List[float]:\\n return self._target_prices\",\n \"def convert_html_to_list(html: str) -> List[dict]:\\n soup = BeautifulSoup(html, 'html.parser')\\n if not (tbodies := soup.find_all('tbody')): # Find the Table\\n raise ValueError('No tables found in the HTML passed!')\\n\\n index_column_map = {\\n 1: 'date',\\n 2: 'open',\\n 3: 'high',\\n 4: 'low',\\n 5: 'close',\\n 6: 'adj_close',\\n 7: 'volume'\\n }\\n\\n stock_data = []\\n current_record = {}\\n index = 1\\n for td in tbodies[0].find_all('td'):\\n\\n if index not in index_column_map: # surpassed the max value in the dict\\n stock_data.append(current_record)\\n index = 1 # Reset index\\n current_record = {} # Reset current record dict\\n\\n value = re.search(r'>(.*)<', str(td.contents[0])).group(1)\\n # Convert to float if possible\\n value = float(val_no_comma) if (val_no_comma := value.replace(',', '')).replace('.', '').isalnum() else value\\n current_record[index_column_map[index]] = value\\n index += 1\\n\\n return stock_data\",\n \"def _do_the_math(a_list):\\n b_list = []\\n for i in a_list:\\n i = i.replace('pi', \\\"np.pi\\\")\\n # ...\\n i = str(eval(i)) # pylint: disable-msg=eval-used\\n b_list.append(i)\\n return b_list\",\n \"def client_units(self) -> List[FlyingUnit]:\\n return [u for u in self.units if u.is_human()]\",\n \"def test_run():\\n for symbol in ['AAPL', 'IBM']:\\n print(\\\"Mean Volume\\\")\\n print(symbol, get_mean_volume(symbol))\",\n \"def build_dividend_lists(portfolio_dict):\\n # ETF dividend list\\n dow_dividends = lookup_dividends(yf.Ticker(\\\"DIA\\\")) \\n sp500_dividends = lookup_dividends(yf.Ticker(\\\"SPY\\\")) \\n nasdaq_dividends = lookup_dividends(yf.Ticker(\\\"QQQ\\\")) \\n totalmarket_dividends = lookup_dividends(yf.Ticker(\\\"VTI\\\")) \\n \\n # Portfolio dividends\\n portfolio_dividend_dict = {}\\n for key in portfolio_dict:\\n portfolio_dividend_dict[key] = lookup_dividends(yf.Ticker(key))\\n \\n return (dow_dividends, sp500_dividends, nasdaq_dividends, totalmarket_dividends, portfolio_dividend_dict)\",\n \"def populateListFormulaUnitsData(self):\\n return _libsbml.Model_populateListFormulaUnitsData(self)\",\n \"def tokenLookup(instrument_df,symbol_list):\\r\\n token_list = []\\r\\n for symbol in symbol_list:\\r\\n token_list.append(int(instrument_df[instrument_df.tradingsymbol==symbol].instrument_token.values[0]))\\r\\n return token_list\",\n \"def tokenLookup(instrument_df,symbol_list):\\r\\n token_list = []\\r\\n for symbol in symbol_list:\\r\\n token_list.append(int(instrument_df[instrument_df.tradingsymbol==symbol].instrument_token.values[0]))\\r\\n return token_list\",\n \"def get(self, *args):\\n return _libsbml.ListOfUnits_get(self, *args)\",\n \"def close_lst(self, f):\\n print(\\\"Closing price running...\\\")\\n close_price = []\\n dates = []\\n for i,j in enumerate(f['Close']):\\n close_price.append(f['Close'][i])\\n dates.append(f.ix[i].name.date())\\n print(\\\"Closing price done!\\\")\\n return dates, close_price\",\n \"def remove_number_symbols(data_lists):\\n billion = 'B'\\n million = 'M'\\n thousand = 'K'\\n percent = '%'\\n dot = '\\\\.'\\n comma = ','\\n B = re.compile(billion)\\n M = re.compile(million)\\n K = re.compile(thousand)\\n Perc = re.compile(percent)\\n Dot = re.compile(dot)\\n Comm = re.compile(comma)\\n\\n fltpoint_dict = {}\\n for symbol, datalist in data_lists.iteritems():\\n new_data_list = []\\n if len(datalist) > 1:\\n for statistic in datalist:\\n if percent in statistic:\\n statistic = Perc.sub('', statistic)\\n statistic = Comm.sub('', statistic)\\n new_data_list.append(float(statistic))\\n elif comma in statistic or 'May' in statistic or 'Mar' in statistic:\\n statistic = Comm.sub('', statistic)\\n try:\\n statistic = float(statistic)\\n except:\\n pass\\n new_data_list.append(statistic)\\n elif billion in statistic or million in statistic or thousand in statistic:\\n statistic = B.sub('0000000', statistic)\\n statistic = M.sub('00000', statistic)\\n statistic = K.sub('0', statistic)\\n statistic = Dot.sub('', statistic)\\n new_data_list.append(float(statistic))\\n else:\\n try:\\n statistic = float(statistic)\\n except:\\n pass\\n new_data_list.append(statistic)\\n fltpoint_dict[symbol] = new_data_list\\n else:\\n fltpoint_dict[symbol] = ['N/A']\\n\\n return fltpoint_dict\",\n \"def get_basket_items_discount(self, offer_info, actual_volume, product_prices):\\n prod_code = offer_info.base_prod_code\\n base_prod_vol = actual_volume.get(prod_code.lower())\\n\\n discount_basket = []\\n\\n if base_prod_vol >= offer_info.min_vol:\\n offer_on_prod = offer_info.offer_on\\n if actual_volume.get(offer_on_prod.lower()):\\n print(f\\\"Base product volume is greater than minimum required volume & product on offer is also available \\\"\\n f\\\"in cart..\\\")\\n if offer_info.is_limited:\\n print(f\\\"Limited offer..\\\")\\n if prod_code == offer_on_prod:\\n # total_allowed_items_on_offer = Limit Volume of base product * (Offer Product Max Volume/Minimum volume of base product)\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume/offer_info.min_vol)\\n max_limit = 1\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n while max_limit <= total_allowed_items_on_offer:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n total_allowed_items_on_offer = offer_info.limit_vol * (offer_info.offer_prod_volume / offer_info.min_vol)\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n max_limit = 1\\n while max_limit <= total_allowed_items_on_offer:\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price *(offer_info.discount_perc/100))*-1\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n max_limit += 1\\n else:\\n print(f\\\"Unlimited offer..\\\")\\n if prod_code == offer_on_prod:\\n if base_prod_vol > offer_info.min_vol:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n if i%2 != 0:\\n discounted_price = (base_prod_actual_price *(offer_info.discount_perc/100))*-1\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n else:\\n for i in range(0, base_prod_vol):\\n base_prod_actual_price = product_prices.get(prod_code.lower()).get('price')\\n discount_basket.append((prod_code, base_prod_actual_price))\\n\\n offer_onprod_actual_price = product_prices.get(offer_on_prod.lower()).get('price')\\n discounted_price = (offer_onprod_actual_price * (offer_info.discount_perc / 100))*-1\\n\\n for j in range(0, actual_volume.get(offer_on_prod.lower())):\\n discount_basket.append((offer_on_prod, offer_onprod_actual_price))\\n discount_basket.append((offer_info.offer_code, discounted_price))\\n\\n\\n return discount_basket\",\n \"def get_list_powers(self):\\r\\n _debug('simq03b_api.get_list_powers')\\r\\n \\r\\n s = self.query('SOUR:LIST:POW?')\\r\\n if s == None: return None\\r\\n a = []\\r\\n n = 0\\r\\n for x in s.split(','):\\r\\n try:\\r\\n a.append(float(x.strip()))\\r\\n except:\\r\\n print('ERROR get_list_powers(): non-float in list ', n, x)\\r\\n n += 1\\r\\n return a\",\n \"def _get_symbols(exchange_code: str, token: str) -> List[mtypes.Symbol]:\\n _LOG.info(\\\"Getting symbols list for exchange: '%s'\\\", exchange_code)\\n response = get_client().service.SymbolList(\\n Token=token, Exchange=exchange_code\\n )\\n\\n if response.SYMBOLS is None:\\n _LOG.error(\\\"No symbols found for exchange: '%s'\\\", exchange_code)\\n return []\\n\\n symbols = [\\n mtypes.Symbol.from_dict(d=obj)\\n for obj in zeep.helpers.serialize_object(response.SYMBOLS[\\\"SYMBOL\\\"])\\n ]\\n\\n _LOG.info(\\\"Got %s symbols for exchange '%s'\\\", len(symbols), exchange_code)\\n return symbols\",\n \"def GenerateUnitList(self):\\n # Verify platform specific terms. Skip whole term if platform does not\\n # match.\\n if self.term.platform:\\n if 'silverpeak' not in self.term.platform:\\n return []\\n if self.term.platform_exclude:\\n if 'silverpeak' in self.term.platform_exclude:\\n return []\\n\\n ret_str = []\\n if 'established' in self.term.option:\\n dst_ports = ['any']\\n else:\\n dst_ports = self._NormalizePortRanges(\\n self._FixLongList(self.term.destination_port, 10)\\n )\\n source_all_ipv6, source_address = self._FixIPv6Address(\\n self.term.source_address\\n )\\n if source_all_ipv6:\\n return ret_str\\n destination_all_ipv6, destination_address = self._FixIPv6Address(\\n self.term.destination_address\\n )\\n if destination_all_ipv6:\\n return ret_str\\n src_ips = self._NormalizeNetblocks(self._FixLongList(source_address, 50))\\n src_ports = self._NormalizePortRanges(\\n self._FixLongList(self.term.source_port, 10)\\n )\\n dst_ips = self._NormalizeNetblocks(\\n self._FixLongList(destination_address, 50)\\n )\\n for src_ip in src_ips:\\n for src_port in src_ports:\\n for dst_ip in dst_ips:\\n for dst_port in dst_ports:\\n ret_str.append((src_ip, src_port, dst_ip, dst_port))\\n return ret_str\",\n \"def _get_stations_by_codes_local(station_codes: List[int]) -> List[WeatherStation]:\\n LOGGER.info('Using pre-generated json to retrieve station by code')\\n with open(weather_stations_file_path) as file_pointer:\\n stations = json.load(file_pointer)\\n results = []\\n for station in stations['weather_stations']:\\n if int(station['code']) in station_codes:\\n results.append(WeatherStation(**station))\\n return results\",\n \"def get_price_range_map(data_map):\\n res_map = defaultdict(lambda: deepcopy(static_constants.UNIT_PRICE_DEFAULT))\\n for key, list_of_price in data_map.items():\\n list_of_price.sort()\\n lower_price = np.percentile(list_of_price, 40)\\n higher_price = np.percentile(list_of_price, 70)\\n median_price = np.percentile(list_of_price, 50)\\n res_map[key] = {'lower_price': lower_price, 'median_price': median_price, 'higher_price': higher_price}\\n return res_map\",\n \"def prices(tickers):\\n try:\\n start = dt.datetime.today()\\n start = start.strftime('%Y-%m-%d') \\n data = pdr.get_data_yahoo(tickers, start=start)\\n price = data['Adj Close']\\n vol = data['Volume']\\n data_dic = {}\\n for stock in tickers:\\n data_dic[str(stock)] = price[str(stock)][0], vol[str(stock)][0]\\n \\n df_data = pd.DataFrame(data_dic.values(), columns=['precio_usa', 'volumen_usa'])\\n df_data['Ticker'] = tickers\\n df_data = df_data.loc[:,['Ticker', 'precio_usa', 'volumen_usa']]\\n\\n except:\\n start = dt.datetime.today()\\n start = start - Day(3)\\n start = start.strftime('%Y-%m-%d') \\n data = pdr.get_data_yahoo(tickers, start=start)\\n price = data['Adj Close']\\n vol = data['Volume']\\n data_dic = {}\\n for stock in tickers:\\n data_dic[str(stock)] = price[str(stock)][0], vol[str(stock)][0]\\n \\n df_data = pd.DataFrame(data_dic.values(), columns=['precio_usa', 'volumen_usa'])\\n df_data['Ticker'] = tickers\\n df_data = df_data.loc[:,['Ticker', 'precio_usa', 'volumen_usa']]\\n\\n return df_data\",\n \"def test_get_stock_price_summary4(self):\\n\\n actual = a1.stock_price_summary([0.02, 0.14, 0.10])\\n expected = (0.26,0)\\n self.assertEqual(actual, expected)\",\n \"def get_prices(self, coin, quote):\\n\\n return self.public_client.ticker(coin, quote)\",\n \"def calculate_prices(self, merged_data):\\n calculated_prices = []\\n for record in merged_data:\\n prices_dict = dict()\\n supplier_price_id = record.get('supplier_detail').get('identifier') # get the supplier price id\\n session_id = record.get('supplier_transaction').get('session_id') # get the transaction session\\n supplier_trans_fee_price = self.compute_fee_price(\\n record) # Get the fee price for each transaction if needed\\n supplier_trans_time_price = self.compute_time_price(\\n record) # Get the time price for each transaction if needed\\n supplier_trans_kwh_price = self.compute_kwh_price(record)\\n total_price = supplier_trans_fee_price + supplier_trans_time_price + supplier_trans_kwh_price\\n prices_dict.update({'fee_price': supplier_trans_fee_price,\\n 'time_price': supplier_trans_time_price,\\n 'kwh_price': supplier_trans_kwh_price,\\n 'total_price': total_price,\\n 'session_id': session_id,\\n 'supplier_price_id': supplier_price_id})\\n calculated_prices.append(prices_dict)\\n\\n return calculated_prices\",\n \"def currency_code_mappings():\\n return [(a, CURRENCIES[a].name) for a in settings.CURRENCIES]\",\n \"def get_prices(self, grab, subject):\\n prices = []\\n try:\\n extras = grab.doc.rex_text(\\n '<h3 class\\\\=\\\"h6 copy-sp-m\\\">.*?%s.*?</h3>(.+?)</ul>' % subject,\\n flags=re.S\\n )\\n except DataNotFound:\\n logging.debug(\\n \\\"Price %s is not found on %s\\\"\\n % (subject, grab.doc.url)\\n )\\n return None\\n\\n sel = XpathSelector(fromstring(extras))\\n prices = []\\n for li in sel.select('//li[@class=\\\"list__item u-cf\\\"]'):\\n obligatory = OrderedDict()\\n obligatory['name'] = li.select('node()').text()\\n money = li.select('node()/strong').text()\\n obligatory['value'] = money[1:].replace(',', '')\\n\\n # Find perweek or perday\\n if li.select(\\n 'span[@class=\\\"boatview__extras-amount\\\"' +\\n ' and contains(text(),\\\"per week\\\")]'\\n ).exists():\\n obligatory['perweek'] = True\\n elif li.select(\\n 'span[@class=\\\"boatview__extras-amount\\\"' +\\n ' and contains(text(),\\\"per day\\\")]'\\n ).exists():\\n obligatory['perday'] = True\\n obligatory['currency'] = money[0]\\n prices.append(obligatory)\\n\\n if len(prices) < 1:\\n logging.debug(\\n \\\"Price %s contains less than one element on: %s\\\"\\n % (subject, grab.doc.url)\\n )\\n return None\\n\\n return prices\",\n \"def pop_stock_list(self, page):\\n soup = bs(page, 'html5lib')\\n table = soup('table', {'class':\\n ['sortable',\\n 'wikitable',\\n 'jquery-tablesorter']})[0]\\n\\n stock_list = OrderedDict()\\n\\n # Grab the headers\\n headers = [self.filter_headers(th.text) for th in table('tr')[0]('th')]\\n\\n for idx,tr in enumerate(table('tr')):\\n # If we specified a maximum number of stocks.\\n if self.count and idx > self.count:\\n break\\n\\n if tr.td:\\n symbol = tr.td.text\\n\\n stock_list[symbol] = {headers[i]: td.text.strip()\\n for i, td in enumerate(tr('td'))}\\n\\n return stock_list\",\n \"def get_prices(uuid, card_format, price_source, price_list, card_type_order, price_data_json):\\n if price_source not in price_data_json[uuid][card_format]:\\n pass\\n #print(f'Price source value of {price_source} is not available for {card_format} and {uuid}')\\n else:\\n source = price_data_json[uuid][card_format][price_source]\\n if price_list not in source:\\n pass\\n #print(f'Price list value of {price_list} is not available for {price_source} and {uuid}')\\n else:\\n retail = source[price_list]\\n for type in card_type_order:\\n if type in retail:\\n return retail[type]\",\n \"def fetch_series(tickers: List[str]) -> List[dict]:\\n with requests.Session() as session:\\n c = suds.client.Client(\\n 'https://www3.bcb.gov.br/sgspub/JSP/sgsgeral/FachadaWSSGS.wsdl',\\n transport=suds_requests.RequestsTransport(session))\\n \\n def _fetch(tck):\\n try:\\n resp = c.service.getUltimoValorVO(tck)\\n if resp is not None:\\n return _process_info(resp)\\n except:\\n tcks_off.append(tck)\\n\\n with executor() as e:\\n ls = list(e.map(_fetch, tickers))\\n return ls\",\n \"def get_products(self):\\n return [item.code for item in self._products]\",\n \"def get_historical_prices(codes, start_date=None, end_date=None,\\n universes=None, conids=None,\\n exclude_universes=None, exclude_conids=None,\\n times=None, cont_fut=None, fields=None,\\n timezone=None, infer_timezone=None,\\n master_fields=None):\\n from quantrocket import get_prices\\n\\n return get_prices(codes,\\n start_date=start_date, end_date=end_date,\\n universes=universes, conids=conids,\\n exclude_universes=exclude_universes,\\n exclude_conids=exclude_conids,\\n times=times, cont_fut=cont_fut,\\n fields=fields, timezone=timezone,\\n infer_timezone=infer_timezone,\\n master_fields=master_fields)\",\n \"def tokenLookup(instrument_df,symbol_list):\\n token_list = []\\n for symbol in symbol_list:\\n token_list.append(int(instrument_df[instrument_df.tradingsymbol==symbol].instrument_token.values[0]))\\n return token_list\",\n \"def inp_item_price(self) -> List[str]:\\n \\n return [str(input(\\\"Enter desired price for item: \\\"))]\",\n \"def get_related_forex_currencies(self, currency = None):\\n new_symbol_names_list = []\\n for symbol_name in self.symbol_names_list:\\n if (symbol_name[:3] == currency ) | (symbol_name[3:] == currency ):\\n new_symbol_names_list.append(symbol_name)\\n symbol_names_list = new_symbol_names_list\\n return symbol_names_list\",\n \"def get_unit_details(driver):\\n unit_details = [\\n a\\n for a in driver.find_elements_by_xpath(\\\"//span[contains(@class, 'tooltip')]\\\")\\n if \\\"$\\\" in a.get_attribute(\\\"title\\\")\\n ]\\n data = []\\n for unit in unit_details:\\n unit_dict = {}\\n unit_dict[\\\"Unit\\\"] = unit.get_attribute(\\\"data-selector\\\")\\n title = unit.get_attribute(\\\"title\\\").replace(\\\"\\\\xa0\\\", \\\" \\\")\\n unit_dict[\\\"Price\\\"] = re.compile(\\\"(\\\\\\\\$.+?)<\\\").findall(title)[0]\\n unit_dict[\\\"Size\\\"] = re.compile(\\\">(\\\\\\\\d.+Sqm)\\\").findall(title)[0]\\n data.append(unit_dict)\\n return data\",\n \"def get_company_and_price(shares: list[Share]) -> list[Share]:\\n\\n for share in shares:\\n\\n share_info = lookup(share.symbol)\\n if share_info is not None:\\n share.company_name = share_info[\\\"name\\\"]\\n share.price = share_info[\\\"price\\\"]\\n share.total = share.price * share.qty\\n else:\\n share.company_name = CMP_NOT_FOUND\\n\\n return shares\",\n \"def names_and_prices():\\n # Just an example\\n cg = CoinGeckoAPI()\\n data = cg.get_price(ids='bitcoin,ethereum', vs_currencies='usd,eur')\\n # Flatten\\n names = list()\\n prices = list()\\n for coin, fx in data.items():\\n for currency, price in fx.items():\\n name = coin+'_'+currency\\n names.append(name)\\n prices.append(price)\\n\\n return names, prices\",\n \"def getActiveCurrencies():\",\n \"def currencies(self):\\r\\n return currencies.Currencies(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6023552","0.5827989","0.5810261","0.57817996","0.57182974","0.5632579","0.55539757","0.5493873","0.5484011","0.54804665","0.54708654","0.5443625","0.5422661","0.5405019","0.5299616","0.5295281","0.527327","0.5272383","0.52600485","0.52459085","0.52217275","0.52160186","0.52093273","0.5187559","0.5186118","0.517191","0.5165962","0.5137883","0.5137879","0.5132765","0.5132293","0.5122199","0.5121893","0.5121529","0.50674784","0.50622284","0.5054525","0.5026209","0.50231344","0.50163037","0.50071716","0.49957815","0.49957815","0.498961","0.4988208","0.4985643","0.49731177","0.49573845","0.49455446","0.49349755","0.49281108","0.49233148","0.49232808","0.49102044","0.48948523","0.487695","0.48752725","0.48702466","0.48636404","0.4861385","0.48597705","0.48582366","0.4850134","0.48358995","0.4828478","0.48277515","0.4809204","0.48084524","0.47996157","0.47982958","0.47971094","0.47971094","0.47941652","0.479046","0.47847673","0.4774322","0.47732782","0.47725254","0.47724","0.47712392","0.4770337","0.47681248","0.47634217","0.47621682","0.47541994","0.4741577","0.4740607","0.47395027","0.47350934","0.4732756","0.4726246","0.472617","0.47241145","0.47212848","0.47210196","0.47203222","0.47160894","0.4714856","0.47139293","0.4712997"],"string":"[\n \"0.6023552\",\n \"0.5827989\",\n \"0.5810261\",\n \"0.57817996\",\n \"0.57182974\",\n \"0.5632579\",\n \"0.55539757\",\n \"0.5493873\",\n \"0.5484011\",\n \"0.54804665\",\n \"0.54708654\",\n \"0.5443625\",\n \"0.5422661\",\n \"0.5405019\",\n \"0.5299616\",\n \"0.5295281\",\n \"0.527327\",\n \"0.5272383\",\n \"0.52600485\",\n \"0.52459085\",\n \"0.52217275\",\n \"0.52160186\",\n \"0.52093273\",\n \"0.5187559\",\n \"0.5186118\",\n \"0.517191\",\n \"0.5165962\",\n \"0.5137883\",\n \"0.5137879\",\n \"0.5132765\",\n \"0.5132293\",\n \"0.5122199\",\n \"0.5121893\",\n \"0.5121529\",\n \"0.50674784\",\n \"0.50622284\",\n \"0.5054525\",\n \"0.5026209\",\n \"0.50231344\",\n \"0.50163037\",\n \"0.50071716\",\n \"0.49957815\",\n \"0.49957815\",\n \"0.498961\",\n \"0.4988208\",\n \"0.4985643\",\n \"0.49731177\",\n \"0.49573845\",\n \"0.49455446\",\n \"0.49349755\",\n \"0.49281108\",\n \"0.49233148\",\n \"0.49232808\",\n \"0.49102044\",\n \"0.48948523\",\n \"0.487695\",\n \"0.48752725\",\n \"0.48702466\",\n \"0.48636404\",\n \"0.4861385\",\n \"0.48597705\",\n \"0.48582366\",\n \"0.4850134\",\n \"0.48358995\",\n \"0.4828478\",\n \"0.48277515\",\n \"0.4809204\",\n \"0.48084524\",\n \"0.47996157\",\n \"0.47982958\",\n \"0.47971094\",\n \"0.47971094\",\n \"0.47941652\",\n \"0.479046\",\n \"0.47847673\",\n \"0.4774322\",\n \"0.47732782\",\n \"0.47725254\",\n \"0.47724\",\n \"0.47712392\",\n \"0.4770337\",\n \"0.47681248\",\n \"0.47634217\",\n \"0.47621682\",\n \"0.47541994\",\n \"0.4741577\",\n \"0.4740607\",\n \"0.47395027\",\n \"0.47350934\",\n \"0.4732756\",\n \"0.4726246\",\n \"0.472617\",\n \"0.47241145\",\n \"0.47212848\",\n \"0.47210196\",\n \"0.47203222\",\n \"0.47160894\",\n \"0.4714856\",\n \"0.47139293\",\n \"0.4712997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8246834"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":1890,"cells":{"query":{"kind":"string","value":"Returns list of items descriptions from list of stock codes."},"document":{"kind":"string","value":"def getDescriptionList(self, list_stockCode=None):\n df = self._df_invoice_original\n\n list_description = list()\n if list_stockCode is None :\n list_description = list(df.Description.unique())\n else:\n for stockCode in list_stockCode:\n description = df[df.StockCode==stockCode].Description.unique()[0]\n list_description.append(description)\n \n return list_description"},"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 getStockCodeList(self, list_description=None):\n list_stockCode = list()\n df = self._df_invoice_original\n \n if list_description is None:\n list_stockCode = list(df.StockCode.unique())\n else :\n for description in list_description:\n stockCode = df[df.Description==description].StockCode.unique()[0]\n list_stockCode.append(stockCode)\n return list_stockCode","def create_not_included_list(codes):\n string = '\\\\begin{itemize}\\n'\n for code in codes:\n title = get_course_title_only(code)\n string += '\\\\item{' + title + '}\\n'\n string += '\\\\end{itemize}\\n'\n return string","def get_order_lists(self, n_items, n_quantities):\n arr_stock_code = self._df_invoice_original.StockCode.unique()\n arr_stock_code = np.random.choice(arr_stock_code, n_items)\n list_stockCode = list(arr_stock_code)\n list_quantities = np.ones(arr_stock_code.shape[0])\n list_quantities *=n_quantities\n\n return list_stockCode, list_quantities","def getUnitPriceList(self, list_stockCode):\n df = self._df_invoice_original\n\n list_unitPrice = list()\n \n for stockCode in list_stockCode:\n unitPrice = df[df.StockCode==stockCode].UnitPrice.unique()[0]\n list_unitPrice.append(unitPrice)\n return list_unitPrice","def _getListing(self):\n\n # lets assure consistent litsting order\n items = self._items.items()\n items.sort()\n return [ \"%s%s%s: %s\" % (_def_sep, str(x[1]), _def_sep, x[1].__doc__)\n for x in items ]","def parse_quantities(quantities):\n codes = []; names = []\n\n for q in quantities:\n c, n = parse_quantity(q)\n codes.append(c)\n names.append(n)\n\n return codes, names","def get_stocks():\n print(\"fetching remote...\")\n code_dataframes = pd.read_html(\n 'http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', header=0)[0]\n # 우리가 필요한 것은 회사명과 종목코드이기 때문에 필요없는 column들은 제외해준다.\n print(\"parsing and filtering data...\")\n code_dataframes.종목코드 = code_dataframes.종목코드.map('{:06d}'.format)\n # 한글로된 컬럼명을 영어로 바꿔준다.\n code_dataframes = code_dataframes[['회사명', '종목코드']]\n code_dataframes = code_dataframes.rename(\n columns={'회사명': 'name', '종목코드': 'code'})\n codes = code_dataframes['code']\n names = code_dataframes['name']\n stocks = []\n for i in range(len(names)):\n stocks.append({\n 'name': names[i],\n 'code': codes[i]\n })\n return stocks","def itemnames():\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\n return(g)","def getAllDescWords(itemList):\r\n itemList = list(set(itemList)) # make itemList unique\r\n descWords = []\r\n for item in itemList:\r\n descWords.extend(worldItems[item][DESCWORDS])\r\n return list(set(descWords))","def get_items_for_catalog(catalog_id):\n pass","def description(self):\n item_counts = [f'{i.quantity}x {i.item.name}' for i in self.items]\n return ','.join(item_counts)","def ConstructList(title, items):\n buf = cStringIO.StringIO()\n fmt = 'list[title=\"{title}\",always-display-title]'.format(title=title)\n resource_printer.Print(sorted(set(items)), fmt, out=buf)\n return buf.getvalue()","def list_items(self):\n click.echo(\"ID --|-- Item Title\")\n for index, item in enumerate(self.items):\n click.echo(\" {} --|-- {}\".format(index, item.title))","def serve_recos(ids, ref_catalog):\r\n desc_list = []\r\n for desc_id in ids:\r\n desc_list.append(ref_catalog[ref_catalog['id'] == desc_id].iloc[0]['description'])\r\n return desc_list","def get_code_mapping( id ):\n returnVal = []\n theCodes = _theRegistry.get_code( id )\n codes = theCodes.get_codes()\n descs = theCodes.get_descriptions()\n for (code, desc) in map(None, codes, descs):\n returnVal.append( { 'code' : code, 'description' : desc } )\n return returnVal","def list():\n\n return cache.codeTableList()","def getArtistsofArtwork(catalog, codes):\n return model.getArtistname(catalog,codes)","def list(self):\n return 'Decks available: \\n{}'.format(\"\\n\".join([\n 'Deck {}: {} ({} cards)'.format(deck['id'], deck['title'], len(deck['cards']))\n for key, deck in self.decks.items()\n ]))","def consult_books(self, bar_code: str):\n try:\n book_data = []\n self.db.cursor.execute('SELECT * from books WHERE bar_code = %s', (bar_code,))\n for i in self.db.cursor.fetchall():\n book_data.append(i)\n except Exception as error:\n print(error)\n else:\n print(f\"ID BOOK: {book_data[0][0]}\\n\"\n f\"TITLE: {book_data[0][1]}\\n\"\n f\"AUTHOR: {book_data[0][2]}\\n\"\n f\"PRICE: R$:{book_data[0][3]}\\n\"\n f\"BAR CODE: {book_data[0][4]}\\n\"\n f\"STOCK: {book_data[0][5]}\")","def hs_code_process(si):\n hs_code = re.sub(r'\\W+', '', si.get('hs_code', ''))\n descrip = re.sub(r'\\W+', '', si.get('description_of_goods', ''))\n bl_type = re.sub(r'\\W+', '', si.get('bl_type', ''))\n hs_codes = []\n if hs_code != '' and hs_code in descrip:\n hs_raw = si.pop('hs_code')\n for raw_line in hs_raw.split('\\n'):\n line = re.sub(r'\\W+', '', raw_line).upper()\n if 'HSCODE' in line:\n remain = line.replace('HSCODE', '').replace('\\n', '')\n remain = re.sub(r'[A-Z]+', '', remain)\n if remain.isdigit() and len(remain) > 4:\n hs_codes.append(remain)\n else:\n # CODE in line below\n hs_line_no = hs_raw.split('\\n').index(raw_line)\n for hs_line in hs_raw.split('\\n')[hs_line_no + 1:]:\n if len(re.findall(r'[a-zA-Z]+', hs_line)) < 1:\n for hs_code in re.findall(r'\\d+', hs_line):\n hs_codes.append(hs_code)\n else:\n break\n\n bl_type = si.get('bl_type', '')\n\n elif hs_code != '' and hs_code in bl_type:\n hs_raw = si.pop('hs_code')\n for raw_info in hs_raw.split('/'):\n info = re.sub(r'\\W+', '', raw_info).upper()\n if 'HSCODE' in info:\n hs_code = info.replace('HSCODE', '').replace('\\n', '')\n hs_code = re.sub(r'[A-Z]+', '', hs_code)\n hs_codes.append(hs_code)\n break\n bl_type = hs_raw.split('/')[0]\n\n else:\n hs_code = re.sub(r'[^\\d]+', '', hs_code)\n hs_codes.append(hs_code)\n bl_type = si.get('bl_type', '')\n\n return hs_codes, bl_type","def getNamesFromDescr(descr):\n i = getIter(descr)\n if not i:\n return\n\n try:\n item = i.next()\n while item:\n if isinstance(item[1], str):\n yield item[0]\n else:\n l = []\n for j in getNamesFromDescr(item[1]):\n l.append(j)\n r = (item[0], l)\n yield r\n item = i.next()\n except StopIteration:\n pass","def codes(self):\n return [card.code for card in self.cards]","def get_snippet_info_list(self):\n snippets = []\n for snippet in self.snippets:\n snippets.append([snippet[\"title\"], snippet[\"description\"]])\n return snippets","def product_db() -> List[Text]:\n\n return [\n \"credit\",\n \"forex\",\n \"debit\",\n \"atm\"\n ]","def run(self):\n logging.debug('List Available Recipes')\n if self.short:\n print(' '.join(pakit.recipe.RDB.names(desc=False)))\n return\n\n available = ['Program Description']\n available.extend(pakit.recipe.RDB.names(desc=True))\n\n msg = 'Available Recipes:'\n msg += PREFIX + PREFIX.join(available)\n print(msg)\n return msg","def list():\n\n click.secho('List of libraries in SJSU-Dev2\\n', fg='white', bold=True)\n package_registry = GetListOfSJSUDev2Repos()\n library_list = [f'{x : <20}: {package_registry[x]}'\n for x in package_registry if x.startswith('lib')]\n print('\\n'.join(library_list))","def codelists():\n return CodelistSet()","def extract_promocodes(self):\n promocode_description = self.text\n\n sentences: list = self._split_by_sentences(promocode_description)\n\n sentence_with_promocode = promocode_description # no needed\n\n promocodes = ()\n\n for sentence in sentences:\n if any(keyword in sentence.lower()\n for keyword in (\"промокод\", \"купон\", \"промо-код\", )):\n\n sentence_with_promocode = sentence\n\n promocodes: list = \\\n self.get_promocodes(sentence_with_promocode,\n parser_constants.instagram_patterns)\n if promocodes:\n break\n # TODO:\n # make probabilities and do not break\n # continue iter by senteces and search4 promo in every\n # after that (we know that here is 1 promo)\n # we can choose the most suitable coupon\n\n for p in promocodes:\n if p and len(p) >= 3:\n promocode = p\n if self.is_valid_promocode_morph_check(promocode):\n break\n else:\n return []\n\n if any(forbidden_promocode in promocode.lower()\n for forbidden_promocode in\n parser_constants.forbidden_promocodes):\n\n return []\n\n expiration_date = self.parse_date(promocode_description)\n\n for key in parser_constants.replacement_table.keys():\n promocode_description = \\\n promocode_description.replace(\n key, parser_constants.replacement_table[key]\n )\n\n return [data_classes.Promocode(\n coupon=promocode,\n promoCodeDescription=promocode_description,\n estimated_date=expiration_date,\n source=self.source\n )]","def get_list_html(self, items):\n html = \"\"\"\n <html>\n\t\t\t<head>\n\t\t\t\t<title>OpenFDA Cool App</title>\n\t\t\t</head>\n\t\t\t<body>\n <ol>\n \"\"\"\n\n for item in items:\n html += \"<li>\" + item + \"</li>\\n\"\n\n html += \"\"\"\n </ol>\n\t\t\t</body>\n </html>\n \"\"\"\n\n return html","def get_pcode_list(self) -> List[str]:\n return self.pcodes","def fill_item_list(self):\n return_list = []\n with Transaction().start(DBNAME, 1):\n self.productlist = self.Product.search([('description', '=', 'Stock'), ('type', '=', 'goods')])\n for i in self.productlist:\n return_list.append(i.template.name)\n return return_list","def list_to_desc_array(data_list):\n\n data_array = _populate_array(data_list, driver.ble_gattc_desc_array)\n return data_array","def inventory(self) -> [str]:\r\n inventory_to_use = []\r\n items = [\"Apple\", \"Sword\", \"Shield\", \"Dagger\"]\r\n\r\n for item_in_items in range(2):\r\n if item_in_items <= 2:\r\n index = randint(0, len(items)) - 1\r\n inventory_to_use.append(items[index])\r\n del items[index]\r\n return inventory_to_use","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 populate_code_list():\n\tletter_code_ST = \"JZIHGFEDCBA\"\n\tletter_code_FG = \"XWUTRQPNMLK\"\n\tfor pos in range(\n\t len(letter_code_ST)): #Interestingly, the values start from 0\n\t\tcode_ST.append(pos) # Number first\n\t\tcode_ST.append(letter_code_ST[pos])\n\tfor pos in range(len(letter_code_FG)):\n\t\tcode_FG.append(pos)\n\t\tcode_FG.append(letter_code_FG[pos])","def getOldCodeList(self):\n if self.modification == 'none':\n old_code = [(x, 'white') for x in self.body.splitlines()]\n elif self.modification == 'change':\n old_code = [self._getOldCodeList(x) for x in self.body.splitlines() \\\n if self._getOldCodeList(x)[0]]\n # we want old_code_list and new_code_list to have the same length\n if(self.old_code_length < self.new_code_length):\n filling = [(None, self.color)] * (self.new_code_length - \\\n self.old_code_length)\n old_code.extend(filling)\n else: # deletion or addition\n old_code = [self._getOldCodeList(x) for x in self.body.splitlines()]\n return old_code","def get_items(data, requisites, formatted):\n returndata = \"\"\n traits = requisites['trait']\n allergens = requisites['allergens']\n\n if formatted:\n prefix = '\\t'\n suffix = '\\n'\n else:\n prefix = ''\n suffix = ', '\n\n for course in data['menu']['meal']['course']:\n item_data = []\n datatype = type(course['menuitem'])\n\n if datatype is list:\n item_data += course['menuitem']\n else:\n item_data.append(course['menuitem'])\n\n for item in item_data:\n if check_item_specifications(item, traits, allergens) and 'No Service at this Time' not in item['name']:\n returndata += (prefix + (item['name']).rstrip(', ') + suffix)\n\n return returndata","def discount_codes(self):\n return [DiscountCode(x) for x in self._dict.get('discount_codes', [])]","def lookup (barcode, ID_TYPES=['ISBN', 'UPC','EAN']):\n\n matches = [] # list of {'desc', 'sku', 'type', 'vnd'}\n\n for idtype in ID_TYPES:\n try:\n result = api.item_lookup(barcode, SearchIndex='All', IdType=idtype)\n for item in result.Items.Item:\n if not _is_duplicate(item.ASIN, matches):\n matches.append({'desc': unicode(item.ItemAttributes.Title),\n 'sku': unicode(item.ASIN),\n 'type': idtype,\n 'vnd': 'AMZN:'+AMZLOCALE}) # vendor id\n\n except (errors.InvalidAccount, errors.InvalidClientTokenId, errors.MissingClientTokenId):\n print >>sys.stderr, \"Amazon Product API lookup: bad account credentials\"\n\n except errors.TooManyRequests, toomanyerr:\n print >>sys.stderr, \"Amazon Product API lookup error:\", toomanyerr\n\n except errors.InternalError, awserr:\n print >>sys.stderr, \"Amazon Product API lookup error:\", awserr\n\n except errors.InvalidParameterValue:\n # this simply means the barcode\n # does not exist for the given type,\n # so no need to do anything explicit\n pass\n\n return matches","def to_iob(text: str, items: List[Instance]) -> List[str]:\n coding = [\"O\"] * len(text)\n for (s, e), label in items:\n b = f\"B-{label}\"\n i = f\"I-{label}\"\n coding[s] = b\n for x in range(s + 1, e):\n coding[x] = i\n\n return coding","def get_details_of_code(self, code):\n row = {}\n try:\n with Transaction().start(DBNAME, 1):\n i = self.Product.search([('code', '=', code),\n ('description', '=', 'Stock'), ('type', '=', 'goods')])\n if i:\n i = i[-1]\n row['item'] = i.template.name\n row['category'] = i.template.category.name\n row['units'] = i.template.default_uom.name\n row['rate'] = i.template.list_price.to_eng()\n suppliers = i.template.product_suppliers\n if suppliers:\n row['supplier'] = suppliers[0].party.name\n return row\n except Exception:\n if settings.level == 10:\n logger.exception('raised exception')\n return row","def sanitize_sample_descriptions(sample_description_list, sanitize_fn=sanitize_text):\n filtered_sample_desc_list = []\n for text in sample_description_list:\n filtered_sample_desc_list.append(sanitize_fn(text))\n\n return filtered_sample_desc_list","def read_stock_codes_from_db():\n\n print('connecting to database...')\n Stocks = get_db()['Stocks']\n print('reading...')\n\n stocks = Stocks.find()\n return stocks","def get_description(self, code):\n try:\n return self.message[str(code)]\n except KeyError:\n return \"Unknown (\" + str(code) + \")\"","def codebook_json_data_factory() -> List[Dict[str, Any]]:\n codebook_data = [\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_A\"\n },\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_B\"\n },\n ]\n return codebook_data","def summarize_food_data(unprocessed_food_list: List[str]) -> List[Dict[str, str]]:\n summary: List[Dict[str, str]] = []\n item_count_data: Dict[str, int] = {}\n\n for item in unprocessed_food_list:\n if item not in item_count_data:\n item_count_data[item] = 1\n else:\n item_count_data[item] += 1\n \n for product in item_count_data:\n item_information: Dict[str, str] = {}\n item_information[\"name\"] = product\n item_information[\"quantity\"] = str(item_count_data[product])\n item_information[\"units\"] = \"-\"\n summary.append(item_information)\n \n return summary","async def _get_stock_data(self, stocks: list):\n\t\tapi_url = 'https://sandbox.tradier.com/v1/markets/quotes'\n\t\tstocks = ','.join(stocks)\n\t\tif not stocks:\n\t\t\treturn []\n\t\ttoken = await self.bot.get_shared_api_tokens('stocks')\n\t\ttoken = token.get('key', None)\n\t\tif not token:\n\t\t\traise ValueError(\n\t\t\t\t'You need to set an API key!\\n'\n\t\t\t\t'Follow this guide for instructions on how to get one:\\n'\n\t\t\t\t'<https://github.com/Flame442/FlameCogs/blob/master/stocks/setup.md>'\n\t\t\t)\n\t\tparams = {'symbols': stocks}\n\t\theaders = {'Authorization': f'Bearer {token}', 'Accept': 'application/json'}\n\t\tasync with aiohttp.ClientSession() as session:\n\t\t\tasync with session.get(api_url, params=params, headers=headers) as r:\n\t\t\t\ttry:\n\t\t\t\t\tr = await r.json()\n\t\t\t\texcept aiohttp.client_exceptions.ContentTypeError:\n\t\t\t\t\t#This might happen when being rate limited, but IDK for sure...\n\t\t\t\t\traise ValueError('Could not get stock data. The API key entered is most likely not valid.')\n\t\tr = r['quotes']\n\t\tif 'quote' not in r:\n\t\t\treturn []\n\t\tr = r['quote']\n\t\tif not isinstance(r, list):\n\t\t\tr = [r]\n\t\tstock = {\n\t\t\tx['symbol']: {\n\t\t\t\t'price': max(1, int(x['last'] * 100)),\n\t\t\t\t#New API does not give this info.\n\t\t\t\t'total_count': None, #int(x['marketCap'] / x['last']) if x['marketCap'] else None\n\t\t\t} for x in r if 'last' in x and x['last'] is not None\n\t\t}\n\t\treturn stock","def getListFromSupplier(self, ddata, supplier):\n # browse through all the components\n head = self.header.getHeaders()\n items = map(lambda cmpn:\n list(map(lambda it: cmpn[it], head)), ddata[supplier])\n for it in items:\n yield it","def suppliers(ticker: str, other_args: List[str]):\n parser = argparse.ArgumentParser(\n prog=\"supplier\",\n add_help=False,\n description=\"List of suppliers from ticker provided. [Source: CSIMarket]\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n url_supply_chain = f\"https://csimarket.com/stocks/competitionNO3.php?supply&code={ticker.upper()}\"\n text_supplier_chain = BeautifulSoup(requests.get(url_supply_chain).text, \"lxml\")\n\n l_supplier = list()\n for supplier in text_supplier_chain.findAll(\n \"td\", {\"class\": \"plavat svjetlirub dae al\"}\n ):\n l_supplier.append(supplier.text)\n\n if l_supplier:\n print(\"List of Suppliers: \" + \", \".join(l_supplier) + \"\\n\")\n else:\n print(\"No suppliers found.\\n\")\n\n except Exception as e:\n print(e, \"\\n\")","def items(self) -> typing.ItemsView[str, Category]:\n return self._primary_code_map.items()","def getDataInterfaces(context, export_only=False):\n from bika.lims.exportimport import instruments\n exims = []\n for exim_id in instruments.__all__:\n exim = instruments.getExim(exim_id)\n if export_only and not hasattr(exim, 'Export'):\n pass\n else:\n exims.append((exim_id, exim.title))\n exims.sort(lambda x, y: cmp(x[1].lower(), y[1].lower()))\n exims.insert(0, ('', t(_('None'))))\n return DisplayList(exims)","def all_currency_codes():\n return [(a, CURRENCIES[a].name) for a in CURRENCIES]","def dishlist_prices(n: list) -> list:\r\n return [dish.price for dish in n]","def list_to_text(ingridients_list):\n to_return = \"List\\n\"\n for (ingridient, quantity) in ingridients_list:\n to_return = f\"{to_return}{ingridient.name} {quantity}\\n\"\n return to_return","def __read_instrumentslist(self):\n available_instruments = []\n with open(\"instrumentslist.txt\", \"r\") as file:\n for line in file:\n splited = line.split(\" \")\n name = \"\"\n for j in splited[1:]:\n name += j\n available_instruments.append(name)\n return available_instruments","def _collect_quantizers_descriptions(self) -> List[QuantizerDescription]:\n # `True` for weight quantizer, `False` otherwise.