= count:\n hold.append(stock)\n count = stock.trend\n \n \n\n context = {\n 'title': 'Trending',\n 'year': datetime.now().year,\n 'user': request.user,\n 'stocks': stocks,\n 'hold': hold,\n\n }\n\n \n return render(\n request,\n 'app/trending.html',\n context,\n )","def on_category(self):\n super(ToolSettings, self).on_category()\n selItems = self.tw_category.selectedItems() or []\n #--- Build Tree ---#\n if selItems:\n if hasattr(selItems[0], 'itemWidget'):\n if selItems[0].itemWidget is not None:\n if not selItems[0].itemWidget.__edited__:\n selItems[0].itemWidget._initWidget()\n selItems[0].itemWidget.buildTree()","def taxonomy_plot(self,seasons):\n print('Formatting data.')\n no_of_ideograms=self.taxonomy_files()\n location=self.place.capitalize()+'-'+str(self.year)\n if seasons==True:\n seasons=self.weather.seasons(self.place)\n print('Done')\n self.conf.taxo_conf(no_of_ideograms, location, self.start_level, self.plot_level, seasons)","def TwitterListener():\n l = StdOutListener()\n auth = OAuthHandler(config.CONSUMER_KEY, config.CONSUMER_SECRET)\n auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\n\n stream = Stream(auth, l)\n api = API(auth_handler=auth)\n config.HASHTAGS = [x['name'] for x in api.trends_place(id=44418)[0]['trends']]\n\n print(\"Stream listener is up and running\")\n stream.filter(track=config.HASHTAGS)","def get_regional_trends(region):\n trends = []\n doc = reg_trends_coll.find_one({REGION_KEY: region})\n if doc:\n trends = format_trends(doc[\"trends\"])\n return trends","def _on_next_event(self, **kwargs):\n self.append_trends()\n self.grab_shots()","def get_top_trends_from_twitter_api(country='Japan', exclude_hashtags=True):\n # this stupid WOEID requires yweather to get (a library), because YAHOO itself has stopped supporting it\n # WOEID\n woeid_client = yweather.Client()\n woeid = woeid_client.fetch_woeid(location=country)\n\n check_rate_limit()\n\n if exclude_hashtags :\n trends = api.GetTrendsWoeid(woeid, exclude='hashtags')\n else:\n trends = api.GetTrendsWoeid(woeid, exclude=None)\n\n output = []\n images_output = []\n for trend in trends:\n trend = trend.AsDict()\n\n # get volumes\n try:\n tw_volume = int(trend['tweet_volume']),\n except:\n tw_volume = [0]\n\n # match time with timezone\n timestamp_str = trend['timestamp'] # this is utc\n timestamp_dt = str_2_datetime(timestamp_str, input_format=time_format_twitter_trends).replace(tzinfo=pytz.utc)\n\n # timestamp_local = timestamp_dt.astimezone(tz=pytz.utc)\n timestamp_utc_str = datetime_2_str(timestamp_dt, output_format=time_format_full_with_timezone)\n\n output.append({\n \"label\": trend['name'],\n \"volume\": tw_volume,\n \"time\": timestamp_utc_str,\n \"query\": trend['query'],\n \"url\": trend['url'],\n })\n\n images_output.append({\n \"label\": trend['name'],\n \"time\": timestamp_utc_str,\n \"tweets\": analyze_trending_keyword(trend['name'], count=50)\n })\n\n output_json = json.dumps(output, ensure_ascii=False)\n images_output_json = json.dumps(images_output, ensure_ascii=False)\n return output_json, images_output_json","def on_category(self):\n super(ProjectSettings, self).on_category()\n selItems = self.tw_category.selectedItems() or []\n #--- Build Tree ---#\n if selItems:\n if hasattr(selItems[0], 'itemWidget'):\n if selItems[0].itemWidget is not None:\n if not selItems[0].itemWidget.__edited__:\n selItems[0].itemWidget._initWidget()\n selItems[0].itemWidget.buildTree()","def plot_mult_timetrends(data, geoids, cols, area, colors, markers, sharex,\n ylim_bottom = -150, ylim_top = 150, ylabel = 'Pct change in mobility', xlabels=None):\n ax = plt.axes(area, sharex = None)\n \n cols = cols\n plt.hlines(0,data.num_date.min(),data.num_date.max())\n i = 0\n for y in cols:\n pts = y[:12]\n \n# lim = ylim\n# plt.xlabel('date', fontsize=18)\n plt.ylabel(ylabel, fontsize=22)\n\n plt.yticks(fontsize=30) \n\n x_locator = FixedLocator(data.num_date[np.arange(0,data.shape[0],7)].tolist())\n ax.xaxis.set_minor_locator(x_locator)\n plt.grid(axis='x', which = 'both') \n \n plt.plot(data['num_date'], data[y], color = colors[i], linewidth=5)\n i = i+ 1\n plt.xticks(ticks = data.num_date[np.arange(0,data.shape[0],28)].tolist(),\n labels = xlabels, rotation=30, ha='right',\n fontsize=30)\n plt.ylim(ylim_bottom,ylim_top)\n\n return ax","def youtube_trending(session=None):\n data = []\n url = 'https://www.youtube.com/feed/trending'\n soup = ph.get_soup(url, session)\n if not soup:\n ph.logger.error('No soup found for {}'.format(url))\n return data\n\n uls = soup.find_all('ul', attrs={'class': 'expanded-shelf-content-list'})\n i = 0\n for ul in uls:\n for li in ul.find_all('li'):\n result_data = {}\n try:\n result_data['link'] = 'https://www.youtube.com' + li.h3.a.attrs['href']\n result_data['title'] = li.h3.a.attrs['title']\n except AttributeError:\n continue\n else:\n result_data['position'] = i\n try:\n result_data['duration'] = _clean_youtube_duration(li.h3.span.text)\n except AttributeError:\n result_data['duration'] = ''\n try:\n result_data['user'] = li.find(attrs={'class': 'yt-lockup-byline'}).a.text\n except AttributeError:\n result_data['user'] = ''\n try:\n metadata = li.find(attrs={'class': 'yt-lockup-meta-info'})\n metadata = [x.text for x in metadata.findChildren()]\n except AttributeError:\n metadata = []\n try:\n result_data['uploaded'] = metadata[0]\n result_data['views'] = metadata[1]\n except IndexError:\n result_data['uploaded'] = ''\n result_data['views'] = ''\n\n data.append(result_data)\n i += 1\n\n return data","def get_national_trends():\n trends = format_trends(list(nat_trends_coll.find({})))\n return trends","def windowMenuActions( self, action ):\n\tif (action.text() == 'Weeklies'):\n if not hasattr(sharedDB, 'myWeekliesWidget'):\n\t\tsharedDB.myWeekliesWidget = weeklieswidget.WeekliesWidget(sharedDB.mainWindow)\n\t\t\n\t #sharedDB.myWeekliesWidget.CalculateWeeklies()\n\t sharedDB.myWeekliesWidget.dockWidget.show()\n\tif (action.text() == 'Assignments'):\t\t\n\t sharedDB.myAssignmentsWidget.dockWidget.show()\n\tif (action.text() == 'Attribute Editor'):\t\t\n\t sharedDB.myAttributeEditorWidget.dockWidget.show()","def top_twenty(request):","def on_double_click(self):\r\n index = self.rssTable.selectedIndexes()[0]\r\n url = QUrl(self.rssModel.url(index))\r\n self.browser.load(url)","def getdata_timeline():\n session = Session()\n sites = session.query(Sites).limit(10).order_by(desc(Sites.id))\n session.close()\n\n return render_template('data.html',sites=sites)","def sponsored_bar():\n articles = []\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored First')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Second')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Third')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Fourth')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Fifth')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n exclude = ['Sponsored First', 'Sponsored Second', 'Sponsored Third',\n 'Sponsored Fourth', 'Sponsored Fifth']\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Article')\n .exclude(tags__text__in=exclude)\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:5 - len(articles)]])\n \"\"\"\n Enable this code (and disable the above) for sponsored articles returning\n by date (without the tag preferences)\n\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Article')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:5 - len(articles)]])\n \"\"\"\n\n return {'articles': articles}","def index():\n last_places=query_db('''select * from places ORDER BY place_id DESC LIMIT 10''')\n last_reviews=query_db('''select places.name, places.place_id, review_id, title FROM reviews, places WHERE places.place_id=reviews.place_id ORDER BY review_id DESC LIMIT 10''')\n return render_template('main.html', places=last_places, reviews=last_reviews)","def sidebar_click(sidebar_item):\n #print('sidebar_click')\n sidebar = driver.find_element_by_class_name('m-menu__nav')\n return click_on(sidebar_item, scope=sidebar)","def show_data(self):\r\n selected_items = self.treeview.selection()\r\n if len(selected_items) > 0:\r\n ticker = self.treeview.item(selected_items, 'values')[0]\r\n self.graph_data(ticker)\r\n self.ticker = ticker\r\n else:\r\n return None","def score_selector(request):\n\n\t# Look for the list of gymnasts entered by the user\n\tgymnast_list = request.GET.get('gymnast_list', False)\n\n\tif gymnast_list:\n\n\t\t# Get the rest of the information submitted through the form\n\t\tgymnasts = gymnast_list.split(\"\\r\\n\")\n\t\tevent = request.GET.get('event', False)\n\t\tsumstat = request.GET.get('sumstat', False)\n\t\ttime = request.GET.get('time', False)\n\n\t\t# Set the date range \n\t\tnow = datetime.datetime.now()\n\t\tif time==\"year\":\n\t\t\tdate_range = [now-relativedelta(years=1), now]\n\t\telif time == \"season\":\n\t\t\tdate_range = [datetime.date(2019, 10, 13), now] # Since last world championships\n\t\telse:\n\t\t\tdate_range = [datetime.date(2016, 8, 21), now] # Since last olympics\n\n\t\t# Get the score data for the results table\n\t\ttable_data = []\n\t\tfor gymnast in gymnasts:\n\t\t\tgymnast = Gymnast.objects.get(name=gymnast)\n\t\t\tthis_gymnast_scores = []\n\t\t\tthis_gymnast_scores.append(gymnast)\n\t\t\tif event == \"AA\":\n\t\t\t\tfor sub_event in [\"VT\", \"UB\", \"BB\", \"FX\"]:\n\t\t\t\t\tscores = Score.objects.filter(gymnast=gymnast, \n\t\t\t\t\t\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=sub_event), score_num=1)\n\t\t\t\t\tif scores.count() > 0:\n\t\t\t\t\t\tif sumstat == \"avg\":\n\t\t\t\t\t\t\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\n\t\t\t\t\t\telif sumstat == \"max\":\n\t\t\t\t\t\t\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\n\t\t\t\t\telse:\n\t\t\t\t\t\tscores_sumstat = \"\"\n\t\t\t\t\tthis_gymnast_scores.append(scores_sumstat)\n\t\t\t\t# Add up AA average\n\t\t\t\tif isinstance(this_gymnast_scores[1], float) and isinstance(this_gymnast_scores[2], float) and isinstance(this_gymnast_scores[3], float) and isinstance(this_gymnast_scores[4], float):\n\t\t\t\t\taa_total = float(this_gymnast_scores[1]) + float(this_gymnast_scores[2]) + float(this_gymnast_scores[3]) + float(this_gymnast_scores[4])\n\t\t\t\t\tthis_gymnast_scores.append(aa_total)\n\t\t\t\telse:\n\t\t\t\t\tthis_gymnast_scores.append(\"\")\n\t\t\telif event == \"VT\":\n\t\t\t\tfor vt_num in [1, 2]:\n\t\t\t\t\tscores = Score.objects.filter(gymnast=gymnast, \n\t\t\t\t\t\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=\"VT\"), score_num=vt_num)\n\t\t\t\t\tif scores.count() > 0:\n\t\t\t\t\t\tif sumstat == \"avg\":\n\t\t\t\t\t\t\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\n\t\t\t\t\t\telif sumstat == \"max\":\n\t\t\t\t\t\t\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\n\t\t\t\t\telse:\n\t\t\t\t\t\tscores_sumstat = \"\"\n\t\t\t\t\tthis_gymnast_scores.append(scores_sumstat)\n\t\t\t\t# Get two-vault average\n\t\t\t\tif isinstance(this_gymnast_scores[1], float) and isinstance(this_gymnast_scores[2], float):\n\t\t\t\t\tvt_avg = (float(this_gymnast_scores[1]) + float(this_gymnast_scores[2]))/2\n\t\t\t\t\tthis_gymnast_scores.append(vt_avg)\n\t\t\t\telse:\n\t\t\t\t\tthis_gymnast_scores.append(\"\")\n\t\t\telse:\n\t\t\t\tscores = Score.objects.filter(gymnast=gymnast, \n\t\t\t\t\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=event))\n\t\t\t\tif scores.count() > 0:\n\t\t\t\t\tif sumstat == \"avg\":\n\t\t\t\t\t\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\n\t\t\t\t\telif sumstat == \"max\":\n\t\t\t\t\t\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\n\t\t\t\telse:\n\t\t\t\t\tscores_sumstat = \"\"\n\t\t\t\tthis_gymnast_scores.append(scores_sumstat)\n\t\t\ttable_data.append(this_gymnast_scores)\n\telse: \n\t\tgymnast_list = \"\"\n\t\tgymnasts = []\n\t\ttable_data = []\n\t\tevent = \"AA\"\n\t\tsumstat = \"avg\"\n\t\ttime = \"year\"\n\n\tcontext = {\n\t\t'gymnast_list': gymnast_list, # Return what they entered so that it shows up again with the results of their request\n\t\t'gymnasts': gymnasts,\n\t\t'table_data': table_data,\n\t\t'event': event,\n\t\t'sumstat': sumstat,\n\t\t'time': time,\n\t}\n\treturn render(request, 'score_selector.html', context=context)","def get_vax_trends_data(area=None):\n if area is None:\n pipe = [\n {\n '$group': {\n '_id': f'${VAX_DATE_KEY}',\n VAX_FIRST_DOSE_KEY: {'$sum': f'${VAX_FIRST_DOSE_KEY}'},\n VAX_SECOND_DOSE_KEY: {'$sum': f'${VAX_SECOND_DOSE_KEY}'},\n VAX_BOOSTER_DOSE_KEY: {'$sum': f'${VAX_BOOSTER_DOSE_KEY}'}\n }\n },\n {\"$sort\": {\"_id\": -1}},\n {'$limit': 7}\n ]\n else:\n pipe = [\n {'$match': {VAX_AREA_KEY: area}},\n {\n '$group': {\n '_id': f'${VAX_DATE_KEY}',\n VAX_FIRST_DOSE_KEY: {'$sum': f'${VAX_FIRST_DOSE_KEY}'},\n VAX_SECOND_DOSE_KEY: {'$sum': f'${VAX_SECOND_DOSE_KEY}'},\n VAX_BOOSTER_DOSE_KEY: {'$sum': f'${VAX_BOOSTER_DOSE_KEY}'}\n }\n },\n {\"$sort\": {\"_id\": -1}},\n {'$limit': 7}\n ]\n data = list(vax_admins_coll.aggregate(pipeline=pipe))\n return data","def get_trending(region='US', **kwargs):\n url = '{}/v1/finance/trending/{}'.format(BASE_URL, region)\n return _make_request(url, 'finance')[0]","def visit_sidebar(self, node):\n\n return BaseTranslator.visit_admonition(self, node)","def visualize_plotly(self, topics):\r\n \r\n df_palette = pd.DataFrame([\r\n [0, '#C03028'],\r\n [1, '#F08030'],\r\n [2, '#6890F0'],\r\n [3, '#78C850'],\r\n [4, '#A890F0'],\r\n [5, '#B22222'],\r\n [6, '#F8D030'],\r\n [7, '#D3D3D3'],\r\n [8, '#F85888'],\r\n [9, '#7FFFD4']])\r\n #[10, '#98D8D8']])\r\n \r\n #[11, '#A8B820'],\r\n #[12, '#7038F8'],\r\n #[13, '#705898'],\r\n #[14, '#705848'],\r\n #[15, '#B8B8D0'],\r\n #[16, '#A8A878'],\r\n #[17, '#EE99AC']])\r\n\r\n df_palette.columns = ['labels', 'typecolor']\r\n self.tweet_dataframe.merge(df_palette, on = 'labels')\r\n\r\n #Divide up the tsne data\r\n\r\n plot_list = []\r\n\r\n for idx, (label, color) in df_palette.iterrows():\r\n\r\n df_filter = self.tweet_dataframe[self.tweet_dataframe['labels'] == label]\r\n \r\n df_filter['custom_text'] = df_filter[['username', 'text']].apply('<br />'.join, axis = 1) \r\n sentiment_boxplot = go.Box(\r\n x = df_filter['vader_polarity'],\r\n name = \"{}\".format(topics[label]),\r\n #text = pd.Series(self.tweet_dataframe['text']),\r\n boxmean = True,\r\n jitter = .5,\r\n boxpoints = 'all',\r\n hoverinfo = 'x+text',\r\n text = df_filter['custom_text'],\r\n marker = dict(color = color) \r\n )\r\n plot_list.append(sentiment_boxplot) \r\n\r\n # Override plotly \r\n axis_layout = dict(zeroline=False, showaxeslabels=False, autotick = True, ticks = '', showticklabels=False, title='')\r\n\r\n layout = go.Layout(\r\n yaxis = axis_layout,\r\n hovermode = \"closest\",\r\n title = \"Sentiment distribution per topic\",\r\n showlegend = True)\r\n\r\n fig = dict(data=plot_list, layout=layout)\r\n #plot_url = py.plot(fig)\r\n offline_plot.plot(fig, filename='data/sentiment_boxplot.html', auto_open = False)\r\n\r\n return plot_list, layout","def sentiment_plot(self, top_words=25):\n if top_words > 25:\n warnings.warn('Including more than 25 words on the X-axis will cause words to be excluded from the axis')\n\n daily_comments = self.comments[(self.comments['days_after_release'].\\\n isin(list(range(self.day_window[0], self.day_window[1] + 1))))]\n if len(daily_comments) == 0:\n warnings.warn('No comments found for this day, trying future dates until comments are found')\n\n while len(daily_comments) == 0:\n if self.day_window[1] > self.comments['days_after_release'].max():\n raise KeyError('Reached bounds of comment dates available. Make sure all comments are present')\n self.day_window[1] += 1\n daily_comments = self.comments[(self.comments['days_after_release'].\\\n isin(list(range(self.day_window[0], self.day_window[1] + 1))))]\n\n print('Now looking at {} to {} days after release'.format(self.day_window[0], self.day_window[1]))\n\n if 'pos' not in daily_comments['sentiment'].values or 'neu' not in daily_comments['sentiment'].values or \\\n 'neg' not in daily_comments['sentiment'].values:\n warnings.warn('No negative or positive sentiments found on this day, trying future dates until positive or negative comments are found')\n\n while 'pos' not in daily_comments['sentiment'].values or 'neu' not in daily_comments['sentiment'].values or \\\n 'neg' not in daily_comments['sentiment'].values:\n if self.day_window[1] > self.comments['days_after_release'].max():\n raise KeyError('Reached bounds of comment dates available. Make sure all comments are present')\n self.day_window[1] += 1\n daily_comments = self.comments[(self.comments['days_after_release']. \\\n isin(list(range(self.day_window[0], self.day_window[1] + 1))))]\n\n print('Now looking at {} to {} days after release'.format(self.day_window[0], self.day_window[1]))\n\n res_positive = daily_comments[(daily_comments['sentiment']=='pos')]['comment_message'].str.split(expand=True)\\\n .stack().value_counts().to_dict()\n res_neutral = daily_comments[(daily_comments['sentiment']=='neu')]['comment_message'].str.split(expand=True)\\\n .stack().value_counts().to_dict()\n res_negative = daily_comments[daily_comments['sentiment']=='neg']['comment_message'].str.split(expand=True)\\\n .stack().value_counts().to_dict()\n\n fig = make_subplots(rows=3, cols=1,\n y_title='Count',\n subplot_titles=('Positive', 'Neutral', 'Negative'))\n trace = fig.add_trace(px.bar(x=list(res_positive.keys())[:top_words], y=list(res_positive.values())[:top_words]).data[0],\n row=1, col=1)\n fig.append_trace(px.bar(x=list(res_neutral.keys())[:top_words], y=list(res_neutral.values())[:top_words]).data[0],\n row=2, col=1)\n fig.append_trace(px.bar(x=list(res_negative.keys())[:top_words], y=list(res_negative.values())[:top_words]).data[0],\n row=3, col=1)\n\n left = np.where(self.day_window[0] < 0, 'Before', 'After')\n right = np.where(self.day_window[1] < 0, 'Before', 'After')\n fig.update_layout(\n title='Top {} Words at {} Days {} Release to {} Days {} Release'.format(top_words,\n self.day_window[0], left,\n self.day_window[1], right)\n )\n fig.show()","def show_derivativeCursor(self, var):\n if self.derivativeThreshold.isChecked(): \n self.plotWidget.derivativeCursorThsPos = 0\n self.plotWidget.derivativeCursorThs = pg.InfiniteLine(pos=self.plotWidget.derivativeCursorThsPos,\n angle=0, movable=True, pen=pg.mkPen('#FFD000', width=2))\n self.plotWidget.addItem(self.plotWidget.derivativeCursorThs)\n self.plotWidget.derivativeCursorThs.sigPositionChanged.connect(self.update_derivativeThreshold)\n else:\n self.plotWidget.derivativeCursorThsPos = []\n self.plotWidget.removeItem(self.plotWidget.derivativeCursorThs)","def draw_html():\n station_reference = request.args.get(\"stationReference\")\n station_name = request.args.get(\"stationName\")\n station_name = station_name.replace(\" \",\"+\")\n # print(\"**********\")\n # print(station_name)\n # print(\"**********\")\n # print(\"**********\")\n # print(station_data.stationName)\n # print(\"**********\") \n if station_name:\n station = station_data.loc[station_data.stationName == station_name]\n else:\n station = station_data.loc[station_data.stationReference == station_reference]\n # print(station)\n # print(\"**********\")\n # print(station)\n # print(\"**********\")\n result_station = station.iloc[0]\n # print(\"**********\")\n # print(result_station)\n # print(\"**********\")\n time_from = request.args.get(\"from\")\n time_to = request.args.get(\"to\")\n if time_from:\n pass\n else:\n time_from = None\n if time_to:\n pass\n else:\n time_to = None\n\n statistic = request.args.get(\"statistic\")\n if statistic is not None:\n statistic = statistic.split(',')\n\n if result_station is not None:\n tide_values = data.station_tides(result_station.stationName, time_from, time_to).reset_index()\n tide_values.rename(columns={result_station.stationName: 'tideValue'}, inplace=True)\n return tide_values.to_html(index=False)\n\n if statistic is not None:\n # print(\"**********\")\n # print(statistic)\n # print(\"**********\")\n frames = []\n for statistic_method in statistic:\n if statistic_method == 'max':\n frames.append(data.max_tides(time_from, time_to))\n elif statistic_method == 'min':\n frames.append(data.min_tides(time_from, time_to))\n else:\n frames.append(data.mean_tides(time_from, time_to))\n # print(\"**********\")\n # print(result)\n # print(\"**********\")\n\n result = pd.concat(frames, axis=1, keys=statistic).reset_index()\n return result.to_html(index=False)","def widget(self, request, group):","def index():\n graphs = [\n message_genre_bar_chart(df),\n category_bar_chart(df),\n top_words_bar_chart(df)\n ]\n \n # encode plotly graphs in JSON\n ids = [\"graph-{}\".format(i) for i, _ in enumerate(graphs)]\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\n \n # render web page with plotly graphs\n return render_template('master.html', ids=ids, graphJSON=graphJSON)","def interactive_utility_report_with_buttons(year: int):\n utility_category_id = Category().get_category_id_by_name('Utility')\n monthly_utility_total = Spending().get_specific_category_monthly_spending(year, [utility_category_id])\n monthly_utility_details = Spending().get_sub_category_monthly_spending_of_a_category(year, [utility_category_id])\n\n df_monthly_total = pd.DataFrame(monthly_utility_total, columns=['sum', 'mon'])\n df_monthly_details = pd.DataFrame(monthly_utility_details, columns=['sum', 'name', 'mon'])\n\n df_water = df_monthly_details[df_monthly_details['name'] == 'Water and Waste']\n df_electricity = df_monthly_details[df_monthly_details['name'] == 'Electricity']\n df_internet = df_monthly_details[df_monthly_details['name'] == 'Internet']\n df_gas = df_monthly_details[df_monthly_details['name'] == 'Nature Gas']\n df_mobile = df_monthly_details[df_monthly_details['name'] == 'Mobile Bill']\n\n fig = go.Figure()\n # Add the total amount into figure as bar chart\n fig.add_trace(\n go.Bar(\n x=df_monthly_total['mon'],\n y=df_monthly_total['sum'],\n name=f\"{year} utility\"\n )\n )\n # Add water\n fig.add_trace(\n go.Scatter(x=df_water['mon'], y=df_water['sum'], name='Water',\n visible=False, line=dict(color=\"#5DADE2\"))\n )\n # Add Electricity\n fig.add_trace(\n go.Scatter(x=df_electricity['mon'], y=df_electricity['sum'], name='Electricity',\n visible=False, line=dict(color=\"#F7DC6F\"))\n )\n # Add Internet\n fig.add_trace(\n go.Scatter(x=df_internet['mon'], y=df_internet['sum'], name='Internet',\n visible=False, line=dict(color=\"#82E0AA\"))\n )\n # Add Gas\n fig.add_trace(\n go.Scatter(x=df_gas['mon'], y=df_gas['sum'], name='Gas',\n visible=False, line=dict(color=\"#E74C3C\"))\n )\n # Add Mobile\n fig.add_trace(\n go.Scatter(x=df_mobile['mon'], y=df_mobile['sum'], name='Mobile',\n visible=False, line=dict(color=\"#BB8FCE\"))\n )\n\n fig.update_layout(\n updatemenus=[\n dict(\n type='buttons',\n direction='right',\n active=0,\n x=0.57,\n y=1.2,\n buttons=list([\n dict(\n label='Total Utility Spending',\n method='update',\n args=[\n {'visible': [True, False, False, False, False, False]},\n {'title': 'Total Utility Spending', \"annotations\": []}\n ]\n ),\n dict(\n label='Water',\n method='update',\n args=[\n {'visible': [True, True, False, False, False, False]},\n {'title': 'Total vs Water', \"annotations\": []}\n ]\n ),\n dict(\n label='Electricity',\n method='update',\n args=[\n {'visible': [True, False, True, False, False, False]},\n {'title': 'Total vs Electricity', \"annotations\": []}\n ]\n ),\n dict(\n label='Internet',\n method='update',\n args=[\n {'visible': [True, False, False, True, False, False]},\n {'title': 'Total vs Internet', \"annotations\": []}\n ]\n ),\n dict(\n label='Gas',\n method='update',\n args=[\n {'visible': [True, False, False, False, True, False]},\n {'title': 'Total vs Gas', \"annotations\": []}\n ]\n ),\n dict(\n label='Mobile',\n method='update',\n args=[\n {'visible': [True, False, False, False, False, True]},\n {'title': 'Total vs Mobile', \"annotations\": []}\n ]\n ),\n ])\n )\n ]\n )\n\n fig.update_layout(\n {\n 'width': 1200,\n 'height': 550\n },\n overwrite=True\n )\n\n aPlot = plotly.offline.plot(fig,\n config={\"displayModeBar\": False},\n show_link=False,\n include_plotlyjs=False,\n output_type='div')\n\n return aPlot","def analyze(request, *args, **kwargs):\n\n mode = 'lines+markers'\n\n tickers = Stock.objects.distinct(\n 'ticker').values_list('ticker', flat=True)\n tickers_dict = {ticker: [] for ticker in tickers}\n tickers_count = tickers.count()\n\n actual_dates = Stock.objects.values('date').annotate(\n dcount=Count('date')).filter(dcount=tickers_count).values_list(\n 'date', flat=True).order_by('date')\n date_list = list(actual_dates)\n\n data = Stock.objects.filter(date__in=actual_dates).order_by('date')\n\n for item in data.values('ticker', 'close', 'oopen'):\n tickers_dict[item['ticker']].append(\n round((item['close']-item['oopen'])*100/item['oopen'], 2)\n )\n\n scatters = [Scatter(x=date_list, y=tickers_dict[obj], mode=mode, name=obj,\n opacity=0.8, visible='legendonly') for obj in tickers_dict]\n figure = {'data': scatters, 'layout': {\n 'title': {\n 'text': 'Open-Closed comparision', 'y': 0.9, 'x': 0.5,\n 'xanchor': 'center','yanchor': 'top'},\n 'yaxis_title': \"Daily percent\",\n 'xaxis_title': \"Years\",\n }}\n\n return render(request, \"analyze.html\", context={\n 'plot_div': plot(figure, output_type='div')})","def trackVocabulary(context):\n ct = getToolByName(context,'portal_catalog')\n tracks = ct.searchResults(portal_type='apyb.papers.track', sort_on='getObjPositionInParent')\n items = [SimpleTerm(b.UID,b.UID,b.Title) for b in tracks]\n return SimpleVocabulary(items)","def on_scatter_toolbar_table_click(self):\n #print('*** on table click ***')\n row = self.myTableWidget.currentRow()\n if row == -1 or row is None:\n return\n yStat = self.myTableWidget.item(row,0).text()\n self.myParent.replot()","def root_wdgt(self):\n self.summarize()\n modes = ['Global', 'Single-Image']\n\n def logic(mode):\n # cache the widget later\n if mode == modes[0]:\n if self.global_walk is None:\n self.global_walk = self.global_walk_specifier()\n ipy_display(self.global_walk)\n elif mode == modes[1]:\n self.image_view = self.single_image_selector()\n # if self.image_view is None:\n # self.image_view = self.single_image_selector()\n # ipy_display(self.image_view)\n\n UI = interactive(\n logic, mode=widgets.ToggleButtons(options=modes, value=modes[0])\n )\n UI.children[-1].layout.height = '1000px'\n ipy_display(UI)","def views(request):\n tag = Tag.objects.filter(name=\"Global\")\n gauges = Gauge.objects.filter(tags=tag)\n return render_to_response('dashboard/views.js',{'gauges': gauges} )","def widgets(self):\r\n url = '{0}/{1}/'.format(self.get_url(), 'widgets')\r\n\r\n return http.Request('GET', url), parsers.parse_json","def plot_trends(group, country=\"US\", state=None, place=None, predictive_method=\"ARIMA\"):\n print(f\"* Plotting Google Trends of `{group}` for {country} - {state or 'All'}\")\n group_queries = get_group_queries(group, only_root=True)\n\n n_queries = len(group_queries)\n n_cols = 3\n n_rows = int(n_queries / n_cols) + (1 if n_queries % n_cols else 0)\n\n # Annotations\n annotations = []\n\n # Initialize figure with subplots\n subplot_titles = [\"%s...\" % t[:22] if len(t) >= 22 else t for t in group_queries]\n fig = make_subplots(\n rows=n_rows, cols=n_cols, subplot_titles=subplot_titles,\n shared_yaxes=True,\n print_grid=True\n )\n\n # Marked Dates\n covid_start_date = COVID_START_DATE\n reopen_date = REOPEN_DATE\n reopen_date_minus_1 = REOPEN_DATE_MINUS_1\n data_start_date = DATA_START_DATE\n data_end_date = DATA_END_DATE\n\n # Figure variable\n baseline = 0\n value_range = [0, 100]\n\n # Model params\n model_params = []\n\n for idx, query in enumerate(group_queries):\n row = int(idx / n_cols) + 1\n col = idx % n_cols + 1\n showlegend = idx == 0\n\n query_file_path = get_data_filename(group, query, country=country, state=state, full=True)\n df = pd.read_csv(query_file_path, parse_dates=True)\n count = df[\"date\"].count()\n\n # ARIMA Model\n if query in df.columns:\n print(\"Query: \", query)\n # get_arima_params(df[query])\n df, model = arima_predict(df, from_date=PREDICT_FROM_DATE, value_col=query)\n params = model.get_params()\n model_params.append([query, str(params[\"order\"])])\n # return False\n \n # No data\n if count == 0:\n continue\n\n # Process\n stayhome_order_date = place.get(\"ClosedFrom\") if place else SOCIAL_DISTANCE_ORDER_DATE\n\n df = df[(df[\"date\"] >= data_start_date) & (df[\"date\"] <= data_end_date)]\n df_before = df[(df[\"date\"] <= reopen_date)]\n df_after = df[(df[\"date\"] >= reopen_date_minus_1)]\n df_prediction = df[df[\"is_predicted\"] == 1]\n\n # Normalize\n if config.TRENDS_APPLY_NORMALIZATION:\n max_value = df[query].max()\n baseline = df_before[query].median()\n df[\"value\"] = df[query].apply(lambda x: (x - baseline) / max_value)\n df_before[\"value\"] = df_before[query].apply(lambda x: (x - baseline) / max_value)\n df_after[\"value\"] = df_after[query].apply(lambda x: (x - baseline) / max_value)\n baseline = 0\n value_range = [-1, 1]\n else:\n max_value = df[query].max()\n baseline = df_before[query].median()\n df[\"value\"] = df[query]\n df_before[\"value\"] = df_before[query]\n df_after[\"value\"] = df_after[query]\n\n # Compute difference\n query_text = query.split(\"+\")[0].strip() + \" + ...\" if \"+\" in query else query\n actual_mean, actual_meanCI95min, actual_meanCI95max = mean_confidence_interval(df_prediction[query])\n predict_mean = df_prediction[\"prediction\"].mean()\n diff = round(100 * (actual_mean - predict_mean) / predict_mean, 1)\n diffCI95min = round(100 * (actual_meanCI95min - predict_mean) / predict_mean, 1)\n diffCI95max = round(100 * (actual_meanCI95max - predict_mean) / predict_mean, 1)\n x_date = list(df['date'])[int(df[\"date\"].count()/2)]\n diff_annot = go.layout.Annotation(\n text=f'<b>{query_text}</b><br><sub><b style=\"color:{config.COLOR_UPTREND if diff >= 0 else config.COLOR_DOWNTREND}\">{diff}%</b>; 95%CI, [{diffCI95min}%, {diffCI95max}%]</sub>',\n showarrow=False, xanchor=\"center\", yanchor=\"top\", \n x=x_date,\n y=0.0,\n xshift=0,\n yshift=-5,\n xref=f\"x{'' if idx == 0 else idx + 1}\",\n yref=f\"y{'' if idx == 0 else idx + 1}\"\n )\n annotations.append(diff_annot)\n\n # Lockdown period\n max_y = max(df[query].max(), abs(df[query].min()))\n min_y = -max_y\n shape_lockdown = go.layout.Shape(**{\"type\": \"rect\",\"y0\":100,\"y1\": -100,\"x0\":COVID_START_DATE, \n \"x1\":REOPEN_DATE,\"xref\":\"x1\",\"yref\":\"y1\",\"layer\":\"below\",\n \"fillcolor\":\"#eeeeee\", \"line\":dict(width=0), \"line_width\": 0})\n fig.add_shape(shape_lockdown, row=row, col=col)\n\n # Horizontal line \n shape = go.layout.Shape(**{\"type\": \"line\",\"y0\":baseline,\"y1\": baseline,\"x0\":str(df[\"date\"].values[0]), \n \"x1\":str(df[\"date\"].values[-1]),\"xref\":\"x1\",\"yref\":\"y1\",\"layer\":\"below\",\n \"line\": {\"color\": \"rgb(200, 200, 200)\",\"width\": 1.5}})\n fig.add_shape(shape, row=row, col=col)\n\n # Stay home order\n if stayhome_order_date:\n shape_stayhome_order = go.layout.Shape(**{\"type\": \"line\",\"y0\":-0.25,\"y1\": 0.25,\"x0\":stayhome_order_date, \n \"x1\":stayhome_order_date,\"xref\":\"x1\",\"yref\":\"y1\",\n \"line\": {\"color\": \"blue\",\"width\": 1.5, \"dash\": \"dot\"}})\n fig.add_shape(shape_stayhome_order, row=row, col=col)\n\n # Plot\n subplot_before = go.Scatter(x=df_before[\"date\"], y=df_before[\"value\"], \n mode=\"lines\", name=\"Before Lockdown\",\n line=dict(width=1, color=config.LINE_COLOR_BEFORE), \n line_shape=\"linear\", showlegend=False) # linear or spline \n subplot_after = go.Scatter(x=df_after[\"date\"], y=df_after[\"value\"], \n mode=\"lines\", name=\"Actual Queries\",\n line=dict(width=1.5, color=config.LINE_COLOR_AFTER), \n line_shape=\"linear\", showlegend=showlegend) # linear or spline \n subplot_prediction = go.Scatter(x=df_prediction[\"date\"], y=df_prediction[\"prediction\"], \n mode=\"lines\", name=\"Expected Queries\",\n line=dict(width=2, color=config.LINE_COLOR_BEFORE, dash=\"dot\"), \n line_shape=\"linear\", showlegend=showlegend) # linear or spline \n subplot_lockdown_legend = go.Bar(x=[reopen_date,], y=[0,], \n name=\"Early Lockdown Phase\", \n showlegend=showlegend,\n marker_color=\"#eeeeee\")\n fig.add_trace(subplot_before, row=row, col=col)\n fig.add_trace(subplot_after, row=row, col=col)\n fig.add_trace(subplot_prediction, row=row, col=col)\n if idx == 0:\n fig.add_trace(subplot_lockdown_legend, row=row, col=col)\n\n # break\n\n # Caption\n # caption = go.layout.Annotation(\n # showarrow=False,\n # text=\"\",\n # xanchor=\"center\",\n # x=0.5,\n # yanchor=\"top\",\n # y=0.0,\n # yshift=0,\n # )\n\n # Layout\n # location = f\"{country}.{state}\" if state else country\n # fig_title = f\"\"\"Term: {group}. Location: {location}<br>\n # <span style=\"font-size: 14px;line-height:1\">Period: {data_start_date} - {data_end_date}\n # <br>Lockdown Period: {covid_start_date} - {PREDICT_FROM_DATE}</span>\"\"\"\n fig_title = \"\"\n fig.update_layout(title={\"text\": fig_title, \"x\":0.5, \"xanchor\": \"center\"}, \n title_font=dict(size=12),\n height=50 + n_rows * 175, width=250 * n_cols, coloraxis=dict(colorscale=\"Bluered_r\"), \n showlegend=True, plot_bgcolor=\"rgb(255,255,255)\", titlefont={\"size\": 30},\n margin={\"t\": 50},\n annotations=annotations,\n legend=dict(\n orientation=\"v\",\n yanchor=\"bottom\",\n y=0,\n xanchor=\"right\",\n x=1,\n bgcolor=\"white\",\n bordercolor=\"#333\",\n borderwidth=1\n )\n )\n fig.update_xaxes(showgrid=False, showticklabels=False, showline=False)\n fig.update_yaxes(showgrid=False, showticklabels=False, showline=True, range=value_range)\n\n # Store model parameters\n mkdir_if_not_exist(config.TRENDS_OUTPUT_DIR)\n df_params = pd.DataFrame(model_params, columns=[\"Query\", \"Order\"])\n df_params.to_csv(\"%s/ARIMA_orders_%s.csv\" % (config.TRENDS_OUTPUT_DIR, group), index=False)\n\n # Create online URL\n url = py.iplot(fig, filename=group, file_id=group)\n print(\"URL:\", url.src)\n\n if config.TRENDS_EXPORT_FIGURES:\n # Save\n mkdir_if_not_exist(config.TRENDS_FIGURES_DIR)\n fig.write_image(\"%s/%s_%s_%s.jpg\" % (config.TRENDS_FIGURES_DIR, country, state or \"All\", group))\n # fig.show()\n else:\n # Show\n fig.show()","def show_dashboard():\n script, div = plots.make_plot()\n script_tab, div_tab = plots.make_tabs()\n script_trend, div_trend = plots.make_trend()\n\n return render_template('layout.html',\n script=script,\n div=div,\n script_trend=script_trend,\n div_trend=div_trend,\n script_tab=script_tab,\n div_tab=div_tab)","def getWidget(self):","def heatmap_termlevel_dropdown(topic_index, embed_id):\n\t\tlog.info(\"Callback: heatmap_termlevel_dropdown: topic_index=%s embed_id=%s\" % (topic_index, embed_id))\n\t\tlayout.current_topic_index = int(topic_index)\n\t\tlayout.current_embed_id = embed_id\n\t\treturn layout.generate_termlevel_heatmap()","def showTopStories(self):\n\t\tif self.newestOrTop == \"newest\":\n\t\t\tself.newestOrTop = \"top\"\n\t\t\tprint \"Getting the latest stories from HN...\"\n\t\t\tself.refreshStories()\n\t\telse:\n\t\t\tinput = raw_input(\"Already showing top stories. Press Return to continue.\")\n\t\tself.printStories()","def render_investip():\n\tlinewidth = 2\n\n\tst.sidebar.markdown('# Dashboard')\n\tstock = st.sidebar.selectbox('Stock:', stocks)\n\n\tstartdd = datetime.datetime(2020, 3, 1)\n\tstartdd = st.sidebar.date_input('start-date', value=startdd)\n\n\tendd = datetime.datetime.now()\n\tendd = st.sidebar.date_input('end-date', value=endd)\n\n\tt0 = stock\n\tt0_ohlc = extract(ticker=t0, start_date=startdd, end_date=endd)\n\tt0_df = pd.DataFrame({f'{t0}-Close': t0_ohlc.Close})\n\n\t# st.write(t0_ohlc)\n\tmpf.plot(t0_ohlc, type='candle',volume=True,show_nontrading=False, title=t0, figscale=1.)\n\t# tdf = plot_ticker(t0, df=t0_df, start_date=startdd, end_date=endd)\n\tst.pyplot()\n\n\n\tst.sidebar.markdown('## Stock Correlation')\n\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_corrs')\n\tif stock_returns:\n\t\tst.markdown('## Stock Correlation')\n\t\tstock_selection = st.sidebar.multiselect('Stocks', stocks, def_stocks)\n\t\tplot_stock_correlations(stock_selection, startdd, endd)\n\t\tst.pyplot()\n\n\t# trading_context = True\n\tst.sidebar.markdown('## Returns')\n\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_returns')\n\tif stock_returns:\n\t\tst.markdown('## Stock Returns')\n\t\tst.markdown('''### Daily Stock returns\n[EWMA](https://www.investopedia.com/articles/07/ewma.asp)''')\n\t\tspan = st.sidebar.slider('span', 2, 21, value=5)\n\t\tplot_historical(t0, t0_ohlc, span=span, linewidth=linewidth)\n\t\tst.pyplot()\n\n\n\t# trading_context = True\n\tst.sidebar.markdown('## Volatility')\n\ttrading_context = st.sidebar.checkbox('Enable', value=False, key='cb_volatility')\n\tif trading_context:\n\t\tst.markdown('## Volatility & Risk')\n\t\tst.markdown('''### Daily differences between High & Low\nWe model these ranges with [Inverse Gamma PDF](https://en.wikipedia.org/wiki/Inverse-gamma_distribution).\nGreen lines denote +/- 1 stdev.\n''')\n\t\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\n\t\tf.suptitle(f'{t0} High-Low Daily')\n\t\tmmd = t0_ohlc.High - t0_ohlc.Low\n\t\t# mmd.dropna(inplace=True)\n\t\tmmd.plot(color='r', ax=ax[0], lw=linewidth)\n\n\t\tmu, sigma = mmd.dropna().mean(), mmd.dropna().std()\n\t\tzval = 1.#96\n\t\t# TODO: try one-tail limit to get outliers\n\t\t_=ax[0].axhline(y=mu, color='k', lw=linewidth)\n\t\t_=ax[0].axhline(y=mu-zval*sigma, color='g', lw=linewidth)\n\t\t_=ax[0].axhline(y=mu+zval*sigma, color='g', lw=linewidth)\n\n\t\tp95 = mmd.dropna().quantile(.95)\n\t\t_=ax[0].axhline(y=p95, color='b', lw=linewidth, label='p95')\n\t\t_=ax[1].axvline(p95, color='b', lw=linewidth, label='p95')\n\n\t\twith warnings.catch_warnings():\n\t\t warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\t\t print(invgamma.fit(mmd))\n\t\t sns.distplot(mmd, fit=invgamma, kde=False, ax=ax[1])\n\t\t_=ax[1].axvline(mmd.values[-1], color='r', label='last', lw=linewidth)\n\t\t_=ax[1].axvline(mu, color='k', label='mean', lw=linewidth)\n\t\t_=ax[1].legend()\n\t\tst.pyplot()\n\n\t\tst.markdown('''### Daily Average True Range (ATR)\nImplementation follows [ATR](https://kodify.net/tradingview/indicators/average-true-range/).\nCheck [Investopedia](https://www.investopedia.com/terms/a/atr.asp) for more info.''')\n\n\t\tatr_df = pd.DataFrame({\n\t\t\tf'{t0}-High-Low': t0_ohlc.High - t0_ohlc.Low,\n\t\t\tf'{t0}-High-PrevCloseAbs': abs(t0_ohlc.High - t0_ohlc.Close.shift(1)),\n\t\t\tf'{t0}-Low-PrevCloseAbs': abs(t0_ohlc.Low - t0_ohlc.Close.shift(1)),\n\t\t}).max(axis=1)\n\t\tatr_df = pd.DataFrame({\n\t\t\tf'{t0}-true-range': atr_df,\n\t\t})\n\t\tatr_df[f'{t0}-ATR14'] = atr_df.iloc[:, 0].rolling(14).mean()\n\t\t# st.write(atr_df)\n\n\t\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\n\t\tf.suptitle(f'{t0} True Range & SMA14')\n\t\tatr_df.plot(ax=ax[0], lw=linewidth)\n\n\t\twith warnings.catch_warnings():\n\t\t warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\t\t #print(invgamma.fit(f'{t0}-true-range'))\n\t\t sns.distplot(atr_df[f'{t0}-true-range'], fit=invgamma, kde=False, ax=ax[1])\n\t\t_=ax[1].axvline(atr_df[f'{t0}-true-range'].values[-1], color='b', label='last', lw=linewidth)\n\t\t_=ax[1].axvline(atr_df[f'{t0}-ATR14'].values[-1], color='r', label='last', lw=linewidth)\n\t\t_=ax[1].legend()\n\t\tst.pyplot()\n\n\n\n\t# do_strategy_analysis = True\n\tst.sidebar.markdown('## Trading Strategy')\n\tdo_strategy_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_stra')\n\tif do_strategy_analysis:\n\t\tst.markdown('## Trading Strategy')\n\t\tst.markdown('[investopedia](https://www.investopedia.com/articles/active-trading/052014/how-use-moving-average-buy-stocks.asp)')\n\t\tshort_window = st.sidebar.slider('short_window', 2, 21, 3)\n\t\tlong_window = st.sidebar.slider('long_window', 3, 50, 5)\n\t\tplot_strategy(t0, t0_df, short_window, long_window)\n\t\tst.pyplot()\n\n\t# do_corr_analysis = False\n\tst.sidebar.markdown('## Correlation analysis')\n\tdo_corr_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_corr')\n\tif do_corr_analysis:\n\t\tst.markdown('## Correlation analysis')\n\t\tt1= 'GC=F' # # SP500 'GC=F'\n\t\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\n\t\tt1 = st.sidebar.selectbox('REF1:', stocks, index=stocks.index(t1))\n\t\tt2 = st.sidebar.selectbox('REF2:', stocks, index=stocks.index(t2))\n\t\tif st.sidebar.button('Reset'):\n\t\t\tt1 = 'GC=F' # # SP500 'GC=F'\n\t\t\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\n\t\t\t# t1 = st.sidebar.selectbox('ref1:', stocks, index=stocks.index(t1))\n\t\t\t# t2 = st.sidebar.selectbox('ref2:', stocks, index=stocks.index(t2))\n\n\t\t@st.cache(persist=True, show_spinner=False)\n\t\tdef get_dataframes(t1, t2, startdd, endd):\n\t\t\tt1_ohlc = extract(ticker=t1, start_date=startdd, end_date=endd)\n\t\t\tt2_ohlc = extract(ticker=t2, start_date=startdd, end_date=endd)\n\t\t\treturn t1_ohlc, t2_ohlc\n\n\t\tt1_ohlc, t2_ohlc = get_dataframes(t1, t2, startdd, endd)\n\t\tt1_df = pd.DataFrame({f'{t1}-Close': t1_ohlc.Close})\n\t\tt2_df = pd.DataFrame({f'{t2}-Close': t2_ohlc.Close})\n\n\t\t#print(t0_ohlc.shape)\n\t\t#t0_ohlc.head()\n\t\t# print(t1_ohlc.shape)\n\t\t# ticker_ohlc.head()\n\t\t# ticker_ohlc.info()\n\n\t\ttdf = t0_df.join(t1_df).join(t2_df).interpolate().dropna()\n\t\t# tdf.head(10)\n\n\t\t# t0_ohlc.corr(t1_ohlc)\n\t\t#ax = t0_ohlc.Close.plot()\n\t\t#t1_ohlc.Close.plot(ax=ax)\n\n\t\timport numpy as np\n\t\tprint('glocal corrleation1: ', t0_ohlc.Close.corr(t1_ohlc.Close))\n\t\tprint('glocal corrleation2: ', t0_ohlc.Close.corr(t2_ohlc.Close))\n\n\t\tp_window_size = 5\n\t\tr_window_size = 5\n\t\tcentering = False\n\n\n\t\tmodf = lambda x: x\n\t\t#modf = np.log10\n\n\n\t\tmain_stat = f'[{t0}]-mean-roll{p_window_size}'\n\t\talt_stat_1 = f'[{t1}]-mean-roll{p_window_size}'\n\t\talt_stat_2 = f'[{t2}]-mean-roll{p_window_size}'\n\t\t# df_rc = pd.DataFrame({\n\t\t# main_stat : tdf.iloc[:, 0].apply(modf).rolling(window=p_window_size,center=centering).mean(),\n\t\t# alt_stat_1: tdf.iloc[:, 1].apply(modf).rolling(window=p_window_size,center=centering).mean(),\n\t\t# alt_stat_2: tdf.iloc[:, 2].apply(modf).rolling(window=p_window_size,center=centering).mean(),\n\t\t# })\n\t\tcom_val = 0.2\n\t\tdf_rc = pd.DataFrame({\n\t\t main_stat : tdf.iloc[:, 0].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\n\t\t alt_stat_1: tdf.iloc[:, 1].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\n\t\t alt_stat_2: tdf.iloc[:, 2].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\n\t\t})\n\n\t\tdf_rc = df_rc.interpolate()\n\t\tdf_rc[f'[{t0}]-[{t1}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_1])\n\t\tdf_rc[f'[{t0}]-[{t2}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_2])\n\n\t\tf, ax = plt.subplots(3,1,figsize=(16,10),sharex=True)\n\t\t#df_rc.iloc[:,0].plot(ax=ax[0], legend=True)\n\t\tdf_rc.iloc[:,1].plot(ax=ax[0], legend=True, color='gold')\n\t\tdf_rc.iloc[:,2].plot(ax=ax[1], legend=True, color='darkred')\n\t\tdf_rc.iloc[:,3].plot(ax=ax[2], legend=True, color='gold')\n\t\tdf_rc.iloc[:,4].plot(ax=ax[2], legend=True, color='darkred')\n\t\tax[2].axhline(y=0, lw=1, color='black')\n\t\t#t0_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[0])\n\t\t#t1_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[1])\n\t\t# ax[0].set(xlabel='Frame',ylabel='Smiling Evidence')\n\t\t# ax[1].set(xlabel='Frame',ylabel='Pearson r')\n\t\t_=plt.suptitle(f\"{t0} Close rolling correlation to {t1}, {t2}\")\n\n\t\tst.pyplot()\n\n\n\t\tf,ax=plt.subplots(1, 2, figsize=(16,8),sharex=False)\n\n\t\t_= df_rc.plot.scatter(x=df_rc.columns[1],\n\t\t y=df_rc.columns[2],\n\t\t c=df_rc.columns[0],\n\t\t colormap='viridis',\n\t\t # legend=None,\n\t\t ax=ax[0])\n\n\t\tprint(df_rc.columns)\n\t\tnewr_p = df_rc.iloc[-1, 0]\n\t\tt1_p = df_rc.iloc[-1, 1]\n\t\tt2_p = df_rc.iloc[-1, 2]\n\t\tt1_c = df_rc.dropna().iloc[-1, 3]\n\t\tt2_c = df_rc.dropna().iloc[-1, 4]\n\t\tprint('current_corr:', (t1_c, t2_c))\n\n\t\t# figure out circle size\n\t\taaaa = df_rc.iloc[:, 1].aggregate([np.max, np.min])\n\t\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\n\t\tprint(aaaa.values[0], aaaa.values[1], xrange)\n\t\txradius = xrange / 20.\n\n\t\tcircle = plt.Circle((t1_p, t2_p), xradius, color='r', fill=False)\n\t\tax[0].add_artist(circle)\n\t\t#ax[0].set_xlabel(f'GOLD Price {t1_p:.4f}')\n\t\t#ax[0].set_ylabel(f'OIL Price {t2_p:.4f}')\n\t\t# ax[0].legend().set_visible(False)\n\n\t\t_= df_rc.plot.scatter(x=df_rc.columns[-2],\n\t\t y=df_rc.columns[-1],\n\t\t c=df_rc.columns[0],\n\t\t colormap='viridis',\n\t\t # legend=True,\n\t\t #linestyle=\n\t\t ax=ax[1])\n\n\t\t# figure out circle size\n\t\taaaa = df_rc.iloc[:, -2].aggregate([np.max, np.min])\n\t\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\n\t\tprint(aaaa.values[0], aaaa.values[1], xrange)\n\t\txradius = xrange / 20.\n\n\t\tcircle1 = plt.Circle((t1_c, t2_c), xradius, color='r', fill=False)\n\t\tax[1].add_artist(circle1)\n\t\t#ax[1].set_ylabel('OIL Correlation')\n\t\t#_= ax[1].set_xlabel('GOLD Correlation')\n\n\n\t\tst.pyplot()","def main():\n option = st.sidebar.radio(\"Use Navigation Here:\",(\"Home\", \"Data Metrics\",\n \"Customer Segmentation\",\"Customer Value Prediction\",\n \"Churn Prediction\", \"Next Purchase Day\", \"Predicting Sales\",\n \"Market Response\"\n ))\n if option == \"Home\":\n with st.spinner(\"Wait for it...\"):\n st.write(home_page())\n elif option == \"Data Metrics\":\n with st.spinner(\"Wait for it...\"):\n st.write(metrics_of_data())\n elif option == \"Customer Segmentation\":\n with st.spinner(\"In Progress\"):\n st.write(customer_segmentation())\n elif option == \"Customer Value Prediction\":\n with st.spinner(\"In Progress\"):\n st.write(customer_value_prediction())\n elif option == \"Churn Prediction\":\n with st.spinner(\"In Progress\"):\n st.write(churn_prediction())\n elif option == \"Next Purchase Day\":\n with st.spinner(\"In Progress\"):\n st.write(predict_next_purchase_day())\n elif option == \"Predicting Sales\":\n with st.spinner(\"In Progress\"):\n st.write(predicting_sales())\n elif option == \"Market Response\":\n with st.spinner(\"In Progress\"):\n st.write(market_response_model())","def __sidebar(self, conf):\n result = \"\"\n\n i = conf[\"conf_json\"][0]\n result += \"\"\"\n\n <h4>%s</h4>\n <ul id=\"sidebar_menu\" class=\"sidebar_menu\">\n <li><a class=\"btn active\" href=\"#description\">DESCRIPTION</a></li>\n <li><a class=\"btn\" href=\"#parameters\">PARAMETERS</a></li>\n <li><a class=\"btn\" href=\"#operations\">OPERATIONS</a>\n <ul class=\"sidebar_submenu\">%s</ul>\n </li>\n <li><a class=\"btn active\" href=\"/\">HOME</a></li>\n </ul>\n \"\"\" % (\n i[\"title\"],\n \"\".join(\n [\n \"<li><a class='btn' href='#%s'>%s</a></li>\" % (op[\"url\"], op[\"url\"])\n for op in conf[\"conf_json\"][1:]\n ]\n ),\n )\n return result","def get_trending_categories(self):\n # TODO: Assuming first server is good - need to make fallback logic\n return self.session.get_any(\"{base}{request_url}\".format(base=self.servers[0],\n request_url=F\"/Trending/Categories/\"))","def index():\r\n\r\n title = \"Global Statistics\"\r\n\r\n n_auth = n_auth_users()\r\n\r\n n_anon = n_anon_users()\r\n\r\n n_total_users = n_anon + n_auth\r\n\r\n n_published_apps = cached_apps.n_published()\r\n n_draft_apps = cached_apps.n_draft()\r\n n_total_apps = n_published_apps + n_draft_apps\r\n\r\n n_tasks = n_tasks_site()\r\n\r\n n_task_runs = n_task_runs_site()\r\n\r\n top5_apps_24_hours = get_top5_apps_24_hours()\r\n\r\n top5_users_24_hours = get_top5_users_24_hours()\r\n\r\n locs = get_locs()\r\n\r\n show_locs = False\r\n if len(locs) > 0:\r\n show_locs = True\r\n\r\n stats = dict(n_total_users=n_total_users, n_auth=n_auth, n_anon=n_anon,\r\n n_published_apps=n_published_apps,\r\n n_draft_apps=n_draft_apps,\r\n n_total_apps=n_total_apps,\r\n n_tasks=n_tasks,\r\n n_task_runs=n_task_runs)\r\n\r\n users = dict(label=\"User Statistics\",\r\n values=[\r\n dict(label='Anonymous', value=[0, n_anon]),\r\n dict(label='Authenticated', value=[0, n_auth])])\r\n\r\n apps = dict(label=\"Apps Statistics\",\r\n values=[\r\n dict(label='Published', value=[0, n_published_apps]),\r\n dict(label='Draft', value=[0, n_draft_apps])])\r\n\r\n tasks = dict(label=\"Task and Task Run Statistics\",\r\n values=[\r\n dict(label='Tasks', value=[0, n_tasks]),\r\n dict(label='Answers', value=[1, n_task_runs])])\r\n\r\n return render_template('/stats/global.html', title=title,\r\n users=json.dumps(users),\r\n apps=json.dumps(apps),\r\n tasks=json.dumps(tasks),\r\n locs=json.dumps(locs),\r\n show_locs=show_locs,\r\n top5_users_24_hours=top5_users_24_hours,\r\n top5_apps_24_hours=top5_apps_24_hours,\r\n stats=stats)","def display_station_stats(self):\n\n self.station_frame = stat_display_labels(\n self.stats_frame,\n \"Station Stats\",\n [\n \"The most popular start station was:\",\n \"The most popular end station was:\",\n \"The most popular start/end station combination was:\",\n ],\n row=1,\n columnspan=5,\n )\n self.station_stats_data = tk.Label(self.station_frame, justify=\"left\")\n self.station_stats_data.grid(row=0, column=1)","def search_tweets(request):\n return render(request, 'ede/search.html')","def viewWidgetCreated(self, view, plot):\n return","def disqus_popular_threads_widget(context, shortname='', num_items=5):\n shortname = getattr(settings, 'DISQUS_WEBSITE_SHORTNAME', shortname)\n return {\n 'shortname': shortname,\n 'num_items': int(num_items),\n }","def main():\r\n root = tk.Tk()\r\n root.geometry(\"250x100+300+300\")\r\n TrendProg(root)\r\n root.mainloop()","def main():\n \n\n\t# Creates a main title and subheader on your page -\n\t# these are static across all pages\n\t#st.title(\"Tweet Classifer\")\n\n\n #image= Image.open('resources/imgs/global_warming_img.jpg')\n\n \n\n\n\t# Creating sidebar with selection box -\n\t# you can create multiple pages this way\n\toptions = [\"Background\",\"Prediction\",\"Data visualization\", \"Information\",\"Project Team\"]\n\tselection = st.sidebar.radio(\"Choose page:\",options )\n\t#selection = st.sidebar.selectbox(\"Choose Option\", options)\n\n\t# Builing out the \"Background\" page\n\tif selection == \"Background\":\n\t\tst.info('This works');\t\n\t\tst.write('Explorers !!!')\t\n\t\timage= Image.open('resources/imgs/global_warming_img.jpg')\n\t\tst.image(image, caption='https://www.azocleantech.com/article.aspx?ArticleID=898', use_column_width=True)\n\t\t\n\n\n\tif selection == \"Project Team\" :\n\n\n\t\t#First row of pictures\n\n\t\tcol1, col2,col3 = st.beta_columns(3)\n\t\tRic_Pic =Image.open('resources/imgs/Rickie_pic.png') \n\t\tcol1.image(Ric_Pic,caption=\"Rickie Mogale Mohale\", width=150)\n\t\t\n \n\t\tCot_Pic =Image.open('resources/imgs/courtney_pic.png') \n\t\tcol2.image(Cot_Pic,caption=\"Courtney Murugan\", width=150)\n\t\t\n\n\t\tCot_Pic =Image.open('resources/imgs/jacques_pic.png') \n\t\tcol3.image(Cot_Pic,caption=\"Jacques Stander\", width=150)\n\t\t\n\n #Second row of pictures\n\t\tcol4, col5,col6 = st.beta_columns(3)\n\t\tvas_Pic =Image.open('resources/imgs/Rickie_pic.png') \n\t\tcol4.image(Ric_Pic,caption=\"Veshen Naidoo\", width=150)\n\t\t\n \n\t\tPhiw_Pic =Image.open('resources/imgs/Rickie_pic.png') \n\t\tcol5.image(Phiw_Pic,caption=\"Phiweka Mthini\", width=150)\n\n\t\tnor_Pic =Image.open('resources/imgs/Rickie_pic.png') \n\t\tcol6.image(nor_Pic,caption=\"Nourhan ALfalous\", width=150)\n\n\t\t#Third row of picture \n\t\tcol7, col8,col9 = st.beta_columns(3)\n\n\t\tzin_Pic =Image.open('resources/imgs/zintle_pic.png') \n\t\tcol8.image(zin_Pic,caption='Zintle Faltein-Maqubela', width=150)\n\t\tcol8.header(\"Role : Team Supervisor\")\n\t\t\n\t# Building out the \"Information\" page\n\tif selection == \"Information\":\n\n\t\tst.info(\"General Information\");\n\t\t# You can read a markdown file from supporting resources folder\n\t\tst.markdown(\"Some information here\");\n\n\t\tst.subheader(\"Raw Twitter data and label\");\n\t\tif st.checkbox('Show raw data'): # data is hidden if box is unchecked\n\t\t\tst.write(raw[['sentiment', 'message']]) # will write the df to the page\n\n\t# Building out the predication page\n\tif selection == \"Prediction\":\n\t\n\n\t\t\n\t\t\n\t\tst.markdown(\"\")\n\t\tst.info('This page will help you predict an individual\\'s position on global warming base on their tweet')\n\t\tst.subheader('To make predictions, please follow the three steps below')\n\t\t\n\t\t#selecting input text\n\t\ttext_type_selection = ['Single tweet','multiple tweets'] \n\t\ttext_selection = st.selectbox('Step 1 ) : Text input', text_type_selection)\n\n\t\t\n\t\t#C\n\t\tdef get_keys(val,my_dict):\n\t\t\tfor key,value in my_dict.items():\n\t\t\t\tif val == value:\n\t\t\t\t\t return key\n\t\t#C\n\n\n\t\t# User selecting prediction model\n\t\t#Models = [\"Logistic regression\",\"Decision tree\",\"Random Forest Classifier\",\"Naive Bayes\",\"XGboost\",\"Linear SVC\"]\n\t\t#selected_model =st.selectbox(\"Step 3 ) : Choose prediction model \",Models )\n \n\n\t\tif text_selection== 'Single tweet':\n ### SINGLE TWEET CLASSIFICATION ###\n\t\t\t\n # Creating a text box for user input\n\t\t\tinput_text = st.text_area(\"Step 2 ) : Enter Your Single Text Below :\") ##user entering a single text to classify and predict\n\t\t\tModels = [\"Logistic regression\",\"Decision tree\",\"Random Forest\",\"Naive Bayes\",\"XGboost\",\"Linear SVC\" ]\n\t\t\tselected_model = st.selectbox(\"Step 3 ) : Choose prediction model \",Models)\n\t\t\tdef load_prediction_models(model_file):\n\n\t\t\t\tloaded_models = joblib.load(open(os.path.join(model_file),\"rb\"))\n\t\t\t\treturn loaded_models\n\n\t\t\tprediction_labels = {'Negative':-1,'Neutral':0,'Positive':1}\n\t\t\tif st.button(\"Classify\"):\n\t\t\t\t## showing the user original text\n\t\t\t\tst.text(\"Input tweet is :\\n{}\".format(input_text))\n\n\t\t\t\t## Calling a function to process the text\n\t\t\t\t#tweet_text = cleaner(input_text) \n\n\t\t\t\t# Transforming user input with vectorizer\n\t\t\t\tvect_text = tweet_cv.transform([input_text]).toarray()\n\t\t\t\t# Load your .pkl file with the model of your choice + make predictions\n\t\t\t\t# Try loading in multiple models to give the user a choice\n\t\t\t\tpredictor = joblib.load(open(os.path.join(\"resources/Logistic_regression.pkl\"),\"rb\"))\n\t\t\t\tprediction = predictor.predict(vect_text)\n\n \t#M Model_ Selection\n\t\t\t\tif selected_model == \"Logistic regression\":\n\n\t\t\t\t\tpredictor = load_prediction_models(\"resources/Logistic_regression.pkl\")\n\t\t\t\t\tprediction = predictor.predict(vect_text)\n \t # st.write(prediction)\n\t\t\t\telif selected_model == \"Decision tree\":\n\n\t\t\t\t\tpredictor = load_prediction_models(\"resources/Logistic_regression.pkl\")\n\t\t\t\t\tprediction = predictor.predict(vect_text)\n # st.write(prediction)\n\t\t\t\telif selected_model == \"Random Forest Classifier\":\n\t\t\t\t\tpredictor = load_prediction_models(\"resources/Logistic_regression.pkl\")\n\t\t\t\t\tprediction = predictor.predict(vect_text)\n # st.write(prediction)\n\t\t\t\telif selected_model == \"Naive Bayes\":\n\t\t\t\t\tpredictor = load_prediction_models(\"resources/Logistic_regression.pkl\")\n\t\t\t\t\tprediction = predictor.predict(vect_text)\n\t\t\t\telif selected_model ==\"XGboost\" :\n\t\t\t\t\t predictor = load_prediction_models(\"resources/Logistic_regression.pkl\")\n\t\t\t\t\t prediction = predictor.predict(vect_text)\n\t\t\t\telif selected_model == \"Linear SVC\" :\n\t\t\t\t\tpredictor = load_prediction_models(\"resources/Logistic_regression.pkl\")\n\t\t\t\t\tprediction = predictor.predict(vect_text)\n\t\t\t\t# st.write(prediction)\n\t\t\t # When model has successfully run, will print prediction\n\t\t\t # You can use a dictionary or similar structure to make this output\n\t\t\t # more human interpretable.\n\t\t\t # st.write(prediction)\n\t\t\t\tfinal_result = get_keys(prediction,prediction_labels)\n\t\t\t\tst.success(\"Tweet Categorized as : {}\".format(final_result))","def update_top_sources_lollipop(data):\n try:\n fig = get_lollipop_plot(data)\n except Exception as e:\n # Failsafe in case all values from dropdown are deleted\n fig = {}\n # logger.error(\"Top sources app error:\", e)\n return fig","def muckrack_trending_topics():\n\tresponse = requests.get('http://muckrack.com')\n\thtml = response.text\n\tdom = pq(html)\n\ttrending_list = dom('.trending')\n\treturn [topic.text for topic in trending_list]","def tips():\n category = list(mongo.db.tips.find().sort(\"tip_date\", -1))\n return render_template(\"tips.html\", category=category)","def showSideBar(self):\n\t\treturn False","def create_widgets(self):","def display_All_T_Confidence(self):\n for s in ('300', '310', '323', '343'):\n SVM = ResampleSVM(self.FormatDataFromCSV(\"PD_P1_SVM/Experimental Data P1/ER2_T\" + s + \".csv\"))\n self.DisplayTernaryPhaseScatter(\n self.FormatDataFromCSV(\"PD_P1_SVM/Experimental Data P1/ER2_T\" + s + \".csv\")[1], 10, s + \" Lab Data\")\n optimal = pd.read_csv(\"T\" + s + \"-OUT.csv\").values.tolist()[0][1:3]\n print(\"Optimal Parameters used: \")\n print(optimal)\n self.DisplayTernaryPhaseScatter(SVM.generate_all_phase(SVM.data_in[1], optimal[0], optimal[1], .01), 7,\n s + \" SVM Optimized\")\n data_in = pd.read_csv(\"Confidence\" + s + \".csv\").values.tolist()\n\n conf = list(map(lambda x: x[4], data_in))\n fig = go.Figure(go.Scatterternary({\n 'mode': 'markers',\n 'a': list(map(lambda x: x[3], data_in)),\n 'b': list(map(lambda x: x[2], data_in)),\n 'c': list(map(lambda x: x[1], data_in)),\n 'text': conf,\n 'marker': {\n 'symbol': 0,\n 'color': conf,\n 'size': 7,\n 'colorbar': dict(title=\"Colorbar\"),\n 'colorscale': \"Viridis\"}\n })\n )\n\n fig.update_layout(\n title=go.layout.Title(\n text=\"Confidence T-\" + s))\n plotly.offline.plot(fig, filename=\"ConfidenceT\" + s + \".html\")\n return","def index(request):\n template = loader.get_template('global.html')\n hot_topics = serializers.serialize(\"json\", Topic.objects.order_by('-updated_at')[:5])\n random_topic = serializers.serialize(\"json\", Topic.objects.order_by('?')[:1])\n\n context = {\n 'hot_topics': hot_topics,\n 'random_topic': random_topic,\n 'request': request,\n }\n return HttpResponse(template.render(context, request))","def get_trends(self, keywords, start, end, region=None, category=None):\n # If only a single keyword is given, i.e. as a string and not as a list, put the single string into a list\n if not isinstance(keywords, list):\n keywords = [keywords]\n # Convert the date strings to Google's format:\n start = adjust_date_format(\n start, self.date_format, self._google_date_format)\n end = adjust_date_format(\n end, self.date_format, self._google_date_format)\n # Create datetime objects from the date-strings:\n start_datetime = datetime.strptime(start, self._google_date_format)\n end_datetime = datetime.strptime(end, self._google_date_format)\n data_keywords_list = []\n for keywords_i in get_chunks(keywords, 5):\n # Get the trends over the entire sample:\n url_all_i = self.create_url(keywords_i,\n previous_weekday(start_datetime, 0), next_weekday(\n end_datetime, 6),\n region, category)\n data_all_i, frequency_i = self.get_data(url_all_i)\n # If the data for the entire sample is already at the daily frequency we are done. Otherwise we need to\n # get the trends for sub-periods\n if frequency_i == 'Daily':\n data_i = data_all_i\n else:\n # Iterate over the URLs of the sub-periods and retrieve the Google Trend data for each\n data_time_list = []\n for url in self.create_urls_subperiods(keywords_i, start_datetime, end_datetime, region, category):\n data_time_list.append(self.get_data(url)[0])\n # Concatenate the so obtained set of DataFrames to a single DataFrame\n data_i = concat_data(\n data_time_list, data_all_i, keywords_i, frequency_i)\n # Add the data for the current list of keywords to a list\n data_keywords_list.append(data_i)\n # Merge the multiple keyword chunks\n data = merge_keyword_chunks(data_keywords_list)\n # Cut data to return only the desired period:\n data = data.loc[data.index.isin(pd.date_range(\n start_datetime, end_datetime, freq='D'))]\n return data","def interactive_annual_monthly_bar_report_with_buttons(year: int):\n condition_dict = {'key': 'category', 'operation': '!=', 'value': 16}\n average_monthly_amount = Spending().get_average_monthly_spending_amount(condition_dict=condition_dict)\n current_annual_monthly_amount = Spending().get_monthly_total_spending_of_a_year(year, include_doctor=False)\n\n df_average_monthly = pd.DataFrame(average_monthly_amount, columns=['avg', 'mon'])\n df_current_annual_monthly = pd.DataFrame(current_annual_monthly_amount, columns=['sum', 'mon'])\n\n fig = go.Figure()\n\n fig.add_trace(\n go.Bar(\n x=df_current_annual_monthly['mon'],\n y=df_current_annual_monthly['sum'],\n name=f'{year} monthly spending'\n )\n )\n\n fig.add_trace(\n go.Scatter(\n x=df_average_monthly['mon'],\n y=df_average_monthly['avg'],\n name='Average Monthly Spending',\n visible=True,\n line=dict(color=\"#33CFA5\", dash=\"dash\")\n )\n )\n\n fig.update_layout(\n updatemenus=[\n dict(\n type='buttons',\n direction='right',\n active=1,\n x=0.57,\n y=1.2,\n buttons=list([\n dict(\n label='Current Year',\n method='update',\n args=[\n {'visible': [True, False]},\n {'title': f'{year} monthly spending', \"annotations\": []}\n ]\n ),\n dict(\n label='Average',\n method='update',\n args=[\n {'visible': [True, True]},\n {'title': 'Compare with Average monthly', \"annotations\": []}\n ]\n ),\n ]),\n )\n ]\n )\n\n fig.update_layout(\n {\n 'width': 960,\n 'height': 550\n },\n overwrite=True\n )\n\n aPlot = plotly.offline.plot(fig,\n config={\"displayModeBar\": False},\n show_link=False,\n include_plotlyjs=False,\n output_type='div')\n\n return aPlot","def menu_python_daily(self, event=None):\n self.link('http://www.pythonware.com/daily/')","def admin_sidebar(context):\n request = context.get('request')\n\n request_context = {\n 'count_users': User.objects.count(),\n 'count_review_groups': Group.objects.count(),\n 'count_default_reviewers': DefaultReviewer.objects.count(),\n 'count_oauth_applications': Application.objects.count(),\n 'count_repository': Repository.objects.accessible(\n request.user, visible_only=False).count(),\n 'count_webhooks': WebHookTarget.objects.count(),\n 'count_hosting_accounts': HostingServiceAccount.objects.count(),\n 'version': get_version_string(),\n }\n\n # We're precomputing URLs in here, rather than computing them in the\n # template, because we need to always ensure that reverse() will be\n # searching all available URL patterns and not just the ones bound to\n # request.current_app.\n #\n # current_app gets set by AdminSite views, and if we're in an extension's\n # AdminSite view, we'll fail to resolve these URLs from within the\n # template. We don't have that problem if calling reverse() ourselves.\n request_context.update({\n 'url_%s' % url_name: reverse('admin:%s' % url_name)\n for url_name in ('auth_user_add',\n 'auth_user_changelist',\n 'hostingsvcs_hostingserviceaccount_add',\n 'hostingsvcs_hostingserviceaccount_changelist',\n 'notifications_webhooktarget_add',\n 'notifications_webhooktarget_changelist',\n 'oauth_application_add',\n 'oauth_application_changelist',\n 'reviews_defaultreviewer_add',\n 'reviews_defaultreviewer_changelist',\n 'reviews_group_add',\n 'reviews_group_changelist',\n 'scmtools_repository_add',\n 'scmtools_repository_changelist')\n })\n\n return RequestContext(request, request_context)","def CLI_trend( RFinfo,var, den, fireyear=False, plot=True, testmethod=\"OLS\"):\n\n\t# warn.warn(\n\t# \t'''\n\t# \tThis is currently only in alpha testing form\n\t# \ti'm going to using a simple trend test without\n\t# \tany consideration of significance. i used cdo\n\t# \tregres on copernicious NDVI data to start. \n\t# \t''')\n\t\n\t# warn.warn(\n\t# \t'''\n\t# \tThis approach is currently ambivilant to when fire \n\t# \tevents occured. This may need to be incoperated with \n\t# \tsome form of breakpoint detection (Chow test or MVregression)\n\t# \t''')\n\n\t# ========== Load in the trend data using xarray ==========\n\tif fireyear:\n\t\tif var == \"ppt\":\n\t\t\tncin = \"./data/cli/1.TERRACLIMATE/TerraClimate_merged_1980to2017_ppt_yearsum_RUSSIA.nc\"\n\t# \t# COnsidering the fireyear\n\t# \tncin = \"./data/veg/COPERN/%s_anmax_Russia.nc\" % var\n\telse:\n\t\t# \t# Only looking at years since the fire\n\t\tif var == \"ppt\":\n\t\t\tnctrend = \"./data/cli/1.TERRACLIMATE/TerraClimate_merged_1980to2017_ppt_yearsum_RUSSIA_cdoregres.nc\"\n\t\t\tdst = xr.open_dataset(nctrend)\n\t\t\tncin = \"./data/cli/1.TERRACLIMATE/TerraClimate_merged_1980to2017_ppt_yearsum_RUSSIA.nc\"\n\t\t# ncin = \"./data/veg/COPERN/%s_anmax_Russia_cdoregres.nc\" % var\n\tds = xr.open_dataset(ncin)\n\n\n\t# ========== Find the recuitment failure in the netcdf ==========\n\tif fireyear:\n\t\t# warn.warn('''\n\t\t# \tTheilsen implemented quickly. check in the future \n\t\t# \t''')\n\t\t# RFshort = RFinfo[RFinfo.fireyear<=2012] \n\t\ttestnm = \"Four Year Mean Postfire anomoly\" \n\t\tCLtrend = []\n\t\tfor index, row in RFinfo.iterrows():\n\t\t\tif row.fireyear<=2013:\n\t\t\t\tarray = ds[var].sel(\n\t\t\t\t\t{\"lat\":row.lat, \"lon\":row.lon},\n\t\t\t\t\tmethod=\"nearest\")#.sel(time=slice(sty, '2017-12-31'))\n\t\t\t\tCLtrend.append(ClimateAnom(array, row.fireyear))\n\t\t\telse:\n\t\t\t\tCLtrend.append(np.NAN)\n\n\telse:\n\t\ttestnm = \"1999-2017 OLS slope\"\n\t\tCLtrend = [float(ds[var].sel(\n\t\t\t{\"lat\":row.lat, \"lon\":row.lon}, method=\"nearest\").values) for index, row in RFinfo.iterrows()]\n\n\tRFinfo[\"CLtrend\"] = CLtrend\n\tRFinfo.dropna(inplace=True, subset=['sn', 'lat', 'lon', den, 'RF17', 'CLtrend'])\n\n\t\n\tslope, intercept, r_value, p_value, std_err = stats.linregress(x=RFinfo[den], y=RFinfo.CLtrend)\n\t# r2val = r_val**2\n\ttau, p_value = stats.kendalltau(x=RFinfo[den], y=RFinfo.CLtrend)\n\tprint(var, den, testnm)\n\tprint(\"r-squared:\", r_value**2)\n\tprint(\"kendalltau:\", tau)\n\t# make a quick plot\n\n\tif plot:\n\t\t# ========== Map of the regional trend data ==========\n\t\t# build the figure and grid\n\t\tif not fireyear:\n\t\t\tplt.figure(num=var+r\"$_{max}$\"+\" trend\", figsize=(12, 6))\n\t\t\tax = plt.axes(projection=ccrs.PlateCarree())\n\t\t\tax.add_feature(cpf.BORDERS, linestyle='--', zorder=102)\n\t\t\tax.add_feature(cpf.LAKES, alpha=0.5, zorder=103)\n\t\t\tax.add_feature(cpf.RIVERS, zorder=104)\n\t\t\t# add lat long linse\n\t\t\tgl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n\t\t\t\tlinewidth=1, color='gray', alpha=0.5, linestyle='--')\n\t\t\tgl.xlabels_top = False\n\t\t\tgl.ylabels_right = False\n\t\t\tgl.xformatter = LONGITUDE_FORMATTER\n\t\t\tgl.yformatter = LATITUDE_FORMATTER\n\n\t\t\t# ========== create the colormap ==========\n\t\t\tcmap = mpc.ListedColormap(palettable.cmocean.diverging.Balance_20_r.mpl_colors)\n\t\t\t# if var == \"NDVI\":\n\t\t\t# \tvmin = -0.02\n\t\t\t# \tvmax = 0.02\n\t\t\t# elif var ==\"LAI\":\n\t\t\t# \tvmin = -0.2\n\t\t\t# \tvmax = 0.2\n\t\t\t# ========== Make the map ==========\n\t\t\tdst[var].plot(ax=ax, transform=ccrs.PlateCarree(),\n\t\t\t\tcmap=cmap, cbar_kwargs={\"extend\":\"both\"})#, vmin=vmin, vmax=vmax)\n\n\t\t\t# ========== Add site markers ==========\n\t\t\t# ipdb.set_trace()\n\t\t\tfor vas, cl in zip(RFinfo.RF17.unique().tolist(), ['yx', \"r*\",\"k.\"]):\n\t\t\t\tax.plot(RFinfo[RFinfo.RF17 == vas].lon.values, RFinfo[RFinfo.RF17 == vas].lat.values, \n\t\t\t\t\tcl, markersize=8, transform=ccrs.PlateCarree())\n\t\t\tplt.show()\n\n\t\t# ========== Scatter plot of the trend vs field data ==========\n\t\tpp = sns.lmplot( x=den, y=\"CLtrend\", \n\t\t\tdata=RFinfo, fit_reg=False, \n\t\t\thue='RF17', height=4, aspect=2)\n\t\t# plt.title('Trend in %smax vs %s' %(var, den))\n\t\taxf = pp.axes[0,0]\n\t\taxf.text(0.85,0.85,\"R_squ: %1.5f \\n K_tau: %1.5f\" % (r_value**2, tau), transform=axf.transAxes)\n\t\tpp.fig.canvas.set_window_title('%s (%smax) vs %s' %(name, var, den))\n\t\tplt.savefig(\"./plots/%s_%s_%s.pdf\" % (name,var,den))\n\t\tplt.savefig(\"./plots/%s_%s_%s.png\" % (name,var,den))\n\t\tplt.show()\n\t\t# ipdb.set_trace()\n\treturn r_value**2, tau","def run():\n #Initialise variables\n data = build_station_list()\n update_water_levels(data)\n ls = []\n ID = []\n \n #Number of days in past taken data from\n dt = 7\n #How many graphs per window\n limit = 4\n #How many stations\n number = 6\n \n #Create list of measuring_id's sorted by water level\n for station in data:\n if station.typical_range_consistent() == True and station.relative_water_level() != None:\n ls.append((station, station.relative_water_level()))\n\n ls = sorted_by_key(ls, 1)\n \n for station in ls:\n ID.append(station[0])\n \n s = count_inconsistent_sets(ID[:number], dt)\n \n ID = ID[:number+s]\n\n plot_water_levels(ID, dt, limit, s)","def create_widgets( self ):","def handle_trending_hashtags(api_pipeline, trending_hashtags_from_db):\n\n trending_hashtags = api_pipeline.get_top_hashtags_worldwide()\n trending_hashtags.sort(key=lambda hashtag: hashtag.tweet_volume, reverse=True)\n trending_hashtags_chart = PlotPainter.plot_hashtags(trending_hashtags)\n trending_hashtags_from_db_today = trending_hashtags_from_db.filter(save_date=datetime.datetime.today().date())\n for trending_hashtag in trending_hashtags:\n if trending_hashtag not in trending_hashtags_from_db_today:\n trending_hashtag.save()\n return trending_hashtags, trending_hashtags_chart","def get_sidebar_layout(self):\n return [\n dbc.Card(children=self.get_sidebar_filters(), body=True),\n dbc.Card(children=self.get_sidebar_filter_save(), body=True, className=\"mt-2\"),\n dbc.Card(children=self.get_sidebar_saved_filter_names(), body=True, className=\"mt-2\")\n ]","def __init__(self):\r\n self.tweets = []\r\n self.lcs = \"outliers\"\r\n self.importance = 0","def update_tweets_feed(n):\n \n # Retrieve the tweets\n first_tweet = get_value(df_1t, n)\n second_tweet = get_value(df_2t, n) \n third_tweet = get_value(df_3t, n)\n fourth_tweet = get_value(df_4t, n)\n fifth_tweet = get_value(df_5t, n)\n sixth_tweet = get_value(df_6t, n)\n seventh_tweet = get_value(df_7t, n)\n eighth_tweet = get_value(df_8t, n)\n nineth_tweet = get_value(df_9t, n)\n tenth_tweet = get_value(df_10t, n) \n \n # Compute the sentiment of each tweet\n sa_first_tweet = sentiment_analyzer_scores(first_tweet)\n sa_second_tweet = sentiment_analyzer_scores(second_tweet)\n sa_third_tweet = sentiment_analyzer_scores(third_tweet)\n sa_fourth_tweet = sentiment_analyzer_scores(fourth_tweet)\n sa_fifth_tweet = sentiment_analyzer_scores(fifth_tweet)\n sa_sixth_tweet = sentiment_analyzer_scores(sixth_tweet)\n sa_seventh_tweet = sentiment_analyzer_scores(seventh_tweet)\n sa_eighth_tweet = sentiment_analyzer_scores(eighth_tweet)\n sa_nineth_tweet = sentiment_analyzer_scores(nineth_tweet)\n sa_tenth_tweet = sentiment_analyzer_scores(tenth_tweet)\n \n # Return the tweet contents and a pie graph of the sentiment.\n \n return html.Div([\n html.Div([\n\n# First Tweet\n html.Div([\n html.Div([\n html.Pre(str(first_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '2px 2px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px',\n }\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_first_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\", }\n ),\n ], \n className = 'row' \n ),\n \n# Second Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(second_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_second_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n \n # Third Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(third_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_third_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n \n # Fourth Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(fourth_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_fourth_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n\n\n # Fifth Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(fifth_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_fifth_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n \n\n # Sixth Tweet\n html.Div([\n html.Div([\n html.Pre(str(sixth_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_sixth_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n \n # Seventh Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(seventh_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n \n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_seventh_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n\n # Eighth Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(eighth_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n \n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_eighth_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n\n # Nineth\n \n html.Div([\n html.Div([\n html.Pre(str(nineth_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_nineth_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n\n # Tenth Tweet\n \n html.Div([\n html.Div([\n html.Pre(str(tenth_tweet)),\n ], \n className = 'ten columns',\n style = {\n 'backgroundColor': 'white',\n 'box-shadow': '3px 3px 10px #ccc',\n 'padding': '10px',\n 'padding-bottom': '25px',\n 'margin': '30px',\n 'overflowX': 'scroll',\n 'fontSize': '22px'}\n ),\n html.Div([\n dcc.Graph(figure = piegraph_asset(sa_tenth_tweet))\n ],\n className = 'nine columns',\n style = {\"padding-left\": \"550px\"}\n ),\n ], \n className = 'row' \n ),\n ], style = {'overflowY': 'scroll', 'overflowX': 'hidden',\n 'maxHeight': '105ex', 'backgroundColor' : '#eaeaea'}\n ),\n \n ])","def get_google_trends_data(keyword, from_date, to_date):\r\n \r\n from_year, from_month = datetime.date.fromisoformat(from_date).year, datetime.date.fromisoformat(from_date).month\r\n to_year, to_month = datetime.date.fromisoformat(to_date).year, datetime.date.fromisoformat(to_date).month\r\n\r\n data = dailydata.get_daily_data(keyword, from_year, from_month, to_year, to_month)\r\n \r\n return data[keyword]","def get_index_trend():\n symbol = request.args.get('symbol', type=str)\n today = datetime.date.today()\n last_year_today = today - datetime.timedelta(days=365)\n data, series = {}, {'data' : [] }\n if symbol is not None:\n data = te.fetchMarkets(\n symbol=symbol,initDate=last_year_today.strftime('%Y-%m-%d'), endDate=today.strftime('%Y-%m-%d')\n ).dropna()\n data['date'] = list(map(lambda x: x.strftime('%m/%d/%Y'), data.index.tolist()))\n return jsonify(data.to_dict(orient='records'))","def get_trend_urls():\n\n trends = []\n twitter = 'http://search.twitter.com/'\n tmp = 'tmp' + str(random.randint(0,1000))\n os.system('wget %s --output-document=%s' % (twitter, tmp))\n with open(tmp) as f:\n for line in f:\n if 'a href' in line and 'search?q' in line:\n trends.append(twitter\n + line.split('a href=\\\"/')[1].split('\\\"')[0])\n os.system('rm %s' % tmp)\n return trends","def general_timeline():\n return render_template('timeline.html', general=True, show_username=True)","def view_category(category_id, date_string):\n\n if date_string.startswith('<<'):\n format = \"<< %d.%m.%Y\"\n elif date_string.endswith('>>'):\n format = \"%d.%m.%Y >>\"\n else:\n format = None\n date = datetime.today()\n\n if format:\n date = scraper.strptime(date_string, format)\n\n url = \"http://www.ruv.is/sarpur/app/json/{0}/{1}\".format(category_id, date.strftime(\"%Y%m%d\"))\n shows = json.loads(requests.get(url).content)\n\n day_before = date + timedelta(days=-1)\n next_day = date + timedelta(days=1)\n INTERFACE.add_dir(u\"<< {0}\".format(day_before.strftime(\"%d.%m.%Y\")),\n 'view_category',\n category_id)\n INTERFACE.add_dir(\"{0} >>\".format((next_day.strftime(\"%d.%m.%Y\"))),\n 'view_category',\n category_id)\n\n for show in shows['events']:\n ev = show['event']\n showtime = datetime.fromtimestamp(float(ev['start_time']))\n end_time = datetime.fromtimestamp(float(ev['end_time']))\n in_progress = showtime < datetime.now() < end_time\n duration = (end_time - showtime).seconds / 60\n\n title = u\"{1} - {0}\".format(\n ev['title'],\n in_progress and u\"[COLOR blue]Í GANGI[/COLOR]\" or showtime.strftime(\"%H:%M\"))\n original_title = ev.get('orginal_title')\n description = ev.get('description', '').strip()\n if original_title and description:\n plot = u\"{0} - {1}\".format(original_title, description)\n elif description:\n plot = description\n elif original_title:\n plot = original_title\n else:\n plot = u\"\"\n\n meta = {\n 'TVShowTitle': title,\n 'Episode': ev['episode_number'],\n 'Premiered': showtime.strftime(\"%d.%m.%Y\"),\n 'TotalEpisodes': ev['number_of_episodes'],\n 'Plot': plot,\n 'Duration': duration,\n 'fanart_image': ev.get('picture')\n }\n\n if in_progress:\n INTERFACE.add_item(title,\n 'play_live',\n category_id,\n image=ev.get('picture'),\n extra_info=meta)\n\n elif ev.get('media'):\n INTERFACE.add_item(title,\n 'play_file',\n ev.get('media'),\n image=ev.get('picture'),\n extra_info=meta)\n else:\n INTERFACE.add_item(title,\n 'play_url',\n ev.get('url'),\n image=ev.get('picture'),\n extra_info=meta)","def burn_in_selector(mcmc_tables: List[pd.DataFrame]):\n min_length = min([len(t) for t in mcmc_tables])\n return st.sidebar.slider(\"Burn-in\", 0, min_length, 0)","def plot(self, context=None):\n\n response = requests.get(self.url).content\n table = pd.read_html(response, attrs={\"id\": \"main_table_countries_today\"})\n df = table[0].fillna(0)\n # df.drop(df.index[0], inplace=True) # World\n df.drop([\"ActiveCases\", 'Serious,Critical', 'Serious,Critical', 'Deaths/1M pop', 'Tests/ 1M pop'], axis=1, inplace=True)\n df.drop(df.columns[6], axis=1, inplace=True)\n\n if len(context) > 3:\n context = context.lower().capitalize()\n df = df.loc[df[\"Country,Other\"] == context]\n if 4 > len(context) > 1:\n context = context.upper()\n df = df.loc[df[\"Country,Other\"] == context]\n if len(context) <= 1:\n df = df[1:]\n\n C_Names = df[\"Country,Other\"].head(n=10).values.tolist()\n T_Cases = df[\"TotalCases\"].head(n=10).values.tolist()\n # N_Cases = df[\"NewCases\"].head(n=10).values.tolist() # not plotted\n T_Deaths = df[\"TotalDeaths\"].head(n=10).values.tolist()\n # N_Deaths = df[\"NewDeaths\"].head(n=10).values.tolist() # not plotted\n T_Recovered = df[\"TotalRecovered\"].head(n=10).values.tolist()\n T_Tests = df[\"TotalTests\"].head(n=10).values.tolist()\n\n x = np.arange(len(C_Names))\n width = 0.20\n\n fig, ax = plt.subplots()\n\n ax.bar(x - 0.30, T_Cases, width, label='TotalCases', color=\"Blue\")\n ax.bar(x - 0.10, T_Deaths, width, label='TotalDeaths', color=\"Red\")\n ax.bar(x + 0.10, T_Tests, width, label='TotalTests', color=\"Green\")\n ax.bar(x + 0.30, T_Recovered, width, label='TotalRecovered', color=\"Orange\")\n\n if len(context) > 1:\n ax.set_title(\"{}'s Situation\".format(context))\n else:\n ax.set_title(\"World's Top10 Situation\")\n\n ax.set_xticks(x)\n ax.set_xticklabels(C_Names)\n ax.legend()\n plt.ticklabel_format(style='plain', axis=\"y\")\n fig.set_size_inches(18.5, 10.5)\n fig.tight_layout()\n plt.grid()\n\n if len(context) > 1:\n font1 = {'family': 'serif',\n 'color': 'blue',\n 'weight': 'bold',\n 'size': 20}\n font2 = {'family': 'serif',\n 'color': 'red',\n 'weight': 'normal',\n 'size': 20}\n font3 = {'family': 'serif',\n 'color': 'green',\n 'weight': 'normal',\n 'size': 20}\n font4 = {'family': 'serif',\n 'color': 'orange',\n 'weight': 'normal',\n 'size': 20}\n\n # bbox=dict(facecolor='black', alpha=0.5)\n plt.text(0.863, 0.67, \"Total Cases:\\n{:,}\".format(int(T_Cases[0])), fontdict=font1, transform=ax.transAxes)\n plt.text(0.863, 0.57, \"Total Deaths:\\n{:,}\".format(int(T_Deaths[0])), fontdict=font2, transform=ax.transAxes)\n plt.text(0.863, 0.47, \"Total Tests:\\n{:,}\".format(int(T_Tests[0])), fontdict=font3, transform=ax.transAxes)\n plt.text(0.863, 0.37, \"Total Recovered:\\n{:,}\".format(int(T_Recovered[0])), fontdict=font4, transform=ax.transAxes)\n\n # plt.savefig('corona.png') # Uncomment it to save the figure\n plt.show()","def action_atom_site_l(obj: AtomSiteL, thread: QtCore.QThread):\n w_actions = []\n if obj.is_attribute(\"type_symbol\"):\n qtb_1 = QtWidgets.QToolButton()\n qtb_1.setText(\"Show b_scat\")\n qtb_1.clicked.connect(lambda: run_function(\n obj.report, (), thread))\n w_actions.append(qtb_1)\n return w_actions","def __r7(soup):\n news = []\n ps = soup.find_all('p', class_='trends-thermometer-description')\n i = 0\n\n for p in ps:\n if i == 6: # Six trending topics\n break\n i += 1\n a = p.parent.parent.parent.a\n news.append(dict(title=a['title'], link=a['href']))\n return news","def charts(self,req):\n self.player.overviewing=True","def setWidget(self, widget: QtWidgets.QWidget):\n super().setWidget(widget)\n if globalstuff.theme == 'dark':\n w = self.widget()\n w.setPalette(globalstuff.textpal)\n if hasattr(w, 'TreeWidget'):\n w.TreeWidget.setStyleSheet(globalstuff.treeqss)","def get_top_trends_from_twitter(country='Japan', exclude_hashtags=False, debug=False, cache_duration_mins=15, append_db=True):\n # load main db\n cache_db = load_db(database_path=db_path, debug=False)\n trends_db = cache_db['trends']\n\n if exclude_hashtags:\n trends_cache = trends_db['exclude_hashtags']\n else:\n trends_cache = trends_db['include_hashtags']\n\n # load trends + top retweets db\n trend_search_db = load_db(database_path=trends_db_path, debug=False)\n\n\n\n # MAIN_DB ONLY\n try:\n db_timestamp = str_2_datetime(trends_cache['timestamp'], input_format=time_format_full_with_timezone)\n except ValueError:\n db_timestamp = str_2_datetime(trends_cache['timestamp'], input_format=time_format_full_no_timezone)\n db_timestamp = db_timestamp.astimezone(tz=pytz.utc)\n\n rq_timestamp = datetime.datetime.now(tz=pytz.utc)\n\n time_diff = rq_timestamp - db_timestamp\n print('time since last trends API call: {} (h:m:s)'.format(time_diff))\n print('time diff in seconds: {}'.format(time_diff.seconds))\n print('time in db: {}'.format(db_timestamp))\n print('time in rq: {}'.format(rq_timestamp))\n\n if time_diff.seconds < cache_duration_mins*60:\n print('less than cache duration, returning cache')\n output_json = json.dumps(trends_cache['content'], ensure_ascii=False)\n return output_json\n else:\n output_json, img_output_json = get_top_trends_from_twitter_api(country=country, exclude_hashtags=exclude_hashtags)\n # update\n output_list = json.loads(output_json)\n trend_search_list = json.loads(img_output_json)\n\n if append_db:\n output_list = trends_cache['content'] + output_list\n trend_search_list = trend_search_db['trends'] + trend_search_list\n \n if exclude_hashtags:\n cache_db['trends']['exclude_hashtags']['content'] = output_list\n cache_db['trends']['exclude_hashtags']['timestamp'] = datetime_2_str(rq_timestamp, output_format=time_format_full_with_timezone)\n else:\n cache_db['trends']['include_hashtags']['content'] = output_list\n cache_db['trends']['include_hashtags']['timestamp'] = datetime_2_str(rq_timestamp, output_format=time_format_full_with_timezone)\n\n trend_search_db['trends'] = trend_search_list\n\n update_db(cache_db, database_path=db_path, debug=debug)\n update_db(trend_search_db, database_path=trends_db_path, debug=debug)\n\n print('trends and image database updated.')\n\n del cache_db\n del trends_db\n del trends_cache\n del trend_search_db\n del trend_search_list\n del output_list\n del output_json\n del img_output_json\n\n print('memory freed.')","def update_chart():\n selected_team = variable.get()\n selected_period = period_variable.get()\n if selected_period == \"All\":\n selected_period = -1\n elif selected_period == \"OT\":\n selected_period = 4\n selected_id = -1\n if selected_team == home_team:\n selected_id = data[\"data\"][\"game\"][\"hometeamid\"]\n elif selected_team == away_team:\n selected_id = data[\"data\"][\"game\"][\"awayteamid\"]\n clear_events()\n for i in range(0, len(plays)):\n add_to_chart(plays[i], {\"teamid\":selected_id, \"period\":selected_period})"],"string":"[\n \"def getTrends(): \\n api = authentication()\\n names = [i.name for i in api.GetTrendsCurrent()]\\n stringTrends = [i.strip('#') for i in names and ]\\n trends = [i for i in stringTrends if i != \\\"\\\"]\\n return trends\",\n \"def get_trends():\\n return api.trends_available()\",\n \"def trending(request):\\n\\titems = Item.objects.all()\\n\\ttrending = []\\n\\n\\tfor item in Item.objects.order_by('-dailyVisits'):\\n\\t\\t#Include items that have been uploaded within the past day and havent been sold\\n\\t\\tif (date.today() - item.datePosted).days <= 0 and item.sold_to == None:\\n\\t\\t\\tif (len(trending) <= 5):\\n\\t\\t\\t\\ttrending.append(item)\\n\\t\\telse:\\n\\t\\t\\titem.dailyVisits = 0\\n\\t\\t\\titem.save()\\n\\n\\t#If there are not enough items in the trending list, add older items to the list\\n\\tif len(trending) <= 5:\\n\\t\\tfor item in Item.objects.order_by('-dailyVisits'):\\n\\t\\t\\tif ((len(trending) <= 5) and (item.sold_to == None) and (item not in trending)):\\n\\t\\t\\t\\ttrending.append(item)\\n\\n\\tcontext_dict = {\\\"trendingItems\\\": trending[0:3], \\\"search_bar\\\" :Search_bar()}\\n\\treturn render(request, 'tailored/index.html', context_dict)\",\n \"def page_dashboard(state):\\n\\n st.title(\\\":chart_with_upwards_trend: Prediction Results Dashboard\\\")\\n\\n st.markdown(\\\"# Select Stocks to View Results:\\\")\\n if state.finalized_data:\\n for stock_data in state.finalized_data:\\n st.write(\\\"---\\\")\\n st.markdown(\\\"## \\\" + stock_data[\\\"stock\\\"])\\n if st.checkbox(\\\"View Results for \\\" + stock_data[\\\"stock\\\"]):\\n\\n ############################################\\n\\n st.markdown(\\\"### Historical Predictions:\\\")\\n\\n df2 = pd.DataFrame.from_dict(stock_data[\\\"prev_predictions\\\"])\\n\\n select_lbl = (\\n \\\"Enter the names of models for \\\" + stock_data[\\\"stock\\\"] + \\\":\\\"\\n )\\n models_selections = st.multiselect(\\n label=select_lbl,\\n options=df2.columns,\\n ) # allow users to display specific model results on dataframe graph\\n\\n if not models_selections: # if nothing is selected show all models!\\n st.line_chart(df2)\\n else:\\n st.line_chart(df2[models_selections])\\n\\n st.markdown(\\n \\\"*Note:* 'Prices' are the actual prices for those days. The rest are model predictions for those days.\\\\nPrices (in USD) are on the y-axis, the day number in the data is on the x-axis.\\\"\\n )\\n\\n ############################################\\n\\n st.markdown(\\\"### Future (Next-Day) Predictions:\\\")\\n\\n df = pd.DataFrame()\\n df = df.append(\\n pd.DataFrame(\\n [stock_data[\\\"prediction_results\\\"][\\\"swing_predictions\\\"]]\\n )\\n )\\n df = df.append(\\n pd.DataFrame(\\n [stock_data[\\\"prediction_results\\\"][\\\"next_day_predictions\\\"]]\\n )\\n )\\n df = df.append(\\n pd.DataFrame([stock_data[\\\"prediction_results\\\"][\\\"model_scores\\\"]])\\n )\\n\\n df.index = [\\n \\\"Swing Predicton\\\",\\n \\\"Price Prediction ($)\\\",\\n \\\"Model Fit Score\\\",\\n ]\\n df = df.transpose()\\n df # display chart\\n\\n st.markdown(\\n \\\"- The current price of the stock is *$\\\"\\n + str(\\n round(stock_data[\\\"prediction_results\\\"][\\\"current_prev_close\\\"], 2)\\n )\\n + \\\"*.\\\"\\n )\\n\\n if state.period == \\\"1mo\\\":\\n st.markdown(\\\"- *Recommended Model (for 1mo):* SVR-RBF\\\")\\n st.markdown(\\n \\\"- *View the homescreen for more model & dataset size combination recommendations.*\\\"\\n )\\n elif state.period == \\\"6mo\\\":\\n st.markdown(\\n \\\"- *Recommended Model (for 6mo):* SVR-Poly (most recommended), LR, EN, or Lasso.\\\"\\n )\\n st.markdown(\\n \\\"- *View the homescreen for more model & dataset size combination recommendations.*\\\"\\n )\\n elif state.period == \\\"1y\\\":\\n st.markdown(\\\"- *Recommended Model (for 1yr):* SVR-Poly\\\")\\n st.markdown(\\n \\\"- *View the homescreen for more model & dataset size combination recommendations.*\\\"\\n )\\n else:\\n st.markdown(\\n \\\"- *Note:* View the home screen for information about the best models and training data size combinations.\\\"\\n )\\n\\n ############################################\\n st.markdown(\\\"### View Other Information:\\\")\\n\\n if st.checkbox(\\n \\\"View \\\" + stock_data[\\\"stock\\\"] + \\\"'s Model Efficiency Timings\\\"\\n ):\\n st.markdown(\\\"#### Model Efficiencies:\\\")\\n st.markdown(\\n \\\"Shows the time in seconds it took models to complete specific tasks:\\\"\\n )\\n df3 = pd.DataFrame()\\n df3 = df3.append(\\n pd.DataFrame(\\n [stock_data[\\\"prediction_results\\\"][\\\"training_times\\\"]]\\n )\\n )\\n df3 = df3.append(\\n pd.DataFrame(\\n [stock_data[\\\"prediction_results\\\"][\\\"testing_times\\\"]]\\n )\\n )\\n df3 = df3.append(\\n pd.DataFrame(\\n [stock_data[\\\"prediction_results\\\"][\\\"new_predictions_times\\\"]]\\n )\\n )\\n df3 = df3.append(\\n pd.DataFrame(\\n [stock_data[\\\"prediction_results\\\"][\\\"prev_predictions_times\\\"]]\\n )\\n )\\n df3.index = [\\n \\\"Training\\\",\\n \\\"Testing/Scoring\\\",\\n \\\"Future Predictions\\\",\\n \\\"Historical Predictions\\\",\\n ]\\n df3 = df3.transpose()\\n df3\\n\\n ############################################\\n\\n if st.checkbox(\\\"View \\\" + stock_data[\\\"stock\\\"] + \\\"'s Information\\\"):\\n st.markdown(\\\"#### Company Information:\\\")\\n for key in stock_data[\\\"stock_info\\\"].keys():\\n st.write(\\\"*\\\", key + \\\":\\\", stock_data[\\\"stock_info\\\"][key])\\n else:\\n st.markdown(\\n \\\"## Generate data to populate and initialize this page by going to the 'Settings' page and running the tool!\\\"\\n )\",\n \"def trends(max: int = None, until: str = None):\\n for post in client.trends(max=max, until=until):\\n print(json.dumps(post))\",\n \"def add_trends(self, doc=None):\\n if doc is None:\\n doc = self.doc\\n\\n trend = Trends(self.palette['trends'])\\n doc.add_root(trend.layout())\\n LOG.info('trends added')\\n return doc\",\n \"def trendingTweets():\\n api = twitter.Api()\\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\\n for topic in trending_topics:\\n topicSearchTerm = topic.name\\n trending_tweets = api.GetSearch(topicSearchTerm)\\n for tweet in trending_tweets:\\n util.safe_print(tweet.GetText())\\n # pass\",\n \"def get_vax_trends(area=None):\\n data = get_vax_trends_data(area)\\n trends = []\\n for d in VAX_DOSES:\\n last_week_dt = data[6]['_id']\\n count = data[0][d]\\n last_week_count = data[6][d]\\n diff = count - last_week_count\\n if diff > 0:\\n status = 'increase'\\n elif diff < 0:\\n status = 'decrease'\\n else:\\n status = 'stable'\\n try:\\n perc = f'{round(diff / last_week_count * 100)}%'\\n except (ValueError, ZeroDivisionError):\\n perc = 'n/a'\\n trends.append({\\n 'id': d,\\n 'last_week_count': format_number(last_week_count),\\n 'percentage': perc,\\n 'title': VARS[d]['title'],\\n 'colour': VARS[d][status]['colour'],\\n \\\"icon\\\": VARS[d]['icon'],\\n 'status_icon': VARS[d][status]['icon'],\\n 'count': format_number(count),\\n 'last_week_dt': format_datetime(last_week_dt, DOW_FMTY)\\n })\\n return trends\",\n \"def trendingTopics():\\n api = twitter.Api()\\n\\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\\n for topic in trending_topics:\\n util.safe_print(topic.name)\",\n \"def get_trending():\\n trending = r.get('trending_store')\\n return trending\",\n \"def trend_click(self, instance):\\n if self.change_top_graph:\\n self.top_plot.current_param = instance.text()\\n self.change_top_graph = False\\n for header in self.headers:\\n if self.top_plot.current_param in header:\\n self.top_plot.setLabel(axis='left', text=header)\\n else:\\n self.bottom_plot.current_param = instance.text()\\n self.change_top_graph = True\\n for header in self.headers:\\n if self.bottom_plot.current_param in header:\\n self.bottom_plot.setLabel(axis='left', text=header)\",\n \"def get(self):\\n\\n # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword.\\n keyword = self.request.get(\\\"keyword\\\")\\n if not keyword:\\n suggested_keywords = [\\\"alarm clocks\\\", \\\"the future\\\", \\\"miller lite\\\", \\\"taco bell\\\", \\\"yoga\\\", \\\"netflix\\\",\\n \\\"life\\\", \\\"traffic\\\", \\\"elon musk\\\", \\\"beards\\\", \\\"world trade\\\", \\\"pepsi\\\", \\\"amazon\\\"]\\n indices = np.arange(len(suggested_keywords))\\n random.shuffle(indices)\\n keyword = suggested_keywords[indices[0]]\\n\\n # Get recent tweets based on the keyword, up to 300 maximum tweets.\\n tweets = get_tweets(keyword, max_tweets=300)\\n\\n # Compute the sentiment of each tweet.\\n v = VaderSentimentModel()\\n sentiment_scores = [v.classify_sentiment(tw) for tw in tweets] # shape (ntweets,)\\n\\n # Label sentiment categorically, e.g. \\\"negative\\\" or \\\"positive\\\"\\n M_sent = np.mean(sentiment_scores)\\n map = {1 : \\\"positive\\\", 0 : \\\"negative\\\"}\\n valence = map[int(M_sent > 0)]\\n\\n \\\"\\\"\\\"\\n Create plots. \\n \\\"\\\"\\\"\\n\\n #############\\n # Plot #1:\\n ############\\n # Plot the distribution of tweets and sentiment.\\n # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots.\\n # Script1 is javascript for this plot.\\n # Div1 is an HTML container for the plot. Goes where you want the plot to appear.\\n resources, script1, div1 = plot_tweets(tweets=tweets, sentiment_scores=sentiment_scores)\\n\\n #############\\n # Plot #2:\\n ############\\n # Plot the key words that lead us to this classification.\\n # Script2 is javascript for this plot.\\n # Div2 is an HTML container for this plot. Goes where you want the plot to appear.\\n # Requires the HTML to include the shared resources, generated above, in the <HEAD>\\n script2, div2 = plot_reason(tweets=tweets, sentiment_scores=sentiment_scores)\\n\\n \\\"\\\"\\\"\\n Create HTML output. \\n \\\"\\\"\\\"\\n\\n # Load HTML template.\\n # This is a functioning webpage, with some placeholders for the keywords and plots we have created.\\n html_p = os.path.join(\\\"html\\\", \\\"index.html\\\")\\n html = open(html_p, \\\"r\\\").read()\\n\\n # Fill in placeholders in the HTML with varibles we have created.\\n term_to_value = {\\n \\\"[[!KEYWORD]]\\\" : keyword,\\n \\\"[[!VALENCE]]\\\" : valence,\\n \\\"[[!BOKEH_SCRIPT]]\\\" : script1,\\n \\\"[[!BOKEH_SCRIPT2]]\\\": script2,\\n \\\"[[!BOKEH_DIV]]\\\" : div1,\\n \\\"[[!BOKEH_RESOURCES]]\\\" : resources,\\n \\\"[[!BOKEH_DIV2]]\\\" : div2\\n }\\n for term, val in term_to_value.items():\\n html = html.replace(term, val)\\n\\n \\\"\\\"\\\"\\n Write a response.\\n This essentially returns HTML to the google app engine.\\n This will render a webpage visible to the user. \\n \\\"\\\"\\\"\\n self.response.headers[\\\"Content-Type\\\"] = \\\"text/html\\\"\\n self.response.write(html)\",\n \"def test_youtube_trends(dates):\\n test = pycmc.charts.youtube.videos(dates[\\\"start\\\"])\\n assert isinstance(test, list)\\n assert len(test) > 90\\n assert test[0][\\\"name\\\"] != \\\"\\\"\\n assert test[0][\\\"id\\\"] != \\\"\\\"\",\n \"def various(self):\\n # Changer le default d'un tb, ici ne rien mettre au niveau le plus haut\\n context = self\\n from imio.dashboard.utils import getCollectionLinkCriterion\\n criterion = getCollectionLinkCriterion(context)\\n criterion.default = u''\\n from eea.facetednavigation.criteria.interfaces import ICriteria\\n ICriteria(context).criteria._p_changed = True\\n\\n # Réparer la vue de la page pst\\n context.setLayout('view')\\n from imio.project.pst.setuphandlers import configure_faceted_folder\\n configure_faceted_folder(context, xml='default_dashboard_widgets.xml', default_UID=None)\",\n \"def layout():\\n # Needs db connection! (Set up tunnel if testing app locally)\\n _ids = get_doc_ids_from_db() \\n dropdown_dates = {num2str_month(_id): _id for _id in _ids}\\n\\n children_list = [\\n html.Div([\\n html.H2('Top women and men quoted'),\\n html.P('''\\n In this section, we display the top women and men quoted as sources in a\\n given month. First, select a month from the dropdown menu below.\\n ''')],\\n ),\\n html.Br(),\\n dcc.Dropdown(\\n id='date-dropdown',\\n options=[\\n {'label': date_str, 'value': date_num}\\n for date_str, date_num in dropdown_dates.items()\\n ],\\n value=_ids[-1],\\n className='dropdown'\\n ),\\n html.Br(),\\n html.Div(\\n dcc.Loading(\\n id='topic-load-progress',\\n children=[\\n dcc.Store(id='top-sources-topic-data'),\\n dcc.Store(id='top-sources-stats'),\\n ])\\n ),\\n html.Br(),\\n html.H4('Topics and gender representation among sources'),\\n dcc.Markdown('''\\n First, we display the *gender prominence* for each topic discovered in the \\n selected month. Gender prominence is a measure we use to study whether a\\n given topic features quotes by men or women more prominently.\\n '''),\\n html.Div([\\n dcc.Graph(id='top-sources-outlet-gender-heatmap'),\\n ]),\\n html.Br(),\\n html.Div([\\n html.H4('Top quoted sources by gender'),\\n html.P(f'''\\n Next, we display the top {NUM_SOURCES_TO_SHOW} sources per gender for the given month.\\n Hover over the dots to display the total number of articles in which each source was \\n quoted.\\n '''),\\n html.Div([\\n dcc.Graph(id='top-sources-dotplot', className='chart'),\\n ]),\\n ]),\\n html.H4('Observations'),\\n dcc.Markdown(id='user-comment-display'),\\n ]\\n return children_list\",\n \"def get_trends(self, types=None, limit=10, page=0, from_language='en'):\\n if not types:\\n types = ['DECK', 'DECK_GROUP']\\n\\n trendables = self.data_source.get_trends(types, limit, page,\\n from_language)\\n\\n return trendables\",\n \"def dashboard():\",\n \"def trending(request):\\n assert isinstance(request, HttpRequest)\\n try:\\n stocks = StockList.objects.all()\\n hold = []\\n count = 0\\n except StockList.DoesNotExist:\\n return print(\\\"No Stocks Available\\\")\\n\\n\\n\\n while len(hold) < 8:\\n for stock in stocks:\\n stock.trend = stock.positiveSentimentCount + stock.negativeSentimentCount\\n if stock.trend>= count:\\n hold.append(stock)\\n count = stock.trend\\n \\n \\n\\n context = {\\n 'title': 'Trending',\\n 'year': datetime.now().year,\\n 'user': request.user,\\n 'stocks': stocks,\\n 'hold': hold,\\n\\n }\\n\\n \\n return render(\\n request,\\n 'app/trending.html',\\n context,\\n )\",\n \"def on_category(self):\\n super(ToolSettings, self).on_category()\\n selItems = self.tw_category.selectedItems() or []\\n #--- Build Tree ---#\\n if selItems:\\n if hasattr(selItems[0], 'itemWidget'):\\n if selItems[0].itemWidget is not None:\\n if not selItems[0].itemWidget.__edited__:\\n selItems[0].itemWidget._initWidget()\\n selItems[0].itemWidget.buildTree()\",\n \"def taxonomy_plot(self,seasons):\\n print('Formatting data.')\\n no_of_ideograms=self.taxonomy_files()\\n location=self.place.capitalize()+'-'+str(self.year)\\n if seasons==True:\\n seasons=self.weather.seasons(self.place)\\n print('Done')\\n self.conf.taxo_conf(no_of_ideograms, location, self.start_level, self.plot_level, seasons)\",\n \"def TwitterListener():\\n l = StdOutListener()\\n auth = OAuthHandler(config.CONSUMER_KEY, config.CONSUMER_SECRET)\\n auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\\n\\n stream = Stream(auth, l)\\n api = API(auth_handler=auth)\\n config.HASHTAGS = [x['name'] for x in api.trends_place(id=44418)[0]['trends']]\\n\\n print(\\\"Stream listener is up and running\\\")\\n stream.filter(track=config.HASHTAGS)\",\n \"def get_regional_trends(region):\\n trends = []\\n doc = reg_trends_coll.find_one({REGION_KEY: region})\\n if doc:\\n trends = format_trends(doc[\\\"trends\\\"])\\n return trends\",\n \"def _on_next_event(self, **kwargs):\\n self.append_trends()\\n self.grab_shots()\",\n \"def get_top_trends_from_twitter_api(country='Japan', exclude_hashtags=True):\\n # this stupid WOEID requires yweather to get (a library), because YAHOO itself has stopped supporting it\\n # WOEID\\n woeid_client = yweather.Client()\\n woeid = woeid_client.fetch_woeid(location=country)\\n\\n check_rate_limit()\\n\\n if exclude_hashtags :\\n trends = api.GetTrendsWoeid(woeid, exclude='hashtags')\\n else:\\n trends = api.GetTrendsWoeid(woeid, exclude=None)\\n\\n output = []\\n images_output = []\\n for trend in trends:\\n trend = trend.AsDict()\\n\\n # get volumes\\n try:\\n tw_volume = int(trend['tweet_volume']),\\n except:\\n tw_volume = [0]\\n\\n # match time with timezone\\n timestamp_str = trend['timestamp'] # this is utc\\n timestamp_dt = str_2_datetime(timestamp_str, input_format=time_format_twitter_trends).replace(tzinfo=pytz.utc)\\n\\n # timestamp_local = timestamp_dt.astimezone(tz=pytz.utc)\\n timestamp_utc_str = datetime_2_str(timestamp_dt, output_format=time_format_full_with_timezone)\\n\\n output.append({\\n \\\"label\\\": trend['name'],\\n \\\"volume\\\": tw_volume,\\n \\\"time\\\": timestamp_utc_str,\\n \\\"query\\\": trend['query'],\\n \\\"url\\\": trend['url'],\\n })\\n\\n images_output.append({\\n \\\"label\\\": trend['name'],\\n \\\"time\\\": timestamp_utc_str,\\n \\\"tweets\\\": analyze_trending_keyword(trend['name'], count=50)\\n })\\n\\n output_json = json.dumps(output, ensure_ascii=False)\\n images_output_json = json.dumps(images_output, ensure_ascii=False)\\n return output_json, images_output_json\",\n \"def on_category(self):\\n super(ProjectSettings, self).on_category()\\n selItems = self.tw_category.selectedItems() or []\\n #--- Build Tree ---#\\n if selItems:\\n if hasattr(selItems[0], 'itemWidget'):\\n if selItems[0].itemWidget is not None:\\n if not selItems[0].itemWidget.__edited__:\\n selItems[0].itemWidget._initWidget()\\n selItems[0].itemWidget.buildTree()\",\n \"def plot_mult_timetrends(data, geoids, cols, area, colors, markers, sharex,\\n ylim_bottom = -150, ylim_top = 150, ylabel = 'Pct change in mobility', xlabels=None):\\n ax = plt.axes(area, sharex = None)\\n \\n cols = cols\\n plt.hlines(0,data.num_date.min(),data.num_date.max())\\n i = 0\\n for y in cols:\\n pts = y[:12]\\n \\n# lim = ylim\\n# plt.xlabel('date', fontsize=18)\\n plt.ylabel(ylabel, fontsize=22)\\n\\n plt.yticks(fontsize=30) \\n\\n x_locator = FixedLocator(data.num_date[np.arange(0,data.shape[0],7)].tolist())\\n ax.xaxis.set_minor_locator(x_locator)\\n plt.grid(axis='x', which = 'both') \\n \\n plt.plot(data['num_date'], data[y], color = colors[i], linewidth=5)\\n i = i+ 1\\n plt.xticks(ticks = data.num_date[np.arange(0,data.shape[0],28)].tolist(),\\n labels = xlabels, rotation=30, ha='right',\\n fontsize=30)\\n plt.ylim(ylim_bottom,ylim_top)\\n\\n return ax\",\n \"def youtube_trending(session=None):\\n data = []\\n url = 'https://www.youtube.com/feed/trending'\\n soup = ph.get_soup(url, session)\\n if not soup:\\n ph.logger.error('No soup found for {}'.format(url))\\n return data\\n\\n uls = soup.find_all('ul', attrs={'class': 'expanded-shelf-content-list'})\\n i = 0\\n for ul in uls:\\n for li in ul.find_all('li'):\\n result_data = {}\\n try:\\n result_data['link'] = 'https://www.youtube.com' + li.h3.a.attrs['href']\\n result_data['title'] = li.h3.a.attrs['title']\\n except AttributeError:\\n continue\\n else:\\n result_data['position'] = i\\n try:\\n result_data['duration'] = _clean_youtube_duration(li.h3.span.text)\\n except AttributeError:\\n result_data['duration'] = ''\\n try:\\n result_data['user'] = li.find(attrs={'class': 'yt-lockup-byline'}).a.text\\n except AttributeError:\\n result_data['user'] = ''\\n try:\\n metadata = li.find(attrs={'class': 'yt-lockup-meta-info'})\\n metadata = [x.text for x in metadata.findChildren()]\\n except AttributeError:\\n metadata = []\\n try:\\n result_data['uploaded'] = metadata[0]\\n result_data['views'] = metadata[1]\\n except IndexError:\\n result_data['uploaded'] = ''\\n result_data['views'] = ''\\n\\n data.append(result_data)\\n i += 1\\n\\n return data\",\n \"def get_national_trends():\\n trends = format_trends(list(nat_trends_coll.find({})))\\n return trends\",\n \"def windowMenuActions( self, action ):\\n\\tif (action.text() == 'Weeklies'):\\n if not hasattr(sharedDB, 'myWeekliesWidget'):\\n\\t\\tsharedDB.myWeekliesWidget = weeklieswidget.WeekliesWidget(sharedDB.mainWindow)\\n\\t\\t\\n\\t #sharedDB.myWeekliesWidget.CalculateWeeklies()\\n\\t sharedDB.myWeekliesWidget.dockWidget.show()\\n\\tif (action.text() == 'Assignments'):\\t\\t\\n\\t sharedDB.myAssignmentsWidget.dockWidget.show()\\n\\tif (action.text() == 'Attribute Editor'):\\t\\t\\n\\t sharedDB.myAttributeEditorWidget.dockWidget.show()\",\n \"def top_twenty(request):\",\n \"def on_double_click(self):\\r\\n index = self.rssTable.selectedIndexes()[0]\\r\\n url = QUrl(self.rssModel.url(index))\\r\\n self.browser.load(url)\",\n \"def getdata_timeline():\\n session = Session()\\n sites = session.query(Sites).limit(10).order_by(desc(Sites.id))\\n session.close()\\n\\n return render_template('data.html',sites=sites)\",\n \"def sponsored_bar():\\n articles = []\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored First')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Second')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Third')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Fourth')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Fifth')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n exclude = ['Sponsored First', 'Sponsored Second', 'Sponsored Third',\\n 'Sponsored Fourth', 'Sponsored Fifth']\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Article')\\n .exclude(tags__text__in=exclude)\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:5 - len(articles)]])\\n \\\"\\\"\\\"\\n Enable this code (and disable the above) for sponsored articles returning\\n by date (without the tag preferences)\\n\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Article')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:5 - len(articles)]])\\n \\\"\\\"\\\"\\n\\n return {'articles': articles}\",\n \"def index():\\n last_places=query_db('''select * from places ORDER BY place_id DESC LIMIT 10''')\\n last_reviews=query_db('''select places.name, places.place_id, review_id, title FROM reviews, places WHERE places.place_id=reviews.place_id ORDER BY review_id DESC LIMIT 10''')\\n return render_template('main.html', places=last_places, reviews=last_reviews)\",\n \"def sidebar_click(sidebar_item):\\n #print('sidebar_click')\\n sidebar = driver.find_element_by_class_name('m-menu__nav')\\n return click_on(sidebar_item, scope=sidebar)\",\n \"def show_data(self):\\r\\n selected_items = self.treeview.selection()\\r\\n if len(selected_items) > 0:\\r\\n ticker = self.treeview.item(selected_items, 'values')[0]\\r\\n self.graph_data(ticker)\\r\\n self.ticker = ticker\\r\\n else:\\r\\n return None\",\n \"def score_selector(request):\\n\\n\\t# Look for the list of gymnasts entered by the user\\n\\tgymnast_list = request.GET.get('gymnast_list', False)\\n\\n\\tif gymnast_list:\\n\\n\\t\\t# Get the rest of the information submitted through the form\\n\\t\\tgymnasts = gymnast_list.split(\\\"\\\\r\\\\n\\\")\\n\\t\\tevent = request.GET.get('event', False)\\n\\t\\tsumstat = request.GET.get('sumstat', False)\\n\\t\\ttime = request.GET.get('time', False)\\n\\n\\t\\t# Set the date range \\n\\t\\tnow = datetime.datetime.now()\\n\\t\\tif time==\\\"year\\\":\\n\\t\\t\\tdate_range = [now-relativedelta(years=1), now]\\n\\t\\telif time == \\\"season\\\":\\n\\t\\t\\tdate_range = [datetime.date(2019, 10, 13), now] # Since last world championships\\n\\t\\telse:\\n\\t\\t\\tdate_range = [datetime.date(2016, 8, 21), now] # Since last olympics\\n\\n\\t\\t# Get the score data for the results table\\n\\t\\ttable_data = []\\n\\t\\tfor gymnast in gymnasts:\\n\\t\\t\\tgymnast = Gymnast.objects.get(name=gymnast)\\n\\t\\t\\tthis_gymnast_scores = []\\n\\t\\t\\tthis_gymnast_scores.append(gymnast)\\n\\t\\t\\tif event == \\\"AA\\\":\\n\\t\\t\\t\\tfor sub_event in [\\\"VT\\\", \\\"UB\\\", \\\"BB\\\", \\\"FX\\\"]:\\n\\t\\t\\t\\t\\tscores = Score.objects.filter(gymnast=gymnast, \\n\\t\\t\\t\\t\\t\\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=sub_event), score_num=1)\\n\\t\\t\\t\\t\\tif scores.count() > 0:\\n\\t\\t\\t\\t\\t\\tif sumstat == \\\"avg\\\":\\n\\t\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\\n\\t\\t\\t\\t\\t\\telif sumstat == \\\"max\\\":\\n\\t\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tscores_sumstat = \\\"\\\"\\n\\t\\t\\t\\t\\tthis_gymnast_scores.append(scores_sumstat)\\n\\t\\t\\t\\t# Add up AA average\\n\\t\\t\\t\\tif isinstance(this_gymnast_scores[1], float) and isinstance(this_gymnast_scores[2], float) and isinstance(this_gymnast_scores[3], float) and isinstance(this_gymnast_scores[4], float):\\n\\t\\t\\t\\t\\taa_total = float(this_gymnast_scores[1]) + float(this_gymnast_scores[2]) + float(this_gymnast_scores[3]) + float(this_gymnast_scores[4])\\n\\t\\t\\t\\t\\tthis_gymnast_scores.append(aa_total)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tthis_gymnast_scores.append(\\\"\\\")\\n\\t\\t\\telif event == \\\"VT\\\":\\n\\t\\t\\t\\tfor vt_num in [1, 2]:\\n\\t\\t\\t\\t\\tscores = Score.objects.filter(gymnast=gymnast, \\n\\t\\t\\t\\t\\t\\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=\\\"VT\\\"), score_num=vt_num)\\n\\t\\t\\t\\t\\tif scores.count() > 0:\\n\\t\\t\\t\\t\\t\\tif sumstat == \\\"avg\\\":\\n\\t\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\\n\\t\\t\\t\\t\\t\\telif sumstat == \\\"max\\\":\\n\\t\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tscores_sumstat = \\\"\\\"\\n\\t\\t\\t\\t\\tthis_gymnast_scores.append(scores_sumstat)\\n\\t\\t\\t\\t# Get two-vault average\\n\\t\\t\\t\\tif isinstance(this_gymnast_scores[1], float) and isinstance(this_gymnast_scores[2], float):\\n\\t\\t\\t\\t\\tvt_avg = (float(this_gymnast_scores[1]) + float(this_gymnast_scores[2]))/2\\n\\t\\t\\t\\t\\tthis_gymnast_scores.append(vt_avg)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tthis_gymnast_scores.append(\\\"\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tscores = Score.objects.filter(gymnast=gymnast, \\n\\t\\t\\t\\t\\tmeet__in=Meet.objects.filter(start_date__range=date_range), event__in=Event.objects.filter(name=event))\\n\\t\\t\\t\\tif scores.count() > 0:\\n\\t\\t\\t\\t\\tif sumstat == \\\"avg\\\":\\n\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Avg('score'))['score__avg']\\n\\t\\t\\t\\t\\telif sumstat == \\\"max\\\":\\n\\t\\t\\t\\t\\t\\tscores_sumstat = scores.aggregate(Max('score'))['score__max']\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tscores_sumstat = \\\"\\\"\\n\\t\\t\\t\\tthis_gymnast_scores.append(scores_sumstat)\\n\\t\\t\\ttable_data.append(this_gymnast_scores)\\n\\telse: \\n\\t\\tgymnast_list = \\\"\\\"\\n\\t\\tgymnasts = []\\n\\t\\ttable_data = []\\n\\t\\tevent = \\\"AA\\\"\\n\\t\\tsumstat = \\\"avg\\\"\\n\\t\\ttime = \\\"year\\\"\\n\\n\\tcontext = {\\n\\t\\t'gymnast_list': gymnast_list, # Return what they entered so that it shows up again with the results of their request\\n\\t\\t'gymnasts': gymnasts,\\n\\t\\t'table_data': table_data,\\n\\t\\t'event': event,\\n\\t\\t'sumstat': sumstat,\\n\\t\\t'time': time,\\n\\t}\\n\\treturn render(request, 'score_selector.html', context=context)\",\n \"def get_vax_trends_data(area=None):\\n if area is None:\\n pipe = [\\n {\\n '$group': {\\n '_id': f'${VAX_DATE_KEY}',\\n VAX_FIRST_DOSE_KEY: {'$sum': f'${VAX_FIRST_DOSE_KEY}'},\\n VAX_SECOND_DOSE_KEY: {'$sum': f'${VAX_SECOND_DOSE_KEY}'},\\n VAX_BOOSTER_DOSE_KEY: {'$sum': f'${VAX_BOOSTER_DOSE_KEY}'}\\n }\\n },\\n {\\\"$sort\\\": {\\\"_id\\\": -1}},\\n {'$limit': 7}\\n ]\\n else:\\n pipe = [\\n {'$match': {VAX_AREA_KEY: area}},\\n {\\n '$group': {\\n '_id': f'${VAX_DATE_KEY}',\\n VAX_FIRST_DOSE_KEY: {'$sum': f'${VAX_FIRST_DOSE_KEY}'},\\n VAX_SECOND_DOSE_KEY: {'$sum': f'${VAX_SECOND_DOSE_KEY}'},\\n VAX_BOOSTER_DOSE_KEY: {'$sum': f'${VAX_BOOSTER_DOSE_KEY}'}\\n }\\n },\\n {\\\"$sort\\\": {\\\"_id\\\": -1}},\\n {'$limit': 7}\\n ]\\n data = list(vax_admins_coll.aggregate(pipeline=pipe))\\n return data\",\n \"def get_trending(region='US', **kwargs):\\n url = '{}/v1/finance/trending/{}'.format(BASE_URL, region)\\n return _make_request(url, 'finance')[0]\",\n \"def visit_sidebar(self, node):\\n\\n return BaseTranslator.visit_admonition(self, node)\",\n \"def visualize_plotly(self, topics):\\r\\n \\r\\n df_palette = pd.DataFrame([\\r\\n [0, '#C03028'],\\r\\n [1, '#F08030'],\\r\\n [2, '#6890F0'],\\r\\n [3, '#78C850'],\\r\\n [4, '#A890F0'],\\r\\n [5, '#B22222'],\\r\\n [6, '#F8D030'],\\r\\n [7, '#D3D3D3'],\\r\\n [8, '#F85888'],\\r\\n [9, '#7FFFD4']])\\r\\n #[10, '#98D8D8']])\\r\\n \\r\\n #[11, '#A8B820'],\\r\\n #[12, '#7038F8'],\\r\\n #[13, '#705898'],\\r\\n #[14, '#705848'],\\r\\n #[15, '#B8B8D0'],\\r\\n #[16, '#A8A878'],\\r\\n #[17, '#EE99AC']])\\r\\n\\r\\n df_palette.columns = ['labels', 'typecolor']\\r\\n self.tweet_dataframe.merge(df_palette, on = 'labels')\\r\\n\\r\\n #Divide up the tsne data\\r\\n\\r\\n plot_list = []\\r\\n\\r\\n for idx, (label, color) in df_palette.iterrows():\\r\\n\\r\\n df_filter = self.tweet_dataframe[self.tweet_dataframe['labels'] == label]\\r\\n \\r\\n df_filter['custom_text'] = df_filter[['username', 'text']].apply('<br />'.join, axis = 1) \\r\\n sentiment_boxplot = go.Box(\\r\\n x = df_filter['vader_polarity'],\\r\\n name = \\\"{}\\\".format(topics[label]),\\r\\n #text = pd.Series(self.tweet_dataframe['text']),\\r\\n boxmean = True,\\r\\n jitter = .5,\\r\\n boxpoints = 'all',\\r\\n hoverinfo = 'x+text',\\r\\n text = df_filter['custom_text'],\\r\\n marker = dict(color = color) \\r\\n )\\r\\n plot_list.append(sentiment_boxplot) \\r\\n\\r\\n # Override plotly \\r\\n axis_layout = dict(zeroline=False, showaxeslabels=False, autotick = True, ticks = '', showticklabels=False, title='')\\r\\n\\r\\n layout = go.Layout(\\r\\n yaxis = axis_layout,\\r\\n hovermode = \\\"closest\\\",\\r\\n title = \\\"Sentiment distribution per topic\\\",\\r\\n showlegend = True)\\r\\n\\r\\n fig = dict(data=plot_list, layout=layout)\\r\\n #plot_url = py.plot(fig)\\r\\n offline_plot.plot(fig, filename='data/sentiment_boxplot.html', auto_open = False)\\r\\n\\r\\n return plot_list, layout\",\n \"def sentiment_plot(self, top_words=25):\\n if top_words > 25:\\n warnings.warn('Including more than 25 words on the X-axis will cause words to be excluded from the axis')\\n\\n daily_comments = self.comments[(self.comments['days_after_release'].\\\\\\n isin(list(range(self.day_window[0], self.day_window[1] + 1))))]\\n if len(daily_comments) == 0:\\n warnings.warn('No comments found for this day, trying future dates until comments are found')\\n\\n while len(daily_comments) == 0:\\n if self.day_window[1] > self.comments['days_after_release'].max():\\n raise KeyError('Reached bounds of comment dates available. Make sure all comments are present')\\n self.day_window[1] += 1\\n daily_comments = self.comments[(self.comments['days_after_release'].\\\\\\n isin(list(range(self.day_window[0], self.day_window[1] + 1))))]\\n\\n print('Now looking at {} to {} days after release'.format(self.day_window[0], self.day_window[1]))\\n\\n if 'pos' not in daily_comments['sentiment'].values or 'neu' not in daily_comments['sentiment'].values or \\\\\\n 'neg' not in daily_comments['sentiment'].values:\\n warnings.warn('No negative or positive sentiments found on this day, trying future dates until positive or negative comments are found')\\n\\n while 'pos' not in daily_comments['sentiment'].values or 'neu' not in daily_comments['sentiment'].values or \\\\\\n 'neg' not in daily_comments['sentiment'].values:\\n if self.day_window[1] > self.comments['days_after_release'].max():\\n raise KeyError('Reached bounds of comment dates available. Make sure all comments are present')\\n self.day_window[1] += 1\\n daily_comments = self.comments[(self.comments['days_after_release']. \\\\\\n isin(list(range(self.day_window[0], self.day_window[1] + 1))))]\\n\\n print('Now looking at {} to {} days after release'.format(self.day_window[0], self.day_window[1]))\\n\\n res_positive = daily_comments[(daily_comments['sentiment']=='pos')]['comment_message'].str.split(expand=True)\\\\\\n .stack().value_counts().to_dict()\\n res_neutral = daily_comments[(daily_comments['sentiment']=='neu')]['comment_message'].str.split(expand=True)\\\\\\n .stack().value_counts().to_dict()\\n res_negative = daily_comments[daily_comments['sentiment']=='neg']['comment_message'].str.split(expand=True)\\\\\\n .stack().value_counts().to_dict()\\n\\n fig = make_subplots(rows=3, cols=1,\\n y_title='Count',\\n subplot_titles=('Positive', 'Neutral', 'Negative'))\\n trace = fig.add_trace(px.bar(x=list(res_positive.keys())[:top_words], y=list(res_positive.values())[:top_words]).data[0],\\n row=1, col=1)\\n fig.append_trace(px.bar(x=list(res_neutral.keys())[:top_words], y=list(res_neutral.values())[:top_words]).data[0],\\n row=2, col=1)\\n fig.append_trace(px.bar(x=list(res_negative.keys())[:top_words], y=list(res_negative.values())[:top_words]).data[0],\\n row=3, col=1)\\n\\n left = np.where(self.day_window[0] < 0, 'Before', 'After')\\n right = np.where(self.day_window[1] < 0, 'Before', 'After')\\n fig.update_layout(\\n title='Top {} Words at {} Days {} Release to {} Days {} Release'.format(top_words,\\n self.day_window[0], left,\\n self.day_window[1], right)\\n )\\n fig.show()\",\n \"def show_derivativeCursor(self, var):\\n if self.derivativeThreshold.isChecked(): \\n self.plotWidget.derivativeCursorThsPos = 0\\n self.plotWidget.derivativeCursorThs = pg.InfiniteLine(pos=self.plotWidget.derivativeCursorThsPos,\\n angle=0, movable=True, pen=pg.mkPen('#FFD000', width=2))\\n self.plotWidget.addItem(self.plotWidget.derivativeCursorThs)\\n self.plotWidget.derivativeCursorThs.sigPositionChanged.connect(self.update_derivativeThreshold)\\n else:\\n self.plotWidget.derivativeCursorThsPos = []\\n self.plotWidget.removeItem(self.plotWidget.derivativeCursorThs)\",\n \"def draw_html():\\n station_reference = request.args.get(\\\"stationReference\\\")\\n station_name = request.args.get(\\\"stationName\\\")\\n station_name = station_name.replace(\\\" \\\",\\\"+\\\")\\n # print(\\\"**********\\\")\\n # print(station_name)\\n # print(\\\"**********\\\")\\n # print(\\\"**********\\\")\\n # print(station_data.stationName)\\n # print(\\\"**********\\\") \\n if station_name:\\n station = station_data.loc[station_data.stationName == station_name]\\n else:\\n station = station_data.loc[station_data.stationReference == station_reference]\\n # print(station)\\n # print(\\\"**********\\\")\\n # print(station)\\n # print(\\\"**********\\\")\\n result_station = station.iloc[0]\\n # print(\\\"**********\\\")\\n # print(result_station)\\n # print(\\\"**********\\\")\\n time_from = request.args.get(\\\"from\\\")\\n time_to = request.args.get(\\\"to\\\")\\n if time_from:\\n pass\\n else:\\n time_from = None\\n if time_to:\\n pass\\n else:\\n time_to = None\\n\\n statistic = request.args.get(\\\"statistic\\\")\\n if statistic is not None:\\n statistic = statistic.split(',')\\n\\n if result_station is not None:\\n tide_values = data.station_tides(result_station.stationName, time_from, time_to).reset_index()\\n tide_values.rename(columns={result_station.stationName: 'tideValue'}, inplace=True)\\n return tide_values.to_html(index=False)\\n\\n if statistic is not None:\\n # print(\\\"**********\\\")\\n # print(statistic)\\n # print(\\\"**********\\\")\\n frames = []\\n for statistic_method in statistic:\\n if statistic_method == 'max':\\n frames.append(data.max_tides(time_from, time_to))\\n elif statistic_method == 'min':\\n frames.append(data.min_tides(time_from, time_to))\\n else:\\n frames.append(data.mean_tides(time_from, time_to))\\n # print(\\\"**********\\\")\\n # print(result)\\n # print(\\\"**********\\\")\\n\\n result = pd.concat(frames, axis=1, keys=statistic).reset_index()\\n return result.to_html(index=False)\",\n \"def widget(self, request, group):\",\n \"def index():\\n graphs = [\\n message_genre_bar_chart(df),\\n category_bar_chart(df),\\n top_words_bar_chart(df)\\n ]\\n \\n # encode plotly graphs in JSON\\n ids = [\\\"graph-{}\\\".format(i) for i, _ in enumerate(graphs)]\\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\\n \\n # render web page with plotly graphs\\n return render_template('master.html', ids=ids, graphJSON=graphJSON)\",\n \"def interactive_utility_report_with_buttons(year: int):\\n utility_category_id = Category().get_category_id_by_name('Utility')\\n monthly_utility_total = Spending().get_specific_category_monthly_spending(year, [utility_category_id])\\n monthly_utility_details = Spending().get_sub_category_monthly_spending_of_a_category(year, [utility_category_id])\\n\\n df_monthly_total = pd.DataFrame(monthly_utility_total, columns=['sum', 'mon'])\\n df_monthly_details = pd.DataFrame(monthly_utility_details, columns=['sum', 'name', 'mon'])\\n\\n df_water = df_monthly_details[df_monthly_details['name'] == 'Water and Waste']\\n df_electricity = df_monthly_details[df_monthly_details['name'] == 'Electricity']\\n df_internet = df_monthly_details[df_monthly_details['name'] == 'Internet']\\n df_gas = df_monthly_details[df_monthly_details['name'] == 'Nature Gas']\\n df_mobile = df_monthly_details[df_monthly_details['name'] == 'Mobile Bill']\\n\\n fig = go.Figure()\\n # Add the total amount into figure as bar chart\\n fig.add_trace(\\n go.Bar(\\n x=df_monthly_total['mon'],\\n y=df_monthly_total['sum'],\\n name=f\\\"{year} utility\\\"\\n )\\n )\\n # Add water\\n fig.add_trace(\\n go.Scatter(x=df_water['mon'], y=df_water['sum'], name='Water',\\n visible=False, line=dict(color=\\\"#5DADE2\\\"))\\n )\\n # Add Electricity\\n fig.add_trace(\\n go.Scatter(x=df_electricity['mon'], y=df_electricity['sum'], name='Electricity',\\n visible=False, line=dict(color=\\\"#F7DC6F\\\"))\\n )\\n # Add Internet\\n fig.add_trace(\\n go.Scatter(x=df_internet['mon'], y=df_internet['sum'], name='Internet',\\n visible=False, line=dict(color=\\\"#82E0AA\\\"))\\n )\\n # Add Gas\\n fig.add_trace(\\n go.Scatter(x=df_gas['mon'], y=df_gas['sum'], name='Gas',\\n visible=False, line=dict(color=\\\"#E74C3C\\\"))\\n )\\n # Add Mobile\\n fig.add_trace(\\n go.Scatter(x=df_mobile['mon'], y=df_mobile['sum'], name='Mobile',\\n visible=False, line=dict(color=\\\"#BB8FCE\\\"))\\n )\\n\\n fig.update_layout(\\n updatemenus=[\\n dict(\\n type='buttons',\\n direction='right',\\n active=0,\\n x=0.57,\\n y=1.2,\\n buttons=list([\\n dict(\\n label='Total Utility Spending',\\n method='update',\\n args=[\\n {'visible': [True, False, False, False, False, False]},\\n {'title': 'Total Utility Spending', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Water',\\n method='update',\\n args=[\\n {'visible': [True, True, False, False, False, False]},\\n {'title': 'Total vs Water', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Electricity',\\n method='update',\\n args=[\\n {'visible': [True, False, True, False, False, False]},\\n {'title': 'Total vs Electricity', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Internet',\\n method='update',\\n args=[\\n {'visible': [True, False, False, True, False, False]},\\n {'title': 'Total vs Internet', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Gas',\\n method='update',\\n args=[\\n {'visible': [True, False, False, False, True, False]},\\n {'title': 'Total vs Gas', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Mobile',\\n method='update',\\n args=[\\n {'visible': [True, False, False, False, False, True]},\\n {'title': 'Total vs Mobile', \\\"annotations\\\": []}\\n ]\\n ),\\n ])\\n )\\n ]\\n )\\n\\n fig.update_layout(\\n {\\n 'width': 1200,\\n 'height': 550\\n },\\n overwrite=True\\n )\\n\\n aPlot = plotly.offline.plot(fig,\\n config={\\\"displayModeBar\\\": False},\\n show_link=False,\\n include_plotlyjs=False,\\n output_type='div')\\n\\n return aPlot\",\n \"def analyze(request, *args, **kwargs):\\n\\n mode = 'lines+markers'\\n\\n tickers = Stock.objects.distinct(\\n 'ticker').values_list('ticker', flat=True)\\n tickers_dict = {ticker: [] for ticker in tickers}\\n tickers_count = tickers.count()\\n\\n actual_dates = Stock.objects.values('date').annotate(\\n dcount=Count('date')).filter(dcount=tickers_count).values_list(\\n 'date', flat=True).order_by('date')\\n date_list = list(actual_dates)\\n\\n data = Stock.objects.filter(date__in=actual_dates).order_by('date')\\n\\n for item in data.values('ticker', 'close', 'oopen'):\\n tickers_dict[item['ticker']].append(\\n round((item['close']-item['oopen'])*100/item['oopen'], 2)\\n )\\n\\n scatters = [Scatter(x=date_list, y=tickers_dict[obj], mode=mode, name=obj,\\n opacity=0.8, visible='legendonly') for obj in tickers_dict]\\n figure = {'data': scatters, 'layout': {\\n 'title': {\\n 'text': 'Open-Closed comparision', 'y': 0.9, 'x': 0.5,\\n 'xanchor': 'center','yanchor': 'top'},\\n 'yaxis_title': \\\"Daily percent\\\",\\n 'xaxis_title': \\\"Years\\\",\\n }}\\n\\n return render(request, \\\"analyze.html\\\", context={\\n 'plot_div': plot(figure, output_type='div')})\",\n \"def trackVocabulary(context):\\n ct = getToolByName(context,'portal_catalog')\\n tracks = ct.searchResults(portal_type='apyb.papers.track', sort_on='getObjPositionInParent')\\n items = [SimpleTerm(b.UID,b.UID,b.Title) for b in tracks]\\n return SimpleVocabulary(items)\",\n \"def on_scatter_toolbar_table_click(self):\\n #print('*** on table click ***')\\n row = self.myTableWidget.currentRow()\\n if row == -1 or row is None:\\n return\\n yStat = self.myTableWidget.item(row,0).text()\\n self.myParent.replot()\",\n \"def root_wdgt(self):\\n self.summarize()\\n modes = ['Global', 'Single-Image']\\n\\n def logic(mode):\\n # cache the widget later\\n if mode == modes[0]:\\n if self.global_walk is None:\\n self.global_walk = self.global_walk_specifier()\\n ipy_display(self.global_walk)\\n elif mode == modes[1]:\\n self.image_view = self.single_image_selector()\\n # if self.image_view is None:\\n # self.image_view = self.single_image_selector()\\n # ipy_display(self.image_view)\\n\\n UI = interactive(\\n logic, mode=widgets.ToggleButtons(options=modes, value=modes[0])\\n )\\n UI.children[-1].layout.height = '1000px'\\n ipy_display(UI)\",\n \"def views(request):\\n tag = Tag.objects.filter(name=\\\"Global\\\")\\n gauges = Gauge.objects.filter(tags=tag)\\n return render_to_response('dashboard/views.js',{'gauges': gauges} )\",\n \"def widgets(self):\\r\\n url = '{0}/{1}/'.format(self.get_url(), 'widgets')\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def plot_trends(group, country=\\\"US\\\", state=None, place=None, predictive_method=\\\"ARIMA\\\"):\\n print(f\\\"* Plotting Google Trends of `{group}` for {country} - {state or 'All'}\\\")\\n group_queries = get_group_queries(group, only_root=True)\\n\\n n_queries = len(group_queries)\\n n_cols = 3\\n n_rows = int(n_queries / n_cols) + (1 if n_queries % n_cols else 0)\\n\\n # Annotations\\n annotations = []\\n\\n # Initialize figure with subplots\\n subplot_titles = [\\\"%s...\\\" % t[:22] if len(t) >= 22 else t for t in group_queries]\\n fig = make_subplots(\\n rows=n_rows, cols=n_cols, subplot_titles=subplot_titles,\\n shared_yaxes=True,\\n print_grid=True\\n )\\n\\n # Marked Dates\\n covid_start_date = COVID_START_DATE\\n reopen_date = REOPEN_DATE\\n reopen_date_minus_1 = REOPEN_DATE_MINUS_1\\n data_start_date = DATA_START_DATE\\n data_end_date = DATA_END_DATE\\n\\n # Figure variable\\n baseline = 0\\n value_range = [0, 100]\\n\\n # Model params\\n model_params = []\\n\\n for idx, query in enumerate(group_queries):\\n row = int(idx / n_cols) + 1\\n col = idx % n_cols + 1\\n showlegend = idx == 0\\n\\n query_file_path = get_data_filename(group, query, country=country, state=state, full=True)\\n df = pd.read_csv(query_file_path, parse_dates=True)\\n count = df[\\\"date\\\"].count()\\n\\n # ARIMA Model\\n if query in df.columns:\\n print(\\\"Query: \\\", query)\\n # get_arima_params(df[query])\\n df, model = arima_predict(df, from_date=PREDICT_FROM_DATE, value_col=query)\\n params = model.get_params()\\n model_params.append([query, str(params[\\\"order\\\"])])\\n # return False\\n \\n # No data\\n if count == 0:\\n continue\\n\\n # Process\\n stayhome_order_date = place.get(\\\"ClosedFrom\\\") if place else SOCIAL_DISTANCE_ORDER_DATE\\n\\n df = df[(df[\\\"date\\\"] >= data_start_date) & (df[\\\"date\\\"] <= data_end_date)]\\n df_before = df[(df[\\\"date\\\"] <= reopen_date)]\\n df_after = df[(df[\\\"date\\\"] >= reopen_date_minus_1)]\\n df_prediction = df[df[\\\"is_predicted\\\"] == 1]\\n\\n # Normalize\\n if config.TRENDS_APPLY_NORMALIZATION:\\n max_value = df[query].max()\\n baseline = df_before[query].median()\\n df[\\\"value\\\"] = df[query].apply(lambda x: (x - baseline) / max_value)\\n df_before[\\\"value\\\"] = df_before[query].apply(lambda x: (x - baseline) / max_value)\\n df_after[\\\"value\\\"] = df_after[query].apply(lambda x: (x - baseline) / max_value)\\n baseline = 0\\n value_range = [-1, 1]\\n else:\\n max_value = df[query].max()\\n baseline = df_before[query].median()\\n df[\\\"value\\\"] = df[query]\\n df_before[\\\"value\\\"] = df_before[query]\\n df_after[\\\"value\\\"] = df_after[query]\\n\\n # Compute difference\\n query_text = query.split(\\\"+\\\")[0].strip() + \\\" + ...\\\" if \\\"+\\\" in query else query\\n actual_mean, actual_meanCI95min, actual_meanCI95max = mean_confidence_interval(df_prediction[query])\\n predict_mean = df_prediction[\\\"prediction\\\"].mean()\\n diff = round(100 * (actual_mean - predict_mean) / predict_mean, 1)\\n diffCI95min = round(100 * (actual_meanCI95min - predict_mean) / predict_mean, 1)\\n diffCI95max = round(100 * (actual_meanCI95max - predict_mean) / predict_mean, 1)\\n x_date = list(df['date'])[int(df[\\\"date\\\"].count()/2)]\\n diff_annot = go.layout.Annotation(\\n text=f'<b>{query_text}</b><br><sub><b style=\\\"color:{config.COLOR_UPTREND if diff >= 0 else config.COLOR_DOWNTREND}\\\">{diff}%</b>; 95%CI, [{diffCI95min}%, {diffCI95max}%]</sub>',\\n showarrow=False, xanchor=\\\"center\\\", yanchor=\\\"top\\\", \\n x=x_date,\\n y=0.0,\\n xshift=0,\\n yshift=-5,\\n xref=f\\\"x{'' if idx == 0 else idx + 1}\\\",\\n yref=f\\\"y{'' if idx == 0 else idx + 1}\\\"\\n )\\n annotations.append(diff_annot)\\n\\n # Lockdown period\\n max_y = max(df[query].max(), abs(df[query].min()))\\n min_y = -max_y\\n shape_lockdown = go.layout.Shape(**{\\\"type\\\": \\\"rect\\\",\\\"y0\\\":100,\\\"y1\\\": -100,\\\"x0\\\":COVID_START_DATE, \\n \\\"x1\\\":REOPEN_DATE,\\\"xref\\\":\\\"x1\\\",\\\"yref\\\":\\\"y1\\\",\\\"layer\\\":\\\"below\\\",\\n \\\"fillcolor\\\":\\\"#eeeeee\\\", \\\"line\\\":dict(width=0), \\\"line_width\\\": 0})\\n fig.add_shape(shape_lockdown, row=row, col=col)\\n\\n # Horizontal line \\n shape = go.layout.Shape(**{\\\"type\\\": \\\"line\\\",\\\"y0\\\":baseline,\\\"y1\\\": baseline,\\\"x0\\\":str(df[\\\"date\\\"].values[0]), \\n \\\"x1\\\":str(df[\\\"date\\\"].values[-1]),\\\"xref\\\":\\\"x1\\\",\\\"yref\\\":\\\"y1\\\",\\\"layer\\\":\\\"below\\\",\\n \\\"line\\\": {\\\"color\\\": \\\"rgb(200, 200, 200)\\\",\\\"width\\\": 1.5}})\\n fig.add_shape(shape, row=row, col=col)\\n\\n # Stay home order\\n if stayhome_order_date:\\n shape_stayhome_order = go.layout.Shape(**{\\\"type\\\": \\\"line\\\",\\\"y0\\\":-0.25,\\\"y1\\\": 0.25,\\\"x0\\\":stayhome_order_date, \\n \\\"x1\\\":stayhome_order_date,\\\"xref\\\":\\\"x1\\\",\\\"yref\\\":\\\"y1\\\",\\n \\\"line\\\": {\\\"color\\\": \\\"blue\\\",\\\"width\\\": 1.5, \\\"dash\\\": \\\"dot\\\"}})\\n fig.add_shape(shape_stayhome_order, row=row, col=col)\\n\\n # Plot\\n subplot_before = go.Scatter(x=df_before[\\\"date\\\"], y=df_before[\\\"value\\\"], \\n mode=\\\"lines\\\", name=\\\"Before Lockdown\\\",\\n line=dict(width=1, color=config.LINE_COLOR_BEFORE), \\n line_shape=\\\"linear\\\", showlegend=False) # linear or spline \\n subplot_after = go.Scatter(x=df_after[\\\"date\\\"], y=df_after[\\\"value\\\"], \\n mode=\\\"lines\\\", name=\\\"Actual Queries\\\",\\n line=dict(width=1.5, color=config.LINE_COLOR_AFTER), \\n line_shape=\\\"linear\\\", showlegend=showlegend) # linear or spline \\n subplot_prediction = go.Scatter(x=df_prediction[\\\"date\\\"], y=df_prediction[\\\"prediction\\\"], \\n mode=\\\"lines\\\", name=\\\"Expected Queries\\\",\\n line=dict(width=2, color=config.LINE_COLOR_BEFORE, dash=\\\"dot\\\"), \\n line_shape=\\\"linear\\\", showlegend=showlegend) # linear or spline \\n subplot_lockdown_legend = go.Bar(x=[reopen_date,], y=[0,], \\n name=\\\"Early Lockdown Phase\\\", \\n showlegend=showlegend,\\n marker_color=\\\"#eeeeee\\\")\\n fig.add_trace(subplot_before, row=row, col=col)\\n fig.add_trace(subplot_after, row=row, col=col)\\n fig.add_trace(subplot_prediction, row=row, col=col)\\n if idx == 0:\\n fig.add_trace(subplot_lockdown_legend, row=row, col=col)\\n\\n # break\\n\\n # Caption\\n # caption = go.layout.Annotation(\\n # showarrow=False,\\n # text=\\\"\\\",\\n # xanchor=\\\"center\\\",\\n # x=0.5,\\n # yanchor=\\\"top\\\",\\n # y=0.0,\\n # yshift=0,\\n # )\\n\\n # Layout\\n # location = f\\\"{country}.{state}\\\" if state else country\\n # fig_title = f\\\"\\\"\\\"Term: {group}. Location: {location}<br>\\n # <span style=\\\"font-size: 14px;line-height:1\\\">Period: {data_start_date} - {data_end_date}\\n # <br>Lockdown Period: {covid_start_date} - {PREDICT_FROM_DATE}</span>\\\"\\\"\\\"\\n fig_title = \\\"\\\"\\n fig.update_layout(title={\\\"text\\\": fig_title, \\\"x\\\":0.5, \\\"xanchor\\\": \\\"center\\\"}, \\n title_font=dict(size=12),\\n height=50 + n_rows * 175, width=250 * n_cols, coloraxis=dict(colorscale=\\\"Bluered_r\\\"), \\n showlegend=True, plot_bgcolor=\\\"rgb(255,255,255)\\\", titlefont={\\\"size\\\": 30},\\n margin={\\\"t\\\": 50},\\n annotations=annotations,\\n legend=dict(\\n orientation=\\\"v\\\",\\n yanchor=\\\"bottom\\\",\\n y=0,\\n xanchor=\\\"right\\\",\\n x=1,\\n bgcolor=\\\"white\\\",\\n bordercolor=\\\"#333\\\",\\n borderwidth=1\\n )\\n )\\n fig.update_xaxes(showgrid=False, showticklabels=False, showline=False)\\n fig.update_yaxes(showgrid=False, showticklabels=False, showline=True, range=value_range)\\n\\n # Store model parameters\\n mkdir_if_not_exist(config.TRENDS_OUTPUT_DIR)\\n df_params = pd.DataFrame(model_params, columns=[\\\"Query\\\", \\\"Order\\\"])\\n df_params.to_csv(\\\"%s/ARIMA_orders_%s.csv\\\" % (config.TRENDS_OUTPUT_DIR, group), index=False)\\n\\n # Create online URL\\n url = py.iplot(fig, filename=group, file_id=group)\\n print(\\\"URL:\\\", url.src)\\n\\n if config.TRENDS_EXPORT_FIGURES:\\n # Save\\n mkdir_if_not_exist(config.TRENDS_FIGURES_DIR)\\n fig.write_image(\\\"%s/%s_%s_%s.jpg\\\" % (config.TRENDS_FIGURES_DIR, country, state or \\\"All\\\", group))\\n # fig.show()\\n else:\\n # Show\\n fig.show()\",\n \"def show_dashboard():\\n script, div = plots.make_plot()\\n script_tab, div_tab = plots.make_tabs()\\n script_trend, div_trend = plots.make_trend()\\n\\n return render_template('layout.html',\\n script=script,\\n div=div,\\n script_trend=script_trend,\\n div_trend=div_trend,\\n script_tab=script_tab,\\n div_tab=div_tab)\",\n \"def getWidget(self):\",\n \"def heatmap_termlevel_dropdown(topic_index, embed_id):\\n\\t\\tlog.info(\\\"Callback: heatmap_termlevel_dropdown: topic_index=%s embed_id=%s\\\" % (topic_index, embed_id))\\n\\t\\tlayout.current_topic_index = int(topic_index)\\n\\t\\tlayout.current_embed_id = embed_id\\n\\t\\treturn layout.generate_termlevel_heatmap()\",\n \"def showTopStories(self):\\n\\t\\tif self.newestOrTop == \\\"newest\\\":\\n\\t\\t\\tself.newestOrTop = \\\"top\\\"\\n\\t\\t\\tprint \\\"Getting the latest stories from HN...\\\"\\n\\t\\t\\tself.refreshStories()\\n\\t\\telse:\\n\\t\\t\\tinput = raw_input(\\\"Already showing top stories. Press Return to continue.\\\")\\n\\t\\tself.printStories()\",\n \"def render_investip():\\n\\tlinewidth = 2\\n\\n\\tst.sidebar.markdown('# Dashboard')\\n\\tstock = st.sidebar.selectbox('Stock:', stocks)\\n\\n\\tstartdd = datetime.datetime(2020, 3, 1)\\n\\tstartdd = st.sidebar.date_input('start-date', value=startdd)\\n\\n\\tendd = datetime.datetime.now()\\n\\tendd = st.sidebar.date_input('end-date', value=endd)\\n\\n\\tt0 = stock\\n\\tt0_ohlc = extract(ticker=t0, start_date=startdd, end_date=endd)\\n\\tt0_df = pd.DataFrame({f'{t0}-Close': t0_ohlc.Close})\\n\\n\\t# st.write(t0_ohlc)\\n\\tmpf.plot(t0_ohlc, type='candle',volume=True,show_nontrading=False, title=t0, figscale=1.)\\n\\t# tdf = plot_ticker(t0, df=t0_df, start_date=startdd, end_date=endd)\\n\\tst.pyplot()\\n\\n\\n\\tst.sidebar.markdown('## Stock Correlation')\\n\\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_corrs')\\n\\tif stock_returns:\\n\\t\\tst.markdown('## Stock Correlation')\\n\\t\\tstock_selection = st.sidebar.multiselect('Stocks', stocks, def_stocks)\\n\\t\\tplot_stock_correlations(stock_selection, startdd, endd)\\n\\t\\tst.pyplot()\\n\\n\\t# trading_context = True\\n\\tst.sidebar.markdown('## Returns')\\n\\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_returns')\\n\\tif stock_returns:\\n\\t\\tst.markdown('## Stock Returns')\\n\\t\\tst.markdown('''### Daily Stock returns\\n[EWMA](https://www.investopedia.com/articles/07/ewma.asp)''')\\n\\t\\tspan = st.sidebar.slider('span', 2, 21, value=5)\\n\\t\\tplot_historical(t0, t0_ohlc, span=span, linewidth=linewidth)\\n\\t\\tst.pyplot()\\n\\n\\n\\t# trading_context = True\\n\\tst.sidebar.markdown('## Volatility')\\n\\ttrading_context = st.sidebar.checkbox('Enable', value=False, key='cb_volatility')\\n\\tif trading_context:\\n\\t\\tst.markdown('## Volatility & Risk')\\n\\t\\tst.markdown('''### Daily differences between High & Low\\nWe model these ranges with [Inverse Gamma PDF](https://en.wikipedia.org/wiki/Inverse-gamma_distribution).\\nGreen lines denote +/- 1 stdev.\\n''')\\n\\t\\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\\n\\t\\tf.suptitle(f'{t0} High-Low Daily')\\n\\t\\tmmd = t0_ohlc.High - t0_ohlc.Low\\n\\t\\t# mmd.dropna(inplace=True)\\n\\t\\tmmd.plot(color='r', ax=ax[0], lw=linewidth)\\n\\n\\t\\tmu, sigma = mmd.dropna().mean(), mmd.dropna().std()\\n\\t\\tzval = 1.#96\\n\\t\\t# TODO: try one-tail limit to get outliers\\n\\t\\t_=ax[0].axhline(y=mu, color='k', lw=linewidth)\\n\\t\\t_=ax[0].axhline(y=mu-zval*sigma, color='g', lw=linewidth)\\n\\t\\t_=ax[0].axhline(y=mu+zval*sigma, color='g', lw=linewidth)\\n\\n\\t\\tp95 = mmd.dropna().quantile(.95)\\n\\t\\t_=ax[0].axhline(y=p95, color='b', lw=linewidth, label='p95')\\n\\t\\t_=ax[1].axvline(p95, color='b', lw=linewidth, label='p95')\\n\\n\\t\\twith warnings.catch_warnings():\\n\\t\\t warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\\t\\t print(invgamma.fit(mmd))\\n\\t\\t sns.distplot(mmd, fit=invgamma, kde=False, ax=ax[1])\\n\\t\\t_=ax[1].axvline(mmd.values[-1], color='r', label='last', lw=linewidth)\\n\\t\\t_=ax[1].axvline(mu, color='k', label='mean', lw=linewidth)\\n\\t\\t_=ax[1].legend()\\n\\t\\tst.pyplot()\\n\\n\\t\\tst.markdown('''### Daily Average True Range (ATR)\\nImplementation follows [ATR](https://kodify.net/tradingview/indicators/average-true-range/).\\nCheck [Investopedia](https://www.investopedia.com/terms/a/atr.asp) for more info.''')\\n\\n\\t\\tatr_df = pd.DataFrame({\\n\\t\\t\\tf'{t0}-High-Low': t0_ohlc.High - t0_ohlc.Low,\\n\\t\\t\\tf'{t0}-High-PrevCloseAbs': abs(t0_ohlc.High - t0_ohlc.Close.shift(1)),\\n\\t\\t\\tf'{t0}-Low-PrevCloseAbs': abs(t0_ohlc.Low - t0_ohlc.Close.shift(1)),\\n\\t\\t}).max(axis=1)\\n\\t\\tatr_df = pd.DataFrame({\\n\\t\\t\\tf'{t0}-true-range': atr_df,\\n\\t\\t})\\n\\t\\tatr_df[f'{t0}-ATR14'] = atr_df.iloc[:, 0].rolling(14).mean()\\n\\t\\t# st.write(atr_df)\\n\\n\\t\\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\\n\\t\\tf.suptitle(f'{t0} True Range & SMA14')\\n\\t\\tatr_df.plot(ax=ax[0], lw=linewidth)\\n\\n\\t\\twith warnings.catch_warnings():\\n\\t\\t warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\\t\\t #print(invgamma.fit(f'{t0}-true-range'))\\n\\t\\t sns.distplot(atr_df[f'{t0}-true-range'], fit=invgamma, kde=False, ax=ax[1])\\n\\t\\t_=ax[1].axvline(atr_df[f'{t0}-true-range'].values[-1], color='b', label='last', lw=linewidth)\\n\\t\\t_=ax[1].axvline(atr_df[f'{t0}-ATR14'].values[-1], color='r', label='last', lw=linewidth)\\n\\t\\t_=ax[1].legend()\\n\\t\\tst.pyplot()\\n\\n\\n\\n\\t# do_strategy_analysis = True\\n\\tst.sidebar.markdown('## Trading Strategy')\\n\\tdo_strategy_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_stra')\\n\\tif do_strategy_analysis:\\n\\t\\tst.markdown('## Trading Strategy')\\n\\t\\tst.markdown('[investopedia](https://www.investopedia.com/articles/active-trading/052014/how-use-moving-average-buy-stocks.asp)')\\n\\t\\tshort_window = st.sidebar.slider('short_window', 2, 21, 3)\\n\\t\\tlong_window = st.sidebar.slider('long_window', 3, 50, 5)\\n\\t\\tplot_strategy(t0, t0_df, short_window, long_window)\\n\\t\\tst.pyplot()\\n\\n\\t# do_corr_analysis = False\\n\\tst.sidebar.markdown('## Correlation analysis')\\n\\tdo_corr_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_corr')\\n\\tif do_corr_analysis:\\n\\t\\tst.markdown('## Correlation analysis')\\n\\t\\tt1= 'GC=F' # # SP500 'GC=F'\\n\\t\\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\\n\\t\\tt1 = st.sidebar.selectbox('REF1:', stocks, index=stocks.index(t1))\\n\\t\\tt2 = st.sidebar.selectbox('REF2:', stocks, index=stocks.index(t2))\\n\\t\\tif st.sidebar.button('Reset'):\\n\\t\\t\\tt1 = 'GC=F' # # SP500 'GC=F'\\n\\t\\t\\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\\n\\t\\t\\t# t1 = st.sidebar.selectbox('ref1:', stocks, index=stocks.index(t1))\\n\\t\\t\\t# t2 = st.sidebar.selectbox('ref2:', stocks, index=stocks.index(t2))\\n\\n\\t\\t@st.cache(persist=True, show_spinner=False)\\n\\t\\tdef get_dataframes(t1, t2, startdd, endd):\\n\\t\\t\\tt1_ohlc = extract(ticker=t1, start_date=startdd, end_date=endd)\\n\\t\\t\\tt2_ohlc = extract(ticker=t2, start_date=startdd, end_date=endd)\\n\\t\\t\\treturn t1_ohlc, t2_ohlc\\n\\n\\t\\tt1_ohlc, t2_ohlc = get_dataframes(t1, t2, startdd, endd)\\n\\t\\tt1_df = pd.DataFrame({f'{t1}-Close': t1_ohlc.Close})\\n\\t\\tt2_df = pd.DataFrame({f'{t2}-Close': t2_ohlc.Close})\\n\\n\\t\\t#print(t0_ohlc.shape)\\n\\t\\t#t0_ohlc.head()\\n\\t\\t# print(t1_ohlc.shape)\\n\\t\\t# ticker_ohlc.head()\\n\\t\\t# ticker_ohlc.info()\\n\\n\\t\\ttdf = t0_df.join(t1_df).join(t2_df).interpolate().dropna()\\n\\t\\t# tdf.head(10)\\n\\n\\t\\t# t0_ohlc.corr(t1_ohlc)\\n\\t\\t#ax = t0_ohlc.Close.plot()\\n\\t\\t#t1_ohlc.Close.plot(ax=ax)\\n\\n\\t\\timport numpy as np\\n\\t\\tprint('glocal corrleation1: ', t0_ohlc.Close.corr(t1_ohlc.Close))\\n\\t\\tprint('glocal corrleation2: ', t0_ohlc.Close.corr(t2_ohlc.Close))\\n\\n\\t\\tp_window_size = 5\\n\\t\\tr_window_size = 5\\n\\t\\tcentering = False\\n\\n\\n\\t\\tmodf = lambda x: x\\n\\t\\t#modf = np.log10\\n\\n\\n\\t\\tmain_stat = f'[{t0}]-mean-roll{p_window_size}'\\n\\t\\talt_stat_1 = f'[{t1}]-mean-roll{p_window_size}'\\n\\t\\talt_stat_2 = f'[{t2}]-mean-roll{p_window_size}'\\n\\t\\t# df_rc = pd.DataFrame({\\n\\t\\t# main_stat : tdf.iloc[:, 0].apply(modf).rolling(window=p_window_size,center=centering).mean(),\\n\\t\\t# alt_stat_1: tdf.iloc[:, 1].apply(modf).rolling(window=p_window_size,center=centering).mean(),\\n\\t\\t# alt_stat_2: tdf.iloc[:, 2].apply(modf).rolling(window=p_window_size,center=centering).mean(),\\n\\t\\t# })\\n\\t\\tcom_val = 0.2\\n\\t\\tdf_rc = pd.DataFrame({\\n\\t\\t main_stat : tdf.iloc[:, 0].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\\n\\t\\t alt_stat_1: tdf.iloc[:, 1].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\\n\\t\\t alt_stat_2: tdf.iloc[:, 2].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\\n\\t\\t})\\n\\n\\t\\tdf_rc = df_rc.interpolate()\\n\\t\\tdf_rc[f'[{t0}]-[{t1}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_1])\\n\\t\\tdf_rc[f'[{t0}]-[{t2}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_2])\\n\\n\\t\\tf, ax = plt.subplots(3,1,figsize=(16,10),sharex=True)\\n\\t\\t#df_rc.iloc[:,0].plot(ax=ax[0], legend=True)\\n\\t\\tdf_rc.iloc[:,1].plot(ax=ax[0], legend=True, color='gold')\\n\\t\\tdf_rc.iloc[:,2].plot(ax=ax[1], legend=True, color='darkred')\\n\\t\\tdf_rc.iloc[:,3].plot(ax=ax[2], legend=True, color='gold')\\n\\t\\tdf_rc.iloc[:,4].plot(ax=ax[2], legend=True, color='darkred')\\n\\t\\tax[2].axhline(y=0, lw=1, color='black')\\n\\t\\t#t0_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[0])\\n\\t\\t#t1_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[1])\\n\\t\\t# ax[0].set(xlabel='Frame',ylabel='Smiling Evidence')\\n\\t\\t# ax[1].set(xlabel='Frame',ylabel='Pearson r')\\n\\t\\t_=plt.suptitle(f\\\"{t0} Close rolling correlation to {t1}, {t2}\\\")\\n\\n\\t\\tst.pyplot()\\n\\n\\n\\t\\tf,ax=plt.subplots(1, 2, figsize=(16,8),sharex=False)\\n\\n\\t\\t_= df_rc.plot.scatter(x=df_rc.columns[1],\\n\\t\\t y=df_rc.columns[2],\\n\\t\\t c=df_rc.columns[0],\\n\\t\\t colormap='viridis',\\n\\t\\t # legend=None,\\n\\t\\t ax=ax[0])\\n\\n\\t\\tprint(df_rc.columns)\\n\\t\\tnewr_p = df_rc.iloc[-1, 0]\\n\\t\\tt1_p = df_rc.iloc[-1, 1]\\n\\t\\tt2_p = df_rc.iloc[-1, 2]\\n\\t\\tt1_c = df_rc.dropna().iloc[-1, 3]\\n\\t\\tt2_c = df_rc.dropna().iloc[-1, 4]\\n\\t\\tprint('current_corr:', (t1_c, t2_c))\\n\\n\\t\\t# figure out circle size\\n\\t\\taaaa = df_rc.iloc[:, 1].aggregate([np.max, np.min])\\n\\t\\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\\n\\t\\tprint(aaaa.values[0], aaaa.values[1], xrange)\\n\\t\\txradius = xrange / 20.\\n\\n\\t\\tcircle = plt.Circle((t1_p, t2_p), xradius, color='r', fill=False)\\n\\t\\tax[0].add_artist(circle)\\n\\t\\t#ax[0].set_xlabel(f'GOLD Price {t1_p:.4f}')\\n\\t\\t#ax[0].set_ylabel(f'OIL Price {t2_p:.4f}')\\n\\t\\t# ax[0].legend().set_visible(False)\\n\\n\\t\\t_= df_rc.plot.scatter(x=df_rc.columns[-2],\\n\\t\\t y=df_rc.columns[-1],\\n\\t\\t c=df_rc.columns[0],\\n\\t\\t colormap='viridis',\\n\\t\\t # legend=True,\\n\\t\\t #linestyle=\\n\\t\\t ax=ax[1])\\n\\n\\t\\t# figure out circle size\\n\\t\\taaaa = df_rc.iloc[:, -2].aggregate([np.max, np.min])\\n\\t\\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\\n\\t\\tprint(aaaa.values[0], aaaa.values[1], xrange)\\n\\t\\txradius = xrange / 20.\\n\\n\\t\\tcircle1 = plt.Circle((t1_c, t2_c), xradius, color='r', fill=False)\\n\\t\\tax[1].add_artist(circle1)\\n\\t\\t#ax[1].set_ylabel('OIL Correlation')\\n\\t\\t#_= ax[1].set_xlabel('GOLD Correlation')\\n\\n\\n\\t\\tst.pyplot()\",\n \"def main():\\n option = st.sidebar.radio(\\\"Use Navigation Here:\\\",(\\\"Home\\\", \\\"Data Metrics\\\",\\n \\\"Customer Segmentation\\\",\\\"Customer Value Prediction\\\",\\n \\\"Churn Prediction\\\", \\\"Next Purchase Day\\\", \\\"Predicting Sales\\\",\\n \\\"Market Response\\\"\\n ))\\n if option == \\\"Home\\\":\\n with st.spinner(\\\"Wait for it...\\\"):\\n st.write(home_page())\\n elif option == \\\"Data Metrics\\\":\\n with st.spinner(\\\"Wait for it...\\\"):\\n st.write(metrics_of_data())\\n elif option == \\\"Customer Segmentation\\\":\\n with st.spinner(\\\"In Progress\\\"):\\n st.write(customer_segmentation())\\n elif option == \\\"Customer Value Prediction\\\":\\n with st.spinner(\\\"In Progress\\\"):\\n st.write(customer_value_prediction())\\n elif option == \\\"Churn Prediction\\\":\\n with st.spinner(\\\"In Progress\\\"):\\n st.write(churn_prediction())\\n elif option == \\\"Next Purchase Day\\\":\\n with st.spinner(\\\"In Progress\\\"):\\n st.write(predict_next_purchase_day())\\n elif option == \\\"Predicting Sales\\\":\\n with st.spinner(\\\"In Progress\\\"):\\n st.write(predicting_sales())\\n elif option == \\\"Market Response\\\":\\n with st.spinner(\\\"In Progress\\\"):\\n st.write(market_response_model())\",\n \"def __sidebar(self, conf):\\n result = \\\"\\\"\\n\\n i = conf[\\\"conf_json\\\"][0]\\n result += \\\"\\\"\\\"\\n\\n <h4>%s</h4>\\n <ul id=\\\"sidebar_menu\\\" class=\\\"sidebar_menu\\\">\\n <li><a class=\\\"btn active\\\" href=\\\"#description\\\">DESCRIPTION</a></li>\\n <li><a class=\\\"btn\\\" href=\\\"#parameters\\\">PARAMETERS</a></li>\\n <li><a class=\\\"btn\\\" href=\\\"#operations\\\">OPERATIONS</a>\\n <ul class=\\\"sidebar_submenu\\\">%s</ul>\\n </li>\\n <li><a class=\\\"btn active\\\" href=\\\"/\\\">HOME</a></li>\\n </ul>\\n \\\"\\\"\\\" % (\\n i[\\\"title\\\"],\\n \\\"\\\".join(\\n [\\n \\\"<li><a class='btn' href='#%s'>%s</a></li>\\\" % (op[\\\"url\\\"], op[\\\"url\\\"])\\n for op in conf[\\\"conf_json\\\"][1:]\\n ]\\n ),\\n )\\n return result\",\n \"def get_trending_categories(self):\\n # TODO: Assuming first server is good - need to make fallback logic\\n return self.session.get_any(\\\"{base}{request_url}\\\".format(base=self.servers[0],\\n request_url=F\\\"/Trending/Categories/\\\"))\",\n \"def index():\\r\\n\\r\\n title = \\\"Global Statistics\\\"\\r\\n\\r\\n n_auth = n_auth_users()\\r\\n\\r\\n n_anon = n_anon_users()\\r\\n\\r\\n n_total_users = n_anon + n_auth\\r\\n\\r\\n n_published_apps = cached_apps.n_published()\\r\\n n_draft_apps = cached_apps.n_draft()\\r\\n n_total_apps = n_published_apps + n_draft_apps\\r\\n\\r\\n n_tasks = n_tasks_site()\\r\\n\\r\\n n_task_runs = n_task_runs_site()\\r\\n\\r\\n top5_apps_24_hours = get_top5_apps_24_hours()\\r\\n\\r\\n top5_users_24_hours = get_top5_users_24_hours()\\r\\n\\r\\n locs = get_locs()\\r\\n\\r\\n show_locs = False\\r\\n if len(locs) > 0:\\r\\n show_locs = True\\r\\n\\r\\n stats = dict(n_total_users=n_total_users, n_auth=n_auth, n_anon=n_anon,\\r\\n n_published_apps=n_published_apps,\\r\\n n_draft_apps=n_draft_apps,\\r\\n n_total_apps=n_total_apps,\\r\\n n_tasks=n_tasks,\\r\\n n_task_runs=n_task_runs)\\r\\n\\r\\n users = dict(label=\\\"User Statistics\\\",\\r\\n values=[\\r\\n dict(label='Anonymous', value=[0, n_anon]),\\r\\n dict(label='Authenticated', value=[0, n_auth])])\\r\\n\\r\\n apps = dict(label=\\\"Apps Statistics\\\",\\r\\n values=[\\r\\n dict(label='Published', value=[0, n_published_apps]),\\r\\n dict(label='Draft', value=[0, n_draft_apps])])\\r\\n\\r\\n tasks = dict(label=\\\"Task and Task Run Statistics\\\",\\r\\n values=[\\r\\n dict(label='Tasks', value=[0, n_tasks]),\\r\\n dict(label='Answers', value=[1, n_task_runs])])\\r\\n\\r\\n return render_template('/stats/global.html', title=title,\\r\\n users=json.dumps(users),\\r\\n apps=json.dumps(apps),\\r\\n tasks=json.dumps(tasks),\\r\\n locs=json.dumps(locs),\\r\\n show_locs=show_locs,\\r\\n top5_users_24_hours=top5_users_24_hours,\\r\\n top5_apps_24_hours=top5_apps_24_hours,\\r\\n stats=stats)\",\n \"def display_station_stats(self):\\n\\n self.station_frame = stat_display_labels(\\n self.stats_frame,\\n \\\"Station Stats\\\",\\n [\\n \\\"The most popular start station was:\\\",\\n \\\"The most popular end station was:\\\",\\n \\\"The most popular start/end station combination was:\\\",\\n ],\\n row=1,\\n columnspan=5,\\n )\\n self.station_stats_data = tk.Label(self.station_frame, justify=\\\"left\\\")\\n self.station_stats_data.grid(row=0, column=1)\",\n \"def search_tweets(request):\\n return render(request, 'ede/search.html')\",\n \"def viewWidgetCreated(self, view, plot):\\n return\",\n \"def disqus_popular_threads_widget(context, shortname='', num_items=5):\\n shortname = getattr(settings, 'DISQUS_WEBSITE_SHORTNAME', shortname)\\n return {\\n 'shortname': shortname,\\n 'num_items': int(num_items),\\n }\",\n \"def main():\\r\\n root = tk.Tk()\\r\\n root.geometry(\\\"250x100+300+300\\\")\\r\\n TrendProg(root)\\r\\n root.mainloop()\",\n \"def main():\\n \\n\\n\\t# Creates a main title and subheader on your page -\\n\\t# these are static across all pages\\n\\t#st.title(\\\"Tweet Classifer\\\")\\n\\n\\n #image= Image.open('resources/imgs/global_warming_img.jpg')\\n\\n \\n\\n\\n\\t# Creating sidebar with selection box -\\n\\t# you can create multiple pages this way\\n\\toptions = [\\\"Background\\\",\\\"Prediction\\\",\\\"Data visualization\\\", \\\"Information\\\",\\\"Project Team\\\"]\\n\\tselection = st.sidebar.radio(\\\"Choose page:\\\",options )\\n\\t#selection = st.sidebar.selectbox(\\\"Choose Option\\\", options)\\n\\n\\t# Builing out the \\\"Background\\\" page\\n\\tif selection == \\\"Background\\\":\\n\\t\\tst.info('This works');\\t\\n\\t\\tst.write('Explorers !!!')\\t\\n\\t\\timage= Image.open('resources/imgs/global_warming_img.jpg')\\n\\t\\tst.image(image, caption='https://www.azocleantech.com/article.aspx?ArticleID=898', use_column_width=True)\\n\\t\\t\\n\\n\\n\\tif selection == \\\"Project Team\\\" :\\n\\n\\n\\t\\t#First row of pictures\\n\\n\\t\\tcol1, col2,col3 = st.beta_columns(3)\\n\\t\\tRic_Pic =Image.open('resources/imgs/Rickie_pic.png') \\n\\t\\tcol1.image(Ric_Pic,caption=\\\"Rickie Mogale Mohale\\\", width=150)\\n\\t\\t\\n \\n\\t\\tCot_Pic =Image.open('resources/imgs/courtney_pic.png') \\n\\t\\tcol2.image(Cot_Pic,caption=\\\"Courtney Murugan\\\", width=150)\\n\\t\\t\\n\\n\\t\\tCot_Pic =Image.open('resources/imgs/jacques_pic.png') \\n\\t\\tcol3.image(Cot_Pic,caption=\\\"Jacques Stander\\\", width=150)\\n\\t\\t\\n\\n #Second row of pictures\\n\\t\\tcol4, col5,col6 = st.beta_columns(3)\\n\\t\\tvas_Pic =Image.open('resources/imgs/Rickie_pic.png') \\n\\t\\tcol4.image(Ric_Pic,caption=\\\"Veshen Naidoo\\\", width=150)\\n\\t\\t\\n \\n\\t\\tPhiw_Pic =Image.open('resources/imgs/Rickie_pic.png') \\n\\t\\tcol5.image(Phiw_Pic,caption=\\\"Phiweka Mthini\\\", width=150)\\n\\n\\t\\tnor_Pic =Image.open('resources/imgs/Rickie_pic.png') \\n\\t\\tcol6.image(nor_Pic,caption=\\\"Nourhan ALfalous\\\", width=150)\\n\\n\\t\\t#Third row of picture \\n\\t\\tcol7, col8,col9 = st.beta_columns(3)\\n\\n\\t\\tzin_Pic =Image.open('resources/imgs/zintle_pic.png') \\n\\t\\tcol8.image(zin_Pic,caption='Zintle Faltein-Maqubela', width=150)\\n\\t\\tcol8.header(\\\"Role : Team Supervisor\\\")\\n\\t\\t\\n\\t# Building out the \\\"Information\\\" page\\n\\tif selection == \\\"Information\\\":\\n\\n\\t\\tst.info(\\\"General Information\\\");\\n\\t\\t# You can read a markdown file from supporting resources folder\\n\\t\\tst.markdown(\\\"Some information here\\\");\\n\\n\\t\\tst.subheader(\\\"Raw Twitter data and label\\\");\\n\\t\\tif st.checkbox('Show raw data'): # data is hidden if box is unchecked\\n\\t\\t\\tst.write(raw[['sentiment', 'message']]) # will write the df to the page\\n\\n\\t# Building out the predication page\\n\\tif selection == \\\"Prediction\\\":\\n\\t\\n\\n\\t\\t\\n\\t\\t\\n\\t\\tst.markdown(\\\"\\\")\\n\\t\\tst.info('This page will help you predict an individual\\\\'s position on global warming base on their tweet')\\n\\t\\tst.subheader('To make predictions, please follow the three steps below')\\n\\t\\t\\n\\t\\t#selecting input text\\n\\t\\ttext_type_selection = ['Single tweet','multiple tweets'] \\n\\t\\ttext_selection = st.selectbox('Step 1 ) : Text input', text_type_selection)\\n\\n\\t\\t\\n\\t\\t#C\\n\\t\\tdef get_keys(val,my_dict):\\n\\t\\t\\tfor key,value in my_dict.items():\\n\\t\\t\\t\\tif val == value:\\n\\t\\t\\t\\t\\t return key\\n\\t\\t#C\\n\\n\\n\\t\\t# User selecting prediction model\\n\\t\\t#Models = [\\\"Logistic regression\\\",\\\"Decision tree\\\",\\\"Random Forest Classifier\\\",\\\"Naive Bayes\\\",\\\"XGboost\\\",\\\"Linear SVC\\\"]\\n\\t\\t#selected_model =st.selectbox(\\\"Step 3 ) : Choose prediction model \\\",Models )\\n \\n\\n\\t\\tif text_selection== 'Single tweet':\\n ### SINGLE TWEET CLASSIFICATION ###\\n\\t\\t\\t\\n # Creating a text box for user input\\n\\t\\t\\tinput_text = st.text_area(\\\"Step 2 ) : Enter Your Single Text Below :\\\") ##user entering a single text to classify and predict\\n\\t\\t\\tModels = [\\\"Logistic regression\\\",\\\"Decision tree\\\",\\\"Random Forest\\\",\\\"Naive Bayes\\\",\\\"XGboost\\\",\\\"Linear SVC\\\" ]\\n\\t\\t\\tselected_model = st.selectbox(\\\"Step 3 ) : Choose prediction model \\\",Models)\\n\\t\\t\\tdef load_prediction_models(model_file):\\n\\n\\t\\t\\t\\tloaded_models = joblib.load(open(os.path.join(model_file),\\\"rb\\\"))\\n\\t\\t\\t\\treturn loaded_models\\n\\n\\t\\t\\tprediction_labels = {'Negative':-1,'Neutral':0,'Positive':1}\\n\\t\\t\\tif st.button(\\\"Classify\\\"):\\n\\t\\t\\t\\t## showing the user original text\\n\\t\\t\\t\\tst.text(\\\"Input tweet is :\\\\n{}\\\".format(input_text))\\n\\n\\t\\t\\t\\t## Calling a function to process the text\\n\\t\\t\\t\\t#tweet_text = cleaner(input_text) \\n\\n\\t\\t\\t\\t# Transforming user input with vectorizer\\n\\t\\t\\t\\tvect_text = tweet_cv.transform([input_text]).toarray()\\n\\t\\t\\t\\t# Load your .pkl file with the model of your choice + make predictions\\n\\t\\t\\t\\t# Try loading in multiple models to give the user a choice\\n\\t\\t\\t\\tpredictor = joblib.load(open(os.path.join(\\\"resources/Logistic_regression.pkl\\\"),\\\"rb\\\"))\\n\\t\\t\\t\\tprediction = predictor.predict(vect_text)\\n\\n \\t#M Model_ Selection\\n\\t\\t\\t\\tif selected_model == \\\"Logistic regression\\\":\\n\\n\\t\\t\\t\\t\\tpredictor = load_prediction_models(\\\"resources/Logistic_regression.pkl\\\")\\n\\t\\t\\t\\t\\tprediction = predictor.predict(vect_text)\\n \\t # st.write(prediction)\\n\\t\\t\\t\\telif selected_model == \\\"Decision tree\\\":\\n\\n\\t\\t\\t\\t\\tpredictor = load_prediction_models(\\\"resources/Logistic_regression.pkl\\\")\\n\\t\\t\\t\\t\\tprediction = predictor.predict(vect_text)\\n # st.write(prediction)\\n\\t\\t\\t\\telif selected_model == \\\"Random Forest Classifier\\\":\\n\\t\\t\\t\\t\\tpredictor = load_prediction_models(\\\"resources/Logistic_regression.pkl\\\")\\n\\t\\t\\t\\t\\tprediction = predictor.predict(vect_text)\\n # st.write(prediction)\\n\\t\\t\\t\\telif selected_model == \\\"Naive Bayes\\\":\\n\\t\\t\\t\\t\\tpredictor = load_prediction_models(\\\"resources/Logistic_regression.pkl\\\")\\n\\t\\t\\t\\t\\tprediction = predictor.predict(vect_text)\\n\\t\\t\\t\\telif selected_model ==\\\"XGboost\\\" :\\n\\t\\t\\t\\t\\t predictor = load_prediction_models(\\\"resources/Logistic_regression.pkl\\\")\\n\\t\\t\\t\\t\\t prediction = predictor.predict(vect_text)\\n\\t\\t\\t\\telif selected_model == \\\"Linear SVC\\\" :\\n\\t\\t\\t\\t\\tpredictor = load_prediction_models(\\\"resources/Logistic_regression.pkl\\\")\\n\\t\\t\\t\\t\\tprediction = predictor.predict(vect_text)\\n\\t\\t\\t\\t# st.write(prediction)\\n\\t\\t\\t # When model has successfully run, will print prediction\\n\\t\\t\\t # You can use a dictionary or similar structure to make this output\\n\\t\\t\\t # more human interpretable.\\n\\t\\t\\t # st.write(prediction)\\n\\t\\t\\t\\tfinal_result = get_keys(prediction,prediction_labels)\\n\\t\\t\\t\\tst.success(\\\"Tweet Categorized as : {}\\\".format(final_result))\",\n \"def update_top_sources_lollipop(data):\\n try:\\n fig = get_lollipop_plot(data)\\n except Exception as e:\\n # Failsafe in case all values from dropdown are deleted\\n fig = {}\\n # logger.error(\\\"Top sources app error:\\\", e)\\n return fig\",\n \"def muckrack_trending_topics():\\n\\tresponse = requests.get('http://muckrack.com')\\n\\thtml = response.text\\n\\tdom = pq(html)\\n\\ttrending_list = dom('.trending')\\n\\treturn [topic.text for topic in trending_list]\",\n \"def tips():\\n category = list(mongo.db.tips.find().sort(\\\"tip_date\\\", -1))\\n return render_template(\\\"tips.html\\\", category=category)\",\n \"def showSideBar(self):\\n\\t\\treturn False\",\n \"def create_widgets(self):\",\n \"def display_All_T_Confidence(self):\\n for s in ('300', '310', '323', '343'):\\n SVM = ResampleSVM(self.FormatDataFromCSV(\\\"PD_P1_SVM/Experimental Data P1/ER2_T\\\" + s + \\\".csv\\\"))\\n self.DisplayTernaryPhaseScatter(\\n self.FormatDataFromCSV(\\\"PD_P1_SVM/Experimental Data P1/ER2_T\\\" + s + \\\".csv\\\")[1], 10, s + \\\" Lab Data\\\")\\n optimal = pd.read_csv(\\\"T\\\" + s + \\\"-OUT.csv\\\").values.tolist()[0][1:3]\\n print(\\\"Optimal Parameters used: \\\")\\n print(optimal)\\n self.DisplayTernaryPhaseScatter(SVM.generate_all_phase(SVM.data_in[1], optimal[0], optimal[1], .01), 7,\\n s + \\\" SVM Optimized\\\")\\n data_in = pd.read_csv(\\\"Confidence\\\" + s + \\\".csv\\\").values.tolist()\\n\\n conf = list(map(lambda x: x[4], data_in))\\n fig = go.Figure(go.Scatterternary({\\n 'mode': 'markers',\\n 'a': list(map(lambda x: x[3], data_in)),\\n 'b': list(map(lambda x: x[2], data_in)),\\n 'c': list(map(lambda x: x[1], data_in)),\\n 'text': conf,\\n 'marker': {\\n 'symbol': 0,\\n 'color': conf,\\n 'size': 7,\\n 'colorbar': dict(title=\\\"Colorbar\\\"),\\n 'colorscale': \\\"Viridis\\\"}\\n })\\n )\\n\\n fig.update_layout(\\n title=go.layout.Title(\\n text=\\\"Confidence T-\\\" + s))\\n plotly.offline.plot(fig, filename=\\\"ConfidenceT\\\" + s + \\\".html\\\")\\n return\",\n \"def index(request):\\n template = loader.get_template('global.html')\\n hot_topics = serializers.serialize(\\\"json\\\", Topic.objects.order_by('-updated_at')[:5])\\n random_topic = serializers.serialize(\\\"json\\\", Topic.objects.order_by('?')[:1])\\n\\n context = {\\n 'hot_topics': hot_topics,\\n 'random_topic': random_topic,\\n 'request': request,\\n }\\n return HttpResponse(template.render(context, request))\",\n \"def get_trends(self, keywords, start, end, region=None, category=None):\\n # If only a single keyword is given, i.e. as a string and not as a list, put the single string into a list\\n if not isinstance(keywords, list):\\n keywords = [keywords]\\n # Convert the date strings to Google's format:\\n start = adjust_date_format(\\n start, self.date_format, self._google_date_format)\\n end = adjust_date_format(\\n end, self.date_format, self._google_date_format)\\n # Create datetime objects from the date-strings:\\n start_datetime = datetime.strptime(start, self._google_date_format)\\n end_datetime = datetime.strptime(end, self._google_date_format)\\n data_keywords_list = []\\n for keywords_i in get_chunks(keywords, 5):\\n # Get the trends over the entire sample:\\n url_all_i = self.create_url(keywords_i,\\n previous_weekday(start_datetime, 0), next_weekday(\\n end_datetime, 6),\\n region, category)\\n data_all_i, frequency_i = self.get_data(url_all_i)\\n # If the data for the entire sample is already at the daily frequency we are done. Otherwise we need to\\n # get the trends for sub-periods\\n if frequency_i == 'Daily':\\n data_i = data_all_i\\n else:\\n # Iterate over the URLs of the sub-periods and retrieve the Google Trend data for each\\n data_time_list = []\\n for url in self.create_urls_subperiods(keywords_i, start_datetime, end_datetime, region, category):\\n data_time_list.append(self.get_data(url)[0])\\n # Concatenate the so obtained set of DataFrames to a single DataFrame\\n data_i = concat_data(\\n data_time_list, data_all_i, keywords_i, frequency_i)\\n # Add the data for the current list of keywords to a list\\n data_keywords_list.append(data_i)\\n # Merge the multiple keyword chunks\\n data = merge_keyword_chunks(data_keywords_list)\\n # Cut data to return only the desired period:\\n data = data.loc[data.index.isin(pd.date_range(\\n start_datetime, end_datetime, freq='D'))]\\n return data\",\n \"def interactive_annual_monthly_bar_report_with_buttons(year: int):\\n condition_dict = {'key': 'category', 'operation': '!=', 'value': 16}\\n average_monthly_amount = Spending().get_average_monthly_spending_amount(condition_dict=condition_dict)\\n current_annual_monthly_amount = Spending().get_monthly_total_spending_of_a_year(year, include_doctor=False)\\n\\n df_average_monthly = pd.DataFrame(average_monthly_amount, columns=['avg', 'mon'])\\n df_current_annual_monthly = pd.DataFrame(current_annual_monthly_amount, columns=['sum', 'mon'])\\n\\n fig = go.Figure()\\n\\n fig.add_trace(\\n go.Bar(\\n x=df_current_annual_monthly['mon'],\\n y=df_current_annual_monthly['sum'],\\n name=f'{year} monthly spending'\\n )\\n )\\n\\n fig.add_trace(\\n go.Scatter(\\n x=df_average_monthly['mon'],\\n y=df_average_monthly['avg'],\\n name='Average Monthly Spending',\\n visible=True,\\n line=dict(color=\\\"#33CFA5\\\", dash=\\\"dash\\\")\\n )\\n )\\n\\n fig.update_layout(\\n updatemenus=[\\n dict(\\n type='buttons',\\n direction='right',\\n active=1,\\n x=0.57,\\n y=1.2,\\n buttons=list([\\n dict(\\n label='Current Year',\\n method='update',\\n args=[\\n {'visible': [True, False]},\\n {'title': f'{year} monthly spending', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Average',\\n method='update',\\n args=[\\n {'visible': [True, True]},\\n {'title': 'Compare with Average monthly', \\\"annotations\\\": []}\\n ]\\n ),\\n ]),\\n )\\n ]\\n )\\n\\n fig.update_layout(\\n {\\n 'width': 960,\\n 'height': 550\\n },\\n overwrite=True\\n )\\n\\n aPlot = plotly.offline.plot(fig,\\n config={\\\"displayModeBar\\\": False},\\n show_link=False,\\n include_plotlyjs=False,\\n output_type='div')\\n\\n return aPlot\",\n \"def menu_python_daily(self, event=None):\\n self.link('http://www.pythonware.com/daily/')\",\n \"def admin_sidebar(context):\\n request = context.get('request')\\n\\n request_context = {\\n 'count_users': User.objects.count(),\\n 'count_review_groups': Group.objects.count(),\\n 'count_default_reviewers': DefaultReviewer.objects.count(),\\n 'count_oauth_applications': Application.objects.count(),\\n 'count_repository': Repository.objects.accessible(\\n request.user, visible_only=False).count(),\\n 'count_webhooks': WebHookTarget.objects.count(),\\n 'count_hosting_accounts': HostingServiceAccount.objects.count(),\\n 'version': get_version_string(),\\n }\\n\\n # We're precomputing URLs in here, rather than computing them in the\\n # template, because we need to always ensure that reverse() will be\\n # searching all available URL patterns and not just the ones bound to\\n # request.current_app.\\n #\\n # current_app gets set by AdminSite views, and if we're in an extension's\\n # AdminSite view, we'll fail to resolve these URLs from within the\\n # template. We don't have that problem if calling reverse() ourselves.\\n request_context.update({\\n 'url_%s' % url_name: reverse('admin:%s' % url_name)\\n for url_name in ('auth_user_add',\\n 'auth_user_changelist',\\n 'hostingsvcs_hostingserviceaccount_add',\\n 'hostingsvcs_hostingserviceaccount_changelist',\\n 'notifications_webhooktarget_add',\\n 'notifications_webhooktarget_changelist',\\n 'oauth_application_add',\\n 'oauth_application_changelist',\\n 'reviews_defaultreviewer_add',\\n 'reviews_defaultreviewer_changelist',\\n 'reviews_group_add',\\n 'reviews_group_changelist',\\n 'scmtools_repository_add',\\n 'scmtools_repository_changelist')\\n })\\n\\n return RequestContext(request, request_context)\",\n \"def CLI_trend( RFinfo,var, den, fireyear=False, plot=True, testmethod=\\\"OLS\\\"):\\n\\n\\t# warn.warn(\\n\\t# \\t'''\\n\\t# \\tThis is currently only in alpha testing form\\n\\t# \\ti'm going to using a simple trend test without\\n\\t# \\tany consideration of significance. i used cdo\\n\\t# \\tregres on copernicious NDVI data to start. \\n\\t# \\t''')\\n\\t\\n\\t# warn.warn(\\n\\t# \\t'''\\n\\t# \\tThis approach is currently ambivilant to when fire \\n\\t# \\tevents occured. This may need to be incoperated with \\n\\t# \\tsome form of breakpoint detection (Chow test or MVregression)\\n\\t# \\t''')\\n\\n\\t# ========== Load in the trend data using xarray ==========\\n\\tif fireyear:\\n\\t\\tif var == \\\"ppt\\\":\\n\\t\\t\\tncin = \\\"./data/cli/1.TERRACLIMATE/TerraClimate_merged_1980to2017_ppt_yearsum_RUSSIA.nc\\\"\\n\\t# \\t# COnsidering the fireyear\\n\\t# \\tncin = \\\"./data/veg/COPERN/%s_anmax_Russia.nc\\\" % var\\n\\telse:\\n\\t\\t# \\t# Only looking at years since the fire\\n\\t\\tif var == \\\"ppt\\\":\\n\\t\\t\\tnctrend = \\\"./data/cli/1.TERRACLIMATE/TerraClimate_merged_1980to2017_ppt_yearsum_RUSSIA_cdoregres.nc\\\"\\n\\t\\t\\tdst = xr.open_dataset(nctrend)\\n\\t\\t\\tncin = \\\"./data/cli/1.TERRACLIMATE/TerraClimate_merged_1980to2017_ppt_yearsum_RUSSIA.nc\\\"\\n\\t\\t# ncin = \\\"./data/veg/COPERN/%s_anmax_Russia_cdoregres.nc\\\" % var\\n\\tds = xr.open_dataset(ncin)\\n\\n\\n\\t# ========== Find the recuitment failure in the netcdf ==========\\n\\tif fireyear:\\n\\t\\t# warn.warn('''\\n\\t\\t# \\tTheilsen implemented quickly. check in the future \\n\\t\\t# \\t''')\\n\\t\\t# RFshort = RFinfo[RFinfo.fireyear<=2012] \\n\\t\\ttestnm = \\\"Four Year Mean Postfire anomoly\\\" \\n\\t\\tCLtrend = []\\n\\t\\tfor index, row in RFinfo.iterrows():\\n\\t\\t\\tif row.fireyear<=2013:\\n\\t\\t\\t\\tarray = ds[var].sel(\\n\\t\\t\\t\\t\\t{\\\"lat\\\":row.lat, \\\"lon\\\":row.lon},\\n\\t\\t\\t\\t\\tmethod=\\\"nearest\\\")#.sel(time=slice(sty, '2017-12-31'))\\n\\t\\t\\t\\tCLtrend.append(ClimateAnom(array, row.fireyear))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tCLtrend.append(np.NAN)\\n\\n\\telse:\\n\\t\\ttestnm = \\\"1999-2017 OLS slope\\\"\\n\\t\\tCLtrend = [float(ds[var].sel(\\n\\t\\t\\t{\\\"lat\\\":row.lat, \\\"lon\\\":row.lon}, method=\\\"nearest\\\").values) for index, row in RFinfo.iterrows()]\\n\\n\\tRFinfo[\\\"CLtrend\\\"] = CLtrend\\n\\tRFinfo.dropna(inplace=True, subset=['sn', 'lat', 'lon', den, 'RF17', 'CLtrend'])\\n\\n\\t\\n\\tslope, intercept, r_value, p_value, std_err = stats.linregress(x=RFinfo[den], y=RFinfo.CLtrend)\\n\\t# r2val = r_val**2\\n\\ttau, p_value = stats.kendalltau(x=RFinfo[den], y=RFinfo.CLtrend)\\n\\tprint(var, den, testnm)\\n\\tprint(\\\"r-squared:\\\", r_value**2)\\n\\tprint(\\\"kendalltau:\\\", tau)\\n\\t# make a quick plot\\n\\n\\tif plot:\\n\\t\\t# ========== Map of the regional trend data ==========\\n\\t\\t# build the figure and grid\\n\\t\\tif not fireyear:\\n\\t\\t\\tplt.figure(num=var+r\\\"$_{max}$\\\"+\\\" trend\\\", figsize=(12, 6))\\n\\t\\t\\tax = plt.axes(projection=ccrs.PlateCarree())\\n\\t\\t\\tax.add_feature(cpf.BORDERS, linestyle='--', zorder=102)\\n\\t\\t\\tax.add_feature(cpf.LAKES, alpha=0.5, zorder=103)\\n\\t\\t\\tax.add_feature(cpf.RIVERS, zorder=104)\\n\\t\\t\\t# add lat long linse\\n\\t\\t\\tgl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\\n\\t\\t\\t\\tlinewidth=1, color='gray', alpha=0.5, linestyle='--')\\n\\t\\t\\tgl.xlabels_top = False\\n\\t\\t\\tgl.ylabels_right = False\\n\\t\\t\\tgl.xformatter = LONGITUDE_FORMATTER\\n\\t\\t\\tgl.yformatter = LATITUDE_FORMATTER\\n\\n\\t\\t\\t# ========== create the colormap ==========\\n\\t\\t\\tcmap = mpc.ListedColormap(palettable.cmocean.diverging.Balance_20_r.mpl_colors)\\n\\t\\t\\t# if var == \\\"NDVI\\\":\\n\\t\\t\\t# \\tvmin = -0.02\\n\\t\\t\\t# \\tvmax = 0.02\\n\\t\\t\\t# elif var ==\\\"LAI\\\":\\n\\t\\t\\t# \\tvmin = -0.2\\n\\t\\t\\t# \\tvmax = 0.2\\n\\t\\t\\t# ========== Make the map ==========\\n\\t\\t\\tdst[var].plot(ax=ax, transform=ccrs.PlateCarree(),\\n\\t\\t\\t\\tcmap=cmap, cbar_kwargs={\\\"extend\\\":\\\"both\\\"})#, vmin=vmin, vmax=vmax)\\n\\n\\t\\t\\t# ========== Add site markers ==========\\n\\t\\t\\t# ipdb.set_trace()\\n\\t\\t\\tfor vas, cl in zip(RFinfo.RF17.unique().tolist(), ['yx', \\\"r*\\\",\\\"k.\\\"]):\\n\\t\\t\\t\\tax.plot(RFinfo[RFinfo.RF17 == vas].lon.values, RFinfo[RFinfo.RF17 == vas].lat.values, \\n\\t\\t\\t\\t\\tcl, markersize=8, transform=ccrs.PlateCarree())\\n\\t\\t\\tplt.show()\\n\\n\\t\\t# ========== Scatter plot of the trend vs field data ==========\\n\\t\\tpp = sns.lmplot( x=den, y=\\\"CLtrend\\\", \\n\\t\\t\\tdata=RFinfo, fit_reg=False, \\n\\t\\t\\thue='RF17', height=4, aspect=2)\\n\\t\\t# plt.title('Trend in %smax vs %s' %(var, den))\\n\\t\\taxf = pp.axes[0,0]\\n\\t\\taxf.text(0.85,0.85,\\\"R_squ: %1.5f \\\\n K_tau: %1.5f\\\" % (r_value**2, tau), transform=axf.transAxes)\\n\\t\\tpp.fig.canvas.set_window_title('%s (%smax) vs %s' %(name, var, den))\\n\\t\\tplt.savefig(\\\"./plots/%s_%s_%s.pdf\\\" % (name,var,den))\\n\\t\\tplt.savefig(\\\"./plots/%s_%s_%s.png\\\" % (name,var,den))\\n\\t\\tplt.show()\\n\\t\\t# ipdb.set_trace()\\n\\treturn r_value**2, tau\",\n \"def run():\\n #Initialise variables\\n data = build_station_list()\\n update_water_levels(data)\\n ls = []\\n ID = []\\n \\n #Number of days in past taken data from\\n dt = 7\\n #How many graphs per window\\n limit = 4\\n #How many stations\\n number = 6\\n \\n #Create list of measuring_id's sorted by water level\\n for station in data:\\n if station.typical_range_consistent() == True and station.relative_water_level() != None:\\n ls.append((station, station.relative_water_level()))\\n\\n ls = sorted_by_key(ls, 1)\\n \\n for station in ls:\\n ID.append(station[0])\\n \\n s = count_inconsistent_sets(ID[:number], dt)\\n \\n ID = ID[:number+s]\\n\\n plot_water_levels(ID, dt, limit, s)\",\n \"def create_widgets( self ):\",\n \"def handle_trending_hashtags(api_pipeline, trending_hashtags_from_db):\\n\\n trending_hashtags = api_pipeline.get_top_hashtags_worldwide()\\n trending_hashtags.sort(key=lambda hashtag: hashtag.tweet_volume, reverse=True)\\n trending_hashtags_chart = PlotPainter.plot_hashtags(trending_hashtags)\\n trending_hashtags_from_db_today = trending_hashtags_from_db.filter(save_date=datetime.datetime.today().date())\\n for trending_hashtag in trending_hashtags:\\n if trending_hashtag not in trending_hashtags_from_db_today:\\n trending_hashtag.save()\\n return trending_hashtags, trending_hashtags_chart\",\n \"def get_sidebar_layout(self):\\n return [\\n dbc.Card(children=self.get_sidebar_filters(), body=True),\\n dbc.Card(children=self.get_sidebar_filter_save(), body=True, className=\\\"mt-2\\\"),\\n dbc.Card(children=self.get_sidebar_saved_filter_names(), body=True, className=\\\"mt-2\\\")\\n ]\",\n \"def __init__(self):\\r\\n self.tweets = []\\r\\n self.lcs = \\\"outliers\\\"\\r\\n self.importance = 0\",\n \"def update_tweets_feed(n):\\n \\n # Retrieve the tweets\\n first_tweet = get_value(df_1t, n)\\n second_tweet = get_value(df_2t, n) \\n third_tweet = get_value(df_3t, n)\\n fourth_tweet = get_value(df_4t, n)\\n fifth_tweet = get_value(df_5t, n)\\n sixth_tweet = get_value(df_6t, n)\\n seventh_tweet = get_value(df_7t, n)\\n eighth_tweet = get_value(df_8t, n)\\n nineth_tweet = get_value(df_9t, n)\\n tenth_tweet = get_value(df_10t, n) \\n \\n # Compute the sentiment of each tweet\\n sa_first_tweet = sentiment_analyzer_scores(first_tweet)\\n sa_second_tweet = sentiment_analyzer_scores(second_tweet)\\n sa_third_tweet = sentiment_analyzer_scores(third_tweet)\\n sa_fourth_tweet = sentiment_analyzer_scores(fourth_tweet)\\n sa_fifth_tweet = sentiment_analyzer_scores(fifth_tweet)\\n sa_sixth_tweet = sentiment_analyzer_scores(sixth_tweet)\\n sa_seventh_tweet = sentiment_analyzer_scores(seventh_tweet)\\n sa_eighth_tweet = sentiment_analyzer_scores(eighth_tweet)\\n sa_nineth_tweet = sentiment_analyzer_scores(nineth_tweet)\\n sa_tenth_tweet = sentiment_analyzer_scores(tenth_tweet)\\n \\n # Return the tweet contents and a pie graph of the sentiment.\\n \\n return html.Div([\\n html.Div([\\n\\n# First Tweet\\n html.Div([\\n html.Div([\\n html.Pre(str(first_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '2px 2px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px',\\n }\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_first_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\", }\\n ),\\n ], \\n className = 'row' \\n ),\\n \\n# Second Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(second_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_second_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n \\n # Third Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(third_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_third_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n \\n # Fourth Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(fourth_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_fourth_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n\\n\\n # Fifth Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(fifth_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_fifth_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n \\n\\n # Sixth Tweet\\n html.Div([\\n html.Div([\\n html.Pre(str(sixth_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_sixth_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n \\n # Seventh Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(seventh_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n \\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_seventh_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n\\n # Eighth Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(eighth_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n \\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_eighth_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n\\n # Nineth\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(nineth_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_nineth_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n\\n # Tenth Tweet\\n \\n html.Div([\\n html.Div([\\n html.Pre(str(tenth_tweet)),\\n ], \\n className = 'ten columns',\\n style = {\\n 'backgroundColor': 'white',\\n 'box-shadow': '3px 3px 10px #ccc',\\n 'padding': '10px',\\n 'padding-bottom': '25px',\\n 'margin': '30px',\\n 'overflowX': 'scroll',\\n 'fontSize': '22px'}\\n ),\\n html.Div([\\n dcc.Graph(figure = piegraph_asset(sa_tenth_tweet))\\n ],\\n className = 'nine columns',\\n style = {\\\"padding-left\\\": \\\"550px\\\"}\\n ),\\n ], \\n className = 'row' \\n ),\\n ], style = {'overflowY': 'scroll', 'overflowX': 'hidden',\\n 'maxHeight': '105ex', 'backgroundColor' : '#eaeaea'}\\n ),\\n \\n ])\",\n \"def get_google_trends_data(keyword, from_date, to_date):\\r\\n \\r\\n from_year, from_month = datetime.date.fromisoformat(from_date).year, datetime.date.fromisoformat(from_date).month\\r\\n to_year, to_month = datetime.date.fromisoformat(to_date).year, datetime.date.fromisoformat(to_date).month\\r\\n\\r\\n data = dailydata.get_daily_data(keyword, from_year, from_month, to_year, to_month)\\r\\n \\r\\n return data[keyword]\",\n \"def get_index_trend():\\n symbol = request.args.get('symbol', type=str)\\n today = datetime.date.today()\\n last_year_today = today - datetime.timedelta(days=365)\\n data, series = {}, {'data' : [] }\\n if symbol is not None:\\n data = te.fetchMarkets(\\n symbol=symbol,initDate=last_year_today.strftime('%Y-%m-%d'), endDate=today.strftime('%Y-%m-%d')\\n ).dropna()\\n data['date'] = list(map(lambda x: x.strftime('%m/%d/%Y'), data.index.tolist()))\\n return jsonify(data.to_dict(orient='records'))\",\n \"def get_trend_urls():\\n\\n trends = []\\n twitter = 'http://search.twitter.com/'\\n tmp = 'tmp' + str(random.randint(0,1000))\\n os.system('wget %s --output-document=%s' % (twitter, tmp))\\n with open(tmp) as f:\\n for line in f:\\n if 'a href' in line and 'search?q' in line:\\n trends.append(twitter\\n + line.split('a href=\\\\\\\"/')[1].split('\\\\\\\"')[0])\\n os.system('rm %s' % tmp)\\n return trends\",\n \"def general_timeline():\\n return render_template('timeline.html', general=True, show_username=True)\",\n \"def view_category(category_id, date_string):\\n\\n if date_string.startswith('<<'):\\n format = \\\"<< %d.%m.%Y\\\"\\n elif date_string.endswith('>>'):\\n format = \\\"%d.%m.%Y >>\\\"\\n else:\\n format = None\\n date = datetime.today()\\n\\n if format:\\n date = scraper.strptime(date_string, format)\\n\\n url = \\\"http://www.ruv.is/sarpur/app/json/{0}/{1}\\\".format(category_id, date.strftime(\\\"%Y%m%d\\\"))\\n shows = json.loads(requests.get(url).content)\\n\\n day_before = date + timedelta(days=-1)\\n next_day = date + timedelta(days=1)\\n INTERFACE.add_dir(u\\\"<< {0}\\\".format(day_before.strftime(\\\"%d.%m.%Y\\\")),\\n 'view_category',\\n category_id)\\n INTERFACE.add_dir(\\\"{0} >>\\\".format((next_day.strftime(\\\"%d.%m.%Y\\\"))),\\n 'view_category',\\n category_id)\\n\\n for show in shows['events']:\\n ev = show['event']\\n showtime = datetime.fromtimestamp(float(ev['start_time']))\\n end_time = datetime.fromtimestamp(float(ev['end_time']))\\n in_progress = showtime < datetime.now() < end_time\\n duration = (end_time - showtime).seconds / 60\\n\\n title = u\\\"{1} - {0}\\\".format(\\n ev['title'],\\n in_progress and u\\\"[COLOR blue]Í GANGI[/COLOR]\\\" or showtime.strftime(\\\"%H:%M\\\"))\\n original_title = ev.get('orginal_title')\\n description = ev.get('description', '').strip()\\n if original_title and description:\\n plot = u\\\"{0} - {1}\\\".format(original_title, description)\\n elif description:\\n plot = description\\n elif original_title:\\n plot = original_title\\n else:\\n plot = u\\\"\\\"\\n\\n meta = {\\n 'TVShowTitle': title,\\n 'Episode': ev['episode_number'],\\n 'Premiered': showtime.strftime(\\\"%d.%m.%Y\\\"),\\n 'TotalEpisodes': ev['number_of_episodes'],\\n 'Plot': plot,\\n 'Duration': duration,\\n 'fanart_image': ev.get('picture')\\n }\\n\\n if in_progress:\\n INTERFACE.add_item(title,\\n 'play_live',\\n category_id,\\n image=ev.get('picture'),\\n extra_info=meta)\\n\\n elif ev.get('media'):\\n INTERFACE.add_item(title,\\n 'play_file',\\n ev.get('media'),\\n image=ev.get('picture'),\\n extra_info=meta)\\n else:\\n INTERFACE.add_item(title,\\n 'play_url',\\n ev.get('url'),\\n image=ev.get('picture'),\\n extra_info=meta)\",\n \"def burn_in_selector(mcmc_tables: List[pd.DataFrame]):\\n min_length = min([len(t) for t in mcmc_tables])\\n return st.sidebar.slider(\\\"Burn-in\\\", 0, min_length, 0)\",\n \"def plot(self, context=None):\\n\\n response = requests.get(self.url).content\\n table = pd.read_html(response, attrs={\\\"id\\\": \\\"main_table_countries_today\\\"})\\n df = table[0].fillna(0)\\n # df.drop(df.index[0], inplace=True) # World\\n df.drop([\\\"ActiveCases\\\", 'Serious,Critical', 'Serious,Critical', 'Deaths/1M pop', 'Tests/ 1M pop'], axis=1, inplace=True)\\n df.drop(df.columns[6], axis=1, inplace=True)\\n\\n if len(context) > 3:\\n context = context.lower().capitalize()\\n df = df.loc[df[\\\"Country,Other\\\"] == context]\\n if 4 > len(context) > 1:\\n context = context.upper()\\n df = df.loc[df[\\\"Country,Other\\\"] == context]\\n if len(context) <= 1:\\n df = df[1:]\\n\\n C_Names = df[\\\"Country,Other\\\"].head(n=10).values.tolist()\\n T_Cases = df[\\\"TotalCases\\\"].head(n=10).values.tolist()\\n # N_Cases = df[\\\"NewCases\\\"].head(n=10).values.tolist() # not plotted\\n T_Deaths = df[\\\"TotalDeaths\\\"].head(n=10).values.tolist()\\n # N_Deaths = df[\\\"NewDeaths\\\"].head(n=10).values.tolist() # not plotted\\n T_Recovered = df[\\\"TotalRecovered\\\"].head(n=10).values.tolist()\\n T_Tests = df[\\\"TotalTests\\\"].head(n=10).values.tolist()\\n\\n x = np.arange(len(C_Names))\\n width = 0.20\\n\\n fig, ax = plt.subplots()\\n\\n ax.bar(x - 0.30, T_Cases, width, label='TotalCases', color=\\\"Blue\\\")\\n ax.bar(x - 0.10, T_Deaths, width, label='TotalDeaths', color=\\\"Red\\\")\\n ax.bar(x + 0.10, T_Tests, width, label='TotalTests', color=\\\"Green\\\")\\n ax.bar(x + 0.30, T_Recovered, width, label='TotalRecovered', color=\\\"Orange\\\")\\n\\n if len(context) > 1:\\n ax.set_title(\\\"{}'s Situation\\\".format(context))\\n else:\\n ax.set_title(\\\"World's Top10 Situation\\\")\\n\\n ax.set_xticks(x)\\n ax.set_xticklabels(C_Names)\\n ax.legend()\\n plt.ticklabel_format(style='plain', axis=\\\"y\\\")\\n fig.set_size_inches(18.5, 10.5)\\n fig.tight_layout()\\n plt.grid()\\n\\n if len(context) > 1:\\n font1 = {'family': 'serif',\\n 'color': 'blue',\\n 'weight': 'bold',\\n 'size': 20}\\n font2 = {'family': 'serif',\\n 'color': 'red',\\n 'weight': 'normal',\\n 'size': 20}\\n font3 = {'family': 'serif',\\n 'color': 'green',\\n 'weight': 'normal',\\n 'size': 20}\\n font4 = {'family': 'serif',\\n 'color': 'orange',\\n 'weight': 'normal',\\n 'size': 20}\\n\\n # bbox=dict(facecolor='black', alpha=0.5)\\n plt.text(0.863, 0.67, \\\"Total Cases:\\\\n{:,}\\\".format(int(T_Cases[0])), fontdict=font1, transform=ax.transAxes)\\n plt.text(0.863, 0.57, \\\"Total Deaths:\\\\n{:,}\\\".format(int(T_Deaths[0])), fontdict=font2, transform=ax.transAxes)\\n plt.text(0.863, 0.47, \\\"Total Tests:\\\\n{:,}\\\".format(int(T_Tests[0])), fontdict=font3, transform=ax.transAxes)\\n plt.text(0.863, 0.37, \\\"Total Recovered:\\\\n{:,}\\\".format(int(T_Recovered[0])), fontdict=font4, transform=ax.transAxes)\\n\\n # plt.savefig('corona.png') # Uncomment it to save the figure\\n plt.show()\",\n \"def action_atom_site_l(obj: AtomSiteL, thread: QtCore.QThread):\\n w_actions = []\\n if obj.is_attribute(\\\"type_symbol\\\"):\\n qtb_1 = QtWidgets.QToolButton()\\n qtb_1.setText(\\\"Show b_scat\\\")\\n qtb_1.clicked.connect(lambda: run_function(\\n obj.report, (), thread))\\n w_actions.append(qtb_1)\\n return w_actions\",\n \"def __r7(soup):\\n news = []\\n ps = soup.find_all('p', class_='trends-thermometer-description')\\n i = 0\\n\\n for p in ps:\\n if i == 6: # Six trending topics\\n break\\n i += 1\\n a = p.parent.parent.parent.a\\n news.append(dict(title=a['title'], link=a['href']))\\n return news\",\n \"def charts(self,req):\\n self.player.overviewing=True\",\n \"def setWidget(self, widget: QtWidgets.QWidget):\\n super().setWidget(widget)\\n if globalstuff.theme == 'dark':\\n w = self.widget()\\n w.setPalette(globalstuff.textpal)\\n if hasattr(w, 'TreeWidget'):\\n w.TreeWidget.setStyleSheet(globalstuff.treeqss)\",\n \"def get_top_trends_from_twitter(country='Japan', exclude_hashtags=False, debug=False, cache_duration_mins=15, append_db=True):\\n # load main db\\n cache_db = load_db(database_path=db_path, debug=False)\\n trends_db = cache_db['trends']\\n\\n if exclude_hashtags:\\n trends_cache = trends_db['exclude_hashtags']\\n else:\\n trends_cache = trends_db['include_hashtags']\\n\\n # load trends + top retweets db\\n trend_search_db = load_db(database_path=trends_db_path, debug=False)\\n\\n\\n\\n # MAIN_DB ONLY\\n try:\\n db_timestamp = str_2_datetime(trends_cache['timestamp'], input_format=time_format_full_with_timezone)\\n except ValueError:\\n db_timestamp = str_2_datetime(trends_cache['timestamp'], input_format=time_format_full_no_timezone)\\n db_timestamp = db_timestamp.astimezone(tz=pytz.utc)\\n\\n rq_timestamp = datetime.datetime.now(tz=pytz.utc)\\n\\n time_diff = rq_timestamp - db_timestamp\\n print('time since last trends API call: {} (h:m:s)'.format(time_diff))\\n print('time diff in seconds: {}'.format(time_diff.seconds))\\n print('time in db: {}'.format(db_timestamp))\\n print('time in rq: {}'.format(rq_timestamp))\\n\\n if time_diff.seconds < cache_duration_mins*60:\\n print('less than cache duration, returning cache')\\n output_json = json.dumps(trends_cache['content'], ensure_ascii=False)\\n return output_json\\n else:\\n output_json, img_output_json = get_top_trends_from_twitter_api(country=country, exclude_hashtags=exclude_hashtags)\\n # update\\n output_list = json.loads(output_json)\\n trend_search_list = json.loads(img_output_json)\\n\\n if append_db:\\n output_list = trends_cache['content'] + output_list\\n trend_search_list = trend_search_db['trends'] + trend_search_list\\n \\n if exclude_hashtags:\\n cache_db['trends']['exclude_hashtags']['content'] = output_list\\n cache_db['trends']['exclude_hashtags']['timestamp'] = datetime_2_str(rq_timestamp, output_format=time_format_full_with_timezone)\\n else:\\n cache_db['trends']['include_hashtags']['content'] = output_list\\n cache_db['trends']['include_hashtags']['timestamp'] = datetime_2_str(rq_timestamp, output_format=time_format_full_with_timezone)\\n\\n trend_search_db['trends'] = trend_search_list\\n\\n update_db(cache_db, database_path=db_path, debug=debug)\\n update_db(trend_search_db, database_path=trends_db_path, debug=debug)\\n\\n print('trends and image database updated.')\\n\\n del cache_db\\n del trends_db\\n del trends_cache\\n del trend_search_db\\n del trend_search_list\\n del output_list\\n del output_json\\n del img_output_json\\n\\n print('memory freed.')\",\n \"def update_chart():\\n selected_team = variable.get()\\n selected_period = period_variable.get()\\n if selected_period == \\\"All\\\":\\n selected_period = -1\\n elif selected_period == \\\"OT\\\":\\n selected_period = 4\\n selected_id = -1\\n if selected_team == home_team:\\n selected_id = data[\\\"data\\\"][\\\"game\\\"][\\\"hometeamid\\\"]\\n elif selected_team == away_team:\\n selected_id = data[\\\"data\\\"][\\\"game\\\"][\\\"awayteamid\\\"]\\n clear_events()\\n for i in range(0, len(plays)):\\n add_to_chart(plays[i], {\\\"teamid\\\":selected_id, \\\"period\\\":selected_period})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6033017","0.5886845","0.54061174","0.5199153","0.51406914","0.512278","0.5105741","0.50310594","0.49992782","0.49274763","0.481228","0.48026663","0.47352293","0.46986964","0.46903226","0.4664409","0.466313","0.46275526","0.45833886","0.45830315","0.4561449","0.45554802","0.4545247","0.45321473","0.44909775","0.44756022","0.44709644","0.4469059","0.4458835","0.4458129","0.44359052","0.439915","0.43803674","0.43699792","0.43601048","0.43494445","0.4347051","0.4337293","0.4316043","0.43012097","0.4293629","0.42861196","0.42755753","0.42724457","0.4270648","0.42694274","0.42636898","0.42605215","0.42519227","0.42493075","0.42464876","0.42388752","0.42384195","0.42355636","0.42223594","0.4217531","0.42145392","0.42093343","0.42065415","0.4206531","0.4205771","0.41973788","0.41835567","0.4180881","0.41798225","0.4178738","0.4177619","0.41731903","0.41661432","0.41612786","0.4159925","0.4150494","0.41436303","0.41426948","0.4136567","0.41352442","0.41310316","0.4129399","0.41248456","0.41203547","0.41184726","0.41160753","0.41157818","0.41142315","0.4097947","0.40921128","0.40914047","0.40908056","0.40887344","0.40866187","0.4084452","0.40824872","0.40796745","0.4076853","0.40652364","0.40611038","0.4050612","0.40456176","0.40441126","0.40397128"],"string":"[\n \"0.6033017\",\n \"0.5886845\",\n \"0.54061174\",\n \"0.5199153\",\n \"0.51406914\",\n \"0.512278\",\n \"0.5105741\",\n \"0.50310594\",\n \"0.49992782\",\n \"0.49274763\",\n \"0.481228\",\n \"0.48026663\",\n \"0.47352293\",\n \"0.46986964\",\n \"0.46903226\",\n \"0.4664409\",\n \"0.466313\",\n \"0.46275526\",\n \"0.45833886\",\n \"0.45830315\",\n \"0.4561449\",\n \"0.45554802\",\n \"0.4545247\",\n \"0.45321473\",\n \"0.44909775\",\n \"0.44756022\",\n \"0.44709644\",\n \"0.4469059\",\n \"0.4458835\",\n \"0.4458129\",\n \"0.44359052\",\n \"0.439915\",\n \"0.43803674\",\n \"0.43699792\",\n \"0.43601048\",\n \"0.43494445\",\n \"0.4347051\",\n \"0.4337293\",\n \"0.4316043\",\n \"0.43012097\",\n \"0.4293629\",\n \"0.42861196\",\n \"0.42755753\",\n \"0.42724457\",\n \"0.4270648\",\n \"0.42694274\",\n \"0.42636898\",\n \"0.42605215\",\n \"0.42519227\",\n \"0.42493075\",\n \"0.42464876\",\n \"0.42388752\",\n \"0.42384195\",\n \"0.42355636\",\n \"0.42223594\",\n \"0.4217531\",\n \"0.42145392\",\n \"0.42093343\",\n \"0.42065415\",\n \"0.4206531\",\n \"0.4205771\",\n \"0.41973788\",\n \"0.41835567\",\n \"0.4180881\",\n \"0.41798225\",\n \"0.4178738\",\n \"0.4177619\",\n \"0.41731903\",\n \"0.41661432\",\n \"0.41612786\",\n \"0.4159925\",\n \"0.4150494\",\n \"0.41436303\",\n \"0.41426948\",\n \"0.4136567\",\n \"0.41352442\",\n \"0.41310316\",\n \"0.4129399\",\n \"0.41248456\",\n \"0.41203547\",\n \"0.41184726\",\n \"0.41160753\",\n \"0.41157818\",\n \"0.41142315\",\n \"0.4097947\",\n \"0.40921128\",\n \"0.40914047\",\n \"0.40908056\",\n \"0.40887344\",\n \"0.40866187\",\n \"0.4084452\",\n \"0.40824872\",\n \"0.40796745\",\n \"0.4076853\",\n \"0.40652364\",\n \"0.40611038\",\n \"0.4050612\",\n \"0.40456176\",\n \"0.40441126\",\n \"0.40397128\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6717184"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22373,"cells":{"query":{"kind":"string","value":"Sidebar widget to select minimum salary for Highest Earners page"},"document":{"kind":"string","value":"def select_minimum_salary(df, step, college_select: str = ''):\n\n st.sidebar.markdown('### Enter minimum FTE salary:')\n sal_describe = df[SALARY_COLUMN].describe()\n\n number_input_settings = {\n 'min_value': 100000,\n 'max_value': int(sal_describe['max']),\n 'value': 500000,\n 'step': step\n }\n\n if college_select:\n t_df = df.loc[df[COLLEGE_NAME] == college_select]\n sal_describe = t_df[SALARY_COLUMN].describe()\n max_value = int(sal_describe['max'])\n number_input_settings['max_value'] = max_value\n\n if max_value > 100000:\n number_input_settings['min_value'] = 75000\n number_input_settings['value'] = 100000\n else:\n number_input_settings['min_value'] = 65000\n number_input_settings['value'] = 75000\n\n min_salary = st.sidebar.number_input('', **number_input_settings)\n\n return min_salary"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_min_expense(self):\n pass","def setMinMax(self):\n currentIndustryNum = self.myParent.myIndustry[self.myIndustryData.id]\n oldIndustryNum = self.myParent.myOldIndustry[self.myIndustryData.id]\n self.setMinValue(-currentIndustryNum)\n if oldIndustryNum > currentIndustryNum:\n self.setMaxValue(oldIndustryNum-currentIndustryNum)\n elif self.isIndustryResearched() == 0:\n self.setMaxValue(0)\n else:\n max = self.getMaxFromFundsAvail()\n cityNum = (self.myParent.cities-self.myParent.citiesUsed)/self.myIndustryData.cities\n if max < cityNum:\n self.setMaxValue(max)\n else:\n self.setMaxValue(cityNum)","def view_batter_bysalary(self):\n conn = rs.create_connection(\"dailyfantasyscraper.db\")\n salary = \"$\" + sal.get()\n position = \"P\"\n batter_salary = (salary, position)\n cur = conn.cursor()\n cur.execute(\n \"SELECT * FROM rotowiredk WHERE salary <= ? and position != ? \", batter_salary)\n result = cur.fetchall()\n conn.commit()\n conn.close()\n\n for item in result:\n print(item)\n tree.insert('', 'end', values=item)","def largest_two():\n # Add your code below!","def second_lowest():\n copy = movies.copy()\n name = copy.sort_values(['Total Gross']).reset_index(drop = True)['#1 Movie'].loc[1]\n return ('second_lowest', name)","def best_sell(self):\n return Library.functions.best_sell(self._book)","def _choose_best_option(self):","def main():\n filename1=input(\"Enter the first filename :\")\n filename2 = input(\"Enter the second filename :\")\n city1, hilltownDict=readFile(filename1)\n city2, valleydaleDict=readFile(filename2)\n max=int (input(\"Enter the maximum number of items :\"))\n\n hilltownDict, valleydaleDict=profitcal(hilltownDict,valleydaleDict)\n hilltownList=convertToList(hilltownDict)\n valleydaleList=convertToList(valleydaleDict)\n\n sort_valley= selectionSort(valleydaleList)\n sort_hill = selectionSort(hilltownList)\n\n\n resultH,tpH=totalProfit(city1,sort_hill,max)\n resultV,tpV=totalProfit(city2,sort_valley,max)\n\n if tpH>tpV:\n print(resultH)\n\n elif tpV>tpH:\n print(resultV)\n\n else:\n print(\"Both city has same profit\" +\"\\n\" + resultH +\"\\n\" + resultV)","def limit_weight(self, weight_max):\n # remove items with low values\n if self.total_weight > weight_max:\n items_sorted_by_fitness = sorted(self.items, key=lambda item: item.fitness, reverse=False)\n while items_sorted_by_fitness and self.total_weight > weight_max:\n least_fit_item = items_sorted_by_fitness.pop(0)\n if self.item_stats[least_fit_item.id] == 1:\n self.item_stats[least_fit_item.id] = 0\n self.update_values() # have to update each time an item is change to recompute weight","def Salvage(self):\n pass","def _check_amount_with_priority(self):\n\t\tfor slc in self:\n\t\t\tif slc.max_amount and self.search([('priority', '<', slc.priority), ('max_amount', '>=', slc.max_amount)]):\n\t\t\t\traise Warning(_(\"There are below slides [Priority less than %s] with bigger amount from [%s]\"\n\t\t\t\t \" which against the logic!!!\\n You can increase amount or handel priority\")\n\t\t\t\t % (slc.priority, slc.max_amount))","def get_greatest_stock_price():\n greatest_stock_price = 0\n // your code here","def select_sort_method():\n st.sidebar.markdown('### Sort method:')\n sort_select = st.sidebar.selectbox('', ['Alphabetically', 'FTE Salary'],\n index=1)\n return sort_select","def best_price(self):\n # TODO rename this to \"display_lowest_price\" or something...\n price = self.lowest_price\n if price:\n return Decimal.quantize(price.per_kg, settings.DECIMAL_CENTS) # round to cents","def youngest(self):\n # Your implementation here","def main():\r\n\r\n st.title(\"Summarizer App\")\r\n menu = ['Home', 'About']\r\n choice = st.sidebar.selectbox(\"Menu\", menu)\r\n\r\n if choice == \"Home\":\r\n st.subheader(\"Summarization\")\r\n raw_text = st.text_area(\"Enter Text Here\")\r\n if st.button(\"Summarize\"):\r\n \r\n with st.expander(\"original Text\"):\r\n st.write(raw_text)\r\n\r\n # Layout\r\n c1, c2 = st.columns(2)\r\n with c1:\r\n with st.expander(\"LexRank Summary\"):\r\n my_summary = sumy_summarizer(raw_text)\r\n document_len = {\"Original\":len(raw_text), \r\n \"Summary\":len(my_summary)}\r\n st.write(document_len)\r\n st.write(my_summary) \r\n st.info(\"Rouge Score\")\r\n eval_df = evaluate_summary(my_summary, raw_text)\r\n st.dataframe(eval_df.T)\r\n eval_df['metrics'] = eval_df.index\r\n c = alt.Chart(eval_df).mark_bar().encode(\r\n x = 'metrics', y = 'rouge-1')\r\n st.altair_chart(c)\r\n\r\n with c2:\r\n with st.expander(\"LexRank Summary\"):\r\n my_summary = summarize(raw_text)\r\n document_len = {\"Original\":len(raw_text), \r\n \"Summary\":len(my_summary)}\r\n st.write(document_len)\r\n st.write(my_summary)\r\n\r\n st.info(\"Rouge Score\")\r\n eval_df = evaluate_summary(my_summary, raw_text)\r\n st.dataframe(eval_df.T)\r\n eval_df['metrics'] = eval_df.index\r\n c = alt.Chart(eval_df).mark_bar().encode(\r\n x = 'metrics', y = 'rouge-1')\r\n st.altair_chart(c)\r\n\r\n else:\r\n st.subheader(\"About\")\r\n st.write(\"Webpage for Summarization using NLP\")\r\n st.write(\"***\")\r\n st.write(\"### Packages Used\")\r\n st.write(\"pip install streamlit gensim==3.8.3 sumy gensim_sum_ext pandas altair seaborn rouge\")\r\n st.write(\"***\")\r\n st.write(\"This webpage is maintained by Ramakrishnan\")","def display_score(self):\n if self.args:\n return self.display_score_for_group()\n return self.display_top_donor_for_each_group()","def __statistics_students_enrolled(self):\n try:\n discipline_name = input(\"Give discipline discipline_name: \")\n self.__discipline_validator.validate_name(discipline_name)\n except DisciplineException as de:\n print(de)\n return\n\n menu_string = \"\\t1. Sort alphabetically \\n\\t\" \\\n \"2. Sort descending by average \\n\\t\" \\\n \"0. Exit\"\n command = self.__ui_read_command(menu_string)\n\n if command == '1':\n self.__ui_statistics_sort_alpha(discipline_name)\n elif command == '2':\n self.__ui_statistics_sort_avg(discipline_name)\n elif command == '0':\n return","def main():\r\n print(\"Please enter the grade of two subjects in which you are struggling with\")\r\n subject1_grade = int(input(\"First subject's Grade: \"))\r\n subject2_grade = int(input(\"Second subject's Grade: \"))\r\n\r\n smaller_number = getSmaller(subject1_grade, subject2_grade)\r\n print(\"What is the name of the subject in which your grade is\", smaller_number, \"?\")\r\n subject_lowest_grade = input(\"Enter the subject's name: \")\r\n print(\"To improve your grade in\", subject_lowest_grade + \",\", \"you should talk with you professor.\")\r\n print(\"Also, you should schedule an appointment with an Academic Advisor\")","def clicked_checkbox_upper_limit(self):\n spectral_model, proxy_index, index = self._get_selected_model(True)\n spectral_model.metadata[\"is_upper_limit\"] \\\n = self.checkbox_upper_limit.isChecked()\n self.measurement_view.update_row(proxy_index.row())\n self.summarize_current_table()\n self.refresh_plots()\n return None","def worst_score(self):\r\n pass","def calculate_risk_tol(*args):\n global total_score\n risk_tol_start = 0.0\n\n for risk_per_pg in risk_tol_per_qs.iterkeys():\n try:\n risk_tol_start = risk_tol_start + risk_tol_per_qs[risk_per_pg][-1] # this is the last item in the list of each information in the page\n except IndexError:\n pass\n total_score = risk_tol_start","def top_students(grade_book, num_students=3):\n return sorted(grade_book, key=grade_book.get, reverse=True)[:num_students]","def update_lowest_sell(self, limit):\n if limit.size == 0:\n #successor case\n limit = self.sell_tree.successor(limit)\n if limit is None:\n #no successor\n self.lowest_sell = None\n else: #have a successor, but dont know if it has orders or not\n if limit.size == 0:#limit has no orders but other limits in the tree might have orders\n if self.sell_tree.size == 0: #we know, no other limits have an order\n self.lowest_sell = None\n else: #other limits have an order\n while limit.size == 0:\n limit = self.sell_tree.successor(limit)\n # now our limit has a valid order, and we've found the first valid successor\n self.lowest_sell = limit.price\n else: #limit has an order, we found the valid successor!\n self.lowest_sell = limit.price","def clicked_btn_find_upper_limit(self):\n spectral_model, proxy_index, index = self._get_selected_model(True)\n # Find the upper limit \n try:\n sigma = round(float(self.edit_ul_sigma.text()),1)\n except:\n logger.debug(\"Invalid sigma for finding limit\")\n return None\n upper_limit = spectral_model.find_upper_limit(sigma=sigma, start_at_current=True)\n # Refresh GUI\n self.measurement_view.update_row(proxy_index.row())\n self.summarize_current_table()\n self.update_fitting_options()\n self.refresh_plots()\n return None","def _company_health_insurance(self, total_salary):\n company_health_insurance = 0\n\n if total_salary <= self.health_max_fee:\n company_health_insurance = total_salary *\\\n self.company_health_insurance_rate\n else:\n company_health_insurance = self.health_max_fee\\\n * self.company_health_insurance_rate\n #end if\n return company_health_insurance","def valuable_customer():\r\n valuable_customer_dictionary = {}\r\n for customer in Records.customers_list:\r\n valuable_customer_dictionary.update({customer.name: customer.total})\r\n search_value = max(valuable_customer_dictionary.values())\r\n for name, value in valuable_customer_dictionary.items():\r\n if value == search_value:\r\n search_name = name\r\n print('The current most valuable customer is', search_name, 'with a total order value of $',\r\n search_value)\r\n input('Press enter to return to main menu\\n')\r\n return","def upper_earning_limit(self):\n\t\treturn self._upper_earning_limit","def adjust_salary_range(driver, salary):\n if salary == 'All': \n return\n index = ['', 'All', '40+', '60+', '80+', '100+', \n '120+', '160+', '180+', '200+'].index(salary)\n salary_button = \"html/body/div[3]/div/div[2]/div[1]/div[4]/form/div/ul/\" \\\n \"li[4]/fieldset/button\"\n salary_path = \"html/body/div[3]/div/div[2]/div[1]/div[4]/\" \\\n \"form/div/ul/li[4]/fieldset/div[1]/ol/li[{}\" \\\n \"]/div/label\".format(index)\n attempts = 1\n while True:\n try:\n elem = driver.find_element_by_xpath(salary_button)\n time.sleep(3)\n except Exception as e:\n attempts += 1\n if attempts > 25: \n break\n else:\n elem.click()\n time.sleep(3)\n driver.find_element_by_xpath(salary_path).click()\n break","def _get_max_expense(self):\n pass","def min_sale_qty(self):\n return self._min_sale_qty","def create_layout() -> None:\n\n st.sidebar.title(\"Menu\")\n app_mode = st.sidebar.selectbox(\"Please select a page\", [' I. Homepage',\n \"II. Download data\" ,\n \"III. Statistic Data\",\n ' IV. AGF Indices',\n ' V. Notes',\n \" VI. Rank of patient\" ])\n \n if app_mode == ' I. Homepage':\n load_homepage() \n elif app_mode == \"III. Statistic Data\":\n leyer.leyer() \n elif app_mode == ' IV. AGF Indices':\n single.AGF_indices() \n elif app_mode == \"II. Download data\":\n download_data.download_data() \n elif app_mode == ' V. Notes':\n text_input.text_input()\n elif app_mode == \" VI. Rank of patient\":\n rank_of_patient.rank_of_patient()","def policy(self):\n element = self.find_css('#grading_type')[0]\n return self.get_selected_option_text(element)","def promjeni_broj_sati(self, x):\r\n value = int(self.brojSatiCombo.currentText())\r\n self.emit(QtCore.SIGNAL('promjeni_max_broj_sati_minutnog(PyQt_PyObject)'),\r\n value)","def calculate_profit(self):","def clicked_checkbox_upper_limit_2(self):\n spectral_model, proxy_index, index = self._get_selected_model(True)\n spectral_model.metadata[\"is_upper_limit\"] \\\n = self.checkbox_upper_limit_2.isChecked()\n self.measurement_view.update_row(proxy_index.row())\n self.summarize_current_table()\n self.refresh_plots()\n return None","def _get_belt(self, new_score):\n return max([(s, b) for s, b in BELTS.items() if s <= new_score])[1]","def evaluate_elf(calories):\n if calories > top_three[2]:\n top_three.append(calories)\n elif calories > top_three[1]:\n top_three.popleft()\n top_three.insert(1, calories)\n elif calories > top_three[0]:\n top_three[0] = calories","def main():\n # create a list of test employees and managers\n testList = [\n {'type': 'employee', 'firstName': 'Mickey', 'lastName': 'Mouse', 'SSN': '100-12-3456', 'salary': 1500.00},\n {'type': 'manager', 'firstName': 'Walt', 'lastName': 'Disney', 'SSN': '100-00-0000', 'salary': 5000.00,\n 'title': 'Head Of Disneyland', 'yearBonus': 1000.00},\n {'type': 'employee', 'firstName': 'Donald', 'lastName': 'Duck', 'SSN': '100-65-4321', 'salary': 1000.00},\n {'type': 'manager', 'firstName': 'Minnie', 'lastName': 'Mouse', 'SSN': '999-99-999', 'salary': 10000.00,\n 'title': 'Head Of Mouse HouseHold', 'yearBonus': 15000.00},\n {'type': 'manager', 'firstName': 'Daisy', 'lastName': 'Duck', 'SSN': '100-65-4321', 'salary': 12000.00,\n 'title': 'Head Of Duck HouseHold', 'yearBonus': 10000.00}]\n\n # Define percentRaise (0.1 == 10%)\n percentRaise = 0.1\n\n # Create Employees and Managers Object using the Test data\n employeeList = loadEmployees(testList)\n\n # Sort employee List, which will ustilize Employee's __lt__ and __eq__ methods\n employeeList.sort()\n\n # Loop over Employee and Manager Objects\n print(\"Employees and Manager should be sorted by last name, then first\\n\")\n for employee in employeeList:\n if type(employee) == Manager:\n print(\"Manager:\")\n else:\n print(\"Employee:\")\n # Print Employee or Manager\n print(employee)\n # Give Raise to Employee or Manager\n employee.giveRaise(percentRaise)\n # Print New Salary\n print(\"With %.2f%% Raise, Salary: $%.2f\\n\" % (percentRaise * 100, employee.salary))\n\n # Employee docStrings\n print(\"\\nEmployee docstring for each method\")\n print(\"Employee.__doc__=\" + Employee.__doc__)\n print(\"Employee.__init__.__doc__=\" + Employee.__init__.__doc__)\n print(\"Employee.giveRaise.__doc__=\" + Employee.giveRaise.__doc__)\n print(\"Employee.__str__.__doc__=\" + Employee.__str__.__doc__)\n print(\"Employee.__eq__.__doc__=\" + Employee.__eq__.__doc__)\n print(\"Employee.__lt__.__doc__=\" + Employee.__lt__.__doc__)\n\n print(\"\\nManger docstring for each method\")\n print(\n \"Since Manager inherits from Employee, several of the methods ('giveRaise', '__eq__' and '__lt__') and the corresponding docstring will originate from the Employee class\\n\")\n print(\"Manager.__doc__=\" + Manager.__doc__)\n print(\"Manager.__init__.__doc__=\" + Manager.__init__.__doc__)\n print(\"Manager.giveRaise.__doc__=\" + Manager.giveRaise.__doc__)\n print(\"Manager.__str__.__doc__=\" + Manager.__str__.__doc__)\n print(\"Manager.__eq__.__doc__=\" + Manager.__eq__.__doc__)\n print(\"Manager.__lt__.__doc__=\" + Manager.__lt__.__doc__)","def execQ3():\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\n tuna = frame[dFrame.Series_title_1 == \"Tuna - canned (supermarket only), 185g\"]\n cheapest = tuna.sort_values(by=\"Price\").head(1)\n return cheapest","def render_investip():\n\tlinewidth = 2\n\n\tst.sidebar.markdown('# Dashboard')\n\tstock = st.sidebar.selectbox('Stock:', stocks)\n\n\tstartdd = datetime.datetime(2020, 3, 1)\n\tstartdd = st.sidebar.date_input('start-date', value=startdd)\n\n\tendd = datetime.datetime.now()\n\tendd = st.sidebar.date_input('end-date', value=endd)\n\n\tt0 = stock\n\tt0_ohlc = extract(ticker=t0, start_date=startdd, end_date=endd)\n\tt0_df = pd.DataFrame({f'{t0}-Close': t0_ohlc.Close})\n\n\t# st.write(t0_ohlc)\n\tmpf.plot(t0_ohlc, type='candle',volume=True,show_nontrading=False, title=t0, figscale=1.)\n\t# tdf = plot_ticker(t0, df=t0_df, start_date=startdd, end_date=endd)\n\tst.pyplot()\n\n\n\tst.sidebar.markdown('## Stock Correlation')\n\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_corrs')\n\tif stock_returns:\n\t\tst.markdown('## Stock Correlation')\n\t\tstock_selection = st.sidebar.multiselect('Stocks', stocks, def_stocks)\n\t\tplot_stock_correlations(stock_selection, startdd, endd)\n\t\tst.pyplot()\n\n\t# trading_context = True\n\tst.sidebar.markdown('## Returns')\n\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_returns')\n\tif stock_returns:\n\t\tst.markdown('## Stock Returns')\n\t\tst.markdown('''### Daily Stock returns\n[EWMA](https://www.investopedia.com/articles/07/ewma.asp)''')\n\t\tspan = st.sidebar.slider('span', 2, 21, value=5)\n\t\tplot_historical(t0, t0_ohlc, span=span, linewidth=linewidth)\n\t\tst.pyplot()\n\n\n\t# trading_context = True\n\tst.sidebar.markdown('## Volatility')\n\ttrading_context = st.sidebar.checkbox('Enable', value=False, key='cb_volatility')\n\tif trading_context:\n\t\tst.markdown('## Volatility & Risk')\n\t\tst.markdown('''### Daily differences between High & Low\nWe model these ranges with [Inverse Gamma PDF](https://en.wikipedia.org/wiki/Inverse-gamma_distribution).\nGreen lines denote +/- 1 stdev.\n''')\n\t\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\n\t\tf.suptitle(f'{t0} High-Low Daily')\n\t\tmmd = t0_ohlc.High - t0_ohlc.Low\n\t\t# mmd.dropna(inplace=True)\n\t\tmmd.plot(color='r', ax=ax[0], lw=linewidth)\n\n\t\tmu, sigma = mmd.dropna().mean(), mmd.dropna().std()\n\t\tzval = 1.#96\n\t\t# TODO: try one-tail limit to get outliers\n\t\t_=ax[0].axhline(y=mu, color='k', lw=linewidth)\n\t\t_=ax[0].axhline(y=mu-zval*sigma, color='g', lw=linewidth)\n\t\t_=ax[0].axhline(y=mu+zval*sigma, color='g', lw=linewidth)\n\n\t\tp95 = mmd.dropna().quantile(.95)\n\t\t_=ax[0].axhline(y=p95, color='b', lw=linewidth, label='p95')\n\t\t_=ax[1].axvline(p95, color='b', lw=linewidth, label='p95')\n\n\t\twith warnings.catch_warnings():\n\t\t warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\t\t print(invgamma.fit(mmd))\n\t\t sns.distplot(mmd, fit=invgamma, kde=False, ax=ax[1])\n\t\t_=ax[1].axvline(mmd.values[-1], color='r', label='last', lw=linewidth)\n\t\t_=ax[1].axvline(mu, color='k', label='mean', lw=linewidth)\n\t\t_=ax[1].legend()\n\t\tst.pyplot()\n\n\t\tst.markdown('''### Daily Average True Range (ATR)\nImplementation follows [ATR](https://kodify.net/tradingview/indicators/average-true-range/).\nCheck [Investopedia](https://www.investopedia.com/terms/a/atr.asp) for more info.''')\n\n\t\tatr_df = pd.DataFrame({\n\t\t\tf'{t0}-High-Low': t0_ohlc.High - t0_ohlc.Low,\n\t\t\tf'{t0}-High-PrevCloseAbs': abs(t0_ohlc.High - t0_ohlc.Close.shift(1)),\n\t\t\tf'{t0}-Low-PrevCloseAbs': abs(t0_ohlc.Low - t0_ohlc.Close.shift(1)),\n\t\t}).max(axis=1)\n\t\tatr_df = pd.DataFrame({\n\t\t\tf'{t0}-true-range': atr_df,\n\t\t})\n\t\tatr_df[f'{t0}-ATR14'] = atr_df.iloc[:, 0].rolling(14).mean()\n\t\t# st.write(atr_df)\n\n\t\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\n\t\tf.suptitle(f'{t0} True Range & SMA14')\n\t\tatr_df.plot(ax=ax[0], lw=linewidth)\n\n\t\twith warnings.catch_warnings():\n\t\t warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n\t\t #print(invgamma.fit(f'{t0}-true-range'))\n\t\t sns.distplot(atr_df[f'{t0}-true-range'], fit=invgamma, kde=False, ax=ax[1])\n\t\t_=ax[1].axvline(atr_df[f'{t0}-true-range'].values[-1], color='b', label='last', lw=linewidth)\n\t\t_=ax[1].axvline(atr_df[f'{t0}-ATR14'].values[-1], color='r', label='last', lw=linewidth)\n\t\t_=ax[1].legend()\n\t\tst.pyplot()\n\n\n\n\t# do_strategy_analysis = True\n\tst.sidebar.markdown('## Trading Strategy')\n\tdo_strategy_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_stra')\n\tif do_strategy_analysis:\n\t\tst.markdown('## Trading Strategy')\n\t\tst.markdown('[investopedia](https://www.investopedia.com/articles/active-trading/052014/how-use-moving-average-buy-stocks.asp)')\n\t\tshort_window = st.sidebar.slider('short_window', 2, 21, 3)\n\t\tlong_window = st.sidebar.slider('long_window', 3, 50, 5)\n\t\tplot_strategy(t0, t0_df, short_window, long_window)\n\t\tst.pyplot()\n\n\t# do_corr_analysis = False\n\tst.sidebar.markdown('## Correlation analysis')\n\tdo_corr_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_corr')\n\tif do_corr_analysis:\n\t\tst.markdown('## Correlation analysis')\n\t\tt1= 'GC=F' # # SP500 'GC=F'\n\t\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\n\t\tt1 = st.sidebar.selectbox('REF1:', stocks, index=stocks.index(t1))\n\t\tt2 = st.sidebar.selectbox('REF2:', stocks, index=stocks.index(t2))\n\t\tif st.sidebar.button('Reset'):\n\t\t\tt1 = 'GC=F' # # SP500 'GC=F'\n\t\t\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\n\t\t\t# t1 = st.sidebar.selectbox('ref1:', stocks, index=stocks.index(t1))\n\t\t\t# t2 = st.sidebar.selectbox('ref2:', stocks, index=stocks.index(t2))\n\n\t\t@st.cache(persist=True, show_spinner=False)\n\t\tdef get_dataframes(t1, t2, startdd, endd):\n\t\t\tt1_ohlc = extract(ticker=t1, start_date=startdd, end_date=endd)\n\t\t\tt2_ohlc = extract(ticker=t2, start_date=startdd, end_date=endd)\n\t\t\treturn t1_ohlc, t2_ohlc\n\n\t\tt1_ohlc, t2_ohlc = get_dataframes(t1, t2, startdd, endd)\n\t\tt1_df = pd.DataFrame({f'{t1}-Close': t1_ohlc.Close})\n\t\tt2_df = pd.DataFrame({f'{t2}-Close': t2_ohlc.Close})\n\n\t\t#print(t0_ohlc.shape)\n\t\t#t0_ohlc.head()\n\t\t# print(t1_ohlc.shape)\n\t\t# ticker_ohlc.head()\n\t\t# ticker_ohlc.info()\n\n\t\ttdf = t0_df.join(t1_df).join(t2_df).interpolate().dropna()\n\t\t# tdf.head(10)\n\n\t\t# t0_ohlc.corr(t1_ohlc)\n\t\t#ax = t0_ohlc.Close.plot()\n\t\t#t1_ohlc.Close.plot(ax=ax)\n\n\t\timport numpy as np\n\t\tprint('glocal corrleation1: ', t0_ohlc.Close.corr(t1_ohlc.Close))\n\t\tprint('glocal corrleation2: ', t0_ohlc.Close.corr(t2_ohlc.Close))\n\n\t\tp_window_size = 5\n\t\tr_window_size = 5\n\t\tcentering = False\n\n\n\t\tmodf = lambda x: x\n\t\t#modf = np.log10\n\n\n\t\tmain_stat = f'[{t0}]-mean-roll{p_window_size}'\n\t\talt_stat_1 = f'[{t1}]-mean-roll{p_window_size}'\n\t\talt_stat_2 = f'[{t2}]-mean-roll{p_window_size}'\n\t\t# df_rc = pd.DataFrame({\n\t\t# main_stat : tdf.iloc[:, 0].apply(modf).rolling(window=p_window_size,center=centering).mean(),\n\t\t# alt_stat_1: tdf.iloc[:, 1].apply(modf).rolling(window=p_window_size,center=centering).mean(),\n\t\t# alt_stat_2: tdf.iloc[:, 2].apply(modf).rolling(window=p_window_size,center=centering).mean(),\n\t\t# })\n\t\tcom_val = 0.2\n\t\tdf_rc = pd.DataFrame({\n\t\t main_stat : tdf.iloc[:, 0].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\n\t\t alt_stat_1: tdf.iloc[:, 1].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\n\t\t alt_stat_2: tdf.iloc[:, 2].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\n\t\t})\n\n\t\tdf_rc = df_rc.interpolate()\n\t\tdf_rc[f'[{t0}]-[{t1}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_1])\n\t\tdf_rc[f'[{t0}]-[{t2}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_2])\n\n\t\tf, ax = plt.subplots(3,1,figsize=(16,10),sharex=True)\n\t\t#df_rc.iloc[:,0].plot(ax=ax[0], legend=True)\n\t\tdf_rc.iloc[:,1].plot(ax=ax[0], legend=True, color='gold')\n\t\tdf_rc.iloc[:,2].plot(ax=ax[1], legend=True, color='darkred')\n\t\tdf_rc.iloc[:,3].plot(ax=ax[2], legend=True, color='gold')\n\t\tdf_rc.iloc[:,4].plot(ax=ax[2], legend=True, color='darkred')\n\t\tax[2].axhline(y=0, lw=1, color='black')\n\t\t#t0_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[0])\n\t\t#t1_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[1])\n\t\t# ax[0].set(xlabel='Frame',ylabel='Smiling Evidence')\n\t\t# ax[1].set(xlabel='Frame',ylabel='Pearson r')\n\t\t_=plt.suptitle(f\"{t0} Close rolling correlation to {t1}, {t2}\")\n\n\t\tst.pyplot()\n\n\n\t\tf,ax=plt.subplots(1, 2, figsize=(16,8),sharex=False)\n\n\t\t_= df_rc.plot.scatter(x=df_rc.columns[1],\n\t\t y=df_rc.columns[2],\n\t\t c=df_rc.columns[0],\n\t\t colormap='viridis',\n\t\t # legend=None,\n\t\t ax=ax[0])\n\n\t\tprint(df_rc.columns)\n\t\tnewr_p = df_rc.iloc[-1, 0]\n\t\tt1_p = df_rc.iloc[-1, 1]\n\t\tt2_p = df_rc.iloc[-1, 2]\n\t\tt1_c = df_rc.dropna().iloc[-1, 3]\n\t\tt2_c = df_rc.dropna().iloc[-1, 4]\n\t\tprint('current_corr:', (t1_c, t2_c))\n\n\t\t# figure out circle size\n\t\taaaa = df_rc.iloc[:, 1].aggregate([np.max, np.min])\n\t\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\n\t\tprint(aaaa.values[0], aaaa.values[1], xrange)\n\t\txradius = xrange / 20.\n\n\t\tcircle = plt.Circle((t1_p, t2_p), xradius, color='r', fill=False)\n\t\tax[0].add_artist(circle)\n\t\t#ax[0].set_xlabel(f'GOLD Price {t1_p:.4f}')\n\t\t#ax[0].set_ylabel(f'OIL Price {t2_p:.4f}')\n\t\t# ax[0].legend().set_visible(False)\n\n\t\t_= df_rc.plot.scatter(x=df_rc.columns[-2],\n\t\t y=df_rc.columns[-1],\n\t\t c=df_rc.columns[0],\n\t\t colormap='viridis',\n\t\t # legend=True,\n\t\t #linestyle=\n\t\t ax=ax[1])\n\n\t\t# figure out circle size\n\t\taaaa = df_rc.iloc[:, -2].aggregate([np.max, np.min])\n\t\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\n\t\tprint(aaaa.values[0], aaaa.values[1], xrange)\n\t\txradius = xrange / 20.\n\n\t\tcircle1 = plt.Circle((t1_c, t2_c), xradius, color='r', fill=False)\n\t\tax[1].add_artist(circle1)\n\t\t#ax[1].set_ylabel('OIL Correlation')\n\t\t#_= ax[1].set_xlabel('GOLD Correlation')\n\n\n\t\tst.pyplot()","def best(self, side):\n return Library.functions.best(self._book, side)","def __call__(self, auctioneer):\n curr_bid = auctioneer.current_bid\n bid_price = curr_bid * self._bid_increase_perc\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\n self._highest_bid = bid_price\n return bid_price\n return 0","def set_greatest(self,widget,greatest):\n self.greatest = greatest\n self.queue_draw()","def getValue(self, state):\n \"\"\"Description:\n first get legal actions of current state and find the max q-value among all legalaction. \n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n legalActions = self.getLegalActions(state)\n if len(legalActions) == 0:\n return 0.0\n maxValues = max([ self.getQValue(state, a) for a in legalActions])\n return maxValues\n \n \"\"\" END CODE \"\"\"","def __init__(self, parent=None):\n super().__init__(parent, modal=True)\n self.setWindowTitle(\"Half-Elf Stat Selection\")\n self.resize(400, 100)\n layout = qtw.QGridLayout()\n self.setLayout(layout)\n label = qtw.QLabel('Select 2 abilities to improve by +1:')\n label.setAlignment(qtc.Qt.AlignCenter)\n layout.addWidget(label, 0, 0, 1, 3)\n self.num_checked = 0\n self.str_checkbox = qtw.QCheckBox('Strength')\n self.dex_checkbox = qtw.QCheckBox('Dexterity')\n self.con_checkbox = qtw.QCheckBox('Constitution')\n self.int_checkbox = qtw.QCheckBox('Intelligence')\n self.wis_checkbox = qtw.QCheckBox('Wisdom')\n self.checkboxes = [\n self.str_checkbox, self.dex_checkbox, self.con_checkbox,\n self.int_checkbox, self.wis_checkbox\n ]\n for checkbox in self.checkboxes:\n checkbox.toggled.connect(self.adjust_nums_checked)\n\n layout.addWidget(self.str_checkbox, 1, 0)\n layout.addWidget(self.dex_checkbox, 1, 1)\n layout.addWidget(self.con_checkbox, 1, 2)\n layout.addWidget(self.int_checkbox, 2, 0)\n layout.addWidget(self.wis_checkbox, 2, 1)\n reset_checkboxes = qtw.QPushButton('Clear', self)\n self.submit_btn = qtw.QPushButton('Ok', self, clicked=self.accept)\n self.submit_btn.setDisabled(True)\n layout.addWidget(reset_checkboxes, 3, 1)\n layout.addWidget(self.submit_btn, 3, 2)\n reset_checkboxes.clicked.connect(self.remove_checks)","def k(rating):\n if rating < 100: return 40\n if rating < 200: return 20\n return 10","def showTopStories(self):\n\t\tif self.newestOrTop == \"newest\":\n\t\t\tself.newestOrTop = \"top\"\n\t\t\tprint \"Getting the latest stories from HN...\"\n\t\t\tself.refreshStories()\n\t\telse:\n\t\t\tinput = raw_input(\"Already showing top stories. Press Return to continue.\")\n\t\tself.printStories()","def min_earned(table):\n money_earned = employees_earning(table)\n temp = 0\n min_earned_employee = [0]\n for employee in money_earned:\n if temp == 0:\n temp = money_earned[employee]\n if money_earned[employee] <= temp:\n temp = money_earned[employee]\n if money_earned[employee] > 0:\n min_earned_employee[0] = (str(employee) + \": \" + str(money_earned[employee]))\n elif money_earned[employee] == 0:\n min_earned_employee.append(str(employee) + \": \" + str(money_earned[employee]))\n if len(min_earned_employee) > 1:\n min_earned_employee.pop(0)\n return min_earned_employee","def fuction_call(chest):\n\n for i in chest:\n max_i = maximum(chest,i)\n if max_i >= 2:\n print(\"The maximum size of a set Matyoshka Dolls with outermost doll\",i,\"is\",max_i)","def give_raise(self):\r\n self.salary = 45000","def min_weight(self):\n return self.experiences.min_weight","def minimum(self):\n return self.properties.get('minimum')","def test_view_manager_report_only_one_highest_course(self):\n self.setup_vars()\n q = self.generate_query(\"view_manager_report\", ())\n res = self.execute_query(q)\n expected = [('(John,3,3,121.00,{OS})',)]\n assert res == expected, f'\\nOutput: {res}\\nExpected: {expected}'","def personal_best(scores):\n return max(scores)","def choose_luggage(self, *args, **kwargs):","def personal_best(scores):\n# return sorted(scores, reverse=True)[0]\n return max(scores)","def get_user_risk_tolerance_port():\n risk_tolerance = questionary.select(\"What's your risk tolerance\", \n choices=[\"Low\", \"Medium\", \"High\"]).ask()\n\n indexes = questionary.select(\"Of the following indexes, select the index that best reflects your current portfolio.\", \n choices=[\"NASDAQ\", \"Russel\", \"S&P 500\", \"EAFE\"]).ask()\n\n # crypto_benchmark = questionary.select(\"Would you like to benchmark your crypto against an\" + \n # \" index, a specific crypto, or a composite of cryptos?\", choices=[\"Index\", \"Crypto\", \"Crypto Composite\"]).ask()\n\n goals = questionary.text(\"Would you like to acomplish a dollar goal or a percentage return goal?\" +\n \" (Start with a '$' if dollar goal and with '%' if percent goal\").ask()\n\n invest_amount = int(questionary.text(\"How much would you like to invest?\").ask())\n\n stock_portfolio = {}\n adding = True\n while adding:\n sp = questionary.text(\"Enter stock from stock portfolio: \").ask()\n sw = int(questionary.text(\"Enter Share Amount: \").ask())\n cont = questionary.confirm(\"Continue?\").ask()\n stock_portfolio[sp] = sw\n if cont == False:\n adding = False\n\n user_dictionary = {\"Risk Tolerance\": risk_tolerance,\n \"Index\": indexes,\n \"Investment Goals\": goals,\n \"Investment Amount\": invest_amount,\n \"Stock Portfolio\": stock_portfolio}\n\n return user_dictionary","def calculate_greatest(self):\n greatest = 0\n for resourceList in self.loading.values():\n for time, use in resourceList:\n if use > greatest:\n greatest = use\n self.emit(\"greatest_calculated\",greatest)\n return greatest","def youngest():\n # fill it out\n newlist = sorted(PEOPLE_LIST, key=itemgetter('age'))\n return newlist","def get_highest_priority(self):\n for i in self.query.index.values.tolist():\n if not int(self.query.loc[i,'in_%s'%self.program]):\n pick = self.query.loc[i]\n break\n return pick","def _individual_health_insurance(self, total_salary):\n individual_health_insurance = 0\n\n if total_salary <= self.health_max_fee:\n individual_health_insurance = total_salary \\\n * self.individual_health_insurance_rate\n else:\n individual_health_insurance = self.health_max_fee\\\n * self.individual_health_insurance_rate\n #end if\n return individual_health_insurance","def college_selectivity():","def plot_fundingOvertime(df, col1, col2, col_transform = 1000000000, left=2015, right=2016.5):\n\n print('\\n*** INTERACTIVE MODE: HOVER OVER THE GRAPH TO SEE AWARD TOTALS FOR EACH YEAR***')\n grouped = pd.DataFrame(df.groupby([col1])[col2].sum())\n grouped.reset_index(inplace=True)\n\n# set amounts by billion dollars\n grouped[col2]=grouped[col2]/col_transform\n source = ColumnDataSource(grouped)\n\n# initialize the figure\n p = figure(plot_width = 1000,\n plot_height = 450,\n title = 'Award funding has increased over time with 2011 seeing the largest funding amounts')\n\n # create the plot\n p.line(x=col1,\n y=col2,\n line_width=6,\n source=source, color = 'green')\n\n # set formating parameters\n p.xgrid.grid_line_color = None\n p.ygrid.grid_line_color = None\n p.background_fill_color = \"AliceBlue\"\n p.title.text_font_size = \"16pt\"\n p.title.text_color = 'MidnightBlue'\n p.xaxis.axis_label_text_font_size = '15pt'\n p.yaxis.axis_label_text_font_size = '15pt'\n p.yaxis.axis_label = 'Amount awarded in US Billion'\n p.xaxis.major_label_text_font_size = '12pt'\n\n # add shaded box to highlight year with greatest funding\n box = BoxAnnotation(left=left, right=right,\n line_width=1,\n line_color='black',\n line_dash='dashed',\n fill_alpha=0.2,\n fill_color='green')\n # add box to plot\n p.add_layout(box)\n\n # create label for the box\n label = Label(x=2016,\n y=6.220,\n x_offset=12,\n text=\"$6.22 b.awarded in 2016\",\n text_baseline=\"middle\")\n\n # add to plot\n p.add_layout(label)\n\n # add interactive hover tool that shows the amount awarded\n hover = HoverTool()\n hover.tooltips = [(\"Total amount awarded \", \"@AwardAmount\")]\n\n hover.mode = 'vline'\n p.add_tools(hover)\n\n # export plots\n _=export_png(p, filename = img_path / 'fundingovertime.png')\n output_file(img_path/'fundingovertime.html')\n\n p.output_backend = \"svg\"\n export_svgs(p, filename=img_path/\"fundingovertime.svg\")\n\n #display plot\n show(p)","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\n return pulumi.get(self, \"y_axis_left_min\")","def best_value(self):\r\n return self._best_value","def show_max_age_label(self):\n self.draw_max_age = True","def display_top_lobbying(\n limit: int = 10,\n raw: bool = False,\n export: str = \"\",\n sheet_name: Optional[str] = None,\n external_axes: bool = False,\n):\n df_lobbying = quiverquant_model.get_top_lobbying()\n\n if df_lobbying.empty:\n return\n\n df_lobbying[\"Amount\"] = df_lobbying.Amount.astype(float).fillna(0) / 100_000\n\n lobbying_by_ticker = pd.DataFrame(\n df_lobbying.groupby(\"Ticker\")[\"Amount\"].agg(\"sum\")\n ).sort_values(by=\"Amount\", ascending=False)\n\n df = lobbying_by_ticker.head(limit)\n\n fig = OpenBBFigure(xaxis_title=\"Ticker\", yaxis_title=\"Total Amount ($100k)\")\n fig.set_title(f\"Corporate Lobbying Spent since {df_lobbying['Date'].min()}\")\n\n fig.add_bar(\n x=df.index,\n y=df.Amount,\n name=\"Amount ($100k)\",\n marker_color=theme.get_colors(),\n )\n\n export_data(\n export,\n os.path.dirname(os.path.abspath(__file__)),\n \"lobbying\",\n df_lobbying,\n sheet_name,\n fig,\n )\n\n if raw:\n return print_rich_table(\n lobbying_by_ticker,\n headers=[\"Amount ($100k)\"],\n show_index=True,\n title=\"Top Lobbying Tickers\",\n export=bool(export),\n limit=limit,\n )\n\n return fig.show(external=external_axes)","def highest_rated_book(self):\n rating_max = 0\n best_rated_book = \"\"\n for book in self.books.keys():\n rating = book.get_average_rating()\n if rating > rating_max:\n rating_max = rating\n best_rated_book = book\n else:\n continue\n return best_rated_book","def get_best( self ):\n if len(self.listScore) < 1:\n if self.bMinimumIsBest: return 9999,\"Unknown\"\n else: return -1,\"Unknown\"\n return self.listScore[0]","def test_lowest_price(self):\n listings = steam_market.get_lowest_price(soup=get_soup_from_path(TEST_FILE_NORMAL_LISTING))\n self.assertEqual('11,59€', listings)","def showNewestStories(self):\n\t\tif self.newestOrTop == \"top\":\n\t\t\tself.newestOrTop = \"newest\"\n\t\t\tprint \"Getting newest stories submitted to HN...\"\n\t\t\tself.refreshStories()\n\t\telse:\n\t\t\tinput = raw_input(\"Already showing newest stories. Press Return to continue.\")\n\t\tself.printStories()","def get_youngest_student(students):\n youngest_index = 0 \n youngest = students[0][3]\n for counter, row in enumerate(students[1:], 1):\n if int(row[3]) > int(youngest):\n youngest = students[counter][3]\n youngest_index = counter \n return students[youngest_index]","def main():\n data = get_sales_data()\n sales_data = [int(num) for num in data]\n update_worksheet(sales_data, 'sales')\n new_surplus_data = calculate_surplus_sandwiches(sales_data)\n update_worksheet(new_surplus_data, 'surplus')\n list_of_last_five_sales = get_last_five_sales_entries()\n stock_data = get_average_sales(list_of_last_five_sales)\n update_worksheet(stock_data, 'stock')\n return stock_data","def execQ5():\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\n cheapest = frame.sort_values(by='Price', ascending=True).head(1)\n return cheapest","def update_fodder(self):\n self.remaining_food['Herbivore'] = self.parameters['f_max']","def create_risk_analysis_page(self, notebook):\r\n\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n # Build-up the containers for the tab. #\r\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\r\n _hpaned = gtk.HPaned()\r\n self.pack1(_hpaned, resize=True, shrink=True)\r\n\r\n # Create the organizational risk pane.\r\n _fixed = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Organization\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _hpaned.pack1(_frame, True, True)\r\n\r\n _labels = [_(u\"1. There are separate design and coding \"\r\n u\"organizations.\"),\r\n _(u\"2. There is an independent software test \"\r\n u\"organization.\"),\r\n _(u\"3. There is an independent software quality \"\r\n u\"assurance organization.\"),\r\n _(u\"4. There is an independent software configuration \"\r\n u\"management organization.\"),\r\n _(u\"5. There is an independent software verification \"\r\n u\"and validation organization.\"),\r\n _(u\"6. A structured programming team will develop the \"\r\n u\"software.\"),\r\n _(u\"7. The educational level of the software team members \"\r\n u\"is above average.\"),\r\n _(u\"8. The experience level of the software team members \"\r\n u\"is above average.\")]\r\n (_x_pos,\r\n _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\r\n _x_pos += 125\r\n\r\n _fixed.put(self.chkDevEnvQ1, _x_pos, _y_pos[0])\r\n _fixed.put(self.chkDevEnvQ2, _x_pos, _y_pos[1])\r\n _fixed.put(self.chkDevEnvQ3, _x_pos, _y_pos[2])\r\n _fixed.put(self.chkDevEnvQ4, _x_pos, _y_pos[3])\r\n _fixed.put(self.chkDevEnvQ5, _x_pos, _y_pos[4])\r\n _fixed.put(self.chkDevEnvQ6, _x_pos, _y_pos[5])\r\n _fixed.put(self.chkDevEnvQ7, _x_pos, _y_pos[6])\r\n _fixed.put(self.chkDevEnvQ8, _x_pos, _y_pos[7])\r\n\r\n # Create the methods risk pane.\r\n _fixed = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Methods\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _hpaned.pack2(_frame, True, True)\r\n\r\n _labels = [_(u\"1. Standards are defined and will be enforced.\"),\r\n _(u\"2. Software will be developed using a higher order \"\r\n u\"language.\"),\r\n _(u\"3. The development process will include formal \"\r\n u\"reviews (PDR, CDR, etc.).\"),\r\n _(u\"4. The development process will include frequent \"\r\n u\"walkthroughs.\"),\r\n _(u\"5. Development will take a top-down and \"\r\n u\"structured approach.\"),\r\n _(u\"6. Unit development folders will be used.\"),\r\n _(u\"7. A software development library will be used.\"),\r\n _(u\"8. A formal change and error reporting process \"\r\n u\"will be used.\"),\r\n _(u\"9. Progress and status will routinely be \"\r\n u\"reported.\")]\r\n (__, _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\r\n\r\n _fixed.put(self.chkDevEnvQ9, _x_pos, _y_pos[0])\r\n _fixed.put(self.chkDevEnvQ10, _x_pos, _y_pos[1])\r\n _fixed.put(self.chkDevEnvQ11, _x_pos, _y_pos[2])\r\n _fixed.put(self.chkDevEnvQ12, _x_pos, _y_pos[3])\r\n _fixed.put(self.chkDevEnvQ13, _x_pos, _y_pos[4])\r\n _fixed.put(self.chkDevEnvQ14, _x_pos, _y_pos[5])\r\n _fixed.put(self.chkDevEnvQ15, _x_pos, _y_pos[6])\r\n _fixed.put(self.chkDevEnvQ16, _x_pos, _y_pos[7])\r\n _fixed.put(self.chkDevEnvQ17, _x_pos, _y_pos[8])\r\n\r\n # Create the documentation risk pane.\r\n _hpaned = gtk.HPaned()\r\n self.pack2(_hpaned, resize=True, shrink=True)\r\n\r\n _fixed = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Documentation\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _hpaned.pack1(_frame, True, True)\r\n\r\n _labels = [_(u\" 1. System requirements specifications will be \"\r\n u\"documented.\"),\r\n _(u\" 2. Software requirements specifications will be \"\r\n u\"documented.\"),\r\n _(u\" 3. Interface design specifications will be \"\r\n u\"documented.\"),\r\n _(u\" 4. Software design specification will be \"\r\n u\"documented.\"),\r\n _(u\" 5. Test plans, procedures, and reports will be \"\r\n u\"documented.\"),\r\n _(u\" 6. The software development plan will be \"\r\n u\"documented.\"),\r\n _(u\" 7. The software quality assurance plan will be \"\r\n u\"documented.\"),\r\n _(u\" 8. The software configuration management plan will \"\r\n u\"be documented.\"),\r\n _(u\" 9. A requirements traceability matrix will be \"\r\n u\"used.\"),\r\n _(u\"10. The software version description will be \"\r\n u\"documented.\"),\r\n _(u\"11. All software discrepancies will be \"\r\n u\"documented.\")]\r\n (__, _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\r\n\r\n _fixed.put(self.chkDevEnvQ18, _x_pos, _y_pos[0])\r\n _fixed.put(self.chkDevEnvQ19, _x_pos, _y_pos[1])\r\n _fixed.put(self.chkDevEnvQ20, _x_pos, _y_pos[2])\r\n _fixed.put(self.chkDevEnvQ21, _x_pos, _y_pos[3])\r\n _fixed.put(self.chkDevEnvQ22, _x_pos, _y_pos[4])\r\n _fixed.put(self.chkDevEnvQ23, _x_pos, _y_pos[5])\r\n _fixed.put(self.chkDevEnvQ24, _x_pos, _y_pos[6])\r\n _fixed.put(self.chkDevEnvQ25, _x_pos, _y_pos[7])\r\n _fixed.put(self.chkDevEnvQ26, _x_pos, _y_pos[8])\r\n _fixed.put(self.chkDevEnvQ27, _x_pos, _y_pos[9])\r\n _fixed.put(self.chkDevEnvQ28, _x_pos, _y_pos[10])\r\n\r\n # Create the tools and test techniques risk pane.\r\n _fixed = gtk.Fixed()\r\n\r\n _scrollwindow = gtk.ScrolledWindow()\r\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\r\n _scrollwindow.add_with_viewport(_fixed)\r\n\r\n _frame = Widgets.make_frame(label=_(u\"Tools & Test Techniques\"))\r\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\r\n _frame.add(_scrollwindow)\r\n\r\n _hpaned.pack2(_frame, True, True)\r\n\r\n _labels = [_(u\" 1. The software language requirements will be \"\r\n u\"specified.\"),\r\n _(u\" 2. Formal program design language will be used.\"),\r\n _(u\" 3. Program design graphical techniques \"\r\n u\"(flowcharts, HIPO, etc.) will be used.\"),\r\n _(u\" 4. Simulation/emulation tools will be used.\"),\r\n _(u\" 5. Configuration management tools will be used.\"),\r\n _(u\" 6. A code auditing tool will be used.\"),\r\n _(u\" 7. A data flow analyzer will be used.\"),\r\n _(u\" 8. A programmer's workbench will be used.\"),\r\n _(u\" 9. Measurement tools will be used.\"),\r\n _(u\"10. Software code reviews will be used.\"),\r\n _(u\"11. Software branch testing will be used.\"),\r\n _(u\"12. Random testing will be used.\"),\r\n _(u\"13. Functional testing will be used.\"),\r\n _(u\"14. Error and anomaly detection testing will be \"\r\n u\"used.\"),\r\n _(u\"15. Structure analysis will be used.\")]\r\n (__, _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\r\n\r\n _fixed.put(self.chkDevEnvQ29, _x_pos, _y_pos[0])\r\n _fixed.put(self.chkDevEnvQ30, _x_pos, _y_pos[1])\r\n _fixed.put(self.chkDevEnvQ31, _x_pos, _y_pos[2])\r\n _fixed.put(self.chkDevEnvQ32, _x_pos, _y_pos[3])\r\n _fixed.put(self.chkDevEnvQ33, _x_pos, _y_pos[4])\r\n _fixed.put(self.chkDevEnvQ34, _x_pos, _y_pos[5])\r\n _fixed.put(self.chkDevEnvQ35, _x_pos, _y_pos[6])\r\n _fixed.put(self.chkDevEnvQ36, _x_pos, _y_pos[7])\r\n _fixed.put(self.chkDevEnvQ37, _x_pos, _y_pos[8])\r\n _fixed.put(self.chkDevEnvQ38, _x_pos, _y_pos[9])\r\n _fixed.put(self.chkDevEnvQ39, _x_pos, _y_pos[10])\r\n _fixed.put(self.chkDevEnvQ40, _x_pos, _y_pos[11])\r\n _fixed.put(self.chkDevEnvQ41, _x_pos, _y_pos[12])\r\n _fixed.put(self.chkDevEnvQ42, _x_pos, _y_pos[13])\r\n _fixed.put(self.chkDevEnvQ43, _x_pos, _y_pos[14])\r\n\r\n _label = gtk.Label()\r\n _label.set_markup(\"<span weight='bold'>\" +\r\n _(u\"Development\\nEnvironment\") +\r\n \"</span>\")\r\n _label.set_alignment(xalign=0.5, yalign=0.5)\r\n _label.set_justify(gtk.JUSTIFY_CENTER)\r\n _label.set_angle(0)\r\n _label.show_all()\r\n _label.set_tooltip_text(_(u\"Assesses risk due to the development \"\r\n u\"environment.\"))\r\n notebook.insert_page(self, tab_label=_label, position=-1)\r\n\r\n return False","def get_lowest_ask(self):\n orders = self.returnOrderBook(1)\n return orders[\"asks\"][0]","def min_value(tree):\n min_utility = float(\"inf\")\n \n if (is_terminal(tree)):\n return tree\n else:\n #options = []\n for node in tree:\n #options.append(max_value(node))\n min_utility = min(min_utility, max_value(node))\n return min_utility","def home(request):\n\n # Big Sale!\n big_sale = BuyProduct.objects.first()\n\n for _ in BuyProduct.objects.all():\n if _.customer_price > big_sale.customer_price:\n big_sale = _\n\n return render(request, \"core/index.html\", {\n \"products\": Product.objects.all(),\n \"latest\": Product.objects.first(),\n \"big_sale\": big_sale\n })","def topSash(parent,defaultSize=(100,100),onSashDrag=None):\r\n sash = wx.SashLayoutWindow(parent,style=wx.SW_3D)\r\n sash.SetDefaultSize(defaultSize)\r\n sash.SetOrientation(wx.LAYOUT_HORIZONTAL)\r\n sash.SetAlignment(wx.LAYOUT_TOP)\r\n sash.SetSashVisible(wx.SASH_BOTTOM, True)\r\n if onSashDrag:\r\n id = sash.GetId()\r\n sash.Bind(wx.EVT_SASH_DRAGGED_RANGE, onSashDrag,id=id,id2=id)\r\n return sash","def book_value():\r\n a = float(input(\"Please Enter Total Stockholders Equity Value: \"))\r\n b = float(input(\"Please Enter Preferred Equity Value: \"))\r\n c = float(input(\"Please Enter Number of Shares of Common Stock Outstanding: \"))\r\n d = (float(a)-float(b))/float(c)\r\n print \">> Your Book Value Per Share of Common Stock is\",round(d,2)","def get_grade(self) -> int :\n return self.grade","def rd1leaderboard(request):\n\n #Add views\n playing_players = Rd1SlotModel.objects.filter(player_name__isnull=False)\n\n endurance_leader = Rd1SlotModel.objects.aggregate(Max('endurance_score'))\n\n #Add context\n context = {\n 'playing_players': playing_players,\n 'endurance_leader': endurance_leader,\n }\n\n return render(request, 'rd1Leaderboard.html', context=context)","def _get_minth(self):\n return self.__minth","def _get_minth(self):\n return self.__minth","def _get_minth(self):\n return self.__minth"],"string":"[\n \"def _get_min_expense(self):\\n pass\",\n \"def setMinMax(self):\\n currentIndustryNum = self.myParent.myIndustry[self.myIndustryData.id]\\n oldIndustryNum = self.myParent.myOldIndustry[self.myIndustryData.id]\\n self.setMinValue(-currentIndustryNum)\\n if oldIndustryNum > currentIndustryNum:\\n self.setMaxValue(oldIndustryNum-currentIndustryNum)\\n elif self.isIndustryResearched() == 0:\\n self.setMaxValue(0)\\n else:\\n max = self.getMaxFromFundsAvail()\\n cityNum = (self.myParent.cities-self.myParent.citiesUsed)/self.myIndustryData.cities\\n if max < cityNum:\\n self.setMaxValue(max)\\n else:\\n self.setMaxValue(cityNum)\",\n \"def view_batter_bysalary(self):\\n conn = rs.create_connection(\\\"dailyfantasyscraper.db\\\")\\n salary = \\\"$\\\" + sal.get()\\n position = \\\"P\\\"\\n batter_salary = (salary, position)\\n cur = conn.cursor()\\n cur.execute(\\n \\\"SELECT * FROM rotowiredk WHERE salary <= ? and position != ? \\\", batter_salary)\\n result = cur.fetchall()\\n conn.commit()\\n conn.close()\\n\\n for item in result:\\n print(item)\\n tree.insert('', 'end', values=item)\",\n \"def largest_two():\\n # Add your code below!\",\n \"def second_lowest():\\n copy = movies.copy()\\n name = copy.sort_values(['Total Gross']).reset_index(drop = True)['#1 Movie'].loc[1]\\n return ('second_lowest', name)\",\n \"def best_sell(self):\\n return Library.functions.best_sell(self._book)\",\n \"def _choose_best_option(self):\",\n \"def main():\\n filename1=input(\\\"Enter the first filename :\\\")\\n filename2 = input(\\\"Enter the second filename :\\\")\\n city1, hilltownDict=readFile(filename1)\\n city2, valleydaleDict=readFile(filename2)\\n max=int (input(\\\"Enter the maximum number of items :\\\"))\\n\\n hilltownDict, valleydaleDict=profitcal(hilltownDict,valleydaleDict)\\n hilltownList=convertToList(hilltownDict)\\n valleydaleList=convertToList(valleydaleDict)\\n\\n sort_valley= selectionSort(valleydaleList)\\n sort_hill = selectionSort(hilltownList)\\n\\n\\n resultH,tpH=totalProfit(city1,sort_hill,max)\\n resultV,tpV=totalProfit(city2,sort_valley,max)\\n\\n if tpH>tpV:\\n print(resultH)\\n\\n elif tpV>tpH:\\n print(resultV)\\n\\n else:\\n print(\\\"Both city has same profit\\\" +\\\"\\\\n\\\" + resultH +\\\"\\\\n\\\" + resultV)\",\n \"def limit_weight(self, weight_max):\\n # remove items with low values\\n if self.total_weight > weight_max:\\n items_sorted_by_fitness = sorted(self.items, key=lambda item: item.fitness, reverse=False)\\n while items_sorted_by_fitness and self.total_weight > weight_max:\\n least_fit_item = items_sorted_by_fitness.pop(0)\\n if self.item_stats[least_fit_item.id] == 1:\\n self.item_stats[least_fit_item.id] = 0\\n self.update_values() # have to update each time an item is change to recompute weight\",\n \"def Salvage(self):\\n pass\",\n \"def _check_amount_with_priority(self):\\n\\t\\tfor slc in self:\\n\\t\\t\\tif slc.max_amount and self.search([('priority', '<', slc.priority), ('max_amount', '>=', slc.max_amount)]):\\n\\t\\t\\t\\traise Warning(_(\\\"There are below slides [Priority less than %s] with bigger amount from [%s]\\\"\\n\\t\\t\\t\\t \\\" which against the logic!!!\\\\n You can increase amount or handel priority\\\")\\n\\t\\t\\t\\t % (slc.priority, slc.max_amount))\",\n \"def get_greatest_stock_price():\\n greatest_stock_price = 0\\n // your code here\",\n \"def select_sort_method():\\n st.sidebar.markdown('### Sort method:')\\n sort_select = st.sidebar.selectbox('', ['Alphabetically', 'FTE Salary'],\\n index=1)\\n return sort_select\",\n \"def best_price(self):\\n # TODO rename this to \\\"display_lowest_price\\\" or something...\\n price = self.lowest_price\\n if price:\\n return Decimal.quantize(price.per_kg, settings.DECIMAL_CENTS) # round to cents\",\n \"def youngest(self):\\n # Your implementation here\",\n \"def main():\\r\\n\\r\\n st.title(\\\"Summarizer App\\\")\\r\\n menu = ['Home', 'About']\\r\\n choice = st.sidebar.selectbox(\\\"Menu\\\", menu)\\r\\n\\r\\n if choice == \\\"Home\\\":\\r\\n st.subheader(\\\"Summarization\\\")\\r\\n raw_text = st.text_area(\\\"Enter Text Here\\\")\\r\\n if st.button(\\\"Summarize\\\"):\\r\\n \\r\\n with st.expander(\\\"original Text\\\"):\\r\\n st.write(raw_text)\\r\\n\\r\\n # Layout\\r\\n c1, c2 = st.columns(2)\\r\\n with c1:\\r\\n with st.expander(\\\"LexRank Summary\\\"):\\r\\n my_summary = sumy_summarizer(raw_text)\\r\\n document_len = {\\\"Original\\\":len(raw_text), \\r\\n \\\"Summary\\\":len(my_summary)}\\r\\n st.write(document_len)\\r\\n st.write(my_summary) \\r\\n st.info(\\\"Rouge Score\\\")\\r\\n eval_df = evaluate_summary(my_summary, raw_text)\\r\\n st.dataframe(eval_df.T)\\r\\n eval_df['metrics'] = eval_df.index\\r\\n c = alt.Chart(eval_df).mark_bar().encode(\\r\\n x = 'metrics', y = 'rouge-1')\\r\\n st.altair_chart(c)\\r\\n\\r\\n with c2:\\r\\n with st.expander(\\\"LexRank Summary\\\"):\\r\\n my_summary = summarize(raw_text)\\r\\n document_len = {\\\"Original\\\":len(raw_text), \\r\\n \\\"Summary\\\":len(my_summary)}\\r\\n st.write(document_len)\\r\\n st.write(my_summary)\\r\\n\\r\\n st.info(\\\"Rouge Score\\\")\\r\\n eval_df = evaluate_summary(my_summary, raw_text)\\r\\n st.dataframe(eval_df.T)\\r\\n eval_df['metrics'] = eval_df.index\\r\\n c = alt.Chart(eval_df).mark_bar().encode(\\r\\n x = 'metrics', y = 'rouge-1')\\r\\n st.altair_chart(c)\\r\\n\\r\\n else:\\r\\n st.subheader(\\\"About\\\")\\r\\n st.write(\\\"Webpage for Summarization using NLP\\\")\\r\\n st.write(\\\"***\\\")\\r\\n st.write(\\\"### Packages Used\\\")\\r\\n st.write(\\\"pip install streamlit gensim==3.8.3 sumy gensim_sum_ext pandas altair seaborn rouge\\\")\\r\\n st.write(\\\"***\\\")\\r\\n st.write(\\\"This webpage is maintained by Ramakrishnan\\\")\",\n \"def display_score(self):\\n if self.args:\\n return self.display_score_for_group()\\n return self.display_top_donor_for_each_group()\",\n \"def __statistics_students_enrolled(self):\\n try:\\n discipline_name = input(\\\"Give discipline discipline_name: \\\")\\n self.__discipline_validator.validate_name(discipline_name)\\n except DisciplineException as de:\\n print(de)\\n return\\n\\n menu_string = \\\"\\\\t1. Sort alphabetically \\\\n\\\\t\\\" \\\\\\n \\\"2. Sort descending by average \\\\n\\\\t\\\" \\\\\\n \\\"0. Exit\\\"\\n command = self.__ui_read_command(menu_string)\\n\\n if command == '1':\\n self.__ui_statistics_sort_alpha(discipline_name)\\n elif command == '2':\\n self.__ui_statistics_sort_avg(discipline_name)\\n elif command == '0':\\n return\",\n \"def main():\\r\\n print(\\\"Please enter the grade of two subjects in which you are struggling with\\\")\\r\\n subject1_grade = int(input(\\\"First subject's Grade: \\\"))\\r\\n subject2_grade = int(input(\\\"Second subject's Grade: \\\"))\\r\\n\\r\\n smaller_number = getSmaller(subject1_grade, subject2_grade)\\r\\n print(\\\"What is the name of the subject in which your grade is\\\", smaller_number, \\\"?\\\")\\r\\n subject_lowest_grade = input(\\\"Enter the subject's name: \\\")\\r\\n print(\\\"To improve your grade in\\\", subject_lowest_grade + \\\",\\\", \\\"you should talk with you professor.\\\")\\r\\n print(\\\"Also, you should schedule an appointment with an Academic Advisor\\\")\",\n \"def clicked_checkbox_upper_limit(self):\\n spectral_model, proxy_index, index = self._get_selected_model(True)\\n spectral_model.metadata[\\\"is_upper_limit\\\"] \\\\\\n = self.checkbox_upper_limit.isChecked()\\n self.measurement_view.update_row(proxy_index.row())\\n self.summarize_current_table()\\n self.refresh_plots()\\n return None\",\n \"def worst_score(self):\\r\\n pass\",\n \"def calculate_risk_tol(*args):\\n global total_score\\n risk_tol_start = 0.0\\n\\n for risk_per_pg in risk_tol_per_qs.iterkeys():\\n try:\\n risk_tol_start = risk_tol_start + risk_tol_per_qs[risk_per_pg][-1] # this is the last item in the list of each information in the page\\n except IndexError:\\n pass\\n total_score = risk_tol_start\",\n \"def top_students(grade_book, num_students=3):\\n return sorted(grade_book, key=grade_book.get, reverse=True)[:num_students]\",\n \"def update_lowest_sell(self, limit):\\n if limit.size == 0:\\n #successor case\\n limit = self.sell_tree.successor(limit)\\n if limit is None:\\n #no successor\\n self.lowest_sell = None\\n else: #have a successor, but dont know if it has orders or not\\n if limit.size == 0:#limit has no orders but other limits in the tree might have orders\\n if self.sell_tree.size == 0: #we know, no other limits have an order\\n self.lowest_sell = None\\n else: #other limits have an order\\n while limit.size == 0:\\n limit = self.sell_tree.successor(limit)\\n # now our limit has a valid order, and we've found the first valid successor\\n self.lowest_sell = limit.price\\n else: #limit has an order, we found the valid successor!\\n self.lowest_sell = limit.price\",\n \"def clicked_btn_find_upper_limit(self):\\n spectral_model, proxy_index, index = self._get_selected_model(True)\\n # Find the upper limit \\n try:\\n sigma = round(float(self.edit_ul_sigma.text()),1)\\n except:\\n logger.debug(\\\"Invalid sigma for finding limit\\\")\\n return None\\n upper_limit = spectral_model.find_upper_limit(sigma=sigma, start_at_current=True)\\n # Refresh GUI\\n self.measurement_view.update_row(proxy_index.row())\\n self.summarize_current_table()\\n self.update_fitting_options()\\n self.refresh_plots()\\n return None\",\n \"def _company_health_insurance(self, total_salary):\\n company_health_insurance = 0\\n\\n if total_salary <= self.health_max_fee:\\n company_health_insurance = total_salary *\\\\\\n self.company_health_insurance_rate\\n else:\\n company_health_insurance = self.health_max_fee\\\\\\n * self.company_health_insurance_rate\\n #end if\\n return company_health_insurance\",\n \"def valuable_customer():\\r\\n valuable_customer_dictionary = {}\\r\\n for customer in Records.customers_list:\\r\\n valuable_customer_dictionary.update({customer.name: customer.total})\\r\\n search_value = max(valuable_customer_dictionary.values())\\r\\n for name, value in valuable_customer_dictionary.items():\\r\\n if value == search_value:\\r\\n search_name = name\\r\\n print('The current most valuable customer is', search_name, 'with a total order value of $',\\r\\n search_value)\\r\\n input('Press enter to return to main menu\\\\n')\\r\\n return\",\n \"def upper_earning_limit(self):\\n\\t\\treturn self._upper_earning_limit\",\n \"def adjust_salary_range(driver, salary):\\n if salary == 'All': \\n return\\n index = ['', 'All', '40+', '60+', '80+', '100+', \\n '120+', '160+', '180+', '200+'].index(salary)\\n salary_button = \\\"html/body/div[3]/div/div[2]/div[1]/div[4]/form/div/ul/\\\" \\\\\\n \\\"li[4]/fieldset/button\\\"\\n salary_path = \\\"html/body/div[3]/div/div[2]/div[1]/div[4]/\\\" \\\\\\n \\\"form/div/ul/li[4]/fieldset/div[1]/ol/li[{}\\\" \\\\\\n \\\"]/div/label\\\".format(index)\\n attempts = 1\\n while True:\\n try:\\n elem = driver.find_element_by_xpath(salary_button)\\n time.sleep(3)\\n except Exception as e:\\n attempts += 1\\n if attempts > 25: \\n break\\n else:\\n elem.click()\\n time.sleep(3)\\n driver.find_element_by_xpath(salary_path).click()\\n break\",\n \"def _get_max_expense(self):\\n pass\",\n \"def min_sale_qty(self):\\n return self._min_sale_qty\",\n \"def create_layout() -> None:\\n\\n st.sidebar.title(\\\"Menu\\\")\\n app_mode = st.sidebar.selectbox(\\\"Please select a page\\\", [' I. Homepage',\\n \\\"II. Download data\\\" ,\\n \\\"III. Statistic Data\\\",\\n ' IV. AGF Indices',\\n ' V. Notes',\\n \\\" VI. Rank of patient\\\" ])\\n \\n if app_mode == ' I. Homepage':\\n load_homepage() \\n elif app_mode == \\\"III. Statistic Data\\\":\\n leyer.leyer() \\n elif app_mode == ' IV. AGF Indices':\\n single.AGF_indices() \\n elif app_mode == \\\"II. Download data\\\":\\n download_data.download_data() \\n elif app_mode == ' V. Notes':\\n text_input.text_input()\\n elif app_mode == \\\" VI. Rank of patient\\\":\\n rank_of_patient.rank_of_patient()\",\n \"def policy(self):\\n element = self.find_css('#grading_type')[0]\\n return self.get_selected_option_text(element)\",\n \"def promjeni_broj_sati(self, x):\\r\\n value = int(self.brojSatiCombo.currentText())\\r\\n self.emit(QtCore.SIGNAL('promjeni_max_broj_sati_minutnog(PyQt_PyObject)'),\\r\\n value)\",\n \"def calculate_profit(self):\",\n \"def clicked_checkbox_upper_limit_2(self):\\n spectral_model, proxy_index, index = self._get_selected_model(True)\\n spectral_model.metadata[\\\"is_upper_limit\\\"] \\\\\\n = self.checkbox_upper_limit_2.isChecked()\\n self.measurement_view.update_row(proxy_index.row())\\n self.summarize_current_table()\\n self.refresh_plots()\\n return None\",\n \"def _get_belt(self, new_score):\\n return max([(s, b) for s, b in BELTS.items() if s <= new_score])[1]\",\n \"def evaluate_elf(calories):\\n if calories > top_three[2]:\\n top_three.append(calories)\\n elif calories > top_three[1]:\\n top_three.popleft()\\n top_three.insert(1, calories)\\n elif calories > top_three[0]:\\n top_three[0] = calories\",\n \"def main():\\n # create a list of test employees and managers\\n testList = [\\n {'type': 'employee', 'firstName': 'Mickey', 'lastName': 'Mouse', 'SSN': '100-12-3456', 'salary': 1500.00},\\n {'type': 'manager', 'firstName': 'Walt', 'lastName': 'Disney', 'SSN': '100-00-0000', 'salary': 5000.00,\\n 'title': 'Head Of Disneyland', 'yearBonus': 1000.00},\\n {'type': 'employee', 'firstName': 'Donald', 'lastName': 'Duck', 'SSN': '100-65-4321', 'salary': 1000.00},\\n {'type': 'manager', 'firstName': 'Minnie', 'lastName': 'Mouse', 'SSN': '999-99-999', 'salary': 10000.00,\\n 'title': 'Head Of Mouse HouseHold', 'yearBonus': 15000.00},\\n {'type': 'manager', 'firstName': 'Daisy', 'lastName': 'Duck', 'SSN': '100-65-4321', 'salary': 12000.00,\\n 'title': 'Head Of Duck HouseHold', 'yearBonus': 10000.00}]\\n\\n # Define percentRaise (0.1 == 10%)\\n percentRaise = 0.1\\n\\n # Create Employees and Managers Object using the Test data\\n employeeList = loadEmployees(testList)\\n\\n # Sort employee List, which will ustilize Employee's __lt__ and __eq__ methods\\n employeeList.sort()\\n\\n # Loop over Employee and Manager Objects\\n print(\\\"Employees and Manager should be sorted by last name, then first\\\\n\\\")\\n for employee in employeeList:\\n if type(employee) == Manager:\\n print(\\\"Manager:\\\")\\n else:\\n print(\\\"Employee:\\\")\\n # Print Employee or Manager\\n print(employee)\\n # Give Raise to Employee or Manager\\n employee.giveRaise(percentRaise)\\n # Print New Salary\\n print(\\\"With %.2f%% Raise, Salary: $%.2f\\\\n\\\" % (percentRaise * 100, employee.salary))\\n\\n # Employee docStrings\\n print(\\\"\\\\nEmployee docstring for each method\\\")\\n print(\\\"Employee.__doc__=\\\" + Employee.__doc__)\\n print(\\\"Employee.__init__.__doc__=\\\" + Employee.__init__.__doc__)\\n print(\\\"Employee.giveRaise.__doc__=\\\" + Employee.giveRaise.__doc__)\\n print(\\\"Employee.__str__.__doc__=\\\" + Employee.__str__.__doc__)\\n print(\\\"Employee.__eq__.__doc__=\\\" + Employee.__eq__.__doc__)\\n print(\\\"Employee.__lt__.__doc__=\\\" + Employee.__lt__.__doc__)\\n\\n print(\\\"\\\\nManger docstring for each method\\\")\\n print(\\n \\\"Since Manager inherits from Employee, several of the methods ('giveRaise', '__eq__' and '__lt__') and the corresponding docstring will originate from the Employee class\\\\n\\\")\\n print(\\\"Manager.__doc__=\\\" + Manager.__doc__)\\n print(\\\"Manager.__init__.__doc__=\\\" + Manager.__init__.__doc__)\\n print(\\\"Manager.giveRaise.__doc__=\\\" + Manager.giveRaise.__doc__)\\n print(\\\"Manager.__str__.__doc__=\\\" + Manager.__str__.__doc__)\\n print(\\\"Manager.__eq__.__doc__=\\\" + Manager.__eq__.__doc__)\\n print(\\\"Manager.__lt__.__doc__=\\\" + Manager.__lt__.__doc__)\",\n \"def execQ3():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n tuna = frame[dFrame.Series_title_1 == \\\"Tuna - canned (supermarket only), 185g\\\"]\\n cheapest = tuna.sort_values(by=\\\"Price\\\").head(1)\\n return cheapest\",\n \"def render_investip():\\n\\tlinewidth = 2\\n\\n\\tst.sidebar.markdown('# Dashboard')\\n\\tstock = st.sidebar.selectbox('Stock:', stocks)\\n\\n\\tstartdd = datetime.datetime(2020, 3, 1)\\n\\tstartdd = st.sidebar.date_input('start-date', value=startdd)\\n\\n\\tendd = datetime.datetime.now()\\n\\tendd = st.sidebar.date_input('end-date', value=endd)\\n\\n\\tt0 = stock\\n\\tt0_ohlc = extract(ticker=t0, start_date=startdd, end_date=endd)\\n\\tt0_df = pd.DataFrame({f'{t0}-Close': t0_ohlc.Close})\\n\\n\\t# st.write(t0_ohlc)\\n\\tmpf.plot(t0_ohlc, type='candle',volume=True,show_nontrading=False, title=t0, figscale=1.)\\n\\t# tdf = plot_ticker(t0, df=t0_df, start_date=startdd, end_date=endd)\\n\\tst.pyplot()\\n\\n\\n\\tst.sidebar.markdown('## Stock Correlation')\\n\\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_corrs')\\n\\tif stock_returns:\\n\\t\\tst.markdown('## Stock Correlation')\\n\\t\\tstock_selection = st.sidebar.multiselect('Stocks', stocks, def_stocks)\\n\\t\\tplot_stock_correlations(stock_selection, startdd, endd)\\n\\t\\tst.pyplot()\\n\\n\\t# trading_context = True\\n\\tst.sidebar.markdown('## Returns')\\n\\tstock_returns = st.sidebar.checkbox('Enable', value=True, key='cb_returns')\\n\\tif stock_returns:\\n\\t\\tst.markdown('## Stock Returns')\\n\\t\\tst.markdown('''### Daily Stock returns\\n[EWMA](https://www.investopedia.com/articles/07/ewma.asp)''')\\n\\t\\tspan = st.sidebar.slider('span', 2, 21, value=5)\\n\\t\\tplot_historical(t0, t0_ohlc, span=span, linewidth=linewidth)\\n\\t\\tst.pyplot()\\n\\n\\n\\t# trading_context = True\\n\\tst.sidebar.markdown('## Volatility')\\n\\ttrading_context = st.sidebar.checkbox('Enable', value=False, key='cb_volatility')\\n\\tif trading_context:\\n\\t\\tst.markdown('## Volatility & Risk')\\n\\t\\tst.markdown('''### Daily differences between High & Low\\nWe model these ranges with [Inverse Gamma PDF](https://en.wikipedia.org/wiki/Inverse-gamma_distribution).\\nGreen lines denote +/- 1 stdev.\\n''')\\n\\t\\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\\n\\t\\tf.suptitle(f'{t0} High-Low Daily')\\n\\t\\tmmd = t0_ohlc.High - t0_ohlc.Low\\n\\t\\t# mmd.dropna(inplace=True)\\n\\t\\tmmd.plot(color='r', ax=ax[0], lw=linewidth)\\n\\n\\t\\tmu, sigma = mmd.dropna().mean(), mmd.dropna().std()\\n\\t\\tzval = 1.#96\\n\\t\\t# TODO: try one-tail limit to get outliers\\n\\t\\t_=ax[0].axhline(y=mu, color='k', lw=linewidth)\\n\\t\\t_=ax[0].axhline(y=mu-zval*sigma, color='g', lw=linewidth)\\n\\t\\t_=ax[0].axhline(y=mu+zval*sigma, color='g', lw=linewidth)\\n\\n\\t\\tp95 = mmd.dropna().quantile(.95)\\n\\t\\t_=ax[0].axhline(y=p95, color='b', lw=linewidth, label='p95')\\n\\t\\t_=ax[1].axvline(p95, color='b', lw=linewidth, label='p95')\\n\\n\\t\\twith warnings.catch_warnings():\\n\\t\\t warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\\t\\t print(invgamma.fit(mmd))\\n\\t\\t sns.distplot(mmd, fit=invgamma, kde=False, ax=ax[1])\\n\\t\\t_=ax[1].axvline(mmd.values[-1], color='r', label='last', lw=linewidth)\\n\\t\\t_=ax[1].axvline(mu, color='k', label='mean', lw=linewidth)\\n\\t\\t_=ax[1].legend()\\n\\t\\tst.pyplot()\\n\\n\\t\\tst.markdown('''### Daily Average True Range (ATR)\\nImplementation follows [ATR](https://kodify.net/tradingview/indicators/average-true-range/).\\nCheck [Investopedia](https://www.investopedia.com/terms/a/atr.asp) for more info.''')\\n\\n\\t\\tatr_df = pd.DataFrame({\\n\\t\\t\\tf'{t0}-High-Low': t0_ohlc.High - t0_ohlc.Low,\\n\\t\\t\\tf'{t0}-High-PrevCloseAbs': abs(t0_ohlc.High - t0_ohlc.Close.shift(1)),\\n\\t\\t\\tf'{t0}-Low-PrevCloseAbs': abs(t0_ohlc.Low - t0_ohlc.Close.shift(1)),\\n\\t\\t}).max(axis=1)\\n\\t\\tatr_df = pd.DataFrame({\\n\\t\\t\\tf'{t0}-true-range': atr_df,\\n\\t\\t})\\n\\t\\tatr_df[f'{t0}-ATR14'] = atr_df.iloc[:, 0].rolling(14).mean()\\n\\t\\t# st.write(atr_df)\\n\\n\\t\\tf, ax = plt.subplots(1, 2, figsize=(14,6), sharex=False)\\n\\t\\tf.suptitle(f'{t0} True Range & SMA14')\\n\\t\\tatr_df.plot(ax=ax[0], lw=linewidth)\\n\\n\\t\\twith warnings.catch_warnings():\\n\\t\\t warnings.filterwarnings(\\\"ignore\\\", category=RuntimeWarning)\\n\\t\\t #print(invgamma.fit(f'{t0}-true-range'))\\n\\t\\t sns.distplot(atr_df[f'{t0}-true-range'], fit=invgamma, kde=False, ax=ax[1])\\n\\t\\t_=ax[1].axvline(atr_df[f'{t0}-true-range'].values[-1], color='b', label='last', lw=linewidth)\\n\\t\\t_=ax[1].axvline(atr_df[f'{t0}-ATR14'].values[-1], color='r', label='last', lw=linewidth)\\n\\t\\t_=ax[1].legend()\\n\\t\\tst.pyplot()\\n\\n\\n\\n\\t# do_strategy_analysis = True\\n\\tst.sidebar.markdown('## Trading Strategy')\\n\\tdo_strategy_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_stra')\\n\\tif do_strategy_analysis:\\n\\t\\tst.markdown('## Trading Strategy')\\n\\t\\tst.markdown('[investopedia](https://www.investopedia.com/articles/active-trading/052014/how-use-moving-average-buy-stocks.asp)')\\n\\t\\tshort_window = st.sidebar.slider('short_window', 2, 21, 3)\\n\\t\\tlong_window = st.sidebar.slider('long_window', 3, 50, 5)\\n\\t\\tplot_strategy(t0, t0_df, short_window, long_window)\\n\\t\\tst.pyplot()\\n\\n\\t# do_corr_analysis = False\\n\\tst.sidebar.markdown('## Correlation analysis')\\n\\tdo_corr_analysis = st.sidebar.checkbox('Enable', value=False, key='cb_corr')\\n\\tif do_corr_analysis:\\n\\t\\tst.markdown('## Correlation analysis')\\n\\t\\tt1= 'GC=F' # # SP500 'GC=F'\\n\\t\\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\\n\\t\\tt1 = st.sidebar.selectbox('REF1:', stocks, index=stocks.index(t1))\\n\\t\\tt2 = st.sidebar.selectbox('REF2:', stocks, index=stocks.index(t2))\\n\\t\\tif st.sidebar.button('Reset'):\\n\\t\\t\\tt1 = 'GC=F' # # SP500 'GC=F'\\n\\t\\t\\tt2 = 'CL=F' # '^GSPC' # '^DJI' # DJ30 'CL=F'\\n\\t\\t\\t# t1 = st.sidebar.selectbox('ref1:', stocks, index=stocks.index(t1))\\n\\t\\t\\t# t2 = st.sidebar.selectbox('ref2:', stocks, index=stocks.index(t2))\\n\\n\\t\\t@st.cache(persist=True, show_spinner=False)\\n\\t\\tdef get_dataframes(t1, t2, startdd, endd):\\n\\t\\t\\tt1_ohlc = extract(ticker=t1, start_date=startdd, end_date=endd)\\n\\t\\t\\tt2_ohlc = extract(ticker=t2, start_date=startdd, end_date=endd)\\n\\t\\t\\treturn t1_ohlc, t2_ohlc\\n\\n\\t\\tt1_ohlc, t2_ohlc = get_dataframes(t1, t2, startdd, endd)\\n\\t\\tt1_df = pd.DataFrame({f'{t1}-Close': t1_ohlc.Close})\\n\\t\\tt2_df = pd.DataFrame({f'{t2}-Close': t2_ohlc.Close})\\n\\n\\t\\t#print(t0_ohlc.shape)\\n\\t\\t#t0_ohlc.head()\\n\\t\\t# print(t1_ohlc.shape)\\n\\t\\t# ticker_ohlc.head()\\n\\t\\t# ticker_ohlc.info()\\n\\n\\t\\ttdf = t0_df.join(t1_df).join(t2_df).interpolate().dropna()\\n\\t\\t# tdf.head(10)\\n\\n\\t\\t# t0_ohlc.corr(t1_ohlc)\\n\\t\\t#ax = t0_ohlc.Close.plot()\\n\\t\\t#t1_ohlc.Close.plot(ax=ax)\\n\\n\\t\\timport numpy as np\\n\\t\\tprint('glocal corrleation1: ', t0_ohlc.Close.corr(t1_ohlc.Close))\\n\\t\\tprint('glocal corrleation2: ', t0_ohlc.Close.corr(t2_ohlc.Close))\\n\\n\\t\\tp_window_size = 5\\n\\t\\tr_window_size = 5\\n\\t\\tcentering = False\\n\\n\\n\\t\\tmodf = lambda x: x\\n\\t\\t#modf = np.log10\\n\\n\\n\\t\\tmain_stat = f'[{t0}]-mean-roll{p_window_size}'\\n\\t\\talt_stat_1 = f'[{t1}]-mean-roll{p_window_size}'\\n\\t\\talt_stat_2 = f'[{t2}]-mean-roll{p_window_size}'\\n\\t\\t# df_rc = pd.DataFrame({\\n\\t\\t# main_stat : tdf.iloc[:, 0].apply(modf).rolling(window=p_window_size,center=centering).mean(),\\n\\t\\t# alt_stat_1: tdf.iloc[:, 1].apply(modf).rolling(window=p_window_size,center=centering).mean(),\\n\\t\\t# alt_stat_2: tdf.iloc[:, 2].apply(modf).rolling(window=p_window_size,center=centering).mean(),\\n\\t\\t# })\\n\\t\\tcom_val = 0.2\\n\\t\\tdf_rc = pd.DataFrame({\\n\\t\\t main_stat : tdf.iloc[:, 0].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\\n\\t\\t alt_stat_1: tdf.iloc[:, 1].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\\n\\t\\t alt_stat_2: tdf.iloc[:, 2].apply(modf).ewm(span=p_window_size, adjust=False).mean(),\\n\\t\\t})\\n\\n\\t\\tdf_rc = df_rc.interpolate()\\n\\t\\tdf_rc[f'[{t0}]-[{t1}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_1])\\n\\t\\tdf_rc[f'[{t0}]-[{t2}]-corr-roll{r_window_size}'] = df_rc[main_stat].rolling(window=r_window_size, center=centering).corr(df_rc[alt_stat_2])\\n\\n\\t\\tf, ax = plt.subplots(3,1,figsize=(16,10),sharex=True)\\n\\t\\t#df_rc.iloc[:,0].plot(ax=ax[0], legend=True)\\n\\t\\tdf_rc.iloc[:,1].plot(ax=ax[0], legend=True, color='gold')\\n\\t\\tdf_rc.iloc[:,2].plot(ax=ax[1], legend=True, color='darkred')\\n\\t\\tdf_rc.iloc[:,3].plot(ax=ax[2], legend=True, color='gold')\\n\\t\\tdf_rc.iloc[:,4].plot(ax=ax[2], legend=True, color='darkred')\\n\\t\\tax[2].axhline(y=0, lw=1, color='black')\\n\\t\\t#t0_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[0])\\n\\t\\t#t1_ohlc.Close.rolling(window=r_window_size,center=True).mean().plot(ax=ax[1])\\n\\t\\t# ax[0].set(xlabel='Frame',ylabel='Smiling Evidence')\\n\\t\\t# ax[1].set(xlabel='Frame',ylabel='Pearson r')\\n\\t\\t_=plt.suptitle(f\\\"{t0} Close rolling correlation to {t1}, {t2}\\\")\\n\\n\\t\\tst.pyplot()\\n\\n\\n\\t\\tf,ax=plt.subplots(1, 2, figsize=(16,8),sharex=False)\\n\\n\\t\\t_= df_rc.plot.scatter(x=df_rc.columns[1],\\n\\t\\t y=df_rc.columns[2],\\n\\t\\t c=df_rc.columns[0],\\n\\t\\t colormap='viridis',\\n\\t\\t # legend=None,\\n\\t\\t ax=ax[0])\\n\\n\\t\\tprint(df_rc.columns)\\n\\t\\tnewr_p = df_rc.iloc[-1, 0]\\n\\t\\tt1_p = df_rc.iloc[-1, 1]\\n\\t\\tt2_p = df_rc.iloc[-1, 2]\\n\\t\\tt1_c = df_rc.dropna().iloc[-1, 3]\\n\\t\\tt2_c = df_rc.dropna().iloc[-1, 4]\\n\\t\\tprint('current_corr:', (t1_c, t2_c))\\n\\n\\t\\t# figure out circle size\\n\\t\\taaaa = df_rc.iloc[:, 1].aggregate([np.max, np.min])\\n\\t\\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\\n\\t\\tprint(aaaa.values[0], aaaa.values[1], xrange)\\n\\t\\txradius = xrange / 20.\\n\\n\\t\\tcircle = plt.Circle((t1_p, t2_p), xradius, color='r', fill=False)\\n\\t\\tax[0].add_artist(circle)\\n\\t\\t#ax[0].set_xlabel(f'GOLD Price {t1_p:.4f}')\\n\\t\\t#ax[0].set_ylabel(f'OIL Price {t2_p:.4f}')\\n\\t\\t# ax[0].legend().set_visible(False)\\n\\n\\t\\t_= df_rc.plot.scatter(x=df_rc.columns[-2],\\n\\t\\t y=df_rc.columns[-1],\\n\\t\\t c=df_rc.columns[0],\\n\\t\\t colormap='viridis',\\n\\t\\t # legend=True,\\n\\t\\t #linestyle=\\n\\t\\t ax=ax[1])\\n\\n\\t\\t# figure out circle size\\n\\t\\taaaa = df_rc.iloc[:, -2].aggregate([np.max, np.min])\\n\\t\\txrange = np.ceil(aaaa.values[0] - aaaa.values[1])\\n\\t\\tprint(aaaa.values[0], aaaa.values[1], xrange)\\n\\t\\txradius = xrange / 20.\\n\\n\\t\\tcircle1 = plt.Circle((t1_c, t2_c), xradius, color='r', fill=False)\\n\\t\\tax[1].add_artist(circle1)\\n\\t\\t#ax[1].set_ylabel('OIL Correlation')\\n\\t\\t#_= ax[1].set_xlabel('GOLD Correlation')\\n\\n\\n\\t\\tst.pyplot()\",\n \"def best(self, side):\\n return Library.functions.best(self._book, side)\",\n \"def __call__(self, auctioneer):\\n curr_bid = auctioneer.current_bid\\n bid_price = curr_bid * self._bid_increase_perc\\n if bid_price <= self._budget and self.get_bid_probability() > 0.3:\\n self._highest_bid = bid_price\\n return bid_price\\n return 0\",\n \"def set_greatest(self,widget,greatest):\\n self.greatest = greatest\\n self.queue_draw()\",\n \"def getValue(self, state):\\n \\\"\\\"\\\"Description:\\n first get legal actions of current state and find the max q-value among all legalaction. \\n \\\"\\\"\\\"\\n \\\"\\\"\\\" YOUR CODE HERE \\\"\\\"\\\"\\n legalActions = self.getLegalActions(state)\\n if len(legalActions) == 0:\\n return 0.0\\n maxValues = max([ self.getQValue(state, a) for a in legalActions])\\n return maxValues\\n \\n \\\"\\\"\\\" END CODE \\\"\\\"\\\"\",\n \"def __init__(self, parent=None):\\n super().__init__(parent, modal=True)\\n self.setWindowTitle(\\\"Half-Elf Stat Selection\\\")\\n self.resize(400, 100)\\n layout = qtw.QGridLayout()\\n self.setLayout(layout)\\n label = qtw.QLabel('Select 2 abilities to improve by +1:')\\n label.setAlignment(qtc.Qt.AlignCenter)\\n layout.addWidget(label, 0, 0, 1, 3)\\n self.num_checked = 0\\n self.str_checkbox = qtw.QCheckBox('Strength')\\n self.dex_checkbox = qtw.QCheckBox('Dexterity')\\n self.con_checkbox = qtw.QCheckBox('Constitution')\\n self.int_checkbox = qtw.QCheckBox('Intelligence')\\n self.wis_checkbox = qtw.QCheckBox('Wisdom')\\n self.checkboxes = [\\n self.str_checkbox, self.dex_checkbox, self.con_checkbox,\\n self.int_checkbox, self.wis_checkbox\\n ]\\n for checkbox in self.checkboxes:\\n checkbox.toggled.connect(self.adjust_nums_checked)\\n\\n layout.addWidget(self.str_checkbox, 1, 0)\\n layout.addWidget(self.dex_checkbox, 1, 1)\\n layout.addWidget(self.con_checkbox, 1, 2)\\n layout.addWidget(self.int_checkbox, 2, 0)\\n layout.addWidget(self.wis_checkbox, 2, 1)\\n reset_checkboxes = qtw.QPushButton('Clear', self)\\n self.submit_btn = qtw.QPushButton('Ok', self, clicked=self.accept)\\n self.submit_btn.setDisabled(True)\\n layout.addWidget(reset_checkboxes, 3, 1)\\n layout.addWidget(self.submit_btn, 3, 2)\\n reset_checkboxes.clicked.connect(self.remove_checks)\",\n \"def k(rating):\\n if rating < 100: return 40\\n if rating < 200: return 20\\n return 10\",\n \"def showTopStories(self):\\n\\t\\tif self.newestOrTop == \\\"newest\\\":\\n\\t\\t\\tself.newestOrTop = \\\"top\\\"\\n\\t\\t\\tprint \\\"Getting the latest stories from HN...\\\"\\n\\t\\t\\tself.refreshStories()\\n\\t\\telse:\\n\\t\\t\\tinput = raw_input(\\\"Already showing top stories. Press Return to continue.\\\")\\n\\t\\tself.printStories()\",\n \"def min_earned(table):\\n money_earned = employees_earning(table)\\n temp = 0\\n min_earned_employee = [0]\\n for employee in money_earned:\\n if temp == 0:\\n temp = money_earned[employee]\\n if money_earned[employee] <= temp:\\n temp = money_earned[employee]\\n if money_earned[employee] > 0:\\n min_earned_employee[0] = (str(employee) + \\\": \\\" + str(money_earned[employee]))\\n elif money_earned[employee] == 0:\\n min_earned_employee.append(str(employee) + \\\": \\\" + str(money_earned[employee]))\\n if len(min_earned_employee) > 1:\\n min_earned_employee.pop(0)\\n return min_earned_employee\",\n \"def fuction_call(chest):\\n\\n for i in chest:\\n max_i = maximum(chest,i)\\n if max_i >= 2:\\n print(\\\"The maximum size of a set Matyoshka Dolls with outermost doll\\\",i,\\\"is\\\",max_i)\",\n \"def give_raise(self):\\r\\n self.salary = 45000\",\n \"def min_weight(self):\\n return self.experiences.min_weight\",\n \"def minimum(self):\\n return self.properties.get('minimum')\",\n \"def test_view_manager_report_only_one_highest_course(self):\\n self.setup_vars()\\n q = self.generate_query(\\\"view_manager_report\\\", ())\\n res = self.execute_query(q)\\n expected = [('(John,3,3,121.00,{OS})',)]\\n assert res == expected, f'\\\\nOutput: {res}\\\\nExpected: {expected}'\",\n \"def personal_best(scores):\\n return max(scores)\",\n \"def choose_luggage(self, *args, **kwargs):\",\n \"def personal_best(scores):\\n# return sorted(scores, reverse=True)[0]\\n return max(scores)\",\n \"def get_user_risk_tolerance_port():\\n risk_tolerance = questionary.select(\\\"What's your risk tolerance\\\", \\n choices=[\\\"Low\\\", \\\"Medium\\\", \\\"High\\\"]).ask()\\n\\n indexes = questionary.select(\\\"Of the following indexes, select the index that best reflects your current portfolio.\\\", \\n choices=[\\\"NASDAQ\\\", \\\"Russel\\\", \\\"S&P 500\\\", \\\"EAFE\\\"]).ask()\\n\\n # crypto_benchmark = questionary.select(\\\"Would you like to benchmark your crypto against an\\\" + \\n # \\\" index, a specific crypto, or a composite of cryptos?\\\", choices=[\\\"Index\\\", \\\"Crypto\\\", \\\"Crypto Composite\\\"]).ask()\\n\\n goals = questionary.text(\\\"Would you like to acomplish a dollar goal or a percentage return goal?\\\" +\\n \\\" (Start with a '$' if dollar goal and with '%' if percent goal\\\").ask()\\n\\n invest_amount = int(questionary.text(\\\"How much would you like to invest?\\\").ask())\\n\\n stock_portfolio = {}\\n adding = True\\n while adding:\\n sp = questionary.text(\\\"Enter stock from stock portfolio: \\\").ask()\\n sw = int(questionary.text(\\\"Enter Share Amount: \\\").ask())\\n cont = questionary.confirm(\\\"Continue?\\\").ask()\\n stock_portfolio[sp] = sw\\n if cont == False:\\n adding = False\\n\\n user_dictionary = {\\\"Risk Tolerance\\\": risk_tolerance,\\n \\\"Index\\\": indexes,\\n \\\"Investment Goals\\\": goals,\\n \\\"Investment Amount\\\": invest_amount,\\n \\\"Stock Portfolio\\\": stock_portfolio}\\n\\n return user_dictionary\",\n \"def calculate_greatest(self):\\n greatest = 0\\n for resourceList in self.loading.values():\\n for time, use in resourceList:\\n if use > greatest:\\n greatest = use\\n self.emit(\\\"greatest_calculated\\\",greatest)\\n return greatest\",\n \"def youngest():\\n # fill it out\\n newlist = sorted(PEOPLE_LIST, key=itemgetter('age'))\\n return newlist\",\n \"def get_highest_priority(self):\\n for i in self.query.index.values.tolist():\\n if not int(self.query.loc[i,'in_%s'%self.program]):\\n pick = self.query.loc[i]\\n break\\n return pick\",\n \"def _individual_health_insurance(self, total_salary):\\n individual_health_insurance = 0\\n\\n if total_salary <= self.health_max_fee:\\n individual_health_insurance = total_salary \\\\\\n * self.individual_health_insurance_rate\\n else:\\n individual_health_insurance = self.health_max_fee\\\\\\n * self.individual_health_insurance_rate\\n #end if\\n return individual_health_insurance\",\n \"def college_selectivity():\",\n \"def plot_fundingOvertime(df, col1, col2, col_transform = 1000000000, left=2015, right=2016.5):\\n\\n print('\\\\n*** INTERACTIVE MODE: HOVER OVER THE GRAPH TO SEE AWARD TOTALS FOR EACH YEAR***')\\n grouped = pd.DataFrame(df.groupby([col1])[col2].sum())\\n grouped.reset_index(inplace=True)\\n\\n# set amounts by billion dollars\\n grouped[col2]=grouped[col2]/col_transform\\n source = ColumnDataSource(grouped)\\n\\n# initialize the figure\\n p = figure(plot_width = 1000,\\n plot_height = 450,\\n title = 'Award funding has increased over time with 2011 seeing the largest funding amounts')\\n\\n # create the plot\\n p.line(x=col1,\\n y=col2,\\n line_width=6,\\n source=source, color = 'green')\\n\\n # set formating parameters\\n p.xgrid.grid_line_color = None\\n p.ygrid.grid_line_color = None\\n p.background_fill_color = \\\"AliceBlue\\\"\\n p.title.text_font_size = \\\"16pt\\\"\\n p.title.text_color = 'MidnightBlue'\\n p.xaxis.axis_label_text_font_size = '15pt'\\n p.yaxis.axis_label_text_font_size = '15pt'\\n p.yaxis.axis_label = 'Amount awarded in US Billion'\\n p.xaxis.major_label_text_font_size = '12pt'\\n\\n # add shaded box to highlight year with greatest funding\\n box = BoxAnnotation(left=left, right=right,\\n line_width=1,\\n line_color='black',\\n line_dash='dashed',\\n fill_alpha=0.2,\\n fill_color='green')\\n # add box to plot\\n p.add_layout(box)\\n\\n # create label for the box\\n label = Label(x=2016,\\n y=6.220,\\n x_offset=12,\\n text=\\\"$6.22 b.awarded in 2016\\\",\\n text_baseline=\\\"middle\\\")\\n\\n # add to plot\\n p.add_layout(label)\\n\\n # add interactive hover tool that shows the amount awarded\\n hover = HoverTool()\\n hover.tooltips = [(\\\"Total amount awarded \\\", \\\"@AwardAmount\\\")]\\n\\n hover.mode = 'vline'\\n p.add_tools(hover)\\n\\n # export plots\\n _=export_png(p, filename = img_path / 'fundingovertime.png')\\n output_file(img_path/'fundingovertime.html')\\n\\n p.output_backend = \\\"svg\\\"\\n export_svgs(p, filename=img_path/\\\"fundingovertime.svg\\\")\\n\\n #display plot\\n show(p)\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def y_axis_left_min(self) -> Optional[pulumi.Input[float]]:\\n return pulumi.get(self, \\\"y_axis_left_min\\\")\",\n \"def best_value(self):\\r\\n return self._best_value\",\n \"def show_max_age_label(self):\\n self.draw_max_age = True\",\n \"def display_top_lobbying(\\n limit: int = 10,\\n raw: bool = False,\\n export: str = \\\"\\\",\\n sheet_name: Optional[str] = None,\\n external_axes: bool = False,\\n):\\n df_lobbying = quiverquant_model.get_top_lobbying()\\n\\n if df_lobbying.empty:\\n return\\n\\n df_lobbying[\\\"Amount\\\"] = df_lobbying.Amount.astype(float).fillna(0) / 100_000\\n\\n lobbying_by_ticker = pd.DataFrame(\\n df_lobbying.groupby(\\\"Ticker\\\")[\\\"Amount\\\"].agg(\\\"sum\\\")\\n ).sort_values(by=\\\"Amount\\\", ascending=False)\\n\\n df = lobbying_by_ticker.head(limit)\\n\\n fig = OpenBBFigure(xaxis_title=\\\"Ticker\\\", yaxis_title=\\\"Total Amount ($100k)\\\")\\n fig.set_title(f\\\"Corporate Lobbying Spent since {df_lobbying['Date'].min()}\\\")\\n\\n fig.add_bar(\\n x=df.index,\\n y=df.Amount,\\n name=\\\"Amount ($100k)\\\",\\n marker_color=theme.get_colors(),\\n )\\n\\n export_data(\\n export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"lobbying\\\",\\n df_lobbying,\\n sheet_name,\\n fig,\\n )\\n\\n if raw:\\n return print_rich_table(\\n lobbying_by_ticker,\\n headers=[\\\"Amount ($100k)\\\"],\\n show_index=True,\\n title=\\\"Top Lobbying Tickers\\\",\\n export=bool(export),\\n limit=limit,\\n )\\n\\n return fig.show(external=external_axes)\",\n \"def highest_rated_book(self):\\n rating_max = 0\\n best_rated_book = \\\"\\\"\\n for book in self.books.keys():\\n rating = book.get_average_rating()\\n if rating > rating_max:\\n rating_max = rating\\n best_rated_book = book\\n else:\\n continue\\n return best_rated_book\",\n \"def get_best( self ):\\n if len(self.listScore) < 1:\\n if self.bMinimumIsBest: return 9999,\\\"Unknown\\\"\\n else: return -1,\\\"Unknown\\\"\\n return self.listScore[0]\",\n \"def test_lowest_price(self):\\n listings = steam_market.get_lowest_price(soup=get_soup_from_path(TEST_FILE_NORMAL_LISTING))\\n self.assertEqual('11,59€', listings)\",\n \"def showNewestStories(self):\\n\\t\\tif self.newestOrTop == \\\"top\\\":\\n\\t\\t\\tself.newestOrTop = \\\"newest\\\"\\n\\t\\t\\tprint \\\"Getting newest stories submitted to HN...\\\"\\n\\t\\t\\tself.refreshStories()\\n\\t\\telse:\\n\\t\\t\\tinput = raw_input(\\\"Already showing newest stories. Press Return to continue.\\\")\\n\\t\\tself.printStories()\",\n \"def get_youngest_student(students):\\n youngest_index = 0 \\n youngest = students[0][3]\\n for counter, row in enumerate(students[1:], 1):\\n if int(row[3]) > int(youngest):\\n youngest = students[counter][3]\\n youngest_index = counter \\n return students[youngest_index]\",\n \"def main():\\n data = get_sales_data()\\n sales_data = [int(num) for num in data]\\n update_worksheet(sales_data, 'sales')\\n new_surplus_data = calculate_surplus_sandwiches(sales_data)\\n update_worksheet(new_surplus_data, 'surplus')\\n list_of_last_five_sales = get_last_five_sales_entries()\\n stock_data = get_average_sales(list_of_last_five_sales)\\n update_worksheet(stock_data, 'stock')\\n return stock_data\",\n \"def execQ5():\\n frame = pan.DataFrame(data, columns=['Product', 'Price', 'Period'])\\n cheapest = frame.sort_values(by='Price', ascending=True).head(1)\\n return cheapest\",\n \"def update_fodder(self):\\n self.remaining_food['Herbivore'] = self.parameters['f_max']\",\n \"def create_risk_analysis_page(self, notebook):\\r\\n\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n # Build-up the containers for the tab. #\\r\\n # +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\\r\\n _hpaned = gtk.HPaned()\\r\\n self.pack1(_hpaned, resize=True, shrink=True)\\r\\n\\r\\n # Create the organizational risk pane.\\r\\n _fixed = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Organization\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _hpaned.pack1(_frame, True, True)\\r\\n\\r\\n _labels = [_(u\\\"1. There are separate design and coding \\\"\\r\\n u\\\"organizations.\\\"),\\r\\n _(u\\\"2. There is an independent software test \\\"\\r\\n u\\\"organization.\\\"),\\r\\n _(u\\\"3. There is an independent software quality \\\"\\r\\n u\\\"assurance organization.\\\"),\\r\\n _(u\\\"4. There is an independent software configuration \\\"\\r\\n u\\\"management organization.\\\"),\\r\\n _(u\\\"5. There is an independent software verification \\\"\\r\\n u\\\"and validation organization.\\\"),\\r\\n _(u\\\"6. A structured programming team will develop the \\\"\\r\\n u\\\"software.\\\"),\\r\\n _(u\\\"7. The educational level of the software team members \\\"\\r\\n u\\\"is above average.\\\"),\\r\\n _(u\\\"8. The experience level of the software team members \\\"\\r\\n u\\\"is above average.\\\")]\\r\\n (_x_pos,\\r\\n _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\\r\\n _x_pos += 125\\r\\n\\r\\n _fixed.put(self.chkDevEnvQ1, _x_pos, _y_pos[0])\\r\\n _fixed.put(self.chkDevEnvQ2, _x_pos, _y_pos[1])\\r\\n _fixed.put(self.chkDevEnvQ3, _x_pos, _y_pos[2])\\r\\n _fixed.put(self.chkDevEnvQ4, _x_pos, _y_pos[3])\\r\\n _fixed.put(self.chkDevEnvQ5, _x_pos, _y_pos[4])\\r\\n _fixed.put(self.chkDevEnvQ6, _x_pos, _y_pos[5])\\r\\n _fixed.put(self.chkDevEnvQ7, _x_pos, _y_pos[6])\\r\\n _fixed.put(self.chkDevEnvQ8, _x_pos, _y_pos[7])\\r\\n\\r\\n # Create the methods risk pane.\\r\\n _fixed = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Methods\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _hpaned.pack2(_frame, True, True)\\r\\n\\r\\n _labels = [_(u\\\"1. Standards are defined and will be enforced.\\\"),\\r\\n _(u\\\"2. Software will be developed using a higher order \\\"\\r\\n u\\\"language.\\\"),\\r\\n _(u\\\"3. The development process will include formal \\\"\\r\\n u\\\"reviews (PDR, CDR, etc.).\\\"),\\r\\n _(u\\\"4. The development process will include frequent \\\"\\r\\n u\\\"walkthroughs.\\\"),\\r\\n _(u\\\"5. Development will take a top-down and \\\"\\r\\n u\\\"structured approach.\\\"),\\r\\n _(u\\\"6. Unit development folders will be used.\\\"),\\r\\n _(u\\\"7. A software development library will be used.\\\"),\\r\\n _(u\\\"8. A formal change and error reporting process \\\"\\r\\n u\\\"will be used.\\\"),\\r\\n _(u\\\"9. Progress and status will routinely be \\\"\\r\\n u\\\"reported.\\\")]\\r\\n (__, _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\\r\\n\\r\\n _fixed.put(self.chkDevEnvQ9, _x_pos, _y_pos[0])\\r\\n _fixed.put(self.chkDevEnvQ10, _x_pos, _y_pos[1])\\r\\n _fixed.put(self.chkDevEnvQ11, _x_pos, _y_pos[2])\\r\\n _fixed.put(self.chkDevEnvQ12, _x_pos, _y_pos[3])\\r\\n _fixed.put(self.chkDevEnvQ13, _x_pos, _y_pos[4])\\r\\n _fixed.put(self.chkDevEnvQ14, _x_pos, _y_pos[5])\\r\\n _fixed.put(self.chkDevEnvQ15, _x_pos, _y_pos[6])\\r\\n _fixed.put(self.chkDevEnvQ16, _x_pos, _y_pos[7])\\r\\n _fixed.put(self.chkDevEnvQ17, _x_pos, _y_pos[8])\\r\\n\\r\\n # Create the documentation risk pane.\\r\\n _hpaned = gtk.HPaned()\\r\\n self.pack2(_hpaned, resize=True, shrink=True)\\r\\n\\r\\n _fixed = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Documentation\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _hpaned.pack1(_frame, True, True)\\r\\n\\r\\n _labels = [_(u\\\" 1. System requirements specifications will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 2. Software requirements specifications will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 3. Interface design specifications will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 4. Software design specification will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 5. Test plans, procedures, and reports will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 6. The software development plan will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 7. The software quality assurance plan will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\" 8. The software configuration management plan will \\\"\\r\\n u\\\"be documented.\\\"),\\r\\n _(u\\\" 9. A requirements traceability matrix will be \\\"\\r\\n u\\\"used.\\\"),\\r\\n _(u\\\"10. The software version description will be \\\"\\r\\n u\\\"documented.\\\"),\\r\\n _(u\\\"11. All software discrepancies will be \\\"\\r\\n u\\\"documented.\\\")]\\r\\n (__, _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\\r\\n\\r\\n _fixed.put(self.chkDevEnvQ18, _x_pos, _y_pos[0])\\r\\n _fixed.put(self.chkDevEnvQ19, _x_pos, _y_pos[1])\\r\\n _fixed.put(self.chkDevEnvQ20, _x_pos, _y_pos[2])\\r\\n _fixed.put(self.chkDevEnvQ21, _x_pos, _y_pos[3])\\r\\n _fixed.put(self.chkDevEnvQ22, _x_pos, _y_pos[4])\\r\\n _fixed.put(self.chkDevEnvQ23, _x_pos, _y_pos[5])\\r\\n _fixed.put(self.chkDevEnvQ24, _x_pos, _y_pos[6])\\r\\n _fixed.put(self.chkDevEnvQ25, _x_pos, _y_pos[7])\\r\\n _fixed.put(self.chkDevEnvQ26, _x_pos, _y_pos[8])\\r\\n _fixed.put(self.chkDevEnvQ27, _x_pos, _y_pos[9])\\r\\n _fixed.put(self.chkDevEnvQ28, _x_pos, _y_pos[10])\\r\\n\\r\\n # Create the tools and test techniques risk pane.\\r\\n _fixed = gtk.Fixed()\\r\\n\\r\\n _scrollwindow = gtk.ScrolledWindow()\\r\\n _scrollwindow.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_AUTOMATIC)\\r\\n _scrollwindow.add_with_viewport(_fixed)\\r\\n\\r\\n _frame = Widgets.make_frame(label=_(u\\\"Tools & Test Techniques\\\"))\\r\\n _frame.set_shadow_type(gtk.SHADOW_ETCHED_OUT)\\r\\n _frame.add(_scrollwindow)\\r\\n\\r\\n _hpaned.pack2(_frame, True, True)\\r\\n\\r\\n _labels = [_(u\\\" 1. The software language requirements will be \\\"\\r\\n u\\\"specified.\\\"),\\r\\n _(u\\\" 2. Formal program design language will be used.\\\"),\\r\\n _(u\\\" 3. Program design graphical techniques \\\"\\r\\n u\\\"(flowcharts, HIPO, etc.) will be used.\\\"),\\r\\n _(u\\\" 4. Simulation/emulation tools will be used.\\\"),\\r\\n _(u\\\" 5. Configuration management tools will be used.\\\"),\\r\\n _(u\\\" 6. A code auditing tool will be used.\\\"),\\r\\n _(u\\\" 7. A data flow analyzer will be used.\\\"),\\r\\n _(u\\\" 8. A programmer's workbench will be used.\\\"),\\r\\n _(u\\\" 9. Measurement tools will be used.\\\"),\\r\\n _(u\\\"10. Software code reviews will be used.\\\"),\\r\\n _(u\\\"11. Software branch testing will be used.\\\"),\\r\\n _(u\\\"12. Random testing will be used.\\\"),\\r\\n _(u\\\"13. Functional testing will be used.\\\"),\\r\\n _(u\\\"14. Error and anomaly detection testing will be \\\"\\r\\n u\\\"used.\\\"),\\r\\n _(u\\\"15. Structure analysis will be used.\\\")]\\r\\n (__, _y_pos) = Widgets.make_labels(_labels, _fixed, 5, 5, wrap=False)\\r\\n\\r\\n _fixed.put(self.chkDevEnvQ29, _x_pos, _y_pos[0])\\r\\n _fixed.put(self.chkDevEnvQ30, _x_pos, _y_pos[1])\\r\\n _fixed.put(self.chkDevEnvQ31, _x_pos, _y_pos[2])\\r\\n _fixed.put(self.chkDevEnvQ32, _x_pos, _y_pos[3])\\r\\n _fixed.put(self.chkDevEnvQ33, _x_pos, _y_pos[4])\\r\\n _fixed.put(self.chkDevEnvQ34, _x_pos, _y_pos[5])\\r\\n _fixed.put(self.chkDevEnvQ35, _x_pos, _y_pos[6])\\r\\n _fixed.put(self.chkDevEnvQ36, _x_pos, _y_pos[7])\\r\\n _fixed.put(self.chkDevEnvQ37, _x_pos, _y_pos[8])\\r\\n _fixed.put(self.chkDevEnvQ38, _x_pos, _y_pos[9])\\r\\n _fixed.put(self.chkDevEnvQ39, _x_pos, _y_pos[10])\\r\\n _fixed.put(self.chkDevEnvQ40, _x_pos, _y_pos[11])\\r\\n _fixed.put(self.chkDevEnvQ41, _x_pos, _y_pos[12])\\r\\n _fixed.put(self.chkDevEnvQ42, _x_pos, _y_pos[13])\\r\\n _fixed.put(self.chkDevEnvQ43, _x_pos, _y_pos[14])\\r\\n\\r\\n _label = gtk.Label()\\r\\n _label.set_markup(\\\"<span weight='bold'>\\\" +\\r\\n _(u\\\"Development\\\\nEnvironment\\\") +\\r\\n \\\"</span>\\\")\\r\\n _label.set_alignment(xalign=0.5, yalign=0.5)\\r\\n _label.set_justify(gtk.JUSTIFY_CENTER)\\r\\n _label.set_angle(0)\\r\\n _label.show_all()\\r\\n _label.set_tooltip_text(_(u\\\"Assesses risk due to the development \\\"\\r\\n u\\\"environment.\\\"))\\r\\n notebook.insert_page(self, tab_label=_label, position=-1)\\r\\n\\r\\n return False\",\n \"def get_lowest_ask(self):\\n orders = self.returnOrderBook(1)\\n return orders[\\\"asks\\\"][0]\",\n \"def min_value(tree):\\n min_utility = float(\\\"inf\\\")\\n \\n if (is_terminal(tree)):\\n return tree\\n else:\\n #options = []\\n for node in tree:\\n #options.append(max_value(node))\\n min_utility = min(min_utility, max_value(node))\\n return min_utility\",\n \"def home(request):\\n\\n # Big Sale!\\n big_sale = BuyProduct.objects.first()\\n\\n for _ in BuyProduct.objects.all():\\n if _.customer_price > big_sale.customer_price:\\n big_sale = _\\n\\n return render(request, \\\"core/index.html\\\", {\\n \\\"products\\\": Product.objects.all(),\\n \\\"latest\\\": Product.objects.first(),\\n \\\"big_sale\\\": big_sale\\n })\",\n \"def topSash(parent,defaultSize=(100,100),onSashDrag=None):\\r\\n sash = wx.SashLayoutWindow(parent,style=wx.SW_3D)\\r\\n sash.SetDefaultSize(defaultSize)\\r\\n sash.SetOrientation(wx.LAYOUT_HORIZONTAL)\\r\\n sash.SetAlignment(wx.LAYOUT_TOP)\\r\\n sash.SetSashVisible(wx.SASH_BOTTOM, True)\\r\\n if onSashDrag:\\r\\n id = sash.GetId()\\r\\n sash.Bind(wx.EVT_SASH_DRAGGED_RANGE, onSashDrag,id=id,id2=id)\\r\\n return sash\",\n \"def book_value():\\r\\n a = float(input(\\\"Please Enter Total Stockholders Equity Value: \\\"))\\r\\n b = float(input(\\\"Please Enter Preferred Equity Value: \\\"))\\r\\n c = float(input(\\\"Please Enter Number of Shares of Common Stock Outstanding: \\\"))\\r\\n d = (float(a)-float(b))/float(c)\\r\\n print \\\">> Your Book Value Per Share of Common Stock is\\\",round(d,2)\",\n \"def get_grade(self) -> int :\\n return self.grade\",\n \"def rd1leaderboard(request):\\n\\n #Add views\\n playing_players = Rd1SlotModel.objects.filter(player_name__isnull=False)\\n\\n endurance_leader = Rd1SlotModel.objects.aggregate(Max('endurance_score'))\\n\\n #Add context\\n context = {\\n 'playing_players': playing_players,\\n 'endurance_leader': endurance_leader,\\n }\\n\\n return render(request, 'rd1Leaderboard.html', context=context)\",\n \"def _get_minth(self):\\n return self.__minth\",\n \"def _get_minth(self):\\n return self.__minth\",\n \"def _get_minth(self):\\n return self.__minth\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.50486606","0.5035432","0.5007743","0.49070513","0.48892468","0.4817341","0.47293594","0.4686217","0.468501","0.4654785","0.4652862","0.464481","0.46400693","0.46108285","0.46024165","0.45971134","0.45901182","0.45752954","0.45622933","0.45582008","0.45527396","0.45513022","0.45391023","0.45382112","0.45357856","0.4510129","0.45096976","0.44835946","0.44796285","0.44731036","0.44555226","0.4437624","0.4424244","0.4423796","0.44235292","0.44221243","0.44191077","0.44162902","0.44140095","0.44042602","0.44042","0.44028193","0.43975103","0.43971887","0.43919265","0.43871102","0.43727326","0.4371139","0.43531874","0.43519735","0.43418932","0.434015","0.43389025","0.43367514","0.43363085","0.43248504","0.43232277","0.432282","0.43213537","0.43187758","0.43061897","0.43038943","0.42801726","0.42781556","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42664585","0.42599958","0.42589462","0.4256292","0.42562434","0.42551863","0.42531776","0.4251332","0.42511985","0.42500925","0.42459878","0.42459598","0.42410702","0.42391098","0.42351824","0.42348963","0.42345175","0.4229552","0.42294046","0.422673","0.4222404","0.4222404","0.4222404"],"string":"[\n \"0.50486606\",\n \"0.5035432\",\n \"0.5007743\",\n \"0.49070513\",\n \"0.48892468\",\n \"0.4817341\",\n \"0.47293594\",\n \"0.4686217\",\n \"0.468501\",\n \"0.4654785\",\n \"0.4652862\",\n \"0.464481\",\n \"0.46400693\",\n \"0.46108285\",\n \"0.46024165\",\n \"0.45971134\",\n \"0.45901182\",\n \"0.45752954\",\n \"0.45622933\",\n \"0.45582008\",\n \"0.45527396\",\n \"0.45513022\",\n \"0.45391023\",\n \"0.45382112\",\n \"0.45357856\",\n \"0.4510129\",\n \"0.45096976\",\n \"0.44835946\",\n \"0.44796285\",\n \"0.44731036\",\n \"0.44555226\",\n \"0.4437624\",\n \"0.4424244\",\n \"0.4423796\",\n \"0.44235292\",\n \"0.44221243\",\n \"0.44191077\",\n \"0.44162902\",\n \"0.44140095\",\n \"0.44042602\",\n \"0.44042\",\n \"0.44028193\",\n \"0.43975103\",\n \"0.43971887\",\n \"0.43919265\",\n \"0.43871102\",\n \"0.43727326\",\n \"0.4371139\",\n \"0.43531874\",\n \"0.43519735\",\n \"0.43418932\",\n \"0.434015\",\n \"0.43389025\",\n \"0.43367514\",\n \"0.43363085\",\n \"0.43248504\",\n \"0.43232277\",\n \"0.432282\",\n \"0.43213537\",\n \"0.43187758\",\n \"0.43061897\",\n \"0.43038943\",\n \"0.42801726\",\n \"0.42781556\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42664585\",\n \"0.42599958\",\n \"0.42589462\",\n \"0.4256292\",\n \"0.42562434\",\n \"0.42551863\",\n \"0.42531776\",\n \"0.4251332\",\n \"0.42511985\",\n \"0.42500925\",\n \"0.42459878\",\n \"0.42459598\",\n \"0.42410702\",\n \"0.42391098\",\n \"0.42351824\",\n \"0.42348963\",\n \"0.42345175\",\n \"0.4229552\",\n \"0.42294046\",\n \"0.422673\",\n \"0.4222404\",\n \"0.4222404\",\n \"0.4222404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60446465"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22374,"cells":{"query":{"kind":"string","value":"Sidebar widget to select salary bin size for histogram plots"},"document":{"kind":"string","value":"def select_bin_size(pay_norm: int, index: int = 2,\n markdown_text: str = 'salary') -> float:\n\n st.sidebar.markdown(f'### Select {markdown_text} bin size:')\n if pay_norm == 1:\n bin_size = st.sidebar.selectbox('', ['$1,000', '$2,500', '$5,000', '$10,000'],\n index=index)\n else:\n bin_size = st.sidebar.selectbox('', ['$0.50', '$1.25', '$2.50', '$5.00'],\n index=index)\n\n bin_size = float(re.sub('[$,]', '', bin_size))\n\n return bin_size"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_histogram(self):\n n_rows = self.df.shape[0]\n if n_rows > 250:\n fig, ax = plt.subplots()\n ax.hist(self.df[self.col_name], bins=50)\n else: \n fig, ax = plt.subplots()\n ax.hist(self.df[self.col_name], bins=int(round(n_rows/5,0)))\n return fig","def makeHistogram(values, numBins, xLabel, yLabel, title=None):","def plot_size_histogram(cluster_size):\n\n fig = px.histogram(x=cluster_size, nbins=50,\n width=500, height=300)\n st.plotly_chart(fig)","def distribution_sentimentscore_histogram(cur, var, table, label):\n x = select(cur,var, table)\n print(\"Number of entries: \", len(x))\n print(\"Maximum: \", max(x))\n print(\"Minimum: \", min(x))\n \n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n ax.set_xlabel(\"Sentiment Score\")\n ax.set_ylabel(\"Number of Loans\")\n fig.suptitle(label)\n ax.hist(x, bins = 15)\n plt.show()","def distribution_magnitude_histogram(cur, var, table, label):\n x = select(cur,var, table)\n print(\"Number of entries: \", len(x))\n print(\"Maximum: \", max(x))\n print(\"Minimum: \", min(x))\n \n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n ax.set_xlabel(\"Sentiment Magnitude\")\n ax.set_ylabel(\"Number of Sentences\")\n fig.suptitle(label)\n ax.hist(x, bins = 20)\n plt.show()","def plot_hist(df, num_bins=8):\n df.hist(figsize=(24, 20), bins=num_bins)\n plt.axes","def hist(self):\r\n plt.hist(self.data_array, bins='auto', density=False, facecolor='b')\r\n plt.title(self.column_name)\r\n plt.savefig(self.column_name + \".svg\")\r\n plt.close()","def get_histogram(self, column):\n\n df_histogram = pd.DataFrame(self.serie)\n #histogramcol = alt.Chart(df_histogram).mark_bar().encode(alt.X(column, bin=True), y='count()')\n histogramcol = alt.Chart(df_histogram).mark_bar().encode(alt.X(column, bin=alt.Bin(maxbins=50)),y='count()')\n return histogramcol","def histogram(self, cond, numbins):\n colname = \"axs_hist_col\"\n res = self._df.select(cond.alias(colname))\n return res.withColumn(\"bin\", (res[colname] / numbins).cast(\"int\")).groupBy(\"bin\").count()","def display(self, bin_size):\n xs = np.linspace(self.sample_min, self.sample_max, 2000)\n ys = np.zeros_like(xs)\n for (l, s), w in zip(self.gauss_params, self.dist_weights):\n ys += ss.norm.pdf(xs, loc=l, scale=s) * w\n plt.plot(xs, ys, color=\"blue\")\n plt.hist(self.samples, density=True, bins=bin_size, color=\"palegreen\")\n plt.xlabel(\"duration\")\n plt.ylabel(\"density\")\n _, _, ymin, ymax = plt.axis()\n if self.lower_bound > 0:\n plt.vlines([self.lower_bound], ymin, ymax, color=\"crimson\")\n if self.upper_bound < float(\"inf\"):\n plt.vlines([self.upper_bound], ymin, ymax, color=\"crimson\")\n plt.show()","def add_histogram(self, tag, values, global_step=None, bins='tensorflow'):\n values = make_np(values)\n self.vis.histogram(make_np(values), opts={'title': tag})","def numBinsChanged(self, val):\n self.numBins = val","def getHistogramWidget(self):\n return self._getToolWindow()","def histogram(self):\r\n channel = self.ui.channel_selection.itemData(self.ui.channel_selection.currentIndex())\r\n\r\n #create a window, the reference must be stored, because the window\r\n #gets destroyed when its reference is garbage collected\r\n #make plotWindow a list and append to that if multiple windows should be possible\r\n title = \"histogram of {:s} channel\".format(self.ui.channel_selection.currentText())\r\n self.plotWindow = pyguitools.SimplePlotWindow(name = title)\r\n self.plotWindow.ax1.hist(self.npImg[self.ui.y0.value():self.ui.y1.value(),\r\n self.ui.x0.value():self.ui.x1.value(), \r\n channel].flatten(),\r\n bins=self.settings[\"histogramm bins\"],\r\n range=(self.settings[\"histogramm min\"],self.settings[\"histogramm max\"]))\r\n self.plotWindow.ax1.set_xlim(self.settings[\"histogramm min\"],self.settings[\"histogramm max\"]) \r\n self.plotWindow.show()","def draw_histo(df, col_name, x_start, bin_width, n_bins, title, output_dir, output_filename, \n log_y = False, describe = False):\n my_plt = df[col_name].plot.hist(\n bins=[bin_width * (x + 0.5) + x_start for x in np.arange(0, n_bins)], \n edgecolor='black', \n color='#88d498',\n log=log_y)\n my_plt.set_title(title)\n pd.set_option('precision', 3)\n plt.tight_layout(pad=5)\n if(describe): print(df[col_name].describe())\n plt.figtext(0.01,0.01, df[col_name].describe().loc[['mean','std','count']].to_string(),fontsize='x-large')\n my_plt.ticklabel_format(style='sci', axis='x', scilimits=(0,3)) \n my_plt.get_figure().savefig(os.path.join(output_dir,output_filename))\n my_plt.get_figure().clf()","def drawHist(data, xLabel, unit, binSize, title):\n mean = np.mean(data)\n median = np.median(data)\n mode = stats.mode(data)[0].astype(float)\n \n q1, q3 = np.percentile(data, [25, 75])\n iqr = q3 - q1\n sigma = np.std(data)\n \n \n bins = np.arange(min(data), max(data) + 1, binSize)\n plt.style.use('dark_background')\n fig, ax = plt.subplots(figsize=(12,7))\n plt.hist(data, bins=bins, histtype='bar') \n plt.title(title)\n plt.xlabel(xLabel + \" \" + unit)\n plt.ylabel('count')\n ymax = ax.get_ylim()[1]\n ax.vlines(mean, 0, ymax, color='red', label='mean')\n ax.vlines(mean-sigma, 0, ymax, color='red', linestyle='--', \n label='mean +/- std')\n ax.vlines(mean+sigma, 0, ymax, color='red', linestyle='--')\n plt.legend()\n plt.show()\n \n print(\"Einheit: \", unit)\n print(\"Minimum: \", round(data.min(),3))\n print(\"Maximum: \", round(data.max(),3))\n print(\"Mittelwert: \", round(mean,3))\n print(\"Median: \", round(median,3))\n print(\"Modus: \", round(mode[0],3))\n print(\"Standardabweichung: \", round(sigma, 3))\n print(\"1. Quartil: \", round(q1,3))\n print(\"3. Quartil: \", round(q3,3))\n print(\"Quartilsdifferenz: \", round(iqr,3))","def __updateHistogramFromControls(self, *args):\n\n hist = self.getHistogram(copy=False)\n if hist is not None:\n count, edges = hist\n if (len(count) == self.__nbinsLineEdit.getValue() and\n (edges[0], edges[-1]) == self.__rangeSlider.getValues()):\n return # Nothing has changed\n\n self._updateFromItem()","def hist_of_numeric(X):\n figsize(10,3)\n for col in get_numeric(X):\n print(col)\n X[col].hist(bins=50)\n show()","def make_voc_histogram_figure(voc_hist_x, voc_hist_y, voc_summary_for_plot,\n annotation_voc_histogram_choice):\n return dcc.Graph(\n id='Voc-histogram',\n figure={\n 'data': [\n {'x': voc_hist_x,\n 'y': voc_hist_y,\n 'type': 'bar',\n 'name': 'Voc'}\n ],\n 'layout': go.Layout(\n title=go.layout.Title(\n text='Figure 1. Histogram of Voc values over the simulation time.',\n xref='paper',\n x=0\n ),\n xaxis={'title': 'Voc (Volts)'},\n yaxis={'title': 'hours/year'},\n margin={'l': 60, 'b': 120, 't': 30, 'r': 60},\n hovermode='closest',\n annotations=[\n dict(\n dict(\n x=voc_summary_for_plot['plot_voltage'][s],\n y=voc_hist_y[np.argmin(\n np.abs(voc_summary_for_plot['plot_voltage'][\n s] - voc_hist_x))],\n xref='x',\n yref='y',\n xanchor='left',\n yanchor='middle',\n text=s,\n hovertext=voc_summary_for_plot['long_note'][s],\n textangle=-90,\n font=dict(\n color=voc_summary_for_plot['plot_color'][s]\n ),\n arrowcolor=voc_summary_for_plot['plot_color'][s],\n # bordercolor=plot_color[s],\n showarrow=True,\n align='left',\n standoff=2,\n arrowhead=4,\n ax=0,\n ay=-40\n ),\n align='left'\n )\n for s in list(annotation_voc_histogram_choice)]\n )\n },\n config=dict(\n toImageButtonOptions=dict(\n scale=5,\n filename='Voc_histogram',\n )\n )\n )","def histogram_summary(self, tag, values, step, bins=1000):\n self.writer.add_histogram(tag, values, step, bins='auto')","def hist(self, color=\"#FFFFFF\", axes_style=\"darkgrid\", context=\"notebook\",\n col_wrap=4, exhibit_path=None, **kwargs):\n import matplotlib.pyplot as plt\n import seaborn as sns\n sns.set_context(context)\n\n # data0 = self.sims_data[[\"sim\", \"origin\", \"dev\", \"rectype\", \"latest\", \"reserve\",]]\n # data0 = data0[(data0[\"dev\"]==data0[\"dev\"].max()) & (data0[\"rectype\"]==\"forecast\")].reset_index(drop=True)\n # data0 = data0.drop([\"dev\", \"rectype\", \"latest\"], axis=1)\n #\n # # Include additional origin representing aggregate distribution.\n # data1 = data0.groupby(\"sim\", as_index=False)[[\"reserve\"]].sum()\n # data1[\"origin\"] =\"total\"\n # data = pd.concat([data0, data1])\n data = self.reserve_dist\n\n # Get mean, min and max ultimate and reserve by origin.\n med_data = data.groupby(\"origin\", as_index=False)[[\"reserve\"]].median().rename(\n {\"reserve\": \"med_res\"}, axis=1).set_index(\"origin\")\n min_data = data.groupby(\"origin\", as_index=False)[[\"reserve\"]].min().rename(\n {\"reserve\": \"min_res\"}, axis=1).set_index(\"origin\")\n max_data = data.groupby(\"origin\", as_index=False)[[\"reserve\"]].max().rename(\n {\"reserve\": \"max_res\"}, axis=1).set_index(\"origin\")\n dfmetrics = functools.reduce(lambda df1, df2: df1.join(df2), (med_data, min_data, max_data))\n dfmetrics = dfmetrics.applymap(lambda v: 0 if v < 0 else v).reset_index(drop=False)\n\n with sns.axes_style(axes_style):\n\n pltkwargs = {\"color\": color, \"bins\": 20, \"edgecolor\": \"#484848\",\n \"alpha\": 1., \"linewidth\": .45}\n\n if kwargs is not None:\n pltkwargs.update(kwargs)\n\n grid = sns.FacetGrid(\n data, col=\"origin\", col_wrap=col_wrap, margin_titles=False,\n despine=True, sharex=False, sharey=False,\n )\n\n hists = grid.map(plt.hist, \"reserve\", **pltkwargs)\n grid.set_axis_labels(\"\", \"\")\n grid.set_titles(\"\", size=6)\n\n # Change ticklabel font size and place legend on each facet.\n origin_vals = sorted([int(ii) for ii in data[\"origin\"].unique() if ii != \"total\"])\n dindex = {jj: ii for ii, jj in enumerate(origin_vals)}\n dindex.update({\"total\": max(dindex.values()) + 1})\n data[\"origin_index\"] = data[\"origin\"].map(dindex)\n origin_order = data[[\"origin_index\", \"origin\"]].drop_duplicates().sort_values(\n \"origin_index\"\n ).origin.values\n\n with warnings.catch_warnings():\n\n warnings.simplefilter(\"ignore\")\n\n for origin, ax_ii in zip(origin_order, grid.axes):\n\n # xmin = np.max([0, dfmetrics[dfmetrics.origin == origin][\"min_res\"].item()])\n xmax = dfmetrics[dfmetrics.origin == origin][\"max_res\"].item() * 1.025\n xmed = dfmetrics[dfmetrics.origin == origin][\"med_res\"].item()\n origin_str = \"{}\".format(origin)\n ax_ii.set_xlim([0, xmax])\n ax_ii.axvline(xmed, color=\"#E02C70\", linestyle=\"--\", linewidth=1.5)\n ax_ii.grid(False)\n\n ymedloc = max(rect.get_height() for rect in ax_ii.patches) * .30\n ax_ii.set_yticks([])\n ax_ii.set_yticklabels([])\n ax_ii.tick_params(\n axis=\"x\", which=\"both\", bottom=True, top=False, labelbottom=True\n )\n ax_ii.set_xticklabels(\n [\"{:,.0f}\".format(jj) for jj in ax_ii.get_xticks()], size=7\n )\n ax_ii.annotate(\n origin_str, xy=(.85, .925), xycoords='axes fraction',\n textcoords='axes fraction', fontsize=9, rotation=0, color=\"#000000\",\n )\n ax_ii.annotate(\n \"median = {:,.0f}\".format(xmed), (xmed, ymedloc), xytext=(7.5, 0),\n textcoords=\"offset points\", ha=\"center\", va=\"bottom\", fontsize=7,\n rotation=90, color=\"#000000\"\n )\n\n # Draw border around each facet.\n for _, spine in ax_ii.spines.items():\n spine.set(visible=True, color=\"#000000\", linewidth=.50)\n\n if exhibit_path is not None:\n plt.savefig(exhibit_path)\n else:\n plt.show()","def _getNormalBinWidth(self, style):\n \n bin_size = style.get(\"normalize_bin_size\", \"auto\")\n if bin_size == \"auto\":\n # Use the size of the first bin.\n x0, x1 = self.histogram.axis.getBinRange(0)\n bin_size = x1 - x0\n return bin_size","def makeHistogram(values, numBins, xLabel, yLabel, title=None):\n pylab.hist(values, bins = numBins)\n pylab.xlabel(xLabel)\n pylab.ylabel(yLabel)\n if not title == None:\n pylab.title(title)\n pylab.show()","def bin_histogram (modified_df, v_to_bin):\n for variable in v_to_bin:\n # Remove Nas\n df = modified_df[modified_df[variable].notnull()]\n # Create surv filter\n hist_filter = df[\"Survived\"] == 1\n # Create Histogram\n plt.hist([df[variable][hist_filter], df[variable][~hist_filter]],\n stacked=True, label=['Survived', 'Not Survived'], color=['g', 'r'])\n plt.legend()\n # Save and reset fig\n plt.savefig(variable+\"_histogram\")\n plt.clf()","def plt_hist(pop, bin_limit=fit_range):\n plt.hist(pop, bins=range(0,bin_limit+1))\n plt.grid(True)\n plt.title('Distribution of Population')\n plt.show()","def bins(self, value):\n self.num_bins = int(value)","def makeHistogram(values, numBins, xLabel, yLabel, title=None):\r\n pylab.hist(values, bins = numBins)\r\n pylab.xlabel(xLabel)\r\n pylab.ylabel(yLabel)\r\n if title != None:\r\n pylab.title(title)\r\n pylab.show()","def histogram(values, title, fig_size=(4,3), path=None):\n plt.clf()\n f, ax = plt.subplots(1, figsize=fig_size)\n ax.hist(values, bins=60)\n ax.set_title(title)\n f.tight_layout()\n if(path != None):\n f.savefig(path+'/hist_'+title+'.png')","def test_histogram_with_varying_number_of_bin(self):\n # this data use number of bins less than the max limit\n df1 = pd.Series([1, 2, 3, 4]).apply(str)\n profiler1 = FloatColumn(df1.name)\n profiler1.max_histogram_bin = 50\n profiler1.update(df1)\n num_bins = len(profiler1.profile['histogram']['bin_counts'])\n self.assertEqual(num_bins, 4)\n\n # this data uses large number of bins, which will be set to\n # the max limit\n df2 = pd.Series([3.195103249264023e+18, 9999995.0, 9999999.0,\n 0.0, -10 ** 10]).apply(str)\n profiler2 = FloatColumn(df2.name)\n profiler2.max_histogram_bin = 50\n profiler2.update(df2)\n num_bins = len(profiler2.profile['histogram']['bin_counts'])\n self.assertEqual(num_bins, 50)\n\n # max number of bin is increased to 10000\n profiler2 = FloatColumn(df2.name)\n profiler2.max_histogram_bin = 10000\n profiler2.update(df2)\n num_bins = len(profiler2.profile['histogram']['bin_counts'])\n self.assertEqual(num_bins, 10000)","def histogram(data, bins=50, nmb_data_to_use=None, ignored_row=0,\n col_to_read=1, output_file=None, verbose=0):\n # prepare arguments\n args = \"-x{} -c{} -b{} -V{}\" \\\n .format(ignored_row, col_to_read, bins, verbose)\n if nmb_data_to_use is not None:\n args += \"-l{}\".format(nmb_data_to_use)\n args = args.split(\" \")\n # run command\n res, msg = tisean('histogram', args, input_data=data,\n output_file=output_file)\n # return\n if msg != \"\":\n print(msg)\n return res","def setBarWidth(w):\n dislin.barwth(w)","def histogram(input, num_bins, filename, title = None, xlabel = None, ylabel = None, **bar_kwargs): \n # Checking for errors in the input\n if not isinstance(num_bins, int):\n raise TypeError('Please enter an integer number of bins.')\n if 200 % num_bins != 0:\n raise ValueError('The original number of intervals must be divisible by the number of bins inputted.')\n \n x_bins = np.linspace(0, 10, num = num_bins, endpoint = False) # Generates list of x-values (in this case energies) at the start of each bin interval\n bin_size = x_bins[1] - x_bins[0] # Calculating size of bins\n midpoints = [i + (bin_size/2) for i in x_bins] # List of midpoints generated for the centre of each bar\n\n down_fact = len(input) / num_bins # Calculating the downsampling factor\n if down_fact != 1: \n down_arr = hist_downsample(input, down_fact) # Downsampling the input data\n else:\n down_arr = input # If the number of bins needed is equal to the length of the input data, no need to downsample\n\n plot_settings(grid = True) # Defining plot settings\n plt.bar(midpoints, height = down_arr, width = bin_size, **bar_kwargs) # Width of bar is set to the size of the histogram bin\n\n # Adds a title or axis labels if specified by the user\n if title != None:\n plt.title(title)\n if xlabel != None:\n plt.xlabel(xlabel)\n if ylabel != None:\n plt.ylabel(ylabel)\n \n # f-string allows save filepath to be set inside the plt.savefig() function\n plt.savefig(f'{os.path.join(plot_path,filename)}.pdf', dpi = 200) # Saving the plot in the 'plots' folder (filepath set using the 'os' package)","def weightHistogram(self, min=None, max=None, nbins=10):\n raise NotImplementedError","def __init__(self, **kwargs): \n self.kwargs = kwargs\n\n # pretty figure up\n prettyplot() \n pretty_colors = prettycolors() \n \n self.fig = plt.figure(1) \n self.sub = self.fig.add_subplot(111) \n\n self.hist_max = 0.0","def make_histogram(datasource,\n counts_name=\"counts\",\n centers_name=\"centers\",\n x_range=None,\n bar_width=0.7,\n plot_width=500,\n plot_height=500,\n min_border=40,\n tools=None,\n title_text_font_size=\"12pt\"):\n start = np.min(datasource.data[centers_name]) - bar_width\n end = np.max(datasource.data[centers_name]) - bar_width\n plot = make_bar_plot(\n datasource, counts_name=counts_name, centers_name=centers_name,\n x_range=[start, end], plot_width=plot_width, plot_height=plot_height,\n tools=tools, title_text_font_size=title_text_font_size)\n return plot","def show_bryant(data_length=200000, bins=100):\n\n data = np.random.normal(0,1,data_length)\n plt.hist(data, bins)\n plt.show()","def makeHistogram(values, numBins, xLabel, yLabel, title=None):\r\n # TODO\r\n pylab.hist(values, bins = numBins)\r\n pylab.xlabel(xLabel)\r\n pylab.ylabel(yLabel)\r\n if title != None:\r\n pylab.title(title)\r\n pylab.show()","def _getNormalBinWidths(self, style):\n \n bin_size = style.get(\"normalize_bin_size\", \"auto\")\n if bin_size == \"auto\":\n # Use the size of the first bins.\n x0, x1 = self.histogram.axes[0].getBinRange(0)\n y0, y1 = self.histogram.axes[1].getBinRange(0)\n return x1 - x0, y1 - y0\n elif bin_size is None:\n return None\n else:\n size_x, size_y = bin_size\n return size_x, size_y","def __init__(self, param, lower, upper, binCount = 50,\n xscale = None, yweight = None, autoFollow = True):\n logging.debug('Hist init: {} [{}, {}]'\n .format(param.name(), lower, upper))\n super(Histogram, self).__init__(title = \"({0}, {1})\".format(lower, upper))\n # add it to the parameter here\n if isinstance(param, ParameterBase):\n self.param = param # parameter we belong to is mandatory\n self.binCount = int(binCount) # bin count is mandatory\n self.xrange = (float(lower), float(upper))\n # setter chose the first option available for invalid options\n self.xscale = xscale\n self.yweight = yweight\n if not isinstance(autoFollow, bool):\n autoFollow = (autoFollow.title() == \"True\")\n self.autoFollow = autoFollow","def histogram(self, x = \"Predictor\", y = None, is_horizontal = False, color = None, facet_col = None, facet_row = None, \n bins = 20, opacity = 1, marginal = None, template = \"ggplot2\", has_title = True, title = None):\n x_clean, df_clean = clean_varname(self._df, var = x)\n\n #These can probably be converted into Lambda functions... but for now, this works.\n if color:\n color_clean, df_clean = clean_varname(df_clean, var = color)\n else:\n color_clean = color\n if y:\n y_clean, df_clean = clean_varname(df_clean, var = y)\n else:\n y_clean = y\n if facet_col:\n facet_col_clean, df_clean = clean_varname(df_clean, var = facet_col)\n else:\n facet_col_clean = facet_col\n if facet_row:\n facet_row_clean, df_clean = clean_varname(df_clean, var = facet_row)\n else:\n facet_row_clean = facet_row\n\n if has_title:\n if not title:\n title = f\"Histogram of {x_clean}\"\n\n if is_horizontal:\n fig = px.histogram(df_clean, y=x_clean, x = y_clean, color=color_clean, title = title,\n marginal = marginal, template = template, opacity = opacity, \n nbins=bins, facet_col = facet_col_clean, facet_row = facet_row_clean)\n else:\n fig = px.histogram(df_clean, x=x_clean, y = y_clean, color=color_clean, title = title,\n marginal = marginal, template = template, opacity = opacity, \n nbins=bins, facet_col = facet_col_clean, facet_row = facet_row_clean)\n return fig","def histogram_type1(self):\n\n # this will show grids at ticks. This style is applied to all plots.\n sns.set(style=\"whitegrid\")\n \n # set x-axis and y-axis limits so that it is easier to compare histograms over time\n g = sns.FacetGrid(self.data, col=\"Region\", col_wrap=3, size=3, xlim=(0, 100000),ylim=(0,0.0005))\n g = g.map(plt.hist, \"Income\", normed = True, bins=15,color=\"r\")\n g.set_xticklabels(fontsize=10, rotation=30)\n g.set_xlabels(\"Income per capita\",fontsize = 10)\n g.set_ylabels(\"Distribution\",fontsize = 10)\n plt.tight_layout()\n plt.subplots_adjust(top=0.9)\n g.fig.suptitle(\"Histograms of Income Per Capita in \" + str(self.year)+ \", by Region\")\n plt.savefig('Histograms for Year ' + str(self.year) +'.pdf')\n pylab.show()","def plot_modelparametercollections_addhist(paramcol_SF,paramcol_AGN,stat_SF,stat_AGN,\n SFcol,AGNcol,LW,bindefs=None,Nbins=10):\n if (paramcol_SF is None) & (paramcol_AGN is not None):\n if len(paramcol_AGN) > 0:\n if bindefs is None:\n xmin = np.min(paramcol_AGN)-np.abs(np.min(paramcol_AGN))*0.05\n xmax = np.max(paramcol_AGN)+np.abs(np.min(paramcol_AGN))*0.05\n binwidth_x = np.diff([xmin,xmax])/Nbins\n bindefs = np.arange(xmin, xmax+binwidth_x, binwidth_x)\n\n plt.hist(paramcol_AGN, bins=bindefs,histtype='step',color=AGNcol)\n\n yminsys, ymaxsys = plt.ylim()\n\n plt.plot([stat_AGN[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=AGNcol) # mean\n plt.plot([stat_AGN[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # median\n plt.plot([stat_AGN[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\n plt.plot([stat_AGN[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\n\n elif (paramcol_SF is not None) & (paramcol_AGN is None):\n if len(paramcol_SF) > 0:\n\n if bindefs is None:\n xmin = np.min(paramcol_SF)-np.abs(np.min(paramcol_SF))*0.05\n xmax = np.max(paramcol_SF)+np.abs(np.min(paramcol_SF))*0.05\n binwidth_x = np.diff([xmin,xmax])/Nbins\n bindefs = np.arange(xmin, xmax+binwidth_x, binwidth_x)\n\n plt.hist(paramcol_SF, bins=bindefs,histtype='step',color=SFcol)\n\n yminsys, ymaxsys = plt.ylim()\n\n plt.plot([stat_SF[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=SFcol) # mean\n plt.plot([stat_SF[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # median\n plt.plot([stat_SF[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\n plt.plot([stat_SF[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\n\n else:\n\n if (len(paramcol_SF) == 0) & (len(paramcol_AGN) == 0):\n pass\n else:\n if bindefs is None:\n xmin = np.min(np.append(paramcol_SF,paramcol_AGN))*0.95\n xmax = np.max(np.append(paramcol_SF,paramcol_AGN))*1.05\n binwidth_x = np.diff([xmin,xmax])/Nbins\n bindefs = np.arange(xmin, xmax+binwidth_x, binwidth_x)\n # if xlog:\n # bindefs = np.logspace(np.log10(bindefs[0]),np.log10(bindefs[-1]),len(bindefs))\n # axHistax.set_xscale('log')\n\n plt.hist(paramcol_SF, bins=bindefs,histtype='step',color=SFcol)\n plt.hist(paramcol_AGN, bins=bindefs,histtype='step',color=AGNcol)\n\n yminsys, ymaxsys = plt.ylim()\n\n if len(paramcol_SF) > 0:\n plt.plot([stat_SF[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=SFcol) # mean\n plt.plot([stat_SF[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # median\n plt.plot([stat_SF[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\n plt.plot([stat_SF[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\n\n if len(paramcol_AGN) > 0:\n plt.plot([stat_AGN[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=AGNcol) # mean\n plt.plot([stat_AGN[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # median\n plt.plot([stat_AGN[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\n plt.plot([stat_AGN[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower","def create_histogram(a, trim_p, bin_size, p_title, p_ylabel, p_xlabel, \r\n file_prefix, dec_prec=0, trim_type=\"both\"):\r\n a.sort()\r\n sample_size = len(a)\r\n print(\"a length pre-trim_p\", len(a))\r\n if trim_type == \"left\" or trim_type == \"right\":\r\n a = stats.trim1(a, trim_p, trim_type)\r\n else:\r\n a = stats.trimboth(a, trim_p)\r\n print(\"a length post-trim_p\", len(a))\r\n bin_min = math.floor(min(a)) # TODO Round down to dec_prec instead\r\n bin_max = round(max(a), dec_prec)\r\n print(\"bin size=\" + str(bin_size) + \r\n \", bin min=\" + str(bin_min) + \r\n \", bin max=\" + str(bin_max))\r\n # Create histogram of values\r\n n, bins, patches = pylab.hist(a, \r\n bins=pylab.frange(bin_min, \r\n bin_max, \r\n bin_size), \r\n normed=False, \r\n histtype=\"stepfilled\")\r\n pylab.setp(patches, \"facecolor\", \"g\", \"alpha\", 0.75)\r\n pylab.title(p_title)\r\n pylab.xlabel(p_xlabel)\r\n pylab.ylabel(p_ylabel)\r\n \r\n if trim_p > 0:\r\n pylab.savefig(file_prefix + \"_trimmed.png\")\r\n else:\r\n pylab.savefig(file_prefix + \".png\")\r\n pylab.show()","def hist_bin_opt (x, minbin=20, maxbin=600, spacing=10, N_trials=1):\n bin_checks = np.arange(minbin, maxbin, spacing)\n # bin_checks = np.linspace(150, 300, 16)\n costs = np.zeros(len(bin_checks))\n i = 0\n # this might be vectorizable in np\n for n_bins in bin_checks:\n # use np.histogram to do the numerical minimization\n pdf, bin_edges = np.histogram(x, n_bins)\n # calculate bin width\n # some discrepancy here but should be fine\n w_bin = np.unique(np.diff(bin_edges))\n if len(w_bin) > 1: w_bin = w_bin[0]\n # calc mean and var\n kbar = np.mean(pdf)\n kvar = np.var(pdf)\n # calc cost\n costs[i] = (2.*kbar - kvar) / (N_trials * w_bin)**2.\n i += 1\n # find the bin size corresponding to a minimization of the costs\n bin_opt_list = bin_checks[costs.min() == costs]\n bin_opt = bin_opt_list[0]\n return bin_opt","def hist(self, bins):\n x = self.x\n plt.hist(x, bins)\n plt.xlabel('Observed Data')\n plt.ylabel('Frequency')\n plt.show()","def cat_hist(val, rug, side, shade, ax, **shade_kwargs):\n bins = get_bins(val)\n _, binned_d, _ = histogram(val, bins=bins)\n\n bin_edges = np.linspace(np.min(val), np.max(val), len(bins))\n heights = np.diff(bin_edges)\n centers = bin_edges[:-1] + heights.mean() / 2\n\n if rug and side == \"both\":\n side = \"right\"\n\n if side == \"right\":\n left = None\n elif side == \"left\":\n left = -binned_d\n elif side == \"both\":\n left = -0.5 * binned_d\n\n ax.barh(centers, binned_d, height=heights, left=left, alpha=shade, **shade_kwargs)\n return binned_d","def btn_equalize_hist_callback(self):\n self.show_as_waiting(True)\n self.image_proc_selected('Histogram Equalization')\n self.show_as_waiting(False)","def histogram(self):\n if np.size(self.stats['Counts']): # don't do anything to an empty list\n if np.size(self.bins) and not self.redo:\n return self.bins, self.occs, self.thresh\n elif np.size(self.bin_array) > 0: \n self.occs, self.bins = np.histogram(self.stats['Counts'], self.bin_array) # fixed bins. \n else:\n try:\n lo, hi = min(self.stats['Counts'])*0.97, max(self.stats['Counts'])*1.02\n # scale number of bins with number of files in histogram and with separation of peaks\n num_bins = int(15 + self.ind//100 + (abs(hi - abs(lo))/hi)**2*15) \n self.occs, self.bins = np.histogram(self.stats['Counts'], bins=np.linspace(lo, hi, num_bins+1)) # no bins provided by user\n except: \n self.occs, self.bins = np.histogram(self.stats['Counts'])\n else: self.occs, self.bins = np.zeros(10), np.arange(0,1.1,0.1)\n return self.bins, self.occs, self.thresh","def histogram(arr, xlbl, xrng=None, nbins=20, alpha=1.):\n if xrng is None:\n xrng = (np.min(arr),np.max(arr))\n p = figure(plot_width=600, plot_height=400)\n # Histogram\n hist, edges = np.histogram(arr, range=xrng, density=True, bins=nbins)\n p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color='blue', alpha=alpha)\n # Label\n p.xaxis.axis_label = xlbl\n # Show\n show(p)","def plot_hist(self):\n labels = [self.get_class_str(action, obj)\n for (action, obj, subj, rec, beg, end) in self.action_clips]\n visualize.plot_hist(labels, proportion=True)","def _bokeh_output_histogram(\n y_true: np.ndarray,\n y_pred: np.ndarray,\n class_names: Sequence[str],\n title_rows: Sequence[str],\n sample_weights: Optional[np.ndarray] = None,\n x_label_rotation: Union[str, float] = \"horizontal\",\n) -> Callable[[], Figure]:\n\n n = len(class_names)\n\n assert_that(np.shape(y_true)).is_equal_to(np.shape(y_pred))\n\n if sample_weights is None:\n sample_weights = np.ones_like(y_true)\n\n assert_that(np.shape(y_true)).is_equal_to(np.shape(sample_weights))\n\n bins = np.arange(0, n + 1, 1)\n normalize = not np.allclose(sample_weights, 1.0)\n\n def figure() -> Figure:\n p = plotting.figure(\n x_range=class_names,\n plot_height=350,\n plot_width=350,\n tools=TOOLS,\n toolbar_location=TOOLBAR_LOCATION,\n )\n\n # class distribution in prediction\n p.vbar(\n x=class_names,\n top=np.histogram(\n y_pred, bins=bins, weights=sample_weights, density=normalize\n )[0],\n width=0.85,\n color=DARK_BLUE,\n alpha=HISTOGRAM_ALPHA,\n legend_label=\"Prediction\",\n )\n\n # class distribution in ground truth\n p.vbar(\n x=class_names,\n top=np.histogram(\n y_true, bins=bins, weights=sample_weights, density=normalize\n )[0],\n width=0.85,\n alpha=0.6,\n legend_label=\"Ground Truth\",\n fill_color=None,\n line_color=\"black\",\n line_width=2.5,\n )\n\n add_title_rows(p, title_rows)\n apply_default_style(p)\n\n p.yaxis.axis_label = (\n \"Fraction of Instances\" if normalize else \"Number of Instances\"\n )\n\n p.xaxis.major_label_orientation = x_label_rotation\n\n p.xgrid.grid_line_color = None\n\n # prevent panning to empty regions\n p.x_range.bounds = (-0.5, 0.5 + len(class_names))\n return p\n\n return figure","def regioncounts(ribo, out, experiments, \n upperlength, lowerlength, title, horizontal, dump):\n\n return plot_region_counts_wrapper(\n ribo_file = ribo, \n experiment_list = experiments, \n range_lower = lowerlength, \n range_upper = upperlength, \n title = title,\n output_file = out, \n dump_to_file = dump,\n horizontal = horizontal)","def update_histo_frame():\n min_histo.text = str(MIN_RANGE_F) # Display the legend\n max_histo.text = str(MAX_RANGE_F)\n\n histogram = np.zeros(GRID_AXIS) # Clear histogram accumulation array\n # Collect camera data and calculate the histogram\n for _row in range(0, GRID_AXIS):\n for _col in range(0, GRID_AXIS):\n histo_index = int(map_range(GRID_DATA[_col, _row], 0, 1, 0, GRID_AXIS - 1))\n histogram[histo_index] = histogram[histo_index] + 1\n\n histo_scale = np.max(histogram) / (GRID_AXIS - 1)\n if histo_scale <= 0:\n histo_scale = 1\n\n # Display the histogram\n for _col in range(0, GRID_AXIS):\n for _row in range(0, GRID_AXIS):\n if histogram[_col] / histo_scale > GRID_AXIS - 1 - _row:\n image_group[((_row * GRID_AXIS) + _col)].fill = index_to_rgb(\n round((_col / GRID_AXIS), 3)\n )\n else:\n image_group[((_row * GRID_AXIS) + _col)].fill = BLACK","def histo ( self ,\n xbins = 20 , xmin = None , xmax = None ,\n ybins = 20 , ymin = None , ymax = None ,\n hpars = () , \n histo = None ,\n integral = False ,\n errors = False , \n density = False ) :\n \n \n histos = self.make_histo ( xbins = xbins , xmin = xmin , xmax = xmax ,\n ybins = ybins , ymin = ymin , ymax = ymax ,\n hpars = hpars ,\n histo = histo )\n\n # loop over the historgam bins \n for ix,iy,x,y,z in histo.items() :\n\n xv , xe = x.value() , x.error()\n yv , ye = y.value() , y.error()\n \n # value at the bin center \n c = self ( xv , yv , error = errors ) \n\n if not integral : \n histo[ix,iy] = c\n continue\n\n # integral over the bin \n v = self.integral( xv - xe , xv + xe , yv - ye , yv + ye )\n \n if errors :\n if 0 == c.cov2 () : pass\n elif 0 != c.value() and 0 != v : \n v = c * ( v / c.value() )\n \n histo[ix,iy] = v \n\n ## coovert to density historgam, if requested \n if density : histo = histo.density()\n \n return histo","def addHistogram2D(self, name, title, n_bins_x, minimum_x, maximum_x, n_bins_y, minimum_y, maximum_y):\n\t\tself.histograms[ name ] = ROOT.TH2F(name, title, n_bins_x, minimum_x, maximum_x, n_bins_y, minimum_y, maximum_y)","def draw_histogram(x: pd.Series, x_label: str):\n\n x.plot.hist(grid=True, bins=20, rwidth=0.9, color='#607c8e')\n plt.title('Histogram for %s' % x_label)\n plt.show()","def histo_param(result_file,output_fig=None,**kwargs):\n ###\n \n old_dic=file2dic(result_file)\n print(kwargs)\n new_dic=select_dic(old_dic,**kwargs)\n \n len_new=len(new_dic['MODEL_KEY'])\n len_old=len(old_dic['MODEL_KEY'])\n \n ### \n \n param_keys=[key for key in old_dic if key not in ['MODEL_KEY','RMS_P','RMS_S','NUM_OBS']]\n \n ### plot\n \n f, ax = plt.subplots(int(len(param_keys)/2),2)\n ax=ax.reshape(-1)\n plt.suptitle('%i Models selected over %i'%(len_new,len_old))\n kk=0\n for key in param_keys:\n \n print(key)\n \n x_best=np.unique(old_dic[key])\n x_tick=[str(x) for x in x_best]\n x_hist=list(range(len(x_best)))\n \n diff_val=x_best[:-1]+np.diff(x_best)/2\n new_val=np.append(x_best[0],diff_val)\n new_val=np.append(new_val,x_best[-1])\n \n n_el,_=np.histogram(new_dic[key],new_val)\n plt.sca(ax[kk])\n plt.bar(x_hist,n_el,facecolor='0.9',width=1,edgecolor=['k' for i in range(len(n_el))])\n plt.xticks(x_hist,x_tick)\n plt.xlabel('Value')\n plt.ylabel('Counts')\n ax[kk].set_title('%s'%key)\n kk=kk+1\n \n \n plt.tight_layout(rect=[0, 0, 1, 0.9])","def _single_histogram(self, plot_name, histogram_data):\n hist, left_edges, right_edges = histogram_data\n\n # figure\n kwargs = {\n \"plot_height\": 250,\n \"height_policy\": \"fit\",\n \"plot_width\": 250,\n \"title\": plot_name,\n }\n p = default_figure(kwargs)\n\n # histogram\n fcolor = self.plot_design.base_color_tints[-3]\n p.quad(top=hist, bottom=0, left=left_edges, right=right_edges, fill_color=fcolor, line_color=fcolor)\n\n # plot specific styling\n p.y_range.start = 0\n p.xaxis.ticker = BasicTicker(desired_num_ticks=5)\n p.xaxis.formatter = NumeralTickFormatter(format=\"0.[0]\")\n\n return p","def make_histogram(outpath, plotdata_y, bins=None, color='red',\n xlabel='', ylabel='', x_range=None):\n if bins is None:\n bins = get_optimum_bins(plotdata_y)\n pyplot.hist(plotdata_y, bins=bins, color=color, range=x_range)\n pyplot.grid(True, which='major', linestyle='-')\n pyplot.grid(True, which='minor')\n pyplot.xlabel(xlabel, fontsize=20)\n pyplot.ylabel(ylabel, fontsize=20)\n pyplot.tick_params(axis='both', which='major', labelsize=16)\n pyplot.tick_params(axis='both', which='minor', labelsize=8)\n pyplot.tight_layout()\n pyplot.savefig(outpath)\n pyplot.close()\n return outpath","def import_histo(w,hz,xmin,xmax):\n print 'Loading histogram with',hz.GetEntries(),'entries'\n data = RooDataHist('data','Zmumu MC',RooArgList(w.var('x')),hz)\n return data","def _create_histogram_plot(self, source):\n # figure\n kwargs = {\n \"plot_height\": 300,\n \"height_policy\": \"fit\",\n \"plot_width\": 300,\n \"title\": self._histogram_title,\n \"css_classes\": [self._histogram]\n }\n p = default_figure(kwargs)\n\n # histogram\n fcolor = self.plot_design.fill_color\n p.quad(top=self._hist_source_data,\n bottom=0,\n left=self._hist_source_left_edges,\n right=self._hist_source_right_edges,\n source=source,\n fill_color=fcolor,\n line_color=fcolor\n )\n\n # plot specific styling\n p.y_range.start = 0\n p.yaxis.visible = False\n p.xaxis.ticker = BasicTicker(desired_num_ticks=5)\n p.xaxis.formatter = NumeralTickFormatter(format=\"0.[0]\")\n\n return p","def draw_histogram(m, rolls, width):\n # Count the number of rolls of each side and store them in a list\n roll_count = []\n for i in list(range(1, m+1)):\n count = rolls.count(i)\n roll_count.append(count)\n\n # Finding the the maximum count from the list\n max_count = max(roll_count)\n\n print(\"Frequency Histogram: \" + str(m) + \"-sided Die\")\n\n # Counting the number of same outputs and then scaling these outputs\n # to the given width\n for j in list(range(1, m+1)):\n count = rolls.count(j)\n scaled_count = round(count*(width/max_count))\n print(str(j)+'.', end='')\n print('#'*scaled_count, end='')\n print('-'*(width-scaled_count))","def setBarWidth(w=0.75):\n dislin.barwth(w)","def create_histograms(PrimaryParticleName, LongVectorSignals, LongVectorSignalsCher,\n\tShortVectorSignals, ShortVectorSignalsCher, LongScinMaxFiber, LongCherMaxFiber, \n\tShortScinMaxFiber, ShortCherMaxFiber, EnergyTotContainer, MaxEnergyTotContainer):\n\n\t#Set ROOT histograms\n\tTH1LongScin = TH1F(\"LongScintillation\", PrimaryParticleName, 100, 0.0, LongScinMaxFiber+200.)\n\tTH1LongCher = TH1F(\"LongCherenkov\", PrimaryParticleName, 100, 0.0, LongCherMaxFiber+200.)\n\tTH1ShortScin = TH1F(\"ShortScintillation\", PrimaryParticleName, 100, 0.0, ShortScinMaxFiber+200.)\n\tTH1ShortCher = TH1F(\"ShortCherenkov\", PrimaryParticleName, 100, 0.0, ShortCherMaxFiber+200.)\n\tTH1EnergyTot = TH1F(\"EnergyTot\", PrimaryParticleName, 100, MaxEnergyTotContainer-10000., MaxEnergyTotContainer+500.) \n\n\t#Fill histograms in for loop\n\tfor index in range(len(LongVectorSignals)):\n\t\tTH1LongScin.Fill(LongVectorSignals[index])\n\t\tTH1LongCher.Fill(LongVectorSignalsCher[index])\n\t\tTH1ShortScin.Fill(ShortVectorSignals[index])\n\t\tTH1ShortCher.Fill(ShortVectorSignalsCher[index])\n\t\tTH1EnergyTot.Fill(EnergyTotContainer[index])\n\n\t#Draw + DrawOptions\n\tStyle = gStyle\n\tStyle.SetOptStat(1) #Show statistics\n\tStyle.SetLineWidth(1)\n\tXAxis = TH1LongScin.GetXaxis() #TH1LongScin\n\tXAxis.SetTitle(\"Energy (MeV)\")\n\tXAxis.SetTitleOffset(1.2)\n\tYAxis = TH1LongScin.GetYaxis()\n\tYAxis.SetTitle(\"Entries\")\n\tTH1LongScin.Draw()\n\tgPad.SaveAs(\"EnergyLongScin.eps\")\n\tXAxis = TH1LongCher.GetXaxis() #TH1LongCher\n\tXAxis.SetTitle(\"# Cher p.e.\")\n\tXAxis.SetTitleOffset(1.2)\n\tYAxis = TH1LongCher.GetYaxis()\n\tYAxis.SetTitle(\"Entries\")\n\tTH1LongCher.Draw()\n\tgPad.SaveAs(\"CherpeLong.eps\")\n\tXAxis = TH1ShortScin.GetXaxis() #TH1ShortScin\n\tXAxis.SetTitle(\"Energy (MeV)\")\n\tXAxis.SetTitleOffset(1.2)\n\tYAxis = TH1ShortScin.GetYaxis()\n\tYAxis.SetTitle(\"Entries\")\n\tTH1ShortScin.Draw()\n\tgPad.SaveAs(\"EnergyShortScin.eps\")\n\tXAxis = TH1ShortCher.GetXaxis() #TH1ShortCher\n\tXAxis.SetTitle(\"# Cher p.e.\")\n\tXAxis.SetTitleOffset(1.2)\n\tYAxis = TH1ShortCher.GetYaxis()\n\tYAxis.SetTitle(\"Entries\")\n\tTH1ShortCher.Draw()\n\tgPad.SaveAs(\"CherpeShort.eps\")\n\tXAxis = TH1EnergyTot.GetXaxis() #TH1EnergyTot\n\tXAxis.SetTitle(\"Energy (MeV)\")\n\tXAxis.SetTitleOffset(1.2)\n\tYAxis = TH1EnergyTot.GetYaxis()\n\tYAxis.SetTitle(\"Entries\")\n\tTH1EnergyTot.Draw()\n\tgPad.SaveAs(\"EnergyTot.eps\")","def __init__(self, bin_edges, **kwargs):\n super().__init__(**kwargs)\n self.bin_edges = bin_edges\n self.num_bins = bin_edges.size + 1\n self.sigma = Parameter(1.0, transform=positive())","def test_format_histograms_two_bins(self):\r\n self.assertEqual(format_histograms_two_bins(array([0, 1, 0, 2, 2, 3]),\r\n array(\r\n [2, 1, 0, 2, 0, 0]), array(\r\n [100, 110, 120, 130, 140, 150, 160])),\r\n \"\"\"Length\\tBefore\\tAfter\\n100\\t0\\t2\\n110\\t1\\t1\\n120\\t0\\t0\\n130\\t2\\t2\\n140\\t2\\t0\\n150\\t3\\t0\"\"\")","def cli_text_histogram(logy, max_pos, bin_width, graph_width, input_file):\n values = []\n for line in input_file:\n try:\n values.append(float(line.strip()))\n except ValueError:\n click.echo(f'Warning: \"{line.strip()}\" could not be parsed', err=True)\n click.echo(text_histogram(\n values, logy=logy, max_pos=max_pos, bin_width=bin_width, graph_width=graph_width\n ))","def rebin_plot(histogram, bins_array):\n newname = histogram.GetName()+'_rebinned'\n newplot = histogram.Rebin(len(bins_array)-1, newname, bins_array)\n newplot.SetDirectory(0)\n\n #print \"found overflow for\", newname, \"of\", overflow\n #newplot.SetBinContent(newplot.GetNbinsX(),newplot.GetBinContent(newplot.GetNbinsX())+newplot.GetBinContent(newplot. GetNbinsX()+1))\n #newplot.SetBinError(newplot.GetNbinsX(),math.sqrt(newplot.GetBinError(newplot.GetNbinsX())**2 + newplot. GetBinError(newplot.GetNbinsX()+1)**2 ) )\n #newplot.SetBinContent(newplot.GetNbinsX()+1,0) # Set overflow to 0\n\n return newplot","def __init__(self, nbins):\n self.nbins = nbins\n # Since the kernel used to compute the Parzen histogram covers more\n # than one bin, we need to add extra bins to both sides of the\n # histogram to account for the contributions of the minimum and maximum\n # intensities. Padding is the number of extra bins used at each side\n # of the histogram (a total of [2 * padding] extra bins). Since the\n # support of the cubic spline is 5 bins (the center plus 2 bins at each\n # side) we need a padding of 2, in the case of cubic splines.\n self.padding = 2\n self.setup_called = False","def __init__(self, bins):\n self.bins = bins","def init_distribution_plot(self, width=200, height=200, mode='fixed'):\n\n self.distribution_plot = figure(title=\"Utility distributions\",\n x_range=(0, 1),\n plot_width=width,\n sizing_mode=mode,\n output_backend=\"webgl\")\n\n hist_data = self.data[self.data.agent_type == 'household'][[\n 'res_utility', 'school_utility'\n ]]\n\n # Residential utility\n hist, edges = np.histogram(hist_data.res_utility,\n density=True,\n bins=50)\n self.res_quads = self.distribution_plot.quad(\n top=hist,\n bottom=0,\n left=edges[:-1],\n right=edges[1:],\n fill_color=\"green\",\n line_color=\"white\",\n alpha=0.7,\n legend_label='Residential utility')\n\n # School utility\n if not self.residential:\n hist, edges = np.histogram(hist_data.school_utility,\n density=True,\n bins=50)\n # Do not show school utilities yet as they are all zero!\n hist = np.zeros(len(hist))\n self.school_quads = self.distribution_plot.quad(\n top=hist,\n bottom=0,\n left=edges[:-1],\n right=edges[1:],\n fill_color=\"orange\",\n line_color=\"white\",\n alpha=0.7,\n legend_label='School utility')","def np_histogram(data, title, bins=\"auto\"):\n figure = plt.figure()\n canvas = figure.canvas\n plt.hist(data, bins=bins)\n plt.title(title)\n\n canvas.draw()\n w, h = canvas.get_width_height()\n np_hist = np.fromstring(canvas.get_renderer().tostring_rgb(), dtype=np.uint8).reshape(h, w, 3)\n plt.close(figure)\n util.np_info(np_hist)\n return np_hist","def draw_histogram(data, # type: thelper.typedefs.ArrayType\n bins=50, # type: Optional[int]\n xlabel=\"\", # type: Optional[thelper.typedefs.LabelType]\n ylabel=\"Proportion\", # type: Optional[thelper.typedefs.LabelType]\n show=False, # type: Optional[bool]\n block=False, # type: Optional[bool]\n ): # type: (...) -> thelper.typedefs.DrawingType\n fig, ax = plt.subplots()\n ax.hist(data, density=True, bins=bins)\n if len(ylabel) > 0:\n ax.set_ylabel(ylabel)\n if len(xlabel) > 0:\n ax.set_xlabel(xlabel)\n ax.set_xlim(xmin=0)\n if show:\n fig.show()\n if block:\n plt.show(block=block)\n return fig\n plt.pause(0.5)\n return fig, ax","def _make_histogram(\n dict_,\n data,\n bins=25,\n show_output=False,\n figsize=(10, 6),\n fontsize=15,\n plot_title=False,\n):\n indicator = dict_[\"ESTIMATION\"][\"indicator\"]\n\n treated = data[[indicator, \"prop_score\"]][data[indicator] == 1].values\n untreated = data[[indicator, \"prop_score\"]][data[indicator] == 0].values\n\n treated = treated[:, 1].tolist()\n untreated = untreated[:, 1].tolist()\n\n # Make the histogram using a list of lists\n fig = plt.figure(figsize=figsize)\n hist = plt.hist(\n [treated, untreated],\n bins=bins,\n weights=[\n np.ones(len(treated)) / len(treated),\n np.ones(len(untreated)) / len(untreated),\n ],\n density=0,\n alpha=0.55,\n label=[\"Treated\", \"Unreated\"],\n )\n\n if show_output is True:\n plt.tick_params(axis=\"both\", labelsize=14)\n plt.legend(loc=\"upper right\", prop={\"size\": 14})\n plt.xticks(np.arange(0, 1.1, step=0.1))\n plt.grid(axis=\"y\", alpha=0.25)\n plt.xlabel(\"$P$\", fontsize=fontsize)\n plt.ylabel(\"$f(P)$\", fontsize=fontsize)\n\n if plot_title is True:\n plt.title(r\"Support of $P(\\hat{Z})$ for $D=1$ and $D=0$\")\n\n else:\n plt.close(fig)\n\n return hist, treated, untreated","def gen_age_histogram(open_plot=True):\n\n df = read_csv(f'{data_files_path}/subject_data')\n male = go.Histogram(x=df[df['Gender'] == 'Male']['Age'],\n xbins=dict(start=min(df['Age']),\n end=max(df['Age'])),\n name='Male',\n opacity=0.75)\n\n female = go.Histogram(x=df[df['Gender'] == 'Female']['Age'],\n xbins=dict(start=min(df['Age']),\n end=max(df['Age'])),\n name='Female',\n opacity=0.75)\n\n data = [male, female]\n layout = go.Layout(title='Distribution of Subjects by Age',\n barmode='overlay',\n font=dict(family='arial', size=18, color='#000000'))\n\n fig = go.Figure(data=data, layout=layout)\n filename = 'age_distribution_by_gender'\n if not os.path.exists(data_files_path):\n print(f'\\nWARNING: The path does not exist. Creating new directory...\\n{data_files_path}\\n')\n os.mkdir(data_files_path)\n pyo.plot(fig, filename=f'{data_files_path}/{filename}.html', auto_open=open_plot)\n print(f'\\nAge Distribution Histogram generated.\\n'\n f'Location: {f\"{data_files_path}/{filename.html}\"}\\n')","def update_view_to_bins(self, *args):\n with delay_callback(self, 'x_min', 'x_max'):\n self.x_min = self.hist_x_min\n self.x_max = self.hist_x_max","def update_view_to_bins(self, *args):\n with delay_callback(self, 'x_min', 'x_max'):\n self.x_min = self.hist_x_min\n self.x_max = self.hist_x_max","def _printHist(self):\n bins = [0] * self.displayBins\n binSize = self.maxValue / self.displayBins\n displaySamps = self.samples[:]\n displaySamps.sort()\n currLimit = binSize\n binIndex = 0\n partIndex = 0\n while partIndex < self.numParticles:\n part = displaySamps[partIndex]\n if binIndex >= self.displayBins:\n # if value beyond max value, add to last bin and go on to next particle\n bins[19] += 1\n partIndex += 1\n elif part < currLimit:\n # count particle in current bin, go on to next particle\n bins[binIndex] += 1\n partIndex += 1\n else:\n # go on to next bin, don't go on to next particle\n binIndex += 1\n currLimit += binSize\n self.printMapPattern(binSize)\n histStr = \"{0:3d} \"\n printStr = \"\"\n for b in bins:\n printStr += histStr.format(b)\n print(printStr)","def plot_bacteria_hist(folder, depth=6, mid_quantile=False):\n\n # Get the stool dataset and discretize it\n ds = parser.get_dataset()\n ds = compute_relative_values(ds)\n t = Tree(ds)\n ds = t.dataset_at_depth(depth)\n\n # Get header names to priint on the plots\n headers = ds[0][2:]\n\n for index, header in enumerate(headers):\n\n node = t.node_for_clade_name(header)\n abundances = t.abundance_column_in_subtree(node)\n abundances = [round(x,3) for x in abundances]\n\n if mid_quantile:\n abundances.sort()\n abundances = abundances[int(len(abundances)*0.25): -int(len(abundances)*0.25)]\n\n xlabel('Relative abundance')\n ylabel('Bin size')\n\n title_text = header.replace('/','-').replace('|', '-')\n title(title_text)\n binwidth = 0.001\n bins, bin_sizes, patches = hist(abundances, bins=np.arange(min(abundances), max(abundances) + binwidth, binwidth), color='#0066FF')\n\n # Write discretized values\n threshold, discretized_abundances = discretize_row(abundances, maxent_discretization_splitter)\n _0 = '0: ' + str(len([x for x in discretized_abundances if x == 0]))\n _1 = '1: ' + str(len([x for x in discretized_abundances if x == 1]))\n\n text_x = 0.7\n\n smaples_text = 'Samples: %d' % len(abundances)\n figtext(text_x, 0.85, smaples_text, fontsize=10)\n\n threshold_text = 'Splitter: %f' % threshold\n figtext(text_x, 0.82, threshold_text, fontsize=10)\n figtext(text_x, 0.79, _0, fontsize=10)\n figtext(text_x, 0.76, _1, fontsize=10)\n\n # Draw threshold line\n max_bin = len(abundances)\n if len(bins) != 0:\n max_bin = max(bins)\n\n a, b = [threshold, threshold], [0, max_bin]\n plot(a, b, c='r')\n\n grid(True)\n\n # Write max and avg\n # max_abundance = 'max: %f' % max(abundances)\n # avg_abundance = 'avg: %f' % (sum(abundances) / float(len(abundances)))\n # figtext(text_x, 0.76, max_abundance, fontsize=10)\n # figtext(text_x, 0.73, avg_abundance, fontsize=10)\n\n # write variance\n # variance = 'var: %f' % tvar(abundances)\n # figtext(text_x, 0.70, variance, fontsize=10)\n\n # Save fig to folder\n if not (os.path.exists(folder)):\n os.makedirs(folder)\n file_name = os.path.join(folder, title_text)\n print 'Hist: ', file_name\n savefig(file_name)\n\n close()","def getHistogram(self, copy: bool=True):\n for item in self.getPlotWidget().getItems():\n if item.getName() == 'histogram':\n return (item.getValueData(copy=copy),\n item.getBinEdgesData(copy=copy))\n else:\n return None","def roo_histo ( self ,\n xbins = 20 , xmin = None , xmax = None ,\n ybins = 20 , ymin = None , ymax = None ,\n hpars = () , \n histo = None , \n events = True ) :\n \n histo = self.make_histo ( xbins = xbins , xmin = xmin , xmax = xmax ,\n ybins = ybins , ymin = ymin , ymax = ymax ,\n hpars = hpars ,\n histo = histo )\n \n hh = self.pdf.createHistogram (\n hID() ,\n self.xvar , self.binning ( histo.GetXaxis() , 'histo2x' ) ,\n ROOT.RooFit.YVar ( self.yvar , self.binning ( histo.GetYaxis() , 'histo2y' ) ) , \n ROOT.RooFit.Scaling ( False ) , \n ROOT.RooFit.Extended ( False ) ) \n\n for i in hh : hh.SetBinError ( i , 0 ) \n \n if events and self.pdf.mustBeExtended() :\n \n for ix , iy , x , y , z in hh.items() :\n volume = 4 * x.error() * y.error() \n hh [ ix , iy ] *= volume\n \n hh *= self.pdf.expectedEvents ( self.vars ) / hh.sum() \n \n histo += hh\n \n return histo","def histogram(data,binwidth, xmin,xmax):\n bins = arange(xmin,xmax, binwidth)\n binsc = bins + (0.5 * binwidth)\n try: #FIXME: use updated numpy.histogram\n histo = numpyhisto(data, bins, new=False)[0]\n except:\n histo = numpyhisto(data, bins)[0]\n return binsc[:len(histo)], histo","def parameters_histograms(w, dw, a, da, b, db):\n w = w.cpu()\n dw = dw.cpu()\n a = a.cpu()\n da = da.cpu()\n b = b.cpu()\n db = db.cpu()\n \n fig = plt.figure(figsize=(10,6))\n ax = fig.add_subplot(231)\n ax.hist(w.reshape(1, w.shape[0] * w.shape[1]))\n ax.set_title('Weights', fontsize = 11)\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n ax = fig.add_subplot(232)\n ax.hist(dw.reshape(1, dw.shape[0] * dw.shape[1]))\n ax.set_title('Weights variations', fontsize = 11)\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n ax = fig.add_subplot(233)\n ax.hist(a)\n ax.set_title('Visible bias', fontsize = 11)\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n ax = fig.add_subplot(234)\n ax.hist(da)\n ax.set_title('Visible bias variations', fontsize = 11)\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n ax = fig.add_subplot(235)\n ax.hist(b)\n ax.set_title('Hidden bias', fontsize = 11)\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n ax = fig.add_subplot(236)\n ax.hist(db)\n ax.set_title('Hidden bias variations', fontsize = 11)\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n plt.subplots_adjust(hspace=0.25)\n plt.show()\n plt.close('all')","def plot_hist_blo(blocking_occ, SOM_nodenum, savefig_str, num_nodes, show_plots, caption=0):\r\n alpha_val = 0.6\r\n nodes_notblo = SOM_nodenum.values*(blocking_occ==0).values\r\n nodes_blo = SOM_nodenum.values*blocking_occ.values\r\n freq_blo, freq_notblo = int(float(blocking_occ.mean())*1000)/10, int(float((blocking_occ==0).mean()*1000))/10\r\n nodes_blo_rem, nodes_notblo_rem = nodes_blo[nodes_blo != 0], nodes_notblo[nodes_notblo != 0]\r\n\r\n fig, ax = plt.subplots(1,1)\r\n ax.hist(nodes_notblo_rem-0.5, bins = np.arange(0.5,num_nodes+1.5,1), density = True, alpha = alpha_val, label = f\"not blocked ({freq_notblo}% of days)\", ec = \"k\") \r\n ax.hist(nodes_blo_rem-0.5, bins = np.arange(0.5,num_nodes+1.5,1), density = True, alpha = alpha_val, label = f\"blocked ({freq_blo}% of days)\", ec = \"k\") \r\n\r\n if caption != 0:\r\n ax.set_xlabel(f\"SOM node number \\n \\n {caption}\")\r\n else:\r\n ax.set_xlabel(f\"SOM node number\")\r\n ax.set_ylabel(\"Frequency of occurence\")\r\n ax.set_xticks(np.arange(1,num_nodes+1,1))\r\n ax.legend()\r\n if show_plots == False:\r\n plt.close();\r\n\r\n fig.savefig(savefig_str, bbox_inches=\"tight\", dpi = 300)","def plot_uv_hist(df, colname, nbins='auto', xlogflag=False, colorid=0):\n\n # Set figure size \n fig, ax = plt.subplots(figsize=(8,6))\n\n # set colorid for bar plot\n base_color = sns.color_palette()[colorid]\n \n # plotting... histogram\n if (xlogflag):\n \tsns.histplot(ax = ax, data = df\n \t, x = colname\n \t, bins = nbins\n \t, color = base_color\n \t\t\t\t\t\t, log_scale= True)\n else:\n \tsns.histplot(ax = ax, data = df\n \t, x = colname\n \t, color = base_color\n \t, bins = nbins)\n\n \n #plt.legend(prop={'size': 12})\n plt.title('Distribution of '+colname, fontsize=20)\n plt.xlabel(colname+' (units)', fontsize=16)\n plt.ylabel('Frequency', fontsize=16)\n plt.xticks(fontsize=16)\n plt.yticks(fontsize=16)\n \n return plt.show()","def oneNumBar(df, colName):\n bins = pd.qcut(x=df[colName[0]], q=15, duplicates='drop')\n ax = bins.value_counts()\n bins = bins.cat.as_ordered()\n bins = bins.cat.categories\n bounds = bins.left \n bounds = list(bounds)\n bounds.append(bins[len(bounds)-1].right)\n texts = []\n for x,y in zip(bounds[0::],bounds[1::]):\n texts.append(\"(\" + str(x) + \", \" + str(y) + \"]\") \n barData = [go.Bar(x=texts, \n y=ax,\n marker=dict(\n color = '#92c5de',\n opacity=0.8)\n )] \n layout = go.Layout(\n title=\"Bar Plot Showing Count of Values for \" + str(colName[0]),\n xaxis=dict(\n title= colName[0]\n ),\n yaxis=dict(\n title= \"NUMBER OF RECORDS\", \n )\n )\n fig = go.Figure(data=barData, layout=layout)\n return {\"label\":\"Frequency\", \"plot\":fig}","def n_particles_bins(DG, bins=[0, 0.5, 3, 10, 100]):\n radii = fid.rss(DG.gas['Coordinates'][()])\n hist, bin_edges = np.histogram(radii, bins)\n\n return hist, bin_edges","def histogram(data, title, path):\n plt.hist(data,\n bins=60)\n plt.xticks(size=22)\n plt.yticks(size=22)\n plt.title(title,\n fontsize=30)\n plt.savefig(path)\n plt.clf()","def interactive_histograms(adata, keys=['n_counts', 'n_genes'],\n bins=100, min_bins=1, max_bins=1000,\n tools='pan, reset, wheel_zoom, save',\n groups=None, fill_alpha=0.4,\n palette=Set1[9] + Set2[8] + Set3[12],\n legend_loc='top_right', display_all=True,\n *args, **kwargs):\n\n from itertools import product\n from functools import reduce\n from bokeh.plotting import figure, show, ColumnDataSource\n from bokeh.models.widgets import CheckboxGroup\n from bokeh.models.widgets.buttons import Button\n from bokeh.models import Slider\n from bokeh.models.callbacks import CustomJS\n from bokeh.io import output_notebook\n from bokeh.layouts import layout, column, row\n\n from copy import copy\n from numpy import array_split, ceil\n output_notebook()\n\n if min_bins < 1:\n raise ValueError(f'Expected min_bins >= 1, got min_bins={min_bins}.')\n if max_bins < min_bins:\n raise ValueError(f'Expected min_bins <= max_bins, got min_bins={min_bins}, max_bins={max_bins}.')\n if not (bins >= min_bins and bins <= max_bins):\n raise ValueError(f'Expected min_bins <= bins <= max_bins, got min_bins={min_bins}, bins={bins}, max_bins={max_bins}.')\n\n # check the input\n for key in keys:\n if key not in adata.obs.keys() and \\\n key not in adata.var.keys() and \\\n key not in adata.var_names:\n raise ValueError(f'The key `{key}` does not exist in adata.obs, adata.var or adata.var_names.')\n\n def _create_adata_groups():\n if groups is None:\n return [('all',)], [adata]\n\n combs = list(product(*[set(adata.obs[g]) for g in groups]))\n adatas= [adata[reduce(lambda l, r: l & r,\n (adata.obs[k] == v for k, v in zip(groups, vals)), True)]\n for vals in combs] + [adata]\n\n if display_all:\n combs += [('all',)]\n adatas += [adata]\n\n return combs, adatas\n\n # group_v_combs contains the value combinations\n # used for grupping\n group_v_combs, adatas = _create_adata_groups()\n n_plots = len(group_v_combs)\n checkbox_group = CheckboxGroup(active=list(range(n_plots)), width=200)\n\n for key in keys:\n # create histogram\n cols, legends, callbacks = [], [], []\n plot_map = dict()\n slider = Slider(start=min_bins, end=max_bins, value=bins, step=1,\n title='Bins')\n\n fig = figure(*args, tools=tools, **kwargs)\n\n plot_ids = []\n for j, (ad, group_vs) in enumerate(zip(adatas, group_v_combs)):\n\n if ad.n_obs == 0:\n continue\n\n plot_ids.append(j)\n color = palette[len(plot_ids) - 1]\n\n if key in ad.obs.keys():\n orig = ad.obs[key]\n hist, edges = np.histogram(orig, density=True, bins=bins)\n elif key in ad.var.keys():\n orig = ad.var[key]\n hist, edges = np.histogram(orig, density=True, bins=bins)\n else:\n orig = ad[:, key].X\n hist, edges = np.histogram(orig, density=True, bins=bins)\n\n # original data, used for recalculation of histogram in JS code\n orig = ColumnDataSource(data=dict(values=orig))\n # data that we update in JS code\n source = ColumnDataSource(data=dict(hist=hist, l_edges=edges[:-1], r_edges=edges[1:]))\n\n legend = ', '.join(': '.join(map(str, gv)) for gv in zip(groups, group_vs)) \\\n if groups is not None else 'all'\n legends.append(legend)\n # create figure\n p = fig.quad(source=source, top='hist', bottom=0,\n left='l_edges', right='r_edges',\n fill_color=color, legend=legend,\n line_color=\"#555555\", fill_alpha=fill_alpha)\n\n # create callback and slider\n callback = CustomJS(args=dict(source=source, orig=orig), code=_inter_hist_js_code)\n callback.args['bins'] = slider\n callbacks.append(callback)\n\n # add the current plot so that we can set it\n # visible/invisible in JS code\n plot_map[f'p_{j}'] = p\n\n # slider now updates all values\n slider.js_on_change('value', *callbacks)\n plot_map['cb'] = checkbox_group\n\n button = Button(label='Toggle All', button_type='primary')\n code_t='\\n'.join(f'p_{p_id}.visible = false;' for i, p_id in enumerate(plot_ids))\n code_f ='\\n'.join(f'p_{p_id}.visible = true;' for i, p_id in enumerate(plot_ids))\n button.callback = CustomJS(\n args=plot_map,\n code=f'''if (cb.active.length == {len(plot_map) - 1}) {{\n console.log(cb.active);\n cb.active = Array();\n {code_t};\n }} else {{\n console.log(cb.active);\n cb.active = Array.from(Array({len(plot_map) - 1}).keys());\n {code_f};\n }}'''\n )\n\n checkbox_group.callback = CustomJS(\n args=plot_map,\n code='\\n'.join(f'p_{p_id}.visible = cb.active.includes({i});' for i, p_id in enumerate(plot_ids))\n )\n checkbox_group.labels = legends\n\n fig.legend.location = legend_loc\n fig.xaxis.axis_label = key\n fig.yaxis.axis_label = 'normalized frequency'\n fig.plot_width = kwargs.get('plot_width', 400)\n fig.plot_height = kwargs.get('plot_height', 400)\n\n cols.append(column(slider, button, row(fig, checkbox_group)))\n\n\n # transform list of pairs of figures and sliders into list of lists, where\n # each sublist has length <= 2\n # note that bokeh does not like np.arrays\n grid = list(map(list, array_split(cols, ceil(len(cols) / 2))))\n\n show(layout(children=grid, sizing_mode='fixed', ncols=2))","def get_histogram(self):\n\n for bin in range(self.bins.size):\n bin_inf = self.bins[bin]\n try: bin_sup = self.bins[bin + 1]\n except IndexError: bin_sup = self.vmax\n self.hist[bin] = np.sum(\n (self.values >= bin_inf)*(self.values < bin_sup))\n\n binned_values = np.sum(self.hist)\n if binned_values == 0: return self.hist # no binned value\n else: self.hist /= np.sum(self.hist)\n return self.hist","def histogram_single_values(self, x_axis:str, title:str, max_bins:int=.1, density: bool=False, show:bool=True, dest_path:str=None):\n d = {}\n for pop in self.populations:\n df = pd.read_csv(self.path + '\\\\{}.csv'.format(pop))\n trials = df['label'].tolist()\n values = df['Component 1'].tolist()\n response = df['response'].tolist()\n \n for i in range(len(response)):\n if response[i] in d:\n d[response[i]].add(values[i])\n else:\n # Removing day 4 trials\n if eval(trials[i])[0] != 4:\n d[response[i]] = set()\n d[response[i]].add(values[i])\n\n data = []\n labels = d.keys()\n if len(labels)==4:\n labels = ['0->0', '0->1', '1->1', '1->0']\n for key in labels:\n data.append(list(d[key]))\n \n bins = np.linspace(0, max_bins, 500)\n colors = self.__multiclass_to_color(deepcopy(labels))\n for i in range(len(labels)):\n plt.hist(d[labels[i]], bins, alpha=0.5, label=labels[i], color=colors[i], histtype='step', density=density)\n plt.axvline(np.array(list(d[labels[i]])).mean(), ls='--', color=colors[i], linewidth=1, label=\"{} mean\".format(labels[i]))\n plt.title(title)\n plt.xlabel(x_axis)\n if density:\n plt.ylabel(\"density\")\n else:\n plt.ylabel(\"Occurences\")\n plt.legend(loc='upper right')\n\n if show:\n plt.show()\n\n if dest_path !=None:\n plt.savefig(dest_path + '\\\\{}.png'.format(title))\n\n plt.clf()\n plt.cla()\n plt.close()","def show_histo(dict, orient=\"horiz\", label=\"counts\", title=\"title\"):\n plt.clf()\n plt.cla()\n if orient==\"horiz\":\n bar_fun = plt.barh \n bar_ticks = plt.yticks\n bar_label = plt.xlabel\n elif orient==\"vert\":\n bar_fun = plt.bar\n bar_ticks = plt.xticks\n bar_label = plt.ylabel\n else:\n raise Exception(\"show_histo: Unknown orientation: %s \".format % orient)\n n = len(dict)\n bar_fun(range(n), list(dict.values()), align='center', alpha=0.4, color = 'g')\n bar_ticks(range(n), list(dict.keys())) \n bar_label(label)\n plt.title(title)\n plt.show()","def simple_hist(vals, samplename, bins=100, min=None, max=None):\n if not min:\n min = vals.min()\n if not max:\n max = vals.max()\n fig = plt.figure()\n plt.hist(vals.dropna(), bins=bins, range=[min, max])\n calc_vals = vals.dropna()\n plt.xlabel('Bins')\n plt.ylabel('Frequency')\n plt.title(samplename)\n plt.axvline(np.median(calc_vals),\n color='r', linestyle='dashed', linewidth=1)\n plt.grid(True)\n\n return fig","def display(\n self, start=None, stop=None, step=None, format=\"%14.4f\", show_empty=False\n ):\n collection = self.collect(start, stop, step)\n print(\"Histogram\")\n print(\"=========\")\n size = len(format % 0)\n sformat = \"%\" + str(size) + \"s\"\n columns = [\"Range Start\", \"Range End\", \"Count\", \"Bins\"]\n formats = [sformat % s for s in columns]\n print(*formats)\n print(\"-\" * (size * 4 + 3))\n total = 0.0\n for row in collection:\n count = row[\"count\"]\n total += count\n if show_empty or count > 0:\n print(\n format % row[\"value_start\"],\n format % row[\"value_stop\"],\n format % count,\n (\n sformat\n % (\"[%s-%s]\" % (row[\"bin_index_start\"], row[\"bin_index_stop\"]))\n ),\n )\n print(\"-\" * (size * 4 + 3))\n print((\"Total: \" + format) % total)","def main(x_axis, y_axis, filtered, unfiltered, name, histogram, total, true_max):\n axes = [x_axis, y_axis, 'description']\n uf_dict, f_dict, min_x, max_x, min_y, max_y = data_from_sc_file(axes, filtered, unfiltered, true_max)\n gen_plots(uf_dict, f_dict, min_x, max_x, min_y, max_y, axes, name, histogram, total)","def mpl_hist_arg(value=True):\n import matplotlib\n\n density_kwarg = 'density' if matplotlib.__version__ >= '2.1.0'\\\n else 'normed'\n return {density_kwarg: value}","def end_hist(pulse, trap):\n all_trial_n, all_trial_n_ave = trap.sideband_cool_sch(pulse)\n n_max = np.amax(all_trial_n)\n hist_xar = sp.arange(n_max + 1) - 0.5\n \n # fig, ax = plt.subplots()\n plt.hist(all_trial_n[:, -1], bins = hist_xar)\n plt.xlabel('Phonon State')\n plt.ylabel('Distribution')\n # return ax","def hist_save(self, d, bin1, name, no):\n\t\tfor i in range(0,no):\n\t\t\ts = d[:,i]\n\t\t\tplt.hist(s, bin1, normed=True, color='c')\t# Extracting the parameters from the histogram\n\t\t\tplt.title('Probability Distribution Fnction of %s' %name, fontsize=20)\n\t\t\tplt.xlabel(\"Filter tap values\", fontsize=20)\n\t\t\tplt.ylabel(\"Probability Distribution\", fontsize=20)\n#\t\t\tplt.xlim(0,0.10)\n\t\t\tplt.ylim(0,100)\n#\t\t\tplt.legend(fontsize = 'xx-large')\n\t\t\tplt.savefig('/home/abhishek/Results/comparison_all_sets/Curve fitting/test/set_1/hist_%s_index_%d' %(name,i))\n\t\t\tplt.close()","def numeric_attribute_charts(self):\n\n file_or_case = \"case\"\n if self.ui.radioButton_file.isChecked():\n file_or_case = \"file\"\n attribute = self.ui.comboBox_num_attributes.currentText()\n title = _(\"Attribute histogram\")\n subtitle = \"<br><sup>\" + _(file_or_case) + _(\" attribute: \") + attribute\n self.ui.comboBox_num_attributes.blockSignals(True)\n self.ui.comboBox_num_attributes.setCurrentIndex(0)\n self.ui.comboBox_num_attributes.blockSignals(False)\n\n cur = self.app.conn.cursor()\n cur.execute(\"select cast(value as int) from attribute where attr_type=? and name=?\",\n [file_or_case, attribute])\n res = cur.fetchall()\n values = []\n for r in res:\n values.append(r[0])\n # Create pandas DataFrame\n data = {attribute: values}\n df = pd.DataFrame(data)\n fig = px.histogram(df, x=attribute, title=title + subtitle)\n fig.show()\n self.helper_export_html(fig)","def super_hist(self, data_list, alpha=0.5, log_scale=True, bins=45):\r\n\r\n fig, _ = mp.subplots(1, 1, figsize=(15, 10), constrained_layout=True)\r\n\r\n names = []\r\n for data in data_list:\r\n plot_data = data[data.Day_First_N_Infections != \"None\"]\r\n column_data = plot_data[\"Day_First_N_Infections\"].values\r\n sns.distplot(column_data,\r\n kde=False,\r\n bins=bins,\r\n hist_kws={\r\n \"linewidth\": 1,\r\n \"alpha\": alpha,\r\n \"edgecolor\": 'black',\r\n \"log\": log_scale\r\n })\r\n\r\n mp.legend(loc='upper left', fontsize=20)\r\n mp.xlabel(\"Days from outbreak to case number \" + str(data_list[0].N) +\r\n \" in county\",\r\n fontsize=18)\r\n mp.ylabel(\"Frequency\", fontsize=18)\r\n\r\n fig.savefig(\"hist_N\" + str(data_list[0].N) + \"_\" + \"_\".join(names) +\r\n \".png\")"],"string":"[\n \"def get_histogram(self):\\n n_rows = self.df.shape[0]\\n if n_rows > 250:\\n fig, ax = plt.subplots()\\n ax.hist(self.df[self.col_name], bins=50)\\n else: \\n fig, ax = plt.subplots()\\n ax.hist(self.df[self.col_name], bins=int(round(n_rows/5,0)))\\n return fig\",\n \"def makeHistogram(values, numBins, xLabel, yLabel, title=None):\",\n \"def plot_size_histogram(cluster_size):\\n\\n fig = px.histogram(x=cluster_size, nbins=50,\\n width=500, height=300)\\n st.plotly_chart(fig)\",\n \"def distribution_sentimentscore_histogram(cur, var, table, label):\\n x = select(cur,var, table)\\n print(\\\"Number of entries: \\\", len(x))\\n print(\\\"Maximum: \\\", max(x))\\n print(\\\"Minimum: \\\", min(x))\\n \\n fig = plt.figure()\\n ax = fig.add_subplot(1,1,1)\\n ax.set_xlabel(\\\"Sentiment Score\\\")\\n ax.set_ylabel(\\\"Number of Loans\\\")\\n fig.suptitle(label)\\n ax.hist(x, bins = 15)\\n plt.show()\",\n \"def distribution_magnitude_histogram(cur, var, table, label):\\n x = select(cur,var, table)\\n print(\\\"Number of entries: \\\", len(x))\\n print(\\\"Maximum: \\\", max(x))\\n print(\\\"Minimum: \\\", min(x))\\n \\n fig = plt.figure()\\n ax = fig.add_subplot(1,1,1)\\n ax.set_xlabel(\\\"Sentiment Magnitude\\\")\\n ax.set_ylabel(\\\"Number of Sentences\\\")\\n fig.suptitle(label)\\n ax.hist(x, bins = 20)\\n plt.show()\",\n \"def plot_hist(df, num_bins=8):\\n df.hist(figsize=(24, 20), bins=num_bins)\\n plt.axes\",\n \"def hist(self):\\r\\n plt.hist(self.data_array, bins='auto', density=False, facecolor='b')\\r\\n plt.title(self.column_name)\\r\\n plt.savefig(self.column_name + \\\".svg\\\")\\r\\n plt.close()\",\n \"def get_histogram(self, column):\\n\\n df_histogram = pd.DataFrame(self.serie)\\n #histogramcol = alt.Chart(df_histogram).mark_bar().encode(alt.X(column, bin=True), y='count()')\\n histogramcol = alt.Chart(df_histogram).mark_bar().encode(alt.X(column, bin=alt.Bin(maxbins=50)),y='count()')\\n return histogramcol\",\n \"def histogram(self, cond, numbins):\\n colname = \\\"axs_hist_col\\\"\\n res = self._df.select(cond.alias(colname))\\n return res.withColumn(\\\"bin\\\", (res[colname] / numbins).cast(\\\"int\\\")).groupBy(\\\"bin\\\").count()\",\n \"def display(self, bin_size):\\n xs = np.linspace(self.sample_min, self.sample_max, 2000)\\n ys = np.zeros_like(xs)\\n for (l, s), w in zip(self.gauss_params, self.dist_weights):\\n ys += ss.norm.pdf(xs, loc=l, scale=s) * w\\n plt.plot(xs, ys, color=\\\"blue\\\")\\n plt.hist(self.samples, density=True, bins=bin_size, color=\\\"palegreen\\\")\\n plt.xlabel(\\\"duration\\\")\\n plt.ylabel(\\\"density\\\")\\n _, _, ymin, ymax = plt.axis()\\n if self.lower_bound > 0:\\n plt.vlines([self.lower_bound], ymin, ymax, color=\\\"crimson\\\")\\n if self.upper_bound < float(\\\"inf\\\"):\\n plt.vlines([self.upper_bound], ymin, ymax, color=\\\"crimson\\\")\\n plt.show()\",\n \"def add_histogram(self, tag, values, global_step=None, bins='tensorflow'):\\n values = make_np(values)\\n self.vis.histogram(make_np(values), opts={'title': tag})\",\n \"def numBinsChanged(self, val):\\n self.numBins = val\",\n \"def getHistogramWidget(self):\\n return self._getToolWindow()\",\n \"def histogram(self):\\r\\n channel = self.ui.channel_selection.itemData(self.ui.channel_selection.currentIndex())\\r\\n\\r\\n #create a window, the reference must be stored, because the window\\r\\n #gets destroyed when its reference is garbage collected\\r\\n #make plotWindow a list and append to that if multiple windows should be possible\\r\\n title = \\\"histogram of {:s} channel\\\".format(self.ui.channel_selection.currentText())\\r\\n self.plotWindow = pyguitools.SimplePlotWindow(name = title)\\r\\n self.plotWindow.ax1.hist(self.npImg[self.ui.y0.value():self.ui.y1.value(),\\r\\n self.ui.x0.value():self.ui.x1.value(), \\r\\n channel].flatten(),\\r\\n bins=self.settings[\\\"histogramm bins\\\"],\\r\\n range=(self.settings[\\\"histogramm min\\\"],self.settings[\\\"histogramm max\\\"]))\\r\\n self.plotWindow.ax1.set_xlim(self.settings[\\\"histogramm min\\\"],self.settings[\\\"histogramm max\\\"]) \\r\\n self.plotWindow.show()\",\n \"def draw_histo(df, col_name, x_start, bin_width, n_bins, title, output_dir, output_filename, \\n log_y = False, describe = False):\\n my_plt = df[col_name].plot.hist(\\n bins=[bin_width * (x + 0.5) + x_start for x in np.arange(0, n_bins)], \\n edgecolor='black', \\n color='#88d498',\\n log=log_y)\\n my_plt.set_title(title)\\n pd.set_option('precision', 3)\\n plt.tight_layout(pad=5)\\n if(describe): print(df[col_name].describe())\\n plt.figtext(0.01,0.01, df[col_name].describe().loc[['mean','std','count']].to_string(),fontsize='x-large')\\n my_plt.ticklabel_format(style='sci', axis='x', scilimits=(0,3)) \\n my_plt.get_figure().savefig(os.path.join(output_dir,output_filename))\\n my_plt.get_figure().clf()\",\n \"def drawHist(data, xLabel, unit, binSize, title):\\n mean = np.mean(data)\\n median = np.median(data)\\n mode = stats.mode(data)[0].astype(float)\\n \\n q1, q3 = np.percentile(data, [25, 75])\\n iqr = q3 - q1\\n sigma = np.std(data)\\n \\n \\n bins = np.arange(min(data), max(data) + 1, binSize)\\n plt.style.use('dark_background')\\n fig, ax = plt.subplots(figsize=(12,7))\\n plt.hist(data, bins=bins, histtype='bar') \\n plt.title(title)\\n plt.xlabel(xLabel + \\\" \\\" + unit)\\n plt.ylabel('count')\\n ymax = ax.get_ylim()[1]\\n ax.vlines(mean, 0, ymax, color='red', label='mean')\\n ax.vlines(mean-sigma, 0, ymax, color='red', linestyle='--', \\n label='mean +/- std')\\n ax.vlines(mean+sigma, 0, ymax, color='red', linestyle='--')\\n plt.legend()\\n plt.show()\\n \\n print(\\\"Einheit: \\\", unit)\\n print(\\\"Minimum: \\\", round(data.min(),3))\\n print(\\\"Maximum: \\\", round(data.max(),3))\\n print(\\\"Mittelwert: \\\", round(mean,3))\\n print(\\\"Median: \\\", round(median,3))\\n print(\\\"Modus: \\\", round(mode[0],3))\\n print(\\\"Standardabweichung: \\\", round(sigma, 3))\\n print(\\\"1. Quartil: \\\", round(q1,3))\\n print(\\\"3. Quartil: \\\", round(q3,3))\\n print(\\\"Quartilsdifferenz: \\\", round(iqr,3))\",\n \"def __updateHistogramFromControls(self, *args):\\n\\n hist = self.getHistogram(copy=False)\\n if hist is not None:\\n count, edges = hist\\n if (len(count) == self.__nbinsLineEdit.getValue() and\\n (edges[0], edges[-1]) == self.__rangeSlider.getValues()):\\n return # Nothing has changed\\n\\n self._updateFromItem()\",\n \"def hist_of_numeric(X):\\n figsize(10,3)\\n for col in get_numeric(X):\\n print(col)\\n X[col].hist(bins=50)\\n show()\",\n \"def make_voc_histogram_figure(voc_hist_x, voc_hist_y, voc_summary_for_plot,\\n annotation_voc_histogram_choice):\\n return dcc.Graph(\\n id='Voc-histogram',\\n figure={\\n 'data': [\\n {'x': voc_hist_x,\\n 'y': voc_hist_y,\\n 'type': 'bar',\\n 'name': 'Voc'}\\n ],\\n 'layout': go.Layout(\\n title=go.layout.Title(\\n text='Figure 1. Histogram of Voc values over the simulation time.',\\n xref='paper',\\n x=0\\n ),\\n xaxis={'title': 'Voc (Volts)'},\\n yaxis={'title': 'hours/year'},\\n margin={'l': 60, 'b': 120, 't': 30, 'r': 60},\\n hovermode='closest',\\n annotations=[\\n dict(\\n dict(\\n x=voc_summary_for_plot['plot_voltage'][s],\\n y=voc_hist_y[np.argmin(\\n np.abs(voc_summary_for_plot['plot_voltage'][\\n s] - voc_hist_x))],\\n xref='x',\\n yref='y',\\n xanchor='left',\\n yanchor='middle',\\n text=s,\\n hovertext=voc_summary_for_plot['long_note'][s],\\n textangle=-90,\\n font=dict(\\n color=voc_summary_for_plot['plot_color'][s]\\n ),\\n arrowcolor=voc_summary_for_plot['plot_color'][s],\\n # bordercolor=plot_color[s],\\n showarrow=True,\\n align='left',\\n standoff=2,\\n arrowhead=4,\\n ax=0,\\n ay=-40\\n ),\\n align='left'\\n )\\n for s in list(annotation_voc_histogram_choice)]\\n )\\n },\\n config=dict(\\n toImageButtonOptions=dict(\\n scale=5,\\n filename='Voc_histogram',\\n )\\n )\\n )\",\n \"def histogram_summary(self, tag, values, step, bins=1000):\\n self.writer.add_histogram(tag, values, step, bins='auto')\",\n \"def hist(self, color=\\\"#FFFFFF\\\", axes_style=\\\"darkgrid\\\", context=\\\"notebook\\\",\\n col_wrap=4, exhibit_path=None, **kwargs):\\n import matplotlib.pyplot as plt\\n import seaborn as sns\\n sns.set_context(context)\\n\\n # data0 = self.sims_data[[\\\"sim\\\", \\\"origin\\\", \\\"dev\\\", \\\"rectype\\\", \\\"latest\\\", \\\"reserve\\\",]]\\n # data0 = data0[(data0[\\\"dev\\\"]==data0[\\\"dev\\\"].max()) & (data0[\\\"rectype\\\"]==\\\"forecast\\\")].reset_index(drop=True)\\n # data0 = data0.drop([\\\"dev\\\", \\\"rectype\\\", \\\"latest\\\"], axis=1)\\n #\\n # # Include additional origin representing aggregate distribution.\\n # data1 = data0.groupby(\\\"sim\\\", as_index=False)[[\\\"reserve\\\"]].sum()\\n # data1[\\\"origin\\\"] =\\\"total\\\"\\n # data = pd.concat([data0, data1])\\n data = self.reserve_dist\\n\\n # Get mean, min and max ultimate and reserve by origin.\\n med_data = data.groupby(\\\"origin\\\", as_index=False)[[\\\"reserve\\\"]].median().rename(\\n {\\\"reserve\\\": \\\"med_res\\\"}, axis=1).set_index(\\\"origin\\\")\\n min_data = data.groupby(\\\"origin\\\", as_index=False)[[\\\"reserve\\\"]].min().rename(\\n {\\\"reserve\\\": \\\"min_res\\\"}, axis=1).set_index(\\\"origin\\\")\\n max_data = data.groupby(\\\"origin\\\", as_index=False)[[\\\"reserve\\\"]].max().rename(\\n {\\\"reserve\\\": \\\"max_res\\\"}, axis=1).set_index(\\\"origin\\\")\\n dfmetrics = functools.reduce(lambda df1, df2: df1.join(df2), (med_data, min_data, max_data))\\n dfmetrics = dfmetrics.applymap(lambda v: 0 if v < 0 else v).reset_index(drop=False)\\n\\n with sns.axes_style(axes_style):\\n\\n pltkwargs = {\\\"color\\\": color, \\\"bins\\\": 20, \\\"edgecolor\\\": \\\"#484848\\\",\\n \\\"alpha\\\": 1., \\\"linewidth\\\": .45}\\n\\n if kwargs is not None:\\n pltkwargs.update(kwargs)\\n\\n grid = sns.FacetGrid(\\n data, col=\\\"origin\\\", col_wrap=col_wrap, margin_titles=False,\\n despine=True, sharex=False, sharey=False,\\n )\\n\\n hists = grid.map(plt.hist, \\\"reserve\\\", **pltkwargs)\\n grid.set_axis_labels(\\\"\\\", \\\"\\\")\\n grid.set_titles(\\\"\\\", size=6)\\n\\n # Change ticklabel font size and place legend on each facet.\\n origin_vals = sorted([int(ii) for ii in data[\\\"origin\\\"].unique() if ii != \\\"total\\\"])\\n dindex = {jj: ii for ii, jj in enumerate(origin_vals)}\\n dindex.update({\\\"total\\\": max(dindex.values()) + 1})\\n data[\\\"origin_index\\\"] = data[\\\"origin\\\"].map(dindex)\\n origin_order = data[[\\\"origin_index\\\", \\\"origin\\\"]].drop_duplicates().sort_values(\\n \\\"origin_index\\\"\\n ).origin.values\\n\\n with warnings.catch_warnings():\\n\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n for origin, ax_ii in zip(origin_order, grid.axes):\\n\\n # xmin = np.max([0, dfmetrics[dfmetrics.origin == origin][\\\"min_res\\\"].item()])\\n xmax = dfmetrics[dfmetrics.origin == origin][\\\"max_res\\\"].item() * 1.025\\n xmed = dfmetrics[dfmetrics.origin == origin][\\\"med_res\\\"].item()\\n origin_str = \\\"{}\\\".format(origin)\\n ax_ii.set_xlim([0, xmax])\\n ax_ii.axvline(xmed, color=\\\"#E02C70\\\", linestyle=\\\"--\\\", linewidth=1.5)\\n ax_ii.grid(False)\\n\\n ymedloc = max(rect.get_height() for rect in ax_ii.patches) * .30\\n ax_ii.set_yticks([])\\n ax_ii.set_yticklabels([])\\n ax_ii.tick_params(\\n axis=\\\"x\\\", which=\\\"both\\\", bottom=True, top=False, labelbottom=True\\n )\\n ax_ii.set_xticklabels(\\n [\\\"{:,.0f}\\\".format(jj) for jj in ax_ii.get_xticks()], size=7\\n )\\n ax_ii.annotate(\\n origin_str, xy=(.85, .925), xycoords='axes fraction',\\n textcoords='axes fraction', fontsize=9, rotation=0, color=\\\"#000000\\\",\\n )\\n ax_ii.annotate(\\n \\\"median = {:,.0f}\\\".format(xmed), (xmed, ymedloc), xytext=(7.5, 0),\\n textcoords=\\\"offset points\\\", ha=\\\"center\\\", va=\\\"bottom\\\", fontsize=7,\\n rotation=90, color=\\\"#000000\\\"\\n )\\n\\n # Draw border around each facet.\\n for _, spine in ax_ii.spines.items():\\n spine.set(visible=True, color=\\\"#000000\\\", linewidth=.50)\\n\\n if exhibit_path is not None:\\n plt.savefig(exhibit_path)\\n else:\\n plt.show()\",\n \"def _getNormalBinWidth(self, style):\\n \\n bin_size = style.get(\\\"normalize_bin_size\\\", \\\"auto\\\")\\n if bin_size == \\\"auto\\\":\\n # Use the size of the first bin.\\n x0, x1 = self.histogram.axis.getBinRange(0)\\n bin_size = x1 - x0\\n return bin_size\",\n \"def makeHistogram(values, numBins, xLabel, yLabel, title=None):\\n pylab.hist(values, bins = numBins)\\n pylab.xlabel(xLabel)\\n pylab.ylabel(yLabel)\\n if not title == None:\\n pylab.title(title)\\n pylab.show()\",\n \"def bin_histogram (modified_df, v_to_bin):\\n for variable in v_to_bin:\\n # Remove Nas\\n df = modified_df[modified_df[variable].notnull()]\\n # Create surv filter\\n hist_filter = df[\\\"Survived\\\"] == 1\\n # Create Histogram\\n plt.hist([df[variable][hist_filter], df[variable][~hist_filter]],\\n stacked=True, label=['Survived', 'Not Survived'], color=['g', 'r'])\\n plt.legend()\\n # Save and reset fig\\n plt.savefig(variable+\\\"_histogram\\\")\\n plt.clf()\",\n \"def plt_hist(pop, bin_limit=fit_range):\\n plt.hist(pop, bins=range(0,bin_limit+1))\\n plt.grid(True)\\n plt.title('Distribution of Population')\\n plt.show()\",\n \"def bins(self, value):\\n self.num_bins = int(value)\",\n \"def makeHistogram(values, numBins, xLabel, yLabel, title=None):\\r\\n pylab.hist(values, bins = numBins)\\r\\n pylab.xlabel(xLabel)\\r\\n pylab.ylabel(yLabel)\\r\\n if title != None:\\r\\n pylab.title(title)\\r\\n pylab.show()\",\n \"def histogram(values, title, fig_size=(4,3), path=None):\\n plt.clf()\\n f, ax = plt.subplots(1, figsize=fig_size)\\n ax.hist(values, bins=60)\\n ax.set_title(title)\\n f.tight_layout()\\n if(path != None):\\n f.savefig(path+'/hist_'+title+'.png')\",\n \"def test_histogram_with_varying_number_of_bin(self):\\n # this data use number of bins less than the max limit\\n df1 = pd.Series([1, 2, 3, 4]).apply(str)\\n profiler1 = FloatColumn(df1.name)\\n profiler1.max_histogram_bin = 50\\n profiler1.update(df1)\\n num_bins = len(profiler1.profile['histogram']['bin_counts'])\\n self.assertEqual(num_bins, 4)\\n\\n # this data uses large number of bins, which will be set to\\n # the max limit\\n df2 = pd.Series([3.195103249264023e+18, 9999995.0, 9999999.0,\\n 0.0, -10 ** 10]).apply(str)\\n profiler2 = FloatColumn(df2.name)\\n profiler2.max_histogram_bin = 50\\n profiler2.update(df2)\\n num_bins = len(profiler2.profile['histogram']['bin_counts'])\\n self.assertEqual(num_bins, 50)\\n\\n # max number of bin is increased to 10000\\n profiler2 = FloatColumn(df2.name)\\n profiler2.max_histogram_bin = 10000\\n profiler2.update(df2)\\n num_bins = len(profiler2.profile['histogram']['bin_counts'])\\n self.assertEqual(num_bins, 10000)\",\n \"def histogram(data, bins=50, nmb_data_to_use=None, ignored_row=0,\\n col_to_read=1, output_file=None, verbose=0):\\n # prepare arguments\\n args = \\\"-x{} -c{} -b{} -V{}\\\" \\\\\\n .format(ignored_row, col_to_read, bins, verbose)\\n if nmb_data_to_use is not None:\\n args += \\\"-l{}\\\".format(nmb_data_to_use)\\n args = args.split(\\\" \\\")\\n # run command\\n res, msg = tisean('histogram', args, input_data=data,\\n output_file=output_file)\\n # return\\n if msg != \\\"\\\":\\n print(msg)\\n return res\",\n \"def setBarWidth(w):\\n dislin.barwth(w)\",\n \"def histogram(input, num_bins, filename, title = None, xlabel = None, ylabel = None, **bar_kwargs): \\n # Checking for errors in the input\\n if not isinstance(num_bins, int):\\n raise TypeError('Please enter an integer number of bins.')\\n if 200 % num_bins != 0:\\n raise ValueError('The original number of intervals must be divisible by the number of bins inputted.')\\n \\n x_bins = np.linspace(0, 10, num = num_bins, endpoint = False) # Generates list of x-values (in this case energies) at the start of each bin interval\\n bin_size = x_bins[1] - x_bins[0] # Calculating size of bins\\n midpoints = [i + (bin_size/2) for i in x_bins] # List of midpoints generated for the centre of each bar\\n\\n down_fact = len(input) / num_bins # Calculating the downsampling factor\\n if down_fact != 1: \\n down_arr = hist_downsample(input, down_fact) # Downsampling the input data\\n else:\\n down_arr = input # If the number of bins needed is equal to the length of the input data, no need to downsample\\n\\n plot_settings(grid = True) # Defining plot settings\\n plt.bar(midpoints, height = down_arr, width = bin_size, **bar_kwargs) # Width of bar is set to the size of the histogram bin\\n\\n # Adds a title or axis labels if specified by the user\\n if title != None:\\n plt.title(title)\\n if xlabel != None:\\n plt.xlabel(xlabel)\\n if ylabel != None:\\n plt.ylabel(ylabel)\\n \\n # f-string allows save filepath to be set inside the plt.savefig() function\\n plt.savefig(f'{os.path.join(plot_path,filename)}.pdf', dpi = 200) # Saving the plot in the 'plots' folder (filepath set using the 'os' package)\",\n \"def weightHistogram(self, min=None, max=None, nbins=10):\\n raise NotImplementedError\",\n \"def __init__(self, **kwargs): \\n self.kwargs = kwargs\\n\\n # pretty figure up\\n prettyplot() \\n pretty_colors = prettycolors() \\n \\n self.fig = plt.figure(1) \\n self.sub = self.fig.add_subplot(111) \\n\\n self.hist_max = 0.0\",\n \"def make_histogram(datasource,\\n counts_name=\\\"counts\\\",\\n centers_name=\\\"centers\\\",\\n x_range=None,\\n bar_width=0.7,\\n plot_width=500,\\n plot_height=500,\\n min_border=40,\\n tools=None,\\n title_text_font_size=\\\"12pt\\\"):\\n start = np.min(datasource.data[centers_name]) - bar_width\\n end = np.max(datasource.data[centers_name]) - bar_width\\n plot = make_bar_plot(\\n datasource, counts_name=counts_name, centers_name=centers_name,\\n x_range=[start, end], plot_width=plot_width, plot_height=plot_height,\\n tools=tools, title_text_font_size=title_text_font_size)\\n return plot\",\n \"def show_bryant(data_length=200000, bins=100):\\n\\n data = np.random.normal(0,1,data_length)\\n plt.hist(data, bins)\\n plt.show()\",\n \"def makeHistogram(values, numBins, xLabel, yLabel, title=None):\\r\\n # TODO\\r\\n pylab.hist(values, bins = numBins)\\r\\n pylab.xlabel(xLabel)\\r\\n pylab.ylabel(yLabel)\\r\\n if title != None:\\r\\n pylab.title(title)\\r\\n pylab.show()\",\n \"def _getNormalBinWidths(self, style):\\n \\n bin_size = style.get(\\\"normalize_bin_size\\\", \\\"auto\\\")\\n if bin_size == \\\"auto\\\":\\n # Use the size of the first bins.\\n x0, x1 = self.histogram.axes[0].getBinRange(0)\\n y0, y1 = self.histogram.axes[1].getBinRange(0)\\n return x1 - x0, y1 - y0\\n elif bin_size is None:\\n return None\\n else:\\n size_x, size_y = bin_size\\n return size_x, size_y\",\n \"def __init__(self, param, lower, upper, binCount = 50,\\n xscale = None, yweight = None, autoFollow = True):\\n logging.debug('Hist init: {} [{}, {}]'\\n .format(param.name(), lower, upper))\\n super(Histogram, self).__init__(title = \\\"({0}, {1})\\\".format(lower, upper))\\n # add it to the parameter here\\n if isinstance(param, ParameterBase):\\n self.param = param # parameter we belong to is mandatory\\n self.binCount = int(binCount) # bin count is mandatory\\n self.xrange = (float(lower), float(upper))\\n # setter chose the first option available for invalid options\\n self.xscale = xscale\\n self.yweight = yweight\\n if not isinstance(autoFollow, bool):\\n autoFollow = (autoFollow.title() == \\\"True\\\")\\n self.autoFollow = autoFollow\",\n \"def histogram(self, x = \\\"Predictor\\\", y = None, is_horizontal = False, color = None, facet_col = None, facet_row = None, \\n bins = 20, opacity = 1, marginal = None, template = \\\"ggplot2\\\", has_title = True, title = None):\\n x_clean, df_clean = clean_varname(self._df, var = x)\\n\\n #These can probably be converted into Lambda functions... but for now, this works.\\n if color:\\n color_clean, df_clean = clean_varname(df_clean, var = color)\\n else:\\n color_clean = color\\n if y:\\n y_clean, df_clean = clean_varname(df_clean, var = y)\\n else:\\n y_clean = y\\n if facet_col:\\n facet_col_clean, df_clean = clean_varname(df_clean, var = facet_col)\\n else:\\n facet_col_clean = facet_col\\n if facet_row:\\n facet_row_clean, df_clean = clean_varname(df_clean, var = facet_row)\\n else:\\n facet_row_clean = facet_row\\n\\n if has_title:\\n if not title:\\n title = f\\\"Histogram of {x_clean}\\\"\\n\\n if is_horizontal:\\n fig = px.histogram(df_clean, y=x_clean, x = y_clean, color=color_clean, title = title,\\n marginal = marginal, template = template, opacity = opacity, \\n nbins=bins, facet_col = facet_col_clean, facet_row = facet_row_clean)\\n else:\\n fig = px.histogram(df_clean, x=x_clean, y = y_clean, color=color_clean, title = title,\\n marginal = marginal, template = template, opacity = opacity, \\n nbins=bins, facet_col = facet_col_clean, facet_row = facet_row_clean)\\n return fig\",\n \"def histogram_type1(self):\\n\\n # this will show grids at ticks. This style is applied to all plots.\\n sns.set(style=\\\"whitegrid\\\")\\n \\n # set x-axis and y-axis limits so that it is easier to compare histograms over time\\n g = sns.FacetGrid(self.data, col=\\\"Region\\\", col_wrap=3, size=3, xlim=(0, 100000),ylim=(0,0.0005))\\n g = g.map(plt.hist, \\\"Income\\\", normed = True, bins=15,color=\\\"r\\\")\\n g.set_xticklabels(fontsize=10, rotation=30)\\n g.set_xlabels(\\\"Income per capita\\\",fontsize = 10)\\n g.set_ylabels(\\\"Distribution\\\",fontsize = 10)\\n plt.tight_layout()\\n plt.subplots_adjust(top=0.9)\\n g.fig.suptitle(\\\"Histograms of Income Per Capita in \\\" + str(self.year)+ \\\", by Region\\\")\\n plt.savefig('Histograms for Year ' + str(self.year) +'.pdf')\\n pylab.show()\",\n \"def plot_modelparametercollections_addhist(paramcol_SF,paramcol_AGN,stat_SF,stat_AGN,\\n SFcol,AGNcol,LW,bindefs=None,Nbins=10):\\n if (paramcol_SF is None) & (paramcol_AGN is not None):\\n if len(paramcol_AGN) > 0:\\n if bindefs is None:\\n xmin = np.min(paramcol_AGN)-np.abs(np.min(paramcol_AGN))*0.05\\n xmax = np.max(paramcol_AGN)+np.abs(np.min(paramcol_AGN))*0.05\\n binwidth_x = np.diff([xmin,xmax])/Nbins\\n bindefs = np.arange(xmin, xmax+binwidth_x, binwidth_x)\\n\\n plt.hist(paramcol_AGN, bins=bindefs,histtype='step',color=AGNcol)\\n\\n yminsys, ymaxsys = plt.ylim()\\n\\n plt.plot([stat_AGN[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=AGNcol) # mean\\n plt.plot([stat_AGN[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # median\\n plt.plot([stat_AGN[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\\n plt.plot([stat_AGN[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\\n\\n elif (paramcol_SF is not None) & (paramcol_AGN is None):\\n if len(paramcol_SF) > 0:\\n\\n if bindefs is None:\\n xmin = np.min(paramcol_SF)-np.abs(np.min(paramcol_SF))*0.05\\n xmax = np.max(paramcol_SF)+np.abs(np.min(paramcol_SF))*0.05\\n binwidth_x = np.diff([xmin,xmax])/Nbins\\n bindefs = np.arange(xmin, xmax+binwidth_x, binwidth_x)\\n\\n plt.hist(paramcol_SF, bins=bindefs,histtype='step',color=SFcol)\\n\\n yminsys, ymaxsys = plt.ylim()\\n\\n plt.plot([stat_SF[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=SFcol) # mean\\n plt.plot([stat_SF[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # median\\n plt.plot([stat_SF[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\\n plt.plot([stat_SF[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\\n\\n else:\\n\\n if (len(paramcol_SF) == 0) & (len(paramcol_AGN) == 0):\\n pass\\n else:\\n if bindefs is None:\\n xmin = np.min(np.append(paramcol_SF,paramcol_AGN))*0.95\\n xmax = np.max(np.append(paramcol_SF,paramcol_AGN))*1.05\\n binwidth_x = np.diff([xmin,xmax])/Nbins\\n bindefs = np.arange(xmin, xmax+binwidth_x, binwidth_x)\\n # if xlog:\\n # bindefs = np.logspace(np.log10(bindefs[0]),np.log10(bindefs[-1]),len(bindefs))\\n # axHistax.set_xscale('log')\\n\\n plt.hist(paramcol_SF, bins=bindefs,histtype='step',color=SFcol)\\n plt.hist(paramcol_AGN, bins=bindefs,histtype='step',color=AGNcol)\\n\\n yminsys, ymaxsys = plt.ylim()\\n\\n if len(paramcol_SF) > 0:\\n plt.plot([stat_SF[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=SFcol) # mean\\n plt.plot([stat_SF[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # median\\n plt.plot([stat_SF[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\\n plt.plot([stat_SF[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=SFcol) # 68% confidence interval lower\\n\\n if len(paramcol_AGN) > 0:\\n plt.plot([stat_AGN[0]]*2, [yminsys, ymaxsys], '--',lw=LW,alpha=0.5,color=AGNcol) # mean\\n plt.plot([stat_AGN[2]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # median\\n plt.plot([stat_AGN[4]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\\n plt.plot([stat_AGN[8]]*2, [yminsys, ymaxsys], ':', lw=LW,alpha=0.5,color=AGNcol) # 68% confidence interval lower\",\n \"def create_histogram(a, trim_p, bin_size, p_title, p_ylabel, p_xlabel, \\r\\n file_prefix, dec_prec=0, trim_type=\\\"both\\\"):\\r\\n a.sort()\\r\\n sample_size = len(a)\\r\\n print(\\\"a length pre-trim_p\\\", len(a))\\r\\n if trim_type == \\\"left\\\" or trim_type == \\\"right\\\":\\r\\n a = stats.trim1(a, trim_p, trim_type)\\r\\n else:\\r\\n a = stats.trimboth(a, trim_p)\\r\\n print(\\\"a length post-trim_p\\\", len(a))\\r\\n bin_min = math.floor(min(a)) # TODO Round down to dec_prec instead\\r\\n bin_max = round(max(a), dec_prec)\\r\\n print(\\\"bin size=\\\" + str(bin_size) + \\r\\n \\\", bin min=\\\" + str(bin_min) + \\r\\n \\\", bin max=\\\" + str(bin_max))\\r\\n # Create histogram of values\\r\\n n, bins, patches = pylab.hist(a, \\r\\n bins=pylab.frange(bin_min, \\r\\n bin_max, \\r\\n bin_size), \\r\\n normed=False, \\r\\n histtype=\\\"stepfilled\\\")\\r\\n pylab.setp(patches, \\\"facecolor\\\", \\\"g\\\", \\\"alpha\\\", 0.75)\\r\\n pylab.title(p_title)\\r\\n pylab.xlabel(p_xlabel)\\r\\n pylab.ylabel(p_ylabel)\\r\\n \\r\\n if trim_p > 0:\\r\\n pylab.savefig(file_prefix + \\\"_trimmed.png\\\")\\r\\n else:\\r\\n pylab.savefig(file_prefix + \\\".png\\\")\\r\\n pylab.show()\",\n \"def hist_bin_opt (x, minbin=20, maxbin=600, spacing=10, N_trials=1):\\n bin_checks = np.arange(minbin, maxbin, spacing)\\n # bin_checks = np.linspace(150, 300, 16)\\n costs = np.zeros(len(bin_checks))\\n i = 0\\n # this might be vectorizable in np\\n for n_bins in bin_checks:\\n # use np.histogram to do the numerical minimization\\n pdf, bin_edges = np.histogram(x, n_bins)\\n # calculate bin width\\n # some discrepancy here but should be fine\\n w_bin = np.unique(np.diff(bin_edges))\\n if len(w_bin) > 1: w_bin = w_bin[0]\\n # calc mean and var\\n kbar = np.mean(pdf)\\n kvar = np.var(pdf)\\n # calc cost\\n costs[i] = (2.*kbar - kvar) / (N_trials * w_bin)**2.\\n i += 1\\n # find the bin size corresponding to a minimization of the costs\\n bin_opt_list = bin_checks[costs.min() == costs]\\n bin_opt = bin_opt_list[0]\\n return bin_opt\",\n \"def hist(self, bins):\\n x = self.x\\n plt.hist(x, bins)\\n plt.xlabel('Observed Data')\\n plt.ylabel('Frequency')\\n plt.show()\",\n \"def cat_hist(val, rug, side, shade, ax, **shade_kwargs):\\n bins = get_bins(val)\\n _, binned_d, _ = histogram(val, bins=bins)\\n\\n bin_edges = np.linspace(np.min(val), np.max(val), len(bins))\\n heights = np.diff(bin_edges)\\n centers = bin_edges[:-1] + heights.mean() / 2\\n\\n if rug and side == \\\"both\\\":\\n side = \\\"right\\\"\\n\\n if side == \\\"right\\\":\\n left = None\\n elif side == \\\"left\\\":\\n left = -binned_d\\n elif side == \\\"both\\\":\\n left = -0.5 * binned_d\\n\\n ax.barh(centers, binned_d, height=heights, left=left, alpha=shade, **shade_kwargs)\\n return binned_d\",\n \"def btn_equalize_hist_callback(self):\\n self.show_as_waiting(True)\\n self.image_proc_selected('Histogram Equalization')\\n self.show_as_waiting(False)\",\n \"def histogram(self):\\n if np.size(self.stats['Counts']): # don't do anything to an empty list\\n if np.size(self.bins) and not self.redo:\\n return self.bins, self.occs, self.thresh\\n elif np.size(self.bin_array) > 0: \\n self.occs, self.bins = np.histogram(self.stats['Counts'], self.bin_array) # fixed bins. \\n else:\\n try:\\n lo, hi = min(self.stats['Counts'])*0.97, max(self.stats['Counts'])*1.02\\n # scale number of bins with number of files in histogram and with separation of peaks\\n num_bins = int(15 + self.ind//100 + (abs(hi - abs(lo))/hi)**2*15) \\n self.occs, self.bins = np.histogram(self.stats['Counts'], bins=np.linspace(lo, hi, num_bins+1)) # no bins provided by user\\n except: \\n self.occs, self.bins = np.histogram(self.stats['Counts'])\\n else: self.occs, self.bins = np.zeros(10), np.arange(0,1.1,0.1)\\n return self.bins, self.occs, self.thresh\",\n \"def histogram(arr, xlbl, xrng=None, nbins=20, alpha=1.):\\n if xrng is None:\\n xrng = (np.min(arr),np.max(arr))\\n p = figure(plot_width=600, plot_height=400)\\n # Histogram\\n hist, edges = np.histogram(arr, range=xrng, density=True, bins=nbins)\\n p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], fill_color='blue', alpha=alpha)\\n # Label\\n p.xaxis.axis_label = xlbl\\n # Show\\n show(p)\",\n \"def plot_hist(self):\\n labels = [self.get_class_str(action, obj)\\n for (action, obj, subj, rec, beg, end) in self.action_clips]\\n visualize.plot_hist(labels, proportion=True)\",\n \"def _bokeh_output_histogram(\\n y_true: np.ndarray,\\n y_pred: np.ndarray,\\n class_names: Sequence[str],\\n title_rows: Sequence[str],\\n sample_weights: Optional[np.ndarray] = None,\\n x_label_rotation: Union[str, float] = \\\"horizontal\\\",\\n) -> Callable[[], Figure]:\\n\\n n = len(class_names)\\n\\n assert_that(np.shape(y_true)).is_equal_to(np.shape(y_pred))\\n\\n if sample_weights is None:\\n sample_weights = np.ones_like(y_true)\\n\\n assert_that(np.shape(y_true)).is_equal_to(np.shape(sample_weights))\\n\\n bins = np.arange(0, n + 1, 1)\\n normalize = not np.allclose(sample_weights, 1.0)\\n\\n def figure() -> Figure:\\n p = plotting.figure(\\n x_range=class_names,\\n plot_height=350,\\n plot_width=350,\\n tools=TOOLS,\\n toolbar_location=TOOLBAR_LOCATION,\\n )\\n\\n # class distribution in prediction\\n p.vbar(\\n x=class_names,\\n top=np.histogram(\\n y_pred, bins=bins, weights=sample_weights, density=normalize\\n )[0],\\n width=0.85,\\n color=DARK_BLUE,\\n alpha=HISTOGRAM_ALPHA,\\n legend_label=\\\"Prediction\\\",\\n )\\n\\n # class distribution in ground truth\\n p.vbar(\\n x=class_names,\\n top=np.histogram(\\n y_true, bins=bins, weights=sample_weights, density=normalize\\n )[0],\\n width=0.85,\\n alpha=0.6,\\n legend_label=\\\"Ground Truth\\\",\\n fill_color=None,\\n line_color=\\\"black\\\",\\n line_width=2.5,\\n )\\n\\n add_title_rows(p, title_rows)\\n apply_default_style(p)\\n\\n p.yaxis.axis_label = (\\n \\\"Fraction of Instances\\\" if normalize else \\\"Number of Instances\\\"\\n )\\n\\n p.xaxis.major_label_orientation = x_label_rotation\\n\\n p.xgrid.grid_line_color = None\\n\\n # prevent panning to empty regions\\n p.x_range.bounds = (-0.5, 0.5 + len(class_names))\\n return p\\n\\n return figure\",\n \"def regioncounts(ribo, out, experiments, \\n upperlength, lowerlength, title, horizontal, dump):\\n\\n return plot_region_counts_wrapper(\\n ribo_file = ribo, \\n experiment_list = experiments, \\n range_lower = lowerlength, \\n range_upper = upperlength, \\n title = title,\\n output_file = out, \\n dump_to_file = dump,\\n horizontal = horizontal)\",\n \"def update_histo_frame():\\n min_histo.text = str(MIN_RANGE_F) # Display the legend\\n max_histo.text = str(MAX_RANGE_F)\\n\\n histogram = np.zeros(GRID_AXIS) # Clear histogram accumulation array\\n # Collect camera data and calculate the histogram\\n for _row in range(0, GRID_AXIS):\\n for _col in range(0, GRID_AXIS):\\n histo_index = int(map_range(GRID_DATA[_col, _row], 0, 1, 0, GRID_AXIS - 1))\\n histogram[histo_index] = histogram[histo_index] + 1\\n\\n histo_scale = np.max(histogram) / (GRID_AXIS - 1)\\n if histo_scale <= 0:\\n histo_scale = 1\\n\\n # Display the histogram\\n for _col in range(0, GRID_AXIS):\\n for _row in range(0, GRID_AXIS):\\n if histogram[_col] / histo_scale > GRID_AXIS - 1 - _row:\\n image_group[((_row * GRID_AXIS) + _col)].fill = index_to_rgb(\\n round((_col / GRID_AXIS), 3)\\n )\\n else:\\n image_group[((_row * GRID_AXIS) + _col)].fill = BLACK\",\n \"def histo ( self ,\\n xbins = 20 , xmin = None , xmax = None ,\\n ybins = 20 , ymin = None , ymax = None ,\\n hpars = () , \\n histo = None ,\\n integral = False ,\\n errors = False , \\n density = False ) :\\n \\n \\n histos = self.make_histo ( xbins = xbins , xmin = xmin , xmax = xmax ,\\n ybins = ybins , ymin = ymin , ymax = ymax ,\\n hpars = hpars ,\\n histo = histo )\\n\\n # loop over the historgam bins \\n for ix,iy,x,y,z in histo.items() :\\n\\n xv , xe = x.value() , x.error()\\n yv , ye = y.value() , y.error()\\n \\n # value at the bin center \\n c = self ( xv , yv , error = errors ) \\n\\n if not integral : \\n histo[ix,iy] = c\\n continue\\n\\n # integral over the bin \\n v = self.integral( xv - xe , xv + xe , yv - ye , yv + ye )\\n \\n if errors :\\n if 0 == c.cov2 () : pass\\n elif 0 != c.value() and 0 != v : \\n v = c * ( v / c.value() )\\n \\n histo[ix,iy] = v \\n\\n ## coovert to density historgam, if requested \\n if density : histo = histo.density()\\n \\n return histo\",\n \"def addHistogram2D(self, name, title, n_bins_x, minimum_x, maximum_x, n_bins_y, minimum_y, maximum_y):\\n\\t\\tself.histograms[ name ] = ROOT.TH2F(name, title, n_bins_x, minimum_x, maximum_x, n_bins_y, minimum_y, maximum_y)\",\n \"def draw_histogram(x: pd.Series, x_label: str):\\n\\n x.plot.hist(grid=True, bins=20, rwidth=0.9, color='#607c8e')\\n plt.title('Histogram for %s' % x_label)\\n plt.show()\",\n \"def histo_param(result_file,output_fig=None,**kwargs):\\n ###\\n \\n old_dic=file2dic(result_file)\\n print(kwargs)\\n new_dic=select_dic(old_dic,**kwargs)\\n \\n len_new=len(new_dic['MODEL_KEY'])\\n len_old=len(old_dic['MODEL_KEY'])\\n \\n ### \\n \\n param_keys=[key for key in old_dic if key not in ['MODEL_KEY','RMS_P','RMS_S','NUM_OBS']]\\n \\n ### plot\\n \\n f, ax = plt.subplots(int(len(param_keys)/2),2)\\n ax=ax.reshape(-1)\\n plt.suptitle('%i Models selected over %i'%(len_new,len_old))\\n kk=0\\n for key in param_keys:\\n \\n print(key)\\n \\n x_best=np.unique(old_dic[key])\\n x_tick=[str(x) for x in x_best]\\n x_hist=list(range(len(x_best)))\\n \\n diff_val=x_best[:-1]+np.diff(x_best)/2\\n new_val=np.append(x_best[0],diff_val)\\n new_val=np.append(new_val,x_best[-1])\\n \\n n_el,_=np.histogram(new_dic[key],new_val)\\n plt.sca(ax[kk])\\n plt.bar(x_hist,n_el,facecolor='0.9',width=1,edgecolor=['k' for i in range(len(n_el))])\\n plt.xticks(x_hist,x_tick)\\n plt.xlabel('Value')\\n plt.ylabel('Counts')\\n ax[kk].set_title('%s'%key)\\n kk=kk+1\\n \\n \\n plt.tight_layout(rect=[0, 0, 1, 0.9])\",\n \"def _single_histogram(self, plot_name, histogram_data):\\n hist, left_edges, right_edges = histogram_data\\n\\n # figure\\n kwargs = {\\n \\\"plot_height\\\": 250,\\n \\\"height_policy\\\": \\\"fit\\\",\\n \\\"plot_width\\\": 250,\\n \\\"title\\\": plot_name,\\n }\\n p = default_figure(kwargs)\\n\\n # histogram\\n fcolor = self.plot_design.base_color_tints[-3]\\n p.quad(top=hist, bottom=0, left=left_edges, right=right_edges, fill_color=fcolor, line_color=fcolor)\\n\\n # plot specific styling\\n p.y_range.start = 0\\n p.xaxis.ticker = BasicTicker(desired_num_ticks=5)\\n p.xaxis.formatter = NumeralTickFormatter(format=\\\"0.[0]\\\")\\n\\n return p\",\n \"def make_histogram(outpath, plotdata_y, bins=None, color='red',\\n xlabel='', ylabel='', x_range=None):\\n if bins is None:\\n bins = get_optimum_bins(plotdata_y)\\n pyplot.hist(plotdata_y, bins=bins, color=color, range=x_range)\\n pyplot.grid(True, which='major', linestyle='-')\\n pyplot.grid(True, which='minor')\\n pyplot.xlabel(xlabel, fontsize=20)\\n pyplot.ylabel(ylabel, fontsize=20)\\n pyplot.tick_params(axis='both', which='major', labelsize=16)\\n pyplot.tick_params(axis='both', which='minor', labelsize=8)\\n pyplot.tight_layout()\\n pyplot.savefig(outpath)\\n pyplot.close()\\n return outpath\",\n \"def import_histo(w,hz,xmin,xmax):\\n print 'Loading histogram with',hz.GetEntries(),'entries'\\n data = RooDataHist('data','Zmumu MC',RooArgList(w.var('x')),hz)\\n return data\",\n \"def _create_histogram_plot(self, source):\\n # figure\\n kwargs = {\\n \\\"plot_height\\\": 300,\\n \\\"height_policy\\\": \\\"fit\\\",\\n \\\"plot_width\\\": 300,\\n \\\"title\\\": self._histogram_title,\\n \\\"css_classes\\\": [self._histogram]\\n }\\n p = default_figure(kwargs)\\n\\n # histogram\\n fcolor = self.plot_design.fill_color\\n p.quad(top=self._hist_source_data,\\n bottom=0,\\n left=self._hist_source_left_edges,\\n right=self._hist_source_right_edges,\\n source=source,\\n fill_color=fcolor,\\n line_color=fcolor\\n )\\n\\n # plot specific styling\\n p.y_range.start = 0\\n p.yaxis.visible = False\\n p.xaxis.ticker = BasicTicker(desired_num_ticks=5)\\n p.xaxis.formatter = NumeralTickFormatter(format=\\\"0.[0]\\\")\\n\\n return p\",\n \"def draw_histogram(m, rolls, width):\\n # Count the number of rolls of each side and store them in a list\\n roll_count = []\\n for i in list(range(1, m+1)):\\n count = rolls.count(i)\\n roll_count.append(count)\\n\\n # Finding the the maximum count from the list\\n max_count = max(roll_count)\\n\\n print(\\\"Frequency Histogram: \\\" + str(m) + \\\"-sided Die\\\")\\n\\n # Counting the number of same outputs and then scaling these outputs\\n # to the given width\\n for j in list(range(1, m+1)):\\n count = rolls.count(j)\\n scaled_count = round(count*(width/max_count))\\n print(str(j)+'.', end='')\\n print('#'*scaled_count, end='')\\n print('-'*(width-scaled_count))\",\n \"def setBarWidth(w=0.75):\\n dislin.barwth(w)\",\n \"def create_histograms(PrimaryParticleName, LongVectorSignals, LongVectorSignalsCher,\\n\\tShortVectorSignals, ShortVectorSignalsCher, LongScinMaxFiber, LongCherMaxFiber, \\n\\tShortScinMaxFiber, ShortCherMaxFiber, EnergyTotContainer, MaxEnergyTotContainer):\\n\\n\\t#Set ROOT histograms\\n\\tTH1LongScin = TH1F(\\\"LongScintillation\\\", PrimaryParticleName, 100, 0.0, LongScinMaxFiber+200.)\\n\\tTH1LongCher = TH1F(\\\"LongCherenkov\\\", PrimaryParticleName, 100, 0.0, LongCherMaxFiber+200.)\\n\\tTH1ShortScin = TH1F(\\\"ShortScintillation\\\", PrimaryParticleName, 100, 0.0, ShortScinMaxFiber+200.)\\n\\tTH1ShortCher = TH1F(\\\"ShortCherenkov\\\", PrimaryParticleName, 100, 0.0, ShortCherMaxFiber+200.)\\n\\tTH1EnergyTot = TH1F(\\\"EnergyTot\\\", PrimaryParticleName, 100, MaxEnergyTotContainer-10000., MaxEnergyTotContainer+500.) \\n\\n\\t#Fill histograms in for loop\\n\\tfor index in range(len(LongVectorSignals)):\\n\\t\\tTH1LongScin.Fill(LongVectorSignals[index])\\n\\t\\tTH1LongCher.Fill(LongVectorSignalsCher[index])\\n\\t\\tTH1ShortScin.Fill(ShortVectorSignals[index])\\n\\t\\tTH1ShortCher.Fill(ShortVectorSignalsCher[index])\\n\\t\\tTH1EnergyTot.Fill(EnergyTotContainer[index])\\n\\n\\t#Draw + DrawOptions\\n\\tStyle = gStyle\\n\\tStyle.SetOptStat(1) #Show statistics\\n\\tStyle.SetLineWidth(1)\\n\\tXAxis = TH1LongScin.GetXaxis() #TH1LongScin\\n\\tXAxis.SetTitle(\\\"Energy (MeV)\\\")\\n\\tXAxis.SetTitleOffset(1.2)\\n\\tYAxis = TH1LongScin.GetYaxis()\\n\\tYAxis.SetTitle(\\\"Entries\\\")\\n\\tTH1LongScin.Draw()\\n\\tgPad.SaveAs(\\\"EnergyLongScin.eps\\\")\\n\\tXAxis = TH1LongCher.GetXaxis() #TH1LongCher\\n\\tXAxis.SetTitle(\\\"# Cher p.e.\\\")\\n\\tXAxis.SetTitleOffset(1.2)\\n\\tYAxis = TH1LongCher.GetYaxis()\\n\\tYAxis.SetTitle(\\\"Entries\\\")\\n\\tTH1LongCher.Draw()\\n\\tgPad.SaveAs(\\\"CherpeLong.eps\\\")\\n\\tXAxis = TH1ShortScin.GetXaxis() #TH1ShortScin\\n\\tXAxis.SetTitle(\\\"Energy (MeV)\\\")\\n\\tXAxis.SetTitleOffset(1.2)\\n\\tYAxis = TH1ShortScin.GetYaxis()\\n\\tYAxis.SetTitle(\\\"Entries\\\")\\n\\tTH1ShortScin.Draw()\\n\\tgPad.SaveAs(\\\"EnergyShortScin.eps\\\")\\n\\tXAxis = TH1ShortCher.GetXaxis() #TH1ShortCher\\n\\tXAxis.SetTitle(\\\"# Cher p.e.\\\")\\n\\tXAxis.SetTitleOffset(1.2)\\n\\tYAxis = TH1ShortCher.GetYaxis()\\n\\tYAxis.SetTitle(\\\"Entries\\\")\\n\\tTH1ShortCher.Draw()\\n\\tgPad.SaveAs(\\\"CherpeShort.eps\\\")\\n\\tXAxis = TH1EnergyTot.GetXaxis() #TH1EnergyTot\\n\\tXAxis.SetTitle(\\\"Energy (MeV)\\\")\\n\\tXAxis.SetTitleOffset(1.2)\\n\\tYAxis = TH1EnergyTot.GetYaxis()\\n\\tYAxis.SetTitle(\\\"Entries\\\")\\n\\tTH1EnergyTot.Draw()\\n\\tgPad.SaveAs(\\\"EnergyTot.eps\\\")\",\n \"def __init__(self, bin_edges, **kwargs):\\n super().__init__(**kwargs)\\n self.bin_edges = bin_edges\\n self.num_bins = bin_edges.size + 1\\n self.sigma = Parameter(1.0, transform=positive())\",\n \"def test_format_histograms_two_bins(self):\\r\\n self.assertEqual(format_histograms_two_bins(array([0, 1, 0, 2, 2, 3]),\\r\\n array(\\r\\n [2, 1, 0, 2, 0, 0]), array(\\r\\n [100, 110, 120, 130, 140, 150, 160])),\\r\\n \\\"\\\"\\\"Length\\\\tBefore\\\\tAfter\\\\n100\\\\t0\\\\t2\\\\n110\\\\t1\\\\t1\\\\n120\\\\t0\\\\t0\\\\n130\\\\t2\\\\t2\\\\n140\\\\t2\\\\t0\\\\n150\\\\t3\\\\t0\\\"\\\"\\\")\",\n \"def cli_text_histogram(logy, max_pos, bin_width, graph_width, input_file):\\n values = []\\n for line in input_file:\\n try:\\n values.append(float(line.strip()))\\n except ValueError:\\n click.echo(f'Warning: \\\"{line.strip()}\\\" could not be parsed', err=True)\\n click.echo(text_histogram(\\n values, logy=logy, max_pos=max_pos, bin_width=bin_width, graph_width=graph_width\\n ))\",\n \"def rebin_plot(histogram, bins_array):\\n newname = histogram.GetName()+'_rebinned'\\n newplot = histogram.Rebin(len(bins_array)-1, newname, bins_array)\\n newplot.SetDirectory(0)\\n\\n #print \\\"found overflow for\\\", newname, \\\"of\\\", overflow\\n #newplot.SetBinContent(newplot.GetNbinsX(),newplot.GetBinContent(newplot.GetNbinsX())+newplot.GetBinContent(newplot. GetNbinsX()+1))\\n #newplot.SetBinError(newplot.GetNbinsX(),math.sqrt(newplot.GetBinError(newplot.GetNbinsX())**2 + newplot. GetBinError(newplot.GetNbinsX()+1)**2 ) )\\n #newplot.SetBinContent(newplot.GetNbinsX()+1,0) # Set overflow to 0\\n\\n return newplot\",\n \"def __init__(self, nbins):\\n self.nbins = nbins\\n # Since the kernel used to compute the Parzen histogram covers more\\n # than one bin, we need to add extra bins to both sides of the\\n # histogram to account for the contributions of the minimum and maximum\\n # intensities. Padding is the number of extra bins used at each side\\n # of the histogram (a total of [2 * padding] extra bins). Since the\\n # support of the cubic spline is 5 bins (the center plus 2 bins at each\\n # side) we need a padding of 2, in the case of cubic splines.\\n self.padding = 2\\n self.setup_called = False\",\n \"def __init__(self, bins):\\n self.bins = bins\",\n \"def init_distribution_plot(self, width=200, height=200, mode='fixed'):\\n\\n self.distribution_plot = figure(title=\\\"Utility distributions\\\",\\n x_range=(0, 1),\\n plot_width=width,\\n sizing_mode=mode,\\n output_backend=\\\"webgl\\\")\\n\\n hist_data = self.data[self.data.agent_type == 'household'][[\\n 'res_utility', 'school_utility'\\n ]]\\n\\n # Residential utility\\n hist, edges = np.histogram(hist_data.res_utility,\\n density=True,\\n bins=50)\\n self.res_quads = self.distribution_plot.quad(\\n top=hist,\\n bottom=0,\\n left=edges[:-1],\\n right=edges[1:],\\n fill_color=\\\"green\\\",\\n line_color=\\\"white\\\",\\n alpha=0.7,\\n legend_label='Residential utility')\\n\\n # School utility\\n if not self.residential:\\n hist, edges = np.histogram(hist_data.school_utility,\\n density=True,\\n bins=50)\\n # Do not show school utilities yet as they are all zero!\\n hist = np.zeros(len(hist))\\n self.school_quads = self.distribution_plot.quad(\\n top=hist,\\n bottom=0,\\n left=edges[:-1],\\n right=edges[1:],\\n fill_color=\\\"orange\\\",\\n line_color=\\\"white\\\",\\n alpha=0.7,\\n legend_label='School utility')\",\n \"def np_histogram(data, title, bins=\\\"auto\\\"):\\n figure = plt.figure()\\n canvas = figure.canvas\\n plt.hist(data, bins=bins)\\n plt.title(title)\\n\\n canvas.draw()\\n w, h = canvas.get_width_height()\\n np_hist = np.fromstring(canvas.get_renderer().tostring_rgb(), dtype=np.uint8).reshape(h, w, 3)\\n plt.close(figure)\\n util.np_info(np_hist)\\n return np_hist\",\n \"def draw_histogram(data, # type: thelper.typedefs.ArrayType\\n bins=50, # type: Optional[int]\\n xlabel=\\\"\\\", # type: Optional[thelper.typedefs.LabelType]\\n ylabel=\\\"Proportion\\\", # type: Optional[thelper.typedefs.LabelType]\\n show=False, # type: Optional[bool]\\n block=False, # type: Optional[bool]\\n ): # type: (...) -> thelper.typedefs.DrawingType\\n fig, ax = plt.subplots()\\n ax.hist(data, density=True, bins=bins)\\n if len(ylabel) > 0:\\n ax.set_ylabel(ylabel)\\n if len(xlabel) > 0:\\n ax.set_xlabel(xlabel)\\n ax.set_xlim(xmin=0)\\n if show:\\n fig.show()\\n if block:\\n plt.show(block=block)\\n return fig\\n plt.pause(0.5)\\n return fig, ax\",\n \"def _make_histogram(\\n dict_,\\n data,\\n bins=25,\\n show_output=False,\\n figsize=(10, 6),\\n fontsize=15,\\n plot_title=False,\\n):\\n indicator = dict_[\\\"ESTIMATION\\\"][\\\"indicator\\\"]\\n\\n treated = data[[indicator, \\\"prop_score\\\"]][data[indicator] == 1].values\\n untreated = data[[indicator, \\\"prop_score\\\"]][data[indicator] == 0].values\\n\\n treated = treated[:, 1].tolist()\\n untreated = untreated[:, 1].tolist()\\n\\n # Make the histogram using a list of lists\\n fig = plt.figure(figsize=figsize)\\n hist = plt.hist(\\n [treated, untreated],\\n bins=bins,\\n weights=[\\n np.ones(len(treated)) / len(treated),\\n np.ones(len(untreated)) / len(untreated),\\n ],\\n density=0,\\n alpha=0.55,\\n label=[\\\"Treated\\\", \\\"Unreated\\\"],\\n )\\n\\n if show_output is True:\\n plt.tick_params(axis=\\\"both\\\", labelsize=14)\\n plt.legend(loc=\\\"upper right\\\", prop={\\\"size\\\": 14})\\n plt.xticks(np.arange(0, 1.1, step=0.1))\\n plt.grid(axis=\\\"y\\\", alpha=0.25)\\n plt.xlabel(\\\"$P$\\\", fontsize=fontsize)\\n plt.ylabel(\\\"$f(P)$\\\", fontsize=fontsize)\\n\\n if plot_title is True:\\n plt.title(r\\\"Support of $P(\\\\hat{Z})$ for $D=1$ and $D=0$\\\")\\n\\n else:\\n plt.close(fig)\\n\\n return hist, treated, untreated\",\n \"def gen_age_histogram(open_plot=True):\\n\\n df = read_csv(f'{data_files_path}/subject_data')\\n male = go.Histogram(x=df[df['Gender'] == 'Male']['Age'],\\n xbins=dict(start=min(df['Age']),\\n end=max(df['Age'])),\\n name='Male',\\n opacity=0.75)\\n\\n female = go.Histogram(x=df[df['Gender'] == 'Female']['Age'],\\n xbins=dict(start=min(df['Age']),\\n end=max(df['Age'])),\\n name='Female',\\n opacity=0.75)\\n\\n data = [male, female]\\n layout = go.Layout(title='Distribution of Subjects by Age',\\n barmode='overlay',\\n font=dict(family='arial', size=18, color='#000000'))\\n\\n fig = go.Figure(data=data, layout=layout)\\n filename = 'age_distribution_by_gender'\\n if not os.path.exists(data_files_path):\\n print(f'\\\\nWARNING: The path does not exist. Creating new directory...\\\\n{data_files_path}\\\\n')\\n os.mkdir(data_files_path)\\n pyo.plot(fig, filename=f'{data_files_path}/{filename}.html', auto_open=open_plot)\\n print(f'\\\\nAge Distribution Histogram generated.\\\\n'\\n f'Location: {f\\\"{data_files_path}/{filename.html}\\\"}\\\\n')\",\n \"def update_view_to_bins(self, *args):\\n with delay_callback(self, 'x_min', 'x_max'):\\n self.x_min = self.hist_x_min\\n self.x_max = self.hist_x_max\",\n \"def update_view_to_bins(self, *args):\\n with delay_callback(self, 'x_min', 'x_max'):\\n self.x_min = self.hist_x_min\\n self.x_max = self.hist_x_max\",\n \"def _printHist(self):\\n bins = [0] * self.displayBins\\n binSize = self.maxValue / self.displayBins\\n displaySamps = self.samples[:]\\n displaySamps.sort()\\n currLimit = binSize\\n binIndex = 0\\n partIndex = 0\\n while partIndex < self.numParticles:\\n part = displaySamps[partIndex]\\n if binIndex >= self.displayBins:\\n # if value beyond max value, add to last bin and go on to next particle\\n bins[19] += 1\\n partIndex += 1\\n elif part < currLimit:\\n # count particle in current bin, go on to next particle\\n bins[binIndex] += 1\\n partIndex += 1\\n else:\\n # go on to next bin, don't go on to next particle\\n binIndex += 1\\n currLimit += binSize\\n self.printMapPattern(binSize)\\n histStr = \\\"{0:3d} \\\"\\n printStr = \\\"\\\"\\n for b in bins:\\n printStr += histStr.format(b)\\n print(printStr)\",\n \"def plot_bacteria_hist(folder, depth=6, mid_quantile=False):\\n\\n # Get the stool dataset and discretize it\\n ds = parser.get_dataset()\\n ds = compute_relative_values(ds)\\n t = Tree(ds)\\n ds = t.dataset_at_depth(depth)\\n\\n # Get header names to priint on the plots\\n headers = ds[0][2:]\\n\\n for index, header in enumerate(headers):\\n\\n node = t.node_for_clade_name(header)\\n abundances = t.abundance_column_in_subtree(node)\\n abundances = [round(x,3) for x in abundances]\\n\\n if mid_quantile:\\n abundances.sort()\\n abundances = abundances[int(len(abundances)*0.25): -int(len(abundances)*0.25)]\\n\\n xlabel('Relative abundance')\\n ylabel('Bin size')\\n\\n title_text = header.replace('/','-').replace('|', '-')\\n title(title_text)\\n binwidth = 0.001\\n bins, bin_sizes, patches = hist(abundances, bins=np.arange(min(abundances), max(abundances) + binwidth, binwidth), color='#0066FF')\\n\\n # Write discretized values\\n threshold, discretized_abundances = discretize_row(abundances, maxent_discretization_splitter)\\n _0 = '0: ' + str(len([x for x in discretized_abundances if x == 0]))\\n _1 = '1: ' + str(len([x for x in discretized_abundances if x == 1]))\\n\\n text_x = 0.7\\n\\n smaples_text = 'Samples: %d' % len(abundances)\\n figtext(text_x, 0.85, smaples_text, fontsize=10)\\n\\n threshold_text = 'Splitter: %f' % threshold\\n figtext(text_x, 0.82, threshold_text, fontsize=10)\\n figtext(text_x, 0.79, _0, fontsize=10)\\n figtext(text_x, 0.76, _1, fontsize=10)\\n\\n # Draw threshold line\\n max_bin = len(abundances)\\n if len(bins) != 0:\\n max_bin = max(bins)\\n\\n a, b = [threshold, threshold], [0, max_bin]\\n plot(a, b, c='r')\\n\\n grid(True)\\n\\n # Write max and avg\\n # max_abundance = 'max: %f' % max(abundances)\\n # avg_abundance = 'avg: %f' % (sum(abundances) / float(len(abundances)))\\n # figtext(text_x, 0.76, max_abundance, fontsize=10)\\n # figtext(text_x, 0.73, avg_abundance, fontsize=10)\\n\\n # write variance\\n # variance = 'var: %f' % tvar(abundances)\\n # figtext(text_x, 0.70, variance, fontsize=10)\\n\\n # Save fig to folder\\n if not (os.path.exists(folder)):\\n os.makedirs(folder)\\n file_name = os.path.join(folder, title_text)\\n print 'Hist: ', file_name\\n savefig(file_name)\\n\\n close()\",\n \"def getHistogram(self, copy: bool=True):\\n for item in self.getPlotWidget().getItems():\\n if item.getName() == 'histogram':\\n return (item.getValueData(copy=copy),\\n item.getBinEdgesData(copy=copy))\\n else:\\n return None\",\n \"def roo_histo ( self ,\\n xbins = 20 , xmin = None , xmax = None ,\\n ybins = 20 , ymin = None , ymax = None ,\\n hpars = () , \\n histo = None , \\n events = True ) :\\n \\n histo = self.make_histo ( xbins = xbins , xmin = xmin , xmax = xmax ,\\n ybins = ybins , ymin = ymin , ymax = ymax ,\\n hpars = hpars ,\\n histo = histo )\\n \\n hh = self.pdf.createHistogram (\\n hID() ,\\n self.xvar , self.binning ( histo.GetXaxis() , 'histo2x' ) ,\\n ROOT.RooFit.YVar ( self.yvar , self.binning ( histo.GetYaxis() , 'histo2y' ) ) , \\n ROOT.RooFit.Scaling ( False ) , \\n ROOT.RooFit.Extended ( False ) ) \\n\\n for i in hh : hh.SetBinError ( i , 0 ) \\n \\n if events and self.pdf.mustBeExtended() :\\n \\n for ix , iy , x , y , z in hh.items() :\\n volume = 4 * x.error() * y.error() \\n hh [ ix , iy ] *= volume\\n \\n hh *= self.pdf.expectedEvents ( self.vars ) / hh.sum() \\n \\n histo += hh\\n \\n return histo\",\n \"def histogram(data,binwidth, xmin,xmax):\\n bins = arange(xmin,xmax, binwidth)\\n binsc = bins + (0.5 * binwidth)\\n try: #FIXME: use updated numpy.histogram\\n histo = numpyhisto(data, bins, new=False)[0]\\n except:\\n histo = numpyhisto(data, bins)[0]\\n return binsc[:len(histo)], histo\",\n \"def parameters_histograms(w, dw, a, da, b, db):\\n w = w.cpu()\\n dw = dw.cpu()\\n a = a.cpu()\\n da = da.cpu()\\n b = b.cpu()\\n db = db.cpu()\\n \\n fig = plt.figure(figsize=(10,6))\\n ax = fig.add_subplot(231)\\n ax.hist(w.reshape(1, w.shape[0] * w.shape[1]))\\n ax.set_title('Weights', fontsize = 11)\\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\\n ax = fig.add_subplot(232)\\n ax.hist(dw.reshape(1, dw.shape[0] * dw.shape[1]))\\n ax.set_title('Weights variations', fontsize = 11)\\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\\n ax = fig.add_subplot(233)\\n ax.hist(a)\\n ax.set_title('Visible bias', fontsize = 11)\\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\\n ax = fig.add_subplot(234)\\n ax.hist(da)\\n ax.set_title('Visible bias variations', fontsize = 11)\\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\\n ax = fig.add_subplot(235)\\n ax.hist(b)\\n ax.set_title('Hidden bias', fontsize = 11)\\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\\n ax = fig.add_subplot(236)\\n ax.hist(db)\\n ax.set_title('Hidden bias variations', fontsize = 11)\\n ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\\n plt.subplots_adjust(hspace=0.25)\\n plt.show()\\n plt.close('all')\",\n \"def plot_hist_blo(blocking_occ, SOM_nodenum, savefig_str, num_nodes, show_plots, caption=0):\\r\\n alpha_val = 0.6\\r\\n nodes_notblo = SOM_nodenum.values*(blocking_occ==0).values\\r\\n nodes_blo = SOM_nodenum.values*blocking_occ.values\\r\\n freq_blo, freq_notblo = int(float(blocking_occ.mean())*1000)/10, int(float((blocking_occ==0).mean()*1000))/10\\r\\n nodes_blo_rem, nodes_notblo_rem = nodes_blo[nodes_blo != 0], nodes_notblo[nodes_notblo != 0]\\r\\n\\r\\n fig, ax = plt.subplots(1,1)\\r\\n ax.hist(nodes_notblo_rem-0.5, bins = np.arange(0.5,num_nodes+1.5,1), density = True, alpha = alpha_val, label = f\\\"not blocked ({freq_notblo}% of days)\\\", ec = \\\"k\\\") \\r\\n ax.hist(nodes_blo_rem-0.5, bins = np.arange(0.5,num_nodes+1.5,1), density = True, alpha = alpha_val, label = f\\\"blocked ({freq_blo}% of days)\\\", ec = \\\"k\\\") \\r\\n\\r\\n if caption != 0:\\r\\n ax.set_xlabel(f\\\"SOM node number \\\\n \\\\n {caption}\\\")\\r\\n else:\\r\\n ax.set_xlabel(f\\\"SOM node number\\\")\\r\\n ax.set_ylabel(\\\"Frequency of occurence\\\")\\r\\n ax.set_xticks(np.arange(1,num_nodes+1,1))\\r\\n ax.legend()\\r\\n if show_plots == False:\\r\\n plt.close();\\r\\n\\r\\n fig.savefig(savefig_str, bbox_inches=\\\"tight\\\", dpi = 300)\",\n \"def plot_uv_hist(df, colname, nbins='auto', xlogflag=False, colorid=0):\\n\\n # Set figure size \\n fig, ax = plt.subplots(figsize=(8,6))\\n\\n # set colorid for bar plot\\n base_color = sns.color_palette()[colorid]\\n \\n # plotting... histogram\\n if (xlogflag):\\n \\tsns.histplot(ax = ax, data = df\\n \\t, x = colname\\n \\t, bins = nbins\\n \\t, color = base_color\\n \\t\\t\\t\\t\\t\\t, log_scale= True)\\n else:\\n \\tsns.histplot(ax = ax, data = df\\n \\t, x = colname\\n \\t, color = base_color\\n \\t, bins = nbins)\\n\\n \\n #plt.legend(prop={'size': 12})\\n plt.title('Distribution of '+colname, fontsize=20)\\n plt.xlabel(colname+' (units)', fontsize=16)\\n plt.ylabel('Frequency', fontsize=16)\\n plt.xticks(fontsize=16)\\n plt.yticks(fontsize=16)\\n \\n return plt.show()\",\n \"def oneNumBar(df, colName):\\n bins = pd.qcut(x=df[colName[0]], q=15, duplicates='drop')\\n ax = bins.value_counts()\\n bins = bins.cat.as_ordered()\\n bins = bins.cat.categories\\n bounds = bins.left \\n bounds = list(bounds)\\n bounds.append(bins[len(bounds)-1].right)\\n texts = []\\n for x,y in zip(bounds[0::],bounds[1::]):\\n texts.append(\\\"(\\\" + str(x) + \\\", \\\" + str(y) + \\\"]\\\") \\n barData = [go.Bar(x=texts, \\n y=ax,\\n marker=dict(\\n color = '#92c5de',\\n opacity=0.8)\\n )] \\n layout = go.Layout(\\n title=\\\"Bar Plot Showing Count of Values for \\\" + str(colName[0]),\\n xaxis=dict(\\n title= colName[0]\\n ),\\n yaxis=dict(\\n title= \\\"NUMBER OF RECORDS\\\", \\n )\\n )\\n fig = go.Figure(data=barData, layout=layout)\\n return {\\\"label\\\":\\\"Frequency\\\", \\\"plot\\\":fig}\",\n \"def n_particles_bins(DG, bins=[0, 0.5, 3, 10, 100]):\\n radii = fid.rss(DG.gas['Coordinates'][()])\\n hist, bin_edges = np.histogram(radii, bins)\\n\\n return hist, bin_edges\",\n \"def histogram(data, title, path):\\n plt.hist(data,\\n bins=60)\\n plt.xticks(size=22)\\n plt.yticks(size=22)\\n plt.title(title,\\n fontsize=30)\\n plt.savefig(path)\\n plt.clf()\",\n \"def interactive_histograms(adata, keys=['n_counts', 'n_genes'],\\n bins=100, min_bins=1, max_bins=1000,\\n tools='pan, reset, wheel_zoom, save',\\n groups=None, fill_alpha=0.4,\\n palette=Set1[9] + Set2[8] + Set3[12],\\n legend_loc='top_right', display_all=True,\\n *args, **kwargs):\\n\\n from itertools import product\\n from functools import reduce\\n from bokeh.plotting import figure, show, ColumnDataSource\\n from bokeh.models.widgets import CheckboxGroup\\n from bokeh.models.widgets.buttons import Button\\n from bokeh.models import Slider\\n from bokeh.models.callbacks import CustomJS\\n from bokeh.io import output_notebook\\n from bokeh.layouts import layout, column, row\\n\\n from copy import copy\\n from numpy import array_split, ceil\\n output_notebook()\\n\\n if min_bins < 1:\\n raise ValueError(f'Expected min_bins >= 1, got min_bins={min_bins}.')\\n if max_bins < min_bins:\\n raise ValueError(f'Expected min_bins <= max_bins, got min_bins={min_bins}, max_bins={max_bins}.')\\n if not (bins >= min_bins and bins <= max_bins):\\n raise ValueError(f'Expected min_bins <= bins <= max_bins, got min_bins={min_bins}, bins={bins}, max_bins={max_bins}.')\\n\\n # check the input\\n for key in keys:\\n if key not in adata.obs.keys() and \\\\\\n key not in adata.var.keys() and \\\\\\n key not in adata.var_names:\\n raise ValueError(f'The key `{key}` does not exist in adata.obs, adata.var or adata.var_names.')\\n\\n def _create_adata_groups():\\n if groups is None:\\n return [('all',)], [adata]\\n\\n combs = list(product(*[set(adata.obs[g]) for g in groups]))\\n adatas= [adata[reduce(lambda l, r: l & r,\\n (adata.obs[k] == v for k, v in zip(groups, vals)), True)]\\n for vals in combs] + [adata]\\n\\n if display_all:\\n combs += [('all',)]\\n adatas += [adata]\\n\\n return combs, adatas\\n\\n # group_v_combs contains the value combinations\\n # used for grupping\\n group_v_combs, adatas = _create_adata_groups()\\n n_plots = len(group_v_combs)\\n checkbox_group = CheckboxGroup(active=list(range(n_plots)), width=200)\\n\\n for key in keys:\\n # create histogram\\n cols, legends, callbacks = [], [], []\\n plot_map = dict()\\n slider = Slider(start=min_bins, end=max_bins, value=bins, step=1,\\n title='Bins')\\n\\n fig = figure(*args, tools=tools, **kwargs)\\n\\n plot_ids = []\\n for j, (ad, group_vs) in enumerate(zip(adatas, group_v_combs)):\\n\\n if ad.n_obs == 0:\\n continue\\n\\n plot_ids.append(j)\\n color = palette[len(plot_ids) - 1]\\n\\n if key in ad.obs.keys():\\n orig = ad.obs[key]\\n hist, edges = np.histogram(orig, density=True, bins=bins)\\n elif key in ad.var.keys():\\n orig = ad.var[key]\\n hist, edges = np.histogram(orig, density=True, bins=bins)\\n else:\\n orig = ad[:, key].X\\n hist, edges = np.histogram(orig, density=True, bins=bins)\\n\\n # original data, used for recalculation of histogram in JS code\\n orig = ColumnDataSource(data=dict(values=orig))\\n # data that we update in JS code\\n source = ColumnDataSource(data=dict(hist=hist, l_edges=edges[:-1], r_edges=edges[1:]))\\n\\n legend = ', '.join(': '.join(map(str, gv)) for gv in zip(groups, group_vs)) \\\\\\n if groups is not None else 'all'\\n legends.append(legend)\\n # create figure\\n p = fig.quad(source=source, top='hist', bottom=0,\\n left='l_edges', right='r_edges',\\n fill_color=color, legend=legend,\\n line_color=\\\"#555555\\\", fill_alpha=fill_alpha)\\n\\n # create callback and slider\\n callback = CustomJS(args=dict(source=source, orig=orig), code=_inter_hist_js_code)\\n callback.args['bins'] = slider\\n callbacks.append(callback)\\n\\n # add the current plot so that we can set it\\n # visible/invisible in JS code\\n plot_map[f'p_{j}'] = p\\n\\n # slider now updates all values\\n slider.js_on_change('value', *callbacks)\\n plot_map['cb'] = checkbox_group\\n\\n button = Button(label='Toggle All', button_type='primary')\\n code_t='\\\\n'.join(f'p_{p_id}.visible = false;' for i, p_id in enumerate(plot_ids))\\n code_f ='\\\\n'.join(f'p_{p_id}.visible = true;' for i, p_id in enumerate(plot_ids))\\n button.callback = CustomJS(\\n args=plot_map,\\n code=f'''if (cb.active.length == {len(plot_map) - 1}) {{\\n console.log(cb.active);\\n cb.active = Array();\\n {code_t};\\n }} else {{\\n console.log(cb.active);\\n cb.active = Array.from(Array({len(plot_map) - 1}).keys());\\n {code_f};\\n }}'''\\n )\\n\\n checkbox_group.callback = CustomJS(\\n args=plot_map,\\n code='\\\\n'.join(f'p_{p_id}.visible = cb.active.includes({i});' for i, p_id in enumerate(plot_ids))\\n )\\n checkbox_group.labels = legends\\n\\n fig.legend.location = legend_loc\\n fig.xaxis.axis_label = key\\n fig.yaxis.axis_label = 'normalized frequency'\\n fig.plot_width = kwargs.get('plot_width', 400)\\n fig.plot_height = kwargs.get('plot_height', 400)\\n\\n cols.append(column(slider, button, row(fig, checkbox_group)))\\n\\n\\n # transform list of pairs of figures and sliders into list of lists, where\\n # each sublist has length <= 2\\n # note that bokeh does not like np.arrays\\n grid = list(map(list, array_split(cols, ceil(len(cols) / 2))))\\n\\n show(layout(children=grid, sizing_mode='fixed', ncols=2))\",\n \"def get_histogram(self):\\n\\n for bin in range(self.bins.size):\\n bin_inf = self.bins[bin]\\n try: bin_sup = self.bins[bin + 1]\\n except IndexError: bin_sup = self.vmax\\n self.hist[bin] = np.sum(\\n (self.values >= bin_inf)*(self.values < bin_sup))\\n\\n binned_values = np.sum(self.hist)\\n if binned_values == 0: return self.hist # no binned value\\n else: self.hist /= np.sum(self.hist)\\n return self.hist\",\n \"def histogram_single_values(self, x_axis:str, title:str, max_bins:int=.1, density: bool=False, show:bool=True, dest_path:str=None):\\n d = {}\\n for pop in self.populations:\\n df = pd.read_csv(self.path + '\\\\\\\\{}.csv'.format(pop))\\n trials = df['label'].tolist()\\n values = df['Component 1'].tolist()\\n response = df['response'].tolist()\\n \\n for i in range(len(response)):\\n if response[i] in d:\\n d[response[i]].add(values[i])\\n else:\\n # Removing day 4 trials\\n if eval(trials[i])[0] != 4:\\n d[response[i]] = set()\\n d[response[i]].add(values[i])\\n\\n data = []\\n labels = d.keys()\\n if len(labels)==4:\\n labels = ['0->0', '0->1', '1->1', '1->0']\\n for key in labels:\\n data.append(list(d[key]))\\n \\n bins = np.linspace(0, max_bins, 500)\\n colors = self.__multiclass_to_color(deepcopy(labels))\\n for i in range(len(labels)):\\n plt.hist(d[labels[i]], bins, alpha=0.5, label=labels[i], color=colors[i], histtype='step', density=density)\\n plt.axvline(np.array(list(d[labels[i]])).mean(), ls='--', color=colors[i], linewidth=1, label=\\\"{} mean\\\".format(labels[i]))\\n plt.title(title)\\n plt.xlabel(x_axis)\\n if density:\\n plt.ylabel(\\\"density\\\")\\n else:\\n plt.ylabel(\\\"Occurences\\\")\\n plt.legend(loc='upper right')\\n\\n if show:\\n plt.show()\\n\\n if dest_path !=None:\\n plt.savefig(dest_path + '\\\\\\\\{}.png'.format(title))\\n\\n plt.clf()\\n plt.cla()\\n plt.close()\",\n \"def show_histo(dict, orient=\\\"horiz\\\", label=\\\"counts\\\", title=\\\"title\\\"):\\n plt.clf()\\n plt.cla()\\n if orient==\\\"horiz\\\":\\n bar_fun = plt.barh \\n bar_ticks = plt.yticks\\n bar_label = plt.xlabel\\n elif orient==\\\"vert\\\":\\n bar_fun = plt.bar\\n bar_ticks = plt.xticks\\n bar_label = plt.ylabel\\n else:\\n raise Exception(\\\"show_histo: Unknown orientation: %s \\\".format % orient)\\n n = len(dict)\\n bar_fun(range(n), list(dict.values()), align='center', alpha=0.4, color = 'g')\\n bar_ticks(range(n), list(dict.keys())) \\n bar_label(label)\\n plt.title(title)\\n plt.show()\",\n \"def simple_hist(vals, samplename, bins=100, min=None, max=None):\\n if not min:\\n min = vals.min()\\n if not max:\\n max = vals.max()\\n fig = plt.figure()\\n plt.hist(vals.dropna(), bins=bins, range=[min, max])\\n calc_vals = vals.dropna()\\n plt.xlabel('Bins')\\n plt.ylabel('Frequency')\\n plt.title(samplename)\\n plt.axvline(np.median(calc_vals),\\n color='r', linestyle='dashed', linewidth=1)\\n plt.grid(True)\\n\\n return fig\",\n \"def display(\\n self, start=None, stop=None, step=None, format=\\\"%14.4f\\\", show_empty=False\\n ):\\n collection = self.collect(start, stop, step)\\n print(\\\"Histogram\\\")\\n print(\\\"=========\\\")\\n size = len(format % 0)\\n sformat = \\\"%\\\" + str(size) + \\\"s\\\"\\n columns = [\\\"Range Start\\\", \\\"Range End\\\", \\\"Count\\\", \\\"Bins\\\"]\\n formats = [sformat % s for s in columns]\\n print(*formats)\\n print(\\\"-\\\" * (size * 4 + 3))\\n total = 0.0\\n for row in collection:\\n count = row[\\\"count\\\"]\\n total += count\\n if show_empty or count > 0:\\n print(\\n format % row[\\\"value_start\\\"],\\n format % row[\\\"value_stop\\\"],\\n format % count,\\n (\\n sformat\\n % (\\\"[%s-%s]\\\" % (row[\\\"bin_index_start\\\"], row[\\\"bin_index_stop\\\"]))\\n ),\\n )\\n print(\\\"-\\\" * (size * 4 + 3))\\n print((\\\"Total: \\\" + format) % total)\",\n \"def main(x_axis, y_axis, filtered, unfiltered, name, histogram, total, true_max):\\n axes = [x_axis, y_axis, 'description']\\n uf_dict, f_dict, min_x, max_x, min_y, max_y = data_from_sc_file(axes, filtered, unfiltered, true_max)\\n gen_plots(uf_dict, f_dict, min_x, max_x, min_y, max_y, axes, name, histogram, total)\",\n \"def mpl_hist_arg(value=True):\\n import matplotlib\\n\\n density_kwarg = 'density' if matplotlib.__version__ >= '2.1.0'\\\\\\n else 'normed'\\n return {density_kwarg: value}\",\n \"def end_hist(pulse, trap):\\n all_trial_n, all_trial_n_ave = trap.sideband_cool_sch(pulse)\\n n_max = np.amax(all_trial_n)\\n hist_xar = sp.arange(n_max + 1) - 0.5\\n \\n # fig, ax = plt.subplots()\\n plt.hist(all_trial_n[:, -1], bins = hist_xar)\\n plt.xlabel('Phonon State')\\n plt.ylabel('Distribution')\\n # return ax\",\n \"def hist_save(self, d, bin1, name, no):\\n\\t\\tfor i in range(0,no):\\n\\t\\t\\ts = d[:,i]\\n\\t\\t\\tplt.hist(s, bin1, normed=True, color='c')\\t# Extracting the parameters from the histogram\\n\\t\\t\\tplt.title('Probability Distribution Fnction of %s' %name, fontsize=20)\\n\\t\\t\\tplt.xlabel(\\\"Filter tap values\\\", fontsize=20)\\n\\t\\t\\tplt.ylabel(\\\"Probability Distribution\\\", fontsize=20)\\n#\\t\\t\\tplt.xlim(0,0.10)\\n\\t\\t\\tplt.ylim(0,100)\\n#\\t\\t\\tplt.legend(fontsize = 'xx-large')\\n\\t\\t\\tplt.savefig('/home/abhishek/Results/comparison_all_sets/Curve fitting/test/set_1/hist_%s_index_%d' %(name,i))\\n\\t\\t\\tplt.close()\",\n \"def numeric_attribute_charts(self):\\n\\n file_or_case = \\\"case\\\"\\n if self.ui.radioButton_file.isChecked():\\n file_or_case = \\\"file\\\"\\n attribute = self.ui.comboBox_num_attributes.currentText()\\n title = _(\\\"Attribute histogram\\\")\\n subtitle = \\\"<br><sup>\\\" + _(file_or_case) + _(\\\" attribute: \\\") + attribute\\n self.ui.comboBox_num_attributes.blockSignals(True)\\n self.ui.comboBox_num_attributes.setCurrentIndex(0)\\n self.ui.comboBox_num_attributes.blockSignals(False)\\n\\n cur = self.app.conn.cursor()\\n cur.execute(\\\"select cast(value as int) from attribute where attr_type=? and name=?\\\",\\n [file_or_case, attribute])\\n res = cur.fetchall()\\n values = []\\n for r in res:\\n values.append(r[0])\\n # Create pandas DataFrame\\n data = {attribute: values}\\n df = pd.DataFrame(data)\\n fig = px.histogram(df, x=attribute, title=title + subtitle)\\n fig.show()\\n self.helper_export_html(fig)\",\n \"def super_hist(self, data_list, alpha=0.5, log_scale=True, bins=45):\\r\\n\\r\\n fig, _ = mp.subplots(1, 1, figsize=(15, 10), constrained_layout=True)\\r\\n\\r\\n names = []\\r\\n for data in data_list:\\r\\n plot_data = data[data.Day_First_N_Infections != \\\"None\\\"]\\r\\n column_data = plot_data[\\\"Day_First_N_Infections\\\"].values\\r\\n sns.distplot(column_data,\\r\\n kde=False,\\r\\n bins=bins,\\r\\n hist_kws={\\r\\n \\\"linewidth\\\": 1,\\r\\n \\\"alpha\\\": alpha,\\r\\n \\\"edgecolor\\\": 'black',\\r\\n \\\"log\\\": log_scale\\r\\n })\\r\\n\\r\\n mp.legend(loc='upper left', fontsize=20)\\r\\n mp.xlabel(\\\"Days from outbreak to case number \\\" + str(data_list[0].N) +\\r\\n \\\" in county\\\",\\r\\n fontsize=18)\\r\\n mp.ylabel(\\\"Frequency\\\", fontsize=18)\\r\\n\\r\\n fig.savefig(\\\"hist_N\\\" + str(data_list[0].N) + \\\"_\\\" + \\\"_\\\".join(names) +\\r\\n \\\".png\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6282638","0.6234359","0.61944395","0.6177648","0.6122733","0.5965984","0.5894797","0.58317345","0.5807618","0.57804626","0.57664645","0.57572645","0.57370436","0.57100546","0.56775147","0.5642595","0.5595545","0.55602545","0.55257535","0.55219674","0.55189705","0.5497796","0.5493891","0.54838043","0.5476191","0.54727304","0.54638034","0.5459779","0.5449384","0.5444597","0.54333997","0.54157573","0.5412461","0.5408798","0.5405536","0.537496","0.5373606","0.5370503","0.5363792","0.53637326","0.53567445","0.5355046","0.53524023","0.5351958","0.53441447","0.5339047","0.53377295","0.5332459","0.5324484","0.53120375","0.5304075","0.5301985","0.5300126","0.5280566","0.5271257","0.5267413","0.5264763","0.52596253","0.525932","0.52402604","0.52280277","0.52239805","0.522201","0.5219383","0.52149385","0.5214657","0.5212815","0.5202921","0.5196633","0.51952916","0.519331","0.5190634","0.51860964","0.5177787","0.5176912","0.5174981","0.5174981","0.5165744","0.51608056","0.51550347","0.5149416","0.5146642","0.51465863","0.51444834","0.5142322","0.51385814","0.5129873","0.5121097","0.51156676","0.51152074","0.5114491","0.5114365","0.5111026","0.5110931","0.5105091","0.5104461","0.51028734","0.51014197","0.5090549","0.5089571"],"string":"[\n \"0.6282638\",\n \"0.6234359\",\n \"0.61944395\",\n \"0.6177648\",\n \"0.6122733\",\n \"0.5965984\",\n \"0.5894797\",\n \"0.58317345\",\n \"0.5807618\",\n \"0.57804626\",\n \"0.57664645\",\n \"0.57572645\",\n \"0.57370436\",\n \"0.57100546\",\n \"0.56775147\",\n \"0.5642595\",\n \"0.5595545\",\n \"0.55602545\",\n \"0.55257535\",\n \"0.55219674\",\n \"0.55189705\",\n \"0.5497796\",\n \"0.5493891\",\n \"0.54838043\",\n \"0.5476191\",\n \"0.54727304\",\n \"0.54638034\",\n \"0.5459779\",\n \"0.5449384\",\n \"0.5444597\",\n \"0.54333997\",\n \"0.54157573\",\n \"0.5412461\",\n \"0.5408798\",\n \"0.5405536\",\n \"0.537496\",\n \"0.5373606\",\n \"0.5370503\",\n \"0.5363792\",\n \"0.53637326\",\n \"0.53567445\",\n \"0.5355046\",\n \"0.53524023\",\n \"0.5351958\",\n \"0.53441447\",\n \"0.5339047\",\n \"0.53377295\",\n \"0.5332459\",\n \"0.5324484\",\n \"0.53120375\",\n \"0.5304075\",\n \"0.5301985\",\n \"0.5300126\",\n \"0.5280566\",\n \"0.5271257\",\n \"0.5267413\",\n \"0.5264763\",\n \"0.52596253\",\n \"0.525932\",\n \"0.52402604\",\n \"0.52280277\",\n \"0.52239805\",\n \"0.522201\",\n \"0.5219383\",\n \"0.52149385\",\n \"0.5214657\",\n \"0.5212815\",\n \"0.5202921\",\n \"0.5196633\",\n \"0.51952916\",\n \"0.519331\",\n \"0.5190634\",\n \"0.51860964\",\n \"0.5177787\",\n \"0.5176912\",\n \"0.5174981\",\n \"0.5174981\",\n \"0.5165744\",\n \"0.51608056\",\n \"0.51550347\",\n \"0.5149416\",\n \"0.5146642\",\n \"0.51465863\",\n \"0.51444834\",\n \"0.5142322\",\n \"0.51385814\",\n \"0.5129873\",\n \"0.5121097\",\n \"0.51156676\",\n \"0.51152074\",\n \"0.5114491\",\n \"0.5114365\",\n \"0.5111026\",\n \"0.5110931\",\n \"0.5105091\",\n \"0.5104461\",\n \"0.51028734\",\n \"0.51014197\",\n \"0.5090549\",\n \"0.5089571\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5996436"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":22375,"cells":{"query":{"kind":"string","value":"Sidebar widget to identify search method for individual search page"},"document":{"kind":"string","value":"def select_search_method():\n st.sidebar.markdown('### Search method:')\n search_method = st.sidebar.selectbox('', ['Individual', 'Department'], index=0)\n return search_method"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def searchWidget(self):\n return self.__searchWidget","def search_page():\n return render_template('page_query.html', search_label=g_search_type)","def on_searchButton_clicked(self):\n self.__search()","def search_btn_clicked(self, widget, data=None):\n # Method to handle search here.\n search_text = self.get_text(\"txt_search\")\n print search_text","def search(request):\n raise NotImplementedError","def search(self, *args, **kwargs):","def name(self): \n return \"search_form\"","def handler_search_changed(self, widget):\n #we set the current language filter to the button's label\n self.filter_on = widget.get_text()\n text = widget.get_text()\n #we update the filter, which updates in turn the view\n self.emit('search-show',text)\n #self.log_filter.refilter()","def search():\r\n return render_template(\"/home/search.html\")","def search():\n pass","def search_page(request):\n if request.method == \"GET\":\n page = request.GET.get('q')\n entries = util.list_entries()\n entries_set=set(entries)\n\n if page in entries_set:\n return render(request, \"encyclopedia/visit_entry.html\",{\n \"entry\": util.get_entry(page),\n \"title\": page\n })\n \n else:\n results = list(filter(lambda x: page in x, entries))\n return render(request, \"encyclopedia/search_page.html\",{\n \"results\": results\n })","def search(request):\n if 'q' in request.GET:\n term = request.GET['q'].lower()\n thispushqueryset = pushitem.objects.filter(Q(searchfield__contains= term) )\n message = _('Searching for %s')%str(term)\n else:\n thispushqueryset = pushitem.objects.none()\n message = _('No search query specified')\n r = makepage(request,thispushqueryset,{'search_query':request.GET['q'].lower(), 'showall': 1,'message':message,}, template='search.html')\n return r","def on_search(self, widget):\n if isinstance(self.ref_object, DistributedDetection):\n self.gtk_def.clear()\n Gtk_Main.Gtk_Main().notebook._add_distributed_anomaly(self.ref_object.error_path,\n self.gtk_def,\n self.entry.get_text().lower())\n elif isinstance(self.ref_object, gtk.TextView):\n self._conf_highlight()\n else:\n self.gtk_def.search(self.entry.get_text().lower())","def onSearch(self):\n self.mainGrid.showSearchPopup()\n self.popupActive = True","def filter_search(self, request, search, view):\n raise NotImplementedError(\".filter_search() must be overridden.\")","def search(self, *args, **kwargs): # real signature unknown\n pass","def advanced_search():\n\n return render_template('Advanced_Search.html')","def name(self):\n return \"action_news_search\"","def new_search(self, widget, data=None):\n self.artist_name.set_text(\"\")\n self.song_name.set_text(\"\")\n self.status_bar.hide()\n self.lyrics_view.hide()\n self.scroll.hide()\n self.window.resize(self.width, self.height)","def search():\n # Check for database tables\n check_db()\n # Check for GET data\n search_query = request.args.get(\"q\", None)\n # Format search results as HTML\n search_results = get_search_results_html(search_query)\n # Format recent searches as HTML\n recent_searches = get_recent_searches_html()\n\n return html_wrapper('<h1>' + SITE_NAME + '''</h1>\n <form action=\"/\" method=\"GET\">\n <input type=\"text\" name=\"q\">\n <input type=\"submit\" value=\"search\">\n </form>''' + search_results + recent_searches)","def load_search_gui(self):\n pass","def search_box(self):\n tags = SearchBox(\"Search Tags: \", self.handle_tag_search)\n tag_search = urwid.LineBox(urwid.AttrWrap(tags, 'editbx', 'editfc'))\n \n text = SearchBox(\"Search Documents: \", self.handle_text_search)\n text_search = urwid.LineBox(urwid.AttrWrap(text, 'editbx', 'editfc'))\n\n l = [ tag_search,\n urwid.Divider(),\n text_search]\n w = urwid.LineBox(urwid.ListBox(urwid.SimpleListWalker(l)))\n return w","def on_search(self, data: Any = None):\n raise NotImplementedError","def search(request):\n context = {}\n q = \"\"\n try:\n if request.POST:\n q = request.POST['q']\n else:\n q = request.GET['q']\n except MultiValueDictKeyError:\n pass\n context['query'] = q\n context['search_entry_list'] = watson.search(q)\n return render(request, 'search.html', context)","def search(self, term):","def search(request):\n\n # get form data \n searchItem = request.GET.get(\"q\")\n # if searchItem is an exact match redirect to that page\n if (util.get_entry(searchItem) is not None):\n return HttpResponseRedirect(reverse(\"entry\", kwargs={\n \"title\": searchItem\n }))\n # add any pages with the string in it to results list \n else: \n results = []\n substring = False\n for title in util.list_entries():\n if searchItem.upper() in title.upper():\n results.append(title)\n if results:\n substring = True\n # return results\n return render(request, \"encyclopedia/search.html\", {\n \"searchItem\": searchItem,\n \"substring\": substring,\n \"results\": results\n })","def search(self, search):\n raise NotImplementedError","def search_bar(request, search = None):\n\tcategories = Category.objects.all()\n\n\tif request.method == 'POST':\n\t\tsearch_form = Search_bar(request.POST)\n\t\tif search_form.is_valid():\n\t\t\tif search_form.cleaned_data['search']:\n\t\t\t\tparameters = '-'.join(search_form.cleaned_data['search'].split(' '))\n\t\t\t\treturn HttpResponseRedirect(reverse('tailored:search', kwargs = {'search': parameters}))\n\t\t\telse:\n\t\t\t\treturn HttpResponseRedirect('all/')\n\n\tcontext_dict = {}\n\tform = Search_bar()\n\tcontext_dict['search_bar'] = form\n\tcontext_dict['categories'] = categories\n\titems = []\n\n\tif search == 'all' or search == None or search == '':\n\t\tsearch = 'all'\n\t\titems = Item.objects.filter(sold_to = None)\n\telse:\n\t\tif search != None:\n\t\t\tsearch = search.split('-')\n\t\t\tfor word in search:\n\t\t\t\titems += Item.objects.filter((Q(description__contains = word ) | Q(title__contains = word)\n\t\t\t\t\t| Q(category = word) | Q(section = word)) & Q(sold_to = None))\n\t\t\tsearchS = '_'.join(search)\n\t\t\tcontext_dict['search'] = searchS\n\t\telse:\n\t\t\treturn home_page(request)\n\t\n\tmaxi = 0\n\t\n\tfor item in items:\n\t\tif item.price > maxi:\n\t\t\tmaxi = item.price\n\t\n\tcontext_dict['maxi'] = maxi + 5\n\tcontext_dict['items'] = items\n\tcontext_dict['pages'] = int(len(items)/6)\n\t\n\treturn render(request, 'tailored/shop_bootstrap.html',context_dict)","def __search(self):\n self.resultList.clear()\n self.infoLabel.clear()\n \n self.buttonBox.button(QDialogButtonBox.Close).setEnabled(False)\n self.buttonBox.button(QDialogButtonBox.Cancel).setEnabled(True)\n self.buttonBox.button(QDialogButtonBox.Cancel).setDefault(True)\n self.searchButton.setEnabled(False)\n QApplication.processEvents(QEventLoop.ExcludeUserInputEvents)\n \n QApplication.setOverrideCursor(Qt.WaitCursor)\n QApplication.processEvents(QEventLoop.ExcludeUserInputEvents)\n \n self.__canceled = False\n \n self.__query = [term for term in self.searchEdit.text().strip().split()\n if term not in PipSearchDialog.Stopwords]\n self.__client.call(\n \"search\",\n ({\"name\": self.__query, \"summary\": self.__query}, \"or\"),\n self.__processSearchResult,\n self.__searchError\n )","def search_api(request):\n data = ApiViewFilters(request.GET, queryset=ApiView.objects.all())\n return render(request, 'template.html', {'filter': data})","def add_search_form():\n g.form = forms.SearchPlaces(formdata=None)\n g.action = url_for(\"page.search_query\")","def search(request):\n title = \"Voices search\"\n search_term = request.params.get('search_term','')\n form = Form(request)\n searchstring = u'%%%s%%' % search_term\n\n # generic_filter can be applied to all Node (and subclassed) objects\n\n generic_filter = or_(\n Content.title.like(searchstring),\n Content.body.like(searchstring),\n )\n\n results = DBSession.query(Content).filter(Content.type !='listing').filter(generic_filter).\\\n order_by(Content.title.asc()).all()\n\n\n page_url = PageURL_WebOb(request)\n page = int(request.params.get(\"page\", 1))\n paginator = Page(results,\n page=page,\n items_per_page=10,\n url=page_url)\n\n return render_to_response(\"buddy:templates/home/searchresult.mako\",\n dict(paginator=paginator,title=title,\n form=FormRenderer(form)),request=request)","def createSearchButton(dash_instance):\n\t\n\tsearchButton = html.Button(id='searchButton', n_clicks=0, children='View', style=dict(marginTop='25', marginLeft=450, width=100, height=50))\n\tupdatingLabel = html.Div(id='updatingArea')\n\treturn searchButton, updatingLabel","def search_for_books(main_page): # Add information to the printout if the book is rented\n\n type_of_search = 0\n\n header = \"\"\"\n Do you want to search for books by the first letter of the title\n or by the type?\n \"\"\"\n search_choices= (\n (\"To search by letter\", search_by_letter),\n (\"To search by type\", search_by_type),\n (\"To exit\",exit.exit_to_main)\n )\n\n book_search = Screen(header,search_choices,\n main_page.login, main_page.password)\n book_search.activate()","def search(request):\n if request.method==\"GET\":\n data = request.GET\n else:\n data = request.POST\n\n keywords = data.get('keywords', '').split(' ')\n\n keywords = filter(None, keywords)\n\n results = {}\n\n signals.application_search.send(None, request=request, keywords=keywords, results=results)\n \n context = RequestContext(request, {\n 'results': results,\n 'keywords' : keywords})\n return render_to_response('localsite/search.html', context)","def question_new_search():","def search(request):\n if 'q' in request.GET:\n term = request.GET['q']\n story_list = Story.objects.filter(Q(title__contains=term)|Q(markdown_content__contains=term))\n heading = \"Search results\"\n return render_to_response(\"cms/story_list.html\",locals())","def __init__(self, name=\"\"):\n super().__init__(\"search\", name)","def on_searchin_changed(self):\r\n\r\n self.check_searchin()","def search_param(command):\n namespace = app.main(command)\n assert namespace.command == 'sv' or namespace.command == \"searchparam\"\n assert namespace.term == \"test\"","def search(self):\r\n return resources.Search(self)","def search():\n import booksearch as bs\n\n opt = var.get()\n term = searchBox.get()\n term2 = dateBox.get()\n\n # Case statement (substitute) for different search areas\n # Each key is an option in the OptionMenu\n searchBy = {\n \"Title & Author\" : bs.search(term),\n \"ID\" : bs.bookID(term),\n \"Date\" : bs.dateRange(term, term2),\n }\n query = searchBy[opt] # Make & stores a query (2D list)\n\n # Repopulates table\n if term != \"\":\n populate(query)","def search_layout():\n all_input_processors = all_processors_for_searching()\n search_toolbar = SearchToolbar(\"Search Toolbar\", vi_mode=True)\n control = BufferControl(\n Buffer(document=Document(), read_only=True),\n search_buffer_control=search_toolbar.control,\n preview_search=True,\n include_default_input_processors=False,\n input_processors=all_input_processors,\n )\n style = style_from_pygments_cls(pygments_style)\n # Apparently theres something wrong with the style other put it in the\n # HSplit constructor\n buf_container = Window(control, style=style)\n search_container = Window(search_toolbar, style=style)\n container = HSplit([buf_container, search_container])\n return container","def search(request):\n return render(request, \"search.html\")","def search(self, query):","def __doSearch(self):\n if (\n self.__replaceMode and\n not e5App().getObject(\"ViewManager\").checkAllDirty()\n ):\n return\n \n self.__cancelSearch = False\n \n if self.filterCheckBox.isChecked():\n fileFilter = self.filterEdit.text()\n fileFilterList = [\n \"^{0}$\".format(filter.replace(\".\", r\"\\.\").replace(\"*\", \".*\"))\n for filter in fileFilter.split(\";\")\n ]\n filterRe = re.compile(\"|\".join(fileFilterList))\n \n if self.projectButton.isChecked():\n if self.filterCheckBox.isChecked():\n files = [self.project.getRelativePath(file)\n for file in\n self.__getFileList(\n self.project.getProjectPath(), filterRe)]\n else:\n files = []\n if self.sourcesCheckBox.isChecked():\n files += self.project.pdata[\"SOURCES\"]\n if self.formsCheckBox.isChecked():\n files += self.project.pdata[\"FORMS\"]\n if self.interfacesCheckBox.isChecked():\n files += self.project.pdata[\"INTERFACES\"]\n if self.protocolsCheckBox.isChecked():\n files += self.project.pdata[\"PROTOCOLS\"]\n if self.resourcesCheckBox.isChecked():\n files += self.project.pdata[\"RESOURCES\"]\n elif self.dirButton.isChecked():\n if not self.filterCheckBox.isChecked():\n filters = []\n if self.sourcesCheckBox.isChecked():\n filters.extend(\n [\"^{0}$\".format(\n assoc.replace(\".\", r\"\\.\").replace(\"*\", \".*\"))\n for assoc in list(\n Preferences.getEditorLexerAssocs().keys())\n if assoc not in self.formsExt + self.interfacesExt +\n self.protocolsExt])\n if self.formsCheckBox.isChecked():\n filters.append(self.filterForms)\n if self.interfacesCheckBox.isChecked():\n filters.append(self.filterInterfaces)\n if self.protocolsCheckBox.isChecked():\n filters.append(self.filterProtocols)\n if self.resourcesCheckBox.isChecked():\n filters.append(self.filterResources)\n filterString = \"|\".join(filters)\n filterRe = re.compile(filterString)\n files = self.__getFileList(\n os.path.abspath(self.dirPicker.currentText()),\n filterRe)\n elif self.openFilesButton.isChecked():\n vm = e5App().getObject(\"ViewManager\")\n vm.checkAllDirty()\n files = vm.getOpenFilenames()\n \n self.findList.clear()\n QApplication.processEvents()\n QApplication.processEvents()\n self.findProgress.setMaximum(len(files))\n \n # retrieve the values\n reg = self.regexpCheckBox.isChecked()\n wo = self.wordCheckBox.isChecked()\n cs = self.caseCheckBox.isChecked()\n ct = self.findtextCombo.currentText()\n if reg:\n txt = ct\n else:\n txt = re.escape(ct)\n if wo:\n txt = \"\\\\b{0}\\\\b\".format(txt)\n flags = re.UNICODE\n if not cs:\n flags |= re.IGNORECASE\n try:\n search = re.compile(txt, flags)\n except re.error as why:\n E5MessageBox.critical(\n self,\n self.tr(\"Invalid search expression\"),\n self.tr(\"\"\"<p>The search expression is not valid.</p>\"\"\"\n \"\"\"<p>Error: {0}</p>\"\"\").format(str(why)))\n self.stopButton.setEnabled(False)\n self.findButton.setEnabled(True)\n self.findButton.setDefault(True)\n return\n # reset the findtextCombo\n if ct in self.searchHistory:\n self.searchHistory.remove(ct)\n self.searchHistory.insert(0, ct)\n self.findtextCombo.clear()\n self.findtextCombo.addItems(self.searchHistory)\n Preferences.Prefs.settings.setValue(\n \"FindFileDialog/SearchHistory\",\n self.searchHistory[:30])\n \n if self.__replaceMode:\n replTxt = self.replacetextCombo.currentText()\n if replTxt in self.replaceHistory:\n self.replaceHistory.remove(replTxt)\n self.replaceHistory.insert(0, replTxt)\n self.replacetextCombo.clear()\n self.replacetextCombo.addItems(self.replaceHistory)\n Preferences.Prefs.settings.setValue(\n \"FindFileDialog/ReplaceHistory\",\n self.replaceHistory[:30])\n \n if self.dirButton.isChecked():\n searchDir = self.dirPicker.currentText()\n if searchDir in self.dirHistory:\n self.dirHistory.remove(searchDir)\n self.dirHistory.insert(0, searchDir)\n self.dirPicker.clear()\n self.dirPicker.addItems(self.dirHistory)\n Preferences.Prefs.settings.setValue(\n \"FindFileDialog/DirectoryHistory\",\n self.dirHistory[:30])\n \n # set the button states\n self.stopButton.setEnabled(True)\n self.stopButton.setDefault(True)\n self.findButton.setEnabled(False)\n \n # now go through all the files\n self.__populating = True\n self.findList.setUpdatesEnabled(False)\n progress = 0\n breakSearch = False\n occurrences = 0\n fileOccurrences = 0\n for file in files:\n self.__lastFileItem = None\n found = False\n if self.__cancelSearch or breakSearch:\n break\n \n self.findProgressLabel.setPath(file)\n \n if self.projectButton.isChecked():\n fn = os.path.join(self.project.ppath, file)\n else:\n fn = file\n # read the file and split it into textlines\n try:\n text, encoding, hashStr = Utilities.readEncodedFileWithHash(fn)\n lines = text.splitlines(True)\n except (UnicodeError, IOError):\n progress += 1\n self.findProgress.setValue(progress)\n continue\n \n # now perform the search and display the lines found\n count = 0\n for line in lines:\n if self.__cancelSearch:\n break\n \n count += 1\n contains = search.search(line)\n if contains:\n occurrences += 1\n found = True\n start = contains.start()\n end = contains.end()\n if self.__replaceMode:\n rline = search.sub(replTxt, line)\n else:\n rline = \"\"\n line = self.__stripEol(line)\n if len(line) > 1024:\n line = \"{0} ...\".format(line[:1024])\n if self.__replaceMode:\n if len(rline) > 1024:\n rline = \"{0} ...\".format(line[:1024])\n line = \"- {0}\\n+ {1}\".format(\n line, self.__stripEol(rline))\n self.__createItem(file, count, line, start, end,\n rline, hashStr)\n \n if self.feelLikeCheckBox.isChecked():\n fn = os.path.join(self.project.ppath, file)\n self.sourceFile.emit(fn, count, \"\", start, end)\n QApplication.processEvents()\n breakSearch = True\n break\n \n QApplication.processEvents()\n \n if found:\n fileOccurrences += 1\n progress += 1\n self.findProgress.setValue(progress)\n \n if not files:\n self.findProgress.setMaximum(1)\n self.findProgress.setValue(1)\n \n resultFormat = self.tr(\"{0} / {1}\", \"occurrences / files\")\n self.findProgressLabel.setPath(resultFormat.format(\n self.tr(\"%n occurrence(s)\", \"\", occurrences),\n self.tr(\"%n file(s)\", \"\", fileOccurrences)))\n \n self.findList.setUpdatesEnabled(True)\n self.findList.sortItems(self.findList.sortColumn(),\n self.findList.header().sortIndicatorOrder())\n self.findList.resizeColumnToContents(1)\n if self.__replaceMode:\n self.findList.header().resizeSection(0, self.__section0Size + 30)\n self.findList.header().setStretchLastSection(True)\n self.__populating = False\n \n self.stopButton.setEnabled(False)\n self.findButton.setEnabled(True)\n self.findButton.setDefault(True)\n \n if breakSearch:\n self.close()","def on_pre_enter(self, *args):\n self.ids['search'].text = ''\n self.filter()","def page_body():\r\n st.header(\"Search\")\r\n st.subheader(\"Search For SMEs With A Few Different Options\")\r\n\r\n search_mode_selection = st.radio(\r\n help=\"Search For SMEs That Have Particular Connections, Titles, Or Names...\",\r\n label=\"Search By\",\r\n options=(SearchMode.Connection.value, SearchMode.JobTitle.value, SearchMode.Name.value),\r\n )\r\n\r\n search_form = st.form(key=\"search_form\", clear_on_submit=False)\r\n search_query = search_form.text_input(label=\"\", value=\"Search...\", max_chars=50)\r\n search_button = search_form.form_submit_button(label=\"Search\")\r\n\r\n if search_button:\r\n results = get_search_results(search_query, SearchMode[str(search_mode_selection).replace(\" \", \"\")])\r\n\r\n # Loop through the results returned from the database query\r\n for result in results:\r\n result_dict = result.to_dict() # Convert internally to a Python dict\r\n\r\n # dict keys here are actually database keys in Firestore. You would need to be signed in to see the proper values\r\n with st.expander(result_dict[\"name\"] + \" - \" + str(result_dict[\"age\"]) + \" years old\"):\r\n st.header(result_dict[\"name\"])\r\n st.write(result_dict[\"jobTitle\"])\r\n\r\n st.subheader(\"Personal Summary\")\r\n st.write(result_dict[\"personalSummary\"])\r\n\r\n if result_dict[\"companyName\"]:\r\n st.subheader(\"Works At\")\r\n st.write(result_dict[\"companyName\"])\r\n\r\n if result_dict[\"connections\"]:\r\n st.subheader(result_dict[\"name\"] + \"'s Connections\")\r\n st.write(\", \".join(result_dict[\"connections\"]))","def click_search_button(self):\n self.click_element(self.generic_search_button_locator)","def showSearchWidget(self):\n self.__replaceWidget.hide()\n self.__searchWidget.show()\n self.__searchWidget.show(self.textForFind())","def search_google(self, widget):\n if not self.searchstring:\n return\n base_uri = \"https://www.google.com/search?q=%s\"\n uri = base_uri % urllib.parse.quote(self.searchstring.encode(\"utf-8\"))\n Gtk.show_uri_on_window(None, uri, Gdk.CURRENT_TIME)","def search_resources(self,searchtext):\n\n self.search.value = searchtext\n self.submit.click()","def show_search_resuls(builder, show: bool):\n show_ui_component(builder, 'findYourInstituteScrolledWindow', show)","def processSearchLanguage(dataField):\n\tvalidCommands=[\"FILTER\",\"TYPE\",\"ORDER\",\"SORT\"]\n\trawData=getDataFromWidget(dataField)\n\t#Split the data\n\tsplitData=rawData.split(\"=\")\n\tif len(splitData) > 1:\n\t\tcommand=splitData[0]\n\t\tcommandData=splitData[1]\n\t\tif len(commandData.split()) > 0:\n\t\t\tcommandName=str(command).upper()\n\t\t\tif commandName in validCommands:\n\t\t\t\t#Filter\n\t\t\t\tif commandName == \"FILTER\" or commandName == \"TYPE\":\n\t\t\t\t\tfilterPodListbox(commandData)\n\t\t\t\telif commandName == \"ORDER\" or commandName == \"SORT\":\n\t\t\t\t\torderPodListbox(commandData)","def search_frame():\r\n global file, split, detail, search_question_frame, search\r\n\r\n add_question_Frame.grid_forget()\r\n quiz_frame.grid_forget()\r\n one_person_quiz_frame.grid_forget()\r\n question_list_frame.grid_forget()\r\n search_question_frame.grid_forget()\r\n select_question_frame.grid_forget()\r\n\r\n search_question_frame.grid(row=0, column=0, rowspan=7, columnspan=5, sticky=N + E + S + W)\r\n\r\n Label(search_question_frame, text='Search for question').grid()\r\n search_entry = Entry(search_question_frame, text=search)\r\n search.get()\r\n search_entry.grid()\r\n\r\n search_button.grid()","def search():\n\n # Make sure user is logged in\n if \"username\" not in session:\n return render_template(\"index.html\", message=\"Please login to view that page!\")\n\n if request.method == \"POST\":\n # Get search form\n search = request.form.get(\"search\").strip()\n search_partial = search + '%'\n\n books = db.execute(\"SELECT * FROM books WHERE isbn LIKE :search OR author LIKE :search OR title LIKE :search\", {\"search\": search_partial}).fetchall()\n\n return render_template(\"search.html\", username=session[\"username\"], search=search, books=books)\n # Actually you cant get this without being logged in\n return render_template(\"search.html\")","def search(self):\n premium = self.config.get('premium', False)\n\n self.params[self.opts['keyword']['query_key']] = self.config[self.opts['keyword']['config_key']] # keyword\n # Selection params\n self.append_param('tag_mode', 'selection')\n if premium:\n self.append_param('order_premium', 'selection')\n else:\n self.append_param('order_not_premium', 'selection')\n\n self.append_param('type', 'selection')\n self.append_param('tool', 'selection')\n self.append_param('ratio', 'selection')\n self.append_param('mode', 'selection')\n\n # Number params\n self.append_param('min_width', 'number')\n self.append_param('max_width', 'number')\n self.append_param('min_height', 'number')\n self.append_param('max_height', 'number')\n if premium:\n self.append_param('min_bookmark', 'number')\n self.append_param('max_bookmark', 'number')\n else:\n self.set_bookmark_filter()\n\n # Date params\n self.append_param('start_time', 'date')\n self.append_param('end_time', 'date')\n\n # multi work filter\n self.filters['multi'] = self.config.get('download_multi', False)\n\n for i in range(self.config['start_page'], self.config['end_page'] + 1):\n self.params['p'] = i\n self.headers['Referer'] = 'https://www.pixiv.net/'\n url ='https://www.pixiv.net/search.php'\n html = self.session.get(url, headers = self.headers, params = self.params, timeout = 10, proxies = self.proxies)\n\n soup = BeautifulSoup(html.text, 'lxml')\n data_items = json.loads(soup.find('input', id = 'js-mount-point-search-result-list')['data-items'])\n\n return self.extract_work_info(data_items)","def search_form_servee(context, cl):\r\n return {\r\n \"request\": context[\"request\"],\r\n \"cl\": cl,\r\n \"show_result_count\": cl.result_count != cl.full_result_count,\r\n \"search_var\": \"q\"\r\n }","def navsearch():\r\n\r\n # initializes page title and header\r\n page_title = 'Ingredient search'\r\n page_header = 'Ingredient search results:'\r\n\r\n # determines the limit and the offset for the pagination\r\n if 'limit' in request.args:\r\n try:\r\n limit = int(request.args['limit'])\r\n\r\n except:\r\n # default limit on error\r\n limit = 4\r\n\r\n else:\r\n # default limit\r\n limit = 4\r\n\r\n if 'offset' in request.args:\r\n try:\r\n offset = int(request.args['offset'])\r\n\r\n except:\r\n # default offset on error\r\n offset = 0\r\n\r\n else:\r\n # default offset\r\n offset = 0\r\n\r\n # determines if search data comes from GET or POST method\r\n if request.method == 'GET':\r\n if 'find' in request.args:\r\n # search text in case if argument exists\r\n find_text = request.args['find']\r\n\r\n else:\r\n # returns error template\r\n return render_template(\r\n 'error.html',\r\n msg='Bad argument error! (/navsearch)'\r\n )\r\n\r\n elif request.method == 'POST':\r\n # search text from the request form\r\n find_text = request.form.get('search')\r\n\r\n else:\r\n # returns error template\r\n return render_template(\r\n 'error.html',\r\n msg='Unhandled method error! (/navsearch)'\r\n )\r\n\r\n # determines all recipe titles in ascendent order\r\n search_text = re.compile(find_text, re.IGNORECASE)\r\n get_titles = mongo.db.recipes.find(\r\n {'ingredients': {'$in': [search_text]}}).sort('title')\r\n\r\n try:\r\n # determines the title at the offset\r\n start = get_titles[offset]['title']\r\n\r\n except IndexError:\r\n # returns an error page on IndexError\r\n return render_template(\r\n 'error.html',\r\n msg='Invalid index error! (/navsearch)'\r\n )\r\n\r\n # determines the previous link for the template\r\n if offset - limit < 0:\r\n prev_link = ''\r\n else:\r\n prev_link = url_for(\r\n 'navsearch',\r\n find=find_text,\r\n limit=str(limit),\r\n offset=str(offset - limit)\r\n )\r\n\r\n if offset + limit >= get_titles.count():\r\n next_link = ''\r\n else:\r\n next_link = url_for(\r\n 'navsearch',\r\n find=find_text,\r\n limit=str(limit),\r\n offset=str(offset + limit)\r\n )\r\n\r\n # determines the subset\r\n recipes = mongo.db.recipes.find(\r\n {'ingredients': {'$in': [search_text]}, 'title': {'$gte': start}}\r\n ).sort('title').limit(limit)\r\n\r\n if recipes.count() > 0:\r\n # returns the subset\r\n return render_template(\r\n 'navsearch.html',\r\n recipes=recipes,\r\n page_title=page_title,\r\n page_header=page_header,\r\n prev=prev_link,\r\n next=next_link\r\n )\r\n\r\n else:\r\n # returns to an error message if there is no reult\r\n return render_template('error.html', msg='No result found!')","def runSearch():\n\tglobal processLanguageOn\n\tdataToFind=getDataFromWidget(podSearchEntry)\n\t#Search through the keys otherwise data changes\n\tdataSource=podListbox.data.keys()\n\t#Store the results of the search\n\tresults=[]\n\t#Search the data source\n\tfor item in dataSource:\n\t\tif searchDataSource(dataToFind,[item],capital=True,full=False):\n\t\t\tresults.append(item)\n\n\t#Add the results to screen\n\tpodListbox.delete(0,END)\n\tfor item in results:\n\t\tpodListbox.addExisting(item)\n\n\tif processLanguageOn:\n\t\tprocessSearchLanguage(podSearchEntry)","def shop_items(request):\n\n items = Item.objects.all()\n\n query = None\n\n \"\"\" Used Code Institute Search logic from Tutorial \"\"\"\n if 'query' in request.GET:\n query = request.GET['query']\n if not query:\n messages.error(request, \"Please enter your search\")\n return redirect(reverse('items'))\n \n queries = Q(name__icontains=query) | Q(item_description__icontains=query)\n items = items.filter(queries)\n\n context = {\n 'items': items,\n 'search_term': query,\n }\n\n return render(request, 'items/items.html', context)","def search_category(self):\n return _(self.child_class)","def createSearchOption(dash_instance):\n\tsearchLabel = html.Label(id='SearchLabel', \n\t style=dict(fontWeight='bold', width=50, marginLeft='100', marginRight='20', display='block'), \n\t\t\t\t\t\t\t children='Search By: ')\n\tradioOptions = [{'label': 'Group Caption', 'value': 'GName'}, {'label': 'Node Caption', 'value': 'NName'}, {'label': 'IP Address', 'value': 'IP'}]\n\tsearchRadio = dcc.RadioItems(id=\"SearchRadio\", \n\t\t\t\t\t\t\t\t options=radioOptions, \n\t\t\t\t\t\t\t\t style=dict(width=350, marginRight='25', marginLeft='50', marginTop='30', display='block', textAlign='left'), \n\t\t\t\t\t\t\t\t labelStyle={'display': 'block'}, value='NName')\n\tsearchBar = dcc.Input(id='SearchBar', \n\t\t\t\t\t\t style=dict(width=240, height=30, marginLeft='175', marginBottom='30', marginRight='25'), \n\t\t\t\t\t\t value='', type='text', placeholder='Enter search term...')\n\t\t\t\t\t\t \n\treturn searchLabel, searchRadio, searchBar","async def search(self, *args, **kwargs):\n pass","def search():\n\n # POST\n if request.method == \"POST\":\n\n # validate form submission\n if not request.form.get(\"intervention\"):\n return render_template(\"results.html\", results=entries.values())\n ''' \n elif not request.form.get(\"setting\"):\n return apology(\"missing setting\")\n elif not request.form.get(\"emrpref\"):\n return apology(\"missing emr pref\")\n elif not request.form.get(\"budget\"):\n return apology(\"missing budget\")'''\n \n results = []\n for k in entries:\n print('entries', entries[k]['Keywords'])\n print('term', request.form.get(\"intervention\"))\n if request.form.get(\"intervention\") in entries[k]['Keywords']:\n print('ya')\n results.append(entries[k])\n\n\n return render_template(\"results.html\", results=results)\n\n\n # GET\n else:\n return render_template(\"search.html\")","def search(self):\r\n return resource.Search(self)","def on_buttonBox_clicked(self, button):\n if button == self.findButton:\n self.__doSearch()\n elif button == self.stopButton:\n self.__stopSearch()","def search():\n try:\n query = request.args.get(\"q\").lower()\n except AttributeError:\n query = request.args.get(\"q\")\n\n # Adding browse functionality\n browse = request.args.get(\"browse\")\n\n if browse is None:\n # Select all rows with a column value that includes query\n results = db.execute(\"SELECT * FROM books \"\n \"WHERE LOWER(isbn) LIKE CONCAT('%', :q, '%')\"\n \"OR LOWER(title) LIKE CONCAT('%', :q, '%') \"\n \"OR LOWER(author) LIKE CONCAT('%', :q, '%') \"\n \"ORDER BY title LIMIT 100\", {'q': query}).fetchall()\n else:\n # Select titles starting with letter\n results = db.execute(\n \"SELECT * FROM books \"\n \"WHERE LOWER(title) LIKE CONCAT(:q, '%') \"\n \"ORDER BY title\", {'q': query}).fetchall()\n\n return render_template(\"search.html\", browse=browse, query=query, results=results)","def search_wikipedia(self):\n self.summary.delete('1.0', tk.END)\n possibilities = wk.search(self.search_box.get('1.0',tk.END).replace(\"\\n\",\"\"))\n if len(possibilities) > 0:\n try:\n p = wk.page(possibilities[0])\n except wk.DisambiguationError as e:\n p = wk.page(e.options[0])\n self.summary.configure(state=\"normal\")\n self.summary.delete('1.0', tk.END)\n self.summary.insert('1.0', p.summary)\n self.summary.configure(state=\"disabled\")\n self.p = p\n self.update_category_map(p.categories)\n self.get_news()\n return None","def search():\n query = input('Please enter your search query\\n')\n # For now, we will just print the whole database\n #db_actions.display()\n db_actions.search(query)","def other_search(self):\n test = self.ask_zoekarg.text()\n if test:\n self.parent().search_arg = test\n self.parent().do_select()","def click_inbound_statement_search_button(self):\n self.click_element(self.page_header_locator)\n self.click_element(self.inbound_statement_search_button_locator, True)","def search_venues_form():\n # seach for Hop should return \"The Musical Hop\".\n # search for \"Music\" should return \"The Musical Hop\" and \"Park Square Live Music & Coffee\"\n return render_template(\n 'pages/search_venues.html'\n )","def search():\n student_to_find=request.args.get(\"student\", None)\n print(f\"A buscar: {student_to_find}\")\n student_list=search_student(student_to_find)\n return render_template(\"search.html\",student_list_result=student_list)","def search_book():\n\n title = request.form.get(\"search\")\n books = book_search_results(GR_KEY, title)\n acct = get_current_account(session['acct'])\n search = True\n\n return render_template(\"index.html\", books=books, acct=acct, search=search)","def startSearch(self, fn, params, searchType):\n\t\tself.mySearchAnnotation = bimpy.interface.bSearchAnnotations(self.mySimpleStack.slabList,\n\t\t\t\t\t\t\tfn = fn,\n\t\t\t\t\t\t\tparams = params,\n\t\t\t\t\t\t\tsearchType = searchType)\n\t\t# self.searchWidget.searchTable()\n\t\tself.mySearchAnnotation.searchFinishedSignal.connect(self.searchWidget.searchTable().slot_SearchFinished)\n\t\tself.mySearchAnnotation.searchNewHitSignal.connect(self.searchWidget.searchTable().slot_newSearchHit)\n\t\tself.mySearchAnnotation.start()","def search(request):\n\n term = \"\"\n organizations = None\n memberships = None\n events = None\n persons = None\n airports = None\n training_requests = None\n comments = None\n only_result = None\n\n if request.method == \"GET\" and \"term\" in request.GET:\n form = SearchForm(request.GET)\n if form.is_valid():\n term = form.cleaned_data.get(\"term\", \"\")\n tokens = re.split(r\"\\s+\", term)\n\n organizations = Organization.objects.filter(\n Q(domain__icontains=term) | Q(fullname__icontains=term)\n ).order_by(\"fullname\")\n if len(organizations) == 1 and not only_result:\n only_result = organizations[0]\n\n memberships = Membership.objects.filter(\n registration_code__icontains=term\n ).order_by(\"-agreement_start\")\n if len(memberships) == 1 and not only_result:\n only_result = memberships[0]\n\n events = Event.objects.filter(\n Q(slug__icontains=term)\n | Q(host__domain__icontains=term)\n | Q(host__fullname__icontains=term)\n | Q(url__icontains=term)\n | Q(contact__icontains=term)\n | Q(venue__icontains=term)\n | Q(address__icontains=term)\n ).order_by(\"-slug\")\n if len(events) == 1 and not only_result:\n only_result = events[0]\n\n # if user searches for two words, assume they mean a person\n # name\n if len(tokens) == 2:\n name1, name2 = tokens\n complex_q = (\n (Q(personal__icontains=name1) & Q(family__icontains=name2))\n | (Q(personal__icontains=name2) & Q(family__icontains=name1))\n | Q(email__icontains=term)\n | Q(secondary_email__icontains=term)\n | Q(github__icontains=term)\n )\n persons = Person.objects.filter(complex_q)\n else:\n persons = Person.objects.filter(\n Q(personal__icontains=term)\n | Q(family__icontains=term)\n | Q(email__icontains=term)\n | Q(secondary_email__icontains=term)\n | Q(github__icontains=term)\n ).order_by(\"family\")\n\n if len(persons) == 1 and not only_result:\n only_result = persons[0]\n\n airports = Airport.objects.filter(\n Q(iata__icontains=term) | Q(fullname__icontains=term)\n ).order_by(\"iata\")\n if len(airports) == 1 and not only_result:\n only_result = airports[0]\n\n training_requests = TrainingRequest.objects.filter(\n Q(group_name__icontains=term)\n | Q(family__icontains=term)\n | Q(email__icontains=term)\n | Q(github__icontains=term)\n | Q(affiliation__icontains=term)\n | Q(location__icontains=term)\n | Q(user_notes__icontains=term)\n )\n if len(training_requests) == 1 and not only_result:\n only_result = training_requests[0]\n\n comments = Comment.objects.filter(\n Q(comment__icontains=term)\n | Q(user_name__icontains=term)\n | Q(user_email__icontains=term)\n | Q(user__personal__icontains=term)\n | Q(user__family__icontains=term)\n | Q(user__email__icontains=term)\n | Q(user__github__icontains=term)\n ).prefetch_related(\"content_object\")\n if len(comments) == 1 and not only_result:\n only_result = comments[0]\n\n # only 1 record found? Let's move to it immediately\n if only_result and not form.cleaned_data[\"no_redirect\"]:\n msg = format_html(\n \"You were moved to this page, because your search <i>{}</i> \"\n \"yields only this result.\",\n term,\n )\n if isinstance(only_result, Comment):\n messages.success(request, msg)\n return redirect(\n only_result.content_object.get_absolute_url()\n + \"#c{}\".format(only_result.id)\n )\n elif hasattr(only_result, \"get_absolute_url\"):\n messages.success(request, msg)\n return redirect(only_result.get_absolute_url())\n\n else:\n messages.error(request, \"Fix errors below.\")\n\n # if empty GET, we'll create a blank form\n else:\n form = SearchForm()\n\n context = {\n \"title\": \"Search\",\n \"form\": form,\n \"term\": term,\n \"organisations\": organizations,\n \"memberships\": memberships,\n \"events\": events,\n \"persons\": persons,\n \"airports\": airports,\n \"comments\": comments,\n \"training_requests\": training_requests,\n }\n return render(request, \"dashboard/search.html\", context)","def do_search(request):\n products = Product.objects.filter(title__icontains=request.GET['q'])\n return render(request, \"products.html\", {\"products\": products})","def searchResultsView(request):\n bloggerName = request.GET.get(\"search_blogger\")\n bloggers = Blogger.objects.all().filter(name__icontains=bloggerName)\n\n context = {\n \"bloggers\": bloggers,\n }\n return render(request, \"blog/search_results.html\", context)","def admin_search_student(request):\n\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\n if request.session['type'] == 'S' or request.session['type'] == 'R': return redirect(reverse(URL_FORBIDDEN))\n\n if request.method == \"GET\":\n return render(\n request,\n 'app/admin/admin_search_student.html',\n {\n 'title':'Student Info',\n 'layout_data' : get_layout_data(request),\n }\n )\n else:\n return redirect(reverse(URL_BAD_REQUEST))","def search(self, filters=None):\n raise NotImplementedError","def search_action(self):\n #TODO: No access control checks for dataproxy resources!\n request_data = self._get_request_data(try_url_params=True)\n resource = Resource.get(request_data['resource_id'])\n if resource is not None and resource.url_type == 'dataproxy':\n pylons.response.headers['Content-Type'] = 'application/json;charset=utf-8'\n return self.dataproxy_search(request_data, resource)\n\n #Default action otherwise\n return self.action('datastore_search', ver=3)","def run_search(self, evt):\n search_input = self.search_input_txtctrl.GetValue()\n self.execute_google_search(str(search_input))\n self.set_result_to_dict_for_page_scroller()\n self.clear_result_screen()\n self.trigger_scroller_event()","def on_filterCheckBox_clicked(self):\n self.__enableFindButton()","def sidebar_click(sidebar_item):\n #print('sidebar_click')\n sidebar = driver.find_element_by_class_name('m-menu__nav')\n return click_on(sidebar_item, scope=sidebar)","def search(request, is_my_list=\"False\"):\n\n search_type = request.GET.get(\"submit\")\n if search_type:\n\n # get query field\n query = ''\n if request.GET.get(search_type):\n query = request.GET.get(search_type)\n\n proj_ids = []\n cod_ids = []\n\n valid_searches = [constants.STRING_TITLE, constants.STRING_DESCRIPTION, constants.STRING_PROTOCOL,\n constants.STRING_CODER, constants.STRING_AREA, constants.STRING_WORKINGGROUP]\n\n search_in_all = True\n for v in valid_searches:\n if v in request.GET:\n search_in_all = False\n break\n\n if search_in_all or request.GET.get(constants.STRING_TITLE):\n codings = CodingProject.objects.all()\n for cod in codings:\n if query.lower() in cod.title.lower():\n cod_ids.append(cod.id)\n\n if search_in_all or request.GET.get(constants.STRING_DESCRIPTION):\n codings = CodingProject.objects.all()\n for cod in codings:\n if query.lower() in cod.additional_information.lower():\n cod_ids.append(cod.id)\n\n if request.GET.get(constants.STRING_PROTOCOL):\n proj_ids += ProjectContainer.objects.filter(protocol__icontains=query).values_list('id', flat=True)\n\n if search_in_all or request.GET.get(constants.STRING_CODER):\n for pr in ProjectContainer.objects.all():\n for cd in pr.codings.all():\n user = Person.objects.using('datatracker').get(id=cd.coder)\n if query.lower() in user.name.lower():\n proj_ids.append(pr.id)\n break\n\n if search_in_all or request.GET.get(constants.STRING_AREA):\n for project_container in ProjectContainer.objects.all():\n docs = []\n if not project_container.docs or project_container.docs == '':\n continue\n keys = filter(None, project_container.docs.split(';'))\n docs.extend(list(DocAlias.objects.using('datatracker').filter(name__in=keys).values_list(\n 'document__group__parent__name')))\n for doc in docs:\n if query.lower() in doc[0].lower():\n proj_ids.append(project_container.id)\n break\n # ids += ProjectContainer.objects.filter(docs__document__group__parent__name__icontains=query).values_list(\n # 'id', flat=True)\n\n if search_in_all or request.GET.get(constants.STRING_WORKINGGROUP):\n for project_container in ProjectContainer.objects.all():\n docs = []\n if not project_container.docs or project_container.docs == '':\n continue\n keys = filter(None, project_container.docs.split(';'))\n docs.extend(list(\n DocAlias.objects.using('datatracker').filter(name__in=keys).values_list('document__group__name')))\n for doc in docs:\n if query.lower() in doc[0].lower():\n proj_ids.append(project_container.id)\n break\n \n if cod_ids:\n cod_ids = list(set(cod_ids))\n proj_ids += ProjectContainer.objects.filter(codings__id__in=cod_ids).values_list('id', flat=True)\n project_containers = ProjectContainer.objects.filter(id__in=list(set(proj_ids)))\n \n request.session[constants.ALL_CODINGS] = cod_ids\n request.session[constants.ALL_PROJECTS] = project_containers\n\n request.session[constants.MAINTAIN_STATE] = True\n\n return HttpResponseRedirect(\n settings.CODESTAND_PREFIX + '/codestand/matches/show_list/' + \n is_my_list + '/{0}/'.format(constants.ATT_CREATION_DATE) + 'True')\n\n else:\n return render_page(request, constants.TEMPLATE_MATCHES_SEARCH, {\n \"form\": SearchForm()\n })","def _on_repository_search(self, evt):\n \n # init query\n query = getattr(evt, \"query\", \"\")\n \n # get selected articles\n articles = self._articles_view.GetSelectedArticles()\n \n # make requested query from first article\n if articles:\n article = articles[0]\n \n if evt.GetId() == ID_REPOSITORY_RECENT_FIRST_AUTHOR and article.authors:\n query = \"%s[AU]\" % article.authors[0].shortname\n \n elif evt.GetId() == ID_REPOSITORY_RECENT_LAST_AUTHOR and article.authors:\n query = \"%s[AU]\" % article.authors[-1].shortname\n \n elif evt.GetId() == ID_REPOSITORY_RECENT_JOURNAL and article.journal:\n query = \"%s[JT]\" % article.journal.abbreviation\n \n # search repository\n self._search_repository(query)","def page_search_notes(self):\n\n task_store_name = \"task_store\"\n alt_task_store = None\n alt_task_store_name = self.helper_get_alt_task_store_name()\n if alt_task_store_name == \"task_store_trash\":\n task_store_name = alt_task_store_name\n alt_task_store = self.task_store_trash\n\n search_text = self.last_request_get_dict[\"search_text\"][0].lower()\n list_taskid_desc, highlight_list = self.ui_backend.search_notes(task_store_name, search_text)\n\n matching_virtual_folder = \"\"\n for _virtfldr, _searchstring in self.woolnote_config.virtual_folders.items():\n if _searchstring.strip().lower() == search_text.strip().lower():\n matching_virtual_folder = \"\"\"virtual folder \"{}\" - \"\"\".format(_virtfldr)\n break\n\n # in the rest of the function, the variable should be None if it is the default task store\n if task_store_name == \"task_store\":\n task_store_name = None\n\n history_id = self.save_history([\"search_text\", \"action\"], alt_task_store_name=alt_task_store_name)\n\n title = \"woolnote - search \" + matching_virtual_folder + search_text\n\n page_header_first_text = \"search \" + matching_virtual_folder + search_text\n page_header_link_button_name = \"reset filter\"\n page_header_link_request_dict = {\"action\": \"show_list\"}\n page_header_list_of_warnings = None\n\n if self.error_msg_queue_list:\n page_header_list_of_warnings = self.helper_convert_msg_queue_list_to_list_for_output()\n\n return html_page_templates.page_list_notes_template(list_taskid_desc=list_taskid_desc,\n self_sess_action_auth=self.sess_action_auth, title=title,\n highlight_in_notes=highlight_list, primary_task_store=self.task_store,\n alt_task_store=alt_task_store, alt_task_store_name=alt_task_store_name,\n history_back_id=history_id,\n virtual_folders=self.woolnote_config.virtual_folders,\n single_task_line_ids=set(self.woolnote_config.single_note_line_id.keys()),\n page_header_first_text=page_header_first_text,\n page_header_optional_link_button_name=page_header_link_button_name,\n page_header_optional_link_button_request_dict=page_header_link_request_dict,\n page_header_optional_list_of_warnings=page_header_list_of_warnings)","def run_search(self, text: str, limit=100, similarity_thresh=50, **kwargs):\n\n class_tree = \"/wdt:P279*\" if \"include_class_tree\" in kwargs else \"\"\n sparq_property_filter = \"\"\n\n if \"instanceof_filter\" in kwargs:\n property_id = kwargs[\"instanceof_filter\"]\n if isinstance(property_id, str):\n # one instanceof in filter\n sparq_instanceof = f\"?item wdt:P31{class_tree} wd:{property_id}.\"\n elif isinstance(property_id, list):\n if len(property_id) == 1:\n sparq_instanceof = f\"?item wdt:P31{class_tree} wd:{property_id[0]}.\"\n else:\n ids = \", \".join([\"wd:\" + x for x in property_id])\n sparq_instanceof = f\" ?item wdt:P31{class_tree} ?tree. \\n FILTER (?tree in ({ids}))\"\n\n else:\n sparq_instanceof = \"\"\n\n if \"property_filters\" in kwargs:\n for prop, value in kwargs[\"property_filters\"].items():\n if isinstance(value, str):\n assert_qid_format(value)\n sparq_property_filter += f\"\\n ?item wdt:{prop} wd:{value} .\"\n elif isinstance(value, list):\n if len(value) == 1:\n assert_qid_format(value[0])\n sparq_property_filter += f\"\\n ?item wdt:{prop} wd:{value[0]} .\"\n else:\n ids = \", \".join([\"wd:\" + x for x in value])\n sparq_instanceof = (\n f\" ?item wdt:{prop} ?tree. \\n FILTER (?tree in ({ids}))\"\n )\n\n endpoint_url = config.WIKIDATA_SPARQL_ENDPOINT\n query = f\"\"\"\n SELECT ?item ?wikipedia_title {{\n SERVICE wikibase:mwapi {{\n bd:serviceParam wikibase:endpoint \"en.wikipedia.org\" .\n bd:serviceParam wikibase:api \"Generator\" .\n bd:serviceParam mwapi:generator \"search\" .\n bd:serviceParam mwapi:gsrsearch \"{text}\" .\n bd:serviceParam mwapi:gsrlimit \"max\" .\n ?item wikibase:apiOutputItem mwapi:item . \n ?wikipedia_title wikibase:apiOutput mwapi:title .\n }}\n hint:Prior hint:runFirst \"true\".\n {sparq_instanceof}\n {sparq_property_filter}\n }} LIMIT {limit}\n \"\"\"\n\n res = get_sparql_results(endpoint_url, query)[\"results\"][\"bindings\"]\n\n res_df = pd.json_normalize(res)\n\n if len(res_df) > 0:\n res_df = res_df[[\"item.value\", \"wikipedia_title.value\"]].rename(\n columns=lambda x: x.replace(\".value\", \"\")\n )\n\n res_df[\"text_similarity\"] = res_df[\"wikipedia_title\"].apply(\n lambda s: self.calculate_label_similarity(text, s)\n )\n res_df = (\n res_df[res_df[\"text_similarity\"] >= similarity_thresh]\n .sort_values(\"text_similarity\", ascending=False)\n .reset_index(drop=True)\n )\n res_df = res_df.drop(columns=\"text_similarity\")\n res_df = res_df.reset_index().rename(columns={\"index\": \"rank\"})\n res_df[\"rank\"] = res_df[\"rank\"] + 1\n\n res_df = self.add_score_to_search_results_df(res_df, rank_col=\"rank\")\n\n return res_df","def showFind(self, txt=\"\"):\n self.__searchWidget.showFind(txt)","def searchInspector(search):\n \n try:\n from PyQt4.QtGui import QApplication, QDialog\n from dialog import Ui_Dialog\n except Exception:\n print \"Missing a required library - please install pyQt4.\"\n return\n \n app = QApplication(sys.argv)\n window = QDialog()\n ui = Ui_Dialog()\n ui.setupUi(window)\n ui.updateList(search)\n window.show()\n app.exec_()","def search_for_barbarian():\n search_button = Utils.wait_and_find('search_search')\n if search_button is not None:\n search_button_location = (search_button.x, search_button.y)\n Utils.click(search_button_location)\n else:\n Utils.wait_and_click('search')\n Utils.wait_and_click('search_barbarian')\n Utils.wait_and_click('search_search')","def do_search(request):\n products = Product.objects.filter(name__icontains=request.GET['q'])\n return render(request, \"search_results.html\", {\"products\": products})","def search(request):\n template_var = base_template_vals(request)\n if request.method==\"GET\":\n event_id_list = []\n events_found = []\n events_found_advanced = []\n events_found_basic = []\n checkbox_session = []\n advanced_search = False\n tags = Tag.objects.all()\n template_var[\"tags\"] = tags\n\n query_tags = request.GET.getlist('query_tag')\n if len(query_tags) > 0: # at least one checkbox selected\n advanced_search = True\n query_tag_list = []\n \n #strip >>u''<< prefix\n for query_tag in query_tags:\n checkbox_session.append(str(query_tag))\n query_tag_list.append(str(query_tag))\n \n events_found_advanced = Event.objects.filter(is_approved=True\n ).filter(tags__name__in=query_tag_list)\n \n query = request.GET.get('query', '').strip('\\t\\n\\r')\n if query == '' : #first came in/accidentily type space/..\n if advanced_search:\n events_found_advanced = events_found_advanced #TODO: Bin this line does nothing.\n else:\n events_found_basic = Event.objects.filter(is_approved=True\n ).order_by(\"-created\") \n else:\n if advanced_search:\n events_found_advanced = events_found_advanced.filter(\n tags__name__in=[query])\n else:\n events_found_basic = Event.objects.filter(\n tags__name__in=[query])\n\n #finally\n if advanced_search:\n #take out duplicated result\n for event_found_advanced in events_found_advanced:\n if(event_found_advanced.id not in event_id_list):\n event_id_list.append(event_found_advanced.id)\n events_found.append(event_found_advanced)\n else:\n for event in events_found_basic:\n events_found.append(event)\n\n #sort result\n sorted_method = request.GET.get('sorted_method', 'desc') #default is desc\n if sorted_method == 'desc':\n events_found.sort(key=lambda event: event.event_time, reverse=True) \n elif sorted_method == 'asc':\n events_found.sort(key=lambda event: event.event_time,\n reverse=False) \n elif sorted_method == 'alphabet':\n events_found.sort(key=lambda event: event.title.lower(),\n reverse=False) \n \n template_var[\"events_found\"] = events_found\n\n request.session[\"sorted_method_session\"] = sorted_method\n request.session[\"checkbox_session\"] = checkbox_session\n request.session[\"query_session\"] = query\n return render_to_response(\"event/event_search_results.html\",\n template_var,\n context_instance=RequestContext(request))\n\n return render_to_response(\"event/event_search_results.html\", template_var,\n context_instance=RequestContext(request))","def search(request):\n current_user = request.user\n\n return render(request,'main_templates/search.html')","def searchbar_changed(data):\n print('searching for ' + data['query'])\n if data['query'] != '':\n options = queue.instantiate_options()\n query = data['query'].replace(' ', '+')\n response = get_request(search_uri + query)\n songs = []\n is_explicit_list = []\n\n for track_obj in response.json()['tracks']['items']:\n song_obj, is_explicit = create_song(track_obj, return_is_explicit=True)\n songs.append(song_obj)\n is_explicit_list.append(is_explicit)\n \n if options['safe_mode'] == 'true':\n temp_songs = []\n for i in range(len(songs)):\n if not is_explicit_list[i]:\n temp_songs.append(songs[i])\n songs = temp_songs\n\n if len(songs) > 5:\n songs = songs[:5]\n\n serialized_songs = [song.to_dict() for song in songs]\n emit('suggestions_changed', serialized_songs)","def do_search(request):\n tickets = Ticket.objects.filter(issue_type__icontains=request.GET['q'])\n return render(request, 'tickets.html', {\"tickets\":tickets})","def search_current_auctions(request):\n query = request.GET.get('q')\n auction = Auction.objects.all()\n\n if query:\n results = auction.filter(Q(antiques__name__icontains=query) | Q(antiques__description__icontains=query))\n\n else:\n results = Auction.objects.all()\n\n pages = pagination(request, results, num=4)\n context = {\n 'items': pages[0],\n 'page_range': pages[1]\n }\n\n return render(request, \"showallauctions.html\", context)","def _on_articles_search(self, evt=None):\n \n # set focus\n self._articles_view.SetFocusToQuery()","def search(request):\n\n filterList = ['everyone', 'black', 'hispanic', 'female', 'lgbt', 'immigrants', 'disabled', 'poor']\n sorts = {\"Title(A-Z)\":\"title\", \"Title(Z-A)\":\"-title\", \"Value Increasing\":\"value\", \"Value Decreasing\":\"-value\", \"Due Date\":\"dueDate\"}\n\n #urls agruments\n query = request.GET.get('query', '')\n Filter = request.GET.get('filter', 'Default')\n sortby = request.GET.get('sortby', 'Default')\n pageNum = request.GET.get('page', '1')\n \n postResult = Post.objects.all().order_by(sorts.get(sortby,\"UUID\"))\n\n if(query):\n #search for words in the title, description or organizaton name, then filter out the tags\n postResult = Post.objects.filter((Q(title__icontains=query)|Q(description__icontains=query))|Q(organization__username__icontains=query))\n if(Filter !='Default' and Filter.lower() in filterList): \n #check if the filter the user entered is a tag\n postResult = postResult.filter(tags__name__in=[Filter.lower()]) \n\n #function to help divide our query into groups of 12 for pagination\n pageObj = Paginator(postResult, 12)\n\n #select the page that we want\n page = pageObj.page(pageNum)\n\n\n context = {\n 'result': postResult,\n 'page_obj': page,\n 'query': query,\n 'filter': Filter,\n 'sortby': sortby,\n }\n return render(request, 'search.html', context=context)"],"string":"[\n \"def searchWidget(self):\\n return self.__searchWidget\",\n \"def search_page():\\n return render_template('page_query.html', search_label=g_search_type)\",\n \"def on_searchButton_clicked(self):\\n self.__search()\",\n \"def search_btn_clicked(self, widget, data=None):\\n # Method to handle search here.\\n search_text = self.get_text(\\\"txt_search\\\")\\n print search_text\",\n \"def search(request):\\n raise NotImplementedError\",\n \"def search(self, *args, **kwargs):\",\n \"def name(self): \\n return \\\"search_form\\\"\",\n \"def handler_search_changed(self, widget):\\n #we set the current language filter to the button's label\\n self.filter_on = widget.get_text()\\n text = widget.get_text()\\n #we update the filter, which updates in turn the view\\n self.emit('search-show',text)\\n #self.log_filter.refilter()\",\n \"def search():\\r\\n return render_template(\\\"/home/search.html\\\")\",\n \"def search():\\n pass\",\n \"def search_page(request):\\n if request.method == \\\"GET\\\":\\n page = request.GET.get('q')\\n entries = util.list_entries()\\n entries_set=set(entries)\\n\\n if page in entries_set:\\n return render(request, \\\"encyclopedia/visit_entry.html\\\",{\\n \\\"entry\\\": util.get_entry(page),\\n \\\"title\\\": page\\n })\\n \\n else:\\n results = list(filter(lambda x: page in x, entries))\\n return render(request, \\\"encyclopedia/search_page.html\\\",{\\n \\\"results\\\": results\\n })\",\n \"def search(request):\\n if 'q' in request.GET:\\n term = request.GET['q'].lower()\\n thispushqueryset = pushitem.objects.filter(Q(searchfield__contains= term) )\\n message = _('Searching for %s')%str(term)\\n else:\\n thispushqueryset = pushitem.objects.none()\\n message = _('No search query specified')\\n r = makepage(request,thispushqueryset,{'search_query':request.GET['q'].lower(), 'showall': 1,'message':message,}, template='search.html')\\n return r\",\n \"def on_search(self, widget):\\n if isinstance(self.ref_object, DistributedDetection):\\n self.gtk_def.clear()\\n Gtk_Main.Gtk_Main().notebook._add_distributed_anomaly(self.ref_object.error_path,\\n self.gtk_def,\\n self.entry.get_text().lower())\\n elif isinstance(self.ref_object, gtk.TextView):\\n self._conf_highlight()\\n else:\\n self.gtk_def.search(self.entry.get_text().lower())\",\n \"def onSearch(self):\\n self.mainGrid.showSearchPopup()\\n self.popupActive = True\",\n \"def filter_search(self, request, search, view):\\n raise NotImplementedError(\\\".filter_search() must be overridden.\\\")\",\n \"def search(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def advanced_search():\\n\\n return render_template('Advanced_Search.html')\",\n \"def name(self):\\n return \\\"action_news_search\\\"\",\n \"def new_search(self, widget, data=None):\\n self.artist_name.set_text(\\\"\\\")\\n self.song_name.set_text(\\\"\\\")\\n self.status_bar.hide()\\n self.lyrics_view.hide()\\n self.scroll.hide()\\n self.window.resize(self.width, self.height)\",\n \"def search():\\n # Check for database tables\\n check_db()\\n # Check for GET data\\n search_query = request.args.get(\\\"q\\\", None)\\n # Format search results as HTML\\n search_results = get_search_results_html(search_query)\\n # Format recent searches as HTML\\n recent_searches = get_recent_searches_html()\\n\\n return html_wrapper('<h1>' + SITE_NAME + '''</h1>\\n <form action=\\\"/\\\" method=\\\"GET\\\">\\n <input type=\\\"text\\\" name=\\\"q\\\">\\n <input type=\\\"submit\\\" value=\\\"search\\\">\\n </form>''' + search_results + recent_searches)\",\n \"def load_search_gui(self):\\n pass\",\n \"def search_box(self):\\n tags = SearchBox(\\\"Search Tags: \\\", self.handle_tag_search)\\n tag_search = urwid.LineBox(urwid.AttrWrap(tags, 'editbx', 'editfc'))\\n \\n text = SearchBox(\\\"Search Documents: \\\", self.handle_text_search)\\n text_search = urwid.LineBox(urwid.AttrWrap(text, 'editbx', 'editfc'))\\n\\n l = [ tag_search,\\n urwid.Divider(),\\n text_search]\\n w = urwid.LineBox(urwid.ListBox(urwid.SimpleListWalker(l)))\\n return w\",\n \"def on_search(self, data: Any = None):\\n raise NotImplementedError\",\n \"def search(request):\\n context = {}\\n q = \\\"\\\"\\n try:\\n if request.POST:\\n q = request.POST['q']\\n else:\\n q = request.GET['q']\\n except MultiValueDictKeyError:\\n pass\\n context['query'] = q\\n context['search_entry_list'] = watson.search(q)\\n return render(request, 'search.html', context)\",\n \"def search(self, term):\",\n \"def search(request):\\n\\n # get form data \\n searchItem = request.GET.get(\\\"q\\\")\\n # if searchItem is an exact match redirect to that page\\n if (util.get_entry(searchItem) is not None):\\n return HttpResponseRedirect(reverse(\\\"entry\\\", kwargs={\\n \\\"title\\\": searchItem\\n }))\\n # add any pages with the string in it to results list \\n else: \\n results = []\\n substring = False\\n for title in util.list_entries():\\n if searchItem.upper() in title.upper():\\n results.append(title)\\n if results:\\n substring = True\\n # return results\\n return render(request, \\\"encyclopedia/search.html\\\", {\\n \\\"searchItem\\\": searchItem,\\n \\\"substring\\\": substring,\\n \\\"results\\\": results\\n })\",\n \"def search(self, search):\\n raise NotImplementedError\",\n \"def search_bar(request, search = None):\\n\\tcategories = Category.objects.all()\\n\\n\\tif request.method == 'POST':\\n\\t\\tsearch_form = Search_bar(request.POST)\\n\\t\\tif search_form.is_valid():\\n\\t\\t\\tif search_form.cleaned_data['search']:\\n\\t\\t\\t\\tparameters = '-'.join(search_form.cleaned_data['search'].split(' '))\\n\\t\\t\\t\\treturn HttpResponseRedirect(reverse('tailored:search', kwargs = {'search': parameters}))\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn HttpResponseRedirect('all/')\\n\\n\\tcontext_dict = {}\\n\\tform = Search_bar()\\n\\tcontext_dict['search_bar'] = form\\n\\tcontext_dict['categories'] = categories\\n\\titems = []\\n\\n\\tif search == 'all' or search == None or search == '':\\n\\t\\tsearch = 'all'\\n\\t\\titems = Item.objects.filter(sold_to = None)\\n\\telse:\\n\\t\\tif search != None:\\n\\t\\t\\tsearch = search.split('-')\\n\\t\\t\\tfor word in search:\\n\\t\\t\\t\\titems += Item.objects.filter((Q(description__contains = word ) | Q(title__contains = word)\\n\\t\\t\\t\\t\\t| Q(category = word) | Q(section = word)) & Q(sold_to = None))\\n\\t\\t\\tsearchS = '_'.join(search)\\n\\t\\t\\tcontext_dict['search'] = searchS\\n\\t\\telse:\\n\\t\\t\\treturn home_page(request)\\n\\t\\n\\tmaxi = 0\\n\\t\\n\\tfor item in items:\\n\\t\\tif item.price > maxi:\\n\\t\\t\\tmaxi = item.price\\n\\t\\n\\tcontext_dict['maxi'] = maxi + 5\\n\\tcontext_dict['items'] = items\\n\\tcontext_dict['pages'] = int(len(items)/6)\\n\\t\\n\\treturn render(request, 'tailored/shop_bootstrap.html',context_dict)\",\n \"def __search(self):\\n self.resultList.clear()\\n self.infoLabel.clear()\\n \\n self.buttonBox.button(QDialogButtonBox.Close).setEnabled(False)\\n self.buttonBox.button(QDialogButtonBox.Cancel).setEnabled(True)\\n self.buttonBox.button(QDialogButtonBox.Cancel).setDefault(True)\\n self.searchButton.setEnabled(False)\\n QApplication.processEvents(QEventLoop.ExcludeUserInputEvents)\\n \\n QApplication.setOverrideCursor(Qt.WaitCursor)\\n QApplication.processEvents(QEventLoop.ExcludeUserInputEvents)\\n \\n self.__canceled = False\\n \\n self.__query = [term for term in self.searchEdit.text().strip().split()\\n if term not in PipSearchDialog.Stopwords]\\n self.__client.call(\\n \\\"search\\\",\\n ({\\\"name\\\": self.__query, \\\"summary\\\": self.__query}, \\\"or\\\"),\\n self.__processSearchResult,\\n self.__searchError\\n )\",\n \"def search_api(request):\\n data = ApiViewFilters(request.GET, queryset=ApiView.objects.all())\\n return render(request, 'template.html', {'filter': data})\",\n \"def add_search_form():\\n g.form = forms.SearchPlaces(formdata=None)\\n g.action = url_for(\\\"page.search_query\\\")\",\n \"def search(request):\\n title = \\\"Voices search\\\"\\n search_term = request.params.get('search_term','')\\n form = Form(request)\\n searchstring = u'%%%s%%' % search_term\\n\\n # generic_filter can be applied to all Node (and subclassed) objects\\n\\n generic_filter = or_(\\n Content.title.like(searchstring),\\n Content.body.like(searchstring),\\n )\\n\\n results = DBSession.query(Content).filter(Content.type !='listing').filter(generic_filter).\\\\\\n order_by(Content.title.asc()).all()\\n\\n\\n page_url = PageURL_WebOb(request)\\n page = int(request.params.get(\\\"page\\\", 1))\\n paginator = Page(results,\\n page=page,\\n items_per_page=10,\\n url=page_url)\\n\\n return render_to_response(\\\"buddy:templates/home/searchresult.mako\\\",\\n dict(paginator=paginator,title=title,\\n form=FormRenderer(form)),request=request)\",\n \"def createSearchButton(dash_instance):\\n\\t\\n\\tsearchButton = html.Button(id='searchButton', n_clicks=0, children='View', style=dict(marginTop='25', marginLeft=450, width=100, height=50))\\n\\tupdatingLabel = html.Div(id='updatingArea')\\n\\treturn searchButton, updatingLabel\",\n \"def search_for_books(main_page): # Add information to the printout if the book is rented\\n\\n type_of_search = 0\\n\\n header = \\\"\\\"\\\"\\n Do you want to search for books by the first letter of the title\\n or by the type?\\n \\\"\\\"\\\"\\n search_choices= (\\n (\\\"To search by letter\\\", search_by_letter),\\n (\\\"To search by type\\\", search_by_type),\\n (\\\"To exit\\\",exit.exit_to_main)\\n )\\n\\n book_search = Screen(header,search_choices,\\n main_page.login, main_page.password)\\n book_search.activate()\",\n \"def search(request):\\n if request.method==\\\"GET\\\":\\n data = request.GET\\n else:\\n data = request.POST\\n\\n keywords = data.get('keywords', '').split(' ')\\n\\n keywords = filter(None, keywords)\\n\\n results = {}\\n\\n signals.application_search.send(None, request=request, keywords=keywords, results=results)\\n \\n context = RequestContext(request, {\\n 'results': results,\\n 'keywords' : keywords})\\n return render_to_response('localsite/search.html', context)\",\n \"def question_new_search():\",\n \"def search(request):\\n if 'q' in request.GET:\\n term = request.GET['q']\\n story_list = Story.objects.filter(Q(title__contains=term)|Q(markdown_content__contains=term))\\n heading = \\\"Search results\\\"\\n return render_to_response(\\\"cms/story_list.html\\\",locals())\",\n \"def __init__(self, name=\\\"\\\"):\\n super().__init__(\\\"search\\\", name)\",\n \"def on_searchin_changed(self):\\r\\n\\r\\n self.check_searchin()\",\n \"def search_param(command):\\n namespace = app.main(command)\\n assert namespace.command == 'sv' or namespace.command == \\\"searchparam\\\"\\n assert namespace.term == \\\"test\\\"\",\n \"def search(self):\\r\\n return resources.Search(self)\",\n \"def search():\\n import booksearch as bs\\n\\n opt = var.get()\\n term = searchBox.get()\\n term2 = dateBox.get()\\n\\n # Case statement (substitute) for different search areas\\n # Each key is an option in the OptionMenu\\n searchBy = {\\n \\\"Title & Author\\\" : bs.search(term),\\n \\\"ID\\\" : bs.bookID(term),\\n \\\"Date\\\" : bs.dateRange(term, term2),\\n }\\n query = searchBy[opt] # Make & stores a query (2D list)\\n\\n # Repopulates table\\n if term != \\\"\\\":\\n populate(query)\",\n \"def search_layout():\\n all_input_processors = all_processors_for_searching()\\n search_toolbar = SearchToolbar(\\\"Search Toolbar\\\", vi_mode=True)\\n control = BufferControl(\\n Buffer(document=Document(), read_only=True),\\n search_buffer_control=search_toolbar.control,\\n preview_search=True,\\n include_default_input_processors=False,\\n input_processors=all_input_processors,\\n )\\n style = style_from_pygments_cls(pygments_style)\\n # Apparently theres something wrong with the style other put it in the\\n # HSplit constructor\\n buf_container = Window(control, style=style)\\n search_container = Window(search_toolbar, style=style)\\n container = HSplit([buf_container, search_container])\\n return container\",\n \"def search(request):\\n return render(request, \\\"search.html\\\")\",\n \"def search(self, query):\",\n \"def __doSearch(self):\\n if (\\n self.__replaceMode and\\n not e5App().getObject(\\\"ViewManager\\\").checkAllDirty()\\n ):\\n return\\n \\n self.__cancelSearch = False\\n \\n if self.filterCheckBox.isChecked():\\n fileFilter = self.filterEdit.text()\\n fileFilterList = [\\n \\\"^{0}$\\\".format(filter.replace(\\\".\\\", r\\\"\\\\.\\\").replace(\\\"*\\\", \\\".*\\\"))\\n for filter in fileFilter.split(\\\";\\\")\\n ]\\n filterRe = re.compile(\\\"|\\\".join(fileFilterList))\\n \\n if self.projectButton.isChecked():\\n if self.filterCheckBox.isChecked():\\n files = [self.project.getRelativePath(file)\\n for file in\\n self.__getFileList(\\n self.project.getProjectPath(), filterRe)]\\n else:\\n files = []\\n if self.sourcesCheckBox.isChecked():\\n files += self.project.pdata[\\\"SOURCES\\\"]\\n if self.formsCheckBox.isChecked():\\n files += self.project.pdata[\\\"FORMS\\\"]\\n if self.interfacesCheckBox.isChecked():\\n files += self.project.pdata[\\\"INTERFACES\\\"]\\n if self.protocolsCheckBox.isChecked():\\n files += self.project.pdata[\\\"PROTOCOLS\\\"]\\n if self.resourcesCheckBox.isChecked():\\n files += self.project.pdata[\\\"RESOURCES\\\"]\\n elif self.dirButton.isChecked():\\n if not self.filterCheckBox.isChecked():\\n filters = []\\n if self.sourcesCheckBox.isChecked():\\n filters.extend(\\n [\\\"^{0}$\\\".format(\\n assoc.replace(\\\".\\\", r\\\"\\\\.\\\").replace(\\\"*\\\", \\\".*\\\"))\\n for assoc in list(\\n Preferences.getEditorLexerAssocs().keys())\\n if assoc not in self.formsExt + self.interfacesExt +\\n self.protocolsExt])\\n if self.formsCheckBox.isChecked():\\n filters.append(self.filterForms)\\n if self.interfacesCheckBox.isChecked():\\n filters.append(self.filterInterfaces)\\n if self.protocolsCheckBox.isChecked():\\n filters.append(self.filterProtocols)\\n if self.resourcesCheckBox.isChecked():\\n filters.append(self.filterResources)\\n filterString = \\\"|\\\".join(filters)\\n filterRe = re.compile(filterString)\\n files = self.__getFileList(\\n os.path.abspath(self.dirPicker.currentText()),\\n filterRe)\\n elif self.openFilesButton.isChecked():\\n vm = e5App().getObject(\\\"ViewManager\\\")\\n vm.checkAllDirty()\\n files = vm.getOpenFilenames()\\n \\n self.findList.clear()\\n QApplication.processEvents()\\n QApplication.processEvents()\\n self.findProgress.setMaximum(len(files))\\n \\n # retrieve the values\\n reg = self.regexpCheckBox.isChecked()\\n wo = self.wordCheckBox.isChecked()\\n cs = self.caseCheckBox.isChecked()\\n ct = self.findtextCombo.currentText()\\n if reg:\\n txt = ct\\n else:\\n txt = re.escape(ct)\\n if wo:\\n txt = \\\"\\\\\\\\b{0}\\\\\\\\b\\\".format(txt)\\n flags = re.UNICODE\\n if not cs:\\n flags |= re.IGNORECASE\\n try:\\n search = re.compile(txt, flags)\\n except re.error as why:\\n E5MessageBox.critical(\\n self,\\n self.tr(\\\"Invalid search expression\\\"),\\n self.tr(\\\"\\\"\\\"<p>The search expression is not valid.</p>\\\"\\\"\\\"\\n \\\"\\\"\\\"<p>Error: {0}</p>\\\"\\\"\\\").format(str(why)))\\n self.stopButton.setEnabled(False)\\n self.findButton.setEnabled(True)\\n self.findButton.setDefault(True)\\n return\\n # reset the findtextCombo\\n if ct in self.searchHistory:\\n self.searchHistory.remove(ct)\\n self.searchHistory.insert(0, ct)\\n self.findtextCombo.clear()\\n self.findtextCombo.addItems(self.searchHistory)\\n Preferences.Prefs.settings.setValue(\\n \\\"FindFileDialog/SearchHistory\\\",\\n self.searchHistory[:30])\\n \\n if self.__replaceMode:\\n replTxt = self.replacetextCombo.currentText()\\n if replTxt in self.replaceHistory:\\n self.replaceHistory.remove(replTxt)\\n self.replaceHistory.insert(0, replTxt)\\n self.replacetextCombo.clear()\\n self.replacetextCombo.addItems(self.replaceHistory)\\n Preferences.Prefs.settings.setValue(\\n \\\"FindFileDialog/ReplaceHistory\\\",\\n self.replaceHistory[:30])\\n \\n if self.dirButton.isChecked():\\n searchDir = self.dirPicker.currentText()\\n if searchDir in self.dirHistory:\\n self.dirHistory.remove(searchDir)\\n self.dirHistory.insert(0, searchDir)\\n self.dirPicker.clear()\\n self.dirPicker.addItems(self.dirHistory)\\n Preferences.Prefs.settings.setValue(\\n \\\"FindFileDialog/DirectoryHistory\\\",\\n self.dirHistory[:30])\\n \\n # set the button states\\n self.stopButton.setEnabled(True)\\n self.stopButton.setDefault(True)\\n self.findButton.setEnabled(False)\\n \\n # now go through all the files\\n self.__populating = True\\n self.findList.setUpdatesEnabled(False)\\n progress = 0\\n breakSearch = False\\n occurrences = 0\\n fileOccurrences = 0\\n for file in files:\\n self.__lastFileItem = None\\n found = False\\n if self.__cancelSearch or breakSearch:\\n break\\n \\n self.findProgressLabel.setPath(file)\\n \\n if self.projectButton.isChecked():\\n fn = os.path.join(self.project.ppath, file)\\n else:\\n fn = file\\n # read the file and split it into textlines\\n try:\\n text, encoding, hashStr = Utilities.readEncodedFileWithHash(fn)\\n lines = text.splitlines(True)\\n except (UnicodeError, IOError):\\n progress += 1\\n self.findProgress.setValue(progress)\\n continue\\n \\n # now perform the search and display the lines found\\n count = 0\\n for line in lines:\\n if self.__cancelSearch:\\n break\\n \\n count += 1\\n contains = search.search(line)\\n if contains:\\n occurrences += 1\\n found = True\\n start = contains.start()\\n end = contains.end()\\n if self.__replaceMode:\\n rline = search.sub(replTxt, line)\\n else:\\n rline = \\\"\\\"\\n line = self.__stripEol(line)\\n if len(line) > 1024:\\n line = \\\"{0} ...\\\".format(line[:1024])\\n if self.__replaceMode:\\n if len(rline) > 1024:\\n rline = \\\"{0} ...\\\".format(line[:1024])\\n line = \\\"- {0}\\\\n+ {1}\\\".format(\\n line, self.__stripEol(rline))\\n self.__createItem(file, count, line, start, end,\\n rline, hashStr)\\n \\n if self.feelLikeCheckBox.isChecked():\\n fn = os.path.join(self.project.ppath, file)\\n self.sourceFile.emit(fn, count, \\\"\\\", start, end)\\n QApplication.processEvents()\\n breakSearch = True\\n break\\n \\n QApplication.processEvents()\\n \\n if found:\\n fileOccurrences += 1\\n progress += 1\\n self.findProgress.setValue(progress)\\n \\n if not files:\\n self.findProgress.setMaximum(1)\\n self.findProgress.setValue(1)\\n \\n resultFormat = self.tr(\\\"{0} / {1}\\\", \\\"occurrences / files\\\")\\n self.findProgressLabel.setPath(resultFormat.format(\\n self.tr(\\\"%n occurrence(s)\\\", \\\"\\\", occurrences),\\n self.tr(\\\"%n file(s)\\\", \\\"\\\", fileOccurrences)))\\n \\n self.findList.setUpdatesEnabled(True)\\n self.findList.sortItems(self.findList.sortColumn(),\\n self.findList.header().sortIndicatorOrder())\\n self.findList.resizeColumnToContents(1)\\n if self.__replaceMode:\\n self.findList.header().resizeSection(0, self.__section0Size + 30)\\n self.findList.header().setStretchLastSection(True)\\n self.__populating = False\\n \\n self.stopButton.setEnabled(False)\\n self.findButton.setEnabled(True)\\n self.findButton.setDefault(True)\\n \\n if breakSearch:\\n self.close()\",\n \"def on_pre_enter(self, *args):\\n self.ids['search'].text = ''\\n self.filter()\",\n \"def page_body():\\r\\n st.header(\\\"Search\\\")\\r\\n st.subheader(\\\"Search For SMEs With A Few Different Options\\\")\\r\\n\\r\\n search_mode_selection = st.radio(\\r\\n help=\\\"Search For SMEs That Have Particular Connections, Titles, Or Names...\\\",\\r\\n label=\\\"Search By\\\",\\r\\n options=(SearchMode.Connection.value, SearchMode.JobTitle.value, SearchMode.Name.value),\\r\\n )\\r\\n\\r\\n search_form = st.form(key=\\\"search_form\\\", clear_on_submit=False)\\r\\n search_query = search_form.text_input(label=\\\"\\\", value=\\\"Search...\\\", max_chars=50)\\r\\n search_button = search_form.form_submit_button(label=\\\"Search\\\")\\r\\n\\r\\n if search_button:\\r\\n results = get_search_results(search_query, SearchMode[str(search_mode_selection).replace(\\\" \\\", \\\"\\\")])\\r\\n\\r\\n # Loop through the results returned from the database query\\r\\n for result in results:\\r\\n result_dict = result.to_dict() # Convert internally to a Python dict\\r\\n\\r\\n # dict keys here are actually database keys in Firestore. You would need to be signed in to see the proper values\\r\\n with st.expander(result_dict[\\\"name\\\"] + \\\" - \\\" + str(result_dict[\\\"age\\\"]) + \\\" years old\\\"):\\r\\n st.header(result_dict[\\\"name\\\"])\\r\\n st.write(result_dict[\\\"jobTitle\\\"])\\r\\n\\r\\n st.subheader(\\\"Personal Summary\\\")\\r\\n st.write(result_dict[\\\"personalSummary\\\"])\\r\\n\\r\\n if result_dict[\\\"companyName\\\"]:\\r\\n st.subheader(\\\"Works At\\\")\\r\\n st.write(result_dict[\\\"companyName\\\"])\\r\\n\\r\\n if result_dict[\\\"connections\\\"]:\\r\\n st.subheader(result_dict[\\\"name\\\"] + \\\"'s Connections\\\")\\r\\n st.write(\\\", \\\".join(result_dict[\\\"connections\\\"]))\",\n \"def click_search_button(self):\\n self.click_element(self.generic_search_button_locator)\",\n \"def showSearchWidget(self):\\n self.__replaceWidget.hide()\\n self.__searchWidget.show()\\n self.__searchWidget.show(self.textForFind())\",\n \"def search_google(self, widget):\\n if not self.searchstring:\\n return\\n base_uri = \\\"https://www.google.com/search?q=%s\\\"\\n uri = base_uri % urllib.parse.quote(self.searchstring.encode(\\\"utf-8\\\"))\\n Gtk.show_uri_on_window(None, uri, Gdk.CURRENT_TIME)\",\n \"def search_resources(self,searchtext):\\n\\n self.search.value = searchtext\\n self.submit.click()\",\n \"def show_search_resuls(builder, show: bool):\\n show_ui_component(builder, 'findYourInstituteScrolledWindow', show)\",\n \"def processSearchLanguage(dataField):\\n\\tvalidCommands=[\\\"FILTER\\\",\\\"TYPE\\\",\\\"ORDER\\\",\\\"SORT\\\"]\\n\\trawData=getDataFromWidget(dataField)\\n\\t#Split the data\\n\\tsplitData=rawData.split(\\\"=\\\")\\n\\tif len(splitData) > 1:\\n\\t\\tcommand=splitData[0]\\n\\t\\tcommandData=splitData[1]\\n\\t\\tif len(commandData.split()) > 0:\\n\\t\\t\\tcommandName=str(command).upper()\\n\\t\\t\\tif commandName in validCommands:\\n\\t\\t\\t\\t#Filter\\n\\t\\t\\t\\tif commandName == \\\"FILTER\\\" or commandName == \\\"TYPE\\\":\\n\\t\\t\\t\\t\\tfilterPodListbox(commandData)\\n\\t\\t\\t\\telif commandName == \\\"ORDER\\\" or commandName == \\\"SORT\\\":\\n\\t\\t\\t\\t\\torderPodListbox(commandData)\",\n \"def search_frame():\\r\\n global file, split, detail, search_question_frame, search\\r\\n\\r\\n add_question_Frame.grid_forget()\\r\\n quiz_frame.grid_forget()\\r\\n one_person_quiz_frame.grid_forget()\\r\\n question_list_frame.grid_forget()\\r\\n search_question_frame.grid_forget()\\r\\n select_question_frame.grid_forget()\\r\\n\\r\\n search_question_frame.grid(row=0, column=0, rowspan=7, columnspan=5, sticky=N + E + S + W)\\r\\n\\r\\n Label(search_question_frame, text='Search for question').grid()\\r\\n search_entry = Entry(search_question_frame, text=search)\\r\\n search.get()\\r\\n search_entry.grid()\\r\\n\\r\\n search_button.grid()\",\n \"def search():\\n\\n # Make sure user is logged in\\n if \\\"username\\\" not in session:\\n return render_template(\\\"index.html\\\", message=\\\"Please login to view that page!\\\")\\n\\n if request.method == \\\"POST\\\":\\n # Get search form\\n search = request.form.get(\\\"search\\\").strip()\\n search_partial = search + '%'\\n\\n books = db.execute(\\\"SELECT * FROM books WHERE isbn LIKE :search OR author LIKE :search OR title LIKE :search\\\", {\\\"search\\\": search_partial}).fetchall()\\n\\n return render_template(\\\"search.html\\\", username=session[\\\"username\\\"], search=search, books=books)\\n # Actually you cant get this without being logged in\\n return render_template(\\\"search.html\\\")\",\n \"def search(self):\\n premium = self.config.get('premium', False)\\n\\n self.params[self.opts['keyword']['query_key']] = self.config[self.opts['keyword']['config_key']] # keyword\\n # Selection params\\n self.append_param('tag_mode', 'selection')\\n if premium:\\n self.append_param('order_premium', 'selection')\\n else:\\n self.append_param('order_not_premium', 'selection')\\n\\n self.append_param('type', 'selection')\\n self.append_param('tool', 'selection')\\n self.append_param('ratio', 'selection')\\n self.append_param('mode', 'selection')\\n\\n # Number params\\n self.append_param('min_width', 'number')\\n self.append_param('max_width', 'number')\\n self.append_param('min_height', 'number')\\n self.append_param('max_height', 'number')\\n if premium:\\n self.append_param('min_bookmark', 'number')\\n self.append_param('max_bookmark', 'number')\\n else:\\n self.set_bookmark_filter()\\n\\n # Date params\\n self.append_param('start_time', 'date')\\n self.append_param('end_time', 'date')\\n\\n # multi work filter\\n self.filters['multi'] = self.config.get('download_multi', False)\\n\\n for i in range(self.config['start_page'], self.config['end_page'] + 1):\\n self.params['p'] = i\\n self.headers['Referer'] = 'https://www.pixiv.net/'\\n url ='https://www.pixiv.net/search.php'\\n html = self.session.get(url, headers = self.headers, params = self.params, timeout = 10, proxies = self.proxies)\\n\\n soup = BeautifulSoup(html.text, 'lxml')\\n data_items = json.loads(soup.find('input', id = 'js-mount-point-search-result-list')['data-items'])\\n\\n return self.extract_work_info(data_items)\",\n \"def search_form_servee(context, cl):\\r\\n return {\\r\\n \\\"request\\\": context[\\\"request\\\"],\\r\\n \\\"cl\\\": cl,\\r\\n \\\"show_result_count\\\": cl.result_count != cl.full_result_count,\\r\\n \\\"search_var\\\": \\\"q\\\"\\r\\n }\",\n \"def navsearch():\\r\\n\\r\\n # initializes page title and header\\r\\n page_title = 'Ingredient search'\\r\\n page_header = 'Ingredient search results:'\\r\\n\\r\\n # determines the limit and the offset for the pagination\\r\\n if 'limit' in request.args:\\r\\n try:\\r\\n limit = int(request.args['limit'])\\r\\n\\r\\n except:\\r\\n # default limit on error\\r\\n limit = 4\\r\\n\\r\\n else:\\r\\n # default limit\\r\\n limit = 4\\r\\n\\r\\n if 'offset' in request.args:\\r\\n try:\\r\\n offset = int(request.args['offset'])\\r\\n\\r\\n except:\\r\\n # default offset on error\\r\\n offset = 0\\r\\n\\r\\n else:\\r\\n # default offset\\r\\n offset = 0\\r\\n\\r\\n # determines if search data comes from GET or POST method\\r\\n if request.method == 'GET':\\r\\n if 'find' in request.args:\\r\\n # search text in case if argument exists\\r\\n find_text = request.args['find']\\r\\n\\r\\n else:\\r\\n # returns error template\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Bad argument error! (/navsearch)'\\r\\n )\\r\\n\\r\\n elif request.method == 'POST':\\r\\n # search text from the request form\\r\\n find_text = request.form.get('search')\\r\\n\\r\\n else:\\r\\n # returns error template\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Unhandled method error! (/navsearch)'\\r\\n )\\r\\n\\r\\n # determines all recipe titles in ascendent order\\r\\n search_text = re.compile(find_text, re.IGNORECASE)\\r\\n get_titles = mongo.db.recipes.find(\\r\\n {'ingredients': {'$in': [search_text]}}).sort('title')\\r\\n\\r\\n try:\\r\\n # determines the title at the offset\\r\\n start = get_titles[offset]['title']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/navsearch)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'navsearch',\\r\\n find=find_text,\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n if offset + limit >= get_titles.count():\\r\\n next_link = ''\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'navsearch',\\r\\n find=find_text,\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n recipes = mongo.db.recipes.find(\\r\\n {'ingredients': {'$in': [search_text]}, 'title': {'$gte': start}}\\r\\n ).sort('title').limit(limit)\\r\\n\\r\\n if recipes.count() > 0:\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'navsearch.html',\\r\\n recipes=recipes,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n else:\\r\\n # returns to an error message if there is no reult\\r\\n return render_template('error.html', msg='No result found!')\",\n \"def runSearch():\\n\\tglobal processLanguageOn\\n\\tdataToFind=getDataFromWidget(podSearchEntry)\\n\\t#Search through the keys otherwise data changes\\n\\tdataSource=podListbox.data.keys()\\n\\t#Store the results of the search\\n\\tresults=[]\\n\\t#Search the data source\\n\\tfor item in dataSource:\\n\\t\\tif searchDataSource(dataToFind,[item],capital=True,full=False):\\n\\t\\t\\tresults.append(item)\\n\\n\\t#Add the results to screen\\n\\tpodListbox.delete(0,END)\\n\\tfor item in results:\\n\\t\\tpodListbox.addExisting(item)\\n\\n\\tif processLanguageOn:\\n\\t\\tprocessSearchLanguage(podSearchEntry)\",\n \"def shop_items(request):\\n\\n items = Item.objects.all()\\n\\n query = None\\n\\n \\\"\\\"\\\" Used Code Institute Search logic from Tutorial \\\"\\\"\\\"\\n if 'query' in request.GET:\\n query = request.GET['query']\\n if not query:\\n messages.error(request, \\\"Please enter your search\\\")\\n return redirect(reverse('items'))\\n \\n queries = Q(name__icontains=query) | Q(item_description__icontains=query)\\n items = items.filter(queries)\\n\\n context = {\\n 'items': items,\\n 'search_term': query,\\n }\\n\\n return render(request, 'items/items.html', context)\",\n \"def search_category(self):\\n return _(self.child_class)\",\n \"def createSearchOption(dash_instance):\\n\\tsearchLabel = html.Label(id='SearchLabel', \\n\\t style=dict(fontWeight='bold', width=50, marginLeft='100', marginRight='20', display='block'), \\n\\t\\t\\t\\t\\t\\t\\t children='Search By: ')\\n\\tradioOptions = [{'label': 'Group Caption', 'value': 'GName'}, {'label': 'Node Caption', 'value': 'NName'}, {'label': 'IP Address', 'value': 'IP'}]\\n\\tsearchRadio = dcc.RadioItems(id=\\\"SearchRadio\\\", \\n\\t\\t\\t\\t\\t\\t\\t\\t options=radioOptions, \\n\\t\\t\\t\\t\\t\\t\\t\\t style=dict(width=350, marginRight='25', marginLeft='50', marginTop='30', display='block', textAlign='left'), \\n\\t\\t\\t\\t\\t\\t\\t\\t labelStyle={'display': 'block'}, value='NName')\\n\\tsearchBar = dcc.Input(id='SearchBar', \\n\\t\\t\\t\\t\\t\\t style=dict(width=240, height=30, marginLeft='175', marginBottom='30', marginRight='25'), \\n\\t\\t\\t\\t\\t\\t value='', type='text', placeholder='Enter search term...')\\n\\t\\t\\t\\t\\t\\t \\n\\treturn searchLabel, searchRadio, searchBar\",\n \"async def search(self, *args, **kwargs):\\n pass\",\n \"def search():\\n\\n # POST\\n if request.method == \\\"POST\\\":\\n\\n # validate form submission\\n if not request.form.get(\\\"intervention\\\"):\\n return render_template(\\\"results.html\\\", results=entries.values())\\n ''' \\n elif not request.form.get(\\\"setting\\\"):\\n return apology(\\\"missing setting\\\")\\n elif not request.form.get(\\\"emrpref\\\"):\\n return apology(\\\"missing emr pref\\\")\\n elif not request.form.get(\\\"budget\\\"):\\n return apology(\\\"missing budget\\\")'''\\n \\n results = []\\n for k in entries:\\n print('entries', entries[k]['Keywords'])\\n print('term', request.form.get(\\\"intervention\\\"))\\n if request.form.get(\\\"intervention\\\") in entries[k]['Keywords']:\\n print('ya')\\n results.append(entries[k])\\n\\n\\n return render_template(\\\"results.html\\\", results=results)\\n\\n\\n # GET\\n else:\\n return render_template(\\\"search.html\\\")\",\n \"def search(self):\\r\\n return resource.Search(self)\",\n \"def on_buttonBox_clicked(self, button):\\n if button == self.findButton:\\n self.__doSearch()\\n elif button == self.stopButton:\\n self.__stopSearch()\",\n \"def search():\\n try:\\n query = request.args.get(\\\"q\\\").lower()\\n except AttributeError:\\n query = request.args.get(\\\"q\\\")\\n\\n # Adding browse functionality\\n browse = request.args.get(\\\"browse\\\")\\n\\n if browse is None:\\n # Select all rows with a column value that includes query\\n results = db.execute(\\\"SELECT * FROM books \\\"\\n \\\"WHERE LOWER(isbn) LIKE CONCAT('%', :q, '%')\\\"\\n \\\"OR LOWER(title) LIKE CONCAT('%', :q, '%') \\\"\\n \\\"OR LOWER(author) LIKE CONCAT('%', :q, '%') \\\"\\n \\\"ORDER BY title LIMIT 100\\\", {'q': query}).fetchall()\\n else:\\n # Select titles starting with letter\\n results = db.execute(\\n \\\"SELECT * FROM books \\\"\\n \\\"WHERE LOWER(title) LIKE CONCAT(:q, '%') \\\"\\n \\\"ORDER BY title\\\", {'q': query}).fetchall()\\n\\n return render_template(\\\"search.html\\\", browse=browse, query=query, results=results)\",\n \"def search_wikipedia(self):\\n self.summary.delete('1.0', tk.END)\\n possibilities = wk.search(self.search_box.get('1.0',tk.END).replace(\\\"\\\\n\\\",\\\"\\\"))\\n if len(possibilities) > 0:\\n try:\\n p = wk.page(possibilities[0])\\n except wk.DisambiguationError as e:\\n p = wk.page(e.options[0])\\n self.summary.configure(state=\\\"normal\\\")\\n self.summary.delete('1.0', tk.END)\\n self.summary.insert('1.0', p.summary)\\n self.summary.configure(state=\\\"disabled\\\")\\n self.p = p\\n self.update_category_map(p.categories)\\n self.get_news()\\n return None\",\n \"def search():\\n query = input('Please enter your search query\\\\n')\\n # For now, we will just print the whole database\\n #db_actions.display()\\n db_actions.search(query)\",\n \"def other_search(self):\\n test = self.ask_zoekarg.text()\\n if test:\\n self.parent().search_arg = test\\n self.parent().do_select()\",\n \"def click_inbound_statement_search_button(self):\\n self.click_element(self.page_header_locator)\\n self.click_element(self.inbound_statement_search_button_locator, True)\",\n \"def search_venues_form():\\n # seach for Hop should return \\\"The Musical Hop\\\".\\n # search for \\\"Music\\\" should return \\\"The Musical Hop\\\" and \\\"Park Square Live Music & Coffee\\\"\\n return render_template(\\n 'pages/search_venues.html'\\n )\",\n \"def search():\\n student_to_find=request.args.get(\\\"student\\\", None)\\n print(f\\\"A buscar: {student_to_find}\\\")\\n student_list=search_student(student_to_find)\\n return render_template(\\\"search.html\\\",student_list_result=student_list)\",\n \"def search_book():\\n\\n title = request.form.get(\\\"search\\\")\\n books = book_search_results(GR_KEY, title)\\n acct = get_current_account(session['acct'])\\n search = True\\n\\n return render_template(\\\"index.html\\\", books=books, acct=acct, search=search)\",\n \"def startSearch(self, fn, params, searchType):\\n\\t\\tself.mySearchAnnotation = bimpy.interface.bSearchAnnotations(self.mySimpleStack.slabList,\\n\\t\\t\\t\\t\\t\\t\\tfn = fn,\\n\\t\\t\\t\\t\\t\\t\\tparams = params,\\n\\t\\t\\t\\t\\t\\t\\tsearchType = searchType)\\n\\t\\t# self.searchWidget.searchTable()\\n\\t\\tself.mySearchAnnotation.searchFinishedSignal.connect(self.searchWidget.searchTable().slot_SearchFinished)\\n\\t\\tself.mySearchAnnotation.searchNewHitSignal.connect(self.searchWidget.searchTable().slot_newSearchHit)\\n\\t\\tself.mySearchAnnotation.start()\",\n \"def search(request):\\n\\n term = \\\"\\\"\\n organizations = None\\n memberships = None\\n events = None\\n persons = None\\n airports = None\\n training_requests = None\\n comments = None\\n only_result = None\\n\\n if request.method == \\\"GET\\\" and \\\"term\\\" in request.GET:\\n form = SearchForm(request.GET)\\n if form.is_valid():\\n term = form.cleaned_data.get(\\\"term\\\", \\\"\\\")\\n tokens = re.split(r\\\"\\\\s+\\\", term)\\n\\n organizations = Organization.objects.filter(\\n Q(domain__icontains=term) | Q(fullname__icontains=term)\\n ).order_by(\\\"fullname\\\")\\n if len(organizations) == 1 and not only_result:\\n only_result = organizations[0]\\n\\n memberships = Membership.objects.filter(\\n registration_code__icontains=term\\n ).order_by(\\\"-agreement_start\\\")\\n if len(memberships) == 1 and not only_result:\\n only_result = memberships[0]\\n\\n events = Event.objects.filter(\\n Q(slug__icontains=term)\\n | Q(host__domain__icontains=term)\\n | Q(host__fullname__icontains=term)\\n | Q(url__icontains=term)\\n | Q(contact__icontains=term)\\n | Q(venue__icontains=term)\\n | Q(address__icontains=term)\\n ).order_by(\\\"-slug\\\")\\n if len(events) == 1 and not only_result:\\n only_result = events[0]\\n\\n # if user searches for two words, assume they mean a person\\n # name\\n if len(tokens) == 2:\\n name1, name2 = tokens\\n complex_q = (\\n (Q(personal__icontains=name1) & Q(family__icontains=name2))\\n | (Q(personal__icontains=name2) & Q(family__icontains=name1))\\n | Q(email__icontains=term)\\n | Q(secondary_email__icontains=term)\\n | Q(github__icontains=term)\\n )\\n persons = Person.objects.filter(complex_q)\\n else:\\n persons = Person.objects.filter(\\n Q(personal__icontains=term)\\n | Q(family__icontains=term)\\n | Q(email__icontains=term)\\n | Q(secondary_email__icontains=term)\\n | Q(github__icontains=term)\\n ).order_by(\\\"family\\\")\\n\\n if len(persons) == 1 and not only_result:\\n only_result = persons[0]\\n\\n airports = Airport.objects.filter(\\n Q(iata__icontains=term) | Q(fullname__icontains=term)\\n ).order_by(\\\"iata\\\")\\n if len(airports) == 1 and not only_result:\\n only_result = airports[0]\\n\\n training_requests = TrainingRequest.objects.filter(\\n Q(group_name__icontains=term)\\n | Q(family__icontains=term)\\n | Q(email__icontains=term)\\n | Q(github__icontains=term)\\n | Q(affiliation__icontains=term)\\n | Q(location__icontains=term)\\n | Q(user_notes__icontains=term)\\n )\\n if len(training_requests) == 1 and not only_result:\\n only_result = training_requests[0]\\n\\n comments = Comment.objects.filter(\\n Q(comment__icontains=term)\\n | Q(user_name__icontains=term)\\n | Q(user_email__icontains=term)\\n | Q(user__personal__icontains=term)\\n | Q(user__family__icontains=term)\\n | Q(user__email__icontains=term)\\n | Q(user__github__icontains=term)\\n ).prefetch_related(\\\"content_object\\\")\\n if len(comments) == 1 and not only_result:\\n only_result = comments[0]\\n\\n # only 1 record found? Let's move to it immediately\\n if only_result and not form.cleaned_data[\\\"no_redirect\\\"]:\\n msg = format_html(\\n \\\"You were moved to this page, because your search <i>{}</i> \\\"\\n \\\"yields only this result.\\\",\\n term,\\n )\\n if isinstance(only_result, Comment):\\n messages.success(request, msg)\\n return redirect(\\n only_result.content_object.get_absolute_url()\\n + \\\"#c{}\\\".format(only_result.id)\\n )\\n elif hasattr(only_result, \\\"get_absolute_url\\\"):\\n messages.success(request, msg)\\n return redirect(only_result.get_absolute_url())\\n\\n else:\\n messages.error(request, \\\"Fix errors below.\\\")\\n\\n # if empty GET, we'll create a blank form\\n else:\\n form = SearchForm()\\n\\n context = {\\n \\\"title\\\": \\\"Search\\\",\\n \\\"form\\\": form,\\n \\\"term\\\": term,\\n \\\"organisations\\\": organizations,\\n \\\"memberships\\\": memberships,\\n \\\"events\\\": events,\\n \\\"persons\\\": persons,\\n \\\"airports\\\": airports,\\n \\\"comments\\\": comments,\\n \\\"training_requests\\\": training_requests,\\n }\\n return render(request, \\\"dashboard/search.html\\\", context)\",\n \"def do_search(request):\\n products = Product.objects.filter(title__icontains=request.GET['q'])\\n return render(request, \\\"products.html\\\", {\\\"products\\\": products})\",\n \"def searchResultsView(request):\\n bloggerName = request.GET.get(\\\"search_blogger\\\")\\n bloggers = Blogger.objects.all().filter(name__icontains=bloggerName)\\n\\n context = {\\n \\\"bloggers\\\": bloggers,\\n }\\n return render(request, \\\"blog/search_results.html\\\", context)\",\n \"def admin_search_student(request):\\n\\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\\n if request.session['type'] == 'S' or request.session['type'] == 'R': return redirect(reverse(URL_FORBIDDEN))\\n\\n if request.method == \\\"GET\\\":\\n return render(\\n request,\\n 'app/admin/admin_search_student.html',\\n {\\n 'title':'Student Info',\\n 'layout_data' : get_layout_data(request),\\n }\\n )\\n else:\\n return redirect(reverse(URL_BAD_REQUEST))\",\n \"def search(self, filters=None):\\n raise NotImplementedError\",\n \"def search_action(self):\\n #TODO: No access control checks for dataproxy resources!\\n request_data = self._get_request_data(try_url_params=True)\\n resource = Resource.get(request_data['resource_id'])\\n if resource is not None and resource.url_type == 'dataproxy':\\n pylons.response.headers['Content-Type'] = 'application/json;charset=utf-8'\\n return self.dataproxy_search(request_data, resource)\\n\\n #Default action otherwise\\n return self.action('datastore_search', ver=3)\",\n \"def run_search(self, evt):\\n search_input = self.search_input_txtctrl.GetValue()\\n self.execute_google_search(str(search_input))\\n self.set_result_to_dict_for_page_scroller()\\n self.clear_result_screen()\\n self.trigger_scroller_event()\",\n \"def on_filterCheckBox_clicked(self):\\n self.__enableFindButton()\",\n \"def sidebar_click(sidebar_item):\\n #print('sidebar_click')\\n sidebar = driver.find_element_by_class_name('m-menu__nav')\\n return click_on(sidebar_item, scope=sidebar)\",\n \"def search(request, is_my_list=\\\"False\\\"):\\n\\n search_type = request.GET.get(\\\"submit\\\")\\n if search_type:\\n\\n # get query field\\n query = ''\\n if request.GET.get(search_type):\\n query = request.GET.get(search_type)\\n\\n proj_ids = []\\n cod_ids = []\\n\\n valid_searches = [constants.STRING_TITLE, constants.STRING_DESCRIPTION, constants.STRING_PROTOCOL,\\n constants.STRING_CODER, constants.STRING_AREA, constants.STRING_WORKINGGROUP]\\n\\n search_in_all = True\\n for v in valid_searches:\\n if v in request.GET:\\n search_in_all = False\\n break\\n\\n if search_in_all or request.GET.get(constants.STRING_TITLE):\\n codings = CodingProject.objects.all()\\n for cod in codings:\\n if query.lower() in cod.title.lower():\\n cod_ids.append(cod.id)\\n\\n if search_in_all or request.GET.get(constants.STRING_DESCRIPTION):\\n codings = CodingProject.objects.all()\\n for cod in codings:\\n if query.lower() in cod.additional_information.lower():\\n cod_ids.append(cod.id)\\n\\n if request.GET.get(constants.STRING_PROTOCOL):\\n proj_ids += ProjectContainer.objects.filter(protocol__icontains=query).values_list('id', flat=True)\\n\\n if search_in_all or request.GET.get(constants.STRING_CODER):\\n for pr in ProjectContainer.objects.all():\\n for cd in pr.codings.all():\\n user = Person.objects.using('datatracker').get(id=cd.coder)\\n if query.lower() in user.name.lower():\\n proj_ids.append(pr.id)\\n break\\n\\n if search_in_all or request.GET.get(constants.STRING_AREA):\\n for project_container in ProjectContainer.objects.all():\\n docs = []\\n if not project_container.docs or project_container.docs == '':\\n continue\\n keys = filter(None, project_container.docs.split(';'))\\n docs.extend(list(DocAlias.objects.using('datatracker').filter(name__in=keys).values_list(\\n 'document__group__parent__name')))\\n for doc in docs:\\n if query.lower() in doc[0].lower():\\n proj_ids.append(project_container.id)\\n break\\n # ids += ProjectContainer.objects.filter(docs__document__group__parent__name__icontains=query).values_list(\\n # 'id', flat=True)\\n\\n if search_in_all or request.GET.get(constants.STRING_WORKINGGROUP):\\n for project_container in ProjectContainer.objects.all():\\n docs = []\\n if not project_container.docs or project_container.docs == '':\\n continue\\n keys = filter(None, project_container.docs.split(';'))\\n docs.extend(list(\\n DocAlias.objects.using('datatracker').filter(name__in=keys).values_list('document__group__name')))\\n for doc in docs:\\n if query.lower() in doc[0].lower():\\n proj_ids.append(project_container.id)\\n break\\n \\n if cod_ids:\\n cod_ids = list(set(cod_ids))\\n proj_ids += ProjectContainer.objects.filter(codings__id__in=cod_ids).values_list('id', flat=True)\\n project_containers = ProjectContainer.objects.filter(id__in=list(set(proj_ids)))\\n \\n request.session[constants.ALL_CODINGS] = cod_ids\\n request.session[constants.ALL_PROJECTS] = project_containers\\n\\n request.session[constants.MAINTAIN_STATE] = True\\n\\n return HttpResponseRedirect(\\n settings.CODESTAND_PREFIX + '/codestand/matches/show_list/' + \\n is_my_list + '/{0}/'.format(constants.ATT_CREATION_DATE) + 'True')\\n\\n else:\\n return render_page(request, constants.TEMPLATE_MATCHES_SEARCH, {\\n \\\"form\\\": SearchForm()\\n })\",\n \"def _on_repository_search(self, evt):\\n \\n # init query\\n query = getattr(evt, \\\"query\\\", \\\"\\\")\\n \\n # get selected articles\\n articles = self._articles_view.GetSelectedArticles()\\n \\n # make requested query from first article\\n if articles:\\n article = articles[0]\\n \\n if evt.GetId() == ID_REPOSITORY_RECENT_FIRST_AUTHOR and article.authors:\\n query = \\\"%s[AU]\\\" % article.authors[0].shortname\\n \\n elif evt.GetId() == ID_REPOSITORY_RECENT_LAST_AUTHOR and article.authors:\\n query = \\\"%s[AU]\\\" % article.authors[-1].shortname\\n \\n elif evt.GetId() == ID_REPOSITORY_RECENT_JOURNAL and article.journal:\\n query = \\\"%s[JT]\\\" % article.journal.abbreviation\\n \\n # search repository\\n self._search_repository(query)\",\n \"def page_search_notes(self):\\n\\n task_store_name = \\\"task_store\\\"\\n alt_task_store = None\\n alt_task_store_name = self.helper_get_alt_task_store_name()\\n if alt_task_store_name == \\\"task_store_trash\\\":\\n task_store_name = alt_task_store_name\\n alt_task_store = self.task_store_trash\\n\\n search_text = self.last_request_get_dict[\\\"search_text\\\"][0].lower()\\n list_taskid_desc, highlight_list = self.ui_backend.search_notes(task_store_name, search_text)\\n\\n matching_virtual_folder = \\\"\\\"\\n for _virtfldr, _searchstring in self.woolnote_config.virtual_folders.items():\\n if _searchstring.strip().lower() == search_text.strip().lower():\\n matching_virtual_folder = \\\"\\\"\\\"virtual folder \\\"{}\\\" - \\\"\\\"\\\".format(_virtfldr)\\n break\\n\\n # in the rest of the function, the variable should be None if it is the default task store\\n if task_store_name == \\\"task_store\\\":\\n task_store_name = None\\n\\n history_id = self.save_history([\\\"search_text\\\", \\\"action\\\"], alt_task_store_name=alt_task_store_name)\\n\\n title = \\\"woolnote - search \\\" + matching_virtual_folder + search_text\\n\\n page_header_first_text = \\\"search \\\" + matching_virtual_folder + search_text\\n page_header_link_button_name = \\\"reset filter\\\"\\n page_header_link_request_dict = {\\\"action\\\": \\\"show_list\\\"}\\n page_header_list_of_warnings = None\\n\\n if self.error_msg_queue_list:\\n page_header_list_of_warnings = self.helper_convert_msg_queue_list_to_list_for_output()\\n\\n return html_page_templates.page_list_notes_template(list_taskid_desc=list_taskid_desc,\\n self_sess_action_auth=self.sess_action_auth, title=title,\\n highlight_in_notes=highlight_list, primary_task_store=self.task_store,\\n alt_task_store=alt_task_store, alt_task_store_name=alt_task_store_name,\\n history_back_id=history_id,\\n virtual_folders=self.woolnote_config.virtual_folders,\\n single_task_line_ids=set(self.woolnote_config.single_note_line_id.keys()),\\n page_header_first_text=page_header_first_text,\\n page_header_optional_link_button_name=page_header_link_button_name,\\n page_header_optional_link_button_request_dict=page_header_link_request_dict,\\n page_header_optional_list_of_warnings=page_header_list_of_warnings)\",\n \"def run_search(self, text: str, limit=100, similarity_thresh=50, **kwargs):\\n\\n class_tree = \\\"/wdt:P279*\\\" if \\\"include_class_tree\\\" in kwargs else \\\"\\\"\\n sparq_property_filter = \\\"\\\"\\n\\n if \\\"instanceof_filter\\\" in kwargs:\\n property_id = kwargs[\\\"instanceof_filter\\\"]\\n if isinstance(property_id, str):\\n # one instanceof in filter\\n sparq_instanceof = f\\\"?item wdt:P31{class_tree} wd:{property_id}.\\\"\\n elif isinstance(property_id, list):\\n if len(property_id) == 1:\\n sparq_instanceof = f\\\"?item wdt:P31{class_tree} wd:{property_id[0]}.\\\"\\n else:\\n ids = \\\", \\\".join([\\\"wd:\\\" + x for x in property_id])\\n sparq_instanceof = f\\\" ?item wdt:P31{class_tree} ?tree. \\\\n FILTER (?tree in ({ids}))\\\"\\n\\n else:\\n sparq_instanceof = \\\"\\\"\\n\\n if \\\"property_filters\\\" in kwargs:\\n for prop, value in kwargs[\\\"property_filters\\\"].items():\\n if isinstance(value, str):\\n assert_qid_format(value)\\n sparq_property_filter += f\\\"\\\\n ?item wdt:{prop} wd:{value} .\\\"\\n elif isinstance(value, list):\\n if len(value) == 1:\\n assert_qid_format(value[0])\\n sparq_property_filter += f\\\"\\\\n ?item wdt:{prop} wd:{value[0]} .\\\"\\n else:\\n ids = \\\", \\\".join([\\\"wd:\\\" + x for x in value])\\n sparq_instanceof = (\\n f\\\" ?item wdt:{prop} ?tree. \\\\n FILTER (?tree in ({ids}))\\\"\\n )\\n\\n endpoint_url = config.WIKIDATA_SPARQL_ENDPOINT\\n query = f\\\"\\\"\\\"\\n SELECT ?item ?wikipedia_title {{\\n SERVICE wikibase:mwapi {{\\n bd:serviceParam wikibase:endpoint \\\"en.wikipedia.org\\\" .\\n bd:serviceParam wikibase:api \\\"Generator\\\" .\\n bd:serviceParam mwapi:generator \\\"search\\\" .\\n bd:serviceParam mwapi:gsrsearch \\\"{text}\\\" .\\n bd:serviceParam mwapi:gsrlimit \\\"max\\\" .\\n ?item wikibase:apiOutputItem mwapi:item . \\n ?wikipedia_title wikibase:apiOutput mwapi:title .\\n }}\\n hint:Prior hint:runFirst \\\"true\\\".\\n {sparq_instanceof}\\n {sparq_property_filter}\\n }} LIMIT {limit}\\n \\\"\\\"\\\"\\n\\n res = get_sparql_results(endpoint_url, query)[\\\"results\\\"][\\\"bindings\\\"]\\n\\n res_df = pd.json_normalize(res)\\n\\n if len(res_df) > 0:\\n res_df = res_df[[\\\"item.value\\\", \\\"wikipedia_title.value\\\"]].rename(\\n columns=lambda x: x.replace(\\\".value\\\", \\\"\\\")\\n )\\n\\n res_df[\\\"text_similarity\\\"] = res_df[\\\"wikipedia_title\\\"].apply(\\n lambda s: self.calculate_label_similarity(text, s)\\n )\\n res_df = (\\n res_df[res_df[\\\"text_similarity\\\"] >= similarity_thresh]\\n .sort_values(\\\"text_similarity\\\", ascending=False)\\n .reset_index(drop=True)\\n )\\n res_df = res_df.drop(columns=\\\"text_similarity\\\")\\n res_df = res_df.reset_index().rename(columns={\\\"index\\\": \\\"rank\\\"})\\n res_df[\\\"rank\\\"] = res_df[\\\"rank\\\"] + 1\\n\\n res_df = self.add_score_to_search_results_df(res_df, rank_col=\\\"rank\\\")\\n\\n return res_df\",\n \"def showFind(self, txt=\\\"\\\"):\\n self.__searchWidget.showFind(txt)\",\n \"def searchInspector(search):\\n \\n try:\\n from PyQt4.QtGui import QApplication, QDialog\\n from dialog import Ui_Dialog\\n except Exception:\\n print \\\"Missing a required library - please install pyQt4.\\\"\\n return\\n \\n app = QApplication(sys.argv)\\n window = QDialog()\\n ui = Ui_Dialog()\\n ui.setupUi(window)\\n ui.updateList(search)\\n window.show()\\n app.exec_()\",\n \"def search_for_barbarian():\\n search_button = Utils.wait_and_find('search_search')\\n if search_button is not None:\\n search_button_location = (search_button.x, search_button.y)\\n Utils.click(search_button_location)\\n else:\\n Utils.wait_and_click('search')\\n Utils.wait_and_click('search_barbarian')\\n Utils.wait_and_click('search_search')\",\n \"def do_search(request):\\n products = Product.objects.filter(name__icontains=request.GET['q'])\\n return render(request, \\\"search_results.html\\\", {\\\"products\\\": products})\",\n \"def search(request):\\n template_var = base_template_vals(request)\\n if request.method==\\\"GET\\\":\\n event_id_list = []\\n events_found = []\\n events_found_advanced = []\\n events_found_basic = []\\n checkbox_session = []\\n advanced_search = False\\n tags = Tag.objects.all()\\n template_var[\\\"tags\\\"] = tags\\n\\n query_tags = request.GET.getlist('query_tag')\\n if len(query_tags) > 0: # at least one checkbox selected\\n advanced_search = True\\n query_tag_list = []\\n \\n #strip >>u''<< prefix\\n for query_tag in query_tags:\\n checkbox_session.append(str(query_tag))\\n query_tag_list.append(str(query_tag))\\n \\n events_found_advanced = Event.objects.filter(is_approved=True\\n ).filter(tags__name__in=query_tag_list)\\n \\n query = request.GET.get('query', '').strip('\\\\t\\\\n\\\\r')\\n if query == '' : #first came in/accidentily type space/..\\n if advanced_search:\\n events_found_advanced = events_found_advanced #TODO: Bin this line does nothing.\\n else:\\n events_found_basic = Event.objects.filter(is_approved=True\\n ).order_by(\\\"-created\\\") \\n else:\\n if advanced_search:\\n events_found_advanced = events_found_advanced.filter(\\n tags__name__in=[query])\\n else:\\n events_found_basic = Event.objects.filter(\\n tags__name__in=[query])\\n\\n #finally\\n if advanced_search:\\n #take out duplicated result\\n for event_found_advanced in events_found_advanced:\\n if(event_found_advanced.id not in event_id_list):\\n event_id_list.append(event_found_advanced.id)\\n events_found.append(event_found_advanced)\\n else:\\n for event in events_found_basic:\\n events_found.append(event)\\n\\n #sort result\\n sorted_method = request.GET.get('sorted_method', 'desc') #default is desc\\n if sorted_method == 'desc':\\n events_found.sort(key=lambda event: event.event_time, reverse=True) \\n elif sorted_method == 'asc':\\n events_found.sort(key=lambda event: event.event_time,\\n reverse=False) \\n elif sorted_method == 'alphabet':\\n events_found.sort(key=lambda event: event.title.lower(),\\n reverse=False) \\n \\n template_var[\\\"events_found\\\"] = events_found\\n\\n request.session[\\\"sorted_method_session\\\"] = sorted_method\\n request.session[\\\"checkbox_session\\\"] = checkbox_session\\n request.session[\\\"query_session\\\"] = query\\n return render_to_response(\\\"event/event_search_results.html\\\",\\n template_var,\\n context_instance=RequestContext(request))\\n\\n return render_to_response(\\\"event/event_search_results.html\\\", template_var,\\n context_instance=RequestContext(request))\",\n \"def search(request):\\n current_user = request.user\\n\\n return render(request,'main_templates/search.html')\",\n \"def searchbar_changed(data):\\n print('searching for ' + data['query'])\\n if data['query'] != '':\\n options = queue.instantiate_options()\\n query = data['query'].replace(' ', '+')\\n response = get_request(search_uri + query)\\n songs = []\\n is_explicit_list = []\\n\\n for track_obj in response.json()['tracks']['items']:\\n song_obj, is_explicit = create_song(track_obj, return_is_explicit=True)\\n songs.append(song_obj)\\n is_explicit_list.append(is_explicit)\\n \\n if options['safe_mode'] == 'true':\\n temp_songs = []\\n for i in range(len(songs)):\\n if not is_explicit_list[i]:\\n temp_songs.append(songs[i])\\n songs = temp_songs\\n\\n if len(songs) > 5:\\n songs = songs[:5]\\n\\n serialized_songs = [song.to_dict() for song in songs]\\n emit('suggestions_changed', serialized_songs)\",\n \"def do_search(request):\\n tickets = Ticket.objects.filter(issue_type__icontains=request.GET['q'])\\n return render(request, 'tickets.html', {\\\"tickets\\\":tickets})\",\n \"def search_current_auctions(request):\\n query = request.GET.get('q')\\n auction = Auction.objects.all()\\n\\n if query:\\n results = auction.filter(Q(antiques__name__icontains=query) | Q(antiques__description__icontains=query))\\n\\n else:\\n results = Auction.objects.all()\\n\\n pages = pagination(request, results, num=4)\\n context = {\\n 'items': pages[0],\\n 'page_range': pages[1]\\n }\\n\\n return render(request, \\\"showallauctions.html\\\", context)\",\n \"def _on_articles_search(self, evt=None):\\n \\n # set focus\\n self._articles_view.SetFocusToQuery()\",\n \"def search(request):\\n\\n filterList = ['everyone', 'black', 'hispanic', 'female', 'lgbt', 'immigrants', 'disabled', 'poor']\\n sorts = {\\\"Title(A-Z)\\\":\\\"title\\\", \\\"Title(Z-A)\\\":\\\"-title\\\", \\\"Value Increasing\\\":\\\"value\\\", \\\"Value Decreasing\\\":\\\"-value\\\", \\\"Due Date\\\":\\\"dueDate\\\"}\\n\\n #urls agruments\\n query = request.GET.get('query', '')\\n Filter = request.GET.get('filter', 'Default')\\n sortby = request.GET.get('sortby', 'Default')\\n pageNum = request.GET.get('page', '1')\\n \\n postResult = Post.objects.all().order_by(sorts.get(sortby,\\\"UUID\\\"))\\n\\n if(query):\\n #search for words in the title, description or organizaton name, then filter out the tags\\n postResult = Post.objects.filter((Q(title__icontains=query)|Q(description__icontains=query))|Q(organization__username__icontains=query))\\n if(Filter !='Default' and Filter.lower() in filterList): \\n #check if the filter the user entered is a tag\\n postResult = postResult.filter(tags__name__in=[Filter.lower()]) \\n\\n #function to help divide our query into groups of 12 for pagination\\n pageObj = Paginator(postResult, 12)\\n\\n #select the page that we want\\n page = pageObj.page(pageNum)\\n\\n\\n context = {\\n 'result': postResult,\\n 'page_obj': page,\\n 'query': query,\\n 'filter': Filter,\\n 'sortby': sortby,\\n }\\n return render(request, 'search.html', context=context)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65629","0.64431345","0.6246589","0.6217658","0.61798435","0.6041973","0.5982113","0.5897964","0.5824327","0.57609326","0.57595426","0.5737622","0.5707807","0.5639201","0.5621058","0.5614636","0.55623174","0.5536656","0.5523965","0.5495684","0.5465359","0.5464474","0.5452865","0.5448908","0.54451627","0.5439077","0.5423182","0.54134303","0.5400333","0.5377085","0.5368981","0.5353603","0.53136","0.53034234","0.5300925","0.53004616","0.5295282","0.52919406","0.5287739","0.528152","0.52703834","0.5234408","0.52244973","0.52169555","0.5216014","0.5214135","0.52103096","0.51956314","0.51796794","0.51736444","0.5172213","0.51700544","0.5169521","0.5156543","0.5154849","0.51449215","0.5144448","0.51282525","0.51179504","0.5110511","0.50957096","0.5087712","0.50866723","0.5075649","0.5071562","0.5065298","0.50544304","0.5034244","0.50250715","0.50176466","0.50138587","0.50035954","0.49983546","0.49957553","0.49924806","0.49895835","0.49861017","0.49800962","0.49754354","0.49698883","0.49635762","0.4958993","0.49565628","0.49490455","0.49481007","0.4941581","0.49399912","0.49356285","0.49292776","0.49239215","0.4923906","0.49209303","0.49180743","0.49170122","0.49103686","0.4908271","0.49059153","0.4903934","0.48978278","0.48942846"],"string":"[\n \"0.65629\",\n \"0.64431345\",\n \"0.6246589\",\n \"0.6217658\",\n \"0.61798435\",\n \"0.6041973\",\n \"0.5982113\",\n \"0.5897964\",\n \"0.5824327\",\n \"0.57609326\",\n \"0.57595426\",\n \"0.5737622\",\n \"0.5707807\",\n \"0.5639201\",\n \"0.5621058\",\n \"0.5614636\",\n \"0.55623174\",\n \"0.5536656\",\n \"0.5523965\",\n \"0.5495684\",\n \"0.5465359\",\n \"0.5464474\",\n \"0.5452865\",\n \"0.5448908\",\n \"0.54451627\",\n \"0.5439077\",\n \"0.5423182\",\n \"0.54134303\",\n \"0.5400333\",\n \"0.5377085\",\n \"0.5368981\",\n \"0.5353603\",\n \"0.53136\",\n \"0.53034234\",\n \"0.5300925\",\n \"0.53004616\",\n \"0.5295282\",\n \"0.52919406\",\n \"0.5287739\",\n \"0.528152\",\n \"0.52703834\",\n \"0.5234408\",\n \"0.52244973\",\n \"0.52169555\",\n \"0.5216014\",\n \"0.5214135\",\n \"0.52103096\",\n \"0.51956314\",\n \"0.51796794\",\n \"0.51736444\",\n \"0.5172213\",\n \"0.51700544\",\n \"0.5169521\",\n \"0.5156543\",\n \"0.5154849\",\n \"0.51449215\",\n \"0.5144448\",\n \"0.51282525\",\n \"0.51179504\",\n \"0.5110511\",\n \"0.50957096\",\n \"0.5087712\",\n \"0.50866723\",\n \"0.5075649\",\n \"0.5071562\",\n \"0.5065298\",\n \"0.50544304\",\n \"0.5034244\",\n \"0.50250715\",\n \"0.50176466\",\n \"0.50138587\",\n \"0.50035954\",\n \"0.49983546\",\n \"0.49957553\",\n \"0.49924806\",\n \"0.49895835\",\n \"0.49861017\",\n \"0.49800962\",\n \"0.49754354\",\n \"0.49698883\",\n \"0.49635762\",\n \"0.4958993\",\n \"0.49565628\",\n \"0.49490455\",\n \"0.49481007\",\n \"0.4941581\",\n \"0.49399912\",\n \"0.49356285\",\n \"0.49292776\",\n \"0.49239215\",\n \"0.4923906\",\n \"0.49209303\",\n \"0.49180743\",\n \"0.49170122\",\n \"0.49103686\",\n \"0.4908271\",\n \"0.49059153\",\n \"0.4903934\",\n \"0.48978278\",\n \"0.48942846\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6213812"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":22376,"cells":{"query":{"kind":"string","value":"Sidebar widget to indicate sorting method"},"document":{"kind":"string","value":"def select_sort_method():\n st.sidebar.markdown('### Sort method:')\n sort_select = st.sidebar.selectbox('', ['Alphabetically', 'FTE Salary'],\n index=1)\n return sort_select"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def mouseDoubleClickEvent(self, event):\n if event.button() == QtCore.Qt.LeftButton:\n sw = self.spw.windows['Sort']\n sw.clear()","def OnColumnClick(self, event):\r\n columns = self.data.getParam('columns')\r\n self.SortItems(columns[event.GetColumn()],'INVERT')","def _sortHandler(self) -> None:\n response, columnIndex, ascending = self._sortDialog()\n order = Qt.AscendingOrder if ascending else Qt.DescendingOrder\n if response:\n self._mainFileView.sortByColumn(columnIndex, order)","def sortby(self):\n ...","def get_sidebar_layout(self):\n return [\n dbc.Card(children=self.get_sidebar_filters(), body=True),\n dbc.Card(children=self.get_sidebar_filter_save(), body=True, className=\"mt-2\"),\n dbc.Card(children=self.get_sidebar_saved_filter_names(), body=True, className=\"mt-2\")\n ]","def on_sort_complete(self):\n if self.get_search_active_thread_count() == 0:\n self.clear_widgets()","def click_timed_sorting_button(self):\n self.my_sorted_list = self.sorting.sorting_alg(self.my_list)\n self.label_2[\"text\"] = self.set_my_sorted_list_label()","def start_sort():\n global data\n if algo_box.get() == \"Bubble Sort\":\n bubble_sort(data, display_data, speed_scale.get())\n elif algo_box.get() == \"Merge Sort\":\n merge_sort(data, display_data, speed_scale.get())","def __init__(self, parent):\n self.order_by = []\n assert(isinstance(parent, gtk_view.gtk_view))\n self._parent = parent\n def shorter(name, action, *method):\n self._parent.window.builder.get_object(name).connect(action, *method)\n self.positions_list = list_view_sort_control(self._parent.window.builder.get_object(\"positions_view\"),\n [[u'id', int],\n (u'Дата Откр.', gtk.CellRendererText(), str, u'open_datetime'),\n (u'время Откр.', gtk.CellRendererText(), str, u'open_time'),\n (u'Дата Закр.', gtk.CellRendererText(), str, u'close_datetime'),\n (u'Время Закр.', gtk.CellRendererText(), str, u'close_time'),\n (u'В позиции', gtk.CellRendererText(), str, u'duration'),\n (u'Инструмент', gtk.CellRendererText(), str, u'paper_name'),\n (u'Кол-во', gtk.CellRendererText(), str, u'count'),\n (u'Тип', gtk.CellRendererText(), str, u'direction'),\n (u'Цена Откр.', gtk.CellRendererText(), str, u'open_price'),\n (u'Цена Закр.', gtk.CellRendererText(), str, u'close_price'),\n (u'Ход', gtk.CellRendererText(), str, u'steps_range'),\n (u'Gross Bfr.', gtk.CellRendererText(), str, u'gross_before'),\n (u'Gross Aftr.', gtk.CellRendererText(), str, u'gross_after'),\n (u'P/L Gross', gtk.CellRendererText(), str, u'pl_gross_abs'),\n (u'Net Bfr.', gtk.CellRendererText(), str, u'net_before'),\n (u'Net Aftr.', gtk.CellRendererText(), str, u'net_after'),\n (u'P/L Net', gtk.CellRendererText(), str, u'pl_net_abs'),\n (u'% Изменения', gtk.CellRendererText(), str, u'percent_range_abs')],\n self_sorting = False,\n sort_callback = self.sort_callback)\n self._parent.window.builder.get_object(\"positions_view\").get_selection().set_mode(gtk.SELECTION_MULTIPLE)\n shorter(\"positions_make\", \"activate\", self.make_positions_activate)\n shorter(\"call_positions_filter\", \"activate\", self.filter_activate)\n shorter(\"delete_positions\", \"activate\", self.delete_positions_activate)\n shorter(\"add_position\", \"activate\", self.add_position_activate)\n shorter(\"update_positions\", \"activate\", self.update_positions_activate)","def __sidebar(self, conf):\n result = \"\"\n\n i = conf[\"conf_json\"][0]\n result += \"\"\"\n\n <h4>%s</h4>\n <ul id=\"sidebar_menu\" class=\"sidebar_menu\">\n <li><a class=\"btn active\" href=\"#description\">DESCRIPTION</a></li>\n <li><a class=\"btn\" href=\"#parameters\">PARAMETERS</a></li>\n <li><a class=\"btn\" href=\"#operations\">OPERATIONS</a>\n <ul class=\"sidebar_submenu\">%s</ul>\n </li>\n <li><a class=\"btn active\" href=\"/\">HOME</a></li>\n </ul>\n \"\"\" % (\n i[\"title\"],\n \"\".join(\n [\n \"<li><a class='btn' href='#%s'>%s</a></li>\" % (op[\"url\"], op[\"url\"])\n for op in conf[\"conf_json\"][1:]\n ]\n ),\n )\n return result","def on_sort(self, param, state):\n if state > 0: # From unchecked to checked\n self.grouped = False\n self.uncheck_group()\n if param not in self.param_list:\n self.param_list.append(param)\n else: # From checked to unchecked\n if param in self.param_list:\n if len(self.param_list) == 1:\n self.param_list = []\n else:\n self.param_list.remove(param)\n self.sorted_labels = utils.sort_by_param(self.nwb_path_list.values(), self.param_list)\n if self.param_list:\n self.sorted = True\n else:\n self.sorted = False\n self.musketeers_widget.session_widget.update_text_filter()\n self.musketeers_widget.session_widget.populate(self.sorted_labels)","def click_on_sort(self):\r\n self.driver.find_element_by_xpath('//*[@id=\"dataTable\"]/thead/tr[3]/th[4]').click()","def on_combo_sort_col_names_currentIndexChanged(self, index):\n if self.ui.sort_radio_asc.isChecked():\n self.model.setSort(index, Qt.AscendingOrder)\n else:\n self.model.setSort(index, Qt.DescendingOrder)\n self.model.select()","def sorter(Plugin):\n return Plugin.order","def gallery_sort(request, item_container):\n\n return do_sort(request, item_container, 'pool', _(u'Bilder umordnen'))","def change_sort(self, sorting_choice):\r\n self.message = \"place have been sorted by: {}\".format(sorting_choice)\r\n self.place_list.sort(sorting_choice)\r\n self.root.ids.entriesBox.clear_widgets()\r\n self.create_widget()\r\n sort_index = self.sort_choices.index(sorting_choice)\r\n self.current_sort = self.sort_choices[sort_index]","def sort(self, args):\n if not args:\n self.err_print('One argument required')\n return\n\n _key = args[0]\n cur = self.ui.leftwin.highlighted().data\n try:\n ind = song.tags.index(_key)\n cur.change_sort(ind)\n self.ui.rightwin.disp()\n except:\n self.err_print('\"{}\" is not a valid key to sort by'.format(_key))","def get_widget_sorted_item(widget):\n for panel in widget['metadata']['panels']:\n for item in panel['items']:\n if 'sort' in item['jaql']:\n return item['jaql']['title']\n return None","def sidebar_menu(items, selected):\n\n return {\n 'items': items,\n 'selected': selected,\n }","def testSortDescending(self):\n self.request.GET['sort'] = \"-name\"\n self.datagrid.load_state()\n\n self.assertEqual(self.datagrid.sort_list, [\"-name\"])\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\n self.assertEqual(self.datagrid.rows[0]['object'].name, \"Group 99\")\n self.assertEqual(self.datagrid.rows[1]['object'].name, \"Group 98\")\n self.assertEqual(self.datagrid.rows[2]['object'].name, \"Group 97\")\n\n # Exercise the code paths when rendering\n self.datagrid.render_listview()","def populate_buttons(self):\n\n # Figure out which index we'll sort on\n if self.sort_group.checkedButton() == self.button_mtime:\n to_sort = self.sort_mtime_idx\n reverse = True\n else:\n to_sort = self.sort_alpha_idx\n reverse = False\n\n # Now add things. This'll automatically shuffle stuff around without\n # us having to worry about removing things first.\n for row, (_, _, button) in enumerate(\n sorted(self.buttons, reverse=reverse, key=lambda i: i[to_sort])\n ):\n self.grid.addWidget(button, row, 0)","def sort(self, col, order):\r\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\r\n self.mylist = sorted(self.mylist,\r\n key=operator.itemgetter(col))\r\n if order == QtCore.Qt.DescendingOrder:\r\n self.mylist.reverse()\r\n self.emit(SIGNAL(\"layoutChanged()\"))","def PaneSortFunc(p1, p2):\r\n \r\n return (p1.dock_pos < p2.dock_pos and [-1] or [1])[0]","def sort(self, col, order):\r\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\r\n self.mylist = sorted(self.mylist,\r\n key=operator.itemgetter(col))\r\n if order == Qt.DescendingOrder:\r\n self.mylist.reverse()\r\n self.emit(SIGNAL(\"layoutChanged()\"))","def sort(self, col, order):\n self.layoutAboutToBeChanged.emit()\n self.mylist = sorted(self.mylist,\n key=operator.itemgetter(col))\n if order == Qt.DescendingOrder:\n self.mylist.reverse()\n self.layoutChanged.emit()","def sort_songs(self, current_sort_option):\n self.songs.sort_songs(current_sort_option)\n SongsToLearnApp.create_widgets(self)","def sort_by_type(self):\n # sort_by_type_sitem = self.locator_finder_by_idx(self.sort_by_type_id, 30)\n # sort_by_type_sitem = sort_by_type_sitem.find_element_by_xpath(\"./..\")\n # while True:\n # try:\n # sort_by_type_sitem.click()\n # break\n # except ElementNotInteractableException:\n # time.sleep(1) \n if self.current_package_version() == semver.VersionInfo.parse(\"3.8.0\"):\n sort_by_type = '//*[@id=\"collectionsDropdown\"]/ul[3]/li[3]/a/label'\n sort_by_type_sitem = self.locator_finder_by_xpath(sort_by_type)\n else:\n sort_by_type_sitem = self.locator_finder_by_xpath(self.sort_by_type_id)\n\n sort_by_type_sitem.click()\n time.sleep(2)","def sort_key(self):\n ...","def orderby():\n pass","def testSortNoDbAscending(self):\n self.request.GET['sort'] = \"custom\"\n self.datagrid.load_state()\n self.assertEqual(self.datagrid.sort_list, [\"custom\"])\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\n self.assertEqual(self.datagrid.rows[0]['object'].name, \"Group 04\")\n self.assertEqual(self.datagrid.rows[1]['object'].name, \"Group 08\")\n self.assertEqual(self.datagrid.rows[2]['object'].name, \"Group 12\")\n\n # Exercise the code paths when rendering\n self.datagrid.render_listview()","def sort(request):\n stars = Product.objects.annotate(\n avg_review=Avg('productreview__rating'),\n )\n select = request.GET['sort']\n if select == 'LtoH':\n results = Product.objects.order_by('price')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})\n elif select == 'HtoL':\n results = Product.objects.order_by('-price')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})\n elif select == 'AtoZ':\n results = Product.objects.order_by('name')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})\n elif select == 'ZtoA':\n results = Product.objects.order_by('-name')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})","def sorted(self): \n pass","def testSortNoDbDescending(self):\n self.request.GET['sort'] = \"-custom\"\n self.datagrid.load_state()\n self.assertEqual(self.datagrid.sort_list, [\"-custom\"])\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\n self.assertEqual(self.datagrid.rows[0]['object'].name, \"Group 03\")\n self.assertEqual(self.datagrid.rows[1]['object'].name, \"Group 07\")\n self.assertEqual(self.datagrid.rows[2]['object'].name, \"Group 11\")\n\n # Exercise the code paths when rendering\n self.datagrid.render_listview()","def sort(request, code):\n filtered = Listing.objects.filter(category=code, is_active=True)\n\n # Returns page with variables. \n return render(request, \"auctions/categories.html\", {\n \"bo\": True,\n \"listings\": filtered,\n })","def testSortAscending(self):\n self.request.GET['sort'] = \"name,objid\"\n self.datagrid.load_state()\n\n self.assertEqual(self.datagrid.sort_list, [\"name\", \"objid\"])\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\n self.assertEqual(self.datagrid.rows[0]['object'].name, \"Group 01\")\n self.assertEqual(self.datagrid.rows[1]['object'].name, \"Group 02\")\n self.assertEqual(self.datagrid.rows[2]['object'].name, \"Group 03\")\n\n # Exercise the code paths when rendering\n self.datagrid.render_listview()","def OnReorder( self, event ):\n column = self.columns[event.GetColumn()]\n if column.sortOn:\n # multiple sorts for the click...\n columns = [ self.columnByAttribute( attr ) for attr in column.sortOn ]\n diff = [ (a,b) for a,b in zip( self.sortOrder, columns ) if b is not a[1]]\n if not diff:\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\n else:\n self.sortOrder = [\n (c.defaultOrder,c) for c in columns \n ] + [ (a,b) for (a,b) in self.sortOrder if b not in columns]\n else:\n if column is self.sortOrder[0][1]:\n # reverse current major order\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\n else:\n self.sortOrder = [(column.defaultOrder,column)] + [\n (a,b) \n for (a,b) in self.sortOrder if b is not column \n ]\n # TODO: store current selection and re-select after sorting...\n self.reorder()\n self.Refresh()","def _sortDialog(self) -> Tuple[Any, Any, Any]:\n dialog = QDialog()\n dialog.setFixedSize(300, 150)\n dialog.setWindowTitle('Sort')\n dialog.setWindowIcon(QIcon(':sort.png'))\n\n # layouts\n dialogLayout = QVBoxLayout()\n formLayout = QFormLayout()\n orderRadioLayout = QVBoxLayout()\n buttonBoxLayout = QHBoxLayout()\n\n # sort by column combobox\n sortLabel = QLabel('Sort by:')\n sortComboBox = QComboBox()\n sortComboBox.addItems(self._modelHeaders[:4])\n\n # order choosing radio buttons\n orderLabel = QLabel('Order:')\n orderRadioAsc = QRadioButton('ascending')\n orderRadioAsc.setChecked(True)\n orderRadioDesc = QRadioButton('descending')\n orderRadioLayout.addWidget(orderRadioAsc)\n orderRadioLayout.addWidget(orderRadioDesc)\n\n formLayout.addRow(sortLabel, sortComboBox)\n formLayout.addRow(orderLabel, orderRadioLayout)\n\n # Ok and Cancel buttons\n buttonBox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\n buttonBox.accepted.connect(dialog.accept)\n buttonBox.rejected.connect(dialog.reject)\n buttonBoxLayout.addStretch()\n buttonBoxLayout.addWidget(buttonBox)\n buttonBoxLayout.addStretch()\n\n dialogLayout.addLayout(formLayout)\n dialogLayout.addLayout(buttonBoxLayout)\n dialogLayout.setAlignment(Qt.AlignCenter)\n\n dialog.setLayout(dialogLayout)\n\n dialogResponse = dialog.exec()\n\n return dialogResponse, sortComboBox.currentIndex(), orderRadioAsc.isChecked()","def __init__(self, data=None, *args, **kwargs):\n super().__init__(data=data, *args, **kwargs)\n\n # if a keyword search term is not present, relevance sort is disabled\n if not data or not data.get(\"q\", None):\n self.fields[\"sort\"].widget.choices[0] = (\n self.SORT_CHOICES[0][0],\n {\"label\": self.SORT_CHOICES[0][1], \"disabled\": True},\n )","def do_sort(request, item_container, app_name, my_title, app_types=[]):\n\n class dms_itemForm ( forms.Form ) :\n sections = forms.CharField(required=False,\n widget=forms.Textarea( attrs={'rows':5, 'cols':40,\n 'style':'width:50%;'}) )\n\n my_item = item_container.item\n objs = []\n has_user_folder = False\n user_perms = UserEditPerms(request.user.username,request.path)\n\n # wurden Zwischentitel geloescht, ergaenzt, umgeordnet?\n if request.POST.has_key('sections_form'):\n s = request.POST['sections']\n if s != item_container.container.sections:\n item_container.container.sections = s\n item_container.container.save()\n change_values = request.POST.has_key('drag_item_form')\n if change_values:\n items, sections, d_sections = get_folder_content(item_container, False, app_types)\n order_by_ids, new_sections_str = do_resort(request.POST['var_order_by_0'])\n n = 0\n c = []\n for i in items:\n if app_types==[] or i.item.app.name in app_types:\n has_changed = False\n if order_by_ids[i.item.id][0] != i.order_by:\n i.order_by = order_by_ids[i.item.id][0]\n has_changed = True\n sec = order_by_ids[i.item.id][1]\n if sec != i.section:\n i.section = sec\n has_changed = True\n if has_changed:\n i.save()\n # --- wurde die Reihenfolge der Zwischentitel geaendert?\n n = new_sections_str\n if item_container.container.sections != n:\n item_container.container.sections = n\n item_container.container.save()\n items, sections, d_sections = get_folder_content(item_container, False, app_types)\n js_head, drag_list, input_str, n_drag_titles = get_drag_list(sections, d_sections)\n max_items = len(items)\n # --- Zwischentitel\n data_init = {'sections' : decode_html(item_container.container.sections,) }\n f = dms_itemForm(data_init)\n tabs = [('tab_sections' , ['sections',]), ]\n sec_content = get_tabbed_form(tabs, help_form, 'lecture' , f, False)\n\n vars, user_perms = get_base_vars(request, item_container, 'frame-main-manage')\n v = { 'objs' : objs,\n 'js_head' : js_head,\n 'drag_list' : drag_list,\n 'input_str' : input_str,\n 'max_items' : n_drag_titles,\n 'id' : my_item.id,\n 'title' : my_title,\n 'sub_title' : my_item.title,\n 'name' : my_item.name,\n 'action' : get_folderish_actions(request, user_perms, item_container, app_name,\n item_container.item.has_comments,\n {'browseable_mode': False,\n 'navigation_mode': False}),\n 'sec_content': sec_content\n }\n vars.update(v)\n vars['image_url'] = ''\n vars['text'] = ''\n vars['text_more'] = ''\n return render_to_response ( 'app/base_sort.html', vars )","def sort_by_name(self):\n # sort_by_name_sitem = self.locator_finder_by_idx(self.sort_by_name_id)\n # sort_by_name_sitem = sort_by_name_sitem.find_element_by_xpath(\"./..\")\n # while True:\n # try:\n # sort_by_name_sitem.click()\n # break\n # except ElementNotInteractableException:\n # time.sleep(1)\n \n if self.current_package_version() == semver.VersionInfo.parse(\"3.8.0\"):\n name = '//*[@id=\"collectionsDropdown\"]/ul[3]/li[2]/a/label'\n sort_by_name_sitem = self.locator_finder_by_xpath(name)\n else:\n sort_by_name_sitem = self.locator_finder_by_xpath(self.sort_by_name_id)\n sort_by_name_sitem.click()\n time.sleep(2)","def folder_sort(request, item_container):\n return do_sort(request, item_container, 'folder', _(u'Ordner, Seiten etc. umordnen'))","def add_arguments(self, actions):\n actions = sorted(actions, key=attrgetter('option_strings'))\n super(SortingHelpFormatter, self).add_arguments(actions)","def __editSortSelectedLines(self):\n editor = self.activeWindow()\n if editor:\n editor.sortLines()","def sort_descending(self):\n # sort_descending_sitem = self.locator_finder_by_idx(self.sort_descending_id)\n # sort_descending_sitem = sort_descending_sitem.find_element_by_xpath(\"./..\")\n # sort_descending_sitem.click()\n # time.sleep(2)\n \n if self.current_package_version() == semver.VersionInfo.parse(\"3.8.0\"):\n sort_by_descending = '//*[@id=\"collectionsDropdown\"]/ul[3]/li[4]/a/label/i'\n sort_descending_sitem = self.locator_finder_by_xpath(sort_by_descending)\n else:\n sort_descending_sitem = self.locator_finder_by_xpath(self.sort_descending_id)\n sort_descending_sitem.click()\n time.sleep(2)","def SortBy(self,tree, col, descending):\n \n # grab values to sort\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\n \n # now sort the data in place\n data.sort(reverse=descending)\n \n for ix, item in enumerate(data):\n tree.move(item[1], '', ix)\n \n # switch the heading so it will sort in the opposite direction\n tree.heading(col,command=lambda col=col: self.SortBy(tree, col,int(not descending)))","def admin_sidebar(context):\n request = context.get('request')\n\n request_context = {\n 'count_users': User.objects.count(),\n 'count_review_groups': Group.objects.count(),\n 'count_default_reviewers': DefaultReviewer.objects.count(),\n 'count_oauth_applications': Application.objects.count(),\n 'count_repository': Repository.objects.accessible(\n request.user, visible_only=False).count(),\n 'count_webhooks': WebHookTarget.objects.count(),\n 'count_hosting_accounts': HostingServiceAccount.objects.count(),\n 'version': get_version_string(),\n }\n\n # We're precomputing URLs in here, rather than computing them in the\n # template, because we need to always ensure that reverse() will be\n # searching all available URL patterns and not just the ones bound to\n # request.current_app.\n #\n # current_app gets set by AdminSite views, and if we're in an extension's\n # AdminSite view, we'll fail to resolve these URLs from within the\n # template. We don't have that problem if calling reverse() ourselves.\n request_context.update({\n 'url_%s' % url_name: reverse('admin:%s' % url_name)\n for url_name in ('auth_user_add',\n 'auth_user_changelist',\n 'hostingsvcs_hostingserviceaccount_add',\n 'hostingsvcs_hostingserviceaccount_changelist',\n 'notifications_webhooktarget_add',\n 'notifications_webhooktarget_changelist',\n 'oauth_application_add',\n 'oauth_application_changelist',\n 'reviews_defaultreviewer_add',\n 'reviews_defaultreviewer_changelist',\n 'reviews_group_add',\n 'reviews_group_changelist',\n 'scmtools_repository_add',\n 'scmtools_repository_changelist')\n })\n\n return RequestContext(request, request_context)","def has_right_sidebar(self):\n return True","def add_sidebar(self, widget: Component) -> None:\n self._root.add_sidebar(widget)","def main_menu_toolbar():\n\n pass","def __str__(self):\n return 'predsort'","def operator(self, sort):\r\n return None","def item_condition(request):\n\n items = Product.objects.all()\n is_used = Product.objects.filter(is_used=True)\n sort = None\n direction = None\n\n if request.GET:\n if 'sort' in request.GET:\n sortkey = request.GET['sort']\n sort = sortkey\n if sortkey == 'name':\n sortkey = 'lower_name'\n items = items.annotate(lower_name=Lower('name'))\n if sortkey == 'category':\n sortkey = 'category__name'\n if 'direction' in request.GET:\n direction = request.GET['direction']\n if direction == 'desc':\n sortkey = f'-{sortkey}'\n items = items.order_by(sortkey)\n\n current_sorting = f'{sort}_{direction}'\n\n context = {\n 'is_used': is_used,\n 'items': items,\n 'current_sorting': current_sorting,\n }\n \n return render(request, 'products/second_hand.html', context)","def sort_by_user_arg(user_argument = 'sort by ex. artist'):\n pass","def _config_sortable(self, sortable):\n for col in self[\"columns\"]:\n command = (lambda c=col: self._sort_column(c, True)) if sortable else \"\"\n self.heading(col, command=command)\n self._sortable = sortable","def arrange_button(self):\r\n self.update_settings()\r\n if self.arrange_call is not None:\r\n self.arrange_call()","def sort_apparel(request):\n stars = Product.objects.annotate(\n avg_review=Avg('productreview__rating'),\n )\n select = request.GET['sorta']\n items = Product.objects.filter(category__icontains='A')\n if select == 'LtoH':\n results = items.order_by('price')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})\n elif select == 'HtoL':\n results = items.order_by('-price')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})\n elif select == 'AtoZ':\n results = items.order_by('name')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})\n elif select == 'ZtoA':\n results = items.order_by('-name')\n return render(request, \"products.html\",\n {\"products\": results, 'stars': stars})","def showSideBar(self):\n\t\treturn False","def sort_by_key(request):\n return request.param","def sort_by_key(request):\n return request.param","def products_view(request):\n from_database = Product.objects.all()\n\n sort_type = request.POST.get('sort-selector')\n print(sort_type)\n if sort_type is None:\n from_database = from_database.order_by('title')\n my_context = {\n 'from_database': from_database,\n 'sort_type': sort_type,\n }\n return render(request, \"products.html\", my_context)\n if sort_type == 'name_asc':\n from_database = from_database.order_by('title')\n my_context = {\n 'from_database': from_database,\n 'sort_type': sort_type,\n }\n return render(request, \"products.html\", my_context)\n if sort_type == 'name_desc':\n from_database = from_database.order_by('-title')\n my_context = {\n 'from_database': from_database,\n 'sort_type': sort_type,\n }\n return render(request, \"products.html\", my_context)\n if sort_type == 'price_asc':\n from_database = from_database.order_by('price')\n my_context = {\n 'from_database': from_database,\n 'sort_type': sort_type,\n }\n return render(request, \"products.html\", my_context)\n if sort_type == 'price_desc':\n from_database = from_database.order_by('-price')\n my_context = {\n 'from_database': from_database,\n 'sort_type': sort_type,\n }\n return render(request, \"products.html\", my_context)","def _sort_dataframe(self, dataframe):\r\n print('Not allowed')","def sidebar_click(sidebar_item):\n #print('sidebar_click')\n sidebar = driver.find_element_by_class_name('m-menu__nav')\n return click_on(sidebar_item, scope=sidebar)","def sort(self, Ncol, order):\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \n if order == Qt.DescendingOrder:\n self.arraydata.reverse()\n self.emit(SIGNAL(\"layoutChanged()\"))","def add_column(self):\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"ID\", cell, text=0, foreground=8, background=9)\n column.set_sort_column_id(0)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Name\", cell, text=1, foreground=8, background=9)\n column.set_sort_column_id(1)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Protocol\", cell, text=2, foreground=8, background=9)\n column.set_sort_column_id(2)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Ip source\", cell, text=3, foreground=8, background=9)\n column.set_sort_column_id(3)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Port source\", cell, text=4, foreground=8, background=9)\n column.set_sort_column_id(4)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Ip dest\", cell, text=5, foreground=8, background=9)\n column.set_sort_column_id(5)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Port dest\", cell, text=6, foreground=8, background=9)\n column.set_sort_column_id(6)\n self.tree_view.append_column(column)\n cell = gtk.CellRendererText()\n column = gtk.TreeViewColumn(\"Action\", cell, text=7, foreground=8, background=9)\n column.set_sort_column_id(7)\n self.tree_view.append_column(column)","def treeview_sort_column(self, treeview, column, reverse):\r\n data = [(treeview.set(ticker, column), ticker) for ticker in treeview.get_children('')]\r\n data.sort(reverse=reverse)\r\n\r\n # sort the stock(s)\r\n for index, (val, k) in enumerate(data):\r\n treeview.move(k, '', index)\r\n\r\n # reverse sort next time\r\n treeview.heading(column, command=lambda: self.treeview_sort_column(treeview, column, not reverse))","def _sidepanel(self):\n global curdir\n global sidepanel_layout\n global root\n\n #Create the sidepanel layout.\n sidepanel_layout = BoxLayout(orientation=\"vertical\", pos_hint={\"x\": 0.0, \"top\": 0.92}, size_hint=(0.1, 0.92))\n\n #If \"curdir\" contains folders, a button is created for each, and bind the button to update the\n # showphotos layout.\n if curdir == \" \":\n return sidepanel_layout\n else:\n root.remove_widget(sidepanel_layout)\n for folders in sorted(glob(join(curdir, \"thumb\", \"*\"))):\n name = basename(folders)\n btn = Button(text=name, on_press=lambda n=name: self._update_scroll(n.text))\n sidepanel_layout.add_widget(btn)\n root.add_widget(sidepanel_layout)\n sidepanel_layout.do_layout()","def XPHideWidget(inWidget):\n pass","async def bucketsort(self, ctx:commands.Context, sort_type:str, sort_parameter:str, member:Member = None):\r\n\r\n if not sort_type in ['name', 'school', 'rarity', '']:\r\n await ctx.send(f'{sort_type} must be one of the following:\\n```name\\nschool\\nrarity```')\r\n return\r\n\r\n if member is None:\r\n member = ctx.message.author\r\n\r\n embeds = []\r\n currfish = await self.config.member(member).bucket()\r\n display_length = await self.GetSetting(ctx.guild, 'bucket_display_length')\r\n newEmbed = Embed(title=f'{member.display_name}\\'s Bucket' + (' (' + sort_parameter + ' only)' if not sort_type == '' else '') + ':')\r\n newEmbed.set_thumbnail(url=bucket_image)\r\n for i in range(len(currfish)):\r\n if i != 0 and i % display_length == 0:\r\n embeds.append(newEmbed)\r\n\r\n if i == len(currfish) - 1:\r\n break\r\n\r\n newEmbed = Embed(title=f'{member.display_name}\\'s Bucket:')\r\n newEmbed.set_thumbnail(url=bucket_image)\r\n\r\n if sort_type == '' or currfish[i][sort_type] == sort_parameter:\r\n newEmbed = FishToEmbed(newEmbed, currfish[i]['name'], currfish[i]['size'], currfish[i]['value'])\r\n else:\r\n embeds.append(newEmbed)\r\n\r\n await menu(ctx, embeds, DEFAULT_CONTROLS, timeout=45)","def sort(self, Ncol, order):\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \n if order != Qt.DescendingOrder:\n self.arraydata.reverse()\n self.emit(SIGNAL(\"layoutChanged()\"))","def add_sort_filter(source, args, index):\n tags = hxl.TagPattern.parse_list(args.get('sort-tags%02d' % index, ''))\n reverse = (args.get('sort-reverse%02d' % index) == 'on')\n return source.sort(tags, reverse)","def toggle_filter(self):\n\t\t#print('toggling filter')\n\t\tself.filter_button.click()\n\t\t#WDW(self.driver, 10).until(EC.presence_of_element_located((By.ID, 'filter_terminated')))\n\t\ttime.sleep(1)\n\t\t# update filter/sort options\n\t\tself.try_load_filter_opts()\n\t\tif not main.is_desktop():\n\t\t\tself.try_load_sort_opts()\n\t\t#raw_input('what happening?')\n\t\ttime.sleep(1)","def get_sidebar_filter_save(self):\n return [\n dbc.FormGroup(\n children=[\n dbc.Label(\"Filter name\", className=\"mr-2\"),\n dbc.Input(\n type=\"text\",\n placeholder=\"filter name\",\n id=\"filter_name\"\n ),\n ]\n ),\n dbc.Button(\"Save\", color=\"warning\", className=\"mt-2\", id=\"filter_save_btn\",\n n_clicks=0),\n ]","def test_sort(self):\n sort_field = MoveSearchForm.sort\n for value, label in sort_field.kwargs['choices']:\n response = self.do_search(id=u'1', sort=value)\n self.assert_(\n response.tmpl_context.results,\n \"\"\"Sort by {0} doesn't crash\"\"\".format(value)\n )","def generateModelSort(data):\n\n model = data[\"model\"]\n cont = data[\"container\"]\n code = data[\"code\"]\n global h_model\n h_model.append(\"\")\n h_model.append(\"\\t// Sort support:\")\n h_model.append(\"\\tvirtual void sort(int column, Qt::SortOrder order=Qt::AscendingOrder);\")\n\n global c_model\n\n # First the static sort functions\n for field in data[\"fields\"]:\n if not field.has_key(\"head\"):\n continue\n c_model.append(\"static bool sort%s%s(const %s &one, const %s two)\" % (\n model[\"name\"], field[\"name\"].capitalize(),\n name, name))\n c_model.append(\"{\")\n order = get(field, \"sort_order\", \"ascending\")\n if order == \"ascending\":\n order = \"Qt::AscendingOrder\"\n elif order == \"descending\":\n order = \"Qt::DescendingOrder\";\n s1 = \"one.%s\" % field[\"name\"]\n s2 = \"two.%s\" % field[\"name\"]\n if field.has_key(\"sort_code\"):\n s1 = \"s1\"\n s2 = \"s2\"\n appendCode(c_model, \"\\t\", code[field[\"sort_code\"]])\n c_model.append(\"\\tif (sortOrder == %s)\" % order)\n c_model.append(\"\\t\\treturn %s < %s;\" % (s1, s2))\n c_model.append(\"\\telse\")\n c_model.append(\"\\t\\treturn %s > %s;\" % (s1, s2))\n c_model.append(\"}\\n\")\n\n # and then the class member:\n c_model.append(\"void %s::sort(int column, Qt::SortOrder order)\" % model[\"name\"])\n c_model.append(\"{\")\n c_model.append(\"\\tsortOrder = order;\")\n c_model.append(\"\")\n c_model.append(\"\\tswitch (column) {\")\n for field in data[\"fields\"]:\n if not field.has_key(\"head\"):\n continue\n c_model.append(\"\\tcase %s_%s:\" % (col_prefix, field[\"name\"].upper()) )\n c_model.append(\"\\t\\tqStableSort(%s.begin(), %s.end(), sort%s%s);\" % (\n cont[\"name\"], cont[\"name\"],\n model[\"name\"], field[\"name\"].capitalize(),\n ))\n c_model.append(\"\\t\\tbreak;\")\n c_model.append(\"\\tdefault:\")\n c_model.append(\"\\t\\treturn;\")\n c_model.append(\"\\t}\")\n c_model.append(\"\\temit dataChanged(index(0, 0), index(chars.count(), %s_LAST));\" % col_prefix)\n c_model.append(\"}\\n\")","def sort_by_date(arg):\n return render_template(\n \"overview/subrecipe.html\",\n title=\"Subrecetas\",\n recipe_form=SearchRecipeForm(),\n last_recipes=get_last_recipes(4),\n subrecipes=overview.sort_by_date(arg)[1],\n arg=arg,\n search_form=SearchForm()\n )","def sortby(self, col, descending):\n\t\t# grab values to sort\n\t\ttree = self.albumList\n\t\tdata = [(tree.set(child, col), child) for child in tree.get_children('')]\n\n\t\t# reorder data\n\t\tdata.sort(reverse=descending)\n\t\tfor indx, item in enumerate(data):\n\t\t tree.move(item[1], '', indx)\n\n\t\t# switch the heading so that it will sort in the opposite direction\n\t\ttree.heading(col,\n\t\t command=lambda col=col: self.sortby(col, int(not descending)))","def get_queryset(self):\n qs = super(SortForm, self).get_queryset()\n\n qs = self.pre_sort(qs)\n\n # Ensure that the form is valid\n if not self.is_valid():\n return qs\n\n # Do Sorting\n sorts = self.cleaned_data.get('sort', [])\n order_by = []\n for sort in sorts:\n param = self.HEADERS[abs(sort) - 1]['column']\n if sort < 0:\n param = '-' + param\n order_by.append(param)\n\n if order_by:\n qs = qs.order_by(*order_by)\n\n qs = self.post_sort(qs)\n\n return qs","def sort_column(self, column):\n if column == 1: # type\n self.sorted_keys = sorted(self.data_dict.keys(),\n key=lambda x: (self.data_dict[x]['type']),\n reverse=self.sorted_type_top)\n # Invert sorting method\n self.sorted_type_top = not self.sorted_type_top\n\n elif column == 2: # Score\n self.sorted_keys = sorted(self.data_dict.keys(),\n key=lambda x: (float(self.data_dict[x]['score'])),\n reverse=self.sorted_score_top)\n # Invert sorting method\n self.sorted_score_top = not self.sorted_score_top\n\n elif column == 4: # Duration\n d = dict()\n for k in self.sorted_keys:\n duration_string = self.data_dict[k]['duration']\n\n # Get amount of episodes\n if 'episode' in duration_string:\n if 'Some' in duration_string:\n episodes = 0\n else:\n episodes = int(duration_string.split(' episodes')[0])\n else:\n episodes = 1\n\n # Get the duration in minutes\n minutes = 0\n if 'min' in duration_string:\n minutes = int(re.findall('([0-9]+)min', duration_string)[0])\n if 'h' in duration_string:\n minutes += int(re.findall('([0-9]+)h', duration_string)[0]) * 60\n\n # Get total duration of the whole show\n minutes *= episodes\n\n # Store it for sorting\n d[k] = minutes\n\n # Sort titles based on duration\n self.sorted_keys = sorted(d.keys(),\n key=lambda x: d[x],\n reverse=self.sorted_duration_top)\n # Invert sorting method\n self.sorted_duration_top = not self.sorted_duration_top\n\n elif column == 5: # release year\n self.sorted_keys = sorted(self.data_dict.keys(),\n key=lambda x: (float(self.data_dict[x]['released'])),\n reverse=self.sorted_year_top)\n # Invert sorting method\n self.sorted_year_top = not self.sorted_year_top\n\n if column != 2:\n # Make sure next time we click to sort by score,\n # the highest score is on top\n self.sorted_score_top = True\n\n # Redraw the table\n self.setup_tableview()","def _swap_columns(self, side):\n displayed_cols = self._displayed_cols\n i1 = self._dragged_col_index\n i2 = i1 + 1 if side == 'right' else i1 - 1\n if 0 <= i2 < len(displayed_cols):\n # there is a neighbor, swap columns:\n displayed_cols[i1] = displayed_cols[i2]\n displayed_cols[i2] = self._dragged_col\n self[\"displaycolumns\"] = displayed_cols\n if side == 'left':\n right = self._dragged_col_neighbor_widths[0]\n self._dragged_col_x -= right # update dragged column x coordinate\n # set new left neighbor width\n if i2 > 0:\n left = ttk.Treeview.column(self, displayed_cols[i2 - 1], 'width')\n else:\n left = None\n else:\n left = self._dragged_col_neighbor_widths[1]\n self._dragged_col_x += left # update x coordinate of dragged column\n # set new right neighbor width\n if i2 < len(displayed_cols) - 1:\n right = ttk.Treeview.column(self, displayed_cols[i2 + 1], 'width')\n else:\n right = None\n self._dragged_col_index = i2 # update dragged column index\n self._dragged_col_neighbor_widths = (left, right)","def add_sidebar(self, widget: Component) -> None:\n if not self.sidebar:\n raise NoSidebarError('Set `sidebar=True` if you want to use the sidebar.')\n\n assert isinstance(widget, Component)\n\n # pylint: disable=protected-access\n if widget._PACKAGE:\n self._packages.add(widget._PACKAGE)\n self._templates.add(widget._TEMPLATE)\n self._imports.add(_Import(component=widget._COMPONENT,\n module=widget._TEMPLATE[:widget._TEMPLATE.find('.')]))\n self._controllers.append(_Control(instantiate=widget._instantiate,\n caption=getattr(widget, 'caption', None)))","def get_sort_query(self, kind, order, is_number):\n pass","def sort(self,desc):\n\tself.__sort(\"\",\"\",desc)","def sort():\n return -1","async def sidebar(self, ctx, *, caption=None):\r\n if caption == \"manual\": # Obsolete method.\r\n caption = None\r\n \r\n async with ctx.typing():\r\n # Check if message has an attachment, for the new sidebar image.\r\n if caption is not None:\r\n await self.edit_caption(caption)\r\n \r\n if ctx.message.attachments:\r\n await ctx.message.attachments[0].save(\"sidebar.png\")\r\n await self.bot.loop.run_in_executor(None, self.upload_image, \"sidebar.png\", \"sidebar\",\r\n f\"Sidebar image updated by {ctx.author} via discord\")\r\n # Build\r\n markdown = await self.make_sidebar()\r\n\r\n # Post\r\n await self.bot.loop.run_in_executor(None, self.post_sidebar, markdown, 'NUFC')\r\n \r\n # Embed.\r\n e = discord.Embed(color=0xff4500)\r\n th = \"http://vignette2.wikia.nocookie.net/valkyriecrusade/images/b/b5/Reddit-The-Official-App-Icon.png\"\r\n e.set_author(icon_url=th, name=\"Sidebar updater\")\r\n e.description = f\"Sidebar for http://www.reddit.com/r/NUFC updated.\"\r\n e.timestamp = datetime.datetime.now()\r\n await ctx.reply(embed=e, mention_author=False)","def start_sorting(self):\n if self.sorting:\n return None\n self.sorting = True\n\n passes = 0\n while self.sorting:\n swap_done = False\n for i in range(len(self.sort_list)-passes-1):\n if not self.sorting:\n break\n if self.sort_list[i][0] > self.sort_list[i+1][0]:\n self.sort_list[i], self.sort_list[i+1] = self.sort_list[i+1], self.sort_list[i]\n self.window.coords(self.sort_list[i][1], 4*i+50, 20, 4*i+50, self.sort_list[i][0])\n self.window.coords(self.sort_list[i+1][1], 4*(i+1)+50, 20, 4*(i+1)+50, self.sort_list[i+1][0])\n self.window.itemconfig(self.sort_list[i][1], fill='red')\n self.window.itemconfig(self.sort_list[i+1][1], fill='red')\n swap_done = True\n self.window.update()\n self.window.itemconfig(self.sort_list[i][1], fill='black')\n self.window.itemconfig(self.sort_list[i+1][1], fill='black')\n self.window.update()\n passes += 1\n\n if not swap_done:\n self.sorting = False\n for line in self.sort_list:\n self.window.itemconfig(line[1], fill='green')\n else:\n self.window.itemconfig(self.sort_list[i+1][1], fill='blue')\n self.window.update()","def SortChildren(self, item):\r\n\r\n if not self._attr_set:\r\n setattr(self._main_win, \"OnCompareItems\", self.OnCompareItems)\r\n self._attr_set = True\r\n \r\n self._main_win.SortChildren(item)","def post_sort(self, qs):\n return qs","def sortChoices(self):\n self.formatList.sort()","def _sort_column(self, column, reverse):\n if tk.DISABLED in self.state():\n return\n # get list of (value, item) tuple where value is the value in column for the item\n l = [(self.set(child, column), child) for child in self.get_children('')]\n # sort list using the column type\n l.sort(reverse=reverse, key=lambda x: self._column_types[column](x[0]))\n # reorder items\n for index, (val, child) in enumerate(l):\n self.move(child, \"\", index)\n # reverse sorting direction for the next time\n self.heading(column, command=lambda: self._sort_column(column, not reverse))","def sort_column(self, column, reverse):\n data = [(self.treeview.set(child, column), child) for child in self.treeview.get_children(\"\")]\n data.sort(reverse=reverse)\n\n # set sort symbol on column heading\n if reverse:\n self.treeview.heading(column, text=f\"{column} ▼\")\n else:\n self.treeview.heading(column, text=f\"{column} ▲\")\n\n # rearrange items in sorted positions\n for index, (value, child) in enumerate(data):\n self.treeview.move(child, \"\", index)\n\n # reverse sort direction\n self.treeview.heading(column, command=lambda: self.sort_column(column, not reverse))","def addToolBarButtons(self):","def on_sort_column_change(self, model):\n id, order = self.tv.get_model().get_sort_column_id()\n if id is None and order is None:\n return False\n if self.prev_sort_column_id and self.prev_sort_column_id[0][0] == id:\n self.prev_sort_column_id.pop(0)\n self.prev_sort_column_id.insert(0, (id, order))\n if len(self.prev_sort_column_id) > 2:\n self.prev_sort_column_id.pop()","def widget(self, request, group):","def data_for_sorting() -> NoReturn:\n raise NotImplementedError","def data_for_sorting() -> NoReturn:\n raise NotImplementedError","def sort_results(self):\n pass","def execute(self, item):\n items = item[\"items\"]\n reverse = not item.get(\"descending\", True)\n items.sort(key=operator.itemgetter(\"distance\"), reverse=reverse)\n\n return item","def build(self):\n global scroll\n global layout\n global curdir\n global root\n global filtering_queue\n global sorting_queue\n\n\n #Creates global variables to be used by other functions.\n curdir = \" \"\n filtering_queue = mp.Queue()\n sorting_queue = mp.Queue()\n\n # Creating the basic layouts.\n root = FloatLayout()\n scroll = ScrollView(pos_hint={\"x\": 0.12, \"top\": 0.92}, size_hint=(0.9, 1))\n layout = GridLayout(cols=5, padding=0, spacing=5)\n layout.bind(minimum_height=layout.setter(\"height\"))\n\n # Create the ActionBar with buttons.\n actionbar = F.ActionBar(pos_hint={'top': 1})\n av = F.ActionView()\n av.add_widget(F.ActionPrevious(title='SPAI', with_previous=False, app_icon=\"icon.png\"))\n av.add_widget(F.ActionOverflow())\n av.add_widget(F.ActionButton(text='Import'.format(), on_press=self._pop))\n av.add_widget(F.ActionButton(text='Report'.format()))\n\n actionbar.add_widget(av)\n av.use_separator = False\n\n # Adding the layouts to the root layout\n root.add_widget(actionbar)\n root.add_widget(self._sidepanel())\n root.add_widget(scroll)\n scroll.add_widget(layout)\n\n return root","def update_top_categories(self):\n cats = self.sorted_categories()\n display = \"\"\n for cat in cats:\n hit = \"hits\" if self.category_map[cat] > 1 else \"hit\"\n display += f\"{cat}, {self.category_map[cat]} {hit}\\n\"\n self.top_categories.configure(state=\"normal\")\n self.top_categories.delete('1.0', tk.END)\n self.top_categories.insert('1.0', display)\n self.top_categories.configure(state=\"disabled\")","def on_sort(self, callback):\n self._sort_callback = callback if callable(callback) else _void"],"string":"[\n \"def mouseDoubleClickEvent(self, event):\\n if event.button() == QtCore.Qt.LeftButton:\\n sw = self.spw.windows['Sort']\\n sw.clear()\",\n \"def OnColumnClick(self, event):\\r\\n columns = self.data.getParam('columns')\\r\\n self.SortItems(columns[event.GetColumn()],'INVERT')\",\n \"def _sortHandler(self) -> None:\\n response, columnIndex, ascending = self._sortDialog()\\n order = Qt.AscendingOrder if ascending else Qt.DescendingOrder\\n if response:\\n self._mainFileView.sortByColumn(columnIndex, order)\",\n \"def sortby(self):\\n ...\",\n \"def get_sidebar_layout(self):\\n return [\\n dbc.Card(children=self.get_sidebar_filters(), body=True),\\n dbc.Card(children=self.get_sidebar_filter_save(), body=True, className=\\\"mt-2\\\"),\\n dbc.Card(children=self.get_sidebar_saved_filter_names(), body=True, className=\\\"mt-2\\\")\\n ]\",\n \"def on_sort_complete(self):\\n if self.get_search_active_thread_count() == 0:\\n self.clear_widgets()\",\n \"def click_timed_sorting_button(self):\\n self.my_sorted_list = self.sorting.sorting_alg(self.my_list)\\n self.label_2[\\\"text\\\"] = self.set_my_sorted_list_label()\",\n \"def start_sort():\\n global data\\n if algo_box.get() == \\\"Bubble Sort\\\":\\n bubble_sort(data, display_data, speed_scale.get())\\n elif algo_box.get() == \\\"Merge Sort\\\":\\n merge_sort(data, display_data, speed_scale.get())\",\n \"def __init__(self, parent):\\n self.order_by = []\\n assert(isinstance(parent, gtk_view.gtk_view))\\n self._parent = parent\\n def shorter(name, action, *method):\\n self._parent.window.builder.get_object(name).connect(action, *method)\\n self.positions_list = list_view_sort_control(self._parent.window.builder.get_object(\\\"positions_view\\\"),\\n [[u'id', int],\\n (u'Дата Откр.', gtk.CellRendererText(), str, u'open_datetime'),\\n (u'время Откр.', gtk.CellRendererText(), str, u'open_time'),\\n (u'Дата Закр.', gtk.CellRendererText(), str, u'close_datetime'),\\n (u'Время Закр.', gtk.CellRendererText(), str, u'close_time'),\\n (u'В позиции', gtk.CellRendererText(), str, u'duration'),\\n (u'Инструмент', gtk.CellRendererText(), str, u'paper_name'),\\n (u'Кол-во', gtk.CellRendererText(), str, u'count'),\\n (u'Тип', gtk.CellRendererText(), str, u'direction'),\\n (u'Цена Откр.', gtk.CellRendererText(), str, u'open_price'),\\n (u'Цена Закр.', gtk.CellRendererText(), str, u'close_price'),\\n (u'Ход', gtk.CellRendererText(), str, u'steps_range'),\\n (u'Gross Bfr.', gtk.CellRendererText(), str, u'gross_before'),\\n (u'Gross Aftr.', gtk.CellRendererText(), str, u'gross_after'),\\n (u'P/L Gross', gtk.CellRendererText(), str, u'pl_gross_abs'),\\n (u'Net Bfr.', gtk.CellRendererText(), str, u'net_before'),\\n (u'Net Aftr.', gtk.CellRendererText(), str, u'net_after'),\\n (u'P/L Net', gtk.CellRendererText(), str, u'pl_net_abs'),\\n (u'% Изменения', gtk.CellRendererText(), str, u'percent_range_abs')],\\n self_sorting = False,\\n sort_callback = self.sort_callback)\\n self._parent.window.builder.get_object(\\\"positions_view\\\").get_selection().set_mode(gtk.SELECTION_MULTIPLE)\\n shorter(\\\"positions_make\\\", \\\"activate\\\", self.make_positions_activate)\\n shorter(\\\"call_positions_filter\\\", \\\"activate\\\", self.filter_activate)\\n shorter(\\\"delete_positions\\\", \\\"activate\\\", self.delete_positions_activate)\\n shorter(\\\"add_position\\\", \\\"activate\\\", self.add_position_activate)\\n shorter(\\\"update_positions\\\", \\\"activate\\\", self.update_positions_activate)\",\n \"def __sidebar(self, conf):\\n result = \\\"\\\"\\n\\n i = conf[\\\"conf_json\\\"][0]\\n result += \\\"\\\"\\\"\\n\\n <h4>%s</h4>\\n <ul id=\\\"sidebar_menu\\\" class=\\\"sidebar_menu\\\">\\n <li><a class=\\\"btn active\\\" href=\\\"#description\\\">DESCRIPTION</a></li>\\n <li><a class=\\\"btn\\\" href=\\\"#parameters\\\">PARAMETERS</a></li>\\n <li><a class=\\\"btn\\\" href=\\\"#operations\\\">OPERATIONS</a>\\n <ul class=\\\"sidebar_submenu\\\">%s</ul>\\n </li>\\n <li><a class=\\\"btn active\\\" href=\\\"/\\\">HOME</a></li>\\n </ul>\\n \\\"\\\"\\\" % (\\n i[\\\"title\\\"],\\n \\\"\\\".join(\\n [\\n \\\"<li><a class='btn' href='#%s'>%s</a></li>\\\" % (op[\\\"url\\\"], op[\\\"url\\\"])\\n for op in conf[\\\"conf_json\\\"][1:]\\n ]\\n ),\\n )\\n return result\",\n \"def on_sort(self, param, state):\\n if state > 0: # From unchecked to checked\\n self.grouped = False\\n self.uncheck_group()\\n if param not in self.param_list:\\n self.param_list.append(param)\\n else: # From checked to unchecked\\n if param in self.param_list:\\n if len(self.param_list) == 1:\\n self.param_list = []\\n else:\\n self.param_list.remove(param)\\n self.sorted_labels = utils.sort_by_param(self.nwb_path_list.values(), self.param_list)\\n if self.param_list:\\n self.sorted = True\\n else:\\n self.sorted = False\\n self.musketeers_widget.session_widget.update_text_filter()\\n self.musketeers_widget.session_widget.populate(self.sorted_labels)\",\n \"def click_on_sort(self):\\r\\n self.driver.find_element_by_xpath('//*[@id=\\\"dataTable\\\"]/thead/tr[3]/th[4]').click()\",\n \"def on_combo_sort_col_names_currentIndexChanged(self, index):\\n if self.ui.sort_radio_asc.isChecked():\\n self.model.setSort(index, Qt.AscendingOrder)\\n else:\\n self.model.setSort(index, Qt.DescendingOrder)\\n self.model.select()\",\n \"def sorter(Plugin):\\n return Plugin.order\",\n \"def gallery_sort(request, item_container):\\n\\n return do_sort(request, item_container, 'pool', _(u'Bilder umordnen'))\",\n \"def change_sort(self, sorting_choice):\\r\\n self.message = \\\"place have been sorted by: {}\\\".format(sorting_choice)\\r\\n self.place_list.sort(sorting_choice)\\r\\n self.root.ids.entriesBox.clear_widgets()\\r\\n self.create_widget()\\r\\n sort_index = self.sort_choices.index(sorting_choice)\\r\\n self.current_sort = self.sort_choices[sort_index]\",\n \"def sort(self, args):\\n if not args:\\n self.err_print('One argument required')\\n return\\n\\n _key = args[0]\\n cur = self.ui.leftwin.highlighted().data\\n try:\\n ind = song.tags.index(_key)\\n cur.change_sort(ind)\\n self.ui.rightwin.disp()\\n except:\\n self.err_print('\\\"{}\\\" is not a valid key to sort by'.format(_key))\",\n \"def get_widget_sorted_item(widget):\\n for panel in widget['metadata']['panels']:\\n for item in panel['items']:\\n if 'sort' in item['jaql']:\\n return item['jaql']['title']\\n return None\",\n \"def sidebar_menu(items, selected):\\n\\n return {\\n 'items': items,\\n 'selected': selected,\\n }\",\n \"def testSortDescending(self):\\n self.request.GET['sort'] = \\\"-name\\\"\\n self.datagrid.load_state()\\n\\n self.assertEqual(self.datagrid.sort_list, [\\\"-name\\\"])\\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\\n self.assertEqual(self.datagrid.rows[0]['object'].name, \\\"Group 99\\\")\\n self.assertEqual(self.datagrid.rows[1]['object'].name, \\\"Group 98\\\")\\n self.assertEqual(self.datagrid.rows[2]['object'].name, \\\"Group 97\\\")\\n\\n # Exercise the code paths when rendering\\n self.datagrid.render_listview()\",\n \"def populate_buttons(self):\\n\\n # Figure out which index we'll sort on\\n if self.sort_group.checkedButton() == self.button_mtime:\\n to_sort = self.sort_mtime_idx\\n reverse = True\\n else:\\n to_sort = self.sort_alpha_idx\\n reverse = False\\n\\n # Now add things. This'll automatically shuffle stuff around without\\n # us having to worry about removing things first.\\n for row, (_, _, button) in enumerate(\\n sorted(self.buttons, reverse=reverse, key=lambda i: i[to_sort])\\n ):\\n self.grid.addWidget(button, row, 0)\",\n \"def sort(self, col, order):\\r\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\r\\n self.mylist = sorted(self.mylist,\\r\\n key=operator.itemgetter(col))\\r\\n if order == QtCore.Qt.DescendingOrder:\\r\\n self.mylist.reverse()\\r\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def PaneSortFunc(p1, p2):\\r\\n \\r\\n return (p1.dock_pos < p2.dock_pos and [-1] or [1])[0]\",\n \"def sort(self, col, order):\\r\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\r\\n self.mylist = sorted(self.mylist,\\r\\n key=operator.itemgetter(col))\\r\\n if order == Qt.DescendingOrder:\\r\\n self.mylist.reverse()\\r\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def sort(self, col, order):\\n self.layoutAboutToBeChanged.emit()\\n self.mylist = sorted(self.mylist,\\n key=operator.itemgetter(col))\\n if order == Qt.DescendingOrder:\\n self.mylist.reverse()\\n self.layoutChanged.emit()\",\n \"def sort_songs(self, current_sort_option):\\n self.songs.sort_songs(current_sort_option)\\n SongsToLearnApp.create_widgets(self)\",\n \"def sort_by_type(self):\\n # sort_by_type_sitem = self.locator_finder_by_idx(self.sort_by_type_id, 30)\\n # sort_by_type_sitem = sort_by_type_sitem.find_element_by_xpath(\\\"./..\\\")\\n # while True:\\n # try:\\n # sort_by_type_sitem.click()\\n # break\\n # except ElementNotInteractableException:\\n # time.sleep(1) \\n if self.current_package_version() == semver.VersionInfo.parse(\\\"3.8.0\\\"):\\n sort_by_type = '//*[@id=\\\"collectionsDropdown\\\"]/ul[3]/li[3]/a/label'\\n sort_by_type_sitem = self.locator_finder_by_xpath(sort_by_type)\\n else:\\n sort_by_type_sitem = self.locator_finder_by_xpath(self.sort_by_type_id)\\n\\n sort_by_type_sitem.click()\\n time.sleep(2)\",\n \"def sort_key(self):\\n ...\",\n \"def orderby():\\n pass\",\n \"def testSortNoDbAscending(self):\\n self.request.GET['sort'] = \\\"custom\\\"\\n self.datagrid.load_state()\\n self.assertEqual(self.datagrid.sort_list, [\\\"custom\\\"])\\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\\n self.assertEqual(self.datagrid.rows[0]['object'].name, \\\"Group 04\\\")\\n self.assertEqual(self.datagrid.rows[1]['object'].name, \\\"Group 08\\\")\\n self.assertEqual(self.datagrid.rows[2]['object'].name, \\\"Group 12\\\")\\n\\n # Exercise the code paths when rendering\\n self.datagrid.render_listview()\",\n \"def sort(request):\\n stars = Product.objects.annotate(\\n avg_review=Avg('productreview__rating'),\\n )\\n select = request.GET['sort']\\n if select == 'LtoH':\\n results = Product.objects.order_by('price')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\\n elif select == 'HtoL':\\n results = Product.objects.order_by('-price')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\\n elif select == 'AtoZ':\\n results = Product.objects.order_by('name')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\\n elif select == 'ZtoA':\\n results = Product.objects.order_by('-name')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\",\n \"def sorted(self): \\n pass\",\n \"def testSortNoDbDescending(self):\\n self.request.GET['sort'] = \\\"-custom\\\"\\n self.datagrid.load_state()\\n self.assertEqual(self.datagrid.sort_list, [\\\"-custom\\\"])\\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\\n self.assertEqual(self.datagrid.rows[0]['object'].name, \\\"Group 03\\\")\\n self.assertEqual(self.datagrid.rows[1]['object'].name, \\\"Group 07\\\")\\n self.assertEqual(self.datagrid.rows[2]['object'].name, \\\"Group 11\\\")\\n\\n # Exercise the code paths when rendering\\n self.datagrid.render_listview()\",\n \"def sort(request, code):\\n filtered = Listing.objects.filter(category=code, is_active=True)\\n\\n # Returns page with variables. \\n return render(request, \\\"auctions/categories.html\\\", {\\n \\\"bo\\\": True,\\n \\\"listings\\\": filtered,\\n })\",\n \"def testSortAscending(self):\\n self.request.GET['sort'] = \\\"name,objid\\\"\\n self.datagrid.load_state()\\n\\n self.assertEqual(self.datagrid.sort_list, [\\\"name\\\", \\\"objid\\\"])\\n self.assertEqual(len(self.datagrid.rows), self.datagrid.paginate_by)\\n self.assertEqual(self.datagrid.rows[0]['object'].name, \\\"Group 01\\\")\\n self.assertEqual(self.datagrid.rows[1]['object'].name, \\\"Group 02\\\")\\n self.assertEqual(self.datagrid.rows[2]['object'].name, \\\"Group 03\\\")\\n\\n # Exercise the code paths when rendering\\n self.datagrid.render_listview()\",\n \"def OnReorder( self, event ):\\n column = self.columns[event.GetColumn()]\\n if column.sortOn:\\n # multiple sorts for the click...\\n columns = [ self.columnByAttribute( attr ) for attr in column.sortOn ]\\n diff = [ (a,b) for a,b in zip( self.sortOrder, columns ) if b is not a[1]]\\n if not diff:\\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\\n else:\\n self.sortOrder = [\\n (c.defaultOrder,c) for c in columns \\n ] + [ (a,b) for (a,b) in self.sortOrder if b not in columns]\\n else:\\n if column is self.sortOrder[0][1]:\\n # reverse current major order\\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\\n else:\\n self.sortOrder = [(column.defaultOrder,column)] + [\\n (a,b) \\n for (a,b) in self.sortOrder if b is not column \\n ]\\n # TODO: store current selection and re-select after sorting...\\n self.reorder()\\n self.Refresh()\",\n \"def _sortDialog(self) -> Tuple[Any, Any, Any]:\\n dialog = QDialog()\\n dialog.setFixedSize(300, 150)\\n dialog.setWindowTitle('Sort')\\n dialog.setWindowIcon(QIcon(':sort.png'))\\n\\n # layouts\\n dialogLayout = QVBoxLayout()\\n formLayout = QFormLayout()\\n orderRadioLayout = QVBoxLayout()\\n buttonBoxLayout = QHBoxLayout()\\n\\n # sort by column combobox\\n sortLabel = QLabel('Sort by:')\\n sortComboBox = QComboBox()\\n sortComboBox.addItems(self._modelHeaders[:4])\\n\\n # order choosing radio buttons\\n orderLabel = QLabel('Order:')\\n orderRadioAsc = QRadioButton('ascending')\\n orderRadioAsc.setChecked(True)\\n orderRadioDesc = QRadioButton('descending')\\n orderRadioLayout.addWidget(orderRadioAsc)\\n orderRadioLayout.addWidget(orderRadioDesc)\\n\\n formLayout.addRow(sortLabel, sortComboBox)\\n formLayout.addRow(orderLabel, orderRadioLayout)\\n\\n # Ok and Cancel buttons\\n buttonBox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\\n buttonBox.accepted.connect(dialog.accept)\\n buttonBox.rejected.connect(dialog.reject)\\n buttonBoxLayout.addStretch()\\n buttonBoxLayout.addWidget(buttonBox)\\n buttonBoxLayout.addStretch()\\n\\n dialogLayout.addLayout(formLayout)\\n dialogLayout.addLayout(buttonBoxLayout)\\n dialogLayout.setAlignment(Qt.AlignCenter)\\n\\n dialog.setLayout(dialogLayout)\\n\\n dialogResponse = dialog.exec()\\n\\n return dialogResponse, sortComboBox.currentIndex(), orderRadioAsc.isChecked()\",\n \"def __init__(self, data=None, *args, **kwargs):\\n super().__init__(data=data, *args, **kwargs)\\n\\n # if a keyword search term is not present, relevance sort is disabled\\n if not data or not data.get(\\\"q\\\", None):\\n self.fields[\\\"sort\\\"].widget.choices[0] = (\\n self.SORT_CHOICES[0][0],\\n {\\\"label\\\": self.SORT_CHOICES[0][1], \\\"disabled\\\": True},\\n )\",\n \"def do_sort(request, item_container, app_name, my_title, app_types=[]):\\n\\n class dms_itemForm ( forms.Form ) :\\n sections = forms.CharField(required=False,\\n widget=forms.Textarea( attrs={'rows':5, 'cols':40,\\n 'style':'width:50%;'}) )\\n\\n my_item = item_container.item\\n objs = []\\n has_user_folder = False\\n user_perms = UserEditPerms(request.user.username,request.path)\\n\\n # wurden Zwischentitel geloescht, ergaenzt, umgeordnet?\\n if request.POST.has_key('sections_form'):\\n s = request.POST['sections']\\n if s != item_container.container.sections:\\n item_container.container.sections = s\\n item_container.container.save()\\n change_values = request.POST.has_key('drag_item_form')\\n if change_values:\\n items, sections, d_sections = get_folder_content(item_container, False, app_types)\\n order_by_ids, new_sections_str = do_resort(request.POST['var_order_by_0'])\\n n = 0\\n c = []\\n for i in items:\\n if app_types==[] or i.item.app.name in app_types:\\n has_changed = False\\n if order_by_ids[i.item.id][0] != i.order_by:\\n i.order_by = order_by_ids[i.item.id][0]\\n has_changed = True\\n sec = order_by_ids[i.item.id][1]\\n if sec != i.section:\\n i.section = sec\\n has_changed = True\\n if has_changed:\\n i.save()\\n # --- wurde die Reihenfolge der Zwischentitel geaendert?\\n n = new_sections_str\\n if item_container.container.sections != n:\\n item_container.container.sections = n\\n item_container.container.save()\\n items, sections, d_sections = get_folder_content(item_container, False, app_types)\\n js_head, drag_list, input_str, n_drag_titles = get_drag_list(sections, d_sections)\\n max_items = len(items)\\n # --- Zwischentitel\\n data_init = {'sections' : decode_html(item_container.container.sections,) }\\n f = dms_itemForm(data_init)\\n tabs = [('tab_sections' , ['sections',]), ]\\n sec_content = get_tabbed_form(tabs, help_form, 'lecture' , f, False)\\n\\n vars, user_perms = get_base_vars(request, item_container, 'frame-main-manage')\\n v = { 'objs' : objs,\\n 'js_head' : js_head,\\n 'drag_list' : drag_list,\\n 'input_str' : input_str,\\n 'max_items' : n_drag_titles,\\n 'id' : my_item.id,\\n 'title' : my_title,\\n 'sub_title' : my_item.title,\\n 'name' : my_item.name,\\n 'action' : get_folderish_actions(request, user_perms, item_container, app_name,\\n item_container.item.has_comments,\\n {'browseable_mode': False,\\n 'navigation_mode': False}),\\n 'sec_content': sec_content\\n }\\n vars.update(v)\\n vars['image_url'] = ''\\n vars['text'] = ''\\n vars['text_more'] = ''\\n return render_to_response ( 'app/base_sort.html', vars )\",\n \"def sort_by_name(self):\\n # sort_by_name_sitem = self.locator_finder_by_idx(self.sort_by_name_id)\\n # sort_by_name_sitem = sort_by_name_sitem.find_element_by_xpath(\\\"./..\\\")\\n # while True:\\n # try:\\n # sort_by_name_sitem.click()\\n # break\\n # except ElementNotInteractableException:\\n # time.sleep(1)\\n \\n if self.current_package_version() == semver.VersionInfo.parse(\\\"3.8.0\\\"):\\n name = '//*[@id=\\\"collectionsDropdown\\\"]/ul[3]/li[2]/a/label'\\n sort_by_name_sitem = self.locator_finder_by_xpath(name)\\n else:\\n sort_by_name_sitem = self.locator_finder_by_xpath(self.sort_by_name_id)\\n sort_by_name_sitem.click()\\n time.sleep(2)\",\n \"def folder_sort(request, item_container):\\n return do_sort(request, item_container, 'folder', _(u'Ordner, Seiten etc. umordnen'))\",\n \"def add_arguments(self, actions):\\n actions = sorted(actions, key=attrgetter('option_strings'))\\n super(SortingHelpFormatter, self).add_arguments(actions)\",\n \"def __editSortSelectedLines(self):\\n editor = self.activeWindow()\\n if editor:\\n editor.sortLines()\",\n \"def sort_descending(self):\\n # sort_descending_sitem = self.locator_finder_by_idx(self.sort_descending_id)\\n # sort_descending_sitem = sort_descending_sitem.find_element_by_xpath(\\\"./..\\\")\\n # sort_descending_sitem.click()\\n # time.sleep(2)\\n \\n if self.current_package_version() == semver.VersionInfo.parse(\\\"3.8.0\\\"):\\n sort_by_descending = '//*[@id=\\\"collectionsDropdown\\\"]/ul[3]/li[4]/a/label/i'\\n sort_descending_sitem = self.locator_finder_by_xpath(sort_by_descending)\\n else:\\n sort_descending_sitem = self.locator_finder_by_xpath(self.sort_descending_id)\\n sort_descending_sitem.click()\\n time.sleep(2)\",\n \"def SortBy(self,tree, col, descending):\\n \\n # grab values to sort\\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\\n \\n # now sort the data in place\\n data.sort(reverse=descending)\\n \\n for ix, item in enumerate(data):\\n tree.move(item[1], '', ix)\\n \\n # switch the heading so it will sort in the opposite direction\\n tree.heading(col,command=lambda col=col: self.SortBy(tree, col,int(not descending)))\",\n \"def admin_sidebar(context):\\n request = context.get('request')\\n\\n request_context = {\\n 'count_users': User.objects.count(),\\n 'count_review_groups': Group.objects.count(),\\n 'count_default_reviewers': DefaultReviewer.objects.count(),\\n 'count_oauth_applications': Application.objects.count(),\\n 'count_repository': Repository.objects.accessible(\\n request.user, visible_only=False).count(),\\n 'count_webhooks': WebHookTarget.objects.count(),\\n 'count_hosting_accounts': HostingServiceAccount.objects.count(),\\n 'version': get_version_string(),\\n }\\n\\n # We're precomputing URLs in here, rather than computing them in the\\n # template, because we need to always ensure that reverse() will be\\n # searching all available URL patterns and not just the ones bound to\\n # request.current_app.\\n #\\n # current_app gets set by AdminSite views, and if we're in an extension's\\n # AdminSite view, we'll fail to resolve these URLs from within the\\n # template. We don't have that problem if calling reverse() ourselves.\\n request_context.update({\\n 'url_%s' % url_name: reverse('admin:%s' % url_name)\\n for url_name in ('auth_user_add',\\n 'auth_user_changelist',\\n 'hostingsvcs_hostingserviceaccount_add',\\n 'hostingsvcs_hostingserviceaccount_changelist',\\n 'notifications_webhooktarget_add',\\n 'notifications_webhooktarget_changelist',\\n 'oauth_application_add',\\n 'oauth_application_changelist',\\n 'reviews_defaultreviewer_add',\\n 'reviews_defaultreviewer_changelist',\\n 'reviews_group_add',\\n 'reviews_group_changelist',\\n 'scmtools_repository_add',\\n 'scmtools_repository_changelist')\\n })\\n\\n return RequestContext(request, request_context)\",\n \"def has_right_sidebar(self):\\n return True\",\n \"def add_sidebar(self, widget: Component) -> None:\\n self._root.add_sidebar(widget)\",\n \"def main_menu_toolbar():\\n\\n pass\",\n \"def __str__(self):\\n return 'predsort'\",\n \"def operator(self, sort):\\r\\n return None\",\n \"def item_condition(request):\\n\\n items = Product.objects.all()\\n is_used = Product.objects.filter(is_used=True)\\n sort = None\\n direction = None\\n\\n if request.GET:\\n if 'sort' in request.GET:\\n sortkey = request.GET['sort']\\n sort = sortkey\\n if sortkey == 'name':\\n sortkey = 'lower_name'\\n items = items.annotate(lower_name=Lower('name'))\\n if sortkey == 'category':\\n sortkey = 'category__name'\\n if 'direction' in request.GET:\\n direction = request.GET['direction']\\n if direction == 'desc':\\n sortkey = f'-{sortkey}'\\n items = items.order_by(sortkey)\\n\\n current_sorting = f'{sort}_{direction}'\\n\\n context = {\\n 'is_used': is_used,\\n 'items': items,\\n 'current_sorting': current_sorting,\\n }\\n \\n return render(request, 'products/second_hand.html', context)\",\n \"def sort_by_user_arg(user_argument = 'sort by ex. artist'):\\n pass\",\n \"def _config_sortable(self, sortable):\\n for col in self[\\\"columns\\\"]:\\n command = (lambda c=col: self._sort_column(c, True)) if sortable else \\\"\\\"\\n self.heading(col, command=command)\\n self._sortable = sortable\",\n \"def arrange_button(self):\\r\\n self.update_settings()\\r\\n if self.arrange_call is not None:\\r\\n self.arrange_call()\",\n \"def sort_apparel(request):\\n stars = Product.objects.annotate(\\n avg_review=Avg('productreview__rating'),\\n )\\n select = request.GET['sorta']\\n items = Product.objects.filter(category__icontains='A')\\n if select == 'LtoH':\\n results = items.order_by('price')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\\n elif select == 'HtoL':\\n results = items.order_by('-price')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\\n elif select == 'AtoZ':\\n results = items.order_by('name')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\\n elif select == 'ZtoA':\\n results = items.order_by('-name')\\n return render(request, \\\"products.html\\\",\\n {\\\"products\\\": results, 'stars': stars})\",\n \"def showSideBar(self):\\n\\t\\treturn False\",\n \"def sort_by_key(request):\\n return request.param\",\n \"def sort_by_key(request):\\n return request.param\",\n \"def products_view(request):\\n from_database = Product.objects.all()\\n\\n sort_type = request.POST.get('sort-selector')\\n print(sort_type)\\n if sort_type is None:\\n from_database = from_database.order_by('title')\\n my_context = {\\n 'from_database': from_database,\\n 'sort_type': sort_type,\\n }\\n return render(request, \\\"products.html\\\", my_context)\\n if sort_type == 'name_asc':\\n from_database = from_database.order_by('title')\\n my_context = {\\n 'from_database': from_database,\\n 'sort_type': sort_type,\\n }\\n return render(request, \\\"products.html\\\", my_context)\\n if sort_type == 'name_desc':\\n from_database = from_database.order_by('-title')\\n my_context = {\\n 'from_database': from_database,\\n 'sort_type': sort_type,\\n }\\n return render(request, \\\"products.html\\\", my_context)\\n if sort_type == 'price_asc':\\n from_database = from_database.order_by('price')\\n my_context = {\\n 'from_database': from_database,\\n 'sort_type': sort_type,\\n }\\n return render(request, \\\"products.html\\\", my_context)\\n if sort_type == 'price_desc':\\n from_database = from_database.order_by('-price')\\n my_context = {\\n 'from_database': from_database,\\n 'sort_type': sort_type,\\n }\\n return render(request, \\\"products.html\\\", my_context)\",\n \"def _sort_dataframe(self, dataframe):\\r\\n print('Not allowed')\",\n \"def sidebar_click(sidebar_item):\\n #print('sidebar_click')\\n sidebar = driver.find_element_by_class_name('m-menu__nav')\\n return click_on(sidebar_item, scope=sidebar)\",\n \"def sort(self, Ncol, order):\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \\n if order == Qt.DescendingOrder:\\n self.arraydata.reverse()\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def add_column(self):\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"ID\\\", cell, text=0, foreground=8, background=9)\\n column.set_sort_column_id(0)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Name\\\", cell, text=1, foreground=8, background=9)\\n column.set_sort_column_id(1)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Protocol\\\", cell, text=2, foreground=8, background=9)\\n column.set_sort_column_id(2)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Ip source\\\", cell, text=3, foreground=8, background=9)\\n column.set_sort_column_id(3)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Port source\\\", cell, text=4, foreground=8, background=9)\\n column.set_sort_column_id(4)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Ip dest\\\", cell, text=5, foreground=8, background=9)\\n column.set_sort_column_id(5)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Port dest\\\", cell, text=6, foreground=8, background=9)\\n column.set_sort_column_id(6)\\n self.tree_view.append_column(column)\\n cell = gtk.CellRendererText()\\n column = gtk.TreeViewColumn(\\\"Action\\\", cell, text=7, foreground=8, background=9)\\n column.set_sort_column_id(7)\\n self.tree_view.append_column(column)\",\n \"def treeview_sort_column(self, treeview, column, reverse):\\r\\n data = [(treeview.set(ticker, column), ticker) for ticker in treeview.get_children('')]\\r\\n data.sort(reverse=reverse)\\r\\n\\r\\n # sort the stock(s)\\r\\n for index, (val, k) in enumerate(data):\\r\\n treeview.move(k, '', index)\\r\\n\\r\\n # reverse sort next time\\r\\n treeview.heading(column, command=lambda: self.treeview_sort_column(treeview, column, not reverse))\",\n \"def _sidepanel(self):\\n global curdir\\n global sidepanel_layout\\n global root\\n\\n #Create the sidepanel layout.\\n sidepanel_layout = BoxLayout(orientation=\\\"vertical\\\", pos_hint={\\\"x\\\": 0.0, \\\"top\\\": 0.92}, size_hint=(0.1, 0.92))\\n\\n #If \\\"curdir\\\" contains folders, a button is created for each, and bind the button to update the\\n # showphotos layout.\\n if curdir == \\\" \\\":\\n return sidepanel_layout\\n else:\\n root.remove_widget(sidepanel_layout)\\n for folders in sorted(glob(join(curdir, \\\"thumb\\\", \\\"*\\\"))):\\n name = basename(folders)\\n btn = Button(text=name, on_press=lambda n=name: self._update_scroll(n.text))\\n sidepanel_layout.add_widget(btn)\\n root.add_widget(sidepanel_layout)\\n sidepanel_layout.do_layout()\",\n \"def XPHideWidget(inWidget):\\n pass\",\n \"async def bucketsort(self, ctx:commands.Context, sort_type:str, sort_parameter:str, member:Member = None):\\r\\n\\r\\n if not sort_type in ['name', 'school', 'rarity', '']:\\r\\n await ctx.send(f'{sort_type} must be one of the following:\\\\n```name\\\\nschool\\\\nrarity```')\\r\\n return\\r\\n\\r\\n if member is None:\\r\\n member = ctx.message.author\\r\\n\\r\\n embeds = []\\r\\n currfish = await self.config.member(member).bucket()\\r\\n display_length = await self.GetSetting(ctx.guild, 'bucket_display_length')\\r\\n newEmbed = Embed(title=f'{member.display_name}\\\\'s Bucket' + (' (' + sort_parameter + ' only)' if not sort_type == '' else '') + ':')\\r\\n newEmbed.set_thumbnail(url=bucket_image)\\r\\n for i in range(len(currfish)):\\r\\n if i != 0 and i % display_length == 0:\\r\\n embeds.append(newEmbed)\\r\\n\\r\\n if i == len(currfish) - 1:\\r\\n break\\r\\n\\r\\n newEmbed = Embed(title=f'{member.display_name}\\\\'s Bucket:')\\r\\n newEmbed.set_thumbnail(url=bucket_image)\\r\\n\\r\\n if sort_type == '' or currfish[i][sort_type] == sort_parameter:\\r\\n newEmbed = FishToEmbed(newEmbed, currfish[i]['name'], currfish[i]['size'], currfish[i]['value'])\\r\\n else:\\r\\n embeds.append(newEmbed)\\r\\n\\r\\n await menu(ctx, embeds, DEFAULT_CONTROLS, timeout=45)\",\n \"def sort(self, Ncol, order):\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \\n if order != Qt.DescendingOrder:\\n self.arraydata.reverse()\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def add_sort_filter(source, args, index):\\n tags = hxl.TagPattern.parse_list(args.get('sort-tags%02d' % index, ''))\\n reverse = (args.get('sort-reverse%02d' % index) == 'on')\\n return source.sort(tags, reverse)\",\n \"def toggle_filter(self):\\n\\t\\t#print('toggling filter')\\n\\t\\tself.filter_button.click()\\n\\t\\t#WDW(self.driver, 10).until(EC.presence_of_element_located((By.ID, 'filter_terminated')))\\n\\t\\ttime.sleep(1)\\n\\t\\t# update filter/sort options\\n\\t\\tself.try_load_filter_opts()\\n\\t\\tif not main.is_desktop():\\n\\t\\t\\tself.try_load_sort_opts()\\n\\t\\t#raw_input('what happening?')\\n\\t\\ttime.sleep(1)\",\n \"def get_sidebar_filter_save(self):\\n return [\\n dbc.FormGroup(\\n children=[\\n dbc.Label(\\\"Filter name\\\", className=\\\"mr-2\\\"),\\n dbc.Input(\\n type=\\\"text\\\",\\n placeholder=\\\"filter name\\\",\\n id=\\\"filter_name\\\"\\n ),\\n ]\\n ),\\n dbc.Button(\\\"Save\\\", color=\\\"warning\\\", className=\\\"mt-2\\\", id=\\\"filter_save_btn\\\",\\n n_clicks=0),\\n ]\",\n \"def test_sort(self):\\n sort_field = MoveSearchForm.sort\\n for value, label in sort_field.kwargs['choices']:\\n response = self.do_search(id=u'1', sort=value)\\n self.assert_(\\n response.tmpl_context.results,\\n \\\"\\\"\\\"Sort by {0} doesn't crash\\\"\\\"\\\".format(value)\\n )\",\n \"def generateModelSort(data):\\n\\n model = data[\\\"model\\\"]\\n cont = data[\\\"container\\\"]\\n code = data[\\\"code\\\"]\\n global h_model\\n h_model.append(\\\"\\\")\\n h_model.append(\\\"\\\\t// Sort support:\\\")\\n h_model.append(\\\"\\\\tvirtual void sort(int column, Qt::SortOrder order=Qt::AscendingOrder);\\\")\\n\\n global c_model\\n\\n # First the static sort functions\\n for field in data[\\\"fields\\\"]:\\n if not field.has_key(\\\"head\\\"):\\n continue\\n c_model.append(\\\"static bool sort%s%s(const %s &one, const %s two)\\\" % (\\n model[\\\"name\\\"], field[\\\"name\\\"].capitalize(),\\n name, name))\\n c_model.append(\\\"{\\\")\\n order = get(field, \\\"sort_order\\\", \\\"ascending\\\")\\n if order == \\\"ascending\\\":\\n order = \\\"Qt::AscendingOrder\\\"\\n elif order == \\\"descending\\\":\\n order = \\\"Qt::DescendingOrder\\\";\\n s1 = \\\"one.%s\\\" % field[\\\"name\\\"]\\n s2 = \\\"two.%s\\\" % field[\\\"name\\\"]\\n if field.has_key(\\\"sort_code\\\"):\\n s1 = \\\"s1\\\"\\n s2 = \\\"s2\\\"\\n appendCode(c_model, \\\"\\\\t\\\", code[field[\\\"sort_code\\\"]])\\n c_model.append(\\\"\\\\tif (sortOrder == %s)\\\" % order)\\n c_model.append(\\\"\\\\t\\\\treturn %s < %s;\\\" % (s1, s2))\\n c_model.append(\\\"\\\\telse\\\")\\n c_model.append(\\\"\\\\t\\\\treturn %s > %s;\\\" % (s1, s2))\\n c_model.append(\\\"}\\\\n\\\")\\n\\n # and then the class member:\\n c_model.append(\\\"void %s::sort(int column, Qt::SortOrder order)\\\" % model[\\\"name\\\"])\\n c_model.append(\\\"{\\\")\\n c_model.append(\\\"\\\\tsortOrder = order;\\\")\\n c_model.append(\\\"\\\")\\n c_model.append(\\\"\\\\tswitch (column) {\\\")\\n for field in data[\\\"fields\\\"]:\\n if not field.has_key(\\\"head\\\"):\\n continue\\n c_model.append(\\\"\\\\tcase %s_%s:\\\" % (col_prefix, field[\\\"name\\\"].upper()) )\\n c_model.append(\\\"\\\\t\\\\tqStableSort(%s.begin(), %s.end(), sort%s%s);\\\" % (\\n cont[\\\"name\\\"], cont[\\\"name\\\"],\\n model[\\\"name\\\"], field[\\\"name\\\"].capitalize(),\\n ))\\n c_model.append(\\\"\\\\t\\\\tbreak;\\\")\\n c_model.append(\\\"\\\\tdefault:\\\")\\n c_model.append(\\\"\\\\t\\\\treturn;\\\")\\n c_model.append(\\\"\\\\t}\\\")\\n c_model.append(\\\"\\\\temit dataChanged(index(0, 0), index(chars.count(), %s_LAST));\\\" % col_prefix)\\n c_model.append(\\\"}\\\\n\\\")\",\n \"def sort_by_date(arg):\\n return render_template(\\n \\\"overview/subrecipe.html\\\",\\n title=\\\"Subrecetas\\\",\\n recipe_form=SearchRecipeForm(),\\n last_recipes=get_last_recipes(4),\\n subrecipes=overview.sort_by_date(arg)[1],\\n arg=arg,\\n search_form=SearchForm()\\n )\",\n \"def sortby(self, col, descending):\\n\\t\\t# grab values to sort\\n\\t\\ttree = self.albumList\\n\\t\\tdata = [(tree.set(child, col), child) for child in tree.get_children('')]\\n\\n\\t\\t# reorder data\\n\\t\\tdata.sort(reverse=descending)\\n\\t\\tfor indx, item in enumerate(data):\\n\\t\\t tree.move(item[1], '', indx)\\n\\n\\t\\t# switch the heading so that it will sort in the opposite direction\\n\\t\\ttree.heading(col,\\n\\t\\t command=lambda col=col: self.sortby(col, int(not descending)))\",\n \"def get_queryset(self):\\n qs = super(SortForm, self).get_queryset()\\n\\n qs = self.pre_sort(qs)\\n\\n # Ensure that the form is valid\\n if not self.is_valid():\\n return qs\\n\\n # Do Sorting\\n sorts = self.cleaned_data.get('sort', [])\\n order_by = []\\n for sort in sorts:\\n param = self.HEADERS[abs(sort) - 1]['column']\\n if sort < 0:\\n param = '-' + param\\n order_by.append(param)\\n\\n if order_by:\\n qs = qs.order_by(*order_by)\\n\\n qs = self.post_sort(qs)\\n\\n return qs\",\n \"def sort_column(self, column):\\n if column == 1: # type\\n self.sorted_keys = sorted(self.data_dict.keys(),\\n key=lambda x: (self.data_dict[x]['type']),\\n reverse=self.sorted_type_top)\\n # Invert sorting method\\n self.sorted_type_top = not self.sorted_type_top\\n\\n elif column == 2: # Score\\n self.sorted_keys = sorted(self.data_dict.keys(),\\n key=lambda x: (float(self.data_dict[x]['score'])),\\n reverse=self.sorted_score_top)\\n # Invert sorting method\\n self.sorted_score_top = not self.sorted_score_top\\n\\n elif column == 4: # Duration\\n d = dict()\\n for k in self.sorted_keys:\\n duration_string = self.data_dict[k]['duration']\\n\\n # Get amount of episodes\\n if 'episode' in duration_string:\\n if 'Some' in duration_string:\\n episodes = 0\\n else:\\n episodes = int(duration_string.split(' episodes')[0])\\n else:\\n episodes = 1\\n\\n # Get the duration in minutes\\n minutes = 0\\n if 'min' in duration_string:\\n minutes = int(re.findall('([0-9]+)min', duration_string)[0])\\n if 'h' in duration_string:\\n minutes += int(re.findall('([0-9]+)h', duration_string)[0]) * 60\\n\\n # Get total duration of the whole show\\n minutes *= episodes\\n\\n # Store it for sorting\\n d[k] = minutes\\n\\n # Sort titles based on duration\\n self.sorted_keys = sorted(d.keys(),\\n key=lambda x: d[x],\\n reverse=self.sorted_duration_top)\\n # Invert sorting method\\n self.sorted_duration_top = not self.sorted_duration_top\\n\\n elif column == 5: # release year\\n self.sorted_keys = sorted(self.data_dict.keys(),\\n key=lambda x: (float(self.data_dict[x]['released'])),\\n reverse=self.sorted_year_top)\\n # Invert sorting method\\n self.sorted_year_top = not self.sorted_year_top\\n\\n if column != 2:\\n # Make sure next time we click to sort by score,\\n # the highest score is on top\\n self.sorted_score_top = True\\n\\n # Redraw the table\\n self.setup_tableview()\",\n \"def _swap_columns(self, side):\\n displayed_cols = self._displayed_cols\\n i1 = self._dragged_col_index\\n i2 = i1 + 1 if side == 'right' else i1 - 1\\n if 0 <= i2 < len(displayed_cols):\\n # there is a neighbor, swap columns:\\n displayed_cols[i1] = displayed_cols[i2]\\n displayed_cols[i2] = self._dragged_col\\n self[\\\"displaycolumns\\\"] = displayed_cols\\n if side == 'left':\\n right = self._dragged_col_neighbor_widths[0]\\n self._dragged_col_x -= right # update dragged column x coordinate\\n # set new left neighbor width\\n if i2 > 0:\\n left = ttk.Treeview.column(self, displayed_cols[i2 - 1], 'width')\\n else:\\n left = None\\n else:\\n left = self._dragged_col_neighbor_widths[1]\\n self._dragged_col_x += left # update x coordinate of dragged column\\n # set new right neighbor width\\n if i2 < len(displayed_cols) - 1:\\n right = ttk.Treeview.column(self, displayed_cols[i2 + 1], 'width')\\n else:\\n right = None\\n self._dragged_col_index = i2 # update dragged column index\\n self._dragged_col_neighbor_widths = (left, right)\",\n \"def add_sidebar(self, widget: Component) -> None:\\n if not self.sidebar:\\n raise NoSidebarError('Set `sidebar=True` if you want to use the sidebar.')\\n\\n assert isinstance(widget, Component)\\n\\n # pylint: disable=protected-access\\n if widget._PACKAGE:\\n self._packages.add(widget._PACKAGE)\\n self._templates.add(widget._TEMPLATE)\\n self._imports.add(_Import(component=widget._COMPONENT,\\n module=widget._TEMPLATE[:widget._TEMPLATE.find('.')]))\\n self._controllers.append(_Control(instantiate=widget._instantiate,\\n caption=getattr(widget, 'caption', None)))\",\n \"def get_sort_query(self, kind, order, is_number):\\n pass\",\n \"def sort(self,desc):\\n\\tself.__sort(\\\"\\\",\\\"\\\",desc)\",\n \"def sort():\\n return -1\",\n \"async def sidebar(self, ctx, *, caption=None):\\r\\n if caption == \\\"manual\\\": # Obsolete method.\\r\\n caption = None\\r\\n \\r\\n async with ctx.typing():\\r\\n # Check if message has an attachment, for the new sidebar image.\\r\\n if caption is not None:\\r\\n await self.edit_caption(caption)\\r\\n \\r\\n if ctx.message.attachments:\\r\\n await ctx.message.attachments[0].save(\\\"sidebar.png\\\")\\r\\n await self.bot.loop.run_in_executor(None, self.upload_image, \\\"sidebar.png\\\", \\\"sidebar\\\",\\r\\n f\\\"Sidebar image updated by {ctx.author} via discord\\\")\\r\\n # Build\\r\\n markdown = await self.make_sidebar()\\r\\n\\r\\n # Post\\r\\n await self.bot.loop.run_in_executor(None, self.post_sidebar, markdown, 'NUFC')\\r\\n \\r\\n # Embed.\\r\\n e = discord.Embed(color=0xff4500)\\r\\n th = \\\"http://vignette2.wikia.nocookie.net/valkyriecrusade/images/b/b5/Reddit-The-Official-App-Icon.png\\\"\\r\\n e.set_author(icon_url=th, name=\\\"Sidebar updater\\\")\\r\\n e.description = f\\\"Sidebar for http://www.reddit.com/r/NUFC updated.\\\"\\r\\n e.timestamp = datetime.datetime.now()\\r\\n await ctx.reply(embed=e, mention_author=False)\",\n \"def start_sorting(self):\\n if self.sorting:\\n return None\\n self.sorting = True\\n\\n passes = 0\\n while self.sorting:\\n swap_done = False\\n for i in range(len(self.sort_list)-passes-1):\\n if not self.sorting:\\n break\\n if self.sort_list[i][0] > self.sort_list[i+1][0]:\\n self.sort_list[i], self.sort_list[i+1] = self.sort_list[i+1], self.sort_list[i]\\n self.window.coords(self.sort_list[i][1], 4*i+50, 20, 4*i+50, self.sort_list[i][0])\\n self.window.coords(self.sort_list[i+1][1], 4*(i+1)+50, 20, 4*(i+1)+50, self.sort_list[i+1][0])\\n self.window.itemconfig(self.sort_list[i][1], fill='red')\\n self.window.itemconfig(self.sort_list[i+1][1], fill='red')\\n swap_done = True\\n self.window.update()\\n self.window.itemconfig(self.sort_list[i][1], fill='black')\\n self.window.itemconfig(self.sort_list[i+1][1], fill='black')\\n self.window.update()\\n passes += 1\\n\\n if not swap_done:\\n self.sorting = False\\n for line in self.sort_list:\\n self.window.itemconfig(line[1], fill='green')\\n else:\\n self.window.itemconfig(self.sort_list[i+1][1], fill='blue')\\n self.window.update()\",\n \"def SortChildren(self, item):\\r\\n\\r\\n if not self._attr_set:\\r\\n setattr(self._main_win, \\\"OnCompareItems\\\", self.OnCompareItems)\\r\\n self._attr_set = True\\r\\n \\r\\n self._main_win.SortChildren(item)\",\n \"def post_sort(self, qs):\\n return qs\",\n \"def sortChoices(self):\\n self.formatList.sort()\",\n \"def _sort_column(self, column, reverse):\\n if tk.DISABLED in self.state():\\n return\\n # get list of (value, item) tuple where value is the value in column for the item\\n l = [(self.set(child, column), child) for child in self.get_children('')]\\n # sort list using the column type\\n l.sort(reverse=reverse, key=lambda x: self._column_types[column](x[0]))\\n # reorder items\\n for index, (val, child) in enumerate(l):\\n self.move(child, \\\"\\\", index)\\n # reverse sorting direction for the next time\\n self.heading(column, command=lambda: self._sort_column(column, not reverse))\",\n \"def sort_column(self, column, reverse):\\n data = [(self.treeview.set(child, column), child) for child in self.treeview.get_children(\\\"\\\")]\\n data.sort(reverse=reverse)\\n\\n # set sort symbol on column heading\\n if reverse:\\n self.treeview.heading(column, text=f\\\"{column} ▼\\\")\\n else:\\n self.treeview.heading(column, text=f\\\"{column} ▲\\\")\\n\\n # rearrange items in sorted positions\\n for index, (value, child) in enumerate(data):\\n self.treeview.move(child, \\\"\\\", index)\\n\\n # reverse sort direction\\n self.treeview.heading(column, command=lambda: self.sort_column(column, not reverse))\",\n \"def addToolBarButtons(self):\",\n \"def on_sort_column_change(self, model):\\n id, order = self.tv.get_model().get_sort_column_id()\\n if id is None and order is None:\\n return False\\n if self.prev_sort_column_id and self.prev_sort_column_id[0][0] == id:\\n self.prev_sort_column_id.pop(0)\\n self.prev_sort_column_id.insert(0, (id, order))\\n if len(self.prev_sort_column_id) > 2:\\n self.prev_sort_column_id.pop()\",\n \"def widget(self, request, group):\",\n \"def data_for_sorting() -> NoReturn:\\n raise NotImplementedError\",\n \"def data_for_sorting() -> NoReturn:\\n raise NotImplementedError\",\n \"def sort_results(self):\\n pass\",\n \"def execute(self, item):\\n items = item[\\\"items\\\"]\\n reverse = not item.get(\\\"descending\\\", True)\\n items.sort(key=operator.itemgetter(\\\"distance\\\"), reverse=reverse)\\n\\n return item\",\n \"def build(self):\\n global scroll\\n global layout\\n global curdir\\n global root\\n global filtering_queue\\n global sorting_queue\\n\\n\\n #Creates global variables to be used by other functions.\\n curdir = \\\" \\\"\\n filtering_queue = mp.Queue()\\n sorting_queue = mp.Queue()\\n\\n # Creating the basic layouts.\\n root = FloatLayout()\\n scroll = ScrollView(pos_hint={\\\"x\\\": 0.12, \\\"top\\\": 0.92}, size_hint=(0.9, 1))\\n layout = GridLayout(cols=5, padding=0, spacing=5)\\n layout.bind(minimum_height=layout.setter(\\\"height\\\"))\\n\\n # Create the ActionBar with buttons.\\n actionbar = F.ActionBar(pos_hint={'top': 1})\\n av = F.ActionView()\\n av.add_widget(F.ActionPrevious(title='SPAI', with_previous=False, app_icon=\\\"icon.png\\\"))\\n av.add_widget(F.ActionOverflow())\\n av.add_widget(F.ActionButton(text='Import'.format(), on_press=self._pop))\\n av.add_widget(F.ActionButton(text='Report'.format()))\\n\\n actionbar.add_widget(av)\\n av.use_separator = False\\n\\n # Adding the layouts to the root layout\\n root.add_widget(actionbar)\\n root.add_widget(self._sidepanel())\\n root.add_widget(scroll)\\n scroll.add_widget(layout)\\n\\n return root\",\n \"def update_top_categories(self):\\n cats = self.sorted_categories()\\n display = \\\"\\\"\\n for cat in cats:\\n hit = \\\"hits\\\" if self.category_map[cat] > 1 else \\\"hit\\\"\\n display += f\\\"{cat}, {self.category_map[cat]} {hit}\\\\n\\\"\\n self.top_categories.configure(state=\\\"normal\\\")\\n self.top_categories.delete('1.0', tk.END)\\n self.top_categories.insert('1.0', display)\\n self.top_categories.configure(state=\\\"disabled\\\")\",\n \"def on_sort(self, callback):\\n self._sort_callback = callback if callable(callback) else _void\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64066905","0.61197245","0.5869867","0.57673573","0.5749316","0.53632724","0.5358934","0.53537744","0.5340585","0.53209037","0.5320746","0.53140193","0.5310284","0.5279612","0.52369547","0.5233304","0.52298254","0.51825815","0.51570153","0.51435727","0.51420707","0.51410043","0.51408374","0.50870776","0.50501317","0.5048362","0.5037489","0.5036181","0.501152","0.5009106","0.49877167","0.49867406","0.498611","0.49844348","0.49809846","0.49612406","0.49015704","0.48922098","0.485556","0.48517305","0.48501384","0.48390615","0.4808991","0.4806933","0.47861513","0.47801876","0.4774429","0.4770969","0.47612643","0.4760166","0.4760002","0.4742374","0.47407547","0.47404706","0.47386396","0.47334856","0.47251174","0.47196007","0.47196007","0.4719432","0.47038913","0.47028103","0.4696002","0.46916002","0.46844205","0.46827072","0.4679703","0.46780437","0.46662676","0.46547598","0.46441615","0.4642311","0.46329346","0.4629454","0.46214455","0.46144336","0.46022338","0.46022","0.4598575","0.4596871","0.45952058","0.45890132","0.4586344","0.4575909","0.45697412","0.45660734","0.45657498","0.4564219","0.4555835","0.4550319","0.45502988","0.45457762","0.45447734","0.45366907","0.45366907","0.45228085","0.45178053","0.45174","0.45121935","0.45066327"],"string":"[\n \"0.64066905\",\n \"0.61197245\",\n \"0.5869867\",\n \"0.57673573\",\n \"0.5749316\",\n \"0.53632724\",\n \"0.5358934\",\n \"0.53537744\",\n \"0.5340585\",\n \"0.53209037\",\n \"0.5320746\",\n \"0.53140193\",\n \"0.5310284\",\n \"0.5279612\",\n \"0.52369547\",\n \"0.5233304\",\n \"0.52298254\",\n \"0.51825815\",\n \"0.51570153\",\n \"0.51435727\",\n \"0.51420707\",\n \"0.51410043\",\n \"0.51408374\",\n \"0.50870776\",\n \"0.50501317\",\n \"0.5048362\",\n \"0.5037489\",\n \"0.5036181\",\n \"0.501152\",\n \"0.5009106\",\n \"0.49877167\",\n \"0.49867406\",\n \"0.498611\",\n \"0.49844348\",\n \"0.49809846\",\n \"0.49612406\",\n \"0.49015704\",\n \"0.48922098\",\n \"0.485556\",\n \"0.48517305\",\n \"0.48501384\",\n \"0.48390615\",\n \"0.4808991\",\n \"0.4806933\",\n \"0.47861513\",\n \"0.47801876\",\n \"0.4774429\",\n \"0.4770969\",\n \"0.47612643\",\n \"0.4760166\",\n \"0.4760002\",\n \"0.4742374\",\n \"0.47407547\",\n \"0.47404706\",\n \"0.47386396\",\n \"0.47334856\",\n \"0.47251174\",\n \"0.47196007\",\n \"0.47196007\",\n \"0.4719432\",\n \"0.47038913\",\n \"0.47028103\",\n \"0.4696002\",\n \"0.46916002\",\n \"0.46844205\",\n \"0.46827072\",\n \"0.4679703\",\n \"0.46780437\",\n \"0.46662676\",\n \"0.46547598\",\n \"0.46441615\",\n \"0.4642311\",\n \"0.46329346\",\n \"0.4629454\",\n \"0.46214455\",\n \"0.46144336\",\n \"0.46022338\",\n \"0.46022\",\n \"0.4598575\",\n \"0.4596871\",\n \"0.45952058\",\n \"0.45890132\",\n \"0.4586344\",\n \"0.4575909\",\n \"0.45697412\",\n \"0.45660734\",\n \"0.45657498\",\n \"0.4564219\",\n \"0.4555835\",\n \"0.4550319\",\n \"0.45502988\",\n \"0.45457762\",\n \"0.45447734\",\n \"0.45366907\",\n \"0.45366907\",\n \"0.45228085\",\n \"0.45178053\",\n \"0.45174\",\n \"0.45121935\",\n \"0.45066327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5715584"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":22377,"cells":{"query":{"kind":"string","value":"Wrapper around k8s.load_and_create_resource to create a SageMaker resource"},"document":{"kind":"string","value":"def create_sagemaker_resource(\n resource_plural, resource_name, spec_file, replacements, namespace=\"default\"\n):\n\n reference, spec, resource = k8s.load_and_create_resource(\n resource_directory,\n CRD_GROUP,\n CRD_VERSION,\n resource_plural,\n resource_name,\n spec_file,\n replacements,\n namespace,\n )\n\n return reference, spec, resource"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_resource(\n service_name: str, config_name: str = None, **resource_args\n):\n session = get_session(config_name)\n return session.resource(service_name, **resource_args)","def create_resource():\n return wsgi.Resource(Controller())","def create_resource():\n return wsgi.Resource(Controller(), serializer=ImageSerialize())","def create_resource():\n deserializer = wsgi.JSONRequestDeserializer()\n serializer = wsgi.JSONResponseSerializer()\n return wsgi.Resource(Controller(), deserializer, serializer)","def __init__(__self__, resource_name, opts=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['block_device_mappings'] = block_device_mappings\n __props__['capacity_reservation_specification'] = capacity_reservation_specification\n __props__['credit_specification'] = credit_specification\n __props__['description'] = description\n __props__['disable_api_termination'] = disable_api_termination\n __props__['ebs_optimized'] = ebs_optimized\n __props__['elastic_gpu_specifications'] = elastic_gpu_specifications\n __props__['elastic_inference_accelerator'] = elastic_inference_accelerator\n __props__['iam_instance_profile'] = iam_instance_profile\n __props__['image_id'] = image_id\n __props__['instance_initiated_shutdown_behavior'] = instance_initiated_shutdown_behavior\n __props__['instance_market_options'] = instance_market_options\n __props__['instance_type'] = instance_type\n __props__['kernel_id'] = kernel_id\n __props__['key_name'] = key_name\n __props__['license_specifications'] = license_specifications\n __props__['monitoring'] = monitoring\n __props__['name'] = name\n __props__['name_prefix'] = name_prefix\n __props__['network_interfaces'] = network_interfaces\n __props__['placement'] = placement\n __props__['ram_disk_id'] = ram_disk_id\n __props__['security_group_names'] = security_group_names\n __props__['tag_specifications'] = tag_specifications\n __props__['tags'] = tags\n __props__['user_data'] = user_data\n __props__['vpc_security_group_ids'] = vpc_security_group_ids\n __props__['arn'] = None\n __props__['default_version'] = None\n __props__['latest_version'] = None\n super(LaunchTemplate, __self__).__init__(\n 'aws:ec2/launchTemplate:LaunchTemplate',\n resource_name,\n __props__,\n opts)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n backend_type: Optional[pulumi.Input['InstanceBackendType']] = None,\n connection_name: Optional[pulumi.Input[str]] = None,\n current_disk_size: Optional[pulumi.Input[str]] = None,\n database_version: Optional[pulumi.Input['InstanceDatabaseVersion']] = None,\n disk_encryption_configuration: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionConfigurationArgs']]] = None,\n disk_encryption_status: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionStatusArgs']]] = None,\n etag: Optional[pulumi.Input[str]] = None,\n failover_replica: Optional[pulumi.Input[pulumi.InputType['InstanceFailoverReplicaArgs']]] = None,\n gce_zone: Optional[pulumi.Input[str]] = None,\n instance_type: Optional[pulumi.Input['InstanceInstanceType']] = None,\n ip_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpMappingArgs']]]]] = None,\n ipv6_address: Optional[pulumi.Input[str]] = None,\n kind: Optional[pulumi.Input[str]] = None,\n maintenance_version: Optional[pulumi.Input[str]] = None,\n master_instance_name: Optional[pulumi.Input[str]] = None,\n max_disk_size: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n on_premises_configuration: Optional[pulumi.Input[pulumi.InputType['OnPremisesConfigurationArgs']]] = None,\n out_of_disk_report: Optional[pulumi.Input[pulumi.InputType['SqlOutOfDiskReportArgs']]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n replica_configuration: Optional[pulumi.Input[pulumi.InputType['ReplicaConfigurationArgs']]] = None,\n replica_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n root_password: Optional[pulumi.Input[str]] = None,\n satisfies_pzs: Optional[pulumi.Input[bool]] = None,\n scheduled_maintenance: Optional[pulumi.Input[pulumi.InputType['SqlScheduledMaintenanceArgs']]] = None,\n secondary_gce_zone: Optional[pulumi.Input[str]] = None,\n self_link: Optional[pulumi.Input[str]] = None,\n server_ca_cert: Optional[pulumi.Input[pulumi.InputType['SslCertArgs']]] = None,\n service_account_email_address: Optional[pulumi.Input[str]] = None,\n settings: Optional[pulumi.Input[pulumi.InputType['SettingsArgs']]] = None,\n state: Optional[pulumi.Input['InstanceState']] = None,\n suspension_reason: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceSuspensionReasonItem']]]] = None,\n __props__=None):\n ...","def __init__(__self__, resource_name, opts=None, allocated_capacity=None, command=None, connections=None, default_arguments=None, description=None, execution_property=None, glue_version=None, max_capacity=None, max_retries=None, name=None, number_of_workers=None, role_arn=None, security_configuration=None, tags=None, timeout=None, worker_type=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['allocated_capacity'] = allocated_capacity\n if command is None:\n raise TypeError(\"Missing required property 'command'\")\n __props__['command'] = command\n __props__['connections'] = connections\n __props__['default_arguments'] = default_arguments\n __props__['description'] = description\n __props__['execution_property'] = execution_property\n __props__['glue_version'] = glue_version\n __props__['max_capacity'] = max_capacity\n __props__['max_retries'] = max_retries\n __props__['name'] = name\n __props__['number_of_workers'] = number_of_workers\n if role_arn is None:\n raise TypeError(\"Missing required property 'role_arn'\")\n __props__['role_arn'] = role_arn\n __props__['security_configuration'] = security_configuration\n __props__['tags'] = tags\n __props__['timeout'] = timeout\n __props__['worker_type'] = worker_type\n __props__['arn'] = None\n super(Job, __self__).__init__(\n 'aws:glue/job:Job',\n resource_name,\n __props__,\n opts)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n description: Optional[pulumi.Input[str]] = None,\n etag: Optional[pulumi.Input[str]] = None,\n file_shares: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FileShareConfigArgs']]]]] = None,\n instance_id: Optional[pulumi.Input[str]] = None,\n kms_key_name: Optional[pulumi.Input[str]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['NetworkConfigArgs']]]]] = None,\n project: Optional[pulumi.Input[str]] = None,\n tier: Optional[pulumi.Input['InstanceTier']] = None,\n __props__=None):\n ...","def create_resource():\n #deserializer = ImageDeserializer()\n #serializer = ImageSerializer()\n return wsgi.Resource(Controller())","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_scaling_configuration_arn: Optional[pulumi.Input[str]] = None,\n encryption_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceEncryptionConfigurationArgs']]] = None,\n health_check_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceHealthCheckConfigurationArgs']]] = None,\n instance_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceInstanceConfigurationArgs']]] = None,\n network_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceNetworkConfigurationArgs']]] = None,\n observability_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceObservabilityConfigurationArgs']]] = None,\n service_name: Optional[pulumi.Input[str]] = None,\n source_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceSourceConfigurationArgs']]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def create_resource():\n return wsgi.Resource(WorkersController())","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n acl_name: Optional[pulumi.Input[str]] = None,\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\n data_tiering: Optional[pulumi.Input[bool]] = None,\n description: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n final_snapshot_name: Optional[pulumi.Input[str]] = None,\n kms_key_arn: Optional[pulumi.Input[str]] = None,\n maintenance_window: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n name_prefix: Optional[pulumi.Input[str]] = None,\n node_type: Optional[pulumi.Input[str]] = None,\n num_replicas_per_shard: Optional[pulumi.Input[int]] = None,\n num_shards: Optional[pulumi.Input[int]] = None,\n parameter_group_name: Optional[pulumi.Input[str]] = None,\n port: Optional[pulumi.Input[int]] = None,\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n snapshot_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n snapshot_name: Optional[pulumi.Input[str]] = None,\n snapshot_retention_limit: Optional[pulumi.Input[int]] = None,\n snapshot_window: Optional[pulumi.Input[str]] = None,\n sns_topic_arn: Optional[pulumi.Input[str]] = None,\n subnet_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tls_enabled: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def create(ctx, iface, resource_config, params, **_):\n\n lb_name = params.get(LB_NAME)\n if not lb_name:\n targs = \\\n utils.find_rels_by_node_type(\n ctx.instance,\n LB_TYPE)\n lb_name = \\\n targs[0].target.instance.runtime_properties[\n EXTERNAL_RESOURCE_ID]\n params.update({LB_NAME: lb_name})\n\n ctx.instance.runtime_properties[LB_NAME] = \\\n lb_name\n\n # Actually create the resource\n iface.create(params)","def __init__(__self__,\n resource_name: str,\n args: RuntimeArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ObjectStorageKeyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n agent_upgrade: Optional[pulumi.Input[pulumi.InputType['AgentUpgradeArgs']]] = None,\n client_public_key: Optional[pulumi.Input[str]] = None,\n extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['MachineExtensionInstanceViewArgs']]]]] = None,\n identity: Optional[pulumi.Input[pulumi.InputType['IdentityArgs']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n location_data: Optional[pulumi.Input[pulumi.InputType['LocationDataArgs']]] = None,\n machine_name: Optional[pulumi.Input[str]] = None,\n mssql_discovered: Optional[pulumi.Input[str]] = None,\n os_profile: Optional[pulumi.Input[pulumi.InputType['OSProfileArgs']]] = None,\n os_type: Optional[pulumi.Input[str]] = None,\n parent_cluster_resource_id: Optional[pulumi.Input[str]] = None,\n private_link_scope_resource_id: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n service_statuses: Optional[pulumi.Input[pulumi.InputType['ServiceStatusesArgs']]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n vm_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n acr: Optional[pulumi.Input[pulumi.InputType['ACRArgs']]] = None,\n aks_resource_id: Optional[pulumi.Input[str]] = None,\n app_name: Optional[pulumi.Input[str]] = None,\n branch_name: Optional[pulumi.Input[str]] = None,\n builder_version: Optional[pulumi.Input[str]] = None,\n deployment_properties: Optional[pulumi.Input[pulumi.InputType['DeploymentPropertiesArgs']]] = None,\n docker_build_context: Optional[pulumi.Input[str]] = None,\n dockerfile: Optional[pulumi.Input[str]] = None,\n dockerfile_generation_mode: Optional[pulumi.Input[Union[str, 'DockerfileGenerationMode']]] = None,\n dockerfile_output_directory: Optional[pulumi.Input[str]] = None,\n generation_language: Optional[pulumi.Input[Union[str, 'GenerationLanguage']]] = None,\n image_name: Optional[pulumi.Input[str]] = None,\n image_tag: Optional[pulumi.Input[str]] = None,\n language_version: Optional[pulumi.Input[str]] = None,\n last_workflow_run: Optional[pulumi.Input[pulumi.InputType['WorkflowRunArgs']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n manifest_generation_mode: Optional[pulumi.Input[Union[str, 'ManifestGenerationMode']]] = None,\n manifest_output_directory: Optional[pulumi.Input[str]] = None,\n manifest_type: Optional[pulumi.Input[Union[str, 'GenerationManifestType']]] = None,\n namespace: Optional[pulumi.Input[str]] = None,\n oidc_credentials: Optional[pulumi.Input[pulumi.InputType['GitHubWorkflowProfileOidcCredentialsArgs']]] = None,\n port: Optional[pulumi.Input[str]] = None,\n repository_name: Optional[pulumi.Input[str]] = None,\n repository_owner: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n workflow_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n block_size_bytes: Optional[pulumi.Input[int]] = None,\n container_id: Optional[pulumi.Input[str]] = None,\n disk_file_format: Optional[pulumi.Input[Union[str, 'DiskFileFormat']]] = None,\n disk_size_gb: Optional[pulumi.Input[float]] = None,\n dynamic: Optional[pulumi.Input[bool]] = None,\n extended_location: Optional[pulumi.Input[pulumi.InputType['ExtendedLocationArgs']]] = None,\n hyper_v_generation: Optional[pulumi.Input[Union[str, 'HyperVGeneration']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n logical_sector_bytes: Optional[pulumi.Input[int]] = None,\n physical_sector_bytes: Optional[pulumi.Input[int]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n virtual_hard_disk_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__, resource_name, opts=None, client_id=None, client_secret=None, client_timeout=None, initial_login=None, password=None, realm=None, root_ca_certificate=None, tls_insecure_skip_verify=None, url=None, username=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n if client_id is None:\n client_id = utilities.get_env('KEYCLOAK_CLIENT_ID')\n __props__['client_id'] = client_id\n if client_secret is None:\n client_secret = utilities.get_env('KEYCLOAK_CLIENT_SECRET')\n __props__['client_secret'] = client_secret\n if client_timeout is None:\n client_timeout = (utilities.get_env_int('KEYCLOAK_CLIENT_TIMEOUT') or 5)\n __props__['client_timeout'] = pulumi.Output.from_input(client_timeout).apply(json.dumps) if client_timeout is not None else None\n __props__['initial_login'] = pulumi.Output.from_input(initial_login).apply(json.dumps) if initial_login is not None else None\n if password is None:\n password = utilities.get_env('KEYCLOAK_PASSWORD')\n __props__['password'] = password\n if realm is None:\n realm = (utilities.get_env('KEYCLOAK_REALM') or 'master')\n __props__['realm'] = realm\n __props__['root_ca_certificate'] = root_ca_certificate\n __props__['tls_insecure_skip_verify'] = pulumi.Output.from_input(tls_insecure_skip_verify).apply(json.dumps) if tls_insecure_skip_verify is not None else None\n if url is None:\n url = utilities.get_env('KEYCLOAK_URL')\n __props__['url'] = url\n if username is None:\n username = utilities.get_env('KEYCLOAK_USER')\n __props__['username'] = username\n super(Provider, __self__).__init__(\n 'keycloak',\n resource_name,\n __props__,\n opts)","def __init__(self,\n name: str = None,\n k8s_name: str = None,\n run_name: str = None,\n spec: {} = None,\n job_name: str = None,\n jar_params: {} = None,\n python_params: {} = None,\n spark_submit_params: {} = None,\n notebook_params: {} = None,\n existing_cluster_id: str = None,\n new_cluster: {} = None,\n libraries: {} = None,\n spark_jar_task: {} = None,\n spark_python_task: {} = None,\n spark_submit_task: {} = None,\n notebook_task: {} = None,\n timeout_seconds: int = None):\n\n if not spec:\n spec = {}\n\n if run_name:\n spec[\"run_name\"] = run_name\n if job_name:\n spec[\"job_name\"] = job_name\n if jar_params:\n spec[\"jar_params\"] = jar_params\n if python_params:\n spec[\"python_params\"] = python_params\n if spark_submit_params:\n spec[\"spark_submit_params\"] = spark_submit_params\n if notebook_params:\n spec[\"notebook_params\"] = notebook_params\n if new_cluster:\n spec[\"new_cluster\"] = new_cluster\n if existing_cluster_id:\n spec[\"existing_cluster_id\"] = existing_cluster_id\n if spark_jar_task:\n spec[\"spark_jar_task\"] = spark_jar_task\n if spark_python_task:\n spec[\"spark_python_task\"] = spark_python_task\n if spark_submit_task:\n spec[\"spark_submit_task\"] = spark_submit_task\n if notebook_task:\n spec[\"notebook_task\"] = notebook_task\n if libraries:\n spec[\"libraries\"] = libraries\n if timeout_seconds:\n spec[\"timeout_seconds\"] = timeout_seconds\n\n if not k8s_name and \"run_name\" in spec:\n k8s_name = spec[\"run_name\"]\n elif not k8s_name:\n raise ValueError(\"You need to provide a k8s_name or a run_name.\")\n\n super().__init__(\n k8s_resource={\n \"apiVersion\": \"databricks.microsoft.com/v1alpha1\",\n \"kind\": \"Run\",\n \"metadata\": {\n \"name\": k8s_name\n },\n \"spec\": spec\n },\n action=\"create\",\n success_condition=(\"status.metadata.state.life_cycle_state in \"\n \"(TERMINATED, SKIPPED, INTERNAL_ERROR)\"),\n attribute_outputs={\n \"name\": \"{.metadata.name}\",\n \"job_id\": \"{.status.metadata.job_id}\",\n \"number_in_job\": \"{.status.metadata.number_in_job}\",\n \"run_id\": \"{.status.metadata.run_id}\",\n \"run_name\": \"{.status.metadata.run_name}\",\n \"life_cycle_state\": \"{.status.metadata.state.life_cycle_state}\",\n \"result_state\": \"{.status.metadata.state.result_state}\",\n \"notebook_output_result\": \"{.status.notebook_output.result}\",\n \"notebook_output_truncated\": \"{.status.notebook_output.truncated}\",\n \"error\": \"{.status.error}\"\n },\n name=name)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n application_insights_id: Optional[pulumi.Input[str]] = None,\n container_registry_id: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n encryption: Optional[pulumi.Input[pulumi.InputType['WorkspaceEncryptionArgs']]] = None,\n friendly_name: Optional[pulumi.Input[str]] = None,\n high_business_impact: Optional[pulumi.Input[bool]] = None,\n identity: Optional[pulumi.Input[pulumi.InputType['WorkspaceIdentityArgs']]] = None,\n image_build_compute_name: Optional[pulumi.Input[str]] = None,\n key_vault_id: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n primary_user_assigned_identity: Optional[pulumi.Input[str]] = None,\n public_access_behind_virtual_network_enabled: Optional[pulumi.Input[bool]] = None,\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n sku_name: Optional[pulumi.Input[str]] = None,\n storage_account_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n v1_legacy_mode_enabled: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: Optional[InstanceArgs] = None,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_renew: Optional[pulumi.Input[bool]] = None,\n auto_renew_period: Optional[pulumi.Input[int]] = None,\n cluster_name: Optional[pulumi.Input[str]] = None,\n data_center_name: Optional[pulumi.Input[str]] = None,\n disk_size: Optional[pulumi.Input[int]] = None,\n disk_type: Optional[pulumi.Input[str]] = None,\n enable_public: Optional[pulumi.Input[bool]] = None,\n instance_type: Optional[pulumi.Input[str]] = None,\n ip_white: Optional[pulumi.Input[str]] = None,\n maintain_end_time: Optional[pulumi.Input[str]] = None,\n maintain_start_time: Optional[pulumi.Input[str]] = None,\n major_version: Optional[pulumi.Input[str]] = None,\n node_count: Optional[pulumi.Input[int]] = None,\n password: Optional[pulumi.Input[str]] = None,\n pay_type: Optional[pulumi.Input[str]] = None,\n period: Optional[pulumi.Input[int]] = None,\n period_unit: Optional[pulumi.Input[str]] = None,\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n vswitch_id: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n blueprint_id: Optional[pulumi.Input[str]] = None,\n bundle_id: Optional[pulumi.Input[str]] = None,\n ip_address_type: Optional[pulumi.Input[str]] = None,\n key_pair_name: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n user_data: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__, resource_name, opts=None, aws_kms_key_arn=None, content_config=None, content_config_permissions=None, input_bucket=None, name=None, notifications=None, output_bucket=None, role=None, thumbnail_config=None, thumbnail_config_permissions=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['aws_kms_key_arn'] = aws_kms_key_arn\n __props__['content_config'] = content_config\n __props__['content_config_permissions'] = content_config_permissions\n if input_bucket is None:\n raise TypeError(\"Missing required property 'input_bucket'\")\n __props__['input_bucket'] = input_bucket\n __props__['name'] = name\n __props__['notifications'] = notifications\n __props__['output_bucket'] = output_bucket\n if role is None:\n raise TypeError(\"Missing required property 'role'\")\n __props__['role'] = role\n __props__['thumbnail_config'] = thumbnail_config\n __props__['thumbnail_config_permissions'] = thumbnail_config_permissions\n __props__['arn'] = None\n super(Pipeline, __self__).__init__(\n 'aws:elastictranscoder/pipeline:Pipeline',\n resource_name,\n __props__,\n opts)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n crypto_key_backend: Optional[pulumi.Input[str]] = None,\n crypto_key_id: Optional[pulumi.Input[str]] = None,\n destroy_scheduled_duration: Optional[pulumi.Input[str]] = None,\n import_only: Optional[pulumi.Input[bool]] = None,\n key_ring_id: Optional[pulumi.Input[str]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n next_rotation_time: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n purpose: Optional[pulumi.Input['CryptoKeyPurpose']] = None,\n rotation_period: Optional[pulumi.Input[str]] = None,\n skip_initial_version_creation: Optional[pulumi.Input[bool]] = None,\n version_template: Optional[pulumi.Input[pulumi.InputType['CryptoKeyVersionTemplateArgs']]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n container_registry_name: Optional[pulumi.Input[str]] = None,\n instance_count: Optional[pulumi.Input[int]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tier: Optional[pulumi.Input[str]] = None,\n virtual_network_subnet_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def create(self, resource_name, data_dict):\n try:\n resource_cls = getattr(self, resource_name).resource\n except AttributeError:\n raise AttributeError(\"No resource named %s is defined.\" % resource_name)\n\n return resource_cls.create(self, data_dict)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n cluster_id: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n private_network_uuid: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[Union[str, 'Region']]] = None,\n size: Optional[pulumi.Input[Union[str, 'DatabaseSlug']]] = None,\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: RegionPerInstanceConfigArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: EnvironmentArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n automatic_update: Optional[pulumi.Input[pulumi.InputType['ProtectionContainerMappingAutomaticUpdateArgs']]] = None,\n name: Optional[pulumi.Input[str]] = None,\n recovery_fabric_name: Optional[pulumi.Input[str]] = None,\n recovery_replication_policy_id: Optional[pulumi.Input[str]] = None,\n recovery_source_protection_container_name: Optional[pulumi.Input[str]] = None,\n recovery_target_protection_container_id: Optional[pulumi.Input[str]] = None,\n recovery_vault_name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ProvisioningTemplateArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n allow_null_values: Optional[pulumi.Input[bool]] = None,\n atomic: Optional[pulumi.Input[bool]] = None,\n chart: Optional[pulumi.Input[str]] = None,\n cleanup_on_fail: Optional[pulumi.Input[bool]] = None,\n compat: Optional[pulumi.Input[str]] = None,\n create_namespace: Optional[pulumi.Input[bool]] = None,\n dependency_update: Optional[pulumi.Input[bool]] = None,\n description: Optional[pulumi.Input[str]] = None,\n devel: Optional[pulumi.Input[bool]] = None,\n disable_crd_hooks: Optional[pulumi.Input[bool]] = None,\n disable_openapi_validation: Optional[pulumi.Input[bool]] = None,\n disable_webhooks: Optional[pulumi.Input[bool]] = None,\n force_update: Optional[pulumi.Input[bool]] = None,\n keyring: Optional[pulumi.Input[str]] = None,\n lint: Optional[pulumi.Input[bool]] = None,\n manifest: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n max_history: Optional[pulumi.Input[int]] = None,\n name: Optional[pulumi.Input[str]] = None,\n namespace: Optional[pulumi.Input[str]] = None,\n postrender: Optional[pulumi.Input[str]] = None,\n recreate_pods: Optional[pulumi.Input[bool]] = None,\n render_subchart_notes: Optional[pulumi.Input[bool]] = None,\n replace: Optional[pulumi.Input[bool]] = None,\n repository_opts: Optional[pulumi.Input[pulumi.InputType['RepositoryOptsArgs']]] = None,\n reset_values: Optional[pulumi.Input[bool]] = None,\n resource_names: Optional[pulumi.Input[Mapping[str, pulumi.Input[Sequence[pulumi.Input[str]]]]]] = None,\n reuse_values: Optional[pulumi.Input[bool]] = None,\n skip_await: Optional[pulumi.Input[bool]] = None,\n skip_crds: Optional[pulumi.Input[bool]] = None,\n timeout: Optional[pulumi.Input[int]] = None,\n value_yaml_files: Optional[pulumi.Input[Sequence[pulumi.Input[Union[pulumi.Asset, pulumi.Archive]]]]] = None,\n values: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n verify: Optional[pulumi.Input[bool]] = None,\n version: Optional[pulumi.Input[str]] = None,\n wait_for_jobs: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def resource_create(resource_id, resource_type, resource_options=None, cibfile=None):\n return item_create(\n item=\"resource\",\n item_id=resource_id,\n item_type=resource_type,\n extra_args=resource_options,\n cibfile=cibfile,\n )","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n additional_locations: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceAdditionalLocationArgs']]]]] = None,\n certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceCertificateArgs']]]]] = None,\n client_certificate_enabled: Optional[pulumi.Input[bool]] = None,\n delegation: Optional[pulumi.Input[pulumi.InputType['ServiceDelegationArgs']]] = None,\n gateway_disabled: Optional[pulumi.Input[bool]] = None,\n hostname_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceHostnameConfigurationArgs']]] = None,\n identity: Optional[pulumi.Input[pulumi.InputType['ServiceIdentityArgs']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n min_api_version: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n notification_sender_email: Optional[pulumi.Input[str]] = None,\n policy: Optional[pulumi.Input[pulumi.InputType['ServicePolicyArgs']]] = None,\n protocols: Optional[pulumi.Input[pulumi.InputType['ServiceProtocolsArgs']]] = None,\n public_ip_address_id: Optional[pulumi.Input[str]] = None,\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\n publisher_email: Optional[pulumi.Input[str]] = None,\n publisher_name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n security: Optional[pulumi.Input[pulumi.InputType['ServiceSecurityArgs']]] = None,\n sign_in: Optional[pulumi.Input[pulumi.InputType['ServiceSignInArgs']]] = None,\n sign_up: Optional[pulumi.Input[pulumi.InputType['ServiceSignUpArgs']]] = None,\n sku_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tenant_access: Optional[pulumi.Input[pulumi.InputType['ServiceTenantAccessArgs']]] = None,\n virtual_network_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceVirtualNetworkConfigurationArgs']]] = None,\n virtual_network_type: Optional[pulumi.Input[str]] = None,\n zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\n arn: Optional[pulumi.Input[str]] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n blueprint_id: Optional[pulumi.Input[str]] = None,\n bundle_id: Optional[pulumi.Input[str]] = None,\n cpu_count: Optional[pulumi.Input[int]] = None,\n created_at: Optional[pulumi.Input[str]] = None,\n ip_address_type: Optional[pulumi.Input[str]] = None,\n ipv6_address: Optional[pulumi.Input[str]] = None,\n ipv6_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n is_static_ip: Optional[pulumi.Input[bool]] = None,\n key_pair_name: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n private_ip_address: Optional[pulumi.Input[str]] = None,\n public_ip_address: Optional[pulumi.Input[str]] = None,\n ram_size: Optional[pulumi.Input[float]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n user_data: Optional[pulumi.Input[str]] = None,\n username: Optional[pulumi.Input[str]] = None) -> 'Instance':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _InstanceState.__new__(_InstanceState)\n\n __props__.__dict__[\"add_on\"] = add_on\n __props__.__dict__[\"arn\"] = arn\n __props__.__dict__[\"availability_zone\"] = availability_zone\n __props__.__dict__[\"blueprint_id\"] = blueprint_id\n __props__.__dict__[\"bundle_id\"] = bundle_id\n __props__.__dict__[\"cpu_count\"] = cpu_count\n __props__.__dict__[\"created_at\"] = created_at\n __props__.__dict__[\"ip_address_type\"] = ip_address_type\n __props__.__dict__[\"ipv6_address\"] = ipv6_address\n __props__.__dict__[\"ipv6_addresses\"] = ipv6_addresses\n __props__.__dict__[\"is_static_ip\"] = is_static_ip\n __props__.__dict__[\"key_pair_name\"] = key_pair_name\n __props__.__dict__[\"name\"] = name\n __props__.__dict__[\"private_ip_address\"] = private_ip_address\n __props__.__dict__[\"public_ip_address\"] = public_ip_address\n __props__.__dict__[\"ram_size\"] = ram_size\n __props__.__dict__[\"tags\"] = tags\n __props__.__dict__[\"tags_all\"] = tags_all\n __props__.__dict__[\"user_data\"] = user_data\n __props__.__dict__[\"username\"] = username\n return Instance(resource_name, opts=opts, __props__=__props__)","def create_resource(self, namespace: \"str\" = None):\n names = [\"create_namespaced_csistorage_capacity\", \"create_csistorage_capacity\"]\n\n _kube_api.execute(\n action=\"create\",\n resource=self,\n names=names,\n namespace=namespace,\n api_client=None,\n api_args={\"body\": self.to_dict()},\n )","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n arm_template_display_name: Optional[pulumi.Input[str]] = None,\n deployment_properties: Optional[pulumi.Input[pulumi.InputType['EnvironmentDeploymentPropertiesArgs']]] = None,\n lab_name: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n user_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: Optional[ResourceDefaultVersionArgs] = None,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: AppArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def create_resource(self, **kwargs):\n results = self.api.action.resource_create(**kwargs)\n # TODO: use `results` rather than re-download, using an isolation layer to standardize the re-structure\n self.get_ckan_metadata(True)\n if 'id' in results:\n self._import_resource_to_cache(kwargs['upload'], results['id'])\n return results","def __init__(__self__,\n resource_name: str,\n args: ApplicationArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ApplicationArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ApplicationArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ServiceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ServiceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def create_sticky(ctx, iface, resource_config, **_):\n\n # Create a copy of the resource config for clean manipulation.\n params = \\\n dict() if not resource_config else resource_config.copy()\n\n lb_name = params.get(LB_NAME)\n policy_name = params.get(RESOURCE_NAME)\n\n if not lb_name:\n targs = \\\n utils.find_rels_by_node_type(\n ctx.instance,\n LB_TYPE)\n lb_name = \\\n targs[0].target.instance.runtime_properties[\n EXTERNAL_RESOURCE_ID]\n params.update({LB_NAME: lb_name})\n\n ctx.instance.runtime_properties[LB_NAME] = \\\n lb_name\n ctx.instance.runtime_properties[RESOURCE_NAME] = \\\n policy_name\n\n # Actually create the resource\n iface.create_sticky(params)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n backup_pool: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n failover_ratio: Optional[pulumi.Input[float]] = None,\n health_checks: Optional[pulumi.Input[str]] = None,\n instances: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n session_affinity: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n kind: Optional[pulumi.Input[Union[str, 'Kind']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n managed_network_group_name: Optional[pulumi.Input[str]] = None,\n managed_network_name: Optional[pulumi.Input[str]] = None,\n management_groups: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n subnets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n subscriptions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n virtual_networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n __props__=None):\n ...","def get(resource_name, id, opts=None, arn=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, default_version=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, latest_version=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None):\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = dict()\n __props__[\"arn\"] = arn\n __props__[\"block_device_mappings\"] = block_device_mappings\n __props__[\"capacity_reservation_specification\"] = capacity_reservation_specification\n __props__[\"credit_specification\"] = credit_specification\n __props__[\"default_version\"] = default_version\n __props__[\"description\"] = description\n __props__[\"disable_api_termination\"] = disable_api_termination\n __props__[\"ebs_optimized\"] = ebs_optimized\n __props__[\"elastic_gpu_specifications\"] = elastic_gpu_specifications\n __props__[\"elastic_inference_accelerator\"] = elastic_inference_accelerator\n __props__[\"iam_instance_profile\"] = iam_instance_profile\n __props__[\"image_id\"] = image_id\n __props__[\"instance_initiated_shutdown_behavior\"] = instance_initiated_shutdown_behavior\n __props__[\"instance_market_options\"] = instance_market_options\n __props__[\"instance_type\"] = instance_type\n __props__[\"kernel_id\"] = kernel_id\n __props__[\"key_name\"] = key_name\n __props__[\"latest_version\"] = latest_version\n __props__[\"license_specifications\"] = license_specifications\n __props__[\"monitoring\"] = monitoring\n __props__[\"name\"] = name\n __props__[\"name_prefix\"] = name_prefix\n __props__[\"network_interfaces\"] = network_interfaces\n __props__[\"placement\"] = placement\n __props__[\"ram_disk_id\"] = ram_disk_id\n __props__[\"security_group_names\"] = security_group_names\n __props__[\"tag_specifications\"] = tag_specifications\n __props__[\"tags\"] = tags\n __props__[\"user_data\"] = user_data\n __props__[\"vpc_security_group_ids\"] = vpc_security_group_ids\n return LaunchTemplate(resource_name, opts=opts, __props__=__props__)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n description: Optional[pulumi.Input[str]] = None,\n enabled: Optional[pulumi.Input[bool]] = None,\n pre_provisioning_hook: Optional[pulumi.Input[pulumi.InputType['ProvisioningTemplateProvisioningHookArgs']]] = None,\n provisioning_role_arn: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ProvisioningTemplateTagArgs']]]]] = None,\n template_body: Optional[pulumi.Input[str]] = None,\n template_name: Optional[pulumi.Input[str]] = None,\n template_type: Optional[pulumi.Input['ProvisioningTemplateTemplateType']] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n admin_site_name: Optional[pulumi.Input[str]] = None,\n allow_pending_state: Optional[pulumi.Input[bool]] = None,\n hosting_environment_profile: Optional[pulumi.Input[pulumi.InputType['HostingEnvironmentProfileArgs']]] = None,\n id: Optional[pulumi.Input[str]] = None,\n kind: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n maximum_number_of_workers: Optional[pulumi.Input[int]] = None,\n name: Optional[pulumi.Input[str]] = None,\n per_site_scaling: Optional[pulumi.Input[bool]] = None,\n reserved: Optional[pulumi.Input[bool]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n sku: Optional[pulumi.Input[pulumi.InputType['SkuDescriptionArgs']]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n type: Optional[pulumi.Input[str]] = None,\n worker_tier_name: Optional[pulumi.Input[str]] = None,\n __props__=None,\n __name__=None,\n __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = _utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['admin_site_name'] = admin_site_name\n __props__['allow_pending_state'] = allow_pending_state\n __props__['hosting_environment_profile'] = hosting_environment_profile\n __props__['id'] = id\n __props__['kind'] = kind\n __props__['location'] = location\n __props__['maximum_number_of_workers'] = maximum_number_of_workers\n __props__['name'] = name\n __props__['per_site_scaling'] = per_site_scaling\n __props__['reserved'] = reserved\n if resource_group_name is None and not opts.urn:\n raise TypeError(\"Missing required property 'resource_group_name'\")\n __props__['resource_group_name'] = resource_group_name\n __props__['sku'] = sku\n __props__['tags'] = tags\n __props__['type'] = type\n __props__['worker_tier_name'] = worker_tier_name\n __props__['geo_region'] = None\n __props__['number_of_sites'] = None\n __props__['resource_group'] = None\n __props__['status'] = None\n __props__['subscription'] = None\n alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:web/v20150801:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/latest:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/latest:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/v20160901:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/v20160901:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/v20180201:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/v20180201:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/v20190801:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/v20190801:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/v20200601:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/v20200601:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/v20200901:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/v20200901:ServerFarm\"), pulumi.Alias(type_=\"azure-native:web/v20201001:ServerFarm\"), pulumi.Alias(type_=\"azure-nextgen:web/v20201001:ServerFarm\")])\n opts = pulumi.ResourceOptions.merge(opts, alias_opts)\n super(ServerFarm, __self__).__init__(\n 'azure-native:web/v20150801:ServerFarm',\n resource_name,\n __props__,\n opts)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n dry_run: Optional[pulumi.Input[bool]] = None,\n load_balancer_id: Optional[pulumi.Input[str]] = None,\n security_group_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n create_sample_data: Optional[pulumi.Input[bool]] = None,\n db_instance_category: Optional[pulumi.Input[str]] = None,\n db_instance_class: Optional[pulumi.Input[str]] = None,\n db_instance_mode: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n encryption_key: Optional[pulumi.Input[str]] = None,\n encryption_type: Optional[pulumi.Input[str]] = None,\n engine: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n instance_charge_type: Optional[pulumi.Input[str]] = None,\n instance_group_count: Optional[pulumi.Input[int]] = None,\n instance_network_type: Optional[pulumi.Input[str]] = None,\n instance_spec: Optional[pulumi.Input[str]] = None,\n ip_whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceIpWhitelistArgs']]]]] = None,\n maintain_end_time: Optional[pulumi.Input[str]] = None,\n maintain_start_time: Optional[pulumi.Input[str]] = None,\n master_node_num: Optional[pulumi.Input[int]] = None,\n payment_type: Optional[pulumi.Input[str]] = None,\n period: Optional[pulumi.Input[str]] = None,\n private_ip_address: Optional[pulumi.Input[str]] = None,\n resource_group_id: Optional[pulumi.Input[str]] = None,\n security_ip_lists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n seg_node_num: Optional[pulumi.Input[int]] = None,\n seg_storage_type: Optional[pulumi.Input[str]] = None,\n ssl_enabled: Optional[pulumi.Input[int]] = None,\n storage_size: Optional[pulumi.Input[int]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n used_time: Optional[pulumi.Input[str]] = None,\n vector_configuration_status: Optional[pulumi.Input[str]] = None,\n vpc_id: Optional[pulumi.Input[str]] = None,\n vswitch_id: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n access_config: Optional[pulumi.Input[pulumi.InputType['RuntimeAccessConfigArgs']]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n request_id: Optional[pulumi.Input[str]] = None,\n runtime_id: Optional[pulumi.Input[str]] = None,\n software_config: Optional[pulumi.Input[pulumi.InputType['RuntimeSoftwareConfigArgs']]] = None,\n virtual_machine: Optional[pulumi.Input[pulumi.InputType['VirtualMachineArgs']]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n attestation_authority: Optional[pulumi.Input[pulumi.InputType['AttestationAuthorityArgs']]] = None,\n base_image: Optional[pulumi.Input[pulumi.InputType['BasisArgs']]] = None,\n build_type: Optional[pulumi.Input[pulumi.InputType['BuildTypeArgs']]] = None,\n compliance: Optional[pulumi.Input[pulumi.InputType['ComplianceNoteArgs']]] = None,\n deployable: Optional[pulumi.Input[pulumi.InputType['DeployableArgs']]] = None,\n discovery: Optional[pulumi.Input[pulumi.InputType['DiscoveryArgs']]] = None,\n dsse_attestation: Optional[pulumi.Input[pulumi.InputType['DSSEAttestationNoteArgs']]] = None,\n expiration_time: Optional[pulumi.Input[str]] = None,\n long_description: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n note_id: Optional[pulumi.Input[str]] = None,\n package: Optional[pulumi.Input[pulumi.InputType['PackageArgs']]] = None,\n project: Optional[pulumi.Input[str]] = None,\n related_url: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RelatedUrlArgs']]]]] = None,\n sbom: Optional[pulumi.Input[pulumi.InputType['DocumentNoteArgs']]] = None,\n sbom_reference: Optional[pulumi.Input[pulumi.InputType['SBOMReferenceNoteArgs']]] = None,\n short_description: Optional[pulumi.Input[str]] = None,\n spdx_file: Optional[pulumi.Input[pulumi.InputType['FileNoteArgs']]] = None,\n spdx_package: Optional[pulumi.Input[pulumi.InputType['PackageInfoNoteArgs']]] = None,\n spdx_relationship: Optional[pulumi.Input[pulumi.InputType['RelationshipNoteArgs']]] = None,\n upgrade: Optional[pulumi.Input[pulumi.InputType['UpgradeNoteArgs']]] = None,\n vulnerability_assessment: Optional[pulumi.Input[pulumi.InputType['VulnerabilityAssessmentNoteArgs']]] = None,\n vulnerability_type: Optional[pulumi.Input[pulumi.InputType['VulnerabilityTypeArgs']]] = None,\n __props__=None):\n ...","def create(self, resource):\n if not resource:\n raise NoneReferenceError(\"resource to create can't be None\")\n\n if not isinstance(resource, self._resource_class):\n raise TypeError(\n \"resource %s is not %s instance\"\n % (resource, self._resource_class.__name__))\n\n return self.build_resource(\n self._os_resource_manager.create(**vars(resource)))","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n client_id: Optional[pulumi.Input[str]] = None,\n client_secret: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n display_name: Optional[pulumi.Input[str]] = None,\n metadata_endpoint: Optional[pulumi.Input[str]] = None,\n opid: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n service_name: Optional[pulumi.Input[str]] = None,\n use_in_api_documentation: Optional[pulumi.Input[bool]] = None,\n use_in_test_console: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n charset: Optional[pulumi.Input[str]] = None,\n collation: Optional[pulumi.Input[str]] = None,\n deletion_policy: Optional[pulumi.Input[str]] = None,\n instance: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n app_name: Optional[pulumi.Input[str]] = None,\n bundle_id: Optional[pulumi.Input[str]] = None,\n encoded_icon: Optional[pulumi.Input[str]] = None,\n industry_id: Optional[pulumi.Input[str]] = None,\n package_name: Optional[pulumi.Input[str]] = None,\n product_id: Optional[pulumi.Input[str]] = None,\n type: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n cluster_name: Optional[pulumi.Input[str]] = None,\n node_setup: Optional[pulumi.Input[pulumi.InputType['NodeSetupArgs']]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n scale_settings: Optional[pulumi.Input[pulumi.InputType['ScaleSettingsArgs']]] = None,\n subnet: Optional[pulumi.Input[pulumi.InputType['ResourceIdArgs']]] = None,\n user_account_settings: Optional[pulumi.Input[pulumi.InputType['UserAccountSettingsArgs']]] = None,\n virtual_machine_configuration: Optional[pulumi.Input[pulumi.InputType['VirtualMachineConfigurationArgs']]] = None,\n vm_priority: Optional[pulumi.Input['VmPriority']] = None,\n vm_size: Optional[pulumi.Input[str]] = None,\n workspace_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: Optional[TargetPoolArgs] = None,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: BundleArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def create(self):\n raise WufooException(\"InstanceResource creation not supported\")","def make_instance(self, include_optional):\n # model = yapily.models.resource.Resource() # noqa: E501\n if include_optional :\n return Resource(\n description = '0', \n file = yapily.models.file.File(\n absolute = True, \n absolute_file = yapily.models.file.File(\n absolute = True, \n absolute_path = '0', \n canonical_file = yapily.models.file.File(\n absolute = True, \n absolute_path = '0', \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n parent_file = yapily.models.file.File(\n absolute = True, \n absolute_path = '0', \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n parent_file = yapily.models.file.File(\n absolute = True, \n absolute_path = '0', \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n absolute_path = '0', \n canonical_file = yapily.models.file.File(\n absolute = True, \n absolute_path = '0', \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n parent_file = yapily.models.file.File(\n absolute = True, \n absolute_path = '0', \n canonical_path = '0', \n directory = True, \n file = True, \n free_space = 56, \n hidden = True, \n name = '0', \n parent = '0', \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n path = '0', \n total_space = 56, \n usable_space = 56, ), \n filename = '0', \n input_stream = None, \n open = True, \n readable = True, \n uri = yapily.models.uri.URI(\n absolute = True, \n authority = '0', \n fragment = '0', \n host = '0', \n opaque = True, \n path = '0', \n port = 56, \n query = '0', \n raw_authority = '0', \n raw_fragment = '0', \n raw_path = '0', \n raw_query = '0', \n raw_scheme_specific_part = '0', \n raw_user_info = '0', \n scheme = '0', \n scheme_specific_part = '0', \n user_info = '0', ), \n url = yapily.models.url.URL(\n authority = '0', \n content = yapily.models.content.content(), \n default_port = 56, \n file = '0', \n host = '0', \n path = '0', \n port = 56, \n protocol = '0', \n query = '0', \n ref = '0', \n user_info = '0', )\n )\n else :\n return Resource(\n )","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n bundle_name: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n desktop_type: Optional[pulumi.Input[str]] = None,\n image_id: Optional[pulumi.Input[str]] = None,\n language: Optional[pulumi.Input[str]] = None,\n root_disk_performance_level: Optional[pulumi.Input[str]] = None,\n root_disk_size_gib: Optional[pulumi.Input[int]] = None,\n user_disk_performance_level: Optional[pulumi.Input[str]] = None,\n user_disk_size_gibs: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None,\n __props__=None):\n ...","def create_sally_resources(region_name, app_name, image_name,\n storage_enckey=None,\n s3_logs_bucket=None,\n identities_url=None,\n ssh_key_name=None,\n ssh_port=None,\n company_domain=None,\n ldap_host=None,\n security_group_name=None,\n instance_type=None,\n web_subnet_id=None,\n vpc_id=None,\n vpc_cidr=None,\n tag_prefix=None,\n dry_run=False):\n subnet_id = web_subnet_id\n sg_tag_prefix = tag_prefix\n if not security_group_name:\n security_group_name = 'kitchen-door'\n sg_name = _get_security_group_names(\n [security_group_name], tag_prefix=sg_tag_prefix)[0]\n\n ec2_client = boto3.client('ec2', region_name=region_name)\n if not vpc_id:\n vpc_id, _ = _get_vpc_id(tag_prefix, ec2_client=ec2_client,\n region_name=region_name)\n if not subnet_id:\n #pylint:disable=unused-variable\n web_subnet_cidrs, _, _ = _split_cidrs(\n vpc_cidr, ec2_client=ec2_client, region_name=region_name)\n web_subnet_by_cidrs = _get_subnet_by_cidrs(\n web_subnet_cidrs, tag_prefix,\n vpc_id=vpc_id, ec2_client=ec2_client)\n # Use first valid subnet that does not require a public IP.\n subnet_id = next(iter(web_subnet_by_cidrs.values()))['SubnetId']\n\n group_ids = _get_security_group_ids(\n [sg_name], tag_prefix,\n vpc_id=vpc_id, ec2_client=ec2_client)\n\n iam_client = boto3.client('iam')\n instance_profile_arn = create_instance_profile(\n create_logs_role(sg_name,\n s3_logs_bucket=s3_logs_bucket,\n iam_client=iam_client, tag_prefix=tag_prefix),\n iam_client=iam_client, region_name=region_name,\n tag_prefix=tag_prefix, dry_run=dry_run)\n\n instances = create_instances(region_name, app_name, image_name,\n storage_enckey=storage_enckey,\n s3_logs_bucket=s3_logs_bucket,\n identities_url=identities_url,\n ssh_key_name=ssh_key_name,\n company_domain=company_domain,\n ldap_host=ldap_host,\n instance_type=instance_type,\n instance_profile_arn=instance_profile_arn,\n security_group_ids=group_ids,\n subnet_type=SUBNET_PUBLIC,\n subnet_id=subnet_id,\n vpc_id=vpc_id,\n vpc_cidr=vpc_cidr,\n tag_prefix=tag_prefix,\n dry_run=dry_run,\n ec2_client=ec2_client,\n ssh_port=ssh_port)\n\n return [instance['InstanceId'] for instance in instances]","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n address_family: Optional[pulumi.Input[str]] = None,\n allocation_default_netmask_length: Optional[pulumi.Input[int]] = None,\n allocation_max_netmask_length: Optional[pulumi.Input[int]] = None,\n allocation_min_netmask_length: Optional[pulumi.Input[int]] = None,\n allocation_resource_tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n auto_import: Optional[pulumi.Input[bool]] = None,\n aws_service: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n ipam_scope_id: Optional[pulumi.Input[str]] = None,\n locale: Optional[pulumi.Input[str]] = None,\n public_ip_source: Optional[pulumi.Input[str]] = None,\n publicly_advertisable: Optional[pulumi.Input[bool]] = None,\n source_ipam_pool_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def create(name, storage, backend, memory, timeout, config, debug):\n setup_lithops_logger(logging.DEBUG)\n\n verify_runtime_name(name)\n\n if config:\n config = load_yaml_config(config)\n\n config_ow = set_config_ow(backend, storage, runtime_name=name)\n config = default_config(config, config_ow)\n\n if config['lithops']['mode'] != SERVERLESS:\n raise Exception('\"lithops runtime create\" command is only valid for serverless backends')\n\n logger.info('Creating new lithops runtime: {}'.format(name))\n storage_config = extract_storage_config(config)\n internal_storage = InternalStorage(storage_config)\n\n compute_config = extract_serverless_config(config)\n compute_handler = ServerlessHandler(compute_config, internal_storage)\n mem = memory if memory else compute_config['runtime_memory']\n to = timeout if timeout else compute_config['runtime_timeout']\n runtime_key = compute_handler.get_runtime_key(name, mem)\n runtime_meta = compute_handler.create_runtime(name, mem, timeout=to)\n\n try:\n internal_storage.put_runtime_meta(runtime_key, runtime_meta)\n except Exception:\n raise (\"Unable to upload 'preinstalled-modules' file into {}\".format(internal_storage.backend))","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n minimal_action: Optional[pulumi.Input[str]] = None,\n most_disruptive_allowed_action: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n preserved_state: Optional[pulumi.Input[pulumi.InputType['RegionPerInstanceConfigPreservedStateArgs']]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n region_instance_group_manager: Optional[pulumi.Input[str]] = None,\n remove_instance_state_on_destroy: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def __init__(self,\n name: str = None,\n k8s_name: str = None,\n job_name: str = None,\n spec: {} = None,\n existing_cluster_id: str = None,\n new_cluster: {} = None,\n libraries: {} = None,\n spark_jar_task: {} = None,\n spark_python_task: {} = None,\n spark_submit_task: {} = None,\n notebook_task: {} = None,\n timeout_seconds: int = None,\n max_retries: int = None,\n min_retry_interval_millis: int = None,\n retry_on_timeout: bool = None,\n schedule: {} = None,\n max_concurrent_runs: int = None,\n email_notifications: {} = None):\n\n if not spec:\n spec = {}\n\n if job_name:\n spec[\"name\"] = job_name\n if new_cluster:\n spec[\"new_cluster\"] = new_cluster\n if existing_cluster_id:\n spec[\"existing_cluster_id\"] = existing_cluster_id\n if spark_jar_task:\n spec[\"spark_jar_task\"] = spark_jar_task\n if spark_python_task:\n spec[\"spark_python_task\"] = spark_python_task\n if spark_submit_task:\n spec[\"spark_submit_task\"] = spark_submit_task\n if notebook_task:\n spec[\"notebook_task\"] = notebook_task\n if libraries:\n spec[\"libraries\"] = libraries\n if timeout_seconds:\n spec[\"timeout_seconds\"] = timeout_seconds\n if max_retries:\n spec[\"max_retries\"] = max_retries\n if min_retry_interval_millis:\n spec[\"min_retry_interval_millis\"] = min_retry_interval_millis\n if retry_on_timeout:\n spec[\"retry_on_timeout\"] = retry_on_timeout\n if schedule:\n spec[\"schedule\"] = schedule\n if max_concurrent_runs:\n spec[\"max_concurrent_runs\"] = max_concurrent_runs\n if email_notifications:\n spec[\"email_notifications\"] = email_notifications\n\n if not k8s_name and \"name\" in spec:\n k8s_name = spec[\"name\"]\n elif not k8s_name:\n raise ValueError(\"You need to provide a k8s_name or a job_name.\")\n\n super().__init__(\n k8s_resource={\n \"apiVersion\": \"databricks.microsoft.com/v1alpha1\",\n \"kind\": \"Djob\",\n \"metadata\": {\n \"name\": k8s_name\n },\n \"spec\": spec\n },\n action=\"create\",\n success_condition=\"status.job_status.job_id > 0\",\n attribute_outputs={\n \"name\": \"{.metadata.name}\",\n \"job_id\": \"{.status.job_status.job_id}\",\n \"job_name\": \"{.status.job_status.settings.name}\"\n },\n name=name)","def create_TestServiceResource(test_case, # type: AnyMagpieTestCaseType\n override_service_name=null, # type: Optional[Str]\n override_service_type=null, # type: Optional[Str]\n override_resource_name=null, # type: Optional[Str]\n override_resource_type=null, # type: Optional[Str]\n override_data=null, # type: Optional[JSON]\n override_headers=null, # type: Optional[HeadersType]\n override_cookies=null, # type: Optional[CookiesType]\n ): # type: (...) -> JSON\n app_or_url = get_app_or_url(test_case)\n svc_name = override_service_name if override_service_name is not null else test_case.test_service_name\n svc_type = override_service_type if override_service_type is not null else test_case.test_service_type\n TestSetup.create_TestService(test_case,\n override_service_name=svc_name, override_service_type=svc_type,\n override_headers=override_headers, override_cookies=override_cookies)\n path = \"/services/{svc}/resources\".format(svc=svc_name)\n data = override_data if override_data is not null else {\n \"resource_name\": override_resource_name or test_case.test_resource_name,\n \"resource_type\": override_resource_type or test_case.test_resource_type,\n }\n resp = test_request(app_or_url, \"POST\", path, json=data,\n headers=override_headers if override_headers is not null else test_case.json_headers,\n cookies=override_cookies if override_cookies is not null else test_case.cookies)\n return check_response_basic_info(resp, 201, expected_method=\"POST\")","def __init__(__self__,\n resource_name: str,\n args: VirtualHardDiskArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__, resource_name, opts=None, destination_cidr_block=None, destination_ipv6_cidr_block=None, egress_only_gateway_id=None, gateway_id=None, instance_id=None, nat_gateway_id=None, network_interface_id=None, route_table_id=None, transit_gateway_id=None, vpc_peering_connection_id=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['destination_cidr_block'] = destination_cidr_block\n __props__['destination_ipv6_cidr_block'] = destination_ipv6_cidr_block\n __props__['egress_only_gateway_id'] = egress_only_gateway_id\n __props__['gateway_id'] = gateway_id\n __props__['instance_id'] = instance_id\n __props__['nat_gateway_id'] = nat_gateway_id\n __props__['network_interface_id'] = network_interface_id\n if route_table_id is None:\n raise TypeError(\"Missing required property 'route_table_id'\")\n __props__['route_table_id'] = route_table_id\n __props__['transit_gateway_id'] = transit_gateway_id\n __props__['vpc_peering_connection_id'] = vpc_peering_connection_id\n __props__['destination_prefix_list_id'] = None\n __props__['instance_owner_id'] = None\n __props__['origin'] = None\n __props__['state'] = None\n super(Route, __self__).__init__(\n 'aws:ec2/route:Route',\n resource_name,\n __props__,\n opts)","def create():\n\n # remember what is created or not\n vpc = False\n igw = False\n sg = False\n sub = False\n vm = False\n\n vpc = _create_resource('vpc', CidrBlock=args.cidr, InstanceTenancy='default')\n igw = _create_resource('igw')\n\n if vpc and igw:\n _attach_vpc_igw(vpc=_existing.vpc, igw=_existing.igw)\n else:\n print('Cannot attach an igw to a vpc as at least one of them could not be created.')\n\n if vpc:\n sg = _create_resource(\n 'sg',\n GroupName=args.role,\n Description='SG for ' + args.role,\n VpcId=getattr(_existing.vpc, 'id', None)\n )\n else:\n print('Cannot create a sg as the vpc to attach it to could not be created.')\n\n if sg:\n _add_ingress_rules()\n else:\n print('Cannot create ingress rule as the sg could not be created.')\n\n if vpc:\n sub = _create_resource(\n 'sub',\n VpcId=getattr(_existing.vpc, 'id', None),\n CidrBlock=args.cidr\n )\n else:\n print('Cannot create a subnet as the vpc to attach it to could not be created.')\n\n if vpc and sub:\n _link_route_table()\n else:\n print('Cannot link subnet and VPC in the route table as vpc or sub not created.')\n\n if sub and sg:\n vm = _create_resource(\n 'vm',\n ImageId=args.ami,\n MinCount=1,\n MaxCount=1,\n KeyName=args.keypair,\n InstanceType=args.instance,\n # Note that there will be no internal name.\n # To get one, create first a DHCP options set and associate it with the VPC.\n NetworkInterfaces=[{\n 'AssociatePublicIpAddress': True,\n 'DeviceIndex': 0, # needs to be 0 to get a public IP\n 'SubnetId': getattr(_existing.sub, 'id', None),\n 'Groups': [getattr(_existing.sg, 'id', None)],\n }],\n )\n else:\n print('Cannot create an instance as the sub or sg to use could not be created.')\n\n if vm:\n if not dry:\n print('Waiting for the instance to be up and running, usually done in less than 45 seconds...')