\n quantizers = chain(\n map(lambda x: (True, x), self._weight_quantizers.values()),\n map(lambda x: (False, x), self._non_weight_quantizers.values()),\n )\n\n quantizers_descriptions = []\n for is_weight_quantizer, q in quantizers:\n is_symmetric = isinstance(q, SymmetricQuantizer)\n\n quantizers_descriptions.append(\n QuantizerDescription(\n q.num_bits, q.per_channel, q.signed, is_symmetric, is_weight_quantizer, q.is_enabled_quantization()\n )\n )\n\n return quantizers_descriptions","def drug_names_on_drug_list(drug_list):\n return [dl[\"Drug (brand name)\"] for dl in drug_list]","def load_description():\n with open('description.txt') as description:\n return [line.strip() for line in description]","def get_products(self):\n return [item.code for item in self._products]","def getAllFirstDescWords(itemList):\r\n itemList = list(set(itemList)) # make itemList unique\r\n descWords = []\r\n for item in itemList:\r\n descWords.append(worldItems[item][DESCWORDS][0])\r\n return list(set(descWords))","def get_book_titles(self, lib_db):\n titles = []\n conn = sqlite3.connect(lib_db)\n c = conn.cursor()\n for row in c.execute(\"SELECT ZTITLE FROM ZBKLIBRARYASSET WHERE ZTITLE <> '' AND ZTITLE <> 'none'\"):\n titles.append(row[0])\n conn.close()\n return titles","def get_packages(module, repo_mgr, list_type, package):\n rc_code, out, err = module.run_command(\"/usr/bin/%s -q list %s %s\"\n % (repo_mgr, list_type, package), environ_update=ENV_LOCALE)\n if rc_code is 0:\n return out.splitlines()\n else:\n if rc_code == 1 and str(err) == 'Error: No matching Packages to list\\n':\n return out.splitlines()\n else:\n module.fail_json(msg=\"Unable to collect \" + repo_mgr + \" list \" + list_type + \" : \" + str(err) + \" - \" + str(out))","def print_items():\n for items in inventory:\n print(f\"- {items.upper()}\")","def license_codes(self) -> Sequence[str]:\n return pulumi.get(self, \"license_codes\")","def build_dividend_lists(portfolio_dict):\n # ETF dividend list\n dow_dividends = lookup_dividends(yf.Ticker(\"DIA\")) \n sp500_dividends = lookup_dividends(yf.Ticker(\"SPY\")) \n nasdaq_dividends = lookup_dividends(yf.Ticker(\"QQQ\")) \n totalmarket_dividends = lookup_dividends(yf.Ticker(\"VTI\")) \n \n # Portfolio dividends\n portfolio_dividend_dict = {}\n for key in portfolio_dict:\n portfolio_dividend_dict[key] = lookup_dividends(yf.Ticker(key))\n \n return (dow_dividends, sp500_dividends, nasdaq_dividends, totalmarket_dividends, portfolio_dividend_dict)","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 getNewCodeList(self):\n if self.modification == 'none':\n new_code = [(x, 'white') for x in self.body.splitlines()]\n elif self.modification == 'change':\n new_code = [self._getNewCodeList(x) for x in self.body.splitlines() \\\n if self._getNewCodeList(x)[0]]\n # we want old_code_list and new_code_list to have the same length\n if(self.new_code_length < self.old_code_length):\n filling = [(None, self.color)] * (self.old_code_length - \\\n self.new_code_length)\n new_code.extend(filling)\n else: # deletion or addition\n new_code = [self._getNewCodeList(x) for x in self.body.splitlines()]\n return new_code","def list_item(self, text):\n #return [[MdStyleInstructionList(), *text]]\n return [[MdStyleInstructionListItem()] + text]","def getFormatsFromDescr(descr):\n i = getIter(descr)\n if not i:\n return\n\n try:\n item = i.next()\n while item:\n item1 = item[1]\n if isinstance(item1, str):\n yield normalize_format(item1)\n else:\n l = []\n for j in getFormatsFromDescr(item1):\n l.append(j)\n yield l\n item = i.next()\n except StopIteration:\n pass","def get_todo_list():\n\n # assume that a \"h264\" encoded file is complete\n return models.LibraryItem.objects.filter(h264=False)","def get_description(self):\n return COMPONENT_LIST[self.index][1]","def _create_pull_requests_descriptions(self, pull_requests: list) -> list:\n descriptions = []\n for pull_request in pull_requests:\n description = deepcopy(self.blocks['description'])\n reviewers = map(\n lambda name: self._get_user_mention(name),\n pull_request['reviewers'],\n )\n description['text']['text'] = ' '.join(reviewers)\n description['accessory']['url'] = pull_request['url']\n descriptions.append(description)\n return descriptions","def GetAllDifferentDescriptionOfCost():\n\n logs.logger.debug(\n \"Start to get back all different description of \"\n \"Cost objects from database.\")\n try:\n ListOfAllDifferentDescriptionOfCost = []\n searchedCostsItems = GetAllDescriptionOfCost()\n for item in searchedCostsItems:\n if item not in ListOfAllDifferentDescriptionOfCost:\n ListOfAllDifferentDescriptionOfCost.append(item)\n logs.logger.info(\n \"Start to get back all different description of \"\n \"Cost objects from database.\")\n return ListOfAllDifferentDescriptionOfCost\n except Exception as e:\n logs.logger.error(e, exc_info=True)","def pull_list():\n URL = 'https://salsa.debian.org/security-tracker-team/security-tracker/raw/master/data/CVE/list'\n file = urllib.request.urlopen(URL).readlines()\n generic = [line.strip().decode() for line in file]\n return generic","def list_tick_dates(self, stock_code):\n\n conn = self.db_engine.connect()\n try:\n get_tick_dates_sql = \"\"\"\n SELECT DISTINCT \"update_date\" FROM \"{0}\".\"{1}\"\n WHERE end_update_time IS NOT NULL AND stock_code='{2}'\n \"\"\".format(Schemas.SCHEMA_META, Tables.TABLE_TICK_UPDATE_LOGS, stock_code)\n\n res = pd.read_sql(get_tick_dates_sql, conn)['update_date'].tolist()\n res.sort()\n return Error.SUCCESS, res\n except Exception:\n self.logger.log_error(traceback.format_exc())\n return Error.ERROR_DB_EXECUTION_FAILED, None\n finally:\n conn.close()","def _get_package_items(self):\r\n mask = \"mask[description,capacity,prices.id,categories[name,id]]\"\r\n package = self.client['Product_Package']\r\n return package.getItems(id=46, mask=mask)","def __init__(self):\n # note: We could have implemented the list as a dictionary, with\n # the barcode as the key, however if the barcode for the item\n # changes we might have problems.\n self.stocklist = [] # a list of stock items","def obtain_stock_names():\n\n url = 'https://en.wikipedia.org/wiki/List_of_S%26P_400_companies'\n stock_names = []\n response = requests.get(url, timeout=5)\n content = BeautifulSoup(response.content, \"html.parser\")\n\n # We get stock_names from the web page\n for stock in content.findAll('a', attrs={\"class\": \"external text\"}):\n if(len(stock.text)<=5):\n stock_names.append(stock.text)\n\n # We persist the Stock Names\n save_dir = Path(__file__).parent.parent\n filename = (save_dir / \"../data/stock_names.joblib\").resolve()\n PersistenceAPI.persist_stock_data(stock_names, filename)\n\n return stock_names","def read_stock_list():\n print(\"Reading list of stocks.\")\n stocks = {}\n with open(STOCKS_FILE) as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n stocks[row['Symbol']] = (row['Name'], row['Sector'])\n return stocks","def list(self, body, ordered=True):\n return [[MdStyleInstructionListStart(ordered)]] + body + [[MdStyleInstructionListEnd()]]","def build_item_list(client, word=''):\n\titem_group = client.search_metadata('collection_name:cooee')\n\titems = item_group.get_all()\n\titem_list_name = word + '_list'\n\titem_urls = []\n\tfor item in items:\n\t\tprimary_text = item.get_primary_text()\n\t\tif word in primary_text:\n\t\t\tprint item.url()\n\t\t\titem_urls.append(item.url())\n\t\t\t#client.add_to_item_list_by_name([item.url()], item_list_name)\n\n\tfor url in item_urls:\n\t\tclient.add_to_item_list_by_name(item_urls, item_list_name)","def obtain_parse_snp500():\n\n # Stores the current time, for the created_at record\n now = datetime.datetime.utcnow()\n\n # Use finsymbols to download stock symbols, industry and S&P 500 data\n sp500=finsymbols.get_sp500_symbols()\n # Obtain the symbol information for each row in the S&P500 constituent table\n symbols = []\n for index, symbol in enumerate(sp500):\n sd = {'ticker': sp500[index]['symbol'],\n 'name': sp500[index]['company'],\n 'sector': sp500[index]['industry']}\n # Create a tuple (for the DB format) and append to the grand list\n symbols.append( (sd['ticker'], 'stock', sd['name'], \n sd['sector'], 'USD', now, now) )\n \n return symbols","def program_list():\n items = []\n\n soup = abcradionational.get_soup(URL + \"/podcasts/program\")\n \n program_heading = abcradionational.get_podcast_heading(soup)\n\n for program in program_heading:\n items.append({\n 'label': program['title'],\n 'path': plugin.url_for('program_item', url=program['url']),\n })\n\n return items","def show_item_list():\n # 3 items per line\n line = []\n linecounter = 0\n item_string = \"\"\n counter = 0\n text_spacer = 20\n clear_messages(0)\n\n for i in range(0, len(ITEMS)):\n space = text_spacer - len(ITEMS[i])\n item_string = item_string + ITEMS[i] + (' ' * space)\n counter += 1\n if counter == 3:\n line.append(item_string)\n linecounter += 1\n item_string = \"\"\n counter = 0\n if counter < 3:\n line.append(item_string)\n\n for i in range(0, linecounter + 1):\n printmessage(line[i], i + 1, MAGENTA, 0)\n clear_messages(3)","def get_weather_description(weather):\n for num_moods in range(0, 10):\n description = weather['list'][num_moods]['weather'][0]['description']\n weather_description_data.append(description)\n return weather_description_data","def get_deck_as_str_list(self):\n\n ls = []\n for card in self.deck:\n ls.append(card.get_card())\n return ls","def alfred_items_for_value(value):\n index = 0\n results = []\n\n config_list = [\n ('t2s.json', u'繁體到簡體', 'SimplifiedChinese.png'),\n ('s2t.json', u'簡體到繁體', 'TraditionalChinese.png'),\n ('s2tw.json', u'簡體到臺灣正體', 'TW_taiwan.png'),\n ('tw2s.json', u'臺灣正體到簡體', 'CN_china.png'),\n ('s2hk.json', u'簡體到香港繁體', 'HK_hongKong.png'),\n ('hk2s.json', u'香港繁體（香港小學學習字詞表標準）到簡體', 'CN_china.png'),\n ('tw2sp.json', u'繁體（臺灣正體標準）到簡體並轉換爲中國大陸常用詞彙', 'CN_china.png'),\n ('s2twp.json', u'簡體到繁體（臺灣正體標準）並轉換爲臺灣常用詞彙', 'TW_taiwan.png'),\n ]\n for config_file, description, icon in config_list:\n converter = opencc.OpenCC(\n config=config_file, opencc_path='/usr/local/bin/opencc')\n item_value = converter.convert(value)\n results.append(alfred.Item(\n title=item_value,\n subtitle=description,\n attributes={\n 'uid': alfred.uid(index),\n 'arg': item_value,\n },\n icon=icon,\n ))\n index += 1\n\n return results","def get_raw(self):\n \n out_items = []\n for order in self.order_lst:\n out_items += [i.get_metadata() for i in order.get_items()]\n \n return out_items","def show_list(self, desc, lst, writeln):\n if not lst:\n return\n val = ', '.join([list_escape(v) for v in lst])\n writeln(\"%s: %s\" % (desc, val))","def lcode(self):\n###############################################################################\n lcode = []\n for M in list(self.estimates.values()):\n if (M.code not in lcode):lcode.append(M.code)\n return(lcode)","def read_publisher_qualified_isocode(self):\n self.CATEGORIES = kpi_from_db_config.CATEGORIES\n self.SELECTED_ISOCODE = kpi_from_db_config.SELECTED_ISOCODE\n self.ID_PUBLISHER_QUALIFIED_ISOCODE = kpi_from_db_config.ID_PUBLISHER_QUALIFIED_ISOCODE\n\n size_m = len(self.CATEGORIES)\n size_n = len(self.SELECTED_ISOCODE)\n list_id = self.ID_PUBLISHER_QUALIFIED_ISOCODE\n\n self.cursor.execute('''\n SELECT value\n FROM public.kpi_report\n WHERE id = %s\n ORDER BY created_at DESC\n LIMIT %s\n ''', [list_id, size_m*size_n])\n\n list_result = [[0]*size_n for _ in range(size_m)]\n\n count_result = size_m*size_n - 1\n for doc in self.cursor:\n list_result[count_result%size_m][count_result/size_m] = doc[0]\n count_result -= 1\n\n for i in range(size_m):\n list_result[i][0:0] = [sum([list_result[i][j] for j in range(size_n)])]\n\n list_result[0:0] = [[0]*(size_n + 1)]\n for i in range(size_n + 1):\n list_result[0][i] = sum([list_result[j][i] for j in range(size_m + 1)])\n\n categories = ['--All--'] + self.CATEGORIES\n\n return list_result, categories","def snippetList(requeset, format = None):","def _list_of_availability_strings():\n names = [availability.name for availability in Availability]\n return names","def CARD_SUITS() -> tuple:\n return \"Diamonds\", \"Hearts\", \"Clubs\", \"Spades\"","def get_list_of_str2(self):\n pass","def getIngredients():\n ingredients = ['Whiskey', 'Tequila', 'Vodka', 'Blue Curacao', 'Orange Juice',\n 'Pineapple Juice', 'Cranberry Juice', 'Sour Mix']\n return ingredients","def listRestock(self):\n # TODO return a list of items that need restocking\n # hint: Need to loop through the stocklist\n # No. 3\n flag = 0\n mystr = \"\"\n for item in self.stocklist:\n if StockItem.needRestock(item):\n mystr = mystr + StockItem.getName(item) + '\\n'\n flag = flag + 1 # item that needs restocking found\n\n # No. 4\n if flag == 0:\n return \"All items stocked\"\n else:\n return mystr","def scrape_descriptions_sync():\n # прочитать Symbols, for symbol in tqdm(symbols)\n # исользовать urllib get запросы на yahoo и полученное записывать в файл с помощью\n # добавить tqdm(symbols)\n\n myheader = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'\n }\n\n symbols = read_symbols()\n YAHOO_HTMLS.mkdir(parents=True, exist_ok=True)\n\n\n for symbol in tqdm(symbols):\n #Example myurl = \"https://finance.yahoo.com/quote/AAPL/profile?p=AAPL\"\n myurl = f'https://finance.yahoo.com/quote/{symbol}/profile?p={symbol}'\n\n try:\n req = request.Request(myurl, headers=myheader)\n response = request.urlopen(req)\n text = response.read()\n response.close()\n\n except Exception:\n print(\"Error occuried during web request!!\")\n print(sys.exc_info()[1])\n\n f = open(YAHOO_HTMLS / f'{symbol}.html', 'wb')\n f.write(text)\n f.close()","def nameList(self):\r\n return [self.name.lower(), self.code] + self._otherNames","def find_item_codes(transaction):\n t = transaction\n item_codes = []\n if t['transaction_type'] in ('SALE', 'REFUND'):\n # Search using line item IDs and order_id\n for oli in (t['order_line_items'] or []):\n li_id = oli['line_item_id']\n item_codes.append(\n get_item_code_for_order(t['order_id'], order_line_item_id=li_id)\n )\n else:\n # Search for ITEM reference\n for ref in (transaction['references'] or []):\n if ref['reference_type'] == 'ITEM_ID':\n item_codes.append(\n get_item_code_for_item_id(ref['reference_id'])\n )\n\n return item_codes"],"string":"[\n \"def getStockCodeList(self, list_description=None):\\n list_stockCode = list()\\n df = self._df_invoice_original\\n \\n if list_description is None:\\n list_stockCode = list(df.StockCode.unique())\\n else :\\n for description in list_description:\\n stockCode = df[df.Description==description].StockCode.unique()[0]\\n list_stockCode.append(stockCode)\\n return list_stockCode\",\n \"def create_not_included_list(codes):\\n string = '\\\\\\\\begin{itemize}\\\\n'\\n for code in codes:\\n title = get_course_title_only(code)\\n string += '\\\\\\\\item{' + title + '}\\\\n'\\n string += '\\\\\\\\end{itemize}\\\\n'\\n return string\",\n \"def get_order_lists(self, n_items, n_quantities):\\n arr_stock_code = self._df_invoice_original.StockCode.unique()\\n arr_stock_code = np.random.choice(arr_stock_code, n_items)\\n list_stockCode = list(arr_stock_code)\\n list_quantities = np.ones(arr_stock_code.shape[0])\\n list_quantities *=n_quantities\\n\\n return list_stockCode, list_quantities\",\n \"def getUnitPriceList(self, list_stockCode):\\n df = self._df_invoice_original\\n\\n list_unitPrice = list()\\n \\n for stockCode in list_stockCode:\\n unitPrice = df[df.StockCode==stockCode].UnitPrice.unique()[0]\\n list_unitPrice.append(unitPrice)\\n return list_unitPrice\",\n \"def _getListing(self):\\n\\n # lets assure consistent litsting order\\n items = self._items.items()\\n items.sort()\\n return [ \\\"%s%s%s: %s\\\" % (_def_sep, str(x[1]), _def_sep, x[1].__doc__)\\n for x in items ]\",\n \"def parse_quantities(quantities):\\n codes = []; names = []\\n\\n for q in quantities:\\n c, n = parse_quantity(q)\\n codes.append(c)\\n names.append(n)\\n\\n return codes, names\",\n \"def get_stocks():\\n print(\\\"fetching remote...\\\")\\n code_dataframes = pd.read_html(\\n 'http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', header=0)[0]\\n # 우리가 필요한 것은 회사명과 종목코드이기 때문에 필요없는 column들은 제외해준다.\\n print(\\\"parsing and filtering data...\\\")\\n code_dataframes.종목코드 = code_dataframes.종목코드.map('{:06d}'.format)\\n # 한글로된 컬럼명을 영어로 바꿔준다.\\n code_dataframes = code_dataframes[['회사명', '종목코드']]\\n code_dataframes = code_dataframes.rename(\\n columns={'회사명': 'name', '종목코드': 'code'})\\n codes = code_dataframes['code']\\n names = code_dataframes['name']\\n stocks = []\\n for i in range(len(names)):\\n stocks.append({\\n 'name': names[i],\\n 'code': codes[i]\\n })\\n return stocks\",\n \"def itemnames():\\n g = ['KIS_NA_39', 'VII_57', 'MX_48', 'MX_56', 'KIS_NA_42', 'VII_54',\\n 'MX_S_48', 'MX_S_52', 'MX_52', 'KIS_NA_45', 'KIS_NA_51', 'MIP_45',\\n 'MIP_49', 'MIP_52', 'MIP_plus_48', 'MIP_plus_51', 'MX_42', 'MX_45',\\n 'MIP_G_42', 'KIS_42', 'KIS_NA_48']\\n return(g)\",\n \"def getAllDescWords(itemList):\\r\\n itemList = list(set(itemList)) # make itemList unique\\r\\n descWords = []\\r\\n for item in itemList:\\r\\n descWords.extend(worldItems[item][DESCWORDS])\\r\\n return list(set(descWords))\",\n \"def get_items_for_catalog(catalog_id):\\n pass\",\n \"def description(self):\\n item_counts = [f'{i.quantity}x {i.item.name}' for i in self.items]\\n return ','.join(item_counts)\",\n \"def ConstructList(title, items):\\n buf = cStringIO.StringIO()\\n fmt = 'list[title=\\\"{title}\\\",always-display-title]'.format(title=title)\\n resource_printer.Print(sorted(set(items)), fmt, out=buf)\\n return buf.getvalue()\",\n \"def list_items(self):\\n click.echo(\\\"ID --|-- Item Title\\\")\\n for index, item in enumerate(self.items):\\n click.echo(\\\" {} --|-- {}\\\".format(index, item.title))\",\n \"def serve_recos(ids, ref_catalog):\\r\\n desc_list = []\\r\\n for desc_id in ids:\\r\\n desc_list.append(ref_catalog[ref_catalog['id'] == desc_id].iloc[0]['description'])\\r\\n return desc_list\",\n \"def get_code_mapping( id ):\\n returnVal = []\\n theCodes = _theRegistry.get_code( id )\\n codes = theCodes.get_codes()\\n descs = theCodes.get_descriptions()\\n for (code, desc) in map(None, codes, descs):\\n returnVal.append( { 'code' : code, 'description' : desc } )\\n return returnVal\",\n \"def list():\\n\\n return cache.codeTableList()\",\n \"def getArtistsofArtwork(catalog, codes):\\n return model.getArtistname(catalog,codes)\",\n \"def list(self):\\n return 'Decks available: \\\\n{}'.format(\\\"\\\\n\\\".join([\\n 'Deck {}: {} ({} cards)'.format(deck['id'], deck['title'], len(deck['cards']))\\n for key, deck in self.decks.items()\\n ]))\",\n \"def consult_books(self, bar_code: str):\\n try:\\n book_data = []\\n self.db.cursor.execute('SELECT * from books WHERE bar_code = %s', (bar_code,))\\n for i in self.db.cursor.fetchall():\\n book_data.append(i)\\n except Exception as error:\\n print(error)\\n else:\\n print(f\\\"ID BOOK: {book_data[0][0]}\\\\n\\\"\\n f\\\"TITLE: {book_data[0][1]}\\\\n\\\"\\n f\\\"AUTHOR: {book_data[0][2]}\\\\n\\\"\\n f\\\"PRICE: R$:{book_data[0][3]}\\\\n\\\"\\n f\\\"BAR CODE: {book_data[0][4]}\\\\n\\\"\\n f\\\"STOCK: {book_data[0][5]}\\\")\",\n \"def hs_code_process(si):\\n hs_code = re.sub(r'\\\\W+', '', si.get('hs_code', ''))\\n descrip = re.sub(r'\\\\W+', '', si.get('description_of_goods', ''))\\n bl_type = re.sub(r'\\\\W+', '', si.get('bl_type', ''))\\n hs_codes = []\\n if hs_code != '' and hs_code in descrip:\\n hs_raw = si.pop('hs_code')\\n for raw_line in hs_raw.split('\\\\n'):\\n line = re.sub(r'\\\\W+', '', raw_line).upper()\\n if 'HSCODE' in line:\\n remain = line.replace('HSCODE', '').replace('\\\\n', '')\\n remain = re.sub(r'[A-Z]+', '', remain)\\n if remain.isdigit() and len(remain) > 4:\\n hs_codes.append(remain)\\n else:\\n # CODE in line below\\n hs_line_no = hs_raw.split('\\\\n').index(raw_line)\\n for hs_line in hs_raw.split('\\\\n')[hs_line_no + 1:]:\\n if len(re.findall(r'[a-zA-Z]+', hs_line)) < 1:\\n for hs_code in re.findall(r'\\\\d+', hs_line):\\n hs_codes.append(hs_code)\\n else:\\n break\\n\\n bl_type = si.get('bl_type', '')\\n\\n elif hs_code != '' and hs_code in bl_type:\\n hs_raw = si.pop('hs_code')\\n for raw_info in hs_raw.split('/'):\\n info = re.sub(r'\\\\W+', '', raw_info).upper()\\n if 'HSCODE' in info:\\n hs_code = info.replace('HSCODE', '').replace('\\\\n', '')\\n hs_code = re.sub(r'[A-Z]+', '', hs_code)\\n hs_codes.append(hs_code)\\n break\\n bl_type = hs_raw.split('/')[0]\\n\\n else:\\n hs_code = re.sub(r'[^\\\\d]+', '', hs_code)\\n hs_codes.append(hs_code)\\n bl_type = si.get('bl_type', '')\\n\\n return hs_codes, bl_type\",\n \"def getNamesFromDescr(descr):\\n i = getIter(descr)\\n if not i:\\n return\\n\\n try:\\n item = i.next()\\n while item:\\n if isinstance(item[1], str):\\n yield item[0]\\n else:\\n l = []\\n for j in getNamesFromDescr(item[1]):\\n l.append(j)\\n r = (item[0], l)\\n yield r\\n item = i.next()\\n except StopIteration:\\n pass\",\n \"def codes(self):\\n return [card.code for card in self.cards]\",\n \"def get_snippet_info_list(self):\\n snippets = []\\n for snippet in self.snippets:\\n snippets.append([snippet[\\\"title\\\"], snippet[\\\"description\\\"]])\\n return snippets\",\n \"def product_db() -> List[Text]:\\n\\n return [\\n \\\"credit\\\",\\n \\\"forex\\\",\\n \\\"debit\\\",\\n \\\"atm\\\"\\n ]\",\n \"def run(self):\\n logging.debug('List Available Recipes')\\n if self.short:\\n print(' '.join(pakit.recipe.RDB.names(desc=False)))\\n return\\n\\n available = ['Program Description']\\n available.extend(pakit.recipe.RDB.names(desc=True))\\n\\n msg = 'Available Recipes:'\\n msg += PREFIX + PREFIX.join(available)\\n print(msg)\\n return msg\",\n \"def list():\\n\\n click.secho('List of libraries in SJSU-Dev2\\\\n', fg='white', bold=True)\\n package_registry = GetListOfSJSUDev2Repos()\\n library_list = [f'{x : <20}: {package_registry[x]}'\\n for x in package_registry if x.startswith('lib')]\\n print('\\\\n'.join(library_list))\",\n \"def codelists():\\n return CodelistSet()\",\n \"def extract_promocodes(self):\\n promocode_description = self.text\\n\\n sentences: list = self._split_by_sentences(promocode_description)\\n\\n sentence_with_promocode = promocode_description # no needed\\n\\n promocodes = ()\\n\\n for sentence in sentences:\\n if any(keyword in sentence.lower()\\n for keyword in (\\\"промокод\\\", \\\"купон\\\", \\\"промо-код\\\", )):\\n\\n sentence_with_promocode = sentence\\n\\n promocodes: list = \\\\\\n self.get_promocodes(sentence_with_promocode,\\n parser_constants.instagram_patterns)\\n if promocodes:\\n break\\n # TODO:\\n # make probabilities and do not break\\n # continue iter by senteces and search4 promo in every\\n # after that (we know that here is 1 promo)\\n # we can choose the most suitable coupon\\n\\n for p in promocodes:\\n if p and len(p) >= 3:\\n promocode = p\\n if self.is_valid_promocode_morph_check(promocode):\\n break\\n else:\\n return []\\n\\n if any(forbidden_promocode in promocode.lower()\\n for forbidden_promocode in\\n parser_constants.forbidden_promocodes):\\n\\n return []\\n\\n expiration_date = self.parse_date(promocode_description)\\n\\n for key in parser_constants.replacement_table.keys():\\n promocode_description = \\\\\\n promocode_description.replace(\\n key, parser_constants.replacement_table[key]\\n )\\n\\n return [data_classes.Promocode(\\n coupon=promocode,\\n promoCodeDescription=promocode_description,\\n estimated_date=expiration_date,\\n source=self.source\\n )]\",\n \"def get_list_html(self, items):\\n html = \\\"\\\"\\\"\\n <html>\\n\\t\\t\\t<head>\\n\\t\\t\\t\\t<title>OpenFDA Cool App</title>\\n\\t\\t\\t</head>\\n\\t\\t\\t<body>\\n <ol>\\n \\\"\\\"\\\"\\n\\n for item in items:\\n html += \\\"<li>\\\" + item + \\\"</li>\\\\n\\\"\\n\\n html += \\\"\\\"\\\"\\n </ol>\\n\\t\\t\\t</body>\\n </html>\\n \\\"\\\"\\\"\\n\\n return html\",\n \"def get_pcode_list(self) -> List[str]:\\n return self.pcodes\",\n \"def fill_item_list(self):\\n return_list = []\\n with Transaction().start(DBNAME, 1):\\n self.productlist = self.Product.search([('description', '=', 'Stock'), ('type', '=', 'goods')])\\n for i in self.productlist:\\n return_list.append(i.template.name)\\n return return_list\",\n \"def list_to_desc_array(data_list):\\n\\n data_array = _populate_array(data_list, driver.ble_gattc_desc_array)\\n return data_array\",\n \"def inventory(self) -> [str]:\\r\\n inventory_to_use = []\\r\\n items = [\\\"Apple\\\", \\\"Sword\\\", \\\"Shield\\\", \\\"Dagger\\\"]\\r\\n\\r\\n for item_in_items in range(2):\\r\\n if item_in_items <= 2:\\r\\n index = randint(0, len(items)) - 1\\r\\n inventory_to_use.append(items[index])\\r\\n del items[index]\\r\\n return inventory_to_use\",\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 populate_code_list():\\n\\tletter_code_ST = \\\"JZIHGFEDCBA\\\"\\n\\tletter_code_FG = \\\"XWUTRQPNMLK\\\"\\n\\tfor pos in range(\\n\\t len(letter_code_ST)): #Interestingly, the values start from 0\\n\\t\\tcode_ST.append(pos) # Number first\\n\\t\\tcode_ST.append(letter_code_ST[pos])\\n\\tfor pos in range(len(letter_code_FG)):\\n\\t\\tcode_FG.append(pos)\\n\\t\\tcode_FG.append(letter_code_FG[pos])\",\n \"def getOldCodeList(self):\\n if self.modification == 'none':\\n old_code = [(x, 'white') for x in self.body.splitlines()]\\n elif self.modification == 'change':\\n old_code = [self._getOldCodeList(x) for x in self.body.splitlines() \\\\\\n if self._getOldCodeList(x)[0]]\\n # we want old_code_list and new_code_list to have the same length\\n if(self.old_code_length < self.new_code_length):\\n filling = [(None, self.color)] * (self.new_code_length - \\\\\\n self.old_code_length)\\n old_code.extend(filling)\\n else: # deletion or addition\\n old_code = [self._getOldCodeList(x) for x in self.body.splitlines()]\\n return old_code\",\n \"def get_items(data, requisites, formatted):\\n returndata = \\\"\\\"\\n traits = requisites['trait']\\n allergens = requisites['allergens']\\n\\n if formatted:\\n prefix = '\\\\t'\\n suffix = '\\\\n'\\n else:\\n prefix = ''\\n suffix = ', '\\n\\n for course in data['menu']['meal']['course']:\\n item_data = []\\n datatype = type(course['menuitem'])\\n\\n if datatype is list:\\n item_data += course['menuitem']\\n else:\\n item_data.append(course['menuitem'])\\n\\n for item in item_data:\\n if check_item_specifications(item, traits, allergens) and 'No Service at this Time' not in item['name']:\\n returndata += (prefix + (item['name']).rstrip(', ') + suffix)\\n\\n return returndata\",\n \"def discount_codes(self):\\n return [DiscountCode(x) for x in self._dict.get('discount_codes', [])]\",\n \"def lookup (barcode, ID_TYPES=['ISBN', 'UPC','EAN']):\\n\\n matches = [] # list of {'desc', 'sku', 'type', 'vnd'}\\n\\n for idtype in ID_TYPES:\\n try:\\n result = api.item_lookup(barcode, SearchIndex='All', IdType=idtype)\\n for item in result.Items.Item:\\n if not _is_duplicate(item.ASIN, matches):\\n matches.append({'desc': unicode(item.ItemAttributes.Title),\\n 'sku': unicode(item.ASIN),\\n 'type': idtype,\\n 'vnd': 'AMZN:'+AMZLOCALE}) # vendor id\\n\\n except (errors.InvalidAccount, errors.InvalidClientTokenId, errors.MissingClientTokenId):\\n print >>sys.stderr, \\\"Amazon Product API lookup: bad account credentials\\\"\\n\\n except errors.TooManyRequests, toomanyerr:\\n print >>sys.stderr, \\\"Amazon Product API lookup error:\\\", toomanyerr\\n\\n except errors.InternalError, awserr:\\n print >>sys.stderr, \\\"Amazon Product API lookup error:\\\", awserr\\n\\n except errors.InvalidParameterValue:\\n # this simply means the barcode\\n # does not exist for the given type,\\n # so no need to do anything explicit\\n pass\\n\\n return matches\",\n \"def to_iob(text: str, items: List[Instance]) -> List[str]:\\n coding = [\\\"O\\\"] * len(text)\\n for (s, e), label in items:\\n b = f\\\"B-{label}\\\"\\n i = f\\\"I-{label}\\\"\\n coding[s] = b\\n for x in range(s + 1, e):\\n coding[x] = i\\n\\n return coding\",\n \"def get_details_of_code(self, code):\\n row = {}\\n try:\\n with Transaction().start(DBNAME, 1):\\n i = self.Product.search([('code', '=', code),\\n ('description', '=', 'Stock'), ('type', '=', 'goods')])\\n if i:\\n i = i[-1]\\n row['item'] = i.template.name\\n row['category'] = i.template.category.name\\n row['units'] = i.template.default_uom.name\\n row['rate'] = i.template.list_price.to_eng()\\n suppliers = i.template.product_suppliers\\n if suppliers:\\n row['supplier'] = suppliers[0].party.name\\n return row\\n except Exception:\\n if settings.level == 10:\\n logger.exception('raised exception')\\n return row\",\n \"def sanitize_sample_descriptions(sample_description_list, sanitize_fn=sanitize_text):\\n filtered_sample_desc_list = []\\n for text in sample_description_list:\\n filtered_sample_desc_list.append(sanitize_fn(text))\\n\\n return filtered_sample_desc_list\",\n \"def read_stock_codes_from_db():\\n\\n print('connecting to database...')\\n Stocks = get_db()['Stocks']\\n print('reading...')\\n\\n stocks = Stocks.find()\\n return stocks\",\n \"def get_description(self, code):\\n try:\\n return self.message[str(code)]\\n except KeyError:\\n return \\\"Unknown (\\\" + str(code) + \\\")\\\"\",\n \"def codebook_json_data_factory() -> List[Dict[str, Any]]:\\n codebook_data = [\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_A\\\"\\n },\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_B\\\"\\n },\\n ]\\n return codebook_data\",\n \"def summarize_food_data(unprocessed_food_list: List[str]) -> List[Dict[str, str]]:\\n summary: List[Dict[str, str]] = []\\n item_count_data: Dict[str, int] = {}\\n\\n for item in unprocessed_food_list:\\n if item not in item_count_data:\\n item_count_data[item] = 1\\n else:\\n item_count_data[item] += 1\\n \\n for product in item_count_data:\\n item_information: Dict[str, str] = {}\\n item_information[\\\"name\\\"] = product\\n item_information[\\\"quantity\\\"] = str(item_count_data[product])\\n item_information[\\\"units\\\"] = \\\"-\\\"\\n summary.append(item_information)\\n \\n return summary\",\n \"async def _get_stock_data(self, stocks: list):\\n\\t\\tapi_url = 'https://sandbox.tradier.com/v1/markets/quotes'\\n\\t\\tstocks = ','.join(stocks)\\n\\t\\tif not stocks:\\n\\t\\t\\treturn []\\n\\t\\ttoken = await self.bot.get_shared_api_tokens('stocks')\\n\\t\\ttoken = token.get('key', None)\\n\\t\\tif not token:\\n\\t\\t\\traise ValueError(\\n\\t\\t\\t\\t'You need to set an API key!\\\\n'\\n\\t\\t\\t\\t'Follow this guide for instructions on how to get one:\\\\n'\\n\\t\\t\\t\\t'<https://github.com/Flame442/FlameCogs/blob/master/stocks/setup.md>'\\n\\t\\t\\t)\\n\\t\\tparams = {'symbols': stocks}\\n\\t\\theaders = {'Authorization': f'Bearer {token}', 'Accept': 'application/json'}\\n\\t\\tasync with aiohttp.ClientSession() as session:\\n\\t\\t\\tasync with session.get(api_url, params=params, headers=headers) as r:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tr = await r.json()\\n\\t\\t\\t\\texcept aiohttp.client_exceptions.ContentTypeError:\\n\\t\\t\\t\\t\\t#This might happen when being rate limited, but IDK for sure...\\n\\t\\t\\t\\t\\traise ValueError('Could not get stock data. The API key entered is most likely not valid.')\\n\\t\\tr = r['quotes']\\n\\t\\tif 'quote' not in r:\\n\\t\\t\\treturn []\\n\\t\\tr = r['quote']\\n\\t\\tif not isinstance(r, list):\\n\\t\\t\\tr = [r]\\n\\t\\tstock = {\\n\\t\\t\\tx['symbol']: {\\n\\t\\t\\t\\t'price': max(1, int(x['last'] * 100)),\\n\\t\\t\\t\\t#New API does not give this info.\\n\\t\\t\\t\\t'total_count': None, #int(x['marketCap'] / x['last']) if x['marketCap'] else None\\n\\t\\t\\t} for x in r if 'last' in x and x['last'] is not None\\n\\t\\t}\\n\\t\\treturn stock\",\n \"def getListFromSupplier(self, ddata, supplier):\\n # browse through all the components\\n head = self.header.getHeaders()\\n items = map(lambda cmpn:\\n list(map(lambda it: cmpn[it], head)), ddata[supplier])\\n for it in items:\\n yield it\",\n \"def suppliers(ticker: str, other_args: List[str]):\\n parser = argparse.ArgumentParser(\\n prog=\\\"supplier\\\",\\n add_help=False,\\n description=\\\"List of suppliers from ticker provided. [Source: CSIMarket]\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n url_supply_chain = f\\\"https://csimarket.com/stocks/competitionNO3.php?supply&code={ticker.upper()}\\\"\\n text_supplier_chain = BeautifulSoup(requests.get(url_supply_chain).text, \\\"lxml\\\")\\n\\n l_supplier = list()\\n for supplier in text_supplier_chain.findAll(\\n \\\"td\\\", {\\\"class\\\": \\\"plavat svjetlirub dae al\\\"}\\n ):\\n l_supplier.append(supplier.text)\\n\\n if l_supplier:\\n print(\\\"List of Suppliers: \\\" + \\\", \\\".join(l_supplier) + \\\"\\\\n\\\")\\n else:\\n print(\\\"No suppliers found.\\\\n\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\",\n \"def items(self) -> typing.ItemsView[str, Category]:\\n return self._primary_code_map.items()\",\n \"def getDataInterfaces(context, export_only=False):\\n from bika.lims.exportimport import instruments\\n exims = []\\n for exim_id in instruments.__all__:\\n exim = instruments.getExim(exim_id)\\n if export_only and not hasattr(exim, 'Export'):\\n pass\\n else:\\n exims.append((exim_id, exim.title))\\n exims.sort(lambda x, y: cmp(x[1].lower(), y[1].lower()))\\n exims.insert(0, ('', t(_('None'))))\\n return DisplayList(exims)\",\n \"def all_currency_codes():\\n return [(a, CURRENCIES[a].name) for a in CURRENCIES]\",\n \"def dishlist_prices(n: list) -> list:\\r\\n return [dish.price for dish in n]\",\n \"def list_to_text(ingridients_list):\\n to_return = \\\"List\\\\n\\\"\\n for (ingridient, quantity) in ingridients_list:\\n to_return = f\\\"{to_return}{ingridient.name} {quantity}\\\\n\\\"\\n return to_return\",\n \"def __read_instrumentslist(self):\\n available_instruments = []\\n with open(\\\"instrumentslist.txt\\\", \\\"r\\\") as file:\\n for line in file:\\n splited = line.split(\\\" \\\")\\n name = \\\"\\\"\\n for j in splited[1:]:\\n name += j\\n available_instruments.append(name)\\n return available_instruments\",\n \"def _collect_quantizers_descriptions(self) -> List[QuantizerDescription]:\\n # `True` for weight quantizer, `False` otherwise.\\n quantizers = chain(\\n map(lambda x: (True, x), self._weight_quantizers.values()),\\n map(lambda x: (False, x), self._non_weight_quantizers.values()),\\n )\\n\\n quantizers_descriptions = []\\n for is_weight_quantizer, q in quantizers:\\n is_symmetric = isinstance(q, SymmetricQuantizer)\\n\\n quantizers_descriptions.append(\\n QuantizerDescription(\\n q.num_bits, q.per_channel, q.signed, is_symmetric, is_weight_quantizer, q.is_enabled_quantization()\\n )\\n )\\n\\n return quantizers_descriptions\",\n \"def drug_names_on_drug_list(drug_list):\\n return [dl[\\\"Drug (brand name)\\\"] for dl in drug_list]\",\n \"def load_description():\\n with open('description.txt') as description:\\n return [line.strip() for line in description]\",\n \"def get_products(self):\\n return [item.code for item in self._products]\",\n \"def getAllFirstDescWords(itemList):\\r\\n itemList = list(set(itemList)) # make itemList unique\\r\\n descWords = []\\r\\n for item in itemList:\\r\\n descWords.append(worldItems[item][DESCWORDS][0])\\r\\n return list(set(descWords))\",\n \"def get_book_titles(self, lib_db):\\n titles = []\\n conn = sqlite3.connect(lib_db)\\n c = conn.cursor()\\n for row in c.execute(\\\"SELECT ZTITLE FROM ZBKLIBRARYASSET WHERE ZTITLE <> '' AND ZTITLE <> 'none'\\\"):\\n titles.append(row[0])\\n conn.close()\\n return titles\",\n \"def get_packages(module, repo_mgr, list_type, package):\\n rc_code, out, err = module.run_command(\\\"/usr/bin/%s -q list %s %s\\\"\\n % (repo_mgr, list_type, package), environ_update=ENV_LOCALE)\\n if rc_code is 0:\\n return out.splitlines()\\n else:\\n if rc_code == 1 and str(err) == 'Error: No matching Packages to list\\\\n':\\n return out.splitlines()\\n else:\\n module.fail_json(msg=\\\"Unable to collect \\\" + repo_mgr + \\\" list \\\" + list_type + \\\" : \\\" + str(err) + \\\" - \\\" + str(out))\",\n \"def print_items():\\n for items in inventory:\\n print(f\\\"- {items.upper()}\\\")\",\n \"def license_codes(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"license_codes\\\")\",\n \"def build_dividend_lists(portfolio_dict):\\n # ETF dividend list\\n dow_dividends = lookup_dividends(yf.Ticker(\\\"DIA\\\")) \\n sp500_dividends = lookup_dividends(yf.Ticker(\\\"SPY\\\")) \\n nasdaq_dividends = lookup_dividends(yf.Ticker(\\\"QQQ\\\")) \\n totalmarket_dividends = lookup_dividends(yf.Ticker(\\\"VTI\\\")) \\n \\n # Portfolio dividends\\n portfolio_dividend_dict = {}\\n for key in portfolio_dict:\\n portfolio_dividend_dict[key] = lookup_dividends(yf.Ticker(key))\\n \\n return (dow_dividends, sp500_dividends, nasdaq_dividends, totalmarket_dividends, portfolio_dividend_dict)\",\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 getNewCodeList(self):\\n if self.modification == 'none':\\n new_code = [(x, 'white') for x in self.body.splitlines()]\\n elif self.modification == 'change':\\n new_code = [self._getNewCodeList(x) for x in self.body.splitlines() \\\\\\n if self._getNewCodeList(x)[0]]\\n # we want old_code_list and new_code_list to have the same length\\n if(self.new_code_length < self.old_code_length):\\n filling = [(None, self.color)] * (self.old_code_length - \\\\\\n self.new_code_length)\\n new_code.extend(filling)\\n else: # deletion or addition\\n new_code = [self._getNewCodeList(x) for x in self.body.splitlines()]\\n return new_code\",\n \"def list_item(self, text):\\n #return [[MdStyleInstructionList(), *text]]\\n return [[MdStyleInstructionListItem()] + text]\",\n \"def getFormatsFromDescr(descr):\\n i = getIter(descr)\\n if not i:\\n return\\n\\n try:\\n item = i.next()\\n while item:\\n item1 = item[1]\\n if isinstance(item1, str):\\n yield normalize_format(item1)\\n else:\\n l = []\\n for j in getFormatsFromDescr(item1):\\n l.append(j)\\n yield l\\n item = i.next()\\n except StopIteration:\\n pass\",\n \"def get_todo_list():\\n\\n # assume that a \\\"h264\\\" encoded file is complete\\n return models.LibraryItem.objects.filter(h264=False)\",\n \"def get_description(self):\\n return COMPONENT_LIST[self.index][1]\",\n \"def _create_pull_requests_descriptions(self, pull_requests: list) -> list:\\n descriptions = []\\n for pull_request in pull_requests:\\n description = deepcopy(self.blocks['description'])\\n reviewers = map(\\n lambda name: self._get_user_mention(name),\\n pull_request['reviewers'],\\n )\\n description['text']['text'] = ' '.join(reviewers)\\n description['accessory']['url'] = pull_request['url']\\n descriptions.append(description)\\n return descriptions\",\n \"def GetAllDifferentDescriptionOfCost():\\n\\n logs.logger.debug(\\n \\\"Start to get back all different description of \\\"\\n \\\"Cost objects from database.\\\")\\n try:\\n ListOfAllDifferentDescriptionOfCost = []\\n searchedCostsItems = GetAllDescriptionOfCost()\\n for item in searchedCostsItems:\\n if item not in ListOfAllDifferentDescriptionOfCost:\\n ListOfAllDifferentDescriptionOfCost.append(item)\\n logs.logger.info(\\n \\\"Start to get back all different description of \\\"\\n \\\"Cost objects from database.\\\")\\n return ListOfAllDifferentDescriptionOfCost\\n except Exception as e:\\n logs.logger.error(e, exc_info=True)\",\n \"def pull_list():\\n URL = 'https://salsa.debian.org/security-tracker-team/security-tracker/raw/master/data/CVE/list'\\n file = urllib.request.urlopen(URL).readlines()\\n generic = [line.strip().decode() for line in file]\\n return generic\",\n \"def list_tick_dates(self, stock_code):\\n\\n conn = self.db_engine.connect()\\n try:\\n get_tick_dates_sql = \\\"\\\"\\\"\\n SELECT DISTINCT \\\"update_date\\\" FROM \\\"{0}\\\".