\n _existing.vm.wait_until_running()\n _tag_volume()\n print('you can reach your VM at ' + _existing.vm.public_ip_address)\n\n else:\n print('VM not created for some reason.')","def get(resource_name, id, opts=None, cluster=None, deployment_controller=None, deployment_maximum_percent=None, deployment_minimum_healthy_percent=None, desired_count=None, enable_ecs_managed_tags=None, health_check_grace_period_seconds=None, iam_role=None, launch_type=None, load_balancers=None, name=None, network_configuration=None, ordered_placement_strategies=None, placement_constraints=None, platform_version=None, propagate_tags=None, scheduling_strategy=None, service_registries=None, tags=None, task_definition=None, wait_for_steady_state=None):\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = dict()\n __props__[\"cluster\"] = cluster\n __props__[\"deployment_controller\"] = deployment_controller\n __props__[\"deployment_maximum_percent\"] = deployment_maximum_percent\n __props__[\"deployment_minimum_healthy_percent\"] = deployment_minimum_healthy_percent\n __props__[\"desired_count\"] = desired_count\n __props__[\"enable_ecs_managed_tags\"] = enable_ecs_managed_tags\n __props__[\"health_check_grace_period_seconds\"] = health_check_grace_period_seconds\n __props__[\"iam_role\"] = iam_role\n __props__[\"launch_type\"] = launch_type\n __props__[\"load_balancers\"] = load_balancers\n __props__[\"name\"] = name\n __props__[\"network_configuration\"] = network_configuration\n __props__[\"ordered_placement_strategies\"] = ordered_placement_strategies\n __props__[\"placement_constraints\"] = placement_constraints\n __props__[\"platform_version\"] = platform_version\n __props__[\"propagate_tags\"] = propagate_tags\n __props__[\"scheduling_strategy\"] = scheduling_strategy\n __props__[\"service_registries\"] = service_registries\n __props__[\"tags\"] = tags\n __props__[\"task_definition\"] = task_definition\n __props__[\"wait_for_steady_state\"] = wait_for_steady_state\n return Service(resource_name, opts=opts, __props__=__props__)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n allow_forwarded_traffic: Optional[pulumi.Input[bool]] = None,\n allow_gateway_transit: Optional[pulumi.Input[bool]] = None,\n allow_virtual_network_access: Optional[pulumi.Input[bool]] = None,\n name: Optional[pulumi.Input[str]] = None,\n remote_address_space_prefixes: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n remote_virtual_network_id: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n use_remote_gateways: Optional[pulumi.Input[bool]] = None,\n workspace_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n authorized_network: Optional[pulumi.Input[str]] = None,\n display_name: Optional[pulumi.Input[str]] = None,\n instance_id: Optional[pulumi.Input[str]] = None,\n instance_messages: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceMessageArgs']]]]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n maintenance_policy: Optional[pulumi.Input[pulumi.InputType['GoogleCloudMemcacheV1beta2MaintenancePolicyArgs']]] = None,\n memcache_version: Optional[pulumi.Input['InstanceMemcacheVersion']] = None,\n name: Optional[pulumi.Input[str]] = None,\n node_config: Optional[pulumi.Input[pulumi.InputType['NodeConfigArgs']]] = None,\n node_count: Optional[pulumi.Input[int]] = None,\n parameters: Optional[pulumi.Input[pulumi.InputType['MemcacheParametersArgs']]] = None,\n project: Optional[pulumi.Input[str]] = None,\n zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def pre_namespace_create(self, resource_dict):\n pass","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n application_name: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n resource_group_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n copy: Optional[pulumi.Input[pulumi.InputType['JobCopyArgs']]] = None,\n extract: Optional[pulumi.Input[pulumi.InputType['JobExtractArgs']]] = None,\n job_id: Optional[pulumi.Input[str]] = None,\n job_timeout_ms: Optional[pulumi.Input[str]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n load: Optional[pulumi.Input[pulumi.InputType['JobLoadArgs']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n query: Optional[pulumi.Input[pulumi.InputType['JobQueryArgs']]] = None,\n __props__=None,\n __name__=None,\n __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = _utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['copy'] = copy\n __props__['extract'] = extract\n if job_id is None:\n raise TypeError(\"Missing required property 'job_id'\")\n __props__['job_id'] = job_id\n __props__['job_timeout_ms'] = job_timeout_ms\n __props__['labels'] = labels\n __props__['load'] = load\n __props__['location'] = location\n __props__['project'] = project\n __props__['query'] = query\n __props__['job_type'] = None\n __props__['user_email'] = None\n super(Job, __self__).__init__(\n 'gcp:bigquery/job:Job',\n resource_name,\n __props__,\n opts)","def create(ctx, resource_config, **_):\n # Create a resource (if necessary)\n ctx.logger.warn(\n 'Azure customData implementation is dependent on '\n 'Virtual Machine image support.')\n azure_config = utils.get_client_config(ctx.node.properties)\n name = utils.get_resource_name(ctx)\n resource_group_name = utils.get_resource_group(ctx)\n vm_name = ctx.node.properties['virtual_machine_name']\n vm_extension_params = {\n 'location': ctx.node.properties.get('location'),\n 'tags': ctx.node.properties.get('tags'),\n 'publisher': resource_config.get('publisher'),\n 'type_properties_type': resource_config.get('ext_type'),\n 'type_handler_version': resource_config.get('typeHandlerVersion'),\n 'settings': resource_config.get('settings'),\n }\n api_version = \\\n ctx.node.properties.get('api_version', constants.API_VER_COMPUTE)\n vm_extension = VirtualMachineExtension(azure_config, ctx.logger,\n api_version)\n # clean empty values from params\n vm_extension_params = \\\n utils.cleanup_empty_params(vm_extension_params)\n result = utils.handle_create(\n vm_extension,\n resource_group_name,\n name,\n vm_name,\n vm_extension_params)\n ctx.instance.runtime_properties['resource_group'] = resource_group_name\n ctx.instance.runtime_properties['virtual_machine'] = vm_name\n ctx.instance.runtime_properties['resource'] = result\n ctx.instance.runtime_properties['resource_id'] = result.get(\"id\", \"\")","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n type_name: Optional[pulumi.Input[str]] = None,\n type_version_arn: Optional[pulumi.Input[str]] = None,\n version_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auth_mode: Optional[pulumi.Input[str]] = None,\n default_s3_location: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n engine_security_group_id: Optional[pulumi.Input[str]] = None,\n idp_auth_url: Optional[pulumi.Input[str]] = None,\n idp_relay_state_parameter_name: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n service_role: Optional[pulumi.Input[str]] = None,\n subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n user_role: Optional[pulumi.Input[str]] = None,\n vpc_id: Optional[pulumi.Input[str]] = None,\n workspace_security_group_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: PrefixArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def pre_service_instance_create(self, resource_dict):\n pass","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n apply_immediately: Optional[pulumi.Input[bool]] = None,\n authentication_strategy: Optional[pulumi.Input[str]] = None,\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\n broker_name: Optional[pulumi.Input[str]] = None,\n configuration: Optional[pulumi.Input[pulumi.InputType['BrokerConfigurationArgs']]] = None,\n deployment_mode: Optional[pulumi.Input[str]] = None,\n encryption_options: Optional[pulumi.Input[pulumi.InputType['BrokerEncryptionOptionsArgs']]] = None,\n engine_type: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n host_instance_type: Optional[pulumi.Input[str]] = None,\n ldap_server_metadata: Optional[pulumi.Input[pulumi.InputType['BrokerLdapServerMetadataArgs']]] = None,\n logs: Optional[pulumi.Input[pulumi.InputType['BrokerLogsArgs']]] = None,\n maintenance_window_start_time: Optional[pulumi.Input[pulumi.InputType['BrokerMaintenanceWindowStartTimeArgs']]] = None,\n publicly_accessible: Optional[pulumi.Input[bool]] = None,\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n storage_type: Optional[pulumi.Input[str]] = None,\n subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n users: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BrokerUserArgs']]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n config: Optional[pulumi.Input[pulumi.InputType['ConfigArgs']]] = None,\n instance_id: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def add_resource(self, name, controller, ipaddress, ram, cpus, storage, owner=None, flavor='', compute=None, huge_pages=False):\n if compute is None: compute = controller\n args = { 'vm': name,\n 'controller': controller,\n 'ipaddress': ipaddress,\n 'ram': ram,\n 'cpus': cpus,\n 'storage': storage,\n 'flavor': flavor,\n 'compute': compute,\n 'hugepages': huge_pages,\n }\n if owner is not None:\n args['owner'] = owner\n self._NDL_API('addresource', args, None)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n description: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n management_cluster: Optional[pulumi.Input[pulumi.InputType['PrivateCloudManagementClusterArgs']]] = None,\n name: Optional[pulumi.Input[str]] = None,\n network_config: Optional[pulumi.Input[pulumi.InputType['PrivateCloudNetworkConfigArgs']]] = None,\n project: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n description: Optional[pulumi.Input[str]] = None,\n metadata: Optional[pulumi.Input[pulumi.InputType['SyntheticsPrivateLocationMetadataArgs']]] = None,\n name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ProtectionContainerMappingArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n instance_id: Optional[pulumi.Input[int]] = None,\n __props__=None):\n ..."],"string":"[\n \"def create_resource(\\n service_name: str, config_name: str = None, **resource_args\\n):\\n session = get_session(config_name)\\n return session.resource(service_name, **resource_args)\",\n \"def create_resource():\\n return wsgi.Resource(Controller())\",\n \"def create_resource():\\n return wsgi.Resource(Controller(), serializer=ImageSerialize())\",\n \"def create_resource():\\n deserializer = wsgi.JSONRequestDeserializer()\\n serializer = wsgi.JSONResponseSerializer()\\n return wsgi.Resource(Controller(), deserializer, serializer)\",\n \"def __init__(__self__, resource_name, opts=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['block_device_mappings'] = block_device_mappings\\n __props__['capacity_reservation_specification'] = capacity_reservation_specification\\n __props__['credit_specification'] = credit_specification\\n __props__['description'] = description\\n __props__['disable_api_termination'] = disable_api_termination\\n __props__['ebs_optimized'] = ebs_optimized\\n __props__['elastic_gpu_specifications'] = elastic_gpu_specifications\\n __props__['elastic_inference_accelerator'] = elastic_inference_accelerator\\n __props__['iam_instance_profile'] = iam_instance_profile\\n __props__['image_id'] = image_id\\n __props__['instance_initiated_shutdown_behavior'] = instance_initiated_shutdown_behavior\\n __props__['instance_market_options'] = instance_market_options\\n __props__['instance_type'] = instance_type\\n __props__['kernel_id'] = kernel_id\\n __props__['key_name'] = key_name\\n __props__['license_specifications'] = license_specifications\\n __props__['monitoring'] = monitoring\\n __props__['name'] = name\\n __props__['name_prefix'] = name_prefix\\n __props__['network_interfaces'] = network_interfaces\\n __props__['placement'] = placement\\n __props__['ram_disk_id'] = ram_disk_id\\n __props__['security_group_names'] = security_group_names\\n __props__['tag_specifications'] = tag_specifications\\n __props__['tags'] = tags\\n __props__['user_data'] = user_data\\n __props__['vpc_security_group_ids'] = vpc_security_group_ids\\n __props__['arn'] = None\\n __props__['default_version'] = None\\n __props__['latest_version'] = None\\n super(LaunchTemplate, __self__).__init__(\\n 'aws:ec2/launchTemplate:LaunchTemplate',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n backend_type: Optional[pulumi.Input['InstanceBackendType']] = None,\\n connection_name: Optional[pulumi.Input[str]] = None,\\n current_disk_size: Optional[pulumi.Input[str]] = None,\\n database_version: Optional[pulumi.Input['InstanceDatabaseVersion']] = None,\\n disk_encryption_configuration: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionConfigurationArgs']]] = None,\\n disk_encryption_status: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionStatusArgs']]] = None,\\n etag: Optional[pulumi.Input[str]] = None,\\n failover_replica: Optional[pulumi.Input[pulumi.InputType['InstanceFailoverReplicaArgs']]] = None,\\n gce_zone: Optional[pulumi.Input[str]] = None,\\n instance_type: Optional[pulumi.Input['InstanceInstanceType']] = None,\\n ip_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpMappingArgs']]]]] = None,\\n ipv6_address: Optional[pulumi.Input[str]] = None,\\n kind: Optional[pulumi.Input[str]] = None,\\n maintenance_version: Optional[pulumi.Input[str]] = None,\\n master_instance_name: Optional[pulumi.Input[str]] = None,\\n max_disk_size: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n on_premises_configuration: Optional[pulumi.Input[pulumi.InputType['OnPremisesConfigurationArgs']]] = None,\\n out_of_disk_report: Optional[pulumi.Input[pulumi.InputType['SqlOutOfDiskReportArgs']]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n replica_configuration: Optional[pulumi.Input[pulumi.InputType['ReplicaConfigurationArgs']]] = None,\\n replica_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n root_password: Optional[pulumi.Input[str]] = None,\\n satisfies_pzs: Optional[pulumi.Input[bool]] = None,\\n scheduled_maintenance: Optional[pulumi.Input[pulumi.InputType['SqlScheduledMaintenanceArgs']]] = None,\\n secondary_gce_zone: Optional[pulumi.Input[str]] = None,\\n self_link: Optional[pulumi.Input[str]] = None,\\n server_ca_cert: Optional[pulumi.Input[pulumi.InputType['SslCertArgs']]] = None,\\n service_account_email_address: Optional[pulumi.Input[str]] = None,\\n settings: Optional[pulumi.Input[pulumi.InputType['SettingsArgs']]] = None,\\n state: Optional[pulumi.Input['InstanceState']] = None,\\n suspension_reason: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceSuspensionReasonItem']]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__, resource_name, opts=None, allocated_capacity=None, command=None, connections=None, default_arguments=None, description=None, execution_property=None, glue_version=None, max_capacity=None, max_retries=None, name=None, number_of_workers=None, role_arn=None, security_configuration=None, tags=None, timeout=None, worker_type=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['allocated_capacity'] = allocated_capacity\\n if command is None:\\n raise TypeError(\\\"Missing required property 'command'\\\")\\n __props__['command'] = command\\n __props__['connections'] = connections\\n __props__['default_arguments'] = default_arguments\\n __props__['description'] = description\\n __props__['execution_property'] = execution_property\\n __props__['glue_version'] = glue_version\\n __props__['max_capacity'] = max_capacity\\n __props__['max_retries'] = max_retries\\n __props__['name'] = name\\n __props__['number_of_workers'] = number_of_workers\\n if role_arn is None:\\n raise TypeError(\\\"Missing required property 'role_arn'\\\")\\n __props__['role_arn'] = role_arn\\n __props__['security_configuration'] = security_configuration\\n __props__['tags'] = tags\\n __props__['timeout'] = timeout\\n __props__['worker_type'] = worker_type\\n __props__['arn'] = None\\n super(Job, __self__).__init__(\\n 'aws:glue/job:Job',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n etag: Optional[pulumi.Input[str]] = None,\\n file_shares: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FileShareConfigArgs']]]]] = None,\\n instance_id: Optional[pulumi.Input[str]] = None,\\n kms_key_name: Optional[pulumi.Input[str]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['NetworkConfigArgs']]]]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n tier: Optional[pulumi.Input['InstanceTier']] = None,\\n __props__=None):\\n ...\",\n \"def create_resource():\\n #deserializer = ImageDeserializer()\\n #serializer = ImageSerializer()\\n return wsgi.Resource(Controller())\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_scaling_configuration_arn: Optional[pulumi.Input[str]] = None,\\n encryption_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceEncryptionConfigurationArgs']]] = None,\\n health_check_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceHealthCheckConfigurationArgs']]] = None,\\n instance_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceInstanceConfigurationArgs']]] = None,\\n network_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceNetworkConfigurationArgs']]] = None,\\n observability_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceObservabilityConfigurationArgs']]] = None,\\n service_name: Optional[pulumi.Input[str]] = None,\\n source_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceSourceConfigurationArgs']]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def create_resource():\\n return wsgi.Resource(WorkersController())\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n acl_name: Optional[pulumi.Input[str]] = None,\\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\\n data_tiering: Optional[pulumi.Input[bool]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n final_snapshot_name: Optional[pulumi.Input[str]] = None,\\n kms_key_arn: Optional[pulumi.Input[str]] = None,\\n maintenance_window: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n name_prefix: Optional[pulumi.Input[str]] = None,\\n node_type: Optional[pulumi.Input[str]] = None,\\n num_replicas_per_shard: Optional[pulumi.Input[int]] = None,\\n num_shards: Optional[pulumi.Input[int]] = None,\\n parameter_group_name: Optional[pulumi.Input[str]] = None,\\n port: Optional[pulumi.Input[int]] = None,\\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n snapshot_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n snapshot_name: Optional[pulumi.Input[str]] = None,\\n snapshot_retention_limit: Optional[pulumi.Input[int]] = None,\\n snapshot_window: Optional[pulumi.Input[str]] = None,\\n sns_topic_arn: Optional[pulumi.Input[str]] = None,\\n subnet_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tls_enabled: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def create(ctx, iface, resource_config, params, **_):\\n\\n lb_name = params.get(LB_NAME)\\n if not lb_name:\\n targs = \\\\\\n utils.find_rels_by_node_type(\\n ctx.instance,\\n LB_TYPE)\\n lb_name = \\\\\\n targs[0].target.instance.runtime_properties[\\n EXTERNAL_RESOURCE_ID]\\n params.update({LB_NAME: lb_name})\\n\\n ctx.instance.runtime_properties[LB_NAME] = \\\\\\n lb_name\\n\\n # Actually create the resource\\n iface.create(params)\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: RuntimeArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ObjectStorageKeyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n agent_upgrade: Optional[pulumi.Input[pulumi.InputType['AgentUpgradeArgs']]] = None,\\n client_public_key: Optional[pulumi.Input[str]] = None,\\n extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['MachineExtensionInstanceViewArgs']]]]] = None,\\n identity: Optional[pulumi.Input[pulumi.InputType['IdentityArgs']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n location_data: Optional[pulumi.Input[pulumi.InputType['LocationDataArgs']]] = None,\\n machine_name: Optional[pulumi.Input[str]] = None,\\n mssql_discovered: Optional[pulumi.Input[str]] = None,\\n os_profile: Optional[pulumi.Input[pulumi.InputType['OSProfileArgs']]] = None,\\n os_type: Optional[pulumi.Input[str]] = None,\\n parent_cluster_resource_id: Optional[pulumi.Input[str]] = None,\\n private_link_scope_resource_id: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n service_statuses: Optional[pulumi.Input[pulumi.InputType['ServiceStatusesArgs']]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n vm_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n acr: Optional[pulumi.Input[pulumi.InputType['ACRArgs']]] = None,\\n aks_resource_id: Optional[pulumi.Input[str]] = None,\\n app_name: Optional[pulumi.Input[str]] = None,\\n branch_name: Optional[pulumi.Input[str]] = None,\\n builder_version: Optional[pulumi.Input[str]] = None,\\n deployment_properties: Optional[pulumi.Input[pulumi.InputType['DeploymentPropertiesArgs']]] = None,\\n docker_build_context: Optional[pulumi.Input[str]] = None,\\n dockerfile: Optional[pulumi.Input[str]] = None,\\n dockerfile_generation_mode: Optional[pulumi.Input[Union[str, 'DockerfileGenerationMode']]] = None,\\n dockerfile_output_directory: Optional[pulumi.Input[str]] = None,\\n generation_language: Optional[pulumi.Input[Union[str, 'GenerationLanguage']]] = None,\\n image_name: Optional[pulumi.Input[str]] = None,\\n image_tag: Optional[pulumi.Input[str]] = None,\\n language_version: Optional[pulumi.Input[str]] = None,\\n last_workflow_run: Optional[pulumi.Input[pulumi.InputType['WorkflowRunArgs']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n manifest_generation_mode: Optional[pulumi.Input[Union[str, 'ManifestGenerationMode']]] = None,\\n manifest_output_directory: Optional[pulumi.Input[str]] = None,\\n manifest_type: Optional[pulumi.Input[Union[str, 'GenerationManifestType']]] = None,\\n namespace: Optional[pulumi.Input[str]] = None,\\n oidc_credentials: Optional[pulumi.Input[pulumi.InputType['GitHubWorkflowProfileOidcCredentialsArgs']]] = None,\\n port: Optional[pulumi.Input[str]] = None,\\n repository_name: Optional[pulumi.Input[str]] = None,\\n repository_owner: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n workflow_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n block_size_bytes: Optional[pulumi.Input[int]] = None,\\n container_id: Optional[pulumi.Input[str]] = None,\\n disk_file_format: Optional[pulumi.Input[Union[str, 'DiskFileFormat']]] = None,\\n disk_size_gb: Optional[pulumi.Input[float]] = None,\\n dynamic: Optional[pulumi.Input[bool]] = None,\\n extended_location: Optional[pulumi.Input[pulumi.InputType['ExtendedLocationArgs']]] = None,\\n hyper_v_generation: Optional[pulumi.Input[Union[str, 'HyperVGeneration']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n logical_sector_bytes: Optional[pulumi.Input[int]] = None,\\n physical_sector_bytes: Optional[pulumi.Input[int]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n virtual_hard_disk_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__, resource_name, opts=None, client_id=None, client_secret=None, client_timeout=None, initial_login=None, password=None, realm=None, root_ca_certificate=None, tls_insecure_skip_verify=None, url=None, username=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n if client_id is None:\\n client_id = utilities.get_env('KEYCLOAK_CLIENT_ID')\\n __props__['client_id'] = client_id\\n if client_secret is None:\\n client_secret = utilities.get_env('KEYCLOAK_CLIENT_SECRET')\\n __props__['client_secret'] = client_secret\\n if client_timeout is None:\\n client_timeout = (utilities.get_env_int('KEYCLOAK_CLIENT_TIMEOUT') or 5)\\n __props__['client_timeout'] = pulumi.Output.from_input(client_timeout).apply(json.dumps) if client_timeout is not None else None\\n __props__['initial_login'] = pulumi.Output.from_input(initial_login).apply(json.dumps) if initial_login is not None else None\\n if password is None:\\n password = utilities.get_env('KEYCLOAK_PASSWORD')\\n __props__['password'] = password\\n if realm is None:\\n realm = (utilities.get_env('KEYCLOAK_REALM') or 'master')\\n __props__['realm'] = realm\\n __props__['root_ca_certificate'] = root_ca_certificate\\n __props__['tls_insecure_skip_verify'] = pulumi.Output.from_input(tls_insecure_skip_verify).apply(json.dumps) if tls_insecure_skip_verify is not None else None\\n if url is None:\\n url = utilities.get_env('KEYCLOAK_URL')\\n __props__['url'] = url\\n if username is None:\\n username = utilities.get_env('KEYCLOAK_USER')\\n __props__['username'] = username\\n super(Provider, __self__).__init__(\\n 'keycloak',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(self,\\n name: str = None,\\n k8s_name: str = None,\\n run_name: str = None,\\n spec: {} = None,\\n job_name: str = None,\\n jar_params: {} = None,\\n python_params: {} = None,\\n spark_submit_params: {} = None,\\n notebook_params: {} = None,\\n existing_cluster_id: str = None,\\n new_cluster: {} = None,\\n libraries: {} = None,\\n spark_jar_task: {} = None,\\n spark_python_task: {} = None,\\n spark_submit_task: {} = None,\\n notebook_task: {} = None,\\n timeout_seconds: int = None):\\n\\n if not spec:\\n spec = {}\\n\\n if run_name:\\n spec[\\\"run_name\\\"] = run_name\\n if job_name:\\n spec[\\\"job_name\\\"] = job_name\\n if jar_params:\\n spec[\\\"jar_params\\\"] = jar_params\\n if python_params:\\n spec[\\\"python_params\\\"] = python_params\\n if spark_submit_params:\\n spec[\\\"spark_submit_params\\\"] = spark_submit_params\\n if notebook_params:\\n spec[\\\"notebook_params\\\"] = notebook_params\\n if new_cluster:\\n spec[\\\"new_cluster\\\"] = new_cluster\\n if existing_cluster_id:\\n spec[\\\"existing_cluster_id\\\"] = existing_cluster_id\\n if spark_jar_task:\\n spec[\\\"spark_jar_task\\\"] = spark_jar_task\\n if spark_python_task:\\n spec[\\\"spark_python_task\\\"] = spark_python_task\\n if spark_submit_task:\\n spec[\\\"spark_submit_task\\\"] = spark_submit_task\\n if notebook_task:\\n spec[\\\"notebook_task\\\"] = notebook_task\\n if libraries:\\n spec[\\\"libraries\\\"] = libraries\\n if timeout_seconds:\\n spec[\\\"timeout_seconds\\\"] = timeout_seconds\\n\\n if not k8s_name and \\\"run_name\\\" in spec:\\n k8s_name = spec[\\\"run_name\\\"]\\n elif not k8s_name:\\n raise ValueError(\\\"You need to provide a k8s_name or a run_name.\\\")\\n\\n super().__init__(\\n k8s_resource={\\n \\\"apiVersion\\\": \\\"databricks.microsoft.com/v1alpha1\\\",\\n \\\"kind\\\": \\\"Run\\\",\\n \\\"metadata\\\": {\\n \\\"name\\\": k8s_name\\n },\\n \\\"spec\\\": spec\\n },\\n action=\\\"create\\\",\\n success_condition=(\\\"status.metadata.state.life_cycle_state in \\\"\\n \\\"(TERMINATED, SKIPPED, INTERNAL_ERROR)\\\"),\\n attribute_outputs={\\n \\\"name\\\": \\\"{.metadata.name}\\\",\\n \\\"job_id\\\": \\\"{.status.metadata.job_id}\\\",\\n \\\"number_in_job\\\": \\\"{.status.metadata.number_in_job}\\\",\\n \\\"run_id\\\": \\\"{.status.metadata.run_id}\\\",\\n \\\"run_name\\\": \\\"{.status.metadata.run_name}\\\",\\n \\\"life_cycle_state\\\": \\\"{.status.metadata.state.life_cycle_state}\\\",\\n \\\"result_state\\\": \\\"{.status.metadata.state.result_state}\\\",\\n \\\"notebook_output_result\\\": \\\"{.status.notebook_output.result}\\\",\\n \\\"notebook_output_truncated\\\": \\\"{.status.notebook_output.truncated}\\\",\\n \\\"error\\\": \\\"{.status.error}\\\"\\n },\\n name=name)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n application_insights_id: Optional[pulumi.Input[str]] = None,\\n container_registry_id: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n encryption: Optional[pulumi.Input[pulumi.InputType['WorkspaceEncryptionArgs']]] = None,\\n friendly_name: Optional[pulumi.Input[str]] = None,\\n high_business_impact: Optional[pulumi.Input[bool]] = None,\\n identity: Optional[pulumi.Input[pulumi.InputType['WorkspaceIdentityArgs']]] = None,\\n image_build_compute_name: Optional[pulumi.Input[str]] = None,\\n key_vault_id: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n primary_user_assigned_identity: Optional[pulumi.Input[str]] = None,\\n public_access_behind_virtual_network_enabled: Optional[pulumi.Input[bool]] = None,\\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n sku_name: Optional[pulumi.Input[str]] = None,\\n storage_account_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n v1_legacy_mode_enabled: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: Optional[InstanceArgs] = None,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_renew: Optional[pulumi.Input[bool]] = None,\\n auto_renew_period: Optional[pulumi.Input[int]] = None,\\n cluster_name: Optional[pulumi.Input[str]] = None,\\n data_center_name: Optional[pulumi.Input[str]] = None,\\n disk_size: Optional[pulumi.Input[int]] = None,\\n disk_type: Optional[pulumi.Input[str]] = None,\\n enable_public: Optional[pulumi.Input[bool]] = None,\\n instance_type: Optional[pulumi.Input[str]] = None,\\n ip_white: Optional[pulumi.Input[str]] = None,\\n maintain_end_time: Optional[pulumi.Input[str]] = None,\\n maintain_start_time: Optional[pulumi.Input[str]] = None,\\n major_version: Optional[pulumi.Input[str]] = None,\\n node_count: Optional[pulumi.Input[int]] = None,\\n password: Optional[pulumi.Input[str]] = None,\\n pay_type: Optional[pulumi.Input[str]] = None,\\n period: Optional[pulumi.Input[int]] = None,\\n period_unit: Optional[pulumi.Input[str]] = None,\\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n vswitch_id: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n blueprint_id: Optional[pulumi.Input[str]] = None,\\n bundle_id: Optional[pulumi.Input[str]] = None,\\n ip_address_type: Optional[pulumi.Input[str]] = None,\\n key_pair_name: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n user_data: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__, resource_name, opts=None, aws_kms_key_arn=None, content_config=None, content_config_permissions=None, input_bucket=None, name=None, notifications=None, output_bucket=None, role=None, thumbnail_config=None, thumbnail_config_permissions=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['aws_kms_key_arn'] = aws_kms_key_arn\\n __props__['content_config'] = content_config\\n __props__['content_config_permissions'] = content_config_permissions\\n if input_bucket is None:\\n raise TypeError(\\\"Missing required property 'input_bucket'\\\")\\n __props__['input_bucket'] = input_bucket\\n __props__['name'] = name\\n __props__['notifications'] = notifications\\n __props__['output_bucket'] = output_bucket\\n if role is None:\\n raise TypeError(\\\"Missing required property 'role'\\\")\\n __props__['role'] = role\\n __props__['thumbnail_config'] = thumbnail_config\\n __props__['thumbnail_config_permissions'] = thumbnail_config_permissions\\n __props__['arn'] = None\\n super(Pipeline, __self__).__init__(\\n 'aws:elastictranscoder/pipeline:Pipeline',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n crypto_key_backend: Optional[pulumi.Input[str]] = None,\\n crypto_key_id: Optional[pulumi.Input[str]] = None,\\n destroy_scheduled_duration: Optional[pulumi.Input[str]] = None,\\n import_only: Optional[pulumi.Input[bool]] = None,\\n key_ring_id: Optional[pulumi.Input[str]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n next_rotation_time: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n purpose: Optional[pulumi.Input['CryptoKeyPurpose']] = None,\\n rotation_period: Optional[pulumi.Input[str]] = None,\\n skip_initial_version_creation: Optional[pulumi.Input[bool]] = None,\\n version_template: Optional[pulumi.Input[pulumi.InputType['CryptoKeyVersionTemplateArgs']]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n container_registry_name: Optional[pulumi.Input[str]] = None,\\n instance_count: Optional[pulumi.Input[int]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tier: Optional[pulumi.Input[str]] = None,\\n virtual_network_subnet_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def create(self, resource_name, data_dict):\\n try:\\n resource_cls = getattr(self, resource_name).resource\\n except AttributeError:\\n raise AttributeError(\\\"No resource named %s is defined.\\\" % resource_name)\\n\\n return resource_cls.create(self, data_dict)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n cluster_id: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n private_network_uuid: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[Union[str, 'Region']]] = None,\\n size: Optional[pulumi.Input[Union[str, 'DatabaseSlug']]] = None,\\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: RegionPerInstanceConfigArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: EnvironmentArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n automatic_update: Optional[pulumi.Input[pulumi.InputType['ProtectionContainerMappingAutomaticUpdateArgs']]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n recovery_fabric_name: Optional[pulumi.Input[str]] = None,\\n recovery_replication_policy_id: Optional[pulumi.Input[str]] = None,\\n recovery_source_protection_container_name: Optional[pulumi.Input[str]] = None,\\n recovery_target_protection_container_id: Optional[pulumi.Input[str]] = None,\\n recovery_vault_name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ProvisioningTemplateArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n allow_null_values: Optional[pulumi.Input[bool]] = None,\\n atomic: Optional[pulumi.Input[bool]] = None,\\n chart: Optional[pulumi.Input[str]] = None,\\n cleanup_on_fail: Optional[pulumi.Input[bool]] = None,\\n compat: Optional[pulumi.Input[str]] = None,\\n create_namespace: Optional[pulumi.Input[bool]] = None,\\n dependency_update: Optional[pulumi.Input[bool]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n devel: Optional[pulumi.Input[bool]] = None,\\n disable_crd_hooks: Optional[pulumi.Input[bool]] = None,\\n disable_openapi_validation: Optional[pulumi.Input[bool]] = None,\\n disable_webhooks: Optional[pulumi.Input[bool]] = None,\\n force_update: Optional[pulumi.Input[bool]] = None,\\n keyring: Optional[pulumi.Input[str]] = None,\\n lint: Optional[pulumi.Input[bool]] = None,\\n manifest: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n max_history: Optional[pulumi.Input[int]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n namespace: Optional[pulumi.Input[str]] = None,\\n postrender: Optional[pulumi.Input[str]] = None,\\n recreate_pods: Optional[pulumi.Input[bool]] = None,\\n render_subchart_notes: Optional[pulumi.Input[bool]] = None,\\n replace: Optional[pulumi.Input[bool]] = None,\\n repository_opts: Optional[pulumi.Input[pulumi.InputType['RepositoryOptsArgs']]] = None,\\n reset_values: Optional[pulumi.Input[bool]] = None,\\n resource_names: Optional[pulumi.Input[Mapping[str, pulumi.Input[Sequence[pulumi.Input[str]]]]]] = None,\\n reuse_values: Optional[pulumi.Input[bool]] = None,\\n skip_await: Optional[pulumi.Input[bool]] = None,\\n skip_crds: Optional[pulumi.Input[bool]] = None,\\n timeout: Optional[pulumi.Input[int]] = None,\\n value_yaml_files: Optional[pulumi.Input[Sequence[pulumi.Input[Union[pulumi.Asset, pulumi.Archive]]]]] = None,\\n values: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n verify: Optional[pulumi.Input[bool]] = None,\\n version: Optional[pulumi.Input[str]] = None,\\n wait_for_jobs: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def resource_create(resource_id, resource_type, resource_options=None, cibfile=None):\\n return item_create(\\n item=\\\"resource\\\",\\n item_id=resource_id,\\n item_type=resource_type,\\n extra_args=resource_options,\\n cibfile=cibfile,\\n )\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n additional_locations: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceAdditionalLocationArgs']]]]] = None,\\n certificates: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ServiceCertificateArgs']]]]] = None,\\n client_certificate_enabled: Optional[pulumi.Input[bool]] = None,\\n delegation: Optional[pulumi.Input[pulumi.InputType['ServiceDelegationArgs']]] = None,\\n gateway_disabled: Optional[pulumi.Input[bool]] = None,\\n hostname_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceHostnameConfigurationArgs']]] = None,\\n identity: Optional[pulumi.Input[pulumi.InputType['ServiceIdentityArgs']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n min_api_version: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n notification_sender_email: Optional[pulumi.Input[str]] = None,\\n policy: Optional[pulumi.Input[pulumi.InputType['ServicePolicyArgs']]] = None,\\n protocols: Optional[pulumi.Input[pulumi.InputType['ServiceProtocolsArgs']]] = None,\\n public_ip_address_id: Optional[pulumi.Input[str]] = None,\\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\\n publisher_email: Optional[pulumi.Input[str]] = None,\\n publisher_name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n security: Optional[pulumi.Input[pulumi.InputType['ServiceSecurityArgs']]] = None,\\n sign_in: Optional[pulumi.Input[pulumi.InputType['ServiceSignInArgs']]] = None,\\n sign_up: Optional[pulumi.Input[pulumi.InputType['ServiceSignUpArgs']]] = None,\\n sku_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tenant_access: Optional[pulumi.Input[pulumi.InputType['ServiceTenantAccessArgs']]] = None,\\n virtual_network_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceVirtualNetworkConfigurationArgs']]] = None,\\n virtual_network_type: Optional[pulumi.Input[str]] = None,\\n zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\\n arn: Optional[pulumi.Input[str]] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n blueprint_id: Optional[pulumi.Input[str]] = None,\\n bundle_id: Optional[pulumi.Input[str]] = None,\\n cpu_count: Optional[pulumi.Input[int]] = None,\\n created_at: Optional[pulumi.Input[str]] = None,\\n ip_address_type: Optional[pulumi.Input[str]] = None,\\n ipv6_address: Optional[pulumi.Input[str]] = None,\\n ipv6_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n is_static_ip: Optional[pulumi.Input[bool]] = None,\\n key_pair_name: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n private_ip_address: Optional[pulumi.Input[str]] = None,\\n public_ip_address: Optional[pulumi.Input[str]] = None,\\n ram_size: Optional[pulumi.Input[float]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n user_data: Optional[pulumi.Input[str]] = None,\\n username: Optional[pulumi.Input[str]] = None) -> 'Instance':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _InstanceState.__new__(_InstanceState)\\n\\n __props__.__dict__[\\\"add_on\\\"] = add_on\\n __props__.__dict__[\\\"arn\\\"] = arn\\n __props__.__dict__[\\\"availability_zone\\\"] = availability_zone\\n __props__.__dict__[\\\"blueprint_id\\\"] = blueprint_id\\n __props__.__dict__[\\\"bundle_id\\\"] = bundle_id\\n __props__.__dict__[\\\"cpu_count\\\"] = cpu_count\\n __props__.__dict__[\\\"created_at\\\"] = created_at\\n __props__.__dict__[\\\"ip_address_type\\\"] = ip_address_type\\n __props__.__dict__[\\\"ipv6_address\\\"] = ipv6_address\\n __props__.__dict__[\\\"ipv6_addresses\\\"] = ipv6_addresses\\n __props__.__dict__[\\\"is_static_ip\\\"] = is_static_ip\\n __props__.__dict__[\\\"key_pair_name\\\"] = key_pair_name\\n __props__.__dict__[\\\"name\\\"] = name\\n __props__.__dict__[\\\"private_ip_address\\\"] = private_ip_address\\n __props__.__dict__[\\\"public_ip_address\\\"] = public_ip_address\\n __props__.__dict__[\\\"ram_size\\\"] = ram_size\\n __props__.__dict__[\\\"tags\\\"] = tags\\n __props__.__dict__[\\\"tags_all\\\"] = tags_all\\n __props__.__dict__[\\\"user_data\\\"] = user_data\\n __props__.__dict__[\\\"username\\\"] = username\\n return Instance(resource_name, opts=opts, __props__=__props__)\",\n \"def create_resource(self, namespace: \\\"str\\\" = None):\\n names = [\\\"create_namespaced_csistorage_capacity\\\", \\\"create_csistorage_capacity\\\"]\\n\\n _kube_api.execute(\\n action=\\\"create\\\",\\n resource=self,\\n names=names,\\n namespace=namespace,\\n api_client=None,\\n api_args={\\\"body\\\": self.to_dict()},\\n )\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n arm_template_display_name: Optional[pulumi.Input[str]] = None,\\n deployment_properties: Optional[pulumi.Input[pulumi.InputType['EnvironmentDeploymentPropertiesArgs']]] = None,\\n lab_name: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n user_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: Optional[ResourceDefaultVersionArgs] = None,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: AppArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def create_resource(self, **kwargs):\\n results = self.api.action.resource_create(**kwargs)\\n # TODO: use `results` rather than re-download, using an isolation layer to standardize the re-structure\\n self.get_ckan_metadata(True)\\n if 'id' in results:\\n self._import_resource_to_cache(kwargs['upload'], results['id'])\\n return results\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ApplicationArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ApplicationArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ApplicationArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ServiceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ServiceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def create_sticky(ctx, iface, resource_config, **_):\\n\\n # Create a copy of the resource config for clean manipulation.\\n params = \\\\\\n dict() if not resource_config else resource_config.copy()\\n\\n lb_name = params.get(LB_NAME)\\n policy_name = params.get(RESOURCE_NAME)\\n\\n if not lb_name:\\n targs = \\\\\\n utils.find_rels_by_node_type(\\n ctx.instance,\\n LB_TYPE)\\n lb_name = \\\\\\n targs[0].target.instance.runtime_properties[\\n EXTERNAL_RESOURCE_ID]\\n params.update({LB_NAME: lb_name})\\n\\n ctx.instance.runtime_properties[LB_NAME] = \\\\\\n lb_name\\n ctx.instance.runtime_properties[RESOURCE_NAME] = \\\\\\n policy_name\\n\\n # Actually create the resource\\n iface.create_sticky(params)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n backup_pool: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n failover_ratio: Optional[pulumi.Input[float]] = None,\\n health_checks: Optional[pulumi.Input[str]] = None,\\n instances: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n session_affinity: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n kind: Optional[pulumi.Input[Union[str, 'Kind']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n managed_network_group_name: Optional[pulumi.Input[str]] = None,\\n managed_network_name: Optional[pulumi.Input[str]] = None,\\n management_groups: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n subnets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n subscriptions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n virtual_networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n __props__=None):\\n ...\",\n \"def get(resource_name, id, opts=None, arn=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, default_version=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, latest_version=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None):\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = dict()\\n __props__[\\\"arn\\\"] = arn\\n __props__[\\\"block_device_mappings\\\"] = block_device_mappings\\n __props__[\\\"capacity_reservation_specification\\\"] = capacity_reservation_specification\\n __props__[\\\"credit_specification\\\"] = credit_specification\\n __props__[\\\"default_version\\\"] = default_version\\n __props__[\\\"description\\\"] = description\\n __props__[\\\"disable_api_termination\\\"] = disable_api_termination\\n __props__[\\\"ebs_optimized\\\"] = ebs_optimized\\n __props__[\\\"elastic_gpu_specifications\\\"] = elastic_gpu_specifications\\n __props__[\\\"elastic_inference_accelerator\\\"] = elastic_inference_accelerator\\n __props__[\\\"iam_instance_profile\\\"] = iam_instance_profile\\n __props__[\\\"image_id\\\"] = image_id\\n __props__[\\\"instance_initiated_shutdown_behavior\\\"] = instance_initiated_shutdown_behavior\\n __props__[\\\"instance_market_options\\\"] = instance_market_options\\n __props__[\\\"instance_type\\\"] = instance_type\\n __props__[\\\"kernel_id\\\"] = kernel_id\\n __props__[\\\"key_name\\\"] = key_name\\n __props__[\\\"latest_version\\\"] = latest_version\\n __props__[\\\"license_specifications\\\"] = license_specifications\\n __props__[\\\"monitoring\\\"] = monitoring\\n __props__[\\\"name\\\"] = name\\n __props__[\\\"name_prefix\\\"] = name_prefix\\n __props__[\\\"network_interfaces\\\"] = network_interfaces\\n __props__[\\\"placement\\\"] = placement\\n __props__[\\\"ram_disk_id\\\"] = ram_disk_id\\n __props__[\\\"security_group_names\\\"] = security_group_names\\n __props__[\\\"tag_specifications\\\"] = tag_specifications\\n __props__[\\\"tags\\\"] = tags\\n __props__[\\\"user_data\\\"] = user_data\\n __props__[\\\"vpc_security_group_ids\\\"] = vpc_security_group_ids\\n return LaunchTemplate(resource_name, opts=opts, __props__=__props__)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n enabled: Optional[pulumi.Input[bool]] = None,\\n pre_provisioning_hook: Optional[pulumi.Input[pulumi.InputType['ProvisioningTemplateProvisioningHookArgs']]] = None,\\n provisioning_role_arn: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ProvisioningTemplateTagArgs']]]]] = None,\\n template_body: Optional[pulumi.Input[str]] = None,\\n template_name: Optional[pulumi.Input[str]] = None,\\n template_type: Optional[pulumi.Input['ProvisioningTemplateTemplateType']] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n admin_site_name: Optional[pulumi.Input[str]] = None,\\n allow_pending_state: Optional[pulumi.Input[bool]] = None,\\n hosting_environment_profile: Optional[pulumi.Input[pulumi.InputType['HostingEnvironmentProfileArgs']]] = None,\\n id: Optional[pulumi.Input[str]] = None,\\n kind: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n maximum_number_of_workers: Optional[pulumi.Input[int]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n per_site_scaling: Optional[pulumi.Input[bool]] = None,\\n reserved: Optional[pulumi.Input[bool]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n sku: Optional[pulumi.Input[pulumi.InputType['SkuDescriptionArgs']]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n type: Optional[pulumi.Input[str]] = None,\\n worker_tier_name: Optional[pulumi.Input[str]] = None,\\n __props__=None,\\n __name__=None,\\n __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = _utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['admin_site_name'] = admin_site_name\\n __props__['allow_pending_state'] = allow_pending_state\\n __props__['hosting_environment_profile'] = hosting_environment_profile\\n __props__['id'] = id\\n __props__['kind'] = kind\\n __props__['location'] = location\\n __props__['maximum_number_of_workers'] = maximum_number_of_workers\\n __props__['name'] = name\\n __props__['per_site_scaling'] = per_site_scaling\\n __props__['reserved'] = reserved\\n if resource_group_name is None and not opts.urn:\\n raise TypeError(\\\"Missing required property 'resource_group_name'\\\")\\n __props__['resource_group_name'] = resource_group_name\\n __props__['sku'] = sku\\n __props__['tags'] = tags\\n __props__['type'] = type\\n __props__['worker_tier_name'] = worker_tier_name\\n __props__['geo_region'] = None\\n __props__['number_of_sites'] = None\\n __props__['resource_group'] = None\\n __props__['status'] = None\\n __props__['subscription'] = None\\n alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\\\"azure-nextgen:web/v20150801:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/latest:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/latest:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/v20160901:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/v20160901:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/v20180201:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/v20180201:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/v20190801:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/v20190801:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/v20200601:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/v20200601:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/v20200901:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/v20200901:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-native:web/v20201001:ServerFarm\\\"), pulumi.Alias(type_=\\\"azure-nextgen:web/v20201001:ServerFarm\\\")])\\n opts = pulumi.ResourceOptions.merge(opts, alias_opts)\\n super(ServerFarm, __self__).__init__(\\n 'azure-native:web/v20150801:ServerFarm',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n dry_run: Optional[pulumi.Input[bool]] = None,\\n load_balancer_id: Optional[pulumi.Input[str]] = None,\\n security_group_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n create_sample_data: Optional[pulumi.Input[bool]] = None,\\n db_instance_category: Optional[pulumi.Input[str]] = None,\\n db_instance_class: Optional[pulumi.Input[str]] = None,\\n db_instance_mode: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n encryption_key: Optional[pulumi.Input[str]] = None,\\n encryption_type: Optional[pulumi.Input[str]] = None,\\n engine: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n instance_charge_type: Optional[pulumi.Input[str]] = None,\\n instance_group_count: Optional[pulumi.Input[int]] = None,\\n instance_network_type: Optional[pulumi.Input[str]] = None,\\n instance_spec: Optional[pulumi.Input[str]] = None,\\n ip_whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceIpWhitelistArgs']]]]] = None,\\n maintain_end_time: Optional[pulumi.Input[str]] = None,\\n maintain_start_time: Optional[pulumi.Input[str]] = None,\\n master_node_num: Optional[pulumi.Input[int]] = None,\\n payment_type: Optional[pulumi.Input[str]] = None,\\n period: Optional[pulumi.Input[str]] = None,\\n private_ip_address: Optional[pulumi.Input[str]] = None,\\n resource_group_id: Optional[pulumi.Input[str]] = None,\\n security_ip_lists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n seg_node_num: Optional[pulumi.Input[int]] = None,\\n seg_storage_type: Optional[pulumi.Input[str]] = None,\\n ssl_enabled: Optional[pulumi.Input[int]] = None,\\n storage_size: Optional[pulumi.Input[int]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n used_time: Optional[pulumi.Input[str]] = None,\\n vector_configuration_status: Optional[pulumi.Input[str]] = None,\\n vpc_id: Optional[pulumi.Input[str]] = None,\\n vswitch_id: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n access_config: Optional[pulumi.Input[pulumi.InputType['RuntimeAccessConfigArgs']]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n request_id: Optional[pulumi.Input[str]] = None,\\n runtime_id: Optional[pulumi.Input[str]] = None,\\n software_config: Optional[pulumi.Input[pulumi.InputType['RuntimeSoftwareConfigArgs']]] = None,\\n virtual_machine: Optional[pulumi.Input[pulumi.InputType['VirtualMachineArgs']]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n attestation_authority: Optional[pulumi.Input[pulumi.InputType['AttestationAuthorityArgs']]] = None,\\n base_image: Optional[pulumi.Input[pulumi.InputType['BasisArgs']]] = None,\\n build_type: Optional[pulumi.Input[pulumi.InputType['BuildTypeArgs']]] = None,\\n compliance: Optional[pulumi.Input[pulumi.InputType['ComplianceNoteArgs']]] = None,\\n deployable: Optional[pulumi.Input[pulumi.InputType['DeployableArgs']]] = None,\\n discovery: Optional[pulumi.Input[pulumi.InputType['DiscoveryArgs']]] = None,\\n dsse_attestation: Optional[pulumi.Input[pulumi.InputType['DSSEAttestationNoteArgs']]] = None,\\n expiration_time: Optional[pulumi.Input[str]] = None,\\n long_description: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n note_id: Optional[pulumi.Input[str]] = None,\\n package: Optional[pulumi.Input[pulumi.InputType['PackageArgs']]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n related_url: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RelatedUrlArgs']]]]] = None,\\n sbom: Optional[pulumi.Input[pulumi.InputType['DocumentNoteArgs']]] = None,\\n sbom_reference: Optional[pulumi.Input[pulumi.InputType['SBOMReferenceNoteArgs']]] = None,\\n short_description: Optional[pulumi.Input[str]] = None,\\n spdx_file: Optional[pulumi.Input[pulumi.InputType['FileNoteArgs']]] = None,\\n spdx_package: Optional[pulumi.Input[pulumi.InputType['PackageInfoNoteArgs']]] = None,\\n spdx_relationship: Optional[pulumi.Input[pulumi.InputType['RelationshipNoteArgs']]] = None,\\n upgrade: Optional[pulumi.Input[pulumi.InputType['UpgradeNoteArgs']]] = None,\\n vulnerability_assessment: Optional[pulumi.Input[pulumi.InputType['VulnerabilityAssessmentNoteArgs']]] = None,\\n vulnerability_type: Optional[pulumi.Input[pulumi.InputType['VulnerabilityTypeArgs']]] = None,\\n __props__=None):\\n ...\",\n \"def create(self, resource):\\n if not resource:\\n raise NoneReferenceError(\\\"resource to create can't be None\\\")\\n\\n if not isinstance(resource, self._resource_class):\\n raise TypeError(\\n \\\"resource %s is not %s instance\\\"\\n % (resource, self._resource_class.__name__))\\n\\n return self.build_resource(\\n self._os_resource_manager.create(**vars(resource)))\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n client_id: Optional[pulumi.Input[str]] = None,\\n client_secret: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n display_name: Optional[pulumi.Input[str]] = None,\\n metadata_endpoint: Optional[pulumi.Input[str]] = None,\\n opid: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n service_name: Optional[pulumi.Input[str]] = None,\\n use_in_api_documentation: Optional[pulumi.Input[bool]] = None,\\n use_in_test_console: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n charset: Optional[pulumi.Input[str]] = None,\\n collation: Optional[pulumi.Input[str]] = None,\\n deletion_policy: Optional[pulumi.Input[str]] = None,\\n instance: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n app_name: Optional[pulumi.Input[str]] = None,\\n bundle_id: Optional[pulumi.Input[str]] = None,\\n encoded_icon: Optional[pulumi.Input[str]] = None,\\n industry_id: Optional[pulumi.Input[str]] = None,\\n package_name: Optional[pulumi.Input[str]] = None,\\n product_id: Optional[pulumi.Input[str]] = None,\\n type: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n cluster_name: Optional[pulumi.Input[str]] = None,\\n node_setup: Optional[pulumi.Input[pulumi.InputType['NodeSetupArgs']]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n scale_settings: Optional[pulumi.Input[pulumi.InputType['ScaleSettingsArgs']]] = None,\\n subnet: Optional[pulumi.Input[pulumi.InputType['ResourceIdArgs']]] = None,\\n user_account_settings: Optional[pulumi.Input[pulumi.InputType['UserAccountSettingsArgs']]] = None,\\n virtual_machine_configuration: Optional[pulumi.Input[pulumi.InputType['VirtualMachineConfigurationArgs']]] = None,\\n vm_priority: Optional[pulumi.Input['VmPriority']] = None,\\n vm_size: Optional[pulumi.Input[str]] = None,\\n workspace_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: Optional[TargetPoolArgs] = None,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: BundleArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def create(self):\\n raise WufooException(\\\"InstanceResource creation not supported\\\")\",\n \"def make_instance(self, include_optional):\\n # model = yapily.models.resource.Resource() # noqa: E501\\n if include_optional :\\n return Resource(\\n description = '0', \\n file = yapily.models.file.File(\\n absolute = True, \\n absolute_file = yapily.models.file.File(\\n absolute = True, \\n absolute_path = '0', \\n canonical_file = yapily.models.file.File(\\n absolute = True, \\n absolute_path = '0', \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n parent_file = yapily.models.file.File(\\n absolute = True, \\n absolute_path = '0', \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n parent_file = yapily.models.file.File(\\n absolute = True, \\n absolute_path = '0', \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n absolute_path = '0', \\n canonical_file = yapily.models.file.File(\\n absolute = True, \\n absolute_path = '0', \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n parent_file = yapily.models.file.File(\\n absolute = True, \\n absolute_path = '0', \\n canonical_path = '0', \\n directory = True, \\n file = True, \\n free_space = 56, \\n hidden = True, \\n name = '0', \\n parent = '0', \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n path = '0', \\n total_space = 56, \\n usable_space = 56, ), \\n filename = '0', \\n input_stream = None, \\n open = True, \\n readable = True, \\n uri = yapily.models.uri.URI(\\n absolute = True, \\n authority = '0', \\n fragment = '0', \\n host = '0', \\n opaque = True, \\n path = '0', \\n port = 56, \\n query = '0', \\n raw_authority = '0', \\n raw_fragment = '0', \\n raw_path = '0', \\n raw_query = '0', \\n raw_scheme_specific_part = '0', \\n raw_user_info = '0', \\n scheme = '0', \\n scheme_specific_part = '0', \\n user_info = '0', ), \\n url = yapily.models.url.URL(\\n authority = '0', \\n content = yapily.models.content.content(), \\n default_port = 56, \\n file = '0', \\n host = '0', \\n path = '0', \\n port = 56, \\n protocol = '0', \\n query = '0', \\n ref = '0', \\n user_info = '0', )\\n )\\n else :\\n return Resource(\\n )\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n bundle_name: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n desktop_type: Optional[pulumi.Input[str]] = None,\\n image_id: Optional[pulumi.Input[str]] = None,\\n language: Optional[pulumi.Input[str]] = None,\\n root_disk_performance_level: Optional[pulumi.Input[str]] = None,\\n root_disk_size_gib: Optional[pulumi.Input[int]] = None,\\n user_disk_performance_level: Optional[pulumi.Input[str]] = None,\\n user_disk_size_gibs: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None,\\n __props__=None):\\n ...\",\n \"def create_sally_resources(region_name, app_name, image_name,\\n storage_enckey=None,\\n s3_logs_bucket=None,\\n identities_url=None,\\n ssh_key_name=None,\\n ssh_port=None,\\n company_domain=None,\\n ldap_host=None,\\n security_group_name=None,\\n instance_type=None,\\n web_subnet_id=None,\\n vpc_id=None,\\n vpc_cidr=None,\\n tag_prefix=None,\\n dry_run=False):\\n subnet_id = web_subnet_id\\n sg_tag_prefix = tag_prefix\\n if not security_group_name:\\n security_group_name = 'kitchen-door'\\n sg_name = _get_security_group_names(\\n [security_group_name], tag_prefix=sg_tag_prefix)[0]\\n\\n ec2_client = boto3.client('ec2', region_name=region_name)\\n if not vpc_id:\\n vpc_id, _ = _get_vpc_id(tag_prefix, ec2_client=ec2_client,\\n region_name=region_name)\\n if not subnet_id:\\n #pylint:disable=unused-variable\\n web_subnet_cidrs, _, _ = _split_cidrs(\\n vpc_cidr, ec2_client=ec2_client, region_name=region_name)\\n web_subnet_by_cidrs = _get_subnet_by_cidrs(\\n web_subnet_cidrs, tag_prefix,\\n vpc_id=vpc_id, ec2_client=ec2_client)\\n # Use first valid subnet that does not require a public IP.\\n subnet_id = next(iter(web_subnet_by_cidrs.values()))['SubnetId']\\n\\n group_ids = _get_security_group_ids(\\n [sg_name], tag_prefix,\\n vpc_id=vpc_id, ec2_client=ec2_client)\\n\\n iam_client = boto3.client('iam')\\n instance_profile_arn = create_instance_profile(\\n create_logs_role(sg_name,\\n s3_logs_bucket=s3_logs_bucket,\\n iam_client=iam_client, tag_prefix=tag_prefix),\\n iam_client=iam_client, region_name=region_name,\\n tag_prefix=tag_prefix, dry_run=dry_run)\\n\\n instances = create_instances(region_name, app_name, image_name,\\n storage_enckey=storage_enckey,\\n s3_logs_bucket=s3_logs_bucket,\\n identities_url=identities_url,\\n ssh_key_name=ssh_key_name,\\n company_domain=company_domain,\\n ldap_host=ldap_host,\\n instance_type=instance_type,\\n instance_profile_arn=instance_profile_arn,\\n security_group_ids=group_ids,\\n subnet_type=SUBNET_PUBLIC,\\n subnet_id=subnet_id,\\n vpc_id=vpc_id,\\n vpc_cidr=vpc_cidr,\\n tag_prefix=tag_prefix,\\n dry_run=dry_run,\\n ec2_client=ec2_client,\\n ssh_port=ssh_port)\\n\\n return [instance['InstanceId'] for instance in instances]\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n address_family: Optional[pulumi.Input[str]] = None,\\n allocation_default_netmask_length: Optional[pulumi.Input[int]] = None,\\n allocation_max_netmask_length: Optional[pulumi.Input[int]] = None,\\n allocation_min_netmask_length: Optional[pulumi.Input[int]] = None,\\n allocation_resource_tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n auto_import: Optional[pulumi.Input[bool]] = None,\\n aws_service: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n ipam_scope_id: Optional[pulumi.Input[str]] = None,\\n locale: Optional[pulumi.Input[str]] = None,\\n public_ip_source: Optional[pulumi.Input[str]] = None,\\n publicly_advertisable: Optional[pulumi.Input[bool]] = None,\\n source_ipam_pool_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def create(name, storage, backend, memory, timeout, config, debug):\\n setup_lithops_logger(logging.DEBUG)\\n\\n verify_runtime_name(name)\\n\\n if config:\\n config = load_yaml_config(config)\\n\\n config_ow = set_config_ow(backend, storage, runtime_name=name)\\n config = default_config(config, config_ow)\\n\\n if config['lithops']['mode'] != SERVERLESS:\\n raise Exception('\\\"lithops runtime create\\\" command is only valid for serverless backends')\\n\\n logger.info('Creating new lithops runtime: {}'.format(name))\\n storage_config = extract_storage_config(config)\\n internal_storage = InternalStorage(storage_config)\\n\\n compute_config = extract_serverless_config(config)\\n compute_handler = ServerlessHandler(compute_config, internal_storage)\\n mem = memory if memory else compute_config['runtime_memory']\\n to = timeout if timeout else compute_config['runtime_timeout']\\n runtime_key = compute_handler.get_runtime_key(name, mem)\\n runtime_meta = compute_handler.create_runtime(name, mem, timeout=to)\\n\\n try:\\n internal_storage.put_runtime_meta(runtime_key, runtime_meta)\\n except Exception:\\n raise (\\\"Unable to upload 'preinstalled-modules' file into {}\\\".format(internal_storage.backend))\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n minimal_action: Optional[pulumi.Input[str]] = None,\\n most_disruptive_allowed_action: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n preserved_state: Optional[pulumi.Input[pulumi.InputType['RegionPerInstanceConfigPreservedStateArgs']]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n region_instance_group_manager: Optional[pulumi.Input[str]] = None,\\n remove_instance_state_on_destroy: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(self,\\n name: str = None,\\n k8s_name: str = None,\\n job_name: str = None,\\n spec: {} = None,\\n existing_cluster_id: str = None,\\n new_cluster: {} = None,\\n libraries: {} = None,\\n spark_jar_task: {} = None,\\n spark_python_task: {} = None,\\n spark_submit_task: {} = None,\\n notebook_task: {} = None,\\n timeout_seconds: int = None,\\n max_retries: int = None,\\n min_retry_interval_millis: int = None,\\n retry_on_timeout: bool = None,\\n schedule: {} = None,\\n max_concurrent_runs: int = None,\\n email_notifications: {} = None):\\n\\n if not spec:\\n spec = {}\\n\\n if job_name:\\n spec[\\\"name\\\"] = job_name\\n if new_cluster:\\n spec[\\\"new_cluster\\\"] = new_cluster\\n if existing_cluster_id:\\n spec[\\\"existing_cluster_id\\\"] = existing_cluster_id\\n if spark_jar_task:\\n spec[\\\"spark_jar_task\\\"] = spark_jar_task\\n if spark_python_task:\\n spec[\\\"spark_python_task\\\"] = spark_python_task\\n if spark_submit_task:\\n spec[\\\"spark_submit_task\\\"] = spark_submit_task\\n if notebook_task:\\n spec[\\\"notebook_task\\\"] = notebook_task\\n if libraries:\\n spec[\\\"libraries\\\"] = libraries\\n if timeout_seconds:\\n spec[\\\"timeout_seconds\\\"] = timeout_seconds\\n if max_retries:\\n spec[\\\"max_retries\\\"] = max_retries\\n if min_retry_interval_millis:\\n spec[\\\"min_retry_interval_millis\\\"] = min_retry_interval_millis\\n if retry_on_timeout:\\n spec[\\\"retry_on_timeout\\\"] = retry_on_timeout\\n if schedule:\\n spec[\\\"schedule\\\"] = schedule\\n if max_concurrent_runs:\\n spec[\\\"max_concurrent_runs\\\"] = max_concurrent_runs\\n if email_notifications:\\n spec[\\\"email_notifications\\\"] = email_notifications\\n\\n if not k8s_name and \\\"name\\\" in spec:\\n k8s_name = spec[\\\"name\\\"]\\n elif not k8s_name:\\n raise ValueError(\\\"You need to provide a k8s_name or a job_name.\\\")\\n\\n super().__init__(\\n k8s_resource={\\n \\\"apiVersion\\\": \\\"databricks.microsoft.com/v1alpha1\\\",\\n \\\"kind\\\": \\\"Djob\\\",\\n \\\"metadata\\\": {\\n \\\"name\\\": k8s_name\\n },\\n \\\"spec\\\": spec\\n },\\n action=\\\"create\\\",\\n success_condition=\\\"status.job_status.job_id > 0\\\",\\n attribute_outputs={\\n \\\"name\\\": \\\"{.metadata.name}\\\",\\n \\\"job_id\\\": \\\"{.status.job_status.job_id}\\\",\\n \\\"job_name\\\": \\\"{.status.job_status.settings.name}\\\"\\n },\\n name=name)\",\n \"def create_TestServiceResource(test_case, # type: AnyMagpieTestCaseType\\n override_service_name=null, # type: Optional[Str]\\n override_service_type=null, # type: Optional[Str]\\n override_resource_name=null, # type: Optional[Str]\\n override_resource_type=null, # type: Optional[Str]\\n override_data=null, # type: Optional[JSON]\\n override_headers=null, # type: Optional[HeadersType]\\n override_cookies=null, # type: Optional[CookiesType]\\n ): # type: (...) -> JSON\\n app_or_url = get_app_or_url(test_case)\\n svc_name = override_service_name if override_service_name is not null else test_case.test_service_name\\n svc_type = override_service_type if override_service_type is not null else test_case.test_service_type\\n TestSetup.create_TestService(test_case,\\n override_service_name=svc_name, override_service_type=svc_type,\\n override_headers=override_headers, override_cookies=override_cookies)\\n path = \\\"/services/{svc}/resources\\\".format(svc=svc_name)\\n data = override_data if override_data is not null else {\\n \\\"resource_name\\\": override_resource_name or test_case.test_resource_name,\\n \\\"resource_type\\\": override_resource_type or test_case.test_resource_type,\\n }\\n resp = test_request(app_or_url, \\\"POST\\\", path, json=data,\\n headers=override_headers if override_headers is not null else test_case.json_headers,\\n cookies=override_cookies if override_cookies is not null else test_case.cookies)\\n return check_response_basic_info(resp, 201, expected_method=\\\"POST\\\")\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: VirtualHardDiskArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__, resource_name, opts=None, destination_cidr_block=None, destination_ipv6_cidr_block=None, egress_only_gateway_id=None, gateway_id=None, instance_id=None, nat_gateway_id=None, network_interface_id=None, route_table_id=None, transit_gateway_id=None, vpc_peering_connection_id=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['destination_cidr_block'] = destination_cidr_block\\n __props__['destination_ipv6_cidr_block'] = destination_ipv6_cidr_block\\n __props__['egress_only_gateway_id'] = egress_only_gateway_id\\n __props__['gateway_id'] = gateway_id\\n __props__['instance_id'] = instance_id\\n __props__['nat_gateway_id'] = nat_gateway_id\\n __props__['network_interface_id'] = network_interface_id\\n if route_table_id is None:\\n raise TypeError(\\\"Missing required property 'route_table_id'\\\")\\n __props__['route_table_id'] = route_table_id\\n __props__['transit_gateway_id'] = transit_gateway_id\\n __props__['vpc_peering_connection_id'] = vpc_peering_connection_id\\n __props__['destination_prefix_list_id'] = None\\n __props__['instance_owner_id'] = None\\n __props__['origin'] = None\\n __props__['state'] = None\\n super(Route, __self__).__init__(\\n 'aws:ec2/route:Route',\\n resource_name,\\n __props__,\\n opts)\",\n \"def create():\\n\\n # remember what is created or not\\n vpc = False\\n igw = False\\n sg = False\\n sub = False\\n vm = False\\n\\n vpc = _create_resource('vpc', CidrBlock=args.cidr, InstanceTenancy='default')\\n igw = _create_resource('igw')\\n\\n if vpc and igw:\\n _attach_vpc_igw(vpc=_existing.vpc, igw=_existing.igw)\\n else:\\n print('Cannot attach an igw to a vpc as at least one of them could not be created.')\\n\\n if vpc:\\n sg = _create_resource(\\n 'sg',\\n GroupName=args.role,\\n Description='SG for ' + args.role,\\n VpcId=getattr(_existing.vpc, 'id', None)\\n )\\n else:\\n print('Cannot create a sg as the vpc to attach it to could not be created.')\\n\\n if sg:\\n _add_ingress_rules()\\n else:\\n print('Cannot create ingress rule as the sg could not be created.')\\n\\n if vpc:\\n sub = _create_resource(\\n 'sub',\\n VpcId=getattr(_existing.vpc, 'id', None),\\n CidrBlock=args.cidr\\n )\\n else:\\n print('Cannot create a subnet as the vpc to attach it to could not be created.')\\n\\n if vpc and sub:\\n _link_route_table()\\n else:\\n print('Cannot link subnet and VPC in the route table as vpc or sub not created.')\\n\\n if sub and sg:\\n vm = _create_resource(\\n 'vm',\\n ImageId=args.ami,\\n MinCount=1,\\n MaxCount=1,\\n KeyName=args.keypair,\\n InstanceType=args.instance,\\n # Note that there will be no internal name.\\n # To get one, create first a DHCP options set and associate it with the VPC.\\n NetworkInterfaces=[{\\n 'AssociatePublicIpAddress': True,\\n 'DeviceIndex': 0, # needs to be 0 to get a public IP\\n 'SubnetId': getattr(_existing.sub, 'id', None),\\n 'Groups': [getattr(_existing.sg, 'id', None)],\\n }],\\n )\\n else:\\n print('Cannot create an instance as the sub or sg to use could not be created.')\\n\\n if vm:\\n if not dry:\\n print('Waiting for the instance to be up and running, usually done in less than 45 seconds...')\\n _existing.vm.wait_until_running()\\n _tag_volume()\\n print('you can reach your VM at ' + _existing.vm.public_ip_address)\\n\\n else:\\n print('VM not created for some reason.')\",\n \"def get(resource_name, id, opts=None, cluster=None, deployment_controller=None, deployment_maximum_percent=None, deployment_minimum_healthy_percent=None, desired_count=None, enable_ecs_managed_tags=None, health_check_grace_period_seconds=None, iam_role=None, launch_type=None, load_balancers=None, name=None, network_configuration=None, ordered_placement_strategies=None, placement_constraints=None, platform_version=None, propagate_tags=None, scheduling_strategy=None, service_registries=None, tags=None, task_definition=None, wait_for_steady_state=None):\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = dict()\\n __props__[\\\"cluster\\\"] = cluster\\n __props__[\\\"deployment_controller\\\"] = deployment_controller\\n __props__[\\\"deployment_maximum_percent\\\"] = deployment_maximum_percent\\n __props__[\\\"deployment_minimum_healthy_percent\\\"] = deployment_minimum_healthy_percent\\n __props__[\\\"desired_count\\\"] = desired_count\\n __props__[\\\"enable_ecs_managed_tags\\\"] = enable_ecs_managed_tags\\n __props__[\\\"health_check_grace_period_seconds\\\"] = health_check_grace_period_seconds\\n __props__[\\\"iam_role\\\"] = iam_role\\n __props__[\\\"launch_type\\\"] = launch_type\\n __props__[\\\"load_balancers\\\"] = load_balancers\\n __props__[\\\"name\\\"] = name\\n __props__[\\\"network_configuration\\\"] = network_configuration\\n __props__[\\\"ordered_placement_strategies\\\"] = ordered_placement_strategies\\n __props__[\\\"placement_constraints\\\"] = placement_constraints\\n __props__[\\\"platform_version\\\"] = platform_version\\n __props__[\\\"propagate_tags\\\"] = propagate_tags\\n __props__[\\\"scheduling_strategy\\\"] = scheduling_strategy\\n __props__[\\\"service_registries\\\"] = service_registries\\n __props__[\\\"tags\\\"] = tags\\n __props__[\\\"task_definition\\\"] = task_definition\\n __props__[\\\"wait_for_steady_state\\\"] = wait_for_steady_state\\n return Service(resource_name, opts=opts, __props__=__props__)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n allow_forwarded_traffic: Optional[pulumi.Input[bool]] = None,\\n allow_gateway_transit: Optional[pulumi.Input[bool]] = None,\\n allow_virtual_network_access: Optional[pulumi.Input[bool]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n remote_address_space_prefixes: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n remote_virtual_network_id: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n use_remote_gateways: Optional[pulumi.Input[bool]] = None,\\n workspace_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n authorized_network: Optional[pulumi.Input[str]] = None,\\n display_name: Optional[pulumi.Input[str]] = None,\\n instance_id: Optional[pulumi.Input[str]] = None,\\n instance_messages: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceMessageArgs']]]]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n maintenance_policy: Optional[pulumi.Input[pulumi.InputType['GoogleCloudMemcacheV1beta2MaintenancePolicyArgs']]] = None,\\n memcache_version: Optional[pulumi.Input['InstanceMemcacheVersion']] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n node_config: Optional[pulumi.Input[pulumi.InputType['NodeConfigArgs']]] = None,\\n node_count: Optional[pulumi.Input[int]] = None,\\n parameters: Optional[pulumi.Input[pulumi.InputType['MemcacheParametersArgs']]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def pre_namespace_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n application_name: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n resource_group_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n copy: Optional[pulumi.Input[pulumi.InputType['JobCopyArgs']]] = None,\\n extract: Optional[pulumi.Input[pulumi.InputType['JobExtractArgs']]] = None,\\n job_id: Optional[pulumi.Input[str]] = None,\\n job_timeout_ms: Optional[pulumi.Input[str]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n load: Optional[pulumi.Input[pulumi.InputType['JobLoadArgs']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n query: Optional[pulumi.Input[pulumi.InputType['JobQueryArgs']]] = None,\\n __props__=None,\\n __name__=None,\\n __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = _utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['copy'] = copy\\n __props__['extract'] = extract\\n if job_id is None:\\n raise TypeError(\\\"Missing required property 'job_id'\\\")\\n __props__['job_id'] = job_id\\n __props__['job_timeout_ms'] = job_timeout_ms\\n __props__['labels'] = labels\\n __props__['load'] = load\\n __props__['location'] = location\\n __props__['project'] = project\\n __props__['query'] = query\\n __props__['job_type'] = None\\n __props__['user_email'] = None\\n super(Job, __self__).__init__(\\n 'gcp:bigquery/job:Job',\\n resource_name,\\n __props__,\\n opts)\",\n \"def create(ctx, resource_config, **_):\\n # Create a resource (if necessary)\\n ctx.logger.warn(\\n 'Azure customData implementation is dependent on '\\n 'Virtual Machine image support.')\\n azure_config = utils.get_client_config(ctx.node.properties)\\n name = utils.get_resource_name(ctx)\\n resource_group_name = utils.get_resource_group(ctx)\\n vm_name = ctx.node.properties['virtual_machine_name']\\n vm_extension_params = {\\n 'location': ctx.node.properties.get('location'),\\n 'tags': ctx.node.properties.get('tags'),\\n 'publisher': resource_config.get('publisher'),\\n 'type_properties_type': resource_config.get('ext_type'),\\n 'type_handler_version': resource_config.get('typeHandlerVersion'),\\n 'settings': resource_config.get('settings'),\\n }\\n api_version = \\\\\\n ctx.node.properties.get('api_version', constants.API_VER_COMPUTE)\\n vm_extension = VirtualMachineExtension(azure_config, ctx.logger,\\n api_version)\\n # clean empty values from params\\n vm_extension_params = \\\\\\n utils.cleanup_empty_params(vm_extension_params)\\n result = utils.handle_create(\\n vm_extension,\\n resource_group_name,\\n name,\\n vm_name,\\n vm_extension_params)\\n ctx.instance.runtime_properties['resource_group'] = resource_group_name\\n ctx.instance.runtime_properties['virtual_machine'] = vm_name\\n ctx.instance.runtime_properties['resource'] = result\\n ctx.instance.runtime_properties['resource_id'] = result.get(\\\"id\\\", \\\"\\\")\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n type_name: Optional[pulumi.Input[str]] = None,\\n type_version_arn: Optional[pulumi.Input[str]] = None,\\n version_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auth_mode: Optional[pulumi.Input[str]] = None,\\n default_s3_location: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n engine_security_group_id: Optional[pulumi.Input[str]] = None,\\n idp_auth_url: Optional[pulumi.Input[str]] = None,\\n idp_relay_state_parameter_name: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n service_role: Optional[pulumi.Input[str]] = None,\\n subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n user_role: Optional[pulumi.Input[str]] = None,\\n vpc_id: Optional[pulumi.Input[str]] = None,\\n workspace_security_group_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: PrefixArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def pre_service_instance_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n apply_immediately: Optional[pulumi.Input[bool]] = None,\\n authentication_strategy: Optional[pulumi.Input[str]] = None,\\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\\n broker_name: Optional[pulumi.Input[str]] = None,\\n configuration: Optional[pulumi.Input[pulumi.InputType['BrokerConfigurationArgs']]] = None,\\n deployment_mode: Optional[pulumi.Input[str]] = None,\\n encryption_options: Optional[pulumi.Input[pulumi.InputType['BrokerEncryptionOptionsArgs']]] = None,\\n engine_type: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n host_instance_type: Optional[pulumi.Input[str]] = None,\\n ldap_server_metadata: Optional[pulumi.Input[pulumi.InputType['BrokerLdapServerMetadataArgs']]] = None,\\n logs: Optional[pulumi.Input[pulumi.InputType['BrokerLogsArgs']]] = None,\\n maintenance_window_start_time: Optional[pulumi.Input[pulumi.InputType['BrokerMaintenanceWindowStartTimeArgs']]] = None,\\n publicly_accessible: Optional[pulumi.Input[bool]] = None,\\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n storage_type: Optional[pulumi.Input[str]] = None,\\n subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n users: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['BrokerUserArgs']]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n config: Optional[pulumi.Input[pulumi.InputType['ConfigArgs']]] = None,\\n instance_id: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def add_resource(self, name, controller, ipaddress, ram, cpus, storage, owner=None, flavor='', compute=None, huge_pages=False):\\n if compute is None: compute = controller\\n args = { 'vm': name,\\n 'controller': controller,\\n 'ipaddress': ipaddress,\\n 'ram': ram,\\n 'cpus': cpus,\\n 'storage': storage,\\n 'flavor': flavor,\\n 'compute': compute,\\n 'hugepages': huge_pages,\\n }\\n if owner is not None:\\n args['owner'] = owner\\n self._NDL_API('addresource', args, None)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n management_cluster: Optional[pulumi.Input[pulumi.InputType['PrivateCloudManagementClusterArgs']]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n network_config: Optional[pulumi.Input[pulumi.InputType['PrivateCloudNetworkConfigArgs']]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n metadata: Optional[pulumi.Input[pulumi.InputType['SyntheticsPrivateLocationMetadataArgs']]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ProtectionContainerMappingArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n instance_id: Optional[pulumi.Input[int]] = None,\\n __props__=None):\\n ...\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6695489","0.65566033","0.6527681","0.63303506","0.61349356","0.6096821","0.6095365","0.6082456","0.60742486","0.6072159","0.6070179","0.60697085","0.6067538","0.6065695","0.60196674","0.60108477","0.59737706","0.59575313","0.59364146","0.5927485","0.5908221","0.587884","0.5864971","0.5854595","0.5852691","0.5818908","0.5812263","0.5801604","0.57875985","0.5774461","0.5774461","0.5774461","0.5774461","0.5774461","0.5774461","0.5769677","0.5763825","0.5763176","0.5757273","0.57554984","0.57504493","0.57480097","0.5716022","0.57149774","0.57082474","0.57011783","0.56961095","0.5695902","0.5689477","0.5689477","0.5689477","0.5687453","0.5687453","0.56830776","0.5680007","0.567756","0.5675721","0.56730896","0.5672778","0.5666744","0.5654419","0.5644919","0.5642726","0.5641352","0.5632817","0.56241953","0.56221503","0.5618592","0.5616002","0.56133914","0.55994225","0.5593165","0.5592439","0.55879223","0.55868566","0.55740327","0.5572936","0.55708146","0.5566437","0.55653894","0.5564775","0.556362","0.5562706","0.5558941","0.5558306","0.55373204","0.5535603","0.5530817","0.5527701","0.5526485","0.5524204","0.5523932","0.551362","0.5504651","0.5502196","0.5496499","0.5495147","0.5493566","0.54930615","0.5490511"],"string":"[\n \"0.6695489\",\n \"0.65566033\",\n \"0.6527681\",\n \"0.63303506\",\n \"0.61349356\",\n \"0.6096821\",\n \"0.6095365\",\n \"0.6082456\",\n \"0.60742486\",\n \"0.6072159\",\n \"0.6070179\",\n \"0.60697085\",\n \"0.6067538\",\n \"0.6065695\",\n \"0.60196674\",\n \"0.60108477\",\n \"0.59737706\",\n \"0.59575313\",\n \"0.59364146\",\n \"0.5927485\",\n \"0.5908221\",\n \"0.587884\",\n \"0.5864971\",\n \"0.5854595\",\n \"0.5852691\",\n \"0.5818908\",\n \"0.5812263\",\n \"0.5801604\",\n \"0.57875985\",\n \"0.5774461\",\n \"0.5774461\",\n \"0.5774461\",\n \"0.5774461\",\n \"0.5774461\",\n \"0.5774461\",\n \"0.5769677\",\n \"0.5763825\",\n \"0.5763176\",\n \"0.5757273\",\n \"0.57554984\",\n \"0.57504493\",\n \"0.57480097\",\n \"0.5716022\",\n \"0.57149774\",\n \"0.57082474\",\n \"0.57011783\",\n \"0.56961095\",\n \"0.5695902\",\n \"0.5689477\",\n \"0.5689477\",\n \"0.5689477\",\n \"0.5687453\",\n \"0.5687453\",\n \"0.56830776\",\n \"0.5680007\",\n \"0.567756\",\n \"0.5675721\",\n \"0.56730896\",\n \"0.5672778\",\n \"0.5666744\",\n \"0.5654419\",\n \"0.5644919\",\n \"0.5642726\",\n \"0.5641352\",\n \"0.5632817\",\n \"0.56241953\",\n \"0.56221503\",\n \"0.5618592\",\n \"0.5616002\",\n \"0.56133914\",\n \"0.55994225\",\n \"0.5593165\",\n \"0.5592439\",\n \"0.55879223\",\n \"0.55868566\",\n \"0.55740327\",\n \"0.5572936\",\n \"0.55708146\",\n \"0.5566437\",\n \"0.55653894\",\n \"0.5564775\",\n \"0.556362\",\n \"0.5562706\",\n \"0.5558941\",\n \"0.5558306\",\n \"0.55373204\",\n \"0.5535603\",\n \"0.5530817\",\n \"0.5527701\",\n \"0.5526485\",\n \"0.5524204\",\n \"0.5523932\",\n \"0.551362\",\n \"0.5504651\",\n \"0.5502196\",\n \"0.5496499\",\n \"0.5495147\",\n \"0.5493566\",\n \"0.54930615\",\n \"0.5490511\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7768747"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22378,"cells":{"query":{"kind":"string","value":"Wrapper around k8s.load_and_create_resource to create a Adopoted resource"},"document":{"kind":"string","value":"def create_adopted_resource(replacements, namespace=\"default\"):\n\n reference, spec, resource = k8s.load_and_create_resource(\n resource_directory,\n ADOPTED_RESOURCE_CRD_GROUP,\n CRD_VERSION,\n \"adoptedresources\",\n replacements[\"ADOPTED_RESOURCE_NAME\"],\n \"adopted_resource_base\",\n replacements,\n namespace,\n )\n\n return reference, spec, resource"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_resource(\n service_name: str, config_name: str = None, **resource_args\n):\n session = get_session(config_name)\n return session.resource(service_name, **resource_args)","def create_sagemaker_resource(\n resource_plural, resource_name, spec_file, replacements, namespace=\"default\"\n):\n\n reference, spec, resource = k8s.load_and_create_resource(\n resource_directory,\n CRD_GROUP,\n CRD_VERSION,\n resource_plural,\n resource_name,\n spec_file,\n replacements,\n namespace,\n )\n\n return reference, spec, resource","def create(ctx, iface, resource_config, params, **_):\n\n lb_name = params.get(LB_NAME)\n if not lb_name:\n targs = \\\n utils.find_rels_by_node_type(\n ctx.instance,\n LB_TYPE)\n lb_name = \\\n targs[0].target.instance.runtime_properties[\n EXTERNAL_RESOURCE_ID]\n params.update({LB_NAME: lb_name})\n\n ctx.instance.runtime_properties[LB_NAME] = \\\n lb_name\n\n # Actually create the resource\n iface.create(params)","def create_sticky(ctx, iface, resource_config, **_):\n\n # Create a copy of the resource config for clean manipulation.\n params = \\\n dict() if not resource_config else resource_config.copy()\n\n lb_name = params.get(LB_NAME)\n policy_name = params.get(RESOURCE_NAME)\n\n if not lb_name:\n targs = \\\n utils.find_rels_by_node_type(\n ctx.instance,\n LB_TYPE)\n lb_name = \\\n targs[0].target.instance.runtime_properties[\n EXTERNAL_RESOURCE_ID]\n params.update({LB_NAME: lb_name})\n\n ctx.instance.runtime_properties[LB_NAME] = \\\n lb_name\n ctx.instance.runtime_properties[RESOURCE_NAME] = \\\n policy_name\n\n # Actually create the resource\n iface.create_sticky(params)","def create_resource():\n return wsgi.Resource(Controller(), serializer=ImageSerialize())","def create_resource(self, **kwargs):\n results = self.api.action.resource_create(**kwargs)\n # TODO: use `results` rather than re-download, using an isolation layer to standardize the re-structure\n self.get_ckan_metadata(True)\n if 'id' in results:\n self._import_resource_to_cache(kwargs['upload'], results['id'])\n return results","def create_resource():\n return wsgi.Resource(Controller())","def add_resource(client, api_id, parent_resource, sub_path):\n response = client.create_resource(\n restApiId=api_id,\n parentId=parent_resource['id'],\n pathPart=sub_path)\n file_name = \"{0}_resource.pickle\".format(sub_path)\n pickle_dictionary_to_file(response, file_name)","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\n arn: Optional[pulumi.Input[str]] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n blueprint_id: Optional[pulumi.Input[str]] = None,\n bundle_id: Optional[pulumi.Input[str]] = None,\n cpu_count: Optional[pulumi.Input[int]] = None,\n created_at: Optional[pulumi.Input[str]] = None,\n ip_address_type: Optional[pulumi.Input[str]] = None,\n ipv6_address: Optional[pulumi.Input[str]] = None,\n ipv6_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n is_static_ip: Optional[pulumi.Input[bool]] = None,\n key_pair_name: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n private_ip_address: Optional[pulumi.Input[str]] = None,\n public_ip_address: Optional[pulumi.Input[str]] = None,\n ram_size: Optional[pulumi.Input[float]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n user_data: Optional[pulumi.Input[str]] = None,\n username: Optional[pulumi.Input[str]] = None) -> 'Instance':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _InstanceState.__new__(_InstanceState)\n\n __props__.__dict__[\"add_on\"] = add_on\n __props__.__dict__[\"arn\"] = arn\n __props__.__dict__[\"availability_zone\"] = availability_zone\n __props__.__dict__[\"blueprint_id\"] = blueprint_id\n __props__.__dict__[\"bundle_id\"] = bundle_id\n __props__.__dict__[\"cpu_count\"] = cpu_count\n __props__.__dict__[\"created_at\"] = created_at\n __props__.__dict__[\"ip_address_type\"] = ip_address_type\n __props__.__dict__[\"ipv6_address\"] = ipv6_address\n __props__.__dict__[\"ipv6_addresses\"] = ipv6_addresses\n __props__.__dict__[\"is_static_ip\"] = is_static_ip\n __props__.__dict__[\"key_pair_name\"] = key_pair_name\n __props__.__dict__[\"name\"] = name\n __props__.__dict__[\"private_ip_address\"] = private_ip_address\n __props__.__dict__[\"public_ip_address\"] = public_ip_address\n __props__.__dict__[\"ram_size\"] = ram_size\n __props__.__dict__[\"tags\"] = tags\n __props__.__dict__[\"tags_all\"] = tags_all\n __props__.__dict__[\"user_data\"] = user_data\n __props__.__dict__[\"username\"] = username\n return Instance(resource_name, opts=opts, __props__=__props__)","def __init__(__self__, resource_name, opts=None, allocated_capacity=None, command=None, connections=None, default_arguments=None, description=None, execution_property=None, glue_version=None, max_capacity=None, max_retries=None, name=None, number_of_workers=None, role_arn=None, security_configuration=None, tags=None, timeout=None, worker_type=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['allocated_capacity'] = allocated_capacity\n if command is None:\n raise TypeError(\"Missing required property 'command'\")\n __props__['command'] = command\n __props__['connections'] = connections\n __props__['default_arguments'] = default_arguments\n __props__['description'] = description\n __props__['execution_property'] = execution_property\n __props__['glue_version'] = glue_version\n __props__['max_capacity'] = max_capacity\n __props__['max_retries'] = max_retries\n __props__['name'] = name\n __props__['number_of_workers'] = number_of_workers\n if role_arn is None:\n raise TypeError(\"Missing required property 'role_arn'\")\n __props__['role_arn'] = role_arn\n __props__['security_configuration'] = security_configuration\n __props__['tags'] = tags\n __props__['timeout'] = timeout\n __props__['worker_type'] = worker_type\n __props__['arn'] = None\n super(Job, __self__).__init__(\n 'aws:glue/job:Job',\n resource_name,\n __props__,\n opts)","def create_resource():\n deserializer = wsgi.JSONRequestDeserializer()\n serializer = wsgi.JSONResponseSerializer()\n return wsgi.Resource(Controller(), deserializer, serializer)","def pre_namespace_create(self, resource_dict):\n pass","def __init__(__self__,\n resource_name: str,\n args: ObjectStorageKeyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: RuntimeArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def create(self, resource_name, data_dict):\n try:\n resource_cls = getattr(self, resource_name).resource\n except AttributeError:\n raise AttributeError(\"No resource named %s is defined.\" % resource_name)\n\n return resource_cls.create(self, data_dict)","def create_resource():\n #deserializer = ImageDeserializer()\n #serializer = ImageSerializer()\n return wsgi.Resource(Controller())","def create(ctx, iface, resource_config, **_):\n resource_id = \\\n utils.get_resource_id(\n ctx.node,\n ctx.instance,\n resource_config.get(VPN_CONNECTION_ID),\n use_instance_id=True\n )\n utils.update_resource_id(ctx.instance, resource_id)\n # Actually create the resource\n create_response = iface.create(resource_config)\n ctx.instance.runtime_properties['create_response'] = \\\n utils.JsonCleanuper(create_response).to_dict()\n ctx.instance.runtime_properties['VPN_CONNECTION_ID'] = \\\n resource_config.get(VPN_CONNECTION_ID)\n ctx.instance.runtime_properties['DESTINATION_CIDR_BLOCK'] = \\\n resource_config.get(DESTINATION_CIDR_BLOCK)","def __init__(__self__, resource_name, opts=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['block_device_mappings'] = block_device_mappings\n __props__['capacity_reservation_specification'] = capacity_reservation_specification\n __props__['credit_specification'] = credit_specification\n __props__['description'] = description\n __props__['disable_api_termination'] = disable_api_termination\n __props__['ebs_optimized'] = ebs_optimized\n __props__['elastic_gpu_specifications'] = elastic_gpu_specifications\n __props__['elastic_inference_accelerator'] = elastic_inference_accelerator\n __props__['iam_instance_profile'] = iam_instance_profile\n __props__['image_id'] = image_id\n __props__['instance_initiated_shutdown_behavior'] = instance_initiated_shutdown_behavior\n __props__['instance_market_options'] = instance_market_options\n __props__['instance_type'] = instance_type\n __props__['kernel_id'] = kernel_id\n __props__['key_name'] = key_name\n __props__['license_specifications'] = license_specifications\n __props__['monitoring'] = monitoring\n __props__['name'] = name\n __props__['name_prefix'] = name_prefix\n __props__['network_interfaces'] = network_interfaces\n __props__['placement'] = placement\n __props__['ram_disk_id'] = ram_disk_id\n __props__['security_group_names'] = security_group_names\n __props__['tag_specifications'] = tag_specifications\n __props__['tags'] = tags\n __props__['user_data'] = user_data\n __props__['vpc_security_group_ids'] = vpc_security_group_ids\n __props__['arn'] = None\n __props__['default_version'] = None\n __props__['latest_version'] = None\n super(LaunchTemplate, __self__).__init__(\n 'aws:ec2/launchTemplate:LaunchTemplate',\n resource_name,\n __props__,\n opts)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n blueprint_id: Optional[pulumi.Input[str]] = None,\n bundle_id: Optional[pulumi.Input[str]] = None,\n ip_address_type: Optional[pulumi.Input[str]] = None,\n key_pair_name: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n user_data: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def add_resource(self, name, controller, ipaddress, ram, cpus, storage, owner=None, flavor='', compute=None, huge_pages=False):\n if compute is None: compute = controller\n args = { 'vm': name,\n 'controller': controller,\n 'ipaddress': ipaddress,\n 'ram': ram,\n 'cpus': cpus,\n 'storage': storage,\n 'flavor': flavor,\n 'compute': compute,\n 'hugepages': huge_pages,\n }\n if owner is not None:\n args['owner'] = owner\n self._NDL_API('addresource', args, None)","def __init__(__self__,\n resource_name: str,\n args: Optional[InstanceArgs] = None,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def _create_resource(resource, **options):\n global _existing\n\n if _existing[resource]:\n print('{r} {k}:{v} already exists with id {i}.'.format(\n r=resource,\n k=args.tag,\n v=args.role,\n i=_existing[resource].id\n ))\n return True\n\n print('{v} a {r} with parameters: {p}...'.format(\n v='Would create' if dry else 'Creating',\n r=resource,\n p=str(options)\n ))\n\n if dry:\n return True\n\n # All easy cases out of the way, we now need to actually create something.\n r = None\n try:\n r = getattr(ec2, definitions[resource].create)(** options)\n # In some cases (instance) a list is returned instead of one item. Quack!\n try:\n r = r[0]\n except:\n pass\n _tag_resource(r)\n print('... {r} id {i} created.'.format(\n r=resource,\n i=r.id\n ))\n _existing[resource] = r\n return True\n except Exception as e:\n if r is None:\n print('Could not create resource {r}.'.format(\n r=resource\n ))\n traceback.print_exc()\n else:\n print('Could not tag resource {r}, id {i}.'.format(\n r=resource,\n i=r.id\n ))\n traceback.print_exc()\n _destroy_resource(resource)\n return False","def __init__(__self__, resource_name, opts=None, destination_cidr_block=None, destination_ipv6_cidr_block=None, egress_only_gateway_id=None, gateway_id=None, instance_id=None, nat_gateway_id=None, network_interface_id=None, route_table_id=None, transit_gateway_id=None, vpc_peering_connection_id=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n __props__['destination_cidr_block'] = destination_cidr_block\n __props__['destination_ipv6_cidr_block'] = destination_ipv6_cidr_block\n __props__['egress_only_gateway_id'] = egress_only_gateway_id\n __props__['gateway_id'] = gateway_id\n __props__['instance_id'] = instance_id\n __props__['nat_gateway_id'] = nat_gateway_id\n __props__['network_interface_id'] = network_interface_id\n if route_table_id is None:\n raise TypeError(\"Missing required property 'route_table_id'\")\n __props__['route_table_id'] = route_table_id\n __props__['transit_gateway_id'] = transit_gateway_id\n __props__['vpc_peering_connection_id'] = vpc_peering_connection_id\n __props__['destination_prefix_list_id'] = None\n __props__['instance_owner_id'] = None\n __props__['origin'] = None\n __props__['state'] = None\n super(Route, __self__).__init__(\n 'aws:ec2/route:Route',\n resource_name,\n __props__,\n opts)","def resource_create(resource_id, resource_type, resource_options=None, cibfile=None):\n return item_create(\n item=\"resource\",\n item_id=resource_id,\n item_type=resource_type,\n extra_args=resource_options,\n cibfile=cibfile,\n )","def create(self, validated_data):\n resource = Resource.objects.create(**validated_data.get(\"resource\"))\n return Secret.objects.create(resource=resource)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n dry_run: Optional[pulumi.Input[bool]] = None,\n load_balancer_id: Optional[pulumi.Input[str]] = None,\n security_group_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__, resource_name, opts=None, client_id=None, client_secret=None, client_timeout=None, initial_login=None, password=None, realm=None, root_ca_certificate=None, tls_insecure_skip_verify=None, url=None, username=None, __props__=None, __name__=None, __opts__=None):\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n if client_id is None:\n client_id = utilities.get_env('KEYCLOAK_CLIENT_ID')\n __props__['client_id'] = client_id\n if client_secret is None:\n client_secret = utilities.get_env('KEYCLOAK_CLIENT_SECRET')\n __props__['client_secret'] = client_secret\n if client_timeout is None:\n client_timeout = (utilities.get_env_int('KEYCLOAK_CLIENT_TIMEOUT') or 5)\n __props__['client_timeout'] = pulumi.Output.from_input(client_timeout).apply(json.dumps) if client_timeout is not None else None\n __props__['initial_login'] = pulumi.Output.from_input(initial_login).apply(json.dumps) if initial_login is not None else None\n if password is None:\n password = utilities.get_env('KEYCLOAK_PASSWORD')\n __props__['password'] = password\n if realm is None:\n realm = (utilities.get_env('KEYCLOAK_REALM') or 'master')\n __props__['realm'] = realm\n __props__['root_ca_certificate'] = root_ca_certificate\n __props__['tls_insecure_skip_verify'] = pulumi.Output.from_input(tls_insecure_skip_verify).apply(json.dumps) if tls_insecure_skip_verify is not None else None\n if url is None:\n url = utilities.get_env('KEYCLOAK_URL')\n __props__['url'] = url\n if username is None:\n username = utilities.get_env('KEYCLOAK_USER')\n __props__['username'] = username\n super(Provider, __self__).__init__(\n 'keycloak',\n resource_name,\n __props__,\n opts)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n description: Optional[pulumi.Input[str]] = None,\n etag: Optional[pulumi.Input[str]] = None,\n file_shares: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FileShareConfigArgs']]]]] = None,\n instance_id: Optional[pulumi.Input[str]] = None,\n kms_key_name: Optional[pulumi.Input[str]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['NetworkConfigArgs']]]]] = None,\n project: Optional[pulumi.Input[str]] = None,\n tier: Optional[pulumi.Input['InstanceTier']] = None,\n __props__=None):\n ...","def GenerateAssetForCreateRequest(args):\n module = dataplex_api.GetMessageModule()\n resource_spec_field = module.GoogleCloudDataplexV1AssetResourceSpec\n resource_spec = module.GoogleCloudDataplexV1AssetResourceSpec(\n name=args.resource_name,\n type=resource_spec_field.TypeValueValuesEnum(args.resource_type),\n )\n request = module.GoogleCloudDataplexV1Asset(\n description=args.description,\n displayName=args.display_name,\n labels=dataplex_api.CreateLabels(module.GoogleCloudDataplexV1Asset, args),\n resourceSpec=resource_spec,\n )\n discovery = GenerateDiscoverySpec(args)\n if discovery != module.GoogleCloudDataplexV1AssetDiscoverySpec():\n setattr(request, 'discoverySpec', discovery)\n return request","def __init__(__self__,\n resource_name: str,\n args: Optional[TargetPoolArgs] = None,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def pre_routing_instance_create(self, resource_dict):\n pass","def pre_service_instance_create(self, resource_dict):\n pass","def __init__(__self__,\n resource_name: str,\n args: PrefixArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def GenerateAssetForCreateRequestAlpha(args):\n module = dataplex_api.GetMessageModule()\n resource_spec_field = module.GoogleCloudDataplexV1AssetResourceSpec\n resource_spec = module.GoogleCloudDataplexV1AssetResourceSpec(\n name=args.resource_name,\n type=resource_spec_field.TypeValueValuesEnum(args.resource_type),\n )\n if args.IsSpecified('resource_read_access_mode'):\n resource_spec.readAccessMode = (\n resource_spec_field.ReadAccessModeValueValuesEnum(\n args.resource_read_access_mode\n )\n )\n request = module.GoogleCloudDataplexV1Asset(\n description=args.description,\n displayName=args.display_name,\n labels=dataplex_api.CreateLabels(module.GoogleCloudDataplexV1Asset, args),\n resourceSpec=resource_spec)\n discovery = GenerateDiscoverySpec(args)\n if discovery != module.GoogleCloudDataplexV1AssetDiscoverySpec():\n setattr(request, 'discoverySpec', discovery)\n return request","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_scaling_configuration_arn: Optional[pulumi.Input[str]] = None,\n encryption_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceEncryptionConfigurationArgs']]] = None,\n health_check_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceHealthCheckConfigurationArgs']]] = None,\n instance_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceInstanceConfigurationArgs']]] = None,\n network_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceNetworkConfigurationArgs']]] = None,\n observability_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceObservabilityConfigurationArgs']]] = None,\n service_name: Optional[pulumi.Input[str]] = None,\n source_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceSourceConfigurationArgs']]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def get(resource_name, id, opts=None, arn=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, default_version=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, latest_version=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None):\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = dict()\n __props__[\"arn\"] = arn\n __props__[\"block_device_mappings\"] = block_device_mappings\n __props__[\"capacity_reservation_specification\"] = capacity_reservation_specification\n __props__[\"credit_specification\"] = credit_specification\n __props__[\"default_version\"] = default_version\n __props__[\"description\"] = description\n __props__[\"disable_api_termination\"] = disable_api_termination\n __props__[\"ebs_optimized\"] = ebs_optimized\n __props__[\"elastic_gpu_specifications\"] = elastic_gpu_specifications\n __props__[\"elastic_inference_accelerator\"] = elastic_inference_accelerator\n __props__[\"iam_instance_profile\"] = iam_instance_profile\n __props__[\"image_id\"] = image_id\n __props__[\"instance_initiated_shutdown_behavior\"] = instance_initiated_shutdown_behavior\n __props__[\"instance_market_options\"] = instance_market_options\n __props__[\"instance_type\"] = instance_type\n __props__[\"kernel_id\"] = kernel_id\n __props__[\"key_name\"] = key_name\n __props__[\"latest_version\"] = latest_version\n __props__[\"license_specifications\"] = license_specifications\n __props__[\"monitoring\"] = monitoring\n __props__[\"name\"] = name\n __props__[\"name_prefix\"] = name_prefix\n __props__[\"network_interfaces\"] = network_interfaces\n __props__[\"placement\"] = placement\n __props__[\"ram_disk_id\"] = ram_disk_id\n __props__[\"security_group_names\"] = security_group_names\n __props__[\"tag_specifications\"] = tag_specifications\n __props__[\"tags\"] = tags\n __props__[\"user_data\"] = user_data\n __props__[\"vpc_security_group_ids\"] = vpc_security_group_ids\n return LaunchTemplate(resource_name, opts=opts, __props__=__props__)","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: InstanceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n enabled: Optional[pulumi.Input[bool]] = None,\n flow_analytics_configuration: Optional[pulumi.Input[pulumi.InputType['TrafficAnalyticsPropertiesArgs']]] = None,\n flow_log_name: Optional[pulumi.Input[str]] = None,\n format: Optional[pulumi.Input[pulumi.InputType['FlowLogFormatParametersArgs']]] = None,\n id: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n network_watcher_name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n retention_policy: Optional[pulumi.Input[pulumi.InputType['RetentionPolicyParametersArgs']]] = None,\n storage_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n target_resource_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def create(self, resource, timeout=None):\n req = ResourceCreateRequest()\n\n if resource is not None:\n req.resource.CopyFrom(\n plumbing.convert_resource_to_plumbing(resource))\n tries = 0\n plumbing_response = None\n while True:\n try:\n plumbing_response = self.stub.Create(\n req,\n metadata=self.parent.get_metadata('Resources.Create', req),\n timeout=timeout)\n except Exception as e:\n if self.parent.shouldRetry(tries, e):\n tries += 1\n self.parent.jitterSleep(tries)\n continue\n raise plumbing.convert_error_to_porcelain(e) from e\n break\n\n resp = models.ResourceCreateResponse()\n resp.meta = plumbing.convert_create_response_metadata_to_porcelain(\n plumbing_response.meta)\n resp.resource = plumbing.convert_resource_to_porcelain(\n plumbing_response.resource)\n resp.rate_limit = plumbing.convert_rate_limit_metadata_to_porcelain(\n plumbing_response.rate_limit)\n return resp","def create():\n\n # remember what is created or not\n vpc = False\n igw = False\n sg = False\n sub = False\n vm = False\n\n vpc = _create_resource('vpc', CidrBlock=args.cidr, InstanceTenancy='default')\n igw = _create_resource('igw')\n\n if vpc and igw:\n _attach_vpc_igw(vpc=_existing.vpc, igw=_existing.igw)\n else:\n print('Cannot attach an igw to a vpc as at least one of them could not be created.')\n\n if vpc:\n sg = _create_resource(\n 'sg',\n GroupName=args.role,\n Description='SG for ' + args.role,\n VpcId=getattr(_existing.vpc, 'id', None)\n )\n else:\n print('Cannot create a sg as the vpc to attach it to could not be created.')\n\n if sg:\n _add_ingress_rules()\n else:\n print('Cannot create ingress rule as the sg could not be created.')\n\n if vpc:\n sub = _create_resource(\n 'sub',\n VpcId=getattr(_existing.vpc, 'id', None),\n CidrBlock=args.cidr\n )\n else:\n print('Cannot create a subnet as the vpc to attach it to could not be created.')\n\n if vpc and sub:\n _link_route_table()\n else:\n print('Cannot link subnet and VPC in the route table as vpc or sub not created.')\n\n if sub and sg:\n vm = _create_resource(\n 'vm',\n ImageId=args.ami,\n MinCount=1,\n MaxCount=1,\n KeyName=args.keypair,\n InstanceType=args.instance,\n # Note that there will be no internal name.\n # To get one, create first a DHCP options set and associate it with the VPC.\n NetworkInterfaces=[{\n 'AssociatePublicIpAddress': True,\n 'DeviceIndex': 0, # needs to be 0 to get a public IP\n 'SubnetId': getattr(_existing.sub, 'id', None),\n 'Groups': [getattr(_existing.sg, 'id', None)],\n }],\n )\n else:\n print('Cannot create an instance as the sub or sg to use could not be created.')\n\n if vm:\n if not dry:\n print('Waiting for the instance to be up and running, usually done in less than 45 seconds...')\n _existing.vm.wait_until_running()\n _tag_volume()\n print('you can reach your VM at ' + _existing.vm.public_ip_address)\n\n else:\n print('VM not created for some reason.')","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n backend_type: Optional[pulumi.Input['InstanceBackendType']] = None,\n connection_name: Optional[pulumi.Input[str]] = None,\n current_disk_size: Optional[pulumi.Input[str]] = None,\n database_version: Optional[pulumi.Input['InstanceDatabaseVersion']] = None,\n disk_encryption_configuration: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionConfigurationArgs']]] = None,\n disk_encryption_status: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionStatusArgs']]] = None,\n etag: Optional[pulumi.Input[str]] = None,\n failover_replica: Optional[pulumi.Input[pulumi.InputType['InstanceFailoverReplicaArgs']]] = None,\n gce_zone: Optional[pulumi.Input[str]] = None,\n instance_type: Optional[pulumi.Input['InstanceInstanceType']] = None,\n ip_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpMappingArgs']]]]] = None,\n ipv6_address: Optional[pulumi.Input[str]] = None,\n kind: Optional[pulumi.Input[str]] = None,\n maintenance_version: Optional[pulumi.Input[str]] = None,\n master_instance_name: Optional[pulumi.Input[str]] = None,\n max_disk_size: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n on_premises_configuration: Optional[pulumi.Input[pulumi.InputType['OnPremisesConfigurationArgs']]] = None,\n out_of_disk_report: Optional[pulumi.Input[pulumi.InputType['SqlOutOfDiskReportArgs']]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n replica_configuration: Optional[pulumi.Input[pulumi.InputType['ReplicaConfigurationArgs']]] = None,\n replica_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n root_password: Optional[pulumi.Input[str]] = None,\n satisfies_pzs: Optional[pulumi.Input[bool]] = None,\n scheduled_maintenance: Optional[pulumi.Input[pulumi.InputType['SqlScheduledMaintenanceArgs']]] = None,\n secondary_gce_zone: Optional[pulumi.Input[str]] = None,\n self_link: Optional[pulumi.Input[str]] = None,\n server_ca_cert: Optional[pulumi.Input[pulumi.InputType['SslCertArgs']]] = None,\n service_account_email_address: Optional[pulumi.Input[str]] = None,\n settings: Optional[pulumi.Input[pulumi.InputType['SettingsArgs']]] = None,\n state: Optional[pulumi.Input['InstanceState']] = None,\n suspension_reason: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceSuspensionReasonItem']]]] = None,\n __props__=None):\n ...","def get(resource_name: str,\n id: pulumi.Input[str],\n opts: Optional[pulumi.ResourceOptions] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n connection_string: Optional[pulumi.Input[str]] = None,\n create_sample_data: Optional[pulumi.Input[bool]] = None,\n db_instance_category: Optional[pulumi.Input[str]] = None,\n db_instance_class: Optional[pulumi.Input[str]] = None,\n db_instance_mode: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n encryption_key: Optional[pulumi.Input[str]] = None,\n encryption_type: Optional[pulumi.Input[str]] = None,\n engine: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n instance_charge_type: Optional[pulumi.Input[str]] = None,\n instance_group_count: Optional[pulumi.Input[int]] = None,\n instance_network_type: Optional[pulumi.Input[str]] = None,\n instance_spec: Optional[pulumi.Input[str]] = None,\n ip_whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceIpWhitelistArgs']]]]] = None,\n maintain_end_time: Optional[pulumi.Input[str]] = None,\n maintain_start_time: Optional[pulumi.Input[str]] = None,\n master_node_num: Optional[pulumi.Input[int]] = None,\n payment_type: Optional[pulumi.Input[str]] = None,\n period: Optional[pulumi.Input[str]] = None,\n port: Optional[pulumi.Input[str]] = None,\n private_ip_address: Optional[pulumi.Input[str]] = None,\n resource_group_id: Optional[pulumi.Input[str]] = None,\n security_ip_lists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n seg_node_num: Optional[pulumi.Input[int]] = None,\n seg_storage_type: Optional[pulumi.Input[str]] = None,\n ssl_enabled: Optional[pulumi.Input[int]] = None,\n status: Optional[pulumi.Input[str]] = None,\n storage_size: Optional[pulumi.Input[int]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n used_time: Optional[pulumi.Input[str]] = None,\n vector_configuration_status: Optional[pulumi.Input[str]] = None,\n vpc_id: Optional[pulumi.Input[str]] = None,\n vswitch_id: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None) -> 'Instance':\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\n\n __props__ = _InstanceState.__new__(_InstanceState)\n\n __props__.__dict__[\"availability_zone\"] = availability_zone\n __props__.__dict__[\"connection_string\"] = connection_string\n __props__.__dict__[\"create_sample_data\"] = create_sample_data\n __props__.__dict__[\"db_instance_category\"] = db_instance_category\n __props__.__dict__[\"db_instance_class\"] = db_instance_class\n __props__.__dict__[\"db_instance_mode\"] = db_instance_mode\n __props__.__dict__[\"description\"] = description\n __props__.__dict__[\"encryption_key\"] = encryption_key\n __props__.__dict__[\"encryption_type\"] = encryption_type\n __props__.__dict__[\"engine\"] = engine\n __props__.__dict__[\"engine_version\"] = engine_version\n __props__.__dict__[\"instance_charge_type\"] = instance_charge_type\n __props__.__dict__[\"instance_group_count\"] = instance_group_count\n __props__.__dict__[\"instance_network_type\"] = instance_network_type\n __props__.__dict__[\"instance_spec\"] = instance_spec\n __props__.__dict__[\"ip_whitelists\"] = ip_whitelists\n __props__.__dict__[\"maintain_end_time\"] = maintain_end_time\n __props__.__dict__[\"maintain_start_time\"] = maintain_start_time\n __props__.__dict__[\"master_node_num\"] = master_node_num\n __props__.__dict__[\"payment_type\"] = payment_type\n __props__.__dict__[\"period\"] = period\n __props__.__dict__[\"port\"] = port\n __props__.__dict__[\"private_ip_address\"] = private_ip_address\n __props__.__dict__[\"resource_group_id\"] = resource_group_id\n __props__.__dict__[\"security_ip_lists\"] = security_ip_lists\n __props__.__dict__[\"seg_node_num\"] = seg_node_num\n __props__.__dict__[\"seg_storage_type\"] = seg_storage_type\n __props__.__dict__[\"ssl_enabled\"] = ssl_enabled\n __props__.__dict__[\"status\"] = status\n __props__.__dict__[\"storage_size\"] = storage_size\n __props__.__dict__[\"tags\"] = tags\n __props__.__dict__[\"used_time\"] = used_time\n __props__.__dict__[\"vector_configuration_status\"] = vector_configuration_status\n __props__.__dict__[\"vpc_id\"] = vpc_id\n __props__.__dict__[\"vswitch_id\"] = vswitch_id\n __props__.__dict__[\"zone_id\"] = zone_id\n return Instance(resource_name, opts=opts, __props__=__props__)","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n instance_id: Optional[pulumi.Input[int]] = None,\n __props__=None):\n ...","def create_resource(self, namespace: \"str\" = None):\n names = [\"create_namespaced_csistorage_capacity\", \"create_csistorage_capacity\"]\n\n _kube_api.execute(\n action=\"create\",\n resource=self,\n names=names,\n namespace=namespace,\n api_client=None,\n api_args={\"body\": self.to_dict()},\n )","def create(ctx, resource_config, **_):\n # Create a resource (if necessary)\n ctx.logger.warn(\n 'Azure customData implementation is dependent on '\n 'Virtual Machine image support.')\n azure_config = utils.get_client_config(ctx.node.properties)\n name = utils.get_resource_name(ctx)\n resource_group_name = utils.get_resource_group(ctx)\n vm_name = ctx.node.properties['virtual_machine_name']\n vm_extension_params = {\n 'location': ctx.node.properties.get('location'),\n 'tags': ctx.node.properties.get('tags'),\n 'publisher': resource_config.get('publisher'),\n 'type_properties_type': resource_config.get('ext_type'),\n 'type_handler_version': resource_config.get('typeHandlerVersion'),\n 'settings': resource_config.get('settings'),\n }\n api_version = \\\n ctx.node.properties.get('api_version', constants.API_VER_COMPUTE)\n vm_extension = VirtualMachineExtension(azure_config, ctx.logger,\n api_version)\n # clean empty values from params\n vm_extension_params = \\\n utils.cleanup_empty_params(vm_extension_params)\n result = utils.handle_create(\n vm_extension,\n resource_group_name,\n name,\n vm_name,\n vm_extension_params)\n ctx.instance.runtime_properties['resource_group'] = resource_group_name\n ctx.instance.runtime_properties['virtual_machine'] = vm_name\n ctx.instance.runtime_properties['resource'] = result\n ctx.instance.runtime_properties['resource_id'] = result.get(\"id\", \"\")","def __init__(__self__,\n resource_name: str,\n args: KafkaConfigurationArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n client_id: Optional[pulumi.Input[str]] = None,\n client_secret: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n display_name: Optional[pulumi.Input[str]] = None,\n metadata_endpoint: Optional[pulumi.Input[str]] = None,\n opid: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n service_name: Optional[pulumi.Input[str]] = None,\n use_in_api_documentation: Optional[pulumi.Input[bool]] = None,\n use_in_test_console: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n accelerator_id: Optional[pulumi.Input[str]] = None,\n basic_endpoint_group_name: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n endpoint_address: Optional[pulumi.Input[str]] = None,\n endpoint_group_region: Optional[pulumi.Input[str]] = None,\n endpoint_sub_address: Optional[pulumi.Input[str]] = None,\n endpoint_type: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n allocation_policy: Optional[pulumi.Input[str]] = None,\n data_residency_enabled: Optional[pulumi.Input[bool]] = None,\n ip_filter_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IotHubDpsIpFilterRuleArgs']]]]] = None,\n linked_hubs: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IotHubDpsLinkedHubArgs']]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n sku: Optional[pulumi.Input[pulumi.InputType['IotHubDpsSkuArgs']]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: RegionPerInstanceConfigArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: EndpointArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n policy: Optional[pulumi.Input[str]] = None,\n resource_arn: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: Optional[ResourceDefaultVersionArgs] = None,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: TagArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def pre_virtual_DNS_create(self, resource_dict):\n pass","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n auto_renew: Optional[pulumi.Input[bool]] = None,\n auto_renew_period: Optional[pulumi.Input[int]] = None,\n cluster_name: Optional[pulumi.Input[str]] = None,\n data_center_name: Optional[pulumi.Input[str]] = None,\n disk_size: Optional[pulumi.Input[int]] = None,\n disk_type: Optional[pulumi.Input[str]] = None,\n enable_public: Optional[pulumi.Input[bool]] = None,\n instance_type: Optional[pulumi.Input[str]] = None,\n ip_white: Optional[pulumi.Input[str]] = None,\n maintain_end_time: Optional[pulumi.Input[str]] = None,\n maintain_start_time: Optional[pulumi.Input[str]] = None,\n major_version: Optional[pulumi.Input[str]] = None,\n node_count: Optional[pulumi.Input[int]] = None,\n password: Optional[pulumi.Input[str]] = None,\n pay_type: Optional[pulumi.Input[str]] = None,\n period: Optional[pulumi.Input[int]] = None,\n period_unit: Optional[pulumi.Input[str]] = None,\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n vswitch_id: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n additional_egress_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['EgressEndpointArgs']]]]] = None,\n cloud_services_network_name: Optional[pulumi.Input[str]] = None,\n enable_default_egress_endpoints: Optional[pulumi.Input[Union[str, 'CloudServicesNetworkEnableDefaultEgressEndpoints']]] = None,\n extended_location: Optional[pulumi.Input[pulumi.InputType['ExtendedLocationArgs']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n charset: Optional[pulumi.Input[str]] = None,\n collation: Optional[pulumi.Input[str]] = None,\n deletion_policy: Optional[pulumi.Input[str]] = None,\n instance: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: AppArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n kind: Optional[pulumi.Input[Union[str, 'Kind']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n managed_network_group_name: Optional[pulumi.Input[str]] = None,\n managed_network_name: Optional[pulumi.Input[str]] = None,\n management_groups: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n subnets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n subscriptions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n virtual_networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ProvisioningTemplateArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n backup_pool: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n failover_ratio: Optional[pulumi.Input[float]] = None,\n health_checks: Optional[pulumi.Input[str]] = None,\n instances: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n session_affinity: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n cache_name: Optional[pulumi.Input[str]] = None,\n end_ip: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n rule_name: Optional[pulumi.Input[str]] = None,\n start_ip: Optional[pulumi.Input[str]] = None,\n __props__=None,\n __name__=None,\n __opts__=None):\n pulumi.log.warn(\"\"\"FirewallRule is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:cache:FirewallRule'.\"\"\")\n if __name__ is not None:\n warnings.warn(\"explicit use of __name__ is deprecated\", DeprecationWarning)\n resource_name = __name__\n if __opts__ is not None:\n warnings.warn(\"explicit use of __opts__ is deprecated, use 'opts' instead\", DeprecationWarning)\n opts = __opts__\n if opts is None:\n opts = pulumi.ResourceOptions()\n if not isinstance(opts, pulumi.ResourceOptions):\n raise TypeError('Expected resource options to be a ResourceOptions instance')\n if opts.version is None:\n opts.version = _utilities.get_version()\n if opts.id is None:\n if __props__ is not None:\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\n __props__ = dict()\n\n if cache_name is None and not opts.urn:\n raise TypeError(\"Missing required property 'cache_name'\")\n __props__['cache_name'] = cache_name\n if end_ip is None and not opts.urn:\n raise TypeError(\"Missing required property 'end_ip'\")\n __props__['end_ip'] = end_ip\n if resource_group_name is None and not opts.urn:\n raise TypeError(\"Missing required property 'resource_group_name'\")\n __props__['resource_group_name'] = resource_group_name\n __props__['rule_name'] = rule_name\n if start_ip is None and not opts.urn:\n raise TypeError(\"Missing required property 'start_ip'\")\n __props__['start_ip'] = start_ip\n __props__['name'] = None\n __props__['type'] = None\n alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\"azure-nextgen:cache/latest:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache/v20160401:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache/v20160401:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache/v20170201:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache/v20170201:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache/v20171001:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache/v20171001:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache/v20180301:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache/v20180301:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache/v20190701:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache/v20190701:FirewallRule\"), pulumi.Alias(type_=\"azure-native:cache/v20200601:FirewallRule\"), pulumi.Alias(type_=\"azure-nextgen:cache/v20200601:FirewallRule\")])\n opts = pulumi.ResourceOptions.merge(opts, alias_opts)\n super(FirewallRule, __self__).__init__(\n 'azure-native:cache/latest:FirewallRule',\n resource_name,\n __props__,\n opts)","def create(self):\n raise WufooException(\"InstanceResource creation not supported\")","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n account_id: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n secret: Optional[pulumi.Input[str]] = None,\n url: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: EndpointAclPolicyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: CustomRoutingEndpointTrafficPolicyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n allow_forwarded_traffic: Optional[pulumi.Input[bool]] = None,\n allow_gateway_transit: Optional[pulumi.Input[bool]] = None,\n allow_virtual_network_access: Optional[pulumi.Input[bool]] = None,\n name: Optional[pulumi.Input[str]] = None,\n remote_address_space_prefixes: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n remote_virtual_network_id: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n use_remote_gateways: Optional[pulumi.Input[bool]] = None,\n workspace_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n agent_upgrade: Optional[pulumi.Input[pulumi.InputType['AgentUpgradeArgs']]] = None,\n client_public_key: Optional[pulumi.Input[str]] = None,\n extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['MachineExtensionInstanceViewArgs']]]]] = None,\n identity: Optional[pulumi.Input[pulumi.InputType['IdentityArgs']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n location_data: Optional[pulumi.Input[pulumi.InputType['LocationDataArgs']]] = None,\n machine_name: Optional[pulumi.Input[str]] = None,\n mssql_discovered: Optional[pulumi.Input[str]] = None,\n os_profile: Optional[pulumi.Input[pulumi.InputType['OSProfileArgs']]] = None,\n os_type: Optional[pulumi.Input[str]] = None,\n parent_cluster_resource_id: Optional[pulumi.Input[str]] = None,\n private_link_scope_resource_id: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n service_statuses: Optional[pulumi.Input[pulumi.InputType['ServiceStatusesArgs']]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n vm_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n crypto_key_backend: Optional[pulumi.Input[str]] = None,\n crypto_key_id: Optional[pulumi.Input[str]] = None,\n destroy_scheduled_duration: Optional[pulumi.Input[str]] = None,\n import_only: Optional[pulumi.Input[bool]] = None,\n key_ring_id: Optional[pulumi.Input[str]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n next_rotation_time: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n purpose: Optional[pulumi.Input['CryptoKeyPurpose']] = None,\n rotation_period: Optional[pulumi.Input[str]] = None,\n skip_initial_version_creation: Optional[pulumi.Input[bool]] = None,\n version_template: Optional[pulumi.Input[pulumi.InputType['CryptoKeyVersionTemplateArgs']]] = None,\n __props__=None):\n ...","def create_advert():\r\n advertiser, category, zone = create_objects()\r\n ad = AdBase.objects.create(\r\n title='Ad Title',\r\n url='www.example.com',\r\n advertiser=advertiser,\r\n category=category,\r\n zone=zone,\r\n )\r\n return ad","def __init__(__self__,\n resource_name: str,\n args: PrivatelinkAzureArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n create_sample_data: Optional[pulumi.Input[bool]] = None,\n db_instance_category: Optional[pulumi.Input[str]] = None,\n db_instance_class: Optional[pulumi.Input[str]] = None,\n db_instance_mode: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n encryption_key: Optional[pulumi.Input[str]] = None,\n encryption_type: Optional[pulumi.Input[str]] = None,\n engine: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n instance_charge_type: Optional[pulumi.Input[str]] = None,\n instance_group_count: Optional[pulumi.Input[int]] = None,\n instance_network_type: Optional[pulumi.Input[str]] = None,\n instance_spec: Optional[pulumi.Input[str]] = None,\n ip_whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceIpWhitelistArgs']]]]] = None,\n maintain_end_time: Optional[pulumi.Input[str]] = None,\n maintain_start_time: Optional[pulumi.Input[str]] = None,\n master_node_num: Optional[pulumi.Input[int]] = None,\n payment_type: Optional[pulumi.Input[str]] = None,\n period: Optional[pulumi.Input[str]] = None,\n private_ip_address: Optional[pulumi.Input[str]] = None,\n resource_group_id: Optional[pulumi.Input[str]] = None,\n security_ip_lists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n seg_node_num: Optional[pulumi.Input[int]] = None,\n seg_storage_type: Optional[pulumi.Input[str]] = None,\n ssl_enabled: Optional[pulumi.Input[int]] = None,\n storage_size: Optional[pulumi.Input[int]] = None,\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\n used_time: Optional[pulumi.Input[str]] = None,\n vector_configuration_status: Optional[pulumi.Input[str]] = None,\n vpc_id: Optional[pulumi.Input[str]] = None,\n vswitch_id: Optional[pulumi.Input[str]] = None,\n zone_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n cluster_id: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n private_network_uuid: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[Union[str, 'Region']]] = None,\n size: Optional[pulumi.Input[Union[str, 'DatabaseSlug']]] = None,\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n address_family: Optional[pulumi.Input[str]] = None,\n allocation_default_netmask_length: Optional[pulumi.Input[int]] = None,\n allocation_max_netmask_length: Optional[pulumi.Input[int]] = None,\n allocation_min_netmask_length: Optional[pulumi.Input[int]] = None,\n allocation_resource_tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n auto_import: Optional[pulumi.Input[bool]] = None,\n aws_service: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n ipam_scope_id: Optional[pulumi.Input[str]] = None,\n locale: Optional[pulumi.Input[str]] = None,\n public_ip_source: Optional[pulumi.Input[str]] = None,\n publicly_advertisable: Optional[pulumi.Input[bool]] = None,\n source_ipam_pool_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: PolicyArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ManagedPrivateEndpointArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def pre_loadbalancer_pool_create(self, resource_dict):\n pass","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n activation_key: Optional[pulumi.Input[str]] = None,\n ip_address: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n private_link_endpoint: Optional[pulumi.Input[str]] = None,\n security_group_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n subnet_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n vpc_endpoint_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: VirtualHardDiskArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n application_insights_id: Optional[pulumi.Input[str]] = None,\n container_registry_id: Optional[pulumi.Input[str]] = None,\n description: Optional[pulumi.Input[str]] = None,\n encryption: Optional[pulumi.Input[pulumi.InputType['WorkspaceEncryptionArgs']]] = None,\n friendly_name: Optional[pulumi.Input[str]] = None,\n high_business_impact: Optional[pulumi.Input[bool]] = None,\n identity: Optional[pulumi.Input[pulumi.InputType['WorkspaceIdentityArgs']]] = None,\n image_build_compute_name: Optional[pulumi.Input[str]] = None,\n key_vault_id: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n primary_user_assigned_identity: Optional[pulumi.Input[str]] = None,\n public_access_behind_virtual_network_enabled: Optional[pulumi.Input[bool]] = None,\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n sku_name: Optional[pulumi.Input[str]] = None,\n storage_account_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n v1_legacy_mode_enabled: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n access_config: Optional[pulumi.Input[pulumi.InputType['RuntimeAccessConfigArgs']]] = None,\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n location: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n request_id: Optional[pulumi.Input[str]] = None,\n runtime_id: Optional[pulumi.Input[str]] = None,\n software_config: Optional[pulumi.Input[pulumi.InputType['RuntimeSoftwareConfigArgs']]] = None,\n virtual_machine: Optional[pulumi.Input[pulumi.InputType['VirtualMachineArgs']]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n name: Optional[pulumi.Input[str]] = None,\n priority: Optional[pulumi.Input[str]] = None,\n ttl: Optional[pulumi.Input[str]] = None,\n type: Optional[pulumi.Input[str]] = None,\n value: Optional[pulumi.Input[str]] = None,\n zone_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def create_TestServiceResource(test_case, # type: AnyMagpieTestCaseType\n override_service_name=null, # type: Optional[Str]\n override_service_type=null, # type: Optional[Str]\n override_resource_name=null, # type: Optional[Str]\n override_resource_type=null, # type: Optional[Str]\n override_data=null, # type: Optional[JSON]\n override_headers=null, # type: Optional[HeadersType]\n override_cookies=null, # type: Optional[CookiesType]\n ): # type: (...) -> JSON\n app_or_url = get_app_or_url(test_case)\n svc_name = override_service_name if override_service_name is not null else test_case.test_service_name\n svc_type = override_service_type if override_service_type is not null else test_case.test_service_type\n TestSetup.create_TestService(test_case,\n override_service_name=svc_name, override_service_type=svc_type,\n override_headers=override_headers, override_cookies=override_cookies)\n path = \"/services/{svc}/resources\".format(svc=svc_name)\n data = override_data if override_data is not null else {\n \"resource_name\": override_resource_name or test_case.test_resource_name,\n \"resource_type\": override_resource_type or test_case.test_resource_type,\n }\n resp = test_request(app_or_url, \"POST\", path, json=data,\n headers=override_headers if override_headers is not null else test_case.json_headers,\n cookies=override_cookies if override_cookies is not null else test_case.cookies)\n return check_response_basic_info(resp, 201, expected_method=\"POST\")","def create_one(cls, attrs=None):\n attrs = attrs or {}\n\n resource = cls.generate()\n\n resource.update(attrs)\n\n return resource","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n azure_subscription_id: Optional[pulumi.Input[str]] = None,\n peer_azure_app_id: Optional[pulumi.Input[str]] = None,\n peer_azure_tenant_id: Optional[pulumi.Input[str]] = None,\n peer_resource_group: Optional[pulumi.Input[str]] = None,\n vnet_name: Optional[pulumi.Input[str]] = None,\n vpc_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def create(self, resource):\n if not resource:\n raise NoneReferenceError(\"resource to create can't be None\")\n\n if not isinstance(resource, self._resource_class):\n raise TypeError(\n \"resource %s is not %s instance\"\n % (resource, self._resource_class.__name__))\n\n return self.build_resource(\n self._os_resource_manager.create(**vars(resource)))","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n acl_name: Optional[pulumi.Input[str]] = None,\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\n data_tiering: Optional[pulumi.Input[bool]] = None,\n description: Optional[pulumi.Input[str]] = None,\n engine_version: Optional[pulumi.Input[str]] = None,\n final_snapshot_name: Optional[pulumi.Input[str]] = None,\n kms_key_arn: Optional[pulumi.Input[str]] = None,\n maintenance_window: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n name_prefix: Optional[pulumi.Input[str]] = None,\n node_type: Optional[pulumi.Input[str]] = None,\n num_replicas_per_shard: Optional[pulumi.Input[int]] = None,\n num_shards: Optional[pulumi.Input[int]] = None,\n parameter_group_name: Optional[pulumi.Input[str]] = None,\n port: Optional[pulumi.Input[int]] = None,\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n snapshot_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n snapshot_name: Optional[pulumi.Input[str]] = None,\n snapshot_retention_limit: Optional[pulumi.Input[int]] = None,\n snapshot_window: Optional[pulumi.Input[str]] = None,\n sns_topic_arn: Optional[pulumi.Input[str]] = None,\n subnet_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tls_enabled: Optional[pulumi.Input[bool]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n container_registry_name: Optional[pulumi.Input[str]] = None,\n instance_count: Optional[pulumi.Input[int]] = None,\n location: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n tier: Optional[pulumi.Input[str]] = None,\n virtual_network_subnet_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ReplicatedBucketArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n block_size_bytes: Optional[pulumi.Input[int]] = None,\n container_id: Optional[pulumi.Input[str]] = None,\n disk_file_format: Optional[pulumi.Input[Union[str, 'DiskFileFormat']]] = None,\n disk_size_gb: Optional[pulumi.Input[float]] = None,\n dynamic: Optional[pulumi.Input[bool]] = None,\n extended_location: Optional[pulumi.Input[pulumi.InputType['ExtendedLocationArgs']]] = None,\n hyper_v_generation: Optional[pulumi.Input[Union[str, 'HyperVGeneration']]] = None,\n location: Optional[pulumi.Input[str]] = None,\n logical_sector_bytes: Optional[pulumi.Input[int]] = None,\n physical_sector_bytes: Optional[pulumi.Input[int]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\n virtual_hard_disk_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def model_resource_factory(acl, obj):\r\n return _model_identity_factory(obj, resource_identity, acl.add_resource)","def __init__(__self__,\n resource_name: str,\n args: ServiceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n args: ServiceArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n data_factory_id: Optional[pulumi.Input[str]] = None,\n fqdns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n name: Optional[pulumi.Input[str]] = None,\n subresource_name: Optional[pulumi.Input[str]] = None,\n target_resource_id: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ..."],"string":"[\n \"def create_resource(\\n service_name: str, config_name: str = None, **resource_args\\n):\\n session = get_session(config_name)\\n return session.resource(service_name, **resource_args)\",\n \"def create_sagemaker_resource(\\n resource_plural, resource_name, spec_file, replacements, namespace=\\\"default\\\"\\n):\\n\\n reference, spec, resource = k8s.load_and_create_resource(\\n resource_directory,\\n CRD_GROUP,\\n CRD_VERSION,\\n resource_plural,\\n resource_name,\\n spec_file,\\n replacements,\\n namespace,\\n )\\n\\n return reference, spec, resource\",\n \"def create(ctx, iface, resource_config, params, **_):\\n\\n lb_name = params.get(LB_NAME)\\n if not lb_name:\\n targs = \\\\\\n utils.find_rels_by_node_type(\\n ctx.instance,\\n LB_TYPE)\\n lb_name = \\\\\\n targs[0].target.instance.runtime_properties[\\n EXTERNAL_RESOURCE_ID]\\n params.update({LB_NAME: lb_name})\\n\\n ctx.instance.runtime_properties[LB_NAME] = \\\\\\n lb_name\\n\\n # Actually create the resource\\n iface.create(params)\",\n \"def create_sticky(ctx, iface, resource_config, **_):\\n\\n # Create a copy of the resource config for clean manipulation.\\n params = \\\\\\n dict() if not resource_config else resource_config.copy()\\n\\n lb_name = params.get(LB_NAME)\\n policy_name = params.get(RESOURCE_NAME)\\n\\n if not lb_name:\\n targs = \\\\\\n utils.find_rels_by_node_type(\\n ctx.instance,\\n LB_TYPE)\\n lb_name = \\\\\\n targs[0].target.instance.runtime_properties[\\n EXTERNAL_RESOURCE_ID]\\n params.update({LB_NAME: lb_name})\\n\\n ctx.instance.runtime_properties[LB_NAME] = \\\\\\n lb_name\\n ctx.instance.runtime_properties[RESOURCE_NAME] = \\\\\\n policy_name\\n\\n # Actually create the resource\\n iface.create_sticky(params)\",\n \"def create_resource():\\n return wsgi.Resource(Controller(), serializer=ImageSerialize())\",\n \"def create_resource(self, **kwargs):\\n results = self.api.action.resource_create(**kwargs)\\n # TODO: use `results` rather than re-download, using an isolation layer to standardize the re-structure\\n self.get_ckan_metadata(True)\\n if 'id' in results:\\n self._import_resource_to_cache(kwargs['upload'], results['id'])\\n return results\",\n \"def create_resource():\\n return wsgi.Resource(Controller())\",\n \"def add_resource(client, api_id, parent_resource, sub_path):\\n response = client.create_resource(\\n restApiId=api_id,\\n parentId=parent_resource['id'],\\n pathPart=sub_path)\\n file_name = \\\"{0}_resource.pickle\\\".format(sub_path)\\n pickle_dictionary_to_file(response, file_name)\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\\n arn: Optional[pulumi.Input[str]] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n blueprint_id: Optional[pulumi.Input[str]] = None,\\n bundle_id: Optional[pulumi.Input[str]] = None,\\n cpu_count: Optional[pulumi.Input[int]] = None,\\n created_at: Optional[pulumi.Input[str]] = None,\\n ip_address_type: Optional[pulumi.Input[str]] = None,\\n ipv6_address: Optional[pulumi.Input[str]] = None,\\n ipv6_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n is_static_ip: Optional[pulumi.Input[bool]] = None,\\n key_pair_name: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n private_ip_address: Optional[pulumi.Input[str]] = None,\\n public_ip_address: Optional[pulumi.Input[str]] = None,\\n ram_size: Optional[pulumi.Input[float]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tags_all: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n user_data: Optional[pulumi.Input[str]] = None,\\n username: Optional[pulumi.Input[str]] = None) -> 'Instance':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _InstanceState.__new__(_InstanceState)\\n\\n __props__.__dict__[\\\"add_on\\\"] = add_on\\n __props__.__dict__[\\\"arn\\\"] = arn\\n __props__.__dict__[\\\"availability_zone\\\"] = availability_zone\\n __props__.__dict__[\\\"blueprint_id\\\"] = blueprint_id\\n __props__.__dict__[\\\"bundle_id\\\"] = bundle_id\\n __props__.__dict__[\\\"cpu_count\\\"] = cpu_count\\n __props__.__dict__[\\\"created_at\\\"] = created_at\\n __props__.__dict__[\\\"ip_address_type\\\"] = ip_address_type\\n __props__.__dict__[\\\"ipv6_address\\\"] = ipv6_address\\n __props__.__dict__[\\\"ipv6_addresses\\\"] = ipv6_addresses\\n __props__.__dict__[\\\"is_static_ip\\\"] = is_static_ip\\n __props__.__dict__[\\\"key_pair_name\\\"] = key_pair_name\\n __props__.__dict__[\\\"name\\\"] = name\\n __props__.__dict__[\\\"private_ip_address\\\"] = private_ip_address\\n __props__.__dict__[\\\"public_ip_address\\\"] = public_ip_address\\n __props__.__dict__[\\\"ram_size\\\"] = ram_size\\n __props__.__dict__[\\\"tags\\\"] = tags\\n __props__.__dict__[\\\"tags_all\\\"] = tags_all\\n __props__.__dict__[\\\"user_data\\\"] = user_data\\n __props__.__dict__[\\\"username\\\"] = username\\n return Instance(resource_name, opts=opts, __props__=__props__)\",\n \"def __init__(__self__, resource_name, opts=None, allocated_capacity=None, command=None, connections=None, default_arguments=None, description=None, execution_property=None, glue_version=None, max_capacity=None, max_retries=None, name=None, number_of_workers=None, role_arn=None, security_configuration=None, tags=None, timeout=None, worker_type=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['allocated_capacity'] = allocated_capacity\\n if command is None:\\n raise TypeError(\\\"Missing required property 'command'\\\")\\n __props__['command'] = command\\n __props__['connections'] = connections\\n __props__['default_arguments'] = default_arguments\\n __props__['description'] = description\\n __props__['execution_property'] = execution_property\\n __props__['glue_version'] = glue_version\\n __props__['max_capacity'] = max_capacity\\n __props__['max_retries'] = max_retries\\n __props__['name'] = name\\n __props__['number_of_workers'] = number_of_workers\\n if role_arn is None:\\n raise TypeError(\\\"Missing required property 'role_arn'\\\")\\n __props__['role_arn'] = role_arn\\n __props__['security_configuration'] = security_configuration\\n __props__['tags'] = tags\\n __props__['timeout'] = timeout\\n __props__['worker_type'] = worker_type\\n __props__['arn'] = None\\n super(Job, __self__).__init__(\\n 'aws:glue/job:Job',\\n resource_name,\\n __props__,\\n opts)\",\n \"def create_resource():\\n deserializer = wsgi.JSONRequestDeserializer()\\n serializer = wsgi.JSONResponseSerializer()\\n return wsgi.Resource(Controller(), deserializer, serializer)\",\n \"def pre_namespace_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ObjectStorageKeyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: RuntimeArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def create(self, resource_name, data_dict):\\n try:\\n resource_cls = getattr(self, resource_name).resource\\n except AttributeError:\\n raise AttributeError(\\\"No resource named %s is defined.\\\" % resource_name)\\n\\n return resource_cls.create(self, data_dict)\",\n \"def create_resource():\\n #deserializer = ImageDeserializer()\\n #serializer = ImageSerializer()\\n return wsgi.Resource(Controller())\",\n \"def create(ctx, iface, resource_config, **_):\\n resource_id = \\\\\\n utils.get_resource_id(\\n ctx.node,\\n ctx.instance,\\n resource_config.get(VPN_CONNECTION_ID),\\n use_instance_id=True\\n )\\n utils.update_resource_id(ctx.instance, resource_id)\\n # Actually create the resource\\n create_response = iface.create(resource_config)\\n ctx.instance.runtime_properties['create_response'] = \\\\\\n utils.JsonCleanuper(create_response).to_dict()\\n ctx.instance.runtime_properties['VPN_CONNECTION_ID'] = \\\\\\n resource_config.get(VPN_CONNECTION_ID)\\n ctx.instance.runtime_properties['DESTINATION_CIDR_BLOCK'] = \\\\\\n resource_config.get(DESTINATION_CIDR_BLOCK)\",\n \"def __init__(__self__, resource_name, opts=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['block_device_mappings'] = block_device_mappings\\n __props__['capacity_reservation_specification'] = capacity_reservation_specification\\n __props__['credit_specification'] = credit_specification\\n __props__['description'] = description\\n __props__['disable_api_termination'] = disable_api_termination\\n __props__['ebs_optimized'] = ebs_optimized\\n __props__['elastic_gpu_specifications'] = elastic_gpu_specifications\\n __props__['elastic_inference_accelerator'] = elastic_inference_accelerator\\n __props__['iam_instance_profile'] = iam_instance_profile\\n __props__['image_id'] = image_id\\n __props__['instance_initiated_shutdown_behavior'] = instance_initiated_shutdown_behavior\\n __props__['instance_market_options'] = instance_market_options\\n __props__['instance_type'] = instance_type\\n __props__['kernel_id'] = kernel_id\\n __props__['key_name'] = key_name\\n __props__['license_specifications'] = license_specifications\\n __props__['monitoring'] = monitoring\\n __props__['name'] = name\\n __props__['name_prefix'] = name_prefix\\n __props__['network_interfaces'] = network_interfaces\\n __props__['placement'] = placement\\n __props__['ram_disk_id'] = ram_disk_id\\n __props__['security_group_names'] = security_group_names\\n __props__['tag_specifications'] = tag_specifications\\n __props__['tags'] = tags\\n __props__['user_data'] = user_data\\n __props__['vpc_security_group_ids'] = vpc_security_group_ids\\n __props__['arn'] = None\\n __props__['default_version'] = None\\n __props__['latest_version'] = None\\n super(LaunchTemplate, __self__).__init__(\\n 'aws:ec2/launchTemplate:LaunchTemplate',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n add_on: Optional[pulumi.Input[pulumi.InputType['InstanceAddOnArgs']]] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n blueprint_id: Optional[pulumi.Input[str]] = None,\\n bundle_id: Optional[pulumi.Input[str]] = None,\\n ip_address_type: Optional[pulumi.Input[str]] = None,\\n key_pair_name: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n user_data: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def add_resource(self, name, controller, ipaddress, ram, cpus, storage, owner=None, flavor='', compute=None, huge_pages=False):\\n if compute is None: compute = controller\\n args = { 'vm': name,\\n 'controller': controller,\\n 'ipaddress': ipaddress,\\n 'ram': ram,\\n 'cpus': cpus,\\n 'storage': storage,\\n 'flavor': flavor,\\n 'compute': compute,\\n 'hugepages': huge_pages,\\n }\\n if owner is not None:\\n args['owner'] = owner\\n self._NDL_API('addresource', args, None)\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: Optional[InstanceArgs] = None,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def _create_resource(resource, **options):\\n global _existing\\n\\n if _existing[resource]:\\n print('{r} {k}:{v} already exists with id {i}.'.format(\\n r=resource,\\n k=args.tag,\\n v=args.role,\\n i=_existing[resource].id\\n ))\\n return True\\n\\n print('{v} a {r} with parameters: {p}...'.format(\\n v='Would create' if dry else 'Creating',\\n r=resource,\\n p=str(options)\\n ))\\n\\n if dry:\\n return True\\n\\n # All easy cases out of the way, we now need to actually create something.\\n r = None\\n try:\\n r = getattr(ec2, definitions[resource].create)(** options)\\n # In some cases (instance) a list is returned instead of one item. Quack!\\n try:\\n r = r[0]\\n except:\\n pass\\n _tag_resource(r)\\n print('... {r} id {i} created.'.format(\\n r=resource,\\n i=r.id\\n ))\\n _existing[resource] = r\\n return True\\n except Exception as e:\\n if r is None:\\n print('Could not create resource {r}.'.format(\\n r=resource\\n ))\\n traceback.print_exc()\\n else:\\n print('Could not tag resource {r}, id {i}.'.format(\\n r=resource,\\n i=r.id\\n ))\\n traceback.print_exc()\\n _destroy_resource(resource)\\n return False\",\n \"def __init__(__self__, resource_name, opts=None, destination_cidr_block=None, destination_ipv6_cidr_block=None, egress_only_gateway_id=None, gateway_id=None, instance_id=None, nat_gateway_id=None, network_interface_id=None, route_table_id=None, transit_gateway_id=None, vpc_peering_connection_id=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n __props__['destination_cidr_block'] = destination_cidr_block\\n __props__['destination_ipv6_cidr_block'] = destination_ipv6_cidr_block\\n __props__['egress_only_gateway_id'] = egress_only_gateway_id\\n __props__['gateway_id'] = gateway_id\\n __props__['instance_id'] = instance_id\\n __props__['nat_gateway_id'] = nat_gateway_id\\n __props__['network_interface_id'] = network_interface_id\\n if route_table_id is None:\\n raise TypeError(\\\"Missing required property 'route_table_id'\\\")\\n __props__['route_table_id'] = route_table_id\\n __props__['transit_gateway_id'] = transit_gateway_id\\n __props__['vpc_peering_connection_id'] = vpc_peering_connection_id\\n __props__['destination_prefix_list_id'] = None\\n __props__['instance_owner_id'] = None\\n __props__['origin'] = None\\n __props__['state'] = None\\n super(Route, __self__).__init__(\\n 'aws:ec2/route:Route',\\n resource_name,\\n __props__,\\n opts)\",\n \"def resource_create(resource_id, resource_type, resource_options=None, cibfile=None):\\n return item_create(\\n item=\\\"resource\\\",\\n item_id=resource_id,\\n item_type=resource_type,\\n extra_args=resource_options,\\n cibfile=cibfile,\\n )\",\n \"def create(self, validated_data):\\n resource = Resource.objects.create(**validated_data.get(\\\"resource\\\"))\\n return Secret.objects.create(resource=resource)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n dry_run: Optional[pulumi.Input[bool]] = None,\\n load_balancer_id: Optional[pulumi.Input[str]] = None,\\n security_group_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__, resource_name, opts=None, client_id=None, client_secret=None, client_timeout=None, initial_login=None, password=None, realm=None, root_ca_certificate=None, tls_insecure_skip_verify=None, url=None, username=None, __props__=None, __name__=None, __opts__=None):\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n if client_id is None:\\n client_id = utilities.get_env('KEYCLOAK_CLIENT_ID')\\n __props__['client_id'] = client_id\\n if client_secret is None:\\n client_secret = utilities.get_env('KEYCLOAK_CLIENT_SECRET')\\n __props__['client_secret'] = client_secret\\n if client_timeout is None:\\n client_timeout = (utilities.get_env_int('KEYCLOAK_CLIENT_TIMEOUT') or 5)\\n __props__['client_timeout'] = pulumi.Output.from_input(client_timeout).apply(json.dumps) if client_timeout is not None else None\\n __props__['initial_login'] = pulumi.Output.from_input(initial_login).apply(json.dumps) if initial_login is not None else None\\n if password is None:\\n password = utilities.get_env('KEYCLOAK_PASSWORD')\\n __props__['password'] = password\\n if realm is None:\\n realm = (utilities.get_env('KEYCLOAK_REALM') or 'master')\\n __props__['realm'] = realm\\n __props__['root_ca_certificate'] = root_ca_certificate\\n __props__['tls_insecure_skip_verify'] = pulumi.Output.from_input(tls_insecure_skip_verify).apply(json.dumps) if tls_insecure_skip_verify is not None else None\\n if url is None:\\n url = utilities.get_env('KEYCLOAK_URL')\\n __props__['url'] = url\\n if username is None:\\n username = utilities.get_env('KEYCLOAK_USER')\\n __props__['username'] = username\\n super(Provider, __self__).__init__(\\n 'keycloak',\\n resource_name,\\n __props__,\\n opts)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n etag: Optional[pulumi.Input[str]] = None,\\n file_shares: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['FileShareConfigArgs']]]]] = None,\\n instance_id: Optional[pulumi.Input[str]] = None,\\n kms_key_name: Optional[pulumi.Input[str]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['NetworkConfigArgs']]]]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n tier: Optional[pulumi.Input['InstanceTier']] = None,\\n __props__=None):\\n ...\",\n \"def GenerateAssetForCreateRequest(args):\\n module = dataplex_api.GetMessageModule()\\n resource_spec_field = module.GoogleCloudDataplexV1AssetResourceSpec\\n resource_spec = module.GoogleCloudDataplexV1AssetResourceSpec(\\n name=args.resource_name,\\n type=resource_spec_field.TypeValueValuesEnum(args.resource_type),\\n )\\n request = module.GoogleCloudDataplexV1Asset(\\n description=args.description,\\n displayName=args.display_name,\\n labels=dataplex_api.CreateLabels(module.GoogleCloudDataplexV1Asset, args),\\n resourceSpec=resource_spec,\\n )\\n discovery = GenerateDiscoverySpec(args)\\n if discovery != module.GoogleCloudDataplexV1AssetDiscoverySpec():\\n setattr(request, 'discoverySpec', discovery)\\n return request\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: Optional[TargetPoolArgs] = None,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def pre_routing_instance_create(self, resource_dict):\\n pass\",\n \"def pre_service_instance_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: PrefixArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def GenerateAssetForCreateRequestAlpha(args):\\n module = dataplex_api.GetMessageModule()\\n resource_spec_field = module.GoogleCloudDataplexV1AssetResourceSpec\\n resource_spec = module.GoogleCloudDataplexV1AssetResourceSpec(\\n name=args.resource_name,\\n type=resource_spec_field.TypeValueValuesEnum(args.resource_type),\\n )\\n if args.IsSpecified('resource_read_access_mode'):\\n resource_spec.readAccessMode = (\\n resource_spec_field.ReadAccessModeValueValuesEnum(\\n args.resource_read_access_mode\\n )\\n )\\n request = module.GoogleCloudDataplexV1Asset(\\n description=args.description,\\n displayName=args.display_name,\\n labels=dataplex_api.CreateLabels(module.GoogleCloudDataplexV1Asset, args),\\n resourceSpec=resource_spec)\\n discovery = GenerateDiscoverySpec(args)\\n if discovery != module.GoogleCloudDataplexV1AssetDiscoverySpec():\\n setattr(request, 'discoverySpec', discovery)\\n return request\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_scaling_configuration_arn: Optional[pulumi.Input[str]] = None,\\n encryption_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceEncryptionConfigurationArgs']]] = None,\\n health_check_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceHealthCheckConfigurationArgs']]] = None,\\n instance_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceInstanceConfigurationArgs']]] = None,\\n network_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceNetworkConfigurationArgs']]] = None,\\n observability_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceObservabilityConfigurationArgs']]] = None,\\n service_name: Optional[pulumi.Input[str]] = None,\\n source_configuration: Optional[pulumi.Input[pulumi.InputType['ServiceSourceConfigurationArgs']]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def get(resource_name, id, opts=None, arn=None, block_device_mappings=None, capacity_reservation_specification=None, credit_specification=None, default_version=None, description=None, disable_api_termination=None, ebs_optimized=None, elastic_gpu_specifications=None, elastic_inference_accelerator=None, iam_instance_profile=None, image_id=None, instance_initiated_shutdown_behavior=None, instance_market_options=None, instance_type=None, kernel_id=None, key_name=None, latest_version=None, license_specifications=None, monitoring=None, name=None, name_prefix=None, network_interfaces=None, placement=None, ram_disk_id=None, security_group_names=None, tag_specifications=None, tags=None, user_data=None, vpc_security_group_ids=None):\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = dict()\\n __props__[\\\"arn\\\"] = arn\\n __props__[\\\"block_device_mappings\\\"] = block_device_mappings\\n __props__[\\\"capacity_reservation_specification\\\"] = capacity_reservation_specification\\n __props__[\\\"credit_specification\\\"] = credit_specification\\n __props__[\\\"default_version\\\"] = default_version\\n __props__[\\\"description\\\"] = description\\n __props__[\\\"disable_api_termination\\\"] = disable_api_termination\\n __props__[\\\"ebs_optimized\\\"] = ebs_optimized\\n __props__[\\\"elastic_gpu_specifications\\\"] = elastic_gpu_specifications\\n __props__[\\\"elastic_inference_accelerator\\\"] = elastic_inference_accelerator\\n __props__[\\\"iam_instance_profile\\\"] = iam_instance_profile\\n __props__[\\\"image_id\\\"] = image_id\\n __props__[\\\"instance_initiated_shutdown_behavior\\\"] = instance_initiated_shutdown_behavior\\n __props__[\\\"instance_market_options\\\"] = instance_market_options\\n __props__[\\\"instance_type\\\"] = instance_type\\n __props__[\\\"kernel_id\\\"] = kernel_id\\n __props__[\\\"key_name\\\"] = key_name\\n __props__[\\\"latest_version\\\"] = latest_version\\n __props__[\\\"license_specifications\\\"] = license_specifications\\n __props__[\\\"monitoring\\\"] = monitoring\\n __props__[\\\"name\\\"] = name\\n __props__[\\\"name_prefix\\\"] = name_prefix\\n __props__[\\\"network_interfaces\\\"] = network_interfaces\\n __props__[\\\"placement\\\"] = placement\\n __props__[\\\"ram_disk_id\\\"] = ram_disk_id\\n __props__[\\\"security_group_names\\\"] = security_group_names\\n __props__[\\\"tag_specifications\\\"] = tag_specifications\\n __props__[\\\"tags\\\"] = tags\\n __props__[\\\"user_data\\\"] = user_data\\n __props__[\\\"vpc_security_group_ids\\\"] = vpc_security_group_ids\\n return LaunchTemplate(resource_name, opts=opts, __props__=__props__)\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: InstanceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n enabled: Optional[pulumi.Input[bool]] = None,\\n flow_analytics_configuration: Optional[pulumi.Input[pulumi.InputType['TrafficAnalyticsPropertiesArgs']]] = None,\\n flow_log_name: Optional[pulumi.Input[str]] = None,\\n format: Optional[pulumi.Input[pulumi.InputType['FlowLogFormatParametersArgs']]] = None,\\n id: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n network_watcher_name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n retention_policy: Optional[pulumi.Input[pulumi.InputType['RetentionPolicyParametersArgs']]] = None,\\n storage_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n target_resource_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def create(self, resource, timeout=None):\\n req = ResourceCreateRequest()\\n\\n if resource is not None:\\n req.resource.CopyFrom(\\n plumbing.convert_resource_to_plumbing(resource))\\n tries = 0\\n plumbing_response = None\\n while True:\\n try:\\n plumbing_response = self.stub.Create(\\n req,\\n metadata=self.parent.get_metadata('Resources.Create', req),\\n timeout=timeout)\\n except Exception as e:\\n if self.parent.shouldRetry(tries, e):\\n tries += 1\\n self.parent.jitterSleep(tries)\\n continue\\n raise plumbing.convert_error_to_porcelain(e) from e\\n break\\n\\n resp = models.ResourceCreateResponse()\\n resp.meta = plumbing.convert_create_response_metadata_to_porcelain(\\n plumbing_response.meta)\\n resp.resource = plumbing.convert_resource_to_porcelain(\\n plumbing_response.resource)\\n resp.rate_limit = plumbing.convert_rate_limit_metadata_to_porcelain(\\n plumbing_response.rate_limit)\\n return resp\",\n \"def create():\\n\\n # remember what is created or not\\n vpc = False\\n igw = False\\n sg = False\\n sub = False\\n vm = False\\n\\n vpc = _create_resource('vpc', CidrBlock=args.cidr, InstanceTenancy='default')\\n igw = _create_resource('igw')\\n\\n if vpc and igw:\\n _attach_vpc_igw(vpc=_existing.vpc, igw=_existing.igw)\\n else:\\n print('Cannot attach an igw to a vpc as at least one of them could not be created.')\\n\\n if vpc:\\n sg = _create_resource(\\n 'sg',\\n GroupName=args.role,\\n Description='SG for ' + args.role,\\n VpcId=getattr(_existing.vpc, 'id', None)\\n )\\n else:\\n print('Cannot create a sg as the vpc to attach it to could not be created.')\\n\\n if sg:\\n _add_ingress_rules()\\n else:\\n print('Cannot create ingress rule as the sg could not be created.')\\n\\n if vpc:\\n sub = _create_resource(\\n 'sub',\\n VpcId=getattr(_existing.vpc, 'id', None),\\n CidrBlock=args.cidr\\n )\\n else:\\n print('Cannot create a subnet as the vpc to attach it to could not be created.')\\n\\n if vpc and sub:\\n _link_route_table()\\n else:\\n print('Cannot link subnet and VPC in the route table as vpc or sub not created.')\\n\\n if sub and sg:\\n vm = _create_resource(\\n 'vm',\\n ImageId=args.ami,\\n MinCount=1,\\n MaxCount=1,\\n KeyName=args.keypair,\\n InstanceType=args.instance,\\n # Note that there will be no internal name.\\n # To get one, create first a DHCP options set and associate it with the VPC.\\n NetworkInterfaces=[{\\n 'AssociatePublicIpAddress': True,\\n 'DeviceIndex': 0, # needs to be 0 to get a public IP\\n 'SubnetId': getattr(_existing.sub, 'id', None),\\n 'Groups': [getattr(_existing.sg, 'id', None)],\\n }],\\n )\\n else:\\n print('Cannot create an instance as the sub or sg to use could not be created.')\\n\\n if vm:\\n if not dry:\\n print('Waiting for the instance to be up and running, usually done in less than 45 seconds...')\\n _existing.vm.wait_until_running()\\n _tag_volume()\\n print('you can reach your VM at ' + _existing.vm.public_ip_address)\\n\\n else:\\n print('VM not created for some reason.')\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n backend_type: Optional[pulumi.Input['InstanceBackendType']] = None,\\n connection_name: Optional[pulumi.Input[str]] = None,\\n current_disk_size: Optional[pulumi.Input[str]] = None,\\n database_version: Optional[pulumi.Input['InstanceDatabaseVersion']] = None,\\n disk_encryption_configuration: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionConfigurationArgs']]] = None,\\n disk_encryption_status: Optional[pulumi.Input[pulumi.InputType['DiskEncryptionStatusArgs']]] = None,\\n etag: Optional[pulumi.Input[str]] = None,\\n failover_replica: Optional[pulumi.Input[pulumi.InputType['InstanceFailoverReplicaArgs']]] = None,\\n gce_zone: Optional[pulumi.Input[str]] = None,\\n instance_type: Optional[pulumi.Input['InstanceInstanceType']] = None,\\n ip_addresses: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IpMappingArgs']]]]] = None,\\n ipv6_address: Optional[pulumi.Input[str]] = None,\\n kind: Optional[pulumi.Input[str]] = None,\\n maintenance_version: Optional[pulumi.Input[str]] = None,\\n master_instance_name: Optional[pulumi.Input[str]] = None,\\n max_disk_size: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n on_premises_configuration: Optional[pulumi.Input[pulumi.InputType['OnPremisesConfigurationArgs']]] = None,\\n out_of_disk_report: Optional[pulumi.Input[pulumi.InputType['SqlOutOfDiskReportArgs']]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n replica_configuration: Optional[pulumi.Input[pulumi.InputType['ReplicaConfigurationArgs']]] = None,\\n replica_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n root_password: Optional[pulumi.Input[str]] = None,\\n satisfies_pzs: Optional[pulumi.Input[bool]] = None,\\n scheduled_maintenance: Optional[pulumi.Input[pulumi.InputType['SqlScheduledMaintenanceArgs']]] = None,\\n secondary_gce_zone: Optional[pulumi.Input[str]] = None,\\n self_link: Optional[pulumi.Input[str]] = None,\\n server_ca_cert: Optional[pulumi.Input[pulumi.InputType['SslCertArgs']]] = None,\\n service_account_email_address: Optional[pulumi.Input[str]] = None,\\n settings: Optional[pulumi.Input[pulumi.InputType['SettingsArgs']]] = None,\\n state: Optional[pulumi.Input['InstanceState']] = None,\\n suspension_reason: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceSuspensionReasonItem']]]] = None,\\n __props__=None):\\n ...\",\n \"def get(resource_name: str,\\n id: pulumi.Input[str],\\n opts: Optional[pulumi.ResourceOptions] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n connection_string: Optional[pulumi.Input[str]] = None,\\n create_sample_data: Optional[pulumi.Input[bool]] = None,\\n db_instance_category: Optional[pulumi.Input[str]] = None,\\n db_instance_class: Optional[pulumi.Input[str]] = None,\\n db_instance_mode: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n encryption_key: Optional[pulumi.Input[str]] = None,\\n encryption_type: Optional[pulumi.Input[str]] = None,\\n engine: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n instance_charge_type: Optional[pulumi.Input[str]] = None,\\n instance_group_count: Optional[pulumi.Input[int]] = None,\\n instance_network_type: Optional[pulumi.Input[str]] = None,\\n instance_spec: Optional[pulumi.Input[str]] = None,\\n ip_whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceIpWhitelistArgs']]]]] = None,\\n maintain_end_time: Optional[pulumi.Input[str]] = None,\\n maintain_start_time: Optional[pulumi.Input[str]] = None,\\n master_node_num: Optional[pulumi.Input[int]] = None,\\n payment_type: Optional[pulumi.Input[str]] = None,\\n period: Optional[pulumi.Input[str]] = None,\\n port: Optional[pulumi.Input[str]] = None,\\n private_ip_address: Optional[pulumi.Input[str]] = None,\\n resource_group_id: Optional[pulumi.Input[str]] = None,\\n security_ip_lists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n seg_node_num: Optional[pulumi.Input[int]] = None,\\n seg_storage_type: Optional[pulumi.Input[str]] = None,\\n ssl_enabled: Optional[pulumi.Input[int]] = None,\\n status: Optional[pulumi.Input[str]] = None,\\n storage_size: Optional[pulumi.Input[int]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n used_time: Optional[pulumi.Input[str]] = None,\\n vector_configuration_status: Optional[pulumi.Input[str]] = None,\\n vpc_id: Optional[pulumi.Input[str]] = None,\\n vswitch_id: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None) -> 'Instance':\\n opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))\\n\\n __props__ = _InstanceState.__new__(_InstanceState)\\n\\n __props__.__dict__[\\\"availability_zone\\\"] = availability_zone\\n __props__.__dict__[\\\"connection_string\\\"] = connection_string\\n __props__.__dict__[\\\"create_sample_data\\\"] = create_sample_data\\n __props__.__dict__[\\\"db_instance_category\\\"] = db_instance_category\\n __props__.__dict__[\\\"db_instance_class\\\"] = db_instance_class\\n __props__.__dict__[\\\"db_instance_mode\\\"] = db_instance_mode\\n __props__.__dict__[\\\"description\\\"] = description\\n __props__.__dict__[\\\"encryption_key\\\"] = encryption_key\\n __props__.__dict__[\\\"encryption_type\\\"] = encryption_type\\n __props__.__dict__[\\\"engine\\\"] = engine\\n __props__.__dict__[\\\"engine_version\\\"] = engine_version\\n __props__.__dict__[\\\"instance_charge_type\\\"] = instance_charge_type\\n __props__.__dict__[\\\"instance_group_count\\\"] = instance_group_count\\n __props__.__dict__[\\\"instance_network_type\\\"] = instance_network_type\\n __props__.__dict__[\\\"instance_spec\\\"] = instance_spec\\n __props__.__dict__[\\\"ip_whitelists\\\"] = ip_whitelists\\n __props__.__dict__[\\\"maintain_end_time\\\"] = maintain_end_time\\n __props__.__dict__[\\\"maintain_start_time\\\"] = maintain_start_time\\n __props__.__dict__[\\\"master_node_num\\\"] = master_node_num\\n __props__.__dict__[\\\"payment_type\\\"] = payment_type\\n __props__.__dict__[\\\"period\\\"] = period\\n __props__.__dict__[\\\"port\\\"] = port\\n __props__.__dict__[\\\"private_ip_address\\\"] = private_ip_address\\n __props__.__dict__[\\\"resource_group_id\\\"] = resource_group_id\\n __props__.__dict__[\\\"security_ip_lists\\\"] = security_ip_lists\\n __props__.__dict__[\\\"seg_node_num\\\"] = seg_node_num\\n __props__.__dict__[\\\"seg_storage_type\\\"] = seg_storage_type\\n __props__.__dict__[\\\"ssl_enabled\\\"] = ssl_enabled\\n __props__.__dict__[\\\"status\\\"] = status\\n __props__.__dict__[\\\"storage_size\\\"] = storage_size\\n __props__.__dict__[\\\"tags\\\"] = tags\\n __props__.__dict__[\\\"used_time\\\"] = used_time\\n __props__.__dict__[\\\"vector_configuration_status\\\"] = vector_configuration_status\\n __props__.__dict__[\\\"vpc_id\\\"] = vpc_id\\n __props__.__dict__[\\\"vswitch_id\\\"] = vswitch_id\\n __props__.__dict__[\\\"zone_id\\\"] = zone_id\\n return Instance(resource_name, opts=opts, __props__=__props__)\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n instance_id: Optional[pulumi.Input[int]] = None,\\n __props__=None):\\n ...\",\n \"def create_resource(self, namespace: \\\"str\\\" = None):\\n names = [\\\"create_namespaced_csistorage_capacity\\\", \\\"create_csistorage_capacity\\\"]\\n\\n _kube_api.execute(\\n action=\\\"create\\\",\\n resource=self,\\n names=names,\\n namespace=namespace,\\n api_client=None,\\n api_args={\\\"body\\\": self.to_dict()},\\n )\",\n \"def create(ctx, resource_config, **_):\\n # Create a resource (if necessary)\\n ctx.logger.warn(\\n 'Azure customData implementation is dependent on '\\n 'Virtual Machine image support.')\\n azure_config = utils.get_client_config(ctx.node.properties)\\n name = utils.get_resource_name(ctx)\\n resource_group_name = utils.get_resource_group(ctx)\\n vm_name = ctx.node.properties['virtual_machine_name']\\n vm_extension_params = {\\n 'location': ctx.node.properties.get('location'),\\n 'tags': ctx.node.properties.get('tags'),\\n 'publisher': resource_config.get('publisher'),\\n 'type_properties_type': resource_config.get('ext_type'),\\n 'type_handler_version': resource_config.get('typeHandlerVersion'),\\n 'settings': resource_config.get('settings'),\\n }\\n api_version = \\\\\\n ctx.node.properties.get('api_version', constants.API_VER_COMPUTE)\\n vm_extension = VirtualMachineExtension(azure_config, ctx.logger,\\n api_version)\\n # clean empty values from params\\n vm_extension_params = \\\\\\n utils.cleanup_empty_params(vm_extension_params)\\n result = utils.handle_create(\\n vm_extension,\\n resource_group_name,\\n name,\\n vm_name,\\n vm_extension_params)\\n ctx.instance.runtime_properties['resource_group'] = resource_group_name\\n ctx.instance.runtime_properties['virtual_machine'] = vm_name\\n ctx.instance.runtime_properties['resource'] = result\\n ctx.instance.runtime_properties['resource_id'] = result.get(\\\"id\\\", \\\"\\\")\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: KafkaConfigurationArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n client_id: Optional[pulumi.Input[str]] = None,\\n client_secret: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n display_name: Optional[pulumi.Input[str]] = None,\\n metadata_endpoint: Optional[pulumi.Input[str]] = None,\\n opid: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n service_name: Optional[pulumi.Input[str]] = None,\\n use_in_api_documentation: Optional[pulumi.Input[bool]] = None,\\n use_in_test_console: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n accelerator_id: Optional[pulumi.Input[str]] = None,\\n basic_endpoint_group_name: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n endpoint_address: Optional[pulumi.Input[str]] = None,\\n endpoint_group_region: Optional[pulumi.Input[str]] = None,\\n endpoint_sub_address: Optional[pulumi.Input[str]] = None,\\n endpoint_type: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n allocation_policy: Optional[pulumi.Input[str]] = None,\\n data_residency_enabled: Optional[pulumi.Input[bool]] = None,\\n ip_filter_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IotHubDpsIpFilterRuleArgs']]]]] = None,\\n linked_hubs: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['IotHubDpsLinkedHubArgs']]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n sku: Optional[pulumi.Input[pulumi.InputType['IotHubDpsSkuArgs']]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: RegionPerInstanceConfigArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: EndpointArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n policy: Optional[pulumi.Input[str]] = None,\\n resource_arn: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: Optional[ResourceDefaultVersionArgs] = None,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: TagArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def pre_virtual_DNS_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n auto_renew: Optional[pulumi.Input[bool]] = None,\\n auto_renew_period: Optional[pulumi.Input[int]] = None,\\n cluster_name: Optional[pulumi.Input[str]] = None,\\n data_center_name: Optional[pulumi.Input[str]] = None,\\n disk_size: Optional[pulumi.Input[int]] = None,\\n disk_type: Optional[pulumi.Input[str]] = None,\\n enable_public: Optional[pulumi.Input[bool]] = None,\\n instance_type: Optional[pulumi.Input[str]] = None,\\n ip_white: Optional[pulumi.Input[str]] = None,\\n maintain_end_time: Optional[pulumi.Input[str]] = None,\\n maintain_start_time: Optional[pulumi.Input[str]] = None,\\n major_version: Optional[pulumi.Input[str]] = None,\\n node_count: Optional[pulumi.Input[int]] = None,\\n password: Optional[pulumi.Input[str]] = None,\\n pay_type: Optional[pulumi.Input[str]] = None,\\n period: Optional[pulumi.Input[int]] = None,\\n period_unit: Optional[pulumi.Input[str]] = None,\\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n vswitch_id: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n additional_egress_endpoints: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['EgressEndpointArgs']]]]] = None,\\n cloud_services_network_name: Optional[pulumi.Input[str]] = None,\\n enable_default_egress_endpoints: Optional[pulumi.Input[Union[str, 'CloudServicesNetworkEnableDefaultEgressEndpoints']]] = None,\\n extended_location: Optional[pulumi.Input[pulumi.InputType['ExtendedLocationArgs']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n charset: Optional[pulumi.Input[str]] = None,\\n collation: Optional[pulumi.Input[str]] = None,\\n deletion_policy: Optional[pulumi.Input[str]] = None,\\n instance: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: AppArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n kind: Optional[pulumi.Input[Union[str, 'Kind']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n managed_network_group_name: Optional[pulumi.Input[str]] = None,\\n managed_network_name: Optional[pulumi.Input[str]] = None,\\n management_groups: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n subnets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n subscriptions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n virtual_networks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ResourceIdArgs']]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ProvisioningTemplateArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n backup_pool: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n failover_ratio: Optional[pulumi.Input[float]] = None,\\n health_checks: Optional[pulumi.Input[str]] = None,\\n instances: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n session_affinity: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n cache_name: Optional[pulumi.Input[str]] = None,\\n end_ip: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n rule_name: Optional[pulumi.Input[str]] = None,\\n start_ip: Optional[pulumi.Input[str]] = None,\\n __props__=None,\\n __name__=None,\\n __opts__=None):\\n pulumi.log.warn(\\\"\\\"\\\"FirewallRule is deprecated: The 'latest' version is deprecated. Please migrate to the resource in the top-level module: 'azure-native:cache:FirewallRule'.\\\"\\\"\\\")\\n if __name__ is not None:\\n warnings.warn(\\\"explicit use of __name__ is deprecated\\\", DeprecationWarning)\\n resource_name = __name__\\n if __opts__ is not None:\\n warnings.warn(\\\"explicit use of __opts__ is deprecated, use 'opts' instead\\\", DeprecationWarning)\\n opts = __opts__\\n if opts is None:\\n opts = pulumi.ResourceOptions()\\n if not isinstance(opts, pulumi.ResourceOptions):\\n raise TypeError('Expected resource options to be a ResourceOptions instance')\\n if opts.version is None:\\n opts.version = _utilities.get_version()\\n if opts.id is None:\\n if __props__ is not None:\\n raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')\\n __props__ = dict()\\n\\n if cache_name is None and not opts.urn:\\n raise TypeError(\\\"Missing required property 'cache_name'\\\")\\n __props__['cache_name'] = cache_name\\n if end_ip is None and not opts.urn:\\n raise TypeError(\\\"Missing required property 'end_ip'\\\")\\n __props__['end_ip'] = end_ip\\n if resource_group_name is None and not opts.urn:\\n raise TypeError(\\\"Missing required property 'resource_group_name'\\\")\\n __props__['resource_group_name'] = resource_group_name\\n __props__['rule_name'] = rule_name\\n if start_ip is None and not opts.urn:\\n raise TypeError(\\\"Missing required property 'start_ip'\\\")\\n __props__['start_ip'] = start_ip\\n __props__['name'] = None\\n __props__['type'] = None\\n alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_=\\\"azure-nextgen:cache/latest:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache/v20160401:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache/v20160401:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache/v20170201:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache/v20170201:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache/v20171001:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache/v20171001:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache/v20180301:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache/v20180301:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache/v20190701:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache/v20190701:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-native:cache/v20200601:FirewallRule\\\"), pulumi.Alias(type_=\\\"azure-nextgen:cache/v20200601:FirewallRule\\\")])\\n opts = pulumi.ResourceOptions.merge(opts, alias_opts)\\n super(FirewallRule, __self__).__init__(\\n 'azure-native:cache/latest:FirewallRule',\\n resource_name,\\n __props__,\\n opts)\",\n \"def create(self):\\n raise WufooException(\\\"InstanceResource creation not supported\\\")\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n account_id: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n secret: Optional[pulumi.Input[str]] = None,\\n url: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: EndpointAclPolicyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: CustomRoutingEndpointTrafficPolicyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n allow_forwarded_traffic: Optional[pulumi.Input[bool]] = None,\\n allow_gateway_transit: Optional[pulumi.Input[bool]] = None,\\n allow_virtual_network_access: Optional[pulumi.Input[bool]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n remote_address_space_prefixes: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n remote_virtual_network_id: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n use_remote_gateways: Optional[pulumi.Input[bool]] = None,\\n workspace_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n agent_upgrade: Optional[pulumi.Input[pulumi.InputType['AgentUpgradeArgs']]] = None,\\n client_public_key: Optional[pulumi.Input[str]] = None,\\n extensions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['MachineExtensionInstanceViewArgs']]]]] = None,\\n identity: Optional[pulumi.Input[pulumi.InputType['IdentityArgs']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n location_data: Optional[pulumi.Input[pulumi.InputType['LocationDataArgs']]] = None,\\n machine_name: Optional[pulumi.Input[str]] = None,\\n mssql_discovered: Optional[pulumi.Input[str]] = None,\\n os_profile: Optional[pulumi.Input[pulumi.InputType['OSProfileArgs']]] = None,\\n os_type: Optional[pulumi.Input[str]] = None,\\n parent_cluster_resource_id: Optional[pulumi.Input[str]] = None,\\n private_link_scope_resource_id: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n service_statuses: Optional[pulumi.Input[pulumi.InputType['ServiceStatusesArgs']]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n vm_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n crypto_key_backend: Optional[pulumi.Input[str]] = None,\\n crypto_key_id: Optional[pulumi.Input[str]] = None,\\n destroy_scheduled_duration: Optional[pulumi.Input[str]] = None,\\n import_only: Optional[pulumi.Input[bool]] = None,\\n key_ring_id: Optional[pulumi.Input[str]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n next_rotation_time: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n purpose: Optional[pulumi.Input['CryptoKeyPurpose']] = None,\\n rotation_period: Optional[pulumi.Input[str]] = None,\\n skip_initial_version_creation: Optional[pulumi.Input[bool]] = None,\\n version_template: Optional[pulumi.Input[pulumi.InputType['CryptoKeyVersionTemplateArgs']]] = None,\\n __props__=None):\\n ...\",\n \"def create_advert():\\r\\n advertiser, category, zone = create_objects()\\r\\n ad = AdBase.objects.create(\\r\\n title='Ad Title',\\r\\n url='www.example.com',\\r\\n advertiser=advertiser,\\r\\n category=category,\\r\\n zone=zone,\\r\\n )\\r\\n return ad\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: PrivatelinkAzureArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n create_sample_data: Optional[pulumi.Input[bool]] = None,\\n db_instance_category: Optional[pulumi.Input[str]] = None,\\n db_instance_class: Optional[pulumi.Input[str]] = None,\\n db_instance_mode: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n encryption_key: Optional[pulumi.Input[str]] = None,\\n encryption_type: Optional[pulumi.Input[str]] = None,\\n engine: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n instance_charge_type: Optional[pulumi.Input[str]] = None,\\n instance_group_count: Optional[pulumi.Input[int]] = None,\\n instance_network_type: Optional[pulumi.Input[str]] = None,\\n instance_spec: Optional[pulumi.Input[str]] = None,\\n ip_whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['InstanceIpWhitelistArgs']]]]] = None,\\n maintain_end_time: Optional[pulumi.Input[str]] = None,\\n maintain_start_time: Optional[pulumi.Input[str]] = None,\\n master_node_num: Optional[pulumi.Input[int]] = None,\\n payment_type: Optional[pulumi.Input[str]] = None,\\n period: Optional[pulumi.Input[str]] = None,\\n private_ip_address: Optional[pulumi.Input[str]] = None,\\n resource_group_id: Optional[pulumi.Input[str]] = None,\\n security_ip_lists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n seg_node_num: Optional[pulumi.Input[int]] = None,\\n seg_storage_type: Optional[pulumi.Input[str]] = None,\\n ssl_enabled: Optional[pulumi.Input[int]] = None,\\n storage_size: Optional[pulumi.Input[int]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,\\n used_time: Optional[pulumi.Input[str]] = None,\\n vector_configuration_status: Optional[pulumi.Input[str]] = None,\\n vpc_id: Optional[pulumi.Input[str]] = None,\\n vswitch_id: Optional[pulumi.Input[str]] = None,\\n zone_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n cluster_id: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n private_network_uuid: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[Union[str, 'Region']]] = None,\\n size: Optional[pulumi.Input[Union[str, 'DatabaseSlug']]] = None,\\n tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n address_family: Optional[pulumi.Input[str]] = None,\\n allocation_default_netmask_length: Optional[pulumi.Input[int]] = None,\\n allocation_max_netmask_length: Optional[pulumi.Input[int]] = None,\\n allocation_min_netmask_length: Optional[pulumi.Input[int]] = None,\\n allocation_resource_tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n auto_import: Optional[pulumi.Input[bool]] = None,\\n aws_service: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n ipam_scope_id: Optional[pulumi.Input[str]] = None,\\n locale: Optional[pulumi.Input[str]] = None,\\n public_ip_source: Optional[pulumi.Input[str]] = None,\\n publicly_advertisable: Optional[pulumi.Input[bool]] = None,\\n source_ipam_pool_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: PolicyArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ManagedPrivateEndpointArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def pre_loadbalancer_pool_create(self, resource_dict):\\n pass\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n activation_key: Optional[pulumi.Input[str]] = None,\\n ip_address: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n private_link_endpoint: Optional[pulumi.Input[str]] = None,\\n security_group_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n subnet_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n vpc_endpoint_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: VirtualHardDiskArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n application_insights_id: Optional[pulumi.Input[str]] = None,\\n container_registry_id: Optional[pulumi.Input[str]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n encryption: Optional[pulumi.Input[pulumi.InputType['WorkspaceEncryptionArgs']]] = None,\\n friendly_name: Optional[pulumi.Input[str]] = None,\\n high_business_impact: Optional[pulumi.Input[bool]] = None,\\n identity: Optional[pulumi.Input[pulumi.InputType['WorkspaceIdentityArgs']]] = None,\\n image_build_compute_name: Optional[pulumi.Input[str]] = None,\\n key_vault_id: Optional[pulumi.Input[str]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n primary_user_assigned_identity: Optional[pulumi.Input[str]] = None,\\n public_access_behind_virtual_network_enabled: Optional[pulumi.Input[bool]] = None,\\n public_network_access_enabled: Optional[pulumi.Input[bool]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n sku_name: Optional[pulumi.Input[str]] = None,\\n storage_account_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n v1_legacy_mode_enabled: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n access_config: Optional[pulumi.Input[pulumi.InputType['RuntimeAccessConfigArgs']]] = None,\\n labels: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n request_id: Optional[pulumi.Input[str]] = None,\\n runtime_id: Optional[pulumi.Input[str]] = None,\\n software_config: Optional[pulumi.Input[pulumi.InputType['RuntimeSoftwareConfigArgs']]] = None,\\n virtual_machine: Optional[pulumi.Input[pulumi.InputType['VirtualMachineArgs']]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n priority: Optional[pulumi.Input[str]] = None,\\n ttl: Optional[pulumi.Input[str]] = None,\\n type: Optional[pulumi.Input[str]] = None,\\n value: Optional[pulumi.Input[str]] = None,\\n zone_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def create_TestServiceResource(test_case, # type: AnyMagpieTestCaseType\\n override_service_name=null, # type: Optional[Str]\\n override_service_type=null, # type: Optional[Str]\\n override_resource_name=null, # type: Optional[Str]\\n override_resource_type=null, # type: Optional[Str]\\n override_data=null, # type: Optional[JSON]\\n override_headers=null, # type: Optional[HeadersType]\\n override_cookies=null, # type: Optional[CookiesType]\\n ): # type: (...) -> JSON\\n app_or_url = get_app_or_url(test_case)\\n svc_name = override_service_name if override_service_name is not null else test_case.test_service_name\\n svc_type = override_service_type if override_service_type is not null else test_case.test_service_type\\n TestSetup.create_TestService(test_case,\\n override_service_name=svc_name, override_service_type=svc_type,\\n override_headers=override_headers, override_cookies=override_cookies)\\n path = \\\"/services/{svc}/resources\\\".format(svc=svc_name)\\n data = override_data if override_data is not null else {\\n \\\"resource_name\\\": override_resource_name or test_case.test_resource_name,\\n \\\"resource_type\\\": override_resource_type or test_case.test_resource_type,\\n }\\n resp = test_request(app_or_url, \\\"POST\\\", path, json=data,\\n headers=override_headers if override_headers is not null else test_case.json_headers,\\n cookies=override_cookies if override_cookies is not null else test_case.cookies)\\n return check_response_basic_info(resp, 201, expected_method=\\\"POST\\\")\",\n \"def create_one(cls, attrs=None):\\n attrs = attrs or {}\\n\\n resource = cls.generate()\\n\\n resource.update(attrs)\\n\\n return resource\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n azure_subscription_id: Optional[pulumi.Input[str]] = None,\\n peer_azure_app_id: Optional[pulumi.Input[str]] = None,\\n peer_azure_tenant_id: Optional[pulumi.Input[str]] = None,\\n peer_resource_group: Optional[pulumi.Input[str]] = None,\\n vnet_name: Optional[pulumi.Input[str]] = None,\\n vpc_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def create(self, resource):\\n if not resource:\\n raise NoneReferenceError(\\\"resource to create can't be None\\\")\\n\\n if not isinstance(resource, self._resource_class):\\n raise TypeError(\\n \\\"resource %s is not %s instance\\\"\\n % (resource, self._resource_class.__name__))\\n\\n return self.build_resource(\\n self._os_resource_manager.create(**vars(resource)))\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n acl_name: Optional[pulumi.Input[str]] = None,\\n auto_minor_version_upgrade: Optional[pulumi.Input[bool]] = None,\\n data_tiering: Optional[pulumi.Input[bool]] = None,\\n description: Optional[pulumi.Input[str]] = None,\\n engine_version: Optional[pulumi.Input[str]] = None,\\n final_snapshot_name: Optional[pulumi.Input[str]] = None,\\n kms_key_arn: Optional[pulumi.Input[str]] = None,\\n maintenance_window: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n name_prefix: Optional[pulumi.Input[str]] = None,\\n node_type: Optional[pulumi.Input[str]] = None,\\n num_replicas_per_shard: Optional[pulumi.Input[int]] = None,\\n num_shards: Optional[pulumi.Input[int]] = None,\\n parameter_group_name: Optional[pulumi.Input[str]] = None,\\n port: Optional[pulumi.Input[int]] = None,\\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n snapshot_arns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n snapshot_name: Optional[pulumi.Input[str]] = None,\\n snapshot_retention_limit: Optional[pulumi.Input[int]] = None,\\n snapshot_window: Optional[pulumi.Input[str]] = None,\\n sns_topic_arn: Optional[pulumi.Input[str]] = None,\\n subnet_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tls_enabled: Optional[pulumi.Input[bool]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n container_registry_name: Optional[pulumi.Input[str]] = None,\\n instance_count: Optional[pulumi.Input[int]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n tier: Optional[pulumi.Input[str]] = None,\\n virtual_network_subnet_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ReplicatedBucketArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n block_size_bytes: Optional[pulumi.Input[int]] = None,\\n container_id: Optional[pulumi.Input[str]] = None,\\n disk_file_format: Optional[pulumi.Input[Union[str, 'DiskFileFormat']]] = None,\\n disk_size_gb: Optional[pulumi.Input[float]] = None,\\n dynamic: Optional[pulumi.Input[bool]] = None,\\n extended_location: Optional[pulumi.Input[pulumi.InputType['ExtendedLocationArgs']]] = None,\\n hyper_v_generation: Optional[pulumi.Input[Union[str, 'HyperVGeneration']]] = None,\\n location: Optional[pulumi.Input[str]] = None,\\n logical_sector_bytes: Optional[pulumi.Input[int]] = None,\\n physical_sector_bytes: Optional[pulumi.Input[int]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,\\n virtual_hard_disk_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def model_resource_factory(acl, obj):\\r\\n return _model_identity_factory(obj, resource_identity, acl.add_resource)\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ServiceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ServiceArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n data_factory_id: Optional[pulumi.Input[str]] = None,\\n fqdns: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n subresource_name: Optional[pulumi.Input[str]] = None,\\n target_resource_id: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6452652","0.6253703","0.6171513","0.6096159","0.59922796","0.59912336","0.595014","0.58938444","0.5844623","0.5840295","0.58089936","0.57912517","0.57580245","0.5738343","0.573344","0.5730226","0.5724765","0.5722394","0.5716222","0.5712392","0.56978524","0.5697366","0.56828666","0.56782913","0.56513584","0.5622816","0.5604982","0.5604254","0.56011033","0.55805737","0.5575721","0.55746305","0.5561922","0.55588144","0.55479366","0.5520979","0.55010575","0.55010575","0.55010575","0.55010575","0.55010575","0.55010575","0.54960454","0.5493873","0.5483175","0.54693073","0.54622185","0.54604614","0.545845","0.5448328","0.54476416","0.5443371","0.5434641","0.54275465","0.54220235","0.54098254","0.5400507","0.539941","0.53993636","0.538981","0.5379709","0.5377062","0.5375751","0.5374794","0.5369402","0.5368437","0.5364827","0.5362824","0.53616166","0.5360451","0.5357631","0.5355669","0.5355481","0.53545105","0.53532517","0.53497905","0.53492624","0.5347708","0.5344598","0.53386724","0.53348005","0.53339636","0.5322797","0.5322062","0.5321442","0.531738","0.53146","0.530934","0.5307275","0.530549","0.53037816","0.5300947","0.529823","0.52981836","0.52882814","0.52871865","0.5285954","0.52821106","0.52821106","0.52812785"],"string":"[\n \"0.6452652\",\n \"0.6253703\",\n \"0.6171513\",\n \"0.6096159\",\n \"0.59922796\",\n \"0.59912336\",\n \"0.595014\",\n \"0.58938444\",\n \"0.5844623\",\n \"0.5840295\",\n \"0.58089936\",\n \"0.57912517\",\n \"0.57580245\",\n \"0.5738343\",\n \"0.573344\",\n \"0.5730226\",\n \"0.5724765\",\n \"0.5722394\",\n \"0.5716222\",\n \"0.5712392\",\n \"0.56978524\",\n \"0.5697366\",\n \"0.56828666\",\n \"0.56782913\",\n \"0.56513584\",\n \"0.5622816\",\n \"0.5604982\",\n \"0.5604254\",\n \"0.56011033\",\n \"0.55805737\",\n \"0.5575721\",\n \"0.55746305\",\n \"0.5561922\",\n \"0.55588144\",\n \"0.55479366\",\n \"0.5520979\",\n \"0.55010575\",\n \"0.55010575\",\n \"0.55010575\",\n \"0.55010575\",\n \"0.55010575\",\n \"0.55010575\",\n \"0.54960454\",\n \"0.5493873\",\n \"0.5483175\",\n \"0.54693073\",\n \"0.54622185\",\n \"0.54604614\",\n \"0.545845\",\n \"0.5448328\",\n \"0.54476416\",\n \"0.5443371\",\n \"0.5434641\",\n \"0.54275465\",\n \"0.54220235\",\n \"0.54098254\",\n \"0.5400507\",\n \"0.539941\",\n \"0.53993636\",\n \"0.538981\",\n \"0.5379709\",\n \"0.5377062\",\n \"0.5375751\",\n \"0.5374794\",\n \"0.5369402\",\n \"0.5368437\",\n \"0.5364827\",\n \"0.5362824\",\n \"0.53616166\",\n \"0.5360451\",\n \"0.5357631\",\n \"0.5355669\",\n \"0.5355481\",\n \"0.53545105\",\n \"0.53532517\",\n \"0.53497905\",\n \"0.53492624\",\n \"0.5347708\",\n \"0.5344598\",\n \"0.53386724\",\n \"0.53348005\",\n \"0.53339636\",\n \"0.5322797\",\n \"0.5322062\",\n \"0.5321442\",\n \"0.531738\",\n \"0.53146\",\n \"0.530934\",\n \"0.5307275\",\n \"0.530549\",\n \"0.53037816\",\n \"0.5300947\",\n \"0.529823\",\n \"0.52981836\",\n \"0.52882814\",\n \"0.52871865\",\n \"0.5285954\",\n \"0.52821106\",\n \"0.52821106\",\n \"0.52812785\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71058977"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22379,"cells":{"query":{"kind":"string","value":"Get the scale for a unit"},"document":{"kind":"string","value":"def get_scale(units, compartmentId, volume, extracellularVolume):\r\n if compartmentId == 'c':\r\n V = volume\r\n else:\r\n V = extracellularVolume\r\n\r\n if units == 'uM':\r\n return 1. / N_AVOGADRO / V * 1e6\r\n elif units == 'mM':\r\n return 1. / N_AVOGADRO / V * 1e3\r\n elif units == 'molecules':\r\n return 1.\r\n else:\r\n raise Exception('Invalid units \"%s\"' % units)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getScale(self):\n return _libsbml.Unit_getScale(self)","def scale(self):\n return self.scale_factor / CONSTANTS.AU","def GetScale(self):\n ...","def scale(self):\n return self._scale","def scale(self) -> Tuple[float, float]:\n return self._scale","def get_unit(scale):\n scale2unit = { 1e-9: 'nm',\n 1e-6: u'\\N{MICRO SIGN}m', #or hex id (lookup): u'\\u00B5'\n 1e-3: 'mm',\n 0.01: 'cm',\n 0.1:'dm',\n 1:'m',\n 1000:'km',\n # time\n 8.6400e4:'day',\n 3.1536e7:'yr',\n 3.1536e10:'ka',\n 3.1536e13:'Ma',\n #Pressure\n 1e9: 'GPa',\n 1e6: 'MPa',\n }\n return scale2unit[scale]","def get_scale():\r\n\r\n \r\n return 0.5","def dim_unit_scaling(in_unit, out_unit):\n\n unit_vals = {\n 'nm': 1e-9,\n 'um': 1e-6,\n 'mm': 1e-3,\n 'cm': 1e-2,\n 'm': 1.0,\n 'km': 1e3,\n }\n\n if in_unit not in unit_vals:\n raise ValueError(\n 'Invalid input unit {}. Must be one of {}'.format(\n in_unit, list(unit_vals.keys())\n )\n )\n if out_unit not in unit_vals:\n raise ValueError(\n 'Invalid input unit {}. Must be one of {}'.format(\n in_unit, list(unit_vals.keys())\n )\n )\n\n return unit_vals[in_unit]/unit_vals[out_unit]","def getScale(self):\n \n dag_node = OpenMaya.MFnDagNode(self.thisObj)\n transform_node = OpenMaya.MFnTransform(dag_node.parent( 0 ))\n \n util = OpenMaya.MScriptUtil()\n util.createFromDouble(0.0, 0.0, 0.0)\n pointeur = util.asDoublePtr()\n transform_node.getScale(pointeur)\n \n sx = util.getDoubleArrayItem(pointeur, 0)\n sy = util.getDoubleArrayItem(pointeur, 1)\n sz = util.getDoubleArrayItem(pointeur, 2)\n\n return sx, sy, sz","def get_scale(self):\n\n if not hasattr(self, \"scale\"):\n raise NotImplementedError(\"\"\"All end-use subclasses of Color should define\n a get_scale method.\"\"\")\n\n return self.scale","def scale(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"scale\")","def scale(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"scale\")","def scale(self):\n return self.distribution.scale","def get_scale(self):\r\n try: return self.scale[0], self.scale[1], self.scale[2]\r\n except: return self.scale, self.scale, self.scale","def GetUserScale(*args, **kwargs):\n return _gdi_.DC_GetUserScale(*args, **kwargs)","def unitsize(self, unit):\n\t\treturn self._unitsize[unit]","def scaling(self):\n return self.__scaling","def scale(self):\n return self._gev_bijector.scale","def getScale(self):\n return self.factor**self.turnOn","def _get_scaling(root):\n dpi = root.winfo_fpixels(\"1i\")\n scaling = dpi / 72.0\n logger.debug(\"dpi: %s, scaling: %s'\", dpi, scaling)\n return scaling","def scaling(self):\n return self._scaling","def scaling(self):\n return self._scaling","def scale(self):\n return self._a","def _scale_coord_to_meters(coord, unit):\n if unit == 'cm':\n return np.divide(coord, 100.)\n elif unit == 'mm':\n return np.divide(coord, 1000.)\n else:\n return coord","def setScale(self, *args):\n return _libsbml.Unit_setScale(self, *args)","def scale(self) -> Optional[pulumi.Input['ScaleArgs']]:\n return pulumi.get(self, \"scale\")","def scale_value(self):\n return self._scale_value[2]","def get_scale_op(self):\n\t\treturn self.variables.get('scale')","def scale_parameter(self):\n return self._scale_parameter","def get_zoom(self) -> float:\n transform = self.transform()\n cur_scale = (transform.m11(), transform.m22())\n return float(f\"{cur_scale[0] - 1.0:0.2f}\")","def _pixel_scale(self, width=None, height=None, scale=None):\n if numpy.count_nonzero([width is not None, height is not None, scale is not None]) > 1:\n raise ValueError(\"Specify only one of width, height, or scale.\")\n if width is not None:\n scale = width / self._width\n elif height is not None:\n scale = height / self._height\n elif scale is None:\n scale = 1.0\n return scale","def _get_unit_factor(self, unit: str) -> np.ndarray:\n\n unit_factors = {\n 'vx': np.array((1, 1, 1)),\n 'nm': np.array(self.parameters.scale),\n 'um': np.array(self.parameters.scale)/1000\n }\n assert unit in unit_factors.keys(), 'Invalid unit'\n unit_factor = unit_factors[unit]\n\n return unit_factor","def unit_scale(x, eps=1e-8):\n\tx = x.copy()\n\tx -= x.min()\n\tx *= 1.0 / (x.max() + eps)\n\treturn x","def _point_scale(self, width=None, height=None, scale=None):\n if numpy.count_nonzero([width is not None, height is not None, scale is not None]) > 1:\n raise ValueError(\"Specify only one of width, height, or scale.\")\n\n if width is not None:\n scale = toyplot.units.points(width) / self._width\n elif height is not None:\n scale = toyplot.units.points(height) / self._height\n elif scale is None:\n scale = 1.0\n return scale","def scale(self):","def convert_unit(size_in_bytes, unit):\n if unit == 'KB':\n return size_in_bytes/1024\n elif unit == 'MB':\n return size_in_bytes/(1024*1024)\n elif unit == 'GB':\n return size_in_bytes/(1024*1024*1024)\n else:\n return size_in_bytes","def temperature_scale(self) -> str:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"temperature_scale\"))\r\n return self._temperature_scale","def scale_to_factor(scale):\n return (B.pi / 2) / (2 * scale**2)","def scale(self, value):\n return (float(value) - float(self.minimum)) / \\\n float(self.maximum - self.minimum) * 2.0 - 1.0","def removeScale(*args):\n return _libsbml.Unit_removeScale(*args)","def unsetScale(self):\n return _libsbml.Unit_unsetScale(self)","def Unit_removeScale(*args):\n return _libsbml.Unit_removeScale(*args)","def get_scale_parameter(self):\r\n \r\n if self.scale_parameter == 0.0: \r\n shape_in_gamma_func = float(1+(1/self.shape_parameter))\r\n gamma_func = special.gamma(shape_in_gamma_func)\r\n self.scale_parameter = (self.mean_fire_recurrence/gamma_func)\r\n return self.scale_parameter\r\n else:\r\n return self.scale_parameter","def _scale(x):\n scaleFactor = 1\n _ret = int(x/scaleFactor)\n return _ret","def getnscale(self):\n return self.nscale","def get_scale_parameter(self):\n\n if self.scale_parameter == 0.0:\n shape_in_gamma_func = float(1+(1/self.shape_parameter))\n gamma_func = special.gamma(shape_in_gamma_func)\n self.scale_parameter = (self.mean_fire_recurrence/gamma_func)\n return self.scale_parameter\n else:\n return self.scale_parameter","def doppler_scale(self):\n return self._dopplerscale","def getscales(self):\n return self.scales","def castSize(self, scale):\n return self.camera.sensorSize * scale","def factor_to_scale(factor):\n return 1 / B.sqrt(4 * factor / B.pi)","def scale(self, value):\r\n return (float(value)-float(self.minimum))/float(self.maximum-self.minimum)*2.0 - 1.0","def scaling(self):\n return self.stacked._box_scaling[1]","def getMultiplier(self):\n return _libsbml.Unit_getMultiplier(self)","def colorscale(self):\n return self[\"colorscale\"]","def get_loss_scale(self):\n return self._loss_scale","def colorscale(self):\n return self['colorscale']","def Scale(*args, **kwargs):\n return _gdi_.Font_Scale(*args, **kwargs)","def get_size(size):\n if size.isdigit():\n return int(size)\n\n def do_get_size(num, unit):\n u = units[unit]\n if num.find('.') == -1:\n return int(num) * u\n return int(float(num) * u)\n\n s = size.strip().upper()\n if s.find(' ') == -1:\n num, unit = re.sub(r\"([\\d.]+)\", r\"\\1 \", s).split()\n else:\n num, unit = s.split()\n\n try:\n return do_get_size(num, unit)\n except KeyError:\n\traise Exception('unknown size unit[%s]' % size)","def scaling_factor(self):\n bin_scale = self.spabins * self.spebins\n return bin_scale * self.int_time","def FindScale(self):\n\n ## 6 and from the cv code the distance is 6 then we are good\n print(\"TODO: Very hard\")","def scale(input):\n return (input - np.min(input)) / ((np.max(input) - np.min(input)))","def loss_scale(self):\n return self._loss_scale","def cm2inch(size):\n return size / 2.54","def cm2inch(size):\n return size / 2.54","def cm2inch(size):\n return size / 2.54","def any_scale(scale):\n return scale","def scale(self, value):\n\t\toldscale = self.oldmax - self.oldmin\n\t\tnewscale = self.newmax - self.newmin\n\t\treturn (newscale * (value - self.oldmin) / oldscale) + self.newmin","def plate_scale(self):\n\n return 206265 * uu.arcsec / (self.diameter.to('mm') * self.f)","def get_scaled_value(self, value):\r\n raise NotImplementedError()","def getScale(self, mode='ACC'):\t#good\r\n\t\tif mode.upper() == 'ACC':\r\n\t\t\treg = 0x1C\r\n\t\telif mode.upper() == 'GYR':\r\n\t\t\treg = 0x1B\t\t\r\n\t\telse:\r\n\t\t\treturn False\r\n\t\tcurrentVal = self.read(reg)\r\n\t\tcurrentVal = self.dec2BinList(currentVal)\r\n\t\tscaleSetting = (currentVal[4]*2) + (currentVal[3]*1) \r\n\t\tif mode.upper() == 'ACC':\r\n\t\t\tscale = 2**(scaleSetting+1) \r\n\t\telif mode.upper() == 'GYR':\r\n\t\t\tscale = (2**(scaleSetting+1))*125\r\n\t\telse:\r\n\t\t\treturn False\r\n\t\treturn scale,scaleSetting","def pixel_scale(self):\n return np.abs(float(self.header[\"CDELT1\"]))","def _get_target_scale(self, im_size_min, im_size_max, target_size, max_size):\n im_scale = float(target_size) / float(im_size_min)\n # Prevent the biggest axis from being more than max_size\n if np.round(im_scale * im_size_max) > max_size:\n im_scale = float(max_size) / float(im_size_max)\n return im_scale","def scale(self,s):\n return Vector(self.x * s, self.y * s, self.z * s)","def get_unit(self):\n return self.unit","def get_units(self):\r\n msg = struct.pack('>2B', 56, 14)\r\n response = self.query(msg)\r\n\r\n if response[1] == 2:\r\n units = 'A'\r\n to_nm_multiplier = 1 / 10\r\n elif response[1] == 1:\r\n units = 'nm'\r\n to_nm_multiplier = 1\r\n elif response[1] == 0:\r\n units = 'um'\r\n to_nm_multiplier = 1000\r\n else:\r\n raise ValueError('Units not recognised.')\r\n\r\n # Save results locally too for quick re-use\r\n self._current_units = units\r\n self._current_to_nm_multiplier = to_nm_multiplier\r\n\r\n return units, to_nm_multiplier","def scale_it(val):\n return scale(val, 0, 1, bpm_range[0], bpm_range[1])","def scale_pos(q_unsc):\n q_sc = (q_unsc - 0.125)/0.125\n return q_sc","def __get_size_multiplier(self, multiplier):\n if multiplier is None:\n result = 1\n elif multiplier in ['k', 'K']:\n result = self.__k_multiplier\n elif multiplier in ['m', 'M']:\n result = self.__m_multiplier\n elif multiplier in ['g', 'G']:\n result = self.__g_multiplier\n else:\n result = 0\n return result","def Scale(*args, **kwargs):\n return _gdi_.GraphicsContext_Scale(*args, **kwargs)","def get_scaled_value_at(self, offset):\r\n return self.get_scaled_value(self.get_value_at(offset))","def Scale(*args, **kwargs):\n return _gdi_.GraphicsMatrix_Scale(*args, **kwargs)","def _get_unit_factor(cls, unit):\n try:\n if isinstance(unit, str):\n unit = cls.UNIT_FACTOR_NAMES[unit]\n return cls.UNIT_FACTORS[unit]\n except KeyError:\n raise UnsupportedUnitError()","def scale(self, fname, **kw):\n return self.scales.scale(fname, **kw)","def unit(self,unit_str,unit_scale):\n self.units[unit_str] = unit_scale\n return self","def unit_of_measurement(self):\n return self.sensor_type[\"unit\"]","def scale(m, scale = 1):\n try:\n scaleX = abs(scale[0])\n scaleY = abs(scale[1])\n except:\n scaleX = abs(scale)\n scaleY = abs(scale)\n resolution = (int(m.getWidth() * scaleX), int(m.getHeight() * scaleY))\n return createBySurface(pygame.transform.scale(m.toSurface(), resolution))","def get_scale_factor(rec, stack):\n \n rec_pixel_size = get_pixel_size_rec(rec)\n stack_pixel_size = get_pixel_size_stack(stack)\n \n return rec_pixel_size / stack_pixel_size","def get_scales(min_scale=0.2, max_scale=0.9,num_layers=6):\n\n # this code follows the original implementation of wei liu\n # for more, look at ssd/score_ssd_pascal.py:310 in the original caffe implementation\n min_ratio = int(min_scale * 100)\n max_ratio = int(max_scale * 100)\n step = int(np.floor((max_ratio - min_ratio) / (num_layers - 2)))\n min_sizes = []\n max_sizes = []\n for ratio in xrange(min_ratio, max_ratio + 1, step):\n min_sizes.append(ratio / 100.)\n max_sizes.append((ratio + step) / 100.)\n min_sizes = [int(100*min_scale / 2.0) / 100.0] + min_sizes\n max_sizes = [min_scale] + max_sizes\n\n # convert it back to this implementation's notation:\n scales = []\n for layer_idx in range(num_layers):\n scales.append([min_sizes[layer_idx], np.single(np.sqrt(min_sizes[layer_idx] * max_sizes[layer_idx]))])\n return scales","def get_wcs_pscale(wcs):\n from numpy import linalg\n det = linalg.det(wcs.wcs.cd)\n pscale = np.sqrt(np.abs(det))*3600.\n return pscale","def get_scale_parameter(self):\n\n shape_in_gamma_func = float(1 + (1 / self._shape_parameter))\n gamma_func = special.gamma(shape_in_gamma_func)\n self._scale_parameter = self._mean_fire_recurrence / gamma_func","def unit_of_measurement(self):\n return self._metadata[1]","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def _scale_param(self, resid_us):\n return((resid_us**2).sum().sum() / self.dof)","def getTCHEMScaleFactor(pt):\n #see https://twiki.cern.ch/twiki/pub/CMS/BtagPOG/SFb-mujet_payload.txt\n if pt > 670: pt = 670\n if pt < 30: pt = 30\n return 0.932251*((1.+(0.00335634*pt))/(1.+(0.00305994*pt)))"],"string":"[\n \"def getScale(self):\\n return _libsbml.Unit_getScale(self)\",\n \"def scale(self):\\n return self.scale_factor / CONSTANTS.AU\",\n \"def GetScale(self):\\n ...\",\n \"def scale(self):\\n return self._scale\",\n \"def scale(self) -> Tuple[float, float]:\\n return self._scale\",\n \"def get_unit(scale):\\n scale2unit = { 1e-9: 'nm',\\n 1e-6: u'\\\\N{MICRO SIGN}m', #or hex id (lookup): u'\\\\u00B5'\\n 1e-3: 'mm',\\n 0.01: 'cm',\\n 0.1:'dm',\\n 1:'m',\\n 1000:'km',\\n # time\\n 8.6400e4:'day',\\n 3.1536e7:'yr',\\n 3.1536e10:'ka',\\n 3.1536e13:'Ma',\\n #Pressure\\n 1e9: 'GPa',\\n 1e6: 'MPa',\\n }\\n return scale2unit[scale]\",\n \"def get_scale():\\r\\n\\r\\n \\r\\n return 0.5\",\n \"def dim_unit_scaling(in_unit, out_unit):\\n\\n unit_vals = {\\n 'nm': 1e-9,\\n 'um': 1e-6,\\n 'mm': 1e-3,\\n 'cm': 1e-2,\\n 'm': 1.0,\\n 'km': 1e3,\\n }\\n\\n if in_unit not in unit_vals:\\n raise ValueError(\\n 'Invalid input unit {}. Must be one of {}'.format(\\n in_unit, list(unit_vals.keys())\\n )\\n )\\n if out_unit not in unit_vals:\\n raise ValueError(\\n 'Invalid input unit {}. Must be one of {}'.format(\\n in_unit, list(unit_vals.keys())\\n )\\n )\\n\\n return unit_vals[in_unit]/unit_vals[out_unit]\",\n \"def getScale(self):\\n \\n dag_node = OpenMaya.MFnDagNode(self.thisObj)\\n transform_node = OpenMaya.MFnTransform(dag_node.parent( 0 ))\\n \\n util = OpenMaya.MScriptUtil()\\n util.createFromDouble(0.0, 0.0, 0.0)\\n pointeur = util.asDoublePtr()\\n transform_node.getScale(pointeur)\\n \\n sx = util.getDoubleArrayItem(pointeur, 0)\\n sy = util.getDoubleArrayItem(pointeur, 1)\\n sz = util.getDoubleArrayItem(pointeur, 2)\\n\\n return sx, sy, sz\",\n \"def get_scale(self):\\n\\n if not hasattr(self, \\\"scale\\\"):\\n raise NotImplementedError(\\\"\\\"\\\"All end-use subclasses of Color should define\\n a get_scale method.\\\"\\\"\\\")\\n\\n return self.scale\",\n \"def scale(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"scale\\\")\",\n \"def scale(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"scale\\\")\",\n \"def scale(self):\\n return self.distribution.scale\",\n \"def get_scale(self):\\r\\n try: return self.scale[0], self.scale[1], self.scale[2]\\r\\n except: return self.scale, self.scale, self.scale\",\n \"def GetUserScale(*args, **kwargs):\\n return _gdi_.DC_GetUserScale(*args, **kwargs)\",\n \"def unitsize(self, unit):\\n\\t\\treturn self._unitsize[unit]\",\n \"def scaling(self):\\n return self.__scaling\",\n \"def scale(self):\\n return self._gev_bijector.scale\",\n \"def getScale(self):\\n return self.factor**self.turnOn\",\n \"def _get_scaling(root):\\n dpi = root.winfo_fpixels(\\\"1i\\\")\\n scaling = dpi / 72.0\\n logger.debug(\\\"dpi: %s, scaling: %s'\\\", dpi, scaling)\\n return scaling\",\n \"def scaling(self):\\n return self._scaling\",\n \"def scaling(self):\\n return self._scaling\",\n \"def scale(self):\\n return self._a\",\n \"def _scale_coord_to_meters(coord, unit):\\n if unit == 'cm':\\n return np.divide(coord, 100.)\\n elif unit == 'mm':\\n return np.divide(coord, 1000.)\\n else:\\n return coord\",\n \"def setScale(self, *args):\\n return _libsbml.Unit_setScale(self, *args)\",\n \"def scale(self) -> Optional[pulumi.Input['ScaleArgs']]:\\n return pulumi.get(self, \\\"scale\\\")\",\n \"def scale_value(self):\\n return self._scale_value[2]\",\n \"def get_scale_op(self):\\n\\t\\treturn self.variables.get('scale')\",\n \"def scale_parameter(self):\\n return self._scale_parameter\",\n \"def get_zoom(self) -> float:\\n transform = self.transform()\\n cur_scale = (transform.m11(), transform.m22())\\n return float(f\\\"{cur_scale[0] - 1.0:0.2f}\\\")\",\n \"def _pixel_scale(self, width=None, height=None, scale=None):\\n if numpy.count_nonzero([width is not None, height is not None, scale is not None]) > 1:\\n raise ValueError(\\\"Specify only one of width, height, or scale.\\\")\\n if width is not None:\\n scale = width / self._width\\n elif height is not None:\\n scale = height / self._height\\n elif scale is None:\\n scale = 1.0\\n return scale\",\n \"def _get_unit_factor(self, unit: str) -> np.ndarray:\\n\\n unit_factors = {\\n 'vx': np.array((1, 1, 1)),\\n 'nm': np.array(self.parameters.scale),\\n 'um': np.array(self.parameters.scale)/1000\\n }\\n assert unit in unit_factors.keys(), 'Invalid unit'\\n unit_factor = unit_factors[unit]\\n\\n return unit_factor\",\n \"def unit_scale(x, eps=1e-8):\\n\\tx = x.copy()\\n\\tx -= x.min()\\n\\tx *= 1.0 / (x.max() + eps)\\n\\treturn x\",\n \"def _point_scale(self, width=None, height=None, scale=None):\\n if numpy.count_nonzero([width is not None, height is not None, scale is not None]) > 1:\\n raise ValueError(\\\"Specify only one of width, height, or scale.\\\")\\n\\n if width is not None:\\n scale = toyplot.units.points(width) / self._width\\n elif height is not None:\\n scale = toyplot.units.points(height) / self._height\\n elif scale is None:\\n scale = 1.0\\n return scale\",\n \"def scale(self):\",\n \"def convert_unit(size_in_bytes, unit):\\n if unit == 'KB':\\n return size_in_bytes/1024\\n elif unit == 'MB':\\n return size_in_bytes/(1024*1024)\\n elif unit == 'GB':\\n return size_in_bytes/(1024*1024*1024)\\n else:\\n return size_in_bytes\",\n \"def temperature_scale(self) -> str:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"temperature_scale\\\"))\\r\\n return self._temperature_scale\",\n \"def scale_to_factor(scale):\\n return (B.pi / 2) / (2 * scale**2)\",\n \"def scale(self, value):\\n return (float(value) - float(self.minimum)) / \\\\\\n float(self.maximum - self.minimum) * 2.0 - 1.0\",\n \"def removeScale(*args):\\n return _libsbml.Unit_removeScale(*args)\",\n \"def unsetScale(self):\\n return _libsbml.Unit_unsetScale(self)\",\n \"def Unit_removeScale(*args):\\n return _libsbml.Unit_removeScale(*args)\",\n \"def get_scale_parameter(self):\\r\\n \\r\\n if self.scale_parameter == 0.0: \\r\\n shape_in_gamma_func = float(1+(1/self.shape_parameter))\\r\\n gamma_func = special.gamma(shape_in_gamma_func)\\r\\n self.scale_parameter = (self.mean_fire_recurrence/gamma_func)\\r\\n return self.scale_parameter\\r\\n else:\\r\\n return self.scale_parameter\",\n \"def _scale(x):\\n scaleFactor = 1\\n _ret = int(x/scaleFactor)\\n return _ret\",\n \"def getnscale(self):\\n return self.nscale\",\n \"def get_scale_parameter(self):\\n\\n if self.scale_parameter == 0.0:\\n shape_in_gamma_func = float(1+(1/self.shape_parameter))\\n gamma_func = special.gamma(shape_in_gamma_func)\\n self.scale_parameter = (self.mean_fire_recurrence/gamma_func)\\n return self.scale_parameter\\n else:\\n return self.scale_parameter\",\n \"def doppler_scale(self):\\n return self._dopplerscale\",\n \"def getscales(self):\\n return self.scales\",\n \"def castSize(self, scale):\\n return self.camera.sensorSize * scale\",\n \"def factor_to_scale(factor):\\n return 1 / B.sqrt(4 * factor / B.pi)\",\n \"def scale(self, value):\\r\\n return (float(value)-float(self.minimum))/float(self.maximum-self.minimum)*2.0 - 1.0\",\n \"def scaling(self):\\n return self.stacked._box_scaling[1]\",\n \"def getMultiplier(self):\\n return _libsbml.Unit_getMultiplier(self)\",\n \"def colorscale(self):\\n return self[\\\"colorscale\\\"]\",\n \"def get_loss_scale(self):\\n return self._loss_scale\",\n \"def colorscale(self):\\n return self['colorscale']\",\n \"def Scale(*args, **kwargs):\\n return _gdi_.Font_Scale(*args, **kwargs)\",\n \"def get_size(size):\\n if size.isdigit():\\n return int(size)\\n\\n def do_get_size(num, unit):\\n u = units[unit]\\n if num.find('.') == -1:\\n return int(num) * u\\n return int(float(num) * u)\\n\\n s = size.strip().upper()\\n if s.find(' ') == -1:\\n num, unit = re.sub(r\\\"([\\\\d.]+)\\\", r\\\"\\\\1 \\\", s).split()\\n else:\\n num, unit = s.split()\\n\\n try:\\n return do_get_size(num, unit)\\n except KeyError:\\n\\traise Exception('unknown size unit[%s]' % size)\",\n \"def scaling_factor(self):\\n bin_scale = self.spabins * self.spebins\\n return bin_scale * self.int_time\",\n \"def FindScale(self):\\n\\n ## 6 and from the cv code the distance is 6 then we are good\\n print(\\\"TODO: Very hard\\\")\",\n \"def scale(input):\\n return (input - np.min(input)) / ((np.max(input) - np.min(input)))\",\n \"def loss_scale(self):\\n return self._loss_scale\",\n \"def cm2inch(size):\\n return size / 2.54\",\n \"def cm2inch(size):\\n return size / 2.54\",\n \"def cm2inch(size):\\n return size / 2.54\",\n \"def any_scale(scale):\\n return scale\",\n \"def scale(self, value):\\n\\t\\toldscale = self.oldmax - self.oldmin\\n\\t\\tnewscale = self.newmax - self.newmin\\n\\t\\treturn (newscale * (value - self.oldmin) / oldscale) + self.newmin\",\n \"def plate_scale(self):\\n\\n return 206265 * uu.arcsec / (self.diameter.to('mm') * self.f)\",\n \"def get_scaled_value(self, value):\\r\\n raise NotImplementedError()\",\n \"def getScale(self, mode='ACC'):\\t#good\\r\\n\\t\\tif mode.upper() == 'ACC':\\r\\n\\t\\t\\treg = 0x1C\\r\\n\\t\\telif mode.upper() == 'GYR':\\r\\n\\t\\t\\treg = 0x1B\\t\\t\\r\\n\\t\\telse:\\r\\n\\t\\t\\treturn False\\r\\n\\t\\tcurrentVal = self.read(reg)\\r\\n\\t\\tcurrentVal = self.dec2BinList(currentVal)\\r\\n\\t\\tscaleSetting = (currentVal[4]*2) + (currentVal[3]*1) \\r\\n\\t\\tif mode.upper() == 'ACC':\\r\\n\\t\\t\\tscale = 2**(scaleSetting+1) \\r\\n\\t\\telif mode.upper() == 'GYR':\\r\\n\\t\\t\\tscale = (2**(scaleSetting+1))*125\\r\\n\\t\\telse:\\r\\n\\t\\t\\treturn False\\r\\n\\t\\treturn scale,scaleSetting\",\n \"def pixel_scale(self):\\n return np.abs(float(self.header[\\\"CDELT1\\\"]))\",\n \"def _get_target_scale(self, im_size_min, im_size_max, target_size, max_size):\\n im_scale = float(target_size) / float(im_size_min)\\n # Prevent the biggest axis from being more than max_size\\n if np.round(im_scale * im_size_max) > max_size:\\n im_scale = float(max_size) / float(im_size_max)\\n return im_scale\",\n \"def scale(self,s):\\n return Vector(self.x * s, self.y * s, self.z * s)\",\n \"def get_unit(self):\\n return self.unit\",\n \"def get_units(self):\\r\\n msg = struct.pack('>2B', 56, 14)\\r\\n response = self.query(msg)\\r\\n\\r\\n if response[1] == 2:\\r\\n units = 'A'\\r\\n to_nm_multiplier = 1 / 10\\r\\n elif response[1] == 1:\\r\\n units = 'nm'\\r\\n to_nm_multiplier = 1\\r\\n elif response[1] == 0:\\r\\n units = 'um'\\r\\n to_nm_multiplier = 1000\\r\\n else:\\r\\n raise ValueError('Units not recognised.')\\r\\n\\r\\n # Save results locally too for quick re-use\\r\\n self._current_units = units\\r\\n self._current_to_nm_multiplier = to_nm_multiplier\\r\\n\\r\\n return units, to_nm_multiplier\",\n \"def scale_it(val):\\n return scale(val, 0, 1, bpm_range[0], bpm_range[1])\",\n \"def scale_pos(q_unsc):\\n q_sc = (q_unsc - 0.125)/0.125\\n return q_sc\",\n \"def __get_size_multiplier(self, multiplier):\\n if multiplier is None:\\n result = 1\\n elif multiplier in ['k', 'K']:\\n result = self.__k_multiplier\\n elif multiplier in ['m', 'M']:\\n result = self.__m_multiplier\\n elif multiplier in ['g', 'G']:\\n result = self.__g_multiplier\\n else:\\n result = 0\\n return result\",\n \"def Scale(*args, **kwargs):\\n return _gdi_.GraphicsContext_Scale(*args, **kwargs)\",\n \"def get_scaled_value_at(self, offset):\\r\\n return self.get_scaled_value(self.get_value_at(offset))\",\n \"def Scale(*args, **kwargs):\\n return _gdi_.GraphicsMatrix_Scale(*args, **kwargs)\",\n \"def _get_unit_factor(cls, unit):\\n try:\\n if isinstance(unit, str):\\n unit = cls.UNIT_FACTOR_NAMES[unit]\\n return cls.UNIT_FACTORS[unit]\\n except KeyError:\\n raise UnsupportedUnitError()\",\n \"def scale(self, fname, **kw):\\n return self.scales.scale(fname, **kw)\",\n \"def unit(self,unit_str,unit_scale):\\n self.units[unit_str] = unit_scale\\n return self\",\n \"def unit_of_measurement(self):\\n return self.sensor_type[\\\"unit\\\"]\",\n \"def scale(m, scale = 1):\\n try:\\n scaleX = abs(scale[0])\\n scaleY = abs(scale[1])\\n except:\\n scaleX = abs(scale)\\n scaleY = abs(scale)\\n resolution = (int(m.getWidth() * scaleX), int(m.getHeight() * scaleY))\\n return createBySurface(pygame.transform.scale(m.toSurface(), resolution))\",\n \"def get_scale_factor(rec, stack):\\n \\n rec_pixel_size = get_pixel_size_rec(rec)\\n stack_pixel_size = get_pixel_size_stack(stack)\\n \\n return rec_pixel_size / stack_pixel_size\",\n \"def get_scales(min_scale=0.2, max_scale=0.9,num_layers=6):\\n\\n # this code follows the original implementation of wei liu\\n # for more, look at ssd/score_ssd_pascal.py:310 in the original caffe implementation\\n min_ratio = int(min_scale * 100)\\n max_ratio = int(max_scale * 100)\\n step = int(np.floor((max_ratio - min_ratio) / (num_layers - 2)))\\n min_sizes = []\\n max_sizes = []\\n for ratio in xrange(min_ratio, max_ratio + 1, step):\\n min_sizes.append(ratio / 100.)\\n max_sizes.append((ratio + step) / 100.)\\n min_sizes = [int(100*min_scale / 2.0) / 100.0] + min_sizes\\n max_sizes = [min_scale] + max_sizes\\n\\n # convert it back to this implementation's notation:\\n scales = []\\n for layer_idx in range(num_layers):\\n scales.append([min_sizes[layer_idx], np.single(np.sqrt(min_sizes[layer_idx] * max_sizes[layer_idx]))])\\n return scales\",\n \"def get_wcs_pscale(wcs):\\n from numpy import linalg\\n det = linalg.det(wcs.wcs.cd)\\n pscale = np.sqrt(np.abs(det))*3600.\\n return pscale\",\n \"def get_scale_parameter(self):\\n\\n shape_in_gamma_func = float(1 + (1 / self._shape_parameter))\\n gamma_func = special.gamma(shape_in_gamma_func)\\n self._scale_parameter = self._mean_fire_recurrence / gamma_func\",\n \"def unit_of_measurement(self):\\n return self._metadata[1]\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def _scale_param(self, resid_us):\\n return((resid_us**2).sum().sum() / self.dof)\",\n \"def getTCHEMScaleFactor(pt):\\n #see https://twiki.cern.ch/twiki/pub/CMS/BtagPOG/SFb-mujet_payload.txt\\n if pt > 670: pt = 670\\n if pt < 30: pt = 30\\n return 0.932251*((1.+(0.00335634*pt))/(1.+(0.00305994*pt)))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80368865","0.7562799","0.74804133","0.73843277","0.7313599","0.73030704","0.72814995","0.7198944","0.7182465","0.71608347","0.71563214","0.71075463","0.7067282","0.7034129","0.7005473","0.69814765","0.6953271","0.69395196","0.6914729","0.6903628","0.6891157","0.6891157","0.6853376","0.68275654","0.67949283","0.6703553","0.66870934","0.664717","0.66379094","0.6575207","0.655788","0.65518683","0.65396684","0.6533925","0.6502524","0.64991266","0.64933854","0.6479481","0.64765626","0.6458448","0.6453984","0.64480054","0.6439325","0.642277","0.64145684","0.6408731","0.6395655","0.63948","0.63821155","0.63736033","0.63646805","0.6362619","0.63256127","0.631416","0.6271201","0.6268285","0.6250987","0.62373054","0.6231856","0.6214014","0.6199948","0.61983603","0.61972344","0.61972344","0.61972344","0.6192277","0.61771053","0.6154555","0.61539066","0.6152098","0.6151612","0.6150235","0.61321706","0.6126702","0.6125803","0.61224324","0.6121554","0.61144793","0.6103211","0.60993683","0.60874546","0.60769516","0.60601383","0.6055562","0.6055152","0.6047669","0.6028864","0.6027122","0.6026857","0.6016533","0.6000294","0.5993728","0.5993728","0.5993728","0.5993728","0.5993728","0.5993728","0.5993728","0.59833103","0.5981906"],"string":"[\n \"0.80368865\",\n \"0.7562799\",\n \"0.74804133\",\n \"0.73843277\",\n \"0.7313599\",\n \"0.73030704\",\n \"0.72814995\",\n \"0.7198944\",\n \"0.7182465\",\n \"0.71608347\",\n \"0.71563214\",\n \"0.71075463\",\n \"0.7067282\",\n \"0.7034129\",\n \"0.7005473\",\n \"0.69814765\",\n \"0.6953271\",\n \"0.69395196\",\n \"0.6914729\",\n \"0.6903628\",\n \"0.6891157\",\n \"0.6891157\",\n \"0.6853376\",\n \"0.68275654\",\n \"0.67949283\",\n \"0.6703553\",\n \"0.66870934\",\n \"0.664717\",\n \"0.66379094\",\n \"0.6575207\",\n \"0.655788\",\n \"0.65518683\",\n \"0.65396684\",\n \"0.6533925\",\n \"0.6502524\",\n \"0.64991266\",\n \"0.64933854\",\n \"0.6479481\",\n \"0.64765626\",\n \"0.6458448\",\n \"0.6453984\",\n \"0.64480054\",\n \"0.6439325\",\n \"0.642277\",\n \"0.64145684\",\n \"0.6408731\",\n \"0.6395655\",\n \"0.63948\",\n \"0.63821155\",\n \"0.63736033\",\n \"0.63646805\",\n \"0.6362619\",\n \"0.63256127\",\n \"0.631416\",\n \"0.6271201\",\n \"0.6268285\",\n \"0.6250987\",\n \"0.62373054\",\n \"0.6231856\",\n \"0.6214014\",\n \"0.6199948\",\n \"0.61983603\",\n \"0.61972344\",\n \"0.61972344\",\n \"0.61972344\",\n \"0.6192277\",\n \"0.61771053\",\n \"0.6154555\",\n \"0.61539066\",\n \"0.6152098\",\n \"0.6151612\",\n \"0.6150235\",\n \"0.61321706\",\n \"0.6126702\",\n \"0.6125803\",\n \"0.61224324\",\n \"0.6121554\",\n \"0.61144793\",\n \"0.6103211\",\n \"0.60993683\",\n \"0.60874546\",\n \"0.60769516\",\n \"0.60601383\",\n \"0.6055562\",\n \"0.6055152\",\n \"0.6047669\",\n \"0.6028864\",\n \"0.6027122\",\n \"0.6026857\",\n \"0.6016533\",\n \"0.6000294\",\n \"0.5993728\",\n \"0.5993728\",\n \"0.5993728\",\n \"0.5993728\",\n \"0.5993728\",\n \"0.5993728\",\n \"0.5993728\",\n \"0.59833103\",\n \"0.5981906\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7647739"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22380,"cells":{"query":{"kind":"string","value":"Cosine similarity between vector and matrix."},"document":{"kind":"string","value":"def cos_sim(vec, mat):\n numer = np.dot(mat, vec)\n vec_norm = np.linalg.norm(vec)\n mat_norm = np.linalg.norm(mat, axis=1)\n\n return np.divide(numer, vec_norm * mat_norm)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))","def compute_cosine_similarity(self):\n cos_matrix = []\n for i in range(len(self.train_vec)):\n val = self.vec1 * self.train_vec[i]\n cos_matrix.append(val[0])\n out = np.argmax(cos_matrix)\n print(self.train_output[out])","def cosine_similarity(vector_x, vector_y):\n if(len(vector_x)!=len(vector_y)):\n raise Exception('Vectors must be the same dimensions')\n \n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))","def calculate_cosine_similarity(self):\n tfidf_matrix = self.calculate_tfidf()\n\n cosine_similarity = linear_kernel(tfidf_matrix, tfidf_matrix) # Cosine similarity matrix calculation\n\n return cosine_similarity","def cosine_similarity(X):\n matrix = X.dot(X.transpose()).todense()\n mat_len = len(matrix)\n norms = [0] * mat_len\n for i in range(0, mat_len):\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\n norm_mat = np.matrix(norms)\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)","def cosine_sim(matrix):\n if type(matrix) is not csr_matrix:\n matrix = csr_matrix(matrix)\n\n return cosine_similarity(matrix, dense_output=False)","def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n # return cosine_similarity(v1, v2)[0][0]","def cosine_similarity(v1: Vector, v2: Vector) -> float:\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))","def cosine_similarity(v1, v2):\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def cosine_similarity(vec1, vec2) -> float:\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))","def CosineSimilarity(test_vec, source_vecs):\n cos_dist = 0\n for source_vec in source_vecs:\n cos_dist += FacePredictor.findCosineDistance(test_vec, source_vec)\n return cos_dist / len(source_vecs)","def cosine_similarity(v1, v2):\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\n return sim","def cosine_similarity(u: np.ndarray, v: np.ndarray) -> np.float64:\n assert u.shape[0] == v.shape[0], \"Input vector must have same shape.\"\n uv = 0\n uu = 0\n vv = 0\n for i in range(u.shape[0]):\n uv += u[i] * v[i]\n uu += u[i] * u[i]\n vv += v[i] * v[i]\n cosine_score = 0\n if uu != 0 and vv != 0:\n cosine_score = uv / np.sqrt(uu * vv)\n return np.float64(cosine_score)","def cosineDistanceMatrix():\n\n\tmatrix = movieMatrix()\n\tsimilarity = np.dot(matrix, matrix.T)\n\tsquareMag = np.diag(similarity)\n\tinvSquareMag = 1/squareMag\n\tinvSquareMag[np.isinf(invSquareMag)]=0\n\tinvMag = np.sqrt(invSquareMag)\n\tcosine = similarity * invMag\n\tcosine = cosine.T * invMag\n\treturn cosine","def cosine_similarity(u, v):\n\n distance = 0.0\n\n ### START CODE HERE ###\n # Compute the dot product between u and v (≈1 line)\n dot = np.dot(u, v)\n # Compute the L2 norm of u (≈1 line)\n norm_u = np.sqrt(np.dot(u, u))\n\n # Compute the L2 norm of v (≈1 line)\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\n # Compute the cosine similarity defined by formula (1) (≈1 line)\n cosine_similarity = dot / (norm_u * norm_v)\n ### END CODE HERE ###\n\n return cosine_similarity","def cosine_similarity(self, x, y):\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))","def cosine_similarity(self, source_doc, input_doc):\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\n return ((tfidf * tfidf.T).A)[0, 1]","def compute_cosine_sim(vec1, vec2):\n numer = np.dot(vec1.reshape((300,)), vec2.reshape((300,)))\n denom = np.sqrt(np.sum(np.square(vec1.reshape(300, )))) * np.sqrt(\n np.sum(np.square(vec2.reshape(300, ))))\n\n similarity = numer / denom\n\n return similarity","def cosine_distance(u, v):\n #print u,v\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))","def cosine_sim(self, u_name, v_name):\n u_vector = self.get_vector(u_name)\n v_vector = self.get_vector(v_name)\n u_norm = self.vectors_norm_db.get(u_name)\n v_norm = self.vectors_norm_db.get(v_name)\n\n numerator = self.scalar_product(u_vector.items, v_vector.items)\n denominator = float(u_norm) * float(v_norm)\n \n try:\n # round the cosine similarity two digits after the decimal point \n cosine = round(numerator / denominator, 2)\n except ZeroDivisionError:\n logging.error(\"division by zero for %s and %s !\" \\\n % (u_name, v_name))\n cosine = 0\n \n logging.debug(\"%s %s = %s \" \\\n % (u_name, v_name, cosine))\n \n return cosine","def compute_cosine_similarity(base_vector, target_vector):\n\n np.seterr(all='print')\n cosine_similarity = 0\n\n try:\n base_vector = np.longdouble(base_vector)\n target_vector = np.longdouble(target_vector)\n vector_dot_products = np.dot(base_vector, target_vector)\n vector_norms = np.linalg.norm(base_vector) * np.linalg.norm(target_vector)\n cosine_similarity = np.divide(vector_dot_products, vector_norms)\n\n if vector_norms == 0.0:\n print 'Error in vec in compute_cosine_similarity'\n print target_vector\n\n except Exception, e:\n print(str(e))\n\n return cosine_similarity","def findCosineDistance(vector1, vector2):\n vec1 = vector1.flatten()\n vec2 = vector2.flatten()\n\n a = np.dot(vec1.T, vec2)\n b = np.dot(vec1.T, vec1)\n c = np.dot(vec2.T, vec2)\n return 1 - (a / (np.sqrt(b) * np.sqrt(c)))","def get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\n return cosine_similarity(word2vec.wv.vectors)","def compute_cosine_sim(vec1, vec2):\r\n\r\n vec1 = np.array(vec1)\r\n vec2 = np.array(vec2)\r\n return np.dot(vec1, vec2)/(norm(vec1) * norm(vec2))","def cosine_similarity(vec_x, vec_y):\n sim_prod = 0.0\n len_x = 0\n len_y = 0\n\n for ngram in vec_x:\n len_x += vec_x[ngram] ** 2\n\n for ngram in vec_y:\n len_y += vec_y[ngram] ** 2\n\n len_x = math.sqrt(len_x)\n len_y = math.sqrt(len_y)\n\n for ngram in vec_x:\n if ngram in vec_y:\n sim_prod += vec_x[ngram] * vec_y[ngram]\n\n return sim_prod / (len_x * len_y)","def cos_sim(u, v):\n return np.vdot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))","def test_cosine_similarity_matrix():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def test_cosine_similarity_compiled():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def cos_sim(vec1, vec2):\n if len(vec1) != len(vec2):\n print 'dimension does not agree.'\n numerator_sum = 0 \n for i in range(len(vec1)):\n numerator_sum = numerator_sum + vec1[i]*vec2[i]\n \n denom = np.linalg.norm(vec1) * np.linalg.norm(vec2)\n \n return numerator_sum/denom","def cosine_distance(u, v):\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))","def test_cosine_similarity_matrix_compiled():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def cosine_sim(a: np.ndarray, \n b: np.ndarray \n ) -> float:\n return (\n 1 + a.dot(b) / \n (np.linalg.norm(a)*np.linalg.norm(b))\n ) / 2","def cos_sim(v1: Union[np.ndarray, np.iterable, int, float], v2: Union[np.ndarray, np.iterable, int, float]) -> float:\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def get_cosine_similarity(doc1, doc2):\n count_vectorizer = CountVectorizer(stop_words='english')\n sparse_matrix = count_vectorizer.fit_transform(raw_documents=[doc1, doc2])\n dtm = sparse_matrix.todense()\n df_dtm = pd.DataFrame(data=dtm, \n columns=count_vectorizer.get_feature_names(), \n index=['doc1', 'doc2'])\n similarity_matrix = cosine_similarity(df_dtm, df_dtm)\n similarity_score = round(similarity_matrix[0][1], 6)\n return similarity_score","def cosine_similarity(a, b):\n cs = dot_product(a, b)/(norm(a) * norm(b))\n return cs","def test_cosine_similarity():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity.py_func(vector1, vector1)\n score12 = cosine_similarity.py_func(vector1, vector2)\n score22 = cosine_similarity.py_func(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def adjusted_cosine_sim(matrix):\n if type(matrix) is not csr_matrix:\n matrix = csr_matrix(matrix)\n\n matrix = _subtract_row_mean(matrix)\n return cosine_similarity(matrix, dense_output=False)","def cosine_sim_cf(matrix):\n if type(matrix) is not lil_matrix:\n matrix = lil_matrix(matrix)\n\n n = matrix.shape[0]\n rows, cols, data = [], [], []\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\n\n for i in range(n):\n i_ratings, i_items = matrix.data[i], user_items[i]\n for j in range(i, n):\n j_ratings, j_items = matrix.data[j], user_items[j]\n sum_numerator, sum_denominator_i, sum_denominator_j = 0, 0, 0\n i_item_ctd, j_item_ctd = 0, 0\n while i_item_ctd < len(i_items) and j_item_ctd < len(j_items):\n if i_items[i_item_ctd][0] > j_items[j_item_ctd][0]:\n j_item_ctd += 1\n elif i_items[i_item_ctd][0] < j_items[j_item_ctd][0]:\n i_item_ctd += 1\n else:\n i_idx = i_items[i_item_ctd][1]\n j_idx = j_items[j_item_ctd][1]\n sum_numerator += i_ratings[i_idx] * j_ratings[j_idx]\n sum_denominator_i += i_ratings[i_idx] ** 2\n sum_denominator_j += j_ratings[j_idx] ** 2\n i_item_ctd += 1\n j_item_ctd += 1\n\n if sum_numerator == 0: continue\n s = sum_numerator / (math.sqrt(sum_denominator_i) * math.sqrt(sum_denominator_j))\n rows.append(i), cols.append(j), data.append(s)\n if i != j: rows.append(j), cols.append(i), data.append(s)\n\n return csr_matrix((data, (rows, cols)))","def __cos_sim(self, v1, v2):\n if np.count_nonzero(v1) == 0 or np.count_nonzero(v2) == 0:\n # whenever at least one of the vectors is all zeros, spatial.distance.cosine will fail by returning nan\n ret = 0\n else:\n ret = 1 - spatial.distance.cosine(v1, v2)\n return ret","def cos_sim(v1, v2):\r\n return np.inner(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def cosine_sim_numpy(im, s):\n return im.dot(s.T)","def cosine(vector_1, vector_2):\n\n def _norm(_v):\n return np.sqrt(sum([x ** 2 for x in _v.values()]))\n\n numerator = dot_product(vector_1, vector_2)\n denominator = _norm(vector_1) * _norm(vector_2)\n if denominator == 0:\n return -1\n return numerator / denominator","def cosine_sim(im, s):\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices ","def cosine_similarity(document, cluster):\n num = dot(document.vector, cluster.centroid)\n den = linalg.norm(document.vector) * linalg.norm(cluster.centroid)\n\n return num / den","def get_cosine_similarity(self, query: list):\n question_vector = self.get_vector(query)\n\n return cosine_similarity(question_vector, self.vectors).flatten()","def cosine_similarity(v1, v2):\n # Cosine Sim:\n # Get the words that both have in common\n\n v1words = set(v1.keys())\n v2words = set(v2.keys())\n\n numerator_words = v1words.intersection(v2words)\n\n # Multiply and sum those counts\n numerator = 0.0\n for word in numerator_words:\n numerator += v1[word] * v2[word]\n\n\n # Divide by the sqrt of the product of the sum of the squares of the counts\n denominator = math.sqrt(math.magnitude(list(v1.values())) * math.magnitude(list(v2.values())))\n\n return numerator/denominator","def similarity_vec(self, vec1: numpy.ndarray, vec2: numpy.ndarray, metric='cosine') -> float:\n if numpy.count_nonzero(vec1) == 0 or numpy.count_nonzero(vec2) == 0:\n if metric == 'cosine':\n return 0.\n else:\n return 0.\n\n vec1 = vec1.reshape((1, -1))\n vec2 = vec2.reshape((1, -1))\n if metric == 'cosine':\n return (1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1))[0]\n else:\n return distance.cdist(vec1, vec2, metric=metric).reshape(-1)[0]","def cosine_similarity(a, b):\n if a.ndim != 1 or b.ndim != 1:\n raise InvalidShapeException(a,b)\n\n if len(a) != len(b):\n raise InvalidLengthException(a,b)\n \n mag_a = np.linalg.norm(a)\n mag_b = np.linalg.norm(b)\n\n return np.dot(a,b)/(mag_a*mag_b)","def _cosine_matrix(self, x1, x2):\n # expand h1 shape to (batch_size, x1_timesteps, 1, embedding_size)\n x1 = K.expand_dims(x1, axis=2)\n # expand x2 shape to (batch_size, 1, x2_timesteps, embedding_size)\n x2 = K.expand_dims(x2, axis=1)\n # cosine matrix (batch_size, h1_timesteps, h2_timesteps)\n cos_matrix = self._cosine_similarity(x1, x2)\n return cos_matrix","def cos_vecs(x, y):\r\n _t = np.sum((x * y), axis=1)\r\n norm_x = np.linalg.norm(x, axis=1, keepdims=True)\r\n norm_y = np.linalg.norm(y, axis=1, keepdims=True)\r\n _t = np.reshape(_t, (-1, 1))\r\n ret = _t / (norm_x * norm_y + 1e-10)\r\n return ret","def cosine_sim(im, s):\n return im.mm(s.t())","def get_cosine_sim(self):\r\n return CosineSimilarity().calculate_similarity(self.tweets)","def cosine_value(self,doc_vector,query_vector):\n\t\tvalue=0;i=0;\n\t\tunit_vector_query=self.unit_vector(query_vector);\n\t\tunit_vector_doc=self.unit_vector(doc_vector);\n\t\titerate=0\n\t\tfor word in query_vector:\n\t\t\tif word in doc_vector:\n\t\t\t\tvalue+=query_vector[word]*doc_vector[word]\n\t\tif unit_vector_query != 0:\n\t\t\tvalue = value/(unit_vector_query*unit_vector_doc)\n\t\telse:\n\t\t\tvalue = 0\n\t\treturn value","def cosine_sim(im, s,):\n return im.mm(s.t())","def pairwise_cosine(mat):\n def cosine_similarity(a, b):\n return (a * b).sum() / (np.linalg.norm(a) * np.linalg.norm(b))\n\n n = len(mat)\n dist_mat = np.zeros((n, n))\n for i in range(n):\n for j in range(i + 1, n):\n dist = cosine_similarity(mat[i], mat[j])\n dist_mat[i, j] = dist\n dist_mat[j, i] = dist\n\n dist_mat[np.isnan(dist_mat)] = 0\n\n return dist_mat","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\n w12 = torch.sum(x1 * x2, dim)\n w1 = torch.norm(x1, 2, dim)\n w2 = torch.norm(x2, 2, dim)\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\r\n w12 = torch.sum(x1 * x2, dim)\r\n w1 = torch.norm(x1, 2, dim)\r\n w2 = torch.norm(x2, 2, dim)\r\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def test_cossim(self):\n metrics = SimilarityMetrics()\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test1,0.0)\n\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test2,-1.0)\n\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\n np.testing.assert_almost_equal(test3,1.0)","def get_cosine(vec1, vec2):\n OPS = get_current_ops()\n v1 = OPS.to_numpy(OPS.asarray(vec1))\n v2 = OPS.to_numpy(OPS.asarray(vec2))\n return numpy.dot(v1, v2) / (numpy.linalg.norm(v1) * numpy.linalg.norm(v2))","def pcosine(u, v):\n\n # validate vectors like scipy does\n u = ssd._validate_vector(u)\n v = ssd._validate_vector(v)\n\n dist = 1. - np.abs(np.dot(u, v) / (linalg.norm(u) * linalg.norm(v)))\n\n return dist","def get_similarity(df):\n count = CountVectorizer()\n count_matrix = count.fit_transform(df[\"bag_of_words\"])\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\n return cosine_sim","def cosine(\n x_mat: 'Tensor', y_mat: 'Tensor', eps: float = 1e-7, device: str = 'cpu'\n) -> 'numpy.ndarray':\n with _get_tf_device(device):\n normalize_a = tf.nn.l2_normalize(x_mat, 1, epsilon=eps)\n normalize_b = tf.nn.l2_normalize(y_mat, 1, epsilon=eps)\n distance = 1 - tf.matmul(normalize_a, normalize_b, transpose_b=True)\n return distance.numpy()","def test_cosine_similarity_all_zeros_compiled():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","def cosine(xs: Tensor, ys: Tensor, epsilon: float = 1e-8) -> Tensor:\n mat = xs @ ys.t()\n x_norm = xs.norm(2, dim=1) + epsilon\n y_norm = ys.norm(2, dim=1) + epsilon\n x_diag = (1 / x_norm).diag()\n y_diag = (1 / y_norm).diag()\n return x_diag @ mat @ y_diag","def cosine(X,Y=None,dense_output=True):\n #Reemplace NaN with Zero\n X[np.isnan(X)] = 0\n if Y is not None:\n Y[np.isnan(Y)] = 0\n \n return cosine_similarity(X,Y,dense_output)","def pairwise_cosine_similarity(x, y):\n x = torch.div(x, torch.sqrt(torch.max(torch.sum(x ** 2), 1e-12)))\n y = torch.div(y, torch.sqrt(torch.max(torch.sum(y ** 2), 1e-12)))\n return torch.mm(x, torch.transpose(y, 1, 0))","def acos_sim(col_pair):\n col1, col2 = col_pair\n c1 = col1[1][0]\n c2 = col2[1][0]\n dp = dot_prod(c1, c2)\n cs = dp / (col1[1][1] * col2[1][1])\n return ((col1[0], col2[0]), cs)","def cosine_similarity(v1, v2):\n v1_len = 0\n v2_len = 0\n dot_product = 0\n\n for context_id, count in v1.items():\n v1_len += count ** 2\n if context_id in v2:\n dot_product += count*v2[context_id]\n for count in v2.values():\n v2_len += count ** 2\n\n v1_len = math.sqrt(v1_len)\n v2_len = math.sqrt(v2_len)\n return dot_product/(v1_len * v2_len)","def cosine(arr1, arr2):\n\n if arr1 is None or arr2 is None:\n return np.NaN\n if not isinstance(arr1, list):\n arr1 = [arr1]\n if any(pd.isnull(arr1)):\n return np.NaN\n if not isinstance(arr2, list):\n arr2 = [arr2]\n if any(pd.isnull(arr2)):\n return np.NaN\n # Create cosine measure object\n measure = sm.Cosine()\n # Call the function to compute the cosine measure.\n return measure.get_raw_score(arr1, arr2)","def cos_sim(com_feat,ref_feat):\n # Fill this in\n a = numpy.squeeze(com_feat)\n b = numpy.squeeze(ref_feat)\n return numpy.dot(a, b) / (numpy.linalg.norm(a) * numpy.linalg.norm(b))","def get_cosine_similarities(keywords: List[str],\n matrices: Matrices,\n word2onehot: Dict[str, int]\n ) -> None:\n for i in combinations(keywords, 2):\n print(i[0], i[1], cosine_sim(\n matrices.embedding[\n word2onehot[i[0]]], matrices.embedding[word2onehot[i[1]]\n ]\n ))","def self_similarity_matrix(feature_vectors):\n norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T])\n norm_feature_vectors = norm_feature_vectors[0].T\n sim_matrix = 1.0 - distance.squareform(\n distance.pdist(norm_feature_vectors.T, 'cosine'))\n return sim_matrix","def test_cosine_similarity_all_zeros():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity.py_func(vector1, vector1)\n score12 = cosine_similarity.py_func(vector1, vector2)\n score22 = cosine_similarity.py_func(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","def cosine_sim_counters(a, b):\n union_ab = sorted((a | b).keys())\n veca = np.array([a[element] if element in a else 0 for element in union_ab])\n vecb = np.array([b[element] if element in b else 0 for element in union_ab])\n return np.dot(veca, vecb) / (np.linalg.norm(veca) * np.linalg.norm(vecb))","def cosinesimilarity_cal(CTRDM1, CTRDM2):\n\n # get number of conditions\n n_cons = np.shape(CTRDM1)[0]\n\n # calculate the number of value above the diagonal in RDM\n n = n_cons * (n_cons - 1)\n\n # initialize two vectors to store the values above the diagnal of two RDMs\n v1 = np.zeros([n], dtype=np.float64)\n v2 = np.zeros([n], dtype=np.float64)\n\n # assignment\n nn = 0\n for i in range(n_cons):\n for j in range(n_cons):\n if i != j:\n v1[nn] = CTRDM1[i, j]\n v2[nn] = CTRDM2[i, j]\n nn = nn + 1\n\n # calculate the Cosine Similarity\n V1 = np.mat(v1)\n V2 = np.mat(v2)\n num = float(V1 * V2.T)\n denom = np.linalg.norm(V1) * np.linalg.norm(V2)\n cos = num / denom\n similarity = 0.5 + 0.5 * cos\n\n return similarity","def cosine_sim_collections(a, b):\n setab = sorted(set(a) | set(b))\n countera, counterb = Counter(a), Counter(b)\n veca = [countera[element] if element in a else 0 for element in setab]\n vecb = [counterb[element] if element in b else 0 for element in setab]\n return dot(veca, vecb) / (norm(veca) * norm(vecb))","def centrality(similarity, vector, vectors):\n return 1.0/len(vectors)*sum([similarity(vector,y) for y in vectors\\\n if y != vector])","def calculate_similarity(self, tweets):\r\n if (len(tweets) == 1):\r\n return 0\r\n vectors = self.vectorizer.vectorize_data(tweets, False)\r\n\r\n temp = cosine_similarity(vectors[0:-1], vectors)\r\n temp = [item for sublist in temp for item in sublist]\r\n sim = sum(temp) / len(temp)\r\n return sim","def getGloveoCosineSimilarity(question1, question2):\n questions = [question1, question2]\n\n ## for the sentences we need to get the count vectors\n vec = CountVectorizer(max_features=5000, stop_words=None,binary=True)\n count_vectors = vec.fit_transform(questions)\n\n ## get the vocabulary of words from the questions\n vocab_index = vec.vocabulary_\n\n ## get the index of the words and embeddings\n index_word = {v:k for k, v in vocab_index.items()}\n\n ## get the question vectors\n question_vectors = np.zeros((count_vectors.shape[0], 300))\n\n ## iterate through count vectors for each word get the embeddings\n ## for each embedding, we will then average by the number of words\n ## this will be then used for cosine similarity\n for i in range(count_vectors.shape[0]):\n row = count_vectors[i, :].toarray()\n word_ids = np.where(row > 0)[1]\n word_counts = row[:, word_ids][0]\n numWords = np.sum(word_counts)\n\n ## if there are no words, continue\n if numWords == 0:\n continue\n\n ## initialize the word embeddings to 0\n word_embeddings = np.zeros((word_ids.shape[0], 300))\n\n ## update the word embeddings\n for j in range(word_ids.shape[0]):\n word_id = word_ids[j]\n word_embeddings[j, :] = word_counts[j] * gloveDict[index_word[word_id]]\n question_vectors[i, :] = np.sum(word_embeddings, axis=0) / numWords\n\n return(cosine_similarity(question_vectors[0], question_vectors[1])[0][0])","def get_cosin_sim(question, contexts):\r\n cos_sim_for_question = []\r\n for context in contexts :\r\n cv = CountVectorizer(stop_words=MY_STOPWORDS, lowercase=False)\r\n matrix = cv.fit_transform(pd.DataFrame([question, context])[0]).toarray()\r\n cos_sim = dot(matrix[0], matrix[1])/(norm(matrix[0])*norm(matrix[1]))\r\n cos_sim_for_question.append(cos_sim)\r\n return pd.Series(cos_sim_for_question)","def cosine_similarity_matrix(references: np.ndarray, queries: np.ndarray) -> np.ndarray:\n size1 = references.shape[0]\n size2 = queries.shape[0]\n scores = np.zeros((size1, size2))\n for i in range(size1):\n for j in range(size2):\n scores[i, j] = cosine_similarity(references[i, :], queries[j, :])\n return scores","def cosine_similarity(user, business):\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))","def tf_cosine_distance(self, a, b):\n normalize_a = tf.nn.l2_normalize(a, -1)\n normalize_b = tf.nn.l2_normalize(b, -1)\n cos_similarity = tf.reduce_sum(\n tf.multiply(normalize_a, normalize_b), axis=-1, keep_dims=True\n )\n return (1.0 - cos_similarity) / 2.0","def cosine_similarity(a, b):\n\n numerator = tf.reduce_sum(tf.multiply(a, b), axis=1)\n denominator = tf.multiply(tf.norm(a, axis=1), tf.norm(b, axis=1))\n cos_similarity = numerator/denominator\n return cos_similarity","def compute_cosine(pred, obs):\n assert(pred.numel() == obs.numel()), \\\n 'Size of observation and prediction tensors much match. Received: pred %s, obs %s.'%(\n str(pred.size()), str(obs.size()))\n\n def normalise(x, dim=1):\n \"\"\" compute L2 norm and normalise x \"\"\"\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\n if dim>0:\n x /= norm.unsqueeze(dim)\n return x\n\n # if we have one-dimensional tensors, compute cosine similarity along first dimension (0).\n # if we have two-dimensional tensors, compute cosine similarity along second dimension (1).\n # if we have three-dimensional tensors, compute cosine similarity along third dimension (2).\n # i.e. first dimension is considered the feature vector (will be reduced to a scalar, the cos.sim.)\n dim = len(pred.size()) - 1\n assert(dim>=0 and dim <=2), \\\n 'This function only computes cosine similarity between 1D, 2D or 3D tensors! Received dim==%i'%(dim)\n\n p_norm = normalise(pred, dim=dim)\n v_norm = normalise(obs, dim=dim)\n return torch.nn.functional.cosine_similarity( p_norm, v_norm, dim=dim )","def cos(\r\n vec1: torch.FloatTensor, vec2: torch.FloatTensor, dim: int = -1\r\n) -> torch.FloatTensor:\r\n return torch.sum(vec1 * vec2, dim=dim) / (\r\n vec1.norm(dim=dim) * vec2.norm(dim=dim) + EPS\r\n )","def cosine_similarity_pure(X, Y=None):\n X, Y = _check_pairwise_arrays(X, Y)\n X_normalized = normalize_pure(X, copy=True)\n if X is Y:\n Y_normalized = X_normalized\n else:\n Y_normalized = normalize_pure(Y, copy=True)\n K = dot_2d(X_normalized, Y_normalized)\n return K","def _do_action_calculate_similarity_cosine_express(self):\n self._run_express_job(\"com.directv.recommend.express.CosineCFTrainer\")\n self._scan_table(\"content/item_item_similarities\")","def cosine_distance(x1, x2):\n x1 = tf.cast(x1, dtype=tf.float32)\n x2 = tf.cast(x2, dtype=tf.float32)\n\n # dot product between rows of `x_1` and rows of `x_2`\n # \"ij,ij->i\" := output[i] = sum_j x1[i, j] * x2[i, j]\n cos_thetas = tf.linalg.einsum(\"ij,ij->i\", x1, x2)\n cos_distances = 1 - cos_thetas\n\n # deal with numerical inaccuracies setting small negatives to zero\n cos_distances = tf.maximum(cos_distances, 0.0)\n\n return cos_distances","def safe_cosine_sim(x, y):\n l2x = fluid.layers.l2_normalize(x, axis=-1)\n l2y = fluid.layers.l2_normalize(y, axis=-1)\n cos = fluid.layers.reduce_sum(l2x * l2y, dim=1, keep_dim=True)\n return cos","def similarity_matrix(feat_mat):\n sim_mat = cosine_similarity(feat_mat)\n np.fill_diagonal(sim_mat, 0)\n return sim_mat","def cosine_distances(X, Y):\n # should not need X_norm_squared because if you could precompute that as\n # well as Y, then you should just pre-compute the output and not even\n # call this function.\n if X is Y:\n X = Y = np.asanyarray(X)\n else:\n X = np.asanyarray(X)\n Y = np.asanyarray(Y)\n\n if X.shape[1] != Y.shape[1]:\n raise ValueError(\"Incompatible dimension for X and Y matrices\")\n\n return 1. - ssd.cdist(X, Y, 'cosine')","def cosine_distance(m, f):\n return (1 - ((m * f).sum() / (norm(m) * norm(f))))","def _arccosine(self, s1, s2, tf_embs):\n tf_pi = tf.constant(np.pi, dtype=tf.float64)\n mat1 = tf.gather(tf_embs, s1)\n mat2 = tf.gather(tf_embs, s2)\n tf_norms = tf.constant(self.norms, dtype=tf.float64, name='norms')\n norms1 = tf.gather(tf_norms, s1)\n norms2 = tf.gather(tf_norms, s2)\n dot = tf.matmul(mat1, tf.transpose(mat2))\n norms = tf.matmul(norms1, tf.transpose(norms2))\n # We clip values due to numerical errors\n # which put some values outside the arccosine range.\n cosine = tf.clip_by_value(dot / norms, -1, 1)\n angle = tf.acos(cosine)\n # The 0 vector has norm 0, which generates a NaN.\n # We catch these NaNs and replace them with pi,\n # which ends up returning 0 similarity.\n angle = tf.select(tf.is_nan(angle), tf.ones_like(angle) * tf_pi, angle)\n return 1 - (angle / tf_pi)","def cosine_similarity(y_true, y_pred, axis=-1):\n y_true = nn.l2_normalize(y_true, axis=axis)\n y_pred = nn.l2_normalize(y_pred, axis=axis)\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)","def cosine_collection_distance(x1, x2):\n x1 = tf.cast(x1, dtype=tf.float32)\n x2 = tf.cast(x2, dtype=tf.float32)\n\n # dot product between rows of `x1` and columns of `x2` transpose\n cos_thetas = tf.linalg.matmul(x1, x2, transpose_b=True)\n pairwise_distances = 1 - cos_thetas\n\n # deal with numerical inaccuracies setting small negatives to zero\n pairwise_distances = tf.maximum(pairwise_distances, 0.0)\n\n return pairwise_distances","def cossim(vA, vB):\n return np.dot(vA, vB) / (np.sqrt(np.dot(vA, vA)) * np.sqrt(np.dot(vB, vB)))","def cos_sim(a, b):\n numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom\n for i in movies: #replace movies\n numerator += (ratingai * ratingbi)\n sosA += ratingai * ratingai\n sosB += ratingbi * ratingbi\n denom = sqrt(sosA * sosB) #make sure denom won't be 0!\n return numerator / denom","def cosineFunction(self):\n \n w = np.zeros((self.N))\n l = self.l\n for i in range(self.r.shape[0]):\n r = np.abs(self.r[i])\n if r <= l:\n tf = lambda r,l : 0.5 * (np.cos(np.pi * r/l) + 1)\n w[i] = tf(r,l)\n else:\n w[i] = 0\n self.w = w","def discrete_cosine_tranform(self, matrix):\n\n\n x = matrix.shape[0]\n y = matrix.shape[1]\n N = x\n\n #Fourier Transform matrix:\n dct = np.zeros([x,y])\n\n for u in range(0, x):\n for v in range(0, y):\n sum_ft = 0\n for i in range(0, x):\n for j in range(0, y):\n\n sum_ft = sum_ft + matrix[i, j] * (np.cos(((2*np.pi)/N)*(u*i + v*j)))\n\n dct[u, v] = sum_ft\n\n return dct"],"string":"[\n \"def cosine_similarity(cls, vec_a, vec_b):\\n return np.dot(vec_a, vec_b) / \\\\\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))\",\n \"def compute_cosine_similarity(self):\\n cos_matrix = []\\n for i in range(len(self.train_vec)):\\n val = self.vec1 * self.train_vec[i]\\n cos_matrix.append(val[0])\\n out = np.argmax(cos_matrix)\\n print(self.train_output[out])\",\n \"def cosine_similarity(vector_x, vector_y):\\n if(len(vector_x)!=len(vector_y)):\\n raise Exception('Vectors must be the same dimensions')\\n \\n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))\",\n \"def calculate_cosine_similarity(self):\\n tfidf_matrix = self.calculate_tfidf()\\n\\n cosine_similarity = linear_kernel(tfidf_matrix, tfidf_matrix) # Cosine similarity matrix calculation\\n\\n return cosine_similarity\",\n \"def cosine_similarity(X):\\n matrix = X.dot(X.transpose()).todense()\\n mat_len = len(matrix)\\n norms = [0] * mat_len\\n for i in range(0, mat_len):\\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\\n norm_mat = np.matrix(norms)\\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)\",\n \"def cosine_sim(matrix):\\n if type(matrix) is not csr_matrix:\\n matrix = csr_matrix(matrix)\\n\\n return cosine_similarity(matrix, dense_output=False)\",\n \"def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\\n # return cosine_similarity(v1, v2)[0][0]\",\n \"def cosine_similarity(v1: Vector, v2: Vector) -> float:\\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))\",\n \"def cosine_similarity(v1, v2):\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def cosine_similarity(vec1, vec2) -> float:\\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))\",\n \"def CosineSimilarity(test_vec, source_vecs):\\n cos_dist = 0\\n for source_vec in source_vecs:\\n cos_dist += FacePredictor.findCosineDistance(test_vec, source_vec)\\n return cos_dist / len(source_vecs)\",\n \"def cosine_similarity(v1, v2):\\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\\n return sim\",\n \"def cosine_similarity(u: np.ndarray, v: np.ndarray) -> np.float64:\\n assert u.shape[0] == v.shape[0], \\\"Input vector must have same shape.\\\"\\n uv = 0\\n uu = 0\\n vv = 0\\n for i in range(u.shape[0]):\\n uv += u[i] * v[i]\\n uu += u[i] * u[i]\\n vv += v[i] * v[i]\\n cosine_score = 0\\n if uu != 0 and vv != 0:\\n cosine_score = uv / np.sqrt(uu * vv)\\n return np.float64(cosine_score)\",\n \"def cosineDistanceMatrix():\\n\\n\\tmatrix = movieMatrix()\\n\\tsimilarity = np.dot(matrix, matrix.T)\\n\\tsquareMag = np.diag(similarity)\\n\\tinvSquareMag = 1/squareMag\\n\\tinvSquareMag[np.isinf(invSquareMag)]=0\\n\\tinvMag = np.sqrt(invSquareMag)\\n\\tcosine = similarity * invMag\\n\\tcosine = cosine.T * invMag\\n\\treturn cosine\",\n \"def cosine_similarity(u, v):\\n\\n distance = 0.0\\n\\n ### START CODE HERE ###\\n # Compute the dot product between u and v (≈1 line)\\n dot = np.dot(u, v)\\n # Compute the L2 norm of u (≈1 line)\\n norm_u = np.sqrt(np.dot(u, u))\\n\\n # Compute the L2 norm of v (≈1 line)\\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\\n # Compute the cosine similarity defined by formula (1) (≈1 line)\\n cosine_similarity = dot / (norm_u * norm_v)\\n ### END CODE HERE ###\\n\\n return cosine_similarity\",\n \"def cosine_similarity(self, x, y):\\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\",\n \"def cosine_similarity(self, source_doc, input_doc):\\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\\n return ((tfidf * tfidf.T).A)[0, 1]\",\n \"def compute_cosine_sim(vec1, vec2):\\n numer = np.dot(vec1.reshape((300,)), vec2.reshape((300,)))\\n denom = np.sqrt(np.sum(np.square(vec1.reshape(300, )))) * np.sqrt(\\n np.sum(np.square(vec2.reshape(300, ))))\\n\\n similarity = numer / denom\\n\\n return similarity\",\n \"def cosine_distance(u, v):\\n #print u,v\\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))\",\n \"def cosine_sim(self, u_name, v_name):\\n u_vector = self.get_vector(u_name)\\n v_vector = self.get_vector(v_name)\\n u_norm = self.vectors_norm_db.get(u_name)\\n v_norm = self.vectors_norm_db.get(v_name)\\n\\n numerator = self.scalar_product(u_vector.items, v_vector.items)\\n denominator = float(u_norm) * float(v_norm)\\n \\n try:\\n # round the cosine similarity two digits after the decimal point \\n cosine = round(numerator / denominator, 2)\\n except ZeroDivisionError:\\n logging.error(\\\"division by zero for %s and %s !\\\" \\\\\\n % (u_name, v_name))\\n cosine = 0\\n \\n logging.debug(\\\"%s %s = %s \\\" \\\\\\n % (u_name, v_name, cosine))\\n \\n return cosine\",\n \"def compute_cosine_similarity(base_vector, target_vector):\\n\\n np.seterr(all='print')\\n cosine_similarity = 0\\n\\n try:\\n base_vector = np.longdouble(base_vector)\\n target_vector = np.longdouble(target_vector)\\n vector_dot_products = np.dot(base_vector, target_vector)\\n vector_norms = np.linalg.norm(base_vector) * np.linalg.norm(target_vector)\\n cosine_similarity = np.divide(vector_dot_products, vector_norms)\\n\\n if vector_norms == 0.0:\\n print 'Error in vec in compute_cosine_similarity'\\n print target_vector\\n\\n except Exception, e:\\n print(str(e))\\n\\n return cosine_similarity\",\n \"def findCosineDistance(vector1, vector2):\\n vec1 = vector1.flatten()\\n vec2 = vector2.flatten()\\n\\n a = np.dot(vec1.T, vec2)\\n b = np.dot(vec1.T, vec1)\\n c = np.dot(vec2.T, vec2)\\n return 1 - (a / (np.sqrt(b) * np.sqrt(c)))\",\n \"def get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\\n return cosine_similarity(word2vec.wv.vectors)\",\n \"def compute_cosine_sim(vec1, vec2):\\r\\n\\r\\n vec1 = np.array(vec1)\\r\\n vec2 = np.array(vec2)\\r\\n return np.dot(vec1, vec2)/(norm(vec1) * norm(vec2))\",\n \"def cosine_similarity(vec_x, vec_y):\\n sim_prod = 0.0\\n len_x = 0\\n len_y = 0\\n\\n for ngram in vec_x:\\n len_x += vec_x[ngram] ** 2\\n\\n for ngram in vec_y:\\n len_y += vec_y[ngram] ** 2\\n\\n len_x = math.sqrt(len_x)\\n len_y = math.sqrt(len_y)\\n\\n for ngram in vec_x:\\n if ngram in vec_y:\\n sim_prod += vec_x[ngram] * vec_y[ngram]\\n\\n return sim_prod / (len_x * len_y)\",\n \"def cos_sim(u, v):\\n return np.vdot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))\",\n \"def test_cosine_similarity_matrix():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def test_cosine_similarity_compiled():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cos_sim(vec1, vec2):\\n if len(vec1) != len(vec2):\\n print 'dimension does not agree.'\\n numerator_sum = 0 \\n for i in range(len(vec1)):\\n numerator_sum = numerator_sum + vec1[i]*vec2[i]\\n \\n denom = np.linalg.norm(vec1) * np.linalg.norm(vec2)\\n \\n return numerator_sum/denom\",\n \"def cosine_distance(u, v):\\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))\",\n \"def test_cosine_similarity_matrix_compiled():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def cosine_sim(a: np.ndarray, \\n b: np.ndarray \\n ) -> float:\\n return (\\n 1 + a.dot(b) / \\n (np.linalg.norm(a)*np.linalg.norm(b))\\n ) / 2\",\n \"def cos_sim(v1: Union[np.ndarray, np.iterable, int, float], v2: Union[np.ndarray, np.iterable, int, float]) -> float:\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def get_cosine_similarity(doc1, doc2):\\n count_vectorizer = CountVectorizer(stop_words='english')\\n sparse_matrix = count_vectorizer.fit_transform(raw_documents=[doc1, doc2])\\n dtm = sparse_matrix.todense()\\n df_dtm = pd.DataFrame(data=dtm, \\n columns=count_vectorizer.get_feature_names(), \\n index=['doc1', 'doc2'])\\n similarity_matrix = cosine_similarity(df_dtm, df_dtm)\\n similarity_score = round(similarity_matrix[0][1], 6)\\n return similarity_score\",\n \"def cosine_similarity(a, b):\\n cs = dot_product(a, b)/(norm(a) * norm(b))\\n return cs\",\n \"def test_cosine_similarity():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity.py_func(vector1, vector1)\\n score12 = cosine_similarity.py_func(vector1, vector2)\\n score22 = cosine_similarity.py_func(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def adjusted_cosine_sim(matrix):\\n if type(matrix) is not csr_matrix:\\n matrix = csr_matrix(matrix)\\n\\n matrix = _subtract_row_mean(matrix)\\n return cosine_similarity(matrix, dense_output=False)\",\n \"def cosine_sim_cf(matrix):\\n if type(matrix) is not lil_matrix:\\n matrix = lil_matrix(matrix)\\n\\n n = matrix.shape[0]\\n rows, cols, data = [], [], []\\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\\n\\n for i in range(n):\\n i_ratings, i_items = matrix.data[i], user_items[i]\\n for j in range(i, n):\\n j_ratings, j_items = matrix.data[j], user_items[j]\\n sum_numerator, sum_denominator_i, sum_denominator_j = 0, 0, 0\\n i_item_ctd, j_item_ctd = 0, 0\\n while i_item_ctd < len(i_items) and j_item_ctd < len(j_items):\\n if i_items[i_item_ctd][0] > j_items[j_item_ctd][0]:\\n j_item_ctd += 1\\n elif i_items[i_item_ctd][0] < j_items[j_item_ctd][0]:\\n i_item_ctd += 1\\n else:\\n i_idx = i_items[i_item_ctd][1]\\n j_idx = j_items[j_item_ctd][1]\\n sum_numerator += i_ratings[i_idx] * j_ratings[j_idx]\\n sum_denominator_i += i_ratings[i_idx] ** 2\\n sum_denominator_j += j_ratings[j_idx] ** 2\\n i_item_ctd += 1\\n j_item_ctd += 1\\n\\n if sum_numerator == 0: continue\\n s = sum_numerator / (math.sqrt(sum_denominator_i) * math.sqrt(sum_denominator_j))\\n rows.append(i), cols.append(j), data.append(s)\\n if i != j: rows.append(j), cols.append(i), data.append(s)\\n\\n return csr_matrix((data, (rows, cols)))\",\n \"def __cos_sim(self, v1, v2):\\n if np.count_nonzero(v1) == 0 or np.count_nonzero(v2) == 0:\\n # whenever at least one of the vectors is all zeros, spatial.distance.cosine will fail by returning nan\\n ret = 0\\n else:\\n ret = 1 - spatial.distance.cosine(v1, v2)\\n return ret\",\n \"def cos_sim(v1, v2):\\r\\n return np.inner(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def cosine_sim_numpy(im, s):\\n return im.dot(s.T)\",\n \"def cosine(vector_1, vector_2):\\n\\n def _norm(_v):\\n return np.sqrt(sum([x ** 2 for x in _v.values()]))\\n\\n numerator = dot_product(vector_1, vector_2)\\n denominator = _norm(vector_1) * _norm(vector_2)\\n if denominator == 0:\\n return -1\\n return numerator / denominator\",\n \"def cosine_sim(im, s):\\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices \",\n \"def cosine_similarity(document, cluster):\\n num = dot(document.vector, cluster.centroid)\\n den = linalg.norm(document.vector) * linalg.norm(cluster.centroid)\\n\\n return num / den\",\n \"def get_cosine_similarity(self, query: list):\\n question_vector = self.get_vector(query)\\n\\n return cosine_similarity(question_vector, self.vectors).flatten()\",\n \"def cosine_similarity(v1, v2):\\n # Cosine Sim:\\n # Get the words that both have in common\\n\\n v1words = set(v1.keys())\\n v2words = set(v2.keys())\\n\\n numerator_words = v1words.intersection(v2words)\\n\\n # Multiply and sum those counts\\n numerator = 0.0\\n for word in numerator_words:\\n numerator += v1[word] * v2[word]\\n\\n\\n # Divide by the sqrt of the product of the sum of the squares of the counts\\n denominator = math.sqrt(math.magnitude(list(v1.values())) * math.magnitude(list(v2.values())))\\n\\n return numerator/denominator\",\n \"def similarity_vec(self, vec1: numpy.ndarray, vec2: numpy.ndarray, metric='cosine') -> float:\\n if numpy.count_nonzero(vec1) == 0 or numpy.count_nonzero(vec2) == 0:\\n if metric == 'cosine':\\n return 0.\\n else:\\n return 0.\\n\\n vec1 = vec1.reshape((1, -1))\\n vec2 = vec2.reshape((1, -1))\\n if metric == 'cosine':\\n return (1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1))[0]\\n else:\\n return distance.cdist(vec1, vec2, metric=metric).reshape(-1)[0]\",\n \"def cosine_similarity(a, b):\\n if a.ndim != 1 or b.ndim != 1:\\n raise InvalidShapeException(a,b)\\n\\n if len(a) != len(b):\\n raise InvalidLengthException(a,b)\\n \\n mag_a = np.linalg.norm(a)\\n mag_b = np.linalg.norm(b)\\n\\n return np.dot(a,b)/(mag_a*mag_b)\",\n \"def _cosine_matrix(self, x1, x2):\\n # expand h1 shape to (batch_size, x1_timesteps, 1, embedding_size)\\n x1 = K.expand_dims(x1, axis=2)\\n # expand x2 shape to (batch_size, 1, x2_timesteps, embedding_size)\\n x2 = K.expand_dims(x2, axis=1)\\n # cosine matrix (batch_size, h1_timesteps, h2_timesteps)\\n cos_matrix = self._cosine_similarity(x1, x2)\\n return cos_matrix\",\n \"def cos_vecs(x, y):\\r\\n _t = np.sum((x * y), axis=1)\\r\\n norm_x = np.linalg.norm(x, axis=1, keepdims=True)\\r\\n norm_y = np.linalg.norm(y, axis=1, keepdims=True)\\r\\n _t = np.reshape(_t, (-1, 1))\\r\\n ret = _t / (norm_x * norm_y + 1e-10)\\r\\n return ret\",\n \"def cosine_sim(im, s):\\n return im.mm(s.t())\",\n \"def get_cosine_sim(self):\\r\\n return CosineSimilarity().calculate_similarity(self.tweets)\",\n \"def cosine_value(self,doc_vector,query_vector):\\n\\t\\tvalue=0;i=0;\\n\\t\\tunit_vector_query=self.unit_vector(query_vector);\\n\\t\\tunit_vector_doc=self.unit_vector(doc_vector);\\n\\t\\titerate=0\\n\\t\\tfor word in query_vector:\\n\\t\\t\\tif word in doc_vector:\\n\\t\\t\\t\\tvalue+=query_vector[word]*doc_vector[word]\\n\\t\\tif unit_vector_query != 0:\\n\\t\\t\\tvalue = value/(unit_vector_query*unit_vector_doc)\\n\\t\\telse:\\n\\t\\t\\tvalue = 0\\n\\t\\treturn value\",\n \"def cosine_sim(im, s,):\\n return im.mm(s.t())\",\n \"def pairwise_cosine(mat):\\n def cosine_similarity(a, b):\\n return (a * b).sum() / (np.linalg.norm(a) * np.linalg.norm(b))\\n\\n n = len(mat)\\n dist_mat = np.zeros((n, n))\\n for i in range(n):\\n for j in range(i + 1, n):\\n dist = cosine_similarity(mat[i], mat[j])\\n dist_mat[i, j] = dist\\n dist_mat[j, i] = dist\\n\\n dist_mat[np.isnan(dist_mat)] = 0\\n\\n return dist_mat\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\n w12 = torch.sum(x1 * x2, dim)\\n w1 = torch.norm(x1, 2, dim)\\n w2 = torch.norm(x2, 2, dim)\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\r\\n w12 = torch.sum(x1 * x2, dim)\\r\\n w1 = torch.norm(x1, 2, dim)\\r\\n w2 = torch.norm(x2, 2, dim)\\r\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def test_cossim(self):\\n metrics = SimilarityMetrics()\\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test1,0.0)\\n\\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test2,-1.0)\\n\\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\\n np.testing.assert_almost_equal(test3,1.0)\",\n \"def get_cosine(vec1, vec2):\\n OPS = get_current_ops()\\n v1 = OPS.to_numpy(OPS.asarray(vec1))\\n v2 = OPS.to_numpy(OPS.asarray(vec2))\\n return numpy.dot(v1, v2) / (numpy.linalg.norm(v1) * numpy.linalg.norm(v2))\",\n \"def pcosine(u, v):\\n\\n # validate vectors like scipy does\\n u = ssd._validate_vector(u)\\n v = ssd._validate_vector(v)\\n\\n dist = 1. - np.abs(np.dot(u, v) / (linalg.norm(u) * linalg.norm(v)))\\n\\n return dist\",\n \"def get_similarity(df):\\n count = CountVectorizer()\\n count_matrix = count.fit_transform(df[\\\"bag_of_words\\\"])\\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\\n return cosine_sim\",\n \"def cosine(\\n x_mat: 'Tensor', y_mat: 'Tensor', eps: float = 1e-7, device: str = 'cpu'\\n) -> 'numpy.ndarray':\\n with _get_tf_device(device):\\n normalize_a = tf.nn.l2_normalize(x_mat, 1, epsilon=eps)\\n normalize_b = tf.nn.l2_normalize(y_mat, 1, epsilon=eps)\\n distance = 1 - tf.matmul(normalize_a, normalize_b, transpose_b=True)\\n return distance.numpy()\",\n \"def test_cosine_similarity_all_zeros_compiled():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cosine(xs: Tensor, ys: Tensor, epsilon: float = 1e-8) -> Tensor:\\n mat = xs @ ys.t()\\n x_norm = xs.norm(2, dim=1) + epsilon\\n y_norm = ys.norm(2, dim=1) + epsilon\\n x_diag = (1 / x_norm).diag()\\n y_diag = (1 / y_norm).diag()\\n return x_diag @ mat @ y_diag\",\n \"def cosine(X,Y=None,dense_output=True):\\n #Reemplace NaN with Zero\\n X[np.isnan(X)] = 0\\n if Y is not None:\\n Y[np.isnan(Y)] = 0\\n \\n return cosine_similarity(X,Y,dense_output)\",\n \"def pairwise_cosine_similarity(x, y):\\n x = torch.div(x, torch.sqrt(torch.max(torch.sum(x ** 2), 1e-12)))\\n y = torch.div(y, torch.sqrt(torch.max(torch.sum(y ** 2), 1e-12)))\\n return torch.mm(x, torch.transpose(y, 1, 0))\",\n \"def acos_sim(col_pair):\\n col1, col2 = col_pair\\n c1 = col1[1][0]\\n c2 = col2[1][0]\\n dp = dot_prod(c1, c2)\\n cs = dp / (col1[1][1] * col2[1][1])\\n return ((col1[0], col2[0]), cs)\",\n \"def cosine_similarity(v1, v2):\\n v1_len = 0\\n v2_len = 0\\n dot_product = 0\\n\\n for context_id, count in v1.items():\\n v1_len += count ** 2\\n if context_id in v2:\\n dot_product += count*v2[context_id]\\n for count in v2.values():\\n v2_len += count ** 2\\n\\n v1_len = math.sqrt(v1_len)\\n v2_len = math.sqrt(v2_len)\\n return dot_product/(v1_len * v2_len)\",\n \"def cosine(arr1, arr2):\\n\\n if arr1 is None or arr2 is None:\\n return np.NaN\\n if not isinstance(arr1, list):\\n arr1 = [arr1]\\n if any(pd.isnull(arr1)):\\n return np.NaN\\n if not isinstance(arr2, list):\\n arr2 = [arr2]\\n if any(pd.isnull(arr2)):\\n return np.NaN\\n # Create cosine measure object\\n measure = sm.Cosine()\\n # Call the function to compute the cosine measure.\\n return measure.get_raw_score(arr1, arr2)\",\n \"def cos_sim(com_feat,ref_feat):\\n # Fill this in\\n a = numpy.squeeze(com_feat)\\n b = numpy.squeeze(ref_feat)\\n return numpy.dot(a, b) / (numpy.linalg.norm(a) * numpy.linalg.norm(b))\",\n \"def get_cosine_similarities(keywords: List[str],\\n matrices: Matrices,\\n word2onehot: Dict[str, int]\\n ) -> None:\\n for i in combinations(keywords, 2):\\n print(i[0], i[1], cosine_sim(\\n matrices.embedding[\\n word2onehot[i[0]]], matrices.embedding[word2onehot[i[1]]\\n ]\\n ))\",\n \"def self_similarity_matrix(feature_vectors):\\n norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T])\\n norm_feature_vectors = norm_feature_vectors[0].T\\n sim_matrix = 1.0 - distance.squareform(\\n distance.pdist(norm_feature_vectors.T, 'cosine'))\\n return sim_matrix\",\n \"def test_cosine_similarity_all_zeros():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity.py_func(vector1, vector1)\\n score12 = cosine_similarity.py_func(vector1, vector2)\\n score22 = cosine_similarity.py_func(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cosine_sim_counters(a, b):\\n union_ab = sorted((a | b).keys())\\n veca = np.array([a[element] if element in a else 0 for element in union_ab])\\n vecb = np.array([b[element] if element in b else 0 for element in union_ab])\\n return np.dot(veca, vecb) / (np.linalg.norm(veca) * np.linalg.norm(vecb))\",\n \"def cosinesimilarity_cal(CTRDM1, CTRDM2):\\n\\n # get number of conditions\\n n_cons = np.shape(CTRDM1)[0]\\n\\n # calculate the number of value above the diagonal in RDM\\n n = n_cons * (n_cons - 1)\\n\\n # initialize two vectors to store the values above the diagnal of two RDMs\\n v1 = np.zeros([n], dtype=np.float64)\\n v2 = np.zeros([n], dtype=np.float64)\\n\\n # assignment\\n nn = 0\\n for i in range(n_cons):\\n for j in range(n_cons):\\n if i != j:\\n v1[nn] = CTRDM1[i, j]\\n v2[nn] = CTRDM2[i, j]\\n nn = nn + 1\\n\\n # calculate the Cosine Similarity\\n V1 = np.mat(v1)\\n V2 = np.mat(v2)\\n num = float(V1 * V2.T)\\n denom = np.linalg.norm(V1) * np.linalg.norm(V2)\\n cos = num / denom\\n similarity = 0.5 + 0.5 * cos\\n\\n return similarity\",\n \"def cosine_sim_collections(a, b):\\n setab = sorted(set(a) | set(b))\\n countera, counterb = Counter(a), Counter(b)\\n veca = [countera[element] if element in a else 0 for element in setab]\\n vecb = [counterb[element] if element in b else 0 for element in setab]\\n return dot(veca, vecb) / (norm(veca) * norm(vecb))\",\n \"def centrality(similarity, vector, vectors):\\n return 1.0/len(vectors)*sum([similarity(vector,y) for y in vectors\\\\\\n if y != vector])\",\n \"def calculate_similarity(self, tweets):\\r\\n if (len(tweets) == 1):\\r\\n return 0\\r\\n vectors = self.vectorizer.vectorize_data(tweets, False)\\r\\n\\r\\n temp = cosine_similarity(vectors[0:-1], vectors)\\r\\n temp = [item for sublist in temp for item in sublist]\\r\\n sim = sum(temp) / len(temp)\\r\\n return sim\",\n \"def getGloveoCosineSimilarity(question1, question2):\\n questions = [question1, question2]\\n\\n ## for the sentences we need to get the count vectors\\n vec = CountVectorizer(max_features=5000, stop_words=None,binary=True)\\n count_vectors = vec.fit_transform(questions)\\n\\n ## get the vocabulary of words from the questions\\n vocab_index = vec.vocabulary_\\n\\n ## get the index of the words and embeddings\\n index_word = {v:k for k, v in vocab_index.items()}\\n\\n ## get the question vectors\\n question_vectors = np.zeros((count_vectors.shape[0], 300))\\n\\n ## iterate through count vectors for each word get the embeddings\\n ## for each embedding, we will then average by the number of words\\n ## this will be then used for cosine similarity\\n for i in range(count_vectors.shape[0]):\\n row = count_vectors[i, :].toarray()\\n word_ids = np.where(row > 0)[1]\\n word_counts = row[:, word_ids][0]\\n numWords = np.sum(word_counts)\\n\\n ## if there are no words, continue\\n if numWords == 0:\\n continue\\n\\n ## initialize the word embeddings to 0\\n word_embeddings = np.zeros((word_ids.shape[0], 300))\\n\\n ## update the word embeddings\\n for j in range(word_ids.shape[0]):\\n word_id = word_ids[j]\\n word_embeddings[j, :] = word_counts[j] * gloveDict[index_word[word_id]]\\n question_vectors[i, :] = np.sum(word_embeddings, axis=0) / numWords\\n\\n return(cosine_similarity(question_vectors[0], question_vectors[1])[0][0])\",\n \"def get_cosin_sim(question, contexts):\\r\\n cos_sim_for_question = []\\r\\n for context in contexts :\\r\\n cv = CountVectorizer(stop_words=MY_STOPWORDS, lowercase=False)\\r\\n matrix = cv.fit_transform(pd.DataFrame([question, context])[0]).toarray()\\r\\n cos_sim = dot(matrix[0], matrix[1])/(norm(matrix[0])*norm(matrix[1]))\\r\\n cos_sim_for_question.append(cos_sim)\\r\\n return pd.Series(cos_sim_for_question)\",\n \"def cosine_similarity_matrix(references: np.ndarray, queries: np.ndarray) -> np.ndarray:\\n size1 = references.shape[0]\\n size2 = queries.shape[0]\\n scores = np.zeros((size1, size2))\\n for i in range(size1):\\n for j in range(size2):\\n scores[i, j] = cosine_similarity(references[i, :], queries[j, :])\\n return scores\",\n \"def cosine_similarity(user, business):\\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))\",\n \"def tf_cosine_distance(self, a, b):\\n normalize_a = tf.nn.l2_normalize(a, -1)\\n normalize_b = tf.nn.l2_normalize(b, -1)\\n cos_similarity = tf.reduce_sum(\\n tf.multiply(normalize_a, normalize_b), axis=-1, keep_dims=True\\n )\\n return (1.0 - cos_similarity) / 2.0\",\n \"def cosine_similarity(a, b):\\n\\n numerator = tf.reduce_sum(tf.multiply(a, b), axis=1)\\n denominator = tf.multiply(tf.norm(a, axis=1), tf.norm(b, axis=1))\\n cos_similarity = numerator/denominator\\n return cos_similarity\",\n \"def compute_cosine(pred, obs):\\n assert(pred.numel() == obs.numel()), \\\\\\n 'Size of observation and prediction tensors much match. Received: pred %s, obs %s.'%(\\n str(pred.size()), str(obs.size()))\\n\\n def normalise(x, dim=1):\\n \\\"\\\"\\\" compute L2 norm and normalise x \\\"\\\"\\\"\\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\\n if dim>0:\\n x /= norm.unsqueeze(dim)\\n return x\\n\\n # if we have one-dimensional tensors, compute cosine similarity along first dimension (0).\\n # if we have two-dimensional tensors, compute cosine similarity along second dimension (1).\\n # if we have three-dimensional tensors, compute cosine similarity along third dimension (2).\\n # i.e. first dimension is considered the feature vector (will be reduced to a scalar, the cos.sim.)\\n dim = len(pred.size()) - 1\\n assert(dim>=0 and dim <=2), \\\\\\n 'This function only computes cosine similarity between 1D, 2D or 3D tensors! Received dim==%i'%(dim)\\n\\n p_norm = normalise(pred, dim=dim)\\n v_norm = normalise(obs, dim=dim)\\n return torch.nn.functional.cosine_similarity( p_norm, v_norm, dim=dim )\",\n \"def cos(\\r\\n vec1: torch.FloatTensor, vec2: torch.FloatTensor, dim: int = -1\\r\\n) -> torch.FloatTensor:\\r\\n return torch.sum(vec1 * vec2, dim=dim) / (\\r\\n vec1.norm(dim=dim) * vec2.norm(dim=dim) + EPS\\r\\n )\",\n \"def cosine_similarity_pure(X, Y=None):\\n X, Y = _check_pairwise_arrays(X, Y)\\n X_normalized = normalize_pure(X, copy=True)\\n if X is Y:\\n Y_normalized = X_normalized\\n else:\\n Y_normalized = normalize_pure(Y, copy=True)\\n K = dot_2d(X_normalized, Y_normalized)\\n return K\",\n \"def _do_action_calculate_similarity_cosine_express(self):\\n self._run_express_job(\\\"com.directv.recommend.express.CosineCFTrainer\\\")\\n self._scan_table(\\\"content/item_item_similarities\\\")\",\n \"def cosine_distance(x1, x2):\\n x1 = tf.cast(x1, dtype=tf.float32)\\n x2 = tf.cast(x2, dtype=tf.float32)\\n\\n # dot product between rows of `x_1` and rows of `x_2`\\n # \\\"ij,ij->i\\\" := output[i] = sum_j x1[i, j] * x2[i, j]\\n cos_thetas = tf.linalg.einsum(\\\"ij,ij->i\\\", x1, x2)\\n cos_distances = 1 - cos_thetas\\n\\n # deal with numerical inaccuracies setting small negatives to zero\\n cos_distances = tf.maximum(cos_distances, 0.0)\\n\\n return cos_distances\",\n \"def safe_cosine_sim(x, y):\\n l2x = fluid.layers.l2_normalize(x, axis=-1)\\n l2y = fluid.layers.l2_normalize(y, axis=-1)\\n cos = fluid.layers.reduce_sum(l2x * l2y, dim=1, keep_dim=True)\\n return cos\",\n \"def similarity_matrix(feat_mat):\\n sim_mat = cosine_similarity(feat_mat)\\n np.fill_diagonal(sim_mat, 0)\\n return sim_mat\",\n \"def cosine_distances(X, Y):\\n # should not need X_norm_squared because if you could precompute that as\\n # well as Y, then you should just pre-compute the output and not even\\n # call this function.\\n if X is Y:\\n X = Y = np.asanyarray(X)\\n else:\\n X = np.asanyarray(X)\\n Y = np.asanyarray(Y)\\n\\n if X.shape[1] != Y.shape[1]:\\n raise ValueError(\\\"Incompatible dimension for X and Y matrices\\\")\\n\\n return 1. - ssd.cdist(X, Y, 'cosine')\",\n \"def cosine_distance(m, f):\\n return (1 - ((m * f).sum() / (norm(m) * norm(f))))\",\n \"def _arccosine(self, s1, s2, tf_embs):\\n tf_pi = tf.constant(np.pi, dtype=tf.float64)\\n mat1 = tf.gather(tf_embs, s1)\\n mat2 = tf.gather(tf_embs, s2)\\n tf_norms = tf.constant(self.norms, dtype=tf.float64, name='norms')\\n norms1 = tf.gather(tf_norms, s1)\\n norms2 = tf.gather(tf_norms, s2)\\n dot = tf.matmul(mat1, tf.transpose(mat2))\\n norms = tf.matmul(norms1, tf.transpose(norms2))\\n # We clip values due to numerical errors\\n # which put some values outside the arccosine range.\\n cosine = tf.clip_by_value(dot / norms, -1, 1)\\n angle = tf.acos(cosine)\\n # The 0 vector has norm 0, which generates a NaN.\\n # We catch these NaNs and replace them with pi,\\n # which ends up returning 0 similarity.\\n angle = tf.select(tf.is_nan(angle), tf.ones_like(angle) * tf_pi, angle)\\n return 1 - (angle / tf_pi)\",\n \"def cosine_similarity(y_true, y_pred, axis=-1):\\n y_true = nn.l2_normalize(y_true, axis=axis)\\n y_pred = nn.l2_normalize(y_pred, axis=axis)\\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)\",\n \"def cosine_collection_distance(x1, x2):\\n x1 = tf.cast(x1, dtype=tf.float32)\\n x2 = tf.cast(x2, dtype=tf.float32)\\n\\n # dot product between rows of `x1` and columns of `x2` transpose\\n cos_thetas = tf.linalg.matmul(x1, x2, transpose_b=True)\\n pairwise_distances = 1 - cos_thetas\\n\\n # deal with numerical inaccuracies setting small negatives to zero\\n pairwise_distances = tf.maximum(pairwise_distances, 0.0)\\n\\n return pairwise_distances\",\n \"def cossim(vA, vB):\\n return np.dot(vA, vB) / (np.sqrt(np.dot(vA, vA)) * np.sqrt(np.dot(vB, vB)))\",\n \"def cos_sim(a, b):\\n numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom\\n for i in movies: #replace movies\\n numerator += (ratingai * ratingbi)\\n sosA += ratingai * ratingai\\n sosB += ratingbi * ratingbi\\n denom = sqrt(sosA * sosB) #make sure denom won't be 0!\\n return numerator / denom\",\n \"def cosineFunction(self):\\n \\n w = np.zeros((self.N))\\n l = self.l\\n for i in range(self.r.shape[0]):\\n r = np.abs(self.r[i])\\n if r <= l:\\n tf = lambda r,l : 0.5 * (np.cos(np.pi * r/l) + 1)\\n w[i] = tf(r,l)\\n else:\\n w[i] = 0\\n self.w = w\",\n \"def discrete_cosine_tranform(self, matrix):\\n\\n\\n x = matrix.shape[0]\\n y = matrix.shape[1]\\n N = x\\n\\n #Fourier Transform matrix:\\n dct = np.zeros([x,y])\\n\\n for u in range(0, x):\\n for v in range(0, y):\\n sum_ft = 0\\n for i in range(0, x):\\n for j in range(0, y):\\n\\n sum_ft = sum_ft + matrix[i, j] * (np.cos(((2*np.pi)/N)*(u*i + v*j)))\\n\\n dct[u, v] = sum_ft\\n\\n return dct\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8128745","0.7961435","0.79325265","0.7881812","0.7870864","0.7867077","0.7831051","0.7827189","0.7769225","0.7692766","0.76837415","0.7670993","0.76513463","0.76232105","0.76175356","0.75908995","0.74540925","0.74253607","0.73605967","0.7359785","0.73473644","0.7333759","0.7321355","0.7316518","0.73022366","0.7300665","0.7266292","0.72640115","0.72512174","0.7245701","0.72330326","0.7214554","0.72068137","0.7197146","0.7164008","0.7150619","0.7144001","0.710846","0.70981705","0.7081177","0.7056436","0.704077","0.7036345","0.7027179","0.70191175","0.69638306","0.69508445","0.69467133","0.694444","0.688386","0.68812215","0.68801767","0.6871488","0.68639827","0.6861643","0.68546426","0.68459445","0.6835493","0.68002117","0.6794632","0.6793435","0.67918915","0.67712593","0.6712412","0.6658573","0.66561633","0.66484237","0.6645491","0.6641563","0.6632033","0.6625607","0.66165036","0.65922385","0.6581627","0.65771544","0.6556629","0.65466833","0.6522416","0.6509871","0.6500315","0.64486337","0.6422805","0.6402486","0.6400684","0.6375428","0.63645005","0.63433725","0.63104093","0.6299787","0.62970066","0.6281906","0.62742263","0.62671554","0.62642616","0.6256641","0.62491214","0.6244359","0.6234856","0.62252516","0.62150264"],"string":"[\n \"0.8128745\",\n \"0.7961435\",\n \"0.79325265\",\n \"0.7881812\",\n \"0.7870864\",\n \"0.7867077\",\n \"0.7831051\",\n \"0.7827189\",\n \"0.7769225\",\n \"0.7692766\",\n \"0.76837415\",\n \"0.7670993\",\n \"0.76513463\",\n \"0.76232105\",\n \"0.76175356\",\n \"0.75908995\",\n \"0.74540925\",\n \"0.74253607\",\n \"0.73605967\",\n \"0.7359785\",\n \"0.73473644\",\n \"0.7333759\",\n \"0.7321355\",\n \"0.7316518\",\n \"0.73022366\",\n \"0.7300665\",\n \"0.7266292\",\n \"0.72640115\",\n \"0.72512174\",\n \"0.7245701\",\n \"0.72330326\",\n \"0.7214554\",\n \"0.72068137\",\n \"0.7197146\",\n \"0.7164008\",\n \"0.7150619\",\n \"0.7144001\",\n \"0.710846\",\n \"0.70981705\",\n \"0.7081177\",\n \"0.7056436\",\n \"0.704077\",\n \"0.7036345\",\n \"0.7027179\",\n \"0.70191175\",\n \"0.69638306\",\n \"0.69508445\",\n \"0.69467133\",\n \"0.694444\",\n \"0.688386\",\n \"0.68812215\",\n \"0.68801767\",\n \"0.6871488\",\n \"0.68639827\",\n \"0.6861643\",\n \"0.68546426\",\n \"0.68459445\",\n \"0.6835493\",\n \"0.68002117\",\n \"0.6794632\",\n \"0.6793435\",\n \"0.67918915\",\n \"0.67712593\",\n \"0.6712412\",\n \"0.6658573\",\n \"0.66561633\",\n \"0.66484237\",\n \"0.6645491\",\n \"0.6641563\",\n \"0.6632033\",\n \"0.6625607\",\n \"0.66165036\",\n \"0.65922385\",\n \"0.6581627\",\n \"0.65771544\",\n \"0.6556629\",\n \"0.65466833\",\n \"0.6522416\",\n \"0.6509871\",\n \"0.6500315\",\n \"0.64486337\",\n \"0.6422805\",\n \"0.6402486\",\n \"0.6400684\",\n \"0.6375428\",\n \"0.63645005\",\n \"0.63433725\",\n \"0.63104093\",\n \"0.6299787\",\n \"0.62970066\",\n \"0.6281906\",\n \"0.62742263\",\n \"0.62671554\",\n \"0.62642616\",\n \"0.6256641\",\n \"0.62491214\",\n \"0.6244359\",\n \"0.6234856\",\n \"0.62252516\",\n \"0.62150264\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.758455"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":22381,"cells":{"query":{"kind":"string","value":"Euclidean distance between vector and matrix."},"document":{"kind":"string","value":"def euclid_dist(vec, mat):\n return np.linalg.norm(mat - vec, axis=1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def euclidean_distance(vector_x, vector_y):\n if len(vector_x) != len(vector_y):\n raise Exception('Vectors must be same dimensions')\n return math.sqrt(sum((vector_x[dim] - vector_y[dim]) ** 2 for dim in range(len(vector_x))))","def get_distance(self,row_vector):\n d = row_vector-self.X_test\n \n return np.sqrt(np.dot(d,d)) # return the euclidean distance","def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\n distance_vector: np.ndarray = x - y\n distance = compute_norm(distance_vector)\n return distance","def calculate_euclidean_distance(self, matrix, input, output_neuron):\n result = 0\n\n # Loop over all input data.\n diff = input - matrix[output_neuron]\n return np.sqrt(sum(diff*diff))","def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:\n return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))","def euclidean_distance(vec1, vec2):\n return numpy.linalg.norm(vec1 - vec2)","def get_euclidean_vector(vector):\n\n return np.subtract(vector[1], vector[0])","def calculateEuclideanDistance(vector):\r\n global euclideanDistance\r\n # create linkage matrix with the distance metric as euclidean distance\r\n # calculate the distances of the clusters by starting as singletons\r\n # and in each iteration will merge the two clusters which have the smallest distance\r\n # returns array of length n - 1\r\n # Z[i] will tell us which clusters were merged in the i-th iteration\r\n # each row has format [cluster1, cluster1, dist, sample_count].\r\n euclideanDistance = linkage(vector, metric='euclidean')","def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\n\n distance = np.linalg.norm(x - y)\n\n return distance","def euclidean_distance(vec):\n\n x, y = vec\n distance = tf.math.sqrt(tf.math.reduce_sum(tf.math.square(x - y), axis=-1, keepdims=True))\n return distance","def distance_metric(u, v):\n if len(u) != len(v):\n raise Exception(\n \"Distance metric not valid for differently sized vectors\")\n sum = 0.\n for i in range(len(u)):\n sum += ((u[i] - v[i]) ** 2)\n return math.sqrt(sum)","def compute_euclidean_dist(vec1, vec2):\r\n assert len(vec1) == len(vec2)\r\n vec1 = np.array(vec1)\r\n vec2 = np.array(vec2)\r\n return np.sqrt(np.sum(np.square(vec2 - vec1)))","def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:\n return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)","def metric_euclid(vector):\n return la.norm(vector)","def euclidean_distance(vector1, vector2):\n e_dist = [(v1 - v2) ** 2 for v1, v2 in zip(vector1, vector2)]\n e_dist = math.sqrt(sum(e_dist))\n return e_dist","def euclidean(x_mat: 'Tensor', y_mat: 'Tensor', device: str = 'cpu') -> 'numpy.ndarray':\n device = tf.device('/GPU:0') if device == 'cuda' else tf.device('/CPU:0')\n\n with _get_tf_device(device):\n return tf.sqrt(\n tf.reduce_sum((tf.expand_dims(x_mat, 1) - tf.expand_dims(y_mat, 0)) ** 2, 2)\n ).numpy()","def euclidean_distance(x1: np.ndarray, x2: np.ndarray) -> float:\n return np.sqrt(np.square(x1 - x2).sum())","def _vector_dist(self, vec1, vec2):\r\n return sqrt(sum([(float(v1) - float(v2)) ** 2 for v1, v2 in\r\n zip(vec1, vec2)]))","def get_euclid_dist(vec_1, vec_2):\n\n\treturn np.sqrt(np.sum(np.fabs(vec_1 - vec_2), axis=1)).flatten()","def euclidean_distance(a, b):\n return np.linalg.norm(a - b)","def get_distance(self, vec):\r\n\r\n sum = 0\r\n if len(self.weights) == len(vec):\r\n for i in range(len(vec)):\r\n sum += (self.weights[i] - vec[i]) * (self.weights[i] - vec[i])\r\n return np.sqrt(sum)\r\n else:\r\n sys.exit(\"Error: dimension of nodes != input data dimension!\")","def euclidean_distance(vector_1, vector_2) -> float:\n\n\n before_square_root = 0\n for i in range(len(vector_1)):\n before_square_root += (vector_1[i] - vector_2[i])**2\n\n d = math.sqrt(before_square_root)\n print(d)\n return(d)","def pairwise_euclidean_distance(M, axis=0):\n\n return pairwise_distance(M, 'euclidean', axis=axis)","def distance(self, u, v):\n numerator = np.dot(u,v)\n denominator = np.linalg.norm(u) * np.linalg.norm(v)\n similarity = numerator/(denominator +1e-7)\n return similarity","def __dist(u, v):\n return spatial.distance.euclidean(u, v)","def distance(self, vector1, vector2):\n\t\tsum_sq = 0\n\t\tfor i in range(28):\n\t\t\tfor j in range(28):\n\t\t\t\tsum_sq += (vector1[i][j] - vector2[i][j])**2\n\t\treturn math.sqrt(sum_sq)","def euclidean_distance(x1, x2):\n return np.sqrt(np.sum(np.square(np.subtract(x1, x2))))","def _euclid_distance(self, A, B, axis=1):\n return np.linalg.norm(A - B, axis=axis)","def distance_matrix(data):\n D = numpy.zeros( (data.shape[0], data.shape[0]) )\n for i in xrange(data.shape[0]):\n for j in xrange(i):\n D[i,j] = numpy.linalg.norm(data[i,:]-data[j,:])\n D[j,i] = D[i,j]\n\n return D","def distancematrix(vec1, vec2):\n v1, v2 = np.meshgrid(vec1, vec2)\n return np.abs(v1 - v2)","def euclidean_dist(X, y):\n return np.sqrt(np.sum((X - y) ** 2, 1)) # broadcasted calculations","def Euclidean(X):\n \n sanitycheck(X,np.ndarray)\n return np.sqrt(np.power(X, 2).sum(axis=1))","def euclidean_metric(x, y):\n if len(x) != len(y):\n raise ValueError(\"Incompatible dimensions.\")\n return np.linalg.norm(x - y)\n \n # Or a slightly longer way:\n return np.sqrt(np.sum(np.subtract(x, y)**2))\n # Or the longest/worst way:\n total = 0\n for i in xrange(len(x)):\n term = x[i] - y[i]\n term = term**2\n total += term\n total = np.sqrt(total)\n return total","def distance(self, u, v):\n # Implement the distance function between vectors u and v]\n # Note: you can also think of this as computing a similarity measure\n # Use of cosine similarity measure, assumes u and v have equal length\n num = np.dot(u,v)\n # den_u = np.sum(u**2)\n # den_v = np.sum(v**2)\n den_u = np.linalg.norm(u)\n den_v = np.linalg.norm(v)\n if den_u == 0.0 or den_v == 0.0:\n return 0.0\n # return num / (math.sqrt(den_u) * math.sqrt(den_v))\n return num / (den_u * den_v)","def euclidean_distance(v, w):\n\n # Guard against empty lists.\n if len(v) is 0:\n return 0\n\n # Note that this is the same as vector subtraction.\n differences = [v[idx] - w[idx] for idx in range(len(v))]\n squares = [diff ** 2 for diff in differences]\n sum_of_squares = sum(squares)\n\n return 1 / (1 + math.sqrt(sum_of_squares))","def euclidean_distance(self,):\n return sqrt(pow((self.pose1.x - self.pose2.x), 2) +\n pow((self.pose1.y - self.pose2.y), 2))","def euclidean_distance(point_one, point_two):\n return np.linalg.norm(point_one-point_two)","def _derive_euclidean_dm(self, cat_mat, dim):\r\n res_mat = []\r\n\r\n for i in range(dim):\r\n res_mat.append([0 for k in range(dim)])\r\n for j in range(i):\r\n res_mat[i][j] = self._vector_dist(cat_mat[i], cat_mat[j])\r\n res_mat[j][i] = res_mat[i][j]\r\n\r\n return DistanceMatrix(res_mat, self.DistanceMatrices[0].ids)","def sqeuclidean(\n x_mat: 'Tensor', y_mat: 'Tensor', device: str = 'cpu'\n) -> 'numpy.ndarray':\n device = tf.device('/GPU:0') if device == 'cuda' else tf.device('/CPU:0')\n\n with _get_tf_device(device):\n return tf.reduce_sum(\n (tf.expand_dims(x_mat, 1) - tf.expand_dims(y_mat, 0)) ** 2, 2\n ).numpy()","def _euclidian_distance(self, x1, x2):\n a= x1-x2\n a2 = a**2\n b = np.sum(a2, axis=1)\n c = np.sqrt(b)\n return c","def euclidean_distance(x1, x2):\n return (x2[0] - x1[0])**2 + (x2[1] - x1[1])**2","def distance(self, other_vector):\n return self.distance_sq(other_vector) ** 0.5","def euclidean(x, y):\n ed = np.sqrt(np.sum((x-y)**2))\n # print ed\n return ed","def euclidean_distance(A, B):\n aa = T.sum(A * A, axis=1)\n bb = T.sum(B * B, axis=1)\n AB = T.dot(A, T.transpose(B))\n\n AA = T.transpose(T.tile(aa, (bb.shape[0], 1)))\n BB = T.tile(bb, (aa.shape[0], 1))\n\n D = AA + BB - 2 * AB\n\n # Sanity checks\n D = T.maximum(D, 0)\n D = T.sqrt(D)\n return D","def euclidean_distance(x1, x2):\n return np.sqrt(np.sum(np.power(x1 - x2, 2)))","def norm_dist(face_vectors, f_vector):\n if len(face_vectors) == 0:\n return np.empty((0))\n return np.linalg.norm(face_vectors - f_vector, axis=1)","def euclidean_distances(X, Y):\r\n\r\n D = np.zeros((X.shape[0],Y.shape[0]))\r\n \r\n for X_idx in range(X.shape[0]):\r\n for Y_idx in range(Y.shape[0]): \r\n \r\n D[X_idx,Y_idx] = np.sqrt(np.sum((X[X_idx,:]-Y[Y_idx,:])**2))\r\n \r\n return D","def euclidean(self, other):\n return linalg.norm([self.x - other.x, self.y - other.y])","def distance(N, M):\n\n\td = np.sqrt( (N[:,0, np.newaxis] - M[:,0])**2 + (N[:,1, np.newaxis] - M[:,1])**2 )\n\treturn d","def test_vector_distance(self):\n\n # Example 1.2\n vector_p = np.array([0.5, 0.0, 0.5])\n vector_q = np.array([0.5, 0.5, 0.0])\n crystal = crystal_system.Tetragonal(0.5, 1.0)\n d_ref_nm = np.sqrt(5.0)/4.0\n\n g_ij_nm2 = crystal.gij_nm2\n\n vector_d = vector_p - vector_q\n d_nm = vector.distance(crystal, vector_d, vector_d)\n\n self.assertAlmostEqual(d_ref_nm, d_nm, 5)\n\n #self.fail(\"Test if the testcase is working.\")","def euclidian_distance(x: np.arrays, y: np.arrays):\r\n diff = x - np.mean(y, axis=0)\r\n return np.sqrt(np.dot(diff.T, diff))","def euclidean_dist_vec(y1, x1, y2, x2):\n\n # euclid's formula\n distance = ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5\n return distance","def euclidean_distance(self, point: List[int]) -> float:\n return sqrt(point[0] ** 2 + point[1] ** 2)","def euclidean_distance(point1, point2):\n return np.linalg.norm(np.array(point1) - np.array(point2))","def euclidean_squared_distance(input1, input2):\n m, n = input1.size(0), input2.size(0)\n mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)\n mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()\n distmat = mat1 + mat2\n distmat.addmm_(input1, input2.t(), beta=1, alpha=-2)\n return distmat","def euclidean(x,y): \n\treturn np.sqrt(np.sum((x-y)**2))","def distance_sq(self, other_vector):\n return sum((x - y) ** 2 for x, y in zip(self.vector, other_vector))","def compute_distance (uVector, uOther):\n ## since each element can be either 0 or 1,\n ## no need for square roots and pow\n d = 0\n for i in range (len(uVector)):\n d = d + math.pow((int(uVector [i]) - int(uOther [i])), 2)\n\n return d","def _euclideanDistance(A, B):\n if len(A) != len(B):\n raise ValueError(\"A and B must have the same number of dimensions\")\n sqr_dist = 0\n for i in range(len(A)):\n sqr_dist += (A[i] - B[i])**2\n return np.sqrt(sqr_dist)","def dist_euclidean(datamtx, strict=True):\n datamtx = asarray(datamtx, 'd')\n if strict:\n if not all(isfinite(datamtx)):\n raise ValueError(\"non finite number in input matrix\")\n if rank(datamtx) != 2:\n raise ValueError(\"input matrix not 2D\")\n numrows, numcols = shape(datamtx)\n else:\n try:\n numrows, numcols = shape(datamtx)\n except ValueError:\n return zeros((0,0),'d')\n\n if numrows == 0 or numcols == 0:\n return zeros((0,0),'d')\n dists = zeros((numrows,numrows),'d')\n for r in range(numrows):\n for c in range(r):\n dist = norm(datamtx[r] - datamtx[c])\n if isnan(dist):\n raise RuntimeError('ERROR: overflow when computing euclidean distance')\n dists[r,c] = dists[c,r] = dist\n\n return dists","def test_euclidean_distance_Ndimension(self):\n\n self.assertEqual(15, euclidean_distance([0, 0, 0], [10, 10, 5]))\n self.assertEqual(15, euclidean_distance([0, 0, 0], [-10, -10, -5]))\n\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [10, 10, 8, 5]))\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [-10, -10, -8, -5]))\n\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [5, 1, 1, 1, 6]))\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [-5, -1, -1, -1, -6]))","def pairwise_euclidean_distance(x, y):\n x_norm = (x ** 2).sum(1).view(-1, 1)\n if y is not None:\n y_t = torch.transpose(y, 0, 1)\n y_norm = (y ** 2).sum(1).view(1, -1)\n else:\n y_t = torch.transpose(x, 0, 1)\n y_norm = x_norm.view(1, -1)\n\n dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t)\n # Ensure diagonal is zero if x=y\n # if y is None:\n # dist = dist - torch.diag(dist.diag)\n return torch.sqrt(torch.clamp(dist, 0.0, np.inf))","def euclidean(x, y):\n return np.sqrt(np.sum((x - y) ** 2))","def euclidean_distance(x, y):\n distance = 0\n for i, j in zip(x, y):\n distance += (i - j) ** 2\n return math.sqrt(distance)","def distance_matrix_vector(anchor, positive):\n \n # here anchor*anchor is equal torch.mul(anchor, anchor)\n # the element-wise value multiplication is returned\n d1_sq = torch.sum(anchor * anchor, dim=1).unsqueeze(-1)\n d2_sq = torch.sum(positive * positive, dim=1).unsqueeze(-1)\n\n eps = 1e-6\n # tensor.repeat(): repeat at each dims, and dims from right to left\n return torch.sqrt((d1_sq.repeat(1, anchor.size(0)) + torch.t(d2_sq.repeat(1, positive.size(0)))\n - 2.0 * torch.bmm(anchor.unsqueeze(0), torch.t(positive).unsqueeze(0)).squeeze(0))\n +eps)","def pairwise_euclidean_distance(x, y):\n m, n = x.size(0), y.size(0)\n dist_mat = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) + \\\n torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t() \\\n - 2 * torch.matmul(x, y.t())\n # for numerical stability\n dist_mat = dist_mat.clamp(min=1e-12).sqrt()\n return dist_mat","def euclidean_distance(x, y):\n return sqrt(sum(pow(a - b, 2) for a, b in zip(x, y)))","def GetDistance(vec1,vec2):\n diff = np.asarray(vec1) - np.asarray(vec2)\n squareDistance = np.dot(diff.T, diff)\n return math.sqrt(squareDistance)","def distance(v: Vector, w: Vector) -> float:\n return magnitude(subtract(v, w))","def euclidean_distance(x, y):\n x1, y1 = x\n x2, y2 = y\n return sqrt((x1 - x2)**2 + (y1 - y2)**2)","def cosine_distance(u, v):\n #print u,v\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))","def get_euclid_distance_to(self, atom):\n return linalg.norm(self.get_coords() - atom.get_coords())","def vector_dist(v, w):\n if isinstance(v, list):\n v = np.asarray(v)\n return vector_mag(v - w)","def compute_distance(node1, node2):\n return np.linalg.norm(node1 - node2)","def euclidean_distance(x1, x2):\n\tdistance = 0\n\t# Squared distance between each coordinate\n\tfor i in range(len(x1)):\n\t\tdistance += pow((x1[i], x2[i]), 2)\n\treturn math.sqrt(distance)","def euclidean_distance(tensor1, tensor2):\n tensor1 = tf.convert_to_tensor(tensor1)\n tensor2 = tf.convert_to_tensor(tensor2)\n\n distance = tf.sqrt(tf.reduce_sum(tf.square(tensor1 - tensor2), axis=-1))\n\n return distance","def distance(self, location):\n return numpy.linalg.norm(self.vector_to(location))","def cosine_distance(u, v):\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))","def _dist(A, B):\n return np.sqrt(np.einsum(\"ijk->ij\", (A[:, None, :] - B) ** 2))","def euclidianDistance(row1, row2):\n\n dist = 0.0\n for i in range(len(row1)-1):\n dist += (row1[i] - row2[i])**2\n \n return sqrt(dist)","def test_distance(self):\n self.assertTrue(np.allclose(self.vectors.distance('dog.n.01', 'mammal.n.01'), 4.5278745))\n self.assertEqual(self.vectors.distance('dog.n.01', 'dog.n.01'), 0)","def test_distances_with_vector_input(self):\n input_vector = self.vectors['dog.n.01']\n distances = self.vectors.distances(input_vector, ['mammal.n.01', 'dog.n.01'])\n self.assertTrue(np.allclose(distances, [4.5278745, 0]))\n\n distances = self.vectors.distances(input_vector)\n self.assertEqual(len(distances), len(self.vectors.vocab))\n self.assertTrue(np.allclose(distances[-1], 10.04756))","def euclidean_distance(a, b):\n return sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)","def norm(vec):\n return np.linalg.norm(vec)","def test_vector_dist(self):\r\n v1 = [1, 4, 2]\r\n v2 = [-1, 12, 4]\r\n\r\n exp = 8.48528137424\r\n obs = self.best._vector_dist(v1, v2)\r\n assert_almost_equal(exp, obs)\r\n\r\n v1 = [1, 2, 100, 4, 2]\r\n v2 = [-1, 12, 4, 12, 99]\r\n\r\n exp = 137.087563258\r\n obs = self.best._vector_dist(v1, v2)\r\n assert_almost_equal(exp, obs)","def cal_distances(embeddings):\n # calculate\n dist = np.zeros([len(embeddings), len(embeddings)], dtype=float)\n for ii in xrange(len(embeddings)):\n for jj in xrange(ii + 1, len(embeddings)):\n dist[ii, jj] = np.linalg.norm(embeddings[ii] - embeddings[jj])\n dist[jj, ii] = dist[ii, jj] \n \n # return\n return dist","def _nn_euclidean_distance(x, y):\n distances = _pdist(x, y)\n return np.maximum(0.0, distances.min(axis=0))","def _nn_euclidean_distance(x, y):\n distances = _pdist(x, y)\n return np.maximum(0.0, distances.min(axis=0))","def euclideanDistance(a, b):\n vec = [pow(a[i] - b[i], 2) for i in range(len(a)) if None not in [a[i],b[i]]]\n return (sum(vec) / len(vec)) if len(vec) > 0 else NaN","def euclidean_distance(start, end):\n\n value = np.sqrt(np.sum(np.square(np.subtract(start, end)), axis=-1))\n return value","def squared_distance(v: Vector, w: Vector) -> float:\n return sum_of_squares(subtract(v, w))","def norm_distance(self):\n graph_size = self.N + self.M\n return self.distance() / (1. * graph_size)","def dist(x,u):\r\n\t# https://github.com/dwf/rescued-scipy-wiki/blob/master/EricsBroadcastingDoc.rst\r\n\tdiff = x[np.newaxis,:,:] - u[:,np.newaxis,:]\r\n\tdist = np.sum(diff**2,axis=-1)\r\n\treturn dist","def EuclideanDistanceSq( self, a, b ):\n if not (type(a) == list or type(a) == Vector):\n a = [a]\n if not (type(b) == list or type(a) == Vector):\n b = [b]\n assert len(a) == len(b)\n sqDist = 0\n for x,y in zip(a,b):\n sqDist += (x-y)**2\n return sqDist","def det(self):\n\n if self.rows != self.columns:\n raise ValueError(\"Matrix must be square\")\n\n if self.rows == 1:\n return self.row(1)[0]\n\n if self.rows == 2:\n return self.entry(1,1) * self.entry(2,2) - self.entry(1,2) * self.entry(2,1)\n\n det = 0\n row_to_expand = 1\n\n for i in range(1, self.columns + 1):\n det += self.entry(row_to_expand, i) * self._cofactor(row_to_expand, i)\n\n return det","def calc_euclidean_similarity(vec_1, vec_2):\n sim = 0\n vec_1 = vec_1.reshape((vec_1.shape[1],))\n vec_2 = vec_2.reshape((vec_2.shape[1],))\n vec_1_nnz = np.nonzero(vec_1)[0]\n print vec_1_nnz\n # import ipdb; ipdb.set_trace()\n vec_2_nnz = np.nonzero(vec_2)[0]\n print vec_2_nnz\n intersect = set(vec_1_nnz) & set(vec_2_nnz)\n if len(intersect) > 0:\n error_squares = [pow(vec_1[arg] - vec_2[arg], 2) for arg in intersect]\n sim = 1.0 / (1 + np.sqrt(np.sum(error_squares)))\n return sim","def similarity_euclid(matrix, business1, business2):\n selected_features = matrix.loc[business1].notna() & matrix.loc[business2].notna()\n\n if not selected_features.any():\n return 0\n\n features1 = matrix.loc[business1][selected_features]\n features2 = matrix.loc[business2][selected_features]\n distance = math.sqrt(((features1 - features2) ** 2).sum())\n\n if distance is np.nan:\n return 0\n\n return 1 / (1 + distance)","def distance(cls, atom_1, atom_2):\n\t\t\n\t\treturn np.linalg.norm((atom_1-atom_2).atom_loc)","def compare_vectors(v1, v2):\n if len(v1) == len(v2):\n distance = 0\n for i in xrange(len(v1)):\n distance += (v1[i] - v2[i]) ** 2\n return distance\n else:\n print \"vector not match in dimensions\"","def euclidean(x,y):\n\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\n\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\n\n\tdistance = np.sum( np.sqrt( ( x.flat_cpt() - y.flat_cpt() )**2 ) )\n\treturn distance"],"string":"[\n \"def euclidean_distance(vector_x, vector_y):\\n if len(vector_x) != len(vector_y):\\n raise Exception('Vectors must be same dimensions')\\n return math.sqrt(sum((vector_x[dim] - vector_y[dim]) ** 2 for dim in range(len(vector_x))))\",\n \"def get_distance(self,row_vector):\\n d = row_vector-self.X_test\\n \\n return np.sqrt(np.dot(d,d)) # return the euclidean distance\",\n \"def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\\n distance_vector: np.ndarray = x - y\\n distance = compute_norm(distance_vector)\\n return distance\",\n \"def calculate_euclidean_distance(self, matrix, input, output_neuron):\\n result = 0\\n\\n # Loop over all input data.\\n diff = input - matrix[output_neuron]\\n return np.sqrt(sum(diff*diff))\",\n \"def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:\\n return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))\",\n \"def euclidean_distance(vec1, vec2):\\n return numpy.linalg.norm(vec1 - vec2)\",\n \"def get_euclidean_vector(vector):\\n\\n return np.subtract(vector[1], vector[0])\",\n \"def calculateEuclideanDistance(vector):\\r\\n global euclideanDistance\\r\\n # create linkage matrix with the distance metric as euclidean distance\\r\\n # calculate the distances of the clusters by starting as singletons\\r\\n # and in each iteration will merge the two clusters which have the smallest distance\\r\\n # returns array of length n - 1\\r\\n # Z[i] will tell us which clusters were merged in the i-th iteration\\r\\n # each row has format [cluster1, cluster1, dist, sample_count].\\r\\n euclideanDistance = linkage(vector, metric='euclidean')\",\n \"def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\\n\\n distance = np.linalg.norm(x - y)\\n\\n return distance\",\n \"def euclidean_distance(vec):\\n\\n x, y = vec\\n distance = tf.math.sqrt(tf.math.reduce_sum(tf.math.square(x - y), axis=-1, keepdims=True))\\n return distance\",\n \"def distance_metric(u, v):\\n if len(u) != len(v):\\n raise Exception(\\n \\\"Distance metric not valid for differently sized vectors\\\")\\n sum = 0.\\n for i in range(len(u)):\\n sum += ((u[i] - v[i]) ** 2)\\n return math.sqrt(sum)\",\n \"def compute_euclidean_dist(vec1, vec2):\\r\\n assert len(vec1) == len(vec2)\\r\\n vec1 = np.array(vec1)\\r\\n vec2 = np.array(vec2)\\r\\n return np.sqrt(np.sum(np.square(vec2 - vec1)))\",\n \"def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:\\n return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)\",\n \"def metric_euclid(vector):\\n return la.norm(vector)\",\n \"def euclidean_distance(vector1, vector2):\\n e_dist = [(v1 - v2) ** 2 for v1, v2 in zip(vector1, vector2)]\\n e_dist = math.sqrt(sum(e_dist))\\n return e_dist\",\n \"def euclidean(x_mat: 'Tensor', y_mat: 'Tensor', device: str = 'cpu') -> 'numpy.ndarray':\\n device = tf.device('/GPU:0') if device == 'cuda' else tf.device('/CPU:0')\\n\\n with _get_tf_device(device):\\n return tf.sqrt(\\n tf.reduce_sum((tf.expand_dims(x_mat, 1) - tf.expand_dims(y_mat, 0)) ** 2, 2)\\n ).numpy()\",\n \"def euclidean_distance(x1: np.ndarray, x2: np.ndarray) -> float:\\n return np.sqrt(np.square(x1 - x2).sum())\",\n \"def _vector_dist(self, vec1, vec2):\\r\\n return sqrt(sum([(float(v1) - float(v2)) ** 2 for v1, v2 in\\r\\n zip(vec1, vec2)]))\",\n \"def get_euclid_dist(vec_1, vec_2):\\n\\n\\treturn np.sqrt(np.sum(np.fabs(vec_1 - vec_2), axis=1)).flatten()\",\n \"def euclidean_distance(a, b):\\n return np.linalg.norm(a - b)\",\n \"def get_distance(self, vec):\\r\\n\\r\\n sum = 0\\r\\n if len(self.weights) == len(vec):\\r\\n for i in range(len(vec)):\\r\\n sum += (self.weights[i] - vec[i]) * (self.weights[i] - vec[i])\\r\\n return np.sqrt(sum)\\r\\n else:\\r\\n sys.exit(\\\"Error: dimension of nodes != input data dimension!\\\")\",\n \"def euclidean_distance(vector_1, vector_2) -> float:\\n\\n\\n before_square_root = 0\\n for i in range(len(vector_1)):\\n before_square_root += (vector_1[i] - vector_2[i])**2\\n\\n d = math.sqrt(before_square_root)\\n print(d)\\n return(d)\",\n \"def pairwise_euclidean_distance(M, axis=0):\\n\\n return pairwise_distance(M, 'euclidean', axis=axis)\",\n \"def distance(self, u, v):\\n numerator = np.dot(u,v)\\n denominator = np.linalg.norm(u) * np.linalg.norm(v)\\n similarity = numerator/(denominator +1e-7)\\n return similarity\",\n \"def __dist(u, v):\\n return spatial.distance.euclidean(u, v)\",\n \"def distance(self, vector1, vector2):\\n\\t\\tsum_sq = 0\\n\\t\\tfor i in range(28):\\n\\t\\t\\tfor j in range(28):\\n\\t\\t\\t\\tsum_sq += (vector1[i][j] - vector2[i][j])**2\\n\\t\\treturn math.sqrt(sum_sq)\",\n \"def euclidean_distance(x1, x2):\\n return np.sqrt(np.sum(np.square(np.subtract(x1, x2))))\",\n \"def _euclid_distance(self, A, B, axis=1):\\n return np.linalg.norm(A - B, axis=axis)\",\n \"def distance_matrix(data):\\n D = numpy.zeros( (data.shape[0], data.shape[0]) )\\n for i in xrange(data.shape[0]):\\n for j in xrange(i):\\n D[i,j] = numpy.linalg.norm(data[i,:]-data[j,:])\\n D[j,i] = D[i,j]\\n\\n return D\",\n \"def distancematrix(vec1, vec2):\\n v1, v2 = np.meshgrid(vec1, vec2)\\n return np.abs(v1 - v2)\",\n \"def euclidean_dist(X, y):\\n return np.sqrt(np.sum((X - y) ** 2, 1)) # broadcasted calculations\",\n \"def Euclidean(X):\\n \\n sanitycheck(X,np.ndarray)\\n return np.sqrt(np.power(X, 2).sum(axis=1))\",\n \"def euclidean_metric(x, y):\\n if len(x) != len(y):\\n raise ValueError(\\\"Incompatible dimensions.\\\")\\n return np.linalg.norm(x - y)\\n \\n # Or a slightly longer way:\\n return np.sqrt(np.sum(np.subtract(x, y)**2))\\n # Or the longest/worst way:\\n total = 0\\n for i in xrange(len(x)):\\n term = x[i] - y[i]\\n term = term**2\\n total += term\\n total = np.sqrt(total)\\n return total\",\n \"def distance(self, u, v):\\n # Implement the distance function between vectors u and v]\\n # Note: you can also think of this as computing a similarity measure\\n # Use of cosine similarity measure, assumes u and v have equal length\\n num = np.dot(u,v)\\n # den_u = np.sum(u**2)\\n # den_v = np.sum(v**2)\\n den_u = np.linalg.norm(u)\\n den_v = np.linalg.norm(v)\\n if den_u == 0.0 or den_v == 0.0:\\n return 0.0\\n # return num / (math.sqrt(den_u) * math.sqrt(den_v))\\n return num / (den_u * den_v)\",\n \"def euclidean_distance(v, w):\\n\\n # Guard against empty lists.\\n if len(v) is 0:\\n return 0\\n\\n # Note that this is the same as vector subtraction.\\n differences = [v[idx] - w[idx] for idx in range(len(v))]\\n squares = [diff ** 2 for diff in differences]\\n sum_of_squares = sum(squares)\\n\\n return 1 / (1 + math.sqrt(sum_of_squares))\",\n \"def euclidean_distance(self,):\\n return sqrt(pow((self.pose1.x - self.pose2.x), 2) +\\n pow((self.pose1.y - self.pose2.y), 2))\",\n \"def euclidean_distance(point_one, point_two):\\n return np.linalg.norm(point_one-point_two)\",\n \"def _derive_euclidean_dm(self, cat_mat, dim):\\r\\n res_mat = []\\r\\n\\r\\n for i in range(dim):\\r\\n res_mat.append([0 for k in range(dim)])\\r\\n for j in range(i):\\r\\n res_mat[i][j] = self._vector_dist(cat_mat[i], cat_mat[j])\\r\\n res_mat[j][i] = res_mat[i][j]\\r\\n\\r\\n return DistanceMatrix(res_mat, self.DistanceMatrices[0].ids)\",\n \"def sqeuclidean(\\n x_mat: 'Tensor', y_mat: 'Tensor', device: str = 'cpu'\\n) -> 'numpy.ndarray':\\n device = tf.device('/GPU:0') if device == 'cuda' else tf.device('/CPU:0')\\n\\n with _get_tf_device(device):\\n return tf.reduce_sum(\\n (tf.expand_dims(x_mat, 1) - tf.expand_dims(y_mat, 0)) ** 2, 2\\n ).numpy()\",\n \"def _euclidian_distance(self, x1, x2):\\n a= x1-x2\\n a2 = a**2\\n b = np.sum(a2, axis=1)\\n c = np.sqrt(b)\\n return c\",\n \"def euclidean_distance(x1, x2):\\n return (x2[0] - x1[0])**2 + (x2[1] - x1[1])**2\",\n \"def distance(self, other_vector):\\n return self.distance_sq(other_vector) ** 0.5\",\n \"def euclidean(x, y):\\n ed = np.sqrt(np.sum((x-y)**2))\\n # print ed\\n return ed\",\n \"def euclidean_distance(A, B):\\n aa = T.sum(A * A, axis=1)\\n bb = T.sum(B * B, axis=1)\\n AB = T.dot(A, T.transpose(B))\\n\\n AA = T.transpose(T.tile(aa, (bb.shape[0], 1)))\\n BB = T.tile(bb, (aa.shape[0], 1))\\n\\n D = AA + BB - 2 * AB\\n\\n # Sanity checks\\n D = T.maximum(D, 0)\\n D = T.sqrt(D)\\n return D\",\n \"def euclidean_distance(x1, x2):\\n return np.sqrt(np.sum(np.power(x1 - x2, 2)))\",\n \"def norm_dist(face_vectors, f_vector):\\n if len(face_vectors) == 0:\\n return np.empty((0))\\n return np.linalg.norm(face_vectors - f_vector, axis=1)\",\n \"def euclidean_distances(X, Y):\\r\\n\\r\\n D = np.zeros((X.shape[0],Y.shape[0]))\\r\\n \\r\\n for X_idx in range(X.shape[0]):\\r\\n for Y_idx in range(Y.shape[0]): \\r\\n \\r\\n D[X_idx,Y_idx] = np.sqrt(np.sum((X[X_idx,:]-Y[Y_idx,:])**2))\\r\\n \\r\\n return D\",\n \"def euclidean(self, other):\\n return linalg.norm([self.x - other.x, self.y - other.y])\",\n \"def distance(N, M):\\n\\n\\td = np.sqrt( (N[:,0, np.newaxis] - M[:,0])**2 + (N[:,1, np.newaxis] - M[:,1])**2 )\\n\\treturn d\",\n \"def test_vector_distance(self):\\n\\n # Example 1.2\\n vector_p = np.array([0.5, 0.0, 0.5])\\n vector_q = np.array([0.5, 0.5, 0.0])\\n crystal = crystal_system.Tetragonal(0.5, 1.0)\\n d_ref_nm = np.sqrt(5.0)/4.0\\n\\n g_ij_nm2 = crystal.gij_nm2\\n\\n vector_d = vector_p - vector_q\\n d_nm = vector.distance(crystal, vector_d, vector_d)\\n\\n self.assertAlmostEqual(d_ref_nm, d_nm, 5)\\n\\n #self.fail(\\\"Test if the testcase is working.\\\")\",\n \"def euclidian_distance(x: np.arrays, y: np.arrays):\\r\\n diff = x - np.mean(y, axis=0)\\r\\n return np.sqrt(np.dot(diff.T, diff))\",\n \"def euclidean_dist_vec(y1, x1, y2, x2):\\n\\n # euclid's formula\\n distance = ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5\\n return distance\",\n \"def euclidean_distance(self, point: List[int]) -> float:\\n return sqrt(point[0] ** 2 + point[1] ** 2)\",\n \"def euclidean_distance(point1, point2):\\n return np.linalg.norm(np.array(point1) - np.array(point2))\",\n \"def euclidean_squared_distance(input1, input2):\\n m, n = input1.size(0), input2.size(0)\\n mat1 = torch.pow(input1, 2).sum(dim=1, keepdim=True).expand(m, n)\\n mat2 = torch.pow(input2, 2).sum(dim=1, keepdim=True).expand(n, m).t()\\n distmat = mat1 + mat2\\n distmat.addmm_(input1, input2.t(), beta=1, alpha=-2)\\n return distmat\",\n \"def euclidean(x,y): \\n\\treturn np.sqrt(np.sum((x-y)**2))\",\n \"def distance_sq(self, other_vector):\\n return sum((x - y) ** 2 for x, y in zip(self.vector, other_vector))\",\n \"def compute_distance (uVector, uOther):\\n ## since each element can be either 0 or 1,\\n ## no need for square roots and pow\\n d = 0\\n for i in range (len(uVector)):\\n d = d + math.pow((int(uVector [i]) - int(uOther [i])), 2)\\n\\n return d\",\n \"def _euclideanDistance(A, B):\\n if len(A) != len(B):\\n raise ValueError(\\\"A and B must have the same number of dimensions\\\")\\n sqr_dist = 0\\n for i in range(len(A)):\\n sqr_dist += (A[i] - B[i])**2\\n return np.sqrt(sqr_dist)\",\n \"def dist_euclidean(datamtx, strict=True):\\n datamtx = asarray(datamtx, 'd')\\n if strict:\\n if not all(isfinite(datamtx)):\\n raise ValueError(\\\"non finite number in input matrix\\\")\\n if rank(datamtx) != 2:\\n raise ValueError(\\\"input matrix not 2D\\\")\\n numrows, numcols = shape(datamtx)\\n else:\\n try:\\n numrows, numcols = shape(datamtx)\\n except ValueError:\\n return zeros((0,0),'d')\\n\\n if numrows == 0 or numcols == 0:\\n return zeros((0,0),'d')\\n dists = zeros((numrows,numrows),'d')\\n for r in range(numrows):\\n for c in range(r):\\n dist = norm(datamtx[r] - datamtx[c])\\n if isnan(dist):\\n raise RuntimeError('ERROR: overflow when computing euclidean distance')\\n dists[r,c] = dists[c,r] = dist\\n\\n return dists\",\n \"def test_euclidean_distance_Ndimension(self):\\n\\n self.assertEqual(15, euclidean_distance([0, 0, 0], [10, 10, 5]))\\n self.assertEqual(15, euclidean_distance([0, 0, 0], [-10, -10, -5]))\\n\\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [10, 10, 8, 5]))\\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [-10, -10, -8, -5]))\\n\\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [5, 1, 1, 1, 6]))\\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [-5, -1, -1, -1, -6]))\",\n \"def pairwise_euclidean_distance(x, y):\\n x_norm = (x ** 2).sum(1).view(-1, 1)\\n if y is not None:\\n y_t = torch.transpose(y, 0, 1)\\n y_norm = (y ** 2).sum(1).view(1, -1)\\n else:\\n y_t = torch.transpose(x, 0, 1)\\n y_norm = x_norm.view(1, -1)\\n\\n dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t)\\n # Ensure diagonal is zero if x=y\\n # if y is None:\\n # dist = dist - torch.diag(dist.diag)\\n return torch.sqrt(torch.clamp(dist, 0.0, np.inf))\",\n \"def euclidean(x, y):\\n return np.sqrt(np.sum((x - y) ** 2))\",\n \"def euclidean_distance(x, y):\\n distance = 0\\n for i, j in zip(x, y):\\n distance += (i - j) ** 2\\n return math.sqrt(distance)\",\n \"def distance_matrix_vector(anchor, positive):\\n \\n # here anchor*anchor is equal torch.mul(anchor, anchor)\\n # the element-wise value multiplication is returned\\n d1_sq = torch.sum(anchor * anchor, dim=1).unsqueeze(-1)\\n d2_sq = torch.sum(positive * positive, dim=1).unsqueeze(-1)\\n\\n eps = 1e-6\\n # tensor.repeat(): repeat at each dims, and dims from right to left\\n return torch.sqrt((d1_sq.repeat(1, anchor.size(0)) + torch.t(d2_sq.repeat(1, positive.size(0)))\\n - 2.0 * torch.bmm(anchor.unsqueeze(0), torch.t(positive).unsqueeze(0)).squeeze(0))\\n +eps)\",\n \"def pairwise_euclidean_distance(x, y):\\n m, n = x.size(0), y.size(0)\\n dist_mat = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) + \\\\\\n torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t() \\\\\\n - 2 * torch.matmul(x, y.t())\\n # for numerical stability\\n dist_mat = dist_mat.clamp(min=1e-12).sqrt()\\n return dist_mat\",\n \"def euclidean_distance(x, y):\\n return sqrt(sum(pow(a - b, 2) for a, b in zip(x, y)))\",\n \"def GetDistance(vec1,vec2):\\n diff = np.asarray(vec1) - np.asarray(vec2)\\n squareDistance = np.dot(diff.T, diff)\\n return math.sqrt(squareDistance)\",\n \"def distance(v: Vector, w: Vector) -> float:\\n return magnitude(subtract(v, w))\",\n \"def euclidean_distance(x, y):\\n x1, y1 = x\\n x2, y2 = y\\n return sqrt((x1 - x2)**2 + (y1 - y2)**2)\",\n \"def cosine_distance(u, v):\\n #print u,v\\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))\",\n \"def get_euclid_distance_to(self, atom):\\n return linalg.norm(self.get_coords() - atom.get_coords())\",\n \"def vector_dist(v, w):\\n if isinstance(v, list):\\n v = np.asarray(v)\\n return vector_mag(v - w)\",\n \"def compute_distance(node1, node2):\\n return np.linalg.norm(node1 - node2)\",\n \"def euclidean_distance(x1, x2):\\n\\tdistance = 0\\n\\t# Squared distance between each coordinate\\n\\tfor i in range(len(x1)):\\n\\t\\tdistance += pow((x1[i], x2[i]), 2)\\n\\treturn math.sqrt(distance)\",\n \"def euclidean_distance(tensor1, tensor2):\\n tensor1 = tf.convert_to_tensor(tensor1)\\n tensor2 = tf.convert_to_tensor(tensor2)\\n\\n distance = tf.sqrt(tf.reduce_sum(tf.square(tensor1 - tensor2), axis=-1))\\n\\n return distance\",\n \"def distance(self, location):\\n return numpy.linalg.norm(self.vector_to(location))\",\n \"def cosine_distance(u, v):\\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))\",\n \"def _dist(A, B):\\n return np.sqrt(np.einsum(\\\"ijk->ij\\\", (A[:, None, :] - B) ** 2))\",\n \"def euclidianDistance(row1, row2):\\n\\n dist = 0.0\\n for i in range(len(row1)-1):\\n dist += (row1[i] - row2[i])**2\\n \\n return sqrt(dist)\",\n \"def test_distance(self):\\n self.assertTrue(np.allclose(self.vectors.distance('dog.n.01', 'mammal.n.01'), 4.5278745))\\n self.assertEqual(self.vectors.distance('dog.n.01', 'dog.n.01'), 0)\",\n \"def test_distances_with_vector_input(self):\\n input_vector = self.vectors['dog.n.01']\\n distances = self.vectors.distances(input_vector, ['mammal.n.01', 'dog.n.01'])\\n self.assertTrue(np.allclose(distances, [4.5278745, 0]))\\n\\n distances = self.vectors.distances(input_vector)\\n self.assertEqual(len(distances), len(self.vectors.vocab))\\n self.assertTrue(np.allclose(distances[-1], 10.04756))\",\n \"def euclidean_distance(a, b):\\n return sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)\",\n \"def norm(vec):\\n return np.linalg.norm(vec)\",\n \"def test_vector_dist(self):\\r\\n v1 = [1, 4, 2]\\r\\n v2 = [-1, 12, 4]\\r\\n\\r\\n exp = 8.48528137424\\r\\n obs = self.best._vector_dist(v1, v2)\\r\\n assert_almost_equal(exp, obs)\\r\\n\\r\\n v1 = [1, 2, 100, 4, 2]\\r\\n v2 = [-1, 12, 4, 12, 99]\\r\\n\\r\\n exp = 137.087563258\\r\\n obs = self.best._vector_dist(v1, v2)\\r\\n assert_almost_equal(exp, obs)\",\n \"def cal_distances(embeddings):\\n # calculate\\n dist = np.zeros([len(embeddings), len(embeddings)], dtype=float)\\n for ii in xrange(len(embeddings)):\\n for jj in xrange(ii + 1, len(embeddings)):\\n dist[ii, jj] = np.linalg.norm(embeddings[ii] - embeddings[jj])\\n dist[jj, ii] = dist[ii, jj] \\n \\n # return\\n return dist\",\n \"def _nn_euclidean_distance(x, y):\\n distances = _pdist(x, y)\\n return np.maximum(0.0, distances.min(axis=0))\",\n \"def _nn_euclidean_distance(x, y):\\n distances = _pdist(x, y)\\n return np.maximum(0.0, distances.min(axis=0))\",\n \"def euclideanDistance(a, b):\\n vec = [pow(a[i] - b[i], 2) for i in range(len(a)) if None not in [a[i],b[i]]]\\n return (sum(vec) / len(vec)) if len(vec) > 0 else NaN\",\n \"def euclidean_distance(start, end):\\n\\n value = np.sqrt(np.sum(np.square(np.subtract(start, end)), axis=-1))\\n return value\",\n \"def squared_distance(v: Vector, w: Vector) -> float:\\n return sum_of_squares(subtract(v, w))\",\n \"def norm_distance(self):\\n graph_size = self.N + self.M\\n return self.distance() / (1. * graph_size)\",\n \"def dist(x,u):\\r\\n\\t# https://github.com/dwf/rescued-scipy-wiki/blob/master/EricsBroadcastingDoc.rst\\r\\n\\tdiff = x[np.newaxis,:,:] - u[:,np.newaxis,:]\\r\\n\\tdist = np.sum(diff**2,axis=-1)\\r\\n\\treturn dist\",\n \"def EuclideanDistanceSq( self, a, b ):\\n if not (type(a) == list or type(a) == Vector):\\n a = [a]\\n if not (type(b) == list or type(a) == Vector):\\n b = [b]\\n assert len(a) == len(b)\\n sqDist = 0\\n for x,y in zip(a,b):\\n sqDist += (x-y)**2\\n return sqDist\",\n \"def det(self):\\n\\n if self.rows != self.columns:\\n raise ValueError(\\\"Matrix must be square\\\")\\n\\n if self.rows == 1:\\n return self.row(1)[0]\\n\\n if self.rows == 2:\\n return self.entry(1,1) * self.entry(2,2) - self.entry(1,2) * self.entry(2,1)\\n\\n det = 0\\n row_to_expand = 1\\n\\n for i in range(1, self.columns + 1):\\n det += self.entry(row_to_expand, i) * self._cofactor(row_to_expand, i)\\n\\n return det\",\n \"def calc_euclidean_similarity(vec_1, vec_2):\\n sim = 0\\n vec_1 = vec_1.reshape((vec_1.shape[1],))\\n vec_2 = vec_2.reshape((vec_2.shape[1],))\\n vec_1_nnz = np.nonzero(vec_1)[0]\\n print vec_1_nnz\\n # import ipdb; ipdb.set_trace()\\n vec_2_nnz = np.nonzero(vec_2)[0]\\n print vec_2_nnz\\n intersect = set(vec_1_nnz) & set(vec_2_nnz)\\n if len(intersect) > 0:\\n error_squares = [pow(vec_1[arg] - vec_2[arg], 2) for arg in intersect]\\n sim = 1.0 / (1 + np.sqrt(np.sum(error_squares)))\\n return sim\",\n \"def similarity_euclid(matrix, business1, business2):\\n selected_features = matrix.loc[business1].notna() & matrix.loc[business2].notna()\\n\\n if not selected_features.any():\\n return 0\\n\\n features1 = matrix.loc[business1][selected_features]\\n features2 = matrix.loc[business2][selected_features]\\n distance = math.sqrt(((features1 - features2) ** 2).sum())\\n\\n if distance is np.nan:\\n return 0\\n\\n return 1 / (1 + distance)\",\n \"def distance(cls, atom_1, atom_2):\\n\\t\\t\\n\\t\\treturn np.linalg.norm((atom_1-atom_2).atom_loc)\",\n \"def compare_vectors(v1, v2):\\n if len(v1) == len(v2):\\n distance = 0\\n for i in xrange(len(v1)):\\n distance += (v1[i] - v2[i]) ** 2\\n return distance\\n else:\\n print \\\"vector not match in dimensions\\\"\",\n \"def euclidean(x,y):\\n\\tassert (isinstance(x, BayesNet) and isinstance(y, BayesNet)), 'Must pass in BayesNet objects.'\\n\\tassert (x==y), 'Passed-in BayesNet objects are not structurally equal.'\\n\\n\\tdistance = np.sum( np.sqrt( ( x.flat_cpt() - y.flat_cpt() )**2 ) )\\n\\treturn distance\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7465319","0.73555523","0.7279082","0.7155775","0.70827484","0.7002339","0.6978049","0.69625634","0.69552696","0.68833405","0.6845088","0.684352","0.6810883","0.6807818","0.6792588","0.675617","0.67391354","0.668006","0.6631312","0.6624541","0.6609537","0.6573236","0.65625995","0.6560832","0.653255","0.65312594","0.65285546","0.65049106","0.6495866","0.6495561","0.64673597","0.6458835","0.645746","0.6432975","0.64013165","0.63539815","0.6347139","0.63147205","0.63098574","0.63087684","0.6305372","0.6305181","0.6303986","0.62722504","0.62700176","0.6269701","0.6259155","0.62562025","0.6242768","0.62360126","0.6231945","0.6231705","0.6228321","0.6218717","0.6194705","0.61904836","0.6178734","0.6166925","0.6163196","0.61545944","0.61478204","0.6145413","0.61299783","0.6124249","0.61240715","0.61146635","0.61099666","0.6085439","0.60835737","0.60805655","0.6073289","0.6071226","0.604865","0.60310054","0.60236984","0.60163486","0.59951746","0.599257","0.59916353","0.5982093","0.59727126","0.59603083","0.595485","0.5951243","0.5950035","0.594738","0.5942231","0.5942231","0.5936682","0.5935512","0.5935003","0.5923977","0.591622","0.59079653","0.5907415","0.5898916","0.58923286","0.587459","0.5868705","0.58592236"],"string":"[\n \"0.7465319\",\n \"0.73555523\",\n \"0.7279082\",\n \"0.7155775\",\n \"0.70827484\",\n \"0.7002339\",\n \"0.6978049\",\n \"0.69625634\",\n \"0.69552696\",\n \"0.68833405\",\n \"0.6845088\",\n \"0.684352\",\n \"0.6810883\",\n \"0.6807818\",\n \"0.6792588\",\n \"0.675617\",\n \"0.67391354\",\n \"0.668006\",\n \"0.6631312\",\n \"0.6624541\",\n \"0.6609537\",\n \"0.6573236\",\n \"0.65625995\",\n \"0.6560832\",\n \"0.653255\",\n \"0.65312594\",\n \"0.65285546\",\n \"0.65049106\",\n \"0.6495866\",\n \"0.6495561\",\n \"0.64673597\",\n \"0.6458835\",\n \"0.645746\",\n \"0.6432975\",\n \"0.64013165\",\n \"0.63539815\",\n \"0.6347139\",\n \"0.63147205\",\n \"0.63098574\",\n \"0.63087684\",\n \"0.6305372\",\n \"0.6305181\",\n \"0.6303986\",\n \"0.62722504\",\n \"0.62700176\",\n \"0.6269701\",\n \"0.6259155\",\n \"0.62562025\",\n \"0.6242768\",\n \"0.62360126\",\n \"0.6231945\",\n \"0.6231705\",\n \"0.6228321\",\n \"0.6218717\",\n \"0.6194705\",\n \"0.61904836\",\n \"0.6178734\",\n \"0.6166925\",\n \"0.6163196\",\n \"0.61545944\",\n \"0.61478204\",\n \"0.6145413\",\n \"0.61299783\",\n \"0.6124249\",\n \"0.61240715\",\n \"0.61146635\",\n \"0.61099666\",\n \"0.6085439\",\n \"0.60835737\",\n \"0.60805655\",\n \"0.6073289\",\n \"0.6071226\",\n \"0.604865\",\n \"0.60310054\",\n \"0.60236984\",\n \"0.60163486\",\n \"0.59951746\",\n \"0.599257\",\n \"0.59916353\",\n \"0.5982093\",\n \"0.59727126\",\n \"0.59603083\",\n \"0.595485\",\n \"0.5951243\",\n \"0.5950035\",\n \"0.594738\",\n \"0.5942231\",\n \"0.5942231\",\n \"0.5936682\",\n \"0.5935512\",\n \"0.5935003\",\n \"0.5923977\",\n \"0.591622\",\n \"0.59079653\",\n \"0.5907415\",\n \"0.5898916\",\n \"0.58923286\",\n \"0.587459\",\n \"0.5868705\",\n \"0.58592236\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77098405"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22382,"cells":{"query":{"kind":"string","value":"Hybrid similarity between vector and matrix."},"document":{"kind":"string","value":"def hybrid_sim(vec, mat, top_k=5, alpha=0.1):\n euc_vec = 0.\n idx = np.array(range(mat.shape[0]))\n\n if alpha > 0:\n # step 1: caculate euclidean distance\n euc_vec = euclid_dist(vec, mat)\n if len(euc_vec) > top_k:\n idx = np.argsort(euc_vec)[:top_k]\n euc_vec = euc_vec[idx]\n mat = mat[idx, :]\n euc_vec = _euc_sim(euc_vec)\n\n cos_vec = cos_sim(vec, mat)\n hyb_vec = (1 - alpha) * (0.5 * cos_vec + 0.5) + alpha * euc_vec\n\n return idx, hyb_vec"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def self_similarity_matrix(feature_vectors):\n norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T])\n norm_feature_vectors = norm_feature_vectors[0].T\n sim_matrix = 1.0 - distance.squareform(\n distance.pdist(norm_feature_vectors.T, 'cosine'))\n return sim_matrix","def test_similarity(self):\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'dog.n.01'), 1))\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'mammal.n.01'), 0.180901358))","def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n # return cosine_similarity(v1, v2)[0][0]","def calculate_similarity(self, tweets):\r\n if (len(tweets) == 1):\r\n return 0\r\n vectors = self.vectorizer.vectorize_data(tweets, False)\r\n\r\n temp = cosine_similarity(vectors[0:-1], vectors)\r\n temp = [item for sublist in temp for item in sublist]\r\n sim = sum(temp) / len(temp)\r\n return sim","def similarity_vector(self, x):\n r_x = self.encoder(x)[1]\n return self.prototypes_layer(r_x)","def cosine_similarity(u, v):\n\n distance = 0.0\n\n ### START CODE HERE ###\n # Compute the dot product between u and v (≈1 line)\n dot = np.dot(u, v)\n # Compute the L2 norm of u (≈1 line)\n norm_u = np.sqrt(np.dot(u, u))\n\n # Compute the L2 norm of v (≈1 line)\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\n # Compute the cosine similarity defined by formula (1) (≈1 line)\n cosine_similarity = dot / (norm_u * norm_v)\n ### END CODE HERE ###\n\n return cosine_similarity","def cosine_similarity(v1, v2):\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def similarity_function_old(feature1, feature2):\n f1Magnitude = feature1.dot(feature1)\n f2Magnitude = feature2.dot(feature2)\n return 1 - feature1.dot(feature2) / (f1Magnitude * f2Magnitude)","def similarity(self, token1, token2):\n vec1 = self.get_vector(token1)\n vec2 = self.get_vector(token2)\n assert vec1 is not None and vec2 is not None, \"Cannot compute similarity between None type vectors.\"\n if not self.normalize:\n # if model not loaded as normalized embeddings \n vec1 = vec1 / np.linalg.norm(vec1)\n vec2 = vec2 / np.linalg.norm(vec2)\n return np.dot(vec1, vec2)","def similarity_matrix(feat_mat):\n sim_mat = cosine_similarity(feat_mat)\n np.fill_diagonal(sim_mat, 0)\n return sim_mat","def get_similarity(self, ):\r\n customer_cos_similarity = cosine_similarity(self.rating_matrix, self.rating_matrix)\r\n customer_cos_similarity = pd.DataFrame(customer_cos_similarity,\r\n index=self.customer_vendor_matrix.index,\r\n columns=self.customer_vendor_matrix.index)\r\n # customer_pearson_similarity = np.corrcoef(self.rating_matrix,\r\n # self.rating_matrix,)\r\n # customer_pearson_similarity = pd.DataFrame(customer_pearson_similarity,\r\n # index=self.customer_vendor_matrix.index,\r\n # columns=self.customer_vendor_matrix.index)\r\n return customer_cos_similarity,\r\n # return customer_pearson_similarity run too slowly\r","def centrality(similarity, vector, vectors):\n return 1.0/len(vectors)*sum([similarity(vector,y) for y in vectors\\\n if y != vector])","def cosine_similarity(vector_x, vector_y):\n if(len(vector_x)!=len(vector_y)):\n raise Exception('Vectors must be the same dimensions')\n \n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))","def similarity_function(feature1, feature2):\n # 256 HOG, 18 HSV, 512 Encoder\n # weight color more if using the full vector\n if len(feature1) > 785:\n salient1 = feature1[256:256 + 18].copy() # be careful not to modify feature vector in place\n salient2 = feature2[256:256 + 18].copy()\n feature1 = feature1.copy()\n feature2 = feature2.copy()\n feature1[256:256 + 18] = salient1 * 10\n feature2[256:256 + 18] = salient2 * 10\n\n abs_distance = np.abs(feature1 - feature2)\n return np.sum(abs_distance)","def distance(self, u, v):\n numerator = np.dot(u,v)\n denominator = np.linalg.norm(u) * np.linalg.norm(v)\n similarity = numerator/(denominator +1e-7)\n return similarity","def similarity_transformation(rot, mat):\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))","def similarity_transformation(rot, mat):\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))","def test_cosine_similarity_matrix():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def similarity_matrix(P, similarity_measure, normalize=True, inverse=True):\n N = len(P) \n S = np.zeros((N, N))\n for i in range(N): \n for j in range(i): \n S[i][j] = similarity_measure(P[i], P[j])\n\n S = square(S)\n if normalize: \n S = S / np.max(S)\n if inverse:\n S = 1 - S # Higher value = more similar\n\n return S","def compute_similarity(site_a, site_b):\n return np.linalg.norm(site_a - site_b)","def cosine_similarity(v1: Vector, v2: Vector) -> float:\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))","def test_cosine_similarity_matrix_compiled():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def semantic_similarity(self,sentence_1, sentence_2, info_content_norm):\n\t words_1 = sentence_1.getList_of_words()\n\t words_2 = sentence_2.getList_of_words()\n\t joint_words = set(words_1).union(set(words_2))\n\t vec_1 = self.semantic_vector(words_1, joint_words, info_content_norm)\n\t vec_2 = self.semantic_vector(words_2, joint_words, info_content_norm)\n\t return np.dot(vec_1, vec_2.T) / (np.linalg.norm(vec_1) * np.linalg.norm(vec_2))","def vi_similarity(self, normalize=True, model='m1'):\n R, C = self.shape\n N = self.grand_total\n\n max_dist = log(N)\n dist = self.vi_distance(normalize=False)\n score = max_dist - dist\n\n if model is None:\n null_score = 0\n elif model == 'm1': # only N is fixed\n null_dist = log(R) + log(C)\n null_score = max_dist - null_dist\n elif model == 'm2r': # fixed row margin\n null_dist = log(C) + fentropy(self.row_totals) / N\n null_score = max_dist - null_dist\n elif model == 'm2c': # fixed column margin\n null_dist = log(R) + fentropy(self.col_totals) / N\n null_score = max_dist - null_dist\n elif model == 'm3': # both row and column margins fixed\n null_dist = (fentropy(self.row_totals) + fentropy(self.col_totals)) / N\n null_score = max_dist - null_dist\n else:\n expected = self.expected(model)\n null_score = expected.vi_similarity(normalize=False, model=None)\n\n score -= null_score\n if normalize:\n max_score = max_dist - null_score\n score = 1.0 if score == max_score else _div(score, max_score)\n\n return score","def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))","def cosine_similarity(X):\n matrix = X.dot(X.transpose()).todense()\n mat_len = len(matrix)\n norms = [0] * mat_len\n for i in range(0, mat_len):\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\n norm_mat = np.matrix(norms)\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)","def similarity_matrix(points, sigma):\n distances_squared = spherical_distances(points, points)**2\n\n \n return np.exp( -distances_squared / (2.0 * sigma) )","def similarity(self, word1, word2):\n common_vect = +np.ones(self.nEmbed) * 10000\n if word1 not in self.vocab and word2 in self.vocab:\n id_word_2 = self.w2id[word2]\n w1 = common_vect\n w2 = self.U[id_word_2]\n elif word1 in self.vocab and word2 not in self.vocab:\n id_word_1 = self.w2id[word1]\n w1 = self.U[id_word_1]\n w2 = common_vect\n elif word1 not in self.vocab and word2 not in self.vocab:\n w1 = common_vect\n w2 = common_vect\n else:\n id_word_1 = self.w2id[word1]\n id_word_2 = self.w2id[word2]\n w1 = self.U[id_word_1]\n w2 = self.U[id_word_2]\n\n # scalair = w1.dot(w2)/np.linalg.norm(w1,w2)\n similarity = w1.dot(w2) / (np.linalg.norm(w1) * np.linalg.norm(w2))\n # similarity = 1 / (1 + np.exp(-scalair))\n # similarity = scalair / (np.linalg.norm(w1) * np.linalg.norm(w2))\n return similarity","def build_matrix(self):\n \n for p1 in self._properties: \n p1 = p1.get_vectorized_data()\n \n for p2 in self._properties:\n p2 = p2.get_vectorized_data()\n v1, v2 = self.prepare_vectors(p1, p2)\n self._similarity_matrix.append(cosine_similarity([v1],[v2]))","def similarity_euclid(matrix, business1, business2):\n selected_features = matrix.loc[business1].notna() & matrix.loc[business2].notna()\n\n if not selected_features.any():\n return 0\n\n features1 = matrix.loc[business1][selected_features]\n features2 = matrix.loc[business2][selected_features]\n distance = math.sqrt(((features1 - features2) ** 2).sum())\n\n if distance is np.nan:\n return 0\n\n return 1 / (1 + distance)","def test_jaccard_similarity_matrix():\n vectors1 = np.array([[1, 1, 0, 0],\n [0, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [1, 0, 1, 1]])\n\n scores = jaccard_similarity_matrix.py_func(vectors1, vectors2)\n expected_scores = np.array([[1/3, 1/4],\n [1/3, 2/3]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def get_similarity(df):\n count = CountVectorizer()\n count_matrix = count.fit_transform(df[\"bag_of_words\"])\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\n return cosine_sim","def cosine_similarity(u: np.ndarray, v: np.ndarray) -> np.float64:\n assert u.shape[0] == v.shape[0], \"Input vector must have same shape.\"\n uv = 0\n uu = 0\n vv = 0\n for i in range(u.shape[0]):\n uv += u[i] * v[i]\n uu += u[i] * u[i]\n vv += v[i] * v[i]\n cosine_score = 0\n if uu != 0 and vv != 0:\n cosine_score = uv / np.sqrt(uu * vv)\n return np.float64(cosine_score)","def pairwise_dot_product_similarity(x, y):\n return torch.mm(x, torch.transpose(y, 1, 0))","def cosine_similarity(v1, v2):\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\n return sim","def test_dice_similarity_matrix():\n vectors1 = np.array([[1, 1, 0, 0],\n [0, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [1, 0, 1, 1]])\n\n scores = dice_similarity_matrix.py_func(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.4],\n [0.5, 0.8]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def cosine_similarity(vec1, vec2) -> float:\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))","def similarity(self, w1, w2):\r\n sim = self.represent(w1).dot(self.represent(w2))\r\n return sim","def compute_similarities_from_vec(self, dataset, a):\n self.model.fit(dataset.X, a)\n return self.model.coef_","def test_cosine_similarity():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity.py_func(vector1, vector1)\n score12 = cosine_similarity.py_func(vector1, vector2)\n score22 = cosine_similarity.py_func(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def test_cosine_similarity_all_zeros():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity.py_func(vector1, vector1)\n score12 = cosine_similarity.py_func(vector1, vector2)\n score22 = cosine_similarity.py_func(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","def compute_all_similarities(self,A,a):\n pass","def compute_cosine_similarity(self):\n cos_matrix = []\n for i in range(len(self.train_vec)):\n val = self.vec1 * self.train_vec[i]\n cos_matrix.append(val[0])\n out = np.argmax(cos_matrix)\n print(self.train_output[out])","def test_cosine_similarity_compiled():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def calculate_user_similarity_profile(self, ratings_vector):\r\n num_users, num_movies = self.ratings_matrix.get_shape()\r\n\r\n user_similarities = sp.dok_matrix((1, num_users))\r\n for i in range(num_users):\r\n\r\n user_similarities[0, i] = self.calculate_pairwise_user_similarity(self.ratings_matrix.getrow(i), ratings_vector)\r\n\r\n return user_similarities.tocsr()","def similarity_vec(self, vec1: numpy.ndarray, vec2: numpy.ndarray, metric='cosine') -> float:\n if numpy.count_nonzero(vec1) == 0 or numpy.count_nonzero(vec2) == 0:\n if metric == 'cosine':\n return 0.\n else:\n return 0.\n\n vec1 = vec1.reshape((1, -1))\n vec2 = vec2.reshape((1, -1))\n if metric == 'cosine':\n return (1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1))[0]\n else:\n return distance.cdist(vec1, vec2, metric=metric).reshape(-1)[0]","def magni(vector):\n return(np.linalg.norm(vector))","def similarity(self, w1, w2):\r\n return self.represent(w1).dot(self.represent(w2))","def similarity(self, w1, w2):\r\n return self.represent(w1).dot(self.represent(w2))","def _calculate_trilinear_similarity(self, context, query, context_max_len, query_max_len,\n w4mlu, bias):\n\n subres0 = nd.tile(self.w4c(context), [1, 1, query_max_len])\n subres1 = nd.tile(nd.transpose(\n self.w4q(query), axes=(0, 2, 1)), [1, context_max_len, 1])\n subres2 = nd.batch_dot(w4mlu * context,\n nd.transpose(query, axes=(0, 2, 1)))\n similarity_mat = subres0 + subres1 + subres2 + bias\n return similarity_mat","def align_reconstruction_naive_similarity(X, Xp):\n\t# Compute similarity Xp = s A X + b\n\tT = superimposition_matrix(X.T, Xp.T, scale=True)\n\tA, b = T[:3, :3], T[:3, 3]\n\ts = np.linalg.det(A)**(1. / 3)\n\tA /= s\n\treturn s, A, b","def test_dice_similarity_matrix_compiled():\n vectors1 = np.array([[1, 1, 0, 0],\n [0, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [1, 0, 1, 1]])\n\n scores = dice_similarity_matrix(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.4],\n [0.5, 0.8]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def test_jaccard_similarity_matrix_compiled():\n vectors1 = np.array([[1, 1, 0, 0],\n [0, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [1, 0, 1, 1]])\n\n scores = jaccard_similarity_matrix(vectors1, vectors2)\n expected_scores = np.array([[1/3, 1/4],\n [1/3, 2/3]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def similarity_score(self, lhs, rhs):\n pass","def cosine_sim(matrix):\n if type(matrix) is not csr_matrix:\n matrix = csr_matrix(matrix)\n\n return cosine_similarity(matrix, dense_output=False)","def similarity(self, e1, e2):\n\t\tpass","def pairwise_euclidean_similarity(x, y):\n s = 2 * torch.mm(x, torch.transpose(y, 1, 0))\n diag_x = torch.sum(x * x, dim=-1)\n diag_x = torch.unsqueeze(diag_x, 0)\n diag_y = torch.reshape(torch.sum(y * y, dim=-1), (1, -1))\n\n return s - diag_x - diag_y","def similarity(pair: Tuple[Text, Text]) -> float:\n (a, b) = pair\n missing = (\n True\n if any(symbol not in Metrics.realine.feature_matrix for symbol in pair)\n else False\n )\n return 0.0 if missing else 1 - Metrics.realine.delta(a, b)","def cosine_similarity(self, x, y):\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))","def test_cosine_similarity_all_zeros_compiled():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","def similarity_transform_3d(v, mapping, alpha = 1):\r\n p_wgt = vec3(0, 0, 0)\r\n q_wgt = vec3(0, 0, 0)\r\n w = len(mapping)*[None]\r\n w_sum = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n x = mp[0].x - v.x\r\n y = mp[0].y - v.y\r\n z = mp[0].z - v.z\r\n if (x == 0 and y == 0 and z == 0): return mp[1]\r\n w[i] = 1/((x*x + y*y + z*z) ** alpha)\r\n p_wgt += mp[0]*w[i]\r\n q_wgt += mp[1]*w[i]\r\n w_sum += w[i]\r\n p_wgt /= w_sum\r\n q_wgt /= w_sum\r\n A = mat3(0)\r\n k = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n q_adj = mp[1] - q_wgt\r\n A += w[i]*p_adj.transpose_multiply(q_adj)\r\n k += w[i]*p_adj.dot(p_adj)\r\n A_arr = np.array(A.matrix).reshape(3, 3)\r\n U, S, V = np.linalg.svd(A_arr)\r\n M_arr = np.matmul(np.transpose(V), np.transpose(U))\r\n M = mat3(M_arr.ravel().tolist())\r\n k = np.sum(S)/k\r\n v_out = k*M*(v - p_wgt) + q_wgt\r\n return v_out","def similarity_function(x, y):\n\n def safe_get(field, row, default_value):\n # Safely get a value from the Row. If the value is None, get the\n # default value.\n return row[field] if row[field] is not None else default_value\n\n # Extract the values for the categorical and continuous features for both\n # the x and y samples. Use an empty string as the default value for missing\n # categorical fields and 0 for the continuous ones.\n x_categorical_features = [safe_get(k, x, \"\") for k in CATEGORICAL_FEATURES]\n x_continuous_features = [safe_get(k, x, 0) for k in CONTINUOUS_FEATURES]\n y_categorical_features = [safe_get(k, y, \"\") for k in CATEGORICAL_FEATURES]\n y_continuous_features = [safe_get(k, y, 0) for k in CONTINUOUS_FEATURES]\n\n # Here a larger distance indicates a poorer match between categorical variables.\n j_d = distance.hamming(x_categorical_features, y_categorical_features)\n j_c = distance.canberra(x_continuous_features, y_continuous_features)\n\n # Take the product of similarities to attain a univariate similarity score.\n # Add a minimal constant to prevent zero values from categorical features.\n # Note: since both the distance function return a Numpy type, we need to\n # call the |item| function to get the underlying Python type. If we don't\n # do that this job will fail when performing KDE due to SPARK-20803 on\n # Spark 2.2.0.