\\\"{1}\\\"\\n WHERE end_update_time IS NOT NULL AND stock_code='{2}'\\n \\\"\\\"\\\".format(Schemas.SCHEMA_META, Tables.TABLE_TICK_UPDATE_LOGS, stock_code)\\n\\n res = pd.read_sql(get_tick_dates_sql, conn)['update_date'].tolist()\\n res.sort()\\n return Error.SUCCESS, res\\n except Exception:\\n self.logger.log_error(traceback.format_exc())\\n return Error.ERROR_DB_EXECUTION_FAILED, None\\n finally:\\n conn.close()\",\n \"def _get_package_items(self):\\r\\n mask = \\\"mask[description,capacity,prices.id,categories[name,id]]\\\"\\r\\n package = self.client['Product_Package']\\r\\n return package.getItems(id=46, mask=mask)\",\n \"def __init__(self):\\n # note: We could have implemented the list as a dictionary, with\\n # the barcode as the key, however if the barcode for the item\\n # changes we might have problems.\\n self.stocklist = [] # a list of stock items\",\n \"def obtain_stock_names():\\n\\n url = 'https://en.wikipedia.org/wiki/List_of_S%26P_400_companies'\\n stock_names = []\\n response = requests.get(url, timeout=5)\\n content = BeautifulSoup(response.content, \\\"html.parser\\\")\\n\\n # We get stock_names from the web page\\n for stock in content.findAll('a', attrs={\\\"class\\\": \\\"external text\\\"}):\\n if(len(stock.text)<=5):\\n stock_names.append(stock.text)\\n\\n # We persist the Stock Names\\n save_dir = Path(__file__).parent.parent\\n filename = (save_dir / \\\"../data/stock_names.joblib\\\").resolve()\\n PersistenceAPI.persist_stock_data(stock_names, filename)\\n\\n return stock_names\",\n \"def read_stock_list():\\n print(\\\"Reading list of stocks.\\\")\\n stocks = {}\\n with open(STOCKS_FILE) as csvfile:\\n reader = csv.DictReader(csvfile)\\n for row in reader:\\n stocks[row['Symbol']] = (row['Name'], row['Sector'])\\n return stocks\",\n \"def list(self, body, ordered=True):\\n return [[MdStyleInstructionListStart(ordered)]] + body + [[MdStyleInstructionListEnd()]]\",\n \"def build_item_list(client, word=''):\\n\\titem_group = client.search_metadata('collection_name:cooee')\\n\\titems = item_group.get_all()\\n\\titem_list_name = word + '_list'\\n\\titem_urls = []\\n\\tfor item in items:\\n\\t\\tprimary_text = item.get_primary_text()\\n\\t\\tif word in primary_text:\\n\\t\\t\\tprint item.url()\\n\\t\\t\\titem_urls.append(item.url())\\n\\t\\t\\t#client.add_to_item_list_by_name([item.url()], item_list_name)\\n\\n\\tfor url in item_urls:\\n\\t\\tclient.add_to_item_list_by_name(item_urls, item_list_name)\",\n \"def obtain_parse_snp500():\\n\\n # Stores the current time, for the created_at record\\n now = datetime.datetime.utcnow()\\n\\n # Use finsymbols to download stock symbols, industry and S&P 500 data\\n sp500=finsymbols.get_sp500_symbols()\\n # Obtain the symbol information for each row in the S&P500 constituent table\\n symbols = []\\n for index, symbol in enumerate(sp500):\\n sd = {'ticker': sp500[index]['symbol'],\\n 'name': sp500[index]['company'],\\n 'sector': sp500[index]['industry']}\\n # Create a tuple (for the DB format) and append to the grand list\\n symbols.append( (sd['ticker'], 'stock', sd['name'], \\n sd['sector'], 'USD', now, now) )\\n \\n return symbols\",\n \"def program_list():\\n items = []\\n\\n soup = abcradionational.get_soup(URL + \\\"/podcasts/program\\\")\\n \\n program_heading = abcradionational.get_podcast_heading(soup)\\n\\n for program in program_heading:\\n items.append({\\n 'label': program['title'],\\n 'path': plugin.url_for('program_item', url=program['url']),\\n })\\n\\n return items\",\n \"def show_item_list():\\n # 3 items per line\\n line = []\\n linecounter = 0\\n item_string = \\\"\\\"\\n counter = 0\\n text_spacer = 20\\n clear_messages(0)\\n\\n for i in range(0, len(ITEMS)):\\n space = text_spacer - len(ITEMS[i])\\n item_string = item_string + ITEMS[i] + (' ' * space)\\n counter += 1\\n if counter == 3:\\n line.append(item_string)\\n linecounter += 1\\n item_string = \\\"\\\"\\n counter = 0\\n if counter < 3:\\n line.append(item_string)\\n\\n for i in range(0, linecounter + 1):\\n printmessage(line[i], i + 1, MAGENTA, 0)\\n clear_messages(3)\",\n \"def get_weather_description(weather):\\n for num_moods in range(0, 10):\\n description = weather['list'][num_moods]['weather'][0]['description']\\n weather_description_data.append(description)\\n return weather_description_data\",\n \"def get_deck_as_str_list(self):\\n\\n ls = []\\n for card in self.deck:\\n ls.append(card.get_card())\\n return ls\",\n \"def alfred_items_for_value(value):\\n index = 0\\n results = []\\n\\n config_list = [\\n ('t2s.json', u'繁體到簡體', 'SimplifiedChinese.png'),\\n ('s2t.json', u'簡體到繁體', 'TraditionalChinese.png'),\\n ('s2tw.json', u'簡體到臺灣正體', 'TW_taiwan.png'),\\n ('tw2s.json', u'臺灣正體到簡體', 'CN_china.png'),\\n ('s2hk.json', u'簡體到香港繁體', 'HK_hongKong.png'),\\n ('hk2s.json', u'香港繁體（香港小學學習字詞表標準）到簡體', 'CN_china.png'),\\n ('tw2sp.json', u'繁體（臺灣正體標準）到簡體並轉換爲中國大陸常用詞彙', 'CN_china.png'),\\n ('s2twp.json', u'簡體到繁體（臺灣正體標準）並轉換爲臺灣常用詞彙', 'TW_taiwan.png'),\\n ]\\n for config_file, description, icon in config_list:\\n converter = opencc.OpenCC(\\n config=config_file, opencc_path='/usr/local/bin/opencc')\\n item_value = converter.convert(value)\\n results.append(alfred.Item(\\n title=item_value,\\n subtitle=description,\\n attributes={\\n 'uid': alfred.uid(index),\\n 'arg': item_value,\\n },\\n icon=icon,\\n ))\\n index += 1\\n\\n return results\",\n \"def get_raw(self):\\n \\n out_items = []\\n for order in self.order_lst:\\n out_items += [i.get_metadata() for i in order.get_items()]\\n \\n return out_items\",\n \"def show_list(self, desc, lst, writeln):\\n if not lst:\\n return\\n val = ', '.join([list_escape(v) for v in lst])\\n writeln(\\\"%s: %s\\\" % (desc, val))\",\n \"def lcode(self):\\n###############################################################################\\n lcode = []\\n for M in list(self.estimates.values()):\\n if (M.code not in lcode):lcode.append(M.code)\\n return(lcode)\",\n \"def read_publisher_qualified_isocode(self):\\n self.CATEGORIES = kpi_from_db_config.CATEGORIES\\n self.SELECTED_ISOCODE = kpi_from_db_config.SELECTED_ISOCODE\\n self.ID_PUBLISHER_QUALIFIED_ISOCODE = kpi_from_db_config.ID_PUBLISHER_QUALIFIED_ISOCODE\\n\\n size_m = len(self.CATEGORIES)\\n size_n = len(self.SELECTED_ISOCODE)\\n list_id = self.ID_PUBLISHER_QUALIFIED_ISOCODE\\n\\n self.cursor.execute('''\\n SELECT value\\n FROM public.kpi_report\\n WHERE id = %s\\n ORDER BY created_at DESC\\n LIMIT %s\\n ''', [list_id, size_m*size_n])\\n\\n list_result = [[0]*size_n for _ in range(size_m)]\\n\\n count_result = size_m*size_n - 1\\n for doc in self.cursor:\\n list_result[count_result%size_m][count_result/size_m] = doc[0]\\n count_result -= 1\\n\\n for i in range(size_m):\\n list_result[i][0:0] = [sum([list_result[i][j] for j in range(size_n)])]\\n\\n list_result[0:0] = [[0]*(size_n + 1)]\\n for i in range(size_n + 1):\\n list_result[0][i] = sum([list_result[j][i] for j in range(size_m + 1)])\\n\\n categories = ['--All--'] + self.CATEGORIES\\n\\n return list_result, categories\",\n \"def snippetList(requeset, format = None):\",\n \"def _list_of_availability_strings():\\n names = [availability.name for availability in Availability]\\n return names\",\n \"def CARD_SUITS() -> tuple:\\n return \\\"Diamonds\\\", \\\"Hearts\\\", \\\"Clubs\\\", \\\"Spades\\\"\",\n \"def get_list_of_str2(self):\\n pass\",\n \"def getIngredients():\\n ingredients = ['Whiskey', 'Tequila', 'Vodka', 'Blue Curacao', 'Orange Juice',\\n 'Pineapple Juice', 'Cranberry Juice', 'Sour Mix']\\n return ingredients\",\n \"def listRestock(self):\\n # TODO return a list of items that need restocking\\n # hint: Need to loop through the stocklist\\n # No. 3\\n flag = 0\\n mystr = \\\"\\\"\\n for item in self.stocklist:\\n if StockItem.needRestock(item):\\n mystr = mystr + StockItem.getName(item) + '\\\\n'\\n flag = flag + 1 # item that needs restocking found\\n\\n # No. 4\\n if flag == 0:\\n return \\\"All items stocked\\\"\\n else:\\n return mystr\",\n \"def scrape_descriptions_sync():\\n # прочитать Symbols, for symbol in tqdm(symbols)\\n # исользовать urllib get запросы на yahoo и полученное записывать в файл с помощью\\n # добавить tqdm(symbols)\\n\\n myheader = {\\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36'\\n }\\n\\n symbols = read_symbols()\\n YAHOO_HTMLS.mkdir(parents=True, exist_ok=True)\\n\\n\\n for symbol in tqdm(symbols):\\n #Example myurl = \\\"https://finance.yahoo.com/quote/AAPL/profile?p=AAPL\\\"\\n myurl = f'https://finance.yahoo.com/quote/{symbol}/profile?p={symbol}'\\n\\n try:\\n req = request.Request(myurl, headers=myheader)\\n response = request.urlopen(req)\\n text = response.read()\\n response.close()\\n\\n except Exception:\\n print(\\\"Error occuried during web request!!\\\")\\n print(sys.exc_info()[1])\\n\\n f = open(YAHOO_HTMLS / f'{symbol}.html', 'wb')\\n f.write(text)\\n f.close()\",\n \"def nameList(self):\\r\\n return [self.name.lower(), self.code] + self._otherNames\",\n \"def find_item_codes(transaction):\\n t = transaction\\n item_codes = []\\n if t['transaction_type'] in ('SALE', 'REFUND'):\\n # Search using line item IDs and order_id\\n for oli in (t['order_line_items'] or []):\\n li_id = oli['line_item_id']\\n item_codes.append(\\n get_item_code_for_order(t['order_id'], order_line_item_id=li_id)\\n )\\n else:\\n # Search for ITEM reference\\n for ref in (transaction['references'] or []):\\n if ref['reference_type'] == 'ITEM_ID':\\n item_codes.append(\\n get_item_code_for_item_id(ref['reference_id'])\\n )\\n\\n return item_codes\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65685076","0.566973","0.5336407","0.5295758","0.51715976","0.51711005","0.5148782","0.5139008","0.508993","0.5061929","0.5032067","0.49966037","0.49956048","0.4957243","0.4957215","0.4956653","0.49447757","0.49301252","0.4907753","0.49050713","0.4902797","0.48980734","0.4886963","0.4886797","0.48835567","0.48771304","0.48710713","0.48617464","0.4855102","0.4849284","0.48486084","0.4825037","0.4807281","0.47881213","0.4759703","0.4755037","0.4743091","0.47387505","0.4730555","0.47223693","0.47157344","0.47130832","0.4687379","0.4683173","0.4675325","0.46687117","0.46561867","0.46542323","0.4647789","0.46424294","0.463977","0.46371582","0.46366236","0.4635237","0.46331847","0.463248","0.46308693","0.4621067","0.46051142","0.46022415","0.4601089","0.4596357","0.4585938","0.45784062","0.45743236","0.45722234","0.4572153","0.45650458","0.4564572","0.45640412","0.45627335","0.45621595","0.4560962","0.45424607","0.45402586","0.4529362","0.45192727","0.45182914","0.45170325","0.45073357","0.45057997","0.45057723","0.45028102","0.4501431","0.4500666","0.44983116","0.44970977","0.44960308","0.44957414","0.44900882","0.44895336","0.44875553","0.44870132","0.4485799","0.44831684","0.44823903","0.4478319","0.44778916","0.4471342","0.44671074"],"string":"[\n \"0.65685076\",\n \"0.566973\",\n \"0.5336407\",\n \"0.5295758\",\n \"0.51715976\",\n \"0.51711005\",\n \"0.5148782\",\n \"0.5139008\",\n \"0.508993\",\n \"0.5061929\",\n \"0.5032067\",\n \"0.49966037\",\n \"0.49956048\",\n \"0.4957243\",\n \"0.4957215\",\n \"0.4956653\",\n \"0.49447757\",\n \"0.49301252\",\n \"0.4907753\",\n \"0.49050713\",\n \"0.4902797\",\n \"0.48980734\",\n \"0.4886963\",\n \"0.4886797\",\n \"0.48835567\",\n \"0.48771304\",\n \"0.48710713\",\n \"0.48617464\",\n \"0.4855102\",\n \"0.4849284\",\n \"0.48486084\",\n \"0.4825037\",\n \"0.4807281\",\n \"0.47881213\",\n \"0.4759703\",\n \"0.4755037\",\n \"0.4743091\",\n \"0.47387505\",\n \"0.4730555\",\n \"0.47223693\",\n \"0.47157344\",\n \"0.47130832\",\n \"0.4687379\",\n \"0.4683173\",\n \"0.4675325\",\n \"0.46687117\",\n \"0.46561867\",\n \"0.46542323\",\n \"0.4647789\",\n \"0.46424294\",\n \"0.463977\",\n \"0.46371582\",\n \"0.46366236\",\n \"0.4635237\",\n \"0.46331847\",\n \"0.463248\",\n \"0.46308693\",\n \"0.4621067\",\n \"0.46051142\",\n \"0.46022415\",\n \"0.4601089\",\n \"0.4596357\",\n \"0.4585938\",\n \"0.45784062\",\n \"0.45743236\",\n \"0.45722234\",\n \"0.4572153\",\n \"0.45650458\",\n \"0.4564572\",\n \"0.45640412\",\n \"0.45627335\",\n \"0.45621595\",\n \"0.4560962\",\n \"0.45424607\",\n \"0.45402586\",\n \"0.4529362\",\n \"0.45192727\",\n \"0.45182914\",\n \"0.45170325\",\n \"0.45073357\",\n \"0.45057997\",\n \"0.45057723\",\n \"0.45028102\",\n \"0.4501431\",\n \"0.4500666\",\n \"0.44983116\",\n \"0.44970977\",\n \"0.44960308\",\n \"0.44957414\",\n \"0.44900882\",\n \"0.44895336\",\n \"0.44875553\",\n \"0.44870132\",\n \"0.4485799\",\n \"0.44831684\",\n \"0.44823903\",\n \"0.4478319\",\n \"0.44778916\",\n \"0.4471342\",\n \"0.44671074\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7398652"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":1891,"cells":{"query":{"kind":"string","value":"Creates new dataframe with invoices lines issued from given parameters. Once done, the new dataframe is aggregated with original one."},"document":{"kind":"string","value":"def create_customer_df_invoice_line(self, customerID, list_stockCode\\\n , list_quantity, invoiceDate):\n \n dict_invoice = dict()\n\n dict_invoice['Quantity'] = list_quantity\n dict_invoice['StockCode'] = list_stockCode\n\n #------------------------------------------------------------------------\n # Build invoiceDate from local current time\n #------------------------------------------------------------------------\n if invoiceDate is None:\n time_struct = time.localtime()\n invoiceDate = str(time_struct.tm_year)+'-'+str(time_struct.tm_mon)\\\n +'-'+str(time_struct.tm_mday)\n invoiceDate +=' '\n invoiceDate +=str(time_struct.tm_hour)+':'+str(time_struct.tm_min)\\\n +':'+str(time_struct.tm_sec)\n invoiceDate = pd.Timestamp(invoiceDate)\n else:\n pass\n\n\n #------------------------------------------------------------------------\n # Lists initialization\n #------------------------------------------------------------------------\n list_customerID = list()\n list_invoiceNo = list()\n list_invoiceDate = list()\n list_invoice_line_index = list()\n \n #------------------------------------------------------------------------\n # Increase Invoice number\n #------------------------------------------------------------------------\n invoiceNo = max(self._df_invoice_original.InvoiceNo)\n invoiceNo += 1\n\n #------------------------------------------------------------------------\n # Get latest invoice line index value\n #------------------------------------------------------------------------\n invoice_line_index = max(self._df_invoice_original.index)\n\n #------------------------------------------------------------------------\n # Build lists for CustomerID, InvoiceNo, InvoiceDate\n # A list of incremented indexes is built for new rows.\n #------------------------------------------------------------------------\n for quantity in list_quantity:\n list_customerID.append(customerID)\n list_invoiceNo.append(invoiceNo)\n list_invoiceDate.append(invoiceDate)\n invoice_line_index += 1\n list_invoice_line_index.append(invoice_line_index) \n\n \n dict_invoice['CustomerID'] = list_customerID\n dict_invoice['InvoiceNo'] = list_invoiceNo\n dict_invoice['InvoiceDate'] = list_invoiceDate\n\n #------------------------------------------------------------------------\n # Get description list from list of stock codes.\n #------------------------------------------------------------------------\n list_description = self.getDescriptionList(list_stockCode)\n \n dict_invoice['Description'] = list_description\n\n #------------------------------------------------------------------------\n # Get unit price list from list of stock codes.\n #------------------------------------------------------------------------\n list_unitPrice = self.getUnitPriceList(list_stockCode)\n \n dict_invoice['UnitPrice'] = list_unitPrice\n\n #------------------------------------------------------------------------\n # Dataframe with new invoices lines is created.\n #------------------------------------------------------------------------\n df_invoice_line \\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys()\\\n , index=list_invoice_line_index)\n \n return df_invoice_line"},"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 merge_purchase_invoice(self):\r\n active_id = self.env['purchase.order'].browse(self.env['purchase.order']._context.get('active_ids'))\r\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')]) \r\n active_id_count = 0\r\n active_count = 0\r\n exist_vendor = []; invoice = [];exist_vendors = [];ctx = ();invoice_id = []\r\n for rec in active_id : \r\n po_reference = self.env['account.invoice'].search([('origin', 'like', rec.name)])\r\n active_count = len(active_id)\r\n if rec.picking_count >= 1 and rec.picking_count != rec.invoice_count:\r\n len_name = [] \r\n for inv in po_reference: \r\n len_name = inv.origin.split(\":\") \r\n if rec.name in len_name:\r\n if po_reference.state == 'draft':\r\n for record in po_reference.invoice_line_ids:\r\n print (record.line_id)\r\n for res in rec.order_line:\r\n if res.id == record.line_id: \r\n record.write({'quantity':res.qty_received})\r\n res.write({'qty_invoiced':record.quantity})\r\n \r\n else:\r\n \r\n po_list = [];line_values = {};lines = {};purchase = []\r\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\r\n purchase.append(rec.id)\r\n active_id_count = len(purchase)\r\n if rec.partner_id.id in exist_vendor:\r\n for inv in invoice:\r\n if inv['partner_id'] == rec.partner_id.id:\r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received \r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received ,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id ,\r\n 'line_id':recc.id\r\n }) \r\n inv['invoice_line_ids'].append(line_values)\r\n inv['origin'] = inv['origin'] + ':' + rec.name\r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendors.append(rec.partner_id.id) \r\n else: \r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received\r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id,\r\n 'line_id':recc.id\r\n }) \r\n print (rec.id)\r\n po_list.append(line_values) \r\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':datetime.today()}) \r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendor.append(rec.partner_id.id) \r\n \r\n else:\r\n po_list = [];line_values = {};lines = {};purchase = []\r\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\r\n purchase.append(rec.id)\r\n active_id_count = len(purchase)\r\n if rec.partner_id.id in exist_vendor:\r\n for inv in invoice:\r\n if inv['partner_id'] == rec.partner_id.id:\r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received\r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received ,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id ,\r\n 'line_id':recc.id\r\n }) \r\n inv['invoice_line_ids'].append(line_values)\r\n inv['origin'] = inv['origin'] + ':' + rec.name\r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendors.append(rec.partner_id.id) \r\n else: \r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received\r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id,\r\n 'line_id':recc.id\r\n }) \r\n print (rec.id)\r\n po_list.append(line_values) \r\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':date.today()}) \r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendor.append(rec.partner_id.id) \r\n \r\n invoices = []\r\n invoice_counts = 0\r\n for record in invoice:\r\n invoice_id = self.env['account.invoice'].create(record)\r\n invoices.append(invoice_id.id)\r\n invoice_counts = len(invoices)\r\n if active_id_count == 1:\r\n if invoice_counts == 1:\r\n form_view = self.env.ref('purchase.view_invoice_supplier_purchase_form').id\r\n tree_view = self.env.ref('account.invoice_tree').id \r\n return{\r\n 'name': _('Invoice'),\r\n 'type':'ir.actions.act_window',\r\n 'view_type':'form',\r\n 'view_mode':'form,tree',\r\n 'res_model':'account.invoice',\r\n 'res_id':invoices[0],\r\n 'views_id':False,\r\n 'views':[(form_view , 'form'), (tree_view , 'tree')],\r\n 'domain':[('id', 'in', invoices)],\r\n 'target': 'current',\r\n } \r\n else: \r\n form_view = self.env.ref('account.invoice_supplier_form').id\r\n tree_view = self.env.ref('account.invoice_supplier_tree').id \r\n return{\r\n 'name': _('Invoice'),\r\n 'type':'ir.actions.act_window',\r\n 'view_type':'form',\r\n 'view_mode':'form,tree',\r\n 'res_model':'account.invoice',\r\n 'views_id':True,\r\n 'views':[(tree_view , 'tree'), (form_view , 'form')],\r\n 'domain':[('id', 'in', invoices)],\r\n 'target': 'current',\r\n }","def _prepare_invoice_lines(self, exchange_line, order_line):\n invoice_type = {\n 'sale.order.line': {\n 'higher': 'out_invoice', 'lower': 'out_refund',\n 'type': 'sale', 'field': 'exchange_sale_line_id'\n },\n 'purchase.order.line': {\n 'higher': 'in_invoice', 'lower': 'in_refund',\n 'type': 'purchase', 'field': 'exchange_purchase_line_id'\n },\n }\n product = exchange_line.exchange_product_id or exchange_line.product_id\n data = {\n 'invoice_type': False,\n 'values': {\n 'product_id': product.id,\n 'quantity': exchange_line.quantity,\n 'name': 'Exchange for [%s]' % exchange_line.product_id.display_name,\n }\n }\n if exchange_line.exchange_product_id or \\\n exchange_line.price_subtotal > order_line.price_subtotal:\n data['invoice_type'] = invoice_type[order_line._name]['higher']\n elif exchange_line.price_subtotal < order_line.price_subtotal:\n data['invoice_type'] = invoice_type[order_line._name]['lower']\n else:\n return {}\n data[invoice_type[order_line._name]['type']] = order_line.order_id\n data['values'][invoice_type[order_line._name]['field']] = order_line.id\n data['values']['price_unit'] = exchange_line.price_unit\n # TODO i think we should take the different between prices NOT the all price\n # abs(exchange_line.price_unit - order_line.price_unit)\n return data","def make_invoices(self):\n for invoice in self.policy.invoices:\n db.session.delete(invoice)\n db.session.commit()\n\n billing_schedules = {'Annual': None, 'Semi-Annual': 3, 'Quarterly': 4, 'Monthly': 12}\n\n invoices = []\n first_invoice = Invoice(self.policy.id,\n self.policy.effective_date, # bill_date\n self.policy.effective_date + relativedelta(months=1), # due\n self.policy.effective_date + relativedelta(months=1, days=14), # cancel\n self.policy.annual_premium)\n invoices.append(first_invoice)\n\n if self.policy.billing_schedule == \"Annual\":\n pass\n elif self.policy.billing_schedule == \"Two-Pay\":\n first_invoice.amount_due = first_invoice.amount_due / billing_schedules.get(self.policy.billing_schedule)\n for i in range(1, billing_schedules.get(self.policy.billing_schedule)):\n months_after_eff_date = i*6\n bill_date = self.policy.effective_date + relativedelta(months=months_after_eff_date)\n invoice = Invoice(self.policy.id,\n bill_date,\n bill_date + relativedelta(months=1),\n bill_date + relativedelta(months=1, days=14),\n self.policy.annual_premium / billing_schedules.get(self.policy.billing_schedule))\n invoices.append(invoice)\n elif self.policy.billing_schedule == \"Quarterly\":\n first_invoice.amount_due = first_invoice.amount_due / billing_schedules.get(self.policy.billing_schedule)\n for i in range(1, billing_schedules.get(self.policy.billing_schedule)):\n months_after_eff_date = i*3\n bill_date = self.policy.effective_date + relativedelta(months=months_after_eff_date)\n invoice = Invoice(self.policy.id,\n bill_date,\n bill_date + relativedelta(months=1),\n bill_date + relativedelta(months=1, days=14),\n self.policy.annual_premium / billing_schedules.get(self.policy.billing_schedule))\n invoices.append(invoice)\n elif self.policy.billing_schedule == \"Monthly\":\n first_invoice.amount_due = first_invoice.amount_due / billing_schedules.get(self.policy.billing_schedule)\n for i in range(1, billing_schedules.get(self.policy.billing_schedule)):\n months_after_eff_date = i\n bill_date = self.policy.effective_date + relativedelta(months=months_after_eff_date)\n invoice = Invoice(self.policy.id,\n bill_date,\n bill_date + relativedelta(months=1),\n bill_date + relativedelta(months=1, days=14),\n self.policy.annual_premium / billing_schedules.get(self.policy.billing_schedule))\n invoices.append(invoice)\n else:\n print \"You have chosen a bad billing schedule.\"\n\n logger.info(str(len(invoices)) + \" invoices generated for policy %s\" % self.policy.id)\n\n for invoice in invoices:\n db.session.add(invoice)\n db.session.commit()","def _create_invoice(self):\n self.ensure_one()\n partner = self.member_id.partner_id\n invoice = self.env['account.invoice'].create({\n 'partner_id': partner.id,\n 'account_id': partner.property_account_receivable_id.id,\n 'fiscal_position_id': partner.property_account_position_id.id\n })\n for line in self.line_ids:\n product = line.activity_id.product_id\n # Handling of invoice lines : needs cache record for onchange, then\n # real writing...\n invoice_line = self.env['account.invoice.line'].new({\n 'product_id': product.id,\n 'invoice_id': invoice.id\n })\n invoice_line._onchange_product_id()\n line_values = dict(invoice_line._cache)\n line_values['price_unit'] = line.price\n invoice_line = self.env['account.invoice.line'].create(line_values)\n invoice.compute_taxes()\n line.registration_id.invoice_line_id = invoice_line.id\n return invoice","def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\n , UnitPrice ):\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\n dict_invoice['CustomerID'] = CustomerID\n dict_invoice['InvoiceNo'] = InvoiceNo\n df_invoice_line \\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])\n \n self.data_transform(df_invoice_line)\n\n #self.feature_rfm_encode()\n\n self.feature_scale()\n\n self.list_feature_drop()\n\n self.feature_description_nlp()\n return","def invoice_items(self,org_id=None,query={}):\n if org_id is None:\n org_id = self.org_id\n query_end_date = datetime.strptime(query['endDate'],'%Y-%m-%dT%H:%M:%SZ')\n # Given a 'query_end_date' to find the invoice containing the\n # line items for that date we need to find the invoice which \n # has 'endDate' equal to the end of the month of the `query_end_date`\n query_first_next_month = query_end_date + relativedelta(months=+1) - relativedelta(days=(query_end_date.day-1))\n target_invoices = []\n invoices = self.invoices(org_id)\n if self.verbose:\n print('Searching invoices org_id={}'.format(org_id))\n print('query={} query_end_date={}'.format(query,query_end_date))\n print('Result keys: {}'.format( invoices['content'].keys() ))\n print('Total result count: {}'.format( invoices['content']['totalCount'] ))\n for invoice in invoices['content']['results']:\n #pprint.pprint(invoice)\n end_date = datetime.strptime(invoice['endDate'],'%Y-%m-%dT%H:%M:%SZ')\n if self.verbose: \n print('invoice({})[\\'endDate\\']={} end_date={}'.format(invoice['id'],invoice['endDate'],end_date))\n if end_date == query_first_next_month:\n target_invoices.append(invoice)\n \n if self.verbose: \n print('Target invoices: {}'.format(target_invoices))\n \n\n target_line_items = []\n for invoice in target_invoices:\n invoice_details = self.invoices(org_id,invoice['id']) \n print('invoice_details: {}'.format(invoice_details))\n for item in invoice_details['content']['lineItems']:\n end_date = datetime.strptime(item['endDate'],'%Y-%m-%dT%H:%M:%SZ')\n if end_date == query_end_date:\n target_line_items.append(item)\n if self.verbose:\n print('target_line_items: {}'.format(target_line_items)) \n return target_line_items","def _prepare_invoice(self, cr, uid, order, lines, context=None):\n if context is None:\n context = {}\n journal_ids = self.pool.get('account.journal').search(cr, uid,\n [('type', '=', 'sale'), ('company_id', '=', order.company_id.id)],\n limit=1)\n if not journal_ids:\n raise osv.except_osv(_('Error !'),\n _('There is no sales journal defined for this company: \"%s\" (id:%d)') % (order.company_id.name, order.company_id.id))\n\n invoice_vals = {\n 'name': order.client_order_ref or '',\n 'origin': order.name,\n 'type': 'out_invoice',\n 'reference': order.client_order_ref or order.name,\n 'account_id': order.partner_id.property_account_receivable.id,\n 'journal_id': order.partner_id.property_default_sale_invoice_journal.id,\n 'partner_id': order.partner_id.id,\n 'address_invoice_id': order.partner_invoice_id.id,\n #'address_contact_id': order.partner_order_id.id,\n 'invoice_line': [(6, 0, lines)],\n 'currency_id': order.pricelist_id.currency_id.id,\n 'comment': order.note,\n 'payment_term': order.payment_term and order.payment_term.id or False,\n 'fiscal_position': order.fiscal_position.id or order.partner_id.property_account_position.id,\n 'date_invoice': context.get('date_invoice', False),\n 'company_id': order.company_id.id,\n 'user_id': order.user_id and order.user_id.id or False\n }\n\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\n\n return invoice_vals","def get_dataframe() -> pandas.DataFrame:\n database_connection = processing.establish_connection(database_path)\n dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)\n database_connection.close()\n dataframe = processing.get_invoice_date_fixed(dataframe)\n analyze_dataframe = dataframe.copy()\n total_sum_dataframe = processing.get_column_sum(analyze_dataframe)\n\n customer_count_dataframe = processing.drop_duplicates(analyze_dataframe)\n customer_count_dataframe = processing.get_column_count(customer_count_dataframe)\n return customer_count_dataframe.merge(total_sum_dataframe, how='inner', on='InvoiceDate')","def _prepare_invoice(self, cr, uid, order, lines, context=None):\n if context is None:\n context = {}\n journal_id = self.pool['account.invoice'].default_get(cr, uid, ['journal_id'], context=context)['journal_id']\n if not journal_id:\n raise osv.except_osv(_('Error!'),\n _('Please define sales journal for this company: \"%s\" (id:%d).') % (order.company_id.name, order.company_id.id))\n invoice_vals = {\n 'name': order.client_order_ref or '',\n 'origin': order.name,\n 'type': 'out_invoice',\n 'reference': order.client_order_ref or order.name,\n 'account_id': order.partner_invoice_id.property_account_receivable.id,\n 'partner_id': order.partner_invoice_id.id,\n 'journal_id': journal_id,\n 'invoice_line': [(6, 0, lines)],\n 'currency_id': order.pricelist_id.currency_id.id,\n 'comment': order.note,\n 'payment_term': order.payment_term and order.payment_term.id or False,\n 'fiscal_position': order.fiscal_position.id or order.partner_invoice_id.property_account_position.id,\n 'date_invoice': context.get('date_invoice', False),\n 'company_id': order.company_id.id,\n 'user_id': order.user_id and order.user_id.id or False,\n 'section_id' : order.section_id.id,\n 'test_1' :order.test\n }\n\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\n return invoice_vals","def make_claim_df(claim_list, columns = ['Sl','Name of Bank','Name of Branch','A/C Number (15 digit)','A/C Title','Amount of Remittance in BDT','Date of A/C Credit','Remittance Received through BEFTN/RTGS','Name of Remittance Collecting/BEFTN Processing Bank','Date of Claim']):\n sl=[]\n nrbc_bank = []\n branch = []\n ac_no = []\n ac_title = []\n amount=[]\n date_account_credit=[]\n channel = []\n other_bank=[]\n claim_date=[]\n i=1\n for claim in claim_list:\n sl.append(i)\n i=i+1\n nrbc_bank.append(\"NRBC Bank Ltd.\")\n branch.append(claim.branch.name.upper())\n ac_no.append(claim.account_no)\n ac_title.append(claim.account_title)\n amount.append(claim.remittance_amount)\n date_account_credit.append(claim.date_account_credit)\n channel.append(claim.get_channel_display())\n other_bank.append(claim.collecting_bank.name)\n claim_date.append(claim.date_claim.date())\n dc = {\n 'SL':sl,\n 'Name of Bank':nrbc_bank,\n 'Name of Branch': branch,\n 'A/C Number': ac_no,\n 'A/C Title': ac_title,\n 'Amount of Remittance in BDT': amount,\n 'Date of A/C Credit': date_account_credit,\n 'Remittance Received Through BEFTN/RTGS': channel,\n 'Name of Remittance Processing Bank': other_bank,\n 'Date of Claim': claim_date\n }\n df = pd.DataFrame(dc)\n return df.sort_values(by=['Name of Remittance Processing Bank',])","def order_process(self, customerID, list_stockCode, list_quantity\\\n , orderDate=None):\n\n segmentID = -1\n\n #-------------------------------------------------------------------------\n # A new customer is created and inserted into data-set.\n #-------------------------------------------------------------------------\n if customerID is None:\n customerID = int(self.createCustomerID())\n else:\n pass\n \n #-------------------------------------------------------------------------\n # A new dataframe with new invoice lines are created.\n #-------------------------------------------------------------------------\n df_invoice_line = self.create_customer_df_invoice_line(customerID\\\n , list_stockCode, list_quantity, orderDate)\n \n #-------------------------------------------------------------------------\n # Original dataframe is updated with customer invoices lines.\n #-------------------------------------------------------------------------\n print(\"order_process : shape before concat= \"+str(self._df_invoice_original.shape))\n self._df_invoice_original \\\n = pd.concat([self._df_invoice_original, df_invoice_line], axis=0)\n print(\"order_process : shape after concat= \"+str(self._df_invoice_original.shape))\n \n #-------------------------------------------------------------------------\n # All invoices lines (including new one) related to customer is retrieved \n # from original dataframe.\n #-------------------------------------------------------------------------\n df_invoice_line_customer \\\n = self.get_customer_history_df_invoice_line(customerID)\n\n #-------------------------------------------------------------------------\n # When calling get_customer_marketSegment(), df_invoice_line_customer is\n # concatened to the original dataframe.\n #------------------------------------------------------------------------- \n segmentID = self.get_customer_marketSegment(df_invoice_line_customer)\n \n return segmentID, customerID","def create_invoice(self):\n for line in self:\n # if not line.account_id:\n # raise UserError(_('Please Add the incoming Account !!'))\n self.ensure_one()\n journal_id = self.env['account.journal'].search([\n ('type', '=', 'sale')], limit=1)\n inv_line_main = {\n 'name': line.description.name,\n 'price_unit': line.amount or 0.00,\n 'quantity': 1,\n 'discount': line.discount,\n 'account_id': line.description.property_account_income_id.id or line.description.categ_id.property_account_income_categ_id.id or False,\n }\n inv_values = {\n 'partner_id': line.patient_id.partner_id.id,\n 'patient_id': line.patient_id.id,\n 'dentist': line.dentist.id,\n 'move_type': 'out_invoice',\n 'invoice_date': datetime.now().strftime(DF) or False,\n 'journal_id': journal_id and journal_id.id or False,\n 'teeth_id': line.patient_id and line.patient_id.id or False,\n }\n acc_id = self.env['account.move'].create(inv_values)\n acc_id.write({'invoice_line_ids': [(0, 0, inv_line_main)]})\n\n self.write({'invc_id': acc_id.id, 'inv': True})\n context = dict(self._context or {})\n wiz_form_id = self.env['ir.model.data'].get_object_reference(\n 'account', 'view_move_form')[1]\n\n return {\n 'view_type': 'form',\n 'view_id': wiz_form_id,\n 'view_mode': 'form',\n 'res_model': 'account.move',\n 'res_id': self.invc_id.id,\n 'type': 'ir.actions.act_window',\n 'target': 'current',\n 'context': context,\n }","def action_create_invoices(self, data):\n invoice_obj = self.env['account.invoice']\n values = {}\n for val in data:\n values.setdefault(val['invoice_type'], {\n 'order': val.get('sale', val.get('purchase')),\n 'values': []\n })\n values[val['invoice_type']]['values'].append((0, 0, val['values']))\n\n for inv_type, inv_data in values.items():\n invoice = invoice_obj.new(self._prepare_invoice(inv_type))\n invoice._onchange_partner_id()\n inv = invoice._convert_to_write({\n name: invoice[name] for name in invoice._cache\n })\n for _, _, line in inv_data['values']:\n line['account_id'] = inv['account_id']\n inv['invoice_line_ids'] = inv_data['values']\n new_invoice = invoice_obj.sudo().create(inv)\n new_invoice.action_invoice_open()\n inv_data['order'].write({\n 'exchange_invoice_ids': [(4, new_invoice.id)]\n })","def _get_lines(self, cr, uid, ids, context=None):\n List=[]\n if ids:\n line = self.pool.get('payment.enrich.lines').browse(cr, uid, ids[0], context=context)\n \n record = line.enrich_id\n val = 0.0\n for line in record.enrich_lines:\n if line.state == 'done' :\n val += line.cost\n res = {\n 'paid_amount':val,\n 'residual_amount':record.amount - val,\n }\n record.write(res)\n return List","def data_transform(self, df) :\n\n #-------------------------------------------------------------------------\n # Copy of given dataframe to be transformed\n #-------------------------------------------------------------------------\n self.df_invoice_line = df\n \n #-------------------------------------------------------------------------\n # Features issued from InvoiceDate are created\n #-------------------------------------------------------------------------\n if self.is_transform_timeFeature is True:\n self.strprint(\"\\n*** Time features transformation ***\")\n self.data_transform_timeFeature()\n\n #-------------------------------------------------------------------------\n # RFM is computed and encoded\n #-------------------------------------------------------------------------\n if self.is_transform_rfm is True:\n self.strprint(\"\\n*** RFM transformation ***\")\n self.data_transform_rfm()\n\n #-------------------------------------------------------------------------\n # NLP features issued from Description are created\n #-------------------------------------------------------------------------\n if self.is_transform_nlp is True:\n self.strprint(\"\\n*** NLP transformation ***\")\n self.data_transform_nlp()\n \n return self.df_invoice_line","def create_invoices(self, cr, uid, ids, context=None):\n invoice_list = []\n po_obj = self.pool.get('purchase.order')\n inv_line_obj = self.pool.get('account.invoice.line')\n inv_obj = self.pool.get('account.invoice')\n addr_obj = self.pool.get('res.partner')\n journal_obj = self.pool.get('account.journal')\n if context is None:\n context = {}\n\n for purchase_adv_obj in self.browse(cr, uid, ids, context=context):\n for purchase_order in po_obj.browse(cr, uid, context.get('active_ids', []), context=context):\n inv_line_ids = []\n invoice_ids = []\n val = inv_line_obj.product_id_change(cr, uid, [], purchase_adv_obj.product_id.id,\n uom_id=False, partner_id=purchase_order.partner_id.id, fposition_id=purchase_order.fiscal_position.id)\n line_id = inv_line_obj.create(cr, uid, {\n 'name': val['value']['name'],\n 'account_id': val['value']['account_id'],\n 'price_unit': purchase_adv_obj.amount,\n 'quantity': purchase_adv_obj.qtty,\n 'discount': False,\n 'uos_id': val['value']['uos_id'],\n 'product_id': purchase_adv_obj.product_id.id,\n 'invoice_line_tax_id': [(6, 0, val['value']['invoice_line_tax_id'])],\n })\n inv_line_ids.append(line_id)\n addr = addr_obj.address_get(cr, uid, [purchase_order.partner_id.id], ['invoice'])\n journal_ids = journal_obj.search(cr, uid, [('type', '=', 'purchase')])\n context.update({'type':'in_invoice','journal_type':'purchase'})\n inv_vals = {\n 'name': purchase_order.partner_ref or purchase_order.name,\n 'origin': purchase_order.name,\n 'type': 'in_invoice',\n 'reference': False,\n 'account_id': purchase_order.partner_id.property_account_payable.id,\n 'journal_id':journal_ids and journal_ids[0] or False,\n 'partner_id': purchase_order.partner_id.id,\n 'address_invoice_id': addr['invoice'],\n 'invoice_line': [(6, 0, inv_line_ids)],\n 'currency_id': purchase_order.pricelist_id.currency_id.id,\n 'comment': '',\n 'payment_term': purchase_order.payment_term_id and purchase_order.payment_term_id.id or False,\n 'fiscal_position': purchase_order.fiscal_position.id or purchase_order.partner_id.property_account_position.id,\n 'prepaid': True\n }\n\n inv_id = inv_obj.create(cr, uid, inv_vals, context=context)\n inv_obj.button_reset_taxes(cr, uid, [inv_id], context=context)\n for invoice in purchase_order.invoice_ids:\n invoice_ids.append(invoice.id)\n invoice_ids.append(inv_id)\n po_obj.write(cr, uid, purchase_order.id, {'invoice_ids': [(6, 0, invoice_ids)]})\n invoice_list.append(inv_id)\n\n if purchase_order.invoice_method in ('picking','order'):\n self.pool.get('purchase.order.line').create(cr, uid, {\n 'order_id': purchase_order.id,\n 'name': val['value']['name'],\n 'date_planned':purchase_order.date_order,\n 'price_unit': -purchase_adv_obj.amount,\n 'product_uom_qty': purchase_adv_obj.qtty,\n 'product_uos': val['value']['uos_id'],\n 'product_uom': val['value']['uos_id'],\n 'product_id': purchase_adv_obj.product_id.id,\n 'adavance_product':True,\n 'discount': False,\n 'taxes_id': [(6, 0, val['value']['invoice_line_tax_id'])],\n }, context=context)\n\n\n context.update({'invoice_id':invoice_list})\n return {\n 'name': 'Open Invoice',\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'purchase.open.invoice',\n 'type': 'ir.actions.act_window',\n 'target': 'new',\n 'context': context\n }","def prepare_report(user, from_date, to_date,\n show_which=\"worked\", # \"worked\", \"invoiced\", or \"paid\"\n client_ids=[], project_ids=[]):\n\n if show_which == \"worked\":\n sessions = Session.objects.filter(\n project__client__user=user,\n date__gte=from_date,\n date__lte=to_date\n )\n elif show_which == \"invoiced\":\n sessions = Session.objects.filter(\n project__client__user=user,\n invoice__invoice_date__gte=from_date,\n invoice__invoice_date__lte=to_date\n )\n elif show_which == \"paid\":\n sessions = Session.objects.filter(\n project__client__user=user,\n invoice__paid_date__gte=from_date,\n invoice__paid_date__lte=to_date\n )\n else:\n raise ValueError(\"Invalid value for the 'show_which' argument \"\n \"supplied\")\n\n if client_ids != []:\n sessions = sessions.filter(\n project__client__in=client_ids\n )\n if project_ids != []:\n sessions = sessions.filter(\n project__in=project_ids\n )\n\n # Starting Python 3.6, the dict maintains order as inserted\n # When running this on a different computer with older Python,\n # the sessions_per_date was all jumbled-up.\n # https://stackoverflow.com/questions/1867861/dictionaries-how-to-keep-keys-values-in-same-order-as-declared\n date_range = pd.date_range(from_date, to_date).date\n sessions_per_date = {today: sessions.filter(date=today)\n for today in date_range}\n\n total_earned = sum([sesh.get_money_earned() for sesh in sessions])\n\n context = {\n 'sessions': sessions, # obsolete if sessions_per_date will work\n 'from': from_date,\n 'to': to_date,\n 'date_range': date_range,\n 'sessions_per_date': sessions_per_date,\n 'total_earned': total_earned,\n }\n\n if client_ids != []:\n context['clients'] = Client.objects.filter(pk__in=client_ids)\n\n if project_ids != []:\n context['projects'] = Project.objects.filter(pk__in=project_ids)\n\n return context","def generate_eob(\n self, date_of_service, date_of_eob, insured, invoice_id, cpt_code, charge_amount\n ):\n if insured == \"insured\":\n # first copayments\n copay_amount = np.random.choice(\n self.distributions[\"copay_amounts\"],\n 1,\n p=self.distributions[\"copay_distribution\"],\n )[0]\n if copay_amount > 0:\n invoicelineitemeob = pd.DataFrame(\n {\n \"invoice_id\": [invoice_id],\n \"cpt_code\": [cpt_code],\n \"created_on\": [date_of_service],\n \"copay_amount\": [copay_amount],\n \"adjustment_amount\": [0],\n \"paid_amount\": [0],\n }\n )\n self.InvoiceLineItemsEob = self.InvoiceLineItemsEob.append(\n invoicelineitemeob\n )\n remaining_charge = charge_amount - copay_amount\n else:\n remaining_charge = charge_amount\n # next eob discounts\n eob_discount_percent = np.random.choice(\n self.distributions[\"eob_discount_percentages\"],\n 1,\n p=self.distributions[\"eob_discount_distribution\"],\n )[0]\n if eob_discount_percent > 0:\n insurance_adjustment = remaining_charge * eob_discount_percent / 100\n remaining_charge = remaining_charge - insurance_adjustment\n invoicelineitemeob = pd.DataFrame(\n {\n \"invoice_id\": [invoice_id],\n \"cpt_code\": [cpt_code],\n \"created_on\": [date_of_eob],\n \"copay_amount\": [0],\n \"adjustment_amount\": [insurance_adjustment],\n \"paid_amount\": [0],\n }\n )\n self.InvoiceLineItemsEob = self.InvoiceLineItemsEob.append(\n invoicelineitemeob\n )\n # next handle eob payments where relevant\n eob_payment_percentage = np.random.choice(\n self.distributions[\"eob_payment_percentages\"],\n 1,\n p=self.distributions[\"eob_payment_distribution\"],\n )[0]\n eob_payment_amount = remaining_charge * (eob_payment_percentage / 100.0)\n if eob_payment_amount > 0:\n invoicelineitemeob = pd.DataFrame(\n {\n \"invoice_id\": [invoice_id],\n \"cpt_code\": [cpt_code],\n \"created_on\": [date_of_eob],\n \"copay_amount\": [0],\n \"adjustment_amount\": [0],\n \"paid_amount\": [eob_payment_amount],\n }\n )\n self.InvoiceLineItemsEob = self.InvoiceLineItemsEob.append(\n invoicelineitemeob\n )\n remaining_charge = remaining_charge - eob_payment_amount\n else:\n remaining_charge = charge_amount\n return remaining_charge","def action_invoice_create(self, grouped=False, final=False):\n if self.invoice_option == 'before_delivery':\n inv_obj = self.env['account.invoice']\n for order in self:\n inv_data = order._prepare_invoice()\n invoice = inv_obj.create(inv_data)\n for inv_line in order.order_line:\n inv_line.invoice_line_create(invoice.id, inv_line.product_uom_qty)\n\n else:\n inv_obj = self.env['account.invoice']\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\n invoices = {}\n references = {}\n invoices_origin = {}\n invoices_name = {}\n\n # Keep track of the sequences of the lines\n # To keep lines under their section\n inv_line_sequence = 0\n for order in self:\n group_key = order.id if grouped else (order.partner_invoice_id.id, order.currency_id.id)\n\n # We only want to create sections that have at least one invoiceable line\n pending_section = None\n\n # Create lines in batch to avoid performance problems\n line_vals_list = []\n # sequence is the natural order of order_lines\n for line in order.order_line:\n if line.display_type == 'line_section':\n pending_section = line\n continue\n if float_is_zero(line.qty_to_invoice, precision_digits=precision):\n continue\n if group_key not in invoices:\n inv_data = order._prepare_invoice()\n invoice = inv_obj.create(inv_data)\n references[invoice] = order\n invoices[group_key] = invoice\n invoices_origin[group_key] = [invoice.origin]\n invoices_name[group_key] = [invoice.name]\n elif group_key in invoices:\n if order.name not in invoices_origin[group_key]:\n invoices_origin[group_key].append(order.name)\n if order.client_order_ref and order.client_order_ref not in invoices_name[group_key]:\n invoices_name[group_key].append(order.client_order_ref)\n\n if line.qty_to_invoice > 0 or (line.qty_to_invoice < 0 and final):\n if pending_section:\n section_invoice = pending_section.invoice_line_create_vals(\n invoices[group_key].id,\n pending_section.qty_to_invoice\n )\n inv_line_sequence += 1\n section_invoice[0]['sequence'] = inv_line_sequence\n line_vals_list.extend(section_invoice)\n pending_section = None\n\n inv_line_sequence += 1\n inv_line = line.invoice_line_create_vals(\n invoices[group_key].id, line.qty_to_invoice\n )\n inv_line[0]['sequence'] = inv_line_sequence\n line_vals_list.extend(inv_line)\n\n if references.get(invoices.get(group_key)):\n if order not in references[invoices[group_key]]:\n references[invoices[group_key]] |= order\n\n self.env['account.invoice.line'].create(line_vals_list)\n\n for group_key in invoices:\n invoices[group_key].write({'name': ', '.join(invoices_name[group_key]),\n 'origin': ', '.join(invoices_origin[group_key])})\n sale_orders = references[invoices[group_key]]\n if len(sale_orders) == 1:\n invoices[group_key].reference = sale_orders.reference\n\n if not invoices:\n raise UserError(_(\n 'There is no invoiceable line. If a product has a Delivered quantities invoicing policy, please make sure that a quantity has been delivered.'))\n\n for invoice in invoices.values():\n invoice.compute_taxes()\n if not invoice.invoice_line_ids:\n raise UserError(_(\n 'There is no invoiceable line. If a product has a Delivered quantities invoicing policy, please make sure that a quantity has been delivered.'))\n # If invoice is negative, do a refund invoice instead\n if invoice.amount_total < 0:\n invoice.type = 'out_refund'\n for line in invoice.invoice_line_ids:\n line.quantity = -line.quantity\n # Use additional field helper function (for account extensions)\n for line in invoice.invoice_line_ids:\n line._set_additional_fields(invoice)\n # Necessary to force computation of taxes. In account_invoice, they are triggered\n # by onchanges, which are not triggered when doing a create.\n invoice.compute_taxes()\n # Idem for partner\n so_payment_term_id = invoice.payment_term_id.id\n fp_invoice = invoice.fiscal_position_id\n invoice._onchange_partner_id()\n invoice.fiscal_position_id = fp_invoice\n # To keep the payment terms set on the SO\n invoice.payment_term_id = so_payment_term_id\n invoice.message_post_with_view('mail.message_origin_link',\n values={'self': invoice, 'origin': references[invoice]},\n subtype_id=self.env.ref('mail.mt_note').id)\n return [inv.id for inv in invoices.values()]","def _get_query(self, type, date_from=False, date_to=False, users=None, products=None):\n # TODO: Revisar def _create_invoice(self, order, so_line, amount):...\n # so.user_id AS id_salesman\n # AND so.user_id IN (%s)\n # AND pp.id IN (%s)\n # GROUP BY salesman\n\n if type == 'most_sold':\n sql = \"\"\"\n SELECT min(sol.id) AS id, \n so.user_id AS salesman, \n sol.product_id AS product,\n AVG(sol.price_reduce_taxexcl) AS price, \n pp.product_tmpl_id AS product_template,\n so.company_id AS company,\n SUM(sol.product_uom_qty) AS qty,\n SUM(sol.price_subtotal) AS subtotal\n FROM sale_order_line sol\n LEFT JOIN sale_order so ON so.id = sol.order_id\n LEFT JOIN product_product pp ON pp.id = sol.product_id\n LEFT JOIN product_template pt ON pt.id = pp.product_tmpl_id\n WHERE so.state NOT IN ('draft', 'sent', 'cancel')\n AND so.date_order BETWEEN '%s' AND '%s'\n AND so.user_id IN (%s)\n AND pp.id IN (%s)\n GROUP BY salesman, sol.product_id, pp.product_tmpl_id, so.company_id\n ORDER BY qty DESC;\n \"\"\" % (date_from, date_to, ', '.join(str(u) for u in users), ', '.join(str(p) for p in products))\n else:\n sql = \"\"\" \n \"\"\"\n self.env.cr.execute(sql)\n return self.env.cr.dictfetchall()","def get_customer_history_df_invoice_line(self, customerID):\n df_invoice_line \\\n = self._df_invoice_original[self._df_invoice_original.CustomerID \\\n == customerID]\n return df_invoice_line","def _prepare_invoice_line(self, inv_id):\n res = {}\n account_id = self.product_id.property_account_income_id.id or self.product_id.categ_id.property_account_income_categ_id.id\n if not account_id:\n raise UserError(_('Please define income account for this product: \"%s\" (id:%d).') % \\\n (self.product_id.name, self.product_id.id,))\n price_unit = self.product_id.lst_price\n res = {\n 'invoice_id': inv_id.id,\n 'name': self.name,\n 'origin': self.order_id.name,\n 'account_id': account_id,\n 'uom_id': self.product_uom_id.id,\n 'quantity': self.product_uom_qty,\n 'price_unit': price_unit,\n 'product_id': self.product_id.id,\n 'invoice_line_tax_id': False,\n 'order_line_id': self.id\n }\n return res","def action_invoice_create(self, grouped=False, final=False):\n inv_obj = self.env['account.invoice']\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\n invoices = {}\n references = {}\n invoices_origin = {}\n invoices_name = {}\n for order in self:\n group_key = order.id if grouped else (order.partner_id.id, order.currency_id.id)\n for line in order.order_line.sorted(key=lambda l: l.qty_received - l.qty_invoiced < 0):\n if float_is_zero(line.qty_received - line.qty_invoiced, precision_digits=precision):\n continue\n if group_key not in invoices:\n inv_data = order._prepare_invoice()\n invoice = inv_obj.create(inv_data)\n references[invoice] = order\n invoices[group_key] = invoice\n invoices_origin[group_key] = [invoice.origin]\n invoices_name[group_key] = [invoice.name]\n elif group_key in invoices:\n if order.name not in invoices_origin[group_key]:\n invoices_origin[group_key].append(order.name)\n if order.partner_ref and order.partner_ref not in invoices_name[group_key]:\n invoices_name[group_key].append(order.partner_ref)\n\n if line.qty_received - line.qty_invoiced > 0:\n line.invoice_line_create(invoices[group_key].id, line.qty_received - line.qty_invoiced)\n elif line.qty_received - line.qty_invoiced < 0 and final:\n line.invoice_line_create(invoices[group_key].id, line.qty_received - line.qty_invoiced)\n\n if references.get(invoices.get(group_key)):\n if order not in references[invoices[group_key]]:\n references[invoices[group_key]] |= order\n\n for group_key in invoices:\n invoices[group_key].write({'name': ', '.join(invoices_name[group_key]),\n 'origin': ', '.join(invoices_origin[group_key])})\n\n if not invoices:\n raise UserError(_('There is no invoicable line.'))\n\n for invoice in invoices.values():\n if not invoice.invoice_line_ids:\n raise UserError(_('There is no invoicable line.'))\n # If invoice is negative, do a refund invoice instead\n if invoice.amount_total < 0:\n invoice.type = 'in_refund'\n for line in invoice.invoice_line_ids:\n line.quantity = -line.quantity\n # Necessary to force computation of taxes. In account_invoice, they are triggered\n # by onchanges, which are not triggered when doing a create.\n invoice.compute_taxes()\n invoice.message_post_with_view('mail.message_origin_link',\n values={'self': invoice, 'origin': references[invoice]},\n subtype_id=self.env.ref('mail.mt_note').id)\n return [inv.id for inv in invoices.values()]","def action_invoice_create(self, grouped=False, final=False):\n inv_obj = self.env['account.invoice']\n precision = self.env['decimal.precision'].sudo().precision_get('Product Unit of Measure')\n invoices = {}\n references = {}\n for order in self:\n group_key = order.id if grouped else (order.partner_invoice_id.id, order.currency_id.id)\n for line in order.order_line.sorted(key=lambda l: l.qty_to_invoice < 0):\n if float_is_zero(line.qty_to_invoice, precision_digits=precision):\n continue\n if group_key not in invoices:\n inv_data = order._prepare_invoice()\n invoice = inv_obj.sudo().create(inv_data)\n references[invoice] = order\n invoices[group_key] = invoice\n invoice['sale_order_id'] = order.id\n elif group_key in invoices:\n vals = {}\n if order.name not in invoices[group_key].origin.split(', '):\n vals['origin'] = invoices[group_key].origin + ', ' + order.name\n if order.client_order_ref and order.client_order_ref not in invoices[group_key].name.split(\n ', ') and order.client_order_ref != invoices[group_key].name:\n vals['name'] = invoices[group_key].name + ', ' + order.client_order_ref\n invoices[group_key].sudo().write(vals)\n if line.qty_to_invoice > 0:\n line.invoice_line_create(invoices[group_key].id, line.qty_to_invoice)\n elif line.qty_to_invoice < 0 and final:\n line.invoice_line_create(invoices[group_key].id, line.qty_to_invoice)\n\n if references.get(invoices.get(group_key)):\n if order not in references[invoices[group_key]]:\n references[invoices[group_key]] |= order\n if not invoices:\n raise UserError(_('There is no invoiceable line.'))\n for invoice in invoices.values():\n if not invoice.invoice_line_ids:\n raise UserError(_('There is no invoiceable line.'))\n # If invoice is negative, do a refund invoice instead\n if invoice.amount_untaxed < 0:\n invoice.type = 'out_refund'\n for line in invoice.invoice_line_ids:\n line.quantity = -line.quantity\n # Use additional field helper function (for account extensions)\n for line in invoice.invoice_line_ids:\n line._set_additional_fields(invoice)\n # Necessary to force computation of taxes. In account_invoice, they are triggered\n # by onchanges, which are not triggered when doing a create.\n invoice.compute_taxes()\n invoice.message_post_with_view('mail.message_origin_link',\n values={'self': invoice, 'origin': references[invoice]},\n subtype_id=self.env.ref('mail.mt_note').id)\n return [inv.id for inv in invoices.values()]","def merge_invoice(self, cr, uid, invoices, context=None):\n order_ids = []\n pick_ids = []\n if len(invoices) <= 1:\n return False\n parent = self.pool.get('account.invoice').browse(cr, uid, context['active_id'])\n for inv in invoices:\n if parent.partner_id != inv.partner_id:\n raise osv.except_osv(_(\"Partners don't match!\"), _(\"Can not merge invoice(s) on different partners or states !.\"))\n\n if inv.state != 'draft':\n raise osv.except_osv(_(\"Invalid action !\"), _(\"You can merge only invoices in draft state.\"))\n\n # Merge invoices that are in draft state\n inv_line_obj = self.pool.get('account.invoice.line')\n name = parent.name\n comment = parent.comment\n origin = parent.origin\n for inv in invoices:\n if inv.id == parent.id:\n continue\n\n # check if a line with the same product already exist. if so add quantity. else hang up invoice line to first invoice head.\n if inv.name:\n # Find if the same name already exist, if yes, skip to add.\n name_list = name.replace(' ', '').split(',')\n if inv.name not in name_list:\n name += ', %s' % inv.name\n if inv.comment:\n comment = comment and comment + ', %s' % inv.comment or inv.comment\n if inv.origin:\n origin += ', %s' % inv.origin\n line_ids = inv_line_obj.search(cr, uid, [('invoice_id', '=', inv.id)])\n for inv_lin in inv_line_obj.browse(cr, uid, line_ids):\n mrg_pdt_ids = inv_line_obj.search(cr, uid, [('invoice_id', '=', parent.id), ('product_id', '=', inv_lin.product_id.id),\n ('uos_id', '=', inv_lin.uos_id.id), ('price_unit', '=', inv_lin.price_unit) # kittiu: extra condition, unit price must also be the same.\n ])\n if len(mrg_pdt_ids) == 1 and inv.type == parent.type: # product found --> add quantity\n inv_line_obj.write(cr, uid, mrg_pdt_ids, {'quantity': inv_line_obj._can_merge_quantity(cr, uid, mrg_pdt_ids[0], inv_lin.id)})\n inv_line_obj.unlink(cr, uid, inv_lin.id)\n elif inv.type == parent.type:\n inv_line_obj.write(cr, uid, inv_lin.id, {'invoice_id': parent.id})\n else:\n inv_line_obj.write(cr, uid, inv_lin.id, {'invoice_id': parent.id, 'quantity': -inv_lin.quantity})\n\n if inv.sale_order_ids:\n order_ids += [order.id for order in inv.sale_order_ids]\n if inv.picking_ids:\n pick_ids += [picking.id for picking in inv.picking_ids]\n\n self.write(cr, uid, parent.id, {'origin': origin, 'name': name, 'comment': comment})\n\n #Remove By DRB\n #cr.execute('update sale_order_invoice_rel set invoice_id = %s where invoice_id = %s', (parent.id, inv.id))\n #cr.execute('update picking_invoice_rel set invoice_id = %s where invoice_id = %s', (parent.id, inv.id))\n\n self.unlink(cr, uid, [inv.id])\n #Distinct List\n order_ids = list(set(order_ids))\n pick_ids = list(set(pick_ids))\n\n self.write(cr, uid, parent.id, {'sale_order_ids': [(6, 0, order_ids)], 'picking_ids': [(6, 0, pick_ids)]})\n self.button_reset_taxes(cr, uid, [parent.id])\n return parent.id","def invoices(self):\r\n return Invoices(self)","def generate_report(df, start_date, end_date):\n # Remove any transactions that had to do with collecting or returning security\n security_df = df[(df[CATEGORY] == 'Security') | (df[CATEGORY] == 'Security-Income')]\n df = df[(df[CATEGORY] != 'Security')]\n\n # Exclude the data for everything except our quarter\n period_data = df.loc[start_date:end_date] # Note: NOT using extended quarter range\n rental_income = period_data[period_data[CATEGORY] == 'Rent']\n utilities = period_data[(period_data[CATEGORY] == 'Utilities')]\n other_income = period_data[(period_data['Transaction Type'] == 'credit') & (period_data[CATEGORY] != 'Rent')]\n expenses = period_data[(period_data['Transaction Type'] == 'debit')]\n unpaid_util_overages = float(0)\n\n # print(rental_income)\n # print(other_income)\n # print(expenses)\n \n html_config.initialize()\n print(html_config.HTML_OPEN)\n\n print('<H1>Income and Expense Report for %s-%s:' % (start_date, end_date), '</H1><p>')\n\n # List all unit specific rents and expenses for the quarter\n for UNIT in sorted(rental_income['Unit'].unique()):\n # Show rental income info\n temp_df = rental_income[rental_income['Unit'] == UNIT]\n print('<br><H2>Total rent for Unit ', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</H2>')\n print(temp_df[['Description', 'Amount']].to_html())\n \n if not SKIP_UTIL_ANALYSIS:\n # Show utilities payments and calculate any overage due\n temp_df = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'debit')]\n print('<br><H2>Utilities Expenses for Unit', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\n overage = temp_df.assign(Overage=lambda x: x.Amount - limit_df.loc[UNIT].Amount)\n # Disable warning when setting negative overage values to zero\n pd.set_option('mode.chained_assignment', None)\n overage.Overage[overage.Overage < 0] = 0\n pd.set_option('mode.chained_assignment', 'warn')\n print(overage[['Description', 'Amount', 'Overage']].to_html())\n # Show if utilties costs exceeded allotted amount\n if (overage['Overage'].sum() > 0):\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\n unpaid_util_overages += overage['Overage'].sum()\n # Show any untilities that were collected \n overage_collected = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'credit')]\n if not overage_collected.empty:\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\n \n\n \n # Generate unit specific Utility usage reports\n if GEN_TENANT_UTIL_REPORTS and OUTPUT_DIRECTORY:\n TENANT_FILE = '%s/122-Spring-St-%s-%s-Unit-%s-utils.html' % (OUTPUT_DIRECTORY, start_date, end_date, UNIT)\n TENANT_REPORTS.append(TENANT_FILE)\n sys.stdout = open(TENANT_FILE, 'w')\n print(html_config.HTML_OPEN)\n\n print('<H1>Unit', UNIT, '</H1>')\n print('<br><H2>Utilities Expenses for: %s-%s' % (start_date, end_date))\n print('<br>Utilites included in rent: ${:,.2f}'.format(limit_df.loc[UNIT].Amount))\n print('</H2>')\n print(overage[['Description', 'Amount', 'Overage']].to_html())\n # Show if any utilties overage may be due\n if (overage['Overage'].sum() > 0):\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\n if not overage_collected.empty:\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\n\n print(html_config.HTML_CLOSE)\n\n # Restore stdout to the main report file\n sys.stdout = open(REPORT_FILE, 'a')\n \n # Show other unit specific transactions\n if SKIP_UTIL_ANALYSIS:\n unit_exp = expenses[(expenses['Unit'] == UNIT)]\n unit_income = other_income[other_income['Unit'] == UNIT]\n else:\n unit_exp = expenses[(expenses['Unit'] == UNIT) & (expenses[CATEGORY] != 'Utilities')]\n unit_income = other_income[(other_income['Unit'] == UNIT) & (other_income[CATEGORY] != 'Utilities')]\n \n if not unit_exp.empty:\n print('<br><H2>Other Unit specific expenses for: ', UNIT, ': ${:,.2f}'.format(unit_exp['Amount'].sum()), '</h2>')\n print(unit_exp[['Description', 'Amount', 'Unit', CATEGORY]].to_html())\n print('<p>')\n \n # Show any other unit specific credit\n other_income = other_income[other_income['Unit'] == UNIT]\n if not other_income.empty:\n print('<br><H2>Expense offsets for Unit ', UNIT, ': ${:,.2f}'.format(other_income['Amount'].sum()), '</H2>')\n print(other_income[['Description', 'Amount', CATEGORY]].to_html())\n \n # Add a line between units\n print('<hr>')\n \n # List the shared income and expenses for the quarter\n temp_df = other_income[other_income['Unit'].isnull()]\n if not temp_df.empty:\n print ('<br><H2>Non unit specific income: ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\n gen_expenses = expenses[expenses['Unit'].isnull()]\n if not gen_expenses.empty:\n print ('<br><H1>Non unit specific expenses</h1>')\n # Get the list of expense categories and generate summary for each\n for category in sorted(gen_expenses[CATEGORY].unique()):\n temp_df = gen_expenses[(gen_expenses[CATEGORY] == category)]\n print ('<br><H2>'+ category +': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\n \n # If there were any security transactions in the period give a security report\n if not security_df.loc[start_date:end_date].empty:\n temp_df = security_df.loc[start_date:end_date] \n print('<hr><H2>Security related transactions:</H2>')\n print(temp_df[['Description', 'Amount', 'Transaction Type', 'Unit']].to_html())\n for UNIT in sorted(rental_income['Unit'].unique()):\n unit_df = security_df[security_df['Unit'] == UNIT]\n collected = unit_df[(unit_df['Transaction Type'] == 'credit')]['Amount'].sum()\n returned = unit_df[(unit_df['Transaction Type'] == 'debit')]['Amount'].sum()\n print('<center><H4>Current Liability on Unit '+str(UNIT)+': ${:,.2f}'.format(collected-returned), '</H4></center>')\n \n # # Summarize the periods income and expenses -- old way to be discarded...\n # print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()), '</H3>')\n # print('<H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\n # print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\n # Summarize the periods income and expenses\n print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()))\n print('<br><H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\n print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\n# print('</H3>')\n \n print(html_config.HTML_CLOSE)\n sys.stdout.flush()","def _prepare_invoice_grp(self, cr, uid, order, line_ids, context=None):\n if context is None:\n context = {}\n context = dict(context)\n\n inv_data = super(grp_orden_compra, self)._prepare_invoice_grp(cr, uid, order, line_ids, context=context)\n\n # adicionando campos numero compromiso y no obligacion desde la OC\n monto_oc = math.floor(order.total_llavep or 0)\n monto_oc = int(monto_oc)\n inv_data.update({'nro_compromiso': order.nro_compromiso or False, 'monto_comprometido': monto_oc or 0, 'currency_id':order.currency_oc.id})\n\n # adicionando campos no afectacion y monto autorizado desde la primera APG\n if order.pc_apg_id:\n first_apg = order.pc_apg_id\n monto_apg = math.floor(first_apg.total_llavep)\n monto_apg = int(monto_apg)\n # TODO R SPRING X ADICIONANDO CABEZALES SIIF A LA FACTURA A PARTIR DE LA APG\n inv_data.update({'nro_afectacion': first_apg.nro_afectacion_siif or False,\n 'monto_afectado': monto_apg or 0,\n 'siif_tipo_ejecucion':first_apg.siif_tipo_ejecucion.id,\n 'siif_concepto_gasto':first_apg.siif_concepto_gasto.id,\n 'siif_financiamiento':first_apg.siif_financiamiento.id,\n 'siif_codigo_sir':first_apg.siif_codigo_sir.id,\n 'siif_nro_fondo_rot':first_apg.siif_nro_fondo_rot.id,\n }) # cambiando nro_afectacion 23/10\n # inv.update({'nro_afectacion': first_apg.nro_afectacion_apg or False, 'monto_afectado': monto_apg or 0})\n\n # # TODO R SPRING X NO LLEVAR LAS LLAVES PRESUPUESTALES POR DEFECTO\n # if order.pc_apg_id.llpapg_ids:\n # llavep_ids = []\n # for llavep in order.pc_apg_id.llpapg_ids:\n # llavep_ids.append((0, 0, {\n # 'programa_id': llavep.programa_id.id,\n # 'odg_id': llavep.odg_id.id,\n # 'auxiliar_id': llavep.auxiliar_id.id,\n # 'disponible': llavep.disponible,\n # 'proyecto_id': llavep.proyecto_id.id,\n # 'fin_id': llavep.fin_id.id,\n # 'mon_id': llavep.mon_id.id,\n # 'tc_id': llavep.tc_id.id,\n # 'importe': llavep.importe\n # }))\n # inv_data.update({'llpapg_ids': llavep_ids})\n\n return inv_data","def _prepare_invoice_line(self, qty):\n self.ensure_one()\n res = {\n 'name': self.name,\n 'sequence': self.sequence,\n 'origin': self.order_id.name,\n 'account_id': self.product_id.product_tmpl_id._get_product_accounts()['stock_input'].id,\n 'price_unit': self.price_unit,\n 'quantity': qty,\n 'uom_id': self.product_uom.id,\n 'product_id': self.product_id.id or False,\n 'invoice_line_tax_ids': [(6, 0, self.taxes_id.ids)],\n 'account_analytic_id': self.account_analytic_id.id,\n 'analytic_tag_ids': [(6, 0, self.analytic_tag_ids.ids)],\n }\n return res","def invoice(self,context,params):\n url = f\"https://api.freshbooks.com/accounting/account/{params['account_id']}/invoices/invoices/{params['invoice_id']}\"\n result = json.loads(util.rest(\"GET\", url, {}, context[\"headers\"][\"access_token\"]).text)\n invoice = result[\"response\"][\"result\"][\"invoice\"]\n invoice_obj = FreshbooksInvoice(\n account_id=invoice['accountid'],\n customerid=invoice['customerid'], \n invoice_id=invoice['invoiceid'],\n currency_code=invoice['currency_code'],\n language=invoice['language'],\n terms=invoice['terms'],\n discount_value=invoice['discount_value'],\n discount_amount=invoice['discount_total']['amount'],\n invoice_number=invoice['invoice_number'],\n po_number=invoice['po_number'],\n amount=invoice['amount']['amount'],\n code=invoice['amount']['code'],\n create_date=invoice['create_date']\n )\n return invoice_obj.__dict__","def clean_qpcr_data(df) -> pd.DataFrame:\n ASSAYDATE_COL = \"Assay Date\"\n INSTRUMENT_COL = \"Instrument\"\n POOL_COL = \"Pool\"\n\n # Names and types of all our columns. We also add the columns INSTRUMENT_COL and POOL_COL\n original_columns = [ASSAYDATE_COL, DATE_COL, \"Sample ID\", \"GENE\", \"Ct [1]\", \"Ct [2]\", \"Ct [3]\", \"Ct Avg\", \"Ct Stdev\", \"Copies [1]\", \"Copies [2]\", \"Copies [3]\", \"Copies AVG\", \"Copies Stdev\", \"PMMoV Ct [1]\", \"PMMoV Ct [2]\", \"PMMoV Ct [3]\", \"PMMoV Avg\", \"PMMoV Stdev\", \"Empty tube weight (g)\", \"Full tube weight (g)\", \"Pellet weight (g)\", \"Extracted Mass (in 100 uL) (g)\", \"Copies per Extracted Mass (copies/g) [1]\", \"Copies per Extracted Mass (copies/g) [2]\", \"Copies per Extracted Mass (copies/g) [3]\", \"Copies per Extracted Mass (copies/g) Avg\", \"Copies per Extracted Mass Stdev\", \"2^Ct\", \"2^Ct normalized to a value\", \"(2^Ct normalized to a value per Extracted Mass)\", \"PMMoV Copies [1]\", \"PMMoV Copies [2]\", \"PMMoV Copies [3]\", \"PMMoV Copies Avg\", \"PMMoV Copies Stdev\", \"Copies per Copies of PMMoV Avg\", \"Copies per Copies of PMMoV * 10^3 Avg\", \"Copies per Copies normalized to a value\", \"Copies per Copies of PMMoV Stdev\", \"Copies per Copies of PMMoV * 10^3 Stdev\", \"Copies per Copies normalized to a value Stdev\", \"Date [2]\", \"Gene\", \"APPROVED: Copies per Copies of PMMoV [1]\", \"APPROVED: Copies per Copies of PMMoV [2]\", \"APPROVED: Copies per Copies of PMMoV [3]\", \"APPROVED: Copies per Copies of PMMoV Avg\", \"APPROVED: Copies per Copies of PMMoV Stdev\", \"APPROVED: Copies per Extracted Mass (copies/g) [1]\", \"APPROVED: Copies per Extracted Mass (copies/g) [2]\", \"APPROVED: Copies per Extracted Mass (copies/g) [3]\", \"APPROVED: Copies per Extracted Mass (copies/g) Avg\", \"APPROVED: Copies/L [1]\", \"APPROVED: Copies/L [2]\", \"APPROVED: Copies/L [3]\", \"APPROVED: Copies/L Avg\", \"MESP UPLOAD: PMMoV Copies per Extracted Mass (copies/g)\", \"MESP UPLOAD: PMMoV Copies/L\", \"INHIBITION CTRL: Date\", \"INHIBITION CTRL: Sample Name\", \"INHIBITION CTRL: Pepper 1/10 [1]\", \"INHIBITION CTRL: Pepper 1/10 [2]\", \"INHIBITION CTRL: Pepper 1/10 [3]\", \"INHIBITION CTRL: Pepper 1/10 Avg\", \"INHIBITION CTRL: Pepper 1/40 [1]\", \"INHIBITION CTRL: Pepper 1/40 [2]\", \"INHIBITION CTRL: Pepper 1/40 [3]\", \"INHIBITION CTRL: Pepper 1/40 Avg\", \"INHIBITION CTRL: Pepper No Dilution [1]\", \"INHIBITION CTRL: Pepper No Dilution [2]\", \"INHIBITION CTRL: Pepper No Dilution [3]\", \"INHIBITION CTRL: Pepper No Dilution Avg\", \"Pepper 1/10 ΔCt\", \"Pepper 1/40 ΔCt\"]\n original_types = [\"date\", \"date\", \"text\", \"text\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"date\", \"text\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"date\", \"text\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\"]\n new_columns = original_columns.copy()\n new_columns[2:2] = [INSTRUMENT_COL, POOL_COL]\n new_types = original_types.copy()\n new_types[2:2] = [\"text\", \"text\"]\n\n # Remove unwanted columns at end of dataframe, and add new columns in the correct order\n df = df[df.columns[:len(original_columns)]].copy()\n df.columns = original_columns\n df[list(set(new_columns) - set(original_columns))] = None\n df = df[new_columns]\n\n # Go through all rows and clean them, assign appropriate values for current instrument, pool, assay date, etc\n cur_instrument, cur_pool = None, None\n cur_assaydate = None\n has_inhibition_controls = False\n has_copies_per_liter = False\n for i,row in df.iterrows():\n cur_instrument, cur_pool = get_inst_and_pool(row, cur_instrument, cur_pool)\n if row[ASSAYDATE_COL]:\n row_date = parse_date(row[ASSAYDATE_COL])\n cur_assaydate = row_date or cur_assaydate\n if row_date is not None:\n has_inhibition_controls = False\n has_copies_per_liter = False\n\n if np.any([\"Pepper 1/10\" in c for c in row if isinstance(c, str)]):\n has_inhibition_controls = True\n if np.any([\"Copies / VS\" in c or \"Copies / L\" in c for c in row if isinstance(c, str)]):\n has_copies_per_liter = True\n df.loc[i, ASSAYDATE_COL] = cur_assaydate\n df.loc[i, INSTRUMENT_COL] = cur_instrument\n df.loc[i, POOL_COL] = cur_pool\n if not has_inhibition_controls:\n df.loc[i, [c for c in df.columns if c.startswith(\"INHIBITION CTRL: \")]] = \"\"\n if not has_copies_per_liter:\n index = [idx for idx, c in enumerate(df.columns) if c.startswith(\"APPROVED: Copies/L\")][0]\n df.loc[i, df.columns[index:]] = \"\"\n\n # Remove invalid rows (where no valid \"Date\" is specified)\n df[DATE_COL] = pd.to_datetime(df[DATE_COL], errors=\"coerce\")\n df = df.loc[~df[DATE_COL].isna()]\n\n # 6 rows have a year of 1900 (from \"Septembe 27th\" assay date), change these to 2020.\n # 12 rows have a year of 2014 (from \"Septembe 17th\" assay date), change these to 2020.\n f = (df[DATE_COL].dt.year == 1900) | (df[DATE_COL].dt.year == 2014)\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\"2020-%m-%d\"), format=\"%Y-%m-%d\")\n\n # Rows from assay date \"Tuesday, December 22, 2020\" and \"Wednesday, December 23, 2020\" have incorrect\n # year in \"Date\" column (2021 instead of 2020)\n df[ASSAYDATE_COL] = pd.to_datetime(df[ASSAYDATE_COL], errors=\"coerce\")\n f = (df[ASSAYDATE_COL] == pd.to_datetime(\"December 22, 2020\")) | (df[ASSAYDATE_COL] == pd.to_datetime(\"December 23, 2020\"))\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\"2020-%m-%d\"), format=\"%Y-%m-%d\")\n\n # Add row at start that specifies the column types (date, text, number)\n df.loc[-1] = new_types\n df.index = df.index + 1\n df = df.sort_index()\n df.index = list(range(len(df.index)))\n\n return df","def create_invoice(self):\n sales_tax = 0.06\n item_sum = 0\n inv = f'Invoice#: {self.invoice_id}\\n'\n for key, value in self.items_with_price.items():\n item_sum += value\n inv += f'{key}.....${value:.2f}\\n'\n\n tax = item_sum * sales_tax\n inv += f'Tax.....${tax:.2f}\\n'\n inv += f'Total.....${tax + item_sum:.2f}'\n # print(inv)\n # returning for unit testing purposes\n return inv","def get_rows(contract_address: str, file_name: str, receipts_filename: str) -> pd.DataFrame():\n receipts_df = pd.read_csv(receipts_filename)\n receipts_df = receipts_df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\n df = pd.read_csv(file_name)\n df = df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\n\n df = df.loc[receipts_df['status'] == 1] \n df = df.loc[df[\"to_address\"] == contract_address.lower()]\n df = df.reset_index()\n df = df.drop(columns='index')\n return df","def cash_flow_response_to_df(\n portfolio_cash_flows_response: lusid.ResourceListOfPortfolioCashFlow,\n sum_by_date: bool = True\n) -> pd.DataFrame:\n\n def select_cols(\n df: pd.DataFrame,\n filter_col: str,\n filter_value: str,\n cols_to_keep: list,\n ) -> pd.DataFrame:\n return df[df[filter_col] == filter_value][cols_to_keep]\n\n # Extract cash payment data from cash flow response\n cash_flows_dict = portfolio_cash_flows_response.to_dict()\n cash_flow_data = pd.json_normalize(cash_flows_dict[\"values\"])\n\n # Split pays and receives and handle -ve signage for pay outflows\n pay_data = select_cols(\n cash_flow_data,\n \"diagnostics.PayReceive\",\n \"Pay\",[\"payment_date\", \"amount\", \"source_transaction_id\"]\n )\n pay_data[\"amount\"] = pay_data[\"amount\"].apply(lambda x: -1 * x)\n pay_data.rename(columns={\"amount\": \"payAmount\"}, inplace=True)\n rec_data = select_cols(\n cash_flow_data,\n \"diagnostics.PayReceive\",\n \"Receive\",\n [\"payment_date\", \"amount\", \"source_transaction_id\"]\n )\n rec_data.rename(columns={\"amount\": \"receiveAmount\"}, inplace=True)\n\n # Merge on payment date and ignore join dupes\n merged_df = pay_data.merge(rec_data, on=[\"payment_date\", \"source_transaction_id\"])\n merged_df.drop_duplicates(subset=[\"payment_date\", \"source_transaction_id\"], keep=\"first\", inplace=True,\n ignore_index=True)\n\n # Add net flows and reduce index to dates\n merged_df['netAmount'] = merged_df['payAmount'] + merged_df['receiveAmount']\n merged_df[\"payment_date\"] = merged_df[\"payment_date\"].apply(lambda x: x.date())\n merged_df.set_index(keys=\"payment_date\", inplace=True)\n\n # Aggregate sub-holdings\n if sum_by_date:\n merged_df = merged_df.groupby(merged_df.index).sum()\n\n return merged_df","def investment_line(self):\n inv, marks = self._get_marks()\n fig = plt.figure(figsize=(4, 2), dpi=200)\n fig.patch.set_facecolor('#ececec')\n ax = fig.add_subplot(111)\n investmentValues = inv['Invested']\n #investmentValues = pd.Series([0], index=[investmentValues.index[0]-timedelta(1)]).append(investmentValues)\n ax.plot(investmentValues, lw=1.2, color=\"blue\", label='Invested', marker=\"o\", markersize=3, markerfacecolor=\"grey\")\n ax.set_xlabel('Time')\n ax.set_ylabel('Investments (€)')\n ax.set_title('Investment Amount (€) - Daily')\n ax.xaxis.set_major_locator(dates.MonthLocator())\n ax.xaxis.set_major_formatter(dates.DateFormatter('%b-%Y'))\n for x, y, mark in zip(marks.index, marks['Invested'], marks['Marks']):\n a = ax.get_ylim()\n if x == marks.index[0]:\n ax.annotate(str(mark) + \" €\", xy=(x + timedelta(abs((self.data.index[0] - self.data.index[-1]).days) / 80), y + (a[1]-a[0])/35), fontsize=5)\n else:\n ax.annotate(str(mark) + \" €\", xy=(x + timedelta(abs((self.data.index[0] - self.data.index[-1]).days) / 50), y - (a[1]-a[0])/35), fontsize=5)\n ax.grid(True)\n fig.autofmt_xdate()\n ax.legend()\n return fig, ax","def invoice_line_create(self, invoice_id, qty):\n invoice_lines = self.env['account.invoice.line']\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\n for line in self:\n if not float_is_zero(qty, precision_digits=precision):\n vals = line._prepare_invoice_line(qty=qty)\n vals.update({'invoice_id': invoice_id, 'purchase_line_id': line.id})\n invoice_lines |= self.env['account.invoice.line'].create(vals)\n return invoice_lines","def emissions_baseline(self):\n baseline = DataFrame(columns=[\"CO2\", \"NOx\", \"PM10\", \"PM2.5\", \"SO2\"])\n baseline = baseline.append(year_1(self.plant.emissions()))\n baseline = baseline.append(year_1(self.plant.fuel_reseller().emissions()))\n baseline = baseline.append(year_1(self.farmer.emissions_exante))\n baseline.loc[\"Total\"] = baseline.sum()\n baseline.loc[\"Total_plant\"] = baseline.iloc[0]\n baseline.loc[\"Total_transport\"] = baseline.iloc[1]\n baseline.loc[\"Total_field\"] = baseline.iloc[2]\n return baseline","def from_invoice_and_line_item(cls, invoice: InvoiceModel, line_item: LineItemModel, line_number: int,\n distribution: str):\n # Note the invoice_date should be the payment_date in the future.\n return cls(total=line_item.total, invoice_number=invoice.id,\n line_number=line_number,\n is_reversal=invoice.invoice_status_code in\n [InvoiceStatus.REFUNDED.value, InvoiceStatus.REFUND_REQUESTED.value],\n distribution=distribution)","def submit_invoices(self, **kwargs) -> ApiResponse:\n \n return self._request(kwargs.pop('path'), data=kwargs)","def create_equity_curve_dataframe(self):\n # returns the cumulative product for percent change over every timestamp in the index\n curve = pd.DataFrame(self.all_holdings)\n curve.set_index('datetime', inplace=True)\n curve['returns'] = curve['total'].pct_change()\n #curve['equity_curve'] = (1.0+curve['returns']).cumprod()\n curve['equity_curve'] = curve['returns']\n curve['equity_curve'] += 1\n curve['equity_curve'] = curve['equity_curve'].cumprod()\n self.equity_curve = curve\n print(curve)","def _prepare_invoice(self):\n # get current logged in user's timezone\n local = pytz.timezone(self.env['res.users'].browse(self._uid).tz) or pytz.utc\n\n self.ensure_one()\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')], limit=1).id\n if not journal_id:\n raise UserError(_('Please define an accounting purchase journal for this company.'))