\n return abs((j_c + 0.001) * j_d).item()","def get_similarity(user1: Rating, user2: Rating) -> float:\n shared = 0.0\n for m_id in user1:\n if m_id in user2:\n shared += user1[m_id] * user2[m_id]\n norm1 = 0.0\n for m_id in user1:\n norm1 = norm1 + user1[m_id] ** 2\n norm2 = 0.0\n for m_id in user2:\n norm2 = norm2 + user2[m_id] ** 2\n return (shared * shared) / (norm1 * norm2)","def cosine_sim_cf(matrix):\n if type(matrix) is not lil_matrix:\n matrix = lil_matrix(matrix)\n\n n = matrix.shape[0]\n rows, cols, data = [], [], []\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\n\n for i in range(n):\n i_ratings, i_items = matrix.data[i], user_items[i]\n for j in range(i, n):\n j_ratings, j_items = matrix.data[j], user_items[j]\n sum_numerator, sum_denominator_i, sum_denominator_j = 0, 0, 0\n i_item_ctd, j_item_ctd = 0, 0\n while i_item_ctd < len(i_items) and j_item_ctd < len(j_items):\n if i_items[i_item_ctd][0] > j_items[j_item_ctd][0]:\n j_item_ctd += 1\n elif i_items[i_item_ctd][0] < j_items[j_item_ctd][0]:\n i_item_ctd += 1\n else:\n i_idx = i_items[i_item_ctd][1]\n j_idx = j_items[j_item_ctd][1]\n sum_numerator += i_ratings[i_idx] * j_ratings[j_idx]\n sum_denominator_i += i_ratings[i_idx] ** 2\n sum_denominator_j += j_ratings[j_idx] ** 2\n i_item_ctd += 1\n j_item_ctd += 1\n\n if sum_numerator == 0: continue\n s = sum_numerator / (math.sqrt(sum_denominator_i) * math.sqrt(sum_denominator_j))\n rows.append(i), cols.append(j), data.append(s)\n if i != j: rows.append(j), cols.append(i), data.append(s)\n\n return csr_matrix((data, (rows, cols)))","def __modelSimilarity(self, mOrig: nn.Module, mDest: nn.Module) -> torch.Tensor:\n cos = nn.CosineSimilarity(0)\n d1 = nn.utils.parameters_to_vector(mOrig.parameters())\n d2 = nn.utils.parameters_to_vector(mDest.parameters())\n sim: torch.Tensor = cos(d1, d2)\n return sim","def similarity(self, mass_function):\n return round(0.5 * (math.cos(math.pi * self.distance(mass_function)) + 1), 6)","def plot_similarity(self) -> None:\n if isinstance(self.model, FastTextWrapper):\n self.valid_data[\"vector\"] = self.valid_data[\"text\"].apply(\n lambda x: self.model.inference(word_tokenize(x), sentence_level=True))\n else:\n self.valid_data[\"vector\"] = self.valid_data[\"text\"].apply(\n lambda x: self.model.inference(word_tokenize(x))[0])\n messages = list(self.valid_data[\"label\"])\n vectors = list(self.valid_data[\"vector\"])\n similarity_matrix(messages=messages, vectors=vectors, name=self.folder, save_path=self.base_path)","def hadamard(u: np.ndarray, v: np.ndarray) -> np.ndarray:\n\n return u * v","def similarity_unsafe(self, mass_function):\n return round(0.5 * (math.cos(math.pi * self.distance_unsafe(mass_function)) + 1), 6)","def similarity_transform_2d(v, mapping, alpha = 1):\r\n p_wgt = vec2(0, 0)\r\n q_wgt = vec2(0, 0)\r\n w = len(mapping)*[None]\r\n w_sum = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n x = mp[0].x - v.x\r\n y = mp[0].y - v.y\r\n if (x == 0 and y == 0): return mp[1]\r\n w[i] = 1/((x*x + y*y) ** alpha)\r\n p_wgt += mp[0]*w[i]\r\n q_wgt += mp[1]*w[i]\r\n w_sum += w[i]\r\n p_wgt /= w_sum\r\n q_wgt /= w_sum\r\n mu = 0\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n mu += w[i]*(p_adj.dot(p_adj))\r\n A_fac = mat2([v.x - p_wgt.x, v.y - p_wgt.y, v.y - p_wgt.y, p_wgt.x - v.x])\r\n v_out = vec2(0, 0)\r\n for i in range(len(mapping)):\r\n mp = mapping[i]\r\n p_adj = mp[0] - p_wgt\r\n q_adj = mp[1] - q_wgt\r\n A = mat2([p_adj.x, p_adj.y, p_adj.y, -p_adj.x])*A_fac*w[i]\r\n A = A.transpose()\r\n v_out += A*q_adj/mu\r\n v_out += q_wgt\r\n return v_out","def compute_similarity(self, seq_node, **kwargs):\n pass","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\n w12 = torch.sum(x1 * x2, dim)\n w1 = torch.norm(x1, 2, dim)\n w2 = torch.norm(x2, 2, dim)\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def compare_stability_matrices(ism1, ism2): \n \n import scipy as sp\n import sklearn as sk\n\n ism1=sk.preprocessing.normalize(ism1,norm='l2')\n ism2=sk.preprocessing.normalize(ism2,norm='l2')\n distance=sp.spatial.distance.correlation(ism1.ravel(), ism2.ravel())\n similarity= 1-distance\n return similarity","def _attentive_matching(self, h1, h2, cosine_matrix, w):\n # h1 * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\n h1 = self._time_distributed_multiply(h1, w)\n # attentive vector (batch_size, h1_timesteps, embedding_szie)\n attentive_vec = self._mean_attentive_vectors(h2, cosine_matrix)\n # attentive_vec * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\n attentive_vec = self._time_distributed_multiply(attentive_vec, w)\n # matching vector, (batch_size, h1_timesteps, mp_dim)\n matching = self._cosine_similarity(h1, attentive_vec)\n return matching","def similarity(self, other, ignore_abundance=False, downsample=False):\n return self._methodcall(lib.kmerminhash_similarity,\n other._get_objptr(),\n ignore_abundance, downsample)","def smat(v):\n \n k = len(v)\n n = TriLengthToSquare(k)\n \n A = np.zeros([n,n])\n A[np.triu_indices(n)] = v\n A[np.triu_indices(n,1)] *= 2 / np.sqrt(2)\n return (A + A.T) / 2","def lev_sim(s1, s2):\n\n if s1 is None or s2 is None:\n return np.NaN\n if pd.isnull(s1) or pd.isnull(s2):\n return np.NaN\n\n # Create the similarity measure object\n measure = sm.Levenshtein()\n\n s1 = gh.convert_to_str_unicode(s1)\n s2 = gh.convert_to_str_unicode(s2)\n\n # Call the function to compute the similarity measure\n return measure.get_sim_score(s1, s2)","def test_coherence(self):\n all_sim = self.similarity.all_to_all(left=self.left, right=self.right)\n\n # broadcast\n left_rep = self.left.repeat_interleave(repeats=self.num_right, dim=0)\n assert left_rep.shape == (self.num_right * self.num_left, self.dimension)\n right_rep = self.right.repeat(self.num_left, 1)\n assert right_rep.shape == (self.num_right * self.num_left, self.dimension)\n one_sim = self.similarity.one_to_one(left=left_rep, right=right_rep).view(self.num_left, self.num_right)\n\n assert torch.allclose(one_sim, all_sim)","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\r\n w12 = torch.sum(x1 * x2, dim)\r\n w1 = torch.norm(x1, 2, dim)\r\n w2 = torch.norm(x2, 2, dim)\r\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def _generate_similarity_mat(labels):\n l_mat = np.repeat(labels, len(labels), axis=1)\n l_mat_t = l_mat.T\n\n sim_mat = np.equal(l_mat, l_mat_t).astype(int)\n return sim_mat","def overlap_similarity(box, other_boxes):\n return jaccard(np.expand_dims(box, axis=0), other_boxes).squeeze(0)","def average_similarity(messages1, messages2):\n if np.array_equal(messages2, messages1):\n return 1\n corr = np.corrcoef(messages1, messages2)\n return np.average(corr)","def distance_metric(u, v):\n if len(u) != len(v):\n raise Exception(\n \"Distance metric not valid for differently sized vectors\")\n sum = 0.\n for i in range(len(u)):\n sum += ((u[i] - v[i]) ** 2)\n return math.sqrt(sum)","def similarity(centroid_a, centroid_b):\n \n vector_a = centroid_a.centroid_vector\n vector_b = centroid_b.centroid_vector\n \n length_a = centroid_a.length\n length_b = centroid_b.length\n \n dotproduct = 0.0\n\n for key, value in vector_a.iteritems():\n if key in vector_b: # if both vectors have the key\n dotproduct += (value * vector_b[key])\n\n return float(dotproduct / (length_a * length_b))","def cosine_similarity(vec_x, vec_y):\n sim_prod = 0.0\n len_x = 0\n len_y = 0\n\n for ngram in vec_x:\n len_x += vec_x[ngram] ** 2\n\n for ngram in vec_y:\n len_y += vec_y[ngram] ** 2\n\n len_x = math.sqrt(len_x)\n len_y = math.sqrt(len_y)\n\n for ngram in vec_x:\n if ngram in vec_y:\n sim_prod += vec_x[ngram] * vec_y[ngram]\n\n return sim_prod / (len_x * len_y)","def test_sim(vec_x, vec_y, feature_list, func):\n feature_map_x = create_feature_map(vec_x, feature_list)\n feature_map_y = create_feature_map(vec_y, feature_list)\n\n if func == 0:\n return cosine_similarity(feature_map_x, feature_map_y)\n\n return minmax(feature_map_x, feature_map_y)","def householder(vector):\n identity_mat = np.eye(len(vector))\n v = vector[np.newaxis]\n denominator = np.matmul(v, v.T)\n numerator = np.matmul(v.T, v)\n rot_mat = identity_mat - (2 * numerator / denominator)\n return rot_mat","def calculate_similarities(self) -> List[float]:\n sims = list()\n for i, r in self.sim_data.iterrows():\n if isinstance(self.model, FastTextWrapper):\n vecs = self.model.inference([r[\"Word 1\"], r[\"Word 2\"]])\n else:\n vecs = self.model.inference_batches([[r[\"Word 1\"]], [r[\"Word 2\"]]])\n vecs = [x[0] for x in vecs]\n if len(vecs) == 2:\n s = cosine_similarity([vecs[0]], [vecs[1]])[0][0]\n sims.append(s)\n else:\n sims.append(np.nan)\n self.sim_data[\"assigned_sim\"] = sims\n self.sim_data = self.sim_data.dropna()\n self.mean_error()\n self.correlation()\n return sims","def _gu_matvec(x1, x2):\n return (x1 @ x2[..., np.newaxis])[..., 0]","def calc_euclidean_similarity(vec_1, vec_2):\n sim = 0\n vec_1 = vec_1.reshape((vec_1.shape[1],))\n vec_2 = vec_2.reshape((vec_2.shape[1],))\n vec_1_nnz = np.nonzero(vec_1)[0]\n print vec_1_nnz\n # import ipdb; ipdb.set_trace()\n vec_2_nnz = np.nonzero(vec_2)[0]\n print vec_2_nnz\n intersect = set(vec_1_nnz) & set(vec_2_nnz)\n if len(intersect) > 0:\n error_squares = [pow(vec_1[arg] - vec_2[arg], 2) for arg in intersect]\n sim = 1.0 / (1 + np.sqrt(np.sum(error_squares)))\n return sim","def model_book_similarities(data):\n\n data = data.T\n U2 = distance.squareform(distance.pdist(data, metric='cosine'))\n sim_matrix = pd.DataFrame(U2)\n\n return sim_matrix","def test_cossim(self):\n metrics = SimilarityMetrics()\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test1,0.0)\n\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test2,-1.0)\n\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\n np.testing.assert_almost_equal(test3,1.0)","def _symmetric_matrix_division(\n matrix: Union[np.ndarray, scipy.sparse.spmatrix],\n vec: np.ndarray,\n vec_right: Optional[np.ndarray] = None,\n scalar: float = 1.0,\n value_zero_division: Union[str, float] = \"raise\",\n) -> Union[np.ndarray, scipy.sparse.csr_matrix,]:\n\n if matrix.ndim != 2:\n raise ValueError(\"Parameter 'matrix' must be a two dimensional array.\")\n\n if matrix.shape[0] != matrix.shape[1] and vec_right is None:\n raise ValueError(\n \"If 'matrix' is non-square, then 'vec_right' must be provided.\"\n )\n\n vec = vec.astype(float)\n\n if (vec == 0.0).any():\n if value_zero_division == \"raise\":\n raise ZeroDivisionError(\n f\"Encountered zero values in division in {(vec == 0).sum()} points.\"\n )\n else:\n # division results into 'nan' without raising a ZeroDivisionWarning. The\n # nan values will be replaced later\n vec[vec == 0.0] = np.nan\n\n vec_inv_left = np.reciprocal(vec)\n\n if vec_right is None:\n vec_inv_right = vec_inv_left.view()\n else:\n vec_right = vec_right.astype(float)\n if (vec_right == 0.0).any():\n if value_zero_division == \"raise\":\n raise ZeroDivisionError(\n f\"Encountered zero values in division in {(vec == 0).sum()}\"\n )\n else:\n vec_right[vec_right == 0.0] = np.inf\n\n vec_inv_right = np.reciprocal(vec_right.astype(float))\n\n if vec_inv_left.ndim != 1 or vec_inv_left.shape[0] != matrix.shape[0]:\n raise ValueError(\n f\"Invalid input: 'vec.shape={vec.shape}' is not compatible with \"\n f\"'matrix.shape={matrix.shape}'.\"\n )\n\n if vec_inv_right.ndim != 1 or vec_inv_right.shape[0] != matrix.shape[1]:\n raise ValueError(\n f\"Invalid input: 'vec_right.shape={vec_inv_right.shape}' is not compatible \"\n f\"with 'matrix.shape={matrix.shape}'.\"\n )\n\n if scipy.sparse.issparse(matrix):\n left_inv_diag_sparse = scipy.sparse.spdiags(\n vec_inv_left, 0, m=matrix.shape[0], n=matrix.shape[0]\n )\n right_inv_diag_sparse = scipy.sparse.spdiags(\n vec_inv_right, 0, m=matrix.shape[1], n=matrix.shape[1]\n )\n\n # The performance of DIA-sparse matrices is good if the matrix is actually\n # sparse. I.e. the performance drops for a sparse-dense-sparse multiplication.\n\n # The zeros are removed in the matrix multiplication, but because 'matrix' is\n # usually a distance matrix we need to preserve the \"true zeros\"!\n matrix.data[matrix.data == 0] = np.inf\n matrix = left_inv_diag_sparse @ matrix @ right_inv_diag_sparse\n\n matrix.data[np.isinf(matrix.data)] = 0\n\n # this imposes precedence order\n # --> np.inf/np.nan -> np.nan\n # i.e. for cases with 0/0, set 'value_zero_division'\n if isinstance(value_zero_division, (int, float)):\n matrix.data[np.isnan(matrix.data)] = value_zero_division\n\n else:\n # This computes efficiently:\n # np.diag(1/vector_elements) @ matrix @ np.diag(1/vector_elements)\n matrix = diagmat_dot_mat(vec_inv_left, matrix, out=matrix)\n matrix = mat_dot_diagmat(matrix, vec_inv_right, out=matrix)\n\n if isinstance(value_zero_division, (int, float)):\n matrix[np.isnan(matrix)] = value_zero_division\n\n # sparse and dense\n if vec_right is None:\n matrix = remove_numeric_noise_symmetric_matrix(matrix)\n\n if scalar != 1.0:\n scalar = 1.0 / scalar\n matrix = np.multiply(matrix, scalar, out=matrix)\n\n return matrix","def w2v_sim(self, s1, s2):\n v1 = self.word2vec.get_centroid_vector(s1)\n v2 = self.word2vec.get_centroid_vector(s2)\n return self.__cos_sim(v1, v2)","def similarity(self, x, y, keyboard_weight=None):\r\n dist = self.distance(x, y, keyboard_weight)\r\n max_len = max(len(x), len(y))\r\n max_dissimilarity = max_len * self.scale_coef\r\n similarity = 1 - dist / max_dissimilarity\r\n return similarity","def test_dice_similarity():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = dice_similarity.py_func(vector1, vector1)\n score12 = dice_similarity.py_func(vector1, vector2)\n score22 = dice_similarity.py_func(vector2, vector2)\n\n assert score12 == 2 * 2/6, \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def cosine_similarity(y_true, y_pred, axis=-1):\n y_true = nn.l2_normalize(y_true, axis=axis)\n y_pred = nn.l2_normalize(y_pred, axis=axis)\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)","def word_order_similarity(self,sentence_1, sentence_2):\n\t words_1 = sentence_1.getList_of_words()\n\t words_2 = sentence_2.getList_of_words()\n\t joint_words = list(set(words_1).union(set(words_2)))\n\t windex = {x[1]: x[0] for x in enumerate(joint_words)}\n\t r1 = self.word_order_vector(words_1, joint_words, windex)\n\t r2 = self.word_order_vector(words_2, joint_words, windex)\n\t return 1.0 - (np.linalg.norm(r1 - r2) / np.linalg.norm(r1 + r2))","def main():\n\n measures = Similarity()\n\n input1=sys.argv[1]\n vect1=np.loadtxt(fname = input1)\n \n input2=sys.argv[2]\n vect2=np.loadtxt(fname = input2)\n\n print measures.cosine_similarity(normBySum(vect1), normBySum(vect2))\n \n\n\n #print measures.cosine_similarity2(vect1, vect2)\n\n #print measures.jaccard_similarity([0,1,2,5,6],[0,2,3,5,7,9])","def test_dice_similarity_all_zeros():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = dice_similarity.py_func(vector1, vector1)\n score12 = dice_similarity.py_func(vector1, vector2)\n score22 = dice_similarity.py_func(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\""],"string":"[\n \"def self_similarity_matrix(feature_vectors):\\n norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T])\\n norm_feature_vectors = norm_feature_vectors[0].T\\n sim_matrix = 1.0 - distance.squareform(\\n distance.pdist(norm_feature_vectors.T, 'cosine'))\\n return sim_matrix\",\n \"def test_similarity(self):\\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'dog.n.01'), 1))\\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'mammal.n.01'), 0.180901358))\",\n \"def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\\n # return cosine_similarity(v1, v2)[0][0]\",\n \"def calculate_similarity(self, tweets):\\r\\n if (len(tweets) == 1):\\r\\n return 0\\r\\n vectors = self.vectorizer.vectorize_data(tweets, False)\\r\\n\\r\\n temp = cosine_similarity(vectors[0:-1], vectors)\\r\\n temp = [item for sublist in temp for item in sublist]\\r\\n sim = sum(temp) / len(temp)\\r\\n return sim\",\n \"def similarity_vector(self, x):\\n r_x = self.encoder(x)[1]\\n return self.prototypes_layer(r_x)\",\n \"def cosine_similarity(u, v):\\n\\n distance = 0.0\\n\\n ### START CODE HERE ###\\n # Compute the dot product between u and v (≈1 line)\\n dot = np.dot(u, v)\\n # Compute the L2 norm of u (≈1 line)\\n norm_u = np.sqrt(np.dot(u, u))\\n\\n # Compute the L2 norm of v (≈1 line)\\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\\n # Compute the cosine similarity defined by formula (1) (≈1 line)\\n cosine_similarity = dot / (norm_u * norm_v)\\n ### END CODE HERE ###\\n\\n return cosine_similarity\",\n \"def cosine_similarity(v1, v2):\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def similarity_function_old(feature1, feature2):\\n f1Magnitude = feature1.dot(feature1)\\n f2Magnitude = feature2.dot(feature2)\\n return 1 - feature1.dot(feature2) / (f1Magnitude * f2Magnitude)\",\n \"def similarity(self, token1, token2):\\n vec1 = self.get_vector(token1)\\n vec2 = self.get_vector(token2)\\n assert vec1 is not None and vec2 is not None, \\\"Cannot compute similarity between None type vectors.\\\"\\n if not self.normalize:\\n # if model not loaded as normalized embeddings \\n vec1 = vec1 / np.linalg.norm(vec1)\\n vec2 = vec2 / np.linalg.norm(vec2)\\n return np.dot(vec1, vec2)\",\n \"def similarity_matrix(feat_mat):\\n sim_mat = cosine_similarity(feat_mat)\\n np.fill_diagonal(sim_mat, 0)\\n return sim_mat\",\n \"def get_similarity(self, ):\\r\\n customer_cos_similarity = cosine_similarity(self.rating_matrix, self.rating_matrix)\\r\\n customer_cos_similarity = pd.DataFrame(customer_cos_similarity,\\r\\n index=self.customer_vendor_matrix.index,\\r\\n columns=self.customer_vendor_matrix.index)\\r\\n # customer_pearson_similarity = np.corrcoef(self.rating_matrix,\\r\\n # self.rating_matrix,)\\r\\n # customer_pearson_similarity = pd.DataFrame(customer_pearson_similarity,\\r\\n # index=self.customer_vendor_matrix.index,\\r\\n # columns=self.customer_vendor_matrix.index)\\r\\n return customer_cos_similarity,\\r\\n # return customer_pearson_similarity run too slowly\\r\",\n \"def centrality(similarity, vector, vectors):\\n return 1.0/len(vectors)*sum([similarity(vector,y) for y in vectors\\\\\\n if y != vector])\",\n \"def cosine_similarity(vector_x, vector_y):\\n if(len(vector_x)!=len(vector_y)):\\n raise Exception('Vectors must be the same dimensions')\\n \\n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))\",\n \"def similarity_function(feature1, feature2):\\n # 256 HOG, 18 HSV, 512 Encoder\\n # weight color more if using the full vector\\n if len(feature1) > 785:\\n salient1 = feature1[256:256 + 18].copy() # be careful not to modify feature vector in place\\n salient2 = feature2[256:256 + 18].copy()\\n feature1 = feature1.copy()\\n feature2 = feature2.copy()\\n feature1[256:256 + 18] = salient1 * 10\\n feature2[256:256 + 18] = salient2 * 10\\n\\n abs_distance = np.abs(feature1 - feature2)\\n return np.sum(abs_distance)\",\n \"def distance(self, u, v):\\n numerator = np.dot(u,v)\\n denominator = np.linalg.norm(u) * np.linalg.norm(v)\\n similarity = numerator/(denominator +1e-7)\\n return similarity\",\n \"def similarity_transformation(rot, mat):\\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))\",\n \"def similarity_transformation(rot, mat):\\n return np.dot(rot, np.dot(mat, np.linalg.inv(rot)))\",\n \"def test_cosine_similarity_matrix():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def similarity_matrix(P, similarity_measure, normalize=True, inverse=True):\\n N = len(P) \\n S = np.zeros((N, N))\\n for i in range(N): \\n for j in range(i): \\n S[i][j] = similarity_measure(P[i], P[j])\\n\\n S = square(S)\\n if normalize: \\n S = S / np.max(S)\\n if inverse:\\n S = 1 - S # Higher value = more similar\\n\\n return S\",\n \"def compute_similarity(site_a, site_b):\\n return np.linalg.norm(site_a - site_b)\",\n \"def cosine_similarity(v1: Vector, v2: Vector) -> float:\\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))\",\n \"def test_cosine_similarity_matrix_compiled():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def semantic_similarity(self,sentence_1, sentence_2, info_content_norm):\\n\\t words_1 = sentence_1.getList_of_words()\\n\\t words_2 = sentence_2.getList_of_words()\\n\\t joint_words = set(words_1).union(set(words_2))\\n\\t vec_1 = self.semantic_vector(words_1, joint_words, info_content_norm)\\n\\t vec_2 = self.semantic_vector(words_2, joint_words, info_content_norm)\\n\\t return np.dot(vec_1, vec_2.T) / (np.linalg.norm(vec_1) * np.linalg.norm(vec_2))\",\n \"def vi_similarity(self, normalize=True, model='m1'):\\n R, C = self.shape\\n N = self.grand_total\\n\\n max_dist = log(N)\\n dist = self.vi_distance(normalize=False)\\n score = max_dist - dist\\n\\n if model is None:\\n null_score = 0\\n elif model == 'm1': # only N is fixed\\n null_dist = log(R) + log(C)\\n null_score = max_dist - null_dist\\n elif model == 'm2r': # fixed row margin\\n null_dist = log(C) + fentropy(self.row_totals) / N\\n null_score = max_dist - null_dist\\n elif model == 'm2c': # fixed column margin\\n null_dist = log(R) + fentropy(self.col_totals) / N\\n null_score = max_dist - null_dist\\n elif model == 'm3': # both row and column margins fixed\\n null_dist = (fentropy(self.row_totals) + fentropy(self.col_totals)) / N\\n null_score = max_dist - null_dist\\n else:\\n expected = self.expected(model)\\n null_score = expected.vi_similarity(normalize=False, model=None)\\n\\n score -= null_score\\n if normalize:\\n max_score = max_dist - null_score\\n score = 1.0 if score == max_score else _div(score, max_score)\\n\\n return score\",\n \"def cosine_similarity(cls, vec_a, vec_b):\\n return np.dot(vec_a, vec_b) / \\\\\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))\",\n \"def cosine_similarity(X):\\n matrix = X.dot(X.transpose()).todense()\\n mat_len = len(matrix)\\n norms = [0] * mat_len\\n for i in range(0, mat_len):\\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\\n norm_mat = np.matrix(norms)\\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)\",\n \"def similarity_matrix(points, sigma):\\n distances_squared = spherical_distances(points, points)**2\\n\\n \\n return np.exp( -distances_squared / (2.0 * sigma) )\",\n \"def similarity(self, word1, word2):\\n common_vect = +np.ones(self.nEmbed) * 10000\\n if word1 not in self.vocab and word2 in self.vocab:\\n id_word_2 = self.w2id[word2]\\n w1 = common_vect\\n w2 = self.U[id_word_2]\\n elif word1 in self.vocab and word2 not in self.vocab:\\n id_word_1 = self.w2id[word1]\\n w1 = self.U[id_word_1]\\n w2 = common_vect\\n elif word1 not in self.vocab and word2 not in self.vocab:\\n w1 = common_vect\\n w2 = common_vect\\n else:\\n id_word_1 = self.w2id[word1]\\n id_word_2 = self.w2id[word2]\\n w1 = self.U[id_word_1]\\n w2 = self.U[id_word_2]\\n\\n # scalair = w1.dot(w2)/np.linalg.norm(w1,w2)\\n similarity = w1.dot(w2) / (np.linalg.norm(w1) * np.linalg.norm(w2))\\n # similarity = 1 / (1 + np.exp(-scalair))\\n # similarity = scalair / (np.linalg.norm(w1) * np.linalg.norm(w2))\\n return similarity\",\n \"def build_matrix(self):\\n \\n for p1 in self._properties: \\n p1 = p1.get_vectorized_data()\\n \\n for p2 in self._properties:\\n p2 = p2.get_vectorized_data()\\n v1, v2 = self.prepare_vectors(p1, p2)\\n self._similarity_matrix.append(cosine_similarity([v1],[v2]))\",\n \"def similarity_euclid(matrix, business1, business2):\\n selected_features = matrix.loc[business1].notna() & matrix.loc[business2].notna()\\n\\n if not selected_features.any():\\n return 0\\n\\n features1 = matrix.loc[business1][selected_features]\\n features2 = matrix.loc[business2][selected_features]\\n distance = math.sqrt(((features1 - features2) ** 2).sum())\\n\\n if distance is np.nan:\\n return 0\\n\\n return 1 / (1 + distance)\",\n \"def test_jaccard_similarity_matrix():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [0, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [1, 0, 1, 1]])\\n\\n scores = jaccard_similarity_matrix.py_func(vectors1, vectors2)\\n expected_scores = np.array([[1/3, 1/4],\\n [1/3, 2/3]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def get_similarity(df):\\n count = CountVectorizer()\\n count_matrix = count.fit_transform(df[\\\"bag_of_words\\\"])\\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\\n return cosine_sim\",\n \"def cosine_similarity(u: np.ndarray, v: np.ndarray) -> np.float64:\\n assert u.shape[0] == v.shape[0], \\\"Input vector must have same shape.\\\"\\n uv = 0\\n uu = 0\\n vv = 0\\n for i in range(u.shape[0]):\\n uv += u[i] * v[i]\\n uu += u[i] * u[i]\\n vv += v[i] * v[i]\\n cosine_score = 0\\n if uu != 0 and vv != 0:\\n cosine_score = uv / np.sqrt(uu * vv)\\n return np.float64(cosine_score)\",\n \"def pairwise_dot_product_similarity(x, y):\\n return torch.mm(x, torch.transpose(y, 1, 0))\",\n \"def cosine_similarity(v1, v2):\\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\\n return sim\",\n \"def test_dice_similarity_matrix():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [0, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [1, 0, 1, 1]])\\n\\n scores = dice_similarity_matrix.py_func(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.4],\\n [0.5, 0.8]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def cosine_similarity(vec1, vec2) -> float:\\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))\",\n \"def similarity(self, w1, w2):\\r\\n sim = self.represent(w1).dot(self.represent(w2))\\r\\n return sim\",\n \"def compute_similarities_from_vec(self, dataset, a):\\n self.model.fit(dataset.X, a)\\n return self.model.coef_\",\n \"def test_cosine_similarity():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity.py_func(vector1, vector1)\\n score12 = cosine_similarity.py_func(vector1, vector2)\\n score22 = cosine_similarity.py_func(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def test_cosine_similarity_all_zeros():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity.py_func(vector1, vector1)\\n score12 = cosine_similarity.py_func(vector1, vector2)\\n score22 = cosine_similarity.py_func(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def compute_all_similarities(self,A,a):\\n pass\",\n \"def compute_cosine_similarity(self):\\n cos_matrix = []\\n for i in range(len(self.train_vec)):\\n val = self.vec1 * self.train_vec[i]\\n cos_matrix.append(val[0])\\n out = np.argmax(cos_matrix)\\n print(self.train_output[out])\",\n \"def test_cosine_similarity_compiled():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def calculate_user_similarity_profile(self, ratings_vector):\\r\\n num_users, num_movies = self.ratings_matrix.get_shape()\\r\\n\\r\\n user_similarities = sp.dok_matrix((1, num_users))\\r\\n for i in range(num_users):\\r\\n\\r\\n user_similarities[0, i] = self.calculate_pairwise_user_similarity(self.ratings_matrix.getrow(i), ratings_vector)\\r\\n\\r\\n return user_similarities.tocsr()\",\n \"def similarity_vec(self, vec1: numpy.ndarray, vec2: numpy.ndarray, metric='cosine') -> float:\\n if numpy.count_nonzero(vec1) == 0 or numpy.count_nonzero(vec2) == 0:\\n if metric == 'cosine':\\n return 0.\\n else:\\n return 0.\\n\\n vec1 = vec1.reshape((1, -1))\\n vec2 = vec2.reshape((1, -1))\\n if metric == 'cosine':\\n return (1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1))[0]\\n else:\\n return distance.cdist(vec1, vec2, metric=metric).reshape(-1)[0]\",\n \"def magni(vector):\\n return(np.linalg.norm(vector))\",\n \"def similarity(self, w1, w2):\\r\\n return self.represent(w1).dot(self.represent(w2))\",\n \"def similarity(self, w1, w2):\\r\\n return self.represent(w1).dot(self.represent(w2))\",\n \"def _calculate_trilinear_similarity(self, context, query, context_max_len, query_max_len,\\n w4mlu, bias):\\n\\n subres0 = nd.tile(self.w4c(context), [1, 1, query_max_len])\\n subres1 = nd.tile(nd.transpose(\\n self.w4q(query), axes=(0, 2, 1)), [1, context_max_len, 1])\\n subres2 = nd.batch_dot(w4mlu * context,\\n nd.transpose(query, axes=(0, 2, 1)))\\n similarity_mat = subres0 + subres1 + subres2 + bias\\n return similarity_mat\",\n \"def align_reconstruction_naive_similarity(X, Xp):\\n\\t# Compute similarity Xp = s A X + b\\n\\tT = superimposition_matrix(X.T, Xp.T, scale=True)\\n\\tA, b = T[:3, :3], T[:3, 3]\\n\\ts = np.linalg.det(A)**(1. / 3)\\n\\tA /= s\\n\\treturn s, A, b\",\n \"def test_dice_similarity_matrix_compiled():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [0, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [1, 0, 1, 1]])\\n\\n scores = dice_similarity_matrix(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.4],\\n [0.5, 0.8]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def test_jaccard_similarity_matrix_compiled():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [0, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [1, 0, 1, 1]])\\n\\n scores = jaccard_similarity_matrix(vectors1, vectors2)\\n expected_scores = np.array([[1/3, 1/4],\\n [1/3, 2/3]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def similarity_score(self, lhs, rhs):\\n pass\",\n \"def cosine_sim(matrix):\\n if type(matrix) is not csr_matrix:\\n matrix = csr_matrix(matrix)\\n\\n return cosine_similarity(matrix, dense_output=False)\",\n \"def similarity(self, e1, e2):\\n\\t\\tpass\",\n \"def pairwise_euclidean_similarity(x, y):\\n s = 2 * torch.mm(x, torch.transpose(y, 1, 0))\\n diag_x = torch.sum(x * x, dim=-1)\\n diag_x = torch.unsqueeze(diag_x, 0)\\n diag_y = torch.reshape(torch.sum(y * y, dim=-1), (1, -1))\\n\\n return s - diag_x - diag_y\",\n \"def similarity(pair: Tuple[Text, Text]) -> float:\\n (a, b) = pair\\n missing = (\\n True\\n if any(symbol not in Metrics.realine.feature_matrix for symbol in pair)\\n else False\\n )\\n return 0.0 if missing else 1 - Metrics.realine.delta(a, b)\",\n \"def cosine_similarity(self, x, y):\\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\",\n \"def test_cosine_similarity_all_zeros_compiled():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def similarity_transform_3d(v, mapping, alpha = 1):\\r\\n p_wgt = vec3(0, 0, 0)\\r\\n q_wgt = vec3(0, 0, 0)\\r\\n w = len(mapping)*[None]\\r\\n w_sum = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n x = mp[0].x - v.x\\r\\n y = mp[0].y - v.y\\r\\n z = mp[0].z - v.z\\r\\n if (x == 0 and y == 0 and z == 0): return mp[1]\\r\\n w[i] = 1/((x*x + y*y + z*z) ** alpha)\\r\\n p_wgt += mp[0]*w[i]\\r\\n q_wgt += mp[1]*w[i]\\r\\n w_sum += w[i]\\r\\n p_wgt /= w_sum\\r\\n q_wgt /= w_sum\\r\\n A = mat3(0)\\r\\n k = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n q_adj = mp[1] - q_wgt\\r\\n A += w[i]*p_adj.transpose_multiply(q_adj)\\r\\n k += w[i]*p_adj.dot(p_adj)\\r\\n A_arr = np.array(A.matrix).reshape(3, 3)\\r\\n U, S, V = np.linalg.svd(A_arr)\\r\\n M_arr = np.matmul(np.transpose(V), np.transpose(U))\\r\\n M = mat3(M_arr.ravel().tolist())\\r\\n k = np.sum(S)/k\\r\\n v_out = k*M*(v - p_wgt) + q_wgt\\r\\n return v_out\",\n \"def similarity_function(x, y):\\n\\n def safe_get(field, row, default_value):\\n # Safely get a value from the Row. If the value is None, get the\\n # default value.\\n return row[field] if row[field] is not None else default_value\\n\\n # Extract the values for the categorical and continuous features for both\\n # the x and y samples. Use an empty string as the default value for missing\\n # categorical fields and 0 for the continuous ones.\\n x_categorical_features = [safe_get(k, x, \\\"\\\") for k in CATEGORICAL_FEATURES]\\n x_continuous_features = [safe_get(k, x, 0) for k in CONTINUOUS_FEATURES]\\n y_categorical_features = [safe_get(k, y, \\\"\\\") for k in CATEGORICAL_FEATURES]\\n y_continuous_features = [safe_get(k, y, 0) for k in CONTINUOUS_FEATURES]\\n\\n # Here a larger distance indicates a poorer match between categorical variables.\\n j_d = distance.hamming(x_categorical_features, y_categorical_features)\\n j_c = distance.canberra(x_continuous_features, y_continuous_features)\\n\\n # Take the product of similarities to attain a univariate similarity score.\\n # Add a minimal constant to prevent zero values from categorical features.\\n # Note: since both the distance function return a Numpy type, we need to\\n # call the |item| function to get the underlying Python type. If we don't\\n # do that this job will fail when performing KDE due to SPARK-20803 on\\n # Spark 2.2.0.\\n return abs((j_c + 0.001) * j_d).item()\",\n \"def get_similarity(user1: Rating, user2: Rating) -> float:\\n shared = 0.0\\n for m_id in user1:\\n if m_id in user2:\\n shared += user1[m_id] * user2[m_id]\\n norm1 = 0.0\\n for m_id in user1:\\n norm1 = norm1 + user1[m_id] ** 2\\n norm2 = 0.0\\n for m_id in user2:\\n norm2 = norm2 + user2[m_id] ** 2\\n return (shared * shared) / (norm1 * norm2)\",\n \"def cosine_sim_cf(matrix):\\n if type(matrix) is not lil_matrix:\\n matrix = lil_matrix(matrix)\\n\\n n = matrix.shape[0]\\n rows, cols, data = [], [], []\\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\\n\\n for i in range(n):\\n i_ratings, i_items = matrix.data[i], user_items[i]\\n for j in range(i, n):\\n j_ratings, j_items = matrix.data[j], user_items[j]\\n sum_numerator, sum_denominator_i, sum_denominator_j = 0, 0, 0\\n i_item_ctd, j_item_ctd = 0, 0\\n while i_item_ctd < len(i_items) and j_item_ctd < len(j_items):\\n if i_items[i_item_ctd][0] > j_items[j_item_ctd][0]:\\n j_item_ctd += 1\\n elif i_items[i_item_ctd][0] < j_items[j_item_ctd][0]:\\n i_item_ctd += 1\\n else:\\n i_idx = i_items[i_item_ctd][1]\\n j_idx = j_items[j_item_ctd][1]\\n sum_numerator += i_ratings[i_idx] * j_ratings[j_idx]\\n sum_denominator_i += i_ratings[i_idx] ** 2\\n sum_denominator_j += j_ratings[j_idx] ** 2\\n i_item_ctd += 1\\n j_item_ctd += 1\\n\\n if sum_numerator == 0: continue\\n s = sum_numerator / (math.sqrt(sum_denominator_i) * math.sqrt(sum_denominator_j))\\n rows.append(i), cols.append(j), data.append(s)\\n if i != j: rows.append(j), cols.append(i), data.append(s)\\n\\n return csr_matrix((data, (rows, cols)))\",\n \"def __modelSimilarity(self, mOrig: nn.Module, mDest: nn.Module) -> torch.Tensor:\\n cos = nn.CosineSimilarity(0)\\n d1 = nn.utils.parameters_to_vector(mOrig.parameters())\\n d2 = nn.utils.parameters_to_vector(mDest.parameters())\\n sim: torch.Tensor = cos(d1, d2)\\n return sim\",\n \"def similarity(self, mass_function):\\n return round(0.5 * (math.cos(math.pi * self.distance(mass_function)) + 1), 6)\",\n \"def plot_similarity(self) -> None:\\n if isinstance(self.model, FastTextWrapper):\\n self.valid_data[\\\"vector\\\"] = self.valid_data[\\\"text\\\"].apply(\\n lambda x: self.model.inference(word_tokenize(x), sentence_level=True))\\n else:\\n self.valid_data[\\\"vector\\\"] = self.valid_data[\\\"text\\\"].apply(\\n lambda x: self.model.inference(word_tokenize(x))[0])\\n messages = list(self.valid_data[\\\"label\\\"])\\n vectors = list(self.valid_data[\\\"vector\\\"])\\n similarity_matrix(messages=messages, vectors=vectors, name=self.folder, save_path=self.base_path)\",\n \"def hadamard(u: np.ndarray, v: np.ndarray) -> np.ndarray:\\n\\n return u * v\",\n \"def similarity_unsafe(self, mass_function):\\n return round(0.5 * (math.cos(math.pi * self.distance_unsafe(mass_function)) + 1), 6)\",\n \"def similarity_transform_2d(v, mapping, alpha = 1):\\r\\n p_wgt = vec2(0, 0)\\r\\n q_wgt = vec2(0, 0)\\r\\n w = len(mapping)*[None]\\r\\n w_sum = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n x = mp[0].x - v.x\\r\\n y = mp[0].y - v.y\\r\\n if (x == 0 and y == 0): return mp[1]\\r\\n w[i] = 1/((x*x + y*y) ** alpha)\\r\\n p_wgt += mp[0]*w[i]\\r\\n q_wgt += mp[1]*w[i]\\r\\n w_sum += w[i]\\r\\n p_wgt /= w_sum\\r\\n q_wgt /= w_sum\\r\\n mu = 0\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n mu += w[i]*(p_adj.dot(p_adj))\\r\\n A_fac = mat2([v.x - p_wgt.x, v.y - p_wgt.y, v.y - p_wgt.y, p_wgt.x - v.x])\\r\\n v_out = vec2(0, 0)\\r\\n for i in range(len(mapping)):\\r\\n mp = mapping[i]\\r\\n p_adj = mp[0] - p_wgt\\r\\n q_adj = mp[1] - q_wgt\\r\\n A = mat2([p_adj.x, p_adj.y, p_adj.y, -p_adj.x])*A_fac*w[i]\\r\\n A = A.transpose()\\r\\n v_out += A*q_adj/mu\\r\\n v_out += q_wgt\\r\\n return v_out\",\n \"def compute_similarity(self, seq_node, **kwargs):\\n pass\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\n w12 = torch.sum(x1 * x2, dim)\\n w1 = torch.norm(x1, 2, dim)\\n w2 = torch.norm(x2, 2, dim)\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def compare_stability_matrices(ism1, ism2): \\n \\n import scipy as sp\\n import sklearn as sk\\n\\n ism1=sk.preprocessing.normalize(ism1,norm='l2')\\n ism2=sk.preprocessing.normalize(ism2,norm='l2')\\n distance=sp.spatial.distance.correlation(ism1.ravel(), ism2.ravel())\\n similarity= 1-distance\\n return similarity\",\n \"def _attentive_matching(self, h1, h2, cosine_matrix, w):\\n # h1 * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\\n h1 = self._time_distributed_multiply(h1, w)\\n # attentive vector (batch_size, h1_timesteps, embedding_szie)\\n attentive_vec = self._mean_attentive_vectors(h2, cosine_matrix)\\n # attentive_vec * weights, (batch_size, h1_timesteps, mp_dim, embedding_size)\\n attentive_vec = self._time_distributed_multiply(attentive_vec, w)\\n # matching vector, (batch_size, h1_timesteps, mp_dim)\\n matching = self._cosine_similarity(h1, attentive_vec)\\n return matching\",\n \"def similarity(self, other, ignore_abundance=False, downsample=False):\\n return self._methodcall(lib.kmerminhash_similarity,\\n other._get_objptr(),\\n ignore_abundance, downsample)\",\n \"def smat(v):\\n \\n k = len(v)\\n n = TriLengthToSquare(k)\\n \\n A = np.zeros([n,n])\\n A[np.triu_indices(n)] = v\\n A[np.triu_indices(n,1)] *= 2 / np.sqrt(2)\\n return (A + A.T) / 2\",\n \"def lev_sim(s1, s2):\\n\\n if s1 is None or s2 is None:\\n return np.NaN\\n if pd.isnull(s1) or pd.isnull(s2):\\n return np.NaN\\n\\n # Create the similarity measure object\\n measure = sm.Levenshtein()\\n\\n s1 = gh.convert_to_str_unicode(s1)\\n s2 = gh.convert_to_str_unicode(s2)\\n\\n # Call the function to compute the similarity measure\\n return measure.get_sim_score(s1, s2)\",\n \"def test_coherence(self):\\n all_sim = self.similarity.all_to_all(left=self.left, right=self.right)\\n\\n # broadcast\\n left_rep = self.left.repeat_interleave(repeats=self.num_right, dim=0)\\n assert left_rep.shape == (self.num_right * self.num_left, self.dimension)\\n right_rep = self.right.repeat(self.num_left, 1)\\n assert right_rep.shape == (self.num_right * self.num_left, self.dimension)\\n one_sim = self.similarity.one_to_one(left=left_rep, right=right_rep).view(self.num_left, self.num_right)\\n\\n assert torch.allclose(one_sim, all_sim)\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\r\\n w12 = torch.sum(x1 * x2, dim)\\r\\n w1 = torch.norm(x1, 2, dim)\\r\\n w2 = torch.norm(x2, 2, dim)\\r\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def _generate_similarity_mat(labels):\\n l_mat = np.repeat(labels, len(labels), axis=1)\\n l_mat_t = l_mat.T\\n\\n sim_mat = np.equal(l_mat, l_mat_t).astype(int)\\n return sim_mat\",\n \"def overlap_similarity(box, other_boxes):\\n return jaccard(np.expand_dims(box, axis=0), other_boxes).squeeze(0)\",\n \"def average_similarity(messages1, messages2):\\n if np.array_equal(messages2, messages1):\\n return 1\\n corr = np.corrcoef(messages1, messages2)\\n return np.average(corr)\",\n \"def distance_metric(u, v):\\n if len(u) != len(v):\\n raise Exception(\\n \\\"Distance metric not valid for differently sized vectors\\\")\\n sum = 0.\\n for i in range(len(u)):\\n sum += ((u[i] - v[i]) ** 2)\\n return math.sqrt(sum)\",\n \"def similarity(centroid_a, centroid_b):\\n \\n vector_a = centroid_a.centroid_vector\\n vector_b = centroid_b.centroid_vector\\n \\n length_a = centroid_a.length\\n length_b = centroid_b.length\\n \\n dotproduct = 0.0\\n\\n for key, value in vector_a.iteritems():\\n if key in vector_b: # if both vectors have the key\\n dotproduct += (value * vector_b[key])\\n\\n return float(dotproduct / (length_a * length_b))\",\n \"def cosine_similarity(vec_x, vec_y):\\n sim_prod = 0.0\\n len_x = 0\\n len_y = 0\\n\\n for ngram in vec_x:\\n len_x += vec_x[ngram] ** 2\\n\\n for ngram in vec_y:\\n len_y += vec_y[ngram] ** 2\\n\\n len_x = math.sqrt(len_x)\\n len_y = math.sqrt(len_y)\\n\\n for ngram in vec_x:\\n if ngram in vec_y:\\n sim_prod += vec_x[ngram] * vec_y[ngram]\\n\\n return sim_prod / (len_x * len_y)\",\n \"def test_sim(vec_x, vec_y, feature_list, func):\\n feature_map_x = create_feature_map(vec_x, feature_list)\\n feature_map_y = create_feature_map(vec_y, feature_list)\\n\\n if func == 0:\\n return cosine_similarity(feature_map_x, feature_map_y)\\n\\n return minmax(feature_map_x, feature_map_y)\",\n \"def householder(vector):\\n identity_mat = np.eye(len(vector))\\n v = vector[np.newaxis]\\n denominator = np.matmul(v, v.T)\\n numerator = np.matmul(v.T, v)\\n rot_mat = identity_mat - (2 * numerator / denominator)\\n return rot_mat\",\n \"def calculate_similarities(self) -> List[float]:\\n sims = list()\\n for i, r in self.sim_data.iterrows():\\n if isinstance(self.model, FastTextWrapper):\\n vecs = self.model.inference([r[\\\"Word 1\\\"], r[\\\"Word 2\\\"]])\\n else:\\n vecs = self.model.inference_batches([[r[\\\"Word 1\\\"]], [r[\\\"Word 2\\\"]]])\\n vecs = [x[0] for x in vecs]\\n if len(vecs) == 2:\\n s = cosine_similarity([vecs[0]], [vecs[1]])[0][0]\\n sims.append(s)\\n else:\\n sims.append(np.nan)\\n self.sim_data[\\\"assigned_sim\\\"] = sims\\n self.sim_data = self.sim_data.dropna()\\n self.mean_error()\\n self.correlation()\\n return sims\",\n \"def _gu_matvec(x1, x2):\\n return (x1 @ x2[..., np.newaxis])[..., 0]\",\n \"def calc_euclidean_similarity(vec_1, vec_2):\\n sim = 0\\n vec_1 = vec_1.reshape((vec_1.shape[1],))\\n vec_2 = vec_2.reshape((vec_2.shape[1],))\\n vec_1_nnz = np.nonzero(vec_1)[0]\\n print vec_1_nnz\\n # import ipdb; ipdb.set_trace()\\n vec_2_nnz = np.nonzero(vec_2)[0]\\n print vec_2_nnz\\n intersect = set(vec_1_nnz) & set(vec_2_nnz)\\n if len(intersect) > 0:\\n error_squares = [pow(vec_1[arg] - vec_2[arg], 2) for arg in intersect]\\n sim = 1.0 / (1 + np.sqrt(np.sum(error_squares)))\\n return sim\",\n \"def model_book_similarities(data):\\n\\n data = data.T\\n U2 = distance.squareform(distance.pdist(data, metric='cosine'))\\n sim_matrix = pd.DataFrame(U2)\\n\\n return sim_matrix\",\n \"def test_cossim(self):\\n metrics = SimilarityMetrics()\\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test1,0.0)\\n\\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test2,-1.0)\\n\\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\\n np.testing.assert_almost_equal(test3,1.0)\",\n \"def _symmetric_matrix_division(\\n matrix: Union[np.ndarray, scipy.sparse.spmatrix],\\n vec: np.ndarray,\\n vec_right: Optional[np.ndarray] = None,\\n scalar: float = 1.0,\\n value_zero_division: Union[str, float] = \\\"raise\\\",\\n) -> Union[np.ndarray, scipy.sparse.csr_matrix,]:\\n\\n if matrix.ndim != 2:\\n raise ValueError(\\\"Parameter 'matrix' must be a two dimensional array.\\\")\\n\\n if matrix.shape[0] != matrix.shape[1] and vec_right is None:\\n raise ValueError(\\n \\\"If 'matrix' is non-square, then 'vec_right' must be provided.\\\"\\n )\\n\\n vec = vec.astype(float)\\n\\n if (vec == 0.0).any():\\n if value_zero_division == \\\"raise\\\":\\n raise ZeroDivisionError(\\n f\\\"Encountered zero values in division in {(vec == 0).sum()} points.\\\"\\n )\\n else:\\n # division results into 'nan' without raising a ZeroDivisionWarning. The\\n # nan values will be replaced later\\n vec[vec == 0.0] = np.nan\\n\\n vec_inv_left = np.reciprocal(vec)\\n\\n if vec_right is None:\\n vec_inv_right = vec_inv_left.view()\\n else:\\n vec_right = vec_right.astype(float)\\n if (vec_right == 0.0).any():\\n if value_zero_division == \\\"raise\\\":\\n raise ZeroDivisionError(\\n f\\\"Encountered zero values in division in {(vec == 0).sum()}\\\"\\n )\\n else:\\n vec_right[vec_right == 0.0] = np.inf\\n\\n vec_inv_right = np.reciprocal(vec_right.astype(float))\\n\\n if vec_inv_left.ndim != 1 or vec_inv_left.shape[0] != matrix.shape[0]:\\n raise ValueError(\\n f\\\"Invalid input: 'vec.shape={vec.shape}' is not compatible with \\\"\\n f\\\"'matrix.shape={matrix.shape}'.\\\"\\n )\\n\\n if vec_inv_right.ndim != 1 or vec_inv_right.shape[0] != matrix.shape[1]:\\n raise ValueError(\\n f\\\"Invalid input: 'vec_right.shape={vec_inv_right.shape}' is not compatible \\\"\\n f\\\"with 'matrix.shape={matrix.shape}'.\\\"\\n )\\n\\n if scipy.sparse.issparse(matrix):\\n left_inv_diag_sparse = scipy.sparse.spdiags(\\n vec_inv_left, 0, m=matrix.shape[0], n=matrix.shape[0]\\n )\\n right_inv_diag_sparse = scipy.sparse.spdiags(\\n vec_inv_right, 0, m=matrix.shape[1], n=matrix.shape[1]\\n )\\n\\n # The performance of DIA-sparse matrices is good if the matrix is actually\\n # sparse. I.e. the performance drops for a sparse-dense-sparse multiplication.\\n\\n # The zeros are removed in the matrix multiplication, but because 'matrix' is\\n # usually a distance matrix we need to preserve the \\\"true zeros\\\"!\\n matrix.data[matrix.data == 0] = np.inf\\n matrix = left_inv_diag_sparse @ matrix @ right_inv_diag_sparse\\n\\n matrix.data[np.isinf(matrix.data)] = 0\\n\\n # this imposes precedence order\\n # --> np.inf/np.nan -> np.nan\\n # i.e. for cases with 0/0, set 'value_zero_division'\\n if isinstance(value_zero_division, (int, float)):\\n matrix.data[np.isnan(matrix.data)] = value_zero_division\\n\\n else:\\n # This computes efficiently:\\n # np.diag(1/vector_elements) @ matrix @ np.diag(1/vector_elements)\\n matrix = diagmat_dot_mat(vec_inv_left, matrix, out=matrix)\\n matrix = mat_dot_diagmat(matrix, vec_inv_right, out=matrix)\\n\\n if isinstance(value_zero_division, (int, float)):\\n matrix[np.isnan(matrix)] = value_zero_division\\n\\n # sparse and dense\\n if vec_right is None:\\n matrix = remove_numeric_noise_symmetric_matrix(matrix)\\n\\n if scalar != 1.0:\\n scalar = 1.0 / scalar\\n matrix = np.multiply(matrix, scalar, out=matrix)\\n\\n return matrix\",\n \"def w2v_sim(self, s1, s2):\\n v1 = self.word2vec.get_centroid_vector(s1)\\n v2 = self.word2vec.get_centroid_vector(s2)\\n return self.__cos_sim(v1, v2)\",\n \"def similarity(self, x, y, keyboard_weight=None):\\r\\n dist = self.distance(x, y, keyboard_weight)\\r\\n max_len = max(len(x), len(y))\\r\\n max_dissimilarity = max_len * self.scale_coef\\r\\n similarity = 1 - dist / max_dissimilarity\\r\\n return similarity\",\n \"def test_dice_similarity():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = dice_similarity.py_func(vector1, vector1)\\n score12 = dice_similarity.py_func(vector1, vector2)\\n score22 = dice_similarity.py_func(vector2, vector2)\\n\\n assert score12 == 2 * 2/6, \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cosine_similarity(y_true, y_pred, axis=-1):\\n y_true = nn.l2_normalize(y_true, axis=axis)\\n y_pred = nn.l2_normalize(y_pred, axis=axis)\\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)\",\n \"def word_order_similarity(self,sentence_1, sentence_2):\\n\\t words_1 = sentence_1.getList_of_words()\\n\\t words_2 = sentence_2.getList_of_words()\\n\\t joint_words = list(set(words_1).union(set(words_2)))\\n\\t windex = {x[1]: x[0] for x in enumerate(joint_words)}\\n\\t r1 = self.word_order_vector(words_1, joint_words, windex)\\n\\t r2 = self.word_order_vector(words_2, joint_words, windex)\\n\\t return 1.0 - (np.linalg.norm(r1 - r2) / np.linalg.norm(r1 + r2))\",\n \"def main():\\n\\n measures = Similarity()\\n\\n input1=sys.argv[1]\\n vect1=np.loadtxt(fname = input1)\\n \\n input2=sys.argv[2]\\n vect2=np.loadtxt(fname = input2)\\n\\n print measures.cosine_similarity(normBySum(vect1), normBySum(vect2))\\n \\n\\n\\n #print measures.cosine_similarity2(vect1, vect2)\\n\\n #print measures.jaccard_similarity([0,1,2,5,6],[0,2,3,5,7,9])\",\n \"def test_dice_similarity_all_zeros():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = dice_similarity.py_func(vector1, vector1)\\n score12 = dice_similarity.py_func(vector1, vector2)\\n score22 = dice_similarity.py_func(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.669418","0.6659926","0.63767517","0.6368019","0.6360521","0.6356867","0.63391316","0.63353467","0.63267857","0.62889796","0.62807196","0.627655","0.624859","0.6224308","0.62191266","0.61848396","0.61848396","0.6180708","0.61560977","0.6126581","0.61166716","0.60962766","0.60699177","0.60540307","0.60407007","0.5985299","0.59630984","0.5949737","0.594262","0.5927078","0.5921665","0.5915536","0.58962405","0.58942604","0.5883141","0.5876525","0.58743995","0.5866719","0.5865895","0.5862684","0.58317816","0.5815946","0.5806081","0.5804533","0.5803521","0.58029675","0.5795866","0.5779139","0.5779139","0.57710826","0.57584906","0.5753188","0.5738744","0.572955","0.5725434","0.5720426","0.57096374","0.5705635","0.5698599","0.569351","0.5682791","0.5621274","0.56197244","0.5599247","0.55892533","0.55886513","0.55831087","0.55762684","0.5570999","0.5562591","0.5559922","0.55595076","0.55526745","0.553096","0.5526922","0.5525384","0.55147266","0.5514418","0.55127394","0.55101967","0.5508339","0.55067825","0.54899555","0.54842144","0.547827","0.54744714","0.54689366","0.5467966","0.5461188","0.5455481","0.54482746","0.5442541","0.54301786","0.54226196","0.5418912","0.54184484","0.5403715","0.5400089","0.53985274","0.5393399"],"string":"[\n \"0.669418\",\n \"0.6659926\",\n \"0.63767517\",\n \"0.6368019\",\n \"0.6360521\",\n \"0.6356867\",\n \"0.63391316\",\n \"0.63353467\",\n \"0.63267857\",\n \"0.62889796\",\n \"0.62807196\",\n \"0.627655\",\n \"0.624859\",\n \"0.6224308\",\n \"0.62191266\",\n \"0.61848396\",\n \"0.61848396\",\n \"0.6180708\",\n \"0.61560977\",\n \"0.6126581\",\n \"0.61166716\",\n \"0.60962766\",\n \"0.60699177\",\n \"0.60540307\",\n \"0.60407007\",\n \"0.5985299\",\n \"0.59630984\",\n \"0.5949737\",\n \"0.594262\",\n \"0.5927078\",\n \"0.5921665\",\n \"0.5915536\",\n \"0.58962405\",\n \"0.58942604\",\n \"0.5883141\",\n \"0.5876525\",\n \"0.58743995\",\n \"0.5866719\",\n \"0.5865895\",\n \"0.5862684\",\n \"0.58317816\",\n \"0.5815946\",\n \"0.5806081\",\n \"0.5804533\",\n \"0.5803521\",\n \"0.58029675\",\n \"0.5795866\",\n \"0.5779139\",\n \"0.5779139\",\n \"0.57710826\",\n \"0.57584906\",\n \"0.5753188\",\n \"0.5738744\",\n \"0.572955\",\n \"0.5725434\",\n \"0.5720426\",\n \"0.57096374\",\n \"0.5705635\",\n \"0.5698599\",\n \"0.569351\",\n \"0.5682791\",\n \"0.5621274\",\n \"0.56197244\",\n \"0.5599247\",\n \"0.55892533\",\n \"0.55886513\",\n \"0.55831087\",\n \"0.55762684\",\n \"0.5570999\",\n \"0.5562591\",\n \"0.5559922\",\n \"0.55595076\",\n \"0.55526745\",\n \"0.553096\",\n \"0.5526922\",\n \"0.5525384\",\n \"0.55147266\",\n \"0.5514418\",\n \"0.55127394\",\n \"0.55101967\",\n \"0.5508339\",\n \"0.55067825\",\n \"0.54899555\",\n \"0.54842144\",\n \"0.547827\",\n \"0.54744714\",\n \"0.54689366\",\n \"0.5467966\",\n \"0.5461188\",\n \"0.5455481\",\n \"0.54482746\",\n \"0.5442541\",\n \"0.54301786\",\n \"0.54226196\",\n \"0.5418912\",\n \"0.54184484\",\n \"0.5403715\",\n \"0.5400089\",\n \"0.53985274\",\n \"0.5393399\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55794203"},"document_rank":{"kind":"string","value":"67"}}},{"rowIdx":22383,"cells":{"query":{"kind":"string","value":"The unit of measurement similarity. Espicially for `oz`, `lb`, `meter` etc."},"document":{"kind":"string","value":"def mm_similarity(s1, s2):\n if filter(str.isalpha, s1) == filter(str.isalpha, s2):\n if len(s1) < len(s2):\n return float(len(s1)) / len(s2)\n else:\n return float(len(s2)) / len(s1)\n else:\n return 0."},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def unit_of_measurement(self):\n return self.values.primary.units","def unit_of_measurement(self):\n return \"%\"","def unit_of_measurement(self):\n return self._tasmota_entity.unit","def unit_of_measurement(self) -> str:\n return self._unit_of_measurement","def unit_of_measurement(self) -> str:\n return MS","def unit_of_measurement(self):\n return self.device.unit()","def unit_of_measurement(self):\n return None","def unit_of_measurement(self) -> str:\n return self._unit","def unit_of_measurement(self):\n return LENGTH_METERS","def unit_of_measurement(self):\n return '%'","def unit_of_measurement(self):\n return self._metadata[1]","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self) -> str:\n return \"objects\"","def unit_of_measurement(self):\n return self.sensor_type[\"unit\"]","def unit_of_measurement(self):\n return SENSOR_TYPES[self.sensor][1]","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return self._unit","def unit_of_measurement(self):\n return DEVICE_MAP[self._sensor_type][DEVICE_MAP_INDEX.index('UNIT_OF_MEASURE_INDEX')]","def unit_of_measurement(self):\n return self.var_units","def getDistanceUnits(self) -> Unit:\n ...","def unit_of_measurement(self) -> Any:\n return PERCENTAGE","def unit_of_measurement(self):\n return SENSOR_TYPES[self.variable][1]","def unit_of_measurement(self):\n return SENSOR_TYPES[self.variable][1]","def unit_of_measurement(self):\n return SENSOR_TYPES[self.type][1]","def unit_of_measure(self):\n try:\n uom = self.metadata['geosoft']['dataset']['geo:unitofmeasurement']['#text']\n except KeyError:\n uom = ''\n return uom","def get_units(self):\r\n msg = struct.pack('>2B', 56, 14)\r\n response = self.query(msg)\r\n\r\n if response[1] == 2:\r\n units = 'A'\r\n to_nm_multiplier = 1 / 10\r\n elif response[1] == 1:\r\n units = 'nm'\r\n to_nm_multiplier = 1\r\n elif response[1] == 0:\r\n units = 'um'\r\n to_nm_multiplier = 1000\r\n else:\r\n raise ValueError('Units not recognised.')\r\n\r\n # Save results locally too for quick re-use\r\n self._current_units = units\r\n self._current_to_nm_multiplier = to_nm_multiplier\r\n\r\n return units, to_nm_multiplier","def unit_of_measurement(self):\n return self._var_unit","def unit_of_measurement(self):\n return self._var_unit","def unit_of_measurement(self) -> str:\n return \"%\"","def unit_of_measurement(self) -> str:\n return \"%\"","def unit_of_measurement(self):\n return self._sensor.unit","def unit_of_measurement(self):\n if self._xfinity_data.unit is not None:\n return self._xfinity_data.unit","def native_unit_of_measurement(self):\n return self._unit_of_measurement","def unit_of_measurement(self):\n return self._sensor_type.unit","def unit_of_measurement(self):\n return SENSOR_TYPES[self._type][2]","def unit_of_measurement(self):\n return SENSOR_TYPES[self._type][2]","def unit_of_measurement(self):\n return 'index'","def unit_of_measurement(self):\n return self._units","def unit_of_measurement(self):\n set_req = self.gateway.const.SetReq\n unit_map = {\n set_req.V_TEMP: (TEMP_CELSIUS\n if self.gateway.metric else TEMP_FAHRENHEIT),\n set_req.V_HUM: '%',\n set_req.V_DIMMER: '%',\n set_req.V_LIGHT_LEVEL: '%',\n set_req.V_WEIGHT: 'kg',\n set_req.V_DISTANCE: 'm',\n set_req.V_IMPEDANCE: 'ohm',\n set_req.V_WATT: 'W',\n set_req.V_KWH: 'kWh',\n set_req.V_FLOW: 'm',\n set_req.V_VOLUME: 'm3',\n set_req.V_VOLTAGE: 'V',\n set_req.V_CURRENT: 'A',\n }\n if float(self.gateway.protocol_version) >= 1.5:\n if set_req.V_UNIT_PREFIX in self._values:\n return self._values[\n set_req.V_UNIT_PREFIX]\n unit_map.update({set_req.V_PERCENTAGE: '%'})\n if float(self.gateway.protocol_version) >= 2.0:\n unit_map.update({\n set_req.V_ORP: 'mV',\n set_req.V_EC: 'μS/cm',\n set_req.V_VAR: 'var',\n set_req.V_VA: 'VA',\n })\n return unit_map.get(self.value_type)","def unit_of_measurement(self):\n return SENSOR_TYPES[self._type][1]","def unit(self) -> str:","def unit_of_measurement(self):\n return SENSOR_TYPES[self._sensor][0]","def units(self):\n pass","def unit_of_measurement(self):\n if self.values.primary.units == \"C\":\n return TEMP_CELSIUS\n if self.values.primary.units == \"F\":\n return TEMP_FAHRENHEIT\n\n return self.values.primary.units","def native_unit_of_measurement(self) -> str:\n return f\"{CURRENCY_CENT}/{UnitOfVolume.LITERS}\"","def unit(self):\n return self._unit","def unit_of_measurement(self):\n\n if self._sensor_class == DEVICE_CLASS_TEMPERATURE:\n return TEMP_CELSIUS\n\n elif self._sensor_class == DEVICE_CLASS_HUMIDITY:\n return '%'","def unit_of_measurement(self):\n return self.display_currency","def unit_of_measurement(self):\n return SENSOR_TYPES.get(self._sensor_type)[1]","def unit_of_measurement(self):\n return SENSOR_TYPES.get(self._sensor_type)[1]","def unitsofmeasure(self):\n return self._unitsofmeasure","def human_m(v):\n if v < 1e-2:\n return (v*1.0e3, 'mm')\n if v < 1:\n return (v*1.0e2, 'cm')\n if v < 1000:\n return (v, 'm')\n return (v/1.0e3, 'km')","def unit_of_measurement(self):\n return SENSOR_UNITS.get(self._sensor_name, None)","def unit_of_measurement(self):\n return SENSOR_UNITS.get(self._sensor_name, None)","def unit_of_measurement(self):\n if self._coordinator.data:\n return self._coordinator.data['unit']\n return None","def unit_of_measurement(self):\n return 'shows'","def test_norm(self):\n self.assertEqual(\"Maximaal 3 dagen. Meer dan 7 dagen is rood.\", self.__metric.norm())","def get_M(self):\n return 1.0","def unit_of_measurement(self) -> Any:\n return TEMP_CELSIUS","def unit_of_measurement(self):\n return self.temperature_unit","def get_unit(self):\n return self.unit","def unit_of_measurement(self):\n return SENSOR_TYPES[self._type][1] if self._type in SENSOR_TYPES else None","def unit_of_measurement(self):\n if self.api_unit in TEMPERATURE_UNITS:\n return self.hass.config.units.temperature_unit\n\n if self.api_unit in LENGTH_UNITS:\n return self.hass.config.units.length_unit\n\n if self.api_unit in PRESSURE_UNITS:\n if self.hass.config.units == IMPERIAL_SYSTEM:\n return self.hass.config.units.pressure_unit\n return PRESSURE_HPA\n\n if self.api_unit in FUEL_CONSUMPTION_UNITS:\n if self.hass.config.units == IMPERIAL_SYSTEM:\n return FUEL_CONSUMPTION_MPG\n return FUEL_CONSUMPTION_L_PER_100KM\n\n return self.api_unit","def similarityMetric(Est, GT, options):\n\n if options == None:\n options = {}\n if not 'metric' in options:\n options['metric'] = 'basic'\n\n#########################################################\n## YOU MUST REMOVE THE REST OF THE CODE OF THIS FUNCTION\n## AND CHANGE FOR YOUR OWN CODE\n#########################################################\n comptador = 0\n if options['metric'].lower() == 'basic':\n for i in Est:\n if i in GT[1]:\n comptador = comptador + 1\n return comptador / len(Est)\n\n else:\n return 0","def unit_of_measurement(self) -> str | None:\n set_req = self.gateway.const.SetReq\n if (\n AwesomeVersion(self.gateway.protocol_version) >= AwesomeVersion(\"1.5\")\n and set_req.V_UNIT_PREFIX in self._values\n ):\n custom_unit: str = self._values[set_req.V_UNIT_PREFIX]\n return custom_unit\n\n if set_req(self.value_type) == set_req.V_TEMP:\n if self.hass.config.units.is_metric:\n return TEMP_CELSIUS\n return TEMP_FAHRENHEIT\n\n unit = self._get_sensor_type()[0]\n return unit","def calc_unit_factor(self, ui_unit):\n # namedView = self.document.getroot().find(inkex.addNS('namedview', 'sodipodi'))\n # doc_units = self.getunittouu(str(1.0) + namedView.get(inkex.addNS('document-units', 'inkscape')))\n unit_factor = self.getunittouu(str(1.0) + ui_unit)\n return unit_factor","def unit(self):\n return self.__unit","def molar_mass_amu():\n return Equivalency([(si.g / si.mol, misc.u)], \"molar_mass_amu\")","def base_unit() -> ureg:\n return ureg.meter","def unit_of_measurement(self) -> str:\n return FPS","def unit_of_measurement(self) -> str:\n return FPS","def dim_unit_scaling(in_unit, out_unit):\n\n unit_vals = {\n 'nm': 1e-9,\n 'um': 1e-6,\n 'mm': 1e-3,\n 'cm': 1e-2,\n 'm': 1.0,\n 'km': 1e3,\n }\n\n if in_unit not in unit_vals:\n raise ValueError(\n 'Invalid input unit {}. Must be one of {}'.format(\n in_unit, list(unit_vals.keys())\n )\n )\n if out_unit not in unit_vals:\n raise ValueError(\n 'Invalid input unit {}. Must be one of {}'.format(\n in_unit, list(unit_vals.keys())\n )\n )\n\n return unit_vals[in_unit]/unit_vals[out_unit]","def get_unit(scale):\n scale2unit = { 1e-9: 'nm',\n 1e-6: u'\\N{MICRO SIGN}m', #or hex id (lookup): u'\\u00B5'\n 1e-3: 'mm',\n 0.01: 'cm',\n 0.1:'dm',\n 1:'m',\n 1000:'km',\n # time\n 8.6400e4:'day',\n 3.1536e7:'yr',\n 3.1536e10:'ka',\n 3.1536e13:'Ma',\n #Pressure\n 1e9: 'GPa',\n 1e6: 'MPa',\n }\n return scale2unit[scale]","def distance2m(d, unit):\n if unit == UOM_M:\n d_m = d\n elif unit == UOM_KM:\n d_m = d * 1000\n elif unit == UOM_F:\n d_m = d * F_FEET2M\n elif unit == UOM_SM:\n d_m = d * F_SM2M\n elif unit == UOM_NM:\n d_m = d * F_NM2M\n \n return d_m","def unit(self):\n return self._unit","def unit(self):\n return self._unit","def get_units(self):\n return str(self._modeler.GetModelUnits())","def native_unit_of_measurement(self):\n return self._target","def unit_of_measurement(self):\r\n return self._sensor_cfg[1]","def unit_of_measurement(self):\r\n return self._sensor_cfg[1]","def test_similarity(self):\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'dog.n.01'), 1))\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'mammal.n.01'), 0.180901358))","def test_mixing_ratio_dimensions():\n p = 998. * units.mbar\n e = 73.75 * units.hPa\n assert str(mixing_ratio(e, p).units) == 'dimensionless'","def unit_of_measurement(self):\n unit = get_uom_from_status(self._device.status)\n if unit == HS_UNIT_LUX:\n return LIGHT_LUX\n elif unit == HS_UNIT_CELSIUS:\n return TEMP_CELSIUS\n elif unit == HS_UNIT_FAHRENHEIT:\n return TEMP_FAHRENHEIT\n elif unit == HS_UNIT_PERCENTAGE:\n return PERCENTAGE\n elif unit == HS_UNIT_A or unit == HS_UNIT_AMPERES:\n return ELECTRIC_CURRENT_AMPERE\n elif unit == HS_UNIT_KW:\n return POWER_KILO_WATT\n elif unit == HS_UNIT_KWH:\n return ENERGY_KILO_WATT_HOUR\n elif unit == HS_UNIT_V or unit == HS_UNIT_VOLTS:\n return ELECTRIC_POTENTIAL_VOLT\n elif unit == HS_UNIT_W or unit == HS_UNIT_WATTS:\n return POWER_WATT\n return None"],"string":"[\n \"def unit_of_measurement(self):\\n return self.values.primary.units\",\n \"def unit_of_measurement(self):\\n return \\\"%\\\"\",\n \"def unit_of_measurement(self):\\n return self._tasmota_entity.unit\",\n \"def unit_of_measurement(self) -> str:\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self) -> str:\\n return MS\",\n \"def unit_of_measurement(self):\\n return self.device.unit()\",\n \"def unit_of_measurement(self):\\n return None\",\n \"def unit_of_measurement(self) -> str:\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return LENGTH_METERS\",\n \"def unit_of_measurement(self):\\n return '%'\",\n \"def unit_of_measurement(self):\\n return self._metadata[1]\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"objects\\\"\",\n \"def unit_of_measurement(self):\\n return self.sensor_type[\\\"unit\\\"]\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self.sensor][1]\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n return DEVICE_MAP[self._sensor_type][DEVICE_MAP_INDEX.index('UNIT_OF_MEASURE_INDEX')]\",\n \"def unit_of_measurement(self):\\n return self.var_units\",\n \"def getDistanceUnits(self) -> Unit:\\n ...\",\n \"def unit_of_measurement(self) -> Any:\\n return PERCENTAGE\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self.variable][1]\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self.variable][1]\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self.type][1]\",\n \"def unit_of_measure(self):\\n try:\\n uom = self.metadata['geosoft']['dataset']['geo:unitofmeasurement']['#text']\\n except KeyError:\\n uom = ''\\n return uom\",\n \"def get_units(self):\\r\\n msg = struct.pack('>2B', 56, 14)\\r\\n response = self.query(msg)\\r\\n\\r\\n if response[1] == 2:\\r\\n units = 'A'\\r\\n to_nm_multiplier = 1 / 10\\r\\n elif response[1] == 1:\\r\\n units = 'nm'\\r\\n to_nm_multiplier = 1\\r\\n elif response[1] == 0:\\r\\n units = 'um'\\r\\n to_nm_multiplier = 1000\\r\\n else:\\r\\n raise ValueError('Units not recognised.')\\r\\n\\r\\n # Save results locally too for quick re-use\\r\\n self._current_units = units\\r\\n self._current_to_nm_multiplier = to_nm_multiplier\\r\\n\\r\\n return units, to_nm_multiplier\",\n \"def unit_of_measurement(self):\\n return self._var_unit\",\n \"def unit_of_measurement(self):\\n return self._var_unit\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"%\\\"\",\n \"def unit_of_measurement(self) -> str:\\n return \\\"%\\\"\",\n \"def unit_of_measurement(self):\\n return self._sensor.unit\",\n \"def unit_of_measurement(self):\\n if self._xfinity_data.unit is not None:\\n return self._xfinity_data.unit\",\n \"def native_unit_of_measurement(self):\\n return self._unit_of_measurement\",\n \"def unit_of_measurement(self):\\n return self._sensor_type.unit\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self._type][2]\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self._type][2]\",\n \"def unit_of_measurement(self):\\n return 'index'\",\n \"def unit_of_measurement(self):\\n return self._units\",\n \"def unit_of_measurement(self):\\n set_req = self.gateway.const.SetReq\\n unit_map = {\\n set_req.V_TEMP: (TEMP_CELSIUS\\n if self.gateway.metric else TEMP_FAHRENHEIT),\\n set_req.V_HUM: '%',\\n set_req.V_DIMMER: '%',\\n set_req.V_LIGHT_LEVEL: '%',\\n set_req.V_WEIGHT: 'kg',\\n set_req.V_DISTANCE: 'm',\\n set_req.V_IMPEDANCE: 'ohm',\\n set_req.V_WATT: 'W',\\n set_req.V_KWH: 'kWh',\\n set_req.V_FLOW: 'm',\\n set_req.V_VOLUME: 'm3',\\n set_req.V_VOLTAGE: 'V',\\n set_req.V_CURRENT: 'A',\\n }\\n if float(self.gateway.protocol_version) >= 1.5:\\n if set_req.V_UNIT_PREFIX in self._values:\\n return self._values[\\n set_req.V_UNIT_PREFIX]\\n unit_map.update({set_req.V_PERCENTAGE: '%'})\\n if float(self.gateway.protocol_version) >= 2.0:\\n unit_map.update({\\n set_req.V_ORP: 'mV',\\n set_req.V_EC: 'μS/cm',\\n set_req.V_VAR: 'var',\\n set_req.V_VA: 'VA',\\n })\\n return unit_map.get(self.value_type)\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self._type][1]\",\n \"def unit(self) -> str:\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self._sensor][0]\",\n \"def units(self):\\n pass\",\n \"def unit_of_measurement(self):\\n if self.values.primary.units == \\\"C\\\":\\n return TEMP_CELSIUS\\n if self.values.primary.units == \\\"F\\\":\\n return TEMP_FAHRENHEIT\\n\\n return self.values.primary.units\",\n \"def native_unit_of_measurement(self) -> str:\\n return f\\\"{CURRENCY_CENT}/{UnitOfVolume.LITERS}\\\"\",\n \"def unit(self):\\n return self._unit\",\n \"def unit_of_measurement(self):\\n\\n if self._sensor_class == DEVICE_CLASS_TEMPERATURE:\\n return TEMP_CELSIUS\\n\\n elif self._sensor_class == DEVICE_CLASS_HUMIDITY:\\n return '%'\",\n \"def unit_of_measurement(self):\\n return self.display_currency\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES.get(self._sensor_type)[1]\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES.get(self._sensor_type)[1]\",\n \"def unitsofmeasure(self):\\n return self._unitsofmeasure\",\n \"def human_m(v):\\n if v < 1e-2:\\n return (v*1.0e3, 'mm')\\n if v < 1:\\n return (v*1.0e2, 'cm')\\n if v < 1000:\\n return (v, 'm')\\n return (v/1.0e3, 'km')\",\n \"def unit_of_measurement(self):\\n return SENSOR_UNITS.get(self._sensor_name, None)\",\n \"def unit_of_measurement(self):\\n return SENSOR_UNITS.get(self._sensor_name, None)\",\n \"def unit_of_measurement(self):\\n if self._coordinator.data:\\n return self._coordinator.data['unit']\\n return None\",\n \"def unit_of_measurement(self):\\n return 'shows'\",\n \"def test_norm(self):\\n self.assertEqual(\\\"Maximaal 3 dagen. Meer dan 7 dagen is rood.\\\", self.__metric.norm())\",\n \"def get_M(self):\\n return 1.0\",\n \"def unit_of_measurement(self) -> Any:\\n return TEMP_CELSIUS\",\n \"def unit_of_measurement(self):\\n return self.temperature_unit\",\n \"def get_unit(self):\\n return self.unit\",\n \"def unit_of_measurement(self):\\n return SENSOR_TYPES[self._type][1] if self._type in SENSOR_TYPES else None\",\n \"def unit_of_measurement(self):\\n if self.api_unit in TEMPERATURE_UNITS:\\n return self.hass.config.units.temperature_unit\\n\\n if self.api_unit in LENGTH_UNITS:\\n return self.hass.config.units.length_unit\\n\\n if self.api_unit in PRESSURE_UNITS:\\n if self.hass.config.units == IMPERIAL_SYSTEM:\\n return self.hass.config.units.pressure_unit\\n return PRESSURE_HPA\\n\\n if self.api_unit in FUEL_CONSUMPTION_UNITS:\\n if self.hass.config.units == IMPERIAL_SYSTEM:\\n return FUEL_CONSUMPTION_MPG\\n return FUEL_CONSUMPTION_L_PER_100KM\\n\\n return self.api_unit\",\n \"def similarityMetric(Est, GT, options):\\n\\n if options == None:\\n options = {}\\n if not 'metric' in options:\\n options['metric'] = 'basic'\\n\\n#########################################################\\n## YOU MUST REMOVE THE REST OF THE CODE OF THIS FUNCTION\\n## AND CHANGE FOR YOUR OWN CODE\\n#########################################################\\n comptador = 0\\n if options['metric'].lower() == 'basic':\\n for i in Est:\\n if i in GT[1]:\\n comptador = comptador + 1\\n return comptador / len(Est)\\n\\n else:\\n return 0\",\n \"def unit_of_measurement(self) -> str | None:\\n set_req = self.gateway.const.SetReq\\n if (\\n AwesomeVersion(self.gateway.protocol_version) >= AwesomeVersion(\\\"1.5\\\")\\n and set_req.V_UNIT_PREFIX in self._values\\n ):\\n custom_unit: str = self._values[set_req.V_UNIT_PREFIX]\\n return custom_unit\\n\\n if set_req(self.value_type) == set_req.V_TEMP:\\n if self.hass.config.units.is_metric:\\n return TEMP_CELSIUS\\n return TEMP_FAHRENHEIT\\n\\n unit = self._get_sensor_type()[0]\\n return unit\",\n \"def calc_unit_factor(self, ui_unit):\\n # namedView = self.document.getroot().find(inkex.addNS('namedview', 'sodipodi'))\\n # doc_units = self.getunittouu(str(1.0) + namedView.get(inkex.addNS('document-units', 'inkscape')))\\n unit_factor = self.getunittouu(str(1.0) + ui_unit)\\n return unit_factor\",\n \"def unit(self):\\n return self.__unit\",\n \"def molar_mass_amu():\\n return Equivalency([(si.g / si.mol, misc.u)], \\\"molar_mass_amu\\\")\",\n \"def base_unit() -> ureg:\\n return ureg.meter\",\n \"def unit_of_measurement(self) -> str:\\n return FPS\",\n \"def unit_of_measurement(self) -> str:\\n return FPS\",\n \"def dim_unit_scaling(in_unit, out_unit):\\n\\n unit_vals = {\\n 'nm': 1e-9,\\n 'um': 1e-6,\\n 'mm': 1e-3,\\n 'cm': 1e-2,\\n 'm': 1.0,\\n 'km': 1e3,\\n }\\n\\n if in_unit not in unit_vals:\\n raise ValueError(\\n 'Invalid input unit {}. Must be one of {}'.format(\\n in_unit, list(unit_vals.keys())\\n )\\n )\\n if out_unit not in unit_vals:\\n raise ValueError(\\n 'Invalid input unit {}. Must be one of {}'.format(\\n in_unit, list(unit_vals.keys())\\n )\\n )\\n\\n return unit_vals[in_unit]/unit_vals[out_unit]\",\n \"def get_unit(scale):\\n scale2unit = { 1e-9: 'nm',\\n 1e-6: u'\\\\N{MICRO SIGN}m', #or hex id (lookup): u'\\\\u00B5'\\n 1e-3: 'mm',\\n 0.01: 'cm',\\n 0.1:'dm',\\n 1:'m',\\n 1000:'km',\\n # time\\n 8.6400e4:'day',\\n 3.1536e7:'yr',\\n 3.1536e10:'ka',\\n 3.1536e13:'Ma',\\n #Pressure\\n 1e9: 'GPa',\\n 1e6: 'MPa',\\n }\\n return scale2unit[scale]\",\n \"def distance2m(d, unit):\\n if unit == UOM_M:\\n d_m = d\\n elif unit == UOM_KM:\\n d_m = d * 1000\\n elif unit == UOM_F:\\n d_m = d * F_FEET2M\\n elif unit == UOM_SM:\\n d_m = d * F_SM2M\\n elif unit == UOM_NM:\\n d_m = d * F_NM2M\\n \\n return d_m\",\n \"def unit(self):\\n return self._unit\",\n \"def unit(self):\\n return self._unit\",\n \"def get_units(self):\\n return str(self._modeler.GetModelUnits())\",\n \"def native_unit_of_measurement(self):\\n return self._target\",\n \"def unit_of_measurement(self):\\r\\n return self._sensor_cfg[1]\",\n \"def unit_of_measurement(self):\\r\\n return self._sensor_cfg[1]\",\n \"def test_similarity(self):\\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'dog.n.01'), 1))\\n self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'mammal.n.01'), 0.180901358))\",\n \"def test_mixing_ratio_dimensions():\\n p = 998. * units.mbar\\n e = 73.75 * units.hPa\\n assert str(mixing_ratio(e, p).units) == 'dimensionless'\",\n \"def unit_of_measurement(self):\\n unit = get_uom_from_status(self._device.status)\\n if unit == HS_UNIT_LUX:\\n return LIGHT_LUX\\n elif unit == HS_UNIT_CELSIUS:\\n return TEMP_CELSIUS\\n elif unit == HS_UNIT_FAHRENHEIT:\\n return TEMP_FAHRENHEIT\\n elif unit == HS_UNIT_PERCENTAGE:\\n return PERCENTAGE\\n elif unit == HS_UNIT_A or unit == HS_UNIT_AMPERES:\\n return ELECTRIC_CURRENT_AMPERE\\n elif unit == HS_UNIT_KW:\\n return POWER_KILO_WATT\\n elif unit == HS_UNIT_KWH:\\n return ENERGY_KILO_WATT_HOUR\\n elif unit == HS_UNIT_V or unit == HS_UNIT_VOLTS:\\n return ELECTRIC_POTENTIAL_VOLT\\n elif unit == HS_UNIT_W or unit == HS_UNIT_WATTS:\\n return POWER_WATT\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68982303","0.6858285","0.68522996","0.68046606","0.6801904","0.67825514","0.6745059","0.6737694","0.6725472","0.6720259","0.67040163","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6688806","0.6672877","0.66688603","0.66638196","0.6653748","0.6653748","0.6653748","0.6653748","0.6653748","0.6653748","0.6653748","0.6649739","0.6627368","0.6596036","0.65894246","0.65841913","0.65841913","0.65761983","0.6563555","0.6551916","0.65467024","0.65467024","0.6526658","0.6526658","0.65229404","0.6510593","0.6505076","0.65032893","0.6495727","0.6495727","0.6495378","0.64752495","0.6474231","0.64615035","0.6459632","0.64457333","0.6438163","0.64330965","0.6426287","0.6415585","0.63925415","0.6362482","0.63519156","0.63519156","0.6341042","0.63077354","0.63050413","0.63050413","0.62988794","0.6290247","0.62640005","0.62585247","0.62559384","0.6215068","0.6214749","0.6212586","0.6199605","0.61848897","0.61806816","0.61791563","0.6175874","0.61751026","0.6170958","0.6156556","0.6156556","0.615578","0.6152348","0.6150663","0.6149465","0.6149465","0.61434585","0.61372066","0.61299926","0.61299926","0.6098392","0.60849625","0.6067289"],"string":"[\n \"0.68982303\",\n \"0.6858285\",\n \"0.68522996\",\n \"0.68046606\",\n \"0.6801904\",\n \"0.67825514\",\n \"0.6745059\",\n \"0.6737694\",\n \"0.6725472\",\n \"0.6720259\",\n \"0.67040163\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6688806\",\n \"0.6672877\",\n \"0.66688603\",\n \"0.66638196\",\n \"0.6653748\",\n \"0.6653748\",\n \"0.6653748\",\n \"0.6653748\",\n \"0.6653748\",\n \"0.6653748\",\n \"0.6653748\",\n \"0.6649739\",\n \"0.6627368\",\n \"0.6596036\",\n \"0.65894246\",\n \"0.65841913\",\n \"0.65841913\",\n \"0.65761983\",\n \"0.6563555\",\n \"0.6551916\",\n \"0.65467024\",\n \"0.65467024\",\n \"0.6526658\",\n \"0.6526658\",\n \"0.65229404\",\n \"0.6510593\",\n \"0.6505076\",\n \"0.65032893\",\n \"0.6495727\",\n \"0.6495727\",\n \"0.6495378\",\n \"0.64752495\",\n \"0.6474231\",\n \"0.64615035\",\n \"0.6459632\",\n \"0.64457333\",\n \"0.6438163\",\n \"0.64330965\",\n \"0.6426287\",\n \"0.6415585\",\n \"0.63925415\",\n \"0.6362482\",\n \"0.63519156\",\n \"0.63519156\",\n \"0.6341042\",\n \"0.63077354\",\n \"0.63050413\",\n \"0.63050413\",\n \"0.62988794\",\n \"0.6290247\",\n \"0.62640005\",\n \"0.62585247\",\n \"0.62559384\",\n \"0.6215068\",\n \"0.6214749\",\n \"0.6212586\",\n \"0.6199605\",\n \"0.61848897\",\n \"0.61806816\",\n \"0.61791563\",\n \"0.6175874\",\n \"0.61751026\",\n \"0.6170958\",\n \"0.6156556\",\n \"0.6156556\",\n \"0.615578\",\n \"0.6152348\",\n \"0.6150663\",\n \"0.6149465\",\n \"0.6149465\",\n \"0.61434585\",\n \"0.61372066\",\n \"0.61299926\",\n \"0.61299926\",\n \"0.6098392\",\n \"0.60849625\",\n \"0.6067289\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22384,"cells":{"query":{"kind":"string","value":"The longest common substring similarity. A special verison."},"document":{"kind":"string","value":"def lcs_similarity(s1, s2):\n max_len = 0\n i = 0\n\n while s1[i] == s2[i]:\n max_len += 1\n i += 1\n if len(s1) == i or len(s2) == i:\n break\n\n if len(s1) < len(s2):\n return float(max_len) / len(s2)\n else:\n return float(max_len) / len(s1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def findLongestCommonSubstringManyStrings(listOfStrings):","def m_longest_common_subsequence_2(s):\n\n # Correct me if I'm wrong but I think unsorted list is faster\n lst = list(s)\n\n lcs = lst[0]\n for string in lst[1:]:\n lcs = LCS(lcs, string)\n\n return lcs","def findLongestCommonSubstring(self, string1, string2):\n countingArray = [[0]*len(string2) for i in range(len(string1))]\n ##we set up an array to locate the possible substrings\n lcss = \"\"\n for row in range(len(countingArray)):\n for column in range(len(countingArray[row])):\n if string1[row] == string2[column]:\n countingArray[row][column] += 1 ##if there's a match the common string length is at least 1\n if not (row == 0 or column == 0):\n countingArray[row][column] += countingArray[row-1][column-1] ##memoize the length of the common string so far\n if countingArray[row][column] > longestLength: ##if it's longer than our current longest string, we'll plan on returning it\n longestLength = countingArray[row][column]\n lcss = string1[row-longestLength+1:row+1]\n return lcss","def longest_common_prefix(strings: list):\n raise NotImplemented","def longest_common_subsequence(seq1, seq2):\n mat = DPMatrix(seq1, seq2, match=1, mismatch=0, gap=0)\n mat.fill()\n\n lcs = []\n augSeq1, augSeq2 = mat.augmented_sequences()\n for c1, c2 in zip(augSeq1, augSeq2):\n if c1 == c2:\n lcs.append(c1)\n\n return ''.join(lcs)","def longestAwesome(self, s: str) -> int:\n\n # So we are moving right, and reducing length by 1\n # for every time we move right - we start from the longest substring that can be formed to lowest one\n # So the moment, we find something we can instantly breal\n\n max_length = 0\n\n if s == s[::-1]:\n return len(s)\n\n for i in range(0, len(s)):\n left = i\n right = len(s)\n\n if right - left > max_length:\n\n while right > left:\n\n candidate = s[left:right]\n # print(f\"The candidate is: {candidate}\")\n ctr = Counter(candidate)\n\n # initial base check\n odd_cnt = 0\n fl = False\n for k, v in ctr.items():\n if v & 1:\n odd_cnt += 1\n if odd_cnt > 1:\n fl = True\n break\n\n if not fl:\n if max_length < (right - left):\n max_length = right - left\n # max_length = max(max_length, len(candidate))\n\n right -= 1\n\n return max_length","def longest_word_length(words):","def longest_common_substring_bottom_up(s1, s2):\n lcs_array = lcs(s1, s2)\n n = len(s1)\n m = len(s2)\n l = lcs_array[n][m]\n result = \"\"\n while l != 0:\n if s1[n - 1] == s2[m - 1]:\n result = s1[n - 1] + result\n l -= 1\n n -= 1\n m -= 1\n elif lcs_array[n][m - 1] >= lcs_array[n - 1][m]:\n m -= 1\n else:\n n -= 1\n return result","def longest_common_subsequence(x, y):\n\n # find the length of the strings\n m = len(x)\n n = len(y)\n\n # declaring the array for storing the dp values\n lcs = np.zeros((m + 1, n + 1))\n\n # iterate through each sub problem\n for i in range(m + 1):\n for j in range(n + 1):\n if i == 0 or j == 0:\n lcs[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n lcs[i, j] = lcs[i - 1, j - 1] + 1\n else:\n # use the optimal substructure property\n # of using already computed results previous subproblems\n lcs[i, j] = max(lcs[i - 1, j], lcs[i, j - 1])\n\n # L[m,n] contains the length of LCS of X[0..n-1] & Y[0..m-1]\n return lcs[m, n]","def longest_ORF(dna):\n both_strings=find_all_ORFs_both_strands(dna)\n L=max(both_strings,key=len)\n Q=len(L)\n return Q\n\n #save out put of find all orfboth string to some variable","def lengthOfLongestSubstringTwoDistinct(self, s):\n if not s:\n return 0\n\n counters = collections.Counter()\n\n start = end = 0\n max_len = -1\n char_count = 0\n while end < len(s):\n if counters[s[end]] == 0:\n char_count += 1\n counters[s[end]] += 1\n\n while char_count > 2:\n counters[s[start]] -= 1\n if counters[s[start]] == 0:\n char_count -= 1\n start += 1\n\n if max_len < end - start + 1:\n max_len = end - start + 1\n\n return max_len","def optimal_string_alignment_similarity(s1, s2):\n max_cost = max(len(s1), len(s2))\n\n if max_cost == 0:\n return 1.0\n\n return 1.0 - float(optimal_string_alignment_distance(s1, s2)) / max_cost","def longestCommonSubsequence(self, text1: str, text2: str) -> int:\n if len(text1) == 0 or len(text2) == 0:\n return 0\n if text1[0] == text2[0]:\n return 1 + self.longestCommonSubsequence(text1[1:], text2[1:])\n else:\n return max(self.longestCommonSubsequence(text1[1:], text2),\n self.longestCommonSubsequence(text1, text2[1:]))","def length_of_longest_substring(s):\n def compare():\n return max(cur_length, longest_substring)\n\n longest_substring = 0\n cur_substring = \"\"\n for c in s:\n if c in cur_substring:\n cur_length = len(cur_substring)\n longest_substring = compare()\n cur_substring = c\n else:\n cur_substring += c\n return compare()","def longest_common_substring_memoization(s1, s2, i, j, count, cache):\n\n if i == len(s1) or j == len(s2):\n return 0\n\n if cache[i][j] != -1:\n return cache[i][j]\n\n if s1[i-1] == s2[j-1]:\n count = 1 + longest_common_substring_memoization(s1, s2, i+1, j+1, count, cache)\n\n else:\n count = max(count,\n max(longest_common_substring_memoization(s1, s2, i+1, j, count, cache),\n longest_common_substring_memoization(s1, s2, i, j+1, count, cache)))\n\n cache[i][j] = count\n return cache[i][j]","def lengthOfLongestSubstring(self, s):\n longest = 0\n i = 0\n j = 0\n n = len(s)\n seen = {}\n while i < n and j < n:\n c = s[j]\n if c in seen:\n i = seen[c] + 1\n seen[c] = j\n j += 1\n longest = max(longest, j-i)\n\n return longest","def longest_common_substring_backtrack(s1, s2, i, j, count):\n if i == len(s1) or j == len(s2):\n return 0\n\n if s1[i-1] == s2[j-1]:\n count = 1 + longest_common_substring_backtrack(s1, s2, i+1, j+1, count)\n\n else:\n count = max(count,\n max(longest_common_substring_backtrack(s1, s2, i+1, j, count),\n longest_common_substring_backtrack(s1, s2, i, j+1, count)))\n\n return count","def longest_subsequence(s1: str, s2: str, s3: str) -> int:\n # Find the shortest string\n s = min(s1, s2, s3, key=lambda x: len(x))\n total = 0\n ok = False\n while s1:\n for i1, c1 in enumerate(s1):\n for i2, c2 in enumerate(s2):\n if ok:\n ok = False\n break\n if c1 == c2:\n for i3, c3 in enumerate(s3):\n if c1 == c3:\n total += 1\n s1 = s1[i1 + 1:]\n s2 = s2[i2 + 1:]\n s3 = s3[i3 + 1:]\n ok = True\n break\n\n return total","def get_similarity(s1, s2):\n t0 = sorted(list(set(s1.split(' ')).intersection(set(s2.split(' ')))))\n t1 = sorted(list(set(t0 + s1.split(' '))))\n t2 = sorted(list(set(t0 + s2.split(' '))))\n\n r01 = SequenceMatcher(None, t0, t1).ratio()\n r02 = SequenceMatcher(None, t0, t2).ratio()\n r12 = SequenceMatcher(None, t1, t2).ratio()\n return max(r01, r02, r12)","def longestCommonPrefix(self, strs: List[str]) -> str:\r\n common = \"\"\r\n if not strs:\r\n return common\r\n shortest_str = min(strs, key=len)\r\n for i in range(len(shortest_str)):\r\n char = shortest_str[i]\r\n for item in strs:\r\n if item[i] != char:\r\n return common\r\n common += char\r\n return common","def compare_str(seq1, seq2):\n if seq1 == seq2:\n return 1\n ld = Levenshtein.distance(seq1, seq2)\n longest = len(seq1 if len(seq1) > len(seq2) else seq2)\n return (longest - ld) / longest","def lcs(s1, s2):\n\n shape = (len(s1) + 1, len(s2) + 1)\n M = np.zeros(shape)\n\n max_length_so_far = 0\n candidates = set()\n\n for i in range(len(s1)):\n for j in range(len(s2)):\n\n if s1[i] == s2[j]:\n M[i+1,j+1] = M[i,j] + 1\n\n if M[i+1,j+1] > max_length_so_far:\n max_length_so_far = int(M[i+1,j+1])\n candidates = {s1[i - max_length_so_far + 1: i + 1]}\n\n elif M[i+1,j+1] == max_length_so_far:\n candidates.add(s1[i - max_length_so_far + 1: i + 1])\n\n else:\n M[i,j] = 0\n\n candidates.add('')\n\n return candidates","def longest(s1, s2):\n return ''.join(sorted(set(s1+s2)))","def longest_common_prefix(fst: str, snd: str) -> str:\n bound = 0\n for a, b in zip(fst, snd):\n if a != b:\n break\n bound += 1\n return fst[:bound]","def find_longest_common_prefix_reduce(words:list):\n if not words:\n return ''\n \n def common_start(w1, w2):\n shorter = w1 if len(w1) < len(w2) else w2\n for i in range(0, len(shorter)):\n if w1[i] != w2[i]:\n return shorter[:i]\n return shorter\n \n return reduce(common_start, words)","def m_longest_common_subsequence_3(iterable):\n lst = list(iterable)\n\n lcs = lst[0]\n for string in lst[1:]:\n lcs = all_lcs(lcs, string).pop() # TODO select a LCS instead of popping the last one\n\n return lcs","def longest_ORF(dna):\n\n\n\n\n #use the find_all_orfs_both_strands function and then compute the max length of these\n ORFs = find_all_ORFs_both_strands(dna)\n return max(ORFs,key=len)","def lengthOfLongestSubstring(s):\n arr = [1] * len(s)\n i = 0\n j = 1\n while j < len(s):\n if s[j] not in s[i:j]:\n arr[i] += 1\n j = j + 1\n else:\n i = i + 1\n j = i + 1\n return max(arr)","def sm_longest_match(s1,s2):\n anslist=[]\n match = SM(None, s1, s2).find_longest_match(0, len(s1), 0, len(s2))\n match_pat = s1[match.a: match.a + match.size]\n while match.size > 1:\n anslist.append( (match_pat, match.a) )\n\n #replace match pattern from string\n s1=s1.replace(match_pat,'')\n s2 = s2.replace(match_pat, '')\n\n match = SM(None, s1, s2).find_longest_match(0, len(s1), 0, len(s2))\n\n match_pat = s1[match.a: match.a + match.size]\n\n return anslist","def longest_common_substring_iterative_bottom_up(s1, s2):\n\n cache = [[0] * (len(s2)+1) for _ in range(-1, len(s1))]\n\n for i in range(1, len(s1)+1):\n for j in range(1, len(s2)+1):\n\n if s1[i-1] == s2[j-1]:\n\n # previous count is cache[i-1][j-1]\n cache[i][j] = cache[i-1][j-1] + 1\n else:\n cache[i][j] = max(cache[i-1][j-1],\n max(cache[i-1][j],\n cache[i][j-1]))\n\n return cache[-1][-1]","def similarity(a, b):\n distance = Levenshtein.distance(a, b)\n return 1 - (distance / max((len(a), len(b))))","def longest(self):\n longest = None\n max_len = 0\n for vector in self._vectors:\n len = vector.length2\n if len > max_len:\n longest = vector\n max_len = len\n return longest","def most_probable_match(string, possible_strings, n_matches=1):\n if not _is_def_lcs:\n raise ImportError(\"`pylcs` not loaded try running 'pip install pylcs'\")\n def return_match(arg_most_prob, probable_match, i_match=1):\n print(\n f\"\\tMatch {i_match} of {n_matches} : \"\n + f\"'{possible_strings[arg_most_prob]}' == '{string}' \"\n + f\"with a match score of {probable_match[arg_most_prob]}/{4*len(string)}.\"\n )\n return possible_strings[arg_most_prob]\n\n possible_strings_l = [s.lower() for s in possible_strings]\n string_l = string.lower()\n lcsubsequence_list_lower = pylcs.lcs_of_list(string_l, possible_strings_l)\n probable_match = (np.add(np.add(\n pylcs.lcs_of_list(string, possible_strings), # longest subsequence \n pylcs.lcs2_of_list(string, possible_strings) # longest substring\n ), np.add(\n lcsubsequence_list_lower, # longest subsequence case insensitive\n pylcs.lcs2_of_list(string_l, possible_strings_l) # longest substring case insensitive\n )).astype(np.int) - (\n np.array([2*(len(t) - m) for t, m in zip(possible_strings_l, lcsubsequence_list_lower)]) # Penalise extra characters in the match\n ))\n if n_matches == 1:\n arg_most_prob = np.argmax(probable_match)\n return return_match(arg_most_prob, probable_match)\n elif n_matches > 1:\n args_most_prob = np.argpartition(probable_match, -n_matches)[-1:-n_matches-1:-1]\n return [return_match(a, probable_match, i+1) for i, a in enumerate(args_most_prob)]\n else: \n raise AttributeError(\"Invalid number of matches requested\")","def longest_ORF(dna):\n orfs = find_all_ORFs_both_strands(dna)\n maxorf =orfs[1];\n for s in orfs:\n if len(s)>len(maxorf):\n maxorf=s\n return maxorf","def find_longest_word(s):\n return sorted(map(lambda si: (si, len(si)), s.split()), key=lambda item: item[1], reverse=True)[0][0]","def longest_ORF(dna):\n\n # YOUR IMPLEMENTATION HERE","def test_cont(self):\n s = 'aaa'\n result = Solution().lengthOfLongestSubstring2(s)\n self.assertEqual(result, 1)","def _get_overlap(word1, word2):\n max_overlap = min(len(word1), len(word2))\n max_found = 0\n for size in range(1, max_overlap+1):\n suffix = word1[-size:]\n prefix = word2[:size]\n if suffix == prefix:\n max_found = size\n return max_found","def __lcsm(protein_seqs):\n\tmax_motif = ''\n\tif not (protein_seqs and isinstance(protein_seqs, list)):\n\t\traise Exception(\n\t\t\t\"Invalid protein sequences! Please check your data.\")\n\tprotein_seqs.sort(key = lambda seq: len(seq))\n\tshortest_seq_len = len(protein_seqs[0])\n\tfor l in range(0, shortest_seq_len):\n\t\tr = shortest_seq_len-1\n\t\twhile r >= l:\n\t\t\tfound = True\n\t\t\tmotif = protein_seqs[0][l:r+1]\n\t\t\tfor seq in protein_seqs[1:]:\n\t\t\t\tif motif not in seq:\n\t\t\t\t\tfound = False\n\t\t\t\t\tbreak\n\t\t\tif found and len(motif)>=len(max_motif):\n\t\t\t\tmax_motif = motif\n\t\t\t\tbreak\n\t\t\tr-=1\n\treturn max_motif","def longest_ORF(dna):\n ORFs = find_all_ORFs_both_strands(dna)\n longest = ''\n for ORF in ORFs:\n if len(ORF) > len(longest):\n longest = ORF\n return longest","def longest_ORF(dna):\n l = find_all_ORFs_both_strands(dna)\n longest=''\n if len(l)>=1:\n\t longest =max(l,key=len)\n return longest","def most_similar_word(self,word, word_set):\n\t max_sim = -1.0\n\t sim_word = \"\"\n\t for ref_word in word_set:\n\t sim = self.word_similarity(word, ref_word)\n\t if sim > max_sim:\n\t max_sim = sim\n\t sim_word = ref_word\n\t return sim_word, max_sim","def find_similarity(long, short):\n similarity1 = 0\n similarity2 = 0\n for i in range(len(long)-len(short)+1):\n a = 0\n part = long[i:i+len(short)]\n for j in range(len(part)):\n if part[j] == short[j]:\n a += 1\n if a == len(short):\n similarity1 = part\n return similarity1\n elif a > similarity2:\n similarity2 = a\n similarity1 = part\n return similarity1","def longest_word_len(text):\r\n return np.max(np.array([len(word) for word in tokenization(text)]))","def longest_ORF(dna):\n orfs = find_all_ORFs_both_strands(dna)\n longest = max(orfs, key=len)\n return longest","def damerau_levenshtein_similarity(s1, s2):\n max_cost = max(len(s1), len(s2))\n\n if max_cost == 0:\n return 1.0\n\n return 1.0 - float(damerau_levenshtein_distance(s1, s2)) / max_cost","def find_longest_word(words):\n \n # lengths = []\n\n # for word in words: \n # l = len(word)\n # lengths.append(l)\n # result = max(lengths)\n # return result\n\n #ALT SOLUTION ONE LINE \n return len(max(words, key=lambda words: len(words)))","def find_longest_common_prefix(words:list):\n trie = Trie(words)\n\n head = trie.head\n\n prefix = []\n\n while len(head) == 1 and trie.eof not in head:\n key, value = head.popitem()\n prefix.append(key)\n head = value\n \n return \"\".join(prefix)","def longest_common_substring_top_down(s1, s2, q=1, r=1, cache=None):\n if cache is None:\n cache = {}\n if (q, r) in cache:\n return cache[(q, r)]\n n = len(s1)\n m = len(s2)\n if n == q - 1 or m == r - 1:\n cache[(q, r)] = \"\"\n return \"\"\n elif s1[n - q] == s2[m - r]: #\n ss = longest_common_substring_top_down(s1, s2, q + 1, r + 1, cache) + s1[n - q]\n cache[(q, r)] = ss\n return ss\n ss1 = longest_common_substring_top_down(s1, s2, q, r + 1, cache)\n ss2 = longest_common_substring_top_down(s1, s2, q + 1, r, cache)\n if (len(ss1) >= len(ss2)):\n cache[(q, r)] = ss1\n return ss1\n else:\n cache[(q, r)] = ss2\n return ss2","def match_all_cui(s,max_len = 10, Eterm_cui = Eterm_cui):\n if len(s) == 0: \n return []\n sub_label = np.zeros(len(s),dtype = 'int')\n location_term = {}\n i = 0\n while i < len(s):\n for j in range(max_len+1,0,-1):\n temp = ' '.join(s[i:i+j])\n if temp in Eterm_cui:\n sub_label[i:i+j] = 1\n location_term[i] = [Eterm_cui[temp]]\n break#matched maximum string, so break\n i += j\n output = []\n for i in range(len(s)):\n if sub_label[i] == 0:#no match\n output += [s[i]]\n elif i in location_term:\n for cui in location_term[i][: :-1]:\n output += [cui]\n return output","def longest_ORF(dna):\n longest_ORF = []\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# initialize empty list\n longest_ORF = longest_ORF + find_all_ORFs_both_strands(dna)\t\t\t\t\t\t\t\t# adds all ORFs both strands to list\n\n for n in range(len(longest_ORF)):\t\t\t\t\t\t\t\t\t\t\t\t\t\t# searches through the list\n\t if longest_ORF[n] == max(longest_ORF, key=len):\t\t\t\t\t\t\t\t\t\t# finds longest string\n\t \treturn longest_ORF[n]\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t# returns longest string","def longest_ORF(dna):\n l = find_all_ORFs_both_strands(dna)\n max_len = 0\n r = \"\"\n # [???] what if there are ORFs with same length?\n # this function just get the first one.\n if len(l) == 0:\n return \"\"\n for o in l:\n if len(o) > max_len:\n r = o\n max_len = len(o)\n return r","def contig_complementary_score(s1, s2) -> int:\n matcher = SequenceMatcher()\n matcher.set_seq1(str(s1))\n matcher.set_seq2(str(s2.complement()))\n _, _, size = matcher.find_longest_match(0, len(s1), 0, len(s2))\n return size","def longest_shared_between(cls, seq_1, seq_2):\n tree = cls(seq_2 + '$', 'B').append_named_sequence(seq_1 + '$', 'A')\n condition = lambda n, c, p: 'B' not in c.seq_ids\n out = lambda n, c, p: p + n.label\n unshared = tree.root.bfs_paths(condition, out)\n shortest = next(unshared)\n common = [shortest]\n for possible in unshared:\n if len(possible) > len(shortest) and not possible[-1] == '$':\n shortest = possible\n common = [possible]\n elif len(possible) == len(shortest) and not possible[-1] == '$':\n common.append(possible)\n return min(common)","def best_string(L, a, b):\n st = ''\n i = len(L) - 1\n j = len(L[0]) - 1\n\n while i > 0 and j > 0:\n if a[i-1] == b[j-1]:\n st = a[i-1] + st\n i -= 1\n j -= 1\n else:\n if L[i-1][j] > L[i][j-1]:\n i -= 1\n else:\n j -= 1\n return st","def find_best_candidate(s_array):\n best_string = ''\n max_val = 0\n for s in s_array:\n score = compare(s)\n if score > max_val:\n max_val = score\n best_string = s\n return best_string","def compare_eq_len(a, b):\n n = len(a)\n m = len(b)\n\n # slide b across a from left to right till from just overlapping till full overlap\n overlap = 0 # stores length of the overlap\n lconcat = \"\" # this stores the shortest common superstring\n for j in range(m):\n starta = 0\n enda = j+1\n startb = m - (j+1)\n endb = m\n if a[starta:enda] == b[startb:endb]:\n # if an overlap is found, check if it is larger than the previously detected one\n # print(\"overlap found\")\n if len(a[starta:enda]) > overlap: \n overlap = len(a[starta:enda]) \n lconcat = b + a[enda:] # this is the current shortest common superstring\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\n\n # print(\"-\")\n\n # slide b across a so that b starts from one element past a after full overlap\n rconcat = \"\"\n for j in range(m-1):\n starta = j+1\n enda = m\n startb = 0\n endb = m - (j+1)\n if a[starta:enda] == b[startb:endb]:\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \n # print(\"overlap found\")\n overlap = len(a[starta:enda]) \n rconcat = a + b[endb:]\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\n\n # after checking for overlaps there may be 1 or no shortest common\n # superstrings stored in both lconcat and rconcat. Choose the shortest one if it exists\n # or the concatenation of a and b if there are no overlaps. We may have to make some\n # arbitrary choices here.\n\n if not lconcat and not rconcat: # both lconcat and rconcat are empty, no overlaps\n superstring = a + b # append b to a (could prepend here too, this is an arbitrary choice)\n elif lconcat and not rconcat: # lconcat contains overlap and rconcat is empty\n superstring = lconcat \n elif rconcat and not lconcat: # rconcat contains overlap and lconcat is empty\n superstring = rconcat\n elif rconcat and lconcat and (len(lconcat) <= len(rconcat)): # use lconcat if it is shorter or equal len to rconat\n superstring = lconcat\n elif rconcat and lconcat and (len(rconcat) < len(lconcat)): # use rconcat only if it is shorter than lconat\n superstring = rconcat\n return superstring","def levenshteinDistance(s, t, asRatioOfMax = False):\n\n if s == None:\n s = \"\"\n if t == None:\n t = \"\"\n\n if t == s:\n return 0\n if len(s) == 0:\n return len(t)\n if len(t) == 0:\n return len(s)\n\n v0 = [x for x in range(len(t)+1)]\n v1 = [0 for x in range(len(t)+1)]\n\n for i, si in enumerate(s):\n\n v1[0] = i + 1\n\n for j, tj in enumerate(t):\n\n if si == tj:\n cost = 0\n else:\n cost = 1\n\n j1 = v1[j] + 1\n j2 = v0[j + 1] + 1\n j3 = v0[j] + cost\n \n if j1 < j2 and j1 < j3:\n v1[j + 1] = j1\n continue\n \n if j2 < j3:\n v1[j+1] = j2\n else:\n v1[j+1] = j3\n \n v0 = v1.copy()\n\n if not asRatioOfMax:\n return v1[len(t)]\n\n return 1 - v1[len(t)]/max([len(t), len(s)])","def most_consonants(words):\n cos = [[len(x)-num_vowels(x),x] for x in words]\n best_pair = max(cos)\n return best_pair[1]","def string_similarity(a, b):\n return SequenceMatcher(a=a, b=b).ratio()","def longest_prefix_length(s, i, j):\n l = 0\n while (i+l < len(s)) and (j+l < len(s)):\n if s[i+l] != s[j+l]:\n break\n l += 1\n return l","def longest_ORF(dna):\n i = 0\n both_ORFs = find_all_ORFs_both_strands(dna)\n longest = both_ORFs[0]\n while i + 1 < len(both_ORFs):\n if len(both_ORFs[i+1]) > len(longest):\n longest = both_ORFs[i+1]\n i+=1\n return longest","def overlap(string_one: str, string_two: str):\n len_s_one = len(string_one)\n len_s_two = len(string_two)\n\n # Content of the tuple => (start_index, stop_index, string from start_index to stop_index)\n largest_overlap = {\"suffix_string\": string_one,\n \"prefix_string\": string_two,\n \"overlap\": None,\n \"weight\": 0}\n len_overlap = 0\n # Iterate over every suffix from string one from right to left\n for i in range(len_s_one, 0, -1):\n # Current suffix from string one\n current_suffix_one = string_one[i - 1:len_s_one]\n\n # Iterate over every prefix of string two from left to right\n for j in range(len_s_two + 1):\n current_prefix_two = string_two[:j]\n if current_suffix_one == current_prefix_two:\n tmp_len = len(current_prefix_two)\n if tmp_len > len_overlap:\n len_overlap = tmp_len\n\n largest_overlap[\"suffix_start\"] = i - 1\n largest_overlap[\"suffix_end\"] = len_s_one\n\n largest_overlap[\"prefix_start\"] = 0\n largest_overlap[\"prefix_end\"] = j\n\n largest_overlap[\"weight\"] = tmp_len\n largest_overlap[\"overlap\"] = current_suffix_one\n return largest_overlap","def find_largest_overlaps(string_one, string_two):\n two_overlaps_suffix_one = overlap(string_one, string_two)\n one_overlaps_suffix_two = overlap(string_two, string_one)\n\n return two_overlaps_suffix_one, one_overlaps_suffix_two","def levenshtein(str1, str2, normalise=False):\n\ttmp = Levenshtein.distance(str1, str2)\n\tif(normalise) and (len(str1) + len(str2)): tmp /= max(len(str1), len(str2))\n\treturn tmp","def getCommon(self, st1, st2):\n fl = len(st1)\n shorter = len(st2)\n if fl < shorter:\n shorter = fl\n \n i = 0\n while i < shorter:\n if st1[i] != st2[i]:\n break\n i += 1\n return i","def levenshtein(seq1: str, seq2: str) -> int:\n if seq1 == \"\":\n return len(seq2)\n if seq2 == \"\":\n return len(seq1)\n if seq1[-1] == seq2[-1]:\n cost = 0\n else:\n cost = 1\n \n result = min([levenshtein(seq1[:-1], seq2) + 1,\n levenshtein(seq1, seq2[:-1]) + 1,\n levenshtein(seq1[:-1], seq2[:-1]) + cost ])\n return result","def scientific_match_ratio(str1, str2, keywords):\n\n # Get rid of the numbers\n str1_numberless = remove_numbers(str1)\n str2_numberless = remove_numbers(str2)\n\n # Get the keywords and whatever remains after removing the keywords\n str1_keywords, str1_remainder = get_common_words_in_description(str1_numberless, keywords)\n str2_keywords, str2_remainder = get_common_words_in_description(str2_numberless, keywords)\n\n remainder_dist = string_num_matches(str1_remainder, str2_remainder)\n common_keywords = str1_keywords.intersection(str2_keywords)\n\n common_keyword_total_len = 0\n for common_kword in common_keywords:\n common_keyword_total_len += len(common_kword)\n\n return (remainder_dist + common_keyword_total_len) * 1.0 / max(len(str1_numberless), len(str2_numberless))","def name_similarity(name_1, name_2, nickname_2=None):\n name_similarity = string_similarity(name_1, name_2)\n nickname_similarity = 0\n if nickname_2:\n nickname_similarity = string_similarity(name_1, nickname_2)\n return max(name_similarity, nickname_similarity)","def levenshtein(s1, s2):\n if len(s1) < len(s2):\n return levenshtein(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 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 \n return previous_row[-1]","def find_max_corrcoef_and_word(self, test_samples, max_length):\n new_max_length = max(self.max_length, max_length)\n if new_max_length != self.max_length:\n self.need_count_fft = True\n self.need_correct_length = True\n self.max_length = new_max_length\n\n self.correct_length_of_all_items()\n self.count_fft_for_all_items()\n word, coefficient = None, 0\n test_fft = self.fft(correct_len(test_samples, self.max_length))\n for i in self.items:\n tmp_coefficient = np.corrcoef(abs(i.fft), abs(test_fft))\n if tmp_coefficient[0][1] > coefficient:\n word = i.word\n coefficient = tmp_coefficient[0][1]\n return word, coefficient","def longest_ORF_noncoding(dna, num_trials):\n longest=[]\n for i in range(0,num_trials):\n \tshuffled_str=shuffle_string(dna)\n \tlongest.append(longest_ORF(shuffled_str))\n long_ORF=max(longest,key=len)\n return len(long_ORF)","def common():\r\n full_song = \"\"\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n full_song += str(song_lyrics(song))\r\n split_lyrics = full_song.lower().split()\r\n counter = collections.Counter(split_lyrics)\r\n most_words = counter.most_common(50)\r\n return most_words","def longestPalindrome(self, s: str) -> int:\n # approach #1 -- using hashset\n # approach 2 -- using hashmap\n hashmap = defaultdict(int)\n odd = 0\n out = 0\n for char in s:\n hashmap[char] += 1\n\n for key, val in hashmap.items():\n if val % 2 == 1:\n odd = 1\n out += (val -1)\n else:\n out += val\n return out +odd","def longestPalindromeSubseq(self, s: str) -> int:\n n = len(s)\n dp = [[1] * n for _ in range(n)]\n for length in range(1, n + 1):\n for i in range(n - length + 1):\n j = i + length - 1\n print(i, j)\n if length == 1:\n dp[i][j] = 1\n elif s[i] == s[j]:\n dp[i][j] = dp[i + 1][j - 1] + 2\n else:\n dp[i][j] = max(dp[i][j - 1], dp[i + 1][j])\n return dp[0][n - 1]","def compare(seq1, seq2):\n if seq1 == seq2:\n return 1\n len_diff = len(seq1) / len(seq2)\n if len_diff > 1:\n len_diff = 1 / len_diff\n\n ngrams1 = {tuple(ng) for ng in get_all_ngrams(seq1)}\n ngrams2 = {tuple(ng) for ng in get_all_ngrams(seq2)}\n\n overall = len(ngrams1 & ngrams2) / len(ngrams1 | ngrams2)\n if overall == 1 or overall == 0:\n return overall\n\n try:\n max_match = len(max(ngrams1 & ngrams2, key=len)) / len(seq1)\n except ValueError:\n return 0\n\n return (len_diff + max_match + overall) / 3","def longest_ORF(dna):\n MyList = find_all_ORFs_both_strands(dna)\n if MyList == []:\n maximum_orf = 'none'\n else:\n maximum_orf = max(MyList, key=len)\n return maximum_orf","def main():\n long = give_long()\n short = give_short()\n similarity1 = find_similarity(long, short)\n print('The best match is '+similarity1+'.')","def novelty(sentence: str, tokenized_sentences: str, similarity_metric: str) -> float:\n # sentences = nltk.sent_tokenize(document)\n max_sim_sentence = ''\n sentence = sentence.lower()\n tokenized_sentences = [sent.lower() for sent in tokenized_sentences]\n\n if similarity_metric == 'jaccard':\n max_sim = - np.inf\n \n for ref_sentence in tokenized_sentences:\n jaccard_sim = jaccard_similarity_words(sentence, ref_sentence)\n if jaccard_sim > max_sim:\n max_sim_sentence = ref_sentence\n max_sim = jaccard_sim\n\n return 1 - max_sim, max_sim_sentence\n\n elif similarity_metric == 'levenshtein':\n min_edit_distance = np.inf\n for ref_sentence in tokenized_sentences:\n edit_distance = levenshtein(sentence, ref_sentence) \\\n / max(len(sentence), len(ref_sentence))\n if edit_distance < min_edit_distance:\n max_sim_sentence = ref_sentence\n min_edit_distance = edit_distance\n\n return min_edit_distance, max_sim_sentence","def levenshtein_distance(s1,s2):\n\n\t\tif len(s1) < len(s2):\n\t\t\treturn Searcher.levenshtein_distance(s2, s1)\n\n\t\t# len(s1) >= len(s2)\n\t\tif len(s2) == 0:\n\t\t\treturn len(s1)\n\n\t\tprevious_row = range(len(s2) + 1)\n\t\tfor i, c1 in enumerate(s1):\n\t\t\tcurrent_row = [i + 1]\n\t\t\tfor j, c2 in enumerate(s2):\n\t\t\t\tinsertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer\n\t\t\t\tdeletions = current_row[j] + 1 # than s2\n\t\t\t\tsubstitutions = previous_row[j] + (c1 != c2)\n\t\t\t\tcurrent_row.append(min(insertions, deletions, substitutions))\n\t\t\tprevious_row = current_row\n\t\t\n\t\treturn previous_row[-1]","def test_string_similarity_constraint():\n f = SimilarityConstraint(func=LevenshteinDistance(), pred=GreaterThan(0.5))\n assert f('BROOKLYN', 'BROKLYN')\n assert not f('BROOKLYN', 'QUEENS')","def find_lcs(l1: str, l2: str, length1: int, length2: int):\n \"\"\" Theorem:{\n Initialize matrix with 0 for first row and colm\n If s1[i] = s2[j], update matrix[i][j] with value\n of matrix[i-1][j-1]+1\n Else update matrix[i][j] with max of value among\n matrix[i][j-1],matrix[i-1][j]\n Matrix[n][m] will be lcs\n }\n \"\"\"\n matrix = [[None]*(length1+1) for i in range(0, length2+1)]\n for i in range(0, length2+1):\n for j in range(0, length1+1):\n if i == 0 or j == 0:\n matrix[i][j] = 0\n elif l1[j-1] == l2[i-1]:\n matrix[i][j] = matrix[i-1][j-1] + 1\n else:\n matrix[i][j] = max(matrix[i][j-1], matrix[i-1][j])\n lcs = [None for i in range(0, matrix[length2][length1])]\n index = matrix[length2][length1]\n m = length2 \n n = length1\n while(m > -1 and n > -1):\n if l2[m-1] == l1[n-1]:\n lcs[index-1] = l2[m-1]\n index -= 1\n m -= 1\n n -= 1\n elif matrix[m-1][n] > matrix[m][n-1]:\n m -= 1\n else:\n n -= 1\n return lcs","def longest_ORF_noncoding(dna, num_trials):\n\n # YOUR IMPLEMENTATION HERE","def _l_distance(first_string, second_string):\n if len(first_string) > len(second_string):\n first_string, second_string = second_string, first_string\n distances = list(range(len(first_string) + 1))\n for index2, char2 in enumerate(second_string):\n new_distances = [index2 + 1]\n for index1, char1 in enumerate(first_string):\n if char1 == char2:\n new_distances.append(distances[index1])\n else:\n new_distances.append(1 + min((distances[index1], distances[index1 + 1], new_distances[-1])))\n distances = new_distances\n return distances[-1]","def longest_word(text):\r\n try:\r\n # replace special characters in the input sentence\r\n text = re.sub('[^A-Za-z0-9]+', ' ', text).lower()\r\n\r\n # split the text by spaces and put it in a list\r\n text_list = list(set(text.split(' ')))\r\n\r\n # remove blanks from list\r\n if \"\" in text_list:\r\n text_list.remove(\"\")\r\n\r\n # find longest word's length & longest word\r\n matches = []\r\n if len(text_list) != 0:\r\n text_list.sort(key=len, reverse=True)\r\n x = len(text_list[0])\r\n for i in text_list:\r\n if len(i) == x:\r\n matches.append(i)\r\n else:\r\n break\r\n matches.sort()\r\n return (\"Longest word/s: \" + str(matches) + \" & its length is: \" + str(x))\r\n else:\r\n return None\r\n except Exception as e:\r\n print(\"Following exception occured while finding longest word - {}\".format(e))\r\n return None","def longest_ORF_unit_tests():\n\n input_a=\"ATGCGAATGTAGCATCAAA\"\n expected_output=''\n actual_output=longest_ORF(input_a)\n print 'Expected Output is ' + expected_output\n print \"actual output: \", actual_output","def common_suffix(s1, s2):\n if not s1 or not s2:\n return None\n # revert the seqs and get a common prefix\n common = common_prefix(s1[::-1], s2[::-1])\n # revert back\n if common:\n return common[::-1]\n else:\n return common","def get_longest_valid_repeated_nonoverlapping_substring(string, validity_function):\n string_length=len(string) \n table=[[0 for i2 in range(string_length+1)] for i in range(string_length+1)] \n\n repeated_substring_length=0\n repeated_substring=[]\n for i in range(1, string_length+1): \n for i2 in range(i+1, string_length+1): \n if (string[i-1]==string[i2-1] and table[i-1][i2-1]<(i2-i)): \n table[i][i2]=table[i-1][i2-1]+1\n if table[i][i2]>repeated_substring_length:\n candidate_string=string[i-table[i][i2]:i]\n if validity_function(candidate_string):\n repeated_substring_length=table[i][i2]\n repeated_substring=candidate_string\n else: \n table[i][i2]=0\n\n return repeated_substring","def similar_string_fast(first_string, second_string):\n partial_score = fuzz.ratio(first_string, second_string)\n token_score = fuzz.token_set_ratio(first_string, second_string)\n\n if max(partial_score, token_score) >= SCORE_THRESHOLD_FAST:\n return True\n\n return False","def lcs_length(s1, s2):\n n = len(s1)\n m = len(s2)\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\n for i in xrange(1, n+1):\n for j in xrange(1, m+1):\n if(s1[i-1]==s2[j-1]):\n matrix[i][j] = matrix[i-1][j-1] + 1\n else:\n matrix[i][j] = max(matrix[i-1][j], matrix[i][j-1])\n return matrix[n][m]","def longest_substring_no_repetitions(string: str) -> str:\n start, end = 0, 0\n longest_substring = ''\n\n limit = len(string)\n while end < limit:\n while True:\n substring = string[start:end+1]\n\n if has_duplicates(substring):\n start += 1\n continue\n\n if len(substring) > len(longest_substring):\n longest_substring = substring\n\n break\n\n end += 1\n\n return longest_substring","def longest_palindromic_substring(s):\n longest = s[0] if len(s) > 0 else \"\"\n for i in range(len(s)):\n j = len(s)\n while s[i] in s[i+1:j] and j <= len(s):\n j = s[i + 1:j].rfind(s[i]) + i + 2\n print(i, j)\n if is_palindrome(s[i:j]) and len(longest) < len(s[i:j]):\n longest = s[i:j]\n j = len(s) + 1\n else:\n j -= 1\n if len(s) - len(longest) <= i:\n break\n return longest","def longest_word_length(words):\n\n longest = len(word[0])\n\n for word in words:\n if longest < len(word):\n longest = len(word)\n\n return longest","def longest_ORF(dna):\n\n reverseComp = get_reverse_complement(dna)\n openFramesfromReverse = find_all_ORFs(reverseComp)\n openFramesfromNormal = find_all_ORFs(dna)\n all_ORFs = openFramesfromNormal + openFramesfromReverse\n longest = max(all_ORFs, key=len)\n return longest","def common(s1, s2):\n cl = commonlen(s1, s2)\n return s2[:cl]","def string_similarity_score(left: str, right: str):\n return SequenceMatcher(None, left, right).ratio()","def most_reducible(wordlist):\n\n\t# We create a memo for reducible words since is_reducible is \n\t# recursive. The keys are the words and the values are the \n\t# number of characters\n\tglobal reducible_words\n\treducible_words = dict()\n\treducible_words['a'], reducible_words['i'] = 1, 1\n\t\n\tword_dict = to_dictionary(wordlist)\n\tfor line in word_dict:\n\t\tis_reducible(line, word_dict)\n\n\t# Varible that will search the memo for the longest word\n\tcurrent_greatest = ''\n\tfor word in reducible_words:\n\t\tif reducible_words[word] > len(current_greatest):\n\t\t\tcurrent_greatest = word\n\tprint(current_greatest)","def levenshtein_distance(str1, str2):\n m = len(str1)\n n = len(str2)\n lensum = float(m + n)\n d = [] \n for i in range(m+1):\n d.append([i]) \n del d[0][0] \n for j in range(n+1):\n d[0].append(j) \n for j in range(1,n+1):\n for i in range(1,m+1):\n if str1[i-1] == str2[j-1]:\n d[i].insert(j,d[i-1][j-1]) \n else:\n minimum = min(d[i-1][j]+1, d[i][j-1]+1, d[i-1][j-1]+2) \n d[i].insert(j, minimum)\n ldist = d[-1][-1]\n ratio = (lensum - ldist)/lensum\n return {'distance':ldist, 'ratio':ratio}","def optimal_string_alignment_distance(s1, s2):\n\n utils.check_for_none(s1, s2)\n utils.check_for_type(str, s1, s2)\n\n # s1 = utils.unicode_normalize(s1)\n # s2 = utils.unicode_normalize(s2)\n\n n1, n2 = len(s1), len(s2)\n\n dp = [[0] * (n2 + 1) for _ in range(n1 + 1)]\n\n for i in range(0, n1 + 1):\n dp[i][0] = i\n for j in range(0, n2 + 1):\n dp[0][j] = j\n\n for i in range(1, n1 + 1):\n for j in range(1, n2 + 1):\n cost = 0 if s1[i - 1] == s2[j - 1] else 1\n\n dp[i][j] = min(dp[i][j - 1] + 1,\n dp[i - 1][j] + 1,\n dp[i - 1][j - 1] + cost)\n\n if i > 1 and j > 1 and s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1]:\n dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + cost)\n\n return dp[n1][n2]","def longest_ORF_noncoding(dna, num_trials):\n longest = ''\n lenl = 0\n for i in range(num_trials):\n shuffle = shuffle_string(dna)\n if len(longest_ORF(shuffle)) > lenl:\n longest = longest_ORF(shuffle)\n lenl = len(longest)\n return len(longest)"],"string":"[\n \"def findLongestCommonSubstringManyStrings(listOfStrings):\",\n \"def m_longest_common_subsequence_2(s):\\n\\n # Correct me if I'm wrong but I think unsorted list is faster\\n lst = list(s)\\n\\n lcs = lst[0]\\n for string in lst[1:]:\\n lcs = LCS(lcs, string)\\n\\n return lcs\",\n \"def findLongestCommonSubstring(self, string1, string2):\\n countingArray = [[0]*len(string2) for i in range(len(string1))]\\n ##we set up an array to locate the possible substrings\\n lcss = \\\"\\\"\\n for row in range(len(countingArray)):\\n for column in range(len(countingArray[row])):\\n if string1[row] == string2[column]:\\n countingArray[row][column] += 1 ##if there's a match the common string length is at least 1\\n if not (row == 0 or column == 0):\\n countingArray[row][column] += countingArray[row-1][column-1] ##memoize the length of the common string so far\\n if countingArray[row][column] > longestLength: ##if it's longer than our current longest string, we'll plan on returning it\\n longestLength = countingArray[row][column]\\n lcss = string1[row-longestLength+1:row+1]\\n return lcss\",\n \"def longest_common_prefix(strings: list):\\n raise NotImplemented\",\n \"def longest_common_subsequence(seq1, seq2):\\n mat = DPMatrix(seq1, seq2, match=1, mismatch=0, gap=0)\\n mat.fill()\\n\\n lcs = []\\n augSeq1, augSeq2 = mat.augmented_sequences()\\n for c1, c2 in zip(augSeq1, augSeq2):\\n if c1 == c2:\\n lcs.append(c1)\\n\\n return ''.join(lcs)\",\n \"def longestAwesome(self, s: str) -> int:\\n\\n # So we are moving right, and reducing length by 1\\n # for every time we move right - we start from the longest substring that can be formed to lowest one\\n # So the moment, we find something we can instantly breal\\n\\n max_length = 0\\n\\n if s == s[::-1]:\\n return len(s)\\n\\n for i in range(0, len(s)):\\n left = i\\n right = len(s)\\n\\n if right - left > max_length:\\n\\n while right > left:\\n\\n candidate = s[left:right]\\n # print(f\\\"The candidate is: {candidate}\\\")\\n ctr = Counter(candidate)\\n\\n # initial base check\\n odd_cnt = 0\\n fl = False\\n for k, v in ctr.items():\\n if v & 1:\\n odd_cnt += 1\\n if odd_cnt > 1:\\n fl = True\\n break\\n\\n if not fl:\\n if max_length < (right - left):\\n max_length = right - left\\n # max_length = max(max_length, len(candidate))\\n\\n right -= 1\\n\\n return max_length\",\n \"def longest_word_length(words):\",\n \"def longest_common_substring_bottom_up(s1, s2):\\n lcs_array = lcs(s1, s2)\\n n = len(s1)\\n m = len(s2)\\n l = lcs_array[n][m]\\n result = \\\"\\\"\\n while l != 0:\\n if s1[n - 1] == s2[m - 1]:\\n result = s1[n - 1] + result\\n l -= 1\\n n -= 1\\n m -= 1\\n elif lcs_array[n][m - 1] >= lcs_array[n - 1][m]:\\n m -= 1\\n else:\\n n -= 1\\n return result\",\n \"def longest_common_subsequence(x, y):\\n\\n # find the length of the strings\\n m = len(x)\\n n = len(y)\\n\\n # declaring the array for storing the dp values\\n lcs = np.zeros((m + 1, n + 1))\\n\\n # iterate through each sub problem\\n for i in range(m + 1):\\n for j in range(n + 1):\\n if i == 0 or j == 0:\\n lcs[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n lcs[i, j] = lcs[i - 1, j - 1] + 1\\n else:\\n # use the optimal substructure property\\n # of using already computed results previous subproblems\\n lcs[i, j] = max(lcs[i - 1, j], lcs[i, j - 1])\\n\\n # L[m,n] contains the length of LCS of X[0..n-1] & Y[0..m-1]\\n return lcs[m, n]\",\n \"def longest_ORF(dna):\\n both_strings=find_all_ORFs_both_strands(dna)\\n L=max(both_strings,key=len)\\n Q=len(L)\\n return Q\\n\\n #save out put of find all orfboth string to some variable\",\n \"def lengthOfLongestSubstringTwoDistinct(self, s):\\n if not s:\\n return 0\\n\\n counters = collections.Counter()\\n\\n start = end = 0\\n max_len = -1\\n char_count = 0\\n while end < len(s):\\n if counters[s[end]] == 0:\\n char_count += 1\\n counters[s[end]] += 1\\n\\n while char_count > 2:\\n counters[s[start]] -= 1\\n if counters[s[start]] == 0:\\n char_count -= 1\\n start += 1\\n\\n if max_len < end - start + 1:\\n max_len = end - start + 1\\n\\n return max_len\",\n \"def optimal_string_alignment_similarity(s1, s2):\\n max_cost = max(len(s1), len(s2))\\n\\n if max_cost == 0:\\n return 1.0\\n\\n return 1.0 - float(optimal_string_alignment_distance(s1, s2)) / max_cost\",\n \"def longestCommonSubsequence(self, text1: str, text2: str) -> int:\\n if len(text1) == 0 or len(text2) == 0:\\n return 0\\n if text1[0] == text2[0]:\\n return 1 + self.longestCommonSubsequence(text1[1:], text2[1:])\\n else:\\n return max(self.longestCommonSubsequence(text1[1:], text2),\\n self.longestCommonSubsequence(text1, text2[1:]))\",\n \"def length_of_longest_substring(s):\\n def compare():\\n return max(cur_length, longest_substring)\\n\\n longest_substring = 0\\n cur_substring = \\\"\\\"\\n for c in s:\\n if c in cur_substring:\\n cur_length = len(cur_substring)\\n longest_substring = compare()\\n cur_substring = c\\n else:\\n cur_substring += c\\n return compare()\",\n \"def longest_common_substring_memoization(s1, s2, i, j, count, cache):\\n\\n if i == len(s1) or j == len(s2):\\n return 0\\n\\n if cache[i][j] != -1:\\n return cache[i][j]\\n\\n if s1[i-1] == s2[j-1]:\\n count = 1 + longest_common_substring_memoization(s1, s2, i+1, j+1, count, cache)\\n\\n else:\\n count = max(count,\\n max(longest_common_substring_memoization(s1, s2, i+1, j, count, cache),\\n longest_common_substring_memoization(s1, s2, i, j+1, count, cache)))\\n\\n cache[i][j] = count\\n return cache[i][j]\",\n \"def lengthOfLongestSubstring(self, s):\\n longest = 0\\n i = 0\\n j = 0\\n n = len(s)\\n seen = {}\\n while i < n and j < n:\\n c = s[j]\\n if c in seen:\\n i = seen[c] + 1\\n seen[c] = j\\n j += 1\\n longest = max(longest, j-i)\\n\\n return longest\",\n \"def longest_common_substring_backtrack(s1, s2, i, j, count):\\n if i == len(s1) or j == len(s2):\\n return 0\\n\\n if s1[i-1] == s2[j-1]:\\n count = 1 + longest_common_substring_backtrack(s1, s2, i+1, j+1, count)\\n\\n else:\\n count = max(count,\\n max(longest_common_substring_backtrack(s1, s2, i+1, j, count),\\n longest_common_substring_backtrack(s1, s2, i, j+1, count)))\\n\\n return count\",\n \"def longest_subsequence(s1: str, s2: str, s3: str) -> int:\\n # Find the shortest string\\n s = min(s1, s2, s3, key=lambda x: len(x))\\n total = 0\\n ok = False\\n while s1:\\n for i1, c1 in enumerate(s1):\\n for i2, c2 in enumerate(s2):\\n if ok:\\n ok = False\\n break\\n if c1 == c2:\\n for i3, c3 in enumerate(s3):\\n if c1 == c3:\\n total += 1\\n s1 = s1[i1 + 1:]\\n s2 = s2[i2 + 1:]\\n s3 = s3[i3 + 1:]\\n ok = True\\n break\\n\\n return total\",\n \"def get_similarity(s1, s2):\\n t0 = sorted(list(set(s1.split(' ')).intersection(set(s2.split(' ')))))\\n t1 = sorted(list(set(t0 + s1.split(' '))))\\n t2 = sorted(list(set(t0 + s2.split(' '))))\\n\\n r01 = SequenceMatcher(None, t0, t1).ratio()\\n r02 = SequenceMatcher(None, t0, t2).ratio()\\n r12 = SequenceMatcher(None, t1, t2).ratio()\\n return max(r01, r02, r12)\",\n \"def longestCommonPrefix(self, strs: List[str]) -> str:\\r\\n common = \\\"\\\"\\r\\n if not strs:\\r\\n return common\\r\\n shortest_str = min(strs, key=len)\\r\\n for i in range(len(shortest_str)):\\r\\n char = shortest_str[i]\\r\\n for item in strs:\\r\\n if item[i] != char:\\r\\n return common\\r\\n common += char\\r\\n return common\",\n \"def compare_str(seq1, seq2):\\n if seq1 == seq2:\\n return 1\\n ld = Levenshtein.distance(seq1, seq2)\\n longest = len(seq1 if len(seq1) > len(seq2) else seq2)\\n return (longest - ld) / longest\",\n \"def lcs(s1, s2):\\n\\n shape = (len(s1) + 1, len(s2) + 1)\\n M = np.zeros(shape)\\n\\n max_length_so_far = 0\\n candidates = set()\\n\\n for i in range(len(s1)):\\n for j in range(len(s2)):\\n\\n if s1[i] == s2[j]:\\n M[i+1,j+1] = M[i,j] + 1\\n\\n if M[i+1,j+1] > max_length_so_far:\\n max_length_so_far = int(M[i+1,j+1])\\n candidates = {s1[i - max_length_so_far + 1: i + 1]}\\n\\n elif M[i+1,j+1] == max_length_so_far:\\n candidates.add(s1[i - max_length_so_far + 1: i + 1])\\n\\n else:\\n M[i,j] = 0\\n\\n candidates.add('')\\n\\n return candidates\",\n \"def longest(s1, s2):\\n return ''.join(sorted(set(s1+s2)))\",\n \"def longest_common_prefix(fst: str, snd: str) -> str:\\n bound = 0\\n for a, b in zip(fst, snd):\\n if a != b:\\n break\\n bound += 1\\n return fst[:bound]\",\n \"def find_longest_common_prefix_reduce(words:list):\\n if not words:\\n return ''\\n \\n def common_start(w1, w2):\\n shorter = w1 if len(w1) < len(w2) else w2\\n for i in range(0, len(shorter)):\\n if w1[i] != w2[i]:\\n return shorter[:i]\\n return shorter\\n \\n return reduce(common_start, words)\",\n \"def m_longest_common_subsequence_3(iterable):\\n lst = list(iterable)\\n\\n lcs = lst[0]\\n for string in lst[1:]:\\n lcs = all_lcs(lcs, string).pop() # TODO select a LCS instead of popping the last one\\n\\n return lcs\",\n \"def longest_ORF(dna):\\n\\n\\n\\n\\n #use the find_all_orfs_both_strands function and then compute the max length of these\\n ORFs = find_all_ORFs_both_strands(dna)\\n return max(ORFs,key=len)\",\n \"def lengthOfLongestSubstring(s):\\n arr = [1] * len(s)\\n i = 0\\n j = 1\\n while j < len(s):\\n if s[j] not in s[i:j]:\\n arr[i] += 1\\n j = j + 1\\n else:\\n i = i + 1\\n j = i + 1\\n return max(arr)\",\n \"def sm_longest_match(s1,s2):\\n anslist=[]\\n match = SM(None, s1, s2).find_longest_match(0, len(s1), 0, len(s2))\\n match_pat = s1[match.a: match.a + match.size]\\n while match.size > 1:\\n anslist.append( (match_pat, match.a) )\\n\\n #replace match pattern from string\\n s1=s1.replace(match_pat,'')\\n s2 = s2.replace(match_pat, '')\\n\\n match = SM(None, s1, s2).find_longest_match(0, len(s1), 0, len(s2))\\n\\n match_pat = s1[match.a: match.a + match.size]\\n\\n return anslist\",\n \"def longest_common_substring_iterative_bottom_up(s1, s2):\\n\\n cache = [[0] * (len(s2)+1) for _ in range(-1, len(s1))]\\n\\n for i in range(1, len(s1)+1):\\n for j in range(1, len(s2)+1):\\n\\n if s1[i-1] == s2[j-1]:\\n\\n # previous count is cache[i-1][j-1]\\n cache[i][j] = cache[i-1][j-1] + 1\\n else:\\n cache[i][j] = max(cache[i-1][j-1],\\n max(cache[i-1][j],\\n cache[i][j-1]))\\n\\n return cache[-1][-1]\",\n \"def similarity(a, b):\\n distance = Levenshtein.distance(a, b)\\n return 1 - (distance / max((len(a), len(b))))\",\n \"def longest(self):\\n longest = None\\n max_len = 0\\n for vector in self._vectors:\\n len = vector.length2\\n if len > max_len:\\n longest = vector\\n max_len = len\\n return longest\",\n \"def most_probable_match(string, possible_strings, n_matches=1):\\n if not _is_def_lcs:\\n raise ImportError(\\\"`pylcs` not loaded try running 'pip install pylcs'\\\")\\n def return_match(arg_most_prob, probable_match, i_match=1):\\n print(\\n f\\\"\\\\tMatch {i_match} of {n_matches} : \\\"\\n + f\\\"'{possible_strings[arg_most_prob]}' == '{string}' \\\"\\n + f\\\"with a match score of {probable_match[arg_most_prob]}/{4*len(string)}.\\\"\\n )\\n return possible_strings[arg_most_prob]\\n\\n possible_strings_l = [s.lower() for s in possible_strings]\\n string_l = string.lower()\\n lcsubsequence_list_lower = pylcs.lcs_of_list(string_l, possible_strings_l)\\n probable_match = (np.add(np.add(\\n pylcs.lcs_of_list(string, possible_strings), # longest subsequence \\n pylcs.lcs2_of_list(string, possible_strings) # longest substring\\n ), np.add(\\n lcsubsequence_list_lower, # longest subsequence case insensitive\\n pylcs.lcs2_of_list(string_l, possible_strings_l) # longest substring case insensitive\\n )).astype(np.int) - (\\n np.array([2*(len(t) - m) for t, m in zip(possible_strings_l, lcsubsequence_list_lower)]) # Penalise extra characters in the match\\n ))\\n if n_matches == 1:\\n arg_most_prob = np.argmax(probable_match)\\n return return_match(arg_most_prob, probable_match)\\n elif n_matches > 1:\\n args_most_prob = np.argpartition(probable_match, -n_matches)[-1:-n_matches-1:-1]\\n return [return_match(a, probable_match, i+1) for i, a in enumerate(args_most_prob)]\\n else: \\n raise AttributeError(\\\"Invalid number of matches requested\\\")\",\n \"def longest_ORF(dna):\\n orfs = find_all_ORFs_both_strands(dna)\\n maxorf =orfs[1];\\n for s in orfs:\\n if len(s)>len(maxorf):\\n maxorf=s\\n return maxorf\",\n \"def find_longest_word(s):\\n return sorted(map(lambda si: (si, len(si)), s.split()), key=lambda item: item[1], reverse=True)[0][0]\",\n \"def longest_ORF(dna):\\n\\n # YOUR IMPLEMENTATION HERE\",\n \"def test_cont(self):\\n s = 'aaa'\\n result = Solution().lengthOfLongestSubstring2(s)\\n self.assertEqual(result, 1)\",\n \"def _get_overlap(word1, word2):\\n max_overlap = min(len(word1), len(word2))\\n max_found = 0\\n for size in range(1, max_overlap+1):\\n suffix = word1[-size:]\\n prefix = word2[:size]\\n if suffix == prefix:\\n max_found = size\\n return max_found\",\n \"def __lcsm(protein_seqs):\\n\\tmax_motif = ''\\n\\tif not (protein_seqs and isinstance(protein_seqs, list)):\\n\\t\\traise Exception(\\n\\t\\t\\t\\\"Invalid protein sequences! Please check your data.\\\")\\n\\tprotein_seqs.sort(key = lambda seq: len(seq))\\n\\tshortest_seq_len = len(protein_seqs[0])\\n\\tfor l in range(0, shortest_seq_len):\\n\\t\\tr = shortest_seq_len-1\\n\\t\\twhile r >= l:\\n\\t\\t\\tfound = True\\n\\t\\t\\tmotif = protein_seqs[0][l:r+1]\\n\\t\\t\\tfor seq in protein_seqs[1:]:\\n\\t\\t\\t\\tif motif not in seq:\\n\\t\\t\\t\\t\\tfound = False\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tif found and len(motif)>=len(max_motif):\\n\\t\\t\\t\\tmax_motif = motif\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tr-=1\\n\\treturn max_motif\",\n \"def longest_ORF(dna):\\n ORFs = find_all_ORFs_both_strands(dna)\\n longest = ''\\n for ORF in ORFs:\\n if len(ORF) > len(longest):\\n longest = ORF\\n return longest\",\n \"def longest_ORF(dna):\\n l = find_all_ORFs_both_strands(dna)\\n longest=''\\n if len(l)>=1:\\n\\t longest =max(l,key=len)\\n return longest\",\n \"def most_similar_word(self,word, word_set):\\n\\t max_sim = -1.0\\n\\t sim_word = \\\"\\\"\\n\\t for ref_word in word_set:\\n\\t sim = self.word_similarity(word, ref_word)\\n\\t if sim > max_sim:\\n\\t max_sim = sim\\n\\t sim_word = ref_word\\n\\t return sim_word, max_sim\",\n \"def find_similarity(long, short):\\n similarity1 = 0\\n similarity2 = 0\\n for i in range(len(long)-len(short)+1):\\n a = 0\\n part = long[i:i+len(short)]\\n for j in range(len(part)):\\n if part[j] == short[j]:\\n a += 1\\n if a == len(short):\\n similarity1 = part\\n return similarity1\\n elif a > similarity2:\\n similarity2 = a\\n similarity1 = part\\n return similarity1\",\n \"def longest_word_len(text):\\r\\n return np.max(np.array([len(word) for word in tokenization(text)]))\",\n \"def longest_ORF(dna):\\n orfs = find_all_ORFs_both_strands(dna)\\n longest = max(orfs, key=len)\\n return longest\",\n \"def damerau_levenshtein_similarity(s1, s2):\\n max_cost = max(len(s1), len(s2))\\n\\n if max_cost == 0:\\n return 1.0\\n\\n return 1.0 - float(damerau_levenshtein_distance(s1, s2)) / max_cost\",\n \"def find_longest_word(words):\\n \\n # lengths = []\\n\\n # for word in words: \\n # l = len(word)\\n # lengths.append(l)\\n # result = max(lengths)\\n # return result\\n\\n #ALT SOLUTION ONE LINE \\n return len(max(words, key=lambda words: len(words)))\",\n \"def find_longest_common_prefix(words:list):\\n trie = Trie(words)\\n\\n head = trie.head\\n\\n prefix = []\\n\\n while len(head) == 1 and trie.eof not in head:\\n key, value = head.popitem()\\n prefix.append(key)\\n head = value\\n \\n return \\\"\\\".join(prefix)\",\n \"def longest_common_substring_top_down(s1, s2, q=1, r=1, cache=None):\\n if cache is None:\\n cache = {}\\n if (q, r) in cache:\\n return cache[(q, r)]\\n n = len(s1)\\n m = len(s2)\\n if n == q - 1 or m == r - 1:\\n cache[(q, r)] = \\\"\\\"\\n return \\\"\\\"\\n elif s1[n - q] == s2[m - r]: #\\n ss = longest_common_substring_top_down(s1, s2, q + 1, r + 1, cache) + s1[n - q]\\n cache[(q, r)] = ss\\n return ss\\n ss1 = longest_common_substring_top_down(s1, s2, q, r + 1, cache)\\n ss2 = longest_common_substring_top_down(s1, s2, q + 1, r, cache)\\n if (len(ss1) >= len(ss2)):\\n cache[(q, r)] = ss1\\n return ss1\\n else:\\n cache[(q, r)] = ss2\\n return ss2\",\n \"def match_all_cui(s,max_len = 10, Eterm_cui = Eterm_cui):\\n if len(s) == 0: \\n return []\\n sub_label = np.zeros(len(s),dtype = 'int')\\n location_term = {}\\n i = 0\\n while i < len(s):\\n for j in range(max_len+1,0,-1):\\n temp = ' '.join(s[i:i+j])\\n if temp in Eterm_cui:\\n sub_label[i:i+j] = 1\\n location_term[i] = [Eterm_cui[temp]]\\n break#matched maximum string, so break\\n i += j\\n output = []\\n for i in range(len(s)):\\n if sub_label[i] == 0:#no match\\n output += [s[i]]\\n elif i in location_term:\\n for cui in location_term[i][: :-1]:\\n output += [cui]\\n return output\",\n \"def longest_ORF(dna):\\n longest_ORF = []\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# initialize empty list\\n longest_ORF = longest_ORF + find_all_ORFs_both_strands(dna)\\t\\t\\t\\t\\t\\t\\t\\t# adds all ORFs both strands to list\\n\\n for n in range(len(longest_ORF)):\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# searches through the list\\n\\t if longest_ORF[n] == max(longest_ORF, key=len):\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# finds longest string\\n\\t \\treturn longest_ORF[n]\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# returns longest string\",\n \"def longest_ORF(dna):\\n l = find_all_ORFs_both_strands(dna)\\n max_len = 0\\n r = \\\"\\\"\\n # [???] what if there are ORFs with same length?\\n # this function just get the first one.\\n if len(l) == 0:\\n return \\\"\\\"\\n for o in l:\\n if len(o) > max_len:\\n r = o\\n max_len = len(o)\\n return r\",\n \"def contig_complementary_score(s1, s2) -> int:\\n matcher = SequenceMatcher()\\n matcher.set_seq1(str(s1))\\n matcher.set_seq2(str(s2.complement()))\\n _, _, size = matcher.find_longest_match(0, len(s1), 0, len(s2))\\n return size\",\n \"def longest_shared_between(cls, seq_1, seq_2):\\n tree = cls(seq_2 + '$', 'B').append_named_sequence(seq_1 + '$', 'A')\\n condition = lambda n, c, p: 'B' not in c.seq_ids\\n out = lambda n, c, p: p + n.label\\n unshared = tree.root.bfs_paths(condition, out)\\n shortest = next(unshared)\\n common = [shortest]\\n for possible in unshared:\\n if len(possible) > len(shortest) and not possible[-1] == '$':\\n shortest = possible\\n common = [possible]\\n elif len(possible) == len(shortest) and not possible[-1] == '$':\\n common.append(possible)\\n return min(common)\",\n \"def best_string(L, a, b):\\n st = ''\\n i = len(L) - 1\\n j = len(L[0]) - 1\\n\\n while i > 0 and j > 0:\\n if a[i-1] == b[j-1]:\\n st = a[i-1] + st\\n i -= 1\\n j -= 1\\n else:\\n if L[i-1][j] > L[i][j-1]:\\n i -= 1\\n else:\\n j -= 1\\n return st\",\n \"def find_best_candidate(s_array):\\n best_string = ''\\n max_val = 0\\n for s in s_array:\\n score = compare(s)\\n if score > max_val:\\n max_val = score\\n best_string = s\\n return best_string\",\n \"def compare_eq_len(a, b):\\n n = len(a)\\n m = len(b)\\n\\n # slide b across a from left to right till from just overlapping till full overlap\\n overlap = 0 # stores length of the overlap\\n lconcat = \\\"\\\" # this stores the shortest common superstring\\n for j in range(m):\\n starta = 0\\n enda = j+1\\n startb = m - (j+1)\\n endb = m\\n if a[starta:enda] == b[startb:endb]:\\n # if an overlap is found, check if it is larger than the previously detected one\\n # print(\\\"overlap found\\\")\\n if len(a[starta:enda]) > overlap: \\n overlap = len(a[starta:enda]) \\n lconcat = b + a[enda:] # this is the current shortest common superstring\\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\\n\\n # print(\\\"-\\\")\\n\\n # slide b across a so that b starts from one element past a after full overlap\\n rconcat = \\\"\\\"\\n for j in range(m-1):\\n starta = j+1\\n enda = m\\n startb = 0\\n endb = m - (j+1)\\n if a[starta:enda] == b[startb:endb]:\\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \\n # print(\\\"overlap found\\\")\\n overlap = len(a[starta:enda]) \\n rconcat = a + b[endb:]\\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\\n\\n # after checking for overlaps there may be 1 or no shortest common\\n # superstrings stored in both lconcat and rconcat. Choose the shortest one if it exists\\n # or the concatenation of a and b if there are no overlaps. We may have to make some\\n # arbitrary choices here.\\n\\n if not lconcat and not rconcat: # both lconcat and rconcat are empty, no overlaps\\n superstring = a + b # append b to a (could prepend here too, this is an arbitrary choice)\\n elif lconcat and not rconcat: # lconcat contains overlap and rconcat is empty\\n superstring = lconcat \\n elif rconcat and not lconcat: # rconcat contains overlap and lconcat is empty\\n superstring = rconcat\\n elif rconcat and lconcat and (len(lconcat) <= len(rconcat)): # use lconcat if it is shorter or equal len to rconat\\n superstring = lconcat\\n elif rconcat and lconcat and (len(rconcat) < len(lconcat)): # use rconcat only if it is shorter than lconat\\n superstring = rconcat\\n return superstring\",\n \"def levenshteinDistance(s, t, asRatioOfMax = False):\\n\\n if s == None:\\n s = \\\"\\\"\\n if t == None:\\n t = \\\"\\\"\\n\\n if t == s:\\n return 0\\n if len(s) == 0:\\n return len(t)\\n if len(t) == 0:\\n return len(s)\\n\\n v0 = [x for x in range(len(t)+1)]\\n v1 = [0 for x in range(len(t)+1)]\\n\\n for i, si in enumerate(s):\\n\\n v1[0] = i + 1\\n\\n for j, tj in enumerate(t):\\n\\n if si == tj:\\n cost = 0\\n else:\\n cost = 1\\n\\n j1 = v1[j] + 1\\n j2 = v0[j + 1] + 1\\n j3 = v0[j] + cost\\n \\n if j1 < j2 and j1 < j3:\\n v1[j + 1] = j1\\n continue\\n \\n if j2 < j3:\\n v1[j+1] = j2\\n else:\\n v1[j+1] = j3\\n \\n v0 = v1.copy()\\n\\n if not asRatioOfMax:\\n return v1[len(t)]\\n\\n return 1 - v1[len(t)]/max([len(t), len(s)])\",\n \"def most_consonants(words):\\n cos = [[len(x)-num_vowels(x),x] for x in words]\\n best_pair = max(cos)\\n return best_pair[1]\",\n \"def string_similarity(a, b):\\n return SequenceMatcher(a=a, b=b).ratio()\",\n \"def longest_prefix_length(s, i, j):\\n l = 0\\n while (i+l < len(s)) and (j+l < len(s)):\\n if s[i+l] != s[j+l]:\\n break\\n l += 1\\n return l\",\n \"def longest_ORF(dna):\\n i = 0\\n both_ORFs = find_all_ORFs_both_strands(dna)\\n longest = both_ORFs[0]\\n while i + 1 < len(both_ORFs):\\n if len(both_ORFs[i+1]) > len(longest):\\n longest = both_ORFs[i+1]\\n i+=1\\n return longest\",\n \"def overlap(string_one: str, string_two: str):\\n len_s_one = len(string_one)\\n len_s_two = len(string_two)\\n\\n # Content of the tuple => (start_index, stop_index, string from start_index to stop_index)\\n largest_overlap = {\\\"suffix_string\\\": string_one,\\n \\\"prefix_string\\\": string_two,\\n \\\"overlap\\\": None,\\n \\\"weight\\\": 0}\\n len_overlap = 0\\n # Iterate over every suffix from string one from right to left\\n for i in range(len_s_one, 0, -1):\\n # Current suffix from string one\\n current_suffix_one = string_one[i - 1:len_s_one]\\n\\n # Iterate over every prefix of string two from left to right\\n for j in range(len_s_two + 1):\\n current_prefix_two = string_two[:j]\\n if current_suffix_one == current_prefix_two:\\n tmp_len = len(current_prefix_two)\\n if tmp_len > len_overlap:\\n len_overlap = tmp_len\\n\\n largest_overlap[\\\"suffix_start\\\"] = i - 1\\n largest_overlap[\\\"suffix_end\\\"] = len_s_one\\n\\n largest_overlap[\\\"prefix_start\\\"] = 0\\n largest_overlap[\\\"prefix_end\\\"] = j\\n\\n largest_overlap[\\\"weight\\\"] = tmp_len\\n largest_overlap[\\\"overlap\\\"] = current_suffix_one\\n return largest_overlap\",\n \"def find_largest_overlaps(string_one, string_two):\\n two_overlaps_suffix_one = overlap(string_one, string_two)\\n one_overlaps_suffix_two = overlap(string_two, string_one)\\n\\n return two_overlaps_suffix_one, one_overlaps_suffix_two\",\n \"def levenshtein(str1, str2, normalise=False):\\n\\ttmp = Levenshtein.distance(str1, str2)\\n\\tif(normalise) and (len(str1) + len(str2)): tmp /= max(len(str1), len(str2))\\n\\treturn tmp\",\n \"def getCommon(self, st1, st2):\\n fl = len(st1)\\n shorter = len(st2)\\n if fl < shorter:\\n shorter = fl\\n \\n i = 0\\n while i < shorter:\\n if st1[i] != st2[i]:\\n break\\n i += 1\\n return i\",\n \"def levenshtein(seq1: str, seq2: str) -> int:\\n if seq1 == \\\"\\\":\\n return len(seq2)\\n if seq2 == \\\"\\\":\\n return len(seq1)\\n if seq1[-1] == seq2[-1]:\\n cost = 0\\n else:\\n cost = 1\\n \\n result = min([levenshtein(seq1[:-1], seq2) + 1,\\n levenshtein(seq1, seq2[:-1]) + 1,\\n levenshtein(seq1[:-1], seq2[:-1]) + cost ])\\n return result\",\n \"def scientific_match_ratio(str1, str2, keywords):\\n\\n # Get rid of the numbers\\n str1_numberless = remove_numbers(str1)\\n str2_numberless = remove_numbers(str2)\\n\\n # Get the keywords and whatever remains after removing the keywords\\n str1_keywords, str1_remainder = get_common_words_in_description(str1_numberless, keywords)\\n str2_keywords, str2_remainder = get_common_words_in_description(str2_numberless, keywords)\\n\\n remainder_dist = string_num_matches(str1_remainder, str2_remainder)\\n common_keywords = str1_keywords.intersection(str2_keywords)\\n\\n common_keyword_total_len = 0\\n for common_kword in common_keywords:\\n common_keyword_total_len += len(common_kword)\\n\\n return (remainder_dist + common_keyword_total_len) * 1.0 / max(len(str1_numberless), len(str2_numberless))\",\n \"def name_similarity(name_1, name_2, nickname_2=None):\\n name_similarity = string_similarity(name_1, name_2)\\n nickname_similarity = 0\\n if nickname_2:\\n nickname_similarity = string_similarity(name_1, nickname_2)\\n return max(name_similarity, nickname_similarity)\",\n \"def levenshtein(s1, s2):\\n if len(s1) < len(s2):\\n return levenshtein(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 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 \\n return previous_row[-1]\",\n \"def find_max_corrcoef_and_word(self, test_samples, max_length):\\n new_max_length = max(self.max_length, max_length)\\n if new_max_length != self.max_length:\\n self.need_count_fft = True\\n self.need_correct_length = True\\n self.max_length = new_max_length\\n\\n self.correct_length_of_all_items()\\n self.count_fft_for_all_items()\\n word, coefficient = None, 0\\n test_fft = self.fft(correct_len(test_samples, self.max_length))\\n for i in self.items:\\n tmp_coefficient = np.corrcoef(abs(i.fft), abs(test_fft))\\n if tmp_coefficient[0][1] > coefficient:\\n word = i.word\\n coefficient = tmp_coefficient[0][1]\\n return word, coefficient\",\n \"def longest_ORF_noncoding(dna, num_trials):\\n longest=[]\\n for i in range(0,num_trials):\\n \\tshuffled_str=shuffle_string(dna)\\n \\tlongest.append(longest_ORF(shuffled_str))\\n long_ORF=max(longest,key=len)\\n return len(long_ORF)\",\n \"def common():\\r\\n full_song = \\\"\\\"\\r\\n albums = simple_album_list()\\r\\n for album in albums:\\r\\n songs = simple_songs_list(album)\\r\\n for song in songs:\\r\\n full_song += str(song_lyrics(song))\\r\\n split_lyrics = full_song.lower().split()\\r\\n counter = collections.Counter(split_lyrics)\\r\\n most_words = counter.most_common(50)\\r\\n return most_words\",\n \"def longestPalindrome(self, s: str) -> int:\\n # approach #1 -- using hashset\\n # approach 2 -- using hashmap\\n hashmap = defaultdict(int)\\n odd = 0\\n out = 0\\n for char in s:\\n hashmap[char] += 1\\n\\n for key, val in hashmap.items():\\n if val % 2 == 1:\\n odd = 1\\n out += (val -1)\\n else:\\n out += val\\n return out +odd\",\n \"def longestPalindromeSubseq(self, s: str) -> int:\\n n = len(s)\\n dp = [[1] * n for _ in range(n)]\\n for length in range(1, n + 1):\\n for i in range(n - length + 1):\\n j = i + length - 1\\n print(i, j)\\n if length == 1:\\n dp[i][j] = 1\\n elif s[i] == s[j]:\\n dp[i][j] = dp[i + 1][j - 1] + 2\\n else:\\n dp[i][j] = max(dp[i][j - 1], dp[i + 1][j])\\n return dp[0][n - 1]\",\n \"def compare(seq1, seq2):\\n if seq1 == seq2:\\n return 1\\n len_diff = len(seq1) / len(seq2)\\n if len_diff > 1:\\n len_diff = 1 / len_diff\\n\\n ngrams1 = {tuple(ng) for ng in get_all_ngrams(seq1)}\\n ngrams2 = {tuple(ng) for ng in get_all_ngrams(seq2)}\\n\\n overall = len(ngrams1 & ngrams2) / len(ngrams1 | ngrams2)\\n if overall == 1 or overall == 0:\\n return overall\\n\\n try:\\n max_match = len(max(ngrams1 & ngrams2, key=len)) / len(seq1)\\n except ValueError:\\n return 0\\n\\n return (len_diff + max_match + overall) / 3\",\n \"def longest_ORF(dna):\\n MyList = find_all_ORFs_both_strands(dna)\\n if MyList == []:\\n maximum_orf = 'none'\\n else:\\n maximum_orf = max(MyList, key=len)\\n return maximum_orf\",\n \"def main():\\n long = give_long()\\n short = give_short()\\n similarity1 = find_similarity(long, short)\\n print('The best match is '+similarity1+'.')\",\n \"def novelty(sentence: str, tokenized_sentences: str, similarity_metric: str) -> float:\\n # sentences = nltk.sent_tokenize(document)\\n max_sim_sentence = ''\\n sentence = sentence.lower()\\n tokenized_sentences = [sent.lower() for sent in tokenized_sentences]\\n\\n if similarity_metric == 'jaccard':\\n max_sim = - np.inf\\n \\n for ref_sentence in tokenized_sentences:\\n jaccard_sim = jaccard_similarity_words(sentence, ref_sentence)\\n if jaccard_sim > max_sim:\\n max_sim_sentence = ref_sentence\\n max_sim = jaccard_sim\\n\\n return 1 - max_sim, max_sim_sentence\\n\\n elif similarity_metric == 'levenshtein':\\n min_edit_distance = np.inf\\n for ref_sentence in tokenized_sentences:\\n edit_distance = levenshtein(sentence, ref_sentence) \\\\\\n / max(len(sentence), len(ref_sentence))\\n if edit_distance < min_edit_distance:\\n max_sim_sentence = ref_sentence\\n min_edit_distance = edit_distance\\n\\n return min_edit_distance, max_sim_sentence\",\n \"def levenshtein_distance(s1,s2):\\n\\n\\t\\tif len(s1) < len(s2):\\n\\t\\t\\treturn Searcher.levenshtein_distance(s2, s1)\\n\\n\\t\\t# len(s1) >= len(s2)\\n\\t\\tif len(s2) == 0:\\n\\t\\t\\treturn len(s1)\\n\\n\\t\\tprevious_row = range(len(s2) + 1)\\n\\t\\tfor i, c1 in enumerate(s1):\\n\\t\\t\\tcurrent_row = [i + 1]\\n\\t\\t\\tfor j, c2 in enumerate(s2):\\n\\t\\t\\t\\tinsertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer\\n\\t\\t\\t\\tdeletions = current_row[j] + 1 # than s2\\n\\t\\t\\t\\tsubstitutions = previous_row[j] + (c1 != c2)\\n\\t\\t\\t\\tcurrent_row.append(min(insertions, deletions, substitutions))\\n\\t\\t\\tprevious_row = current_row\\n\\t\\t\\n\\t\\treturn previous_row[-1]\",\n \"def test_string_similarity_constraint():\\n f = SimilarityConstraint(func=LevenshteinDistance(), pred=GreaterThan(0.5))\\n assert f('BROOKLYN', 'BROKLYN')\\n assert not f('BROOKLYN', 'QUEENS')\",\n \"def find_lcs(l1: str, l2: str, length1: int, length2: int):\\n \\\"\\\"\\\" Theorem:{\\n Initialize matrix with 0 for first row and colm\\n If s1[i] = s2[j], update matrix[i][j] with value\\n of matrix[i-1][j-1]+1\\n Else update matrix[i][j] with max of value among\\n matrix[i][j-1],matrix[i-1][j]\\n Matrix[n][m] will be lcs\\n }\\n \\\"\\\"\\\"\\n matrix = [[None]*(length1+1) for i in range(0, length2+1)]\\n for i in range(0, length2+1):\\n for j in range(0, length1+1):\\n if i == 0 or j == 0:\\n matrix[i][j] = 0\\n elif l1[j-1] == l2[i-1]:\\n matrix[i][j] = matrix[i-1][j-1] + 1\\n else:\\n matrix[i][j] = max(matrix[i][j-1], matrix[i-1][j])\\n lcs = [None for i in range(0, matrix[length2][length1])]\\n index = matrix[length2][length1]\\n m = length2 \\n n = length1\\n while(m > -1 and n > -1):\\n if l2[m-1] == l1[n-1]:\\n lcs[index-1] = l2[m-1]\\n index -= 1\\n m -= 1\\n n -= 1\\n elif matrix[m-1][n] > matrix[m][n-1]:\\n m -= 1\\n else:\\n n -= 1\\n return lcs\",\n \"def longest_ORF_noncoding(dna, num_trials):\\n\\n # YOUR IMPLEMENTATION HERE\",\n \"def _l_distance(first_string, second_string):\\n if len(first_string) > len(second_string):\\n first_string, second_string = second_string, first_string\\n distances = list(range(len(first_string) + 1))\\n for index2, char2 in enumerate(second_string):\\n new_distances = [index2 + 1]\\n for index1, char1 in enumerate(first_string):\\n if char1 == char2:\\n new_distances.append(distances[index1])\\n else:\\n new_distances.append(1 + min((distances[index1], distances[index1 + 1], new_distances[-1])))\\n distances = new_distances\\n return distances[-1]\",\n \"def longest_word(text):\\r\\n try:\\r\\n # replace special characters in the input sentence\\r\\n text = re.sub('[^A-Za-z0-9]+', ' ', text).lower()\\r\\n\\r\\n # split the text by spaces and put it in a list\\r\\n text_list = list(set(text.split(' ')))\\r\\n\\r\\n # remove blanks from list\\r\\n if \\\"\\\" in text_list:\\r\\n text_list.remove(\\\"\\\")\\r\\n\\r\\n # find longest word's length & longest word\\r\\n matches = []\\r\\n if len(text_list) != 0:\\r\\n text_list.sort(key=len, reverse=True)\\r\\n x = len(text_list[0])\\r\\n for i in text_list:\\r\\n if len(i) == x:\\r\\n matches.append(i)\\r\\n else:\\r\\n break\\r\\n matches.sort()\\r\\n return (\\\"Longest word/s: \\\" + str(matches) + \\\" & its length is: \\\" + str(x))\\r\\n else:\\r\\n return None\\r\\n except Exception as e:\\r\\n print(\\\"Following exception occured while finding longest word - {}\\\".format(e))\\r\\n return None\",\n \"def longest_ORF_unit_tests():\\n\\n input_a=\\\"ATGCGAATGTAGCATCAAA\\\"\\n expected_output=''\\n actual_output=longest_ORF(input_a)\\n print 'Expected Output is ' + expected_output\\n print \\\"actual output: \\\", actual_output\",\n \"def common_suffix(s1, s2):\\n if not s1 or not s2:\\n return None\\n # revert the seqs and get a common prefix\\n common = common_prefix(s1[::-1], s2[::-1])\\n # revert back\\n if common:\\n return common[::-1]\\n else:\\n return common\",\n \"def get_longest_valid_repeated_nonoverlapping_substring(string, validity_function):\\n string_length=len(string) \\n table=[[0 for i2 in range(string_length+1)] for i in range(string_length+1)] \\n\\n repeated_substring_length=0\\n repeated_substring=[]\\n for i in range(1, string_length+1): \\n for i2 in range(i+1, string_length+1): \\n if (string[i-1]==string[i2-1] and table[i-1][i2-1]<(i2-i)): \\n table[i][i2]=table[i-1][i2-1]+1\\n if table[i][i2]>repeated_substring_length:\\n candidate_string=string[i-table[i][i2]:i]\\n if validity_function(candidate_string):\\n repeated_substring_length=table[i][i2]\\n repeated_substring=candidate_string\\n else: \\n table[i][i2]=0\\n\\n return repeated_substring\",\n \"def similar_string_fast(first_string, second_string):\\n partial_score = fuzz.ratio(first_string, second_string)\\n token_score = fuzz.token_set_ratio(first_string, second_string)\\n\\n if max(partial_score, token_score) >= SCORE_THRESHOLD_FAST:\\n return True\\n\\n return False\",\n \"def lcs_length(s1, s2):\\n n = len(s1)\\n m = len(s2)\\n matrix = [([0]*(m+1)) for i in xrange(n+1)]\\n for i in xrange(1, n+1):\\n for j in xrange(1, m+1):\\n if(s1[i-1]==s2[j-1]):\\n matrix[i][j] = matrix[i-1][j-1] + 1\\n else:\\n matrix[i][j] = max(matrix[i-1][j], matrix[i][j-1])\\n return matrix[n][m]\",\n \"def longest_substring_no_repetitions(string: str) -> str:\\n start, end = 0, 0\\n longest_substring = ''\\n\\n limit = len(string)\\n while end < limit:\\n while True:\\n substring = string[start:end+1]\\n\\n if has_duplicates(substring):\\n start += 1\\n continue\\n\\n if len(substring) > len(longest_substring):\\n longest_substring = substring\\n\\n break\\n\\n end += 1\\n\\n return longest_substring\",\n \"def longest_palindromic_substring(s):\\n longest = s[0] if len(s) > 0 else \\\"\\\"\\n for i in range(len(s)):\\n j = len(s)\\n while s[i] in s[i+1:j] and j <= len(s):\\n j = s[i + 1:j].rfind(s[i]) + i + 2\\n print(i, j)\\n if is_palindrome(s[i:j]) and len(longest) < len(s[i:j]):\\n longest = s[i:j]\\n j = len(s) + 1\\n else:\\n j -= 1\\n if len(s) - len(longest) <= i:\\n break\\n return longest\",\n \"def longest_word_length(words):\\n\\n longest = len(word[0])\\n\\n for word in words:\\n if longest < len(word):\\n longest = len(word)\\n\\n return longest\",\n \"def longest_ORF(dna):\\n\\n reverseComp = get_reverse_complement(dna)\\n openFramesfromReverse = find_all_ORFs(reverseComp)\\n openFramesfromNormal = find_all_ORFs(dna)\\n all_ORFs = openFramesfromNormal + openFramesfromReverse\\n longest = max(all_ORFs, key=len)\\n return longest\",\n \"def common(s1, s2):\\n cl = commonlen(s1, s2)\\n return s2[:cl]\",\n \"def string_similarity_score(left: str, right: str):\\n return SequenceMatcher(None, left, right).ratio()\",\n \"def most_reducible(wordlist):\\n\\n\\t# We create a memo for reducible words since is_reducible is \\n\\t# recursive. The keys are the words and the values are the \\n\\t# number of characters\\n\\tglobal reducible_words\\n\\treducible_words = dict()\\n\\treducible_words['a'], reducible_words['i'] = 1, 1\\n\\t\\n\\tword_dict = to_dictionary(wordlist)\\n\\tfor line in word_dict:\\n\\t\\tis_reducible(line, word_dict)\\n\\n\\t# Varible that will search the memo for the longest word\\n\\tcurrent_greatest = ''\\n\\tfor word in reducible_words:\\n\\t\\tif reducible_words[word] > len(current_greatest):\\n\\t\\t\\tcurrent_greatest = word\\n\\tprint(current_greatest)\",\n \"def levenshtein_distance(str1, str2):\\n m = len(str1)\\n n = len(str2)\\n lensum = float(m + n)\\n d = [] \\n for i in range(m+1):\\n d.append([i]) \\n del d[0][0] \\n for j in range(n+1):\\n d[0].append(j) \\n for j in range(1,n+1):\\n for i in range(1,m+1):\\n if str1[i-1] == str2[j-1]:\\n d[i].insert(j,d[i-1][j-1]) \\n else:\\n minimum = min(d[i-1][j]+1, d[i][j-1]+1, d[i-1][j-1]+2) \\n d[i].insert(j, minimum)\\n ldist = d[-1][-1]\\n ratio = (lensum - ldist)/lensum\\n return {'distance':ldist, 'ratio':ratio}\",\n \"def optimal_string_alignment_distance(s1, s2):\\n\\n utils.check_for_none(s1, s2)\\n utils.check_for_type(str, s1, s2)\\n\\n # s1 = utils.unicode_normalize(s1)\\n # s2 = utils.unicode_normalize(s2)\\n\\n n1, n2 = len(s1), len(s2)\\n\\n dp = [[0] * (n2 + 1) for _ in range(n1 + 1)]\\n\\n for i in range(0, n1 + 1):\\n dp[i][0] = i\\n for j in range(0, n2 + 1):\\n dp[0][j] = j\\n\\n for i in range(1, n1 + 1):\\n for j in range(1, n2 + 1):\\n cost = 0 if s1[i - 1] == s2[j - 1] else 1\\n\\n dp[i][j] = min(dp[i][j - 1] + 1,\\n dp[i - 1][j] + 1,\\n dp[i - 1][j - 1] + cost)\\n\\n if i > 1 and j > 1 and s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1]:\\n dp[i][j] = min(dp[i][j], dp[i - 2][j - 2] + cost)\\n\\n return dp[n1][n2]\",\n \"def longest_ORF_noncoding(dna, num_trials):\\n longest = ''\\n lenl = 0\\n for i in range(num_trials):\\n shuffle = shuffle_string(dna)\\n if len(longest_ORF(shuffle)) > lenl:\\n longest = longest_ORF(shuffle)\\n lenl = len(longest)\\n return len(longest)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7570705","0.7322224","0.71764874","0.71665967","0.71261054","0.7063779","0.7052958","0.70349896","0.697746","0.6941382","0.6925402","0.6925334","0.6887414","0.6853402","0.6841317","0.68183476","0.676528","0.67370135","0.67279613","0.67115736","0.6691741","0.6641049","0.66375303","0.66238415","0.66110456","0.651613","0.6450298","0.643781","0.64136666","0.6413216","0.64083254","0.6404016","0.6387621","0.63778424","0.6351663","0.63268334","0.63205314","0.6296471","0.6292714","0.6282782","0.6258423","0.62473464","0.62318295","0.62297916","0.6224941","0.6218803","0.6209131","0.620489","0.6196495","0.6160357","0.6139194","0.6129233","0.6126957","0.6121625","0.61148876","0.6108743","0.6095661","0.60522234","0.6048368","0.6014295","0.60139","0.60075283","0.59909695","0.597944","0.59777117","0.5969332","0.5967951","0.5964472","0.5958253","0.59466827","0.5939188","0.5939023","0.59363544","0.5929288","0.5924494","0.5918255","0.5908473","0.59080505","0.59069306","0.5903156","0.59025866","0.58908564","0.58726305","0.5869878","0.58661854","0.58509","0.58504015","0.58467036","0.5843158","0.58406466","0.58278716","0.5824138","0.5820746","0.58188635","0.5814337","0.58134073","0.5812977","0.58099836","0.58090264","0.5804497"],"string":"[\n \"0.7570705\",\n \"0.7322224\",\n \"0.71764874\",\n \"0.71665967\",\n \"0.71261054\",\n \"0.7063779\",\n \"0.7052958\",\n \"0.70349896\",\n \"0.697746\",\n \"0.6941382\",\n \"0.6925402\",\n \"0.6925334\",\n \"0.6887414\",\n \"0.6853402\",\n \"0.6841317\",\n \"0.68183476\",\n \"0.676528\",\n \"0.67370135\",\n \"0.67279613\",\n \"0.67115736\",\n \"0.6691741\",\n \"0.6641049\",\n \"0.66375303\",\n \"0.66238415\",\n \"0.66110456\",\n \"0.651613\",\n \"0.6450298\",\n \"0.643781\",\n \"0.64136666\",\n \"0.6413216\",\n \"0.64083254\",\n \"0.6404016\",\n \"0.6387621\",\n \"0.63778424\",\n \"0.6351663\",\n \"0.63268334\",\n \"0.63205314\",\n \"0.6296471\",\n \"0.6292714\",\n \"0.6282782\",\n \"0.6258423\",\n \"0.62473464\",\n \"0.62318295\",\n \"0.62297916\",\n \"0.6224941\",\n \"0.6218803\",\n \"0.6209131\",\n \"0.620489\",\n \"0.6196495\",\n \"0.6160357\",\n \"0.6139194\",\n \"0.6129233\",\n \"0.6126957\",\n \"0.6121625\",\n \"0.61148876\",\n \"0.6108743\",\n \"0.6095661\",\n \"0.60522234\",\n \"0.6048368\",\n \"0.6014295\",\n \"0.60139\",\n \"0.60075283\",\n \"0.59909695\",\n \"0.597944\",\n \"0.59777117\",\n \"0.5969332\",\n \"0.5967951\",\n \"0.5964472\",\n \"0.5958253\",\n \"0.59466827\",\n \"0.5939188\",\n \"0.5939023\",\n \"0.59363544\",\n \"0.5929288\",\n \"0.5924494\",\n \"0.5918255\",\n \"0.5908473\",\n \"0.59080505\",\n \"0.59069306\",\n \"0.5903156\",\n \"0.59025866\",\n \"0.58908564\",\n \"0.58726305\",\n \"0.5869878\",\n \"0.58661854\",\n \"0.58509\",\n \"0.58504015\",\n \"0.58467036\",\n \"0.5843158\",\n \"0.58406466\",\n \"0.58278716\",\n \"0.5824138\",\n \"0.5820746\",\n \"0.58188635\",\n \"0.5814337\",\n \"0.58134073\",\n \"0.5812977\",\n \"0.58099836\",\n \"0.58090264\",\n \"0.5804497\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70683295"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":22385,"cells":{"query":{"kind":"string","value":"Generate a description for the combination of well, tile, channel and, optionaly, depth and/or time"},"document":{"kind":"string","value":"def generate_tile_description(tile, time = None, depth = None):\n \n desc = \"s\"+ str(tile)\n if depth is not None:\n desc = desc + \"_z\" + str(depth)\n if time is not None:\n desc = desc + \"_t\" + str(time)\n return desc"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def describe(self):\n branch = randint(0,62)\n \n if 0 <= branch <= 29: \n if self.casteOrder[0] == 'soldiers':\n if self.genesis == 'escape persecution': \n self.description = '{2}: A full service {3} for retired {1}'.format(choice(self.__class__.badjectives), choice(self.__class__.soldierSynonyms), self.name, choice(self.__class__.settlements[self.tech])) \n elif self.genesis == 'maintain control': \n self.description = 'The penal mining colony for {0} {1} on {2}'.format(choice(self.__class__.badjectives), choice(self.__class__.casteSynonyms[self.casteOrder[5]]), self.name, choice(self.__class__.settlements[self.tech])) \n elif self.genesis == 'explore the unknown':\n self.description = 'The frontier garrison and {2} {0} recently conquered {1}'.format(choice(self.__class__.techPreps[self.tech]), self.name, choice(self.__class__.settlements[self.tech]))\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'The militarized {3} and bonsai garden for the mandatory contemplation of {0} {1} {2}'.format(choice(self.__class__.seedIdeals), choice(self.__class__.techPreps[self.tech]), self.name, choice(self.__class__.settlements[self.tech]))\n elif self.genesis == 'spread the gospel':\n self.description = 'An outpost for crusaders who took up arms to defend {2} {3} on {0} {1}'.format(choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.badjectives), choice(self.__class__.priestSynonyms)) \n elif self.casteOrder[0] == 'scientists':\n if self.genesis == 'escape persecution': \n self.description = 'A sanctuary for {0} who challenged the dogma of powerful {1} {2} {3}'.format(choice(self.__class__.scientistSynonyms), choice(self.__class__.priestSynonyms), choice(self.__class__.techPreps[self.tech]), self.name) \n elif self.genesis == 'maintain control': \n self.description = '{0}, home to a coalition of {1} eugenicists and their {2} servants'.format(self.name, choice(self.__class__.scientistAdjectives), choice(self.__class__.casteAdjectives[self.casteOrder[5]]))\n elif self.genesis == 'explore the unknown':\n self.description = 'An Extremely Large Hadron Collider {0} {1} {2}'.format(choice(self.__class__.techPreps[self.tech]), choice(self.__class__.techAdjectives[self.tech]), self.name)\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'The research institute and {2} for abiogenesis on {0} {1}'.format(choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.settlements[self.tech]))\n elif self.genesis == 'spread the gospel':\n self.description = 'The Galactic Academy of Sciences founded by {0} {1} {2} {3}'.format(choice(self.__class__.scientistAdjectives), choice(self.__class__.scientistSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.casteOrder[0] == 'laborers':\n if self.genesis == 'escape persecution':\n self.description = 'The {0} for unionized {1} {2} {3}'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.laborerSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.genesis == 'maintain control':\n self.description = '{0} {1}: divisional headquarters for the communist party in this region of the galaxy (where evidence of {2} has been redacted)'.format(choice(self.__class__.techAdjectives[self.tech]).capitalize(), self.name, choice(self.__class__.controlIdeals))\n elif self.genesis == 'explore the unknown':\n self.description = 'A team of robot {0} sent to survey {1} {2}'.format(choice(self.__class__.laborerSynonyms), choice(self.__class__.techAdjectives[self.tech]), self.name)\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'Drones sent to terraform {0} {1} and build {2}'.format(choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.seedPlaces))\n elif self.genesis == 'spread the gospel':\n self.description = 'The {0} {1} {2} where a hard day\\'s work is highly valued'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.casteOrder[0] == 'merchants':\n if self.genesis == 'escape persecution':\n self.description = '{0} {1}: a refuge for {2} who fled a communist revolution on their home planet'.format(choice(self.__class__.techAdjectives[self.tech]).capitalize(), self.name, choice(self.__class__.merchantSynonyms))\n elif self.genesis == 'maintain control':\n self.description = 'The monopolistic conglomerate of {0} {1} {2} {3}'.format(choice(self.__class__.merchantAdjectives), choice(self.__class__.merchantSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.genesis == 'explore the unknown':\n self.description = '{3} {0}, home to a consortium of {1} seeking to monetize {2}'.format(self.name, choice(self.__class__.merchantSynonyms), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]), choice(self.__class__.techAdjectives[self.tech]).capitalize())\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'An oligarchy of wealthy {0} who recently opened a for-profit {1} on {2}'.format(choice(self.__class__.merchantSynonyms), choice(self.__class__.settlements[self.tech]), self.name)\n elif self.genesis == 'spread the gospel':\n self.description = 'An orbital printing press that rains down copies of {0} onto {1} {2}'.format(choice(self.__class__.merchantBooks), choice(self.__class__.techAdjectives[self.tech]), self.name)\n elif self.casteOrder[0] == 'artists':\n if self.genesis == 'escape persecution':\n self.description = 'The {0}\\' commune for the free and naked expression of {1} {2} {3}'.format(choice(self.__class__.artistSynonyms), choice(self.__class__.escapeIdeals), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.genesis == 'maintain control':\n self.description = 'The {0} with mandatory art classes on {1} {2}'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.techAdjectives[self.tech]), self.name)\n elif self.genesis == 'explore the unknown':\n self.description = '{0} {1}, an observation deck where {2} observe naked and confined {3} to better understand the mysteries of {4}'.format(choice(self.__class__.techPreps[self.tech]).capitalize(), self.name, choice(self.__class__.artistSynonyms), choice(self.__class__.casteSynonyms[self.casteOrder[5]]), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]))\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'The {0} for {1} {2} who choose to better understand their craft through lovemaking on {3}'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.seedAdjectives), choice(self.__class__.artistSynonyms), self.name)\n elif self.genesis == 'spread the gospel':\n self.description = 'A {0} on {1} that hosts an annual conference for pop {2}'.format(choice(self.__class__.settlements[self.tech]), self.name, choice(self.__class__.artistSynonyms))\n elif self.casteOrder[0] == 'priests':\n if self.genesis == 'escape persecution':\n self.description = '{3} {0}, home to an order of heretical {1} who reject the {2} doctrine of their people'.format(self.name, choice(self.__class__.priestSynonyms), choice(self.__class__.gospelAdjectives), choice(self.__class__.techAdjectives[self.tech]).capitalize())\n elif self.genesis == 'maintain control':\n self.description = 'The orthodox {0} for {1} {2} {3} {4}'.format(choice(self.__class__.gospelPlaces), choice(self.__class__.gospelAdjectives), choice(self.__class__.priestSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.genesis == 'explore the unknown':\n self.description = '{0} {1}, a seminary for the metaphysical contemplation of {2}'.format(choice(self.__class__.techPreps[self.tech]).capitalize(), self.name, choice(self.__class__.gospelIdeals))\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'The marriage counseling clinic and devotional {2} {0} {1}'.format(choice(self.__class__.techPreps[self.tech]), self.name, choice(self.__class__.settlements[self.tech])) \n elif self.genesis == 'spread the gospel':\n self.description = 'A {0} mission on {1} {2} for converting {3} natives'.format(choice(self.__class__.gospelAdjectives), choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.badjectives))\n \n elif 30 <= branch <= 44:\n if self.tech == 'pre-industrial technology':\n if self.genesis == 'explore the unknown':\n self.description = '{0}, home to a historical reenactment society of {2} {1}'.format(self.name, choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.badjectives))\n elif self.genesis == 'escape persecution':\n self.description = '{1}: a {2} of {0} Luddites'.format(choice(self.__class__.casteAdjectives[self.casteOrder[0]]), self.name, choice(self.__class__.preindustrialSettlements))\n elif self.genesis == 'maintain control':\n self.description = 'The {0} fiefdoms of {1}, where {2} serve their {3} liege lord'.format(choice(self.__class__.preindustrialAdjectives), self.name, choice(self.__class__.casteSynonyms[self.casteOrder[5]]), choice(self.__class__.casteAdjectives[self.casteOrder[0]]))\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'A harem of {0} {1} on medieval {2}'.format(choice(self.__class__.seedAdjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\n elif self.genesis == 'spread the gospel':\n self.description = 'The Puritan {0} for evangelical {1} on {2}'.format(choice(self.__class__.preindustrialSettlements), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\n elif self.tech == 'machine technology':\n if self.genesis == 'explore the unknown':\n self.description = 'An array of radio telescopes outside the {0} on {1} {2}'.format(choice(self.__class__.machineSettlements), choice(self.__class__.machineAdjectives), self.name)\n elif self.genesis == 'escape persecution':\n self.description = 'Public housing for {0} {1} gentrified {2}'.format(choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.machinePreps), self.name) \n elif self.genesis == 'maintain control':\n self.description = 'The juvenile detention center for {0} children who obsess over {1} on {2}'.format(choice(self.__class__.badjectives), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]), self.name)\n elif self.genesis == 'seed the galaxy with life':\n self.description = 'Just a giant cruise ship full of {0} {1} on {2}'.format(choice(self.__class__.seedAdjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\n elif self.genesis == 'spread the gospel':\n self.description = 'A megachurch run by {0} {1}, broadcasting live from {2}'.format(choice(self.__class__.priestAdjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\n elif self.tech == 'ubiquitous technology':\n if self.genesis == 'explore the unknown':\n self.description = 'A quantum data center for simulating the lives of {0} {1} {2} {3}'.format(choice(self.__class__.casteAdjectives[self.casteOrder[0]]), choice(self.__class__.casteSynonyms[choice(self.__class__.castes)]), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.genesis == 'escape persecution':\n self.description = 'The cryogenics facility for the preservation of {0} {1} {2} {3}'.format(choice(self.__class__.badjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.techPreps[self.tech]), self.name)\n elif self.genesis == 'maintain control':\n self.description = 'Orbiting {0}: a socially stratified {1} governed by {2}'.format(self.name, choice(self.__class__.ubiqitousSettlements), choice(self.__class__.casteSynonyms[self.casteOrder[0]]))\n elif self.genesis == 'seed the galaxy with life':\n self.description = '{0} {1}, home to an assembly plant for android {2}'.format(choice(self.__class__.ubiqitousAdjectives).capitalize(), self.name, choice(self.__class__.casteSynonyms[self.casteOrder[0]]))\n elif self.genesis == 'spread the gospel':\n self.description = '{0}, home to an anthropological society of {1} who covertly inject the theme of {2} into the folklore of other civilizations'.format(self.name, choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]))\n \n elif 45 <= branch <= 62:\n if self.casteOrder[0] == 'laborers':\n if self.tech == 'pre-industrial technology':\n self.description = 'A kibbutz for {0} {1} on {2}'.format(choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.laborerSynonyms), self.name)\n elif self.tech == 'machine technology':\n self.description = 'The worker-owned and operated {1} of {2} {0}'.format(self.name, choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.machineAdjectives))\n elif self.tech == 'ubiquitous technology':\n self.description = '{0}, host to a psychic hivemind of {1} {2}'.format(self.name, choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.__class__.laborerSynonyms))\n elif self.casteOrder[0] == 'artists':\n if self.tech == 'pre-industrial technology':\n self.description = 'The conservatory for neoclassical {0} who explore the theme of {1} on {2}'.format(choice(self.__class__.artistSynonyms), choice(self.__class__.genesisIdeals[self.genesis]), self.name)\n elif self.tech == 'machine technology':\n self.description = 'A series of art installations conceived by {1} at various {0} on {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.artistSynonyms), self.name)\n elif self.tech == 'ubiquitous technology':\n self.description = '{0}: a collective of AI {1} who parody the human theme of {2}'.format(self.name, choice(self.__class__.artistSynonyms), choice(self.__class__.genesisIdeals[self.genesis]))\n elif self.casteOrder[0] == 'priests':\n if self.tech == 'pre-industrial technology':\n self.description = '{0} {1}, home to a scriptorium dedicated to the penning of meditations on {2}'.format(choice(self.__class__.preindustrialAdjectives).capitalize(), self.name, choice(self.__class__.genesisIdeals[self.genesis]))\n elif self.tech == 'machine technology':\n self.description = 'Polygamous cultists who live among the {0} of {1} {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.machineAdjectives), self.name)\n elif self.tech == 'ubiquitous technology':\n self.description = 'A pyramidic burial chamber and monument to {0} {1} {2}'.format(choice(self.__class__.genesisIdeals[self.genesis]), choice(self.__class__.ubiquitousPreps), self.name)\n elif self.casteOrder[0] == 'scientists':\n if self.tech == 'pre-industrial technology':\n self.description = '{0}, home to a collective of {1} {2} who reproduce scientific experiments from pre-industrial Earth'.format(self.name, choice(self.genesisAdjectives[self.genesis]), choice(self.__class__.scientistSynonyms))\n elif self.tech == 'machine technology':\n self.description = 'A society of {0} who work simulated jobs at {1} on {3} to better understand {2}'.format(choice(self.__class__.scientistSynonyms), choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.genesisIdeals[self.genesis]), self.name)\n elif self.tech == 'ubiquitous technology':\n self.description = 'The postdoctoral program for {0} tenure-track {1} {2} {3}'.format(choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.__class__.scientistSynonyms), choice(self.__class__.ubiquitousPreps), self.name)\n elif self.casteOrder[0] == 'soldiers':\n if self.tech == 'pre-industrial technology':\n self.description = '{0}, where {1} {2} study their martial arts in the quiet isolation of a {3}'.format(self.name, choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.__class__.soldierSynonyms), choice(self.__class__.preindustrialSettlements))\n elif self.tech == 'machine technology':\n self.description = '{0}, where {1} {2} spar each other to overcome their frustrations surrounding {3}'.format(self.name, choice(self.__class__.badjectives), choice(self.__class__.soldierSynonyms), choice(self.__class__.genesisIdeals[self.genesis]))\n elif self.tech == 'ubiquitous technology':\n self.description = 'The highly militarized {0} {1} {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.ubiquitousPreps), self.name)\n elif self.casteOrder[0] == 'merchants':\n if self.tech == 'pre-industrial technology':\n self.description = 'An antique fair on {0} where 1% of the proceeds are donated to the study of {1}'.format(self.name, choice(self.__class__.genesisIdeals[self.genesis]))\n elif self.tech == 'machine technology':\n self.description = 'A chain of retail {0} near the {1} on {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.machineSettlements), self.name)\n elif self.tech == 'ubiquitous technology':\n self.description = 'A pay-by-the-hour computer simulation {0} {1} where patrons can experience {2}'.format(choice(self.__class__.ubiquitousPreps), self.name, choice(self.__class__.genesisIdeals[self.genesis]))","def get_description():\n desc = dict()\n today = datetime.date.today()\n desc['data'] = True\n desc['description'] = \"\"\"This plot presents one of two metrics indicating\n daily humidity levels as reported by a surface weather station. The\n first being mixing ratio, which is a measure of the amount of water\n vapor in the air. The second being vapor pressure deficit, which reports\n the difference between vapor pressure and saturated vapor pressure.\n The vapor pressure calculation shown here makes no accounting for\n leaf tempeature. The saturation vapor pressure is computed by the\n Tetens formula (Buck, 1981).\n\n <br />On 22 June 2016, this plot was modified to display the range of\n daily averages and not the range of instantaneous observations.\n \"\"\"\n desc['arguments'] = [\n dict(type='zstation', name='zstation', default='AMW',\n label='Select Station:', network='IA_ASOS'),\n dict(type='year', name='year', default=today.year,\n label='Select Year to Plot'),\n dict(type='select', name='var', default='mixing_ratio',\n label='Which Humidity Variable to Compute?', options=PDICT),\n ]\n return desc","def generate_basic(self, time):\n output_text = ''\n try:\n output_text = 'for x=1 to ' + str(int(time)//4) + ':\\n'\n except:\n return\n for i, layer in enumerate(self.c_layers):\n c = ['0'] * 8\n b = ['0'] * 8\n d = ['0'] * 8\n\n c[self.num_sides - i - 1] = '1'\n for point in layer:\n if not point.on:\n continue\n\n index = 7 - int(point.toplevel[1])\n if point.toplevel[0] == \"b\":\n b[index] = '1'\n else:\n d[index] = '1'\n\n output_text += f' portb = %{\"\".join(b)}\\n'\n output_text += f' portd = %{\"\".join(d)}\\n'\n output_text += f' portc = %{\"\".join(c)}\\n'\n output_text += ' pause 1\\n'\n output_text += '\\n'\n\n output_text += ' next x\\n'\n return output_text","def summary(self):\n self.tiles.refreshnames()\n self.glues.refreshnames()\n # self.check_consistent()\n info = {\n \"ntiles\": len(self.tiles),\n \"nrt\": len([x for x in self.tiles if not x.is_fake]),\n \"nft\": len([x for x in self.tiles if x.is_fake]),\n \"nends\": len(self.glues),\n \"ntends\": len(self.tiles.glues_from_tiles()),\n \"tns\": \" \".join(x.name for x in self.tiles if x.name),\n \"ens\": \" \".join(x.name for x in self.glues if x.name)\n # if (\"info\" in self.keys() and \"name\" in self[\"info\"].keys())\n # else \"\",\n }\n tun = sum(1 for x in self.tiles if x.name is None)\n if tun > 0:\n info[\"tns\"] += \" ({} unnamed)\".format(tun)\n eun = sum(1 for x in self.glues if x.name is None)\n if eun > 0:\n info[\"ens\"] += \" ({} unnamed)\".format(eun)\n if info[\"nft\"] > 0:\n info[\"nft\"] = \" (+ {} fake)\".format(info[\"nft\"])\n else:\n info[\"nft\"] = \"\"\n return \"TileSet: {nrt} tiles{nft}, {nends} ends, {ntends} ends in tiles.\\nTiles: {tns}\\nEnds: {ens}\".format(\n **info\n )","def get_description():\n desc = {\"description\": __doc__, \"data\": True, \"cache\": 600}\n today = datetime.date.today()\n desc[\"arguments\"] = [\n dict(\n type=\"csector\",\n name=\"csector\",\n default=\"IA\",\n label=\"Select state/sector to plot\",\n ),\n dict(\n type=\"date\",\n name=\"sdate\",\n default=f\"{today.year}/01/01\",\n label=\"Start Date:\",\n min=\"2000/01/04\",\n max=today.strftime(\"%Y/%m/%d\"),\n ),\n dict(\n type=\"date\",\n name=\"edate\",\n default=today.strftime(\"%Y/%m/%d\"),\n label=\"End Date:\",\n min=\"2000/01/04\",\n max=today.strftime(\"%Y/%m/%d\"),\n ),\n dict(\n type=\"select\",\n name=\"d\",\n default=\"0\",\n options=PDICT,\n label=\"Select Drought Classification (at and above counted):\",\n ),\n dict(\n type=\"select\",\n name=\"w\",\n default=\"percent\",\n options=PDICT2,\n label=\"How to express time for plot:\",\n ),\n dict(type=\"cmap\", name=\"cmap\", default=\"plasma\", label=\"Color Ramp:\"),\n ]\n return desc","def description():","def get_description():\n desc = {\"description\": __doc__}\n sts = utc() - timedelta(hours=26)\n ets = utc() - timedelta(hours=2)\n desc[\"arguments\"] = [\n {\n \"type\": \"datetime\",\n \"name\": \"sts\",\n \"default\": sts.strftime(\"%Y/%m/%d %H00\"),\n \"label\": \"Start Timestamp (UTC):\",\n \"min\": \"1986/01/01 0000\",\n },\n {\n \"type\": \"datetime\",\n \"name\": \"ets\",\n \"default\": ets.strftime(\"%Y/%m/%d %H00\"),\n \"label\": (\n \"End Timestamp [inclusive] (UTC), \"\n \"interval must be less than 4 days\"\n ),\n \"min\": \"1986/01/01 0000\",\n },\n {\n \"type\": \"select\",\n \"options\": PDICT,\n \"default\": \"min\",\n \"name\": \"w\",\n \"label\": \"Which statistic to compute\",\n },\n {\n \"type\": \"csector\",\n \"name\": \"csector\",\n \"default\": \"IA\",\n \"label\": \"Select state/sector\",\n },\n {\n \"type\": \"select\",\n \"options\": PDICT2,\n \"default\": \"user\",\n \"label\": \"Plotting mode (user defined color-ramp or freezing)\",\n \"name\": \"mode\",\n },\n {\n \"type\": \"cmap\",\n \"name\": \"cmap\",\n \"default\": \"gnuplot2\",\n \"label\": \"Color Ramp:\",\n },\n ]\n return desc","def get_description():\n desc = dict()\n desc['data'] = True\n desc['description'] = \"\"\"This plot displays the maximum and minimum change\n in high temperature between a given day and a given number of days prior\n to that date. The red bars are the largest difference between a maximum\n high over a period of days and the given day. The blue bars are the\n opposite.\"\"\"\n desc['arguments'] = [\n dict(type='station', name='station', default='IA0200',\n label='Select Station:', network='IACLIMATE'),\n dict(type='int', name='days', default='4',\n label='Number of Trailing Days:'),\n ]\n return desc","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def description(self) -> str:\r\n description = \"The player must aim to put the most possible units \" + \\\r\n \"of \" + colour_name(self.colour) + \" on the outer\" +\\\r\n \" perimeter.\"\r\n return description","def get_description():\n desc = dict()\n desc['cache'] = 86400\n desc['data'] = True\n desc['description'] = \"\"\"This chart presents a histogram of issuance times\n for a given watch, warning, or advisory type for a given office.\"\"\"\n desc['arguments'] = [\n dict(type='networkselect', name='station', network='WFO',\n default='DMX', label='Select WFO:'),\n dict(type='phenomena', name='phenomena',\n default='WC', label='Select Watch/Warning Phenomena Type:'),\n dict(type='significance', name='significance',\n default='W', label='Select Watch/Warning Significance Level:'),\n ]\n return desc","def description(self) -> str:\r\n descrip = 'The player must aim to put the most possible units of a ' \\\r\n 'given colour c on the outer perimeter of ' \\\r\n 'the board. The ' \\\r\n 'player’s score is the total number of unit cells ' \\\r\n 'of colour ' \\\r\n 'c that are on the perimeter. There is a ' \\\r\n 'premium on corner ' \\\r\n 'cells: they count twice towards the score. '\r\n return descrip","def describe():","def get_description():\n desc = {}\n desc[\"data\"] = True\n desc[\n \"description\"\n ] = \"\"\"For each year, the average first and last date of\n a given temperature is computed. The values are then averaged and plotted\n to represent the period between these occurences and also the number of\n days represented by the period.\n \"\"\"\n desc[\"arguments\"] = [\n dict(\n type=\"station\",\n name=\"station\",\n default=\"IATDSM\",\n label=\"Select Station\",\n network=\"IACLIMATE\",\n )\n ]\n return desc","def _generate_overlay_file_name(self, well, channel, desc):\n \n return \"c\" + channel + \"_w\" + well + \"_\" + desc + \".png\"","def GetDescription(cls):\n return textwrap.dedent('''\n This trace step includes a diagram of the Ego long. acceleration in the report.\n ''').strip()","def get_description():\r\n return{\"I'll never yield!\":\"Grants a shield.\",\r\n \"Stay still!\":\"Affected unit cannot act in their turn.\"\r\n }","def get_description():\n desc = {\"description\": __doc__, \"data\": True}\n desc[\"arguments\"] = [\n dict(\n type=\"station\",\n name=\"station\",\n default=\"IATDSM\",\n label=\"Select Station:\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"select\",\n name=\"var\",\n default=\"spi\",\n options=PDICT,\n label=\"Select which metric to plot:\",\n ),\n dict(\n type=\"select\",\n name=\"c\",\n default=\"ncei91\",\n options=PDICT2,\n label=\"Which climatology to use for averages:\",\n ),\n dict(\n type=\"int\",\n name=\"days\",\n default=90,\n label=\"Over how many trailing days to compute the metric?\",\n ),\n ]\n return desc","def get_description():\n desc = dict()\n desc[\"cache\"] = 3600\n desc[\"data\"] = True\n desc[\n \"description\"\n ] = \"\"\"This plot is not meant for interactive use, but a backend for\n SPS plots.\n \"\"\"\n desc[\"arguments\"] = [\n dict(\n type=\"text\",\n name=\"pid\",\n default=\"202012300005-KDVN-WWUS83-SPSDVN\",\n label=\"IEM generated up to 35 char product identifier:\",\n ),\n dict(\n type=\"int\",\n default=0,\n name=\"segnum\",\n label=\"Product Segment Number (starts at 0):\",\n ),\n ]\n return desc","def description(self) -> str:\r\n descrip = 'The player must aim for the largest “blob” of a given ' \\\r\n 'colour c. A blob is a group of connected blocks with the ' \\\r\n 'same colour. Two blocks are connected if their sides ' \\\r\n 'touch; touching corners doesn’t count. The player’s score ' \\\r\n 'is the number of unit cells in the largest blob of colour ' \\\r\n 'c. '\r\n return descrip","def __str__(self):\n return '<TuebingenMEG: %i samples, %i timepoints, %i channels>' \\\n % (self.nsamples, self.ntimepoints, len(self.channelids))","def desc(self):\n return LandCell.desc(self) + \"; plant=\" + str(self.plant)","def description(self):\n\n\t\treturn \"%d %s %s\" % (self.vintage, self.winery.name, self.name)","def get_description():\n desc = {\"description\": __doc__}\n desc[\"data\"] = True\n desc[\"arguments\"] = [\n dict(\n type=\"select\",\n options=PDICT3,\n default=\"temps\",\n name=\"v\",\n label=\"Which Variable(s) to Plot\",\n ),\n dict(\n type=\"station\",\n name=\"station1\",\n default=\"IATDSM\",\n label=\"Select First Station:\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"select\",\n name=\"c1\",\n label=\"Climatology Source for First Station:\",\n default=\"1951\",\n options=PDICT,\n ),\n dict(\n type=\"station\",\n name=\"station2\",\n default=\"IATDSM\",\n optional=True,\n label=\"Select Second Station (Optional):\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"select\",\n name=\"c2\",\n label=\"Climatology Source for Second Station:\",\n default=\"1951\",\n options=PDICT,\n ),\n dict(\n type=\"select\",\n name=\"s\",\n label=\"For difference plot, should smoother be applied:\",\n default=\"0\",\n options=PDICT2,\n ),\n dict(\n type=\"year\",\n min=1880,\n name=\"sy1\",\n default=1991,\n label=\"Inclusive Start Year for First Station Period of Years:\",\n ),\n dict(\n type=\"year\",\n min=1880,\n name=\"ey1\",\n default=2020,\n label=\"Inclusive End Year for First Station Period of Years:\",\n ),\n dict(\n type=\"year\",\n min=1880,\n name=\"sy2\",\n default=1981,\n label=\"Inclusive Start Year for Second Station Period of Years:\",\n ),\n dict(\n type=\"year\",\n min=1880,\n name=\"ey2\",\n default=2010,\n label=\"Inclusive End Year for Second Station Period of Years:\",\n ),\n ]\n return desc","def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def __init__(self, name=\"\", description=\"\", time_units=\"s\", len_units=\"m\"):\n\n # Set general info\n self._type = 2 # observation well id\n self.time_units = time_units\n self.len_units = len_units\n\n self.parameters = {'full': True, # is full penetrating?\n 'r': 1., # radius, distance until pumping well in length units\n 'd': 0., # depth of well screen (from top) in length units\n 'l': 1.} # depth of well bottom in length units\n\n # Create drawdown data\n self.drawdown = _Data(dtype=1, name=name, description=description)\n self.drawdown.set_units(self.time_units, self.len_units)\n\n # Set results from models\n self.data = []","def get_description():\n desc = {\"description\": __doc__, \"data\": True}\n dt = datetime.date.today() - datetime.timedelta(days=1)\n desc[\"arguments\"] = [\n {\n \"type\": \"select\",\n \"name\": \"mode\",\n \"default\": \"normal\",\n \"label\": \"Application Mode:\",\n \"options\": PDICT,\n },\n dict(\n type=\"state\",\n name=\"state\",\n default=\"IA\",\n label=\"Select State:\",\n ),\n dict(\n type=\"date\",\n name=\"date\",\n default=dt.strftime(\"%Y/%m/%d\"),\n label=\"Retroactive Date of Plot\",\n ),\n dict(\n type=\"int\",\n name=\"d1\",\n default=30,\n label=\"Over how many trailing days to compute the metric?\",\n ),\n dict(\n type=\"int\",\n name=\"d2\",\n default=60,\n label=\"Over how many trailing days to compute the metric?\",\n ),\n dict(\n type=\"int\",\n name=\"d3\",\n default=90,\n label=\"Over how many trailing days to compute the metric?\",\n ),\n ]\n return desc","def get_description():\n d = dict()\n d['data'] = True\n d['report'] = True\n d['description'] = \"\"\" \"\"\"\n d['arguments'] = [\n dict(type='station', name='station', default='IA2203',\n label='Select Station'),\n ]\n return d","def get_description():\n desc = {\"description\": __doc__, \"data\": True}\n today = datetime.date.today()\n thisyear = today.year\n desc[\"arguments\"] = [\n dict(\n type=\"station\",\n name=\"station\",\n default=\"IATDSM\",\n label=\"Select Station:\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"select\",\n options=PDICT,\n name=\"var\",\n default=\"precip\",\n label=\"Accumulate Precipitation or Snow?\",\n ),\n dict(\n type=\"year\",\n name=\"year1\",\n default=thisyear,\n label=\"Additional Year to Plot:\",\n ),\n dict(\n type=\"year\",\n name=\"year2\",\n optional=True,\n default=(thisyear - 1),\n label=\"Additional Year to Plot: (optional)\",\n ),\n dict(\n type=\"year\",\n name=\"year3\",\n optional=True,\n default=(thisyear - 2),\n label=\"Additional Year to Plot: (optional)\",\n ),\n dict(\n type=\"sday\",\n name=\"sdate\",\n default=\"0101\",\n label=\"Start Day of Year for Plot:\",\n ),\n dict(\n optional=True,\n type=\"sday\",\n name=\"edate\",\n default=f\"{today:%m%d}\",\n label=\"End Day of Year for Plot:\",\n ),\n dict(\n type=\"int\",\n default=\"3\",\n label=\"Number of missing days to allow before excluding year\",\n name=\"m\",\n ),\n ]\n return desc","def GenBrief(self):\n\n brief = \"[{0}] {1} \".format(self.TimeStamp.strftime( \"%H:%M\"), ActionType.actionNames[self.Type])\n\n if self.Type in {ActionType.Feed, ActionType.Notes, ActionType.WakeUp, ActionType.SleepTime,\\\n ActionType.ComFood, ActionType.ErrStatus, ActionType.Pills, ActionType.Snacks}:\n brief += self.Detail\n elif self.Type == ActionType.Poop:\n pass\n elif self.Type == ActionType.AD:\n pass\n elif self.Type == ActionType.UnKnown:\n brief += \"可以尝试 '总结' 或 '一周总结' 查看萝卜成长状态。\"\n elif self.Type == ActionType.FallSleep:\n tnow = cn_utility.GetNowForUTC8()\n delta_minutes = int((tnow - self.TimeStamp).total_seconds()/60)\n #brief += \"已经睡了{0}小时{1}分钟.\\n天气干，记得涂唇膏。\".format(int(delta_minutes/60), delta_minutes%60)\n brief += \"已经睡了{0}小时{1}分钟.\".format(int(delta_minutes/60), delta_minutes%60)\n else:\n pass\n\n if len(brief) > 300:\n brief = brief[0:300]\n\n return brief","def descString(self):\n return \"\".join ([self.Name, \" (\", str(self.RollCount), \"d\"\\\n , str(self.RollMax), \"; \", str(self.CritRollMin), \"-\"\\\n , str(self.CritRollMax), \"x\", str (self.CritRollMult)\\\n , \") - \", str(self.Value), \" gp\"])","def info():\n return r\"\"\"Tseng, Lin-Yu, and Chun Chen. \"Multiple trajectory search for unconstrained/constrained multi-objective optimization.\" Evolutionary Computation, 2009. CEC'09. IEEE Congress on. IEEE, 2009.\"\"\"","def info():\n return r\"\"\"Lin-Yu Tseng and Chun Chen, \"Multiple trajectory search for Large Scale Global Optimization,\" 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, 2008, pp. 3052-3059. doi: 10.1109/CEC.2008.4631210\"\"\"","def description(self):","def get_description():\n desc = {\"description\": __doc__, \"data\": True}\n desc[\"arguments\"] = [\n dict(\n type=\"station\",\n name=\"station\",\n default=\"IATDSM\",\n label=\"Select Station\",\n network=\"IACLIMATE\",\n ),\n dict(\n type=\"select\",\n name=\"var\",\n default=\"precip\",\n label=\"Which Variable:\",\n options=PDICT,\n ),\n ]\n return desc","def get_description(self):\n return \"-\".join(\n map(str, (self.release, self.chromosome, self.start, self.reference, self.alternative))\n )","def __str__(self):\n if len(self.label) > 0:\n descr = [\"'%s', target='%s' [%s]\" % (self.label, self.target.name, self.target.body_type)]\n else:\n descr = [\"target='%s' [%s]\" % (self.target.name, self.target.body_type)]\n if self.baseline:\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\n if self.beam:\n descr[0] += ', beam height=%f' % (self.beam.height,)\n for scan_ind, scan in enumerate(self.scans):\n descr.append('%4d: %s' % (scan_ind, str(scan)))\n return '\\n'.join(descr)","def __str__(self):\n rep=\"This system has \"+str(self.NL)+\" layers.\\n\"\n rep+=\"The parameters for the each layers are:\\n\"\n for i in range(self.NL-1):\n rep+=\"Layer no. \"+str(i)+\":\\t \"+str(self.layers[i])\n rep+=\"Coupled to the next layer with strength:\\t\"+str(self.couplings[i])+\"\\n\"\n rep+=\"Layer no. \"+str(self.NL-1)+\":\\t \"+str(self.layers[self.NL-1])\n \n return rep","def AMdepth(depthPerc=100,channel=1):\r\n inst.write(\":AM%i:DEPT %iPCT\" %(channel,depthPerc))","def getDesc():\n\treturn \"Create an animation showing the dataset with the Animator\"","def plane_desc(self) -> str:\n return self.planes[0].join(' ') + self.planes[1].join(' ') + self.planes[2].join(' ')","def describe(self) -> str:","def __str__(self):\n description = \"-------- %s --------\" % (self.name)\n description += \"\\nnmax = %i\" % (self.nmax)\n description += \"\\nnslots = %i\" % (self.nslots)\n description += \"\\nbonus_power = %i\" % (self.bonus_power)\n description += \"\\nbonus_initiative = %.1f\" % (self.bonus_initiative)\n description += \"\\nneeds_drive = %i\" % (self.needs_drive)\n description += \"\\nis_mobile = %i\" % (self.is_mobile)\n description += \"\\n----- Default Parts -----\"\n for i in range(len(self.default_parts)):\n description += \"\\n%i) %s\" % (i + 1, self.default_parts[i].name)\n return description","def describe_me():\n # this is quite different than the `describe_me` in ARAX_overlay and ARAX_filter_kg due to expander being less\n # of a dispatcher (like overlay and filter_kg) and more of a single self contained class\n brief_description = \"\"\"\n`expand` effectively takes a query graph (QG) and reaches out to various knowledge providers (KP's) to find all bioentity subgraphs\nthat satisfy that QG and augments the knowledge graph (KG) with them. As currently implemented, `expand` can utilize the ARA Expander\nteam KG1 and KG2 Neo4j instances as well as BioThings Explorer to fulfill QG's, with functionality built in to reach out to other KP's as they are rolled out.\n \"\"\"\n description_list = []\n params_dict = dict()\n params_dict['brief_description'] = brief_description\n params_dict['edge_id'] = {\"a query graph edge ID or list of such IDs to expand (optional, default is to expand entire query graph)\"} # this is a workaround due to how self.parameters is utilized in this class\n params_dict['node_id'] = {\"a query graph node ID to expand (optional, default is to expand entire query graph)\"}\n params_dict['kp'] = {\"the knowledge provider to use - current options are `ARAX/KG1`, `ARAX/KG2`, or `BTE` (optional, default is `ARAX/KG1`)\"}\n params_dict['enforce_directionality'] = {\"whether to obey (vs. ignore) edge directions in query graph - options are `true` or `false` (optional, default is `false`)\"}\n params_dict['use_synonyms'] = {\"whether to consider synonym curies for query nodes with a curie specified - options are `true` or `false` (optional, default is `true`)\"}\n params_dict['synonym_handling'] = {\"how to handle synonyms in the answer - options are `map_back` (default; map edges using a synonym back to the original curie) or `add_all` (add synonym nodes as they are - no mapping/merging)\"}\n params_dict['continue_if_no_results'] = {\"whether to continue execution if no paths are found matching the query graph - options are `true` or `false` (optional, default is `false`)\"}\n description_list.append(params_dict)\n return description_list","def description(self) -> str:\n return \"put units of a given colour on the perimeter of the board.\"","def description() -> str:\n content = \"Demonstrates usage of blackbord remappings.\\n\"\n content += \"\\n\"\n content += \"Demonstration is via an exemplar behaviour making use of remappings..\\n\"\n\n if py_trees.console.has_colours:\n banner_line = console.green + \"*\" * 79 + \"\\n\" + console.reset\n s = banner_line\n s += console.bold_white + \"Blackboard\".center(79) + \"\\n\" + console.reset\n s += banner_line\n s += \"\\n\"\n s += content\n s += \"\\n\"\n s += banner_line\n else:\n s = content\n return s","def get_descr_short(self):\n desc_text = []\n stack = [(self._desc, 0)]\n while stack:\n dl, di = stack.pop()\n while di < len(dl):\n if dl[di] == 0:\n di += 1\n elif dl[di] > 0 and dl[di] < 100000:\n desc_text.append(\"%06d\" % dl[di])\n elif dl[di] >= 100000 and dl[di] < 200000:\n desc_text.append(\"%06d LOOP\" % dl[di])\n elif dl[di] >= 200000 and dl[di] < 300000:\n desc_text.append(\"%06d OPER\" % dl[di])\n elif dl[di] >= 300000 and dl[di] < 400000:\n desc_text.append(\"%06d SEQ\" % dl[di])\n di += 1\n return desc_text","def describe(self, level=0):\n \n d = {'Nodes': self.num_nodes,\n 'Links': self.num_links,\n 'Patterns': self.num_patterns,\n 'Curves': self.num_curves,\n 'Sources': self.num_sources,\n 'Controls': self.num_controls}\n \n if level >= 1:\n d['Nodes'] = {\n 'Junctions': self.num_junctions,\n 'Tanks': self.num_tanks,\n 'Reservoirs': self.num_reservoirs}\n d['Links'] = {\n 'Pipes': self.num_pipes,\n 'Pumps': self.num_pumps,\n 'Valves': self.num_valves}\n d['Curves'] = {\n 'Pump': len(self._curve_reg._pump_curves), \n 'Efficiency': len(self._curve_reg._efficiency_curves), \n 'Headloss': len(self._curve_reg._headloss_curves), \n 'Volume': len(self._curve_reg._volume_curves)}\n \n if level >= 2:\n d['Links']['Pumps'] = {\n 'Head': len(list(self.head_pumps())),\n 'Power': len(list(self.power_pumps()))}\n d['Links']['Valves'] = {\n 'PRV': len(list(self.prvs())),\n 'PSV': len(list(self.psvs())),\n 'PBV': len(list(self.pbvs())),\n 'TCV': len(list(self.tcvs())),\n 'FCV': len(list(self.fcvs())),\n 'GPV': len(list(self.gpvs()))}\n \n return d","def describe(self):\n composition = str()\n for n, comp in enumerate(self.components):\n if self.molefractions[n] > 0.0:\n composition += comp.name\n composition += \" %.2f\" % self.molefractions[n]\n composition += \"; \"\n return composition","def get_description():\n desc = {\"description\": __doc__, \"data\": True, \"cache\": 300}\n desc[\"defaults\"] = {\"_r\": \"t\"}\n yest = date.today() - timedelta(days=1)\n desc[\"arguments\"] = [\n dict(\n type=\"networkselect\",\n name=\"station\",\n default=\"KDSM\",\n label=\"Select Station:\",\n network=\"NWSCLI\",\n ),\n dict(\n type=\"date\",\n name=\"date\",\n default=yest.strftime(\"%Y/%m/%d\"),\n label=\"Select Date\",\n min=\"2000/01/01\",\n ),\n ]\n return desc","def __str__(self):\n description = \"Object class Critter.\"\n description += \"\\nName: \" + self.name + \"\\nMood: \" + self.mood + \"\\nHunger: \" + str(self.hunger) + \"\\nBoredom: \" + str(self.boredom) + \"\\n\"\n return description","def description(self):\n return '%s and a CD' % self.component.description()","def generate_file_name(well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def _info(self, N):\n IDs = self.chemicals.IDs\n data = self.data\n IDs, data = nonzeros(IDs, data)\n N_IDs = len(IDs)\n if N_IDs == 0:\n return f\"{type(self).__name__}: (empty)\"\n elif self.units:\n basic_info = f\"{type(self).__name__} ({self.units}):\\n\"\n else:\n basic_info = f\"{type(self).__name__}:\\n\"\n beginning = f'({self.phase}) ' if self.phase else \" \"\n new_line = '\\n' + len(beginning) * ' '\n data_info = ''\n lengths = [len(i) for i in IDs]\n maxlen = max(lengths) + 1\n N_max = N or tmo.Stream.display_units.N\n too_many_chemicals = N_IDs > N_max\n N = N_max if too_many_chemicals else N_IDs\n for i in range(N):\n spaces = ' ' * (maxlen - lengths[i])\n if i != 0:\n data_info += new_line\n data_info += IDs[i] + spaces + f' {data[i]:.3g}'\n if too_many_chemicals:\n data_info += new_line + '...'\n return (basic_info\n + beginning\n + data_info)","def card_format(card):\n pretty_output = '%s pts: %d powers: %s' % (\n card['name'],\n card['points'],\n ', '.join(card['powers'])\n )\n pretty_output += ' desc: %s' % card['description']\n return pretty_output","def write_data_card(spec, data_card, channels, path):\n with open(path, \"w\") as f:\n f.write(f\"imax {str(size(data_card.bins))}\" + \"\\n\")\n f.write(\n \"jmax \"\n + str(size(data_card.processes) - size(data_card.isSignal.keys()))\n + \"\\n\"\n )\n f.write(f\"kmax {str(size(data_card.systs, 0))}\" + \"\\n\")\n\n if data_card.hasShapes:\n for channel in data_card.shapeMap.keys():\n for sample in data_card.shapeMap[channel].keys():\n f.write(\n f\"shapes {sample} {channel} {data_card.shapeMap[channel][sample][0]} {data_card.shapeMap[channel][sample][1]}\"\n )\n if size(data_card.shapeMap[channel][sample]) > 2:\n f.write(f\" {data_card.shapeMap[channel][sample][2]}\" + \"\\n\")\n else:\n f.write(\"\\n\")\n\n f.write(\"\\n---------------------------------\\n\")\n f.write(\"bin \")\n for bin in data_card.obs.keys():\n f.write(f\"{bin} \")\n f.write(\"\\n\")\n f.write(\"observation \")\n for channel in data_card.obs.keys():\n f.write(f\"{str(data_card.obs[channel])} \")\n f.write(\"\\n---------------------------------\\n\")\n f.write(\"bin \")\n for channel in data_card.obs.keys():\n for sample in data_card.exp[channel].keys():\n f.write(f\"{channel} \")\n f.write(\"\\n\")\n f.write(\"process \")\n for channel in data_card.bins:\n for sample in data_card.exp[channel].keys():\n f.write(f\"{sample} \")\n f.write(\"\\n\")\n f.write(\"process \")\n for channel in data_card.bins:\n for sample in data_card.exp[channel].keys():\n if sample in data_card.signals:\n f.write(f\"{str(-1 * data_card.processes.index(sample))} \")\n else:\n f.write(f\"{str(data_card.processes.index(sample) + 1)} \")\n f.write(\"\\n\")\n f.write(\"rate \")\n for channel in data_card.bins:\n for sample in data_card.exp[channel].keys():\n\n f.write(f\"{str(data_card.exp[channel][sample])} \")\n f.write(\"\\n---------------------------------\\n\")\n for syst in data_card.systs:\n f.write(f\"{syst[0]} {syst[2]} \")\n for bin in syst[4].keys():\n for sample in data_card.exp[bin].keys():\n if syst[4][bin][sample] != 0:\n f.write(f\"{str(syst[4][bin][sample])} \")\n else:\n f.write(\"- \")\n\n f.write(\"\\n\")\n f.write(\"\\n---------------------------------\\n\")\n for cAp in data_card.rateParams.keys():\n _dir = cAp.split(\"AND\")\n for i in range(size(data_card.rateParams[cAp], 0)):\n if size(data_card.rateParams[cAp][i][0]) > 3:\n f.write(\n f\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])} {data_card.rateParams[cAp][i][0][3]}\"\n )\n else:\n f.write(\n f\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])}\"\n )\n f.write(\"\\n\")\n f.write(\"\\n---------------------------------\\n\")\n for idxc, channel in enumerate(channels):\n if (\n channel in data_card.binParFlags.keys()\n and data_card.binParFlags[channel] == True\n ):\n # double check to be safe\n shapesys = False\n staterror = False\n for sample in spec[\"channels\"][idxc][\"samples\"]:\n mod_types = [mod[\"type\"] for mod in sample[\"modifiers\"]]\n if \"shapesys\" in mod_types:\n shapesys = True\n elif \"staterror\" in mod_types:\n staterror = True\n\n if shapesys:\n f.write(f\"{channel} autoMCStats 100000 0 2\" + \"\\n\")\n if staterror:\n f.write(f\"{channel} autoMCStats 0 0 2\" + \"\\n\")","def _get_description_str(epoch_idx):\n total_digits = len(str(cfg.epochs))\n cur_digits = len(str(epoch_idx))\n sup_digits = total_digits - cur_digits\n desc_str = f\"Epoch {'0' * (sup_digits) + str(epoch_idx + 1)}/{cfg.epochs}\"\n\n return desc_str","def printHam(ham: Dict[str, Any], digits: int = 4) -> None:\n\n qubitNum = ham['circuit']['qubits']\n\n print(f\"\\n====================\\n1. Basic information\\n====================\\n\")\n print(f\"Title: `{ham['file']['title']}`\")\n print(f\"Qubits: {qubitNum}\")\n print(f\"System energy level: {ham['circuit']['sys_level']}\")\n print(f\"Sampling interval: {ham['circuit']['dt']} ns\")\n print(f\"Circuit duration: {ham['circuit']['max_time_ns']} ns\")\n print(f\"Calculation steps: {ham['circuit']['max_time_dt']}\")\n\n # Obtain the max length name\n maxNameLengthDrift = 0 if len(ham[\"drift\"]) == 0 else max([len(key) for key in ham[\"drift\"]])\n maxNameLengthControl = 0 if len(ham[\"control\"]) == 0 else max([len(key) for key in ham[\"control\"]])\n maxNameLength = str(max(max(maxNameLengthDrift, maxNameLengthControl), 10))\n\n # Print abstract of operator\n print(f\"\\n============\\n2. Operators\\n============\\n\")\n qubitFormat = \"{0: <5} {1: <7} {2: <\" + maxNameLength + \"} {3: <9} {4: <6} {5: <6}\"\n print(qubitFormat.format('-' * 5, '-' * 7, '-' * 10, '-' * 9, '-' * 6, '-' * 6))\n print(qubitFormat.format(\"Qubit\", \"Type\", \"Name\", \"On qubits\", \"Pulses\", \"Amp\"))\n for qubit in range(qubitNum):\n print(qubitFormat.format('-' * 5, '-' * 7, '-' * 10, '-' * 9, '-' * 6, '-' * 6))\n for key in ham[\"drift\"]:\n drifts = ham[\"drift\"][key]\n if qubit in drifts[\"on_qubits\"]:\n print(qubitFormat.format(qubit, \"Drift\", key, f\"{drifts['on_qubits']}\", 0, f\"{drifts['amp']}\"))\n for key in ham[\"control\"]:\n ctrls = ham[\"control\"][key]\n if qubit in ctrls[\"on_qubits\"]:\n print(qubitFormat.format(qubit, \"Control\", key, f\"{ctrls['on_qubits']}\",\n len(ctrls['waveforms']), \"-\"))\n\n # Print abstract of waveforms\n def paraRound(para: Dict[str, Any]) -> Union[Dict[str, Any], None]:\n \"\"\" Reduce the length of pulse Parameters \"\"\"\n if para is None:\n return None\n else:\n for key in para:\n para[key] = round(para[key], digits)\n return para\n\n print(f\"\\n============\\n3. Waveforms\\n============\\n\")\n qubitFormat = \"{0: <9} {1: <\" + maxNameLength + \"} {2: <20} {3: <5} {4: <7} {5: <45}\"\n print(qubitFormat.format('-' * 9, '-' * 10, '-' * 15, '-' * 5, '-' * 7, '-' * 45))\n print(qubitFormat.format(\"On qubits\", \"Control\", \"Waveform\", \"Start\", \"Duration\", \"Params (Sequences)\"))\n for key in ham[\"control\"]:\n ctrls = ham[\"control\"][key]\n if len(ctrls['waveforms']) > 0:\n print(qubitFormat.format('-' * 9, '-' * 10, '-' * 15, '-' * 5, '-' * 7, '-' * 45))\n for wave in ctrls['waveforms']:\n waveName = \"\"\n wavePara = \"\"\n if wave['func'] is None:\n waveName = \"Manual Sequence\"\n wavePara = f\"Sequence contains {len(wave['sequence'])} pieces\"\n elif callable(wave['func']):\n waveName = \"Manual Wave\"\n wavePara = f\"{paraRound(wave['para'])}\"\n elif isinstance(wave['func'], str):\n waveName = wave['func']\n wavePara = f\"{paraRound(wave['para'])}\"\n print(qubitFormat.format(f\"{ctrls['on_qubits']}\", key, waveName, wave['insert_ns'],\n wave['duration_ns'], wavePara))","def get_descr_full(self):\n desc_text = []\n stack = [(self._desc, 0)]\n while stack:\n dl, di = stack.pop()\n while di < len(dl):\n if dl[di] == 0:\n di += 1\n elif dl[di] > 0 and dl[di] < 100000:\n desc_text.append(str(self._tables.tab_b[dl[di]]))\n di += 1\n elif dl[di] >= 100000 and dl[di] < 200000:\n lm = dl[di] // 1000 - 100\n ln = dl[di] % 1000\n desc_text.append(\"%06d : LOOP, %d desc., %d times\" % (dl[di], lm , ln))\n di += 1\n elif dl[di] >= 200000 and dl[di] < 300000:\n en = self._tables.tab_c.get(dl[di])\n am = dl[di] // 1000 - 200\n an = dl[di] % 1000\n if en is None:\n en = (str(am), \"\")\n if dl[di] < 222000:\n desc_text.append(\"%06d : OPERATOR %s: %d\" % (dl[di], en[0], an))\n else:\n desc_text.append(\"%06d : OPERATOR '%s'\" % (dl[di], en[0]))\n di += 1\n elif dl[di] >= 300000 and dl[di] < 400000:\n stack.append((dl, di + 1))\n da = dl[di]\n dl = self._tables.tab_d[dl[di]]\n di = 0\n desc_text.append(\"%06d : SEQUENCE, %d desc.\" % (da, len(dl)))\n return desc_text","def description(self, depth=1, indent=0, itemize=\"\", output=None):\n if depth is None or depth < 0:\n return\n output_strio = StringIO()\n if self.label is None:\n label = \" (%s)\" % self.oid\n else:\n label = \" (%s: '%s')\" % (self.oid, self.label)\n output_strio.write('%s%s%s object at %s%s'\n % (indent*' ',\n itemize,\n self.__class__.__name__,\n hex(id(self)),\n label))\n if depth >= 1:\n output_strio.write(': %d Sequences' % len(self))\n if depth >= 2:\n if self.taxon_set is not None:\n tlead = \"\\n%s[Taxon Set]\\n\" % (\" \" * (indent+4))\n output_strio.write(tlead)\n self.taxon_set.description(depth=depth-1, indent=indent+8, itemize=\"\", output=output_strio)\n tlead = \"\\n%s[Characters]\\n\" % (\" \" * (indent+4))\n output_strio.write(tlead)\n indent += 8\n maxlabel = max([len(str(t.label)) for t in self.taxon_set])\n for i, t in enumerate(self.taxon_set):\n output_strio.write('%s%s%s : %s characters\\n' \\\n % (\" \" * indent,\n \"[%d] \" % i,\n str(t.label),\n len(self.taxon_seq_map[t])))\n\n s = output_strio.getvalue()\n if output is not None:\n output.write(s)\n return s","def __str__(self):\n s = \"Filename : %s\\n\" % self.fname\n s += \"Data size : %d x %d x %d\\n\" % (self._size[::-1])\n s += \"CCD Chip Size : %d x %d\\n\" % self._chipSize[::-1]\n s += \"File date : %s\\n\" % time.asctime(self._filedate)\n s += \"Exposure Time : %f\\n\" % self.Exposure\n s += \"Num ROI : %d\\n\" % self.NumROI\n s += \"Num ROI Exp : %d\\n\" % self.NumROIExperiment\n s += \"Contoller Ver.: %d\\n\" % self.ControllerVersion\n s += \"Logic Output : %d\\n\" % self.LogicOutput\n #self.AppHiCapLowNoise = self._readInt(4)\n s += \"Timing Mode : %d\\n\" % self.TimingMode\n s += \"Det. Temp : %d\\n\" % self.DetTemperature\n s += \"Det. Type : %d\\n\" % self.DetectorType\n s += \"Trigger Diode : %d\\n\" % self.TriggerDiode\n s += \"Delay Time : %d\\n\" % self.DelayTime\n s += \"Shutter Cont. : %d\\n\" % self.ShutterControl\n s += \"Absorb Live : %d\\n\" % self.AbsorbLive\n s += \"Absorb Mode : %d\\n\" % self.AbsorbMode\n s += \"Virtual Chip : %d\\n\" % self.CanDoVirtualChip\n s += \"Thresh. Min L : %d\\n\" % self.ThresholdMinLive\n s += \"Thresh. Min : %d\\n\" % self.ThresholdMin\n s += \"Thresh. Max L : %d\\n\" % self.ThresholdMaxLive\n s += \"Thresh. Max : %d\\n\" % self.ThresholdMax\n s += \"Geometric Op : %d\\n\" % self.GeometricOps\n s += \"ADC Offset : %d\\n\" % self.ADCOffset\n s += \"ADC Rate : %d\\n\" % self.ADCRate\n s += \"ADC Type : %d\\n\" % self.ADCType\n s += \"ADC Resol. : %d\\n\" % self.ADCRes\n s += \"ADC Bit. Adj. : %d\\n\" % self.ADCBitAdj\n s += \"ADC Gain : %d\\n\" % self.Gain\n \n i = 0\n for roi in self.allROI:\n s += \"ROI %-4d : %-5d %-5d %-5d %-5d %-5d %-5d\\n\" % (i,roi[0], roi[1], roi[2],\n roi[3], roi[4], roi[5])\n i += 1\n \n s += \"\\nComments :\\n\"\n i = 0\n for c in self._comments:\n s += \"%-3d : \" % i\n i += 1\n s += c\n s += \"\\n\"\n return s","def create_basic_description(result, season, country, var):\n if country!='all':\n return f'In the {season} season {(len(result))} tourists visited Tuscany from {country.title()}, we managed to cluster {np.round(len(result[pd.notnull(result[var])])/len(result)*100,2)}% of them.'\n else:\n return f'In the {season} season {(len(result))} tourists visited Tuscany, we managed to cluster {np.round(len(result[pd.notnull(result[var])])/len(result)*100,2)}% of them.'","def _info(self, N):\n from thermosteam import Stream\n N_max = N or Stream.display_units.N\n IDs = self.chemicals.IDs\n index, = self.data.any(0).nonzero()\n len_ = len(index)\n if len_ == 0:\n return f\"{type(self).__name__}: (empty)\"\n elif self.units:\n basic_info = f\"{type(self).__name__} ({self.units}):\\n\"\n else:\n basic_info = f\"{type(self).__name__}:\\n\"\n all_IDs = tuple([IDs[i] for i in index])\n\n # Length of chemical column\n all_lengths = [len(i) for i in IDs]\n maxlen = max(all_lengths + [8])\n\n # Set up chemical data for all phases\n phases_data_info = ''\n for phase in self._phases:\n phase_data = self[phase, all_IDs]\n IDs, data = nonzeros(all_IDs, phase_data)\n if not IDs: continue\n \n # Get basic structure for phase data\n beginning = f'({phase}) '\n new_line = '\\n' + len(beginning) * ' '\n\n # Set chemical data\n data_info = ''\n N_IDs = len(data)\n too_many_chemicals = N_IDs > N_max\n N = N_max if too_many_chemicals else N_IDs\n lengths = [len(i) for i in IDs]\n for i in range(N):\n spaces = ' ' * (maxlen - lengths[i])\n if i: data_info += new_line\n data_info += f'{IDs[i]} ' + spaces + f' {data[i]:.3g}'\n if too_many_chemicals: data += new_line + '...'\n # Put it together\n phases_data_info += beginning + data_info + '\\n'\n \n return basic_info + phases_data_info.rstrip('\\n')","def get_obj_desc():\n\n attributes = {'results' : ['aperiodic_params_', 'peak_params_',\n 'r_squared_', 'error_',\n '_gaussian_params'],\n 'settings' : ['peak_width_limits', 'max_n_peaks',\n 'min_peak_height', 'peak_threshold',\n 'aperiodic_mode'],\n 'data' : ['power_spectrum', 'freq_range', 'freq_res'],\n 'data_info' : ['freq_range', 'freq_res'],\n 'arrays' : ['freqs', 'power_spectrum', 'aperiodic_params_',\n 'peak_params_', '_gaussian_params'],\n 'model_components' : ['_spectrum_flat', '_spectrum_peak_rm',\n '_ap_fit', '_peak_fit']}\n\n return attributes","def prescription(self):\n prescription = \"\\n{0:>10}\\t{1:>10}\\t{2:>10}\\t{3:>10}\\n\".format(\"R\",\"Material\",\"d\",\"diameter\")\n for surface in self.lensSurfaces():\n prescription += \"{0:>10.2f}\\t{1:>10}\\t{2:>10.2f}\\t{3:>10.2f}\\n\".format(surface.R, str(surface.mat), surface.spacing, surface.diameter)\n return prescription","def summary_line_and_description():","def get_description(self):\n print(\"This Iron door.\")","def get_d_string(self):\n\t\tif self.difficulty == 1:\n\t\t\treturn 'EASY'\n\t\tif self.difficulty == 2:\n\t\t\treturn 'MEDIUM'\n\t\tif self.difficulty == 3:\n\t\t\treturn 'HARD'\n\t\tif self.difficulty > 3:\n\t\t\treturn 'ELITE'","def summarize(self):\n txtSumm = ''\n\n if self.legtype == 'Takeoff':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Landing':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Other':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Observing':\n txtSumm = \"%02d -- %s, RA: %s, Dec: %s, LegDur: %s, ObsDur: %s\" %\\\n (self.legno, self.target, self.ra, self.dec,\n str(self.duration),\n str(self.obsdur))\n txtSumm += \"\\n\"\n if self.nonsid is True:\n txtSumm += \"NONSIDERIAL TARGET -- NAIFID: %d\" % (self.naifid)\n txtSumm += \"\\n\"\n txtSumm += \"(The SOFIA project sincerely hopes you enjoy \"\n txtSumm += \"your observing breaks due to XFORMS crashes)\"\n txtSumm += \"\\n\"\n txtSumm += \"ObsPlan: %s, ObsBlk: %s\" % (self.obsplan, self.obsblk)\n txtSumm += \"\\n\\n\"\n txtSumm += \"Elevation Range: %.1f, %.1f\" % (self.range_elev[0],\n self.range_elev[1])\n txtSumm += \"\\n\\n\"\n txtSumm += \"ROF Range: %.1f, %.1f\" % (self.range_rof[0],\n self.range_rof[1])\n txtSumm += \"\\n\"\n txtSumm += \"ROF Rate Range: %.1f, %.1f %s\" % (self.range_rofrt[0],\n self.range_rofrt[1],\n self.range_rofrtu)\n txtSumm += \"\\n\\n\"\n txtSumm += \"True Heading Range: %.1f, %.1f\" % (self.range_thdg[0],\n self.range_thdg[1])\n txtSumm += \"\\n\"\n txtSumm += \"True Heading Rate Range: %.1f, %.1f %s\" %\\\n (self.range_thdgrt[0],\n self.range_thdgrt[1],\n self.range_thdgrtu)\n txtSumm += \"\\n\"\n txtSumm += \"Moon Angle: %.1f, Moon Illumination: %s\" %\\\n (self.moonangle, self.moonillum)\n\n return txtSumm","def tilestr(tile, show_details=False, show_unit=True):\n # TODO: show_details\n \n if tile.get('unit_name') in [None, \"\"]:\n unit_name = \"----\"\n else:\n unit_name = tile['unit_name'][0:4]\n if tile['unit_army_id'] != '1':\n army_id = int(tile['unit_army_id'])\n if army_id > 5:\n raise Exception(\"not implemented: more than 5 armies: {}\".format(army_id))\n unit_name = unit_name[0:(army_id-2)].upper() + unit_name[(army_id-2):].lower()\n details = \"\"\n if show_details:\n details = \" \"+tile_details_str(tile)\n return \"{}@{:02d},{:02d}{}{}\".format(\n tile['terrain_name'][0:4], tile['x'], tile['y'], (\":\"+unit_name) if show_unit else \"\",\n details)","def __str__(self):\n output = 'Pathogens:\\n'\n for x in self.extant_p:\n output += ' n %s h %f d %f host %s extant\\n' % (x.name, x.height, x.dist, x.host.name)\n for x in self.not_extant_p:\n output += ' n %s h %f d %f host %s not extant\\n' % (x.name, x.height, x.dist, x.host.name)\n for x in self.not_yet_sampled_p:\n output += ' n %s h %f d %f host %s not yet sampled\\n' % (x.name, x.height, x.dist, x.host.name)\n\n output += 'Hosts:\\n'\n for x in self.extant_h:\n output += ' %s %f %f extant\\n' % (x.name, x.height, x.dist)\n for x in self.not_extant_h:\n output += ' %s %f %f not extant\\n' % (x.name, x.height, x.dist)\n for x in self.not_yet_sampled_h:\n output += ' %s %f %f not yet sampled\\n' % (x.name, x.height, x.dist)\n\n return output","def _repr_(self):\n desc = ''\n if self.n_vertices()==0:\n desc += 'The empty polyhedron'\n else:\n desc += 'A ' + repr(self.dim()) + '-dimensional polyhedron'\n desc += ' in '\n if self.field()==QQ: desc += 'QQ'\n else: desc += 'RDF'\n desc += '^' + repr(self.ambient_dim()) \n\n if self.n_vertices()>0:\n desc += ' defined as the convex hull of ' \n desc += repr(self.n_vertices())\n if self.n_vertices()==1: desc += ' vertex'\n else: desc += ' vertices'\n \n if self.n_rays()>0:\n if self.n_lines()>0: desc += \", \"\n else: desc += \" and \"\n desc += repr(self.n_rays())\n if self.n_rays()==1: desc += ' ray'\n else: desc += ' rays'\n \n if self.n_lines()>0:\n if self.n_rays()>0: desc += \", \"\n else: desc += \" and \"\n desc += repr(self.n_lines())\n if self.n_lines()==1: desc +=' line'\n else: desc +=' lines'\n\n return desc + \".\\n\";","def get_description(self):","def difficulty(mag):\n mag = float(mag)\n if mag <= -4:\n return \"Visible in daytime.\"\n elif mag <= 6:\n return \"Visible at night.\"\n else:\n flux = mag_def(\"%s x\" % mag)\n needed_flux = mag_def(\"6 x\")\n eye_area = math.pi * (0.005**2)\n needed_power = needed_flux * eye_area\n diameter = 2 * math.sqrt(needed_power / (flux*math.pi))\n return \"%s m telescope needed.\" % diameter","def writeDataCards(opt,sigExp,bkgExp,shapesURL):\n\n #create a card per category\n dcList=[]\n for icat in range(len(opt.categs)):\n cat='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\n dcTxt='%s/shapes-parametric.datacard_%s.dat'%(opt.output,cat)\n dcList.append(dcTxt)\n with open(dcTxt,'w') as dc:\n dc.write('#\\n')\n dc.write('# datacard was automatically generated with generateWorkspace.py\\n')\n dc.write('# the options passed are printed below\\n')\n dc.write('# %s\\n'%opt)\n dc.write('#\\n')\n dc.write('imax *\\n')\n dc.write('jmax *\\n')\n dc.write('kmax *\\n')\n dc.write('-'*50+'\\n')\n dc.write('shapes * * {0} $PROCESS_{1} $PROCESS_$SYSTEMATIC\\n'.format(shapesURL,cat))\n dc.write('shapes data_obs * {0} $PROCESS_{1}\\n'.format(shapesURL,cat))\n dc.write('-'*50+'\\n')\n dc.write('bin %s\\n'%cat)\n dc.write('observation -1\\n')\n dc.write('-'*50+'\\n')\n dc.write('%15s %15s %15s\\n'%('bin',cat,cat))\n dc.write('%15s %15s %15s\\n'%('process','sig','bkg'))\n dc.write('%15s %15s %15s\\n'%('process','0', '1'))\n dc.write('%15s %15s %15s\\n'%('rate','%3.2f'%sigExp[icat], '%3.2f'%bkgExp[icat]))\n dc.write('-'*50+'\\n')\n \n #float the background normalization as well as the signal\n dc.write('mu_bkg{0} rateParam {0} bkg 1\\n'.format(cat))\n\n #uncertainties\n dc.write('lumi %8s %15s %15s\\n'%('lnN','1.027','-'))\n dc.write('%s_sigShape %8s %15s %15s\\n'%(cat,'shape','1','-'))\n dc.write('%s_bkgShape %8s %15s %15s\\n'%(cat,'shape','-','1'))\n dc.write('{0} autoMCStats 0.0 1\\n'.format(cat))\n \n print '\\tshapes available @',shapesURL\n print '\\tgenerated the following datacards',dcList","def describe(self):\n\n print(\"Correlation length: {0}\".format(self.cl))\n print(\"icoordchange: {0}\".format(self.icoordchange))\n print(\"ispec: {0}\".format(self.ispec))\n print(\"ireg: {0}\".format(self.ireg))\n print(\"Domain: x-axis: from {0} to {1} with {2} steps of {3}\".format(self.xori, self.xend,\n self.nx, self.dx))\n print(\"Domain: y-axis: from {0} to {1} with {2} steps of {3}\".format(self.yori, self.yend,\n self.ny, self.dy))\n print(\"Exclusion value: {0}\".format(self.valex))\n print(\"Signal-to-noise ratio: {0}\".format(self.snr))\n print(\"Variance of the background field: {0}\".format(self.varbak))","def __str__(self):\n return f\"{self._desc:16s}\"","def Description(self) -> str:","def Description(self) -> str:","def summary_stats(tile_summary):\n return \"Original Dimensions: %dx%d\\n\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\n \"Original Tile Size: %dx%d\\n\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\n \"Scale Factor: 1/%dx\\n\" % tile_summary.scale_factor + \\\n \"Scaled Dimensions: %dx%d\\n\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\n \"Scaled Tile Size: %dx%d\\n\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\n \"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\n\" % (\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\n \"Tiles: %dx%d = %d\\n\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\n \" %5d (%5.2f%%) tiles >=%d%% tissue\\n\" % (\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\n\" % (\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\n TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\n\" % (\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\n \" %5d (%5.2f%%) tiles =0%% tissue\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)","def print_image_info(self):\r\n\r\n maxt = np.max(self.times)\r\n\r\n print (\" Duration of Image Stack: %9.3f s (%8.3f min) period = %8.3f s\" % (maxt, maxt/60.0, self.period))\r\n\r\n print (' Image shape: ', self.imageData.shape)\r\n\r\n print (' nFrames: %d framerate: %9.3f\\n' % (self.nFrames, self.framerate))","def get_more_info(model):# pragma: no cover\n\n group_time = model.meta.exposure.group_time\n nframes_used = model.meta.exposure.nframes\n saturated_flag = dqflags.group['SATURATED']\n jump_flag = dqflags.group['JUMP_DET']\n\n return (group_time, nframes_used, saturated_flag, jump_flag)","def format(self):\r\n\r\n earth = \"???\" if self.maskearth else self.earth\r\n air = \"???\" if self.maskair else self.air\r\n fire = \"???\" if self.maskfire else self.fire\r\n water = \"???\" if self.maskwater else self.water\r\n\r\n if any((self.earth, self.fire, self.water)):\r\n statsline = f'Stats: {earth}/{air}/{fire}/{water}'\r\n elif self.air:\r\n statsline = f'Air: {air}'\r\n else:\r\n statsline = ''\r\n\r\n return (\r\n f'Character {self.name}, [{self.token}]. '\r\n f'Init: {self.init} {statsline} Owner: {self.user.name}'\r\n )","def draw(self):\n drawing = \"\"\n \n if self.get_gene().get_choice_at(1) is 'c':\n drawing += 'o'\n else:\n drawing += 'x'\n \n if self.is_tft():\n drawing += \"tft\"\n return drawing\n elif self.is_t2t():\n drawing += \"t2t\"\n return drawing\n elif self.is_ftf():\n drawing += \"ftf\"\n return drawing\n\n rule = self.get_gene().get_defect_fraction()\n fraction_display = 0.166\n\n if rule >= 1.0:\n drawing += \"ddd\"\n elif rule > (5*fraction_display):\n drawing += \"ddc\"\n elif rule > (4*fraction_display):\n drawing += \"dcd\"\n elif rule > (3*fraction_display):\n drawing += \"dcc\"\n elif rule > (2*fraction_display):\n drawing += \"cdd\"\n elif rule > (1*fraction_display):\n drawing += \"cdc\"\n elif rule > (0*fraction_display):\n drawing += \"ccd\"\n else:\n drawing += \"ccc\"\n\n return drawing","def describe_game_state(hand, battlefield, graveyard, library):\n\tlog(\"\")\n\tlog(\"Hand is now:\")\n\tlog(hand)\n\tlog(\"Battlefield is now:\")\n\tlog(battlefield)\n\tlog(\"Graveyard is now:\")\n\tlog(graveyard)\n\tlog(\"Library is now:\")\n\tlog(Counter(library))\n\tlog(\"\")","def extract_desc(desc_line, desc_filepath, numwavelets, include_mismatch=True, wavelet=None):\n line = desc_line\n model_filename = desc_filepath + line.split(\" \")[0]\n params = line[line.find(\"(\")+1:line.find('train')-4]\n if \")\" in params:\n hidden_layers, remaining = params.split(\"), \")\n hidden_layers = hidden_layers + \")\"\n activation, alpha = remaining.split(\", \")\n else:\n hidden_layers, activation, alpha = params.split(\", \")\n hidden_layers = \"(\" + hidden_layers + \")\"\n scores, time = line.split(\"\\t\")[1:]\n train, test = scores[1:-1].split(\" - \")\n time = time.split(\" \")[2]\n outline = hidden_layers + \"\\t\" + activation + \"\\t\" + alpha + \"\\t\" +\\\n train + \"\\t\" + test + \"\\t\" + time + \"\\t\" + model_filename\n if include_mismatch:\n #datafile = model_filename.split(\"NN\")[0] + \"Test.\" + wavelet + \".dat\"\n datafile = \"../wavelets-\" + str(numwavelets) + \"/Test.\" + wavelet + \".dat\"\n #classfile = model_filename.split(\"NN\")[0] + \"Test.\" + wavelet + \".cls\"\n classfile = \"../wavelets-\" + str(numwavelets) + \"/Test.\" + wavelet + \".cls\"\n outline += \"\\t\" + str(run_model(model_filename, datafile, classfile))\n return outline","def __str__(self):\n reprStr = 'Help Mario build Iron Man suit!'+'\\n' +'To make the ' + self._name + ',you need:'+'\\n'\n for part in self._supplies:\n reprStr = reprStr + str(part.getCount()) + ' ' + part.getData() + '\\n'\n return reprStr","def cal_desc(self):\n desc = \"\"\n desc += \"Requested by \"\n orgs = self.org.all()\n if len(orgs) > 0:\n for org in orgs:\n desc += org.name + \", \"\n desc = desc[:-2] + \".\\n\" # removes trailing comma\n ccs = self.ccinstances.all()\n if len(ccs) > 0:\n desc += \"Crew Chiefs: \"\n for cc in ccs:\n desc += cc.crew_chief.get_full_name() + \" [\" + (cc.service.shortname if cc.service else cc.category.name) + \"], \"\n desc = desc[:-2] + \".\\n\" # removes trailing comma\n if self.description:\n desc += self.description + \"\\n\"\n return desc","def describe_detailed(self) -> str:\n one_to_one = []\n one_to_many = []\n many_to_one = []\n many_to_many = []\n cats_a: set[Category] = set()\n cats_b: set[Category] = set()\n for rule in self.rules:\n cats_a.update(rule.factors_categories_a.keys())\n cats_b.update(rule.factors_categories_b.keys())\n if rule.cardinality_a == \"one\" and rule.cardinality_b == \"one\":\n one_to_one.append(rule)\n elif rule.cardinality_a == \"one\":\n one_to_many.append(rule)\n elif rule.cardinality_b == \"one\":\n many_to_one.append(rule)\n else:\n many_to_many.append(rule)\n\n cat_a, cat_b = self.categorization_a.name, self.categorization_b.name\n\n r = f\"# Mapping between {cat_a} and {cat_b}\\n\\n\"\n r += \"## Simple direct mappings\\n\\n\"\n r += \"\\n\".join(\n rule.format_human_readable(categorization_separator=\"\")\n for rule in one_to_one\n )\n r += \"\\n\\n\"\n r += f\"## One-to-many mappings - one {cat_a} to many {cat_b}\\n\\n\"\n r += \"\\n\".join((rule.format_human_readable()) for rule in one_to_many)\n r += \"\\n\\n\"\n r += f\"## Many-to-one mappings - many {cat_a} to one {cat_b}\\n\\n\"\n r += \"\\n\".join((rule.format_human_readable()) for rule in many_to_one)\n r += \"\\n\\n\"\n r += f\"## Many-to-many mappings - many {cat_a} to many {cat_b}\\n\\n\"\n r += \"\\n\".join((rule.format_human_readable()) for rule in many_to_many)\n r += \"\\n\\n\"\n\n r += \"## Unmapped categories\\n\\n\"\n cats_missing_a = set(self.categorization_a.values()) - cats_a\n cats_missing_b = set(self.categorization_b.values()) - cats_b\n r += f\"### {cat_a}\\n\"\n r += \"\\n\".join(sorted(str(x) for x in cats_missing_a)) + \"\\n\\n\"\n r += f\"### {cat_b}\\n\"\n r += \"\\n\".join(sorted(str(x) for x in cats_missing_b)) + \"\\n\\n\"\n\n return r","def print_info(case, title='Case', kolom=25):\n def print_desc(desc, val, unit):\n print('| {:<{k:d}s} | {:> {k:d}f} | {:^{k:d}s} |'.format(\n desc, val, unit, k=kolom-2))\n \n lebar = kolom*3+4\n print('='*lebar)\n print('|{:^{l:d}s}|'.format(title, l=lebar-2))\n print('='*lebar)\n print('|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|'.format('description', 'value', 'unit', k=kolom))\n print('-'*lebar)\n \n L, k, q, TA, TB, nodes, dx = get_valdict(case, 'L,k,q,TA,TB,nodes,dx')\n \n print_desc('L (length)', L, 'm')\n print_desc('k (conductivity)', k, 'W/(m.K)')\n print_desc('q (heat generation)', q, 'kW/(m^3)')\n print_desc('TA (temperature at A)', TA, 'Celcius')\n print_desc('TB (temperature at B)', TB, 'Celcius')\n print_desc('nodes', nodes, '-')\n print_desc('dx (grid space)', dx, 'm')\n\n print('='*lebar)\n print()","def print(self):\n print(\"-----\", self.name, \"-----\")\n print(\"Enable index\", self.enable_index)\n print(\"End index:\", self.stop_index)\n print(\"Measurement length (from the very beginning to the end index):\", self.time_vec[self.stop_index])\n print()\n print(\"Start temperature (hot):\", self.temp_hot_start)\n print(\"Start temperature (cold):\", self.temp_cold_start)\n print(\"Start temperature (mean):\", self.temp_start)\n print(\"End temperature (hot):\", self.temp_hot[self.stop_index])\n print(\"End temperature (cold)\", self.temp_cold[self.stop_index])\n print()\n print(\"Max temperature\", self.temp_max)\n print(\"Min temperature\", self.temp_min)\n print()\n print(\"Heat pump\")\n print(\"Energy input:\", self.work_inp)\n print(\"Q_hot\", self.qhot_pump)\n print(\"Q_cold\", self.qcold_pump)\n print(\"Q_cold + W\", self.qcold_pump + self.work_inp)\n print(\"E_lost\", self.qcold_pump + self.work_inp - self.qhot_pump)\n print(\"Coefficient of performance COP_hot\", self.qhot_pump / self.work_inp)\n print(\"Coefficient of performance COP_cold\", self.qcold_pump / self.work_inp)\n print(\"Ideal COP_hot with the setup\", self.qhot_pump/(self.qhot_pump-self.qcold_pump))\n print(\"Ideal COP_cold with the setup\", self.qcold_pump / (self.qhot_pump - self.qcold_pump))\n print(\"Ideal Carnot COP_hot\", (self.temp_max+273.15)/(self.temp_max-self.temp_min))\n print(\"Ideal Carnot COP_cold\", (self.temp_min+273.15)/(self.temp_max-self.temp_min))\n print(\"Efficiency fraction out of ideal Carnot cooler\", (self.qcold_pump / self.work_inp)/((self.temp_min+273.15)/(self.temp_max-self.temp_min)))\n\n if self.not_air:\n print(\"Heat transfer through insulator, hot side\", self.heat_loss_pump_hot)\n print(\"Heat transfer through insulator, cold side\", self.heat_loss_pump_cold)\n print(\"Estimated Q_hot with resistor\", self.qhot_resistor)\n else:\n print(\"Estimated Q_hot with resistor (=energy input)\", self.qhot_resistor)\n #\n # I think it should be defined for Q_hot too. Yep, TODO that\n # Also calculate heatloss due to conduction TODO remove these comments when ready\n # Todo implement resistive heater calculations\n print()\n print(\"Heat engine\")\n print(\"Energy generated:\", self.work_gen)\n print(\"Q_hot\", self.qhot_engine)\n print(\"Q_cold\", self.qcold_engine)\n print(\"Q_hot - Q_cold\", self.qhot_engine - self.qcold_engine)\n print(\"E_lost\", -self.qcold_engine - self.work_gen + self.qhot_engine)\n print(\"\\\"Heat transfer efficiency\\\" (%)\", self.work_gen / (self.qhot_engine - self.qcold_engine) * 100)\n print(\"Efficiency e\", self.work_gen / self.qhot_engine)\n print(\"Ideal efficiency with the setup\", 1 - (self.qcold_engine / self.qhot_engine))\n print(\"Ideal Carnot efficiency\", (self.temp_max-self.temp_min)/(self.temp_max+273.15))\n if self.not_air:\n print(\"Heat transfer through insulator, hot side\", self.heat_loss_gen_hot)\n print(\"Heat transfer through insulator, cold side\", self.heat_loss_gen_cold)\n print()\n print(\"Total efficiency of cycle\", self.work_gen/self.work_inp)\n # About the efficiency of peltier elements (#telok@IRCnet, 2016-07-27)\n # 19:10 < AgenttiX> Oletteko kokeilleet TECin ohjaamista Arduinolla? Toimisiko tämä kytkentä? http://garagelab.com/profiles/blogs/how-to-use-a-peltier-with-arduino\n # --\n # 20:21 <@hrst> Ei toimi. Peltieriä ei voi ohjata PWM:llä.\n # 20:22 <@hrst> Hyötysuhde on PWM:llä paska, mikä on ongelma koska se on muutenkin liian paska, ja sen lisäksi se hajoaa mekaaniseen värähtelyyn ennemmin tai myöhemmin.\n print(\"-----\\n\")","def description() -> str:\n content = \"Demonstrates usage of blackboard namespaces.\\n\"\n content += \"\\n\"\n\n if py_trees.console.has_colours:\n banner_line = console.green + \"*\" * 79 + \"\\n\" + console.reset\n s = banner_line\n s += console.bold_white + \"Blackboard\".center(79) + \"\\n\" + console.reset\n s += banner_line\n s += \"\\n\"\n s += content\n s += \"\\n\"\n s += banner_line\n else:\n s = content\n return s","def get_figure_desc(figure):\n return figure['output']","def test_contents(dataset):\n print dataset.info\n\n depth = dataset.variables['depth']\n\n print depth.time.info","def generate_description(self, which, world):\n global parsed_kd_des\n if parsed_kd_des is None:\n parsed_kd_des = util.parseDes('killdude_background', KillDudeBackground)[0]\n s = choice(parsed_kd_des.__dict__[which].strip().splitlines())\n substs = util.LambdaMap()\n #here we parse following options: $depart_village, $who (dude.name) and $name (dude.unique_name)\n substs['who'] = self.dude.name\n substs['name'] = self.dude.unique_name\n substs['current_city'] = lambda : current_city(world)\n substs['village_name'] = lambda : util.gen_name('city')\n\n res = string.Template(s).safe_substitute(substs)\n return res","def desc(self):\n kpi_name = 'id:{idx}, resource: {rsc}, group: {grp}, metric: {mtr}'\\\n .format(idx=self.idx,\n rsc=self.resource,\n grp=self.group,\n mtr=self.metric)\n return kpi_name","def template(title, steps, loops):\n return \"%-8s %-20s %20d\" % (title, format_power(steps), loops)","def description(self) -> str:\n return \" get many blocks of given colour adjacent\"","def TIMEchannels(tree, TIMEnum, CHnum):\n chanpath = ('.SETTINGS.NI.NI_6602_TIME.TCARD_'\n + str(TIMEnum) + '.CHANNEL_' + str(CHnum))\n tree.addNode(chanpath)\n AddNodeWithTag(tree, chanpath + ':ACTIVE', 'NUMERIC', 'TIMACTIVE_CARD' +\n str(TIMEnum) + 'CH' + str(CHnum))\n AddNodeWithTag(tree, chanpath + ':CHANNEL_NAME', 'TEXT', \n 'TIMEUSERNAME_CARD' + str(TIMEnum) + 'CH' + str(CHnum))\n AddNumericWithUnit(tree, chanpath + ':TRIG_TIME', 'TRIGGERTIME_CARD' +\n str(TIMEnum) + 'CH' + str(CHnum), 's')\n AddNumericWithUnit(tree, chanpath + ':PULSE_WIDTH', 'PWM_CARD' +\n str(TIMEnum) + 'CH' + str(CHnum), 's')\n AddNodeWithTag(tree, chanpath + ':NI_NAME', 'TEXT', 'TIMENINAME_CARD' +\n str(TIMEnum) + 'CH' + str(CHnum))","def __str__(self):\n output = 'Light curve name: {}\\n'.format(self.name)\n try:\n output += ('Initial time: {} UTC\\n'\n 'End time: {} UTC\\n'\n 'Duration: {:.3f} minutes\\n'.format(\n self.initial_time.iso, self.end_time.iso,\n (self.end_time - self.initial_time).value*u.d.to('min'))\n )\n except:\n pass\n output += 'Time offset: {:.3f} seconds\\n\\n'.format(self.dt)\n try:\n output += 'Exposure time: {:.4f} seconds\\n'.format(self.exptime)\n output += 'Cycle time: {:.4f} seconds\\n'.format(self.cycle)\n output += 'Num. data points: {}\\n\\n'.format(len(self.time))\n except:\n output += 'Object LightCurve was not instantiated with time and flux.\\n\\n'\n try:\n output += ('Bandpass: {:.3f} +/- {:.3f} microns\\n'\n 'Object Distance: {:.2f} AU\\n'\n 'Used shadow velocity: {:.3f} km/s\\n'\n 'Fresnel scale: {:.3f} seconds or {:.2f} km\\n'\n 'Stellar size effect: {:.3f} seconds or {:.2f} km\\n'.format(\n self.lambda_0, self.delta_lambda, self.dist, self.vel,\n self.fresnel_scale/self.vel, self.fresnel_scale,\n self.d_star/self.vel, self.d_star)\n )\n except:\n output += '\\nThere is no occultation associated with this light curve.\\n'\n try:\n output += ('Inst. response: {:.3f} seconds or {:.2f} km\\n'\n 'Dead time effect: {:.3f} seconds or {:.2f} km\\n'\n 'Model resolution: {:.3f} seconds or {:.2f} km\\n'\n 'Modelled baseflux: {:.3f}\\n'\n 'Modelled bottomflux: {:.3f}\\n'\n 'Light curve sigma: {:.3f}\\n\\n'.format(\n self.exptime, self.exptime*self.vel, self.cycle-self.exptime,\n (self.cycle-self.exptime)*self.vel, self.model_resolution,\n self.model_resolution*self.vel, self.baseflux, self.bottomflux,\n self.lc_sigma.mean())\n )\n except:\n output += '\\nObject LightCurve model was not fitted.\\n\\n'\n try:\n output += ('Immersion time: {} UTC +/- {:.3f} seconds\\n'\n 'Emersion time: {} UTC +/- {:.3f} seconds\\n\\n'.format(\n self.immersion.iso, self.immersion_err,\n self.emersion.iso, self.emersion_err)\n )\n except:\n output += 'Immersion and emersion times were not fitted or instantiated.\\n\\n'\n\n try:\n output += 'Monte Carlo chi square fit.\\n\\n' + self.chisquare.__str__() + '\\n'\n except:\n pass\n return output"],"string":"[\n \"def describe(self):\\n branch = randint(0,62)\\n \\n if 0 <= branch <= 29: \\n if self.casteOrder[0] == 'soldiers':\\n if self.genesis == 'escape persecution': \\n self.description = '{2}: A full service {3} for retired {1}'.format(choice(self.__class__.badjectives), choice(self.__class__.soldierSynonyms), self.name, choice(self.__class__.settlements[self.tech])) \\n elif self.genesis == 'maintain control': \\n self.description = 'The penal mining colony for {0} {1} on {2}'.format(choice(self.__class__.badjectives), choice(self.__class__.casteSynonyms[self.casteOrder[5]]), self.name, choice(self.__class__.settlements[self.tech])) \\n elif self.genesis == 'explore the unknown':\\n self.description = 'The frontier garrison and {2} {0} recently conquered {1}'.format(choice(self.__class__.techPreps[self.tech]), self.name, choice(self.__class__.settlements[self.tech]))\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'The militarized {3} and bonsai garden for the mandatory contemplation of {0} {1} {2}'.format(choice(self.__class__.seedIdeals), choice(self.__class__.techPreps[self.tech]), self.name, choice(self.__class__.settlements[self.tech]))\\n elif self.genesis == 'spread the gospel':\\n self.description = 'An outpost for crusaders who took up arms to defend {2} {3} on {0} {1}'.format(choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.badjectives), choice(self.__class__.priestSynonyms)) \\n elif self.casteOrder[0] == 'scientists':\\n if self.genesis == 'escape persecution': \\n self.description = 'A sanctuary for {0} who challenged the dogma of powerful {1} {2} {3}'.format(choice(self.__class__.scientistSynonyms), choice(self.__class__.priestSynonyms), choice(self.__class__.techPreps[self.tech]), self.name) \\n elif self.genesis == 'maintain control': \\n self.description = '{0}, home to a coalition of {1} eugenicists and their {2} servants'.format(self.name, choice(self.__class__.scientistAdjectives), choice(self.__class__.casteAdjectives[self.casteOrder[5]]))\\n elif self.genesis == 'explore the unknown':\\n self.description = 'An Extremely Large Hadron Collider {0} {1} {2}'.format(choice(self.__class__.techPreps[self.tech]), choice(self.__class__.techAdjectives[self.tech]), self.name)\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'The research institute and {2} for abiogenesis on {0} {1}'.format(choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.settlements[self.tech]))\\n elif self.genesis == 'spread the gospel':\\n self.description = 'The Galactic Academy of Sciences founded by {0} {1} {2} {3}'.format(choice(self.__class__.scientistAdjectives), choice(self.__class__.scientistSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.casteOrder[0] == 'laborers':\\n if self.genesis == 'escape persecution':\\n self.description = 'The {0} for unionized {1} {2} {3}'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.laborerSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.genesis == 'maintain control':\\n self.description = '{0} {1}: divisional headquarters for the communist party in this region of the galaxy (where evidence of {2} has been redacted)'.format(choice(self.__class__.techAdjectives[self.tech]).capitalize(), self.name, choice(self.__class__.controlIdeals))\\n elif self.genesis == 'explore the unknown':\\n self.description = 'A team of robot {0} sent to survey {1} {2}'.format(choice(self.__class__.laborerSynonyms), choice(self.__class__.techAdjectives[self.tech]), self.name)\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'Drones sent to terraform {0} {1} and build {2}'.format(choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.seedPlaces))\\n elif self.genesis == 'spread the gospel':\\n self.description = 'The {0} {1} {2} where a hard day\\\\'s work is highly valued'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.casteOrder[0] == 'merchants':\\n if self.genesis == 'escape persecution':\\n self.description = '{0} {1}: a refuge for {2} who fled a communist revolution on their home planet'.format(choice(self.__class__.techAdjectives[self.tech]).capitalize(), self.name, choice(self.__class__.merchantSynonyms))\\n elif self.genesis == 'maintain control':\\n self.description = 'The monopolistic conglomerate of {0} {1} {2} {3}'.format(choice(self.__class__.merchantAdjectives), choice(self.__class__.merchantSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.genesis == 'explore the unknown':\\n self.description = '{3} {0}, home to a consortium of {1} seeking to monetize {2}'.format(self.name, choice(self.__class__.merchantSynonyms), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]), choice(self.__class__.techAdjectives[self.tech]).capitalize())\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'An oligarchy of wealthy {0} who recently opened a for-profit {1} on {2}'.format(choice(self.__class__.merchantSynonyms), choice(self.__class__.settlements[self.tech]), self.name)\\n elif self.genesis == 'spread the gospel':\\n self.description = 'An orbital printing press that rains down copies of {0} onto {1} {2}'.format(choice(self.__class__.merchantBooks), choice(self.__class__.techAdjectives[self.tech]), self.name)\\n elif self.casteOrder[0] == 'artists':\\n if self.genesis == 'escape persecution':\\n self.description = 'The {0}\\\\' commune for the free and naked expression of {1} {2} {3}'.format(choice(self.__class__.artistSynonyms), choice(self.__class__.escapeIdeals), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.genesis == 'maintain control':\\n self.description = 'The {0} with mandatory art classes on {1} {2}'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.techAdjectives[self.tech]), self.name)\\n elif self.genesis == 'explore the unknown':\\n self.description = '{0} {1}, an observation deck where {2} observe naked and confined {3} to better understand the mysteries of {4}'.format(choice(self.__class__.techPreps[self.tech]).capitalize(), self.name, choice(self.__class__.artistSynonyms), choice(self.__class__.casteSynonyms[self.casteOrder[5]]), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]))\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'The {0} for {1} {2} who choose to better understand their craft through lovemaking on {3}'.format(choice(self.__class__.settlements[self.tech]), choice(self.__class__.seedAdjectives), choice(self.__class__.artistSynonyms), self.name)\\n elif self.genesis == 'spread the gospel':\\n self.description = 'A {0} on {1} that hosts an annual conference for pop {2}'.format(choice(self.__class__.settlements[self.tech]), self.name, choice(self.__class__.artistSynonyms))\\n elif self.casteOrder[0] == 'priests':\\n if self.genesis == 'escape persecution':\\n self.description = '{3} {0}, home to an order of heretical {1} who reject the {2} doctrine of their people'.format(self.name, choice(self.__class__.priestSynonyms), choice(self.__class__.gospelAdjectives), choice(self.__class__.techAdjectives[self.tech]).capitalize())\\n elif self.genesis == 'maintain control':\\n self.description = 'The orthodox {0} for {1} {2} {3} {4}'.format(choice(self.__class__.gospelPlaces), choice(self.__class__.gospelAdjectives), choice(self.__class__.priestSynonyms), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.genesis == 'explore the unknown':\\n self.description = '{0} {1}, a seminary for the metaphysical contemplation of {2}'.format(choice(self.__class__.techPreps[self.tech]).capitalize(), self.name, choice(self.__class__.gospelIdeals))\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'The marriage counseling clinic and devotional {2} {0} {1}'.format(choice(self.__class__.techPreps[self.tech]), self.name, choice(self.__class__.settlements[self.tech])) \\n elif self.genesis == 'spread the gospel':\\n self.description = 'A {0} mission on {1} {2} for converting {3} natives'.format(choice(self.__class__.gospelAdjectives), choice(self.__class__.techAdjectives[self.tech]), self.name, choice(self.__class__.badjectives))\\n \\n elif 30 <= branch <= 44:\\n if self.tech == 'pre-industrial technology':\\n if self.genesis == 'explore the unknown':\\n self.description = '{0}, home to a historical reenactment society of {2} {1}'.format(self.name, choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.badjectives))\\n elif self.genesis == 'escape persecution':\\n self.description = '{1}: a {2} of {0} Luddites'.format(choice(self.__class__.casteAdjectives[self.casteOrder[0]]), self.name, choice(self.__class__.preindustrialSettlements))\\n elif self.genesis == 'maintain control':\\n self.description = 'The {0} fiefdoms of {1}, where {2} serve their {3} liege lord'.format(choice(self.__class__.preindustrialAdjectives), self.name, choice(self.__class__.casteSynonyms[self.casteOrder[5]]), choice(self.__class__.casteAdjectives[self.casteOrder[0]]))\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'A harem of {0} {1} on medieval {2}'.format(choice(self.__class__.seedAdjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\\n elif self.genesis == 'spread the gospel':\\n self.description = 'The Puritan {0} for evangelical {1} on {2}'.format(choice(self.__class__.preindustrialSettlements), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\\n elif self.tech == 'machine technology':\\n if self.genesis == 'explore the unknown':\\n self.description = 'An array of radio telescopes outside the {0} on {1} {2}'.format(choice(self.__class__.machineSettlements), choice(self.__class__.machineAdjectives), self.name)\\n elif self.genesis == 'escape persecution':\\n self.description = 'Public housing for {0} {1} gentrified {2}'.format(choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.machinePreps), self.name) \\n elif self.genesis == 'maintain control':\\n self.description = 'The juvenile detention center for {0} children who obsess over {1} on {2}'.format(choice(self.__class__.badjectives), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]), self.name)\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = 'Just a giant cruise ship full of {0} {1} on {2}'.format(choice(self.__class__.seedAdjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\\n elif self.genesis == 'spread the gospel':\\n self.description = 'A megachurch run by {0} {1}, broadcasting live from {2}'.format(choice(self.__class__.priestAdjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), self.name)\\n elif self.tech == 'ubiquitous technology':\\n if self.genesis == 'explore the unknown':\\n self.description = 'A quantum data center for simulating the lives of {0} {1} {2} {3}'.format(choice(self.__class__.casteAdjectives[self.casteOrder[0]]), choice(self.__class__.casteSynonyms[choice(self.__class__.castes)]), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.genesis == 'escape persecution':\\n self.description = 'The cryogenics facility for the preservation of {0} {1} {2} {3}'.format(choice(self.__class__.badjectives), choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.techPreps[self.tech]), self.name)\\n elif self.genesis == 'maintain control':\\n self.description = 'Orbiting {0}: a socially stratified {1} governed by {2}'.format(self.name, choice(self.__class__.ubiqitousSettlements), choice(self.__class__.casteSynonyms[self.casteOrder[0]]))\\n elif self.genesis == 'seed the galaxy with life':\\n self.description = '{0} {1}, home to an assembly plant for android {2}'.format(choice(self.__class__.ubiqitousAdjectives).capitalize(), self.name, choice(self.__class__.casteSynonyms[self.casteOrder[0]]))\\n elif self.genesis == 'spread the gospel':\\n self.description = '{0}, home to an anthropological society of {1} who covertly inject the theme of {2} into the folklore of other civilizations'.format(self.name, choice(self.__class__.casteSynonyms[self.casteOrder[0]]), choice(self.__class__.genesisIdeals[choice(self.__class__.genesisReasons)]))\\n \\n elif 45 <= branch <= 62:\\n if self.casteOrder[0] == 'laborers':\\n if self.tech == 'pre-industrial technology':\\n self.description = 'A kibbutz for {0} {1} on {2}'.format(choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.laborerSynonyms), self.name)\\n elif self.tech == 'machine technology':\\n self.description = 'The worker-owned and operated {1} of {2} {0}'.format(self.name, choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.machineAdjectives))\\n elif self.tech == 'ubiquitous technology':\\n self.description = '{0}, host to a psychic hivemind of {1} {2}'.format(self.name, choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.__class__.laborerSynonyms))\\n elif self.casteOrder[0] == 'artists':\\n if self.tech == 'pre-industrial technology':\\n self.description = 'The conservatory for neoclassical {0} who explore the theme of {1} on {2}'.format(choice(self.__class__.artistSynonyms), choice(self.__class__.genesisIdeals[self.genesis]), self.name)\\n elif self.tech == 'machine technology':\\n self.description = 'A series of art installations conceived by {1} at various {0} on {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.artistSynonyms), self.name)\\n elif self.tech == 'ubiquitous technology':\\n self.description = '{0}: a collective of AI {1} who parody the human theme of {2}'.format(self.name, choice(self.__class__.artistSynonyms), choice(self.__class__.genesisIdeals[self.genesis]))\\n elif self.casteOrder[0] == 'priests':\\n if self.tech == 'pre-industrial technology':\\n self.description = '{0} {1}, home to a scriptorium dedicated to the penning of meditations on {2}'.format(choice(self.__class__.preindustrialAdjectives).capitalize(), self.name, choice(self.__class__.genesisIdeals[self.genesis]))\\n elif self.tech == 'machine technology':\\n self.description = 'Polygamous cultists who live among the {0} of {1} {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.machineAdjectives), self.name)\\n elif self.tech == 'ubiquitous technology':\\n self.description = 'A pyramidic burial chamber and monument to {0} {1} {2}'.format(choice(self.__class__.genesisIdeals[self.genesis]), choice(self.__class__.ubiquitousPreps), self.name)\\n elif self.casteOrder[0] == 'scientists':\\n if self.tech == 'pre-industrial technology':\\n self.description = '{0}, home to a collective of {1} {2} who reproduce scientific experiments from pre-industrial Earth'.format(self.name, choice(self.genesisAdjectives[self.genesis]), choice(self.__class__.scientistSynonyms))\\n elif self.tech == 'machine technology':\\n self.description = 'A society of {0} who work simulated jobs at {1} on {3} to better understand {2}'.format(choice(self.__class__.scientistSynonyms), choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.genesisIdeals[self.genesis]), self.name)\\n elif self.tech == 'ubiquitous technology':\\n self.description = 'The postdoctoral program for {0} tenure-track {1} {2} {3}'.format(choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.__class__.scientistSynonyms), choice(self.__class__.ubiquitousPreps), self.name)\\n elif self.casteOrder[0] == 'soldiers':\\n if self.tech == 'pre-industrial technology':\\n self.description = '{0}, where {1} {2} study their martial arts in the quiet isolation of a {3}'.format(self.name, choice(self.__class__.genesisAdjectives[self.genesis]), choice(self.__class__.soldierSynonyms), choice(self.__class__.preindustrialSettlements))\\n elif self.tech == 'machine technology':\\n self.description = '{0}, where {1} {2} spar each other to overcome their frustrations surrounding {3}'.format(self.name, choice(self.__class__.badjectives), choice(self.__class__.soldierSynonyms), choice(self.__class__.genesisIdeals[self.genesis]))\\n elif self.tech == 'ubiquitous technology':\\n self.description = 'The highly militarized {0} {1} {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.ubiquitousPreps), self.name)\\n elif self.casteOrder[0] == 'merchants':\\n if self.tech == 'pre-industrial technology':\\n self.description = 'An antique fair on {0} where 1% of the proceeds are donated to the study of {1}'.format(self.name, choice(self.__class__.genesisIdeals[self.genesis]))\\n elif self.tech == 'machine technology':\\n self.description = 'A chain of retail {0} near the {1} on {2}'.format(choice(self.__class__.genesisPlaces[self.genesis]), choice(self.__class__.machineSettlements), self.name)\\n elif self.tech == 'ubiquitous technology':\\n self.description = 'A pay-by-the-hour computer simulation {0} {1} where patrons can experience {2}'.format(choice(self.__class__.ubiquitousPreps), self.name, choice(self.__class__.genesisIdeals[self.genesis]))\",\n \"def get_description():\\n desc = dict()\\n today = datetime.date.today()\\n desc['data'] = True\\n desc['description'] = \\\"\\\"\\\"This plot presents one of two metrics indicating\\n daily humidity levels as reported by a surface weather station. The\\n first being mixing ratio, which is a measure of the amount of water\\n vapor in the air. The second being vapor pressure deficit, which reports\\n the difference between vapor pressure and saturated vapor pressure.\\n The vapor pressure calculation shown here makes no accounting for\\n leaf tempeature. The saturation vapor pressure is computed by the\\n Tetens formula (Buck, 1981).\\n\\n <br />On 22 June 2016, this plot was modified to display the range of\\n daily averages and not the range of instantaneous observations.\\n \\\"\\\"\\\"\\n desc['arguments'] = [\\n dict(type='zstation', name='zstation', default='AMW',\\n label='Select Station:', network='IA_ASOS'),\\n dict(type='year', name='year', default=today.year,\\n label='Select Year to Plot'),\\n dict(type='select', name='var', default='mixing_ratio',\\n label='Which Humidity Variable to Compute?', options=PDICT),\\n ]\\n return desc\",\n \"def generate_basic(self, time):\\n output_text = ''\\n try:\\n output_text = 'for x=1 to ' + str(int(time)//4) + ':\\\\n'\\n except:\\n return\\n for i, layer in enumerate(self.c_layers):\\n c = ['0'] * 8\\n b = ['0'] * 8\\n d = ['0'] * 8\\n\\n c[self.num_sides - i - 1] = '1'\\n for point in layer:\\n if not point.on:\\n continue\\n\\n index = 7 - int(point.toplevel[1])\\n if point.toplevel[0] == \\\"b\\\":\\n b[index] = '1'\\n else:\\n d[index] = '1'\\n\\n output_text += f' portb = %{\\\"\\\".join(b)}\\\\n'\\n output_text += f' portd = %{\\\"\\\".join(d)}\\\\n'\\n output_text += f' portc = %{\\\"\\\".join(c)}\\\\n'\\n output_text += ' pause 1\\\\n'\\n output_text += '\\\\n'\\n\\n output_text += ' next x\\\\n'\\n return output_text\",\n \"def summary(self):\\n self.tiles.refreshnames()\\n self.glues.refreshnames()\\n # self.check_consistent()\\n info = {\\n \\\"ntiles\\\": len(self.tiles),\\n \\\"nrt\\\": len([x for x in self.tiles if not x.is_fake]),\\n \\\"nft\\\": len([x for x in self.tiles if x.is_fake]),\\n \\\"nends\\\": len(self.glues),\\n \\\"ntends\\\": len(self.tiles.glues_from_tiles()),\\n \\\"tns\\\": \\\" \\\".join(x.name for x in self.tiles if x.name),\\n \\\"ens\\\": \\\" \\\".join(x.name for x in self.glues if x.name)\\n # if (\\\"info\\\" in self.keys() and \\\"name\\\" in self[\\\"info\\\"].keys())\\n # else \\\"\\\",\\n }\\n tun = sum(1 for x in self.tiles if x.name is None)\\n if tun > 0:\\n info[\\\"tns\\\"] += \\\" ({} unnamed)\\\".format(tun)\\n eun = sum(1 for x in self.glues if x.name is None)\\n if eun > 0:\\n info[\\\"ens\\\"] += \\\" ({} unnamed)\\\".format(eun)\\n if info[\\\"nft\\\"] > 0:\\n info[\\\"nft\\\"] = \\\" (+ {} fake)\\\".format(info[\\\"nft\\\"])\\n else:\\n info[\\\"nft\\\"] = \\\"\\\"\\n return \\\"TileSet: {nrt} tiles{nft}, {nends} ends, {ntends} ends in tiles.\\\\nTiles: {tns}\\\\nEnds: {ens}\\\".format(\\n **info\\n )\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__, \\\"data\\\": True, \\\"cache\\\": 600}\\n today = datetime.date.today()\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"csector\\\",\\n name=\\\"csector\\\",\\n default=\\\"IA\\\",\\n label=\\\"Select state/sector to plot\\\",\\n ),\\n dict(\\n type=\\\"date\\\",\\n name=\\\"sdate\\\",\\n default=f\\\"{today.year}/01/01\\\",\\n label=\\\"Start Date:\\\",\\n min=\\\"2000/01/04\\\",\\n max=today.strftime(\\\"%Y/%m/%d\\\"),\\n ),\\n dict(\\n type=\\\"date\\\",\\n name=\\\"edate\\\",\\n default=today.strftime(\\\"%Y/%m/%d\\\"),\\n label=\\\"End Date:\\\",\\n min=\\\"2000/01/04\\\",\\n max=today.strftime(\\\"%Y/%m/%d\\\"),\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"d\\\",\\n default=\\\"0\\\",\\n options=PDICT,\\n label=\\\"Select Drought Classification (at and above counted):\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"w\\\",\\n default=\\\"percent\\\",\\n options=PDICT2,\\n label=\\\"How to express time for plot:\\\",\\n ),\\n dict(type=\\\"cmap\\\", name=\\\"cmap\\\", default=\\\"plasma\\\", label=\\\"Color Ramp:\\\"),\\n ]\\n return desc\",\n \"def description():\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__}\\n sts = utc() - timedelta(hours=26)\\n ets = utc() - timedelta(hours=2)\\n desc[\\\"arguments\\\"] = [\\n {\\n \\\"type\\\": \\\"datetime\\\",\\n \\\"name\\\": \\\"sts\\\",\\n \\\"default\\\": sts.strftime(\\\"%Y/%m/%d %H00\\\"),\\n \\\"label\\\": \\\"Start Timestamp (UTC):\\\",\\n \\\"min\\\": \\\"1986/01/01 0000\\\",\\n },\\n {\\n \\\"type\\\": \\\"datetime\\\",\\n \\\"name\\\": \\\"ets\\\",\\n \\\"default\\\": ets.strftime(\\\"%Y/%m/%d %H00\\\"),\\n \\\"label\\\": (\\n \\\"End Timestamp [inclusive] (UTC), \\\"\\n \\\"interval must be less than 4 days\\\"\\n ),\\n \\\"min\\\": \\\"1986/01/01 0000\\\",\\n },\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"options\\\": PDICT,\\n \\\"default\\\": \\\"min\\\",\\n \\\"name\\\": \\\"w\\\",\\n \\\"label\\\": \\\"Which statistic to compute\\\",\\n },\\n {\\n \\\"type\\\": \\\"csector\\\",\\n \\\"name\\\": \\\"csector\\\",\\n \\\"default\\\": \\\"IA\\\",\\n \\\"label\\\": \\\"Select state/sector\\\",\\n },\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"options\\\": PDICT2,\\n \\\"default\\\": \\\"user\\\",\\n \\\"label\\\": \\\"Plotting mode (user defined color-ramp or freezing)\\\",\\n \\\"name\\\": \\\"mode\\\",\\n },\\n {\\n \\\"type\\\": \\\"cmap\\\",\\n \\\"name\\\": \\\"cmap\\\",\\n \\\"default\\\": \\\"gnuplot2\\\",\\n \\\"label\\\": \\\"Color Ramp:\\\",\\n },\\n ]\\n return desc\",\n \"def get_description():\\n desc = dict()\\n desc['data'] = True\\n desc['description'] = \\\"\\\"\\\"This plot displays the maximum and minimum change\\n in high temperature between a given day and a given number of days prior\\n to that date. The red bars are the largest difference between a maximum\\n high over a period of days and the given day. The blue bars are the\\n opposite.\\\"\\\"\\\"\\n desc['arguments'] = [\\n dict(type='station', name='station', default='IA0200',\\n label='Select Station:', network='IACLIMATE'),\\n dict(type='int', name='days', default='4',\\n label='Number of Trailing Days:'),\\n ]\\n return desc\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def description(self) -> str:\\r\\n description = \\\"The player must aim to put the most possible units \\\" + \\\\\\r\\n \\\"of \\\" + colour_name(self.colour) + \\\" on the outer\\\" +\\\\\\r\\n \\\" perimeter.\\\"\\r\\n return description\",\n \"def get_description():\\n desc = dict()\\n desc['cache'] = 86400\\n desc['data'] = True\\n desc['description'] = \\\"\\\"\\\"This chart presents a histogram of issuance times\\n for a given watch, warning, or advisory type for a given office.\\\"\\\"\\\"\\n desc['arguments'] = [\\n dict(type='networkselect', name='station', network='WFO',\\n default='DMX', label='Select WFO:'),\\n dict(type='phenomena', name='phenomena',\\n default='WC', label='Select Watch/Warning Phenomena Type:'),\\n dict(type='significance', name='significance',\\n default='W', label='Select Watch/Warning Significance Level:'),\\n ]\\n return desc\",\n \"def description(self) -> str:\\r\\n descrip = 'The player must aim to put the most possible units of a ' \\\\\\r\\n 'given colour c on the outer perimeter of ' \\\\\\r\\n 'the board. The ' \\\\\\r\\n 'player’s score is the total number of unit cells ' \\\\\\r\\n 'of colour ' \\\\\\r\\n 'c that are on the perimeter. There is a ' \\\\\\r\\n 'premium on corner ' \\\\\\r\\n 'cells: they count twice towards the score. '\\r\\n return descrip\",\n \"def describe():\",\n \"def get_description():\\n desc = {}\\n desc[\\\"data\\\"] = True\\n desc[\\n \\\"description\\\"\\n ] = \\\"\\\"\\\"For each year, the average first and last date of\\n a given temperature is computed. The values are then averaged and plotted\\n to represent the period between these occurences and also the number of\\n days represented by the period.\\n \\\"\\\"\\\"\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"station\\\",\\n name=\\\"station\\\",\\n default=\\\"IATDSM\\\",\\n label=\\\"Select Station\\\",\\n network=\\\"IACLIMATE\\\",\\n )\\n ]\\n return desc\",\n \"def _generate_overlay_file_name(self, well, channel, desc):\\n \\n return \\\"c\\\" + channel + \\\"_w\\\" + well + \\\"_\\\" + desc + \\\".png\\\"\",\n \"def GetDescription(cls):\\n return textwrap.dedent('''\\n This trace step includes a diagram of the Ego long. acceleration in the report.\\n ''').strip()\",\n \"def get_description():\\r\\n return{\\\"I'll never yield!\\\":\\\"Grants a shield.\\\",\\r\\n \\\"Stay still!\\\":\\\"Affected unit cannot act in their turn.\\\"\\r\\n }\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__, \\\"data\\\": True}\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"station\\\",\\n name=\\\"station\\\",\\n default=\\\"IATDSM\\\",\\n label=\\\"Select Station:\\\",\\n network=\\\"IACLIMATE\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"var\\\",\\n default=\\\"spi\\\",\\n options=PDICT,\\n label=\\\"Select which metric to plot:\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"c\\\",\\n default=\\\"ncei91\\\",\\n options=PDICT2,\\n label=\\\"Which climatology to use for averages:\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n name=\\\"days\\\",\\n default=90,\\n label=\\\"Over how many trailing days to compute the metric?\\\",\\n ),\\n ]\\n return desc\",\n \"def get_description():\\n desc = dict()\\n desc[\\\"cache\\\"] = 3600\\n desc[\\\"data\\\"] = True\\n desc[\\n \\\"description\\\"\\n ] = \\\"\\\"\\\"This plot is not meant for interactive use, but a backend for\\n SPS plots.\\n \\\"\\\"\\\"\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"text\\\",\\n name=\\\"pid\\\",\\n default=\\\"202012300005-KDVN-WWUS83-SPSDVN\\\",\\n label=\\\"IEM generated up to 35 char product identifier:\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n default=0,\\n name=\\\"segnum\\\",\\n label=\\\"Product Segment Number (starts at 0):\\\",\\n ),\\n ]\\n return desc\",\n \"def description(self) -> str:\\r\\n descrip = 'The player must aim for the largest “blob” of a given ' \\\\\\r\\n 'colour c. A blob is a group of connected blocks with the ' \\\\\\r\\n 'same colour. Two blocks are connected if their sides ' \\\\\\r\\n 'touch; touching corners doesn’t count. The player’s score ' \\\\\\r\\n 'is the number of unit cells in the largest blob of colour ' \\\\\\r\\n 'c. '\\r\\n return descrip\",\n \"def __str__(self):\\n return '<TuebingenMEG: %i samples, %i timepoints, %i channels>' \\\\\\n % (self.nsamples, self.ntimepoints, len(self.channelids))\",\n \"def desc(self):\\n return LandCell.desc(self) + \\\"; plant=\\\" + str(self.plant)\",\n \"def description(self):\\n\\n\\t\\treturn \\\"%d %s %s\\\" % (self.vintage, self.winery.name, self.name)\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__}\\n desc[\\\"data\\\"] = True\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"select\\\",\\n options=PDICT3,\\n default=\\\"temps\\\",\\n name=\\\"v\\\",\\n label=\\\"Which Variable(s) to Plot\\\",\\n ),\\n dict(\\n type=\\\"station\\\",\\n name=\\\"station1\\\",\\n default=\\\"IATDSM\\\",\\n label=\\\"Select First Station:\\\",\\n network=\\\"IACLIMATE\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"c1\\\",\\n label=\\\"Climatology Source for First Station:\\\",\\n default=\\\"1951\\\",\\n options=PDICT,\\n ),\\n dict(\\n type=\\\"station\\\",\\n name=\\\"station2\\\",\\n default=\\\"IATDSM\\\",\\n optional=True,\\n label=\\\"Select Second Station (Optional):\\\",\\n network=\\\"IACLIMATE\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"c2\\\",\\n label=\\\"Climatology Source for Second Station:\\\",\\n default=\\\"1951\\\",\\n options=PDICT,\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"s\\\",\\n label=\\\"For difference plot, should smoother be applied:\\\",\\n default=\\\"0\\\",\\n options=PDICT2,\\n ),\\n dict(\\n type=\\\"year\\\",\\n min=1880,\\n name=\\\"sy1\\\",\\n default=1991,\\n label=\\\"Inclusive Start Year for First Station Period of Years:\\\",\\n ),\\n dict(\\n type=\\\"year\\\",\\n min=1880,\\n name=\\\"ey1\\\",\\n default=2020,\\n label=\\\"Inclusive End Year for First Station Period of Years:\\\",\\n ),\\n dict(\\n type=\\\"year\\\",\\n min=1880,\\n name=\\\"sy2\\\",\\n default=1981,\\n label=\\\"Inclusive Start Year for Second Station Period of Years:\\\",\\n ),\\n dict(\\n type=\\\"year\\\",\\n min=1880,\\n name=\\\"ey2\\\",\\n default=2010,\\n label=\\\"Inclusive End Year for Second Station Period of Years:\\\",\\n ),\\n ]\\n return desc\",\n \"def _generate_raw_file_name(self, well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def __init__(self, name=\\\"\\\", description=\\\"\\\", time_units=\\\"s\\\", len_units=\\\"m\\\"):\\n\\n # Set general info\\n self._type = 2 # observation well id\\n self.time_units = time_units\\n self.len_units = len_units\\n\\n self.parameters = {'full': True, # is full penetrating?\\n 'r': 1., # radius, distance until pumping well in length units\\n 'd': 0., # depth of well screen (from top) in length units\\n 'l': 1.} # depth of well bottom in length units\\n\\n # Create drawdown data\\n self.drawdown = _Data(dtype=1, name=name, description=description)\\n self.drawdown.set_units(self.time_units, self.len_units)\\n\\n # Set results from models\\n self.data = []\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__, \\\"data\\\": True}\\n dt = datetime.date.today() - datetime.timedelta(days=1)\\n desc[\\\"arguments\\\"] = [\\n {\\n \\\"type\\\": \\\"select\\\",\\n \\\"name\\\": \\\"mode\\\",\\n \\\"default\\\": \\\"normal\\\",\\n \\\"label\\\": \\\"Application Mode:\\\",\\n \\\"options\\\": PDICT,\\n },\\n dict(\\n type=\\\"state\\\",\\n name=\\\"state\\\",\\n default=\\\"IA\\\",\\n label=\\\"Select State:\\\",\\n ),\\n dict(\\n type=\\\"date\\\",\\n name=\\\"date\\\",\\n default=dt.strftime(\\\"%Y/%m/%d\\\"),\\n label=\\\"Retroactive Date of Plot\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n name=\\\"d1\\\",\\n default=30,\\n label=\\\"Over how many trailing days to compute the metric?\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n name=\\\"d2\\\",\\n default=60,\\n label=\\\"Over how many trailing days to compute the metric?\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n name=\\\"d3\\\",\\n default=90,\\n label=\\\"Over how many trailing days to compute the metric?\\\",\\n ),\\n ]\\n return desc\",\n \"def get_description():\\n d = dict()\\n d['data'] = True\\n d['report'] = True\\n d['description'] = \\\"\\\"\\\" \\\"\\\"\\\"\\n d['arguments'] = [\\n dict(type='station', name='station', default='IA2203',\\n label='Select Station'),\\n ]\\n return d\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__, \\\"data\\\": True}\\n today = datetime.date.today()\\n thisyear = today.year\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"station\\\",\\n name=\\\"station\\\",\\n default=\\\"IATDSM\\\",\\n label=\\\"Select Station:\\\",\\n network=\\\"IACLIMATE\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n options=PDICT,\\n name=\\\"var\\\",\\n default=\\\"precip\\\",\\n label=\\\"Accumulate Precipitation or Snow?\\\",\\n ),\\n dict(\\n type=\\\"year\\\",\\n name=\\\"year1\\\",\\n default=thisyear,\\n label=\\\"Additional Year to Plot:\\\",\\n ),\\n dict(\\n type=\\\"year\\\",\\n name=\\\"year2\\\",\\n optional=True,\\n default=(thisyear - 1),\\n label=\\\"Additional Year to Plot: (optional)\\\",\\n ),\\n dict(\\n type=\\\"year\\\",\\n name=\\\"year3\\\",\\n optional=True,\\n default=(thisyear - 2),\\n label=\\\"Additional Year to Plot: (optional)\\\",\\n ),\\n dict(\\n type=\\\"sday\\\",\\n name=\\\"sdate\\\",\\n default=\\\"0101\\\",\\n label=\\\"Start Day of Year for Plot:\\\",\\n ),\\n dict(\\n optional=True,\\n type=\\\"sday\\\",\\n name=\\\"edate\\\",\\n default=f\\\"{today:%m%d}\\\",\\n label=\\\"End Day of Year for Plot:\\\",\\n ),\\n dict(\\n type=\\\"int\\\",\\n default=\\\"3\\\",\\n label=\\\"Number of missing days to allow before excluding year\\\",\\n name=\\\"m\\\",\\n ),\\n ]\\n return desc\",\n \"def GenBrief(self):\\n\\n brief = \\\"[{0}] {1} \\\".format(self.TimeStamp.strftime( \\\"%H:%M\\\"), ActionType.actionNames[self.Type])\\n\\n if self.Type in {ActionType.Feed, ActionType.Notes, ActionType.WakeUp, ActionType.SleepTime,\\\\\\n ActionType.ComFood, ActionType.ErrStatus, ActionType.Pills, ActionType.Snacks}:\\n brief += self.Detail\\n elif self.Type == ActionType.Poop:\\n pass\\n elif self.Type == ActionType.AD:\\n pass\\n elif self.Type == ActionType.UnKnown:\\n brief += \\\"可以尝试 '总结' 或 '一周总结' 查看萝卜成长状态。\\\"\\n elif self.Type == ActionType.FallSleep:\\n tnow = cn_utility.GetNowForUTC8()\\n delta_minutes = int((tnow - self.TimeStamp).total_seconds()/60)\\n #brief += \\\"已经睡了{0}小时{1}分钟.\\\\n天气干，记得涂唇膏。\\\".format(int(delta_minutes/60), delta_minutes%60)\\n brief += \\\"已经睡了{0}小时{1}分钟.\\\".format(int(delta_minutes/60), delta_minutes%60)\\n else:\\n pass\\n\\n if len(brief) > 300:\\n brief = brief[0:300]\\n\\n return brief\",\n \"def descString(self):\\n return \\\"\\\".join ([self.Name, \\\" (\\\", str(self.RollCount), \\\"d\\\"\\\\\\n , str(self.RollMax), \\\"; \\\", str(self.CritRollMin), \\\"-\\\"\\\\\\n , str(self.CritRollMax), \\\"x\\\", str (self.CritRollMult)\\\\\\n , \\\") - \\\", str(self.Value), \\\" gp\\\"])\",\n \"def info():\\n return r\\\"\\\"\\\"Tseng, Lin-Yu, and Chun Chen. \\\"Multiple trajectory search for unconstrained/constrained multi-objective optimization.\\\" Evolutionary Computation, 2009. CEC'09. IEEE Congress on. IEEE, 2009.\\\"\\\"\\\"\",\n \"def info():\\n return r\\\"\\\"\\\"Lin-Yu Tseng and Chun Chen, \\\"Multiple trajectory search for Large Scale Global Optimization,\\\" 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, 2008, pp. 3052-3059. doi: 10.1109/CEC.2008.4631210\\\"\\\"\\\"\",\n \"def description(self):\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__, \\\"data\\\": True}\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"station\\\",\\n name=\\\"station\\\",\\n default=\\\"IATDSM\\\",\\n label=\\\"Select Station\\\",\\n network=\\\"IACLIMATE\\\",\\n ),\\n dict(\\n type=\\\"select\\\",\\n name=\\\"var\\\",\\n default=\\\"precip\\\",\\n label=\\\"Which Variable:\\\",\\n options=PDICT,\\n ),\\n ]\\n return desc\",\n \"def get_description(self):\\n return \\\"-\\\".join(\\n map(str, (self.release, self.chromosome, self.start, self.reference, self.alternative))\\n )\",\n \"def __str__(self):\\n if len(self.label) > 0:\\n descr = [\\\"'%s', target='%s' [%s]\\\" % (self.label, self.target.name, self.target.body_type)]\\n else:\\n descr = [\\\"target='%s' [%s]\\\" % (self.target.name, self.target.body_type)]\\n if self.baseline:\\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\\n if self.beam:\\n descr[0] += ', beam height=%f' % (self.beam.height,)\\n for scan_ind, scan in enumerate(self.scans):\\n descr.append('%4d: %s' % (scan_ind, str(scan)))\\n return '\\\\n'.join(descr)\",\n \"def __str__(self):\\n rep=\\\"This system has \\\"+str(self.NL)+\\\" layers.\\\\n\\\"\\n rep+=\\\"The parameters for the each layers are:\\\\n\\\"\\n for i in range(self.NL-1):\\n rep+=\\\"Layer no. \\\"+str(i)+\\\":\\\\t \\\"+str(self.layers[i])\\n rep+=\\\"Coupled to the next layer with strength:\\\\t\\\"+str(self.couplings[i])+\\\"\\\\n\\\"\\n rep+=\\\"Layer no. \\\"+str(self.NL-1)+\\\":\\\\t \\\"+str(self.layers[self.NL-1])\\n \\n return rep\",\n \"def AMdepth(depthPerc=100,channel=1):\\r\\n inst.write(\\\":AM%i:DEPT %iPCT\\\" %(channel,depthPerc))\",\n \"def getDesc():\\n\\treturn \\\"Create an animation showing the dataset with the Animator\\\"\",\n \"def plane_desc(self) -> str:\\n return self.planes[0].join(' ') + self.planes[1].join(' ') + self.planes[2].join(' ')\",\n \"def describe(self) -> str:\",\n \"def __str__(self):\\n description = \\\"-------- %s --------\\\" % (self.name)\\n description += \\\"\\\\nnmax = %i\\\" % (self.nmax)\\n description += \\\"\\\\nnslots = %i\\\" % (self.nslots)\\n description += \\\"\\\\nbonus_power = %i\\\" % (self.bonus_power)\\n description += \\\"\\\\nbonus_initiative = %.1f\\\" % (self.bonus_initiative)\\n description += \\\"\\\\nneeds_drive = %i\\\" % (self.needs_drive)\\n description += \\\"\\\\nis_mobile = %i\\\" % (self.is_mobile)\\n description += \\\"\\\\n----- Default Parts -----\\\"\\n for i in range(len(self.default_parts)):\\n description += \\\"\\\\n%i) %s\\\" % (i + 1, self.default_parts[i].name)\\n return description\",\n \"def describe_me():\\n # this is quite different than the `describe_me` in ARAX_overlay and ARAX_filter_kg due to expander being less\\n # of a dispatcher (like overlay and filter_kg) and more of a single self contained class\\n brief_description = \\\"\\\"\\\"\\n`expand` effectively takes a query graph (QG) and reaches out to various knowledge providers (KP's) to find all bioentity subgraphs\\nthat satisfy that QG and augments the knowledge graph (KG) with them. As currently implemented, `expand` can utilize the ARA Expander\\nteam KG1 and KG2 Neo4j instances as well as BioThings Explorer to fulfill QG's, with functionality built in to reach out to other KP's as they are rolled out.\\n \\\"\\\"\\\"\\n description_list = []\\n params_dict = dict()\\n params_dict['brief_description'] = brief_description\\n params_dict['edge_id'] = {\\\"a query graph edge ID or list of such IDs to expand (optional, default is to expand entire query graph)\\\"} # this is a workaround due to how self.parameters is utilized in this class\\n params_dict['node_id'] = {\\\"a query graph node ID to expand (optional, default is to expand entire query graph)\\\"}\\n params_dict['kp'] = {\\\"the knowledge provider to use - current options are `ARAX/KG1`, `ARAX/KG2`, or `BTE` (optional, default is `ARAX/KG1`)\\\"}\\n params_dict['enforce_directionality'] = {\\\"whether to obey (vs. ignore) edge directions in query graph - options are `true` or `false` (optional, default is `false`)\\\"}\\n params_dict['use_synonyms'] = {\\\"whether to consider synonym curies for query nodes with a curie specified - options are `true` or `false` (optional, default is `true`)\\\"}\\n params_dict['synonym_handling'] = {\\\"how to handle synonyms in the answer - options are `map_back` (default; map edges using a synonym back to the original curie) or `add_all` (add synonym nodes as they are - no mapping/merging)\\\"}\\n params_dict['continue_if_no_results'] = {\\\"whether to continue execution if no paths are found matching the query graph - options are `true` or `false` (optional, default is `false`)\\\"}\\n description_list.append(params_dict)\\n return description_list\",\n \"def description(self) -> str:\\n return \\\"put units of a given colour on the perimeter of the board.\\\"\",\n \"def description() -> str:\\n content = \\\"Demonstrates usage of blackbord remappings.\\\\n\\\"\\n content += \\\"\\\\n\\\"\\n content += \\\"Demonstration is via an exemplar behaviour making use of remappings..\\\\n\\\"\\n\\n if py_trees.console.has_colours:\\n banner_line = console.green + \\\"*\\\" * 79 + \\\"\\\\n\\\" + console.reset\\n s = banner_line\\n s += console.bold_white + \\\"Blackboard\\\".center(79) + \\\"\\\\n\\\" + console.reset\\n s += banner_line\\n s += \\\"\\\\n\\\"\\n s += content\\n s += \\\"\\\\n\\\"\\n s += banner_line\\n else:\\n s = content\\n return s\",\n \"def get_descr_short(self):\\n desc_text = []\\n stack = [(self._desc, 0)]\\n while stack:\\n dl, di = stack.pop()\\n while di < len(dl):\\n if dl[di] == 0:\\n di += 1\\n elif dl[di] > 0 and dl[di] < 100000:\\n desc_text.append(\\\"%06d\\\" % dl[di])\\n elif dl[di] >= 100000 and dl[di] < 200000:\\n desc_text.append(\\\"%06d LOOP\\\" % dl[di])\\n elif dl[di] >= 200000 and dl[di] < 300000:\\n desc_text.append(\\\"%06d OPER\\\" % dl[di])\\n elif dl[di] >= 300000 and dl[di] < 400000:\\n desc_text.append(\\\"%06d SEQ\\\" % dl[di])\\n di += 1\\n return desc_text\",\n \"def describe(self, level=0):\\n \\n d = {'Nodes': self.num_nodes,\\n 'Links': self.num_links,\\n 'Patterns': self.num_patterns,\\n 'Curves': self.num_curves,\\n 'Sources': self.num_sources,\\n 'Controls': self.num_controls}\\n \\n if level >= 1:\\n d['Nodes'] = {\\n 'Junctions': self.num_junctions,\\n 'Tanks': self.num_tanks,\\n 'Reservoirs': self.num_reservoirs}\\n d['Links'] = {\\n 'Pipes': self.num_pipes,\\n 'Pumps': self.num_pumps,\\n 'Valves': self.num_valves}\\n d['Curves'] = {\\n 'Pump': len(self._curve_reg._pump_curves), \\n 'Efficiency': len(self._curve_reg._efficiency_curves), \\n 'Headloss': len(self._curve_reg._headloss_curves), \\n 'Volume': len(self._curve_reg._volume_curves)}\\n \\n if level >= 2:\\n d['Links']['Pumps'] = {\\n 'Head': len(list(self.head_pumps())),\\n 'Power': len(list(self.power_pumps()))}\\n d['Links']['Valves'] = {\\n 'PRV': len(list(self.prvs())),\\n 'PSV': len(list(self.psvs())),\\n 'PBV': len(list(self.pbvs())),\\n 'TCV': len(list(self.tcvs())),\\n 'FCV': len(list(self.fcvs())),\\n 'GPV': len(list(self.gpvs()))}\\n \\n return d\",\n \"def describe(self):\\n composition = str()\\n for n, comp in enumerate(self.components):\\n if self.molefractions[n] > 0.0:\\n composition += comp.name\\n composition += \\\" %.2f\\\" % self.molefractions[n]\\n composition += \\\"; \\\"\\n return composition\",\n \"def get_description():\\n desc = {\\\"description\\\": __doc__, \\\"data\\\": True, \\\"cache\\\": 300}\\n desc[\\\"defaults\\\"] = {\\\"_r\\\": \\\"t\\\"}\\n yest = date.today() - timedelta(days=1)\\n desc[\\\"arguments\\\"] = [\\n dict(\\n type=\\\"networkselect\\\",\\n name=\\\"station\\\",\\n default=\\\"KDSM\\\",\\n label=\\\"Select Station:\\\",\\n network=\\\"NWSCLI\\\",\\n ),\\n dict(\\n type=\\\"date\\\",\\n name=\\\"date\\\",\\n default=yest.strftime(\\\"%Y/%m/%d\\\"),\\n label=\\\"Select Date\\\",\\n min=\\\"2000/01/01\\\",\\n ),\\n ]\\n return desc\",\n \"def __str__(self):\\n description = \\\"Object class Critter.\\\"\\n description += \\\"\\\\nName: \\\" + self.name + \\\"\\\\nMood: \\\" + self.mood + \\\"\\\\nHunger: \\\" + str(self.hunger) + \\\"\\\\nBoredom: \\\" + str(self.boredom) + \\\"\\\\n\\\"\\n return description\",\n \"def description(self):\\n return '%s and a CD' % self.component.description()\",\n \"def generate_file_name(well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def _info(self, N):\\n IDs = self.chemicals.IDs\\n data = self.data\\n IDs, data = nonzeros(IDs, data)\\n N_IDs = len(IDs)\\n if N_IDs == 0:\\n return f\\\"{type(self).__name__}: (empty)\\\"\\n elif self.units:\\n basic_info = f\\\"{type(self).__name__} ({self.units}):\\\\n\\\"\\n else:\\n basic_info = f\\\"{type(self).__name__}:\\\\n\\\"\\n beginning = f'({self.phase}) ' if self.phase else \\\" \\\"\\n new_line = '\\\\n' + len(beginning) * ' '\\n data_info = ''\\n lengths = [len(i) for i in IDs]\\n maxlen = max(lengths) + 1\\n N_max = N or tmo.Stream.display_units.N\\n too_many_chemicals = N_IDs > N_max\\n N = N_max if too_many_chemicals else N_IDs\\n for i in range(N):\\n spaces = ' ' * (maxlen - lengths[i])\\n if i != 0:\\n data_info += new_line\\n data_info += IDs[i] + spaces + f' {data[i]:.3g}'\\n if too_many_chemicals:\\n data_info += new_line + '...'\\n return (basic_info\\n + beginning\\n + data_info)\",\n \"def card_format(card):\\n pretty_output = '%s pts: %d powers: %s' % (\\n card['name'],\\n card['points'],\\n ', '.join(card['powers'])\\n )\\n pretty_output += ' desc: %s' % card['description']\\n return pretty_output\",\n \"def write_data_card(spec, data_card, channels, path):\\n with open(path, \\\"w\\\") as f:\\n f.write(f\\\"imax {str(size(data_card.bins))}\\\" + \\\"\\\\n\\\")\\n f.write(\\n \\\"jmax \\\"\\n + str(size(data_card.processes) - size(data_card.isSignal.keys()))\\n + \\\"\\\\n\\\"\\n )\\n f.write(f\\\"kmax {str(size(data_card.systs, 0))}\\\" + \\\"\\\\n\\\")\\n\\n if data_card.hasShapes:\\n for channel in data_card.shapeMap.keys():\\n for sample in data_card.shapeMap[channel].keys():\\n f.write(\\n f\\\"shapes {sample} {channel} {data_card.shapeMap[channel][sample][0]} {data_card.shapeMap[channel][sample][1]}\\\"\\n )\\n if size(data_card.shapeMap[channel][sample]) > 2:\\n f.write(f\\\" {data_card.shapeMap[channel][sample][2]}\\\" + \\\"\\\\n\\\")\\n else:\\n f.write(\\\"\\\\n\\\")\\n\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n f.write(\\\"bin \\\")\\n for bin in data_card.obs.keys():\\n f.write(f\\\"{bin} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"observation \\\")\\n for channel in data_card.obs.keys():\\n f.write(f\\\"{str(data_card.obs[channel])} \\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n f.write(\\\"bin \\\")\\n for channel in data_card.obs.keys():\\n for sample in data_card.exp[channel].keys():\\n f.write(f\\\"{channel} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"process \\\")\\n for channel in data_card.bins:\\n for sample in data_card.exp[channel].keys():\\n f.write(f\\\"{sample} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"process \\\")\\n for channel in data_card.bins:\\n for sample in data_card.exp[channel].keys():\\n if sample in data_card.signals:\\n f.write(f\\\"{str(-1 * data_card.processes.index(sample))} \\\")\\n else:\\n f.write(f\\\"{str(data_card.processes.index(sample) + 1)} \\\")\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"rate \\\")\\n for channel in data_card.bins:\\n for sample in data_card.exp[channel].keys():\\n\\n f.write(f\\\"{str(data_card.exp[channel][sample])} \\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n for syst in data_card.systs:\\n f.write(f\\\"{syst[0]} {syst[2]} \\\")\\n for bin in syst[4].keys():\\n for sample in data_card.exp[bin].keys():\\n if syst[4][bin][sample] != 0:\\n f.write(f\\\"{str(syst[4][bin][sample])} \\\")\\n else:\\n f.write(\\\"- \\\")\\n\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n for cAp in data_card.rateParams.keys():\\n _dir = cAp.split(\\\"AND\\\")\\n for i in range(size(data_card.rateParams[cAp], 0)):\\n if size(data_card.rateParams[cAp][i][0]) > 3:\\n f.write(\\n f\\\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])} {data_card.rateParams[cAp][i][0][3]}\\\"\\n )\\n else:\\n f.write(\\n f\\\"{str(data_card.rateParams[cAp][i][0][0])} rateParam {_dir[0]} {_dir[1]} {str(data_card.rateParams[cAp][i][0][1])}\\\"\\n )\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"\\\\n---------------------------------\\\\n\\\")\\n for idxc, channel in enumerate(channels):\\n if (\\n channel in data_card.binParFlags.keys()\\n and data_card.binParFlags[channel] == True\\n ):\\n # double check to be safe\\n shapesys = False\\n staterror = False\\n for sample in spec[\\\"channels\\\"][idxc][\\\"samples\\\"]:\\n mod_types = [mod[\\\"type\\\"] for mod in sample[\\\"modifiers\\\"]]\\n if \\\"shapesys\\\" in mod_types:\\n shapesys = True\\n elif \\\"staterror\\\" in mod_types:\\n staterror = True\\n\\n if shapesys:\\n f.write(f\\\"{channel} autoMCStats 100000 0 2\\\" + \\\"\\\\n\\\")\\n if staterror:\\n f.write(f\\\"{channel} autoMCStats 0 0 2\\\" + \\\"\\\\n\\\")\",\n \"def _get_description_str(epoch_idx):\\n total_digits = len(str(cfg.epochs))\\n cur_digits = len(str(epoch_idx))\\n sup_digits = total_digits - cur_digits\\n desc_str = f\\\"Epoch {'0' * (sup_digits) + str(epoch_idx + 1)}/{cfg.epochs}\\\"\\n\\n return desc_str\",\n \"def printHam(ham: Dict[str, Any], digits: int = 4) -> None:\\n\\n qubitNum = ham['circuit']['qubits']\\n\\n print(f\\\"\\\\n====================\\\\n1. Basic information\\\\n====================\\\\n\\\")\\n print(f\\\"Title: `{ham['file']['title']}`\\\")\\n print(f\\\"Qubits: {qubitNum}\\\")\\n print(f\\\"System energy level: {ham['circuit']['sys_level']}\\\")\\n print(f\\\"Sampling interval: {ham['circuit']['dt']} ns\\\")\\n print(f\\\"Circuit duration: {ham['circuit']['max_time_ns']} ns\\\")\\n print(f\\\"Calculation steps: {ham['circuit']['max_time_dt']}\\\")\\n\\n # Obtain the max length name\\n maxNameLengthDrift = 0 if len(ham[\\\"drift\\\"]) == 0 else max([len(key) for key in ham[\\\"drift\\\"]])\\n maxNameLengthControl = 0 if len(ham[\\\"control\\\"]) == 0 else max([len(key) for key in ham[\\\"control\\\"]])\\n maxNameLength = str(max(max(maxNameLengthDrift, maxNameLengthControl), 10))\\n\\n # Print abstract of operator\\n print(f\\\"\\\\n============\\\\n2. Operators\\\\n============\\\\n\\\")\\n qubitFormat = \\\"{0: <5} {1: <7} {2: <\\\" + maxNameLength + \\\"} {3: <9} {4: <6} {5: <6}\\\"\\n print(qubitFormat.format('-' * 5, '-' * 7, '-' * 10, '-' * 9, '-' * 6, '-' * 6))\\n print(qubitFormat.format(\\\"Qubit\\\", \\\"Type\\\", \\\"Name\\\", \\\"On qubits\\\", \\\"Pulses\\\", \\\"Amp\\\"))\\n for qubit in range(qubitNum):\\n print(qubitFormat.format('-' * 5, '-' * 7, '-' * 10, '-' * 9, '-' * 6, '-' * 6))\\n for key in ham[\\\"drift\\\"]:\\n drifts = ham[\\\"drift\\\"][key]\\n if qubit in drifts[\\\"on_qubits\\\"]:\\n print(qubitFormat.format(qubit, \\\"Drift\\\", key, f\\\"{drifts['on_qubits']}\\\", 0, f\\\"{drifts['amp']}\\\"))\\n for key in ham[\\\"control\\\"]:\\n ctrls = ham[\\\"control\\\"][key]\\n if qubit in ctrls[\\\"on_qubits\\\"]:\\n print(qubitFormat.format(qubit, \\\"Control\\\", key, f\\\"{ctrls['on_qubits']}\\\",\\n len(ctrls['waveforms']), \\\"-\\\"))\\n\\n # Print abstract of waveforms\\n def paraRound(para: Dict[str, Any]) -> Union[Dict[str, Any], None]:\\n \\\"\\\"\\\" Reduce the length of pulse Parameters \\\"\\\"\\\"\\n if para is None:\\n return None\\n else:\\n for key in para:\\n para[key] = round(para[key], digits)\\n return para\\n\\n print(f\\\"\\\\n============\\\\n3. Waveforms\\\\n============\\\\n\\\")\\n qubitFormat = \\\"{0: <9} {1: <\\\" + maxNameLength + \\\"} {2: <20} {3: <5} {4: <7} {5: <45}\\\"\\n print(qubitFormat.format('-' * 9, '-' * 10, '-' * 15, '-' * 5, '-' * 7, '-' * 45))\\n print(qubitFormat.format(\\\"On qubits\\\", \\\"Control\\\", \\\"Waveform\\\", \\\"Start\\\", \\\"Duration\\\", \\\"Params (Sequences)\\\"))\\n for key in ham[\\\"control\\\"]:\\n ctrls = ham[\\\"control\\\"][key]\\n if len(ctrls['waveforms']) > 0:\\n print(qubitFormat.format('-' * 9, '-' * 10, '-' * 15, '-' * 5, '-' * 7, '-' * 45))\\n for wave in ctrls['waveforms']:\\n waveName = \\\"\\\"\\n wavePara = \\\"\\\"\\n if wave['func'] is None:\\n waveName = \\\"Manual Sequence\\\"\\n wavePara = f\\\"Sequence contains {len(wave['sequence'])} pieces\\\"\\n elif callable(wave['func']):\\n waveName = \\\"Manual Wave\\\"\\n wavePara = f\\\"{paraRound(wave['para'])}\\\"\\n elif isinstance(wave['func'], str):\\n waveName = wave['func']\\n wavePara = f\\\"{paraRound(wave['para'])}\\\"\\n print(qubitFormat.format(f\\\"{ctrls['on_qubits']}\\\", key, waveName, wave['insert_ns'],\\n wave['duration_ns'], wavePara))\",\n \"def get_descr_full(self):\\n desc_text = []\\n stack = [(self._desc, 0)]\\n while stack:\\n dl, di = stack.pop()\\n while di < len(dl):\\n if dl[di] == 0:\\n di += 1\\n elif dl[di] > 0 and dl[di] < 100000:\\n desc_text.append(str(self._tables.tab_b[dl[di]]))\\n di += 1\\n elif dl[di] >= 100000 and dl[di] < 200000:\\n lm = dl[di] // 1000 - 100\\n ln = dl[di] % 1000\\n desc_text.append(\\\"%06d : LOOP, %d desc., %d times\\\" % (dl[di], lm , ln))\\n di += 1\\n elif dl[di] >= 200000 and dl[di] < 300000:\\n en = self._tables.tab_c.get(dl[di])\\n am = dl[di] // 1000 - 200\\n an = dl[di] % 1000\\n if en is None:\\n en = (str(am), \\\"\\\")\\n if dl[di] < 222000:\\n desc_text.append(\\\"%06d : OPERATOR %s: %d\\\" % (dl[di], en[0], an))\\n else:\\n desc_text.append(\\\"%06d : OPERATOR '%s'\\\" % (dl[di], en[0]))\\n di += 1\\n elif dl[di] >= 300000 and dl[di] < 400000:\\n stack.append((dl, di + 1))\\n da = dl[di]\\n dl = self._tables.tab_d[dl[di]]\\n di = 0\\n desc_text.append(\\\"%06d : SEQUENCE, %d desc.\\\" % (da, len(dl)))\\n return desc_text\",\n \"def description(self, depth=1, indent=0, itemize=\\\"\\\", output=None):\\n if depth is None or depth < 0:\\n return\\n output_strio = StringIO()\\n if self.label is None:\\n label = \\\" (%s)\\\" % self.oid\\n else:\\n label = \\\" (%s: '%s')\\\" % (self.oid, self.label)\\n output_strio.write('%s%s%s object at %s%s'\\n % (indent*' ',\\n itemize,\\n self.__class__.__name__,\\n hex(id(self)),\\n label))\\n if depth >= 1:\\n output_strio.write(': %d Sequences' % len(self))\\n if depth >= 2:\\n if self.taxon_set is not None:\\n tlead = \\\"\\\\n%s[Taxon Set]\\\\n\\\" % (\\\" \\\" * (indent+4))\\n output_strio.write(tlead)\\n self.taxon_set.description(depth=depth-1, indent=indent+8, itemize=\\\"\\\", output=output_strio)\\n tlead = \\\"\\\\n%s[Characters]\\\\n\\\" % (\\\" \\\" * (indent+4))\\n output_strio.write(tlead)\\n indent += 8\\n maxlabel = max([len(str(t.label)) for t in self.taxon_set])\\n for i, t in enumerate(self.taxon_set):\\n output_strio.write('%s%s%s : %s characters\\\\n' \\\\\\n % (\\\" \\\" * indent,\\n \\\"[%d] \\\" % i,\\n str(t.label),\\n len(self.taxon_seq_map[t])))\\n\\n s = output_strio.getvalue()\\n if output is not None:\\n output.write(s)\\n return s\",\n \"def __str__(self):\\n s = \\\"Filename : %s\\\\n\\\" % self.fname\\n s += \\\"Data size : %d x %d x %d\\\\n\\\" % (self._size[::-1])\\n s += \\\"CCD Chip Size : %d x %d\\\\n\\\" % self._chipSize[::-1]\\n s += \\\"File date : %s\\\\n\\\" % time.asctime(self._filedate)\\n s += \\\"Exposure Time : %f\\\\n\\\" % self.Exposure\\n s += \\\"Num ROI : %d\\\\n\\\" % self.NumROI\\n s += \\\"Num ROI Exp : %d\\\\n\\\" % self.NumROIExperiment\\n s += \\\"Contoller Ver.: %d\\\\n\\\" % self.ControllerVersion\\n s += \\\"Logic Output : %d\\\\n\\\" % self.LogicOutput\\n #self.AppHiCapLowNoise = self._readInt(4)\\n s += \\\"Timing Mode : %d\\\\n\\\" % self.TimingMode\\n s += \\\"Det. Temp : %d\\\\n\\\" % self.DetTemperature\\n s += \\\"Det. Type : %d\\\\n\\\" % self.DetectorType\\n s += \\\"Trigger Diode : %d\\\\n\\\" % self.TriggerDiode\\n s += \\\"Delay Time : %d\\\\n\\\" % self.DelayTime\\n s += \\\"Shutter Cont. : %d\\\\n\\\" % self.ShutterControl\\n s += \\\"Absorb Live : %d\\\\n\\\" % self.AbsorbLive\\n s += \\\"Absorb Mode : %d\\\\n\\\" % self.AbsorbMode\\n s += \\\"Virtual Chip : %d\\\\n\\\" % self.CanDoVirtualChip\\n s += \\\"Thresh. Min L : %d\\\\n\\\" % self.ThresholdMinLive\\n s += \\\"Thresh. Min : %d\\\\n\\\" % self.ThresholdMin\\n s += \\\"Thresh. Max L : %d\\\\n\\\" % self.ThresholdMaxLive\\n s += \\\"Thresh. Max : %d\\\\n\\\" % self.ThresholdMax\\n s += \\\"Geometric Op : %d\\\\n\\\" % self.GeometricOps\\n s += \\\"ADC Offset : %d\\\\n\\\" % self.ADCOffset\\n s += \\\"ADC Rate : %d\\\\n\\\" % self.ADCRate\\n s += \\\"ADC Type : %d\\\\n\\\" % self.ADCType\\n s += \\\"ADC Resol. : %d\\\\n\\\" % self.ADCRes\\n s += \\\"ADC Bit. Adj. : %d\\\\n\\\" % self.ADCBitAdj\\n s += \\\"ADC Gain : %d\\\\n\\\" % self.Gain\\n \\n i = 0\\n for roi in self.allROI:\\n s += \\\"ROI %-4d : %-5d %-5d %-5d %-5d %-5d %-5d\\\\n\\\" % (i,roi[0], roi[1], roi[2],\\n roi[3], roi[4], roi[5])\\n i += 1\\n \\n s += \\\"\\\\nComments :\\\\n\\\"\\n i = 0\\n for c in self._comments:\\n s += \\\"%-3d : \\\" % i\\n i += 1\\n s += c\\n s += \\\"\\\\n\\\"\\n return s\",\n \"def create_basic_description(result, season, country, var):\\n if country!='all':\\n return f'In the {season} season {(len(result))} tourists visited Tuscany from {country.title()}, we managed to cluster {np.round(len(result[pd.notnull(result[var])])/len(result)*100,2)}% of them.'\\n else:\\n return f'In the {season} season {(len(result))} tourists visited Tuscany, we managed to cluster {np.round(len(result[pd.notnull(result[var])])/len(result)*100,2)}% of them.'\",\n \"def _info(self, N):\\n from thermosteam import Stream\\n N_max = N or Stream.display_units.N\\n IDs = self.chemicals.IDs\\n index, = self.data.any(0).nonzero()\\n len_ = len(index)\\n if len_ == 0:\\n return f\\\"{type(self).__name__}: (empty)\\\"\\n elif self.units:\\n basic_info = f\\\"{type(self).__name__} ({self.units}):\\\\n\\\"\\n else:\\n basic_info = f\\\"{type(self).__name__}:\\\\n\\\"\\n all_IDs = tuple([IDs[i] for i in index])\\n\\n # Length of chemical column\\n all_lengths = [len(i) for i in IDs]\\n maxlen = max(all_lengths + [8])\\n\\n # Set up chemical data for all phases\\n phases_data_info = ''\\n for phase in self._phases:\\n phase_data = self[phase, all_IDs]\\n IDs, data = nonzeros(all_IDs, phase_data)\\n if not IDs: continue\\n \\n # Get basic structure for phase data\\n beginning = f'({phase}) '\\n new_line = '\\\\n' + len(beginning) * ' '\\n\\n # Set chemical data\\n data_info = ''\\n N_IDs = len(data)\\n too_many_chemicals = N_IDs > N_max\\n N = N_max if too_many_chemicals else N_IDs\\n lengths = [len(i) for i in IDs]\\n for i in range(N):\\n spaces = ' ' * (maxlen - lengths[i])\\n if i: data_info += new_line\\n data_info += f'{IDs[i]} ' + spaces + f' {data[i]:.3g}'\\n if too_many_chemicals: data += new_line + '...'\\n # Put it together\\n phases_data_info += beginning + data_info + '\\\\n'\\n \\n return basic_info + phases_data_info.rstrip('\\\\n')\",\n \"def get_obj_desc():\\n\\n attributes = {'results' : ['aperiodic_params_', 'peak_params_',\\n 'r_squared_', 'error_',\\n '_gaussian_params'],\\n 'settings' : ['peak_width_limits', 'max_n_peaks',\\n 'min_peak_height', 'peak_threshold',\\n 'aperiodic_mode'],\\n 'data' : ['power_spectrum', 'freq_range', 'freq_res'],\\n 'data_info' : ['freq_range', 'freq_res'],\\n 'arrays' : ['freqs', 'power_spectrum', 'aperiodic_params_',\\n 'peak_params_', '_gaussian_params'],\\n 'model_components' : ['_spectrum_flat', '_spectrum_peak_rm',\\n '_ap_fit', '_peak_fit']}\\n\\n return attributes\",\n \"def prescription(self):\\n prescription = \\\"\\\\n{0:>10}\\\\t{1:>10}\\\\t{2:>10}\\\\t{3:>10}\\\\n\\\".format(\\\"R\\\",\\\"Material\\\",\\\"d\\\",\\\"diameter\\\")\\n for surface in self.lensSurfaces():\\n prescription += \\\"{0:>10.2f}\\\\t{1:>10}\\\\t{2:>10.2f}\\\\t{3:>10.2f}\\\\n\\\".format(surface.R, str(surface.mat), surface.spacing, surface.diameter)\\n return prescription\",\n \"def summary_line_and_description():\",\n \"def get_description(self):\\n print(\\\"This Iron door.\\\")\",\n \"def get_d_string(self):\\n\\t\\tif self.difficulty == 1:\\n\\t\\t\\treturn 'EASY'\\n\\t\\tif self.difficulty == 2:\\n\\t\\t\\treturn 'MEDIUM'\\n\\t\\tif self.difficulty == 3:\\n\\t\\t\\treturn 'HARD'\\n\\t\\tif self.difficulty > 3:\\n\\t\\t\\treturn 'ELITE'\",\n \"def summarize(self):\\n txtSumm = ''\\n\\n if self.legtype == 'Takeoff':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Landing':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Other':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Observing':\\n txtSumm = \\\"%02d -- %s, RA: %s, Dec: %s, LegDur: %s, ObsDur: %s\\\" %\\\\\\n (self.legno, self.target, self.ra, self.dec,\\n str(self.duration),\\n str(self.obsdur))\\n txtSumm += \\\"\\\\n\\\"\\n if self.nonsid is True:\\n txtSumm += \\\"NONSIDERIAL TARGET -- NAIFID: %d\\\" % (self.naifid)\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"(The SOFIA project sincerely hopes you enjoy \\\"\\n txtSumm += \\\"your observing breaks due to XFORMS crashes)\\\"\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"ObsPlan: %s, ObsBlk: %s\\\" % (self.obsplan, self.obsblk)\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"Elevation Range: %.1f, %.1f\\\" % (self.range_elev[0],\\n self.range_elev[1])\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"ROF Range: %.1f, %.1f\\\" % (self.range_rof[0],\\n self.range_rof[1])\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"ROF Rate Range: %.1f, %.1f %s\\\" % (self.range_rofrt[0],\\n self.range_rofrt[1],\\n self.range_rofrtu)\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"True Heading Range: %.1f, %.1f\\\" % (self.range_thdg[0],\\n self.range_thdg[1])\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"True Heading Rate Range: %.1f, %.1f %s\\\" %\\\\\\n (self.range_thdgrt[0],\\n self.range_thdgrt[1],\\n self.range_thdgrtu)\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"Moon Angle: %.1f, Moon Illumination: %s\\\" %\\\\\\n (self.moonangle, self.moonillum)\\n\\n return txtSumm\",\n \"def tilestr(tile, show_details=False, show_unit=True):\\n # TODO: show_details\\n \\n if tile.get('unit_name') in [None, \\\"\\\"]:\\n unit_name = \\\"----\\\"\\n else:\\n unit_name = tile['unit_name'][0:4]\\n if tile['unit_army_id'] != '1':\\n army_id = int(tile['unit_army_id'])\\n if army_id > 5:\\n raise Exception(\\\"not implemented: more than 5 armies: {}\\\".format(army_id))\\n unit_name = unit_name[0:(army_id-2)].upper() + unit_name[(army_id-2):].lower()\\n details = \\\"\\\"\\n if show_details:\\n details = \\\" \\\"+tile_details_str(tile)\\n return \\\"{}@{:02d},{:02d}{}{}\\\".format(\\n tile['terrain_name'][0:4], tile['x'], tile['y'], (\\\":\\\"+unit_name) if show_unit else \\\"\\\",\\n details)\",\n \"def __str__(self):\\n output = 'Pathogens:\\\\n'\\n for x in self.extant_p:\\n output += ' n %s h %f d %f host %s extant\\\\n' % (x.name, x.height, x.dist, x.host.name)\\n for x in self.not_extant_p:\\n output += ' n %s h %f d %f host %s not extant\\\\n' % (x.name, x.height, x.dist, x.host.name)\\n for x in self.not_yet_sampled_p:\\n output += ' n %s h %f d %f host %s not yet sampled\\\\n' % (x.name, x.height, x.dist, x.host.name)\\n\\n output += 'Hosts:\\\\n'\\n for x in self.extant_h:\\n output += ' %s %f %f extant\\\\n' % (x.name, x.height, x.dist)\\n for x in self.not_extant_h:\\n output += ' %s %f %f not extant\\\\n' % (x.name, x.height, x.dist)\\n for x in self.not_yet_sampled_h:\\n output += ' %s %f %f not yet sampled\\\\n' % (x.name, x.height, x.dist)\\n\\n return output\",\n \"def _repr_(self):\\n desc = ''\\n if self.n_vertices()==0:\\n desc += 'The empty polyhedron'\\n else:\\n desc += 'A ' + repr(self.dim()) + '-dimensional polyhedron'\\n desc += ' in '\\n if self.field()==QQ: desc += 'QQ'\\n else: desc += 'RDF'\\n desc += '^' + repr(self.ambient_dim()) \\n\\n if self.n_vertices()>0:\\n desc += ' defined as the convex hull of ' \\n desc += repr(self.n_vertices())\\n if self.n_vertices()==1: desc += ' vertex'\\n else: desc += ' vertices'\\n \\n if self.n_rays()>0:\\n if self.n_lines()>0: desc += \\\", \\\"\\n else: desc += \\\" and \\\"\\n desc += repr(self.n_rays())\\n if self.n_rays()==1: desc += ' ray'\\n else: desc += ' rays'\\n \\n if self.n_lines()>0:\\n if self.n_rays()>0: desc += \\\", \\\"\\n else: desc += \\\" and \\\"\\n desc += repr(self.n_lines())\\n if self.n_lines()==1: desc +=' line'\\n else: desc +=' lines'\\n\\n return desc + \\\".\\\\n\\\";\",\n \"def get_description(self):\",\n \"def difficulty(mag):\\n mag = float(mag)\\n if mag <= -4:\\n return \\\"Visible in daytime.\\\"\\n elif mag <= 6:\\n return \\\"Visible at night.\\\"\\n else:\\n flux = mag_def(\\\"%s x\\\" % mag)\\n needed_flux = mag_def(\\\"6 x\\\")\\n eye_area = math.pi * (0.005**2)\\n needed_power = needed_flux * eye_area\\n diameter = 2 * math.sqrt(needed_power / (flux*math.pi))\\n return \\\"%s m telescope needed.\\\" % diameter\",\n \"def writeDataCards(opt,sigExp,bkgExp,shapesURL):\\n\\n #create a card per category\\n dcList=[]\\n for icat in range(len(opt.categs)):\\n cat='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\\n dcTxt='%s/shapes-parametric.datacard_%s.dat'%(opt.output,cat)\\n dcList.append(dcTxt)\\n with open(dcTxt,'w') as dc:\\n dc.write('#\\\\n')\\n dc.write('# datacard was automatically generated with generateWorkspace.py\\\\n')\\n dc.write('# the options passed are printed below\\\\n')\\n dc.write('# %s\\\\n'%opt)\\n dc.write('#\\\\n')\\n dc.write('imax *\\\\n')\\n dc.write('jmax *\\\\n')\\n dc.write('kmax *\\\\n')\\n dc.write('-'*50+'\\\\n')\\n dc.write('shapes * * {0} $PROCESS_{1} $PROCESS_$SYSTEMATIC\\\\n'.format(shapesURL,cat))\\n dc.write('shapes data_obs * {0} $PROCESS_{1}\\\\n'.format(shapesURL,cat))\\n dc.write('-'*50+'\\\\n')\\n dc.write('bin %s\\\\n'%cat)\\n dc.write('observation -1\\\\n')\\n dc.write('-'*50+'\\\\n')\\n dc.write('%15s %15s %15s\\\\n'%('bin',cat,cat))\\n dc.write('%15s %15s %15s\\\\n'%('process','sig','bkg'))\\n dc.write('%15s %15s %15s\\\\n'%('process','0', '1'))\\n dc.write('%15s %15s %15s\\\\n'%('rate','%3.2f'%sigExp[icat], '%3.2f'%bkgExp[icat]))\\n dc.write('-'*50+'\\\\n')\\n \\n #float the background normalization as well as the signal\\n dc.write('mu_bkg{0} rateParam {0} bkg 1\\\\n'.format(cat))\\n\\n #uncertainties\\n dc.write('lumi %8s %15s %15s\\\\n'%('lnN','1.027','-'))\\n dc.write('%s_sigShape %8s %15s %15s\\\\n'%(cat,'shape','1','-'))\\n dc.write('%s_bkgShape %8s %15s %15s\\\\n'%(cat,'shape','-','1'))\\n dc.write('{0} autoMCStats 0.0 1\\\\n'.format(cat))\\n \\n print '\\\\tshapes available @',shapesURL\\n print '\\\\tgenerated the following datacards',dcList\",\n \"def describe(self):\\n\\n print(\\\"Correlation length: {0}\\\".format(self.cl))\\n print(\\\"icoordchange: {0}\\\".format(self.icoordchange))\\n print(\\\"ispec: {0}\\\".format(self.ispec))\\n print(\\\"ireg: {0}\\\".format(self.ireg))\\n print(\\\"Domain: x-axis: from {0} to {1} with {2} steps of {3}\\\".format(self.xori, self.xend,\\n self.nx, self.dx))\\n print(\\\"Domain: y-axis: from {0} to {1} with {2} steps of {3}\\\".format(self.yori, self.yend,\\n self.ny, self.dy))\\n print(\\\"Exclusion value: {0}\\\".format(self.valex))\\n print(\\\"Signal-to-noise ratio: {0}\\\".format(self.snr))\\n print(\\\"Variance of the background field: {0}\\\".format(self.varbak))\",\n \"def __str__(self):\\n return f\\\"{self._desc:16s}\\\"\",\n \"def Description(self) -> str:\",\n \"def Description(self) -> str:\",\n \"def summary_stats(tile_summary):\\n return \\\"Original Dimensions: %dx%d\\\\n\\\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\\\\n \\\"Original Tile Size: %dx%d\\\\n\\\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\\\\n \\\"Scale Factor: 1/%dx\\\\n\\\" % tile_summary.scale_factor + \\\\\\n \\\"Scaled Dimensions: %dx%d\\\\n\\\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\\\\n \\\"Scaled Tile Size: %dx%d\\\\n\\\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\\\\n \\\"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\\\n\\\" % (\\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\\\\n \\\"Tiles: %dx%d = %d\\\\n\\\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% tissue\\\\n\\\" % (\\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\\n TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles =0%% tissue\\\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)\",\n \"def print_image_info(self):\\r\\n\\r\\n maxt = np.max(self.times)\\r\\n\\r\\n print (\\\" Duration of Image Stack: %9.3f s (%8.3f min) period = %8.3f s\\\" % (maxt, maxt/60.0, self.period))\\r\\n\\r\\n print (' Image shape: ', self.imageData.shape)\\r\\n\\r\\n print (' nFrames: %d framerate: %9.3f\\\\n' % (self.nFrames, self.framerate))\",\n \"def get_more_info(model):# pragma: no cover\\n\\n group_time = model.meta.exposure.group_time\\n nframes_used = model.meta.exposure.nframes\\n saturated_flag = dqflags.group['SATURATED']\\n jump_flag = dqflags.group['JUMP_DET']\\n\\n return (group_time, nframes_used, saturated_flag, jump_flag)\",\n \"def format(self):\\r\\n\\r\\n earth = \\\"???\\\" if self.maskearth else self.earth\\r\\n air = \\\"???\\\" if self.maskair else self.air\\r\\n fire = \\\"???\\\" if self.maskfire else self.fire\\r\\n water = \\\"???\\\" if self.maskwater else self.water\\r\\n\\r\\n if any((self.earth, self.fire, self.water)):\\r\\n statsline = f'Stats: {earth}/{air}/{fire}/{water}'\\r\\n elif self.air:\\r\\n statsline = f'Air: {air}'\\r\\n else:\\r\\n statsline = ''\\r\\n\\r\\n return (\\r\\n f'Character {self.name}, [{self.token}]. '\\r\\n f'Init: {self.init} {statsline} Owner: {self.user.name}'\\r\\n )\",\n \"def draw(self):\\n drawing = \\\"\\\"\\n \\n if self.get_gene().get_choice_at(1) is 'c':\\n drawing += 'o'\\n else:\\n drawing += 'x'\\n \\n if self.is_tft():\\n drawing += \\\"tft\\\"\\n return drawing\\n elif self.is_t2t():\\n drawing += \\\"t2t\\\"\\n return drawing\\n elif self.is_ftf():\\n drawing += \\\"ftf\\\"\\n return drawing\\n\\n rule = self.get_gene().get_defect_fraction()\\n fraction_display = 0.166\\n\\n if rule >= 1.0:\\n drawing += \\\"ddd\\\"\\n elif rule > (5*fraction_display):\\n drawing += \\\"ddc\\\"\\n elif rule > (4*fraction_display):\\n drawing += \\\"dcd\\\"\\n elif rule > (3*fraction_display):\\n drawing += \\\"dcc\\\"\\n elif rule > (2*fraction_display):\\n drawing += \\\"cdd\\\"\\n elif rule > (1*fraction_display):\\n drawing += \\\"cdc\\\"\\n elif rule > (0*fraction_display):\\n drawing += \\\"ccd\\\"\\n else:\\n drawing += \\\"ccc\\\"\\n\\n return drawing\",\n \"def describe_game_state(hand, battlefield, graveyard, library):\\n\\tlog(\\\"\\\")\\n\\tlog(\\\"Hand is now:\\\")\\n\\tlog(hand)\\n\\tlog(\\\"Battlefield is now:\\\")\\n\\tlog(battlefield)\\n\\tlog(\\\"Graveyard is now:\\\")\\n\\tlog(graveyard)\\n\\tlog(\\\"Library is now:\\\")\\n\\tlog(Counter(library))\\n\\tlog(\\\"\\\")\",\n \"def extract_desc(desc_line, desc_filepath, numwavelets, include_mismatch=True, wavelet=None):\\n line = desc_line\\n model_filename = desc_filepath + line.split(\\\" \\\")[0]\\n params = line[line.find(\\\"(\\\")+1:line.find('train')-4]\\n if \\\")\\\" in params:\\n hidden_layers, remaining = params.split(\\\"), \\\")\\n hidden_layers = hidden_layers + \\\")\\\"\\n activation, alpha = remaining.split(\\\", \\\")\\n else:\\n hidden_layers, activation, alpha = params.split(\\\", \\\")\\n hidden_layers = \\\"(\\\" + hidden_layers + \\\")\\\"\\n scores, time = line.split(\\\"\\\\t\\\")[1:]\\n train, test = scores[1:-1].split(\\\" - \\\")\\n time = time.split(\\\" \\\")[2]\\n outline = hidden_layers + \\\"\\\\t\\\" + activation + \\\"\\\\t\\\" + alpha + \\\"\\\\t\\\" +\\\\\\n train + \\\"\\\\t\\\" + test + \\\"\\\\t\\\" + time + \\\"\\\\t\\\" + model_filename\\n if include_mismatch:\\n #datafile = model_filename.split(\\\"NN\\\")[0] + \\\"Test.\\\" + wavelet + \\\".dat\\\"\\n datafile = \\\"../wavelets-\\\" + str(numwavelets) + \\\"/Test.\\\" + wavelet + \\\".dat\\\"\\n #classfile = model_filename.split(\\\"NN\\\")[0] + \\\"Test.\\\" + wavelet + \\\".cls\\\"\\n classfile = \\\"../wavelets-\\\" + str(numwavelets) + \\\"/Test.\\\" + wavelet + \\\".cls\\\"\\n outline += \\\"\\\\t\\\" + str(run_model(model_filename, datafile, classfile))\\n return outline\",\n \"def __str__(self):\\n reprStr = 'Help Mario build Iron Man suit!'+'\\\\n' +'To make the ' + self._name + ',you need:'+'\\\\n'\\n for part in self._supplies:\\n reprStr = reprStr + str(part.getCount()) + ' ' + part.getData() + '\\\\n'\\n return reprStr\",\n \"def cal_desc(self):\\n desc = \\\"\\\"\\n desc += \\\"Requested by \\\"\\n orgs = self.org.all()\\n if len(orgs) > 0:\\n for org in orgs:\\n desc += org.name + \\\", \\\"\\n desc = desc[:-2] + \\\".\\\\n\\\" # removes trailing comma\\n ccs = self.ccinstances.all()\\n if len(ccs) > 0:\\n desc += \\\"Crew Chiefs: \\\"\\n for cc in ccs:\\n desc += cc.crew_chief.get_full_name() + \\\" [\\\" + (cc.service.shortname if cc.service else cc.category.name) + \\\"], \\\"\\n desc = desc[:-2] + \\\".\\\\n\\\" # removes trailing comma\\n if self.description:\\n desc += self.description + \\\"\\\\n\\\"\\n return desc\",\n \"def describe_detailed(self) -> str:\\n one_to_one = []\\n one_to_many = []\\n many_to_one = []\\n many_to_many = []\\n cats_a: set[Category] = set()\\n cats_b: set[Category] = set()\\n for rule in self.rules:\\n cats_a.update(rule.factors_categories_a.keys())\\n cats_b.update(rule.factors_categories_b.keys())\\n if rule.cardinality_a == \\\"one\\\" and rule.cardinality_b == \\\"one\\\":\\n one_to_one.append(rule)\\n elif rule.cardinality_a == \\\"one\\\":\\n one_to_many.append(rule)\\n elif rule.cardinality_b == \\\"one\\\":\\n many_to_one.append(rule)\\n else:\\n many_to_many.append(rule)\\n\\n cat_a, cat_b = self.categorization_a.name, self.categorization_b.name\\n\\n r = f\\\"# Mapping between {cat_a} and {cat_b}\\\\n\\\\n\\\"\\n r += \\\"## Simple direct mappings\\\\n\\\\n\\\"\\n r += \\\"\\\\n\\\".join(\\n rule.format_human_readable(categorization_separator=\\\"\\\")\\n for rule in one_to_one\\n )\\n r += \\\"\\\\n\\\\n\\\"\\n r += f\\\"## One-to-many mappings - one {cat_a} to many {cat_b}\\\\n\\\\n\\\"\\n r += \\\"\\\\n\\\".join((rule.format_human_readable()) for rule in one_to_many)\\n r += \\\"\\\\n\\\\n\\\"\\n r += f\\\"## Many-to-one mappings - many {cat_a} to one {cat_b}\\\\n\\\\n\\\"\\n r += \\\"\\\\n\\\".join((rule.format_human_readable()) for rule in many_to_one)\\n r += \\\"\\\\n\\\\n\\\"\\n r += f\\\"## Many-to-many mappings - many {cat_a} to many {cat_b}\\\\n\\\\n\\\"\\n r += \\\"\\\\n\\\".join((rule.format_human_readable()) for rule in many_to_many)\\n r += \\\"\\\\n\\\\n\\\"\\n\\n r += \\\"## Unmapped categories\\\\n\\\\n\\\"\\n cats_missing_a = set(self.categorization_a.values()) - cats_a\\n cats_missing_b = set(self.categorization_b.values()) - cats_b\\n r += f\\\"### {cat_a}\\\\n\\\"\\n r += \\\"\\\\n\\\".join(sorted(str(x) for x in cats_missing_a)) + \\\"\\\\n\\\\n\\\"\\n r += f\\\"### {cat_b}\\\\n\\\"\\n r += \\\"\\\\n\\\".join(sorted(str(x) for x in cats_missing_b)) + \\\"\\\\n\\\\n\\\"\\n\\n return r\",\n \"def print_info(case, title='Case', kolom=25):\\n def print_desc(desc, val, unit):\\n print('| {:<{k:d}s} | {:> {k:d}f} | {:^{k:d}s} |'.format(\\n desc, val, unit, k=kolom-2))\\n \\n lebar = kolom*3+4\\n print('='*lebar)\\n print('|{:^{l:d}s}|'.format(title, l=lebar-2))\\n print('='*lebar)\\n print('|{:^{k:d}s}|{:^{k:d}s}|{:^{k:d}s}|'.format('description', 'value', 'unit', k=kolom))\\n print('-'*lebar)\\n \\n L, k, q, TA, TB, nodes, dx = get_valdict(case, 'L,k,q,TA,TB,nodes,dx')\\n \\n print_desc('L (length)', L, 'm')\\n print_desc('k (conductivity)', k, 'W/(m.K)')\\n print_desc('q (heat generation)', q, 'kW/(m^3)')\\n print_desc('TA (temperature at A)', TA, 'Celcius')\\n print_desc('TB (temperature at B)', TB, 'Celcius')\\n print_desc('nodes', nodes, '-')\\n print_desc('dx (grid space)', dx, 'm')\\n\\n print('='*lebar)\\n print()\",\n \"def print(self):\\n print(\\\"-----\\\", self.name, \\\"-----\\\")\\n print(\\\"Enable index\\\", self.enable_index)\\n print(\\\"End index:\\\", self.stop_index)\\n print(\\\"Measurement length (from the very beginning to the end index):\\\", self.time_vec[self.stop_index])\\n print()\\n print(\\\"Start temperature (hot):\\\", self.temp_hot_start)\\n print(\\\"Start temperature (cold):\\\", self.temp_cold_start)\\n print(\\\"Start temperature (mean):\\\", self.temp_start)\\n print(\\\"End temperature (hot):\\\", self.temp_hot[self.stop_index])\\n print(\\\"End temperature (cold)\\\", self.temp_cold[self.stop_index])\\n print()\\n print(\\\"Max temperature\\\", self.temp_max)\\n print(\\\"Min temperature\\\", self.temp_min)\\n print()\\n print(\\\"Heat pump\\\")\\n print(\\\"Energy input:\\\", self.work_inp)\\n print(\\\"Q_hot\\\", self.qhot_pump)\\n print(\\\"Q_cold\\\", self.qcold_pump)\\n print(\\\"Q_cold + W\\\", self.qcold_pump + self.work_inp)\\n print(\\\"E_lost\\\", self.qcold_pump + self.work_inp - self.qhot_pump)\\n print(\\\"Coefficient of performance COP_hot\\\", self.qhot_pump / self.work_inp)\\n print(\\\"Coefficient of performance COP_cold\\\", self.qcold_pump / self.work_inp)\\n print(\\\"Ideal COP_hot with the setup\\\", self.qhot_pump/(self.qhot_pump-self.qcold_pump))\\n print(\\\"Ideal COP_cold with the setup\\\", self.qcold_pump / (self.qhot_pump - self.qcold_pump))\\n print(\\\"Ideal Carnot COP_hot\\\", (self.temp_max+273.15)/(self.temp_max-self.temp_min))\\n print(\\\"Ideal Carnot COP_cold\\\", (self.temp_min+273.15)/(self.temp_max-self.temp_min))\\n print(\\\"Efficiency fraction out of ideal Carnot cooler\\\", (self.qcold_pump / self.work_inp)/((self.temp_min+273.15)/(self.temp_max-self.temp_min)))\\n\\n if self.not_air:\\n print(\\\"Heat transfer through insulator, hot side\\\", self.heat_loss_pump_hot)\\n print(\\\"Heat transfer through insulator, cold side\\\", self.heat_loss_pump_cold)\\n print(\\\"Estimated Q_hot with resistor\\\", self.qhot_resistor)\\n else:\\n print(\\\"Estimated Q_hot with resistor (=energy input)\\\", self.qhot_resistor)\\n #\\n # I think it should be defined for Q_hot too. Yep, TODO that\\n # Also calculate heatloss due to conduction TODO remove these comments when ready\\n # Todo implement resistive heater calculations\\n print()\\n print(\\\"Heat engine\\\")\\n print(\\\"Energy generated:\\\", self.work_gen)\\n print(\\\"Q_hot\\\", self.qhot_engine)\\n print(\\\"Q_cold\\\", self.qcold_engine)\\n print(\\\"Q_hot - Q_cold\\\", self.qhot_engine - self.qcold_engine)\\n print(\\\"E_lost\\\", -self.qcold_engine - self.work_gen + self.qhot_engine)\\n print(\\\"\\\\\\\"Heat transfer efficiency\\\\\\\" (%)\\\", self.work_gen / (self.qhot_engine - self.qcold_engine) * 100)\\n print(\\\"Efficiency e\\\", self.work_gen / self.qhot_engine)\\n print(\\\"Ideal efficiency with the setup\\\", 1 - (self.qcold_engine / self.qhot_engine))\\n print(\\\"Ideal Carnot efficiency\\\", (self.temp_max-self.temp_min)/(self.temp_max+273.15))\\n if self.not_air:\\n print(\\\"Heat transfer through insulator, hot side\\\", self.heat_loss_gen_hot)\\n print(\\\"Heat transfer through insulator, cold side\\\", self.heat_loss_gen_cold)\\n print()\\n print(\\\"Total efficiency of cycle\\\", self.work_gen/self.work_inp)\\n # About the efficiency of peltier elements (#telok@IRCnet, 2016-07-27)\\n # 19:10 < AgenttiX> Oletteko kokeilleet TECin ohjaamista Arduinolla? Toimisiko tämä kytkentä? http://garagelab.com/profiles/blogs/how-to-use-a-peltier-with-arduino\\n # --\\n # 20:21 <@hrst> Ei toimi. Peltieriä ei voi ohjata PWM:llä.\\n # 20:22 <@hrst> Hyötysuhde on PWM:llä paska, mikä on ongelma koska se on muutenkin liian paska, ja sen lisäksi se hajoaa mekaaniseen värähtelyyn ennemmin tai myöhemmin.\\n print(\\\"-----\\\\n\\\")\",\n \"def description() -> str:\\n content = \\\"Demonstrates usage of blackboard namespaces.\\\\n\\\"\\n content += \\\"\\\\n\\\"\\n\\n if py_trees.console.has_colours:\\n banner_line = console.green + \\\"*\\\" * 79 + \\\"\\\\n\\\" + console.reset\\n s = banner_line\\n s += console.bold_white + \\\"Blackboard\\\".center(79) + \\\"\\\\n\\\" + console.reset\\n s += banner_line\\n s += \\\"\\\\n\\\"\\n s += content\\n s += \\\"\\\\n\\\"\\n s += banner_line\\n else:\\n s = content\\n return s\",\n \"def get_figure_desc(figure):\\n return figure['output']\",\n \"def test_contents(dataset):\\n print dataset.info\\n\\n depth = dataset.variables['depth']\\n\\n print depth.time.info\",\n \"def generate_description(self, which, world):\\n global parsed_kd_des\\n if parsed_kd_des is None:\\n parsed_kd_des = util.parseDes('killdude_background', KillDudeBackground)[0]\\n s = choice(parsed_kd_des.__dict__[which].strip().splitlines())\\n substs = util.LambdaMap()\\n #here we parse following options: $depart_village, $who (dude.name) and $name (dude.unique_name)\\n substs['who'] = self.dude.name\\n substs['name'] = self.dude.unique_name\\n substs['current_city'] = lambda : current_city(world)\\n substs['village_name'] = lambda : util.gen_name('city')\\n\\n res = string.Template(s).safe_substitute(substs)\\n return res\",\n \"def desc(self):\\n kpi_name = 'id:{idx}, resource: {rsc}, group: {grp}, metric: {mtr}'\\\\\\n .format(idx=self.idx,\\n rsc=self.resource,\\n grp=self.group,\\n mtr=self.metric)\\n return kpi_name\",\n \"def template(title, steps, loops):\\n return \\\"%-8s %-20s %20d\\\" % (title, format_power(steps), loops)\",\n \"def description(self) -> str:\\n return \\\" get many blocks of given colour adjacent\\\"\",\n \"def TIMEchannels(tree, TIMEnum, CHnum):\\n chanpath = ('.SETTINGS.NI.NI_6602_TIME.TCARD_'\\n + str(TIMEnum) + '.CHANNEL_' + str(CHnum))\\n tree.addNode(chanpath)\\n AddNodeWithTag(tree, chanpath + ':ACTIVE', 'NUMERIC', 'TIMACTIVE_CARD' +\\n str(TIMEnum) + 'CH' + str(CHnum))\\n AddNodeWithTag(tree, chanpath + ':CHANNEL_NAME', 'TEXT', \\n 'TIMEUSERNAME_CARD' + str(TIMEnum) + 'CH' + str(CHnum))\\n AddNumericWithUnit(tree, chanpath + ':TRIG_TIME', 'TRIGGERTIME_CARD' +\\n str(TIMEnum) + 'CH' + str(CHnum), 's')\\n AddNumericWithUnit(tree, chanpath + ':PULSE_WIDTH', 'PWM_CARD' +\\n str(TIMEnum) + 'CH' + str(CHnum), 's')\\n AddNodeWithTag(tree, chanpath + ':NI_NAME', 'TEXT', 'TIMENINAME_CARD' +\\n str(TIMEnum) + 'CH' + str(CHnum))\",\n \"def __str__(self):\\n output = 'Light curve name: {}\\\\n'.format(self.name)\\n try:\\n output += ('Initial time: {} UTC\\\\n'\\n 'End time: {} UTC\\\\n'\\n 'Duration: {:.3f} minutes\\\\n'.format(\\n self.initial_time.iso, self.end_time.iso,\\n (self.end_time - self.initial_time).value*u.d.to('min'))\\n )\\n except:\\n pass\\n output += 'Time offset: {:.3f} seconds\\\\n\\\\n'.format(self.dt)\\n try:\\n output += 'Exposure time: {:.4f} seconds\\\\n'.format(self.exptime)\\n output += 'Cycle time: {:.4f} seconds\\\\n'.format(self.cycle)\\n output += 'Num. data points: {}\\\\n\\\\n'.format(len(self.time))\\n except:\\n output += 'Object LightCurve was not instantiated with time and flux.\\\\n\\\\n'\\n try:\\n output += ('Bandpass: {:.3f} +/- {:.3f} microns\\\\n'\\n 'Object Distance: {:.2f} AU\\\\n'\\n 'Used shadow velocity: {:.3f} km/s\\\\n'\\n 'Fresnel scale: {:.3f} seconds or {:.2f} km\\\\n'\\n 'Stellar size effect: {:.3f} seconds or {:.2f} km\\\\n'.format(\\n self.lambda_0, self.delta_lambda, self.dist, self.vel,\\n self.fresnel_scale/self.vel, self.fresnel_scale,\\n self.d_star/self.vel, self.d_star)\\n )\\n except:\\n output += '\\\\nThere is no occultation associated with this light curve.\\\\n'\\n try:\\n output += ('Inst. response: {:.3f} seconds or {:.2f} km\\\\n'\\n 'Dead time effect: {:.3f} seconds or {:.2f} km\\\\n'\\n 'Model resolution: {:.3f} seconds or {:.2f} km\\\\n'\\n 'Modelled baseflux: {:.3f}\\\\n'\\n 'Modelled bottomflux: {:.3f}\\\\n'\\n 'Light curve sigma: {:.3f}\\\\n\\\\n'.format(\\n self.exptime, self.exptime*self.vel, self.cycle-self.exptime,\\n (self.cycle-self.exptime)*self.vel, self.model_resolution,\\n self.model_resolution*self.vel, self.baseflux, self.bottomflux,\\n self.lc_sigma.mean())\\n )\\n except:\\n output += '\\\\nObject LightCurve model was not fitted.\\\\n\\\\n'\\n try:\\n output += ('Immersion time: {} UTC +/- {:.3f} seconds\\\\n'\\n 'Emersion time: {} UTC +/- {:.3f} seconds\\\\n\\\\n'.format(\\n self.immersion.iso, self.immersion_err,\\n self.emersion.iso, self.emersion_err)\\n )\\n except:\\n output += 'Immersion and emersion times were not fitted or instantiated.\\\\n\\\\n'\\n\\n try:\\n output += 'Monte Carlo chi square fit.\\\\n\\\\n' + self.chisquare.__str__() + '\\\\n'\\n except:\\n pass\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6358604","0.61127687","0.60675085","0.6044572","0.59665823","0.592374","0.59180605","0.59102285","0.5901158","0.5849729","0.5839126","0.5819639","0.5782906","0.5768085","0.5766401","0.57455444","0.5734563","0.5732122","0.57276","0.57193375","0.5670834","0.56679654","0.56610614","0.56345844","0.56201404","0.5610995","0.55990946","0.55932605","0.5580501","0.55771893","0.55635864","0.55287105","0.55111456","0.55104756","0.5506927","0.54927367","0.54819626","0.5475388","0.547414","0.5468172","0.5454715","0.5453633","0.5421119","0.54160565","0.5414548","0.5393292","0.537484","0.53726965","0.53676623","0.53648174","0.533061","0.5326983","0.5326701","0.5326314","0.5321077","0.53135276","0.5305987","0.5301724","0.52924657","0.52773786","0.52529037","0.52297056","0.52222186","0.52168787","0.52123255","0.52069044","0.5205781","0.5205464","0.5204711","0.5198787","0.5198053","0.51953965","0.5169354","0.5168628","0.5162007","0.51579136","0.51573193","0.5153911","0.5153911","0.51479053","0.514727","0.51419556","0.5139908","0.5136519","0.5131391","0.5131214","0.5130579","0.51242477","0.5123696","0.5123326","0.51204586","0.5116313","0.51161134","0.5112893","0.5112631","0.5112106","0.51115394","0.5109475","0.51090795","0.50991887"],"string":"[\n \"0.6358604\",\n \"0.61127687\",\n \"0.60675085\",\n \"0.6044572\",\n \"0.59665823\",\n \"0.592374\",\n \"0.59180605\",\n \"0.59102285\",\n \"0.5901158\",\n \"0.5849729\",\n \"0.5839126\",\n \"0.5819639\",\n \"0.5782906\",\n \"0.5768085\",\n \"0.5766401\",\n \"0.57455444\",\n \"0.5734563\",\n \"0.5732122\",\n \"0.57276\",\n \"0.57193375\",\n \"0.5670834\",\n \"0.56679654\",\n \"0.56610614\",\n \"0.56345844\",\n \"0.56201404\",\n \"0.5610995\",\n \"0.55990946\",\n \"0.55932605\",\n \"0.5580501\",\n \"0.55771893\",\n \"0.55635864\",\n \"0.55287105\",\n \"0.55111456\",\n \"0.55104756\",\n \"0.5506927\",\n \"0.54927367\",\n \"0.54819626\",\n \"0.5475388\",\n \"0.547414\",\n \"0.5468172\",\n \"0.5454715\",\n \"0.5453633\",\n \"0.5421119\",\n \"0.54160565\",\n \"0.5414548\",\n \"0.5393292\",\n \"0.537484\",\n \"0.53726965\",\n \"0.53676623\",\n \"0.53648174\",\n \"0.533061\",\n \"0.5326983\",\n \"0.5326701\",\n \"0.5326314\",\n \"0.5321077\",\n \"0.53135276\",\n \"0.5305987\",\n \"0.5301724\",\n \"0.52924657\",\n \"0.52773786\",\n \"0.52529037\",\n \"0.52297056\",\n \"0.52222186\",\n \"0.52168787\",\n \"0.52123255\",\n \"0.52069044\",\n \"0.5205781\",\n \"0.5205464\",\n \"0.5204711\",\n \"0.5198787\",\n \"0.5198053\",\n \"0.51953965\",\n \"0.5169354\",\n \"0.5168628\",\n \"0.5162007\",\n \"0.51579136\",\n \"0.51573193\",\n \"0.5153911\",\n \"0.5153911\",\n \"0.51479053\",\n \"0.514727\",\n \"0.51419556\",\n \"0.5139908\",\n \"0.5136519\",\n \"0.5131391\",\n \"0.5131214\",\n \"0.5130579\",\n \"0.51242477\",\n \"0.5123696\",\n \"0.5123326\",\n \"0.51204586\",\n \"0.5116313\",\n \"0.51161134\",\n \"0.5112893\",\n \"0.5112631\",\n \"0.5112106\",\n \"0.51115394\",\n \"0.5109475\",\n \"0.51090795\",\n \"0.50991887\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7623472"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22386,"cells":{"query":{"kind":"string","value":"Generate a name for a file using the description and channel"},"document":{"kind":"string","value":"def generate_file_name(well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def file_name(id, title, kind=\"src\"):\n fn_template = conf.template_source_file_name\n if kind == \"tst\":\n fn_template = conf.template_test_file_name\n\n return fn_template.format(id=id, title=title.replace(\"-\", \"_\"))","def _generate_overlay_file_name(self, well, channel, desc):\n \n return \"c\" + channel + \"_w\" + well + \"_\" + desc + \".png\"","def generate_file_name(entry):\n return str_for_file(u'{name}, {year}, {title}'.format(\n year=entry['year'],\n name=get_last_name(entry['author'][0]),\n title=entry['title']\n ))","def create_file_name(self):\n # create a unique id for the file name\n index = self.helpers.alpha_uuid()\n\n filename = self.form['FieldStorage'][self.image_cid].filename\n extension = guess_extension(guess_type(filename)[0])\n return ( # concatenates the following data\n self.articleData.get('directory') + # directory\n '/' + # slash\n self.articleData.get('article_name') + # the article name\n '-' + # hyphen character\n index + # the id of the image\n extension\n )","def create_final_name(fname, date, fc_id, sample_name):\n \n # Split the file name according to CASAVA convention\n m = re.match(r'(\\S+?)_(?:[ACGTN\\-]+|NoIndex|Undetermined)_L0*(\\d+)_R(\\d)_\\d+\\.fastq(.*)', fname)\n if m is not None:\n lane = m.group(2)\n read = m.group(3)\n ext = m.group(4)\n else:\n # Split the file name according to bcbb convention\n m = re.match(r'(\\d+)_(\\d+)_([^_]+)_(\\d+)_(?:nophix_)?(\\d+)_fastq.txt(.*)', fname)\n if m is None:\n raise ValueError(\"Could not parse file name {:s} correctly!\".format(fname))\n lane = m.group(1)\n read = m.group(5)\n ext = m.group(6)\n \n dest_file_name = \"{:s}.fastq{:s}\".format(\"_\".join([lane,\n date,\n fc_id,\n sample_name,\n read]),\n ext.replace('..','.'))\n return dest_file_name","def collected_filename(cfg, collect_dir, i=None):\n if i is not None:\n file = cfg[\"files\"][i]\n else:\n file = cfg[\"file\"]\n ext = path.splitext(file)[1]\n name = cfg[\"id\"]\n if i is not None:\n name += \"_\" + str(i)\n return path.join(collect_dir, name + ext)","def _generate_filename(doc_type, login, *args):\n filename = []\n filename.append(doc_type)\n filename.append(login)\n for item in args:\n filename.append(item)\n filename.append(datetime.datetime.now().isoformat(timespec='microseconds'))\n filename = '_'.join(filename)\n return filename","def content_file_name(instance, filename):\r\n return '/'.join([str(instance.app.publisher.id), str(instance.app.id), filename])","def construct_name_file(size_sample, randomness, pos_equal_neg, kernel):\n if randomness:\n randomness = \"rand\"\n else:\n randomness = \"nrand\"\n\n if pos_equal_neg:\n pos_equal_neg = \"pos-neg-eq\"\n else:\n pos_equal_neg = \"pos-neg-neq\"\n\n return \"{}_{}_{}_{}.json\".format(size_sample, randomness, pos_equal_neg, kernel)","def generate_filename(player_name):\n name = player_name.split()\n filename = '_'.join(name).lower()\n return filename","def get_nomenclature_channel_fname(czi_fname, nomenclature_file, channel_name, ext='.inr.gz'):\n # - Read NOMENCLATURE file defining naming conventions:\n n_names = get_nomenclature_name(nomenclature_file)\n return n_names[czi_fname]+\"/\", n_names[czi_fname] + \"_\" + channel_name + ext","def generate_glider_filename(description):\n filename = (\n \"{glider}-{year:d}-{day:03d}-{mission:d}-{segment}.{type}\".format(**description)\n )\n return os.path.join(description['path'], filename)","def generate_namefile(pathfolder, methodvalues):\n datestr = datetime.datetime.now().date().strftime('%F')\n paramsstr = str(hash(str(methodvalues)))\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\n namefile = os.path.join(pathfolder, namefile)\n return namefile","def generate_filename(filename: str) -> str:\n return f\"{str(uuid.uuid4())}.{get_extension(filename)}\"","def channel_name(radio_id: int, channel_id: int) -> str:\n return f\"COMM{radio_id} Ch {channel_id}\"","async def filename_generator(self):\n chars = list(string.ascii_letters+string.digits)\n name = ''\n for i in range(random.randint(9, 25)):\n name += random.choice(chars)\n\n if name not in self.player['audio_files']:\n return name\n\n return await self.filename_generator()","def gen_file_name(filename, path=UPLOAD_FOLDER):\n\n i = 1\n while os.path.exists(os.path.join(path, filename)):\n name, extension = os.path.splitext(filename)\n filename = '%s_%s%s' % (name, str(i), extension)\n i += 1\n\n return filename","def generate_filename(playlist_or_album_name, user_id_or_artist_id=None):\n filename = ''\n if user_id_or_artist_id:\n filename += user_id_or_artist_id + '_'\n filename += playlist_or_album_name + '_' + str(time_ns())\n return filename","def _get_file_name(name: types.TSeedName) -> str:\n return f\"{name}.yml\"","def get_file_name(self):\n\n return \"%s - %s\" % (self.get_tags()[\"artist\"], self.get_tags()[\"title\"])","def generateFilename(self, name):\n return self.context.generateUniqueId(type_name='Module')","def filename(i):\n rand_name = os.path.join(os.getcwd(), \"input-%d.txt\" % i)\n ref_name = os.path.join(os.getcwd(), \"input-%d.ref\" % i)\n return rand_name, ref_name","def create_filename (self):\n\t\tassert self.__patient_name and self.__location_name, \"New filename could not be determined, one or more needed arguments is empty!\"\n\t\t_patient_name = self.__patient_name.split(' ')\n\t\t_patient_name.reverse()\n\t\t\n\t\treturn os.path.join(os.path.dirname(self.file._path), \"%s MR %s%s\" % (self.__location_name, ', '.join(_patient_name).upper(), self._file.extension))","def filename(self):\n return '%s%s' % (self.identifier, self.extension)","def generate_report_file_name(args: Dict[str, Any]) -> str:\n return (\n f\"{args.get('report_type', '').lower().replace(' ', '_')}_fireeye_\"\n f\"{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.\"\n f\"{args.get('type', REPORT_TYPE_ALLOWED_FORMAT[args.get('report_type', '')][0])}\"\n )","def generate_file_name(hour, minute):\n hour = str(hour)\n if len(hour) == 1:\n hour = \"0\" + hour\n minute = str(minute)\n if len(minute) == 1:\n minute = \"0\" + minute\n file_name = date + \"--\" + hour + special_char + minute + special_char + \"00,00.mvol\"\n\n # print \"filename: \",file_name\n return file_name","def filename(self):\n translator = {ord(\" \"): \"_\", ord(\",\"): None}\n return f'{self._full_name.translate(translator)}.txt'","def generate_file_filename(instance, filename):\n return _generate_filename(instance, filename, 'photos')","def temp_name(self, filename):\n if self.params.get('nopart', False) or filename == '-' or \\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\n return filename\n return filename + '.part'","def get_alternative_name(self, file_root, file_ext):\n return \"%s_%s%s\" % (file_root, get_random_string(7), file_ext)","def get_display_name(self, short=False):\n if self.filename is None:\n return '[New file]'\n elif short:\n return os.path.basename(self.filename)\n else:\n return self.filename","def file_name(product, ext='json'):\n return f\"./output/{product}_{datetime.now().strftime('%Y-%m-%d_%H%M%S')}_transformed_{version}.{ext}\"","def generate_name(file_name, index):\n name = ''\n with open(file_name) as names:\n for i, line in enumerate(names):\n if i == index:\n name += line.split(None, 1)[0]\n return name","def get_file_name(self):\n return str(self.get_file())","def get_download_file_name(self):\n # Use 'unknown' if the course instance does not have a term\n if self.course_instance.term:\n term = self.course_instance.term.get_url_name()\n else:\n term = 'unknown'\n\n return '{course}-{term}-{number}-{instructors}-{type}{ext}'.format(\n course=self.course_instance.course.get_url_name(),\n term=term,\n number=self.exam_number,\n instructors='_'.join([i.last_name for i in self.instructors]),\n type=self.exam_type,\n ext=self.file_ext)","def get_file_name() -> str:\n import uuid\n uniq_append_string = uuid.uuid4().hex\n return \"LOCAL_STORAGE_{}\".format(uniq_append_string)","def generateFileName(self):\n return 'Covid' + self.map_type + '.html'","def getNextFilename(self, cmd):\n \n self.actor.exposureID += 1\n path = os.path.join(\"$ICS_MHS_DATA_ROOT\", 'mcs')\n path = os.path.expandvars(os.path.expanduser(path))\n\n if not os.path.isdir(path):\n os.makedirs(path, 0o755)\n \n return os.path.join(path, 'MCSA%010d.fits' % (self.actor.exposureID))","def generate_image_filename():\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\n return 'CCTV_{0}.jpg'.format(now)","def generate_file_name(self):\n self._session_iterator = None # New file invalidate old interator\n self._img_count += 1\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\n return self.current_file","def mk_filename(pattern, queryresponserow, resp, url):\n name = None\n if resp:\n cdheader = resp.headers.get(\"Content-Disposition\", None)\n if cdheader:\n _, params = parse_header(cdheader)\n name = params.get('filename', \"\")\n # Work around https://github.com/sunpy/sunpy/issues/3372\n if name.count('\"') >= 2:\n name = name.split('\"')[1]\n\n # This is a hack to to prevent IRIS data from being labelled as XML files\n if name is None and \"VOEvent_IRIS\" not in queryresponserow['fileid']:\n # Advice from the VSO is to fallback to providerid + fileid for a filename\n # As it's possible multiple providers give the same fileid.\n # However, I haven't implemented this yet as it would be a breaking\n # change to the filenames we expect.\n fileid = queryresponserow['fileid']\n\n # Some providers make fileid a path\n # Some also don't specify a file extension, but not a lot we can do\n # about that.\n name = fileid.split(\"/\")[-1]\n\n # If somehow we have got this far with an empty string, fallback to url segment\n if not name:\n name = url.split('/')[-1]\n\n # Remove any not-filename appropriate characters\n name = slugify(name)\n\n # If absolutely everything else fails make a filename based on download time\n if not name:\n name = f\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\"\n\n fname = pattern.format(file=name,\n **queryresponserow.response_block_map)\n\n return fname","def init_data_file_name():\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\n filename = 'show-commands-' + now + \".txt\"\n return filename","def create_random_file_name():\n\n def random_file_name_factory():\n length = random.randint(10, 15)\n chars = string.ascii_letters + string.digits + \"-_\"\n return f\"{''.join(random.choice(chars) for _ in range(length))}.jpg\"\n\n return random_file_name_factory","def filename(self):\n return self.config.get('filename', self.id) + f'_{self.file_suffix}'","def get_title(src_name, src_type=None):\n if src_type == 'tcp':\n return '{0}:{1}'.format(*src_name)\n return os.path.basename(src_name)","def get_resampled_filename(filepath):\n try:\n filepath = filepath.encode('utf-8')\n except UnicodeDecodeError:\n pass\n return 'tmp_%s.flac' % md5(filepath).hexdigest()","def format_filename(title: str, id: Any, ext: str = \".\", dirFormat=None):\r\n ...","def _generate_filename(instance, filename, prefix):\n md5 = hashlib.md5()\n md5.update(struct.pack('f', time.time()))\n for chunk in instance.file.chunks():\n md5.update(chunk)\n extension = os.path.splitext(filename)[1]\n return os.path.join(prefix, md5.hexdigest() + extension)","def modpricesetter_get_file_name(self):\r\n year, month, day = self._get_market_year_month_day_as_str()\r\n interval_number = self._get_interval_number_as_str()\r\n base_name = \"NEMPriceSetter_{year}{month}{day}{interval_number}00.xml\"\r\n name = base_name.format(\r\n year=year, month=month, day=day, interval_number=interval_number\r\n )\r\n path_name = Path(self.cache_folder) / name\r\n name_OCD = name.replace(\".xml\", \"_OCD.xml\")\r\n path_name_OCD = Path(self.cache_folder) / name_OCD\r\n name_zero = name.replace(\".xml\", \"00.xml\")\r\n path_name_zero = Path(self.cache_folder) / name_zero\r\n if os.path.exists(path_name):\r\n return name\r\n elif os.path.exists(path_name_OCD):\r\n return name_OCD\r\n elif os.path.exists(path_name_zero):\r\n return name_zero\r\n else:\r\n return name","def source_file_name_feature(self):\n return \"_\".join((C_FILE_NAME, self.file_image_name.value))","def get_filename(out_dir, file_date, extension):\n return path.join(out_dir, f'CrossrefCitations_{file_date}.{extension}')","def create_filename(value):\n return '%s.mp3' % slugify(value, u'_')","def generate_filename(\n radar, field, sweep, ext=\"png\", datetime_format=\"%Y%m%d%H%M%S\", use_sweep_time=False\n):\n name_s = generate_radar_name(radar).replace(\" \", \"_\")\n field_s = field.replace(\" \", \"_\")\n if use_sweep_time:\n time_s = generate_radar_time_sweep(radar, sweep).strftime(datetime_format)\n else:\n time_s = generate_radar_time_begin(radar).strftime(datetime_format)\n sweep_s = str(sweep).zfill(2)\n return f\"{name_s}_{field_s}_{sweep_s}_{time_s}.{ext}\"","def filename(N, Dr, g, launch):\n\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\n (N, Dr, g, launch)))","def unique_filename(data):\n file = data\n get_ext = file.filename.split(\".\")[-1]\n new_name = \"%s.%s\" % (uuid.uuid4().hex, get_ext)\n return new_name","def file_on_disk_name(instance, filename):\n return generate_file_on_disk_name(instance.checksum, filename)","def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\n\tnew_filename = time.strftime(\"%d-%m-%Y_\")\n\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\n\tnew_filename = new_filename + \"_P\" + str(image_class)\n\treturn new_filename","def content_file_name(self, filename):\n ext = filename.split('.')[-1]\n filename = \"%s_%s.%s\" % (filename, self.id, ext)\n return os.path.join('pictures/static/pictures/', filename)","def _build_name(self):\n self.ctrl_name = NameUtils.get_unique_name(self.asset,\n self.side,\n self.part,\n \"ctrl\")","def output_file_name_maker(args):\n log.debug(\"Entering output_file_name_maker()\")\n path = os.getcwd() + '/out_files/'\n if not os.path.isdir(path):\n os.mkdir(path)\n\n if args.output is None:\n out_file_name = path + args.input[:-4] + '_' + args.type + '_' + args.layer\n else:\n out_file_name = path + args.output\n\n log.debug(\"Exiting output_file_name_maker()\")\n return out_file_name","def get_custom_filename(existing_files: List[str]) -> str:\n questions = [\n {\n 'type': 'input',\n 'name': 'custom_filename',\n 'message': 'Name your new sample :',\n 'default': get_default_filename(),\n 'validate': lambda text: (\n (len(re.findall(r'^[A-Za-z0-9_\\-.]{3,40}$', text)) > 0\n and text+'.smp' not in existing_files\n ) or\n 'Typed file name contains illegal characters or already exist'\n )\n }\n ]\n return prompt(questions, style=custom_style_2)['custom_filename']+'.smp'","def _filename(self, corotid):\n from datasource import DataSource\n self.corotid = corotid\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\n \n query = \"\"\"SELECT run_code, hlfccdid, win_id \n FROM corot \n WHERE corotid = %d;\"\"\" % self.corotid\n result = self.corot.query(query)\n \n par = {'run': result[0][0],\n 'half': result[0][1].rstrip('RL'), \n 'win': result[0][2]}\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\n logger.info('%d = %s' % (corotid,filename))\n return filename","def make_img_name(file_ext='.png'):\r\n fn = []\r\n # format seqs and write out to temp file\r\n for i in range(0, 30):\r\n fn.append(choice(ALPHABET))\r\n return ''.join(fn) + file_ext","def update_filename(instance, filename):\n path = os.path.join(\"documents_analizer\", \"documents\")\n name = \"{}{}\".format(highly_random_name(),\n os.path.splitext(filename)[1])\n return os.path.join(path, name)","def download_filename_full(self, doc):\n # todo modify\n authors = \",\".join([x['name'] for x in doc.artists])\n author = re.sub(\"[\\\\\\\\/:*?\\\"<>|]\", '', authors.strip())\n mp3_name = re.sub(\"[\\\\\\\\/:*?\\\"<>|]\", '', doc['name'])\n name = os.path.join(author, \"%s - %s.mp4\" % (author, mp3_name))\n return name","def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\n filename = self.filename(filetype=filetype, format=format)\n #return Path(dir) / filename\n return filename","def filename(lang):\n filename = lang.replace('-', '_')\n filename = filename.lower()\n return filename","def _file_name(size):\n timestamp = str(int(time.time()))\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')","def _prettyfilename(self):\n return f'{self.title} ({self.subtype})'","def generate_subdir(channel, energy=13):\n return os.path.join('%dTeV' % energy, channel, strftime(\"%d_%b_%y\"))","def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\n filename = self.filename(filetype=filetype, format=format)\n # return Path(dir) / filename\n return filename","def _generate_output_name(extension):\n output_name = 'TDG_{:%Y-%m-%d_%H-%M-%S}.{}'.format(datetime.now(), extension)\n return output_name","def out_filename(self, filetype, dir, format='old'):\n filename = self.filename(filetype=filetype, format=format)\n return Path(dir) / filename","def _create_file_name(file_path):\r\n file_base, file_ext = os.path.splitext(file_path)\r\n if os.path.isfile(file_path):\r\n nfile = 1\r\n check = True\r\n while check:\r\n name_add = '0000' + str(nfile)\r\n file_path = file_base + \"_\" + name_add[-4:] + file_ext\r\n if os.path.isfile(file_path):\r\n nfile = nfile + 1\r\n else:\r\n check = False\r\n return file_path","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\n ext='.mif'):\n\n if basename == '':\n msg = 'Unable to generate filename for command %s. ' % self.cmd\n msg += 'basename is not set!'\n raise ValueError(msg)\n if cwd is None:\n cwd = os.getcwd()\n if change_ext:\n if suffix:\n suffix = ''.join((suffix, ext))\n else:\n suffix = ext\n if suffix is None:\n suffix = ''\n fname = fname_presuffix(basename, suffix=suffix,\n use_ext=False, newpath=cwd)\n return fname","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\n ext='.mif'):\n\n if basename == '':\n msg = 'Unable to generate filename for command %s. ' % self.cmd\n msg += 'basename is not set!'\n raise ValueError(msg)\n if cwd is None:\n cwd = os.getcwd()\n if change_ext:\n if suffix:\n suffix = ''.join((suffix, ext))\n else:\n suffix = ext\n if suffix is None:\n suffix = ''\n fname = fname_presuffix(basename, suffix=suffix,\n use_ext=False, newpath=cwd)\n return fname","def newCalFileName(self, type, runBegin, runEnd='end'):\n \n path=os.path.join(self.cdir)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup,self.src)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup,self.src,type)\n if not os.path.exists(path): \n os.mkdir(path)\n return path+'/'+str(runBegin)+'-'+str(runEnd)+'.data'","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):\n if not basename:\n msg = \"Unable to generate filename for command %s. \" % self.cmd\n msg += \"basename is not set!\"\n raise ValueError(msg)\n\n if cwd is None:\n cwd = os.getcwd()\n if ext is None:\n ext = Info.output_type_to_ext(self.inputs.outputtype)\n if change_ext:\n suffix = \"\".join((suffix, ext)) if suffix else ext\n\n if suffix is None:\n suffix = \"\"\n fname = fname_presuffix(basename, suffix=suffix, use_ext=False, newpath=cwd)\n return fname","def get_file_name(replay_dir, template_name):\n suffix = '.json' if not template_name.endswith('.json') else ''\n file_name = f'{template_name}{suffix}'\n return os.path.join(replay_dir, file_name)","def _get_buildspec_filename(\n lambda_name, file_name):\n return '.'.join(\n ('buildspecs', lambda_name, file_name))","def generate_filename(ext,sha512base16_hash=None):\n## # Timestamp filename\n## timestamp = str(get_current_unix_time())\n## filename = timestamp+\".\"+ext\n # Base16 hash filename\n filename = sha512base16_hash+\".\"+ext\n return filename","def _make_fname(song, ext=None, av=None, subdir=None):\n # pylint: disable=E1103\n # Instance of 'bool' has no 'extension' member (some types not inferable)\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\n if not os.path.exists(ddir):\n os.makedirs(ddir)\n\n if ext:\n extension = ext\n\n else:\n stream = streams.select(streams.get(song),\n audio=av == \"audio\", m4a_ok=True)\n extension = stream['ext']\n\n # filename = song.title[:59] + \".\" + extension\n filename = song.title + \".\" + extension\n filename = os.path.join(ddir, mswinfn(filename.replace(\"/\", \"-\")))\n filename = filename.replace('\"', '')\n return filename","def get_output_filename(item: str, root: str, i: int) -> str:\n element_split = item.split(\"/\")\n item, ext = element_split[-1].split(\".\")\n if i < 0:\n return f\"{root}/{'/'.join(element_split[:-1])}/{item}.{ext}\"\n else:\n return f\"{root}/{'/'.join(element_split[:-1])}/{item}_aug{i}.{ext}\"","def outputFilename(name=\"\", ext=\"\", time=True):\n # get the date in the format specifed\n dateTime = datetime.now()\n dateTimeFormat = \"%Y-%m-%d__%H-%M-%S\" if time else \"%Y-%m-%d\"\n fileName = dateTime.strftime(dateTimeFormat)\n\n # construct the filename\n fileName = fileName + \"_\" + name if fileName != \"\" else fileName\n ext = \".\" + ext if ext != \"\" else \"\"\n\n return fileName + ext","def generate_random_name(filename):\n ext = filename.split('.')[-1]\n rns = [random.randint(0, len(LETTER_SET) - 1) for _ in range(3)]\n name = ''.join([LETTER_SET[rn] for rn in rns])\n return \"{new_fn}.{ext}\".format(new_fn=name, ext=ext)","def format_image_filename(device_image):\n return \"{}-{}-{}-{}.bit\".format(device_image.bitstream_type,\n device_image.pci_vendor,\n device_image.pci_device,\n device_image.uuid)","def friendly_name(self):\n if not self.collection:\n print('YIKES no filepath %s %s' % (self.title,self.slug))\n print (self)\n return ''\n fpath = self.collection.filePath\n if '/' in fpath:\n bg = fpath.index('/')+1\n return fpath[bg:]+ '/' + self.fileName\n return fpath+'/'+self.fileName","def update_destination_file_name (file_name):\n\tglobal COUNTER \n\tCOUNTER += 1\n\tsplitted = file_name.split('/')\n\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]","def genBaseName(fileName):\n return fileName.split(\"_\")[0].split(\".\")[0]","def getOutputFilename(self, filename):\n return filename[:-4] + \".txt\"","def appendDescription(dir, file_name, description):\n\tpath = os.path.join(dir, file_name)\n\tbase, ext = os.path.splitext(path)\n\treturn base + '.' + description + ext","def getNextFilename(self, expType):\n with self.lock:\n self.exposureID += 1\n exposureID = self.exposureID\n path = os.path.join(\"$ICS_MHS_DATA_ROOT\", 'agcc')\n path = os.path.expandvars(os.path.expanduser(path))\n if not os.path.isdir(path):\n os.makedirs(path, 0o755)\n with self.lock:\n timestamp = self.timestamp\n return os.path.join(path, 'AGC%d_%s_%06d_%s.fits' % \\\n (self.agcid + 1, expType, exposureID, timestamp))","def _create_name(self) -> str:\n return self.stream.__class__.__name__","def _get_track_name(self, filename):\n return os.path.basename(filename)","def _generate_processed_key_name(process_to, upload_name):\n timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\n name, extension = os.path.splitext(upload_name)\n digest = md5(''.join([timestamp, upload_name])).hexdigest()\n return os.path.join(process_to, '{0}.{1}'.format(digest, extension))","def _build_fname_templ(n):\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\n for i in range(1, n):\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\n var='{var%i}' % i)]\n return globals.ds_fn_sep.join(parts) + '.nc'","def format_filename(prefix, suffix, seq_len, uncased):\n seq_str = \"seq-{}\".format(seq_len)\n if uncased:\n case_str = \"uncased\"\n else:\n case_str = \"cased\"\n\n file_name = \"{}.{}.{}.{}\".format(prefix, seq_str, case_str, suffix)\n\n return file_name","def playlist_num_filename(**kwargs):\n return f\"{kwargs['track_num']} - {default_filename(**kwargs)}\"","def new_filename(fname=None,ndigits=3):\n if fname is None:\n ext = (\"%%.%ii\" % ndigits) % 1\n fname = \"%s.%s\" % (random_string(6), ext)\n \n if os.path.exists(fname): \n fname = increment_filename(fname,ndigits=ndigits)\n\n return fname"],"string":"[\n \"def _generate_raw_file_name(self, well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def file_name(id, title, kind=\\\"src\\\"):\\n fn_template = conf.template_source_file_name\\n if kind == \\\"tst\\\":\\n fn_template = conf.template_test_file_name\\n\\n return fn_template.format(id=id, title=title.replace(\\\"-\\\", \\\"_\\\"))\",\n \"def _generate_overlay_file_name(self, well, channel, desc):\\n \\n return \\\"c\\\" + channel + \\\"_w\\\" + well + \\\"_\\\" + desc + \\\".png\\\"\",\n \"def generate_file_name(entry):\\n return str_for_file(u'{name}, {year}, {title}'.format(\\n year=entry['year'],\\n name=get_last_name(entry['author'][0]),\\n title=entry['title']\\n ))\",\n \"def create_file_name(self):\\n # create a unique id for the file name\\n index = self.helpers.alpha_uuid()\\n\\n filename = self.form['FieldStorage'][self.image_cid].filename\\n extension = guess_extension(guess_type(filename)[0])\\n return ( # concatenates the following data\\n self.articleData.get('directory') + # directory\\n '/' + # slash\\n self.articleData.get('article_name') + # the article name\\n '-' + # hyphen character\\n index + # the id of the image\\n extension\\n )\",\n \"def create_final_name(fname, date, fc_id, sample_name):\\n \\n # Split the file name according to CASAVA convention\\n m = re.match(r'(\\\\S+?)_(?:[ACGTN\\\\-]+|NoIndex|Undetermined)_L0*(\\\\d+)_R(\\\\d)_\\\\d+\\\\.fastq(.*)', fname)\\n if m is not None:\\n lane = m.group(2)\\n read = m.group(3)\\n ext = m.group(4)\\n else:\\n # Split the file name according to bcbb convention\\n m = re.match(r'(\\\\d+)_(\\\\d+)_([^_]+)_(\\\\d+)_(?:nophix_)?(\\\\d+)_fastq.txt(.*)', fname)\\n if m is None:\\n raise ValueError(\\\"Could not parse file name {:s} correctly!\\\".format(fname))\\n lane = m.group(1)\\n read = m.group(5)\\n ext = m.group(6)\\n \\n dest_file_name = \\\"{:s}.fastq{:s}\\\".format(\\\"_\\\".join([lane,\\n date,\\n fc_id,\\n sample_name,\\n read]),\\n ext.replace('..','.'))\\n return dest_file_name\",\n \"def collected_filename(cfg, collect_dir, i=None):\\n if i is not None:\\n file = cfg[\\\"files\\\"][i]\\n else:\\n file = cfg[\\\"file\\\"]\\n ext = path.splitext(file)[1]\\n name = cfg[\\\"id\\\"]\\n if i is not None:\\n name += \\\"_\\\" + str(i)\\n return path.join(collect_dir, name + ext)\",\n \"def _generate_filename(doc_type, login, *args):\\n filename = []\\n filename.append(doc_type)\\n filename.append(login)\\n for item in args:\\n filename.append(item)\\n filename.append(datetime.datetime.now().isoformat(timespec='microseconds'))\\n filename = '_'.join(filename)\\n return filename\",\n \"def content_file_name(instance, filename):\\r\\n return '/'.join([str(instance.app.publisher.id), str(instance.app.id), filename])\",\n \"def construct_name_file(size_sample, randomness, pos_equal_neg, kernel):\\n if randomness:\\n randomness = \\\"rand\\\"\\n else:\\n randomness = \\\"nrand\\\"\\n\\n if pos_equal_neg:\\n pos_equal_neg = \\\"pos-neg-eq\\\"\\n else:\\n pos_equal_neg = \\\"pos-neg-neq\\\"\\n\\n return \\\"{}_{}_{}_{}.json\\\".format(size_sample, randomness, pos_equal_neg, kernel)\",\n \"def generate_filename(player_name):\\n name = player_name.split()\\n filename = '_'.join(name).lower()\\n return filename\",\n \"def get_nomenclature_channel_fname(czi_fname, nomenclature_file, channel_name, ext='.inr.gz'):\\n # - Read NOMENCLATURE file defining naming conventions:\\n n_names = get_nomenclature_name(nomenclature_file)\\n return n_names[czi_fname]+\\\"/\\\", n_names[czi_fname] + \\\"_\\\" + channel_name + ext\",\n \"def generate_glider_filename(description):\\n filename = (\\n \\\"{glider}-{year:d}-{day:03d}-{mission:d}-{segment}.