\n invoice_vals = {\n 'name': self.partner_ref or '',\n 'origin': self.name,\n 'type': 'in_invoice',\n 'account_id': self.partner_id.property_account_payable_id.id,\n 'partner_id': self.partner_id.id,\n 'journal_id': journal_id,\n 'currency_id': self.currency_id.id,\n 'comment': self.notes,\n 'payment_term_id': self.payment_term_id.id,\n 'fiscal_position_id': self.fiscal_position_id.id or self.partner_id.property_account_position_id.id,\n 'company_id': self.company_id.id,\n 'purchase_id': self.id,\n 'date_invoice':pytz.utc.localize(datetime.datetime.now()).astimezone(local).strftime('%Y-%m-%d'),\n }\n return invoice_vals","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","def create_invoice(sender, invoice, issuer_details, **kwargs):\n if not invoice.items:\n return\n\n price = sum([item.price for item in invoice.items.all()])\n\n if not price:\n return\n\n paypal_invoice = models.Invoice(\n customer=invoice.customer,\n year=invoice.year,\n month=invoice.month,\n invoice_date=invoice.invoice_date,\n end_date=invoice.due_date,\n tax_percent=invoice.tax_percent,\n issuer_details=issuer_details,\n )\n\n paypal_invoice.payment_details = {\n 'name': invoice.customer.name,\n 'address': invoice.customer.address,\n 'country': invoice.customer.country,\n 'country_name': invoice.customer.get_country_display(),\n 'email': invoice.customer.email,\n 'postal': invoice.customer.postal,\n 'phone_number': invoice.customer.phone_number,\n 'bank_name': invoice.customer.bank_name,\n 'bank_account': invoice.customer.bank_account,\n }\n\n paypal_invoice.save()\n\n for item in invoice.items.all():\n models.InvoiceItem.objects.create(\n invoice=paypal_invoice,\n price=item.price,\n tax=item.tax,\n quantity=item.quantity,\n unit_price=item.unit_price,\n unit_of_measure=helpers.convert_unit_of_measure(item.unit),\n name=item.name,\n start=item.start,\n end=item.end,\n )","def invoices_report_filter(request):\n qry = DBSession.query(Invoice)\n supplierlist = []\n for supplier in qry:\n if supplier.SupplierID:\n entry = {'Name': supplier.Order.Supplier.Name,\n 'ID': supplier.SupplierID}\n if entry not in supplierlist:\n supplierlist.append(entry)\n\n projectlist = []\n for project in qry:\n if project.ProjectID:\n entry = {'Name': project.Order.Project.Name,\n 'ID': project.ProjectID}\n if entry not in projectlist:\n projectlist.append(entry)\n\n paymentdatelist = []\n for paymentdate in qry:\n if paymentdate.PaymentDate:\n entry = paymentdate.PaymentDate.strftime(\"%d %B %Y\")\n if entry not in paymentdatelist:\n paymentdatelist.append(entry)\n\n return {'projects': sorted(projectlist, key=lambda k: k['Name'].upper()),\n 'suppliers': sorted(supplierlist, key=lambda k: k['Name'].upper()),\n 'paymentdates': sorted(paymentdatelist),\n 'paymentdates_exist': paymentdatelist != [],\n 'statuses': ['Draft', 'Due', 'Paid']}","def invoices(self,org_id=None,invoice_id=''):\n if org_id is None:\n org_id = self.org_id\n return self.get('{}/orgs/{}/invoices/{}'.format(ApiVersion.A1.value,org_id,invoice_id))","def sub_tax_sales_transfer_pricing(manager, df_fields, seq_recs, seq_reservas):\n # df_hotel = manager.get_dataframe(tables['dwc_bok_t_canco_hotel'])\n # df_circuit = manager.get_dataframe(tables['dwc_bok_t_canco_hotel_circuit'])\n # df_other = manager.get_dataframe(tables['dwc_bok_t_canco_other'])\n # df_transfer = manager.get_dataframe(tables['dwc_bok_t_canco_transfer'])\n # df_endow = manager.get_dataframe(tables['dwc_bok_t_canco_endowments'])\n # df_extra = manager.get_dataframe(tables['dwc_bok_t_canco_extra'])\n\n df_aux = df_fields.select(\"operative_incoming\", \"booking_id\")\n\n df_hotel = sub_tax_sales_transfer_pricing_aux(manager, df_hotelt, seq_recs, seq_reservas, df_aux)\n df_circuit = sub_tax_sales_transfer_pricing_aux(manager, df_circuitt, seq_recs, seq_reservas, df_aux)\n df_other = sub_tax_sales_transfer_pricing_aux(manager, df_othert, seq_recs, seq_reservas, df_aux)\n df_transfer = sub_tax_sales_transfer_pricing_aux(manager, df_transfert, seq_recs, seq_reservas, df_aux)\n df_endow = sub_tax_sales_transfer_pricing_aux(manager, df_endowt, seq_recs, seq_reservas, df_aux)\n df_extra = sub_tax_sales_transfer_pricing_aux_extra(manager, df_extrat, seq_recs, seq_reservas, df_aux)\n\n df_impuesto_canal = df_hotel.union(df_circuit).union(df_other).union(df_transfer).union(df_endow).union(\n df_extra)\n\n df_impuesto_canal = df_impuesto_canal.groupBy(\"seq_rec\", \"seq_reserva\") \\\n .agg({'impuesto_canal': 'sum'}).withColumnRenamed(\"SUM(impuesto_canal)\", \"Tax_Sales_Transfer_pricing\")\n\n df_fields = df_fields.join(df_impuesto_canal, [df_fields.operative_incoming == df_impuesto_canal.seq_rec,\n df_fields.booking_id == df_impuesto_canal.seq_reserva],\n 'left_outer').drop(df_impuesto_canal.seq_rec).drop(df_impuesto_canal.seq_reserva)\n\n df_fields = df_fields.na.fill({\"Tax_Sales_Transfer_pricing\": 0})\n\n df_fields = df_fields.withColumn(\"Tax_Sales_Transfer_pricing\",\n udf_round_ccy(df_fields.Tax_Sales_Transfer_pricing,\n df_fields.booking_currency))\n\n del df_hotel, df_circuit, df_other, df_transfer, df_endow, df_extra, df_impuesto_canal\n\n return df_fields","def income_report_gen(start, end):\n payments = get_income(start, end)\n row_title = [\"Name\", \"Boat\", \"Rent Day\", \"Pay Day\", \"Amount\"]\n data = []\n for payment in payments:\n temp = []\n for title, value in payment.items():\n temp.append(str(value))\n data.append(temp)\n row_format = \"{:>15}\" * (len(row_title)+1)\n print(row_format.format(\"\", *row_title))\n total_income = 0\n for i in range(len(data)):\n print(row_format.format(i+1, *data[i]))\n total_income += int(data[i][4])\n print(row_format.format(\"SUM\", *([\"--------------\"] * 4), str(total_income)))","def get_equal_dataframe() -> pandas.DataFrame:\n data = {\n 'InvoiceDate': ['2009-01', '2012-01'],\n 'Count': [4, 4],\n 'Total': [8198.79, 5323.15]\n }\n return pandas.DataFrame(data, columns=['InvoiceDate', 'Count', 'Total'])","def order_report():","def _prepare_invoice(self, cr, uid, order, lines, context=None):\n invoice_vals = super(my_sale_order, self)._prepare_invoice(cr, uid, order,\n lines, context)\n\n invoice_vals.update({\n 'partner_shipping_id': order.partner_shipping_id.id,\n })\n\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\n\n return invoice_vals","def action_invoice_create(self, cr, uid, ids, context=None):\n res = False\n\n journal_obj = self.pool.get('account.journal')\n inv_obj = self.pool.get('account.invoice')\n inv_line_obj = self.pool.get('account.invoice.line')\n fiscal_obj = self.pool.get('account.fiscal.position')\n\n for order in self.browse(cr, uid, ids, context=context):\n# pay_acc_id = order.partner_id.property_account_payable.id\n #use a new method to get the account_id\n pay_acc_id = self._get_inv_pay_acc_id(cr,uid,order) \n journal_ids = journal_obj.search(cr, uid, [('type', '=','purchase'),('company_id', '=', order.company_id.id)], limit=1)\n if not journal_ids:\n raise osv.except_osv(_('Error!'),\n _('Define purchase journal for this company: \"%s\" (id:%d).') % (order.company_id.name, order.company_id.id))\n\n # generate invoice line correspond to PO line and link that to created invoice (inv_id) and PO line\n inv_lines = []\n for po_line in order.order_line:\n #check if this line have quantity to generate invoice, by johnw\n if po_line.product_qty <= po_line.invoice_qty:\n continue \n# if po_line.product_id:\n# acc_id = po_line.product_id.property_account_expense.id\n# if not acc_id:\n# acc_id = po_line.product_id.categ_id.property_account_expense_categ.id\n# if not acc_id:\n# raise osv.except_osv(_('Error!'), _('Define expense account for this company: \"%s\" (id:%d).') % (po_line.product_id.name, po_line.product_id.id,))\n# else:\n# acc_id = property_obj.get(cr, uid, 'property_account_expense_categ', 'product.category').id \n #use a new method to get the account_id, by johnw \n acc_id = self._get_inv_line_exp_acc_id(cr,uid,order,po_line)\n fpos = order.fiscal_position or False\n acc_id = fiscal_obj.map_account(cr, uid, fpos, acc_id)\n\n inv_line_data = self._prepare_inv_line(cr, uid, acc_id, po_line, context=context)\n #update the quantity to the quantity, by johnw\n inv_line_data.update({'quantity':(po_line.product_qty - po_line.invoice_qty)})\n inv_line_id = inv_line_obj.create(cr, uid, inv_line_data, context=context)\n inv_lines.append(inv_line_id)\n\n po_line.write({'invoiced':True, 'invoice_lines': [(4, inv_line_id)]}, context=context)\n \n #if no lines then return direct, by johnw\n if len(inv_lines) == 0:\n continue\n \n # get invoice data and create invoice\n inv_data = {\n 'name': order.partner_ref or order.name,\n 'reference': order.partner_ref or order.name,\n 'account_id': pay_acc_id,\n 'type': 'in_invoice',\n 'partner_id': order.partner_id.id,\n 'currency_id': order.pricelist_id.currency_id.id,\n 'journal_id': len(journal_ids) and journal_ids[0] or False,\n 'invoice_line': [(6, 0, inv_lines)],\n 'origin': order.name,\n 'fiscal_position': order.fiscal_position.id or False,\n 'payment_term': order.payment_term_id.id or False,\n 'company_id': order.company_id.id,\n }\n inv_id = inv_obj.create(cr, uid, inv_data, context=context)\n\n # compute the invoice\n inv_obj.button_compute(cr, uid, [inv_id], context=context, set_total=True)\n\n # Link this new invoice to related purchase order\n order.write({'invoice_ids': [(4, inv_id)]}, context=context)\n res = inv_id\n return res","def _prepare_invoice(self):\n self.ensure_one()\n journal_id = self.env['account.invoice'].default_get(['journal_id'])['journal_id']\n if not journal_id:\n raise UserError(_('Please define an accounting sales journal for this company.'))\n invoice_vals = {\n 'name': self.client_order_ref or '',\n 'origin': self.name,\n 'type': 'out_invoice',\n 'account_id': self.partner_invoice_id.property_account_receivable_id.id,\n 'partner_id': self.partner_invoice_id.id,\n 'partner_shipping_id': self.partner_shipping_id.id,\n 'journal_id': journal_id,\n 'currency_id': self.pricelist_id.currency_id.id,\n 'comment': self.note,\n 'payment_term_id': self.payment_term_id.id,\n 'fiscal_position_id': self.fiscal_position_id.id or self.partner_invoice_id.property_account_position_id.id,\n 'company_id': self.company_id.id,\n 'user_id': self.user_id and self.user_id.id,\n 'team_id': self.team_id.id,\n 'x_studio_field_rgEdd': self.x_studio_field_icWOZ.id,\n 'x_studio_car_type_1': self.vehicle.id,\n 'x_studio_job_card_1': self.x_studio_agency_job_card,\n 'x_studio_car_type_name': self.vehicle.model_id.name,\n 'x_studio_plate_num': self.vehicle.license_plate,\n 'x_studio_claim_num': self.claim_no,\n\n 'x_studio_is_insured':self.is_insured,\n 'x_studio_service_provider': self.service_advisor.id,\n 'date_invoice': fields.Date.today(),\n 'transaction_ids': [(6, 0, self.transaction_ids.ids)],\n }\n return invoice_vals\n\n # 'x_studio_field_rgEdd':order.x_studio_field_icWOZ.id,","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 _calculate(self):\n source = self.source\n res = {}\n l_cols = [[], [], [], []]\n r_lines = {}\n dateline=None\n ###delete the below code when fetch data from database(assume: data in database has been pretreatment)\n if source[t.ror].min() > -99.0:\n pass\n else:\n source[t.ror] = np.where(\n source[t.ror] > -99.0, source[t.ror], -99.0)\n ###\n for account in self.accounts:\n source_account = source[source[t.account] == account]\n source_account = source_account.reset_index(drop=True)\n dateline=source_account[t.effective_date]\n ror=source_account[t.ror]/100\n returns_cum = ROR.ror_cum_ann(source_account, self.annualized)\n # double_return_cum=round(double_return_cum,2)+1\n returns_cum = returns_cum + 1\n growth_amounts = returns_cum * self.starting_value\n returns_cum, growth_amounts = round(returns_cum - 1, 4), \\\n round(growth_amounts, 2)\n l_cols[0].append(growth_amounts.iloc[-1, 0])#account growth amount\n l_cols[1].append(growth_amounts.iloc[-1, 1])#bench growth amount\n l_cols[2].append(returns_cum.iloc[-1, 0])#account return\n l_cols[3].append(returns_cum.iloc[-1, 1])#bench return\n r_lines[account] = [list(returns_cum.iloc[:,0]), list(growth_amounts.iloc[:, 0]),#list(returns_cum.iloc[:, 0])\n list(growth_amounts.iloc[:, 1])]#account return, account growth amount, bench growth amount\n res['account_vs_benchmark'] = {'xAxis': self.accounts,\n 'series': l_cols}\n res['growth_of_unit'] = {'xAxis': list(dateline),\n 'series': r_lines}\n return res\n # ret_dict = self._ret(accounts, starting_value, source, annualized)\n # return ret_dict","def quick_report(qbo_session, filter_attributes={}, headers=True):\n\n #basically, you can filter on any attribute massage.ledgerize() kicks out\n filterable_attributes = {\n \"TxnDate\":0, \"qbbo_type\":1, \"entity_id\":2, \n \"line_number\":3, \"document_type\":4,\n \"domain\":5, \"user_number\":6,\n \"CreateTime\":7, \"LastUpdatedTime\":8, \"SyncToken\":9, \"Adjustment\":10,\n \"account\":11, \"amount\":13, \"description\":14, \"name\":15,\n \"linked_transactions\":16\n }\n \n fa = copy.deepcopy(filter_attributes)\n\n for a in filter_attributes:\n \n if not a in filterable_attributes:\n raise Exception(\"QuickReport() doesn't know how to filter on\"+\n \" %s. Please use one of:\\n%s\" % \n (a, filterable_attributes))\n \n #yes, we're being permissive\n if isinstance(filter_attributes[a],(int,float,long,str)):\n \n fa[a]=[filter_attributes[a]]\n\n elif isinstance(filter_attributes[a],(list,tuple)):\n \n fa[a]=filter_attributes[a]\n\n else:\n \n raise Exception(\"filter_attributes items must be lists,\" + \\\n \"tuples, or stand-alone values\")\n\n filtered_lines = [qbo_session.ledgerize(\"_\", headers=True)]\n\n for ledger_line in qbo_session.ledger_lines():\n\n #now let's apply the filter, white-list style\n\n for a in fa:\n\n white_list = fa[a]\n\n #sometimes a Line will just HAVE the attribute\n #e.g. a JournalEntry line will always have an account\n #othertimes, we'll have to look it up with a cross reference\n #e.g. an Invoice line will NOT have an account, it'll have\n #an item, so we need to look up the account in the item\n\n #so we're breaking that functionality out into it's own function\n\n i = filterable_attributes[a]\n\n if ledger_line[i] in white_list:\n\n filtered_lines.append(ledger_line)\n\n return filtered_lines","def create_equity_curve_dataframe(self):\n # returns the cumulative product for percent change over every timestamp in the index\n curve = pd.DataFrame(self.all_holdings)\n curve.set_index('datetime', inplace=True)\n curve['returns'] = curve['total'].pct_change()\n #curve['equity_curve'] = (1.0+curve['returns']).cumprod()\n curve['equity_curve'] = curve['returns']\n curve['equity_curve'] += 1\n curve['equity_curve'] = curve['equity_curve'].cumprod()\n self.equity_curve = curve","def pnl(qbo_session, period = \"YEARLY\", start_date=\"first\", end_date=\"last\",\n **kwargs):\n\n pnl_account_types = [\n \n \"Income\", \"Other Income\",\n \"Expense\", \"Other Expense\", \"Cost of Goods Sold\"\n \n ]\n\n \n\n # go through the accounts, collecting a list of those that are \n # pnl accounts\n\n relevant_accounts = []\n\n coa = qbo_session.chart_of_accounts()\n\n AccountType_i = coa[0].index(\"AccountType\")\n fqa_i = coa[0].index(\"FullyQualifiedName\")\n\n for a in coa:\n\n AccountType = a[AccountType_i]\n\n if AccountType in pnl_account_types:\n\n relevant_accounts.append(a[fqa_i])\n \n # now collect the ledger_lines that are even relevant to the time\n # period and pnl accounts (and we'll handle presentation last)\n\n relevant_activity = {} #{account:[relevant lines]}\n\n all_ledger_lines = qbo_session.ledger_lines(None, None, None, True,\n **kwargs)\n\n headers = all_ledger_lines[0]\n\n account_i = headers.index(\"account\") \n amount_i = headers.index(\"amount\")\n date_i = headers.index(\"TxnDate\")\n \n earliest_date = datetime(2100,1,1)\n latest_date = datetime(1900,1,1)\n\n for line in all_ledger_lines[1:]:\n\n account = line[account_i]\n line_date = line[date_i]\n\n #first apply the date filter!\n if not start_date == \"first\" and line_date < start_date:\n continue\n \n if not end_date == \"last\" and line_date > end_date:\n continue\n \n #if it's made the cut, we can update the report date bounds\n earliest_date = min(line_date,earliest_date)\n latest_date = max(line_date,latest_date)\n\n #then apply the account filter!\n\n if not account in relevant_activity:\n #then let's confirm that its account type is a pnl one\n \n if not account in relevant_accounts:\n \n continue\n\n else:\n relevant_activity[account] = []\n\n relevant_activity[account].append(line)\n\n #now let's do presentation\n #TODO -- incorporate pandas tables...do only minimal work on it until then\n\n pnl_lines = []\n\n if period == \"YEARLY\":\n\n report_start_date = datetime(earliest_date.year,1,1)\n report_end_date = datetime(latest_date.year,12,31)\n\n period_start_dates = list(rrule(YEARLY, bymonth=1, bymonthday=1,\n dtstart=report_start_date,\n until=report_end_date))\n\n period_end_dates = list(rrule(YEARLY, bymonth=12, bymonthday=-1,\n dtstart=report_start_date,\n until=report_end_date))\n\n elif period == \"MONTHLY\":\n\n report_start_date = datetime(earliest_date.year,\n earliest_date.month,\n 1)\n report_end_date = datetime(latest_date.year,\n latest_date.month,\n calendar.monthrange(latest_date.year,\n latest_date.month)[1])\n\n period_start_dates = list(rrule(MONTHLY, bymonthday=1,\n dtstart=report_start_date,\n until=report_end_date))\n\n period_end_dates = list(rrule(YEARLY, bymonthday=-1,\n dtstart=report_start_date,\n until=report_end_date)) \n\n header_1 = [\"\", \"Period Start -->\"] + period_start_dates\n header_2 = [\"Account\", \"Period End -->\"] + period_end_dates\n\n pnl_lines.append(header_1)\n pnl_lines.append(header_2)\n\n \"\"\"Clearly, there's a way to do this with only one pass of the data...\n let's get that right in the first re-write...probably with pandas\"\"\"\n\n #now let's fill up the pnl_lines with what we know to be the relevant data\n #for now, we'll rely on the knowledge that the data is coming to us in\n #date order, but that should be fixed too...\n\n for account in relevant_activity:\n\n account_row = [account, \"\"] #one value per period \n\n current_period_index = 0 #primitive counter, yes!\n this_period_total = 0 #this will be this period's total\n\n for line in relevant_activity[account]:\n \n line_amount = line[amount_i]\n line_date = line[date_i] \n\n if line_date > period_end_dates[current_period_index]:\n\n account_row.append(this_period_total)\n this_period_total = line_amount\n current_period_index +=1\n\n else:\n \n this_period_total = round(this_period_total +\n line_amount, 2)\n\n \"\"\"super sloppy...\"\"\"\n account_row.append(this_period_total) #for the last period\n current_period_index +=1\n\n while current_period_index < len(period_end_dates):\n account_row.append(0)\n current_period_index +=1\n\n pnl_lines.append(account_row)\n\n return pnl_lines","def action_generate_lines_txt(self):\n rp_obj = self.env['res.partner']\n voucher_obj = self.env['account.wh.iva']\n txt_iva_obj = self.env['txt.iva.line']\n vouchers = []\n txt_brw = self.browse(self._ids)[0]\n txt_ids = txt_iva_obj.search([('txt_id', '=', txt_brw.id)])\n if txt_ids:\n txt_ids.unlink()\n\n if txt_brw.type:\n vouchers = voucher_obj.search([\n ('date_ret', '>=', txt_brw.date_start),\n ('date_ret', '<=', txt_brw.date_end),\n ('period_id', '=', txt_brw.period_id.id),\n ('state', '=', 'done'),\n ('type', 'in', ['in_invoice', 'in_refund'])])\n else:\n vouchers = voucher_obj.search([\n ('date_ret', '>=', txt_brw.date_start),\n ('date_ret', '<=', txt_brw.date_end),\n ('period_id', '=', txt_brw.period_id.id),\n ('state', '=', 'done'),\n ('type', 'in', ['out_invoice', 'out_refund'])])\n\n for voucher in vouchers:\n acc_part_id = rp_obj._find_accounting_partner(voucher.partner_id)\n for voucher_lines in voucher.wh_lines:\n if voucher_lines.invoice_id.state not in ['open', 'paid']:\n continue\n for voucher_tax_line in voucher_lines.tax_line:\n txt_iva_obj.create(\n {'partner_id': acc_part_id.id,\n 'voucher_id': voucher.id,\n 'invoice_id': voucher_lines.invoice_id.id,\n 'txt_id': txt_brw.id,\n 'untaxed': voucher_tax_line.base,\n 'amount_withheld': voucher_tax_line.amount_ret,\n 'tax_wh_iva_id': voucher_tax_line.id,\n })\n return True","def invoices(self):\r\n return inv.Invoices(self)","def features_orders(df_customers, df_receipts):\n df_customers.sort_values(by=['customerId2'], ascending=[True], inplace=True)\n # total amount of all the orders of a cusrtomer\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')['price'].sum(), 'sum_orders')\n # the min amount paid in one receipt by a customer\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)[\n 'price'].sum().groupby('customerId2').min()['price'], 'min_order')\n # the mean amount paid per receipt by a customer\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\n .groupby('customerId2').mean()['price'], 'mean_order')\n # the max amount paid per receipt by a customer\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\n .groupby('customerId2').max()['price'], 'max_order')\n # the number of orders\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\n .groupby('customerId2').count()['price'], 'count_orders')\n\n # the total amount of items bought by a user\n df_customers = _add_column(df_customers,\n df_receipts.groupby('customerId2')['itemQty'].sum(), 'sum_itemQty')\n # the min amount of items bought by a user in a receipt\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\n .groupby('customerId2').min()['itemQty'], 'min_itemQty')\n # the mean amount of items bought by a user in a receipt\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\n .groupby('customerId2').mean()['itemQty'], 'mean_itemQty')\n # the max amount of items bought by a user in a receipt\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\n .groupby('customerId2').max()['itemQty'], 'max_itemQty')\n # from which dividion type a user buys\n for i in DIVISIONID_VALS:\n k = 'divisionId_' + str(i)\n df_receipts[k] = np.where(df_receipts['divisionId'] == i, 1, 0)\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\n # which source type a user uses to pay\n for i in SOURCEID_VALS:\n k = 'sourceId_' + str(i)\n df_receipts[k] = np.where(df_receipts['sourceId'] == i, 1, 0)\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\n\n logger.info(\"Features from the returns table: {shape} {dtypes}\"\n .format(shape=df_customers.shape, dtypes=df_customers.dtypes))\n return df_customers","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 df(client_ids, start, end):\n obj = search(client_ids, start, end)\n df = DataFrame.from_dict(obj).T\n\n if df.empty:\n return df\n\n df.index.name = 'client_id'\n df = df.rename(columns={ 0: 'inactive', 1: 'active' })\n df['total'] = df.sum(axis=1)\n df = df.fillna(0).astype('int64')\n\n return df","def _prepare_invoice(self):\n self.ensure_one()\n # journal_id = self.env['account.invoice'].with_context(force_company=self.env.user.company_id.id).default_get(['journal_id'])['journal_id']\n journal_id = self.company_id.journal_id.id\n if not journal_id:\n raise UserError(_('Please define an accounting sales journal for this company.'))\n invoice_vals = {\n 'name': self.client_order_ref or '',\n 'origin': self.name,\n 'type': 'out_invoice',\n 'account_id': self.partner_invoice_id.property_account_receivable_id.id,\n 'partner_id': self.partner_invoice_id.id,\n 'partner_shipping_id': self.partner_shipping_id.id,\n 'journal_id': journal_id,\n 'currency_id': self.pricelist_id.currency_id.id,\n 'comment': self.note,\n 'payment_term_id': self.payment_term_id.id,\n 'fiscal_position_id': self.fiscal_position_id.id or self.partner_invoice_id.property_account_position_id.id,\n 'company_id': self.company_id.id,\n 'user_id': self.user_id and self.user_id.id,\n 'team_id': self.team_id.id\n }\n return invoice_vals","def sub_transfer_pricing(manager, df_fields, seq_recs, seq_reservas):\n # df_hotel = manager.get_dataframe(tables[\"dwc_bok_t_canco_hotel\"])\n # df_circuit = manager.get_dataframe(tables[\"dwc_bok_t_canco_hotel_circuit\"])\n # df_other = manager.get_dataframe(tables[\"dwc_bok_t_canco_other\"])\n # df_transfer = manager.get_dataframe(tables[\"dwc_bok_t_canco_transfer\"])\n # df_endow = manager.get_dataframe(tables[\"dwc_bok_t_canco_endowments\"])\n # df_extra = manager.get_dataframe(tables[\"dwc_bok_t_canco_extra\"])\n\n df_aux = df_fields.select(\"operative_incoming\", \"booking_id\", \"invoicing_company\",\n \"creation_date\", \"booking_currency\")\n\n df_hotel = sub_transfer_pricing_aux(manager, df_hotelt, seq_recs, seq_reservas, df_aux)\n df_circuit = sub_transfer_pricing_aux(manager, df_circuitt, seq_recs, seq_reservas, df_aux)\n df_other = sub_transfer_pricing_aux(manager, df_othert, seq_recs, seq_reservas, df_aux)\n df_transfer = sub_transfer_pricing_aux(manager, df_transfert, seq_recs, seq_reservas, df_aux)\n df_endow = sub_transfer_pricing_aux(manager, df_endowt, seq_recs, seq_reservas, df_aux)\n df_extra = sub_transfer_pricing_aux_extra(manager, df_extrat, seq_recs, seq_reservas, df_aux)\n\n df_impuesto_canco = df_hotel.union(df_circuit).union(df_other).union(df_transfer).union(df_endow).union(\n df_extra)\n\n df_impuesto_canco = df_impuesto_canco.groupBy(\"operative_incoming\", \"booking_id\") \\\n .agg({'impuesto_canco': 'sum'}).withColumnRenamed(\"SUM(impuesto_canco)\", \"impuesto_canco\")\n\n df_impuesto_canco = df_impuesto_canco.withColumnRenamed(\"operative_incoming\", \"seq_rec\") \\\n .withColumnRenamed(\"booking_id\", \"seq_res\")\n\n # add impuesto_canco\n df_fields = df_fields.join(df_impuesto_canco, [df_fields.operative_incoming == df_impuesto_canco.seq_rec,\n df_fields.booking_id == df_impuesto_canco.seq_res],\n 'left_outer').drop(\"seq_rec\", \"seq_res\")\n\n df_addcanco = sub_transfer_pricing_aux_add_canco(manager, df_fields, seq_recs, seq_reservas)\n\n df_addcanco = df_addcanco.withColumnRenamed(\"operative_incoming\", \"seq_rec\") \\\n .withColumnRenamed(\"booking_id\", \"seq_res\")\n\n # add add_impuesto_canco\n df_fields = df_fields.join(df_addcanco, [df_fields.operative_incoming == df_addcanco.seq_rec,\n df_fields.booking_id == df_addcanco.seq_res],\n \"left_outer\").drop(df_addcanco.seq_rec).drop(df_addcanco.seq_res)\n\n df_fields = df_fields.na.fill({'impuesto_canco': 0, 'add_impuesto_canco': 0})\n\n df_fields = df_fields.withColumn(\"Transfer_pricing\", df_fields.impuesto_canco + df_fields.add_impuesto_canco) \\\n .drop(\"impuesto_canco\", \"add_impuesto_canco\")\n\n df_fields = df_fields.withColumn(\"Transfer_pricing\", udf_round_ccy(df_fields.Transfer_pricing,\n df_fields.booking_currency))\n\n del df_hotel, df_circuit, df_other, df_transfer, df_endow, df_extra, df_impuesto_canco, df_addcanco, df_aux\n\n return df_fields","def _create_payments(self, invoice):\n self.ensure_one()\n if self.schedule_id and self.schedule_id.occurences > 0:\n # TODO: make more intelligent price cut\n amount = invoice.amount_total\n amount_per_occurence = amount / self.schedule_id.occurences\n for day in self.schedule_id.day_ids:\n payment = self.env['account.payment'].new({\n 'payment_type': 'inbound',\n 'partner_type': 'customer',\n 'partner_id': self.member_id.partner_id.id,\n 'amount': amount_per_occurence,\n 'payment_date': day.day,\n 'journal_id': self.journal_id.id,\n })\n payment._onchange_journal()\n payment_values = dict(payment._cache)\n payment = self.env['account.payment'].create(payment_values)\n payment.invoice_ids = [(4, invoice.id, False)]","def _getdata(self, data):\n lines = []\n start_date = str(data['form']['start_date'])\n end_date = str(data['form']['end_date'])\n department_ids = data['form']['department_ids']\n\n vehicles_ids = self.pool.get('fleet.vehicle').search(self.cr, self.uid,\\\n [('department_id', 'in', department_ids)], context=self.context)\n\n fuel_qty_line_obj = self.pool.get('fuel.qty.line')\n\n sdate = datetime.strptime(start_date, \"%Y-%m-%d\")\n syear = sdate.year\n smonth = sdate.month\n edate = datetime.strptime(end_date, \"%Y-%m-%d\")\n eyear = edate.year\n emonth = edate.month\n\n fuel_qty_line_ids = fuel_qty_line_obj.search(self.cr, self.uid,\\\n [('vehicles_id', 'in', vehicles_ids)], context=self.context)\n\n\n\n counter = 1\n for qty_line in fuel_qty_line_obj.browse(self.cr, self.uid, \\\n fuel_qty_line_ids, context=self.context):\n current_m = int(qty_line.month)\n current_y = int(qty_line.year)\n start = current_m >= smonth and current_y >= syear\n end = current_m <= emonth and current_y <= eyear\n if start and end:\n line = {'type':str(counter)+\" : \"+\\\n qty_line.vehicles_id.type.name}\n line['vehicle_no'] = qty_line.vehicles_id.vin_sn\n line['spent'] = qty_line.spent_qty\n line['counter_no'] = str(qty_line.vehicles_id.odometer)+\" \"+\\\n qty_line.vehicles_id.odometer_unit\n line['date'] = qty_line.month+\"/\"+qty_line.year\n lines.append(line)\n counter += 1\n return lines","def invoice_onsettled(invoice):\n\n db = current.db\n s3db = current.s3db\n\n # Look up claim, invoice number, program and billing\n btable = s3db.fin_voucher_billing\n ctable = s3db.fin_voucher_claim\n itable = s3db.fin_voucher_invoice\n ptable = s3db.fin_voucher_program\n join = [ptable.on(ptable.id == ctable.program_id),\n btable.on(btable.id == ctable.billing_id),\n itable.on(itable.id == ctable.invoice_id),\n ]\n query = (ctable.invoice_id == invoice.id) & \\\n (ctable.deleted == False)\n row = db(query).select(ctable.id,\n ctable.program_id,\n ctable.billing_id,\n ctable.pe_id,\n btable.date,\n itable.invoice_no,\n ptable.name,\n ptable.organisation_id,\n join = join,\n limitby = (0, 1),\n ).first()\n if not row:\n return\n program = row.fin_voucher_program\n billing = row.fin_voucher_billing\n claim = row.fin_voucher_claim\n invoice_no = row.fin_voucher_invoice.invoice_no\n\n error = None\n\n # Look up the provider organisation\n pe_id = claim.pe_id\n otable = s3db.org_organisation\n provider = db(otable.pe_id == pe_id).select(otable.id,\n otable.name,\n limitby = (0, 1),\n ).first()\n\n from .helpers import get_role_emails\n provider_accountants = get_role_emails(\"PROVIDER_ACCOUNTANT\", pe_id)\n if not provider_accountants:\n error = \"No provider accountant found\"\n\n if not error:\n # Lookup the template variables\n base_url = current.deployment_settings.get_base_public_url()\n appname = current.request.application\n data = {\"program\": program.name,\n \"date\": btable.date.represent(billing.date),\n \"invoice\": invoice_no,\n \"organisation\": provider.name,\n \"url\": \"%s/%s/fin/voucher_claim/%s\" % (base_url, appname, claim.id),\n }\n\n # Send the email notification\n from .notifications import CMSNotifications\n error = CMSNotifications.send(provider_accountants,\n \"InvoiceSettled\",\n data,\n module = \"fin\",\n resource = \"voucher_invoice\",\n )\n if error:\n msg = \"%s could not be notified about invoice settlement: %s\"\n current.log.error(msg % (provider.name, error))\n else:\n msg = \"%s notified about invoice settlement\"\n current.log.debug(msg % provider.name)","def generate_agreement_orders(self, start_date, end_date):\n self.ensure_one()\n if not self.active:\n return\n lines_to_order = {}\n exp_date = fields.Date.from_string(self.next_expiration_date)\n if exp_date < end_date and self.prolong != 'unlimited':\n end_date = exp_date\n for line in self.agreement_line:\n # Check if there is any agreement line to order\n if not line.active_chk:\n continue\n # Check future orders for this line until end_date\n next_order_date = self._get_next_order_date(line, start_date)\n while next_order_date <= end_date:\n # Add to a list to order all lines together\n if not lines_to_order.get(next_order_date):\n lines_to_order[next_order_date] = self.env[\n 'sale.recurring_orders.agreement.line']\n lines_to_order[next_order_date] |= line\n next_order_date = self._get_next_order_date(\n line, next_order_date)\n # Order all pending lines\n dates = lines_to_order.keys()\n dates.sort()\n for date in dates:\n # Check if an order exists for that date\n order = self.order_line.filtered(\n lambda x: (\n fields.Date.to_string(\n fields.Datetime.from_string(x.date_order)) ==\n fields.Date.to_string(date)))\n if not order:\n # create it if not exists\n self.create_order(\n fields.Date.to_string(date), lines_to_order[date])","def get_invoices(self, since, until):\n return self._request('getInvoices', data={\n 'date_from': since.strftime('%d/%m/%Y'),\n 'date_to': until.strftime('%d/%m/%Y')\n })","def duplicate_invoice(invoice):\n from invoicer.models import Invoice\n from invoicer.models import LineItem\n\n # copy main attributes\n new_invoice = Invoice(\n company=invoice.company,\n invoice_date=datetime.now(),\n client=invoice.client,\n location=invoice.location,\n tax_rate=invoice.tax_rate,\n left_address=invoice.left_address,\n right_address=invoice.right_address,\n terms=invoice.terms,\n footer=invoice.footer\n )\n new_invoice.save()\n\n # now line items\n for line_item in invoice.line_items.all():\n new_invoice.line_items.add(LineItem(\n name=line_item.name,\n description=line_item.description,\n price=line_item.price,\n taxable=line_item.taxable,\n item=line_item.item,\n quantity=line_item.quantity\n ))\n\n return new_invoice","def _prepare_invoice_line(self, cr, uid, group, picking, move_line, invoice_id,\n invoice_vals, context=None):\n result = super(stock_picking, self)._prepare_invoice_line(cr, uid, group, picking, move_line, invoice_id,\n invoice_vals, context=None)\n \n result['discount'] = self._get_discount_invoice(cr, uid, move_line)\n result['discount2'] = self._get_discount2_invoice(cr, uid, move_line)\n result['price_unit'] = self._get_price_unit_invoice(cr, uid, move_line, invoice_vals['type'], context=None)\n return result","def invoices_in_route(self):\n from invoicing.models import Invoice\n invoices = Invoice.objects.filter(\n route=self.number, print_date__range=(date.today() - timedelta(6), date.today()),\n canceled=False).count()\n return invoices","def get_historic_rates(cls, client, product, start_date, end_date,\n granularity=1):\n startDate = dt.datetime.strptime(start_date, \"%Y-%m-%d\")\n startDateTimestamp = startDate.timestamp()\n endDate = dt.datetime.strptime(end_date, \"%Y-%m-%d\")\n endDateTimestamp = endDate.timestamp()\n\n # List of time divisions for retrieving data.\n timeRange = range(int(startDateTimestamp), int(endDateTimestamp),\n 200 * granularity)\n timeRange = list(timeRange) + [endDateTimestamp]\n\n # New DataFrame.\n columns = ['time', 'low', 'high', 'open', 'close', 'volume']\n data = pd.DataFrame(columns=columns)\n\n # Populating dataframe.\n for i in tqdm(range(len(timeRange) - 1)):\n try:\n data = cls.append_data(client, data, product, columns,\n timeRange[i], timeRange[i + 1])\n except ValueError:\n time.sleep(3)\n data = cls.append_data(data, columns, product,\n timeRange[i], timeRange[i + 1])\n\n # Reindexing dataframe.\n data['time'] = data.time.apply(dt.datetime.fromtimestamp)\n data.set_index('time', inplace=True)\n\n # Using data points where the price has changed.\n data = data.where(data.close != data.close.shift()).dropna().sort_index()\n\n return data","def execute(filters=None):\n\tcolumns, data = [\n\t\t\"Accident:Link/Accident:150\",\n\t\t\"Victim:Data:200\",\n\t\t\"Nature of Accident:Data:200\",\n\t\t\"Fire Extinguisher Used:Link/Fire Extinguisher:150\",\n\t\t\"Fire Extinguisher Location: Data:150\",\n\t\t\"Reported By:Data:100\",\n\t\t\"Reported Date:Data:100\"\n\t], []\n\n\tconditions = \"tabAcc.fire_related = 1\" # Only Fire related accidents\n\n\tif filters.get('modified_from') and filters.get('modified_to'):\n\t\tconditions += ' and tabAcc.prepared_date BETWEEN \\'{0}\\' and \\'{1}\\''.format(filters.get('modified_from'),filters.get('modified_to'))\n\tif filters.get('fire_extinguisher'):\n\t\tconditions += ' and tabAcc.fire_extinguisher_used = \\'{0}\\''.format(filters.get('fire_extinguisher'))\n\tif filters.get('nature_of_accident'):\n\t\tconditions += ' and tabAcc.type_of_accident = \\'{0}\\''.format(filters.get('nature_of_accident'))\n\tif filters.get('reported_by'):\n\t\tconditions += ' and tabAcc.prepared_by = \\'{0}\\''.format(filters.get('reported_by'))\n\n\tsql = '''select tabAcc.name accident,tabAcc.employee_name victim, tabAcc.type_of_accident nature_of_accident , tabAcc.fire_extinguisher_used fire_extinguisher_used,\n\ttabFire.location fire_extinguisher_used, tabAcc.prepared_by reported_by,tabAcc.prepared_date reported_date from `tabFire Extinguisher` tabFire LEFT JOIN `tabAccident` tabAcc\n\tON (tabFire.name = tabAcc.fire_extinguisher_used) WHERE {0}'''\n\n\tfrappe.errprint(sql.format(conditions))\n\tdata = frappe.db.sql(sql.format(conditions))\n\treturn columns,data","def generate_orders(self, cr, uid, ids, context=None):\n voucher_pool = self.pool.get('account.voucher')\n payment_term_obj = self.pool.get('account.payment.term')\n account_budget_confirmation_obj = self.pool.get('account.budget.confirmation')\n period_obj = self.pool.get('account.period')\n if context is None:\n context = {}\n for order in self.browse(cr, uid, ids, context=context):\n #################################to remind\n total_fixed = total_percent = 0\n for line in order.payment_term.line_ids:\n if line.value == 'fixed':\n total_fixed += line.value_amount\n if line.value == 'procent':\n total_percent += line.value_amount\n total_fixed = (total_fixed * 100) / (order.amount or 1.0)\n if (total_fixed + total_percent) > 100:\n raise orm.except_orm(_('Error!'), _(\"Can not create the payments !\\n\\\n The related payment term is probably miss configured as it gives a computed amount greater than the total permanent payment amount. \\\n The latest line of your payment term must be of type 'balance' to avoid rounding issues.