{type}\\\".format(**description)\\n )\\n return os.path.join(description['path'], filename)\",\n \"def generate_namefile(pathfolder, methodvalues):\\n datestr = datetime.datetime.now().date().strftime('%F')\\n paramsstr = str(hash(str(methodvalues)))\\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\\n namefile = os.path.join(pathfolder, namefile)\\n return namefile\",\n \"def generate_filename(filename: str) -> str:\\n return f\\\"{str(uuid.uuid4())}.{get_extension(filename)}\\\"\",\n \"def channel_name(radio_id: int, channel_id: int) -> str:\\n return f\\\"COMM{radio_id} Ch {channel_id}\\\"\",\n \"async def filename_generator(self):\\n chars = list(string.ascii_letters+string.digits)\\n name = ''\\n for i in range(random.randint(9, 25)):\\n name += random.choice(chars)\\n\\n if name not in self.player['audio_files']:\\n return name\\n\\n return await self.filename_generator()\",\n \"def gen_file_name(filename, path=UPLOAD_FOLDER):\\n\\n i = 1\\n while os.path.exists(os.path.join(path, filename)):\\n name, extension = os.path.splitext(filename)\\n filename = '%s_%s%s' % (name, str(i), extension)\\n i += 1\\n\\n return filename\",\n \"def generate_filename(playlist_or_album_name, user_id_or_artist_id=None):\\n filename = ''\\n if user_id_or_artist_id:\\n filename += user_id_or_artist_id + '_'\\n filename += playlist_or_album_name + '_' + str(time_ns())\\n return filename\",\n \"def _get_file_name(name: types.TSeedName) -> str:\\n return f\\\"{name}.yml\\\"\",\n \"def get_file_name(self):\\n\\n return \\\"%s - %s\\\" % (self.get_tags()[\\\"artist\\\"], self.get_tags()[\\\"title\\\"])\",\n \"def generateFilename(self, name):\\n return self.context.generateUniqueId(type_name='Module')\",\n \"def filename(i):\\n rand_name = os.path.join(os.getcwd(), \\\"input-%d.txt\\\" % i)\\n ref_name = os.path.join(os.getcwd(), \\\"input-%d.ref\\\" % i)\\n return rand_name, ref_name\",\n \"def create_filename (self):\\n\\t\\tassert self.__patient_name and self.__location_name, \\\"New filename could not be determined, one or more needed arguments is empty!\\\"\\n\\t\\t_patient_name = self.__patient_name.split(' ')\\n\\t\\t_patient_name.reverse()\\n\\t\\t\\n\\t\\treturn os.path.join(os.path.dirname(self.file._path), \\\"%s MR %s%s\\\" % (self.__location_name, ', '.join(_patient_name).upper(), self._file.extension))\",\n \"def filename(self):\\n return '%s%s' % (self.identifier, self.extension)\",\n \"def generate_report_file_name(args: Dict[str, Any]) -> str:\\n return (\\n f\\\"{args.get('report_type', '').lower().replace(' ', '_')}_fireeye_\\\"\\n f\\\"{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.\\\"\\n f\\\"{args.get('type', REPORT_TYPE_ALLOWED_FORMAT[args.get('report_type', '')][0])}\\\"\\n )\",\n \"def generate_file_name(hour, minute):\\n hour = str(hour)\\n if len(hour) == 1:\\n hour = \\\"0\\\" + hour\\n minute = str(minute)\\n if len(minute) == 1:\\n minute = \\\"0\\\" + minute\\n file_name = date + \\\"--\\\" + hour + special_char + minute + special_char + \\\"00,00.mvol\\\"\\n\\n # print \\\"filename: \\\",file_name\\n return file_name\",\n \"def filename(self):\\n translator = {ord(\\\" \\\"): \\\"_\\\", ord(\\\",\\\"): None}\\n return f'{self._full_name.translate(translator)}.txt'\",\n \"def generate_file_filename(instance, filename):\\n return _generate_filename(instance, filename, 'photos')\",\n \"def temp_name(self, filename):\\n if self.params.get('nopart', False) or filename == '-' or \\\\\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\\n return filename\\n return filename + '.part'\",\n \"def get_alternative_name(self, file_root, file_ext):\\n return \\\"%s_%s%s\\\" % (file_root, get_random_string(7), file_ext)\",\n \"def get_display_name(self, short=False):\\n if self.filename is None:\\n return '[New file]'\\n elif short:\\n return os.path.basename(self.filename)\\n else:\\n return self.filename\",\n \"def file_name(product, ext='json'):\\n return f\\\"./output/{product}_{datetime.now().strftime('%Y-%m-%d_%H%M%S')}_transformed_{version}.{ext}\\\"\",\n \"def generate_name(file_name, index):\\n name = ''\\n with open(file_name) as names:\\n for i, line in enumerate(names):\\n if i == index:\\n name += line.split(None, 1)[0]\\n return name\",\n \"def get_file_name(self):\\n return str(self.get_file())\",\n \"def get_download_file_name(self):\\n # Use 'unknown' if the course instance does not have a term\\n if self.course_instance.term:\\n term = self.course_instance.term.get_url_name()\\n else:\\n term = 'unknown'\\n\\n return '{course}-{term}-{number}-{instructors}-{type}{ext}'.format(\\n course=self.course_instance.course.get_url_name(),\\n term=term,\\n number=self.exam_number,\\n instructors='_'.join([i.last_name for i in self.instructors]),\\n type=self.exam_type,\\n ext=self.file_ext)\",\n \"def get_file_name() -> str:\\n import uuid\\n uniq_append_string = uuid.uuid4().hex\\n return \\\"LOCAL_STORAGE_{}\\\".format(uniq_append_string)\",\n \"def generateFileName(self):\\n return 'Covid' + self.map_type + '.html'\",\n \"def getNextFilename(self, cmd):\\n \\n self.actor.exposureID += 1\\n path = os.path.join(\\\"$ICS_MHS_DATA_ROOT\\\", 'mcs')\\n path = os.path.expandvars(os.path.expanduser(path))\\n\\n if not os.path.isdir(path):\\n os.makedirs(path, 0o755)\\n \\n return os.path.join(path, 'MCSA%010d.fits' % (self.actor.exposureID))\",\n \"def generate_image_filename():\\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\\n return 'CCTV_{0}.jpg'.format(now)\",\n \"def generate_file_name(self):\\n self._session_iterator = None # New file invalidate old interator\\n self._img_count += 1\\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\\n return self.current_file\",\n \"def mk_filename(pattern, queryresponserow, resp, url):\\n name = None\\n if resp:\\n cdheader = resp.headers.get(\\\"Content-Disposition\\\", None)\\n if cdheader:\\n _, params = parse_header(cdheader)\\n name = params.get('filename', \\\"\\\")\\n # Work around https://github.com/sunpy/sunpy/issues/3372\\n if name.count('\\\"') >= 2:\\n name = name.split('\\\"')[1]\\n\\n # This is a hack to to prevent IRIS data from being labelled as XML files\\n if name is None and \\\"VOEvent_IRIS\\\" not in queryresponserow['fileid']:\\n # Advice from the VSO is to fallback to providerid + fileid for a filename\\n # As it's possible multiple providers give the same fileid.\\n # However, I haven't implemented this yet as it would be a breaking\\n # change to the filenames we expect.\\n fileid = queryresponserow['fileid']\\n\\n # Some providers make fileid a path\\n # Some also don't specify a file extension, but not a lot we can do\\n # about that.\\n name = fileid.split(\\\"/\\\")[-1]\\n\\n # If somehow we have got this far with an empty string, fallback to url segment\\n if not name:\\n name = url.split('/')[-1]\\n\\n # Remove any not-filename appropriate characters\\n name = slugify(name)\\n\\n # If absolutely everything else fails make a filename based on download time\\n if not name:\\n name = f\\\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\\\"\\n\\n fname = pattern.format(file=name,\\n **queryresponserow.response_block_map)\\n\\n return fname\",\n \"def init_data_file_name():\\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\\n filename = 'show-commands-' + now + \\\".txt\\\"\\n return filename\",\n \"def create_random_file_name():\\n\\n def random_file_name_factory():\\n length = random.randint(10, 15)\\n chars = string.ascii_letters + string.digits + \\\"-_\\\"\\n return f\\\"{''.join(random.choice(chars) for _ in range(length))}.jpg\\\"\\n\\n return random_file_name_factory\",\n \"def filename(self):\\n return self.config.get('filename', self.id) + f'_{self.file_suffix}'\",\n \"def get_title(src_name, src_type=None):\\n if src_type == 'tcp':\\n return '{0}:{1}'.format(*src_name)\\n return os.path.basename(src_name)\",\n \"def get_resampled_filename(filepath):\\n try:\\n filepath = filepath.encode('utf-8')\\n except UnicodeDecodeError:\\n pass\\n return 'tmp_%s.flac' % md5(filepath).hexdigest()\",\n \"def format_filename(title: str, id: Any, ext: str = \\\".\\\", dirFormat=None):\\r\\n ...\",\n \"def _generate_filename(instance, filename, prefix):\\n md5 = hashlib.md5()\\n md5.update(struct.pack('f', time.time()))\\n for chunk in instance.file.chunks():\\n md5.update(chunk)\\n extension = os.path.splitext(filename)[1]\\n return os.path.join(prefix, md5.hexdigest() + extension)\",\n \"def modpricesetter_get_file_name(self):\\r\\n year, month, day = self._get_market_year_month_day_as_str()\\r\\n interval_number = self._get_interval_number_as_str()\\r\\n base_name = \\\"NEMPriceSetter_{year}{month}{day}{interval_number}00.xml\\\"\\r\\n name = base_name.format(\\r\\n year=year, month=month, day=day, interval_number=interval_number\\r\\n )\\r\\n path_name = Path(self.cache_folder) / name\\r\\n name_OCD = name.replace(\\\".xml\\\", \\\"_OCD.xml\\\")\\r\\n path_name_OCD = Path(self.cache_folder) / name_OCD\\r\\n name_zero = name.replace(\\\".xml\\\", \\\"00.xml\\\")\\r\\n path_name_zero = Path(self.cache_folder) / name_zero\\r\\n if os.path.exists(path_name):\\r\\n return name\\r\\n elif os.path.exists(path_name_OCD):\\r\\n return name_OCD\\r\\n elif os.path.exists(path_name_zero):\\r\\n return name_zero\\r\\n else:\\r\\n return name\",\n \"def source_file_name_feature(self):\\n return \\\"_\\\".join((C_FILE_NAME, self.file_image_name.value))\",\n \"def get_filename(out_dir, file_date, extension):\\n return path.join(out_dir, f'CrossrefCitations_{file_date}.{extension}')\",\n \"def create_filename(value):\\n return '%s.mp3' % slugify(value, u'_')\",\n \"def generate_filename(\\n radar, field, sweep, ext=\\\"png\\\", datetime_format=\\\"%Y%m%d%H%M%S\\\", use_sweep_time=False\\n):\\n name_s = generate_radar_name(radar).replace(\\\" \\\", \\\"_\\\")\\n field_s = field.replace(\\\" \\\", \\\"_\\\")\\n if use_sweep_time:\\n time_s = generate_radar_time_sweep(radar, sweep).strftime(datetime_format)\\n else:\\n time_s = generate_radar_time_begin(radar).strftime(datetime_format)\\n sweep_s = str(sweep).zfill(2)\\n return f\\\"{name_s}_{field_s}_{sweep_s}_{time_s}.{ext}\\\"\",\n \"def filename(N, Dr, g, launch):\\n\\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\\n (N, Dr, g, launch)))\",\n \"def unique_filename(data):\\n file = data\\n get_ext = file.filename.split(\\\".\\\")[-1]\\n new_name = \\\"%s.%s\\\" % (uuid.uuid4().hex, get_ext)\\n return new_name\",\n \"def file_on_disk_name(instance, filename):\\n return generate_file_on_disk_name(instance.checksum, filename)\",\n \"def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\\n\\tnew_filename = time.strftime(\\\"%d-%m-%Y_\\\")\\n\\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\\n\\tnew_filename = new_filename + \\\"_P\\\" + str(image_class)\\n\\treturn new_filename\",\n \"def content_file_name(self, filename):\\n ext = filename.split('.')[-1]\\n filename = \\\"%s_%s.%s\\\" % (filename, self.id, ext)\\n return os.path.join('pictures/static/pictures/', filename)\",\n \"def _build_name(self):\\n self.ctrl_name = NameUtils.get_unique_name(self.asset,\\n self.side,\\n self.part,\\n \\\"ctrl\\\")\",\n \"def output_file_name_maker(args):\\n log.debug(\\\"Entering output_file_name_maker()\\\")\\n path = os.getcwd() + '/out_files/'\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n\\n if args.output is None:\\n out_file_name = path + args.input[:-4] + '_' + args.type + '_' + args.layer\\n else:\\n out_file_name = path + args.output\\n\\n log.debug(\\\"Exiting output_file_name_maker()\\\")\\n return out_file_name\",\n \"def get_custom_filename(existing_files: List[str]) -> str:\\n questions = [\\n {\\n 'type': 'input',\\n 'name': 'custom_filename',\\n 'message': 'Name your new sample :',\\n 'default': get_default_filename(),\\n 'validate': lambda text: (\\n (len(re.findall(r'^[A-Za-z0-9_\\\\-.]{3,40}$', text)) > 0\\n and text+'.smp' not in existing_files\\n ) or\\n 'Typed file name contains illegal characters or already exist'\\n )\\n }\\n ]\\n return prompt(questions, style=custom_style_2)['custom_filename']+'.smp'\",\n \"def _filename(self, corotid):\\n from datasource import DataSource\\n self.corotid = corotid\\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\\n \\n query = \\\"\\\"\\\"SELECT run_code, hlfccdid, win_id \\n FROM corot \\n WHERE corotid = %d;\\\"\\\"\\\" % self.corotid\\n result = self.corot.query(query)\\n \\n par = {'run': result[0][0],\\n 'half': result[0][1].rstrip('RL'), \\n 'win': result[0][2]}\\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\\n logger.info('%d = %s' % (corotid,filename))\\n return filename\",\n \"def make_img_name(file_ext='.png'):\\r\\n fn = []\\r\\n # format seqs and write out to temp file\\r\\n for i in range(0, 30):\\r\\n fn.append(choice(ALPHABET))\\r\\n return ''.join(fn) + file_ext\",\n \"def update_filename(instance, filename):\\n path = os.path.join(\\\"documents_analizer\\\", \\\"documents\\\")\\n name = \\\"{}{}\\\".format(highly_random_name(),\\n os.path.splitext(filename)[1])\\n return os.path.join(path, name)\",\n \"def download_filename_full(self, doc):\\n # todo modify\\n authors = \\\",\\\".join([x['name'] for x in doc.artists])\\n author = re.sub(\\\"[\\\\\\\\\\\\\\\\/:*?\\\\\\\"<>|]\\\", '', authors.strip())\\n mp3_name = re.sub(\\\"[\\\\\\\\\\\\\\\\/:*?\\\\\\\"<>|]\\\", '', doc['name'])\\n name = os.path.join(author, \\\"%s - %s.mp4\\\" % (author, mp3_name))\\n return name\",\n \"def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\\n filename = self.filename(filetype=filetype, format=format)\\n #return Path(dir) / filename\\n return filename\",\n \"def filename(lang):\\n filename = lang.replace('-', '_')\\n filename = filename.lower()\\n return filename\",\n \"def _file_name(size):\\n timestamp = str(int(time.time()))\\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')\",\n \"def _prettyfilename(self):\\n return f'{self.title} ({self.subtype})'\",\n \"def generate_subdir(channel, energy=13):\\n return os.path.join('%dTeV' % energy, channel, strftime(\\\"%d_%b_%y\\\"))\",\n \"def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\\n filename = self.filename(filetype=filetype, format=format)\\n # return Path(dir) / filename\\n return filename\",\n \"def _generate_output_name(extension):\\n output_name = 'TDG_{:%Y-%m-%d_%H-%M-%S}.{}'.format(datetime.now(), extension)\\n return output_name\",\n \"def out_filename(self, filetype, dir, format='old'):\\n filename = self.filename(filetype=filetype, format=format)\\n return Path(dir) / filename\",\n \"def _create_file_name(file_path):\\r\\n file_base, file_ext = os.path.splitext(file_path)\\r\\n if os.path.isfile(file_path):\\r\\n nfile = 1\\r\\n check = True\\r\\n while check:\\r\\n name_add = '0000' + str(nfile)\\r\\n file_path = file_base + \\\"_\\\" + name_add[-4:] + file_ext\\r\\n if os.path.isfile(file_path):\\r\\n nfile = nfile + 1\\r\\n else:\\r\\n check = False\\r\\n return file_path\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\\n ext='.mif'):\\n\\n if basename == '':\\n msg = 'Unable to generate filename for command %s. ' % self.cmd\\n msg += 'basename is not set!'\\n raise ValueError(msg)\\n if cwd is None:\\n cwd = os.getcwd()\\n if change_ext:\\n if suffix:\\n suffix = ''.join((suffix, ext))\\n else:\\n suffix = ext\\n if suffix is None:\\n suffix = ''\\n fname = fname_presuffix(basename, suffix=suffix,\\n use_ext=False, newpath=cwd)\\n return fname\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\\n ext='.mif'):\\n\\n if basename == '':\\n msg = 'Unable to generate filename for command %s. ' % self.cmd\\n msg += 'basename is not set!'\\n raise ValueError(msg)\\n if cwd is None:\\n cwd = os.getcwd()\\n if change_ext:\\n if suffix:\\n suffix = ''.join((suffix, ext))\\n else:\\n suffix = ext\\n if suffix is None:\\n suffix = ''\\n fname = fname_presuffix(basename, suffix=suffix,\\n use_ext=False, newpath=cwd)\\n return fname\",\n \"def newCalFileName(self, type, runBegin, runEnd='end'):\\n \\n path=os.path.join(self.cdir)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup,self.src)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup,self.src,type)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n return path+'/'+str(runBegin)+'-'+str(runEnd)+'.data'\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):\\n if not basename:\\n msg = \\\"Unable to generate filename for command %s. \\\" % self.cmd\\n msg += \\\"basename is not set!\\\"\\n raise ValueError(msg)\\n\\n if cwd is None:\\n cwd = os.getcwd()\\n if ext is None:\\n ext = Info.output_type_to_ext(self.inputs.outputtype)\\n if change_ext:\\n suffix = \\\"\\\".join((suffix, ext)) if suffix else ext\\n\\n if suffix is None:\\n suffix = \\\"\\\"\\n fname = fname_presuffix(basename, suffix=suffix, use_ext=False, newpath=cwd)\\n return fname\",\n \"def get_file_name(replay_dir, template_name):\\n suffix = '.json' if not template_name.endswith('.json') else ''\\n file_name = f'{template_name}{suffix}'\\n return os.path.join(replay_dir, file_name)\",\n \"def _get_buildspec_filename(\\n lambda_name, file_name):\\n return '.'.join(\\n ('buildspecs', lambda_name, file_name))\",\n \"def generate_filename(ext,sha512base16_hash=None):\\n## # Timestamp filename\\n## timestamp = str(get_current_unix_time())\\n## filename = timestamp+\\\".\\\"+ext\\n # Base16 hash filename\\n filename = sha512base16_hash+\\\".\\\"+ext\\n return filename\",\n \"def _make_fname(song, ext=None, av=None, subdir=None):\\n # pylint: disable=E1103\\n # Instance of 'bool' has no 'extension' member (some types not inferable)\\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\\n if not os.path.exists(ddir):\\n os.makedirs(ddir)\\n\\n if ext:\\n extension = ext\\n\\n else:\\n stream = streams.select(streams.get(song),\\n audio=av == \\\"audio\\\", m4a_ok=True)\\n extension = stream['ext']\\n\\n # filename = song.title[:59] + \\\".\\\" + extension\\n filename = song.title + \\\".\\\" + extension\\n filename = os.path.join(ddir, mswinfn(filename.replace(\\\"/\\\", \\\"-\\\")))\\n filename = filename.replace('\\\"', '')\\n return filename\",\n \"def get_output_filename(item: str, root: str, i: int) -> str:\\n element_split = item.split(\\\"/\\\")\\n item, ext = element_split[-1].split(\\\".\\\")\\n if i < 0:\\n return f\\\"{root}/{'/'.join(element_split[:-1])}/{item}.{ext}\\\"\\n else:\\n return f\\\"{root}/{'/'.join(element_split[:-1])}/{item}_aug{i}.{ext}\\\"\",\n \"def outputFilename(name=\\\"\\\", ext=\\\"\\\", time=True):\\n # get the date in the format specifed\\n dateTime = datetime.now()\\n dateTimeFormat = \\\"%Y-%m-%d__%H-%M-%S\\\" if time else \\\"%Y-%m-%d\\\"\\n fileName = dateTime.strftime(dateTimeFormat)\\n\\n # construct the filename\\n fileName = fileName + \\\"_\\\" + name if fileName != \\\"\\\" else fileName\\n ext = \\\".\\\" + ext if ext != \\\"\\\" else \\\"\\\"\\n\\n return fileName + ext\",\n \"def generate_random_name(filename):\\n ext = filename.split('.')[-1]\\n rns = [random.randint(0, len(LETTER_SET) - 1) for _ in range(3)]\\n name = ''.join([LETTER_SET[rn] for rn in rns])\\n return \\\"{new_fn}.{ext}\\\".format(new_fn=name, ext=ext)\",\n \"def format_image_filename(device_image):\\n return \\\"{}-{}-{}-{}.bit\\\".format(device_image.bitstream_type,\\n device_image.pci_vendor,\\n device_image.pci_device,\\n device_image.uuid)\",\n \"def friendly_name(self):\\n if not self.collection:\\n print('YIKES no filepath %s %s' % (self.title,self.slug))\\n print (self)\\n return ''\\n fpath = self.collection.filePath\\n if '/' in fpath:\\n bg = fpath.index('/')+1\\n return fpath[bg:]+ '/' + self.fileName\\n return fpath+'/'+self.fileName\",\n \"def update_destination_file_name (file_name):\\n\\tglobal COUNTER \\n\\tCOUNTER += 1\\n\\tsplitted = file_name.split('/')\\n\\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]\",\n \"def genBaseName(fileName):\\n return fileName.split(\\\"_\\\")[0].split(\\\".\\\")[0]\",\n \"def getOutputFilename(self, filename):\\n return filename[:-4] + \\\".txt\\\"\",\n \"def appendDescription(dir, file_name, description):\\n\\tpath = os.path.join(dir, file_name)\\n\\tbase, ext = os.path.splitext(path)\\n\\treturn base + '.' + description + ext\",\n \"def getNextFilename(self, expType):\\n with self.lock:\\n self.exposureID += 1\\n exposureID = self.exposureID\\n path = os.path.join(\\\"$ICS_MHS_DATA_ROOT\\\", 'agcc')\\n path = os.path.expandvars(os.path.expanduser(path))\\n if not os.path.isdir(path):\\n os.makedirs(path, 0o755)\\n with self.lock:\\n timestamp = self.timestamp\\n return os.path.join(path, 'AGC%d_%s_%06d_%s.fits' % \\\\\\n (self.agcid + 1, expType, exposureID, timestamp))\",\n \"def _create_name(self) -> str:\\n return self.stream.__class__.__name__\",\n \"def _get_track_name(self, filename):\\n return os.path.basename(filename)\",\n \"def _generate_processed_key_name(process_to, upload_name):\\n timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\\n name, extension = os.path.splitext(upload_name)\\n digest = md5(''.join([timestamp, upload_name])).hexdigest()\\n return os.path.join(process_to, '{0}.{1}'.format(digest, extension))\",\n \"def _build_fname_templ(n):\\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\\n for i in range(1, n):\\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\\n var='{var%i}' % i)]\\n return globals.ds_fn_sep.join(parts) + '.nc'\",\n \"def format_filename(prefix, suffix, seq_len, uncased):\\n seq_str = \\\"seq-{}\\\".format(seq_len)\\n if uncased:\\n case_str = \\\"uncased\\\"\\n else:\\n case_str = \\\"cased\\\"\\n\\n file_name = \\\"{}.{}.{}.{}\\\".format(prefix, seq_str, case_str, suffix)\\n\\n return file_name\",\n \"def playlist_num_filename(**kwargs):\\n return f\\\"{kwargs['track_num']} - {default_filename(**kwargs)}\\\"\",\n \"def new_filename(fname=None,ndigits=3):\\n if fname is None:\\n ext = (\\\"%%.%ii\\\" % ndigits) % 1\\n fname = \\\"%s.%s\\\" % (random_string(6), ext)\\n \\n if os.path.exists(fname): \\n fname = increment_filename(fname,ndigits=ndigits)\\n\\n return fname\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75195915","0.7167804","0.71012443","0.70670754","0.69943404","0.67369324","0.6670187","0.66342044","0.66167426","0.6494215","0.64797497","0.6479064","0.6476831","0.64566636","0.6450938","0.64458376","0.6431859","0.6416797","0.6390564","0.63887256","0.6379263","0.63707864","0.6364709","0.6351363","0.63499355","0.6347175","0.634275","0.63143575","0.63116884","0.62697697","0.625603","0.6248453","0.6239961","0.6211779","0.6208297","0.62011933","0.61814475","0.61797863","0.6174575","0.6140057","0.61364377","0.61182743","0.61140096","0.6110816","0.61029804","0.6100755","0.6082549","0.607765","0.60764325","0.607335","0.60630345","0.6059483","0.60574424","0.6048166","0.60345787","0.60343796","0.60306704","0.6026986","0.6013758","0.599426","0.59873575","0.59872776","0.5985263","0.59812826","0.59781265","0.59761965","0.5967784","0.59631765","0.59560066","0.5950423","0.59501016","0.5946216","0.5941753","0.5938588","0.59275806","0.5922075","0.5922075","0.5916932","0.5910453","0.59010506","0.58981514","0.5884643","0.5869485","0.58673143","0.5863101","0.5860955","0.58598673","0.5853558","0.58517426","0.58480245","0.58445394","0.5843368","0.5841723","0.5836151","0.58358467","0.58334386","0.58319485","0.5830244","0.58291346","0.5821115"],"string":"[\n \"0.75195915\",\n \"0.7167804\",\n \"0.71012443\",\n \"0.70670754\",\n \"0.69943404\",\n \"0.67369324\",\n \"0.6670187\",\n \"0.66342044\",\n \"0.66167426\",\n \"0.6494215\",\n \"0.64797497\",\n \"0.6479064\",\n \"0.6476831\",\n \"0.64566636\",\n \"0.6450938\",\n \"0.64458376\",\n \"0.6431859\",\n \"0.6416797\",\n \"0.6390564\",\n \"0.63887256\",\n \"0.6379263\",\n \"0.63707864\",\n \"0.6364709\",\n \"0.6351363\",\n \"0.63499355\",\n \"0.6347175\",\n \"0.634275\",\n \"0.63143575\",\n \"0.63116884\",\n \"0.62697697\",\n \"0.625603\",\n \"0.6248453\",\n \"0.6239961\",\n \"0.6211779\",\n \"0.6208297\",\n \"0.62011933\",\n \"0.61814475\",\n \"0.61797863\",\n \"0.6174575\",\n \"0.6140057\",\n \"0.61364377\",\n \"0.61182743\",\n \"0.61140096\",\n \"0.6110816\",\n \"0.61029804\",\n \"0.6100755\",\n \"0.6082549\",\n \"0.607765\",\n \"0.60764325\",\n \"0.607335\",\n \"0.60630345\",\n \"0.6059483\",\n \"0.60574424\",\n \"0.6048166\",\n \"0.60345787\",\n \"0.60343796\",\n \"0.60306704\",\n \"0.6026986\",\n \"0.6013758\",\n \"0.599426\",\n \"0.59873575\",\n \"0.59872776\",\n \"0.5985263\",\n \"0.59812826\",\n \"0.59781265\",\n \"0.59761965\",\n \"0.5967784\",\n \"0.59631765\",\n \"0.59560066\",\n \"0.5950423\",\n \"0.59501016\",\n \"0.5946216\",\n \"0.5941753\",\n \"0.5938588\",\n \"0.59275806\",\n \"0.5922075\",\n \"0.5922075\",\n \"0.5916932\",\n \"0.5910453\",\n \"0.59010506\",\n \"0.58981514\",\n \"0.5884643\",\n \"0.5869485\",\n \"0.58673143\",\n \"0.5863101\",\n \"0.5860955\",\n \"0.58598673\",\n \"0.5853558\",\n \"0.58517426\",\n \"0.58480245\",\n \"0.58445394\",\n \"0.5843368\",\n \"0.5841723\",\n \"0.5836151\",\n \"0.58358467\",\n \"0.58334386\",\n \"0.58319485\",\n \"0.5830244\",\n \"0.58291346\",\n \"0.5821115\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80092597"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22387,"cells":{"query":{"kind":"string","value":"Constructor that takes the config and the well we are generating images for."},"document":{"kind":"string","value":"def __init__(self, config, well, directory):\n self.config = config\n self.well = well\n self.directory = directory"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, config):\n logging.info(\"Creating footprint\")\n # self.infra = yaml.load(config)\n self.infra = config\n self.footprint_name = self.infra.get(\"footprint\", \"ehw\")\n self.images = self.infra.get(\"images\")\n self.old_images = self.infra.get(\"old_images\", [])\n self.container_name = \"%s-metadata\" % self.footprint_name\n \n self.admin_password = self.infra.get('admin-password')\n self.savefile = self.infra.get(\"footprint\", \"outfile\") + \"-save.yaml\"\n if os.path.exists(self.savefile):\n self.saved_images = yaml.load(open(self.savefile))\n self.footprint_status=self.infra.get(\"footprint_status\", None)\n logging.debug(\"Loaded saved images: %s\" % self.saved_images)\n # sys.exit(0) ","def __init__(self, generate_image_pyramid: bool = True):\n if not os.path.exists(MANIFEST_OUTPUT_DIR):\n os.makedirs(MANIFEST_OUTPUT_DIR)\n self._manifest_factory = ManifestFactory()\n self._manifest_factory.set_base_prezi_uri(MANIFEST_BASE_URL)\n self._manifest_factory.set_base_prezi_dir(MANIFEST_OUTPUT_DIR)\n self._manifest_factory.set_base_image_uri(IMAGE_BASE_URL)\n self._manifest_factory.set_iiif_image_info(2.0, 1) # Version, ComplianceLevel\n\n self._image_reader = ImageReader(IMAGE_SOURCE_DIR)\n\n if generate_image_pyramid:\n self._tile_generator = IIIFStatic(dst=IMAGE_FILE_OUTPUT_DIR, prefix=IMAGE_BASE_URL)\n\n self._generate_images = generate_image_pyramid","def __init__(self, train_path='train/image', label_path='train/label', merge_path='train/merge', aug_merge_path='train/aug_merge', aug_train_path='train/aug_train', aug_label_path='train/aug_label', img_type=\"nii\"):\n\n\t\tself.train_imgs = glob.glob(\"/*.\"+img_type)\n\t\tself.label_imgs = glob.glob(\"/*.\"+img_type)\n\t\tself.train_path = train_path\n\t\tself.label_path = label_path\n\t\tself.merge_path = merge_path\n\t\tself.img_type = img_type\n\t\tself.aug_merge_path = aug_merge_path\n\t\tself.aug_train_path = aug_train_path\n\t\tself.aug_label_path = aug_label_path\n\t\tself.slices = len(self.train_imgs)\n\t\tself.datagen = ImageDataGenerator(\n\t\t\t\t\t\t\t rotation_range=0.2,\n\t\t\t\t\t\t\t width_shift_range=0.05,\n\t\t\t\t\t\t\t height_shift_range=0.05,\n\t\t\t\t\t\t\t shear_range=0.05,\n\t\t\t\t\t\t\t zoom_range=0.05,\n\t\t\t\t\t\t\t horizontal_flip=True,\n\t\t\t\t\t\t\t fill_mode='nearest')","def __init__(self, img):\n self.img = img","def __init__(self, train_path=\"./data/train/image\", label_path=\"./data/train/label\",\n\t\t\t\t merge_path=\"./data/train/merge\", aug_merge_path=\"./data/train/aug_merge\", \n\t\t\t\t aug_train_path=\"./data/train/aug_images\", \n\t\t\t\t aug_label_path=\"./data/train/aug_masks\", img_type=\"tif\"):\n\n\t\tself.train_imgs = glob.glob(train_path+\"/*.\"+img_type)\n\t\tself.label_imgs = glob.glob(label_path+\"/*.\"+img_type)\n\t\tself.train_path = train_path\n\t\tself.label_path = label_path\n\t\tself.merge_path = merge_path\n\t\tself.img_type = img_type\n\t\tself.aug_merge_path = aug_merge_path\n\t\tself.aug_train_path = aug_train_path\n\t\tself.aug_label_path = aug_label_path\n\n\t\tif not os.path.exists(merge_path):\n\t\t\tos.mkdir(merge_path)\n\t\t\tos.mkdir(aug_merge_path)\n\t\t\tos.mkdir(aug_train_path)\n\t\t\tos.mkdir(aug_label_path)\n\n\t\tself.slices = len(self.train_imgs)\n\t\tself.datagen = ImageDataGenerator(\n\t\t\t\t\t\t\t\t\tpreprocessing_function=self.preprocess,\n\t\t\t\t\t\t\t\t\trotation_range=0.2,\n\t\t\t\t\t\t\t\t\twidth_shift_range=0.1,\n\t\t\t\t\t\t\t\t\theight_shift_range=0.1,\n\t\t\t\t\t\t\t\t\tshear_range=0.05,\n\t\t\t\t\t\t\t\t\tzoom_range=0.05,\n\t\t\t\t\t\t\t\t\thorizontal_flip=True,\n\t\t\t\t\t\t\t\t\tfill_mode='nearest')","def build_filler_images(self):","def __init__ (self):\r\n\r\n self.path = 'c:\\\\python22\\\\work\\\\'\r\n self.bfh_vals = (BM, 0, 0, 0, 0)\r\n self.bih_vals = (BIH_SIZE, 0, 0, 1, 32, 0, 0, 0, 0, 0, 0)\r\n self.the_file = None\r\n self.image = []\r\n self.colourmap = []","def __init__(self, config, set_name, preprocess_image):\n\t\t\tself.data_dir = config['data_dir']\n\t\t\tself.set_name = set_name\n\t\t\tself.coco = COCO(os.path.join(self.data_dir, 'annotations', 'instances_' + set_name + '.json'))\n\t\t\tself.image_ids = self.coco.getImgIds()\n\t\t\tself.mask = config['mask']\n\n\t\t\tself.load_classes()\n\n\t\t\tsuper(CocoGenerator, self).__from_config__(config, preprocess_image=preprocess_image)","def __init__(self, gdmix_config_obj, jar_path=\"\", namespace=\"\", secret_name=\"\", image=\"\", service_account=\"\", job_suffix=\"\", *, prev_model_name):\n super().__init__(gdmix_config_obj, jar_path, namespace, secret_name, image, service_account, job_suffix)\n self.prev_model_name = prev_model_name\n self.root_output_dir = gdmix_config_obj.output_dir","def __init__(self, images, loader):\n super().__init__()\n self._images = images\n self._loader = loader","def initImages(self):\n pass","def initImages(self):\n pass","def initImages(self):\n pass","def __init__(self, cfg):\r\n\r\n\t\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\r\n\t\tanchor_config = cfg.MODEL.ANCHORS\r\n\t\tself.feature_maps = anchor_config.FEATURE_MAPS\r\n\t\tself.min_sizes = anchor_config.MIN_SIZES\r\n\t\tself.max_sizes = anchor_config.MAX_SIZES \r\n\t\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\r\n\t\tself.clip = anchor_config.CLIP","def generate_image(self):\n pass","def __init__(self, image):\n self.image = image","def create_base_image(self, builder, template, parameters):","def __init__(self, path, margin=25, folder=\"output\"):\n self.image = Image.open(path)\n self.width, self.height = self.image.size\n self.path = path\n self.margin = margin\n self.output_path = os.path.join(os.path.dirname(self.path),\n folder,\n os.path.basename(self.path))","def __build__(self,data_index=0):\n \n super(Image,self).__build__()\n # -- How to read the image\n self._build_properties = dict(\n data_index = data_index,\n header_exptime = \"EXPTIME\",\n dataslice0=\"undefined\",\n dataslice1=\"undefined\",\n bkgdbox={\"bh\":100,\"bw\":100,\"fh\":3,\"fw\":3},\n )","def __init__(self, **kwargs):\n super(ImageExporter, self).__init__(**kwargs)","def configure(self):\n self.data_batch_file = self.get_value_from_config('data_batch_file')\n self.batch_meta_file = self.get_value_from_config('batch_meta_file')\n self.has_background = self.get_value_from_config('has_background')\n self.num_classes = self.get_value_from_config('num_classes')\n self.converted_images_dir = self.get_value_from_config('converted_images_dir')\n if not self.converted_images_dir:\n self.converted_images_dir = self.data_batch_file.parent / 'converted_images'\n self.convert_images = self.get_value_from_config('convert_images')\n # create directory for storing images if it is necessary\n if self.convert_images and not self.converted_images_dir.exists():\n self.converted_images_dir.mkdir(parents=True)\n self.dataset_meta = self.get_value_from_config('dataset_meta_file')","def __init__(self, boardSize, width = 750, height = 750, borderSize = 75, boardTexture = BOARD_TEXTURE, stoneBlackTexture = STONE_BLACK_TEXTURE, stoneWhiteTexture = STONE_WHITE_TEXTURE, markerBlackTexture = MARKER_BLACK_TEXTURE, markerWhiteTexture = MARKER_WHITE_TEXTURE):\n\n if (boardSize == 19): self.settings = BOARD_SETTINGS_19\n elif (boardSize == 13):\n self.settings = BOARD_SETTINGS_13\n borderSize += 10\n elif (boardSize == 9):\n self.settings = BOARD_SETTINGS_9\n borderSize += 25\n\n self.width = width\n self.height = height\n self.innerWidth = width - borderSize * 2\n self.innerHeight = height - borderSize * 2\n self.borderSize = borderSize\n self.baseImage = Image.new(\"RGBA\", (width, height), (255, 255, 255, 255))\n\n (boardSize, _, _) = self.settings\n stoneWidth = int(self.innerWidth / (boardSize - 1))\n stoneHeight = int(self.innerWidth / (boardSize - 1))\n markerWidth = int(stoneWidth / 2)\n markerHeight = int(stoneHeight / 2)\n\n self.stoneBlackTexture = stoneBlackTexture.resize((stoneWidth, stoneHeight), Image.BICUBIC)\n self.stoneWhiteTexture = stoneWhiteTexture.resize((stoneWidth, stoneHeight), Image.BICUBIC)\n self.markerBlackTexture = markerBlackTexture.resize((markerWidth, markerHeight), Image.BICUBIC)\n self.markerWhiteTexture = markerWhiteTexture.resize((markerWidth, markerHeight), Image.BICUBIC)\n\n self.__draw_board_texture(boardTexture)\n self.__draw_board()","def __init__(self, img, settings):\r\n self.img_orig = img\r\n self.settings = settings","def __init__(self):\n self.index = 'r11_07_06c'\n self.parameters = {'run_index': 'r11_07_06c',\n 'h_1': 0.25,\n 'rho_0': 1.150,\n 'rho_1': 1.100,\n 'rho_2': 1.000,\n 'alpha': 0.5,\n 'D': 0.4,\n 'H': 0.25,\n 'sample': 1.0,\n 'perspective': 'old'}\n self.run_data = {'run_index': 'r11_07_06c',\n 'l0x': 2796,\n 'l0y': 1151,\n 'lsx': 2793,\n 'lsy': 716,\n 'j10x': 210,\n 'j10y': 1165,\n 'j1sx': 208,\n 'j1sy': 727,\n 'leakage': -76,\n 'odd_1': 'n',\n 'j20x': 2728,\n 'j20y': 1086,\n 'j2sx': 2730,\n 'j2sy': 670,\n 'r0x': 1097,\n 'r0y': 1095,\n 'rsx': 1093,\n 'rsy': 683,\n 'odd_2': 'n'}\n self.raw_image = 'tests/data/synced/r11_07_06c/cam1/img_0001.jpg'\n self.bc_image = 'tests/data/bc/r11_07_06c/cam1/img_0001.jpg'\n self.processed_path = 'tests/data/processed_ref/r11_07_06c/cam1/img_0001.jpg'","def __init__(self, image1, image2):\n self.image1 = image1\n self.image2 = image2","def __init__(self, config_file = 'config.yaml'):\n\n self.name = ''\n self.img_dir = ''\n self.out_dir = ''\n self.cam_file = ''\n self.options_file = ''\n self.output_xml_file = ''\n\n # If there is an options file, it will overwrite the defaults \n if config_file is not None:\n self.load(config_file)","def __init__(self, opt):\r\n super().__init__(opt)\r\n\r\n self.image_color = []\r\n for folder in self.annotations.keys():\r\n for image in self.annotations[folder].keys():\r\n img_path = os.path.join(self.root_dir, folder, image)\r\n camera, spec, n = image.split('_')\r\n if camera == 'BB':\r\n continue\r\n else:\r\n if spec == 'color':\r\n self.image_color.append(img_path)\r\n\r\n def sort_priority(x):\r\n *_, folder, name = x.split('/')\r\n folder_n = int(folder[1])\r\n folder_t = folder[2]\r\n name = int(name[0:-4].split('_')[-1])\r\n return folder_n, folder_t, name\r\n\r\n self.image_source, self.image_target = self._get_src_tgt(\r\n opt.augmentation_ratio, self.image_color, sort_priority)","def __init__(self, metadata, environment, component, image, version, s3_bucket, exec_env):\n self.environment = environment\n self.component = component\n self.s3_bucket = s3_bucket\n self.exec_env = exec_env\n self.image = image\n self.version = version\n self.metadata = metadata\n\n # generate Terragrunt config as part of object initialisation\n self.config()","def __init__(self, *args):\n super().__init__(*args)\n\n self.output_dir = os.path.join(self.config.results_dir, \"cowinner\")\n self.merged_dir = os.path.join(self.output_dir, \"merged\")","def __init__(self, data_dir, pairs_filepath, img_ext):\n self.data_dir = data_dir\n self.pairs_filepath = pairs_filepath\n self.img_ext = img_ext","def __init__(self, config: str, wdir=os.getcwd()):\n\n self.config_path = config\n self.wdir = wdir\n\n with open(self.config_path) as fin:\n self.config_dic = yaml.load(fin, Loader=yaml.BaseLoader)\n\n # default is 00 for all interaction types\n self.config_parameters_dic = None\n\n # This parameter defines whether self_loops will be kept or not\n self.self_loops = True\n if 'self_loops' in self.config_dic:\n self.self_loops = bool(int(self.config_dic['self_loops']))","def __init__(self, ms, srcdict=None):\n Imaging.__init__(self, ms)\n self.srcdict = srcdict","def __init__(self, args):\n self.cropped_dir = args.cropped_dir\n self.output_dir = args.output_dir\n self.txt_info_dir = args.txt_info_dir\n\n self.HR_dim = []\n self.HR_dim.append(int(args.HR_dim.split(' ')[0]))\n self.HR_dim.append(int(args.HR_dim.split(' ')[1]))\n self.HR_dim.append(int(args.HR_dim.split(' ')[2]))\n\n self.HR_pixdim = []\n self.HR_pixdim.append(float(args.HR_pixdim.split(' ')[0]))\n self.HR_pixdim.append(float(args.HR_pixdim.split(' ')[1]))\n self.HR_pixdim.append(float(args.HR_pixdim.split(' ')[2]))","def __init__(__self__,\n resource_name: str,\n args: ImageInitArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","def __init__(\n self,\n world: \"SimWorld\",\n image_size: Optional[Tuple[int, int]] = None,\n pixel_size: float = 0.015,\n images: Optional[str] = None,\n max_mag: float = 20.0,\n seeing: float = 3.0,\n **kwargs: Any,\n ):\n Object.__init__(self, **kwargs)\n\n # store\n self.world = world\n self.telescope = world.telescope\n self.full_frame: Tuple[int, int, int, int] = (\n (0, 0, image_size[0], image_size[1]) if image_size is not None else (0, 0, 512, 512)\n )\n self.window = self.full_frame\n self.binning = (1, 1)\n self.pixel_size = pixel_size\n self.image_format = ImageFormat.INT16\n self.images = (\n [] if images is None else sorted(glob.glob(images)) if \"*\" in images or \"?\" in images else [images]\n )\n self._max_mag = max_mag\n self._seeing = seeing\n\n # private stuff\n self._catalog = None\n self._catalog_coords = None","def __init__(self, binning: int = 2, **kwargs: Any):\n ImageProcessor.__init__(self, **kwargs)\n\n # store\n self.binning = binning","def __init__ (self, config, logger):\n self.logger = logger\n self.logger.add('loading AREA')\n config['data_type'] = np.float32\n self.area = AreaGrid(config,logger = self.logger)\n self.area.config['dataset_name'] = 'Area Data'\n self.area.config['description'] = \\\n \"\"\"Area Data contains fractional cohort data for each year the ATM\n was run. \n \"\"\"\n self.logger.add('performing post AREA setup')\n self.shape = self.area.config['grid_shape']\n self.aoi = self.area.area_of_interest()\n config['shape'] = self.shape\n config['grid_shape'] = self.area.config['grid_shape']\n config['AOI mask'] = self.aoi\n config['cohort list'] = self.area.get_cohort_list()\n self.logger.add('loading ALD')\n self.ald = ALDGrid(config,logger = self.logger)\n self.ald.config['dataset_name'] = 'ALD Data'\n self.ald.config['description'] = \\\n \"\"\"ALD Data contains ALD, and Protective Layer data for each year \n the ATM was run.\n \"\"\"\n self.logger.add('loading POI')\n self.poi = POIGrid(config,logger = self.logger)\n self.poi.config['dataset_name'] = 'POI Data'\n self.poi.config['description'] = \\\n \"\"\"POI Data contains Poi data for each year the ATM was run. \n \"\"\"\n self.logger.add('loading ICE')\n self.ice = IceGrid(config,logger = self.logger)\n self.ice.config['dataset_name'] = 'Ice Data'\n self.ice.config['description'] = \\\n \"\"\"\n Ice Data contains the ice content grid for the ATM model run\n \"\"\"\n self.logger.add('loading LAKE POND')\n self.lake_pond = LakePondGrid(config,logger = self.logger)\n self.lake_pond.config['dataset_name'] = 'Lake Pond Data'\n self.lake_pond.config['description'] = \\\n \"\"\"Lake-Pond Data contains Lake and Pond depth and count data for \n each year the ATM was run. \n \"\"\"\n self.logger.add('loading CLIMATE EVENT')\n self.climate_event = ClimateEventGrid(config,logger = self.logger)\n self.climate_event.config['dataset_name'] = 'Climate Event Data'\n self.climate_event.config['description'] = \\\n \"\"\"Climate Event Data contains climate event data for each \n year the ATM was run. \n \"\"\"\n ## TODO:redo masks here\n # for lpt in config['pond types'] + config['lake types']:\n # #~ print lpt\n # mask = self.area[lpt][0] > 0 # all cells in first ts > 0\n # self.lake_pond.apply_mask(lpt, mask)\n self.logger.add('loading DRAINGAGE')\n self.drainage = DrainageGrid(config,logger = self.logger)\n self.drainage.config['dataset_name'] = 'Drainage Data'\n self.drainage.config['description'] = \"\"\"\n Drainage contains the drainage grid for the ATM model run\n \"\"\"\n \n self.logger.add('loading DEGREE DAY')\n self.degreedays = DegreeDayGrids(\n os.path.join(\n config['Input_dir'], config['Met_Control']['FDD_file']),\n os.path.join(\n config['Input_dir'], config['Met_Control']['TDD_file'])\n )\n \n ## what does this do?\n self.ald.setup_ald_constants(\n self.degreedays.thawing[config['start year']]\n )","def __init__(self, config):\n self.config = config\n self.observation_space = spaces.Box(\n low=np.array([-15, -15, -15, -15, -15]),\n high=np.array([15, 15, 15, 15, 15])\n )\n self.action_space = spaces.Box(\n low=np.array([0, 0]),\n high=np.array([15, 5])\n )\n if config[\"backend\"] == \"EH\":\n self.params = {\n \"a_1\": 1,\n \"b_1\": 3,\n \"c_1\": 1,\n \"d_1\": 5,\n \"I_ext1\": 3.1,\n \"m\": 0,\n \"a_2\": 6,\n \"tau_2\": 10,\n \"I_ext2\": .45,\n \"gamma\": .01,\n \"r\": .00035,\n \"s\": 4,\n \"x0\": -1.6,\n }\n self.sim = EHSim()\n else:\n self.params = {\n \"x0\": -1.6,\n \"y0\": 1,\n \"tau0\": 2857,\n \"tau1\": 1,\n \"tau2\": 10,\n \"I_rst1\": 3.1,\n \"I_rst2\": .45,\n \"gamma\": .01,\n }\n self.sim = JSim()\n self.x1 = 0\n self.y1 = -5\n self.z = 5.5\n self.x2 = 0\n self.y2 = 0\n self.frame = 0\n self.curr_stim = []\n self.reset()\n self.history = list()","def __init__(self, nb_sub_images, window_size, recovery, image_horiz_size):\n self.nb_sub_images = nb_sub_images\n self.window_size = window_size\n self.recovery = recovery\n self.image_horiz_size = image_horiz_size","def __init__(self, fig):\n # Initialize the system\n self.set_sys()\n\n #################################################################\n #\n # Initialize plotting facility\n #\n #################################################################\n\n self.fig = fig\n\n # import utility to visualize the wigner function\n from wigner_normalize import WignerNormalize, WignerSymLogNorm\n\n img_params = dict(\n origin='lower',\n cmap='seismic',\n norm=WignerNormalize(vmin=-0.01, vmax=0.05),\n # norm=WignerSymLogNorm(linthresh=1e-5, vmin=-0.01, vmax=0.1),\n )\n\n ax = fig.add_subplot(121)\n ax.set_title('Classical density, $\\\\rho(x,p,t)$')\n\n # generate empty plots\n self.img_clasical_rho = ax.imshow(\n [[]],\n **img_params,\n extent=[self.quant_sys.X.min(), self.quant_sys.X.max(), self.quant_sys.P.min(), self.quant_sys.P.max()],\n )\n\n ax.set_xlabel('$x$ (a.u.)')\n ax.set_ylabel('$p$ (a.u.)')\n\n ax = fig.add_subplot(122)\n ax.set_title('Real part quantum density matrix, $\\Re\\hat{\\\\rho}$')\n\n # generate empty plots\n self.img_Upsilon2 = ax.imshow([[]], **img_params, extent=[1, 2, 1, 2])\n\n #ax.set_xlabel('$x$ (a.u.)')\n #ax.set_ylabel('$p$ (a.u.)')\n\n #self.fig.colorbar(self.img_clasical_rho)","def __init__(self):\n # Effective batch size\n self.BATCH_SIZE = self.IMAGES_PER_GPU * self.GPU_COUNT\n\n # Input image size\n if self.IMAGE_RESIZE_MODE == \"crop\":\n self.IMAGE_SHAPE = np.array([self.IMAGE_MIN_DIM, self.IMAGE_MIN_DIM,\n self.IMAGE_CHANNEL_COUNT])\n else:\n self.IMAGE_SHAPE = np.array([self.IMAGE_MAX_DIM, self.IMAGE_MAX_DIM,\n self.IMAGE_CHANNEL_COUNT])\n\n # Image meta data length\n # See compose_image_meta() for details\n self.IMAGE_META_SIZE = 1 + 3 + 3 + 4 + 1 + self.NUM_CLASSES","def __init__(self, *args, **kwargs):\n super(AscatL2Image, self).__init__(*args, **kwargs)","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n pi0eeg_name = '{0}Pi0EEG'.format(name)\n \n d2pipi0eeg_name = '{0}D2PiPi0EEG'.format(name) \n d2kpi0eeg_name = '{0}D2KPi0EEG'.format(name)\n\n d02pi0pi0eeg_name = '{0}D02Pi0Pi0EEG'.format(name) \n dst2d0pieeg_name = '{0}Dst2D0PiEEG'.format(name)\n\n self.selPi0EEG = makeEEGResonance(\n pi0eeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.Pi0EEG\n )\n \n self.selD2PiPi0EEG = makeD(\n d2pipi0eeg_name,\n config,\n inputSel=[self.selPi0EEG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPi0,\n useBachelorPID=False\n )\n\n usePIDforKaon = False\n if (config['Bachelor_PIDK_MIN'] > -10):\n usePIDforKaon = True\n else :\n usePIDforKaon = False\n \n self.selD2KPi0EEG = makeD(\n d2kpi0eeg_name, \n config,\n inputSel=[self.selPi0EEG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPi0,\n useBachelorPID=usePIDforKaon\n )\n\n self.selD02Pi0Pi0EEG = makeD(\n d02pi0pi0eeg_name, \n config,\n inputSel=[self.selPi0EEG],\n decDescriptors=self.D02Pi0Pi0,\n useBachelorPID=False,\n )\n\n self.selDst2D0PiEEG = makeDstar(\n dst2d0pieeg_name,\n config,\n inputSel=[self.selD02Pi0Pi0EEG, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.line_D2PiPi0EEG = make_line(\n self,\n '{0}Line'.format(d2pipi0eeg_name),\n prescale=config['PrescaleD2PiPi0EEG'],\n postscale=config['PostscaleD2PiPi0EEG'],\n selection=self.selD2PiPi0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KPi0EEG = make_line(\n self,\n '{0}Line'.format(d2kpi0eeg_name),\n prescale=config['PrescaleD2KPi0EEG'],\n postscale=config['PostscaleD2KPi0EEG'],\n selection=self.selD2KPi0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_Dst2D0PiEEG = make_line(\n self,\n '{0}Line'.format(dst2d0pieeg_name),\n prescale=config['PrescaleDst2D0PiEEG'],\n postscale=config['PostscaleDst2D0PiEEG'],\n selection=self.selDst2D0PiEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n if not os.path.isfile(CONFIG_FILENAME):\n first_time_run()\n raise SystemExit()\n\n \"\"\"\n Init file is present, read and parse it:\n \"\"\"\n conf = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation(), inline_comment_prefixes='#')\n conf.read(CONFIG_FILENAME)\n \"\"\"\n Process certain paths:\n \"\"\"\n\n path = conf['Paths']\n self.ffc_dir = path['FlatFieldCalDir']\n self.capture_dir = path['CaptureDir']\n self.image_dir = path['ImageDir']\n\n \"\"\"\n Process options\n \"\"\"\n\n self.cal_auto_save = conf.getboolean('Options', 'CalAutoSave', fallback=True)\n self.cal_auto_load = conf.getboolean('Options', 'CalAutoLoad', fallback=True)\n self.sound_on_capture = conf.getboolean('Options', 'SoundOnCapture', fallback=True)\n self.exp_init1 = conf.getint('Options', 'ExpInit1', fallback=100)\n self.exp_init2 = conf.getint('Options', 'ExpInit2', fallback=100)\n self.black_correct = conf.getboolean('Options', 'BlackCorrect', fallback=True)\n # Setup square window, default of full-screen height\n self.tiff_seq_x_window = conf.getint('Options', 'TiffSeqXWindow', fallback=cameras.FRAME_HEIGHT)\n self.tiff_seq_y_window = conf.getint('Options', 'TiffSeqYWindow', fallback=cameras.FRAME_HEIGHT)\n self.tiff_seq_rebin = conf.getint('Options', 'TiffSeqRebin', fallback = 2)","def __init__(self, config_path):\n cfg = Config.fromfile(config_path)\n self.cfg = cfg\n\n # Now make the dataloader\n self.dataset = build_dataset(cfg.data.test)\n\n self.loader = build_dataloader(\n self.dataset,\n imgs_per_gpu=1,\n workers_per_gpu=0,\n dist=False,\n shuffle=False\n )","def __init__(self,config,typ='train'):\n\n self._config = config\n self.type = typ\n self.reader = JsonlReader(self._config.annotations.as_dict()[typ])\n self.annotations = self.reader.read()\n self.transform = get_image_processor(self._config.image_processor)","def __init__(self, im, lab):\n\n #self.ROOT = root\n self.images = im\n self.labels = lab","def __init__(self, config):\n\n self._config = config\n self._is_chef = config.is_chef\n\n # create source and target environment\n self._envs = {}\n for env_name in config.envs:\n env = make_env(env_name, copy.copy(config), \"target\")\n self._envs[env_name] = env\n ob_space = env.observation_space\n env_ob_space = env.env_observation_space\n ac_space = env.action_space\n logger.info(\"Observation space: \" + str(ob_space))\n logger.info(\"Action space: \" + str(ac_space))\n\n # create a new observation space after data augmentation (random crop)\n if config.encoder_type == \"cnn\":\n assert (\n not config.ob_norm\n ), \"Turn off the observation norm (--ob_norm False) for pixel inputs\"\n # env\n ob_space = gym.spaces.Dict(spaces=dict(ob_space.spaces))\n for k in ob_space.spaces.keys():\n if len(ob_space.spaces[k].shape) == 3:\n shape = [\n ob_space.spaces[k].shape[0],\n config.encoder_image_size,\n config.encoder_image_size,\n ]\n ob_space.spaces[k] = gym.spaces.Box(\n low=0, high=255, shape=shape, dtype=np.uint8\n )\n\n # build agent and networks for algorithm\n self._agent = get_multi_stage_agent_by_name(config.algo)(\n config, ob_space, ac_space, env_ob_space, ob_space, ac_space, env_ob_space,\n )\n\n # build rollout runner\n self._runners = {}\n for k, env in self._envs.items():\n self._runners[k] = RolloutRunner(\n config,\n env,\n env,\n self._agent.run_in(\"target\", stage=\"policy_init\"),\n \"target\",\n )","def __init__(self):\n super(ISimpleITKImageMetric, self).__init__()\n self.metric = 'ISimpleITKImageMetric'\n self.ground_truth = None # SimpleITK.Image\n self.segmentation = None # SimpleITK.Image","def __init__(self, metadata_folder='./'):\n self.metadata = self.load_metadata(metadata_folder)\n self.prefix = 'data/miap/images/'\n return","def __init__(self, domains, image_dir, transform, hold_out_size):\n self.image_dir = image_dir\n self.transform = transform\n self.dataset = []\n self.attr2idx = {}\n self.idx2attr = {}\n self.domains=domains\n self.sample_count = {}\n self.img_cache = {}\n\n \n self.valid_set_size=hold_out_size\n if self.valid_set_size >0:\n self.valid_set_dir=\"./validation_set\"\n utils.create_empty_dir(self.valid_set_dir)\n \n self.preprocess()\n \n self.num_images = len(self.dataset)","def __init__(self, **attrs):\n \n self.element_id = None\n self.design_id = None\n \n self.part = None\n self.color = None\n \n self.img_url = None\n \n self.count = None\n self.is_spare = None\n \n super().__init__(**attrs)","def __init__(self, root_dir, figs_dir, fig_name, species, alpha, loop_length, tpulse, solver, scaling_suffix='', aspect_ratio_factor=1.0, em_cutoff=1e+25, em_peak_falloff=0.99, Tn=np.arange(250,5250,250), **kwargs):\n #set up paths\n child_path = os.path.join(root_dir, species+'_heating_runs', 'alpha'+str(alpha), 'data')\n self.file_path = 'ebtel_L'+str(loop_length)+'_tn%d'+scaling_suffix+'_tpulse'+str(tpulse)+'_'+solver\n self.root_path = os.path.join(child_path, self.file_path)\n self.em_res_top_dir = os.path.join(os.path.split(child_path)[0].replace(root_dir.strip('/'),figs_dir.strip('/')),fig_name+'.lvl1_em')\n #configure logger\n self.logger = logging.getLogger(type(self).__name__)\n #configure keyword arguments\n self.Tn=Tn\n self.tpulse = tpulse\n self.aspect_ratio_factor = aspect_ratio_factor\n self.em_cutoff = em_cutoff\n self.em_peak_falloff = em_peak_falloff\n #instantiate binner class\n self.binner = emb.EM_Binner(2.*loop_length*1.e+8)","def __init__(self,\n registration_type: RegistrationType = RegistrationType.RIGID,\n number_of_histogram_bins: int = 200,\n learning_rate: float = 1.0,\n step_size: float = 0.001,\n number_of_iterations: int = 200,\n relaxation_factor: float = 0.5,\n shrink_factors: typing.List[int] = (2, 1, 1),\n smoothing_sigmas: typing.List[float] = (2, 1, 0),\n sampling_percentage: float = 0.2,\n sampling_seed: int = sitk.sitkWallClock,\n resampling_interpolator=sitk.sitkBSpline):\n super().__init__()\n\n if len(shrink_factors) != len(smoothing_sigmas):\n raise ValueError(\"shrink_factors and smoothing_sigmas need to be same length\")\n\n self.registration_type = registration_type\n self.number_of_histogram_bins = number_of_histogram_bins\n self.learning_rate = learning_rate\n self.step_size = step_size\n self.number_of_iterations = number_of_iterations\n self.relaxation_factor = relaxation_factor\n self.shrink_factors = shrink_factors\n self.smoothing_sigmas = smoothing_sigmas\n self.sampling_percentage = sampling_percentage\n self.sampling_seed = sampling_seed\n self.resampling_interpolator = resampling_interpolator\n\n registration = sitk.ImageRegistrationMethod()\n\n # similarity metric\n # will compare how well the two images match each other\n # registration.SetMetricAsJointHistogramMutualInformation(self.number_of_histogram_bins, 1.5)\n registration.SetMetricAsMattesMutualInformation(self.number_of_histogram_bins)\n registration.SetMetricSamplingStrategy(registration.RANDOM)\n registration.SetMetricSamplingPercentage(self.sampling_percentage, self.sampling_seed)\n\n # An image gradient calculator based on ImageFunction is used instead of image gradient filters\n # set to True uses GradientRecursiveGaussianImageFilter\n # set to False uses CentralDifferenceImageFunction\n # see also https://itk.org/Doxygen/html/classitk_1_1ImageToImageMetricv4.html\n registration.SetMetricUseFixedImageGradientFilter(False)\n registration.SetMetricUseMovingImageGradientFilter(False)\n\n # interpolator\n # will evaluate the intensities of the moving image at non-rigid positions\n registration.SetInterpolator(sitk.sitkLinear)\n\n # optimizer\n # is required to explore the parameter space of the transform in search of optimal values of the metric\n if self.registration_type == RegistrationType.BSPLINE:\n registration.SetOptimizerAsLBFGSB()\n else:\n registration.SetOptimizerAsRegularStepGradientDescent(learningRate=self.learning_rate,\n minStep=self.step_size,\n numberOfIterations=self.number_of_iterations,\n relaxationFactor=self.relaxation_factor,\n gradientMagnitudeTolerance=1e-4,\n estimateLearningRate=registration.EachIteration,\n maximumStepSizeInPhysicalUnits=0.0)\n registration.SetOptimizerScalesFromPhysicalShift()\n\n # setup for the multi-resolution framework\n registration.SetShrinkFactorsPerLevel(self.shrink_factors)\n registration.SetSmoothingSigmasPerLevel(self.smoothing_sigmas)\n registration.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()\n\n self.registration = registration\n self.transform = None","def __init__(self, path_image, path_imagefile, path_bndboxfile, transform):\r\n # -------------------- DATA ARGUMENT\r\n self.shape = 446\r\n self.hue = 0.1\r\n self.saturation = 1.5\r\n self.exposure = 1.5\r\n self.imagelist = []\r\n self.labellist = []\r\n self.transform = transform\r\n label_dir = os.listdir(path_bndboxfile)\r\n image_dir = os.listdir(path_imagefile)\r\n\r\n # read imagepath\r\n for file in image_dir:\r\n file_name = os.path.join(path_imagefile, file)\r\n with open(file_name) as f:\r\n lines = f.readlines()\r\n for line in lines:\r\n image_name = line.split()[0] + '.JPEG'\r\n image = os.path.join(path_image, image_name)\r\n self.imagelist.append(image)\r\n\r\n # read imagelabel, i.e, (name, xmin, xmax, ymin, ymax)\r\n for file in label_dir:\r\n if file.split('.')[1] == 'xml':\r\n file_name = os.path.join(path_bndboxfile, file)\r\n with open(file_name) as f:\r\n xml_tree = parse(f).documentElement\r\n objects = xml_tree.getElementsByTagName('object')\r\n for object in objects:\r\n label = []\r\n name = object.getElementsByTagName('name')[0]\r\n label.append(name.childNodes[0].data)\r\n bndbox = object.getElementsByTagName('bndbox')[0]\r\n for node in bndbox.childNodes:\r\n if node.nodeType == node.ELEMENT_NODE:\r\n label.append(node.childNodes[0].data)\r\n self.labellist.append(label)\r\n else:\r\n print('Expect files in xml format. but get {}'.format(file.split('.')[1]))","def __init__(self,\n ann_file,\n img_prefix,\n img_means,\n img_stds,\n img_expected_sizes,\n size_divisor=None,\n flip_ratio=0,\n be_cell_size=32,\n be_random_ratio=0.5,\n proposal_file=None,\n num_max_proposals=1000,\n with_mask=False,\n with_crowd=False,\n with_label=True,\n test_mode=False,\n with_background_erasing=False,\n debug=False):\n # load annotations from annotation file\n self.img_infos = self.load_annotations(ann_file)\n # prefix of image path\n self.img_prefix = img_prefix\n # image normalization parameters\n self.img_means, self.img_stds = img_means, img_stds\n # img_expected_sizes list\n self.img_expected_sizes = img_expected_sizes \\\n if isinstance(img_expected_sizes, list) else [img_expected_sizes]\n assert is_list_of(self.img_expected_sizes, tuple)\n # size divisor\n self.size_divisor = size_divisor\n # flip ratio\n self.flip_ratio = flip_ratio\n # background erasing settings\n self.be_cell_size = be_cell_size\n self.be_random_ratio = be_random_ratio\n\n # load the pkl proposal file\n if proposal_file is not None:\n self.proposals = self.load_proposals(proposal_file)\n else:\n self.proposals = None\n self.num_max_proposals = num_max_proposals\n\n # filter images with no annotation during training\n if not test_mode:\n valid_inds = self._filter_imgs()\n self.img_infos = [self.img_infos[i] for i in valid_inds]\n if self.proposals is not None:\n self.proposals = [self.proposals[i] for i in valid_inds]\n\n # with mask or not\n self.with_mask = with_mask\n # with crowd object or not\n self.with_crowd = with_crowd\n # training with label or not\n self.with_label = with_label\n # test mode\n self.test_mode = test_mode\n # with background erasing or not\n self.with_background_erasing = with_background_erasing\n # debug mode\n self.debug = debug\n if self.debug:\n self.img_infos = self.img_infos[:50]\n\n # set aspect ratio flag for images\n if not self.test_mode:\n self._set_group_flag()\n\n # transforms\n self.img_transforms = ImageTransforms(img_means=self.img_means,\n img_stds=self.img_stds,\n size_divisor=self.size_divisor)\n self.bbox_transforms = BboxTransforms()\n self.mask_transforms = MaskTransforms()\n self.background_erasing = BackgroundErasing()","def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0","def __init__(\n self,\n title,\n body,\n img_path_xs,\n img_path_sm,\n img_path_md,\n img_path_lg,\n is_active):\n\n self.title = title\n self.body = body\n self.img_path_xs = img_path_xs\n self.img_path_sm = img_path_sm\n self.img_path_md = img_path_md\n self.img_path_lg = img_path_lg\n self.is_active = is_active","def __init__(self, width, height):\n\n self._width = width\n self._height = height\n \n # The images in the cache!\n self._images = {} # {filename : bitmap}\n\n return","def __init__(\n self,\n darks: Union[list, np.ndarray] = None,\n flats: Union[list, np.ndarray] = None,\n bias: Union[list, np.ndarray] = None,\n loader=FITSImage,\n easy_ram: bool = True,\n verbose: bool = True,\n shared: bool = False,\n **kwargs,\n ):\n\n super().__init__(**kwargs)\n\n self.loader = loader\n self.easy_ram = easy_ram\n\n self.shapes = {}\n\n def check_input(value):\n if value is None:\n value = []\n elif isinstance(value, np.ndarray):\n if len(value) == 0:\n value = []\n elif value.ndim == 2:\n value = [value]\n # ndim 1 or 3\n else:\n value = value.tolist()\n if not isinstance(value, (list, np.ndarray)):\n value = [value]\n\n return value\n\n self.master_bias = self._produce_master(check_input(bias), \"bias\")\n self.master_dark = self._produce_master(check_input(darks), \"dark\")\n self.master_flat = self._produce_master(check_input(flats), \"flat\")\n\n if shared:\n self._share()\n self.verbose = verbose\n\n self.calibration = self._calibration_shared if shared else self._calibration\n self._parallel_friendly = shared","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n d0eeg_name = '{0}D0EEG'.format(name)\n dst2pid0eeg_name = '{0}Dst2PiD0EEG'.format(name)\n \n d0ee_name = '{0}D0EE'.format(name)\n dst2pid0ee_name = '{0}Dst2PiD0EE'.format(name)\n\n self.selD0EEG = makeEEGResonance(\n d0eeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.D0EEG\n ) \n\n self.selD0EE = makeEEResonance(\n d0ee_name, \n config,\n inputSel=[StdAllLooseElectrons],\n decDescriptors=self.D0EE\n )\n \n self.selDst2PiD0EEG = makeDstar(\n dst2pid0eeg_name,\n config,\n inputSel=[self.selD0EEG, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.selDst2PiD0EE = makeDstar(\n dst2pid0ee_name,\n config,\n inputSel=[self.selD0EE, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.line_Dst2PiD0EEG = make_line(\n self,\n '{0}Line'.format(dst2pid0eeg_name),\n prescale=config['PrescaleDst2PiD0EEG'],\n postscale=config['PostscaleDst2PiD0EEG'],\n selection=self.selDst2PiD0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_Dst2PiD0EE = make_line(\n self,\n '{0}Line'.format(dst2pid0ee_name),\n prescale=config['PrescaleDst2PiD0EE'],\n postscale=config['PostscaleDst2PiD0EE'],\n selection=self.selDst2PiD0EE,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, eod):\n self.eod = eod\n self.img_lst = []","def __init__(self):\n\n # self.threshold = 3.\n self.gamma_min = 3\n self.gamma_max = 12\n self.n_samples = 40\n # self.do_plots = False\n # self.do_albedo = True\n # self.verbose = True\n\n self.nbands = 7\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\n\n # Determine 250 or 500 meters product\n # self.resolution = 500\n\n # self.pixelWidth = 500\n # self.pixelHeight = 500","def __init__(self):\n #variabile dove si trova il dataset\n self.data_dir=\"/Users/Eric/Desktop/eric/Programmazione/python/DeepLearning/data/knifey-spoony\"\n #Url dove posso scaricare un dataset\n self.data_url=\"https://github.com/Hvass-Labs/knifey-spoony/raw/master/knifey-spoony.tar.gz\"\n #path della cartella di train\n self.train_dir=os.path.join(self.data_dir,\"train/\")\n #path della cartella di test\n self.test_dir=os.path.join(self.data_dir,\"test/\")\n #dimensione immagine\n self.image_size=200\n #canali immagine\n self.num_channels=3\n self.img_shape=[self.image_size,self.image_size,self.num_channels]\n self.img_size_flat=self.image_size*self.image_size*self.num_channels\n #numero di classi del dataset\n self.num_classes=3\n self.download=Download()","def __init__(self, params, out):\n self.configurations = []\n self.out = out\n self.add_configuration(params)","def __init__(self, a):\n self.files = a.imagefiles\n self.nfiles = len(self.files)\n self.nempty = a.empty\n self.maxframes = a.max_frames\n #self._setloglevel(a)\n\n self._info()\n self.h5opts = self._seth5opts(a)\n\n\n self.ntowrite = numpy.min((self.maxframes, self.nframes))\\\n if self.maxframes > 0 else self.nframes\n\n self.outfile = a.outfile\n self.dgrppath = a.dset\n self.dsetpath = '/'.join((a.dset, 'images'))","def __init__(self, images, batch_size, ctx, multisp):\n self.ctx = ctx\n self.batch_size = batch_size\n self.images = []\n self.multisp = multisp\n\n self.images=images\n\n if self.images:\n self.channels, self.imgsize, _ = self._read_img(self.images[0]['data']).shape\n\n logging.info(\"Found a total of {} images\".format(len(self.images)))","def buildImages(self, config, base, file_num, image_num, obj_num, ignore, logger):\n # This is basically the same as the base class version. Just a few extra things to\n # add to the ignore list.\n ignore += [ 'file_name', 'dir', 'nfiles', 'det_num',\n 'only_dets', 'readout', 'exptime', 'camera' ]\n\n opt = {\n 'cosmic_ray_rate': float,\n 'cosmic_ray_catalog': str,\n 'header': dict\n }\n params, safe = galsim.config.GetAllParams(config, base, opt=opt, ignore=ignore)\n\n image = galsim.config.BuildImage(base, image_num, obj_num, logger=logger)\n\n # Add cosmic rays.\n cosmic_ray_rate = params.get('cosmic_ray_rate', 0)\n if cosmic_ray_rate > 0:\n cosmic_ray_catalog = params.get('cosmic_ray_catalog', None)\n if cosmic_ray_catalog is None:\n cosmic_ray_catalog = os.path.join(data_dir, 'cosmic_rays_itl_2017.fits.gz')\n if not os.path.isfile(cosmic_ray_catalog):\n raise FileNotFoundError(f'{cosmic_ray_catalog} not found')\n\n logger.info('Adding cosmic rays with rate %f using %s.',\n cosmic_ray_rate, cosmic_ray_catalog)\n exptime = base['exptime']\n det_name = base['det_name']\n cosmic_rays = CosmicRays(cosmic_ray_rate, cosmic_ray_catalog)\n rng = galsim.config.GetRNG(config, base)\n cosmic_rays.paint(image.array, rng, exptime=exptime)\n\n # Add header keywords for various values written to the primary\n # header of the simulated raw output file, so that all the needed\n # information is in the eimage file.\n image.header = galsim.FitsHeader()\n exptime = base['exptime']\n image.header['EXPTIME'] = exptime\n image.header['DET_NAME'] = base['det_name']\n\n header_vals = copy.deepcopy(params.get('header', {}))\n opsim_data = get_opsim_data(config, base)\n\n # Helper function to parse a value with priority:\n # 1. from header_vals (popped from dict if present)\n # 2. from opsim_data\n # 3. specified default\n def parse(item, type, default):\n if item in header_vals:\n val = galsim.config.ParseValue(header_vals, item, base, type)[0]\n del header_vals[item]\n else:\n val = opsim_data.get(item, default)\n return val\n\n # Get a few items needed more than once first\n mjd = parse('mjd', float, 51444.0)\n mjd_obs = parse('observationStartMJD', float, mjd)\n mjd_end = mjd_obs + exptime/86400.0\n seqnum = parse('seqnum', int, 0)\n ratel = parse('fieldRA', float, 0.0)\n dectel = parse('fieldDec', float, 0.0)\n airmass = parse('airmass', float, 'N/A')\n\n # Now construct the image header\n image.header['MJD'] = mjd\n image.header['MJD-OBS'] = mjd_obs, 'Start of exposure'\n # NOTE: Should this day be the current day,\n # or the day at the time of the most recent noon?\n dayobs = astropy.time.Time(mjd_obs, format='mjd').strftime('%Y%m%d')\n image.header['DAYOBS'] = dayobs\n image.header['SEQNUM'] = seqnum\n image.header['CONTRLLR'] = 'P', 'simulated data'\n image.header['RUNNUM'] = parse('observationId', int, -999)\n image.header['OBSID'] = f\"IM_P_{dayobs}_{seqnum:06d}\"\n image.header['IMGTYPE'] = parse('image_type', str, 'SKYEXP')\n image.header['REASON'] = parse('reason', str, 'survey')\n image.header['RATEL'] = ratel\n image.header['DECTEL'] = dectel\n with warnings.catch_warnings():\n # Silence FITS warning about long header keyword\n warnings.simplefilter('ignore')\n image.header['ROTTELPOS'] = parse('rotTelPos', float, 0.0)\n image.header['FILTER'] = parse('band', str, 'N/A/')\n image.header['CAMERA'] = base['output']['camera']\n image.header['HASTART'] = opsim_data.getHourAngle(mjd_obs, ratel)\n image.header['HAEND'] = opsim_data.getHourAngle(mjd_end, ratel)\n image.header['AMSTART'] = airmass\n image.header['AMEND'] = airmass # wrong, does anyone care?\n\n # If there's anything left in header_vals, add it to the header.\n for k in header_vals:\n image.header[k] = galsim.config.ParseValue(\n header_vals, k, base, None\n )[0]\n\n return [ image ]","def __init__(self,workspace,gridin,df,csim):\n #Constructor begins here\n self.__workspace=workspace\n self.__grid=gridin\n self.__dflst=pd.read_csv(workspace+'WELL\\\\'+'filelist.csv',sep=',')\n print('*--- Preparing simulation, reading csv wells ---*')\n self.__dfwells=pd.read_csv(workspace+'WELL\\\\'+'arraydata.csv',sep=',')\n self.__nwells=self.__dflst.shape[0]\n self.__df=df\n self.__dfclst={}\n self.__dfcwells={}\n self.__ncwells=0\n if(csim==True):\n self.buildCwells()","def __init__(self, image: np.ndarray) -> None:\n self.image = image","def __init__(self, inputfile, imlist=True, use_dic=True):\n self.inputfile = inputfile\n self.imlist = imlist\n self.use_dic = use_dic\n if use_dic:\n self.n, self.home, self.dic = createDictionaryFromFile(inputfile)\n else:\n self.n, self.home, self.dic = createPathY(inputfile)\n self.data = 0\n self.activity = None\n self.imsize = None\n self.img = 0\n self.i = 0\n self.labels = []\n self.allimgs = []\n self.alllabels = []\n self.fout = None\n self.record_mode = False","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n phi3hm_name = '{0}Phi3HM'.format(name)\n phi3hr_name = '{0}Phi3HR'.format(name)\n \n d2piphi3hm_name = '{0}D2PiPhi3HM'.format(name)\n d2piphi3hr_name = '{0}D2PiPhi3HR'.format(name)\n \n d2kphi3hm_name = '{0}D2KPhi3HM'.format(name)\n d2kphi3hr_name = '{0}D2KPhi3HR'.format(name)\n \n\n self.selPhi3HM = makeResonance(\n phi3hm_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\n decDescriptors=self.Phi3H\n )\n\n self.selPhi3HR = makeResonance(\n phi3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\n decDescriptors=self.Phi3H\n )\n \n self.selD2PiPhi3HM = makeD(\n d2piphi3hm_name,\n config,\n inputSel=[self.selPhi3HM, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPhi,\n useBachelorPID=False,\n )\n \n self.selD2KPhi3HM = makeD(\n d2kphi3hm_name,\n config,\n inputSel=[self.selPhi3HM, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPhi,\n useBachelorPID=False,\n )\n \n self.selD2PiPhi3HR = makeD(\n d2piphi3hr_name,\n config,\n inputSel=[self.selPhi3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPhi,\n useBachelorPID=False,\n )\n \n self.selD2KPhi3HR = makeD(\n d2kphi3hr_name,\n config,\n inputSel=[self.selPhi3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPhi,\n useBachelorPID=False,\n )\n \n self.line_D2PiPhi3HM = make_line(\n self,\n name='{0}Line'.format(d2piphi3hm_name),\n prescale=config['PrescaleD2PiPhi3HM'],\n postscale=config['PostscaleD2PiPhi3HM'],\n selection=self.selD2PiPhi3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2PiPhi3HR = make_line(\n self,\n name='{0}Line'.format(d2piphi3hr_name),\n prescale=config['PrescaleD2PiPhi3HR'],\n postscale=config['PostscaleD2PiPhi3HR'],\n selection=self.selD2PiPhi3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n self.line_D2KPhi3HM = make_line(\n self,\n name='{0}Line'.format(d2kphi3hm_name),\n prescale=config['PrescaleD2KPhi3HM'],\n postscale=config['PostscaleD2KPhi3HM'],\n selection=self.selD2KPhi3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KPhi3HR = make_line(\n self,\n name='{0}Line'.format(d2kphi3hr_name),\n prescale=config['PrescaleD2KPhi3HR'],\n postscale=config['PostscaleD2KPhi3HR'],\n selection=self.selD2KPhi3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def pibooth_setup_picture_factory(cfg, opt_index, factory):","def __init__(self, *args, **kwargs):\n # Training/prediction data volume\n self.train_volume: np.ndarray = None\n # Training label volume\n self.train_label_volume: Optional[np.ndarray] = None\n # Per-voxel training error weighting volume\n self.train_weight_volume: Optional[np.ndarray] = None\n # Validation data volume\n self.eval_volume: Optional[np.ndarray] = None\n # Validation label volume\n self.eval_label_volume: Optional[np.ndarray] = None\n\n # Number of label classes\n self.n_classes: int = None\n\n # RandomState used to control random number generation\n self.random_state = np.random.RandomState()\n\n # Image augmentation instance_settings for use during training\n self.augmentation_settings: Dict[str, Any] = None\n\n # Load data if arguments are supplied\n if len(args) > 0 or len(kwargs) > 0:\n self.load(*args, **kwargs)\n\n pass","def __init__(self, seriesDir):\n # initialize attributes\n self.seriesDir = seriesDir\n self.niftiImage = None\n\n # make a list of all of the par files in this dir\n par_pattern = re.compile(fnmatch.translate('*.par'), re.IGNORECASE)\n parFiles = [x for x in os.listdir(self.seriesDir) if par_pattern.match(x)]\n\n # figure out what type of image this is, 4d or 3d\n self.scanType = self._determineScanType(parFiles[0])\n\n # build nifti image\n if self.scanType == 'anat':\n self.niftiImage = self.buildAnat(parFiles)\n elif self.scanType == 'func':\n self.niftiImage = self.buildFunc(parFiles)","def __init__(self):\n self.model = {'mol':[], 'nmol':0}\n self.template = {} \n self.config = {}\n self.config['tfile'] = 'gau-template-bsse.gjf'\n self.config['xyzfile'] = 'model.xyz'\n self.config['jobfile'] = 'gau.gjf'\n self.config['job_prefix'] = self.config['jobfile'].split(\".\")[0]\n self.config['incr'] = 1\n \n self.rd_cmd_stream()\n return","def __init__(self, dataset_path, use_augmentation, batch_size):\n\n self.dataset_path = dataset_path\n self.train_dictionary = {}\n self.evaluation_dictionary = {}\n self.image_width = 105\n self.image_height = 105\n self.batch_size = batch_size\n self.use_augmentation = use_augmentation\n self._train_alphabets = []\n self._validation_alphabets = []\n self._evaluation_alphabets = []\n self._current_train_alphabet_index = 0\n self._current_validation_alphabet_index = 0\n self._current_evaluation_alphabet_index = 0\n\n self.load_dataset()\n\n if (self.use_augmentation):\n self.image_augmentor = self.createAugmentor()\n else:\n self.use_augmentation = []","def step_run(cls, config):\n\n nir_paw_image_fname = config.get(cls.step_name, 'nir_paw_image')\n nir_paw_conf_fname = config.get(cls.step_name, 'nir_paw_conf')\n output_template = config.get(cls.step_name, 'output_template')\n conf_limit = config.getfloat(cls.step_name, 'conf_limit')\n\n# logger.info('reading Comparison image from %s', comp_fname)\n# comp_im = DESImage.load(comp_fname)\n\n ret_code = cls.__call__(nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit)\n return ret_code","def __init__(self):\n\n super().__init__()\n\n self.active = True\n self.driver = Driver.instance()\n self.camera = Camera.instance()\n\n # define directories and file paths\n date_str = datetime.today().strftime(\"%Y-%m-%d-%H-%M-%S\")\n self.log_dir = f\"{const.Storage.DATA}/{date_str}\"\n self.img_dir = f\"{self.log_dir}/img/\"\n self.log_path = f\"{self.log_dir}/log.csv\"\n self.img_extension = \"npy\"\n\n # ensure that the necessary directories exist\n os.mkdir(self.log_dir)\n os.mkdir(self.img_dir)\n assert os.path.isdir(self.log_dir), \"data directory could not be created\"\n assert os.path.isdir(self.img_dir), \"image directory could not be created\"","def __init__(__self__, *,\n component_arn: Optional[str] = None,\n parameters: Optional[Sequence['outputs.ImageRecipeComponentParameter']] = None):\n if component_arn is not None:\n pulumi.set(__self__, \"component_arn\", component_arn)\n if parameters is not None:\n pulumi.set(__self__, \"parameters\", parameters)","def __init__(self,imageObject):\n getJsonEnv()\n self.meta=imageObject\n self.imageNames=self.getImageName()\n self.gcsBucket=self.setUpCredentials()\n self.sources = []","def __init__(self, sizes, image_size):\n super(CraterNetwork, self).__init__(sizes);\n self.im_size = image_size\n self.validating = False","def __init__(self, args):\n self.train_img_file = os.path.join(args.data_dir, args.train_img_file)\n self.train_lbl_file = os.path.join(args.data_dir, args.train_lbl_file)\n self.test_img_file = os.path.join(args.data_dir, args.test_img_file)\n self.test_lbl_file = os.path.join(args.data_dir, args.test_lbl_file)\n self.batch_size = args.batch_size\n self.num_workers = args.data_workders\n self.shuffle = True\n self.dataset_name = args.dataset_name\n self.pin_memory = False #args.cuda\n\n # check dataset files exist\n files = [self.train_img_file, self.train_lbl_file,\n self.test_img_file, self.test_lbl_file]\n for file in files:\n if not os.path.isfile(file):\n msg = \"Data file not found. Please check the path \" +\\\n \"or download files using scripts/download_files.py \"\n raise IOError(msg)","def load_image(self, **kwargs):\n ...","def __init__(self, prod_obj, log_level=logutil.logging.NOTSET, use_defaults=True, input_custom_pars_file=None,\n output_custom_pars_file=None):\n log.setLevel(log_level)\n if input_custom_pars_file and input_custom_pars_file and input_custom_pars_file == output_custom_pars_file:\n sys.exit(\"ERROR: Input and output parameter files must have unique names!\")\n self.label = \"hap_config\"\n self.description = \"A set of routines to generate appropriate set of configuration parameters\"\n self.instrument = prod_obj.instrument\n self.detector = prod_obj.detector\n self.inst_det = \"{}_{}\".format(prod_obj.instrument, prod_obj.detector).lower()\n self.use_defaults = use_defaults\n self.input_custom_pars_file = input_custom_pars_file\n self.output_custom_pars_file = output_custom_pars_file\n\n # The filters attribute is populated by _determine_conditions()\n self.filters = None\n\n self._determine_conditions(prod_obj)\n self.full_cfg_index, self.pars_dir = read_index(self.instrument,\n self.detector)\n\n # Instantiate the parameter set\n self.pars = {}\n\n # open input parameter file if specified by user\n if self.input_custom_pars_file:\n with open(self.input_custom_pars_file) as f_cfg:\n self.input_cfg_json_data = json.load(f_cfg)[prod_obj.product_basename]\n else:\n self.input_cfg_json_data = None\n\n # generate parameter sets for each pipeline step\n # step_name_list = [AstrodrizzlePars, CatalogGenerationPars, QualityControlPars]\n # step_title_list = ['astrodrizzle', 'catalog generation', 'quality control']\n for step_title, step_name in zip(step_title_list, step_name_list):\n cfg_index = self.full_cfg_index[step_title]\n self.pars[step_title] = step_name(cfg_index,\n self.conditions,\n self.pars_dir,\n step_title,\n self.use_defaults,\n self.input_cfg_json_data)\n\n # write out all parameters to file if specified by user\n if output_custom_pars_file:\n self.write_pars(prod_obj)","def __init__(self, **kwargs):\n\n def paraChck(**kwargs):\n \"\"\"\n check and validate the keyword argument input\n \"\"\"\n import sys\n\n \n def_val = {\n 'x_train':None,\n 'y_train':None,\n 'x_test':None,\n 'y_test':None,\n 'channel':1,\n 'input_img_cols':72,\n 'input_img_rows':72,\n 'nb_classes':13,\n 'nb_epoch': 5,\n 'batch_size' : 16,\n 'dict_label' : None} # default parameteters value\n\n diff = set(kwargs.keys()) - set(def_val.keys())\n if diff:\n print(\"Invalid args:\",tuple(diff),file=sys.stderr)\n return\n\n def_val.update(kwargs)\n return def_val\n \n def_val = paraChck(**kwargs)\n\n class Bunch(object):\n def __init__(self, adict):\n self.__dict__.update(adict)\n \n self.x_train = def_val['x_train']\n self.y_train = def_val['y_train']\n self.x_test = def_val['x_test']\n self.y_test = def_val['y_test']\n self.channels = def_val['channel']\n self.input_img_rows = def_val['input_img_rows']\n self.input_img_cols = def_val['input_img_cols']\n self.nb_classes = def_val['nb_classes']\n self.plot_model = None\n self.model = None\n self.nb_epoch = def_val['nb_epoch']\n self.batch_size = def_val['batch_size']\n self.dict_label = def_val['dict_label']\n \n # default label dictionary if users do not provide\n values = ['label_' + str(i).zfill(2) for i in range(0,self.nb_classes)]\n keys = range(self.nb_classes) \n \n if self.dict_label is None:\n self.dict_label = dict(zip(keys, values))\n else:\n self.dict_label = kwargs['dict_label']\n \n self.dict_factor = {v: k for k, v in self.dict_label.items()}","def __init__(self, width, height, rand_seed=None, filepath=None):\r\n self.height = height\r\n self.width = width\r\n\r\n if rand_seed:\r\n seed(rand_seed)","def __init__(self, img_root, gt_json_meta_fname, pd_json_meta_fname):\n self.img_root = img_root\n\n analyzer = PanopticEvalAnalyzer(gt_json_meta_fname, pd_json_meta_fname)\n self.gt, self.pd = analyzer.gt, analyzer.pd\n self.imgIds = analyzer.imgIds\n self.res_dframe, self.overall_table, self.cat_table = analyzer.summarize()\n # cached widgets\n self.global_walk = None\n self.__init_global_state__()","def __init__(self, generator, discriminator, noise_dim, save_path):\n self.generator = generator\n self.discriminator = discriminator\n self.noise_dim = noise_dim\n self.save_path = save_path\n self.check_points_path = os.path.join(save_path, 'check_points')\n self.output_image_path = os.path.join(save_path, 'images_during_training')\n self.generator.generate()","def __init__(self, book_config,\n soft_cover_options=None, hard_cover_options=None):\n self.__book_config = book_config\n if soft_cover_options:\n self.__soft_cover_options = soft_cover_options\n else:\n self.__soft_cover_options = {\n 'page-height': '221',\n 'page-width': '551',\n 'margin-top': '3',\n 'margin-right': '3',\n 'margin-bottom': '3',\n 'margin-left': '3',\n 'dpi': '350',\n }\n if hard_cover_options:\n self.__hard_cover_options = hard_cover_options\n else:\n self.__hard_cover_options = {\n 'page-height': '221',\n 'page-width': '551',\n 'margin-top': '3',\n 'margin-right': '3',\n 'margin-bottom': '3',\n 'margin-left': '3',\n 'dpi': '350',\n }","def __init__(self, n_pixels=(250,150),\n submarine=None,\n air_velocity=0.1,\n water_velocity=1.0,\n rock_velocity=10.0,\n **kwargs):\n\n GeneratedGalleryModel.__init__(self)\n\n self.air_velocity = air_velocity\n self.water_velocity = water_velocity\n self.rock_velocity = rock_velocity\n\n if len(n_pixels) not in [2,3]:\n raise ValueError('Submarine-sonar model only works for dimensions greater than 1.')\n\n if submarine is None:\n if len(n_pixels) == 2:\n submarine = default_submarine_2D\n else: # len(n_pixels) == 3\n submarine = default_submarine_3D\n self.submarine = submarine\n\n config_list = list()\n\n # Configure X direction\n x_lbc = kwargs['x_lbc'] if ('x_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n x_rbc = kwargs['x_rbc'] if ('x_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n\n xmin, xmax = 0.0, 2.5\n x_config = (xmin, xmax, x_lbc, x_rbc)\n config_list.append(x_config)\n\n\n if len(n_pixels) == 3:\n\n # If it is there, configure Y direction\n y_lbc = kwargs['y_lbc'] if ('y_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n y_rbc = kwargs['y_rbc'] if ('y_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n\n ymin, ymax = 0.0, 2.5\n y_config = (ymin, ymax, y_lbc, y_rbc)\n config_list.append(y_config)\n\n # Configure Z direction\n z_lbc = kwargs['z_lbc'] if ('z_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n z_rbc = kwargs['z_rbc'] if ('z_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\n\n zmin, zmax = 0.0, 1.5\n z_config = (zmin, zmax, z_lbc, z_rbc)\n config_list.append(z_config)\n\n domain = RectangularDomain(*config_list)\n\n mesh_args = [domain] + list(n_pixels)\n mesh = CartesianMesh(*mesh_args)\n\n\n self._mesh = mesh\n self._domain = mesh.domain\n\n # Set _initial_model and _true_model\n self.rebuild_models()","def __init__(self, content_image_dir,style_image_dir,\n selectedContent,selectedStyle,\n content_transform,style_transform,\n subffix='jpg', random_seed=1234):\n self.content_image_dir= content_image_dir\n self.style_image_dir = style_image_dir\n self.content_transform= content_transform\n self.style_transform = style_transform\n self.selectedContent = selectedContent\n self.selectedStyle = selectedStyle\n self.subffix = subffix\n self.content_dataset = []\n self.art_dataset = []\n self.random_seed= random_seed\n self.preprocess()\n self.num_images = len(self.content_dataset)\n self.art_num = len(self.art_dataset)","def __init__(self, opt):\r\n super().__init__()\r\n self.opt = opt\r\n # if self.opt.dataset_mode not in ['aligned', 'unaligned', 'heatmap', 'jointsAligned', 'jointsAligned2',\r\n # \"jointsUnaligned\", \"hand3d\"]:\r\n # raise ValueError\r\n\r\n # self.mode = self.opt.dataset_mode\r\n\r\n self.root_dir = self.opt.dataroot\r\n with open(os.path.join(self.root_dir, \"annotation.pickle\"),\r\n \"rb\") as handle:\r\n self.annotations = pickle.load(handle)\r\n\r\n self.image_color_sk = []\r\n for folder in self.annotations.keys():\r\n for image in self.annotations[folder].keys():\r\n img_path = os.path.join(self.root_dir, folder, image)\r\n camera, spec, n = image.split('_')\r\n if camera == 'BB':\r\n continue\r\n else:\r\n if spec == 'color':\r\n # ceate pair SK_color / SK_depth\r\n self.image_color_sk.append(img_path)\r\n\r\n def sort_priority(x):\r\n *_, folder, name = x.split('/')\r\n folder_n = int(folder[1])\r\n folder_t = folder[2]\r\n name = int(name[0:-4].split('_')[-1])\r\n return folder_n, folder_t, name\r\n\r\n self.image_color_sk.sort(key=lambda x: sort_priority(x))\r\n\r\n if self.opt.isTrain:\r\n self.augmentation_map = np.zeros(self.__len__(), dtype=np.bool)\r\n n = int((1 - self.opt.augmentation_ratio) * self.__len__())\r\n if self.opt.dataset_mode == 'generate':\r\n self.augmentation_map[0:int(n)] = True\r\n else:\r\n self.augmentation_map[int(n)::] = True\r\n print(f\"train on {np.sum(self.augmentation_map)} images\")\r\n\r\n self.image_color_sk = [\r\n self.image_color_sk[i]\r\n for i, state in enumerate(self.augmentation_map)\r\n if state == True\r\n ]\r\n self.image_color_pair = self.image_color_sk.copy()\r\n random.shuffle(self.image_color_pair)","def __init__(self, data_dir, file_prefix, num_images):\n self.file_prefix = file_prefix\n self.files = [os.path.join(data_dir, '%s%03d.jpg' % (file_prefix, i + 1)) for i in range(num_images)]\n self.files = list(filter(os.path.exists, self.files))\n self.panoramas = None\n self.homographies = None\n self.images = []\n self.display_match = False\n self.useBlending = False\n print('found %d images' % len(self.files))","def __init__(self, name, cfg={}) -> None:\n self.name = name\n self.top_k_words = cfg[\"top_k_words\"]\n self._cfg = cfg\n self.train_im_to_caption: Dict[Any, List[str]] = collections.defaultdict(list)\n self.val_im_to_caption: Dict[Any, List[str]] = collections.defaultdict(list)\n self.train_image_caption_pairs: List[Tuple[FilePath, str]] = []\n self.val_image_caption_pairs: List[Tuple[FilePath, str]] = []\n self.create_image_caption_pairs()\n self.preprocess()\n self.vocab_size = self.tokenizer.num_words\n\n logger.info(\"Done creating dataset\")","def __init__(self, config_path='configs/sample_config.ini', one_hot=True):\n # Set one-hot encoding setting\n self.ONE_HOT = one_hot\n\n # Parse config file\n config = configparser.ConfigParser()\n config.read(config_path)\n\n # Get (and check) configuration\n # Get dataset info\n self._TRAIN_DATA_DIR = config.get('DATASET', 'TRAIN_DATA_DIR')\n self._TEST_DATA_DIR = config.get('DATASET', 'TEST_DATA_DIR')\n self._LABEL_FILE = config.get('DATASET', 'LABEL_FILE')\n\n # Get image info\n self._HEIGHT = config.getint('IMAGE', 'HEIGHT')\n self._WIDTH = config.getint('IMAGE', 'WIDTH')\n self._GRAYSCALE_IN = config.getboolean('IMAGE', 'GRAYSCALE_IN')\n self._GRAYSCALE_OUT = config.getboolean('IMAGE', 'GRAYSCALE_OUT')\n\n # Get traning and testing spec\n self._TRAIN_BATCH_SIZE = config.getint('TRAIN_TEST_SPEC',\n 'TRAIN_BATCH_SIZE')\n self._TEST_BATCH_SIZE = config.getint('TRAIN_TEST_SPEC',\n 'TEST_BATCH_SIZE')\n self._SHUFFLE_BUFFER_SIZE = config.getint('TRAIN_TEST_SPEC',\n 'SHUFFLE_BUFFER_SIZE')\n self._PREFETCH_BUFFER_SIZE = config.getint('TRAIN_TEST_SPEC',\n 'PREFETCH_BUFFER_SIZE')\n\n # Get data augmentation spec\n self._RANDOM_ROTATE = config.getboolean('DATA_AUGMENTATION',\n 'RANDOM_ROTATE')\n self._ROTATE_STDDEV = config.getfloat('DATA_AUGMENTATION',\n 'ROTATE_STDDEV')\n\n self._RANDOM_ZOOM = config.getboolean('DATA_AUGMENTATION',\n 'RANDOM_ZOOM')\n self._ZOOM_MAX_PERCENT = config.getfloat('DATA_AUGMENTATION',\n 'ZOOM_MAX_PERCENT')\n self._ZOOM_STDDEV = config.getfloat('DATA_AUGMENTATION', 'ZOOM_STDDEV')\n\n self._RESIZE = config.getboolean('DATA_AUGMENTATION', 'RESIZE')\n self._RESIZE_HEIGHT = config.getint('DATA_AUGMENTATION',\n 'RESIZE_HEIGHT')\n self._RESIZE_WIDTH = config.getint('DATA_AUGMENTATION', 'RESIZE_WIDTH')\n\n # Label conversion\n self._CLASS_NAMES = [line.strip() for line in\n open(self._LABEL_FILE).readlines()]\n self._NUM_CLASSES = len(self._CLASS_NAMES) # Number of classes","def __init__(self, imageLabel, imageHint, hardwareConstraints=None, pool=None, serverIDWildcard=\"\"):\n self.imageLabel = imageLabel\n self.imageHint = imageHint\n self.hardwareConstraints = dict(\n minimumCPUs=1, minimumRAMGB=2,\n minimumDisk1SizeGB=16, minimumDisk2SizeGB=16)\n if hardwareConstraints is not None:\n self.hardwareConstraints.update(hardwareConstraints)\n self.pool = pool\n self.serverIDWildcard = serverIDWildcard","def __init__(self, footprint, mconf, name):\n logging.debug(\"%s entered __init__\" % name)\n self.image_id = mconf[\"image-id\"]\n self.networks = mconf[\"networks\"]\n self.ip_addresses = mconf[\"ip-addresses\"]\n self.flavor=mconf[\"flavor\"]\n self.machine_name = \"%s%s\" % (footprint.footprint_name, name)\n self.footprint = footprint","def __init__(\n self,\n root: str = folder,\n train_subset: bool = True,\n suffix: str = '.png',\n min_num_cls: int = 5,\n max_num_cls: int = 20,\n k_shot: int = 20,\n expand_dim: bool = False,\n load_images: bool = True\n ) -> None:\n self.root = os.path.join(root, 'images_background' if train_subset else 'images_evaluation')\n self.suffix = suffix\n self.min_num_cls = min_num_cls\n self.max_num_cls = max_num_cls\n self.k_shot = k_shot\n self.expand_dim = expand_dim\n self.load_images = load_images\n\n # create a nested dictionary to store data\n self.data = dict.fromkeys(list_dir(root=self.root))\n for alphabet in self.data:\n self.data[alphabet] = dict.fromkeys(list_dir(root=os.path.join(self.root, alphabet)))\n\n # loop through each alphabet\n for character in self.data[alphabet]:\n self.data[alphabet][character] = []\n\n # loop through all images in an alphabet character\n for img_name in list_files(root=os.path.join(self.root, alphabet, character), suffix=suffix):\n if self.load_images:\n # load images\n img = _load_image(img_url=os.path.join(self.root, alphabet, character, img_name), expand_dim=self.expand_dim)\n else:\n img = img_name\n\n self.data[alphabet][character].append(img)","def __init__(self, config, screenshot_func=None):\n\n self._config = config\n self._fov = (self._config.aimbot_train_image_size, self._config.aimbot_train_image_size)\n self._screenshot_func = screenshot_func\n # Do not initialize the model here, prevent tensorflow from loading\n self._model_root_path = os.path.join(self._config.training_env_path, 'aimbot_model')\n self._model = None\n\n self._hook_screenshot = None\n\n # Setup image paths\n self._image_path = os.path.join(self._config.training_env_path, 'aimbot_images')\n self._curr_image_inc = None\n\n self._train_data_tfrecord_path = os.path.join(self._model_root_path, 'train.tfrecord')\n self._test_data_tfrecord_path = os.path.join(self._model_root_path, 'test.tfrecord')\n\n self._train_data_set = None\n self._test_data_set = None"],"string":"[\n \"def __init__(self, config):\\n logging.info(\\\"Creating footprint\\\")\\n # self.infra = yaml.load(config)\\n self.infra = config\\n self.footprint_name = self.infra.get(\\\"footprint\\\", \\\"ehw\\\")\\n self.images = self.infra.get(\\\"images\\\")\\n self.old_images = self.infra.get(\\\"old_images\\\", [])\\n self.container_name = \\\"%s-metadata\\\" % self.footprint_name\\n \\n self.admin_password = self.infra.get('admin-password')\\n self.savefile = self.infra.get(\\\"footprint\\\", \\\"outfile\\\") + \\\"-save.yaml\\\"\\n if os.path.exists(self.savefile):\\n self.saved_images = yaml.load(open(self.savefile))\\n self.footprint_status=self.infra.get(\\\"footprint_status\\\", None)\\n logging.debug(\\\"Loaded saved images: %s\\\" % self.saved_images)\\n # sys.exit(0) \",\n \"def __init__(self, generate_image_pyramid: bool = True):\\n if not os.path.exists(MANIFEST_OUTPUT_DIR):\\n os.makedirs(MANIFEST_OUTPUT_DIR)\\n self._manifest_factory = ManifestFactory()\\n self._manifest_factory.set_base_prezi_uri(MANIFEST_BASE_URL)\\n self._manifest_factory.set_base_prezi_dir(MANIFEST_OUTPUT_DIR)\\n self._manifest_factory.set_base_image_uri(IMAGE_BASE_URL)\\n self._manifest_factory.set_iiif_image_info(2.0, 1) # Version, ComplianceLevel\\n\\n self._image_reader = ImageReader(IMAGE_SOURCE_DIR)\\n\\n if generate_image_pyramid:\\n self._tile_generator = IIIFStatic(dst=IMAGE_FILE_OUTPUT_DIR, prefix=IMAGE_BASE_URL)\\n\\n self._generate_images = generate_image_pyramid\",\n \"def __init__(self, train_path='train/image', label_path='train/label', merge_path='train/merge', aug_merge_path='train/aug_merge', aug_train_path='train/aug_train', aug_label_path='train/aug_label', img_type=\\\"nii\\\"):\\n\\n\\t\\tself.train_imgs = glob.glob(\\\"/*.\\\"+img_type)\\n\\t\\tself.label_imgs = glob.glob(\\\"/*.\\\"+img_type)\\n\\t\\tself.train_path = train_path\\n\\t\\tself.label_path = label_path\\n\\t\\tself.merge_path = merge_path\\n\\t\\tself.img_type = img_type\\n\\t\\tself.aug_merge_path = aug_merge_path\\n\\t\\tself.aug_train_path = aug_train_path\\n\\t\\tself.aug_label_path = aug_label_path\\n\\t\\tself.slices = len(self.train_imgs)\\n\\t\\tself.datagen = ImageDataGenerator(\\n\\t\\t\\t\\t\\t\\t\\t rotation_range=0.2,\\n\\t\\t\\t\\t\\t\\t\\t width_shift_range=0.05,\\n\\t\\t\\t\\t\\t\\t\\t height_shift_range=0.05,\\n\\t\\t\\t\\t\\t\\t\\t shear_range=0.05,\\n\\t\\t\\t\\t\\t\\t\\t zoom_range=0.05,\\n\\t\\t\\t\\t\\t\\t\\t horizontal_flip=True,\\n\\t\\t\\t\\t\\t\\t\\t fill_mode='nearest')\",\n \"def __init__(self, img):\\n self.img = img\",\n \"def __init__(self, train_path=\\\"./data/train/image\\\", label_path=\\\"./data/train/label\\\",\\n\\t\\t\\t\\t merge_path=\\\"./data/train/merge\\\", aug_merge_path=\\\"./data/train/aug_merge\\\", \\n\\t\\t\\t\\t aug_train_path=\\\"./data/train/aug_images\\\", \\n\\t\\t\\t\\t aug_label_path=\\\"./data/train/aug_masks\\\", img_type=\\\"tif\\\"):\\n\\n\\t\\tself.train_imgs = glob.glob(train_path+\\\"/*.\\\"+img_type)\\n\\t\\tself.label_imgs = glob.glob(label_path+\\\"/*.\\\"+img_type)\\n\\t\\tself.train_path = train_path\\n\\t\\tself.label_path = label_path\\n\\t\\tself.merge_path = merge_path\\n\\t\\tself.img_type = img_type\\n\\t\\tself.aug_merge_path = aug_merge_path\\n\\t\\tself.aug_train_path = aug_train_path\\n\\t\\tself.aug_label_path = aug_label_path\\n\\n\\t\\tif not os.path.exists(merge_path):\\n\\t\\t\\tos.mkdir(merge_path)\\n\\t\\t\\tos.mkdir(aug_merge_path)\\n\\t\\t\\tos.mkdir(aug_train_path)\\n\\t\\t\\tos.mkdir(aug_label_path)\\n\\n\\t\\tself.slices = len(self.train_imgs)\\n\\t\\tself.datagen = ImageDataGenerator(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tpreprocessing_function=self.preprocess,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\trotation_range=0.2,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\twidth_shift_range=0.1,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\theight_shift_range=0.1,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tshear_range=0.05,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tzoom_range=0.05,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\thorizontal_flip=True,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfill_mode='nearest')\",\n \"def build_filler_images(self):\",\n \"def __init__ (self):\\r\\n\\r\\n self.path = 'c:\\\\\\\\python22\\\\\\\\work\\\\\\\\'\\r\\n self.bfh_vals = (BM, 0, 0, 0, 0)\\r\\n self.bih_vals = (BIH_SIZE, 0, 0, 1, 32, 0, 0, 0, 0, 0, 0)\\r\\n self.the_file = None\\r\\n self.image = []\\r\\n self.colourmap = []\",\n \"def __init__(self, config, set_name, preprocess_image):\\n\\t\\t\\tself.data_dir = config['data_dir']\\n\\t\\t\\tself.set_name = set_name\\n\\t\\t\\tself.coco = COCO(os.path.join(self.data_dir, 'annotations', 'instances_' + set_name + '.json'))\\n\\t\\t\\tself.image_ids = self.coco.getImgIds()\\n\\t\\t\\tself.mask = config['mask']\\n\\n\\t\\t\\tself.load_classes()\\n\\n\\t\\t\\tsuper(CocoGenerator, self).__from_config__(config, preprocess_image=preprocess_image)\",\n \"def __init__(self, gdmix_config_obj, jar_path=\\\"\\\", namespace=\\\"\\\", secret_name=\\\"\\\", image=\\\"\\\", service_account=\\\"\\\", job_suffix=\\\"\\\", *, prev_model_name):\\n super().__init__(gdmix_config_obj, jar_path, namespace, secret_name, image, service_account, job_suffix)\\n self.prev_model_name = prev_model_name\\n self.root_output_dir = gdmix_config_obj.output_dir\",\n \"def __init__(self, images, loader):\\n super().__init__()\\n self._images = images\\n self._loader = loader\",\n \"def initImages(self):\\n pass\",\n \"def initImages(self):\\n pass\",\n \"def initImages(self):\\n pass\",\n \"def __init__(self, cfg):\\r\\n\\r\\n\\t\\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\\r\\n\\t\\tanchor_config = cfg.MODEL.ANCHORS\\r\\n\\t\\tself.feature_maps = anchor_config.FEATURE_MAPS\\r\\n\\t\\tself.min_sizes = anchor_config.MIN_SIZES\\r\\n\\t\\tself.max_sizes = anchor_config.MAX_SIZES \\r\\n\\t\\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\\r\\n\\t\\tself.clip = anchor_config.CLIP\",\n \"def generate_image(self):\\n pass\",\n \"def __init__(self, image):\\n self.image = image\",\n \"def create_base_image(self, builder, template, parameters):\",\n \"def __init__(self, path, margin=25, folder=\\\"output\\\"):\\n self.image = Image.open(path)\\n self.width, self.height = self.image.size\\n self.path = path\\n self.margin = margin\\n self.output_path = os.path.join(os.path.dirname(self.path),\\n folder,\\n os.path.basename(self.path))\",\n \"def __build__(self,data_index=0):\\n \\n super(Image,self).__build__()\\n # -- How to read the image\\n self._build_properties = dict(\\n data_index = data_index,\\n header_exptime = \\\"EXPTIME\\\",\\n dataslice0=\\\"undefined\\\",\\n dataslice1=\\\"undefined\\\",\\n bkgdbox={\\\"bh\\\":100,\\\"bw\\\":100,\\\"fh\\\":3,\\\"fw\\\":3},\\n )\",\n \"def __init__(self, **kwargs):\\n super(ImageExporter, self).__init__(**kwargs)\",\n \"def configure(self):\\n self.data_batch_file = self.get_value_from_config('data_batch_file')\\n self.batch_meta_file = self.get_value_from_config('batch_meta_file')\\n self.has_background = self.get_value_from_config('has_background')\\n self.num_classes = self.get_value_from_config('num_classes')\\n self.converted_images_dir = self.get_value_from_config('converted_images_dir')\\n if not self.converted_images_dir:\\n self.converted_images_dir = self.data_batch_file.parent / 'converted_images'\\n self.convert_images = self.get_value_from_config('convert_images')\\n # create directory for storing images if it is necessary\\n if self.convert_images and not self.converted_images_dir.exists():\\n self.converted_images_dir.mkdir(parents=True)\\n self.dataset_meta = self.get_value_from_config('dataset_meta_file')\",\n \"def __init__(self, boardSize, width = 750, height = 750, borderSize = 75, boardTexture = BOARD_TEXTURE, stoneBlackTexture = STONE_BLACK_TEXTURE, stoneWhiteTexture = STONE_WHITE_TEXTURE, markerBlackTexture = MARKER_BLACK_TEXTURE, markerWhiteTexture = MARKER_WHITE_TEXTURE):\\n\\n if (boardSize == 19): self.settings = BOARD_SETTINGS_19\\n elif (boardSize == 13):\\n self.settings = BOARD_SETTINGS_13\\n borderSize += 10\\n elif (boardSize == 9):\\n self.settings = BOARD_SETTINGS_9\\n borderSize += 25\\n\\n self.width = width\\n self.height = height\\n self.innerWidth = width - borderSize * 2\\n self.innerHeight = height - borderSize * 2\\n self.borderSize = borderSize\\n self.baseImage = Image.new(\\\"RGBA\\\", (width, height), (255, 255, 255, 255))\\n\\n (boardSize, _, _) = self.settings\\n stoneWidth = int(self.innerWidth / (boardSize - 1))\\n stoneHeight = int(self.innerWidth / (boardSize - 1))\\n markerWidth = int(stoneWidth / 2)\\n markerHeight = int(stoneHeight / 2)\\n\\n self.stoneBlackTexture = stoneBlackTexture.resize((stoneWidth, stoneHeight), Image.BICUBIC)\\n self.stoneWhiteTexture = stoneWhiteTexture.resize((stoneWidth, stoneHeight), Image.BICUBIC)\\n self.markerBlackTexture = markerBlackTexture.resize((markerWidth, markerHeight), Image.BICUBIC)\\n self.markerWhiteTexture = markerWhiteTexture.resize((markerWidth, markerHeight), Image.BICUBIC)\\n\\n self.__draw_board_texture(boardTexture)\\n self.__draw_board()\",\n \"def __init__(self, img, settings):\\r\\n self.img_orig = img\\r\\n self.settings = settings\",\n \"def __init__(self):\\n self.index = 'r11_07_06c'\\n self.parameters = {'run_index': 'r11_07_06c',\\n 'h_1': 0.25,\\n 'rho_0': 1.150,\\n 'rho_1': 1.100,\\n 'rho_2': 1.000,\\n 'alpha': 0.5,\\n 'D': 0.4,\\n 'H': 0.25,\\n 'sample': 1.0,\\n 'perspective': 'old'}\\n self.run_data = {'run_index': 'r11_07_06c',\\n 'l0x': 2796,\\n 'l0y': 1151,\\n 'lsx': 2793,\\n 'lsy': 716,\\n 'j10x': 210,\\n 'j10y': 1165,\\n 'j1sx': 208,\\n 'j1sy': 727,\\n 'leakage': -76,\\n 'odd_1': 'n',\\n 'j20x': 2728,\\n 'j20y': 1086,\\n 'j2sx': 2730,\\n 'j2sy': 670,\\n 'r0x': 1097,\\n 'r0y': 1095,\\n 'rsx': 1093,\\n 'rsy': 683,\\n 'odd_2': 'n'}\\n self.raw_image = 'tests/data/synced/r11_07_06c/cam1/img_0001.jpg'\\n self.bc_image = 'tests/data/bc/r11_07_06c/cam1/img_0001.jpg'\\n self.processed_path = 'tests/data/processed_ref/r11_07_06c/cam1/img_0001.jpg'\",\n \"def __init__(self, image1, image2):\\n self.image1 = image1\\n self.image2 = image2\",\n \"def __init__(self, config_file = 'config.yaml'):\\n\\n self.name = ''\\n self.img_dir = ''\\n self.out_dir = ''\\n self.cam_file = ''\\n self.options_file = ''\\n self.output_xml_file = ''\\n\\n # If there is an options file, it will overwrite the defaults \\n if config_file is not None:\\n self.load(config_file)\",\n \"def __init__(self, opt):\\r\\n super().__init__(opt)\\r\\n\\r\\n self.image_color = []\\r\\n for folder in self.annotations.keys():\\r\\n for image in self.annotations[folder].keys():\\r\\n img_path = os.path.join(self.root_dir, folder, image)\\r\\n camera, spec, n = image.split('_')\\r\\n if camera == 'BB':\\r\\n continue\\r\\n else:\\r\\n if spec == 'color':\\r\\n self.image_color.append(img_path)\\r\\n\\r\\n def sort_priority(x):\\r\\n *_, folder, name = x.split('/')\\r\\n folder_n = int(folder[1])\\r\\n folder_t = folder[2]\\r\\n name = int(name[0:-4].split('_')[-1])\\r\\n return folder_n, folder_t, name\\r\\n\\r\\n self.image_source, self.image_target = self._get_src_tgt(\\r\\n opt.augmentation_ratio, self.image_color, sort_priority)\",\n \"def __init__(self, metadata, environment, component, image, version, s3_bucket, exec_env):\\n self.environment = environment\\n self.component = component\\n self.s3_bucket = s3_bucket\\n self.exec_env = exec_env\\n self.image = image\\n self.version = version\\n self.metadata = metadata\\n\\n # generate Terragrunt config as part of object initialisation\\n self.config()\",\n \"def __init__(self, *args):\\n super().__init__(*args)\\n\\n self.output_dir = os.path.join(self.config.results_dir, \\\"cowinner\\\")\\n self.merged_dir = os.path.join(self.output_dir, \\\"merged\\\")\",\n \"def __init__(self, data_dir, pairs_filepath, img_ext):\\n self.data_dir = data_dir\\n self.pairs_filepath = pairs_filepath\\n self.img_ext = img_ext\",\n \"def __init__(self, config: str, wdir=os.getcwd()):\\n\\n self.config_path = config\\n self.wdir = wdir\\n\\n with open(self.config_path) as fin:\\n self.config_dic = yaml.load(fin, Loader=yaml.BaseLoader)\\n\\n # default is 00 for all interaction types\\n self.config_parameters_dic = None\\n\\n # This parameter defines whether self_loops will be kept or not\\n self.self_loops = True\\n if 'self_loops' in self.config_dic:\\n self.self_loops = bool(int(self.config_dic['self_loops']))\",\n \"def __init__(self, ms, srcdict=None):\\n Imaging.__init__(self, ms)\\n self.srcdict = srcdict\",\n \"def __init__(self, args):\\n self.cropped_dir = args.cropped_dir\\n self.output_dir = args.output_dir\\n self.txt_info_dir = args.txt_info_dir\\n\\n self.HR_dim = []\\n self.HR_dim.append(int(args.HR_dim.split(' ')[0]))\\n self.HR_dim.append(int(args.HR_dim.split(' ')[1]))\\n self.HR_dim.append(int(args.HR_dim.split(' ')[2]))\\n\\n self.HR_pixdim = []\\n self.HR_pixdim.append(float(args.HR_pixdim.split(' ')[0]))\\n self.HR_pixdim.append(float(args.HR_pixdim.split(' ')[1]))\\n self.HR_pixdim.append(float(args.HR_pixdim.split(' ')[2]))\",\n \"def __init__(__self__,\\n resource_name: str,\\n args: ImageInitArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"def __init__(\\n self,\\n world: \\\"SimWorld\\\",\\n image_size: Optional[Tuple[int, int]] = None,\\n pixel_size: float = 0.015,\\n images: Optional[str] = None,\\n max_mag: float = 20.0,\\n seeing: float = 3.0,\\n **kwargs: Any,\\n ):\\n Object.__init__(self, **kwargs)\\n\\n # store\\n self.world = world\\n self.telescope = world.telescope\\n self.full_frame: Tuple[int, int, int, int] = (\\n (0, 0, image_size[0], image_size[1]) if image_size is not None else (0, 0, 512, 512)\\n )\\n self.window = self.full_frame\\n self.binning = (1, 1)\\n self.pixel_size = pixel_size\\n self.image_format = ImageFormat.INT16\\n self.images = (\\n [] if images is None else sorted(glob.glob(images)) if \\\"*\\\" in images or \\\"?\\\" in images else [images]\\n )\\n self._max_mag = max_mag\\n self._seeing = seeing\\n\\n # private stuff\\n self._catalog = None\\n self._catalog_coords = None\",\n \"def __init__(self, binning: int = 2, **kwargs: Any):\\n ImageProcessor.__init__(self, **kwargs)\\n\\n # store\\n self.binning = binning\",\n \"def __init__ (self, config, logger):\\n self.logger = logger\\n self.logger.add('loading AREA')\\n config['data_type'] = np.float32\\n self.area = AreaGrid(config,logger = self.logger)\\n self.area.config['dataset_name'] = 'Area Data'\\n self.area.config['description'] = \\\\\\n \\\"\\\"\\\"Area Data contains fractional cohort data for each year the ATM\\n was run. \\n \\\"\\\"\\\"\\n self.logger.add('performing post AREA setup')\\n self.shape = self.area.config['grid_shape']\\n self.aoi = self.area.area_of_interest()\\n config['shape'] = self.shape\\n config['grid_shape'] = self.area.config['grid_shape']\\n config['AOI mask'] = self.aoi\\n config['cohort list'] = self.area.get_cohort_list()\\n self.logger.add('loading ALD')\\n self.ald = ALDGrid(config,logger = self.logger)\\n self.ald.config['dataset_name'] = 'ALD Data'\\n self.ald.config['description'] = \\\\\\n \\\"\\\"\\\"ALD Data contains ALD, and Protective Layer data for each year \\n the ATM was run.\\n \\\"\\\"\\\"\\n self.logger.add('loading POI')\\n self.poi = POIGrid(config,logger = self.logger)\\n self.poi.config['dataset_name'] = 'POI Data'\\n self.poi.config['description'] = \\\\\\n \\\"\\\"\\\"POI Data contains Poi data for each year the ATM was run. \\n \\\"\\\"\\\"\\n self.logger.add('loading ICE')\\n self.ice = IceGrid(config,logger = self.logger)\\n self.ice.config['dataset_name'] = 'Ice Data'\\n self.ice.config['description'] = \\\\\\n \\\"\\\"\\\"\\n Ice Data contains the ice content grid for the ATM model run\\n \\\"\\\"\\\"\\n self.logger.add('loading LAKE POND')\\n self.lake_pond = LakePondGrid(config,logger = self.logger)\\n self.lake_pond.config['dataset_name'] = 'Lake Pond Data'\\n self.lake_pond.config['description'] = \\\\\\n \\\"\\\"\\\"Lake-Pond Data contains Lake and Pond depth and count data for \\n each year the ATM was run. \\n \\\"\\\"\\\"\\n self.logger.add('loading CLIMATE EVENT')\\n self.climate_event = ClimateEventGrid(config,logger = self.logger)\\n self.climate_event.config['dataset_name'] = 'Climate Event Data'\\n self.climate_event.config['description'] = \\\\\\n \\\"\\\"\\\"Climate Event Data contains climate event data for each \\n year the ATM was run. \\n \\\"\\\"\\\"\\n ## TODO:redo masks here\\n # for lpt in config['pond types'] + config['lake types']:\\n # #~ print lpt\\n # mask = self.area[lpt][0] > 0 # all cells in first ts > 0\\n # self.lake_pond.apply_mask(lpt, mask)\\n self.logger.add('loading DRAINGAGE')\\n self.drainage = DrainageGrid(config,logger = self.logger)\\n self.drainage.config['dataset_name'] = 'Drainage Data'\\n self.drainage.config['description'] = \\\"\\\"\\\"\\n Drainage contains the drainage grid for the ATM model run\\n \\\"\\\"\\\"\\n \\n self.logger.add('loading DEGREE DAY')\\n self.degreedays = DegreeDayGrids(\\n os.path.join(\\n config['Input_dir'], config['Met_Control']['FDD_file']),\\n os.path.join(\\n config['Input_dir'], config['Met_Control']['TDD_file'])\\n )\\n \\n ## what does this do?\\n self.ald.setup_ald_constants(\\n self.degreedays.thawing[config['start year']]\\n )\",\n \"def __init__(self, config):\\n self.config = config\\n self.observation_space = spaces.Box(\\n low=np.array([-15, -15, -15, -15, -15]),\\n high=np.array([15, 15, 15, 15, 15])\\n )\\n self.action_space = spaces.Box(\\n low=np.array([0, 0]),\\n high=np.array([15, 5])\\n )\\n if config[\\\"backend\\\"] == \\\"EH\\\":\\n self.params = {\\n \\\"a_1\\\": 1,\\n \\\"b_1\\\": 3,\\n \\\"c_1\\\": 1,\\n \\\"d_1\\\": 5,\\n \\\"I_ext1\\\": 3.1,\\n \\\"m\\\": 0,\\n \\\"a_2\\\": 6,\\n \\\"tau_2\\\": 10,\\n \\\"I_ext2\\\": .45,\\n \\\"gamma\\\": .01,\\n \\\"r\\\": .00035,\\n \\\"s\\\": 4,\\n \\\"x0\\\": -1.6,\\n }\\n self.sim = EHSim()\\n else:\\n self.params = {\\n \\\"x0\\\": -1.6,\\n \\\"y0\\\": 1,\\n \\\"tau0\\\": 2857,\\n \\\"tau1\\\": 1,\\n \\\"tau2\\\": 10,\\n \\\"I_rst1\\\": 3.1,\\n \\\"I_rst2\\\": .45,\\n \\\"gamma\\\": .01,\\n }\\n self.sim = JSim()\\n self.x1 = 0\\n self.y1 = -5\\n self.z = 5.5\\n self.x2 = 0\\n self.y2 = 0\\n self.frame = 0\\n self.curr_stim = []\\n self.reset()\\n self.history = list()\",\n \"def __init__(self, nb_sub_images, window_size, recovery, image_horiz_size):\\n self.nb_sub_images = nb_sub_images\\n self.window_size = window_size\\n self.recovery = recovery\\n self.image_horiz_size = image_horiz_size\",\n \"def __init__(self, fig):\\n # Initialize the system\\n self.set_sys()\\n\\n #################################################################\\n #\\n # Initialize plotting facility\\n #\\n #################################################################\\n\\n self.fig = fig\\n\\n # import utility to visualize the wigner function\\n from wigner_normalize import WignerNormalize, WignerSymLogNorm\\n\\n img_params = dict(\\n origin='lower',\\n cmap='seismic',\\n norm=WignerNormalize(vmin=-0.01, vmax=0.05),\\n # norm=WignerSymLogNorm(linthresh=1e-5, vmin=-0.01, vmax=0.1),\\n )\\n\\n ax = fig.add_subplot(121)\\n ax.set_title('Classical density, $\\\\\\\\rho(x,p,t)$')\\n\\n # generate empty plots\\n self.img_clasical_rho = ax.imshow(\\n [[]],\\n **img_params,\\n extent=[self.quant_sys.X.min(), self.quant_sys.X.max(), self.quant_sys.P.min(), self.quant_sys.P.max()],\\n )\\n\\n ax.set_xlabel('$x$ (a.u.)')\\n ax.set_ylabel('$p$ (a.u.)')\\n\\n ax = fig.add_subplot(122)\\n ax.set_title('Real part quantum density matrix, $\\\\Re\\\\hat{\\\\\\\\rho}$')\\n\\n # generate empty plots\\n self.img_Upsilon2 = ax.imshow([[]], **img_params, extent=[1, 2, 1, 2])\\n\\n #ax.set_xlabel('$x$ (a.u.)')\\n #ax.set_ylabel('$p$ (a.u.)')\\n\\n #self.fig.colorbar(self.img_clasical_rho)\",\n \"def __init__(self):\\n # Effective batch size\\n self.BATCH_SIZE = self.IMAGES_PER_GPU * self.GPU_COUNT\\n\\n # Input image size\\n if self.IMAGE_RESIZE_MODE == \\\"crop\\\":\\n self.IMAGE_SHAPE = np.array([self.IMAGE_MIN_DIM, self.IMAGE_MIN_DIM,\\n self.IMAGE_CHANNEL_COUNT])\\n else:\\n self.IMAGE_SHAPE = np.array([self.IMAGE_MAX_DIM, self.IMAGE_MAX_DIM,\\n self.IMAGE_CHANNEL_COUNT])\\n\\n # Image meta data length\\n # See compose_image_meta() for details\\n self.IMAGE_META_SIZE = 1 + 3 + 3 + 4 + 1 + self.NUM_CLASSES\",\n \"def __init__(self, *args, **kwargs):\\n super(AscatL2Image, self).__init__(*args, **kwargs)\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n pi0eeg_name = '{0}Pi0EEG'.format(name)\\n \\n d2pipi0eeg_name = '{0}D2PiPi0EEG'.format(name) \\n d2kpi0eeg_name = '{0}D2KPi0EEG'.format(name)\\n\\n d02pi0pi0eeg_name = '{0}D02Pi0Pi0EEG'.format(name) \\n dst2d0pieeg_name = '{0}Dst2D0PiEEG'.format(name)\\n\\n self.selPi0EEG = makeEEGResonance(\\n pi0eeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.Pi0EEG\\n )\\n \\n self.selD2PiPi0EEG = makeD(\\n d2pipi0eeg_name,\\n config,\\n inputSel=[self.selPi0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPi0,\\n useBachelorPID=False\\n )\\n\\n usePIDforKaon = False\\n if (config['Bachelor_PIDK_MIN'] > -10):\\n usePIDforKaon = True\\n else :\\n usePIDforKaon = False\\n \\n self.selD2KPi0EEG = makeD(\\n d2kpi0eeg_name, \\n config,\\n inputSel=[self.selPi0EEG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPi0,\\n useBachelorPID=usePIDforKaon\\n )\\n\\n self.selD02Pi0Pi0EEG = makeD(\\n d02pi0pi0eeg_name, \\n config,\\n inputSel=[self.selPi0EEG],\\n decDescriptors=self.D02Pi0Pi0,\\n useBachelorPID=False,\\n )\\n\\n self.selDst2D0PiEEG = makeDstar(\\n dst2d0pieeg_name,\\n config,\\n inputSel=[self.selD02Pi0Pi0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.line_D2PiPi0EEG = make_line(\\n self,\\n '{0}Line'.format(d2pipi0eeg_name),\\n prescale=config['PrescaleD2PiPi0EEG'],\\n postscale=config['PostscaleD2PiPi0EEG'],\\n selection=self.selD2PiPi0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KPi0EEG = make_line(\\n self,\\n '{0}Line'.format(d2kpi0eeg_name),\\n prescale=config['PrescaleD2KPi0EEG'],\\n postscale=config['PostscaleD2KPi0EEG'],\\n selection=self.selD2KPi0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_Dst2D0PiEEG = make_line(\\n self,\\n '{0}Line'.format(dst2d0pieeg_name),\\n prescale=config['PrescaleDst2D0PiEEG'],\\n postscale=config['PostscaleDst2D0PiEEG'],\\n selection=self.selDst2D0PiEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n if not os.path.isfile(CONFIG_FILENAME):\\n first_time_run()\\n raise SystemExit()\\n\\n \\\"\\\"\\\"\\n Init file is present, read and parse it:\\n \\\"\\\"\\\"\\n conf = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation(), inline_comment_prefixes='#')\\n conf.read(CONFIG_FILENAME)\\n \\\"\\\"\\\"\\n Process certain paths:\\n \\\"\\\"\\\"\\n\\n path = conf['Paths']\\n self.ffc_dir = path['FlatFieldCalDir']\\n self.capture_dir = path['CaptureDir']\\n self.image_dir = path['ImageDir']\\n\\n \\\"\\\"\\\"\\n Process options\\n \\\"\\\"\\\"\\n\\n self.cal_auto_save = conf.getboolean('Options', 'CalAutoSave', fallback=True)\\n self.cal_auto_load = conf.getboolean('Options', 'CalAutoLoad', fallback=True)\\n self.sound_on_capture = conf.getboolean('Options', 'SoundOnCapture', fallback=True)\\n self.exp_init1 = conf.getint('Options', 'ExpInit1', fallback=100)\\n self.exp_init2 = conf.getint('Options', 'ExpInit2', fallback=100)\\n self.black_correct = conf.getboolean('Options', 'BlackCorrect', fallback=True)\\n # Setup square window, default of full-screen height\\n self.tiff_seq_x_window = conf.getint('Options', 'TiffSeqXWindow', fallback=cameras.FRAME_HEIGHT)\\n self.tiff_seq_y_window = conf.getint('Options', 'TiffSeqYWindow', fallback=cameras.FRAME_HEIGHT)\\n self.tiff_seq_rebin = conf.getint('Options', 'TiffSeqRebin', fallback = 2)\",\n \"def __init__(self, config_path):\\n cfg = Config.fromfile(config_path)\\n self.cfg = cfg\\n\\n # Now make the dataloader\\n self.dataset = build_dataset(cfg.data.test)\\n\\n self.loader = build_dataloader(\\n self.dataset,\\n imgs_per_gpu=1,\\n workers_per_gpu=0,\\n dist=False,\\n shuffle=False\\n )\",\n \"def __init__(self,config,typ='train'):\\n\\n self._config = config\\n self.type = typ\\n self.reader = JsonlReader(self._config.annotations.as_dict()[typ])\\n self.annotations = self.reader.read()\\n self.transform = get_image_processor(self._config.image_processor)\",\n \"def __init__(self, im, lab):\\n\\n #self.ROOT = root\\n self.images = im\\n self.labels = lab\",\n \"def __init__(self, config):\\n\\n self._config = config\\n self._is_chef = config.is_chef\\n\\n # create source and target environment\\n self._envs = {}\\n for env_name in config.envs:\\n env = make_env(env_name, copy.copy(config), \\\"target\\\")\\n self._envs[env_name] = env\\n ob_space = env.observation_space\\n env_ob_space = env.env_observation_space\\n ac_space = env.action_space\\n logger.info(\\\"Observation space: \\\" + str(ob_space))\\n logger.info(\\\"Action space: \\\" + str(ac_space))\\n\\n # create a new observation space after data augmentation (random crop)\\n if config.encoder_type == \\\"cnn\\\":\\n assert (\\n not config.ob_norm\\n ), \\\"Turn off the observation norm (--ob_norm False) for pixel inputs\\\"\\n # env\\n ob_space = gym.spaces.Dict(spaces=dict(ob_space.spaces))\\n for k in ob_space.spaces.keys():\\n if len(ob_space.spaces[k].shape) == 3:\\n shape = [\\n ob_space.spaces[k].shape[0],\\n config.encoder_image_size,\\n config.encoder_image_size,\\n ]\\n ob_space.spaces[k] = gym.spaces.Box(\\n low=0, high=255, shape=shape, dtype=np.uint8\\n )\\n\\n # build agent and networks for algorithm\\n self._agent = get_multi_stage_agent_by_name(config.algo)(\\n config, ob_space, ac_space, env_ob_space, ob_space, ac_space, env_ob_space,\\n )\\n\\n # build rollout runner\\n self._runners = {}\\n for k, env in self._envs.items():\\n self._runners[k] = RolloutRunner(\\n config,\\n env,\\n env,\\n self._agent.run_in(\\\"target\\\", stage=\\\"policy_init\\\"),\\n \\\"target\\\",\\n )\",\n \"def __init__(self):\\n super(ISimpleITKImageMetric, self).__init__()\\n self.metric = 'ISimpleITKImageMetric'\\n self.ground_truth = None # SimpleITK.Image\\n self.segmentation = None # SimpleITK.Image\",\n \"def __init__(self, metadata_folder='./'):\\n self.metadata = self.load_metadata(metadata_folder)\\n self.prefix = 'data/miap/images/'\\n return\",\n \"def __init__(self, domains, image_dir, transform, hold_out_size):\\n self.image_dir = image_dir\\n self.transform = transform\\n self.dataset = []\\n self.attr2idx = {}\\n self.idx2attr = {}\\n self.domains=domains\\n self.sample_count = {}\\n self.img_cache = {}\\n\\n \\n self.valid_set_size=hold_out_size\\n if self.valid_set_size >0:\\n self.valid_set_dir=\\\"./validation_set\\\"\\n utils.create_empty_dir(self.valid_set_dir)\\n \\n self.preprocess()\\n \\n self.num_images = len(self.dataset)\",\n \"def __init__(self, **attrs):\\n \\n self.element_id = None\\n self.design_id = None\\n \\n self.part = None\\n self.color = None\\n \\n self.img_url = None\\n \\n self.count = None\\n self.is_spare = None\\n \\n super().__init__(**attrs)\",\n \"def __init__(self, root_dir, figs_dir, fig_name, species, alpha, loop_length, tpulse, solver, scaling_suffix='', aspect_ratio_factor=1.0, em_cutoff=1e+25, em_peak_falloff=0.99, Tn=np.arange(250,5250,250), **kwargs):\\n #set up paths\\n child_path = os.path.join(root_dir, species+'_heating_runs', 'alpha'+str(alpha), 'data')\\n self.file_path = 'ebtel_L'+str(loop_length)+'_tn%d'+scaling_suffix+'_tpulse'+str(tpulse)+'_'+solver\\n self.root_path = os.path.join(child_path, self.file_path)\\n self.em_res_top_dir = os.path.join(os.path.split(child_path)[0].replace(root_dir.strip('/'),figs_dir.strip('/')),fig_name+'.lvl1_em')\\n #configure logger\\n self.logger = logging.getLogger(type(self).__name__)\\n #configure keyword arguments\\n self.Tn=Tn\\n self.tpulse = tpulse\\n self.aspect_ratio_factor = aspect_ratio_factor\\n self.em_cutoff = em_cutoff\\n self.em_peak_falloff = em_peak_falloff\\n #instantiate binner class\\n self.binner = emb.EM_Binner(2.*loop_length*1.e+8)\",\n \"def __init__(self,\\n registration_type: RegistrationType = RegistrationType.RIGID,\\n number_of_histogram_bins: int = 200,\\n learning_rate: float = 1.0,\\n step_size: float = 0.001,\\n number_of_iterations: int = 200,\\n relaxation_factor: float = 0.5,\\n shrink_factors: typing.List[int] = (2, 1, 1),\\n smoothing_sigmas: typing.List[float] = (2, 1, 0),\\n sampling_percentage: float = 0.2,\\n sampling_seed: int = sitk.sitkWallClock,\\n resampling_interpolator=sitk.sitkBSpline):\\n super().__init__()\\n\\n if len(shrink_factors) != len(smoothing_sigmas):\\n raise ValueError(\\\"shrink_factors and smoothing_sigmas need to be same length\\\")\\n\\n self.registration_type = registration_type\\n self.number_of_histogram_bins = number_of_histogram_bins\\n self.learning_rate = learning_rate\\n self.step_size = step_size\\n self.number_of_iterations = number_of_iterations\\n self.relaxation_factor = relaxation_factor\\n self.shrink_factors = shrink_factors\\n self.smoothing_sigmas = smoothing_sigmas\\n self.sampling_percentage = sampling_percentage\\n self.sampling_seed = sampling_seed\\n self.resampling_interpolator = resampling_interpolator\\n\\n registration = sitk.ImageRegistrationMethod()\\n\\n # similarity metric\\n # will compare how well the two images match each other\\n # registration.SetMetricAsJointHistogramMutualInformation(self.number_of_histogram_bins, 1.5)\\n registration.SetMetricAsMattesMutualInformation(self.number_of_histogram_bins)\\n registration.SetMetricSamplingStrategy(registration.RANDOM)\\n registration.SetMetricSamplingPercentage(self.sampling_percentage, self.sampling_seed)\\n\\n # An image gradient calculator based on ImageFunction is used instead of image gradient filters\\n # set to True uses GradientRecursiveGaussianImageFilter\\n # set to False uses CentralDifferenceImageFunction\\n # see also https://itk.org/Doxygen/html/classitk_1_1ImageToImageMetricv4.html\\n registration.SetMetricUseFixedImageGradientFilter(False)\\n registration.SetMetricUseMovingImageGradientFilter(False)\\n\\n # interpolator\\n # will evaluate the intensities of the moving image at non-rigid positions\\n registration.SetInterpolator(sitk.sitkLinear)\\n\\n # optimizer\\n # is required to explore the parameter space of the transform in search of optimal values of the metric\\n if self.registration_type == RegistrationType.BSPLINE:\\n registration.SetOptimizerAsLBFGSB()\\n else:\\n registration.SetOptimizerAsRegularStepGradientDescent(learningRate=self.learning_rate,\\n minStep=self.step_size,\\n numberOfIterations=self.number_of_iterations,\\n relaxationFactor=self.relaxation_factor,\\n gradientMagnitudeTolerance=1e-4,\\n estimateLearningRate=registration.EachIteration,\\n maximumStepSizeInPhysicalUnits=0.0)\\n registration.SetOptimizerScalesFromPhysicalShift()\\n\\n # setup for the multi-resolution framework\\n registration.SetShrinkFactorsPerLevel(self.shrink_factors)\\n registration.SetSmoothingSigmasPerLevel(self.smoothing_sigmas)\\n registration.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()\\n\\n self.registration = registration\\n self.transform = None\",\n \"def __init__(self, path_image, path_imagefile, path_bndboxfile, transform):\\r\\n # -------------------- DATA ARGUMENT\\r\\n self.shape = 446\\r\\n self.hue = 0.1\\r\\n self.saturation = 1.5\\r\\n self.exposure = 1.5\\r\\n self.imagelist = []\\r\\n self.labellist = []\\r\\n self.transform = transform\\r\\n label_dir = os.listdir(path_bndboxfile)\\r\\n image_dir = os.listdir(path_imagefile)\\r\\n\\r\\n # read imagepath\\r\\n for file in image_dir:\\r\\n file_name = os.path.join(path_imagefile, file)\\r\\n with open(file_name) as f:\\r\\n lines = f.readlines()\\r\\n for line in lines:\\r\\n image_name = line.split()[0] + '.JPEG'\\r\\n image = os.path.join(path_image, image_name)\\r\\n self.imagelist.append(image)\\r\\n\\r\\n # read imagelabel, i.e, (name, xmin, xmax, ymin, ymax)\\r\\n for file in label_dir:\\r\\n if file.split('.')[1] == 'xml':\\r\\n file_name = os.path.join(path_bndboxfile, file)\\r\\n with open(file_name) as f:\\r\\n xml_tree = parse(f).documentElement\\r\\n objects = xml_tree.getElementsByTagName('object')\\r\\n for object in objects:\\r\\n label = []\\r\\n name = object.getElementsByTagName('name')[0]\\r\\n label.append(name.childNodes[0].data)\\r\\n bndbox = object.getElementsByTagName('bndbox')[0]\\r\\n for node in bndbox.childNodes:\\r\\n if node.nodeType == node.ELEMENT_NODE:\\r\\n label.append(node.childNodes[0].data)\\r\\n self.labellist.append(label)\\r\\n else:\\r\\n print('Expect files in xml format. but get {}'.format(file.split('.')[1]))\",\n \"def __init__(self,\\n ann_file,\\n img_prefix,\\n img_means,\\n img_stds,\\n img_expected_sizes,\\n size_divisor=None,\\n flip_ratio=0,\\n be_cell_size=32,\\n be_random_ratio=0.5,\\n proposal_file=None,\\n num_max_proposals=1000,\\n with_mask=False,\\n with_crowd=False,\\n with_label=True,\\n test_mode=False,\\n with_background_erasing=False,\\n debug=False):\\n # load annotations from annotation file\\n self.img_infos = self.load_annotations(ann_file)\\n # prefix of image path\\n self.img_prefix = img_prefix\\n # image normalization parameters\\n self.img_means, self.img_stds = img_means, img_stds\\n # img_expected_sizes list\\n self.img_expected_sizes = img_expected_sizes \\\\\\n if isinstance(img_expected_sizes, list) else [img_expected_sizes]\\n assert is_list_of(self.img_expected_sizes, tuple)\\n # size divisor\\n self.size_divisor = size_divisor\\n # flip ratio\\n self.flip_ratio = flip_ratio\\n # background erasing settings\\n self.be_cell_size = be_cell_size\\n self.be_random_ratio = be_random_ratio\\n\\n # load the pkl proposal file\\n if proposal_file is not None:\\n self.proposals = self.load_proposals(proposal_file)\\n else:\\n self.proposals = None\\n self.num_max_proposals = num_max_proposals\\n\\n # filter images with no annotation during training\\n if not test_mode:\\n valid_inds = self._filter_imgs()\\n self.img_infos = [self.img_infos[i] for i in valid_inds]\\n if self.proposals is not None:\\n self.proposals = [self.proposals[i] for i in valid_inds]\\n\\n # with mask or not\\n self.with_mask = with_mask\\n # with crowd object or not\\n self.with_crowd = with_crowd\\n # training with label or not\\n self.with_label = with_label\\n # test mode\\n self.test_mode = test_mode\\n # with background erasing or not\\n self.with_background_erasing = with_background_erasing\\n # debug mode\\n self.debug = debug\\n if self.debug:\\n self.img_infos = self.img_infos[:50]\\n\\n # set aspect ratio flag for images\\n if not self.test_mode:\\n self._set_group_flag()\\n\\n # transforms\\n self.img_transforms = ImageTransforms(img_means=self.img_means,\\n img_stds=self.img_stds,\\n size_divisor=self.size_divisor)\\n self.bbox_transforms = BboxTransforms()\\n self.mask_transforms = MaskTransforms()\\n self.background_erasing = BackgroundErasing()\",\n \"def __init__(self):\\n parameters_list = []\\n self.config_dict = self.open_config(parameters_list)\\n\\n # Define defaults\\n self.disc_gt = 0.0\\n self.disc_out = 0.0\",\n \"def __init__(\\n self,\\n title,\\n body,\\n img_path_xs,\\n img_path_sm,\\n img_path_md,\\n img_path_lg,\\n is_active):\\n\\n self.title = title\\n self.body = body\\n self.img_path_xs = img_path_xs\\n self.img_path_sm = img_path_sm\\n self.img_path_md = img_path_md\\n self.img_path_lg = img_path_lg\\n self.is_active = is_active\",\n \"def __init__(self, width, height):\\n\\n self._width = width\\n self._height = height\\n \\n # The images in the cache!\\n self._images = {} # {filename : bitmap}\\n\\n return\",\n \"def __init__(\\n self,\\n darks: Union[list, np.ndarray] = None,\\n flats: Union[list, np.ndarray] = None,\\n bias: Union[list, np.ndarray] = None,\\n loader=FITSImage,\\n easy_ram: bool = True,\\n verbose: bool = True,\\n shared: bool = False,\\n **kwargs,\\n ):\\n\\n super().__init__(**kwargs)\\n\\n self.loader = loader\\n self.easy_ram = easy_ram\\n\\n self.shapes = {}\\n\\n def check_input(value):\\n if value is None:\\n value = []\\n elif isinstance(value, np.ndarray):\\n if len(value) == 0:\\n value = []\\n elif value.ndim == 2:\\n value = [value]\\n # ndim 1 or 3\\n else:\\n value = value.tolist()\\n if not isinstance(value, (list, np.ndarray)):\\n value = [value]\\n\\n return value\\n\\n self.master_bias = self._produce_master(check_input(bias), \\\"bias\\\")\\n self.master_dark = self._produce_master(check_input(darks), \\\"dark\\\")\\n self.master_flat = self._produce_master(check_input(flats), \\\"flat\\\")\\n\\n if shared:\\n self._share()\\n self.verbose = verbose\\n\\n self.calibration = self._calibration_shared if shared else self._calibration\\n self._parallel_friendly = shared\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n d0eeg_name = '{0}D0EEG'.format(name)\\n dst2pid0eeg_name = '{0}Dst2PiD0EEG'.format(name)\\n \\n d0ee_name = '{0}D0EE'.format(name)\\n dst2pid0ee_name = '{0}Dst2PiD0EE'.format(name)\\n\\n self.selD0EEG = makeEEGResonance(\\n d0eeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.D0EEG\\n ) \\n\\n self.selD0EE = makeEEResonance(\\n d0ee_name, \\n config,\\n inputSel=[StdAllLooseElectrons],\\n decDescriptors=self.D0EE\\n )\\n \\n self.selDst2PiD0EEG = makeDstar(\\n dst2pid0eeg_name,\\n config,\\n inputSel=[self.selD0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.selDst2PiD0EE = makeDstar(\\n dst2pid0ee_name,\\n config,\\n inputSel=[self.selD0EE, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.line_Dst2PiD0EEG = make_line(\\n self,\\n '{0}Line'.format(dst2pid0eeg_name),\\n prescale=config['PrescaleDst2PiD0EEG'],\\n postscale=config['PostscaleDst2PiD0EEG'],\\n selection=self.selDst2PiD0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_Dst2PiD0EE = make_line(\\n self,\\n '{0}Line'.format(dst2pid0ee_name),\\n prescale=config['PrescaleDst2PiD0EE'],\\n postscale=config['PostscaleDst2PiD0EE'],\\n selection=self.selDst2PiD0EE,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, eod):\\n self.eod = eod\\n self.img_lst = []\",\n \"def __init__(self):\\n\\n # self.threshold = 3.\\n self.gamma_min = 3\\n self.gamma_max = 12\\n self.n_samples = 40\\n # self.do_plots = False\\n # self.do_albedo = True\\n # self.verbose = True\\n\\n self.nbands = 7\\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\\n\\n # Determine 250 or 500 meters product\\n # self.resolution = 500\\n\\n # self.pixelWidth = 500\\n # self.pixelHeight = 500\",\n \"def __init__(self):\\n #variabile dove si trova il dataset\\n self.data_dir=\\\"/Users/Eric/Desktop/eric/Programmazione/python/DeepLearning/data/knifey-spoony\\\"\\n #Url dove posso scaricare un dataset\\n self.data_url=\\\"https://github.com/Hvass-Labs/knifey-spoony/raw/master/knifey-spoony.tar.gz\\\"\\n #path della cartella di train\\n self.train_dir=os.path.join(self.data_dir,\\\"train/\\\")\\n #path della cartella di test\\n self.test_dir=os.path.join(self.data_dir,\\\"test/\\\")\\n #dimensione immagine\\n self.image_size=200\\n #canali immagine\\n self.num_channels=3\\n self.img_shape=[self.image_size,self.image_size,self.num_channels]\\n self.img_size_flat=self.image_size*self.image_size*self.num_channels\\n #numero di classi del dataset\\n self.num_classes=3\\n self.download=Download()\",\n \"def __init__(self, params, out):\\n self.configurations = []\\n self.out = out\\n self.add_configuration(params)\",\n \"def __init__(self, a):\\n self.files = a.imagefiles\\n self.nfiles = len(self.files)\\n self.nempty = a.empty\\n self.maxframes = a.max_frames\\n #self._setloglevel(a)\\n\\n self._info()\\n self.h5opts = self._seth5opts(a)\\n\\n\\n self.ntowrite = numpy.min((self.maxframes, self.nframes))\\\\\\n if self.maxframes > 0 else self.nframes\\n\\n self.outfile = a.outfile\\n self.dgrppath = a.dset\\n self.dsetpath = '/'.join((a.dset, 'images'))\",\n \"def __init__(self, images, batch_size, ctx, multisp):\\n self.ctx = ctx\\n self.batch_size = batch_size\\n self.images = []\\n self.multisp = multisp\\n\\n self.images=images\\n\\n if self.images:\\n self.channels, self.imgsize, _ = self._read_img(self.images[0]['data']).shape\\n\\n logging.info(\\\"Found a total of {} images\\\".format(len(self.images)))\",\n \"def buildImages(self, config, base, file_num, image_num, obj_num, ignore, logger):\\n # This is basically the same as the base class version. Just a few extra things to\\n # add to the ignore list.\\n ignore += [ 'file_name', 'dir', 'nfiles', 'det_num',\\n 'only_dets', 'readout', 'exptime', 'camera' ]\\n\\n opt = {\\n 'cosmic_ray_rate': float,\\n 'cosmic_ray_catalog': str,\\n 'header': dict\\n }\\n params, safe = galsim.config.GetAllParams(config, base, opt=opt, ignore=ignore)\\n\\n image = galsim.config.BuildImage(base, image_num, obj_num, logger=logger)\\n\\n # Add cosmic rays.\\n cosmic_ray_rate = params.get('cosmic_ray_rate', 0)\\n if cosmic_ray_rate > 0:\\n cosmic_ray_catalog = params.get('cosmic_ray_catalog', None)\\n if cosmic_ray_catalog is None:\\n cosmic_ray_catalog = os.path.join(data_dir, 'cosmic_rays_itl_2017.fits.gz')\\n if not os.path.isfile(cosmic_ray_catalog):\\n raise FileNotFoundError(f'{cosmic_ray_catalog} not found')\\n\\n logger.info('Adding cosmic rays with rate %f using %s.',\\n cosmic_ray_rate, cosmic_ray_catalog)\\n exptime = base['exptime']\\n det_name = base['det_name']\\n cosmic_rays = CosmicRays(cosmic_ray_rate, cosmic_ray_catalog)\\n rng = galsim.config.GetRNG(config, base)\\n cosmic_rays.paint(image.array, rng, exptime=exptime)\\n\\n # Add header keywords for various values written to the primary\\n # header of the simulated raw output file, so that all the needed\\n # information is in the eimage file.\\n image.header = galsim.FitsHeader()\\n exptime = base['exptime']\\n image.header['EXPTIME'] = exptime\\n image.header['DET_NAME'] = base['det_name']\\n\\n header_vals = copy.deepcopy(params.get('header', {}))\\n opsim_data = get_opsim_data(config, base)\\n\\n # Helper function to parse a value with priority:\\n # 1. from header_vals (popped from dict if present)\\n # 2. from opsim_data\\n # 3. specified default\\n def parse(item, type, default):\\n if item in header_vals:\\n val = galsim.config.ParseValue(header_vals, item, base, type)[0]\\n del header_vals[item]\\n else:\\n val = opsim_data.get(item, default)\\n return val\\n\\n # Get a few items needed more than once first\\n mjd = parse('mjd', float, 51444.0)\\n mjd_obs = parse('observationStartMJD', float, mjd)\\n mjd_end = mjd_obs + exptime/86400.0\\n seqnum = parse('seqnum', int, 0)\\n ratel = parse('fieldRA', float, 0.0)\\n dectel = parse('fieldDec', float, 0.0)\\n airmass = parse('airmass', float, 'N/A')\\n\\n # Now construct the image header\\n image.header['MJD'] = mjd\\n image.header['MJD-OBS'] = mjd_obs, 'Start of exposure'\\n # NOTE: Should this day be the current day,\\n # or the day at the time of the most recent noon?\\n dayobs = astropy.time.Time(mjd_obs, format='mjd').strftime('%Y%m%d')\\n image.header['DAYOBS'] = dayobs\\n image.header['SEQNUM'] = seqnum\\n image.header['CONTRLLR'] = 'P', 'simulated data'\\n image.header['RUNNUM'] = parse('observationId', int, -999)\\n image.header['OBSID'] = f\\\"IM_P_{dayobs}_{seqnum:06d}\\\"\\n image.header['IMGTYPE'] = parse('image_type', str, 'SKYEXP')\\n image.header['REASON'] = parse('reason', str, 'survey')\\n image.header['RATEL'] = ratel\\n image.header['DECTEL'] = dectel\\n with warnings.catch_warnings():\\n # Silence FITS warning about long header keyword\\n warnings.simplefilter('ignore')\\n image.header['ROTTELPOS'] = parse('rotTelPos', float, 0.0)\\n image.header['FILTER'] = parse('band', str, 'N/A/')\\n image.header['CAMERA'] = base['output']['camera']\\n image.header['HASTART'] = opsim_data.getHourAngle(mjd_obs, ratel)\\n image.header['HAEND'] = opsim_data.getHourAngle(mjd_end, ratel)\\n image.header['AMSTART'] = airmass\\n image.header['AMEND'] = airmass # wrong, does anyone care?\\n\\n # If there's anything left in header_vals, add it to the header.\\n for k in header_vals:\\n image.header[k] = galsim.config.ParseValue(\\n header_vals, k, base, None\\n )[0]\\n\\n return [ image ]\",\n \"def __init__(self,workspace,gridin,df,csim):\\n #Constructor begins here\\n self.__workspace=workspace\\n self.__grid=gridin\\n self.__dflst=pd.read_csv(workspace+'WELL\\\\\\\\'+'filelist.csv',sep=',')\\n print('*--- Preparing simulation, reading csv wells ---*')\\n self.__dfwells=pd.read_csv(workspace+'WELL\\\\\\\\'+'arraydata.csv',sep=',')\\n self.__nwells=self.__dflst.shape[0]\\n self.__df=df\\n self.__dfclst={}\\n self.__dfcwells={}\\n self.__ncwells=0\\n if(csim==True):\\n self.buildCwells()\",\n \"def __init__(self, image: np.ndarray) -> None:\\n self.image = image\",\n \"def __init__(self, inputfile, imlist=True, use_dic=True):\\n self.inputfile = inputfile\\n self.imlist = imlist\\n self.use_dic = use_dic\\n if use_dic:\\n self.n, self.home, self.dic = createDictionaryFromFile(inputfile)\\n else:\\n self.n, self.home, self.dic = createPathY(inputfile)\\n self.data = 0\\n self.activity = None\\n self.imsize = None\\n self.img = 0\\n self.i = 0\\n self.labels = []\\n self.allimgs = []\\n self.alllabels = []\\n self.fout = None\\n self.record_mode = False\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n phi3hm_name = '{0}Phi3HM'.format(name)\\n phi3hr_name = '{0}Phi3HR'.format(name)\\n \\n d2piphi3hm_name = '{0}D2PiPhi3HM'.format(name)\\n d2piphi3hr_name = '{0}D2PiPhi3HR'.format(name)\\n \\n d2kphi3hm_name = '{0}D2KPhi3HM'.format(name)\\n d2kphi3hr_name = '{0}D2KPhi3HR'.format(name)\\n \\n\\n self.selPhi3HM = makeResonance(\\n phi3hm_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\\n decDescriptors=self.Phi3H\\n )\\n\\n self.selPhi3HR = makeResonance(\\n phi3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\\n decDescriptors=self.Phi3H\\n )\\n \\n self.selD2PiPhi3HM = makeD(\\n d2piphi3hm_name,\\n config,\\n inputSel=[self.selPhi3HM, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KPhi3HM = makeD(\\n d2kphi3hm_name,\\n config,\\n inputSel=[self.selPhi3HM, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiPhi3HR = makeD(\\n d2piphi3hr_name,\\n config,\\n inputSel=[self.selPhi3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KPhi3HR = makeD(\\n d2kphi3hr_name,\\n config,\\n inputSel=[self.selPhi3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPhi,\\n useBachelorPID=False,\\n )\\n \\n self.line_D2PiPhi3HM = make_line(\\n self,\\n name='{0}Line'.format(d2piphi3hm_name),\\n prescale=config['PrescaleD2PiPhi3HM'],\\n postscale=config['PostscaleD2PiPhi3HM'],\\n selection=self.selD2PiPhi3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2PiPhi3HR = make_line(\\n self,\\n name='{0}Line'.format(d2piphi3hr_name),\\n prescale=config['PrescaleD2PiPhi3HR'],\\n postscale=config['PostscaleD2PiPhi3HR'],\\n selection=self.selD2PiPhi3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n self.line_D2KPhi3HM = make_line(\\n self,\\n name='{0}Line'.format(d2kphi3hm_name),\\n prescale=config['PrescaleD2KPhi3HM'],\\n postscale=config['PostscaleD2KPhi3HM'],\\n selection=self.selD2KPhi3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KPhi3HR = make_line(\\n self,\\n name='{0}Line'.format(d2kphi3hr_name),\\n prescale=config['PrescaleD2KPhi3HR'],\\n postscale=config['PostscaleD2KPhi3HR'],\\n selection=self.selD2KPhi3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def pibooth_setup_picture_factory(cfg, opt_index, factory):\",\n \"def __init__(self, *args, **kwargs):\\n # Training/prediction data volume\\n self.train_volume: np.ndarray = None\\n # Training label volume\\n self.train_label_volume: Optional[np.ndarray] = None\\n # Per-voxel training error weighting volume\\n self.train_weight_volume: Optional[np.ndarray] = None\\n # Validation data volume\\n self.eval_volume: Optional[np.ndarray] = None\\n # Validation label volume\\n self.eval_label_volume: Optional[np.ndarray] = None\\n\\n # Number of label classes\\n self.n_classes: int = None\\n\\n # RandomState used to control random number generation\\n self.random_state = np.random.RandomState()\\n\\n # Image augmentation instance_settings for use during training\\n self.augmentation_settings: Dict[str, Any] = None\\n\\n # Load data if arguments are supplied\\n if len(args) > 0 or len(kwargs) > 0:\\n self.load(*args, **kwargs)\\n\\n pass\",\n \"def __init__(self, seriesDir):\\n # initialize attributes\\n self.seriesDir = seriesDir\\n self.niftiImage = None\\n\\n # make a list of all of the par files in this dir\\n par_pattern = re.compile(fnmatch.translate('*.par'), re.IGNORECASE)\\n parFiles = [x for x in os.listdir(self.seriesDir) if par_pattern.match(x)]\\n\\n # figure out what type of image this is, 4d or 3d\\n self.scanType = self._determineScanType(parFiles[0])\\n\\n # build nifti image\\n if self.scanType == 'anat':\\n self.niftiImage = self.buildAnat(parFiles)\\n elif self.scanType == 'func':\\n self.niftiImage = self.buildFunc(parFiles)\",\n \"def __init__(self):\\n self.model = {'mol':[], 'nmol':0}\\n self.template = {} \\n self.config = {}\\n self.config['tfile'] = 'gau-template-bsse.gjf'\\n self.config['xyzfile'] = 'model.xyz'\\n self.config['jobfile'] = 'gau.gjf'\\n self.config['job_prefix'] = self.config['jobfile'].split(\\\".\\\")[0]\\n self.config['incr'] = 1\\n \\n self.rd_cmd_stream()\\n return\",\n \"def __init__(self, dataset_path, use_augmentation, batch_size):\\n\\n self.dataset_path = dataset_path\\n self.train_dictionary = {}\\n self.evaluation_dictionary = {}\\n self.image_width = 105\\n self.image_height = 105\\n self.batch_size = batch_size\\n self.use_augmentation = use_augmentation\\n self._train_alphabets = []\\n self._validation_alphabets = []\\n self._evaluation_alphabets = []\\n self._current_train_alphabet_index = 0\\n self._current_validation_alphabet_index = 0\\n self._current_evaluation_alphabet_index = 0\\n\\n self.load_dataset()\\n\\n if (self.use_augmentation):\\n self.image_augmentor = self.createAugmentor()\\n else:\\n self.use_augmentation = []\",\n \"def step_run(cls, config):\\n\\n nir_paw_image_fname = config.get(cls.step_name, 'nir_paw_image')\\n nir_paw_conf_fname = config.get(cls.step_name, 'nir_paw_conf')\\n output_template = config.get(cls.step_name, 'output_template')\\n conf_limit = config.getfloat(cls.step_name, 'conf_limit')\\n\\n# logger.info('reading Comparison image from %s', comp_fname)\\n# comp_im = DESImage.load(comp_fname)\\n\\n ret_code = cls.__call__(nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit)\\n return ret_code\",\n \"def __init__(self):\\n\\n super().__init__()\\n\\n self.active = True\\n self.driver = Driver.instance()\\n self.camera = Camera.instance()\\n\\n # define directories and file paths\\n date_str = datetime.today().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\")\\n self.log_dir = f\\\"{const.Storage.DATA}/{date_str}\\\"\\n self.img_dir = f\\\"{self.log_dir}/img/\\\"\\n self.log_path = f\\\"{self.log_dir}/log.csv\\\"\\n self.img_extension = \\\"npy\\\"\\n\\n # ensure that the necessary directories exist\\n os.mkdir(self.log_dir)\\n os.mkdir(self.img_dir)\\n assert os.path.isdir(self.log_dir), \\\"data directory could not be created\\\"\\n assert os.path.isdir(self.img_dir), \\\"image directory could not be created\\\"\",\n \"def __init__(__self__, *,\\n component_arn: Optional[str] = None,\\n parameters: Optional[Sequence['outputs.ImageRecipeComponentParameter']] = None):\\n if component_arn is not None:\\n pulumi.set(__self__, \\\"component_arn\\\", component_arn)\\n if parameters is not None:\\n pulumi.set(__self__, \\\"parameters\\\", parameters)\",\n \"def __init__(self,imageObject):\\n getJsonEnv()\\n self.meta=imageObject\\n self.imageNames=self.getImageName()\\n self.gcsBucket=self.setUpCredentials()\\n self.sources = []\",\n \"def __init__(self, sizes, image_size):\\n super(CraterNetwork, self).__init__(sizes);\\n self.im_size = image_size\\n self.validating = False\",\n \"def __init__(self, args):\\n self.train_img_file = os.path.join(args.data_dir, args.train_img_file)\\n self.train_lbl_file = os.path.join(args.data_dir, args.train_lbl_file)\\n self.test_img_file = os.path.join(args.data_dir, args.test_img_file)\\n self.test_lbl_file = os.path.join(args.data_dir, args.test_lbl_file)\\n self.batch_size = args.batch_size\\n self.num_workers = args.data_workders\\n self.shuffle = True\\n self.dataset_name = args.dataset_name\\n self.pin_memory = False #args.cuda\\n\\n # check dataset files exist\\n files = [self.train_img_file, self.train_lbl_file,\\n self.test_img_file, self.test_lbl_file]\\n for file in files:\\n if not os.path.isfile(file):\\n msg = \\\"Data file not found. Please check the path \\\" +\\\\\\n \\\"or download files using scripts/download_files.py \\\"\\n raise IOError(msg)\",\n \"def load_image(self, **kwargs):\\n ...\",\n \"def __init__(self, prod_obj, log_level=logutil.logging.NOTSET, use_defaults=True, input_custom_pars_file=None,\\n output_custom_pars_file=None):\\n log.setLevel(log_level)\\n if input_custom_pars_file and input_custom_pars_file and input_custom_pars_file == output_custom_pars_file:\\n sys.exit(\\\"ERROR: Input and output parameter files must have unique names!\\\")\\n self.label = \\\"hap_config\\\"\\n self.description = \\\"A set of routines to generate appropriate set of configuration parameters\\\"\\n self.instrument = prod_obj.instrument\\n self.detector = prod_obj.detector\\n self.inst_det = \\\"{}_{}\\\".format(prod_obj.instrument, prod_obj.detector).lower()\\n self.use_defaults = use_defaults\\n self.input_custom_pars_file = input_custom_pars_file\\n self.output_custom_pars_file = output_custom_pars_file\\n\\n # The filters attribute is populated by _determine_conditions()\\n self.filters = None\\n\\n self._determine_conditions(prod_obj)\\n self.full_cfg_index, self.pars_dir = read_index(self.instrument,\\n self.detector)\\n\\n # Instantiate the parameter set\\n self.pars = {}\\n\\n # open input parameter file if specified by user\\n if self.input_custom_pars_file:\\n with open(self.input_custom_pars_file) as f_cfg:\\n self.input_cfg_json_data = json.load(f_cfg)[prod_obj.product_basename]\\n else:\\n self.input_cfg_json_data = None\\n\\n # generate parameter sets for each pipeline step\\n # step_name_list = [AstrodrizzlePars, CatalogGenerationPars, QualityControlPars]\\n # step_title_list = ['astrodrizzle', 'catalog generation', 'quality control']\\n for step_title, step_name in zip(step_title_list, step_name_list):\\n cfg_index = self.full_cfg_index[step_title]\\n self.pars[step_title] = step_name(cfg_index,\\n self.conditions,\\n self.pars_dir,\\n step_title,\\n self.use_defaults,\\n self.input_cfg_json_data)\\n\\n # write out all parameters to file if specified by user\\n if output_custom_pars_file:\\n self.write_pars(prod_obj)\",\n \"def __init__(self, **kwargs):\\n\\n def paraChck(**kwargs):\\n \\\"\\\"\\\"\\n check and validate the keyword argument input\\n \\\"\\\"\\\"\\n import sys\\n\\n \\n def_val = {\\n 'x_train':None,\\n 'y_train':None,\\n 'x_test':None,\\n 'y_test':None,\\n 'channel':1,\\n 'input_img_cols':72,\\n 'input_img_rows':72,\\n 'nb_classes':13,\\n 'nb_epoch': 5,\\n 'batch_size' : 16,\\n 'dict_label' : None} # default parameteters value\\n\\n diff = set(kwargs.keys()) - set(def_val.keys())\\n if diff:\\n print(\\\"Invalid args:\\\",tuple(diff),file=sys.stderr)\\n return\\n\\n def_val.update(kwargs)\\n return def_val\\n \\n def_val = paraChck(**kwargs)\\n\\n class Bunch(object):\\n def __init__(self, adict):\\n self.__dict__.update(adict)\\n \\n self.x_train = def_val['x_train']\\n self.y_train = def_val['y_train']\\n self.x_test = def_val['x_test']\\n self.y_test = def_val['y_test']\\n self.channels = def_val['channel']\\n self.input_img_rows = def_val['input_img_rows']\\n self.input_img_cols = def_val['input_img_cols']\\n self.nb_classes = def_val['nb_classes']\\n self.plot_model = None\\n self.model = None\\n self.nb_epoch = def_val['nb_epoch']\\n self.batch_size = def_val['batch_size']\\n self.dict_label = def_val['dict_label']\\n \\n # default label dictionary if users do not provide\\n values = ['label_' + str(i).zfill(2) for i in range(0,self.nb_classes)]\\n keys = range(self.nb_classes) \\n \\n if self.dict_label is None:\\n self.dict_label = dict(zip(keys, values))\\n else:\\n self.dict_label = kwargs['dict_label']\\n \\n self.dict_factor = {v: k for k, v in self.dict_label.items()}\",\n \"def __init__(self, width, height, rand_seed=None, filepath=None):\\r\\n self.height = height\\r\\n self.width = width\\r\\n\\r\\n if rand_seed:\\r\\n seed(rand_seed)\",\n \"def __init__(self, img_root, gt_json_meta_fname, pd_json_meta_fname):\\n self.img_root = img_root\\n\\n analyzer = PanopticEvalAnalyzer(gt_json_meta_fname, pd_json_meta_fname)\\n self.gt, self.pd = analyzer.gt, analyzer.pd\\n self.imgIds = analyzer.imgIds\\n self.res_dframe, self.overall_table, self.cat_table = analyzer.summarize()\\n # cached widgets\\n self.global_walk = None\\n self.__init_global_state__()\",\n \"def __init__(self, generator, discriminator, noise_dim, save_path):\\n self.generator = generator\\n self.discriminator = discriminator\\n self.noise_dim = noise_dim\\n self.save_path = save_path\\n self.check_points_path = os.path.join(save_path, 'check_points')\\n self.output_image_path = os.path.join(save_path, 'images_during_training')\\n self.generator.generate()\",\n \"def __init__(self, book_config,\\n soft_cover_options=None, hard_cover_options=None):\\n self.__book_config = book_config\\n if soft_cover_options:\\n self.__soft_cover_options = soft_cover_options\\n else:\\n self.__soft_cover_options = {\\n 'page-height': '221',\\n 'page-width': '551',\\n 'margin-top': '3',\\n 'margin-right': '3',\\n 'margin-bottom': '3',\\n 'margin-left': '3',\\n 'dpi': '350',\\n }\\n if hard_cover_options:\\n self.__hard_cover_options = hard_cover_options\\n else:\\n self.__hard_cover_options = {\\n 'page-height': '221',\\n 'page-width': '551',\\n 'margin-top': '3',\\n 'margin-right': '3',\\n 'margin-bottom': '3',\\n 'margin-left': '3',\\n 'dpi': '350',\\n }\",\n \"def __init__(self, n_pixels=(250,150),\\n submarine=None,\\n air_velocity=0.1,\\n water_velocity=1.0,\\n rock_velocity=10.0,\\n **kwargs):\\n\\n GeneratedGalleryModel.__init__(self)\\n\\n self.air_velocity = air_velocity\\n self.water_velocity = water_velocity\\n self.rock_velocity = rock_velocity\\n\\n if len(n_pixels) not in [2,3]:\\n raise ValueError('Submarine-sonar model only works for dimensions greater than 1.')\\n\\n if submarine is None:\\n if len(n_pixels) == 2:\\n submarine = default_submarine_2D\\n else: # len(n_pixels) == 3\\n submarine = default_submarine_3D\\n self.submarine = submarine\\n\\n config_list = list()\\n\\n # Configure X direction\\n x_lbc = kwargs['x_lbc'] if ('x_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n x_rbc = kwargs['x_rbc'] if ('x_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n\\n xmin, xmax = 0.0, 2.5\\n x_config = (xmin, xmax, x_lbc, x_rbc)\\n config_list.append(x_config)\\n\\n\\n if len(n_pixels) == 3:\\n\\n # If it is there, configure Y direction\\n y_lbc = kwargs['y_lbc'] if ('y_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n y_rbc = kwargs['y_rbc'] if ('y_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n\\n ymin, ymax = 0.0, 2.5\\n y_config = (ymin, ymax, y_lbc, y_rbc)\\n config_list.append(y_config)\\n\\n # Configure Z direction\\n z_lbc = kwargs['z_lbc'] if ('z_lbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n z_rbc = kwargs['z_rbc'] if ('z_rbc' in list(kwargs.keys())) else PML(0.1, 100.0)\\n\\n zmin, zmax = 0.0, 1.5\\n z_config = (zmin, zmax, z_lbc, z_rbc)\\n config_list.append(z_config)\\n\\n domain = RectangularDomain(*config_list)\\n\\n mesh_args = [domain] + list(n_pixels)\\n mesh = CartesianMesh(*mesh_args)\\n\\n\\n self._mesh = mesh\\n self._domain = mesh.domain\\n\\n # Set _initial_model and _true_model\\n self.rebuild_models()\",\n \"def __init__(self, content_image_dir,style_image_dir,\\n selectedContent,selectedStyle,\\n content_transform,style_transform,\\n subffix='jpg', random_seed=1234):\\n self.content_image_dir= content_image_dir\\n self.style_image_dir = style_image_dir\\n self.content_transform= content_transform\\n self.style_transform = style_transform\\n self.selectedContent = selectedContent\\n self.selectedStyle = selectedStyle\\n self.subffix = subffix\\n self.content_dataset = []\\n self.art_dataset = []\\n self.random_seed= random_seed\\n self.preprocess()\\n self.num_images = len(self.content_dataset)\\n self.art_num = len(self.art_dataset)\",\n \"def __init__(self, opt):\\r\\n super().__init__()\\r\\n self.opt = opt\\r\\n # if self.opt.dataset_mode not in ['aligned', 'unaligned', 'heatmap', 'jointsAligned', 'jointsAligned2',\\r\\n # \\\"jointsUnaligned\\\", \\\"hand3d\\\"]:\\r\\n # raise ValueError\\r\\n\\r\\n # self.mode = self.opt.dataset_mode\\r\\n\\r\\n self.root_dir = self.opt.dataroot\\r\\n with open(os.path.join(self.root_dir, \\\"annotation.pickle\\\"),\\r\\n \\\"rb\\\") as handle:\\r\\n self.annotations = pickle.load(handle)\\r\\n\\r\\n self.image_color_sk = []\\r\\n for folder in self.annotations.keys():\\r\\n for image in self.annotations[folder].keys():\\r\\n img_path = os.path.join(self.root_dir, folder, image)\\r\\n camera, spec, n = image.split('_')\\r\\n if camera == 'BB':\\r\\n continue\\r\\n else:\\r\\n if spec == 'color':\\r\\n # ceate pair SK_color / SK_depth\\r\\n self.image_color_sk.append(img_path)\\r\\n\\r\\n def sort_priority(x):\\r\\n *_, folder, name = x.split('/')\\r\\n folder_n = int(folder[1])\\r\\n folder_t = folder[2]\\r\\n name = int(name[0:-4].split('_')[-1])\\r\\n return folder_n, folder_t, name\\r\\n\\r\\n self.image_color_sk.sort(key=lambda x: sort_priority(x))\\r\\n\\r\\n if self.opt.isTrain:\\r\\n self.augmentation_map = np.zeros(self.__len__(), dtype=np.bool)\\r\\n n = int((1 - self.opt.augmentation_ratio) * self.__len__())\\r\\n if self.opt.dataset_mode == 'generate':\\r\\n self.augmentation_map[0:int(n)] = True\\r\\n else:\\r\\n self.augmentation_map[int(n)::] = True\\r\\n print(f\\\"train on {np.sum(self.augmentation_map)} images\\\")\\r\\n\\r\\n self.image_color_sk = [\\r\\n self.image_color_sk[i]\\r\\n for i, state in enumerate(self.augmentation_map)\\r\\n if state == True\\r\\n ]\\r\\n self.image_color_pair = self.image_color_sk.copy()\\r\\n random.shuffle(self.image_color_pair)\",\n \"def __init__(self, data_dir, file_prefix, num_images):\\n self.file_prefix = file_prefix\\n self.files = [os.path.join(data_dir, '%s%03d.jpg' % (file_prefix, i + 1)) for i in range(num_images)]\\n self.files = list(filter(os.path.exists, self.files))\\n self.panoramas = None\\n self.homographies = None\\n self.images = []\\n self.display_match = False\\n self.useBlending = False\\n print('found %d images' % len(self.files))\",\n \"def __init__(self, name, cfg={}) -> None:\\n self.name = name\\n self.top_k_words = cfg[\\\"top_k_words\\\"]\\n self._cfg = cfg\\n self.train_im_to_caption: Dict[Any, List[str]] = collections.defaultdict(list)\\n self.val_im_to_caption: Dict[Any, List[str]] = collections.defaultdict(list)\\n self.train_image_caption_pairs: List[Tuple[FilePath, str]] = []\\n self.val_image_caption_pairs: List[Tuple[FilePath, str]] = []\\n self.create_image_caption_pairs()\\n self.preprocess()\\n self.vocab_size = self.tokenizer.num_words\\n\\n logger.info(\\\"Done creating dataset\\\")\",\n \"def __init__(self, config_path='configs/sample_config.ini', one_hot=True):\\n # Set one-hot encoding setting\\n self.ONE_HOT = one_hot\\n\\n # Parse config file\\n config = configparser.ConfigParser()\\n config.read(config_path)\\n\\n # Get (and check) configuration\\n # Get dataset info\\n self._TRAIN_DATA_DIR = config.get('DATASET', 'TRAIN_DATA_DIR')\\n self._TEST_DATA_DIR = config.get('DATASET', 'TEST_DATA_DIR')\\n self._LABEL_FILE = config.get('DATASET', 'LABEL_FILE')\\n\\n # Get image info\\n self._HEIGHT = config.getint('IMAGE', 'HEIGHT')\\n self._WIDTH = config.getint('IMAGE', 'WIDTH')\\n self._GRAYSCALE_IN = config.getboolean('IMAGE', 'GRAYSCALE_IN')\\n self._GRAYSCALE_OUT = config.getboolean('IMAGE', 'GRAYSCALE_OUT')\\n\\n # Get traning and testing spec\\n self._TRAIN_BATCH_SIZE = config.getint('TRAIN_TEST_SPEC',\\n 'TRAIN_BATCH_SIZE')\\n self._TEST_BATCH_SIZE = config.getint('TRAIN_TEST_SPEC',\\n 'TEST_BATCH_SIZE')\\n self._SHUFFLE_BUFFER_SIZE = config.getint('TRAIN_TEST_SPEC',\\n 'SHUFFLE_BUFFER_SIZE')\\n self._PREFETCH_BUFFER_SIZE = config.getint('TRAIN_TEST_SPEC',\\n 'PREFETCH_BUFFER_SIZE')\\n\\n # Get data augmentation spec\\n self._RANDOM_ROTATE = config.getboolean('DATA_AUGMENTATION',\\n 'RANDOM_ROTATE')\\n self._ROTATE_STDDEV = config.getfloat('DATA_AUGMENTATION',\\n 'ROTATE_STDDEV')\\n\\n self._RANDOM_ZOOM = config.getboolean('DATA_AUGMENTATION',\\n 'RANDOM_ZOOM')\\n self._ZOOM_MAX_PERCENT = config.getfloat('DATA_AUGMENTATION',\\n 'ZOOM_MAX_PERCENT')\\n self._ZOOM_STDDEV = config.getfloat('DATA_AUGMENTATION', 'ZOOM_STDDEV')\\n\\n self._RESIZE = config.getboolean('DATA_AUGMENTATION', 'RESIZE')\\n self._RESIZE_HEIGHT = config.getint('DATA_AUGMENTATION',\\n 'RESIZE_HEIGHT')\\n self._RESIZE_WIDTH = config.getint('DATA_AUGMENTATION', 'RESIZE_WIDTH')\\n\\n # Label conversion\\n self._CLASS_NAMES = [line.strip() for line in\\n open(self._LABEL_FILE).readlines()]\\n self._NUM_CLASSES = len(self._CLASS_NAMES) # Number of classes\",\n \"def __init__(self, imageLabel, imageHint, hardwareConstraints=None, pool=None, serverIDWildcard=\\\"\\\"):\\n self.imageLabel = imageLabel\\n self.imageHint = imageHint\\n self.hardwareConstraints = dict(\\n minimumCPUs=1, minimumRAMGB=2,\\n minimumDisk1SizeGB=16, minimumDisk2SizeGB=16)\\n if hardwareConstraints is not None:\\n self.hardwareConstraints.update(hardwareConstraints)\\n self.pool = pool\\n self.serverIDWildcard = serverIDWildcard\",\n \"def __init__(self, footprint, mconf, name):\\n logging.debug(\\\"%s entered __init__\\\" % name)\\n self.image_id = mconf[\\\"image-id\\\"]\\n self.networks = mconf[\\\"networks\\\"]\\n self.ip_addresses = mconf[\\\"ip-addresses\\\"]\\n self.flavor=mconf[\\\"flavor\\\"]\\n self.machine_name = \\\"%s%s\\\" % (footprint.footprint_name, name)\\n self.footprint = footprint\",\n \"def __init__(\\n self,\\n root: str = folder,\\n train_subset: bool = True,\\n suffix: str = '.png',\\n min_num_cls: int = 5,\\n max_num_cls: int = 20,\\n k_shot: int = 20,\\n expand_dim: bool = False,\\n load_images: bool = True\\n ) -> None:\\n self.root = os.path.join(root, 'images_background' if train_subset else 'images_evaluation')\\n self.suffix = suffix\\n self.min_num_cls = min_num_cls\\n self.max_num_cls = max_num_cls\\n self.k_shot = k_shot\\n self.expand_dim = expand_dim\\n self.load_images = load_images\\n\\n # create a nested dictionary to store data\\n self.data = dict.fromkeys(list_dir(root=self.root))\\n for alphabet in self.data:\\n self.data[alphabet] = dict.fromkeys(list_dir(root=os.path.join(self.root, alphabet)))\\n\\n # loop through each alphabet\\n for character in self.data[alphabet]:\\n self.data[alphabet][character] = []\\n\\n # loop through all images in an alphabet character\\n for img_name in list_files(root=os.path.join(self.root, alphabet, character), suffix=suffix):\\n if self.load_images:\\n # load images\\n img = _load_image(img_url=os.path.join(self.root, alphabet, character, img_name), expand_dim=self.expand_dim)\\n else:\\n img = img_name\\n\\n self.data[alphabet][character].append(img)\",\n \"def __init__(self, config, screenshot_func=None):\\n\\n self._config = config\\n self._fov = (self._config.aimbot_train_image_size, self._config.aimbot_train_image_size)\\n self._screenshot_func = screenshot_func\\n # Do not initialize the model here, prevent tensorflow from loading\\n self._model_root_path = os.path.join(self._config.training_env_path, 'aimbot_model')\\n self._model = None\\n\\n self._hook_screenshot = None\\n\\n # Setup image paths\\n self._image_path = os.path.join(self._config.training_env_path, 'aimbot_images')\\n self._curr_image_inc = None\\n\\n self._train_data_tfrecord_path = os.path.join(self._model_root_path, 'train.tfrecord')\\n self._test_data_tfrecord_path = os.path.join(self._model_root_path, 'test.tfrecord')\\n\\n self._train_data_set = None\\n self._test_data_set = None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66241646","0.6582035","0.6469281","0.6385457","0.634032","0.6314989","0.62429136","0.6220419","0.6145161","0.61328727","0.61167324","0.61167324","0.61167324","0.61101764","0.6095435","0.60907704","0.60626227","0.6061309","0.60489887","0.6046789","0.60387987","0.60380125","0.6019408","0.5997857","0.5964925","0.59556377","0.59543175","0.59528095","0.59427613","0.59380376","0.59374756","0.59352756","0.5920931","0.59182876","0.58610284","0.5859589","0.5853912","0.58493465","0.5843845","0.5842604","0.58130896","0.58127433","0.57973623","0.578951","0.5779316","0.5779141","0.5771777","0.5760507","0.57458043","0.57390016","0.57363534","0.572789","0.5725661","0.5711055","0.5710172","0.57078904","0.57047445","0.56994444","0.56977147","0.5686123","0.5680434","0.56785965","0.56778926","0.56692195","0.5665979","0.56586254","0.56531835","0.56524247","0.5641168","0.5637912","0.56293815","0.5625749","0.5623189","0.56222934","0.56219095","0.5613691","0.56033146","0.55995643","0.55987316","0.5598509","0.5594772","0.5593214","0.55871433","0.55857766","0.55817574","0.55744475","0.5574436","0.5573829","0.55681396","0.5565143","0.55632865","0.556087","0.55597717","0.5553554","0.5547061","0.55460465","0.55424494","0.55404437","0.5538452","0.55372036"],"string":"[\n \"0.66241646\",\n \"0.6582035\",\n \"0.6469281\",\n \"0.6385457\",\n \"0.634032\",\n \"0.6314989\",\n \"0.62429136\",\n \"0.6220419\",\n \"0.6145161\",\n \"0.61328727\",\n \"0.61167324\",\n \"0.61167324\",\n \"0.61167324\",\n \"0.61101764\",\n \"0.6095435\",\n \"0.60907704\",\n \"0.60626227\",\n \"0.6061309\",\n \"0.60489887\",\n \"0.6046789\",\n \"0.60387987\",\n \"0.60380125\",\n \"0.6019408\",\n \"0.5997857\",\n \"0.5964925\",\n \"0.59556377\",\n \"0.59543175\",\n \"0.59528095\",\n \"0.59427613\",\n \"0.59380376\",\n \"0.59374756\",\n \"0.59352756\",\n \"0.5920931\",\n \"0.59182876\",\n \"0.58610284\",\n \"0.5859589\",\n \"0.5853912\",\n \"0.58493465\",\n \"0.5843845\",\n \"0.5842604\",\n \"0.58130896\",\n \"0.58127433\",\n \"0.57973623\",\n \"0.578951\",\n \"0.5779316\",\n \"0.5779141\",\n \"0.5771777\",\n \"0.5760507\",\n \"0.57458043\",\n \"0.57390016\",\n \"0.57363534\",\n \"0.572789\",\n \"0.5725661\",\n \"0.5711055\",\n \"0.5710172\",\n \"0.57078904\",\n \"0.57047445\",\n \"0.56994444\",\n \"0.56977147\",\n \"0.5686123\",\n \"0.5680434\",\n \"0.56785965\",\n \"0.56778926\",\n \"0.56692195\",\n \"0.5665979\",\n \"0.56586254\",\n \"0.56531835\",\n \"0.56524247\",\n \"0.5641168\",\n \"0.5637912\",\n \"0.56293815\",\n \"0.5625749\",\n \"0.5623189\",\n \"0.56222934\",\n \"0.56219095\",\n \"0.5613691\",\n \"0.56033146\",\n \"0.55995643\",\n \"0.55987316\",\n \"0.5598509\",\n \"0.5594772\",\n \"0.5593214\",\n \"0.55871433\",\n \"0.55857766\",\n \"0.55817574\",\n \"0.55744475\",\n \"0.5574436\",\n \"0.5573829\",\n \"0.55681396\",\n \"0.5565143\",\n \"0.55632865\",\n \"0.556087\",\n \"0.55597717\",\n \"0.5553554\",\n \"0.5547061\",\n \"0.55460465\",\n \"0.55424494\",\n \"0.55404437\",\n \"0.5538452\",\n \"0.55372036\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7127226"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22388,"cells":{"query":{"kind":"string","value":"Generate overlay images containing text x, y The arguments x and y may be negative, in which case the position will be offset from the right/top edge of the image."},"document":{"kind":"string","value":"def generate_overlay_images(self, overlay_name, x, y):\n for tile in range(1, self.config.number_of_tiles + 1):\n for time in self.config.time_points:\n for depth in self.config.depth_points:\n desc = self.config.tile_description_generator(tile, time, depth)\n file_name = self._generate_overlay_file_name(self.well, overlay_name, desc)\n self._generate_overlay(file_name, overlay_name + '-' + self.well + '-' + desc, x, y)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _overlay_text(\n image_file_name, x_offset_from_left_px, y_offset_from_top_px,\n text_string, font_size, use_north_gravity):\n\n command_string = '\"{0:s}\" \"{1:s}\"'.format(CONVERT_EXE_NAME, image_file_name)\n if use_north_gravity:\n command_string += ' -gravity North'\n\n command_string += (\n ' -pointsize {0:d} -font \"{1:s}\" '\n '-fill \"rgb(0, 0, 0)\" -annotate {2:+d}{3:+d} \"{4:s}\" \"{5:s}\"'\n ).format(\n font_size, TITLE_FONT_NAME,\n x_offset_from_left_px, y_offset_from_top_px, text_string,\n image_file_name\n )\n\n exit_code = os.system(command_string)\n if exit_code == 0:\n return\n\n raise ValueError(imagemagick_utils.ERROR_STRING)","def image_text(self, img: Image, text, x_pos=0, y_pos=0) -> Image.Image:\n # Make blank image for text\n txt = Image.new(\"RGBA\", img.size, (255, 255, 255, 0))\n d = ImageDraw.Draw(txt)\n # offset with black text to fake outline\n d.text(\n (x_pos + 1, y_pos + 1), text, font=self.font, fill=(0, 0, 0, 255)\n )\n d.text((x_pos + 1, y_pos), text, font=self.font, fill=(0, 0, 0, 255))\n d.text((x_pos, y_pos + 1), text, font=self.font, fill=(0, 0, 0, 255))\n # text in white\n d.text((x_pos, y_pos), text, font=self.font, fill=(255, 255, 255, 255))\n out = Image.alpha_composite(img.convert(\"RGBA\"), txt)\n return out","def generateImage(self):\n self.image = self.font.render(self.text, True, self.color)\n self.rect = self.image.get_rect()\n self.rect.center = self.xy","def text_on_img(text=\"Hello\", lbl_w=None, lbl_h=None, font_size=14, filepath=None):\n\n font = ImageFont.truetype(\"lte50712.ttf\", font_size)\n\n if lbl_w is None:\n lbl_w = 150\n if lbl_h is None:\n lbl_h = 150\n\n assert(text)\n\n text = text.upper()\n\n m_width, m_heigth = font.getsize(\"M\")\n char_num_per_line = lbl_w // m_width\n\n image = Image.new(mode=\"RGB\", size=(lbl_w, lbl_h), color=\"white\")\n draw = ImageDraw.Draw(image)\n\n words = text.split()\n if len(words) <= 2 and all(font.getsize(w)[0] < lbl_w for w in words):\n t_wrapper = words\n else:\n t_wrapper = textwrap.wrap(text, char_num_per_line)\n\n offset = (lbl_h - (m_heigth * len(t_wrapper))) // 2\n\n for line in t_wrapper:\n line_w, line_h = font.getsize(line)\n draw.text(((lbl_w - line_w) // 2, offset), line, font=font, fill=(0, 0, 0))\n offset += line_h\n\n if filepath:\n image.save(filepath)\n\n return image","def _draw_x_label(self):\n overlay = self.image.getOverlay()\n TextRoi.setGlobalJustification(TextRoi.CENTER)\n offset = self.image.getHeight() - self.extend_label\n label_pos = self.image.getWidth() / 2\n text = TextRoi(label_pos, offset, 'Energy loss [eV]', self.font)\n text_width = text.getFloatWidth()\n text_y = text.getYBase()\n text.setLocation(label_pos - text_width / 2, text_y)\n text.setStrokeColor(Color(1.00, 1.00, 1.00))\n overlay.add(text)","def _build_texts(self, image):\n offset_generator = self._iter_texts_rects()\n draw = ImageDraw.Draw(image)\n for text, font_name, color, align in self._texts:\n text_x, text_y, max_width, max_height = next(offset_generator)\n if not text: # Empty string: go to next text position\n continue\n # Use PIL to draw text because better support for fonts than OpenCV\n font = fonts.get_pil_font(text, font_name, max_width, max_height)\n _, text_height = font.getsize(text)\n (text_width, _baseline), (offset_x, offset_y) = font.font.getsize(text)\n if align == self.CENTER:\n text_x += (max_width - text_width) // 2\n elif align == self.RIGHT:\n text_x += (max_width - text_width)\n\n draw.text((text_x - offset_x // 2,\n text_y + (max_height - text_height) // 2 - offset_y // 2),\n text, color, font=font)","def addText(img, left_curverad, right_curverad, offset):\n # Average the curvature radius of two lanes\n avg_curverad = (left_curverad + right_curverad)/2\n annotation = 'Radius of curvature: {:5.1f} m Vehicle offset from the center: {:5.1f} m'.format(avg_curverad, offset)\n result = cv2.putText(img, annotation, (30,40), 0, 1, (0,0,0), 2, cv2.LINE_AA)\n\n return result","def draw_text(img, text, pos):\n font = cv2.FONT_HERSHEY_SIMPLEX\n return cv2.putText(img, text, pos, font, 1, (255, 255, 255), 1, cv2.LINE_AA)","def draw_boxes_info(image, current_data):\n\n font_position1 = (50, 600)\n font_position2 = (50, 650)\n font_scale = .4\n font_thickness = 1\n\n locations = current_data[\"locations\"] #returns x1, y1, x2, y2\n frame_num = \"Frame Number: \" + str(current_data[\"frame_num\"])\n\n for box in locations:\n box_text = (\"Box locations are x1: {0}, y1: {1}, x2: {2}, y2: {3}\").format(box[1],box[3],box[0],box[2])\n\n cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 3)\n cv2.putText(image, box_text, font_position1, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\n font_thickness, cv2.LINE_AA)\n\n cv2.putText(image, frame_num, font_position2, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\n font_thickness, cv2.LINE_AA)\n\n return image","def make_images(self, text_on_button):\n text_color = pg.Color('black')\n idle_color = pg.Color('#FCF0C8')\n hover_color = pg.Color('#E84A5F')\n blocked_color = pg.Color('#DCDADA')\n blocked_text_color = pg.Color('#A79E8B')\n # idle_image = self.font.render(text, True, idle_color)\n # blocked_image = self.font.render(text, True, blocked_color)\n # hover_image = self.font.render(text, True, hover_color, hover_fill)\n text = self.font.render(text_on_button, True, text_color)\n blocked_text = self.font.render(\n text_on_button, True, blocked_text_color)\n width = text.get_width() + 30\n height = text.get_height() + 20\n\n image = pg.Surface((width, height)).convert_alpha()\n image.fill((0, 0, 0, 0))\n\n idle_image = image.copy()\n small_rect = pg.rect.Rect(3, 3, width - 6, height - 6)\n pg.draw.ellipse(idle_image, idle_color, small_rect)\n\n blocked_image = image.copy()\n small_rect = pg.rect.Rect(3, 3, width - 6, height - 6)\n pg.draw.ellipse(blocked_image, blocked_color, small_rect)\n\n hover_image = image.copy()\n big_rect = pg.rect.Rect(0, 0, width, height)\n pg.draw.ellipse(hover_image, hover_color, big_rect)\n small_rect = pg.rect.Rect(3, 3, width - 6, height - 6)\n pg.draw.ellipse(hover_image, idle_color, small_rect)\n\n text_rect = pg.rect.Rect(0, 0, text.get_width(), text.get_height())\n text_rect.center = small_rect.center\n idle_image.blit(text, text_rect)\n blocked_image.blit(blocked_text, text_rect)\n hover_image.blit(text, text_rect)\n\n return idle_image, blocked_image, hover_image","def _draw_text_on_image(self, string, position, color = (77, 255, 9)):\n cv2.putText(self.image, string, (position),\n fontFace = self._font_face, fontScale = self._font_scale,\n color = color, thickness = self._font_thickness)","def draw_text(self, text, i, j, **params):","def drawText(filepath, fontsize, text, target_size, color):\n image = Image.open(filepath)\n image = image.resize(target_size)\n draw = ImageDraw.Draw(image)\n # Import the arial font style\n font = ImageFont.truetype(font=os.path.join(\"inpainting\", \"arial.ttf\"), size=fontsize)\n size = font.getsize(text)\n x = randint(size[0] + 16, 255 - 16 - size[0])\n y = randint(size[1] + 16, 255 - 16 - size[1])\n draw.text((x, y), text, fill=color, font=font)\n # Determine the bounds of the text area\n x0 = x + 0.5 * size[0] - 16\n y1 = y + 0.5 * fontsize + 16\n x1 = x + 0.5 * size[0] + 16\n y0 = y + 0.5 * fontsize - 16\n return image, (int(x0), int(y0), int(x1), int(y1))","def text(self, x, y, text):\n for i, char in enumerate(text):\n self.point(x + i, y, char)","def create_image_search(image_name, saved_image, text):\n with Image(filename=image_name) as img:\n with img.clone() as cloned:\n cloned.format = 'png'\n with Drawing() as draw:\n draw.font_family = 'Liberation Mono'\n draw.font_size = 30\n text = SpecialActions.simplifying(text)\n draw.text(140, 785, text)\n draw(cloned)\n cloned.save(filename=saved_image)","def _write(self, pos, txt, align, font, pencolor):\n x, y = pos\n x = x * self.xscale\n y = y * self.yscale\n anchor = {\"left\":\"sw\", \"center\":\"s\", \"right\":\"se\" }\n item = self.cv.create_text(x-1, -y, text = txt, anchor = anchor[align],\n fill = pencolor, font = font)\n x0, y0, x1, y1 = self.cv.bbox(item)\n self.cv.update()\n return item, x1-1","def text(self, text: str, xo: int, yo: int, color: int):\n for offset, letter in enumerate(text):\n template = font.get(letter)\n for x, line in enumerate(template):\n line_str = '{:08b}'.format(line).replace('0b', '')\n if self.portrait:\n line_str = reversed(line_str)\n for y, pix in enumerate(line_str):\n if pix == '1':\n self.pixel(xo + x + (offset * 8), yo + y, color)","def createNew(string):\n image=Image.open('imageGenerator/images/images.jpg').convert('RGBA')\n\n fnt = ImageFont.truetype(\"fonts/arial.ttf\", 25)\n d = ImageDraw.Draw(image)\n\n d.text((10,10), string, font=fnt, fill=(255,255,255,128))\n\n d.text((10,60), \"World\", fill=(255,255,255,255))\n file=byt()\n image.save(file,'jpeg')\n return file.getvalue()","def AddText(img,text, px=0.98,py=0.96, color = 255,s=0.9,lw=1,stroke=1):\n h,w=img.shape\n \n font = cv2.FONT_HERSHEY_SIMPLEX#CV_FONT_HERSHEY_SIMPLEX normal size sans-serif font\n D,sd=cv2.getTextSize(text, font, s, lw)\n wt=int(D[0])\n bcolor=255-color #color of stroke\n bw =lw+1 #stroke thickness\n \n x=int(w*px-wt)\n y=int(h*py)\n if stroke == 1:\n cv2.putText(img,text,(x,y), font, s, bcolor, bw, cv2.LINE_AA) #Stroke \n cv2.putText(img,text,(x,y), font, s, color, lw, cv2.LINE_AA)","def text_draw(self, x, y, text, style={}):","def add_text_to_image(image, text):\n\n font = cv2.FONT_HERSHEY_SIMPLEX\n fontScale = 0.7\n color = (255, 255, 255)\n thickness = 1\n height, width, dim = image.shape\n org = (20, height-50)\n number_of_characters = len(text)\n print(number_of_characters)\n if number_of_characters < 65:\n image = cv2.rectangle(image, (0, height), (0 + width, height-50-1*25 - 25), (0, 0, 0), -1)\n return cv2.putText(image, text, org, font, fontScale, color, thickness, cv2.LINE_AA)\n else:\n number_of_sentences = math.ceil(number_of_characters/65)\n words_split = text.split()\n sentences = np.array_split(words_split, number_of_sentences)\n image = cv2.rectangle(image, (0, height), (0 + width, height-50-len(sentences)*25 - 25), (0, 0, 0), -1)\n\n for i, sentence in enumerate(reversed(sentences)):\n sentence = \" \".join(sentence)\n org = (20, height-50-i*25)\n image = cv2.putText(image, sentence, org, font, fontScale, color, thickness, cv2.LINE_AA)\n return image","def draw_text(self, text: str, x_offset: int = 0,\n font_name: str = \"Apple_II_mod.ttf\", font_pt: int = 8) -> Image.Image:\n if self.rows < 5:\n raise ValueError(\n f\"Unable to scroll text on a board with rows < 5 px. Board Rows: {self.rows} \")\n\n text = str(text)\n\n if not font_name.endswith('.ttf'):\n font_name = f'{font_name}.ttf'\n\n if not os.path.exists(f\"{RESOURCES}/{font_name}\"):\n raise ValueError(\n f\"The font: '{font_name}' could not be found, please try a font in ./resources\")\n\n font = ImageFont.truetype(f\"{RESOURCES}/{font_name}\", font_pt)\n text_width, text_height = font.getsize(text)\n\n # FIXME :\n # For some reason we need to remove 1 px from the image width\n # for it to clear smoothly at the end\n # Instead of messing with the math, a workaround of clearing the panel\n # after the scroll was added\n image = Image.new('P', (text_width + (self.cols * 2), self.rows), 0)\n draw = ImageDraw.Draw(image)\n\n draw.text((self.cols, x_offset), text, font=font, fill=255)\n\n return image","def draw_pos_text(self, text):\n fw, fh = self.font.size(text) # fw: font width, fh: font height\n surface = self.font.render(text, True, (0, 255, 0))\n # // makes integer division in python3\n self.screen.blit(surface, ((self.width - fw) // 2, (self.height - fh) // 2))","def _draw_vertical_text(\n text,\n image_font,\n text_color,\n text_background_color,\n character_spacing=2,\n space_width=1,\n):\n\n space_height = int(image_font.getsize(\" \")[1] * space_width)\n\n char_heights = [\n image_font.getsize(c)[1] if c != \" \" else space_height for c in text\n ]\n text_width = max([image_font.getsize(c)[0] for c in text])\n text_height = sum(char_heights) + character_spacing * len(text)\n\n txt_img = Image.new(\"RGBA\", (text_width, text_height), color=text_background_color)\n txt_mask = Image.new(\"RGBA\", (text_width, text_height), color=text_background_color)\n\n txt_img_draw = ImageDraw.Draw(txt_img)\n txt_mask_draw = ImageDraw.Draw(txt_mask)\n\n for i, c in enumerate(text):\n txt_img_draw.text(\n (0, sum(char_heights[0:i]) + i * character_spacing),\n c,\n fill=text_color,\n font=image_font,\n )\n\n return txt_img.crop(txt_img.getbbox())","def invert(draw, x, y, text, center):\n \n font = ImageFont.load_default()\n draw.rectangle((x, y, x+120, y+10), outline=255, fill=255)\n if (center):\n x=74-4*len(text) - 4*(len(text)%2) \n draw.text((x, y), text, font=font, outline=0,fill=\"black\")","def text(self, str: str, x: int, y: int, colour: int, /) -> None:","def overlayImage(self, filename):\n overlayTime = time.time()\n\n # local refs\n ren = self.ren\n renWinInteract = self.renWinInteract\n renIndex = self.parent.rendererIndex\n\n # to do this we change cam pos to far away and\n # save temp image with just text in\n\n # so move the camera far away\n camera = ren.GetActiveCamera()\n origCamPos = camera.GetPosition()\n newCamPos = [0] * 3\n newCamPos[0] = origCamPos[0] * 100000\n newCamPos[1] = origCamPos[1] * 100000\n newCamPos[2] = origCamPos[2] * 100000\n camera.SetPosition(newCamPos)\n\n # also hide axes if visible\n if self.axes.isEnabled():\n axesHidden = True\n self.toggleAxes()\n else:\n axesHidden = False\n\n try:\n # save image\n overlayFilePrefix = os.path.join(self.mainWindow.tmpDirectory, \"renderer%d_overlay\" % renIndex)\n overlayFile = self.saveImage(\"VTK\", \"jpg\", overlayFilePrefix, False)\n if not os.path.exists(overlayFile):\n print(\"WARNING: overlay file does not exist: %s\" % overlayFile)\n return\n\n try:\n # open POV-Ray image\n povim = Image.open(filename)\n modified = False\n\n # find text in top left corner\n im = Image.open(overlayFile)\n\n # start point\n xmin = ymin = 0\n xmax = int(im.size[0] * 0.5)\n ymax = int(im.size[1] * 0.8)\n\n # find extremes\n xmin, xmax, ymin, ymax = self.findOverlayExtremes(im, xmin, xmax, ymin, ymax)\n\n # crop\n region = im.crop((xmin, ymin, xmax + 2, ymax + 2))\n\n # add to povray image\n if region.size[0] != 0:\n region = region.resize((region.size[0], region.size[1]), Image.ANTIALIAS)\n povim.paste(region, (0, 0))\n modified = True\n\n # now look for anything at the bottom => scalar bar\n im = Image.open(overlayFile)\n\n # start point\n xmin = 0\n ymin = im.size[1] - 80\n xmax = im.size[0]\n ymax = im.size[1]\n\n # find extremes\n xmin, xmax, ymin, ymax = self.findOverlayExtremes(im, xmin, xmax, ymin, ymax)\n\n # crop\n region = im.crop((xmin, ymin, xmax, ymax))\n\n # add?\n if region.size[0] != 0:\n newregiondimx = int(povim.size[0] * 0.8)\n dx = (float(povim.size[0]) * 0.8 - float(region.size[0])) / float(region.size[0])\n newregiondimy = region.size[1] + int(region.size[1] * dx)\n region = region.resize((newregiondimx, newregiondimy), Image.ANTIALIAS)\n\n xpos = int((povim.size[0] - region.size[0]) / 2.0)\n povim.paste(region, (xpos, int(povim.size[1] - region.size[1])))\n\n modified = True\n\n # now look for text in top right corner\n im = Image.open(overlayFile)\n\n # start point\n xmin = int(im.size[0] * 0.5)\n ymin = 0\n xmax = im.size[0]\n ymax = int(im.size[1] * 0.6)\n\n # find extremes\n xmin, xmax, ymin, ymax = self.findOverlayExtremes(im, xmin, xmax, ymin, ymax)\n\n # crop\n region = im.crop((xmin - 2, ymin, xmax, ymax + 2))\n\n if region.size[0] != 0:\n region = region.resize((region.size[0], region.size[1]), Image.ANTIALIAS)\n xpos = povim.size[0] - 220\n povim.paste(region, (xpos, 0))\n\n modified = True\n\n # save image\n if modified:\n povim.save(filename)\n\n finally:\n os.unlink(overlayFile)\n\n finally:\n # return to original cam pos\n camera.SetPosition(origCamPos)\n renWinInteract.ReInitialize()\n\n # show axes again\n if axesHidden:\n self.toggleAxes()\n\n overlayTime = time.time() - overlayTime\n self.logger.debug(\"Overlay time: %f s\", overlayTime)","def overlay_boxes(self, image, predictions):\n for prediction in predictions:\n template = ''\n for idx in range(min(3, len(prediction['score']))):\n template += \"{}:{:.2f},\".format(prediction['category'][idx], prediction['score'][idx])\n color = [0, 0, 255]\n top_left, bottom_right = prediction['bbox'][:2], prediction['bbox'][2:]\n image = cv2.rectangle(\n image, tuple(top_left), tuple(bottom_right), tuple(color), 1\n )\n cv2.putText(\n image, template, (top_left[0], top_left[1]+15), cv2.FONT_HERSHEY_SIMPLEX, .6, tuple(color), 1\n )\n\n return image","def _set_text_pos(xv, yv, xlims, ylims, ax, offsetx=0.08, offsety=0.07):\n\n # check log or linear scale\n xlim = np.log10(xlims) if ax.get_xscale() == \"log\" else xlims\n ylim = np.log10(ylims) if ax.get_yscale() == \"log\" else ylims\n x = np.log10(xv) if ax.get_xscale() == \"log\" else xv\n y = np.log10(yv) if ax.get_yscale() == \"log\" else yv\n\n # get mid-points\n xmid = np.mean(xlim)\n ymid = np.mean(ylim)\n\n # get extent of axes\n xextent = xlim[1] - xlim[0]\n yextent = ylim[1] - ylim[0]\n\n # set test positions for each quadrant\n if x <= xmid and y <= ymid:\n # lower left\n textx = x - xextent * offsetx\n texty = y - yextent * offsety\n elif x <= xmid and y > ymid:\n # upper left\n textx = x + xextent * offsetx\n texty = y + yextent * offsety\n elif x > xmid and y > ymid:\n # upper right\n textx = x - xextent * offsetx\n texty = y + yextent * offsety\n else:\n # lower right\n textx = x + xextent * offsetx\n texty = y - yextent * offsety\n\n if ax.get_xscale() == \"log\":\n textx = 10**textx\n if ax.get_yscale() == \"log\":\n texty = 10**texty\n\n return textx, texty","def __init__(self, text='', x=0, y=0):\n \n #Some defaults\n self.text = text\n self.text_color = (255, 255, 255)\n self.text_size = 36\n self.x = x\n self.y = y\n self.font = pygame.font.Font(None, self.text_size)\n self.text_img = self.font.render(self.text, True, self.text_color)\n self.text_rect = self.text_img.get_rect()\n self.text_rect.left = self.x\n self.text_rect.top = self.y","def add_watermark(image, text, font=watermark_font) -> Image:\n rgba_image = image.convert('RGBA')\n text_overlay = Image.new('RGBA', rgba_image.size, (255, 255, 255, 0))\n image_draw = ImageDraw.Draw(text_overlay)\n\n text_size_x, text_size_y = image_draw.textsize(text, font=font)\n\n # Set text position.\n text_xy = (rgba_image.size[0] - text_size_x, rgba_image.size[1] - text_size_y)\n\n # Set text color and transparency.\n image_draw.text(text_xy, text, font=font, fill=(128, 128, 128, 180))\n image_with_watermark = Image.alpha_composite(rgba_image, text_overlay)\n return image_with_watermark","def make_images(self, text, width, height):\n idle_color = pg.Color('black')\n hover_color = pg.Color('white')\n hover_fill = pg.Color('purple')\n locked_color = pg.Color('white')\n locked_fill = pg.Color('red')\n if width is not None and height is not None:\n idle_image = pg.Surface((width, height)).convert()\n idle_image.set_alpha(0)\n idle_image = idle_image.convert_alpha()\n idle_text = self.font.render(text, True, idle_color)\n idle_image.blit(idle_text, idle_text.get_rect(\n center=idle_image.get_rect().center))\n hover_image = pg.Surface((width, height)).convert()\n hover_image.fill(hover_fill)\n hover_text = self.font.render(text, True, hover_color, hover_fill)\n hover_image.blit(hover_text, hover_text.get_rect(\n center=hover_image.get_rect().center))\n locked_image = pg.Surface((width, height)).convert()\n locked_image.fill(locked_fill)\n locked_text = self.font.render(\n text, True, locked_color, locked_fill)\n locked_image.blit(locked_text, locked_text.get_rect(\n center=locked_image.get_rect().center))\n else:\n idle_image = self.font.render(text, True, idle_color)\n hover_image = self.font.render(text, True, hover_color, hover_fill)\n locked_image = self.font.render(\n text, True, locked_color, locked_fill)\n\n self.idle_image = idle_image\n self.hover_image = hover_image\n self.locked_image = locked_image","def draw_box_and_text(img, text, box, color):\n cv2.putText(\n img, text,\n (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX,\n 0.5, color, 2)\n # cv2.rectangle(\n # img,\n # (box[1], box[0]),\n # (box[3], box[2]),\n # color, 2)\n cv2.rectangle(\n img,\n (box[0], box[1]),\n (box[2], box[3]),\n color, 2)","def draw_bounding_box( y1, x1, y2, x2, \n img, \n thickness=4, \n fontsize=15,\n outlineColor=(255, 255, 0),\n textColor=(255, 255, 0),\n display_str=() ):\n\n img = Image.fromarray( img )\n draw = ImageDraw.Draw( img )\n\n for x in range( 0, thickness ):\n draw.rectangle( [(x1-x, y1-x), (x2-x, y2-x)], outline=outlineColor )\n\n for i in range(1, len(fonts)):\n if fonts[i].fontformat == 'TrueType':\n for j in range(1,len(fonts[i].style)):\n if fonts[i].style[j][1] == 'Regular':\n absolute_path = fonts[i].file\n break;\n try:\n absolute_path\n except NameError:\n font = ImageFont.load_default()\n else:\n font = ImageFont.truetype(absolute_path, fontsize)\n\n draw.text( (x1, y1), display_str, font=font, fill=textColor )\n\n return numpy.array( img )","def _render_static_image_annotation(self):\n cv2.rectangle(self._image,\n (0,0), (640, 40),\n (0, 0, 0),\n -1)\n \n cv2.putText(self._image,self._current_mode, (40, 25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)\n\n cv2.putText(self._image, time.asctime(), (400, 460),\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)","def draw_text_lb_corner(dc, text, pos_x, pos_y, size_multiplier=1):\r\n tw, th = dc.GetTextExtent(text, size_multiplier)\r\n dc.DrawText(text, (pos_x-tw), (pos_y-th), size_multiplier=size_multiplier)","def draw_text(self, text, font, color, surface, x, y): #use for narrative in end sequence\n text_obj = font.render(text, True, color)\n text_rect = text_obj.get_rect()\n text_rect.center = (x, y)\n surface.blit(text_obj, text_rect)","def draw_text(SCREEN, text, x, y):\n text = constants.CALIBRI_25.render(text, True, constants.BLACK)\n SCREEN.blit(text, (x, y))","def add_text(text, x, y, rgb = [0, 0, 0], font_name = 'times new roman', size = 32, bold = False, italics = False, merged = False, slot = 0):\r\n \r\n font = pygame.font.SysFont(font_name, size, bold, italics)\r\n text = font.render(text, 1, rgb)\r\n \r\n if slot not in __g.keys():\r\n raise IndexError('No image loaded at slot %i' %(slot))\r\n \r\n surface = __g.values()[slot]\r\n if merged:\r\n surface.image.blit(text, (x, y))\r\n else:\r\n surface.text_add.append([x, y, text])","def encode_image(text_to_encode, template_image=\"images/samoyed.jpg\", output_image=\"images/samoyed.secret.png\"):\n\n image = Image.open(template_image)\n pixels = image.load()\n\n x_size = image.size[0]\n y_size = image.size[1]\n\n for x in range(x_size):\n for y in range(y_size):\n if lsb_of_red_pixel(image, x, y):\n pixels[x,y] = (image.getpixel((x, y))[0] - 1, image.getpixel((x, y))[1], image.getpixel((x, y))[2])\n\n text_image = Image.new(\"RGB\", image.size)\n\n usr_font = ImageFont.truetype(\"ComicNeue.otf\", 25)\n d_usr = ImageDraw.Draw(text_image)\n d_usr = d_usr.text((10,10), text_to_encode, (255,255,255), font=usr_font)\n\n for x in range(x_size):\n for y in range(y_size):\n if lsb_of_red_pixel(text_image, x, y):\n pixels[x,y] = (image.getpixel((x, y))[0] + 1, image.getpixel((x, y))[1], image.getpixel((x, y))[2])\n\n image.save(output_image)","def show(self, cv_img, text, X=0, Y=0):\n w, h, pads = self.get_textSize(text)\n x = X + pads\n y = Y + h + pads*2\n cv2.putText( cv_img, text, (x, y),\n self.fontFace, self.fontScale,\n self.fontColor, self.fontThickness, 255)","def pil_text(text, w_border=TILE_TEXT_W_BORDER, h_border=TILE_TEXT_H_BORDER,\n font_size=TILE_TEXT_SIZE, text_color=TILE_TEXT_COLOR, background=TILE_TEXT_BACKGROUND_COLOR):\n\n x, y = ImageDraw.Draw(Image.new(\"RGB\", (1, 1), background)).textsize(text)\n image = Image.new(\"RGB\", (x + 2 * w_border, y + 2 * h_border), background)\n draw = ImageDraw.Draw(image)\n draw.text((w_border, h_border), text, text_color)\n return image","def pos_image(image, x,y):\n image.anchor_x = x\n image.anchor_y = y","def add_text(self, x, y, text):\n elem = TextElement()\n elem.text = str(text)\n elem.style = {\n 'font-size': self.svg.unittouu('10pt'),\n 'fill-opacity': '1.0',\n 'stroke': 'none',\n 'font-weight': 'normal',\n 'font-style': 'normal' }\n elem.update(x=str(x), y=str(y))\n return elem","def tagviews(tab,text,x,y):\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n viseg=cv2.putText(tab,text,(x, y), font,0.3,white,1)\r\n return viseg","def titled_overlay(image, overlay, title, org, border_thickness=2):\n # Place title\n title_img = np.ones((50, overlay.shape[1], 3)).astype('uint8') * 255\n cv2.putText(title_img, title, org=(10, 35), fontScale=1, thickness=2, color=(0, 0, 0),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX, lineType=cv2.LINE_AA)\n\n # Add title to overlay\n overlay = np.concatenate((title_img, overlay), axis=0)\n\n # Add border to overlay\n overlay[:border_thickness, :, :] = 255\n overlay[-border_thickness:, :, :] = 255\n overlay[:, :border_thickness, :] = 255\n overlay[:, -border_thickness:, :] = 255\n\n # Place overlay onto image\n x_offset, y_offset = org\n image[y_offset:y_offset + overlay.shape[0], x_offset:x_offset + overlay.shape[1]] = overlay\n\n # Add a white border","def draw_text(self, text, position=(0, 0), color='black', font=None,\n font_size=12, rotation=0, **kwargs):\n font = self.font(font_size)\n\n text_image = Image.new('L', self.dimensions, 'black')\n draw_text_image = ImageDraw.Draw(text_image)\n draw_text_image.text(position, text, font=font, fill='white')\n\n alpha = Image.new('L', self.dimensions)\n alpha = ImageChops.lighter(alpha, text_image)\n\n solidcolor = Image.new('RGBA', self.dimensions, color)\n image_mask = Image.eval(text_image, lambda p: 255 * (int(p != 0)))\n self.base_image = Image.composite(solidcolor, self.base_image, image_mask)\n self.base_image.putalpha(alpha)","def add_image_text(image: imageType, text: str, underline: bool = False) -> imageType:\n\n text_image: imageType = cv2.cvtColor(image.copy(), cv2.COLOR_GRAY2RGB)\n h, w = text_image.shape[:2]\n text_font = cv2.FONT_HERSHEY_SIMPLEX\n text_origin_pos = (int(w / 10), int(9 * h / 10)) # from topleft\n text_scale = 0.4\n text_color = (154, 205, 50)\n text_line_thick = 1\n cv2.putText(text_image, text, text_origin_pos, text_font, text_scale, text_color, text_line_thick, lineType=cv2.LINE_AA)\n\n if underline:\n under_start_offset = (-5, 10) # -x to the left, y underneath\n line_length = 155\n under_start_pos = tuple(x + y for x, y in zip(text_origin_pos, under_start_offset))\n under_finish_pos = tuple(x + y for x, y in zip(under_start_pos, (line_length, 0)))\n under_color = (50, 100, 250)\n under_line_thick = 2\n cv2.line(text_image, under_start_pos, under_finish_pos, under_color, under_line_thick)\n\n return text_image","def AddText(img, new_dimensions, text):\n font_file = DownloadFont()\n with Drawing() as draw:\n font_size = new_dimensions[1] * .045\n xpos = round(new_dimensions[0] / 2)\n ypos = round(new_dimensions[1] - 1.5 * font_size)\n outline = font_size / 10\n draw.font = font_file\n draw.font_size = font_size\n draw.text_alignment = 'center'\n draw.text_antialias = True\n invis = draw.stroke_color\n draw.stroke_color = Color('#000000')\n draw.fill_color = Color('#ffff00')\n draw.stroke_width = outline\n draw.text(xpos, ypos, text)\n draw.stroke_color = invis\n draw.text(xpos, ypos, text)\n draw(img)\n os.remove(font_file)","def drawRectBox(image, rect, addText):\n fontC = ImageFont.truetype(\"./Font/platech.ttf\", 14, 0)\n\n cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0, 0, 255), 2,\n cv2.LINE_AA)\n cv2.rectangle(image, (int(rect[0] - 1), int(rect[1]) - 16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,\n cv2.LINE_AA)\n img = Image.fromarray(image)\n draw = ImageDraw.Draw(img)\n draw.text((int(rect[0] + 1), int(rect[1] - 16)), addText, (255, 255, 255), font=fontC)\n imagex = np.array(img)\n return imagex","def annotate(axis, text, x, y):\n text_annotation = Annotation(text, xy=(x, y), xycoords='data')\n axis.add_artist(text_annotation)","def put_text(img: np.ndarray,\n bbox: Tuple[float, float, float, float],\n person: Person,\n all_frames_cnt: int) -> np.ndarray:\n x, y, w, h = bbox\n\n pivot_img_size = 112\n resolution_x = img.shape[1];\n\n overlay = img.copy()\n opacity = 0.4\n\n img = cv2.rectangle(img, (x, y), (x + w, y + h), (67, 67, 67), 1)\n if x + w + pivot_img_size < resolution_x:\n # right\n cv2.rectangle(img, (x + w, y), (x + w + pivot_img_size + 20, y + h), (64, 64, 64), cv2.FILLED)\n cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)\n\n elif x - pivot_img_size > 0:\n # left\n cv2.rectangle(img, (x - pivot_img_size - 20, y), (x, y + h), (64, 64, 64), cv2.FILLED)\n cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)\n\n gender_label = person.gender\n race_label = person.race\n age_label = person.age\n num_frames = person.percent_in_video(all_frames_cnt)\n\n attributes = [gender_label, race_label, age_label, num_frames]\n\n for index, attribute in enumerate(attributes):\n if x + w + pivot_img_size < resolution_x:\n\n text_location_y = y + 20 + (index + 1) * 20\n text_location_x = x + w\n\n if text_location_y < y + h:\n cv2.putText(img, attribute, (text_location_x + 10, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n (255, 255, 255), 1)\n\n elif x - pivot_img_size > 0:\n\n text_location_y = y + 20 + (index + 1) * 20\n text_location_x = x - pivot_img_size\n\n if text_location_y <= y + h:\n cv2.putText(img, attribute, (text_location_x + 10, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n (255, 255, 255), 1)\n\n return img","def draw_text(self, text, origin, font=cv2.FONT_HERSHEY_SIMPLEX, text_scale=0.7, text_color=(255,0,0), thickness=2):\r\n cv2.putText(self.image, text, origin, font, text_scale, text_color, thickness)","def yolo_show_img(image, class_ids, boxes, labels, confidences, colors):\n for i, box in enumerate(boxes):\n # extract the bounding box coordinates\n (x, y) = (box[0], box[1])\n (w, h) = (box[2], box[3])\n\n # draw a bounding box rectangle and label on the image\n color = [int(c) for c in colors[class_ids[i]]]\n cv2.rectangle(image, (x, y), (x + w, y + h), color, 3)\n text = '{}: {:.4f}'.format(labels[i], confidences[i])\n print(text)\n\n font_scale = 1.3\n # set the rectangle background to white\n rectangle_bgr = color\n # set some text\n # get the width and height of the text box\n (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=1)[0]\n # set the text start position\n text_offset_x = x\n text_offset_y = y - 3 \n # make the coords of the box with a small padding of two pixels\n box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 10, text_offset_y - text_height - 10 ))\n cv2.rectangle(image, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)\n cv2.putText(image, text, (text_offset_x, text_offset_y), cv2.FONT_HERSHEY_SIMPLEX, \n fontScale=font_scale, color=(255, 255, 255), thickness=2)\n\n cv2.imshow('yolo prediction', image)\n cv2.waitKey(0)","def draw(font_string,font_size,lang,alphabets,outdir=\".\"): # language, font file name, font full path, font size, characters\n \n \n image_dir=lang+\".\"+\"images\"\n if(os.path.exists(image_dir)):\n pass\n else:\n os.mkdir(image_dir)\n \n #Using a font\n #font= ImageFont.truetype(font,fsz)\n boxfile=image_dir+\"/\"+\"bigimage.box\"\n f=open(boxfile,\"w\")\n wt = 4000\n ht = 4000 #modified later using a separate script\n\t\n bigimage=Image.new(\"L\",(wt,ht),255)\t#change here for inverting\n bigdraw=ImageDraw.Draw(bigimage)\n x=y=10\n count=0\n for akshar in alphabets:\n akshar.strip() #remove nasty characters\n \n #I shall now create an image with black bgc and white font color. One\n #getbbox() determines the bounding box values I shall invert the image.\n #This has to be done since getbbox() only finds bounding box values for\n #non-zero pixels (read as white), but tesseract-ocr runs on the exact\n #opposite bgc fgc combination. Contact debayanin@gmail.com.\n \n \n #The lines below are pango/cairo code \n surface = cairo.ImageSurface(cairo.FORMAT_A8, font_size*4, font_size*3)\n context = cairo.Context(surface)\n\n pc = pangocairo.CairoContext(context)\n\n layout = pc.create_layout()\n layout.set_font_description(pango.FontDescription(font_string))\n layout.set_text(akshar)\n print akshar\n\n # lines take care of centering the text.\n width, height = surface.get_width(), surface.get_height()\n w, h = layout.get_pixel_size()\n position = (10,10) #most likely this part messes up when you try to change the size within this script. It is suggested to use the separate script.\n context.move_to(*position)\n pc.show_layout(layout)\n surface.write_to_png(\"pango.png\")\n\t\n #Here we open the generated image using PIL functions\n temp_image=Image.open(\"pango.png\") #black background, white text\n draw = ImageDraw.Draw(temp_image)\n bbox = temp_image.getbbox()\n deltax=bbox[2]-bbox[0]\n deltay=bbox[3]-bbox[1]\n\n \n print bbox\n new_image=temp_image.crop(bbox)\n temp_image=temp_image.load()\n inverted_image = ImageChops.invert(new_image) #White background, black text\n\t\n\tinverted_image.save(image_dir+\"/\"+str(count)+\".png\")\n\tbigimage.paste(inverted_image,(x,y))\n\tos.unlink(image_dir+\"/\"+str(count)+\".png\")\n\tcount = count+1\n\t#bigimage.load()\n bigbox=(x,y,x+deltax,y+deltay)\n print bigbox\n draw=ImageDraw.Draw(bigimage)\n\t#draw.rectangle(bigbox,None,100)\n x=bigbox[2]+5\n if x>(wt-10):\n x=10; y=y+40\n\n os.unlink(\"pango.png\") #delete the pango generated png\n\n line=akshar+\" \"+str(bigbox[0]-1)+\" \"+str(ht-(bigbox[1]+deltay)-1)+\" \"+str(bigbox[2]+1)+\" \"+str(ht-(bigbox[3]-deltay)+1) # this is the line to be added to the box file\n\tf.write(line+'\\n')\n\n\t#degrade code starts\n\tstrip=[deltax*.2,deltax*.4,deltax*.7]\n\tfor values in range(0,2):\n\t\tdistort2=inverted_image\n\t\tfor wai in range(0,deltay):\n\t\t\tfor ex in range(strip[values],strip[values]+1):\n\t\t\t\tdistort2.putpixel((ex,wai),255)\n\t\tbigbox=(x,y,x+deltax,y+deltay)\n\t\t#draw.rectangle(bigbox,None,10)\n\t\tline=akshar+\" \"+str(bigbox[0]-1)+\" \"+str(ht-(bigbox[1]+deltay)-1)+\" \"+str(bigbox[2]+1)+\" \"+str(ht-(bigbox[3]-deltay)+1) # this is the line to be added to the box file\n \tf.write(line+'\\n')\n\t\tbigimage.paste(distort2,(x,y))\n\t\tx=bigbox[2]+5\n \tif x>(wt-10):\n \t\tx=10; y=y+40\n\t\t\n\t\t\t\n\t#degrade code ends\n \n #distort.distort(filename2,bbox,fsz,akshar)\n \n \n \n #bigimage.save(image_dir+\"/\"+\"bigimage.tif\",\"TIFF\") #useful to generate merged file for all images when using default sizes.\n f.close()\n train.train(lang,outdir)","def draw_text(self, display, text, size, x, y , mode):\n font = pygame.font.Font(self.font_name, size)\n text_surface = font.render(text, True, (0,0,0))\n text_rect = text_surface.get_rect()\n if mode == \"left\":\n text_rect.topleft = (x,y)\n elif mode == \"center\":\n text_rect.center = (x,y)\n display.blit(text_surface, text_rect)","def update(self):\n rect = self.img_rect.center\n self.font_img = self.font.render(self.text, True, color1)\n self.img_rect = self.font_img.get_rect()\n # self.img_rect.center = [self.title_img.get_width() + self.font_img.get_width()//2 + padding, 5*Y//8]\n self.img_rect.center = rect","def plot_extent_text(data_obj, test_num, yi, xpos, ha, va, fs, dfs):\n\n plt.text(xpos[0], yi, 'Test ' + str(test_num) + ': Image extent', ha=ha, va=va, fontsize=fs)\n if data_obj.image_extent == None:\n str1 = 'None'\n else:\n str1 = \"{0:.0f}\".format(data_obj.image_extent) + ' mm' + '$^\\clubsuit$'\n\n plt.text(xpos[1], yi, str1, ha=ha, va=va, fontsize=fs - dfs)\n plt.text(xpos[3], yi, '$\\leq$ 10 mm', ha=ha, va=va, fontsize=fs - dfs)","def draw_on_image(D):\n \n #Set up rectangle's position within window\n lower_left_x = 20 \n lower_left_y = 42\n dx = 5\n dy = 5\n\n #Display border for rectangle\n #Border is a black rectangle under white text rectangle\n bord_upper_left = (lower_left_x-dx-3, lower_left_y-dy-20-3)\n bord_lower_right = (lower_left_x+dx+160+3, lower_left_y+dy+50+3)\n cv.Rectangle(D.image, bord_upper_left, bord_lower_right, D.black, cv.CV_FILLED)\n \n #Display white rectangle under text\n rect_upper_left = (lower_left_x-dx, lower_left_y-dy-20)\n rect_lower_right = (lower_left_x+dx+160, lower_left_y+dy+50)\n cv.Rectangle(D.image, rect_upper_left, rect_lower_right, D.white, cv.CV_FILLED)\n \n ####\n hive = \"hi!\"\n hiveStat = \"hive\"\n \n #Build Strings\n robotAssignment = (\"Assignment #: %.lf\"%R.assignment)\n robotConverged = (\"Converged: %s\"%R.converged)\n robotStatus = (\"Robot Status: \" + R.status)\n hiveCommand = (\"Hive Command: \" + hive)\n hiveStatus = (\"Hive Status: \" + hiveStat)\n \n # Position strings in a box so they won't overlap\n firstLineString = (lower_left_x,lower_left_y) \n secondLineString = (lower_left_x, lower_left_y + 20) \n thirdLineString = (lower_left_x, lower_left_y + 40)\n fourthLineString = (lower_left_x, lower_left_y + 60)\n fifthLineString = (lower_left_x, lower_left_y + 80)\n\n #Display strings in window\n cv.PutText(D.image, robotAssignment, firstLineString, D.font, cv.RGB(0,0,255)) \n cv.PutText(D.image, robotConverged, secondLineString, D.font, cv.RGB(0,0,255))\n cv.PutText(D.image, robotStatus, thirdLineString, D.font, cv.RGB(0,0,255))\n cv.PutText(D.image, hiveCommand, fourthLineString, D.font, cv.RGB(0,0,255))\n cv.PutText(D.image, hiveStatus, fifthLineString, D.font, cv.RGB(0,0,255))","def add_annotations(self):\n for i in range(8):\n self.text.append(self.canvas.create_text(-self.width / 2,\n (self.width / 2) + (i * self.width),\n font=(\"Purisa\", 12), anchor=\"nw\"))\n self.canvas.itemconfig(self.text[i], text=str((i - 8) * -1))\n for i in range(8):\n self.text.append(self.canvas.create_text((self.width / 2) + (i * self.width),\n self.width * 8 + 10, font=(\"Purisa\", 12), anchor=\"nw\"))\n self.canvas.itemconfig(self.text[i + 8], text=string.ascii_lowercase[i])","def waterMarkImages(imageFile,\n imageOutputPath,\n text,\n fontName=\"Bangers\",\n size=\"M\",\n opacity=255,\n position=\"BR\"):\n # TODO : Try to have color as an argument as well\n fileName = imageFile.split(os.path.sep)[-1]\n try:\n photo = Image.open(imageFile).convert(\"RGBA\")\n except IOError:\n raise WatermarkException(4, val=imageFile)\n\n width, height = photo.size\n\n COLOR = (255, 255, 255)\n\n textImage = Image.new('RGBA', (width, height), COLOR + (0, ))\n\n fontsize = _setSize(width, height, size)\n\n drawing = ImageDraw.Draw(textImage)\n\n if fontName in FONT_MAP:\n # * fontPath = \"PyWaterMark\\\\fonts\\\\\" + FONT_MAP[fontName] - ONLY DEVS\n fontPath = pkg_resources.resource_filename(\n 'PyWatermark', 'fonts/' + FONT_MAP[fontName])\n if not os.path.isfile(fontPath):\n raise WatermarkException(2, val=fontName)\n else:\n if not os.path.isfile(fontName):\n raise WatermarkException(3, val=fontName)\n\n font = ImageFont.truetype(fontPath, fontsize)\n\n textWidth, textHeight = drawing.textsize(text, font)\n\n pos = _setPosition(width, height, textWidth, textHeight, position)\n\n drawing.text(pos, text, fill=COLOR + (opacity, ), font=font)\n\n combined = Image.alpha_composite(photo, textImage)\n\n filenameSave = fileName.split('.')\n filenameSave[1] = 'wm.png'\n filenameSave = ''.join(filenameSave)\n # * Why you can't store JPEG images with transparency\n # * https://stackoverflow.com/questions/41413956/pil-unable-to-change-the-transparency-level-for-jpeg-image\n combined.save(imageOutputPath + '\\\\' + filenameSave)\n\n return filenameSave","def add_text(self, x, y, text, style=None):\n style = self.__prepare_style(style, ' ')\n for i, c in enumerate(text):\n if self.check_coord_in_range(x + i, y):\n text_style = Style(c, style.fg_color, style.bg_color, style.font_style)\n self.canvas[y][x + i] = text_style","def insert_line(self, text, x, y, color=defcolor):\n for i in range(len(text)):\n x2 = x + (i * (self.font['width'] + 1))\n if text[i] in self.font:\n self.insert_bitmap(self.font[text[i]], x2, y, color)\n else:\n self.insert_bitmap(self.font['?'], x2, y, color)","def draw_single_bbox(self, image, x, color=None, label=None, line_thickness=2):\n\n # get pixel locations of top left and bottom right corners\n corner1 = (int(x[0] * self.x), int(x[1] * self.y))\n corner2 = (int(x[2] * self.x), int(x[3] * self.y))\n\n cv2.rectangle(image, corner1, corner2, color, thickness=line_thickness, lineType=cv2.LINE_AA)\n\n if label:\n font_thickness = max(line_thickness - 1, 1)\n t_size = cv2.getTextSize(label, 0, fontScale= line_thickness / 3, thickness=font_thickness)[0]\n corner2 = corner1[0] + t_size[0], corner1[1] - t_size[1] - 3\n\n cv2.rectangle(image, corner1, corner2, color, -1, cv2.LINE_AA) # filled\n\n cv2.putText(image, \n label, \n (corner1[0], corner1[1] - 2), 0, \n line_thickness / 3, \n [225, 255, 255], \n thickness=font_thickness, \n lineType=cv2.LINE_AA)\n\n return image","def create_overlay():\n c = canvas.Canvas('simple_form_overlay.pdf')\n\n c.drawString(115, 650, 'Mike')\n c.drawString(115, 600, 'Driscoll')\n c.drawString(115, 550, '123 Greenway Road')\n c.drawString(115, 500, 'Everytown')\n c.drawString(355, 500, 'IA')\n c.drawString(115, 450, '55555')\n\n c.save()","def drawText(text, font, surface, x, y, textcolour):\r\n textobj = font.render(text, 1, textcolour)\r\n textrect = textobj.get_rect()\r\n textrect.topleft = (x, y)\r\n surface.blit(textobj, textrect)","def display_text(text, x, y, size):\r\n font = pygame.font.Font('freesansbold.ttf', size)\r\n text_surf, text_rect = text_objects(text, font)\r\n text_rect.center = (x, y)\r\n display.blit(text_surf, text_rect)","def tagview(tab,label,x,y):\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n col=classifc[label]\r\n labnow=classif[label]\r\n# print (labnow, text)\r\n if label == 'back_ground':\r\n deltay=30\r\n else:\r\n# deltay=25*((labnow-1)%5)\r\n deltay=40+10*(labnow-1)\r\n\r\n viseg=cv2.putText(tab,label,(x, y+deltay), font,0.3,col,1)\r\n return viseg","def watermark_text(self, text, color, font_type, font_size, pos_name):\n image = Image.open(self.path)\n drawing = ImageDraw.Draw(image)\n text = text\n font = ImageFont.truetype(font_type, font_size)\n self.watermark_width, self.watermark_height = drawing.textsize(text, font)\n pos = self.watermark_position(pos_name)\n\n drawing.text(pos, text, fill=color, font=font)\n parent_dir = os.path.dirname(self.output_path)\n\n if not os.path.exists(parent_dir):\n os.makedirs(parent_dir)\n\n image.save(self.output_path)\n return os.path.exists(self.output_path)","def __init__(self,text,position,xmid = False,fontsize = 36,backgroundcolor = (200,200,200),surface = None):\n pygame.font.init()\n basicfont = pygame.font.Font(None,fontsize)\n\n # Figure out the size of the image that will be drawn on and create that\n # image\n self.linewidths = []\n for x in text:\n self.texttemp = basicfont.render(x,0,(1,1,1))\n self.linewidths.append(self.texttemp.get_width())\n self.imagewidth = basicfont.render(text[self.linewidths.index(max(self.linewidths))],0,(1,1,1)).get_width()\n self.imageheight = len(text) * fontsize + (len(text)-1) * 10\n self.image = pygame.Surface((self.imagewidth,self.imageheight))\n self.image.fill(backgroundcolor)\n\n # Draw the text to the image\n n = 0\n for x in text:\n self.texttemp = basicfont.render(x,0,(1,1,1))\n self.image.blit(self.texttemp,(0,n * fontsize + n * 10))\n n +=1\n\n # Set the position of the text. If xmid is passed in as true set the\n # pos to the top middle pixel of the text\n if xmid:\n self.pos = (position[0] - int(self.image.get_width() / 2),position[1])\n else:\n self.pos = position\n\n # Set up the information that will be needed to blit the image to a\n # surface\n self.blitinfo = (self.image, self.pos)\n\n # automatically blit the text onto an input surface\n if surface:\n surface.blit(*self.blitinfo)","def plot_area_text(data_obj, test_num, yi, xpos, ha, va, fs, dfs):\n\n plt.text(xpos[0], yi, 'Test ' + str(test_num) + ': Image area', ha=ha, va=va, fontsize=fs)\n str1 = str(data_obj.image_area[0]) + ' by ' + str(data_obj.image_area[1]) + ' pixels'\n plt.text(xpos[1], yi, str1, ha=ha, va=va, fontsize=fs - dfs)\n plt.text(xpos[3], yi, '$\\geq$ 1000 by 1000 pixels', ha=ha, va=va, fontsize=fs - dfs)","def write_text(text_to_write, image_size):\n image_text = Image.new(\"RGB\", image_size)\n font = ImageFont.load_default().font\n drawer = ImageDraw.Draw(image_text)\n\n #Text wrapping. Change parameters for different text formatting\n margin = offset = 10\n for line in textwrap.wrap(text_to_write, width=60):\n drawer.text((margin,offset), line, font=font)\n offset += 10\n return image_text","def draw_text(image, text, font, size, color, pos_name, shadow=False):\n # Make a copy of the image\n image = image.copy()\n\n draw = ImageDraw.Draw(image)\n\n width, height = get_size(image)\n\n rgb_colors = assigning_colors()\n\n try:\n font_type = ImageFont.truetype(font, size)\n except ValueError:\n raise Exception(\"Fonte ou tamanho de fonte inválidos\")\n\n w, h = draw.textsize(text, font=font_type)\n\n position = get_position(height, h, width, w, pos_name, shadow=shadow)\n\n if color in rgb_colors:\n color = rgb_colors[color]\n else:\n raise Exception(\"Cor inválida\")\n\n draw.text(xy=position, text=text, fill=color,\n font=font_type, align=check_alignment(pos_name))\n\n return image","async def outline_text(draw_surface, coords, draw_text, font):\n draw = partial(draw_surface.text, text=draw_text, font=font,\n fill=\"black\")\n for offset_pair in product(range(-1, 2), repeat=2):\n draw((coords[0]+offset_pair[0], coords[1]+offset_pair[1]))\n draw(coords, fill=\"white\")","def draw_label(self, image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=0.8, thickness=1):\n # gets the size of the label\n size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n # where the position is\n x, y = point\n # gets the rectangle size\n cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\n cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)","def mytext(x,y,s,**kwargs):\n # we take care of this one\n model = kwargs.pop('model', None)\n if model:\n th = text(x,y,model,**kwargs)\n draw()\n x0,y0,w,h = th.get_window_extent().bounds\n gca().texts.remove(th)\n x = x0\n y = y0\n kwargs['transform'] = matplotlib.transforms.IdentityTransform()\n kwargs['horizontalalignment'] = 'left'\n kwargs['verticalalignment'] = 'baseline'\n# print x,y,kwargs\n return text(x,y,s,**kwargs)","def _write_overlay_info(self):\n cv2.putText(\n img=self.output,\n text=f'X: {float(self.estimated_distance[0]):6.2f} m',\n org=(25, 25),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n color=(0, 0, 255),\n fontScale=0.5\n )\n cv2.putText(\n img=self.output,\n text=f'Y: {float(self.estimated_distance[1]):6.2f} m',\n org=(25, 50),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n color=(0, 0, 255),\n fontScale=0.5\n )\n cv2.putText(\n img=self.output,\n text=f'Z: {float(self.estimated_distance[2]):6.2f} m',\n org=(25, 75),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n color=(0, 0, 255),\n fontScale=0.5\n )\n cv2.putText(\n img=self.output,\n text=f'Rotation: {float(self.estimated_rotation):6.2f} rad',\n org=(25, 100),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n color=(0, 0, 255),\n fontScale=0.5\n )","def print_text(TINY_FONT, x, y, text, color = white):\n text_image = TINY_FONT.render(text, True, color)\n gameDisplay.blit(text_image, (x,y))","def draw_date(img,txs):\n fontSize=img.size[1]/20\n draw = ImageDraw.Draw(img) # new draw instance \n arial=load_font(fontSize)\n tsize=draw.textsize(txs,font=arial)\n #draw.text((img.size[0]-tsize[0]-10,img.size[1]-(fontSize+5)),txs,fill=(200,200,255),font=arial)\n draw.text((10,img.size[1]-(fontSize+5)),txs,fill=(50,75,255),font=arial)\n return img","def draw_text(screen, text, size, x, y):\r\n font = pygame.font.Font(font_name, size)\r\n text_surface = font.render(text, True, WHITE)\r\n text_rect = text_surface.get_rect()\r\n text_rect.midtop = (x, y)\r\n screen.blit(text_surface, text_rect)\r\n pygame.display.update()","def annotate(image_path, bounds, classes, scores, labels):\n image = Image.open(image_path)\n draw = ImageDraw.Draw(image)\n for bound, cls, score in zip(bounds, classes, scores):\n draw.rectangle([(bound[0], bound[1]), (bound[2], bound[3])], None, (0, 255, 255))\n text = str(labels[cls]) + str(round(score, 3))\n text_size = draw.textsize(text)\n draw.rectangle([(bound[0], bound[1]), (bound[0] + text_size[0], bound[1] + text_size[1])], (0, 255, 255))\n draw.text((bound[0], bound[1]), text, (0, 0, 0))\n return image","def make_annotation_vertical(x, y):\n text = str(x)\n if len(text) > 33:\n text = text[0:33]+\"..\"\n return Annotation(\n text=text, # text is the y-coord\n showarrow=False, # annotation w/o arrows, default is True\n x=x, # set x position\n xref='x', # position text horizontally with x-coords\n xanchor='center', # x position corresp. to center of text\n yref='y', # set y position\n yanchor='bottom', # position text vertically with y-coords\n y=0, # y position corresp. to top of text\n textangle=-90, # Rotating the text upwards\n font=annotation.Font(\n color='#FFFFFF', # set font color\n size=13 # and size\n )\n )","def setScreenText(text: str, x: int, y: int):\n pass","def apply_png_overlay(image, overlay, start_row, start_col, height=200, width=200):\n\n alpha_s = overlay[:, :, 3] / 255.0\n alpha_l = 1.0 - alpha_s\n for c in range(0, 3):\n image[start_row:start_row+height, start_col:start_col+width, c] = (\n alpha_s * overlay[:, :, c] +\n alpha_l * image[start_row:start_row+height, start_col:start_col+width, c])\n \n return image","def draw_grid(image, bbox, label, outline=\"white\", input_format=\"xyxy\"):\n draw = ImageDraw.Draw(image)\n if input_format == \"xywh\":\n bbox = xywh_xyxy(bbox)\n \n for num, i in enumerate(bbox):\n x0, y0, x1, y1 = i\n l = label[num]\n draw.rectangle([x0, y0, x1, y1], outline=outline)\n draw.text((x0,y0), l, fill=(255, 0, 0))\n return image","def _draw_text_outline(img: Image.Image, font: ImageFont.ImageFont, text: str, fill_color: Color, line_color: Color, origin: Tuple[int, int]) -> None:\n draw = ImageDraw.Draw(img)\n # set to render text unaliased (https://mail.python.org/pipermail/image-sig/2005-August/003497.html)\n draw.fontmode = \"1\"\n # correct for font offset\n x_offset, y_offset, _, _ = font.getbbox(text)\n # These values were tweaked by hand to get a better centered text\n x, y = (origin[0]-2*x_offset, origin[1]-y_offset//2)\n origin = (x, y)\n # draw border\n draw.text((x - 1, y), text, font=font, fill=line_color)\n draw.text((x + 1, y), text, font=font, fill=line_color)\n draw.text((x, y - 1), text, font=font, fill=line_color)\n draw.text((x, y + 1), text, font=font, fill=line_color)\n # draw text over it\n draw.text(origin, text, font=font, fill=fill_color)","def add_text(img, text, type):\n assert type in ['title', 'caption']\n txt = PIL.Image.new('RGBA', img.size, (255,255,255,0))\n d = PIL.ImageDraw.Draw(txt)\n\n if type == 'caption':\n size = 10\n else:\n size = 25\n\n try:\n fnt = PIL.ImageFont.truetype('FreeSans.ttf', size=size)\n except IOError:\n fnt = PIL.ImageFont.load_default()\n\n font_size = fnt.getsize(text)\n\n if type == 'caption':\n tupper = txt.height - font_size[1] - 20\n tleft = 10\n elif type == 'title':\n tupper = 10\n tleft = (img.width / 2) - (font_size[0] / 2)\n\n d.text((tleft, tupper), text, font=fnt, fill=(255, 255, 255, 255))\n img = PIL.Image.alpha_composite(img, txt)\n\n return(img)","def draw_on_image(self, img):\n image = Image(img)\n\n # If the overlay has not expired, draw on the plate highlight and/or the status message\n if not self.has_expired():\n image.draw_text(self._text, image.center(), self._color,\n centered=True, scale=2, thickness=3)","def draw_boxes_texts(img,\n boxes,\n texts=None,\n colors=None,\n line_width=1,\n draw_start=False,\n box_format='x1y1x2y2'):\n assert box_format in ('x1y1x2y2', 'x1y1wh', 'xywh', 'xywha',\n 'polygon'), 'not supported box format!'\n img = imread(img)\n if len(boxes) == 0:\n return img\n boxes = copy.deepcopy(boxes)\n # convert bbox type to int\n if not isinstance(boxes, np.ndarray):\n if box_format != 'polygon':\n boxes = np.array(boxes)\n if box_format != 'xywha':\n boxes = boxes.astype(np.int)\n if len(boxes.shape) == 1:\n boxes = [boxes]\n else:\n boxes = [list(map(int, box)) for box in boxes]\n else:\n boxes = boxes.astype(np.int)\n if texts is not None and not isinstance(texts, (list, np.ndarray)):\n texts = [texts]\n if isinstance(img, Image.Image):\n img = cv.cvtColor(np.asarray(img), cv.COLOR_RGB2BGR)\n if not isinstance(img, np.ndarray):\n return\n if colors == 'random':\n colors = np.random.randint(0, 255, size=(len(boxes), 3))\n colors = [tuple(map(int, color)) for color in colors]\n text_color = (0, 255, 255)\n thickness = line_width\n font = cv.FONT_HERSHEY_SIMPLEX\n for idx, box in enumerate(boxes):\n # default color: red, BGR order\n box_color = (0, 0, 255) if colors is None else colors[idx]\n if box_format == 'x1y1x2y2':\n cv.rectangle(img, tuple(box[0:2]),\n tuple(box[2:4]), box_color, thickness)\n elif box_format == 'x1y1wh':\n box[0:4] = cvtools.x1y1wh_to_x1y1x2y2(list(box[0:4]))\n cv.rectangle(img, tuple(box[0:2]),\n tuple(box[2:4]), box_color, thickness)\n elif box_format == 'xywh':\n box[0:4] = cvtools.xywh_to_x1y1x2y2(list(box[0:4]))\n cv.rectangle(img, tuple(box[0:2]),\n tuple(box[2:4]), box_color, thickness)\n elif box_format == 'xywha':\n rrect = tuple(box[:2]), tuple(box[2:4]), box[4]\n box = cv.boxPoints(rrect).astype(np.int)\n # box = np.int0(box)\n cv.drawContours(img, [box], 0, box_color, thickness)\n box = box.reshape((-1,))\n elif box_format == 'polygon':\n # for i in np.arange(2, len(box), 2):\n # cv.line(img, tuple(box[i-2:i]),\n # tuple(box[i:i+2]), box_color, thickness)\n # cv.line(img, tuple(box[-2:]),\n # tuple(box[:2]), box_color, thickness)\n # 如果img内存非连续，cv的所有绘制都会失效\n cv.polylines(img, np.int32([np.array(box).reshape(-1, 2)]), 1, box_color, thickness)\n # cv.line(img, tuple(box[:2]), tuple(box[2:4]), box_color, thickness)\n # cv.line(img, tuple(box[2:4]), tuple(box[4:6]), box_color, thickness)\n # cv.line(img, tuple(box[4:6]), tuple(box[6:8]), box_color, thickness)\n # cv.line(img, tuple(box[6:]), tuple(box[:2]), box_color, thickness)\n if draw_start:\n cv.circle(img, tuple(box[:2]),\n radius=5, color=text_color, thickness=-1)\n if texts is not None:\n cv.putText(img, texts[idx],\n (box[0]+2, box[1]-2), font, 0.5, text_color, 1)\n return img","def write_text(text_to_write, image_size):\n image_text = Image.new(\"RGB\", image_size)\n font = ImageFont.load_default().font\n drawer = ImageDraw.Draw(image_text)\n\n # Text wrapping. Change parameters for different text formatting\n margin = offset = 10\n for line in textwrap.wrap(text_to_write, width=60):\n drawer.text((margin, offset), line, font=font)\n offset += 10\n return image_text","def draw_text(display, font_name, text, size, color, x, y):\n font = pg.font.Font(font_name, size)\n text_surface = font.render(text, True, color)\n text_rect = text_surface.get_rect()\n text_rect.midtop = (x, y)\n display.blit(text_surface, text_rect)","def np_text(text, w_border=TILE_TEXT_W_BORDER, h_border=TILE_TEXT_H_BORDER, \n font_size=TILE_TEXT_SIZE, text_color=TILE_TEXT_COLOR, background=TILE_TEXT_BACKGROUND_COLOR):\n pil_img = pil_text(text, w_border, h_border, font_size,\n text_color, background)\n np_img = util.pil_to_np_rgb(pil_img)\n return np_img","def draw_bboxes_withindex(img,boxes, uids):\n source = Image.fromarray(img)\n draw = ImageDraw.Draw(source)\n w2,h2 = (img.shape[0],img.shape[1])\n \n font = ImageFont.truetype('/usr/share/fonts/truetype/freefont/FreeSerif.ttf', 40)\n #font = ImageFont.truetype('arial.ttf', 24)\n\n\n idx = 0\n\n for b in boxes:\n xmin,ymin,xmax,ymax = b\n \n for j in range(3):\n draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline=\"red\")\n draw.text((xmin+20, ymin+70), str(uids[idx]), font = font)\n idx +=1\n return source","def write_text(size, text):\n img = Image.new('RGB', size, (255, 255, 255))\n draw = ImageDraw.Draw(img)\n font = ImageFont.truetype('/Library/Fonts/Arial.ttf', size)\n draw.text((10,10), text, (0, 0, 0), font=font)\n return img","def _draw_text_outline(img, font, text, fill_color, line_color, origin):\n draw = ImageDraw.Draw(img)\n # set to render text unaliased (https://mail.python.org/pipermail/image-sig/2005-August/003497.html)\n draw.fontmode = \"1\"\n # correct for font offset\n x_offset, y_offset = font.getoffset(text)\n # These values were tweaked by hand to get a better centered text\n x, y = (origin[0]-2*x_offset, origin[1]-y_offset//2)\n origin = (x, y)\n # draw border\n draw.text((x - 1, y), text, font=font, fill=line_color)\n draw.text((x + 1, y), text, font=font, fill=line_color)\n draw.text((x, y - 1), text, font=font, fill=line_color)\n draw.text((x, y + 1), text, font=font, fill=line_color)\n # draw text over it\n draw.text(origin, text, font=font, fill=fill_color)","def render(self):\n if self.frame_pos:\n self.pos = [\n self.frame_pos[0] + self.position[0] - (self.size[0] / 2),\n self.frame_pos[1] + self.position[1] - (self.size[1] / 2),\n ]\n if self.variable_text:\n self.image = self.fontA.render(self.text, 1, self.color)","def img_syll_over_text(data_word):\n\n step = 200\n\n x,y = syll_over_text(data_word)\n\n imgdata = BytesIO()\n fig, ax = plt.subplots()\n ax.plot(x,y)\n plt.ioff()\n plt.yticks(range(0,int(max(y) + 2),1))\n plt.ylim(1, 4)\n plt.xlim(step, len(x)+step)\n plt.title(\"Syllables per Word (\" + str(step) + \" Word Moving Average)\")\n plt.xlabel(\"Word Number\")\n plt.tight_layout()\n fig.savefig(imgdata, format = 'png', dpi = 200)\n\n return imgdata","def DrawTextOneLine( x, y, text, textColor = 1, textSize = 0.04, NDC = True, halign = \"left\", valign = \"bottom\", skipLines = 0 ):\n \n halignMap = {\"left\":1, \"center\":2, \"right\":3}\n valignMap = {\"bottom\":1, \"center\":2, \"top\":3}\n \n scaleLineHeight = 1.0\n if valign == \"top\": scaleLineHeight = 0.8\n if skipLines: text = \"#lower[%.1f]{%s}\" % (skipLines*scaleLineHeight,text)\n \n # Draw the text quite simply:\n import ROOT\n l = ROOT.TLatex()\n if NDC: l.SetNDC()\n l.SetTextAlign( 10*halignMap[halign] + valignMap[valign] )\n l.SetTextColor( textColor )\n l.SetTextSize( textSize )\n l.DrawLatex( x, y, text )\n return l","def DrawTextOneLine( x, y, text, textColor = 1, textSize = 0.04, NDC = True, halign = \"left\", valign = \"bottom\", skipLines = 0 ):\n \n halignMap = {\"left\":1, \"center\":2, \"right\":3}\n valignMap = {\"bottom\":1, \"center\":2, \"top\":3}\n \n scaleLineHeight = 1.0\n if valign == \"top\": scaleLineHeight = 0.8\n if skipLines: text = \"#lower[%.1f]{%s}\" % (skipLines*scaleLineHeight,text)\n \n # Draw the text quite simply:\n import ROOT\n l = ROOT.TLatex()\n if NDC: l.SetNDC()\n l.SetTextAlign( 10*halignMap[halign] + valignMap[valign] )\n l.SetTextColor( textColor )\n l.SetTextSize( textSize )\n l.DrawLatex( x, y, text )\n return l","def draw_text(frame, text, coordinate, line_color=(0, 255, 124), normalized=True):\n\n x1 = coordinate[0]\n y1 = coordinate[1]\n x2 = coordinate[2]\n y2 = coordinate[3]\n\n if normalized:\n h = frame.shape[0]\n w = frame.shape[1]\n\n x1 = int(x1 * w)\n x2 = int(x2 * w)\n\n y1 = int(y1 * h)\n y2 = int(y2 * h)\n\n font = cv.FONT_HERSHEY_SIMPLEX\n bottom_left_corner_of_text = (x2, y1 + 10)\n font_scale = 0.4\n font_color = line_color\n line_type = 1\n\n cv.putText(frame,\n text,\n bottom_left_corner_of_text,\n font,\n font_scale,\n font_color,\n line_type)"],"string":"[\n \"def _overlay_text(\\n image_file_name, x_offset_from_left_px, y_offset_from_top_px,\\n text_string, font_size, use_north_gravity):\\n\\n command_string = '\\\"{0:s}\\\" \\\"{1:s}\\\"'.format(CONVERT_EXE_NAME, image_file_name)\\n if use_north_gravity:\\n command_string += ' -gravity North'\\n\\n command_string += (\\n ' -pointsize {0:d} -font \\\"{1:s}\\\" '\\n '-fill \\\"rgb(0, 0, 0)\\\" -annotate {2:+d}{3:+d} \\\"{4:s}\\\" \\\"{5:s}\\\"'\\n ).format(\\n font_size, TITLE_FONT_NAME,\\n x_offset_from_left_px, y_offset_from_top_px, text_string,\\n image_file_name\\n )\\n\\n exit_code = os.system(command_string)\\n if exit_code == 0:\\n return\\n\\n raise ValueError(imagemagick_utils.ERROR_STRING)\",\n \"def image_text(self, img: Image, text, x_pos=0, y_pos=0) -> Image.Image:\\n # Make blank image for text\\n txt = Image.new(\\\"RGBA\\\", img.size, (255, 255, 255, 0))\\n d = ImageDraw.Draw(txt)\\n # offset with black text to fake outline\\n d.text(\\n (x_pos + 1, y_pos + 1), text, font=self.font, fill=(0, 0, 0, 255)\\n )\\n d.text((x_pos + 1, y_pos), text, font=self.font, fill=(0, 0, 0, 255))\\n d.text((x_pos, y_pos + 1), text, font=self.font, fill=(0, 0, 0, 255))\\n # text in white\\n d.text((x_pos, y_pos), text, font=self.font, fill=(255, 255, 255, 255))\\n out = Image.alpha_composite(img.convert(\\\"RGBA\\\"), txt)\\n return out\",\n \"def generateImage(self):\\n self.image = self.font.render(self.text, True, self.color)\\n self.rect = self.image.get_rect()\\n self.rect.center = self.xy\",\n \"def text_on_img(text=\\\"Hello\\\", lbl_w=None, lbl_h=None, font_size=14, filepath=None):\\n\\n font = ImageFont.truetype(\\\"lte50712.ttf\\\", font_size)\\n\\n if lbl_w is None:\\n lbl_w = 150\\n if lbl_h is None:\\n lbl_h = 150\\n\\n assert(text)\\n\\n text = text.upper()\\n\\n m_width, m_heigth = font.getsize(\\\"M\\\")\\n char_num_per_line = lbl_w // m_width\\n\\n image = Image.new(mode=\\\"RGB\\\", size=(lbl_w, lbl_h), color=\\\"white\\\")\\n draw = ImageDraw.Draw(image)\\n\\n words = text.split()\\n if len(words) <= 2 and all(font.getsize(w)[0] < lbl_w for w in words):\\n t_wrapper = words\\n else:\\n t_wrapper = textwrap.wrap(text, char_num_per_line)\\n\\n offset = (lbl_h - (m_heigth * len(t_wrapper))) // 2\\n\\n for line in t_wrapper:\\n line_w, line_h = font.getsize(line)\\n draw.text(((lbl_w - line_w) // 2, offset), line, font=font, fill=(0, 0, 0))\\n offset += line_h\\n\\n if filepath:\\n image.save(filepath)\\n\\n return image\",\n \"def _draw_x_label(self):\\n overlay = self.image.getOverlay()\\n TextRoi.setGlobalJustification(TextRoi.CENTER)\\n offset = self.image.getHeight() - self.extend_label\\n label_pos = self.image.getWidth() / 2\\n text = TextRoi(label_pos, offset, 'Energy loss [eV]', self.font)\\n text_width = text.getFloatWidth()\\n text_y = text.getYBase()\\n text.setLocation(label_pos - text_width / 2, text_y)\\n text.setStrokeColor(Color(1.00, 1.00, 1.00))\\n overlay.add(text)\",\n \"def _build_texts(self, image):\\n offset_generator = self._iter_texts_rects()\\n draw = ImageDraw.Draw(image)\\n for text, font_name, color, align in self._texts:\\n text_x, text_y, max_width, max_height = next(offset_generator)\\n if not text: # Empty string: go to next text position\\n continue\\n # Use PIL to draw text because better support for fonts than OpenCV\\n font = fonts.get_pil_font(text, font_name, max_width, max_height)\\n _, text_height = font.getsize(text)\\n (text_width, _baseline), (offset_x, offset_y) = font.font.getsize(text)\\n if align == self.CENTER:\\n text_x += (max_width - text_width) // 2\\n elif align == self.RIGHT:\\n text_x += (max_width - text_width)\\n\\n draw.text((text_x - offset_x // 2,\\n text_y + (max_height - text_height) // 2 - offset_y // 2),\\n text, color, font=font)\",\n \"def addText(img, left_curverad, right_curverad, offset):\\n # Average the curvature radius of two lanes\\n avg_curverad = (left_curverad + right_curverad)/2\\n annotation = 'Radius of curvature: {:5.1f} m Vehicle offset from the center: {:5.1f} m'.format(avg_curverad, offset)\\n result = cv2.putText(img, annotation, (30,40), 0, 1, (0,0,0), 2, cv2.LINE_AA)\\n\\n return result\",\n \"def draw_text(img, text, pos):\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n return cv2.putText(img, text, pos, font, 1, (255, 255, 255), 1, cv2.LINE_AA)\",\n \"def draw_boxes_info(image, current_data):\\n\\n font_position1 = (50, 600)\\n font_position2 = (50, 650)\\n font_scale = .4\\n font_thickness = 1\\n\\n locations = current_data[\\\"locations\\\"] #returns x1, y1, x2, y2\\n frame_num = \\\"Frame Number: \\\" + str(current_data[\\\"frame_num\\\"])\\n\\n for box in locations:\\n box_text = (\\\"Box locations are x1: {0}, y1: {1}, x2: {2}, y2: {3}\\\").format(box[1],box[3],box[0],box[2])\\n\\n cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 3)\\n cv2.putText(image, box_text, font_position1, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\\n font_thickness, cv2.LINE_AA)\\n\\n cv2.putText(image, frame_num, font_position2, cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255),\\n font_thickness, cv2.LINE_AA)\\n\\n return image\",\n \"def make_images(self, text_on_button):\\n text_color = pg.Color('black')\\n idle_color = pg.Color('#FCF0C8')\\n hover_color = pg.Color('#E84A5F')\\n blocked_color = pg.Color('#DCDADA')\\n blocked_text_color = pg.Color('#A79E8B')\\n # idle_image = self.font.render(text, True, idle_color)\\n # blocked_image = self.font.render(text, True, blocked_color)\\n # hover_image = self.font.render(text, True, hover_color, hover_fill)\\n text = self.font.render(text_on_button, True, text_color)\\n blocked_text = self.font.render(\\n text_on_button, True, blocked_text_color)\\n width = text.get_width() + 30\\n height = text.get_height() + 20\\n\\n image = pg.Surface((width, height)).convert_alpha()\\n image.fill((0, 0, 0, 0))\\n\\n idle_image = image.copy()\\n small_rect = pg.rect.Rect(3, 3, width - 6, height - 6)\\n pg.draw.ellipse(idle_image, idle_color, small_rect)\\n\\n blocked_image = image.copy()\\n small_rect = pg.rect.Rect(3, 3, width - 6, height - 6)\\n pg.draw.ellipse(blocked_image, blocked_color, small_rect)\\n\\n hover_image = image.copy()\\n big_rect = pg.rect.Rect(0, 0, width, height)\\n pg.draw.ellipse(hover_image, hover_color, big_rect)\\n small_rect = pg.rect.Rect(3, 3, width - 6, height - 6)\\n pg.draw.ellipse(hover_image, idle_color, small_rect)\\n\\n text_rect = pg.rect.Rect(0, 0, text.get_width(), text.get_height())\\n text_rect.center = small_rect.center\\n idle_image.blit(text, text_rect)\\n blocked_image.blit(blocked_text, text_rect)\\n hover_image.blit(text, text_rect)\\n\\n return idle_image, blocked_image, hover_image\",\n \"def _draw_text_on_image(self, string, position, color = (77, 255, 9)):\\n cv2.putText(self.image, string, (position),\\n fontFace = self._font_face, fontScale = self._font_scale,\\n color = color, thickness = self._font_thickness)\",\n \"def draw_text(self, text, i, j, **params):\",\n \"def drawText(filepath, fontsize, text, target_size, color):\\n image = Image.open(filepath)\\n image = image.resize(target_size)\\n draw = ImageDraw.Draw(image)\\n # Import the arial font style\\n font = ImageFont.truetype(font=os.path.join(\\\"inpainting\\\", \\\"arial.ttf\\\"), size=fontsize)\\n size = font.getsize(text)\\n x = randint(size[0] + 16, 255 - 16 - size[0])\\n y = randint(size[1] + 16, 255 - 16 - size[1])\\n draw.text((x, y), text, fill=color, font=font)\\n # Determine the bounds of the text area\\n x0 = x + 0.5 * size[0] - 16\\n y1 = y + 0.5 * fontsize + 16\\n x1 = x + 0.5 * size[0] + 16\\n y0 = y + 0.5 * fontsize - 16\\n return image, (int(x0), int(y0), int(x1), int(y1))\",\n \"def text(self, x, y, text):\\n for i, char in enumerate(text):\\n self.point(x + i, y, char)\",\n \"def create_image_search(image_name, saved_image, text):\\n with Image(filename=image_name) as img:\\n with img.clone() as cloned:\\n cloned.format = 'png'\\n with Drawing() as draw:\\n draw.font_family = 'Liberation Mono'\\n draw.font_size = 30\\n text = SpecialActions.simplifying(text)\\n draw.text(140, 785, text)\\n draw(cloned)\\n cloned.save(filename=saved_image)\",\n \"def _write(self, pos, txt, align, font, pencolor):\\n x, y = pos\\n x = x * self.xscale\\n y = y * self.yscale\\n anchor = {\\\"left\\\":\\\"sw\\\", \\\"center\\\":\\\"s\\\", \\\"right\\\":\\\"se\\\" }\\n item = self.cv.create_text(x-1, -y, text = txt, anchor = anchor[align],\\n fill = pencolor, font = font)\\n x0, y0, x1, y1 = self.cv.bbox(item)\\n self.cv.update()\\n return item, x1-1\",\n \"def text(self, text: str, xo: int, yo: int, color: int):\\n for offset, letter in enumerate(text):\\n template = font.get(letter)\\n for x, line in enumerate(template):\\n line_str = '{:08b}'.format(line).replace('0b', '')\\n if self.portrait:\\n line_str = reversed(line_str)\\n for y, pix in enumerate(line_str):\\n if pix == '1':\\n self.pixel(xo + x + (offset * 8), yo + y, color)\",\n \"def createNew(string):\\n image=Image.open('imageGenerator/images/images.jpg').convert('RGBA')\\n\\n fnt = ImageFont.truetype(\\\"fonts/arial.ttf\\\", 25)\\n d = ImageDraw.Draw(image)\\n\\n d.text((10,10), string, font=fnt, fill=(255,255,255,128))\\n\\n d.text((10,60), \\\"World\\\", fill=(255,255,255,255))\\n file=byt()\\n image.save(file,'jpeg')\\n return file.getvalue()\",\n \"def AddText(img,text, px=0.98,py=0.96, color = 255,s=0.9,lw=1,stroke=1):\\n h,w=img.shape\\n \\n font = cv2.FONT_HERSHEY_SIMPLEX#CV_FONT_HERSHEY_SIMPLEX normal size sans-serif font\\n D,sd=cv2.getTextSize(text, font, s, lw)\\n wt=int(D[0])\\n bcolor=255-color #color of stroke\\n bw =lw+1 #stroke thickness\\n \\n x=int(w*px-wt)\\n y=int(h*py)\\n if stroke == 1:\\n cv2.putText(img,text,(x,y), font, s, bcolor, bw, cv2.LINE_AA) #Stroke \\n cv2.putText(img,text,(x,y), font, s, color, lw, cv2.LINE_AA)\",\n \"def text_draw(self, x, y, text, style={}):\",\n \"def add_text_to_image(image, text):\\n\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n fontScale = 0.7\\n color = (255, 255, 255)\\n thickness = 1\\n height, width, dim = image.shape\\n org = (20, height-50)\\n number_of_characters = len(text)\\n print(number_of_characters)\\n if number_of_characters < 65:\\n image = cv2.rectangle(image, (0, height), (0 + width, height-50-1*25 - 25), (0, 0, 0), -1)\\n return cv2.putText(image, text, org, font, fontScale, color, thickness, cv2.LINE_AA)\\n else:\\n number_of_sentences = math.ceil(number_of_characters/65)\\n words_split = text.split()\\n sentences = np.array_split(words_split, number_of_sentences)\\n image = cv2.rectangle(image, (0, height), (0 + width, height-50-len(sentences)*25 - 25), (0, 0, 0), -1)\\n\\n for i, sentence in enumerate(reversed(sentences)):\\n sentence = \\\" \\\".join(sentence)\\n org = (20, height-50-i*25)\\n image = cv2.putText(image, sentence, org, font, fontScale, color, thickness, cv2.LINE_AA)\\n return image\",\n \"def draw_text(self, text: str, x_offset: int = 0,\\n font_name: str = \\\"Apple_II_mod.ttf\\\", font_pt: int = 8) -> Image.Image:\\n if self.rows < 5:\\n raise ValueError(\\n f\\\"Unable to scroll text on a board with rows < 5 px. Board Rows: {self.rows} \\\")\\n\\n text = str(text)\\n\\n if not font_name.endswith('.ttf'):\\n font_name = f'{font_name}.ttf'\\n\\n if not os.path.exists(f\\\"{RESOURCES}/{font_name}\\\"):\\n raise ValueError(\\n f\\\"The font: '{font_name}' could not be found, please try a font in ./resources\\\")\\n\\n font = ImageFont.truetype(f\\\"{RESOURCES}/{font_name}\\\", font_pt)\\n text_width, text_height = font.getsize(text)\\n\\n # FIXME :\\n # For some reason we need to remove 1 px from the image width\\n # for it to clear smoothly at the end\\n # Instead of messing with the math, a workaround of clearing the panel\\n # after the scroll was added\\n image = Image.new('P', (text_width + (self.cols * 2), self.rows), 0)\\n draw = ImageDraw.Draw(image)\\n\\n draw.text((self.cols, x_offset), text, font=font, fill=255)\\n\\n return image\",\n \"def draw_pos_text(self, text):\\n fw, fh = self.font.size(text) # fw: font width, fh: font height\\n surface = self.font.render(text, True, (0, 255, 0))\\n # // makes integer division in python3\\n self.screen.blit(surface, ((self.width - fw) // 2, (self.height - fh) // 2))\",\n \"def _draw_vertical_text(\\n text,\\n image_font,\\n text_color,\\n text_background_color,\\n character_spacing=2,\\n space_width=1,\\n):\\n\\n space_height = int(image_font.getsize(\\\" \\\")[1] * space_width)\\n\\n char_heights = [\\n image_font.getsize(c)[1] if c != \\\" \\\" else space_height for c in text\\n ]\\n text_width = max([image_font.getsize(c)[0] for c in text])\\n text_height = sum(char_heights) + character_spacing * len(text)\\n\\n txt_img = Image.new(\\\"RGBA\\\", (text_width, text_height), color=text_background_color)\\n txt_mask = Image.new(\\\"RGBA\\\", (text_width, text_height), color=text_background_color)\\n\\n txt_img_draw = ImageDraw.Draw(txt_img)\\n txt_mask_draw = ImageDraw.Draw(txt_mask)\\n\\n for i, c in enumerate(text):\\n txt_img_draw.text(\\n (0, sum(char_heights[0:i]) + i * character_spacing),\\n c,\\n fill=text_color,\\n font=image_font,\\n )\\n\\n return txt_img.crop(txt_img.getbbox())\",\n \"def invert(draw, x, y, text, center):\\n \\n font = ImageFont.load_default()\\n draw.rectangle((x, y, x+120, y+10), outline=255, fill=255)\\n if (center):\\n x=74-4*len(text) - 4*(len(text)%2) \\n draw.text((x, y), text, font=font, outline=0,fill=\\\"black\\\")\",\n \"def text(self, str: str, x: int, y: int, colour: int, /) -> None:\",\n \"def overlayImage(self, filename):\\n overlayTime = time.time()\\n\\n # local refs\\n ren = self.ren\\n renWinInteract = self.renWinInteract\\n renIndex = self.parent.rendererIndex\\n\\n # to do this we change cam pos to far away and\\n # save temp image with just text in\\n\\n # so move the camera far away\\n camera = ren.GetActiveCamera()\\n origCamPos = camera.GetPosition()\\n newCamPos = [0] * 3\\n newCamPos[0] = origCamPos[0] * 100000\\n newCamPos[1] = origCamPos[1] * 100000\\n newCamPos[2] = origCamPos[2] * 100000\\n camera.SetPosition(newCamPos)\\n\\n # also hide axes if visible\\n if self.axes.isEnabled():\\n axesHidden = True\\n self.toggleAxes()\\n else:\\n axesHidden = False\\n\\n try:\\n # save image\\n overlayFilePrefix = os.path.join(self.mainWindow.tmpDirectory, \\\"renderer%d_overlay\\\" % renIndex)\\n overlayFile = self.saveImage(\\\"VTK\\\", \\\"jpg\\\", overlayFilePrefix, False)\\n if not os.path.exists(overlayFile):\\n print(\\\"WARNING: overlay file does not exist: %s\\\" % overlayFile)\\n return\\n\\n try:\\n # open POV-Ray image\\n povim = Image.open(filename)\\n modified = False\\n\\n # find text in top left corner\\n im = Image.open(overlayFile)\\n\\n # start point\\n xmin = ymin = 0\\n xmax = int(im.size[0] * 0.5)\\n ymax = int(im.size[1] * 0.8)\\n\\n # find extremes\\n xmin, xmax, ymin, ymax = self.findOverlayExtremes(im, xmin, xmax, ymin, ymax)\\n\\n # crop\\n region = im.crop((xmin, ymin, xmax + 2, ymax + 2))\\n\\n # add to povray image\\n if region.size[0] != 0:\\n region = region.resize((region.size[0], region.size[1]), Image.ANTIALIAS)\\n povim.paste(region, (0, 0))\\n modified = True\\n\\n # now look for anything at the bottom => scalar bar\\n im = Image.open(overlayFile)\\n\\n # start point\\n xmin = 0\\n ymin = im.size[1] - 80\\n xmax = im.size[0]\\n ymax = im.size[1]\\n\\n # find extremes\\n xmin, xmax, ymin, ymax = self.findOverlayExtremes(im, xmin, xmax, ymin, ymax)\\n\\n # crop\\n region = im.crop((xmin, ymin, xmax, ymax))\\n\\n # add?\\n if region.size[0] != 0:\\n newregiondimx = int(povim.size[0] * 0.8)\\n dx = (float(povim.size[0]) * 0.8 - float(region.size[0])) / float(region.size[0])\\n newregiondimy = region.size[1] + int(region.size[1] * dx)\\n region = region.resize((newregiondimx, newregiondimy), Image.ANTIALIAS)\\n\\n xpos = int((povim.size[0] - region.size[0]) / 2.0)\\n povim.paste(region, (xpos, int(povim.size[1] - region.size[1])))\\n\\n modified = True\\n\\n # now look for text in top right corner\\n im = Image.open(overlayFile)\\n\\n # start point\\n xmin = int(im.size[0] * 0.5)\\n ymin = 0\\n xmax = im.size[0]\\n ymax = int(im.size[1] * 0.6)\\n\\n # find extremes\\n xmin, xmax, ymin, ymax = self.findOverlayExtremes(im, xmin, xmax, ymin, ymax)\\n\\n # crop\\n region = im.crop((xmin - 2, ymin, xmax, ymax + 2))\\n\\n if region.size[0] != 0:\\n region = region.resize((region.size[0], region.size[1]), Image.ANTIALIAS)\\n xpos = povim.size[0] - 220\\n povim.paste(region, (xpos, 0))\\n\\n modified = True\\n\\n # save image\\n if modified:\\n povim.save(filename)\\n\\n finally:\\n os.unlink(overlayFile)\\n\\n finally:\\n # return to original cam pos\\n camera.SetPosition(origCamPos)\\n renWinInteract.ReInitialize()\\n\\n # show axes again\\n if axesHidden:\\n self.toggleAxes()\\n\\n overlayTime = time.time() - overlayTime\\n self.logger.debug(\\\"Overlay time: %f s\\\", overlayTime)\",\n \"def overlay_boxes(self, image, predictions):\\n for prediction in predictions:\\n template = ''\\n for idx in range(min(3, len(prediction['score']))):\\n template += \\\"{}:{:.2f},\\\".format(prediction['category'][idx], prediction['score'][idx])\\n color = [0, 0, 255]\\n top_left, bottom_right = prediction['bbox'][:2], prediction['bbox'][2:]\\n image = cv2.rectangle(\\n image, tuple(top_left), tuple(bottom_right), tuple(color), 1\\n )\\n cv2.putText(\\n image, template, (top_left[0], top_left[1]+15), cv2.FONT_HERSHEY_SIMPLEX, .6, tuple(color), 1\\n )\\n\\n return image\",\n \"def _set_text_pos(xv, yv, xlims, ylims, ax, offsetx=0.08, offsety=0.07):\\n\\n # check log or linear scale\\n xlim = np.log10(xlims) if ax.get_xscale() == \\\"log\\\" else xlims\\n ylim = np.log10(ylims) if ax.get_yscale() == \\\"log\\\" else ylims\\n x = np.log10(xv) if ax.get_xscale() == \\\"log\\\" else xv\\n y = np.log10(yv) if ax.get_yscale() == \\\"log\\\" else yv\\n\\n # get mid-points\\n xmid = np.mean(xlim)\\n ymid = np.mean(ylim)\\n\\n # get extent of axes\\n xextent = xlim[1] - xlim[0]\\n yextent = ylim[1] - ylim[0]\\n\\n # set test positions for each quadrant\\n if x <= xmid and y <= ymid:\\n # lower left\\n textx = x - xextent * offsetx\\n texty = y - yextent * offsety\\n elif x <= xmid and y > ymid:\\n # upper left\\n textx = x + xextent * offsetx\\n texty = y + yextent * offsety\\n elif x > xmid and y > ymid:\\n # upper right\\n textx = x - xextent * offsetx\\n texty = y + yextent * offsety\\n else:\\n # lower right\\n textx = x + xextent * offsetx\\n texty = y - yextent * offsety\\n\\n if ax.get_xscale() == \\\"log\\\":\\n textx = 10**textx\\n if ax.get_yscale() == \\\"log\\\":\\n texty = 10**texty\\n\\n return textx, texty\",\n \"def __init__(self, text='', x=0, y=0):\\n \\n #Some defaults\\n self.text = text\\n self.text_color = (255, 255, 255)\\n self.text_size = 36\\n self.x = x\\n self.y = y\\n self.font = pygame.font.Font(None, self.text_size)\\n self.text_img = self.font.render(self.text, True, self.text_color)\\n self.text_rect = self.text_img.get_rect()\\n self.text_rect.left = self.x\\n self.text_rect.top = self.y\",\n \"def add_watermark(image, text, font=watermark_font) -> Image:\\n rgba_image = image.convert('RGBA')\\n text_overlay = Image.new('RGBA', rgba_image.size, (255, 255, 255, 0))\\n image_draw = ImageDraw.Draw(text_overlay)\\n\\n text_size_x, text_size_y = image_draw.textsize(text, font=font)\\n\\n # Set text position.\\n text_xy = (rgba_image.size[0] - text_size_x, rgba_image.size[1] - text_size_y)\\n\\n # Set text color and transparency.\\n image_draw.text(text_xy, text, font=font, fill=(128, 128, 128, 180))\\n image_with_watermark = Image.alpha_composite(rgba_image, text_overlay)\\n return image_with_watermark\",\n \"def make_images(self, text, width, height):\\n idle_color = pg.Color('black')\\n hover_color = pg.Color('white')\\n hover_fill = pg.Color('purple')\\n locked_color = pg.Color('white')\\n locked_fill = pg.Color('red')\\n if width is not None and height is not None:\\n idle_image = pg.Surface((width, height)).convert()\\n idle_image.set_alpha(0)\\n idle_image = idle_image.convert_alpha()\\n idle_text = self.font.render(text, True, idle_color)\\n idle_image.blit(idle_text, idle_text.get_rect(\\n center=idle_image.get_rect().center))\\n hover_image = pg.Surface((width, height)).convert()\\n hover_image.fill(hover_fill)\\n hover_text = self.font.render(text, True, hover_color, hover_fill)\\n hover_image.blit(hover_text, hover_text.get_rect(\\n center=hover_image.get_rect().center))\\n locked_image = pg.Surface((width, height)).convert()\\n locked_image.fill(locked_fill)\\n locked_text = self.font.render(\\n text, True, locked_color, locked_fill)\\n locked_image.blit(locked_text, locked_text.get_rect(\\n center=locked_image.get_rect().center))\\n else:\\n idle_image = self.font.render(text, True, idle_color)\\n hover_image = self.font.render(text, True, hover_color, hover_fill)\\n locked_image = self.font.render(\\n text, True, locked_color, locked_fill)\\n\\n self.idle_image = idle_image\\n self.hover_image = hover_image\\n self.locked_image = locked_image\",\n \"def draw_box_and_text(img, text, box, color):\\n cv2.putText(\\n img, text,\\n (box[0], box[1]-5), cv2.FONT_HERSHEY_SIMPLEX,\\n 0.5, color, 2)\\n # cv2.rectangle(\\n # img,\\n # (box[1], box[0]),\\n # (box[3], box[2]),\\n # color, 2)\\n cv2.rectangle(\\n img,\\n (box[0], box[1]),\\n (box[2], box[3]),\\n color, 2)\",\n \"def draw_bounding_box( y1, x1, y2, x2, \\n img, \\n thickness=4, \\n fontsize=15,\\n outlineColor=(255, 255, 0),\\n textColor=(255, 255, 0),\\n display_str=() ):\\n\\n img = Image.fromarray( img )\\n draw = ImageDraw.Draw( img )\\n\\n for x in range( 0, thickness ):\\n draw.rectangle( [(x1-x, y1-x), (x2-x, y2-x)], outline=outlineColor )\\n\\n for i in range(1, len(fonts)):\\n if fonts[i].fontformat == 'TrueType':\\n for j in range(1,len(fonts[i].style)):\\n if fonts[i].style[j][1] == 'Regular':\\n absolute_path = fonts[i].file\\n break;\\n try:\\n absolute_path\\n except NameError:\\n font = ImageFont.load_default()\\n else:\\n font = ImageFont.truetype(absolute_path, fontsize)\\n\\n draw.text( (x1, y1), display_str, font=font, fill=textColor )\\n\\n return numpy.array( img )\",\n \"def _render_static_image_annotation(self):\\n cv2.rectangle(self._image,\\n (0,0), (640, 40),\\n (0, 0, 0),\\n -1)\\n \\n cv2.putText(self._image,self._current_mode, (40, 25),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)\\n\\n cv2.putText(self._image, time.asctime(), (400, 460),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)\",\n \"def draw_text_lb_corner(dc, text, pos_x, pos_y, size_multiplier=1):\\r\\n tw, th = dc.GetTextExtent(text, size_multiplier)\\r\\n dc.DrawText(text, (pos_x-tw), (pos_y-th), size_multiplier=size_multiplier)\",\n \"def draw_text(self, text, font, color, surface, x, y): #use for narrative in end sequence\\n text_obj = font.render(text, True, color)\\n text_rect = text_obj.get_rect()\\n text_rect.center = (x, y)\\n surface.blit(text_obj, text_rect)\",\n \"def draw_text(SCREEN, text, x, y):\\n text = constants.CALIBRI_25.render(text, True, constants.BLACK)\\n SCREEN.blit(text, (x, y))\",\n \"def add_text(text, x, y, rgb = [0, 0, 0], font_name = 'times new roman', size = 32, bold = False, italics = False, merged = False, slot = 0):\\r\\n \\r\\n font = pygame.font.SysFont(font_name, size, bold, italics)\\r\\n text = font.render(text, 1, rgb)\\r\\n \\r\\n if slot not in __g.keys():\\r\\n raise IndexError('No image loaded at slot %i' %(slot))\\r\\n \\r\\n surface = __g.values()[slot]\\r\\n if merged:\\r\\n surface.image.blit(text, (x, y))\\r\\n else:\\r\\n surface.text_add.append([x, y, text])\",\n \"def encode_image(text_to_encode, template_image=\\\"images/samoyed.jpg\\\", output_image=\\\"images/samoyed.secret.png\\\"):\\n\\n image = Image.open(template_image)\\n pixels = image.load()\\n\\n x_size = image.size[0]\\n y_size = image.size[1]\\n\\n for x in range(x_size):\\n for y in range(y_size):\\n if lsb_of_red_pixel(image, x, y):\\n pixels[x,y] = (image.getpixel((x, y))[0] - 1, image.getpixel((x, y))[1], image.getpixel((x, y))[2])\\n\\n text_image = Image.new(\\\"RGB\\\", image.size)\\n\\n usr_font = ImageFont.truetype(\\\"ComicNeue.otf\\\", 25)\\n d_usr = ImageDraw.Draw(text_image)\\n d_usr = d_usr.text((10,10), text_to_encode, (255,255,255), font=usr_font)\\n\\n for x in range(x_size):\\n for y in range(y_size):\\n if lsb_of_red_pixel(text_image, x, y):\\n pixels[x,y] = (image.getpixel((x, y))[0] + 1, image.getpixel((x, y))[1], image.getpixel((x, y))[2])\\n\\n image.save(output_image)\",\n \"def show(self, cv_img, text, X=0, Y=0):\\n w, h, pads = self.get_textSize(text)\\n x = X + pads\\n y = Y + h + pads*2\\n cv2.putText( cv_img, text, (x, y),\\n self.fontFace, self.fontScale,\\n self.fontColor, self.fontThickness, 255)\",\n \"def pil_text(text, w_border=TILE_TEXT_W_BORDER, h_border=TILE_TEXT_H_BORDER,\\n font_size=TILE_TEXT_SIZE, text_color=TILE_TEXT_COLOR, background=TILE_TEXT_BACKGROUND_COLOR):\\n\\n x, y = ImageDraw.Draw(Image.new(\\\"RGB\\\", (1, 1), background)).textsize(text)\\n image = Image.new(\\\"RGB\\\", (x + 2 * w_border, y + 2 * h_border), background)\\n draw = ImageDraw.Draw(image)\\n draw.text((w_border, h_border), text, text_color)\\n return image\",\n \"def pos_image(image, x,y):\\n image.anchor_x = x\\n image.anchor_y = y\",\n \"def add_text(self, x, y, text):\\n elem = TextElement()\\n elem.text = str(text)\\n elem.style = {\\n 'font-size': self.svg.unittouu('10pt'),\\n 'fill-opacity': '1.0',\\n 'stroke': 'none',\\n 'font-weight': 'normal',\\n 'font-style': 'normal' }\\n elem.update(x=str(x), y=str(y))\\n return elem\",\n \"def tagviews(tab,text,x,y):\\r\\n font = cv2.FONT_HERSHEY_SIMPLEX\\r\\n viseg=cv2.putText(tab,text,(x, y), font,0.3,white,1)\\r\\n return viseg\",\n \"def titled_overlay(image, overlay, title, org, border_thickness=2):\\n # Place title\\n title_img = np.ones((50, overlay.shape[1], 3)).astype('uint8') * 255\\n cv2.putText(title_img, title, org=(10, 35), fontScale=1, thickness=2, color=(0, 0, 0),\\n fontFace=cv2.FONT_HERSHEY_SIMPLEX, lineType=cv2.LINE_AA)\\n\\n # Add title to overlay\\n overlay = np.concatenate((title_img, overlay), axis=0)\\n\\n # Add border to overlay\\n overlay[:border_thickness, :, :] = 255\\n overlay[-border_thickness:, :, :] = 255\\n overlay[:, :border_thickness, :] = 255\\n overlay[:, -border_thickness:, :] = 255\\n\\n # Place overlay onto image\\n x_offset, y_offset = org\\n image[y_offset:y_offset + overlay.shape[0], x_offset:x_offset + overlay.shape[1]] = overlay\\n\\n # Add a white border\",\n \"def draw_text(self, text, position=(0, 0), color='black', font=None,\\n font_size=12, rotation=0, **kwargs):\\n font = self.font(font_size)\\n\\n text_image = Image.new('L', self.dimensions, 'black')\\n draw_text_image = ImageDraw.Draw(text_image)\\n draw_text_image.text(position, text, font=font, fill='white')\\n\\n alpha = Image.new('L', self.dimensions)\\n alpha = ImageChops.lighter(alpha, text_image)\\n\\n solidcolor = Image.new('RGBA', self.dimensions, color)\\n image_mask = Image.eval(text_image, lambda p: 255 * (int(p != 0)))\\n self.base_image = Image.composite(solidcolor, self.base_image, image_mask)\\n self.base_image.putalpha(alpha)\",\n \"def add_image_text(image: imageType, text: str, underline: bool = False) -> imageType:\\n\\n text_image: imageType = cv2.cvtColor(image.copy(), cv2.COLOR_GRAY2RGB)\\n h, w = text_image.shape[:2]\\n text_font = cv2.FONT_HERSHEY_SIMPLEX\\n text_origin_pos = (int(w / 10), int(9 * h / 10)) # from topleft\\n text_scale = 0.4\\n text_color = (154, 205, 50)\\n text_line_thick = 1\\n cv2.putText(text_image, text, text_origin_pos, text_font, text_scale, text_color, text_line_thick, lineType=cv2.LINE_AA)\\n\\n if underline:\\n under_start_offset = (-5, 10) # -x to the left, y underneath\\n line_length = 155\\n under_start_pos = tuple(x + y for x, y in zip(text_origin_pos, under_start_offset))\\n under_finish_pos = tuple(x + y for x, y in zip(under_start_pos, (line_length, 0)))\\n under_color = (50, 100, 250)\\n under_line_thick = 2\\n cv2.line(text_image, under_start_pos, under_finish_pos, under_color, under_line_thick)\\n\\n return text_image\",\n \"def AddText(img, new_dimensions, text):\\n font_file = DownloadFont()\\n with Drawing() as draw:\\n font_size = new_dimensions[1] * .045\\n xpos = round(new_dimensions[0] / 2)\\n ypos = round(new_dimensions[1] - 1.5 * font_size)\\n outline = font_size / 10\\n draw.font = font_file\\n draw.font_size = font_size\\n draw.text_alignment = 'center'\\n draw.text_antialias = True\\n invis = draw.stroke_color\\n draw.stroke_color = Color('#000000')\\n draw.fill_color = Color('#ffff00')\\n draw.stroke_width = outline\\n draw.text(xpos, ypos, text)\\n draw.stroke_color = invis\\n draw.text(xpos, ypos, text)\\n draw(img)\\n os.remove(font_file)\",\n \"def drawRectBox(image, rect, addText):\\n fontC = ImageFont.truetype(\\\"./Font/platech.ttf\\\", 14, 0)\\n\\n cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0, 0, 255), 2,\\n cv2.LINE_AA)\\n cv2.rectangle(image, (int(rect[0] - 1), int(rect[1]) - 16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,\\n cv2.LINE_AA)\\n img = Image.fromarray(image)\\n draw = ImageDraw.Draw(img)\\n draw.text((int(rect[0] + 1), int(rect[1] - 16)), addText, (255, 255, 255), font=fontC)\\n imagex = np.array(img)\\n return imagex\",\n \"def annotate(axis, text, x, y):\\n text_annotation = Annotation(text, xy=(x, y), xycoords='data')\\n axis.add_artist(text_annotation)\",\n \"def put_text(img: np.ndarray,\\n bbox: Tuple[float, float, float, float],\\n person: Person,\\n all_frames_cnt: int) -> np.ndarray:\\n x, y, w, h = bbox\\n\\n pivot_img_size = 112\\n resolution_x = img.shape[1];\\n\\n overlay = img.copy()\\n opacity = 0.4\\n\\n img = cv2.rectangle(img, (x, y), (x + w, y + h), (67, 67, 67), 1)\\n if x + w + pivot_img_size < resolution_x:\\n # right\\n cv2.rectangle(img, (x + w, y), (x + w + pivot_img_size + 20, y + h), (64, 64, 64), cv2.FILLED)\\n cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)\\n\\n elif x - pivot_img_size > 0:\\n # left\\n cv2.rectangle(img, (x - pivot_img_size - 20, y), (x, y + h), (64, 64, 64), cv2.FILLED)\\n cv2.addWeighted(overlay, opacity, img, 1 - opacity, 0, img)\\n\\n gender_label = person.gender\\n race_label = person.race\\n age_label = person.age\\n num_frames = person.percent_in_video(all_frames_cnt)\\n\\n attributes = [gender_label, race_label, age_label, num_frames]\\n\\n for index, attribute in enumerate(attributes):\\n if x + w + pivot_img_size < resolution_x:\\n\\n text_location_y = y + 20 + (index + 1) * 20\\n text_location_x = x + w\\n\\n if text_location_y < y + h:\\n cv2.putText(img, attribute, (text_location_x + 10, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\\n (255, 255, 255), 1)\\n\\n elif x - pivot_img_size > 0:\\n\\n text_location_y = y + 20 + (index + 1) * 20\\n text_location_x = x - pivot_img_size\\n\\n if text_location_y <= y + h:\\n cv2.putText(img, attribute, (text_location_x + 10, text_location_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\\n (255, 255, 255), 1)\\n\\n return img\",\n \"def draw_text(self, text, origin, font=cv2.FONT_HERSHEY_SIMPLEX, text_scale=0.7, text_color=(255,0,0), thickness=2):\\r\\n cv2.putText(self.image, text, origin, font, text_scale, text_color, thickness)\",\n \"def yolo_show_img(image, class_ids, boxes, labels, confidences, colors):\\n for i, box in enumerate(boxes):\\n # extract the bounding box coordinates\\n (x, y) = (box[0], box[1])\\n (w, h) = (box[2], box[3])\\n\\n # draw a bounding box rectangle and label on the image\\n color = [int(c) for c in colors[class_ids[i]]]\\n cv2.rectangle(image, (x, y), (x + w, y + h), color, 3)\\n text = '{}: {:.4f}'.format(labels[i], confidences[i])\\n print(text)\\n\\n font_scale = 1.3\\n # set the rectangle background to white\\n rectangle_bgr = color\\n # set some text\\n # get the width and height of the text box\\n (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=1)[0]\\n # set the text start position\\n text_offset_x = x\\n text_offset_y = y - 3 \\n # make the coords of the box with a small padding of two pixels\\n box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 10, text_offset_y - text_height - 10 ))\\n cv2.rectangle(image, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)\\n cv2.putText(image, text, (text_offset_x, text_offset_y), cv2.FONT_HERSHEY_SIMPLEX, \\n fontScale=font_scale, color=(255, 255, 255), thickness=2)\\n\\n cv2.imshow('yolo prediction', image)\\n cv2.waitKey(0)\",\n \"def draw(font_string,font_size,lang,alphabets,outdir=\\\".\\\"): # language, font file name, font full path, font size, characters\\n \\n \\n image_dir=lang+\\\".\\\"+\\\"images\\\"\\n if(os.path.exists(image_dir)):\\n pass\\n else:\\n os.mkdir(image_dir)\\n \\n #Using a font\\n #font= ImageFont.truetype(font,fsz)\\n boxfile=image_dir+\\\"/\\\"+\\\"bigimage.box\\\"\\n f=open(boxfile,\\\"w\\\")\\n wt = 4000\\n ht = 4000 #modified later using a separate script\\n\\t\\n bigimage=Image.new(\\\"L\\\",(wt,ht),255)\\t#change here for inverting\\n bigdraw=ImageDraw.Draw(bigimage)\\n x=y=10\\n count=0\\n for akshar in alphabets:\\n akshar.strip() #remove nasty characters\\n \\n #I shall now create an image with black bgc and white font color. One\\n #getbbox() determines the bounding box values I shall invert the image.\\n #This has to be done since getbbox() only finds bounding box values for\\n #non-zero pixels (read as white), but tesseract-ocr runs on the exact\\n #opposite bgc fgc combination. Contact debayanin@gmail.com.\\n \\n \\n #The lines below are pango/cairo code \\n surface = cairo.ImageSurface(cairo.FORMAT_A8, font_size*4, font_size*3)\\n context = cairo.Context(surface)\\n\\n pc = pangocairo.CairoContext(context)\\n\\n layout = pc.create_layout()\\n layout.set_font_description(pango.FontDescription(font_string))\\n layout.set_text(akshar)\\n print akshar\\n\\n # lines take care of centering the text.\\n width, height = surface.get_width(), surface.get_height()\\n w, h = layout.get_pixel_size()\\n position = (10,10) #most likely this part messes up when you try to change the size within this script. It is suggested to use the separate script.\\n context.move_to(*position)\\n pc.show_layout(layout)\\n surface.write_to_png(\\\"pango.png\\\")\\n\\t\\n #Here we open the generated image using PIL functions\\n temp_image=Image.open(\\\"pango.png\\\") #black background, white text\\n draw = ImageDraw.Draw(temp_image)\\n bbox = temp_image.getbbox()\\n deltax=bbox[2]-bbox[0]\\n deltay=bbox[3]-bbox[1]\\n\\n \\n print bbox\\n new_image=temp_image.crop(bbox)\\n temp_image=temp_image.load()\\n inverted_image = ImageChops.invert(new_image) #White background, black text\\n\\t\\n\\tinverted_image.save(image_dir+\\\"/\\\"+str(count)+\\\".png\\\")\\n\\tbigimage.paste(inverted_image,(x,y))\\n\\tos.unlink(image_dir+\\\"/\\\"+str(count)+\\\".png\\\")\\n\\tcount = count+1\\n\\t#bigimage.load()\\n bigbox=(x,y,x+deltax,y+deltay)\\n print bigbox\\n draw=ImageDraw.Draw(bigimage)\\n\\t#draw.rectangle(bigbox,None,100)\\n x=bigbox[2]+5\\n if x>(wt-10):\\n x=10; y=y+40\\n\\n os.unlink(\\\"pango.png\\\") #delete the pango generated png\\n\\n line=akshar+\\\" \\\"+str(bigbox[0]-1)+\\\" \\\"+str(ht-(bigbox[1]+deltay)-1)+\\\" \\\"+str(bigbox[2]+1)+\\\" \\\"+str(ht-(bigbox[3]-deltay)+1) # this is the line to be added to the box file\\n\\tf.write(line+'\\\\n')\\n\\n\\t#degrade code starts\\n\\tstrip=[deltax*.2,deltax*.4,deltax*.7]\\n\\tfor values in range(0,2):\\n\\t\\tdistort2=inverted_image\\n\\t\\tfor wai in range(0,deltay):\\n\\t\\t\\tfor ex in range(strip[values],strip[values]+1):\\n\\t\\t\\t\\tdistort2.putpixel((ex,wai),255)\\n\\t\\tbigbox=(x,y,x+deltax,y+deltay)\\n\\t\\t#draw.rectangle(bigbox,None,10)\\n\\t\\tline=akshar+\\\" \\\"+str(bigbox[0]-1)+\\\" \\\"+str(ht-(bigbox[1]+deltay)-1)+\\\" \\\"+str(bigbox[2]+1)+\\\" \\\"+str(ht-(bigbox[3]-deltay)+1) # this is the line to be added to the box file\\n \\tf.write(line+'\\\\n')\\n\\t\\tbigimage.paste(distort2,(x,y))\\n\\t\\tx=bigbox[2]+5\\n \\tif x>(wt-10):\\n \\t\\tx=10; y=y+40\\n\\t\\t\\n\\t\\t\\t\\n\\t#degrade code ends\\n \\n #distort.distort(filename2,bbox,fsz,akshar)\\n \\n \\n \\n #bigimage.save(image_dir+\\\"/\\\"+\\\"bigimage.tif\\\",\\\"TIFF\\\") #useful to generate merged file for all images when using default sizes.\\n f.close()\\n train.train(lang,outdir)\",\n \"def draw_text(self, display, text, size, x, y , mode):\\n font = pygame.font.Font(self.font_name, size)\\n text_surface = font.render(text, True, (0,0,0))\\n text_rect = text_surface.get_rect()\\n if mode == \\\"left\\\":\\n text_rect.topleft = (x,y)\\n elif mode == \\\"center\\\":\\n text_rect.center = (x,y)\\n display.blit(text_surface, text_rect)\",\n \"def update(self):\\n rect = self.img_rect.center\\n self.font_img = self.font.render(self.text, True, color1)\\n self.img_rect = self.font_img.get_rect()\\n # self.img_rect.center = [self.title_img.get_width() + self.font_img.get_width()//2 + padding, 5*Y//8]\\n self.img_rect.center = rect\",\n \"def plot_extent_text(data_obj, test_num, yi, xpos, ha, va, fs, dfs):\\n\\n plt.text(xpos[0], yi, 'Test ' + str(test_num) + ': Image extent', ha=ha, va=va, fontsize=fs)\\n if data_obj.image_extent == None:\\n str1 = 'None'\\n else:\\n str1 = \\\"{0:.0f}\\\".format(data_obj.image_extent) + ' mm' + '$^\\\\clubsuit$'\\n\\n plt.text(xpos[1], yi, str1, ha=ha, va=va, fontsize=fs - dfs)\\n plt.text(xpos[3], yi, '$\\\\leq$ 10 mm', ha=ha, va=va, fontsize=fs - dfs)\",\n \"def draw_on_image(D):\\n \\n #Set up rectangle's position within window\\n lower_left_x = 20 \\n lower_left_y = 42\\n dx = 5\\n dy = 5\\n\\n #Display border for rectangle\\n #Border is a black rectangle under white text rectangle\\n bord_upper_left = (lower_left_x-dx-3, lower_left_y-dy-20-3)\\n bord_lower_right = (lower_left_x+dx+160+3, lower_left_y+dy+50+3)\\n cv.Rectangle(D.image, bord_upper_left, bord_lower_right, D.black, cv.CV_FILLED)\\n \\n #Display white rectangle under text\\n rect_upper_left = (lower_left_x-dx, lower_left_y-dy-20)\\n rect_lower_right = (lower_left_x+dx+160, lower_left_y+dy+50)\\n cv.Rectangle(D.image, rect_upper_left, rect_lower_right, D.white, cv.CV_FILLED)\\n \\n ####\\n hive = \\\"hi!\\\"\\n hiveStat = \\\"hive\\\"\\n \\n #Build Strings\\n robotAssignment = (\\\"Assignment #: %.lf\\\"%R.assignment)\\n robotConverged = (\\\"Converged: %s\\\"%R.converged)\\n robotStatus = (\\\"Robot Status: \\\" + R.status)\\n hiveCommand = (\\\"Hive Command: \\\" + hive)\\n hiveStatus = (\\\"Hive Status: \\\" + hiveStat)\\n \\n # Position strings in a box so they won't overlap\\n firstLineString = (lower_left_x,lower_left_y) \\n secondLineString = (lower_left_x, lower_left_y + 20) \\n thirdLineString = (lower_left_x, lower_left_y + 40)\\n fourthLineString = (lower_left_x, lower_left_y + 60)\\n fifthLineString = (lower_left_x, lower_left_y + 80)\\n\\n #Display strings in window\\n cv.PutText(D.image, robotAssignment, firstLineString, D.font, cv.RGB(0,0,255)) \\n cv.PutText(D.image, robotConverged, secondLineString, D.font, cv.RGB(0,0,255))\\n cv.PutText(D.image, robotStatus, thirdLineString, D.font, cv.RGB(0,0,255))\\n cv.PutText(D.image, hiveCommand, fourthLineString, D.font, cv.RGB(0,0,255))\\n cv.PutText(D.image, hiveStatus, fifthLineString, D.font, cv.RGB(0,0,255))\",\n \"def add_annotations(self):\\n for i in range(8):\\n self.text.append(self.canvas.create_text(-self.width / 2,\\n (self.width / 2) + (i * self.width),\\n font=(\\\"Purisa\\\", 12), anchor=\\\"nw\\\"))\\n self.canvas.itemconfig(self.text[i], text=str((i - 8) * -1))\\n for i in range(8):\\n self.text.append(self.canvas.create_text((self.width / 2) + (i * self.width),\\n self.width * 8 + 10, font=(\\\"Purisa\\\", 12), anchor=\\\"nw\\\"))\\n self.canvas.itemconfig(self.text[i + 8], text=string.ascii_lowercase[i])\",\n \"def waterMarkImages(imageFile,\\n imageOutputPath,\\n text,\\n fontName=\\\"Bangers\\\",\\n size=\\\"M\\\",\\n opacity=255,\\n position=\\\"BR\\\"):\\n # TODO : Try to have color as an argument as well\\n fileName = imageFile.split(os.path.sep)[-1]\\n try:\\n photo = Image.open(imageFile).convert(\\\"RGBA\\\")\\n except IOError:\\n raise WatermarkException(4, val=imageFile)\\n\\n width, height = photo.size\\n\\n COLOR = (255, 255, 255)\\n\\n textImage = Image.new('RGBA', (width, height), COLOR + (0, ))\\n\\n fontsize = _setSize(width, height, size)\\n\\n drawing = ImageDraw.Draw(textImage)\\n\\n if fontName in FONT_MAP:\\n # * fontPath = \\\"PyWaterMark\\\\\\\\fonts\\\\\\\\\\\" + FONT_MAP[fontName] - ONLY DEVS\\n fontPath = pkg_resources.resource_filename(\\n 'PyWatermark', 'fonts/' + FONT_MAP[fontName])\\n if not os.path.isfile(fontPath):\\n raise WatermarkException(2, val=fontName)\\n else:\\n if not os.path.isfile(fontName):\\n raise WatermarkException(3, val=fontName)\\n\\n font = ImageFont.truetype(fontPath, fontsize)\\n\\n textWidth, textHeight = drawing.textsize(text, font)\\n\\n pos = _setPosition(width, height, textWidth, textHeight, position)\\n\\n drawing.text(pos, text, fill=COLOR + (opacity, ), font=font)\\n\\n combined = Image.alpha_composite(photo, textImage)\\n\\n filenameSave = fileName.split('.')\\n filenameSave[1] = 'wm.png'\\n filenameSave = ''.join(filenameSave)\\n # * Why you can't store JPEG images with transparency\\n # * https://stackoverflow.com/questions/41413956/pil-unable-to-change-the-transparency-level-for-jpeg-image\\n combined.save(imageOutputPath + '\\\\\\\\' + filenameSave)\\n\\n return filenameSave\",\n \"def add_text(self, x, y, text, style=None):\\n style = self.__prepare_style(style, ' ')\\n for i, c in enumerate(text):\\n if self.check_coord_in_range(x + i, y):\\n text_style = Style(c, style.fg_color, style.bg_color, style.font_style)\\n self.canvas[y][x + i] = text_style\",\n \"def insert_line(self, text, x, y, color=defcolor):\\n for i in range(len(text)):\\n x2 = x + (i * (self.font['width'] + 1))\\n if text[i] in self.font:\\n self.insert_bitmap(self.font[text[i]], x2, y, color)\\n else:\\n self.insert_bitmap(self.font['?'], x2, y, color)\",\n \"def draw_single_bbox(self, image, x, color=None, label=None, line_thickness=2):\\n\\n # get pixel locations of top left and bottom right corners\\n corner1 = (int(x[0] * self.x), int(x[1] * self.y))\\n corner2 = (int(x[2] * self.x), int(x[3] * self.y))\\n\\n cv2.rectangle(image, corner1, corner2, color, thickness=line_thickness, lineType=cv2.LINE_AA)\\n\\n if label:\\n font_thickness = max(line_thickness - 1, 1)\\n t_size = cv2.getTextSize(label, 0, fontScale= line_thickness / 3, thickness=font_thickness)[0]\\n corner2 = corner1[0] + t_size[0], corner1[1] - t_size[1] - 3\\n\\n cv2.rectangle(image, corner1, corner2, color, -1, cv2.LINE_AA) # filled\\n\\n cv2.putText(image, \\n label, \\n (corner1[0], corner1[1] - 2), 0, \\n line_thickness / 3, \\n [225, 255, 255], \\n thickness=font_thickness, \\n lineType=cv2.LINE_AA)\\n\\n return image\",\n \"def create_overlay():\\n c = canvas.Canvas('simple_form_overlay.pdf')\\n\\n c.drawString(115, 650, 'Mike')\\n c.drawString(115, 600, 'Driscoll')\\n c.drawString(115, 550, '123 Greenway Road')\\n c.drawString(115, 500, 'Everytown')\\n c.drawString(355, 500, 'IA')\\n c.drawString(115, 450, '55555')\\n\\n c.save()\",\n \"def drawText(text, font, surface, x, y, textcolour):\\r\\n textobj = font.render(text, 1, textcolour)\\r\\n textrect = textobj.get_rect()\\r\\n textrect.topleft = (x, y)\\r\\n surface.blit(textobj, textrect)\",\n \"def display_text(text, x, y, size):\\r\\n font = pygame.font.Font('freesansbold.ttf', size)\\r\\n text_surf, text_rect = text_objects(text, font)\\r\\n text_rect.center = (x, y)\\r\\n display.blit(text_surf, text_rect)\",\n \"def tagview(tab,label,x,y):\\r\\n font = cv2.FONT_HERSHEY_SIMPLEX\\r\\n col=classifc[label]\\r\\n labnow=classif[label]\\r\\n# print (labnow, text)\\r\\n if label == 'back_ground':\\r\\n deltay=30\\r\\n else:\\r\\n# deltay=25*((labnow-1)%5)\\r\\n deltay=40+10*(labnow-1)\\r\\n\\r\\n viseg=cv2.putText(tab,label,(x, y+deltay), font,0.3,col,1)\\r\\n return viseg\",\n \"def watermark_text(self, text, color, font_type, font_size, pos_name):\\n image = Image.open(self.path)\\n drawing = ImageDraw.Draw(image)\\n text = text\\n font = ImageFont.truetype(font_type, font_size)\\n self.watermark_width, self.watermark_height = drawing.textsize(text, font)\\n pos = self.watermark_position(pos_name)\\n\\n drawing.text(pos, text, fill=color, font=font)\\n parent_dir = os.path.dirname(self.output_path)\\n\\n if not os.path.exists(parent_dir):\\n os.makedirs(parent_dir)\\n\\n image.save(self.output_path)\\n return os.path.exists(self.output_path)\",\n \"def __init__(self,text,position,xmid = False,fontsize = 36,backgroundcolor = (200,200,200),surface = None):\\n pygame.font.init()\\n basicfont = pygame.font.Font(None,fontsize)\\n\\n # Figure out the size of the image that will be drawn on and create that\\n # image\\n self.linewidths = []\\n for x in text:\\n self.texttemp = basicfont.render(x,0,(1,1,1))\\n self.linewidths.append(self.texttemp.get_width())\\n self.imagewidth = basicfont.render(text[self.linewidths.index(max(self.linewidths))],0,(1,1,1)).get_width()\\n self.imageheight = len(text) * fontsize + (len(text)-1) * 10\\n self.image = pygame.Surface((self.imagewidth,self.imageheight))\\n self.image.fill(backgroundcolor)\\n\\n # Draw the text to the image\\n n = 0\\n for x in text:\\n self.texttemp = basicfont.render(x,0,(1,1,1))\\n self.image.blit(self.texttemp,(0,n * fontsize + n * 10))\\n n +=1\\n\\n # Set the position of the text. If xmid is passed in as true set the\\n # pos to the top middle pixel of the text\\n if xmid:\\n self.pos = (position[0] - int(self.image.get_width() / 2),position[1])\\n else:\\n self.pos = position\\n\\n # Set up the information that will be needed to blit the image to a\\n # surface\\n self.blitinfo = (self.image, self.pos)\\n\\n # automatically blit the text onto an input surface\\n if surface:\\n surface.blit(*self.blitinfo)\",\n \"def plot_area_text(data_obj, test_num, yi, xpos, ha, va, fs, dfs):\\n\\n plt.text(xpos[0], yi, 'Test ' + str(test_num) + ': Image area', ha=ha, va=va, fontsize=fs)\\n str1 = str(data_obj.image_area[0]) + ' by ' + str(data_obj.image_area[1]) + ' pixels'\\n plt.text(xpos[1], yi, str1, ha=ha, va=va, fontsize=fs - dfs)\\n plt.text(xpos[3], yi, '$\\\\geq$ 1000 by 1000 pixels', ha=ha, va=va, fontsize=fs - dfs)\",\n \"def write_text(text_to_write, image_size):\\n image_text = Image.new(\\\"RGB\\\", image_size)\\n font = ImageFont.load_default().font\\n drawer = ImageDraw.Draw(image_text)\\n\\n #Text wrapping. Change parameters for different text formatting\\n margin = offset = 10\\n for line in textwrap.wrap(text_to_write, width=60):\\n drawer.text((margin,offset), line, font=font)\\n offset += 10\\n return image_text\",\n \"def draw_text(image, text, font, size, color, pos_name, shadow=False):\\n # Make a copy of the image\\n image = image.copy()\\n\\n draw = ImageDraw.Draw(image)\\n\\n width, height = get_size(image)\\n\\n rgb_colors = assigning_colors()\\n\\n try:\\n font_type = ImageFont.truetype(font, size)\\n except ValueError:\\n raise Exception(\\\"Fonte ou tamanho de fonte inválidos\\\")\\n\\n w, h = draw.textsize(text, font=font_type)\\n\\n position = get_position(height, h, width, w, pos_name, shadow=shadow)\\n\\n if color in rgb_colors:\\n color = rgb_colors[color]\\n else:\\n raise Exception(\\\"Cor inválida\\\")\\n\\n draw.text(xy=position, text=text, fill=color,\\n font=font_type, align=check_alignment(pos_name))\\n\\n return image\",\n \"async def outline_text(draw_surface, coords, draw_text, font):\\n draw = partial(draw_surface.text, text=draw_text, font=font,\\n fill=\\\"black\\\")\\n for offset_pair in product(range(-1, 2), repeat=2):\\n draw((coords[0]+offset_pair[0], coords[1]+offset_pair[1]))\\n draw(coords, fill=\\\"white\\\")\",\n \"def draw_label(self, image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=0.8, thickness=1):\\n # gets the size of the label\\n size = cv2.getTextSize(label, font, font_scale, thickness)[0]\\n # where the position is\\n x, y = point\\n # gets the rectangle size\\n cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)\\n cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)\",\n \"def mytext(x,y,s,**kwargs):\\n # we take care of this one\\n model = kwargs.pop('model', None)\\n if model:\\n th = text(x,y,model,**kwargs)\\n draw()\\n x0,y0,w,h = th.get_window_extent().bounds\\n gca().texts.remove(th)\\n x = x0\\n y = y0\\n kwargs['transform'] = matplotlib.transforms.IdentityTransform()\\n kwargs['horizontalalignment'] = 'left'\\n kwargs['verticalalignment'] = 'baseline'\\n# print x,y,kwargs\\n return text(x,y,s,**kwargs)\",\n \"def _write_overlay_info(self):\\n cv2.putText(\\n img=self.output,\\n text=f'X: {float(self.estimated_distance[0]):6.2f} m',\\n org=(25, 25),\\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\\n color=(0, 0, 255),\\n fontScale=0.5\\n )\\n cv2.putText(\\n img=self.output,\\n text=f'Y: {float(self.estimated_distance[1]):6.2f} m',\\n org=(25, 50),\\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\\n color=(0, 0, 255),\\n fontScale=0.5\\n )\\n cv2.putText(\\n img=self.output,\\n text=f'Z: {float(self.estimated_distance[2]):6.2f} m',\\n org=(25, 75),\\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\\n color=(0, 0, 255),\\n fontScale=0.5\\n )\\n cv2.putText(\\n img=self.output,\\n text=f'Rotation: {float(self.estimated_rotation):6.2f} rad',\\n org=(25, 100),\\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\\n color=(0, 0, 255),\\n fontScale=0.5\\n )\",\n \"def print_text(TINY_FONT, x, y, text, color = white):\\n text_image = TINY_FONT.render(text, True, color)\\n gameDisplay.blit(text_image, (x,y))\",\n \"def draw_date(img,txs):\\n fontSize=img.size[1]/20\\n draw = ImageDraw.Draw(img) # new draw instance \\n arial=load_font(fontSize)\\n tsize=draw.textsize(txs,font=arial)\\n #draw.text((img.size[0]-tsize[0]-10,img.size[1]-(fontSize+5)),txs,fill=(200,200,255),font=arial)\\n draw.text((10,img.size[1]-(fontSize+5)),txs,fill=(50,75,255),font=arial)\\n return img\",\n \"def draw_text(screen, text, size, x, y):\\r\\n font = pygame.font.Font(font_name, size)\\r\\n text_surface = font.render(text, True, WHITE)\\r\\n text_rect = text_surface.get_rect()\\r\\n text_rect.midtop = (x, y)\\r\\n screen.blit(text_surface, text_rect)\\r\\n pygame.display.update()\",\n \"def annotate(image_path, bounds, classes, scores, labels):\\n image = Image.open(image_path)\\n draw = ImageDraw.Draw(image)\\n for bound, cls, score in zip(bounds, classes, scores):\\n draw.rectangle([(bound[0], bound[1]), (bound[2], bound[3])], None, (0, 255, 255))\\n text = str(labels[cls]) + str(round(score, 3))\\n text_size = draw.textsize(text)\\n draw.rectangle([(bound[0], bound[1]), (bound[0] + text_size[0], bound[1] + text_size[1])], (0, 255, 255))\\n draw.text((bound[0], bound[1]), text, (0, 0, 0))\\n return image\",\n \"def make_annotation_vertical(x, y):\\n text = str(x)\\n if len(text) > 33:\\n text = text[0:33]+\\\"..\\\"\\n return Annotation(\\n text=text, # text is the y-coord\\n showarrow=False, # annotation w/o arrows, default is True\\n x=x, # set x position\\n xref='x', # position text horizontally with x-coords\\n xanchor='center', # x position corresp. to center of text\\n yref='y', # set y position\\n yanchor='bottom', # position text vertically with y-coords\\n y=0, # y position corresp. to top of text\\n textangle=-90, # Rotating the text upwards\\n font=annotation.Font(\\n color='#FFFFFF', # set font color\\n size=13 # and size\\n )\\n )\",\n \"def setScreenText(text: str, x: int, y: int):\\n pass\",\n \"def apply_png_overlay(image, overlay, start_row, start_col, height=200, width=200):\\n\\n alpha_s = overlay[:, :, 3] / 255.0\\n alpha_l = 1.0 - alpha_s\\n for c in range(0, 3):\\n image[start_row:start_row+height, start_col:start_col+width, c] = (\\n alpha_s * overlay[:, :, c] +\\n alpha_l * image[start_row:start_row+height, start_col:start_col+width, c])\\n \\n return image\",\n \"def draw_grid(image, bbox, label, outline=\\\"white\\\", input_format=\\\"xyxy\\\"):\\n draw = ImageDraw.Draw(image)\\n if input_format == \\\"xywh\\\":\\n bbox = xywh_xyxy(bbox)\\n \\n for num, i in enumerate(bbox):\\n x0, y0, x1, y1 = i\\n l = label[num]\\n draw.rectangle([x0, y0, x1, y1], outline=outline)\\n draw.text((x0,y0), l, fill=(255, 0, 0))\\n return image\",\n \"def _draw_text_outline(img: Image.Image, font: ImageFont.ImageFont, text: str, fill_color: Color, line_color: Color, origin: Tuple[int, int]) -> None:\\n draw = ImageDraw.Draw(img)\\n # set to render text unaliased (https://mail.python.org/pipermail/image-sig/2005-August/003497.html)\\n draw.fontmode = \\\"1\\\"\\n # correct for font offset\\n x_offset, y_offset, _, _ = font.getbbox(text)\\n # These values were tweaked by hand to get a better centered text\\n x, y = (origin[0]-2*x_offset, origin[1]-y_offset//2)\\n origin = (x, y)\\n # draw border\\n draw.text((x - 1, y), text, font=font, fill=line_color)\\n draw.text((x + 1, y), text, font=font, fill=line_color)\\n draw.text((x, y - 1), text, font=font, fill=line_color)\\n draw.text((x, y + 1), text, font=font, fill=line_color)\\n # draw text over it\\n draw.text(origin, text, font=font, fill=fill_color)\",\n \"def add_text(img, text, type):\\n assert type in ['title', 'caption']\\n txt = PIL.Image.new('RGBA', img.size, (255,255,255,0))\\n d = PIL.ImageDraw.Draw(txt)\\n\\n if type == 'caption':\\n size = 10\\n else:\\n size = 25\\n\\n try:\\n fnt = PIL.ImageFont.truetype('FreeSans.ttf', size=size)\\n except IOError:\\n fnt = PIL.ImageFont.load_default()\\n\\n font_size = fnt.getsize(text)\\n\\n if type == 'caption':\\n tupper = txt.height - font_size[1] - 20\\n tleft = 10\\n elif type == 'title':\\n tupper = 10\\n tleft = (img.width / 2) - (font_size[0] / 2)\\n\\n d.text((tleft, tupper), text, font=fnt, fill=(255, 255, 255, 255))\\n img = PIL.Image.alpha_composite(img, txt)\\n\\n return(img)\",\n \"def draw_on_image(self, img):\\n image = Image(img)\\n\\n # If the overlay has not expired, draw on the plate highlight and/or the status message\\n if not self.has_expired():\\n image.draw_text(self._text, image.center(), self._color,\\n centered=True, scale=2, thickness=3)\",\n \"def draw_boxes_texts(img,\\n boxes,\\n texts=None,\\n colors=None,\\n line_width=1,\\n draw_start=False,\\n box_format='x1y1x2y2'):\\n assert box_format in ('x1y1x2y2', 'x1y1wh', 'xywh', 'xywha',\\n 'polygon'), 'not supported box format!'\\n img = imread(img)\\n if len(boxes) == 0:\\n return img\\n boxes = copy.deepcopy(boxes)\\n # convert bbox type to int\\n if not isinstance(boxes, np.ndarray):\\n if box_format != 'polygon':\\n boxes = np.array(boxes)\\n if box_format != 'xywha':\\n boxes = boxes.astype(np.int)\\n if len(boxes.shape) == 1:\\n boxes = [boxes]\\n else:\\n boxes = [list(map(int, box)) for box in boxes]\\n else:\\n boxes = boxes.astype(np.int)\\n if texts is not None and not isinstance(texts, (list, np.ndarray)):\\n texts = [texts]\\n if isinstance(img, Image.Image):\\n img = cv.cvtColor(np.asarray(img), cv.COLOR_RGB2BGR)\\n if not isinstance(img, np.ndarray):\\n return\\n if colors == 'random':\\n colors = np.random.randint(0, 255, size=(len(boxes), 3))\\n colors = [tuple(map(int, color)) for color in colors]\\n text_color = (0, 255, 255)\\n thickness = line_width\\n font = cv.FONT_HERSHEY_SIMPLEX\\n for idx, box in enumerate(boxes):\\n # default color: red, BGR order\\n box_color = (0, 0, 255) if colors is None else colors[idx]\\n if box_format == 'x1y1x2y2':\\n cv.rectangle(img, tuple(box[0:2]),\\n tuple(box[2:4]), box_color, thickness)\\n elif box_format == 'x1y1wh':\\n box[0:4] = cvtools.x1y1wh_to_x1y1x2y2(list(box[0:4]))\\n cv.rectangle(img, tuple(box[0:2]),\\n tuple(box[2:4]), box_color, thickness)\\n elif box_format == 'xywh':\\n box[0:4] = cvtools.xywh_to_x1y1x2y2(list(box[0:4]))\\n cv.rectangle(img, tuple(box[0:2]),\\n tuple(box[2:4]), box_color, thickness)\\n elif box_format == 'xywha':\\n rrect = tuple(box[:2]), tuple(box[2:4]), box[4]\\n box = cv.boxPoints(rrect).astype(np.int)\\n # box = np.int0(box)\\n cv.drawContours(img, [box], 0, box_color, thickness)\\n box = box.reshape((-1,))\\n elif box_format == 'polygon':\\n # for i in np.arange(2, len(box), 2):\\n # cv.line(img, tuple(box[i-2:i]),\\n # tuple(box[i:i+2]), box_color, thickness)\\n # cv.line(img, tuple(box[-2:]),\\n # tuple(box[:2]), box_color, thickness)\\n # 如果img内存非连续，cv的所有绘制都会失效\\n cv.polylines(img, np.int32([np.array(box).reshape(-1, 2)]), 1, box_color, thickness)\\n # cv.line(img, tuple(box[:2]), tuple(box[2:4]), box_color, thickness)\\n # cv.line(img, tuple(box[2:4]), tuple(box[4:6]), box_color, thickness)\\n # cv.line(img, tuple(box[4:6]), tuple(box[6:8]), box_color, thickness)\\n # cv.line(img, tuple(box[6:]), tuple(box[:2]), box_color, thickness)\\n if draw_start:\\n cv.circle(img, tuple(box[:2]),\\n radius=5, color=text_color, thickness=-1)\\n if texts is not None:\\n cv.putText(img, texts[idx],\\n (box[0]+2, box[1]-2), font, 0.5, text_color, 1)\\n return img\",\n \"def write_text(text_to_write, image_size):\\n image_text = Image.new(\\\"RGB\\\", image_size)\\n font = ImageFont.load_default().font\\n drawer = ImageDraw.Draw(image_text)\\n\\n # Text wrapping. Change parameters for different text formatting\\n margin = offset = 10\\n for line in textwrap.wrap(text_to_write, width=60):\\n drawer.text((margin, offset), line, font=font)\\n offset += 10\\n return image_text\",\n \"def draw_text(display, font_name, text, size, color, x, y):\\n font = pg.font.Font(font_name, size)\\n text_surface = font.render(text, True, color)\\n text_rect = text_surface.get_rect()\\n text_rect.midtop = (x, y)\\n display.blit(text_surface, text_rect)\",\n \"def np_text(text, w_border=TILE_TEXT_W_BORDER, h_border=TILE_TEXT_H_BORDER, \\n font_size=TILE_TEXT_SIZE, text_color=TILE_TEXT_COLOR, background=TILE_TEXT_BACKGROUND_COLOR):\\n pil_img = pil_text(text, w_border, h_border, font_size,\\n text_color, background)\\n np_img = util.pil_to_np_rgb(pil_img)\\n return np_img\",\n \"def draw_bboxes_withindex(img,boxes, uids):\\n source = Image.fromarray(img)\\n draw = ImageDraw.Draw(source)\\n w2,h2 = (img.shape[0],img.shape[1])\\n \\n font = ImageFont.truetype('/usr/share/fonts/truetype/freefont/FreeSerif.ttf', 40)\\n #font = ImageFont.truetype('arial.ttf', 24)\\n\\n\\n idx = 0\\n\\n for b in boxes:\\n xmin,ymin,xmax,ymax = b\\n \\n for j in range(3):\\n draw.rectangle(((xmin+j, ymin+j), (xmax+j, ymax+j)), outline=\\\"red\\\")\\n draw.text((xmin+20, ymin+70), str(uids[idx]), font = font)\\n idx +=1\\n return source\",\n \"def write_text(size, text):\\n img = Image.new('RGB', size, (255, 255, 255))\\n draw = ImageDraw.Draw(img)\\n font = ImageFont.truetype('/Library/Fonts/Arial.ttf', size)\\n draw.text((10,10), text, (0, 0, 0), font=font)\\n return img\",\n \"def _draw_text_outline(img, font, text, fill_color, line_color, origin):\\n draw = ImageDraw.Draw(img)\\n # set to render text unaliased (https://mail.python.org/pipermail/image-sig/2005-August/003497.html)\\n draw.fontmode = \\\"1\\\"\\n # correct for font offset\\n x_offset, y_offset = font.getoffset(text)\\n # These values were tweaked by hand to get a better centered text\\n x, y = (origin[0]-2*x_offset, origin[1]-y_offset//2)\\n origin = (x, y)\\n # draw border\\n draw.text((x - 1, y), text, font=font, fill=line_color)\\n draw.text((x + 1, y), text, font=font, fill=line_color)\\n draw.text((x, y - 1), text, font=font, fill=line_color)\\n draw.text((x, y + 1), text, font=font, fill=line_color)\\n # draw text over it\\n draw.text(origin, text, font=font, fill=fill_color)\",\n \"def render(self):\\n if self.frame_pos:\\n self.pos = [\\n self.frame_pos[0] + self.position[0] - (self.size[0] / 2),\\n self.frame_pos[1] + self.position[1] - (self.size[1] / 2),\\n ]\\n if self.variable_text:\\n self.image = self.fontA.render(self.text, 1, self.color)\",\n \"def img_syll_over_text(data_word):\\n\\n step = 200\\n\\n x,y = syll_over_text(data_word)\\n\\n imgdata = BytesIO()\\n fig, ax = plt.subplots()\\n ax.plot(x,y)\\n plt.ioff()\\n plt.yticks(range(0,int(max(y) + 2),1))\\n plt.ylim(1, 4)\\n plt.xlim(step, len(x)+step)\\n plt.title(\\\"Syllables per Word (\\\" + str(step) + \\\" Word Moving Average)\\\")\\n plt.xlabel(\\\"Word Number\\\")\\n plt.tight_layout()\\n fig.savefig(imgdata, format = 'png', dpi = 200)\\n\\n return imgdata\",\n \"def DrawTextOneLine( x, y, text, textColor = 1, textSize = 0.04, NDC = True, halign = \\\"left\\\", valign = \\\"bottom\\\", skipLines = 0 ):\\n \\n halignMap = {\\\"left\\\":1, \\\"center\\\":2, \\\"right\\\":3}\\n valignMap = {\\\"bottom\\\":1, \\\"center\\\":2, \\\"top\\\":3}\\n \\n scaleLineHeight = 1.0\\n if valign == \\\"top\\\": scaleLineHeight = 0.8\\n if skipLines: text = \\\"#lower[%.1f]{%s}\\\" % (skipLines*scaleLineHeight,text)\\n \\n # Draw the text quite simply:\\n import ROOT\\n l = ROOT.TLatex()\\n if NDC: l.SetNDC()\\n l.SetTextAlign( 10*halignMap[halign] + valignMap[valign] )\\n l.SetTextColor( textColor )\\n l.SetTextSize( textSize )\\n l.DrawLatex( x, y, text )\\n return l\",\n \"def DrawTextOneLine( x, y, text, textColor = 1, textSize = 0.04, NDC = True, halign = \\\"left\\\", valign = \\\"bottom\\\", skipLines = 0 ):\\n \\n halignMap = {\\\"left\\\":1, \\\"center\\\":2, \\\"right\\\":3}\\n valignMap = {\\\"bottom\\\":1, \\\"center\\\":2, \\\"top\\\":3}\\n \\n scaleLineHeight = 1.0\\n if valign == \\\"top\\\": scaleLineHeight = 0.8\\n if skipLines: text = \\\"#lower[%.1f]{%s}\\\" % (skipLines*scaleLineHeight,text)\\n \\n # Draw the text quite simply:\\n import ROOT\\n l = ROOT.TLatex()\\n if NDC: l.SetNDC()\\n l.SetTextAlign( 10*halignMap[halign] + valignMap[valign] )\\n l.SetTextColor( textColor )\\n l.SetTextSize( textSize )\\n l.DrawLatex( x, y, text )\\n return l\",\n \"def draw_text(frame, text, coordinate, line_color=(0, 255, 124), normalized=True):\\n\\n x1 = coordinate[0]\\n y1 = coordinate[1]\\n x2 = coordinate[2]\\n y2 = coordinate[3]\\n\\n if normalized:\\n h = frame.shape[0]\\n w = frame.shape[1]\\n\\n x1 = int(x1 * w)\\n x2 = int(x2 * w)\\n\\n y1 = int(y1 * h)\\n y2 = int(y2 * h)\\n\\n font = cv.FONT_HERSHEY_SIMPLEX\\n bottom_left_corner_of_text = (x2, y1 + 10)\\n font_scale = 0.4\\n font_color = line_color\\n line_type = 1\\n\\n cv.putText(frame,\\n text,\\n bottom_left_corner_of_text,\\n font,\\n font_scale,\\n font_color,\\n line_type)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.733315","0.66948533","0.64981097","0.64721394","0.6364706","0.63315034","0.6134925","0.6109466","0.6081946","0.6055199","0.6026748","0.60190666","0.6002177","0.59274775","0.5924847","0.5903324","0.58893514","0.588931","0.5851297","0.58502513","0.5820367","0.5816728","0.58058596","0.5803391","0.57989585","0.57944256","0.57913893","0.5768966","0.57591724","0.57586086","0.5757248","0.5742477","0.57401156","0.57372004","0.57270306","0.5726712","0.5725204","0.57193065","0.5697161","0.5693353","0.5687909","0.5687594","0.5682616","0.5663396","0.5659834","0.56264144","0.56138545","0.559965","0.55879253","0.5585728","0.5578236","0.55773395","0.55761194","0.55501366","0.55500674","0.55446947","0.5542606","0.5540511","0.55363154","0.5530586","0.55220103","0.551695","0.5514263","0.55084246","0.5504001","0.5502769","0.5500085","0.54967326","0.54938376","0.5490584","0.5484281","0.54768103","0.547679","0.54660165","0.5463795","0.54617494","0.5456681","0.5456662","0.54526436","0.54524696","0.5435583","0.541874","0.5416494","0.54138386","0.5406636","0.5401241","0.5400036","0.5399814","0.5388916","0.5388072","0.5374038","0.5373112","0.53683215","0.5363357","0.53538394","0.535055","0.53500545","0.53455895","0.53455895","0.5343709"],"string":"[\n \"0.733315\",\n \"0.66948533\",\n \"0.64981097\",\n \"0.64721394\",\n \"0.6364706\",\n \"0.63315034\",\n \"0.6134925\",\n \"0.6109466\",\n \"0.6081946\",\n \"0.6055199\",\n \"0.6026748\",\n \"0.60190666\",\n \"0.6002177\",\n \"0.59274775\",\n \"0.5924847\",\n \"0.5903324\",\n \"0.58893514\",\n \"0.588931\",\n \"0.5851297\",\n \"0.58502513\",\n \"0.5820367\",\n \"0.5816728\",\n \"0.58058596\",\n \"0.5803391\",\n \"0.57989585\",\n \"0.57944256\",\n \"0.57913893\",\n \"0.5768966\",\n \"0.57591724\",\n \"0.57586086\",\n \"0.5757248\",\n \"0.5742477\",\n \"0.57401156\",\n \"0.57372004\",\n \"0.57270306\",\n \"0.5726712\",\n \"0.5725204\",\n \"0.57193065\",\n \"0.5697161\",\n \"0.5693353\",\n \"0.5687909\",\n \"0.5687594\",\n \"0.5682616\",\n \"0.5663396\",\n \"0.5659834\",\n \"0.56264144\",\n \"0.56138545\",\n \"0.559965\",\n \"0.55879253\",\n \"0.5585728\",\n \"0.5578236\",\n \"0.55773395\",\n \"0.55761194\",\n \"0.55501366\",\n \"0.55500674\",\n \"0.55446947\",\n \"0.5542606\",\n \"0.5540511\",\n \"0.55363154\",\n \"0.5530586\",\n \"0.55220103\",\n \"0.551695\",\n \"0.5514263\",\n \"0.55084246\",\n \"0.5504001\",\n \"0.5502769\",\n \"0.5500085\",\n \"0.54967326\",\n \"0.54938376\",\n \"0.5490584\",\n \"0.5484281\",\n \"0.54768103\",\n \"0.547679\",\n \"0.54660165\",\n \"0.5463795\",\n \"0.54617494\",\n \"0.5456681\",\n \"0.5456662\",\n \"0.54526436\",\n \"0.54524696\",\n \"0.5435583\",\n \"0.541874\",\n \"0.5416494\",\n \"0.54138386\",\n \"0.5406636\",\n \"0.5401241\",\n \"0.5400036\",\n \"0.5399814\",\n \"0.5388916\",\n \"0.5388072\",\n \"0.5374038\",\n \"0.5373112\",\n \"0.53683215\",\n \"0.5363357\",\n \"0.53538394\",\n \"0.535055\",\n \"0.53500545\",\n \"0.53455895\",\n \"0.53455895\",\n \"0.5343709\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62469816"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":22389,"cells":{"query":{"kind":"string","value":"Generate a name for a file using the description and channel"},"document":{"kind":"string","value":"def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_file_name(well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def file_name(id, title, kind=\"src\"):\n fn_template = conf.template_source_file_name\n if kind == \"tst\":\n fn_template = conf.template_test_file_name\n\n return fn_template.format(id=id, title=title.replace(\"-\", \"_\"))","def _generate_overlay_file_name(self, well, channel, desc):\n \n return \"c\" + channel + \"_w\" + well + \"_\" + desc + \".png\"","def generate_file_name(entry):\n return str_for_file(u'{name}, {year}, {title}'.format(\n year=entry['year'],\n name=get_last_name(entry['author'][0]),\n title=entry['title']\n ))","def create_file_name(self):\n # create a unique id for the file name\n index = self.helpers.alpha_uuid()\n\n filename = self.form['FieldStorage'][self.image_cid].filename\n extension = guess_extension(guess_type(filename)[0])\n return ( # concatenates the following data\n self.articleData.get('directory') + # directory\n '/' + # slash\n self.articleData.get('article_name') + # the article name\n '-' + # hyphen character\n index + # the id of the image\n extension\n )","def create_final_name(fname, date, fc_id, sample_name):\n \n # Split the file name according to CASAVA convention\n m = re.match(r'(\\S+?)_(?:[ACGTN\\-]+|NoIndex|Undetermined)_L0*(\\d+)_R(\\d)_\\d+\\.fastq(.*)', fname)\n if m is not None:\n lane = m.group(2)\n read = m.group(3)\n ext = m.group(4)\n else:\n # Split the file name according to bcbb convention\n m = re.match(r'(\\d+)_(\\d+)_([^_]+)_(\\d+)_(?:nophix_)?(\\d+)_fastq.txt(.*)', fname)\n if m is None:\n raise ValueError(\"Could not parse file name {:s} correctly!\".format(fname))\n lane = m.group(1)\n read = m.group(5)\n ext = m.group(6)\n \n dest_file_name = \"{:s}.fastq{:s}\".format(\"_\".join([lane,\n date,\n fc_id,\n sample_name,\n read]),\n ext.replace('..','.'))\n return dest_file_name","def collected_filename(cfg, collect_dir, i=None):\n if i is not None:\n file = cfg[\"files\"][i]\n else:\n file = cfg[\"file\"]\n ext = path.splitext(file)[1]\n name = cfg[\"id\"]\n if i is not None:\n name += \"_\" + str(i)\n return path.join(collect_dir, name + ext)","def _generate_filename(doc_type, login, *args):\n filename = []\n filename.append(doc_type)\n filename.append(login)\n for item in args:\n filename.append(item)\n filename.append(datetime.datetime.now().isoformat(timespec='microseconds'))\n filename = '_'.join(filename)\n return filename","def content_file_name(instance, filename):\r\n return '/'.join([str(instance.app.publisher.id), str(instance.app.id), filename])","def construct_name_file(size_sample, randomness, pos_equal_neg, kernel):\n if randomness:\n randomness = \"rand\"\n else:\n randomness = \"nrand\"\n\n if pos_equal_neg:\n pos_equal_neg = \"pos-neg-eq\"\n else:\n pos_equal_neg = \"pos-neg-neq\"\n\n return \"{}_{}_{}_{}.json\".format(size_sample, randomness, pos_equal_neg, kernel)","def get_nomenclature_channel_fname(czi_fname, nomenclature_file, channel_name, ext='.inr.gz'):\n # - Read NOMENCLATURE file defining naming conventions:\n n_names = get_nomenclature_name(nomenclature_file)\n return n_names[czi_fname]+\"/\", n_names[czi_fname] + \"_\" + channel_name + ext","def generate_filename(player_name):\n name = player_name.split()\n filename = '_'.join(name).lower()\n return filename","def generate_glider_filename(description):\n filename = (\n \"{glider}-{year:d}-{day:03d}-{mission:d}-{segment}.{type}\".format(**description)\n )\n return os.path.join(description['path'], filename)","def generate_namefile(pathfolder, methodvalues):\n datestr = datetime.datetime.now().date().strftime('%F')\n paramsstr = str(hash(str(methodvalues)))\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\n namefile = os.path.join(pathfolder, namefile)\n return namefile","def channel_name(radio_id: int, channel_id: int) -> str:\n return f\"COMM{radio_id} Ch {channel_id}\"","def generate_filename(filename: str) -> str:\n return f\"{str(uuid.uuid4())}.{get_extension(filename)}\"","async def filename_generator(self):\n chars = list(string.ascii_letters+string.digits)\n name = ''\n for i in range(random.randint(9, 25)):\n name += random.choice(chars)\n\n if name not in self.player['audio_files']:\n return name\n\n return await self.filename_generator()","def gen_file_name(filename, path=UPLOAD_FOLDER):\n\n i = 1\n while os.path.exists(os.path.join(path, filename)):\n name, extension = os.path.splitext(filename)\n filename = '%s_%s%s' % (name, str(i), extension)\n i += 1\n\n return filename","def generate_filename(playlist_or_album_name, user_id_or_artist_id=None):\n filename = ''\n if user_id_or_artist_id:\n filename += user_id_or_artist_id + '_'\n filename += playlist_or_album_name + '_' + str(time_ns())\n return filename","def _get_file_name(name: types.TSeedName) -> str:\n return f\"{name}.yml\"","def get_file_name(self):\n\n return \"%s - %s\" % (self.get_tags()[\"artist\"], self.get_tags()[\"title\"])","def generateFilename(self, name):\n return self.context.generateUniqueId(type_name='Module')","def filename(i):\n rand_name = os.path.join(os.getcwd(), \"input-%d.txt\" % i)\n ref_name = os.path.join(os.getcwd(), \"input-%d.ref\" % i)\n return rand_name, ref_name","def create_filename (self):\n\t\tassert self.__patient_name and self.__location_name, \"New filename could not be determined, one or more needed arguments is empty!\"\n\t\t_patient_name = self.__patient_name.split(' ')\n\t\t_patient_name.reverse()\n\t\t\n\t\treturn os.path.join(os.path.dirname(self.file._path), \"%s MR %s%s\" % (self.__location_name, ', '.join(_patient_name).upper(), self._file.extension))","def filename(self):\n return '%s%s' % (self.identifier, self.extension)","def generate_report_file_name(args: Dict[str, Any]) -> str:\n return (\n f\"{args.get('report_type', '').lower().replace(' ', '_')}_fireeye_\"\n f\"{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.\"\n f\"{args.get('type', REPORT_TYPE_ALLOWED_FORMAT[args.get('report_type', '')][0])}\"\n )","def generate_file_name(hour, minute):\n hour = str(hour)\n if len(hour) == 1:\n hour = \"0\" + hour\n minute = str(minute)\n if len(minute) == 1:\n minute = \"0\" + minute\n file_name = date + \"--\" + hour + special_char + minute + special_char + \"00,00.mvol\"\n\n # print \"filename: \",file_name\n return file_name","def filename(self):\n translator = {ord(\" \"): \"_\", ord(\",\"): None}\n return f'{self._full_name.translate(translator)}.txt'","def generate_file_filename(instance, filename):\n return _generate_filename(instance, filename, 'photos')","def temp_name(self, filename):\n if self.params.get('nopart', False) or filename == '-' or \\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\n return filename\n return filename + '.part'","def get_alternative_name(self, file_root, file_ext):\n return \"%s_%s%s\" % (file_root, get_random_string(7), file_ext)","def get_display_name(self, short=False):\n if self.filename is None:\n return '[New file]'\n elif short:\n return os.path.basename(self.filename)\n else:\n return self.filename","def file_name(product, ext='json'):\n return f\"./output/{product}_{datetime.now().strftime('%Y-%m-%d_%H%M%S')}_transformed_{version}.{ext}\"","def generate_name(file_name, index):\n name = ''\n with open(file_name) as names:\n for i, line in enumerate(names):\n if i == index:\n name += line.split(None, 1)[0]\n return name","def get_file_name(self):\n return str(self.get_file())","def get_download_file_name(self):\n # Use 'unknown' if the course instance does not have a term\n if self.course_instance.term:\n term = self.course_instance.term.get_url_name()\n else:\n term = 'unknown'\n\n return '{course}-{term}-{number}-{instructors}-{type}{ext}'.format(\n course=self.course_instance.course.get_url_name(),\n term=term,\n number=self.exam_number,\n instructors='_'.join([i.last_name for i in self.instructors]),\n type=self.exam_type,\n ext=self.file_ext)","def get_file_name() -> str:\n import uuid\n uniq_append_string = uuid.uuid4().hex\n return \"LOCAL_STORAGE_{}\".format(uniq_append_string)","def generateFileName(self):\n return 'Covid' + self.map_type + '.html'","def getNextFilename(self, cmd):\n \n self.actor.exposureID += 1\n path = os.path.join(\"$ICS_MHS_DATA_ROOT\", 'mcs')\n path = os.path.expandvars(os.path.expanduser(path))\n\n if not os.path.isdir(path):\n os.makedirs(path, 0o755)\n \n return os.path.join(path, 'MCSA%010d.fits' % (self.actor.exposureID))","def generate_image_filename():\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\n return 'CCTV_{0}.jpg'.format(now)","def generate_file_name(self):\n self._session_iterator = None # New file invalidate old interator\n self._img_count += 1\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\n return self.current_file","def mk_filename(pattern, queryresponserow, resp, url):\n name = None\n if resp:\n cdheader = resp.headers.get(\"Content-Disposition\", None)\n if cdheader:\n _, params = parse_header(cdheader)\n name = params.get('filename', \"\")\n # Work around https://github.com/sunpy/sunpy/issues/3372\n if name.count('\"') >= 2:\n name = name.split('\"')[1]\n\n # This is a hack to to prevent IRIS data from being labelled as XML files\n if name is None and \"VOEvent_IRIS\" not in queryresponserow['fileid']:\n # Advice from the VSO is to fallback to providerid + fileid for a filename\n # As it's possible multiple providers give the same fileid.\n # However, I haven't implemented this yet as it would be a breaking\n # change to the filenames we expect.\n fileid = queryresponserow['fileid']\n\n # Some providers make fileid a path\n # Some also don't specify a file extension, but not a lot we can do\n # about that.\n name = fileid.split(\"/\")[-1]\n\n # If somehow we have got this far with an empty string, fallback to url segment\n if not name:\n name = url.split('/')[-1]\n\n # Remove any not-filename appropriate characters\n name = slugify(name)\n\n # If absolutely everything else fails make a filename based on download time\n if not name:\n name = f\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\"\n\n fname = pattern.format(file=name,\n **queryresponserow.response_block_map)\n\n return fname","def init_data_file_name():\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\n filename = 'show-commands-' + now + \".txt\"\n return filename","def create_random_file_name():\n\n def random_file_name_factory():\n length = random.randint(10, 15)\n chars = string.ascii_letters + string.digits + \"-_\"\n return f\"{''.join(random.choice(chars) for _ in range(length))}.jpg\"\n\n return random_file_name_factory","def filename(self):\n return self.config.get('filename', self.id) + f'_{self.file_suffix}'","def get_title(src_name, src_type=None):\n if src_type == 'tcp':\n return '{0}:{1}'.format(*src_name)\n return os.path.basename(src_name)","def get_resampled_filename(filepath):\n try:\n filepath = filepath.encode('utf-8')\n except UnicodeDecodeError:\n pass\n return 'tmp_%s.flac' % md5(filepath).hexdigest()","def format_filename(title: str, id: Any, ext: str = \".\", dirFormat=None):\r\n ...","def _generate_filename(instance, filename, prefix):\n md5 = hashlib.md5()\n md5.update(struct.pack('f', time.time()))\n for chunk in instance.file.chunks():\n md5.update(chunk)\n extension = os.path.splitext(filename)[1]\n return os.path.join(prefix, md5.hexdigest() + extension)","def modpricesetter_get_file_name(self):\r\n year, month, day = self._get_market_year_month_day_as_str()\r\n interval_number = self._get_interval_number_as_str()\r\n base_name = \"NEMPriceSetter_{year}{month}{day}{interval_number}00.xml\"\r\n name = base_name.format(\r\n year=year, month=month, day=day, interval_number=interval_number\r\n )\r\n path_name = Path(self.cache_folder) / name\r\n name_OCD = name.replace(\".xml\", \"_OCD.xml\")\r\n path_name_OCD = Path(self.cache_folder) / name_OCD\r\n name_zero = name.replace(\".xml\", \"00.xml\")\r\n path_name_zero = Path(self.cache_folder) / name_zero\r\n if os.path.exists(path_name):\r\n return name\r\n elif os.path.exists(path_name_OCD):\r\n return name_OCD\r\n elif os.path.exists(path_name_zero):\r\n return name_zero\r\n else:\r\n return name","def source_file_name_feature(self):\n return \"_\".join((C_FILE_NAME, self.file_image_name.value))","def get_filename(out_dir, file_date, extension):\n return path.join(out_dir, f'CrossrefCitations_{file_date}.{extension}')","def create_filename(value):\n return '%s.mp3' % slugify(value, u'_')","def generate_filename(\n radar, field, sweep, ext=\"png\", datetime_format=\"%Y%m%d%H%M%S\", use_sweep_time=False\n):\n name_s = generate_radar_name(radar).replace(\" \", \"_\")\n field_s = field.replace(\" \", \"_\")\n if use_sweep_time:\n time_s = generate_radar_time_sweep(radar, sweep).strftime(datetime_format)\n else:\n time_s = generate_radar_time_begin(radar).strftime(datetime_format)\n sweep_s = str(sweep).zfill(2)\n return f\"{name_s}_{field_s}_{sweep_s}_{time_s}.{ext}\"","def filename(N, Dr, g, launch):\n\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\n (N, Dr, g, launch)))","def unique_filename(data):\n file = data\n get_ext = file.filename.split(\".\")[-1]\n new_name = \"%s.%s\" % (uuid.uuid4().hex, get_ext)\n return new_name","def file_on_disk_name(instance, filename):\n return generate_file_on_disk_name(instance.checksum, filename)","def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\n\tnew_filename = time.strftime(\"%d-%m-%Y_\")\n\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\n\tnew_filename = new_filename + \"_P\" + str(image_class)\n\treturn new_filename","def content_file_name(self, filename):\n ext = filename.split('.')[-1]\n filename = \"%s_%s.%s\" % (filename, self.id, ext)\n return os.path.join('pictures/static/pictures/', filename)","def _build_name(self):\n self.ctrl_name = NameUtils.get_unique_name(self.asset,\n self.side,\n self.part,\n \"ctrl\")","def get_custom_filename(existing_files: List[str]) -> str:\n questions = [\n {\n 'type': 'input',\n 'name': 'custom_filename',\n 'message': 'Name your new sample :',\n 'default': get_default_filename(),\n 'validate': lambda text: (\n (len(re.findall(r'^[A-Za-z0-9_\\-.]{3,40}$', text)) > 0\n and text+'.smp' not in existing_files\n ) or\n 'Typed file name contains illegal characters or already exist'\n )\n }\n ]\n return prompt(questions, style=custom_style_2)['custom_filename']+'.smp'","def output_file_name_maker(args):\n log.debug(\"Entering output_file_name_maker()\")\n path = os.getcwd() + '/out_files/'\n if not os.path.isdir(path):\n os.mkdir(path)\n\n if args.output is None:\n out_file_name = path + args.input[:-4] + '_' + args.type + '_' + args.layer\n else:\n out_file_name = path + args.output\n\n log.debug(\"Exiting output_file_name_maker()\")\n return out_file_name","def _filename(self, corotid):\n from datasource import DataSource\n self.corotid = corotid\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\n \n query = \"\"\"SELECT run_code, hlfccdid, win_id \n FROM corot \n WHERE corotid = %d;\"\"\" % self.corotid\n result = self.corot.query(query)\n \n par = {'run': result[0][0],\n 'half': result[0][1].rstrip('RL'), \n 'win': result[0][2]}\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\n logger.info('%d = %s' % (corotid,filename))\n return filename","def make_img_name(file_ext='.png'):\r\n fn = []\r\n # format seqs and write out to temp file\r\n for i in range(0, 30):\r\n fn.append(choice(ALPHABET))\r\n return ''.join(fn) + file_ext","def update_filename(instance, filename):\n path = os.path.join(\"documents_analizer\", \"documents\")\n name = \"{}{}\".format(highly_random_name(),\n os.path.splitext(filename)[1])\n return os.path.join(path, name)","def download_filename_full(self, doc):\n # todo modify\n authors = \",\".join([x['name'] for x in doc.artists])\n author = re.sub(\"[\\\\\\\\/:*?\\\"<>|]\", '', authors.strip())\n mp3_name = re.sub(\"[\\\\\\\\/:*?\\\"<>|]\", '', doc['name'])\n name = os.path.join(author, \"%s - %s.mp4\" % (author, mp3_name))\n return name","def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\n filename = self.filename(filetype=filetype, format=format)\n #return Path(dir) / filename\n return filename","def filename(lang):\n filename = lang.replace('-', '_')\n filename = filename.lower()\n return filename","def _file_name(size):\n timestamp = str(int(time.time()))\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')","def generate_subdir(channel, energy=13):\n return os.path.join('%dTeV' % energy, channel, strftime(\"%d_%b_%y\"))","def _prettyfilename(self):\n return f'{self.title} ({self.subtype})'","def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\n filename = self.filename(filetype=filetype, format=format)\n # return Path(dir) / filename\n return filename","def _generate_output_name(extension):\n output_name = 'TDG_{:%Y-%m-%d_%H-%M-%S}.{}'.format(datetime.now(), extension)\n return output_name","def out_filename(self, filetype, dir, format='old'):\n filename = self.filename(filetype=filetype, format=format)\n return Path(dir) / filename","def _create_file_name(file_path):\r\n file_base, file_ext = os.path.splitext(file_path)\r\n if os.path.isfile(file_path):\r\n nfile = 1\r\n check = True\r\n while check:\r\n name_add = '0000' + str(nfile)\r\n file_path = file_base + \"_\" + name_add[-4:] + file_ext\r\n if os.path.isfile(file_path):\r\n nfile = nfile + 1\r\n else:\r\n check = False\r\n return file_path","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\n ext='.mif'):\n\n if basename == '':\n msg = 'Unable to generate filename for command %s. ' % self.cmd\n msg += 'basename is not set!'\n raise ValueError(msg)\n if cwd is None:\n cwd = os.getcwd()\n if change_ext:\n if suffix:\n suffix = ''.join((suffix, ext))\n else:\n suffix = ext\n if suffix is None:\n suffix = ''\n fname = fname_presuffix(basename, suffix=suffix,\n use_ext=False, newpath=cwd)\n return fname","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\n ext='.mif'):\n\n if basename == '':\n msg = 'Unable to generate filename for command %s. ' % self.cmd\n msg += 'basename is not set!'\n raise ValueError(msg)\n if cwd is None:\n cwd = os.getcwd()\n if change_ext:\n if suffix:\n suffix = ''.join((suffix, ext))\n else:\n suffix = ext\n if suffix is None:\n suffix = ''\n fname = fname_presuffix(basename, suffix=suffix,\n use_ext=False, newpath=cwd)\n return fname","def newCalFileName(self, type, runBegin, runEnd='end'):\n \n path=os.path.join(self.cdir)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup,self.src)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup,self.src,type)\n if not os.path.exists(path): \n os.mkdir(path)\n return path+'/'+str(runBegin)+'-'+str(runEnd)+'.data'","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):\n if not basename:\n msg = \"Unable to generate filename for command %s. \" % self.cmd\n msg += \"basename is not set!\"\n raise ValueError(msg)\n\n if cwd is None:\n cwd = os.getcwd()\n if ext is None:\n ext = Info.output_type_to_ext(self.inputs.outputtype)\n if change_ext:\n suffix = \"\".join((suffix, ext)) if suffix else ext\n\n if suffix is None:\n suffix = \"\"\n fname = fname_presuffix(basename, suffix=suffix, use_ext=False, newpath=cwd)\n return fname","def get_file_name(replay_dir, template_name):\n suffix = '.json' if not template_name.endswith('.json') else ''\n file_name = f'{template_name}{suffix}'\n return os.path.join(replay_dir, file_name)","def _get_buildspec_filename(\n lambda_name, file_name):\n return '.'.join(\n ('buildspecs', lambda_name, file_name))","def generate_filename(ext,sha512base16_hash=None):\n## # Timestamp filename\n## timestamp = str(get_current_unix_time())\n## filename = timestamp+\".\"+ext\n # Base16 hash filename\n filename = sha512base16_hash+\".\"+ext\n return filename","def _make_fname(song, ext=None, av=None, subdir=None):\n # pylint: disable=E1103\n # Instance of 'bool' has no 'extension' member (some types not inferable)\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\n if not os.path.exists(ddir):\n os.makedirs(ddir)\n\n if ext:\n extension = ext\n\n else:\n stream = streams.select(streams.get(song),\n audio=av == \"audio\", m4a_ok=True)\n extension = stream['ext']\n\n # filename = song.title[:59] + \".\" + extension\n filename = song.title + \".\" + extension\n filename = os.path.join(ddir, mswinfn(filename.replace(\"/\", \"-\")))\n filename = filename.replace('\"', '')\n return filename","def get_output_filename(item: str, root: str, i: int) -> str:\n element_split = item.split(\"/\")\n item, ext = element_split[-1].split(\".\")\n if i < 0:\n return f\"{root}/{'/'.join(element_split[:-1])}/{item}.{ext}\"\n else:\n return f\"{root}/{'/'.join(element_split[:-1])}/{item}_aug{i}.{ext}\"","def outputFilename(name=\"\", ext=\"\", time=True):\n # get the date in the format specifed\n dateTime = datetime.now()\n dateTimeFormat = \"%Y-%m-%d__%H-%M-%S\" if time else \"%Y-%m-%d\"\n fileName = dateTime.strftime(dateTimeFormat)\n\n # construct the filename\n fileName = fileName + \"_\" + name if fileName != \"\" else fileName\n ext = \".\" + ext if ext != \"\" else \"\"\n\n return fileName + ext","def format_image_filename(device_image):\n return \"{}-{}-{}-{}.bit\".format(device_image.bitstream_type,\n device_image.pci_vendor,\n device_image.pci_device,\n device_image.uuid)","def generate_random_name(filename):\n ext = filename.split('.')[-1]\n rns = [random.randint(0, len(LETTER_SET) - 1) for _ in range(3)]\n name = ''.join([LETTER_SET[rn] for rn in rns])\n return \"{new_fn}.{ext}\".format(new_fn=name, ext=ext)","def friendly_name(self):\n if not self.collection:\n print('YIKES no filepath %s %s' % (self.title,self.slug))\n print (self)\n return ''\n fpath = self.collection.filePath\n if '/' in fpath:\n bg = fpath.index('/')+1\n return fpath[bg:]+ '/' + self.fileName\n return fpath+'/'+self.fileName","def update_destination_file_name (file_name):\n\tglobal COUNTER \n\tCOUNTER += 1\n\tsplitted = file_name.split('/')\n\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]","def genBaseName(fileName):\n return fileName.split(\"_\")[0].split(\".\")[0]","def appendDescription(dir, file_name, description):\n\tpath = os.path.join(dir, file_name)\n\tbase, ext = os.path.splitext(path)\n\treturn base + '.' + description + ext","def getOutputFilename(self, filename):\n return filename[:-4] + \".txt\"","def getNextFilename(self, expType):\n with self.lock:\n self.exposureID += 1\n exposureID = self.exposureID\n path = os.path.join(\"$ICS_MHS_DATA_ROOT\", 'agcc')\n path = os.path.expandvars(os.path.expanduser(path))\n if not os.path.isdir(path):\n os.makedirs(path, 0o755)\n with self.lock:\n timestamp = self.timestamp\n return os.path.join(path, 'AGC%d_%s_%06d_%s.fits' % \\\n (self.agcid + 1, expType, exposureID, timestamp))","def _create_name(self) -> str:\n return self.stream.__class__.__name__","def _get_track_name(self, filename):\n return os.path.basename(filename)","def _generate_processed_key_name(process_to, upload_name):\n timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\n name, extension = os.path.splitext(upload_name)\n digest = md5(''.join([timestamp, upload_name])).hexdigest()\n return os.path.join(process_to, '{0}.{1}'.format(digest, extension))","def _build_fname_templ(n):\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\n for i in range(1, n):\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\n var='{var%i}' % i)]\n return globals.ds_fn_sep.join(parts) + '.nc'","def playlist_num_filename(**kwargs):\n return f\"{kwargs['track_num']} - {default_filename(**kwargs)}\"","def format_filename(prefix, suffix, seq_len, uncased):\n seq_str = \"seq-{}\".format(seq_len)\n if uncased:\n case_str = \"uncased\"\n else:\n case_str = \"cased\"\n\n file_name = \"{}.{}.{}.{}\".format(prefix, seq_str, case_str, suffix)\n\n return file_name","def new_filename(fname=None,ndigits=3):\n if fname is None:\n ext = (\"%%.%ii\" % ndigits) % 1\n fname = \"%s.%s\" % (random_string(6), ext)\n \n if os.path.exists(fname): \n fname = increment_filename(fname,ndigits=ndigits)\n\n return fname"],"string":"[\n \"def generate_file_name(well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def file_name(id, title, kind=\\\"src\\\"):\\n fn_template = conf.template_source_file_name\\n if kind == \\\"tst\\\":\\n fn_template = conf.template_test_file_name\\n\\n return fn_template.format(id=id, title=title.replace(\\\"-\\\", \\\"_\\\"))\",\n \"def _generate_overlay_file_name(self, well, channel, desc):\\n \\n return \\\"c\\\" + channel + \\\"_w\\\" + well + \\\"_\\\" + desc + \\\".png\\\"\",\n \"def generate_file_name(entry):\\n return str_for_file(u'{name}, {year}, {title}'.format(\\n year=entry['year'],\\n name=get_last_name(entry['author'][0]),\\n title=entry['title']\\n ))\",\n \"def create_file_name(self):\\n # create a unique id for the file name\\n index = self.helpers.alpha_uuid()\\n\\n filename = self.form['FieldStorage'][self.image_cid].filename\\n extension = guess_extension(guess_type(filename)[0])\\n return ( # concatenates the following data\\n self.articleData.get('directory') + # directory\\n '/' + # slash\\n self.articleData.get('article_name') + # the article name\\n '-' + # hyphen character\\n index + # the id of the image\\n extension\\n )\",\n \"def create_final_name(fname, date, fc_id, sample_name):\\n \\n # Split the file name according to CASAVA convention\\n m = re.match(r'(\\\\S+?)_(?:[ACGTN\\\\-]+|NoIndex|Undetermined)_L0*(\\\\d+)_R(\\\\d)_\\\\d+\\\\.fastq(.*)', fname)\\n if m is not None:\\n lane = m.group(2)\\n read = m.group(3)\\n ext = m.group(4)\\n else:\\n # Split the file name according to bcbb convention\\n m = re.match(r'(\\\\d+)_(\\\\d+)_([^_]+)_(\\\\d+)_(?:nophix_)?(\\\\d+)_fastq.txt(.*)', fname)\\n if m is None:\\n raise ValueError(\\\"Could not parse file name {:s} correctly!\\\".format(fname))\\n lane = m.group(1)\\n read = m.group(5)\\n ext = m.group(6)\\n \\n dest_file_name = \\\"{:s}.fastq{:s}\\\".format(\\\"_\\\".join([lane,\\n date,\\n fc_id,\\n sample_name,\\n read]),\\n ext.replace('..','.'))\\n return dest_file_name\",\n \"def collected_filename(cfg, collect_dir, i=None):\\n if i is not None:\\n file = cfg[\\\"files\\\"][i]\\n else:\\n file = cfg[\\\"file\\\"]\\n ext = path.splitext(file)[1]\\n name = cfg[\\\"id\\\"]\\n if i is not None:\\n name += \\\"_\\\" + str(i)\\n return path.join(collect_dir, name + ext)\",\n \"def _generate_filename(doc_type, login, *args):\\n filename = []\\n filename.append(doc_type)\\n filename.append(login)\\n for item in args:\\n filename.append(item)\\n filename.append(datetime.datetime.now().isoformat(timespec='microseconds'))\\n filename = '_'.join(filename)\\n return filename\",\n \"def content_file_name(instance, filename):\\r\\n return '/'.join([str(instance.app.publisher.id), str(instance.app.id), filename])\",\n \"def construct_name_file(size_sample, randomness, pos_equal_neg, kernel):\\n if randomness:\\n randomness = \\\"rand\\\"\\n else:\\n randomness = \\\"nrand\\\"\\n\\n if pos_equal_neg:\\n pos_equal_neg = \\\"pos-neg-eq\\\"\\n else:\\n pos_equal_neg = \\\"pos-neg-neq\\\"\\n\\n return \\\"{}_{}_{}_{}.json\\\".format(size_sample, randomness, pos_equal_neg, kernel)\",\n \"def get_nomenclature_channel_fname(czi_fname, nomenclature_file, channel_name, ext='.inr.gz'):\\n # - Read NOMENCLATURE file defining naming conventions:\\n n_names = get_nomenclature_name(nomenclature_file)\\n return n_names[czi_fname]+\\\"/\\\", n_names[czi_fname] + \\\"_\\\" + channel_name + ext\",\n \"def generate_filename(player_name):\\n name = player_name.split()\\n filename = '_'.join(name).lower()\\n return filename\",\n \"def generate_glider_filename(description):\\n filename = (\\n \\\"{glider}-{year:d}-{day:03d}-{mission:d}-{segment}.{type}\\\".format(**description)\\n )\\n return os.path.join(description['path'], filename)\",\n \"def generate_namefile(pathfolder, methodvalues):\\n datestr = datetime.datetime.now().date().strftime('%F')\\n paramsstr = str(hash(str(methodvalues)))\\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\\n namefile = os.path.join(pathfolder, namefile)\\n return namefile\",\n \"def channel_name(radio_id: int, channel_id: int) -> str:\\n return f\\\"COMM{radio_id} Ch {channel_id}\\\"\",\n \"def generate_filename(filename: str) -> str:\\n return f\\\"{str(uuid.uuid4())}.{get_extension(filename)}\\\"\",\n \"async def filename_generator(self):\\n chars = list(string.ascii_letters+string.digits)\\n name = ''\\n for i in range(random.randint(9, 25)):\\n name += random.choice(chars)\\n\\n if name not in self.player['audio_files']:\\n return name\\n\\n return await self.filename_generator()\",\n \"def gen_file_name(filename, path=UPLOAD_FOLDER):\\n\\n i = 1\\n while os.path.exists(os.path.join(path, filename)):\\n name, extension = os.path.splitext(filename)\\n filename = '%s_%s%s' % (name, str(i), extension)\\n i += 1\\n\\n return filename\",\n \"def generate_filename(playlist_or_album_name, user_id_or_artist_id=None):\\n filename = ''\\n if user_id_or_artist_id:\\n filename += user_id_or_artist_id + '_'\\n filename += playlist_or_album_name + '_' + str(time_ns())\\n return filename\",\n \"def _get_file_name(name: types.TSeedName) -> str:\\n return f\\\"{name}.yml\\\"\",\n \"def get_file_name(self):\\n\\n return \\\"%s - %s\\\" % (self.get_tags()[\\\"artist\\\"], self.get_tags()[\\\"title\\\"])\",\n \"def generateFilename(self, name):\\n return self.context.generateUniqueId(type_name='Module')\",\n \"def filename(i):\\n rand_name = os.path.join(os.getcwd(), \\\"input-%d.txt\\\" % i)\\n ref_name = os.path.join(os.getcwd(), \\\"input-%d.ref\\\" % i)\\n return rand_name, ref_name\",\n \"def create_filename (self):\\n\\t\\tassert self.__patient_name and self.__location_name, \\\"New filename could not be determined, one or more needed arguments is empty!\\\"\\n\\t\\t_patient_name = self.__patient_name.split(' ')\\n\\t\\t_patient_name.reverse()\\n\\t\\t\\n\\t\\treturn os.path.join(os.path.dirname(self.file._path), \\\"%s MR %s%s\\\" % (self.__location_name, ', '.join(_patient_name).upper(), self._file.extension))\",\n \"def filename(self):\\n return '%s%s' % (self.identifier, self.extension)\",\n \"def generate_report_file_name(args: Dict[str, Any]) -> str:\\n return (\\n f\\\"{args.get('report_type', '').lower().replace(' ', '_')}_fireeye_\\\"\\n f\\\"{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.\\\"\\n f\\\"{args.get('type', REPORT_TYPE_ALLOWED_FORMAT[args.get('report_type', '')][0])}\\\"\\n )\",\n \"def generate_file_name(hour, minute):\\n hour = str(hour)\\n if len(hour) == 1:\\n hour = \\\"0\\\" + hour\\n minute = str(minute)\\n if len(minute) == 1:\\n minute = \\\"0\\\" + minute\\n file_name = date + \\\"--\\\" + hour + special_char + minute + special_char + \\\"00,00.mvol\\\"\\n\\n # print \\\"filename: \\\",file_name\\n return file_name\",\n \"def filename(self):\\n translator = {ord(\\\" \\\"): \\\"_\\\", ord(\\\",\\\"): None}\\n return f'{self._full_name.translate(translator)}.txt'\",\n \"def generate_file_filename(instance, filename):\\n return _generate_filename(instance, filename, 'photos')\",\n \"def temp_name(self, filename):\\n if self.params.get('nopart', False) or filename == '-' or \\\\\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\\n return filename\\n return filename + '.part'\",\n \"def get_alternative_name(self, file_root, file_ext):\\n return \\\"%s_%s%s\\\" % (file_root, get_random_string(7), file_ext)\",\n \"def get_display_name(self, short=False):\\n if self.filename is None:\\n return '[New file]'\\n elif short:\\n return os.path.basename(self.filename)\\n else:\\n return self.filename\",\n \"def file_name(product, ext='json'):\\n return f\\\"./output/{product}_{datetime.now().strftime('%Y-%m-%d_%H%M%S')}_transformed_{version}.{ext}\\\"\",\n \"def generate_name(file_name, index):\\n name = ''\\n with open(file_name) as names:\\n for i, line in enumerate(names):\\n if i == index:\\n name += line.split(None, 1)[0]\\n return name\",\n \"def get_file_name(self):\\n return str(self.get_file())\",\n \"def get_download_file_name(self):\\n # Use 'unknown' if the course instance does not have a term\\n if self.course_instance.term:\\n term = self.course_instance.term.get_url_name()\\n else:\\n term = 'unknown'\\n\\n return '{course}-{term}-{number}-{instructors}-{type}{ext}'.format(\\n course=self.course_instance.course.get_url_name(),\\n term=term,\\n number=self.exam_number,\\n instructors='_'.join([i.last_name for i in self.instructors]),\\n type=self.exam_type,\\n ext=self.file_ext)\",\n \"def get_file_name() -> str:\\n import uuid\\n uniq_append_string = uuid.uuid4().hex\\n return \\\"LOCAL_STORAGE_{}\\\".format(uniq_append_string)\",\n \"def generateFileName(self):\\n return 'Covid' + self.map_type + '.html'\",\n \"def getNextFilename(self, cmd):\\n \\n self.actor.exposureID += 1\\n path = os.path.join(\\\"$ICS_MHS_DATA_ROOT\\\", 'mcs')\\n path = os.path.expandvars(os.path.expanduser(path))\\n\\n if not os.path.isdir(path):\\n os.makedirs(path, 0o755)\\n \\n return os.path.join(path, 'MCSA%010d.fits' % (self.actor.exposureID))\",\n \"def generate_image_filename():\\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\\n return 'CCTV_{0}.jpg'.format(now)\",\n \"def generate_file_name(self):\\n self._session_iterator = None # New file invalidate old interator\\n self._img_count += 1\\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\\n return self.current_file\",\n \"def mk_filename(pattern, queryresponserow, resp, url):\\n name = None\\n if resp:\\n cdheader = resp.headers.get(\\\"Content-Disposition\\\", None)\\n if cdheader:\\n _, params = parse_header(cdheader)\\n name = params.get('filename', \\\"\\\")\\n # Work around https://github.com/sunpy/sunpy/issues/3372\\n if name.count('\\\"') >= 2:\\n name = name.split('\\\"')[1]\\n\\n # This is a hack to to prevent IRIS data from being labelled as XML files\\n if name is None and \\\"VOEvent_IRIS\\\" not in queryresponserow['fileid']:\\n # Advice from the VSO is to fallback to providerid + fileid for a filename\\n # As it's possible multiple providers give the same fileid.\\n # However, I haven't implemented this yet as it would be a breaking\\n # change to the filenames we expect.\\n fileid = queryresponserow['fileid']\\n\\n # Some providers make fileid a path\\n # Some also don't specify a file extension, but not a lot we can do\\n # about that.\\n name = fileid.split(\\\"/\\\")[-1]\\n\\n # If somehow we have got this far with an empty string, fallback to url segment\\n if not name:\\n name = url.split('/')[-1]\\n\\n # Remove any not-filename appropriate characters\\n name = slugify(name)\\n\\n # If absolutely everything else fails make a filename based on download time\\n if not name:\\n name = f\\\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\\\"\\n\\n fname = pattern.format(file=name,\\n **queryresponserow.response_block_map)\\n\\n return fname\",\n \"def init_data_file_name():\\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\\n filename = 'show-commands-' + now + \\\".txt\\\"\\n return filename\",\n \"def create_random_file_name():\\n\\n def random_file_name_factory():\\n length = random.randint(10, 15)\\n chars = string.ascii_letters + string.digits + \\\"-_\\\"\\n return f\\\"{''.join(random.choice(chars) for _ in range(length))}.jpg\\\"\\n\\n return random_file_name_factory\",\n \"def filename(self):\\n return self.config.get('filename', self.id) + f'_{self.file_suffix}'\",\n \"def get_title(src_name, src_type=None):\\n if src_type == 'tcp':\\n return '{0}:{1}'.format(*src_name)\\n return os.path.basename(src_name)\",\n \"def get_resampled_filename(filepath):\\n try:\\n filepath = filepath.encode('utf-8')\\n except UnicodeDecodeError:\\n pass\\n return 'tmp_%s.flac' % md5(filepath).hexdigest()\",\n \"def format_filename(title: str, id: Any, ext: str = \\\".\\\", dirFormat=None):\\r\\n ...\",\n \"def _generate_filename(instance, filename, prefix):\\n md5 = hashlib.md5()\\n md5.update(struct.pack('f', time.time()))\\n for chunk in instance.file.chunks():\\n md5.update(chunk)\\n extension = os.path.splitext(filename)[1]\\n return os.path.join(prefix, md5.hexdigest() + extension)\",\n \"def modpricesetter_get_file_name(self):\\r\\n year, month, day = self._get_market_year_month_day_as_str()\\r\\n interval_number = self._get_interval_number_as_str()\\r\\n base_name = \\\"NEMPriceSetter_{year}{month}{day}{interval_number}00.xml\\\"\\r\\n name = base_name.format(\\r\\n year=year, month=month, day=day, interval_number=interval_number\\r\\n )\\r\\n path_name = Path(self.cache_folder) / name\\r\\n name_OCD = name.replace(\\\".xml\\\", \\\"_OCD.xml\\\")\\r\\n path_name_OCD = Path(self.cache_folder) / name_OCD\\r\\n name_zero = name.replace(\\\".xml\\\", \\\"00.xml\\\")\\r\\n path_name_zero = Path(self.cache_folder) / name_zero\\r\\n if os.path.exists(path_name):\\r\\n return name\\r\\n elif os.path.exists(path_name_OCD):\\r\\n return name_OCD\\r\\n elif os.path.exists(path_name_zero):\\r\\n return name_zero\\r\\n else:\\r\\n return name\",\n \"def source_file_name_feature(self):\\n return \\\"_\\\".join((C_FILE_NAME, self.file_image_name.value))\",\n \"def get_filename(out_dir, file_date, extension):\\n return path.join(out_dir, f'CrossrefCitations_{file_date}.{extension}')\",\n \"def create_filename(value):\\n return '%s.mp3' % slugify(value, u'_')\",\n \"def generate_filename(\\n radar, field, sweep, ext=\\\"png\\\", datetime_format=\\\"%Y%m%d%H%M%S\\\", use_sweep_time=False\\n):\\n name_s = generate_radar_name(radar).replace(\\\" \\\", \\\"_\\\")\\n field_s = field.replace(\\\" \\\", \\\"_\\\")\\n if use_sweep_time:\\n time_s = generate_radar_time_sweep(radar, sweep).strftime(datetime_format)\\n else:\\n time_s = generate_radar_time_begin(radar).strftime(datetime_format)\\n sweep_s = str(sweep).zfill(2)\\n return f\\\"{name_s}_{field_s}_{sweep_s}_{time_s}.{ext}\\\"\",\n \"def filename(N, Dr, g, launch):\\n\\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\\n (N, Dr, g, launch)))\",\n \"def unique_filename(data):\\n file = data\\n get_ext = file.filename.split(\\\".\\\")[-1]\\n new_name = \\\"%s.%s\\\" % (uuid.uuid4().hex, get_ext)\\n return new_name\",\n \"def file_on_disk_name(instance, filename):\\n return generate_file_on_disk_name(instance.checksum, filename)\",\n \"def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\\n\\tnew_filename = time.strftime(\\\"%d-%m-%Y_\\\")\\n\\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\\n\\tnew_filename = new_filename + \\\"_P\\\" + str(image_class)\\n\\treturn new_filename\",\n \"def content_file_name(self, filename):\\n ext = filename.split('.')[-1]\\n filename = \\\"%s_%s.%s\\\" % (filename, self.id, ext)\\n return os.path.join('pictures/static/pictures/', filename)\",\n \"def _build_name(self):\\n self.ctrl_name = NameUtils.get_unique_name(self.asset,\\n self.side,\\n self.part,\\n \\\"ctrl\\\")\",\n \"def get_custom_filename(existing_files: List[str]) -> str:\\n questions = [\\n {\\n 'type': 'input',\\n 'name': 'custom_filename',\\n 'message': 'Name your new sample :',\\n 'default': get_default_filename(),\\n 'validate': lambda text: (\\n (len(re.findall(r'^[A-Za-z0-9_\\\\-.]{3,40}$', text)) > 0\\n and text+'.smp' not in existing_files\\n ) or\\n 'Typed file name contains illegal characters or already exist'\\n )\\n }\\n ]\\n return prompt(questions, style=custom_style_2)['custom_filename']+'.smp'\",\n \"def output_file_name_maker(args):\\n log.debug(\\\"Entering output_file_name_maker()\\\")\\n path = os.getcwd() + '/out_files/'\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n\\n if args.output is None:\\n out_file_name = path + args.input[:-4] + '_' + args.type + '_' + args.layer\\n else:\\n out_file_name = path + args.output\\n\\n log.debug(\\\"Exiting output_file_name_maker()\\\")\\n return out_file_name\",\n \"def _filename(self, corotid):\\n from datasource import DataSource\\n self.corotid = corotid\\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\\n \\n query = \\\"\\\"\\\"SELECT run_code, hlfccdid, win_id \\n FROM corot \\n WHERE corotid = %d;\\\"\\\"\\\" % self.corotid\\n result = self.corot.query(query)\\n \\n par = {'run': result[0][0],\\n 'half': result[0][1].rstrip('RL'), \\n 'win': result[0][2]}\\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\\n logger.info('%d = %s' % (corotid,filename))\\n return filename\",\n \"def make_img_name(file_ext='.png'):\\r\\n fn = []\\r\\n # format seqs and write out to temp file\\r\\n for i in range(0, 30):\\r\\n fn.append(choice(ALPHABET))\\r\\n return ''.join(fn) + file_ext\",\n \"def update_filename(instance, filename):\\n path = os.path.join(\\\"documents_analizer\\\", \\\"documents\\\")\\n name = \\\"{}{}\\\".format(highly_random_name(),\\n os.path.splitext(filename)[1])\\n return os.path.join(path, name)\",\n \"def download_filename_full(self, doc):\\n # todo modify\\n authors = \\\",\\\".join([x['name'] for x in doc.artists])\\n author = re.sub(\\\"[\\\\\\\\\\\\\\\\/:*?\\\\\\\"<>|]\\\", '', authors.strip())\\n mp3_name = re.sub(\\\"[\\\\\\\\\\\\\\\\/:*?\\\\\\\"<>|]\\\", '', doc['name'])\\n name = os.path.join(author, \\\"%s - %s.mp4\\\" % (author, mp3_name))\\n return name\",\n \"def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\\n filename = self.filename(filetype=filetype, format=format)\\n #return Path(dir) / filename\\n return filename\",\n \"def filename(lang):\\n filename = lang.replace('-', '_')\\n filename = filename.lower()\\n return filename\",\n \"def _file_name(size):\\n timestamp = str(int(time.time()))\\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')\",\n \"def generate_subdir(channel, energy=13):\\n return os.path.join('%dTeV' % energy, channel, strftime(\\\"%d_%b_%y\\\"))\",\n \"def _prettyfilename(self):\\n return f'{self.title} ({self.subtype})'\",\n \"def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\\n filename = self.filename(filetype=filetype, format=format)\\n # return Path(dir) / filename\\n return filename\",\n \"def _generate_output_name(extension):\\n output_name = 'TDG_{:%Y-%m-%d_%H-%M-%S}.{}'.format(datetime.now(), extension)\\n return output_name\",\n \"def out_filename(self, filetype, dir, format='old'):\\n filename = self.filename(filetype=filetype, format=format)\\n return Path(dir) / filename\",\n \"def _create_file_name(file_path):\\r\\n file_base, file_ext = os.path.splitext(file_path)\\r\\n if os.path.isfile(file_path):\\r\\n nfile = 1\\r\\n check = True\\r\\n while check:\\r\\n name_add = '0000' + str(nfile)\\r\\n file_path = file_base + \\\"_\\\" + name_add[-4:] + file_ext\\r\\n if os.path.isfile(file_path):\\r\\n nfile = nfile + 1\\r\\n else:\\r\\n check = False\\r\\n return file_path\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\\n ext='.mif'):\\n\\n if basename == '':\\n msg = 'Unable to generate filename for command %s. ' % self.cmd\\n msg += 'basename is not set!'\\n raise ValueError(msg)\\n if cwd is None:\\n cwd = os.getcwd()\\n if change_ext:\\n if suffix:\\n suffix = ''.join((suffix, ext))\\n else:\\n suffix = ext\\n if suffix is None:\\n suffix = ''\\n fname = fname_presuffix(basename, suffix=suffix,\\n use_ext=False, newpath=cwd)\\n return fname\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\\n ext='.mif'):\\n\\n if basename == '':\\n msg = 'Unable to generate filename for command %s. ' % self.cmd\\n msg += 'basename is not set!'\\n raise ValueError(msg)\\n if cwd is None:\\n cwd = os.getcwd()\\n if change_ext:\\n if suffix:\\n suffix = ''.join((suffix, ext))\\n else:\\n suffix = ext\\n if suffix is None:\\n suffix = ''\\n fname = fname_presuffix(basename, suffix=suffix,\\n use_ext=False, newpath=cwd)\\n return fname\",\n \"def newCalFileName(self, type, runBegin, runEnd='end'):\\n \\n path=os.path.join(self.cdir)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup,self.src)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup,self.src,type)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n return path+'/'+str(runBegin)+'-'+str(runEnd)+'.data'\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):\\n if not basename:\\n msg = \\\"Unable to generate filename for command %s. \\\" % self.cmd\\n msg += \\\"basename is not set!\\\"\\n raise ValueError(msg)\\n\\n if cwd is None:\\n cwd = os.getcwd()\\n if ext is None:\\n ext = Info.output_type_to_ext(self.inputs.outputtype)\\n if change_ext:\\n suffix = \\\"\\\".join((suffix, ext)) if suffix else ext\\n\\n if suffix is None:\\n suffix = \\\"\\\"\\n fname = fname_presuffix(basename, suffix=suffix, use_ext=False, newpath=cwd)\\n return fname\",\n \"def get_file_name(replay_dir, template_name):\\n suffix = '.json' if not template_name.endswith('.json') else ''\\n file_name = f'{template_name}{suffix}'\\n return os.path.join(replay_dir, file_name)\",\n \"def _get_buildspec_filename(\\n lambda_name, file_name):\\n return '.'.join(\\n ('buildspecs', lambda_name, file_name))\",\n \"def generate_filename(ext,sha512base16_hash=None):\\n## # Timestamp filename\\n## timestamp = str(get_current_unix_time())\\n## filename = timestamp+\\\".\\\"+ext\\n # Base16 hash filename\\n filename = sha512base16_hash+\\\".\\\"+ext\\n return filename\",\n \"def _make_fname(song, ext=None, av=None, subdir=None):\\n # pylint: disable=E1103\\n # Instance of 'bool' has no 'extension' member (some types not inferable)\\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\\n if not os.path.exists(ddir):\\n os.makedirs(ddir)\\n\\n if ext:\\n extension = ext\\n\\n else:\\n stream = streams.select(streams.get(song),\\n audio=av == \\\"audio\\\", m4a_ok=True)\\n extension = stream['ext']\\n\\n # filename = song.title[:59] + \\\".\\\" + extension\\n filename = song.title + \\\".\\\" + extension\\n filename = os.path.join(ddir, mswinfn(filename.replace(\\\"/\\\", \\\"-\\\")))\\n filename = filename.replace('\\\"', '')\\n return filename\",\n \"def get_output_filename(item: str, root: str, i: int) -> str:\\n element_split = item.split(\\\"/\\\")\\n item, ext = element_split[-1].split(\\\".\\\")\\n if i < 0:\\n return f\\\"{root}/{'/'.join(element_split[:-1])}/{item}.{ext}\\\"\\n else:\\n return f\\\"{root}/{'/'.join(element_split[:-1])}/{item}_aug{i}.{ext}\\\"\",\n \"def outputFilename(name=\\\"\\\", ext=\\\"\\\", time=True):\\n # get the date in the format specifed\\n dateTime = datetime.now()\\n dateTimeFormat = \\\"%Y-%m-%d__%H-%M-%S\\\" if time else \\\"%Y-%m-%d\\\"\\n fileName = dateTime.strftime(dateTimeFormat)\\n\\n # construct the filename\\n fileName = fileName + \\\"_\\\" + name if fileName != \\\"\\\" else fileName\\n ext = \\\".\\\" + ext if ext != \\\"\\\" else \\\"\\\"\\n\\n return fileName + ext\",\n \"def format_image_filename(device_image):\\n return \\\"{}-{}-{}-{}.bit\\\".format(device_image.bitstream_type,\\n device_image.pci_vendor,\\n device_image.pci_device,\\n device_image.uuid)\",\n \"def generate_random_name(filename):\\n ext = filename.split('.')[-1]\\n rns = [random.randint(0, len(LETTER_SET) - 1) for _ in range(3)]\\n name = ''.join([LETTER_SET[rn] for rn in rns])\\n return \\\"{new_fn}.{ext}\\\".format(new_fn=name, ext=ext)\",\n \"def friendly_name(self):\\n if not self.collection:\\n print('YIKES no filepath %s %s' % (self.title,self.slug))\\n print (self)\\n return ''\\n fpath = self.collection.filePath\\n if '/' in fpath:\\n bg = fpath.index('/')+1\\n return fpath[bg:]+ '/' + self.fileName\\n return fpath+'/'+self.fileName\",\n \"def update_destination_file_name (file_name):\\n\\tglobal COUNTER \\n\\tCOUNTER += 1\\n\\tsplitted = file_name.split('/')\\n\\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]\",\n \"def genBaseName(fileName):\\n return fileName.split(\\\"_\\\")[0].split(\\\".\\\")[0]\",\n \"def appendDescription(dir, file_name, description):\\n\\tpath = os.path.join(dir, file_name)\\n\\tbase, ext = os.path.splitext(path)\\n\\treturn base + '.' + description + ext\",\n \"def getOutputFilename(self, filename):\\n return filename[:-4] + \\\".txt\\\"\",\n \"def getNextFilename(self, expType):\\n with self.lock:\\n self.exposureID += 1\\n exposureID = self.exposureID\\n path = os.path.join(\\\"$ICS_MHS_DATA_ROOT\\\", 'agcc')\\n path = os.path.expandvars(os.path.expanduser(path))\\n if not os.path.isdir(path):\\n os.makedirs(path, 0o755)\\n with self.lock:\\n timestamp = self.timestamp\\n return os.path.join(path, 'AGC%d_%s_%06d_%s.fits' % \\\\\\n (self.agcid + 1, expType, exposureID, timestamp))\",\n \"def _create_name(self) -> str:\\n return self.stream.__class__.__name__\",\n \"def _get_track_name(self, filename):\\n return os.path.basename(filename)\",\n \"def _generate_processed_key_name(process_to, upload_name):\\n timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\\n name, extension = os.path.splitext(upload_name)\\n digest = md5(''.join([timestamp, upload_name])).hexdigest()\\n return os.path.join(process_to, '{0}.{1}'.format(digest, extension))\",\n \"def _build_fname_templ(n):\\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\\n for i in range(1, n):\\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\\n var='{var%i}' % i)]\\n return globals.ds_fn_sep.join(parts) + '.nc'\",\n \"def playlist_num_filename(**kwargs):\\n return f\\\"{kwargs['track_num']} - {default_filename(**kwargs)}\\\"\",\n \"def format_filename(prefix, suffix, seq_len, uncased):\\n seq_str = \\\"seq-{}\\\".format(seq_len)\\n if uncased:\\n case_str = \\\"uncased\\\"\\n else:\\n case_str = \\\"cased\\\"\\n\\n file_name = \\\"{}.{}.{}.{}\\\".format(prefix, seq_str, case_str, suffix)\\n\\n return file_name\",\n \"def new_filename(fname=None,ndigits=3):\\n if fname is None:\\n ext = (\\\"%%.%ii\\\" % ndigits) % 1\\n fname = \\\"%s.%s\\\" % (random_string(6), ext)\\n \\n if os.path.exists(fname): \\n fname = increment_filename(fname,ndigits=ndigits)\\n\\n return fname\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80091065","0.7165928","0.7101471","0.7064337","0.69931364","0.67345184","0.6668517","0.66324764","0.66144115","0.64927953","0.6479","0.6476943","0.64755803","0.6454013","0.6448165","0.6447837","0.6430102","0.6413977","0.63882077","0.63864017","0.63777316","0.636905","0.6362883","0.6348021","0.6347843","0.63450694","0.63408315","0.6311075","0.63088846","0.6267646","0.6253687","0.6246914","0.62364984","0.62089753","0.62055486","0.6199151","0.6178803","0.6176319","0.6173263","0.6138361","0.6134544","0.61154276","0.611337","0.6109585","0.61008054","0.6099955","0.6079365","0.6076334","0.60735756","0.6071808","0.6061241","0.6056225","0.60547364","0.604555","0.6032432","0.60320187","0.6028176","0.60251886","0.6010918","0.5993882","0.598593","0.5984471","0.5982621","0.59794235","0.597504","0.59743035","0.5965455","0.5961188","0.5953797","0.595152","0.59482384","0.5943754","0.59392655","0.59358996","0.5924448","0.5920188","0.5920188","0.59158623","0.59081805","0.58972996","0.5896152","0.5883053","0.58677936","0.586425","0.586006","0.5858712","0.58582944","0.5851622","0.5848807","0.58455294","0.5842946","0.5840959","0.58404493","0.58356863","0.5833883","0.58304435","0.58298725","0.5828068","0.5826516","0.58189064"],"string":"[\n \"0.80091065\",\n \"0.7165928\",\n \"0.7101471\",\n \"0.7064337\",\n \"0.69931364\",\n \"0.67345184\",\n \"0.6668517\",\n \"0.66324764\",\n \"0.66144115\",\n \"0.64927953\",\n \"0.6479\",\n \"0.6476943\",\n \"0.64755803\",\n \"0.6454013\",\n \"0.6448165\",\n \"0.6447837\",\n \"0.6430102\",\n \"0.6413977\",\n \"0.63882077\",\n \"0.63864017\",\n \"0.63777316\",\n \"0.636905\",\n \"0.6362883\",\n \"0.6348021\",\n \"0.6347843\",\n \"0.63450694\",\n \"0.63408315\",\n \"0.6311075\",\n \"0.63088846\",\n \"0.6267646\",\n \"0.6253687\",\n \"0.6246914\",\n \"0.62364984\",\n \"0.62089753\",\n \"0.62055486\",\n \"0.6199151\",\n \"0.6178803\",\n \"0.6176319\",\n \"0.6173263\",\n \"0.6138361\",\n \"0.6134544\",\n \"0.61154276\",\n \"0.611337\",\n \"0.6109585\",\n \"0.61008054\",\n \"0.6099955\",\n \"0.6079365\",\n \"0.6076334\",\n \"0.60735756\",\n \"0.6071808\",\n \"0.6061241\",\n \"0.6056225\",\n \"0.60547364\",\n \"0.604555\",\n \"0.6032432\",\n \"0.60320187\",\n \"0.6028176\",\n \"0.60251886\",\n \"0.6010918\",\n \"0.5993882\",\n \"0.598593\",\n \"0.5984471\",\n \"0.5982621\",\n \"0.59794235\",\n \"0.597504\",\n \"0.59743035\",\n \"0.5965455\",\n \"0.5961188\",\n \"0.5953797\",\n \"0.595152\",\n \"0.59482384\",\n \"0.5943754\",\n \"0.59392655\",\n \"0.59358996\",\n \"0.5924448\",\n \"0.5920188\",\n \"0.5920188\",\n \"0.59158623\",\n \"0.59081805\",\n \"0.58972996\",\n \"0.5896152\",\n \"0.5883053\",\n \"0.58677936\",\n \"0.586425\",\n \"0.586006\",\n \"0.5858712\",\n \"0.58582944\",\n \"0.5851622\",\n \"0.5848807\",\n \"0.58455294\",\n \"0.5842946\",\n \"0.5840959\",\n \"0.58404493\",\n \"0.58356863\",\n \"0.5833883\",\n \"0.58304435\",\n \"0.58298725\",\n \"0.5828068\",\n \"0.5826516\",\n \"0.58189064\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7519707"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":22390,"cells":{"query":{"kind":"string","value":"Generate a name for a file using the description and channel"},"document":{"kind":"string","value":"def _generate_overlay_file_name(self, well, channel, desc):\n \n return \"c\" + channel + \"_w\" + well + \"_\" + desc + \".png\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_file_name(well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"","def file_name(id, title, kind=\"src\"):\n fn_template = conf.template_source_file_name\n if kind == \"tst\":\n fn_template = conf.template_test_file_name\n\n return fn_template.format(id=id, title=title.replace(\"-\", \"_\"))","def generate_file_name(entry):\n return str_for_file(u'{name}, {year}, {title}'.format(\n year=entry['year'],\n name=get_last_name(entry['author'][0]),\n title=entry['title']\n ))","def create_file_name(self):\n # create a unique id for the file name\n index = self.helpers.alpha_uuid()\n\n filename = self.form['FieldStorage'][self.image_cid].filename\n extension = guess_extension(guess_type(filename)[0])\n return ( # concatenates the following data\n self.articleData.get('directory') + # directory\n '/' + # slash\n self.articleData.get('article_name') + # the article name\n '-' + # hyphen character\n index + # the id of the image\n extension\n )","def create_final_name(fname, date, fc_id, sample_name):\n \n # Split the file name according to CASAVA convention\n m = re.match(r'(\\S+?)_(?:[ACGTN\\-]+|NoIndex|Undetermined)_L0*(\\d+)_R(\\d)_\\d+\\.fastq(.*)', fname)\n if m is not None:\n lane = m.group(2)\n read = m.group(3)\n ext = m.group(4)\n else:\n # Split the file name according to bcbb convention\n m = re.match(r'(\\d+)_(\\d+)_([^_]+)_(\\d+)_(?:nophix_)?(\\d+)_fastq.txt(.*)', fname)\n if m is None:\n raise ValueError(\"Could not parse file name {:s} correctly!\".format(fname))\n lane = m.group(1)\n read = m.group(5)\n ext = m.group(6)\n \n dest_file_name = \"{:s}.fastq{:s}\".format(\"_\".join([lane,\n date,\n fc_id,\n sample_name,\n read]),\n ext.replace('..','.'))\n return dest_file_name","def collected_filename(cfg, collect_dir, i=None):\n if i is not None:\n file = cfg[\"files\"][i]\n else:\n file = cfg[\"file\"]\n ext = path.splitext(file)[1]\n name = cfg[\"id\"]\n if i is not None:\n name += \"_\" + str(i)\n return path.join(collect_dir, name + ext)","def _generate_filename(doc_type, login, *args):\n filename = []\n filename.append(doc_type)\n filename.append(login)\n for item in args:\n filename.append(item)\n filename.append(datetime.datetime.now().isoformat(timespec='microseconds'))\n filename = '_'.join(filename)\n return filename","def content_file_name(instance, filename):\r\n return '/'.join([str(instance.app.publisher.id), str(instance.app.id), filename])","def construct_name_file(size_sample, randomness, pos_equal_neg, kernel):\n if randomness:\n randomness = \"rand\"\n else:\n randomness = \"nrand\"\n\n if pos_equal_neg:\n pos_equal_neg = \"pos-neg-eq\"\n else:\n pos_equal_neg = \"pos-neg-neq\"\n\n return \"{}_{}_{}_{}.json\".format(size_sample, randomness, pos_equal_neg, kernel)","def generate_filename(player_name):\n name = player_name.split()\n filename = '_'.join(name).lower()\n return filename","def get_nomenclature_channel_fname(czi_fname, nomenclature_file, channel_name, ext='.inr.gz'):\n # - Read NOMENCLATURE file defining naming conventions:\n n_names = get_nomenclature_name(nomenclature_file)\n return n_names[czi_fname]+\"/\", n_names[czi_fname] + \"_\" + channel_name + ext","def generate_glider_filename(description):\n filename = (\n \"{glider}-{year:d}-{day:03d}-{mission:d}-{segment}.