\"))\n # create one move line for the total and possibly adjust the other lines amount\n totlines1 = []\n for o in order.line_ids:\n totlines1 += payment_term_obj.compute(cr, uid, order.payment_term.id, o.amount, order.date or False, context=context)\n \n d = {}\n for k, v in totlines1:\n d.setdefault(k, [k]).append(v)\n totlines = map(tuple, d.values())\n\n for t in totlines :\n #to substract date from the interval number \n order_date = t[0]\n entered_date = datetime.datetime.strptime(order_date, '%Y-%m-%d')\n entered_date = entered_date.date()\n account_id = (order.partner_id.property_account_payable and order.partner_id.property_account_payable.id) or \\\n (order.journal_id.default_credit_account_id and order.journal_id.default_credit_account_id.id)\n period_id = period_obj.find(cr, uid, t[0], context=context)[0]\n\n list_confirm = [conf.id for conf in o.confirmation_ids]\n confirmations = account_budget_confirmation_obj.search(cr, uid, [('id','in', list_confirm),('period_id','=', period_id)], context=context) #('date','=',t[0]),\n\n for confirm in confirmations:\n confirm_id = confirm\n\n voucher_lines = [(0, 0, {'name':ol.name, 'account_id':ol.account_id.id, 'type':'dr',\n 'amount':t[count + 1], 'account_analytic_id':ol.account_analytic_id.id, 'budget_confirm_id': confirm_id })\n for count, ol in enumerate(order.line_ids)]\n res = voucher_pool.onchange_price(cr, uid, 0, voucher_lines, [], partner_id=order.partner_id.id, context=context).get(\"value\", {})\n voucher_dict = {\n 'partner_id' : order.partner_id.id,\n 'account_id': account_id,\n 'company_id' : order.company_id.id,\n 'journal_id' : order.journal_id.id,\n 'period_id': order.period_id.id,\n 'type':'purchase',\n 'date' : t[0],\n 'reference': order.name,\n 'payment_permanent_voucher_id': order.id,\n 'line_ids':voucher_lines,\n 'amount':res.get(\"amount\", 0.0)\n }\n voucher_pool.create(cr, uid, voucher_dict, context=context)\n return self.write(cr, uid, ids, {'state':'done'}, context=context)","def process_ticket_data():\r\n c = conn.cursor()\r\n c.execute('Select Count(*) from raw_ticket_data')\r\n totalleft = c.fetchone()[0]\r\n print('{} total rows required'.format(totalleft))\r\n np.random.seed(1)\r\n df_total = pd.read_sql_query('Select Ticketnumber, TickIssueDate, TickIssueTime, ViolationDesc, '\r\n ' VehMake, TickRPPlate, TickStreetNo, TickMeter, Agency, TickBadgeIssued, '\r\n 'TickStreetName , TotalPaid, TotalAmtDue from raw_ticket_data ', conn)\r\n columnlist = df_total.columns.tolist()\r\n df_total.sort_values(by = 'TickIssueDate', inplace = True)\r\n n = 500000\r\n totalsize = df_total.shape[0]\r\n indexes = [i for i in range(0,totalsize, n)]\r\n columnlist = df_total.columns.tolist()\r\n columnlist.append('address')\r\n tqdm.pandas()\r\n j = 1\r\n for i in indexes:\r\n df = df_total[i:i+n]\r\n print('Iteration {} started at {}. {} records left'.format(j, dt.datetime.now().strftime(\"%H:%M\"), totalsize))\r\n df['TickStreetNo'] = df['TickStreetNo'].apply(return_num)\r\n df['ViolationDesc'] = df['ViolationDesc'].apply(lambda x: x.replace('METER DTN','MTR OUT DT'))\r\n df['TickStreetName'] = df['TickStreetName'].apply(replace_street)\r\n df['TickStreetName'] = df['TickStreetName'].apply(return_street)\r\n df['TotalPaid'] = df['TotalPaid'].apply(return_cost)\r\n df['TotalAmtDue'] = df['TotalAmtDue'].apply(lambda x: re.sub('[^1-9]', '', str(x)))\r\n df['TickRPPlate'] = df['TickRPPlate'].apply(lambda x: 'None' if len(re.findall('[\\w+]', str(x))) == 0 else str(x).replace('[^\\w+]', ''))\r\n df['Tdelt'] = df['TickIssueTime'].apply(return_time_delta)\r\n\r\n\r\n df_1 = df.merge(single_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\r\n df_2 = df.merge(double_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\r\n\r\n df_2 = df_2.merge(df_1, how = 'left', left_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'], right_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'])\r\n df_3 = df_2[pd.isnull(df_2['Tdelt_y'])]\r\n df_2.dropna(subset = ['Tdelt_y'], inplace = True)\r\n df_2['timedelta'] = df_2.apply(lambda x: np.abs(x['Tdelt_y'] - x['Tdelt_x']), axis = 1)\r\n df_2.sort_values(by = 'timedelta', inplace = True)\r\n\r\n df_2.columns = [col.replace('_x', '') for col in df_2.columns]\r\n df_3.columns = [col.replace('_x', '') for col in df_3.columns]\r\n df_2.drop_duplicates(subset = 'TicketNumber', inplace = True)\r\n print(\"Searching for unmatchable addresses\")\r\n df_3['address'] = df_3.progress_apply(return_address, axis = 1)\r\n\r\n df = df_1.append(df_2)\r\n df = df.append(df_3)\r\n df['TickIssueDate'] = df.apply(Time, axis = 1)\r\n df = df[columnlist]\r\n\r\n if i == 0:\r\n df.to_sql('ticket_data', if_exists = 'replace',con = conn)\r\n else:\r\n df.to_sql('ticket_data', if_exists = 'append',con = conn)\r\n\r\n totalsize -= n\r\n j+=1\r\n\r\n del c\r\n\r\n return","def compute_counterpart_lines(self):\n for item in self:\n move_debit_lines = []\n move_credit_lines = []\n\n # list of all the move lines of the payment's move\n line_list = []\n for entry in item.journal_entry_ids:\n for line in entry.line_ids:\n if line.account_id.treasury_planning:\n line_list.append(line)\n\n # for each line above collect all the reconciled counterpart lines\n for line in line_list:\n if line.credit > 0 and line.debit == 0:\n for match in line.matched_debit_ids:\n move_debit_lines.append(match.debit_move_id.id)\n\n if line.credit == 0 and line.debit > 0:\n for match in line.matched_credit_ids:\n move_credit_lines.append(match.credit_move_id.id)\n\n if move_credit_lines:\n counterpart_move_ids = move_credit_lines\n else:\n counterpart_move_ids = move_debit_lines\n\n # bank move share is transformed to dictionary\n bank_move_dict = (ast.literal_eval(item.cf_share) if\n item.cf_share else {})\n\n # the share of each counterpart line is \"merged or added\"\n # in a weighted manner to the bank line share\n for cpt in counterpart_move_ids:\n dest_move_line = self.env['account.move.line'].browse(cpt)\n weight = round(dest_move_line.balance / item.amount, 2)\n # counterpart share is transformed into dictionary\n move_line_dict = ast.literal_eval(dest_move_line.cf_share)\n\n # each key is finally added to the bank line share\n for key, value in move_line_dict.iteritems():\n draft_dictionary = dictop.sum_dictionary(\n bank_move_dict.get(key, {}), 1,\n move_line_dict.get(key, {}), weight)\n bank_move_dict[key] = dictop.check_dict_total(\n draft_dictionary, 1)\n\n # the dictionary is transformed into string and assigned\n item.cf_share = json.dumps(bank_move_dict)","def _prepare_analytic_line(self, cr, uid, obj_line, context=None):\n return {'name': obj_line.name,\n 'date': obj_line.date,\n 'account_id': obj_line.analytic_account_id.id,\n 'unit_amount': obj_line.quantity,\n 'product_id': obj_line.product_id and obj_line.product_id.id or False,\n 'product_uom_id': obj_line.product_uom_id and obj_line.product_uom_id.id or False,\n 'amount': (obj_line.credit or 0.0) - (obj_line.debit or 0.0),\n 'general_account_id': obj_line.account_id.id,\n 'journal_id': obj_line.journal_id.analytic_journal_id.id,\n 'ref': obj_line.ref,\n 'move_id': obj_line.id,\n 'user_id': uid,\n }","def _prepare_order_line_invoice_line(self, cr, uid, line, account_id=False, context=None):\n res = super(sale_order_line, self)._prepare_order_line_invoice_line(cr, uid, line, account_id=account_id, context=context)\n \n res.update({'part_number': line.part_number, 'internal_part_number' : line.internal_part_number})\n return res","def _prepare_invoice_line(self, qty):\n self.ensure_one()\n res = super(SaleOrderLine, self)._prepare_invoice_line(qty)\n\n res.update({\n 'cost_center_id': self.cost_center_id and self.cost_center_id.id or False\n })\n return res","def billing_history(cls, user=None):\n invoices = Invoice.query.filter(Invoice.user_id == user.id) \\\n .order_by(Invoice.created_on.desc()).limit(12)\n\n return invoices","def _initialize_df(self, df):\n df['values'] = (self.tc.instrument_returns['cumulative'] *\n self.tc.starting_cash).mul(self.target_weights, axis=1).values * (1 - self.tc.commission)\n df['allocations'] = self.df['values'].div(df['values'].sum(axis=1), axis=0)\n df['returns'] = (df['values'].sum(axis=1)).pct_change(1).fillna(0)","def generate_pr_table(start_ref, end_ref):\r\n header = \"|| Merged By || Author || Title || PR || JIRA || Verified? ||\"\r\n pr_link = \"[#{num}|https://github.com/edx/edx-platform/pull/{num}]\"\r\n user_link = \"[@{user}|https://github.com/{user}]\"\r\n rows = [header]\r\n prbe = prs_by_email(start_ref, end_ref)\r\n for email, pull_requests in prbe.items():\r\n for i, pull_request in enumerate(pull_requests):\r\n try:\r\n pr_info = get_pr_info(pull_request)\r\n title = pr_info[\"title\"] or \"\"\r\n body = pr_info[\"body\"] or \"\"\r\n author = pr_info[\"user\"][\"login\"]\r\n except requests.exceptions.RequestException as e:\r\n message = (\r\n \"Warning: could not fetch data for #{num}: \"\r\n \"{message}\".format(num=pull_request, message=e.message)\r\n )\r\n print(colorize(\"red\", message), file=sys.stderr)\r\n title = \"?\"\r\n body = \"?\"\r\n author = \"\"\r\n rows.append(\"| {merged_by} | {author} | {title} | {pull_request} | {jira} | {verified} |\".format(\r\n merged_by=email if i == 0 else \"\",\r\n author=user_link.format(user=author) if author else \"\",\r\n title=title.replace(\"|\", \"\\|\"),\r\n pull_request=pr_link.format(num=pull_request),\r\n jira=\", \".join(parse_ticket_references(body)),\r\n verified=\"\",\r\n ))\r\n return \"\\n\".join(rows)","def _get_report_data(self, request, queryset):\n first_item = queryset[0]\n data = {\n 'id': str(slugify(first_item.invoice_no)),\n 'property_of_id': (\n first_item.property_of.id\n if first_item.property_of else None\n ),\n 'model': queryset.model._meta.model_name,\n 'base_info': {\n 'invoice_no': first_item.invoice_no,\n 'invoice_date': first_item.invoice_date,\n 'provider': first_item.provider,\n 'datetime': datetime.datetime.now().strftime(\n self._invoice_report_datetime_format\n ),\n },\n 'items': list(map(self._parse_item, queryset)),\n 'sum_price': str(\n queryset.aggregate(\n Sum(self._price_field)\n ).get('{}__sum'.format(self._price_field))\n )\n }\n logger.info('Invoice report data: {}'.format(data))\n return data","def add_invoice() -> str:\r\n invoice_details = []\r\n #Catching values user has entered in UI\r\n invoice_number = request.args.get(\"invoice_number\")\r\n invoice_details.append(invoice_number)\r\n customer = request.args.get(\"customer\")\r\n invoice_details.append(customer)\r\n date_required = request.args.get(\"date_required\")\r\n invoice_details.append(date_required)\r\n recipe = request.args.get(\"recipe\")\r\n invoice_details.append(recipe)\r\n gyle_number = request.args.get(\"gyle_number\")\r\n invoice_details.append(gyle_number)\r\n quantity_ordered = request.args.get(\"quantity_ordered\")\r\n invoice_details.append(quantity_ordered)\r\n #Passing list to function which writes list to CSV file\r\n data_add(invoice_details)\r\n invoice_message = \"INVOICE ADDED\"\r\n return render_template(\"singular_message.html\",\r\n user_display=invoice_message)","def create_or_find_b2b_invoices_and_process_ept(self, row, sale_order, invoice_date, tax):\n\n vat_number = row.get('Buyer Tax Registration', False)\n invoice_number = row.get('VAT Invoice Number', False)\n\n invoices = sale_order.invoice_ids.filtered(\n lambda x: x.type == 'out_invoice' and x.state != 'cancel')\n if not invoices:\n lines = sale_order.order_line.filtered(lambda line: line.qty_to_invoice > 0)\n if not lines:\n return False\n invoices = sale_order._create_invoices()\n self.write({'invoice_ids': [(4, invoices and invoices.id)]})\n\n for invoice in invoices:\n if not invoice.partner_id.vat:\n invoice.partner_id.vat = vat_number\n\n payments_lines = []\n if invoice.invoice_payments_widget != 'false':\n payments_dict = json.loads(invoice.invoice_payments_widget)\n payments_content = payments_dict.get('content', [])\n for line in payments_content:\n payments_lines.append(line.get('payment_id', False))\n\n invoice_line = invoice.mapped('invoice_line_ids').filtered(\\\n lambda line: line.tax_ids != tax)\n if invoice_line:\n invoice.button_draft()\n invoice.write({'ref': invoice_number, 'date': invoice_date})\n\n if len(invoice_line) > 1:\n for line in invoice_line:\n line.with_context({'check_move_validity': False}).write( \\\n {'tax_ids': [(6, 0, [tax.id])]})\n else:\n invoice_line.with_context({'check_move_validity': False}).write( \\\n {'tax_ids': [(6, 0, [tax.id])]})\n\n invoice.with_context({'check_move_validity': False})._recompute_tax_lines( \\\n recompute_tax_base_amount=True)\n invoice.action_post()\n for line in payments_lines:\n invoice.js_assign_outstanding_line(line)\n\n return True","def get_invoice(self):\n\n # Check if unclosed invoice for the client exists\n old_inv = connection.Kinko.find_one({'cl': self.cl, 'tid': None,\n 'typ': TYPE_MAP[self.tab_type]})\n\n inv_num = None\n # If it does, update its values and update packages\n if old_inv:\n old_inv.dt = datetime.datetime.today()\n old_inv.range.lt = self.q_dict[\"cs.sd\"].get(\"$lt\", None)\n old_inv.save()\n\n inv_num = old_inv.num\n\n else:\n #kinko dict to be updated in Kinko Collection.\n kdict = {\n \"amt\": 0.0,\n \"cl\": unicode(self.cl),\n \"dt\": datetime.datetime.today(),\n \"typ\": TYPE_MAP[self.tab_type],\n \"range\": {\"lt\": self.q_dict[\"cs.sd\"].get(\"$lt\", None),\n \"gt\": self.q_dict[\"cs.sd\"].get(\"$gte\", None),\n }\n }\n\n k = Kinko(kdict)\n\n k_count = 1\n\n #the get num method of Kinko model generates the unique no for new kinko\n k[\"num\"] = self.get_knum(1)\n while connection.Kinko.collection.find({\"num\": k.num}).count() > 0:\n k[\"num\"] = self.get_knum(k_count+1)\n k_count += k_count\n\n connection.Kinko(k).save()\n\n inv_num = k['num']\n\n if inv_num:\n #after creating a new document in Kinko all packages are updated.\n connection.Package.collection.update(self.q_dict, {'$set': {'inv.num': inv_num}}, safe=True, multi=True)\n \n #Aggrigation of remitted amount for requested client\n non_invoiced = kinko_map_reduce(inv_num, TYPE_MAP[self.tab_type])\n\n if len(non_invoiced) == 0:\n return False\n else:\n inv = connection.Kinko.find_one({'num': inv_num})\n if inv:\n inv.amt = non_invoiced[0]['value']['amt']\n inv.save()\n return inv\n else:\n return False\n else:\n return False","async def daily(self, ctx):\r\n # TODO: Asssess whether this can be cleaned up. \r\n # As it stands, very similar to inv()\r\n author = ctx.author\r\n with DB() as db:\r\n company = await self.get_active_company(ctx, db, author)\r\n stock = self.iex.get_held_stocks(db, company.id)\r\n inventory = []\r\n for s in stock:\r\n close = await self.get_latest_close(ctx, db, s.symbol)\r\n inventory.append([s.symbol, s.quantity, s.purchase_price, close.close, s.quantity*close.close - s.quantity*s.purchase_price ]) \r\n inv_df = pd.DataFrame(inventory, columns=['Symbol', 'Quantity', 'Purchase Price', 'Close', 'Current Value'])\r\n inv_df['sign'] = np.where(inv_df['Current Value']>=0, '+', '-')\r\n inv_df['%'] = abs(((inv_df['Close'] - inv_df['Purchase Price']) / inv_df['Purchase Price']) * 100)\r\n inv_df['%'] = inv_df['%'].round(1)\r\n inv_df = inv_df.sort_values(['Symbol'])\r\n inv_df = inv_df[['sign', '%', 'Symbol', 'Quantity', 'Purchase Price', 'Close', 'Current Value']]\r\n aggregated = tabulate(inv_df.values.tolist(), headers=['Δ', '%', 'Symbol', 'Quantity', 'Purchase Price', 'Close', 'Current Value'])\r\n await ctx.send(f'```diff\\n{aggregated}```')","def records(self, variables=None, **kwargs):\n # Get necessary data for the variables\n all_df = self._data.records_all().set_index(self.DATE)\n variables = self._convert_variables(variables, all_df.columns.tolist())\n df = all_df.loc[:, variables]\n # Figure\n if self._data.complemented:\n title = f\"{self.area}: Cases over time\\nwith {self._data.complemented}\"\n else:\n title = f\"{self.area}: Cases over time\"\n self.line_plot(df=df, title=title, y_integer=True, **kwargs)\n return df.reset_index()","def action_invoice_create(self, cr, uid, ids, context=None):\n res = False\n\n journal_obj = self.pool.get('account.journal')\n inv_obj = self.pool.get('account.invoice')\n inv_line_obj = self.pool.get('account.invoice.line')\n fiscal_obj = self.pool.get('account.fiscal.position')\n property_obj = self.pool.get('ir.property')\n\n for order in self.browse(cr, uid, ids, context=context):\n pay_acc_id = order.partner_id.property_account_payable.id\n journal_ids = journal_obj.search(cr, uid, [('type', '=','purchase'),('company_id', '=', order.company_id.id)], limit=1)\n if not journal_ids:\n raise osv.except_osv(_('Error !'),\n _('There is no purchase journal defined for this company: \"%s\" (id:%d)') % (order.company_id.name, order.company_id.id))\n\n # generate invoice line correspond to PO line and link that to created invoice (inv_id) and PO line\n inv_lines = []\n for po_line in order.order_line:\n if po_line.product_id:\n acc_id = po_line.product_id.product_tmpl_id.property_account_expense.id\n if not acc_id:\n acc_id = po_line.product_id.categ_id.property_account_expense_categ.id\n if not acc_id:\n raise osv.except_osv(_('Error !'), _('There is no expense account defined for this product: \"%s\" (id:%d)') % (po_line.product_id.name, po_line.product_id.id,))\n else:\n acc_id = property_obj.get(cr, uid, 'property_account_expense_categ', 'product.category').id\n fpos = order.fiscal_position or False\n acc_id = fiscal_obj.map_account(cr, uid, fpos, acc_id)\n\n inv_line_data = self._prepare_inv_line(cr, uid, acc_id, po_line, context=context)\n inv_line_id = inv_line_obj.create(cr, uid, inv_line_data, context=context)\n inv_lines.append(inv_line_id)\n\n po_line.write({'invoiced':True, 'invoice_lines': [(4, inv_line_id)]}, context=context)\n\n # get invoice data and create invoice\n inv_data = {\n 'name': order.partner_ref or order.name,\n 'reference': order.partner_ref or order.name,\n 'account_id': pay_acc_id,\n 'type': 'in_invoice',\n 'partner_id': order.partner_id.id,\n 'currency_id': order.pricelist_id.currency_id.id,\n 'address_invoice_id': order.partner_address_id.id,\n 'address_contact_id': order.partner_address_id.id,\n 'journal_id': len(journal_ids) and journal_ids[0] or False,\n 'invoice_line': [(6, 0, inv_lines)], \n 'origin': order.name,\n 'fiscal_position': order.fiscal_position.id or order.partner_id.property_account_position.id,\n 'payment_term': order.partner_id.property_payment_term and order.partner_id.property_payment_term.id or False,\n 'company_id': order.company_id.id,\n 'add_disc': order.add_disc or 0.0\n }\n inv_id = inv_obj.create(cr, uid, inv_data, context=context)\n\n # compute the invoice\n inv_obj.button_compute(cr, uid, [inv_id], context=context, set_total=True)\n\n # Link this new invoice to related purchase order\n order.write({'invoice_ids': [(4, inv_id)]}, context=context)\n res = inv_id\n return res","def GenerateVersion(self):\n pol_obj = self.env['account.analytic.account']\n hist_obj = self.env['account.analytic.account']\n inv_obj = self.env['account.invoice']\n values = self.GeneratePolicy()\n # logger.info('\\n === values = %s' % values)\n policies = values.get('policy', [])\n res = []\n i = 0\n for policy in pol_obj.browse(policies):\n i += 1\n logger.info('version %s -> %s (%s / %s)' % (policy.name, policy.id, i, len(policies)))\n # search invoice\n inv_ids = inv_obj.search([('pol_numpol', '=', policy.name), ('id','in', values.get('invoice'))], order='prm_datedeb')\n # logger.info('=== inv_ids = %s' % inv_ids.mapped('prm_datedeb'))\n inv_len = len(inv_ids)\n c = 0\n for inv_id in inv_ids:\n c += 1\n # logger.info('inv_len = %s ?= %s c' % (inv_len,c))\n hist_buf = {\n 'type': 'contract',\n 'is_insurance': True,\n 'partner_id': policy.partner_id.id,\n 'property_account_position': policy.property_account_position.id,\n 'insured_id': policy.insured_id.id,\n 'manager_id': policy.manager_id.id,\n 'branch_id': policy.branch_id.id,\n 'ins_product_id': policy.ins_product_id.id,\n 'fraction_id': policy.fraction_id.id or False,\n #'name': policy.name + '_' + str(c).zfill(4) + 'AA',\n 'name': policy.name + '_' + str(policy.next_sequence).zfill(4) + 'AA',\n 'parent_id': policy.id,\n 'date_start': inv_id.prm_datedeb,\n 'date': inv_id.prm_datefin,\n 'agency_id': inv_id.journal_id.agency_id.id or False,\n 'invoice_id': inv_id.id,\n 'stage_id': self.env.ref('insurance_management.avenant').id\n }\n if c == inv_len:\n hist_buf['is_last_situation'] = True\n hist_ids = hist_obj.search([('name','=', hist_buf.get('name'))])\n if not hist_ids:\n # logger.info('===> create history')\n res.append(hist_obj.create(hist_buf).id)\n next_sequence = policy.next_sequence + 1\n policy.write({'next_sequence': next_sequence})\n else:\n hist_ids.update(hist_buf)\n res += hist_ids.ids\n return res","def income_model_user_defined_returns(num_of_years=30, trials=100, method='normal'):\n\n print(\"Running method income_model_user_defined_returns()\")\n\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\n\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\n\n read_income_inputs = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_model_inputs',\n index_col=[0])\n\n read_returns_est = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='income_assets_returns_estimates',\n index_col=[0])\n clean_names = list(read_returns_est.index)\n clean_names = [s.split(' ')[0] for s in clean_names]\n read_returns_est.loc[:, 'names'] = clean_names\n read_returns_est.set_index('names', drop=True, inplace=True)\n read_returns_est = read_returns_est[:-1]\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\n\n # ---------------Returns DataFrame based on the user input------------------------------------\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\n read_normal.loc[:, 'FIA'] = user_est_fia_return\n\n read_asset_weights = pd.read_excel(src + \"portfolio_information.xlsx\", sheet_name='asset_weights',\n index_col=[0])\n\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\n\n # read random returns for simulation\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\n\n # ann_ret = np.full((num_of_years+1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\n # read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years+1), columns=read_returns_est.index)\n # # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\n # user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\n # read_normal.loc[:, 'FIA'] = user_est_fia_return\n\n assets_col_names = list(read_normal.columns)\n\n tickers = list(read_asset_weights.index)\n wts = np.array(read_asset_weights.loc[:, 'base'])\n\n def asset_median_returns(data, ticker):\n return data.filter(regex=ticker).median(axis=1)\n\n # dataframe for unsorted returns (normal)\n median_returns_normal = read_normal.copy()\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\n\n years = list(range(0, num_of_years + 1))\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\n 'contract_value']\n\n term = int(read_income_inputs.loc['term', 'inputs'])\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\n spread = float(read_income_inputs.loc['spread', 'inputs'])\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\n premium = float(read_income_inputs.loc['premium', 'inputs'])\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\n\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\n social = float(read_income_inputs.loc['social', 'inputs'])\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\n\n # ---------------INCOME MODEL--------------------------------------------\n runs = 0\n returns_dict = {}\n asset_dict = {}\n fia_dict = {}\n\n income_df = pd.DataFrame(index=years, columns=income_cols)\n income_df.loc[:, 'year'] = years\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\n\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\n lambda x: (1 + x) ** (1 / term) - 1)\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\n income_df.loc[:, 'ann_net_spread'], 0)\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\n\n elif counter <= min(bonus_term, income_starts):\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\n (1 + income_growth)\n else:\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\n\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\n\n for counter in years:\n if counter == 0:\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\n\n elif income_df.loc[counter, 'strategy_term'] == 1:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\n income_df.loc[counter, 'contract_value'] = x2\n\n else:\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\n income_df.loc[counter, 'eoy_income']\n\n income_df.loc[counter, 'contract_value'] = x1\n\n # variable stores the income number that is used in the base and fia portfolio calcs.\n\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\n\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\n\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\n\n # --------------------BASE MODEL---------------------------------------------\n base_wts = read_asset_weights.loc[:, 'base']\n base_assets = list(base_wts.index)\n base_weights = list(base_wts.values)\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\n\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\n\n # -------------------required income----------------------------------\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n cpn_income_base = base_investment * wtd_cpn_yield\n\n # ----------------------RANDOM RETURNS--------------------------\n r_cols = base_assets\n boy_value = ['bv_{}'.format(name) for name in base_assets]\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\n\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\n\n for c in range(len(r_cols)):\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\n\n random_returns = read_normal.copy()\n\n base_df = random_returns.copy()\n fia_portfolio_df = random_returns.copy()\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\n cpn_income_port = port_investment * wtd_cpn_yield\n\n # -------------BASE PORTFOLIO----------------------------\n for name in boy_value:\n base_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\n\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'total_net_fees'] = 0.0\n base_df.loc[counter, 'income'] = 0.0\n base_investment = base_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\n counter, 'adv_fees']\n\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\n\n else:\n\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\n income_needed = income_needed + social\n\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\n base_df.loc[counter, 'adv_fees'] - \\\n base_df.loc[counter, 'income']\n\n base_investment = base_df.loc[counter, 'total_net_fees']\n\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\n\n # ----------------------------FIA PORTFOLIO----------------------------------------------\n for name in boy_value:\n fia_portfolio_df.loc[:, name] = 0.0\n\n for counter in years:\n period_returns = list(random_returns.loc[counter, :])\n if counter == 0:\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\n fia_portfolio_df.loc[counter, 'income'] = 0.0\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\n\n elif (counter > 0) and (counter < income_starts):\n\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\n\n else:\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\n for c in range(len(boy_value))]\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\n\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\n # stops from the year income starts. Req. income is reduced by the coupon payments\n\n if counter == income_starts:\n\n income_needed = req_annual_income - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = req_annual_income\n\n else:\n income_needed = income_needed * (1 + inflation) - social\n income_net_fia_income = max(0, income_needed - income_from_fia)\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\n income_needed = income_needed + social\n\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\n fia_portfolio_df.loc[counter, 'income']\n\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\n\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\n income_df.loc[:, 'contract_value']\n\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\n\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\n lambda x: x if x > 0 else 0)\n\n # ---------income breakdown for Base portfolio----------------------------------\n base_df.to_csv(src + 'base_port_detail.csv')\n sim_base_total.to_csv(src + 'base_ending_values.csv')\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\n income_breakdown_base.loc[:, 'social_security_income'] = social\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\n\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_base.loc[:, 'income_from_portfolio'][\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n\n # ---------income breakdown for FIA portfolio----------------------------------\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\n\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\n income_breakdown_port.loc[:, 'social_security_income'] = social\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\n\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\n income_breakdown_port.loc[:, 'income_from_portfolio'][\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\n axis=1)\n\n # ------------Block Ends-------------------------------------------------------------\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\n sim_base_income[sim_base_total < income_needed] = 0.0\n\n sim_port_income[sim_port_total < income_net_fia_income] = 0\n\n sim_port_income = sim_port_income + income_from_fia\n\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\n\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\n\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\n\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\n\n # ----drop year 0--------\n sim_base_total = sim_base_total[1:]\n sim_port_total = sim_port_total[1:]\n\n # ----------------quantile analysis for base terminal value--------------------------\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\n for c in range(len(cols)):\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\n\n base_qcut.clip(lower=0, inplace=True)\n\n # ----------------------quantile analysis for base income----------------------------\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\n for c in range(len(cols)):\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # base_income_qcut = base_income_qcut.loc[income_starts:]\n\n # -------------quantile analysis for portfolio terminal value ----------------\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\n for c in range(len(cols)):\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\n\n port_qcut.clip(lower=0, inplace=True)\n\n # ---------------quantile analysis for portfolio income----------------------------\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\n for c in range(len(cols)):\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\n\n # ----Remove NaN's prior to the income start years------------\n # port_income_qcut = port_income_qcut.loc[income_starts:]\n\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\n trials + 1)\n\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\n index=['base', 'fia_portfolio', 'Notes'],\n columns=['Ruin Probability'])\n\n # -----------Year-wise probability of ending value greater than 0 -----------------\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\n\n # -----------------------WRITING FILES TO EXCEL ---------------------------\n col_names = ['50th', 'age', 'comment']\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\n\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\n\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\n base_qcut.loc[:, 'age'] = age_index\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\n\n base_income_qcut = base_income_qcut.loc[1:, :]\n base_income_qcut.loc[:, 'age'] = age_index\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\n\n port_qcut.loc[:, 'age'] = age_index\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\n\n port_income_qcut = port_income_qcut.loc[1:, :]\n port_income_qcut.loc[:, 'age'] = age_index\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\n\n income_breakdown_base = income_breakdown_base.loc[1:, :]\n income_breakdown_base.loc[:, 'age'] = age_index\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\n\n income_breakdown_port = income_breakdown_port.loc[1:, :]\n income_breakdown_port.loc[:, 'age'] = age_index\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\n\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\n\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\n\n terminal_val.to_excel(writer, sheet_name='terminal_values')\n ending_val.to_excel(writer, sheet_name='port_ending_values')\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\n\n writer.save()\n\n # -----------------Plotting charts--------------------------------------------\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\n plt.savefig(src + \"quantile_terminal_base.png\")\n plt.close('all')\n\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\n plt.savefig(src + \"quantile_income_base.png\")\n plt.close('all')\n\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\n plt.savefig(src + \"success_probabilty_base.png\")\n plt.close('all')\n\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\n plt.savefig(src + \"ruin_probability_base.png\")\n plt.close('all')\n\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\n plt.savefig(src + \"quantile_terminal_fia.png\")\n plt.close('all')\n\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\n plt.savefig(src + \"quantile_income_fia.png\")\n plt.close('all')\n\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\n plt.savefig(src + \"success_probabilty_fia.png\")\n plt.close('all')\n\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\n plt.savefig(src + \"ruin_probability_fia.png\")\n plt.close('all')\n\n print(\"simulation completed for {}\".format(method))","def prepare_invoice(self):\n journal_id = self.env['account.invoice'].default_get(['journal_id'])['journal_id']\n if not journal_id:\n raise UserError(_('Please define sales journal for this company: \"%s\" (id:%d).') % (self.company_id.name, self.company_id.id))\n invoice_vals = {\n 'order_id': self.id,\n 'name': self.order_no,\n 'origin': self.order_no,\n 'type': 'out_invoice',\n 'reference': self.patient_id.name + ':' + self.name,\n 'account_id': self.patient_id.partner_id.property_account_receivable_id.id,\n 'partner_id': self.patient_id.partner_id.id,\n 'journal_id': journal_id,\n 'comment': self.note,\n 'doctor_id': self.doctor_id.id,\n 'payment_term': False,\n 'user_id': False,\n }\n return invoice_vals","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","def __init__(self, numQueues, rate, start_hour, end_hour, appt_low, appt_high):\n\n self.rate = rate\n self.numQueues = numQueues\n self.start = datetime.datetime.combine(datetime.date.today(), datetime.time(start_hour,0,0))\n self.end = datetime.datetime.combine(datetime.date.today(), datetime.time(end_hour,0,0))\n self.appt_low = appt_low\n self.appt_high = appt_high\n minutes_for_new_items = (end_hour-start_hour)*60 #new patients seen between 9AM and 4PM\n time_between_items = rate #exponential dist. time parameter\n self.expected_count = int(np.ceil(stats.poisson.ppf(.9999, minutes_for_new_items/time_between_items)))\n self.ques = [datetime.datetime.combine(datetime.datetime.today(), datetime.time(start_hour,0,0)) for i in range(0, self.numQueues)]\n cols = ['simulation', 'num_items', 'wait_count', 'avg_wait_time', 'close_time']\n self.results = pd.DataFrame(columns = cols)\n return","def run(params, conn, outputfile):\n date_begin = parse(params['date_begin'] + ' 00:00:00 +0700')\n date_end = parse(params['date_end'] + ' 23:59:59 +0700')\n domain_id = params['domain_id']\n authority_ids = params['authority_ids']\n\n covid_report_type_id = fetch_report_type_id(conn, 'surveillance-covid-19', domain_id)\n main_data = fetch_data(conn, date_begin, date_end, authority_ids, domain_id, covid_report_type_id)\n\n covid_report_type_id = fetch_report_type_id(conn, 'surveillance-covid-19-followup', domain_id)\n follow_data = fetch_data(conn, date_begin, date_end, authority_ids, domain_id, covid_report_type_id)\n\n line_list = join(main_data, follow_data)\n tabular(line_list)\n\n if len(line_list) == 0:\n return False\n\n df = pandas.DataFrame(line_list)\n df['date'] = df['date'].dt.tz_convert(tz)\n df['date'] = df['date'].dt.strftime('%d/%m/%Y %H:%M')\n writer = pandas.ExcelWriter(outputfile)\n df.to_excel(writer, 'covid-19', columns=['report_id', 'name', 'gender', 'age',\n 'village_no', 'village', 'tumbols', 'amphurs',\n 'arrival_date_village', 'mobile_phone',\n 'risk_factor', 'symptom_check', 'symptom_covid',\n 'date', 'latitude', 'longitude',\n '01', '02', '03', '04', '05', '06',\n '07', '08', '09', '10', '11', '12', '13', '14'], index=False)\n ldf = pandas.DataFrame(flat(main_data))\n ldf['date'] = ldf['date'].dt.tz_convert(tz)\n ldf.sort_values(by=['date'], inplace=True)\n ldf['date'] = ldf['date'].dt.strftime('%d/%m/%Y %H:%M')\n\n def is_followup(row):\n return row['report_id'] != row['group_id']\n\n ldf['followup'] = ldf.apply(is_followup, axis=1)\n ldf.to_excel(writer,\n 'all',\n columns=['report_id', 'group_id', 'followup', 'name', 'gender', 'age',\n 'village_no', 'village', 'tumbols', 'amphurs',\n 'arrival_date_village', 'mobile_phone',\n 'risk_factor', 'symptom_check', 'symptom_covid',\n 'total_times', 'activity_other',\n 'date', 'latitude', 'longitude'],\n index=False)\n writer.save()\n return True","def action_invoice_create(self, cr, uid, ids, grouped=False, states=None, date_invoice=False, context=None):\n order = self.browse(cr, uid, ids[0], context=context)\n inv_obj = self.pool.get('account.invoice')\n # create the invoice\n inv_id = super(sale_order, self).action_invoice_create(cr, uid, ids, grouped, states, date_invoice, context=context)\n # modify the invoice\n inv_obj.write(cr, uid, [inv_id], {'past_doc': order.past_doc})\n return inv_id","def get_rows(self):\n rows = []\n common_fields = self.get_common_data()\n\n p_identities = self.data['participantIdentities']\n p_data = self.data['participants']\n winning_team = self.get_winning_team()\n items_purchased = self.get_items_purchased()\n team_champions = self.get_team_champions()\n teams = set(team_champions.keys())\n gold_per_player = self.get_gold_per_player()\n xp_per_player = self.get_xp_per_player()\n gold_diff = self.get_gold_diff()\n xp_diff = self.get_xp_diff()\n dmg_taken_diff = self.get_dmg_taken_diff()\n\n for p in p_identities:\n p_id = int(p['participantId'])\n p_idx = p_id - 1\n team_id = p_data[p_idx]['teamId']\n opposing_team_id = (teams - {team_id}).pop()\n player_purchases = items_purchased[p_idx]\n purchase_list = [item_pair[0] for item_pair in player_purchases]\n items_10min = {\n item_pair[0] for item_pair in player_purchases\n if item_pair[1] < 1000*60*10\n }\n cur_row = {\n 'summonerId': p['player']['summonerId'],\n\n 'allyChampions': team_champions[team_id],\n 'championId': p_data[p_idx]['championId'],\n 'gold5': gold_per_player[p_idx].get(5, None),\n 'gold10': gold_per_player[p_idx].get(10, None),\n 'gold15': gold_per_player[p_idx].get(15, None),\n 'gold20': gold_per_player[p_idx].get(20, None),\n 'xp5': xp_per_player[p_idx].get(5, None),\n 'xp10': xp_per_player[p_idx].get(10, None),\n 'xp15': xp_per_player[p_idx].get(15, None),\n 'xp20': xp_per_player[p_idx].get(20, None),\n 'items10': list(items_10min),\n 'opponentChampions': team_champions[opposing_team_id],\n 'purchases': purchase_list,\n 'lane': p_data[p_idx]['timeline']['lane'],\n 'role': p_data[p_idx]['timeline']['role'],\n 'xpDiff10': xp_diff[p_idx],\n 'damageTakenDiff10': dmg_taken_diff[p_idx],\n 'gdPerMin10': gold_diff[p_idx],\n 'winner': (team_id == winning_team),\n }\n\n cur_row.update(common_fields)\n rows.append(cur_row)\n\n return rows","def show_outstanding_invoices(self, show_outstanding_invoices):\n\n self._show_outstanding_invoices = show_outstanding_invoices","def create_data():\n data_set = pd.DataFrame()\n customer_id = list()\n for i in range(1, 10001):\n customer_id.append(i)\n data_set = pd.DataFrame()\n data_set.loc[:, 'customer_id'] = np.array(customer_id)\n product_name = ('dining chair', 'dining table', 'bed', 'dining set',\n 'stool', 'couch', 'occasional table',\n 'recliner')\n product_name_random = random.choices(product_name, k=10000)\n data_set.loc[:, 'product_name'] = np.array(product_name_random)\n quantity_rented = (1, 2, 3, 4)\n quantity_rented_random = random.choices(quantity_rented, k=10000)\n data_set.loc[:, 'quantity_rented'] = np.array(quantity_rented_random)\n unit_rental_price_monthly = list()\n for i in range(0, 10000):\n unit_rental_price_monthly.append(random.uniform(1.5, 25))\n data_set.loc[:, 'unit_rental_price'] = np.array(unit_rental_price_monthly)\n rental_period_months = list()\n for i in range(0, 10000):\n rental_period_months.append(randint(6, 60))\n data_set.loc[:, 'rental_period_months'] = np.array(rental_period_months)\n return data_set"],"string":"[\n \"def merge_purchase_invoice(self):\\r\\n active_id = self.env['purchase.order'].browse(self.env['purchase.order']._context.get('active_ids'))\\r\\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')]) \\r\\n active_id_count = 0\\r\\n active_count = 0\\r\\n exist_vendor = []; invoice = [];exist_vendors = [];ctx = ();invoice_id = []\\r\\n for rec in active_id : \\r\\n po_reference = self.env['account.invoice'].search([('origin', 'like', rec.name)])\\r\\n active_count = len(active_id)\\r\\n if rec.picking_count >= 1 and rec.picking_count != rec.invoice_count:\\r\\n len_name = [] \\r\\n for inv in po_reference: \\r\\n len_name = inv.origin.split(\\\":\\\") \\r\\n if rec.name in len_name:\\r\\n if po_reference.state == 'draft':\\r\\n for record in po_reference.invoice_line_ids:\\r\\n print (record.line_id)\\r\\n for res in rec.order_line:\\r\\n if res.id == record.line_id: \\r\\n record.write({'quantity':res.qty_received})\\r\\n res.write({'qty_invoiced':record.quantity})\\r\\n \\r\\n else:\\r\\n \\r\\n po_list = [];line_values = {};lines = {};purchase = []\\r\\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\\r\\n purchase.append(rec.id)\\r\\n active_id_count = len(purchase)\\r\\n if rec.partner_id.id in exist_vendor:\\r\\n for inv in invoice:\\r\\n if inv['partner_id'] == rec.partner_id.id:\\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received \\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received ,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id ,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n inv['invoice_line_ids'].append(line_values)\\r\\n inv['origin'] = inv['origin'] + ':' + rec.name\\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendors.append(rec.partner_id.id) \\r\\n else: \\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received\\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n print (rec.id)\\r\\n po_list.append(line_values) \\r\\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':datetime.today()}) \\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendor.append(rec.partner_id.id) \\r\\n \\r\\n else:\\r\\n po_list = [];line_values = {};lines = {};purchase = []\\r\\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\\r\\n purchase.append(rec.id)\\r\\n active_id_count = len(purchase)\\r\\n if rec.partner_id.id in exist_vendor:\\r\\n for inv in invoice:\\r\\n if inv['partner_id'] == rec.partner_id.id:\\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received\\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received ,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id ,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n inv['invoice_line_ids'].append(line_values)\\r\\n inv['origin'] = inv['origin'] + ':' + rec.name\\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendors.append(rec.partner_id.id) \\r\\n else: \\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received\\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n print (rec.id)\\r\\n po_list.append(line_values) \\r\\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':date.today()}) \\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendor.append(rec.partner_id.id) \\r\\n \\r\\n invoices = []\\r\\n invoice_counts = 0\\r\\n for record in invoice:\\r\\n invoice_id = self.env['account.invoice'].create(record)\\r\\n invoices.append(invoice_id.id)\\r\\n invoice_counts = len(invoices)\\r\\n if active_id_count == 1:\\r\\n if invoice_counts == 1:\\r\\n form_view = self.env.ref('purchase.view_invoice_supplier_purchase_form').id\\r\\n tree_view = self.env.ref('account.invoice_tree').id \\r\\n return{\\r\\n 'name': _('Invoice'),\\r\\n 'type':'ir.actions.act_window',\\r\\n 'view_type':'form',\\r\\n 'view_mode':'form,tree',\\r\\n 'res_model':'account.invoice',\\r\\n 'res_id':invoices[0],\\r\\n 'views_id':False,\\r\\n 'views':[(form_view , 'form'), (tree_view , 'tree')],\\r\\n 'domain':[('id', 'in', invoices)],\\r\\n 'target': 'current',\\r\\n } \\r\\n else: \\r\\n form_view = self.env.ref('account.invoice_supplier_form').id\\r\\n tree_view = self.env.ref('account.invoice_supplier_tree').id \\r\\n return{\\r\\n 'name': _('Invoice'),\\r\\n 'type':'ir.actions.act_window',\\r\\n 'view_type':'form',\\r\\n 'view_mode':'form,tree',\\r\\n 'res_model':'account.invoice',\\r\\n 'views_id':True,\\r\\n 'views':[(tree_view , 'tree'), (form_view , 'form')],\\r\\n 'domain':[('id', 'in', invoices)],\\r\\n 'target': 'current',\\r\\n }\",\n \"def _prepare_invoice_lines(self, exchange_line, order_line):\\n invoice_type = {\\n 'sale.order.line': {\\n 'higher': 'out_invoice', 'lower': 'out_refund',\\n 'type': 'sale', 'field': 'exchange_sale_line_id'\\n },\\n 'purchase.order.line': {\\n 'higher': 'in_invoice', 'lower': 'in_refund',\\n 'type': 'purchase', 'field': 'exchange_purchase_line_id'\\n },\\n }\\n product = exchange_line.exchange_product_id or exchange_line.product_id\\n data = {\\n 'invoice_type': False,\\n 'values': {\\n 'product_id': product.id,\\n 'quantity': exchange_line.quantity,\\n 'name': 'Exchange for [%s]' % exchange_line.product_id.display_name,\\n }\\n }\\n if exchange_line.exchange_product_id or \\\\\\n exchange_line.price_subtotal > order_line.price_subtotal:\\n data['invoice_type'] = invoice_type[order_line._name]['higher']\\n elif exchange_line.price_subtotal < order_line.price_subtotal:\\n data['invoice_type'] = invoice_type[order_line._name]['lower']\\n else:\\n return {}\\n data[invoice_type[order_line._name]['type']] = order_line.order_id\\n data['values'][invoice_type[order_line._name]['field']] = order_line.id\\n data['values']['price_unit'] = exchange_line.price_unit\\n # TODO i think we should take the different between prices NOT the all price\\n # abs(exchange_line.price_unit - order_line.price_unit)\\n return data\",\n \"def make_invoices(self):\\n for invoice in self.policy.invoices:\\n db.session.delete(invoice)\\n db.session.commit()\\n\\n billing_schedules = {'Annual': None, 'Semi-Annual': 3, 'Quarterly': 4, 'Monthly': 12}\\n\\n invoices = []\\n first_invoice = Invoice(self.policy.id,\\n self.policy.effective_date, # bill_date\\n self.policy.effective_date + relativedelta(months=1), # due\\n self.policy.effective_date + relativedelta(months=1, days=14), # cancel\\n self.policy.annual_premium)\\n invoices.append(first_invoice)\\n\\n if self.policy.billing_schedule == \\\"Annual\\\":\\n pass\\n elif self.policy.billing_schedule == \\\"Two-Pay\\\":\\n first_invoice.amount_due = first_invoice.amount_due / billing_schedules.get(self.policy.billing_schedule)\\n for i in range(1, billing_schedules.get(self.policy.billing_schedule)):\\n months_after_eff_date = i*6\\n bill_date = self.policy.effective_date + relativedelta(months=months_after_eff_date)\\n invoice = Invoice(self.policy.id,\\n bill_date,\\n bill_date + relativedelta(months=1),\\n bill_date + relativedelta(months=1, days=14),\\n self.policy.annual_premium / billing_schedules.get(self.policy.billing_schedule))\\n invoices.append(invoice)\\n elif self.policy.billing_schedule == \\\"Quarterly\\\":\\n first_invoice.amount_due = first_invoice.amount_due / billing_schedules.get(self.policy.billing_schedule)\\n for i in range(1, billing_schedules.get(self.policy.billing_schedule)):\\n months_after_eff_date = i*3\\n bill_date = self.policy.effective_date + relativedelta(months=months_after_eff_date)\\n invoice = Invoice(self.policy.id,\\n bill_date,\\n bill_date + relativedelta(months=1),\\n bill_date + relativedelta(months=1, days=14),\\n self.policy.annual_premium / billing_schedules.get(self.policy.billing_schedule))\\n invoices.append(invoice)\\n elif self.policy.billing_schedule == \\\"Monthly\\\":\\n first_invoice.amount_due = first_invoice.amount_due / billing_schedules.get(self.policy.billing_schedule)\\n for i in range(1, billing_schedules.get(self.policy.billing_schedule)):\\n months_after_eff_date = i\\n bill_date = self.policy.effective_date + relativedelta(months=months_after_eff_date)\\n invoice = Invoice(self.policy.id,\\n bill_date,\\n bill_date + relativedelta(months=1),\\n bill_date + relativedelta(months=1, days=14),\\n self.policy.annual_premium / billing_schedules.get(self.policy.billing_schedule))\\n invoices.append(invoice)\\n else:\\n print \\\"You have chosen a bad billing schedule.\\\"\\n\\n logger.info(str(len(invoices)) + \\\" invoices generated for policy %s\\\" % self.policy.id)\\n\\n for invoice in invoices:\\n db.session.add(invoice)\\n db.session.commit()\",\n \"def _create_invoice(self):\\n self.ensure_one()\\n partner = self.member_id.partner_id\\n invoice = self.env['account.invoice'].create({\\n 'partner_id': partner.id,\\n 'account_id': partner.property_account_receivable_id.id,\\n 'fiscal_position_id': partner.property_account_position_id.id\\n })\\n for line in self.line_ids:\\n product = line.activity_id.product_id\\n # Handling of invoice lines : needs cache record for onchange, then\\n # real writing...\\n invoice_line = self.env['account.invoice.line'].new({\\n 'product_id': product.id,\\n 'invoice_id': invoice.id\\n })\\n invoice_line._onchange_product_id()\\n line_values = dict(invoice_line._cache)\\n line_values['price_unit'] = line.price\\n invoice_line = self.env['account.invoice.line'].create(line_values)\\n invoice.compute_taxes()\\n line.registration_id.invoice_line_id = invoice_line.id\\n return invoice\",\n \"def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\\\\n , UnitPrice ):\\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\\n dict_invoice['CustomerID'] = CustomerID\\n dict_invoice['InvoiceNo'] = InvoiceNo\\n df_invoice_line \\\\\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])\\n \\n self.data_transform(df_invoice_line)\\n\\n #self.feature_rfm_encode()\\n\\n self.feature_scale()\\n\\n self.list_feature_drop()\\n\\n self.feature_description_nlp()\\n return\",\n \"def invoice_items(self,org_id=None,query={}):\\n if org_id is None:\\n org_id = self.org_id\\n query_end_date = datetime.strptime(query['endDate'],'%Y-%m-%dT%H:%M:%SZ')\\n # Given a 'query_end_date' to find the invoice containing the\\n # line items for that date we need to find the invoice which \\n # has 'endDate' equal to the end of the month of the `query_end_date`\\n query_first_next_month = query_end_date + relativedelta(months=+1) - relativedelta(days=(query_end_date.day-1))\\n target_invoices = []\\n invoices = self.invoices(org_id)\\n if self.verbose:\\n print('Searching invoices org_id={}'.format(org_id))\\n print('query={} query_end_date={}'.format(query,query_end_date))\\n print('Result keys: {}'.format( invoices['content'].keys() ))\\n print('Total result count: {}'.format( invoices['content']['totalCount'] ))\\n for invoice in invoices['content']['results']:\\n #pprint.pprint(invoice)\\n end_date = datetime.strptime(invoice['endDate'],'%Y-%m-%dT%H:%M:%SZ')\\n if self.verbose: \\n print('invoice({})[\\\\'endDate\\\\']={} end_date={}'.format(invoice['id'],invoice['endDate'],end_date))\\n if end_date == query_first_next_month:\\n target_invoices.append(invoice)\\n \\n if self.verbose: \\n print('Target invoices: {}'.format(target_invoices))\\n \\n\\n target_line_items = []\\n for invoice in target_invoices:\\n invoice_details = self.invoices(org_id,invoice['id']) \\n print('invoice_details: {}'.format(invoice_details))\\n for item in invoice_details['content']['lineItems']:\\n end_date = datetime.strptime(item['endDate'],'%Y-%m-%dT%H:%M:%SZ')\\n if end_date == query_end_date:\\n target_line_items.append(item)\\n if self.verbose:\\n print('target_line_items: {}'.format(target_line_items)) \\n return target_line_items\",\n \"def _prepare_invoice(self, cr, uid, order, lines, context=None):\\n if context is None:\\n context = {}\\n journal_ids = self.pool.get('account.journal').search(cr, uid,\\n [('type', '=', 'sale'), ('company_id', '=', order.company_id.id)],\\n limit=1)\\n if not journal_ids:\\n raise osv.except_osv(_('Error !'),\\n _('There is no sales journal defined for this company: \\\"%s\\\" (id:%d)') % (order.company_id.name, order.company_id.id))\\n\\n invoice_vals = {\\n 'name': order.client_order_ref or '',\\n 'origin': order.name,\\n 'type': 'out_invoice',\\n 'reference': order.client_order_ref or order.name,\\n 'account_id': order.partner_id.property_account_receivable.id,\\n 'journal_id': order.partner_id.property_default_sale_invoice_journal.id,\\n 'partner_id': order.partner_id.id,\\n 'address_invoice_id': order.partner_invoice_id.id,\\n #'address_contact_id': order.partner_order_id.id,\\n 'invoice_line': [(6, 0, lines)],\\n 'currency_id': order.pricelist_id.currency_id.id,\\n 'comment': order.note,\\n 'payment_term': order.payment_term and order.payment_term.id or False,\\n 'fiscal_position': order.fiscal_position.id or order.partner_id.property_account_position.id,\\n 'date_invoice': context.get('date_invoice', False),\\n 'company_id': order.company_id.id,\\n 'user_id': order.user_id and order.user_id.id or False\\n }\\n\\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\\n\\n return invoice_vals\",\n \"def get_dataframe() -> pandas.DataFrame:\\n database_connection = processing.establish_connection(database_path)\\n dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)\\n database_connection.close()\\n dataframe = processing.get_invoice_date_fixed(dataframe)\\n analyze_dataframe = dataframe.copy()\\n total_sum_dataframe = processing.get_column_sum(analyze_dataframe)\\n\\n customer_count_dataframe = processing.drop_duplicates(analyze_dataframe)\\n customer_count_dataframe = processing.get_column_count(customer_count_dataframe)\\n return customer_count_dataframe.merge(total_sum_dataframe, how='inner', on='InvoiceDate')\",\n \"def _prepare_invoice(self, cr, uid, order, lines, context=None):\\n if context is None:\\n context = {}\\n journal_id = self.pool['account.invoice'].default_get(cr, uid, ['journal_id'], context=context)['journal_id']\\n if not journal_id:\\n raise osv.except_osv(_('Error!'),\\n _('Please define sales journal for this company: \\\"%s\\\" (id:%d).') % (order.company_id.name, order.company_id.id))\\n invoice_vals = {\\n 'name': order.client_order_ref or '',\\n 'origin': order.name,\\n 'type': 'out_invoice',\\n 'reference': order.client_order_ref or order.name,\\n 'account_id': order.partner_invoice_id.property_account_receivable.id,\\n 'partner_id': order.partner_invoice_id.id,\\n 'journal_id': journal_id,\\n 'invoice_line': [(6, 0, lines)],\\n 'currency_id': order.pricelist_id.currency_id.id,\\n 'comment': order.note,\\n 'payment_term': order.payment_term and order.payment_term.id or False,\\n 'fiscal_position': order.fiscal_position.id or order.partner_invoice_id.property_account_position.id,\\n 'date_invoice': context.get('date_invoice', False),\\n 'company_id': order.company_id.id,\\n 'user_id': order.user_id and order.user_id.id or False,\\n 'section_id' : order.section_id.id,\\n 'test_1' :order.test\\n }\\n\\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\\n return invoice_vals\",\n \"def make_claim_df(claim_list, columns = ['Sl','Name of Bank','Name of Branch','A/C Number (15 digit)','A/C Title','Amount of Remittance in BDT','Date of A/C Credit','Remittance Received through BEFTN/RTGS','Name of Remittance Collecting/BEFTN Processing Bank','Date of Claim']):\\n sl=[]\\n nrbc_bank = []\\n branch = []\\n ac_no = []\\n ac_title = []\\n amount=[]\\n date_account_credit=[]\\n channel = []\\n other_bank=[]\\n claim_date=[]\\n i=1\\n for claim in claim_list:\\n sl.append(i)\\n i=i+1\\n nrbc_bank.append(\\\"NRBC Bank Ltd.\\\")\\n branch.append(claim.branch.name.upper())\\n ac_no.append(claim.account_no)\\n ac_title.append(claim.account_title)\\n amount.append(claim.remittance_amount)\\n date_account_credit.append(claim.date_account_credit)\\n channel.append(claim.get_channel_display())\\n other_bank.append(claim.collecting_bank.name)\\n claim_date.append(claim.date_claim.date())\\n dc = {\\n 'SL':sl,\\n 'Name of Bank':nrbc_bank,\\n 'Name of Branch': branch,\\n 'A/C Number': ac_no,\\n 'A/C Title': ac_title,\\n 'Amount of Remittance in BDT': amount,\\n 'Date of A/C Credit': date_account_credit,\\n 'Remittance Received Through BEFTN/RTGS': channel,\\n 'Name of Remittance Processing Bank': other_bank,\\n 'Date of Claim': claim_date\\n }\\n df = pd.DataFrame(dc)\\n return df.sort_values(by=['Name of Remittance Processing Bank',])\",\n \"def order_process(self, customerID, list_stockCode, list_quantity\\\\\\n , orderDate=None):\\n\\n segmentID = -1\\n\\n #-------------------------------------------------------------------------\\n # A new customer is created and inserted into data-set.\\n #-------------------------------------------------------------------------\\n if customerID is None:\\n customerID = int(self.createCustomerID())\\n else:\\n pass\\n \\n #-------------------------------------------------------------------------\\n # A new dataframe with new invoice lines are created.\\n #-------------------------------------------------------------------------\\n df_invoice_line = self.create_customer_df_invoice_line(customerID\\\\\\n , list_stockCode, list_quantity, orderDate)\\n \\n #-------------------------------------------------------------------------\\n # Original dataframe is updated with customer invoices lines.\\n #-------------------------------------------------------------------------\\n print(\\\"order_process : shape before concat= \\\"+str(self._df_invoice_original.shape))\\n self._df_invoice_original \\\\\\n = pd.concat([self._df_invoice_original, df_invoice_line], axis=0)\\n print(\\\"order_process : shape after concat= \\\"+str(self._df_invoice_original.shape))\\n \\n #-------------------------------------------------------------------------\\n # All invoices lines (including new one) related to customer is retrieved \\n # from original dataframe.\\n #-------------------------------------------------------------------------\\n df_invoice_line_customer \\\\\\n = self.get_customer_history_df_invoice_line(customerID)\\n\\n #-------------------------------------------------------------------------\\n # When calling get_customer_marketSegment(), df_invoice_line_customer is\\n # concatened to the original dataframe.\\n #------------------------------------------------------------------------- \\n segmentID = self.get_customer_marketSegment(df_invoice_line_customer)\\n \\n return segmentID, customerID\",\n \"def create_invoice(self):\\n for line in self:\\n # if not line.account_id:\\n # raise UserError(_('Please Add the incoming Account !!'))\\n self.ensure_one()\\n journal_id = self.env['account.journal'].search([\\n ('type', '=', 'sale')], limit=1)\\n inv_line_main = {\\n 'name': line.description.name,\\n 'price_unit': line.amount or 0.00,\\n 'quantity': 1,\\n 'discount': line.discount,\\n 'account_id': line.description.property_account_income_id.id or line.description.categ_id.property_account_income_categ_id.id or False,\\n }\\n inv_values = {\\n 'partner_id': line.patient_id.partner_id.id,\\n 'patient_id': line.patient_id.id,\\n 'dentist': line.dentist.id,\\n 'move_type': 'out_invoice',\\n 'invoice_date': datetime.now().strftime(DF) or False,\\n 'journal_id': journal_id and journal_id.id or False,\\n 'teeth_id': line.patient_id and line.patient_id.id or False,\\n }\\n acc_id = self.env['account.move'].create(inv_values)\\n acc_id.write({'invoice_line_ids': [(0, 0, inv_line_main)]})\\n\\n self.write({'invc_id': acc_id.id, 'inv': True})\\n context = dict(self._context or {})\\n wiz_form_id = self.env['ir.model.data'].get_object_reference(\\n 'account', 'view_move_form')[1]\\n\\n return {\\n 'view_type': 'form',\\n 'view_id': wiz_form_id,\\n 'view_mode': 'form',\\n 'res_model': 'account.move',\\n 'res_id': self.invc_id.id,\\n 'type': 'ir.actions.act_window',\\n 'target': 'current',\\n 'context': context,\\n }\",\n \"def action_create_invoices(self, data):\\n invoice_obj = self.env['account.invoice']\\n values = {}\\n for val in data:\\n values.setdefault(val['invoice_type'], {\\n 'order': val.get('sale', val.get('purchase')),\\n 'values': []\\n })\\n values[val['invoice_type']]['values'].append((0, 0, val['values']))\\n\\n for inv_type, inv_data in values.items():\\n invoice = invoice_obj.new(self._prepare_invoice(inv_type))\\n invoice._onchange_partner_id()\\n inv = invoice._convert_to_write({\\n name: invoice[name] for name in invoice._cache\\n })\\n for _, _, line in inv_data['values']:\\n line['account_id'] = inv['account_id']\\n inv['invoice_line_ids'] = inv_data['values']\\n new_invoice = invoice_obj.sudo().create(inv)\\n new_invoice.action_invoice_open()\\n inv_data['order'].write({\\n 'exchange_invoice_ids': [(4, new_invoice.id)]\\n })\",\n \"def _get_lines(self, cr, uid, ids, context=None):\\n List=[]\\n if ids:\\n line = self.pool.get('payment.enrich.lines').browse(cr, uid, ids[0], context=context)\\n \\n record = line.enrich_id\\n val = 0.0\\n for line in record.enrich_lines:\\n if line.state == 'done' :\\n val += line.cost\\n res = {\\n 'paid_amount':val,\\n 'residual_amount':record.amount - val,\\n }\\n record.write(res)\\n return List\",\n \"def data_transform(self, df) :\\n\\n #-------------------------------------------------------------------------\\n # Copy of given dataframe to be transformed\\n #-------------------------------------------------------------------------\\n self.df_invoice_line = df\\n \\n #-------------------------------------------------------------------------\\n # Features issued from InvoiceDate are created\\n #-------------------------------------------------------------------------\\n if self.is_transform_timeFeature is True:\\n self.strprint(\\\"\\\\n*** Time features transformation ***\\\")\\n self.data_transform_timeFeature()\\n\\n #-------------------------------------------------------------------------\\n # RFM is computed and encoded\\n #-------------------------------------------------------------------------\\n if self.is_transform_rfm is True:\\n self.strprint(\\\"\\\\n*** RFM transformation ***\\\")\\n self.data_transform_rfm()\\n\\n #-------------------------------------------------------------------------\\n # NLP features issued from Description are created\\n #-------------------------------------------------------------------------\\n if self.is_transform_nlp is True:\\n self.strprint(\\\"\\\\n*** NLP transformation ***\\\")\\n self.data_transform_nlp()\\n \\n return self.df_invoice_line\",\n \"def create_invoices(self, cr, uid, ids, context=None):\\n invoice_list = []\\n po_obj = self.pool.get('purchase.order')\\n inv_line_obj = self.pool.get('account.invoice.line')\\n inv_obj = self.pool.get('account.invoice')\\n addr_obj = self.pool.get('res.partner')\\n journal_obj = self.pool.get('account.journal')\\n if context is None:\\n context = {}\\n\\n for purchase_adv_obj in self.browse(cr, uid, ids, context=context):\\n for purchase_order in po_obj.browse(cr, uid, context.get('active_ids', []), context=context):\\n inv_line_ids = []\\n invoice_ids = []\\n val = inv_line_obj.product_id_change(cr, uid, [], purchase_adv_obj.product_id.id,\\n uom_id=False, partner_id=purchase_order.partner_id.id, fposition_id=purchase_order.fiscal_position.id)\\n line_id = inv_line_obj.create(cr, uid, {\\n 'name': val['value']['name'],\\n 'account_id': val['value']['account_id'],\\n 'price_unit': purchase_adv_obj.amount,\\n 'quantity': purchase_adv_obj.qtty,\\n 'discount': False,\\n 'uos_id': val['value']['uos_id'],\\n 'product_id': purchase_adv_obj.product_id.id,\\n 'invoice_line_tax_id': [(6, 0, val['value']['invoice_line_tax_id'])],\\n })\\n inv_line_ids.append(line_id)\\n addr = addr_obj.address_get(cr, uid, [purchase_order.partner_id.id], ['invoice'])\\n journal_ids = journal_obj.search(cr, uid, [('type', '=', 'purchase')])\\n context.update({'type':'in_invoice','journal_type':'purchase'})\\n inv_vals = {\\n 'name': purchase_order.partner_ref or purchase_order.name,\\n 'origin': purchase_order.name,\\n 'type': 'in_invoice',\\n 'reference': False,\\n 'account_id': purchase_order.partner_id.property_account_payable.id,\\n 'journal_id':journal_ids and journal_ids[0] or False,\\n 'partner_id': purchase_order.partner_id.id,\\n 'address_invoice_id': addr['invoice'],\\n 'invoice_line': [(6, 0, inv_line_ids)],\\n 'currency_id': purchase_order.pricelist_id.currency_id.id,\\n 'comment': '',\\n 'payment_term': purchase_order.payment_term_id and purchase_order.payment_term_id.id or False,\\n 'fiscal_position': purchase_order.fiscal_position.id or purchase_order.partner_id.property_account_position.id,\\n 'prepaid': True\\n }\\n\\n inv_id = inv_obj.create(cr, uid, inv_vals, context=context)\\n inv_obj.button_reset_taxes(cr, uid, [inv_id], context=context)\\n for invoice in purchase_order.invoice_ids:\\n invoice_ids.append(invoice.id)\\n invoice_ids.append(inv_id)\\n po_obj.write(cr, uid, purchase_order.id, {'invoice_ids': [(6, 0, invoice_ids)]})\\n invoice_list.append(inv_id)\\n\\n if purchase_order.invoice_method in ('picking','order'):\\n self.pool.get('purchase.order.line').create(cr, uid, {\\n 'order_id': purchase_order.id,\\n 'name': val['value']['name'],\\n 'date_planned':purchase_order.date_order,\\n 'price_unit': -purchase_adv_obj.amount,\\n 'product_uom_qty': purchase_adv_obj.qtty,\\n 'product_uos': val['value']['uos_id'],\\n 'product_uom': val['value']['uos_id'],\\n 'product_id': purchase_adv_obj.product_id.id,\\n 'adavance_product':True,\\n 'discount': False,\\n 'taxes_id': [(6, 0, val['value']['invoice_line_tax_id'])],\\n }, context=context)\\n\\n\\n context.update({'invoice_id':invoice_list})\\n return {\\n 'name': 'Open Invoice',\\n 'view_type': 'form',\\n 'view_mode': 'form',\\n 'res_model': 'purchase.open.invoice',\\n 'type': 'ir.actions.act_window',\\n 'target': 'new',\\n 'context': context\\n }\",\n \"def prepare_report(user, from_date, to_date,\\n show_which=\\\"worked\\\", # \\\"worked\\\", \\\"invoiced\\\", or \\\"paid\\\"\\n client_ids=[], project_ids=[]):\\n\\n if show_which == \\\"worked\\\":\\n sessions = Session.objects.filter(\\n project__client__user=user,\\n date__gte=from_date,\\n date__lte=to_date\\n )\\n elif show_which == \\\"invoiced\\\":\\n sessions = Session.objects.filter(\\n project__client__user=user,\\n invoice__invoice_date__gte=from_date,\\n invoice__invoice_date__lte=to_date\\n )\\n elif show_which == \\\"paid\\\":\\n sessions = Session.objects.filter(\\n project__client__user=user,\\n invoice__paid_date__gte=from_date,\\n invoice__paid_date__lte=to_date\\n )\\n else:\\n raise ValueError(\\\"Invalid value for the 'show_which' argument \\\"\\n \\\"supplied\\\")\\n\\n if client_ids != []:\\n sessions = sessions.filter(\\n project__client__in=client_ids\\n )\\n if project_ids != []:\\n sessions = sessions.filter(\\n project__in=project_ids\\n )\\n\\n # Starting Python 3.6, the dict maintains order as inserted\\n # When running this on a different computer with older Python,\\n # the sessions_per_date was all jumbled-up.\\n # https://stackoverflow.com/questions/1867861/dictionaries-how-to-keep-keys-values-in-same-order-as-declared\\n date_range = pd.date_range(from_date, to_date).date\\n sessions_per_date = {today: sessions.filter(date=today)\\n for today in date_range}\\n\\n total_earned = sum([sesh.get_money_earned() for sesh in sessions])\\n\\n context = {\\n 'sessions': sessions, # obsolete if sessions_per_date will work\\n 'from': from_date,\\n 'to': to_date,\\n 'date_range': date_range,\\n 'sessions_per_date': sessions_per_date,\\n 'total_earned': total_earned,\\n }\\n\\n if client_ids != []:\\n context['clients'] = Client.objects.filter(pk__in=client_ids)\\n\\n if project_ids != []:\\n context['projects'] = Project.objects.filter(pk__in=project_ids)\\n\\n return context\",\n \"def generate_eob(\\n self, date_of_service, date_of_eob, insured, invoice_id, cpt_code, charge_amount\\n ):\\n if insured == \\\"insured\\\":\\n # first copayments\\n copay_amount = np.random.choice(\\n self.distributions[\\\"copay_amounts\\\"],\\n 1,\\n p=self.distributions[\\\"copay_distribution\\\"],\\n )[0]\\n if copay_amount > 0:\\n invoicelineitemeob = pd.DataFrame(\\n {\\n \\\"invoice_id\\\": [invoice_id],\\n \\\"cpt_code\\\": [cpt_code],\\n \\\"created_on\\\": [date_of_service],\\n \\\"copay_amount\\\": [copay_amount],\\n \\\"adjustment_amount\\\": [0],\\n \\\"paid_amount\\\": [0],\\n }\\n )\\n self.InvoiceLineItemsEob = self.InvoiceLineItemsEob.append(\\n invoicelineitemeob\\n )\\n remaining_charge = charge_amount - copay_amount\\n else:\\n remaining_charge = charge_amount\\n # next eob discounts\\n eob_discount_percent = np.random.choice(\\n self.distributions[\\\"eob_discount_percentages\\\"],\\n 1,\\n p=self.distributions[\\\"eob_discount_distribution\\\"],\\n )[0]\\n if eob_discount_percent > 0:\\n insurance_adjustment = remaining_charge * eob_discount_percent / 100\\n remaining_charge = remaining_charge - insurance_adjustment\\n invoicelineitemeob = pd.DataFrame(\\n {\\n \\\"invoice_id\\\": [invoice_id],\\n \\\"cpt_code\\\": [cpt_code],\\n \\\"created_on\\\": [date_of_eob],\\n \\\"copay_amount\\\": [0],\\n \\\"adjustment_amount\\\": [insurance_adjustment],\\n \\\"paid_amount\\\": [0],\\n }\\n )\\n self.InvoiceLineItemsEob = self.InvoiceLineItemsEob.append(\\n invoicelineitemeob\\n )\\n # next handle eob payments where relevant\\n eob_payment_percentage = np.random.choice(\\n self.distributions[\\\"eob_payment_percentages\\\"],\\n 1,\\n p=self.distributions[\\\"eob_payment_distribution\\\"],\\n )[0]\\n eob_payment_amount = remaining_charge * (eob_payment_percentage / 100.0)\\n if eob_payment_amount > 0:\\n invoicelineitemeob = pd.DataFrame(\\n {\\n \\\"invoice_id\\\": [invoice_id],\\n \\\"cpt_code\\\": [cpt_code],\\n \\\"created_on\\\": [date_of_eob],\\n \\\"copay_amount\\\": [0],\\n \\\"adjustment_amount\\\": [0],\\n \\\"paid_amount\\\": [eob_payment_amount],\\n }\\n )\\n self.InvoiceLineItemsEob = self.InvoiceLineItemsEob.append(\\n invoicelineitemeob\\n )\\n remaining_charge = remaining_charge - eob_payment_amount\\n else:\\n remaining_charge = charge_amount\\n return remaining_charge\",\n \"def action_invoice_create(self, grouped=False, final=False):\\n if self.invoice_option == 'before_delivery':\\n inv_obj = self.env['account.invoice']\\n for order in self:\\n inv_data = order._prepare_invoice()\\n invoice = inv_obj.create(inv_data)\\n for inv_line in order.order_line:\\n inv_line.invoice_line_create(invoice.id, inv_line.product_uom_qty)\\n\\n else:\\n inv_obj = self.env['account.invoice']\\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\\n invoices = {}\\n references = {}\\n invoices_origin = {}\\n invoices_name = {}\\n\\n # Keep track of the sequences of the lines\\n # To keep lines under their section\\n inv_line_sequence = 0\\n for order in self:\\n group_key = order.id if grouped else (order.partner_invoice_id.id, order.currency_id.id)\\n\\n # We only want to create sections that have at least one invoiceable line\\n pending_section = None\\n\\n # Create lines in batch to avoid performance problems\\n line_vals_list = []\\n # sequence is the natural order of order_lines\\n for line in order.order_line:\\n if line.display_type == 'line_section':\\n pending_section = line\\n continue\\n if float_is_zero(line.qty_to_invoice, precision_digits=precision):\\n continue\\n if group_key not in invoices:\\n inv_data = order._prepare_invoice()\\n invoice = inv_obj.create(inv_data)\\n references[invoice] = order\\n invoices[group_key] = invoice\\n invoices_origin[group_key] = [invoice.origin]\\n invoices_name[group_key] = [invoice.name]\\n elif group_key in invoices:\\n if order.name not in invoices_origin[group_key]:\\n invoices_origin[group_key].append(order.name)\\n if order.client_order_ref and order.client_order_ref not in invoices_name[group_key]:\\n invoices_name[group_key].append(order.client_order_ref)\\n\\n if line.qty_to_invoice > 0 or (line.qty_to_invoice < 0 and final):\\n if pending_section:\\n section_invoice = pending_section.invoice_line_create_vals(\\n invoices[group_key].id,\\n pending_section.qty_to_invoice\\n )\\n inv_line_sequence += 1\\n section_invoice[0]['sequence'] = inv_line_sequence\\n line_vals_list.extend(section_invoice)\\n pending_section = None\\n\\n inv_line_sequence += 1\\n inv_line = line.invoice_line_create_vals(\\n invoices[group_key].id, line.qty_to_invoice\\n )\\n inv_line[0]['sequence'] = inv_line_sequence\\n line_vals_list.extend(inv_line)\\n\\n if references.get(invoices.get(group_key)):\\n if order not in references[invoices[group_key]]:\\n references[invoices[group_key]] |= order\\n\\n self.env['account.invoice.line'].create(line_vals_list)\\n\\n for group_key in invoices:\\n invoices[group_key].write({'name': ', '.join(invoices_name[group_key]),\\n 'origin': ', '.join(invoices_origin[group_key])})\\n sale_orders = references[invoices[group_key]]\\n if len(sale_orders) == 1:\\n invoices[group_key].reference = sale_orders.reference\\n\\n if not invoices:\\n raise UserError(_(\\n 'There is no invoiceable line. If a product has a Delivered quantities invoicing policy, please make sure that a quantity has been delivered.'))\\n\\n for invoice in invoices.values():\\n invoice.compute_taxes()\\n if not invoice.invoice_line_ids:\\n raise UserError(_(\\n 'There is no invoiceable line. If a product has a Delivered quantities invoicing policy, please make sure that a quantity has been delivered.'))\\n # If invoice is negative, do a refund invoice instead\\n if invoice.amount_total < 0:\\n invoice.type = 'out_refund'\\n for line in invoice.invoice_line_ids:\\n line.quantity = -line.quantity\\n # Use additional field helper function (for account extensions)\\n for line in invoice.invoice_line_ids:\\n line._set_additional_fields(invoice)\\n # Necessary to force computation of taxes. In account_invoice, they are triggered\\n # by onchanges, which are not triggered when doing a create.\\n invoice.compute_taxes()\\n # Idem for partner\\n so_payment_term_id = invoice.payment_term_id.id\\n fp_invoice = invoice.fiscal_position_id\\n invoice._onchange_partner_id()\\n invoice.fiscal_position_id = fp_invoice\\n # To keep the payment terms set on the SO\\n invoice.payment_term_id = so_payment_term_id\\n invoice.message_post_with_view('mail.message_origin_link',\\n values={'self': invoice, 'origin': references[invoice]},\\n subtype_id=self.env.ref('mail.mt_note').id)\\n return [inv.id for inv in invoices.values()]\",\n \"def _get_query(self, type, date_from=False, date_to=False, users=None, products=None):\\n # TODO: Revisar def _create_invoice(self, order, so_line, amount):...\\n # so.user_id AS id_salesman\\n # AND so.user_id IN (%s)\\n # AND pp.id IN (%s)\\n # GROUP BY salesman\\n\\n if type == 'most_sold':\\n sql = \\\"\\\"\\\"\\n SELECT min(sol.id) AS id, \\n so.user_id AS salesman, \\n sol.product_id AS product,\\n AVG(sol.price_reduce_taxexcl) AS price, \\n pp.product_tmpl_id AS product_template,\\n so.company_id AS company,\\n SUM(sol.product_uom_qty) AS qty,\\n SUM(sol.price_subtotal) AS subtotal\\n FROM sale_order_line sol\\n LEFT JOIN sale_order so ON so.id = sol.order_id\\n LEFT JOIN product_product pp ON pp.id = sol.product_id\\n LEFT JOIN product_template pt ON pt.id = pp.product_tmpl_id\\n WHERE so.state NOT IN ('draft', 'sent', 'cancel')\\n AND so.date_order BETWEEN '%s' AND '%s'\\n AND so.user_id IN (%s)\\n AND pp.id IN (%s)\\n GROUP BY salesman, sol.product_id, pp.product_tmpl_id, so.company_id\\n ORDER BY qty DESC;\\n \\\"\\\"\\\" % (date_from, date_to, ', '.join(str(u) for u in users), ', '.join(str(p) for p in products))\\n else:\\n sql = \\\"\\\"\\\" \\n \\\"\\\"\\\"\\n self.env.cr.execute(sql)\\n return self.env.cr.dictfetchall()\",\n \"def get_customer_history_df_invoice_line(self, customerID):\\n df_invoice_line \\\\\\n = self._df_invoice_original[self._df_invoice_original.CustomerID \\\\\\n == customerID]\\n return df_invoice_line\",\n \"def _prepare_invoice_line(self, inv_id):\\n res = {}\\n account_id = self.product_id.property_account_income_id.id or self.product_id.categ_id.property_account_income_categ_id.id\\n if not account_id:\\n raise UserError(_('Please define income account for this product: \\\"%s\\\" (id:%d).') % \\\\\\n (self.product_id.name, self.product_id.id,))\\n price_unit = self.product_id.lst_price\\n res = {\\n 'invoice_id': inv_id.id,\\n 'name': self.name,\\n 'origin': self.order_id.name,\\n 'account_id': account_id,\\n 'uom_id': self.product_uom_id.id,\\n 'quantity': self.product_uom_qty,\\n 'price_unit': price_unit,\\n 'product_id': self.product_id.id,\\n 'invoice_line_tax_id': False,\\n 'order_line_id': self.id\\n }\\n return res\",\n \"def action_invoice_create(self, grouped=False, final=False):\\n inv_obj = self.env['account.invoice']\\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\\n invoices = {}\\n references = {}\\n invoices_origin = {}\\n invoices_name = {}\\n for order in self:\\n group_key = order.id if grouped else (order.partner_id.id, order.currency_id.id)\\n for line in order.order_line.sorted(key=lambda l: l.qty_received - l.qty_invoiced < 0):\\n if float_is_zero(line.qty_received - line.qty_invoiced, precision_digits=precision):\\n continue\\n if group_key not in invoices:\\n inv_data = order._prepare_invoice()\\n invoice = inv_obj.create(inv_data)\\n references[invoice] = order\\n invoices[group_key] = invoice\\n invoices_origin[group_key] = [invoice.origin]\\n invoices_name[group_key] = [invoice.name]\\n elif group_key in invoices:\\n if order.name not in invoices_origin[group_key]:\\n invoices_origin[group_key].append(order.name)\\n if order.partner_ref and order.partner_ref not in invoices_name[group_key]:\\n invoices_name[group_key].append(order.partner_ref)\\n\\n if line.qty_received - line.qty_invoiced > 0:\\n line.invoice_line_create(invoices[group_key].id, line.qty_received - line.qty_invoiced)\\n elif line.qty_received - line.qty_invoiced < 0 and final:\\n line.invoice_line_create(invoices[group_key].id, line.qty_received - line.qty_invoiced)\\n\\n if references.get(invoices.get(group_key)):\\n if order not in references[invoices[group_key]]:\\n references[invoices[group_key]] |= order\\n\\n for group_key in invoices:\\n invoices[group_key].write({'name': ', '.join(invoices_name[group_key]),\\n 'origin': ', '.join(invoices_origin[group_key])})\\n\\n if not invoices:\\n raise UserError(_('There is no invoicable line.'))\\n\\n for invoice in invoices.values():\\n if not invoice.invoice_line_ids:\\n raise UserError(_('There is no invoicable line.'))\\n # If invoice is negative, do a refund invoice instead\\n if invoice.amount_total < 0:\\n invoice.type = 'in_refund'\\n for line in invoice.invoice_line_ids:\\n line.quantity = -line.quantity\\n # Necessary to force computation of taxes. In account_invoice, they are triggered\\n # by onchanges, which are not triggered when doing a create.\\n invoice.compute_taxes()\\n invoice.message_post_with_view('mail.message_origin_link',\\n values={'self': invoice, 'origin': references[invoice]},\\n subtype_id=self.env.ref('mail.mt_note').id)\\n return [inv.id for inv in invoices.values()]\",\n \"def action_invoice_create(self, grouped=False, final=False):\\n inv_obj = self.env['account.invoice']\\n precision = self.env['decimal.precision'].sudo().precision_get('Product Unit of Measure')\\n invoices = {}\\n references = {}\\n for order in self:\\n group_key = order.id if grouped else (order.partner_invoice_id.id, order.currency_id.id)\\n for line in order.order_line.sorted(key=lambda l: l.qty_to_invoice < 0):\\n if float_is_zero(line.qty_to_invoice, precision_digits=precision):\\n continue\\n if group_key not in invoices:\\n inv_data = order._prepare_invoice()\\n invoice = inv_obj.sudo().create(inv_data)\\n references[invoice] = order\\n invoices[group_key] = invoice\\n invoice['sale_order_id'] = order.id\\n elif group_key in invoices:\\n vals = {}\\n if order.name not in invoices[group_key].origin.split(', '):\\n vals['origin'] = invoices[group_key].origin + ', ' + order.name\\n if order.client_order_ref and order.client_order_ref not in invoices[group_key].name.split(\\n ', ') and order.client_order_ref != invoices[group_key].name:\\n vals['name'] = invoices[group_key].name + ', ' + order.client_order_ref\\n invoices[group_key].sudo().write(vals)\\n if line.qty_to_invoice > 0:\\n line.invoice_line_create(invoices[group_key].id, line.qty_to_invoice)\\n elif line.qty_to_invoice < 0 and final:\\n line.invoice_line_create(invoices[group_key].id, line.qty_to_invoice)\\n\\n if references.get(invoices.get(group_key)):\\n if order not in references[invoices[group_key]]:\\n references[invoices[group_key]] |= order\\n if not invoices:\\n raise UserError(_('There is no invoiceable line.'))