{type}\".format(**description)\n )\n return os.path.join(description['path'], filename)","def generate_namefile(pathfolder, methodvalues):\n datestr = datetime.datetime.now().date().strftime('%F')\n paramsstr = str(hash(str(methodvalues)))\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\n namefile = os.path.join(pathfolder, namefile)\n return namefile","def generate_filename(filename: str) -> str:\n return f\"{str(uuid.uuid4())}.{get_extension(filename)}\"","def channel_name(radio_id: int, channel_id: int) -> str:\n return f\"COMM{radio_id} Ch {channel_id}\"","async def filename_generator(self):\n chars = list(string.ascii_letters+string.digits)\n name = ''\n for i in range(random.randint(9, 25)):\n name += random.choice(chars)\n\n if name not in self.player['audio_files']:\n return name\n\n return await self.filename_generator()","def gen_file_name(filename, path=UPLOAD_FOLDER):\n\n i = 1\n while os.path.exists(os.path.join(path, filename)):\n name, extension = os.path.splitext(filename)\n filename = '%s_%s%s' % (name, str(i), extension)\n i += 1\n\n return filename","def generate_filename(playlist_or_album_name, user_id_or_artist_id=None):\n filename = ''\n if user_id_or_artist_id:\n filename += user_id_or_artist_id + '_'\n filename += playlist_or_album_name + '_' + str(time_ns())\n return filename","def _get_file_name(name: types.TSeedName) -> str:\n return f\"{name}.yml\"","def get_file_name(self):\n\n return \"%s - %s\" % (self.get_tags()[\"artist\"], self.get_tags()[\"title\"])","def generateFilename(self, name):\n return self.context.generateUniqueId(type_name='Module')","def filename(i):\n rand_name = os.path.join(os.getcwd(), \"input-%d.txt\" % i)\n ref_name = os.path.join(os.getcwd(), \"input-%d.ref\" % i)\n return rand_name, ref_name","def create_filename (self):\n\t\tassert self.__patient_name and self.__location_name, \"New filename could not be determined, one or more needed arguments is empty!\"\n\t\t_patient_name = self.__patient_name.split(' ')\n\t\t_patient_name.reverse()\n\t\t\n\t\treturn os.path.join(os.path.dirname(self.file._path), \"%s MR %s%s\" % (self.__location_name, ', '.join(_patient_name).upper(), self._file.extension))","def filename(self):\n return '%s%s' % (self.identifier, self.extension)","def generate_report_file_name(args: Dict[str, Any]) -> str:\n return (\n f\"{args.get('report_type', '').lower().replace(' ', '_')}_fireeye_\"\n f\"{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.\"\n f\"{args.get('type', REPORT_TYPE_ALLOWED_FORMAT[args.get('report_type', '')][0])}\"\n )","def generate_file_name(hour, minute):\n hour = str(hour)\n if len(hour) == 1:\n hour = \"0\" + hour\n minute = str(minute)\n if len(minute) == 1:\n minute = \"0\" + minute\n file_name = date + \"--\" + hour + special_char + minute + special_char + \"00,00.mvol\"\n\n # print \"filename: \",file_name\n return file_name","def filename(self):\n translator = {ord(\" \"): \"_\", ord(\",\"): None}\n return f'{self._full_name.translate(translator)}.txt'","def generate_file_filename(instance, filename):\n return _generate_filename(instance, filename, 'photos')","def temp_name(self, filename):\n if self.params.get('nopart', False) or filename == '-' or \\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\n return filename\n return filename + '.part'","def get_alternative_name(self, file_root, file_ext):\n return \"%s_%s%s\" % (file_root, get_random_string(7), file_ext)","def get_display_name(self, short=False):\n if self.filename is None:\n return '[New file]'\n elif short:\n return os.path.basename(self.filename)\n else:\n return self.filename","def file_name(product, ext='json'):\n return f\"./output/{product}_{datetime.now().strftime('%Y-%m-%d_%H%M%S')}_transformed_{version}.{ext}\"","def generate_name(file_name, index):\n name = ''\n with open(file_name) as names:\n for i, line in enumerate(names):\n if i == index:\n name += line.split(None, 1)[0]\n return name","def get_file_name(self):\n return str(self.get_file())","def get_download_file_name(self):\n # Use 'unknown' if the course instance does not have a term\n if self.course_instance.term:\n term = self.course_instance.term.get_url_name()\n else:\n term = 'unknown'\n\n return '{course}-{term}-{number}-{instructors}-{type}{ext}'.format(\n course=self.course_instance.course.get_url_name(),\n term=term,\n number=self.exam_number,\n instructors='_'.join([i.last_name for i in self.instructors]),\n type=self.exam_type,\n ext=self.file_ext)","def get_file_name() -> str:\n import uuid\n uniq_append_string = uuid.uuid4().hex\n return \"LOCAL_STORAGE_{}\".format(uniq_append_string)","def generateFileName(self):\n return 'Covid' + self.map_type + '.html'","def getNextFilename(self, cmd):\n \n self.actor.exposureID += 1\n path = os.path.join(\"$ICS_MHS_DATA_ROOT\", 'mcs')\n path = os.path.expandvars(os.path.expanduser(path))\n\n if not os.path.isdir(path):\n os.makedirs(path, 0o755)\n \n return os.path.join(path, 'MCSA%010d.fits' % (self.actor.exposureID))","def generate_image_filename():\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\n return 'CCTV_{0}.jpg'.format(now)","def generate_file_name(self):\n self._session_iterator = None # New file invalidate old interator\n self._img_count += 1\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\n return self.current_file","def mk_filename(pattern, queryresponserow, resp, url):\n name = None\n if resp:\n cdheader = resp.headers.get(\"Content-Disposition\", None)\n if cdheader:\n _, params = parse_header(cdheader)\n name = params.get('filename', \"\")\n # Work around https://github.com/sunpy/sunpy/issues/3372\n if name.count('\"') >= 2:\n name = name.split('\"')[1]\n\n # This is a hack to to prevent IRIS data from being labelled as XML files\n if name is None and \"VOEvent_IRIS\" not in queryresponserow['fileid']:\n # Advice from the VSO is to fallback to providerid + fileid for a filename\n # As it's possible multiple providers give the same fileid.\n # However, I haven't implemented this yet as it would be a breaking\n # change to the filenames we expect.\n fileid = queryresponserow['fileid']\n\n # Some providers make fileid a path\n # Some also don't specify a file extension, but not a lot we can do\n # about that.\n name = fileid.split(\"/\")[-1]\n\n # If somehow we have got this far with an empty string, fallback to url segment\n if not name:\n name = url.split('/')[-1]\n\n # Remove any not-filename appropriate characters\n name = slugify(name)\n\n # If absolutely everything else fails make a filename based on download time\n if not name:\n name = f\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\"\n\n fname = pattern.format(file=name,\n **queryresponserow.response_block_map)\n\n return fname","def init_data_file_name():\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\n filename = 'show-commands-' + now + \".txt\"\n return filename","def create_random_file_name():\n\n def random_file_name_factory():\n length = random.randint(10, 15)\n chars = string.ascii_letters + string.digits + \"-_\"\n return f\"{''.join(random.choice(chars) for _ in range(length))}.jpg\"\n\n return random_file_name_factory","def filename(self):\n return self.config.get('filename', self.id) + f'_{self.file_suffix}'","def get_title(src_name, src_type=None):\n if src_type == 'tcp':\n return '{0}:{1}'.format(*src_name)\n return os.path.basename(src_name)","def get_resampled_filename(filepath):\n try:\n filepath = filepath.encode('utf-8')\n except UnicodeDecodeError:\n pass\n return 'tmp_%s.flac' % md5(filepath).hexdigest()","def format_filename(title: str, id: Any, ext: str = \".\", dirFormat=None):\r\n ...","def _generate_filename(instance, filename, prefix):\n md5 = hashlib.md5()\n md5.update(struct.pack('f', time.time()))\n for chunk in instance.file.chunks():\n md5.update(chunk)\n extension = os.path.splitext(filename)[1]\n return os.path.join(prefix, md5.hexdigest() + extension)","def modpricesetter_get_file_name(self):\r\n year, month, day = self._get_market_year_month_day_as_str()\r\n interval_number = self._get_interval_number_as_str()\r\n base_name = \"NEMPriceSetter_{year}{month}{day}{interval_number}00.xml\"\r\n name = base_name.format(\r\n year=year, month=month, day=day, interval_number=interval_number\r\n )\r\n path_name = Path(self.cache_folder) / name\r\n name_OCD = name.replace(\".xml\", \"_OCD.xml\")\r\n path_name_OCD = Path(self.cache_folder) / name_OCD\r\n name_zero = name.replace(\".xml\", \"00.xml\")\r\n path_name_zero = Path(self.cache_folder) / name_zero\r\n if os.path.exists(path_name):\r\n return name\r\n elif os.path.exists(path_name_OCD):\r\n return name_OCD\r\n elif os.path.exists(path_name_zero):\r\n return name_zero\r\n else:\r\n return name","def source_file_name_feature(self):\n return \"_\".join((C_FILE_NAME, self.file_image_name.value))","def get_filename(out_dir, file_date, extension):\n return path.join(out_dir, f'CrossrefCitations_{file_date}.{extension}')","def create_filename(value):\n return '%s.mp3' % slugify(value, u'_')","def generate_filename(\n radar, field, sweep, ext=\"png\", datetime_format=\"%Y%m%d%H%M%S\", use_sweep_time=False\n):\n name_s = generate_radar_name(radar).replace(\" \", \"_\")\n field_s = field.replace(\" \", \"_\")\n if use_sweep_time:\n time_s = generate_radar_time_sweep(radar, sweep).strftime(datetime_format)\n else:\n time_s = generate_radar_time_begin(radar).strftime(datetime_format)\n sweep_s = str(sweep).zfill(2)\n return f\"{name_s}_{field_s}_{sweep_s}_{time_s}.{ext}\"","def filename(N, Dr, g, launch):\n\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\n (N, Dr, g, launch)))","def unique_filename(data):\n file = data\n get_ext = file.filename.split(\".\")[-1]\n new_name = \"%s.%s\" % (uuid.uuid4().hex, get_ext)\n return new_name","def file_on_disk_name(instance, filename):\n return generate_file_on_disk_name(instance.checksum, filename)","def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\n\tnew_filename = time.strftime(\"%d-%m-%Y_\")\n\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\n\tnew_filename = new_filename + \"_P\" + str(image_class)\n\treturn new_filename","def content_file_name(self, filename):\n ext = filename.split('.')[-1]\n filename = \"%s_%s.%s\" % (filename, self.id, ext)\n return os.path.join('pictures/static/pictures/', filename)","def _build_name(self):\n self.ctrl_name = NameUtils.get_unique_name(self.asset,\n self.side,\n self.part,\n \"ctrl\")","def output_file_name_maker(args):\n log.debug(\"Entering output_file_name_maker()\")\n path = os.getcwd() + '/out_files/'\n if not os.path.isdir(path):\n os.mkdir(path)\n\n if args.output is None:\n out_file_name = path + args.input[:-4] + '_' + args.type + '_' + args.layer\n else:\n out_file_name = path + args.output\n\n log.debug(\"Exiting output_file_name_maker()\")\n return out_file_name","def get_custom_filename(existing_files: List[str]) -> str:\n questions = [\n {\n 'type': 'input',\n 'name': 'custom_filename',\n 'message': 'Name your new sample :',\n 'default': get_default_filename(),\n 'validate': lambda text: (\n (len(re.findall(r'^[A-Za-z0-9_\\-.]{3,40}$', text)) > 0\n and text+'.smp' not in existing_files\n ) or\n 'Typed file name contains illegal characters or already exist'\n )\n }\n ]\n return prompt(questions, style=custom_style_2)['custom_filename']+'.smp'","def _filename(self, corotid):\n from datasource import DataSource\n self.corotid = corotid\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\n \n query = \"\"\"SELECT run_code, hlfccdid, win_id \n FROM corot \n WHERE corotid = %d;\"\"\" % self.corotid\n result = self.corot.query(query)\n \n par = {'run': result[0][0],\n 'half': result[0][1].rstrip('RL'), \n 'win': result[0][2]}\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\n logger.info('%d = %s' % (corotid,filename))\n return filename","def make_img_name(file_ext='.png'):\r\n fn = []\r\n # format seqs and write out to temp file\r\n for i in range(0, 30):\r\n fn.append(choice(ALPHABET))\r\n return ''.join(fn) + file_ext","def update_filename(instance, filename):\n path = os.path.join(\"documents_analizer\", \"documents\")\n name = \"{}{}\".format(highly_random_name(),\n os.path.splitext(filename)[1])\n return os.path.join(path, name)","def download_filename_full(self, doc):\n # todo modify\n authors = \",\".join([x['name'] for x in doc.artists])\n author = re.sub(\"[\\\\\\\\/:*?\\\"<>|]\", '', authors.strip())\n mp3_name = re.sub(\"[\\\\\\\\/:*?\\\"<>|]\", '', doc['name'])\n name = os.path.join(author, \"%s - %s.mp4\" % (author, mp3_name))\n return name","def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\n filename = self.filename(filetype=filetype, format=format)\n #return Path(dir) / filename\n return filename","def filename(lang):\n filename = lang.replace('-', '_')\n filename = filename.lower()\n return filename","def _file_name(size):\n timestamp = str(int(time.time()))\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')","def _prettyfilename(self):\n return f'{self.title} ({self.subtype})'","def generate_subdir(channel, energy=13):\n return os.path.join('%dTeV' % energy, channel, strftime(\"%d_%b_%y\"))","def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\n filename = self.filename(filetype=filetype, format=format)\n # return Path(dir) / filename\n return filename","def _generate_output_name(extension):\n output_name = 'TDG_{:%Y-%m-%d_%H-%M-%S}.{}'.format(datetime.now(), extension)\n return output_name","def out_filename(self, filetype, dir, format='old'):\n filename = self.filename(filetype=filetype, format=format)\n return Path(dir) / filename","def _create_file_name(file_path):\r\n file_base, file_ext = os.path.splitext(file_path)\r\n if os.path.isfile(file_path):\r\n nfile = 1\r\n check = True\r\n while check:\r\n name_add = '0000' + str(nfile)\r\n file_path = file_base + \"_\" + name_add[-4:] + file_ext\r\n if os.path.isfile(file_path):\r\n nfile = nfile + 1\r\n else:\r\n check = False\r\n return file_path","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\n ext='.mif'):\n\n if basename == '':\n msg = 'Unable to generate filename for command %s. ' % self.cmd\n msg += 'basename is not set!'\n raise ValueError(msg)\n if cwd is None:\n cwd = os.getcwd()\n if change_ext:\n if suffix:\n suffix = ''.join((suffix, ext))\n else:\n suffix = ext\n if suffix is None:\n suffix = ''\n fname = fname_presuffix(basename, suffix=suffix,\n use_ext=False, newpath=cwd)\n return fname","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\n ext='.mif'):\n\n if basename == '':\n msg = 'Unable to generate filename for command %s. ' % self.cmd\n msg += 'basename is not set!'\n raise ValueError(msg)\n if cwd is None:\n cwd = os.getcwd()\n if change_ext:\n if suffix:\n suffix = ''.join((suffix, ext))\n else:\n suffix = ext\n if suffix is None:\n suffix = ''\n fname = fname_presuffix(basename, suffix=suffix,\n use_ext=False, newpath=cwd)\n return fname","def newCalFileName(self, type, runBegin, runEnd='end'):\n \n path=os.path.join(self.cdir)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup,self.src)\n if not os.path.exists(path): \n os.mkdir(path)\n path=os.path.join(self.cdir,self.calibgroup,self.src,type)\n if not os.path.exists(path): \n os.mkdir(path)\n return path+'/'+str(runBegin)+'-'+str(runEnd)+'.data'","def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):\n if not basename:\n msg = \"Unable to generate filename for command %s. \" % self.cmd\n msg += \"basename is not set!\"\n raise ValueError(msg)\n\n if cwd is None:\n cwd = os.getcwd()\n if ext is None:\n ext = Info.output_type_to_ext(self.inputs.outputtype)\n if change_ext:\n suffix = \"\".join((suffix, ext)) if suffix else ext\n\n if suffix is None:\n suffix = \"\"\n fname = fname_presuffix(basename, suffix=suffix, use_ext=False, newpath=cwd)\n return fname","def get_file_name(replay_dir, template_name):\n suffix = '.json' if not template_name.endswith('.json') else ''\n file_name = f'{template_name}{suffix}'\n return os.path.join(replay_dir, file_name)","def _get_buildspec_filename(\n lambda_name, file_name):\n return '.'.join(\n ('buildspecs', lambda_name, file_name))","def generate_filename(ext,sha512base16_hash=None):\n## # Timestamp filename\n## timestamp = str(get_current_unix_time())\n## filename = timestamp+\".\"+ext\n # Base16 hash filename\n filename = sha512base16_hash+\".\"+ext\n return filename","def _make_fname(song, ext=None, av=None, subdir=None):\n # pylint: disable=E1103\n # Instance of 'bool' has no 'extension' member (some types not inferable)\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\n if not os.path.exists(ddir):\n os.makedirs(ddir)\n\n if ext:\n extension = ext\n\n else:\n stream = streams.select(streams.get(song),\n audio=av == \"audio\", m4a_ok=True)\n extension = stream['ext']\n\n # filename = song.title[:59] + \".\" + extension\n filename = song.title + \".\" + extension\n filename = os.path.join(ddir, mswinfn(filename.replace(\"/\", \"-\")))\n filename = filename.replace('\"', '')\n return filename","def get_output_filename(item: str, root: str, i: int) -> str:\n element_split = item.split(\"/\")\n item, ext = element_split[-1].split(\".\")\n if i < 0:\n return f\"{root}/{'/'.join(element_split[:-1])}/{item}.{ext}\"\n else:\n return f\"{root}/{'/'.join(element_split[:-1])}/{item}_aug{i}.{ext}\"","def outputFilename(name=\"\", ext=\"\", time=True):\n # get the date in the format specifed\n dateTime = datetime.now()\n dateTimeFormat = \"%Y-%m-%d__%H-%M-%S\" if time else \"%Y-%m-%d\"\n fileName = dateTime.strftime(dateTimeFormat)\n\n # construct the filename\n fileName = fileName + \"_\" + name if fileName != \"\" else fileName\n ext = \".\" + ext if ext != \"\" else \"\"\n\n return fileName + ext","def generate_random_name(filename):\n ext = filename.split('.')[-1]\n rns = [random.randint(0, len(LETTER_SET) - 1) for _ in range(3)]\n name = ''.join([LETTER_SET[rn] for rn in rns])\n return \"{new_fn}.{ext}\".format(new_fn=name, ext=ext)","def format_image_filename(device_image):\n return \"{}-{}-{}-{}.bit\".format(device_image.bitstream_type,\n device_image.pci_vendor,\n device_image.pci_device,\n device_image.uuid)","def friendly_name(self):\n if not self.collection:\n print('YIKES no filepath %s %s' % (self.title,self.slug))\n print (self)\n return ''\n fpath = self.collection.filePath\n if '/' in fpath:\n bg = fpath.index('/')+1\n return fpath[bg:]+ '/' + self.fileName\n return fpath+'/'+self.fileName","def update_destination_file_name (file_name):\n\tglobal COUNTER \n\tCOUNTER += 1\n\tsplitted = file_name.split('/')\n\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]","def genBaseName(fileName):\n return fileName.split(\"_\")[0].split(\".\")[0]","def getOutputFilename(self, filename):\n return filename[:-4] + \".txt\"","def appendDescription(dir, file_name, description):\n\tpath = os.path.join(dir, file_name)\n\tbase, ext = os.path.splitext(path)\n\treturn base + '.' + description + ext","def getNextFilename(self, expType):\n with self.lock:\n self.exposureID += 1\n exposureID = self.exposureID\n path = os.path.join(\"$ICS_MHS_DATA_ROOT\", 'agcc')\n path = os.path.expandvars(os.path.expanduser(path))\n if not os.path.isdir(path):\n os.makedirs(path, 0o755)\n with self.lock:\n timestamp = self.timestamp\n return os.path.join(path, 'AGC%d_%s_%06d_%s.fits' % \\\n (self.agcid + 1, expType, exposureID, timestamp))","def _create_name(self) -> str:\n return self.stream.__class__.__name__","def _get_track_name(self, filename):\n return os.path.basename(filename)","def _generate_processed_key_name(process_to, upload_name):\n timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\n name, extension = os.path.splitext(upload_name)\n digest = md5(''.join([timestamp, upload_name])).hexdigest()\n return os.path.join(process_to, '{0}.{1}'.format(digest, extension))","def _build_fname_templ(n):\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\n for i in range(1, n):\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\n var='{var%i}' % i)]\n return globals.ds_fn_sep.join(parts) + '.nc'","def format_filename(prefix, suffix, seq_len, uncased):\n seq_str = \"seq-{}\".format(seq_len)\n if uncased:\n case_str = \"uncased\"\n else:\n case_str = \"cased\"\n\n file_name = \"{}.{}.{}.{}\".format(prefix, seq_str, case_str, suffix)\n\n return file_name","def playlist_num_filename(**kwargs):\n return f\"{kwargs['track_num']} - {default_filename(**kwargs)}\"","def new_filename(fname=None,ndigits=3):\n if fname is None:\n ext = (\"%%.%ii\" % ndigits) % 1\n fname = \"%s.%s\" % (random_string(6), ext)\n \n if os.path.exists(fname): \n fname = increment_filename(fname,ndigits=ndigits)\n\n return fname"],"string":"[\n \"def generate_file_name(well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def _generate_raw_file_name(self, well, channel, desc):\\n \\n return \\\"bPLATE_w\\\" + well + \\\"_\\\" + desc + \\\"_c\\\" + channel + \\\".png\\\"\",\n \"def file_name(id, title, kind=\\\"src\\\"):\\n fn_template = conf.template_source_file_name\\n if kind == \\\"tst\\\":\\n fn_template = conf.template_test_file_name\\n\\n return fn_template.format(id=id, title=title.replace(\\\"-\\\", \\\"_\\\"))\",\n \"def generate_file_name(entry):\\n return str_for_file(u'{name}, {year}, {title}'.format(\\n year=entry['year'],\\n name=get_last_name(entry['author'][0]),\\n title=entry['title']\\n ))\",\n \"def create_file_name(self):\\n # create a unique id for the file name\\n index = self.helpers.alpha_uuid()\\n\\n filename = self.form['FieldStorage'][self.image_cid].filename\\n extension = guess_extension(guess_type(filename)[0])\\n return ( # concatenates the following data\\n self.articleData.get('directory') + # directory\\n '/' + # slash\\n self.articleData.get('article_name') + # the article name\\n '-' + # hyphen character\\n index + # the id of the image\\n extension\\n )\",\n \"def create_final_name(fname, date, fc_id, sample_name):\\n \\n # Split the file name according to CASAVA convention\\n m = re.match(r'(\\\\S+?)_(?:[ACGTN\\\\-]+|NoIndex|Undetermined)_L0*(\\\\d+)_R(\\\\d)_\\\\d+\\\\.fastq(.*)', fname)\\n if m is not None:\\n lane = m.group(2)\\n read = m.group(3)\\n ext = m.group(4)\\n else:\\n # Split the file name according to bcbb convention\\n m = re.match(r'(\\\\d+)_(\\\\d+)_([^_]+)_(\\\\d+)_(?:nophix_)?(\\\\d+)_fastq.txt(.*)', fname)\\n if m is None:\\n raise ValueError(\\\"Could not parse file name {:s} correctly!\\\".format(fname))\\n lane = m.group(1)\\n read = m.group(5)\\n ext = m.group(6)\\n \\n dest_file_name = \\\"{:s}.fastq{:s}\\\".format(\\\"_\\\".join([lane,\\n date,\\n fc_id,\\n sample_name,\\n read]),\\n ext.replace('..','.'))\\n return dest_file_name\",\n \"def collected_filename(cfg, collect_dir, i=None):\\n if i is not None:\\n file = cfg[\\\"files\\\"][i]\\n else:\\n file = cfg[\\\"file\\\"]\\n ext = path.splitext(file)[1]\\n name = cfg[\\\"id\\\"]\\n if i is not None:\\n name += \\\"_\\\" + str(i)\\n return path.join(collect_dir, name + ext)\",\n \"def _generate_filename(doc_type, login, *args):\\n filename = []\\n filename.append(doc_type)\\n filename.append(login)\\n for item in args:\\n filename.append(item)\\n filename.append(datetime.datetime.now().isoformat(timespec='microseconds'))\\n filename = '_'.join(filename)\\n return filename\",\n \"def content_file_name(instance, filename):\\r\\n return '/'.join([str(instance.app.publisher.id), str(instance.app.id), filename])\",\n \"def construct_name_file(size_sample, randomness, pos_equal_neg, kernel):\\n if randomness:\\n randomness = \\\"rand\\\"\\n else:\\n randomness = \\\"nrand\\\"\\n\\n if pos_equal_neg:\\n pos_equal_neg = \\\"pos-neg-eq\\\"\\n else:\\n pos_equal_neg = \\\"pos-neg-neq\\\"\\n\\n return \\\"{}_{}_{}_{}.json\\\".format(size_sample, randomness, pos_equal_neg, kernel)\",\n \"def generate_filename(player_name):\\n name = player_name.split()\\n filename = '_'.join(name).lower()\\n return filename\",\n \"def get_nomenclature_channel_fname(czi_fname, nomenclature_file, channel_name, ext='.inr.gz'):\\n # - Read NOMENCLATURE file defining naming conventions:\\n n_names = get_nomenclature_name(nomenclature_file)\\n return n_names[czi_fname]+\\\"/\\\", n_names[czi_fname] + \\\"_\\\" + channel_name + ext\",\n \"def generate_glider_filename(description):\\n filename = (\\n \\\"{glider}-{year:d}-{day:03d}-{mission:d}-{segment}.{type}\\\".format(**description)\\n )\\n return os.path.join(description['path'], filename)\",\n \"def generate_namefile(pathfolder, methodvalues):\\n datestr = datetime.datetime.now().date().strftime('%F')\\n paramsstr = str(hash(str(methodvalues)))\\n namefile = datestr + '-' + methodvalues['codename'] + '_' + paramsstr\\n namefile = os.path.join(pathfolder, namefile)\\n return namefile\",\n \"def generate_filename(filename: str) -> str:\\n return f\\\"{str(uuid.uuid4())}.{get_extension(filename)}\\\"\",\n \"def channel_name(radio_id: int, channel_id: int) -> str:\\n return f\\\"COMM{radio_id} Ch {channel_id}\\\"\",\n \"async def filename_generator(self):\\n chars = list(string.ascii_letters+string.digits)\\n name = ''\\n for i in range(random.randint(9, 25)):\\n name += random.choice(chars)\\n\\n if name not in self.player['audio_files']:\\n return name\\n\\n return await self.filename_generator()\",\n \"def gen_file_name(filename, path=UPLOAD_FOLDER):\\n\\n i = 1\\n while os.path.exists(os.path.join(path, filename)):\\n name, extension = os.path.splitext(filename)\\n filename = '%s_%s%s' % (name, str(i), extension)\\n i += 1\\n\\n return filename\",\n \"def generate_filename(playlist_or_album_name, user_id_or_artist_id=None):\\n filename = ''\\n if user_id_or_artist_id:\\n filename += user_id_or_artist_id + '_'\\n filename += playlist_or_album_name + '_' + str(time_ns())\\n return filename\",\n \"def _get_file_name(name: types.TSeedName) -> str:\\n return f\\\"{name}.yml\\\"\",\n \"def get_file_name(self):\\n\\n return \\\"%s - %s\\\" % (self.get_tags()[\\\"artist\\\"], self.get_tags()[\\\"title\\\"])\",\n \"def generateFilename(self, name):\\n return self.context.generateUniqueId(type_name='Module')\",\n \"def filename(i):\\n rand_name = os.path.join(os.getcwd(), \\\"input-%d.txt\\\" % i)\\n ref_name = os.path.join(os.getcwd(), \\\"input-%d.ref\\\" % i)\\n return rand_name, ref_name\",\n \"def create_filename (self):\\n\\t\\tassert self.__patient_name and self.__location_name, \\\"New filename could not be determined, one or more needed arguments is empty!\\\"\\n\\t\\t_patient_name = self.__patient_name.split(' ')\\n\\t\\t_patient_name.reverse()\\n\\t\\t\\n\\t\\treturn os.path.join(os.path.dirname(self.file._path), \\\"%s MR %s%s\\\" % (self.__location_name, ', '.join(_patient_name).upper(), self._file.extension))\",\n \"def filename(self):\\n return '%s%s' % (self.identifier, self.extension)\",\n \"def generate_report_file_name(args: Dict[str, Any]) -> str:\\n return (\\n f\\\"{args.get('report_type', '').lower().replace(' ', '_')}_fireeye_\\\"\\n f\\\"{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}.\\\"\\n f\\\"{args.get('type', REPORT_TYPE_ALLOWED_FORMAT[args.get('report_type', '')][0])}\\\"\\n )\",\n \"def generate_file_name(hour, minute):\\n hour = str(hour)\\n if len(hour) == 1:\\n hour = \\\"0\\\" + hour\\n minute = str(minute)\\n if len(minute) == 1:\\n minute = \\\"0\\\" + minute\\n file_name = date + \\\"--\\\" + hour + special_char + minute + special_char + \\\"00,00.mvol\\\"\\n\\n # print \\\"filename: \\\",file_name\\n return file_name\",\n \"def filename(self):\\n translator = {ord(\\\" \\\"): \\\"_\\\", ord(\\\",\\\"): None}\\n return f'{self._full_name.translate(translator)}.txt'\",\n \"def generate_file_filename(instance, filename):\\n return _generate_filename(instance, filename, 'photos')\",\n \"def temp_name(self, filename):\\n if self.params.get('nopart', False) or filename == '-' or \\\\\\n (os.path.exists(encodeFilename(filename)) and not os.path.isfile(encodeFilename(filename))):\\n return filename\\n return filename + '.part'\",\n \"def get_alternative_name(self, file_root, file_ext):\\n return \\\"%s_%s%s\\\" % (file_root, get_random_string(7), file_ext)\",\n \"def get_display_name(self, short=False):\\n if self.filename is None:\\n return '[New file]'\\n elif short:\\n return os.path.basename(self.filename)\\n else:\\n return self.filename\",\n \"def file_name(product, ext='json'):\\n return f\\\"./output/{product}_{datetime.now().strftime('%Y-%m-%d_%H%M%S')}_transformed_{version}.{ext}\\\"\",\n \"def generate_name(file_name, index):\\n name = ''\\n with open(file_name) as names:\\n for i, line in enumerate(names):\\n if i == index:\\n name += line.split(None, 1)[0]\\n return name\",\n \"def get_file_name(self):\\n return str(self.get_file())\",\n \"def get_download_file_name(self):\\n # Use 'unknown' if the course instance does not have a term\\n if self.course_instance.term:\\n term = self.course_instance.term.get_url_name()\\n else:\\n term = 'unknown'\\n\\n return '{course}-{term}-{number}-{instructors}-{type}{ext}'.format(\\n course=self.course_instance.course.get_url_name(),\\n term=term,\\n number=self.exam_number,\\n instructors='_'.join([i.last_name for i in self.instructors]),\\n type=self.exam_type,\\n ext=self.file_ext)\",\n \"def get_file_name() -> str:\\n import uuid\\n uniq_append_string = uuid.uuid4().hex\\n return \\\"LOCAL_STORAGE_{}\\\".format(uniq_append_string)\",\n \"def generateFileName(self):\\n return 'Covid' + self.map_type + '.html'\",\n \"def getNextFilename(self, cmd):\\n \\n self.actor.exposureID += 1\\n path = os.path.join(\\\"$ICS_MHS_DATA_ROOT\\\", 'mcs')\\n path = os.path.expandvars(os.path.expanduser(path))\\n\\n if not os.path.isdir(path):\\n os.makedirs(path, 0o755)\\n \\n return os.path.join(path, 'MCSA%010d.fits' % (self.actor.exposureID))\",\n \"def generate_image_filename():\\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\\n return 'CCTV_{0}.jpg'.format(now)\",\n \"def generate_file_name(self):\\n self._session_iterator = None # New file invalidate old interator\\n self._img_count += 1\\n self._current_file = '{0}/frame_{1}.jpg'.format(self._relative_path,self._img_count)\\n return self.current_file\",\n \"def mk_filename(pattern, queryresponserow, resp, url):\\n name = None\\n if resp:\\n cdheader = resp.headers.get(\\\"Content-Disposition\\\", None)\\n if cdheader:\\n _, params = parse_header(cdheader)\\n name = params.get('filename', \\\"\\\")\\n # Work around https://github.com/sunpy/sunpy/issues/3372\\n if name.count('\\\"') >= 2:\\n name = name.split('\\\"')[1]\\n\\n # This is a hack to to prevent IRIS data from being labelled as XML files\\n if name is None and \\\"VOEvent_IRIS\\\" not in queryresponserow['fileid']:\\n # Advice from the VSO is to fallback to providerid + fileid for a filename\\n # As it's possible multiple providers give the same fileid.\\n # However, I haven't implemented this yet as it would be a breaking\\n # change to the filenames we expect.\\n fileid = queryresponserow['fileid']\\n\\n # Some providers make fileid a path\\n # Some also don't specify a file extension, but not a lot we can do\\n # about that.\\n name = fileid.split(\\\"/\\\")[-1]\\n\\n # If somehow we have got this far with an empty string, fallback to url segment\\n if not name:\\n name = url.split('/')[-1]\\n\\n # Remove any not-filename appropriate characters\\n name = slugify(name)\\n\\n # If absolutely everything else fails make a filename based on download time\\n if not name:\\n name = f\\\"vso_file_{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}\\\"\\n\\n fname = pattern.format(file=name,\\n **queryresponserow.response_block_map)\\n\\n return fname\",\n \"def init_data_file_name():\\n now = datetime.datetime.now().isoformat().split('.')[0].replace(':', '-')\\n filename = 'show-commands-' + now + \\\".txt\\\"\\n return filename\",\n \"def create_random_file_name():\\n\\n def random_file_name_factory():\\n length = random.randint(10, 15)\\n chars = string.ascii_letters + string.digits + \\\"-_\\\"\\n return f\\\"{''.join(random.choice(chars) for _ in range(length))}.jpg\\\"\\n\\n return random_file_name_factory\",\n \"def filename(self):\\n return self.config.get('filename', self.id) + f'_{self.file_suffix}'\",\n \"def get_title(src_name, src_type=None):\\n if src_type == 'tcp':\\n return '{0}:{1}'.format(*src_name)\\n return os.path.basename(src_name)\",\n \"def get_resampled_filename(filepath):\\n try:\\n filepath = filepath.encode('utf-8')\\n except UnicodeDecodeError:\\n pass\\n return 'tmp_%s.flac' % md5(filepath).hexdigest()\",\n \"def format_filename(title: str, id: Any, ext: str = \\\".\\\", dirFormat=None):\\r\\n ...\",\n \"def _generate_filename(instance, filename, prefix):\\n md5 = hashlib.md5()\\n md5.update(struct.pack('f', time.time()))\\n for chunk in instance.file.chunks():\\n md5.update(chunk)\\n extension = os.path.splitext(filename)[1]\\n return os.path.join(prefix, md5.hexdigest() + extension)\",\n \"def modpricesetter_get_file_name(self):\\r\\n year, month, day = self._get_market_year_month_day_as_str()\\r\\n interval_number = self._get_interval_number_as_str()\\r\\n base_name = \\\"NEMPriceSetter_{year}{month}{day}{interval_number}00.xml\\\"\\r\\n name = base_name.format(\\r\\n year=year, month=month, day=day, interval_number=interval_number\\r\\n )\\r\\n path_name = Path(self.cache_folder) / name\\r\\n name_OCD = name.replace(\\\".xml\\\", \\\"_OCD.xml\\\")\\r\\n path_name_OCD = Path(self.cache_folder) / name_OCD\\r\\n name_zero = name.replace(\\\".xml\\\", \\\"00.xml\\\")\\r\\n path_name_zero = Path(self.cache_folder) / name_zero\\r\\n if os.path.exists(path_name):\\r\\n return name\\r\\n elif os.path.exists(path_name_OCD):\\r\\n return name_OCD\\r\\n elif os.path.exists(path_name_zero):\\r\\n return name_zero\\r\\n else:\\r\\n return name\",\n \"def source_file_name_feature(self):\\n return \\\"_\\\".join((C_FILE_NAME, self.file_image_name.value))\",\n \"def get_filename(out_dir, file_date, extension):\\n return path.join(out_dir, f'CrossrefCitations_{file_date}.{extension}')\",\n \"def create_filename(value):\\n return '%s.mp3' % slugify(value, u'_')\",\n \"def generate_filename(\\n radar, field, sweep, ext=\\\"png\\\", datetime_format=\\\"%Y%m%d%H%M%S\\\", use_sweep_time=False\\n):\\n name_s = generate_radar_name(radar).replace(\\\" \\\", \\\"_\\\")\\n field_s = field.replace(\\\" \\\", \\\"_\\\")\\n if use_sweep_time:\\n time_s = generate_radar_time_sweep(radar, sweep).strftime(datetime_format)\\n else:\\n time_s = generate_radar_time_begin(radar).strftime(datetime_format)\\n sweep_s = str(sweep).zfill(2)\\n return f\\\"{name_s}_{field_s}_{sweep_s}_{time_s}.{ext}\\\"\",\n \"def filename(N, Dr, g, launch):\\n\\n return 'N%s_R%s_G%s_E%s.datR' % tuple(map(float_to_letters,\\n (N, Dr, g, launch)))\",\n \"def unique_filename(data):\\n file = data\\n get_ext = file.filename.split(\\\".\\\")[-1]\\n new_name = \\\"%s.%s\\\" % (uuid.uuid4().hex, get_ext)\\n return new_name\",\n \"def file_on_disk_name(instance, filename):\\n return generate_file_on_disk_name(instance.checksum, filename)\",\n \"def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\\n\\tnew_filename = time.strftime(\\\"%d-%m-%Y_\\\")\\n\\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\\n\\tnew_filename = new_filename + \\\"_P\\\" + str(image_class)\\n\\treturn new_filename\",\n \"def content_file_name(self, filename):\\n ext = filename.split('.')[-1]\\n filename = \\\"%s_%s.%s\\\" % (filename, self.id, ext)\\n return os.path.join('pictures/static/pictures/', filename)\",\n \"def _build_name(self):\\n self.ctrl_name = NameUtils.get_unique_name(self.asset,\\n self.side,\\n self.part,\\n \\\"ctrl\\\")\",\n \"def output_file_name_maker(args):\\n log.debug(\\\"Entering output_file_name_maker()\\\")\\n path = os.getcwd() + '/out_files/'\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n\\n if args.output is None:\\n out_file_name = path + args.input[:-4] + '_' + args.type + '_' + args.layer\\n else:\\n out_file_name = path + args.output\\n\\n log.debug(\\\"Exiting output_file_name_maker()\\\")\\n return out_file_name\",\n \"def get_custom_filename(existing_files: List[str]) -> str:\\n questions = [\\n {\\n 'type': 'input',\\n 'name': 'custom_filename',\\n 'message': 'Name your new sample :',\\n 'default': get_default_filename(),\\n 'validate': lambda text: (\\n (len(re.findall(r'^[A-Za-z0-9_\\\\-.]{3,40}$', text)) > 0\\n and text+'.smp' not in existing_files\\n ) or\\n 'Typed file name contains illegal characters or already exist'\\n )\\n }\\n ]\\n return prompt(questions, style=custom_style_2)['custom_filename']+'.smp'\",\n \"def _filename(self, corotid):\\n from datasource import DataSource\\n self.corotid = corotid\\n self.corot = DataSource(database='corot', user='sro', host='pina.aip.de')\\n \\n query = \\\"\\\"\\\"SELECT run_code, hlfccdid, win_id \\n FROM corot \\n WHERE corotid = %d;\\\"\\\"\\\" % self.corotid\\n result = self.corot.query(query)\\n \\n par = {'run': result[0][0],\\n 'half': result[0][1].rstrip('RL'), \\n 'win': result[0][2]}\\n filename = '/work2/jwe/CoRoT/%(run)s/data/%(run)s_%(half)s_%(win)04d.fits' % par\\n logger.info('%d = %s' % (corotid,filename))\\n return filename\",\n \"def make_img_name(file_ext='.png'):\\r\\n fn = []\\r\\n # format seqs and write out to temp file\\r\\n for i in range(0, 30):\\r\\n fn.append(choice(ALPHABET))\\r\\n return ''.join(fn) + file_ext\",\n \"def update_filename(instance, filename):\\n path = os.path.join(\\\"documents_analizer\\\", \\\"documents\\\")\\n name = \\\"{}{}\\\".format(highly_random_name(),\\n os.path.splitext(filename)[1])\\n return os.path.join(path, name)\",\n \"def download_filename_full(self, doc):\\n # todo modify\\n authors = \\\",\\\".join([x['name'] for x in doc.artists])\\n author = re.sub(\\\"[\\\\\\\\\\\\\\\\/:*?\\\\\\\"<>|]\\\", '', authors.strip())\\n mp3_name = re.sub(\\\"[\\\\\\\\\\\\\\\\/:*?\\\\\\\"<>|]\\\", '', doc['name'])\\n name = os.path.join(author, \\\"%s - %s.mp4\\\" % (author, mp3_name))\\n return name\",\n \"def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\\n filename = self.filename(filetype=filetype, format=format)\\n #return Path(dir) / filename\\n return filename\",\n \"def filename(lang):\\n filename = lang.replace('-', '_')\\n filename = filename.lower()\\n return filename\",\n \"def _file_name(size):\\n timestamp = str(int(time.time()))\\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')\",\n \"def _prettyfilename(self):\\n return f'{self.title} ({self.subtype})'\",\n \"def generate_subdir(channel, energy=13):\\n return os.path.join('%dTeV' % energy, channel, strftime(\\\"%d_%b_%y\\\"))\",\n \"def out_filename(self, filetype, format='old', dir=Location.OUT_DIR):\\n filename = self.filename(filetype=filetype, format=format)\\n # return Path(dir) / filename\\n return filename\",\n \"def _generate_output_name(extension):\\n output_name = 'TDG_{:%Y-%m-%d_%H-%M-%S}.{}'.format(datetime.now(), extension)\\n return output_name\",\n \"def out_filename(self, filetype, dir, format='old'):\\n filename = self.filename(filetype=filetype, format=format)\\n return Path(dir) / filename\",\n \"def _create_file_name(file_path):\\r\\n file_base, file_ext = os.path.splitext(file_path)\\r\\n if os.path.isfile(file_path):\\r\\n nfile = 1\\r\\n check = True\\r\\n while check:\\r\\n name_add = '0000' + str(nfile)\\r\\n file_path = file_base + \\\"_\\\" + name_add[-4:] + file_ext\\r\\n if os.path.isfile(file_path):\\r\\n nfile = nfile + 1\\r\\n else:\\r\\n check = False\\r\\n return file_path\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\\n ext='.mif'):\\n\\n if basename == '':\\n msg = 'Unable to generate filename for command %s. ' % self.cmd\\n msg += 'basename is not set!'\\n raise ValueError(msg)\\n if cwd is None:\\n cwd = os.getcwd()\\n if change_ext:\\n if suffix:\\n suffix = ''.join((suffix, ext))\\n else:\\n suffix = ext\\n if suffix is None:\\n suffix = ''\\n fname = fname_presuffix(basename, suffix=suffix,\\n use_ext=False, newpath=cwd)\\n return fname\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,\\n ext='.mif'):\\n\\n if basename == '':\\n msg = 'Unable to generate filename for command %s. ' % self.cmd\\n msg += 'basename is not set!'\\n raise ValueError(msg)\\n if cwd is None:\\n cwd = os.getcwd()\\n if change_ext:\\n if suffix:\\n suffix = ''.join((suffix, ext))\\n else:\\n suffix = ext\\n if suffix is None:\\n suffix = ''\\n fname = fname_presuffix(basename, suffix=suffix,\\n use_ext=False, newpath=cwd)\\n return fname\",\n \"def newCalFileName(self, type, runBegin, runEnd='end'):\\n \\n path=os.path.join(self.cdir)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup,self.src)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n path=os.path.join(self.cdir,self.calibgroup,self.src,type)\\n if not os.path.exists(path): \\n os.mkdir(path)\\n return path+'/'+str(runBegin)+'-'+str(runEnd)+'.data'\",\n \"def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True, ext=None):\\n if not basename:\\n msg = \\\"Unable to generate filename for command %s. \\\" % self.cmd\\n msg += \\\"basename is not set!\\\"\\n raise ValueError(msg)\\n\\n if cwd is None:\\n cwd = os.getcwd()\\n if ext is None:\\n ext = Info.output_type_to_ext(self.inputs.outputtype)\\n if change_ext:\\n suffix = \\\"\\\".join((suffix, ext)) if suffix else ext\\n\\n if suffix is None:\\n suffix = \\\"\\\"\\n fname = fname_presuffix(basename, suffix=suffix, use_ext=False, newpath=cwd)\\n return fname\",\n \"def get_file_name(replay_dir, template_name):\\n suffix = '.json' if not template_name.endswith('.json') else ''\\n file_name = f'{template_name}{suffix}'\\n return os.path.join(replay_dir, file_name)\",\n \"def _get_buildspec_filename(\\n lambda_name, file_name):\\n return '.'.join(\\n ('buildspecs', lambda_name, file_name))\",\n \"def generate_filename(ext,sha512base16_hash=None):\\n## # Timestamp filename\\n## timestamp = str(get_current_unix_time())\\n## filename = timestamp+\\\".\\\"+ext\\n # Base16 hash filename\\n filename = sha512base16_hash+\\\".\\\"+ext\\n return filename\",\n \"def _make_fname(song, ext=None, av=None, subdir=None):\\n # pylint: disable=E1103\\n # Instance of 'bool' has no 'extension' member (some types not inferable)\\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\\n if not os.path.exists(ddir):\\n os.makedirs(ddir)\\n\\n if ext:\\n extension = ext\\n\\n else:\\n stream = streams.select(streams.get(song),\\n audio=av == \\\"audio\\\", m4a_ok=True)\\n extension = stream['ext']\\n\\n # filename = song.title[:59] + \\\".\\\" + extension\\n filename = song.title + \\\".\\\" + extension\\n filename = os.path.join(ddir, mswinfn(filename.replace(\\\"/\\\", \\\"-\\\")))\\n filename = filename.replace('\\\"', '')\\n return filename\",\n \"def get_output_filename(item: str, root: str, i: int) -> str:\\n element_split = item.split(\\\"/\\\")\\n item, ext = element_split[-1].split(\\\".\\\")\\n if i < 0:\\n return f\\\"{root}/{'/'.join(element_split[:-1])}/{item}.{ext}\\\"\\n else:\\n return f\\\"{root}/{'/'.join(element_split[:-1])}/{item}_aug{i}.{ext}\\\"\",\n \"def outputFilename(name=\\\"\\\", ext=\\\"\\\", time=True):\\n # get the date in the format specifed\\n dateTime = datetime.now()\\n dateTimeFormat = \\\"%Y-%m-%d__%H-%M-%S\\\" if time else \\\"%Y-%m-%d\\\"\\n fileName = dateTime.strftime(dateTimeFormat)\\n\\n # construct the filename\\n fileName = fileName + \\\"_\\\" + name if fileName != \\\"\\\" else fileName\\n ext = \\\".\\\" + ext if ext != \\\"\\\" else \\\"\\\"\\n\\n return fileName + ext\",\n \"def generate_random_name(filename):\\n ext = filename.split('.')[-1]\\n rns = [random.randint(0, len(LETTER_SET) - 1) for _ in range(3)]\\n name = ''.join([LETTER_SET[rn] for rn in rns])\\n return \\\"{new_fn}.{ext}\\\".format(new_fn=name, ext=ext)\",\n \"def format_image_filename(device_image):\\n return \\\"{}-{}-{}-{}.bit\\\".format(device_image.bitstream_type,\\n device_image.pci_vendor,\\n device_image.pci_device,\\n device_image.uuid)\",\n \"def friendly_name(self):\\n if not self.collection:\\n print('YIKES no filepath %s %s' % (self.title,self.slug))\\n print (self)\\n return ''\\n fpath = self.collection.filePath\\n if '/' in fpath:\\n bg = fpath.index('/')+1\\n return fpath[bg:]+ '/' + self.fileName\\n return fpath+'/'+self.fileName\",\n \"def update_destination_file_name (file_name):\\n\\tglobal COUNTER \\n\\tCOUNTER += 1\\n\\tsplitted = file_name.split('/')\\n\\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]\",\n \"def genBaseName(fileName):\\n return fileName.split(\\\"_\\\")[0].split(\\\".\\\")[0]\",\n \"def getOutputFilename(self, filename):\\n return filename[:-4] + \\\".txt\\\"\",\n \"def appendDescription(dir, file_name, description):\\n\\tpath = os.path.join(dir, file_name)\\n\\tbase, ext = os.path.splitext(path)\\n\\treturn base + '.' + description + ext\",\n \"def getNextFilename(self, expType):\\n with self.lock:\\n self.exposureID += 1\\n exposureID = self.exposureID\\n path = os.path.join(\\\"$ICS_MHS_DATA_ROOT\\\", 'agcc')\\n path = os.path.expandvars(os.path.expanduser(path))\\n if not os.path.isdir(path):\\n os.makedirs(path, 0o755)\\n with self.lock:\\n timestamp = self.timestamp\\n return os.path.join(path, 'AGC%d_%s_%06d_%s.fits' % \\\\\\n (self.agcid + 1, expType, exposureID, timestamp))\",\n \"def _create_name(self) -> str:\\n return self.stream.__class__.__name__\",\n \"def _get_track_name(self, filename):\\n return os.path.basename(filename)\",\n \"def _generate_processed_key_name(process_to, upload_name):\\n timestamp = datetime.now().strftime('%Y%m%d%H%M%S%f')\\n name, extension = os.path.splitext(upload_name)\\n digest = md5(''.join([timestamp, upload_name])).hexdigest()\\n return os.path.join(process_to, '{0}.{1}'.format(digest, extension))\",\n \"def _build_fname_templ(n):\\n parts =[globals.ds_fn_templ.format(i='{i_ref:d}', ds='{ref}', var='{ref_var}')]\\n for i in range(1, n):\\n parts += [globals.ds_fn_templ.format(i='{i_ds%i:d}' % i, ds='{ds%i}' % i,\\n var='{var%i}' % i)]\\n return globals.ds_fn_sep.join(parts) + '.nc'\",\n \"def format_filename(prefix, suffix, seq_len, uncased):\\n seq_str = \\\"seq-{}\\\".format(seq_len)\\n if uncased:\\n case_str = \\\"uncased\\\"\\n else:\\n case_str = \\\"cased\\\"\\n\\n file_name = \\\"{}.{}.{}.{}\\\".format(prefix, seq_str, case_str, suffix)\\n\\n return file_name\",\n \"def playlist_num_filename(**kwargs):\\n return f\\\"{kwargs['track_num']} - {default_filename(**kwargs)}\\\"\",\n \"def new_filename(fname=None,ndigits=3):\\n if fname is None:\\n ext = (\\\"%%.%ii\\\" % ndigits) % 1\\n fname = \\\"%s.%s\\\" % (random_string(6), ext)\\n \\n if os.path.exists(fname): \\n fname = increment_filename(fname,ndigits=ndigits)\\n\\n return fname\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80092597","0.75195915","0.7167804","0.70670754","0.69943404","0.67369324","0.6670187","0.66342044","0.66167426","0.6494215","0.64797497","0.6479064","0.6476831","0.64566636","0.6450938","0.64458376","0.6431859","0.6416797","0.6390564","0.63887256","0.6379263","0.63707864","0.6364709","0.6351363","0.63499355","0.6347175","0.634275","0.63143575","0.63116884","0.62697697","0.625603","0.6248453","0.6239961","0.6211779","0.6208297","0.62011933","0.61814475","0.61797863","0.6174575","0.6140057","0.61364377","0.61182743","0.61140096","0.6110816","0.61029804","0.6100755","0.6082549","0.607765","0.60764325","0.607335","0.60630345","0.6059483","0.60574424","0.6048166","0.60345787","0.60343796","0.60306704","0.6026986","0.6013758","0.599426","0.59873575","0.59872776","0.5985263","0.59812826","0.59781265","0.59761965","0.5967784","0.59631765","0.59560066","0.5950423","0.59501016","0.5946216","0.5941753","0.5938588","0.59275806","0.5922075","0.5922075","0.5916932","0.5910453","0.59010506","0.58981514","0.5884643","0.5869485","0.58673143","0.5863101","0.5860955","0.58598673","0.5853558","0.58517426","0.58480245","0.58445394","0.5843368","0.5841723","0.5836151","0.58358467","0.58334386","0.58319485","0.5830244","0.58291346","0.5821115"],"string":"[\n \"0.80092597\",\n \"0.75195915\",\n \"0.7167804\",\n \"0.70670754\",\n \"0.69943404\",\n \"0.67369324\",\n \"0.6670187\",\n \"0.66342044\",\n \"0.66167426\",\n \"0.6494215\",\n \"0.64797497\",\n \"0.6479064\",\n \"0.6476831\",\n \"0.64566636\",\n \"0.6450938\",\n \"0.64458376\",\n \"0.6431859\",\n \"0.6416797\",\n \"0.6390564\",\n \"0.63887256\",\n \"0.6379263\",\n \"0.63707864\",\n \"0.6364709\",\n \"0.6351363\",\n \"0.63499355\",\n \"0.6347175\",\n \"0.634275\",\n \"0.63143575\",\n \"0.63116884\",\n \"0.62697697\",\n \"0.625603\",\n \"0.6248453\",\n \"0.6239961\",\n \"0.6211779\",\n \"0.6208297\",\n \"0.62011933\",\n \"0.61814475\",\n \"0.61797863\",\n \"0.6174575\",\n \"0.6140057\",\n \"0.61364377\",\n \"0.61182743\",\n \"0.61140096\",\n \"0.6110816\",\n \"0.61029804\",\n \"0.6100755\",\n \"0.6082549\",\n \"0.607765\",\n \"0.60764325\",\n \"0.607335\",\n \"0.60630345\",\n \"0.6059483\",\n \"0.60574424\",\n \"0.6048166\",\n \"0.60345787\",\n \"0.60343796\",\n \"0.60306704\",\n \"0.6026986\",\n \"0.6013758\",\n \"0.599426\",\n \"0.59873575\",\n \"0.59872776\",\n \"0.5985263\",\n \"0.59812826\",\n \"0.59781265\",\n \"0.59761965\",\n \"0.5967784\",\n \"0.59631765\",\n \"0.59560066\",\n \"0.5950423\",\n \"0.59501016\",\n \"0.5946216\",\n \"0.5941753\",\n \"0.5938588\",\n \"0.59275806\",\n \"0.5922075\",\n \"0.5922075\",\n \"0.5916932\",\n \"0.5910453\",\n \"0.59010506\",\n \"0.58981514\",\n \"0.5884643\",\n \"0.5869485\",\n \"0.58673143\",\n \"0.5863101\",\n \"0.5860955\",\n \"0.58598673\",\n \"0.5853558\",\n \"0.58517426\",\n \"0.58480245\",\n \"0.58445394\",\n \"0.5843368\",\n \"0.5841723\",\n \"0.5836151\",\n \"0.58358467\",\n \"0.58334386\",\n \"0.58319485\",\n \"0.5830244\",\n \"0.58291346\",\n \"0.5821115\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71012443"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":22391,"cells":{"query":{"kind":"string","value":"Constructor that takes the configuration for the plate"},"document":{"kind":"string","value":"def __init__(self, config):\n self.config = config"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0","def __init__():\n self.placa = placa","def __init__(self, projection=None, transform=ccrs.PlateCarree(),\n figure_kws=dict(), drainage_baisins=True):\n self.proj = projection\n self.transform = transform\n self.fig_kws = figure_kws\n self.drainage_baisins = drainage_baisins","def __init__(self):\r\n config = ConfigProvider().getProcessingConfig()\r\n self.xGround = config.get(\"xGround\")\r\n self.yGround = config.get(\"yGround\")","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n pi0eeg_name = '{0}Pi0EEG'.format(name)\n \n d2pipi0eeg_name = '{0}D2PiPi0EEG'.format(name) \n d2kpi0eeg_name = '{0}D2KPi0EEG'.format(name)\n\n d02pi0pi0eeg_name = '{0}D02Pi0Pi0EEG'.format(name) \n dst2d0pieeg_name = '{0}Dst2D0PiEEG'.format(name)\n\n self.selPi0EEG = makeEEGResonance(\n pi0eeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.Pi0EEG\n )\n \n self.selD2PiPi0EEG = makeD(\n d2pipi0eeg_name,\n config,\n inputSel=[self.selPi0EEG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPi0,\n useBachelorPID=False\n )\n\n usePIDforKaon = False\n if (config['Bachelor_PIDK_MIN'] > -10):\n usePIDforKaon = True\n else :\n usePIDforKaon = False\n \n self.selD2KPi0EEG = makeD(\n d2kpi0eeg_name, \n config,\n inputSel=[self.selPi0EEG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPi0,\n useBachelorPID=usePIDforKaon\n )\n\n self.selD02Pi0Pi0EEG = makeD(\n d02pi0pi0eeg_name, \n config,\n inputSel=[self.selPi0EEG],\n decDescriptors=self.D02Pi0Pi0,\n useBachelorPID=False,\n )\n\n self.selDst2D0PiEEG = makeDstar(\n dst2d0pieeg_name,\n config,\n inputSel=[self.selD02Pi0Pi0EEG, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.line_D2PiPi0EEG = make_line(\n self,\n '{0}Line'.format(d2pipi0eeg_name),\n prescale=config['PrescaleD2PiPi0EEG'],\n postscale=config['PostscaleD2PiPi0EEG'],\n selection=self.selD2PiPi0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KPi0EEG = make_line(\n self,\n '{0}Line'.format(d2kpi0eeg_name),\n prescale=config['PrescaleD2KPi0EEG'],\n postscale=config['PostscaleD2KPi0EEG'],\n selection=self.selD2KPi0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_Dst2D0PiEEG = make_line(\n self,\n '{0}Line'.format(dst2d0pieeg_name),\n prescale=config['PrescaleDst2D0PiEEG'],\n postscale=config['PostscaleDst2D0PiEEG'],\n selection=self.selDst2D0PiEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n self._read_calibration_data()\n self.configure_sensor(\n TemperatureOversamplings.x08,\n PressureOversamplings.x16,\n HumidityOversamplings.x08,\n IIRFilterCoefficients.FC_003,\n 250,\n 250)","def __init__(self, cfg):\r\n\r\n\t\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\r\n\t\tanchor_config = cfg.MODEL.ANCHORS\r\n\t\tself.feature_maps = anchor_config.FEATURE_MAPS\r\n\t\tself.min_sizes = anchor_config.MIN_SIZES\r\n\t\tself.max_sizes = anchor_config.MAX_SIZES \r\n\t\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\r\n\t\tself.clip = anchor_config.CLIP","def __init__(self, name: str = None, *plates: Plate):\r\n super().__init__()\r\n self.name = name or f\"{self.__class__.__name__.lower()}_{self.id}\"\r\n self.plates = plates","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n phi3hm_name = '{0}Phi3HM'.format(name)\n phi3hr_name = '{0}Phi3HR'.format(name)\n \n d2piphi3hm_name = '{0}D2PiPhi3HM'.format(name)\n d2piphi3hr_name = '{0}D2PiPhi3HR'.format(name)\n \n d2kphi3hm_name = '{0}D2KPhi3HM'.format(name)\n d2kphi3hr_name = '{0}D2KPhi3HR'.format(name)\n \n\n self.selPhi3HM = makeResonance(\n phi3hm_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\n decDescriptors=self.Phi3H\n )\n\n self.selPhi3HR = makeResonance(\n phi3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\n decDescriptors=self.Phi3H\n )\n \n self.selD2PiPhi3HM = makeD(\n d2piphi3hm_name,\n config,\n inputSel=[self.selPhi3HM, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPhi,\n useBachelorPID=False,\n )\n \n self.selD2KPhi3HM = makeD(\n d2kphi3hm_name,\n config,\n inputSel=[self.selPhi3HM, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPhi,\n useBachelorPID=False,\n )\n \n self.selD2PiPhi3HR = makeD(\n d2piphi3hr_name,\n config,\n inputSel=[self.selPhi3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPhi,\n useBachelorPID=False,\n )\n \n self.selD2KPhi3HR = makeD(\n d2kphi3hr_name,\n config,\n inputSel=[self.selPhi3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPhi,\n useBachelorPID=False,\n )\n \n self.line_D2PiPhi3HM = make_line(\n self,\n name='{0}Line'.format(d2piphi3hm_name),\n prescale=config['PrescaleD2PiPhi3HM'],\n postscale=config['PostscaleD2PiPhi3HM'],\n selection=self.selD2PiPhi3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2PiPhi3HR = make_line(\n self,\n name='{0}Line'.format(d2piphi3hr_name),\n prescale=config['PrescaleD2PiPhi3HR'],\n postscale=config['PostscaleD2PiPhi3HR'],\n selection=self.selD2PiPhi3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n self.line_D2KPhi3HM = make_line(\n self,\n name='{0}Line'.format(d2kphi3hm_name),\n prescale=config['PrescaleD2KPhi3HM'],\n postscale=config['PostscaleD2KPhi3HM'],\n selection=self.selD2KPhi3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KPhi3HR = make_line(\n self,\n name='{0}Line'.format(d2kphi3hr_name),\n prescale=config['PrescaleD2KPhi3HR'],\n postscale=config['PostscaleD2KPhi3HR'],\n selection=self.selD2KPhi3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, config, well, directory):\n self.config = config\n self.well = well\n self.directory = directory","def __init__(self, config_file = 'config.yaml'):\n\n self.name = ''\n self.img_dir = ''\n self.out_dir = ''\n self.cam_file = ''\n self.options_file = ''\n self.output_xml_file = ''\n\n # If there is an options file, it will overwrite the defaults \n if config_file is not None:\n self.load(config_file)","def __init__(self, config):\n super().__init__(config)\n self.collector_host = config.get(\"collector_host\")\n self.schedds = config.get(\"schedds\", [None])\n self.condor_config = config.get(\"condor_config\")\n self.constraint = config.get(\"constraint\", True)\n self.classad_attrs = config.get(\"classad_attrs\")\n self.correction_map = config.get(\"correction_map\")","def __init__(self):\n\n super().__init__()\n\n self.active = True\n self.driver = Driver.instance()\n self.sensor_manager = SensorManager.instance()\n\n self.pwm = Adafruit_PCA9685.PCA9685(address=0x40, busnum=1) # create PCA9685-object at I2C-port\n self.pwm.set_pwm_freq(50)\n\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(20, GPIO.OUT)\n GPIO.setup(21, GPIO.OUT)\n GPIO.setup(26, GPIO.OUT)\n self.driven_distance = 0","def __init__(self, config):\n self.cfg = config\n self.var_combinations = [\"tas:tas\", \"pr:pr\", \"pr:tas\"]\n self.seasons = [\"jja\", \"djf\", \"annual\"]\n self.projects = [\"cmip5\", \"cmip6\"]\n self.variables = [\"tas\", \"pr\"]\n self.scenarios = [\"26\", \"45\", \"85\"]\n\n # generate list of candidate bound limits\n small = np.arange(0.1, 1, 0.1)\n medium = np.arange(1, 11)\n high = np.arange(20, 100, 10)\n v_high = np.arange(150, 400, 50)\n self.bound_candidates = np.concatenate(\n (small, medium, high, v_high)) * 5 / 4","def __init__(self, config: str) -> None:\n self.configuration = config","def __init__(self, config: str) -> None:\n self.configuration = config","def __init__(self):\n self.risepower = 4.\n self.min_event_amplitude = 5.0e-12 # pA default\n self.template = None\n pass","def __init__(self, config: dict) -> None:\n super().__init__(config)","def __init__(self, name, i_node, j_node, m_node, n_node, t, material, model, kx_mod=1.0,\n ky_mod=1.0):\n\n self.name = name\n self.ID = None\n self.type = 'Rect'\n\n self.i_node = i_node\n self.j_node = j_node\n self.m_node = m_node\n self.n_node = n_node\n\n self.t = t\n \n self.kx_mod = kx_mod\n self.ky_mod = ky_mod\n\n self.pressures = [] # A list of surface pressures [pressure, case='Case 1']\n\n # Plates need a link to the model they belong to\n self.model = model\n\n # Get material properties for the plate from the model\n try:\n self.E = self.model.Materials[material].E\n self.nu = self.model.Materials[material].nu\n except:\n raise KeyError('Please define the material ' + str(material) + ' before assigning it to plates.')","def __init__(self, config_file=None, cam_prefix=\"rc\", serial_number=\"\",\n output_dir=\"\", parseport=5001,\n force_serial=True, set_temperature=-40, send_to_remote=False,\n remote_config='nemea.config.json'):\n\n # Load the default parameters from config file\n if not config_file:\n config_file = os.path.join(SR, 'config',\n '%s_config.yaml' % cam_prefix)\n with open(config_file) as df:\n camera_params = yaml.load(df, Loader=yaml.FullLoader)\n self.__dict__.update(camera_params['default'].items())\n\n self.camPrefix = cam_prefix\n self.outputDir = output_dir\n self.forceSerial = force_serial\n self.setTemperature = set_temperature\n self.ExposureTime = 0\n self.lastExposed = None\n self.opt = None\n self.parseport = parseport\n self.send_to_remote = send_to_remote\n if self.send_to_remote:\n self.transfer = None #transfer(**params)\n self.lastError = \"\"","def __init__(self):\n GPIO.setwarnings(False)\n GPIO.cleanup() # Reset the high and low levels of the GPIO port\n #The following code defines the GPIO used to control the L298N chip. This definition is different for different Raspberry Pi driver boards.\n self.Motor_A_EN = 17\n self.Motor_B_EN = 4\n self.Motor_A_Pin1 = 27\n self.Motor_A_Pin2 = 18\n self.Motor_B_Pin1 = 21\n self.Motor_B_Pin2 = 26\n self.setup()","def __init__(self, config=None):\n config_dict = {}\n if config:\n config_dict = json.load(config)\n\n self.android = config_dict.get(\"android\")\n self.linux = config_dict.get(\"linux\")\n self.atf = config_dict.get(\"atf\")\n self.qemu = config_dict.get(\"qemu\", \"qemu-system-aarch64\")","def __init__(self, config):\n super().__init__(config)\n\n # Prepare the timer.\n self.timer = 0\n\n # Set the current player index.\n self.current_player_index = 0\n # If we are the client, the server goes first.\n for i in range(len(sys.argv)):\n if sys.argv[i] == \"--client\":\n self.current_player_index = 1\n\n # Prepare the phase counter.\n self.__current_phase = Game.PHASE_PREPARE\n # Prepare the shot location store.\n self.__current_fire_location = None\n self.__current_fire_effect = None","def __init__(self, config=None):\n pass","def __init__(self, make, model, year):\r\n super().__init__(make, model, year)\r\n self.battery_size = 70\r\n # self.autopilot = autopilot\r","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def __init__(self, configuration):\n self._config = configuration","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n d0eeg_name = '{0}D0EEG'.format(name)\n dst2pid0eeg_name = '{0}Dst2PiD0EEG'.format(name)\n \n d0ee_name = '{0}D0EE'.format(name)\n dst2pid0ee_name = '{0}Dst2PiD0EE'.format(name)\n\n self.selD0EEG = makeEEGResonance(\n d0eeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.D0EEG\n ) \n\n self.selD0EE = makeEEResonance(\n d0ee_name, \n config,\n inputSel=[StdAllLooseElectrons],\n decDescriptors=self.D0EE\n )\n \n self.selDst2PiD0EEG = makeDstar(\n dst2pid0eeg_name,\n config,\n inputSel=[self.selD0EEG, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.selDst2PiD0EE = makeDstar(\n dst2pid0ee_name,\n config,\n inputSel=[self.selD0EE, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.line_Dst2PiD0EEG = make_line(\n self,\n '{0}Line'.format(dst2pid0eeg_name),\n prescale=config['PrescaleDst2PiD0EEG'],\n postscale=config['PostscaleDst2PiD0EEG'],\n selection=self.selDst2PiD0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_Dst2PiD0EE = make_line(\n self,\n '{0}Line'.format(dst2pid0ee_name),\n prescale=config['PrescaleDst2PiD0EE'],\n postscale=config['PostscaleDst2PiD0EE'],\n selection=self.selDst2PiD0EE,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, config, sensor=None):\n if sensor is None:\n from monitor.sensor import SensorDriver\n self.sensor = SensorDriver(config.getint(CONFIG_SECTION, \"trigger_pin\"),\n config.getint(CONFIG_SECTION, \"echo_pin\"))\n else:\n self.sensor = sensor\n self.num_samples = config.getint(CONFIG_SECTION, \"num_samples\")\n self.drop_extremes = config.getboolean(CONFIG_SECTION, \"drop_extremes\")\n self.sample_delay = config.getfloat(CONFIG_SECTION, \"sample_delay\")\n self.is_running = False\n self.dist_to_bottom = config.getfloat(CONFIG_SECTION, \"distance_to_bottom\")","def __init__(self, config_directory, scale_override=None):\n self._config_directory = config_directory\n\n self._config_detector = DetectorConfig(config_directory)\n self._config_align = AlignConfig(config_directory, scale_override=scale_override)\n self._config_crystal = CrystalMatchConfig(config_directory)","def __init__(self, config):\n\n self.config = config","def __init__(self, config: Dict[str, Any]) -> None:\n self.config = config","def __init__(self, config):\n self.name = \"leduc-holdem\"\n self.game = LeducholdemGame()\n super().__init__(config)\n self.actions = [\"call\", \"raise\", \"fold\", \"check\"]\n self.state_shape = [36]\n\n with open(\n os.path.join(rlcard.__path__[0], \"games/leducholdem/card2index.json\"), \"r\"\n ) as file:\n self.card2index = json.load(file)","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n etappg_name = '{0}EtaPPG'.format(name)\n eta3hr_name = '{0}Eta3HR'.format(name)\n eta3hm_name = '{0}Eta3HM'.format(name)\n \n d2pietappg_name = '{0}D2PiEtaPPG'.format(name)\n d2pieta3hr_name = '{0}D2PiEta3HR'.format(name)\n d2pieta3hm_name = '{0}D2PiEta3HM'.format(name)\n \n d2ketappg_name = '{0}D2KEtaPPG'.format(name)\n d2keta3hr_name = '{0}D2KEta3HR'.format(name)\n d2keta3hm_name = '{0}D2KEta3HM'.format(name)\n\n self.selEtaPPG = makeResonance(\n etappg_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\n decDescriptors=self.EtaPPG\n )\n\n self.selEta3HR = makeResonance(\n eta3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\n decDescriptors=self.Eta3H\n )\n\n self.selEta3HM = makeResonance(\n eta3hm_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\n decDescriptors=self.Eta3H\n )\n\n self.selD2PiEtaPPG = makeD(\n d2pietappg_name,\n config,\n inputSel=[self.selEtaPPG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False,\n )\n\n self.selD2KEtaPPG = makeD(\n d2ketappg_name,\n config,\n inputSel=[self.selEtaPPG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=False,\n )\n \n self.selD2PiEta3HR = makeD(\n d2pieta3hr_name,\n config,\n inputSel=[self.selEta3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False,\n )\n\n self.selD2KEta3HR = makeD(\n d2keta3hr_name,\n config,\n inputSel=[self.selEta3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=False,\n )\n \n self.selD2PiEta3HM = makeD(\n d2pieta3hm_name,\n config,\n inputSel=[self.selEta3HM, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False,\n )\n \n\n self.selD2KEta3HM = makeD(\n d2keta3hm_name,\n config,\n inputSel=[self.selEta3HM, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=False,\n )\n \n\n self.line_D2PiEtaPPG = make_line(\n self,\n name='{0}Line'.format(d2pietappg_name),\n prescale=config['PrescaleD2PiEtaPPG'],\n postscale=config['PostscaleD2PiEtaPPG'],\n selection=self.selD2PiEtaPPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2PiEta3HM = make_line(\n self,\n name='{0}Line'.format(d2pieta3hm_name),\n prescale=config['PrescaleD2PiEta3HM'],\n postscale=config['PostscaleD2PiEta3HM'],\n selection=self.selD2PiEta3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2PiEta3HR = make_line(\n self,\n name='{0}Line'.format(d2pieta3hr_name),\n prescale=config['PrescaleD2PiEta3HR'],\n postscale=config['PostscaleD2PiEta3HR'],\n selection=self.selD2PiEta3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KEtaPPG = make_line(\n self,\n name='{0}Line'.format(d2ketappg_name),\n prescale=config['PrescaleD2KEtaPPG'],\n postscale=config['PostscaleD2KEtaPPG'],\n selection=self.selD2KEtaPPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEta3HM = make_line(\n self,\n name='{0}Line'.format(d2keta3hm_name),\n prescale=config['PrescaleD2KEta3HM'],\n postscale=config['PostscaleD2KEta3HM'],\n selection=self.selD2KEta3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEta3HR = make_line(\n self,\n name='{0}Line'.format(d2keta3hr_name),\n prescale=config['PrescaleD2KEta3HR'],\n postscale=config['PostscaleD2KEta3HR'],\n selection=self.selD2KEta3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n self.__parameters: ConfigParams = ConfigParams()","def __init__(self):\n\n # self.threshold = 3.\n self.gamma_min = 3\n self.gamma_max = 12\n self.n_samples = 40\n # self.do_plots = False\n # self.do_albedo = True\n # self.verbose = True\n\n self.nbands = 7\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\n\n # Determine 250 or 500 meters product\n # self.resolution = 500\n\n # self.pixelWidth = 500\n # self.pixelHeight = 500","def __init__(self):\n\n super().__init__(\n dynamics_model=DoorDynamicsModel(),\n virtual_sensor_model=UnimodalVirtualSensorModel(\n virtual_sensor_model=[\n DoorVirtualSensorModel(modalities={\"image\"}),\n DoorVirtualSensorModel(modalities={\"pos\", \"sensors\"}),\n ],\n state_dim=3,\n ),\n )","def __init__(self, footprint, mconf, name):\n logging.debug(\"%s entered __init__\" % name)\n self.image_id = mconf[\"image-id\"]\n self.networks = mconf[\"networks\"]\n self.ip_addresses = mconf[\"ip-addresses\"]\n self.flavor=mconf[\"flavor\"]\n self.machine_name = \"%s%s\" % (footprint.footprint_name, name)\n self.footprint = footprint","def default_plate(self):\n self.name = \"Default Plate Armor\"\n self.rarity = \"Common\"\n self.pdef_value = 20\n self.mdef_value = 5\n self.increase_crit = 0\n self.desc = \"An old piece of iron armor, it's pretty heavy.\"","def __init__(\n self,\n hass,\n name: str,\n config: ConfigEntry,\n coordinator,\n file_path: str,\n ) -> None:\n super().__init__()\n\n self.hass = hass\n self._name = CAMERA_DATA[name][SENSOR_NAME]\n self._type = name\n self.check_file_path_access(file_path)\n self._file_path = file_path\n self._coordinator = coordinator\n self._host = config.data.get(CONF_HOST)\n self._unique_id = config.entry_id\n self._no_mail = (\n None\n if not config.data.get(CONF_CUSTOM_IMG)\n else config.data.get(CONF_CUSTOM_IMG_FILE)\n )","def _configure(self, config):\n self.friction_coef = config['friction_coef']\n self.num_cone_faces = config['num_cone_faces']\n self.num_samples = config['grasp_samples_per_surface_point']\n self.dir_prior = config['dir_prior']\n self.target_num_grasps = config['target_num_grasps']\n if self.target_num_grasps is None:\n self.target_num_grasps = config['min_num_grasps']\n\n self.min_contact_dist = config['min_contact_dist']\n self.num_grasp_rots = config['coll_check_num_grasp_rots']\n if 'max_num_surface_points' in config.keys():\n self.max_num_surface_points_ = config['max_num_surface_points']\n else:\n self.max_num_surface_points_ = 100","def __init__(self, parent):\n super(Demo5, self).__init__(parent)\n self.angle = 0.0\n self.replication = 1.0\n self.offset = 0.0\n self.deltaRep = 1\n self.revolution = 0\n self.stepsPer90 = 180\n self.stepsLeft = self.stepsPer90\n self.deltaAng = 90.0\n self.deltaOff = 0.15\n self.spin = True\n self.x2yAspect = 1.0\n self.texture = None","def __init__(self, config_file):\n Config = ConfigParser.ConfigParser()\n Config.read(config_file)\n \n self.port_id = Config.get(\"SerialPortSection\", \"ComPort\")\n self.baud_rate = Config.get(\"SerialPortSection\", \"BaudRate\")\n self. timeout = Config.get(\"SerialPortSection\", \"TimeOut\")\n self.config_file = config_file","def __init__(self, rings=False, branches=False):\n self.rings = rings\n self.branches = branches","def __init__(self, encut, name=\"relax_settings\"):\n\n ionic = {\"EDIFF\": 1E-17, \"NSW\": 20, \"IBRION\": 2,\"ISIF\": 2, \"ISYM\": -1, \"NBLOCK\": 1, \"KBLOCK\": 20}\n InputParameters.__init__(self, ionic_settings=ionic, name=name)\n self.update_electronic_sttings(\"ENCUT\", encut)","def __init__(self, hass, camera_entity, config, name):\n self.hass = hass\n self._camera_entity = camera_entity\n if name:\n self._name = name\n else:\n self._name = f\"SevenSegment OCR {split_entity_id(camera_entity)[1]}\"\n self._state = None\n\n self.filepath = os.path.join(\n self.hass.config.config_dir,\n \"ssocr-{}.png\".format(self._name.replace(\" \", \"_\")),\n )\n crop = [\n \"crop\",\n str(config[CONF_X_POS]),\n str(config[CONF_Y_POS]),\n str(config[CONF_WIDTH]),\n str(config[CONF_HEIGHT]),\n ]\n digits = [\"-d\", str(config.get(CONF_DIGITS, -1))]\n rotate = [\"rotate\", str(config[CONF_ROTATE])]\n threshold = [\"-t\", str(config[CONF_THRESHOLD])]\n extra_arguments = config[CONF_EXTRA_ARGUMENTS].split(\" \")\n\n self._command = (\n [config[CONF_SSOCR_BIN]]\n + crop\n + digits\n + threshold\n + rotate\n + extra_arguments\n )\n self._command.append(self.filepath)","def __init__(self, name):\n self.cloud = name\n self.cloudtype = \"multipass\"\n config = Config()\n self.default = config[f\"cloudmesh.volume.{self.cloud}.default\"]\n self.cm = CmDatabase()","def __init__(self) -> None:\n self.sensor = serial.Serial(config.DEVICE)\n super().__init__()","def __init__(self):\n #screen Settings\n self.screen_width = 1024\n self.screen_height = 768\n self.bg_color = (32, 32, 32)\n\n #rocket settings\n self.rocket_speed = 1\n\n #laser Settings\n self.laser_speed = 1.0\n self.laser_width = 3\n self.laser_height = 15\n self.laser_color = (0, 255, 255)\n self.lasers_allowed = 3","def __init__(self, config):\n super().__init__(config)\n\n # For NaMaster you need to pass the masks\n self.mask_files = self.config[\"tjpcov\"].get(\"mask_file\")\n self.mask_names = self.config[\"tjpcov\"].get(\"mask_names\")\n\n # Binning info is only needed if workspaces are not passed\n self.binning_info = self.config[\"tjpcov\"].get(\"binning_info\", None)\n\n # nside is needed if mask_files is a hdf5 file\n self.nside = self.config[\"tjpcov\"].get(\"nside\", None)\n\n # Read NaMaster specific options\n self.nmt_conf = self.config.get(\"NaMaster\", {})\n for k in [\"f\", \"w\", \"cw\"]:\n if k not in self.nmt_conf:\n self.nmt_conf[k] = {}\n\n # Read cache from input file. It will update the cache passed as an\n # argument of the different methods\n self.cache = self.config.get(\"cache\", {})","def __init__(self, name, type_name, thickness, grid_front_area, bundles_flow_area, grid_wetted_area, rods_wetted_at_grid_area, P_wet_at_grid, P_bundles, orientation):\n self.name = name\n self.type = type_name\n self.thickness = thickness\n self.length = self.thickness\n self.grid_front_area = grid_front_area\n self.bundles_flow_area = bundles_flow_area\n self.grid_wetted_area = grid_wetted_area\n self.rods_wetted_at_grid_area = rods_wetted_at_grid_area\n self.orientation = orientation\n self.P_wet_at_grid = P_wet_at_grid\n self.P_bundles = P_bundles","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n etaeeg_name = '{0}EtaEEG'.format(name)\n \n d2pietaeeg_name = '{0}D2PiEtaEEG'.format(name) \n d2ketaeeg_name = '{0}D2KEtaEEG'.format(name)\n\n self.selEtaEEG = makeEEGResonance(\n etaeeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.EtaEEG\n )\n \n self.selD2PiEtaEEG = makeD(\n d2pietaeeg_name,\n config,\n inputSel=[self.selEtaEEG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False\n )\n\n usePIDforKaon = False\n if (config['Bachelor_PIDK_MIN'] > -10):\n usePIDforKaon = True\n else :\n usePIDforKaon = False\n \n self.selD2KEtaEEG = makeD(\n d2ketaeeg_name, \n config,\n inputSel=[self.selEtaEEG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=usePIDforKaon\n )\n\n self.line_D2PiEtaEEG = make_line(\n self,\n '{0}Line'.format(d2pietaeeg_name),\n prescale=config['PrescaleD2PiEtaEEG'],\n postscale=config['PostscaleD2PiEtaEEG'],\n selection=self.selD2PiEtaEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KEtaEEG = make_line(\n self,\n '{0}Line'.format(d2ketaeeg_name),\n prescale=config['PrescaleD2KEtaEEG'],\n postscale=config['PostscaleD2KEtaEEG'],\n selection=self.selD2KEtaEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, **kwargs):\n super(VeryCleverBeamsplitter, self).__init__(**kwargs)\n self.shader_source = IL_SHADER_SOURCE\n self.centre = [0.5, 0.5]\n self.blazing_function = np.linspace(0,1,32)\n self.zernike_coefficients = np.zeros(12)","def __init__(self, car_reference, pt_reference):\n\n self.car_reference = car_reference\n self.pt_reference = pt_reference\n\n # We need to provide some mandatory information\n self.number_of_parameters = 3\n self.number_of_states = 2\n\n # We can provide some information that is passed to the output\n self.information = dict(problem = \"ModeShareProblem\")\n\n # We can provide the reference state. Currently, it is not used in any\n # optimizer, but it is helpful for plotting optimization results as\n # the existing plotting scripts will know how to use this information.\n self.reference_state = [self.car_reference, self.pt_reference]","def __init__(self):\n # Screen settings\n self.screen_width = 1860\n self.screen_height = 1020\n self.screen_size = self.screen_width, self.screen_height\n self.bg_color = 230, 230, 230\n\n # Ship static settings\n self.ship_limit = 3\n\n # Bullet static settings\n self.bullet_limit = 3\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = 60, 60, 60\n\n # Alien static settings\n self.fleet_drop_speed = 10\n\n self.speed_up_scale = 1.1\n self.initialize_dynamic_settings()","def __init__(self, pins, image):\n self._pins = pins\n self._image = image","def __init__(self):\n # Screen settings\n self.screen_width = 2400\n self.screen_height = 1600\n self.bg_color = (0, 0, 0)\n\n # Raindrop settings\n self.r_y_speed = 10","def __init__(self, planet, config=None, log=None, **kwargs):\n self.planet = planet.capitalize()\n self.log = logging.setup(log)\n\n if config is None or isinstance(config, str):\n from . import config as pcfg\n config = os.path.join(self.planet, config)\n config = pcfg.planetConfig(self.planet, configFile=config)\n config.update_config(**kwargs)\n self.config = config\n self.chem = None\n self.gas = []\n self.nGas = 0\n self.nConstituent = 0\n self.cloud = []\n self.nCloud = 0\n self.property = []\n if config.path not in sys.path:\n sys.path.insert(0, config.path)","def __init__(self, config, cfg):\n self.config = config\n self.cfg = cfg","def __init__(self, config):\n self.__config = config\n self.__logger = SLoggerHandler().getLogger(LoggerNames.EXPERIMENT_C)\n self.__num_gpus = ConfigProvider().get_config(\"controllerConfig.json\")[\"hardware\"][\"numGPUs\"]","def __init__(self, configfile_name):\n assert os.path.exists(configfile_name), \\\n 'Config file %s does not exist' % (configfile_name)\n\n correct_param_load = self.load_config_file(configfile_name)\n assert correct_param_load,\\\n 'Config params could not be loaded from file'\n\n self.fullpath_input_configfile = configfile_name\n self.word_det_rfc = None\n self.reg_coeffs = None\n self.bb_reg = None\n self.img_files = None","def __init__(self,port=None,config=None):\n\n # Attempt to load a Dorna object with a configuration file, default to the desktop config if none is entered manually.\n\n self.WORKING_DIRECTORY = os.getcwd()\n self.FILE_DIRECTORY = os.path.dirname(__file__)\n self.DEFAULT_CONFIG = os.path.join(self.FILE_DIRECTORY, \"../configuration_store/config.yaml\")\n \n self.positionStore = PositionStore()\n \n config = config if config!= None else self.DEFAULT_CONFIG\n\n try:\n print(config)\n self.robot=Dorna(config)\n print(\"Loaded configuration file\")\n except:\n print(\"Failed to load configuration file, please re-instantiate the arm object with a valid config.\")","def __init__(self, address=0x68, config=0):\r\n\t\tself.i2c = FT232H.I2CDevice(ft232h, address)\r\n\t\tif config == 0:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=0)\r\n\t\t\tself.setScale(mode='GYR',scale=0)\r\n\t\telif config == 1:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=1)\r\n\t\t\tself.setScale(mode='GYR',scale=1)\t\t\t\t\r\n\t\telif config == 2:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=2)\r\n\t\t\tself.setScale(mode='GYR',scale=2)\t\r\n\t\telif config == 3:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=3)\r\n\t\t\tself.setScale(mode='GYR',scale=3)\t\t\t\t\r\n\t\telif config == 4:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=1)\t\r\n\t\t\tself.setTempDisable()\r\n\t\t\tself.setGYRStandby(axis='X')\r\n\t\t\tself.setGYRStandby(axis='Y')\r\n\t\t\tself.setGYRStandby(axis='Z')","def __init__(self):\n self.config = {}","def __init__(self, config):\n try:\n config['volume_id']\n config['access_key']\n config['secret_access_key']\n config['region']\n except KeyError, e:\n logging.error(repr(e))\n raise ImproperlyConfigured()\n\n if not config.has_key('keep'):\n config['keep'] = 5\n\n self.config = config","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n etaprimeppg_name = '{0}EtaPrimePPG'.format(name)\n etaprime3hr_name = '{0}EtaPrime3HR'.format(name)\n \n d2pietaprimeppg_name = '{0}D2PiEtaPrimePPG'.format(name)\n d2pietaprime3hr_name = '{0}D2PiEtaPrime3HR'.format(name)\n \n d2ketaprimeppg_name = '{0}D2KEtaPrimePPG'.format(name)\n d2ketaprime3hr_name = '{0}D2KEtaPrime3HR'.format(name)\n \n self.selEtaPrimePPG = makeResonance(\n etaprimeppg_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\n decDescriptors=self.EtaPrimePPG\n )\n \n self.selEtaPrime3HR = makeResonance(\n etaprime3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedEta],\n decDescriptors=self.EtaPrime3H\n )\n \n self.selD2PiEtaPrimePPG = makeD(\n d2pietaprimeppg_name,\n config,\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEtaPrime,\n useBachelorPID=False,\n )\n \n self.selD2KEtaPrimePPG = makeD(\n d2ketaprimeppg_name,\n config,\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEtaPrime,\n useBachelorPID=False,\n )\n \n self.selD2PiEtaPrime3HR = makeD(\n d2pietaprime3hr_name,\n config,\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEtaPrime,\n useBachelorPID=False, \n )\n \n self.selD2KEtaPrime3HR = makeD(\n d2ketaprime3hr_name,\n config,\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEtaPrime,\n useBachelorPID=False,\n )\n \n self.line_D2PiEtaPrimePPG = make_line(\n self,\n name='{0}Line'.format(d2pietaprimeppg_name),\n prescale=config['PrescaleD2PiEtaPrimePPG'],\n postscale=config['PostscaleD2PiEtaPrimePPG'],\n selection=self.selD2PiEtaPrimePPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2PiEtaPrime3HR = make_line(\n self,\n name='{0}Line'.format(d2pietaprime3hr_name),\n prescale=config['PrescaleD2PiEtaPrime3HR'],\n postscale=config['PostscaleD2PiEtaPrime3HR'],\n selection=self.selD2PiEtaPrime3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEtaPrimePPG = make_line(\n self,\n name='{0}Line'.format(d2ketaprimeppg_name),\n prescale=config['PrescaleD2KEtaPrimePPG'],\n postscale=config['PostscaleD2KEtaPrimePPG'],\n selection=self.selD2KEtaPrimePPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEtaPrime3HR = make_line(\n self,\n name='{0}Line'.format(d2ketaprime3hr_name),\n prescale=config['PrescaleD2KEtaPrime3HR'],\n postscale=config['PostscaleD2KEtaPrime3HR'],\n selection=self.selD2KEtaPrime3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n self.plant_names = list(PLANT_TYPES.keys()) #: list of str: available plant type names.\n self.plant_types = list(PLANT_TYPES.items()) #: list of dicts: model parameters for plant types.\n self.num_plant_types = len(PLANT_TYPES) #: int: amount unique plant types in garden.\n self.plant_centers = [] #: location of plants in garden\n self.non_plant_centers = [] #: additional coordinates without plants\n self.plant_in_bounds = 0 #: int: count of plant centers in bound.","def __init__(self, name: str, instance: PartInstance, controlPoints: ModelDotArray):\n super().__init__()\n pass","def __init__(self):\n super().__init__()\n\n # Gadget state\n \n self.isDoorOpen = False\n self.verified = True\n\n # Ev3dev initialization\n self.leds = Leds()\n self.sound = Sound()\n self.drive = MoveTank(OUTPUT_B, OUTPUT_C)\n \n self.ir_sensor = InfraredSensor()\n self.ir_sensor.mode = self.ir_sensor.MODE_IR_REMOTE\n self.color_sensor = ColorSensor()\n self.color_sensor.mode = 'COL-COLOR' # WHITE\n\n # Start threads\n threading.Thread(target=self._patrol_thread, daemon=True).start()","def __init__(self):\n if not os.path.isfile(CONFIG_FILENAME):\n first_time_run()\n raise SystemExit()\n\n \"\"\"\n Init file is present, read and parse it:\n \"\"\"\n conf = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation(), inline_comment_prefixes='#')\n conf.read(CONFIG_FILENAME)\n \"\"\"\n Process certain paths:\n \"\"\"\n\n path = conf['Paths']\n self.ffc_dir = path['FlatFieldCalDir']\n self.capture_dir = path['CaptureDir']\n self.image_dir = path['ImageDir']\n\n \"\"\"\n Process options\n \"\"\"\n\n self.cal_auto_save = conf.getboolean('Options', 'CalAutoSave', fallback=True)\n self.cal_auto_load = conf.getboolean('Options', 'CalAutoLoad', fallback=True)\n self.sound_on_capture = conf.getboolean('Options', 'SoundOnCapture', fallback=True)\n self.exp_init1 = conf.getint('Options', 'ExpInit1', fallback=100)\n self.exp_init2 = conf.getint('Options', 'ExpInit2', fallback=100)\n self.black_correct = conf.getboolean('Options', 'BlackCorrect', fallback=True)\n # Setup square window, default of full-screen height\n self.tiff_seq_x_window = conf.getint('Options', 'TiffSeqXWindow', fallback=cameras.FRAME_HEIGHT)\n self.tiff_seq_y_window = conf.getint('Options', 'TiffSeqYWindow', fallback=cameras.FRAME_HEIGHT)\n self.tiff_seq_rebin = conf.getint('Options', 'TiffSeqRebin', fallback = 2)","def __init__(self, x, y , planet):\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.planet = planet\n\t\tself.battery = 100","def __init__(self, **kwargs):\n self.max_samples = get_value(kwargs, 'max_samples', types=[int], range_min=0, default_value=0)\n self.obs_filename = get_value(kwargs, 'lba_obs_file', types=[str, None], default_value=None)\n self.sample_rate = get_value(kwargs, 'sample_rate', types=[int, None], default_value=None)\n if self.sample_rate is None:\n LOG.warning(\"No sample rate provided, defaulting to {0}\".format(self._default_sample_rate))\n self.sample_rate = self._default_sample_rate\n self.antenna_name = get_value(kwargs, 'lba_antenna_name', types=[str, None], default_value=None)\n if self.obs_filename is not None and self.antenna_name is None:\n raise RuntimeError(\"LBA file is missing --lba_antenna_name parameter which is needed when using \"\n \"--lba_obs_file\")\n self.chunk_size = get_value(kwargs, 'chunk_size', types=[int], range_min=0, default_value=4096)","def __init__(self, pitch, color, our_side, video_port=0, comm_port='/dev/ttyACM0', penalty=False, comms=1):\n assert pitch in [0, 1]\n assert color in ['yellow', 'blue']\n assert our_side in ['left', 'right']\n\n self.pitch = pitch\n\n # Set up the Arduino communications\n self.arduino = Arduino(comm_port, 115200, 1, comms)\n\n # Set up camera for frames\n self.camera = Camera(port=video_port, pitch=self.pitch)\n frame = self.camera.get_frame()\n center_point = self.camera.get_adjusted_center(frame)\n\n # Set up vision\n self.calibration = tools.get_colors(pitch)\n self.vision = Vision(\n pitch=pitch, color=color, our_side=our_side,\n frame_shape=frame.shape, frame_center=center_point,\n calibration=self.calibration)\n\n # Set up postprocessing for vision\n self.postprocessing = Postprocessing(our_side)\n\n # Set up main planner\n self.planner = Planner(our_side=our_side, pitch_num=self.pitch, isPenalty=penalty)\n\n # Set up GUI\n self.GUI = GUI(calibration=self.calibration, arduino=self.arduino, pitch=self.pitch)\n\n self.color = color\n self.side = our_side\n\n self.preprocessing = Preprocessing()\n\n self.robot = Robot_Controller()","def __init__(self, cpara, dz_soil):\n\n # --- grid ---\n self.z = np.linspace(0, cpara['grid']['zmax'], cpara['grid']['Nlayers']) # grid [m] above ground\n self.dz = self.z[1] - self.z[0] # gridsize [m]\n self.ones = np.ones(len(self.z)) # dummy\n\n # --- switches ---\n self.Switch_Eflow = cpara['ctr']['Eflow'] # True assumes constant U/ustar at upper boundary\n self.Switch_WMA = cpara['ctr']['WMA'] # True solves scalar profiles\n self.Switch_Ebal = cpara['ctr']['Ebal'] # True solves leaf energy balance\n\n logger.info('Eflow: %s, WMA: %s, Ebal: %s',\n self.Switch_Eflow,\n self.Switch_WMA,\n self.Switch_Ebal)\n\n # --- PlantTypes ---\n ptypes = []\n ptnames = list(cpara['planttypes'].keys())\n ptnames.sort()\n for pt in ptnames:\n ptypes.append(PlantType(self.z, cpara['planttypes'][pt], dz_soil, ctr=cpara['ctr'], loc=cpara['loc']))\n self.planttypes = ptypes\n \n # --- stand characteristics: sum over planttypes---\n\n # total leaf area index [m2 m-2]\n self.LAI = sum([pt.LAI for pt in self.planttypes])\n # total leaf area density [m2 m-3]\n self.lad = sum([pt.lad for pt in self.planttypes])\n\n # layerwise mean leaf characteristic dimension [m] for interception model\n self.leaf_length = sum([pt.leafp['lt'] * pt.lad for pt in self.planttypes]) / (self.lad + EPS)\n\n # root area density [m2 m-3]\n rad = np.zeros(np.shape(dz_soil))\n imax = 1\n for pt in self.planttypes:\n rad[:len(pt.Roots.rad)] += pt.Roots.rad\n imax = max(imax, len(pt.Roots.rad))\n \n self.ix_roots = np.array(range(imax)) # soil model layers corresponding to root zone\n self.rad = rad[self.ix_roots]\n \n # total root area index [m2 m-2]\n self.RAI = sum([pt.Roots.RAI for pt in self.planttypes])\n # distribution of roots across soil model layers [-]\n self.root_distr = self.rad * dz_soil[self.ix_roots] / (self.RAI + EPS)\n\n # canopy height [m]\n if len(np.where(self.lad > 0)[0]) > 0:\n f = np.where(self.lad > 0)[0][-1]\n self.hc = self.z[f].copy()\n else:\n self.hc = 0.0\n\n # --- create radiation, micromet, interception, and forestfloor model instances\n self.radiation = Radiation(cpara['radiation'], self.Switch_Ebal)\n\n self.micromet = Micromet(self.z, self.lad, self.hc, cpara['micromet'])\n\n self.interception = Interception(cpara['interception'], self.lad * self.dz)\n\n self.forestfloor = ForestFloor(cpara['forestfloor'])","def __init__(self, region):\r\n self.region = region","def __init__(self):\n\n self.Cp_air0 = config_earth.earth_properties['Cp_air0']\n self.Rsp_air = config_earth.earth_properties['Rsp_air']\n\n self.d = config_earth.balloon_properties['d']\n self.vol = math.pi*4/3*pow((self.d/2),3) #volume m^3\n self.surfArea = math.pi*self.d*self.d #m^2\n self.cs_area = math.pi*self.d*self.d/4.0 #m^2\n\n #self.emissEnv = config_earth.balloon_properties['emissEnv']\n self.areaDensityEnv = config_earth.balloon_properties['areaDensityEnv']\n self.mp = config_earth.balloon_properties['mp']\n self.mdot = 0\n self.massEnv = config_earth.balloon_properties['mEnv']\n self.Upsilon = config_earth.balloon_properties['Upsilon']\n\n self.vent = config_earth.simulation['vent']\n self.coord = config_earth.simulation['start_coord']\n self.t = config_earth.simulation['start_time']\n self.lat = math.radians(self.coord['lat'])\n self.Ls = self.t.timetuple().tm_yday\n self.min_alt = config_earth.simulation['min_alt']\n\n self.vm_coeff = .1 #virtual mass coefficient\n self.k = self.massEnv*config_earth.balloon_properties['cp'] #thermal mass coefficient\n\n self.dt = config_earth.dt","def __init__(self):\n super().__init__()\n self.location = 0.0\n self.scale = 1.0\n self.type = 'Laplace'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, config, town):\n\t\tself.config = config","def initialize(self, **kwargs):\n\n # Defining the configuration object\n self.config = kwargs.get('config')","def __init__(self, filepath):\n try:\n config_file_r = open(filepath)\n self.sim_parametres = yaml.load(config_file_r, Loader=yaml.FullLoader)\n except:\n raise Exception(\"Le fichier de configuration n'a pas été atteint ou n'a pas pu être lu. Veuillez vérifier \"\n \"qu'il n'y ait aucune erreur de syntaxe.\")","def __init__(self, metadata, environment, component, image, version, s3_bucket, exec_env):\n self.environment = environment\n self.component = component\n self.s3_bucket = s3_bucket\n self.exec_env = exec_env\n self.image = image\n self.version = version\n self.metadata = metadata\n\n # generate Terragrunt config as part of object initialisation\n self.config()","def __init__(self,height=7,width=7):\n self.__cars = []\n self.__height = height\n self.__width = width","def initialize_from_config(self):","def __init__(self, name, properties):\n super(WiretapShotProcessorPreset, self).__init__(name, properties)\n \n # Necessary in order for Hiero to view this plugin as different from\n # the default shot processor\n self._parentType = WiretapShotProcessor","def __init__(self, config):\n raise NotImplementedError (\"This is just an abstract interface\")","def __init__(self):\n super(Config, self).__init__()\n self.run_control[\"output_dir\"] = os.getcwd()+\"/output/arctan_baseline/single_turn_injection/tracking\"\n ring_tof = 1149.185123\n field_files = glob.glob(\"output/arctan_baseline/bump_quest_v9/find_bump_r_*_theta_90/find_bump_parameters_001.out\")\n substitutions = config.get_baseline_substitution(0.22, ring_tof)\n substitutions = self.ramp_fields(\n substitutions,\n field_files,\n [i for i, f in enumerate(field_files)],\n ring_tof,\n will_step = False\n )\n self.substitution_list = [substitutions] # used for plotting unperturbed CO\n\n self.run_control[\"find_closed_orbits_4d\"] = False\n self.run_control[\"find_da\"] = False\n self.run_control[\"find_bump_parameters\"] = False\n self.run_control[\"track_bump\"] = False\n self.run_control[\"track_beam\"] = True\n self.find_closed_orbits[\"subs_overrides\"][\"__n_turns__\"] = 0.11\n self.find_closed_orbits[\"subs_overrides\"][\"__do_bump__\"] = False\n self.find_closed_orbits[\"final_subs_overrides\"].update(substitutions)\n self.find_closed_orbits[\"max_iterations\"] = 0\n self.find_closed_orbits[\"do_minuit\"] = True\n self.find_closed_orbits[\"minuit_iterations\"] = 10 \n self.find_closed_orbits[\"us_cell\"] = 0\n self.find_closed_orbits[\"ds_cell\"] = 1\n\n self.find_da = {\n \"run_dir\":\"tmp/find_da/\",\n \"probe_files\":\"RINGPROBE01.h5\",\n \"subs_overrides\":{\"__n_turns__\":101.1, \"__do_magnet_field_maps__\":\"False\", \"__step_size__\":0.01},\n \"get_output_file\":\"get_da\",\n \"scan_output_file\":\"scan_da\",\n \"row_list\":None,\n \"scan_x_list\":[],\n \"scan_y_list\":[],\n \"x_seed\":1.,\n \"y_seed\":1.,\n \"min_delta\":0.9,\n \"max_delta\":1000.,\n \"required_n_hits\":100,\n \"dt_tolerance\":0.5, # fraction of closed orbit dt\n \"max_iterations\":15,\n \"decoupled\":True,\n }\n\n max_amp = 0.01\n self.track_beam_dummy = {\n \"run_dir\":\"tmp/track_beam/\",\n \"save_dir\":\"track_beam\",\n \"print_events\":[0, 1, -1],\n \"settings\":[{\n \"name\":\"forwards\",\n \"direction\":\"forwards\",\n \"probe_files\":\"RINGPROBE01.h5\", \n \"beam\":{\n \"type\":\"beam_gen\",\n \"closed_orbit_file\":\"closed_orbits_cache\",\n \"eigen_emittances\":[[0, 0]]*3,#+[[max_amp, max_amp]],\n \"n_per_dimension\":2,\n \"variables\":[\"x\",\"x'\",\"y\",\"y'\"],\n \"amplitude_dist\":\"uniform\", #\"grid\", # \n \"phase_dist\":\"uniform\", #\"grid\", # \n \"max_amplitude_4d\":max_amp, # amplitude_dist != grid\n \"energy\":3.0,\n },\n \"subs_overrides\":{\n \"__n_turns__\":45.1,\n \"__hdf5__\":\"True\",\n \"__do_magnet_field_maps__\":\"False\",\n \"__do_bump__\":\"True\",\n \"__step_size__\":0.01\n },\n },],\n }\n\n T0 = ring_tof\n self.track_beam = {\n \"run_dir\":\"tmp/track_beam/\",\n \"save_dir\":\"track_beam_rf_on_2\",\n \"print_events\":[i for i in range(1)],\n \"variables\":[\"x\", \"x'\", \"y\", \"y'\", \"t\", \"energy\"],\n \"settings\":[{\n \"name\":\"grid\",\n \"direction\":\"forwards\",\n \"probe_files\":\"RINGPROBE01.h5\", \n \"beam\":{\n \"type\":\"grid\",\n \"energy\":3.0,\n \"start\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, 0.0, 941.272],\n \"stop\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, T0, 941.272],\n \"nsteps\":[1, 1, 1, 1, 4+1, 1],\n },\n \"subs_overrides\":{\n \"__n_turns__\":100.1,\n \"__hdf5__\":True,\n \"__do_magnet_field_maps__\":False,\n \"__do_bump__\":True,\n \"__do_rf__\":True,\n \"__do_foil__\":False,\n \"__step_size__\":0.01\n },\n },],\n }","def __init__(self, hass, config):\n super().__init__()\n self._parent = hass\n self.ezvizService = hass.data[PY_EZVIZ_GATEWAY]\n\n self.deviceSerial = self.ezvizService.deviceSerial\n\n self._interval_snapshots = 30\n self._last_image = \"\"\n self._last_snapshot_time = 0","def __init__(self):\n super().__init__()\n\n # Robot state\n self.ask_mode = False\n\n # Connect two large motors on output ports B and C\n self.sound = Sound()\n self.leds = Leds()\n self.p1 = TouchSensor(INPUT_1)\n self.p2 = TouchSensor(INPUT_2)\n self.p3 = TouchSensor(INPUT_3)\n self.p4 = TouchSensor(INPUT_4)","def __init__(self, parant):\n pass","def __init__(self, board, sample_rate=1e6, bus_width=8):\n\n # Grab a reference to the board and its pattern-gen API.\n self.board = board\n self.api = board.apis.pattern_generator\n\n # Grab a reference to the user's bus parameters.\n self.sample_rate = int(sample_rate)\n self.bus_width = bus_width\n\n # FIXME: These should be read from the board, rather than hardcoded!\n self.upload_chunk_size = 2048\n self.samples_max = 32 * 1024","def initializeParameters(self):\r\n\t\tself.input_raster.enabled = True\r\n\t\tself.approach.enabled = True\r\n\t\tself.predefined_pattern.enabled = False\r\n\t\tself.predefined_pattern.value = 'Mexican Hat wavelet'\r\n\t\tself.pattern_workspace.enabled = False\r\n\t\tself.point_matrix_size.enabled = False\r\n\t\tself.point_matrix_size.value = 3\r\n\t\tself.point_vectors.enabled = False\r\n\t\tself.mapping_field.enabled = False\r\n\t\tself.move_to_max.enabled = False\r\n\t\tself.move_to_max_distance.enabled = False\r\n\t\tself.move_to_max_distance.value = 3\r\n\t\tself.mh_iteration.enabled = False\r\n\t\tself.mh_dil_val.enabled = False\r\n\t\tself.mh_dil_val.value = 1\r\n\t\tself.mh_dil_start.value = 0.01\r\n\t\tself.mh_dil_stop.value = 1\r\n\t\tself.mh_dil_step.value = 0.1\r\n\t\tself.mh_dil_start.enabled = False\r\n\t\tself.mh_dil_stop.enabled = False\r\n\t\tself.mh_dil_step.enabled = False\r\n\t\tself.transform.enabled = False\r\n\t\tself.size_of_the_cell.enabled = False\r\n\t\tself.size_of_the_cell.value = 1\r\n\t\tself.output_sim_matrix.enabled = False\r\n\t\tself.output_table.enabled = False\r\n\t\tself.output_raster_workspace.enabled = False","def __init__(self):\n self._config = copy.copy(self._parameters)","def __init__(self):\r\n\r\n super(Metallized, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.spec_sheet = 0\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0","def __init__(self, portname, devicetype):\n if devicetype == DEVICE_DEBUG_BOARD:\n self.device = debugbox.DebugBox(portname)\n elif devicetype == DEVICE_SFP_BREAKOUT:\n self.device = sfpbreakout.SFP(portname)\n elif devicetype == DEVICE_DUMMY_BOARD:\n self.device = dummybox.DummyBox(portname)\n else:\n raise IOError(\"Invalid Device Type\")\n \n # Set up laser sections\n self.mirror1 = Testrig.LaserSection(self, 814, 10, 90, 728, 844, 860, 864)\n self.laser_phase = Testrig.LaserSection(self, 810, 11, 20, 726, 846, 858, 866)\n self.gain = Testrig.LaserSection(self, 818, 12, 180, 730, 848, None, None)\n# self.gain1.validator.setTop(150)\n self.mirror2 = Testrig.LaserSection(self, 826, 14, 90, 734, 850, 862, 864)\n# self.front.validator.setTop(60)\n self.soa1 = Testrig.LaserSection(self, 822, 15, 300, 736, 852, None, None)\n# self.soa1.validator.setTop(150)\n self.soa2 = Testrig.LaserSection(self, 830, 16, 100, 738, 854, None, None, self.to_display_current_section2, self.to_internal_current_section2)\n self.phase1 = Testrig.LaserSection(self, 834, 13, 100, 732, 856, None, None, self.to_display_current_section2, self.to_internal_current_section2)\n \n self.voltage_max = 2.5 # This is now a constant\n \n self.full_rig = True","def __init__(self, world_map, GRID_LOCK, coordinates=None):\n\n ''' Take parameters, and Sprite Constants '''\n super(BeesSprite, self).__init__(world_map, BeesSprite.IMAGE, GRID_LOCK,\n BeesSprite.HEALTH_BAR, BeesSprite.AVG_SPEED,\n BeesSprite.VISION, coordinates)\n\n self.type = \"bees\"\n self.prey = [\"plant\"]","def __init__(self):\n # Bildschirmeinstellungen\n self.screen_width = 1200\n self.screen_height = 800\n self.bg_color = (230, 230, 230)\n # schiffeinstellungen\n self.ship_limit = 3\n # Geschosseinstellungen\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = (60, 60, 60)\n self.bullets_allowed = 3\n # Invasionsiffeinstellung\n self.fleet_drop_speed = 7 # og drop 10\n # Stärke der Beschleunigung des Spiels\n self.speedup_scale = 1.05\n # Stärke der Punktewerterhöhung bei Treffern\n self.score_scale = 1.5\n\n self.initialize_dynamic_settings()","def __init__(self, config, set_name, preprocess_image):\n\t\t\tself.data_dir = config['data_dir']\n\t\t\tself.set_name = set_name\n\t\t\tself.coco = COCO(os.path.join(self.data_dir, 'annotations', 'instances_' + set_name + '.json'))\n\t\t\tself.image_ids = self.coco.getImgIds()\n\t\t\tself.mask = config['mask']\n\n\t\t\tself.load_classes()\n\n\t\t\tsuper(CocoGenerator, self).__from_config__(config, preprocess_image=preprocess_image)","def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\"DFTCL_settings\"):\n\n cl_settings = {\"ISPIN\": 2, \"MAGMOM\": magmom, \"SAXIS\": None, \"LSORBIT\": None, \"LNONCOLLINEAR\": None}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIMX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"encut\", encut)"],"string":"[\n \"def __init__(self):\\n parameters_list = []\\n self.config_dict = self.open_config(parameters_list)\\n\\n # Define defaults\\n self.disc_gt = 0.0\\n self.disc_out = 0.0\",\n \"def __init__():\\n self.placa = placa\",\n \"def __init__(self, projection=None, transform=ccrs.PlateCarree(),\\n figure_kws=dict(), drainage_baisins=True):\\n self.proj = projection\\n self.transform = transform\\n self.fig_kws = figure_kws\\n self.drainage_baisins = drainage_baisins\",\n \"def __init__(self):\\r\\n config = ConfigProvider().getProcessingConfig()\\r\\n self.xGround = config.get(\\\"xGround\\\")\\r\\n self.yGround = config.get(\\\"yGround\\\")\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n pi0eeg_name = '{0}Pi0EEG'.format(name)\\n \\n d2pipi0eeg_name = '{0}D2PiPi0EEG'.format(name) \\n d2kpi0eeg_name = '{0}D2KPi0EEG'.format(name)\\n\\n d02pi0pi0eeg_name = '{0}D02Pi0Pi0EEG'.format(name) \\n dst2d0pieeg_name = '{0}Dst2D0PiEEG'.format(name)\\n\\n self.selPi0EEG = makeEEGResonance(\\n pi0eeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.Pi0EEG\\n )\\n \\n self.selD2PiPi0EEG = makeD(\\n d2pipi0eeg_name,\\n config,\\n inputSel=[self.selPi0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPi0,\\n useBachelorPID=False\\n )\\n\\n usePIDforKaon = False\\n if (config['Bachelor_PIDK_MIN'] > -10):\\n usePIDforKaon = True\\n else :\\n usePIDforKaon = False\\n \\n self.selD2KPi0EEG = makeD(\\n d2kpi0eeg_name, \\n config,\\n inputSel=[self.selPi0EEG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPi0,\\n useBachelorPID=usePIDforKaon\\n )\\n\\n self.selD02Pi0Pi0EEG = makeD(\\n d02pi0pi0eeg_name, \\n config,\\n inputSel=[self.selPi0EEG],\\n decDescriptors=self.D02Pi0Pi0,\\n useBachelorPID=False,\\n )\\n\\n self.selDst2D0PiEEG = makeDstar(\\n dst2d0pieeg_name,\\n config,\\n inputSel=[self.selD02Pi0Pi0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.line_D2PiPi0EEG = make_line(\\n self,\\n '{0}Line'.format(d2pipi0eeg_name),\\n prescale=config['PrescaleD2PiPi0EEG'],\\n postscale=config['PostscaleD2PiPi0EEG'],\\n selection=self.selD2PiPi0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KPi0EEG = make_line(\\n self,\\n '{0}Line'.format(d2kpi0eeg_name),\\n prescale=config['PrescaleD2KPi0EEG'],\\n postscale=config['PostscaleD2KPi0EEG'],\\n selection=self.selD2KPi0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_Dst2D0PiEEG = make_line(\\n self,\\n '{0}Line'.format(dst2d0pieeg_name),\\n prescale=config['PrescaleDst2D0PiEEG'],\\n postscale=config['PostscaleDst2D0PiEEG'],\\n selection=self.selDst2D0PiEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.configure_sensor(\\n TemperatureOversamplings.x08,\\n PressureOversamplings.x16,\\n HumidityOversamplings.x08,\\n IIRFilterCoefficients.FC_003,\\n 250,\\n 250)\",\n \"def __init__(self, cfg):\\r\\n\\r\\n\\t\\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\\r\\n\\t\\tanchor_config = cfg.MODEL.ANCHORS\\r\\n\\t\\tself.feature_maps = anchor_config.FEATURE_MAPS\\r\\n\\t\\tself.min_sizes = anchor_config.MIN_SIZES\\r\\n\\t\\tself.max_sizes = anchor_config.MAX_SIZES \\r\\n\\t\\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\\r\\n\\t\\tself.clip = anchor_config.CLIP\",\n \"def __init__(self, name: str = None, *plates: Plate):\\r\\n super().__init__()\\r\\n self.name = name or f\\\"{self.__class__.__name__.lower()}_{self.id}\\\"\\r\\n self.plates = plates\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n phi3hm_name = '{0}Phi3HM'.format(name)\\n phi3hr_name = '{0}Phi3HR'.format(name)\\n \\n d2piphi3hm_name = '{0}D2PiPhi3HM'.format(name)\\n d2piphi3hr_name = '{0}D2PiPhi3HR'.format(name)\\n \\n d2kphi3hm_name = '{0}D2KPhi3HM'.format(name)\\n d2kphi3hr_name = '{0}D2KPhi3HR'.format(name)\\n \\n\\n self.selPhi3HM = makeResonance(\\n phi3hm_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\\n decDescriptors=self.Phi3H\\n )\\n\\n self.selPhi3HR = makeResonance(\\n phi3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\\n decDescriptors=self.Phi3H\\n )\\n \\n self.selD2PiPhi3HM = makeD(\\n d2piphi3hm_name,\\n config,\\n inputSel=[self.selPhi3HM, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KPhi3HM = makeD(\\n d2kphi3hm_name,\\n config,\\n inputSel=[self.selPhi3HM, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiPhi3HR = makeD(\\n d2piphi3hr_name,\\n config,\\n inputSel=[self.selPhi3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KPhi3HR = makeD(\\n d2kphi3hr_name,\\n config,\\n inputSel=[self.selPhi3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPhi,\\n useBachelorPID=False,\\n )\\n \\n self.line_D2PiPhi3HM = make_line(\\n self,\\n name='{0}Line'.format(d2piphi3hm_name),\\n prescale=config['PrescaleD2PiPhi3HM'],\\n postscale=config['PostscaleD2PiPhi3HM'],\\n selection=self.selD2PiPhi3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2PiPhi3HR = make_line(\\n self,\\n name='{0}Line'.format(d2piphi3hr_name),\\n prescale=config['PrescaleD2PiPhi3HR'],\\n postscale=config['PostscaleD2PiPhi3HR'],\\n selection=self.selD2PiPhi3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n self.line_D2KPhi3HM = make_line(\\n self,\\n name='{0}Line'.format(d2kphi3hm_name),\\n prescale=config['PrescaleD2KPhi3HM'],\\n postscale=config['PostscaleD2KPhi3HM'],\\n selection=self.selD2KPhi3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KPhi3HR = make_line(\\n self,\\n name='{0}Line'.format(d2kphi3hr_name),\\n prescale=config['PrescaleD2KPhi3HR'],\\n postscale=config['PostscaleD2KPhi3HR'],\\n selection=self.selD2KPhi3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, config, well, directory):\\n self.config = config\\n self.well = well\\n self.directory = directory\",\n \"def __init__(self, config_file = 'config.yaml'):\\n\\n self.name = ''\\n self.img_dir = ''\\n self.out_dir = ''\\n self.cam_file = ''\\n self.options_file = ''\\n self.output_xml_file = ''\\n\\n # If there is an options file, it will overwrite the defaults \\n if config_file is not None:\\n self.load(config_file)\",\n \"def __init__(self, config):\\n super().__init__(config)\\n self.collector_host = config.get(\\\"collector_host\\\")\\n self.schedds = config.get(\\\"schedds\\\", [None])\\n self.condor_config = config.get(\\\"condor_config\\\")\\n self.constraint = config.get(\\\"constraint\\\", True)\\n self.classad_attrs = config.get(\\\"classad_attrs\\\")\\n self.correction_map = config.get(\\\"correction_map\\\")\",\n \"def __init__(self):\\n\\n super().__init__()\\n\\n self.active = True\\n self.driver = Driver.instance()\\n self.sensor_manager = SensorManager.instance()\\n\\n self.pwm = Adafruit_PCA9685.PCA9685(address=0x40, busnum=1) # create PCA9685-object at I2C-port\\n self.pwm.set_pwm_freq(50)\\n\\n GPIO.setwarnings(False)\\n GPIO.setmode(GPIO.BCM)\\n GPIO.setup(20, GPIO.OUT)\\n GPIO.setup(21, GPIO.OUT)\\n GPIO.setup(26, GPIO.OUT)\\n self.driven_distance = 0\",\n \"def __init__(self, config):\\n self.cfg = config\\n self.var_combinations = [\\\"tas:tas\\\", \\\"pr:pr\\\", \\\"pr:tas\\\"]\\n self.seasons = [\\\"jja\\\", \\\"djf\\\", \\\"annual\\\"]\\n self.projects = [\\\"cmip5\\\", \\\"cmip6\\\"]\\n self.variables = [\\\"tas\\\", \\\"pr\\\"]\\n self.scenarios = [\\\"26\\\", \\\"45\\\", \\\"85\\\"]\\n\\n # generate list of candidate bound limits\\n small = np.arange(0.1, 1, 0.1)\\n medium = np.arange(1, 11)\\n high = np.arange(20, 100, 10)\\n v_high = np.arange(150, 400, 50)\\n self.bound_candidates = np.concatenate(\\n (small, medium, high, v_high)) * 5 / 4\",\n \"def __init__(self, config: str) -> None:\\n self.configuration = config\",\n \"def __init__(self, config: str) -> None:\\n self.configuration = config\",\n \"def __init__(self):\\n self.risepower = 4.\\n self.min_event_amplitude = 5.0e-12 # pA default\\n self.template = None\\n pass\",\n \"def __init__(self, config: dict) -> None:\\n super().__init__(config)\",\n \"def __init__(self, name, i_node, j_node, m_node, n_node, t, material, model, kx_mod=1.0,\\n ky_mod=1.0):\\n\\n self.name = name\\n self.ID = None\\n self.type = 'Rect'\\n\\n self.i_node = i_node\\n self.j_node = j_node\\n self.m_node = m_node\\n self.n_node = n_node\\n\\n self.t = t\\n \\n self.kx_mod = kx_mod\\n self.ky_mod = ky_mod\\n\\n self.pressures = [] # A list of surface pressures [pressure, case='Case 1']\\n\\n # Plates need a link to the model they belong to\\n self.model = model\\n\\n # Get material properties for the plate from the model\\n try:\\n self.E = self.model.Materials[material].E\\n self.nu = self.model.Materials[material].nu\\n except:\\n raise KeyError('Please define the material ' + str(material) + ' before assigning it to plates.')\",\n \"def __init__(self, config_file=None, cam_prefix=\\\"rc\\\", serial_number=\\\"\\\",\\n output_dir=\\\"\\\", parseport=5001,\\n force_serial=True, set_temperature=-40, send_to_remote=False,\\n remote_config='nemea.config.json'):\\n\\n # Load the default parameters from config file\\n if not config_file:\\n config_file = os.path.join(SR, 'config',\\n '%s_config.yaml' % cam_prefix)\\n with open(config_file) as df:\\n camera_params = yaml.load(df, Loader=yaml.FullLoader)\\n self.__dict__.update(camera_params['default'].items())\\n\\n self.camPrefix = cam_prefix\\n self.outputDir = output_dir\\n self.forceSerial = force_serial\\n self.setTemperature = set_temperature\\n self.ExposureTime = 0\\n self.lastExposed = None\\n self.opt = None\\n self.parseport = parseport\\n self.send_to_remote = send_to_remote\\n if self.send_to_remote:\\n self.transfer = None #transfer(**params)\\n self.lastError = \\\"\\\"\",\n \"def __init__(self):\\n GPIO.setwarnings(False)\\n GPIO.cleanup() # Reset the high and low levels of the GPIO port\\n #The following code defines the GPIO used to control the L298N chip. This definition is different for different Raspberry Pi driver boards.\\n self.Motor_A_EN = 17\\n self.Motor_B_EN = 4\\n self.Motor_A_Pin1 = 27\\n self.Motor_A_Pin2 = 18\\n self.Motor_B_Pin1 = 21\\n self.Motor_B_Pin2 = 26\\n self.setup()\",\n \"def __init__(self, config=None):\\n config_dict = {}\\n if config:\\n config_dict = json.load(config)\\n\\n self.android = config_dict.get(\\\"android\\\")\\n self.linux = config_dict.get(\\\"linux\\\")\\n self.atf = config_dict.get(\\\"atf\\\")\\n self.qemu = config_dict.get(\\\"qemu\\\", \\\"qemu-system-aarch64\\\")\",\n \"def __init__(self, config):\\n super().__init__(config)\\n\\n # Prepare the timer.\\n self.timer = 0\\n\\n # Set the current player index.\\n self.current_player_index = 0\\n # If we are the client, the server goes first.\\n for i in range(len(sys.argv)):\\n if sys.argv[i] == \\\"--client\\\":\\n self.current_player_index = 1\\n\\n # Prepare the phase counter.\\n self.__current_phase = Game.PHASE_PREPARE\\n # Prepare the shot location store.\\n self.__current_fire_location = None\\n self.__current_fire_effect = None\",\n \"def __init__(self, config=None):\\n pass\",\n \"def __init__(self, make, model, year):\\r\\n super().__init__(make, model, year)\\r\\n self.battery_size = 70\\r\\n # self.autopilot = autopilot\\r\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def __init__(self, configuration):\\n self._config = configuration\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n d0eeg_name = '{0}D0EEG'.format(name)\\n dst2pid0eeg_name = '{0}Dst2PiD0EEG'.format(name)\\n \\n d0ee_name = '{0}D0EE'.format(name)\\n dst2pid0ee_name = '{0}Dst2PiD0EE'.format(name)\\n\\n self.selD0EEG = makeEEGResonance(\\n d0eeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.D0EEG\\n ) \\n\\n self.selD0EE = makeEEResonance(\\n d0ee_name, \\n config,\\n inputSel=[StdAllLooseElectrons],\\n decDescriptors=self.D0EE\\n )\\n \\n self.selDst2PiD0EEG = makeDstar(\\n dst2pid0eeg_name,\\n config,\\n inputSel=[self.selD0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.selDst2PiD0EE = makeDstar(\\n dst2pid0ee_name,\\n config,\\n inputSel=[self.selD0EE, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.line_Dst2PiD0EEG = make_line(\\n self,\\n '{0}Line'.format(dst2pid0eeg_name),\\n prescale=config['PrescaleDst2PiD0EEG'],\\n postscale=config['PostscaleDst2PiD0EEG'],\\n selection=self.selDst2PiD0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_Dst2PiD0EE = make_line(\\n self,\\n '{0}Line'.format(dst2pid0ee_name),\\n prescale=config['PrescaleDst2PiD0EE'],\\n postscale=config['PostscaleDst2PiD0EE'],\\n selection=self.selDst2PiD0EE,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, config, sensor=None):\\n if sensor is None:\\n from monitor.sensor import SensorDriver\\n self.sensor = SensorDriver(config.getint(CONFIG_SECTION, \\\"trigger_pin\\\"),\\n config.getint(CONFIG_SECTION, \\\"echo_pin\\\"))\\n else:\\n self.sensor = sensor\\n self.num_samples = config.getint(CONFIG_SECTION, \\\"num_samples\\\")\\n self.drop_extremes = config.getboolean(CONFIG_SECTION, \\\"drop_extremes\\\")\\n self.sample_delay = config.getfloat(CONFIG_SECTION, \\\"sample_delay\\\")\\n self.is_running = False\\n self.dist_to_bottom = config.getfloat(CONFIG_SECTION, \\\"distance_to_bottom\\\")\",\n \"def __init__(self, config_directory, scale_override=None):\\n self._config_directory = config_directory\\n\\n self._config_detector = DetectorConfig(config_directory)\\n self._config_align = AlignConfig(config_directory, scale_override=scale_override)\\n self._config_crystal = CrystalMatchConfig(config_directory)\",\n \"def __init__(self, config):\\n\\n self.config = config\",\n \"def __init__(self, config: Dict[str, Any]) -> None:\\n self.config = config\",\n \"def __init__(self, config):\\n self.name = \\\"leduc-holdem\\\"\\n self.game = LeducholdemGame()\\n super().__init__(config)\\n self.actions = [\\\"call\\\", \\\"raise\\\", \\\"fold\\\", \\\"check\\\"]\\n self.state_shape = [36]\\n\\n with open(\\n os.path.join(rlcard.__path__[0], \\\"games/leducholdem/card2index.json\\\"), \\\"r\\\"\\n ) as file:\\n self.card2index = json.load(file)\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n etappg_name = '{0}EtaPPG'.format(name)\\n eta3hr_name = '{0}Eta3HR'.format(name)\\n eta3hm_name = '{0}Eta3HM'.format(name)\\n \\n d2pietappg_name = '{0}D2PiEtaPPG'.format(name)\\n d2pieta3hr_name = '{0}D2PiEta3HR'.format(name)\\n d2pieta3hm_name = '{0}D2PiEta3HM'.format(name)\\n \\n d2ketappg_name = '{0}D2KEtaPPG'.format(name)\\n d2keta3hr_name = '{0}D2KEta3HR'.format(name)\\n d2keta3hm_name = '{0}D2KEta3HM'.format(name)\\n\\n self.selEtaPPG = makeResonance(\\n etappg_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\\n decDescriptors=self.EtaPPG\\n )\\n\\n self.selEta3HR = makeResonance(\\n eta3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\\n decDescriptors=self.Eta3H\\n )\\n\\n self.selEta3HM = makeResonance(\\n eta3hm_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\\n decDescriptors=self.Eta3H\\n )\\n\\n self.selD2PiEtaPPG = makeD(\\n d2pietappg_name,\\n config,\\n inputSel=[self.selEtaPPG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False,\\n )\\n\\n self.selD2KEtaPPG = makeD(\\n d2ketappg_name,\\n config,\\n inputSel=[self.selEtaPPG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiEta3HR = makeD(\\n d2pieta3hr_name,\\n config,\\n inputSel=[self.selEta3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False,\\n )\\n\\n self.selD2KEta3HR = makeD(\\n d2keta3hr_name,\\n config,\\n inputSel=[self.selEta3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiEta3HM = makeD(\\n d2pieta3hm_name,\\n config,\\n inputSel=[self.selEta3HM, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False,\\n )\\n \\n\\n self.selD2KEta3HM = makeD(\\n d2keta3hm_name,\\n config,\\n inputSel=[self.selEta3HM, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=False,\\n )\\n \\n\\n self.line_D2PiEtaPPG = make_line(\\n self,\\n name='{0}Line'.format(d2pietappg_name),\\n prescale=config['PrescaleD2PiEtaPPG'],\\n postscale=config['PostscaleD2PiEtaPPG'],\\n selection=self.selD2PiEtaPPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2PiEta3HM = make_line(\\n self,\\n name='{0}Line'.format(d2pieta3hm_name),\\n prescale=config['PrescaleD2PiEta3HM'],\\n postscale=config['PostscaleD2PiEta3HM'],\\n selection=self.selD2PiEta3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2PiEta3HR = make_line(\\n self,\\n name='{0}Line'.format(d2pieta3hr_name),\\n prescale=config['PrescaleD2PiEta3HR'],\\n postscale=config['PostscaleD2PiEta3HR'],\\n selection=self.selD2PiEta3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KEtaPPG = make_line(\\n self,\\n name='{0}Line'.format(d2ketappg_name),\\n prescale=config['PrescaleD2KEtaPPG'],\\n postscale=config['PostscaleD2KEtaPPG'],\\n selection=self.selD2KEtaPPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEta3HM = make_line(\\n self,\\n name='{0}Line'.format(d2keta3hm_name),\\n prescale=config['PrescaleD2KEta3HM'],\\n postscale=config['PostscaleD2KEta3HM'],\\n selection=self.selD2KEta3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEta3HR = make_line(\\n self,\\n name='{0}Line'.format(d2keta3hr_name),\\n prescale=config['PrescaleD2KEta3HR'],\\n postscale=config['PostscaleD2KEta3HR'],\\n selection=self.selD2KEta3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n self.__parameters: ConfigParams = ConfigParams()\",\n \"def __init__(self):\\n\\n # self.threshold = 3.\\n self.gamma_min = 3\\n self.gamma_max = 12\\n self.n_samples = 40\\n # self.do_plots = False\\n # self.do_albedo = True\\n # self.verbose = True\\n\\n self.nbands = 7\\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\\n\\n # Determine 250 or 500 meters product\\n # self.resolution = 500\\n\\n # self.pixelWidth = 500\\n # self.pixelHeight = 500\",\n \"def __init__(self):\\n\\n super().__init__(\\n dynamics_model=DoorDynamicsModel(),\\n virtual_sensor_model=UnimodalVirtualSensorModel(\\n virtual_sensor_model=[\\n DoorVirtualSensorModel(modalities={\\\"image\\\"}),\\n DoorVirtualSensorModel(modalities={\\\"pos\\\", \\\"sensors\\\"}),\\n ],\\n state_dim=3,\\n ),\\n )\",\n \"def __init__(self, footprint, mconf, name):\\n logging.debug(\\\"%s entered __init__\\\" % name)\\n self.image_id = mconf[\\\"image-id\\\"]\\n self.networks = mconf[\\\"networks\\\"]\\n self.ip_addresses = mconf[\\\"ip-addresses\\\"]\\n self.flavor=mconf[\\\"flavor\\\"]\\n self.machine_name = \\\"%s%s\\\" % (footprint.footprint_name, name)\\n self.footprint = footprint\",\n \"def default_plate(self):\\n self.name = \\\"Default Plate Armor\\\"\\n self.rarity = \\\"Common\\\"\\n self.pdef_value = 20\\n self.mdef_value = 5\\n self.increase_crit = 0\\n self.desc = \\\"An old piece of iron armor, it's pretty heavy.\\\"\",\n \"def __init__(\\n self,\\n hass,\\n name: str,\\n config: ConfigEntry,\\n coordinator,\\n file_path: str,\\n ) -> None:\\n super().__init__()\\n\\n self.hass = hass\\n self._name = CAMERA_DATA[name][SENSOR_NAME]\\n self._type = name\\n self.check_file_path_access(file_path)\\n self._file_path = file_path\\n self._coordinator = coordinator\\n self._host = config.data.get(CONF_HOST)\\n self._unique_id = config.entry_id\\n self._no_mail = (\\n None\\n if not config.data.get(CONF_CUSTOM_IMG)\\n else config.data.get(CONF_CUSTOM_IMG_FILE)\\n )\",\n \"def _configure(self, config):\\n self.friction_coef = config['friction_coef']\\n self.num_cone_faces = config['num_cone_faces']\\n self.num_samples = config['grasp_samples_per_surface_point']\\n self.dir_prior = config['dir_prior']\\n self.target_num_grasps = config['target_num_grasps']\\n if self.target_num_grasps is None:\\n self.target_num_grasps = config['min_num_grasps']\\n\\n self.min_contact_dist = config['min_contact_dist']\\n self.num_grasp_rots = config['coll_check_num_grasp_rots']\\n if 'max_num_surface_points' in config.keys():\\n self.max_num_surface_points_ = config['max_num_surface_points']\\n else:\\n self.max_num_surface_points_ = 100\",\n \"def __init__(self, parent):\\n super(Demo5, self).__init__(parent)\\n self.angle = 0.0\\n self.replication = 1.0\\n self.offset = 0.0\\n self.deltaRep = 1\\n self.revolution = 0\\n self.stepsPer90 = 180\\n self.stepsLeft = self.stepsPer90\\n self.deltaAng = 90.0\\n self.deltaOff = 0.15\\n self.spin = True\\n self.x2yAspect = 1.0\\n self.texture = None\",\n \"def __init__(self, config_file):\\n Config = ConfigParser.ConfigParser()\\n Config.read(config_file)\\n \\n self.port_id = Config.get(\\\"SerialPortSection\\\", \\\"ComPort\\\")\\n self.baud_rate = Config.get(\\\"SerialPortSection\\\", \\\"BaudRate\\\")\\n self. timeout = Config.get(\\\"SerialPortSection\\\", \\\"TimeOut\\\")\\n self.config_file = config_file\",\n \"def __init__(self, rings=False, branches=False):\\n self.rings = rings\\n self.branches = branches\",\n \"def __init__(self, encut, name=\\\"relax_settings\\\"):\\n\\n ionic = {\\\"EDIFF\\\": 1E-17, \\\"NSW\\\": 20, \\\"IBRION\\\": 2,\\\"ISIF\\\": 2, \\\"ISYM\\\": -1, \\\"NBLOCK\\\": 1, \\\"KBLOCK\\\": 20}\\n InputParameters.__init__(self, ionic_settings=ionic, name=name)\\n self.update_electronic_sttings(\\\"ENCUT\\\", encut)\",\n \"def __init__(self, hass, camera_entity, config, name):\\n self.hass = hass\\n self._camera_entity = camera_entity\\n if name:\\n self._name = name\\n else:\\n self._name = f\\\"SevenSegment OCR {split_entity_id(camera_entity)[1]}\\\"\\n self._state = None\\n\\n self.filepath = os.path.join(\\n self.hass.config.config_dir,\\n \\\"ssocr-{}.png\\\".format(self._name.replace(\\\" \\\", \\\"_\\\")),\\n )\\n crop = [\\n \\\"crop\\\",\\n str(config[CONF_X_POS]),\\n str(config[CONF_Y_POS]),\\n str(config[CONF_WIDTH]),\\n str(config[CONF_HEIGHT]),\\n ]\\n digits = [\\\"-d\\\", str(config.get(CONF_DIGITS, -1))]\\n rotate = [\\\"rotate\\\", str(config[CONF_ROTATE])]\\n threshold = [\\\"-t\\\", str(config[CONF_THRESHOLD])]\\n extra_arguments = config[CONF_EXTRA_ARGUMENTS].split(\\\" \\\")\\n\\n self._command = (\\n [config[CONF_SSOCR_BIN]]\\n + crop\\n + digits\\n + threshold\\n + rotate\\n + extra_arguments\\n )\\n self._command.append(self.filepath)\",\n \"def __init__(self, name):\\n self.cloud = name\\n self.cloudtype = \\\"multipass\\\"\\n config = Config()\\n self.default = config[f\\\"cloudmesh.volume.{self.cloud}.default\\\"]\\n self.cm = CmDatabase()\",\n \"def __init__(self) -> None:\\n self.sensor = serial.Serial(config.DEVICE)\\n super().__init__()\",\n \"def __init__(self):\\n #screen Settings\\n self.screen_width = 1024\\n self.screen_height = 768\\n self.bg_color = (32, 32, 32)\\n\\n #rocket settings\\n self.rocket_speed = 1\\n\\n #laser Settings\\n self.laser_speed = 1.0\\n self.laser_width = 3\\n self.laser_height = 15\\n self.laser_color = (0, 255, 255)\\n self.lasers_allowed = 3\",\n \"def __init__(self, config):\\n super().__init__(config)\\n\\n # For NaMaster you need to pass the masks\\n self.mask_files = self.config[\\\"tjpcov\\\"].get(\\\"mask_file\\\")\\n self.mask_names = self.config[\\\"tjpcov\\\"].get(\\\"mask_names\\\")\\n\\n # Binning info is only needed if workspaces are not passed\\n self.binning_info = self.config[\\\"tjpcov\\\"].get(\\\"binning_info\\\", None)\\n\\n # nside is needed if mask_files is a hdf5 file\\n self.nside = self.config[\\\"tjpcov\\\"].get(\\\"nside\\\", None)\\n\\n # Read NaMaster specific options\\n self.nmt_conf = self.config.get(\\\"NaMaster\\\", {})\\n for k in [\\\"f\\\", \\\"w\\\", \\\"cw\\\"]:\\n if k not in self.nmt_conf:\\n self.nmt_conf[k] = {}\\n\\n # Read cache from input file. It will update the cache passed as an\\n # argument of the different methods\\n self.cache = self.config.get(\\\"cache\\\", {})\",\n \"def __init__(self, name, type_name, thickness, grid_front_area, bundles_flow_area, grid_wetted_area, rods_wetted_at_grid_area, P_wet_at_grid, P_bundles, orientation):\\n self.name = name\\n self.type = type_name\\n self.thickness = thickness\\n self.length = self.thickness\\n self.grid_front_area = grid_front_area\\n self.bundles_flow_area = bundles_flow_area\\n self.grid_wetted_area = grid_wetted_area\\n self.rods_wetted_at_grid_area = rods_wetted_at_grid_area\\n self.orientation = orientation\\n self.P_wet_at_grid = P_wet_at_grid\\n self.P_bundles = P_bundles\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n etaeeg_name = '{0}EtaEEG'.format(name)\\n \\n d2pietaeeg_name = '{0}D2PiEtaEEG'.format(name) \\n d2ketaeeg_name = '{0}D2KEtaEEG'.format(name)\\n\\n self.selEtaEEG = makeEEGResonance(\\n etaeeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.EtaEEG\\n )\\n \\n self.selD2PiEtaEEG = makeD(\\n d2pietaeeg_name,\\n config,\\n inputSel=[self.selEtaEEG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False\\n )\\n\\n usePIDforKaon = False\\n if (config['Bachelor_PIDK_MIN'] > -10):\\n usePIDforKaon = True\\n else :\\n usePIDforKaon = False\\n \\n self.selD2KEtaEEG = makeD(\\n d2ketaeeg_name, \\n config,\\n inputSel=[self.selEtaEEG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=usePIDforKaon\\n )\\n\\n self.line_D2PiEtaEEG = make_line(\\n self,\\n '{0}Line'.format(d2pietaeeg_name),\\n prescale=config['PrescaleD2PiEtaEEG'],\\n postscale=config['PostscaleD2PiEtaEEG'],\\n selection=self.selD2PiEtaEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KEtaEEG = make_line(\\n self,\\n '{0}Line'.format(d2ketaeeg_name),\\n prescale=config['PrescaleD2KEtaEEG'],\\n postscale=config['PostscaleD2KEtaEEG'],\\n selection=self.selD2KEtaEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, **kwargs):\\n super(VeryCleverBeamsplitter, self).__init__(**kwargs)\\n self.shader_source = IL_SHADER_SOURCE\\n self.centre = [0.5, 0.5]\\n self.blazing_function = np.linspace(0,1,32)\\n self.zernike_coefficients = np.zeros(12)\",\n \"def __init__(self, car_reference, pt_reference):\\n\\n self.car_reference = car_reference\\n self.pt_reference = pt_reference\\n\\n # We need to provide some mandatory information\\n self.number_of_parameters = 3\\n self.number_of_states = 2\\n\\n # We can provide some information that is passed to the output\\n self.information = dict(problem = \\\"ModeShareProblem\\\")\\n\\n # We can provide the reference state. Currently, it is not used in any\\n # optimizer, but it is helpful for plotting optimization results as\\n # the existing plotting scripts will know how to use this information.\\n self.reference_state = [self.car_reference, self.pt_reference]\",\n \"def __init__(self):\\n # Screen settings\\n self.screen_width = 1860\\n self.screen_height = 1020\\n self.screen_size = self.screen_width, self.screen_height\\n self.bg_color = 230, 230, 230\\n\\n # Ship static settings\\n self.ship_limit = 3\\n\\n # Bullet static settings\\n self.bullet_limit = 3\\n self.bullet_width = 3\\n self.bullet_height = 15\\n self.bullet_color = 60, 60, 60\\n\\n # Alien static settings\\n self.fleet_drop_speed = 10\\n\\n self.speed_up_scale = 1.1\\n self.initialize_dynamic_settings()\",\n \"def __init__(self, pins, image):\\n self._pins = pins\\n self._image = image\",\n \"def __init__(self):\\n # Screen settings\\n self.screen_width = 2400\\n self.screen_height = 1600\\n self.bg_color = (0, 0, 0)\\n\\n # Raindrop settings\\n self.r_y_speed = 10\",\n \"def __init__(self, planet, config=None, log=None, **kwargs):\\n self.planet = planet.capitalize()\\n self.log = logging.setup(log)\\n\\n if config is None or isinstance(config, str):\\n from . import config as pcfg\\n config = os.path.join(self.planet, config)\\n config = pcfg.planetConfig(self.planet, configFile=config)\\n config.update_config(**kwargs)\\n self.config = config\\n self.chem = None\\n self.gas = []\\n self.nGas = 0\\n self.nConstituent = 0\\n self.cloud = []\\n self.nCloud = 0\\n self.property = []\\n if config.path not in sys.path:\\n sys.path.insert(0, config.path)\",\n \"def __init__(self, config, cfg):\\n self.config = config\\n self.cfg = cfg\",\n \"def __init__(self, config):\\n self.__config = config\\n self.__logger = SLoggerHandler().getLogger(LoggerNames.EXPERIMENT_C)\\n self.__num_gpus = ConfigProvider().get_config(\\\"controllerConfig.json\\\")[\\\"hardware\\\"][\\\"numGPUs\\\"]\",\n \"def __init__(self, configfile_name):\\n assert os.path.exists(configfile_name), \\\\\\n 'Config file %s does not exist' % (configfile_name)\\n\\n correct_param_load = self.load_config_file(configfile_name)\\n assert correct_param_load,\\\\\\n 'Config params could not be loaded from file'\\n\\n self.fullpath_input_configfile = configfile_name\\n self.word_det_rfc = None\\n self.reg_coeffs = None\\n self.bb_reg = None\\n self.img_files = None\",\n \"def __init__(self,port=None,config=None):\\n\\n # Attempt to load a Dorna object with a configuration file, default to the desktop config if none is entered manually.\\n\\n self.WORKING_DIRECTORY = os.getcwd()\\n self.FILE_DIRECTORY = os.path.dirname(__file__)\\n self.DEFAULT_CONFIG = os.path.join(self.FILE_DIRECTORY, \\\"../configuration_store/config.yaml\\\")\\n \\n self.positionStore = PositionStore()\\n \\n config = config if config!= None else self.DEFAULT_CONFIG\\n\\n try:\\n print(config)\\n self.robot=Dorna(config)\\n print(\\\"Loaded configuration file\\\")\\n except:\\n print(\\\"Failed to load configuration file, please re-instantiate the arm object with a valid config.\\\")\",\n \"def __init__(self, address=0x68, config=0):\\r\\n\\t\\tself.i2c = FT232H.I2CDevice(ft232h, address)\\r\\n\\t\\tif config == 0:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=0)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=0)\\r\\n\\t\\telif config == 1:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=1)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=1)\\t\\t\\t\\t\\r\\n\\t\\telif config == 2:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=2)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=2)\\t\\r\\n\\t\\telif config == 3:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=3)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=3)\\t\\t\\t\\t\\r\\n\\t\\telif config == 4:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=1)\\t\\r\\n\\t\\t\\tself.setTempDisable()\\r\\n\\t\\t\\tself.setGYRStandby(axis='X')\\r\\n\\t\\t\\tself.setGYRStandby(axis='Y')\\r\\n\\t\\t\\tself.setGYRStandby(axis='Z')\",\n \"def __init__(self):\\n self.config = {}\",\n \"def __init__(self, config):\\n try:\\n config['volume_id']\\n config['access_key']\\n config['secret_access_key']\\n config['region']\\n except KeyError, e:\\n logging.error(repr(e))\\n raise ImproperlyConfigured()\\n\\n if not config.has_key('keep'):\\n config['keep'] = 5\\n\\n self.config = config\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n etaprimeppg_name = '{0}EtaPrimePPG'.format(name)\\n etaprime3hr_name = '{0}EtaPrime3HR'.format(name)\\n \\n d2pietaprimeppg_name = '{0}D2PiEtaPrimePPG'.format(name)\\n d2pietaprime3hr_name = '{0}D2PiEtaPrime3HR'.format(name)\\n \\n d2ketaprimeppg_name = '{0}D2KEtaPrimePPG'.format(name)\\n d2ketaprime3hr_name = '{0}D2KEtaPrime3HR'.format(name)\\n \\n self.selEtaPrimePPG = makeResonance(\\n etaprimeppg_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\\n decDescriptors=self.EtaPrimePPG\\n )\\n \\n self.selEtaPrime3HR = makeResonance(\\n etaprime3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedEta],\\n decDescriptors=self.EtaPrime3H\\n )\\n \\n self.selD2PiEtaPrimePPG = makeD(\\n d2pietaprimeppg_name,\\n config,\\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEtaPrime,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KEtaPrimePPG = makeD(\\n d2ketaprimeppg_name,\\n config,\\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEtaPrime,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiEtaPrime3HR = makeD(\\n d2pietaprime3hr_name,\\n config,\\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEtaPrime,\\n useBachelorPID=False, \\n )\\n \\n self.selD2KEtaPrime3HR = makeD(\\n d2ketaprime3hr_name,\\n config,\\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEtaPrime,\\n useBachelorPID=False,\\n )\\n \\n self.line_D2PiEtaPrimePPG = make_line(\\n self,\\n name='{0}Line'.format(d2pietaprimeppg_name),\\n prescale=config['PrescaleD2PiEtaPrimePPG'],\\n postscale=config['PostscaleD2PiEtaPrimePPG'],\\n selection=self.selD2PiEtaPrimePPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2PiEtaPrime3HR = make_line(\\n self,\\n name='{0}Line'.format(d2pietaprime3hr_name),\\n prescale=config['PrescaleD2PiEtaPrime3HR'],\\n postscale=config['PostscaleD2PiEtaPrime3HR'],\\n selection=self.selD2PiEtaPrime3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEtaPrimePPG = make_line(\\n self,\\n name='{0}Line'.format(d2ketaprimeppg_name),\\n prescale=config['PrescaleD2KEtaPrimePPG'],\\n postscale=config['PostscaleD2KEtaPrimePPG'],\\n selection=self.selD2KEtaPrimePPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEtaPrime3HR = make_line(\\n self,\\n name='{0}Line'.format(d2ketaprime3hr_name),\\n prescale=config['PrescaleD2KEtaPrime3HR'],\\n postscale=config['PostscaleD2KEtaPrime3HR'],\\n selection=self.selD2KEtaPrime3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n self.plant_names = list(PLANT_TYPES.keys()) #: list of str: available plant type names.\\n self.plant_types = list(PLANT_TYPES.items()) #: list of dicts: model parameters for plant types.\\n self.num_plant_types = len(PLANT_TYPES) #: int: amount unique plant types in garden.\\n self.plant_centers = [] #: location of plants in garden\\n self.non_plant_centers = [] #: additional coordinates without plants\\n self.plant_in_bounds = 0 #: int: count of plant centers in bound.\",\n \"def __init__(self, name: str, instance: PartInstance, controlPoints: ModelDotArray):\\n super().__init__()\\n pass\",\n \"def __init__(self):\\n super().__init__()\\n\\n # Gadget state\\n \\n self.isDoorOpen = False\\n self.verified = True\\n\\n # Ev3dev initialization\\n self.leds = Leds()\\n self.sound = Sound()\\n self.drive = MoveTank(OUTPUT_B, OUTPUT_C)\\n \\n self.ir_sensor = InfraredSensor()\\n self.ir_sensor.mode = self.ir_sensor.MODE_IR_REMOTE\\n self.color_sensor = ColorSensor()\\n self.color_sensor.mode = 'COL-COLOR' # WHITE\\n\\n # Start threads\\n threading.Thread(target=self._patrol_thread, daemon=True).start()\",\n \"def __init__(self):\\n if not os.path.isfile(CONFIG_FILENAME):\\n first_time_run()\\n raise SystemExit()\\n\\n \\\"\\\"\\\"\\n Init file is present, read and parse it:\\n \\\"\\\"\\\"\\n conf = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation(), inline_comment_prefixes='#')\\n conf.read(CONFIG_FILENAME)\\n \\\"\\\"\\\"\\n Process certain paths:\\n \\\"\\\"\\\"\\n\\n path = conf['Paths']\\n self.ffc_dir = path['FlatFieldCalDir']\\n self.capture_dir = path['CaptureDir']\\n self.image_dir = path['ImageDir']\\n\\n \\\"\\\"\\\"\\n Process options\\n \\\"\\\"\\\"\\n\\n self.cal_auto_save = conf.getboolean('Options', 'CalAutoSave', fallback=True)\\n self.cal_auto_load = conf.getboolean('Options', 'CalAutoLoad', fallback=True)\\n self.sound_on_capture = conf.getboolean('Options', 'SoundOnCapture', fallback=True)\\n self.exp_init1 = conf.getint('Options', 'ExpInit1', fallback=100)\\n self.exp_init2 = conf.getint('Options', 'ExpInit2', fallback=100)\\n self.black_correct = conf.getboolean('Options', 'BlackCorrect', fallback=True)\\n # Setup square window, default of full-screen height\\n self.tiff_seq_x_window = conf.getint('Options', 'TiffSeqXWindow', fallback=cameras.FRAME_HEIGHT)\\n self.tiff_seq_y_window = conf.getint('Options', 'TiffSeqYWindow', fallback=cameras.FRAME_HEIGHT)\\n self.tiff_seq_rebin = conf.getint('Options', 'TiffSeqRebin', fallback = 2)\",\n \"def __init__(self, x, y , planet):\\n\\t\\tself.x = x\\n\\t\\tself.y = y\\n\\t\\tself.planet = planet\\n\\t\\tself.battery = 100\",\n \"def __init__(self, **kwargs):\\n self.max_samples = get_value(kwargs, 'max_samples', types=[int], range_min=0, default_value=0)\\n self.obs_filename = get_value(kwargs, 'lba_obs_file', types=[str, None], default_value=None)\\n self.sample_rate = get_value(kwargs, 'sample_rate', types=[int, None], default_value=None)\\n if self.sample_rate is None:\\n LOG.warning(\\\"No sample rate provided, defaulting to {0}\\\".format(self._default_sample_rate))\\n self.sample_rate = self._default_sample_rate\\n self.antenna_name = get_value(kwargs, 'lba_antenna_name', types=[str, None], default_value=None)\\n if self.obs_filename is not None and self.antenna_name is None:\\n raise RuntimeError(\\\"LBA file is missing --lba_antenna_name parameter which is needed when using \\\"\\n \\\"--lba_obs_file\\\")\\n self.chunk_size = get_value(kwargs, 'chunk_size', types=[int], range_min=0, default_value=4096)\",\n \"def __init__(self, pitch, color, our_side, video_port=0, comm_port='/dev/ttyACM0', penalty=False, comms=1):\\n assert pitch in [0, 1]\\n assert color in ['yellow', 'blue']\\n assert our_side in ['left', 'right']\\n\\n self.pitch = pitch\\n\\n # Set up the Arduino communications\\n self.arduino = Arduino(comm_port, 115200, 1, comms)\\n\\n # Set up camera for frames\\n self.camera = Camera(port=video_port, pitch=self.pitch)\\n frame = self.camera.get_frame()\\n center_point = self.camera.get_adjusted_center(frame)\\n\\n # Set up vision\\n self.calibration = tools.get_colors(pitch)\\n self.vision = Vision(\\n pitch=pitch, color=color, our_side=our_side,\\n frame_shape=frame.shape, frame_center=center_point,\\n calibration=self.calibration)\\n\\n # Set up postprocessing for vision\\n self.postprocessing = Postprocessing(our_side)\\n\\n # Set up main planner\\n self.planner = Planner(our_side=our_side, pitch_num=self.pitch, isPenalty=penalty)\\n\\n # Set up GUI\\n self.GUI = GUI(calibration=self.calibration, arduino=self.arduino, pitch=self.pitch)\\n\\n self.color = color\\n self.side = our_side\\n\\n self.preprocessing = Preprocessing()\\n\\n self.robot = Robot_Controller()\",\n \"def __init__(self, cpara, dz_soil):\\n\\n # --- grid ---\\n self.z = np.linspace(0, cpara['grid']['zmax'], cpara['grid']['Nlayers']) # grid [m] above ground\\n self.dz = self.z[1] - self.z[0] # gridsize [m]\\n self.ones = np.ones(len(self.z)) # dummy\\n\\n # --- switches ---\\n self.Switch_Eflow = cpara['ctr']['Eflow'] # True assumes constant U/ustar at upper boundary\\n self.Switch_WMA = cpara['ctr']['WMA'] # True solves scalar profiles\\n self.Switch_Ebal = cpara['ctr']['Ebal'] # True solves leaf energy balance\\n\\n logger.info('Eflow: %s, WMA: %s, Ebal: %s',\\n self.Switch_Eflow,\\n self.Switch_WMA,\\n self.Switch_Ebal)\\n\\n # --- PlantTypes ---\\n ptypes = []\\n ptnames = list(cpara['planttypes'].keys())\\n ptnames.sort()\\n for pt in ptnames:\\n ptypes.append(PlantType(self.z, cpara['planttypes'][pt], dz_soil, ctr=cpara['ctr'], loc=cpara['loc']))\\n self.planttypes = ptypes\\n \\n # --- stand characteristics: sum over planttypes---\\n\\n # total leaf area index [m2 m-2]\\n self.LAI = sum([pt.LAI for pt in self.planttypes])\\n # total leaf area density [m2 m-3]\\n self.lad = sum([pt.lad for pt in self.planttypes])\\n\\n # layerwise mean leaf characteristic dimension [m] for interception model\\n self.leaf_length = sum([pt.leafp['lt'] * pt.lad for pt in self.planttypes]) / (self.lad + EPS)\\n\\n # root area density [m2 m-3]\\n rad = np.zeros(np.shape(dz_soil))\\n imax = 1\\n for pt in self.planttypes:\\n rad[:len(pt.Roots.rad)] += pt.Roots.rad\\n imax = max(imax, len(pt.Roots.rad))\\n \\n self.ix_roots = np.array(range(imax)) # soil model layers corresponding to root zone\\n self.rad = rad[self.ix_roots]\\n \\n # total root area index [m2 m-2]\\n self.RAI = sum([pt.Roots.RAI for pt in self.planttypes])\\n # distribution of roots across soil model layers [-]\\n self.root_distr = self.rad * dz_soil[self.ix_roots] / (self.RAI + EPS)\\n\\n # canopy height [m]\\n if len(np.where(self.lad > 0)[0]) > 0:\\n f = np.where(self.lad > 0)[0][-1]\\n self.hc = self.z[f].copy()\\n else:\\n self.hc = 0.0\\n\\n # --- create radiation, micromet, interception, and forestfloor model instances\\n self.radiation = Radiation(cpara['radiation'], self.Switch_Ebal)\\n\\n self.micromet = Micromet(self.z, self.lad, self.hc, cpara['micromet'])\\n\\n self.interception = Interception(cpara['interception'], self.lad * self.dz)\\n\\n self.forestfloor = ForestFloor(cpara['forestfloor'])\",\n \"def __init__(self, region):\\r\\n self.region = region\",\n \"def __init__(self):\\n\\n self.Cp_air0 = config_earth.earth_properties['Cp_air0']\\n self.Rsp_air = config_earth.earth_properties['Rsp_air']\\n\\n self.d = config_earth.balloon_properties['d']\\n self.vol = math.pi*4/3*pow((self.d/2),3) #volume m^3\\n self.surfArea = math.pi*self.d*self.d #m^2\\n self.cs_area = math.pi*self.d*self.d/4.0 #m^2\\n\\n #self.emissEnv = config_earth.balloon_properties['emissEnv']\\n self.areaDensityEnv = config_earth.balloon_properties['areaDensityEnv']\\n self.mp = config_earth.balloon_properties['mp']\\n self.mdot = 0\\n self.massEnv = config_earth.balloon_properties['mEnv']\\n self.Upsilon = config_earth.balloon_properties['Upsilon']\\n\\n self.vent = config_earth.simulation['vent']\\n self.coord = config_earth.simulation['start_coord']\\n self.t = config_earth.simulation['start_time']\\n self.lat = math.radians(self.coord['lat'])\\n self.Ls = self.t.timetuple().tm_yday\\n self.min_alt = config_earth.simulation['min_alt']\\n\\n self.vm_coeff = .1 #virtual mass coefficient\\n self.k = self.massEnv*config_earth.balloon_properties['cp'] #thermal mass coefficient\\n\\n self.dt = config_earth.dt\",\n \"def __init__(self):\\n super().__init__()\\n self.location = 0.0\\n self.scale = 1.0\\n self.type = 'Laplace'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, config, town):\\n\\t\\tself.config = config\",\n \"def initialize(self, **kwargs):\\n\\n # Defining the configuration object\\n self.config = kwargs.get('config')\",\n \"def __init__(self, filepath):\\n try:\\n config_file_r = open(filepath)\\n self.sim_parametres = yaml.load(config_file_r, Loader=yaml.FullLoader)\\n except:\\n raise Exception(\\\"Le fichier de configuration n'a pas été atteint ou n'a pas pu être lu. Veuillez vérifier \\\"\\n \\\"qu'il n'y ait aucune erreur de syntaxe.\\\")\",\n \"def __init__(self, metadata, environment, component, image, version, s3_bucket, exec_env):\\n self.environment = environment\\n self.component = component\\n self.s3_bucket = s3_bucket\\n self.exec_env = exec_env\\n self.image = image\\n self.version = version\\n self.metadata = metadata\\n\\n # generate Terragrunt config as part of object initialisation\\n self.config()\",\n \"def __init__(self,height=7,width=7):\\n self.__cars = []\\n self.__height = height\\n self.__width = width\",\n \"def initialize_from_config(self):\",\n \"def __init__(self, name, properties):\\n super(WiretapShotProcessorPreset, self).__init__(name, properties)\\n \\n # Necessary in order for Hiero to view this plugin as different from\\n # the default shot processor\\n self._parentType = WiretapShotProcessor\",\n \"def __init__(self, config):\\n raise NotImplementedError (\\\"This is just an abstract interface\\\")\",\n \"def __init__(self):\\n super(Config, self).__init__()\\n self.run_control[\\\"output_dir\\\"] = os.getcwd()+\\\"/output/arctan_baseline/single_turn_injection/tracking\\\"\\n ring_tof = 1149.185123\\n field_files = glob.glob(\\\"output/arctan_baseline/bump_quest_v9/find_bump_r_*_theta_90/find_bump_parameters_001.out\\\")\\n substitutions = config.get_baseline_substitution(0.22, ring_tof)\\n substitutions = self.ramp_fields(\\n substitutions,\\n field_files,\\n [i for i, f in enumerate(field_files)],\\n ring_tof,\\n will_step = False\\n )\\n self.substitution_list = [substitutions] # used for plotting unperturbed CO\\n\\n self.run_control[\\\"find_closed_orbits_4d\\\"] = False\\n self.run_control[\\\"find_da\\\"] = False\\n self.run_control[\\\"find_bump_parameters\\\"] = False\\n self.run_control[\\\"track_bump\\\"] = False\\n self.run_control[\\\"track_beam\\\"] = True\\n self.find_closed_orbits[\\\"subs_overrides\\\"][\\\"__n_turns__\\\"] = 0.11\\n self.find_closed_orbits[\\\"subs_overrides\\\"][\\\"__do_bump__\\\"] = False\\n self.find_closed_orbits[\\\"final_subs_overrides\\\"].update(substitutions)\\n self.find_closed_orbits[\\\"max_iterations\\\"] = 0\\n self.find_closed_orbits[\\\"do_minuit\\\"] = True\\n self.find_closed_orbits[\\\"minuit_iterations\\\"] = 10 \\n self.find_closed_orbits[\\\"us_cell\\\"] = 0\\n self.find_closed_orbits[\\\"ds_cell\\\"] = 1\\n\\n self.find_da = {\\n \\\"run_dir\\\":\\\"tmp/find_da/\\\",\\n \\\"probe_files\\\":\\\"RINGPROBE01.h5\\\",\\n \\\"subs_overrides\\\":{\\\"__n_turns__\\\":101.1, \\\"__do_magnet_field_maps__\\\":\\\"False\\\", \\\"__step_size__\\\":0.01},\\n \\\"get_output_file\\\":\\\"get_da\\\",\\n \\\"scan_output_file\\\":\\\"scan_da\\\",\\n \\\"row_list\\\":None,\\n \\\"scan_x_list\\\":[],\\n \\\"scan_y_list\\\":[],\\n \\\"x_seed\\\":1.,\\n \\\"y_seed\\\":1.,\\n \\\"min_delta\\\":0.9,\\n \\\"max_delta\\\":1000.,\\n \\\"required_n_hits\\\":100,\\n \\\"dt_tolerance\\\":0.5, # fraction of closed orbit dt\\n \\\"max_iterations\\\":15,\\n \\\"decoupled\\\":True,\\n }\\n\\n max_amp = 0.01\\n self.track_beam_dummy = {\\n \\\"run_dir\\\":\\\"tmp/track_beam/\\\",\\n \\\"save_dir\\\":\\\"track_beam\\\",\\n \\\"print_events\\\":[0, 1, -1],\\n \\\"settings\\\":[{\\n \\\"name\\\":\\\"forwards\\\",\\n \\\"direction\\\":\\\"forwards\\\",\\n \\\"probe_files\\\":\\\"RINGPROBE01.h5\\\", \\n \\\"beam\\\":{\\n \\\"type\\\":\\\"beam_gen\\\",\\n \\\"closed_orbit_file\\\":\\\"closed_orbits_cache\\\",\\n \\\"eigen_emittances\\\":[[0, 0]]*3,#+[[max_amp, max_amp]],\\n \\\"n_per_dimension\\\":2,\\n \\\"variables\\\":[\\\"x\\\",\\\"x'\\\",\\\"y\\\",\\\"y'\\\"],\\n \\\"amplitude_dist\\\":\\\"uniform\\\", #\\\"grid\\\", # \\n \\\"phase_dist\\\":\\\"uniform\\\", #\\\"grid\\\", # \\n \\\"max_amplitude_4d\\\":max_amp, # amplitude_dist != grid\\n \\\"energy\\\":3.0,\\n },\\n \\\"subs_overrides\\\":{\\n \\\"__n_turns__\\\":45.1,\\n \\\"__hdf5__\\\":\\\"True\\\",\\n \\\"__do_magnet_field_maps__\\\":\\\"False\\\",\\n \\\"__do_bump__\\\":\\\"True\\\",\\n \\\"__step_size__\\\":0.01\\n },\\n },],\\n }\\n\\n T0 = ring_tof\\n self.track_beam = {\\n \\\"run_dir\\\":\\\"tmp/track_beam/\\\",\\n \\\"save_dir\\\":\\\"track_beam_rf_on_2\\\",\\n \\\"print_events\\\":[i for i in range(1)],\\n \\\"variables\\\":[\\\"x\\\", \\\"x'\\\", \\\"y\\\", \\\"y'\\\", \\\"t\\\", \\\"energy\\\"],\\n \\\"settings\\\":[{\\n \\\"name\\\":\\\"grid\\\",\\n \\\"direction\\\":\\\"forwards\\\",\\n \\\"probe_files\\\":\\\"RINGPROBE01.h5\\\", \\n \\\"beam\\\":{\\n \\\"type\\\":\\\"grid\\\",\\n \\\"energy\\\":3.0,\\n \\\"start\\\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, 0.0, 941.272],\\n \\\"stop\\\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, T0, 941.272],\\n \\\"nsteps\\\":[1, 1, 1, 1, 4+1, 1],\\n },\\n \\\"subs_overrides\\\":{\\n \\\"__n_turns__\\\":100.1,\\n \\\"__hdf5__\\\":True,\\n \\\"__do_magnet_field_maps__\\\":False,\\n \\\"__do_bump__\\\":True,\\n \\\"__do_rf__\\\":True,\\n \\\"__do_foil__\\\":False,\\n \\\"__step_size__\\\":0.01\\n },\\n },],\\n }\",\n \"def __init__(self, hass, config):\\n super().__init__()\\n self._parent = hass\\n self.ezvizService = hass.data[PY_EZVIZ_GATEWAY]\\n\\n self.deviceSerial = self.ezvizService.deviceSerial\\n\\n self._interval_snapshots = 30\\n self._last_image = \\\"\\\"\\n self._last_snapshot_time = 0\",\n \"def __init__(self):\\n super().__init__()\\n\\n # Robot state\\n self.ask_mode = False\\n\\n # Connect two large motors on output ports B and C\\n self.sound = Sound()\\n self.leds = Leds()\\n self.p1 = TouchSensor(INPUT_1)\\n self.p2 = TouchSensor(INPUT_2)\\n self.p3 = TouchSensor(INPUT_3)\\n self.p4 = TouchSensor(INPUT_4)\",\n \"def __init__(self, parant):\\n pass\",\n \"def __init__(self, board, sample_rate=1e6, bus_width=8):\\n\\n # Grab a reference to the board and its pattern-gen API.\\n self.board = board\\n self.api = board.apis.pattern_generator\\n\\n # Grab a reference to the user's bus parameters.\\n self.sample_rate = int(sample_rate)\\n self.bus_width = bus_width\\n\\n # FIXME: These should be read from the board, rather than hardcoded!\\n self.upload_chunk_size = 2048\\n self.samples_max = 32 * 1024\",\n \"def initializeParameters(self):\\r\\n\\t\\tself.input_raster.enabled = True\\r\\n\\t\\tself.approach.enabled = True\\r\\n\\t\\tself.predefined_pattern.enabled = False\\r\\n\\t\\tself.predefined_pattern.value = 'Mexican Hat wavelet'\\r\\n\\t\\tself.pattern_workspace.enabled = False\\r\\n\\t\\tself.point_matrix_size.enabled = False\\r\\n\\t\\tself.point_matrix_size.value = 3\\r\\n\\t\\tself.point_vectors.enabled = False\\r\\n\\t\\tself.mapping_field.enabled = False\\r\\n\\t\\tself.move_to_max.enabled = False\\r\\n\\t\\tself.move_to_max_distance.enabled = False\\r\\n\\t\\tself.move_to_max_distance.value = 3\\r\\n\\t\\tself.mh_iteration.enabled = False\\r\\n\\t\\tself.mh_dil_val.enabled = False\\r\\n\\t\\tself.mh_dil_val.value = 1\\r\\n\\t\\tself.mh_dil_start.value = 0.01\\r\\n\\t\\tself.mh_dil_stop.value = 1\\r\\n\\t\\tself.mh_dil_step.value = 0.1\\r\\n\\t\\tself.mh_dil_start.enabled = False\\r\\n\\t\\tself.mh_dil_stop.enabled = False\\r\\n\\t\\tself.mh_dil_step.enabled = False\\r\\n\\t\\tself.transform.enabled = False\\r\\n\\t\\tself.size_of_the_cell.enabled = False\\r\\n\\t\\tself.size_of_the_cell.value = 1\\r\\n\\t\\tself.output_sim_matrix.enabled = False\\r\\n\\t\\tself.output_table.enabled = False\\r\\n\\t\\tself.output_raster_workspace.enabled = False\",\n \"def __init__(self):\\n self._config = copy.copy(self._parameters)\",\n \"def __init__(self):\\r\\n\\r\\n super(Metallized, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.spec_sheet = 0\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\",\n \"def __init__(self, portname, devicetype):\\n if devicetype == DEVICE_DEBUG_BOARD:\\n self.device = debugbox.DebugBox(portname)\\n elif devicetype == DEVICE_SFP_BREAKOUT:\\n self.device = sfpbreakout.SFP(portname)\\n elif devicetype == DEVICE_DUMMY_BOARD:\\n self.device = dummybox.DummyBox(portname)\\n else:\\n raise IOError(\\\"Invalid Device Type\\\")\\n \\n # Set up laser sections\\n self.mirror1 = Testrig.LaserSection(self, 814, 10, 90, 728, 844, 860, 864)\\n self.laser_phase = Testrig.LaserSection(self, 810, 11, 20, 726, 846, 858, 866)\\n self.gain = Testrig.LaserSection(self, 818, 12, 180, 730, 848, None, None)\\n# self.gain1.validator.setTop(150)\\n self.mirror2 = Testrig.LaserSection(self, 826, 14, 90, 734, 850, 862, 864)\\n# self.front.validator.setTop(60)\\n self.soa1 = Testrig.LaserSection(self, 822, 15, 300, 736, 852, None, None)\\n# self.soa1.validator.setTop(150)\\n self.soa2 = Testrig.LaserSection(self, 830, 16, 100, 738, 854, None, None, self.to_display_current_section2, self.to_internal_current_section2)\\n self.phase1 = Testrig.LaserSection(self, 834, 13, 100, 732, 856, None, None, self.to_display_current_section2, self.to_internal_current_section2)\\n \\n self.voltage_max = 2.5 # This is now a constant\\n \\n self.full_rig = True\",\n \"def __init__(self, world_map, GRID_LOCK, coordinates=None):\\n\\n ''' Take parameters, and Sprite Constants '''\\n super(BeesSprite, self).__init__(world_map, BeesSprite.IMAGE, GRID_LOCK,\\n BeesSprite.HEALTH_BAR, BeesSprite.AVG_SPEED,\\n BeesSprite.VISION, coordinates)\\n\\n self.type = \\\"bees\\\"\\n self.prey = [\\\"plant\\\"]\",\n \"def __init__(self):\\n # Bildschirmeinstellungen\\n self.screen_width = 1200\\n self.screen_height = 800\\n self.bg_color = (230, 230, 230)\\n # schiffeinstellungen\\n self.ship_limit = 3\\n # Geschosseinstellungen\\n self.bullet_width = 3\\n self.bullet_height = 15\\n self.bullet_color = (60, 60, 60)\\n self.bullets_allowed = 3\\n # Invasionsiffeinstellung\\n self.fleet_drop_speed = 7 # og drop 10\\n # Stärke der Beschleunigung des Spiels\\n self.speedup_scale = 1.05\\n # Stärke der Punktewerterhöhung bei Treffern\\n self.score_scale = 1.5\\n\\n self.initialize_dynamic_settings()\",\n \"def __init__(self, config, set_name, preprocess_image):\\n\\t\\t\\tself.data_dir = config['data_dir']\\n\\t\\t\\tself.set_name = set_name\\n\\t\\t\\tself.coco = COCO(os.path.join(self.data_dir, 'annotations', 'instances_' + set_name + '.json'))\\n\\t\\t\\tself.image_ids = self.coco.getImgIds()\\n\\t\\t\\tself.mask = config['mask']\\n\\n\\t\\t\\tself.load_classes()\\n\\n\\t\\t\\tsuper(CocoGenerator, self).__from_config__(config, preprocess_image=preprocess_image)\",\n \"def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\\\"DFTCL_settings\\\"):\\n\\n cl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": magmom, \\\"SAXIS\\\": None, \\\"LSORBIT\\\": None, \\\"LNONCOLLINEAR\\\": None}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIMX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"encut\\\", encut)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67734075","0.6454328","0.63851225","0.63834906","0.63531744","0.6349419","0.6346132","0.6324723","0.63021517","0.6280164","0.623309","0.6228021","0.6225777","0.6223605","0.62195665","0.62195665","0.6196393","0.61835295","0.61778045","0.6167447","0.614865","0.61357284","0.61340714","0.6132265","0.6125323","0.61092716","0.61043096","0.6091107","0.6082826","0.60793394","0.6066803","0.6063257","0.60628355","0.6050973","0.60487056","0.60466194","0.6043847","0.6033339","0.60316736","0.6007557","0.6006504","0.5992785","0.5991358","0.5990173","0.59888005","0.5983155","0.5981987","0.5977569","0.59744865","0.5955753","0.59516984","0.59357107","0.59342074","0.5926217","0.5925604","0.59201115","0.5901312","0.59011275","0.5900281","0.5898342","0.5895929","0.5889855","0.58896244","0.5885545","0.5876006","0.5874434","0.5872326","0.5869426","0.586883","0.5864588","0.5861911","0.5860412","0.58584666","0.5856277","0.5852469","0.5850696","0.58442646","0.583735","0.58368707","0.5835408","0.58353364","0.583164","0.58269894","0.58247507","0.5814648","0.5811417","0.5805989","0.5804829","0.5803752","0.58036345","0.5801321","0.5800216","0.5799968","0.57991797","0.57871765","0.57817817","0.57792926","0.577539"],"string":"[\n \"0.67734075\",\n \"0.6454328\",\n \"0.63851225\",\n \"0.63834906\",\n \"0.63531744\",\n \"0.6349419\",\n \"0.6346132\",\n \"0.6324723\",\n \"0.63021517\",\n \"0.6280164\",\n \"0.623309\",\n \"0.6228021\",\n \"0.6225777\",\n \"0.6223605\",\n \"0.62195665\",\n \"0.62195665\",\n \"0.6196393\",\n \"0.61835295\",\n \"0.61778045\",\n \"0.6167447\",\n \"0.614865\",\n \"0.61357284\",\n \"0.61340714\",\n \"0.6132265\",\n \"0.6125323\",\n \"0.61092716\",\n \"0.61043096\",\n \"0.6091107\",\n \"0.6082826\",\n \"0.60793394\",\n \"0.6066803\",\n \"0.6063257\",\n \"0.60628355\",\n \"0.6050973\",\n \"0.60487056\",\n \"0.60466194\",\n \"0.6043847\",\n \"0.6033339\",\n \"0.60316736\",\n \"0.6007557\",\n \"0.6006504\",\n \"0.5992785\",\n \"0.5991358\",\n \"0.5990173\",\n \"0.59888005\",\n \"0.5983155\",\n \"0.5981987\",\n \"0.5977569\",\n \"0.59744865\",\n \"0.5955753\",\n \"0.59516984\",\n \"0.59357107\",\n \"0.59342074\",\n \"0.5926217\",\n \"0.5925604\",\n \"0.59201115\",\n \"0.5901312\",\n \"0.59011275\",\n \"0.5900281\",\n \"0.5898342\",\n \"0.5895929\",\n \"0.5889855\",\n \"0.58896244\",\n \"0.5885545\",\n \"0.5876006\",\n \"0.5874434\",\n \"0.5872326\",\n \"0.5869426\",\n \"0.586883\",\n \"0.5864588\",\n \"0.5861911\",\n \"0.5860412\",\n \"0.58584666\",\n \"0.5856277\",\n \"0.5852469\",\n \"0.5850696\",\n \"0.58442646\",\n \"0.583735\",\n \"0.58368707\",\n \"0.5835408\",\n \"0.58353364\",\n \"0.583164\",\n \"0.58269894\",\n \"0.58247507\",\n \"0.5814648\",\n \"0.5811417\",\n \"0.5805989\",\n \"0.5804829\",\n \"0.5803752\",\n \"0.58036345\",\n \"0.5801321\",\n \"0.5800216\",\n \"0.5799968\",\n \"0.57991797\",\n \"0.57871765\",\n \"0.57817817\",\n \"0.57792926\",\n \"0.577539\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60791457"},"document_rank":{"kind":"string","value":"32"}}},{"rowIdx":22392,"cells":{"query":{"kind":"string","value":"Constructor that takes the configuration for the plate"},"document":{"kind":"string","value":"def __init__(self, config):\n self.config = config"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0","def __init__():\n self.placa = placa","def __init__(self, projection=None, transform=ccrs.PlateCarree(),\n figure_kws=dict(), drainage_baisins=True):\n self.proj = projection\n self.transform = transform\n self.fig_kws = figure_kws\n self.drainage_baisins = drainage_baisins","def __init__(self):\r\n config = ConfigProvider().getProcessingConfig()\r\n self.xGround = config.get(\"xGround\")\r\n self.yGround = config.get(\"yGround\")","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n pi0eeg_name = '{0}Pi0EEG'.format(name)\n \n d2pipi0eeg_name = '{0}D2PiPi0EEG'.format(name) \n d2kpi0eeg_name = '{0}D2KPi0EEG'.format(name)\n\n d02pi0pi0eeg_name = '{0}D02Pi0Pi0EEG'.format(name) \n dst2d0pieeg_name = '{0}Dst2D0PiEEG'.format(name)\n\n self.selPi0EEG = makeEEGResonance(\n pi0eeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.Pi0EEG\n )\n \n self.selD2PiPi0EEG = makeD(\n d2pipi0eeg_name,\n config,\n inputSel=[self.selPi0EEG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPi0,\n useBachelorPID=False\n )\n\n usePIDforKaon = False\n if (config['Bachelor_PIDK_MIN'] > -10):\n usePIDforKaon = True\n else :\n usePIDforKaon = False\n \n self.selD2KPi0EEG = makeD(\n d2kpi0eeg_name, \n config,\n inputSel=[self.selPi0EEG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPi0,\n useBachelorPID=usePIDforKaon\n )\n\n self.selD02Pi0Pi0EEG = makeD(\n d02pi0pi0eeg_name, \n config,\n inputSel=[self.selPi0EEG],\n decDescriptors=self.D02Pi0Pi0,\n useBachelorPID=False,\n )\n\n self.selDst2D0PiEEG = makeDstar(\n dst2d0pieeg_name,\n config,\n inputSel=[self.selD02Pi0Pi0EEG, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.line_D2PiPi0EEG = make_line(\n self,\n '{0}Line'.format(d2pipi0eeg_name),\n prescale=config['PrescaleD2PiPi0EEG'],\n postscale=config['PostscaleD2PiPi0EEG'],\n selection=self.selD2PiPi0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KPi0EEG = make_line(\n self,\n '{0}Line'.format(d2kpi0eeg_name),\n prescale=config['PrescaleD2KPi0EEG'],\n postscale=config['PostscaleD2KPi0EEG'],\n selection=self.selD2KPi0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_Dst2D0PiEEG = make_line(\n self,\n '{0}Line'.format(dst2d0pieeg_name),\n prescale=config['PrescaleDst2D0PiEEG'],\n postscale=config['PostscaleDst2D0PiEEG'],\n selection=self.selDst2D0PiEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n self._read_calibration_data()\n self.configure_sensor(\n TemperatureOversamplings.x08,\n PressureOversamplings.x16,\n HumidityOversamplings.x08,\n IIRFilterCoefficients.FC_003,\n 250,\n 250)","def __init__(self, cfg):\r\n\r\n\t\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\r\n\t\tanchor_config = cfg.MODEL.ANCHORS\r\n\t\tself.feature_maps = anchor_config.FEATURE_MAPS\r\n\t\tself.min_sizes = anchor_config.MIN_SIZES\r\n\t\tself.max_sizes = anchor_config.MAX_SIZES \r\n\t\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\r\n\t\tself.clip = anchor_config.CLIP","def __init__(self, name: str = None, *plates: Plate):\r\n super().__init__()\r\n self.name = name or f\"{self.__class__.__name__.lower()}_{self.id}\"\r\n self.plates = plates","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n phi3hm_name = '{0}Phi3HM'.format(name)\n phi3hr_name = '{0}Phi3HR'.format(name)\n \n d2piphi3hm_name = '{0}D2PiPhi3HM'.format(name)\n d2piphi3hr_name = '{0}D2PiPhi3HR'.format(name)\n \n d2kphi3hm_name = '{0}D2KPhi3HM'.format(name)\n d2kphi3hr_name = '{0}D2KPhi3HR'.format(name)\n \n\n self.selPhi3HM = makeResonance(\n phi3hm_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\n decDescriptors=self.Phi3H\n )\n\n self.selPhi3HR = makeResonance(\n phi3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\n decDescriptors=self.Phi3H\n )\n \n self.selD2PiPhi3HM = makeD(\n d2piphi3hm_name,\n config,\n inputSel=[self.selPhi3HM, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPhi,\n useBachelorPID=False,\n )\n \n self.selD2KPhi3HM = makeD(\n d2kphi3hm_name,\n config,\n inputSel=[self.selPhi3HM, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPhi,\n useBachelorPID=False,\n )\n \n self.selD2PiPhi3HR = makeD(\n d2piphi3hr_name,\n config,\n inputSel=[self.selPhi3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiPhi,\n useBachelorPID=False,\n )\n \n self.selD2KPhi3HR = makeD(\n d2kphi3hr_name,\n config,\n inputSel=[self.selPhi3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KPhi,\n useBachelorPID=False,\n )\n \n self.line_D2PiPhi3HM = make_line(\n self,\n name='{0}Line'.format(d2piphi3hm_name),\n prescale=config['PrescaleD2PiPhi3HM'],\n postscale=config['PostscaleD2PiPhi3HM'],\n selection=self.selD2PiPhi3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2PiPhi3HR = make_line(\n self,\n name='{0}Line'.format(d2piphi3hr_name),\n prescale=config['PrescaleD2PiPhi3HR'],\n postscale=config['PostscaleD2PiPhi3HR'],\n selection=self.selD2PiPhi3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n self.line_D2KPhi3HM = make_line(\n self,\n name='{0}Line'.format(d2kphi3hm_name),\n prescale=config['PrescaleD2KPhi3HM'],\n postscale=config['PostscaleD2KPhi3HM'],\n selection=self.selD2KPhi3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KPhi3HR = make_line(\n self,\n name='{0}Line'.format(d2kphi3hr_name),\n prescale=config['PrescaleD2KPhi3HR'],\n postscale=config['PostscaleD2KPhi3HR'],\n selection=self.selD2KPhi3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, config, well, directory):\n self.config = config\n self.well = well\n self.directory = directory","def __init__(self, config_file = 'config.yaml'):\n\n self.name = ''\n self.img_dir = ''\n self.out_dir = ''\n self.cam_file = ''\n self.options_file = ''\n self.output_xml_file = ''\n\n # If there is an options file, it will overwrite the defaults \n if config_file is not None:\n self.load(config_file)","def __init__(self, config):\n super().__init__(config)\n self.collector_host = config.get(\"collector_host\")\n self.schedds = config.get(\"schedds\", [None])\n self.condor_config = config.get(\"condor_config\")\n self.constraint = config.get(\"constraint\", True)\n self.classad_attrs = config.get(\"classad_attrs\")\n self.correction_map = config.get(\"correction_map\")","def __init__(self):\n\n super().__init__()\n\n self.active = True\n self.driver = Driver.instance()\n self.sensor_manager = SensorManager.instance()\n\n self.pwm = Adafruit_PCA9685.PCA9685(address=0x40, busnum=1) # create PCA9685-object at I2C-port\n self.pwm.set_pwm_freq(50)\n\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(20, GPIO.OUT)\n GPIO.setup(21, GPIO.OUT)\n GPIO.setup(26, GPIO.OUT)\n self.driven_distance = 0","def __init__(self, config):\n self.cfg = config\n self.var_combinations = [\"tas:tas\", \"pr:pr\", \"pr:tas\"]\n self.seasons = [\"jja\", \"djf\", \"annual\"]\n self.projects = [\"cmip5\", \"cmip6\"]\n self.variables = [\"tas\", \"pr\"]\n self.scenarios = [\"26\", \"45\", \"85\"]\n\n # generate list of candidate bound limits\n small = np.arange(0.1, 1, 0.1)\n medium = np.arange(1, 11)\n high = np.arange(20, 100, 10)\n v_high = np.arange(150, 400, 50)\n self.bound_candidates = np.concatenate(\n (small, medium, high, v_high)) * 5 / 4","def __init__(self, config: str) -> None:\n self.configuration = config","def __init__(self, config: str) -> None:\n self.configuration = config","def __init__(self):\n self.risepower = 4.\n self.min_event_amplitude = 5.0e-12 # pA default\n self.template = None\n pass","def __init__(self, config: dict) -> None:\n super().__init__(config)","def __init__(self, name, i_node, j_node, m_node, n_node, t, material, model, kx_mod=1.0,\n ky_mod=1.0):\n\n self.name = name\n self.ID = None\n self.type = 'Rect'\n\n self.i_node = i_node\n self.j_node = j_node\n self.m_node = m_node\n self.n_node = n_node\n\n self.t = t\n \n self.kx_mod = kx_mod\n self.ky_mod = ky_mod\n\n self.pressures = [] # A list of surface pressures [pressure, case='Case 1']\n\n # Plates need a link to the model they belong to\n self.model = model\n\n # Get material properties for the plate from the model\n try:\n self.E = self.model.Materials[material].E\n self.nu = self.model.Materials[material].nu\n except:\n raise KeyError('Please define the material ' + str(material) + ' before assigning it to plates.')","def __init__(self, config_file=None, cam_prefix=\"rc\", serial_number=\"\",\n output_dir=\"\", parseport=5001,\n force_serial=True, set_temperature=-40, send_to_remote=False,\n remote_config='nemea.config.json'):\n\n # Load the default parameters from config file\n if not config_file:\n config_file = os.path.join(SR, 'config',\n '%s_config.yaml' % cam_prefix)\n with open(config_file) as df:\n camera_params = yaml.load(df, Loader=yaml.FullLoader)\n self.__dict__.update(camera_params['default'].items())\n\n self.camPrefix = cam_prefix\n self.outputDir = output_dir\n self.forceSerial = force_serial\n self.setTemperature = set_temperature\n self.ExposureTime = 0\n self.lastExposed = None\n self.opt = None\n self.parseport = parseport\n self.send_to_remote = send_to_remote\n if self.send_to_remote:\n self.transfer = None #transfer(**params)\n self.lastError = \"\"","def __init__(self):\n GPIO.setwarnings(False)\n GPIO.cleanup() # Reset the high and low levels of the GPIO port\n #The following code defines the GPIO used to control the L298N chip. This definition is different for different Raspberry Pi driver boards.\n self.Motor_A_EN = 17\n self.Motor_B_EN = 4\n self.Motor_A_Pin1 = 27\n self.Motor_A_Pin2 = 18\n self.Motor_B_Pin1 = 21\n self.Motor_B_Pin2 = 26\n self.setup()","def __init__(self, config=None):\n config_dict = {}\n if config:\n config_dict = json.load(config)\n\n self.android = config_dict.get(\"android\")\n self.linux = config_dict.get(\"linux\")\n self.atf = config_dict.get(\"atf\")\n self.qemu = config_dict.get(\"qemu\", \"qemu-system-aarch64\")","def __init__(self, config):\n super().__init__(config)\n\n # Prepare the timer.\n self.timer = 0\n\n # Set the current player index.\n self.current_player_index = 0\n # If we are the client, the server goes first.\n for i in range(len(sys.argv)):\n if sys.argv[i] == \"--client\":\n self.current_player_index = 1\n\n # Prepare the phase counter.\n self.__current_phase = Game.PHASE_PREPARE\n # Prepare the shot location store.\n self.__current_fire_location = None\n self.__current_fire_effect = None","def __init__(self, config=None):\n pass","def __init__(self, make, model, year):\r\n super().__init__(make, model, year)\r\n self.battery_size = 70\r\n # self.autopilot = autopilot\r","def __init__(self):\n self._read_calibration_data()\n self.set_oversamplings_and_mode(\n HumidityOversampling.x08,\n TemperatureOversampling.x08,\n PressureOversampling.x16,\n SensorMode.Normal)\n self.set_config(\n InactiveDuration.ms1000,\n FilterCoefficient.fc04)","def __init__(self, configuration):\n self._config = configuration","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n d0eeg_name = '{0}D0EEG'.format(name)\n dst2pid0eeg_name = '{0}Dst2PiD0EEG'.format(name)\n \n d0ee_name = '{0}D0EE'.format(name)\n dst2pid0ee_name = '{0}Dst2PiD0EE'.format(name)\n\n self.selD0EEG = makeEEGResonance(\n d0eeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.D0EEG\n ) \n\n self.selD0EE = makeEEResonance(\n d0ee_name, \n config,\n inputSel=[StdAllLooseElectrons],\n decDescriptors=self.D0EE\n )\n \n self.selDst2PiD0EEG = makeDstar(\n dst2pid0eeg_name,\n config,\n inputSel=[self.selD0EEG, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.selDst2PiD0EE = makeDstar(\n dst2pid0ee_name,\n config,\n inputSel=[self.selD0EE, StdAllNoPIDsPions],\n decDescriptors=self.Dst2D0Pi,\n )\n\n self.line_Dst2PiD0EEG = make_line(\n self,\n '{0}Line'.format(dst2pid0eeg_name),\n prescale=config['PrescaleDst2PiD0EEG'],\n postscale=config['PostscaleDst2PiD0EEG'],\n selection=self.selDst2PiD0EEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_Dst2PiD0EE = make_line(\n self,\n '{0}Line'.format(dst2pid0ee_name),\n prescale=config['PrescaleDst2PiD0EE'],\n postscale=config['PostscaleDst2PiD0EE'],\n selection=self.selDst2PiD0EE,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, config, sensor=None):\n if sensor is None:\n from monitor.sensor import SensorDriver\n self.sensor = SensorDriver(config.getint(CONFIG_SECTION, \"trigger_pin\"),\n config.getint(CONFIG_SECTION, \"echo_pin\"))\n else:\n self.sensor = sensor\n self.num_samples = config.getint(CONFIG_SECTION, \"num_samples\")\n self.drop_extremes = config.getboolean(CONFIG_SECTION, \"drop_extremes\")\n self.sample_delay = config.getfloat(CONFIG_SECTION, \"sample_delay\")\n self.is_running = False\n self.dist_to_bottom = config.getfloat(CONFIG_SECTION, \"distance_to_bottom\")","def __init__(self, config_directory, scale_override=None):\n self._config_directory = config_directory\n\n self._config_detector = DetectorConfig(config_directory)\n self._config_align = AlignConfig(config_directory, scale_override=scale_override)\n self._config_crystal = CrystalMatchConfig(config_directory)","def __init__(self, config):\n\n self.config = config","def __init__(self, config: Dict[str, Any]) -> None:\n self.config = config","def __init__(self, config):\n self.name = \"leduc-holdem\"\n self.game = LeducholdemGame()\n super().__init__(config)\n self.actions = [\"call\", \"raise\", \"fold\", \"check\"]\n self.state_shape = [36]\n\n with open(\n os.path.join(rlcard.__path__[0], \"games/leducholdem/card2index.json\"), \"r\"\n ) as file:\n self.card2index = json.load(file)","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n etappg_name = '{0}EtaPPG'.format(name)\n eta3hr_name = '{0}Eta3HR'.format(name)\n eta3hm_name = '{0}Eta3HM'.format(name)\n \n d2pietappg_name = '{0}D2PiEtaPPG'.format(name)\n d2pieta3hr_name = '{0}D2PiEta3HR'.format(name)\n d2pieta3hm_name = '{0}D2PiEta3HM'.format(name)\n \n d2ketappg_name = '{0}D2KEtaPPG'.format(name)\n d2keta3hr_name = '{0}D2KEta3HR'.format(name)\n d2keta3hm_name = '{0}D2KEta3HM'.format(name)\n\n self.selEtaPPG = makeResonance(\n etappg_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\n decDescriptors=self.EtaPPG\n )\n\n self.selEta3HR = makeResonance(\n eta3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\n decDescriptors=self.Eta3H\n )\n\n self.selEta3HM = makeResonance(\n eta3hm_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\n decDescriptors=self.Eta3H\n )\n\n self.selD2PiEtaPPG = makeD(\n d2pietappg_name,\n config,\n inputSel=[self.selEtaPPG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False,\n )\n\n self.selD2KEtaPPG = makeD(\n d2ketappg_name,\n config,\n inputSel=[self.selEtaPPG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=False,\n )\n \n self.selD2PiEta3HR = makeD(\n d2pieta3hr_name,\n config,\n inputSel=[self.selEta3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False,\n )\n\n self.selD2KEta3HR = makeD(\n d2keta3hr_name,\n config,\n inputSel=[self.selEta3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=False,\n )\n \n self.selD2PiEta3HM = makeD(\n d2pieta3hm_name,\n config,\n inputSel=[self.selEta3HM, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False,\n )\n \n\n self.selD2KEta3HM = makeD(\n d2keta3hm_name,\n config,\n inputSel=[self.selEta3HM, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=False,\n )\n \n\n self.line_D2PiEtaPPG = make_line(\n self,\n name='{0}Line'.format(d2pietappg_name),\n prescale=config['PrescaleD2PiEtaPPG'],\n postscale=config['PostscaleD2PiEtaPPG'],\n selection=self.selD2PiEtaPPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2PiEta3HM = make_line(\n self,\n name='{0}Line'.format(d2pieta3hm_name),\n prescale=config['PrescaleD2PiEta3HM'],\n postscale=config['PostscaleD2PiEta3HM'],\n selection=self.selD2PiEta3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2PiEta3HR = make_line(\n self,\n name='{0}Line'.format(d2pieta3hr_name),\n prescale=config['PrescaleD2PiEta3HR'],\n postscale=config['PostscaleD2PiEta3HR'],\n selection=self.selD2PiEta3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KEtaPPG = make_line(\n self,\n name='{0}Line'.format(d2ketappg_name),\n prescale=config['PrescaleD2KEtaPPG'],\n postscale=config['PostscaleD2KEtaPPG'],\n selection=self.selD2KEtaPPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEta3HM = make_line(\n self,\n name='{0}Line'.format(d2keta3hm_name),\n prescale=config['PrescaleD2KEta3HM'],\n postscale=config['PostscaleD2KEta3HM'],\n selection=self.selD2KEta3HM,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEta3HR = make_line(\n self,\n name='{0}Line'.format(d2keta3hr_name),\n prescale=config['PrescaleD2KEta3HR'],\n postscale=config['PostscaleD2KEta3HR'],\n selection=self.selD2KEta3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n self.__parameters: ConfigParams = ConfigParams()","def __init__(self):\n\n # self.threshold = 3.\n self.gamma_min = 3\n self.gamma_max = 12\n self.n_samples = 40\n # self.do_plots = False\n # self.do_albedo = True\n # self.verbose = True\n\n self.nbands = 7\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\n\n # Determine 250 or 500 meters product\n # self.resolution = 500\n\n # self.pixelWidth = 500\n # self.pixelHeight = 500","def __init__(self):\n\n super().__init__(\n dynamics_model=DoorDynamicsModel(),\n virtual_sensor_model=UnimodalVirtualSensorModel(\n virtual_sensor_model=[\n DoorVirtualSensorModel(modalities={\"image\"}),\n DoorVirtualSensorModel(modalities={\"pos\", \"sensors\"}),\n ],\n state_dim=3,\n ),\n )","def default_plate(self):\n self.name = \"Default Plate Armor\"\n self.rarity = \"Common\"\n self.pdef_value = 20\n self.mdef_value = 5\n self.increase_crit = 0\n self.desc = \"An old piece of iron armor, it's pretty heavy.\"","def __init__(self, footprint, mconf, name):\n logging.debug(\"%s entered __init__\" % name)\n self.image_id = mconf[\"image-id\"]\n self.networks = mconf[\"networks\"]\n self.ip_addresses = mconf[\"ip-addresses\"]\n self.flavor=mconf[\"flavor\"]\n self.machine_name = \"%s%s\" % (footprint.footprint_name, name)\n self.footprint = footprint","def __init__(\n self,\n hass,\n name: str,\n config: ConfigEntry,\n coordinator,\n file_path: str,\n ) -> None:\n super().__init__()\n\n self.hass = hass\n self._name = CAMERA_DATA[name][SENSOR_NAME]\n self._type = name\n self.check_file_path_access(file_path)\n self._file_path = file_path\n self._coordinator = coordinator\n self._host = config.data.get(CONF_HOST)\n self._unique_id = config.entry_id\n self._no_mail = (\n None\n if not config.data.get(CONF_CUSTOM_IMG)\n else config.data.get(CONF_CUSTOM_IMG_FILE)\n )","def _configure(self, config):\n self.friction_coef = config['friction_coef']\n self.num_cone_faces = config['num_cone_faces']\n self.num_samples = config['grasp_samples_per_surface_point']\n self.dir_prior = config['dir_prior']\n self.target_num_grasps = config['target_num_grasps']\n if self.target_num_grasps is None:\n self.target_num_grasps = config['min_num_grasps']\n\n self.min_contact_dist = config['min_contact_dist']\n self.num_grasp_rots = config['coll_check_num_grasp_rots']\n if 'max_num_surface_points' in config.keys():\n self.max_num_surface_points_ = config['max_num_surface_points']\n else:\n self.max_num_surface_points_ = 100","def __init__(self, parent):\n super(Demo5, self).__init__(parent)\n self.angle = 0.0\n self.replication = 1.0\n self.offset = 0.0\n self.deltaRep = 1\n self.revolution = 0\n self.stepsPer90 = 180\n self.stepsLeft = self.stepsPer90\n self.deltaAng = 90.0\n self.deltaOff = 0.15\n self.spin = True\n self.x2yAspect = 1.0\n self.texture = None","def __init__(self, config_file):\n Config = ConfigParser.ConfigParser()\n Config.read(config_file)\n \n self.port_id = Config.get(\"SerialPortSection\", \"ComPort\")\n self.baud_rate = Config.get(\"SerialPortSection\", \"BaudRate\")\n self. timeout = Config.get(\"SerialPortSection\", \"TimeOut\")\n self.config_file = config_file","def __init__(self, rings=False, branches=False):\n self.rings = rings\n self.branches = branches","def __init__(self, encut, name=\"relax_settings\"):\n\n ionic = {\"EDIFF\": 1E-17, \"NSW\": 20, \"IBRION\": 2,\"ISIF\": 2, \"ISYM\": -1, \"NBLOCK\": 1, \"KBLOCK\": 20}\n InputParameters.__init__(self, ionic_settings=ionic, name=name)\n self.update_electronic_sttings(\"ENCUT\", encut)","def __init__(self, hass, camera_entity, config, name):\n self.hass = hass\n self._camera_entity = camera_entity\n if name:\n self._name = name\n else:\n self._name = f\"SevenSegment OCR {split_entity_id(camera_entity)[1]}\"\n self._state = None\n\n self.filepath = os.path.join(\n self.hass.config.config_dir,\n \"ssocr-{}.png\".format(self._name.replace(\" \", \"_\")),\n )\n crop = [\n \"crop\",\n str(config[CONF_X_POS]),\n str(config[CONF_Y_POS]),\n str(config[CONF_WIDTH]),\n str(config[CONF_HEIGHT]),\n ]\n digits = [\"-d\", str(config.get(CONF_DIGITS, -1))]\n rotate = [\"rotate\", str(config[CONF_ROTATE])]\n threshold = [\"-t\", str(config[CONF_THRESHOLD])]\n extra_arguments = config[CONF_EXTRA_ARGUMENTS].split(\" \")\n\n self._command = (\n [config[CONF_SSOCR_BIN]]\n + crop\n + digits\n + threshold\n + rotate\n + extra_arguments\n )\n self._command.append(self.filepath)","def __init__(self, name):\n self.cloud = name\n self.cloudtype = \"multipass\"\n config = Config()\n self.default = config[f\"cloudmesh.volume.{self.cloud}.default\"]\n self.cm = CmDatabase()","def __init__(self) -> None:\n self.sensor = serial.Serial(config.DEVICE)\n super().__init__()","def __init__(self):\n #screen Settings\n self.screen_width = 1024\n self.screen_height = 768\n self.bg_color = (32, 32, 32)\n\n #rocket settings\n self.rocket_speed = 1\n\n #laser Settings\n self.laser_speed = 1.0\n self.laser_width = 3\n self.laser_height = 15\n self.laser_color = (0, 255, 255)\n self.lasers_allowed = 3","def __init__(self, config):\n super().__init__(config)\n\n # For NaMaster you need to pass the masks\n self.mask_files = self.config[\"tjpcov\"].get(\"mask_file\")\n self.mask_names = self.config[\"tjpcov\"].get(\"mask_names\")\n\n # Binning info is only needed if workspaces are not passed\n self.binning_info = self.config[\"tjpcov\"].get(\"binning_info\", None)\n\n # nside is needed if mask_files is a hdf5 file\n self.nside = self.config[\"tjpcov\"].get(\"nside\", None)\n\n # Read NaMaster specific options\n self.nmt_conf = self.config.get(\"NaMaster\", {})\n for k in [\"f\", \"w\", \"cw\"]:\n if k not in self.nmt_conf:\n self.nmt_conf[k] = {}\n\n # Read cache from input file. It will update the cache passed as an\n # argument of the different methods\n self.cache = self.config.get(\"cache\", {})","def __init__(self, name, type_name, thickness, grid_front_area, bundles_flow_area, grid_wetted_area, rods_wetted_at_grid_area, P_wet_at_grid, P_bundles, orientation):\n self.name = name\n self.type = type_name\n self.thickness = thickness\n self.length = self.thickness\n self.grid_front_area = grid_front_area\n self.bundles_flow_area = bundles_flow_area\n self.grid_wetted_area = grid_wetted_area\n self.rods_wetted_at_grid_area = rods_wetted_at_grid_area\n self.orientation = orientation\n self.P_wet_at_grid = P_wet_at_grid\n self.P_bundles = P_bundles","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n etaeeg_name = '{0}EtaEEG'.format(name)\n \n d2pietaeeg_name = '{0}D2PiEtaEEG'.format(name) \n d2ketaeeg_name = '{0}D2KEtaEEG'.format(name)\n\n self.selEtaEEG = makeEEGResonance(\n etaeeg_name, \n config,\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\n decDescriptors=self.EtaEEG\n )\n \n self.selD2PiEtaEEG = makeD(\n d2pietaeeg_name,\n config,\n inputSel=[self.selEtaEEG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEta,\n useBachelorPID=False\n )\n\n usePIDforKaon = False\n if (config['Bachelor_PIDK_MIN'] > -10):\n usePIDforKaon = True\n else :\n usePIDforKaon = False\n \n self.selD2KEtaEEG = makeD(\n d2ketaeeg_name, \n config,\n inputSel=[self.selEtaEEG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEta,\n useBachelorPID=usePIDforKaon\n )\n\n self.line_D2PiEtaEEG = make_line(\n self,\n '{0}Line'.format(d2pietaeeg_name),\n prescale=config['PrescaleD2PiEtaEEG'],\n postscale=config['PostscaleD2PiEtaEEG'],\n selection=self.selD2PiEtaEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n\n self.line_D2KEtaEEG = make_line(\n self,\n '{0}Line'.format(d2ketaeeg_name),\n prescale=config['PrescaleD2KEtaEEG'],\n postscale=config['PostscaleD2KEtaEEG'],\n selection=self.selD2KEtaEEG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self, **kwargs):\n super(VeryCleverBeamsplitter, self).__init__(**kwargs)\n self.shader_source = IL_SHADER_SOURCE\n self.centre = [0.5, 0.5]\n self.blazing_function = np.linspace(0,1,32)\n self.zernike_coefficients = np.zeros(12)","def __init__(self, car_reference, pt_reference):\n\n self.car_reference = car_reference\n self.pt_reference = pt_reference\n\n # We need to provide some mandatory information\n self.number_of_parameters = 3\n self.number_of_states = 2\n\n # We can provide some information that is passed to the output\n self.information = dict(problem = \"ModeShareProblem\")\n\n # We can provide the reference state. Currently, it is not used in any\n # optimizer, but it is helpful for plotting optimization results as\n # the existing plotting scripts will know how to use this information.\n self.reference_state = [self.car_reference, self.pt_reference]","def __init__(self):\n # Screen settings\n self.screen_width = 1860\n self.screen_height = 1020\n self.screen_size = self.screen_width, self.screen_height\n self.bg_color = 230, 230, 230\n\n # Ship static settings\n self.ship_limit = 3\n\n # Bullet static settings\n self.bullet_limit = 3\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = 60, 60, 60\n\n # Alien static settings\n self.fleet_drop_speed = 10\n\n self.speed_up_scale = 1.1\n self.initialize_dynamic_settings()","def __init__(self, pins, image):\n self._pins = pins\n self._image = image","def __init__(self):\n # Screen settings\n self.screen_width = 2400\n self.screen_height = 1600\n self.bg_color = (0, 0, 0)\n\n # Raindrop settings\n self.r_y_speed = 10","def __init__(self, planet, config=None, log=None, **kwargs):\n self.planet = planet.capitalize()\n self.log = logging.setup(log)\n\n if config is None or isinstance(config, str):\n from . import config as pcfg\n config = os.path.join(self.planet, config)\n config = pcfg.planetConfig(self.planet, configFile=config)\n config.update_config(**kwargs)\n self.config = config\n self.chem = None\n self.gas = []\n self.nGas = 0\n self.nConstituent = 0\n self.cloud = []\n self.nCloud = 0\n self.property = []\n if config.path not in sys.path:\n sys.path.insert(0, config.path)","def __init__(self, config, cfg):\n self.config = config\n self.cfg = cfg","def __init__(self, config):\n self.__config = config\n self.__logger = SLoggerHandler().getLogger(LoggerNames.EXPERIMENT_C)\n self.__num_gpus = ConfigProvider().get_config(\"controllerConfig.json\")[\"hardware\"][\"numGPUs\"]","def __init__(self, configfile_name):\n assert os.path.exists(configfile_name), \\\n 'Config file %s does not exist' % (configfile_name)\n\n correct_param_load = self.load_config_file(configfile_name)\n assert correct_param_load,\\\n 'Config params could not be loaded from file'\n\n self.fullpath_input_configfile = configfile_name\n self.word_det_rfc = None\n self.reg_coeffs = None\n self.bb_reg = None\n self.img_files = None","def __init__(self, address=0x68, config=0):\r\n\t\tself.i2c = FT232H.I2CDevice(ft232h, address)\r\n\t\tif config == 0:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=0)\r\n\t\t\tself.setScale(mode='GYR',scale=0)\r\n\t\telif config == 1:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=1)\r\n\t\t\tself.setScale(mode='GYR',scale=1)\t\t\t\t\r\n\t\telif config == 2:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=2)\r\n\t\t\tself.setScale(mode='GYR',scale=2)\t\r\n\t\telif config == 3:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=3)\r\n\t\t\tself.setScale(mode='GYR',scale=3)\t\t\t\t\r\n\t\telif config == 4:\r\n\t\t\tself.setWake()\r\n\t\t\tself.setScale(mode='ACC',scale=1)\t\r\n\t\t\tself.setTempDisable()\r\n\t\t\tself.setGYRStandby(axis='X')\r\n\t\t\tself.setGYRStandby(axis='Y')\r\n\t\t\tself.setGYRStandby(axis='Z')","def __init__(self,port=None,config=None):\n\n # Attempt to load a Dorna object with a configuration file, default to the desktop config if none is entered manually.\n\n self.WORKING_DIRECTORY = os.getcwd()\n self.FILE_DIRECTORY = os.path.dirname(__file__)\n self.DEFAULT_CONFIG = os.path.join(self.FILE_DIRECTORY, \"../configuration_store/config.yaml\")\n \n self.positionStore = PositionStore()\n \n config = config if config!= None else self.DEFAULT_CONFIG\n\n try:\n print(config)\n self.robot=Dorna(config)\n print(\"Loaded configuration file\")\n except:\n print(\"Failed to load configuration file, please re-instantiate the arm object with a valid config.\")","def __init__(self):\n self.config = {}","def __init__(self, config):\n try:\n config['volume_id']\n config['access_key']\n config['secret_access_key']\n config['region']\n except KeyError, e:\n logging.error(repr(e))\n raise ImproperlyConfigured()\n\n if not config.has_key('keep'):\n config['keep'] = 5\n\n self.config = config","def __init__(self, name, config):\n self.name = name\n self.config = config\n LineBuilder.__init__(self, name, config)\n \n etaprimeppg_name = '{0}EtaPrimePPG'.format(name)\n etaprime3hr_name = '{0}EtaPrime3HR'.format(name)\n \n d2pietaprimeppg_name = '{0}D2PiEtaPrimePPG'.format(name)\n d2pietaprime3hr_name = '{0}D2PiEtaPrime3HR'.format(name)\n \n d2ketaprimeppg_name = '{0}D2KEtaPrimePPG'.format(name)\n d2ketaprime3hr_name = '{0}D2KEtaPrime3HR'.format(name)\n \n self.selEtaPrimePPG = makeResonance(\n etaprimeppg_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\n decDescriptors=self.EtaPrimePPG\n )\n \n self.selEtaPrime3HR = makeResonance(\n etaprime3hr_name,\n config,\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedEta],\n decDescriptors=self.EtaPrime3H\n )\n \n self.selD2PiEtaPrimePPG = makeD(\n d2pietaprimeppg_name,\n config,\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEtaPrime,\n useBachelorPID=False,\n )\n \n self.selD2KEtaPrimePPG = makeD(\n d2ketaprimeppg_name,\n config,\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEtaPrime,\n useBachelorPID=False,\n )\n \n self.selD2PiEtaPrime3HR = makeD(\n d2pietaprime3hr_name,\n config,\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsPions],\n decDescriptors=self.D2PiEtaPrime,\n useBachelorPID=False, \n )\n \n self.selD2KEtaPrime3HR = makeD(\n d2ketaprime3hr_name,\n config,\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsKaons],\n decDescriptors=self.D2KEtaPrime,\n useBachelorPID=False,\n )\n \n self.line_D2PiEtaPrimePPG = make_line(\n self,\n name='{0}Line'.format(d2pietaprimeppg_name),\n prescale=config['PrescaleD2PiEtaPrimePPG'],\n postscale=config['PostscaleD2PiEtaPrimePPG'],\n selection=self.selD2PiEtaPrimePPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2PiEtaPrime3HR = make_line(\n self,\n name='{0}Line'.format(d2pietaprime3hr_name),\n prescale=config['PrescaleD2PiEtaPrime3HR'],\n postscale=config['PostscaleD2PiEtaPrime3HR'],\n selection=self.selD2PiEtaPrime3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEtaPrimePPG = make_line(\n self,\n name='{0}Line'.format(d2ketaprimeppg_name),\n prescale=config['PrescaleD2KEtaPrimePPG'],\n postscale=config['PostscaleD2KEtaPrimePPG'],\n selection=self.selD2KEtaPrimePPG,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )\n \n self.line_D2KEtaPrime3HR = make_line(\n self,\n name='{0}Line'.format(d2ketaprime3hr_name),\n prescale=config['PrescaleD2KEtaPrime3HR'],\n postscale=config['PostscaleD2KEtaPrime3HR'],\n selection=self.selD2KEtaPrime3HR,\n HLT1=config['Hlt1Filter'],\n HLT2=config['Hlt2Filter']\n )","def __init__(self):\n self.plant_names = list(PLANT_TYPES.keys()) #: list of str: available plant type names.\n self.plant_types = list(PLANT_TYPES.items()) #: list of dicts: model parameters for plant types.\n self.num_plant_types = len(PLANT_TYPES) #: int: amount unique plant types in garden.\n self.plant_centers = [] #: location of plants in garden\n self.non_plant_centers = [] #: additional coordinates without plants\n self.plant_in_bounds = 0 #: int: count of plant centers in bound.","def __init__(self):\n super().__init__()\n\n # Gadget state\n \n self.isDoorOpen = False\n self.verified = True\n\n # Ev3dev initialization\n self.leds = Leds()\n self.sound = Sound()\n self.drive = MoveTank(OUTPUT_B, OUTPUT_C)\n \n self.ir_sensor = InfraredSensor()\n self.ir_sensor.mode = self.ir_sensor.MODE_IR_REMOTE\n self.color_sensor = ColorSensor()\n self.color_sensor.mode = 'COL-COLOR' # WHITE\n\n # Start threads\n threading.Thread(target=self._patrol_thread, daemon=True).start()","def __init__(self, name: str, instance: PartInstance, controlPoints: ModelDotArray):\n super().__init__()\n pass","def __init__(self):\n if not os.path.isfile(CONFIG_FILENAME):\n first_time_run()\n raise SystemExit()\n\n \"\"\"\n Init file is present, read and parse it:\n \"\"\"\n conf = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation(), inline_comment_prefixes='#')\n conf.read(CONFIG_FILENAME)\n \"\"\"\n Process certain paths:\n \"\"\"\n\n path = conf['Paths']\n self.ffc_dir = path['FlatFieldCalDir']\n self.capture_dir = path['CaptureDir']\n self.image_dir = path['ImageDir']\n\n \"\"\"\n Process options\n \"\"\"\n\n self.cal_auto_save = conf.getboolean('Options', 'CalAutoSave', fallback=True)\n self.cal_auto_load = conf.getboolean('Options', 'CalAutoLoad', fallback=True)\n self.sound_on_capture = conf.getboolean('Options', 'SoundOnCapture', fallback=True)\n self.exp_init1 = conf.getint('Options', 'ExpInit1', fallback=100)\n self.exp_init2 = conf.getint('Options', 'ExpInit2', fallback=100)\n self.black_correct = conf.getboolean('Options', 'BlackCorrect', fallback=True)\n # Setup square window, default of full-screen height\n self.tiff_seq_x_window = conf.getint('Options', 'TiffSeqXWindow', fallback=cameras.FRAME_HEIGHT)\n self.tiff_seq_y_window = conf.getint('Options', 'TiffSeqYWindow', fallback=cameras.FRAME_HEIGHT)\n self.tiff_seq_rebin = conf.getint('Options', 'TiffSeqRebin', fallback = 2)","def __init__(self, x, y , planet):\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.planet = planet\n\t\tself.battery = 100","def __init__(self, **kwargs):\n self.max_samples = get_value(kwargs, 'max_samples', types=[int], range_min=0, default_value=0)\n self.obs_filename = get_value(kwargs, 'lba_obs_file', types=[str, None], default_value=None)\n self.sample_rate = get_value(kwargs, 'sample_rate', types=[int, None], default_value=None)\n if self.sample_rate is None:\n LOG.warning(\"No sample rate provided, defaulting to {0}\".format(self._default_sample_rate))\n self.sample_rate = self._default_sample_rate\n self.antenna_name = get_value(kwargs, 'lba_antenna_name', types=[str, None], default_value=None)\n if self.obs_filename is not None and self.antenna_name is None:\n raise RuntimeError(\"LBA file is missing --lba_antenna_name parameter which is needed when using \"\n \"--lba_obs_file\")\n self.chunk_size = get_value(kwargs, 'chunk_size', types=[int], range_min=0, default_value=4096)","def __init__(self, pitch, color, our_side, video_port=0, comm_port='/dev/ttyACM0', penalty=False, comms=1):\n assert pitch in [0, 1]\n assert color in ['yellow', 'blue']\n assert our_side in ['left', 'right']\n\n self.pitch = pitch\n\n # Set up the Arduino communications\n self.arduino = Arduino(comm_port, 115200, 1, comms)\n\n # Set up camera for frames\n self.camera = Camera(port=video_port, pitch=self.pitch)\n frame = self.camera.get_frame()\n center_point = self.camera.get_adjusted_center(frame)\n\n # Set up vision\n self.calibration = tools.get_colors(pitch)\n self.vision = Vision(\n pitch=pitch, color=color, our_side=our_side,\n frame_shape=frame.shape, frame_center=center_point,\n calibration=self.calibration)\n\n # Set up postprocessing for vision\n self.postprocessing = Postprocessing(our_side)\n\n # Set up main planner\n self.planner = Planner(our_side=our_side, pitch_num=self.pitch, isPenalty=penalty)\n\n # Set up GUI\n self.GUI = GUI(calibration=self.calibration, arduino=self.arduino, pitch=self.pitch)\n\n self.color = color\n self.side = our_side\n\n self.preprocessing = Preprocessing()\n\n self.robot = Robot_Controller()","def __init__(self, cpara, dz_soil):\n\n # --- grid ---\n self.z = np.linspace(0, cpara['grid']['zmax'], cpara['grid']['Nlayers']) # grid [m] above ground\n self.dz = self.z[1] - self.z[0] # gridsize [m]\n self.ones = np.ones(len(self.z)) # dummy\n\n # --- switches ---\n self.Switch_Eflow = cpara['ctr']['Eflow'] # True assumes constant U/ustar at upper boundary\n self.Switch_WMA = cpara['ctr']['WMA'] # True solves scalar profiles\n self.Switch_Ebal = cpara['ctr']['Ebal'] # True solves leaf energy balance\n\n logger.info('Eflow: %s, WMA: %s, Ebal: %s',\n self.Switch_Eflow,\n self.Switch_WMA,\n self.Switch_Ebal)\n\n # --- PlantTypes ---\n ptypes = []\n ptnames = list(cpara['planttypes'].keys())\n ptnames.sort()\n for pt in ptnames:\n ptypes.append(PlantType(self.z, cpara['planttypes'][pt], dz_soil, ctr=cpara['ctr'], loc=cpara['loc']))\n self.planttypes = ptypes\n \n # --- stand characteristics: sum over planttypes---\n\n # total leaf area index [m2 m-2]\n self.LAI = sum([pt.LAI for pt in self.planttypes])\n # total leaf area density [m2 m-3]\n self.lad = sum([pt.lad for pt in self.planttypes])\n\n # layerwise mean leaf characteristic dimension [m] for interception model\n self.leaf_length = sum([pt.leafp['lt'] * pt.lad for pt in self.planttypes]) / (self.lad + EPS)\n\n # root area density [m2 m-3]\n rad = np.zeros(np.shape(dz_soil))\n imax = 1\n for pt in self.planttypes:\n rad[:len(pt.Roots.rad)] += pt.Roots.rad\n imax = max(imax, len(pt.Roots.rad))\n \n self.ix_roots = np.array(range(imax)) # soil model layers corresponding to root zone\n self.rad = rad[self.ix_roots]\n \n # total root area index [m2 m-2]\n self.RAI = sum([pt.Roots.RAI for pt in self.planttypes])\n # distribution of roots across soil model layers [-]\n self.root_distr = self.rad * dz_soil[self.ix_roots] / (self.RAI + EPS)\n\n # canopy height [m]\n if len(np.where(self.lad > 0)[0]) > 0:\n f = np.where(self.lad > 0)[0][-1]\n self.hc = self.z[f].copy()\n else:\n self.hc = 0.0\n\n # --- create radiation, micromet, interception, and forestfloor model instances\n self.radiation = Radiation(cpara['radiation'], self.Switch_Ebal)\n\n self.micromet = Micromet(self.z, self.lad, self.hc, cpara['micromet'])\n\n self.interception = Interception(cpara['interception'], self.lad * self.dz)\n\n self.forestfloor = ForestFloor(cpara['forestfloor'])","def __init__(self, region):\r\n self.region = region","def __init__(self):\n\n self.Cp_air0 = config_earth.earth_properties['Cp_air0']\n self.Rsp_air = config_earth.earth_properties['Rsp_air']\n\n self.d = config_earth.balloon_properties['d']\n self.vol = math.pi*4/3*pow((self.d/2),3) #volume m^3\n self.surfArea = math.pi*self.d*self.d #m^2\n self.cs_area = math.pi*self.d*self.d/4.0 #m^2\n\n #self.emissEnv = config_earth.balloon_properties['emissEnv']\n self.areaDensityEnv = config_earth.balloon_properties['areaDensityEnv']\n self.mp = config_earth.balloon_properties['mp']\n self.mdot = 0\n self.massEnv = config_earth.balloon_properties['mEnv']\n self.Upsilon = config_earth.balloon_properties['Upsilon']\n\n self.vent = config_earth.simulation['vent']\n self.coord = config_earth.simulation['start_coord']\n self.t = config_earth.simulation['start_time']\n self.lat = math.radians(self.coord['lat'])\n self.Ls = self.t.timetuple().tm_yday\n self.min_alt = config_earth.simulation['min_alt']\n\n self.vm_coeff = .1 #virtual mass coefficient\n self.k = self.massEnv*config_earth.balloon_properties['cp'] #thermal mass coefficient\n\n self.dt = config_earth.dt","def __init__(self):\n super().__init__()\n self.location = 0.0\n self.scale = 1.0\n self.type = 'Laplace'\n self.distType = 'Continuous'\n self.hasInfiniteBound = True\n self.compatibleQuadrature.append('CDF')\n self.preferredQuadrature = 'CDF'\n self.preferredPolynomials = 'CDF'","def __init__(self, config, town):\n\t\tself.config = config","def initialize(self, **kwargs):\n\n # Defining the configuration object\n self.config = kwargs.get('config')","def __init__(self, filepath):\n try:\n config_file_r = open(filepath)\n self.sim_parametres = yaml.load(config_file_r, Loader=yaml.FullLoader)\n except:\n raise Exception(\"Le fichier de configuration n'a pas été atteint ou n'a pas pu être lu. Veuillez vérifier \"\n \"qu'il n'y ait aucune erreur de syntaxe.\")","def __init__(self, metadata, environment, component, image, version, s3_bucket, exec_env):\n self.environment = environment\n self.component = component\n self.s3_bucket = s3_bucket\n self.exec_env = exec_env\n self.image = image\n self.version = version\n self.metadata = metadata\n\n # generate Terragrunt config as part of object initialisation\n self.config()","def __init__(self,height=7,width=7):\n self.__cars = []\n self.__height = height\n self.__width = width","def initialize_from_config(self):","def __init__(self, name, properties):\n super(WiretapShotProcessorPreset, self).__init__(name, properties)\n \n # Necessary in order for Hiero to view this plugin as different from\n # the default shot processor\n self._parentType = WiretapShotProcessor","def __init__(self, config):\n raise NotImplementedError (\"This is just an abstract interface\")","def __init__(self):\n super(Config, self).__init__()\n self.run_control[\"output_dir\"] = os.getcwd()+\"/output/arctan_baseline/single_turn_injection/tracking\"\n ring_tof = 1149.185123\n field_files = glob.glob(\"output/arctan_baseline/bump_quest_v9/find_bump_r_*_theta_90/find_bump_parameters_001.out\")\n substitutions = config.get_baseline_substitution(0.22, ring_tof)\n substitutions = self.ramp_fields(\n substitutions,\n field_files,\n [i for i, f in enumerate(field_files)],\n ring_tof,\n will_step = False\n )\n self.substitution_list = [substitutions] # used for plotting unperturbed CO\n\n self.run_control[\"find_closed_orbits_4d\"] = False\n self.run_control[\"find_da\"] = False\n self.run_control[\"find_bump_parameters\"] = False\n self.run_control[\"track_bump\"] = False\n self.run_control[\"track_beam\"] = True\n self.find_closed_orbits[\"subs_overrides\"][\"__n_turns__\"] = 0.11\n self.find_closed_orbits[\"subs_overrides\"][\"__do_bump__\"] = False\n self.find_closed_orbits[\"final_subs_overrides\"].update(substitutions)\n self.find_closed_orbits[\"max_iterations\"] = 0\n self.find_closed_orbits[\"do_minuit\"] = True\n self.find_closed_orbits[\"minuit_iterations\"] = 10 \n self.find_closed_orbits[\"us_cell\"] = 0\n self.find_closed_orbits[\"ds_cell\"] = 1\n\n self.find_da = {\n \"run_dir\":\"tmp/find_da/\",\n \"probe_files\":\"RINGPROBE01.h5\",\n \"subs_overrides\":{\"__n_turns__\":101.1, \"__do_magnet_field_maps__\":\"False\", \"__step_size__\":0.01},\n \"get_output_file\":\"get_da\",\n \"scan_output_file\":\"scan_da\",\n \"row_list\":None,\n \"scan_x_list\":[],\n \"scan_y_list\":[],\n \"x_seed\":1.,\n \"y_seed\":1.,\n \"min_delta\":0.9,\n \"max_delta\":1000.,\n \"required_n_hits\":100,\n \"dt_tolerance\":0.5, # fraction of closed orbit dt\n \"max_iterations\":15,\n \"decoupled\":True,\n }\n\n max_amp = 0.01\n self.track_beam_dummy = {\n \"run_dir\":\"tmp/track_beam/\",\n \"save_dir\":\"track_beam\",\n \"print_events\":[0, 1, -1],\n \"settings\":[{\n \"name\":\"forwards\",\n \"direction\":\"forwards\",\n \"probe_files\":\"RINGPROBE01.h5\", \n \"beam\":{\n \"type\":\"beam_gen\",\n \"closed_orbit_file\":\"closed_orbits_cache\",\n \"eigen_emittances\":[[0, 0]]*3,#+[[max_amp, max_amp]],\n \"n_per_dimension\":2,\n \"variables\":[\"x\",\"x'\",\"y\",\"y'\"],\n \"amplitude_dist\":\"uniform\", #\"grid\", # \n \"phase_dist\":\"uniform\", #\"grid\", # \n \"max_amplitude_4d\":max_amp, # amplitude_dist != grid\n \"energy\":3.0,\n },\n \"subs_overrides\":{\n \"__n_turns__\":45.1,\n \"__hdf5__\":\"True\",\n \"__do_magnet_field_maps__\":\"False\",\n \"__do_bump__\":\"True\",\n \"__step_size__\":0.01\n },\n },],\n }\n\n T0 = ring_tof\n self.track_beam = {\n \"run_dir\":\"tmp/track_beam/\",\n \"save_dir\":\"track_beam_rf_on_2\",\n \"print_events\":[i for i in range(1)],\n \"variables\":[\"x\", \"x'\", \"y\", \"y'\", \"t\", \"energy\"],\n \"settings\":[{\n \"name\":\"grid\",\n \"direction\":\"forwards\",\n \"probe_files\":\"RINGPROBE01.h5\", \n \"beam\":{\n \"type\":\"grid\",\n \"energy\":3.0,\n \"start\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, 0.0, 941.272],\n \"stop\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, T0, 941.272],\n \"nsteps\":[1, 1, 1, 1, 4+1, 1],\n },\n \"subs_overrides\":{\n \"__n_turns__\":100.1,\n \"__hdf5__\":True,\n \"__do_magnet_field_maps__\":False,\n \"__do_bump__\":True,\n \"__do_rf__\":True,\n \"__do_foil__\":False,\n \"__step_size__\":0.01\n },\n },],\n }","def __init__(self, hass, config):\n super().__init__()\n self._parent = hass\n self.ezvizService = hass.data[PY_EZVIZ_GATEWAY]\n\n self.deviceSerial = self.ezvizService.deviceSerial\n\n self._interval_snapshots = 30\n self._last_image = \"\"\n self._last_snapshot_time = 0","def __init__(self):\n super().__init__()\n\n # Robot state\n self.ask_mode = False\n\n # Connect two large motors on output ports B and C\n self.sound = Sound()\n self.leds = Leds()\n self.p1 = TouchSensor(INPUT_1)\n self.p2 = TouchSensor(INPUT_2)\n self.p3 = TouchSensor(INPUT_3)\n self.p4 = TouchSensor(INPUT_4)","def __init__(self, board, sample_rate=1e6, bus_width=8):\n\n # Grab a reference to the board and its pattern-gen API.\n self.board = board\n self.api = board.apis.pattern_generator\n\n # Grab a reference to the user's bus parameters.\n self.sample_rate = int(sample_rate)\n self.bus_width = bus_width\n\n # FIXME: These should be read from the board, rather than hardcoded!\n self.upload_chunk_size = 2048\n self.samples_max = 32 * 1024","def __init__(self, parant):\n pass","def initializeParameters(self):\r\n\t\tself.input_raster.enabled = True\r\n\t\tself.approach.enabled = True\r\n\t\tself.predefined_pattern.enabled = False\r\n\t\tself.predefined_pattern.value = 'Mexican Hat wavelet'\r\n\t\tself.pattern_workspace.enabled = False\r\n\t\tself.point_matrix_size.enabled = False\r\n\t\tself.point_matrix_size.value = 3\r\n\t\tself.point_vectors.enabled = False\r\n\t\tself.mapping_field.enabled = False\r\n\t\tself.move_to_max.enabled = False\r\n\t\tself.move_to_max_distance.enabled = False\r\n\t\tself.move_to_max_distance.value = 3\r\n\t\tself.mh_iteration.enabled = False\r\n\t\tself.mh_dil_val.enabled = False\r\n\t\tself.mh_dil_val.value = 1\r\n\t\tself.mh_dil_start.value = 0.01\r\n\t\tself.mh_dil_stop.value = 1\r\n\t\tself.mh_dil_step.value = 0.1\r\n\t\tself.mh_dil_start.enabled = False\r\n\t\tself.mh_dil_stop.enabled = False\r\n\t\tself.mh_dil_step.enabled = False\r\n\t\tself.transform.enabled = False\r\n\t\tself.size_of_the_cell.enabled = False\r\n\t\tself.size_of_the_cell.value = 1\r\n\t\tself.output_sim_matrix.enabled = False\r\n\t\tself.output_table.enabled = False\r\n\t\tself.output_raster_workspace.enabled = False","def __init__(self):\r\n\r\n super(Metallized, self).__init__()\r\n\r\n # Initialize public scalar attributes.\r\n self.spec_sheet = 0\r\n if self.hazard_rate_type < 3: # MIL-HDBK-217\r\n self.reference_temperature = 358.0","def __init__(self, portname, devicetype):\n if devicetype == DEVICE_DEBUG_BOARD:\n self.device = debugbox.DebugBox(portname)\n elif devicetype == DEVICE_SFP_BREAKOUT:\n self.device = sfpbreakout.SFP(portname)\n elif devicetype == DEVICE_DUMMY_BOARD:\n self.device = dummybox.DummyBox(portname)\n else:\n raise IOError(\"Invalid Device Type\")\n \n # Set up laser sections\n self.mirror1 = Testrig.LaserSection(self, 814, 10, 90, 728, 844, 860, 864)\n self.laser_phase = Testrig.LaserSection(self, 810, 11, 20, 726, 846, 858, 866)\n self.gain = Testrig.LaserSection(self, 818, 12, 180, 730, 848, None, None)\n# self.gain1.validator.setTop(150)\n self.mirror2 = Testrig.LaserSection(self, 826, 14, 90, 734, 850, 862, 864)\n# self.front.validator.setTop(60)\n self.soa1 = Testrig.LaserSection(self, 822, 15, 300, 736, 852, None, None)\n# self.soa1.validator.setTop(150)\n self.soa2 = Testrig.LaserSection(self, 830, 16, 100, 738, 854, None, None, self.to_display_current_section2, self.to_internal_current_section2)\n self.phase1 = Testrig.LaserSection(self, 834, 13, 100, 732, 856, None, None, self.to_display_current_section2, self.to_internal_current_section2)\n \n self.voltage_max = 2.5 # This is now a constant\n \n self.full_rig = True","def __init__(self):\n self._config = copy.copy(self._parameters)","def __init__(self, world_map, GRID_LOCK, coordinates=None):\n\n ''' Take parameters, and Sprite Constants '''\n super(BeesSprite, self).__init__(world_map, BeesSprite.IMAGE, GRID_LOCK,\n BeesSprite.HEALTH_BAR, BeesSprite.AVG_SPEED,\n BeesSprite.VISION, coordinates)\n\n self.type = \"bees\"\n self.prey = [\"plant\"]","def __init__(self):\n # Bildschirmeinstellungen\n self.screen_width = 1200\n self.screen_height = 800\n self.bg_color = (230, 230, 230)\n # schiffeinstellungen\n self.ship_limit = 3\n # Geschosseinstellungen\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = (60, 60, 60)\n self.bullets_allowed = 3\n # Invasionsiffeinstellung\n self.fleet_drop_speed = 7 # og drop 10\n # Stärke der Beschleunigung des Spiels\n self.speedup_scale = 1.05\n # Stärke der Punktewerterhöhung bei Treffern\n self.score_scale = 1.5\n\n self.initialize_dynamic_settings()","def __init__(self, config, set_name, preprocess_image):\n\t\t\tself.data_dir = config['data_dir']\n\t\t\tself.set_name = set_name\n\t\t\tself.coco = COCO(os.path.join(self.data_dir, 'annotations', 'instances_' + set_name + '.json'))\n\t\t\tself.image_ids = self.coco.getImgIds()\n\t\t\tself.mask = config['mask']\n\n\t\t\tself.load_classes()\n\n\t\t\tsuper(CocoGenerator, self).__from_config__(config, preprocess_image=preprocess_image)","def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\"DFTCL_settings\"):\n\n cl_settings = {\"ISPIN\": 2, \"MAGMOM\": magmom, \"SAXIS\": None, \"LSORBIT\": None, \"LNONCOLLINEAR\": None}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIMX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"encut\", encut)"],"string":"[\n \"def __init__(self):\\n parameters_list = []\\n self.config_dict = self.open_config(parameters_list)\\n\\n # Define defaults\\n self.disc_gt = 0.0\\n self.disc_out = 0.0\",\n \"def __init__():\\n self.placa = placa\",\n \"def __init__(self, projection=None, transform=ccrs.PlateCarree(),\\n figure_kws=dict(), drainage_baisins=True):\\n self.proj = projection\\n self.transform = transform\\n self.fig_kws = figure_kws\\n self.drainage_baisins = drainage_baisins\",\n \"def __init__(self):\\r\\n config = ConfigProvider().getProcessingConfig()\\r\\n self.xGround = config.get(\\\"xGround\\\")\\r\\n self.yGround = config.get(\\\"yGround\\\")\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n pi0eeg_name = '{0}Pi0EEG'.format(name)\\n \\n d2pipi0eeg_name = '{0}D2PiPi0EEG'.format(name) \\n d2kpi0eeg_name = '{0}D2KPi0EEG'.format(name)\\n\\n d02pi0pi0eeg_name = '{0}D02Pi0Pi0EEG'.format(name) \\n dst2d0pieeg_name = '{0}Dst2D0PiEEG'.format(name)\\n\\n self.selPi0EEG = makeEEGResonance(\\n pi0eeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.Pi0EEG\\n )\\n \\n self.selD2PiPi0EEG = makeD(\\n d2pipi0eeg_name,\\n config,\\n inputSel=[self.selPi0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPi0,\\n useBachelorPID=False\\n )\\n\\n usePIDforKaon = False\\n if (config['Bachelor_PIDK_MIN'] > -10):\\n usePIDforKaon = True\\n else :\\n usePIDforKaon = False\\n \\n self.selD2KPi0EEG = makeD(\\n d2kpi0eeg_name, \\n config,\\n inputSel=[self.selPi0EEG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPi0,\\n useBachelorPID=usePIDforKaon\\n )\\n\\n self.selD02Pi0Pi0EEG = makeD(\\n d02pi0pi0eeg_name, \\n config,\\n inputSel=[self.selPi0EEG],\\n decDescriptors=self.D02Pi0Pi0,\\n useBachelorPID=False,\\n )\\n\\n self.selDst2D0PiEEG = makeDstar(\\n dst2d0pieeg_name,\\n config,\\n inputSel=[self.selD02Pi0Pi0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.line_D2PiPi0EEG = make_line(\\n self,\\n '{0}Line'.format(d2pipi0eeg_name),\\n prescale=config['PrescaleD2PiPi0EEG'],\\n postscale=config['PostscaleD2PiPi0EEG'],\\n selection=self.selD2PiPi0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KPi0EEG = make_line(\\n self,\\n '{0}Line'.format(d2kpi0eeg_name),\\n prescale=config['PrescaleD2KPi0EEG'],\\n postscale=config['PostscaleD2KPi0EEG'],\\n selection=self.selD2KPi0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_Dst2D0PiEEG = make_line(\\n self,\\n '{0}Line'.format(dst2d0pieeg_name),\\n prescale=config['PrescaleDst2D0PiEEG'],\\n postscale=config['PostscaleDst2D0PiEEG'],\\n selection=self.selDst2D0PiEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.configure_sensor(\\n TemperatureOversamplings.x08,\\n PressureOversamplings.x16,\\n HumidityOversamplings.x08,\\n IIRFilterCoefficients.FC_003,\\n 250,\\n 250)\",\n \"def __init__(self, cfg):\\r\\n\\r\\n\\t\\tself.image_size = cfg.MODEL.INPUT.IMAGE_SIZE\\r\\n\\t\\tanchor_config = cfg.MODEL.ANCHORS\\r\\n\\t\\tself.feature_maps = anchor_config.FEATURE_MAPS\\r\\n\\t\\tself.min_sizes = anchor_config.MIN_SIZES\\r\\n\\t\\tself.max_sizes = anchor_config.MAX_SIZES \\r\\n\\t\\tself.aspect_ratios = anchor_config.ASPECT_RATIOS\\r\\n\\t\\tself.clip = anchor_config.CLIP\",\n \"def __init__(self, name: str = None, *plates: Plate):\\r\\n super().__init__()\\r\\n self.name = name or f\\\"{self.__class__.__name__.lower()}_{self.id}\\\"\\r\\n self.plates = plates\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n phi3hm_name = '{0}Phi3HM'.format(name)\\n phi3hr_name = '{0}Phi3HR'.format(name)\\n \\n d2piphi3hm_name = '{0}D2PiPhi3HM'.format(name)\\n d2piphi3hr_name = '{0}D2PiPhi3HR'.format(name)\\n \\n d2kphi3hm_name = '{0}D2KPhi3HM'.format(name)\\n d2kphi3hr_name = '{0}D2KPhi3HR'.format(name)\\n \\n\\n self.selPhi3HM = makeResonance(\\n phi3hm_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\\n decDescriptors=self.Phi3H\\n )\\n\\n self.selPhi3HR = makeResonance(\\n phi3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\\n decDescriptors=self.Phi3H\\n )\\n \\n self.selD2PiPhi3HM = makeD(\\n d2piphi3hm_name,\\n config,\\n inputSel=[self.selPhi3HM, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KPhi3HM = makeD(\\n d2kphi3hm_name,\\n config,\\n inputSel=[self.selPhi3HM, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiPhi3HR = makeD(\\n d2piphi3hr_name,\\n config,\\n inputSel=[self.selPhi3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiPhi,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KPhi3HR = makeD(\\n d2kphi3hr_name,\\n config,\\n inputSel=[self.selPhi3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KPhi,\\n useBachelorPID=False,\\n )\\n \\n self.line_D2PiPhi3HM = make_line(\\n self,\\n name='{0}Line'.format(d2piphi3hm_name),\\n prescale=config['PrescaleD2PiPhi3HM'],\\n postscale=config['PostscaleD2PiPhi3HM'],\\n selection=self.selD2PiPhi3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2PiPhi3HR = make_line(\\n self,\\n name='{0}Line'.format(d2piphi3hr_name),\\n prescale=config['PrescaleD2PiPhi3HR'],\\n postscale=config['PostscaleD2PiPhi3HR'],\\n selection=self.selD2PiPhi3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n self.line_D2KPhi3HM = make_line(\\n self,\\n name='{0}Line'.format(d2kphi3hm_name),\\n prescale=config['PrescaleD2KPhi3HM'],\\n postscale=config['PostscaleD2KPhi3HM'],\\n selection=self.selD2KPhi3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KPhi3HR = make_line(\\n self,\\n name='{0}Line'.format(d2kphi3hr_name),\\n prescale=config['PrescaleD2KPhi3HR'],\\n postscale=config['PostscaleD2KPhi3HR'],\\n selection=self.selD2KPhi3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, config, well, directory):\\n self.config = config\\n self.well = well\\n self.directory = directory\",\n \"def __init__(self, config_file = 'config.yaml'):\\n\\n self.name = ''\\n self.img_dir = ''\\n self.out_dir = ''\\n self.cam_file = ''\\n self.options_file = ''\\n self.output_xml_file = ''\\n\\n # If there is an options file, it will overwrite the defaults \\n if config_file is not None:\\n self.load(config_file)\",\n \"def __init__(self, config):\\n super().__init__(config)\\n self.collector_host = config.get(\\\"collector_host\\\")\\n self.schedds = config.get(\\\"schedds\\\", [None])\\n self.condor_config = config.get(\\\"condor_config\\\")\\n self.constraint = config.get(\\\"constraint\\\", True)\\n self.classad_attrs = config.get(\\\"classad_attrs\\\")\\n self.correction_map = config.get(\\\"correction_map\\\")\",\n \"def __init__(self):\\n\\n super().__init__()\\n\\n self.active = True\\n self.driver = Driver.instance()\\n self.sensor_manager = SensorManager.instance()\\n\\n self.pwm = Adafruit_PCA9685.PCA9685(address=0x40, busnum=1) # create PCA9685-object at I2C-port\\n self.pwm.set_pwm_freq(50)\\n\\n GPIO.setwarnings(False)\\n GPIO.setmode(GPIO.BCM)\\n GPIO.setup(20, GPIO.OUT)\\n GPIO.setup(21, GPIO.OUT)\\n GPIO.setup(26, GPIO.OUT)\\n self.driven_distance = 0\",\n \"def __init__(self, config):\\n self.cfg = config\\n self.var_combinations = [\\\"tas:tas\\\", \\\"pr:pr\\\", \\\"pr:tas\\\"]\\n self.seasons = [\\\"jja\\\", \\\"djf\\\", \\\"annual\\\"]\\n self.projects = [\\\"cmip5\\\", \\\"cmip6\\\"]\\n self.variables = [\\\"tas\\\", \\\"pr\\\"]\\n self.scenarios = [\\\"26\\\", \\\"45\\\", \\\"85\\\"]\\n\\n # generate list of candidate bound limits\\n small = np.arange(0.1, 1, 0.1)\\n medium = np.arange(1, 11)\\n high = np.arange(20, 100, 10)\\n v_high = np.arange(150, 400, 50)\\n self.bound_candidates = np.concatenate(\\n (small, medium, high, v_high)) * 5 / 4\",\n \"def __init__(self, config: str) -> None:\\n self.configuration = config\",\n \"def __init__(self, config: str) -> None:\\n self.configuration = config\",\n \"def __init__(self):\\n self.risepower = 4.\\n self.min_event_amplitude = 5.0e-12 # pA default\\n self.template = None\\n pass\",\n \"def __init__(self, config: dict) -> None:\\n super().__init__(config)\",\n \"def __init__(self, name, i_node, j_node, m_node, n_node, t, material, model, kx_mod=1.0,\\n ky_mod=1.0):\\n\\n self.name = name\\n self.ID = None\\n self.type = 'Rect'\\n\\n self.i_node = i_node\\n self.j_node = j_node\\n self.m_node = m_node\\n self.n_node = n_node\\n\\n self.t = t\\n \\n self.kx_mod = kx_mod\\n self.ky_mod = ky_mod\\n\\n self.pressures = [] # A list of surface pressures [pressure, case='Case 1']\\n\\n # Plates need a link to the model they belong to\\n self.model = model\\n\\n # Get material properties for the plate from the model\\n try:\\n self.E = self.model.Materials[material].E\\n self.nu = self.model.Materials[material].nu\\n except:\\n raise KeyError('Please define the material ' + str(material) + ' before assigning it to plates.')\",\n \"def __init__(self, config_file=None, cam_prefix=\\\"rc\\\", serial_number=\\\"\\\",\\n output_dir=\\\"\\\", parseport=5001,\\n force_serial=True, set_temperature=-40, send_to_remote=False,\\n remote_config='nemea.config.json'):\\n\\n # Load the default parameters from config file\\n if not config_file:\\n config_file = os.path.join(SR, 'config',\\n '%s_config.yaml' % cam_prefix)\\n with open(config_file) as df:\\n camera_params = yaml.load(df, Loader=yaml.FullLoader)\\n self.__dict__.update(camera_params['default'].items())\\n\\n self.camPrefix = cam_prefix\\n self.outputDir = output_dir\\n self.forceSerial = force_serial\\n self.setTemperature = set_temperature\\n self.ExposureTime = 0\\n self.lastExposed = None\\n self.opt = None\\n self.parseport = parseport\\n self.send_to_remote = send_to_remote\\n if self.send_to_remote:\\n self.transfer = None #transfer(**params)\\n self.lastError = \\\"\\\"\",\n \"def __init__(self):\\n GPIO.setwarnings(False)\\n GPIO.cleanup() # Reset the high and low levels of the GPIO port\\n #The following code defines the GPIO used to control the L298N chip. This definition is different for different Raspberry Pi driver boards.\\n self.Motor_A_EN = 17\\n self.Motor_B_EN = 4\\n self.Motor_A_Pin1 = 27\\n self.Motor_A_Pin2 = 18\\n self.Motor_B_Pin1 = 21\\n self.Motor_B_Pin2 = 26\\n self.setup()\",\n \"def __init__(self, config=None):\\n config_dict = {}\\n if config:\\n config_dict = json.load(config)\\n\\n self.android = config_dict.get(\\\"android\\\")\\n self.linux = config_dict.get(\\\"linux\\\")\\n self.atf = config_dict.get(\\\"atf\\\")\\n self.qemu = config_dict.get(\\\"qemu\\\", \\\"qemu-system-aarch64\\\")\",\n \"def __init__(self, config):\\n super().__init__(config)\\n\\n # Prepare the timer.\\n self.timer = 0\\n\\n # Set the current player index.\\n self.current_player_index = 0\\n # If we are the client, the server goes first.\\n for i in range(len(sys.argv)):\\n if sys.argv[i] == \\\"--client\\\":\\n self.current_player_index = 1\\n\\n # Prepare the phase counter.\\n self.__current_phase = Game.PHASE_PREPARE\\n # Prepare the shot location store.\\n self.__current_fire_location = None\\n self.__current_fire_effect = None\",\n \"def __init__(self, config=None):\\n pass\",\n \"def __init__(self, make, model, year):\\r\\n super().__init__(make, model, year)\\r\\n self.battery_size = 70\\r\\n # self.autopilot = autopilot\\r\",\n \"def __init__(self):\\n self._read_calibration_data()\\n self.set_oversamplings_and_mode(\\n HumidityOversampling.x08,\\n TemperatureOversampling.x08,\\n PressureOversampling.x16,\\n SensorMode.Normal)\\n self.set_config(\\n InactiveDuration.ms1000,\\n FilterCoefficient.fc04)\",\n \"def __init__(self, configuration):\\n self._config = configuration\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n d0eeg_name = '{0}D0EEG'.format(name)\\n dst2pid0eeg_name = '{0}Dst2PiD0EEG'.format(name)\\n \\n d0ee_name = '{0}D0EE'.format(name)\\n dst2pid0ee_name = '{0}Dst2PiD0EE'.format(name)\\n\\n self.selD0EEG = makeEEGResonance(\\n d0eeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.D0EEG\\n ) \\n\\n self.selD0EE = makeEEResonance(\\n d0ee_name, \\n config,\\n inputSel=[StdAllLooseElectrons],\\n decDescriptors=self.D0EE\\n )\\n \\n self.selDst2PiD0EEG = makeDstar(\\n dst2pid0eeg_name,\\n config,\\n inputSel=[self.selD0EEG, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.selDst2PiD0EE = makeDstar(\\n dst2pid0ee_name,\\n config,\\n inputSel=[self.selD0EE, StdAllNoPIDsPions],\\n decDescriptors=self.Dst2D0Pi,\\n )\\n\\n self.line_Dst2PiD0EEG = make_line(\\n self,\\n '{0}Line'.format(dst2pid0eeg_name),\\n prescale=config['PrescaleDst2PiD0EEG'],\\n postscale=config['PostscaleDst2PiD0EEG'],\\n selection=self.selDst2PiD0EEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_Dst2PiD0EE = make_line(\\n self,\\n '{0}Line'.format(dst2pid0ee_name),\\n prescale=config['PrescaleDst2PiD0EE'],\\n postscale=config['PostscaleDst2PiD0EE'],\\n selection=self.selDst2PiD0EE,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, config, sensor=None):\\n if sensor is None:\\n from monitor.sensor import SensorDriver\\n self.sensor = SensorDriver(config.getint(CONFIG_SECTION, \\\"trigger_pin\\\"),\\n config.getint(CONFIG_SECTION, \\\"echo_pin\\\"))\\n else:\\n self.sensor = sensor\\n self.num_samples = config.getint(CONFIG_SECTION, \\\"num_samples\\\")\\n self.drop_extremes = config.getboolean(CONFIG_SECTION, \\\"drop_extremes\\\")\\n self.sample_delay = config.getfloat(CONFIG_SECTION, \\\"sample_delay\\\")\\n self.is_running = False\\n self.dist_to_bottom = config.getfloat(CONFIG_SECTION, \\\"distance_to_bottom\\\")\",\n \"def __init__(self, config_directory, scale_override=None):\\n self._config_directory = config_directory\\n\\n self._config_detector = DetectorConfig(config_directory)\\n self._config_align = AlignConfig(config_directory, scale_override=scale_override)\\n self._config_crystal = CrystalMatchConfig(config_directory)\",\n \"def __init__(self, config):\\n\\n self.config = config\",\n \"def __init__(self, config: Dict[str, Any]) -> None:\\n self.config = config\",\n \"def __init__(self, config):\\n self.name = \\\"leduc-holdem\\\"\\n self.game = LeducholdemGame()\\n super().__init__(config)\\n self.actions = [\\\"call\\\", \\\"raise\\\", \\\"fold\\\", \\\"check\\\"]\\n self.state_shape = [36]\\n\\n with open(\\n os.path.join(rlcard.__path__[0], \\\"games/leducholdem/card2index.json\\\"), \\\"r\\\"\\n ) as file:\\n self.card2index = json.load(file)\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n etappg_name = '{0}EtaPPG'.format(name)\\n eta3hr_name = '{0}Eta3HR'.format(name)\\n eta3hm_name = '{0}Eta3HM'.format(name)\\n \\n d2pietappg_name = '{0}D2PiEtaPPG'.format(name)\\n d2pieta3hr_name = '{0}D2PiEta3HR'.format(name)\\n d2pieta3hm_name = '{0}D2PiEta3HM'.format(name)\\n \\n d2ketappg_name = '{0}D2KEtaPPG'.format(name)\\n d2keta3hr_name = '{0}D2KEta3HR'.format(name)\\n d2keta3hm_name = '{0}D2KEta3HM'.format(name)\\n\\n self.selEtaPPG = makeResonance(\\n etappg_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\\n decDescriptors=self.EtaPPG\\n )\\n\\n self.selEta3HR = makeResonance(\\n eta3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedPi0],\\n decDescriptors=self.Eta3H\\n )\\n\\n self.selEta3HM = makeResonance(\\n eta3hm_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseMergedPi0],\\n decDescriptors=self.Eta3H\\n )\\n\\n self.selD2PiEtaPPG = makeD(\\n d2pietappg_name,\\n config,\\n inputSel=[self.selEtaPPG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False,\\n )\\n\\n self.selD2KEtaPPG = makeD(\\n d2ketappg_name,\\n config,\\n inputSel=[self.selEtaPPG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiEta3HR = makeD(\\n d2pieta3hr_name,\\n config,\\n inputSel=[self.selEta3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False,\\n )\\n\\n self.selD2KEta3HR = makeD(\\n d2keta3hr_name,\\n config,\\n inputSel=[self.selEta3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiEta3HM = makeD(\\n d2pieta3hm_name,\\n config,\\n inputSel=[self.selEta3HM, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False,\\n )\\n \\n\\n self.selD2KEta3HM = makeD(\\n d2keta3hm_name,\\n config,\\n inputSel=[self.selEta3HM, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=False,\\n )\\n \\n\\n self.line_D2PiEtaPPG = make_line(\\n self,\\n name='{0}Line'.format(d2pietappg_name),\\n prescale=config['PrescaleD2PiEtaPPG'],\\n postscale=config['PostscaleD2PiEtaPPG'],\\n selection=self.selD2PiEtaPPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2PiEta3HM = make_line(\\n self,\\n name='{0}Line'.format(d2pieta3hm_name),\\n prescale=config['PrescaleD2PiEta3HM'],\\n postscale=config['PostscaleD2PiEta3HM'],\\n selection=self.selD2PiEta3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2PiEta3HR = make_line(\\n self,\\n name='{0}Line'.format(d2pieta3hr_name),\\n prescale=config['PrescaleD2PiEta3HR'],\\n postscale=config['PostscaleD2PiEta3HR'],\\n selection=self.selD2PiEta3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KEtaPPG = make_line(\\n self,\\n name='{0}Line'.format(d2ketappg_name),\\n prescale=config['PrescaleD2KEtaPPG'],\\n postscale=config['PostscaleD2KEtaPPG'],\\n selection=self.selD2KEtaPPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEta3HM = make_line(\\n self,\\n name='{0}Line'.format(d2keta3hm_name),\\n prescale=config['PrescaleD2KEta3HM'],\\n postscale=config['PostscaleD2KEta3HM'],\\n selection=self.selD2KEta3HM,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEta3HR = make_line(\\n self,\\n name='{0}Line'.format(d2keta3hr_name),\\n prescale=config['PrescaleD2KEta3HR'],\\n postscale=config['PostscaleD2KEta3HR'],\\n selection=self.selD2KEta3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n self.__parameters: ConfigParams = ConfigParams()\",\n \"def __init__(self):\\n\\n # self.threshold = 3.\\n self.gamma_min = 3\\n self.gamma_max = 12\\n self.n_samples = 40\\n # self.do_plots = False\\n # self.do_albedo = True\\n # self.verbose = True\\n\\n self.nbands = 7\\n self.bu = np.array([0.004, 0.015, 0.003, 0.004, 0.013, 0.010, 0.006])\\n\\n # Determine 250 or 500 meters product\\n # self.resolution = 500\\n\\n # self.pixelWidth = 500\\n # self.pixelHeight = 500\",\n \"def __init__(self):\\n\\n super().__init__(\\n dynamics_model=DoorDynamicsModel(),\\n virtual_sensor_model=UnimodalVirtualSensorModel(\\n virtual_sensor_model=[\\n DoorVirtualSensorModel(modalities={\\\"image\\\"}),\\n DoorVirtualSensorModel(modalities={\\\"pos\\\", \\\"sensors\\\"}),\\n ],\\n state_dim=3,\\n ),\\n )\",\n \"def default_plate(self):\\n self.name = \\\"Default Plate Armor\\\"\\n self.rarity = \\\"Common\\\"\\n self.pdef_value = 20\\n self.mdef_value = 5\\n self.increase_crit = 0\\n self.desc = \\\"An old piece of iron armor, it's pretty heavy.\\\"\",\n \"def __init__(self, footprint, mconf, name):\\n logging.debug(\\\"%s entered __init__\\\" % name)\\n self.image_id = mconf[\\\"image-id\\\"]\\n self.networks = mconf[\\\"networks\\\"]\\n self.ip_addresses = mconf[\\\"ip-addresses\\\"]\\n self.flavor=mconf[\\\"flavor\\\"]\\n self.machine_name = \\\"%s%s\\\" % (footprint.footprint_name, name)\\n self.footprint = footprint\",\n \"def __init__(\\n self,\\n hass,\\n name: str,\\n config: ConfigEntry,\\n coordinator,\\n file_path: str,\\n ) -> None:\\n super().__init__()\\n\\n self.hass = hass\\n self._name = CAMERA_DATA[name][SENSOR_NAME]\\n self._type = name\\n self.check_file_path_access(file_path)\\n self._file_path = file_path\\n self._coordinator = coordinator\\n self._host = config.data.get(CONF_HOST)\\n self._unique_id = config.entry_id\\n self._no_mail = (\\n None\\n if not config.data.get(CONF_CUSTOM_IMG)\\n else config.data.get(CONF_CUSTOM_IMG_FILE)\\n )\",\n \"def _configure(self, config):\\n self.friction_coef = config['friction_coef']\\n self.num_cone_faces = config['num_cone_faces']\\n self.num_samples = config['grasp_samples_per_surface_point']\\n self.dir_prior = config['dir_prior']\\n self.target_num_grasps = config['target_num_grasps']\\n if self.target_num_grasps is None:\\n self.target_num_grasps = config['min_num_grasps']\\n\\n self.min_contact_dist = config['min_contact_dist']\\n self.num_grasp_rots = config['coll_check_num_grasp_rots']\\n if 'max_num_surface_points' in config.keys():\\n self.max_num_surface_points_ = config['max_num_surface_points']\\n else:\\n self.max_num_surface_points_ = 100\",\n \"def __init__(self, parent):\\n super(Demo5, self).__init__(parent)\\n self.angle = 0.0\\n self.replication = 1.0\\n self.offset = 0.0\\n self.deltaRep = 1\\n self.revolution = 0\\n self.stepsPer90 = 180\\n self.stepsLeft = self.stepsPer90\\n self.deltaAng = 90.0\\n self.deltaOff = 0.15\\n self.spin = True\\n self.x2yAspect = 1.0\\n self.texture = None\",\n \"def __init__(self, config_file):\\n Config = ConfigParser.ConfigParser()\\n Config.read(config_file)\\n \\n self.port_id = Config.get(\\\"SerialPortSection\\\", \\\"ComPort\\\")\\n self.baud_rate = Config.get(\\\"SerialPortSection\\\", \\\"BaudRate\\\")\\n self. timeout = Config.get(\\\"SerialPortSection\\\", \\\"TimeOut\\\")\\n self.config_file = config_file\",\n \"def __init__(self, rings=False, branches=False):\\n self.rings = rings\\n self.branches = branches\",\n \"def __init__(self, encut, name=\\\"relax_settings\\\"):\\n\\n ionic = {\\\"EDIFF\\\": 1E-17, \\\"NSW\\\": 20, \\\"IBRION\\\": 2,\\\"ISIF\\\": 2, \\\"ISYM\\\": -1, \\\"NBLOCK\\\": 1, \\\"KBLOCK\\\": 20}\\n InputParameters.__init__(self, ionic_settings=ionic, name=name)\\n self.update_electronic_sttings(\\\"ENCUT\\\", encut)\",\n \"def __init__(self, hass, camera_entity, config, name):\\n self.hass = hass\\n self._camera_entity = camera_entity\\n if name:\\n self._name = name\\n else:\\n self._name = f\\\"SevenSegment OCR {split_entity_id(camera_entity)[1]}\\\"\\n self._state = None\\n\\n self.filepath = os.path.join(\\n self.hass.config.config_dir,\\n \\\"ssocr-{}.png\\\".format(self._name.replace(\\\" \\\", \\\"_\\\")),\\n )\\n crop = [\\n \\\"crop\\\",\\n str(config[CONF_X_POS]),\\n str(config[CONF_Y_POS]),\\n str(config[CONF_WIDTH]),\\n str(config[CONF_HEIGHT]),\\n ]\\n digits = [\\\"-d\\\", str(config.get(CONF_DIGITS, -1))]\\n rotate = [\\\"rotate\\\", str(config[CONF_ROTATE])]\\n threshold = [\\\"-t\\\", str(config[CONF_THRESHOLD])]\\n extra_arguments = config[CONF_EXTRA_ARGUMENTS].split(\\\" \\\")\\n\\n self._command = (\\n [config[CONF_SSOCR_BIN]]\\n + crop\\n + digits\\n + threshold\\n + rotate\\n + extra_arguments\\n )\\n self._command.append(self.filepath)\",\n \"def __init__(self, name):\\n self.cloud = name\\n self.cloudtype = \\\"multipass\\\"\\n config = Config()\\n self.default = config[f\\\"cloudmesh.volume.{self.cloud}.default\\\"]\\n self.cm = CmDatabase()\",\n \"def __init__(self) -> None:\\n self.sensor = serial.Serial(config.DEVICE)\\n super().__init__()\",\n \"def __init__(self):\\n #screen Settings\\n self.screen_width = 1024\\n self.screen_height = 768\\n self.bg_color = (32, 32, 32)\\n\\n #rocket settings\\n self.rocket_speed = 1\\n\\n #laser Settings\\n self.laser_speed = 1.0\\n self.laser_width = 3\\n self.laser_height = 15\\n self.laser_color = (0, 255, 255)\\n self.lasers_allowed = 3\",\n \"def __init__(self, config):\\n super().__init__(config)\\n\\n # For NaMaster you need to pass the masks\\n self.mask_files = self.config[\\\"tjpcov\\\"].get(\\\"mask_file\\\")\\n self.mask_names = self.config[\\\"tjpcov\\\"].get(\\\"mask_names\\\")\\n\\n # Binning info is only needed if workspaces are not passed\\n self.binning_info = self.config[\\\"tjpcov\\\"].get(\\\"binning_info\\\", None)\\n\\n # nside is needed if mask_files is a hdf5 file\\n self.nside = self.config[\\\"tjpcov\\\"].get(\\\"nside\\\", None)\\n\\n # Read NaMaster specific options\\n self.nmt_conf = self.config.get(\\\"NaMaster\\\", {})\\n for k in [\\\"f\\\", \\\"w\\\", \\\"cw\\\"]:\\n if k not in self.nmt_conf:\\n self.nmt_conf[k] = {}\\n\\n # Read cache from input file. It will update the cache passed as an\\n # argument of the different methods\\n self.cache = self.config.get(\\\"cache\\\", {})\",\n \"def __init__(self, name, type_name, thickness, grid_front_area, bundles_flow_area, grid_wetted_area, rods_wetted_at_grid_area, P_wet_at_grid, P_bundles, orientation):\\n self.name = name\\n self.type = type_name\\n self.thickness = thickness\\n self.length = self.thickness\\n self.grid_front_area = grid_front_area\\n self.bundles_flow_area = bundles_flow_area\\n self.grid_wetted_area = grid_wetted_area\\n self.rods_wetted_at_grid_area = rods_wetted_at_grid_area\\n self.orientation = orientation\\n self.P_wet_at_grid = P_wet_at_grid\\n self.P_bundles = P_bundles\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n etaeeg_name = '{0}EtaEEG'.format(name)\\n \\n d2pietaeeg_name = '{0}D2PiEtaEEG'.format(name) \\n d2ketaeeg_name = '{0}D2KEtaEEG'.format(name)\\n\\n self.selEtaEEG = makeEEGResonance(\\n etaeeg_name, \\n config,\\n inputSel=[StdLooseAllPhotons,StdDiElectronFromTracks],\\n decDescriptors=self.EtaEEG\\n )\\n \\n self.selD2PiEtaEEG = makeD(\\n d2pietaeeg_name,\\n config,\\n inputSel=[self.selEtaEEG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEta,\\n useBachelorPID=False\\n )\\n\\n usePIDforKaon = False\\n if (config['Bachelor_PIDK_MIN'] > -10):\\n usePIDforKaon = True\\n else :\\n usePIDforKaon = False\\n \\n self.selD2KEtaEEG = makeD(\\n d2ketaeeg_name, \\n config,\\n inputSel=[self.selEtaEEG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEta,\\n useBachelorPID=usePIDforKaon\\n )\\n\\n self.line_D2PiEtaEEG = make_line(\\n self,\\n '{0}Line'.format(d2pietaeeg_name),\\n prescale=config['PrescaleD2PiEtaEEG'],\\n postscale=config['PostscaleD2PiEtaEEG'],\\n selection=self.selD2PiEtaEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n\\n self.line_D2KEtaEEG = make_line(\\n self,\\n '{0}Line'.format(d2ketaeeg_name),\\n prescale=config['PrescaleD2KEtaEEG'],\\n postscale=config['PostscaleD2KEtaEEG'],\\n selection=self.selD2KEtaEEG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self, **kwargs):\\n super(VeryCleverBeamsplitter, self).__init__(**kwargs)\\n self.shader_source = IL_SHADER_SOURCE\\n self.centre = [0.5, 0.5]\\n self.blazing_function = np.linspace(0,1,32)\\n self.zernike_coefficients = np.zeros(12)\",\n \"def __init__(self, car_reference, pt_reference):\\n\\n self.car_reference = car_reference\\n self.pt_reference = pt_reference\\n\\n # We need to provide some mandatory information\\n self.number_of_parameters = 3\\n self.number_of_states = 2\\n\\n # We can provide some information that is passed to the output\\n self.information = dict(problem = \\\"ModeShareProblem\\\")\\n\\n # We can provide the reference state. Currently, it is not used in any\\n # optimizer, but it is helpful for plotting optimization results as\\n # the existing plotting scripts will know how to use this information.\\n self.reference_state = [self.car_reference, self.pt_reference]\",\n \"def __init__(self):\\n # Screen settings\\n self.screen_width = 1860\\n self.screen_height = 1020\\n self.screen_size = self.screen_width, self.screen_height\\n self.bg_color = 230, 230, 230\\n\\n # Ship static settings\\n self.ship_limit = 3\\n\\n # Bullet static settings\\n self.bullet_limit = 3\\n self.bullet_width = 3\\n self.bullet_height = 15\\n self.bullet_color = 60, 60, 60\\n\\n # Alien static settings\\n self.fleet_drop_speed = 10\\n\\n self.speed_up_scale = 1.1\\n self.initialize_dynamic_settings()\",\n \"def __init__(self, pins, image):\\n self._pins = pins\\n self._image = image\",\n \"def __init__(self):\\n # Screen settings\\n self.screen_width = 2400\\n self.screen_height = 1600\\n self.bg_color = (0, 0, 0)\\n\\n # Raindrop settings\\n self.r_y_speed = 10\",\n \"def __init__(self, planet, config=None, log=None, **kwargs):\\n self.planet = planet.capitalize()\\n self.log = logging.setup(log)\\n\\n if config is None or isinstance(config, str):\\n from . import config as pcfg\\n config = os.path.join(self.planet, config)\\n config = pcfg.planetConfig(self.planet, configFile=config)\\n config.update_config(**kwargs)\\n self.config = config\\n self.chem = None\\n self.gas = []\\n self.nGas = 0\\n self.nConstituent = 0\\n self.cloud = []\\n self.nCloud = 0\\n self.property = []\\n if config.path not in sys.path:\\n sys.path.insert(0, config.path)\",\n \"def __init__(self, config, cfg):\\n self.config = config\\n self.cfg = cfg\",\n \"def __init__(self, config):\\n self.__config = config\\n self.__logger = SLoggerHandler().getLogger(LoggerNames.EXPERIMENT_C)\\n self.__num_gpus = ConfigProvider().get_config(\\\"controllerConfig.json\\\")[\\\"hardware\\\"][\\\"numGPUs\\\"]\",\n \"def __init__(self, configfile_name):\\n assert os.path.exists(configfile_name), \\\\\\n 'Config file %s does not exist' % (configfile_name)\\n\\n correct_param_load = self.load_config_file(configfile_name)\\n assert correct_param_load,\\\\\\n 'Config params could not be loaded from file'\\n\\n self.fullpath_input_configfile = configfile_name\\n self.word_det_rfc = None\\n self.reg_coeffs = None\\n self.bb_reg = None\\n self.img_files = None\",\n \"def __init__(self, address=0x68, config=0):\\r\\n\\t\\tself.i2c = FT232H.I2CDevice(ft232h, address)\\r\\n\\t\\tif config == 0:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=0)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=0)\\r\\n\\t\\telif config == 1:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=1)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=1)\\t\\t\\t\\t\\r\\n\\t\\telif config == 2:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=2)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=2)\\t\\r\\n\\t\\telif config == 3:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=3)\\r\\n\\t\\t\\tself.setScale(mode='GYR',scale=3)\\t\\t\\t\\t\\r\\n\\t\\telif config == 4:\\r\\n\\t\\t\\tself.setWake()\\r\\n\\t\\t\\tself.setScale(mode='ACC',scale=1)\\t\\r\\n\\t\\t\\tself.setTempDisable()\\r\\n\\t\\t\\tself.setGYRStandby(axis='X')\\r\\n\\t\\t\\tself.setGYRStandby(axis='Y')\\r\\n\\t\\t\\tself.setGYRStandby(axis='Z')\",\n \"def __init__(self,port=None,config=None):\\n\\n # Attempt to load a Dorna object with a configuration file, default to the desktop config if none is entered manually.\\n\\n self.WORKING_DIRECTORY = os.getcwd()\\n self.FILE_DIRECTORY = os.path.dirname(__file__)\\n self.DEFAULT_CONFIG = os.path.join(self.FILE_DIRECTORY, \\\"../configuration_store/config.yaml\\\")\\n \\n self.positionStore = PositionStore()\\n \\n config = config if config!= None else self.DEFAULT_CONFIG\\n\\n try:\\n print(config)\\n self.robot=Dorna(config)\\n print(\\\"Loaded configuration file\\\")\\n except:\\n print(\\\"Failed to load configuration file, please re-instantiate the arm object with a valid config.\\\")\",\n \"def __init__(self):\\n self.config = {}\",\n \"def __init__(self, config):\\n try:\\n config['volume_id']\\n config['access_key']\\n config['secret_access_key']\\n config['region']\\n except KeyError, e:\\n logging.error(repr(e))\\n raise ImproperlyConfigured()\\n\\n if not config.has_key('keep'):\\n config['keep'] = 5\\n\\n self.config = config\",\n \"def __init__(self, name, config):\\n self.name = name\\n self.config = config\\n LineBuilder.__init__(self, name, config)\\n \\n etaprimeppg_name = '{0}EtaPrimePPG'.format(name)\\n etaprime3hr_name = '{0}EtaPrime3HR'.format(name)\\n \\n d2pietaprimeppg_name = '{0}D2PiEtaPrimePPG'.format(name)\\n d2pietaprime3hr_name = '{0}D2PiEtaPrime3HR'.format(name)\\n \\n d2ketaprimeppg_name = '{0}D2KEtaPrimePPG'.format(name)\\n d2ketaprime3hr_name = '{0}D2KEtaPrime3HR'.format(name)\\n \\n self.selEtaPrimePPG = makeResonance(\\n etaprimeppg_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseAllPhotons],\\n decDescriptors=self.EtaPrimePPG\\n )\\n \\n self.selEtaPrime3HR = makeResonance(\\n etaprime3hr_name,\\n config,\\n inputSel=[StdAllNoPIDsPions,StdLooseResolvedEta],\\n decDescriptors=self.EtaPrime3H\\n )\\n \\n self.selD2PiEtaPrimePPG = makeD(\\n d2pietaprimeppg_name,\\n config,\\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEtaPrime,\\n useBachelorPID=False,\\n )\\n \\n self.selD2KEtaPrimePPG = makeD(\\n d2ketaprimeppg_name,\\n config,\\n inputSel=[self.selEtaPrimePPG, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEtaPrime,\\n useBachelorPID=False,\\n )\\n \\n self.selD2PiEtaPrime3HR = makeD(\\n d2pietaprime3hr_name,\\n config,\\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsPions],\\n decDescriptors=self.D2PiEtaPrime,\\n useBachelorPID=False, \\n )\\n \\n self.selD2KEtaPrime3HR = makeD(\\n d2ketaprime3hr_name,\\n config,\\n inputSel=[self.selEtaPrime3HR, StdAllNoPIDsKaons],\\n decDescriptors=self.D2KEtaPrime,\\n useBachelorPID=False,\\n )\\n \\n self.line_D2PiEtaPrimePPG = make_line(\\n self,\\n name='{0}Line'.format(d2pietaprimeppg_name),\\n prescale=config['PrescaleD2PiEtaPrimePPG'],\\n postscale=config['PostscaleD2PiEtaPrimePPG'],\\n selection=self.selD2PiEtaPrimePPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2PiEtaPrime3HR = make_line(\\n self,\\n name='{0}Line'.format(d2pietaprime3hr_name),\\n prescale=config['PrescaleD2PiEtaPrime3HR'],\\n postscale=config['PostscaleD2PiEtaPrime3HR'],\\n selection=self.selD2PiEtaPrime3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEtaPrimePPG = make_line(\\n self,\\n name='{0}Line'.format(d2ketaprimeppg_name),\\n prescale=config['PrescaleD2KEtaPrimePPG'],\\n postscale=config['PostscaleD2KEtaPrimePPG'],\\n selection=self.selD2KEtaPrimePPG,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\\n \\n self.line_D2KEtaPrime3HR = make_line(\\n self,\\n name='{0}Line'.format(d2ketaprime3hr_name),\\n prescale=config['PrescaleD2KEtaPrime3HR'],\\n postscale=config['PostscaleD2KEtaPrime3HR'],\\n selection=self.selD2KEtaPrime3HR,\\n HLT1=config['Hlt1Filter'],\\n HLT2=config['Hlt2Filter']\\n )\",\n \"def __init__(self):\\n self.plant_names = list(PLANT_TYPES.keys()) #: list of str: available plant type names.\\n self.plant_types = list(PLANT_TYPES.items()) #: list of dicts: model parameters for plant types.\\n self.num_plant_types = len(PLANT_TYPES) #: int: amount unique plant types in garden.\\n self.plant_centers = [] #: location of plants in garden\\n self.non_plant_centers = [] #: additional coordinates without plants\\n self.plant_in_bounds = 0 #: int: count of plant centers in bound.\",\n \"def __init__(self):\\n super().__init__()\\n\\n # Gadget state\\n \\n self.isDoorOpen = False\\n self.verified = True\\n\\n # Ev3dev initialization\\n self.leds = Leds()\\n self.sound = Sound()\\n self.drive = MoveTank(OUTPUT_B, OUTPUT_C)\\n \\n self.ir_sensor = InfraredSensor()\\n self.ir_sensor.mode = self.ir_sensor.MODE_IR_REMOTE\\n self.color_sensor = ColorSensor()\\n self.color_sensor.mode = 'COL-COLOR' # WHITE\\n\\n # Start threads\\n threading.Thread(target=self._patrol_thread, daemon=True).start()\",\n \"def __init__(self, name: str, instance: PartInstance, controlPoints: ModelDotArray):\\n super().__init__()\\n pass\",\n \"def __init__(self):\\n if not os.path.isfile(CONFIG_FILENAME):\\n first_time_run()\\n raise SystemExit()\\n\\n \\\"\\\"\\\"\\n Init file is present, read and parse it:\\n \\\"\\\"\\\"\\n conf = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation(), inline_comment_prefixes='#')\\n conf.read(CONFIG_FILENAME)\\n \\\"\\\"\\\"\\n Process certain paths:\\n \\\"\\\"\\\"\\n\\n path = conf['Paths']\\n self.ffc_dir = path['FlatFieldCalDir']\\n self.capture_dir = path['CaptureDir']\\n self.image_dir = path['ImageDir']\\n\\n \\\"\\\"\\\"\\n Process options\\n \\\"\\\"\\\"\\n\\n self.cal_auto_save = conf.getboolean('Options', 'CalAutoSave', fallback=True)\\n self.cal_auto_load = conf.getboolean('Options', 'CalAutoLoad', fallback=True)\\n self.sound_on_capture = conf.getboolean('Options', 'SoundOnCapture', fallback=True)\\n self.exp_init1 = conf.getint('Options', 'ExpInit1', fallback=100)\\n self.exp_init2 = conf.getint('Options', 'ExpInit2', fallback=100)\\n self.black_correct = conf.getboolean('Options', 'BlackCorrect', fallback=True)\\n # Setup square window, default of full-screen height\\n self.tiff_seq_x_window = conf.getint('Options', 'TiffSeqXWindow', fallback=cameras.FRAME_HEIGHT)\\n self.tiff_seq_y_window = conf.getint('Options', 'TiffSeqYWindow', fallback=cameras.FRAME_HEIGHT)\\n self.tiff_seq_rebin = conf.getint('Options', 'TiffSeqRebin', fallback = 2)\",\n \"def __init__(self, x, y , planet):\\n\\t\\tself.x = x\\n\\t\\tself.y = y\\n\\t\\tself.planet = planet\\n\\t\\tself.battery = 100\",\n \"def __init__(self, **kwargs):\\n self.max_samples = get_value(kwargs, 'max_samples', types=[int], range_min=0, default_value=0)\\n self.obs_filename = get_value(kwargs, 'lba_obs_file', types=[str, None], default_value=None)\\n self.sample_rate = get_value(kwargs, 'sample_rate', types=[int, None], default_value=None)\\n if self.sample_rate is None:\\n LOG.warning(\\\"No sample rate provided, defaulting to {0}\\\".format(self._default_sample_rate))\\n self.sample_rate = self._default_sample_rate\\n self.antenna_name = get_value(kwargs, 'lba_antenna_name', types=[str, None], default_value=None)\\n if self.obs_filename is not None and self.antenna_name is None:\\n raise RuntimeError(\\\"LBA file is missing --lba_antenna_name parameter which is needed when using \\\"\\n \\\"--lba_obs_file\\\")\\n self.chunk_size = get_value(kwargs, 'chunk_size', types=[int], range_min=0, default_value=4096)\",\n \"def __init__(self, pitch, color, our_side, video_port=0, comm_port='/dev/ttyACM0', penalty=False, comms=1):\\n assert pitch in [0, 1]\\n assert color in ['yellow', 'blue']\\n assert our_side in ['left', 'right']\\n\\n self.pitch = pitch\\n\\n # Set up the Arduino communications\\n self.arduino = Arduino(comm_port, 115200, 1, comms)\\n\\n # Set up camera for frames\\n self.camera = Camera(port=video_port, pitch=self.pitch)\\n frame = self.camera.get_frame()\\n center_point = self.camera.get_adjusted_center(frame)\\n\\n # Set up vision\\n self.calibration = tools.get_colors(pitch)\\n self.vision = Vision(\\n pitch=pitch, color=color, our_side=our_side,\\n frame_shape=frame.shape, frame_center=center_point,\\n calibration=self.calibration)\\n\\n # Set up postprocessing for vision\\n self.postprocessing = Postprocessing(our_side)\\n\\n # Set up main planner\\n self.planner = Planner(our_side=our_side, pitch_num=self.pitch, isPenalty=penalty)\\n\\n # Set up GUI\\n self.GUI = GUI(calibration=self.calibration, arduino=self.arduino, pitch=self.pitch)\\n\\n self.color = color\\n self.side = our_side\\n\\n self.preprocessing = Preprocessing()\\n\\n self.robot = Robot_Controller()\",\n \"def __init__(self, cpara, dz_soil):\\n\\n # --- grid ---\\n self.z = np.linspace(0, cpara['grid']['zmax'], cpara['grid']['Nlayers']) # grid [m] above ground\\n self.dz = self.z[1] - self.z[0] # gridsize [m]\\n self.ones = np.ones(len(self.z)) # dummy\\n\\n # --- switches ---\\n self.Switch_Eflow = cpara['ctr']['Eflow'] # True assumes constant U/ustar at upper boundary\\n self.Switch_WMA = cpara['ctr']['WMA'] # True solves scalar profiles\\n self.Switch_Ebal = cpara['ctr']['Ebal'] # True solves leaf energy balance\\n\\n logger.info('Eflow: %s, WMA: %s, Ebal: %s',\\n self.Switch_Eflow,\\n self.Switch_WMA,\\n self.Switch_Ebal)\\n\\n # --- PlantTypes ---\\n ptypes = []\\n ptnames = list(cpara['planttypes'].keys())\\n ptnames.sort()\\n for pt in ptnames:\\n ptypes.append(PlantType(self.z, cpara['planttypes'][pt], dz_soil, ctr=cpara['ctr'], loc=cpara['loc']))\\n self.planttypes = ptypes\\n \\n # --- stand characteristics: sum over planttypes---\\n\\n # total leaf area index [m2 m-2]\\n self.LAI = sum([pt.LAI for pt in self.planttypes])\\n # total leaf area density [m2 m-3]\\n self.lad = sum([pt.lad for pt in self.planttypes])\\n\\n # layerwise mean leaf characteristic dimension [m] for interception model\\n self.leaf_length = sum([pt.leafp['lt'] * pt.lad for pt in self.planttypes]) / (self.lad + EPS)\\n\\n # root area density [m2 m-3]\\n rad = np.zeros(np.shape(dz_soil))\\n imax = 1\\n for pt in self.planttypes:\\n rad[:len(pt.Roots.rad)] += pt.Roots.rad\\n imax = max(imax, len(pt.Roots.rad))\\n \\n self.ix_roots = np.array(range(imax)) # soil model layers corresponding to root zone\\n self.rad = rad[self.ix_roots]\\n \\n # total root area index [m2 m-2]\\n self.RAI = sum([pt.Roots.RAI for pt in self.planttypes])\\n # distribution of roots across soil model layers [-]\\n self.root_distr = self.rad * dz_soil[self.ix_roots] / (self.RAI + EPS)\\n\\n # canopy height [m]\\n if len(np.where(self.lad > 0)[0]) > 0:\\n f = np.where(self.lad > 0)[0][-1]\\n self.hc = self.z[f].copy()\\n else:\\n self.hc = 0.0\\n\\n # --- create radiation, micromet, interception, and forestfloor model instances\\n self.radiation = Radiation(cpara['radiation'], self.Switch_Ebal)\\n\\n self.micromet = Micromet(self.z, self.lad, self.hc, cpara['micromet'])\\n\\n self.interception = Interception(cpara['interception'], self.lad * self.dz)\\n\\n self.forestfloor = ForestFloor(cpara['forestfloor'])\",\n \"def __init__(self, region):\\r\\n self.region = region\",\n \"def __init__(self):\\n\\n self.Cp_air0 = config_earth.earth_properties['Cp_air0']\\n self.Rsp_air = config_earth.earth_properties['Rsp_air']\\n\\n self.d = config_earth.balloon_properties['d']\\n self.vol = math.pi*4/3*pow((self.d/2),3) #volume m^3\\n self.surfArea = math.pi*self.d*self.d #m^2\\n self.cs_area = math.pi*self.d*self.d/4.0 #m^2\\n\\n #self.emissEnv = config_earth.balloon_properties['emissEnv']\\n self.areaDensityEnv = config_earth.balloon_properties['areaDensityEnv']\\n self.mp = config_earth.balloon_properties['mp']\\n self.mdot = 0\\n self.massEnv = config_earth.balloon_properties['mEnv']\\n self.Upsilon = config_earth.balloon_properties['Upsilon']\\n\\n self.vent = config_earth.simulation['vent']\\n self.coord = config_earth.simulation['start_coord']\\n self.t = config_earth.simulation['start_time']\\n self.lat = math.radians(self.coord['lat'])\\n self.Ls = self.t.timetuple().tm_yday\\n self.min_alt = config_earth.simulation['min_alt']\\n\\n self.vm_coeff = .1 #virtual mass coefficient\\n self.k = self.massEnv*config_earth.balloon_properties['cp'] #thermal mass coefficient\\n\\n self.dt = config_earth.dt\",\n \"def __init__(self):\\n super().__init__()\\n self.location = 0.0\\n self.scale = 1.0\\n self.type = 'Laplace'\\n self.distType = 'Continuous'\\n self.hasInfiniteBound = True\\n self.compatibleQuadrature.append('CDF')\\n self.preferredQuadrature = 'CDF'\\n self.preferredPolynomials = 'CDF'\",\n \"def __init__(self, config, town):\\n\\t\\tself.config = config\",\n \"def initialize(self, **kwargs):\\n\\n # Defining the configuration object\\n self.config = kwargs.get('config')\",\n \"def __init__(self, filepath):\\n try:\\n config_file_r = open(filepath)\\n self.sim_parametres = yaml.load(config_file_r, Loader=yaml.FullLoader)\\n except:\\n raise Exception(\\\"Le fichier de configuration n'a pas été atteint ou n'a pas pu être lu. Veuillez vérifier \\\"\\n \\\"qu'il n'y ait aucune erreur de syntaxe.\\\")\",\n \"def __init__(self, metadata, environment, component, image, version, s3_bucket, exec_env):\\n self.environment = environment\\n self.component = component\\n self.s3_bucket = s3_bucket\\n self.exec_env = exec_env\\n self.image = image\\n self.version = version\\n self.metadata = metadata\\n\\n # generate Terragrunt config as part of object initialisation\\n self.config()\",\n \"def __init__(self,height=7,width=7):\\n self.__cars = []\\n self.__height = height\\n self.__width = width\",\n \"def initialize_from_config(self):\",\n \"def __init__(self, name, properties):\\n super(WiretapShotProcessorPreset, self).__init__(name, properties)\\n \\n # Necessary in order for Hiero to view this plugin as different from\\n # the default shot processor\\n self._parentType = WiretapShotProcessor\",\n \"def __init__(self, config):\\n raise NotImplementedError (\\\"This is just an abstract interface\\\")\",\n \"def __init__(self):\\n super(Config, self).__init__()\\n self.run_control[\\\"output_dir\\\"] = os.getcwd()+\\\"/output/arctan_baseline/single_turn_injection/tracking\\\"\\n ring_tof = 1149.185123\\n field_files = glob.glob(\\\"output/arctan_baseline/bump_quest_v9/find_bump_r_*_theta_90/find_bump_parameters_001.out\\\")\\n substitutions = config.get_baseline_substitution(0.22, ring_tof)\\n substitutions = self.ramp_fields(\\n substitutions,\\n field_files,\\n [i for i, f in enumerate(field_files)],\\n ring_tof,\\n will_step = False\\n )\\n self.substitution_list = [substitutions] # used for plotting unperturbed CO\\n\\n self.run_control[\\\"find_closed_orbits_4d\\\"] = False\\n self.run_control[\\\"find_da\\\"] = False\\n self.run_control[\\\"find_bump_parameters\\\"] = False\\n self.run_control[\\\"track_bump\\\"] = False\\n self.run_control[\\\"track_beam\\\"] = True\\n self.find_closed_orbits[\\\"subs_overrides\\\"][\\\"__n_turns__\\\"] = 0.11\\n self.find_closed_orbits[\\\"subs_overrides\\\"][\\\"__do_bump__\\\"] = False\\n self.find_closed_orbits[\\\"final_subs_overrides\\\"].update(substitutions)\\n self.find_closed_orbits[\\\"max_iterations\\\"] = 0\\n self.find_closed_orbits[\\\"do_minuit\\\"] = True\\n self.find_closed_orbits[\\\"minuit_iterations\\\"] = 10 \\n self.find_closed_orbits[\\\"us_cell\\\"] = 0\\n self.find_closed_orbits[\\\"ds_cell\\\"] = 1\\n\\n self.find_da = {\\n \\\"run_dir\\\":\\\"tmp/find_da/\\\",\\n \\\"probe_files\\\":\\\"RINGPROBE01.h5\\\",\\n \\\"subs_overrides\\\":{\\\"__n_turns__\\\":101.1, \\\"__do_magnet_field_maps__\\\":\\\"False\\\", \\\"__step_size__\\\":0.01},\\n \\\"get_output_file\\\":\\\"get_da\\\",\\n \\\"scan_output_file\\\":\\\"scan_da\\\",\\n \\\"row_list\\\":None,\\n \\\"scan_x_list\\\":[],\\n \\\"scan_y_list\\\":[],\\n \\\"x_seed\\\":1.,\\n \\\"y_seed\\\":1.,\\n \\\"min_delta\\\":0.9,\\n \\\"max_delta\\\":1000.,\\n \\\"required_n_hits\\\":100,\\n \\\"dt_tolerance\\\":0.5, # fraction of closed orbit dt\\n \\\"max_iterations\\\":15,\\n \\\"decoupled\\\":True,\\n }\\n\\n max_amp = 0.01\\n self.track_beam_dummy = {\\n \\\"run_dir\\\":\\\"tmp/track_beam/\\\",\\n \\\"save_dir\\\":\\\"track_beam\\\",\\n \\\"print_events\\\":[0, 1, -1],\\n \\\"settings\\\":[{\\n \\\"name\\\":\\\"forwards\\\",\\n \\\"direction\\\":\\\"forwards\\\",\\n \\\"probe_files\\\":\\\"RINGPROBE01.h5\\\", \\n \\\"beam\\\":{\\n \\\"type\\\":\\\"beam_gen\\\",\\n \\\"closed_orbit_file\\\":\\\"closed_orbits_cache\\\",\\n \\\"eigen_emittances\\\":[[0, 0]]*3,#+[[max_amp, max_amp]],\\n \\\"n_per_dimension\\\":2,\\n \\\"variables\\\":[\\\"x\\\",\\\"x'\\\",\\\"y\\\",\\\"y'\\\"],\\n \\\"amplitude_dist\\\":\\\"uniform\\\", #\\\"grid\\\", # \\n \\\"phase_dist\\\":\\\"uniform\\\", #\\\"grid\\\", # \\n \\\"max_amplitude_4d\\\":max_amp, # amplitude_dist != grid\\n \\\"energy\\\":3.0,\\n },\\n \\\"subs_overrides\\\":{\\n \\\"__n_turns__\\\":45.1,\\n \\\"__hdf5__\\\":\\\"True\\\",\\n \\\"__do_magnet_field_maps__\\\":\\\"False\\\",\\n \\\"__do_bump__\\\":\\\"True\\\",\\n \\\"__step_size__\\\":0.01\\n },\\n },],\\n }\\n\\n T0 = ring_tof\\n self.track_beam = {\\n \\\"run_dir\\\":\\\"tmp/track_beam/\\\",\\n \\\"save_dir\\\":\\\"track_beam_rf_on_2\\\",\\n \\\"print_events\\\":[i for i in range(1)],\\n \\\"variables\\\":[\\\"x\\\", \\\"x'\\\", \\\"y\\\", \\\"y'\\\", \\\"t\\\", \\\"energy\\\"],\\n \\\"settings\\\":[{\\n \\\"name\\\":\\\"grid\\\",\\n \\\"direction\\\":\\\"forwards\\\",\\n \\\"probe_files\\\":\\\"RINGPROBE01.h5\\\", \\n \\\"beam\\\":{\\n \\\"type\\\":\\\"grid\\\",\\n \\\"energy\\\":3.0,\\n \\\"start\\\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, 0.0, 941.272],\\n \\\"stop\\\":[4357.646683446333, 0.0, -116.7090485272821, 0.0, T0, 941.272],\\n \\\"nsteps\\\":[1, 1, 1, 1, 4+1, 1],\\n },\\n \\\"subs_overrides\\\":{\\n \\\"__n_turns__\\\":100.1,\\n \\\"__hdf5__\\\":True,\\n \\\"__do_magnet_field_maps__\\\":False,\\n \\\"__do_bump__\\\":True,\\n \\\"__do_rf__\\\":True,\\n \\\"__do_foil__\\\":False,\\n \\\"__step_size__\\\":0.01\\n },\\n },],\\n }\",\n \"def __init__(self, hass, config):\\n super().__init__()\\n self._parent = hass\\n self.ezvizService = hass.data[PY_EZVIZ_GATEWAY]\\n\\n self.deviceSerial = self.ezvizService.deviceSerial\\n\\n self._interval_snapshots = 30\\n self._last_image = \\\"\\\"\\n self._last_snapshot_time = 0\",\n \"def __init__(self):\\n super().__init__()\\n\\n # Robot state\\n self.ask_mode = False\\n\\n # Connect two large motors on output ports B and C\\n self.sound = Sound()\\n self.leds = Leds()\\n self.p1 = TouchSensor(INPUT_1)\\n self.p2 = TouchSensor(INPUT_2)\\n self.p3 = TouchSensor(INPUT_3)\\n self.p4 = TouchSensor(INPUT_4)\",\n \"def __init__(self, board, sample_rate=1e6, bus_width=8):\\n\\n # Grab a reference to the board and its pattern-gen API.\\n self.board = board\\n self.api = board.apis.pattern_generator\\n\\n # Grab a reference to the user's bus parameters.\\n self.sample_rate = int(sample_rate)\\n self.bus_width = bus_width\\n\\n # FIXME: These should be read from the board, rather than hardcoded!\\n self.upload_chunk_size = 2048\\n self.samples_max = 32 * 1024\",\n \"def __init__(self, parant):\\n pass\",\n \"def initializeParameters(self):\\r\\n\\t\\tself.input_raster.enabled = True\\r\\n\\t\\tself.approach.enabled = True\\r\\n\\t\\tself.predefined_pattern.enabled = False\\r\\n\\t\\tself.predefined_pattern.value = 'Mexican Hat wavelet'\\r\\n\\t\\tself.pattern_workspace.enabled = False\\r\\n\\t\\tself.point_matrix_size.enabled = False\\r\\n\\t\\tself.point_matrix_size.value = 3\\r\\n\\t\\tself.point_vectors.enabled = False\\r\\n\\t\\tself.mapping_field.enabled = False\\r\\n\\t\\tself.move_to_max.enabled = False\\r\\n\\t\\tself.move_to_max_distance.enabled = False\\r\\n\\t\\tself.move_to_max_distance.value = 3\\r\\n\\t\\tself.mh_iteration.enabled = False\\r\\n\\t\\tself.mh_dil_val.enabled = False\\r\\n\\t\\tself.mh_dil_val.value = 1\\r\\n\\t\\tself.mh_dil_start.value = 0.01\\r\\n\\t\\tself.mh_dil_stop.value = 1\\r\\n\\t\\tself.mh_dil_step.value = 0.1\\r\\n\\t\\tself.mh_dil_start.enabled = False\\r\\n\\t\\tself.mh_dil_stop.enabled = False\\r\\n\\t\\tself.mh_dil_step.enabled = False\\r\\n\\t\\tself.transform.enabled = False\\r\\n\\t\\tself.size_of_the_cell.enabled = False\\r\\n\\t\\tself.size_of_the_cell.value = 1\\r\\n\\t\\tself.output_sim_matrix.enabled = False\\r\\n\\t\\tself.output_table.enabled = False\\r\\n\\t\\tself.output_raster_workspace.enabled = False\",\n \"def __init__(self):\\r\\n\\r\\n super(Metallized, self).__init__()\\r\\n\\r\\n # Initialize public scalar attributes.\\r\\n self.spec_sheet = 0\\r\\n if self.hazard_rate_type < 3: # MIL-HDBK-217\\r\\n self.reference_temperature = 358.0\",\n \"def __init__(self, portname, devicetype):\\n if devicetype == DEVICE_DEBUG_BOARD:\\n self.device = debugbox.DebugBox(portname)\\n elif devicetype == DEVICE_SFP_BREAKOUT:\\n self.device = sfpbreakout.SFP(portname)\\n elif devicetype == DEVICE_DUMMY_BOARD:\\n self.device = dummybox.DummyBox(portname)\\n else:\\n raise IOError(\\\"Invalid Device Type\\\")\\n \\n # Set up laser sections\\n self.mirror1 = Testrig.LaserSection(self, 814, 10, 90, 728, 844, 860, 864)\\n self.laser_phase = Testrig.LaserSection(self, 810, 11, 20, 726, 846, 858, 866)\\n self.gain = Testrig.LaserSection(self, 818, 12, 180, 730, 848, None, None)\\n# self.gain1.validator.setTop(150)\\n self.mirror2 = Testrig.LaserSection(self, 826, 14, 90, 734, 850, 862, 864)\\n# self.front.validator.setTop(60)\\n self.soa1 = Testrig.LaserSection(self, 822, 15, 300, 736, 852, None, None)\\n# self.soa1.validator.setTop(150)\\n self.soa2 = Testrig.LaserSection(self, 830, 16, 100, 738, 854, None, None, self.to_display_current_section2, self.to_internal_current_section2)\\n self.phase1 = Testrig.LaserSection(self, 834, 13, 100, 732, 856, None, None, self.to_display_current_section2, self.to_internal_current_section2)\\n \\n self.voltage_max = 2.5 # This is now a constant\\n \\n self.full_rig = True\",\n \"def __init__(self):\\n self._config = copy.copy(self._parameters)\",\n \"def __init__(self, world_map, GRID_LOCK, coordinates=None):\\n\\n ''' Take parameters, and Sprite Constants '''\\n super(BeesSprite, self).__init__(world_map, BeesSprite.IMAGE, GRID_LOCK,\\n BeesSprite.HEALTH_BAR, BeesSprite.AVG_SPEED,\\n BeesSprite.VISION, coordinates)\\n\\n self.type = \\\"bees\\\"\\n self.prey = [\\\"plant\\\"]\",\n \"def __init__(self):\\n # Bildschirmeinstellungen\\n self.screen_width = 1200\\n self.screen_height = 800\\n self.bg_color = (230, 230, 230)\\n # schiffeinstellungen\\n self.ship_limit = 3\\n # Geschosseinstellungen\\n self.bullet_width = 3\\n self.bullet_height = 15\\n self.bullet_color = (60, 60, 60)\\n self.bullets_allowed = 3\\n # Invasionsiffeinstellung\\n self.fleet_drop_speed = 7 # og drop 10\\n # Stärke der Beschleunigung des Spiels\\n self.speedup_scale = 1.05\\n # Stärke der Punktewerterhöhung bei Treffern\\n self.score_scale = 1.5\\n\\n self.initialize_dynamic_settings()\",\n \"def __init__(self, config, set_name, preprocess_image):\\n\\t\\t\\tself.data_dir = config['data_dir']\\n\\t\\t\\tself.set_name = set_name\\n\\t\\t\\tself.coco = COCO(os.path.join(self.data_dir, 'annotations', 'instances_' + set_name + '.json'))\\n\\t\\t\\tself.image_ids = self.coco.getImgIds()\\n\\t\\t\\tself.mask = config['mask']\\n\\n\\t\\t\\tself.load_classes()\\n\\n\\t\\t\\tsuper(CocoGenerator, self).__from_config__(config, preprocess_image=preprocess_image)\",\n \"def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\\\"DFTCL_settings\\\"):\\n\\n cl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": magmom, \\\"SAXIS\\\": None, \\\"LSORBIT\\\": None, \\\"LNONCOLLINEAR\\\": None}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIMX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"encut\\\", encut)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67721254","0.645361","0.63844484","0.638197","0.6350986","0.6348385","0.6343165","0.6325039","0.6300187","0.6279017","0.622937","0.62258977","0.6225211","0.62215316","0.62173843","0.62173843","0.6196136","0.6180987","0.6177326","0.6164835","0.61487406","0.6132704","0.61322767","0.61299556","0.6123877","0.6108182","0.6102512","0.6088802","0.6082851","0.60768396","0.6064397","0.6060805","0.60606724","0.6048849","0.6046263","0.60457575","0.6042167","0.60320437","0.60316926","0.60049695","0.6003853","0.59917426","0.59892535","0.59889203","0.598709","0.59817076","0.5980498","0.59770167","0.5973248","0.59532636","0.5949576","0.5933463","0.5932077","0.5923972","0.5923384","0.59193426","0.59008366","0.58985865","0.5898176","0.5896825","0.5893421","0.58890295","0.5887847","0.5883232","0.587359","0.5872142","0.58711743","0.5868231","0.5867452","0.58624023","0.58605987","0.5859","0.5857348","0.5855395","0.5851916","0.5849603","0.5842773","0.5835608","0.58351433","0.5833768","0.5833077","0.5830622","0.58243704","0.582316","0.5812146","0.5809584","0.5804939","0.58041626","0.58040524","0.58030313","0.5800098","0.5798616","0.57984316","0.57977235","0.57852083","0.57797605","0.5776814","0.57735395"],"string":"[\n \"0.67721254\",\n \"0.645361\",\n \"0.63844484\",\n \"0.638197\",\n \"0.6350986\",\n \"0.6348385\",\n \"0.6343165\",\n \"0.6325039\",\n \"0.6300187\",\n \"0.6279017\",\n \"0.622937\",\n \"0.62258977\",\n \"0.6225211\",\n \"0.62215316\",\n \"0.62173843\",\n \"0.62173843\",\n \"0.6196136\",\n \"0.6180987\",\n \"0.6177326\",\n \"0.6164835\",\n \"0.61487406\",\n \"0.6132704\",\n \"0.61322767\",\n \"0.61299556\",\n \"0.6123877\",\n \"0.6108182\",\n \"0.6102512\",\n \"0.6088802\",\n \"0.6082851\",\n \"0.60768396\",\n \"0.6064397\",\n \"0.6060805\",\n \"0.60606724\",\n \"0.6048849\",\n \"0.6046263\",\n \"0.60457575\",\n \"0.6042167\",\n \"0.60320437\",\n \"0.60316926\",\n \"0.60049695\",\n \"0.6003853\",\n \"0.59917426\",\n \"0.59892535\",\n \"0.59889203\",\n \"0.598709\",\n \"0.59817076\",\n \"0.5980498\",\n \"0.59770167\",\n \"0.5973248\",\n \"0.59532636\",\n \"0.5949576\",\n \"0.5933463\",\n \"0.5932077\",\n \"0.5923972\",\n \"0.5923384\",\n \"0.59193426\",\n \"0.59008366\",\n \"0.58985865\",\n \"0.5898176\",\n \"0.5896825\",\n \"0.5893421\",\n \"0.58890295\",\n \"0.5887847\",\n \"0.5883232\",\n \"0.587359\",\n \"0.5872142\",\n \"0.58711743\",\n \"0.5868231\",\n \"0.5867452\",\n \"0.58624023\",\n \"0.58605987\",\n \"0.5859\",\n \"0.5857348\",\n \"0.5855395\",\n \"0.5851916\",\n \"0.5849603\",\n \"0.5842773\",\n \"0.5835608\",\n \"0.58351433\",\n \"0.5833768\",\n \"0.5833077\",\n \"0.5830622\",\n \"0.58243704\",\n \"0.582316\",\n \"0.5812146\",\n \"0.5809584\",\n \"0.5804939\",\n \"0.58041626\",\n \"0.58040524\",\n \"0.58030313\",\n \"0.5800098\",\n \"0.5798616\",\n \"0.57984316\",\n \"0.57977235\",\n \"0.57852083\",\n \"0.57797605\",\n \"0.5776814\",\n \"0.57735395\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.607687"},"document_rank":{"kind":"string","value":"30"}}},{"rowIdx":22393,"cells":{"query":{"kind":"string","value":"Initialize the plot text text color instance"},"document":{"kind":"string","value":"def __init__(self, red=Black.red, green=Black.green, blue=Black.blue):\n self.color = Color(red, green, blue)\n\n self.template = '\\ttextcolor = {textcolor};\\n'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def text(self, txt, ax=None, color='black'):\n _plot_text(self, txt, ax, color)","def text(self, txt, ax=None, color='black'):\n _plot_text(self, txt, ax, color)","def text(self, str: str, x: int, y: int, colour: int, /) -> None:","def text_plot(self):\n if self.stext is not None:\n # Create text object :\n self.stextmesh = visu.Text(text=self.stext, color=self.stextcolor,\n font_size=self.stextsize, pos=self.xyz,\n bold=True, name='SourcesText')\n\n # Set text texture :\n self.stextmesh.set_gl_state('translucent', depth_test=True)\n\n # Apply a transformation to text elements to not cover sources :\n self.stextmesh.transform = vist.STTransform(\n translate=self.stextshift)\n else:\n self.stextmesh = visu.Text(name='NoneText')","def __init__(self, plot_design):\n self.plot_design = plot_design\n\n text_color = self.plot_design.text_color\n text_font = self.plot_design.text_font\n sns.set_style(\"white\",\n {\n \"axes.edgecolor\": text_color,\n \"axes.labelcolor\": text_color,\n \"text.color\": text_color,\n \"font.sans-serif\": [text_font],\n \"xtick.color\": text_color,\n \"ytick.color\": text_color,\n }\n )","def __init__(self, font, color, text=\"\", top=0, left=0, bottom=None, right=None):\n self.text = text\n self.font = font\n self.color = color\n self.top = top\n self.left = left\n self.bottom = bottom\n self.right = right\n self.renderLabel()","def __init__(self, text, font, pos, color=(255, 255, 255)):\r\n self.pos = pos\r\n self.label = font.render(text, 1, color)","def get_text_color ( self, object ):\n return self.text_color_","def set_label_text(self, text, warn=False):\n self.text = text\n self.warn = warn\n self.opacity = .8\n if self.warn:\n self.color = 'darksalmon'\n else:\n self.color = 'lightgreen'\n self.on_size()","def _setPlotValText(ax, texts, core, data, labels, labelFmt, fontSize, collection):\n _ = core.getAssemblyPitch()\n for a, val, label in zip(core, data, labels):\n x, y, _ = a.spatialLocator.getLocalCoordinates()\n cmap = collection.get_cmap()\n patchColor = numpy.asarray(cmap(collection.norm(val)))\n luminance = patchColor.dot(LUMINANCE_WEIGHTS)\n dark = luminance < 0.5\n if dark:\n color = \"white\"\n else:\n color = \"black\"\n # Write text on top of patch locations.\n if label is None and labelFmt is not None:\n # Write the value\n labelText = labelFmt.format(val)\n text = ax.text(\n x,\n y,\n labelText,\n zorder=1,\n ha=\"center\",\n va=\"center\",\n fontsize=fontSize,\n color=color,\n )\n elif label is not None:\n text = ax.text(\n x,\n y,\n label,\n zorder=1,\n ha=\"center\",\n va=\"center\",\n fontsize=fontSize,\n color=color,\n )\n else:\n # labelFmt was none, so they don't want any text plotted\n continue\n texts.append(text)","def SetTextColour(self, colText):\r\n \r\n self._colText = colText","def __init__(self, text, start, end, color, alpha=1):\n self.text = text\n self.start = start\n self.end = end\n self.color = color\n self.alpha = alpha","def init_text(self):\n d = self.declaration\n if d.text:\n self.set_text(d.text)\n if d.text_color:\n self.set_text_color(d.text_color)\n if d.text_alignment:\n self.set_text_alignment(d.text_alignment)\n if d.font_family or d.text_size:\n self.refresh_font()\n if hasattr(d, 'max_lines') and d.max_lines:\n self.set_max_lines(d.max_lines)","def render_text(self, title, pos, color):\n if title not in self.text_elements:\n self.text_elements[title] = UIText(\n title, pos, font_size=20, font_color=color)","def _setText(self, text):\n self.text = \"\"\n for ch in text:\n char, vertices, glyph = self._extractGlyph(ch)\n if not vertices is None and self.glyphs in (\n GlyphTypes.BASE, GlyphTypes.LAYER_COLOR):\n vertices['rgba'] = glm.vec4(self.color)\n if not self.text:\n off, kern = self._updateMetric(0, char)\n if char in self.NO_GLYPH_CHARS:\n self.colors.append([char, None])\n else:\n vertices['vtx'] += off + glyph['offset']\n self.allVertices = np.hstack(vertices)\n self.allIndices = self._baseInd\n self.colors.append([char, self.color])\n self.text += char\n else:\n pos = len(self.text)\n nonGlyph = countInSet(self.text, self.NO_GLYPH_CHARS)\n # Set the metric\n off, kern = self._updateMetric(pos, char)\n if char in self.NO_GLYPH_CHARS:\n self.colors.append([char, None])\n else:\n vertices['vtx'] += off + kern + glyph['offset']\n if self.allVertices is None:\n self.allVertices = np.hstack(vertices)\n else:\n self.allVertices = np.append(self.allVertices,\n vertices)\n if self.allIndices is None:\n self.allIndices = self._baseInd\n else:\n self.allIndices = np.vstack((self.allIndices,\n self._baseInd + (pos - nonGlyph) * 4))\n self.colors.append([char, self.color])\n self.text += char\n self.setUniforms()","def text_draw(self, x, y, text, style={}):","def _colorstr(self, args):","def _plot_text(polyreg, txt, ax, color):\n ax = _newax(ax)\n rc, xc = cheby_ball(polyreg)\n ax.text(xc[0], xc[1], txt, color=color)","def __init__(self, text=None, **style):\n\n self.lines = []\n if text is not None:\n self.append(text)\n Figure.__init__(self, style)","def setDefaultTextColor(self, Union, QColor=None, Qt_GlobalColor=None, QGradient=None): # real signature unknown; restored from __doc__\n pass","def set_intro_text(self, text, color):\n text_y_position = (self.border.inner_y() + self.objects['titleimage'].t()) / 2\n self.objects['introtext'] = LCARSText((self.width/2, text_y_position),\n text,\n 36,\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)","def set_text(self, text):\n self.str = text\n self.image = self.font.render(self.str, True, self.font_color)\n self.rect.width = self.image.get_width()\n self.rect.height = self.image.get_height()","def main():\n fout_png = 'color0.png'\n _, axis = plt.subplots(1, 1, figsize=(6, 6))\n colors = [\n '#0032ff',\n '#00ebff',\n '#fdfe02',\n '#ff0000',\n '#8500ff',\n ]\n plt_color_text(colors)\n plt.savefig(fout_png)","def set_intro_text_3(self, text, color):\n text_y_position = self.objects['introtext2'].t() + 80\n self.objects['introtext3'] = LCARSText((self.width/2, text_y_position),\n text,\n 36,\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)","def initialize_plot(self):\n\n self.scat = self.config.ax.scatter(self.lons[0, 0], self.lats[0, 0], c=self.z[0,0]\n # , vmax=self.vmax\n # , vmin=self.vmin\n # ,cmap='coolwarm'\n # , norm=LogNorm()\n , transform=self.config.projection, edgecolor='none', s=0.6)\n cbar = plt.colorbar(self.scat)\n # cbar.set_label('Melt water Flux (mm/yr)')\n\n self.ttl = self.config.ax.text(1.5, 1.05, '', transform=self.config.ax.transAxes, va='center')\n\n return self.scat","def set_text(self, text: str) -> None:\n self._text = text\n self._text_image = self._style.font.render(\n text, True, self._style.primary_color)\n self._redraw()","def __init__(self, a_width, a_height, a_point, a_str, window, text_size, color):\n self.width = a_width\n self.height = a_height\n self.cen_point = a_point\n \n # retrieve the x and y coordinates of the center point and \n # save them as instance variables\n self.cen_point_x = a_point.getX()\n self.cen_point_y = a_point.getY()\n self.text = graphics.Text(self.cen_point, a_str)\n self.text.setSize(text_size)\n self.text.setFill(color)\n self.draw_but(window)","def draw_text(self, text, i, j, **params):","def GetTextColour(self):\r\n \r\n return self._colText","def draw_text(self, text, position, font_size, font_color):\n font_color = check_color(font_color)\n STtext.text(self.canvas, text, position, font_size, font_color)","def text(self, a, b, text, angle=0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\n table = toyplot.data.Table()\n table[\"x\"] = _require_scalar_vector(a)\n table[\"y\"] = _require_scalar_vector(b, table.shape[0])\n table[\"text\"] = _broadcast_object(text, table.shape[0])\n table[\"angle\"] = _broadcast_scalar(angle, table.shape[0])\n table[\"fill\"] = _broadcast_object(fill, table.shape[0])\n table[\"opacity\"] = _broadcast_scalar(opacity, table.shape[0])\n table[\"title\"] = _broadcast_object(title, table.shape[0])\n style = _combine_styles({\"font-weight\":\"normal\", \"stroke\":\"none\", \"text-anchor\":\"middle\", \"alignment-baseline\":\"middle\"}, _require_style(style))\n id = _require_optional_id(id)\n\n default_color = next(self._text_colors)\n table[\"toyplot:fill\"] = toyplot.color._broadcast_color(default_color if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\n\n self._update_domain(table[\"x\"], table[\"y\"])\n\n self._children.append(TextMark(table=table, coordinates=[\"x\", \"y\"], axes=[\"x\", \"y\"], text=\"text\", angle=\"angle\", fill=\"toyplot:fill\", opacity=\"opacity\", title=\"title\", style=style, id=id))\n return self._children[-1]","def set_intro_text_2(self, text, color):\n text_y_position = (self.objects['titleimage'].b() + self.border.bottom()) / 2\n\ttext_y_position = text_y_position - 30\n self.objects['introtext2'] = LCARSText((self.width/2, text_y_position),\n text,\n 36,\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)","def title(self, text, x=0.01, y=0.99, size=15, color=None,\n bgcolor=None, alpha=1, **kwargs):\n if color is None:\n color = 'k' if self._black_bg else 'w'\n if bgcolor is None:\n bgcolor = 'w' if self._black_bg else 'k'\n self.frame_axes.text(x, y, text, \n transform=self.frame_axes.transAxes,\n horizontalalignment='left',\n verticalalignment='top',\n size=size, color=color,\n bbox=dict(boxstyle=\"square,pad=.3\", \n ec=bgcolor, fc=bgcolor, alpha=alpha),\n **kwargs)","def text(self, x, y, text, angle=0.0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\n table = toyplot.data.Table()\n table[\"x\"] = _require_scalar_vector(x)\n table[\"y\"] = _require_scalar_vector(y, table.shape[0])\n table[\"text\"] = _broadcast_object(text, table.shape[0])\n table[\"angle\"] = _broadcast_scalar(angle, table.shape[0])\n table[\"fill\"] = _broadcast_object(fill, table.shape[0])\n table[\"toyplot:fill\"] = toyplot.color._broadcast_color(toyplot.color.near_black if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\n table[\"opacity\"] = _broadcast_scalar(opacity, table.shape[0])\n table[\"title\"] = _broadcast_object(title, table.shape[0])\n style = _combine_styles({\"font-weight\":\"normal\", \"stroke\":\"none\", \"text-anchor\":\"middle\", \"alignment-baseline\":\"middle\"}, _require_style(style))\n id = _require_optional_id(id)\n\n self._children.append(TextMark(table=table, coordinates=[\"x\", \"y\"], axes=[\"x\", \"y\"], text=\"text\", angle=\"angle\", fill=\"toyplot:fill\", opacity=\"opacity\", title=\"title\", style=style, id=id))\n return self._children[-1]","def define_text(cls, position, text, scale=1, rotation=0, text_width=1):\n \n position = np.array(position)\n text_obj = cls()\n text_obj.coord = []\n sep = 0.2*scale\n posref=np.array([0.0,0.0])\n \n #complete text length\n #textlen = 0.5*sep*(len(text)-1)+0.5*scale * np.sum([np.array(Feature.hershey_table[ord(text[x])]['width']) for x in range(len(text))])\n for x in range(len(text)):\n code = ord(text[x])\n letter = Feature.hershey_table[code]\n if x>0:\n posref[0]=posref[0]+ scale*np.array(letter['width'])/2+sep\n #posref[0] = posref[0]-textlen\n #position[0] = position[0] + scale*np.array(letter['width'])/2+sep\n for k in letter['coord']:\n if len(k)>0:\n k = np.array(k)\n coord = [scale*m+posref-np.array([0,0.5*scale]) for m in k]\n text_obj.coord.append(np.squeeze(coord))\n posref[0]=posref[0]+ scale*np.array(letter['width'])/2+sep\n \n #align number\n posref[0]=posref[0]- scale*np.array(letter['width'])/2-sep\n for x in range(len(text_obj.coord)):\n text_obj.coord[x] = np.array([z- np.array([posref[0]/2,0]) for z in text_obj.coord[x]])\n \n #Rotate the number by alpha\n if rotation !=0:\n\n alpha = rotation\n R = np.array([[np.cos(alpha),-np.sin(alpha)],[np.sin(alpha),np.cos(alpha)]])\n\n for i in range(len(text_obj.coord)):\n text_obj.coord[i] = np.squeeze([np.dot([x],R) \n for x in text_obj.coord[i]])\n \n #move the number to position pos\n for x in range(len(text_obj.coord)):\n text_obj.coord[x] = np.array([z+ position for z in text_obj.coord[x]])\n \n #make thick numbers (currently unused)\n '''for n in range(len(text_obj.coord)):\n x = text_obj.coord[n][:,0]\n y = text_obj.coord[n][:,1]\n x2 = np.array([[x[i],(2/3*x[i]+1/3*x[i+1]),(1/3*x[i]+2/3*x[i+1])] for i in range(x.shape[0]-1)])\n y2 = np.array([[y[i],(2/3*y[i]+1/3*y[i+1]),(1/3*y[i]+2/3*y[i+1])] for i in range(y.shape[0]-1)])\n x2 = np.concatenate(x2)\n x2 = np.append(x2,x[-1])\n y2 = np.concatenate(y2)\n y2 = np.append(y2,y[-1])\n x=x2\n y=y2\n\n #nt = np.linspace(0, 1, 40)\n #t = np.zeros(x.shape)\n #t[1:] = np.sqrt((x[1:] - x[:-1])**2 + (y[1:] - y[:-1])**2)\n #t = np.cumsum(t)\n #t /= t[-1]\n #x2 = scipy.interpolate.spline(t, x, nt,order=2)\n #y2 = scipy.interpolate.spline(t, y, nt,order=2)\n newcoord = np.stack((x2,y2),axis=1)\n \n csx = CubicSpline(np.linspace(0, 1, x.shape[0]), x,bc_type = 'clamped')\n csy = CubicSpline(np.linspace(0, 1, y.shape[0]), y,bc_type = 'clamped')\n s\n xs = np.linspace(0, 1,1000)\n newcoord = np.stack((csx(xs),csy(xs)),axis=1)\n \n text_obj.coord[n]=Feature.define_tube(newcoord,0,100).coord[0]'''\n #set object as text type\n text_obj.open = True\n text_obj.text_width = text_width\n return text_obj","def draw_text(text, start_x, start_y, color, size):\n\n if len(color) == 3:\n color = (color[0], color[1], color[2], 255)\n\n label = pyglet.text.Label(text,\n font_name='Times New Roman',\n font_size=size,\n x=start_x, y=start_y,\n color=color)\n GL.glLoadIdentity()\n\n label.draw()","def _set_text(self, text):\n if not self.has_textframe:\n raise TypeError(\"cannot set text of shape with no text frame\")\n self.textframe.text = _to_unicode(text)","def render_text(self, text, x_pos, y_pos, colour=0):\n if colour == 0:\n GL.glColor3f(0.0, 0.0, 0.0) # text is black\n elif colour == 1:\n GL.glColor3f(1.0, 0.0, 0.0)\n elif colour == 2:\n GL.glColor3f(0.0, 1.0, 0.0)\n GL.glRasterPos2f(x_pos, y_pos)\n font = GLUT.GLUT_BITMAP_HELVETICA_12\n\n for character in text:\n if character == '\\n':\n y_pos = y_pos - 20\n GL.glRasterPos2f(x_pos, y_pos)\n else:\n GLUT.glutBitmapCharacter(font, ord(character))","def __init__(self, text: str) -> None:\n\n super().__init__()\n\n self._width = 0\n self._opacity = 255\n self._sprites = []\n self._text = text\n for index, c in enumerate(text):\n y_offset = 0\n if c in Text.characters:\n if Text.characters[c][1]:\n y_offset = 2\n c = Text.characters[c][0]\n elif c.isupper():\n c = c.lower() + \"_maj\"\n self._sprites.append(\n cocos.sprite.Sprite(pyglet.image.load(PATH + '/assets/img/common/font/{0}.png'.format(c))))\n self._sprites[index].position = self._width, (self._sprites[index].height - 11) / 2 - y_offset\n self._width += self._sprites[index].width\n self.add(self._sprites[index])","def __init__(self, text='', x=0, y=0):\n \n #Some defaults\n self.text = text\n self.text_color = (255, 255, 255)\n self.text_size = 36\n self.x = x\n self.y = y\n self.font = pygame.font.Font(None, self.text_size)\n self.text_img = self.font.render(self.text, True, self.text_color)\n self.text_rect = self.text_img.get_rect()\n self.text_rect.left = self.x\n self.text_rect.top = self.y","def __add_texts(self, ax, values, param_location,\n temp_values=None, temp_compare_values=None,\n value_colors=None, value_bck_colors=None,\n compare_values=None, compare_value_colors=None,\n compare_value_bck_colors=None,\n kwargs_params=None, kwargs_values=None, kwargs_compare_values=None):\n # set to empty dict if None\n if kwargs_params is None:\n kwargs_params = dict()\n if kwargs_values is None:\n kwargs_values = dict()\n if kwargs_compare_values is None:\n kwargs_compare_values = dict()\n\n # total length of parameters\n total_params = len(self.params)\n\n # get the rotation angles\n rotation = (2 * np.pi / total_params) * np.arange(total_params)\n\n # flip the rotation if the label is in lower half\n mask_flip_label = (rotation > np.pi / 2) & (rotation < np.pi / 2 * 3)\n rotation[mask_flip_label] = rotation[mask_flip_label] + np.pi\n rotation_degrees = -np.rad2deg(rotation)\n\n # plot params\n for x, rotation, label in zip(self.theta, rotation_degrees, self.params):\n temp_text = ax.text(\n x, param_location, label,\n rotation=rotation, rotation_mode=\"anchor\",\n ha=\"center\", **kwargs_params\n )\n\n self.param_texts.append(temp_text)\n\n # plot values\n for i, (x, value, rotation) in enumerate(zip(self.theta, values, rotation_degrees)):\n if value_colors is not None:\n kwargs_values[\"color\"] = value_colors[i]\n if value_bck_colors is not None and kwargs_values.get(\"bbox\") is not None:\n kwargs_values[\"bbox\"][\"facecolor\"] = value_bck_colors[i]\n\n temp_text = ax.text(\n x, temp_values[i], value, ha=\"center\", **kwargs_values\n )\n\n self.value_texts.append(temp_text)\n\n # plot comparison values\n if compare_values is not None:\n for i, (x, value, rotation) in enumerate(zip(self.theta, compare_values,\n rotation_degrees)):\n if compare_value_colors is not None:\n kwargs_compare_values[\"color\"] = compare_value_colors[i]\n if compare_value_bck_colors is not None and kwargs_values.get(\"bbox\") is not None:\n kwargs_compare_values[\"bbox\"][\"facecolor\"] = compare_value_bck_colors[i]\n\n if temp_compare_values is not None:\n value_1 = temp_compare_values[i]\n value_2 = value\n else:\n value_1 = value_2 = value\n\n temp_text = ax.text(\n x, value_1, value_2, ha=\"center\", **kwargs_compare_values\n )\n\n self.compare_value_texts.append(temp_text)","def _update(self):\n\n if self.rgb:\n self._canvas['bg'] = tks.color_funcs.rgb_to_hex_string(self.rgb)\n self._text['text'] = self._color_info_text()\n else:\n self._canvas['bg'] = self._blank_label_color\n self._text['text'] = ''","def plt_color_text(colors):\n num_vals = len(colors)\n xvals = [10]*num_vals\n yvals = range(num_vals)\n for idx, (xval, yval, color) in enumerate(zip(xvals, yvals, colors)):\n plt.scatter(xval, yval, s=1000, marker='s', color=color)\n plt.text(xval+.004, yval, color, fontsize=20, va='center')","def draw_text(self, text, position=(0, 0), color='black', font=None,\n font_size=12, rotation=0, **kwargs):\n font = self.font(font_size)\n\n text_image = Image.new('L', self.dimensions, 'black')\n draw_text_image = ImageDraw.Draw(text_image)\n draw_text_image.text(position, text, font=font, fill='white')\n\n alpha = Image.new('L', self.dimensions)\n alpha = ImageChops.lighter(alpha, text_image)\n\n solidcolor = Image.new('RGBA', self.dimensions, color)\n image_mask = Image.eval(text_image, lambda p: 255 * (int(p != 0)))\n self.base_image = Image.composite(solidcolor, self.base_image, image_mask)\n self.base_image.putalpha(alpha)","def colorful_text(text, color=Fore.RESET):\n return color + text + Fore.RESET","def set_text_attr(color):\n SetConsoleTextAttribute(stdout_handle, color)","def color_text(txt, foreground=PALETTE['white'], background=PALETTE['black']):\n if isinstance(foreground, str) and foreground.startswith('#'):\n foreground = hex_to_rgb(foreground)\n if isinstance(background, str) and background.startswith('#'):\n background = hex_to_rgb(background)\n return '{}{}{}{}'.format(_f(*foreground), _b(*background), txt, _r())","def use_black_text(self): \n black_foreground = 0\n for color in range(curses.COLORS):\n curses.init_pair(color, black_foreground, color)","def add_text(self, x, y, text, style=None):\n style = self.__prepare_style(style, ' ')\n for i, c in enumerate(text):\n if self.check_coord_in_range(x + i, y):\n text_style = Style(c, style.fg_color, style.bg_color, style.font_style)\n self.canvas[y][x + i] = text_style","def colorText(text, color):\n\tif text is not None and color is not None:\n\t\tif isinstance(color, types.StringTypes):\n\t\t\tcolor = ColorUtil.keyToType(color)\n\t\tif color >= 0:\n\t\t\treturn CyTranslator().changeTextColor(text, color)\n\treturn text","def _labels_pcolor(self, obj, fmt=None, **kwargs):\n # Parse input args and populate _facecolors, which is initially unfilled\n # See: https://stackoverflow.com/a/20998634/4970632\n fmt = _not_none(fmt, pticker.SimpleFormatter())\n labels_kw = {'size': rc['text.labelsize'], 'ha': 'center', 'va': 'center'}\n labels_kw.update(kwargs)\n obj.update_scalarmappable() # populate _facecolors\n\n # Get positions and contour colors\n array = obj.get_array()\n paths = obj.get_paths()\n colors = _to_ndarray(obj.get_facecolors())\n edgecolors = _to_ndarray(obj.get_edgecolors())\n if len(colors) == 1: # weird flex but okay\n colors = np.repeat(colors, len(array), axis=0)\n if len(edgecolors) == 1:\n edgecolors = np.repeat(edgecolors, len(array), axis=0)\n\n # Apply colors\n labs = []\n for i, (color, path, num) in enumerate(zip(colors, paths, array)):\n if not np.isfinite(num):\n edgecolors[i, :] = 0\n continue\n bbox = path.get_extents()\n x = (bbox.xmin + bbox.xmax) / 2\n y = (bbox.ymin + bbox.ymax) / 2\n if 'color' not in kwargs:\n _, _, lum = to_xyz(color, 'hcl')\n if lum < 50:\n color = 'w'\n else:\n color = 'k'\n labels_kw['color'] = color\n lab = self.text(x, y, fmt(num), **labels_kw)\n labs.append(lab)\n obj.set_edgecolors(edgecolors)\n\n return labs","def text(self, font, text, x0, y0, color=WHITE, background=BLACK):\n if font.WIDTH == 8:\n self._text8(font, text, x0, y0, color, background)\n else:\n self._text16(font, text, x0, y0, color, background)","def __init__(self, x, y, w, h, text=''):\n self.rect = pygame.Rect(x, y, w, h)\n self.color = COLOR_INACTIVE\n self.text = text\n self.txt_surface = FONT.render(text, True, self.color)\n self.active = False","def __init__(self, ecran, text, police, taille, pos = (0,0), couleurText = (0,0,0), couleurFond = (255,255,255)):\n self.ecran = ecran\n self.txt = text # val String du texte\n self.police = police\n self.taille = taille\n self.pos = pos\n self.couleurText = couleurText\n self.couleurFond = couleurFond\n self.text = Texte(self.ecran, self.txt, self.police, self.taille, self.pos, self.couleurText) # objet Texte\n self.textRect = self.text.textRend.get_rect()\n self.dim = (self.textRect.w + 5, self.textRect.h + 5) # dims -> dimensions du texte + marge de 5px","def add_text(self, text, color, pos, font):\n text = font.render(text, True, color)\n text_rec = text.get_rect(center=pos)\n self.window.blit(text, text_rec)","def render_text(self, text, x_pos, y_pos, z_pos):\n GL.glDisable(GL.GL_LIGHTING)\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\n font = GLUT.GLUT_BITMAP_HELVETICA_10\n\n for character in text:\n if character == '\\n':\n y_pos = y_pos - 20\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\n else:\n GLUT.glutBitmapCharacter(font, ord(character))\n\n GL.glEnable(GL.GL_LIGHTING)","def __init__(self, text, pos=(0, 0), anchor='ll',\n size=16,\n sizeUnits=POINT_UNITS,\n color=(1, 0, 1, 1),\n dpi=None,\n yPointsUp=None,\n font_file=None,\n angle=0.,\n glyphs=GlyphTypes.BASE,\n filterControl=True):\n self.logger = namedLogger(__name__, self.__class__)\n if dpi:\n if Label.DPI is None:\n Label.DPI = dpi\n else:\n self.DPI = dpi\n if yPointsUp:\n if Label.Y_POINTS_UP is None:\n Label.Y_POINTS_UP = yPointsUp\n else:\n self.Y_POINTS_UP = yPointsUp\n self.color = color\n self.glyphs = glyphs\n self.filterControl = filterControl\n\n # Model matrix\n self.transform = Transform()\n\n if font_file is None:\n font_file = Path(__file__).parent.joinpath(Label.DEFAULT_FONT)\n self.font = Font(font_file)\n self._lineWidth = 0\n self._labelWidth = 0\n self._labelHeight = self.font.table['linespace']\n self.setSize(size, sizeUnits)\n\n self._baseInd = np.array([0, 1, 2, 2, 3, 0], np.uint32)\n self.allVertices = None\n self.allIndices = None\n self.extracted = {}\n # Offet, kerning, next_char_shift\n self._string_metric = []\n\n # Set text\n if self.filterControl:\n text = self._filterControl(text)\n self.shader = Shader.fromString(*self._getShaderCode())\n self._setText(text)\n self._setMesh()\n self.model = Transform()\n self.setPos(*pos, anchor)\n self.setRotation(angle)","def figtext(self, x, y, s='text', fontsize=9, halign='left'):\n plt.figtext(x=x, y=y, s=s, fontsize=self.ftsize1, horizontalalignment=halign)","def rainbow_text(x,y,ls,lc,**kw):\n t = plt.gca().transData\n fig = plt.gcf()\n plt.show()\n \n #horizontal version\n for s,c in zip(ls,lc):\n text = plt.text(x,y,\" \"+s+\" \",color=c, transform=t, **kw)\n text.draw(fig.canvas.get_renderer())\n ex = text.get_window_extent()\n t = transforms.offset_copy(text._transform, x=ex.width, units='dots')","def plot_markup():\n\n plt.rc('text', usetex=True)\n plt.rc('font', family='serif')\n plt.rc('font', size=18)","def add_text(self, text, size=10, x=0.5, y=0.5, ha='center', va='center', bbox='default', **kwargs):\n if bbox == 'default':\n bbox = {'boxstyle': 'square', 'fc': 'none'}\n\n return self.ax.text(x=x, y=y, s=text, size=size, ha=ha, va=va, bbox=bbox, **kwargs)","def __init__(self):\n self.colors = (\n 'BLACK', 'RED', 'GREEN', 'YELLOW',\n 'BLUE', 'MAGENTA', 'CYAN', 'WHITE'\n )\n\n self.disable_color = True\n\n if sys.stdout.isatty():\n self.disable_color = False","def c_prnt(self, text, color):\n if color == 'pink':\n a = self.pink\n elif color == 'blue':\n a = self.blue\n elif color == 'green':\n a = self.green\n elif color == 'dgrn':\n a = self.dgrn\n elif color == 'yel':\n a = self.yellow\n elif color == 'amber':\n a = self.amber\n else:\n raise Exception('The color you selected is not acceptable')\n print(a + text + self.ENDC)","def _on_node_enter(self, *args): # pylint: disable=W0613\r\n self._text.node[\"fg\"] = (self.text_color.x + 0.1, self.text_color.y + 0.1,\r\n self.text_color.z + 0.1, 1.0)","def __init__(self, x, y, align_bottom_left, text, font, color):\n super(TextBox, self).__init__(x, y)\n\n self.align_bottom_left = align_bottom_left\n self.__text = text\n self.font = font\n self.color = color\n self.original_x = x\n self.original_y = y\n self.image = None\n self.image_rect = None\n\n self.set_text(text)","def SetTextBackground(*args, **kwargs):\n return _gdi_.DC_SetTextBackground(*args, **kwargs)","def __init__(self, text=None, settings=None, style='General', language='en'):\n\n self._text = None\n self._settings = None\n self._style = None\n self._language = None\n\n self.text = text\n self.settings = settings\n self.style = style\n self.language = language","def text_objects(self, text, font, color):\n\n text_surface = font.render(text, True, color)\n return text_surface, text_surface.get_rect()","def __init__(self, text):\n\n self.text = text","def __init__(self, width=None, height=None, loads=None, fg=None, bg=None):\n if width is None and height is None and loads is not None:\n # self.width and self.height are set based on the size of the loads string\n loadsLines = loads.splitlines() # TODO - how to handle \\r\\n cases?\n self._width = max([len(line) for line in loadsLines])\n self._height = len(loadsLines)\n else:\n # self.width and self.height are set based on the width and height parameters\n if width is None:\n self._width = DEFAULT_CANVAS_WIDTH\n else:\n if not isinstance(width, int):\n raise PyTextCanvasException('`width` arg must be an int, not %r' % (width.__class__.__name__))\n if width < 1:\n raise PyTextCanvasException('`width` arg must be 1 or greater, not %r' % (width))\n self._width = width\n\n if height is None:\n self._height = DEFAULT_CANVAS_HEIGHT\n else:\n if not isinstance(height, int):\n raise PyTextCanvasException('`height` arg must be an int, not %r' % (height.__class__.__name__))\n if height < 1:\n raise PyTextCanvasException('`height` arg must be 1 or greater, not %r' % (height))\n self._height = height\n\n # The data structure for the characters in this canvas. The None value\n # represents a \"transparent\" character when canvases are layered on\n # top of each other, as opposed to a space ' ' which will cover up\n # that cell with a blank space.\n\n # =====================================\n # ============= IMPORTANT =============\n # =====================================\n # If you directly modify _chars, be sure to set _strDirty to True, otherwise\n # the cached version of the string will be returned and any changes won't\n # be reflected.\n self._chars = [[None] * self._height for i in range(self._width)]\n\n\n # NOTE: A None value for color is like a None value for character, while CLEAR would be similar to a space character.\n if fg is not None and fg not in range(9):\n raise PyTextCanvasException('fg arg must be None or one of the CLEAR, BLACK, WHITE, RED, GREEN, BLUE, CYAN, MAGENTA, YELLOW constants.')\n if bg is not None and bg not in range(9):\n raise PyTextCanvasException('bg arg must be None or one of the CLEAR, BLACK, WHITE, RED, GREEN, BLUE, CYAN, MAGENTA, YELLOW constants.')\n self._fg = fg\n self._bg = bg\n\n # The foreground & background of each cell in the canvas. These are\n # stored as one of the six color constants, which are ints 0-7. The None\n # value represents the RESET'd color in colorama.\n # Currently we're sticking to the 8 colors in the colorama module. More might be added later.\n self._fginfo = [[None] * self._height for i in range(self._width)]\n self._bginfo = [[None] * self._height for i in range(self._width)]\n # TODO - the rest of the color implementation needs to be done.\n\n self._cursor = (0, 0) # The cursor is always set to integers.\n\n if loads is not None:\n # Pre-populate with a string.\n self.loads(loads)\n\n self._strCache = None # Cached returned value from __str__()\n self._strDirty = True # If False, __str__() uses the cached value.","def textObject(text, font, color):\n\n textSurface = font.render(text, True, color)\n return textSurface, textSurface.get_rect()","def __init__(self, text):\n self.text = text","def __init__(self, text):\n self.text = text","def plot_colour(self, label):\n label = label.lower()\n pretty_colours = {}\n # SPIce HD\n pretty_colours['544'] = 'maroon'\n pretty_colours['545'] = 'goldenrod'\n pretty_colours['548'] = 'blueviolet'\n pretty_colours['549'] = 'forestgreen'\n # H2\n ## DOM Efficiency Sets\n pretty_colours['551'] = 'cornflowerblue'\n pretty_colours['552'] = 'cornflowerblue'\n pretty_colours['553'] = 'cornflowerblue'\n pretty_colours['554'] = 'mediumseagreen'\n pretty_colours['555'] = 'mediumseagreen'\n pretty_colours['556'] = 'mediumseagreen'\n ## Hole Ice Sets\n pretty_colours['560'] = 'olive'\n pretty_colours['561'] = 'olive'\n pretty_colours['564'] = 'darkorange'\n pretty_colours['565'] = 'darkorange'\n pretty_colours['572'] = 'teal'\n pretty_colours['573'] = 'teal'\n ## Dima Hole Ice Set without RDE\n pretty_colours['570'] = 'mediumvioletred'\n ## Baseline\n pretty_colours['585'] = 'slategrey'\n # Systematics\n pretty_colours['aeff_scale'] = 'maroon'\n pretty_colours['atm_muon_scale'] = 'goldenrod'\n pretty_colours['deltam31'] = 'blueviolet'\n pretty_colours['theta23'] = 'forestgreen'\n pretty_colours['hole_ice_fwd'] = 'mediumvioletred'\n pretty_colours['dom_eff'] = 'cornflowerblue'\n pretty_colours['genie_ma_qe'] = 'mediumseagreen'\n pretty_colours['genie_ma_res'] = 'olive'\n pretty_colours['hole_ice'] = 'darkorange'\n pretty_colours['nue_numu_ratio'] = 'teal'\n pretty_colours['theta13'] = 'fuchsia'\n pretty_colours['barr_nu_nubar'] = 'thistle'\n pretty_colours['barr_uphor'] = 'orchid'\n pretty_colours['delta_index'] = 'navy'\n # Mass ordering\n pretty_colours['no'] = 'r'\n pretty_colours['io'] = 'b'\n # Asimov fits\n pretty_colours['th_to_wh'] = 'darkviolet'\n pretty_colours['wh_to_th'] = 'deepskyblue'\n colourlabel = None\n for colourkey in pretty_colours.keys():\n if (colourkey in label) or (colourkey == label):\n colourlabel = pretty_colours[colourkey]\n if colourlabel is None:\n logging.debug(\"I do not have a colour scheme for your label %s. \"\n \"Returning black.\"%label)\n colourlabel = 'k'\n return colourlabel","def __init__(self, text, start, end, color, start_marker=None, end_marker=None):\n super().__init__(text, start, end, color)\n self.start_marker = start_marker\n self.end_marker = end_marker","def set_text_f(self, format, *args):\n self._text.set(format % args)\n self.change_bg(\"green\")\n self._label.update_idletasks()","def update_rc():\n rcParams['text.usetex'] = True\n rcParams['text.latex.unicode'] = False\n rcParams['axes.linewidth'] = 2\n rcParams['ytick.labelsize'] = 'large'\n rcParams['xtick.labelsize'] = 'large'\n rcParams['axes.edgecolor'] = 'grey'\n rc('text', usetex=True)","def rainbow_text(axes, x, y, strings, colors, **kwargs):\n fontweights = None\n if \"fontweights\" in kwargs:\n fontweights = kwargs[\"fontweights\"]\n del kwargs[\"fontweights\"]\n\n renderer = axes.figure.canvas.get_renderer()\n for i, s in enumerate(strings):\n kwargs[\"color\"] = colors[i]\n if fontweights is not None:\n kwargs[\"fontweight\"] = fontweights[i]\n tx = axes.text(x, y, s, **kwargs)\n tx.draw(renderer)\n ex = tx.get_window_extent()\n kwargs[\"transform\"] = mtransforms.offset_copy(tx._transform,\n x=ex.width,\n units='dots')","def _create_label(self, x, y, text, width=50, **config):\n\n self.main_canvas.create_text(x, y, text='%6s' % text, width=width, **config)","def SetTextBackground(*args, **kwargs):\n return _gdi_.PseudoDC_SetTextBackground(*args, **kwargs)","def _colored(self, text, *color_args):\n if self.allow_colors and color_args:\n return termcolor.colored(text, *color_args)\n return text","def setText(self, text, color=(1, 1, 1, 1)):\n if self.filterControl:\n text = self._filterControl(text)\n pos = 0\n for diffs in difflib.ndiff(self.text, text):\n c = diffs[2]\n if diffs[0] == ' ':\n self.logger.debug('NO change at pos %d' % pos)\n pos += 1\n else:\n if diffs[0] == '-':\n self._delChar(pos)\n self.logger.info('Removed char at pos %d.' % pos)\n elif diffs[0] == '+':\n self._insChar(c, pos, color)\n self.logger.info(\"Inserted char %r at pos %d.\" % (c, pos))\n pos += 1\n self.mesh.rebuild(self.allVertices, self.allIndices)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def __init__(self, name, attrs={}):\n TextFormat.__init__(self, name, attrs)","def _color_text(text, color):\n color_mapping = {\n 'black': '0;30',\n 'red': '0;31',\n 'green': '0;32',\n 'brown': '0;33',\n 'blue': '0;34',\n 'magenta': '0;35',\n 'cyan': '0;36',\n 'lightgrey': '0;37',\n 'default': '0;39',\n 'darkgrey': '1;30',\n 'lightred': '1;31',\n 'lightgreen': '1;32',\n 'yellow': '1;33',\n 'lightblue': '1;34',\n 'lightmagenta': '1;35',\n 'lightcyan': '1;36',\n 'white': '1;37'}\n\n if sys.platform == 'win32':\n # On Windows do not colorize text unless in IPython\n return text\n\n color_code = color_mapping.get(color, '0;39')\n return '\\033[{0}m{1}\\033[0m'.format(color_code, text)","def _color(self, args):","def drawText(font, textstr, clear_screen=True, color=(250, 10, 10)):\n if clear_screen:\n screen.fill(black) # black screen\n\n # Render font\n pltText = font.render(textstr, 1, color)\n\n # Center text\n textpos = pltText.get_rect()\n textpos.centerx = screen.get_rect().centerx\n textpos.centery = screen.get_rect().centery\n\n # Blit onto screen\n screen.blit(pltText, textpos)\n\n # Update\n pygame.display.update()","def colorize(text, color):\n return COLOR_DICT[color] + str(text) + COLOR_DICT['end']","def draw_text(self, i, j, text, col, bg=None):\n txt = self.font.render(text, True, col, bg)\n rect = txt.get_rect()\n rect.center = self.get_rect(i, j).center\n self.screen.blit(txt, rect)","def mytext(x,y,s,**kwargs):\n # we take care of this one\n model = kwargs.pop('model', None)\n if model:\n th = text(x,y,model,**kwargs)\n draw()\n x0,y0,w,h = th.get_window_extent().bounds\n gca().texts.remove(th)\n x = x0\n y = y0\n kwargs['transform'] = matplotlib.transforms.IdentityTransform()\n kwargs['horizontalalignment'] = 'left'\n kwargs['verticalalignment'] = 'baseline'\n# print x,y,kwargs\n return text(x,y,s,**kwargs)","def text(txt, color, size=20, font_name=None):\n return font(size, font_name).render(str(txt), True, color)","def setup_text_plots(fontsize=8, usetex=True):\n from distutils.version import LooseVersion\n matplotlib.rc('legend', fontsize=fontsize, handlelength=3)\n matplotlib.rc('axes', titlesize=fontsize)\n matplotlib.rc('axes', labelsize=fontsize)\n matplotlib.rc('xtick', labelsize=fontsize)\n matplotlib.rc('ytick', labelsize=fontsize)\n matplotlib.rc('text', usetex=usetex)\n matplotlib.rc('font', size=fontsize, family='serif',\n style='normal', variant='normal',\n stretch='normal', weight='normal')\n matplotlib.rc('patch', force_edgecolor=True)\n if LooseVersion(matplotlib.__version__) < LooseVersion(\"3.1\"):\n matplotlib.rc('_internal', classic_mode=True)\n else:\n # New in mpl 3.1\n matplotlib.rc('scatter.edgecolors', 'b')\n matplotlib.rc('grid', linestyle=':')\n matplotlib.rc('errorbar', capsize=3)\n matplotlib.rc('image', cmap='viridis')\n matplotlib.rc('axes', xmargin=0)\n matplotlib.rc('axes', ymargin=0)\n matplotlib.rc('xtick', direction='in')\n matplotlib.rc('ytick', direction='in')\n matplotlib.rc('xtick', top=True)\n matplotlib.rc('ytick', right=True)","def __init__(self, name, rect, **kwargs):\n self.name = name\n self.rect = pg.Rect(rect)\n self.color = (128, 128, 128)\n self.font = pg.font.SysFont(\"arial\", 12)\n self.text = self.font.render(name, False, pg.Color(\"white\"))\n self.selected_text = self.font.render(name, False, pg.Color(\"black\"))\n self.text_rect = self.text.get_rect(center=self.rect.center)\n self.set_kwargs(kwargs)"],"string":"[\n \"def text(self, txt, ax=None, color='black'):\\n _plot_text(self, txt, ax, color)\",\n \"def text(self, txt, ax=None, color='black'):\\n _plot_text(self, txt, ax, color)\",\n \"def text(self, str: str, x: int, y: int, colour: int, /) -> None:\",\n \"def text_plot(self):\\n if self.stext is not None:\\n # Create text object :\\n self.stextmesh = visu.Text(text=self.stext, color=self.stextcolor,\\n font_size=self.stextsize, pos=self.xyz,\\n bold=True, name='SourcesText')\\n\\n # Set text texture :\\n self.stextmesh.set_gl_state('translucent', depth_test=True)\\n\\n # Apply a transformation to text elements to not cover sources :\\n self.stextmesh.transform = vist.STTransform(\\n translate=self.stextshift)\\n else:\\n self.stextmesh = visu.Text(name='NoneText')\",\n \"def __init__(self, plot_design):\\n self.plot_design = plot_design\\n\\n text_color = self.plot_design.text_color\\n text_font = self.plot_design.text_font\\n sns.set_style(\\\"white\\\",\\n {\\n \\\"axes.edgecolor\\\": text_color,\\n \\\"axes.labelcolor\\\": text_color,\\n \\\"text.color\\\": text_color,\\n \\\"font.sans-serif\\\": [text_font],\\n \\\"xtick.color\\\": text_color,\\n \\\"ytick.color\\\": text_color,\\n }\\n )\",\n \"def __init__(self, font, color, text=\\\"\\\", top=0, left=0, bottom=None, right=None):\\n self.text = text\\n self.font = font\\n self.color = color\\n self.top = top\\n self.left = left\\n self.bottom = bottom\\n self.right = right\\n self.renderLabel()\",\n \"def __init__(self, text, font, pos, color=(255, 255, 255)):\\r\\n self.pos = pos\\r\\n self.label = font.render(text, 1, color)\",\n \"def get_text_color ( self, object ):\\n return self.text_color_\",\n \"def set_label_text(self, text, warn=False):\\n self.text = text\\n self.warn = warn\\n self.opacity = .8\\n if self.warn:\\n self.color = 'darksalmon'\\n else:\\n self.color = 'lightgreen'\\n self.on_size()\",\n \"def _setPlotValText(ax, texts, core, data, labels, labelFmt, fontSize, collection):\\n _ = core.getAssemblyPitch()\\n for a, val, label in zip(core, data, labels):\\n x, y, _ = a.spatialLocator.getLocalCoordinates()\\n cmap = collection.get_cmap()\\n patchColor = numpy.asarray(cmap(collection.norm(val)))\\n luminance = patchColor.dot(LUMINANCE_WEIGHTS)\\n dark = luminance < 0.5\\n if dark:\\n color = \\\"white\\\"\\n else:\\n color = \\\"black\\\"\\n # Write text on top of patch locations.\\n if label is None and labelFmt is not None:\\n # Write the value\\n labelText = labelFmt.format(val)\\n text = ax.text(\\n x,\\n y,\\n labelText,\\n zorder=1,\\n ha=\\\"center\\\",\\n va=\\\"center\\\",\\n fontsize=fontSize,\\n color=color,\\n )\\n elif label is not None:\\n text = ax.text(\\n x,\\n y,\\n label,\\n zorder=1,\\n ha=\\\"center\\\",\\n va=\\\"center\\\",\\n fontsize=fontSize,\\n color=color,\\n )\\n else:\\n # labelFmt was none, so they don't want any text plotted\\n continue\\n texts.append(text)\",\n \"def SetTextColour(self, colText):\\r\\n \\r\\n self._colText = colText\",\n \"def __init__(self, text, start, end, color, alpha=1):\\n self.text = text\\n self.start = start\\n self.end = end\\n self.color = color\\n self.alpha = alpha\",\n \"def init_text(self):\\n d = self.declaration\\n if d.text:\\n self.set_text(d.text)\\n if d.text_color:\\n self.set_text_color(d.text_color)\\n if d.text_alignment:\\n self.set_text_alignment(d.text_alignment)\\n if d.font_family or d.text_size:\\n self.refresh_font()\\n if hasattr(d, 'max_lines') and d.max_lines:\\n self.set_max_lines(d.max_lines)\",\n \"def render_text(self, title, pos, color):\\n if title not in self.text_elements:\\n self.text_elements[title] = UIText(\\n title, pos, font_size=20, font_color=color)\",\n \"def _setText(self, text):\\n self.text = \\\"\\\"\\n for ch in text:\\n char, vertices, glyph = self._extractGlyph(ch)\\n if not vertices is None and self.glyphs in (\\n GlyphTypes.BASE, GlyphTypes.LAYER_COLOR):\\n vertices['rgba'] = glm.vec4(self.color)\\n if not self.text:\\n off, kern = self._updateMetric(0, char)\\n if char in self.NO_GLYPH_CHARS:\\n self.colors.append([char, None])\\n else:\\n vertices['vtx'] += off + glyph['offset']\\n self.allVertices = np.hstack(vertices)\\n self.allIndices = self._baseInd\\n self.colors.append([char, self.color])\\n self.text += char\\n else:\\n pos = len(self.text)\\n nonGlyph = countInSet(self.text, self.NO_GLYPH_CHARS)\\n # Set the metric\\n off, kern = self._updateMetric(pos, char)\\n if char in self.NO_GLYPH_CHARS:\\n self.colors.append([char, None])\\n else:\\n vertices['vtx'] += off + kern + glyph['offset']\\n if self.allVertices is None:\\n self.allVertices = np.hstack(vertices)\\n else:\\n self.allVertices = np.append(self.allVertices,\\n vertices)\\n if self.allIndices is None:\\n self.allIndices = self._baseInd\\n else:\\n self.allIndices = np.vstack((self.allIndices,\\n self._baseInd + (pos - nonGlyph) * 4))\\n self.colors.append([char, self.color])\\n self.text += char\\n self.setUniforms()\",\n \"def text_draw(self, x, y, text, style={}):\",\n \"def _colorstr(self, args):\",\n \"def _plot_text(polyreg, txt, ax, color):\\n ax = _newax(ax)\\n rc, xc = cheby_ball(polyreg)\\n ax.text(xc[0], xc[1], txt, color=color)\",\n \"def __init__(self, text=None, **style):\\n\\n self.lines = []\\n if text is not None:\\n self.append(text)\\n Figure.__init__(self, style)\",\n \"def setDefaultTextColor(self, Union, QColor=None, Qt_GlobalColor=None, QGradient=None): # real signature unknown; restored from __doc__\\n pass\",\n \"def set_intro_text(self, text, color):\\n text_y_position = (self.border.inner_y() + self.objects['titleimage'].t()) / 2\\n self.objects['introtext'] = LCARSText((self.width/2, text_y_position),\\n text,\\n 36,\\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)\",\n \"def set_text(self, text):\\n self.str = text\\n self.image = self.font.render(self.str, True, self.font_color)\\n self.rect.width = self.image.get_width()\\n self.rect.height = self.image.get_height()\",\n \"def main():\\n fout_png = 'color0.png'\\n _, axis = plt.subplots(1, 1, figsize=(6, 6))\\n colors = [\\n '#0032ff',\\n '#00ebff',\\n '#fdfe02',\\n '#ff0000',\\n '#8500ff',\\n ]\\n plt_color_text(colors)\\n plt.savefig(fout_png)\",\n \"def set_intro_text_3(self, text, color):\\n text_y_position = self.objects['introtext2'].t() + 80\\n self.objects['introtext3'] = LCARSText((self.width/2, text_y_position),\\n text,\\n 36,\\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)\",\n \"def initialize_plot(self):\\n\\n self.scat = self.config.ax.scatter(self.lons[0, 0], self.lats[0, 0], c=self.z[0,0]\\n # , vmax=self.vmax\\n # , vmin=self.vmin\\n # ,cmap='coolwarm'\\n # , norm=LogNorm()\\n , transform=self.config.projection, edgecolor='none', s=0.6)\\n cbar = plt.colorbar(self.scat)\\n # cbar.set_label('Melt water Flux (mm/yr)')\\n\\n self.ttl = self.config.ax.text(1.5, 1.05, '', transform=self.config.ax.transAxes, va='center')\\n\\n return self.scat\",\n \"def set_text(self, text: str) -> None:\\n self._text = text\\n self._text_image = self._style.font.render(\\n text, True, self._style.primary_color)\\n self._redraw()\",\n \"def __init__(self, a_width, a_height, a_point, a_str, window, text_size, color):\\n self.width = a_width\\n self.height = a_height\\n self.cen_point = a_point\\n \\n # retrieve the x and y coordinates of the center point and \\n # save them as instance variables\\n self.cen_point_x = a_point.getX()\\n self.cen_point_y = a_point.getY()\\n self.text = graphics.Text(self.cen_point, a_str)\\n self.text.setSize(text_size)\\n self.text.setFill(color)\\n self.draw_but(window)\",\n \"def draw_text(self, text, i, j, **params):\",\n \"def GetTextColour(self):\\r\\n \\r\\n return self._colText\",\n \"def draw_text(self, text, position, font_size, font_color):\\n font_color = check_color(font_color)\\n STtext.text(self.canvas, text, position, font_size, font_color)\",\n \"def text(self, a, b, text, angle=0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\\n table = toyplot.data.Table()\\n table[\\\"x\\\"] = _require_scalar_vector(a)\\n table[\\\"y\\\"] = _require_scalar_vector(b, table.shape[0])\\n table[\\\"text\\\"] = _broadcast_object(text, table.shape[0])\\n table[\\\"angle\\\"] = _broadcast_scalar(angle, table.shape[0])\\n table[\\\"fill\\\"] = _broadcast_object(fill, table.shape[0])\\n table[\\\"opacity\\\"] = _broadcast_scalar(opacity, table.shape[0])\\n table[\\\"title\\\"] = _broadcast_object(title, table.shape[0])\\n style = _combine_styles({\\\"font-weight\\\":\\\"normal\\\", \\\"stroke\\\":\\\"none\\\", \\\"text-anchor\\\":\\\"middle\\\", \\\"alignment-baseline\\\":\\\"middle\\\"}, _require_style(style))\\n id = _require_optional_id(id)\\n\\n default_color = next(self._text_colors)\\n table[\\\"toyplot:fill\\\"] = toyplot.color._broadcast_color(default_color if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\\n\\n self._update_domain(table[\\\"x\\\"], table[\\\"y\\\"])\\n\\n self._children.append(TextMark(table=table, coordinates=[\\\"x\\\", \\\"y\\\"], axes=[\\\"x\\\", \\\"y\\\"], text=\\\"text\\\", angle=\\\"angle\\\", fill=\\\"toyplot:fill\\\", opacity=\\\"opacity\\\", title=\\\"title\\\", style=style, id=id))\\n return self._children[-1]\",\n \"def set_intro_text_2(self, text, color):\\n text_y_position = (self.objects['titleimage'].b() + self.border.bottom()) / 2\\n\\ttext_y_position = text_y_position - 30\\n self.objects['introtext2'] = LCARSText((self.width/2, text_y_position),\\n text,\\n 36,\\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)\",\n \"def title(self, text, x=0.01, y=0.99, size=15, color=None,\\n bgcolor=None, alpha=1, **kwargs):\\n if color is None:\\n color = 'k' if self._black_bg else 'w'\\n if bgcolor is None:\\n bgcolor = 'w' if self._black_bg else 'k'\\n self.frame_axes.text(x, y, text, \\n transform=self.frame_axes.transAxes,\\n horizontalalignment='left',\\n verticalalignment='top',\\n size=size, color=color,\\n bbox=dict(boxstyle=\\\"square,pad=.3\\\", \\n ec=bgcolor, fc=bgcolor, alpha=alpha),\\n **kwargs)\",\n \"def text(self, x, y, text, angle=0.0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\\n table = toyplot.data.Table()\\n table[\\\"x\\\"] = _require_scalar_vector(x)\\n table[\\\"y\\\"] = _require_scalar_vector(y, table.shape[0])\\n table[\\\"text\\\"] = _broadcast_object(text, table.shape[0])\\n table[\\\"angle\\\"] = _broadcast_scalar(angle, table.shape[0])\\n table[\\\"fill\\\"] = _broadcast_object(fill, table.shape[0])\\n table[\\\"toyplot:fill\\\"] = toyplot.color._broadcast_color(toyplot.color.near_black if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\\n table[\\\"opacity\\\"] = _broadcast_scalar(opacity, table.shape[0])\\n table[\\\"title\\\"] = _broadcast_object(title, table.shape[0])\\n style = _combine_styles({\\\"font-weight\\\":\\\"normal\\\", \\\"stroke\\\":\\\"none\\\", \\\"text-anchor\\\":\\\"middle\\\", \\\"alignment-baseline\\\":\\\"middle\\\"}, _require_style(style))\\n id = _require_optional_id(id)\\n\\n self._children.append(TextMark(table=table, coordinates=[\\\"x\\\", \\\"y\\\"], axes=[\\\"x\\\", \\\"y\\\"], text=\\\"text\\\", angle=\\\"angle\\\", fill=\\\"toyplot:fill\\\", opacity=\\\"opacity\\\", title=\\\"title\\\", style=style, id=id))\\n return self._children[-1]\",\n \"def define_text(cls, position, text, scale=1, rotation=0, text_width=1):\\n \\n position = np.array(position)\\n text_obj = cls()\\n text_obj.coord = []\\n sep = 0.2*scale\\n posref=np.array([0.0,0.0])\\n \\n #complete text length\\n #textlen = 0.5*sep*(len(text)-1)+0.5*scale * np.sum([np.array(Feature.hershey_table[ord(text[x])]['width']) for x in range(len(text))])\\n for x in range(len(text)):\\n code = ord(text[x])\\n letter = Feature.hershey_table[code]\\n if x>0:\\n posref[0]=posref[0]+ scale*np.array(letter['width'])/2+sep\\n #posref[0] = posref[0]-textlen\\n #position[0] = position[0] + scale*np.array(letter['width'])/2+sep\\n for k in letter['coord']:\\n if len(k)>0:\\n k = np.array(k)\\n coord = [scale*m+posref-np.array([0,0.5*scale]) for m in k]\\n text_obj.coord.append(np.squeeze(coord))\\n posref[0]=posref[0]+ scale*np.array(letter['width'])/2+sep\\n \\n #align number\\n posref[0]=posref[0]- scale*np.array(letter['width'])/2-sep\\n for x in range(len(text_obj.coord)):\\n text_obj.coord[x] = np.array([z- np.array([posref[0]/2,0]) for z in text_obj.coord[x]])\\n \\n #Rotate the number by alpha\\n if rotation !=0:\\n\\n alpha = rotation\\n R = np.array([[np.cos(alpha),-np.sin(alpha)],[np.sin(alpha),np.cos(alpha)]])\\n\\n for i in range(len(text_obj.coord)):\\n text_obj.coord[i] = np.squeeze([np.dot([x],R) \\n for x in text_obj.coord[i]])\\n \\n #move the number to position pos\\n for x in range(len(text_obj.coord)):\\n text_obj.coord[x] = np.array([z+ position for z in text_obj.coord[x]])\\n \\n #make thick numbers (currently unused)\\n '''for n in range(len(text_obj.coord)):\\n x = text_obj.coord[n][:,0]\\n y = text_obj.coord[n][:,1]\\n x2 = np.array([[x[i],(2/3*x[i]+1/3*x[i+1]),(1/3*x[i]+2/3*x[i+1])] for i in range(x.shape[0]-1)])\\n y2 = np.array([[y[i],(2/3*y[i]+1/3*y[i+1]),(1/3*y[i]+2/3*y[i+1])] for i in range(y.shape[0]-1)])\\n x2 = np.concatenate(x2)\\n x2 = np.append(x2,x[-1])\\n y2 = np.concatenate(y2)\\n y2 = np.append(y2,y[-1])\\n x=x2\\n y=y2\\n\\n #nt = np.linspace(0, 1, 40)\\n #t = np.zeros(x.shape)\\n #t[1:] = np.sqrt((x[1:] - x[:-1])**2 + (y[1:] - y[:-1])**2)\\n #t = np.cumsum(t)\\n #t /= t[-1]\\n #x2 = scipy.interpolate.spline(t, x, nt,order=2)\\n #y2 = scipy.interpolate.spline(t, y, nt,order=2)\\n newcoord = np.stack((x2,y2),axis=1)\\n \\n csx = CubicSpline(np.linspace(0, 1, x.shape[0]), x,bc_type = 'clamped')\\n csy = CubicSpline(np.linspace(0, 1, y.shape[0]), y,bc_type = 'clamped')\\n s\\n xs = np.linspace(0, 1,1000)\\n newcoord = np.stack((csx(xs),csy(xs)),axis=1)\\n \\n text_obj.coord[n]=Feature.define_tube(newcoord,0,100).coord[0]'''\\n #set object as text type\\n text_obj.open = True\\n text_obj.text_width = text_width\\n return text_obj\",\n \"def draw_text(text, start_x, start_y, color, size):\\n\\n if len(color) == 3:\\n color = (color[0], color[1], color[2], 255)\\n\\n label = pyglet.text.Label(text,\\n font_name='Times New Roman',\\n font_size=size,\\n x=start_x, y=start_y,\\n color=color)\\n GL.glLoadIdentity()\\n\\n label.draw()\",\n \"def _set_text(self, text):\\n if not self.has_textframe:\\n raise TypeError(\\\"cannot set text of shape with no text frame\\\")\\n self.textframe.text = _to_unicode(text)\",\n \"def render_text(self, text, x_pos, y_pos, colour=0):\\n if colour == 0:\\n GL.glColor3f(0.0, 0.0, 0.0) # text is black\\n elif colour == 1:\\n GL.glColor3f(1.0, 0.0, 0.0)\\n elif colour == 2:\\n GL.glColor3f(0.0, 1.0, 0.0)\\n GL.glRasterPos2f(x_pos, y_pos)\\n font = GLUT.GLUT_BITMAP_HELVETICA_12\\n\\n for character in text:\\n if character == '\\\\n':\\n y_pos = y_pos - 20\\n GL.glRasterPos2f(x_pos, y_pos)\\n else:\\n GLUT.glutBitmapCharacter(font, ord(character))\",\n \"def __init__(self, text: str) -> None:\\n\\n super().__init__()\\n\\n self._width = 0\\n self._opacity = 255\\n self._sprites = []\\n self._text = text\\n for index, c in enumerate(text):\\n y_offset = 0\\n if c in Text.characters:\\n if Text.characters[c][1]:\\n y_offset = 2\\n c = Text.characters[c][0]\\n elif c.isupper():\\n c = c.lower() + \\\"_maj\\\"\\n self._sprites.append(\\n cocos.sprite.Sprite(pyglet.image.load(PATH + '/assets/img/common/font/{0}.png'.format(c))))\\n self._sprites[index].position = self._width, (self._sprites[index].height - 11) / 2 - y_offset\\n self._width += self._sprites[index].width\\n self.add(self._sprites[index])\",\n \"def __init__(self, text='', x=0, y=0):\\n \\n #Some defaults\\n self.text = text\\n self.text_color = (255, 255, 255)\\n self.text_size = 36\\n self.x = x\\n self.y = y\\n self.font = pygame.font.Font(None, self.text_size)\\n self.text_img = self.font.render(self.text, True, self.text_color)\\n self.text_rect = self.text_img.get_rect()\\n self.text_rect.left = self.x\\n self.text_rect.top = self.y\",\n \"def __add_texts(self, ax, values, param_location,\\n temp_values=None, temp_compare_values=None,\\n value_colors=None, value_bck_colors=None,\\n compare_values=None, compare_value_colors=None,\\n compare_value_bck_colors=None,\\n kwargs_params=None, kwargs_values=None, kwargs_compare_values=None):\\n # set to empty dict if None\\n if kwargs_params is None:\\n kwargs_params = dict()\\n if kwargs_values is None:\\n kwargs_values = dict()\\n if kwargs_compare_values is None:\\n kwargs_compare_values = dict()\\n\\n # total length of parameters\\n total_params = len(self.params)\\n\\n # get the rotation angles\\n rotation = (2 * np.pi / total_params) * np.arange(total_params)\\n\\n # flip the rotation if the label is in lower half\\n mask_flip_label = (rotation > np.pi / 2) & (rotation < np.pi / 2 * 3)\\n rotation[mask_flip_label] = rotation[mask_flip_label] + np.pi\\n rotation_degrees = -np.rad2deg(rotation)\\n\\n # plot params\\n for x, rotation, label in zip(self.theta, rotation_degrees, self.params):\\n temp_text = ax.text(\\n x, param_location, label,\\n rotation=rotation, rotation_mode=\\\"anchor\\\",\\n ha=\\\"center\\\", **kwargs_params\\n )\\n\\n self.param_texts.append(temp_text)\\n\\n # plot values\\n for i, (x, value, rotation) in enumerate(zip(self.theta, values, rotation_degrees)):\\n if value_colors is not None:\\n kwargs_values[\\\"color\\\"] = value_colors[i]\\n if value_bck_colors is not None and kwargs_values.get(\\\"bbox\\\") is not None:\\n kwargs_values[\\\"bbox\\\"][\\\"facecolor\\\"] = value_bck_colors[i]\\n\\n temp_text = ax.text(\\n x, temp_values[i], value, ha=\\\"center\\\", **kwargs_values\\n )\\n\\n self.value_texts.append(temp_text)\\n\\n # plot comparison values\\n if compare_values is not None:\\n for i, (x, value, rotation) in enumerate(zip(self.theta, compare_values,\\n rotation_degrees)):\\n if compare_value_colors is not None:\\n kwargs_compare_values[\\\"color\\\"] = compare_value_colors[i]\\n if compare_value_bck_colors is not None and kwargs_values.get(\\\"bbox\\\") is not None:\\n kwargs_compare_values[\\\"bbox\\\"][\\\"facecolor\\\"] = compare_value_bck_colors[i]\\n\\n if temp_compare_values is not None:\\n value_1 = temp_compare_values[i]\\n value_2 = value\\n else:\\n value_1 = value_2 = value\\n\\n temp_text = ax.text(\\n x, value_1, value_2, ha=\\\"center\\\", **kwargs_compare_values\\n )\\n\\n self.compare_value_texts.append(temp_text)\",\n \"def _update(self):\\n\\n if self.rgb:\\n self._canvas['bg'] = tks.color_funcs.rgb_to_hex_string(self.rgb)\\n self._text['text'] = self._color_info_text()\\n else:\\n self._canvas['bg'] = self._blank_label_color\\n self._text['text'] = ''\",\n \"def plt_color_text(colors):\\n num_vals = len(colors)\\n xvals = [10]*num_vals\\n yvals = range(num_vals)\\n for idx, (xval, yval, color) in enumerate(zip(xvals, yvals, colors)):\\n plt.scatter(xval, yval, s=1000, marker='s', color=color)\\n plt.text(xval+.004, yval, color, fontsize=20, va='center')\",\n \"def draw_text(self, text, position=(0, 0), color='black', font=None,\\n font_size=12, rotation=0, **kwargs):\\n font = self.font(font_size)\\n\\n text_image = Image.new('L', self.dimensions, 'black')\\n draw_text_image = ImageDraw.Draw(text_image)\\n draw_text_image.text(position, text, font=font, fill='white')\\n\\n alpha = Image.new('L', self.dimensions)\\n alpha = ImageChops.lighter(alpha, text_image)\\n\\n solidcolor = Image.new('RGBA', self.dimensions, color)\\n image_mask = Image.eval(text_image, lambda p: 255 * (int(p != 0)))\\n self.base_image = Image.composite(solidcolor, self.base_image, image_mask)\\n self.base_image.putalpha(alpha)\",\n \"def colorful_text(text, color=Fore.RESET):\\n return color + text + Fore.RESET\",\n \"def set_text_attr(color):\\n SetConsoleTextAttribute(stdout_handle, color)\",\n \"def color_text(txt, foreground=PALETTE['white'], background=PALETTE['black']):\\n if isinstance(foreground, str) and foreground.startswith('#'):\\n foreground = hex_to_rgb(foreground)\\n if isinstance(background, str) and background.startswith('#'):\\n background = hex_to_rgb(background)\\n return '{}{}{}{}'.format(_f(*foreground), _b(*background), txt, _r())\",\n \"def use_black_text(self): \\n black_foreground = 0\\n for color in range(curses.COLORS):\\n curses.init_pair(color, black_foreground, color)\",\n \"def add_text(self, x, y, text, style=None):\\n style = self.__prepare_style(style, ' ')\\n for i, c in enumerate(text):\\n if self.check_coord_in_range(x + i, y):\\n text_style = Style(c, style.fg_color, style.bg_color, style.font_style)\\n self.canvas[y][x + i] = text_style\",\n \"def colorText(text, color):\\n\\tif text is not None and color is not None:\\n\\t\\tif isinstance(color, types.StringTypes):\\n\\t\\t\\tcolor = ColorUtil.keyToType(color)\\n\\t\\tif color >= 0:\\n\\t\\t\\treturn CyTranslator().changeTextColor(text, color)\\n\\treturn text\",\n \"def _labels_pcolor(self, obj, fmt=None, **kwargs):\\n # Parse input args and populate _facecolors, which is initially unfilled\\n # See: https://stackoverflow.com/a/20998634/4970632\\n fmt = _not_none(fmt, pticker.SimpleFormatter())\\n labels_kw = {'size': rc['text.labelsize'], 'ha': 'center', 'va': 'center'}\\n labels_kw.update(kwargs)\\n obj.update_scalarmappable() # populate _facecolors\\n\\n # Get positions and contour colors\\n array = obj.get_array()\\n paths = obj.get_paths()\\n colors = _to_ndarray(obj.get_facecolors())\\n edgecolors = _to_ndarray(obj.get_edgecolors())\\n if len(colors) == 1: # weird flex but okay\\n colors = np.repeat(colors, len(array), axis=0)\\n if len(edgecolors) == 1:\\n edgecolors = np.repeat(edgecolors, len(array), axis=0)\\n\\n # Apply colors\\n labs = []\\n for i, (color, path, num) in enumerate(zip(colors, paths, array)):\\n if not np.isfinite(num):\\n edgecolors[i, :] = 0\\n continue\\n bbox = path.get_extents()\\n x = (bbox.xmin + bbox.xmax) / 2\\n y = (bbox.ymin + bbox.ymax) / 2\\n if 'color' not in kwargs:\\n _, _, lum = to_xyz(color, 'hcl')\\n if lum < 50:\\n color = 'w'\\n else:\\n color = 'k'\\n labels_kw['color'] = color\\n lab = self.text(x, y, fmt(num), **labels_kw)\\n labs.append(lab)\\n obj.set_edgecolors(edgecolors)\\n\\n return labs\",\n \"def text(self, font, text, x0, y0, color=WHITE, background=BLACK):\\n if font.WIDTH == 8:\\n self._text8(font, text, x0, y0, color, background)\\n else:\\n self._text16(font, text, x0, y0, color, background)\",\n \"def __init__(self, x, y, w, h, text=''):\\n self.rect = pygame.Rect(x, y, w, h)\\n self.color = COLOR_INACTIVE\\n self.text = text\\n self.txt_surface = FONT.render(text, True, self.color)\\n self.active = False\",\n \"def __init__(self, ecran, text, police, taille, pos = (0,0), couleurText = (0,0,0), couleurFond = (255,255,255)):\\n self.ecran = ecran\\n self.txt = text # val String du texte\\n self.police = police\\n self.taille = taille\\n self.pos = pos\\n self.couleurText = couleurText\\n self.couleurFond = couleurFond\\n self.text = Texte(self.ecran, self.txt, self.police, self.taille, self.pos, self.couleurText) # objet Texte\\n self.textRect = self.text.textRend.get_rect()\\n self.dim = (self.textRect.w + 5, self.textRect.h + 5) # dims -> dimensions du texte + marge de 5px\",\n \"def add_text(self, text, color, pos, font):\\n text = font.render(text, True, color)\\n text_rec = text.get_rect(center=pos)\\n self.window.blit(text, text_rec)\",\n \"def render_text(self, text, x_pos, y_pos, z_pos):\\n GL.glDisable(GL.GL_LIGHTING)\\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\\n font = GLUT.GLUT_BITMAP_HELVETICA_10\\n\\n for character in text:\\n if character == '\\\\n':\\n y_pos = y_pos - 20\\n GL.glRasterPos3f(x_pos, y_pos, z_pos)\\n else:\\n GLUT.glutBitmapCharacter(font, ord(character))\\n\\n GL.glEnable(GL.GL_LIGHTING)\",\n \"def __init__(self, text, pos=(0, 0), anchor='ll',\\n size=16,\\n sizeUnits=POINT_UNITS,\\n color=(1, 0, 1, 1),\\n dpi=None,\\n yPointsUp=None,\\n font_file=None,\\n angle=0.,\\n glyphs=GlyphTypes.BASE,\\n filterControl=True):\\n self.logger = namedLogger(__name__, self.__class__)\\n if dpi:\\n if Label.DPI is None:\\n Label.DPI = dpi\\n else:\\n self.DPI = dpi\\n if yPointsUp:\\n if Label.Y_POINTS_UP is None:\\n Label.Y_POINTS_UP = yPointsUp\\n else:\\n self.Y_POINTS_UP = yPointsUp\\n self.color = color\\n self.glyphs = glyphs\\n self.filterControl = filterControl\\n\\n # Model matrix\\n self.transform = Transform()\\n\\n if font_file is None:\\n font_file = Path(__file__).parent.joinpath(Label.DEFAULT_FONT)\\n self.font = Font(font_file)\\n self._lineWidth = 0\\n self._labelWidth = 0\\n self._labelHeight = self.font.table['linespace']\\n self.setSize(size, sizeUnits)\\n\\n self._baseInd = np.array([0, 1, 2, 2, 3, 0], np.uint32)\\n self.allVertices = None\\n self.allIndices = None\\n self.extracted = {}\\n # Offet, kerning, next_char_shift\\n self._string_metric = []\\n\\n # Set text\\n if self.filterControl:\\n text = self._filterControl(text)\\n self.shader = Shader.fromString(*self._getShaderCode())\\n self._setText(text)\\n self._setMesh()\\n self.model = Transform()\\n self.setPos(*pos, anchor)\\n self.setRotation(angle)\",\n \"def figtext(self, x, y, s='text', fontsize=9, halign='left'):\\n plt.figtext(x=x, y=y, s=s, fontsize=self.ftsize1, horizontalalignment=halign)\",\n \"def rainbow_text(x,y,ls,lc,**kw):\\n t = plt.gca().transData\\n fig = plt.gcf()\\n plt.show()\\n \\n #horizontal version\\n for s,c in zip(ls,lc):\\n text = plt.text(x,y,\\\" \\\"+s+\\\" \\\",color=c, transform=t, **kw)\\n text.draw(fig.canvas.get_renderer())\\n ex = text.get_window_extent()\\n t = transforms.offset_copy(text._transform, x=ex.width, units='dots')\",\n \"def plot_markup():\\n\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='serif')\\n plt.rc('font', size=18)\",\n \"def add_text(self, text, size=10, x=0.5, y=0.5, ha='center', va='center', bbox='default', **kwargs):\\n if bbox == 'default':\\n bbox = {'boxstyle': 'square', 'fc': 'none'}\\n\\n return self.ax.text(x=x, y=y, s=text, size=size, ha=ha, va=va, bbox=bbox, **kwargs)\",\n \"def __init__(self):\\n self.colors = (\\n 'BLACK', 'RED', 'GREEN', 'YELLOW',\\n 'BLUE', 'MAGENTA', 'CYAN', 'WHITE'\\n )\\n\\n self.disable_color = True\\n\\n if sys.stdout.isatty():\\n self.disable_color = False\",\n \"def c_prnt(self, text, color):\\n if color == 'pink':\\n a = self.pink\\n elif color == 'blue':\\n a = self.blue\\n elif color == 'green':\\n a = self.green\\n elif color == 'dgrn':\\n a = self.dgrn\\n elif color == 'yel':\\n a = self.yellow\\n elif color == 'amber':\\n a = self.amber\\n else:\\n raise Exception('The color you selected is not acceptable')\\n print(a + text + self.ENDC)\",\n \"def _on_node_enter(self, *args): # pylint: disable=W0613\\r\\n self._text.node[\\\"fg\\\"] = (self.text_color.x + 0.1, self.text_color.y + 0.1,\\r\\n self.text_color.z + 0.1, 1.0)\",\n \"def __init__(self, x, y, align_bottom_left, text, font, color):\\n super(TextBox, self).__init__(x, y)\\n\\n self.align_bottom_left = align_bottom_left\\n self.__text = text\\n self.font = font\\n self.color = color\\n self.original_x = x\\n self.original_y = y\\n self.image = None\\n self.image_rect = None\\n\\n self.set_text(text)\",\n \"def SetTextBackground(*args, **kwargs):\\n return _gdi_.DC_SetTextBackground(*args, **kwargs)\",\n \"def __init__(self, text=None, settings=None, style='General', language='en'):\\n\\n self._text = None\\n self._settings = None\\n self._style = None\\n self._language = None\\n\\n self.text = text\\n self.settings = settings\\n self.style = style\\n self.language = language\",\n \"def text_objects(self, text, font, color):\\n\\n text_surface = font.render(text, True, color)\\n return text_surface, text_surface.get_rect()\",\n \"def __init__(self, text):\\n\\n self.text = text\",\n \"def __init__(self, width=None, height=None, loads=None, fg=None, bg=None):\\n if width is None and height is None and loads is not None:\\n # self.width and self.height are set based on the size of the loads string\\n loadsLines = loads.splitlines() # TODO - how to handle \\\\r\\\\n cases?\\n self._width = max([len(line) for line in loadsLines])\\n self._height = len(loadsLines)\\n else:\\n # self.width and self.height are set based on the width and height parameters\\n if width is None:\\n self._width = DEFAULT_CANVAS_WIDTH\\n else:\\n if not isinstance(width, int):\\n raise PyTextCanvasException('`width` arg must be an int, not %r' % (width.__class__.__name__))\\n if width < 1:\\n raise PyTextCanvasException('`width` arg must be 1 or greater, not %r' % (width))\\n self._width = width\\n\\n if height is None:\\n self._height = DEFAULT_CANVAS_HEIGHT\\n else:\\n if not isinstance(height, int):\\n raise PyTextCanvasException('`height` arg must be an int, not %r' % (height.__class__.__name__))\\n if height < 1:\\n raise PyTextCanvasException('`height` arg must be 1 or greater, not %r' % (height))\\n self._height = height\\n\\n # The data structure for the characters in this canvas. The None value\\n # represents a \\\"transparent\\\" character when canvases are layered on\\n # top of each other, as opposed to a space ' ' which will cover up\\n # that cell with a blank space.\\n\\n # =====================================\\n # ============= IMPORTANT =============\\n # =====================================\\n # If you directly modify _chars, be sure to set _strDirty to True, otherwise\\n # the cached version of the string will be returned and any changes won't\\n # be reflected.\\n self._chars = [[None] * self._height for i in range(self._width)]\\n\\n\\n # NOTE: A None value for color is like a None value for character, while CLEAR would be similar to a space character.\\n if fg is not None and fg not in range(9):\\n raise PyTextCanvasException('fg arg must be None or one of the CLEAR, BLACK, WHITE, RED, GREEN, BLUE, CYAN, MAGENTA, YELLOW constants.')\\n if bg is not None and bg not in range(9):\\n raise PyTextCanvasException('bg arg must be None or one of the CLEAR, BLACK, WHITE, RED, GREEN, BLUE, CYAN, MAGENTA, YELLOW constants.')\\n self._fg = fg\\n self._bg = bg\\n\\n # The foreground & background of each cell in the canvas. These are\\n # stored as one of the six color constants, which are ints 0-7. The None\\n # value represents the RESET'd color in colorama.\\n # Currently we're sticking to the 8 colors in the colorama module. More might be added later.\\n self._fginfo = [[None] * self._height for i in range(self._width)]\\n self._bginfo = [[None] * self._height for i in range(self._width)]\\n # TODO - the rest of the color implementation needs to be done.\\n\\n self._cursor = (0, 0) # The cursor is always set to integers.\\n\\n if loads is not None:\\n # Pre-populate with a string.\\n self.loads(loads)\\n\\n self._strCache = None # Cached returned value from __str__()\\n self._strDirty = True # If False, __str__() uses the cached value.\",\n \"def textObject(text, font, color):\\n\\n textSurface = font.render(text, True, color)\\n return textSurface, textSurface.get_rect()\",\n \"def __init__(self, text):\\n self.text = text\",\n \"def __init__(self, text):\\n self.text = text\",\n \"def plot_colour(self, label):\\n label = label.lower()\\n pretty_colours = {}\\n # SPIce HD\\n pretty_colours['544'] = 'maroon'\\n pretty_colours['545'] = 'goldenrod'\\n pretty_colours['548'] = 'blueviolet'\\n pretty_colours['549'] = 'forestgreen'\\n # H2\\n ## DOM Efficiency Sets\\n pretty_colours['551'] = 'cornflowerblue'\\n pretty_colours['552'] = 'cornflowerblue'\\n pretty_colours['553'] = 'cornflowerblue'\\n pretty_colours['554'] = 'mediumseagreen'\\n pretty_colours['555'] = 'mediumseagreen'\\n pretty_colours['556'] = 'mediumseagreen'\\n ## Hole Ice Sets\\n pretty_colours['560'] = 'olive'\\n pretty_colours['561'] = 'olive'\\n pretty_colours['564'] = 'darkorange'\\n pretty_colours['565'] = 'darkorange'\\n pretty_colours['572'] = 'teal'\\n pretty_colours['573'] = 'teal'\\n ## Dima Hole Ice Set without RDE\\n pretty_colours['570'] = 'mediumvioletred'\\n ## Baseline\\n pretty_colours['585'] = 'slategrey'\\n # Systematics\\n pretty_colours['aeff_scale'] = 'maroon'\\n pretty_colours['atm_muon_scale'] = 'goldenrod'\\n pretty_colours['deltam31'] = 'blueviolet'\\n pretty_colours['theta23'] = 'forestgreen'\\n pretty_colours['hole_ice_fwd'] = 'mediumvioletred'\\n pretty_colours['dom_eff'] = 'cornflowerblue'\\n pretty_colours['genie_ma_qe'] = 'mediumseagreen'\\n pretty_colours['genie_ma_res'] = 'olive'\\n pretty_colours['hole_ice'] = 'darkorange'\\n pretty_colours['nue_numu_ratio'] = 'teal'\\n pretty_colours['theta13'] = 'fuchsia'\\n pretty_colours['barr_nu_nubar'] = 'thistle'\\n pretty_colours['barr_uphor'] = 'orchid'\\n pretty_colours['delta_index'] = 'navy'\\n # Mass ordering\\n pretty_colours['no'] = 'r'\\n pretty_colours['io'] = 'b'\\n # Asimov fits\\n pretty_colours['th_to_wh'] = 'darkviolet'\\n pretty_colours['wh_to_th'] = 'deepskyblue'\\n colourlabel = None\\n for colourkey in pretty_colours.keys():\\n if (colourkey in label) or (colourkey == label):\\n colourlabel = pretty_colours[colourkey]\\n if colourlabel is None:\\n logging.debug(\\\"I do not have a colour scheme for your label %s. \\\"\\n \\\"Returning black.\\\"%label)\\n colourlabel = 'k'\\n return colourlabel\",\n \"def __init__(self, text, start, end, color, start_marker=None, end_marker=None):\\n super().__init__(text, start, end, color)\\n self.start_marker = start_marker\\n self.end_marker = end_marker\",\n \"def set_text_f(self, format, *args):\\n self._text.set(format % args)\\n self.change_bg(\\\"green\\\")\\n self._label.update_idletasks()\",\n \"def update_rc():\\n rcParams['text.usetex'] = True\\n rcParams['text.latex.unicode'] = False\\n rcParams['axes.linewidth'] = 2\\n rcParams['ytick.labelsize'] = 'large'\\n rcParams['xtick.labelsize'] = 'large'\\n rcParams['axes.edgecolor'] = 'grey'\\n rc('text', usetex=True)\",\n \"def rainbow_text(axes, x, y, strings, colors, **kwargs):\\n fontweights = None\\n if \\\"fontweights\\\" in kwargs:\\n fontweights = kwargs[\\\"fontweights\\\"]\\n del kwargs[\\\"fontweights\\\"]\\n\\n renderer = axes.figure.canvas.get_renderer()\\n for i, s in enumerate(strings):\\n kwargs[\\\"color\\\"] = colors[i]\\n if fontweights is not None:\\n kwargs[\\\"fontweight\\\"] = fontweights[i]\\n tx = axes.text(x, y, s, **kwargs)\\n tx.draw(renderer)\\n ex = tx.get_window_extent()\\n kwargs[\\\"transform\\\"] = mtransforms.offset_copy(tx._transform,\\n x=ex.width,\\n units='dots')\",\n \"def _create_label(self, x, y, text, width=50, **config):\\n\\n self.main_canvas.create_text(x, y, text='%6s' % text, width=width, **config)\",\n \"def SetTextBackground(*args, **kwargs):\\n return _gdi_.PseudoDC_SetTextBackground(*args, **kwargs)\",\n \"def _colored(self, text, *color_args):\\n if self.allow_colors and color_args:\\n return termcolor.colored(text, *color_args)\\n return text\",\n \"def setText(self, text, color=(1, 1, 1, 1)):\\n if self.filterControl:\\n text = self._filterControl(text)\\n pos = 0\\n for diffs in difflib.ndiff(self.text, text):\\n c = diffs[2]\\n if diffs[0] == ' ':\\n self.logger.debug('NO change at pos %d' % pos)\\n pos += 1\\n else:\\n if diffs[0] == '-':\\n self._delChar(pos)\\n self.logger.info('Removed char at pos %d.' % pos)\\n elif diffs[0] == '+':\\n self._insChar(c, pos, color)\\n self.logger.info(\\\"Inserted char %r at pos %d.\\\" % (c, pos))\\n pos += 1\\n self.mesh.rebuild(self.allVertices, self.allIndices)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def __init__(self, name, attrs={}):\\n TextFormat.__init__(self, name, attrs)\",\n \"def _color_text(text, color):\\n color_mapping = {\\n 'black': '0;30',\\n 'red': '0;31',\\n 'green': '0;32',\\n 'brown': '0;33',\\n 'blue': '0;34',\\n 'magenta': '0;35',\\n 'cyan': '0;36',\\n 'lightgrey': '0;37',\\n 'default': '0;39',\\n 'darkgrey': '1;30',\\n 'lightred': '1;31',\\n 'lightgreen': '1;32',\\n 'yellow': '1;33',\\n 'lightblue': '1;34',\\n 'lightmagenta': '1;35',\\n 'lightcyan': '1;36',\\n 'white': '1;37'}\\n\\n if sys.platform == 'win32':\\n # On Windows do not colorize text unless in IPython\\n return text\\n\\n color_code = color_mapping.get(color, '0;39')\\n return '\\\\033[{0}m{1}\\\\033[0m'.format(color_code, text)\",\n \"def _color(self, args):\",\n \"def drawText(font, textstr, clear_screen=True, color=(250, 10, 10)):\\n if clear_screen:\\n screen.fill(black) # black screen\\n\\n # Render font\\n pltText = font.render(textstr, 1, color)\\n\\n # Center text\\n textpos = pltText.get_rect()\\n textpos.centerx = screen.get_rect().centerx\\n textpos.centery = screen.get_rect().centery\\n\\n # Blit onto screen\\n screen.blit(pltText, textpos)\\n\\n # Update\\n pygame.display.update()\",\n \"def colorize(text, color):\\n return COLOR_DICT[color] + str(text) + COLOR_DICT['end']\",\n \"def draw_text(self, i, j, text, col, bg=None):\\n txt = self.font.render(text, True, col, bg)\\n rect = txt.get_rect()\\n rect.center = self.get_rect(i, j).center\\n self.screen.blit(txt, rect)\",\n \"def mytext(x,y,s,**kwargs):\\n # we take care of this one\\n model = kwargs.pop('model', None)\\n if model:\\n th = text(x,y,model,**kwargs)\\n draw()\\n x0,y0,w,h = th.get_window_extent().bounds\\n gca().texts.remove(th)\\n x = x0\\n y = y0\\n kwargs['transform'] = matplotlib.transforms.IdentityTransform()\\n kwargs['horizontalalignment'] = 'left'\\n kwargs['verticalalignment'] = 'baseline'\\n# print x,y,kwargs\\n return text(x,y,s,**kwargs)\",\n \"def text(txt, color, size=20, font_name=None):\\n return font(size, font_name).render(str(txt), True, color)\",\n \"def setup_text_plots(fontsize=8, usetex=True):\\n from distutils.version import LooseVersion\\n matplotlib.rc('legend', fontsize=fontsize, handlelength=3)\\n matplotlib.rc('axes', titlesize=fontsize)\\n matplotlib.rc('axes', labelsize=fontsize)\\n matplotlib.rc('xtick', labelsize=fontsize)\\n matplotlib.rc('ytick', labelsize=fontsize)\\n matplotlib.rc('text', usetex=usetex)\\n matplotlib.rc('font', size=fontsize, family='serif',\\n style='normal', variant='normal',\\n stretch='normal', weight='normal')\\n matplotlib.rc('patch', force_edgecolor=True)\\n if LooseVersion(matplotlib.__version__) < LooseVersion(\\\"3.1\\\"):\\n matplotlib.rc('_internal', classic_mode=True)\\n else:\\n # New in mpl 3.1\\n matplotlib.rc('scatter.edgecolors', 'b')\\n matplotlib.rc('grid', linestyle=':')\\n matplotlib.rc('errorbar', capsize=3)\\n matplotlib.rc('image', cmap='viridis')\\n matplotlib.rc('axes', xmargin=0)\\n matplotlib.rc('axes', ymargin=0)\\n matplotlib.rc('xtick', direction='in')\\n matplotlib.rc('ytick', direction='in')\\n matplotlib.rc('xtick', top=True)\\n matplotlib.rc('ytick', right=True)\",\n \"def __init__(self, name, rect, **kwargs):\\n self.name = name\\n self.rect = pg.Rect(rect)\\n self.color = (128, 128, 128)\\n self.font = pg.font.SysFont(\\\"arial\\\", 12)\\n self.text = self.font.render(name, False, pg.Color(\\\"white\\\"))\\n self.selected_text = self.font.render(name, False, pg.Color(\\\"black\\\"))\\n self.text_rect = self.text.get_rect(center=self.rect.center)\\n self.set_kwargs(kwargs)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7327333","0.7327333","0.7149617","0.7067926","0.7030527","0.6963405","0.68744624","0.6669955","0.65810484","0.65442663","0.6485745","0.6445651","0.63656104","0.63190436","0.6300679","0.6277589","0.62424827","0.62409574","0.61770606","0.6144568","0.6128026","0.6118654","0.6113981","0.6110288","0.61001885","0.6066878","0.6045125","0.6041633","0.60360205","0.6034058","0.59838486","0.59828","0.59803146","0.5968164","0.59444654","0.5942492","0.59257656","0.59145874","0.59057796","0.58992624","0.5895004","0.5893291","0.5890206","0.5883198","0.5881051","0.5880035","0.58737516","0.5872424","0.58656275","0.5865426","0.58334243","0.5822164","0.58180565","0.58154404","0.58072656","0.57981","0.57553893","0.57388866","0.5737892","0.5734946","0.5728869","0.5727563","0.57177234","0.57164735","0.5714802","0.57033145","0.57015824","0.5700166","0.56940776","0.56922746","0.5687543","0.5677869","0.5677869","0.5675553","0.56717294","0.5668803","0.5665592","0.56591135","0.565181","0.5642731","0.56370515","0.5633443","0.56333834","0.56333834","0.56333834","0.56333834","0.56333834","0.56333834","0.56333834","0.56333834","0.56333834","0.5627752","0.5625297","0.5617621","0.561467","0.5612395","0.56120765","0.5606925","0.56026804","0.5601851"],"string":"[\n \"0.7327333\",\n \"0.7327333\",\n \"0.7149617\",\n \"0.7067926\",\n \"0.7030527\",\n \"0.6963405\",\n \"0.68744624\",\n \"0.6669955\",\n \"0.65810484\",\n \"0.65442663\",\n \"0.6485745\",\n \"0.6445651\",\n \"0.63656104\",\n \"0.63190436\",\n \"0.6300679\",\n \"0.6277589\",\n \"0.62424827\",\n \"0.62409574\",\n \"0.61770606\",\n \"0.6144568\",\n \"0.6128026\",\n \"0.6118654\",\n \"0.6113981\",\n \"0.6110288\",\n \"0.61001885\",\n \"0.6066878\",\n \"0.6045125\",\n \"0.6041633\",\n \"0.60360205\",\n \"0.6034058\",\n \"0.59838486\",\n \"0.59828\",\n \"0.59803146\",\n \"0.5968164\",\n \"0.59444654\",\n \"0.5942492\",\n \"0.59257656\",\n \"0.59145874\",\n \"0.59057796\",\n \"0.58992624\",\n \"0.5895004\",\n \"0.5893291\",\n \"0.5890206\",\n \"0.5883198\",\n \"0.5881051\",\n \"0.5880035\",\n \"0.58737516\",\n \"0.5872424\",\n \"0.58656275\",\n \"0.5865426\",\n \"0.58334243\",\n \"0.5822164\",\n \"0.58180565\",\n \"0.58154404\",\n \"0.58072656\",\n \"0.57981\",\n \"0.57553893\",\n \"0.57388866\",\n \"0.5737892\",\n \"0.5734946\",\n \"0.5728869\",\n \"0.5727563\",\n \"0.57177234\",\n \"0.57164735\",\n \"0.5714802\",\n \"0.57033145\",\n \"0.57015824\",\n \"0.5700166\",\n \"0.56940776\",\n \"0.56922746\",\n \"0.5687543\",\n \"0.5677869\",\n \"0.5677869\",\n \"0.5675553\",\n \"0.56717294\",\n \"0.5668803\",\n \"0.5665592\",\n \"0.56591135\",\n \"0.565181\",\n \"0.5642731\",\n \"0.56370515\",\n \"0.5633443\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.56333834\",\n \"0.5627752\",\n \"0.5625297\",\n \"0.5617621\",\n \"0.561467\",\n \"0.5612395\",\n \"0.56120765\",\n \"0.5606925\",\n \"0.56026804\",\n \"0.5601851\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6425525"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":22394,"cells":{"query":{"kind":"string","value":"Converts the plot text TextColor instance to a ztree plot text text color property declaration."},"document":{"kind":"string","value":"def to_str(self):\n return self.template.format(textcolor=self.color.to_str())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_text_color ( self, object ):\n return self.text_color_","def GetTextColour(self):\r\n \r\n return self._colText","def get_text_color ( self, object ):\n if self._is_selected( object ):\n return self.selected_text_color_\n return self.text_color_","def colorText(text, color):\n\tif text is not None and color is not None:\n\t\tif isinstance(color, types.StringTypes):\n\t\t\tcolor = ColorUtil.keyToType(color)\n\t\tif color >= 0:\n\t\t\treturn CyTranslator().changeTextColor(text, color)\n\treturn text","def colorful_text(text, color=Fore.RESET):\n return color + text + Fore.RESET","def text(self, str: str, x: int, y: int, colour: int, /) -> None:","def SetTextColour(self, colText):\r\n \r\n self._colText = colText","def color_text(txt, foreground=PALETTE['white'], background=PALETTE['black']):\n if isinstance(foreground, str) and foreground.startswith('#'):\n foreground = hex_to_rgb(foreground)\n if isinstance(background, str) and background.startswith('#'):\n background = hex_to_rgb(background)\n return '{}{}{}{}'.format(_f(*foreground), _b(*background), txt, _r())","def change_edge_color(self, text):\n self.layer.edge_color = text","def text(self, txt, ax=None, color='black'):\n _plot_text(self, txt, ax, color)","def text(self, txt, ax=None, color='black'):\n _plot_text(self, txt, ax, color)","def colorize(text, color):\n return COLOR_DICT[color] + str(text) + COLOR_DICT['end']","def set_text_attr(color):\n SetConsoleTextAttribute(stdout_handle, color)","def _color_text(text, color):\n color_mapping = {\n 'black': '0;30',\n 'red': '0;31',\n 'green': '0;32',\n 'brown': '0;33',\n 'blue': '0;34',\n 'magenta': '0;35',\n 'cyan': '0;36',\n 'lightgrey': '0;37',\n 'default': '0;39',\n 'darkgrey': '1;30',\n 'lightred': '1;31',\n 'lightgreen': '1;32',\n 'yellow': '1;33',\n 'lightblue': '1;34',\n 'lightmagenta': '1;35',\n 'lightcyan': '1;36',\n 'white': '1;37'}\n\n if sys.platform == 'win32':\n # On Windows do not colorize text unless in IPython\n return text\n\n color_code = color_mapping.get(color, '0;39')\n return '\\033[{0}m{1}\\033[0m'.format(color_code, text)","def _color_info_text(self):\n\n t = ''\n for info in self.color_info:\n if info == 'rgbhex':\n t1 = tks.color_funcs.rgb_to_hex_string(self.rgb)\n elif info == 'rgb':\n t1 = tks.color_funcs.rgb_to_rgb_string(self.rgb, dp=2)\n elif info == 'hsv':\n t1 = tks.color_funcs.rgb_to_hsv_string(self.rgb, dp=2)\n elif info == 'hls':\n t1 = tks.color_funcs.rgb_to_hls_string(self.rgb, dp=2)\n\n t = t + '%s\\n' % t1\n\n return t","def _colorstr(self, args):","def color_text(text, color_name):\n\n if use_color():\n return colorama.Fore.__dict__[color_name.upper()] + text + colorama.Style.RESET_ALL\n else:\n return text","def textColor(colorNumber):\n return '\\033[%dm' % (30 + colorNumber)","def _setPlotValText(ax, texts, core, data, labels, labelFmt, fontSize, collection):\n _ = core.getAssemblyPitch()\n for a, val, label in zip(core, data, labels):\n x, y, _ = a.spatialLocator.getLocalCoordinates()\n cmap = collection.get_cmap()\n patchColor = numpy.asarray(cmap(collection.norm(val)))\n luminance = patchColor.dot(LUMINANCE_WEIGHTS)\n dark = luminance < 0.5\n if dark:\n color = \"white\"\n else:\n color = \"black\"\n # Write text on top of patch locations.\n if label is None and labelFmt is not None:\n # Write the value\n labelText = labelFmt.format(val)\n text = ax.text(\n x,\n y,\n labelText,\n zorder=1,\n ha=\"center\",\n va=\"center\",\n fontsize=fontSize,\n color=color,\n )\n elif label is not None:\n text = ax.text(\n x,\n y,\n label,\n zorder=1,\n ha=\"center\",\n va=\"center\",\n fontsize=fontSize,\n color=color,\n )\n else:\n # labelFmt was none, so they don't want any text plotted\n continue\n texts.append(text)","def c_prnt(self, text, color):\n if color == 'pink':\n a = self.pink\n elif color == 'blue':\n a = self.blue\n elif color == 'green':\n a = self.green\n elif color == 'dgrn':\n a = self.dgrn\n elif color == 'yel':\n a = self.yellow\n elif color == 'amber':\n a = self.amber\n else:\n raise Exception('The color you selected is not acceptable')\n print(a + text + self.ENDC)","def get_coloured_text_string(text, colour):\n if colour==\"red\":\n return (\"\\033[31m\" + text + \"\\033[0m\")\n if colour==\"green\":\n return (\"\\033[32m\" + text + \"\\033[0m\")\n if colour==\"yellow\":\n return (\"\\033[33m\" + text + \"\\033[0m\")\n if colour==\"blue\":\n return (\"\\033[34m\" + text + \"\\033[0m\")\n if colour==\"purple\":\n return (\"\\033[35m\" + text + \"\\033[0m\")\n if colour==\"cyan\":\n return (\"\\033[36m\" + text + \"\\033[0m\")\n if colour==\"white\":\n return (\"\\033[37m\" + text + \"\\033[0m\")\n return text","def __init__(self, plot_design):\n self.plot_design = plot_design\n\n text_color = self.plot_design.text_color\n text_font = self.plot_design.text_font\n sns.set_style(\"white\",\n {\n \"axes.edgecolor\": text_color,\n \"axes.labelcolor\": text_color,\n \"text.color\": text_color,\n \"font.sans-serif\": [text_font],\n \"xtick.color\": text_color,\n \"ytick.color\": text_color,\n }\n )","def text_color(string: str, color: str) -> str:\n return f\"\\x1b{_code(color)}{string}\\x1b[0m\"","def get_text_color(cls, quad):\n\n\t\tr = cls.get_address_value(quad.left_operand)\n\t\tg = cls.get_address_value(quad.right_operand)\n\t\tb = cls.get_address_value(quad.result)\n\n\t\tif r >= 0 and g >= 0 and b >= 0:\n\t\t\treturn [r,g,b]\n\t\telse:\n\t\t\tcls.throwColorError(\"text\", r,g,b)","def printcolor(color, text):\r\n pushcolor()\r\n setcolor(color)\r\n print text\r\n popcolor()","def setDefaultTextColor(self, Union, QColor=None, Qt_GlobalColor=None, QGradient=None): # real signature unknown; restored from __doc__\n pass","def color(color):\n if sys.platform == \"win32\":\n if color == \"green\":\n set_text_attr(FOREGROUND_GREEN | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\n elif color == \"yellow\":\n set_text_attr(FOREGROUND_YELLOW | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\n elif color == \"red\":\n set_text_attr(FOREGROUND_RED | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\n elif color == \"blue\":\n set_text_attr(FOREGROUND_BLUE | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\n elif color == \"reset\":\n set_text_attr(FOREGROUND_GREY | get_text_attr() & 0x0070)\n else :\n if color == \"green\":\n sys.stdout.write('\\033[92m')\n elif color == \"red\":\n sys.stdout.write('\\033[91m')\n elif color == \"blue\":\n sys.stdout.write('\\033[94m')\n elif color == \"reset\":\n sys.stdout.write('\\033[0m')","def colorize(text, color):\n\n if not supports_color():\n return text\n\n return color + text + Colors.ENDC","def ColorsLabel(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorsLabel(*args)","def color_string(self, data, type):\n\n # http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python\n\n if self.options['no_color']:\n return data\n\n CEND = '\\x1b[0m'\n\n if type == ColorType.ok:\n return '\\x1b[1;32;40m{0}{1}'.format(data, CEND)\n if type == ColorType.error:\n return '\\x1b[1;31;40m{0}{1}'.format(data, CEND)\n if type == ColorType.warning:\n return '\\x1b[1;36;40m{0}{1}'.format(data, CEND)\n if type == ColorType.info:\n return '\\x1b[1;34;40m{0}{1}'.format(data, CEND)\n\n return str","def text_plot(self):\n if self.stext is not None:\n # Create text object :\n self.stextmesh = visu.Text(text=self.stext, color=self.stextcolor,\n font_size=self.stextsize, pos=self.xyz,\n bold=True, name='SourcesText')\n\n # Set text texture :\n self.stextmesh.set_gl_state('translucent', depth_test=True)\n\n # Apply a transformation to text elements to not cover sources :\n self.stextmesh.transform = vist.STTransform(\n translate=self.stextshift)\n else:\n self.stextmesh = visu.Text(name='NoneText')","def _colored(self, text, *color_args):\n if self.allow_colors and color_args:\n return termcolor.colored(text, *color_args)\n return text","def __init__(self, red=Black.red, green=Black.green, blue=Black.blue):\n self.color = Color(red, green, blue)\n\n self.template = '\\ttextcolor = {textcolor};\\n'","def _labels_pcolor(self, obj, fmt=None, **kwargs):\n # Parse input args and populate _facecolors, which is initially unfilled\n # See: https://stackoverflow.com/a/20998634/4970632\n fmt = _not_none(fmt, pticker.SimpleFormatter())\n labels_kw = {'size': rc['text.labelsize'], 'ha': 'center', 'va': 'center'}\n labels_kw.update(kwargs)\n obj.update_scalarmappable() # populate _facecolors\n\n # Get positions and contour colors\n array = obj.get_array()\n paths = obj.get_paths()\n colors = _to_ndarray(obj.get_facecolors())\n edgecolors = _to_ndarray(obj.get_edgecolors())\n if len(colors) == 1: # weird flex but okay\n colors = np.repeat(colors, len(array), axis=0)\n if len(edgecolors) == 1:\n edgecolors = np.repeat(edgecolors, len(array), axis=0)\n\n # Apply colors\n labs = []\n for i, (color, path, num) in enumerate(zip(colors, paths, array)):\n if not np.isfinite(num):\n edgecolors[i, :] = 0\n continue\n bbox = path.get_extents()\n x = (bbox.xmin + bbox.xmax) / 2\n y = (bbox.ymin + bbox.ymax) / 2\n if 'color' not in kwargs:\n _, _, lum = to_xyz(color, 'hcl')\n if lum < 50:\n color = 'w'\n else:\n color = 'k'\n labels_kw['color'] = color\n lab = self.text(x, y, fmt(num), **labels_kw)\n labs.append(lab)\n obj.set_edgecolors(edgecolors)\n\n return labs","def get_color(self) -> str:\r\n return self.color","def _change_text_color(text, color_code) -> StyledStr:\n uncolored_fg = _remove_text_colors(text)\n return _apply_ansi_code(color_code, uncolored_fg)","def GetHyperTextNewColour(self):\r\n\r\n return self._hypertextnewcolour","def text_to_qcolor(text):\r\n color = QColor()\r\n text = str(text)\r\n if not is_text_string(text):\r\n return color\r\n if text.startswith('#') and len(text)==7:\r\n correct = '#0123456789abcdef'\r\n for char in text:\r\n if char.lower() not in correct:\r\n return color\r\n elif text not in list(QColor.colorNames()):\r\n return color\r\n color.setNamedColor(text)\r\n return color","def name_id_text(self):\n text = Marker()\n text.header = self._header\n text.type = Marker.TEXT_VIEW_FACING\n text.action = Marker.ADD\n text.scale.z = 0.05\n text.color = self.GREEN\n text.pose = deepcopy(self.POSE)\n text.pose.position.x = self._p1.x\n text.pose.position.y = (self._p1.y + self._p5.y) / 2\n text.pose.position.z = self._p1.z\n text.text = \"{} #{}\".format(self._object.object_name, self._track_id)\n return text","def _remove_text_colors(text) -> StyledStr:\n return _remove_regex(FOREGROUND_COLORS_REGEX, text)","def text(self, x, y, text, angle=0.0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\n table = toyplot.data.Table()\n table[\"x\"] = _require_scalar_vector(x)\n table[\"y\"] = _require_scalar_vector(y, table.shape[0])\n table[\"text\"] = _broadcast_object(text, table.shape[0])\n table[\"angle\"] = _broadcast_scalar(angle, table.shape[0])\n table[\"fill\"] = _broadcast_object(fill, table.shape[0])\n table[\"toyplot:fill\"] = toyplot.color._broadcast_color(toyplot.color.near_black if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\n table[\"opacity\"] = _broadcast_scalar(opacity, table.shape[0])\n table[\"title\"] = _broadcast_object(title, table.shape[0])\n style = _combine_styles({\"font-weight\":\"normal\", \"stroke\":\"none\", \"text-anchor\":\"middle\", \"alignment-baseline\":\"middle\"}, _require_style(style))\n id = _require_optional_id(id)\n\n self._children.append(TextMark(table=table, coordinates=[\"x\", \"y\"], axes=[\"x\", \"y\"], text=\"text\", angle=\"angle\", fill=\"toyplot:fill\", opacity=\"opacity\", title=\"title\", style=style, id=id))\n return self._children[-1]","def anyTextToColor(self, mystr, r=None):\n\n if len(mystr) < 3:\n # pad up with zeros\n while len(mystr) % 3 != 0:\n mystr += \"0\"\n\n i = 0\n sum1 = 0\n sum2 = 0\n sum3 = 0\n for c in mystr:\n if i % 3 == 0:\n sum1 += int( str(ord(c)) + str(i)[::-1])\n if i % 3 == 1:\n sum2 += int(str(ord(c)) + str(i)[::-1])\n if i % 3 == 2:\n sum3 += int(str(ord(c)) + str(i)[::-1])\n i += 1\n\n x1 = sum1 % 255\n x2 = sum2 % 255\n x3 = sum3 % 255\n\n if r is not None:\n x1 = r\n\n # if we wants to force a shade of green\n # x2 = 255\n\n outstr = \"%x%x%x\" % (x1, x2, x3)\n\n while len(outstr) < 6:\n outstr += \"a\"\n\n return outstr","def _on_node_enter(self, *args): # pylint: disable=W0613\r\n self._text.node[\"fg\"] = (self.text_color.x + 0.1, self.text_color.y + 0.1,\r\n self.text_color.z + 0.1, 1.0)","def description_color(self):\n return self._attribute.get(\"color\")","def color(self):\n if \"color\" in self._prop_dict:\n return self._prop_dict[\"color\"]\n else:\n return None","def _setText(self, text):\n self.text = \"\"\n for ch in text:\n char, vertices, glyph = self._extractGlyph(ch)\n if not vertices is None and self.glyphs in (\n GlyphTypes.BASE, GlyphTypes.LAYER_COLOR):\n vertices['rgba'] = glm.vec4(self.color)\n if not self.text:\n off, kern = self._updateMetric(0, char)\n if char in self.NO_GLYPH_CHARS:\n self.colors.append([char, None])\n else:\n vertices['vtx'] += off + glyph['offset']\n self.allVertices = np.hstack(vertices)\n self.allIndices = self._baseInd\n self.colors.append([char, self.color])\n self.text += char\n else:\n pos = len(self.text)\n nonGlyph = countInSet(self.text, self.NO_GLYPH_CHARS)\n # Set the metric\n off, kern = self._updateMetric(pos, char)\n if char in self.NO_GLYPH_CHARS:\n self.colors.append([char, None])\n else:\n vertices['vtx'] += off + kern + glyph['offset']\n if self.allVertices is None:\n self.allVertices = np.hstack(vertices)\n else:\n self.allVertices = np.append(self.allVertices,\n vertices)\n if self.allIndices is None:\n self.allIndices = self._baseInd\n else:\n self.allIndices = np.vstack((self.allIndices,\n self._baseInd + (pos - nonGlyph) * 4))\n self.colors.append([char, self.color])\n self.text += char\n self.setUniforms()","def _color(self, text, color_name=None, bold=False):\n\n if self.disable_color == True:\n return text\n \n if color_name == None:\n color_name = 'YELLOW'\n\n if color_name in self.colors:\n return '\\033[{0};{1}m{2}\\033[0m'.format(\n int(bold), self.colors.index(color_name) + 30, text)\n\n raise Exception('ERROR: \"{0}\" is not a valid color.\\n'.format(color_name))\n raise Exception('VALID COLORS: {0}.\\n'.format(', '.join(self.colors)))","def color(self):\n return self['color']","def _update_axes_label_color(axes_actor, color=None):\n color = Color(color, default_color=pyvista.global_theme.font.color)\n if isinstance(axes_actor, _vtk.vtkAxesActor):\n prop_x = axes_actor.GetXAxisCaptionActor2D().GetCaptionTextProperty()\n prop_y = axes_actor.GetYAxisCaptionActor2D().GetCaptionTextProperty()\n prop_z = axes_actor.GetZAxisCaptionActor2D().GetCaptionTextProperty()\n for prop in [prop_x, prop_y, prop_z]:\n prop.SetColor(color.float_rgb)\n prop.SetShadow(False)\n elif isinstance(axes_actor, _vtk.vtkAnnotatedCubeActor):\n axes_actor.GetTextEdgesProperty().SetColor(color.float_rgb)","def color(self, label):\n if self.grayscale:\n return (\"#ffffff\", \"#555555\", \"#888888\", \"#bbbbbb\", \"#222222\")[label]\n # COC WL WR SL SR\n return (\"#4e73b0\", \"#fdb863\", \"#b2abd2\", \"#e66101\", \"#5e3c99\")[label]","def get_graph_color ( self, object ):\n return self.graph_color_","def paintText(self, text):\n return '@paint '+text * 2","def GetItemTextColour(self, item):\r\n\r\n return item.Attr().GetTextColour()","def colored_text(text, color, bold=False):\n if DISABLE_COLORS:\n return text\n attrs = []\n if bold:\n attrs.append('bold')\n return colored(text, color=color, attrs=attrs)","def get_color(self) -> str:\n return self.color","def get_hex_color(self) -> str:\n return f'#{self.color.hex()}'","def text(self, a, b, text, angle=0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\n table = toyplot.data.Table()\n table[\"x\"] = _require_scalar_vector(a)\n table[\"y\"] = _require_scalar_vector(b, table.shape[0])\n table[\"text\"] = _broadcast_object(text, table.shape[0])\n table[\"angle\"] = _broadcast_scalar(angle, table.shape[0])\n table[\"fill\"] = _broadcast_object(fill, table.shape[0])\n table[\"opacity\"] = _broadcast_scalar(opacity, table.shape[0])\n table[\"title\"] = _broadcast_object(title, table.shape[0])\n style = _combine_styles({\"font-weight\":\"normal\", \"stroke\":\"none\", \"text-anchor\":\"middle\", \"alignment-baseline\":\"middle\"}, _require_style(style))\n id = _require_optional_id(id)\n\n default_color = next(self._text_colors)\n table[\"toyplot:fill\"] = toyplot.color._broadcast_color(default_color if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\n\n self._update_domain(table[\"x\"], table[\"y\"])\n\n self._children.append(TextMark(table=table, coordinates=[\"x\", \"y\"], axes=[\"x\", \"y\"], text=\"text\", angle=\"angle\", fill=\"toyplot:fill\", opacity=\"opacity\", title=\"title\", style=style, id=id))\n return self._children[-1]","def textObject(text, font, color):\n\n textSurface = font.render(text, True, color)\n return textSurface, textSurface.get_rect()","def set_color(objname, rgb):\r\n return f'\\ncmd.set_color(\"{objname}\", {(rgb[0], rgb[1], rgb[2])})'","def _on_node_leave(self, *args): # pylint: disable=W0613\r\n self._text.node[\"fg\"] = (self.text_color.x, self.text_color.y, self.text_color.z, 1.0)","def __repr__(self):\r\n return f\"Color('{self.color}')\"","def get_color(self):\n return \"yellow\"","def create_color(cls, text_color: int, background_color: int) -> int:\n global _COLOR_COUNTER\n unicurses.init_pair(_COLOR_COUNTER, text_color, background_color)\n color = unicurses.color_pair(_COLOR_COUNTER)\n _COLOR_COUNTER += 1\n return color","def fl_textcolor(colr):\n _fl_textcolor = library.cfuncproto(\n library.load_so_libforms(), \"fl_textcolor\",\\\n None, [xfdata.FL_COLOR],\\\n \"\"\"void fl_textcolor(FL_COLOR col)\"\"\")\n library.check_if_flinitialized()\n #library.checknonfatal_allowed_value_in_list(colr, xfdata.COLOR_list)\n ul_colr = library.convert_to_FL_COLOR(colr)\n library.keep_elem_refs(colr, ul_colr)\n _fl_textcolor(ul_colr)","def setText(self, text, color=(1, 1, 1, 1)):\n if self.filterControl:\n text = self._filterControl(text)\n pos = 0\n for diffs in difflib.ndiff(self.text, text):\n c = diffs[2]\n if diffs[0] == ' ':\n self.logger.debug('NO change at pos %d' % pos)\n pos += 1\n else:\n if diffs[0] == '-':\n self._delChar(pos)\n self.logger.info('Removed char at pos %d.' % pos)\n elif diffs[0] == '+':\n self._insChar(c, pos, color)\n self.logger.info(\"Inserted char %r at pos %d.\" % (c, pos))\n pos += 1\n self.mesh.rebuild(self.allVertices, self.allIndices)","def colored (string_, color, attrs):\n return string_","def color_print(txt, foreground=PALETTE['white'], background=PALETTE['black']):\n print(color_text(txt, foreground, background))","def init_text(self):\n d = self.declaration\n if d.text:\n self.set_text(d.text)\n if d.text_color:\n self.set_text_color(d.text_color)\n if d.text_alignment:\n self.set_text_alignment(d.text_alignment)\n if d.font_family or d.text_size:\n self.refresh_font()\n if hasattr(d, 'max_lines') and d.max_lines:\n self.set_max_lines(d.max_lines)","def colored(self):\n return colored(str(self), **self.color_opts)","def color_qualifiers(val: Any) -> str:\n if val == \"NO\":\n return \"color: #dc3545;\"\n elif val == \"YES\":\n return \"color: #198754;\"\n return \"\"","def print_color(text: str, color:bcolors = bcolors.BOLD):\n print(f\"{color}{text}{bcolors.ENDC}\")","def color(self):\n return self.container['color']","def color_str(text, color):\n if not is_cli() or no_color():\n # Disable color output if not in CLI mode or if color is disabled\n return text\n return f'\\033[{_COLORS[color]}m{text}\\033[30m'","def name_color(self):\n return self._item.get(\"name_color\")","def create(data):\n \n # init color\n color = Color(\n color_id = data.get('id'),\n name = data['name'],\n rgb = data['rgb'],\n is_trans = data['is_trans'])\n \n # get external names and IDs\n if 'external_ids' in data:\n for name, value in data['external_ids'].items():\n color.external_names[name] = [n for l in value['ext_descrs'] for n in l]\n color.external_ids[name] = value['ext_ids']\n \n return color","def set_node_text(self, text_func):\n\n self.text_func = text_func","def ColorizeDNA(self, text):\n if (text == 'A'):\n escape = '\\033[92m' # Green\n elif (text == 'G'):\n escape = '\\033[93m' # Yellow\n elif (text == 'T'):\n escape = '\\033[91m' # Red\n elif (text == 'C'):\n escape = '\\033[96m' # Blue\n else:\n return text\n return escape + text + '\\033[0m'","def __init__(self, font, color, text=\"\", top=0, left=0, bottom=None, right=None):\n self.text = text\n self.font = font\n self.color = color\n self.top = top\n self.left = left\n self.bottom = bottom\n self.right = right\n self.renderLabel()","def GetAsString(*args, **kwargs):\n return _gdi_.Colour_GetAsString(*args, **kwargs)","def toMage(self):\r\n h, s, v = self.Coords\r\n return '@hsvcolor {%s} %3.1f %3.1f %3.1f' % (self.Name, h, s, v)","def GetPageTextColour(self, page_idx):\r\n \r\n if page_idx >= self._tabs.GetPageCount():\r\n return wx.NullColour\r\n\r\n # update our own tab catalog\r\n page_info = self._tabs.GetPage(page_idx)\r\n return page_info.text_colour","def XCAFDoc_DocumentTool_ColorsLabel(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorsLabel(*args)","def to_pref_str(t):\n return \"{}@{}@{}\".format(t[0],\n t[1].to_rgba_string(),\n t[2].to_rgba_string())","def test_color_str_to_trio(self):\n for case in self.__class__.TEXTS:\n with self.subTest(case=case):\n self.assertEqual(colors.color_str_to_trio(case[0]), case[1])","def colorize(text='', opts=(), **kwargs):\n code_list = []\n if text == '' and len(opts) == 1 and opts[0] == 'reset':\n return '\\x1b[%sm' % RESET\n for k, value in six.iteritems(kwargs):\n if k == 'fg':\n code_list.append(FOREGROUND[value])\n elif k == 'bg':\n code_list.append(BACKGROUND[value])\n for opt in opts:\n if opt in OPT_DICT:\n code_list.append(OPT_DICT[opt])\n if 'noreset' not in opts:\n text = text + '\\x1b[%sm' % RESET\n return ('\\x1b[%sm' % ';'.join(code_list)) + text","def color(self):\n return 0x2f3136","def set_intro_text_3(self, text, color):\n text_y_position = self.objects['introtext2'].t() + 80\n self.objects['introtext3'] = LCARSText((self.width/2, text_y_position),\n text,\n 36,\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)","def alphanumeric_color(self):\n return self._alphanumeric_color","def translate_to_tkcolor(color):\n return rgb_to_hex(translate_rgb(color))","def _plot_text(polyreg, txt, ax, color):\n ax = _newax(ax)\n rc, xc = cheby_ball(polyreg)\n ax.text(xc[0], xc[1], txt, color=color)","def __init__(self, text, start, end, color, alpha=1):\n self.text = text\n self.start = start\n self.end = end\n self.color = color\n self.alpha = alpha","def text(txt, color, size=20, font_name=None):\n return font(size, font_name).render(str(txt), True, color)","def color(self) -> Optional[str]:\n return self.colour","def from_string(cls, text_color):\n\n a = 255\n try:\n r, g, b, a = text_color.replace('rgb(', '').replace(')', '').split(',')\n except ValueError:\n r, g, b = text_color.replace('rgb(', '').replace(')', '').split(',')\n\n return cls(int(r), int(g), int(b), int(a))","def text_objects(self, text, font, color):\n\n text_surface = font.render(text, True, color)\n return text_surface, text_surface.get_rect()","def color(self):\n return self.settings['color']","def color(self, s, fg=None, style=None):\n return LogStr(s, fg=fg, style=style)","def __init__(self, text, font, pos, color=(255, 255, 255)):\r\n self.pos = pos\r\n self.label = font.render(text, 1, color)","def setColorString(clr):\n dislin.color(clr)","def color(self):\n return self.COLOR"],"string":"[\n \"def get_text_color ( self, object ):\\n return self.text_color_\",\n \"def GetTextColour(self):\\r\\n \\r\\n return self._colText\",\n \"def get_text_color ( self, object ):\\n if self._is_selected( object ):\\n return self.selected_text_color_\\n return self.text_color_\",\n \"def colorText(text, color):\\n\\tif text is not None and color is not None:\\n\\t\\tif isinstance(color, types.StringTypes):\\n\\t\\t\\tcolor = ColorUtil.keyToType(color)\\n\\t\\tif color >= 0:\\n\\t\\t\\treturn CyTranslator().changeTextColor(text, color)\\n\\treturn text\",\n \"def colorful_text(text, color=Fore.RESET):\\n return color + text + Fore.RESET\",\n \"def text(self, str: str, x: int, y: int, colour: int, /) -> None:\",\n \"def SetTextColour(self, colText):\\r\\n \\r\\n self._colText = colText\",\n \"def color_text(txt, foreground=PALETTE['white'], background=PALETTE['black']):\\n if isinstance(foreground, str) and foreground.startswith('#'):\\n foreground = hex_to_rgb(foreground)\\n if isinstance(background, str) and background.startswith('#'):\\n background = hex_to_rgb(background)\\n return '{}{}{}{}'.format(_f(*foreground), _b(*background), txt, _r())\",\n \"def change_edge_color(self, text):\\n self.layer.edge_color = text\",\n \"def text(self, txt, ax=None, color='black'):\\n _plot_text(self, txt, ax, color)\",\n \"def text(self, txt, ax=None, color='black'):\\n _plot_text(self, txt, ax, color)\",\n \"def colorize(text, color):\\n return COLOR_DICT[color] + str(text) + COLOR_DICT['end']\",\n \"def set_text_attr(color):\\n SetConsoleTextAttribute(stdout_handle, color)\",\n \"def _color_text(text, color):\\n color_mapping = {\\n 'black': '0;30',\\n 'red': '0;31',\\n 'green': '0;32',\\n 'brown': '0;33',\\n 'blue': '0;34',\\n 'magenta': '0;35',\\n 'cyan': '0;36',\\n 'lightgrey': '0;37',\\n 'default': '0;39',\\n 'darkgrey': '1;30',\\n 'lightred': '1;31',\\n 'lightgreen': '1;32',\\n 'yellow': '1;33',\\n 'lightblue': '1;34',\\n 'lightmagenta': '1;35',\\n 'lightcyan': '1;36',\\n 'white': '1;37'}\\n\\n if sys.platform == 'win32':\\n # On Windows do not colorize text unless in IPython\\n return text\\n\\n color_code = color_mapping.get(color, '0;39')\\n return '\\\\033[{0}m{1}\\\\033[0m'.format(color_code, text)\",\n \"def _color_info_text(self):\\n\\n t = ''\\n for info in self.color_info:\\n if info == 'rgbhex':\\n t1 = tks.color_funcs.rgb_to_hex_string(self.rgb)\\n elif info == 'rgb':\\n t1 = tks.color_funcs.rgb_to_rgb_string(self.rgb, dp=2)\\n elif info == 'hsv':\\n t1 = tks.color_funcs.rgb_to_hsv_string(self.rgb, dp=2)\\n elif info == 'hls':\\n t1 = tks.color_funcs.rgb_to_hls_string(self.rgb, dp=2)\\n\\n t = t + '%s\\\\n' % t1\\n\\n return t\",\n \"def _colorstr(self, args):\",\n \"def color_text(text, color_name):\\n\\n if use_color():\\n return colorama.Fore.__dict__[color_name.upper()] + text + colorama.Style.RESET_ALL\\n else:\\n return text\",\n \"def textColor(colorNumber):\\n return '\\\\033[%dm' % (30 + colorNumber)\",\n \"def _setPlotValText(ax, texts, core, data, labels, labelFmt, fontSize, collection):\\n _ = core.getAssemblyPitch()\\n for a, val, label in zip(core, data, labels):\\n x, y, _ = a.spatialLocator.getLocalCoordinates()\\n cmap = collection.get_cmap()\\n patchColor = numpy.asarray(cmap(collection.norm(val)))\\n luminance = patchColor.dot(LUMINANCE_WEIGHTS)\\n dark = luminance < 0.5\\n if dark:\\n color = \\\"white\\\"\\n else:\\n color = \\\"black\\\"\\n # Write text on top of patch locations.\\n if label is None and labelFmt is not None:\\n # Write the value\\n labelText = labelFmt.format(val)\\n text = ax.text(\\n x,\\n y,\\n labelText,\\n zorder=1,\\n ha=\\\"center\\\",\\n va=\\\"center\\\",\\n fontsize=fontSize,\\n color=color,\\n )\\n elif label is not None:\\n text = ax.text(\\n x,\\n y,\\n label,\\n zorder=1,\\n ha=\\\"center\\\",\\n va=\\\"center\\\",\\n fontsize=fontSize,\\n color=color,\\n )\\n else:\\n # labelFmt was none, so they don't want any text plotted\\n continue\\n texts.append(text)\",\n \"def c_prnt(self, text, color):\\n if color == 'pink':\\n a = self.pink\\n elif color == 'blue':\\n a = self.blue\\n elif color == 'green':\\n a = self.green\\n elif color == 'dgrn':\\n a = self.dgrn\\n elif color == 'yel':\\n a = self.yellow\\n elif color == 'amber':\\n a = self.amber\\n else:\\n raise Exception('The color you selected is not acceptable')\\n print(a + text + self.ENDC)\",\n \"def get_coloured_text_string(text, colour):\\n if colour==\\\"red\\\":\\n return (\\\"\\\\033[31m\\\" + text + \\\"\\\\033[0m\\\")\\n if colour==\\\"green\\\":\\n return (\\\"\\\\033[32m\\\" + text + \\\"\\\\033[0m\\\")\\n if colour==\\\"yellow\\\":\\n return (\\\"\\\\033[33m\\\" + text + \\\"\\\\033[0m\\\")\\n if colour==\\\"blue\\\":\\n return (\\\"\\\\033[34m\\\" + text + \\\"\\\\033[0m\\\")\\n if colour==\\\"purple\\\":\\n return (\\\"\\\\033[35m\\\" + text + \\\"\\\\033[0m\\\")\\n if colour==\\\"cyan\\\":\\n return (\\\"\\\\033[36m\\\" + text + \\\"\\\\033[0m\\\")\\n if colour==\\\"white\\\":\\n return (\\\"\\\\033[37m\\\" + text + \\\"\\\\033[0m\\\")\\n return text\",\n \"def __init__(self, plot_design):\\n self.plot_design = plot_design\\n\\n text_color = self.plot_design.text_color\\n text_font = self.plot_design.text_font\\n sns.set_style(\\\"white\\\",\\n {\\n \\\"axes.edgecolor\\\": text_color,\\n \\\"axes.labelcolor\\\": text_color,\\n \\\"text.color\\\": text_color,\\n \\\"font.sans-serif\\\": [text_font],\\n \\\"xtick.color\\\": text_color,\\n \\\"ytick.color\\\": text_color,\\n }\\n )\",\n \"def text_color(string: str, color: str) -> str:\\n return f\\\"\\\\x1b{_code(color)}{string}\\\\x1b[0m\\\"\",\n \"def get_text_color(cls, quad):\\n\\n\\t\\tr = cls.get_address_value(quad.left_operand)\\n\\t\\tg = cls.get_address_value(quad.right_operand)\\n\\t\\tb = cls.get_address_value(quad.result)\\n\\n\\t\\tif r >= 0 and g >= 0 and b >= 0:\\n\\t\\t\\treturn [r,g,b]\\n\\t\\telse:\\n\\t\\t\\tcls.throwColorError(\\\"text\\\", r,g,b)\",\n \"def printcolor(color, text):\\r\\n pushcolor()\\r\\n setcolor(color)\\r\\n print text\\r\\n popcolor()\",\n \"def setDefaultTextColor(self, Union, QColor=None, Qt_GlobalColor=None, QGradient=None): # real signature unknown; restored from __doc__\\n pass\",\n \"def color(color):\\n if sys.platform == \\\"win32\\\":\\n if color == \\\"green\\\":\\n set_text_attr(FOREGROUND_GREEN | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\\n elif color == \\\"yellow\\\":\\n set_text_attr(FOREGROUND_YELLOW | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\\n elif color == \\\"red\\\":\\n set_text_attr(FOREGROUND_RED | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\\n elif color == \\\"blue\\\":\\n set_text_attr(FOREGROUND_BLUE | get_text_attr() & 0x0070 | FOREGROUND_INTENSITY)\\n elif color == \\\"reset\\\":\\n set_text_attr(FOREGROUND_GREY | get_text_attr() & 0x0070)\\n else :\\n if color == \\\"green\\\":\\n sys.stdout.write('\\\\033[92m')\\n elif color == \\\"red\\\":\\n sys.stdout.write('\\\\033[91m')\\n elif color == \\\"blue\\\":\\n sys.stdout.write('\\\\033[94m')\\n elif color == \\\"reset\\\":\\n sys.stdout.write('\\\\033[0m')\",\n \"def colorize(text, color):\\n\\n if not supports_color():\\n return text\\n\\n return color + text + Colors.ENDC\",\n \"def ColorsLabel(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorsLabel(*args)\",\n \"def color_string(self, data, type):\\n\\n # http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python\\n\\n if self.options['no_color']:\\n return data\\n\\n CEND = '\\\\x1b[0m'\\n\\n if type == ColorType.ok:\\n return '\\\\x1b[1;32;40m{0}{1}'.format(data, CEND)\\n if type == ColorType.error:\\n return '\\\\x1b[1;31;40m{0}{1}'.format(data, CEND)\\n if type == ColorType.warning:\\n return '\\\\x1b[1;36;40m{0}{1}'.format(data, CEND)\\n if type == ColorType.info:\\n return '\\\\x1b[1;34;40m{0}{1}'.format(data, CEND)\\n\\n return str\",\n \"def text_plot(self):\\n if self.stext is not None:\\n # Create text object :\\n self.stextmesh = visu.Text(text=self.stext, color=self.stextcolor,\\n font_size=self.stextsize, pos=self.xyz,\\n bold=True, name='SourcesText')\\n\\n # Set text texture :\\n self.stextmesh.set_gl_state('translucent', depth_test=True)\\n\\n # Apply a transformation to text elements to not cover sources :\\n self.stextmesh.transform = vist.STTransform(\\n translate=self.stextshift)\\n else:\\n self.stextmesh = visu.Text(name='NoneText')\",\n \"def _colored(self, text, *color_args):\\n if self.allow_colors and color_args:\\n return termcolor.colored(text, *color_args)\\n return text\",\n \"def __init__(self, red=Black.red, green=Black.green, blue=Black.blue):\\n self.color = Color(red, green, blue)\\n\\n self.template = '\\\\ttextcolor = {textcolor};\\\\n'\",\n \"def _labels_pcolor(self, obj, fmt=None, **kwargs):\\n # Parse input args and populate _facecolors, which is initially unfilled\\n # See: https://stackoverflow.com/a/20998634/4970632\\n fmt = _not_none(fmt, pticker.SimpleFormatter())\\n labels_kw = {'size': rc['text.labelsize'], 'ha': 'center', 'va': 'center'}\\n labels_kw.update(kwargs)\\n obj.update_scalarmappable() # populate _facecolors\\n\\n # Get positions and contour colors\\n array = obj.get_array()\\n paths = obj.get_paths()\\n colors = _to_ndarray(obj.get_facecolors())\\n edgecolors = _to_ndarray(obj.get_edgecolors())\\n if len(colors) == 1: # weird flex but okay\\n colors = np.repeat(colors, len(array), axis=0)\\n if len(edgecolors) == 1:\\n edgecolors = np.repeat(edgecolors, len(array), axis=0)\\n\\n # Apply colors\\n labs = []\\n for i, (color, path, num) in enumerate(zip(colors, paths, array)):\\n if not np.isfinite(num):\\n edgecolors[i, :] = 0\\n continue\\n bbox = path.get_extents()\\n x = (bbox.xmin + bbox.xmax) / 2\\n y = (bbox.ymin + bbox.ymax) / 2\\n if 'color' not in kwargs:\\n _, _, lum = to_xyz(color, 'hcl')\\n if lum < 50:\\n color = 'w'\\n else:\\n color = 'k'\\n labels_kw['color'] = color\\n lab = self.text(x, y, fmt(num), **labels_kw)\\n labs.append(lab)\\n obj.set_edgecolors(edgecolors)\\n\\n return labs\",\n \"def get_color(self) -> str:\\r\\n return self.color\",\n \"def _change_text_color(text, color_code) -> StyledStr:\\n uncolored_fg = _remove_text_colors(text)\\n return _apply_ansi_code(color_code, uncolored_fg)\",\n \"def GetHyperTextNewColour(self):\\r\\n\\r\\n return self._hypertextnewcolour\",\n \"def text_to_qcolor(text):\\r\\n color = QColor()\\r\\n text = str(text)\\r\\n if not is_text_string(text):\\r\\n return color\\r\\n if text.startswith('#') and len(text)==7:\\r\\n correct = '#0123456789abcdef'\\r\\n for char in text:\\r\\n if char.lower() not in correct:\\r\\n return color\\r\\n elif text not in list(QColor.colorNames()):\\r\\n return color\\r\\n color.setNamedColor(text)\\r\\n return color\",\n \"def name_id_text(self):\\n text = Marker()\\n text.header = self._header\\n text.type = Marker.TEXT_VIEW_FACING\\n text.action = Marker.ADD\\n text.scale.z = 0.05\\n text.color = self.GREEN\\n text.pose = deepcopy(self.POSE)\\n text.pose.position.x = self._p1.x\\n text.pose.position.y = (self._p1.y + self._p5.y) / 2\\n text.pose.position.z = self._p1.z\\n text.text = \\\"{} #{}\\\".format(self._object.object_name, self._track_id)\\n return text\",\n \"def _remove_text_colors(text) -> StyledStr:\\n return _remove_regex(FOREGROUND_COLORS_REGEX, text)\",\n \"def text(self, x, y, text, angle=0.0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\\n table = toyplot.data.Table()\\n table[\\\"x\\\"] = _require_scalar_vector(x)\\n table[\\\"y\\\"] = _require_scalar_vector(y, table.shape[0])\\n table[\\\"text\\\"] = _broadcast_object(text, table.shape[0])\\n table[\\\"angle\\\"] = _broadcast_scalar(angle, table.shape[0])\\n table[\\\"fill\\\"] = _broadcast_object(fill, table.shape[0])\\n table[\\\"toyplot:fill\\\"] = toyplot.color._broadcast_color(toyplot.color.near_black if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\\n table[\\\"opacity\\\"] = _broadcast_scalar(opacity, table.shape[0])\\n table[\\\"title\\\"] = _broadcast_object(title, table.shape[0])\\n style = _combine_styles({\\\"font-weight\\\":\\\"normal\\\", \\\"stroke\\\":\\\"none\\\", \\\"text-anchor\\\":\\\"middle\\\", \\\"alignment-baseline\\\":\\\"middle\\\"}, _require_style(style))\\n id = _require_optional_id(id)\\n\\n self._children.append(TextMark(table=table, coordinates=[\\\"x\\\", \\\"y\\\"], axes=[\\\"x\\\", \\\"y\\\"], text=\\\"text\\\", angle=\\\"angle\\\", fill=\\\"toyplot:fill\\\", opacity=\\\"opacity\\\", title=\\\"title\\\", style=style, id=id))\\n return self._children[-1]\",\n \"def anyTextToColor(self, mystr, r=None):\\n\\n if len(mystr) < 3:\\n # pad up with zeros\\n while len(mystr) % 3 != 0:\\n mystr += \\\"0\\\"\\n\\n i = 0\\n sum1 = 0\\n sum2 = 0\\n sum3 = 0\\n for c in mystr:\\n if i % 3 == 0:\\n sum1 += int( str(ord(c)) + str(i)[::-1])\\n if i % 3 == 1:\\n sum2 += int(str(ord(c)) + str(i)[::-1])\\n if i % 3 == 2:\\n sum3 += int(str(ord(c)) + str(i)[::-1])\\n i += 1\\n\\n x1 = sum1 % 255\\n x2 = sum2 % 255\\n x3 = sum3 % 255\\n\\n if r is not None:\\n x1 = r\\n\\n # if we wants to force a shade of green\\n # x2 = 255\\n\\n outstr = \\\"%x%x%x\\\" % (x1, x2, x3)\\n\\n while len(outstr) < 6:\\n outstr += \\\"a\\\"\\n\\n return outstr\",\n \"def _on_node_enter(self, *args): # pylint: disable=W0613\\r\\n self._text.node[\\\"fg\\\"] = (self.text_color.x + 0.1, self.text_color.y + 0.1,\\r\\n self.text_color.z + 0.1, 1.0)\",\n \"def description_color(self):\\n return self._attribute.get(\\\"color\\\")\",\n \"def color(self):\\n if \\\"color\\\" in self._prop_dict:\\n return self._prop_dict[\\\"color\\\"]\\n else:\\n return None\",\n \"def _setText(self, text):\\n self.text = \\\"\\\"\\n for ch in text:\\n char, vertices, glyph = self._extractGlyph(ch)\\n if not vertices is None and self.glyphs in (\\n GlyphTypes.BASE, GlyphTypes.LAYER_COLOR):\\n vertices['rgba'] = glm.vec4(self.color)\\n if not self.text:\\n off, kern = self._updateMetric(0, char)\\n if char in self.NO_GLYPH_CHARS:\\n self.colors.append([char, None])\\n else:\\n vertices['vtx'] += off + glyph['offset']\\n self.allVertices = np.hstack(vertices)\\n self.allIndices = self._baseInd\\n self.colors.append([char, self.color])\\n self.text += char\\n else:\\n pos = len(self.text)\\n nonGlyph = countInSet(self.text, self.NO_GLYPH_CHARS)\\n # Set the metric\\n off, kern = self._updateMetric(pos, char)\\n if char in self.NO_GLYPH_CHARS:\\n self.colors.append([char, None])\\n else:\\n vertices['vtx'] += off + kern + glyph['offset']\\n if self.allVertices is None:\\n self.allVertices = np.hstack(vertices)\\n else:\\n self.allVertices = np.append(self.allVertices,\\n vertices)\\n if self.allIndices is None:\\n self.allIndices = self._baseInd\\n else:\\n self.allIndices = np.vstack((self.allIndices,\\n self._baseInd + (pos - nonGlyph) * 4))\\n self.colors.append([char, self.color])\\n self.text += char\\n self.setUniforms()\",\n \"def _color(self, text, color_name=None, bold=False):\\n\\n if self.disable_color == True:\\n return text\\n \\n if color_name == None:\\n color_name = 'YELLOW'\\n\\n if color_name in self.colors:\\n return '\\\\033[{0};{1}m{2}\\\\033[0m'.format(\\n int(bold), self.colors.index(color_name) + 30, text)\\n\\n raise Exception('ERROR: \\\"{0}\\\" is not a valid color.\\\\n'.format(color_name))\\n raise Exception('VALID COLORS: {0}.\\\\n'.format(', '.join(self.colors)))\",\n \"def color(self):\\n return self['color']\",\n \"def _update_axes_label_color(axes_actor, color=None):\\n color = Color(color, default_color=pyvista.global_theme.font.color)\\n if isinstance(axes_actor, _vtk.vtkAxesActor):\\n prop_x = axes_actor.GetXAxisCaptionActor2D().GetCaptionTextProperty()\\n prop_y = axes_actor.GetYAxisCaptionActor2D().GetCaptionTextProperty()\\n prop_z = axes_actor.GetZAxisCaptionActor2D().GetCaptionTextProperty()\\n for prop in [prop_x, prop_y, prop_z]:\\n prop.SetColor(color.float_rgb)\\n prop.SetShadow(False)\\n elif isinstance(axes_actor, _vtk.vtkAnnotatedCubeActor):\\n axes_actor.GetTextEdgesProperty().SetColor(color.float_rgb)\",\n \"def color(self, label):\\n if self.grayscale:\\n return (\\\"#ffffff\\\", \\\"#555555\\\", \\\"#888888\\\", \\\"#bbbbbb\\\", \\\"#222222\\\")[label]\\n # COC WL WR SL SR\\n return (\\\"#4e73b0\\\", \\\"#fdb863\\\", \\\"#b2abd2\\\", \\\"#e66101\\\", \\\"#5e3c99\\\")[label]\",\n \"def get_graph_color ( self, object ):\\n return self.graph_color_\",\n \"def paintText(self, text):\\n return '@paint '+text * 2\",\n \"def GetItemTextColour(self, item):\\r\\n\\r\\n return item.Attr().GetTextColour()\",\n \"def colored_text(text, color, bold=False):\\n if DISABLE_COLORS:\\n return text\\n attrs = []\\n if bold:\\n attrs.append('bold')\\n return colored(text, color=color, attrs=attrs)\",\n \"def get_color(self) -> str:\\n return self.color\",\n \"def get_hex_color(self) -> str:\\n return f'#{self.color.hex()}'\",\n \"def text(self, a, b, text, angle=0, fill=None, colormap=None, palette=None, opacity=1.0, title=None, style=None, id=None):\\n table = toyplot.data.Table()\\n table[\\\"x\\\"] = _require_scalar_vector(a)\\n table[\\\"y\\\"] = _require_scalar_vector(b, table.shape[0])\\n table[\\\"text\\\"] = _broadcast_object(text, table.shape[0])\\n table[\\\"angle\\\"] = _broadcast_scalar(angle, table.shape[0])\\n table[\\\"fill\\\"] = _broadcast_object(fill, table.shape[0])\\n table[\\\"opacity\\\"] = _broadcast_scalar(opacity, table.shape[0])\\n table[\\\"title\\\"] = _broadcast_object(title, table.shape[0])\\n style = _combine_styles({\\\"font-weight\\\":\\\"normal\\\", \\\"stroke\\\":\\\"none\\\", \\\"text-anchor\\\":\\\"middle\\\", \\\"alignment-baseline\\\":\\\"middle\\\"}, _require_style(style))\\n id = _require_optional_id(id)\\n\\n default_color = next(self._text_colors)\\n table[\\\"toyplot:fill\\\"] = toyplot.color._broadcast_color(default_color if fill is None else fill, table.shape[0], colormap=colormap, palette=palette)\\n\\n self._update_domain(table[\\\"x\\\"], table[\\\"y\\\"])\\n\\n self._children.append(TextMark(table=table, coordinates=[\\\"x\\\", \\\"y\\\"], axes=[\\\"x\\\", \\\"y\\\"], text=\\\"text\\\", angle=\\\"angle\\\", fill=\\\"toyplot:fill\\\", opacity=\\\"opacity\\\", title=\\\"title\\\", style=style, id=id))\\n return self._children[-1]\",\n \"def textObject(text, font, color):\\n\\n textSurface = font.render(text, True, color)\\n return textSurface, textSurface.get_rect()\",\n \"def set_color(objname, rgb):\\r\\n return f'\\\\ncmd.set_color(\\\"{objname}\\\", {(rgb[0], rgb[1], rgb[2])})'\",\n \"def _on_node_leave(self, *args): # pylint: disable=W0613\\r\\n self._text.node[\\\"fg\\\"] = (self.text_color.x, self.text_color.y, self.text_color.z, 1.0)\",\n \"def __repr__(self):\\r\\n return f\\\"Color('{self.color}')\\\"\",\n \"def get_color(self):\\n return \\\"yellow\\\"\",\n \"def create_color(cls, text_color: int, background_color: int) -> int:\\n global _COLOR_COUNTER\\n unicurses.init_pair(_COLOR_COUNTER, text_color, background_color)\\n color = unicurses.color_pair(_COLOR_COUNTER)\\n _COLOR_COUNTER += 1\\n return color\",\n \"def fl_textcolor(colr):\\n _fl_textcolor = library.cfuncproto(\\n library.load_so_libforms(), \\\"fl_textcolor\\\",\\\\\\n None, [xfdata.FL_COLOR],\\\\\\n \\\"\\\"\\\"void fl_textcolor(FL_COLOR col)\\\"\\\"\\\")\\n library.check_if_flinitialized()\\n #library.checknonfatal_allowed_value_in_list(colr, xfdata.COLOR_list)\\n ul_colr = library.convert_to_FL_COLOR(colr)\\n library.keep_elem_refs(colr, ul_colr)\\n _fl_textcolor(ul_colr)\",\n \"def setText(self, text, color=(1, 1, 1, 1)):\\n if self.filterControl:\\n text = self._filterControl(text)\\n pos = 0\\n for diffs in difflib.ndiff(self.text, text):\\n c = diffs[2]\\n if diffs[0] == ' ':\\n self.logger.debug('NO change at pos %d' % pos)\\n pos += 1\\n else:\\n if diffs[0] == '-':\\n self._delChar(pos)\\n self.logger.info('Removed char at pos %d.' % pos)\\n elif diffs[0] == '+':\\n self._insChar(c, pos, color)\\n self.logger.info(\\\"Inserted char %r at pos %d.\\\" % (c, pos))\\n pos += 1\\n self.mesh.rebuild(self.allVertices, self.allIndices)\",\n \"def colored (string_, color, attrs):\\n return string_\",\n \"def color_print(txt, foreground=PALETTE['white'], background=PALETTE['black']):\\n print(color_text(txt, foreground, background))\",\n \"def init_text(self):\\n d = self.declaration\\n if d.text:\\n self.set_text(d.text)\\n if d.text_color:\\n self.set_text_color(d.text_color)\\n if d.text_alignment:\\n self.set_text_alignment(d.text_alignment)\\n if d.font_family or d.text_size:\\n self.refresh_font()\\n if hasattr(d, 'max_lines') and d.max_lines:\\n self.set_max_lines(d.max_lines)\",\n \"def colored(self):\\n return colored(str(self), **self.color_opts)\",\n \"def color_qualifiers(val: Any) -> str:\\n if val == \\\"NO\\\":\\n return \\\"color: #dc3545;\\\"\\n elif val == \\\"YES\\\":\\n return \\\"color: #198754;\\\"\\n return \\\"\\\"\",\n \"def print_color(text: str, color:bcolors = bcolors.BOLD):\\n print(f\\\"{color}{text}{bcolors.ENDC}\\\")\",\n \"def color(self):\\n return self.container['color']\",\n \"def color_str(text, color):\\n if not is_cli() or no_color():\\n # Disable color output if not in CLI mode or if color is disabled\\n return text\\n return f'\\\\033[{_COLORS[color]}m{text}\\\\033[30m'\",\n \"def name_color(self):\\n return self._item.get(\\\"name_color\\\")\",\n \"def create(data):\\n \\n # init color\\n color = Color(\\n color_id = data.get('id'),\\n name = data['name'],\\n rgb = data['rgb'],\\n is_trans = data['is_trans'])\\n \\n # get external names and IDs\\n if 'external_ids' in data:\\n for name, value in data['external_ids'].items():\\n color.external_names[name] = [n for l in value['ext_descrs'] for n in l]\\n color.external_ids[name] = value['ext_ids']\\n \\n return color\",\n \"def set_node_text(self, text_func):\\n\\n self.text_func = text_func\",\n \"def ColorizeDNA(self, text):\\n if (text == 'A'):\\n escape = '\\\\033[92m' # Green\\n elif (text == 'G'):\\n escape = '\\\\033[93m' # Yellow\\n elif (text == 'T'):\\n escape = '\\\\033[91m' # Red\\n elif (text == 'C'):\\n escape = '\\\\033[96m' # Blue\\n else:\\n return text\\n return escape + text + '\\\\033[0m'\",\n \"def __init__(self, font, color, text=\\\"\\\", top=0, left=0, bottom=None, right=None):\\n self.text = text\\n self.font = font\\n self.color = color\\n self.top = top\\n self.left = left\\n self.bottom = bottom\\n self.right = right\\n self.renderLabel()\",\n \"def GetAsString(*args, **kwargs):\\n return _gdi_.Colour_GetAsString(*args, **kwargs)\",\n \"def toMage(self):\\r\\n h, s, v = self.Coords\\r\\n return '@hsvcolor {%s} %3.1f %3.1f %3.1f' % (self.Name, h, s, v)\",\n \"def GetPageTextColour(self, page_idx):\\r\\n \\r\\n if page_idx >= self._tabs.GetPageCount():\\r\\n return wx.NullColour\\r\\n\\r\\n # update our own tab catalog\\r\\n page_info = self._tabs.GetPage(page_idx)\\r\\n return page_info.text_colour\",\n \"def XCAFDoc_DocumentTool_ColorsLabel(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_ColorsLabel(*args)\",\n \"def to_pref_str(t):\\n return \\\"{}@{}@{}\\\".format(t[0],\\n t[1].to_rgba_string(),\\n t[2].to_rgba_string())\",\n \"def test_color_str_to_trio(self):\\n for case in self.__class__.TEXTS:\\n with self.subTest(case=case):\\n self.assertEqual(colors.color_str_to_trio(case[0]), case[1])\",\n \"def colorize(text='', opts=(), **kwargs):\\n code_list = []\\n if text == '' and len(opts) == 1 and opts[0] == 'reset':\\n return '\\\\x1b[%sm' % RESET\\n for k, value in six.iteritems(kwargs):\\n if k == 'fg':\\n code_list.append(FOREGROUND[value])\\n elif k == 'bg':\\n code_list.append(BACKGROUND[value])\\n for opt in opts:\\n if opt in OPT_DICT:\\n code_list.append(OPT_DICT[opt])\\n if 'noreset' not in opts:\\n text = text + '\\\\x1b[%sm' % RESET\\n return ('\\\\x1b[%sm' % ';'.join(code_list)) + text\",\n \"def color(self):\\n return 0x2f3136\",\n \"def set_intro_text_3(self, text, color):\\n text_y_position = self.objects['introtext2'].t() + 80\\n self.objects['introtext3'] = LCARSText((self.width/2, text_y_position),\\n text,\\n 36,\\n TextAlign.XALIGN_CENTRE, color, Colours.BG, True)\",\n \"def alphanumeric_color(self):\\n return self._alphanumeric_color\",\n \"def translate_to_tkcolor(color):\\n return rgb_to_hex(translate_rgb(color))\",\n \"def _plot_text(polyreg, txt, ax, color):\\n ax = _newax(ax)\\n rc, xc = cheby_ball(polyreg)\\n ax.text(xc[0], xc[1], txt, color=color)\",\n \"def __init__(self, text, start, end, color, alpha=1):\\n self.text = text\\n self.start = start\\n self.end = end\\n self.color = color\\n self.alpha = alpha\",\n \"def text(txt, color, size=20, font_name=None):\\n return font(size, font_name).render(str(txt), True, color)\",\n \"def color(self) -> Optional[str]:\\n return self.colour\",\n \"def from_string(cls, text_color):\\n\\n a = 255\\n try:\\n r, g, b, a = text_color.replace('rgb(', '').replace(')', '').split(',')\\n except ValueError:\\n r, g, b = text_color.replace('rgb(', '').replace(')', '').split(',')\\n\\n return cls(int(r), int(g), int(b), int(a))\",\n \"def text_objects(self, text, font, color):\\n\\n text_surface = font.render(text, True, color)\\n return text_surface, text_surface.get_rect()\",\n \"def color(self):\\n return self.settings['color']\",\n \"def color(self, s, fg=None, style=None):\\n return LogStr(s, fg=fg, style=style)\",\n \"def __init__(self, text, font, pos, color=(255, 255, 255)):\\r\\n self.pos = pos\\r\\n self.label = font.render(text, 1, color)\",\n \"def setColorString(clr):\\n dislin.color(clr)\",\n \"def color(self):\\n return self.COLOR\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.72018707\",\n \"0.63388044\",\n \"0.62334913\",\n \"0.5977799\",\n \"0.5870951\",\n \"0.58445084\",\n \"0.58192\",\n \"0.57877505\",\n \"0.5772373\",\n \"0.57390344\",\n \"0.57390344\",\n \"0.5726201\",\n \"0.56486714\",\n \"0.5620535\",\n \"0.5599045\",\n \"0.55467296\",\n \"0.55348307\",\n \"0.5521071\",\n \"0.5505261\",\n \"0.54955703\",\n \"0.5466612\",\n \"0.5456736\",\n \"0.54553384\",\n \"0.54425603\",\n \"0.5391722\",\n \"0.5383927\",\n \"0.53799653\",\n \"0.5376057\",\n \"0.5370747\",\n \"0.5346356\",\n \"0.53142685\",\n \"0.52965933\",\n \"0.52882653\",\n \"0.5287732\",\n \"0.52868927\",\n \"0.5282421\",\n \"0.52751505\",\n \"0.5258763\",\n \"0.52309644\",\n \"0.52260184\",\n \"0.5222443\",\n \"0.5220371\",\n \"0.52183396\",\n \"0.52030426\",\n \"0.51998764\",\n \"0.5188662\",\n \"0.518291\",\n \"0.5181797\",\n \"0.5170744\",\n \"0.51559424\",\n \"0.51475435\",\n \"0.51312435\",\n \"0.5130437\",\n \"0.5121843\",\n \"0.51208395\",\n \"0.50963223\",\n \"0.5077899\",\n \"0.50731826\",\n \"0.50676453\",\n \"0.5056245\",\n \"0.50542367\",\n \"0.5045315\",\n \"0.50421804\",\n \"0.5041158\",\n \"0.503929\",\n \"0.5035528\",\n \"0.5033044\",\n \"0.5032944\",\n \"0.50261855\",\n \"0.5021898\",\n \"0.5016655\",\n \"0.50134355\",\n \"0.5002505\",\n \"0.50016665\",\n \"0.50012636\",\n \"0.49892193\",\n \"0.4987342\",\n \"0.49859467\",\n \"0.49814373\",\n \"0.4976809\",\n \"0.49760014\",\n \"0.4974093\",\n \"0.49672133\",\n \"0.49665007\",\n \"0.49634653\",\n \"0.49583673\",\n \"0.49524617\",\n \"0.49488178\",\n \"0.49478236\",\n \"0.4941697\",\n \"0.49381423\",\n \"0.49379575\",\n \"0.49354446\",\n \"0.49304682\",\n \"0.49236298\",\n \"0.4923022\",\n \"0.49223715\",\n \"0.4920699\",\n \"0.49190864\",\n \"0.49175942\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54452705"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":22395,"cells":{"query":{"kind":"string","value":"The function generates and returns a combined table with names of stocks in columns and dates in indexes. Profit tables for individual stocks are taken from stock_retrunrs function."},"document":{"kind":"string","value":"def Generating_stock_daily_return_table():\r\n #Getting Names list\r\n Profitfile='pap//CombProfit.csv'\r\n path='D://Doktorat Marek//dane//'\r\n ProfitsFilePath=path+Profitfile\r\n quarterly_profit=pd.read_csv(ProfitsFilePath,index_col=0,header=0,parse_dates=True)\r\n Names_list=quarterly_profit.columns.tolist()\r\n \r\n Stock_returns=pd.DataFrame(index=pd.date_range('19980101','20180918',freq='D'),columns=Names_list)\r\n for name in Names_list:\r\n Stock_returns[name]=1+stock_returns(name)['Return']/100\r\n Stock_returns[name].fillna(1,inplace=True)\r\n \r\n WIG=pd.read_excel('D://Doktorat Marek//dane//notowania//Infostrefa//PL9999999995.xls')\r\n WIG['Date']=pd.to_datetime(WIG['Data'])\r\n WIG.set_index('Date',inplace=True)\r\n Stock_returns['WIG'] = 1+WIG['Zmiana']/100\r\n Stock_returns['WIG'].fillna(1,inplace=True)\r\n Stock_returns['Average']=Stock_returns.mean(1)\r\n \r\n FileReturns='D://Doktorat Marek//dane//Notowania//Stock_returns.csv'\r\n Stock_returns.to_csv(FileReturns,encoding='UTF-8')\r\n return 0"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setupStockTable(self):\n # Get the date\n # NOTE: This is probably un\n date = datetime.date()\n dateStr = date.month() + \"/\" + date.day() + \"/\" + date.year()\n\n stocks = (\"INTC\", \"AAPL\", \"GOOG\", \"YHOO\", \"SYK\", \"VZ\")\n\n for stock in stocks:\n stockObj = self.securityFactory(stock)\n stockObj.queryAPI()\n\n self.stockDB.query(\"INSERT INTO basic_info (ticker, price, daily_change, company, year_high, year_low, \\\n daily_percent, date, streak) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\", (stockObj.target, stockObj.curr, \\\n stockObj.daily_change, stockObj.company,\\\n stockObj.year_high, stockObj.year_low,\\\n stockObj.daily_percent, dateStr, 0))","def stocks():\n mappings = {key: key for key in Schema.stock_columns}\n return Schema(mappings)","def portfolio_table(self):\n idx = set(name.split('-')[0].split('.')[0] for name, etf in self.etfs.items() if not etf.sold())\n table = pd.DataFrame({'Invested': 0, 'Shares':0, 'Share Price':0, 'Present Value':0, 'P/L':0, 'P/L%':0},index=idx)\n for name, etf in self.etfs.items():\n if not etf.sold():\n table.loc[name.split('-')[0].split('.')[0], 'Invested'] += etf.initial_investment()\n table.loc[name.split('-')[0].split('.')[0], 'Shares'] += etf.n_shares\n table.loc[name.split('-')[0].split('.')[0], 'Share Price'] = etf.stock_price()\n table.loc[name.split('-')[0].split('.')[0], 'Present Value'] += etf.present_value()\n table.loc[name.split('-')[0].split('.')[0], 'P/L'] += etf.profit_loss()\n table.insert(1, 'PMA', round(table['Invested'] / table['Shares'], 2))\n table.insert(3, 'Initial Weight', round(table['Invested'] / table['Invested'].sum() * 100, 2))\n table.insert(4, 'Present Weight', round(table['Present Value'] / table['Present Value'].sum() * 100, 2))\n table['P/L%'] = round(table['P/L'] / table['Invested'] * 100, 2)\n table['P/L'] = round(table['P/L'], 2)\n table['Present Value'] = round(table['Present Value'], 2)\n return table.sort_values('Invested', 0, ascending=False)","def investments_table(self):\n table = pd.DataFrame(index=[etf.buy_date for etf in self.etfs.values()])\n table['Ticker'] = [name.split('-')[0].split('.')[0] for name in self.etfs.keys()]\n table['Buying Price (€)'] = [etf.buy_price for etf in self.etfs.values()]\n table['Number of Shares'] = [etf.n_shares for etf in self.etfs.values()]\n table['Commissions (€)'] = [etf.total_commissions() for etf in self.etfs.values()]\n table['Invested (€)'] = [etf.initial_investment() for etf in self.etfs.values()]\n table['Share Price (€)'] = [etf.stock_price() for etf in self.etfs.values()]\n table['Value (€)'] = [etf.present_value() for etf in self.etfs.values()]\n table['P/L (€)'] = [etf.profit_loss() for etf in self.etfs.values()]\n table['P/L (%)'] = [etf.profit_loss(pct=True) for etf in self.etfs.values()]\n return table","def getHTMLTable(self):\n stocks = self.getTrackedStocks()\n\n html = \" \\\n <html> \\\n <head></head> \\\n <body> \\\n <table> \\\n <tr> \\\n <th>Ticker</th> \\\n <th>Company</th> \\\n <th>Price</th> \\\n <th>Daily Change</th> \\\n <th>Daily Percent Change</th> \\\n <th>Year High</th> \\\n <th>Year Low</th> \\\n </tr>\"\n\n for stock in stocks:\n html += \"<tr>\"\n\n html += \"<td>\"\n html += str(stock.target)\n html += \"</td>\"\n\n html += \"<td>\"\n html += str(stock.company)\n html += \"</td>\"\n\n html += \"<td>\"\n html += str(stock.curr)\n html += \"</td>\"\n\n html += \"<td>\"\n html += str(stock.daily_change)\n html += \"</td>\"\n\n html += \"<td>\"\n html += str(stock.daily_percent)\n html += \"</td>\"\n\n html += \"<td>\"\n html += str(stock.year_high)\n html += \"</td>\"\n\n html += \"<td>\"\n html += str(stock.year_low)\n html += \"</td>\"\n\n html += \"</tr>\"\n\n html += \"</table></body></html>\"\n\n return html","def get_stock_price_df(info, symbols):\n\n df_l = []\n\n for num, i in enumerate(info):\n df = pd.DataFrame.from_dict(i, orient='index')\n df['Symbol'] = symbols[num]\n df_l.append(df)\n\n df_full = pd.concat(df_l)\n df_full = df_full.rename(columns={'1. open': 'Open',\n '2. high': 'High',\n '3. low': 'Low',\n '4. close': 'Close',\n '5. volume': 'Volume'})\n\n return df_full","def get_quarterly_report_tables(self, symbol, date):\n raw_data = self.sec_file_ops.get_raw_quarterly_financial_statement(symbol, date)\n\n cleared_tables = {}\n for title in raw_data:\n cleared_tables[title] = self._remove_unnecessary_tags(raw_data[title])\n # print(cleared_tables)\n\n tables = {}\n for title in cleared_tables:\n table = Table()\n table.read_tablecontent(cleared_tables[title])\n tables[title] = table\n\n for title in tables:\n tables[title].setup_linked_rows()\n # tables[title].print(linked=True)\n\n return tables","def create_query(window,con,input_table_name,output_table_name,input_columns, stat_columns):\n sql = \"CREATE TABLE {} AS \".format(output_table_name)\n sql = sql + \"SELECT\" \n for input_column in input_columns:\n sql = sql + \" {},\".format(input_column)\n for stat_column in stat_columns:\n sql = sql + \" {},\".format(stat_column)\n for stat_column in stat_columns:\n sql = sql + \" AVG({}) OVER(PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS ma{:02.0f}_{},\".format(stat_column,window-1,window,stat_column)\n sql = sql + \" MIN({}) OVER(PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS min{:02.0f}_{},\".format(stat_column,window-1,window,stat_column)\n sql = sql + \" MAX({}) OVER(PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS max{:02.0f}_{},\".format(stat_column,window-1,window,stat_column)\n sql = sql + \" regr_slope({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS slope{:02.0f}_{},\".format(stat_column,window-1,window,stat_column)\n sql = sql + \" regr_intercept({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS intercept{:02.0f}_{},\".format(stat_column,window-1,window,stat_column)\n sql = sql + (\" regr_slope({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) * year \"\n \"+ regr_intercept({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS ols{:02.0f}_{},\".format(stat_column,window-1,stat_column,window-1,window,stat_column))\n \n \n sql = sql[:-1]\n sql = sql + \" FROM {}\".format(input_table_name)\n return sql","def get_back_dataframe(self, end_date=None, stocks=None):\n if end_date is None:\n end_date = self.dates[-1]\n\n if type(end_date) is not datetime.datetime and type(end_date) is not pd.tslib.Timestamp:\n end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n\n if stocks is None:\n stocks = self.stocks\n\n info = {}\n for stock in stocks:\n info[stock] = self.stock_data[stock].to_stock_dataframe_range(start_date=None, end_date=end_date)\n\n return info","def sql_create_big_table():\n return \"\"\"\n SELECT\n m.tube_assembly_id as 'tube_assembly_id'\n , m.quantity_1 as 'quantity_component'\n , c.component_id \n , c.component_type_id \n , c.type as component_type \n , c.connection_type_id\n , c.outside_shape\n , c.base_type\n , c.height_over_tube\n , c.bolt_pattern_long\n , c.bolt_pattern_wide\n , c.groove\n , c.base_diameter\n , c.shoulder_diameter\n , c.unique_feature\n , c.orientation\n , c.weight\n , p.supplier\n , p.quote_date\n , p.annual_usage\n , p.min_order_quantity\n , p.bracket_pricing\n , p.quantity\n , p.cost\n FROM\n stg_bill_of_materials m INNER JOIN stg_comp_boss c\n ON m.component_id_1 = c.component_id\n \n INNER JOIN stg_price_quote p\n ON m.tube_assembly_id = p.tube_assembly_id\n \"\"\"","def get_stock_data(company, start_date_inc, stop_date_inc):\n\n api_key = 'Bo9P_cJnmf5EsQPp1Bdp'\n desired_cols = 'date,close'\n\n# ticker = 'FB'\n# start_date_inc = '20170801'\n# end_date_inc = '20170831'\n\n # format and send the request\n payload = {\n 'date.gte': start_date_inc,\n 'date.lte': stop_date_inc,\n 'ticker': company,\n 'qopts.columns': desired_cols,\n 'api_key': api_key\n }\n meta_url = r'https://www.quandl.com/api/v3/datatables/WIKI/PRICES'\n r = requests.get(meta_url, params=payload)\n\n # convert to a pandas dataframe\n df = pd.DataFrame(r.json()['datatable']['data'])\n if not df.empty:\n df.columns = ['date', 'price']\n df['date'] = pd.to_datetime(df['date'])\n\n return df","def create_order(df_stock, df_signal, moneyness=('OTM', 'ITM'),\n cycle=0, strike=0, expire=(False, True)):\n symbol = df_stock.ix[df_stock.index.values[0]]['symbol']\n\n tb_closes = {\n stock.date.strftime('%Y-%m-%d'): np.float(stock.close) for stock in\n Stock.objects.filter(Q(symbol=symbol) & Q(source='thinkback'))\n }\n\n holding = df_signal['holding'].apply(\n lambda x: int(x / np.timedelta64(1, 'D'))\n ).astype(np.int).min()\n\n data = list()\n dates0, options0 = get_options_by_cycle_strike(\n symbol=symbol,\n name='CALL',\n dates0=df_signal['date0'],\n dte=holding,\n moneyness=moneyness,\n cycle=cycle,\n strike=strike\n )\n\n for date0, (index, signal) in zip(dates0, df_signal.iterrows()):\n date1 = signal['date1']\n\n if date0:\n option0 = options0.get(date=date0)\n\n option1 = None\n if option0 and option0.bid > 0:\n date1, option1 = get_option_by_contract_date(option0.contract, date1)\n\n if option0 and option1:\n stock0 = tb_closes[option0.date.strftime('%Y-%m-%d')]\n close0 = stock0 - np.float(option0.bid)\n\n ask1 = 0\n if int(expire):\n ask1 = np.float(\n tb_closes[option1.date.strftime('%Y-%m-%d')]\n - np.float(option0.contract.strike)\n )\n ask1 = ask1 if ask1 > 0 else 0.0\n\n date1 = option1.date\n stock1 = tb_closes[option1.date.strftime('%Y-%m-%d')]\n close1 = stock1 - np.float(ask1)\n else:\n date1 = option1.date\n stock1 = tb_closes[option1.date.strftime('%Y-%m-%d')]\n close1 = stock1 - np.float(option1.ask)\n\n data.append({\n 'date0': option0.date,\n 'date1': date1,\n 'signal0': 'BUY',\n 'signal1': 'SELL',\n 'stock0': stock0,\n 'stock1': stock1,\n 'option0': option0.bid,\n 'option1': ask1 if expire else option1.ask,\n 'close0': np.round(close0, 2), # buy using ask\n 'close1': np.round(close1, 2), # sell using bid\n 'option_code': option0.contract.option_code,\n 'strike': np.float(option0.contract.strike),\n 'dte0': np.int(option0.dte),\n 'dte1': np.int(option1.dte),\n 'intrinsic0': np.float(option0.intrinsic),\n 'intrinsic1': np.float(option1.intrinsic)\n })\n\n df = DataFrame()\n if len(data):\n df = DataFrame(data, columns=[\n 'date0', 'date1', 'signal0', 'signal1',\n 'stock0', 'stock1', 'option0', 'option1', 'close0', 'close1',\n 'option_code', 'strike', 'dte0', 'dte1',\n 'intrinsic0', 'intrinsic1'\n ])\n\n df['holding'] = df['date1'] - df['date0']\n df['pct_chg'] = np.round((df['close1'] - df['close0']) / df['close0'], 2)\n\n f = lambda x: np.round(x['pct_chg'] * -1 if x['signal0'] == 'SELL' else x['pct_chg'], 2)\n df['pct_chg'] = df.apply(f, axis=1)\n\n df['sqm0'] = 100\n df['sqm1'] = -100\n df['oqm0'] = -1\n df['oqm1'] = 1\n\n return df","def get_dataframes(symbols=(\"sne\", \"goog\", \"tsla\"), source='yahoo', refresh=False):\n symbols = util.make_symbols(list(symbols))\n if refresh:\n symbols_to_refresh = symbols\n else:\n symbols_to_refresh = [sym for sym in symbols if not Equity.objects.filter(symbol=sym).exists()]\n source = source.lower().strip()\n if source in ('yahoo', 'google'):\n source += '_finance'\n if source[:3] == 'fed':\n source = 'federal_reserve_economic_data'\n ccpanda = ccp.ConcurrentPandas()\n # set the data source\n getattr(ccpanda, \"set_source_\" + source)()\n if symbols_to_refresh:\n # tell concurrent pandas which keys/symbols to retrieve\n ccpanda.insert_keys(symbols_to_refresh)\n # start concurrentpandas threads\n ccpanda.consume_keys_asynchronous_threads()\n # FIXME: is there a better/faster iterator to use like `ccpanda.output_map` attribute?\n pseudodict = ccpanda.return_map()\n else:\n pseudodict = {}\n table = {}\n for sym in symbols:\n e, created = None, False\n if not sym in symbols_to_refresh:\n e, created = Equity.objects.get_or_create(symbol=sym)\n if created or not e or not e.time_series or sym in symbols_to_refresh:\n e, created = Equity.objects.get_or_create(\n symbol=sym,\n name=sym, # FIXME: use data source to find equity name!\n time_series=pseudodict[sym].to_json(),\n )\n table[sym] = pd.io.json.read_json(path_or_buf=e.time_series, orient='columns', typ='frame', convert_dates=True)\n return table","def pack_for_create_table(tables: tuple):\n\n all_tables = {'product': \"\"\"CREATE TABLE IF NOT EXISTS product (\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\n PRODUCT_ID CHAR(50),\n GLOBAL_ID CHAR(50),\n TITLE TEXT,\n COUNTRY CHAR(50),\n RETURNS_ACCEPTED CHAR(50),\n IS_MULTI_VARIATION_LISTING CHAR(50),\n CONDITION_ID CHAR(50),\n CONDITION_DISPLAY_NAME CHAR(50),\n CATEGORY_ID CHAR(50),\n CATEGORY_NAME CHAR(100),\n AUTO_PAY CHAR(50),\n LOCATION CHAR(50),\n PAYMENT_METHOD CHAR(50),\n TOP_RATED_LISTING CHAR(50),\n VIEW_ITEM_URL TEXT\n )\"\"\",\n 'listing_info': \"\"\"CREATE TABLE IF NOT EXISTS listing_info (\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\n BEST_OFFER_ENABLED CHAR(20),\n END_TIME CHAR(50),\n GIFT CHAR(20),\n BUY_IT_NOW_AVAILABLE CHAR(20),\n START_TIME CHAR(50),\n FIXED_PRICE CHAR(20)\n )\"\"\",\n 'selling_status': \"\"\"CREATE TABLE IF NOT EXISTS selling_status (\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\n CONVERTED_CURRENT_PRICE_CURRENCY_ID CHAR(20),\n CONVERTED_CURRENT_PRICE_VALUE CHAR(50),\n CURRENT_PRICE_CURRENCY_ID CHAR(20),\n CURRENT_PRICE_VALUE CHAR(50),\n TIME_LEFT CHAR(50),\n SELLING_STATE CHAR(20)\n )\"\"\",\n 'shipping_info': \"\"\"CREATE TABLE IF NOT EXISTS shipping_info (\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\n SHIP_TO_LOCATIONS CHAR(20),\n SHIPPING_TYPE CHAR(100),\n ONE_DAY_SHIPPING_AVAILABLE CHAR(20),\n EXPEDITED_SHIPPING CHAR(20),\n SHIPPING_SERVICE_COST_CURRENCY_ID CHAR(50),\n SHIPPING_SERVICE_COST_VALUE CHAR(50),\n HANDLING_TIME CHAR(50)\n )\"\"\"\n }\n\n # If table was not specified, put all tables in line\n tables = all_tables.keys() if tables is None else tables\n\n return ';'.join([\"{}\".format(all_tables[i]) for i in tables])","def make_historical_balances_and_prices_table(game_id: int, user_id: int, start_time: float = None,\n end_time: float = None) -> pd.DataFrame:\n start_time, end_time = get_time_defaults(game_id, start_time, end_time)\n balances_df = get_user_balance_history(game_id, user_id, start_time, end_time)\n df = add_bookends(balances_df, end_time=end_time)\n df = df.groupby(\"symbol\").apply(resample_values)\n df = df.reset_index().rename(columns={\"level_1\": \"timestamp\"})\n df = append_price_data_to_balance_histories(df) # price appends + resampling happen here\n df.sort_values([\"symbol\", \"timestamp\"])\n df[\"value\"] = df[\"balance\"] * df[\"price\"]\n df = filter_for_trade_time(df)\n df[[\"balance\", \"price\", \"value\"]] = df[[\"balance\", \"price\", \"value\"]].astype(float)\n apply_validation(df, balances_and_prices_table_schema, strict=True)\n return df.reset_index(drop=True).sort_values([\"timestamp\", \"symbol\"])","def make_table(data, portfolio):\n table_data = [\n [\n # Header\n click.style('Rank', fg='white'),\n click.style('Coin', fg='white'),\n click.style('Price', fg='white'),\n click.style('Coins Owned', fg='white'),\n click.style('Net Worth CAD', fg='white'), \n click.style('Net Worth USD', fg='white'),\n click.style('24 Hour Volume', fg='white'),\n click.style('Market Cap', fg='white'),\n click.style('1 Hour', fg='white'),\n click.style('24 Hours', fg='white'),\n click.style('7 Days', fg='white'),\n click.style('Last Updated', fg='white')\n ]\n ]\n\n for row in data:\n id = row['id']\n table_row = [\n click.style(row['rank'], fg='white'),\n click.style(id, fg='cyan'),\n click.style('$' + add_commas(row['price_usd']), fg='green'),\n click.style(add_commas(portfolio[id]), fg='green'),\n click.style('$' + add_commas(str(int(round(float(portfolio[id]) * float(row['price_cad']),)))), fg='green'),\n click.style('$' + add_commas(str(int(round(float(portfolio[id]) * float(row['price_usd']),)))), fg='green'),\n click.style('$' + add_commas(format(float(row['24h_volume_usd']), '.2f')), fg='green'),\n click.style('$' + add_commas(format(float(row['market_cap_usd']), '.2f')), fg='green'),\n style_percent(row['percent_change_1h']),\n style_percent(row['percent_change_24h']),\n style_percent(row['percent_change_7d']),\n click.style(time_since(row['last_updated']), fg='white')\n ]\n table_data.append(table_row)\n \n table_instance = AsciiTable(table_data, 'Crypto Stats')\n cols = len(table_instance.table.split('\\n')[0])\n resize_terminal(total_rows(data), cols)\n\n return table_instance.table","def create_stock_view_list_table(self):\n create_stock_view_list_sql = \"\"\"\n CREATE TABLE IF NOT EXISTS \"{0}\".\"{1}\" (\n stock_view_name text NOT NULL PRIMARY KEY,\n stock_view_relation text NOT NULL\n );\n \"\"\".format(Schemas.SCHEMA_META, Tables.TABLE_TICK_STOCK_VIEW_LIST)\n\n conn = self.db_engine.connect()\n try:\n conn.execute(create_stock_view_list_sql)\n except:\n self._logger.log_error(traceback.format_exc())\n return Error.ERROR_DB_EXECUTION_FAILED\n finally:\n conn.close()","def join_data(df_order, df_stock, df_all):\n df_stock0 = df_stock.set_index('date')\n df_list = []\n for index, data in df_order.iterrows():\n df_both = df_all.query(\n '(option_code == %r | option_code == %r) & date >= %r & date <= %r' % (\n data['code0'], data['code1'], data['date0'], data['date1']\n )\n )[['date', 'option_code', 'dte', 'sell', 'buy', 'strike']]\n\n df0 = df_both.query('option_code == %r' % data['code0'])\n df1 = df_both.query('option_code == %r' % data['code1'])\n df_join = pd.merge(df0, df1, on='date', suffixes=(0, 1))\n\n if data['signal0'] == 'BUY':\n first = df_join['buy0'] - df_join['sell1']\n remain = df_join['sell0'] - df_join['buy1']\n df_join['signal'] = ['BUY'] + ['SELL'] * len(remain[1:])\n df_join['option'] = [first.iloc[0]] + list(remain[1:])\n else:\n first = -df_join['sell0'] + df_join['buy1']\n remain = -df_join['buy0'] + df_join['sell1']\n df_join['signal'] = ['SELL'] + ['BUY'] * len(remain[1:])\n df_join['option'] = [first.iloc[0]] + list(remain[1:])\n\n df_join['pos_net'] = df_join['option'] - df_join['option'].iloc[0]\n df_join['pct_chg'] = df_join['pos_net'] / np.abs(df_join['option'].iloc[0]) + 0\n\n df_join = df_join.drop([\n 'sell0', 'buy0', 'sell1', 'buy1', 'strike0', 'strike1', 'dte1'\n ], axis=1)\n df_close = df_stock0[data['date0']:data['date1']]\n df_close = df_close[['close']].reset_index()\n df_both = pd.merge(df_join, df_close, on='date')\n df_both.rename(index=str, columns={'close': 'stock', 'dte0': 'dte'}, inplace=True)\n\n # print df.to_string(line_width=1000)\n df_both = df_both.replace([np.inf, -np.inf], np.nan)\n df_both = df_both.fillna(0)\n\n df_both = df_both.round({\n 'pos_net': 2,\n 'pct_chg': 2,\n })\n\n df_list.append(df_both)\n\n return df_list","def getStock(symbol, start, end):\n df = data.get_data_yahoo(symbol, start, end)\n\n df.columns.values[-1] = 'AdjClose'\n df.columns = df.columns + '_' + symbol\n df['Return_%s' % symbol] = df['AdjClose_%s' % symbol].pct_change()\n\n return df","def getHistoricalData(stockName, startDate):\n conn = r.connect(db = db.DB)\n stockName = stockName.upper()\n startDate = dateToString(startDate)\n endDate = dateToString(datetime.datetime.now())\n\n if not stockName in db.STOCK_MAP.keys():\n return dict(\n error = 1,\n message = \"The info you want is not what I can give\"\n )\n\n stock = yf.StockInfo(stockName + db.IN_LONDON)\n cachedData = r.table(db.HISTORICAL_TABLE).get(stockName).run(conn)\n infoDict = dict()\n\n if cachedData == None:\n print \"\\n-- DB -- \" + stockName + \" == Inserting New Information ==\\n\"\n histList = stock.historical_prices(startDate, endDate)\n infoDict[\"history_list\"] = createHistoryDictList(histList)\n infoDict[\"index\"] = stockName\n infoDict[\"name\"] = db.STOCK_MAP[stockName]\n infoDict[\"timestamp\"] = getTime()\n r.table(db.HISTORICAL_TABLE).insert(infoDict).run(conn)\n else:\n elapsedTime = (\n getTime() -\n cachedData[\"timestamp\"]\n )\n if elapsedTime > db.HISTORICAL_INTERVAL:\n print \"\\n-- DB -- \" + stockName + \" == Updating Database ==\\n\"\n histList = stock.historical_prices(startDate, endDate)\n infoDict[\"history_list\"] = createHistoryDictList(histList)\n infoDict[\"index\"] = stockName\n infoDict[\"timestamp\"] = getTime()\n r.table(db.HISTORICAL_TABLE).get(stockName).update(\n infoDict\n ).run(conn)\n else:\n print \"\\n-- DB -- \" + stockName + \" == Using Cached Data ==\\n\"\n infoDict = cachedData\n\n infoDict[\"name\"] = db.STOCK_MAP[stockName]\n return infoDict","def download_stock_price_hist(\n\ttickers = [ 'AAPL' ],\n\tprice_column = 'Adj Close',\t\t\t\t\t\t\t\t# assume it's the Adjusted Close price that are interested\n\tstart = datetime.date( 2009, 12, 31 ),\t\t\t\t# assume start is guaranteed to be a weekday\n\tend = datetime.date( 2015, 12, 31 ),\n\tcsv_file = \"stock_price_test.csv\",\n):\n\t# Check validity of inputs\n\tif len( tickers ) <= 0:\n\t\tprint \"Tickers must not be empty\";\n\t\treturn False;\n\tif start > end:\n\t\tprint \"Start date \" + start.isoformat() + \" can't be later than End date \" + end.isoformat();\n\n\tdf = pd.DataFrame();\t\t\t# data frame to return\n\tfor _i in range( len(tickers) ):\n\t\tticker = tickers[_i];\n\t\tprint \"Index\" + str(_i) + \"\\t\" + \"Ticker: \" + ticker;\n\n\t\tstart_str = start.isoformat();\n\t\tend_str = end.isoformat();\n\t\thist = ystockquote.get_historical_prices( ticker, start_str, end_str );\t# dictionary with date string as the key\n\n\t\t# Get time series of stock prices (Don't sort before forming the Series!!!)\n\t\tdate_index = [];\n\t\tprice_data = [];\n\t\tfor key, val in hist.iteritems():\n\t\t\tdate_index.append( datetime.datetime.strptime( key, \"%Y-%m-%d\" ).date() );\n\t\t\tprice_data.append( float( val[ price_column ] ) )\n\n\t\tif min( date_index ) > start:\t\t\t\t\t\t\t\t# Pass if the no stock price is available on Start\n\t\t\tcontinue;\n\t\tstock_ts = pd.Series( price_data, date_index );\n\t\tstock_ts = stock_ts.sort_index();\n\n\t\t# Add current stock TS to the DataFrame\n\t\tdf[ticker] = stock_ts;\n\t\n\tdf.to_csv( csv_file, index_label='Date' );\n\treturn True;","def buildExposureTable(exposures, fields, instruments):\n name = []\n ra = []\n dec= []\n field= []\n inst = []\n airmass = []\n mjd = []\n exptime = []\n epoch = []\n apcorr = []\n index = 0\n for k,e in exposures.items():\n name.append(e.name)\n ra.append(getDegree(e.coords.ra))\n dec.append(getDegree(e.coords.dec))\n field.append(fields[e.field].index)\n if e.instrument in specialInstruments:\n inst.append(specialInstruments[e.instrument])\n else:\n inst.append(instruments[e.instrument].index)\n e.index = index\n index += 1\n\n airmass.append(e.airmass)\n mjd.append(e.mjd)\n exptime.append(e.exptime)\n epoch.append(e.epoch)\n apcorr.append(e.apcorr)\n hdu = pf.BinTableHDU.from_columns(\\\n pf.ColDefs( [pf.Column(name='NAME',format=py_to_fits(name),array=name),\n pf.Column(name='RA',format=py_to_fits(ra),array=ra),\n pf.Column(name='DEC',format=py_to_fits(dec),array=dec),\n pf.Column(name='FIELDNUMBER',format=py_to_fits(field),array=field),\n pf.Column(name='INSTRUMENTNUMBER',format=py_to_fits(inst),\\\n array=inst),\n pf.Column(name=\"MJD\",format=py_to_fits(mjd),array=mjd),\n pf.Column(name=\"AIRMASS\",format=py_to_fits(airmass),array=airmass),\n pf.Column(name=\"EXPTIME\",format=py_to_fits(exptime),array=exptime),\n pf.Column(name=\"EPOCH\",format=py_to_fits(epoch),array=epoch),\n pf.Column(name=\"APCORR\",format=py_to_fits(apcorr),array=apcorr)] ),\n name = 'Exposures')\n # hdu.header['EXTNAME'] = 'Exposures'\n return hdu","def construct_table(self):\n table_str = self.header_row\n row_lbls, col_lbls = self.get_idxvals()\n for r,rlbl in enumerate(row_lbls):\n row_data = [self.data[rlbl,clbl] for clbl in col_lbls]\n table_str += self.construct_row(r, row_data)\n \n return table_str","def getStock(symbol, start, end):\n df = pd.io.data.get_data_yahoo(symbol, start, end)\n\n df.columns.values[-1] = 'AdjClose'\n df.columns = df.columns + '_' + symbol\n df['Return_%s' % symbol] = df['AdjClose_%s' % symbol].pct_change()\n\n return df","def create_tables (cls, env=os.environ):\n\n cur = cls.pri_table_read_cursor (env=env)\n cur.execute ('SPECIALCASE gettablelist')\n ret = cur.fetchall ()\n \n existingtables = set ([x[0].lower() for x in ret])\n\n for tabname in (set (cls.table_desc.keys ()) - existingtables):\n sql, lsd = cls.table_desc[tabname]\n epls, desls, sqlprefix = lsd.get_create_labeling (savels=True)\n\n conn = get_labeled_conn (epls, desls)\n cur = conn.cursor ()\n cur.execute (sql)\n conn.close ()\n lsd.pop_labelset ()\n\n \n import psycopg2\n for sql in cls.sql_createindex:\n conn = get_labeled_conn ()\n cur = conn.cursor ()\n # XXX It would be better to check which indices exist as we do for tables.\n try:\n cur.execute (sql)\n except psycopg2.ProgrammingError, e: \n pass\n conn.close ()","def create_stock_view_table(self, stock_view_name, stock_relation):\n from Core.DAO.ComplicatedTables.StockViewTable import StockViewTable\n create_tick_sql = \"\"\"\n {4};\n CREATE INDEX IF NOT EXISTS stock_view_{3}_date ON \"{0}\".\"{1}{2}\"(\"date\");\n CREATE INDEX IF NOT EXISTS stock_view_{3}_datetime ON \"{0}\".\"{1}{2}\"(\"datetime\");\n \"\"\".format(Schemas.SCHEMA_STOCK_VIEW_DATA, Tables.TABLE_TICK_STOCK_VIEW_PREFIX,\n stock_view_name, stock_view_name.split(\".\")[0],\n StockViewTable(stock_relation).get_table_define_sql(Schemas.SCHEMA_STOCK_VIEW_DATA,\n Tables.TABLE_TICK_STOCK_VIEW_PREFIX +\n stock_view_name))\n\n conn = self.db_engine.connect()\n try:\n conn.execute(create_tick_sql)\n except:\n self._logger.log_error(traceback.format_exc())\n return Error.ERROR_DB_EXECUTION_FAILED\n finally:\n conn.close()\n\n return Error.SUCCESS","def index():\n\n rows = db.execute(\n 'SELECT symbol, SUM(CASE WHEN operation = \"SELL\" THEN -shares ELSE shares END) shares FROM transactions WHERE id = :id GROUP BY symbol;', id=session['user_id'])\n\n cash = db.execute('SELECT cash FROM users WHERE id = :id', id=session['user_id'])[0]['cash']\n\n grand_total = cash\n\n for row in rows:\n stock = lookup(row['symbol'])\n\n row['name'] = stock['name']\n row['price'] = stock['price']\n row['total'] = row['shares'] * stock['price']\n\n grand_total += row['shares'] * stock['price']\n\n rows.append({\n 'symbol': 'CASH',\n 'cash': cash,\n 'total': grand_total\n })\n\n return render_template('index.html', stocks=rows)","def get_data(symbols, dates):\n df = pd.DataFrame(index=dates)\n if \"SPY\" not in symbols:\n symbols.insert(0, \"SPY\")\n for symbol in symbols:\n temp = pd.read_csv(symbol_to_path(symbol, base_dir=\"data\"), \n index_col=\"Date\", \n parse_dates=True, \n usecols=[\"Date\", \"Adj Close\"])\n \n temp = temp.rename(columns={\"Adj Close\": symbol})\n \n df = df.join(temp, how=\"inner\")\n df = df.sort_index(axis=0, ascending=[1])\n \n return df","def get_dividends(self, stock_list, start_date=None, end_date=None):\n df_dict = {}\n df_list = []\n file_in_path = [year.replace(\".csv\", \"\") for year in self.get_csv_in_path(self.dividend_eps_path)]\n if not start_date:\n start_date = file_in_path[0]\n if not end_date:\n end_date = file_in_path[-1]\n if start_date > end_date:\n return df_dict\n for year in range(int(start_date), int(end_date)+1):\n target_path = \"{}/{}.csv\".format(self.dividend_eps_path, year)\n df = pd.read_csv(target_path, index_col=\"名稱\")\n self.replace_nan_to_other(df, \"\")\n for stock in stock_list:\n pd_index = df.index.to_list()\n old_list = []\n if stock in pd_index:\n data = df.loc[stock]\n\n # print(\"日期 = {}\".format(data.get(\"除息交易日\")))\n if df_dict.get(stock):\n old_list = df_dict.get(stock)\n\n # check data is available\n dict = {}\n if data.get(\"現金股利\") != \"\":\n dict.update({\"除息交易日\": \"{}{}\".format(year, data.get(\"除息交易日\").split(\"'\")[1].replace(\"/\", \"\")) if data.get('除息交易日') else \"\",\n \"現金股利\": data.get(\"現金股利\"),\n })\n if data.get(\"股票股利\") != \"\":\n dict.update({\"除權交易日\": \"{}{}\".format(year, data.get(\"除權交易日\").split(\"'\")[1].replace(\"/\", \"\")) if data.get('除權交易日') else \"\",\n \"股票股利\": data.get(\"股票股利\"),\n })\n if dict:\n old_list.append(dict)\n df_dict.update({stock: old_list})\n\n return df_dict","def index():\n\n stocks_owned = db.execute(\"SELECT DISTINCT stock FROM transaction WHERE id = :id;\", id=session['user_id'])\n\n number_of_rows= len(stocks_owned) - 1\n\n i = 0\n\n total_value=0\n\n for stock in stocks_owned:\n\n stock_list=[]\n stock_list[i]=stock\n\n value = db.execute(\"SELECT SUM(total_amount) FROM transaction WHERE id = :id GROUP BY stock HAVING stock=:stock\", id=session['usestockr_id'], stock=stocks_owned[\"stock\"])\n value_list=[]\n value_list[i] = value\n\n amount_owned = db.execute(\"SELECT SUM(amount) FROM transaction WHERE id = :id GROUP BY stock HAVING stock=:stock\", id=session['user_id'], stock = stocks_owned[\"stock\"])\n amount_list=[]\n amount_list[i]= amount_owned\n\n quote_input = stocks_owned[i]\n quote_info = lookup(quote_input)\n price = quote_info['price']\n price_list=[]\n price_list[i] = price\n\n\n total_value+=value\n\n i+=1\n\n cash = db.execute(\"SELECT cash FROM users WHERE id = :id;\", id=session['user_id'])\n\n grand_total = total_value + cash\n\n ###(\"SELECT stock, SUM(total_amount) FROM transaction WHERE id = :id;, id=session['user_id'] GROUP BY stock\")####\n\n\n return render_template(\"index.html\", number_of_rows=number_of_rows, stock_list=stock_list, amount_list=amount_list, value_list=value_list, price_list=price_list, total_value=total_value, grand_total=grand_total)","def get_basics():\n today = _dt.date.today().isoformat()\n log.info('Start to collect HS_basics_all stocks...')\n log.info('Collecting the main stock basics from tushare for %s' % today)\n hs = __get_hs_daily(today)\n \n log.info('Collecting SH50 and prepare to merge into the main frame...')\n sh50 = __get_sh50()\n log.info('Collection HS300 and prepare to merge into the main frame...')\n hs300 = __get_hs300()\n log.info('Collection ZZ500 and prepare to merge into the main frame...') \n zz500 = __get_zz500()\n\n # Add 上证50 column\n log.trace('Adding SH50 to main frame as \\'sh50\\'')\n hs['sh50'] = np.where(hs['code'].isin(sh50['code']), True, False)\n # Merge from hs300 and change column name to 'hs300'\n log.trace('Adding HS300 to main frame as \\'hs300\\'') \n hs = hs.merge(hs300, on='code', how='left')\n hs.fillna({'hs300':0}, axis=0, inplace=True)\n # Add 中证500 column\n log.trace('Adding ZZ500 to main frame as \\'zz500\\'')\n hs['zz500'] = np.where(hs['code'].isin(zz500['code']), True, False)\n # Filte 中小板\n log.trace('Adding SME to main frame as \\'sme\\'')\n __filter_sme(hs)\n # Filte 创业板\n log.trace('Adding GEM to main frame as \\'gem\\'')\n __filter_gem(hs)\n # Filte ST\n log.trace('Filting ST and tag to main frame as \\'st\\'')\n __filter_st(hs)\n # Fill 概念分类\n log.trace('Adding concept classfied \\'concept\\'')\n hs = __concept_classified(hs)\n\n # Reset index, prepare to write to database\n log.info('Reindexing the DataFrame and rename to match the column name in db...')\n hs = hs.reindex(['code','name','industry','concept','sme','gem','st','hs300','sh50','zz500','pe','outstanding','totals','totalAssets','liquidAssets','timeToMarket','pb','rev','profit','holders'], axis=1)\n hs.rename({'totalAssets': 'total_assets', 'liquidAssets': 'liquid_assets', 'timeToMarket': 'time_to_market'}, axis='columns', inplace=True)\n\n log.info('Successfully collect stock basics, got [%s] records.' % len(hs))\n \n return hs","def construct_base_table(self):\n # Get requested biological entities.\n req_biomodels = [exp.biomodel for exp in self.req_exps]\n # Remove duplicates.\n req_biomodels = list(unique_everseen(req_biomodels))\n # Order according to the linearized models.\n relations = Relations(self.app_label, model_superclass=(BiologicalModel, MetaModel))\n relations.start_join()\n linearized_biomodels = relations.linearized\n req_biomodels = sort_by_other(req_biomodels, \n order=linearized_biomodels)\n # Construct the SQL statement.\n sql = self.construct_result_table(req_biomodels)\n return sql","def result_table(fmt='latex_booktabs'):\n \n names = [\n \"ETF EW.\",\n \"Antonacci ETF\",\n \"Antonacci ETF Inv. Vol.\",\n \"Futures EW.\",\n \"Antonacci Futures\",\n \"Antonacci Futures Inv. Vol.\",\n \"TSMOM Futures Low Vol.\",\n \"TSMOM Futures High Vol.\"\n ]\n\n # Get stats for each strategy\n s1 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=7, target_vol=40, periods=6, vol_weight=False)\n s2 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=False)\n s3 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=True)\n s4 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=47, target_vol=40, periods=6, vol_weight=False)\n s5 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\n s6 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=True)\n s7 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\n s8 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=100, periods=6, vol_weight=False)\n\n # The relevant columns from the summary data\n cols = [3, 4, 5, 6]\n num_assets = [7, 2, 2, 47, 10, 10, 47, 47]\n stats = [s1, s2, s3, s4, s5, s6, s7, s8]\n table = [names]\n \n # Collecting the results\n for i, col in enumerate(cols):\n col_list = [round(stat['summary'][col], 2) for stat in stats]\n table.append(col_list)\n\n table.append(num_assets)\n table = list(map(list, zip(*table))) # Transpose\n \n # Creating table headers\n headers = ['Strategy Name', 'Annual Return', 'Annual Vol.', 'Sharpe', 'Max. Drawdown', '# Assets']\n \n # Returning latex table\n tbl = tabulate(table, headers, tablefmt=fmt)\n print(tbl)\n \n return tbl","def get_data(symbols, dates):\n \n df = pd.DataFrame(index=dates)\n if 'SPY' not in symbols:\n symbols.insert(0,'SPY')\n for symbol in symbols:\n \n df1 = pd.read_csv(symbol_to_path(symbol),usecols=['Date','Adj Close'],\n index_col='Date',na_values =['nan'] )\n df1 = df1.rename(columns ={\"Adj Close\": symbol}) \n #print df1\n df = df.join(df1,how='inner')\n \n return df.sort_index()","def get_table(base, engine):\n class w1_temp_table(base):\n __tablename__ = 'w1_temp'\n __table_args__ = {\"useexisting\": True}\n\n id = sa.Column(sa.types.Integer, primary_key=True)\n logger_id = sa.Column(sa.types.Integer)\n value = sa.Column(sa.types.String)\n datetime = sa.Column(sa.types.DateTime)\n return w1_temp_table","def trading_alg(self,table_name = None, buy_now = False, strategy_name = \"sma9\"):\n \n self.bs.buyed_stocks = 0\n self.bs.money = self.bs.startCredit\n spy_stocks = self.load_data(table_name = table_name, symbols = [\"SPY\"])\n spy_stocks = FinI.add_indicators(spy_stocks)\n \n if self.symbols:\n symbols = self.symbols\n else:\n symbols = self.db.get_symbols()\n\n # symbols = [\"INTC\",\"BYND\",\"ZM\",\"NKE\",\"HIMX\",\"JKS\",\"ENPH\",\"DUK\",\"GE\",\"DIS\",\"LEVI\",\"NVAX\",\"SLCA\",\"GPS\"]\n \n for symbol in symbols:\n print(\"symbol: \" + str(symbol))\n \n sub_data = self.load_data(table_name = table_name, symbols = symbol)\n if len(sub_data) < 1:\n break\n\n self.bt_stocks = FinI.add_indicators(sub_data)\n self.bt_stocks = FinI.add_fib(self.bt_stocks)\n # print(self.bt_stocks)\n print(self.bt_stocks[\"sma30\"])\n print(\"calculating percent change:\" + str(symbol))\n # sub_data = self.stocks.loc[self.stocks.sym ==symbol[0]].sort_values(by='index')\n \n self.symbols = symbol[0]\n \n # self.prev_stock = sub_data.iloc[0]\n # self.bt_stocks.iloc[0] = sub_data.iloc[0]\n\n # self.sell_marks = self.sell_marks.iloc[0:0]\n # self.buy_marks = self.buy_marks.iloc[0:0]\n self.bs.transactions = 0\n self.bs.profit_perc = 0\n \n # trend_indicator = \n # TODO mechanism for select strategies\n # self.ts_boll(buy_now = buy_now, at_settings = None, symbol = symbol, spy_stocks = spy_stocks)\n self.ts_eval(buy_now = buy_now, at_settings = None, symbol = symbol, spy_stocks = spy_stocks, strategy_logic = strategy_name)\n\n # call the method with passed and assembled name\n # method = getattr(self, 'ts_' + strategy_name)\n # method(buy_now = buy_now, at_settings = None, symbol = symbol, spy_stocks = spy_stocks, strategy_name = strategy_name)","def main():\n\n bank_lookup = {\n 'Spending': bank_main,\n 'Income': bank_main,\n 'Saving': bank_savings,\n 'Credit card': bank_credit,\n }\n\n sheet_data = petl.fromxlsx('sample-data.xlsx', sheet='Data')\n data = sheet_data.cut(*range(5))\n early_data = data.select('Date', lambda r: r.month <= 2)\n\n for account, table in split_table(early_data, 'Account'):\n modified_table = bank_lookup[account](table)\n # modified_table.tocsv(table['Account'][0]+'.csv')\n print(modified_table)","def stock_1_query(self):\n return f\"\"\"\n SELECT Date, '{self.stock_1}'\n FROM closing_prices;\"\"\"","def __generate_data_table__(self):\n # | - __generate_data_table__\n rows_list = []\n for job in self.job_var_lst:\n revisions = self.job_revision_number(job)\n for revision in range(revisions + 1)[1:]:\n # | - FOR LOOP BODY\n entry_param_dict = {}\n for prop in job:\n entry_param_dict[prop[\"property\"]] = prop[\"value\"]\n\n entry_param_dict[\"variable_list\"] = job\n entry_param_dict[\"path\"] = self.var_lst_to_path(job)\n\n entry_param_dict[\"max_revision\"] = revisions\n entry_param_dict[\"revision_number\"] = revision\n\n rows_list.append(entry_param_dict)\n # __|\n\n data_frame = pd.DataFrame(rows_list)\n\n return(data_frame)\n # __|","def get_data(table_name, end, num, start=None):\n if start == None:\n if table_name == \"days\": start = end - timedelta(days=num-1) \n if table_name == \"weeks\": start = end - timedelta(weeks=num-1) \n if table_name == \"months\": start = end - relativedelta(months=+num-1) \n if table_name == \"years\": start = end - relativedelta(years=+num-1) \n else: \n start = days.get_entry(table_name, start).date\n \n dates = []\n data = []\n weather = []\n density = []\n \n while start <= end:\n entry = days.get_entry(table_name, start)\n data.append(entry.sentiment)\n \n if table_name == \"days\": \n dates.append(entry.date.strftime(\"%B %d, %Y\"))\n start = start + timedelta(days=1)\n if table_name == \"weeks\": \n dates.append(entry.date.strftime(\"%B %d, %Y\"))\n start = start + timedelta(weeks=1) \n if table_name == \"months\": \n dates.append(entry.date.strftime(\"%B %Y\"))\n start = start + relativedelta(months=+1) \n if table_name == \"years\": \n dates.append(entry.date.strftime(\"%Y\"))\n start = start + relativedelta(years=+1) \n\n # 7/15/15 is the last entry in the current weather dictionary\n num_days = (min(start, date(2015,7,15)) - entry.date).days\n temp = {entry.date + timedelta(days=i): weather_dict[entry.date + timedelta(days=i)] for i in range(num_days)}\n weather.append(float(sum(temp.values()))/float(len(temp)))\n\n if density_dict != None:\n d = max(entry.date, date(2014,7,1))\n num_days = (min(start, date(2015,7,28)) - d).days\n rho = {d + timedelta(days=i): density_dict[d + timedelta(days=i)] for i in range(num_days)}\n density.append(float(sum(rho.values()))/float(len(rho)))\n\n return dates, data, weather, density","def product_tables(self): \r\n\r\n self.mycursor.execute('CREATE TABLE IF NOT EXISTS product(\\\r\n PROD_id BIGINT PRIMARY KEY,\\\r\n PROD_name VARCHAR(100) NOT NULL,\\\r\n PROD_grade CHAR(1) NOT NULL,\\\r\n PROD_url VARCHAR(150) NOT NULL UNIQUE)')","def create_tick_data_table(self, stock_code):\n create_tick_sql = \"\"\"\n {4};\n CREATE INDEX IF NOT EXISTS tick_{3}_date ON \"{0}\".\"{1}{2}\"(\"date\");\n CREATE INDEX IF NOT EXISTS tick_{3}_datetime ON \"{0}\".\"{1}{2}\"(\"datetime\");\n \"\"\".format(Schemas.SCHEMA_TICK_DATA, Tables.TABLE_TICK_STOCK_PREFIX,\n stock_code, stock_code.split(\".\")[0],\n TickDataTable().get_table_define_sql(Schemas.SCHEMA_TICK_DATA, Tables.TABLE_TICK_STOCK_PREFIX +\n stock_code))\n\n conn = self.db_engine.connect()\n try:\n conn.execute(create_tick_sql)\n except:\n self._logger.log_error(traceback.format_exc())\n return Error.ERROR_DB_EXECUTION_FAILED\n finally:\n conn.close()\n\n return Error.SUCCESS","def populate(table_name, date):\n\tlog_msg3(\"Populando \" + table_name)\n\n\twsq_to_txt(table_name, date)\n\n\t# si es un nuevo año se crea una nueva tabla\n\tif(is_new_year(table_name) and not new_tables_created):\n\t\tcreate_tables()\n\n\ttxt_to_table(table_name)\n\n\tlog_msg_ok3()","def index():\n user_stocks_list = db.execute(\"SELECT stock FROM transactions WHERE id = :current_id\", current_id=session[\"user_id\"])\n user_stocks = []\n for stock in user_stocks_list:\n if stock['stock'] not in user_stocks:\n user_stocks.append(stock['stock'])\n\n stock_portfolio = []\n\n for possible_stock in user_stocks:\n bought_shares_list = db.execute(\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\",\n current_id=session[\"user_id\"], stock=possible_stock, t='B')\n bought_shares = 0\n bought_shares = bought_shares_list[0][\"SUM(units)\"]\n sold_shares_list = db.execute(\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\",\n current_id=session[\"user_id\"], stock=possible_stock, t='S')\n sold_shares = 0\n sold_shares = sold_shares_list[0][\"SUM(units)\"]\n if sold_shares == None:\n sold_shares = 0\n\n available_shares = 0\n if bought_shares != None and (bought_shares - sold_shares) > 0:\n available_shares = bought_shares - sold_shares\n current_price = int(lookup(possible_stock)[\"price\"])\n market_value = current_price * available_shares\n dict_stock = {}\n dict_stock['name_stock'] = possible_stock\n dict_stock['shares_quantity'] = available_shares\n dict_stock['current_price'] = current_price\n dict_stock['market_value'] = market_value\n stock_portfolio.append(dict_stock)\n else:\n pass\n\n available_money_list = db.execute(\"SELECT cash FROM users WHERE id = :current_id\", current_id=session[\"user_id\"])\n available_money = usd(available_money_list[0]['cash'])\n\n username_list = db.execute(\"SELECT username FROM users WHERE id = :current_id\", current_id=session[\"user_id\"])\n username = username_list[0][\"username\"]\n\n sum_market_values = 0\n for collection in stock_portfolio:\n sum_market_values += int(collection['market_value'])\n\n total_value = usd(available_money_list[0]['cash'] + sum_market_values)\n\n return render_template(\"index.html\", stock_portfolio=stock_portfolio, user_stocks=user_stocks, money=available_money, name=username, total_value=total_value)","async def get_trading_table(self):\n if self.trading_table is None:\n self.trading_table = {}\n wikitext = await Controller.get_wikitext('Trading')\n for match in re.finditer(r\"===='''([^']+)'''====\\n({\\|[^\\n]*\\n(?:[^\\n]*\\n)+?\\|})\", wikitext):\n place = match.group(1)\n trade_list = {'into':{}, 'from':{}}\n for row in match.group(2).strip().split('|-'):\n if len(row) < 5:\n continue\n trade = re.search(r'\\|([0-9,.]+)\\|\\| \\[\\[(?:[^|\\]]+\\|)?([^\\]]+)\\]\\]\\|\\|→\\n\\|align\\=right\\|([0-9,.]+)\\|\\| \\[\\[(?:[^|\\]]+\\|)?([^\\]]+)\\]\\]', row)\n if not trade:\n trade = re.search(r'\\| ?([0-9,.]+) \\[\\[(?:[^|\\]]+\\|)?([^\\]]+)\\]\\]\\|\\| ?([0-9,.]+) \\[\\[(?:[^|\\]]+\\|)?([^\\]]+)\\]\\]', row)\n if not trade:\n logging.warn(f'No trade row in `{row}`')\n continue\n from_amt = int(trade.group(1).replace(',', ''))\n from_itm = trade.group(2).lower()\n to_amt = int(trade.group(3).replace(',', ''))\n to_itm = trade.group(4).lower()\n if from_itm not in trade_list['from']:\n trade_list['from'][from_itm] = []\n if to_itm not in trade_list['into']:\n trade_list['into'][to_itm] = []\n trade_list['from'][from_itm].append((to_itm, from_amt, to_amt))\n trade_list['into'][to_itm].append((from_itm, to_amt, from_amt))\n if '(' in place:\n # Gorenichi (Kiev), Magnitogorsk (trader), Magnitogorsk (fitter)\n if place[0] == 'G':\n place = 'Kiev'\n self.trading_table[place.lower()] = trade_list\n return self.trading_table","def last_day_table(self):\n idx = set(name.split('-')[0].split('.')[0] for name, etf in self.etfs.items() if not etf.sold()).union(['Totale'])\n table = pd.DataFrame({'Value':0, 'P/L':0, 'P/L%':0},index=idx)\n if len(self.etfs) > 0:\n for name, etf in self.etfs.items():\n if not etf.sold():\n table.loc[name.split('-')[0].split('.')[0], 'P/L'] += etf.get_gain()\n table.loc[name.split('-')[0].split('.')[0], 'Value'] += etf.get_value(self.data.index[-1]-timedelta(1))\n table['P/L%'] = round(table['P/L'] / table['Value'] * 100, 2)\n table.loc['Totale', 'P/L'] = table['P/L'].sum()\n table.loc['Totale', 'P/L%'] = self.gains(pct=True)\n return table.sort_values('Value', 0, ascending=False)","def account_df(self, typ='trades', improve=False):\n cols = ['date_open', 'date_close', 'symbol', 'style', 'volume', 'price_open', 'price_stop', 'price_limit', 'price_close', 'comment', 'magic', 'order_id_master', 'order_id_stop', 'order_id_limit', 'direction', 'price_diff', 'price_diff', 'price_diff_d', 'price_diff_rel', 'price_diff_rel_d', 'MAE', 'MFE', 'MAE_rel', 'MFE_rel', 'price_trailing_diff', 'profit']\n d = self._d_orders[typ]\n if len(d)>0:\n df = pd.DataFrame(d.values(), index=d.keys())\n df = df.rename(columns={0: 'bo'})\n df['date_created'] = df['bo'].map(lambda o: o.date_created)\n df['date_open'] = df['bo'].map(lambda o: o.date_open)\n df['date_close'] = df['bo'].map(lambda o: o.date_close)\n df['date_closed'] = df['bo'].map(lambda o: o.date_closed)\n df['symbol'] = df['bo'].map(lambda o: o.symbol)\n #df['style'] = df['bo'].map(lambda o: o.style)\n df['volume'] = df['bo'].map(lambda o: o.volume)\n df['price_open'] = df['bo'].map(lambda o: o.price_open)\n df['price_stop'] = df['bo'].map(lambda o: o.price_stop)\n df['price_limit'] = df['bo'].map(lambda o: o.price_limit)\n df['price_close'] = df['bo'].map(lambda o: o.price_close)\n df['comment'] = df['bo'].map(lambda o: o.comment)\n df['magic'] = df['bo'].map(lambda o: o.magic)\n #df['order_id_master'] = df['bo'].map(lambda o: o.order_id_master)\n #df['order_id_stop'] = df['bo'].map(lambda o: o.order_id_stop)\n #df['order_id_limit'] = df['bo'].map(lambda o: o.order_id_limit)\n\n df['direction'] = df['bo'].map(lambda o: o.direction)\n\n df['price_diff'] = df['bo'].map(lambda o: o.price_diff)\n df['price_diff_d'] = df['bo'].map(lambda o: o.price_diff_d)\n df['price_diff_rel'] = df['bo'].map(lambda o: o.price_diff_rel)\n df['price_diff_rel_d'] = df['bo'].map(lambda o: o.price_diff_rel_d)\n \n df['MAE'] = df['bo'].map(lambda o: o.MAE)\n df['MFE'] = df['bo'].map(lambda o: o.MFE)\n \n #df['MAE_rel'] = df['MAE'] / df['price_open']\n #df['MFE_rel'] = df['MFE'] / df['price_open']\n df['MAE_rel'] = df['bo'].map(lambda o: o.MAE_rel)\n df['MFE_rel'] = df['bo'].map(lambda o: o.MFE_rel)\n \n\n #df['profit'] = df['volume'] * df['price_diff'].fillna(0)\n df['profit'] = df['bo'].map(lambda o: o.profit)\n #df['profit_rel'] = df['bo'].map(lambda o: o.profit_rel)\n \n if improve:\n try:\n df = improve_account_df_with_additional_data(df)\n except Exception as e:\n log.error(\"Can't improve account df with additional data\")\n log.error(\"Reason: %s\" % str(e))\n \n #del df['bo'] \n \n return(df)\n else:\n return(pd.DataFrame(columns=cols))","def _create_performance_contribution_tables(self, performance_df: QFDataFrame) -> List[DFTable]:\n # Create a QFSeries which contains the initial amount of cash in the portfolio for each year / month\n numeric_columns = [col for col in performance_df.columns if is_numeric_dtype(performance_df[col])]\n portfolio_values = performance_df[numeric_columns].sum().shift(fill_value=self._initial_cash).cumsum()\n performance_df[numeric_columns] = performance_df[numeric_columns] / portfolio_values[numeric_columns]\n\n # Add category column and aggregate data accordingly\n ticker_name_to_category = {t.name: category for t, category in self._ticker_to_category.items()}\n performance_df[\"Category\"] = performance_df[\"Asset\"].apply(lambda t: ticker_name_to_category[t])\n all_categories = list(set(ticker_name_to_category.values()))\n performance_df = performance_df.sort_values(by=[\"Category\", \"Asset\"])\n performance_df = performance_df.groupby(\"Category\").apply(\n lambda d: pd.concat([PricesDataFrame({**{\"Asset\": [d.name], \"Category\": [d.name]},\n **{c: [d[c].sum()] for c in numeric_columns}}), d],\n ignore_index=True)).drop(columns=[\"Category\"])\n\n # Add the Total Performance row (divide by 2 as the df contains already aggregated data for each group)\n total_sum_row = performance_df[numeric_columns].sum() / 2\n total_sum_row[\"Asset\"] = \"Total Performance\"\n performance_df = performance_df.append(total_sum_row, ignore_index=True)\n\n # Format the rows using the percentage formatter\n performance_df[numeric_columns] = performance_df[numeric_columns].applymap(lambda x: '{:.2%}'.format(x))\n\n # Divide the performance dataframe into a number of dataframes, so that each of them contains up to\n # self._max_columns_per_page columns\n split_dfs = np.array_split(performance_df.set_index(\"Asset\"),\n np.ceil((performance_df.num_of_columns - 1) / self._max_columns_per_page), axis=1)\n df_tables = [DFTable(df.reset_index(), css_classes=['table', 'shrink-font', 'right-align', 'wide-first-column'])\n for df in split_dfs]\n\n # Get the indices of rows, which contain category info\n category_indices = performance_df[performance_df[\"Asset\"].isin(all_categories)].index\n\n for df_table in df_tables:\n # Add table formatting, highlight rows showing the total contribution of the given category\n df_table.add_rows_styles(category_indices, {\"font-weight\": \"bold\", \"font-size\": \"0.95em\",\n \"background-color\": \"#cbd0d2\"})\n df_table.add_rows_styles([performance_df.index[-1]], {\"font-weight\": \"bold\", \"font-size\": \"0.95em\",\n \"background-color\": \"#b9bcbd\"})\n return df_tables","def get_stock_data_frame(time, stock):\n\n print(\"Getting\", time, \"stock data for\", stock)\n url = 'https://api.iextrading.com/1.0/stock/'+stock+'/chart/'+time\n req = requests.get(url)\n print(url)\n\n print(\"Parsing data.\")\n rjson = req.text\n\n rdata = json.loads(rjson)\n\n dates = []\n openprices = []\n highprices = []\n lowprices = []\n closeprices = []\n volumes = []\n\n for i in rdata:\n date = i['date']\n dates.append(date)\n openprices.append(float(i['open']))\n highprices.append(float(i['high']))\n lowprices.append(float(i['low']))\n closeprices.append(float(i['close']))\n volumes.append(float(i['volume']))\n\n index = pd.DatetimeIndex(dates, dtype='datetime64[ns]')\n _open = pd.Series(openprices, index=index)\n high = pd.Series(highprices, index=index)\n low = pd.Series(lowprices, index=index)\n close = pd.Series(closeprices, index=index)\n data_frame_data = {'Open' : _open, 'High' : high, 'Low' : low, 'Close' : close}\n\n return pd.DataFrame(data_frame_data)","def glue_table(name: str, df: pd.DataFrame, build_path=\"_build\"):\n\n if not os.path.exists(build_path):\n os.mkdir(build_path)\n df.to_excel(os.path.join(build_path, f\"{name}.xlsx\"))\n\n glue(name, df)","def _get_db_create_table(self, frame):\r\n\r\n columns = (u',\\n'.\r\n\r\n join([u' `%s` DECIMAL(20,5) DEFAULT NULL COMMENT \"%s\"' %\r\n\r\n (self._get_db_name(name), name) for name in\r\n\r\n frame.index.values]))\r\n\r\n table_name = self._get_db_table_name(frame)\r\n\r\n return (\r\n\r\n u'CREATE TABLE `%s` (\\n' % table_name +\r\n\r\n u' `ticker` VARCHAR(50) NOT NULL COMMENT \"Exchange:Ticker\",\\n' +\r\n\r\n u' `period` DATE NOT NULL COMMENT \"Period\",\\n' +\r\n\r\n u'%s,\\n' % columns +\r\n\r\n u' PRIMARY KEY USING BTREE (`ticker`, `period`),\\n' +\r\n\r\n u' KEY `ix_ticker` USING BTREE (`ticker`))\\n' +\r\n\r\n u'ENGINE=MyISAM DEFAULT CHARSET=utf8\\n' +\r\n\r\n u'COMMENT = \"%s\"' % frame.index.name)","def get_52_week_high_low_for_stocks(stocks):\n print(\"Fetching stock quotes.\")\n # Build a full list of symbols\n symbols = []\n for key in stocks.keys():\n symbols.append(key)\n\n num_of_batches = int(len(symbols)/BATCH_SIZE) + 1\n\n all_stocks_df = pandas.DataFrame()\n\n #all_stocks_df = pandas.DataFrame()\n\n # Get quotes for all the stocks in batches\n for i in range(0, num_of_batches):\n print(\"Fetching quotes in batch: \" + str(i+1) + \"/\" + str(num_of_batches))\n start = i*BATCH_SIZE\n end = start + BATCH_SIZE\n batch_symbols = symbols[start: end]\n batch_symbols_query = '+'.join(batch_symbols)\n request_url = YAHOO_FINANCE_API + \"?\" + YAHOO_FINANCE_SYMBOL_PARAM + \"=\" + batch_symbols_query +\\\n \"&\" + YAHOO_FINANCE_FORMAT_PARAM + \"=\" + YAHOO_FINANCE_SYMBOL_PARAM + YAHOO_FINANCE_52_ASK_PRICE +\\\n YAHOO_FINANCE_BID_PRICE + YAHOO_FINANCE_52_CLOSE_PRICE + YAHOO_FINANCE_52_WEEK_LOW +\\\n YAHOO_FINANCE_52_WEEK_HIGH + YAHOO_FINANCE_52_LOW_CHANGE +\\\n YAHOO_FINANCE_52_HIGH_CHANGE + YAHOO_FINANCE_DIV_YIELD\n r = requests.get(request_url)\n\n # Read the returned CSV as a pandas table\n # Returned format is NAME,ASK,BID,52-wLow,52-wHigh\n df = pandas.read_table(StringIO(r.text), header=None, sep=',')\n all_stocks_df = all_stocks_df.append(df, ignore_index=True)\n\n # Delay to slow down things\n time.sleep(1)\n\n\n # Assign columns\n print(\"Stock quotes have been fetched. Beginning analysis...\")\n all_stocks_df.columns=['symbol', 'ask', 'bid', 'close', '52w-low', '52w-high', '52w-low-change', '52w-high-change', 'div-iteryield']\n\n # Add the percent change columns\n all_stocks_df['52w-%-low-change'] = all_stocks_df['52w-low-change']/all_stocks_df['52w-low']*100\n all_stocks_df['52w-%-high-change'] = all_stocks_df['52w-high-change'] / all_stocks_df['52w-high'] * 100\n\n # Add the names and sectors\n all_stocks_df['name'] = \"\"\n all_stocks_df['sector'] = \"\"\n for index, row in all_stocks_df.iterrows():\n all_stocks_df.loc[index, 'name'] = stocks[row['symbol']][0]\n all_stocks_df.loc[index, 'sector'] = stocks[row['symbol']][1]\n\n\n # Process the received quotes\n sorted_values = all_stocks_df.sort_values('52w-%-low-change')\n\n # Done\n print(\"Analysis completed.\")\n return sorted_values","def create_tables(*args):\n import pandas\n tables = {}\n for locus in args[0].index:\n tables[locus] = get_table(*args, locus)\n return tables","def table(self):\n\n param=self.x_param\n\n device=self.device\n\n base_params=device.get_params()\n\n data_tot=DataFrame()\n\n for i in range(len(param)):\n\n print_index=1\n\n for name in param.names:\n\n device._set_params(param(i))\n\n device.draw()\n\n df=device.export_all()\n\n if self.labels_bottom is not None:\n\n index=self.labels_bottom[i]\n\n else:\n\n index=str(i)\n\n print(\"Generating table, item {} of {}\\r\".format(print_index,len(param)),end=\"\")\n\n data_tot=data_tot.append(Series(df,name=index))\n\n device._set_params(base_params)\n\n return data_tot","def generate_raport(table):\n\n item_table = store.get_table()\n item_table = store.check_table(item_table)\n raport = []\n\n id_position = 0\n title_position = 1\n price_position = 3\n earnings = 0\n\n for item in item_table:\n price = item[price_position]\n sold_copies = get_sold_copies(table, item[id_position])\n if price and sold_copies:\n earnings = int(price) * int(sold_copies)\n raport_row = [item[title_position], str(earnings)]\n raport.append(raport_row)\n\n return raport","def get_all_available_stock_table(db_path):\n\n try:\n db_connection = sqlite3.connect(db_path)\n except sqlite3.Error as e:\n print(e)\n return False\n\n cursor = db_connection.cursor()\n\n available_tables_1 = cursor.execute(\"SELECT name FROM sqlite_master WHERE type ='table'\").fetchall()\n available_tables = [tab[0] for tab in available_tables_1 if tab[0] != 'Consumption_Index']\n available_tables.sort()\n db_connection.close()\n\n return available_tables","def generate_table(self, rows):\n ...","def get_stock(symbol, start, end):\n df = pdr.DataReader(symbol, 'yahoo', start, end)\n df = df.sort_index(axis=0)\n return df","def renter_accounting_report_gen(sid, start, end):\n results = renter_accounting(sid, start, end)\n print(\"Name: \" + results[0])\n sum_value = 0\n row_title = [\"Date\", \"Boat\", \"Rent\", \"Payment\", \"Sum\"]\n row_format = \"{:>15}\" * len(row_title)\n print(row_format.format(*row_title))\n for result in results[1]:\n temp = list(result.keys()) + [value for key, value in list(result.values())[0].items()]\n if temp[2]:\n sum_value += temp[3]\n temp[2] = \"\"\n else:\n sum_value -= temp[3]\n temp[2] = temp[3]\n temp[3] = \"\"\n temp.append(sum_value)\n print(row_format.format(*[str(x) for x in temp]))","def stock_market(no_profiles: int) -> tuple:\n all_companies = []\n Stocks = namedtuple(\"Stocks\", 'name symbol open high close company_weight')\n MkValue_ = random.uniform(1000, 50000, 100)\n wts_ = random.uniform(0, 1, 100)\n wts_ = wts_/sum(wts_)\n\n for _ in range(100):\n name = fake.company()\n open_ = round(MkValue_[_]*wts_[_],2)\n close = round(open_ * random.uniform(0.7, 1.15), 2)\n high = round(open_ * random.uniform(0.85, 1.15), 2)\n if high < open_:\n high = open_\n if high < close:\n high = close\n\n all_companies.append(\n Stocks(name=name, symbol=symbol(name), open=open_, high=round(high, 2), close=round(close, 2), company_weight=round(wts_[_], 4)))\n\n stock_index = round(\n sum(x.open * x.company_weight for x in all_companies), 4)\n highest_for_day = round(\n sum(x.high * x.company_weight for x in all_companies), 2)\n lowest_close_for_day = round(\n sum(x.close * x.company_weight for x in all_companies), 2)\n\n # print(f\"\\n------------------------------------Top 100 listed companies on Fake Stock Exchange------------------------------------\")\n # [print(x) for x in sorted(all_companies, key=lambda x:x.symbol)]\n # print(f\"\\n--------------Main details on {date.today()}--------------\")\n # print(f\"\\nStart of the day: {stock_index}\")\n # print(f\"Highest for the day: {highest_for_day}\")\n # print(f\"Lowest close for the day: {lowest_close_for_day}\")\n return sorted(all_companies, key=lambda x: x.symbol), stock_index, highest_for_day, lowest_close_for_day","def index():\n rows = db.execute(\"SELECT Symbol, SUM(Shares) as totalShares FROM cash WHERE id=:id GROUP BY Symbol HAVING totalShares > 0\", id=session[\"user_id\"])\n transactions=[]\n grand_total = 0\n for row in rows:\n stock = lookup(row[\"Symbol\"])\n transactions.append({\n \"Symbol\": stock[\"symbol\"],\n \"Name\": stock[\"name\"],\n \"Shares\": row[\"totalShares\"],\n \"Price\": usd(stock[\"price\"]),\n \"Total\": usd(stock[\"price\"] * row[\"totalShares\"])\n })\n grand_total += stock[\"price\"] * row[\"totalShares\"]\n rows = db.execute(\"SELECT cash FROM users WHERE id=:id\", id=session[\"user_id\"])\n cash = rows[0][\"cash\"]\n return render_template(\"table.html\", transactions=transactions, cash=usd(cash), grand_total=usd(grand_total))","def get_com_data_fr_all_stocks(self):\n full_list = self.replace_special_characters_in_list(self.full_stocklist_to_retrieve)\n chunk_of_list = self.break_list_to_sub_list(self.full_stocklist_to_retrieve)\n \n self.temp_full_data_df = None\n for n in chunk_of_list:\n # print the progress\n sys.stdout.write('.')\n\n # set the small chunk of list\n self.set_target_stocks_list(n)\n self.get_com_data()\n\n # convert to dataframe\n self.com_data_allstock_df = pandas.DataFrame(self.com_data_allstock_list)\n self.com_data_allstock_df.rename(columns ={'symbol':'SYMBOL'}, inplace=True)\n \n print 'Done\\n'","def __generate_table(package_response, primary_table_id):\n if isinstance(package_response, Package):\n primary_table = package_response.tables[primary_table_id]\n header_id = primary_table.definition.header_table_id\n header_table = package_response.tables[header_id]\n dimension_columns = list(filter(lambda column_obj: column_obj.is_dimension, primary_table.definition.columns))\n dimension_columns_count = len(dimension_columns)\n row_count = len(primary_table.data.rows)\n header_row_count = len(header_table.data.rows)\n\n headers = list(list())\n # Constructs the column headers by considering dimension columns and header rows\n for series_definition_column in header_table.definition.columns:\n header_row = list()\n for i in range(0, dimension_columns_count, 1):\n if dimension_columns[i].description is \"\":\n header_row.append(\" \")\n else:\n header_row.append(dimension_columns[i].description)\n\n for i in range(0, header_row_count, 1):\n header_row.append(str(_SeriesDataHelper.get_value_helper(header_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\n headers.append(header_row)\n\n data = list(list())\n # Constructs the column data\n for i in range(0, row_count, 1):\n data_row = list()\n for series_definition_column in primary_table.definition.columns:\n data_row.append(str(_SeriesDataHelper.get_value_helper(primary_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\n data.append(data_row)\n\n if len(header_table.definition.columns) > 1:\n data_frame = pd.DataFrame(data=data)\n data_frame.columns = pd.MultiIndex.from_arrays(headers)\n else:\n data_frame = pd.DataFrame(data=data, columns = headers[0])\n\n return data_frame\n\n else:\n ValueError(\"Response data passed should be of package type.\")","def __populate_historical_trade_data(self):\n\n trade_data = self.__transactions.pivot_table(\n index=\"Date\",\n columns=[\"Ticker\"],\n values=[\n \"Quantity\",\n \"Investment\",\n ],\n aggfunc={\"Quantity\": np.sum, \"Investment\": np.sum},\n )\n\n # Make historical prices columns a multi-index. This helps the merging.\n self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(\n [[\"Close\"], self.portfolio_historical_prices.columns]\n )\n\n trade_data = pd.merge(\n trade_data,\n self.portfolio_historical_prices,\n how=\"outer\",\n left_index=True,\n right_index=True,\n )\n\n trade_data[\"Close\"] = trade_data[\"Close\"].fillna(method=\"ffill\")\n trade_data.fillna(0, inplace=True)\n\n trade_data[\"Quantity\"] = trade_data[\"Quantity\"].cumsum()\n trade_data[\"Investment\"] = trade_data[\"Investment\"].cumsum()\n trade_data[\"Investment\", \"Total\"] = trade_data[\"Investment\"].sum(axis=1)\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Investment delta\"], self.tickers_list + [\"Total\"]]\n )\n ] = (trade_data[\"Investment\"].diff(periods=1).fillna(trade_data[\"Investment\"]))\n\n # End Value = Quantity * Close\n trade_data[pd.MultiIndex.from_product([[\"End Value\"], self.tickers_list])] = (\n trade_data[\"Quantity\"][self.tickers_list]\n * trade_data[\"Close\"][self.tickers_list]\n )\n\n trade_data.loc[:, (\"End Value\", \"Total\")] = trade_data[\"End Value\"][\n self.tickers_list\n ].sum(axis=1)\n\n # Initial Value = Previous End Value + Investment changes\n trade_data[\n pd.MultiIndex.from_product(\n [[\"Initial Value\"], self.tickers_list + [\"Total\"]]\n )\n ] = 0\n\n trade_data[\"Initial Value\"] = trade_data[\"End Value\"].shift(1) + trade_data[\n \"Investment\"\n ].diff(periods=1)\n\n # Set first day Initial Value as the Investment (NaNs break first period)\n for t in self.tickers_list + [\"Total\"]:\n trade_data.at[trade_data.index[0], (\"Initial Value\", t)] = trade_data.iloc[\n 0\n ][\"Investment\"][t]\n\n trade_data = trade_data.reindex(\n columns=[\n \"Quantity\",\n \"Investment\",\n \"Investment delta\",\n \"Close\",\n \"Initial Value\",\n \"End Value\",\n ],\n level=0,\n )\n self.historical_trade_data = trade_data","def fetch_all_stocks(config):\n start_date_dt = datetime.datetime.strptime(config['start_date'], '%Y%m%d').date()\n end_date_dt = datetime.datetime.strptime(config['end_date'], '%Y%m%d').date()\n\n with open(DATA_DIR / 'nasdaq_screener_1619356287441.csv', 'r') as fp:\n reader = csv.reader(fp)\n next(reader) # skip header\n symbols = [row[0] for row in reader]\n\n dir_path = DATA_DIR / 'nasdaq_historical' / '{}_{}'.format(config['start_date'], config['end_date'])\n if not os.path.exists(dir_path):\n os.mkdir(dir_path)\n\n for symbol in symbols:\n filename = '{}.csv'.format(symbol)\n full_path = dir_path / filename\n if not os.path.exists(full_path):\n try:\n df = web.get_data_yahoo(symbol, config['start_date'], config['end_date'])\n except:\n # print(traceback.format_exc())\n print('Could not load ' + symbol)\n else:\n if df.index[0].date() == start_date_dt and df.index[-1].date() == end_date_dt:\n print('Loaded: ' + symbol)\n df.to_csv(full_path)","def construct_result_table(self, biomodels):\n select = 'SELECT DISTINCT '\n cached_alias = 'c'\n join_exps = 'FROM %s_%s AS %s' % (self.app_label, \n settings.DISBI['JOINED_TABLENAME'], \n cached_alias)\n left_join_template = '''\n LEFT JOIN (\n %s\n ) AS %s\n ON (%s = %s)\n '''\n subtables_not_null_column = []\n select_bios = []\n relations = Relations(self.app_label, model_superclass=(BiologicalModel, MetaModel))\n for biomodel in biomodels:\n # Requested experiments related to biomodel.\n req_exps_for_bio = [] \n for exp in self.req_exps:\n if exp.biomodel == biomodel:\n req_exps_for_bio.append(exp)\n \n select_bios.extend(self.get_show_columns(biomodel))\n \n\n # Get Meta models for Bio model and the show columns to the \n # SELECT clause.\n metamodels_of_biomodel = relations.get_related_metamodels(biomodel)\n #print(metamodels_of_biomodel)\n for metamodel in metamodels_of_biomodel:\n select_bios.extend(self.get_show_columns(metamodel))\n\n \n for exp in req_exps_for_bio:\n select_bios.extend(self.get_display_names(exp))\n exp_alias = 'exp%s' % exp.pk\n join_exps += left_join_template % (\n self.construct_exptable(exp),\n exp_alias,\n '%s.%s_id' % (cached_alias, biomodel.__name__.lower()),\n '%s.%s' % (exp_alias, exp.biofield.column)\n )\n subtables_not_null_column.append((self.get_notnull_column(exp), \n str(exp.id)))\n exclude_empty = 'WHERE ' + ' OR '.join(['%s_%s IS NOT NULL' % col \n for col in subtables_not_null_column])\n \n sql = '\\n'.join((select + ', '.join(select_bios), join_exps, exclude_empty))\n return re.sub(r'^\\s+', '', sql, flags=re.MULTILINE)","def generate_table(self, outtablename,\n cols=['A', 'B', 'AB'],\n generateTable=True):\n if generateTable:\n new_indices = ['time (s)', 'mean counts']\n for idx in self.data[cols].describe().index[2:]:\n new_indices.append(idx)\n outTable = self.data[cols].describe()\\\n .set_index(pd.Index(new_indices))\n outTable.to_latex(\n self.tables_dir + outtablename + \".tex\", float_format=\"%d\")\n print(\"Outtable: \", outTable)","def index():\n user_name = db.execute(\"SELECT username FROM users WHERE id = ?\", session[\"user_id\"])\n check = db.execute(\"SELECT name FROM main.sqlite_master WHERE type='table'\")\n #print(check)\n #print('stocks' not in check[0]['name'])\n if not any(c['name'] == 'stocks' for c in check):\n return render_template(\"index.html\", user_name=user_name)\n\n stocks = db.execute(\"SELECT * FROM stocks WHERE user_id = ?\", session[\"user_id\"])\n cash = db.execute(\"SELECT cash FROM users WHERE id = ?\", session[\"user_id\"])\n\n user_name = db.execute(\"SELECT username, cash FROM users WHERE id = ?\", session[\"user_id\"])\n total_value = user_name[0][\"cash\"]\n sum_stocks = db.execute(\"SELECT symbol, ammount FROM stocks WHERE user_id = ?\", session[\"user_id\"])\n\n for stock in sum_stocks:\n total_value += stock[\"ammount\"] * lookup(stock[\"symbol\"])['price']\n \n #print(stocks)\n return render_template(\"index.html\", stocks=stocks, user_name=user_name, cash=usd(cash[0]['cash']), total_value=usd(total_value))","def download_all_stocks():\n stocks = get_stocklist()\n dfs = {}\n for i, r in stocks.iterrows():\n start = time.time()\n s = r['Ticker']\n stockfile = '../stockdata/' + s + '.csv.gz'\n print('downloading', s)\n stock = quandl.get('EOD/' + s)\n stock.to_csv(stockfile, compression='gzip')\n dfs[s] = stock\n print('took', time.time() - start, 's')\n\n return dfs","def to_stock_dataframe_range(self, start_date=None, end_date=None):\n if end_date is None:\n end_date = self.dates[-2]\n if type(end_date) is pd.tslib.Timestamp:\n end_date = end_date.strftime(\"%Y-%m-%d\")\n if type(end_date) is not datetime.datetime and type(end_date) is not pd.tslib.Timestamp:\n end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n try:\n end_date = self.dates[list(self.dates).index(end_date) + 1]\n except:\n end_date = \"Last\"\n if start_date is None:\n start_date = self.dates[0]\n\n if type(start_date) is not datetime.datetime and type(start_date) is not pd.tslib.Timestamp:\n start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d\")\n\n class_data = [i for i in dir(self) if not callable(getattr(self, i)) and not i.startswith(\"__\") and\n type(getattr(self, i)) is pd.DataFrame]\n df = pd.DataFrame()\n for i in class_data:\n df = join_features(df, getattr(self, i), fill_method=FillMethod.FUTURE_KROGH)\n if end_date is \"Last\":\n print(df.ix[start_date:, :])\n return df.ix[start_date:, :]\n return df.ix[start_date:end_date, :]","def __gen_datatable__(self):\n # | - __generate_data_table\n rows_list = []\n for Job_i in self.Job_list:\n # | - FOR LOOP BODY\n entry_param_dict = {}\n for prop, value in Job_i.job_params.items():\n entry_param_dict[prop] = value\n\n entry_param_dict[\"Job\"] = Job_i\n entry_param_dict[\"path\"] = Job_i.full_path\n entry_param_dict[\"max_revision\"] = Job_i.max_revision\n entry_param_dict[\"revision_number\"] = Job_i.revision_number\n\n rows_list.append(entry_param_dict)\n # __|\n\n data_frame = pd.DataFrame(rows_list)\n\n return(data_frame)\n # __|","def get_market_inf():\n r = requests.get(vs.DSE_URL+vs.DSE_MARKET_INF_URL)\n soup = BeautifulSoup(r.text, 'html.parser')\n quotes = [] # a list to store quotes\n\n table = soup.find('table', attrs={'class': 'table table-bordered background-white text-center', '_id': 'data-table'})\n\n for row in table.find_all('tr')[1:]:\n cols = row.find_all('td')\n quotes.append({'Date': cols[0].text.strip().replace(\",\", \"\"),\n 'Total Trade': cols[1].text.strip().replace(\",\", \"\"),\n 'Total Volume': cols[2].text.strip().replace(\",\", \"\"),\n 'Total Value (mn)': cols[3].text.strip().replace(\",\", \"\"),\n 'Total Market Cap. (mn)': cols[4].text.strip().replace(\",\", \"\"),\n 'DSEX Index': cols[5].text.strip().replace(\",\", \"\"),\n 'DSES Index': cols[6].text.strip().replace(\",\", \"\"),\n 'DS30 Index': cols[7].text.strip().replace(\",\", \"\"),\n 'DGEN Index': cols[8].text.strip().replace(\",\", \"\")\n })\n df = pd.DataFrame(quotes)\n return df","def MongoStockTable(inputfilename):\n input=open(inputfilename)\n data_string=json.load(input)\n jsonOutput=\"{ \\\"portfolio\\\":[\"\n for portfolio in data_string[\"portfolio\"]:\n if portfolio[\"display\"] == \"yes\":\n jsonOutput+=\"{\\\"portfolioname\\\":\\\"\" + portfolio[\"portfolioName\"]+\"\\\", \\\"portfolioStocks\\\":[\"\n cumulative=Accumulator.Accumulator()\n for data in portfolio[\"portfolioStocks\"]:\n stock=Stock(data)\n cumulative.Add(stock.totalpurchaseprice, stock.commission_to_buy, stock.dollarGain,stock.dailyChange_func() ,stock.currentWorth_func() )\n jsonOutput+=stock.JSON()+\",\" \n jsonOutput=jsonOutput.rstrip(',') # remove that trailing extraneous , [:-1]\n jsonOutput+=\"],\"\n jsonOutput+=\"\\n\" \n jsonOutput+=\"\\\"cumulative Result\\\":\"+cumulative.JSONify()+\"},\" \n jsonOutput=jsonOutput.rstrip(',') \n jsonOutput+=\"] }\"\n MongoSave(ast.literal_eval(jsonOutput))\n input.close()","def get_stock_data(symbol):\n # Set current dates\n start = date(date.today().year, 1, 1) # first of current year\n end = date.today() # today\n\n # Get yahoo Yahoo data\n data = pdr.get_data_yahoo(symbol, start=start, end=end)\n\n # Rename columns\n data.columns = [\"Highest price (USD)\",\n \"Lowest price (USD)\",\n \"Opening price (USD)\",\n \"Closing price (USD)\",\n \"Volume\",\n \"Adjusted closing price (USD)\"]\n\n return data","def internetData(tickerSymbol):\n #this qeureys the SQL database for all stock data\n temp = Stock.query.filter_by(ticker=tickerSymbol).all()\n #init the lists to store data for output\n prices = []#closing price\n volumes = []\n \n for i in temp:\n prices.append(i.close)\n volumes.append(i.volume)\n print tickerSymbol\n print \"prices length is \" + str(len(prices))\n print \"volumes length is \" + str(len(volumes))\n outputDict = {'Prices':prices,'Volumes':volumes}\n return outputDict","def plot_market_index(\n date_range: list, index_col_name: str = INDEX_COLUMN_NAME, table_name: str = TABLE\n) -> pd.DataFrame:\n assert len(date_range) == 2\n date_range = [date.strftime(\"%Y-%m-%d\") for date in date_range]\n partitions = get_partitions(table_name)\n partition = st.sidebar.selectbox(\"Choose Index\", partitions.values[:, 0])\n df_mkt_idx = query_database(table_name, date_range=date_range, key_word=partition)\n\n column_name_num = list(df_mkt_idx.select_dtypes(include=[\"number\"]).columns)\n\n columns_selected = st.sidebar.multiselect(\n \"Choose Columns\", column_name_num, column_name_num[0]\n )\n\n df_vis = df_mkt_idx[columns_selected]\n brush = alt.selection(type=\"interval\", encodings=[\"x\"])\n base_chart_top = base_chart_down = (\n alt.Chart(df_vis.reset_index())\n .transform_fold(columns_selected)\n .mark_line()\n .encode(\n x=f\"{index_col_name}:T\",\n y=alt.X(f\"value:Q\", scale=alt.Scale(zero=False)),\n color=\"key:N\",\n tooltip=[f\"{index_col_name}:T\"],\n )\n )\n upper = base_chart_top.properties(\n width=600, height=400, title=f\"{partition}\"\n ).encode(alt.X(f\"{index_col_name}:T\", scale=alt.Scale(domain=brush)))\n lower = base_chart_down.properties(width=600, height=90).add_selection(brush)\n st.altair_chart(upper & lower, use_container_width=True)\n\n if st.checkbox(\"Show DataFrame\"):\n st.write(df_mkt_idx)\n st.markdown(get_table_download_link(df_mkt_idx), unsafe_allow_html=True)\n\n return df_mkt_idx","def get_or_create_base_table(self, fetch_as='ordereddict'):\n # Make a sorted list of all ids.\n exp_id_str = get_id_str(self.req_exps)\n table_name = '%s_%s_%s' % (self.app_label, \n settings.DISBI['DATATABLE_PREFIX'], \n exp_id_str)\n if not db_table_exists(table_name):\n self.create_base_table(table_name)\n column_names = list(get_columnnames(table_name))\n # Escape all column names.\n column_names = ['%s' % column_name for column_name in column_names]\n # Format all columns with scientific notation that end with underscore and a number.\n # These columns contain numerical data and should be presented in \n # scientific notation.\n datacol_pattern = re.compile(r'_\\d+$')\n for i, column_name in enumerate(column_names):\n if datacol_pattern.search(column_name) is not None:\n column_names[i] = '{} as {}'.format(self.wrap_in_func('to_char', \n column_name, \n self.DB_PRECISION),\n column_name)\n sql = 'SELECT %s FROM %s' % (', '.join(column_names), table_name)\n #return from_db(sql)\n return from_db(sql, fetch_as=fetch_as)","def generate_daily_matrix(full_df, feat_days):\n pred_ticker = full_df.ticker.unique()[0]\n feature_tickers = [i for i in full_df.ticker.unique() if i != pred_ticker]\n dfml = full_df[full_df.ticker == pred_ticker].drop('ticker', axis=1)\n dfml.rename({'percent_change_pred': f'{pred_ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\n for ticker in feature_tickers:\n help_df = full_df[full_df.ticker == ticker][['past_date', 'current_date', 'prediction_date', 'percent_change_pred']]\n help_df.rename({'percent_change_pred': f'{ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\n dfml = pd.merge(dfml, help_df,\n left_on=['past_date', 'current_date', 'prediction_date'],\n right_on=['past_date', 'current_date', 'prediction_date'],\n how='left')\n return dfml.drop('percent_change_feat', axis=1)","def __get_latest_data(table_name='derivatives_economicindicatorstandard'):\n # create query and get data\n query = 'SELECT * FROM ' + table_name\n df = AccessDB().run_read_query(query)\n\n if table_name == 'derivatives_economicindicatorstandard':\n df = pd.DataFrame(df.groupby(['dbcode', 'indicator', 'country', 'freq', 'flow'])['date'].max())\n else:\n df = pd.DataFrame(df.groupby(['dbcode', 'indicator', 'country', 'freq', 'counter_party'])['date'].max())\n df.reset_index(inplace=True)\n return df","def make_summary_tables( res ):\n\n # transform second table to csv and read this as a dataFrame\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\",\",index_col=0)\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\n\n # first table is trickier because the data is spread on to columns, and there is title line\n L = res.tables[0].as_html().split('\\n')\n L.pop(1) # get rid of the title\n tmp = pd.read_html('\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \n\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\n # NB : I exclude the last 2 elements which are empty \n \n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\n \n return result_general_df , result_fit_df","def get_exp_columns(self, wanted_exps):\n # Get the dict.\n exp_id_str = get_id_str(self.req_exps)\n table_name = '%s_datatable_%s' % (self.app_label, exp_id_str)\n # Make experiment unique.\n if not db_table_exists(table_name):\n self.create_base_table(table_name)\n column_names = list(get_columnnames(table_name))\n divisor_col = None\n dividend_col = None\n #exp_cols = []\n #potential_columns = []\n # Iterate over all column names, and make a list of those that hold data.\n datacol_pattern = re.compile(r'_\\d+$')\n dividend_pattern = r'_{}$'.format(str(wanted_exps['dividend'].id))\n divisor_pattern = r'_{}$'.format(str(wanted_exps['divisor'].id))\n for column_name in column_names:\n if datacol_pattern.search(column_name) is not None:\n if divisor_col is None:\n if re.search(divisor_pattern, column_name):\n divisor_col = column_name\n if dividend_col is None:\n if re.search(dividend_pattern, column_name):\n dividend_col = column_name\n \n sql = \"SELECT %s, %s FROM %s;\" % (dividend_col, divisor_col, table_name)\n return from_db(sql, fetch_as='tuple')","def create_order(df_signal, df_stock, df_all,\n side=('follow', 'reverse', 'buy', 'sell'),\n cycle=0, pct0=0, pct1=0, limit=0, stop=0):\n df_signal0 = df_signal.copy()\n closes = df_stock[['date', 'close']].set_index('date')['close']\n\n if side == 'buy':\n df_signal0['signal0'] = 'BUY'\n df_signal0['signal1'] = 'SELL'\n elif side == 'sell':\n df_signal0['signal0'] = 'SELL'\n df_signal0['signal1'] = 'BUY'\n elif side == 'reverse':\n df_signal0['signal0'] = df_signal0['signal0'].apply(reverse)\n df_signal0['signal1'] = df_signal0['signal1'].apply(reverse)\n\n signals = []\n for index, data in df_signal0.iterrows():\n try:\n target0 = data['close0'] * (1 + (pct0 / 100.0))\n target1 = data['close0'] * (1 + (pct1 / 100.0))\n\n option0a, option0b, option1a, option1b = get_cycle_strike2(\n df_all, data['date0'], data['date1'],\n 'CALL', target0, cycle, 0,\n 'PUT', target1, cycle, -1\n )\n\n df_code0 = df_all.query('option_code == %r & date >= %r & date <= %r' % (\n option0a['option_code'], data['date0'], data['date1']\n ))[['date', 'option_code', 'dte', 'sell', 'buy', 'strike']]\n df_code1 = df_all.query('option_code == %r & date >= %r & date <= %r' % (\n option1a['option_code'], data['date0'], data['date1']\n ))[['date', 'option_code', 'dte', 'sell', 'buy', 'strike']]\n df_join = pd.merge(df_code0, df_code1, on='date', suffixes=('0', '1'))\n\n max_profit = data['close0'] * (limit / 100.0)\n max_loss = data['close0'] * (stop / 100.0)\n if data['signal0'] == 'BUY':\n df_join['enter'] = df_join['buy0'] - df_join['sell1']\n df_join['exit'] = df_join['sell0'] - df_join['buy1']\n enter_price = df_join['enter'].iloc[0]\n else:\n df_join['enter'] = -df_join['sell0'] + df_join['buy1']\n df_join['exit'] = -df_join['buy0'] + df_join['sell1']\n enter_price = df_join['enter'].iloc[0]\n\n limit_price = enter_price + max_profit\n stop_price = enter_price - max_loss\n # print enter_price, max_profit, limit_price\n df_join['limit'] = df_join['exit'] >= limit_price\n df_join['stop'] = df_join['exit'] <= stop_price\n\n # ts(df_join)\n df_oco = df_join[(df_join['limit']) | (df_join['stop'])]\n\n data['stock0'] = data['close0']\n data['stock1'] = data['close1']\n data['code0'] = option0a['option_code']\n data['code1'] = option1a['option_code']\n data['strike0'] = option0a['strike']\n data['strike1'] = option1a['strike']\n\n # buy sell are same\n if len(df_oco):\n exit_date = pd.to_datetime(df_oco['date'].iloc[0])\n data['date1'] = exit_date\n data['stock0'] = data['close0']\n data['stock1'] = closes[exit_date]\n\n if data['signal0'] == 'BUY':\n data['price0a'] = option0a['buy']\n data['price1a'] = -option1a['sell']\n data['price0b'] = -df_oco['sell0'].iloc[0]\n data['price1b'] = df_oco['buy1'].iloc[0]\n else: # SELL\n data['price0a'] = -option0a['sell']\n data['price1a'] = option1a['buy']\n data['price0b'] = df_oco['buy0'].iloc[0]\n data['price1b'] = -df_oco['sell1'].iloc[0]\n else:\n data['stock0'] = data['close0']\n data['stock1'] = data['close1']\n if data['signal0'] == 'BUY':\n data['price0a'] = option0a['buy']\n data['price1a'] = -option1a['sell']\n data['price0b'] = -option0b['sell']\n data['price1b'] = option1b['buy']\n else: # SELL\n data['price0a'] = -option0a['sell']\n data['price1a'] = option1a['buy']\n data['price0b'] = option0b['buy']\n data['price1b'] = -option1b['sell']\n\n signals.append(data)\n except (KeyError, ValueError, IndexError):\n logger.info('Not found, date: %s cycle: %s pct0: %s pct1: %s' % (\n data['date0'].strftime('%Y-%m-%d'), cycle, pct0, pct1\n ))\n\n df = pd.DataFrame(signals)\n df['close0'] = df['price0a'] + df['price1a']\n df['close1'] = df['price0b'] + df['price1b']\n df = df[df['close0'] != 0]\n\n df['bp_effect'] = df['stock0'] * 100\n df['net_chg'] = -df['close1'] - df['close0']\n df['pct_chg'] = df['net_chg'] / df['stock0']\n df['holding'] = df['date1'] - df['date0']\n\n df['sqm0'] = 0\n df['sqm1'] = 0\n df['oqm0'] = df.apply(lambda x: -2 if x['signal0'] == 'SELL' else 2, axis=1)\n df['oqm1'] = -df['oqm0']\n\n df = df.round({\n 'pct_chg': 4,\n 'net_chg': 2\n })\n\n return df","def get_data(list_data_tuples):\n \n \n benchmark_symbol=list_data_tuples[0][0]; # First element is the benchmark symbol\n \n #print benchmark_symbol\n \n df=pd.DataFrame(index=list_data_tuples[0][1]['data'].index) # First dataframe index is nothing but date\n \n for tpl in list_data_tuples:\n #print tpl[0]\n df_temp = pd.DataFrame(tpl[1]['data']['Adj. Close'],index=tpl[1]['data'].index)\n df_temp = df_temp.rename(columns={'Adj. Close': tpl[0]}) # tpl[0] is the symbol\n #print df_temp,tpl[0]\n df = df.join(df_temp)\n if tpl[0] == benchmark_symbol: # drop dates SPY did not trade\n df = df.dropna(subset=[benchmark_symbol])\n\n df=df.dropna(axis=0) # This drops any NaN values especially if the stock price has no information\n \n return df","def gather_stock_data(tickers, save=True):\n prices = pd.DataFrame()\n ts = TimeSeries(key='EY2QBMV6MD9FX9CP', output_format='pandas')\n\n for ticker in tickers:\n successful_grab = False\n ticker_daily_adj = None\n\n while successful_grab is not True:\n try:\n ticker_daily_adj = ts.get_daily_adjusted(ticker, outputsize='full')[0]\n successful_grab = True\n except ValueError:\n print('Waiting for API to let me in')\n time.sleep(10)\n\n ticker_daily_adj.loc[:, '0. ticker'] = ticker\n ticker_daily_adj = ticker_daily_adj[sorted(ticker_daily_adj.columns)]\n\n prices = pd.concat([prices, ticker_daily_adj])\n\n prices.sort_index(inplace=True)\n prices.reset_index(inplace=True)\n prices['date'] = pd.to_datetime(prices['date'])\n if save:\n prices.to_csv('stockdata.csv', index=True)\n\n return prices","def get_portfolio_prices(stocks: list, funds: list, etfs: list, start_date: str, end_date=today) -> pd.DataFrame:\r\n data_frames_stocks = get_assets_data_frames(\r\n stocks, inv.get_stock_historical_data, 'brazil', start_date=start_date, end_date=end_date)\r\n data_frames_funds = get_assets_data_frames(\r\n funds, inv.get_fund_historical_data, 'brazil', start_date=start_date, end_date=end_date)\r\n data_frames_etfs = get_assets_data_frames(\r\n etfs, inv.get_etf_historical_data, 'brazil', start_date=start_date, end_date=end_date)\r\n\r\n data_frames = [*data_frames_stocks, *data_frames_funds, *data_frames_etfs]\r\n\r\n assets = [*stocks, *funds, *etfs]\r\n\r\n portfolio_prices = build_multi_index_data_frame(\r\n data_frames, assets, ['Close', 'Open', 'High', 'Low'])\r\n\r\n return portfolio_prices","def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\n datafile = open(output_table, \"w\")\n datafile.write(\n \"Feature_1\" + '\\t' + \"Feature_2\" + \"\\t\" + \"plus_plus\" + '\\t' + \"minus_minus\" + '\\t' + \"plus_minus\" + '\\t' + \"minus_plus\" + '\\t' + \"p_value_same_opposite\" + '\\t' + \"p-value_same_opposite_Bonferoni_corrected\" + '\\t' + \"Ratio_same_opposite\" + '\\t' + \"p_value_divergent_convergent\" + '\\t' + \"p_value_divergent_convergent Bonferoni corrected\" + '\\t' + \"Ratio divergent convergent\" + '\\n')\n for i in range(len(NamesL_pairs)):\n datafile.write(\n NamesL_pairs[i][0] + '\\t' + NamesL_pairs[i][1] + '\\t' + str(p_pL[i]) + '\\t' + str(m_mL[i]) + '\\t' + str(\n p_mL[i]) + '\\t' + str(m_pL[i]) + '\\t' + str(p_valsL[i]) + '\\t' + str(p_vals_BonferoniL[i]) + '\\t' + str(\n RatiosL[i]) + '\\t' + str(p_valsL_divergent_convergent[i]) + '\\t' + str(\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\t' + str(RatiosL_divergent_convergent[i]) + '\\n')\n datafile.close()\n return","def get_back_data(self, end_date=None, stocks=None):\n if end_date is None:\n end_date = self.dates[-1]\n\n if type(end_date) is not datetime.datetime and type(end_date) is not pd.tslib.Timestamp:\n end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n\n if stocks is None:\n stocks = self.stocks\n\n info = {}\n for stock in stocks:\n info[stock] = self.stock_data[stock].to_stock_data_range(start_date=None, end_date=end_date)\n\n return info","def _get_dataset_table(self):\n\n model_dataset = self.get_model_dataset()\n index_as_str = np.expand_dims(model_dataset.index.astype(str), 1)\n observation_data = np.column_stack((index_as_str, model_dataset.as_matrix()))\n observation_data_headers = ['DateTime']\n observation_data_headers.extend(model_dataset.keys())\n observation_table = SimpleTable(data=observation_data,\n headers=observation_data_headers)\n\n return observation_table","def GetSQL(df, table, OutputLocation):\r\n insert_query = \"INSERT INTO {table} (DATE, SPY, GLD) VALUES ('{date}', '{stock1}', '{stock2}');\"\r\n\r\n\r\n queries = []\r\n for index, row in df.iterrows():\r\n date = index.strftime(\"%Y-%m-%d\")\r\n stock1 = row[0]\r\n stock2 = row[1]\r\n queries.append(insert_query.format(table=table, date=date, stock1=stock1, stock2=stock2))\r\n\r\n with open(OutputLocation+\"/\"+datetime.now().strftime(\"%Y%m%d\")+\"_FinanceMarketInsert.sql\", \"w\") as output:\r\n output.write(\"\\n\".join(queries))","def get_price_history(self):\n # Connect to the database and return cursor\n database = DatabaseMySQL()\n\n # Query database.\n sql = \"Select published_at, `close` from company_price_volume_history \\\n where company_id =\" + str(self.company_id)\n df = database.get_query_df(sql)\n\n return df","def get_data(symbols, dates, base_dir=\"../data/\"):\n df = pd.DataFrame(index=dates)\n if 'SPY' not in symbols:\n symbols.insert(0, 'SPY')\n\n for symbol in symbols:\n temp_df = pd.read_csv(symbol_to_path(symbol, base_dir), index_col='Date',\n parse_dates=True, usecols=['Date', 'Close'],\n na_values=['nan'])\n temp_df = temp_df.rename(columns={'Close': symbol})\n df = df.join(temp_df, how='inner')\n return df","def quandl_stocks(symbol, start_date=(2000, 1, 1), end_date=None):\n\n query_list = ['WIKI' + '/' + symbol + '.' + str(k) for k in range(1, 13)]\n\n start_date = datetime.date(*start_date)\n\n if end_date:\n end_date = datetime.date(*end_date)\n else:\n end_date = datetime.date.today()\n\n return quandl.get(query_list,\n returns='pandas',\n start_date=start_date,\n end_date=end_date,\n collapse='daily',\n order='asc'\n )","def history():\n if request.method == \"GET\":\n current_user = session[\"user_id\"]\n\n sales=db.execute(\"SELECT stock_symbol, volume_sold, price, date_sold FROM sales WHERE id = :id\", id=current_user)\n lookedup=[]\n sales_table_stock_name=[]\n sales_table_symbols=[]\n sales_table_stock_price=[]\n sales_table_volumes=[]\n sales_table_date=[]\n\n for row in sales:\n lookedup = lookup(row[\"stock_symbol\"])\n sales_table_stock_name.append(str(lookedup.get(\"name\")))\n sales_table_symbols.append(str(row[\"stock_symbol\"]))\n sales_table_stock_price.append(float(row[\"price\"]))\n sales_table_volumes.append(int(row[\"volume_sold\"]))\n sales_table_date.append(str(row[\"date_sold\"]))\n\n buys = db.execute(\"SELECT stock_symbol, volume_purchased, price, date_purchased FROM purchases WHERE id = :id\", id=current_user)\n\n buy_table_stock_name=[]\n buy_table_symbols=[]\n buy_table_volumes=[]\n buy_table_share_price=[]\n buy_table_date=[]\n\n lookedup=[]\n\n for row in buys:\n\n lookedup = lookup(row[\"stock_symbol\"])\n buy_table_stock_name.append(str(lookedup.get(\"name\")))\n buy_table_symbols.append(str(row[\"stock_symbol\"]))\n buy_table_volumes.append(int(row[\"volume_purchased\"]))\n buy_table_share_price.append(float(row[\"price\"]))\n buy_table_date.append(str(row[\"date_purchased\"]))\n\n\n return render_template(\"history.html\",\n sales_table_stock_name=sales_table_stock_name,\n sales_table_stock_price=sales_table_stock_price,\n sales_table_symbols=sales_table_symbols,\n sales_table_volumes=sales_table_volumes,\n sales_table_date=sales_table_date,\n buy_table_stock_name=buy_table_stock_name,\n buy_table_symbols=buy_table_symbols,\n buy_table_volumes=buy_table_volumes,\n buy_table_share_price = buy_table_share_price,\n buy_table_date=buy_table_date)\n\n else:\n return apology(\"how did u post to history\")\n\n # return apology(\"No history here\")","def get_output(data, capital=100000, leverage=1, commission=0, slippage=0):\n total_capital = capital * leverage\n df = data\n df['cnt'] = df.groupby('timestamp')['symbol'].transform(\n lambda x: len(x))\n df['qty'] = (total_capital/df['cnt']/df['price']).round()\n df['profit'] = df.eval('(sell-buy)*qty')\n df['commission'] = df.eval('(sell+buy)*qty') * commission * 0.01\n df['slippage'] = df.eval('(sell+buy)*qty') * slippage * 0.01\n df['net_profit'] = df.eval('profit - commission - slippage')\n return df","def hist_data(self):\n # database address\n jydb_address = \"117.122.223.35\"\n jydb_user_id = \"zpy\"\n jydb_user_pwd = \"Z1pe1y1@zpy\"\n jydb_db_name = \"jydb02\"\n # preallocate data set\n back_testing_data = {}\n \n # on-line mode\n conn = pymysql.connect(jydb_address, jydb_user_id, jydb_user_pwd, jydb_db_name)\n # not using moving average\n if not self.moving_average: \n # iterate on all ism packages\n for _i, ism_pack in enumerate(tqdm(self.ism_data)):\n ismcode = ism_pack[0][0]\n ProRunDate = ism_pack[0][5]\n ProStopDate = ism_pack[0][6]\n MaxStoreSum = ism_pack[0][7]\n MinInvestShare = ism_pack[0][8]\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\n # collect data from source conn\n flag_run = \"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \" + \\\n \"InnerCode IN (\" + \",\".join(InnerCode_ls) + \") AND \" + \\\n \"TradingDay=\\'\" + ProRunDate + \"\\'\"\n flag_stop = \"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \" + \\\n \"InnerCode IN (\" + \",\".join(InnerCode_ls) + \") AND \" + \\\n \"TradingDay=\\'\" + ProStopDate + \"\\'\"\n run_price = pd.read_sql(flag_run, conn)\n stop_price = pd.read_sql(flag_stop, conn)\n back_testing_data[ismcode] = pd.merge(run_price, stop_price, on='InnerCode', \n suffixes=('_run', '_stop'))\n else: # using moving average \n # iterate on all ism packages\n for _i, ism_pack in enumerate(self.ism_data):\n ismcode = ism_pack[0][0]\n ProRunDate = ism_pack[0][5]\n TradingDay_begin = former_market_date(ProRunDate, self.L_shift, conn)\n TradingDay_end = future_market_date(ProRunDate, self.R_shift, conn)\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\n flag = \"SELECT InnerCode, TradingDay, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \" + \\\n \"InnerCode IN (\" + \",\".join(InnerCode_ls) + \") AND \" + \\\n \"TradingDay BETWEEN \\'\" + TradingDay_begin + \"\\' AND \\'\" + TradingDay_end + \"\\'\"\n back_testing_data[ismcode] = pd.read_sql(flag, conn)\n \n # close sql connection\n conn.close()\n \n return back_testing_data","def get_stock_data(stock_name, ma=[],bollinger=[],exp_ma=[],ma_conv=[]):\n df = xl.parse(stock_name)\n #df.drop(['VOLUME'], 1, inplace=True)\n df.set_index('Date', inplace=True)\n \n # Renaming all the columns so that we can use the old version code\n df.rename(columns={'OPEN': 'Open', 'HIGH': 'High', 'LOW': 'Low', 'NUMBER_TICKS': 'Volume', 'LAST_PRICE': 'Adj Close'}, inplace=True)\n # Percentage change\n df['Pct'] = df['Adj Close'].pct_change()\n df.dropna(inplace=True)\n \n # Moving Average \n if ma != []:\n for moving in ma:\n df['{}ma'.format(moving)] = df['Adj Close'].rolling(window=moving).mean()\n # Bollinger \n if bollinger != []:\n def bbands(price, length=30, numsd=2):\n \"\"\" returns average, upper band, and lower band\"\"\"\n ave = pd.stats.moments.rolling_mean(price,length)\n sd = pd.stats.moments.rolling_std(price,length)\n upband = ave + (sd*numsd)\n dnband = ave - (sd*numsd)\n return np.round(upband,3), np.round(dnband,3)\n for moving in bollinger:\n df['{}bollingerup'.format(moving)],df['{}bollingerdown'.format(moving)] = bbands(df['Adj Close'], length=moving, numsd=2)\n \n # Exponential Moving Average \n if exp_ma != []:\n for moving in exp_ma:\n df['{}exp_ma'.format(moving)] = df['Adj Close'].ewm(min_periods=1, adjust=True,com=moving).mean()\n \n # Moving Average Convergence \n if ma_conv!= []:\n for moving in ma_conv:\n df['{}ma_conv'.format(moving)] = df['Adj Close'].ewm(min_periods=1, adjust=True,com=moving[0]).mean()-df['Adj Close'].ewm(min_periods=1, adjust=True,com=moving[1]).mean()\n \n \n \n df.dropna(inplace=True)\n \n \n # Move Adj Close to the rightmost for the ease of training\n adj_close = df['Adj Close']\n df.drop(labels=['Adj Close'], axis=1, inplace=True)\n df = pd.concat([df, adj_close], axis=1)\n \n return df","def make_tables(self):\n for t in self.tables:\n self.add_table(groupname=t['groupname'],\n tablename=t['tablename'],\n description=t['description'],\n tabletitle=t['tabletitle'])","def generate_for_day(df: pd.DataFrame, dt=None, tickers=None, rounds=None) -> ListOfDicts:\n\n if dt:\n logging.info(f\"Training {rounds} rounds with {len(df)} stocks in dataset for {dt}...\")\n else:\n logging.info(f\"Training {rounds} rounds with {len(df)} stocks in dataset...\")\n\n if len(df) < MIN_TRAINING_SIZE:\n logging.warning(f\"Skipping because the number of stocks < {MIN_TRAINING_SIZE}...\")\n return []\n\n dt_str = dt and dt.strftime(\"%Y-%m-%d\")\n df = df[(df.date == dt_str)]\n\n calculate_overall_r2(df, rounds)\n\n raw_estimate_by_ticker = {}\n\n if tickers:\n unavailable_tickers = set(tickers) - set(list(df['ticker']))\n for ticker in unavailable_tickers:\n logging.warning(f\"Ticker '{ticker}' not found in dataset. Continuing...\")\n tickers.remove(ticker)\n else:\n tickers = set(list(df['ticker']))\n logging.info(f\"Building models for {len(tickers)} stocks.\")\n\n def _gfds(ticker):\n return ticker, generate_ffe_for_ticker(ticker, df=df, rounds=rounds)\n\n for ticker, estimate in fastmap.fastmap(_gfds, list(tickers)):\n market_cap = float(df[(df.ticker == ticker)]['marketCap'])\n logging.info(\"FFER for %r: %.4f\", ticker, market_cap / estimate)\n raw_estimate_by_ticker[ticker] = estimate\n\n estimates = []\n for ticker, pred in raw_estimate_by_ticker.items():\n df_row = df[(df.ticker == ticker)].to_dict(orient='records')[0]\n row = {}\n if dt_str:\n row[\"date\"] = dt_str\n row[\"ticker\"] = ticker\n row[\"num_shares\"] = df_row[\"shares\"]\n row[\"actual_market_cap\"] = df_row['marketCap']\n row[\"estimate_market_cap\"] = pred\n row[\"actual_price\"] = df_row[\"price\"]\n row[\"estimate_price\"] = row[\"estimate_market_cap\"] / row[\"num_shares\"]\n row[\"ffer\"] = row[\"actual_price\"] / row[\"estimate_price\"]\n for col in X_DIMENSIONS:\n row[col] = df_row[col]\n estimates.append(row)\n\n return sorted(estimates, key=lambda p: p[\"ffer\"])","def basic_table_details():\n tbl: pa.table = pa.Table.from_pylist([\n {'first_name': 'Kakashi', 'last_name': 'Hatake', },\n {'first_name': 'Itachi', 'last_name': 'Uchiha', },\n {'first_name': 'Shisui', 'last_name': 'Uchiha', },\n ])\n\n results = {\n 'column_names': tbl.column_names,\n 'columns > map > combine_chunks > to_pylist': [col.combine_chunks().to_pylist() for col in tbl.columns],\n 'nbytes': tbl.nbytes,\n 'num_columns': tbl.num_columns,\n 'num_rows': tbl.num_rows,\n 'schema': tbl.schema,\n 'shape': tbl.shape,\n }\n\n print(results)","def prepare_data(self):\r\n annual_df = self.annual_df\r\n coef_df = self.coef_df\r\n quarter_df = self.quarter_df\r\n # historical_df = self.historical_df\r\n Event_Buffer = self.Event_Buffer\r\n\r\n Tot_Prod = coef_df[\"Product\"].nunique()\r\n # Tot_Week = coef_df[\"wk\"].nunique()\r\n Tot_Week = 52\r\n\r\n EDLP_Events = list(annual_df[\"RP_Events\"])\r\n Min_EDLP_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\r\n ]\r\n Max_EDLP_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in EDLP_Events\r\n ]\r\n\r\n TPR_Events = list(annual_df[\"TPR_Events\"])\r\n Min_TPR_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\r\n ]\r\n Max_TPR_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in TPR_Events\r\n ]\r\n\r\n Target_EDLP_Spend = [i for i in annual_df[\"PPG_RP_Spend\"]]\r\n Target_TPR_Spend = [i for i in annual_df[\"PPG_TPR_Spend\"]]\r\n Target_Trade_Spend = [i for i in annual_df[\"PPG_Total_Spend\"]]\r\n\r\n Mapping = {}\r\n Prod_Ind = coef_df[\"Product\"][0:Tot_Prod]\r\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\r\n Mapping[j] = i\r\n Mapping_reverse = {i: j for j, i in Mapping.items()}\r\n\r\n constants = [i for i in coef_df[\"constant\"]]\r\n\r\n Cat_Coef = coef_df[\"Catalogue\"][0:Tot_Prod]\r\n\r\n Disp_Coef = coef_df[\"Display\"][0:Tot_Prod]\r\n\r\n Base_Price_stg1 = [i for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg1 = []\r\n for pr in range(Tot_Prod):\r\n Intercepts_stg1.append(\r\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\r\n )\r\n\r\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg2 = [\r\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\r\n ] # noqa\r\n\r\n EDLP_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Regular\") == 1]]\r\n )\r\n TPR_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Promoted\") == 1]]\r\n )\r\n\r\n # ################################ Available EDLP Interactions pairs ##############################\r\n\r\n EDLP = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Regular\") > 1\r\n ]\r\n EDLP_Interactions = []\r\n for i in EDLP:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n EDLP_Interactions.append(temp)\r\n\r\n # ###################################### Available TPR Interactions pairs #########################\r\n\r\n TPR = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Promoted\") > 1\r\n ]\r\n TPR_Interactions = []\r\n for i in TPR:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n TPR_Interactions.append(temp)\r\n\r\n # ###################################### EDLP_Interaction_Coef_Values ############################\r\n\r\n EDLP_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Regular\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ###################################### TPR_Interaction_Coef_Values #############################\r\n\r\n TPR_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Promoted\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ##################################### Loading Pantry Loading Coefficients #######################\r\n\r\n Pantry_1 = list(coef_df[\"Pantry_Loading_1\"])\r\n Pantry_1 = [\r\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\r\n ]\r\n Pantry_2 = list(coef_df[\"Pantry_Loading_2\"])\r\n Pantry_2 = [\r\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\r\n ]\r\n\r\n # TE_Coeff = np.array(Promo_df[[\"TE_Promo\",\"TE_NoPromo\"]])\r\n self.Tot_Prod = Tot_Prod\r\n self.Tot_Week = Tot_Week\r\n self.EDLP_Events = EDLP_Events\r\n self.Min_EDLP_Events = Min_EDLP_Events\r\n self.Max_EDLP_Events = Max_EDLP_Events\r\n self.TPR_Events = TPR_Events\r\n self.Min_TPR_Events = Min_TPR_Events\r\n self.Max_TPR_Events = Max_TPR_Events\r\n\r\n self.Target_EDLP_Spend = Target_EDLP_Spend\r\n self.Target_TPR_Spend = Target_TPR_Spend\r\n self.Target_Trade_Spend = Target_Trade_Spend\r\n self.Mapping = Mapping\r\n self.Mapping_reverse = Mapping_reverse\r\n self.constants = constants\r\n self.EDLP_Coef = EDLP_Coef\r\n self.TPR_Coef = TPR_Coef\r\n\r\n self.EDLP_Interactions = EDLP_Interactions\r\n self.TPR_Interactions = TPR_Interactions\r\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\r\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\r\n self.Pantry_1 = Pantry_1\r\n self.Pantry_2 = Pantry_2\r\n\r\n self.Base_Price_stg1 = Base_Price_stg1\r\n self.Intercepts_stg1 = Intercepts_stg1\r\n self.Base_Price_stg2 = Base_Price_stg2\r\n self.Intercepts_stg2 = Intercepts_stg2\r\n\r\n self.Cat_Coef = Cat_Coef\r\n self.Disp_Coef = Disp_Coef"],"string":"[\n \"def setupStockTable(self):\\n # Get the date\\n # NOTE: This is probably un\\n date = datetime.date()\\n dateStr = date.month() + \\\"/\\\" + date.day() + \\\"/\\\" + date.year()\\n\\n stocks = (\\\"INTC\\\", \\\"AAPL\\\", \\\"GOOG\\\", \\\"YHOO\\\", \\\"SYK\\\", \\\"VZ\\\")\\n\\n for stock in stocks:\\n stockObj = self.securityFactory(stock)\\n stockObj.queryAPI()\\n\\n self.stockDB.query(\\\"INSERT INTO basic_info (ticker, price, daily_change, company, year_high, year_low, \\\\\\n daily_percent, date, streak) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\\\", (stockObj.target, stockObj.curr, \\\\\\n stockObj.daily_change, stockObj.company,\\\\\\n stockObj.year_high, stockObj.year_low,\\\\\\n stockObj.daily_percent, dateStr, 0))\",\n \"def stocks():\\n mappings = {key: key for key in Schema.stock_columns}\\n return Schema(mappings)\",\n \"def portfolio_table(self):\\n idx = set(name.split('-')[0].split('.')[0] for name, etf in self.etfs.items() if not etf.sold())\\n table = pd.DataFrame({'Invested': 0, 'Shares':0, 'Share Price':0, 'Present Value':0, 'P/L':0, 'P/L%':0},index=idx)\\n for name, etf in self.etfs.items():\\n if not etf.sold():\\n table.loc[name.split('-')[0].split('.')[0], 'Invested'] += etf.initial_investment()\\n table.loc[name.split('-')[0].split('.')[0], 'Shares'] += etf.n_shares\\n table.loc[name.split('-')[0].split('.')[0], 'Share Price'] = etf.stock_price()\\n table.loc[name.split('-')[0].split('.')[0], 'Present Value'] += etf.present_value()\\n table.loc[name.split('-')[0].split('.')[0], 'P/L'] += etf.profit_loss()\\n table.insert(1, 'PMA', round(table['Invested'] / table['Shares'], 2))\\n table.insert(3, 'Initial Weight', round(table['Invested'] / table['Invested'].sum() * 100, 2))\\n table.insert(4, 'Present Weight', round(table['Present Value'] / table['Present Value'].sum() * 100, 2))\\n table['P/L%'] = round(table['P/L'] / table['Invested'] * 100, 2)\\n table['P/L'] = round(table['P/L'], 2)\\n table['Present Value'] = round(table['Present Value'], 2)\\n return table.sort_values('Invested', 0, ascending=False)\",\n \"def investments_table(self):\\n table = pd.DataFrame(index=[etf.buy_date for etf in self.etfs.values()])\\n table['Ticker'] = [name.split('-')[0].split('.')[0] for name in self.etfs.keys()]\\n table['Buying Price (€)'] = [etf.buy_price for etf in self.etfs.values()]\\n table['Number of Shares'] = [etf.n_shares for etf in self.etfs.values()]\\n table['Commissions (€)'] = [etf.total_commissions() for etf in self.etfs.values()]\\n table['Invested (€)'] = [etf.initial_investment() for etf in self.etfs.values()]\\n table['Share Price (€)'] = [etf.stock_price() for etf in self.etfs.values()]\\n table['Value (€)'] = [etf.present_value() for etf in self.etfs.values()]\\n table['P/L (€)'] = [etf.profit_loss() for etf in self.etfs.values()]\\n table['P/L (%)'] = [etf.profit_loss(pct=True) for etf in self.etfs.values()]\\n return table\",\n \"def getHTMLTable(self):\\n stocks = self.getTrackedStocks()\\n\\n html = \\\" \\\\\\n <html> \\\\\\n <head></head> \\\\\\n <body> \\\\\\n <table> \\\\\\n <tr> \\\\\\n <th>Ticker</th> \\\\\\n <th>Company</th> \\\\\\n <th>Price</th> \\\\\\n <th>Daily Change</th> \\\\\\n <th>Daily Percent Change</th> \\\\\\n <th>Year High</th> \\\\\\n <th>Year Low</th> \\\\\\n </tr>\\\"\\n\\n for stock in stocks:\\n html += \\\"<tr>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.target)\\n html += \\\"</td>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.company)\\n html += \\\"</td>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.curr)\\n html += \\\"</td>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.daily_change)\\n html += \\\"</td>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.daily_percent)\\n html += \\\"</td>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.year_high)\\n html += \\\"</td>\\\"\\n\\n html += \\\"<td>\\\"\\n html += str(stock.year_low)\\n html += \\\"</td>\\\"\\n\\n html += \\\"</tr>\\\"\\n\\n html += \\\"</table></body></html>\\\"\\n\\n return html\",\n \"def get_stock_price_df(info, symbols):\\n\\n df_l = []\\n\\n for num, i in enumerate(info):\\n df = pd.DataFrame.from_dict(i, orient='index')\\n df['Symbol'] = symbols[num]\\n df_l.append(df)\\n\\n df_full = pd.concat(df_l)\\n df_full = df_full.rename(columns={'1. open': 'Open',\\n '2. high': 'High',\\n '3. low': 'Low',\\n '4. close': 'Close',\\n '5. volume': 'Volume'})\\n\\n return df_full\",\n \"def get_quarterly_report_tables(self, symbol, date):\\n raw_data = self.sec_file_ops.get_raw_quarterly_financial_statement(symbol, date)\\n\\n cleared_tables = {}\\n for title in raw_data:\\n cleared_tables[title] = self._remove_unnecessary_tags(raw_data[title])\\n # print(cleared_tables)\\n\\n tables = {}\\n for title in cleared_tables:\\n table = Table()\\n table.read_tablecontent(cleared_tables[title])\\n tables[title] = table\\n\\n for title in tables:\\n tables[title].setup_linked_rows()\\n # tables[title].print(linked=True)\\n\\n return tables\",\n \"def create_query(window,con,input_table_name,output_table_name,input_columns, stat_columns):\\n sql = \\\"CREATE TABLE {} AS \\\".format(output_table_name)\\n sql = sql + \\\"SELECT\\\" \\n for input_column in input_columns:\\n sql = sql + \\\" {},\\\".format(input_column)\\n for stat_column in stat_columns:\\n sql = sql + \\\" {},\\\".format(stat_column)\\n for stat_column in stat_columns:\\n sql = sql + \\\" AVG({}) OVER(PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS ma{:02.0f}_{},\\\".format(stat_column,window-1,window,stat_column)\\n sql = sql + \\\" MIN({}) OVER(PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS min{:02.0f}_{},\\\".format(stat_column,window-1,window,stat_column)\\n sql = sql + \\\" MAX({}) OVER(PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS max{:02.0f}_{},\\\".format(stat_column,window-1,window,stat_column)\\n sql = sql + \\\" regr_slope({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS slope{:02.0f}_{},\\\".format(stat_column,window-1,window,stat_column)\\n sql = sql + \\\" regr_intercept({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS intercept{:02.0f}_{},\\\".format(stat_column,window-1,window,stat_column)\\n sql = sql + (\\\" regr_slope({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) * year \\\"\\n \\\"+ regr_intercept({},year) OVER (PARTITION BY pfafid_30spfaf06, month, temporal_resolution ORDER BY year ROWS BETWEEN {:01.0f} PRECEDING AND CURRENT ROW) AS ols{:02.0f}_{},\\\".format(stat_column,window-1,stat_column,window-1,window,stat_column))\\n \\n \\n sql = sql[:-1]\\n sql = sql + \\\" FROM {}\\\".format(input_table_name)\\n return sql\",\n \"def get_back_dataframe(self, end_date=None, stocks=None):\\n if end_date is None:\\n end_date = self.dates[-1]\\n\\n if type(end_date) is not datetime.datetime and type(end_date) is not pd.tslib.Timestamp:\\n end_date = datetime.datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n\\n if stocks is None:\\n stocks = self.stocks\\n\\n info = {}\\n for stock in stocks:\\n info[stock] = self.stock_data[stock].to_stock_dataframe_range(start_date=None, end_date=end_date)\\n\\n return info\",\n \"def sql_create_big_table():\\n return \\\"\\\"\\\"\\n SELECT\\n m.tube_assembly_id as 'tube_assembly_id'\\n , m.quantity_1 as 'quantity_component'\\n , c.component_id \\n , c.component_type_id \\n , c.type as component_type \\n , c.connection_type_id\\n , c.outside_shape\\n , c.base_type\\n , c.height_over_tube\\n , c.bolt_pattern_long\\n , c.bolt_pattern_wide\\n , c.groove\\n , c.base_diameter\\n , c.shoulder_diameter\\n , c.unique_feature\\n , c.orientation\\n , c.weight\\n , p.supplier\\n , p.quote_date\\n , p.annual_usage\\n , p.min_order_quantity\\n , p.bracket_pricing\\n , p.quantity\\n , p.cost\\n FROM\\n stg_bill_of_materials m INNER JOIN stg_comp_boss c\\n ON m.component_id_1 = c.component_id\\n \\n INNER JOIN stg_price_quote p\\n ON m.tube_assembly_id = p.tube_assembly_id\\n \\\"\\\"\\\"\",\n \"def get_stock_data(company, start_date_inc, stop_date_inc):\\n\\n api_key = 'Bo9P_cJnmf5EsQPp1Bdp'\\n desired_cols = 'date,close'\\n\\n# ticker = 'FB'\\n# start_date_inc = '20170801'\\n# end_date_inc = '20170831'\\n\\n # format and send the request\\n payload = {\\n 'date.gte': start_date_inc,\\n 'date.lte': stop_date_inc,\\n 'ticker': company,\\n 'qopts.columns': desired_cols,\\n 'api_key': api_key\\n }\\n meta_url = r'https://www.quandl.com/api/v3/datatables/WIKI/PRICES'\\n r = requests.get(meta_url, params=payload)\\n\\n # convert to a pandas dataframe\\n df = pd.DataFrame(r.json()['datatable']['data'])\\n if not df.empty:\\n df.columns = ['date', 'price']\\n df['date'] = pd.to_datetime(df['date'])\\n\\n return df\",\n \"def create_order(df_stock, df_signal, moneyness=('OTM', 'ITM'),\\n cycle=0, strike=0, expire=(False, True)):\\n symbol = df_stock.ix[df_stock.index.values[0]]['symbol']\\n\\n tb_closes = {\\n stock.date.strftime('%Y-%m-%d'): np.float(stock.close) for stock in\\n Stock.objects.filter(Q(symbol=symbol) & Q(source='thinkback'))\\n }\\n\\n holding = df_signal['holding'].apply(\\n lambda x: int(x / np.timedelta64(1, 'D'))\\n ).astype(np.int).min()\\n\\n data = list()\\n dates0, options0 = get_options_by_cycle_strike(\\n symbol=symbol,\\n name='CALL',\\n dates0=df_signal['date0'],\\n dte=holding,\\n moneyness=moneyness,\\n cycle=cycle,\\n strike=strike\\n )\\n\\n for date0, (index, signal) in zip(dates0, df_signal.iterrows()):\\n date1 = signal['date1']\\n\\n if date0:\\n option0 = options0.get(date=date0)\\n\\n option1 = None\\n if option0 and option0.bid > 0:\\n date1, option1 = get_option_by_contract_date(option0.contract, date1)\\n\\n if option0 and option1:\\n stock0 = tb_closes[option0.date.strftime('%Y-%m-%d')]\\n close0 = stock0 - np.float(option0.bid)\\n\\n ask1 = 0\\n if int(expire):\\n ask1 = np.float(\\n tb_closes[option1.date.strftime('%Y-%m-%d')]\\n - np.float(option0.contract.strike)\\n )\\n ask1 = ask1 if ask1 > 0 else 0.0\\n\\n date1 = option1.date\\n stock1 = tb_closes[option1.date.strftime('%Y-%m-%d')]\\n close1 = stock1 - np.float(ask1)\\n else:\\n date1 = option1.date\\n stock1 = tb_closes[option1.date.strftime('%Y-%m-%d')]\\n close1 = stock1 - np.float(option1.ask)\\n\\n data.append({\\n 'date0': option0.date,\\n 'date1': date1,\\n 'signal0': 'BUY',\\n 'signal1': 'SELL',\\n 'stock0': stock0,\\n 'stock1': stock1,\\n 'option0': option0.bid,\\n 'option1': ask1 if expire else option1.ask,\\n 'close0': np.round(close0, 2), # buy using ask\\n 'close1': np.round(close1, 2), # sell using bid\\n 'option_code': option0.contract.option_code,\\n 'strike': np.float(option0.contract.strike),\\n 'dte0': np.int(option0.dte),\\n 'dte1': np.int(option1.dte),\\n 'intrinsic0': np.float(option0.intrinsic),\\n 'intrinsic1': np.float(option1.intrinsic)\\n })\\n\\n df = DataFrame()\\n if len(data):\\n df = DataFrame(data, columns=[\\n 'date0', 'date1', 'signal0', 'signal1',\\n 'stock0', 'stock1', 'option0', 'option1', 'close0', 'close1',\\n 'option_code', 'strike', 'dte0', 'dte1',\\n 'intrinsic0', 'intrinsic1'\\n ])\\n\\n df['holding'] = df['date1'] - df['date0']\\n df['pct_chg'] = np.round((df['close1'] - df['close0']) / df['close0'], 2)\\n\\n f = lambda x: np.round(x['pct_chg'] * -1 if x['signal0'] == 'SELL' else x['pct_chg'], 2)\\n df['pct_chg'] = df.apply(f, axis=1)\\n\\n df['sqm0'] = 100\\n df['sqm1'] = -100\\n df['oqm0'] = -1\\n df['oqm1'] = 1\\n\\n return df\",\n \"def get_dataframes(symbols=(\\\"sne\\\", \\\"goog\\\", \\\"tsla\\\"), source='yahoo', refresh=False):\\n symbols = util.make_symbols(list(symbols))\\n if refresh:\\n symbols_to_refresh = symbols\\n else:\\n symbols_to_refresh = [sym for sym in symbols if not Equity.objects.filter(symbol=sym).exists()]\\n source = source.lower().strip()\\n if source in ('yahoo', 'google'):\\n source += '_finance'\\n if source[:3] == 'fed':\\n source = 'federal_reserve_economic_data'\\n ccpanda = ccp.ConcurrentPandas()\\n # set the data source\\n getattr(ccpanda, \\\"set_source_\\\" + source)()\\n if symbols_to_refresh:\\n # tell concurrent pandas which keys/symbols to retrieve\\n ccpanda.insert_keys(symbols_to_refresh)\\n # start concurrentpandas threads\\n ccpanda.consume_keys_asynchronous_threads()\\n # FIXME: is there a better/faster iterator to use like `ccpanda.output_map` attribute?\\n pseudodict = ccpanda.return_map()\\n else:\\n pseudodict = {}\\n table = {}\\n for sym in symbols:\\n e, created = None, False\\n if not sym in symbols_to_refresh:\\n e, created = Equity.objects.get_or_create(symbol=sym)\\n if created or not e or not e.time_series or sym in symbols_to_refresh:\\n e, created = Equity.objects.get_or_create(\\n symbol=sym,\\n name=sym, # FIXME: use data source to find equity name!\\n time_series=pseudodict[sym].to_json(),\\n )\\n table[sym] = pd.io.json.read_json(path_or_buf=e.time_series, orient='columns', typ='frame', convert_dates=True)\\n return table\",\n \"def pack_for_create_table(tables: tuple):\\n\\n all_tables = {'product': \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS product (\\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\\n PRODUCT_ID CHAR(50),\\n GLOBAL_ID CHAR(50),\\n TITLE TEXT,\\n COUNTRY CHAR(50),\\n RETURNS_ACCEPTED CHAR(50),\\n IS_MULTI_VARIATION_LISTING CHAR(50),\\n CONDITION_ID CHAR(50),\\n CONDITION_DISPLAY_NAME CHAR(50),\\n CATEGORY_ID CHAR(50),\\n CATEGORY_NAME CHAR(100),\\n AUTO_PAY CHAR(50),\\n LOCATION CHAR(50),\\n PAYMENT_METHOD CHAR(50),\\n TOP_RATED_LISTING CHAR(50),\\n VIEW_ITEM_URL TEXT\\n )\\\"\\\"\\\",\\n 'listing_info': \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS listing_info (\\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\\n BEST_OFFER_ENABLED CHAR(20),\\n END_TIME CHAR(50),\\n GIFT CHAR(20),\\n BUY_IT_NOW_AVAILABLE CHAR(20),\\n START_TIME CHAR(50),\\n FIXED_PRICE CHAR(20)\\n )\\\"\\\"\\\",\\n 'selling_status': \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS selling_status (\\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\\n CONVERTED_CURRENT_PRICE_CURRENCY_ID CHAR(20),\\n CONVERTED_CURRENT_PRICE_VALUE CHAR(50),\\n CURRENT_PRICE_CURRENCY_ID CHAR(20),\\n CURRENT_PRICE_VALUE CHAR(50),\\n TIME_LEFT CHAR(50),\\n SELLING_STATE CHAR(20)\\n )\\\"\\\"\\\",\\n 'shipping_info': \\\"\\\"\\\"CREATE TABLE IF NOT EXISTS shipping_info (\\n ITEM_ID CHAR(50) NOT NULL UNIQUE,\\n SHIP_TO_LOCATIONS CHAR(20),\\n SHIPPING_TYPE CHAR(100),\\n ONE_DAY_SHIPPING_AVAILABLE CHAR(20),\\n EXPEDITED_SHIPPING CHAR(20),\\n SHIPPING_SERVICE_COST_CURRENCY_ID CHAR(50),\\n SHIPPING_SERVICE_COST_VALUE CHAR(50),\\n HANDLING_TIME CHAR(50)\\n )\\\"\\\"\\\"\\n }\\n\\n # If table was not specified, put all tables in line\\n tables = all_tables.keys() if tables is None else tables\\n\\n return ';'.join([\\\"{}\\\".format(all_tables[i]) for i in tables])\",\n \"def make_historical_balances_and_prices_table(game_id: int, user_id: int, start_time: float = None,\\n end_time: float = None) -> pd.DataFrame:\\n start_time, end_time = get_time_defaults(game_id, start_time, end_time)\\n balances_df = get_user_balance_history(game_id, user_id, start_time, end_time)\\n df = add_bookends(balances_df, end_time=end_time)\\n df = df.groupby(\\\"symbol\\\").apply(resample_values)\\n df = df.reset_index().rename(columns={\\\"level_1\\\": \\\"timestamp\\\"})\\n df = append_price_data_to_balance_histories(df) # price appends + resampling happen here\\n df.sort_values([\\\"symbol\\\", \\\"timestamp\\\"])\\n df[\\\"value\\\"] = df[\\\"balance\\\"] * df[\\\"price\\\"]\\n df = filter_for_trade_time(df)\\n df[[\\\"balance\\\", \\\"price\\\", \\\"value\\\"]] = df[[\\\"balance\\\", \\\"price\\\", \\\"value\\\"]].astype(float)\\n apply_validation(df, balances_and_prices_table_schema, strict=True)\\n return df.reset_index(drop=True).sort_values([\\\"timestamp\\\", \\\"symbol\\\"])\",\n \"def make_table(data, portfolio):\\n table_data = [\\n [\\n # Header\\n click.style('Rank', fg='white'),\\n click.style('Coin', fg='white'),\\n click.style('Price', fg='white'),\\n click.style('Coins Owned', fg='white'),\\n click.style('Net Worth CAD', fg='white'), \\n click.style('Net Worth USD', fg='white'),\\n click.style('24 Hour Volume', fg='white'),\\n click.style('Market Cap', fg='white'),\\n click.style('1 Hour', fg='white'),\\n click.style('24 Hours', fg='white'),\\n click.style('7 Days', fg='white'),\\n click.style('Last Updated', fg='white')\\n ]\\n ]\\n\\n for row in data:\\n id = row['id']\\n table_row = [\\n click.style(row['rank'], fg='white'),\\n click.style(id, fg='cyan'),\\n click.style('$' + add_commas(row['price_usd']), fg='green'),\\n click.style(add_commas(portfolio[id]), fg='green'),\\n click.style('$' + add_commas(str(int(round(float(portfolio[id]) * float(row['price_cad']),)))), fg='green'),\\n click.style('$' + add_commas(str(int(round(float(portfolio[id]) * float(row['price_usd']),)))), fg='green'),\\n click.style('$' + add_commas(format(float(row['24h_volume_usd']), '.2f')), fg='green'),\\n click.style('$' + add_commas(format(float(row['market_cap_usd']), '.2f')), fg='green'),\\n style_percent(row['percent_change_1h']),\\n style_percent(row['percent_change_24h']),\\n style_percent(row['percent_change_7d']),\\n click.style(time_since(row['last_updated']), fg='white')\\n ]\\n table_data.append(table_row)\\n \\n table_instance = AsciiTable(table_data, 'Crypto Stats')\\n cols = len(table_instance.table.split('\\\\n')[0])\\n resize_terminal(total_rows(data), cols)\\n\\n return table_instance.table\",\n \"def create_stock_view_list_table(self):\\n create_stock_view_list_sql = \\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"{0}\\\".\\\"{1}\\\" (\\n stock_view_name text NOT NULL PRIMARY KEY,\\n stock_view_relation text NOT NULL\\n );\\n \\\"\\\"\\\".format(Schemas.SCHEMA_META, Tables.TABLE_TICK_STOCK_VIEW_LIST)\\n\\n conn = self.db_engine.connect()\\n try:\\n conn.execute(create_stock_view_list_sql)\\n except:\\n self._logger.log_error(traceback.format_exc())\\n return Error.ERROR_DB_EXECUTION_FAILED\\n finally:\\n conn.close()\",\n \"def join_data(df_order, df_stock, df_all):\\n df_stock0 = df_stock.set_index('date')\\n df_list = []\\n for index, data in df_order.iterrows():\\n df_both = df_all.query(\\n '(option_code == %r | option_code == %r) & date >= %r & date <= %r' % (\\n data['code0'], data['code1'], data['date0'], data['date1']\\n )\\n )[['date', 'option_code', 'dte', 'sell', 'buy', 'strike']]\\n\\n df0 = df_both.query('option_code == %r' % data['code0'])\\n df1 = df_both.query('option_code == %r' % data['code1'])\\n df_join = pd.merge(df0, df1, on='date', suffixes=(0, 1))\\n\\n if data['signal0'] == 'BUY':\\n first = df_join['buy0'] - df_join['sell1']\\n remain = df_join['sell0'] - df_join['buy1']\\n df_join['signal'] = ['BUY'] + ['SELL'] * len(remain[1:])\\n df_join['option'] = [first.iloc[0]] + list(remain[1:])\\n else:\\n first = -df_join['sell0'] + df_join['buy1']\\n remain = -df_join['buy0'] + df_join['sell1']\\n df_join['signal'] = ['SELL'] + ['BUY'] * len(remain[1:])\\n df_join['option'] = [first.iloc[0]] + list(remain[1:])\\n\\n df_join['pos_net'] = df_join['option'] - df_join['option'].iloc[0]\\n df_join['pct_chg'] = df_join['pos_net'] / np.abs(df_join['option'].iloc[0]) + 0\\n\\n df_join = df_join.drop([\\n 'sell0', 'buy0', 'sell1', 'buy1', 'strike0', 'strike1', 'dte1'\\n ], axis=1)\\n df_close = df_stock0[data['date0']:data['date1']]\\n df_close = df_close[['close']].reset_index()\\n df_both = pd.merge(df_join, df_close, on='date')\\n df_both.rename(index=str, columns={'close': 'stock', 'dte0': 'dte'}, inplace=True)\\n\\n # print df.to_string(line_width=1000)\\n df_both = df_both.replace([np.inf, -np.inf], np.nan)\\n df_both = df_both.fillna(0)\\n\\n df_both = df_both.round({\\n 'pos_net': 2,\\n 'pct_chg': 2,\\n })\\n\\n df_list.append(df_both)\\n\\n return df_list\",\n \"def getStock(symbol, start, end):\\n df = data.get_data_yahoo(symbol, start, end)\\n\\n df.columns.values[-1] = 'AdjClose'\\n df.columns = df.columns + '_' + symbol\\n df['Return_%s' % symbol] = df['AdjClose_%s' % symbol].pct_change()\\n\\n return df\",\n \"def getHistoricalData(stockName, startDate):\\n conn = r.connect(db = db.DB)\\n stockName = stockName.upper()\\n startDate = dateToString(startDate)\\n endDate = dateToString(datetime.datetime.now())\\n\\n if not stockName in db.STOCK_MAP.keys():\\n return dict(\\n error = 1,\\n message = \\\"The info you want is not what I can give\\\"\\n )\\n\\n stock = yf.StockInfo(stockName + db.IN_LONDON)\\n cachedData = r.table(db.HISTORICAL_TABLE).get(stockName).run(conn)\\n infoDict = dict()\\n\\n if cachedData == None:\\n print \\\"\\\\n-- DB -- \\\" + stockName + \\\" == Inserting New Information ==\\\\n\\\"\\n histList = stock.historical_prices(startDate, endDate)\\n infoDict[\\\"history_list\\\"] = createHistoryDictList(histList)\\n infoDict[\\\"index\\\"] = stockName\\n infoDict[\\\"name\\\"] = db.STOCK_MAP[stockName]\\n infoDict[\\\"timestamp\\\"] = getTime()\\n r.table(db.HISTORICAL_TABLE).insert(infoDict).run(conn)\\n else:\\n elapsedTime = (\\n getTime() -\\n cachedData[\\\"timestamp\\\"]\\n )\\n if elapsedTime > db.HISTORICAL_INTERVAL:\\n print \\\"\\\\n-- DB -- \\\" + stockName + \\\" == Updating Database ==\\\\n\\\"\\n histList = stock.historical_prices(startDate, endDate)\\n infoDict[\\\"history_list\\\"] = createHistoryDictList(histList)\\n infoDict[\\\"index\\\"] = stockName\\n infoDict[\\\"timestamp\\\"] = getTime()\\n r.table(db.HISTORICAL_TABLE).get(stockName).update(\\n infoDict\\n ).run(conn)\\n else:\\n print \\\"\\\\n-- DB -- \\\" + stockName + \\\" == Using Cached Data ==\\\\n\\\"\\n infoDict = cachedData\\n\\n infoDict[\\\"name\\\"] = db.STOCK_MAP[stockName]\\n return infoDict\",\n \"def download_stock_price_hist(\\n\\ttickers = [ 'AAPL' ],\\n\\tprice_column = 'Adj Close',\\t\\t\\t\\t\\t\\t\\t\\t# assume it's the Adjusted Close price that are interested\\n\\tstart = datetime.date( 2009, 12, 31 ),\\t\\t\\t\\t# assume start is guaranteed to be a weekday\\n\\tend = datetime.date( 2015, 12, 31 ),\\n\\tcsv_file = \\\"stock_price_test.csv\\\",\\n):\\n\\t# Check validity of inputs\\n\\tif len( tickers ) <= 0:\\n\\t\\tprint \\\"Tickers must not be empty\\\";\\n\\t\\treturn False;\\n\\tif start > end:\\n\\t\\tprint \\\"Start date \\\" + start.isoformat() + \\\" can't be later than End date \\\" + end.isoformat();\\n\\n\\tdf = pd.DataFrame();\\t\\t\\t# data frame to return\\n\\tfor _i in range( len(tickers) ):\\n\\t\\tticker = tickers[_i];\\n\\t\\tprint \\\"Index\\\" + str(_i) + \\\"\\\\t\\\" + \\\"Ticker: \\\" + ticker;\\n\\n\\t\\tstart_str = start.isoformat();\\n\\t\\tend_str = end.isoformat();\\n\\t\\thist = ystockquote.get_historical_prices( ticker, start_str, end_str );\\t# dictionary with date string as the key\\n\\n\\t\\t# Get time series of stock prices (Don't sort before forming the Series!!!)\\n\\t\\tdate_index = [];\\n\\t\\tprice_data = [];\\n\\t\\tfor key, val in hist.iteritems():\\n\\t\\t\\tdate_index.append( datetime.datetime.strptime( key, \\\"%Y-%m-%d\\\" ).date() );\\n\\t\\t\\tprice_data.append( float( val[ price_column ] ) )\\n\\n\\t\\tif min( date_index ) > start:\\t\\t\\t\\t\\t\\t\\t\\t# Pass if the no stock price is available on Start\\n\\t\\t\\tcontinue;\\n\\t\\tstock_ts = pd.Series( price_data, date_index );\\n\\t\\tstock_ts = stock_ts.sort_index();\\n\\n\\t\\t# Add current stock TS to the DataFrame\\n\\t\\tdf[ticker] = stock_ts;\\n\\t\\n\\tdf.to_csv( csv_file, index_label='Date' );\\n\\treturn True;\",\n \"def buildExposureTable(exposures, fields, instruments):\\n name = []\\n ra = []\\n dec= []\\n field= []\\n inst = []\\n airmass = []\\n mjd = []\\n exptime = []\\n epoch = []\\n apcorr = []\\n index = 0\\n for k,e in exposures.items():\\n name.append(e.name)\\n ra.append(getDegree(e.coords.ra))\\n dec.append(getDegree(e.coords.dec))\\n field.append(fields[e.field].index)\\n if e.instrument in specialInstruments:\\n inst.append(specialInstruments[e.instrument])\\n else:\\n inst.append(instruments[e.instrument].index)\\n e.index = index\\n index += 1\\n\\n airmass.append(e.airmass)\\n mjd.append(e.mjd)\\n exptime.append(e.exptime)\\n epoch.append(e.epoch)\\n apcorr.append(e.apcorr)\\n hdu = pf.BinTableHDU.from_columns(\\\\\\n pf.ColDefs( [pf.Column(name='NAME',format=py_to_fits(name),array=name),\\n pf.Column(name='RA',format=py_to_fits(ra),array=ra),\\n pf.Column(name='DEC',format=py_to_fits(dec),array=dec),\\n pf.Column(name='FIELDNUMBER',format=py_to_fits(field),array=field),\\n pf.Column(name='INSTRUMENTNUMBER',format=py_to_fits(inst),\\\\\\n array=inst),\\n pf.Column(name=\\\"MJD\\\",format=py_to_fits(mjd),array=mjd),\\n pf.Column(name=\\\"AIRMASS\\\",format=py_to_fits(airmass),array=airmass),\\n pf.Column(name=\\\"EXPTIME\\\",format=py_to_fits(exptime),array=exptime),\\n pf.Column(name=\\\"EPOCH\\\",format=py_to_fits(epoch),array=epoch),\\n pf.Column(name=\\\"APCORR\\\",format=py_to_fits(apcorr),array=apcorr)] ),\\n name = 'Exposures')\\n # hdu.header['EXTNAME'] = 'Exposures'\\n return hdu\",\n \"def construct_table(self):\\n table_str = self.header_row\\n row_lbls, col_lbls = self.get_idxvals()\\n for r,rlbl in enumerate(row_lbls):\\n row_data = [self.data[rlbl,clbl] for clbl in col_lbls]\\n table_str += self.construct_row(r, row_data)\\n \\n return table_str\",\n \"def getStock(symbol, start, end):\\n df = pd.io.data.get_data_yahoo(symbol, start, end)\\n\\n df.columns.values[-1] = 'AdjClose'\\n df.columns = df.columns + '_' + symbol\\n df['Return_%s' % symbol] = df['AdjClose_%s' % symbol].pct_change()\\n\\n return df\",\n \"def create_tables (cls, env=os.environ):\\n\\n cur = cls.pri_table_read_cursor (env=env)\\n cur.execute ('SPECIALCASE gettablelist')\\n ret = cur.fetchall ()\\n \\n existingtables = set ([x[0].lower() for x in ret])\\n\\n for tabname in (set (cls.table_desc.keys ()) - existingtables):\\n sql, lsd = cls.table_desc[tabname]\\n epls, desls, sqlprefix = lsd.get_create_labeling (savels=True)\\n\\n conn = get_labeled_conn (epls, desls)\\n cur = conn.cursor ()\\n cur.execute (sql)\\n conn.close ()\\n lsd.pop_labelset ()\\n\\n \\n import psycopg2\\n for sql in cls.sql_createindex:\\n conn = get_labeled_conn ()\\n cur = conn.cursor ()\\n # XXX It would be better to check which indices exist as we do for tables.\\n try:\\n cur.execute (sql)\\n except psycopg2.ProgrammingError, e: \\n pass\\n conn.close ()\",\n \"def create_stock_view_table(self, stock_view_name, stock_relation):\\n from Core.DAO.ComplicatedTables.StockViewTable import StockViewTable\\n create_tick_sql = \\\"\\\"\\\"\\n {4};\\n CREATE INDEX IF NOT EXISTS stock_view_{3}_date ON \\\"{0}\\\".\\\"{1}{2}\\\"(\\\"date\\\");\\n CREATE INDEX IF NOT EXISTS stock_view_{3}_datetime ON \\\"{0}\\\".\\\"{1}{2}\\\"(\\\"datetime\\\");\\n \\\"\\\"\\\".format(Schemas.SCHEMA_STOCK_VIEW_DATA, Tables.TABLE_TICK_STOCK_VIEW_PREFIX,\\n stock_view_name, stock_view_name.split(\\\".\\\")[0],\\n StockViewTable(stock_relation).get_table_define_sql(Schemas.SCHEMA_STOCK_VIEW_DATA,\\n Tables.TABLE_TICK_STOCK_VIEW_PREFIX +\\n stock_view_name))\\n\\n conn = self.db_engine.connect()\\n try:\\n conn.execute(create_tick_sql)\\n except:\\n self._logger.log_error(traceback.format_exc())\\n return Error.ERROR_DB_EXECUTION_FAILED\\n finally:\\n conn.close()\\n\\n return Error.SUCCESS\",\n \"def index():\\n\\n rows = db.execute(\\n 'SELECT symbol, SUM(CASE WHEN operation = \\\"SELL\\\" THEN -shares ELSE shares END) shares FROM transactions WHERE id = :id GROUP BY symbol;', id=session['user_id'])\\n\\n cash = db.execute('SELECT cash FROM users WHERE id = :id', id=session['user_id'])[0]['cash']\\n\\n grand_total = cash\\n\\n for row in rows:\\n stock = lookup(row['symbol'])\\n\\n row['name'] = stock['name']\\n row['price'] = stock['price']\\n row['total'] = row['shares'] * stock['price']\\n\\n grand_total += row['shares'] * stock['price']\\n\\n rows.append({\\n 'symbol': 'CASH',\\n 'cash': cash,\\n 'total': grand_total\\n })\\n\\n return render_template('index.html', stocks=rows)\",\n \"def get_data(symbols, dates):\\n df = pd.DataFrame(index=dates)\\n if \\\"SPY\\\" not in symbols:\\n symbols.insert(0, \\\"SPY\\\")\\n for symbol in symbols:\\n temp = pd.read_csv(symbol_to_path(symbol, base_dir=\\\"data\\\"), \\n index_col=\\\"Date\\\", \\n parse_dates=True, \\n usecols=[\\\"Date\\\", \\\"Adj Close\\\"])\\n \\n temp = temp.rename(columns={\\\"Adj Close\\\": symbol})\\n \\n df = df.join(temp, how=\\\"inner\\\")\\n df = df.sort_index(axis=0, ascending=[1])\\n \\n return df\",\n \"def get_dividends(self, stock_list, start_date=None, end_date=None):\\n df_dict = {}\\n df_list = []\\n file_in_path = [year.replace(\\\".csv\\\", \\\"\\\") for year in self.get_csv_in_path(self.dividend_eps_path)]\\n if not start_date:\\n start_date = file_in_path[0]\\n if not end_date:\\n end_date = file_in_path[-1]\\n if start_date > end_date:\\n return df_dict\\n for year in range(int(start_date), int(end_date)+1):\\n target_path = \\\"{}/{}.csv\\\".format(self.dividend_eps_path, year)\\n df = pd.read_csv(target_path, index_col=\\\"名稱\\\")\\n self.replace_nan_to_other(df, \\\"\\\")\\n for stock in stock_list:\\n pd_index = df.index.to_list()\\n old_list = []\\n if stock in pd_index:\\n data = df.loc[stock]\\n\\n # print(\\\"日期 = {}\\\".format(data.get(\\\"除息交易日\\\")))\\n if df_dict.get(stock):\\n old_list = df_dict.get(stock)\\n\\n # check data is available\\n dict = {}\\n if data.get(\\\"現金股利\\\") != \\\"\\\":\\n dict.update({\\\"除息交易日\\\": \\\"{}{}\\\".format(year, data.get(\\\"除息交易日\\\").split(\\\"'\\\")[1].replace(\\\"/\\\", \\\"\\\")) if data.get('除息交易日') else \\\"\\\",\\n \\\"現金股利\\\": data.get(\\\"現金股利\\\"),\\n })\\n if data.get(\\\"股票股利\\\") != \\\"\\\":\\n dict.update({\\\"除權交易日\\\": \\\"{}{}\\\".format(year, data.get(\\\"除權交易日\\\").split(\\\"'\\\")[1].replace(\\\"/\\\", \\\"\\\")) if data.get('除權交易日') else \\\"\\\",\\n \\\"股票股利\\\": data.get(\\\"股票股利\\\"),\\n })\\n if dict:\\n old_list.append(dict)\\n df_dict.update({stock: old_list})\\n\\n return df_dict\",\n \"def index():\\n\\n stocks_owned = db.execute(\\\"SELECT DISTINCT stock FROM transaction WHERE id = :id;\\\", id=session['user_id'])\\n\\n number_of_rows= len(stocks_owned) - 1\\n\\n i = 0\\n\\n total_value=0\\n\\n for stock in stocks_owned:\\n\\n stock_list=[]\\n stock_list[i]=stock\\n\\n value = db.execute(\\\"SELECT SUM(total_amount) FROM transaction WHERE id = :id GROUP BY stock HAVING stock=:stock\\\", id=session['usestockr_id'], stock=stocks_owned[\\\"stock\\\"])\\n value_list=[]\\n value_list[i] = value\\n\\n amount_owned = db.execute(\\\"SELECT SUM(amount) FROM transaction WHERE id = :id GROUP BY stock HAVING stock=:stock\\\", id=session['user_id'], stock = stocks_owned[\\\"stock\\\"])\\n amount_list=[]\\n amount_list[i]= amount_owned\\n\\n quote_input = stocks_owned[i]\\n quote_info = lookup(quote_input)\\n price = quote_info['price']\\n price_list=[]\\n price_list[i] = price\\n\\n\\n total_value+=value\\n\\n i+=1\\n\\n cash = db.execute(\\\"SELECT cash FROM users WHERE id = :id;\\\", id=session['user_id'])\\n\\n grand_total = total_value + cash\\n\\n ###(\\\"SELECT stock, SUM(total_amount) FROM transaction WHERE id = :id;, id=session['user_id'] GROUP BY stock\\\")####\\n\\n\\n return render_template(\\\"index.html\\\", number_of_rows=number_of_rows, stock_list=stock_list, amount_list=amount_list, value_list=value_list, price_list=price_list, total_value=total_value, grand_total=grand_total)\",\n \"def get_basics():\\n today = _dt.date.today().isoformat()\\n log.info('Start to collect HS_basics_all stocks...')\\n log.info('Collecting the main stock basics from tushare for %s' % today)\\n hs = __get_hs_daily(today)\\n \\n log.info('Collecting SH50 and prepare to merge into the main frame...')\\n sh50 = __get_sh50()\\n log.info('Collection HS300 and prepare to merge into the main frame...')\\n hs300 = __get_hs300()\\n log.info('Collection ZZ500 and prepare to merge into the main frame...') \\n zz500 = __get_zz500()\\n\\n # Add 上证50 column\\n log.trace('Adding SH50 to main frame as \\\\'sh50\\\\'')\\n hs['sh50'] = np.where(hs['code'].isin(sh50['code']), True, False)\\n # Merge from hs300 and change column name to 'hs300'\\n log.trace('Adding HS300 to main frame as \\\\'hs300\\\\'') \\n hs = hs.merge(hs300, on='code', how='left')\\n hs.fillna({'hs300':0}, axis=0, inplace=True)\\n # Add 中证500 column\\n log.trace('Adding ZZ500 to main frame as \\\\'zz500\\\\'')\\n hs['zz500'] = np.where(hs['code'].isin(zz500['code']), True, False)\\n # Filte 中小板\\n log.trace('Adding SME to main frame as \\\\'sme\\\\'')\\n __filter_sme(hs)\\n # Filte 创业板\\n log.trace('Adding GEM to main frame as \\\\'gem\\\\'')\\n __filter_gem(hs)\\n # Filte ST\\n log.trace('Filting ST and tag to main frame as \\\\'st\\\\'')\\n __filter_st(hs)\\n # Fill 概念分类\\n log.trace('Adding concept classfied \\\\'concept\\\\'')\\n hs = __concept_classified(hs)\\n\\n # Reset index, prepare to write to database\\n log.info('Reindexing the DataFrame and rename to match the column name in db...')\\n hs = hs.reindex(['code','name','industry','concept','sme','gem','st','hs300','sh50','zz500','pe','outstanding','totals','totalAssets','liquidAssets','timeToMarket','pb','rev','profit','holders'], axis=1)\\n hs.rename({'totalAssets': 'total_assets', 'liquidAssets': 'liquid_assets', 'timeToMarket': 'time_to_market'}, axis='columns', inplace=True)\\n\\n log.info('Successfully collect stock basics, got [%s] records.' % len(hs))\\n \\n return hs\",\n \"def construct_base_table(self):\\n # Get requested biological entities.\\n req_biomodels = [exp.biomodel for exp in self.req_exps]\\n # Remove duplicates.\\n req_biomodels = list(unique_everseen(req_biomodels))\\n # Order according to the linearized models.\\n relations = Relations(self.app_label, model_superclass=(BiologicalModel, MetaModel))\\n relations.start_join()\\n linearized_biomodels = relations.linearized\\n req_biomodels = sort_by_other(req_biomodels, \\n order=linearized_biomodels)\\n # Construct the SQL statement.\\n sql = self.construct_result_table(req_biomodels)\\n return sql\",\n \"def result_table(fmt='latex_booktabs'):\\n \\n names = [\\n \\\"ETF EW.\\\",\\n \\\"Antonacci ETF\\\",\\n \\\"Antonacci ETF Inv. Vol.\\\",\\n \\\"Futures EW.\\\",\\n \\\"Antonacci Futures\\\",\\n \\\"Antonacci Futures Inv. Vol.\\\",\\n \\\"TSMOM Futures Low Vol.\\\",\\n \\\"TSMOM Futures High Vol.\\\"\\n ]\\n\\n # Get stats for each strategy\\n s1 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=7, target_vol=40, periods=6, vol_weight=False)\\n s2 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=False)\\n s3 = calculate.stats_from_parameters(name='Antonacci', price_set='ETF', fee_rate_bps=10, get_top=2, target_vol=40, periods=6, vol_weight=True)\\n s4 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=47, target_vol=40, periods=6, vol_weight=False)\\n s5 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\\n s6 = calculate.stats_from_parameters(name='Antonacci', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=True)\\n s7 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=40, periods=6, vol_weight=False)\\n s8 = calculate.stats_from_parameters(name='TSMOM', price_set='Futures', fee_rate_bps=10, get_top=10, target_vol=100, periods=6, vol_weight=False)\\n\\n # The relevant columns from the summary data\\n cols = [3, 4, 5, 6]\\n num_assets = [7, 2, 2, 47, 10, 10, 47, 47]\\n stats = [s1, s2, s3, s4, s5, s6, s7, s8]\\n table = [names]\\n \\n # Collecting the results\\n for i, col in enumerate(cols):\\n col_list = [round(stat['summary'][col], 2) for stat in stats]\\n table.append(col_list)\\n\\n table.append(num_assets)\\n table = list(map(list, zip(*table))) # Transpose\\n \\n # Creating table headers\\n headers = ['Strategy Name', 'Annual Return', 'Annual Vol.', 'Sharpe', 'Max. Drawdown', '# Assets']\\n \\n # Returning latex table\\n tbl = tabulate(table, headers, tablefmt=fmt)\\n print(tbl)\\n \\n return tbl\",\n \"def get_data(symbols, dates):\\n \\n df = pd.DataFrame(index=dates)\\n if 'SPY' not in symbols:\\n symbols.insert(0,'SPY')\\n for symbol in symbols:\\n \\n df1 = pd.read_csv(symbol_to_path(symbol),usecols=['Date','Adj Close'],\\n index_col='Date',na_values =['nan'] )\\n df1 = df1.rename(columns ={\\\"Adj Close\\\": symbol}) \\n #print df1\\n df = df.join(df1,how='inner')\\n \\n return df.sort_index()\",\n \"def get_table(base, engine):\\n class w1_temp_table(base):\\n __tablename__ = 'w1_temp'\\n __table_args__ = {\\\"useexisting\\\": True}\\n\\n id = sa.Column(sa.types.Integer, primary_key=True)\\n logger_id = sa.Column(sa.types.Integer)\\n value = sa.Column(sa.types.String)\\n datetime = sa.Column(sa.types.DateTime)\\n return w1_temp_table\",\n \"def trading_alg(self,table_name = None, buy_now = False, strategy_name = \\\"sma9\\\"):\\n \\n self.bs.buyed_stocks = 0\\n self.bs.money = self.bs.startCredit\\n spy_stocks = self.load_data(table_name = table_name, symbols = [\\\"SPY\\\"])\\n spy_stocks = FinI.add_indicators(spy_stocks)\\n \\n if self.symbols:\\n symbols = self.symbols\\n else:\\n symbols = self.db.get_symbols()\\n\\n # symbols = [\\\"INTC\\\",\\\"BYND\\\",\\\"ZM\\\",\\\"NKE\\\",\\\"HIMX\\\",\\\"JKS\\\",\\\"ENPH\\\",\\\"DUK\\\",\\\"GE\\\",\\\"DIS\\\",\\\"LEVI\\\",\\\"NVAX\\\",\\\"SLCA\\\",\\\"GPS\\\"]\\n \\n for symbol in symbols:\\n print(\\\"symbol: \\\" + str(symbol))\\n \\n sub_data = self.load_data(table_name = table_name, symbols = symbol)\\n if len(sub_data) < 1:\\n break\\n\\n self.bt_stocks = FinI.add_indicators(sub_data)\\n self.bt_stocks = FinI.add_fib(self.bt_stocks)\\n # print(self.bt_stocks)\\n print(self.bt_stocks[\\\"sma30\\\"])\\n print(\\\"calculating percent change:\\\" + str(symbol))\\n # sub_data = self.stocks.loc[self.stocks.sym ==symbol[0]].sort_values(by='index')\\n \\n self.symbols = symbol[0]\\n \\n # self.prev_stock = sub_data.iloc[0]\\n # self.bt_stocks.iloc[0] = sub_data.iloc[0]\\n\\n # self.sell_marks = self.sell_marks.iloc[0:0]\\n # self.buy_marks = self.buy_marks.iloc[0:0]\\n self.bs.transactions = 0\\n self.bs.profit_perc = 0\\n \\n # trend_indicator = \\n # TODO mechanism for select strategies\\n # self.ts_boll(buy_now = buy_now, at_settings = None, symbol = symbol, spy_stocks = spy_stocks)\\n self.ts_eval(buy_now = buy_now, at_settings = None, symbol = symbol, spy_stocks = spy_stocks, strategy_logic = strategy_name)\\n\\n # call the method with passed and assembled name\\n # method = getattr(self, 'ts_' + strategy_name)\\n # method(buy_now = buy_now, at_settings = None, symbol = symbol, spy_stocks = spy_stocks, strategy_name = strategy_name)\",\n \"def main():\\n\\n bank_lookup = {\\n 'Spending': bank_main,\\n 'Income': bank_main,\\n 'Saving': bank_savings,\\n 'Credit card': bank_credit,\\n }\\n\\n sheet_data = petl.fromxlsx('sample-data.xlsx', sheet='Data')\\n data = sheet_data.cut(*range(5))\\n early_data = data.select('Date', lambda r: r.month <= 2)\\n\\n for account, table in split_table(early_data, 'Account'):\\n modified_table = bank_lookup[account](table)\\n # modified_table.tocsv(table['Account'][0]+'.csv')\\n print(modified_table)\",\n \"def stock_1_query(self):\\n return f\\\"\\\"\\\"\\n SELECT Date, '{self.stock_1}'\\n FROM closing_prices;\\\"\\\"\\\"\",\n \"def __generate_data_table__(self):\\n # | - __generate_data_table__\\n rows_list = []\\n for job in self.job_var_lst:\\n revisions = self.job_revision_number(job)\\n for revision in range(revisions + 1)[1:]:\\n # | - FOR LOOP BODY\\n entry_param_dict = {}\\n for prop in job:\\n entry_param_dict[prop[\\\"property\\\"]] = prop[\\\"value\\\"]\\n\\n entry_param_dict[\\\"variable_list\\\"] = job\\n entry_param_dict[\\\"path\\\"] = self.var_lst_to_path(job)\\n\\n entry_param_dict[\\\"max_revision\\\"] = revisions\\n entry_param_dict[\\\"revision_number\\\"] = revision\\n\\n rows_list.append(entry_param_dict)\\n # __|\\n\\n data_frame = pd.DataFrame(rows_list)\\n\\n return(data_frame)\\n # __|\",\n \"def get_data(table_name, end, num, start=None):\\n if start == None:\\n if table_name == \\\"days\\\": start = end - timedelta(days=num-1) \\n if table_name == \\\"weeks\\\": start = end - timedelta(weeks=num-1) \\n if table_name == \\\"months\\\": start = end - relativedelta(months=+num-1) \\n if table_name == \\\"years\\\": start = end - relativedelta(years=+num-1) \\n else: \\n start = days.get_entry(table_name, start).date\\n \\n dates = []\\n data = []\\n weather = []\\n density = []\\n \\n while start <= end:\\n entry = days.get_entry(table_name, start)\\n data.append(entry.sentiment)\\n \\n if table_name == \\\"days\\\": \\n dates.append(entry.date.strftime(\\\"%B %d, %Y\\\"))\\n start = start + timedelta(days=1)\\n if table_name == \\\"weeks\\\": \\n dates.append(entry.date.strftime(\\\"%B %d, %Y\\\"))\\n start = start + timedelta(weeks=1) \\n if table_name == \\\"months\\\": \\n dates.append(entry.date.strftime(\\\"%B %Y\\\"))\\n start = start + relativedelta(months=+1) \\n if table_name == \\\"years\\\": \\n dates.append(entry.date.strftime(\\\"%Y\\\"))\\n start = start + relativedelta(years=+1) \\n\\n # 7/15/15 is the last entry in the current weather dictionary\\n num_days = (min(start, date(2015,7,15)) - entry.date).days\\n temp = {entry.date + timedelta(days=i): weather_dict[entry.date + timedelta(days=i)] for i in range(num_days)}\\n weather.append(float(sum(temp.values()))/float(len(temp)))\\n\\n if density_dict != None:\\n d = max(entry.date, date(2014,7,1))\\n num_days = (min(start, date(2015,7,28)) - d).days\\n rho = {d + timedelta(days=i): density_dict[d + timedelta(days=i)] for i in range(num_days)}\\n density.append(float(sum(rho.values()))/float(len(rho)))\\n\\n return dates, data, weather, density\",\n \"def product_tables(self): \\r\\n\\r\\n self.mycursor.execute('CREATE TABLE IF NOT EXISTS product(\\\\\\r\\n PROD_id BIGINT PRIMARY KEY,\\\\\\r\\n PROD_name VARCHAR(100) NOT NULL,\\\\\\r\\n PROD_grade CHAR(1) NOT NULL,\\\\\\r\\n PROD_url VARCHAR(150) NOT NULL UNIQUE)')\",\n \"def create_tick_data_table(self, stock_code):\\n create_tick_sql = \\\"\\\"\\\"\\n {4};\\n CREATE INDEX IF NOT EXISTS tick_{3}_date ON \\\"{0}\\\".\\\"{1}{2}\\\"(\\\"date\\\");\\n CREATE INDEX IF NOT EXISTS tick_{3}_datetime ON \\\"{0}\\\".\\\"{1}{2}\\\"(\\\"datetime\\\");\\n \\\"\\\"\\\".format(Schemas.SCHEMA_TICK_DATA, Tables.TABLE_TICK_STOCK_PREFIX,\\n stock_code, stock_code.split(\\\".\\\")[0],\\n TickDataTable().get_table_define_sql(Schemas.SCHEMA_TICK_DATA, Tables.TABLE_TICK_STOCK_PREFIX +\\n stock_code))\\n\\n conn = self.db_engine.connect()\\n try:\\n conn.execute(create_tick_sql)\\n except:\\n self._logger.log_error(traceback.format_exc())\\n return Error.ERROR_DB_EXECUTION_FAILED\\n finally:\\n conn.close()\\n\\n return Error.SUCCESS\",\n \"def populate(table_name, date):\\n\\tlog_msg3(\\\"Populando \\\" + table_name)\\n\\n\\twsq_to_txt(table_name, date)\\n\\n\\t# si es un nuevo año se crea una nueva tabla\\n\\tif(is_new_year(table_name) and not new_tables_created):\\n\\t\\tcreate_tables()\\n\\n\\ttxt_to_table(table_name)\\n\\n\\tlog_msg_ok3()\",\n \"def index():\\n user_stocks_list = db.execute(\\\"SELECT stock FROM transactions WHERE id = :current_id\\\", current_id=session[\\\"user_id\\\"])\\n user_stocks = []\\n for stock in user_stocks_list:\\n if stock['stock'] not in user_stocks:\\n user_stocks.append(stock['stock'])\\n\\n stock_portfolio = []\\n\\n for possible_stock in user_stocks:\\n bought_shares_list = db.execute(\\\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\\\",\\n current_id=session[\\\"user_id\\\"], stock=possible_stock, t='B')\\n bought_shares = 0\\n bought_shares = bought_shares_list[0][\\\"SUM(units)\\\"]\\n sold_shares_list = db.execute(\\\"SELECT SUM(units) FROM transactions WHERE (id = :current_id AND stock = :stock AND type = :t)\\\",\\n current_id=session[\\\"user_id\\\"], stock=possible_stock, t='S')\\n sold_shares = 0\\n sold_shares = sold_shares_list[0][\\\"SUM(units)\\\"]\\n if sold_shares == None:\\n sold_shares = 0\\n\\n available_shares = 0\\n if bought_shares != None and (bought_shares - sold_shares) > 0:\\n available_shares = bought_shares - sold_shares\\n current_price = int(lookup(possible_stock)[\\\"price\\\"])\\n market_value = current_price * available_shares\\n dict_stock = {}\\n dict_stock['name_stock'] = possible_stock\\n dict_stock['shares_quantity'] = available_shares\\n dict_stock['current_price'] = current_price\\n dict_stock['market_value'] = market_value\\n stock_portfolio.append(dict_stock)\\n else:\\n pass\\n\\n available_money_list = db.execute(\\\"SELECT cash FROM users WHERE id = :current_id\\\", current_id=session[\\\"user_id\\\"])\\n available_money = usd(available_money_list[0]['cash'])\\n\\n username_list = db.execute(\\\"SELECT username FROM users WHERE id = :current_id\\\", current_id=session[\\\"user_id\\\"])\\n username = username_list[0][\\\"username\\\"]\\n\\n sum_market_values = 0\\n for collection in stock_portfolio:\\n sum_market_values += int(collection['market_value'])\\n\\n total_value = usd(available_money_list[0]['cash'] + sum_market_values)\\n\\n return render_template(\\\"index.html\\\", stock_portfolio=stock_portfolio, user_stocks=user_stocks, money=available_money, name=username, total_value=total_value)\",\n \"async def get_trading_table(self):\\n if self.trading_table is None:\\n self.trading_table = {}\\n wikitext = await Controller.get_wikitext('Trading')\\n for match in re.finditer(r\\\"===='''([^']+)'''====\\\\n({\\\\|[^\\\\n]*\\\\n(?:[^\\\\n]*\\\\n)+?\\\\|})\\\", wikitext):\\n place = match.group(1)\\n trade_list = {'into':{}, 'from':{}}\\n for row in match.group(2).strip().split('|-'):\\n if len(row) < 5:\\n continue\\n trade = re.search(r'\\\\|([0-9,.]+)\\\\|\\\\| \\\\[\\\\[(?:[^|\\\\]]+\\\\|)?([^\\\\]]+)\\\\]\\\\]\\\\|\\\\|→\\\\n\\\\|align\\\\=right\\\\|([0-9,.]+)\\\\|\\\\| \\\\[\\\\[(?:[^|\\\\]]+\\\\|)?([^\\\\]]+)\\\\]\\\\]', row)\\n if not trade:\\n trade = re.search(r'\\\\| ?([0-9,.]+) \\\\[\\\\[(?:[^|\\\\]]+\\\\|)?([^\\\\]]+)\\\\]\\\\]\\\\|\\\\| ?([0-9,.]+) \\\\[\\\\[(?:[^|\\\\]]+\\\\|)?([^\\\\]]+)\\\\]\\\\]', row)\\n if not trade:\\n logging.warn(f'No trade row in `{row}`')\\n continue\\n from_amt = int(trade.group(1).replace(',', ''))\\n from_itm = trade.group(2).lower()\\n to_amt = int(trade.group(3).replace(',', ''))\\n to_itm = trade.group(4).lower()\\n if from_itm not in trade_list['from']:\\n trade_list['from'][from_itm] = []\\n if to_itm not in trade_list['into']:\\n trade_list['into'][to_itm] = []\\n trade_list['from'][from_itm].append((to_itm, from_amt, to_amt))\\n trade_list['into'][to_itm].append((from_itm, to_amt, from_amt))\\n if '(' in place:\\n # Gorenichi (Kiev), Magnitogorsk (trader), Magnitogorsk (fitter)\\n if place[0] == 'G':\\n place = 'Kiev'\\n self.trading_table[place.lower()] = trade_list\\n return self.trading_table\",\n \"def last_day_table(self):\\n idx = set(name.split('-')[0].split('.')[0] for name, etf in self.etfs.items() if not etf.sold()).union(['Totale'])\\n table = pd.DataFrame({'Value':0, 'P/L':0, 'P/L%':0},index=idx)\\n if len(self.etfs) > 0:\\n for name, etf in self.etfs.items():\\n if not etf.sold():\\n table.loc[name.split('-')[0].split('.')[0], 'P/L'] += etf.get_gain()\\n table.loc[name.split('-')[0].split('.')[0], 'Value'] += etf.get_value(self.data.index[-1]-timedelta(1))\\n table['P/L%'] = round(table['P/L'] / table['Value'] * 100, 2)\\n table.loc['Totale', 'P/L'] = table['P/L'].sum()\\n table.loc['Totale', 'P/L%'] = self.gains(pct=True)\\n return table.sort_values('Value', 0, ascending=False)\",\n \"def account_df(self, typ='trades', improve=False):\\n cols = ['date_open', 'date_close', 'symbol', 'style', 'volume', 'price_open', 'price_stop', 'price_limit', 'price_close', 'comment', 'magic', 'order_id_master', 'order_id_stop', 'order_id_limit', 'direction', 'price_diff', 'price_diff', 'price_diff_d', 'price_diff_rel', 'price_diff_rel_d', 'MAE', 'MFE', 'MAE_rel', 'MFE_rel', 'price_trailing_diff', 'profit']\\n d = self._d_orders[typ]\\n if len(d)>0:\\n df = pd.DataFrame(d.values(), index=d.keys())\\n df = df.rename(columns={0: 'bo'})\\n df['date_created'] = df['bo'].map(lambda o: o.date_created)\\n df['date_open'] = df['bo'].map(lambda o: o.date_open)\\n df['date_close'] = df['bo'].map(lambda o: o.date_close)\\n df['date_closed'] = df['bo'].map(lambda o: o.date_closed)\\n df['symbol'] = df['bo'].map(lambda o: o.symbol)\\n #df['style'] = df['bo'].map(lambda o: o.style)\\n df['volume'] = df['bo'].map(lambda o: o.volume)\\n df['price_open'] = df['bo'].map(lambda o: o.price_open)\\n df['price_stop'] = df['bo'].map(lambda o: o.price_stop)\\n df['price_limit'] = df['bo'].map(lambda o: o.price_limit)\\n df['price_close'] = df['bo'].map(lambda o: o.price_close)\\n df['comment'] = df['bo'].map(lambda o: o.comment)\\n df['magic'] = df['bo'].map(lambda o: o.magic)\\n #df['order_id_master'] = df['bo'].map(lambda o: o.order_id_master)\\n #df['order_id_stop'] = df['bo'].map(lambda o: o.order_id_stop)\\n #df['order_id_limit'] = df['bo'].map(lambda o: o.order_id_limit)\\n\\n df['direction'] = df['bo'].map(lambda o: o.direction)\\n\\n df['price_diff'] = df['bo'].map(lambda o: o.price_diff)\\n df['price_diff_d'] = df['bo'].map(lambda o: o.price_diff_d)\\n df['price_diff_rel'] = df['bo'].map(lambda o: o.price_diff_rel)\\n df['price_diff_rel_d'] = df['bo'].map(lambda o: o.price_diff_rel_d)\\n \\n df['MAE'] = df['bo'].map(lambda o: o.MAE)\\n df['MFE'] = df['bo'].map(lambda o: o.MFE)\\n \\n #df['MAE_rel'] = df['MAE'] / df['price_open']\\n #df['MFE_rel'] = df['MFE'] / df['price_open']\\n df['MAE_rel'] = df['bo'].map(lambda o: o.MAE_rel)\\n df['MFE_rel'] = df['bo'].map(lambda o: o.MFE_rel)\\n \\n\\n #df['profit'] = df['volume'] * df['price_diff'].fillna(0)\\n df['profit'] = df['bo'].map(lambda o: o.profit)\\n #df['profit_rel'] = df['bo'].map(lambda o: o.profit_rel)\\n \\n if improve:\\n try:\\n df = improve_account_df_with_additional_data(df)\\n except Exception as e:\\n log.error(\\\"Can't improve account df with additional data\\\")\\n log.error(\\\"Reason: %s\\\" % str(e))\\n \\n #del df['bo'] \\n \\n return(df)\\n else:\\n return(pd.DataFrame(columns=cols))\",\n \"def _create_performance_contribution_tables(self, performance_df: QFDataFrame) -> List[DFTable]:\\n # Create a QFSeries which contains the initial amount of cash in the portfolio for each year / month\\n numeric_columns = [col for col in performance_df.columns if is_numeric_dtype(performance_df[col])]\\n portfolio_values = performance_df[numeric_columns].sum().shift(fill_value=self._initial_cash).cumsum()\\n performance_df[numeric_columns] = performance_df[numeric_columns] / portfolio_values[numeric_columns]\\n\\n # Add category column and aggregate data accordingly\\n ticker_name_to_category = {t.name: category for t, category in self._ticker_to_category.items()}\\n performance_df[\\\"Category\\\"] = performance_df[\\\"Asset\\\"].apply(lambda t: ticker_name_to_category[t])\\n all_categories = list(set(ticker_name_to_category.values()))\\n performance_df = performance_df.sort_values(by=[\\\"Category\\\", \\\"Asset\\\"])\\n performance_df = performance_df.groupby(\\\"Category\\\").apply(\\n lambda d: pd.concat([PricesDataFrame({**{\\\"Asset\\\": [d.name], \\\"Category\\\": [d.name]},\\n **{c: [d[c].sum()] for c in numeric_columns}}), d],\\n ignore_index=True)).drop(columns=[\\\"Category\\\"])\\n\\n # Add the Total Performance row (divide by 2 as the df contains already aggregated data for each group)\\n total_sum_row = performance_df[numeric_columns].sum() / 2\\n total_sum_row[\\\"Asset\\\"] = \\\"Total Performance\\\"\\n performance_df = performance_df.append(total_sum_row, ignore_index=True)\\n\\n # Format the rows using the percentage formatter\\n performance_df[numeric_columns] = performance_df[numeric_columns].applymap(lambda x: '{:.2%}'.format(x))\\n\\n # Divide the performance dataframe into a number of dataframes, so that each of them contains up to\\n # self._max_columns_per_page columns\\n split_dfs = np.array_split(performance_df.set_index(\\\"Asset\\\"),\\n np.ceil((performance_df.num_of_columns - 1) / self._max_columns_per_page), axis=1)\\n df_tables = [DFTable(df.reset_index(), css_classes=['table', 'shrink-font', 'right-align', 'wide-first-column'])\\n for df in split_dfs]\\n\\n # Get the indices of rows, which contain category info\\n category_indices = performance_df[performance_df[\\\"Asset\\\"].isin(all_categories)].index\\n\\n for df_table in df_tables:\\n # Add table formatting, highlight rows showing the total contribution of the given category\\n df_table.add_rows_styles(category_indices, {\\\"font-weight\\\": \\\"bold\\\", \\\"font-size\\\": \\\"0.95em\\\",\\n \\\"background-color\\\": \\\"#cbd0d2\\\"})\\n df_table.add_rows_styles([performance_df.index[-1]], {\\\"font-weight\\\": \\\"bold\\\", \\\"font-size\\\": \\\"0.95em\\\",\\n \\\"background-color\\\": \\\"#b9bcbd\\\"})\\n return df_tables\",\n \"def get_stock_data_frame(time, stock):\\n\\n print(\\\"Getting\\\", time, \\\"stock data for\\\", stock)\\n url = 'https://api.iextrading.com/1.0/stock/'+stock+'/chart/'+time\\n req = requests.get(url)\\n print(url)\\n\\n print(\\\"Parsing data.\\\")\\n rjson = req.text\\n\\n rdata = json.loads(rjson)\\n\\n dates = []\\n openprices = []\\n highprices = []\\n lowprices = []\\n closeprices = []\\n volumes = []\\n\\n for i in rdata:\\n date = i['date']\\n dates.append(date)\\n openprices.append(float(i['open']))\\n highprices.append(float(i['high']))\\n lowprices.append(float(i['low']))\\n closeprices.append(float(i['close']))\\n volumes.append(float(i['volume']))\\n\\n index = pd.DatetimeIndex(dates, dtype='datetime64[ns]')\\n _open = pd.Series(openprices, index=index)\\n high = pd.Series(highprices, index=index)\\n low = pd.Series(lowprices, index=index)\\n close = pd.Series(closeprices, index=index)\\n data_frame_data = {'Open' : _open, 'High' : high, 'Low' : low, 'Close' : close}\\n\\n return pd.DataFrame(data_frame_data)\",\n \"def glue_table(name: str, df: pd.DataFrame, build_path=\\\"_build\\\"):\\n\\n if not os.path.exists(build_path):\\n os.mkdir(build_path)\\n df.to_excel(os.path.join(build_path, f\\\"{name}.xlsx\\\"))\\n\\n glue(name, df)\",\n \"def _get_db_create_table(self, frame):\\r\\n\\r\\n columns = (u',\\\\n'.\\r\\n\\r\\n join([u' `%s` DECIMAL(20,5) DEFAULT NULL COMMENT \\\"%s\\\"' %\\r\\n\\r\\n (self._get_db_name(name), name) for name in\\r\\n\\r\\n frame.index.values]))\\r\\n\\r\\n table_name = self._get_db_table_name(frame)\\r\\n\\r\\n return (\\r\\n\\r\\n u'CREATE TABLE `%s` (\\\\n' % table_name +\\r\\n\\r\\n u' `ticker` VARCHAR(50) NOT NULL COMMENT \\\"Exchange:Ticker\\\",\\\\n' +\\r\\n\\r\\n u' `period` DATE NOT NULL COMMENT \\\"Period\\\",\\\\n' +\\r\\n\\r\\n u'%s,\\\\n' % columns +\\r\\n\\r\\n u' PRIMARY KEY USING BTREE (`ticker`, `period`),\\\\n' +\\r\\n\\r\\n u' KEY `ix_ticker` USING BTREE (`ticker`))\\\\n' +\\r\\n\\r\\n u'ENGINE=MyISAM DEFAULT CHARSET=utf8\\\\n' +\\r\\n\\r\\n u'COMMENT = \\\"%s\\\"' % frame.index.name)\",\n \"def get_52_week_high_low_for_stocks(stocks):\\n print(\\\"Fetching stock quotes.\\\")\\n # Build a full list of symbols\\n symbols = []\\n for key in stocks.keys():\\n symbols.append(key)\\n\\n num_of_batches = int(len(symbols)/BATCH_SIZE) + 1\\n\\n all_stocks_df = pandas.DataFrame()\\n\\n #all_stocks_df = pandas.DataFrame()\\n\\n # Get quotes for all the stocks in batches\\n for i in range(0, num_of_batches):\\n print(\\\"Fetching quotes in batch: \\\" + str(i+1) + \\\"/\\\" + str(num_of_batches))\\n start = i*BATCH_SIZE\\n end = start + BATCH_SIZE\\n batch_symbols = symbols[start: end]\\n batch_symbols_query = '+'.join(batch_symbols)\\n request_url = YAHOO_FINANCE_API + \\\"?\\\" + YAHOO_FINANCE_SYMBOL_PARAM + \\\"=\\\" + batch_symbols_query +\\\\\\n \\\"&\\\" + YAHOO_FINANCE_FORMAT_PARAM + \\\"=\\\" + YAHOO_FINANCE_SYMBOL_PARAM + YAHOO_FINANCE_52_ASK_PRICE +\\\\\\n YAHOO_FINANCE_BID_PRICE + YAHOO_FINANCE_52_CLOSE_PRICE + YAHOO_FINANCE_52_WEEK_LOW +\\\\\\n YAHOO_FINANCE_52_WEEK_HIGH + YAHOO_FINANCE_52_LOW_CHANGE +\\\\\\n YAHOO_FINANCE_52_HIGH_CHANGE + YAHOO_FINANCE_DIV_YIELD\\n r = requests.get(request_url)\\n\\n # Read the returned CSV as a pandas table\\n # Returned format is NAME,ASK,BID,52-wLow,52-wHigh\\n df = pandas.read_table(StringIO(r.text), header=None, sep=',')\\n all_stocks_df = all_stocks_df.append(df, ignore_index=True)\\n\\n # Delay to slow down things\\n time.sleep(1)\\n\\n\\n # Assign columns\\n print(\\\"Stock quotes have been fetched. Beginning analysis...\\\")\\n all_stocks_df.columns=['symbol', 'ask', 'bid', 'close', '52w-low', '52w-high', '52w-low-change', '52w-high-change', 'div-iteryield']\\n\\n # Add the percent change columns\\n all_stocks_df['52w-%-low-change'] = all_stocks_df['52w-low-change']/all_stocks_df['52w-low']*100\\n all_stocks_df['52w-%-high-change'] = all_stocks_df['52w-high-change'] / all_stocks_df['52w-high'] * 100\\n\\n # Add the names and sectors\\n all_stocks_df['name'] = \\\"\\\"\\n all_stocks_df['sector'] = \\\"\\\"\\n for index, row in all_stocks_df.iterrows():\\n all_stocks_df.loc[index, 'name'] = stocks[row['symbol']][0]\\n all_stocks_df.loc[index, 'sector'] = stocks[row['symbol']][1]\\n\\n\\n # Process the received quotes\\n sorted_values = all_stocks_df.sort_values('52w-%-low-change')\\n\\n # Done\\n print(\\\"Analysis completed.\\\")\\n return sorted_values\",\n \"def create_tables(*args):\\n import pandas\\n tables = {}\\n for locus in args[0].index:\\n tables[locus] = get_table(*args, locus)\\n return tables\",\n \"def table(self):\\n\\n param=self.x_param\\n\\n device=self.device\\n\\n base_params=device.get_params()\\n\\n data_tot=DataFrame()\\n\\n for i in range(len(param)):\\n\\n print_index=1\\n\\n for name in param.names:\\n\\n device._set_params(param(i))\\n\\n device.draw()\\n\\n df=device.export_all()\\n\\n if self.labels_bottom is not None:\\n\\n index=self.labels_bottom[i]\\n\\n else:\\n\\n index=str(i)\\n\\n print(\\\"Generating table, item {} of {}\\\\r\\\".format(print_index,len(param)),end=\\\"\\\")\\n\\n data_tot=data_tot.append(Series(df,name=index))\\n\\n device._set_params(base_params)\\n\\n return data_tot\",\n \"def generate_raport(table):\\n\\n item_table = store.get_table()\\n item_table = store.check_table(item_table)\\n raport = []\\n\\n id_position = 0\\n title_position = 1\\n price_position = 3\\n earnings = 0\\n\\n for item in item_table:\\n price = item[price_position]\\n sold_copies = get_sold_copies(table, item[id_position])\\n if price and sold_copies:\\n earnings = int(price) * int(sold_copies)\\n raport_row = [item[title_position], str(earnings)]\\n raport.append(raport_row)\\n\\n return raport\",\n \"def get_all_available_stock_table(db_path):\\n\\n try:\\n db_connection = sqlite3.connect(db_path)\\n except sqlite3.Error as e:\\n print(e)\\n return False\\n\\n cursor = db_connection.cursor()\\n\\n available_tables_1 = cursor.execute(\\\"SELECT name FROM sqlite_master WHERE type ='table'\\\").fetchall()\\n available_tables = [tab[0] for tab in available_tables_1 if tab[0] != 'Consumption_Index']\\n available_tables.sort()\\n db_connection.close()\\n\\n return available_tables\",\n \"def generate_table(self, rows):\\n ...\",\n \"def get_stock(symbol, start, end):\\n df = pdr.DataReader(symbol, 'yahoo', start, end)\\n df = df.sort_index(axis=0)\\n return df\",\n \"def renter_accounting_report_gen(sid, start, end):\\n results = renter_accounting(sid, start, end)\\n print(\\\"Name: \\\" + results[0])\\n sum_value = 0\\n row_title = [\\\"Date\\\", \\\"Boat\\\", \\\"Rent\\\", \\\"Payment\\\", \\\"Sum\\\"]\\n row_format = \\\"{:>15}\\\" * len(row_title)\\n print(row_format.format(*row_title))\\n for result in results[1]:\\n temp = list(result.keys()) + [value for key, value in list(result.values())[0].items()]\\n if temp[2]:\\n sum_value += temp[3]\\n temp[2] = \\\"\\\"\\n else:\\n sum_value -= temp[3]\\n temp[2] = temp[3]\\n temp[3] = \\\"\\\"\\n temp.append(sum_value)\\n print(row_format.format(*[str(x) for x in temp]))\",\n \"def stock_market(no_profiles: int) -> tuple:\\n all_companies = []\\n Stocks = namedtuple(\\\"Stocks\\\", 'name symbol open high close company_weight')\\n MkValue_ = random.uniform(1000, 50000, 100)\\n wts_ = random.uniform(0, 1, 100)\\n wts_ = wts_/sum(wts_)\\n\\n for _ in range(100):\\n name = fake.company()\\n open_ = round(MkValue_[_]*wts_[_],2)\\n close = round(open_ * random.uniform(0.7, 1.15), 2)\\n high = round(open_ * random.uniform(0.85, 1.15), 2)\\n if high < open_:\\n high = open_\\n if high < close:\\n high = close\\n\\n all_companies.append(\\n Stocks(name=name, symbol=symbol(name), open=open_, high=round(high, 2), close=round(close, 2), company_weight=round(wts_[_], 4)))\\n\\n stock_index = round(\\n sum(x.open * x.company_weight for x in all_companies), 4)\\n highest_for_day = round(\\n sum(x.high * x.company_weight for x in all_companies), 2)\\n lowest_close_for_day = round(\\n sum(x.close * x.company_weight for x in all_companies), 2)\\n\\n # print(f\\\"\\\\n------------------------------------Top 100 listed companies on Fake Stock Exchange------------------------------------\\\")\\n # [print(x) for x in sorted(all_companies, key=lambda x:x.symbol)]\\n # print(f\\\"\\\\n--------------Main details on {date.today()}--------------\\\")\\n # print(f\\\"\\\\nStart of the day: {stock_index}\\\")\\n # print(f\\\"Highest for the day: {highest_for_day}\\\")\\n # print(f\\\"Lowest close for the day: {lowest_close_for_day}\\\")\\n return sorted(all_companies, key=lambda x: x.symbol), stock_index, highest_for_day, lowest_close_for_day\",\n \"def index():\\n rows = db.execute(\\\"SELECT Symbol, SUM(Shares) as totalShares FROM cash WHERE id=:id GROUP BY Symbol HAVING totalShares > 0\\\", id=session[\\\"user_id\\\"])\\n transactions=[]\\n grand_total = 0\\n for row in rows:\\n stock = lookup(row[\\\"Symbol\\\"])\\n transactions.append({\\n \\\"Symbol\\\": stock[\\\"symbol\\\"],\\n \\\"Name\\\": stock[\\\"name\\\"],\\n \\\"Shares\\\": row[\\\"totalShares\\\"],\\n \\\"Price\\\": usd(stock[\\\"price\\\"]),\\n \\\"Total\\\": usd(stock[\\\"price\\\"] * row[\\\"totalShares\\\"])\\n })\\n grand_total += stock[\\\"price\\\"] * row[\\\"totalShares\\\"]\\n rows = db.execute(\\\"SELECT cash FROM users WHERE id=:id\\\", id=session[\\\"user_id\\\"])\\n cash = rows[0][\\\"cash\\\"]\\n return render_template(\\\"table.html\\\", transactions=transactions, cash=usd(cash), grand_total=usd(grand_total))\",\n \"def get_com_data_fr_all_stocks(self):\\n full_list = self.replace_special_characters_in_list(self.full_stocklist_to_retrieve)\\n chunk_of_list = self.break_list_to_sub_list(self.full_stocklist_to_retrieve)\\n \\n self.temp_full_data_df = None\\n for n in chunk_of_list:\\n # print the progress\\n sys.stdout.write('.')\\n\\n # set the small chunk of list\\n self.set_target_stocks_list(n)\\n self.get_com_data()\\n\\n # convert to dataframe\\n self.com_data_allstock_df = pandas.DataFrame(self.com_data_allstock_list)\\n self.com_data_allstock_df.rename(columns ={'symbol':'SYMBOL'}, inplace=True)\\n \\n print 'Done\\\\n'\",\n \"def __generate_table(package_response, primary_table_id):\\n if isinstance(package_response, Package):\\n primary_table = package_response.tables[primary_table_id]\\n header_id = primary_table.definition.header_table_id\\n header_table = package_response.tables[header_id]\\n dimension_columns = list(filter(lambda column_obj: column_obj.is_dimension, primary_table.definition.columns))\\n dimension_columns_count = len(dimension_columns)\\n row_count = len(primary_table.data.rows)\\n header_row_count = len(header_table.data.rows)\\n\\n headers = list(list())\\n # Constructs the column headers by considering dimension columns and header rows\\n for series_definition_column in header_table.definition.columns:\\n header_row = list()\\n for i in range(0, dimension_columns_count, 1):\\n if dimension_columns[i].description is \\\"\\\":\\n header_row.append(\\\" \\\")\\n else:\\n header_row.append(dimension_columns[i].description)\\n\\n for i in range(0, header_row_count, 1):\\n header_row.append(str(_SeriesDataHelper.get_value_helper(header_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\\n headers.append(header_row)\\n\\n data = list(list())\\n # Constructs the column data\\n for i in range(0, row_count, 1):\\n data_row = list()\\n for series_definition_column in primary_table.definition.columns:\\n data_row.append(str(_SeriesDataHelper.get_value_helper(primary_table.data.columns[series_definition_column.id], series_definition_column.type, i, series_definition_column.format.null_format)))\\n data.append(data_row)\\n\\n if len(header_table.definition.columns) > 1:\\n data_frame = pd.DataFrame(data=data)\\n data_frame.columns = pd.MultiIndex.from_arrays(headers)\\n else:\\n data_frame = pd.DataFrame(data=data, columns = headers[0])\\n\\n return data_frame\\n\\n else:\\n ValueError(\\\"Response data passed should be of package type.\\\")\",\n \"def __populate_historical_trade_data(self):\\n\\n trade_data = self.__transactions.pivot_table(\\n index=\\\"Date\\\",\\n columns=[\\\"Ticker\\\"],\\n values=[\\n \\\"Quantity\\\",\\n \\\"Investment\\\",\\n ],\\n aggfunc={\\\"Quantity\\\": np.sum, \\\"Investment\\\": np.sum},\\n )\\n\\n # Make historical prices columns a multi-index. This helps the merging.\\n self.portfolio_historical_prices.columns = pd.MultiIndex.from_product(\\n [[\\\"Close\\\"], self.portfolio_historical_prices.columns]\\n )\\n\\n trade_data = pd.merge(\\n trade_data,\\n self.portfolio_historical_prices,\\n how=\\\"outer\\\",\\n left_index=True,\\n right_index=True,\\n )\\n\\n trade_data[\\\"Close\\\"] = trade_data[\\\"Close\\\"].fillna(method=\\\"ffill\\\")\\n trade_data.fillna(0, inplace=True)\\n\\n trade_data[\\\"Quantity\\\"] = trade_data[\\\"Quantity\\\"].cumsum()\\n trade_data[\\\"Investment\\\"] = trade_data[\\\"Investment\\\"].cumsum()\\n trade_data[\\\"Investment\\\", \\\"Total\\\"] = trade_data[\\\"Investment\\\"].sum(axis=1)\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Investment delta\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = (trade_data[\\\"Investment\\\"].diff(periods=1).fillna(trade_data[\\\"Investment\\\"]))\\n\\n # End Value = Quantity * Close\\n trade_data[pd.MultiIndex.from_product([[\\\"End Value\\\"], self.tickers_list])] = (\\n trade_data[\\\"Quantity\\\"][self.tickers_list]\\n * trade_data[\\\"Close\\\"][self.tickers_list]\\n )\\n\\n trade_data.loc[:, (\\\"End Value\\\", \\\"Total\\\")] = trade_data[\\\"End Value\\\"][\\n self.tickers_list\\n ].sum(axis=1)\\n\\n # Initial Value = Previous End Value + Investment changes\\n trade_data[\\n pd.MultiIndex.from_product(\\n [[\\\"Initial Value\\\"], self.tickers_list + [\\\"Total\\\"]]\\n )\\n ] = 0\\n\\n trade_data[\\\"Initial Value\\\"] = trade_data[\\\"End Value\\\"].shift(1) + trade_data[\\n \\\"Investment\\\"\\n ].diff(periods=1)\\n\\n # Set first day Initial Value as the Investment (NaNs break first period)\\n for t in self.tickers_list + [\\\"Total\\\"]:\\n trade_data.at[trade_data.index[0], (\\\"Initial Value\\\", t)] = trade_data.iloc[\\n 0\\n ][\\\"Investment\\\"][t]\\n\\n trade_data = trade_data.reindex(\\n columns=[\\n \\\"Quantity\\\",\\n \\\"Investment\\\",\\n \\\"Investment delta\\\",\\n \\\"Close\\\",\\n \\\"Initial Value\\\",\\n \\\"End Value\\\",\\n ],\\n level=0,\\n )\\n self.historical_trade_data = trade_data\",\n \"def fetch_all_stocks(config):\\n start_date_dt = datetime.datetime.strptime(config['start_date'], '%Y%m%d').date()\\n end_date_dt = datetime.datetime.strptime(config['end_date'], '%Y%m%d').date()\\n\\n with open(DATA_DIR / 'nasdaq_screener_1619356287441.csv', 'r') as fp:\\n reader = csv.reader(fp)\\n next(reader) # skip header\\n symbols = [row[0] for row in reader]\\n\\n dir_path = DATA_DIR / 'nasdaq_historical' / '{}_{}'.format(config['start_date'], config['end_date'])\\n if not os.path.exists(dir_path):\\n os.mkdir(dir_path)\\n\\n for symbol in symbols:\\n filename = '{}.csv'.format(symbol)\\n full_path = dir_path / filename\\n if not os.path.exists(full_path):\\n try:\\n df = web.get_data_yahoo(symbol, config['start_date'], config['end_date'])\\n except:\\n # print(traceback.format_exc())\\n print('Could not load ' + symbol)\\n else:\\n if df.index[0].date() == start_date_dt and df.index[-1].date() == end_date_dt:\\n print('Loaded: ' + symbol)\\n df.to_csv(full_path)\",\n \"def construct_result_table(self, biomodels):\\n select = 'SELECT DISTINCT '\\n cached_alias = 'c'\\n join_exps = 'FROM %s_%s AS %s' % (self.app_label, \\n settings.DISBI['JOINED_TABLENAME'], \\n cached_alias)\\n left_join_template = '''\\n LEFT JOIN (\\n %s\\n ) AS %s\\n ON (%s = %s)\\n '''\\n subtables_not_null_column = []\\n select_bios = []\\n relations = Relations(self.app_label, model_superclass=(BiologicalModel, MetaModel))\\n for biomodel in biomodels:\\n # Requested experiments related to biomodel.\\n req_exps_for_bio = [] \\n for exp in self.req_exps:\\n if exp.biomodel == biomodel:\\n req_exps_for_bio.append(exp)\\n \\n select_bios.extend(self.get_show_columns(biomodel))\\n \\n\\n # Get Meta models for Bio model and the show columns to the \\n # SELECT clause.\\n metamodels_of_biomodel = relations.get_related_metamodels(biomodel)\\n #print(metamodels_of_biomodel)\\n for metamodel in metamodels_of_biomodel:\\n select_bios.extend(self.get_show_columns(metamodel))\\n\\n \\n for exp in req_exps_for_bio:\\n select_bios.extend(self.get_display_names(exp))\\n exp_alias = 'exp%s' % exp.pk\\n join_exps += left_join_template % (\\n self.construct_exptable(exp),\\n exp_alias,\\n '%s.%s_id' % (cached_alias, biomodel.__name__.lower()),\\n '%s.%s' % (exp_alias, exp.biofield.column)\\n )\\n subtables_not_null_column.append((self.get_notnull_column(exp), \\n str(exp.id)))\\n exclude_empty = 'WHERE ' + ' OR '.join(['%s_%s IS NOT NULL' % col \\n for col in subtables_not_null_column])\\n \\n sql = '\\\\n'.join((select + ', '.join(select_bios), join_exps, exclude_empty))\\n return re.sub(r'^\\\\s+', '', sql, flags=re.MULTILINE)\",\n \"def generate_table(self, outtablename,\\n cols=['A', 'B', 'AB'],\\n generateTable=True):\\n if generateTable:\\n new_indices = ['time (s)', 'mean counts']\\n for idx in self.data[cols].describe().index[2:]:\\n new_indices.append(idx)\\n outTable = self.data[cols].describe()\\\\\\n .set_index(pd.Index(new_indices))\\n outTable.to_latex(\\n self.tables_dir + outtablename + \\\".tex\\\", float_format=\\\"%d\\\")\\n print(\\\"Outtable: \\\", outTable)\",\n \"def index():\\n user_name = db.execute(\\\"SELECT username FROM users WHERE id = ?\\\", session[\\\"user_id\\\"])\\n check = db.execute(\\\"SELECT name FROM main.sqlite_master WHERE type='table'\\\")\\n #print(check)\\n #print('stocks' not in check[0]['name'])\\n if not any(c['name'] == 'stocks' for c in check):\\n return render_template(\\\"index.html\\\", user_name=user_name)\\n\\n stocks = db.execute(\\\"SELECT * FROM stocks WHERE user_id = ?\\\", session[\\\"user_id\\\"])\\n cash = db.execute(\\\"SELECT cash FROM users WHERE id = ?\\\", session[\\\"user_id\\\"])\\n\\n user_name = db.execute(\\\"SELECT username, cash FROM users WHERE id = ?\\\", session[\\\"user_id\\\"])\\n total_value = user_name[0][\\\"cash\\\"]\\n sum_stocks = db.execute(\\\"SELECT symbol, ammount FROM stocks WHERE user_id = ?\\\", session[\\\"user_id\\\"])\\n\\n for stock in sum_stocks:\\n total_value += stock[\\\"ammount\\\"] * lookup(stock[\\\"symbol\\\"])['price']\\n \\n #print(stocks)\\n return render_template(\\\"index.html\\\", stocks=stocks, user_name=user_name, cash=usd(cash[0]['cash']), total_value=usd(total_value))\",\n \"def download_all_stocks():\\n stocks = get_stocklist()\\n dfs = {}\\n for i, r in stocks.iterrows():\\n start = time.time()\\n s = r['Ticker']\\n stockfile = '../stockdata/' + s + '.csv.gz'\\n print('downloading', s)\\n stock = quandl.get('EOD/' + s)\\n stock.to_csv(stockfile, compression='gzip')\\n dfs[s] = stock\\n print('took', time.time() - start, 's')\\n\\n return dfs\",\n \"def to_stock_dataframe_range(self, start_date=None, end_date=None):\\n if end_date is None:\\n end_date = self.dates[-2]\\n if type(end_date) is pd.tslib.Timestamp:\\n end_date = end_date.strftime(\\\"%Y-%m-%d\\\")\\n if type(end_date) is not datetime.datetime and type(end_date) is not pd.tslib.Timestamp:\\n end_date = datetime.datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n try:\\n end_date = self.dates[list(self.dates).index(end_date) + 1]\\n except:\\n end_date = \\\"Last\\\"\\n if start_date is None:\\n start_date = self.dates[0]\\n\\n if type(start_date) is not datetime.datetime and type(start_date) is not pd.tslib.Timestamp:\\n start_date = datetime.datetime.strptime(start_date, \\\"%Y-%m-%d\\\")\\n\\n class_data = [i for i in dir(self) if not callable(getattr(self, i)) and not i.startswith(\\\"__\\\") and\\n type(getattr(self, i)) is pd.DataFrame]\\n df = pd.DataFrame()\\n for i in class_data:\\n df = join_features(df, getattr(self, i), fill_method=FillMethod.FUTURE_KROGH)\\n if end_date is \\\"Last\\\":\\n print(df.ix[start_date:, :])\\n return df.ix[start_date:, :]\\n return df.ix[start_date:end_date, :]\",\n \"def __gen_datatable__(self):\\n # | - __generate_data_table\\n rows_list = []\\n for Job_i in self.Job_list:\\n # | - FOR LOOP BODY\\n entry_param_dict = {}\\n for prop, value in Job_i.job_params.items():\\n entry_param_dict[prop] = value\\n\\n entry_param_dict[\\\"Job\\\"] = Job_i\\n entry_param_dict[\\\"path\\\"] = Job_i.full_path\\n entry_param_dict[\\\"max_revision\\\"] = Job_i.max_revision\\n entry_param_dict[\\\"revision_number\\\"] = Job_i.revision_number\\n\\n rows_list.append(entry_param_dict)\\n # __|\\n\\n data_frame = pd.DataFrame(rows_list)\\n\\n return(data_frame)\\n # __|\",\n \"def get_market_inf():\\n r = requests.get(vs.DSE_URL+vs.DSE_MARKET_INF_URL)\\n soup = BeautifulSoup(r.text, 'html.parser')\\n quotes = [] # a list to store quotes\\n\\n table = soup.find('table', attrs={'class': 'table table-bordered background-white text-center', '_id': 'data-table'})\\n\\n for row in table.find_all('tr')[1:]:\\n cols = row.find_all('td')\\n quotes.append({'Date': cols[0].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Trade': cols[1].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Volume': cols[2].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Value (mn)': cols[3].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'Total Market Cap. (mn)': cols[4].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DSEX Index': cols[5].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DSES Index': cols[6].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DS30 Index': cols[7].text.strip().replace(\\\",\\\", \\\"\\\"),\\n 'DGEN Index': cols[8].text.strip().replace(\\\",\\\", \\\"\\\")\\n })\\n df = pd.DataFrame(quotes)\\n return df\",\n \"def MongoStockTable(inputfilename):\\n input=open(inputfilename)\\n data_string=json.load(input)\\n jsonOutput=\\\"{ \\\\\\\"portfolio\\\\\\\":[\\\"\\n for portfolio in data_string[\\\"portfolio\\\"]:\\n if portfolio[\\\"display\\\"] == \\\"yes\\\":\\n jsonOutput+=\\\"{\\\\\\\"portfolioname\\\\\\\":\\\\\\\"\\\" + portfolio[\\\"portfolioName\\\"]+\\\"\\\\\\\", \\\\\\\"portfolioStocks\\\\\\\":[\\\"\\n cumulative=Accumulator.Accumulator()\\n for data in portfolio[\\\"portfolioStocks\\\"]:\\n stock=Stock(data)\\n cumulative.Add(stock.totalpurchaseprice, stock.commission_to_buy, stock.dollarGain,stock.dailyChange_func() ,stock.currentWorth_func() )\\n jsonOutput+=stock.JSON()+\\\",\\\" \\n jsonOutput=jsonOutput.rstrip(',') # remove that trailing extraneous , [:-1]\\n jsonOutput+=\\\"],\\\"\\n jsonOutput+=\\\"\\\\n\\\" \\n jsonOutput+=\\\"\\\\\\\"cumulative Result\\\\\\\":\\\"+cumulative.JSONify()+\\\"},\\\" \\n jsonOutput=jsonOutput.rstrip(',') \\n jsonOutput+=\\\"] }\\\"\\n MongoSave(ast.literal_eval(jsonOutput))\\n input.close()\",\n \"def get_stock_data(symbol):\\n # Set current dates\\n start = date(date.today().year, 1, 1) # first of current year\\n end = date.today() # today\\n\\n # Get yahoo Yahoo data\\n data = pdr.get_data_yahoo(symbol, start=start, end=end)\\n\\n # Rename columns\\n data.columns = [\\\"Highest price (USD)\\\",\\n \\\"Lowest price (USD)\\\",\\n \\\"Opening price (USD)\\\",\\n \\\"Closing price (USD)\\\",\\n \\\"Volume\\\",\\n \\\"Adjusted closing price (USD)\\\"]\\n\\n return data\",\n \"def internetData(tickerSymbol):\\n #this qeureys the SQL database for all stock data\\n temp = Stock.query.filter_by(ticker=tickerSymbol).all()\\n #init the lists to store data for output\\n prices = []#closing price\\n volumes = []\\n \\n for i in temp:\\n prices.append(i.close)\\n volumes.append(i.volume)\\n print tickerSymbol\\n print \\\"prices length is \\\" + str(len(prices))\\n print \\\"volumes length is \\\" + str(len(volumes))\\n outputDict = {'Prices':prices,'Volumes':volumes}\\n return outputDict\",\n \"def plot_market_index(\\n date_range: list, index_col_name: str = INDEX_COLUMN_NAME, table_name: str = TABLE\\n) -> pd.DataFrame:\\n assert len(date_range) == 2\\n date_range = [date.strftime(\\\"%Y-%m-%d\\\") for date in date_range]\\n partitions = get_partitions(table_name)\\n partition = st.sidebar.selectbox(\\\"Choose Index\\\", partitions.values[:, 0])\\n df_mkt_idx = query_database(table_name, date_range=date_range, key_word=partition)\\n\\n column_name_num = list(df_mkt_idx.select_dtypes(include=[\\\"number\\\"]).columns)\\n\\n columns_selected = st.sidebar.multiselect(\\n \\\"Choose Columns\\\", column_name_num, column_name_num[0]\\n )\\n\\n df_vis = df_mkt_idx[columns_selected]\\n brush = alt.selection(type=\\\"interval\\\", encodings=[\\\"x\\\"])\\n base_chart_top = base_chart_down = (\\n alt.Chart(df_vis.reset_index())\\n .transform_fold(columns_selected)\\n .mark_line()\\n .encode(\\n x=f\\\"{index_col_name}:T\\\",\\n y=alt.X(f\\\"value:Q\\\", scale=alt.Scale(zero=False)),\\n color=\\\"key:N\\\",\\n tooltip=[f\\\"{index_col_name}:T\\\"],\\n )\\n )\\n upper = base_chart_top.properties(\\n width=600, height=400, title=f\\\"{partition}\\\"\\n ).encode(alt.X(f\\\"{index_col_name}:T\\\", scale=alt.Scale(domain=brush)))\\n lower = base_chart_down.properties(width=600, height=90).add_selection(brush)\\n st.altair_chart(upper & lower, use_container_width=True)\\n\\n if st.checkbox(\\\"Show DataFrame\\\"):\\n st.write(df_mkt_idx)\\n st.markdown(get_table_download_link(df_mkt_idx), unsafe_allow_html=True)\\n\\n return df_mkt_idx\",\n \"def get_or_create_base_table(self, fetch_as='ordereddict'):\\n # Make a sorted list of all ids.\\n exp_id_str = get_id_str(self.req_exps)\\n table_name = '%s_%s_%s' % (self.app_label, \\n settings.DISBI['DATATABLE_PREFIX'], \\n exp_id_str)\\n if not db_table_exists(table_name):\\n self.create_base_table(table_name)\\n column_names = list(get_columnnames(table_name))\\n # Escape all column names.\\n column_names = ['%s' % column_name for column_name in column_names]\\n # Format all columns with scientific notation that end with underscore and a number.\\n # These columns contain numerical data and should be presented in \\n # scientific notation.\\n datacol_pattern = re.compile(r'_\\\\d+$')\\n for i, column_name in enumerate(column_names):\\n if datacol_pattern.search(column_name) is not None:\\n column_names[i] = '{} as {}'.format(self.wrap_in_func('to_char', \\n column_name, \\n self.DB_PRECISION),\\n column_name)\\n sql = 'SELECT %s FROM %s' % (', '.join(column_names), table_name)\\n #return from_db(sql)\\n return from_db(sql, fetch_as=fetch_as)\",\n \"def generate_daily_matrix(full_df, feat_days):\\n pred_ticker = full_df.ticker.unique()[0]\\n feature_tickers = [i for i in full_df.ticker.unique() if i != pred_ticker]\\n dfml = full_df[full_df.ticker == pred_ticker].drop('ticker', axis=1)\\n dfml.rename({'percent_change_pred': f'{pred_ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\\n for ticker in feature_tickers:\\n help_df = full_df[full_df.ticker == ticker][['past_date', 'current_date', 'prediction_date', 'percent_change_pred']]\\n help_df.rename({'percent_change_pred': f'{ticker}_percent_change_{feat_days}'}, axis=1, inplace=True)\\n dfml = pd.merge(dfml, help_df,\\n left_on=['past_date', 'current_date', 'prediction_date'],\\n right_on=['past_date', 'current_date', 'prediction_date'],\\n how='left')\\n return dfml.drop('percent_change_feat', axis=1)\",\n \"def __get_latest_data(table_name='derivatives_economicindicatorstandard'):\\n # create query and get data\\n query = 'SELECT * FROM ' + table_name\\n df = AccessDB().run_read_query(query)\\n\\n if table_name == 'derivatives_economicindicatorstandard':\\n df = pd.DataFrame(df.groupby(['dbcode', 'indicator', 'country', 'freq', 'flow'])['date'].max())\\n else:\\n df = pd.DataFrame(df.groupby(['dbcode', 'indicator', 'country', 'freq', 'counter_party'])['date'].max())\\n df.reset_index(inplace=True)\\n return df\",\n \"def make_summary_tables( res ):\\n\\n # transform second table to csv and read this as a dataFrame\\n result_fit_df = pd.read_csv(StringIO( res.tables[1].as_csv() ), sep=\\\",\\\",index_col=0)\\n result_fit_df.columns = [i.strip() for i in result_fit_df.columns]\\n result_fit_df.index = [i.strip() for i in result_fit_df.index]\\n\\n # first table is trickier because the data is spread on to columns, and there is title line\\n L = res.tables[0].as_html().split('\\\\n')\\n L.pop(1) # get rid of the title\\n tmp = pd.read_html('\\\\n'.join(L) , header=None)[0] # read as a dataframe, but with 4 columns \\n\\n names = list(tmp[0]) + list(tmp[2])[:-2] # columns 0 and 2 are metric names\\n values = list(tmp[1]) + list(tmp[3])[:-2] # columns 1 and 3 are the corresponding values\\n # NB : I exclude the last 2 elements which are empty \\n \\n result_general_df = pd.DataFrame( {'Name': names , 'Value' : values}, index = names , columns=['Value'] )\\n \\n return result_general_df , result_fit_df\",\n \"def get_exp_columns(self, wanted_exps):\\n # Get the dict.\\n exp_id_str = get_id_str(self.req_exps)\\n table_name = '%s_datatable_%s' % (self.app_label, exp_id_str)\\n # Make experiment unique.\\n if not db_table_exists(table_name):\\n self.create_base_table(table_name)\\n column_names = list(get_columnnames(table_name))\\n divisor_col = None\\n dividend_col = None\\n #exp_cols = []\\n #potential_columns = []\\n # Iterate over all column names, and make a list of those that hold data.\\n datacol_pattern = re.compile(r'_\\\\d+$')\\n dividend_pattern = r'_{}$'.format(str(wanted_exps['dividend'].id))\\n divisor_pattern = r'_{}$'.format(str(wanted_exps['divisor'].id))\\n for column_name in column_names:\\n if datacol_pattern.search(column_name) is not None:\\n if divisor_col is None:\\n if re.search(divisor_pattern, column_name):\\n divisor_col = column_name\\n if dividend_col is None:\\n if re.search(dividend_pattern, column_name):\\n dividend_col = column_name\\n \\n sql = \\\"SELECT %s, %s FROM %s;\\\" % (dividend_col, divisor_col, table_name)\\n return from_db(sql, fetch_as='tuple')\",\n \"def create_order(df_signal, df_stock, df_all,\\n side=('follow', 'reverse', 'buy', 'sell'),\\n cycle=0, pct0=0, pct1=0, limit=0, stop=0):\\n df_signal0 = df_signal.copy()\\n closes = df_stock[['date', 'close']].set_index('date')['close']\\n\\n if side == 'buy':\\n df_signal0['signal0'] = 'BUY'\\n df_signal0['signal1'] = 'SELL'\\n elif side == 'sell':\\n df_signal0['signal0'] = 'SELL'\\n df_signal0['signal1'] = 'BUY'\\n elif side == 'reverse':\\n df_signal0['signal0'] = df_signal0['signal0'].apply(reverse)\\n df_signal0['signal1'] = df_signal0['signal1'].apply(reverse)\\n\\n signals = []\\n for index, data in df_signal0.iterrows():\\n try:\\n target0 = data['close0'] * (1 + (pct0 / 100.0))\\n target1 = data['close0'] * (1 + (pct1 / 100.0))\\n\\n option0a, option0b, option1a, option1b = get_cycle_strike2(\\n df_all, data['date0'], data['date1'],\\n 'CALL', target0, cycle, 0,\\n 'PUT', target1, cycle, -1\\n )\\n\\n df_code0 = df_all.query('option_code == %r & date >= %r & date <= %r' % (\\n option0a['option_code'], data['date0'], data['date1']\\n ))[['date', 'option_code', 'dte', 'sell', 'buy', 'strike']]\\n df_code1 = df_all.query('option_code == %r & date >= %r & date <= %r' % (\\n option1a['option_code'], data['date0'], data['date1']\\n ))[['date', 'option_code', 'dte', 'sell', 'buy', 'strike']]\\n df_join = pd.merge(df_code0, df_code1, on='date', suffixes=('0', '1'))\\n\\n max_profit = data['close0'] * (limit / 100.0)\\n max_loss = data['close0'] * (stop / 100.0)\\n if data['signal0'] == 'BUY':\\n df_join['enter'] = df_join['buy0'] - df_join['sell1']\\n df_join['exit'] = df_join['sell0'] - df_join['buy1']\\n enter_price = df_join['enter'].iloc[0]\\n else:\\n df_join['enter'] = -df_join['sell0'] + df_join['buy1']\\n df_join['exit'] = -df_join['buy0'] + df_join['sell1']\\n enter_price = df_join['enter'].iloc[0]\\n\\n limit_price = enter_price + max_profit\\n stop_price = enter_price - max_loss\\n # print enter_price, max_profit, limit_price\\n df_join['limit'] = df_join['exit'] >= limit_price\\n df_join['stop'] = df_join['exit'] <= stop_price\\n\\n # ts(df_join)\\n df_oco = df_join[(df_join['limit']) | (df_join['stop'])]\\n\\n data['stock0'] = data['close0']\\n data['stock1'] = data['close1']\\n data['code0'] = option0a['option_code']\\n data['code1'] = option1a['option_code']\\n data['strike0'] = option0a['strike']\\n data['strike1'] = option1a['strike']\\n\\n # buy sell are same\\n if len(df_oco):\\n exit_date = pd.to_datetime(df_oco['date'].iloc[0])\\n data['date1'] = exit_date\\n data['stock0'] = data['close0']\\n data['stock1'] = closes[exit_date]\\n\\n if data['signal0'] == 'BUY':\\n data['price0a'] = option0a['buy']\\n data['price1a'] = -option1a['sell']\\n data['price0b'] = -df_oco['sell0'].iloc[0]\\n data['price1b'] = df_oco['buy1'].iloc[0]\\n else: # SELL\\n data['price0a'] = -option0a['sell']\\n data['price1a'] = option1a['buy']\\n data['price0b'] = df_oco['buy0'].iloc[0]\\n data['price1b'] = -df_oco['sell1'].iloc[0]\\n else:\\n data['stock0'] = data['close0']\\n data['stock1'] = data['close1']\\n if data['signal0'] == 'BUY':\\n data['price0a'] = option0a['buy']\\n data['price1a'] = -option1a['sell']\\n data['price0b'] = -option0b['sell']\\n data['price1b'] = option1b['buy']\\n else: # SELL\\n data['price0a'] = -option0a['sell']\\n data['price1a'] = option1a['buy']\\n data['price0b'] = option0b['buy']\\n data['price1b'] = -option1b['sell']\\n\\n signals.append(data)\\n except (KeyError, ValueError, IndexError):\\n logger.info('Not found, date: %s cycle: %s pct0: %s pct1: %s' % (\\n data['date0'].strftime('%Y-%m-%d'), cycle, pct0, pct1\\n ))\\n\\n df = pd.DataFrame(signals)\\n df['close0'] = df['price0a'] + df['price1a']\\n df['close1'] = df['price0b'] + df['price1b']\\n df = df[df['close0'] != 0]\\n\\n df['bp_effect'] = df['stock0'] * 100\\n df['net_chg'] = -df['close1'] - df['close0']\\n df['pct_chg'] = df['net_chg'] / df['stock0']\\n df['holding'] = df['date1'] - df['date0']\\n\\n df['sqm0'] = 0\\n df['sqm1'] = 0\\n df['oqm0'] = df.apply(lambda x: -2 if x['signal0'] == 'SELL' else 2, axis=1)\\n df['oqm1'] = -df['oqm0']\\n\\n df = df.round({\\n 'pct_chg': 4,\\n 'net_chg': 2\\n })\\n\\n return df\",\n \"def get_data(list_data_tuples):\\n \\n \\n benchmark_symbol=list_data_tuples[0][0]; # First element is the benchmark symbol\\n \\n #print benchmark_symbol\\n \\n df=pd.DataFrame(index=list_data_tuples[0][1]['data'].index) # First dataframe index is nothing but date\\n \\n for tpl in list_data_tuples:\\n #print tpl[0]\\n df_temp = pd.DataFrame(tpl[1]['data']['Adj. Close'],index=tpl[1]['data'].index)\\n df_temp = df_temp.rename(columns={'Adj. Close': tpl[0]}) # tpl[0] is the symbol\\n #print df_temp,tpl[0]\\n df = df.join(df_temp)\\n if tpl[0] == benchmark_symbol: # drop dates SPY did not trade\\n df = df.dropna(subset=[benchmark_symbol])\\n\\n df=df.dropna(axis=0) # This drops any NaN values especially if the stock price has no information\\n \\n return df\",\n \"def gather_stock_data(tickers, save=True):\\n prices = pd.DataFrame()\\n ts = TimeSeries(key='EY2QBMV6MD9FX9CP', output_format='pandas')\\n\\n for ticker in tickers:\\n successful_grab = False\\n ticker_daily_adj = None\\n\\n while successful_grab is not True:\\n try:\\n ticker_daily_adj = ts.get_daily_adjusted(ticker, outputsize='full')[0]\\n successful_grab = True\\n except ValueError:\\n print('Waiting for API to let me in')\\n time.sleep(10)\\n\\n ticker_daily_adj.loc[:, '0. ticker'] = ticker\\n ticker_daily_adj = ticker_daily_adj[sorted(ticker_daily_adj.columns)]\\n\\n prices = pd.concat([prices, ticker_daily_adj])\\n\\n prices.sort_index(inplace=True)\\n prices.reset_index(inplace=True)\\n prices['date'] = pd.to_datetime(prices['date'])\\n if save:\\n prices.to_csv('stockdata.csv', index=True)\\n\\n return prices\",\n \"def get_portfolio_prices(stocks: list, funds: list, etfs: list, start_date: str, end_date=today) -> pd.DataFrame:\\r\\n data_frames_stocks = get_assets_data_frames(\\r\\n stocks, inv.get_stock_historical_data, 'brazil', start_date=start_date, end_date=end_date)\\r\\n data_frames_funds = get_assets_data_frames(\\r\\n funds, inv.get_fund_historical_data, 'brazil', start_date=start_date, end_date=end_date)\\r\\n data_frames_etfs = get_assets_data_frames(\\r\\n etfs, inv.get_etf_historical_data, 'brazil', start_date=start_date, end_date=end_date)\\r\\n\\r\\n data_frames = [*data_frames_stocks, *data_frames_funds, *data_frames_etfs]\\r\\n\\r\\n assets = [*stocks, *funds, *etfs]\\r\\n\\r\\n portfolio_prices = build_multi_index_data_frame(\\r\\n data_frames, assets, ['Close', 'Open', 'High', 'Low'])\\r\\n\\r\\n return portfolio_prices\",\n \"def table_gen(NamesL_pairs, p_pL, m_mL, p_mL, m_pL, p_valsL, p_vals_BonferoniL, RatiosL, p_valsL_divergent_convergent,\\n p_valsL_divergent_convergent_BonferoniL, RatiosL_divergent_convergent, output_table):\\n datafile = open(output_table, \\\"w\\\")\\n datafile.write(\\n \\\"Feature_1\\\" + '\\\\t' + \\\"Feature_2\\\" + \\\"\\\\t\\\" + \\\"plus_plus\\\" + '\\\\t' + \\\"minus_minus\\\" + '\\\\t' + \\\"plus_minus\\\" + '\\\\t' + \\\"minus_plus\\\" + '\\\\t' + \\\"p_value_same_opposite\\\" + '\\\\t' + \\\"p-value_same_opposite_Bonferoni_corrected\\\" + '\\\\t' + \\\"Ratio_same_opposite\\\" + '\\\\t' + \\\"p_value_divergent_convergent\\\" + '\\\\t' + \\\"p_value_divergent_convergent Bonferoni corrected\\\" + '\\\\t' + \\\"Ratio divergent convergent\\\" + '\\\\n')\\n for i in range(len(NamesL_pairs)):\\n datafile.write(\\n NamesL_pairs[i][0] + '\\\\t' + NamesL_pairs[i][1] + '\\\\t' + str(p_pL[i]) + '\\\\t' + str(m_mL[i]) + '\\\\t' + str(\\n p_mL[i]) + '\\\\t' + str(m_pL[i]) + '\\\\t' + str(p_valsL[i]) + '\\\\t' + str(p_vals_BonferoniL[i]) + '\\\\t' + str(\\n RatiosL[i]) + '\\\\t' + str(p_valsL_divergent_convergent[i]) + '\\\\t' + str(\\n p_valsL_divergent_convergent_BonferoniL[i]) + '\\\\t' + str(RatiosL_divergent_convergent[i]) + '\\\\n')\\n datafile.close()\\n return\",\n \"def get_back_data(self, end_date=None, stocks=None):\\n if end_date is None:\\n end_date = self.dates[-1]\\n\\n if type(end_date) is not datetime.datetime and type(end_date) is not pd.tslib.Timestamp:\\n end_date = datetime.datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n\\n if stocks is None:\\n stocks = self.stocks\\n\\n info = {}\\n for stock in stocks:\\n info[stock] = self.stock_data[stock].to_stock_data_range(start_date=None, end_date=end_date)\\n\\n return info\",\n \"def _get_dataset_table(self):\\n\\n model_dataset = self.get_model_dataset()\\n index_as_str = np.expand_dims(model_dataset.index.astype(str), 1)\\n observation_data = np.column_stack((index_as_str, model_dataset.as_matrix()))\\n observation_data_headers = ['DateTime']\\n observation_data_headers.extend(model_dataset.keys())\\n observation_table = SimpleTable(data=observation_data,\\n headers=observation_data_headers)\\n\\n return observation_table\",\n \"def GetSQL(df, table, OutputLocation):\\r\\n insert_query = \\\"INSERT INTO {table} (DATE, SPY, GLD) VALUES ('{date}', '{stock1}', '{stock2}');\\\"\\r\\n\\r\\n\\r\\n queries = []\\r\\n for index, row in df.iterrows():\\r\\n date = index.strftime(\\\"%Y-%m-%d\\\")\\r\\n stock1 = row[0]\\r\\n stock2 = row[1]\\r\\n queries.append(insert_query.format(table=table, date=date, stock1=stock1, stock2=stock2))\\r\\n\\r\\n with open(OutputLocation+\\\"/\\\"+datetime.now().strftime(\\\"%Y%m%d\\\")+\\\"_FinanceMarketInsert.sql\\\", \\\"w\\\") as output:\\r\\n output.write(\\\"\\\\n\\\".join(queries))\",\n \"def get_price_history(self):\\n # Connect to the database and return cursor\\n database = DatabaseMySQL()\\n\\n # Query database.\\n sql = \\\"Select published_at, `close` from company_price_volume_history \\\\\\n where company_id =\\\" + str(self.company_id)\\n df = database.get_query_df(sql)\\n\\n return df\",\n \"def get_data(symbols, dates, base_dir=\\\"../data/\\\"):\\n df = pd.DataFrame(index=dates)\\n if 'SPY' not in symbols:\\n symbols.insert(0, 'SPY')\\n\\n for symbol in symbols:\\n temp_df = pd.read_csv(symbol_to_path(symbol, base_dir), index_col='Date',\\n parse_dates=True, usecols=['Date', 'Close'],\\n na_values=['nan'])\\n temp_df = temp_df.rename(columns={'Close': symbol})\\n df = df.join(temp_df, how='inner')\\n return df\",\n \"def quandl_stocks(symbol, start_date=(2000, 1, 1), end_date=None):\\n\\n query_list = ['WIKI' + '/' + symbol + '.' + str(k) for k in range(1, 13)]\\n\\n start_date = datetime.date(*start_date)\\n\\n if end_date:\\n end_date = datetime.date(*end_date)\\n else:\\n end_date = datetime.date.today()\\n\\n return quandl.get(query_list,\\n returns='pandas',\\n start_date=start_date,\\n end_date=end_date,\\n collapse='daily',\\n order='asc'\\n )\",\n \"def history():\\n if request.method == \\\"GET\\\":\\n current_user = session[\\\"user_id\\\"]\\n\\n sales=db.execute(\\\"SELECT stock_symbol, volume_sold, price, date_sold FROM sales WHERE id = :id\\\", id=current_user)\\n lookedup=[]\\n sales_table_stock_name=[]\\n sales_table_symbols=[]\\n sales_table_stock_price=[]\\n sales_table_volumes=[]\\n sales_table_date=[]\\n\\n for row in sales:\\n lookedup = lookup(row[\\\"stock_symbol\\\"])\\n sales_table_stock_name.append(str(lookedup.get(\\\"name\\\")))\\n sales_table_symbols.append(str(row[\\\"stock_symbol\\\"]))\\n sales_table_stock_price.append(float(row[\\\"price\\\"]))\\n sales_table_volumes.append(int(row[\\\"volume_sold\\\"]))\\n sales_table_date.append(str(row[\\\"date_sold\\\"]))\\n\\n buys = db.execute(\\\"SELECT stock_symbol, volume_purchased, price, date_purchased FROM purchases WHERE id = :id\\\", id=current_user)\\n\\n buy_table_stock_name=[]\\n buy_table_symbols=[]\\n buy_table_volumes=[]\\n buy_table_share_price=[]\\n buy_table_date=[]\\n\\n lookedup=[]\\n\\n for row in buys:\\n\\n lookedup = lookup(row[\\\"stock_symbol\\\"])\\n buy_table_stock_name.append(str(lookedup.get(\\\"name\\\")))\\n buy_table_symbols.append(str(row[\\\"stock_symbol\\\"]))\\n buy_table_volumes.append(int(row[\\\"volume_purchased\\\"]))\\n buy_table_share_price.append(float(row[\\\"price\\\"]))\\n buy_table_date.append(str(row[\\\"date_purchased\\\"]))\\n\\n\\n return render_template(\\\"history.html\\\",\\n sales_table_stock_name=sales_table_stock_name,\\n sales_table_stock_price=sales_table_stock_price,\\n sales_table_symbols=sales_table_symbols,\\n sales_table_volumes=sales_table_volumes,\\n sales_table_date=sales_table_date,\\n buy_table_stock_name=buy_table_stock_name,\\n buy_table_symbols=buy_table_symbols,\\n buy_table_volumes=buy_table_volumes,\\n buy_table_share_price = buy_table_share_price,\\n buy_table_date=buy_table_date)\\n\\n else:\\n return apology(\\\"how did u post to history\\\")\\n\\n # return apology(\\\"No history here\\\")\",\n \"def get_output(data, capital=100000, leverage=1, commission=0, slippage=0):\\n total_capital = capital * leverage\\n df = data\\n df['cnt'] = df.groupby('timestamp')['symbol'].transform(\\n lambda x: len(x))\\n df['qty'] = (total_capital/df['cnt']/df['price']).round()\\n df['profit'] = df.eval('(sell-buy)*qty')\\n df['commission'] = df.eval('(sell+buy)*qty') * commission * 0.01\\n df['slippage'] = df.eval('(sell+buy)*qty') * slippage * 0.01\\n df['net_profit'] = df.eval('profit - commission - slippage')\\n return df\",\n \"def hist_data(self):\\n # database address\\n jydb_address = \\\"117.122.223.35\\\"\\n jydb_user_id = \\\"zpy\\\"\\n jydb_user_pwd = \\\"Z1pe1y1@zpy\\\"\\n jydb_db_name = \\\"jydb02\\\"\\n # preallocate data set\\n back_testing_data = {}\\n \\n # on-line mode\\n conn = pymysql.connect(jydb_address, jydb_user_id, jydb_user_pwd, jydb_db_name)\\n # not using moving average\\n if not self.moving_average: \\n # iterate on all ism packages\\n for _i, ism_pack in enumerate(tqdm(self.ism_data)):\\n ismcode = ism_pack[0][0]\\n ProRunDate = ism_pack[0][5]\\n ProStopDate = ism_pack[0][6]\\n MaxStoreSum = ism_pack[0][7]\\n MinInvestShare = ism_pack[0][8]\\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\\n # collect data from source conn\\n flag_run = \\\"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \\\" + \\\\\\n \\\"InnerCode IN (\\\" + \\\",\\\".join(InnerCode_ls) + \\\") AND \\\" + \\\\\\n \\\"TradingDay=\\\\'\\\" + ProRunDate + \\\"\\\\'\\\"\\n flag_stop = \\\"SELECT InnerCode, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \\\" + \\\\\\n \\\"InnerCode IN (\\\" + \\\",\\\".join(InnerCode_ls) + \\\") AND \\\" + \\\\\\n \\\"TradingDay=\\\\'\\\" + ProStopDate + \\\"\\\\'\\\"\\n run_price = pd.read_sql(flag_run, conn)\\n stop_price = pd.read_sql(flag_stop, conn)\\n back_testing_data[ismcode] = pd.merge(run_price, stop_price, on='InnerCode', \\n suffixes=('_run', '_stop'))\\n else: # using moving average \\n # iterate on all ism packages\\n for _i, ism_pack in enumerate(self.ism_data):\\n ismcode = ism_pack[0][0]\\n ProRunDate = ism_pack[0][5]\\n TradingDay_begin = former_market_date(ProRunDate, self.L_shift, conn)\\n TradingDay_end = future_market_date(ProRunDate, self.R_shift, conn)\\n InnerCode_ls = flatten(list(map(lambda x: [y[0] for y in x[1:]], ism_pack[1:])))\\n SecuCode_ls = flatten(list(map(lambda x: [y[1] for y in x[1:]], ism_pack[1:])))\\n PriorType_ls = flatten(list(map(lambda x: [y[2] for y in x[1:]], ism_pack[1:])))\\n flag = \\\"SELECT InnerCode, TradingDay, OpenPrice, ClosePrice FROM QT_DailyQuote WHERE \\\" + \\\\\\n \\\"InnerCode IN (\\\" + \\\",\\\".join(InnerCode_ls) + \\\") AND \\\" + \\\\\\n \\\"TradingDay BETWEEN \\\\'\\\" + TradingDay_begin + \\\"\\\\' AND \\\\'\\\" + TradingDay_end + \\\"\\\\'\\\"\\n back_testing_data[ismcode] = pd.read_sql(flag, conn)\\n \\n # close sql connection\\n conn.close()\\n \\n return back_testing_data\",\n \"def get_stock_data(stock_name, ma=[],bollinger=[],exp_ma=[],ma_conv=[]):\\n df = xl.parse(stock_name)\\n #df.drop(['VOLUME'], 1, inplace=True)\\n df.set_index('Date', inplace=True)\\n \\n # Renaming all the columns so that we can use the old version code\\n df.rename(columns={'OPEN': 'Open', 'HIGH': 'High', 'LOW': 'Low', 'NUMBER_TICKS': 'Volume', 'LAST_PRICE': 'Adj Close'}, inplace=True)\\n # Percentage change\\n df['Pct'] = df['Adj Close'].pct_change()\\n df.dropna(inplace=True)\\n \\n # Moving Average \\n if ma != []:\\n for moving in ma:\\n df['{}ma'.format(moving)] = df['Adj Close'].rolling(window=moving).mean()\\n # Bollinger \\n if bollinger != []:\\n def bbands(price, length=30, numsd=2):\\n \\\"\\\"\\\" returns average, upper band, and lower band\\\"\\\"\\\"\\n ave = pd.stats.moments.rolling_mean(price,length)\\n sd = pd.stats.moments.rolling_std(price,length)\\n upband = ave + (sd*numsd)\\n dnband = ave - (sd*numsd)\\n return np.round(upband,3), np.round(dnband,3)\\n for moving in bollinger:\\n df['{}bollingerup'.format(moving)],df['{}bollingerdown'.format(moving)] = bbands(df['Adj Close'], length=moving, numsd=2)\\n \\n # Exponential Moving Average \\n if exp_ma != []:\\n for moving in exp_ma:\\n df['{}exp_ma'.format(moving)] = df['Adj Close'].ewm(min_periods=1, adjust=True,com=moving).mean()\\n \\n # Moving Average Convergence \\n if ma_conv!= []:\\n for moving in ma_conv:\\n df['{}ma_conv'.format(moving)] = df['Adj Close'].ewm(min_periods=1, adjust=True,com=moving[0]).mean()-df['Adj Close'].ewm(min_periods=1, adjust=True,com=moving[1]).mean()\\n \\n \\n \\n df.dropna(inplace=True)\\n \\n \\n # Move Adj Close to the rightmost for the ease of training\\n adj_close = df['Adj Close']\\n df.drop(labels=['Adj Close'], axis=1, inplace=True)\\n df = pd.concat([df, adj_close], axis=1)\\n \\n return df\",\n \"def make_tables(self):\\n for t in self.tables:\\n self.add_table(groupname=t['groupname'],\\n tablename=t['tablename'],\\n description=t['description'],\\n tabletitle=t['tabletitle'])\",\n \"def generate_for_day(df: pd.DataFrame, dt=None, tickers=None, rounds=None) -> ListOfDicts:\\n\\n if dt:\\n logging.info(f\\\"Training {rounds} rounds with {len(df)} stocks in dataset for {dt}...\\\")\\n else:\\n logging.info(f\\\"Training {rounds} rounds with {len(df)} stocks in dataset...\\\")\\n\\n if len(df) < MIN_TRAINING_SIZE:\\n logging.warning(f\\\"Skipping because the number of stocks < {MIN_TRAINING_SIZE}...\\\")\\n return []\\n\\n dt_str = dt and dt.strftime(\\\"%Y-%m-%d\\\")\\n df = df[(df.date == dt_str)]\\n\\n calculate_overall_r2(df, rounds)\\n\\n raw_estimate_by_ticker = {}\\n\\n if tickers:\\n unavailable_tickers = set(tickers) - set(list(df['ticker']))\\n for ticker in unavailable_tickers:\\n logging.warning(f\\\"Ticker '{ticker}' not found in dataset. Continuing...\\\")\\n tickers.remove(ticker)\\n else:\\n tickers = set(list(df['ticker']))\\n logging.info(f\\\"Building models for {len(tickers)} stocks.\\\")\\n\\n def _gfds(ticker):\\n return ticker, generate_ffe_for_ticker(ticker, df=df, rounds=rounds)\\n\\n for ticker, estimate in fastmap.fastmap(_gfds, list(tickers)):\\n market_cap = float(df[(df.ticker == ticker)]['marketCap'])\\n logging.info(\\\"FFER for %r: %.4f\\\", ticker, market_cap / estimate)\\n raw_estimate_by_ticker[ticker] = estimate\\n\\n estimates = []\\n for ticker, pred in raw_estimate_by_ticker.items():\\n df_row = df[(df.ticker == ticker)].to_dict(orient='records')[0]\\n row = {}\\n if dt_str:\\n row[\\\"date\\\"] = dt_str\\n row[\\\"ticker\\\"] = ticker\\n row[\\\"num_shares\\\"] = df_row[\\\"shares\\\"]\\n row[\\\"actual_market_cap\\\"] = df_row['marketCap']\\n row[\\\"estimate_market_cap\\\"] = pred\\n row[\\\"actual_price\\\"] = df_row[\\\"price\\\"]\\n row[\\\"estimate_price\\\"] = row[\\\"estimate_market_cap\\\"] / row[\\\"num_shares\\\"]\\n row[\\\"ffer\\\"] = row[\\\"actual_price\\\"] / row[\\\"estimate_price\\\"]\\n for col in X_DIMENSIONS:\\n row[col] = df_row[col]\\n estimates.append(row)\\n\\n return sorted(estimates, key=lambda p: p[\\\"ffer\\\"])\",\n \"def basic_table_details():\\n tbl: pa.table = pa.Table.from_pylist([\\n {'first_name': 'Kakashi', 'last_name': 'Hatake', },\\n {'first_name': 'Itachi', 'last_name': 'Uchiha', },\\n {'first_name': 'Shisui', 'last_name': 'Uchiha', },\\n ])\\n\\n results = {\\n 'column_names': tbl.column_names,\\n 'columns > map > combine_chunks > to_pylist': [col.combine_chunks().to_pylist() for col in tbl.columns],\\n 'nbytes': tbl.nbytes,\\n 'num_columns': tbl.num_columns,\\n 'num_rows': tbl.num_rows,\\n 'schema': tbl.schema,\\n 'shape': tbl.shape,\\n }\\n\\n print(results)\",\n \"def prepare_data(self):\\r\\n annual_df = self.annual_df\\r\\n coef_df = self.coef_df\\r\\n quarter_df = self.quarter_df\\r\\n # historical_df = self.historical_df\\r\\n Event_Buffer = self.Event_Buffer\\r\\n\\r\\n Tot_Prod = coef_df[\\\"Product\\\"].nunique()\\r\\n # Tot_Week = coef_df[\\\"wk\\\"].nunique()\\r\\n Tot_Week = 52\\r\\n\\r\\n EDLP_Events = list(annual_df[\\\"RP_Events\\\"])\\r\\n Min_EDLP_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\\r\\n ]\\r\\n Max_EDLP_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in EDLP_Events\\r\\n ]\\r\\n\\r\\n TPR_Events = list(annual_df[\\\"TPR_Events\\\"])\\r\\n Min_TPR_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\\r\\n ]\\r\\n Max_TPR_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in TPR_Events\\r\\n ]\\r\\n\\r\\n Target_EDLP_Spend = [i for i in annual_df[\\\"PPG_RP_Spend\\\"]]\\r\\n Target_TPR_Spend = [i for i in annual_df[\\\"PPG_TPR_Spend\\\"]]\\r\\n Target_Trade_Spend = [i for i in annual_df[\\\"PPG_Total_Spend\\\"]]\\r\\n\\r\\n Mapping = {}\\r\\n Prod_Ind = coef_df[\\\"Product\\\"][0:Tot_Prod]\\r\\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\\r\\n Mapping[j] = i\\r\\n Mapping_reverse = {i: j for j, i in Mapping.items()}\\r\\n\\r\\n constants = [i for i in coef_df[\\\"constant\\\"]]\\r\\n\\r\\n Cat_Coef = coef_df[\\\"Catalogue\\\"][0:Tot_Prod]\\r\\n\\r\\n Disp_Coef = coef_df[\\\"Display\\\"][0:Tot_Prod]\\r\\n\\r\\n Base_Price_stg1 = [i for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg1 = []\\r\\n for pr in range(Tot_Prod):\\r\\n Intercepts_stg1.append(\\r\\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\\r\\n )\\r\\n\\r\\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg2 = [\\r\\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\\r\\n ] # noqa\\r\\n\\r\\n EDLP_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Regular\\\") == 1]]\\r\\n )\\r\\n TPR_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Promoted\\\") == 1]]\\r\\n )\\r\\n\\r\\n # ################################ Available EDLP Interactions pairs ##############################\\r\\n\\r\\n EDLP = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Regular\\\") > 1\\r\\n ]\\r\\n EDLP_Interactions = []\\r\\n for i in EDLP:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n EDLP_Interactions.append(temp)\\r\\n\\r\\n # ###################################### Available TPR Interactions pairs #########################\\r\\n\\r\\n TPR = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Promoted\\\") > 1\\r\\n ]\\r\\n TPR_Interactions = []\\r\\n for i in TPR:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n TPR_Interactions.append(temp)\\r\\n\\r\\n # ###################################### EDLP_Interaction_Coef_Values ############################\\r\\n\\r\\n EDLP_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Regular\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ###################################### TPR_Interaction_Coef_Values #############################\\r\\n\\r\\n TPR_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Promoted\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ##################################### Loading Pantry Loading Coefficients #######################\\r\\n\\r\\n Pantry_1 = list(coef_df[\\\"Pantry_Loading_1\\\"])\\r\\n Pantry_1 = [\\r\\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\\r\\n ]\\r\\n Pantry_2 = list(coef_df[\\\"Pantry_Loading_2\\\"])\\r\\n Pantry_2 = [\\r\\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\\r\\n ]\\r\\n\\r\\n # TE_Coeff = np.array(Promo_df[[\\\"TE_Promo\\\",\\\"TE_NoPromo\\\"]])\\r\\n self.Tot_Prod = Tot_Prod\\r\\n self.Tot_Week = Tot_Week\\r\\n self.EDLP_Events = EDLP_Events\\r\\n self.Min_EDLP_Events = Min_EDLP_Events\\r\\n self.Max_EDLP_Events = Max_EDLP_Events\\r\\n self.TPR_Events = TPR_Events\\r\\n self.Min_TPR_Events = Min_TPR_Events\\r\\n self.Max_TPR_Events = Max_TPR_Events\\r\\n\\r\\n self.Target_EDLP_Spend = Target_EDLP_Spend\\r\\n self.Target_TPR_Spend = Target_TPR_Spend\\r\\n self.Target_Trade_Spend = Target_Trade_Spend\\r\\n self.Mapping = Mapping\\r\\n self.Mapping_reverse = Mapping_reverse\\r\\n self.constants = constants\\r\\n self.EDLP_Coef = EDLP_Coef\\r\\n self.TPR_Coef = TPR_Coef\\r\\n\\r\\n self.EDLP_Interactions = EDLP_Interactions\\r\\n self.TPR_Interactions = TPR_Interactions\\r\\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\\r\\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\\r\\n self.Pantry_1 = Pantry_1\\r\\n self.Pantry_2 = Pantry_2\\r\\n\\r\\n self.Base_Price_stg1 = Base_Price_stg1\\r\\n self.Intercepts_stg1 = Intercepts_stg1\\r\\n self.Base_Price_stg2 = Base_Price_stg2\\r\\n self.Intercepts_stg2 = Intercepts_stg2\\r\\n\\r\\n self.Cat_Coef = Cat_Coef\\r\\n self.Disp_Coef = Disp_Coef\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63580585","0.61449856","0.60916585","0.59879607","0.586941","0.5752481","0.574493","0.56445813","0.5596955","0.5551755","0.5538056","0.55360204","0.55317575","0.55244833","0.5521858","0.55173093","0.55147207","0.54893124","0.54601693","0.54540384","0.5437737","0.54161805","0.541107","0.5409818","0.54051584","0.53736913","0.537251","0.53687346","0.5362668","0.5362655","0.5350182","0.5347812","0.5345321","0.5343556","0.53332","0.5322726","0.5319458","0.5318018","0.5317219","0.5306712","0.53063095","0.52901036","0.5287743","0.5283358","0.52776384","0.52685124","0.5264576","0.52608335","0.5257239","0.524417","0.5241812","0.52405065","0.5239502","0.5238472","0.5225575","0.5216067","0.52143174","0.52109843","0.52101606","0.52012557","0.52006716","0.519906","0.51877695","0.51847553","0.5183077","0.5154142","0.5153502","0.51450175","0.5142727","0.5138591","0.513658","0.51300764","0.5121206","0.5120772","0.5114458","0.5112668","0.5112266","0.51004255","0.5096089","0.50920177","0.5091172","0.5087495","0.50847894","0.5079082","0.50782204","0.5078139","0.5077259","0.5073554","0.50655115","0.5064889","0.5063461","0.5057288","0.50535256","0.50532764","0.5051656","0.5041171","0.50362957","0.5034352","0.5034192","0.50325924"],"string":"[\n \"0.63580585\",\n \"0.61449856\",\n \"0.60916585\",\n \"0.59879607\",\n \"0.586941\",\n \"0.5752481\",\n \"0.574493\",\n \"0.56445813\",\n \"0.5596955\",\n \"0.5551755\",\n \"0.5538056\",\n \"0.55360204\",\n \"0.55317575\",\n \"0.55244833\",\n \"0.5521858\",\n \"0.55173093\",\n \"0.55147207\",\n \"0.54893124\",\n \"0.54601693\",\n \"0.54540384\",\n \"0.5437737\",\n \"0.54161805\",\n \"0.541107\",\n \"0.5409818\",\n \"0.54051584\",\n \"0.53736913\",\n \"0.537251\",\n \"0.53687346\",\n \"0.5362668\",\n \"0.5362655\",\n \"0.5350182\",\n \"0.5347812\",\n \"0.5345321\",\n \"0.5343556\",\n \"0.53332\",\n \"0.5322726\",\n \"0.5319458\",\n \"0.5318018\",\n \"0.5317219\",\n \"0.5306712\",\n \"0.53063095\",\n \"0.52901036\",\n \"0.5287743\",\n \"0.5283358\",\n \"0.52776384\",\n \"0.52685124\",\n \"0.5264576\",\n \"0.52608335\",\n \"0.5257239\",\n \"0.524417\",\n \"0.5241812\",\n \"0.52405065\",\n \"0.5239502\",\n \"0.5238472\",\n \"0.5225575\",\n \"0.5216067\",\n \"0.52143174\",\n \"0.52109843\",\n \"0.52101606\",\n \"0.52012557\",\n \"0.52006716\",\n \"0.519906\",\n \"0.51877695\",\n \"0.51847553\",\n \"0.5183077\",\n \"0.5154142\",\n \"0.5153502\",\n \"0.51450175\",\n \"0.5142727\",\n \"0.5138591\",\n \"0.513658\",\n \"0.51300764\",\n \"0.5121206\",\n \"0.5120772\",\n \"0.5114458\",\n \"0.5112668\",\n \"0.5112266\",\n \"0.51004255\",\n \"0.5096089\",\n \"0.50920177\",\n \"0.5091172\",\n \"0.5087495\",\n \"0.50847894\",\n \"0.5079082\",\n \"0.50782204\",\n \"0.5078139\",\n \"0.5077259\",\n \"0.5073554\",\n \"0.50655115\",\n \"0.5064889\",\n \"0.5063461\",\n \"0.5057288\",\n \"0.50535256\",\n \"0.50532764\",\n \"0.5051656\",\n \"0.5041171\",\n \"0.50362957\",\n \"0.5034352\",\n \"0.5034192\",\n \"0.50325924\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6858316"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22396,"cells":{"query":{"kind":"string","value":"General method for costing belt filter press. Capital cost is a function of flow in gal/hr."},"document":{"kind":"string","value":"def cost_filter_press(blk):\n t0 = blk.flowsheet().time.first()\n # Add cost variable and constraint\n blk.capital_cost = pyo.Var(\n initialize=1,\n units=blk.config.flowsheet_costing_block.base_currency,\n bounds=(0, None),\n doc=\"Capital cost of unit operation\",\n )\n\n Q = pyo.units.convert(\n blk.unit_model.properties_in[t0].flow_vol,\n to_units=pyo.units.gal / pyo.units.hr,\n )\n\n # Get parameter dict from database\n parameter_dict = blk.unit_model.config.database.get_unit_operation_parameters(\n blk.unit_model._tech_type, subtype=blk.unit_model.config.process_subtype\n )\n\n # Get costing parameter sub-block for this technology\n A, B = blk.unit_model._get_tech_parameters(\n blk,\n parameter_dict,\n blk.unit_model.config.process_subtype,\n [\"capital_a_parameter\", \"capital_b_parameter\"],\n )\n\n # Determine if a costing factor is required\n factor = parameter_dict[\"capital_cost\"][\"cost_factor\"]\n\n expr = pyo.units.convert(\n A * Q + B, to_units=blk.config.flowsheet_costing_block.base_currency\n )\n\n blk.capital_cost_constraint = pyo.Constraint(expr=blk.capital_cost == expr)\n\n # Register flows\n blk.config.flowsheet_costing_block.cost_flow(\n blk.unit_model.electricity[t0], \"electricity\"\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cond_boiler_op_cost(Q_therm_W, Q_design_W, T_return_to_boiler_K):\n if Q_therm_W > 0.0:\n\n # boiler efficiency\n eta_boiler = cond_boiler_operation(Q_therm_W, Q_design_W, T_return_to_boiler_K)\n\n E_aux_Boiler_req_W = BOILER_P_AUX * Q_therm_W\n\n Q_primary_W = Q_therm_W / eta_boiler\n else:\n Q_primary_W = 0.0\n E_aux_Boiler_req_W = 0.0\n\n return Q_primary_W, E_aux_Boiler_req_W","def calc_capital_costs (self):\n self.capital_costs = self.max_boiler_output * \\\n self.comp_specs[\"cost per btu/hrs\"]\n #~ print self.capital_costs","def cost(self) -> float:","def get_PP_costing(self, cost_accounts, scaled_param, units, tech, ccs=\"B\"):\n # ------------------------ Power Plant Cost ------------------------\n\n # check to see if a costing block already exists\n if hasattr(self, \"costing\"):\n raise AttributeError(\n \"{} already has an attribute costing. \"\n \"Check that you are not calling get_costing\"\n \" twice on the same model\".format(self.name)\n )\n\n # create a costing Block\n self.costing = Block()\n self.costing.library = \"PP\"\n\n # find flowsheet block to create global costing parameters\n try:\n fs = self.flowsheet()\n except AttributeError:\n fs = self.parent_block()\n\n # build flowsheet level parameters CE_index = year\n if not hasattr(fs, \"costing\"):\n fs.get_costing(year=\"2018\")\n\n CE_index = fs.costing.CE_index\n\n # define preloaded accounts\n PC_preloaded_accounts = {\n \"Coal Handling\": [\"1.1\", \"1.2\", \"1.3\", \"1.4\", \"1.9a\"],\n \"Sorbent Handling\": [\"1.5\", \"1.6\", \"1.7\", \"1.8\", \"1.9b\"],\n \"Coal Feed\": [\"2.1\", \"2.2\", \"2.9a\"],\n \"Sorbent Feed\": [\"2.5\", \"2.6\", \"2.9b\"],\n \"Feedwater System\": [\"3.1\", \"3.3\"],\n \"PC Boiler\": [\"4.9\"],\n \"Steam Turbine\": [\"8.1\"],\n \"Condenser\": [\"8.3\"],\n \"Cooling Tower\": [\"9.1\"],\n \"Circulating Water System\": [\"9.2\", \"9.3\", \"9.4\", \"9.6\", \"9.7\"],\n \"Ash Handling\": [\"10.6\", \"10.7\", \"10.9\"],\n }\n\n IGCC_preloaded_accounts = {\n \"Coal Handling\": [\"1.1\", \"1.2\", \"1.3\", \"1.4\", \"1.9\"],\n \"Coal Feed\": [\"2.1\", \"2.2\", \"2.3\", \"2.4\", \"2.9\"],\n \"Feedwater System\": [\"3.1\", \"3.3\"],\n \"Gasifier\": [\"4.1\"],\n \"Syngas Cooler\": [\"4.2\"],\n \"ASU\": [\"4.3a\"],\n \"ASU Oxidant Compression\": [\"4.3b\"],\n \"Combustion Turbine\": [\"6.1\", \"6.3\"],\n \"Syngas Expander\": [\"6.2\"],\n \"HRSG\": [\"7.1\", \"7.2\"],\n \"Steam Turbine\": [\"8.1\"],\n \"Condenser\": [\"8.3\"],\n \"Cooling Tower\": [\"9.1\"],\n \"Circulating Water System\": [\"9.2\", \"9.3\", \"9.4\", \"9.6\", \"9.7\"],\n \"Slag Handling\": [\"10.1\", \"10.2\", \"10.3\", \"10.6\", \"10.7\", \"10.8\", \"10.9\"],\n }\n\n NGCC_preloaded_accounts = {\n \"Feedwater System\": [\"3.1\", \"3.3\"],\n \"Combustion Turbine\": [\"6.1\", \"6.3\"],\n \"HRSG\": [\"7.1\", \"7.2\"],\n \"Steam Turbine\": [\"8.1\"],\n \"Condenser\": [\"8.3\"],\n \"Cooling Tower\": [\"9.1\"],\n \"Circulating Water System\": [\"9.2\", \"9.3\", \"9.4\", \"9.6\", \"9.7\"],\n }\n\n AUSC_preloaded_accounts = {\n \"PC Boiler\": [\"4.9\"],\n \"Steam Turbine\": [\"8.1\"],\n \"Steam Piping\": [\"8.4\"],\n }\n\n # preloaded account handling\n if type(cost_accounts) == str:\n if tech in [1, 2]:\n cost_accounts = PC_preloaded_accounts[cost_accounts]\n elif tech in [3, 4, 5]:\n cost_accounts = IGCC_preloaded_accounts[cost_accounts]\n elif tech == 6:\n cost_accounts = NGCC_preloaded_accounts[cost_accounts]\n elif tech == 7:\n cost_accounts = AUSC_preloaded_accounts[cost_accounts]\n else:\n AttributeError(\"{} technology not supported\".format(self.name))\n\n # pull data for each account into dictionaries\n process_params = {}\n reference_units = {}\n account_names = {}\n exponents = {}\n reference_costs = {}\n reference_params = {}\n engineering_fees = {}\n process_contingencies = {}\n project_contingencies = {}\n\n for account in cost_accounts:\n try: # first look for data in json file info\n process_params[account] = BB_costing_exponents[str(tech)][account][\n \"Process Parameter\"\n ]\n reference_units[account] = BB_costing_params[str(tech)][ccs][\n cost_accounts[0]\n ][\"Units\"]\n account_names[account] = BB_costing_exponents[str(tech)][account][\n \"Account Name\"\n ]\n exponents[account] = float(\n BB_costing_exponents[str(tech)][account][\"Exponent\"]\n )\n reference_costs[account] = BB_costing_params[str(tech)][ccs][account][\"BEC\"]\n reference_params[account] = BB_costing_params[str(tech)][ccs][account][\n \"RP Value\"\n ]\n engineering_fees[account] = BB_costing_params[str(tech)][ccs][account][\n \"Eng Fee\"\n ]\n process_contingencies[account] = BB_costing_params[str(tech)][ccs][account][\n \"Process Contingency\"\n ]\n project_contingencies[account] = BB_costing_params[str(tech)][ccs][account][\n \"Project Contingency\"\n ]\n except KeyError:\n try: # next look for data in custom dictionaries\n process_params[account] = custom_costing_exponents[str(tech)][account][\n \"Process Parameter\"\n ]\n reference_units[account] = custom_costing_params[str(tech)][ccs][\n cost_accounts[0]\n ][\"Units\"]\n account_names[account] = custom_costing_exponents[str(tech)][account][\n \"Account Name\"\n ]\n exponents[account] = float(\n custom_costing_exponents[str(tech)][account][\"Exponent\"]\n )\n reference_costs[account] = custom_costing_params[str(tech)][ccs][\n account\n ][\"BEC\"]\n reference_params[account] = custom_costing_params[str(tech)][ccs][\n account\n ][\"RP Value\"]\n engineering_fees[account] = custom_costing_params[str(tech)][ccs][\n account\n ][\"Eng Fee\"]\n process_contingencies[account] = custom_costing_params[str(tech)][ccs][\n account\n ][\"Process Contingency\"]\n project_contingencies[account] = custom_costing_params[str(tech)][ccs][\n account\n ][\"Project Contingency\"]\n except KeyError:\n print(\n \"KeyError: Account {} could not be found in the \"\n \"dictionary for technology {} with CCS {}\".format(\n account, str(tech), ccs\n )\n )\n\n # check that all accounts use the same process parameter\n param_check = None\n for account in cost_accounts:\n param = process_params[account]\n if param_check is None:\n param_check = param\n elif param != param_check:\n raise ValueError(\n \"{} cost accounts selected do not use \"\n \"the same process parameter\".format(self.name)\n )\n\n # check that the user passed the correct units\n for account in cost_accounts:\n ref_units = reference_units[account]\n if units != ref_units:\n raise ValueError(\n \"Account %s uses units of %s. \"\n \"Units of %s were passed.\" % (cost_accounts[0], ref_units, units)\n )\n\n # Used by other functions for reporting results\n self.costing.account_names = account_names\n\n # define parameters\n self.costing.exp = Param(\n cost_accounts,\n mutable=True,\n initialize=exponents,\n doc=\"exponential parameter for account\",\n )\n\n self.costing.ref_cost = Param(\n cost_accounts,\n mutable=True,\n initialize=reference_costs,\n doc=\"reference cost for account\",\n )\n\n self.costing.ref_param = Param(\n cost_accounts,\n mutable=True,\n initialize=reference_params,\n doc=\"reference parameter for account\",\n )\n\n self.costing.eng_fee = Param(\n cost_accounts,\n mutable=True,\n initialize=engineering_fees,\n doc=\"engineering fee percentage\",\n )\n\n self.costing.process_conting = Param(\n cost_accounts,\n mutable=True,\n initialize=process_contingencies,\n doc=\"process contingency percentage\",\n )\n\n self.costing.project_conting = Param(\n cost_accounts,\n mutable=True,\n initialize=project_contingencies,\n doc=\"project contingency percentage\",\n )\n\n # define variables\n self.costing.bare_erected_cost = Var(\n cost_accounts,\n initialize=reference_costs,\n bounds=(0, 1e4),\n doc=\"scaled bare erected cost in $MM\",\n )\n\n self.costing.total_plant_cost = Var(\n cost_accounts,\n initialize=reference_costs,\n bounds=(0, 1e4),\n doc=\"total plant cost in $MM\",\n )\n\n # rule for scaling BEC\n # reference cost is in 2018 dollars, 671.1 is CE index for 2018\n def bare_erected_cost_rule(costing, i):\n return (\n costing.bare_erected_cost[i] * 1e3\n == (CE_index / 671.1)\n * costing.ref_cost[i]\n * (scaled_param / costing.ref_param[i]) ** costing.exp[i]\n )\n\n self.costing.bare_erected_cost_eq = Constraint(\n cost_accounts, rule=bare_erected_cost_rule\n )\n\n # rule for calculating TPC\n def total_plant_cost_rule(costing, i):\n return costing.total_plant_cost[i] == costing.bare_erected_cost[i] * (\n 1 + costing.eng_fee[i] + costing.process_conting[i]\n ) * (1 + costing.project_conting[i])\n\n self.costing.total_plant_cost_eq = Constraint(\n cost_accounts, rule=total_plant_cost_rule\n )\n\n # rule for sum of BEC\n def BEC_sum_rule(costing):\n return sum(costing.bare_erected_cost[i] for i in cost_accounts)\n\n self.costing.bare_erected_cost_sum = Expression(rule=BEC_sum_rule)\n\n # rule for sum of TPC\n def TPC_sum_rule(costing):\n return sum(costing.total_plant_cost[i] for i in cost_accounts)\n\n self.costing.total_plant_cost_sum = Expression(rule=TPC_sum_rule)\n\n # add variable and constraint scaling\n for i in cost_accounts:\n iscale.set_scaling_factor(self.costing.bare_erected_cost[i], 1)\n iscale.set_scaling_factor(self.costing.total_plant_cost[i], 1)\n iscale.constraint_scaling_transform(\n self.costing.bare_erected_cost_eq[i], 1e-3, overwrite=False\n )\n iscale.constraint_scaling_transform(\n self.costing.total_plant_cost_eq[i], 1, overwrite=False\n )","def _cost(self, action):\n raise NotImplementedError","def c(x):\n cost = per_widget_cost * x + fixed_cost\n return cost","def adjust_cost(self) -> None:\n\n n_iterations = self.array.shape[-1]\n n_year = len(self.array.year.values)\n\n # If uncertainty is not considered, the cost factor equals 1.\n # Otherwise, a variability of +/-30% is added.\n\n if n_iterations == 1:\n cost_factor = 1\n else:\n if \"reference\" in self.array.value.values.tolist():\n cost_factor = np.ones((n_iterations, 1))\n else:\n cost_factor = np.random.triangular(0.7, 1, 1.3, (n_iterations, 1))\n\n # Correction of hydrogen tank cost, per kg\n # Correction of fuel cell stack cost, per kW\n if \"FCEV\" in self.array.powertrain:\n self.array.loc[\n dict(powertrain=\"FCEV\", parameter=\"fuel tank cost per kg\")\n ] = np.reshape(\n (1.078e58 * np.exp(-6.32e-2 * self.array.year.values) + 3.43e2)\n * cost_factor,\n (1, n_year, n_iterations),\n )\n\n self.array.loc[\n dict(powertrain=\"FCEV\", parameter=\"fuel tank cost per kg\")\n ] = np.reshape(\n (3.15e66 * np.exp(-7.35e-2 * self.array.year.values) + 2.39e1)\n * cost_factor,\n (1, n_year, n_iterations),\n )\n\n # Correction of energy battery system cost, per kWh\n list_batt = [\n i\n for i in [\"BEV\", \"PHEV-e\", \"PHEV-c-p\", \"PHEV-c-d\"]\n if i in self.array.powertrain\n ]\n if len(list_batt) > 0:\n self.array.loc[\n dict(powertrain=list_batt, parameter=\"energy battery cost per kWh\")\n ] = np.reshape(\n (2.75e86 * np.exp(-9.61e-2 * self.array.year.values) + 5.059e1)\n * cost_factor,\n (1, 1, n_year, n_iterations),\n )\n\n # Correction of power battery system cost, per kW\n list_pwt = [\n i\n for i in [\n \"ICEV-p\",\n \"ICEV-d\",\n \"ICEV-g\",\n \"PHEV-c-p\",\n \"PHEV-c-d\",\n \"FCEV\",\n \"HEV-p\",\n \"HEV-d\",\n ]\n if i in self.array.powertrain\n ]\n\n if len(list_pwt) > 0:\n self.array.loc[\n dict(powertrain=list_pwt, parameter=\"power battery cost per kW\")\n ] = np.reshape(\n (8.337e40 * np.exp(-4.49e-2 * self.array.year.values) + 11.17)\n * cost_factor,\n (1, 1, n_year, n_iterations),\n )\n\n # Correction of combustion powertrain cost for ICEV-g\n if \"ICEV-g\" in self.array.powertrain:\n self.array.loc[\n dict(powertrain=\"ICEV-g\", parameter=\"combustion powertrain cost per kW\")\n ] = np.clip(\n np.reshape(\n (5.92e160 * np.exp(-0.1819 * self.array.year.values) + 26.76)\n * cost_factor,\n (1, n_year, n_iterations),\n ),\n None,\n 100,\n )","def cond_boiler_operation(Q_load_W, Q_design_W, T_return_to_boiler_K):\n\n x = [0, 15.5, 21, 26.7, 32.2, 37.7, 43.3, 49, 54.4, 60, 65.6, 71.1, 100] # Return Temperature Dependency\n y = [96.8, 96.8, 96.2, 95.5, 94.7, 93.2, 91.2, 88.9, 87.3, 86.3, 86.0, 85.9, 85.8] # Return Temperature Dependency\n x1 = [0, 0.05, 0.25, 0.5, 0.75, 1] # Load Point dependency\n y1 = [99.5, 99.3, 98.3, 97.6, 97.1, 96.8] # Load Point Dependency\n\n # do the interpolation\n eff_of_T_return = interp1d(x, y, kind='linear')\n eff_of_phi = interp1d(x1, y1, kind='cubic')\n\n # get input variables\n if Q_design_W > 0:\n phi = float(Q_load_W) / float(Q_design_W)\n else:\n phi = 0\n\n if T_return_to_boiler_K == 0: # accounting with times with no flow\n T_return = 0\n else:\n T_return = T_return_to_boiler_K - 273\n eff_score = eff_of_phi(phi) / eff_of_phi(1)\n boiler_eff = (eff_score * eff_of_T_return(T_return)) / 100.0\n\n return boiler_eff","def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\n Capex_a_Boiler_USD = 0.0\n Opex_a_fix_Boiler_USD = 0.0\n Capex_Boiler_USD = 0.0\n\n if Q_design_W > 0.0:\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\n\n if Q_design_W <= max_boiler_size:\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n else:\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\n Q_nom_W = Q_design_W / number_of_boilers\n\n boiler_cost_data = boiler_cost_data[\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\n\n Inv_a = boiler_cost_data.iloc[0]['a']\n Inv_b = boiler_cost_data.iloc[0]['b']\n Inv_c = boiler_cost_data.iloc[0]['c']\n Inv_d = boiler_cost_data.iloc[0]['d']\n Inv_e = boiler_cost_data.iloc[0]['e']\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\n\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\n\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\n Capex_Boiler_USD = InvC\n\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD","def cost_b(self):\n return self._cost_b","def circuitSat(C):","def compute_cost(AL,Y,cost_function_name):\n cost_functions = {\n \"cost_func_1\": cf.cost_function_1\n } \n\n activ_func = cost_functions.get(cost_function_name,lambda : \"Invalid Cost Function Name !\")\n\n cost,dAL = activ_func(AL,Y)\n\n return cost, dAL","def calc_Cop_boiler(q_load_Wh, Q_nom_W, T_return_to_boiler_K):\n\n if (Q_nom_W > 0.0) and (q_load_Wh > 0.0):\n\n # calculate efficiency according to partload\n phi = float(q_load_Wh) / float(Q_nom_W)\n if phi >=1.0: # avoid rounding error\n phi = 0.98\n T_return_C = np.float(T_return_to_boiler_K - 273.15)\n eff_score = eff_of_phi(phi) / eff_of_phi(1)\n boiler_eff = (eff_score * eff_of_T_return([T_return_C]))[0] / 100.0\n else:\n boiler_eff = 0.0\n\n return boiler_eff","def bc_flower(teff):\n lteff=np.log10(teff)\n if (lteff<3.7):\n bcflow=-0.190537291496456e+05+0.155144866764412e+05*lteff-0.421278819301717e+04*(lteff*lteff)+0.381476328422343e+03*(lteff*lteff*lteff)\n if (lteff>=3.7 and lteff<3.9):\n bcflow=-0.370510203809015e+05+0.385672629965804e+05*lteff-0.150651486316025e+05*(lteff*lteff)+0.261724637119416e+04*(lteff*lteff*lteff)-0.170623810323864e+03*(lteff*lteff*lteff*lteff)\n if (lteff>=3.9):\n bcflow=-0.370510203809015e+05+0.385672629965804e+05*lteff-0.150651486316025e+05*(lteff*lteff)+0.261724637119416e+04*(lteff*lteff*lteff)-0.170623810323864e+03*(lteff*lteff*lteff*lteff)\n return bcflow","def compute_cost(AL, Y):\n pass","def __call__(self, rate:'kW'):\n self.rate = rate\n self.cost = self.price * rate","def obs_cost_fn(self, state):\n # Weights for different terms\n W_PUSHER = 1\n W_GOAL = 2\n W_DIFF = 5\n\n length = state.shape[0]\n # pusher_x, pusher_y = state[:, 0], state[:, 1]\n box_x, box_y = state[:, 2], state[:, 3]\n # goal_x, goal_y = np.tile(self.goal[0], (length, 1)), np.tile(self.goal[1], (length, 1))\n\n pusher = state[:, 0:2]\n box = state[:, 2:4]\n goal = np.tile(self.goal, (length, 1))\n goal_x, goal_y = goal[:, 0], goal[:, 1]\n\n d_box = np.linalg.norm(pusher - box, axis=1, ord=2)\n d_goal = np.linalg.norm(box - goal, axis=1, ord=2)\n\n\n # pusher_box = np.array([box_x - pusher_x, box_y - pusher_y])\n # box_goal = np.array([goal_x - box_x, goal_y - box_y])\n # d_box = np.sqrt(np.dot(pusher_box, pusher_box))\n # d_goal = np.sqrt(np.dot(box_goal, box_goal))\n diff_coord = np.abs(box_x / (box_y + EPSILON) - goal_x / (goal_y + EPSILON))\n # the -0.4 is to adjust for the radius of the box and pusher\n return W_PUSHER * np.max([d_box - 0.4, np.zeros(len(d_box))], axis=0) + W_GOAL * d_goal + W_DIFF * diff_coord","def compute_cost(AL, Y):\n pass","def compute_cost(self, chrome):\n return 1","def taumBday(n_b, n_w, cost_b, cost_w, z):\n # 3 cases for how much it would cost to buy black or white present\n case1 = n_w * cost_w + n_b * cost_b\n case2 = n_w * (cost_b + z) + n_b * cost_b\n case3 = n_w * cost_w + n_b * (cost_w + z)\n return min(case1, case2, case3)","def cost_mabr(blk):\n t0 = blk.flowsheet().time.first()\n\n # Get parameter dict from database\n parameter_dict = blk.unit_model.config.database.get_unit_operation_parameters(\n blk.unit_model._tech_type, subtype=blk.unit_model.config.process_subtype\n )\n\n # Get costing parameter sub-block for this technology\n A, B = blk.unit_model._get_tech_parameters(\n blk,\n parameter_dict,\n blk.unit_model.config.process_subtype,\n [\"reactor_cost\", \"blower_cost\"],\n )\n\n # Add cost variable and constraint\n blk.capital_cost = pyo.Var(\n initialize=1,\n units=blk.config.flowsheet_costing_block.base_currency,\n bounds=(0, None),\n doc=\"Capital cost of unit operation\",\n )\n\n DCC_reactor = pyo.units.convert(\n blk.unit_model.properties_treated[t0].flow_mass_comp[\"ammonium_as_nitrogen\"]\n / blk.unit_model.nitrogen_removal_rate\n * A,\n to_units=blk.config.flowsheet_costing_block.base_currency,\n )\n\n DCC_blower = pyo.units.convert(\n blk.unit_model.reactor_area * blk.unit_model.air_flow_rate[t0] * B,\n to_units=blk.config.flowsheet_costing_block.base_currency,\n )\n\n expr = DCC_reactor + DCC_blower\n\n blk.unit_model._add_cost_factor(\n blk, parameter_dict[\"capital_cost\"][\"cost_factor\"]\n )\n\n blk.capital_cost_constraint = pyo.Constraint(\n expr=blk.capital_cost == blk.cost_factor * expr\n )\n\n # Register flows\n blk.config.flowsheet_costing_block.cost_flow(\n blk.unit_model.electricity[t0], \"electricity\"\n )","def cost_b_v(self):\n return self._cost_b_v","def cost(self,e1,e2):\n pass","def costFun(self, S, x):","def get_prodcost(_craftable, tab):\n # print(f'{tab}{_craftable.name} ({_craftable.value}):')\n if _craftable.prod_cost == 0:\n prodcost = 0\n\n tab = tab + ' '\n\n # If there are Craftable Items as required materials ....\n if len(_craftable.craftables_list) > 0:\n for craft, amount in _craftable.craftables_list:\n # partial_cost = round(float(craftables[craft].value) * amount, 2)\n for loop in range(amount):\n partial_cost = get_prodcost(craftables[craft], tab)\n # If the prod cost of the item is more expensive than the buy cost\n if partial_cost >= float(craftables[craft].value):\n partial_cost = float(craftables[craft].value)\n craftables[craft].buy_or_craft = 'buy'\n # print(f'{tab}Buy item {craft}')\n else:\n craftables[craft].buy_or_craft = 'craft'\n # print(f'{tab}Craft item {craft}')\n prodcost = prodcost + partial_cost\n\n # If the Total PROD Cost (prod_cost + res_totalcost) is cheaper the the market value:\n prodcost = prodcost + _craftable.res_totalcost\n if prodcost < float(_craftable.value):\n for resource, amount in _craftable.resources_list:\n partial_cost = round(float(resources[resource].unit_price) * amount, 2)\n # print(f'{tab}{resource} Cost: {partial_cost}')\n\n # print(f'{tab}Total Prod C ost: {round(prodcost, 2)}')\n if prodcost < float(_craftable.value):\n # print(f'{tab}Craft item {_craftable.name}')\n _craftable.buy_or_craft = 'craft'\n else:\n # print(f'{tab}Buy item {_craftable.name}')\n _craftable.buy_or_craft = 'buy'\n return prodcost\n else:\n return _craftable.prod_cost","def __call__(self, x, u, k):\n first_time_through = True\n for cost, arg, weight in zip(self._costs, self._args, self._weights):\n if arg == \"x\":\n cost_input = x\n else:\n cost_input = u[arg]\n\n current_term = weight * cost(cost_input, k)\n if current_term > 1e8:\n print(\"Warning: cost %s is %f\" % (cost._name, current_term))\n print(\"Input is: \", cost_input)\n\n# if cost._name[:4] == \"bike\":\n# print(cost._name, \": \", current_term)\n\n if first_time_through:\n total_cost = current_term\n else:\n total_cost += current_term\n\n first_time_through = False\n\n return total_cost","def set_costs(self) -> None:\n self[\"glider cost\"] = (\n self[\"glider base mass\"] * self[\"glider cost slope\"]\n + self[\"glider cost intercept\"]\n )\n self[\"lightweighting cost\"] = (\n self[\"glider base mass\"]\n * self[\"lightweighting\"]\n * self[\"glider lightweighting cost per kg\"]\n )\n self[\"electric powertrain cost\"] = (\n self[\"electric powertrain cost per kW\"] * self[\"electric power\"]\n )\n self[\"combustion powertrain cost\"] = (\n self[\"combustion power\"] * self[\"combustion powertrain cost per kW\"]\n )\n self[\"fuel cell cost\"] = self[\"fuel cell power\"] * self[\"fuel cell cost per kW\"]\n self[\"power battery cost\"] = (\n self[\"battery power\"] * self[\"power battery cost per kW\"]\n )\n self[\"energy battery cost\"] = (\n self[\"energy battery cost per kWh\"] * self[\"electric energy stored\"]\n )\n self[\"fuel tank cost\"] = self[\"fuel tank cost per kg\"] * self[\"fuel mass\"]\n # Per km\n self[\"energy cost\"] = self[\"energy cost per kWh\"] * self[\"TtW energy\"] / 3600\n\n # For battery, need to divide cost of electricity\n # at battery by efficiency of charging\n # to get costs at the \"wall socket\".\n\n _ = lambda x: np.where(x == 0, 1, x)\n self[\"energy cost\"] /= _(self[\"battery charge efficiency\"])\n\n self[\"component replacement cost\"] = (\n self[\"energy battery cost\"] * self[\"battery lifetime replacements\"]\n + self[\"fuel cell cost\"] * self[\"fuel cell lifetime replacements\"]\n )\n\n with open(DATA_DIR / \"purchase_cost_params.yaml\", \"r\") as stream:\n to_markup = yaml.safe_load(stream)[\"markup\"]\n\n self[to_markup] *= self[\"markup factor\"]\n\n # calculate costs per km:\n self[\"lifetime\"] = self[\"lifetime kilometers\"] / self[\"kilometers per year\"]\n\n with open(DATA_DIR / \"purchase_cost_params.yaml\", \"r\") as stream:\n purchase_cost_params = yaml.safe_load(stream)[\"purchase\"]\n\n self[\"purchase cost\"] = self[purchase_cost_params].sum(axis=2)\n # per km\n amortisation_factor = self[\"interest rate\"] + (\n self[\"interest rate\"]\n / (\n (np.array(1) + self[\"interest rate\"]) ** self[\"lifetime kilometers\"]\n - np.array(1)\n )\n )\n self[\"amortised purchase cost\"] = (\n self[\"purchase cost\"] * amortisation_factor / self[\"kilometers per year\"]\n )\n\n # per km\n self[\"maintenance cost\"] = (\n self[\"maintenance cost per glider cost\"]\n * self[\"glider cost\"]\n / self[\"kilometers per year\"]\n )\n\n # simple assumption that component replacement\n # occurs at half of life.\n self[\"amortised component replacement cost\"] = (\n (\n self[\"component replacement cost\"]\n * (\n (np.array(1) - self[\"interest rate\"]) ** self[\"lifetime kilometers\"]\n / 2\n )\n )\n * amortisation_factor\n / self[\"kilometers per year\"]\n )\n\n self[\"total cost per km\"] = (\n self[\"energy cost\"]\n + self[\"amortised purchase cost\"]\n + self[\"maintenance cost\"]\n + self[\"amortised component replacement cost\"]\n )","def get_expected_cost(self):","def return_running_cost_func(RunningCost='Minimize Input Energy'):\n if type(RunningCost)==str:\n assert RunningCost in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\"\n else:\n assert type(RunningCost)==list, \"RunningCost must be a list of cost types.\"\n for el in RunningCost:\n assert type(el)==str, \"Each element of RunningCost must be a string. Not \" + str(type(el)) + \".\"\n assert el in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\" + el + \"' not accepted.\"\n\n if \"Minimize Input Energy\" in RunningCost:\n result1 = lambda X,U,dt: np.trapz((k3/2)*U**2,dx=dt)\n else:\n result1 = lambda X,U,dt: 0\n\n if \"Minimize time away from target angle\" in RunningCost:\n result2 = lambda X,U,dt: np.trapz(k1*(1/2)*(X[0,1:]-TargetAngle)**2,dx=dt)\n else:\n result2 = lambda X,U,dt: 0\n\n if \"Minimize time away from target angular velocity\" in RunningCost:\n result3 = lambda X,U,dt:\\\n np.trapz(k2*(1/2)*(X[1,1:]-TargetAngularVelocity)**2,dx=dt)\n else:\n result3 = lambda X,U,dt: 0\n\n result = lambda X,U,dt: result1(X,U,dt) \\\n + result2(X,U,dt) \\\n + result3(X,U,dt)\n return(result)","def calculate_bandwidth(self, complist):\n\n self.type = complist[0]\n self.spot = complist[2]\n self.value = complist[1]\n self.fail = False\n\n if self.spot == \"1\" or self.spot == \"2\":\n # print \"Calculation if Loop 1st filter\"\n if self.type == \"C\":\n if self.c1 == 0:\n self.c1 = self.value\n else:\n self.fail = True\n else:\n if self.r1 == 0:\n self.r1 = self.value\n else:\n self.fail = True\n\n if self.spot == \"3\" or self.spot == \"4\":\n # print \"Calculation if loop 2nd filter\"\n if self.type == \"C\":\n if self.c2 == 0:\n self.c2 = self.value\n else:\n self.fail = True\n else: #self.type == \"R\"\n if self.r2 == 0:\n self.r2 = self.value\n else:\n self.fail = True\n\n if self.c1 != 0 and self.c2 != 0 and self.r1 != 0 and self.r2 != 0:\n cf_low = int((1/(2*pi*(self.r1)*(self.c1))/1000))\n #print \"hello!\"\n cf_low = str(cf_low)\n self.cutoff_freql = cf_low\n self.cutoff_freql_text = cf_low + \"K\"\n print self.cutoff_freql_text\n cf_high = int((1/(2*pi*(self.r2)*(self.c2))/1000))\n cf_high = str(cf_high)\n self.cutoff_freqh = cf_high\n self.cutoff_freqh_text = cf_high + \"K\"\n print self.cutoff_freqh_text\n voltage_change = float((self.r2)/self.r1)\n voltage = 5 + voltage_change\n self.voltage = str(voltage)\n print self.voltage","def cost_func(plist):\n\t\tgamma, alpha = plist\n\t\tk = ac.Moffat2DKernel(gamma, alpha, x_size=nx, y_size=ny)\n\n\t\tarr_out_predict = ac.convolve(arr_in, k)\n\n\t\tarr_out_fit, arr_out_predict_fit = match_dimension(arr_out, arr_out_predict)\n\t\tdiff = (arr_out_fit - arr_out_predict_fit)*scale_factor\n\n\t\treturn np.sum(diff**2)/diff.size","def eval_BC(Teff,logg,FeH,filt=\"g\",allBCs=None):\n if allBCs is None: allBCs = read_bc_table()\n \n BCs = allBCs[filt]\n \n points = np.atleast_2d([np.ravel(Teff),np.ravel(logg),np.ravel(FeH)]).T\n points[points[:,2] < -2.5,2] = -2.5\n out = interpolate.griddata(BCs[:,0:3], BCs[:,3], points, method='linear')\n return out","def calc_capital_costs (self):\n powerhouse_control_cost = 0\n if not self.cd['switchgear suitable for renewables']:\n powerhouse_control_cost = self.cd['switchgear cost']\n\n #~ road_needed = self.comp_specs['road needed for transmission line']\n\n\n if str(self.comp_specs['transmission capital cost'])\\\n != 'UNKNOWN':\n transmission_line_cost = \\\n int(self.comp_specs['transmission capital cost'])\n else:\n if str(self.comp_specs['distance to resource']) \\\n != 'UNKNOWN':\n distance = \\\n float(self.comp_specs\\\n ['distance to resource'])\n transmission_line_cost = \\\n distance*self.comp_specs['est. transmission line cost']\n\n secondary_load_cost = 0\n if self.comp_specs['secondary load']:\n secondary_load_cost = self.comp_specs['secondary load cost']\n\n if str(self.comp_specs['generation capital cost']) \\\n != 'UNKNOWN':\n wind_cost = \\\n int(self.comp_specs['generation capital cost'])\n self.cost_per_kw = np.nan\n else:\n for i in range(len(self.comp_specs['estimated costs'])):\n if int(self.comp_specs['estimated costs'].iloc[i].name) < \\\n self.load_offset_proposed:\n if i == len(self.comp_specs['estimated costs']) - 1:\n cost = float(self.comp_specs['estimated costs'].iloc[i])\n break\n continue\n\n cost = float(self.comp_specs['estimated costs'].iloc[i])\n break\n\n wind_cost = self.load_offset_proposed * cost\n self.cost_per_kw = cost\n\n #~ print powerhouse_control_cost\n #~ print transmission_line_cost\n #~ print secondary_load_cost\n #~ print wind_cost\n self.capital_costs = powerhouse_control_cost + transmission_line_cost +\\\n secondary_load_cost + wind_cost\n\n #~ print 'self.capital_costs',self.capital_costs","def _add_bal(self):\n\n c = self.components\n p = self.pipes\n\n # TODO No mass flow reversal yet\n if self.temperature_driven:\n\n lines = self.params['lines'].v()\n\n self.block.mix_temp = Var(self.TIME, lines)\n\n def _temp_bal_incoming(b, t, l):\n\n incoming_comps = collections.defaultdict(list)\n incoming_pipes = collections.defaultdict(list)\n\n for name, comp in c.items():\n if value(comp.get_mflo(t)) >= 0:\n incoming_comps['supply'].append(name)\n else:\n incoming_comps['return'].append(name)\n\n for name, pipe in p.items():\n if value(pipe.get_edge_mflo(self.name, t)) >= 0:\n incoming_pipes['supply'].append(name)\n else:\n incoming_pipes['return'].append(name)\n # Zero mass flow rate:\n if value(\n sum(c[comp].get_mflo(t) for comp in incoming_comps[l]) + \\\n sum(p[pipe].get_edge_mflo(self.name, t) for pipe in\n incoming_pipes[l])) == 0:\n # mixed temperature is average of all joined pipes, actual value should not matter,\n # because packages in pipes of this time step will have zero size and components do not take over\n # mixed temperature in case there is no mass flow\n\n return b.mix_temp[t, l] == (\n sum(c[comp].get_temperature(t, l) for comp in c) +\n sum(p[pipe].get_temperature(self.name, t, l) for\n pipe in p)) / (\n len(p) + len(c))\n\n\n else: # mass flow rate through the node\n return (sum(\n c[comp].get_mflo(t) for comp in incoming_comps[l]) +\n sum(p[pipe].get_edge_mflo(self.name, t) for pipe in\n incoming_pipes[l])) * b.mix_temp[t, l] == \\\n sum(c[comp].get_mflo(t) * c[comp].get_temperature(t,\n l)\n for comp in incoming_comps[l]) + \\\n sum(p[pipe].get_edge_mflo(self.name, t) * p[\n pipe].get_edge_temperature(self.name, t, l)\n for pipe in incoming_pipes[l])\n\n self.block.def_mixed_temp = Constraint(self.TIME,\n lines,\n rule=_temp_bal_incoming)\n\n def _temp_bal_outgoing(b, t, l, comp):\n\n outgoing_comps = collections.defaultdict(list)\n outgoing_pipes = collections.defaultdict(list)\n\n for name, comp_obj in c.items():\n if comp_obj.get_mflo(t) >= 0:\n outgoing_comps['return'].append(name)\n else:\n outgoing_comps['supply'].append(name)\n\n for name, pipe_obj in p.items():\n if pipe_obj.get_edge_mflo(self.name, t) >= 0:\n outgoing_pipes['return'].append(name)\n else:\n outgoing_pipes['supply'].append(name)\n\n if t == 0:\n return Constraint.Skip\n if comp in outgoing_pipes[l]:\n return p[comp].get_edge_temperature(self.name, t, l) == \\\n b.mix_temp[t, l]\n elif comp in outgoing_comps[l]:\n return c[comp].get_temperature(t, l) == b.mix_temp[t, l]\n else:\n return Constraint.Skip\n\n self.block.outgoing_temp_comps = Constraint(self.TIME,\n lines,\n c.keys(),\n rule=_temp_bal_outgoing)\n self.block.outgoing_temp_pipes = Constraint(self.TIME,\n lines,\n p.keys(),\n rule=_temp_bal_outgoing)\n\n elif self.repr_days is None:\n\n def _heat_bal(b, t):\n return 0 == sum(\n self.components[i].get_heat(t) for i in self.components) \\\n + sum(\n pipe.get_edge_heat(self.name, t) for pipe in p.values())\n\n self.block.ineq_heat_bal = Constraint(self.TIME,\n rule=_heat_bal)\n\n def _mass_bal(b, t):\n return 0 == sum(\n self.components[i].get_mflo(t) for i in self.components) \\\n + sum(\n pipe.get_edge_mflo(self.name, t) for pipe in p.values())\n\n self.block.ineq_mass_bal = Constraint(self.TIME,\n rule=_mass_bal)\n\n else:\n def _heat_bal(b, t, c):\n return 0 == sum(\n self.components[i].get_heat(t, c) for i in\n self.components) \\\n + sum(\n pipe.get_edge_heat(self.name, t, c) for pipe in p.values())\n\n self.block.ineq_heat_bal = Constraint(self.TIME, self.REPR_DAYS,\n rule=_heat_bal)\n\n def _mass_bal(b, t, c):\n return 0 == sum(\n self.components[i].get_mflo(t, c) for i in\n self.components) \\\n + sum(\n pipe.get_edge_mflo(self.name, t, c) for pipe in p.values())\n\n self.block.ineq_mass_bal = Constraint(self.TIME, self.REPR_DAYS,\n rule=_mass_bal)","def shipping_cost(fruit):\n if is_berry(fruit):\n shipping_cost = 0\n else:\n shipping_cost = 5\n return shipping_cost","def calculate_total_cost(state):\n pass","def calc_capital_costs (self):\n road_needed = 'road needed'\n if self.cd['on road system']:\n road_needed = 'road not needed'\n\n dist = self.comp_specs['distance to community']\n self.capital_costs = self.comp_specs['est. intertie cost per mile']\\\n [road_needed] * dist\n #~ print self.capital_costs","def transform_shunt_coeff(g, fc, R0=50, bw_ratio=0.1, flt_type='lowpass'):\r\n lst = []\r\n if flt_type == 'lowpass':\r\n val = g/(R0*2*math.pi*fc)\r\n lst.append({'ref_des':'C', 'value':val}) \r\n \r\n elif flt_type == 'highpass':\r\n val = R0/(2*math.pi*fc*g)\r\n lst.append({'ref_des':'L', 'value':val}) \r\n\r\n elif flt_type == 'bandpass':\r\n val = bw_ratio*R0/(2*math.pi*fc*g)\r\n lst.append({'ref_des':'L', 'value':val}) \r\n val = g/(2*math.pi*fc*bw_ratio*R0) \r\n lst.append({'ref_des':'C', 'value':val}) \r\n\r\n elif flt_type == 'bandstop':\r\n val = R0/(g*2*math.pi*fc*bw_ratio)\r\n lst.append({'ref_des':'L', 'value':val}) \r\n val = g*bw_ratio/(R0*2*math.pi*fc)\r\n lst.append({'ref_des':'C', 'value':val}) \r\n else:\r\n raise ValueError(\"not a valid filter type\")\r\n return lst","def step_cost(self, state, action, result=None):\n return 1 # Override this if actions have different costs","def front_column_model_p_gain():","def getCost(dat, rateBlocks, key=\"Lintel\"):\n\n x = dat[:]\n\n if key == \"Lintel\":\n edges = [s*100 for s in [5, 6, 7, 10,\n 11, 12, 15, 16, 17, 20, 21, 22, 25]]\n else:\n edges = [s*100 for s in [10, 11, 12, 15, 16, 17, 20,\n 21, 22, 25, 26, 27, 30, 31, 32, 35, 36, 37, 40]]\n for i in edges:\n if i >= x[2]:\n x[2] = i\n break\n\n vol = x[0]*600*x[2]/float(1000000000)\n return vol*rateBlocks # *x[3]","def get_action_cost(state: tuple, action: int) -> float:\n assert len(state) == 3 and state[0] < 5 and state[1] < 4 and state[2] < 3 and action < 3\n if state[0] == 0:\n return 0.0\n if state[0] == 1 and action == 0:\n return -20.0 + 10.0 * 0.5\n return -20.0 # Penalty = 20 due to Team Number = 9","def shipping_cost(fruit_name):\n\n\tif is_berry(fruit_name):\n\t\treturn 0\n\telse:\n\t\treturn 5","def cost_pressure_exchanger(blk):\n cost_by_flow_volume(\n blk,\n blk.costing_package.pressure_exchanger.cost,\n pyo.units.convert(\n blk.unit_model.low_pressure_side.properties_in[0].flow_vol,\n (pyo.units.meter**3 / pyo.units.hours),\n ),\n )","def filter_cap(stock):\n return stock['Class'] == 'Small'","def filter_de(stock, thresh = .5):\n \"\"\"ASSUMPTION: FILTER_ROE WAS RUN BEFORE THIS, OR GET_MORINGSTAR_RATIOS WAS CALLED\"\"\"\n \"\"\"TODO: ABSTRACT USING HOF\"\"\"\n return stock['DE'] <= thresh","def filtfilt_gust_opt_func(ics, b, a, x):\n m = max(len(a), len(b)) - 1\n z0f = ics[:m]\n z0b = ics[m:]\n y_f = lfilter(b, a, x, zi=z0f)[0]\n y_fb = lfilter(b, a, y_f[::-1], zi=z0b)[0][::-1]\n\n y_b = lfilter(b, a, x[::-1], zi=z0b)[0][::-1]\n y_bf = lfilter(b, a, y_b, zi=z0f)[0]\n value = np.sum((y_fb - y_bf)**2)\n return value","def bern_metric(pipe_diameter, delta_p, pipe_length):\n fr_c = 0.003 # assuming Reynolds number is 10**5 and pipe material is smooth copper\n fr_reyn = 0.046 / (reynolds_num(pipe_diameter, delta_p, pipe_length) ** 0.2) # Taitel and Dukler approximation\n rho = 1000 # density of water @ 4 deg celsius (kg/m**3)\n\n v = math.sqrt((2 * delta_p) / (rho * (4 * fr_reyn * (pipe_length / pipe_diameter) - 1)))\n flow_rate_turb = v * ((math.pi / 4) * (pipe_diameter ** 2))\n\n return flow_rate_turb, v","def calc_boiler_const(Q_load_Wh, thermal_efficiency):\n Q_fuel_Wh = Q_load_Wh / thermal_efficiency\n Q_losses_Wh = Q_fuel_Wh - Q_load_Wh\n\n return Q_fuel_Wh, Q_losses_Wh","def cost_function(x, N, w, dt):\n yh = np.abs(fftkernel(x, w / dt)) # density\n # formula for density\n C = np.sum(yh ** 2) * dt - 2 * np.sum(yh * x) * \\\n dt + 2 / np.sqrt(2 * np.pi) / w / N\n C = C * N * N\n # formula for rate\n # C = dt*sum( yh.^2 - 2*yh.*y_hist + 2/sqrt(2*pi)/w*y_hist )\n return C, yh","def black_caplet(discount, forward, strike, caplet_vol, reset, maturity, \n notional = 1000000):\n tau = maturity - reset\n d1 = (np.log(forward/strike) + 0.5*caplet_vol**2*reset)/(caplet_vol*np.sqrt(reset))\n d2 = d1 - caplet_vol*np.sqrt(reset)\n return (forward*norm.cdf(d1) - strike*norm.cdf(d2))*discount*tau*notional","def p_funct(self, yi, p, t): \t\t\n\t\t\n\t\tglobal Cg \n\t\tCg = abs(yi[0])\n\t\tglobal Ch \n\t\tCh = abs(yi[1])\n\t\tglobal tin_g \n\t\ttin_g = abs(yi[2])\n\t\tglobal tin_h \n\t\ttin_h = abs(yi[3])\n\t\t\n\t\ty = state_at(t)\n\t\trisk = 1 - (y[-1][0] + y[-1][3]) / sum(y[-1])\n\t\t\n\t\treturn abs(risk - p)","def biz(cps):\n # Head of unit\n mask = cps['tc5_p'] > 0\n cps_valid = cps[mask]\n rand = np.random.uniform(size=len(cps_valid))\n new_vals = np.exp(10.4 + 1. * rand)\n new_vals = np.where(new_vals < 50000., 50000., new_vals)\n cps.loc[mask, 'bilp'] = new_vals\n # spouse of unit\n mask = cps['tc5_s'] > 0\n cps_valid = cps[mask]\n rand = np.random.uniform(size=len(cps_valid))\n new_vals = np.exp(10.4 + 1. * rand)\n new_vals = np.where(new_vals < 50000., 50000., new_vals)\n cps.loc[mask, 'bils'] = new_vals","def filter_smooth(self, transect, setting):\n\n # Set property\n self.smooth_filter = setting\n\n # Compute ens_time\n ens_time = np.nancumsum(transect.date_time.ens_duration_sec)\n n_ensembles = len(ens_time)\n # Determine if smooth filter should be applied\n if self.smooth_filter == 'On':\n # Initialize arrays\n self.smooth_speed = repmat([np.nan], 1, n_ensembles)\n self.smooth_upper_limit = repmat([np.nan], 1, n_ensembles)\n self.smooth_lower_limit = repmat([np.nan], 1, n_ensembles)\n\n # Boat velocity components\n b_vele = np.copy(self.u_mps)\n b_veln = np.copy(self.v_mps)\n\n # Set filter parameters\n filter_width = 10\n half_width = 10\n multiplier = 9\n cycles = 3\n\n # Initialize variables\n bt_bad_idx = []\n upper_limit = 0\n lower_limit = 0\n\n # Compute speed and direction of boat\n direct, speed = cart2pol(b_vele, b_veln)\n\n # Compute residuals from a robust Loess smooth\n speed_smooth = rloess(ens_time, speed, filter_width)\n speed_res = speed - speed_smooth\n\n # Apply a trimmed standard deviation filter multiple times\n for i in range(cycles):\n filter_array = BoatData.run_std_trim(half_width, speed_res.T)\n\n # Compute filter bounds\n upper_limit = speed_smooth + multiplier * filter_array\n lower_limit = speed_smooth - multiplier * filter_array\n\n # Apply filter to residuals\n bt_bad_idx = np.where(np.logical_or(np.greater(speed, upper_limit), np.less(speed, lower_limit)))[0]\n speed_res[bt_bad_idx] = np.nan\n\n # Update valid_data property\n self.valid_data[4, :] = True\n self.valid_data[4, bt_bad_idx] = False\n self.valid_data[4, self.valid_data[1, :] == False] = True\n self.smooth_upper_limit = upper_limit\n self.smooth_lower_limit = lower_limit\n self.smooth_speed = speed_smooth\n\n else:\n\n # No filter applied all data assumed valid\n self.valid_data[4, :] = True\n self.smooth_upper_limit = np.nan\n self.smooth_lower_limit = np.nan\n self.smooth_speed = np.nan\n\n # Combine all filter data to composite valid data\n self.valid_data[0, :] = np.all(self.valid_data[1:, ], 0)\n self.num_invalid = np.sum(self.valid_data[0, :] == False, 0)","def calc_HEX_cooling(Q, UA, thi, tho, tci, ch):\n\n if ch > 0:\n eff = [0.1, 0]\n Flag = False\n tol = 0.00000001\n while abs((eff[0] - eff[1]) / eff[0]) > tol:\n if Flag == True:\n eff[0] = eff[1]\n else:\n cmin = ch * (thi - tho) / ((thi - tci) * eff[0])\n if cmin < ch:\n cc = cmin\n cmax = ch\n else:\n cc = cmin\n cmax = cc\n cmin = ch\n cr = cmin / cmax\n NTU = UA / cmin\n eff[1] = calc_plate_HEX(NTU, cr)\n cmin = ch * (thi - tho) / ((thi - tci) * eff[1])\n tco = tci + eff[1] * cmin * (thi - tci) / cc\n Flag = True\n cc = Q / abs(tci - tco)\n tco = tco - 273\n else:\n tco = 0\n cc = 0\n return np.float(tco), np.float(cc / 1000)","def flowStress(f_hard,eps,d,q,a):\n\n pass","def butter_bp_coe(lowcut, highcut, fs, order=1):\n nyq = 0.5 * fs\n low = lowcut / nyq\n high = highcut / nyq\n b, a = butter(order, [low, high], btype='band')\n return b, a","def penalty_calc(self):\n self.p_budget = (self.tx_oma_min - self.rx_unstressed_sensitivity - self.fiber_conn_loss)*self.l_1\n\n # fiber attenuation,\n self.p_atten = self.alpha*self.length # column B\n\n # calculate bandwidth for RIN test (exclude transmitter)\n rin_inverse_bw = np.sqrt(np.square(1.0/self.bw_cd) + np.square(1.0/self.bw_md) + (0.477/(self.rx_bw**2))*self.l_1)\n rin_bw = 1.0 / rin_inverse_bw\n\n # see FC-MSQS-2 equation B.47 in Annex B.4 for the following k_rin = math.sqrt(2.0/math.pi)*erfinv(0.8)\n k_rin = 0.7\n\n # v_rin,\n self.v_rin = (k_rin*1E6*(self.rin_test_isi**2)*rin_bw*\n math.pow(10.0,0.1*self.rin)) # column AK\n\n # Prin,\n print('v_rin: ', self.v_rin)\n print('Q: ',self.Q)\n print('isi_dj_refl_closed :', self.isi_dj_refl_closed)\n self.p_rin = -10.0*np.log10(np.sqrt(1.0-np.multiply(self.v_rin, np.square(self.Q/self.isi_dj_refl_closed)))) # column R\n print(\"P_rin : \", self.p_rin)\n self.beta = (3.14159E-6*self.speedup*self.br_nominal *self.delta_lambda*self.d1*self.length) # column O\n self.sigma_mpn = (self.k_mpn/math.sqrt(2.0)*(self.l_1 -np.exp(-np.square(self.beta)))) # column P\n self.p_mpn = (-10.0*np.log10(np.sqrt(self.l_1 - (self.Q**2)*np.square(self.sigma_mpn)))) # column Q\n self.p_blw = (-10.0*math.log10(math.sqrt(1.0- ((self.Q*self.sigma_blw)/ self.isi_tp4_rx)**2))*self.l_1) # cell T13\n self.p_reflection = -10.0*np.log10(self.isi_reflection) # column N\n self.v_mn = (((1.0-math.pow(10.0,-0.2*self.pmn))/ (self.Q)**2)*self.l_1) # cell AG7\n print(\"isi_center : \", self.isi_center)\n\n self.p_isi_center = -10.0*np.log10(self.isi_center) # column J\n\n self.p_isi_corners = (-10.0*np.log10(self.isi_corners) - self.p_isi_center) # column K\n self.p_isi_dj_center = (-10.0*np.log10(self.isi_dj_refl_closed) - self.p_isi_center) # column L\n self.p_isi_dj_corners = (-10.0*np.log10(self.isi_dj_corners) -self.p_isi_center -self.p_isi_corners) # column M\n\n\n # calculate the \"cross\" penalty contribution, column S\n arg1 = ((self.sigma_blw**2 + self.v_rin)/ np.square(self.isi_dj_refl_closed))\n arg2 = self.l_1 - (self.Q**2)*(arg1 + self.v_mn + np.square(self.sigma_mpn))\n arg3 = (-10.0*np.log10(np.multiply(self.isi_dj_refl_closed, np.sqrt(arg2))))\n self.p_cross_center = ( # column S\n arg3\n - self.p_blw # cell T13\n - self.p_isi_center # column J\n - self.p_isi_dj_center # column L\n - self.p_mpn # column Q\n - self.p_reflection # column N\n - self.p_rin # column R\n - self.pmn*self.l_1) # cell G13\n print('p_isi_center: ', self.p_isi_center)\n\n # calculate the total power budget evaluated at the center of the eye\n self.p_total_center = ( # column T\n self.p_isi_center # column J\n + self.p_isi_dj_center # column L\n + self.p_atten # column B\n + self.p_mpn # column Q\n + self.p_reflection # column N\n + self.p_rin # column R\n + self.p_cross_center # column S\n + self.pmn*self.l_1) # cell G13\n # calculate the total power budget evaluated at the corner of the eye\n self.p_total_corners = (\n self.p_isi_center # column J\n + self.p_isi_corners # column K\n + self.p_atten # column B\n + self.p_mpn # column Q\n + self.p_reflection # column N\n + self.p_rin # column R\n + self.p_cross_center # column S\n + self.pmn*self.l_1 # cell G13\n + self.p_isi_dj_corners)# column M\n\n # receiver stressed sensitivity\n self.margin = ( self.p_budget\n - self.p_total_center) # column W\n\n self.rx_stressed_sensitivity = (\n self.tx_oma_min*self.l_1\n - self.chil\n - self.p_mpn\n - self.p_reflection\n - self.p_rin\n - 0.5*self.p_cross_center\n - self.pmn*self.l_1\n - self.margin[self.lnum//2]*self.l_1)\n\n\n # end of GbE10.penalty_calc\n #======================================================================+","def vector_substraction(self, th_eur, r_eur, th_sb, r_sb):\n # Initialize the cost vector\n cost = []\n \n # Loop through the fraction of europe and calculate the vector cost\n for i in range(1, len(r_eur)):\n # Check if the location is just sea and nothing else. Do not take this position.\n if (np.sum(r_eur) == 0):\n cost.append(20)\n else:\n cost_zeros = (len([r == 0 for r in r_eur if r == 0])) * 5\n (x1, y1) = (np.cos(th_eur[i])*r_eur[i], np.sin(th_eur[i])*r_eur[i])\n (x2, y2) = (np.cos(th_sb[i])*r_sb[i], np.sin(th_sb[i])*r_sb[i])\n cost.append(np.sqrt(((y2 - y1)**2 + (x2 - x1)**2)) + cost_zeros) \n\n # Make the cost function nonlinear. Favour very good single vectors.\n mask = len([c for c in cost if c > 1])\n cost_sum = sum(el**4 for el in cost)\n if (mask > 2):\n cost_sum += 10\n \n return cost_sum","def _cost_method(self, *args, **kwargs):\n\n cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2\n\n if 'verbose' in kwargs and kwargs['verbose']:\n print(' - DATA FIDELITY (X):', cost_val)\n\n return cost_val","def cost(self):\n\t\treturn self.g + self.h","def get_spike_cost(self, r):\n return self.tc.costs(\n self.pf.XS[:, :, 0].transpose(),\n self.pf.XS[:, :, 1].transpose(),\n r,\n self.pf.WS[:].transpose())[2]","def calcCostFun(self):\n\n self.start()\n F, K = self.model()\n \n return self.costFunction","def eval_cost(self, params, **kwargs):\n raise NotImplementedError","def fuel_cond(T):\n\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\n \n return kc","def get_cost_string(es, t):\n strg = ''\n for item in es:\n strg += '{:011.7f}'.format(item / t) + ' '\n return strg","def run (self, scalers = {'capital costs':1.0}):\n self.was_run = True\n self.reason = \"OK\"\n\n tag = self.cd['file id'].split('+')\n if len(tag) > 1 and tag[1] != 'biomass_pellet':\n self.was_run = False\n self.reason = (\"Not a biomass pellet project.\")\n return\n\n if not self.cd[\"on road system\"]:\n self.diagnostics.add_warning(self.component_name,\n \"not on road system\")\n self.max_boiler_output = 0\n self.heat_displaced_sqft = 0\n self.biomass_fuel_consumed = 0\n self.fuel_price_per_unit = 0\n self.heat_diesel_displaced = 0\n self.reason = \\\n \"Not on road or marine highway system, so it is assumed that\" +\\\n \" pellets cannot be delivered cost effectively.\"\n return\n\n if np.isnan(float(self.comp_specs['peak month % of total'])):\n self.diagnostics.add_warning(self.component_name,\n \"bad config value for 'peak month % of total'\")\n self.max_boiler_output = 0\n self.heat_displaced_sqft = 0\n self.biomass_fuel_consumed = 0\n self.fuel_price_per_unit = 0\n self.heat_diesel_displaced = 0\n self.reason = \"bad config value for 'peak month % of total'\"\n return\n\n if self.cd[\"model heating fuel\"]:\n self.calc_heat_displaced_sqft()\n self.calc_energy_output()\n efficiency = self.comp_specs[\"pellet efficiency\"]\n self.calc_max_boiler_output(efficiency)\n factor = self.comp_specs['capacity factor']\n self.calc_biomass_fuel_consumed(factor)\n self.calc_diesel_displaced()\n\n\n if self.cd[\"model financial\"]:\n self.get_diesel_prices()\n\n self.calc_capital_costs()\n self.calc_maintenance_cost()\n\n\n self.fuel_price_per_unit = self.cd['pellet price']\n\n self.calc_proposed_biomass_cost(self.fuel_price_per_unit)\n self.calc_displaced_heating_oil_price()\n\n\n self.calc_annual_electric_savings()\n self.calc_annual_heating_savings()\n\n self.calc_annual_total_savings()\n self.calc_annual_costs(self.cd['interest rate'],\n scalers['capital costs'])\n self.calc_annual_net_benefit()\n self.calc_npv(self.cd['discount rate'], self.cd[\"current year\"])\n\n fuel_cost = self.biomass_fuel_consumed * self.fuel_price_per_unit\n self.calc_levelized_costs(self.maintenance_cost + fuel_cost)","def capacity_cost_rule(mod, g, p):\n return mod.stor_spec_power_capacity_mw[g, p] \\\n * mod.stor_spec_fixed_cost_per_mw_yr[g, p] \\\n + mod.stor_spec_energy_capacity_mwh[g, p] \\\n * mod.stor_spec_fixed_cost_per_mwh_yr[g, p]","def get_cost(self, action: Action) -> N:\n pass","def Hstep_cost_function(H): \n U = Wold - Yold\n #cost = -np.trace(H.T@K@H) + (self.admm_rho/2)*(norm(H.T@D - Wold + self.Y, 'fro')**2) \n cost = -np.trace(H.T@K@H)/nsamples + (rho/2)*np.trace((H.T@D - U)@(H.T@D-U).T) \n return cost","def getCost(self, state, action):\n util.raiseNotDefined()","def cost_cal(self, Type, Tract_pop, Tractx, Tracty, cnum):\n Geox1 = sf.FeatureScaling(self.Geox)\n Geoy1 = sf.FeatureScaling(self.Geoy)\n Tract_pop1 = sf.FeatureScaling(Tract_pop)\n Tractx1 = sf.FeatureScaling(Tractx)\n Tracty1 = sf.FeatureScaling(Tracty)\n \n self.cost, temp = ans.cost(self.demandloc, Geox1, Geoy1, Tract_pop1, Type, Tractx1, Tracty1, Tract_pop, cnum)","def cost(inventory):\n return inventory.reduce(convert.get_cost)","def transport_cost_per_t(self):\n return safe_divide(self.reseller.operating_expenses(), self.quantity_fieldside)","def __call__(self, T_HTF_inlet, m_in, htf):\n # Function maps inputs ( T [K], P [Pa] ) to outputs (C [J/kg-K])\n # We apply it: inputs ( T [C], P [Pa] ) to outputs (C [J/kg-K])\n T_min, T_max = T_HTF_inlet, self.T_exhaust_inlet\n T_mean = 0.5 * (T_min + T_max)\n C_p_exhaust = PropsSI('C',\n 'T', T_mean + 273.15,\n 'P', self.P_exhaust_inlet,\n 'Air')\n C_exhaust = C_p_exhaust * self.m_exhaust # [J/K]\n C_HTF = htf.lookup('C',T_mean) * m_in\n\n # This should be true\n C_min, C_max = C_exhaust, C_HTF\n C_r = C_min / C_max\n if C_r > 1:\n raise ValueError(\"HTF stream should have greater capacity (C_r = {})\".format(C_r))\n DeltaT_max = self.T_exhaust_inlet - T_HTF_inlet\n Q_max = C_min * DeltaT_max\n Q = self.effectiveness * Q_max\n T_HTF_outlet = T_HTF_inlet + Q / C_HTF\n T_exhaust_outlet = self.T_exhaust_inlet - Q / C_exhaust\n\n # TODO: adjust for (water) flow rate\n DeltaP_HTF = -6e3 # [Pa]\n DeltaP_exhaust = -300 # [Pa]\n \n return Q, T_HTF_outlet, T_exhaust_outlet, DeltaP_HTF, DeltaP_exhaust","def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):\n\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\n # capacity for the corresponding technology from the database\n if CC_size_W < CCGT_cost_data['cap_min'][0]:\n CC_size_W = CCGT_cost_data['cap_min'][0]\n CCGT_cost_data = CCGT_cost_data[\n (CCGT_cost_data['cap_min'] <= CC_size_W) & (CCGT_cost_data['cap_max'] > CC_size_W)]\n\n\n #costs of connection\n connection_costs = ngas.calc_Cinv_gas(CC_size_W)\n\n Inv_a = CCGT_cost_data.iloc[0]['a']\n Inv_b = CCGT_cost_data.iloc[0]['b']\n Inv_c = CCGT_cost_data.iloc[0]['c']\n Inv_d = CCGT_cost_data.iloc[0]['d']\n Inv_e = CCGT_cost_data.iloc[0]['e']\n Inv_IR = CCGT_cost_data.iloc[0]['IR_%']\n Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']\n Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100\n\n InvC = Inv_a + Inv_b * (CC_size_W) ** Inv_c + (Inv_d + Inv_e * CC_size_W) * log(CC_size_W)\n\n Capex_a_CCGT_USD = calc_capex_annualized((InvC+connection_costs), Inv_IR, Inv_LT)\n Opex_fixed_CCGT_USD = InvC * Inv_OM\n Capex_CCGT_USD = InvC\n\n return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD","def calc_calories(gpx_track, wt = 175, activity='Run'):","def test_cost(self):\n self.assertTrue(\n int(self.ospf.parse_state(\n pattern='cost',\n cmd_key='sh_ospf_ints')) == 10, 'OSPF Interface: cost not found')","def cost(ev):\n profData = getProfilingData(ev)\n if profData is not None:\n return profData.PropagatedCost()\n return \"\"","def deredden(EBV,filt):\n conversion_data = ascii.read(datapath+\"/stellar_param_data/sf11.txt\")\n assert filt in conversion_data[\"filter\"], (filt, conversion_data[\"filter\"])\n return EBV * float(conversion_data[\"AB_EBV\"][np.where(conversion_data[\"filter\"]==filt)[0]])","def oil_compressibility(pressure2, P_bubble, temp2, api, Rsb, sg2):\n import numpy as np\n from math import e\n\n # oil isothermal compressibility\n\n coil_array = []\n\n if pressure2 < P_bubble:\n # use McCain\n ln_coil = -7.573 - (1.45 * np.log(pressure2)) - (0.383 * np.log(P_bubble)) + (1.402 * np.log(temp2)) + (0.256 * np.log(api)) + (0.449 * np.log(Rsb)) \n coil = np.exp(ln_coil)\n if pressure2 >= P_bubble:\n # use Vazquez-Beggs\n coil = ((5 * Rsb) + (17.2 * temp2) - (1180 * sg2) + (12.61 * api) - 1433) / (1E+05 * pressure2)\n\n return coil","def total_cost_w_tax(tax_rate, state, cost_amount):\n state.upper()\n default_tax_rate = 0.05\n if state == 'CA':\n total_cost = (cost_amount * .07) + cost_amount\n elif tax_rate != 0.07 or tax_rate != 0.05:\n total_cost = (cost_amount * tax_rate) + cost_amount\n else:\n total_cost = (cost_amount * default_tax_rate) + cost_amount\n return total_cost, state.upper()","def _cost_load_theft(self):\n theft_prob = self.theft_probability\n theft = np.random.choice([0, 1], p=[1-theft_prob, theft_prob])\n if theft == 1:\n cost = self._calculate_profit_weight() * PRICE_PER_KG\n return cost\n else:\n return 0","def thermal_operating_costs_rule(_m, y, s):\r\n\r\n return sum((m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])) * m.p[g, y, s, t]\r\n for g in m.G_THERM for t in m.T)","def stepCost(self, state, action):\n\n j = len(state)\n while (j > 0 and state[j-1] != ' '): j -= 1\n word = state[j:]\n if(action == ' '):\n return self.unigramCost(word)\n cost1 = self.unigramCost(word)\n if(word == ''): cost1 = 0.0\n cost = self.unigramCost(word + action) - cost1\n #print(f'state = {state}, word = {word}, action = {action}, cost = {cost}')\n return cost","def bcost(action):\r\n # An action is an (a, b, arrow) tuple; a and b are\r\n # times; arrow is a string.\r\n a, b, arrow = action\r\n return max(a, b)","def testActionCosts(self):\n from builder import Builder\n\n action = Parser.parse_as(cost_load.split(\"\\n\"), Action, self.domain)\n b = Builder(action)\n \n expected_term = b(\"load-costs\", \"?v\")\n self.assertEqual(action.get_total_cost(), expected_term)\n\n action.set_total_cost(25)\n self.assertEqual(action.get_total_cost(), b(25))\n\n action.set_total_cost(None)\n self.assertEqual(len(action.effect.parts), 1)\n\n action.set_total_cost(b(\"+\", (\"load-costs\", \"?v\"), 5))\n self.assertEqual(len(action.effect.parts), 2)","def hinderedRotor_heatCapacity(T, freq, barr):\n x = constants.h * constants.c * 100. * freq / constants.kB / T\n exp_x = math.exp(x)\n one_minus_exp_x = 1.0 - exp_x\n z = 0.5 * constants.h * constants.c * 100. * barr / constants.kB / T\n BB = scipy.special.i1(z) / scipy.special.i0(z)\n return x * x * exp_x / one_minus_exp_x / one_minus_exp_x - 0.5 + z * (z - BB - z * BB * BB)","def car_cost(self,car,car_stations):\n car_cost = car.capacity # Aqui le deberiamos aumentar el peso\n #de acuerdo a la capacidad del carro y la cantidad de lugares \n #en la estacion\n return car_stations","def get_flow(self, pressure_drop, coeff): \n flow = coeff * pressure_drop**0.5\n return flow","def _cost(self, action):\n return float(self.spacecraft.crashed)","def mce_filter(freq, f_raw, params):\n\tz = np.exp(-2j*np.pi*freq/f_raw)\n\tb11, b12, b21, b22 = np.array(params[:4])*0.5**14\n\tH = (1+z)**4 / (1-b11*z+b12*z**2) / (1-b21*z+b22*z**2)\n\tH /= 2**4 / (1-b11+b12) / (1-b21+b22)\n\treturn H","def bcost(action):\n # An action is an (a, b, arrow) tuple; a and b are\n # times; arrow is a string.\n a, b, arrow = action\n return max(a, b)","def butter_filter(self, data, low_pass, high_pass, fs, order=10):\n\n nyq = fs/2\n low = low_pass/nyq\n high = high_pass/nyq\n\n b, a = signal.butter(order, [low, high], btype='band')\n filt_data = np.abs(signal.hilbert(signal.filtfilt(b, a, data, axis=1), axis=1))\n return filt_data","def cost_function(params, count):\n circuit = models.Circuit(nqubits)\n for l in range(layers):\n for q in range(nqubits):\n circuit.add(gates.RY(q, theta=0))\n for q in range(0, nqubits - 1, 2):\n circuit.add(gates.CZ(q, q + 1))\n for q in range(nqubits):\n circuit.add(gates.RY(q, theta=0))\n for q in range(1, nqubits - 2, 2):\n circuit.add(gates.CZ(q, q + 1))\n circuit.add(gates.CZ(0, nqubits - 1))\n for q in range(nqubits):\n circuit.add(gates.RY(q, theta=0))\n\n cost = 0\n circuit.set_parameters(\n params\n ) # this will change all thetas to the appropriate values\n for i in range(len(ising_groundstates)):\n final_state = circuit(np.copy(ising_groundstates[i]))\n cost += np.real(encoder.expectation(final_state.state()))\n\n if count[0] % 50 == 0:\n print(count[0], cost / len(ising_groundstates))\n count[0] += 1\n\n return cost / len(ising_groundstates)","def make_pipeline():\r\n\r\n mkt_cap_screen = (morningstar.valuation.market_cap.latest > 1e9)\r\n\r\n return Pipeline(\r\n columns={\r\n 'Free Cash Flow': morningstar.cash_flow_statement.free_cash_flow.latest,\r\n }, screen=mkt_cap_screen)","def __init__(self, name, fuel, fanciness):\n super().__init__(name, fuel)\n self.fanciness = float(fanciness)\n self.price_per_km *= self.fanciness","def get_problem():\n\n #User Defined Terrain Elevation\n #def terr( x_pos, y_pos ):\n #Defines terrain elevation [m] as a function of x and y positions [m]\n # elev=100.0*(np.sin(0.5*(x_pos/1000.0)))**2.0 #User defined elevation map\n # return elev\n\n #User Defined Tunnel Cost\n #def tunnel(depth):\n #Defines additional cost for placing a 1 meter length of track a non-zero\n #depth below the ground.\n # TunnelCost=(50e3)/(1+np.exp(-(depth-5))) #Tunneling Cost (2016 USD)\n # return TunnelCost\n\n #def bridge(height):\n #Defines additional cost for placing a 1 meter length of track a non-zero\n #heigh above the ground.\n # BridgeCost=10e3*(height/10)**2 #Bridge Cost (2016 USD)\n # return BridgeCost\n\n # Rename this and/or move to optim package?\n problem = beluga.optim.Problem('surftest_noinc')\n\n #Define independent variables\n problem.independent('t', 's')\n\n # Define equations of motion\n problem.state('x','V*cos(hdg)','m') \\\n .state('y','V*sin(hdg)','m') \\\n .state('V','amax*sin(thrA) + eps*(cos(thrA)+cos(hdgA))','m/s') \\\n .state('hdg','cmax/V*sin(hdgA)','rad')\n\n # Define controls\n #problem.control('thrA','rad') \\\n # .control('hdgA','rad')\n problem.control('hdgA','rad')\n\n # Define Cost Functional\n problem.cost['path'] = Expression('1','s')\n\n #problem.cost['path'] = Expression('TimeToUSD+trk*V', 'USD')\n\n #+ \\\n #'(50e3)/(1.0+exp(-1.0*(z-0.0*(sin(0.5*(x/1000.0)))**2.0-5.0)))+'+ \\\n #'10e3*((0.0*(sin(0.5*(x/1000.0)))**2.0-z)/10.0)**2.0','USD')\n\n #Define constraints\n problem.constraints().initial('x-x_0','m') \\\n .initial('y-y_0','m') \\\n .initial('V-V_0','m/s') \\\n .initial('hdg-hdg_0','rad') \\\n .terminal('x-x_f','m') \\\n .terminal('y-y_f','m')\n #.terminal('V-V_f','m/s')\n #.initial('hdg-hdg_0','rad') \\\n\n #Define constants\n problem.constant('g',9.81,'m/s^2') #Acceleration due to gravity\n problem.constant('trk',1,'USD/m') #Basic cost of 1 m of track on ground (10k per m)\n problem.constant('amax',1.0,'m/s^2') #Maximum thrust acceleration of vehicle\n problem.constant('cmax',1.0,'m/s^2') #Maximum allowed centripetal acceleration\n problem.constant('eps',10,'m/s^2') #Error constant\n problem.constant('TimeToUSD',1,'USD/s') #Time is Money!!\n problem.constant('thrA',0,'rad')\n\n #Unit scaling\n problem.scale.unit('m','x') \\\n .unit('s','x/V') \\\n .unit('rad',1) \\\n .unit('USD',1)\n\n #Configure solver\n problem.bvp_solver = algorithms.MultipleShooting(derivative_method='fd',tolerance=1e-4, max_iterations=1000, verbose = True, cached = False, number_arcs=2)\n\n #Initial Guess\n problem.guess.setup('auto',start=[0.0,0.0,1.0,pi/4-0.2], costate_guess=-0.1) #City A\n\n #Add Continuation Steps\n problem.steps.add_step().num_cases(10) \\\n .terminal('x', 10) \\\n .terminal('y', 0)\n\n problem.steps.add_step().num_cases(10) \\\n .const('eps', 0.2)\n\n #problem.steps.add_step().num_cases(10) \\\n # .terminal('y', 2*pi*1000) \\\n # .terminal('z', 0.0) \\\n # .terminal('inc', 0.0)\n #^ City B\n return problem","def SCB(t,init,rhoS,deltaSC,rhoC,deltaCB,rhoB):\n\n #Declaring useful parameters\n [S0,C0,B0]=init\n lambdaS=deltaSC-rhoS #Introducing global decay rates lambda.\n lambdaC=deltaCB-rhoC\n lambdaB=-rhoB\n\n #Checking for eigenvalues equalitites\n thresh=1e-5 #threshold difference for considering two eignevalues as equal\n bSC=(lambdaC-lambdaS)*(abs(lambdaC-lambdaS)>=thresh)\n bSB=(lambdaB-lambdaS)*(abs(lambdaB-lambdaS)>=thresh)\n bCB=(lambdaB-lambdaC)*(abs(lambdaB-lambdaC)>=thresh)\n\n #S has always the same expression\n S=S0*np.exp(-lambdaS*t)\n\n #there are two cases for C\n if bSC!=0:\n c2=deltaSC*S0/bSC; c1=C0-c2\n C=c1*np.exp(-lambdaC*t)+c2*np.exp(-lambdaS*t)\n\n #there are three subcases for B in that case\n if bCB==0:\n b2=deltaCB*c1; b3=deltaCB*c2/bSB; b1=B0-b3\n B=(b1+b2*t)*np.exp(-lambdaB*t)+b3*np.exp(-lambdaS*t)\n\n elif bSB==0:\n b2=deltaCB*c1/bCB; b3=deltaCB*c2; b1=B0-b2\n B=(b1+b3*t)*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)\n\n else:\n b2=deltaCB*c1/bCB; b3=deltaCB*c2/bSB; b1=B0-b2-b3\n B=b1*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)+b3*np.exp(-lambdaS*t)\n\n else:\n c2=deltaSC*S0\n c1=C0\n C=(c1+c2*t)*np.exp(-lambdaS*t)\n\n #there are two subcases for B in that case\n if bCB!=0:\n b3=deltaCB*c2/bSB; b2=(deltaCB*c1-b3)/bSB; b1=B0-b2\n B=b1*np.exp(-lambdaB*t)+(b2+b3*t)*np.exp(-lambdaC*t)\n\n else:\n b1=B0; b2=deltaCB*c1; b3=deltaCB*c2/2\n B=(b1+b2*t+b3*t**2)*np.exp(-lambdaB*t)\n\n return(np.vstack((S,C,B)))","def calculate_cost(self):\n costs = {}\n if np.abs(self.agent.get_position()[1]) > self.y_lim:\n costs['cost_outside_bounds'] = 1.\n if self.agent.velocity_violation:\n costs['cost_velocity_violation'] = 1.\n # sum all costs in one total cost\n costs['cost'] = min(1, sum(v for k, v in costs.items() if k.startswith('cost_')))\n return costs"],"string":"[\n \"def cond_boiler_op_cost(Q_therm_W, Q_design_W, T_return_to_boiler_K):\\n if Q_therm_W > 0.0:\\n\\n # boiler efficiency\\n eta_boiler = cond_boiler_operation(Q_therm_W, Q_design_W, T_return_to_boiler_K)\\n\\n E_aux_Boiler_req_W = BOILER_P_AUX * Q_therm_W\\n\\n Q_primary_W = Q_therm_W / eta_boiler\\n else:\\n Q_primary_W = 0.0\\n E_aux_Boiler_req_W = 0.0\\n\\n return Q_primary_W, E_aux_Boiler_req_W\",\n \"def calc_capital_costs (self):\\n self.capital_costs = self.max_boiler_output * \\\\\\n self.comp_specs[\\\"cost per btu/hrs\\\"]\\n #~ print self.capital_costs\",\n \"def cost(self) -> float:\",\n \"def get_PP_costing(self, cost_accounts, scaled_param, units, tech, ccs=\\\"B\\\"):\\n # ------------------------ Power Plant Cost ------------------------\\n\\n # check to see if a costing block already exists\\n if hasattr(self, \\\"costing\\\"):\\n raise AttributeError(\\n \\\"{} already has an attribute costing. \\\"\\n \\\"Check that you are not calling get_costing\\\"\\n \\\" twice on the same model\\\".format(self.name)\\n )\\n\\n # create a costing Block\\n self.costing = Block()\\n self.costing.library = \\\"PP\\\"\\n\\n # find flowsheet block to create global costing parameters\\n try:\\n fs = self.flowsheet()\\n except AttributeError:\\n fs = self.parent_block()\\n\\n # build flowsheet level parameters CE_index = year\\n if not hasattr(fs, \\\"costing\\\"):\\n fs.get_costing(year=\\\"2018\\\")\\n\\n CE_index = fs.costing.CE_index\\n\\n # define preloaded accounts\\n PC_preloaded_accounts = {\\n \\\"Coal Handling\\\": [\\\"1.1\\\", \\\"1.2\\\", \\\"1.3\\\", \\\"1.4\\\", \\\"1.9a\\\"],\\n \\\"Sorbent Handling\\\": [\\\"1.5\\\", \\\"1.6\\\", \\\"1.7\\\", \\\"1.8\\\", \\\"1.9b\\\"],\\n \\\"Coal Feed\\\": [\\\"2.1\\\", \\\"2.2\\\", \\\"2.9a\\\"],\\n \\\"Sorbent Feed\\\": [\\\"2.5\\\", \\\"2.6\\\", \\\"2.9b\\\"],\\n \\\"Feedwater System\\\": [\\\"3.1\\\", \\\"3.3\\\"],\\n \\\"PC Boiler\\\": [\\\"4.9\\\"],\\n \\\"Steam Turbine\\\": [\\\"8.1\\\"],\\n \\\"Condenser\\\": [\\\"8.3\\\"],\\n \\\"Cooling Tower\\\": [\\\"9.1\\\"],\\n \\\"Circulating Water System\\\": [\\\"9.2\\\", \\\"9.3\\\", \\\"9.4\\\", \\\"9.6\\\", \\\"9.7\\\"],\\n \\\"Ash Handling\\\": [\\\"10.6\\\", \\\"10.7\\\", \\\"10.9\\\"],\\n }\\n\\n IGCC_preloaded_accounts = {\\n \\\"Coal Handling\\\": [\\\"1.1\\\", \\\"1.2\\\", \\\"1.3\\\", \\\"1.4\\\", \\\"1.9\\\"],\\n \\\"Coal Feed\\\": [\\\"2.1\\\", \\\"2.2\\\", \\\"2.3\\\", \\\"2.4\\\", \\\"2.9\\\"],\\n \\\"Feedwater System\\\": [\\\"3.1\\\", \\\"3.3\\\"],\\n \\\"Gasifier\\\": [\\\"4.1\\\"],\\n \\\"Syngas Cooler\\\": [\\\"4.2\\\"],\\n \\\"ASU\\\": [\\\"4.3a\\\"],\\n \\\"ASU Oxidant Compression\\\": [\\\"4.3b\\\"],\\n \\\"Combustion Turbine\\\": [\\\"6.1\\\", \\\"6.3\\\"],\\n \\\"Syngas Expander\\\": [\\\"6.2\\\"],\\n \\\"HRSG\\\": [\\\"7.1\\\", \\\"7.2\\\"],\\n \\\"Steam Turbine\\\": [\\\"8.1\\\"],\\n \\\"Condenser\\\": [\\\"8.3\\\"],\\n \\\"Cooling Tower\\\": [\\\"9.1\\\"],\\n \\\"Circulating Water System\\\": [\\\"9.2\\\", \\\"9.3\\\", \\\"9.4\\\", \\\"9.6\\\", \\\"9.7\\\"],\\n \\\"Slag Handling\\\": [\\\"10.1\\\", \\\"10.2\\\", \\\"10.3\\\", \\\"10.6\\\", \\\"10.7\\\", \\\"10.8\\\", \\\"10.9\\\"],\\n }\\n\\n NGCC_preloaded_accounts = {\\n \\\"Feedwater System\\\": [\\\"3.1\\\", \\\"3.3\\\"],\\n \\\"Combustion Turbine\\\": [\\\"6.1\\\", \\\"6.3\\\"],\\n \\\"HRSG\\\": [\\\"7.1\\\", \\\"7.2\\\"],\\n \\\"Steam Turbine\\\": [\\\"8.1\\\"],\\n \\\"Condenser\\\": [\\\"8.3\\\"],\\n \\\"Cooling Tower\\\": [\\\"9.1\\\"],\\n \\\"Circulating Water System\\\": [\\\"9.2\\\", \\\"9.3\\\", \\\"9.4\\\", \\\"9.6\\\", \\\"9.7\\\"],\\n }\\n\\n AUSC_preloaded_accounts = {\\n \\\"PC Boiler\\\": [\\\"4.9\\\"],\\n \\\"Steam Turbine\\\": [\\\"8.1\\\"],\\n \\\"Steam Piping\\\": [\\\"8.4\\\"],\\n }\\n\\n # preloaded account handling\\n if type(cost_accounts) == str:\\n if tech in [1, 2]:\\n cost_accounts = PC_preloaded_accounts[cost_accounts]\\n elif tech in [3, 4, 5]:\\n cost_accounts = IGCC_preloaded_accounts[cost_accounts]\\n elif tech == 6:\\n cost_accounts = NGCC_preloaded_accounts[cost_accounts]\\n elif tech == 7:\\n cost_accounts = AUSC_preloaded_accounts[cost_accounts]\\n else:\\n AttributeError(\\\"{} technology not supported\\\".format(self.name))\\n\\n # pull data for each account into dictionaries\\n process_params = {}\\n reference_units = {}\\n account_names = {}\\n exponents = {}\\n reference_costs = {}\\n reference_params = {}\\n engineering_fees = {}\\n process_contingencies = {}\\n project_contingencies = {}\\n\\n for account in cost_accounts:\\n try: # first look for data in json file info\\n process_params[account] = BB_costing_exponents[str(tech)][account][\\n \\\"Process Parameter\\\"\\n ]\\n reference_units[account] = BB_costing_params[str(tech)][ccs][\\n cost_accounts[0]\\n ][\\\"Units\\\"]\\n account_names[account] = BB_costing_exponents[str(tech)][account][\\n \\\"Account Name\\\"\\n ]\\n exponents[account] = float(\\n BB_costing_exponents[str(tech)][account][\\\"Exponent\\\"]\\n )\\n reference_costs[account] = BB_costing_params[str(tech)][ccs][account][\\\"BEC\\\"]\\n reference_params[account] = BB_costing_params[str(tech)][ccs][account][\\n \\\"RP Value\\\"\\n ]\\n engineering_fees[account] = BB_costing_params[str(tech)][ccs][account][\\n \\\"Eng Fee\\\"\\n ]\\n process_contingencies[account] = BB_costing_params[str(tech)][ccs][account][\\n \\\"Process Contingency\\\"\\n ]\\n project_contingencies[account] = BB_costing_params[str(tech)][ccs][account][\\n \\\"Project Contingency\\\"\\n ]\\n except KeyError:\\n try: # next look for data in custom dictionaries\\n process_params[account] = custom_costing_exponents[str(tech)][account][\\n \\\"Process Parameter\\\"\\n ]\\n reference_units[account] = custom_costing_params[str(tech)][ccs][\\n cost_accounts[0]\\n ][\\\"Units\\\"]\\n account_names[account] = custom_costing_exponents[str(tech)][account][\\n \\\"Account Name\\\"\\n ]\\n exponents[account] = float(\\n custom_costing_exponents[str(tech)][account][\\\"Exponent\\\"]\\n )\\n reference_costs[account] = custom_costing_params[str(tech)][ccs][\\n account\\n ][\\\"BEC\\\"]\\n reference_params[account] = custom_costing_params[str(tech)][ccs][\\n account\\n ][\\\"RP Value\\\"]\\n engineering_fees[account] = custom_costing_params[str(tech)][ccs][\\n account\\n ][\\\"Eng Fee\\\"]\\n process_contingencies[account] = custom_costing_params[str(tech)][ccs][\\n account\\n ][\\\"Process Contingency\\\"]\\n project_contingencies[account] = custom_costing_params[str(tech)][ccs][\\n account\\n ][\\\"Project Contingency\\\"]\\n except KeyError:\\n print(\\n \\\"KeyError: Account {} could not be found in the \\\"\\n \\\"dictionary for technology {} with CCS {}\\\".format(\\n account, str(tech), ccs\\n )\\n )\\n\\n # check that all accounts use the same process parameter\\n param_check = None\\n for account in cost_accounts:\\n param = process_params[account]\\n if param_check is None:\\n param_check = param\\n elif param != param_check:\\n raise ValueError(\\n \\\"{} cost accounts selected do not use \\\"\\n \\\"the same process parameter\\\".format(self.name)\\n )\\n\\n # check that the user passed the correct units\\n for account in cost_accounts:\\n ref_units = reference_units[account]\\n if units != ref_units:\\n raise ValueError(\\n \\\"Account %s uses units of %s. \\\"\\n \\\"Units of %s were passed.\\\" % (cost_accounts[0], ref_units, units)\\n )\\n\\n # Used by other functions for reporting results\\n self.costing.account_names = account_names\\n\\n # define parameters\\n self.costing.exp = Param(\\n cost_accounts,\\n mutable=True,\\n initialize=exponents,\\n doc=\\\"exponential parameter for account\\\",\\n )\\n\\n self.costing.ref_cost = Param(\\n cost_accounts,\\n mutable=True,\\n initialize=reference_costs,\\n doc=\\\"reference cost for account\\\",\\n )\\n\\n self.costing.ref_param = Param(\\n cost_accounts,\\n mutable=True,\\n initialize=reference_params,\\n doc=\\\"reference parameter for account\\\",\\n )\\n\\n self.costing.eng_fee = Param(\\n cost_accounts,\\n mutable=True,\\n initialize=engineering_fees,\\n doc=\\\"engineering fee percentage\\\",\\n )\\n\\n self.costing.process_conting = Param(\\n cost_accounts,\\n mutable=True,\\n initialize=process_contingencies,\\n doc=\\\"process contingency percentage\\\",\\n )\\n\\n self.costing.project_conting = Param(\\n cost_accounts,\\n mutable=True,\\n initialize=project_contingencies,\\n doc=\\\"project contingency percentage\\\",\\n )\\n\\n # define variables\\n self.costing.bare_erected_cost = Var(\\n cost_accounts,\\n initialize=reference_costs,\\n bounds=(0, 1e4),\\n doc=\\\"scaled bare erected cost in $MM\\\",\\n )\\n\\n self.costing.total_plant_cost = Var(\\n cost_accounts,\\n initialize=reference_costs,\\n bounds=(0, 1e4),\\n doc=\\\"total plant cost in $MM\\\",\\n )\\n\\n # rule for scaling BEC\\n # reference cost is in 2018 dollars, 671.1 is CE index for 2018\\n def bare_erected_cost_rule(costing, i):\\n return (\\n costing.bare_erected_cost[i] * 1e3\\n == (CE_index / 671.1)\\n * costing.ref_cost[i]\\n * (scaled_param / costing.ref_param[i]) ** costing.exp[i]\\n )\\n\\n self.costing.bare_erected_cost_eq = Constraint(\\n cost_accounts, rule=bare_erected_cost_rule\\n )\\n\\n # rule for calculating TPC\\n def total_plant_cost_rule(costing, i):\\n return costing.total_plant_cost[i] == costing.bare_erected_cost[i] * (\\n 1 + costing.eng_fee[i] + costing.process_conting[i]\\n ) * (1 + costing.project_conting[i])\\n\\n self.costing.total_plant_cost_eq = Constraint(\\n cost_accounts, rule=total_plant_cost_rule\\n )\\n\\n # rule for sum of BEC\\n def BEC_sum_rule(costing):\\n return sum(costing.bare_erected_cost[i] for i in cost_accounts)\\n\\n self.costing.bare_erected_cost_sum = Expression(rule=BEC_sum_rule)\\n\\n # rule for sum of TPC\\n def TPC_sum_rule(costing):\\n return sum(costing.total_plant_cost[i] for i in cost_accounts)\\n\\n self.costing.total_plant_cost_sum = Expression(rule=TPC_sum_rule)\\n\\n # add variable and constraint scaling\\n for i in cost_accounts:\\n iscale.set_scaling_factor(self.costing.bare_erected_cost[i], 1)\\n iscale.set_scaling_factor(self.costing.total_plant_cost[i], 1)\\n iscale.constraint_scaling_transform(\\n self.costing.bare_erected_cost_eq[i], 1e-3, overwrite=False\\n )\\n iscale.constraint_scaling_transform(\\n self.costing.total_plant_cost_eq[i], 1, overwrite=False\\n )\",\n \"def _cost(self, action):\\n raise NotImplementedError\",\n \"def c(x):\\n cost = per_widget_cost * x + fixed_cost\\n return cost\",\n \"def adjust_cost(self) -> None:\\n\\n n_iterations = self.array.shape[-1]\\n n_year = len(self.array.year.values)\\n\\n # If uncertainty is not considered, the cost factor equals 1.\\n # Otherwise, a variability of +/-30% is added.\\n\\n if n_iterations == 1:\\n cost_factor = 1\\n else:\\n if \\\"reference\\\" in self.array.value.values.tolist():\\n cost_factor = np.ones((n_iterations, 1))\\n else:\\n cost_factor = np.random.triangular(0.7, 1, 1.3, (n_iterations, 1))\\n\\n # Correction of hydrogen tank cost, per kg\\n # Correction of fuel cell stack cost, per kW\\n if \\\"FCEV\\\" in self.array.powertrain:\\n self.array.loc[\\n dict(powertrain=\\\"FCEV\\\", parameter=\\\"fuel tank cost per kg\\\")\\n ] = np.reshape(\\n (1.078e58 * np.exp(-6.32e-2 * self.array.year.values) + 3.43e2)\\n * cost_factor,\\n (1, n_year, n_iterations),\\n )\\n\\n self.array.loc[\\n dict(powertrain=\\\"FCEV\\\", parameter=\\\"fuel tank cost per kg\\\")\\n ] = np.reshape(\\n (3.15e66 * np.exp(-7.35e-2 * self.array.year.values) + 2.39e1)\\n * cost_factor,\\n (1, n_year, n_iterations),\\n )\\n\\n # Correction of energy battery system cost, per kWh\\n list_batt = [\\n i\\n for i in [\\\"BEV\\\", \\\"PHEV-e\\\", \\\"PHEV-c-p\\\", \\\"PHEV-c-d\\\"]\\n if i in self.array.powertrain\\n ]\\n if len(list_batt) > 0:\\n self.array.loc[\\n dict(powertrain=list_batt, parameter=\\\"energy battery cost per kWh\\\")\\n ] = np.reshape(\\n (2.75e86 * np.exp(-9.61e-2 * self.array.year.values) + 5.059e1)\\n * cost_factor,\\n (1, 1, n_year, n_iterations),\\n )\\n\\n # Correction of power battery system cost, per kW\\n list_pwt = [\\n i\\n for i in [\\n \\\"ICEV-p\\\",\\n \\\"ICEV-d\\\",\\n \\\"ICEV-g\\\",\\n \\\"PHEV-c-p\\\",\\n \\\"PHEV-c-d\\\",\\n \\\"FCEV\\\",\\n \\\"HEV-p\\\",\\n \\\"HEV-d\\\",\\n ]\\n if i in self.array.powertrain\\n ]\\n\\n if len(list_pwt) > 0:\\n self.array.loc[\\n dict(powertrain=list_pwt, parameter=\\\"power battery cost per kW\\\")\\n ] = np.reshape(\\n (8.337e40 * np.exp(-4.49e-2 * self.array.year.values) + 11.17)\\n * cost_factor,\\n (1, 1, n_year, n_iterations),\\n )\\n\\n # Correction of combustion powertrain cost for ICEV-g\\n if \\\"ICEV-g\\\" in self.array.powertrain:\\n self.array.loc[\\n dict(powertrain=\\\"ICEV-g\\\", parameter=\\\"combustion powertrain cost per kW\\\")\\n ] = np.clip(\\n np.reshape(\\n (5.92e160 * np.exp(-0.1819 * self.array.year.values) + 26.76)\\n * cost_factor,\\n (1, n_year, n_iterations),\\n ),\\n None,\\n 100,\\n )\",\n \"def cond_boiler_operation(Q_load_W, Q_design_W, T_return_to_boiler_K):\\n\\n x = [0, 15.5, 21, 26.7, 32.2, 37.7, 43.3, 49, 54.4, 60, 65.6, 71.1, 100] # Return Temperature Dependency\\n y = [96.8, 96.8, 96.2, 95.5, 94.7, 93.2, 91.2, 88.9, 87.3, 86.3, 86.0, 85.9, 85.8] # Return Temperature Dependency\\n x1 = [0, 0.05, 0.25, 0.5, 0.75, 1] # Load Point dependency\\n y1 = [99.5, 99.3, 98.3, 97.6, 97.1, 96.8] # Load Point Dependency\\n\\n # do the interpolation\\n eff_of_T_return = interp1d(x, y, kind='linear')\\n eff_of_phi = interp1d(x1, y1, kind='cubic')\\n\\n # get input variables\\n if Q_design_W > 0:\\n phi = float(Q_load_W) / float(Q_design_W)\\n else:\\n phi = 0\\n\\n if T_return_to_boiler_K == 0: # accounting with times with no flow\\n T_return = 0\\n else:\\n T_return = T_return_to_boiler_K - 273\\n eff_score = eff_of_phi(phi) / eff_of_phi(1)\\n boiler_eff = (eff_score * eff_of_T_return(T_return)) / 100.0\\n\\n return boiler_eff\",\n \"def calc_Cinv_boiler(Q_design_W, technology_type, boiler_cost_data):\\n Capex_a_Boiler_USD = 0.0\\n Opex_a_fix_Boiler_USD = 0.0\\n Capex_Boiler_USD = 0.0\\n\\n if Q_design_W > 0.0:\\n boiler_cost_data = boiler_cost_data[boiler_cost_data['code'] == technology_type]\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if Q_design_W < boiler_cost_data.iloc[0]['cap_min']:\\n Q_design_W = boiler_cost_data.iloc[0]['cap_min']\\n max_boiler_size = boiler_cost_data.iloc[0]['cap_max']\\n\\n if Q_design_W <= max_boiler_size:\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_design_W) & (boiler_cost_data['cap_max'] > Q_design_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = Inv_a + Inv_b * (Q_design_W) ** Inv_c + (Inv_d + Inv_e * Q_design_W) * log(Q_design_W)\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n else:\\n number_of_boilers = int(ceil(Q_design_W / max_boiler_size))\\n Q_nom_W = Q_design_W / number_of_boilers\\n\\n boiler_cost_data = boiler_cost_data[\\n (boiler_cost_data['cap_min'] <= Q_nom_W) & (boiler_cost_data['cap_max'] > Q_nom_W)]\\n\\n Inv_a = boiler_cost_data.iloc[0]['a']\\n Inv_b = boiler_cost_data.iloc[0]['b']\\n Inv_c = boiler_cost_data.iloc[0]['c']\\n Inv_d = boiler_cost_data.iloc[0]['d']\\n Inv_e = boiler_cost_data.iloc[0]['e']\\n Inv_IR = boiler_cost_data.iloc[0]['IR_%']\\n Inv_LT = boiler_cost_data.iloc[0]['LT_yr']\\n Inv_OM = boiler_cost_data.iloc[0]['O&M_%'] / 100.0\\n\\n InvC = (Inv_a + Inv_b * (Q_nom_W) ** Inv_c + (Inv_d + Inv_e * Q_nom_W) * log(Q_nom_W)) * number_of_boilers\\n\\n Capex_a_Boiler_USD = calc_capex_annualized(InvC, Inv_IR, Inv_LT)\\n Opex_a_fix_Boiler_USD = InvC * Inv_OM\\n Capex_Boiler_USD = InvC\\n\\n return Capex_a_Boiler_USD, Opex_a_fix_Boiler_USD, Capex_Boiler_USD\",\n \"def cost_b(self):\\n return self._cost_b\",\n \"def circuitSat(C):\",\n \"def compute_cost(AL,Y,cost_function_name):\\n cost_functions = {\\n \\\"cost_func_1\\\": cf.cost_function_1\\n } \\n\\n activ_func = cost_functions.get(cost_function_name,lambda : \\\"Invalid Cost Function Name !\\\")\\n\\n cost,dAL = activ_func(AL,Y)\\n\\n return cost, dAL\",\n \"def calc_Cop_boiler(q_load_Wh, Q_nom_W, T_return_to_boiler_K):\\n\\n if (Q_nom_W > 0.0) and (q_load_Wh > 0.0):\\n\\n # calculate efficiency according to partload\\n phi = float(q_load_Wh) / float(Q_nom_W)\\n if phi >=1.0: # avoid rounding error\\n phi = 0.98\\n T_return_C = np.float(T_return_to_boiler_K - 273.15)\\n eff_score = eff_of_phi(phi) / eff_of_phi(1)\\n boiler_eff = (eff_score * eff_of_T_return([T_return_C]))[0] / 100.0\\n else:\\n boiler_eff = 0.0\\n\\n return boiler_eff\",\n \"def bc_flower(teff):\\n lteff=np.log10(teff)\\n if (lteff<3.7):\\n bcflow=-0.190537291496456e+05+0.155144866764412e+05*lteff-0.421278819301717e+04*(lteff*lteff)+0.381476328422343e+03*(lteff*lteff*lteff)\\n if (lteff>=3.7 and lteff<3.9):\\n bcflow=-0.370510203809015e+05+0.385672629965804e+05*lteff-0.150651486316025e+05*(lteff*lteff)+0.261724637119416e+04*(lteff*lteff*lteff)-0.170623810323864e+03*(lteff*lteff*lteff*lteff)\\n if (lteff>=3.9):\\n bcflow=-0.370510203809015e+05+0.385672629965804e+05*lteff-0.150651486316025e+05*(lteff*lteff)+0.261724637119416e+04*(lteff*lteff*lteff)-0.170623810323864e+03*(lteff*lteff*lteff*lteff)\\n return bcflow\",\n \"def compute_cost(AL, Y):\\n pass\",\n \"def __call__(self, rate:'kW'):\\n self.rate = rate\\n self.cost = self.price * rate\",\n \"def obs_cost_fn(self, state):\\n # Weights for different terms\\n W_PUSHER = 1\\n W_GOAL = 2\\n W_DIFF = 5\\n\\n length = state.shape[0]\\n # pusher_x, pusher_y = state[:, 0], state[:, 1]\\n box_x, box_y = state[:, 2], state[:, 3]\\n # goal_x, goal_y = np.tile(self.goal[0], (length, 1)), np.tile(self.goal[1], (length, 1))\\n\\n pusher = state[:, 0:2]\\n box = state[:, 2:4]\\n goal = np.tile(self.goal, (length, 1))\\n goal_x, goal_y = goal[:, 0], goal[:, 1]\\n\\n d_box = np.linalg.norm(pusher - box, axis=1, ord=2)\\n d_goal = np.linalg.norm(box - goal, axis=1, ord=2)\\n\\n\\n # pusher_box = np.array([box_x - pusher_x, box_y - pusher_y])\\n # box_goal = np.array([goal_x - box_x, goal_y - box_y])\\n # d_box = np.sqrt(np.dot(pusher_box, pusher_box))\\n # d_goal = np.sqrt(np.dot(box_goal, box_goal))\\n diff_coord = np.abs(box_x / (box_y + EPSILON) - goal_x / (goal_y + EPSILON))\\n # the -0.4 is to adjust for the radius of the box and pusher\\n return W_PUSHER * np.max([d_box - 0.4, np.zeros(len(d_box))], axis=0) + W_GOAL * d_goal + W_DIFF * diff_coord\",\n \"def compute_cost(AL, Y):\\n pass\",\n \"def compute_cost(self, chrome):\\n return 1\",\n \"def taumBday(n_b, n_w, cost_b, cost_w, z):\\n # 3 cases for how much it would cost to buy black or white present\\n case1 = n_w * cost_w + n_b * cost_b\\n case2 = n_w * (cost_b + z) + n_b * cost_b\\n case3 = n_w * cost_w + n_b * (cost_w + z)\\n return min(case1, case2, case3)\",\n \"def cost_mabr(blk):\\n t0 = blk.flowsheet().time.first()\\n\\n # Get parameter dict from database\\n parameter_dict = blk.unit_model.config.database.get_unit_operation_parameters(\\n blk.unit_model._tech_type, subtype=blk.unit_model.config.process_subtype\\n )\\n\\n # Get costing parameter sub-block for this technology\\n A, B = blk.unit_model._get_tech_parameters(\\n blk,\\n parameter_dict,\\n blk.unit_model.config.process_subtype,\\n [\\\"reactor_cost\\\", \\\"blower_cost\\\"],\\n )\\n\\n # Add cost variable and constraint\\n blk.capital_cost = pyo.Var(\\n initialize=1,\\n units=blk.config.flowsheet_costing_block.base_currency,\\n bounds=(0, None),\\n doc=\\\"Capital cost of unit operation\\\",\\n )\\n\\n DCC_reactor = pyo.units.convert(\\n blk.unit_model.properties_treated[t0].flow_mass_comp[\\\"ammonium_as_nitrogen\\\"]\\n / blk.unit_model.nitrogen_removal_rate\\n * A,\\n to_units=blk.config.flowsheet_costing_block.base_currency,\\n )\\n\\n DCC_blower = pyo.units.convert(\\n blk.unit_model.reactor_area * blk.unit_model.air_flow_rate[t0] * B,\\n to_units=blk.config.flowsheet_costing_block.base_currency,\\n )\\n\\n expr = DCC_reactor + DCC_blower\\n\\n blk.unit_model._add_cost_factor(\\n blk, parameter_dict[\\\"capital_cost\\\"][\\\"cost_factor\\\"]\\n )\\n\\n blk.capital_cost_constraint = pyo.Constraint(\\n expr=blk.capital_cost == blk.cost_factor * expr\\n )\\n\\n # Register flows\\n blk.config.flowsheet_costing_block.cost_flow(\\n blk.unit_model.electricity[t0], \\\"electricity\\\"\\n )\",\n \"def cost_b_v(self):\\n return self._cost_b_v\",\n \"def cost(self,e1,e2):\\n pass\",\n \"def costFun(self, S, x):\",\n \"def get_prodcost(_craftable, tab):\\n # print(f'{tab}{_craftable.name} ({_craftable.value}):')\\n if _craftable.prod_cost == 0:\\n prodcost = 0\\n\\n tab = tab + ' '\\n\\n # If there are Craftable Items as required materials ....\\n if len(_craftable.craftables_list) > 0:\\n for craft, amount in _craftable.craftables_list:\\n # partial_cost = round(float(craftables[craft].value) * amount, 2)\\n for loop in range(amount):\\n partial_cost = get_prodcost(craftables[craft], tab)\\n # If the prod cost of the item is more expensive than the buy cost\\n if partial_cost >= float(craftables[craft].value):\\n partial_cost = float(craftables[craft].value)\\n craftables[craft].buy_or_craft = 'buy'\\n # print(f'{tab}Buy item {craft}')\\n else:\\n craftables[craft].buy_or_craft = 'craft'\\n # print(f'{tab}Craft item {craft}')\\n prodcost = prodcost + partial_cost\\n\\n # If the Total PROD Cost (prod_cost + res_totalcost) is cheaper the the market value:\\n prodcost = prodcost + _craftable.res_totalcost\\n if prodcost < float(_craftable.value):\\n for resource, amount in _craftable.resources_list:\\n partial_cost = round(float(resources[resource].unit_price) * amount, 2)\\n # print(f'{tab}{resource} Cost: {partial_cost}')\\n\\n # print(f'{tab}Total Prod C ost: {round(prodcost, 2)}')\\n if prodcost < float(_craftable.value):\\n # print(f'{tab}Craft item {_craftable.name}')\\n _craftable.buy_or_craft = 'craft'\\n else:\\n # print(f'{tab}Buy item {_craftable.name}')\\n _craftable.buy_or_craft = 'buy'\\n return prodcost\\n else:\\n return _craftable.prod_cost\",\n \"def __call__(self, x, u, k):\\n first_time_through = True\\n for cost, arg, weight in zip(self._costs, self._args, self._weights):\\n if arg == \\\"x\\\":\\n cost_input = x\\n else:\\n cost_input = u[arg]\\n\\n current_term = weight * cost(cost_input, k)\\n if current_term > 1e8:\\n print(\\\"Warning: cost %s is %f\\\" % (cost._name, current_term))\\n print(\\\"Input is: \\\", cost_input)\\n\\n# if cost._name[:4] == \\\"bike\\\":\\n# print(cost._name, \\\": \\\", current_term)\\n\\n if first_time_through:\\n total_cost = current_term\\n else:\\n total_cost += current_term\\n\\n first_time_through = False\\n\\n return total_cost\",\n \"def set_costs(self) -> None:\\n self[\\\"glider cost\\\"] = (\\n self[\\\"glider base mass\\\"] * self[\\\"glider cost slope\\\"]\\n + self[\\\"glider cost intercept\\\"]\\n )\\n self[\\\"lightweighting cost\\\"] = (\\n self[\\\"glider base mass\\\"]\\n * self[\\\"lightweighting\\\"]\\n * self[\\\"glider lightweighting cost per kg\\\"]\\n )\\n self[\\\"electric powertrain cost\\\"] = (\\n self[\\\"electric powertrain cost per kW\\\"] * self[\\\"electric power\\\"]\\n )\\n self[\\\"combustion powertrain cost\\\"] = (\\n self[\\\"combustion power\\\"] * self[\\\"combustion powertrain cost per kW\\\"]\\n )\\n self[\\\"fuel cell cost\\\"] = self[\\\"fuel cell power\\\"] * self[\\\"fuel cell cost per kW\\\"]\\n self[\\\"power battery cost\\\"] = (\\n self[\\\"battery power\\\"] * self[\\\"power battery cost per kW\\\"]\\n )\\n self[\\\"energy battery cost\\\"] = (\\n self[\\\"energy battery cost per kWh\\\"] * self[\\\"electric energy stored\\\"]\\n )\\n self[\\\"fuel tank cost\\\"] = self[\\\"fuel tank cost per kg\\\"] * self[\\\"fuel mass\\\"]\\n # Per km\\n self[\\\"energy cost\\\"] = self[\\\"energy cost per kWh\\\"] * self[\\\"TtW energy\\\"] / 3600\\n\\n # For battery, need to divide cost of electricity\\n # at battery by efficiency of charging\\n # to get costs at the \\\"wall socket\\\".\\n\\n _ = lambda x: np.where(x == 0, 1, x)\\n self[\\\"energy cost\\\"] /= _(self[\\\"battery charge efficiency\\\"])\\n\\n self[\\\"component replacement cost\\\"] = (\\n self[\\\"energy battery cost\\\"] * self[\\\"battery lifetime replacements\\\"]\\n + self[\\\"fuel cell cost\\\"] * self[\\\"fuel cell lifetime replacements\\\"]\\n )\\n\\n with open(DATA_DIR / \\\"purchase_cost_params.yaml\\\", \\\"r\\\") as stream:\\n to_markup = yaml.safe_load(stream)[\\\"markup\\\"]\\n\\n self[to_markup] *= self[\\\"markup factor\\\"]\\n\\n # calculate costs per km:\\n self[\\\"lifetime\\\"] = self[\\\"lifetime kilometers\\\"] / self[\\\"kilometers per year\\\"]\\n\\n with open(DATA_DIR / \\\"purchase_cost_params.yaml\\\", \\\"r\\\") as stream:\\n purchase_cost_params = yaml.safe_load(stream)[\\\"purchase\\\"]\\n\\n self[\\\"purchase cost\\\"] = self[purchase_cost_params].sum(axis=2)\\n # per km\\n amortisation_factor = self[\\\"interest rate\\\"] + (\\n self[\\\"interest rate\\\"]\\n / (\\n (np.array(1) + self[\\\"interest rate\\\"]) ** self[\\\"lifetime kilometers\\\"]\\n - np.array(1)\\n )\\n )\\n self[\\\"amortised purchase cost\\\"] = (\\n self[\\\"purchase cost\\\"] * amortisation_factor / self[\\\"kilometers per year\\\"]\\n )\\n\\n # per km\\n self[\\\"maintenance cost\\\"] = (\\n self[\\\"maintenance cost per glider cost\\\"]\\n * self[\\\"glider cost\\\"]\\n / self[\\\"kilometers per year\\\"]\\n )\\n\\n # simple assumption that component replacement\\n # occurs at half of life.\\n self[\\\"amortised component replacement cost\\\"] = (\\n (\\n self[\\\"component replacement cost\\\"]\\n * (\\n (np.array(1) - self[\\\"interest rate\\\"]) ** self[\\\"lifetime kilometers\\\"]\\n / 2\\n )\\n )\\n * amortisation_factor\\n / self[\\\"kilometers per year\\\"]\\n )\\n\\n self[\\\"total cost per km\\\"] = (\\n self[\\\"energy cost\\\"]\\n + self[\\\"amortised purchase cost\\\"]\\n + self[\\\"maintenance cost\\\"]\\n + self[\\\"amortised component replacement cost\\\"]\\n )\",\n \"def get_expected_cost(self):\",\n \"def return_running_cost_func(RunningCost='Minimize Input Energy'):\\n if type(RunningCost)==str:\\n assert RunningCost in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\\\"\\n else:\\n assert type(RunningCost)==list, \\\"RunningCost must be a list of cost types.\\\"\\n for el in RunningCost:\\n assert type(el)==str, \\\"Each element of RunningCost must be a string. Not \\\" + str(type(el)) + \\\".\\\"\\n assert el in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\\\" + el + \\\"' not accepted.\\\"\\n\\n if \\\"Minimize Input Energy\\\" in RunningCost:\\n result1 = lambda X,U,dt: np.trapz((k3/2)*U**2,dx=dt)\\n else:\\n result1 = lambda X,U,dt: 0\\n\\n if \\\"Minimize time away from target angle\\\" in RunningCost:\\n result2 = lambda X,U,dt: np.trapz(k1*(1/2)*(X[0,1:]-TargetAngle)**2,dx=dt)\\n else:\\n result2 = lambda X,U,dt: 0\\n\\n if \\\"Minimize time away from target angular velocity\\\" in RunningCost:\\n result3 = lambda X,U,dt:\\\\\\n np.trapz(k2*(1/2)*(X[1,1:]-TargetAngularVelocity)**2,dx=dt)\\n else:\\n result3 = lambda X,U,dt: 0\\n\\n result = lambda X,U,dt: result1(X,U,dt) \\\\\\n + result2(X,U,dt) \\\\\\n + result3(X,U,dt)\\n return(result)\",\n \"def calculate_bandwidth(self, complist):\\n\\n self.type = complist[0]\\n self.spot = complist[2]\\n self.value = complist[1]\\n self.fail = False\\n\\n if self.spot == \\\"1\\\" or self.spot == \\\"2\\\":\\n # print \\\"Calculation if Loop 1st filter\\\"\\n if self.type == \\\"C\\\":\\n if self.c1 == 0:\\n self.c1 = self.value\\n else:\\n self.fail = True\\n else:\\n if self.r1 == 0:\\n self.r1 = self.value\\n else:\\n self.fail = True\\n\\n if self.spot == \\\"3\\\" or self.spot == \\\"4\\\":\\n # print \\\"Calculation if loop 2nd filter\\\"\\n if self.type == \\\"C\\\":\\n if self.c2 == 0:\\n self.c2 = self.value\\n else:\\n self.fail = True\\n else: #self.type == \\\"R\\\"\\n if self.r2 == 0:\\n self.r2 = self.value\\n else:\\n self.fail = True\\n\\n if self.c1 != 0 and self.c2 != 0 and self.r1 != 0 and self.r2 != 0:\\n cf_low = int((1/(2*pi*(self.r1)*(self.c1))/1000))\\n #print \\\"hello!\\\"\\n cf_low = str(cf_low)\\n self.cutoff_freql = cf_low\\n self.cutoff_freql_text = cf_low + \\\"K\\\"\\n print self.cutoff_freql_text\\n cf_high = int((1/(2*pi*(self.r2)*(self.c2))/1000))\\n cf_high = str(cf_high)\\n self.cutoff_freqh = cf_high\\n self.cutoff_freqh_text = cf_high + \\\"K\\\"\\n print self.cutoff_freqh_text\\n voltage_change = float((self.r2)/self.r1)\\n voltage = 5 + voltage_change\\n self.voltage = str(voltage)\\n print self.voltage\",\n \"def cost_func(plist):\\n\\t\\tgamma, alpha = plist\\n\\t\\tk = ac.Moffat2DKernel(gamma, alpha, x_size=nx, y_size=ny)\\n\\n\\t\\tarr_out_predict = ac.convolve(arr_in, k)\\n\\n\\t\\tarr_out_fit, arr_out_predict_fit = match_dimension(arr_out, arr_out_predict)\\n\\t\\tdiff = (arr_out_fit - arr_out_predict_fit)*scale_factor\\n\\n\\t\\treturn np.sum(diff**2)/diff.size\",\n \"def eval_BC(Teff,logg,FeH,filt=\\\"g\\\",allBCs=None):\\n if allBCs is None: allBCs = read_bc_table()\\n \\n BCs = allBCs[filt]\\n \\n points = np.atleast_2d([np.ravel(Teff),np.ravel(logg),np.ravel(FeH)]).T\\n points[points[:,2] < -2.5,2] = -2.5\\n out = interpolate.griddata(BCs[:,0:3], BCs[:,3], points, method='linear')\\n return out\",\n \"def calc_capital_costs (self):\\n powerhouse_control_cost = 0\\n if not self.cd['switchgear suitable for renewables']:\\n powerhouse_control_cost = self.cd['switchgear cost']\\n\\n #~ road_needed = self.comp_specs['road needed for transmission line']\\n\\n\\n if str(self.comp_specs['transmission capital cost'])\\\\\\n != 'UNKNOWN':\\n transmission_line_cost = \\\\\\n int(self.comp_specs['transmission capital cost'])\\n else:\\n if str(self.comp_specs['distance to resource']) \\\\\\n != 'UNKNOWN':\\n distance = \\\\\\n float(self.comp_specs\\\\\\n ['distance to resource'])\\n transmission_line_cost = \\\\\\n distance*self.comp_specs['est. transmission line cost']\\n\\n secondary_load_cost = 0\\n if self.comp_specs['secondary load']:\\n secondary_load_cost = self.comp_specs['secondary load cost']\\n\\n if str(self.comp_specs['generation capital cost']) \\\\\\n != 'UNKNOWN':\\n wind_cost = \\\\\\n int(self.comp_specs['generation capital cost'])\\n self.cost_per_kw = np.nan\\n else:\\n for i in range(len(self.comp_specs['estimated costs'])):\\n if int(self.comp_specs['estimated costs'].iloc[i].name) < \\\\\\n self.load_offset_proposed:\\n if i == len(self.comp_specs['estimated costs']) - 1:\\n cost = float(self.comp_specs['estimated costs'].iloc[i])\\n break\\n continue\\n\\n cost = float(self.comp_specs['estimated costs'].iloc[i])\\n break\\n\\n wind_cost = self.load_offset_proposed * cost\\n self.cost_per_kw = cost\\n\\n #~ print powerhouse_control_cost\\n #~ print transmission_line_cost\\n #~ print secondary_load_cost\\n #~ print wind_cost\\n self.capital_costs = powerhouse_control_cost + transmission_line_cost +\\\\\\n secondary_load_cost + wind_cost\\n\\n #~ print 'self.capital_costs',self.capital_costs\",\n \"def _add_bal(self):\\n\\n c = self.components\\n p = self.pipes\\n\\n # TODO No mass flow reversal yet\\n if self.temperature_driven:\\n\\n lines = self.params['lines'].v()\\n\\n self.block.mix_temp = Var(self.TIME, lines)\\n\\n def _temp_bal_incoming(b, t, l):\\n\\n incoming_comps = collections.defaultdict(list)\\n incoming_pipes = collections.defaultdict(list)\\n\\n for name, comp in c.items():\\n if value(comp.get_mflo(t)) >= 0:\\n incoming_comps['supply'].append(name)\\n else:\\n incoming_comps['return'].append(name)\\n\\n for name, pipe in p.items():\\n if value(pipe.get_edge_mflo(self.name, t)) >= 0:\\n incoming_pipes['supply'].append(name)\\n else:\\n incoming_pipes['return'].append(name)\\n # Zero mass flow rate:\\n if value(\\n sum(c[comp].get_mflo(t) for comp in incoming_comps[l]) + \\\\\\n sum(p[pipe].get_edge_mflo(self.name, t) for pipe in\\n incoming_pipes[l])) == 0:\\n # mixed temperature is average of all joined pipes, actual value should not matter,\\n # because packages in pipes of this time step will have zero size and components do not take over\\n # mixed temperature in case there is no mass flow\\n\\n return b.mix_temp[t, l] == (\\n sum(c[comp].get_temperature(t, l) for comp in c) +\\n sum(p[pipe].get_temperature(self.name, t, l) for\\n pipe in p)) / (\\n len(p) + len(c))\\n\\n\\n else: # mass flow rate through the node\\n return (sum(\\n c[comp].get_mflo(t) for comp in incoming_comps[l]) +\\n sum(p[pipe].get_edge_mflo(self.name, t) for pipe in\\n incoming_pipes[l])) * b.mix_temp[t, l] == \\\\\\n sum(c[comp].get_mflo(t) * c[comp].get_temperature(t,\\n l)\\n for comp in incoming_comps[l]) + \\\\\\n sum(p[pipe].get_edge_mflo(self.name, t) * p[\\n pipe].get_edge_temperature(self.name, t, l)\\n for pipe in incoming_pipes[l])\\n\\n self.block.def_mixed_temp = Constraint(self.TIME,\\n lines,\\n rule=_temp_bal_incoming)\\n\\n def _temp_bal_outgoing(b, t, l, comp):\\n\\n outgoing_comps = collections.defaultdict(list)\\n outgoing_pipes = collections.defaultdict(list)\\n\\n for name, comp_obj in c.items():\\n if comp_obj.get_mflo(t) >= 0:\\n outgoing_comps['return'].append(name)\\n else:\\n outgoing_comps['supply'].append(name)\\n\\n for name, pipe_obj in p.items():\\n if pipe_obj.get_edge_mflo(self.name, t) >= 0:\\n outgoing_pipes['return'].append(name)\\n else:\\n outgoing_pipes['supply'].append(name)\\n\\n if t == 0:\\n return Constraint.Skip\\n if comp in outgoing_pipes[l]:\\n return p[comp].get_edge_temperature(self.name, t, l) == \\\\\\n b.mix_temp[t, l]\\n elif comp in outgoing_comps[l]:\\n return c[comp].get_temperature(t, l) == b.mix_temp[t, l]\\n else:\\n return Constraint.Skip\\n\\n self.block.outgoing_temp_comps = Constraint(self.TIME,\\n lines,\\n c.keys(),\\n rule=_temp_bal_outgoing)\\n self.block.outgoing_temp_pipes = Constraint(self.TIME,\\n lines,\\n p.keys(),\\n rule=_temp_bal_outgoing)\\n\\n elif self.repr_days is None:\\n\\n def _heat_bal(b, t):\\n return 0 == sum(\\n self.components[i].get_heat(t) for i in self.components) \\\\\\n + sum(\\n pipe.get_edge_heat(self.name, t) for pipe in p.values())\\n\\n self.block.ineq_heat_bal = Constraint(self.TIME,\\n rule=_heat_bal)\\n\\n def _mass_bal(b, t):\\n return 0 == sum(\\n self.components[i].get_mflo(t) for i in self.components) \\\\\\n + sum(\\n pipe.get_edge_mflo(self.name, t) for pipe in p.values())\\n\\n self.block.ineq_mass_bal = Constraint(self.TIME,\\n rule=_mass_bal)\\n\\n else:\\n def _heat_bal(b, t, c):\\n return 0 == sum(\\n self.components[i].get_heat(t, c) for i in\\n self.components) \\\\\\n + sum(\\n pipe.get_edge_heat(self.name, t, c) for pipe in p.values())\\n\\n self.block.ineq_heat_bal = Constraint(self.TIME, self.REPR_DAYS,\\n rule=_heat_bal)\\n\\n def _mass_bal(b, t, c):\\n return 0 == sum(\\n self.components[i].get_mflo(t, c) for i in\\n self.components) \\\\\\n + sum(\\n pipe.get_edge_mflo(self.name, t, c) for pipe in p.values())\\n\\n self.block.ineq_mass_bal = Constraint(self.TIME, self.REPR_DAYS,\\n rule=_mass_bal)\",\n \"def shipping_cost(fruit):\\n if is_berry(fruit):\\n shipping_cost = 0\\n else:\\n shipping_cost = 5\\n return shipping_cost\",\n \"def calculate_total_cost(state):\\n pass\",\n \"def calc_capital_costs (self):\\n road_needed = 'road needed'\\n if self.cd['on road system']:\\n road_needed = 'road not needed'\\n\\n dist = self.comp_specs['distance to community']\\n self.capital_costs = self.comp_specs['est. intertie cost per mile']\\\\\\n [road_needed] * dist\\n #~ print self.capital_costs\",\n \"def transform_shunt_coeff(g, fc, R0=50, bw_ratio=0.1, flt_type='lowpass'):\\r\\n lst = []\\r\\n if flt_type == 'lowpass':\\r\\n val = g/(R0*2*math.pi*fc)\\r\\n lst.append({'ref_des':'C', 'value':val}) \\r\\n \\r\\n elif flt_type == 'highpass':\\r\\n val = R0/(2*math.pi*fc*g)\\r\\n lst.append({'ref_des':'L', 'value':val}) \\r\\n\\r\\n elif flt_type == 'bandpass':\\r\\n val = bw_ratio*R0/(2*math.pi*fc*g)\\r\\n lst.append({'ref_des':'L', 'value':val}) \\r\\n val = g/(2*math.pi*fc*bw_ratio*R0) \\r\\n lst.append({'ref_des':'C', 'value':val}) \\r\\n\\r\\n elif flt_type == 'bandstop':\\r\\n val = R0/(g*2*math.pi*fc*bw_ratio)\\r\\n lst.append({'ref_des':'L', 'value':val}) \\r\\n val = g*bw_ratio/(R0*2*math.pi*fc)\\r\\n lst.append({'ref_des':'C', 'value':val}) \\r\\n else:\\r\\n raise ValueError(\\\"not a valid filter type\\\")\\r\\n return lst\",\n \"def step_cost(self, state, action, result=None):\\n return 1 # Override this if actions have different costs\",\n \"def front_column_model_p_gain():\",\n \"def getCost(dat, rateBlocks, key=\\\"Lintel\\\"):\\n\\n x = dat[:]\\n\\n if key == \\\"Lintel\\\":\\n edges = [s*100 for s in [5, 6, 7, 10,\\n 11, 12, 15, 16, 17, 20, 21, 22, 25]]\\n else:\\n edges = [s*100 for s in [10, 11, 12, 15, 16, 17, 20,\\n 21, 22, 25, 26, 27, 30, 31, 32, 35, 36, 37, 40]]\\n for i in edges:\\n if i >= x[2]:\\n x[2] = i\\n break\\n\\n vol = x[0]*600*x[2]/float(1000000000)\\n return vol*rateBlocks # *x[3]\",\n \"def get_action_cost(state: tuple, action: int) -> float:\\n assert len(state) == 3 and state[0] < 5 and state[1] < 4 and state[2] < 3 and action < 3\\n if state[0] == 0:\\n return 0.0\\n if state[0] == 1 and action == 0:\\n return -20.0 + 10.0 * 0.5\\n return -20.0 # Penalty = 20 due to Team Number = 9\",\n \"def shipping_cost(fruit_name):\\n\\n\\tif is_berry(fruit_name):\\n\\t\\treturn 0\\n\\telse:\\n\\t\\treturn 5\",\n \"def cost_pressure_exchanger(blk):\\n cost_by_flow_volume(\\n blk,\\n blk.costing_package.pressure_exchanger.cost,\\n pyo.units.convert(\\n blk.unit_model.low_pressure_side.properties_in[0].flow_vol,\\n (pyo.units.meter**3 / pyo.units.hours),\\n ),\\n )\",\n \"def filter_cap(stock):\\n return stock['Class'] == 'Small'\",\n \"def filter_de(stock, thresh = .5):\\n \\\"\\\"\\\"ASSUMPTION: FILTER_ROE WAS RUN BEFORE THIS, OR GET_MORINGSTAR_RATIOS WAS CALLED\\\"\\\"\\\"\\n \\\"\\\"\\\"TODO: ABSTRACT USING HOF\\\"\\\"\\\"\\n return stock['DE'] <= thresh\",\n \"def filtfilt_gust_opt_func(ics, b, a, x):\\n m = max(len(a), len(b)) - 1\\n z0f = ics[:m]\\n z0b = ics[m:]\\n y_f = lfilter(b, a, x, zi=z0f)[0]\\n y_fb = lfilter(b, a, y_f[::-1], zi=z0b)[0][::-1]\\n\\n y_b = lfilter(b, a, x[::-1], zi=z0b)[0][::-1]\\n y_bf = lfilter(b, a, y_b, zi=z0f)[0]\\n value = np.sum((y_fb - y_bf)**2)\\n return value\",\n \"def bern_metric(pipe_diameter, delta_p, pipe_length):\\n fr_c = 0.003 # assuming Reynolds number is 10**5 and pipe material is smooth copper\\n fr_reyn = 0.046 / (reynolds_num(pipe_diameter, delta_p, pipe_length) ** 0.2) # Taitel and Dukler approximation\\n rho = 1000 # density of water @ 4 deg celsius (kg/m**3)\\n\\n v = math.sqrt((2 * delta_p) / (rho * (4 * fr_reyn * (pipe_length / pipe_diameter) - 1)))\\n flow_rate_turb = v * ((math.pi / 4) * (pipe_diameter ** 2))\\n\\n return flow_rate_turb, v\",\n \"def calc_boiler_const(Q_load_Wh, thermal_efficiency):\\n Q_fuel_Wh = Q_load_Wh / thermal_efficiency\\n Q_losses_Wh = Q_fuel_Wh - Q_load_Wh\\n\\n return Q_fuel_Wh, Q_losses_Wh\",\n \"def cost_function(x, N, w, dt):\\n yh = np.abs(fftkernel(x, w / dt)) # density\\n # formula for density\\n C = np.sum(yh ** 2) * dt - 2 * np.sum(yh * x) * \\\\\\n dt + 2 / np.sqrt(2 * np.pi) / w / N\\n C = C * N * N\\n # formula for rate\\n # C = dt*sum( yh.^2 - 2*yh.*y_hist + 2/sqrt(2*pi)/w*y_hist )\\n return C, yh\",\n \"def black_caplet(discount, forward, strike, caplet_vol, reset, maturity, \\n notional = 1000000):\\n tau = maturity - reset\\n d1 = (np.log(forward/strike) + 0.5*caplet_vol**2*reset)/(caplet_vol*np.sqrt(reset))\\n d2 = d1 - caplet_vol*np.sqrt(reset)\\n return (forward*norm.cdf(d1) - strike*norm.cdf(d2))*discount*tau*notional\",\n \"def p_funct(self, yi, p, t): \\t\\t\\n\\t\\t\\n\\t\\tglobal Cg \\n\\t\\tCg = abs(yi[0])\\n\\t\\tglobal Ch \\n\\t\\tCh = abs(yi[1])\\n\\t\\tglobal tin_g \\n\\t\\ttin_g = abs(yi[2])\\n\\t\\tglobal tin_h \\n\\t\\ttin_h = abs(yi[3])\\n\\t\\t\\n\\t\\ty = state_at(t)\\n\\t\\trisk = 1 - (y[-1][0] + y[-1][3]) / sum(y[-1])\\n\\t\\t\\n\\t\\treturn abs(risk - p)\",\n \"def biz(cps):\\n # Head of unit\\n mask = cps['tc5_p'] > 0\\n cps_valid = cps[mask]\\n rand = np.random.uniform(size=len(cps_valid))\\n new_vals = np.exp(10.4 + 1. * rand)\\n new_vals = np.where(new_vals < 50000., 50000., new_vals)\\n cps.loc[mask, 'bilp'] = new_vals\\n # spouse of unit\\n mask = cps['tc5_s'] > 0\\n cps_valid = cps[mask]\\n rand = np.random.uniform(size=len(cps_valid))\\n new_vals = np.exp(10.4 + 1. * rand)\\n new_vals = np.where(new_vals < 50000., 50000., new_vals)\\n cps.loc[mask, 'bils'] = new_vals\",\n \"def filter_smooth(self, transect, setting):\\n\\n # Set property\\n self.smooth_filter = setting\\n\\n # Compute ens_time\\n ens_time = np.nancumsum(transect.date_time.ens_duration_sec)\\n n_ensembles = len(ens_time)\\n # Determine if smooth filter should be applied\\n if self.smooth_filter == 'On':\\n # Initialize arrays\\n self.smooth_speed = repmat([np.nan], 1, n_ensembles)\\n self.smooth_upper_limit = repmat([np.nan], 1, n_ensembles)\\n self.smooth_lower_limit = repmat([np.nan], 1, n_ensembles)\\n\\n # Boat velocity components\\n b_vele = np.copy(self.u_mps)\\n b_veln = np.copy(self.v_mps)\\n\\n # Set filter parameters\\n filter_width = 10\\n half_width = 10\\n multiplier = 9\\n cycles = 3\\n\\n # Initialize variables\\n bt_bad_idx = []\\n upper_limit = 0\\n lower_limit = 0\\n\\n # Compute speed and direction of boat\\n direct, speed = cart2pol(b_vele, b_veln)\\n\\n # Compute residuals from a robust Loess smooth\\n speed_smooth = rloess(ens_time, speed, filter_width)\\n speed_res = speed - speed_smooth\\n\\n # Apply a trimmed standard deviation filter multiple times\\n for i in range(cycles):\\n filter_array = BoatData.run_std_trim(half_width, speed_res.T)\\n\\n # Compute filter bounds\\n upper_limit = speed_smooth + multiplier * filter_array\\n lower_limit = speed_smooth - multiplier * filter_array\\n\\n # Apply filter to residuals\\n bt_bad_idx = np.where(np.logical_or(np.greater(speed, upper_limit), np.less(speed, lower_limit)))[0]\\n speed_res[bt_bad_idx] = np.nan\\n\\n # Update valid_data property\\n self.valid_data[4, :] = True\\n self.valid_data[4, bt_bad_idx] = False\\n self.valid_data[4, self.valid_data[1, :] == False] = True\\n self.smooth_upper_limit = upper_limit\\n self.smooth_lower_limit = lower_limit\\n self.smooth_speed = speed_smooth\\n\\n else:\\n\\n # No filter applied all data assumed valid\\n self.valid_data[4, :] = True\\n self.smooth_upper_limit = np.nan\\n self.smooth_lower_limit = np.nan\\n self.smooth_speed = np.nan\\n\\n # Combine all filter data to composite valid data\\n self.valid_data[0, :] = np.all(self.valid_data[1:, ], 0)\\n self.num_invalid = np.sum(self.valid_data[0, :] == False, 0)\",\n \"def calc_HEX_cooling(Q, UA, thi, tho, tci, ch):\\n\\n if ch > 0:\\n eff = [0.1, 0]\\n Flag = False\\n tol = 0.00000001\\n while abs((eff[0] - eff[1]) / eff[0]) > tol:\\n if Flag == True:\\n eff[0] = eff[1]\\n else:\\n cmin = ch * (thi - tho) / ((thi - tci) * eff[0])\\n if cmin < ch:\\n cc = cmin\\n cmax = ch\\n else:\\n cc = cmin\\n cmax = cc\\n cmin = ch\\n cr = cmin / cmax\\n NTU = UA / cmin\\n eff[1] = calc_plate_HEX(NTU, cr)\\n cmin = ch * (thi - tho) / ((thi - tci) * eff[1])\\n tco = tci + eff[1] * cmin * (thi - tci) / cc\\n Flag = True\\n cc = Q / abs(tci - tco)\\n tco = tco - 273\\n else:\\n tco = 0\\n cc = 0\\n return np.float(tco), np.float(cc / 1000)\",\n \"def flowStress(f_hard,eps,d,q,a):\\n\\n pass\",\n \"def butter_bp_coe(lowcut, highcut, fs, order=1):\\n nyq = 0.5 * fs\\n low = lowcut / nyq\\n high = highcut / nyq\\n b, a = butter(order, [low, high], btype='band')\\n return b, a\",\n \"def penalty_calc(self):\\n self.p_budget = (self.tx_oma_min - self.rx_unstressed_sensitivity - self.fiber_conn_loss)*self.l_1\\n\\n # fiber attenuation,\\n self.p_atten = self.alpha*self.length # column B\\n\\n # calculate bandwidth for RIN test (exclude transmitter)\\n rin_inverse_bw = np.sqrt(np.square(1.0/self.bw_cd) + np.square(1.0/self.bw_md) + (0.477/(self.rx_bw**2))*self.l_1)\\n rin_bw = 1.0 / rin_inverse_bw\\n\\n # see FC-MSQS-2 equation B.47 in Annex B.4 for the following k_rin = math.sqrt(2.0/math.pi)*erfinv(0.8)\\n k_rin = 0.7\\n\\n # v_rin,\\n self.v_rin = (k_rin*1E6*(self.rin_test_isi**2)*rin_bw*\\n math.pow(10.0,0.1*self.rin)) # column AK\\n\\n # Prin,\\n print('v_rin: ', self.v_rin)\\n print('Q: ',self.Q)\\n print('isi_dj_refl_closed :', self.isi_dj_refl_closed)\\n self.p_rin = -10.0*np.log10(np.sqrt(1.0-np.multiply(self.v_rin, np.square(self.Q/self.isi_dj_refl_closed)))) # column R\\n print(\\\"P_rin : \\\", self.p_rin)\\n self.beta = (3.14159E-6*self.speedup*self.br_nominal *self.delta_lambda*self.d1*self.length) # column O\\n self.sigma_mpn = (self.k_mpn/math.sqrt(2.0)*(self.l_1 -np.exp(-np.square(self.beta)))) # column P\\n self.p_mpn = (-10.0*np.log10(np.sqrt(self.l_1 - (self.Q**2)*np.square(self.sigma_mpn)))) # column Q\\n self.p_blw = (-10.0*math.log10(math.sqrt(1.0- ((self.Q*self.sigma_blw)/ self.isi_tp4_rx)**2))*self.l_1) # cell T13\\n self.p_reflection = -10.0*np.log10(self.isi_reflection) # column N\\n self.v_mn = (((1.0-math.pow(10.0,-0.2*self.pmn))/ (self.Q)**2)*self.l_1) # cell AG7\\n print(\\\"isi_center : \\\", self.isi_center)\\n\\n self.p_isi_center = -10.0*np.log10(self.isi_center) # column J\\n\\n self.p_isi_corners = (-10.0*np.log10(self.isi_corners) - self.p_isi_center) # column K\\n self.p_isi_dj_center = (-10.0*np.log10(self.isi_dj_refl_closed) - self.p_isi_center) # column L\\n self.p_isi_dj_corners = (-10.0*np.log10(self.isi_dj_corners) -self.p_isi_center -self.p_isi_corners) # column M\\n\\n\\n # calculate the \\\"cross\\\" penalty contribution, column S\\n arg1 = ((self.sigma_blw**2 + self.v_rin)/ np.square(self.isi_dj_refl_closed))\\n arg2 = self.l_1 - (self.Q**2)*(arg1 + self.v_mn + np.square(self.sigma_mpn))\\n arg3 = (-10.0*np.log10(np.multiply(self.isi_dj_refl_closed, np.sqrt(arg2))))\\n self.p_cross_center = ( # column S\\n arg3\\n - self.p_blw # cell T13\\n - self.p_isi_center # column J\\n - self.p_isi_dj_center # column L\\n - self.p_mpn # column Q\\n - self.p_reflection # column N\\n - self.p_rin # column R\\n - self.pmn*self.l_1) # cell G13\\n print('p_isi_center: ', self.p_isi_center)\\n\\n # calculate the total power budget evaluated at the center of the eye\\n self.p_total_center = ( # column T\\n self.p_isi_center # column J\\n + self.p_isi_dj_center # column L\\n + self.p_atten # column B\\n + self.p_mpn # column Q\\n + self.p_reflection # column N\\n + self.p_rin # column R\\n + self.p_cross_center # column S\\n + self.pmn*self.l_1) # cell G13\\n # calculate the total power budget evaluated at the corner of the eye\\n self.p_total_corners = (\\n self.p_isi_center # column J\\n + self.p_isi_corners # column K\\n + self.p_atten # column B\\n + self.p_mpn # column Q\\n + self.p_reflection # column N\\n + self.p_rin # column R\\n + self.p_cross_center # column S\\n + self.pmn*self.l_1 # cell G13\\n + self.p_isi_dj_corners)# column M\\n\\n # receiver stressed sensitivity\\n self.margin = ( self.p_budget\\n - self.p_total_center) # column W\\n\\n self.rx_stressed_sensitivity = (\\n self.tx_oma_min*self.l_1\\n - self.chil\\n - self.p_mpn\\n - self.p_reflection\\n - self.p_rin\\n - 0.5*self.p_cross_center\\n - self.pmn*self.l_1\\n - self.margin[self.lnum//2]*self.l_1)\\n\\n\\n # end of GbE10.penalty_calc\\n #======================================================================+\",\n \"def vector_substraction(self, th_eur, r_eur, th_sb, r_sb):\\n # Initialize the cost vector\\n cost = []\\n \\n # Loop through the fraction of europe and calculate the vector cost\\n for i in range(1, len(r_eur)):\\n # Check if the location is just sea and nothing else. Do not take this position.\\n if (np.sum(r_eur) == 0):\\n cost.append(20)\\n else:\\n cost_zeros = (len([r == 0 for r in r_eur if r == 0])) * 5\\n (x1, y1) = (np.cos(th_eur[i])*r_eur[i], np.sin(th_eur[i])*r_eur[i])\\n (x2, y2) = (np.cos(th_sb[i])*r_sb[i], np.sin(th_sb[i])*r_sb[i])\\n cost.append(np.sqrt(((y2 - y1)**2 + (x2 - x1)**2)) + cost_zeros) \\n\\n # Make the cost function nonlinear. Favour very good single vectors.\\n mask = len([c for c in cost if c > 1])\\n cost_sum = sum(el**4 for el in cost)\\n if (mask > 2):\\n cost_sum += 10\\n \\n return cost_sum\",\n \"def _cost_method(self, *args, **kwargs):\\n\\n cost_val = 0.5 * np.linalg.norm(self.obs_data - self.op(args[0])) ** 2\\n\\n if 'verbose' in kwargs and kwargs['verbose']:\\n print(' - DATA FIDELITY (X):', cost_val)\\n\\n return cost_val\",\n \"def cost(self):\\n\\t\\treturn self.g + self.h\",\n \"def get_spike_cost(self, r):\\n return self.tc.costs(\\n self.pf.XS[:, :, 0].transpose(),\\n self.pf.XS[:, :, 1].transpose(),\\n r,\\n self.pf.WS[:].transpose())[2]\",\n \"def calcCostFun(self):\\n\\n self.start()\\n F, K = self.model()\\n \\n return self.costFunction\",\n \"def eval_cost(self, params, **kwargs):\\n raise NotImplementedError\",\n \"def fuel_cond(T):\\n\\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\\n \\n return kc\",\n \"def get_cost_string(es, t):\\n strg = ''\\n for item in es:\\n strg += '{:011.7f}'.format(item / t) + ' '\\n return strg\",\n \"def run (self, scalers = {'capital costs':1.0}):\\n self.was_run = True\\n self.reason = \\\"OK\\\"\\n\\n tag = self.cd['file id'].split('+')\\n if len(tag) > 1 and tag[1] != 'biomass_pellet':\\n self.was_run = False\\n self.reason = (\\\"Not a biomass pellet project.\\\")\\n return\\n\\n if not self.cd[\\\"on road system\\\"]:\\n self.diagnostics.add_warning(self.component_name,\\n \\\"not on road system\\\")\\n self.max_boiler_output = 0\\n self.heat_displaced_sqft = 0\\n self.biomass_fuel_consumed = 0\\n self.fuel_price_per_unit = 0\\n self.heat_diesel_displaced = 0\\n self.reason = \\\\\\n \\\"Not on road or marine highway system, so it is assumed that\\\" +\\\\\\n \\\" pellets cannot be delivered cost effectively.\\\"\\n return\\n\\n if np.isnan(float(self.comp_specs['peak month % of total'])):\\n self.diagnostics.add_warning(self.component_name,\\n \\\"bad config value for 'peak month % of total'\\\")\\n self.max_boiler_output = 0\\n self.heat_displaced_sqft = 0\\n self.biomass_fuel_consumed = 0\\n self.fuel_price_per_unit = 0\\n self.heat_diesel_displaced = 0\\n self.reason = \\\"bad config value for 'peak month % of total'\\\"\\n return\\n\\n if self.cd[\\\"model heating fuel\\\"]:\\n self.calc_heat_displaced_sqft()\\n self.calc_energy_output()\\n efficiency = self.comp_specs[\\\"pellet efficiency\\\"]\\n self.calc_max_boiler_output(efficiency)\\n factor = self.comp_specs['capacity factor']\\n self.calc_biomass_fuel_consumed(factor)\\n self.calc_diesel_displaced()\\n\\n\\n if self.cd[\\\"model financial\\\"]:\\n self.get_diesel_prices()\\n\\n self.calc_capital_costs()\\n self.calc_maintenance_cost()\\n\\n\\n self.fuel_price_per_unit = self.cd['pellet price']\\n\\n self.calc_proposed_biomass_cost(self.fuel_price_per_unit)\\n self.calc_displaced_heating_oil_price()\\n\\n\\n self.calc_annual_electric_savings()\\n self.calc_annual_heating_savings()\\n\\n self.calc_annual_total_savings()\\n self.calc_annual_costs(self.cd['interest rate'],\\n scalers['capital costs'])\\n self.calc_annual_net_benefit()\\n self.calc_npv(self.cd['discount rate'], self.cd[\\\"current year\\\"])\\n\\n fuel_cost = self.biomass_fuel_consumed * self.fuel_price_per_unit\\n self.calc_levelized_costs(self.maintenance_cost + fuel_cost)\",\n \"def capacity_cost_rule(mod, g, p):\\n return mod.stor_spec_power_capacity_mw[g, p] \\\\\\n * mod.stor_spec_fixed_cost_per_mw_yr[g, p] \\\\\\n + mod.stor_spec_energy_capacity_mwh[g, p] \\\\\\n * mod.stor_spec_fixed_cost_per_mwh_yr[g, p]\",\n \"def get_cost(self, action: Action) -> N:\\n pass\",\n \"def Hstep_cost_function(H): \\n U = Wold - Yold\\n #cost = -np.trace(H.T@K@H) + (self.admm_rho/2)*(norm(H.T@D - Wold + self.Y, 'fro')**2) \\n cost = -np.trace(H.T@K@H)/nsamples + (rho/2)*np.trace((H.T@D - U)@(H.T@D-U).T) \\n return cost\",\n \"def getCost(self, state, action):\\n util.raiseNotDefined()\",\n \"def cost_cal(self, Type, Tract_pop, Tractx, Tracty, cnum):\\n Geox1 = sf.FeatureScaling(self.Geox)\\n Geoy1 = sf.FeatureScaling(self.Geoy)\\n Tract_pop1 = sf.FeatureScaling(Tract_pop)\\n Tractx1 = sf.FeatureScaling(Tractx)\\n Tracty1 = sf.FeatureScaling(Tracty)\\n \\n self.cost, temp = ans.cost(self.demandloc, Geox1, Geoy1, Tract_pop1, Type, Tractx1, Tracty1, Tract_pop, cnum)\",\n \"def cost(inventory):\\n return inventory.reduce(convert.get_cost)\",\n \"def transport_cost_per_t(self):\\n return safe_divide(self.reseller.operating_expenses(), self.quantity_fieldside)\",\n \"def __call__(self, T_HTF_inlet, m_in, htf):\\n # Function maps inputs ( T [K], P [Pa] ) to outputs (C [J/kg-K])\\n # We apply it: inputs ( T [C], P [Pa] ) to outputs (C [J/kg-K])\\n T_min, T_max = T_HTF_inlet, self.T_exhaust_inlet\\n T_mean = 0.5 * (T_min + T_max)\\n C_p_exhaust = PropsSI('C',\\n 'T', T_mean + 273.15,\\n 'P', self.P_exhaust_inlet,\\n 'Air')\\n C_exhaust = C_p_exhaust * self.m_exhaust # [J/K]\\n C_HTF = htf.lookup('C',T_mean) * m_in\\n\\n # This should be true\\n C_min, C_max = C_exhaust, C_HTF\\n C_r = C_min / C_max\\n if C_r > 1:\\n raise ValueError(\\\"HTF stream should have greater capacity (C_r = {})\\\".format(C_r))\\n DeltaT_max = self.T_exhaust_inlet - T_HTF_inlet\\n Q_max = C_min * DeltaT_max\\n Q = self.effectiveness * Q_max\\n T_HTF_outlet = T_HTF_inlet + Q / C_HTF\\n T_exhaust_outlet = self.T_exhaust_inlet - Q / C_exhaust\\n\\n # TODO: adjust for (water) flow rate\\n DeltaP_HTF = -6e3 # [Pa]\\n DeltaP_exhaust = -300 # [Pa]\\n \\n return Q, T_HTF_outlet, T_exhaust_outlet, DeltaP_HTF, DeltaP_exhaust\",\n \"def calc_Cinv_CCGT(CC_size_W, CCGT_cost_data):\\n\\n # if the Q_design is below the lowest capacity available for the technology, then it is replaced by the least\\n # capacity for the corresponding technology from the database\\n if CC_size_W < CCGT_cost_data['cap_min'][0]:\\n CC_size_W = CCGT_cost_data['cap_min'][0]\\n CCGT_cost_data = CCGT_cost_data[\\n (CCGT_cost_data['cap_min'] <= CC_size_W) & (CCGT_cost_data['cap_max'] > CC_size_W)]\\n\\n\\n #costs of connection\\n connection_costs = ngas.calc_Cinv_gas(CC_size_W)\\n\\n Inv_a = CCGT_cost_data.iloc[0]['a']\\n Inv_b = CCGT_cost_data.iloc[0]['b']\\n Inv_c = CCGT_cost_data.iloc[0]['c']\\n Inv_d = CCGT_cost_data.iloc[0]['d']\\n Inv_e = CCGT_cost_data.iloc[0]['e']\\n Inv_IR = CCGT_cost_data.iloc[0]['IR_%']\\n Inv_LT = CCGT_cost_data.iloc[0]['LT_yr']\\n Inv_OM = CCGT_cost_data.iloc[0]['O&M_%'] / 100\\n\\n InvC = Inv_a + Inv_b * (CC_size_W) ** Inv_c + (Inv_d + Inv_e * CC_size_W) * log(CC_size_W)\\n\\n Capex_a_CCGT_USD = calc_capex_annualized((InvC+connection_costs), Inv_IR, Inv_LT)\\n Opex_fixed_CCGT_USD = InvC * Inv_OM\\n Capex_CCGT_USD = InvC\\n\\n return Capex_a_CCGT_USD, Opex_fixed_CCGT_USD, Capex_CCGT_USD\",\n \"def calc_calories(gpx_track, wt = 175, activity='Run'):\",\n \"def test_cost(self):\\n self.assertTrue(\\n int(self.ospf.parse_state(\\n pattern='cost',\\n cmd_key='sh_ospf_ints')) == 10, 'OSPF Interface: cost not found')\",\n \"def cost(ev):\\n profData = getProfilingData(ev)\\n if profData is not None:\\n return profData.PropagatedCost()\\n return \\\"\\\"\",\n \"def deredden(EBV,filt):\\n conversion_data = ascii.read(datapath+\\\"/stellar_param_data/sf11.txt\\\")\\n assert filt in conversion_data[\\\"filter\\\"], (filt, conversion_data[\\\"filter\\\"])\\n return EBV * float(conversion_data[\\\"AB_EBV\\\"][np.where(conversion_data[\\\"filter\\\"]==filt)[0]])\",\n \"def oil_compressibility(pressure2, P_bubble, temp2, api, Rsb, sg2):\\n import numpy as np\\n from math import e\\n\\n # oil isothermal compressibility\\n\\n coil_array = []\\n\\n if pressure2 < P_bubble:\\n # use McCain\\n ln_coil = -7.573 - (1.45 * np.log(pressure2)) - (0.383 * np.log(P_bubble)) + (1.402 * np.log(temp2)) + (0.256 * np.log(api)) + (0.449 * np.log(Rsb)) \\n coil = np.exp(ln_coil)\\n if pressure2 >= P_bubble:\\n # use Vazquez-Beggs\\n coil = ((5 * Rsb) + (17.2 * temp2) - (1180 * sg2) + (12.61 * api) - 1433) / (1E+05 * pressure2)\\n\\n return coil\",\n \"def total_cost_w_tax(tax_rate, state, cost_amount):\\n state.upper()\\n default_tax_rate = 0.05\\n if state == 'CA':\\n total_cost = (cost_amount * .07) + cost_amount\\n elif tax_rate != 0.07 or tax_rate != 0.05:\\n total_cost = (cost_amount * tax_rate) + cost_amount\\n else:\\n total_cost = (cost_amount * default_tax_rate) + cost_amount\\n return total_cost, state.upper()\",\n \"def _cost_load_theft(self):\\n theft_prob = self.theft_probability\\n theft = np.random.choice([0, 1], p=[1-theft_prob, theft_prob])\\n if theft == 1:\\n cost = self._calculate_profit_weight() * PRICE_PER_KG\\n return cost\\n else:\\n return 0\",\n \"def thermal_operating_costs_rule(_m, y, s):\\r\\n\\r\\n return sum((m.C_MC[g, y] + ((m.EMISSIONS_RATE[g] - m.baseline[y]) * m.permit_price[y])) * m.p[g, y, s, t]\\r\\n for g in m.G_THERM for t in m.T)\",\n \"def stepCost(self, state, action):\\n\\n j = len(state)\\n while (j > 0 and state[j-1] != ' '): j -= 1\\n word = state[j:]\\n if(action == ' '):\\n return self.unigramCost(word)\\n cost1 = self.unigramCost(word)\\n if(word == ''): cost1 = 0.0\\n cost = self.unigramCost(word + action) - cost1\\n #print(f'state = {state}, word = {word}, action = {action}, cost = {cost}')\\n return cost\",\n \"def bcost(action):\\r\\n # An action is an (a, b, arrow) tuple; a and b are\\r\\n # times; arrow is a string.\\r\\n a, b, arrow = action\\r\\n return max(a, b)\",\n \"def testActionCosts(self):\\n from builder import Builder\\n\\n action = Parser.parse_as(cost_load.split(\\\"\\\\n\\\"), Action, self.domain)\\n b = Builder(action)\\n \\n expected_term = b(\\\"load-costs\\\", \\\"?v\\\")\\n self.assertEqual(action.get_total_cost(), expected_term)\\n\\n action.set_total_cost(25)\\n self.assertEqual(action.get_total_cost(), b(25))\\n\\n action.set_total_cost(None)\\n self.assertEqual(len(action.effect.parts), 1)\\n\\n action.set_total_cost(b(\\\"+\\\", (\\\"load-costs\\\", \\\"?v\\\"), 5))\\n self.assertEqual(len(action.effect.parts), 2)\",\n \"def hinderedRotor_heatCapacity(T, freq, barr):\\n x = constants.h * constants.c * 100. * freq / constants.kB / T\\n exp_x = math.exp(x)\\n one_minus_exp_x = 1.0 - exp_x\\n z = 0.5 * constants.h * constants.c * 100. * barr / constants.kB / T\\n BB = scipy.special.i1(z) / scipy.special.i0(z)\\n return x * x * exp_x / one_minus_exp_x / one_minus_exp_x - 0.5 + z * (z - BB - z * BB * BB)\",\n \"def car_cost(self,car,car_stations):\\n car_cost = car.capacity # Aqui le deberiamos aumentar el peso\\n #de acuerdo a la capacidad del carro y la cantidad de lugares \\n #en la estacion\\n return car_stations\",\n \"def get_flow(self, pressure_drop, coeff): \\n flow = coeff * pressure_drop**0.5\\n return flow\",\n \"def _cost(self, action):\\n return float(self.spacecraft.crashed)\",\n \"def mce_filter(freq, f_raw, params):\\n\\tz = np.exp(-2j*np.pi*freq/f_raw)\\n\\tb11, b12, b21, b22 = np.array(params[:4])*0.5**14\\n\\tH = (1+z)**4 / (1-b11*z+b12*z**2) / (1-b21*z+b22*z**2)\\n\\tH /= 2**4 / (1-b11+b12) / (1-b21+b22)\\n\\treturn H\",\n \"def bcost(action):\\n # An action is an (a, b, arrow) tuple; a and b are\\n # times; arrow is a string.\\n a, b, arrow = action\\n return max(a, b)\",\n \"def butter_filter(self, data, low_pass, high_pass, fs, order=10):\\n\\n nyq = fs/2\\n low = low_pass/nyq\\n high = high_pass/nyq\\n\\n b, a = signal.butter(order, [low, high], btype='band')\\n filt_data = np.abs(signal.hilbert(signal.filtfilt(b, a, data, axis=1), axis=1))\\n return filt_data\",\n \"def cost_function(params, count):\\n circuit = models.Circuit(nqubits)\\n for l in range(layers):\\n for q in range(nqubits):\\n circuit.add(gates.RY(q, theta=0))\\n for q in range(0, nqubits - 1, 2):\\n circuit.add(gates.CZ(q, q + 1))\\n for q in range(nqubits):\\n circuit.add(gates.RY(q, theta=0))\\n for q in range(1, nqubits - 2, 2):\\n circuit.add(gates.CZ(q, q + 1))\\n circuit.add(gates.CZ(0, nqubits - 1))\\n for q in range(nqubits):\\n circuit.add(gates.RY(q, theta=0))\\n\\n cost = 0\\n circuit.set_parameters(\\n params\\n ) # this will change all thetas to the appropriate values\\n for i in range(len(ising_groundstates)):\\n final_state = circuit(np.copy(ising_groundstates[i]))\\n cost += np.real(encoder.expectation(final_state.state()))\\n\\n if count[0] % 50 == 0:\\n print(count[0], cost / len(ising_groundstates))\\n count[0] += 1\\n\\n return cost / len(ising_groundstates)\",\n \"def make_pipeline():\\r\\n\\r\\n mkt_cap_screen = (morningstar.valuation.market_cap.latest > 1e9)\\r\\n\\r\\n return Pipeline(\\r\\n columns={\\r\\n 'Free Cash Flow': morningstar.cash_flow_statement.free_cash_flow.latest,\\r\\n }, screen=mkt_cap_screen)\",\n \"def __init__(self, name, fuel, fanciness):\\n super().__init__(name, fuel)\\n self.fanciness = float(fanciness)\\n self.price_per_km *= self.fanciness\",\n \"def get_problem():\\n\\n #User Defined Terrain Elevation\\n #def terr( x_pos, y_pos ):\\n #Defines terrain elevation [m] as a function of x and y positions [m]\\n # elev=100.0*(np.sin(0.5*(x_pos/1000.0)))**2.0 #User defined elevation map\\n # return elev\\n\\n #User Defined Tunnel Cost\\n #def tunnel(depth):\\n #Defines additional cost for placing a 1 meter length of track a non-zero\\n #depth below the ground.\\n # TunnelCost=(50e3)/(1+np.exp(-(depth-5))) #Tunneling Cost (2016 USD)\\n # return TunnelCost\\n\\n #def bridge(height):\\n #Defines additional cost for placing a 1 meter length of track a non-zero\\n #heigh above the ground.\\n # BridgeCost=10e3*(height/10)**2 #Bridge Cost (2016 USD)\\n # return BridgeCost\\n\\n # Rename this and/or move to optim package?\\n problem = beluga.optim.Problem('surftest_noinc')\\n\\n #Define independent variables\\n problem.independent('t', 's')\\n\\n # Define equations of motion\\n problem.state('x','V*cos(hdg)','m') \\\\\\n .state('y','V*sin(hdg)','m') \\\\\\n .state('V','amax*sin(thrA) + eps*(cos(thrA)+cos(hdgA))','m/s') \\\\\\n .state('hdg','cmax/V*sin(hdgA)','rad')\\n\\n # Define controls\\n #problem.control('thrA','rad') \\\\\\n # .control('hdgA','rad')\\n problem.control('hdgA','rad')\\n\\n # Define Cost Functional\\n problem.cost['path'] = Expression('1','s')\\n\\n #problem.cost['path'] = Expression('TimeToUSD+trk*V', 'USD')\\n\\n #+ \\\\\\n #'(50e3)/(1.0+exp(-1.0*(z-0.0*(sin(0.5*(x/1000.0)))**2.0-5.0)))+'+ \\\\\\n #'10e3*((0.0*(sin(0.5*(x/1000.0)))**2.0-z)/10.0)**2.0','USD')\\n\\n #Define constraints\\n problem.constraints().initial('x-x_0','m') \\\\\\n .initial('y-y_0','m') \\\\\\n .initial('V-V_0','m/s') \\\\\\n .initial('hdg-hdg_0','rad') \\\\\\n .terminal('x-x_f','m') \\\\\\n .terminal('y-y_f','m')\\n #.terminal('V-V_f','m/s')\\n #.initial('hdg-hdg_0','rad') \\\\\\n\\n #Define constants\\n problem.constant('g',9.81,'m/s^2') #Acceleration due to gravity\\n problem.constant('trk',1,'USD/m') #Basic cost of 1 m of track on ground (10k per m)\\n problem.constant('amax',1.0,'m/s^2') #Maximum thrust acceleration of vehicle\\n problem.constant('cmax',1.0,'m/s^2') #Maximum allowed centripetal acceleration\\n problem.constant('eps',10,'m/s^2') #Error constant\\n problem.constant('TimeToUSD',1,'USD/s') #Time is Money!!\\n problem.constant('thrA',0,'rad')\\n\\n #Unit scaling\\n problem.scale.unit('m','x') \\\\\\n .unit('s','x/V') \\\\\\n .unit('rad',1) \\\\\\n .unit('USD',1)\\n\\n #Configure solver\\n problem.bvp_solver = algorithms.MultipleShooting(derivative_method='fd',tolerance=1e-4, max_iterations=1000, verbose = True, cached = False, number_arcs=2)\\n\\n #Initial Guess\\n problem.guess.setup('auto',start=[0.0,0.0,1.0,pi/4-0.2], costate_guess=-0.1) #City A\\n\\n #Add Continuation Steps\\n problem.steps.add_step().num_cases(10) \\\\\\n .terminal('x', 10) \\\\\\n .terminal('y', 0)\\n\\n problem.steps.add_step().num_cases(10) \\\\\\n .const('eps', 0.2)\\n\\n #problem.steps.add_step().num_cases(10) \\\\\\n # .terminal('y', 2*pi*1000) \\\\\\n # .terminal('z', 0.0) \\\\\\n # .terminal('inc', 0.0)\\n #^ City B\\n return problem\",\n \"def SCB(t,init,rhoS,deltaSC,rhoC,deltaCB,rhoB):\\n\\n #Declaring useful parameters\\n [S0,C0,B0]=init\\n lambdaS=deltaSC-rhoS #Introducing global decay rates lambda.\\n lambdaC=deltaCB-rhoC\\n lambdaB=-rhoB\\n\\n #Checking for eigenvalues equalitites\\n thresh=1e-5 #threshold difference for considering two eignevalues as equal\\n bSC=(lambdaC-lambdaS)*(abs(lambdaC-lambdaS)>=thresh)\\n bSB=(lambdaB-lambdaS)*(abs(lambdaB-lambdaS)>=thresh)\\n bCB=(lambdaB-lambdaC)*(abs(lambdaB-lambdaC)>=thresh)\\n\\n #S has always the same expression\\n S=S0*np.exp(-lambdaS*t)\\n\\n #there are two cases for C\\n if bSC!=0:\\n c2=deltaSC*S0/bSC; c1=C0-c2\\n C=c1*np.exp(-lambdaC*t)+c2*np.exp(-lambdaS*t)\\n\\n #there are three subcases for B in that case\\n if bCB==0:\\n b2=deltaCB*c1; b3=deltaCB*c2/bSB; b1=B0-b3\\n B=(b1+b2*t)*np.exp(-lambdaB*t)+b3*np.exp(-lambdaS*t)\\n\\n elif bSB==0:\\n b2=deltaCB*c1/bCB; b3=deltaCB*c2; b1=B0-b2\\n B=(b1+b3*t)*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)\\n\\n else:\\n b2=deltaCB*c1/bCB; b3=deltaCB*c2/bSB; b1=B0-b2-b3\\n B=b1*np.exp(-lambdaB*t)+b2*np.exp(-lambdaC*t)+b3*np.exp(-lambdaS*t)\\n\\n else:\\n c2=deltaSC*S0\\n c1=C0\\n C=(c1+c2*t)*np.exp(-lambdaS*t)\\n\\n #there are two subcases for B in that case\\n if bCB!=0:\\n b3=deltaCB*c2/bSB; b2=(deltaCB*c1-b3)/bSB; b1=B0-b2\\n B=b1*np.exp(-lambdaB*t)+(b2+b3*t)*np.exp(-lambdaC*t)\\n\\n else:\\n b1=B0; b2=deltaCB*c1; b3=deltaCB*c2/2\\n B=(b1+b2*t+b3*t**2)*np.exp(-lambdaB*t)\\n\\n return(np.vstack((S,C,B)))\",\n \"def calculate_cost(self):\\n costs = {}\\n if np.abs(self.agent.get_position()[1]) > self.y_lim:\\n costs['cost_outside_bounds'] = 1.\\n if self.agent.velocity_violation:\\n costs['cost_velocity_violation'] = 1.\\n # sum all costs in one total cost\\n costs['cost'] = min(1, sum(v for k, v in costs.items() if k.startswith('cost_')))\\n return costs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60161155","0.5890601","0.5859548","0.5829997","0.5820077","0.58090657","0.57660633","0.57442236","0.5743485","0.5697327","0.56755006","0.56499743","0.5622","0.5602251","0.55968297","0.5590435","0.55680066","0.5539766","0.5538298","0.5469668","0.5467324","0.545357","0.5451566","0.5417329","0.54080707","0.54065144","0.5398749","0.53898406","0.5372717","0.5366814","0.5342058","0.53366333","0.53355926","0.5330968","0.5325407","0.5319396","0.53144","0.5310978","0.53085464","0.53056145","0.5268696","0.5254879","0.5248971","0.5244455","0.52403396","0.5230964","0.5224848","0.52105904","0.5206531","0.5199386","0.51987433","0.51920724","0.51918656","0.5189098","0.5184746","0.51815045","0.51809233","0.5172803","0.51572496","0.5148201","0.51416063","0.51406837","0.5135654","0.5124746","0.51221126","0.5118122","0.51176137","0.5117444","0.5105962","0.5104551","0.5090607","0.5088549","0.508647","0.50796205","0.50783443","0.5067843","0.5064367","0.5063713","0.50578356","0.5057098","0.5054711","0.50541496","0.5051367","0.5051242","0.5050079","0.50456667","0.5045457","0.5043441","0.50426656","0.5041153","0.5040887","0.5040194","0.50394344","0.50381887","0.5036751","0.5033895","0.5029311","0.502644","0.5021936","0.5018307"],"string":"[\n \"0.60161155\",\n \"0.5890601\",\n \"0.5859548\",\n \"0.5829997\",\n \"0.5820077\",\n \"0.58090657\",\n \"0.57660633\",\n \"0.57442236\",\n \"0.5743485\",\n \"0.5697327\",\n \"0.56755006\",\n \"0.56499743\",\n \"0.5622\",\n \"0.5602251\",\n \"0.55968297\",\n \"0.5590435\",\n \"0.55680066\",\n \"0.5539766\",\n \"0.5538298\",\n \"0.5469668\",\n \"0.5467324\",\n \"0.545357\",\n \"0.5451566\",\n \"0.5417329\",\n \"0.54080707\",\n \"0.54065144\",\n \"0.5398749\",\n \"0.53898406\",\n \"0.5372717\",\n \"0.5366814\",\n \"0.5342058\",\n \"0.53366333\",\n \"0.53355926\",\n \"0.5330968\",\n \"0.5325407\",\n \"0.5319396\",\n \"0.53144\",\n \"0.5310978\",\n \"0.53085464\",\n \"0.53056145\",\n \"0.5268696\",\n \"0.5254879\",\n \"0.5248971\",\n \"0.5244455\",\n \"0.52403396\",\n \"0.5230964\",\n \"0.5224848\",\n \"0.52105904\",\n \"0.5206531\",\n \"0.5199386\",\n \"0.51987433\",\n \"0.51920724\",\n \"0.51918656\",\n \"0.5189098\",\n \"0.5184746\",\n \"0.51815045\",\n \"0.51809233\",\n \"0.5172803\",\n \"0.51572496\",\n \"0.5148201\",\n \"0.51416063\",\n \"0.51406837\",\n \"0.5135654\",\n \"0.5124746\",\n \"0.51221126\",\n \"0.5118122\",\n \"0.51176137\",\n \"0.5117444\",\n \"0.5105962\",\n \"0.5104551\",\n \"0.5090607\",\n \"0.5088549\",\n \"0.508647\",\n \"0.50796205\",\n \"0.50783443\",\n \"0.5067843\",\n \"0.5064367\",\n \"0.5063713\",\n \"0.50578356\",\n \"0.5057098\",\n \"0.5054711\",\n \"0.50541496\",\n \"0.5051367\",\n \"0.5051242\",\n \"0.5050079\",\n \"0.50456667\",\n \"0.5045457\",\n \"0.5043441\",\n \"0.50426656\",\n \"0.5041153\",\n \"0.5040887\",\n \"0.5040194\",\n \"0.50394344\",\n \"0.50381887\",\n \"0.5036751\",\n \"0.5033895\",\n \"0.5029311\",\n \"0.502644\",\n \"0.5021936\",\n \"0.5018307\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7558044"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22397,"cells":{"query":{"kind":"string","value":"Test case for create_symlink_file"},"document":{"kind":"string","value":"def test_create_symlink_file(self):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _create_symlink(self, source_path, main):\n main_file = os.path.realpath(os.path.join(source_path, main))\n if not os.path.isfile(main_file):\n main_file += '.js'\n if not os.path.isfile(main_file):\n print('\\tWARNING: Could not create symlink for {}, no such file.'.format(main_file))\n return\n main_file_name = os.path.basename(main_file)\n with change_working_directory(os.path.realpath(self.symlink_dir)) as cd:\n file_path = os.path.join(cd, main_file_name)\n self.created(file_path)\n if os.path.islink(file_path):\n os.remove(file_path)\n symlink(main_file, main_file_name)","def _symlink(source, link_name):\n flags = 0\n\n if source is not None and os.path.isdir(source):\n flags = 1\n\n CreateSymbolicLinkW(link_name, source, flags)","def testCreateSymlinkOutput(self): # pylint: disable=no-self-use\n gcs_client = mock.MagicMock(spec=storage.Client)\n blob = prow.create_symlink(gcs_client, \"gs://bucket/symlink\",\n \"gs://bucket/output\")\n\n blob.upload_from_string.assert_called_once_with(\"gs://bucket/output\")","def create_symbolic_link(file, target):\n try:\n os.symlink(file, target)\n except NotImplementedError:\n logger.critical(\"Symbolic links not supported on this platform\")\n raise\n except OSError:\n logger.critical(\"Not sufficient permissions\")\n raise","def _symlink_file_on_disk(source, link_name):\n link_dir = os.path.dirname(link_name)\n\n # create intermediate dirs if they do not already exist\n if not os.path.isdir(link_dir):\n try:\n os.makedirs(link_dir)\n except OSError as exc:\n logger.error(\"Error creating directory '%s': %s\", link_dir, exc)\n return False\n\n # create symbolic link\n try:\n os.symlink(source, link_name)\n except OSError as exc:\n logger.error(\"Error creating symlink '%s': %s\", link_name, exc)\n return False\n\n logger.debug(\"Created symlink '%s' to '%s'\", link_name, source)\n return True","def testIsSymlink(self):\r\n P=lambda p:ufsi.NativeUnixPath(p)\r\n existingValidSymlinkPath=P(self.existingValidSymlinkFilePathStr)\r\n existingInvalidSymlinkPath=P(self.existingInvalidSymlinkFilePathStr)\r\n nonExistingSymlinkPath=P(self.nonExistingSymlinkPathStr)\r\n\r\n # 1\r\n self.assertEquals(existingValidSymlinkPath.isSymlink(),True,\r\n 'Symlink %r exists'\r\n %str(existingValidSymlinkPath))\r\n\r\n # 2\r\n self.assertEquals(existingInvalidSymlinkPath.isSymlink(),True,\r\n 'Symlink %r exists'\r\n %str(existingInvalidSymlinkPath))\r\n\r\n # 3\r\n self.assertEquals(nonExistingSymlinkPath.isSymlink(),False,\r\n 'Symlink %r does not exist'\r\n %str(nonExistingSymlinkPath))","def ensure_symlink_exists(symlink_path, file_path):\n\n if not (os.path.islink(symlink_path) or (os.path.realpath(symlink_path) != os.path.realpath(file_path))):\n # This is bad.\n raise CronException(\"Path {0} is not a symlink or does not point where expected.\".format(symlink_path))","def _makeSymlink ( target, source, env ) :\n if len(target) != 1 :\n fail ( \"unexpected number of targets for symlink: \"+str(target) )\n if len(source) != 1 :\n fail ( \"unexpected number of sources for symlink: \"+str(source) )\n\n target = str(target[0])\n source = str(source[0].abspath)\n trace ( \"Executing symlink `%s' -> `%s'\" % ( target, source ), \"makeSymlink\", 3 )\n\n os.symlink ( source, target )","def create_symlink(src: str, dst: str) -> bool:\n if exists(src):\n with suppress(Exception):\n if isfile(dst):\n remove(dst)\n else:\n rmtree(dst)\n\n try:\n\n symlink(src, dst)\n return True\n\n except PermissionError as err:\n printer(\n \"User without permission to create the symbolic link.\",\n str(err),\n foreground=FG().ERROR,\n )\n return False\n\n except FileExistsError:\n remove(dst)\n symlink(src, dst)\n return False","def test_history_import_symlink():\n with HistoryArchive() as history_archive:\n history_archive.write_metafiles()\n history_archive.write_link('datasets/Pasted_Entry_1.txt', '../target.txt')\n history_archive.write_file('target.txt', 'insecure')\n _run_jihaw_cleanup_check_secure(history_archive, 'Symlink dataset in import archive allowed')","def create_symlink(source_file, dest_file, sudo=True):\n LOG.info(\"Creating symlink to {} called {}\".format(source_file, dest_file))\n cmd = \"ln -sf {} {}\".format(source_file, dest_file)\n _exec_cmd(cmd=cmd, sudo=sudo, fail_ok=False)","def create_symlink(src, dest):\n sudo('ln -s {} {}'.format(src, dest))","def test_diff_git_symlink_added(self):\n diff = (\n b'diff --git a/link b/link\\n'\n b'new file mode 120000\\n'\n b'index 0000000..100b938\\n'\n b'--- /dev/null\\n'\n b'+++ b/link\\n'\n b'@@ -0,0 +1 @@\\n'\n b'+README\\n'\n b'\\\\ No newline at end of file\\n'\n )\n\n parsed_files = self.tool.get_parser(diff).parse()\n self.assertEqual(len(parsed_files), 1)\n\n self.assert_parsed_diff_file(\n parsed_files[0],\n orig_filename=b'link',\n orig_file_details=PRE_CREATION,\n modified_filename=b'link',\n modified_file_details=b'100b938',\n new_unix_mode='120000',\n is_symlink=True,\n new_symlink_target=b'README',\n insert_count=1,\n data=diff)","def create_symlinks(target_dir: os.PathLike, symlinks_to_create: List[os.PathLike]):\n for src_path in symlinks_to_create:\n trg_path = os.path.join(target_dir, os.path.basename(src_path))\n\n if os.path.islink(src_path):\n # Let's not create symlinks to symlinks\n # Since dropping the current symlink will break the experiment\n os.symlink(os.readlink(src_path), trg_path)\n else:\n print(f'Creating a symlink to {src_path}, so try not to delete it occasionally!')\n os.symlink(src_path, trg_path)","def attempt_symlink_to(path: str, to_path: str) -> None:\n try:\n Path(path).symlink_to(Path(to_path))\n except OSError:\n pytest.skip(\"could not create symbolic link\")","def makeLinks(self, source, target):\n\n if os.path.exists(target): os.unlink(target)\n os.symlink(source, target)","def _safe_setup_link(link_filename, real_filename):\r\n real_filename = os.path.relpath(real_filename, os.path.dirname(link_filename))\r\n\r\n if os.path.exists(link_filename):\r\n try:\r\n os.unlink(link_filename)\r\n except OSError:\r\n pass\r\n try:\r\n os.symlink(real_filename, link_filename)\r\n except OSError as e:\r\n # Typically permission denied.\r\n pass","def _symlink_tar(self):\r\n outsidep = self.unsafe_common_dir / \"unsafe_file.txt\"\r\n symlinkp = self.unsafe_common_dir / \"symlink.txt\"\r\n symlink_tar = self.unsafe_common_dir / \"symlink.tar.gz\"\r\n outsidep.symlink(symlinkp)\r\n with tarfile.open(symlink_tar, \"w:gz\") as tar:\r\n tar.add(symlinkp)\r\n\r\n return symlink_tar","def testGetSymlinkPath(self):\r\n P=lambda p:ufsi.NativeUnixPath(p)\r\n existingValidFileSymlinkPath=P(self.existingValidSymlinkFilePathStr)\r\n existingValidDirSymlinkPath=P(self.existingValidSymlinkDirPathStr)\r\n existingFilePath=P(self.existingFilePathStr)\r\n existingDirPath=P(self.existingDirPathStr)\r\n nonExistingSymlinkPath=P(self.nonExistingSymlinkPathStr)\r\n\r\n # 1\r\n r1=str(existingValidFileSymlinkPath.getSymlinkPath())\r\n r2=self.existingValidFileSymlinkPath\r\n self.assertEquals(r1,r2,'Symlink path expected %r. Got %r'%(r2,r1))\r\n \r\n # 2\r\n r1=str(existingValidDirSymlinkPath.getSymlinkPath())\r\n r2=self.existingValidDirSymlinkPath\r\n self.assertEquals(r1,r2,'Symlink path expected %r. Got %r'%(r2,r1))\r\n\r\n # 3\r\n self.assertRaises(ufsi.NotASymlinkError,\r\n existingFilePath.getSymlinkPath)\r\n\r\n # 4\r\n self.assertRaises(ufsi.NotASymlinkError,\r\n existingDirPath.getSymlinkPath)\r\n\r\n # 5\r\n self.assertRaises(ufsi.NotASymlinkError,\r\n nonExistingSymlinkPath.getSymlinkPath)","def make_symlink(dbconfig, targ):\n if \"latest\" in dbconfig and not dbconfig[\"latest\"]:\n return\n link = re.sub(r'[0-9]+', 'latest', targ)\n try:\n os.symlink(targ, link)\n info(\"create link \" + link + \" --> \" + targ)\n except OSError as e:\n if e.errno == errno.EEXIST:\n os.remove(link)\n os.symlink(targ, link)\n info(\"move link \" + link + \" --> \" + targ)","def symlink(self, filen, link):\n src = os.path.abspath(filen)\n cwd = self.getWorkingDirectory()\n dest = os.path.join(cwd, link)\n os.symlink(os.path.relpath(src, cwd), dest)","def symlink(self, filen, link):\n src = os.path.abspath(filen)\n cwd = self.getWorkingDirectory()\n dest = os.path.join(cwd, link)\n os.symlink(os.path.relpath(src, cwd), dest)","def createLink(self):\n \n if( self.useLink ):\n trymakedir( self.parent.installPath + \"/\" + self.alias )\n\n os.chdir( self.parent.installPath + \"/\" + self.alias )\n \n # check for already existing symlinks or dirs \n if( os.path.islink( self.version )):\n os.unlink( self.version )\n elif( os.path.isdir( self.version )):\n self.abort( \"could not create link to [ \" + self.linkPath + \" ]\\nin [ \" \\\n + os.path.basename( self.installPath ) + \" ]!!!\" )\n\n os.symlink( self.linkPath , self.version )\n print \"+ Linking \" + self.parent.installPath + \"/\" + self.alias + \"/\" + self.version \\\n + \" -> \" + self.linkPath","def _link(filename, existing_filename):\n CreateHardLinkW(filename, existing_filename, 0)","def IsSymlink(info):\n return (info.external_attr >> 16) == 0120777","def ln(src, dst):\n os.symlink(src, dst)","def _sync_symlink(self, binary_name, link_to):\n\n # The symlink we are creating:\n link_path = os.path.join(self.bin_dir, binary_name)\n\n # The expected file we should be linking to:\n link_dest = os.path.join(self.bin_dir, link_to)\n\n if not os.path.exists(link_path) or \\\n not os.path.islink(link_path) or \\\n os.path.realpath(link_path) != os.path.realpath(link_dest):\n if os.path.exists(link_path):\n os.remove(link_path)\n os.symlink(link_to, os.path.join(self.bin_dir, binary_name))\n self.output.append(\"Symlinked %s to %s.\" % (link_path, link_dest))\n self.changed = True","def test_symlink(self, mock_request):\n self.server.hook = UrlRequestHook('test_url', request_method='GET')\n linkpath = b'ugly'\n targetpath = b'ugliest'\n self.server.input_queue = sftpcmd(\n SSH2_FXP_SYMLINK, sftpstring(linkpath), sftpstring(targetpath),\n sftpint(0))\n self.server.process()\n mock_request.assert_called_once_with(\n 'GET', 'test_url/symlink', auth=None,\n data={\n 'method': 'symlink', 'linkpath': linkpath,\n 'targetpath': targetpath})","def symlink(self, req, link, parent, name):\r\n self.reply_err(req, EROFS)","def symlink(source, link_name):\n os_symlink = getattr(os, \"symlink\", None)\n if callable(os_symlink):\n os_symlink(source, link_name)\n else:\n import ctypes\n csl = ctypes.windll.kernel32.CreateSymbolicLinkW\n csl.argtypes = (ctypes.c_wchar_p, ctypes.c_wchar_p, ctypes.c_uint32)\n csl.restype = ctypes.c_ubyte\n flags = 1 if os.path.isdir(source) else 0\n if csl(link_name, source, flags) == 0:\n raise ctypes.WinError()","def symlink(self, name, source, linkname):\n self._assert_absolute_path_or_placeholder(source)\n self._assert_absolute_path_or_placeholder(linkname)\n self._run(name, ['symlink', source, linkname])\n self.m.path.mock_copy_paths(source, linkname)","def _symlink_or_copy(src, dst):\n # try to symlink file\n try:\n os.symlink(src, dst)\n print('Creating symlink \"%s\" pointing to \"%s\"' % (dst, src))\n except Exception as ex_symlink:\n # try to copy file\n try:\n shutil.copyfile(src, dst)\n print('Copying file from \"%s\" to \"%s\"' % (src, dst))\n except Exception as ex_copy:\n raise RuntimeError('Could neither symlink nor copy file \"%s\" to \"%s\":\\n- %s\\n- %s' % (src, dst, str(ex_symlink), str(ex_copy)))","def make_link(self, filepath):\n # Check file exists. It may have been deleted but still in manifest\n if not os.path.exists(self.fullpath(filepath)):\n print('File not found: {filepath}'.format(\n filepath=self.fullpath(filepath)))\n if self.contains(filepath):\n print('removing from manifest')\n self.delete(filepath)\n self.needsync = True\n self.existing_filepaths.discard(filepath)\n else:\n try:\n destdir = os.path.dirname(filepath)\n # Make destination directory if not already exists\n # Necessary because sometimes this is called before\n # individual model setup\n if not os.path.exists(destdir):\n os.makedirs(destdir)\n if self.copy_file(filepath):\n shutil.copy(self.fullpath(filepath), filepath)\n perm = (stat.S_IRUSR | stat.S_IRGRP\n | stat.S_IROTH | stat.S_IWUSR)\n os.chmod(filepath, perm)\n else:\n make_symlink(self.fullpath(filepath), filepath)\n except Exception:\n action = 'copying' if self.copy_file else 'linking'\n print('payu: error: {action} orig: {orig} '\n 'local: {local}'.format(action=action,\n orig=self.fullpath(filepath),\n local=filepath))\n raise\n finally:\n self.existing_filepaths.discard(filepath)","def _symlink(conf, devname, label, remove=False):\n return\n\n linkpath = conf.get('symlink')\n if linkpath:\n linkpath = expanduser(linkpath)\n if lexists(linkpath):\n os.unlink(linkpath)\n if not remove:\n # TODO: handle path errors\n os.symlink(get_mount_target(devname, label), linkpath)","def testLink(self):\n def _check(results):\n self.flushLoggedErrors()\n self.assertEqual(results[0], b'')\n self.assertTrue(results[1].startswith(b'l'), 'link failed')\n return self.runCommand('rm testLink')\n\n d = self.runScript('ln testLink testfile1', 'ls -l testLink')\n d.addCallback(_check)\n d.addCallback(self.assertEqual, b'')\n return d","def test_profile_link_file(profile_manager, test_profile,\n tmpdir, inventory_content):\n myfile = tmpdir.mkdir(\"ir_dir\").join(\"fake_hosts_file\")\n myfile.write(inventory_content)\n org_inventory = myfile.strpath\n\n target_path = test_profile.link_file(org_inventory)\n assert target_path == os.path.join(\n test_profile.path, os.path.basename(org_inventory))\n profile_inventory = py.path.local(target_path)\n assert profile_inventory.islink()\n assert inventory_content == profile_inventory.read()","def test_diff_git_symlink_changed(self):\n diff = (\n b'diff --git a/link b/link\\n'\n b'index 100b937..100b938 120000\\n'\n b'--- a/link\\n'\n b'+++ b/link\\n'\n b'@@ -1 +1 @@\\n'\n b'-README\\n'\n b'\\\\ No newline at end of file\\n'\n b'+README.md\\n'\n b'\\\\ No newline at end of file\\n'\n )\n\n parsed_files = self.tool.get_parser(diff).parse()\n self.assertEqual(len(parsed_files), 1)\n\n self.assert_parsed_diff_file(\n parsed_files[0],\n orig_filename=b'link',\n orig_file_details=b'100b937',\n modified_filename=b'link',\n modified_file_details=b'100b938',\n old_unix_mode='120000',\n new_unix_mode='120000',\n is_symlink=True,\n old_symlink_target=b'README',\n new_symlink_target=b'README.md',\n insert_count=1,\n delete_count=1,\n data=diff)","def __write_file(path, link_name, context):\n file_name = ''\n for strng in ['/', 'http:']:\n if not file_name:\n file_name = link_name.replace(strng, '')\n else:\n file_name = file_name.replace(strng, '')\n\n full_path = os.path.join(path)\n if not os.path.exists(full_path):\n os.makedirs(full_path)\n open(full_path + '/' + file_name, 'w').write(context.encode('utf-8'))\n logging.info('File: %s Created', full_path + '/' + file_name)","def symlink(self, src, dst):\n return os.symlink(src, dst)","def create_link(self):\n if self.link_info:\n link_type = self.file.options['link_type']\n if 'node' in self.link_info:\n target_path = self.link_info['node'].full_path\n if link_type == 'string':\n # create string dataset containing link path\n #- self.file.file_pointer.create_dataset(self.full_path, data=\"h5link:/\" + target_path)\n self.file.create_dataset(self.full_path, data=\"h5link:/\" + target_path)\n elif link_type == 'hard':\n # create hard link to target. This implemented by h5py \"Softlink\". Not sure why.\n #- self.file.file_pointer[self.full_path] = h5py.SoftLink(target_path)\n self.file.create_softlink(self.full_path, target_path)\n else: \n raise Exception('Invalid option value for link_type (%s)' % link_type)\n elif 'extlink' in self.link_info:\n file, path = self.link_info['extlink']\n # link to external file\n if link_type == 'string':\n # create string dataset containing link path\n target_path = \"%s,%s\" % (file, path)\n #- self.file.file_pointer.create_dataset(self.full_path, data=\"h5extlink:/\" + target_path)\n self.file.create_dataset(self.full_path, data=\"h5extlink:/\" + target_path)\n elif link_type == 'hard':\n # create link to external file\n #- self.file.file_pointer[self.full_path] = h5py.ExternalLink(file,path)\n self.file.create_external_link(self.full_path, file, path) \n else:\n raise Exception('Invalid option value for link_type (%s)' % link_type)\n else:\n raise SystemError(\"** Error: invalid key in link_info %s\" % self.link_info)","def _errcheck_link(value, func, args): # pylint: disable=W0613\n # The windows api returns nonzero if the call was successful\n if value != 0:\n return\n\n last_error = ctypes.windll.kernel32.GetLastError()\n # Somehow CreateSymbolicLinkW and CreateHardLinkW retuns zero\n # and the last error is 2 (The system cannot find the file specified)\n # but the link is created successfuly\n # it seems like a bug in the WinAPI\n if last_error == 0 or last_error == 2:\n return\n if last_error == 183:\n raise OSError(errno.EEXIST,\n \"Cannot create a file when that file already exists\",\n args[0])","def _symlink_datafile(self):\n logger.debug(\"Symlinking datafile to '%s'\", self.source)\n\n if os.path.isfile(self.source):\n # construct symlink target path based on source file name\n rel_dst_path = self.datafile.storage.get_available_name(\n file_path(self, os.path.basename(self.source))\n )\n abs_dst_path = os.path.join(settings.FILE_STORE_BASE_DIR,\n rel_dst_path)\n # create symlink\n if _symlink_file_on_disk(self.source, abs_dst_path):\n # update the model with the symlink path\n self.datafile.name = rel_dst_path\n logger.debug(\"Datafile symlinked\")\n return True\n else:\n logger.error(\"Symlinking failed\")\n return False\n else:\n logger.error(\"Symlinking failed: source is not a file\")\n return False","def copy_and_link(file_name):\n if os.path.normpath(output_path) != os.getcwd():\n write_to_runner(f\"mv {file_name} {output_path} \\n\")\n write_to_runner(f\"ln -s {output_path}/{file_name} . \\n\")","def _create_links(self):\n for line in self.iter_files_to_install():\n arcname, link = line.split()\n if link == 'False':\n continue\n self.files.append(create_link(arcname, link, self.prefix))","def _link_files(self, source, target):\n try:\n logging.debug(\"Linking %s and %s\" % (source, target))\n os.link(os.path.realpath(source), target)\n except PermissionError as e:\n msg = \"ERROR: Insufficient rights on {}! \" \\\n \"Possible cause; source file need to be writable/appendable when fs.protect_hardlinks is enabled. \" \\\n \"Permissions: {}\"\n logging.error(msg.format(e.filename, str(AccessControlList.from_file(source))))\n except FileExistsError as e:\n logging.debug(\"File %s already exists!\" % e.filename)","def is_symlink(self):\n return self._security_class == \"lnk_file\"","def link(self):\n\n if self.path_source is not None:\n full_source_path = os.path.join(\n os.path.expandvars(self.path_source), self.name\n )\n full_destination_path = os.path.join(\n os.path.expandvars(self.path_destination), self.name\n )\n\n try:\n if self.sudo:\n spawn.process(\n f'ln -sfv \"{full_source_path}\" \"{full_destination_path}\"',\n sudo=True,\n )\n else:\n os.symlink(full_source_path, full_destination_path)\n except FileExistsError:\n message.error(\n \"Can't symlink, file already exists at destination. Attempting fix.\"\n )\n os.remove(full_destination_path)\n message.info(f\"Removed: '{full_destination_path}'\")\n os.symlink(full_source_path, full_destination_path)\n finally:\n message.info(\n f\"Symlink created: '{full_source_path}' <--> '{full_destination_path}'\"\n )\n else:\n message.error(\n f\"'{self.name}' has no source from which to create a link from.\"\n )","def testGetLinkedFileEntry(self):\n path_spec = path_spec_factory.Factory.NewPathSpec(\n definitions.TYPE_INDICATOR_HFS, identifier=self._IDENTIFIER_A_LINK,\n location='/a_link', parent=self._raw_path_spec)\n file_entry = self._file_system.GetFileEntryByPathSpec(path_spec)\n self.assertIsNotNone(file_entry)\n\n linked_file_entry = file_entry.GetLinkedFileEntry()\n\n self.assertIsNotNone(linked_file_entry)\n\n self.assertEqual(linked_file_entry.name, 'another_file')","def mksymlinkto(self, value, absolute=1):\n if absolute:\n error.checked_call(os.symlink, str(value), self.strpath)\n else:\n base = self.common(value)\n # with posix local paths '/' is always a common base\n relsource = self.__class__(value).relto(base)\n reldest = self.relto(base)\n n = reldest.count(self.sep)\n target = self.sep.join((\"..\",) * n + (relsource,))\n error.checked_call(os.symlink, target, self.strpath)","def symlink(source, target):\n source, target = map(os.path.expanduser, (source, target))\n print(\"Will symlink %s to %s\" % (source, target))\n\n if os.path.exists(target):\n if os.path.islink(target) and os.path.realpath(target) == source:\n logging.info(\"%s exists\" % target)\n return\n\n backup = target + \".old\"\n\n if os.path.exists(backup):\n raise Exception(\"Can't backup to %s: file already exists.\" % backup)\n\n shutil.move(target, backup)\n\n else:\n os.symlink(source, target)\n logging.info(\"%s symlinked to %s\" % (source, target))","def symlink(source, target, use_sudo=True):\n\n # Some older versions of Fabric do not have the is_link method \n try:\n from fabric.contrib.files import is_link\n is_a_link = is_link(target)\n except ImportError:\n with settings(hide(\"everything\"), warn_only=True):\n if run(\"test -L \"+target).failed:\n\t is_a_link = False\n else:\n is_a_link = True\n\n if not is_a_link:\n cmd = \"ln -s \"+source+\" \"+target\n if use_sudo:\n sudo(cmd)\n else:\n run(cmd)","def symlink(path, v=False):\r\n if not os.path.exists(path):\r\n err(path + ' : no such file or directory')\r\n elif not os.path.isdir(path):\r\n err(path + ' : not a directory')\r\n else:\r\n theme_name = os.path.basename(os.path.normpath(path))\r\n theme_path = os.path.join(_THEMES_PATH, theme_name)\r\n if os.path.exists(theme_path):\r\n err(path + ' : already exists')\r\n else:\r\n if v:\r\n print(\"Linking `{p}' to `{t}' ...\".format(p=path, t=theme_path))\r\n try:\r\n os.symlink(path, theme_path)\r\n except Exception as e:\r\n err(\"Cannot link `{p}' to `{t}':\\n{e}\".format(p=path, t=theme_path, e=str(e)))","def create_links(self, name):\n for target, linknames in self._link_map.iteritems():\n for linkname in linknames:\n self._api.path.mock_copy_paths(target, linkname)\n self._api.python(\n name,\n self._resource,\n args = [\n '--link-json',\n self._api.json.input({str(target) : linkname\n for target, linkname in self._link_map.iteritems()\n }),\n ],\n infra_step=True)","def create_target(cls, relpath, target):\r\n cls.create_file(cls.build_path(relpath), target, mode='a')","def make_sym_links(f, source, dest_dir, catalog):\n source = get_key_from_values(f, catalog)\n source_path = f\"{source}/{f}\"\n dest_path = f\"{dest_dir}/{f}\"\n ls = os.listdir(dest_dir)\n if f not in ls: \n os.symlink(source_path, dest_path)","def open_file_link_manager(file):\n pass","def test_diff_git_symlink_removed(self):\n diff = (\n b'diff --git a/link b/link\\n'\n b'deleted file mode 120000\\n'\n b'index 100b938..0000000\\n'\n b'--- a/link\\n'\n b'+++ /dev/null\\n'\n b'@@ -1 +0,0 @@\\n'\n b'-README.txt\\n'\n b'\\\\ No newline at end of file\\n'\n )\n\n parsed_files = self.tool.get_parser(diff).parse()\n self.assertEqual(len(parsed_files), 1)\n\n self.assert_parsed_diff_file(\n parsed_files[0],\n orig_filename=b'link',\n orig_file_details=b'100b938',\n modified_filename=b'link',\n modified_file_details=b'0000000',\n old_unix_mode='120000',\n is_symlink=True,\n old_symlink_target=b'README.txt',\n deleted=True,\n delete_count=1,\n data=diff)","def create_symlink_dir(src_dir, src_list, dst):\n if not src_list:\n return\n message = \"creating symlink directory at {dst} with files {src_list}\".format(\n dst=dst,\n src_list=pformat(src_list))\n logging.info(message)\n if not os.path.exists(dst):\n os.makedirs(dst)\n for src_file in src_list:\n if not src_file:\n continue\n source = os.path.join(src_dir, src_file)\n destination = os.path.join(dst, src_file)\n if os.path.lexists(destination):\n continue\n try:\n os.symlink(source, destination)\n except Exception as e:\n msg = format_debug(e)\n logging.error(e)","def create_file(path):\n command = ['touch', TEST_FILE]\n file_operation(path, command)","def link(target, link_name):\n src = os.path.abspath(target)\n dst = os.path.abspath(link_name)\n os.symlink(src, dst)","def force_symlink(target_path, link_location):\n\n pardir = os.path.dirname(link_location)\n if not os.path.exists(pardir):\n os.makedirs(pardir)\n\n if os.path.lexists(link_location):\n assert os.path.islink(link_location), \\\n \"The path {} exists but is not a symlink\".format(link_location)\n if os.readlink(link_location) != target_path:\n os.remove(link_location)\n os.symlink(target_path, link_location)\n else:\n os.symlink(target_path, link_location)","def symlink_p(src, dst):\n try:\n os.symlink(src, dst)\n except OSError as exc: # Python >2.5\n if exc.errno == errno.EEXIST and os.path.islink(dst):\n if os.path.realpath(dst) == os.path.realpath(src):\n pass\n else:\n print('%s is a link already pointing to %s' % (dst, os.path.realpath(dst)), file=sys.stderr)\n else:\n raise","def test_readlink(self, mock_request):\n self.server.hook = UrlRequestHook(\n 'test_url',\n urls_mapping={\n 'readlink': ['test_url_1', 'test_url_2']},\n paths_mapping={\n 'readlink': ['test_path_1', 'test_path_2']})\n linkpath = b'ugly'\n targetpath = b'ugliest'\n os.symlink(linkpath, targetpath)\n self.server.input_queue = sftpcmd(\n SSH2_FXP_READLINK, sftpstring(targetpath), sftpint(0))\n self.server.process()\n mock_request.assert_has_calls([\n mock.call(\n 'POST', 'test_url_1/test_path_1', auth=None,\n data={'method': 'readlink', 'filename': targetpath}),\n mock.call(\n 'POST', 'test_url_1/test_path_2', auth=None,\n data={'method': 'readlink', 'filename': targetpath}),\n mock.call(\n 'POST', 'test_url_2/test_path_1', auth=None,\n data={'method': 'readlink', 'filename': targetpath}),\n mock.call(\n 'POST', 'test_url_2/test_path_2', auth=None,\n data={'method': 'readlink', 'filename': targetpath}),\n ])","def symlink_force(source, link_name):\n try:\n os.symlink(source, link_name)\n except OSError as e:\n if e.errno == errno.EEXIST:\n os.remove(link_name)\n os.symlink(source, link_name)","def force_symlink(src, dst):\n try:\n os.unlink(dst)\n os.symlink(src, dst)\n except OSError:\n os.symlink(src, dst)","def create_dir(link_dir):\n if not os.path.exists(link_dir):\n os.makedirs(link_dir)","def _tryLink(self,src, dst):\n\n hiero.core.log.info(\"Attempting to link %s to %s\" % (src, dst))\n \n try:\n os.link(util.asUnicode(src), util.asUnicode(dst))\n except OSError as err:\n # If the OS returns an ENOTSUP error (45), for example when trying to set\n # flags on an NFS mounted volume that doesn't support them, Python should\n # absorb this. However, a regression in Python 2.7.3 causes this not to\n # be the case, and the error is thrown as an exception. We therefore\n # catch this explicitly as value 45, since errno.ENOTSUP is not defined\n # in Python 2.7.2 (which is part of the problem). See the following\n # link for further information: http://bugs.python.org/issue14662\n # See TP 199072.\n if err.errno == 45: # ENOTSUP\n pass\n elif err.errno == 17: # FILE EXISTS\n raise\n else:\n raise","def symlink(origin, target):\n # Skip anything in the home directory if the user is admin\n if user_is_admin() and not args.root and check_contain_home_dir(target):\n print(highlight_colour(\"'%s'\") % str(target) +\n warning_colour(\" is inside of home folder. Skipping...\"))\n raise StopTraversing(\"Skipping.\")\n\n # Check for a broken symlink, if true: prompt for replacement.\n # This is done to avoid having any broken symlinks lingering.\n if is_broken_symlink(target):\n if args.yes or prompt(origin, target, \"remove\"):\n target.unlink()\n else:\n return\n\n if args.replace:\n replace_symlink(origin, target)\n elif args.remove:\n remove_symlink(origin, target)\n else:\n create_symlink(origin, target)","def create_home_directory_symbolic_links():\n file_paths = (\n path\n for path in repo_home.rglob(\"*\")\n if path.is_file() and not path.is_symlink()\n )\n\n for file_path in file_paths:\n sym_link_path = translate_home_path(file_path)\n\n if sym_link_path.is_file() and not sym_link_path.is_symlink():\n backup_file(sym_link_path)\n sym_link_path.unlink()\n\n if sym_link_path.is_symlink():\n sym_link_path.unlink()\n\n print(f\"Creating Symlink: {sym_link_path} -> {file_path}\")\n sym_link_path.symlink_to(file_path)","def make_symlink(dst, src, silently_move=False):\n dst_dir = os.path.dirname(dst.rstrip(os.path.sep))\n if not os.path.isdir(dst_dir):\n os.makedirs(dst_dir)\n\n # get a temporary directory\n if os.path.exists(dst):\n if silently_move or (((os.path.isfile(dst) or (os.path.isdir(dst)) and\n query_yes_no('Move NSLS-II from userpackages?')))):\n import tempfile\n temp_dir = tempfile.mkdtemp()\n shutil.move(dst, temp_dir)\n print('Previous NSLS-II folder moved to {0}'.format(temp_dir))\n else:\n print('NSLS-II already exists in userpackages. Please move or delete it'\n 'and then re-run setup.py')\n return False\n\n # this symlink does not get removed when pip uninstall vttools is run...\n # todo figure out how to make pip uninstall remove this symlink\n try:\n # symlink the NSLS-II folder into userpackages\n os.symlink(src, dst)\n except AttributeError:\n # you must be on Windows!\n call(['mklink', '/j', dst, src], shell=True)\n\n return True","def fix_git_symlinked(src, dst):\n # if running from WC there should be a 'doc' dir sibling to nikola package\n if not should_fix_git_symlinked():\n return\n # probabbly in a WC, so symlinks should be fixed\n for root, dirs, files in os.walk(dst):\n for name in files:\n filename = os.path.join(root, name)\n\n # detect if symlinked\n try:\n if not (2 < os.path.getsize(filename) < 500):\n continue\n # which encoding uses a git symlink marker ? betting on default\n with open(filename, 'r') as f:\n text = f.read()\n if text[0] != '.':\n # de facto hint to skip binary files and exclude.meta\n continue\n except Exception:\n # probably encoding: content binary or encoding not defalt,\n # also in py2.6 it can be path encoding\n continue\n dst_dir_relpath = os.path.dirname(os.path.relpath(filename, dst))\n path = os.path.normpath(os.path.join(src, dst_dir_relpath, text))\n if not os.path.exists(path):\n continue\n # most probably it is a git symlinked file\n\n # copy original content to filename\n shutil.copy(path, filename)","def _follow_symlinks(filepath):\n filepath = os.path.abspath(filepath)\n while os.path.islink(filepath):\n filepath = os.path.normpath(\n os.path.join(os.path.dirname(filepath), os.readlink(filepath)))\n return filepath","def test_getLinkrelToSameDirectory(self):\n linkrel = self.builder.getLinkrel(FilePath(\"/foo/bar\"),\n FilePath(\"/foo/bar\"))\n self.assertEquals(linkrel, \"\")","def testIsFile(self):\r\n P=lambda p:ufsi.NativeUnixPath(p)\r\n existingFilePath=P(self.existingFilePathStr)\r\n nonExistingFilePath=P(self.nonExistingFilePathStr)\r\n existingValidFileSymlinkPath=P(self.existingValidSymlinkFilePathStr)\r\n existingInvalidFileSymlinkPath=\\\r\n P(self.existingInvalidSymlinkFilePathStr)\r\n existingDirPath=P(self.existingDirPathStr)\r\n\r\n\r\n # 1\r\n self.assertEquals(existingFilePath.isFile(),True,\r\n '%r is a file'%str(existingFilePath))\r\n\r\n # 2\r\n self.assertEquals(nonExistingFilePath.isFile(),False,\r\n 'File %r does not exist'%str(nonExistingFilePath))\r\n\r\n # 3\r\n self.assertEquals(existingValidFileSymlinkPath.isFile(),True,\r\n '%r is a file'%str(existingValidFileSymlinkPath))\r\n\r\n # 4\r\n self.assertEquals(existingInvalidFileSymlinkPath.isFile(),False,\r\n '%r is an invalid symlink'\r\n %str(existingInvalidFileSymlinkPath))\r\n\r\n # 5\r\n self.assertEquals(existingDirPath.isFile(),False,\r\n '%r is a dir'%str(existingDirPath))","def main(file_paths: Optional[List[Path]] = None):\n if file_paths:\n for file_path in file_paths:\n move_and_symlink_file(file_path)\n\n else:\n create_home_directories()\n create_home_directory_symbolic_links()","def move_and_symlink_file(file_path: Path):\n assert (\n file_path.is_file()\n and not file_path.is_symlink()\n and user_home in file_path.parents\n )\n\n original_path = file_path\n new_path = translate_home_path(original_path)\n\n print(f\"Moving: {original_path} -> {new_path}\")\n if not new_path.parent.exists():\n new_path.parent.mkdir(mode=HOME_DIRECTORY_MODE, parents=True)\n move(str(original_path), str(new_path))\n\n print(f\"Creating Symlink: {original_path} -> {new_path}\")\n original_path.symlink_to(new_path)","def update_symlinks(n):\n\tif n > 0: return\n\tsymlink_dir = sc.text_image_symlink_dir.absolute()\n\tfor tpi, info in sorted(index.items(), key=lambda t: t[0]):\n\t\tsymlink = symlink_dir / info['url']\n\t\tif symlink.is_symlink():\n\t\t\tif symlink.resolve() == info['file']:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tsymlink.unlink()\n\t\tif not symlink.parent.exists():\n\t\t\tsymlink.parent.mkdir(parents=True)\n\t\tsymlink.symlink_to(info['file'])","def make_franny_symlinks(src_dirs, out_dir):\n\n for path, dirs, files in chain.from_iterable(os.walk(path)\n for path in src_dirs):\n print('Looking in %s' % path)\n for sta in ['NS12', 'NS13', 'NS14']:\n for filename in fnmatch.filter(files, '*.%s*' % sta):\n net = filename.split('.')[-7]\n chan = filename.split('.')[-4]\n if chan[-1] == 'N':\n new_chan = 'EH1'\n elif chan[-1] == 'E':\n new_chan = 'EH2'\n else:\n continue\n mseed_nm = filename.split('/')[-1]\n new_mseed = string.replace(mseed_nm, chan, new_chan)\n old_path = os.path.join(path, filename)\n new_path = '%s/%s/%s/%s.D/%s' % (out_dir, net,\n sta, new_chan, new_mseed)\n\n print('Creating symlink for file %s at %s'\n % (old_path, new_path))\n spwd = '*blackmore89'\n cmnd = 'sudo -S ln %s %s' % (old_path, new_path)\n os.system('echo %s | %s' % (spwd, cmnd))\n return","def symlink_input(filegroup_resource_path, temp_dir, strip_prefix=None,\n copy=False):\n assert os.path.isdir(temp_dir)\n manifest = runfiles.Create()\n with open(manifest.Rlocation(filegroup_resource_path)) as f:\n input_filenames = f.read().splitlines()\n for name in input_filenames:\n orig_name = manifest.Rlocation(name)\n assert os.path.exists(orig_name), name\n dest_name = name\n for prefix in (strip_prefix or []):\n if dest_name.startswith(prefix):\n dest_name = dest_name[len(prefix):]\n break\n temp_name = join(temp_dir, dest_name)\n os.makedirs(os.path.dirname(temp_name), exist_ok=True)\n if copy:\n shutil.copy(orig_name, temp_name)\n else:\n os.symlink(orig_name, temp_name)","def symlink_force(target: str, link_name: str):\n\n # os.replace() may fail if files are on different filesystems\n link_dir = os.path.dirname(link_name)\n\n while True:\n temp_link_name = tempfile.mktemp(dir=link_dir)\n try:\n os.symlink(target, temp_link_name)\n break\n except FileExistsError:\n pass\n try:\n os.replace(temp_link_name, link_name)\n except OSError: # e.g. permission denied\n os.remove(temp_link_name)\n raise","def test_single_named_link():\n pass","def fix_link(hook, target_link):\n if os.path.exists(hook):\n os.unlink(hook)\n os.symlink(target_link, hook)","def symlink_tree(self, root):\n return SymlinkTree(root, self.m, self.resource('symlink.py'))","def create_external_link(self, path, target_file, target_path):\n if self.options['storage_method'] == 'hdf5':\n # execute h5py command\n self.file.file_pointer[self.full_path] = h5py.ExternalLink(file,path)\n elif self.options['storage_method'] == 'none':\n # save command for later processing\n self.h5commands.append((\"create_external_link\", path, target_file, target_path))\n else:\n raise Exception('Invalid option value for storage_method (%s)' % storage_method)","def link(self, src, dst, label=None):\n self._tag(dst, label)\n self._mkdir_for(dst)\n abs_src = self._rootjoin(src)\n abs_dst = os.path.join(self.chroot, dst)\n try:\n os.link(abs_src, abs_dst)\n except OSError as e:\n if e.errno == errno.EEXIST:\n # File already exists, skip\n pass\n elif e.errno == errno.EXDEV:\n # Hard link across devices, fall back on copying\n shutil.copyfile(abs_src, abs_dst)\n else:\n raise","def link(from_dir,to_dir,report=False):\n # The paths in to_dir that we will be symlinking to.\n to_paths = get_target_paths(to_dir,report)\n \n # Dictionary of symlinks we will be creating, from_path->to_path\n symlinks = {}\n for to_path in to_paths:\n to_directory, to_filename = os.path.split(to_path)\n # Change leading underscores to leading dots. \n if to_filename.startswith('_'):\n from_filename = '.' + to_filename[1:]\n else:\n from_filename = to_filename\n # Remove hostname specifiers.\n parts = from_filename.split(HOSTNAME_SEPARATOR)\n assert len(parts) == 1 or len(parts) == 2\n from_filename = parts[0]\n from_path = os.path.join(from_dir,from_filename) \n symlinks[from_path] = to_path\n\n # Attempt to create the symlinks that don't already exist.\n for from_path,to_path in symlinks.items(): \n # Check that nothing already exists at from_path.\n if os.path.islink(from_path):\n # A link already exists.\n existing_to_path = os.readlink(from_path)\n existing_to_path = os.path.abspath(os.path.expanduser(existing_to_path))\n if existing_to_path == to_path:\n # It's already a link to the intended target. All is\n # well.\n continue\n else:\n # It's a link to somewhere else.\n print from_path+\" => is already symlinked to \"+existing_to_path\n elif os.path.isfile(from_path):\n print \"There's a file in the way at \"+from_path\n elif os.path.isdir(from_path):\n print \"There's a directory in the way at \"+from_path\n elif os.path.ismount(from_path):\n print \"There's a mount point in the way at \"+from_path\n else:\n # The path is clear, make the symlink.\n if report:\n print 'link would make symlink: %s->%s' % (from_path,to_path)\n else:\n print 'Making symlink %s->%s' % (from_path,to_path)\n os.symlink(to_path,from_path)","def test_normalize_patch_with_git_diff_new_symlink(self):\n self.assertEqual(\n self.tool.normalize_patch(\n patch=(\n b'diff --git /dev/null b/test\\n'\n b'new file mode 120000\\n'\n b'--- /dev/null\\n'\n b'+++ b/test\\n'\n b'@@ -0,0 +1,1 @@\\n'\n b'+target_file\\n'\n b'\\\\ No newline at end of file'\n ),\n filename='test',\n revision=PRE_CREATION),\n (\n b'diff --git /dev/null b/test\\n'\n b'new file mode 100000\\n'\n b'--- /dev/null\\n'\n b'+++ b/test\\n'\n b'@@ -0,0 +1,1 @@\\n'\n b'+target_file\\n'\n b'\\\\ No newline at end of file'\n ))","def convert_relative_symlinks(template_dir, out_dir):\n for root, dirs, files in os.walk(out_dir):\n for filename in files:\n filepath = os.path.join(root, filename)\n if os.path.islink(filepath):\n linkto = os.readlink(filepath)\n if linkto.startswith('.'):\n os.remove(filepath)\n start_dir = os.path.relpath(root, out_dir)\n os.symlink(os.path.join(template_dir, start_dir, filename), filepath)","def update_link(self):\n try:\n relpath = os.path.relpath(self.path, os.path.dirname(self.link_path))\n os.symlink(relpath, self.link_path)\n except OSError as e:\n if e.errno == errno.EEXIST:\n os.unlink(self.link_path)\n os.symlink(self.path, self.link_path)","def test_open_write(self, client, remote_temp_dir):\n\n file_path = posixpath.join(remote_temp_dir, \"test2.txt\")\n assert not client.exists(file_path)\n\n with HdfsHook() as hook:\n with hook.open(file_path, \"wb\") as file_:\n file_.write(b\"Test file\\n\")\n\n assert client.exists(file_path)","def link(self, src, dst, label=None):\r\n self._tag(dst, label)\r\n self._mkdir_for(dst)\r\n abs_src = self._rootjoin(src)\r\n abs_dst = os.path.join(self.chroot, dst)\r\n try:\r\n os.link(abs_src, abs_dst)\r\n except OSError as e:\r\n if e.errno == errno.EEXIST:\r\n # File already exists, skip\r\n pass\r\n elif e.errno == errno.EXDEV:\r\n # Hard link across devices, fall back on copying\r\n shutil.copyfile(abs_src, abs_dst)\r\n else:\r\n raise","def link(self, src, dst, label=None):\r\n self._tag(dst, label)\r\n self._mkdir_for(dst)\r\n abs_src = self._rootjoin(src)\r\n abs_dst = os.path.join(self.chroot, dst)\r\n try:\r\n os.link(abs_src, abs_dst)\r\n except OSError as e:\r\n if e.errno == errno.EEXIST:\r\n # File already exists, skip\r\n pass\r\n elif e.errno == errno.EXDEV:\r\n # Hard link across devices, fall back on copying\r\n shutil.copyfile(abs_src, abs_dst)\r\n else:\r\n raise","def mklinkto(self, oldname):\n error.checked_call(os.link, str(oldname), str(self))","def _make_lib_file_symbolic_links(self):\n so_file_dict = {\n 'rpmio': {\n 'sym_src_dir': self.rpm.lib_dir,\n 'sym_dst_dir': 'rpmio/.libs',\n 'require': True,\n },\n 'rpm': {\n 'sym_src_dir': self.rpm.lib_dir,\n 'sym_dst_dir': 'lib/.libs',\n 'require': True,\n },\n 'rpmbuild': {\n 'sym_src_dir': self.rpm.lib_dir,\n 'sym_dst_dir': 'build/.libs',\n 'require': True,\n },\n 'rpmsign': {\n 'sym_src_dir': self.rpm.lib_dir,\n 'sym_dst_dir': 'sign/.libs',\n },\n }\n\n self._update_sym_src_dirs_conditionally(so_file_dict)\n\n for name in so_file_dict:\n so_dict = so_file_dict[name]\n pattern = 'lib{0}.so*'.format(name)\n so_files = Cmd.find(so_dict['sym_src_dir'], pattern)\n if not so_files:\n is_required = so_dict.get('require', False)\n if not is_required:\n message_format = (\n \"Skip creating symbolic link of \"\n \"not existing so file '{0}'\"\n )\n Log.debug(message_format.format(name))\n continue\n\n message = 'so file pattern {0} not found at {1}'.format(\n pattern, so_dict['sym_src_dir']\n )\n raise InstallError(message)\n sym_dst_dir = os.path.abspath('../{0}'.format(\n so_dict['sym_dst_dir']))\n if not os.path.isdir(sym_dst_dir):\n Cmd.mkdir_p(sym_dst_dir)\n\n cmd = 'ln -sf {0} {1}/lib{2}.so'.format(so_files[0],\n sym_dst_dir,\n name)\n Cmd.sh_e(cmd)","def _post_src_install_soname_symlinks(mysettings, out):\n\n\timage_dir = mysettings[\"D\"]\n\tneeded_filename = os.path.join(mysettings[\"PORTAGE_BUILDDIR\"],\n\t\t\"build-info\", \"NEEDED.ELF.2\")\n\n\tf = None\n\ttry:\n\t\tf = io.open(_unicode_encode(needed_filename,\n\t\t\tencoding=_encodings['fs'], errors='strict'),\n\t\t\tmode='r', encoding=_encodings['repo.content'],\n\t\t\terrors='replace')\n\t\tlines = f.readlines()\n\texcept IOError as e:\n\t\tif e.errno not in (errno.ENOENT, errno.ESTALE):\n\t\t\traise\n\t\treturn\n\tfinally:\n\t\tif f is not None:\n\t\t\tf.close()\n\n\tqa_no_symlink = \"\"\n\tf = None\n\ttry:\n\t\tf = io.open(_unicode_encode(os.path.join(\n\t\t\tmysettings[\"PORTAGE_BUILDDIR\"],\n\t\t\t\"build-info\", \"QA_SONAME_NO_SYMLINK\"),\n\t\t\tencoding=_encodings['fs'], errors='strict'),\n\t\t\tmode='r', encoding=_encodings['repo.content'],\n\t\t\terrors='replace')\n\t\tqa_no_symlink = f.read()\n\texcept IOError as e:\n\t\tif e.errno not in (errno.ENOENT, errno.ESTALE):\n\t\t\traise\n\tfinally:\n\t\tif f is not None:\n\t\t\tf.close()\n\n\tqa_no_symlink = qa_no_symlink.split()\n\tif qa_no_symlink:\n\t\tif len(qa_no_symlink) > 1:\n\t\t\tqa_no_symlink = \"|\".join(\"(%s)\" % x for x in qa_no_symlink)\n\t\t\tqa_no_symlink = \"^(%s)$\" % qa_no_symlink\n\t\telse:\n\t\t\tqa_no_symlink = \"^%s$\" % qa_no_symlink[0]\n\t\tqa_no_symlink = re.compile(qa_no_symlink)\n\n\tlibpaths = set(portage.util.getlibpaths(\n\t\tmysettings[\"ROOT\"], env=mysettings))\n\tlibpath_inodes = set()\n\tfor libpath in libpaths:\n\t\tlibdir = os.path.join(mysettings[\"ROOT\"], libpath.lstrip(os.sep))\n\t\ttry:\n\t\t\ts = os.stat(libdir)\n\t\texcept OSError:\n\t\t\tcontinue\n\t\telse:\n\t\t\tlibpath_inodes.add((s.st_dev, s.st_ino))\n\n\tis_libdir_cache = {}\n\n\tdef is_libdir(obj_parent):\n\t\ttry:\n\t\t\treturn is_libdir_cache[obj_parent]\n\t\texcept KeyError:\n\t\t\tpass\n\n\t\trval = False\n\t\tif obj_parent in libpaths:\n\t\t\trval = True\n\t\telse:\n\t\t\tparent_path = os.path.join(mysettings[\"ROOT\"],\n\t\t\t\tobj_parent.lstrip(os.sep))\n\t\t\ttry:\n\t\t\t\ts = os.stat(parent_path)\n\t\t\texcept OSError:\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tif (s.st_dev, s.st_ino) in libpath_inodes:\n\t\t\t\t\trval = True\n\n\t\tis_libdir_cache[obj_parent] = rval\n\t\treturn rval\n\n\tmissing_symlinks = []\n\n\t# Parse NEEDED.ELF.2 like LinkageMapELF.rebuild() does.\n\tfor l in lines:\n\t\tl = l.rstrip(\"\\n\")\n\t\tif not l:\n\t\t\tcontinue\n\t\tfields = l.split(\";\")\n\t\tif len(fields) < 5:\n\t\t\tportage.util.writemsg_level(_(\"\\nWrong number of fields \" \\\n\t\t\t\t\"in %s: %s\\n\\n\") % (needed_filename, l),\n\t\t\t\tlevel=logging.ERROR, noiselevel=-1)\n\t\t\tcontinue\n\n\t\tobj, soname = fields[1:3]\n\t\tif not soname:\n\t\t\tcontinue\n\t\tif not is_libdir(os.path.dirname(obj)):\n\t\t\tcontinue\n\t\tif qa_no_symlink and qa_no_symlink.match(obj.strip(os.sep)) is not None:\n\t\t\tcontinue\n\n\t\tobj_file_path = os.path.join(image_dir, obj.lstrip(os.sep))\n\t\tsym_file_path = os.path.join(os.path.dirname(obj_file_path), soname)\n\t\ttry:\n\t\t\tos.lstat(sym_file_path)\n\t\texcept OSError as e:\n\t\t\tif e.errno not in (errno.ENOENT, errno.ESTALE):\n\t\t\t\traise\n\t\telse:\n\t\t\tcontinue\n\n\t\tmissing_symlinks.append((obj, soname))\n\n\tif not missing_symlinks:\n\t\treturn\n\n\tqa_msg = [\"QA Notice: Missing soname symlink(s):\"]\n\tqa_msg.append(\"\")\n\tqa_msg.extend(\"\\t%s -> %s\" % (os.path.join(\n\t\tos.path.dirname(obj).lstrip(os.sep), soname),\n\t\tos.path.basename(obj))\n\t\tfor obj, soname in missing_symlinks)\n\tqa_msg.append(\"\")\n\tfor line in qa_msg:\n\t\teqawarn(line, key=mysettings.mycpv, out=out)","def _create_file(self, rel_path, text):\n # FIXME: There are better/more secure APIs for creating tmp file paths.\n file_path = self.filesystem.join(self._temp_dir, rel_path)\n self.filesystem.write_text_file(file_path, text)\n return file_path","def _set_target_symlinks(self):\n try:\n job_id = self.active_queue[0][\"id\"]\n tgt_dir = self.config[\"target\"][\"directory\"]\n for t in [\"objects\", \"config\"]:\n src = self.active_queue[0][t + \"_filename\"]\n tgt = os.path.join(tgt_dir, self.config[\"target\"][t])\n # force symlink creation\n add_symlink(src, tgt, True)\n self.logger.debug(\"[active/%s] set symlink: '%s' -> '%s'\"\n % (job_id, tgt, src))\n\n except KeyError:\n raise ProfileKeyError(\"no value for target.directory\")","def make_link(first, second):\n manager = Actions()\n manager.make_link(first, second)","def createLink(self, source, destination):\n log(\"creating link\")\n\n if \"flix\" in source:\n return \"%s\" % +OSUtils.createLink(source, destination)\n return \"0\"","def absolute_symlink(\n source_path: str,\n dest_path: str\n):\n os.symlink(os.path.abspath(source_path), dest_path)"],"string":"[\n \"def _create_symlink(self, source_path, main):\\n main_file = os.path.realpath(os.path.join(source_path, main))\\n if not os.path.isfile(main_file):\\n main_file += '.js'\\n if not os.path.isfile(main_file):\\n print('\\\\tWARNING: Could not create symlink for {}, no such file.'.format(main_file))\\n return\\n main_file_name = os.path.basename(main_file)\\n with change_working_directory(os.path.realpath(self.symlink_dir)) as cd:\\n file_path = os.path.join(cd, main_file_name)\\n self.created(file_path)\\n if os.path.islink(file_path):\\n os.remove(file_path)\\n symlink(main_file, main_file_name)\",\n \"def _symlink(source, link_name):\\n flags = 0\\n\\n if source is not None and os.path.isdir(source):\\n flags = 1\\n\\n CreateSymbolicLinkW(link_name, source, flags)\",\n \"def testCreateSymlinkOutput(self): # pylint: disable=no-self-use\\n gcs_client = mock.MagicMock(spec=storage.Client)\\n blob = prow.create_symlink(gcs_client, \\\"gs://bucket/symlink\\\",\\n \\\"gs://bucket/output\\\")\\n\\n blob.upload_from_string.assert_called_once_with(\\\"gs://bucket/output\\\")\",\n \"def create_symbolic_link(file, target):\\n try:\\n os.symlink(file, target)\\n except NotImplementedError:\\n logger.critical(\\\"Symbolic links not supported on this platform\\\")\\n raise\\n except OSError:\\n logger.critical(\\\"Not sufficient permissions\\\")\\n raise\",\n \"def _symlink_file_on_disk(source, link_name):\\n link_dir = os.path.dirname(link_name)\\n\\n # create intermediate dirs if they do not already exist\\n if not os.path.isdir(link_dir):\\n try:\\n os.makedirs(link_dir)\\n except OSError as exc:\\n logger.error(\\\"Error creating directory '%s': %s\\\", link_dir, exc)\\n return False\\n\\n # create symbolic link\\n try:\\n os.symlink(source, link_name)\\n except OSError as exc:\\n logger.error(\\\"Error creating symlink '%s': %s\\\", link_name, exc)\\n return False\\n\\n logger.debug(\\\"Created symlink '%s' to '%s'\\\", link_name, source)\\n return True\",\n \"def testIsSymlink(self):\\r\\n P=lambda p:ufsi.NativeUnixPath(p)\\r\\n existingValidSymlinkPath=P(self.existingValidSymlinkFilePathStr)\\r\\n existingInvalidSymlinkPath=P(self.existingInvalidSymlinkFilePathStr)\\r\\n nonExistingSymlinkPath=P(self.nonExistingSymlinkPathStr)\\r\\n\\r\\n # 1\\r\\n self.assertEquals(existingValidSymlinkPath.isSymlink(),True,\\r\\n 'Symlink %r exists'\\r\\n %str(existingValidSymlinkPath))\\r\\n\\r\\n # 2\\r\\n self.assertEquals(existingInvalidSymlinkPath.isSymlink(),True,\\r\\n 'Symlink %r exists'\\r\\n %str(existingInvalidSymlinkPath))\\r\\n\\r\\n # 3\\r\\n self.assertEquals(nonExistingSymlinkPath.isSymlink(),False,\\r\\n 'Symlink %r does not exist'\\r\\n %str(nonExistingSymlinkPath))\",\n \"def ensure_symlink_exists(symlink_path, file_path):\\n\\n if not (os.path.islink(symlink_path) or (os.path.realpath(symlink_path) != os.path.realpath(file_path))):\\n # This is bad.\\n raise CronException(\\\"Path {0} is not a symlink or does not point where expected.\\\".format(symlink_path))\",\n \"def _makeSymlink ( target, source, env ) :\\n if len(target) != 1 :\\n fail ( \\\"unexpected number of targets for symlink: \\\"+str(target) )\\n if len(source) != 1 :\\n fail ( \\\"unexpected number of sources for symlink: \\\"+str(source) )\\n\\n target = str(target[0])\\n source = str(source[0].abspath)\\n trace ( \\\"Executing symlink `%s' -> `%s'\\\" % ( target, source ), \\\"makeSymlink\\\", 3 )\\n\\n os.symlink ( source, target )\",\n \"def create_symlink(src: str, dst: str) -> bool:\\n if exists(src):\\n with suppress(Exception):\\n if isfile(dst):\\n remove(dst)\\n else:\\n rmtree(dst)\\n\\n try:\\n\\n symlink(src, dst)\\n return True\\n\\n except PermissionError as err:\\n printer(\\n \\\"User without permission to create the symbolic link.\\\",\\n str(err),\\n foreground=FG().ERROR,\\n )\\n return False\\n\\n except FileExistsError:\\n remove(dst)\\n symlink(src, dst)\\n return False\",\n \"def test_history_import_symlink():\\n with HistoryArchive() as history_archive:\\n history_archive.write_metafiles()\\n history_archive.write_link('datasets/Pasted_Entry_1.txt', '../target.txt')\\n history_archive.write_file('target.txt', 'insecure')\\n _run_jihaw_cleanup_check_secure(history_archive, 'Symlink dataset in import archive allowed')\",\n \"def create_symlink(source_file, dest_file, sudo=True):\\n LOG.info(\\\"Creating symlink to {} called {}\\\".format(source_file, dest_file))\\n cmd = \\\"ln -sf {} {}\\\".format(source_file, dest_file)\\n _exec_cmd(cmd=cmd, sudo=sudo, fail_ok=False)\",\n \"def create_symlink(src, dest):\\n sudo('ln -s {} {}'.format(src, dest))\",\n \"def test_diff_git_symlink_added(self):\\n diff = (\\n b'diff --git a/link b/link\\\\n'\\n b'new file mode 120000\\\\n'\\n b'index 0000000..100b938\\\\n'\\n b'--- /dev/null\\\\n'\\n b'+++ b/link\\\\n'\\n b'@@ -0,0 +1 @@\\\\n'\\n b'+README\\\\n'\\n b'\\\\\\\\ No newline at end of file\\\\n'\\n )\\n\\n parsed_files = self.tool.get_parser(diff).parse()\\n self.assertEqual(len(parsed_files), 1)\\n\\n self.assert_parsed_diff_file(\\n parsed_files[0],\\n orig_filename=b'link',\\n orig_file_details=PRE_CREATION,\\n modified_filename=b'link',\\n modified_file_details=b'100b938',\\n new_unix_mode='120000',\\n is_symlink=True,\\n new_symlink_target=b'README',\\n insert_count=1,\\n data=diff)\",\n \"def create_symlinks(target_dir: os.PathLike, symlinks_to_create: List[os.PathLike]):\\n for src_path in symlinks_to_create:\\n trg_path = os.path.join(target_dir, os.path.basename(src_path))\\n\\n if os.path.islink(src_path):\\n # Let's not create symlinks to symlinks\\n # Since dropping the current symlink will break the experiment\\n os.symlink(os.readlink(src_path), trg_path)\\n else:\\n print(f'Creating a symlink to {src_path}, so try not to delete it occasionally!')\\n os.symlink(src_path, trg_path)\",\n \"def attempt_symlink_to(path: str, to_path: str) -> None:\\n try:\\n Path(path).symlink_to(Path(to_path))\\n except OSError:\\n pytest.skip(\\\"could not create symbolic link\\\")\",\n \"def makeLinks(self, source, target):\\n\\n if os.path.exists(target): os.unlink(target)\\n os.symlink(source, target)\",\n \"def _safe_setup_link(link_filename, real_filename):\\r\\n real_filename = os.path.relpath(real_filename, os.path.dirname(link_filename))\\r\\n\\r\\n if os.path.exists(link_filename):\\r\\n try:\\r\\n os.unlink(link_filename)\\r\\n except OSError:\\r\\n pass\\r\\n try:\\r\\n os.symlink(real_filename, link_filename)\\r\\n except OSError as e:\\r\\n # Typically permission denied.\\r\\n pass\",\n \"def _symlink_tar(self):\\r\\n outsidep = self.unsafe_common_dir / \\\"unsafe_file.txt\\\"\\r\\n symlinkp = self.unsafe_common_dir / \\\"symlink.txt\\\"\\r\\n symlink_tar = self.unsafe_common_dir / \\\"symlink.tar.gz\\\"\\r\\n outsidep.symlink(symlinkp)\\r\\n with tarfile.open(symlink_tar, \\\"w:gz\\\") as tar:\\r\\n tar.add(symlinkp)\\r\\n\\r\\n return symlink_tar\",\n \"def testGetSymlinkPath(self):\\r\\n P=lambda p:ufsi.NativeUnixPath(p)\\r\\n existingValidFileSymlinkPath=P(self.existingValidSymlinkFilePathStr)\\r\\n existingValidDirSymlinkPath=P(self.existingValidSymlinkDirPathStr)\\r\\n existingFilePath=P(self.existingFilePathStr)\\r\\n existingDirPath=P(self.existingDirPathStr)\\r\\n nonExistingSymlinkPath=P(self.nonExistingSymlinkPathStr)\\r\\n\\r\\n # 1\\r\\n r1=str(existingValidFileSymlinkPath.getSymlinkPath())\\r\\n r2=self.existingValidFileSymlinkPath\\r\\n self.assertEquals(r1,r2,'Symlink path expected %r. Got %r'%(r2,r1))\\r\\n \\r\\n # 2\\r\\n r1=str(existingValidDirSymlinkPath.getSymlinkPath())\\r\\n r2=self.existingValidDirSymlinkPath\\r\\n self.assertEquals(r1,r2,'Symlink path expected %r. Got %r'%(r2,r1))\\r\\n\\r\\n # 3\\r\\n self.assertRaises(ufsi.NotASymlinkError,\\r\\n existingFilePath.getSymlinkPath)\\r\\n\\r\\n # 4\\r\\n self.assertRaises(ufsi.NotASymlinkError,\\r\\n existingDirPath.getSymlinkPath)\\r\\n\\r\\n # 5\\r\\n self.assertRaises(ufsi.NotASymlinkError,\\r\\n nonExistingSymlinkPath.getSymlinkPath)\",\n \"def make_symlink(dbconfig, targ):\\n if \\\"latest\\\" in dbconfig and not dbconfig[\\\"latest\\\"]:\\n return\\n link = re.sub(r'[0-9]+', 'latest', targ)\\n try:\\n os.symlink(targ, link)\\n info(\\\"create link \\\" + link + \\\" --> \\\" + targ)\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n os.remove(link)\\n os.symlink(targ, link)\\n info(\\\"move link \\\" + link + \\\" --> \\\" + targ)\",\n \"def symlink(self, filen, link):\\n src = os.path.abspath(filen)\\n cwd = self.getWorkingDirectory()\\n dest = os.path.join(cwd, link)\\n os.symlink(os.path.relpath(src, cwd), dest)\",\n \"def symlink(self, filen, link):\\n src = os.path.abspath(filen)\\n cwd = self.getWorkingDirectory()\\n dest = os.path.join(cwd, link)\\n os.symlink(os.path.relpath(src, cwd), dest)\",\n \"def createLink(self):\\n \\n if( self.useLink ):\\n trymakedir( self.parent.installPath + \\\"/\\\" + self.alias )\\n\\n os.chdir( self.parent.installPath + \\\"/\\\" + self.alias )\\n \\n # check for already existing symlinks or dirs \\n if( os.path.islink( self.version )):\\n os.unlink( self.version )\\n elif( os.path.isdir( self.version )):\\n self.abort( \\\"could not create link to [ \\\" + self.linkPath + \\\" ]\\\\nin [ \\\" \\\\\\n + os.path.basename( self.installPath ) + \\\" ]!!!\\\" )\\n\\n os.symlink( self.linkPath , self.version )\\n print \\\"+ Linking \\\" + self.parent.installPath + \\\"/\\\" + self.alias + \\\"/\\\" + self.version \\\\\\n + \\\" -> \\\" + self.linkPath\",\n \"def _link(filename, existing_filename):\\n CreateHardLinkW(filename, existing_filename, 0)\",\n \"def IsSymlink(info):\\n return (info.external_attr >> 16) == 0120777\",\n \"def ln(src, dst):\\n os.symlink(src, dst)\",\n \"def _sync_symlink(self, binary_name, link_to):\\n\\n # The symlink we are creating:\\n link_path = os.path.join(self.bin_dir, binary_name)\\n\\n # The expected file we should be linking to:\\n link_dest = os.path.join(self.bin_dir, link_to)\\n\\n if not os.path.exists(link_path) or \\\\\\n not os.path.islink(link_path) or \\\\\\n os.path.realpath(link_path) != os.path.realpath(link_dest):\\n if os.path.exists(link_path):\\n os.remove(link_path)\\n os.symlink(link_to, os.path.join(self.bin_dir, binary_name))\\n self.output.append(\\\"Symlinked %s to %s.\\\" % (link_path, link_dest))\\n self.changed = True\",\n \"def test_symlink(self, mock_request):\\n self.server.hook = UrlRequestHook('test_url', request_method='GET')\\n linkpath = b'ugly'\\n targetpath = b'ugliest'\\n self.server.input_queue = sftpcmd(\\n SSH2_FXP_SYMLINK, sftpstring(linkpath), sftpstring(targetpath),\\n sftpint(0))\\n self.server.process()\\n mock_request.assert_called_once_with(\\n 'GET', 'test_url/symlink', auth=None,\\n data={\\n 'method': 'symlink', 'linkpath': linkpath,\\n 'targetpath': targetpath})\",\n \"def symlink(self, req, link, parent, name):\\r\\n self.reply_err(req, EROFS)\",\n \"def symlink(source, link_name):\\n os_symlink = getattr(os, \\\"symlink\\\", None)\\n if callable(os_symlink):\\n os_symlink(source, link_name)\\n else:\\n import ctypes\\n csl = ctypes.windll.kernel32.CreateSymbolicLinkW\\n csl.argtypes = (ctypes.c_wchar_p, ctypes.c_wchar_p, ctypes.c_uint32)\\n csl.restype = ctypes.c_ubyte\\n flags = 1 if os.path.isdir(source) else 0\\n if csl(link_name, source, flags) == 0:\\n raise ctypes.WinError()\",\n \"def symlink(self, name, source, linkname):\\n self._assert_absolute_path_or_placeholder(source)\\n self._assert_absolute_path_or_placeholder(linkname)\\n self._run(name, ['symlink', source, linkname])\\n self.m.path.mock_copy_paths(source, linkname)\",\n \"def _symlink_or_copy(src, dst):\\n # try to symlink file\\n try:\\n os.symlink(src, dst)\\n print('Creating symlink \\\"%s\\\" pointing to \\\"%s\\\"' % (dst, src))\\n except Exception as ex_symlink:\\n # try to copy file\\n try:\\n shutil.copyfile(src, dst)\\n print('Copying file from \\\"%s\\\" to \\\"%s\\\"' % (src, dst))\\n except Exception as ex_copy:\\n raise RuntimeError('Could neither symlink nor copy file \\\"%s\\\" to \\\"%s\\\":\\\\n- %s\\\\n- %s' % (src, dst, str(ex_symlink), str(ex_copy)))\",\n \"def make_link(self, filepath):\\n # Check file exists. It may have been deleted but still in manifest\\n if not os.path.exists(self.fullpath(filepath)):\\n print('File not found: {filepath}'.format(\\n filepath=self.fullpath(filepath)))\\n if self.contains(filepath):\\n print('removing from manifest')\\n self.delete(filepath)\\n self.needsync = True\\n self.existing_filepaths.discard(filepath)\\n else:\\n try:\\n destdir = os.path.dirname(filepath)\\n # Make destination directory if not already exists\\n # Necessary because sometimes this is called before\\n # individual model setup\\n if not os.path.exists(destdir):\\n os.makedirs(destdir)\\n if self.copy_file(filepath):\\n shutil.copy(self.fullpath(filepath), filepath)\\n perm = (stat.S_IRUSR | stat.S_IRGRP\\n | stat.S_IROTH | stat.S_IWUSR)\\n os.chmod(filepath, perm)\\n else:\\n make_symlink(self.fullpath(filepath), filepath)\\n except Exception:\\n action = 'copying' if self.copy_file else 'linking'\\n print('payu: error: {action} orig: {orig} '\\n 'local: {local}'.format(action=action,\\n orig=self.fullpath(filepath),\\n local=filepath))\\n raise\\n finally:\\n self.existing_filepaths.discard(filepath)\",\n \"def _symlink(conf, devname, label, remove=False):\\n return\\n\\n linkpath = conf.get('symlink')\\n if linkpath:\\n linkpath = expanduser(linkpath)\\n if lexists(linkpath):\\n os.unlink(linkpath)\\n if not remove:\\n # TODO: handle path errors\\n os.symlink(get_mount_target(devname, label), linkpath)\",\n \"def testLink(self):\\n def _check(results):\\n self.flushLoggedErrors()\\n self.assertEqual(results[0], b'')\\n self.assertTrue(results[1].startswith(b'l'), 'link failed')\\n return self.runCommand('rm testLink')\\n\\n d = self.runScript('ln testLink testfile1', 'ls -l testLink')\\n d.addCallback(_check)\\n d.addCallback(self.assertEqual, b'')\\n return d\",\n \"def test_profile_link_file(profile_manager, test_profile,\\n tmpdir, inventory_content):\\n myfile = tmpdir.mkdir(\\\"ir_dir\\\").join(\\\"fake_hosts_file\\\")\\n myfile.write(inventory_content)\\n org_inventory = myfile.strpath\\n\\n target_path = test_profile.link_file(org_inventory)\\n assert target_path == os.path.join(\\n test_profile.path, os.path.basename(org_inventory))\\n profile_inventory = py.path.local(target_path)\\n assert profile_inventory.islink()\\n assert inventory_content == profile_inventory.read()\",\n \"def test_diff_git_symlink_changed(self):\\n diff = (\\n b'diff --git a/link b/link\\\\n'\\n b'index 100b937..100b938 120000\\\\n'\\n b'--- a/link\\\\n'\\n b'+++ b/link\\\\n'\\n b'@@ -1 +1 @@\\\\n'\\n b'-README\\\\n'\\n b'\\\\\\\\ No newline at end of file\\\\n'\\n b'+README.md\\\\n'\\n b'\\\\\\\\ No newline at end of file\\\\n'\\n )\\n\\n parsed_files = self.tool.get_parser(diff).parse()\\n self.assertEqual(len(parsed_files), 1)\\n\\n self.assert_parsed_diff_file(\\n parsed_files[0],\\n orig_filename=b'link',\\n orig_file_details=b'100b937',\\n modified_filename=b'link',\\n modified_file_details=b'100b938',\\n old_unix_mode='120000',\\n new_unix_mode='120000',\\n is_symlink=True,\\n old_symlink_target=b'README',\\n new_symlink_target=b'README.md',\\n insert_count=1,\\n delete_count=1,\\n data=diff)\",\n \"def __write_file(path, link_name, context):\\n file_name = ''\\n for strng in ['/', 'http:']:\\n if not file_name:\\n file_name = link_name.replace(strng, '')\\n else:\\n file_name = file_name.replace(strng, '')\\n\\n full_path = os.path.join(path)\\n if not os.path.exists(full_path):\\n os.makedirs(full_path)\\n open(full_path + '/' + file_name, 'w').write(context.encode('utf-8'))\\n logging.info('File: %s Created', full_path + '/' + file_name)\",\n \"def symlink(self, src, dst):\\n return os.symlink(src, dst)\",\n \"def create_link(self):\\n if self.link_info:\\n link_type = self.file.options['link_type']\\n if 'node' in self.link_info:\\n target_path = self.link_info['node'].full_path\\n if link_type == 'string':\\n # create string dataset containing link path\\n #- self.file.file_pointer.create_dataset(self.full_path, data=\\\"h5link:/\\\" + target_path)\\n self.file.create_dataset(self.full_path, data=\\\"h5link:/\\\" + target_path)\\n elif link_type == 'hard':\\n # create hard link to target. This implemented by h5py \\\"Softlink\\\". Not sure why.\\n #- self.file.file_pointer[self.full_path] = h5py.SoftLink(target_path)\\n self.file.create_softlink(self.full_path, target_path)\\n else: \\n raise Exception('Invalid option value for link_type (%s)' % link_type)\\n elif 'extlink' in self.link_info:\\n file, path = self.link_info['extlink']\\n # link to external file\\n if link_type == 'string':\\n # create string dataset containing link path\\n target_path = \\\"%s,%s\\\" % (file, path)\\n #- self.file.file_pointer.create_dataset(self.full_path, data=\\\"h5extlink:/\\\" + target_path)\\n self.file.create_dataset(self.full_path, data=\\\"h5extlink:/\\\" + target_path)\\n elif link_type == 'hard':\\n # create link to external file\\n #- self.file.file_pointer[self.full_path] = h5py.ExternalLink(file,path)\\n self.file.create_external_link(self.full_path, file, path) \\n else:\\n raise Exception('Invalid option value for link_type (%s)' % link_type)\\n else:\\n raise SystemError(\\\"** Error: invalid key in link_info %s\\\" % self.link_info)\",\n \"def _errcheck_link(value, func, args): # pylint: disable=W0613\\n # The windows api returns nonzero if the call was successful\\n if value != 0:\\n return\\n\\n last_error = ctypes.windll.kernel32.GetLastError()\\n # Somehow CreateSymbolicLinkW and CreateHardLinkW retuns zero\\n # and the last error is 2 (The system cannot find the file specified)\\n # but the link is created successfuly\\n # it seems like a bug in the WinAPI\\n if last_error == 0 or last_error == 2:\\n return\\n if last_error == 183:\\n raise OSError(errno.EEXIST,\\n \\\"Cannot create a file when that file already exists\\\",\\n args[0])\",\n \"def _symlink_datafile(self):\\n logger.debug(\\\"Symlinking datafile to '%s'\\\", self.source)\\n\\n if os.path.isfile(self.source):\\n # construct symlink target path based on source file name\\n rel_dst_path = self.datafile.storage.get_available_name(\\n file_path(self, os.path.basename(self.source))\\n )\\n abs_dst_path = os.path.join(settings.FILE_STORE_BASE_DIR,\\n rel_dst_path)\\n # create symlink\\n if _symlink_file_on_disk(self.source, abs_dst_path):\\n # update the model with the symlink path\\n self.datafile.name = rel_dst_path\\n logger.debug(\\\"Datafile symlinked\\\")\\n return True\\n else:\\n logger.error(\\\"Symlinking failed\\\")\\n return False\\n else:\\n logger.error(\\\"Symlinking failed: source is not a file\\\")\\n return False\",\n \"def copy_and_link(file_name):\\n if os.path.normpath(output_path) != os.getcwd():\\n write_to_runner(f\\\"mv {file_name} {output_path} \\\\n\\\")\\n write_to_runner(f\\\"ln -s {output_path}/{file_name} . \\\\n\\\")\",\n \"def _create_links(self):\\n for line in self.iter_files_to_install():\\n arcname, link = line.split()\\n if link == 'False':\\n continue\\n self.files.append(create_link(arcname, link, self.prefix))\",\n \"def _link_files(self, source, target):\\n try:\\n logging.debug(\\\"Linking %s and %s\\\" % (source, target))\\n os.link(os.path.realpath(source), target)\\n except PermissionError as e:\\n msg = \\\"ERROR: Insufficient rights on {}! \\\" \\\\\\n \\\"Possible cause; source file need to be writable/appendable when fs.protect_hardlinks is enabled. \\\" \\\\\\n \\\"Permissions: {}\\\"\\n logging.error(msg.format(e.filename, str(AccessControlList.from_file(source))))\\n except FileExistsError as e:\\n logging.debug(\\\"File %s already exists!\\\" % e.filename)\",\n \"def is_symlink(self):\\n return self._security_class == \\\"lnk_file\\\"\",\n \"def link(self):\\n\\n if self.path_source is not None:\\n full_source_path = os.path.join(\\n os.path.expandvars(self.path_source), self.name\\n )\\n full_destination_path = os.path.join(\\n os.path.expandvars(self.path_destination), self.name\\n )\\n\\n try:\\n if self.sudo:\\n spawn.process(\\n f'ln -sfv \\\"{full_source_path}\\\" \\\"{full_destination_path}\\\"',\\n sudo=True,\\n )\\n else:\\n os.symlink(full_source_path, full_destination_path)\\n except FileExistsError:\\n message.error(\\n \\\"Can't symlink, file already exists at destination. Attempting fix.\\\"\\n )\\n os.remove(full_destination_path)\\n message.info(f\\\"Removed: '{full_destination_path}'\\\")\\n os.symlink(full_source_path, full_destination_path)\\n finally:\\n message.info(\\n f\\\"Symlink created: '{full_source_path}' <--> '{full_destination_path}'\\\"\\n )\\n else:\\n message.error(\\n f\\\"'{self.name}' has no source from which to create a link from.\\\"\\n )\",\n \"def testGetLinkedFileEntry(self):\\n path_spec = path_spec_factory.Factory.NewPathSpec(\\n definitions.TYPE_INDICATOR_HFS, identifier=self._IDENTIFIER_A_LINK,\\n location='/a_link', parent=self._raw_path_spec)\\n file_entry = self._file_system.GetFileEntryByPathSpec(path_spec)\\n self.assertIsNotNone(file_entry)\\n\\n linked_file_entry = file_entry.GetLinkedFileEntry()\\n\\n self.assertIsNotNone(linked_file_entry)\\n\\n self.assertEqual(linked_file_entry.name, 'another_file')\",\n \"def mksymlinkto(self, value, absolute=1):\\n if absolute:\\n error.checked_call(os.symlink, str(value), self.strpath)\\n else:\\n base = self.common(value)\\n # with posix local paths '/' is always a common base\\n relsource = self.__class__(value).relto(base)\\n reldest = self.relto(base)\\n n = reldest.count(self.sep)\\n target = self.sep.join((\\\"..\\\",) * n + (relsource,))\\n error.checked_call(os.symlink, target, self.strpath)\",\n \"def symlink(source, target):\\n source, target = map(os.path.expanduser, (source, target))\\n print(\\\"Will symlink %s to %s\\\" % (source, target))\\n\\n if os.path.exists(target):\\n if os.path.islink(target) and os.path.realpath(target) == source:\\n logging.info(\\\"%s exists\\\" % target)\\n return\\n\\n backup = target + \\\".old\\\"\\n\\n if os.path.exists(backup):\\n raise Exception(\\\"Can't backup to %s: file already exists.\\\" % backup)\\n\\n shutil.move(target, backup)\\n\\n else:\\n os.symlink(source, target)\\n logging.info(\\\"%s symlinked to %s\\\" % (source, target))\",\n \"def symlink(source, target, use_sudo=True):\\n\\n # Some older versions of Fabric do not have the is_link method \\n try:\\n from fabric.contrib.files import is_link\\n is_a_link = is_link(target)\\n except ImportError:\\n with settings(hide(\\\"everything\\\"), warn_only=True):\\n if run(\\\"test -L \\\"+target).failed:\\n\\t is_a_link = False\\n else:\\n is_a_link = True\\n\\n if not is_a_link:\\n cmd = \\\"ln -s \\\"+source+\\\" \\\"+target\\n if use_sudo:\\n sudo(cmd)\\n else:\\n run(cmd)\",\n \"def symlink(path, v=False):\\r\\n if not os.path.exists(path):\\r\\n err(path + ' : no such file or directory')\\r\\n elif not os.path.isdir(path):\\r\\n err(path + ' : not a directory')\\r\\n else:\\r\\n theme_name = os.path.basename(os.path.normpath(path))\\r\\n theme_path = os.path.join(_THEMES_PATH, theme_name)\\r\\n if os.path.exists(theme_path):\\r\\n err(path + ' : already exists')\\r\\n else:\\r\\n if v:\\r\\n print(\\\"Linking `{p}' to `{t}' ...\\\".format(p=path, t=theme_path))\\r\\n try:\\r\\n os.symlink(path, theme_path)\\r\\n except Exception as e:\\r\\n err(\\\"Cannot link `{p}' to `{t}':\\\\n{e}\\\".format(p=path, t=theme_path, e=str(e)))\",\n \"def create_links(self, name):\\n for target, linknames in self._link_map.iteritems():\\n for linkname in linknames:\\n self._api.path.mock_copy_paths(target, linkname)\\n self._api.python(\\n name,\\n self._resource,\\n args = [\\n '--link-json',\\n self._api.json.input({str(target) : linkname\\n for target, linkname in self._link_map.iteritems()\\n }),\\n ],\\n infra_step=True)\",\n \"def create_target(cls, relpath, target):\\r\\n cls.create_file(cls.build_path(relpath), target, mode='a')\",\n \"def make_sym_links(f, source, dest_dir, catalog):\\n source = get_key_from_values(f, catalog)\\n source_path = f\\\"{source}/{f}\\\"\\n dest_path = f\\\"{dest_dir}/{f}\\\"\\n ls = os.listdir(dest_dir)\\n if f not in ls: \\n os.symlink(source_path, dest_path)\",\n \"def open_file_link_manager(file):\\n pass\",\n \"def test_diff_git_symlink_removed(self):\\n diff = (\\n b'diff --git a/link b/link\\\\n'\\n b'deleted file mode 120000\\\\n'\\n b'index 100b938..0000000\\\\n'\\n b'--- a/link\\\\n'\\n b'+++ /dev/null\\\\n'\\n b'@@ -1 +0,0 @@\\\\n'\\n b'-README.txt\\\\n'\\n b'\\\\\\\\ No newline at end of file\\\\n'\\n )\\n\\n parsed_files = self.tool.get_parser(diff).parse()\\n self.assertEqual(len(parsed_files), 1)\\n\\n self.assert_parsed_diff_file(\\n parsed_files[0],\\n orig_filename=b'link',\\n orig_file_details=b'100b938',\\n modified_filename=b'link',\\n modified_file_details=b'0000000',\\n old_unix_mode='120000',\\n is_symlink=True,\\n old_symlink_target=b'README.txt',\\n deleted=True,\\n delete_count=1,\\n data=diff)\",\n \"def create_symlink_dir(src_dir, src_list, dst):\\n if not src_list:\\n return\\n message = \\\"creating symlink directory at {dst} with files {src_list}\\\".format(\\n dst=dst,\\n src_list=pformat(src_list))\\n logging.info(message)\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n for src_file in src_list:\\n if not src_file:\\n continue\\n source = os.path.join(src_dir, src_file)\\n destination = os.path.join(dst, src_file)\\n if os.path.lexists(destination):\\n continue\\n try:\\n os.symlink(source, destination)\\n except Exception as e:\\n msg = format_debug(e)\\n logging.error(e)\",\n \"def create_file(path):\\n command = ['touch', TEST_FILE]\\n file_operation(path, command)\",\n \"def link(target, link_name):\\n src = os.path.abspath(target)\\n dst = os.path.abspath(link_name)\\n os.symlink(src, dst)\",\n \"def force_symlink(target_path, link_location):\\n\\n pardir = os.path.dirname(link_location)\\n if not os.path.exists(pardir):\\n os.makedirs(pardir)\\n\\n if os.path.lexists(link_location):\\n assert os.path.islink(link_location), \\\\\\n \\\"The path {} exists but is not a symlink\\\".format(link_location)\\n if os.readlink(link_location) != target_path:\\n os.remove(link_location)\\n os.symlink(target_path, link_location)\\n else:\\n os.symlink(target_path, link_location)\",\n \"def symlink_p(src, dst):\\n try:\\n os.symlink(src, dst)\\n except OSError as exc: # Python >2.5\\n if exc.errno == errno.EEXIST and os.path.islink(dst):\\n if os.path.realpath(dst) == os.path.realpath(src):\\n pass\\n else:\\n print('%s is a link already pointing to %s' % (dst, os.path.realpath(dst)), file=sys.stderr)\\n else:\\n raise\",\n \"def test_readlink(self, mock_request):\\n self.server.hook = UrlRequestHook(\\n 'test_url',\\n urls_mapping={\\n 'readlink': ['test_url_1', 'test_url_2']},\\n paths_mapping={\\n 'readlink': ['test_path_1', 'test_path_2']})\\n linkpath = b'ugly'\\n targetpath = b'ugliest'\\n os.symlink(linkpath, targetpath)\\n self.server.input_queue = sftpcmd(\\n SSH2_FXP_READLINK, sftpstring(targetpath), sftpint(0))\\n self.server.process()\\n mock_request.assert_has_calls([\\n mock.call(\\n 'POST', 'test_url_1/test_path_1', auth=None,\\n data={'method': 'readlink', 'filename': targetpath}),\\n mock.call(\\n 'POST', 'test_url_1/test_path_2', auth=None,\\n data={'method': 'readlink', 'filename': targetpath}),\\n mock.call(\\n 'POST', 'test_url_2/test_path_1', auth=None,\\n data={'method': 'readlink', 'filename': targetpath}),\\n mock.call(\\n 'POST', 'test_url_2/test_path_2', auth=None,\\n data={'method': 'readlink', 'filename': targetpath}),\\n ])\",\n \"def symlink_force(source, link_name):\\n try:\\n os.symlink(source, link_name)\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n os.remove(link_name)\\n os.symlink(source, link_name)\",\n \"def force_symlink(src, dst):\\n try:\\n os.unlink(dst)\\n os.symlink(src, dst)\\n except OSError:\\n os.symlink(src, dst)\",\n \"def create_dir(link_dir):\\n if not os.path.exists(link_dir):\\n os.makedirs(link_dir)\",\n \"def _tryLink(self,src, dst):\\n\\n hiero.core.log.info(\\\"Attempting to link %s to %s\\\" % (src, dst))\\n \\n try:\\n os.link(util.asUnicode(src), util.asUnicode(dst))\\n except OSError as err:\\n # If the OS returns an ENOTSUP error (45), for example when trying to set\\n # flags on an NFS mounted volume that doesn't support them, Python should\\n # absorb this. However, a regression in Python 2.7.3 causes this not to\\n # be the case, and the error is thrown as an exception. We therefore\\n # catch this explicitly as value 45, since errno.ENOTSUP is not defined\\n # in Python 2.7.2 (which is part of the problem). See the following\\n # link for further information: http://bugs.python.org/issue14662\\n # See TP 199072.\\n if err.errno == 45: # ENOTSUP\\n pass\\n elif err.errno == 17: # FILE EXISTS\\n raise\\n else:\\n raise\",\n \"def symlink(origin, target):\\n # Skip anything in the home directory if the user is admin\\n if user_is_admin() and not args.root and check_contain_home_dir(target):\\n print(highlight_colour(\\\"'%s'\\\") % str(target) +\\n warning_colour(\\\" is inside of home folder. Skipping...\\\"))\\n raise StopTraversing(\\\"Skipping.\\\")\\n\\n # Check for a broken symlink, if true: prompt for replacement.\\n # This is done to avoid having any broken symlinks lingering.\\n if is_broken_symlink(target):\\n if args.yes or prompt(origin, target, \\\"remove\\\"):\\n target.unlink()\\n else:\\n return\\n\\n if args.replace:\\n replace_symlink(origin, target)\\n elif args.remove:\\n remove_symlink(origin, target)\\n else:\\n create_symlink(origin, target)\",\n \"def create_home_directory_symbolic_links():\\n file_paths = (\\n path\\n for path in repo_home.rglob(\\\"*\\\")\\n if path.is_file() and not path.is_symlink()\\n )\\n\\n for file_path in file_paths:\\n sym_link_path = translate_home_path(file_path)\\n\\n if sym_link_path.is_file() and not sym_link_path.is_symlink():\\n backup_file(sym_link_path)\\n sym_link_path.unlink()\\n\\n if sym_link_path.is_symlink():\\n sym_link_path.unlink()\\n\\n print(f\\\"Creating Symlink: {sym_link_path} -> {file_path}\\\")\\n sym_link_path.symlink_to(file_path)\",\n \"def make_symlink(dst, src, silently_move=False):\\n dst_dir = os.path.dirname(dst.rstrip(os.path.sep))\\n if not os.path.isdir(dst_dir):\\n os.makedirs(dst_dir)\\n\\n # get a temporary directory\\n if os.path.exists(dst):\\n if silently_move or (((os.path.isfile(dst) or (os.path.isdir(dst)) and\\n query_yes_no('Move NSLS-II from userpackages?')))):\\n import tempfile\\n temp_dir = tempfile.mkdtemp()\\n shutil.move(dst, temp_dir)\\n print('Previous NSLS-II folder moved to {0}'.format(temp_dir))\\n else:\\n print('NSLS-II already exists in userpackages. Please move or delete it'\\n 'and then re-run setup.py')\\n return False\\n\\n # this symlink does not get removed when pip uninstall vttools is run...\\n # todo figure out how to make pip uninstall remove this symlink\\n try:\\n # symlink the NSLS-II folder into userpackages\\n os.symlink(src, dst)\\n except AttributeError:\\n # you must be on Windows!\\n call(['mklink', '/j', dst, src], shell=True)\\n\\n return True\",\n \"def fix_git_symlinked(src, dst):\\n # if running from WC there should be a 'doc' dir sibling to nikola package\\n if not should_fix_git_symlinked():\\n return\\n # probabbly in a WC, so symlinks should be fixed\\n for root, dirs, files in os.walk(dst):\\n for name in files:\\n filename = os.path.join(root, name)\\n\\n # detect if symlinked\\n try:\\n if not (2 < os.path.getsize(filename) < 500):\\n continue\\n # which encoding uses a git symlink marker ? betting on default\\n with open(filename, 'r') as f:\\n text = f.read()\\n if text[0] != '.':\\n # de facto hint to skip binary files and exclude.meta\\n continue\\n except Exception:\\n # probably encoding: content binary or encoding not defalt,\\n # also in py2.6 it can be path encoding\\n continue\\n dst_dir_relpath = os.path.dirname(os.path.relpath(filename, dst))\\n path = os.path.normpath(os.path.join(src, dst_dir_relpath, text))\\n if not os.path.exists(path):\\n continue\\n # most probably it is a git symlinked file\\n\\n # copy original content to filename\\n shutil.copy(path, filename)\",\n \"def _follow_symlinks(filepath):\\n filepath = os.path.abspath(filepath)\\n while os.path.islink(filepath):\\n filepath = os.path.normpath(\\n os.path.join(os.path.dirname(filepath), os.readlink(filepath)))\\n return filepath\",\n \"def test_getLinkrelToSameDirectory(self):\\n linkrel = self.builder.getLinkrel(FilePath(\\\"/foo/bar\\\"),\\n FilePath(\\\"/foo/bar\\\"))\\n self.assertEquals(linkrel, \\\"\\\")\",\n \"def testIsFile(self):\\r\\n P=lambda p:ufsi.NativeUnixPath(p)\\r\\n existingFilePath=P(self.existingFilePathStr)\\r\\n nonExistingFilePath=P(self.nonExistingFilePathStr)\\r\\n existingValidFileSymlinkPath=P(self.existingValidSymlinkFilePathStr)\\r\\n existingInvalidFileSymlinkPath=\\\\\\r\\n P(self.existingInvalidSymlinkFilePathStr)\\r\\n existingDirPath=P(self.existingDirPathStr)\\r\\n\\r\\n\\r\\n # 1\\r\\n self.assertEquals(existingFilePath.isFile(),True,\\r\\n '%r is a file'%str(existingFilePath))\\r\\n\\r\\n # 2\\r\\n self.assertEquals(nonExistingFilePath.isFile(),False,\\r\\n 'File %r does not exist'%str(nonExistingFilePath))\\r\\n\\r\\n # 3\\r\\n self.assertEquals(existingValidFileSymlinkPath.isFile(),True,\\r\\n '%r is a file'%str(existingValidFileSymlinkPath))\\r\\n\\r\\n # 4\\r\\n self.assertEquals(existingInvalidFileSymlinkPath.isFile(),False,\\r\\n '%r is an invalid symlink'\\r\\n %str(existingInvalidFileSymlinkPath))\\r\\n\\r\\n # 5\\r\\n self.assertEquals(existingDirPath.isFile(),False,\\r\\n '%r is a dir'%str(existingDirPath))\",\n \"def main(file_paths: Optional[List[Path]] = None):\\n if file_paths:\\n for file_path in file_paths:\\n move_and_symlink_file(file_path)\\n\\n else:\\n create_home_directories()\\n create_home_directory_symbolic_links()\",\n \"def move_and_symlink_file(file_path: Path):\\n assert (\\n file_path.is_file()\\n and not file_path.is_symlink()\\n and user_home in file_path.parents\\n )\\n\\n original_path = file_path\\n new_path = translate_home_path(original_path)\\n\\n print(f\\\"Moving: {original_path} -> {new_path}\\\")\\n if not new_path.parent.exists():\\n new_path.parent.mkdir(mode=HOME_DIRECTORY_MODE, parents=True)\\n move(str(original_path), str(new_path))\\n\\n print(f\\\"Creating Symlink: {original_path} -> {new_path}\\\")\\n original_path.symlink_to(new_path)\",\n \"def update_symlinks(n):\\n\\tif n > 0: return\\n\\tsymlink_dir = sc.text_image_symlink_dir.absolute()\\n\\tfor tpi, info in sorted(index.items(), key=lambda t: t[0]):\\n\\t\\tsymlink = symlink_dir / info['url']\\n\\t\\tif symlink.is_symlink():\\n\\t\\t\\tif symlink.resolve() == info['file']:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsymlink.unlink()\\n\\t\\tif not symlink.parent.exists():\\n\\t\\t\\tsymlink.parent.mkdir(parents=True)\\n\\t\\tsymlink.symlink_to(info['file'])\",\n \"def make_franny_symlinks(src_dirs, out_dir):\\n\\n for path, dirs, files in chain.from_iterable(os.walk(path)\\n for path in src_dirs):\\n print('Looking in %s' % path)\\n for sta in ['NS12', 'NS13', 'NS14']:\\n for filename in fnmatch.filter(files, '*.%s*' % sta):\\n net = filename.split('.')[-7]\\n chan = filename.split('.')[-4]\\n if chan[-1] == 'N':\\n new_chan = 'EH1'\\n elif chan[-1] == 'E':\\n new_chan = 'EH2'\\n else:\\n continue\\n mseed_nm = filename.split('/')[-1]\\n new_mseed = string.replace(mseed_nm, chan, new_chan)\\n old_path = os.path.join(path, filename)\\n new_path = '%s/%s/%s/%s.D/%s' % (out_dir, net,\\n sta, new_chan, new_mseed)\\n\\n print('Creating symlink for file %s at %s'\\n % (old_path, new_path))\\n spwd = '*blackmore89'\\n cmnd = 'sudo -S ln %s %s' % (old_path, new_path)\\n os.system('echo %s | %s' % (spwd, cmnd))\\n return\",\n \"def symlink_input(filegroup_resource_path, temp_dir, strip_prefix=None,\\n copy=False):\\n assert os.path.isdir(temp_dir)\\n manifest = runfiles.Create()\\n with open(manifest.Rlocation(filegroup_resource_path)) as f:\\n input_filenames = f.read().splitlines()\\n for name in input_filenames:\\n orig_name = manifest.Rlocation(name)\\n assert os.path.exists(orig_name), name\\n dest_name = name\\n for prefix in (strip_prefix or []):\\n if dest_name.startswith(prefix):\\n dest_name = dest_name[len(prefix):]\\n break\\n temp_name = join(temp_dir, dest_name)\\n os.makedirs(os.path.dirname(temp_name), exist_ok=True)\\n if copy:\\n shutil.copy(orig_name, temp_name)\\n else:\\n os.symlink(orig_name, temp_name)\",\n \"def symlink_force(target: str, link_name: str):\\n\\n # os.replace() may fail if files are on different filesystems\\n link_dir = os.path.dirname(link_name)\\n\\n while True:\\n temp_link_name = tempfile.mktemp(dir=link_dir)\\n try:\\n os.symlink(target, temp_link_name)\\n break\\n except FileExistsError:\\n pass\\n try:\\n os.replace(temp_link_name, link_name)\\n except OSError: # e.g. permission denied\\n os.remove(temp_link_name)\\n raise\",\n \"def test_single_named_link():\\n pass\",\n \"def fix_link(hook, target_link):\\n if os.path.exists(hook):\\n os.unlink(hook)\\n os.symlink(target_link, hook)\",\n \"def symlink_tree(self, root):\\n return SymlinkTree(root, self.m, self.resource('symlink.py'))\",\n \"def create_external_link(self, path, target_file, target_path):\\n if self.options['storage_method'] == 'hdf5':\\n # execute h5py command\\n self.file.file_pointer[self.full_path] = h5py.ExternalLink(file,path)\\n elif self.options['storage_method'] == 'none':\\n # save command for later processing\\n self.h5commands.append((\\\"create_external_link\\\", path, target_file, target_path))\\n else:\\n raise Exception('Invalid option value for storage_method (%s)' % storage_method)\",\n \"def link(self, src, dst, label=None):\\n self._tag(dst, label)\\n self._mkdir_for(dst)\\n abs_src = self._rootjoin(src)\\n abs_dst = os.path.join(self.chroot, dst)\\n try:\\n os.link(abs_src, abs_dst)\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n # File already exists, skip\\n pass\\n elif e.errno == errno.EXDEV:\\n # Hard link across devices, fall back on copying\\n shutil.copyfile(abs_src, abs_dst)\\n else:\\n raise\",\n \"def link(from_dir,to_dir,report=False):\\n # The paths in to_dir that we will be symlinking to.\\n to_paths = get_target_paths(to_dir,report)\\n \\n # Dictionary of symlinks we will be creating, from_path->to_path\\n symlinks = {}\\n for to_path in to_paths:\\n to_directory, to_filename = os.path.split(to_path)\\n # Change leading underscores to leading dots. \\n if to_filename.startswith('_'):\\n from_filename = '.' + to_filename[1:]\\n else:\\n from_filename = to_filename\\n # Remove hostname specifiers.\\n parts = from_filename.split(HOSTNAME_SEPARATOR)\\n assert len(parts) == 1 or len(parts) == 2\\n from_filename = parts[0]\\n from_path = os.path.join(from_dir,from_filename) \\n symlinks[from_path] = to_path\\n\\n # Attempt to create the symlinks that don't already exist.\\n for from_path,to_path in symlinks.items(): \\n # Check that nothing already exists at from_path.\\n if os.path.islink(from_path):\\n # A link already exists.\\n existing_to_path = os.readlink(from_path)\\n existing_to_path = os.path.abspath(os.path.expanduser(existing_to_path))\\n if existing_to_path == to_path:\\n # It's already a link to the intended target. All is\\n # well.\\n continue\\n else:\\n # It's a link to somewhere else.\\n print from_path+\\\" => is already symlinked to \\\"+existing_to_path\\n elif os.path.isfile(from_path):\\n print \\\"There's a file in the way at \\\"+from_path\\n elif os.path.isdir(from_path):\\n print \\\"There's a directory in the way at \\\"+from_path\\n elif os.path.ismount(from_path):\\n print \\\"There's a mount point in the way at \\\"+from_path\\n else:\\n # The path is clear, make the symlink.\\n if report:\\n print 'link would make symlink: %s->%s' % (from_path,to_path)\\n else:\\n print 'Making symlink %s->%s' % (from_path,to_path)\\n os.symlink(to_path,from_path)\",\n \"def test_normalize_patch_with_git_diff_new_symlink(self):\\n self.assertEqual(\\n self.tool.normalize_patch(\\n patch=(\\n b'diff --git /dev/null b/test\\\\n'\\n b'new file mode 120000\\\\n'\\n b'--- /dev/null\\\\n'\\n b'+++ b/test\\\\n'\\n b'@@ -0,0 +1,1 @@\\\\n'\\n b'+target_file\\\\n'\\n b'\\\\\\\\ No newline at end of file'\\n ),\\n filename='test',\\n revision=PRE_CREATION),\\n (\\n b'diff --git /dev/null b/test\\\\n'\\n b'new file mode 100000\\\\n'\\n b'--- /dev/null\\\\n'\\n b'+++ b/test\\\\n'\\n b'@@ -0,0 +1,1 @@\\\\n'\\n b'+target_file\\\\n'\\n b'\\\\\\\\ No newline at end of file'\\n ))\",\n \"def convert_relative_symlinks(template_dir, out_dir):\\n for root, dirs, files in os.walk(out_dir):\\n for filename in files:\\n filepath = os.path.join(root, filename)\\n if os.path.islink(filepath):\\n linkto = os.readlink(filepath)\\n if linkto.startswith('.'):\\n os.remove(filepath)\\n start_dir = os.path.relpath(root, out_dir)\\n os.symlink(os.path.join(template_dir, start_dir, filename), filepath)\",\n \"def update_link(self):\\n try:\\n relpath = os.path.relpath(self.path, os.path.dirname(self.link_path))\\n os.symlink(relpath, self.link_path)\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n os.unlink(self.link_path)\\n os.symlink(self.path, self.link_path)\",\n \"def test_open_write(self, client, remote_temp_dir):\\n\\n file_path = posixpath.join(remote_temp_dir, \\\"test2.txt\\\")\\n assert not client.exists(file_path)\\n\\n with HdfsHook() as hook:\\n with hook.open(file_path, \\\"wb\\\") as file_:\\n file_.write(b\\\"Test file\\\\n\\\")\\n\\n assert client.exists(file_path)\",\n \"def link(self, src, dst, label=None):\\r\\n self._tag(dst, label)\\r\\n self._mkdir_for(dst)\\r\\n abs_src = self._rootjoin(src)\\r\\n abs_dst = os.path.join(self.chroot, dst)\\r\\n try:\\r\\n os.link(abs_src, abs_dst)\\r\\n except OSError as e:\\r\\n if e.errno == errno.EEXIST:\\r\\n # File already exists, skip\\r\\n pass\\r\\n elif e.errno == errno.EXDEV:\\r\\n # Hard link across devices, fall back on copying\\r\\n shutil.copyfile(abs_src, abs_dst)\\r\\n else:\\r\\n raise\",\n \"def link(self, src, dst, label=None):\\r\\n self._tag(dst, label)\\r\\n self._mkdir_for(dst)\\r\\n abs_src = self._rootjoin(src)\\r\\n abs_dst = os.path.join(self.chroot, dst)\\r\\n try:\\r\\n os.link(abs_src, abs_dst)\\r\\n except OSError as e:\\r\\n if e.errno == errno.EEXIST:\\r\\n # File already exists, skip\\r\\n pass\\r\\n elif e.errno == errno.EXDEV:\\r\\n # Hard link across devices, fall back on copying\\r\\n shutil.copyfile(abs_src, abs_dst)\\r\\n else:\\r\\n raise\",\n \"def mklinkto(self, oldname):\\n error.checked_call(os.link, str(oldname), str(self))\",\n \"def _make_lib_file_symbolic_links(self):\\n so_file_dict = {\\n 'rpmio': {\\n 'sym_src_dir': self.rpm.lib_dir,\\n 'sym_dst_dir': 'rpmio/.libs',\\n 'require': True,\\n },\\n 'rpm': {\\n 'sym_src_dir': self.rpm.lib_dir,\\n 'sym_dst_dir': 'lib/.libs',\\n 'require': True,\\n },\\n 'rpmbuild': {\\n 'sym_src_dir': self.rpm.lib_dir,\\n 'sym_dst_dir': 'build/.libs',\\n 'require': True,\\n },\\n 'rpmsign': {\\n 'sym_src_dir': self.rpm.lib_dir,\\n 'sym_dst_dir': 'sign/.libs',\\n },\\n }\\n\\n self._update_sym_src_dirs_conditionally(so_file_dict)\\n\\n for name in so_file_dict:\\n so_dict = so_file_dict[name]\\n pattern = 'lib{0}.so*'.format(name)\\n so_files = Cmd.find(so_dict['sym_src_dir'], pattern)\\n if not so_files:\\n is_required = so_dict.get('require', False)\\n if not is_required:\\n message_format = (\\n \\\"Skip creating symbolic link of \\\"\\n \\\"not existing so file '{0}'\\\"\\n )\\n Log.debug(message_format.format(name))\\n continue\\n\\n message = 'so file pattern {0} not found at {1}'.format(\\n pattern, so_dict['sym_src_dir']\\n )\\n raise InstallError(message)\\n sym_dst_dir = os.path.abspath('../{0}'.format(\\n so_dict['sym_dst_dir']))\\n if not os.path.isdir(sym_dst_dir):\\n Cmd.mkdir_p(sym_dst_dir)\\n\\n cmd = 'ln -sf {0} {1}/lib{2}.so'.format(so_files[0],\\n sym_dst_dir,\\n name)\\n Cmd.sh_e(cmd)\",\n \"def _post_src_install_soname_symlinks(mysettings, out):\\n\\n\\timage_dir = mysettings[\\\"D\\\"]\\n\\tneeded_filename = os.path.join(mysettings[\\\"PORTAGE_BUILDDIR\\\"],\\n\\t\\t\\\"build-info\\\", \\\"NEEDED.ELF.2\\\")\\n\\n\\tf = None\\n\\ttry:\\n\\t\\tf = io.open(_unicode_encode(needed_filename,\\n\\t\\t\\tencoding=_encodings['fs'], errors='strict'),\\n\\t\\t\\tmode='r', encoding=_encodings['repo.content'],\\n\\t\\t\\terrors='replace')\\n\\t\\tlines = f.readlines()\\n\\texcept IOError as e:\\n\\t\\tif e.errno not in (errno.ENOENT, errno.ESTALE):\\n\\t\\t\\traise\\n\\t\\treturn\\n\\tfinally:\\n\\t\\tif f is not None:\\n\\t\\t\\tf.close()\\n\\n\\tqa_no_symlink = \\\"\\\"\\n\\tf = None\\n\\ttry:\\n\\t\\tf = io.open(_unicode_encode(os.path.join(\\n\\t\\t\\tmysettings[\\\"PORTAGE_BUILDDIR\\\"],\\n\\t\\t\\t\\\"build-info\\\", \\\"QA_SONAME_NO_SYMLINK\\\"),\\n\\t\\t\\tencoding=_encodings['fs'], errors='strict'),\\n\\t\\t\\tmode='r', encoding=_encodings['repo.content'],\\n\\t\\t\\terrors='replace')\\n\\t\\tqa_no_symlink = f.read()\\n\\texcept IOError as e:\\n\\t\\tif e.errno not in (errno.ENOENT, errno.ESTALE):\\n\\t\\t\\traise\\n\\tfinally:\\n\\t\\tif f is not None:\\n\\t\\t\\tf.close()\\n\\n\\tqa_no_symlink = qa_no_symlink.split()\\n\\tif qa_no_symlink:\\n\\t\\tif len(qa_no_symlink) > 1:\\n\\t\\t\\tqa_no_symlink = \\\"|\\\".join(\\\"(%s)\\\" % x for x in qa_no_symlink)\\n\\t\\t\\tqa_no_symlink = \\\"^(%s)$\\\" % qa_no_symlink\\n\\t\\telse:\\n\\t\\t\\tqa_no_symlink = \\\"^%s$\\\" % qa_no_symlink[0]\\n\\t\\tqa_no_symlink = re.compile(qa_no_symlink)\\n\\n\\tlibpaths = set(portage.util.getlibpaths(\\n\\t\\tmysettings[\\\"ROOT\\\"], env=mysettings))\\n\\tlibpath_inodes = set()\\n\\tfor libpath in libpaths:\\n\\t\\tlibdir = os.path.join(mysettings[\\\"ROOT\\\"], libpath.lstrip(os.sep))\\n\\t\\ttry:\\n\\t\\t\\ts = os.stat(libdir)\\n\\t\\texcept OSError:\\n\\t\\t\\tcontinue\\n\\t\\telse:\\n\\t\\t\\tlibpath_inodes.add((s.st_dev, s.st_ino))\\n\\n\\tis_libdir_cache = {}\\n\\n\\tdef is_libdir(obj_parent):\\n\\t\\ttry:\\n\\t\\t\\treturn is_libdir_cache[obj_parent]\\n\\t\\texcept KeyError:\\n\\t\\t\\tpass\\n\\n\\t\\trval = False\\n\\t\\tif obj_parent in libpaths:\\n\\t\\t\\trval = True\\n\\t\\telse:\\n\\t\\t\\tparent_path = os.path.join(mysettings[\\\"ROOT\\\"],\\n\\t\\t\\t\\tobj_parent.lstrip(os.sep))\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ts = os.stat(parent_path)\\n\\t\\t\\texcept OSError:\\n\\t\\t\\t\\tpass\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif (s.st_dev, s.st_ino) in libpath_inodes:\\n\\t\\t\\t\\t\\trval = True\\n\\n\\t\\tis_libdir_cache[obj_parent] = rval\\n\\t\\treturn rval\\n\\n\\tmissing_symlinks = []\\n\\n\\t# Parse NEEDED.ELF.2 like LinkageMapELF.rebuild() does.\\n\\tfor l in lines:\\n\\t\\tl = l.rstrip(\\\"\\\\n\\\")\\n\\t\\tif not l:\\n\\t\\t\\tcontinue\\n\\t\\tfields = l.split(\\\";\\\")\\n\\t\\tif len(fields) < 5:\\n\\t\\t\\tportage.util.writemsg_level(_(\\\"\\\\nWrong number of fields \\\" \\\\\\n\\t\\t\\t\\t\\\"in %s: %s\\\\n\\\\n\\\") % (needed_filename, l),\\n\\t\\t\\t\\tlevel=logging.ERROR, noiselevel=-1)\\n\\t\\t\\tcontinue\\n\\n\\t\\tobj, soname = fields[1:3]\\n\\t\\tif not soname:\\n\\t\\t\\tcontinue\\n\\t\\tif not is_libdir(os.path.dirname(obj)):\\n\\t\\t\\tcontinue\\n\\t\\tif qa_no_symlink and qa_no_symlink.match(obj.strip(os.sep)) is not None:\\n\\t\\t\\tcontinue\\n\\n\\t\\tobj_file_path = os.path.join(image_dir, obj.lstrip(os.sep))\\n\\t\\tsym_file_path = os.path.join(os.path.dirname(obj_file_path), soname)\\n\\t\\ttry:\\n\\t\\t\\tos.lstat(sym_file_path)\\n\\t\\texcept OSError as e:\\n\\t\\t\\tif e.errno not in (errno.ENOENT, errno.ESTALE):\\n\\t\\t\\t\\traise\\n\\t\\telse:\\n\\t\\t\\tcontinue\\n\\n\\t\\tmissing_symlinks.append((obj, soname))\\n\\n\\tif not missing_symlinks:\\n\\t\\treturn\\n\\n\\tqa_msg = [\\\"QA Notice: Missing soname symlink(s):\\\"]\\n\\tqa_msg.append(\\\"\\\")\\n\\tqa_msg.extend(\\\"\\\\t%s -> %s\\\" % (os.path.join(\\n\\t\\tos.path.dirname(obj).lstrip(os.sep), soname),\\n\\t\\tos.path.basename(obj))\\n\\t\\tfor obj, soname in missing_symlinks)\\n\\tqa_msg.append(\\\"\\\")\\n\\tfor line in qa_msg:\\n\\t\\teqawarn(line, key=mysettings.mycpv, out=out)\",\n \"def _create_file(self, rel_path, text):\\n # FIXME: There are better/more secure APIs for creating tmp file paths.\\n file_path = self.filesystem.join(self._temp_dir, rel_path)\\n self.filesystem.write_text_file(file_path, text)\\n return file_path\",\n \"def _set_target_symlinks(self):\\n try:\\n job_id = self.active_queue[0][\\\"id\\\"]\\n tgt_dir = self.config[\\\"target\\\"][\\\"directory\\\"]\\n for t in [\\\"objects\\\", \\\"config\\\"]:\\n src = self.active_queue[0][t + \\\"_filename\\\"]\\n tgt = os.path.join(tgt_dir, self.config[\\\"target\\\"][t])\\n # force symlink creation\\n add_symlink(src, tgt, True)\\n self.logger.debug(\\\"[active/%s] set symlink: '%s' -> '%s'\\\"\\n % (job_id, tgt, src))\\n\\n except KeyError:\\n raise ProfileKeyError(\\\"no value for target.directory\\\")\",\n \"def make_link(first, second):\\n manager = Actions()\\n manager.make_link(first, second)\",\n \"def createLink(self, source, destination):\\n log(\\\"creating link\\\")\\n\\n if \\\"flix\\\" in source:\\n return \\\"%s\\\" % +OSUtils.createLink(source, destination)\\n return \\\"0\\\"\",\n \"def absolute_symlink(\\n source_path: str,\\n dest_path: str\\n):\\n os.symlink(os.path.abspath(source_path), dest_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7453823","0.7333795","0.7322523","0.7264164","0.72327024","0.71856415","0.7102871","0.7052772","0.70179003","0.701752","0.6931252","0.6921266","0.69030124","0.6874251","0.68668115","0.682023","0.67737055","0.6681461","0.66567737","0.6653678","0.6610112","0.6610112","0.6608762","0.6590855","0.657266","0.65319246","0.65244013","0.65209776","0.6516923","0.6515994","0.6514848","0.63721263","0.63451254","0.6337129","0.63273233","0.6302337","0.62805307","0.62245935","0.6191092","0.6173892","0.6158638","0.6130782","0.60964715","0.6095246","0.6082975","0.60739094","0.6057621","0.6021274","0.5984693","0.5980049","0.59270227","0.5919002","0.59037715","0.5887669","0.5887431","0.58818215","0.5881334","0.5875476","0.58740664","0.5850494","0.58487725","0.5822913","0.5792361","0.57819766","0.57805496","0.5742695","0.57416296","0.5712214","0.5710727","0.5710216","0.57002985","0.56917566","0.56836855","0.5682854","0.5677804","0.567482","0.56700516","0.56687176","0.56640226","0.5650547","0.5614922","0.5614723","0.56139183","0.5605815","0.56035644","0.5602759","0.5598956","0.55960613","0.5583662","0.5579033","0.5574386","0.5574386","0.5559396","0.5557987","0.55468065","0.55434096","0.55375576","0.55324674","0.5530489","0.55097663"],"string":"[\n \"0.7453823\",\n \"0.7333795\",\n \"0.7322523\",\n \"0.7264164\",\n \"0.72327024\",\n \"0.71856415\",\n \"0.7102871\",\n \"0.7052772\",\n \"0.70179003\",\n \"0.701752\",\n \"0.6931252\",\n \"0.6921266\",\n \"0.69030124\",\n \"0.6874251\",\n \"0.68668115\",\n \"0.682023\",\n \"0.67737055\",\n \"0.6681461\",\n \"0.66567737\",\n \"0.6653678\",\n \"0.6610112\",\n \"0.6610112\",\n \"0.6608762\",\n \"0.6590855\",\n \"0.657266\",\n \"0.65319246\",\n \"0.65244013\",\n \"0.65209776\",\n \"0.6516923\",\n \"0.6515994\",\n \"0.6514848\",\n \"0.63721263\",\n \"0.63451254\",\n \"0.6337129\",\n \"0.63273233\",\n \"0.6302337\",\n \"0.62805307\",\n \"0.62245935\",\n \"0.6191092\",\n \"0.6173892\",\n \"0.6158638\",\n \"0.6130782\",\n \"0.60964715\",\n \"0.6095246\",\n \"0.6082975\",\n \"0.60739094\",\n \"0.6057621\",\n \"0.6021274\",\n \"0.5984693\",\n \"0.5980049\",\n \"0.59270227\",\n \"0.5919002\",\n \"0.59037715\",\n \"0.5887669\",\n \"0.5887431\",\n \"0.58818215\",\n \"0.5881334\",\n \"0.5875476\",\n \"0.58740664\",\n \"0.5850494\",\n \"0.58487725\",\n \"0.5822913\",\n \"0.5792361\",\n \"0.57819766\",\n \"0.57805496\",\n \"0.5742695\",\n \"0.57416296\",\n \"0.5712214\",\n \"0.5710727\",\n \"0.5710216\",\n \"0.57002985\",\n \"0.56917566\",\n \"0.56836855\",\n \"0.5682854\",\n \"0.5677804\",\n \"0.567482\",\n \"0.56700516\",\n \"0.56687176\",\n \"0.56640226\",\n \"0.5650547\",\n \"0.5614922\",\n \"0.5614723\",\n \"0.56139183\",\n \"0.5605815\",\n \"0.56035644\",\n \"0.5602759\",\n \"0.5598956\",\n \"0.55960613\",\n \"0.5583662\",\n \"0.5579033\",\n \"0.5574386\",\n \"0.5574386\",\n \"0.5559396\",\n \"0.5557987\",\n \"0.55468065\",\n \"0.55434096\",\n \"0.55375576\",\n \"0.55324674\",\n \"0.5530489\",\n \"0.55097663\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.95351255"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22398,"cells":{"query":{"kind":"string","value":"Test case for get_meta_range"},"document":{"kind":"string","value":"def test_get_meta_range(self):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_get_range(self):\n pass","def test_get_range_empty(self):\n\n queryset = mock.Mock()\n queryset.aggregate.return_value = None\n\n dimension = models.QuantitativeDimension(\n key='shares',\n name='Count of shares',\n description='Count of shares',\n field_name='shared_count',\n )\n\n min_val, max_val = dimension.get_range(queryset)\n\n self.assertIsNone(min_val)\n self.assertIsNone(max_val)","def test_data_range(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n lenrange = random.randint(1, 10)\n nreps = random.randint(1, 10)\n\n ex.range = [\"i\", range(lenrange)]\n ex.nreps = nreps\n\n ex.vary[\"X\"][\"along\"] = 0\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n self.assertIn([\"smalloc\", \"X\", nreps * m * n + (nreps - 1) * m], cmds)\n rangeidx = random.randint(0, lenrange - 1)\n repidx = random.randint(0, nreps - 1)\n self.assertIn([\"soffset\", \"X\", repidx * m,\n \"X_%d_%d\" % (rangeidx, repidx)], cmds)","def getRange(self, p_int): # real signature unknown; restored from __doc__\n pass","def GetScalarRange(self):\n ...","def GetTRange(self):\n ...","def _in_range_op(spec):","def range(self):\n\n return time_stat(self, stat=\"range\")","def __dynamic_range_process(info):\n if 'range' in info:\n for i in range(len(info['range'])):\n if info['range'][i][1] == -1:\n info['range'][i][1] = None\n return info","def test_002_range_columns(self):\n assert(len(\n self.range_transformer.fit_transform(\n self.data[self.range_col]\n ).columns\n ) == 1)","def f_has_range(self):\n raise NotImplementedError(\"Should have implemented this.\")","def testRangeQuery(self):\n\n rq = TermRangeQuery(\"sorter\", \"b\", \"d\", True, True)\n filteredquery = FilteredQuery(rq, self.filter)\n scoreDocs = self.searcher.search(filteredquery, 1000).scoreDocs\n self.assertEqual(2, len(scoreDocs))","def getRange(self):\n return self.range","def test_range__no_base_date(self):\n data = self._data()\n data.pop('base_date')\n response = self._get(get_kwargs=data)\n self._check_response(response, 104)","def get_range_value(self, key):\n pass","def f_get_range(self, copy=True):\n raise NotImplementedError(\"Should have implemented this.\")","def _get_sight_range(self):\n raise NotImplementedError","def test_get_pivot_in_correct_range(self):\n list = [5, 6, 7, 8, 9, 2]\n assert 0 <= get_pivot(list) <= 5 # between the first and last indices","def test_data_with_range_view(self):\n\n self.create_model()\n self.create_machine()\n self.insert_data()\n\n date_literal = '%Y-%m-%d'\n start_date = dt.today()\n end_date = start_date + datetime.timedelta(days=1)\n\n self.create_user_account_and_login()\n query_url = self.range_url + '/' + self.data['mid'] + \\\n '/?s=' + dt.strftime(start_date, date_literal) + \\\n '&e=' + dt.strftime(end_date, date_literal)\n\n response = self.client.get(query_url)\n results = json.loads(response.content)\n\n self.assertEquals(len(results), 2)","def test_range__no_end_date(self):\n data = self._data()\n data.pop('end_date')\n response = self._get(get_kwargs=data)\n self._check_response(response, 104)","def provider_range_lookup(self, record):\n pass","def start(self):\n return _uhd_swig.meta_range_t_start(self)","def getSliderRange(*args):\n\n #get timeslider range start\n startF = cmds.playbackOptions(query=True, min=True)\n endF = cmds.playbackOptions(query=True, max=True)\n return(startF, endF)","def test_range_query(self):\r\n start = datetime(*self.base_date.timetuple()[:3])\r\n end = start + timedelta(days=3)\r\n\r\n results = DateTimeQueryTestModel.filter(user=0, day__gte=start, day__lt=end)\r\n assert len(results) == 3","def _hit_range_get(self):\n return (self.hit_start, self.hit_end)","def ex_range(data):\n a, b, step = _cleanse_range_args(data)\n return list(range(a, b+sign(step), step))","def _query_range_get(self):\n return (self.query_start, self.query_end)","def looks_range(self) -> Optional[int]:\n return self._get_property(LOOKS_RANGE_PROP, int)","def isRangeValid(self) -> bool:\n ...","def get_range(self) -> tuple[int, int]:\n return self.range_from, self.range_to","def getRangeMM(self) -> float:\n ...","def get_range(self, start=None, end=None):\n\n # handle the case of no data\n if self.data.shape[0] == 0 or self.source.data[\"index\"].shape[0] == 0:\n return None, None\n\n first_source_idx = self.source.data[\"index\"][0]\n last_source_idx = self.source.data[\"index\"][-1]\n\n # convert to timestamp if necessary\n if isinstance(self.data.index, pd.DatetimeIndex):\n start = pd.to_datetime(start, unit=\"ms\")\n end = pd.to_datetime(end, unit=\"ms\")\n first_source_idx = pd.to_datetime(first_source_idx, unit=\"ms\")\n last_source_idx = pd.to_datetime(last_source_idx, unit=\"ms\")\n\n # get new start and end\n if start is not None:\n if start < first_source_idx:\n start = max(self.data.index[0], start)\n elif start > last_source_idx:\n start = min(self.data.index[-1], start)\n elif start < self.data.index[0]:\n start = self.data.index[0]\n elif start > self.data.index[-1]:\n start = self.data.index[-1]\n elif len(self.source.data[\"index\"]) > 0:\n start = first_source_idx\n else:\n start = self.data.index[0]\n\n if end is not None:\n if end < first_source_idx:\n end = max(self.data.index[0], end)\n elif end > last_source_idx:\n end = min(self.data.index[-1], end)\n elif end < self.data.index[0]:\n end = self.data.index[0]\n elif end > self.data.index[-1]:\n end = self.data.index[-1]\n elif len(self.source.data[\"index\"]) > 0:\n end = last_source_idx\n else:\n end = self.data.index[-1]\n\n return start, end","def test_for_range(self, rnge, parse_input_mocked_metadata):\n bb = parse_input_mocked_metadata(\n \"for float m in {}\\n\\tMZgate(m, m/2) | [0, 1]\".format(rnge)\n )\n # create a list out of the `rnge` string\n rnge_list = np.array(\n range(*np.array(rnge.split(\":\"), dtype=int)),\n dtype=float\n )\n\n assert np.all(bb._forvar[\"m\"] == rnge_list)\n assert bb.operations == [\n {'op': 'MZgate', 'args': [rnge_list[0], rnge_list[0]/2], 'kwargs': {}, 'modes': [0, 1]},\n {'op': 'MZgate', 'args': [rnge_list[1], rnge_list[1]/2], 'kwargs': {}, 'modes': [0, 1]},\n {'op': 'MZgate', 'args': [rnge_list[2], rnge_list[2]/2], 'kwargs': {}, 'modes': [0, 1]}\n ]","def test_get_offenses_with_invalid_range(self, make_request):\n action_params = {\"range\": \"-1-2\"}\n with self.assertRaises(PluginException):\n self.action.run(action_params)","def get_data_range(self, start_position, length):\n pass","def test_simsam_range_functions_without_mapping_file(self):\r\n actual = qiime.simsam.simsam_range(\r\n self.tutorial_otu_table,\r\n self.tutorial_tree,\r\n [1],\r\n [0.1])\r\n self.assertEqual(len(list(actual)), 1)","def range_query(self, start_key, end_key):\n if not self.attached:\n raise CastleCollectionNotAttachedException()\n\n print \"THIS IS FAKE\"\n pycastle_log.info(\"Doing range query from key \"+str(start_key)+\" to key \"+str(end_key))\n try:\n i = 0\n while i < 10:\n yield i\n i+=1\n if i % 5 == 0:\n pycastle_log.info(\"Getting next batch\")\n except GeneratorExit:\n pycastle_log.info(\"User requested stop of range query from key \"+str(start_key)+\" to key \"+str(end_key))","def test_returns_custom_range(self):\n img = np.arange(5)\n\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\n self.assertEqual(actual.min(), 0)\n self.assertEqual(actual.max(), 255)","def test_range__bad_base_date(self):\n self.base_date = 'bad'\n response = self._get(get_kwargs=self._data())\n self._check_response(response, 104)","def get_recordrange(self):\r\n if self.version >= 10.1:\r\n querystr = \"\"\"?where=&outFields=*&returnGeometry=false&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=[{%0D%0A++++\"statisticType\"%3A+\"count\"%2C%0D%0A++++\"onStatisticField\"%3A+\"objectid\"%2C+++++\"outStatisticFieldName\"%3A+\"oidcount\"%0D%0A++}%2C{%0D%0A++++\"statisticType\"%3A+\"min\"%2C%0D%0A++++\"onStatisticField\"%3A+\"objectid\"%2C+++++\"outStatisticFieldName\"%3A+\"oidmin\"%0D%0A++}%2C{%0D%0A++++\"statisticType\"%3A+\"max\"%2C%0D%0A++++\"onStatisticField\"%3A+\"objectid\"%2C+++++\"outStatisticFieldName\"%3A+\"oidmax\"%0D%0A++}]&returnZ=false&returnM=false&returnDistinctValues=false&f=pjson\"\"\"\r\n req = requests.get(self.endpointurl + querystr)\r\n self.recordinfo = req.json()[\"features\"][0][\"attributes\"]\r\n\r\n elif self.version < 10.1:\r\n querystr = \"\"\"?text=&geometry=&geometryType=esriGeometryPoint&inSR=&spatialRel=esriSpatialRelIntersects&relationParam=&objectIds=&where=objectid+>+-1&time=&returnCountOnly=true&returnIdsOnly=false&returnGeometry=false&maxAllowableOffset=&outSR=&outFields=&f=pjson\"\"\"\r\n req = requests.get(self.endpontquerystr + qs)\r\n self.recordinfo = {\"oidmin\": 0, \"oidmax\": req.json()[\"count\"]}\r\n\r\n [\r\n self.iterlist.append([x, x + 999])\r\n for x in range(\r\n self.recordinfo[\"oidmin\"]\r\n if self.recordinfo[\"oidmin\"] != self.recordinfo[\"oidmax\"]\r\n else 1 - self.recordinfo[\"oidmin\"],\r\n self.recordinfo[\"oidmax\"],\r\n 1000,\r\n )\r\n ]","def range_callback(data):\n global D\n D.ranges = data.ranges","def test_date_range_fields():\n now = datetime.datetime(2017, 6, 13, 9, 44, 31, 62870)\n fields = {\n 'estimated_land_date_after': now,\n 'estimated_land_date_before': now,\n 'adviser.id': 1234,\n }\n\n filters, ranges = _split_range_fields(fields)\n\n assert filters == {\n 'adviser.id': 1234,\n }\n assert ranges == {\n 'estimated_land_date': {\n 'gte': now,\n 'lte': now,\n },\n }","def range_(self):\n return self.bset.range_","def test_range__bad_end_date(self):\n self.end_date = 'bad'\n response = self._get(get_kwargs=self._data())\n self._check_response(response, 104)","def test_metrostations_get(self):\n pass","def range (self):\n return self._range","def range (self):\n return self._range","def range(self):\n return self.range_array","def _full_value_range(self):\n min_value, max_value = self._raw_data.data_range\n return max_value - min_value","def test_portrange_get_sensordef(self):\n test_sensordef = {\n \"kind\": self.test_portrange.get_kind(),\n \"name\": \"Port Range\",\n \"description\": \"Checks the availability of a port range on a target system\",\n \"help\": \"Checks the availability of a port range on a target system\",\n \"tag\": \"mpportrangesensor\",\n \"groups\": [\n {\n \"name\": \" portspecific\",\n \"caption\": \"Port specific\",\n \"fields\": [\n {\n \"type\": \"integer\",\n \"name\": \"timeout\",\n \"caption\": \"Timeout (in s)\",\n \"required\": \"1\",\n \"default\": 60,\n \"minimum\": 1,\n \"maximum\": 900,\n \"help\": \"If the reply takes longer than this value the request is aborted \"\n \"and an error message is triggered. Max. value is 900 sec. (=15 min.)\"\n },\n {\n \"type\": \"integer\",\n \"name\": \"startport\",\n \"caption\": \"Port\",\n \"required\": \"1\",\n \"default\": 110,\n \"minimum\": 1,\n \"maximum\": 65534,\n \"help\": \"Specify the port ranges starting port\"\n },\n {\n \"type\": \"integer\",\n \"name\": \"endport\",\n \"caption\": \"Port\",\n \"required\": \"1\",\n \"default\": 110,\n \"minimum\": 1,\n \"maximum\": 65534,\n \"help\": \"Specify the port ranges end port\"\n }\n ]\n }\n ]\n }\n assert_equal(self.test_portrange.get_sensordef(), test_sensordef)","def get_range(self):\n return time_to_range(self.get_time())","def test_split_ranges(self):\n start = datetime.utcnow() - pd.Timedelta(\"5H\")\n end = datetime.utcnow() + pd.Timedelta(\"5min\")\n delta = pd.Timedelta(\"1H\")\n\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\n self.assertEqual(len(ranges), 5)\n self.assertEqual(ranges[0][0], start)\n self.assertEqual(ranges[-1][1], end)\n\n st_times = [start_tm[0] for start_tm in ranges]\n for end_time in (end_tm[1] for end_tm in ranges):\n self.assertNotIn(end_time, st_times)\n\n end = end + pd.Timedelta(\"20min\")\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\n self.assertEqual(len(ranges), 5)\n self.assertEqual(ranges[0][0], start)\n self.assertEqual(ranges[-1][1], end)","def test_range_not_integrated(self):\n self.fbuser.delete()\n steps = [{'dateTime': '2012-06-07', 'value': '10'}]\n TimeSeriesData.objects.create(\n user=self.user,\n resource_type=TimeSeriesDataType.objects.get(\n category=TimeSeriesDataType.activities, resource='steps'),\n date=steps[0]['dateTime'],\n value=steps[0]['value']\n )\n\n response = self._mock_utility(response=steps,\n get_kwargs=self._data())\n self._check_response(response, 100, steps)","def range(self):\n \n return self._range","def parse_range(option):\n return {\"range\": timedelta(days=option)}","def m_to_range(self, data):\n return (data - self._min_range_m) / self._total_range","def test_range_finder_syntax():\n rf = RangeFinder(0)\n rf.range_finder_counter = MagicMock()\n pid_value = rf.pidGet()\n dist = rf._getDistance()","def plugin_fetch(self, tmrange):\n raise NotImplementedError(\"EMFetcher abstract superclass\")","def get_range_info(self):\n with open(self.range_path, 'r') as _file:\n for line in _file.readlines():\n list0 = line.strip().split('-')\n range_dict = {\n 'min': int(list0[0], 16),\n 'max': int(list0[1], 16),\n 'max_offset': int(list0[1], 16) - int(list0[0], 16),\n }\n self.ranges.append(range_dict)","def step(self):\n return _uhd_swig.meta_range_t_step(self)","def byrange(self, start, stop):\n\t\treturn ElementsByRange(self.AEM_want, self, (start, stop))","def range(self):\n return self.timerange()","def visitRange(self, date):\n raise NotImplementedError()","def define_range():\n\n def_range = {'lt': [0.0, 24.0],\n 'lon': [0.0, 360.0],\n 'angle': [0.0, 2.0 * np.pi]}\n\n return def_range","def test_timesheet_range(self):\n response_get = self.client.get('/accounts/timesheet/')\n response = response_get.content.decode()\n\n if self.today.day < 16:\n self.assertIn(u'%s-%s-16' % (self.today.year, '{:02d}'.format(self.today.month-1)), response)\n self.assertIn(u'%s-%s-15' % (self.today.year, '{:02d}'.format(self.today.month)), response)\n else:\n self.assertIn(u'%s-%s-16' % (self.today.year, '{:02d}'.format(self.today.month)), response)\n self.assertIn(u'%s-%s-15' % (self.today.year, '{:02d}'.format(self.today.month+1)), response)\n\n self.assertNotIn(u'#%s: %s' % (self.outofrange_ticket.pk, self.outofrange_ticket.title), response)\n\n #self.assertIn(u'#%s: %s' % (self.inrange_ticket.pk, self.inrange_ticket.title), response)","def _is_range(cls, rng):\n match = re.search(\"([0-9][1-9]*)-([0-9][1-9]*)\", rng)\n # Group is a singular value.\n return match is not None","def GetSRange(self):\n ...","def kind(self):\n return DateValueTypes.RANGE","def _calc_range(self) -> np.ndarray:\n if self._is_ct25k():\n range_resolution = 30\n n_gates = 256\n else:\n n_gates = int(self.metadata[\"number_of_gates\"])\n range_resolution = int(self.metadata[\"range_resolution\"])\n return np.arange(n_gates) * range_resolution + range_resolution / 2","def get_range(self, rel_name):\n return self._declaration[rel_name].range_type","def number_range(low, high, obj_type=int):\n\n def number_range_parser(argument):\n try:\n argument = obj_type(argument)\n except ValueError:\n argparse.ArgumentError('Must be of type {}'.format(obj_type.__name__))\n\n if low <= argument <= high:\n return argument\n else:\n parser.error('Value is not in the range of {} and {}'.format(low, high))\n\n return number_range_parser","def getRange(self, chr, start, end, bins=2000, zoomlvl=-1, metric=\"AVG\", respType=\"DataFrame\"):\n try:\n iter = self.file.fetch(chr, start, end)\n # result = []\n # for x in iter:\n # returnBin = (x.reference_name, x.reference_start, x.reference_end, x.query_alignment_sequence, x.query_sequence)\n # result.append(returnBin)\n\n # if self.columns is None:\n # self.columns = [\"chr\", \"start\", \"end\", \"query_alignment_sequence\", \"query_sequence\"]\n\n # if respType is \"DataFrame\":\n # result = toDataFrame(result, self.columns)\n\n (result, _) = get_range_helper(self.toDF, self.get_bin,\n self.get_col_names, chr, start, end, iter, self.columns, respType)\n\n return result, None\n except ValueError as e:\n raise Exception(\"didn't find chromId with the given name\")","def range_to_m(self, data):\n return data * self._total_range + self._min_range_m","def _computeRangeFromData(data):\n if data is None:\n return None\n\n dataRange = min_max(data, min_positive=True, finite=True)\n if dataRange.minimum is None: # Only non-finite data\n return None\n\n if dataRange is not None:\n min_positive = dataRange.min_positive\n if min_positive is None:\n min_positive = float('nan')\n return dataRange.minimum, min_positive, dataRange.maximum","def test_outside_top_range(self):\n\n with self.assertRaises(ValueError):\n self.expander.get_next_month(13)","def test_range_argument(self):\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\n bins_range = (0, 2)\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\n hist_exp = np.array([1, 2, 0])\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.n_bins,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\n np.testing.assert_array_almost_equal(hist_exp, hist)","def range(self) -> ty.Tuple[float, float]:\r\n ...","def test_parse_valid_time_range(self):\n from azure.servicefabric.models.time_range import (\n TimeRange\n )\n from azure.servicefabric.models.time_of_day import (\n TimeOfDay\n )\n\n res = sf_c.parse_time_range({\n 'StartTime': {\n 'Hour': 0,\n 'Minute': 0\n },\n 'EndTime': {\n 'Hour': 23,\n 'Minute': 59,\n }\n })\n\n self.assertIsInstance(res, TimeRange)\n\n self.assertIsInstance(res.start_time, TimeOfDay)\n self.assertEqual(res.start_time.hour, 0)\n self.assertEqual(res.start_time.minute, 0)\n\n self.assertIsInstance(res.end_time, TimeOfDay)\n self.assertEqual(res.end_time.hour, 23)\n self.assertEqual(res.end_time.minute, 59)","def range_params(self, ran, kw):\n specs = {\"range\": (SchemaNode(\"value\"),\n SchemaNode(\"param\").set_attr(\"name\",\"minInclusive\"),\n SchemaNode(\"param\").set_attr(\"name\",\"maxInclusive\")),\n \"length\": (SchemaNode(\"param\").set_attr(\"name\",\"length\"),\n SchemaNode(\"param\").set_attr(\"name\",\"minLength\"),\n SchemaNode(\"param\").set_attr(\"name\",\"maxLength\"))}\n (exact, min_, max_) = specs[kw]\n if (len(ran) == 1 or ran[0] == ran[1]) and ran[0][0] != \"m\":\n elem = exact\n elem.text = ran[0]\n return [elem]\n res = []\n if ran[0][0] != \"m\":\n elem = min_\n elem.text = ran[0]\n res.append(elem)\n if ran[1][0] != \"m\":\n elem = max_\n elem.text = ran[1]\n res.append(elem)\n return res","def get_cells(worksheet, get_range: str):\n cells = worksheet[get_range]\n\n return cells","def _check_range(r): \n if _is_single_range(r):\n _check_one_range(r)\n elif isinstance(r, collections.Sequence):\n for r2 in r:\n _check_one_range(r2)\n else:\n raise error.RangeSyntaxError(str(r))","def range_field(self):\n return self.db.range_field","def range_table(self):\n raise NotImplementedError('Abstract method.')","def test_pos_operate_with_list_get_range_val_out_of_bounds(self):\n key = (\"test\", \"demo\", \"list_key\")\n list = [{\"op\": aerospike.OP_LIST_GET_RANGE, \"bin\": \"int_bin\", \"index\": 2, \"val\": 9}]\n\n (key, meta, bins) = self.as_connection.operate(key, list)\n\n assert bins == {\"int_bin\": [3, 4]}","def test_seq_rangeExamples(self):\n\n self.assertEqual(MessageSet(2, 4), MessageSet(4, 2))\n self.assertEqual(list(MessageSet(2, 4)), [2, 3, 4])\n\n m = MessageSet(3291, None)\n m.last = 3290\n self.assertEqual(list(m), [3290, 3291])","def do_range_forloop(self, line):\n self.E_str = \"do_range_forloop\"\n\n # Get the range parameters\n line = line.replace(\" \", \"\")\n line = line[line.find(\"range\")+5:]\n range_str, _ = gen_parse.get_str_between_delims(line, \"(\", \")\")\n words = range_str.split(\",\")\n\n if len(words) == 1:\n start, step = 0, 1\n end = int(words[0])\n elif len(words) == 2:\n step = 1\n start, end = [int(i) for i in words]\n else:\n start, end, step = [int(i) for i in words]\n\n return range(start, end, step)","def get_range(self, field, deep=False, axis=None):\n variables = list(self.vars(deep, with_name=field))\n\n if not variables:\n raise KeyError(\"No variable named '%s' was found!\" % field)\n\n start = [np.nanmin(self[var], axis).item(0) for var in variables]\n end = [np.nanmax(self[var], axis).item(0) for var in variables]\n return min(start), max(end)","def test_Op_integers(self):\r\n start, stop, step = iscalars('start', 'stop', 'step')\r\n out = ARange(start.type.dtype)(start, stop, step)\r\n f = function([start, stop, step], out)\r\n\r\n assert numpy.all(f(0, 5, 1) == numpy.arange(0, 5, 1))\r\n assert numpy.all(f(2, 11, 4) == numpy.arange(2, 11, 4))\r\n assert numpy.all(f(-5, 1, 1) == numpy.arange(-5, 1, 1))\r\n assert numpy.all(f(10, 2, -2) == numpy.arange(10, 2, -2))\r\n assert numpy.all(f(10, 2, 2) == numpy.arange(10, 2, 2))\r\n assert numpy.all(f(0, 0, 1) == numpy.arange(0, 0, 1))","def test_ave_age_range(step):\n diff = step[\"ave_birth\"] - step[\"birth\"]\n assert 0 < diff < 15E6","def __getitem__(self, *args):\n return _uhd_swig.range_vector_t___getitem__(self, *args)","def test_creation_bounds_not_inclusive():\n with pytest.raises(ValueError) as __:\n value = -42\n __ = param.Integer(value=value, hardbounds=[-42, 100], inclusive_bounds=[False, False])","def getAFeRange(brand):\n return afe_range[brand]","def getMassRange(brand):\n return mass_range[brand]","def test_date_range(self):\n\n url = '/%s/job-types/status/?started=%s&ended=%s' % ( self.api,\n '2015-01-01T00:00:00Z',\n '2015-01-02T00:00:00Z')\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 4)","def __check_range(self, range_type, name):\n\n # Before we really do anything, make sure a warning or critical \n # threshold were even set.\n\n self.__verbose_print('range was ' + range_type)\n\n range_list = self.__warning\n if range_type == 'critical':\n range_list = self.__critical\n\n if len(range_list) < 1:\n return\n\n threshold = self.__perf[name]['threshold']\n val = self.__perf[name]['val']\n\n # Even if the warning/critical thresholds are required, a user may\n # forget to set all necessary ranges. If the ranges are required,\n # exit with a complaint, otherwise just skip the range test.\n\n if len(range_list) < threshold:\n if range_type not in self.__req_option:\n return\n raise UserWarning (\"Please set part %s of the %s threshold!\" % (threshold, range_type))\n\n # The option parser should have already split these into proper\n # bottom, top, and match inversion, so long as the array element\n # is defined Perform our range test and set the exit status.\n\n self.__verbose_print(range_list[threshold-1])\n (bottom, top, invert, raw) = range_list[threshold-1]\n\n self.__perf[name][\"raw_%s\" % range_type] = raw\n\n if ((not invert and (val < bottom or val > top)) or\n (invert and val >= bottom and val <= top)):\n self.__exit_status = range_type\n self.__perf[name]['state'] = range_type\n\n self.__verbose_print(\"%s:%s:%s = %s\" % (val, bottom, top, ((val < bottom) or (val > top))))","def get_range(self, range, last):\n found = re.match('\\s*([+-]?)([0-9]+)(%?)(\\s*,\\s*([+-]?)([0-9]+)(%?))?\\s*', str(range))\n if not found:\n raise ConfigError('Failed to parse range \"%s\" in \"__size\"!' % str(range))\n (s1, n1, p1, v2, s2, n2, p2) = found.groups()\n if v2:\n d1 = last * int(n1) / 100.0 if p1 else int(n1)\n d2 = last * int(n2) / 100.0 if p2 else int(n2)\n m = last + d1 if s1 == '+' else last - d1 if s1 == '-' or p1 else d1\n M = last - d2 if s2 == '-' else last + d2 if s2 == '+' or p2 else d2\n else:\n d = last * int(n1) / 100.0 if p1 else int(n1)\n m = last - d\n M = last + d\n return m, M","def __init__(\n self, low=None, high=None, exclude_low=False, exclude_high=False\n ):\n vtype = type(high)\n if (low is not None) and (vtype is not float):\n vtype = type(low)\n if vtype not in RangeTypes:\n raise TraitError(\n \"TraitRange can only be use for int, long or \"\n \"float values, but a value of type %s was \"\n \"specified.\" % vtype\n )\n if vtype is float:\n self.validate = self.float_validate\n kind = 4\n self._type_desc = \"a floating point number\"\n if low is not None:\n low = float(low)\n if high is not None:\n high = float(high)\n elif vtype is LONG_TYPE:\n self.validate = self.long_validate\n self._type_desc = \"a long integer\"\n if low is not None:\n low = LONG_TYPE(low)\n if high is not None:\n high = LONG_TYPE(high)\n else:\n self.validate = self.int_validate\n kind = 3\n self._type_desc = \"an integer\"\n if low is not None:\n low = int(low)\n if high is not None:\n high = int(high)\n exclude_mask = 0\n if exclude_low:\n exclude_mask |= 1\n if exclude_high:\n exclude_mask |= 2\n if vtype is not LONG_TYPE:\n self.fast_validate = (kind, low, high, exclude_mask)\n\n # Assign type-corrected arguments to handler attributes\n self._low = low\n self._high = high\n self._exclude_low = exclude_low\n self._exclude_high = exclude_high","def test_ym2mjdmrange():\n\tyears = [1899,2000,2020]\n\tmonths = [12,1,2]\n\tmjd1_true = [14989,51544,58880]\n\tmjd2_true = [15019,51574,58908]\n\tmjd1_test, mjd2_test = date_functions.ym2mjdmrange( years, months )\n\n\tassert mjd1_test[0] == mjd1_true[0]\n\tassert mjd1_test[1] == mjd1_true[1]\n\tassert mjd1_test[2] == mjd1_true[2]\n\tassert mjd2_test[0] == mjd2_true[0]\n\tassert mjd2_test[1] == mjd2_true[1]\n\tassert mjd2_test[2] == mjd2_true[2]","def evaluate_comparison_range(node):\n return None","def test_numeric_range_question_possibilities(self):\n my_step = WorkflowStep.objects.get(\n workflow__workflowcollectionmember__workflow_collection=self.my_collection\n )\n my_step_input = WorkflowStepUserInput.objects.get(workflow_step=my_step)\n my_step_input.type.name = \"numeric_range_question\"\n my_step_input.type.json_schema = {\n \"type\": \"object\",\n \"title\": \"User Input: Numeric Range Question\",\n \"description\": \"A schema representing a numeric range question user input.\",\n \"required\": [\"label\", \"inputOptions\"],\n \"properties\": {\n \"id\": {\n \"type\": \"string\",\n \"title\": \"A string-based user input identifier.\",\n \"description\": \"This value may be managed outside of the object specification and so is optional.\",\n \"examples\": [\"4125-1351-1251-asfd\"],\n },\n \"label\": {\n \"type\": \"string\",\n \"title\": \"UI Label for Input\",\n \"description\": \"Label that should be displayed by user interfaces for this input.\",\n \"examples\": [\"The label to display for the input/question.\"],\n },\n \"inputOptions\": {\n \"type\": \"object\",\n \"properties\": {\n \"minimumValue\": {\n \"$id\": \"#/properties/options\",\n \"type\": \"number\",\n \"title\": \"Minimum Value\",\n \"description\": \"The minimum value to be displayed to the user for this question.\",\n },\n \"maximumValue\": {\n \"$id\": \"#/properties/options\",\n \"type\": \"number\",\n \"title\": \"Maximum Value\",\n \"description\": \"The maximum value to be displayed to the user for this question.\",\n },\n \"step\": {\n \"$id\": \"#/properties/options\",\n \"type\": \"number\",\n \"title\": \"Step Value\",\n \"description\": \"The number between each entry between minimum and maximum.\",\n },\n },\n },\n \"correctInput\": {\n \"description\": \"Indicates which answers are the correct ones.\",\n \"type\": \"number\",\n },\n \"meta\": {\n \"type\": \"object\",\n \"properties\": {\n \"inputRequired\": {\n \"type\": \"boolean\",\n \"description\": \"Whether or not an answer should be required from the user.\",\n },\n \"correctInputRequired\": {\n \"type\": \"boolean\",\n \"description\": \"Whether or not the correct answer should be required from the user.\",\n },\n },\n },\n },\n }\n my_step_input.type.save()\n\n my_step_input.specification = {\n \"label\": \"What is your favorite number?\",\n \"inputOptions\": {\"minimumValue\": 0, \"maximumValue\": 25, \"step\": 1},\n \"correctInput\": 3,\n \"meta\": {\"correctInputRequired\": True, \"inputRequired\": True},\n }\n my_step_input.save()\n\n # Ensure if correct answer is required, that the enum is just the correct answer\n self.assertEqual(\n my_step_input.response_schema[\"properties\"][\"userInput\"][\"type\"], \"number\"\n )\n self.assertEqual(\n my_step_input.response_schema[\"properties\"][\"userInput\"][\"enum\"], [3]\n )\n\n # Change the example so correctInput is not required\n my_step_input.specification = {\n \"label\": \"What is your favorite number?\",\n \"inputOptions\": {\"minimumValue\": 0, \"maximumValue\": 15, \"step\": 1},\n \"correctInput\": 3,\n \"meta\": {\"correctInputRequired\": False, \"inputRequired\": True},\n }\n my_step_input.save()\n\n # Ensure that if the correct answer isn't required, we have all options in the enum\n self.assertEqual(\n my_step_input.response_schema[\"properties\"][\"userInput\"][\"type\"], \"number\"\n )\n self.assertEqual(\n my_step_input.response_schema[\"properties\"][\"userInput\"][\"enum\"],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n )\n\n # Change the example so correctInput and Input are not required\n my_step_input.specification = {\n \"label\": \"What is your favorite number?\",\n \"inputOptions\": {\"minimumValue\": 0, \"maximumValue\": 15, \"step\": 1},\n \"correctInput\": 3,\n \"meta\": {\"correctInputRequired\": False, \"inputRequired\": False},\n }\n my_step_input.save()\n\n # Ensure that if neither are required, we have all options in the enum + None\n self.assertEqual(\n my_step_input.response_schema[\"properties\"][\"userInput\"][\"anyOf\"],\n [{\"type\": \"number\"}, {\"type\": \"null\"}],\n )\n self.assertEqual(\n my_step_input.response_schema[\"properties\"][\"userInput\"][\"enum\"],\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, None],\n )"],"string":"[\n \"def test_get_range(self):\\n pass\",\n \"def test_get_range_empty(self):\\n\\n queryset = mock.Mock()\\n queryset.aggregate.return_value = None\\n\\n dimension = models.QuantitativeDimension(\\n key='shares',\\n name='Count of shares',\\n description='Count of shares',\\n field_name='shared_count',\\n )\\n\\n min_val, max_val = dimension.get_range(queryset)\\n\\n self.assertIsNone(min_val)\\n self.assertIsNone(max_val)\",\n \"def test_data_range(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n lenrange = random.randint(1, 10)\\n nreps = random.randint(1, 10)\\n\\n ex.range = [\\\"i\\\", range(lenrange)]\\n ex.nreps = nreps\\n\\n ex.vary[\\\"X\\\"][\\\"along\\\"] = 0\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n self.assertIn([\\\"smalloc\\\", \\\"X\\\", nreps * m * n + (nreps - 1) * m], cmds)\\n rangeidx = random.randint(0, lenrange - 1)\\n repidx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"soffset\\\", \\\"X\\\", repidx * m,\\n \\\"X_%d_%d\\\" % (rangeidx, repidx)], cmds)\",\n \"def getRange(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def GetScalarRange(self):\\n ...\",\n \"def GetTRange(self):\\n ...\",\n \"def _in_range_op(spec):\",\n \"def range(self):\\n\\n return time_stat(self, stat=\\\"range\\\")\",\n \"def __dynamic_range_process(info):\\n if 'range' in info:\\n for i in range(len(info['range'])):\\n if info['range'][i][1] == -1:\\n info['range'][i][1] = None\\n return info\",\n \"def test_002_range_columns(self):\\n assert(len(\\n self.range_transformer.fit_transform(\\n self.data[self.range_col]\\n ).columns\\n ) == 1)\",\n \"def f_has_range(self):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def testRangeQuery(self):\\n\\n rq = TermRangeQuery(\\\"sorter\\\", \\\"b\\\", \\\"d\\\", True, True)\\n filteredquery = FilteredQuery(rq, self.filter)\\n scoreDocs = self.searcher.search(filteredquery, 1000).scoreDocs\\n self.assertEqual(2, len(scoreDocs))\",\n \"def getRange(self):\\n return self.range\",\n \"def test_range__no_base_date(self):\\n data = self._data()\\n data.pop('base_date')\\n response = self._get(get_kwargs=data)\\n self._check_response(response, 104)\",\n \"def get_range_value(self, key):\\n pass\",\n \"def f_get_range(self, copy=True):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def _get_sight_range(self):\\n raise NotImplementedError\",\n \"def test_get_pivot_in_correct_range(self):\\n list = [5, 6, 7, 8, 9, 2]\\n assert 0 <= get_pivot(list) <= 5 # between the first and last indices\",\n \"def test_data_with_range_view(self):\\n\\n self.create_model()\\n self.create_machine()\\n self.insert_data()\\n\\n date_literal = '%Y-%m-%d'\\n start_date = dt.today()\\n end_date = start_date + datetime.timedelta(days=1)\\n\\n self.create_user_account_and_login()\\n query_url = self.range_url + '/' + self.data['mid'] + \\\\\\n '/?s=' + dt.strftime(start_date, date_literal) + \\\\\\n '&e=' + dt.strftime(end_date, date_literal)\\n\\n response = self.client.get(query_url)\\n results = json.loads(response.content)\\n\\n self.assertEquals(len(results), 2)\",\n \"def test_range__no_end_date(self):\\n data = self._data()\\n data.pop('end_date')\\n response = self._get(get_kwargs=data)\\n self._check_response(response, 104)\",\n \"def provider_range_lookup(self, record):\\n pass\",\n \"def start(self):\\n return _uhd_swig.meta_range_t_start(self)\",\n \"def getSliderRange(*args):\\n\\n #get timeslider range start\\n startF = cmds.playbackOptions(query=True, min=True)\\n endF = cmds.playbackOptions(query=True, max=True)\\n return(startF, endF)\",\n \"def test_range_query(self):\\r\\n start = datetime(*self.base_date.timetuple()[:3])\\r\\n end = start + timedelta(days=3)\\r\\n\\r\\n results = DateTimeQueryTestModel.filter(user=0, day__gte=start, day__lt=end)\\r\\n assert len(results) == 3\",\n \"def _hit_range_get(self):\\n return (self.hit_start, self.hit_end)\",\n \"def ex_range(data):\\n a, b, step = _cleanse_range_args(data)\\n return list(range(a, b+sign(step), step))\",\n \"def _query_range_get(self):\\n return (self.query_start, self.query_end)\",\n \"def looks_range(self) -> Optional[int]:\\n return self._get_property(LOOKS_RANGE_PROP, int)\",\n \"def isRangeValid(self) -> bool:\\n ...\",\n \"def get_range(self) -> tuple[int, int]:\\n return self.range_from, self.range_to\",\n \"def getRangeMM(self) -> float:\\n ...\",\n \"def get_range(self, start=None, end=None):\\n\\n # handle the case of no data\\n if self.data.shape[0] == 0 or self.source.data[\\\"index\\\"].shape[0] == 0:\\n return None, None\\n\\n first_source_idx = self.source.data[\\\"index\\\"][0]\\n last_source_idx = self.source.data[\\\"index\\\"][-1]\\n\\n # convert to timestamp if necessary\\n if isinstance(self.data.index, pd.DatetimeIndex):\\n start = pd.to_datetime(start, unit=\\\"ms\\\")\\n end = pd.to_datetime(end, unit=\\\"ms\\\")\\n first_source_idx = pd.to_datetime(first_source_idx, unit=\\\"ms\\\")\\n last_source_idx = pd.to_datetime(last_source_idx, unit=\\\"ms\\\")\\n\\n # get new start and end\\n if start is not None:\\n if start < first_source_idx:\\n start = max(self.data.index[0], start)\\n elif start > last_source_idx:\\n start = min(self.data.index[-1], start)\\n elif start < self.data.index[0]:\\n start = self.data.index[0]\\n elif start > self.data.index[-1]:\\n start = self.data.index[-1]\\n elif len(self.source.data[\\\"index\\\"]) > 0:\\n start = first_source_idx\\n else:\\n start = self.data.index[0]\\n\\n if end is not None:\\n if end < first_source_idx:\\n end = max(self.data.index[0], end)\\n elif end > last_source_idx:\\n end = min(self.data.index[-1], end)\\n elif end < self.data.index[0]:\\n end = self.data.index[0]\\n elif end > self.data.index[-1]:\\n end = self.data.index[-1]\\n elif len(self.source.data[\\\"index\\\"]) > 0:\\n end = last_source_idx\\n else:\\n end = self.data.index[-1]\\n\\n return start, end\",\n \"def test_for_range(self, rnge, parse_input_mocked_metadata):\\n bb = parse_input_mocked_metadata(\\n \\\"for float m in {}\\\\n\\\\tMZgate(m, m/2) | [0, 1]\\\".format(rnge)\\n )\\n # create a list out of the `rnge` string\\n rnge_list = np.array(\\n range(*np.array(rnge.split(\\\":\\\"), dtype=int)),\\n dtype=float\\n )\\n\\n assert np.all(bb._forvar[\\\"m\\\"] == rnge_list)\\n assert bb.operations == [\\n {'op': 'MZgate', 'args': [rnge_list[0], rnge_list[0]/2], 'kwargs': {}, 'modes': [0, 1]},\\n {'op': 'MZgate', 'args': [rnge_list[1], rnge_list[1]/2], 'kwargs': {}, 'modes': [0, 1]},\\n {'op': 'MZgate', 'args': [rnge_list[2], rnge_list[2]/2], 'kwargs': {}, 'modes': [0, 1]}\\n ]\",\n \"def test_get_offenses_with_invalid_range(self, make_request):\\n action_params = {\\\"range\\\": \\\"-1-2\\\"}\\n with self.assertRaises(PluginException):\\n self.action.run(action_params)\",\n \"def get_data_range(self, start_position, length):\\n pass\",\n \"def test_simsam_range_functions_without_mapping_file(self):\\r\\n actual = qiime.simsam.simsam_range(\\r\\n self.tutorial_otu_table,\\r\\n self.tutorial_tree,\\r\\n [1],\\r\\n [0.1])\\r\\n self.assertEqual(len(list(actual)), 1)\",\n \"def range_query(self, start_key, end_key):\\n if not self.attached:\\n raise CastleCollectionNotAttachedException()\\n\\n print \\\"THIS IS FAKE\\\"\\n pycastle_log.info(\\\"Doing range query from key \\\"+str(start_key)+\\\" to key \\\"+str(end_key))\\n try:\\n i = 0\\n while i < 10:\\n yield i\\n i+=1\\n if i % 5 == 0:\\n pycastle_log.info(\\\"Getting next batch\\\")\\n except GeneratorExit:\\n pycastle_log.info(\\\"User requested stop of range query from key \\\"+str(start_key)+\\\" to key \\\"+str(end_key))\",\n \"def test_returns_custom_range(self):\\n img = np.arange(5)\\n\\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\\n self.assertEqual(actual.min(), 0)\\n self.assertEqual(actual.max(), 255)\",\n \"def test_range__bad_base_date(self):\\n self.base_date = 'bad'\\n response = self._get(get_kwargs=self._data())\\n self._check_response(response, 104)\",\n \"def get_recordrange(self):\\r\\n if self.version >= 10.1:\\r\\n querystr = \\\"\\\"\\\"?where=&outFields=*&returnGeometry=false&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=[{%0D%0A++++\\\"statisticType\\\"%3A+\\\"count\\\"%2C%0D%0A++++\\\"onStatisticField\\\"%3A+\\\"objectid\\\"%2C+++++\\\"outStatisticFieldName\\\"%3A+\\\"oidcount\\\"%0D%0A++}%2C{%0D%0A++++\\\"statisticType\\\"%3A+\\\"min\\\"%2C%0D%0A++++\\\"onStatisticField\\\"%3A+\\\"objectid\\\"%2C+++++\\\"outStatisticFieldName\\\"%3A+\\\"oidmin\\\"%0D%0A++}%2C{%0D%0A++++\\\"statisticType\\\"%3A+\\\"max\\\"%2C%0D%0A++++\\\"onStatisticField\\\"%3A+\\\"objectid\\\"%2C+++++\\\"outStatisticFieldName\\\"%3A+\\\"oidmax\\\"%0D%0A++}]&returnZ=false&returnM=false&returnDistinctValues=false&f=pjson\\\"\\\"\\\"\\r\\n req = requests.get(self.endpointurl + querystr)\\r\\n self.recordinfo = req.json()[\\\"features\\\"][0][\\\"attributes\\\"]\\r\\n\\r\\n elif self.version < 10.1:\\r\\n querystr = \\\"\\\"\\\"?text=&geometry=&geometryType=esriGeometryPoint&inSR=&spatialRel=esriSpatialRelIntersects&relationParam=&objectIds=&where=objectid+>+-1&time=&returnCountOnly=true&returnIdsOnly=false&returnGeometry=false&maxAllowableOffset=&outSR=&outFields=&f=pjson\\\"\\\"\\\"\\r\\n req = requests.get(self.endpontquerystr + qs)\\r\\n self.recordinfo = {\\\"oidmin\\\": 0, \\\"oidmax\\\": req.json()[\\\"count\\\"]}\\r\\n\\r\\n [\\r\\n self.iterlist.append([x, x + 999])\\r\\n for x in range(\\r\\n self.recordinfo[\\\"oidmin\\\"]\\r\\n if self.recordinfo[\\\"oidmin\\\"] != self.recordinfo[\\\"oidmax\\\"]\\r\\n else 1 - self.recordinfo[\\\"oidmin\\\"],\\r\\n self.recordinfo[\\\"oidmax\\\"],\\r\\n 1000,\\r\\n )\\r\\n ]\",\n \"def range_callback(data):\\n global D\\n D.ranges = data.ranges\",\n \"def test_date_range_fields():\\n now = datetime.datetime(2017, 6, 13, 9, 44, 31, 62870)\\n fields = {\\n 'estimated_land_date_after': now,\\n 'estimated_land_date_before': now,\\n 'adviser.id': 1234,\\n }\\n\\n filters, ranges = _split_range_fields(fields)\\n\\n assert filters == {\\n 'adviser.id': 1234,\\n }\\n assert ranges == {\\n 'estimated_land_date': {\\n 'gte': now,\\n 'lte': now,\\n },\\n }\",\n \"def range_(self):\\n return self.bset.range_\",\n \"def test_range__bad_end_date(self):\\n self.end_date = 'bad'\\n response = self._get(get_kwargs=self._data())\\n self._check_response(response, 104)\",\n \"def test_metrostations_get(self):\\n pass\",\n \"def range (self):\\n return self._range\",\n \"def range (self):\\n return self._range\",\n \"def range(self):\\n return self.range_array\",\n \"def _full_value_range(self):\\n min_value, max_value = self._raw_data.data_range\\n return max_value - min_value\",\n \"def test_portrange_get_sensordef(self):\\n test_sensordef = {\\n \\\"kind\\\": self.test_portrange.get_kind(),\\n \\\"name\\\": \\\"Port Range\\\",\\n \\\"description\\\": \\\"Checks the availability of a port range on a target system\\\",\\n \\\"help\\\": \\\"Checks the availability of a port range on a target system\\\",\\n \\\"tag\\\": \\\"mpportrangesensor\\\",\\n \\\"groups\\\": [\\n {\\n \\\"name\\\": \\\" portspecific\\\",\\n \\\"caption\\\": \\\"Port specific\\\",\\n \\\"fields\\\": [\\n {\\n \\\"type\\\": \\\"integer\\\",\\n \\\"name\\\": \\\"timeout\\\",\\n \\\"caption\\\": \\\"Timeout (in s)\\\",\\n \\\"required\\\": \\\"1\\\",\\n \\\"default\\\": 60,\\n \\\"minimum\\\": 1,\\n \\\"maximum\\\": 900,\\n \\\"help\\\": \\\"If the reply takes longer than this value the request is aborted \\\"\\n \\\"and an error message is triggered. Max. value is 900 sec. (=15 min.)\\\"\\n },\\n {\\n \\\"type\\\": \\\"integer\\\",\\n \\\"name\\\": \\\"startport\\\",\\n \\\"caption\\\": \\\"Port\\\",\\n \\\"required\\\": \\\"1\\\",\\n \\\"default\\\": 110,\\n \\\"minimum\\\": 1,\\n \\\"maximum\\\": 65534,\\n \\\"help\\\": \\\"Specify the port ranges starting port\\\"\\n },\\n {\\n \\\"type\\\": \\\"integer\\\",\\n \\\"name\\\": \\\"endport\\\",\\n \\\"caption\\\": \\\"Port\\\",\\n \\\"required\\\": \\\"1\\\",\\n \\\"default\\\": 110,\\n \\\"minimum\\\": 1,\\n \\\"maximum\\\": 65534,\\n \\\"help\\\": \\\"Specify the port ranges end port\\\"\\n }\\n ]\\n }\\n ]\\n }\\n assert_equal(self.test_portrange.get_sensordef(), test_sensordef)\",\n \"def get_range(self):\\n return time_to_range(self.get_time())\",\n \"def test_split_ranges(self):\\n start = datetime.utcnow() - pd.Timedelta(\\\"5H\\\")\\n end = datetime.utcnow() + pd.Timedelta(\\\"5min\\\")\\n delta = pd.Timedelta(\\\"1H\\\")\\n\\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\\n self.assertEqual(len(ranges), 5)\\n self.assertEqual(ranges[0][0], start)\\n self.assertEqual(ranges[-1][1], end)\\n\\n st_times = [start_tm[0] for start_tm in ranges]\\n for end_time in (end_tm[1] for end_tm in ranges):\\n self.assertNotIn(end_time, st_times)\\n\\n end = end + pd.Timedelta(\\\"20min\\\")\\n ranges = QueryProvider._calc_split_ranges(start, end, delta)\\n self.assertEqual(len(ranges), 5)\\n self.assertEqual(ranges[0][0], start)\\n self.assertEqual(ranges[-1][1], end)\",\n \"def test_range_not_integrated(self):\\n self.fbuser.delete()\\n steps = [{'dateTime': '2012-06-07', 'value': '10'}]\\n TimeSeriesData.objects.create(\\n user=self.user,\\n resource_type=TimeSeriesDataType.objects.get(\\n category=TimeSeriesDataType.activities, resource='steps'),\\n date=steps[0]['dateTime'],\\n value=steps[0]['value']\\n )\\n\\n response = self._mock_utility(response=steps,\\n get_kwargs=self._data())\\n self._check_response(response, 100, steps)\",\n \"def range(self):\\n \\n return self._range\",\n \"def parse_range(option):\\n return {\\\"range\\\": timedelta(days=option)}\",\n \"def m_to_range(self, data):\\n return (data - self._min_range_m) / self._total_range\",\n \"def test_range_finder_syntax():\\n rf = RangeFinder(0)\\n rf.range_finder_counter = MagicMock()\\n pid_value = rf.pidGet()\\n dist = rf._getDistance()\",\n \"def plugin_fetch(self, tmrange):\\n raise NotImplementedError(\\\"EMFetcher abstract superclass\\\")\",\n \"def get_range_info(self):\\n with open(self.range_path, 'r') as _file:\\n for line in _file.readlines():\\n list0 = line.strip().split('-')\\n range_dict = {\\n 'min': int(list0[0], 16),\\n 'max': int(list0[1], 16),\\n 'max_offset': int(list0[1], 16) - int(list0[0], 16),\\n }\\n self.ranges.append(range_dict)\",\n \"def step(self):\\n return _uhd_swig.meta_range_t_step(self)\",\n \"def byrange(self, start, stop):\\n\\t\\treturn ElementsByRange(self.AEM_want, self, (start, stop))\",\n \"def range(self):\\n return self.timerange()\",\n \"def visitRange(self, date):\\n raise NotImplementedError()\",\n \"def define_range():\\n\\n def_range = {'lt': [0.0, 24.0],\\n 'lon': [0.0, 360.0],\\n 'angle': [0.0, 2.0 * np.pi]}\\n\\n return def_range\",\n \"def test_timesheet_range(self):\\n response_get = self.client.get('/accounts/timesheet/')\\n response = response_get.content.decode()\\n\\n if self.today.day < 16:\\n self.assertIn(u'%s-%s-16' % (self.today.year, '{:02d}'.format(self.today.month-1)), response)\\n self.assertIn(u'%s-%s-15' % (self.today.year, '{:02d}'.format(self.today.month)), response)\\n else:\\n self.assertIn(u'%s-%s-16' % (self.today.year, '{:02d}'.format(self.today.month)), response)\\n self.assertIn(u'%s-%s-15' % (self.today.year, '{:02d}'.format(self.today.month+1)), response)\\n\\n self.assertNotIn(u'#%s: %s' % (self.outofrange_ticket.pk, self.outofrange_ticket.title), response)\\n\\n #self.assertIn(u'#%s: %s' % (self.inrange_ticket.pk, self.inrange_ticket.title), response)\",\n \"def _is_range(cls, rng):\\n match = re.search(\\\"([0-9][1-9]*)-([0-9][1-9]*)\\\", rng)\\n # Group is a singular value.\\n return match is not None\",\n \"def GetSRange(self):\\n ...\",\n \"def kind(self):\\n return DateValueTypes.RANGE\",\n \"def _calc_range(self) -> np.ndarray:\\n if self._is_ct25k():\\n range_resolution = 30\\n n_gates = 256\\n else:\\n n_gates = int(self.metadata[\\\"number_of_gates\\\"])\\n range_resolution = int(self.metadata[\\\"range_resolution\\\"])\\n return np.arange(n_gates) * range_resolution + range_resolution / 2\",\n \"def get_range(self, rel_name):\\n return self._declaration[rel_name].range_type\",\n \"def number_range(low, high, obj_type=int):\\n\\n def number_range_parser(argument):\\n try:\\n argument = obj_type(argument)\\n except ValueError:\\n argparse.ArgumentError('Must be of type {}'.format(obj_type.__name__))\\n\\n if low <= argument <= high:\\n return argument\\n else:\\n parser.error('Value is not in the range of {} and {}'.format(low, high))\\n\\n return number_range_parser\",\n \"def getRange(self, chr, start, end, bins=2000, zoomlvl=-1, metric=\\\"AVG\\\", respType=\\\"DataFrame\\\"):\\n try:\\n iter = self.file.fetch(chr, start, end)\\n # result = []\\n # for x in iter:\\n # returnBin = (x.reference_name, x.reference_start, x.reference_end, x.query_alignment_sequence, x.query_sequence)\\n # result.append(returnBin)\\n\\n # if self.columns is None:\\n # self.columns = [\\\"chr\\\", \\\"start\\\", \\\"end\\\", \\\"query_alignment_sequence\\\", \\\"query_sequence\\\"]\\n\\n # if respType is \\\"DataFrame\\\":\\n # result = toDataFrame(result, self.columns)\\n\\n (result, _) = get_range_helper(self.toDF, self.get_bin,\\n self.get_col_names, chr, start, end, iter, self.columns, respType)\\n\\n return result, None\\n except ValueError as e:\\n raise Exception(\\\"didn't find chromId with the given name\\\")\",\n \"def range_to_m(self, data):\\n return data * self._total_range + self._min_range_m\",\n \"def _computeRangeFromData(data):\\n if data is None:\\n return None\\n\\n dataRange = min_max(data, min_positive=True, finite=True)\\n if dataRange.minimum is None: # Only non-finite data\\n return None\\n\\n if dataRange is not None:\\n min_positive = dataRange.min_positive\\n if min_positive is None:\\n min_positive = float('nan')\\n return dataRange.minimum, min_positive, dataRange.maximum\",\n \"def test_outside_top_range(self):\\n\\n with self.assertRaises(ValueError):\\n self.expander.get_next_month(13)\",\n \"def test_range_argument(self):\\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\\n bins_range = (0, 2)\\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\\n hist_exp = np.array([1, 2, 0])\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.n_bins,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\\n np.testing.assert_array_almost_equal(hist_exp, hist)\",\n \"def range(self) -> ty.Tuple[float, float]:\\r\\n ...\",\n \"def test_parse_valid_time_range(self):\\n from azure.servicefabric.models.time_range import (\\n TimeRange\\n )\\n from azure.servicefabric.models.time_of_day import (\\n TimeOfDay\\n )\\n\\n res = sf_c.parse_time_range({\\n 'StartTime': {\\n 'Hour': 0,\\n 'Minute': 0\\n },\\n 'EndTime': {\\n 'Hour': 23,\\n 'Minute': 59,\\n }\\n })\\n\\n self.assertIsInstance(res, TimeRange)\\n\\n self.assertIsInstance(res.start_time, TimeOfDay)\\n self.assertEqual(res.start_time.hour, 0)\\n self.assertEqual(res.start_time.minute, 0)\\n\\n self.assertIsInstance(res.end_time, TimeOfDay)\\n self.assertEqual(res.end_time.hour, 23)\\n self.assertEqual(res.end_time.minute, 59)\",\n \"def range_params(self, ran, kw):\\n specs = {\\\"range\\\": (SchemaNode(\\\"value\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"minInclusive\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"maxInclusive\\\")),\\n \\\"length\\\": (SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"length\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"minLength\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"maxLength\\\"))}\\n (exact, min_, max_) = specs[kw]\\n if (len(ran) == 1 or ran[0] == ran[1]) and ran[0][0] != \\\"m\\\":\\n elem = exact\\n elem.text = ran[0]\\n return [elem]\\n res = []\\n if ran[0][0] != \\\"m\\\":\\n elem = min_\\n elem.text = ran[0]\\n res.append(elem)\\n if ran[1][0] != \\\"m\\\":\\n elem = max_\\n elem.text = ran[1]\\n res.append(elem)\\n return res\",\n \"def get_cells(worksheet, get_range: str):\\n cells = worksheet[get_range]\\n\\n return cells\",\n \"def _check_range(r): \\n if _is_single_range(r):\\n _check_one_range(r)\\n elif isinstance(r, collections.Sequence):\\n for r2 in r:\\n _check_one_range(r2)\\n else:\\n raise error.RangeSyntaxError(str(r))\",\n \"def range_field(self):\\n return self.db.range_field\",\n \"def range_table(self):\\n raise NotImplementedError('Abstract method.')\",\n \"def test_pos_operate_with_list_get_range_val_out_of_bounds(self):\\n key = (\\\"test\\\", \\\"demo\\\", \\\"list_key\\\")\\n list = [{\\\"op\\\": aerospike.OP_LIST_GET_RANGE, \\\"bin\\\": \\\"int_bin\\\", \\\"index\\\": 2, \\\"val\\\": 9}]\\n\\n (key, meta, bins) = self.as_connection.operate(key, list)\\n\\n assert bins == {\\\"int_bin\\\": [3, 4]}\",\n \"def test_seq_rangeExamples(self):\\n\\n self.assertEqual(MessageSet(2, 4), MessageSet(4, 2))\\n self.assertEqual(list(MessageSet(2, 4)), [2, 3, 4])\\n\\n m = MessageSet(3291, None)\\n m.last = 3290\\n self.assertEqual(list(m), [3290, 3291])\",\n \"def do_range_forloop(self, line):\\n self.E_str = \\\"do_range_forloop\\\"\\n\\n # Get the range parameters\\n line = line.replace(\\\" \\\", \\\"\\\")\\n line = line[line.find(\\\"range\\\")+5:]\\n range_str, _ = gen_parse.get_str_between_delims(line, \\\"(\\\", \\\")\\\")\\n words = range_str.split(\\\",\\\")\\n\\n if len(words) == 1:\\n start, step = 0, 1\\n end = int(words[0])\\n elif len(words) == 2:\\n step = 1\\n start, end = [int(i) for i in words]\\n else:\\n start, end, step = [int(i) for i in words]\\n\\n return range(start, end, step)\",\n \"def get_range(self, field, deep=False, axis=None):\\n variables = list(self.vars(deep, with_name=field))\\n\\n if not variables:\\n raise KeyError(\\\"No variable named '%s' was found!\\\" % field)\\n\\n start = [np.nanmin(self[var], axis).item(0) for var in variables]\\n end = [np.nanmax(self[var], axis).item(0) for var in variables]\\n return min(start), max(end)\",\n \"def test_Op_integers(self):\\r\\n start, stop, step = iscalars('start', 'stop', 'step')\\r\\n out = ARange(start.type.dtype)(start, stop, step)\\r\\n f = function([start, stop, step], out)\\r\\n\\r\\n assert numpy.all(f(0, 5, 1) == numpy.arange(0, 5, 1))\\r\\n assert numpy.all(f(2, 11, 4) == numpy.arange(2, 11, 4))\\r\\n assert numpy.all(f(-5, 1, 1) == numpy.arange(-5, 1, 1))\\r\\n assert numpy.all(f(10, 2, -2) == numpy.arange(10, 2, -2))\\r\\n assert numpy.all(f(10, 2, 2) == numpy.arange(10, 2, 2))\\r\\n assert numpy.all(f(0, 0, 1) == numpy.arange(0, 0, 1))\",\n \"def test_ave_age_range(step):\\n diff = step[\\\"ave_birth\\\"] - step[\\\"birth\\\"]\\n assert 0 < diff < 15E6\",\n \"def __getitem__(self, *args):\\n return _uhd_swig.range_vector_t___getitem__(self, *args)\",\n \"def test_creation_bounds_not_inclusive():\\n with pytest.raises(ValueError) as __:\\n value = -42\\n __ = param.Integer(value=value, hardbounds=[-42, 100], inclusive_bounds=[False, False])\",\n \"def getAFeRange(brand):\\n return afe_range[brand]\",\n \"def getMassRange(brand):\\n return mass_range[brand]\",\n \"def test_date_range(self):\\n\\n url = '/%s/job-types/status/?started=%s&ended=%s' % ( self.api,\\n '2015-01-01T00:00:00Z',\\n '2015-01-02T00:00:00Z')\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 4)\",\n \"def __check_range(self, range_type, name):\\n\\n # Before we really do anything, make sure a warning or critical \\n # threshold were even set.\\n\\n self.__verbose_print('range was ' + range_type)\\n\\n range_list = self.__warning\\n if range_type == 'critical':\\n range_list = self.__critical\\n\\n if len(range_list) < 1:\\n return\\n\\n threshold = self.__perf[name]['threshold']\\n val = self.__perf[name]['val']\\n\\n # Even if the warning/critical thresholds are required, a user may\\n # forget to set all necessary ranges. If the ranges are required,\\n # exit with a complaint, otherwise just skip the range test.\\n\\n if len(range_list) < threshold:\\n if range_type not in self.__req_option:\\n return\\n raise UserWarning (\\\"Please set part %s of the %s threshold!\\\" % (threshold, range_type))\\n\\n # The option parser should have already split these into proper\\n # bottom, top, and match inversion, so long as the array element\\n # is defined Perform our range test and set the exit status.\\n\\n self.__verbose_print(range_list[threshold-1])\\n (bottom, top, invert, raw) = range_list[threshold-1]\\n\\n self.__perf[name][\\\"raw_%s\\\" % range_type] = raw\\n\\n if ((not invert and (val < bottom or val > top)) or\\n (invert and val >= bottom and val <= top)):\\n self.__exit_status = range_type\\n self.__perf[name]['state'] = range_type\\n\\n self.__verbose_print(\\\"%s:%s:%s = %s\\\" % (val, bottom, top, ((val < bottom) or (val > top))))\",\n \"def get_range(self, range, last):\\n found = re.match('\\\\s*([+-]?)([0-9]+)(%?)(\\\\s*,\\\\s*([+-]?)([0-9]+)(%?))?\\\\s*', str(range))\\n if not found:\\n raise ConfigError('Failed to parse range \\\"%s\\\" in \\\"__size\\\"!' % str(range))\\n (s1, n1, p1, v2, s2, n2, p2) = found.groups()\\n if v2:\\n d1 = last * int(n1) / 100.0 if p1 else int(n1)\\n d2 = last * int(n2) / 100.0 if p2 else int(n2)\\n m = last + d1 if s1 == '+' else last - d1 if s1 == '-' or p1 else d1\\n M = last - d2 if s2 == '-' else last + d2 if s2 == '+' or p2 else d2\\n else:\\n d = last * int(n1) / 100.0 if p1 else int(n1)\\n m = last - d\\n M = last + d\\n return m, M\",\n \"def __init__(\\n self, low=None, high=None, exclude_low=False, exclude_high=False\\n ):\\n vtype = type(high)\\n if (low is not None) and (vtype is not float):\\n vtype = type(low)\\n if vtype not in RangeTypes:\\n raise TraitError(\\n \\\"TraitRange can only be use for int, long or \\\"\\n \\\"float values, but a value of type %s was \\\"\\n \\\"specified.\\\" % vtype\\n )\\n if vtype is float:\\n self.validate = self.float_validate\\n kind = 4\\n self._type_desc = \\\"a floating point number\\\"\\n if low is not None:\\n low = float(low)\\n if high is not None:\\n high = float(high)\\n elif vtype is LONG_TYPE:\\n self.validate = self.long_validate\\n self._type_desc = \\\"a long integer\\\"\\n if low is not None:\\n low = LONG_TYPE(low)\\n if high is not None:\\n high = LONG_TYPE(high)\\n else:\\n self.validate = self.int_validate\\n kind = 3\\n self._type_desc = \\\"an integer\\\"\\n if low is not None:\\n low = int(low)\\n if high is not None:\\n high = int(high)\\n exclude_mask = 0\\n if exclude_low:\\n exclude_mask |= 1\\n if exclude_high:\\n exclude_mask |= 2\\n if vtype is not LONG_TYPE:\\n self.fast_validate = (kind, low, high, exclude_mask)\\n\\n # Assign type-corrected arguments to handler attributes\\n self._low = low\\n self._high = high\\n self._exclude_low = exclude_low\\n self._exclude_high = exclude_high\",\n \"def test_ym2mjdmrange():\\n\\tyears = [1899,2000,2020]\\n\\tmonths = [12,1,2]\\n\\tmjd1_true = [14989,51544,58880]\\n\\tmjd2_true = [15019,51574,58908]\\n\\tmjd1_test, mjd2_test = date_functions.ym2mjdmrange( years, months )\\n\\n\\tassert mjd1_test[0] == mjd1_true[0]\\n\\tassert mjd1_test[1] == mjd1_true[1]\\n\\tassert mjd1_test[2] == mjd1_true[2]\\n\\tassert mjd2_test[0] == mjd2_true[0]\\n\\tassert mjd2_test[1] == mjd2_true[1]\\n\\tassert mjd2_test[2] == mjd2_true[2]\",\n \"def evaluate_comparison_range(node):\\n return None\",\n \"def test_numeric_range_question_possibilities(self):\\n my_step = WorkflowStep.objects.get(\\n workflow__workflowcollectionmember__workflow_collection=self.my_collection\\n )\\n my_step_input = WorkflowStepUserInput.objects.get(workflow_step=my_step)\\n my_step_input.type.name = \\\"numeric_range_question\\\"\\n my_step_input.type.json_schema = {\\n \\\"type\\\": \\\"object\\\",\\n \\\"title\\\": \\\"User Input: Numeric Range Question\\\",\\n \\\"description\\\": \\\"A schema representing a numeric range question user input.\\\",\\n \\\"required\\\": [\\\"label\\\", \\\"inputOptions\\\"],\\n \\\"properties\\\": {\\n \\\"id\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"title\\\": \\\"A string-based user input identifier.\\\",\\n \\\"description\\\": \\\"This value may be managed outside of the object specification and so is optional.\\\",\\n \\\"examples\\\": [\\\"4125-1351-1251-asfd\\\"],\\n },\\n \\\"label\\\": {\\n \\\"type\\\": \\\"string\\\",\\n \\\"title\\\": \\\"UI Label for Input\\\",\\n \\\"description\\\": \\\"Label that should be displayed by user interfaces for this input.\\\",\\n \\\"examples\\\": [\\\"The label to display for the input/question.\\\"],\\n },\\n \\\"inputOptions\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"minimumValue\\\": {\\n \\\"$id\\\": \\\"#/properties/options\\\",\\n \\\"type\\\": \\\"number\\\",\\n \\\"title\\\": \\\"Minimum Value\\\",\\n \\\"description\\\": \\\"The minimum value to be displayed to the user for this question.\\\",\\n },\\n \\\"maximumValue\\\": {\\n \\\"$id\\\": \\\"#/properties/options\\\",\\n \\\"type\\\": \\\"number\\\",\\n \\\"title\\\": \\\"Maximum Value\\\",\\n \\\"description\\\": \\\"The maximum value to be displayed to the user for this question.\\\",\\n },\\n \\\"step\\\": {\\n \\\"$id\\\": \\\"#/properties/options\\\",\\n \\\"type\\\": \\\"number\\\",\\n \\\"title\\\": \\\"Step Value\\\",\\n \\\"description\\\": \\\"The number between each entry between minimum and maximum.\\\",\\n },\\n },\\n },\\n \\\"correctInput\\\": {\\n \\\"description\\\": \\\"Indicates which answers are the correct ones.\\\",\\n \\\"type\\\": \\\"number\\\",\\n },\\n \\\"meta\\\": {\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"inputRequired\\\": {\\n \\\"type\\\": \\\"boolean\\\",\\n \\\"description\\\": \\\"Whether or not an answer should be required from the user.\\\",\\n },\\n \\\"correctInputRequired\\\": {\\n \\\"type\\\": \\\"boolean\\\",\\n \\\"description\\\": \\\"Whether or not the correct answer should be required from the user.\\\",\\n },\\n },\\n },\\n },\\n }\\n my_step_input.type.save()\\n\\n my_step_input.specification = {\\n \\\"label\\\": \\\"What is your favorite number?\\\",\\n \\\"inputOptions\\\": {\\\"minimumValue\\\": 0, \\\"maximumValue\\\": 25, \\\"step\\\": 1},\\n \\\"correctInput\\\": 3,\\n \\\"meta\\\": {\\\"correctInputRequired\\\": True, \\\"inputRequired\\\": True},\\n }\\n my_step_input.save()\\n\\n # Ensure if correct answer is required, that the enum is just the correct answer\\n self.assertEqual(\\n my_step_input.response_schema[\\\"properties\\\"][\\\"userInput\\\"][\\\"type\\\"], \\\"number\\\"\\n )\\n self.assertEqual(\\n my_step_input.response_schema[\\\"properties\\\"][\\\"userInput\\\"][\\\"enum\\\"], [3]\\n )\\n\\n # Change the example so correctInput is not required\\n my_step_input.specification = {\\n \\\"label\\\": \\\"What is your favorite number?\\\",\\n \\\"inputOptions\\\": {\\\"minimumValue\\\": 0, \\\"maximumValue\\\": 15, \\\"step\\\": 1},\\n \\\"correctInput\\\": 3,\\n \\\"meta\\\": {\\\"correctInputRequired\\\": False, \\\"inputRequired\\\": True},\\n }\\n my_step_input.save()\\n\\n # Ensure that if the correct answer isn't required, we have all options in the enum\\n self.assertEqual(\\n my_step_input.response_schema[\\\"properties\\\"][\\\"userInput\\\"][\\\"type\\\"], \\\"number\\\"\\n )\\n self.assertEqual(\\n my_step_input.response_schema[\\\"properties\\\"][\\\"userInput\\\"][\\\"enum\\\"],\\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\\n )\\n\\n # Change the example so correctInput and Input are not required\\n my_step_input.specification = {\\n \\\"label\\\": \\\"What is your favorite number?\\\",\\n \\\"inputOptions\\\": {\\\"minimumValue\\\": 0, \\\"maximumValue\\\": 15, \\\"step\\\": 1},\\n \\\"correctInput\\\": 3,\\n \\\"meta\\\": {\\\"correctInputRequired\\\": False, \\\"inputRequired\\\": False},\\n }\\n my_step_input.save()\\n\\n # Ensure that if neither are required, we have all options in the enum + None\\n self.assertEqual(\\n my_step_input.response_schema[\\\"properties\\\"][\\\"userInput\\\"][\\\"anyOf\\\"],\\n [{\\\"type\\\": \\\"number\\\"}, {\\\"type\\\": \\\"null\\\"}],\\n )\\n self.assertEqual(\\n my_step_input.response_schema[\\\"properties\\\"][\\\"userInput\\\"][\\\"enum\\\"],\\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, None],\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8197148","0.6618275","0.6484984","0.64601654","0.63697124","0.6269415","0.6174672","0.6106884","0.608165","0.6080813","0.60700256","0.6020781","0.60202074","0.5981426","0.59406525","0.58997625","0.5883565","0.5879113","0.5847939","0.5835961","0.5828512","0.5803753","0.5780891","0.5761234","0.5756502","0.57524395","0.57376945","0.5732459","0.5727585","0.5718125","0.57070714","0.56981033","0.56626123","0.56578565","0.5649587","0.56488013","0.56316674","0.56262386","0.56233466","0.5620733","0.5620227","0.56096905","0.5588668","0.5574512","0.5561625","0.55451584","0.55451584","0.5544686","0.5534993","0.5520836","0.5515611","0.55120057","0.5492818","0.5479788","0.54756564","0.5470175","0.5468413","0.54677784","0.5463307","0.5439814","0.5433585","0.54287964","0.542334","0.54219276","0.5418742","0.5409442","0.5405452","0.5399774","0.53987116","0.5389666","0.53698957","0.53651094","0.53543067","0.53509516","0.53374755","0.5337466","0.5323657","0.5304106","0.529587","0.52920014","0.5273509","0.52652586","0.5258356","0.52572155","0.5251569","0.5239977","0.5237124","0.52322674","0.52229756","0.52199906","0.5218302","0.52169424","0.5209079","0.5207248","0.52035713","0.5197141","0.51912653","0.5190202","0.51875883","0.51805"],"string":"[\n \"0.8197148\",\n \"0.6618275\",\n \"0.6484984\",\n \"0.64601654\",\n \"0.63697124\",\n \"0.6269415\",\n \"0.6174672\",\n \"0.6106884\",\n \"0.608165\",\n \"0.6080813\",\n \"0.60700256\",\n \"0.6020781\",\n \"0.60202074\",\n \"0.5981426\",\n \"0.59406525\",\n \"0.58997625\",\n \"0.5883565\",\n \"0.5879113\",\n \"0.5847939\",\n \"0.5835961\",\n \"0.5828512\",\n \"0.5803753\",\n \"0.5780891\",\n \"0.5761234\",\n \"0.5756502\",\n \"0.57524395\",\n \"0.57376945\",\n \"0.5732459\",\n \"0.5727585\",\n \"0.5718125\",\n \"0.57070714\",\n \"0.56981033\",\n \"0.56626123\",\n \"0.56578565\",\n \"0.5649587\",\n \"0.56488013\",\n \"0.56316674\",\n \"0.56262386\",\n \"0.56233466\",\n \"0.5620733\",\n \"0.5620227\",\n \"0.56096905\",\n \"0.5588668\",\n \"0.5574512\",\n \"0.5561625\",\n \"0.55451584\",\n \"0.55451584\",\n \"0.5544686\",\n \"0.5534993\",\n \"0.5520836\",\n \"0.5515611\",\n \"0.55120057\",\n \"0.5492818\",\n \"0.5479788\",\n \"0.54756564\",\n \"0.5470175\",\n \"0.5468413\",\n \"0.54677784\",\n \"0.5463307\",\n \"0.5439814\",\n \"0.5433585\",\n \"0.54287964\",\n \"0.542334\",\n \"0.54219276\",\n \"0.5418742\",\n \"0.5409442\",\n \"0.5405452\",\n \"0.5399774\",\n \"0.53987116\",\n \"0.5389666\",\n \"0.53698957\",\n \"0.53651094\",\n \"0.53543067\",\n \"0.53509516\",\n \"0.53374755\",\n \"0.5337466\",\n \"0.5323657\",\n \"0.5304106\",\n \"0.529587\",\n \"0.52920014\",\n \"0.5273509\",\n \"0.52652586\",\n \"0.5258356\",\n \"0.52572155\",\n \"0.5251569\",\n \"0.5239977\",\n \"0.5237124\",\n \"0.52322674\",\n \"0.52229756\",\n \"0.52199906\",\n \"0.5218302\",\n \"0.52169424\",\n \"0.5209079\",\n \"0.5207248\",\n \"0.52035713\",\n \"0.5197141\",\n \"0.51912653\",\n \"0.5190202\",\n \"0.51875883\",\n \"0.51805\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.9391207"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22399,"cells":{"query":{"kind":"string","value":"Test case for get_range"},"document":{"kind":"string","value":"def test_get_range(self):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_get_meta_range(self):\n pass","def getRange(self, p_int): # real signature unknown; restored from __doc__\n pass","def _in_range_op(spec):","def f_get_range(self, copy=True):\n raise NotImplementedError(\"Should have implemented this.\")","def GetTRange(self):\n ...","def get_range(self) -> tuple[int, int]:\n return self.range_from, self.range_to","def ex_range(data):\n a, b, step = _cleanse_range_args(data)\n return list(range(a, b+sign(step), step))","def getRange(self):\n return self.range","def f_has_range(self):\n raise NotImplementedError(\"Should have implemented this.\")","def _get_sight_range(self):\n raise NotImplementedError","def range(self):\n\n return time_stat(self, stat=\"range\")","def test_data_range(self):\n ex = self.ex\n m = self.m\n n = self.n\n\n lenrange = random.randint(1, 10)\n nreps = random.randint(1, 10)\n\n ex.range = [\"i\", range(lenrange)]\n ex.nreps = nreps\n\n ex.vary[\"X\"][\"along\"] = 0\n ex.vary[\"X\"][\"with\"].add(\"rep\")\n ex.infer_lds()\n\n cmds = ex.generate_cmds()\n\n self.assertIn([\"smalloc\", \"X\", nreps * m * n + (nreps - 1) * m], cmds)\n rangeidx = random.randint(0, lenrange - 1)\n repidx = random.randint(0, nreps - 1)\n self.assertIn([\"soffset\", \"X\", repidx * m,\n \"X_%d_%d\" % (rangeidx, repidx)], cmds)","def get_range(self):\n return time_to_range(self.get_time())","def get_range_value(self, key):\n pass","def get_range( value ):\n return list(range(value))","def GetScalarRange(self):\n ...","def get_range(value):\n return list(range(value))","def range(self):\n return self.timerange()","def range_function(num, start_range, end_range):\n if num > start_range and num < end_range:\n print(num, \"is in the range.\\n\")\n elif num < start_range or num > end_range:\n print(num, \"is not in the range.\\n\")","def test_get_range_empty(self):\n\n queryset = mock.Mock()\n queryset.aggregate.return_value = None\n\n dimension = models.QuantitativeDimension(\n key='shares',\n name='Count of shares',\n description='Count of shares',\n field_name='shared_count',\n )\n\n min_val, max_val = dimension.get_range(queryset)\n\n self.assertIsNone(min_val)\n self.assertIsNone(max_val)","def range(self):\n return self.range_array","def range_inclusive(start, stop):\n return range(start, stop + 1)","def range (self):\n return self._range","def range (self):\n return self._range","def getRange (start, stop, step=1):\r\n result = [n for n in range(start, stop, step)]\r\n return result","def get_range(self, start=None, end=None):\n\n # handle the case of no data\n if self.data.shape[0] == 0 or self.source.data[\"index\"].shape[0] == 0:\n return None, None\n\n first_source_idx = self.source.data[\"index\"][0]\n last_source_idx = self.source.data[\"index\"][-1]\n\n # convert to timestamp if necessary\n if isinstance(self.data.index, pd.DatetimeIndex):\n start = pd.to_datetime(start, unit=\"ms\")\n end = pd.to_datetime(end, unit=\"ms\")\n first_source_idx = pd.to_datetime(first_source_idx, unit=\"ms\")\n last_source_idx = pd.to_datetime(last_source_idx, unit=\"ms\")\n\n # get new start and end\n if start is not None:\n if start < first_source_idx:\n start = max(self.data.index[0], start)\n elif start > last_source_idx:\n start = min(self.data.index[-1], start)\n elif start < self.data.index[0]:\n start = self.data.index[0]\n elif start > self.data.index[-1]:\n start = self.data.index[-1]\n elif len(self.source.data[\"index\"]) > 0:\n start = first_source_idx\n else:\n start = self.data.index[0]\n\n if end is not None:\n if end < first_source_idx:\n end = max(self.data.index[0], end)\n elif end > last_source_idx:\n end = min(self.data.index[-1], end)\n elif end < self.data.index[0]:\n end = self.data.index[0]\n elif end > self.data.index[-1]:\n end = self.data.index[-1]\n elif len(self.source.data[\"index\"]) > 0:\n end = last_source_idx\n else:\n end = self.data.index[-1]\n\n return start, end","def new_range(r):\n if isinstance(r, list) or isinstance(r, tuple) and len(r) == 2:\n lower = r[0]\n upper = r[1]\n else:\n lower = r\n upper = r\n lower = int(lower)\n upper = int(upper)\n return range(lower, upper + 1)","def get_range(start, stop):\n \n nums = []\n\n for num in range(start, stop):\n nums.append(num)\n\n return nums","def range(self):\n \n return self._range","def isRangeValid(self) -> bool:\n ...","def _hit_range_get(self):\n return (self.hit_start, self.hit_end)","def _query_range_get(self):\n return (self.query_start, self.query_end)","def range() -> List[int]:\n pass","def get_data_range(self, start_position, length):\n pass","def range(self) -> ty.Tuple[float, float]:\r\n ...","def get_range(n0: int, n1: int, ns: int) -> List[int]:\n # Return a range as a list\n def lrange(a, b, n=1) -> List[int]:\n return list(range(a, b, n))\n # Get the in-bounds part of the range\n n_range = lrange(max(0, n0), min(ns, n1))\n # Handle out-of-bounds indices by reflection across boundaries\n if n0 < 0:\n # Underflow\n n_range = lrange(-n0, 0, -1) + n_range\n if n1 > ns:\n # Overflow\n n_range = n_range + lrange(ns - 1, 2 * ns - n1 - 1, -1)\n\n return n_range","def _translate_range(self, len_, start, end):\n start = int(start)\n end = int(end)\n if start < 0:\n start += len_\n start = max(0, min(start, len_))\n if end < 0:\n end += len_\n end = max(-1, min(end, len_ - 1))\n return start, end","def range_query(self, start_key, end_key):\n if not self.attached:\n raise CastleCollectionNotAttachedException()\n\n print \"THIS IS FAKE\"\n pycastle_log.info(\"Doing range query from key \"+str(start_key)+\" to key \"+str(end_key))\n try:\n i = 0\n while i < 10:\n yield i\n i+=1\n if i % 5 == 0:\n pycastle_log.info(\"Getting next batch\")\n except GeneratorExit:\n pycastle_log.info(\"User requested stop of range query from key \"+str(start_key)+\" to key \"+str(end_key))","def provider_range_lookup(self, record):\n pass","def range_(self):\n return self.bset.range_","def GetSRange(self):\n ...","def range_callback(data):\n global D\n D.ranges = data.ranges","def testRangeQuery(self):\n\n rq = TermRangeQuery(\"sorter\", \"b\", \"d\", True, True)\n filteredquery = FilteredQuery(rq, self.filter)\n scoreDocs = self.searcher.search(filteredquery, 1000).scoreDocs\n self.assertEqual(2, len(scoreDocs))","def test_Op_integers(self):\r\n start, stop, step = iscalars('start', 'stop', 'step')\r\n out = ARange(start.type.dtype)(start, stop, step)\r\n f = function([start, stop, step], out)\r\n\r\n assert numpy.all(f(0, 5, 1) == numpy.arange(0, 5, 1))\r\n assert numpy.all(f(2, 11, 4) == numpy.arange(2, 11, 4))\r\n assert numpy.all(f(-5, 1, 1) == numpy.arange(-5, 1, 1))\r\n assert numpy.all(f(10, 2, -2) == numpy.arange(10, 2, -2))\r\n assert numpy.all(f(10, 2, 2) == numpy.arange(10, 2, 2))\r\n assert numpy.all(f(0, 0, 1) == numpy.arange(0, 0, 1))","def get_range(start, stop):\n nums = []\n\n for num in range(start, stop):\n nums.append(num)\n print(nums)","def parseSimpleRange_(self, aRange):\n (start, end) = (None, None)\n \n result = []\n minus = string.find(aRange, '-')\n if ( minus < 0 ):\n if isInt(aRange) and int(aRange)>0:\n # FEDE\n #result.append(int(aRange)-1)\n ###\n result.append(int(aRange))\n else:\n common.logger.message(\"parseSimpleRange_ ERROR \"+aRange)\n usage()\n pass\n \n pass\n else:\n (start, end) = string.split(aRange, '-')\n if isInt(start) and isInt(end) and int(start)>0 and int(start)<int(end):\n #result=range(int(start)-1, int(end))\n result=range(int(start), int(end)+1) #Daniele \n else:\n common.logger.message(\"parseSimpleRange_ ERROR \"+start+end)\n\n return result","def rangeFinder(self, startIndex, endIndex, list):\n max = list[startIndex]\n min = list[startIndex]\n for x in range(startIndex, endIndex):\n if list[x] > max:\n max = list[x]\n if list[x] < min:\n min = list[x]\n return max-min","def _check_range(r): \n if _is_single_range(r):\n _check_one_range(r)\n elif isinstance(r, collections.Sequence):\n for r2 in r:\n _check_one_range(r2)\n else:\n raise error.RangeSyntaxError(str(r))","def test_range_argument(self):\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\n bins_range = (0, 2)\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\n hist_exp = np.array([1, 2, 0])\n\n bin_edges, hist, _, _ = hist_w_unc(\n self.input,\n bins=self.n_bins,\n bins_range=bins_range,\n normed=False,\n )\n\n # check if we end up with the same bin edges anyway\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\n np.testing.assert_array_almost_equal(hist_exp, hist)","def looks_range(self) -> Optional[int]:\n return self._get_property(LOOKS_RANGE_PROP, int)","def test_returns_custom_range(self):\n img = np.arange(5)\n\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\n self.assertEqual(actual.min(), 0)\n self.assertEqual(actual.max(), 255)","def get_range(cls, data: tuple or list) -> float:\n cls._data_validation(data)\n max_ = cls.get_max(data)\n min_ = cls.get_min(data)\n return float(max_ - min_)","def _bi_range(start, end):\n if start == end:\n return (start,)\n\n elif end < start:\n return reversed(range(end, start + 1))\n\n else:\n return range(start, end + 1)","def _check_range(range_):\n try:\n if not isinstance(range_, list):\n range_ = list(range_)\n min_, max_ = range_\n except (ValueError, TypeError):\n raise TypeError(\"each range in ising_linear_ranges should be a list of length 2.\")\n if not isinstance(min_, Number) or not isinstance(max_, Number) or min_ > max_:\n raise ValueError((\"each range in ising_linear_ranges should be a 2-tuple \"\n \"(min, max) where min <= max\"))\n return range_","def getRange(self, chr, start, end, bins=2000, zoomlvl=-1, metric=\"AVG\", respType=\"DataFrame\"):\n try:\n iter = self.file.fetch(chr, start, end)\n # result = []\n # for x in iter:\n # returnBin = (x.reference_name, x.reference_start, x.reference_end, x.query_alignment_sequence, x.query_sequence)\n # result.append(returnBin)\n\n # if self.columns is None:\n # self.columns = [\"chr\", \"start\", \"end\", \"query_alignment_sequence\", \"query_sequence\"]\n\n # if respType is \"DataFrame\":\n # result = toDataFrame(result, self.columns)\n\n (result, _) = get_range_helper(self.toDF, self.get_bin,\n self.get_col_names, chr, start, end, iter, self.columns, respType)\n\n return result, None\n except ValueError as e:\n raise Exception(\"didn't find chromId with the given name\")","def test_range__no_end_date(self):\n data = self._data()\n data.pop('end_date')\n response = self._get(get_kwargs=data)\n self._check_response(response, 104)","def test_inclusive_intervals(self):\n dim = Integer(\"yolo\", \"uniform\", -3, 5.5)\n assert dim.interval() == (-3, 3)","def test_range__no_base_date(self):\n data = self._data()\n data.pop('base_date')\n response = self._get(get_kwargs=data)\n self._check_response(response, 104)","def test_get_pivot_in_correct_range(self):\n list = [5, 6, 7, 8, 9, 2]\n assert 0 <= get_pivot(list) <= 5 # between the first and last indices","def getSliderRange(*args):\n\n #get timeslider range start\n startF = cmds.playbackOptions(query=True, min=True)\n endF = cmds.playbackOptions(query=True, max=True)\n return(startF, endF)","def test_creation_bounds_not_inclusive():\n with pytest.raises(ValueError) as __:\n value = -42\n __ = param.Integer(value=value, hardbounds=[-42, 100], inclusive_bounds=[False, False])","def evaluate_comparison_range(node):\n return None","def do_range_forloop(self, line):\n self.E_str = \"do_range_forloop\"\n\n # Get the range parameters\n line = line.replace(\" \", \"\")\n line = line[line.find(\"range\")+5:]\n range_str, _ = gen_parse.get_str_between_delims(line, \"(\", \")\")\n words = range_str.split(\",\")\n\n if len(words) == 1:\n start, step = 0, 1\n end = int(words[0])\n elif len(words) == 2:\n step = 1\n start, end = [int(i) for i in words]\n else:\n start, end, step = [int(i) for i in words]\n\n return range(start, end, step)","def range(self):\n return self._upper - self._lower","def in_range(start, end, x):\n if start <= end:\n return start <= x <= end\n else:\n return start <= x or x <= end","def query_range(tree, start_y, start_x, end_y, end_x):\n res = 0\n start_y -= 1\n\n while end_y > start_y:\n res += bit.query_range(tree[end_y], start_x, end_x)\n end_y -= (end_y & -end_y)\n\n while start_y > end_y:\n res -= bit.query_range(tree[start_y], start_x, end_x)\n start_y -= (start_y & -start_y)\n\n return res","def _adjustRange(self, start, end):\n adjusted_start = start\n if self._start:\n if end < self._start:\n return None\n adjusted_start = max(self._start, start)\n \n adjusted_end = end\n if self._end:\n if self._end < start:\n return None\n adjusted_end = min(self._end, end)\n \n return (adjusted_start, adjusted_end)","def test_data_with_range_view(self):\n\n self.create_model()\n self.create_machine()\n self.insert_data()\n\n date_literal = '%Y-%m-%d'\n start_date = dt.today()\n end_date = start_date + datetime.timedelta(days=1)\n\n self.create_user_account_and_login()\n query_url = self.range_url + '/' + self.data['mid'] + \\\n '/?s=' + dt.strftime(start_date, date_literal) + \\\n '&e=' + dt.strftime(end_date, date_literal)\n\n response = self.client.get(query_url)\n results = json.loads(response.content)\n\n self.assertEquals(len(results), 2)","def number_range(low, high, obj_type=int):\n\n def number_range_parser(argument):\n try:\n argument = obj_type(argument)\n except ValueError:\n argparse.ArgumentError('Must be of type {}'.format(obj_type.__name__))\n\n if low <= argument <= high:\n return argument\n else:\n parser.error('Value is not in the range of {} and {}'.format(low, high))\n\n return number_range_parser","def define_range():\n\n def_range = {'lt': [0.0, 24.0],\n 'lon': [0.0, 360.0],\n 'angle': [0.0, 2.0 * np.pi]}\n\n return def_range","def detect_range(self) -> Union[int, float]:\n return self.proto.detect_range","def get_rangelist(start, end, count):\n if start is not None and end is not None:\n if count != 0 and not (start == 0 and count < end):\n start = int(start)\n end = int(end)\n cnt = end - start\n rangelist = []\n div = int(start) / count + 1\n multiple = round(div, 0)\n start_range = int(count * multiple)\n n = 1\n for itr in range(0, start_range + count, (end - start)):\n if itr < count:\n rangelist.append([itr, itr + cnt, n])\n n += 1\n return rangelist\n return []","def f_get_range(self, copy=True):\n if not self.f_has_range():\n raise TypeError(\n \"Your parameter `%s` is not array, so cannot return array.\"\n % self.v_full_name\n )\n elif copy:\n return self._explored_range[:]\n else:\n return self._explored_range","def test_range_query(self):\r\n start = datetime(*self.base_date.timetuple()[:3])\r\n end = start + timedelta(days=3)\r\n\r\n results = DateTimeQueryTestModel.filter(user=0, day__gte=start, day__lt=end)\r\n assert len(results) == 3","def _validate_val_range(self, proposal):\n val_range = proposal[\"value\"]\n if len(val_range) != 2:\n raise traitlets.TraitError(\"val_range must be of length 2.\")\n if val_range[0] > val_range[1]:\n raise traitlets.TraitError(\n \"val_range[0] must be smaller than val_range[1].\"\n )\n return val_range","def range(start: int, stop: int = None, step: int = None) -> ObservableBase:\n from ..operators.observable.range import from_range\n return from_range(start, stop, step)","def _calc_range(self) -> np.ndarray:\n if self._is_ct25k():\n range_resolution = 30\n n_gates = 256\n else:\n n_gates = int(self.metadata[\"number_of_gates\"])\n range_resolution = int(self.metadata[\"range_resolution\"])\n return np.arange(n_gates) * range_resolution + range_resolution / 2","def getRange(self) -> Tuple[int, int]:\n return self.validator().bottom(), self.validator().top()","def global_range(self):\n raise NotImplementedError","def getRange(self, epRange):\n epRange = list(map(int, epRange.split('-')))\n if len(epRange) > 1:\n return list(range(epRange[0], epRange[1]+1))\n else:\n return epRange","def open_range(start, stop, step):\n return np.arange(start, stop+step/2, step)","def _full_value_range(self):\n min_value, max_value = self._raw_data.data_range\n return max_value - min_value","def byrange(self, start, stop):\n\t\treturn ElementsByRange(self.AEM_want, self, (start, stop))","def getPageRange(base_url, node, page_range, page_size, from_date=None, to_date=None, delay=None):\n\n\tdocs = None\n\n\tfor p in page_range:\n\t\tprint \"Getting page %d\" % (p)\n\n\t\tpage_result = getPage(base_url, node, p, from_date=from_date, to_date=to_date)\n\t\tif docs is None:\n\t\t\tdocs = page_result\n\t\telse:\n\t\t\tdocs = docs.append(page_result)\n\n\t\tif delay is not None:\n\t\t\ttime.sleep(delay)\n\n\treturn docs","def map_to_range(val, old_min, old_max, new_min, new_max):\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)","def randrange(start: int, stop: int, step: int) -> int:\n ...","def range_params(self, ran, kw):\n specs = {\"range\": (SchemaNode(\"value\"),\n SchemaNode(\"param\").set_attr(\"name\",\"minInclusive\"),\n SchemaNode(\"param\").set_attr(\"name\",\"maxInclusive\")),\n \"length\": (SchemaNode(\"param\").set_attr(\"name\",\"length\"),\n SchemaNode(\"param\").set_attr(\"name\",\"minLength\"),\n SchemaNode(\"param\").set_attr(\"name\",\"maxLength\"))}\n (exact, min_, max_) = specs[kw]\n if (len(ran) == 1 or ran[0] == ran[1]) and ran[0][0] != \"m\":\n elem = exact\n elem.text = ran[0]\n return [elem]\n res = []\n if ran[0][0] != \"m\":\n elem = min_\n elem.text = ran[0]\n res.append(elem)\n if ran[1][0] != \"m\":\n elem = max_\n elem.text = ran[1]\n res.append(elem)\n return res","def range100(self):\r\n return self.init(100)","def _rangeQueryIntFeature(self):\n\n # create args\n minToGet = c_int64()\n maxToGet = c_int64()\n\n errorCode = VimbaDLL.featureIntRangeQuery(self._handle,\n self._name,\n byref(minToGet),\n byref(maxToGet))\n if errorCode != 0:\n raise VimbaException(errorCode)\n\n return (int(str(minToGet.value)), int(str(maxToGet.value)))","def grid_range(self, key, row_start, row_end, column_start, column_end, *, encoding=_NOTSET):\n if not isinstance(row_start, int):\n raise TypeError(\"row_start argument must be int\")\n if not isinstance(row_end, int):\n raise TypeError(\"row_end argument must be int\")\n if not isinstance(column_start, int):\n raise TypeError(\"column_start argument must be int\")\n if not isinstance(column_end, int):\n raise TypeError(\"column_end argument must be int\")\n return self.execute(b'GRID.RANGE', key, row_start, row_end, column_start, column_end, encoding=encoding)","def range(self) -> Tuple[Union[int, float], Union[int, float]]:\n return self._range","def subject(self) -> global___Range:","def test_002_range_columns(self):\n assert(len(\n self.range_transformer.fit_transform(\n self.data[self.range_col]\n ).columns\n ) == 1)","def test_pos_operate_with_list_get_range_val_out_of_bounds(self):\n key = (\"test\", \"demo\", \"list_key\")\n list = [{\"op\": aerospike.OP_LIST_GET_RANGE, \"bin\": \"int_bin\", \"index\": 2, \"val\": 9}]\n\n (key, meta, bins) = self.as_connection.operate(key, list)\n\n assert bins == {\"int_bin\": [3, 4]}","def getRangeByCells(self, (cell_start_row, cell_start_col), (cell_end_row, cell_end_col), sheet):\r\n return sheet.Range(sheet.Cells(cell_start_row, cell_start_col), \r\n sheet.Cells(cell_end_row, cell_end_col))","def test_simsam_range_functions_without_mapping_file(self):\r\n actual = qiime.simsam.simsam_range(\r\n self.tutorial_otu_table,\r\n self.tutorial_tree,\r\n [1],\r\n [0.1])\r\n self.assertEqual(len(list(actual)), 1)","def calcrange(a4lim,data):\r\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\r\n return a4range","def test_creation_hardbounds_inclusive():\n value = -42\n hardbounds = [-42, 100]\n\n num_a = param.Integer(value=value, hardbounds=hardbounds, inclusive_bounds=[True, True])\n assert num_a.value == value\n assert num_a.hardbounds == hardbounds","def range_table(self):\n raise NotImplementedError('Abstract method.')","def is_valid_range(parser, arg, minimum=0, maximum=100):\n if arg < minimum:\n parser.error(\"%s < %s\", arg, minimum)\n else:\n if arg > maximum:\n parser.error(\"%s > %s\", arg, maximum)\n\n return arg"],"string":"[\n \"def test_get_meta_range(self):\\n pass\",\n \"def getRange(self, p_int): # real signature unknown; restored from __doc__\\n pass\",\n \"def _in_range_op(spec):\",\n \"def f_get_range(self, copy=True):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def GetTRange(self):\\n ...\",\n \"def get_range(self) -> tuple[int, int]:\\n return self.range_from, self.range_to\",\n \"def ex_range(data):\\n a, b, step = _cleanse_range_args(data)\\n return list(range(a, b+sign(step), step))\",\n \"def getRange(self):\\n return self.range\",\n \"def f_has_range(self):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def _get_sight_range(self):\\n raise NotImplementedError\",\n \"def range(self):\\n\\n return time_stat(self, stat=\\\"range\\\")\",\n \"def test_data_range(self):\\n ex = self.ex\\n m = self.m\\n n = self.n\\n\\n lenrange = random.randint(1, 10)\\n nreps = random.randint(1, 10)\\n\\n ex.range = [\\\"i\\\", range(lenrange)]\\n ex.nreps = nreps\\n\\n ex.vary[\\\"X\\\"][\\\"along\\\"] = 0\\n ex.vary[\\\"X\\\"][\\\"with\\\"].add(\\\"rep\\\")\\n ex.infer_lds()\\n\\n cmds = ex.generate_cmds()\\n\\n self.assertIn([\\\"smalloc\\\", \\\"X\\\", nreps * m * n + (nreps - 1) * m], cmds)\\n rangeidx = random.randint(0, lenrange - 1)\\n repidx = random.randint(0, nreps - 1)\\n self.assertIn([\\\"soffset\\\", \\\"X\\\", repidx * m,\\n \\\"X_%d_%d\\\" % (rangeidx, repidx)], cmds)\",\n \"def get_range(self):\\n return time_to_range(self.get_time())\",\n \"def get_range_value(self, key):\\n pass\",\n \"def get_range( value ):\\n return list(range(value))\",\n \"def GetScalarRange(self):\\n ...\",\n \"def get_range(value):\\n return list(range(value))\",\n \"def range(self):\\n return self.timerange()\",\n \"def range_function(num, start_range, end_range):\\n if num > start_range and num < end_range:\\n print(num, \\\"is in the range.\\\\n\\\")\\n elif num < start_range or num > end_range:\\n print(num, \\\"is not in the range.\\\\n\\\")\",\n \"def test_get_range_empty(self):\\n\\n queryset = mock.Mock()\\n queryset.aggregate.return_value = None\\n\\n dimension = models.QuantitativeDimension(\\n key='shares',\\n name='Count of shares',\\n description='Count of shares',\\n field_name='shared_count',\\n )\\n\\n min_val, max_val = dimension.get_range(queryset)\\n\\n self.assertIsNone(min_val)\\n self.assertIsNone(max_val)\",\n \"def range(self):\\n return self.range_array\",\n \"def range_inclusive(start, stop):\\n return range(start, stop + 1)\",\n \"def range (self):\\n return self._range\",\n \"def range (self):\\n return self._range\",\n \"def getRange (start, stop, step=1):\\r\\n result = [n for n in range(start, stop, step)]\\r\\n return result\",\n \"def get_range(self, start=None, end=None):\\n\\n # handle the case of no data\\n if self.data.shape[0] == 0 or self.source.data[\\\"index\\\"].shape[0] == 0:\\n return None, None\\n\\n first_source_idx = self.source.data[\\\"index\\\"][0]\\n last_source_idx = self.source.data[\\\"index\\\"][-1]\\n\\n # convert to timestamp if necessary\\n if isinstance(self.data.index, pd.DatetimeIndex):\\n start = pd.to_datetime(start, unit=\\\"ms\\\")\\n end = pd.to_datetime(end, unit=\\\"ms\\\")\\n first_source_idx = pd.to_datetime(first_source_idx, unit=\\\"ms\\\")\\n last_source_idx = pd.to_datetime(last_source_idx, unit=\\\"ms\\\")\\n\\n # get new start and end\\n if start is not None:\\n if start < first_source_idx:\\n start = max(self.data.index[0], start)\\n elif start > last_source_idx:\\n start = min(self.data.index[-1], start)\\n elif start < self.data.index[0]:\\n start = self.data.index[0]\\n elif start > self.data.index[-1]:\\n start = self.data.index[-1]\\n elif len(self.source.data[\\\"index\\\"]) > 0:\\n start = first_source_idx\\n else:\\n start = self.data.index[0]\\n\\n if end is not None:\\n if end < first_source_idx:\\n end = max(self.data.index[0], end)\\n elif end > last_source_idx:\\n end = min(self.data.index[-1], end)\\n elif end < self.data.index[0]:\\n end = self.data.index[0]\\n elif end > self.data.index[-1]:\\n end = self.data.index[-1]\\n elif len(self.source.data[\\\"index\\\"]) > 0:\\n end = last_source_idx\\n else:\\n end = self.data.index[-1]\\n\\n return start, end\",\n \"def new_range(r):\\n if isinstance(r, list) or isinstance(r, tuple) and len(r) == 2:\\n lower = r[0]\\n upper = r[1]\\n else:\\n lower = r\\n upper = r\\n lower = int(lower)\\n upper = int(upper)\\n return range(lower, upper + 1)\",\n \"def get_range(start, stop):\\n \\n nums = []\\n\\n for num in range(start, stop):\\n nums.append(num)\\n\\n return nums\",\n \"def range(self):\\n \\n return self._range\",\n \"def isRangeValid(self) -> bool:\\n ...\",\n \"def _hit_range_get(self):\\n return (self.hit_start, self.hit_end)\",\n \"def _query_range_get(self):\\n return (self.query_start, self.query_end)\",\n \"def range() -> List[int]:\\n pass\",\n \"def get_data_range(self, start_position, length):\\n pass\",\n \"def range(self) -> ty.Tuple[float, float]:\\r\\n ...\",\n \"def get_range(n0: int, n1: int, ns: int) -> List[int]:\\n # Return a range as a list\\n def lrange(a, b, n=1) -> List[int]:\\n return list(range(a, b, n))\\n # Get the in-bounds part of the range\\n n_range = lrange(max(0, n0), min(ns, n1))\\n # Handle out-of-bounds indices by reflection across boundaries\\n if n0 < 0:\\n # Underflow\\n n_range = lrange(-n0, 0, -1) + n_range\\n if n1 > ns:\\n # Overflow\\n n_range = n_range + lrange(ns - 1, 2 * ns - n1 - 1, -1)\\n\\n return n_range\",\n \"def _translate_range(self, len_, start, end):\\n start = int(start)\\n end = int(end)\\n if start < 0:\\n start += len_\\n start = max(0, min(start, len_))\\n if end < 0:\\n end += len_\\n end = max(-1, min(end, len_ - 1))\\n return start, end\",\n \"def range_query(self, start_key, end_key):\\n if not self.attached:\\n raise CastleCollectionNotAttachedException()\\n\\n print \\\"THIS IS FAKE\\\"\\n pycastle_log.info(\\\"Doing range query from key \\\"+str(start_key)+\\\" to key \\\"+str(end_key))\\n try:\\n i = 0\\n while i < 10:\\n yield i\\n i+=1\\n if i % 5 == 0:\\n pycastle_log.info(\\\"Getting next batch\\\")\\n except GeneratorExit:\\n pycastle_log.info(\\\"User requested stop of range query from key \\\"+str(start_key)+\\\" to key \\\"+str(end_key))\",\n \"def provider_range_lookup(self, record):\\n pass\",\n \"def range_(self):\\n return self.bset.range_\",\n \"def GetSRange(self):\\n ...\",\n \"def range_callback(data):\\n global D\\n D.ranges = data.ranges\",\n \"def testRangeQuery(self):\\n\\n rq = TermRangeQuery(\\\"sorter\\\", \\\"b\\\", \\\"d\\\", True, True)\\n filteredquery = FilteredQuery(rq, self.filter)\\n scoreDocs = self.searcher.search(filteredquery, 1000).scoreDocs\\n self.assertEqual(2, len(scoreDocs))\",\n \"def test_Op_integers(self):\\r\\n start, stop, step = iscalars('start', 'stop', 'step')\\r\\n out = ARange(start.type.dtype)(start, stop, step)\\r\\n f = function([start, stop, step], out)\\r\\n\\r\\n assert numpy.all(f(0, 5, 1) == numpy.arange(0, 5, 1))\\r\\n assert numpy.all(f(2, 11, 4) == numpy.arange(2, 11, 4))\\r\\n assert numpy.all(f(-5, 1, 1) == numpy.arange(-5, 1, 1))\\r\\n assert numpy.all(f(10, 2, -2) == numpy.arange(10, 2, -2))\\r\\n assert numpy.all(f(10, 2, 2) == numpy.arange(10, 2, 2))\\r\\n assert numpy.all(f(0, 0, 1) == numpy.arange(0, 0, 1))\",\n \"def get_range(start, stop):\\n nums = []\\n\\n for num in range(start, stop):\\n nums.append(num)\\n print(nums)\",\n \"def parseSimpleRange_(self, aRange):\\n (start, end) = (None, None)\\n \\n result = []\\n minus = string.find(aRange, '-')\\n if ( minus < 0 ):\\n if isInt(aRange) and int(aRange)>0:\\n # FEDE\\n #result.append(int(aRange)-1)\\n ###\\n result.append(int(aRange))\\n else:\\n common.logger.message(\\\"parseSimpleRange_ ERROR \\\"+aRange)\\n usage()\\n pass\\n \\n pass\\n else:\\n (start, end) = string.split(aRange, '-')\\n if isInt(start) and isInt(end) and int(start)>0 and int(start)<int(end):\\n #result=range(int(start)-1, int(end))\\n result=range(int(start), int(end)+1) #Daniele \\n else:\\n common.logger.message(\\\"parseSimpleRange_ ERROR \\\"+start+end)\\n\\n return result\",\n \"def rangeFinder(self, startIndex, endIndex, list):\\n max = list[startIndex]\\n min = list[startIndex]\\n for x in range(startIndex, endIndex):\\n if list[x] > max:\\n max = list[x]\\n if list[x] < min:\\n min = list[x]\\n return max-min\",\n \"def _check_range(r): \\n if _is_single_range(r):\\n _check_one_range(r)\\n elif isinstance(r, collections.Sequence):\\n for r2 in r:\\n _check_one_range(r2)\\n else:\\n raise error.RangeSyntaxError(str(r))\",\n \"def test_range_argument(self):\\n # we test with range from 0 to 2, with 3 bins -> [0, 0.66, 1.33, 2] exp. bins\\n bins_range = (0, 2)\\n bins_exp = np.array([0, 2 / 3, 1 + 1 / 3, 2])\\n hist_exp = np.array([1, 2, 0])\\n\\n bin_edges, hist, _, _ = hist_w_unc(\\n self.input,\\n bins=self.n_bins,\\n bins_range=bins_range,\\n normed=False,\\n )\\n\\n # check if we end up with the same bin edges anyway\\n np.testing.assert_array_almost_equal(bins_exp, bin_edges)\\n np.testing.assert_array_almost_equal(hist_exp, hist)\",\n \"def looks_range(self) -> Optional[int]:\\n return self._get_property(LOOKS_RANGE_PROP, int)\",\n \"def test_returns_custom_range(self):\\n img = np.arange(5)\\n\\n actual = util.apply_gamma(img, out_min=0, out_max=255, gamma=1)\\n self.assertEqual(actual.min(), 0)\\n self.assertEqual(actual.max(), 255)\",\n \"def get_range(cls, data: tuple or list) -> float:\\n cls._data_validation(data)\\n max_ = cls.get_max(data)\\n min_ = cls.get_min(data)\\n return float(max_ - min_)\",\n \"def _bi_range(start, end):\\n if start == end:\\n return (start,)\\n\\n elif end < start:\\n return reversed(range(end, start + 1))\\n\\n else:\\n return range(start, end + 1)\",\n \"def _check_range(range_):\\n try:\\n if not isinstance(range_, list):\\n range_ = list(range_)\\n min_, max_ = range_\\n except (ValueError, TypeError):\\n raise TypeError(\\\"each range in ising_linear_ranges should be a list of length 2.\\\")\\n if not isinstance(min_, Number) or not isinstance(max_, Number) or min_ > max_:\\n raise ValueError((\\\"each range in ising_linear_ranges should be a 2-tuple \\\"\\n \\\"(min, max) where min <= max\\\"))\\n return range_\",\n \"def getRange(self, chr, start, end, bins=2000, zoomlvl=-1, metric=\\\"AVG\\\", respType=\\\"DataFrame\\\"):\\n try:\\n iter = self.file.fetch(chr, start, end)\\n # result = []\\n # for x in iter:\\n # returnBin = (x.reference_name, x.reference_start, x.reference_end, x.query_alignment_sequence, x.query_sequence)\\n # result.append(returnBin)\\n\\n # if self.columns is None:\\n # self.columns = [\\\"chr\\\", \\\"start\\\", \\\"end\\\", \\\"query_alignment_sequence\\\", \\\"query_sequence\\\"]\\n\\n # if respType is \\\"DataFrame\\\":\\n # result = toDataFrame(result, self.columns)\\n\\n (result, _) = get_range_helper(self.toDF, self.get_bin,\\n self.get_col_names, chr, start, end, iter, self.columns, respType)\\n\\n return result, None\\n except ValueError as e:\\n raise Exception(\\\"didn't find chromId with the given name\\\")\",\n \"def test_range__no_end_date(self):\\n data = self._data()\\n data.pop('end_date')\\n response = self._get(get_kwargs=data)\\n self._check_response(response, 104)\",\n \"def test_inclusive_intervals(self):\\n dim = Integer(\\\"yolo\\\", \\\"uniform\\\", -3, 5.5)\\n assert dim.interval() == (-3, 3)\",\n \"def test_range__no_base_date(self):\\n data = self._data()\\n data.pop('base_date')\\n response = self._get(get_kwargs=data)\\n self._check_response(response, 104)\",\n \"def test_get_pivot_in_correct_range(self):\\n list = [5, 6, 7, 8, 9, 2]\\n assert 0 <= get_pivot(list) <= 5 # between the first and last indices\",\n \"def getSliderRange(*args):\\n\\n #get timeslider range start\\n startF = cmds.playbackOptions(query=True, min=True)\\n endF = cmds.playbackOptions(query=True, max=True)\\n return(startF, endF)\",\n \"def test_creation_bounds_not_inclusive():\\n with pytest.raises(ValueError) as __:\\n value = -42\\n __ = param.Integer(value=value, hardbounds=[-42, 100], inclusive_bounds=[False, False])\",\n \"def evaluate_comparison_range(node):\\n return None\",\n \"def do_range_forloop(self, line):\\n self.E_str = \\\"do_range_forloop\\\"\\n\\n # Get the range parameters\\n line = line.replace(\\\" \\\", \\\"\\\")\\n line = line[line.find(\\\"range\\\")+5:]\\n range_str, _ = gen_parse.get_str_between_delims(line, \\\"(\\\", \\\")\\\")\\n words = range_str.split(\\\",\\\")\\n\\n if len(words) == 1:\\n start, step = 0, 1\\n end = int(words[0])\\n elif len(words) == 2:\\n step = 1\\n start, end = [int(i) for i in words]\\n else:\\n start, end, step = [int(i) for i in words]\\n\\n return range(start, end, step)\",\n \"def range(self):\\n return self._upper - self._lower\",\n \"def in_range(start, end, x):\\n if start <= end:\\n return start <= x <= end\\n else:\\n return start <= x or x <= end\",\n \"def query_range(tree, start_y, start_x, end_y, end_x):\\n res = 0\\n start_y -= 1\\n\\n while end_y > start_y:\\n res += bit.query_range(tree[end_y], start_x, end_x)\\n end_y -= (end_y & -end_y)\\n\\n while start_y > end_y:\\n res -= bit.query_range(tree[start_y], start_x, end_x)\\n start_y -= (start_y & -start_y)\\n\\n return res\",\n \"def _adjustRange(self, start, end):\\n adjusted_start = start\\n if self._start:\\n if end < self._start:\\n return None\\n adjusted_start = max(self._start, start)\\n \\n adjusted_end = end\\n if self._end:\\n if self._end < start:\\n return None\\n adjusted_end = min(self._end, end)\\n \\n return (adjusted_start, adjusted_end)\",\n \"def test_data_with_range_view(self):\\n\\n self.create_model()\\n self.create_machine()\\n self.insert_data()\\n\\n date_literal = '%Y-%m-%d'\\n start_date = dt.today()\\n end_date = start_date + datetime.timedelta(days=1)\\n\\n self.create_user_account_and_login()\\n query_url = self.range_url + '/' + self.data['mid'] + \\\\\\n '/?s=' + dt.strftime(start_date, date_literal) + \\\\\\n '&e=' + dt.strftime(end_date, date_literal)\\n\\n response = self.client.get(query_url)\\n results = json.loads(response.content)\\n\\n self.assertEquals(len(results), 2)\",\n \"def number_range(low, high, obj_type=int):\\n\\n def number_range_parser(argument):\\n try:\\n argument = obj_type(argument)\\n except ValueError:\\n argparse.ArgumentError('Must be of type {}'.format(obj_type.__name__))\\n\\n if low <= argument <= high:\\n return argument\\n else:\\n parser.error('Value is not in the range of {} and {}'.format(low, high))\\n\\n return number_range_parser\",\n \"def define_range():\\n\\n def_range = {'lt': [0.0, 24.0],\\n 'lon': [0.0, 360.0],\\n 'angle': [0.0, 2.0 * np.pi]}\\n\\n return def_range\",\n \"def detect_range(self) -> Union[int, float]:\\n return self.proto.detect_range\",\n \"def get_rangelist(start, end, count):\\n if start is not None and end is not None:\\n if count != 0 and not (start == 0 and count < end):\\n start = int(start)\\n end = int(end)\\n cnt = end - start\\n rangelist = []\\n div = int(start) / count + 1\\n multiple = round(div, 0)\\n start_range = int(count * multiple)\\n n = 1\\n for itr in range(0, start_range + count, (end - start)):\\n if itr < count:\\n rangelist.append([itr, itr + cnt, n])\\n n += 1\\n return rangelist\\n return []\",\n \"def f_get_range(self, copy=True):\\n if not self.f_has_range():\\n raise TypeError(\\n \\\"Your parameter `%s` is not array, so cannot return array.\\\"\\n % self.v_full_name\\n )\\n elif copy:\\n return self._explored_range[:]\\n else:\\n return self._explored_range\",\n \"def test_range_query(self):\\r\\n start = datetime(*self.base_date.timetuple()[:3])\\r\\n end = start + timedelta(days=3)\\r\\n\\r\\n results = DateTimeQueryTestModel.filter(user=0, day__gte=start, day__lt=end)\\r\\n assert len(results) == 3\",\n \"def _validate_val_range(self, proposal):\\n val_range = proposal[\\\"value\\\"]\\n if len(val_range) != 2:\\n raise traitlets.TraitError(\\\"val_range must be of length 2.\\\")\\n if val_range[0] > val_range[1]:\\n raise traitlets.TraitError(\\n \\\"val_range[0] must be smaller than val_range[1].\\\"\\n )\\n return val_range\",\n \"def range(start: int, stop: int = None, step: int = None) -> ObservableBase:\\n from ..operators.observable.range import from_range\\n return from_range(start, stop, step)\",\n \"def _calc_range(self) -> np.ndarray:\\n if self._is_ct25k():\\n range_resolution = 30\\n n_gates = 256\\n else:\\n n_gates = int(self.metadata[\\\"number_of_gates\\\"])\\n range_resolution = int(self.metadata[\\\"range_resolution\\\"])\\n return np.arange(n_gates) * range_resolution + range_resolution / 2\",\n \"def getRange(self) -> Tuple[int, int]:\\n return self.validator().bottom(), self.validator().top()\",\n \"def global_range(self):\\n raise NotImplementedError\",\n \"def getRange(self, epRange):\\n epRange = list(map(int, epRange.split('-')))\\n if len(epRange) > 1:\\n return list(range(epRange[0], epRange[1]+1))\\n else:\\n return epRange\",\n \"def open_range(start, stop, step):\\n return np.arange(start, stop+step/2, step)\",\n \"def _full_value_range(self):\\n min_value, max_value = self._raw_data.data_range\\n return max_value - min_value\",\n \"def byrange(self, start, stop):\\n\\t\\treturn ElementsByRange(self.AEM_want, self, (start, stop))\",\n \"def getPageRange(base_url, node, page_range, page_size, from_date=None, to_date=None, delay=None):\\n\\n\\tdocs = None\\n\\n\\tfor p in page_range:\\n\\t\\tprint \\\"Getting page %d\\\" % (p)\\n\\n\\t\\tpage_result = getPage(base_url, node, p, from_date=from_date, to_date=to_date)\\n\\t\\tif docs is None:\\n\\t\\t\\tdocs = page_result\\n\\t\\telse:\\n\\t\\t\\tdocs = docs.append(page_result)\\n\\n\\t\\tif delay is not None:\\n\\t\\t\\ttime.sleep(delay)\\n\\n\\treturn docs\",\n \"def map_to_range(val, old_min, old_max, new_min, new_max):\\n return new_max - (val - old_min) * (new_max - new_min) / (old_max - old_min)\",\n \"def randrange(start: int, stop: int, step: int) -> int:\\n ...\",\n \"def range_params(self, ran, kw):\\n specs = {\\\"range\\\": (SchemaNode(\\\"value\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"minInclusive\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"maxInclusive\\\")),\\n \\\"length\\\": (SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"length\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"minLength\\\"),\\n SchemaNode(\\\"param\\\").set_attr(\\\"name\\\",\\\"maxLength\\\"))}\\n (exact, min_, max_) = specs[kw]\\n if (len(ran) == 1 or ran[0] == ran[1]) and ran[0][0] != \\\"m\\\":\\n elem = exact\\n elem.text = ran[0]\\n return [elem]\\n res = []\\n if ran[0][0] != \\\"m\\\":\\n elem = min_\\n elem.text = ran[0]\\n res.append(elem)\\n if ran[1][0] != \\\"m\\\":\\n elem = max_\\n elem.text = ran[1]\\n res.append(elem)\\n return res\",\n \"def range100(self):\\r\\n return self.init(100)\",\n \"def _rangeQueryIntFeature(self):\\n\\n # create args\\n minToGet = c_int64()\\n maxToGet = c_int64()\\n\\n errorCode = VimbaDLL.featureIntRangeQuery(self._handle,\\n self._name,\\n byref(minToGet),\\n byref(maxToGet))\\n if errorCode != 0:\\n raise VimbaException(errorCode)\\n\\n return (int(str(minToGet.value)), int(str(maxToGet.value)))\",\n \"def grid_range(self, key, row_start, row_end, column_start, column_end, *, encoding=_NOTSET):\\n if not isinstance(row_start, int):\\n raise TypeError(\\\"row_start argument must be int\\\")\\n if not isinstance(row_end, int):\\n raise TypeError(\\\"row_end argument must be int\\\")\\n if not isinstance(column_start, int):\\n raise TypeError(\\\"column_start argument must be int\\\")\\n if not isinstance(column_end, int):\\n raise TypeError(\\\"column_end argument must be int\\\")\\n return self.execute(b'GRID.RANGE', key, row_start, row_end, column_start, column_end, encoding=encoding)\",\n \"def range(self) -> Tuple[Union[int, float], Union[int, float]]:\\n return self._range\",\n \"def subject(self) -> global___Range:\",\n \"def test_002_range_columns(self):\\n assert(len(\\n self.range_transformer.fit_transform(\\n self.data[self.range_col]\\n ).columns\\n ) == 1)\",\n \"def test_pos_operate_with_list_get_range_val_out_of_bounds(self):\\n key = (\\\"test\\\", \\\"demo\\\", \\\"list_key\\\")\\n list = [{\\\"op\\\": aerospike.OP_LIST_GET_RANGE, \\\"bin\\\": \\\"int_bin\\\", \\\"index\\\": 2, \\\"val\\\": 9}]\\n\\n (key, meta, bins) = self.as_connection.operate(key, list)\\n\\n assert bins == {\\\"int_bin\\\": [3, 4]}\",\n \"def getRangeByCells(self, (cell_start_row, cell_start_col), (cell_end_row, cell_end_col), sheet):\\r\\n return sheet.Range(sheet.Cells(cell_start_row, cell_start_col), \\r\\n sheet.Cells(cell_end_row, cell_end_col))\",\n \"def test_simsam_range_functions_without_mapping_file(self):\\r\\n actual = qiime.simsam.simsam_range(\\r\\n self.tutorial_otu_table,\\r\\n self.tutorial_tree,\\r\\n [1],\\r\\n [0.1])\\r\\n self.assertEqual(len(list(actual)), 1)\",\n \"def calcrange(a4lim,data):\\r\\n a4range=N.intersect1d(N.where(data>a4lim[0])[0],N.where(data<a4lim[1])[0])\\r\\n return a4range\",\n \"def test_creation_hardbounds_inclusive():\\n value = -42\\n hardbounds = [-42, 100]\\n\\n num_a = param.Integer(value=value, hardbounds=hardbounds, inclusive_bounds=[True, True])\\n assert num_a.value == value\\n assert num_a.hardbounds == hardbounds\",\n \"def range_table(self):\\n raise NotImplementedError('Abstract method.')\",\n \"def is_valid_range(parser, arg, minimum=0, maximum=100):\\n if arg < minimum:\\n parser.error(\\\"%s < %s\\\", arg, minimum)\\n else:\\n if arg > maximum:\\n parser.error(\\\"%s > %s\\\", arg, maximum)\\n\\n return arg\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80703026","0.7837224","0.7255473","0.7147124","0.7073319","0.7031268","0.70060104","0.6990455","0.6969976","0.6872395","0.68121374","0.67698324","0.6767907","0.6744316","0.67391896","0.67225266","0.6692281","0.66836524","0.6643143","0.6600423","0.659429","0.65940017","0.6591628","0.6591628","0.65800357","0.6577428","0.6565064","0.65649545","0.6540213","0.65292394","0.65247214","0.6504584","0.64879274","0.64790994","0.64702034","0.6469029","0.6462531","0.645659","0.64386976","0.64342743","0.6430295","0.6420432","0.6413048","0.64118344","0.6388609","0.6374389","0.6346751","0.63149124","0.6313666","0.63078356","0.62879574","0.6279775","0.6279312","0.62755823","0.6270826","0.6267892","0.6261789","0.6241012","0.6236893","0.6232861","0.6229386","0.6228697","0.6207711","0.6199707","0.6197818","0.61948895","0.61947334","0.6187493","0.617374","0.61657655","0.6162022","0.6153132","0.6149723","0.6146383","0.61432964","0.61402357","0.6135336","0.61330986","0.61296844","0.61280406","0.61243916","0.6117144","0.61034924","0.6103002","0.61013854","0.61005175","0.60973155","0.6089831","0.60814685","0.6079911","0.6063572","0.6052587","0.6052434","0.60463476","0.60441357","0.603915","0.603253","0.6032346","0.6017453","0.60051584"],"string":"[\n \"0.80703026\",\n \"0.7837224\",\n \"0.7255473\",\n \"0.7147124\",\n \"0.7073319\",\n \"0.7031268\",\n \"0.70060104\",\n \"0.6990455\",\n \"0.6969976\",\n \"0.6872395\",\n \"0.68121374\",\n \"0.67698324\",\n \"0.6767907\",\n \"0.6744316\",\n \"0.67391896\",\n \"0.67225266\",\n \"0.6692281\",\n \"0.66836524\",\n \"0.6643143\",\n \"0.6600423\",\n \"0.659429\",\n \"0.65940017\",\n \"0.6591628\",\n \"0.6591628\",\n \"0.65800357\",\n \"0.6577428\",\n \"0.6565064\",\n \"0.65649545\",\n \"0.6540213\",\n \"0.65292394\",\n \"0.65247214\",\n \"0.6504584\",\n \"0.64879274\",\n \"0.64790994\",\n \"0.64702034\",\n \"0.6469029\",\n \"0.6462531\",\n \"0.645659\",\n \"0.64386976\",\n \"0.64342743\",\n \"0.6430295\",\n \"0.6420432\",\n \"0.6413048\",\n \"0.64118344\",\n \"0.6388609\",\n \"0.6374389\",\n \"0.6346751\",\n \"0.63149124\",\n \"0.6313666\",\n \"0.63078356\",\n \"0.62879574\",\n \"0.6279775\",\n \"0.6279312\",\n \"0.62755823\",\n \"0.6270826\",\n \"0.6267892\",\n \"0.6261789\",\n \"0.6241012\",\n \"0.6236893\",\n \"0.6232861\",\n \"0.6229386\",\n \"0.6228697\",\n \"0.6207711\",\n \"0.6199707\",\n \"0.6197818\",\n \"0.61948895\",\n \"0.61947334\",\n \"0.6187493\",\n \"0.617374\",\n \"0.61657655\",\n \"0.6162022\",\n \"0.6153132\",\n \"0.6149723\",\n \"0.6146383\",\n \"0.61432964\",\n \"0.61402357\",\n \"0.6135336\",\n \"0.61330986\",\n \"0.61296844\",\n \"0.61280406\",\n \"0.61243916\",\n \"0.6117144\",\n \"0.61034924\",\n \"0.6103002\",\n \"0.61013854\",\n \"0.61005175\",\n \"0.60973155\",\n \"0.6089831\",\n \"0.60814685\",\n \"0.6079911\",\n \"0.6063572\",\n \"0.6052587\",\n \"0.6052434\",\n \"0.60463476\",\n \"0.60441357\",\n \"0.603915\",\n \"0.603253\",\n \"0.6032346\",\n \"0.6017453\",\n \"0.60051584\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.92907894"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":223,"numItemsPerPage":100,"numTotalItems":95772,"offset":22300,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjY0NTEyOCwianRpIjoiZmVkZTYzZTMtZDM5OC00NzNlLTkzNjEtMGY1MDFhMzA4NmRjIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2NDg3MjgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.mbgYFOK0E3NVDU8ldP4pm8IHxYqXypSIcrWK1xz9sFafz_ZF9-itmxAjczUdtCbyGM7XggLQRu9e4j0hHl6PDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Get index of right child | def get_right_index(i):
pos = i + 1
right_pos = 2 * pos + 1
right_index = right_pos - 1
return right_index | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_right_child_index(self):\n return (2 * self.index) + 2",
"def get_right_child_index(self, parent):\n return 2*parent+2",
"def right_child(self, index):\n return 2 * index + 1",
"def right_child(self, index):\n return 2 * index + 2",
"def right_child_idx(idx):\n return... | [
"0.8933065",
"0.86470544",
"0.85833573",
"0.8481242",
"0.83146423",
"0.80509824",
"0.80138457",
"0.78668714",
"0.78668714",
"0.7804404",
"0.7780275",
"0.77160066",
"0.77091724",
"0.76771295",
"0.7524542",
"0.74409086",
"0.740755",
"0.7335976",
"0.7293008",
"0.72079617",
"0.71... | 0.7053241 | 25 |
Get value at index or np.inf if it doesn't exist | def get_value_at(self, i):
return self.default_value if i > self.last_item else self.heap[i] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def value(self, idx):\n item = self.items[idx]\n if item is None:\n ret = -float('inf')\n else:\n ret = self.fn(item)\n return ret",
"def get_value(_list, _index):\n # print(_list, len(_list))\n if _index >= len(_list):\n return None\n return _lis... | [
"0.6983909",
"0.6865913",
"0.67301875",
"0.66794467",
"0.65117055",
"0.6493914",
"0.63198966",
"0.63175035",
"0.6283424",
"0.62733126",
"0.6259123",
"0.6183555",
"0.6179405",
"0.61543536",
"0.6129469",
"0.6128749",
"0.6111963",
"0.6062199",
"0.6061996",
"0.6040353",
"0.604035... | 0.6355408 | 6 |
Return the height of the heap and the maximum number of digits for any entry in the heap | def get_max_digs(self):
# Get maximum number of digits
# 20200607: np.ceil --> np.floor + 1
# Consider edge case n = 9 vs n = 10
# ceil(log_10(9)) == 1, ceil(log_10(10)) == 1
# floor(log_10(9)) + 1 == 1, floor(log_10(10)) + 1 == 2
max_digs = int(np.floor(np.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def size(heap):\n return len(heap)",
"def height(self):\n if self.children == []:\n return 1 \n else:\n arr = []\n for child in self.children:\n result = 1 + child.height()\n arr.append(result)\n return max(arr)",
"def build_max_heap(heap):\n\tfor j in range(heap.len/... | [
"0.7472827",
"0.7241892",
"0.7016466",
"0.6986129",
"0.6964422",
"0.6905836",
"0.6845946",
"0.68156487",
"0.67743343",
"0.6735504",
"0.67184144",
"0.6671894",
"0.6658428",
"0.6652893",
"0.6619757",
"0.66108006",
"0.66101164",
"0.65899974",
"0.6552006",
"0.6547167",
"0.6538108... | 0.6795792 | 8 |
Set value at index | def set_value_at(self, i, new_value=default_value):
self.heap[i] = new_value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_at_index(self, index: int, value: object) -> None:\n self.data[index] = value",
"def __setitem__(self, index, value):\n self._update_value_at(index, value)",
"def setvalue(self, index, value):\n self._checkIndex(index)\n self._items[index].value = value",
"def __setitem__(... | [
"0.84746706",
"0.8103384",
"0.8065691",
"0.8000954",
"0.7889873",
"0.7880268",
"0.7860388",
"0.7846067",
"0.7758514",
"0.77272445",
"0.7670536",
"0.76695335",
"0.76406544",
"0.7637111",
"0.75609124",
"0.7530689",
"0.7505704",
"0.7435533",
"0.74202067",
"0.74066633",
"0.739702... | 0.0 | -1 |
Swap values at index_0 and index_1 | def swap(self, index_0, index_1):
value_0 = self.get_value_at(index_0)
value_1 = self.get_value_at(index_1)
self.set_value_at(index_0, value_1)
self.set_value_at(index_1, value_0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def swap(A, index1, index2):\r\n \r\n temp = A[index1]\r\n A[index1] = A[index2]\r\n A[index2] = temp",
"def swap_numbers(numbers, index1, index2):\n temp = numbers[index1]\n numbers[index1] = numbers[index2]\n numbers[index2] = temp",
"def __swap(self, index_1, index_2):\n temp = s... | [
"0.8045885",
"0.80415386",
"0.7976427",
"0.7946715",
"0.77399236",
"0.7737298",
"0.7729477",
"0.76621556",
"0.7614902",
"0.7564756",
"0.7564756",
"0.737364",
"0.7371236",
"0.73423225",
"0.727941",
"0.7278792",
"0.72698545",
"0.71813565",
"0.716901",
"0.7142264",
"0.7134006",
... | 0.82595736 | 0 |
use dictionary to store the difference (target nums[i]). | def twoSum(self, nums: List[int], target: int) -> List[int]:
diffRec = {}
for i, v in enumerate(nums):
if v in diffRec:
return [diffRec[v], i]
else:
diffRec[target - v] = i
return -1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def twoSum(self, nums: List[int], target: int) -> List[int]:\n d = {}\n for i, n in enumerate(nums):\n d[n]=i\n \n for i, n in enumerate(nums):\n m = target - n\n if m in d and d[m] != i:\n return [i,d[m... | [
"0.6771178",
"0.6764401",
"0.6693631",
"0.65968764",
"0.6315861",
"0.62800086",
"0.62199515",
"0.62003785",
"0.61241025",
"0.6120513",
"0.61066025",
"0.5899261",
"0.56001204",
"0.55316275",
"0.5446655",
"0.5416312",
"0.5380408",
"0.5378301",
"0.537053",
"0.5293179",
"0.528616... | 0.6832244 | 0 |
checks if user can afford item and deducts item cost from self.resources and adds the item (or the purchases effects) to the user. returns true if purchased, returns false if not. Default usage is to use an item name from purchase.py. If a Balance object "balance" is given INSTEAD of "item", then it is used directly. | def purchase(self, item=None, balance=None):
if item!= None:
cost = purchases.getCost(item)
if self.affords(cost):
self.payFor(cost)
# TODO: actually do whatever was purchased
# self.applyItem(item)
return True
e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def userCanAffordItemObj(self, user : bbUser.bbUser, item : bbItem.bbItem) -> bool:\n return user.credits >= item.getValue()",
"def can_afford(self, item_name):\n item = self.get(item_name)\n for resource in RESOURCES:\n if item.cost.get(resource, 0) > self.game.resources.get(reso... | [
"0.69444394",
"0.6761436",
"0.6278624",
"0.62341774",
"0.6051828",
"0.5915107",
"0.5884033",
"0.5883767",
"0.5861438",
"0.58475995",
"0.5811648",
"0.5739377",
"0.56401324",
"0.560546",
"0.5601953",
"0.5595759",
"0.55743337",
"0.5556562",
"0.5518422",
"0.55150044",
"0.5479447"... | 0.7959187 | 0 |
Returns the dictionary should be called once the dictionary is constructed. | def get_socket_dictionary(self) -> dict:
socket_dictionary = {
"action": self.action,
"car_id": self.car_id,
"username": self.username,
"password": self.password,
"usertoken": self.usertoken,
"info_date_time": self.info_date_time,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_dict(self):\n # type: () -> dict\n self.is_valid()\n return self._get_dict()",
"def dict(self) -> Dict:\r\n return super().dict()",
"def dict(self) -> Dict:\r\n return super().dict()",
"def get_dict(self):\n return",
"def __load(self) -> Dict:\n return dict()",
... | [
"0.7532119",
"0.72821766",
"0.72821766",
"0.7258358",
"0.7140809",
"0.70793706",
"0.7072972",
"0.7072972",
"0.7009909",
"0.6964286",
"0.6956898",
"0.6956898",
"0.6944875",
"0.6912645",
"0.6845601",
"0.6812687",
"0.67669713",
"0.67046356",
"0.66895974",
"0.6679883",
"0.6679883... | 0.0 | -1 |
Returns the date reformated into python datetime format. | def get_python_date(self):
return dateutil.parser.parse(self.iso_date) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def modis_to_from_pydatetime(date):\n \n if isinstance(date, (str, unicode)): \n return dt.datetime.strptime(date[1:], '%Y%j').date()\n return dt.datetime.strftime(date, 'A%Y%j')",
"def _to_date(self, x):\n if isinstance(x, datetime.datetime):\n return x.date()\n return x... | [
"0.71629053",
"0.6894753",
"0.68495804",
"0.6835706",
"0.68110466",
"0.6798551",
"0.678441",
"0.6690061",
"0.6632137",
"0.6628195",
"0.66245985",
"0.66197765",
"0.6619198",
"0.660381",
"0.6570892",
"0.65689874",
"0.653436",
"0.6477465",
"0.64709014",
"0.6451577",
"0.6413728",... | 0.70473415 | 1 |
Renders a page for a particular compound. | def CompoundPage(request):
form = compound_form.CompoundForm(request.GET)
if not form.is_valid():
logging.error(form.errors)
raise Http404
# Compute the delta G estimate.
kegg_id = form.cleaned_compoundId
compound = models.Compound.objects.get(kegg_id=kegg_id)
compound.Stash... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main_page():\n pages=get_accounts()\n return render_template('disp.html',pages=pages)",
"def renderPage():\n return render_template(\"index.html\")",
"def main_page():\n return render_template(\"main_page.html\")",
"def homepage():\n\n pagesClassIDs = {\n \"index\": {\n \"b... | [
"0.59548026",
"0.57890856",
"0.57370317",
"0.5636853",
"0.55066913",
"0.54987985",
"0.54826313",
"0.54345924",
"0.542592",
"0.53984725",
"0.53741604",
"0.537301",
"0.5337757",
"0.5306245",
"0.5297516",
"0.5291385",
"0.5291248",
"0.5286969",
"0.5282097",
"0.5271443",
"0.526328... | 0.7018998 | 0 |
Build protocol data from message. | def toData(self):
lines = []
# 1. Request and protocol version
lines.append(self.request + " " + BANNER)
# 2. Request arguments
lines.extend(['%s: %s' % (arg, self.args[arg]) for arg in self.args])
# 3. End of message (double CR-LF)
data = "\r\n".join(lines) + "\... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode_data(self, msg):\n if len(msg) < 6:\n raise ValueError(\"Data message is too short - minimum length 6 bytes, got %d bytes\" % len(msg))\n\n (x, TIME) = struct.unpack(\"<HL\", msg[0:6])\n\n if x & (2**15) != 0:\n raise ValueError(\"Expected a data message, found... | [
"0.65798396",
"0.64550364",
"0.6363458",
"0.621391",
"0.61582",
"0.6149012",
"0.599604",
"0.59846616",
"0.59781426",
"0.59737766",
"0.59535956",
"0.5951033",
"0.5927215",
"0.5879687",
"0.58785284",
"0.5871454",
"0.58702606",
"0.5868797",
"0.5863226",
"0.58508974",
"0.5833903"... | 0.5936232 | 12 |
Parse and extract message from protocol data. | def fromData(self, data):
self.reset()
request = ""
version = None
args = {}
# Parse raw data to construct message (strip empty lines)
lines = [line.strip() for line in data.splitlines() if line.strip() != ""]
# If message is empty, return false
if not l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_message(self, data):\r\n if TwitchChatStream._check_has_ping(data):\r\n self._maybe_print('got ping')\r\n self._send_pong()\r\n\r\n channel_name_or_false = TwitchChatStream._check_has_channel(data)\r\n if channel_name_or_false:\r\n current_channel = ... | [
"0.75778097",
"0.7159968",
"0.71568024",
"0.71497744",
"0.71487087",
"0.7114772",
"0.70746684",
"0.7059902",
"0.6972103",
"0.69445956",
"0.693621",
"0.69232017",
"0.6908883",
"0.68726164",
"0.68299896",
"0.68240994",
"0.68166345",
"0.66790384",
"0.66693574",
"0.66176164",
"0.... | 0.6679768 | 17 |
Returns true if the queue is empty and false otherwise Must be O(1) | def is_empty(self) -> bool:
if self.num_items == 0:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_empty(self):\n return self.queue == []",
"def is_empty(self):\n return len(self.queue) == 0",
"def is_empty(self):\n return len(self.queue) == 0",
"def empty(self) -> bool: \n if(self.queue is not None and len(self.queue) > 0):\n print(\"len > 0\" )\n ... | [
"0.88570523",
"0.87778527",
"0.87778527",
"0.8776103",
"0.87759084",
"0.8768391",
"0.86971426",
"0.8675233",
"0.8659355",
"0.8658433",
"0.8606875",
"0.8600865",
"0.85989565",
"0.8582111",
"0.8568123",
"0.8497459",
"0.84956324",
"0.8487618",
"0.8420514",
"0.8420514",
"0.832870... | 0.0 | -1 |
enqueues item, adding it to the rear NodeList Must be O(1) | def enqueue(self, item: Any) -> None:
node = Node(item, self.rear)
self.rear = node
self.num_items += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enqueue(self, item):\n\t\tself.items.insert(0, item)",
"def enqueue(self, node):\n self.items.append(node)",
"def enqueue(self, item):\n self.items.insert(0, item)",
"def enqueue(self, item):\n self.items.insert(0, item)",
"def enqueue(self, item):\n old_last = self.last\n ... | [
"0.78006804",
"0.7710979",
"0.76479787",
"0.76479787",
"0.75651515",
"0.75481075",
"0.753136",
"0.7486071",
"0.7372014",
"0.7372014",
"0.7367847",
"0.7348242",
"0.72837245",
"0.7240521",
"0.71723264",
"0.71205115",
"0.7076686",
"0.7066437",
"0.69631815",
"0.6946971",
"0.68910... | 0.7469452 | 8 |
dequeues item, removing first item from front NodeList If front NodeList is empty, remove items from rear NodeList and add to front NodeList until rear NodeList is empty If front NodeList and rear NodeList are both empty, raise IndexError Must be O(1) general case | def dequeue(self) -> Any:
if self.rear is None and self.front is None:
raise IndexError
elif self.front is None:
while self.rear is not None:
node = Node(self.rear.value, self.front)
self.front = node
self.rear = self.rear.rest
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(self, index):\n if index < 0 or index >= len(self):\n raise AttributeError(\"i must be >= 0 and < size of queue\")\n if index == 0:\n oldItem = self._front.data\n self._front = self._front.next\n else:\n probe = self._front\n wh... | [
"0.68911767",
"0.68660563",
"0.658952",
"0.6525972",
"0.6522565",
"0.6490781",
"0.648833",
"0.647087",
"0.644473",
"0.64431846",
"0.6430354",
"0.64276177",
"0.6422422",
"0.64148337",
"0.64119107",
"0.63734394",
"0.63578594",
"0.63180566",
"0.6286091",
"0.6252454",
"0.62177724... | 0.63208413 | 17 |
Returns the number of items in the queue Must be O(1) | def size(self) -> int:
return self.num_items | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def size(self):\r\n return len(self.queue)",
"def size(self):\r\n return len(self.queue)",
"def size(self):\n return len(self.queue)",
"def size(self):\n return len(self.queue)",
"def size(self):\n return len(self.queue)",
"def size(self):\n return len(self.queue... | [
"0.800648",
"0.800648",
"0.7884259",
"0.7884259",
"0.7884259",
"0.7884259",
"0.7839228",
"0.7830114",
"0.7822343",
"0.77353644",
"0.75935245",
"0.7576671",
"0.7454184",
"0.7449153",
"0.73749566",
"0.7319964",
"0.7215429",
"0.7196273",
"0.7196273",
"0.71886265",
"0.71323776",
... | 0.66311985 | 63 |
Run SugarPy on a given .mzML file based on identified peptides from an evidences.csv Translated Ursgal parameters are passed to the SugarPy main function. | def _execute(self):
self.time_point(tag="execution")
main = self.import_engine_as_python_function()
output_file = os.path.join(
self.params["output_dir_path"], self.params["output_file"]
)
input_file = os.path.join(
self.params["input_dir_path"], self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n langs = []\n\n with open(\"sql/07_populate.sql\", 'w', encoding='utf8') as sql:\n sql.write(\"--this file is generated from csv files in data folder\\n\\n\")\n\n langs = write_lang_city(sql)\n write_groups_diets(sql, langs)\n\n with open(\"sql/10_populate_test_data.sql\"... | [
"0.5694265",
"0.5572508",
"0.5570067",
"0.55660534",
"0.55130184",
"0.5468869",
"0.5449003",
"0.54461205",
"0.5285306",
"0.52705884",
"0.52448237",
"0.52008086",
"0.5169603",
"0.5167817",
"0.5165798",
"0.5160243",
"0.5145471",
"0.51369846",
"0.51311797",
"0.512673",
"0.511855... | 0.5930959 | 0 |
Initialize a player at Python Casino, we give new players 100 chips to play | def __init__(self, name="Player"):
self.name = name
self.chips = 100
self.hand1 = []
self.hand2 = []
self.bet = 0
self.lastbet = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newPlayer():\r\n pass",
"def __init__(self):\n\n self.name = 'KuhnPoker'\n self.num_players = 2",
"def initGame(self):\n self.map = {}\n self.blocks = Group()\n self.Coins =Group()\n self.players = Group()\n self.player1 = Player(1525,75,2)\n self.... | [
"0.6672925",
"0.6669602",
"0.6631138",
"0.6495722",
"0.6478701",
"0.6469059",
"0.6440115",
"0.64392686",
"0.64156747",
"0.6413498",
"0.6401054",
"0.63888377",
"0.63830507",
"0.6313076",
"0.62933445",
"0.6278405",
"0.6247309",
"0.62469554",
"0.62101966",
"0.6190493",
"0.618668... | 0.6768657 | 0 |
Request a bet from the player | def get_bet(self):
while newbet := input(f"{self.name}: {self.chips} chips. Last bet: {self.lastbet}. Bet: "):
try:
newbet = int(newbet)
if newbet in range(0, self.chips+1):
self.bet = newbet
self.chips -= newbet
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def bet(message, user: ParamType.MIXER_USER, amount):\n\n username = user.username.lower()\n username_sender = message.username.lower()\n\n mixcord_user = await database.get_user(message.user_id)\n\n # handle if somebody is trying to accept or deny\n if amount == \"accept\" or amount == \"deny... | [
"0.7001438",
"0.6755639",
"0.6732551",
"0.66591",
"0.66209906",
"0.6607337",
"0.63983536",
"0.6385922",
"0.6177575",
"0.61636424",
"0.6158878",
"0.6030608",
"0.6015223",
"0.59681845",
"0.595183",
"0.5890456",
"0.5877548",
"0.5866771",
"0.586661",
"0.58525896",
"0.5830537",
... | 0.5253574 | 59 |
Get a player name to join the game | def get_name(self):
name = input("What is your name? ")
if len(name) > 0:
self.name = name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_name(self):\n return self._player_name",
"def get_name_from_player(player):\r\n return player.name.lower()",
"def get_current_player_name(self) -> str:\n if self.p1_turn:\n return 'p1'\n return 'p2'",
"def get_player_name(self):\n return self._player_name",
... | [
"0.75537586",
"0.7549152",
"0.75380224",
"0.7499969",
"0.74118185",
"0.7327175",
"0.7180175",
"0.7167274",
"0.70668465",
"0.69005984",
"0.68667847",
"0.6844018",
"0.6836805",
"0.6779528",
"0.6658368",
"0.6543344",
"0.65326613",
"0.65127695",
"0.6503237",
"0.64859694",
"0.6484... | 0.0 | -1 |
Setup player for a new round | def prepare_round(self):
self.hand1 = []
self.hand2 = []
# Note, this should already be zero from having bet paid
self.bet = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newPlayer():\r\n pass",
"def set_player(self, new_player):\n self.player = new_player",
"def change_player(self):\n if self.__root.children is None:\n if self.__root.win[0] == 1:\n self.win = Board.PLAYER_0\n elif self.__root.win[1] == 1:\n ... | [
"0.7188657",
"0.68857676",
"0.67688686",
"0.67233884",
"0.6633779",
"0.662217",
"0.6593084",
"0.65605456",
"0.6546107",
"0.65304965",
"0.6521109",
"0.6511719",
"0.6417494",
"0.63130975",
"0.6311738",
"0.63064224",
"0.6296433",
"0.6292017",
"0.62892103",
"0.62458867",
"0.62320... | 0.0 | -1 |
Receive a card dealt during the deal round | def dealt_card(self, card):
self.hand1.append(card)
print(f"{self.name} was dealt a {card}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deal(self):\n dealt_card = self.deck_of_cards.pop()\n print(\"You have been dealt the {} \".format(dealt_card.value) \\\n + \"of {}.\".format(dealt_card.suit) + \"\\n\")",
"def deal_card(self):\n return self._deal(1)[0]",
"def deal(self):\n\n if self.dealer... | [
"0.7164214",
"0.6649279",
"0.6613201",
"0.6463644",
"0.64456797",
"0.6287789",
"0.62788486",
"0.6256954",
"0.6247799",
"0.6185928",
"0.618543",
"0.6180968",
"0.61769384",
"0.61759967",
"0.61480325",
"0.6139512",
"0.613029",
"0.6088372",
"0.6074345",
"0.60608476",
"0.6047473",... | 0.7149964 | 1 |
Return the value of the players hand. Still need to handle split hands somehow | def hand_value(self):
return deck.bj_hand_value(self.hand1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def player_hand_value(self, hand_idx=0):\n return self._get_hand_value(self.players[hand_idx]['hand'])",
"def _get_hand_value(self):\n\t\tvalue_list = []\n\t\tfor index, hand in enumerate(self.player_hand):\n\t\t\tif self.status[index] == 'won':\n\t\t\t\tvalue_list.append(hand.bet)\n\t\t\telif self.status... | [
"0.7838407",
"0.7544803",
"0.7197065",
"0.7069113",
"0.68920386",
"0.68238187",
"0.68197185",
"0.6810919",
"0.68102187",
"0.68066204",
"0.6653587",
"0.66281724",
"0.6549728",
"0.6535241",
"0.6513264",
"0.6500152",
"0.64739597",
"0.63902885",
"0.63755286",
"0.6328881",
"0.6209... | 0.808111 | 0 |
Lost hand, lost bet | def lose(self, dlr):
print(f"Sorry {self.name}, your total of {sum(self.hand1)} didn't beat the dealers {dlr}")
self.lastbet = self.bet
self.bet = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lose(self) -> None:\n self._actual_money -= self._bet",
"def rough_outcome(self) -> float:\n # HUYNH YOU PRICK WHY THE FUCK DO YOU MAKE US WRITE THIS SHIT EVEN IT'S NOT USED ANYWHERE\n # pick move based on this may not be optimal but better than random\n # return 1 if win immediat... | [
"0.69771945",
"0.66398084",
"0.65543616",
"0.6549002",
"0.64223045",
"0.64099866",
"0.6402642",
"0.6375774",
"0.6362669",
"0.6361766",
"0.6273635",
"0.6248365",
"0.6205552",
"0.6203644",
"0.6169682",
"0.61347765",
"0.6123261",
"0.6119863",
"0.61127377",
"0.6111896",
"0.609298... | 0.7257465 | 0 |
Pay a push bet | def push(self, dlr):
print(f"{self.name}'s {dlr} matched the dealers hand, push")
self.chips += self.bet
self.lastbet = self.bet
self.bet = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pay_gold(self, something):\n print(\"GOLD PAID\")",
"def awaiting_payment(self):",
"def post(self):\n \n access_token = accessToken.gerated_access_token\n api_url = \"https://sandbox.safaricom.co.ke/mpesa/stkpush/v1/processrequest\"\n headers = { \"Authorization\": \"Bear... | [
"0.65109074",
"0.6435573",
"0.63549113",
"0.62870425",
"0.6262188",
"0.6180678",
"0.6157217",
"0.5998908",
"0.59986365",
"0.5978201",
"0.59458923",
"0.5945481",
"0.59274006",
"0.5924785",
"0.59118724",
"0.5908018",
"0.58982766",
"0.5818431",
"0.579684",
"0.57960904",
"0.57928... | 0.0 | -1 |
Given a bearer token, send a GET request to the API. | def obtain_bearer_token(host, path):
url = '{0}{1}'.format(host, quote(path.encode('utf8')))
assert CLIENT_ID, "Please supply your client_id."
assert CLIENT_SECRET, "Please supply your client_secret."
data = urlencode({
'client_id': CLIENT_ID,
'client_secret': CLIENT_SECRET,
'gra... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(auth_token):\n session = requests.Session()\n session.headers.update({'Accept': 'application/json',\n 'Authorization': 'Bearer %s' % auth_token})\n return FlattrApi(session)",
"def request(host, path, bearer_token, url_params=None):\n url_params = url_params or {}\n url = '{0}{1}'.f... | [
"0.74230313",
"0.7217766",
"0.7217766",
"0.7217766",
"0.7144044",
"0.6993274",
"0.6922886",
"0.69217604",
"0.6805349",
"0.6731545",
"0.6706645",
"0.65714157",
"0.6518531",
"0.65005594",
"0.64718956",
"0.6424078",
"0.6410442",
"0.63174736",
"0.6309319",
"0.62804854",
"0.623405... | 0.6105324 | 32 |
Given a bearer token, send a GET request to the API. | def request_from_yelp(host, path, bearer_token, url_params=None):
url_params = url_params or {}
url = '{0}{1}'.format(host, quote(path.encode('utf8')))
headers = {
'Authorization': 'Bearer %s' % bearer_token,
}
response = requests.request('GET', url, headers=headers, params=url_params)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(auth_token):\n session = requests.Session()\n session.headers.update({'Accept': 'application/json',\n 'Authorization': 'Bearer %s' % auth_token})\n return FlattrApi(session)",
"def request(host, path, bearer_token, url_params=None):\n url_params = url_params or {}\n url = '{0}{1}'.f... | [
"0.74231493",
"0.7217314",
"0.7217314",
"0.7217314",
"0.7143837",
"0.6995892",
"0.6923943",
"0.6921158",
"0.6802968",
"0.6730711",
"0.6707949",
"0.6570709",
"0.64983624",
"0.64725584",
"0.64226097",
"0.64084786",
"0.63167053",
"0.63087755",
"0.627998",
"0.623275",
"0.6221526"... | 0.6519203 | 12 |
Query the Search API by a search term and location. | def search(bearer_token, term, location):
url_params = {
'term': term.replace(' ', '+'),
'location': location.replace(' ', '+'),
'limit': SEARCH_LIMIT
}
return request_from_yelp(API_HOST, SEARCH_PATH, bearer_token, url_params=url_params) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search(api_key, term, location):\n\n\n\n url_params = {\n\n 'term': term.replace(' ', '+'),\n\n 'location': location.replace(' ', '+'),\n\n 'limit': SEARCH_LIMIT\n\n }\n\n return request(API_HOST, SEARCH_PATH, api_key, url_params=url_params)",
"def search(api_key, term, location... | [
"0.84327716",
"0.8417795",
"0.83943504",
"0.82543755",
"0.8189317",
"0.81802875",
"0.8086794",
"0.7866687",
"0.7748555",
"0.7738864",
"0.72571903",
"0.72518456",
"0.7206588",
"0.7186088",
"0.70739245",
"0.70538163",
"0.7021339",
"0.69804955",
"0.69747436",
"0.69324064",
"0.68... | 0.7719189 | 10 |
Query the Business API by a business ID. | def get_business(bearer_token, business_id):
business_path = BUSINESS_PATH + business_id
return request_from_yelp(API_HOST, business_path, bearer_token) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_business(api_key, business_id):\r\n business_path = BUSINESS_PATH + business_id\r\n\r\n return request(API_HOST, business_path, api_key)",
"def get_business(api_key, business_id):\n\n business_path = BUSINESS_PATH + business_id\n\n\n return request(API_HOST, business_path, api_key)",
"def g... | [
"0.8017975",
"0.79832286",
"0.79655355",
"0.7780533",
"0.7551206",
"0.7551206",
"0.7551206",
"0.7334066",
"0.6866214",
"0.6796703",
"0.62649596",
"0.6192476",
"0.60992557",
"0.60603786",
"0.6021728",
"0.5977142",
"0.5906498",
"0.5848761",
"0.58446676",
"0.5840517",
"0.5755293... | 0.73491096 | 7 |
Queries the API by the input values from the user. | def query_api(term, location):
bearer_token = obtain_bearer_token(API_HOST, TOKEN_PATH)
response = search(bearer_token, term, location)
businesses = response.get('businesses')
if not businesses:
print(u'No businesses for {0} in {1} found.'.format(term, location))
return
final_resu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ask_query(self):\n self.__output = list()\n return input(form('What do you want to search?\\n> '))",
"def query(self, **kwargs):",
"def query(self):",
"def api_query(self, **kwargs):\n with self._api_lock:\n return self._api_query(kwargs)",
"def query(self):\n ... | [
"0.6936234",
"0.67944455",
"0.66436636",
"0.65685666",
"0.651623",
"0.6514857",
"0.6289315",
"0.6279256",
"0.6257811",
"0.62468415",
"0.6245594",
"0.61942685",
"0.61852056",
"0.6142807",
"0.6141403",
"0.60930455",
"0.6082008",
"0.6082008",
"0.6081551",
"0.6076993",
"0.6075414... | 0.5678671 | 53 |
Checks if created data class is of the right class. | def test_creates_data(self, config_filename, expected_class):
data = create_data(read_config_file(config_filename))
self.assertIsInstance(data, expected_class) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_dataclass_instance(obj):\n return hasattr(type(obj), '__dataclass_fields__')",
"def _check_dataclass(self) -> PossibleResult[T]:\n if is_dataclass(self.constructor):\n if not isinstance(self.obj, Mapping):\n raise DeserializeError(\n Mapping, self.ob... | [
"0.73167884",
"0.70281214",
"0.697299",
"0.6734586",
"0.65639776",
"0.6543953",
"0.6501673",
"0.64816797",
"0.6398697",
"0.63877684",
"0.63857174",
"0.62964743",
"0.62771004",
"0.6239756",
"0.6161517",
"0.6121133",
"0.6119971",
"0.60966104",
"0.6038572",
"0.59667945",
"0.5946... | 0.0 | -1 |
Checks if create date raises lookup error when name is incorrect. | def test_create_data_lookup_error(self):
with self.assertRaises(LookupError):
_ = create_data({"name": "fake_data"}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_name(self):\n\t\tpass",
"def check_for_date(date_str):\r\n try:\r\n if rex.match(\"\\d\\d\\d\\d-\\d\\d-\\d\\d\", str(date_str)) is None:\r\n raise sqlErr(\"Not a Date!\")\r\n except Exception as e:\r\n raise e",
"def _validate(self, date, format):\n... | [
"0.65987",
"0.61021996",
"0.60601944",
"0.60601944",
"0.6025887",
"0.60065466",
"0.5996862",
"0.5987295",
"0.5957946",
"0.5957502",
"0.59451586",
"0.59265476",
"0.59222174",
"0.5908189",
"0.5866697",
"0.5861176",
"0.584377",
"0.5835697",
"0.58273",
"0.58071387",
"0.58070785",... | 0.56072104 | 33 |
Checks if creates data collection. | def test_creates_data_collection(self):
data_collection = create_data_collection(read_config_file("test/data_collection.yaml"))
self.assertIsInstance(data_collection, DataCollection) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_collection(self):\n pass",
"def check_for_new_data(self):\n return",
"def _validate_create_data(self, data):\n return",
"def data_loaded_check(self):\n return True",
"def assertExists(self):\n for db in self._db_tree:\n assert(db in self._datast... | [
"0.69012666",
"0.66905195",
"0.6603279",
"0.64278567",
"0.63041574",
"0.61867595",
"0.61684585",
"0.6142971",
"0.6142184",
"0.60509443",
"0.60087866",
"0.60020673",
"0.59476244",
"0.5895808",
"0.5882238",
"0.58364767",
"0.5814158",
"0.58005524",
"0.5793292",
"0.57574344",
"0.... | 0.69225866 | 0 |
(Computed) The etag of the IAM policy. | def etag(self) -> str:
return pulumi.get(self, "etag") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def etag(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"etag\")",
"def etag(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"etag\")",
"def etag(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"etag\")",
"def etag(self) -> pulumi.Output[str]:\n return pul... | [
"0.7073697",
"0.7073697",
"0.7073697",
"0.7073697",
"0.7073697",
"0.7073697",
"0.7073697",
"0.69740593",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
"0.6932817",
... | 0.71071345 | 7 |
The providerassigned unique ID for this managed resource. | def id(self) -> str:
return pulumi.get(self, "id") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def provider_id(self):\n return self.get('_id')",
"def provider_id(self):\n raise NotImplementedError",
"def id(self):\n return self.raw_resource.uuid",
"def healthcare_provider_id(self):\n return self._healthcare_provider_id",
"def unique_identifier(self) -> str:\n retur... | [
"0.8193402",
"0.7851373",
"0.77124894",
"0.7604287",
"0.7477648",
"0.7476093",
"0.7476093",
"0.7476093",
"0.7425807",
"0.7380237",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.7371964",
"0.735787",
"0.735787",
"0.73477197",
"... | 0.0 | -1 |
(Required only by `bigqueryanalyticshub.ListingIamPolicy`) The policy data generated by a `organizations_get_iam_policy` data source. | def policy_data(self) -> str:
return pulumi.get(self, "policy_data") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def policy_data(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"policy_data\")",
"def policy_data(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"policy_data\")",
"def policy_data(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"policy_data\")",
"def pol... | [
"0.76912093",
"0.7415256",
"0.71202403",
"0.69792354",
"0.69779694",
"0.6871238",
"0.67510366",
"0.6739088",
"0.6735609",
"0.6735609",
"0.6735609",
"0.66321325",
"0.6614807",
"0.6489843",
"0.6465918",
"0.6465918",
"0.6463034",
"0.64587224",
"0.6429678",
"0.6399119",
"0.637848... | 0.7500135 | 2 |
Retrieves the current IAM policy data for listing example ```python import pulumi import pulumi_gcp as gcp policy = gcp.bigqueryanalyticshub.get_listing_iam_policy(project=google_bigquery_analytics_hub_listing["listing"]["project"], location=google_bigquery_analytics_hub_listing["listing"]["location"], data_exchange_id... | def get_listing_iam_policy(data_exchange_id: Optional[str] = None,
listing_id: Optional[str] = None,
location: Optional[str] = None,
project: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_listing_iam_policy_output(data_exchange_id: Optional[pulumi.Input[str]] = None,\n listing_id: Optional[pulumi.Input[str]] = None,\n location: Optional[pulumi.Input[Optional[str]]] = None,\n project: Optional[... | [
"0.7828049",
"0.6415348",
"0.6217417",
"0.6160221",
"0.6139532",
"0.6135994",
"0.61296153",
"0.61258733",
"0.61258733",
"0.61258733",
"0.6070894",
"0.6070894",
"0.6063281",
"0.6063281",
"0.6063281",
"0.5996258",
"0.5977472",
"0.59605813",
"0.59031934",
"0.59001213",
"0.589823... | 0.7959973 | 0 |
Retrieves the current IAM policy data for listing example ```python import pulumi import pulumi_gcp as gcp policy = gcp.bigqueryanalyticshub.get_listing_iam_policy(project=google_bigquery_analytics_hub_listing["listing"]["project"], location=google_bigquery_analytics_hub_listing["listing"]["location"], data_exchange_id... | def get_listing_iam_policy_output(data_exchange_id: Optional[pulumi.Input[str]] = None,
listing_id: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[Optional[str]]] = None,
project: Optional[pulumi.I... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_listing_iam_policy(data_exchange_id: Optional[str] = None,\n listing_id: Optional[str] = None,\n location: Optional[str] = None,\n project: Optional[str] = None,\n opts: Optional[pulumi.InvokeOptions] = ... | [
"0.7957954",
"0.6418271",
"0.62199444",
"0.6163126",
"0.61404836",
"0.6138383",
"0.6131887",
"0.6128473",
"0.6128473",
"0.6128473",
"0.6072734",
"0.6072734",
"0.60656893",
"0.60656893",
"0.60656893",
"0.5997886",
"0.59795076",
"0.5963106",
"0.59044737",
"0.59022045",
"0.59003... | 0.7827125 | 1 |
initialize with location of my articles and outdir | def __init__(self, workdir = "archived_links", outdir = "tmp"):
self.workdir = workdir
self.outdir = outdir
self.bigdf = ""
self.ArticlesLoaded = False
self.clf = "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(self) -> None:\n logger.debug(f\"[nbtutorial]: Outdir is: {self.outdir}\")",
"def __init__(self, texts_path, slug, metadata):\n self.texts_path = os.path.abspath(texts_path)\n\n self.slug = slug\n\n self.metadata = metadata",
"def __init__(self, output_dir: str):\n s... | [
"0.6814485",
"0.63777536",
"0.63513637",
"0.6347799",
"0.631205",
"0.62790346",
"0.62379956",
"0.6223603",
"0.61957836",
"0.6175018",
"0.6114833",
"0.6083057",
"0.6074342",
"0.6068505",
"0.6055408",
"0.6051235",
"0.60070103",
"0.59881234",
"0.5972294",
"0.59503233",
"0.592627... | 0.66417944 | 1 |
generates the dataframe and label | def loadArticles(self, pubList=[], outdir = ""):
allArticles = {}
publishers = [x for x in os.listdir(self.workdir) if x.find(".") == -1]
for publisher in publishers:
if len(pubList) > 0:
if publisher not in pubList:
continue
articles ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_generate_df_with_label(self):\n\n data_df = pyjstat.generate_df(self.oecd_datasets['oecd'], 'label')\n line_thirty = ['Unemployment rate', 'Belgium', 2009, 7.891892855]\n dimensions = pyjstat.get_dimensions(self.oecd_datasets['oecd'],\n 'labe... | [
"0.7051935",
"0.68929803",
"0.6859137",
"0.6765094",
"0.66359156",
"0.6608978",
"0.65593594",
"0.6559319",
"0.6543085",
"0.6508285",
"0.6502606",
"0.64988977",
"0.64407736",
"0.6428954",
"0.63835454",
"0.63357615",
"0.63354117",
"0.63071513",
"0.62099665",
"0.62028444",
"0.61... | 0.0 | -1 |
load train, test, and validation sets | def loadSets(self, indir=""):
if indir=="":
print("specify folder")
return -1
self.train = pd.read_pickle("{}/train.pkl".format(indir))
self.valid = pd.read_pickle("{}/valid.pkl".format(indir))
self.test = pd.read_pickle("{}/test.pkl".format(indir))
pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_training_set():\n global training_set\n f = gzip.open('mnist.pkl.gz', 'rb')\n train, valid, test = cPickle.load(f)\n [training_set, training_labels] = train\n [validation_set, validation_labels] = valid\n [testing_set, testing_labels] = test\n training_set = np.concatenate((training_set, validation... | [
"0.77761513",
"0.7662758",
"0.75225306",
"0.7434124",
"0.7416108",
"0.7411986",
"0.7359799",
"0.73430353",
"0.7298566",
"0.72648185",
"0.7188164",
"0.7177405",
"0.7131839",
"0.71224463",
"0.7074946",
"0.7033295",
"0.69873655",
"0.6973563",
"0.6957486",
"0.69372827",
"0.691362... | 0.7525998 | 2 |
fine tune features for awd_listm a few times | def setupEmbeddings(self, path = "awd_lm"):
try:
data_lm = TextLMDataBunch.from_df(path, train_df=self.train, valid_df=self.valid,\
text_cols = "text", label_cols = "label")
except:
print("error creating LM")
return
learn = language_model_learner(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n \n # The following 5 command lines can be outcommented if the features are already created.\n # There is no need to process the data every single time.\n # Fine tuning the learning algorythm is much faster without that extra step.\n \n # by reading the train dataset the feature inde... | [
"0.6061019",
"0.5993656",
"0.593578",
"0.592096",
"0.5915068",
"0.5902306",
"0.5864268",
"0.5861113",
"0.5835116",
"0.58337325",
"0.58134186",
"0.5757614",
"0.57337815",
"0.5727529",
"0.568164",
"0.5675858",
"0.5675187",
"0.5664823",
"0.5611879",
"0.5602952",
"0.55911386",
... | 0.0 | -1 |
Print the function runtime | def timer(func):
@wraps(func)
def wrap_timer(*args, **kwargs):
t0 = perf_counter()
returned = func(*args, **kwargs)
t1 = perf_counter()
print(f"[Time: {t1-t0:.6f} s]")
return returned
return wrap_timer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runtime_print(f):\n def decorated_fun(*args, **kwargs):\n t0 = datetime.now()\n ret = f(*args, **kwargs)\n t1 = datetime.now()\n print(f'Runtime: {t1 - t0}')\n return ret\n\n return decorated_fun",
"def print_func_measuremetns():\n print(\"Measured functions:\")\n ... | [
"0.7667241",
"0.7212847",
"0.67484915",
"0.6739686",
"0.66121894",
"0.6481864",
"0.6399771",
"0.63735133",
"0.6281253",
"0.6276147",
"0.6228342",
"0.6212566",
"0.6120459",
"0.61113286",
"0.6105854",
"0.6098444",
"0.60396963",
"0.6032985",
"0.6006991",
"0.598489",
"0.59652984"... | 0.0 | -1 |
Calculate the crossentropy loss for a batch. | def forward(self, x, y):
# calculate softmax probabilities
x = x - np.max(x, axis=1, keepdims=True)
prob = np.exp(x)
prob = prob / np.sum(prob, axis=1, keepdims=True)
loss = -np.sum(y * np.log(prob + 1e-8))
if self.reduction == "mean":
m, _ = x.shape
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cross_entropy_loss(batch_out, batch_gt):\r\n criterion = torch.nn.CrossEntropyLoss()\r\n target = torch.argmax(batch_gt, 1)\r\n loss = criterion(batch_out, target)\r\n\r\n return loss",
"def loss_calc(pred, label, device):\r\n # out shape batch_size x channels x h x w -> batch_size x channels ... | [
"0.7592742",
"0.7591871",
"0.74489385",
"0.7352859",
"0.7275295",
"0.7253083",
"0.72098607",
"0.7196246",
"0.7168889",
"0.7168013",
"0.71548504",
"0.71115357",
"0.7018222",
"0.69646066",
"0.69580334",
"0.695356",
"0.69529086",
"0.69491434",
"0.69073373",
"0.68900985",
"0.6882... | 0.0 | -1 |
Backward computation of gradients. | def backward(self):
assert self.cache is not None, "Cannot backprop without forward first."
prob, y = self.cache
dX = prob - y
if self.reduction == "mean":
m, _ = prob.shape
dX /= m
# clear cache
self.cache = None
return dX | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def backward(self, gradient):\n #TODO\n pass",
"def backward(self, gradient):\n #TODO\n pass",
"def backward(self, gradient):\n raise NotImplementedError()",
"def backward(self, gradient: Tensor) -> Tensor:\n self.b_grad = np.sum(gradient, axis=0)\n self.w_gra... | [
"0.82506675",
"0.82506675",
"0.82469416",
"0.8232206",
"0.80324847",
"0.80324847",
"0.78823906",
"0.7857436",
"0.78483355",
"0.78429383",
"0.77847767",
"0.7679982",
"0.76649225",
"0.7645512",
"0.7608589",
"0.7604201",
"0.759334",
"0.7587491",
"0.75818443",
"0.7530521",
"0.751... | 0.0 | -1 |
Pick nth member of a randomised list of perturbation fields, and apply it to soil moistire in wrfinput. | def fractalise(fractals,n,fpath):
# Use different fractals for MP
n = nnn-n
# Load numpy array
for f in fractals:
if f.endswith('{:02d}.npy'.format(n)):
frac_arr = N.load(f)
f_out = f
# Normalise to +/- 1%
frac_arr_pc = normalise(frac_arr)
# Load virgin ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_town(self, people=50):\n \n #1.5 acres farm needed per person\n #farmer could farm 20-40 (30) acres\n #30/1.5 = 20 people per farm\n people_to_assign = people\n farms_needed = (self.population + people)/20 + 1\n if people_to_assign >= farms_needed:\n ... | [
"0.5138526",
"0.51018333",
"0.5094111",
"0.49444968",
"0.49331975",
"0.49025005",
"0.49025005",
"0.48996407",
"0.48993465",
"0.48773155",
"0.4877003",
"0.4868825",
"0.4847049",
"0.48081005",
"0.4741778",
"0.47232425",
"0.4711773",
"0.4694738",
"0.46840546",
"0.4678606",
"0.46... | 0.0 | -1 |
Test for the view returning hardcoded data for the template | def test_the_info_view(self):
info_url = resolve('/')
self.assertEqual(info_url.func.__name__, 'Info_view')
self.assertEqual(self.response.status_code, 200) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_home_view_template(self):\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'user_data.html')",
"def test_get_template_data(self, mock_gsiup):\n mock_gsiup.return_value = models.UserPref(\n email='user@... | [
"0.70924264",
"0.6801489",
"0.6795118",
"0.659572",
"0.65819216",
"0.65160805",
"0.64946854",
"0.648827",
"0.64539325",
"0.6453025",
"0.64388144",
"0.64339983",
"0.6404006",
"0.6361182",
"0.6361182",
"0.6361182",
"0.6361182",
"0.6361182",
"0.6319067",
"0.631654",
"0.6288902",... | 0.0 | -1 |
Test for template correctness | def test_for_template(self):
self.assertTemplateUsed(self.response, 'my_info_template.html') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_template_feedback(self):\r\n pass",
"def test_create_template_subsciption(self):\n pass",
"def test_register_template(self):\n pass",
"def validate_template(template):\n if not isinstance(template, Template):\n raise TypeError(\"%s is not a template\" % template)",
"... | [
"0.7268917",
"0.7127761",
"0.7098289",
"0.68893516",
"0.6856749",
"0.6837006",
"0.6832027",
"0.6791806",
"0.6789153",
"0.667286",
"0.65756553",
"0.65742195",
"0.65539366",
"0.6544988",
"0.6526361",
"0.65153843",
"0.6472864",
"0.6436084",
"0.64188963",
"0.63870656",
"0.6362022... | 0.6860066 | 4 |
Test that view renders data from model | def test_the_view_render_Contact_instance(self):
my_info = self.response.context_data['info']
self.assertIsInstance(my_info, Contact)
model_instance = Contact.objects.first()
self.assertIn(model_instance.name, self.response.content)
self.assertIn(model_instance.surname, self.re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_context_data(self):\n self.view.object = self.obj\n context = self.view.get_context_data()\n self.assertIn(\"code\", context)\n self.assertIn(\"edit\", context)\n self.assertTrue(context[\"edit\"])",
"def test():\n return render_template(\n 'test.html',\n... | [
"0.6840645",
"0.66080403",
"0.6523233",
"0.64715046",
"0.6451231",
"0.6444405",
"0.64431345",
"0.6432887",
"0.6404577",
"0.64019704",
"0.6368436",
"0.63485414",
"0.634387",
"0.63366467",
"0.63298786",
"0.63038164",
"0.62876505",
"0.62627774",
"0.62624705",
"0.6258345",
"0.624... | 0.7052593 | 0 |
Test if unicode is in data base | def test_the_unicode_in_data_base(self):
model_instance = Contact.objects.first()
model_instance.name = u'Олег'
model_instance.surname = u'Сенишин'
model_instance.bio = u'Працюю, після роботи, самостійно вивчаю Python, Django, JavaScript.'
model_instance.save()
response... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def IsUnicodeSupported(self):\n return self._common_type.IsUnicodeSupported()",
"def test_unicode_warnings(self):\n\n unicodedata = u\"Alors vous imaginez ma surprise, au lever du jour, quand \"\\\n u\"une drôle de petit voix m’a réveillé. \"\\\n u\"Elle di... | [
"0.6732994",
"0.64144266",
"0.6325585",
"0.61495537",
"0.611053",
"0.6060772",
"0.60573775",
"0.602658",
"0.6022272",
"0.60210747",
"0.59900254",
"0.58709526",
"0.58263814",
"0.5813352",
"0.5812566",
"0.5799742",
"0.57963264",
"0.5767176",
"0.57383955",
"0.5736866",
"0.569727... | 0.58200395 | 13 |
Test that nothing breaks when database is empty | def test_in_case_base_data_is_empty(self):
Contact.objects.all().delete()
obj_list = Contact.objects.all()
self.assertFalse(obj_list)
response = self.client.get(reverse("my_info"))
my_info = response.context_data['info']
self.assertIsNone(my_info)
self.assertEqu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_empty_db(client, test_db):\n rv = client.get(\"/\")\n assert b\"No entries yet. Add some!\" in rv.data",
"def test_empty_db(self):\n rv = self.app.get('/')\n assert b'Your closet is empty.' in rv.data",
"async def test_fetch_users_empty(database):\n await database.setup_database... | [
"0.81646407",
"0.7992816",
"0.7651522",
"0.76504767",
"0.7524679",
"0.7524679",
"0.7524679",
"0.7524679",
"0.7524679",
"0.7524679",
"0.7477498",
"0.74590117",
"0.74297035",
"0.7413825",
"0.73992026",
"0.73721033",
"0.73433846",
"0.73256075",
"0.73021424",
"0.729906",
"0.71927... | 0.64519596 | 86 |
Test for form date field validation | def test_form_date_validation(self):
form = My_add_data_form(data={'date': date(1800, 05, 03)})
self.assertEqual(form.errors['date'], ['You already dead now'])
form = My_add_data_form(data={'date': date(2200, 05, 03)})
self.assertEqual(form.errors['date'], ['You not born yet']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_date_field():",
"def validate(self, test_data):\n if not isinstance(test_data, datetime.date):\n raise ValidationError('Invalid type/value.', 'datetime.date',\n type(test_data))",
"def check_date_format(form, field):\n try:\n field.data = da... | [
"0.79683304",
"0.7457315",
"0.74449104",
"0.73319423",
"0.73196423",
"0.7264576",
"0.7140698",
"0.7116886",
"0.70986587",
"0.70432013",
"0.7036628",
"0.6971",
"0.69465804",
"0.69303936",
"0.69096756",
"0.69046116",
"0.69046116",
"0.68596894",
"0.6810483",
"0.67967176",
"0.678... | 0.84299916 | 0 |
Test for the view returning hardcoded data for the template | def test_the_data_edit_url(self):
my_instance = Contact.objects.first()
info_url = resolve('/to_form/%s/' % my_instance.id)
self.assertEqual(info_url.func.__name__, 'my_edit_data')
self.assertEqual(self.response.status_code, 200) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_home_view_template(self):\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'user_data.html')",
"def test_get_template_data(self, mock_gsiup):\n mock_gsiup.return_value = models.UserPref(\n email='user@... | [
"0.70924264",
"0.6801489",
"0.6795118",
"0.659572",
"0.65819216",
"0.65160805",
"0.64946854",
"0.648827",
"0.64539325",
"0.6453025",
"0.64388144",
"0.64339983",
"0.6404006",
"0.6361182",
"0.6361182",
"0.6361182",
"0.6361182",
"0.6361182",
"0.6319067",
"0.631654",
"0.6288902",... | 0.0 | -1 |
Test that view return errors in Json format | def test_that_view_return_errors_in_json(self):
self.client.login(username='admin', password='admin')
url = reverse("to_form", args=str(self.my_instance.id))
response = self.client.post(url, data={'name': 'Oleg'}, format='json')
self.assertEqual(response.status_code, 200)
for c ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_invalid_json(self):\r\n data = {\"Testing invalid\"}\r\n response = self.client.post(\r\n reverse('verify_student_results_callback'),\r\n data=data,\r\n content_type='application/json',\r\n HTTP_AUTHORIZATION='test BBBBBBBBBBBBBBBBBBBB: testing',\r... | [
"0.6963432",
"0.68196243",
"0.6738075",
"0.6731617",
"0.66677034",
"0.66568595",
"0.6615065",
"0.65761465",
"0.65392554",
"0.6538512",
"0.6524916",
"0.6521766",
"0.6519895",
"0.6510612",
"0.6499493",
"0.6459137",
"0.6440112",
"0.6433431",
"0.64058393",
"0.6382526",
"0.6369236... | 0.8210779 | 0 |
Test that view saves data if form valid | def test_that_view_saves_data_if_form_valid(self):
self.client.login(username='admin', password='admin')
url = reverse("to_form", args=str(self.my_instance.id))
response = self.client.post(url, data={'name': 'Oleg', 'surname': 'Senyshyn', 'date': date(1995, 05, 03),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_submit_form_using_valid_data():",
"def test_valid_form_true(self):\n form = UserRegisterForm(data=self.data)\n self.assertTrue(form.is_valid())",
"def test_form_valid(self):\n form = Mock()\n form.cleaned_data = Mock()\n self.view.form_valid(form)\n self.asser... | [
"0.75905186",
"0.6972983",
"0.68898875",
"0.68876064",
"0.6843631",
"0.68388474",
"0.6799494",
"0.6762418",
"0.6688027",
"0.66799116",
"0.6619247",
"0.6603506",
"0.6595378",
"0.65945286",
"0.6561139",
"0.65168655",
"0.6509908",
"0.64848745",
"0.6478526",
"0.6477208",
"0.64530... | 0.806071 | 0 |
Loads patient Procedure observations | def load(cls):
# Loop through procedures and build patient procedure lists:
procs = csv.reader(file(PROCEDURES_FILE,'U'),dialect='excel-tab')
header = procs.next()
for proc in procs:
cls(dict(zip(header,proc))) # Create a procedure instance | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_patient(self, patient):\n \n # Maintain patient count \n self.count_total_patients += 1\n \n # Allocate patient to subunit/session\n self.allocate_patient(patient)\n \n # Add to appropriate _population lists\n patient_dict = {'negative': s... | [
"0.6553638",
"0.5726791",
"0.5658638",
"0.56560385",
"0.5612213",
"0.5544604",
"0.5427802",
"0.533888",
"0.5330403",
"0.5310503",
"0.5299247",
"0.52846605",
"0.5251733",
"0.5122264",
"0.5118435",
"0.50937766",
"0.50423414",
"0.50294584",
"0.50182426",
"0.4999633",
"0.4999633"... | 0.68310565 | 0 |
Returns a tabseparated string representation of a procedure | def asTabString(self):
dl = [self.pid, self.date, self.snomed, self.name[:20]]
s = ""
for v in dl:
s += "%s\t"%v
return s[0:-1] # Throw away the last tab | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def asTabString(self):\n dl = [self.pid, self.start, self.snomed, self.name[:20]]\n s = \"\"\n for v in dl:\n s += \"%s\\t\"%v \n return s[0:-1] # Throw away the last tab",
"def print_para_table(s):\n if MODE == 1:\n t = [['Parameter', 'Value', 'Unit'],\n ['N... | [
"0.62828636",
"0.61583054",
"0.57344764",
"0.57325906",
"0.57238656",
"0.57168484",
"0.57098097",
"0.57098097",
"0.5668657",
"0.5587102",
"0.55370563",
"0.54839957",
"0.5471588",
"0.54644656",
"0.542793",
"0.53945804",
"0.53495973",
"0.5316048",
"0.5290959",
"0.52773845",
"0.... | 0.62052697 | 1 |
Return a list of dictionaries from the text and attributes of the children under this XML root. | def parse_root(self, root):
return [self.parse_element(child) for child in root.getchildren()]
# [child for child in root.getchildren()] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_attributes_from_child(child):\n return [{'element': child,\n 'attribute': x.attrib,\n 'tag': x.tag,\n 'keys': x.keys()} for x in child]",
"def xml_children_as_dict(node):\n return dict((e.tag, e.text) for e in node)",
"def get_attributes_from_ch... | [
"0.7088785",
"0.70835507",
"0.7002394",
"0.6750578",
"0.66856766",
"0.666509",
"0.6659247",
"0.65232205",
"0.65068334",
"0.63856643",
"0.63711137",
"0.6368282",
"0.6368282",
"0.6367467",
"0.6303724",
"0.6253999",
"0.62409544",
"0.6207389",
"0.6205319",
"0.6199433",
"0.6199433... | 0.56534237 | 65 |
Initiate the root XML, parse it, and return a dataframe | def process_data(self):
structure_data = self.parse_root(self.root)
dict_data = {}
for d in structure_data:
dict_data = {**dict_data, **d}
df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_XML(xml_file, df_cols):\n \n xtree = et.parse(xml_file)\n xroot = xtree.getroot()\n rows = []\n \n for node in xroot: \n res = []\n #res.append(node.attrib.get(df_cols[0]))\n for el in df_cols: \n if node is not None and node.find(el) is not None:\n ... | [
"0.65164536",
"0.6279615",
"0.6141911",
"0.6138031",
"0.6130271",
"0.60411644",
"0.6022583",
"0.60092944",
"0.5845552",
"0.5839543",
"0.58087265",
"0.5732619",
"0.56914544",
"0.5677152",
"0.5674767",
"0.56603354",
"0.56579065",
"0.5635685",
"0.5619518",
"0.5595719",
"0.552725... | 0.6824071 | 0 |
Converts a string into a returned boolean. | def string_to_bool(arg):
if arg.lower() == 'true':
arg = True
elif arg.lower() == 'false':
arg = False
else:
raise ValueError('ValueError: Argument must be either "true" or "false".')
return arg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toBool( string ):\r\n return string == 'true'",
"def __str_to_bool(self, s):\n if s == 'True':\n return True\n elif s == 'False':\n return False\n else:\n raise ValueError",
"def strToBool(s):\n\tassert type(s) == str or type(s) == unicode\n\treturn ... | [
"0.8471315",
"0.84079623",
"0.8271629",
"0.81777906",
"0.8156742",
"0.8126672",
"0.81109166",
"0.80406594",
"0.8026977",
"0.80231994",
"0.80016416",
"0.79439914",
"0.79439867",
"0.7938794",
"0.7926165",
"0.79252136",
"0.7922872",
"0.78702635",
"0.7863978",
"0.7842485",
"0.783... | 0.7725143 | 26 |
Creates and returns a dictionary of parameters. | def set_parameters(targeted_flag='true',
tv_flag='false',
hinge_flag='true',
cos_flag='false',
interpolation='bilinear',
model_type='small',
loss_type='center',
dataset_type='vgg',... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameters_dict(self):\n return",
"def get_params(self) -> dict:\n # initialize dictionary\n params = dict()\n\n # loop through parameters, adding to parameter dictionary\n for key in self._get_param_names():\n params[key] = getattr(self, key)\n\n return p... | [
"0.77072877",
"0.76394325",
"0.75884134",
"0.75218064",
"0.75122553",
"0.7418143",
"0.7411113",
"0.7338234",
"0.7304477",
"0.7296404",
"0.72799194",
"0.727563",
"0.7259348",
"0.72202986",
"0.7213263",
"0.71996874",
"0.7194121",
"0.7186132",
"0.7162062",
"0.714997",
"0.7136379... | 0.0 | -1 |
Constructor. Any message fields that are implicitly/explicitly set to None will be assigned a default value. The recommend use is keyword arguments as this is more robust to future message changes. You cannot mix inorder arguments and keyword arguments. | def __init__(self, *args, **kwds):
if args or kwds:
super(GraspableObjectList, self).__init__(*args, **kwds)
# message fields cannot be None, assign default values for those that are
if self.graspable_objects is None:
self.graspable_objects = []
if self.image is None:
self.im... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, *args, **kwds):\n if args or kwds:\n super(KomodoSpeechRecCommand, self).__init__(*args, **kwds)\n #message fields cannot be None, assign default values for those that are\n if self.header is None:\n self.header = std_msgs.msg.Header()\n if self.cmd is None:\n ... | [
"0.77936506",
"0.7720004",
"0.7720004",
"0.76861507",
"0.76264745",
"0.756367",
"0.74394095",
"0.74168324",
"0.7351523",
"0.7307856",
"0.7280199",
"0.7237893",
"0.72272074",
"0.7223773",
"0.7223773",
"0.7218694",
"0.7211321",
"0.7211321",
"0.7211321",
"0.7211321",
"0.7211321"... | 0.0 | -1 |
serialize message into buffer | def serialize(self, buff):
try:
length = len(self.graspable_objects)
buff.write(_struct_I.pack(length))
for val1 in self.graspable_objects:
_x = val1.reference_frame_id
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize(self, buff):\n try:\n _x = self\n buff.write(_struct_2d2q14dq.pack(_x.tcp, _x.ori, _x.zone, _x.vacuum, _x.workx, _x.worky, _x.workz, _x.workq0, _x.workqx, _x.workqy, _x.workqz, _x.toolx, _x.tooly, _x.toolz, _x.toolq0, _x.toolqx, _x.toolqy, _x.toolqz, _x.ret))\n _x = self.msg\n ... | [
"0.6972838",
"0.6802016",
"0.6796291",
"0.67317253",
"0.66948694",
"0.66847515",
"0.6658851",
"0.6626311",
"0.66227144",
"0.6618303",
"0.6607444",
"0.65361035",
"0.6529462",
"0.65220654",
"0.64863104",
"0.6467052",
"0.64530367",
"0.6438432",
"0.6427743",
"0.6426063",
"0.63947... | 0.0 | -1 |
unpack serialized message in str into this message instance | def deserialize(self, str):
if python3:
codecs.lookup_error("rosmsg").msg_type = self._type
try:
if self.graspable_objects is None:
self.graspable_objects = None
if self.image is None:
self.image = sensor_msgs.msg.Image()
if self.camera_info is None:
self.camera_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deserialize(self, str):\n try:\n end = 0\n _x = self\n start = end\n end += 152\n (_x.tcp, _x.ori, _x.zone, _x.vacuum, _x.workx, _x.worky, _x.workz, _x.workq0, _x.workqx, _x.workqy, _x.workqz, _x.toolx, _x.tooly, _x.toolz, _x.toolq0, _x.toolqx, _x.toolqy, _x.toolqz, _x.ret,) = _stru... | [
"0.7632397",
"0.74631983",
"0.731979",
"0.730263",
"0.7269273",
"0.7222538",
"0.72173554",
"0.7174571",
"0.7169",
"0.70951414",
"0.70948184",
"0.7040944",
"0.7029083",
"0.69617283",
"0.69550794",
"0.69547164",
"0.6943421",
"0.69407064",
"0.69404405",
"0.6910806",
"0.689353",
... | 0.64665866 | 63 |
serialize message with numpy array types into buffer | def serialize_numpy(self, buff, numpy):
try:
length = len(self.graspable_objects)
buff.write(_struct_I.pack(length))
for val1 in self.graspable_objects:
_x = val1.reference_frame_id
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize_numpy(self, buff, numpy):\n try:\n pass\n except struct.error as se: self._check_types(se)\n except TypeError as te: self._check_types(te)",
"def serialize_numpy(self, buff, numpy):\n try:\n _x = self\n buff.write(_struct_2d2q14dq.pack(_x.tcp, _x.ori, _x.zone, _x.vacuum, ... | [
"0.7994329",
"0.7972612",
"0.7893365",
"0.785495",
"0.7740611",
"0.7677824",
"0.7657543",
"0.7626826",
"0.75874037",
"0.7565361",
"0.75626636",
"0.75620574",
"0.7558187",
"0.75431186",
"0.7534563",
"0.7527763",
"0.75262064",
"0.75172293",
"0.75110817",
"0.749996",
"0.7497972"... | 0.6931643 | 90 |
unpack serialized message in str into this message instance using numpy for array types | def deserialize_numpy(self, str, numpy):
if python3:
codecs.lookup_error("rosmsg").msg_type = self._type
try:
if self.graspable_objects is None:
self.graspable_objects = None
if self.image is None:
self.image = sensor_msgs.msg.Image()
if self.camera_info is None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def deserialize_numpy(self, str, numpy):\n try:\n end = 0\n _x = self\n start = end\n end += 152\n (_x.tcp, _x.ori, _x.zone, _x.vacuum, _x.workx, _x.worky, _x.workz, _x.workq0, _x.workqx, _x.workqy, _x.workqz, _x.toolx, _x.tooly, _x.toolz, _x.toolq0, _x.toolqx, _x.toolqy, _x.toolqz, _x.... | [
"0.8089704",
"0.7976802",
"0.7917302",
"0.7906449",
"0.7893566",
"0.7831728",
"0.7812211",
"0.78098595",
"0.7784959",
"0.77591026",
"0.77403194",
"0.77151906",
"0.77145106",
"0.76970345",
"0.76930326",
"0.76857287",
"0.7673268",
"0.7639446",
"0.7636968",
"0.76292497",
"0.7624... | 0.7313426 | 56 |
Prints onto the Console the contents of the given ttk.Entry. In this example, it is used as the function that is "CALLED BACK" when an event (namely, the pressing of a certain Button) occurs. | def print_contents(entry_box):
contents_of_entry_box = entry_box.get()
print(contents_of_entry_box) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_entry(text):\n print \"Text entered: \\n '%s'\" % text",
"def display_entry(self, entry):\n border = '-' * 50\n print(border)\n print('Employee: {}'.format(entry.employee_name))\n print('Task Name: {}'.format(entry.task_name))\n print(\"Date: {}\".format(entry.date)... | [
"0.68677735",
"0.66442794",
"0.6286252",
"0.6025847",
"0.60157746",
"0.5983749",
"0.5923214",
"0.58699477",
"0.5760914",
"0.5722802",
"0.5621972",
"0.55991316",
"0.5574494",
"0.5553222",
"0.5552907",
"0.55416965",
"0.5510099",
"0.54524994",
"0.5447962",
"0.5444517",
"0.543691... | 0.62130547 | 3 |
Generate the doxygen XML. | def __init__(
self,
doxygen_executable,
runner,
recursive,
source_paths,
output_path,
warnings_as_error,
):
self.doxygen_executable = doxygen_executable
self.runner = runner
self.recursive = recursive
self.source_paths = source_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate(self):\n\n # Write Doxyfile\n doxyfile_content = DOXYFILE_TEMPLATE.format(\n name=\"wurfapi\",\n output_path=self.output_path,\n source_path=\" \".join(self.source_paths),\n recursive=\"YES\" if self.recursive else \"NO\",\n extra=\"... | [
"0.7742234",
"0.74330884",
"0.73922634",
"0.67211056",
"0.6559393",
"0.6496459",
"0.6456328",
"0.6445485",
"0.63774556",
"0.6369798",
"0.6321232",
"0.6274118",
"0.622829",
"0.6170425",
"0.6131777",
"0.61040103",
"0.609831",
"0.6032156",
"0.60005504",
"0.59803677",
"0.595689",... | 0.0 | -1 |
Generate the Doxygen XML. We do not have to remove any old XML or similar since we use the index.xml file to parse the rest.. So if some stale information is in the output folder it is ok we will not use it anyway | def generate(self):
# Write Doxyfile
doxyfile_content = DOXYFILE_TEMPLATE.format(
name="wurfapi",
output_path=self.output_path,
source_path=" ".join(self.source_paths),
recursive="YES" if self.recursive else "NO",
extra="",
)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_doxygen(self):\n if not getattr(self, \"doxygen_conf\", None):\n self.generator.bld.fatal(\"No doxygen configuration file supplied.\")\n if not isinstance(self.doxygen_conf, Node.Node):\n self.generator.bld.fatal(\"'doxygen_conf' must be a Node.\")\n\n self.create_task(\n ... | [
"0.70324063",
"0.6946403",
"0.68050563",
"0.66510713",
"0.6509056",
"0.6443243",
"0.6337205",
"0.63226926",
"0.62693423",
"0.62597907",
"0.623974",
"0.62392175",
"0.6145338",
"0.6020383",
"0.6006961",
"0.6003898",
"0.5965673",
"0.5950159",
"0.5910592",
"0.5896202",
"0.5866549... | 0.77589625 | 0 |
Sidebar widget to select your data view | def select_data_view() -> str:
st.sidebar.markdown('### Select your data view:')
view_select = st.sidebar.selectbox('', DATA_VIEWS, index=0). \
replace(' (NEW)', '')
return view_select | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_layout() -> None:\n\n st.sidebar.title(\"Menu\")\n app_mode = st.sidebar.selectbox(\"Please select a page\", [' I. Homepage',\n \"II. Download data\" ,\n \"III. Statistic Data\",... | [
"0.55691195",
"0.5513806",
"0.5493136",
"0.5451674",
"0.5418005",
"0.5393736",
"0.53791744",
"0.5376035",
"0.5333957",
"0.53197217",
"0.53023964",
"0.5265653",
"0.5262883",
"0.52579665",
"0.5177404",
"0.5163641",
"0.51526636",
"0.5104715",
"0.510281",
"0.5094196",
"0.5078733"... | 0.61881065 | 0 |
Sidebar widget to select fiscal year | def select_fiscal_year(view_select) -> str:
if 'Wage Growth' in view_select:
working_fy_list = FY_LIST[:-1]
else:
working_fy_list = FY_LIST
st.sidebar.markdown('### Select fiscal year:')
fy_select = st.sidebar.selectbox('', working_fy_list, index=0).split(' ')[0]
return fy_select | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_calender_year(self, year):\n self.single_selection_from_kendo_dropdown(self.calender_year_kendo_dropdown_locator, year)",
"def set_start_year(self, year):\n return self.form.set_value(\"output period \\\"year from\\\"\", str(year))",
"def MonthYearFieldWidget(field, request):\n return ... | [
"0.6576069",
"0.6364317",
"0.62365025",
"0.6131743",
"0.6076331",
"0.5969902",
"0.5813705",
"0.57639",
"0.5758694",
"0.5715589",
"0.56626064",
"0.5603968",
"0.5560014",
"0.5438583",
"0.54234385",
"0.53938276",
"0.5356743",
"0.5356063",
"0.5349551",
"0.5347137",
"0.53393054",
... | 0.7023844 | 0 |
Sidebar widget to select pay rate conversion (hourly/annual) | def select_pay_conversion(fy_select, pay_norm, view_select) -> int:
st.sidebar.markdown('### Select pay rate conversion:')
conversion_select = st.sidebar.selectbox('', PAY_CONVERSION, index=0)
if conversion_select == 'Hourly':
if view_select != 'Trends':
pay_norm = FISCAL_HOURS[fy_selec... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cb_radio(label):\n global pm_rate\n rate_dict = {'0.2 Step': 0.2, '1.0 Step': 1.0}\n pm_rate = rate_dict[label]",
"def select_rates_tab(self):\n self.select_static_tab(self.rates_tab_locator, True)",
"def render_investip():\n\tlinewidth = 2\n\n\tst.sidebar.markdown('# Dashboard')\n\tstock =... | [
"0.55547404",
"0.53186727",
"0.5312881",
"0.5295051",
"0.5293411",
"0.5169351",
"0.5135066",
"0.511413",
"0.5101465",
"0.50238144",
"0.49694377",
"0.49577525",
"0.4831858",
"0.48169535",
"0.48148766",
"0.4786951",
"0.47772965",
"0.47656235",
"0.4762115",
"0.47487792",
"0.4740... | 0.64868665 | 0 |
Sidebar widget to select trends for Trends page | def select_trends() -> str:
trends_checkbox = st.sidebar.checkbox(f'Show all trends', True)
if trends_checkbox:
trends_select = TRENDS_LIST
else:
trends_select = st.sidebar.multiselect('Select your trends', TRENDS_LIST)
return trends_select | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getTrends(): \n api = authentication()\n names = [i.name for i in api.GetTrendsCurrent()]\n stringTrends = [i.strip('#') for i in names and ]\n trends = [i for i in stringTrends if i != \"\"]\n return trends",
"def get_trends():\n return api.trends_available()",
"def trending(request):\n\titems = I... | [
"0.6033017",
"0.5886845",
"0.54061174",
"0.5199153",
"0.51406914",
"0.512278",
"0.5105741",
"0.50310594",
"0.49992782",
"0.49274763",
"0.481228",
"0.48026663",
"0.47352293",
"0.46986964",
"0.46903226",
"0.4664409",
"0.466313",
"0.46275526",
"0.45833886",
"0.45830315",
"0.4561... | 0.6717184 | 0 |
Sidebar widget to select minimum salary for Highest Earners page | def select_minimum_salary(df, step, college_select: str = ''):
st.sidebar.markdown('### Enter minimum FTE salary:')
sal_describe = df[SALARY_COLUMN].describe()
number_input_settings = {
'min_value': 100000,
'max_value': int(sal_describe['max']),
'value': 500000,
'step': ste... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_min_expense(self):\n pass",
"def setMinMax(self):\n currentIndustryNum = self.myParent.myIndustry[self.myIndustryData.id]\n oldIndustryNum = self.myParent.myOldIndustry[self.myIndustryData.id]\n self.setMinValue(-currentIndustryNum)\n if oldIndustryNum > currentIndustr... | [
"0.50486606",
"0.5035432",
"0.5007743",
"0.49070513",
"0.48892468",
"0.4817341",
"0.47293594",
"0.4686217",
"0.468501",
"0.4654785",
"0.4652862",
"0.464481",
"0.46400693",
"0.46108285",
"0.46024165",
"0.45971134",
"0.45901182",
"0.45752954",
"0.45622933",
"0.45582008",
"0.455... | 0.60446465 | 0 |
Sidebar widget to select salary bin size for histogram plots | def select_bin_size(pay_norm: int, index: int = 2,
markdown_text: str = 'salary') -> float:
st.sidebar.markdown(f'### Select {markdown_text} bin size:')
if pay_norm == 1:
bin_size = st.sidebar.selectbox('', ['$1,000', '$2,500', '$5,000', '$10,000'],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_histogram(self):\n n_rows = self.df.shape[0]\n if n_rows > 250:\n fig, ax = plt.subplots()\n ax.hist(self.df[self.col_name], bins=50)\n else: \n fig, ax = plt.subplots()\n ax.hist(self.df[self.col_name], bins=int(round(n_rows/5,0)))\n return fig",
"def makeHistogram(values... | [
"0.6282638",
"0.6234359",
"0.61944395",
"0.6177648",
"0.6122733",
"0.5965984",
"0.5894797",
"0.58317345",
"0.5807618",
"0.57804626",
"0.57664645",
"0.57572645",
"0.57370436",
"0.57100546",
"0.56775147",
"0.5642595",
"0.5595545",
"0.55602545",
"0.55257535",
"0.55219674",
"0.55... | 0.5996436 | 5 |
Sidebar widget to identify search method for individual search page | def select_search_method():
st.sidebar.markdown('### Search method:')
search_method = st.sidebar.selectbox('', ['Individual', 'Department'], index=0)
return search_method | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def searchWidget(self):\n return self.__searchWidget",
"def search_page():\n return render_template('page_query.html', search_label=g_search_type)",
"def on_searchButton_clicked(self):\n self.__search()",
"def search_btn_clicked(self, widget, data=None):\n # Method to handle search he... | [
"0.65629",
"0.64431345",
"0.6246589",
"0.6217658",
"0.61798435",
"0.6041973",
"0.5982113",
"0.5897964",
"0.5824327",
"0.57609326",
"0.57595426",
"0.5737622",
"0.5707807",
"0.5639201",
"0.5621058",
"0.5614636",
"0.55623174",
"0.5536656",
"0.5523965",
"0.5495684",
"0.5465359",
... | 0.6213812 | 4 |
Sidebar widget to indicate sorting method | def select_sort_method():
st.sidebar.markdown('### Sort method:')
sort_select = st.sidebar.selectbox('', ['Alphabetically', 'FTE Salary'],
index=1)
return sort_select | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mouseDoubleClickEvent(self, event):\n if event.button() == QtCore.Qt.LeftButton:\n sw = self.spw.windows['Sort']\n sw.clear()",
"def OnColumnClick(self, event):\r\n columns = self.data.getParam('columns')\r\n self.SortItems(columns[event.GetColumn()],'INVERT')",
"... | [
"0.64066905",
"0.61197245",
"0.5869867",
"0.57673573",
"0.5749316",
"0.53632724",
"0.5358934",
"0.53537744",
"0.5340585",
"0.53209037",
"0.5320746",
"0.53140193",
"0.5310284",
"0.5279612",
"0.52369547",
"0.5233304",
"0.52298254",
"0.51825815",
"0.51570153",
"0.51435727",
"0.5... | 0.5715584 | 5 |
Wrapper around k8s.load_and_create_resource to create a SageMaker resource | def create_sagemaker_resource(
resource_plural, resource_name, spec_file, replacements, namespace="default"
):
reference, spec, resource = k8s.load_and_create_resource(
resource_directory,
CRD_GROUP,
CRD_VERSION,
resource_plural,
resource_name,
spec_file,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_resource(\n service_name: str, config_name: str = None, **resource_args\n):\n session = get_session(config_name)\n return session.resource(service_name, **resource_args)",
"def create_resource():\n return wsgi.Resource(Controller())",
"def create_resource():\n return wsgi.Resource(Con... | [
"0.6695489",
"0.65566033",
"0.6527681",
"0.63303506",
"0.61349356",
"0.6096821",
"0.6095365",
"0.6082456",
"0.60742486",
"0.6072159",
"0.6070179",
"0.60697085",
"0.6067538",
"0.6065695",
"0.60196674",
"0.60108477",
"0.59737706",
"0.59575313",
"0.59364146",
"0.5927485",
"0.590... | 0.7768747 | 0 |
Wrapper around k8s.load_and_create_resource to create a Adopoted resource | def create_adopted_resource(replacements, namespace="default"):
reference, spec, resource = k8s.load_and_create_resource(
resource_directory,
ADOPTED_RESOURCE_CRD_GROUP,
CRD_VERSION,
"adoptedresources",
replacements["ADOPTED_RESOURCE_NAME"],
"adopted_resource_base",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_resource(\n service_name: str, config_name: str = None, **resource_args\n):\n session = get_session(config_name)\n return session.resource(service_name, **resource_args)",
"def create_sagemaker_resource(\n resource_plural, resource_name, spec_file, replacements, namespace=\"default\"\n):\n... | [
"0.6452652",
"0.6253703",
"0.6171513",
"0.6096159",
"0.59922796",
"0.59912336",
"0.595014",
"0.58938444",
"0.5844623",
"0.5840295",
"0.58089936",
"0.57912517",
"0.57580245",
"0.5738343",
"0.573344",
"0.5730226",
"0.5724765",
"0.5722394",
"0.5716222",
"0.5712392",
"0.56978524"... | 0.71058977 | 0 |
Get the scale for a unit | def get_scale(units, compartmentId, volume, extracellularVolume):
if compartmentId == 'c':
V = volume
else:
V = extracellularVolume
if units == 'uM':
return 1. / N_AVOGADRO / V * 1e6
elif units == 'mM':
return 1. / N_AVOGADRO / V * 1e3
elif units == 'molecu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getScale(self):\n return _libsbml.Unit_getScale(self)",
"def scale(self):\n return self.scale_factor / CONSTANTS.AU",
"def GetScale(self):\n ...",
"def scale(self):\n return self._scale",
"def scale(self) -> Tuple[float, float]:\n return self._scale",
"def get_unit(scal... | [
"0.80368865",
"0.7562799",
"0.74804133",
"0.73843277",
"0.7313599",
"0.73030704",
"0.72814995",
"0.7198944",
"0.7182465",
"0.71608347",
"0.71563214",
"0.71075463",
"0.7067282",
"0.7034129",
"0.7005473",
"0.69814765",
"0.6953271",
"0.69395196",
"0.6914729",
"0.6903628",
"0.689... | 0.7647739 | 1 |
Cosine similarity between vector and matrix. | def cos_sim(vec, mat):
numer = np.dot(mat, vec)
vec_norm = np.linalg.norm(vec)
mat_norm = np.linalg.norm(mat, axis=1)
return np.divide(numer, vec_norm * mat_norm) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))",
"def compute_cosine_similarity(self):\n cos_matrix = []\n for i in range(len(self.train_vec)):\n val = self.vec1 * self.train_vec[i]\n ... | [
"0.8128745",
"0.7961435",
"0.79325265",
"0.7881812",
"0.7870864",
"0.7867077",
"0.7831051",
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"0.7769225",
"0.7692766",
"0.76837415",
"0.7670993",
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"0.76175356",
"0.75908995",
"0.74540925",
"0.74253607",
"0.73605967",
"0.7359785",
"0.7347... | 0.758455 | 16 |
Euclidean distance between vector and matrix. | def euclid_dist(vec, mat):
return np.linalg.norm(mat - vec, axis=1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def euclidean_distance(vector_x, vector_y):\n if len(vector_x) != len(vector_y):\n raise Exception('Vectors must be same dimensions')\n return math.sqrt(sum((vector_x[dim] - vector_y[dim]) ** 2 for dim in range(len(vector_x))))",
"def get_distance(self,row_vector):\n d = row_vector-self.X_test\n ... | [
"0.7465319",
"0.73555523",
"0.7279082",
"0.7155775",
"0.70827484",
"0.7002339",
"0.6978049",
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"0.675617",
"0.67391354",
"0.668006",
"0.6631312",
"0.6624541",
"0.6609537",
... | 0.77098405 | 0 |
Hybrid similarity between vector and matrix. | def hybrid_sim(vec, mat, top_k=5, alpha=0.1):
euc_vec = 0.
idx = np.array(range(mat.shape[0]))
if alpha > 0:
# step 1: caculate euclidean distance
euc_vec = euclid_dist(vec, mat)
if len(euc_vec) > top_k:
idx = np.argsort(euc_vec)[:top_k]
euc_vec = euc_vec[idx... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def self_similarity_matrix(feature_vectors):\n norm_feature_vectors, mean, std = at.normalize_features([feature_vectors.T])\n norm_feature_vectors = norm_feature_vectors[0].T\n sim_matrix = 1.0 - distance.squareform(\n distance.pdist(norm_feature_vectors.T, 'cosine'))\n return sim_matrix",
"de... | [
"0.669418",
"0.6659926",
"0.63767517",
"0.6368019",
"0.6360521",
"0.6356867",
"0.63391316",
"0.63353467",
"0.63267857",
"0.62889796",
"0.62807196",
"0.627655",
"0.624859",
"0.6224308",
"0.62191266",
"0.61848396",
"0.61848396",
"0.6180708",
"0.61560977",
"0.6126581",
"0.611667... | 0.55794203 | 67 |
The unit of measurement similarity. Espicially for `oz`, `lb`, `meter` etc. | def mm_similarity(s1, s2):
if filter(str.isalpha, s1) == filter(str.isalpha, s2):
if len(s1) < len(s2):
return float(len(s1)) / len(s2)
else:
return float(len(s2)) / len(s1)
else:
return 0. | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unit_of_measurement(self):\n return self.values.primary.units",
"def unit_of_measurement(self):\n return \"%\"",
"def unit_of_measurement(self):\n return self._tasmota_entity.unit",
"def unit_of_measurement(self) -> str:\n return self._unit_of_measurement",
"def unit_of_measurem... | [
"0.68982303",
"0.6858285",
"0.68522996",
"0.68046606",
"0.6801904",
"0.67825514",
"0.6745059",
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"0.6725472",
"0.6720259",
"0.67040163",
"0.6688806",
"0.6688806",
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"0.6688806",
"0.6688806",
"0.6688806",
"0.6688806",
"0.6688806",
"0.6688806",
"0.6688806"... | 0.0 | -1 |
The longest common substring similarity. A special verison. | def lcs_similarity(s1, s2):
max_len = 0
i = 0
while s1[i] == s2[i]:
max_len += 1
i += 1
if len(s1) == i or len(s2) == i:
break
if len(s1) < len(s2):
return float(max_len) / len(s2)
else:
return float(max_len) / len(s1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findLongestCommonSubstringManyStrings(listOfStrings):",
"def m_longest_common_subsequence_2(s):\n\n # Correct me if I'm wrong but I think unsorted list is faster\n lst = list(s)\n\n lcs = lst[0]\n for string in lst[1:]:\n lcs = LCS(lcs, string)\n\n return lcs",
"def findLongestCommonS... | [
"0.7570705",
"0.7322224",
"0.71764874",
"0.71665967",
"0.71261054",
"0.7063779",
"0.7052958",
"0.70349896",
"0.697746",
"0.6941382",
"0.6925402",
"0.6925334",
"0.6887414",
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"0.68183476",
"0.676528",
"0.67370135",
"0.67279613",
"0.67115736",
"0.6691741... | 0.70683295 | 5 |
Generate a description for the combination of well, tile, channel and, optionaly, depth and/or time | def generate_tile_description(tile, time = None, depth = None):
desc = "s"+ str(tile)
if depth is not None:
desc = desc + "_z" + str(depth)
if time is not None:
desc = desc + "_t" + str(time)
return desc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def describe(self):\n branch = randint(0,62)\n \n if 0 <= branch <= 29: \n if self.casteOrder[0] == 'soldiers':\n if self.genesis == 'escape persecution': \n self.description = '{2}: A full service {3} for retired {1}'.... | [
"0.6358604",
"0.61127687",
"0.60675085",
"0.6044572",
"0.59665823",
"0.592374",
"0.59180605",
"0.59102285",
"0.5901158",
"0.5849729",
"0.5839126",
"0.5819639",
"0.5782906",
"0.5768085",
"0.5766401",
"0.57455444",
"0.5734563",
"0.5732122",
"0.57276",
"0.57193375",
"0.5670834",... | 0.7623472 | 0 |
Generate a name for a file using the description and channel | def generate_file_name(well, channel, desc):
return "bPLATE_w" + well + "_" + desc + "_c" + channel + ".png" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"",
"def file_name(id, title, kind=\"src\"):\n fn_template = conf.template_source_file_name\n if kind == \"tst\":\n fn_template = conf.template_test_fi... | [
"0.75195915",
"0.7167804",
"0.71012443",
"0.70670754",
"0.69943404",
"0.67369324",
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"0.64458376",
"0.6431859",
"0.6416797",
"0.6390564",
"0.63887256",
"0.63... | 0.80092597 | 0 |
Constructor that takes the config and the well we are generating images for. | def __init__(self, config, well, directory):
self.config = config
self.well = well
self.directory = directory | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, config):\n logging.info(\"Creating footprint\")\n # self.infra = yaml.load(config)\n self.infra = config\n self.footprint_name = self.infra.get(\"footprint\", \"ehw\")\n self.images = self.infra.get(\"images\")\n self.old_images = self.infra.get(\"old_im... | [
"0.66241646",
"0.6582035",
"0.6469281",
"0.6385457",
"0.634032",
"0.6314989",
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"0.60907704",
"0.60626227",
"0.6061309",
"0.60489887",
"0.6046789",
"0.6038... | 0.7127226 | 0 |
Generate overlay images containing text x, y The arguments x and y may be negative, in which case the position will be offset from the right/top edge of the image. | def generate_overlay_images(self, overlay_name, x, y):
for tile in range(1, self.config.number_of_tiles + 1):
for time in self.config.time_points:
for depth in self.config.depth_points:
desc = self.config.tile_description_generator(tile, time, depth)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _overlay_text(\n image_file_name, x_offset_from_left_px, y_offset_from_top_px,\n text_string, font_size, use_north_gravity):\n\n command_string = '\"{0:s}\" \"{1:s}\"'.format(CONVERT_EXE_NAME, image_file_name)\n if use_north_gravity:\n command_string += ' -gravity North'\n\n comma... | [
"0.733315",
"0.66948533",
"0.64981097",
"0.64721394",
"0.6364706",
"0.63315034",
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"0.5924847",
"0.5903324",
"0.58893514",
"0.588931",
"0.5851297",
"0.58502513",
"0.5820367... | 0.62469816 | 6 |
Generate a name for a file using the description and channel | def _generate_raw_file_name(self, well, channel, desc):
return "bPLATE_w" + well + "_" + desc + "_c" + channel + ".png" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_file_name(well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"",
"def file_name(id, title, kind=\"src\"):\n fn_template = conf.template_source_file_name\n if kind == \"tst\":\n fn_template = conf.template_test_file_name\n\n return f... | [
"0.80091065",
"0.7165928",
"0.7101471",
"0.7064337",
"0.69931364",
"0.67345184",
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"0.6448165",
"0.6447837",
"0.6430102",
"0.6413977",
"0.63882077",
"0.63864017",
"0.6377731... | 0.7519707 | 1 |
Generate a name for a file using the description and channel | def _generate_overlay_file_name(self, well, channel, desc):
return "c" + channel + "_w" + well + "_" + desc + ".png" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_file_name(well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"",
"def _generate_raw_file_name(self, well, channel, desc):\n \n return \"bPLATE_w\" + well + \"_\" + desc + \"_c\" + channel + \".png\"",
"def file_name(id, title, k... | [
"0.80092597",
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"0.64458376",
"0.6431859",
"0.6416797",
"0.6390564",
"0.63887256",
"0.63... | 0.71012443 | 3 |
Constructor that takes the configuration for the plate | def __init__(self, config):
self.config = config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0",
"def __init__():\n self.placa = placa",
"def __init__(self, projection=None, transform=ccrs.PlateCarree(... | [
"0.67734075",
"0.6454328",
"0.63851225",
"0.63834906",
"0.63531744",
"0.6349419",
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"0.62195665",
"0.6196393",
"0.61835295",
"0.61778045",
"0.6167447",
"0.61486... | 0.60791457 | 32 |
Constructor that takes the configuration for the plate | def __init__(self, config):
self.config = config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n parameters_list = []\n self.config_dict = self.open_config(parameters_list)\n\n # Define defaults\n self.disc_gt = 0.0\n self.disc_out = 0.0",
"def __init__():\n self.placa = placa",
"def __init__(self, projection=None, transform=ccrs.PlateCarree(... | [
"0.67721254",
"0.645361",
"0.63844484",
"0.638197",
"0.6350986",
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"0.62173843",
"0.6196136",
"0.6180987",
"0.6177326",
"0.6164835",
"0.61487406",... | 0.607687 | 30 |
Initialize the plot text text color instance | def __init__(self, red=Black.red, green=Black.green, blue=Black.blue):
self.color = Color(red, green, blue)
self.template = '\ttextcolor = {textcolor};\n' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def text(self, txt, ax=None, color='black'):\n _plot_text(self, txt, ax, color)",
"def text(self, txt, ax=None, color='black'):\n _plot_text(self, txt, ax, color)",
"def text(self, str: str, x: int, y: int, colour: int, /) -> None:",
"def text_plot(self):\n if self.stext is not None:\n ... | [
"0.7327333",
"0.7327333",
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"0.7030527",
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"0.6277589",
"0.62424827",
"0.62409574",
"0.61770606",
"0.6144568",
"0.61280... | 0.6425525 | 12 |
Converts the plot text TextColor instance to a ztree plot text text color property declaration. | def to_str(self):
return self.template.format(textcolor=self.color.to_str()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_text_color ( self, object ):\n return self.text_color_",
"def GetTextColour(self):\r\n \r\n return self._colText",
"def get_text_color ( self, object ):\n if self._is_selected( object ):\n return self.selected_text_color_\n return self.text_color_",
"def ... | [
"0.72018707",
"0.63388044",
"0.62334913",
"0.5977799",
"0.5870951",
"0.58445084",
"0.58192",
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"0.5599045",
"0.55467296",
"0.55348307",
"0.5521071",
"0.5505261",
"0.54955703",
"0.5466... | 0.54452705 | 23 |
The function generates and returns a combined table with names of stocks in columns and dates in indexes. Profit tables for individual stocks are taken from stock_retrunrs function. | def Generating_stock_daily_return_table():
#Getting Names list
Profitfile='pap//CombProfit.csv'
path='D://Doktorat Marek//dane//'
ProfitsFilePath=path+Profitfile
quarterly_profit=pd.read_csv(ProfitsFilePath,index_col=0,header=0,parse_dates=True)
Names_list=quarterly_profit.columns.tolist()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setupStockTable(self):\n # Get the date\n # NOTE: This is probably un\n date = datetime.date()\n dateStr = date.month() + \"/\" + date.day() + \"/\" + date.year()\n\n stocks = (\"INTC\", \"AAPL\", \"GOOG\", \"YHOO\", \"SYK\", \"VZ\")\n\n for stock in stocks:\n ... | [
"0.63580585",
"0.61449856",
"0.60916585",
"0.59879607",
"0.586941",
"0.5752481",
"0.574493",
"0.56445813",
"0.5596955",
"0.5551755",
"0.5538056",
"0.55360204",
"0.55317575",
"0.55244833",
"0.5521858",
"0.55173093",
"0.55147207",
"0.54893124",
"0.54601693",
"0.54540384",
"0.54... | 0.6858316 | 0 |
General method for costing belt filter press. Capital cost is a function of flow in gal/hr. | def cost_filter_press(blk):
t0 = blk.flowsheet().time.first()
# Add cost variable and constraint
blk.capital_cost = pyo.Var(
initialize=1,
units=blk.config.flowsheet_costing_block.base_currency,
bounds=(0, None),
doc="Capital cost of unit operation... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cond_boiler_op_cost(Q_therm_W, Q_design_W, T_return_to_boiler_K):\n if Q_therm_W > 0.0:\n\n # boiler efficiency\n eta_boiler = cond_boiler_operation(Q_therm_W, Q_design_W, T_return_to_boiler_K)\n\n E_aux_Boiler_req_W = BOILER_P_AUX * Q_therm_W\n\n Q_primary_W = Q_therm_W / eta_bo... | [
"0.60161155",
"0.5890601",
"0.5859548",
"0.5829997",
"0.5820077",
"0.58090657",
"0.57660633",
"0.57442236",
"0.5743485",
"0.5697327",
"0.56755006",
"0.56499743",
"0.5622",
"0.5602251",
"0.55968297",
"0.5590435",
"0.55680066",
"0.5539766",
"0.5538298",
"0.5469668",
"0.5467324"... | 0.7558044 | 0 |
Test case for create_symlink_file | def test_create_symlink_file(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_symlink(self, source_path, main):\n main_file = os.path.realpath(os.path.join(source_path, main))\n if not os.path.isfile(main_file):\n main_file += '.js'\n if not os.path.isfile(main_file):\n print('\\tWARNING: Could not create symlink for {}, no such file.'.... | [
"0.7453823",
"0.7333795",
"0.7322523",
"0.7264164",
"0.72327024",
"0.71856415",
"0.7102871",
"0.7052772",
"0.70179003",
"0.701752",
"0.6931252",
"0.6921266",
"0.69030124",
"0.6874251",
"0.68668115",
"0.682023",
"0.67737055",
"0.6681461",
"0.66567737",
"0.6653678",
"0.6610112"... | 0.95351255 | 0 |
Test case for get_meta_range | def test_get_meta_range(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_range(self):\n pass",
"def test_get_range_empty(self):\n\n queryset = mock.Mock()\n queryset.aggregate.return_value = None\n\n dimension = models.QuantitativeDimension(\n key='shares',\n name='Count of shares',\n description='Count of shar... | [
"0.8197148",
"0.6618275",
"0.6484984",
"0.64601654",
"0.63697124",
"0.6269415",
"0.6174672",
"0.6106884",
"0.608165",
"0.6080813",
"0.60700256",
"0.6020781",
"0.60202074",
"0.5981426",
"0.59406525",
"0.58997625",
"0.5883565",
"0.5879113",
"0.5847939",
"0.5835961",
"0.5828512"... | 0.9391207 | 0 |
Test case for get_range | def test_get_range(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_meta_range(self):\n pass",
"def getRange(self, p_int): # real signature unknown; restored from __doc__\n pass",
"def _in_range_op(spec):",
"def f_get_range(self, copy=True):\n raise NotImplementedError(\"Should have implemented this.\")",
"def GetTRange(self):\n ...... | [
"0.80703026",
"0.7837224",
"0.7255473",
"0.7147124",
"0.7073319",
"0.7031268",
"0.70060104",
"0.6990455",
"0.6969976",
"0.6872395",
"0.68121374",
"0.67698324",
"0.6767907",
"0.6744316",
"0.67391896",
"0.67225266",
"0.6692281",
"0.66836524",
"0.6643143",
"0.6600423",
"0.659429... | 0.92907894 | 0 |
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