\\n for invoice in invoices.values():\\n if not invoice.invoice_line_ids:\\n raise UserError(_('There is no invoiceable line.'))\\n # If invoice is negative, do a refund invoice instead\\n if invoice.amount_untaxed < 0:\\n invoice.type = 'out_refund'\\n for line in invoice.invoice_line_ids:\\n line.quantity = -line.quantity\\n # Use additional field helper function (for account extensions)\\n for line in invoice.invoice_line_ids:\\n line._set_additional_fields(invoice)\\n # Necessary to force computation of taxes. In account_invoice, they are triggered\\n # by onchanges, which are not triggered when doing a create.\\n invoice.compute_taxes()\\n invoice.message_post_with_view('mail.message_origin_link',\\n values={'self': invoice, 'origin': references[invoice]},\\n subtype_id=self.env.ref('mail.mt_note').id)\\n return [inv.id for inv in invoices.values()]\",\n \"def merge_invoice(self, cr, uid, invoices, context=None):\\n order_ids = []\\n pick_ids = []\\n if len(invoices) <= 1:\\n return False\\n parent = self.pool.get('account.invoice').browse(cr, uid, context['active_id'])\\n for inv in invoices:\\n if parent.partner_id != inv.partner_id:\\n raise osv.except_osv(_(\\\"Partners don't match!\\\"), _(\\\"Can not merge invoice(s) on different partners or states !.\\\"))\\n\\n if inv.state != 'draft':\\n raise osv.except_osv(_(\\\"Invalid action !\\\"), _(\\\"You can merge only invoices in draft state.\\\"))\\n\\n # Merge invoices that are in draft state\\n inv_line_obj = self.pool.get('account.invoice.line')\\n name = parent.name\\n comment = parent.comment\\n origin = parent.origin\\n for inv in invoices:\\n if inv.id == parent.id:\\n continue\\n\\n # check if a line with the same product already exist. if so add quantity. else hang up invoice line to first invoice head.\\n if inv.name:\\n # Find if the same name already exist, if yes, skip to add.\\n name_list = name.replace(' ', '').split(',')\\n if inv.name not in name_list:\\n name += ', %s' % inv.name\\n if inv.comment:\\n comment = comment and comment + ', %s' % inv.comment or inv.comment\\n if inv.origin:\\n origin += ', %s' % inv.origin\\n line_ids = inv_line_obj.search(cr, uid, [('invoice_id', '=', inv.id)])\\n for inv_lin in inv_line_obj.browse(cr, uid, line_ids):\\n mrg_pdt_ids = inv_line_obj.search(cr, uid, [('invoice_id', '=', parent.id), ('product_id', '=', inv_lin.product_id.id),\\n ('uos_id', '=', inv_lin.uos_id.id), ('price_unit', '=', inv_lin.price_unit) # kittiu: extra condition, unit price must also be the same.\\n ])\\n if len(mrg_pdt_ids) == 1 and inv.type == parent.type: # product found --> add quantity\\n inv_line_obj.write(cr, uid, mrg_pdt_ids, {'quantity': inv_line_obj._can_merge_quantity(cr, uid, mrg_pdt_ids[0], inv_lin.id)})\\n inv_line_obj.unlink(cr, uid, inv_lin.id)\\n elif inv.type == parent.type:\\n inv_line_obj.write(cr, uid, inv_lin.id, {'invoice_id': parent.id})\\n else:\\n inv_line_obj.write(cr, uid, inv_lin.id, {'invoice_id': parent.id, 'quantity': -inv_lin.quantity})\\n\\n if inv.sale_order_ids:\\n order_ids += [order.id for order in inv.sale_order_ids]\\n if inv.picking_ids:\\n pick_ids += [picking.id for picking in inv.picking_ids]\\n\\n self.write(cr, uid, parent.id, {'origin': origin, 'name': name, 'comment': comment})\\n\\n #Remove By DRB\\n #cr.execute('update sale_order_invoice_rel set invoice_id = %s where invoice_id = %s', (parent.id, inv.id))\\n #cr.execute('update picking_invoice_rel set invoice_id = %s where invoice_id = %s', (parent.id, inv.id))\\n\\n self.unlink(cr, uid, [inv.id])\\n #Distinct List\\n order_ids = list(set(order_ids))\\n pick_ids = list(set(pick_ids))\\n\\n self.write(cr, uid, parent.id, {'sale_order_ids': [(6, 0, order_ids)], 'picking_ids': [(6, 0, pick_ids)]})\\n self.button_reset_taxes(cr, uid, [parent.id])\\n return parent.id\",\n \"def invoices(self):\\r\\n return Invoices(self)\",\n \"def generate_report(df, start_date, end_date):\\n # Remove any transactions that had to do with collecting or returning security\\n security_df = df[(df[CATEGORY] == 'Security') | (df[CATEGORY] == 'Security-Income')]\\n df = df[(df[CATEGORY] != 'Security')]\\n\\n # Exclude the data for everything except our quarter\\n period_data = df.loc[start_date:end_date] # Note: NOT using extended quarter range\\n rental_income = period_data[period_data[CATEGORY] == 'Rent']\\n utilities = period_data[(period_data[CATEGORY] == 'Utilities')]\\n other_income = period_data[(period_data['Transaction Type'] == 'credit') & (period_data[CATEGORY] != 'Rent')]\\n expenses = period_data[(period_data['Transaction Type'] == 'debit')]\\n unpaid_util_overages = float(0)\\n\\n # print(rental_income)\\n # print(other_income)\\n # print(expenses)\\n \\n html_config.initialize()\\n print(html_config.HTML_OPEN)\\n\\n print('<H1>Income and Expense Report for %s-%s:' % (start_date, end_date), '</H1><p>')\\n\\n # List all unit specific rents and expenses for the quarter\\n for UNIT in sorted(rental_income['Unit'].unique()):\\n # Show rental income info\\n temp_df = rental_income[rental_income['Unit'] == UNIT]\\n print('<br><H2>Total rent for Unit ', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</H2>')\\n print(temp_df[['Description', 'Amount']].to_html())\\n \\n if not SKIP_UTIL_ANALYSIS:\\n # Show utilities payments and calculate any overage due\\n temp_df = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'debit')]\\n print('<br><H2>Utilities Expenses for Unit', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\\n overage = temp_df.assign(Overage=lambda x: x.Amount - limit_df.loc[UNIT].Amount)\\n # Disable warning when setting negative overage values to zero\\n pd.set_option('mode.chained_assignment', None)\\n overage.Overage[overage.Overage < 0] = 0\\n pd.set_option('mode.chained_assignment', 'warn')\\n print(overage[['Description', 'Amount', 'Overage']].to_html())\\n # Show if utilties costs exceeded allotted amount\\n if (overage['Overage'].sum() > 0):\\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\\n unpaid_util_overages += overage['Overage'].sum()\\n # Show any untilities that were collected \\n overage_collected = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'credit')]\\n if not overage_collected.empty:\\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\\n \\n\\n \\n # Generate unit specific Utility usage reports\\n if GEN_TENANT_UTIL_REPORTS and OUTPUT_DIRECTORY:\\n TENANT_FILE = '%s/122-Spring-St-%s-%s-Unit-%s-utils.html' % (OUTPUT_DIRECTORY, start_date, end_date, UNIT)\\n TENANT_REPORTS.append(TENANT_FILE)\\n sys.stdout = open(TENANT_FILE, 'w')\\n print(html_config.HTML_OPEN)\\n\\n print('<H1>Unit', UNIT, '</H1>')\\n print('<br><H2>Utilities Expenses for: %s-%s' % (start_date, end_date))\\n print('<br>Utilites included in rent: ${:,.2f}'.format(limit_df.loc[UNIT].Amount))\\n print('</H2>')\\n print(overage[['Description', 'Amount', 'Overage']].to_html())\\n # Show if any utilties overage may be due\\n if (overage['Overage'].sum() > 0):\\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\\n if not overage_collected.empty:\\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\\n\\n print(html_config.HTML_CLOSE)\\n\\n # Restore stdout to the main report file\\n sys.stdout = open(REPORT_FILE, 'a')\\n \\n # Show other unit specific transactions\\n if SKIP_UTIL_ANALYSIS:\\n unit_exp = expenses[(expenses['Unit'] == UNIT)]\\n unit_income = other_income[other_income['Unit'] == UNIT]\\n else:\\n unit_exp = expenses[(expenses['Unit'] == UNIT) & (expenses[CATEGORY] != 'Utilities')]\\n unit_income = other_income[(other_income['Unit'] == UNIT) & (other_income[CATEGORY] != 'Utilities')]\\n \\n if not unit_exp.empty:\\n print('<br><H2>Other Unit specific expenses for: ', UNIT, ': ${:,.2f}'.format(unit_exp['Amount'].sum()), '</h2>')\\n print(unit_exp[['Description', 'Amount', 'Unit', CATEGORY]].to_html())\\n print('<p>')\\n \\n # Show any other unit specific credit\\n other_income = other_income[other_income['Unit'] == UNIT]\\n if not other_income.empty:\\n print('<br><H2>Expense offsets for Unit ', UNIT, ': ${:,.2f}'.format(other_income['Amount'].sum()), '</H2>')\\n print(other_income[['Description', 'Amount', CATEGORY]].to_html())\\n \\n # Add a line between units\\n print('<hr>')\\n \\n # List the shared income and expenses for the quarter\\n temp_df = other_income[other_income['Unit'].isnull()]\\n if not temp_df.empty:\\n print ('<br><H2>Non unit specific income: ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\\n gen_expenses = expenses[expenses['Unit'].isnull()]\\n if not gen_expenses.empty:\\n print ('<br><H1>Non unit specific expenses</h1>')\\n # Get the list of expense categories and generate summary for each\\n for category in sorted(gen_expenses[CATEGORY].unique()):\\n temp_df = gen_expenses[(gen_expenses[CATEGORY] == category)]\\n print ('<br><H2>'+ category +': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\\n \\n # If there were any security transactions in the period give a security report\\n if not security_df.loc[start_date:end_date].empty:\\n temp_df = security_df.loc[start_date:end_date] \\n print('<hr><H2>Security related transactions:</H2>')\\n print(temp_df[['Description', 'Amount', 'Transaction Type', 'Unit']].to_html())\\n for UNIT in sorted(rental_income['Unit'].unique()):\\n unit_df = security_df[security_df['Unit'] == UNIT]\\n collected = unit_df[(unit_df['Transaction Type'] == 'credit')]['Amount'].sum()\\n returned = unit_df[(unit_df['Transaction Type'] == 'debit')]['Amount'].sum()\\n print('<center><H4>Current Liability on Unit '+str(UNIT)+': ${:,.2f}'.format(collected-returned), '</H4></center>')\\n \\n # # Summarize the periods income and expenses -- old way to be discarded...\\n # print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()), '</H3>')\\n # print('<H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\\n # print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\\n # Summarize the periods income and expenses\\n print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()))\\n print('<br><H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\\n print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\\n# print('</H3>')\\n \\n print(html_config.HTML_CLOSE)\\n sys.stdout.flush()\",\n \"def _prepare_invoice_grp(self, cr, uid, order, line_ids, context=None):\\n if context is None:\\n context = {}\\n context = dict(context)\\n\\n inv_data = super(grp_orden_compra, self)._prepare_invoice_grp(cr, uid, order, line_ids, context=context)\\n\\n # adicionando campos numero compromiso y no obligacion desde la OC\\n monto_oc = math.floor(order.total_llavep or 0)\\n monto_oc = int(monto_oc)\\n inv_data.update({'nro_compromiso': order.nro_compromiso or False, 'monto_comprometido': monto_oc or 0, 'currency_id':order.currency_oc.id})\\n\\n # adicionando campos no afectacion y monto autorizado desde la primera APG\\n if order.pc_apg_id:\\n first_apg = order.pc_apg_id\\n monto_apg = math.floor(first_apg.total_llavep)\\n monto_apg = int(monto_apg)\\n # TODO R SPRING X ADICIONANDO CABEZALES SIIF A LA FACTURA A PARTIR DE LA APG\\n inv_data.update({'nro_afectacion': first_apg.nro_afectacion_siif or False,\\n 'monto_afectado': monto_apg or 0,\\n 'siif_tipo_ejecucion':first_apg.siif_tipo_ejecucion.id,\\n 'siif_concepto_gasto':first_apg.siif_concepto_gasto.id,\\n 'siif_financiamiento':first_apg.siif_financiamiento.id,\\n 'siif_codigo_sir':first_apg.siif_codigo_sir.id,\\n 'siif_nro_fondo_rot':first_apg.siif_nro_fondo_rot.id,\\n }) # cambiando nro_afectacion 23/10\\n # inv.update({'nro_afectacion': first_apg.nro_afectacion_apg or False, 'monto_afectado': monto_apg or 0})\\n\\n # # TODO R SPRING X NO LLEVAR LAS LLAVES PRESUPUESTALES POR DEFECTO\\n # if order.pc_apg_id.llpapg_ids:\\n # llavep_ids = []\\n # for llavep in order.pc_apg_id.llpapg_ids:\\n # llavep_ids.append((0, 0, {\\n # 'programa_id': llavep.programa_id.id,\\n # 'odg_id': llavep.odg_id.id,\\n # 'auxiliar_id': llavep.auxiliar_id.id,\\n # 'disponible': llavep.disponible,\\n # 'proyecto_id': llavep.proyecto_id.id,\\n # 'fin_id': llavep.fin_id.id,\\n # 'mon_id': llavep.mon_id.id,\\n # 'tc_id': llavep.tc_id.id,\\n # 'importe': llavep.importe\\n # }))\\n # inv_data.update({'llpapg_ids': llavep_ids})\\n\\n return inv_data\",\n \"def _prepare_invoice_line(self, qty):\\n self.ensure_one()\\n res = {\\n 'name': self.name,\\n 'sequence': self.sequence,\\n 'origin': self.order_id.name,\\n 'account_id': self.product_id.product_tmpl_id._get_product_accounts()['stock_input'].id,\\n 'price_unit': self.price_unit,\\n 'quantity': qty,\\n 'uom_id': self.product_uom.id,\\n 'product_id': self.product_id.id or False,\\n 'invoice_line_tax_ids': [(6, 0, self.taxes_id.ids)],\\n 'account_analytic_id': self.account_analytic_id.id,\\n 'analytic_tag_ids': [(6, 0, self.analytic_tag_ids.ids)],\\n }\\n return res\",\n \"def invoice(self,context,params):\\n url = f\\\"https://api.freshbooks.com/accounting/account/{params['account_id']}/invoices/invoices/{params['invoice_id']}\\\"\\n result = json.loads(util.rest(\\\"GET\\\", url, {}, context[\\\"headers\\\"][\\\"access_token\\\"]).text)\\n invoice = result[\\\"response\\\"][\\\"result\\\"][\\\"invoice\\\"]\\n invoice_obj = FreshbooksInvoice(\\n account_id=invoice['accountid'],\\n customerid=invoice['customerid'], \\n invoice_id=invoice['invoiceid'],\\n currency_code=invoice['currency_code'],\\n language=invoice['language'],\\n terms=invoice['terms'],\\n discount_value=invoice['discount_value'],\\n discount_amount=invoice['discount_total']['amount'],\\n invoice_number=invoice['invoice_number'],\\n po_number=invoice['po_number'],\\n amount=invoice['amount']['amount'],\\n code=invoice['amount']['code'],\\n create_date=invoice['create_date']\\n )\\n return invoice_obj.__dict__\",\n \"def clean_qpcr_data(df) -> pd.DataFrame:\\n ASSAYDATE_COL = \\\"Assay Date\\\"\\n INSTRUMENT_COL = \\\"Instrument\\\"\\n POOL_COL = \\\"Pool\\\"\\n\\n # Names and types of all our columns. We also add the columns INSTRUMENT_COL and POOL_COL\\n original_columns = [ASSAYDATE_COL, DATE_COL, \\\"Sample ID\\\", \\\"GENE\\\", \\\"Ct [1]\\\", \\\"Ct [2]\\\", \\\"Ct [3]\\\", \\\"Ct Avg\\\", \\\"Ct Stdev\\\", \\\"Copies [1]\\\", \\\"Copies [2]\\\", \\\"Copies [3]\\\", \\\"Copies AVG\\\", \\\"Copies Stdev\\\", \\\"PMMoV Ct [1]\\\", \\\"PMMoV Ct [2]\\\", \\\"PMMoV Ct [3]\\\", \\\"PMMoV Avg\\\", \\\"PMMoV Stdev\\\", \\\"Empty tube weight (g)\\\", \\\"Full tube weight (g)\\\", \\\"Pellet weight (g)\\\", \\\"Extracted Mass (in 100 uL) (g)\\\", \\\"Copies per Extracted Mass (copies/g) [1]\\\", \\\"Copies per Extracted Mass (copies/g) [2]\\\", \\\"Copies per Extracted Mass (copies/g) [3]\\\", \\\"Copies per Extracted Mass (copies/g) Avg\\\", \\\"Copies per Extracted Mass Stdev\\\", \\\"2^Ct\\\", \\\"2^Ct normalized to a value\\\", \\\"(2^Ct normalized to a value per Extracted Mass)\\\", \\\"PMMoV Copies [1]\\\", \\\"PMMoV Copies [2]\\\", \\\"PMMoV Copies [3]\\\", \\\"PMMoV Copies Avg\\\", \\\"PMMoV Copies Stdev\\\", \\\"Copies per Copies of PMMoV Avg\\\", \\\"Copies per Copies of PMMoV * 10^3 Avg\\\", \\\"Copies per Copies normalized to a value\\\", \\\"Copies per Copies of PMMoV Stdev\\\", \\\"Copies per Copies of PMMoV * 10^3 Stdev\\\", \\\"Copies per Copies normalized to a value Stdev\\\", \\\"Date [2]\\\", \\\"Gene\\\", \\\"APPROVED: Copies per Copies of PMMoV [1]\\\", \\\"APPROVED: Copies per Copies of PMMoV [2]\\\", \\\"APPROVED: Copies per Copies of PMMoV [3]\\\", \\\"APPROVED: Copies per Copies of PMMoV Avg\\\", \\\"APPROVED: Copies per Copies of PMMoV Stdev\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) [1]\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) [2]\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) [3]\\\", \\\"APPROVED: Copies per Extracted Mass (copies/g) Avg\\\", \\\"APPROVED: Copies/L [1]\\\", \\\"APPROVED: Copies/L [2]\\\", \\\"APPROVED: Copies/L [3]\\\", \\\"APPROVED: Copies/L Avg\\\", \\\"MESP UPLOAD: PMMoV Copies per Extracted Mass (copies/g)\\\", \\\"MESP UPLOAD: PMMoV Copies/L\\\", \\\"INHIBITION CTRL: Date\\\", \\\"INHIBITION CTRL: Sample Name\\\", \\\"INHIBITION CTRL: Pepper 1/10 [1]\\\", \\\"INHIBITION CTRL: Pepper 1/10 [2]\\\", \\\"INHIBITION CTRL: Pepper 1/10 [3]\\\", \\\"INHIBITION CTRL: Pepper 1/10 Avg\\\", \\\"INHIBITION CTRL: Pepper 1/40 [1]\\\", \\\"INHIBITION CTRL: Pepper 1/40 [2]\\\", \\\"INHIBITION CTRL: Pepper 1/40 [3]\\\", \\\"INHIBITION CTRL: Pepper 1/40 Avg\\\", \\\"INHIBITION CTRL: Pepper No Dilution [1]\\\", \\\"INHIBITION CTRL: Pepper No Dilution [2]\\\", \\\"INHIBITION CTRL: Pepper No Dilution [3]\\\", \\\"INHIBITION CTRL: Pepper No Dilution Avg\\\", \\\"Pepper 1/10 ΔCt\\\", \\\"Pepper 1/40 ΔCt\\\"]\\n original_types = [\\\"date\\\", \\\"date\\\", \\\"text\\\", \\\"text\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"date\\\", \\\"text\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"date\\\", \\\"text\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\", \\\"number\\\"]\\n new_columns = original_columns.copy()\\n new_columns[2:2] = [INSTRUMENT_COL, POOL_COL]\\n new_types = original_types.copy()\\n new_types[2:2] = [\\\"text\\\", \\\"text\\\"]\\n\\n # Remove unwanted columns at end of dataframe, and add new columns in the correct order\\n df = df[df.columns[:len(original_columns)]].copy()\\n df.columns = original_columns\\n df[list(set(new_columns) - set(original_columns))] = None\\n df = df[new_columns]\\n\\n # Go through all rows and clean them, assign appropriate values for current instrument, pool, assay date, etc\\n cur_instrument, cur_pool = None, None\\n cur_assaydate = None\\n has_inhibition_controls = False\\n has_copies_per_liter = False\\n for i,row in df.iterrows():\\n cur_instrument, cur_pool = get_inst_and_pool(row, cur_instrument, cur_pool)\\n if row[ASSAYDATE_COL]:\\n row_date = parse_date(row[ASSAYDATE_COL])\\n cur_assaydate = row_date or cur_assaydate\\n if row_date is not None:\\n has_inhibition_controls = False\\n has_copies_per_liter = False\\n\\n if np.any([\\\"Pepper 1/10\\\" in c for c in row if isinstance(c, str)]):\\n has_inhibition_controls = True\\n if np.any([\\\"Copies / VS\\\" in c or \\\"Copies / L\\\" in c for c in row if isinstance(c, str)]):\\n has_copies_per_liter = True\\n df.loc[i, ASSAYDATE_COL] = cur_assaydate\\n df.loc[i, INSTRUMENT_COL] = cur_instrument\\n df.loc[i, POOL_COL] = cur_pool\\n if not has_inhibition_controls:\\n df.loc[i, [c for c in df.columns if c.startswith(\\\"INHIBITION CTRL: \\\")]] = \\\"\\\"\\n if not has_copies_per_liter:\\n index = [idx for idx, c in enumerate(df.columns) if c.startswith(\\\"APPROVED: Copies/L\\\")][0]\\n df.loc[i, df.columns[index:]] = \\\"\\\"\\n\\n # Remove invalid rows (where no valid \\\"Date\\\" is specified)\\n df[DATE_COL] = pd.to_datetime(df[DATE_COL], errors=\\\"coerce\\\")\\n df = df.loc[~df[DATE_COL].isna()]\\n\\n # 6 rows have a year of 1900 (from \\\"Septembe 27th\\\" assay date), change these to 2020.\\n # 12 rows have a year of 2014 (from \\\"Septembe 17th\\\" assay date), change these to 2020.\\n f = (df[DATE_COL].dt.year == 1900) | (df[DATE_COL].dt.year == 2014)\\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\\\"2020-%m-%d\\\"), format=\\\"%Y-%m-%d\\\")\\n\\n # Rows from assay date \\\"Tuesday, December 22, 2020\\\" and \\\"Wednesday, December 23, 2020\\\" have incorrect\\n # year in \\\"Date\\\" column (2021 instead of 2020)\\n df[ASSAYDATE_COL] = pd.to_datetime(df[ASSAYDATE_COL], errors=\\\"coerce\\\")\\n f = (df[ASSAYDATE_COL] == pd.to_datetime(\\\"December 22, 2020\\\")) | (df[ASSAYDATE_COL] == pd.to_datetime(\\\"December 23, 2020\\\"))\\n df.loc[f, DATE_COL] = pd.to_datetime(df.loc[f, DATE_COL].dt.strftime(\\\"2020-%m-%d\\\"), format=\\\"%Y-%m-%d\\\")\\n\\n # Add row at start that specifies the column types (date, text, number)\\n df.loc[-1] = new_types\\n df.index = df.index + 1\\n df = df.sort_index()\\n df.index = list(range(len(df.index)))\\n\\n return df\",\n \"def create_invoice(self):\\n sales_tax = 0.06\\n item_sum = 0\\n inv = f'Invoice#: {self.invoice_id}\\\\n'\\n for key, value in self.items_with_price.items():\\n item_sum += value\\n inv += f'{key}.....${value:.2f}\\\\n'\\n\\n tax = item_sum * sales_tax\\n inv += f'Tax.....${tax:.2f}\\\\n'\\n inv += f'Total.....${tax + item_sum:.2f}'\\n # print(inv)\\n # returning for unit testing purposes\\n return inv\",\n \"def get_rows(contract_address: str, file_name: str, receipts_filename: str) -> pd.DataFrame():\\n receipts_df = pd.read_csv(receipts_filename)\\n receipts_df = receipts_df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\\n df = pd.read_csv(file_name)\\n df = df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\\n\\n df = df.loc[receipts_df['status'] == 1] \\n df = df.loc[df[\\\"to_address\\\"] == contract_address.lower()]\\n df = df.reset_index()\\n df = df.drop(columns='index')\\n return df\",\n \"def cash_flow_response_to_df(\\n portfolio_cash_flows_response: lusid.ResourceListOfPortfolioCashFlow,\\n sum_by_date: bool = True\\n) -> pd.DataFrame:\\n\\n def select_cols(\\n df: pd.DataFrame,\\n filter_col: str,\\n filter_value: str,\\n cols_to_keep: list,\\n ) -> pd.DataFrame:\\n return df[df[filter_col] == filter_value][cols_to_keep]\\n\\n # Extract cash payment data from cash flow response\\n cash_flows_dict = portfolio_cash_flows_response.to_dict()\\n cash_flow_data = pd.json_normalize(cash_flows_dict[\\\"values\\\"])\\n\\n # Split pays and receives and handle -ve signage for pay outflows\\n pay_data = select_cols(\\n cash_flow_data,\\n \\\"diagnostics.PayReceive\\\",\\n \\\"Pay\\\",[\\\"payment_date\\\", \\\"amount\\\", \\\"source_transaction_id\\\"]\\n )\\n pay_data[\\\"amount\\\"] = pay_data[\\\"amount\\\"].apply(lambda x: -1 * x)\\n pay_data.rename(columns={\\\"amount\\\": \\\"payAmount\\\"}, inplace=True)\\n rec_data = select_cols(\\n cash_flow_data,\\n \\\"diagnostics.PayReceive\\\",\\n \\\"Receive\\\",\\n [\\\"payment_date\\\", \\\"amount\\\", \\\"source_transaction_id\\\"]\\n )\\n rec_data.rename(columns={\\\"amount\\\": \\\"receiveAmount\\\"}, inplace=True)\\n\\n # Merge on payment date and ignore join dupes\\n merged_df = pay_data.merge(rec_data, on=[\\\"payment_date\\\", \\\"source_transaction_id\\\"])\\n merged_df.drop_duplicates(subset=[\\\"payment_date\\\", \\\"source_transaction_id\\\"], keep=\\\"first\\\", inplace=True,\\n ignore_index=True)\\n\\n # Add net flows and reduce index to dates\\n merged_df['netAmount'] = merged_df['payAmount'] + merged_df['receiveAmount']\\n merged_df[\\\"payment_date\\\"] = merged_df[\\\"payment_date\\\"].apply(lambda x: x.date())\\n merged_df.set_index(keys=\\\"payment_date\\\", inplace=True)\\n\\n # Aggregate sub-holdings\\n if sum_by_date:\\n merged_df = merged_df.groupby(merged_df.index).sum()\\n\\n return merged_df\",\n \"def investment_line(self):\\n inv, marks = self._get_marks()\\n fig = plt.figure(figsize=(4, 2), dpi=200)\\n fig.patch.set_facecolor('#ececec')\\n ax = fig.add_subplot(111)\\n investmentValues = inv['Invested']\\n #investmentValues = pd.Series([0], index=[investmentValues.index[0]-timedelta(1)]).append(investmentValues)\\n ax.plot(investmentValues, lw=1.2, color=\\\"blue\\\", label='Invested', marker=\\\"o\\\", markersize=3, markerfacecolor=\\\"grey\\\")\\n ax.set_xlabel('Time')\\n ax.set_ylabel('Investments (€)')\\n ax.set_title('Investment Amount (€) - Daily')\\n ax.xaxis.set_major_locator(dates.MonthLocator())\\n ax.xaxis.set_major_formatter(dates.DateFormatter('%b-%Y'))\\n for x, y, mark in zip(marks.index, marks['Invested'], marks['Marks']):\\n a = ax.get_ylim()\\n if x == marks.index[0]:\\n ax.annotate(str(mark) + \\\" €\\\", xy=(x + timedelta(abs((self.data.index[0] - self.data.index[-1]).days) / 80), y + (a[1]-a[0])/35), fontsize=5)\\n else:\\n ax.annotate(str(mark) + \\\" €\\\", xy=(x + timedelta(abs((self.data.index[0] - self.data.index[-1]).days) / 50), y - (a[1]-a[0])/35), fontsize=5)\\n ax.grid(True)\\n fig.autofmt_xdate()\\n ax.legend()\\n return fig, ax\",\n \"def invoice_line_create(self, invoice_id, qty):\\n invoice_lines = self.env['account.invoice.line']\\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\\n for line in self:\\n if not float_is_zero(qty, precision_digits=precision):\\n vals = line._prepare_invoice_line(qty=qty)\\n vals.update({'invoice_id': invoice_id, 'purchase_line_id': line.id})\\n invoice_lines |= self.env['account.invoice.line'].create(vals)\\n return invoice_lines\",\n \"def emissions_baseline(self):\\n baseline = DataFrame(columns=[\\\"CO2\\\", \\\"NOx\\\", \\\"PM10\\\", \\\"PM2.5\\\", \\\"SO2\\\"])\\n baseline = baseline.append(year_1(self.plant.emissions()))\\n baseline = baseline.append(year_1(self.plant.fuel_reseller().emissions()))\\n baseline = baseline.append(year_1(self.farmer.emissions_exante))\\n baseline.loc[\\\"Total\\\"] = baseline.sum()\\n baseline.loc[\\\"Total_plant\\\"] = baseline.iloc[0]\\n baseline.loc[\\\"Total_transport\\\"] = baseline.iloc[1]\\n baseline.loc[\\\"Total_field\\\"] = baseline.iloc[2]\\n return baseline\",\n \"def from_invoice_and_line_item(cls, invoice: InvoiceModel, line_item: LineItemModel, line_number: int,\\n distribution: str):\\n # Note the invoice_date should be the payment_date in the future.\\n return cls(total=line_item.total, invoice_number=invoice.id,\\n line_number=line_number,\\n is_reversal=invoice.invoice_status_code in\\n [InvoiceStatus.REFUNDED.value, InvoiceStatus.REFUND_REQUESTED.value],\\n distribution=distribution)\",\n \"def submit_invoices(self, **kwargs) -> ApiResponse:\\n \\n return self._request(kwargs.pop('path'), data=kwargs)\",\n \"def create_equity_curve_dataframe(self):\\n # returns the cumulative product for percent change over every timestamp in the index\\n curve = pd.DataFrame(self.all_holdings)\\n curve.set_index('datetime', inplace=True)\\n curve['returns'] = curve['total'].pct_change()\\n #curve['equity_curve'] = (1.0+curve['returns']).cumprod()\\n curve['equity_curve'] = curve['returns']\\n curve['equity_curve'] += 1\\n curve['equity_curve'] = curve['equity_curve'].cumprod()\\n self.equity_curve = curve\\n print(curve)\",\n \"def _prepare_invoice(self):\\n # get current logged in user's timezone\\n local = pytz.timezone(self.env['res.users'].browse(self._uid).tz) or pytz.utc\\n\\n self.ensure_one()\\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')], limit=1).id\\n if not journal_id:\\n raise UserError(_('Please define an accounting purchase journal for this company.'))\\n invoice_vals = {\\n 'name': self.partner_ref or '',\\n 'origin': self.name,\\n 'type': 'in_invoice',\\n 'account_id': self.partner_id.property_account_payable_id.id,\\n 'partner_id': self.partner_id.id,\\n 'journal_id': journal_id,\\n 'currency_id': self.currency_id.id,\\n 'comment': self.notes,\\n 'payment_term_id': self.payment_term_id.id,\\n 'fiscal_position_id': self.fiscal_position_id.id or self.partner_id.property_account_position_id.id,\\n 'company_id': self.company_id.id,\\n 'purchase_id': self.id,\\n 'date_invoice':pytz.utc.localize(datetime.datetime.now()).astimezone(local).strftime('%Y-%m-%d'),\\n }\\n return invoice_vals\",\n \"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\",\n \"def create_invoice(sender, invoice, issuer_details, **kwargs):\\n if not invoice.items:\\n return\\n\\n price = sum([item.price for item in invoice.items.all()])\\n\\n if not price:\\n return\\n\\n paypal_invoice = models.Invoice(\\n customer=invoice.customer,\\n year=invoice.year,\\n month=invoice.month,\\n invoice_date=invoice.invoice_date,\\n end_date=invoice.due_date,\\n tax_percent=invoice.tax_percent,\\n issuer_details=issuer_details,\\n )\\n\\n paypal_invoice.payment_details = {\\n 'name': invoice.customer.name,\\n 'address': invoice.customer.address,\\n 'country': invoice.customer.country,\\n 'country_name': invoice.customer.get_country_display(),\\n 'email': invoice.customer.email,\\n 'postal': invoice.customer.postal,\\n 'phone_number': invoice.customer.phone_number,\\n 'bank_name': invoice.customer.bank_name,\\n 'bank_account': invoice.customer.bank_account,\\n }\\n\\n paypal_invoice.save()\\n\\n for item in invoice.items.all():\\n models.InvoiceItem.objects.create(\\n invoice=paypal_invoice,\\n price=item.price,\\n tax=item.tax,\\n quantity=item.quantity,\\n unit_price=item.unit_price,\\n unit_of_measure=helpers.convert_unit_of_measure(item.unit),\\n name=item.name,\\n start=item.start,\\n end=item.end,\\n )\",\n \"def invoices_report_filter(request):\\n qry = DBSession.query(Invoice)\\n supplierlist = []\\n for supplier in qry:\\n if supplier.SupplierID:\\n entry = {'Name': supplier.Order.Supplier.Name,\\n 'ID': supplier.SupplierID}\\n if entry not in supplierlist:\\n supplierlist.append(entry)\\n\\n projectlist = []\\n for project in qry:\\n if project.ProjectID:\\n entry = {'Name': project.Order.Project.Name,\\n 'ID': project.ProjectID}\\n if entry not in projectlist:\\n projectlist.append(entry)\\n\\n paymentdatelist = []\\n for paymentdate in qry:\\n if paymentdate.PaymentDate:\\n entry = paymentdate.PaymentDate.strftime(\\\"%d %B %Y\\\")\\n if entry not in paymentdatelist:\\n paymentdatelist.append(entry)\\n\\n return {'projects': sorted(projectlist, key=lambda k: k['Name'].upper()),\\n 'suppliers': sorted(supplierlist, key=lambda k: k['Name'].upper()),\\n 'paymentdates': sorted(paymentdatelist),\\n 'paymentdates_exist': paymentdatelist != [],\\n 'statuses': ['Draft', 'Due', 'Paid']}\",\n \"def invoices(self,org_id=None,invoice_id=''):\\n if org_id is None:\\n org_id = self.org_id\\n return self.get('{}/orgs/{}/invoices/{}'.format(ApiVersion.A1.value,org_id,invoice_id))\",\n \"def sub_tax_sales_transfer_pricing(manager, df_fields, seq_recs, seq_reservas):\\n # df_hotel = manager.get_dataframe(tables['dwc_bok_t_canco_hotel'])\\n # df_circuit = manager.get_dataframe(tables['dwc_bok_t_canco_hotel_circuit'])\\n # df_other = manager.get_dataframe(tables['dwc_bok_t_canco_other'])\\n # df_transfer = manager.get_dataframe(tables['dwc_bok_t_canco_transfer'])\\n # df_endow = manager.get_dataframe(tables['dwc_bok_t_canco_endowments'])\\n # df_extra = manager.get_dataframe(tables['dwc_bok_t_canco_extra'])\\n\\n df_aux = df_fields.select(\\\"operative_incoming\\\", \\\"booking_id\\\")\\n\\n df_hotel = sub_tax_sales_transfer_pricing_aux(manager, df_hotelt, seq_recs, seq_reservas, df_aux)\\n df_circuit = sub_tax_sales_transfer_pricing_aux(manager, df_circuitt, seq_recs, seq_reservas, df_aux)\\n df_other = sub_tax_sales_transfer_pricing_aux(manager, df_othert, seq_recs, seq_reservas, df_aux)\\n df_transfer = sub_tax_sales_transfer_pricing_aux(manager, df_transfert, seq_recs, seq_reservas, df_aux)\\n df_endow = sub_tax_sales_transfer_pricing_aux(manager, df_endowt, seq_recs, seq_reservas, df_aux)\\n df_extra = sub_tax_sales_transfer_pricing_aux_extra(manager, df_extrat, seq_recs, seq_reservas, df_aux)\\n\\n df_impuesto_canal = df_hotel.union(df_circuit).union(df_other).union(df_transfer).union(df_endow).union(\\n df_extra)\\n\\n df_impuesto_canal = df_impuesto_canal.groupBy(\\\"seq_rec\\\", \\\"seq_reserva\\\") \\\\\\n .agg({'impuesto_canal': 'sum'}).withColumnRenamed(\\\"SUM(impuesto_canal)\\\", \\\"Tax_Sales_Transfer_pricing\\\")\\n\\n df_fields = df_fields.join(df_impuesto_canal, [df_fields.operative_incoming == df_impuesto_canal.seq_rec,\\n df_fields.booking_id == df_impuesto_canal.seq_reserva],\\n 'left_outer').drop(df_impuesto_canal.seq_rec).drop(df_impuesto_canal.seq_reserva)\\n\\n df_fields = df_fields.na.fill({\\\"Tax_Sales_Transfer_pricing\\\": 0})\\n\\n df_fields = df_fields.withColumn(\\\"Tax_Sales_Transfer_pricing\\\",\\n udf_round_ccy(df_fields.Tax_Sales_Transfer_pricing,\\n df_fields.booking_currency))\\n\\n del df_hotel, df_circuit, df_other, df_transfer, df_endow, df_extra, df_impuesto_canal\\n\\n return df_fields\",\n \"def income_report_gen(start, end):\\n payments = get_income(start, end)\\n row_title = [\\\"Name\\\", \\\"Boat\\\", \\\"Rent Day\\\", \\\"Pay Day\\\", \\\"Amount\\\"]\\n data = []\\n for payment in payments:\\n temp = []\\n for title, value in payment.items():\\n temp.append(str(value))\\n data.append(temp)\\n row_format = \\\"{:>15}\\\" * (len(row_title)+1)\\n print(row_format.format(\\\"\\\", *row_title))\\n total_income = 0\\n for i in range(len(data)):\\n print(row_format.format(i+1, *data[i]))\\n total_income += int(data[i][4])\\n print(row_format.format(\\\"SUM\\\", *([\\\"--------------\\\"] * 4), str(total_income)))\",\n \"def get_equal_dataframe() -> pandas.DataFrame:\\n data = {\\n 'InvoiceDate': ['2009-01', '2012-01'],\\n 'Count': [4, 4],\\n 'Total': [8198.79, 5323.15]\\n }\\n return pandas.DataFrame(data, columns=['InvoiceDate', 'Count', 'Total'])\",\n \"def order_report():\",\n \"def _prepare_invoice(self, cr, uid, order, lines, context=None):\\n invoice_vals = super(my_sale_order, self)._prepare_invoice(cr, uid, order,\\n lines, context)\\n\\n invoice_vals.update({\\n 'partner_shipping_id': order.partner_shipping_id.id,\\n })\\n\\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\\n\\n return invoice_vals\",\n \"def action_invoice_create(self, cr, uid, ids, context=None):\\n res = False\\n\\n journal_obj = self.pool.get('account.journal')\\n inv_obj = self.pool.get('account.invoice')\\n inv_line_obj = self.pool.get('account.invoice.line')\\n fiscal_obj = self.pool.get('account.fiscal.position')\\n\\n for order in self.browse(cr, uid, ids, context=context):\\n# pay_acc_id = order.partner_id.property_account_payable.id\\n #use a new method to get the account_id\\n pay_acc_id = self._get_inv_pay_acc_id(cr,uid,order) \\n journal_ids = journal_obj.search(cr, uid, [('type', '=','purchase'),('company_id', '=', order.company_id.id)], limit=1)\\n if not journal_ids:\\n raise osv.except_osv(_('Error!'),\\n _('Define purchase journal for this company: \\\"%s\\\" (id:%d).') % (order.company_id.name, order.company_id.id))\\n\\n # generate invoice line correspond to PO line and link that to created invoice (inv_id) and PO line\\n inv_lines = []\\n for po_line in order.order_line:\\n #check if this line have quantity to generate invoice, by johnw\\n if po_line.product_qty <= po_line.invoice_qty:\\n continue \\n# if po_line.product_id:\\n# acc_id = po_line.product_id.property_account_expense.id\\n# if not acc_id:\\n# acc_id = po_line.product_id.categ_id.property_account_expense_categ.id\\n# if not acc_id:\\n# raise osv.except_osv(_('Error!'), _('Define expense account for this company: \\\"%s\\\" (id:%d).') % (po_line.product_id.name, po_line.product_id.id,))\\n# else:\\n# acc_id = property_obj.get(cr, uid, 'property_account_expense_categ', 'product.category').id \\n #use a new method to get the account_id, by johnw \\n acc_id = self._get_inv_line_exp_acc_id(cr,uid,order,po_line)\\n fpos = order.fiscal_position or False\\n acc_id = fiscal_obj.map_account(cr, uid, fpos, acc_id)\\n\\n inv_line_data = self._prepare_inv_line(cr, uid, acc_id, po_line, context=context)\\n #update the quantity to the quantity, by johnw\\n inv_line_data.update({'quantity':(po_line.product_qty - po_line.invoice_qty)})\\n inv_line_id = inv_line_obj.create(cr, uid, inv_line_data, context=context)\\n inv_lines.append(inv_line_id)\\n\\n po_line.write({'invoiced':True, 'invoice_lines': [(4, inv_line_id)]}, context=context)\\n \\n #if no lines then return direct, by johnw\\n if len(inv_lines) == 0:\\n continue\\n \\n # get invoice data and create invoice\\n inv_data = {\\n 'name': order.partner_ref or order.name,\\n 'reference': order.partner_ref or order.name,\\n 'account_id': pay_acc_id,\\n 'type': 'in_invoice',\\n 'partner_id': order.partner_id.id,\\n 'currency_id': order.pricelist_id.currency_id.id,\\n 'journal_id': len(journal_ids) and journal_ids[0] or False,\\n 'invoice_line': [(6, 0, inv_lines)],\\n 'origin': order.name,\\n 'fiscal_position': order.fiscal_position.id or False,\\n 'payment_term': order.payment_term_id.id or False,\\n 'company_id': order.company_id.id,\\n }\\n inv_id = inv_obj.create(cr, uid, inv_data, context=context)\\n\\n # compute the invoice\\n inv_obj.button_compute(cr, uid, [inv_id], context=context, set_total=True)\\n\\n # Link this new invoice to related purchase order\\n order.write({'invoice_ids': [(4, inv_id)]}, context=context)\\n res = inv_id\\n return res\",\n \"def _prepare_invoice(self):\\n self.ensure_one()\\n journal_id = self.env['account.invoice'].default_get(['journal_id'])['journal_id']\\n if not journal_id:\\n raise UserError(_('Please define an accounting sales journal for this company.'))\\n invoice_vals = {\\n 'name': self.client_order_ref or '',\\n 'origin': self.name,\\n 'type': 'out_invoice',\\n 'account_id': self.partner_invoice_id.property_account_receivable_id.id,\\n 'partner_id': self.partner_invoice_id.id,\\n 'partner_shipping_id': self.partner_shipping_id.id,\\n 'journal_id': journal_id,\\n 'currency_id': self.pricelist_id.currency_id.id,\\n 'comment': self.note,\\n 'payment_term_id': self.payment_term_id.id,\\n 'fiscal_position_id': self.fiscal_position_id.id or self.partner_invoice_id.property_account_position_id.id,\\n 'company_id': self.company_id.id,\\n 'user_id': self.user_id and self.user_id.id,\\n 'team_id': self.team_id.id,\\n 'x_studio_field_rgEdd': self.x_studio_field_icWOZ.id,\\n 'x_studio_car_type_1': self.vehicle.id,\\n 'x_studio_job_card_1': self.x_studio_agency_job_card,\\n 'x_studio_car_type_name': self.vehicle.model_id.name,\\n 'x_studio_plate_num': self.vehicle.license_plate,\\n 'x_studio_claim_num': self.claim_no,\\n\\n 'x_studio_is_insured':self.is_insured,\\n 'x_studio_service_provider': self.service_advisor.id,\\n 'date_invoice': fields.Date.today(),\\n 'transaction_ids': [(6, 0, self.transaction_ids.ids)],\\n }\\n return invoice_vals\\n\\n # 'x_studio_field_rgEdd':order.x_studio_field_icWOZ.id,\",\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 _calculate(self):\\n source = self.source\\n res = {}\\n l_cols = [[], [], [], []]\\n r_lines = {}\\n dateline=None\\n ###delete the below code when fetch data from database(assume: data in database has been pretreatment)\\n if source[t.ror].min() > -99.0:\\n pass\\n else:\\n source[t.ror] = np.where(\\n source[t.ror] > -99.0, source[t.ror], -99.0)\\n ###\\n for account in self.accounts:\\n source_account = source[source[t.account] == account]\\n source_account = source_account.reset_index(drop=True)\\n dateline=source_account[t.effective_date]\\n ror=source_account[t.ror]/100\\n returns_cum = ROR.ror_cum_ann(source_account, self.annualized)\\n # double_return_cum=round(double_return_cum,2)+1\\n returns_cum = returns_cum + 1\\n growth_amounts = returns_cum * self.starting_value\\n returns_cum, growth_amounts = round(returns_cum - 1, 4), \\\\\\n round(growth_amounts, 2)\\n l_cols[0].append(growth_amounts.iloc[-1, 0])#account growth amount\\n l_cols[1].append(growth_amounts.iloc[-1, 1])#bench growth amount\\n l_cols[2].append(returns_cum.iloc[-1, 0])#account return\\n l_cols[3].append(returns_cum.iloc[-1, 1])#bench return\\n r_lines[account] = [list(returns_cum.iloc[:,0]), list(growth_amounts.iloc[:, 0]),#list(returns_cum.iloc[:, 0])\\n list(growth_amounts.iloc[:, 1])]#account return, account growth amount, bench growth amount\\n res['account_vs_benchmark'] = {'xAxis': self.accounts,\\n 'series': l_cols}\\n res['growth_of_unit'] = {'xAxis': list(dateline),\\n 'series': r_lines}\\n return res\\n # ret_dict = self._ret(accounts, starting_value, source, annualized)\\n # return ret_dict\",\n \"def quick_report(qbo_session, filter_attributes={}, headers=True):\\n\\n #basically, you can filter on any attribute massage.ledgerize() kicks out\\n filterable_attributes = {\\n \\\"TxnDate\\\":0, \\\"qbbo_type\\\":1, \\\"entity_id\\\":2, \\n \\\"line_number\\\":3, \\\"document_type\\\":4,\\n \\\"domain\\\":5, \\\"user_number\\\":6,\\n \\\"CreateTime\\\":7, \\\"LastUpdatedTime\\\":8, \\\"SyncToken\\\":9, \\\"Adjustment\\\":10,\\n \\\"account\\\":11, \\\"amount\\\":13, \\\"description\\\":14, \\\"name\\\":15,\\n \\\"linked_transactions\\\":16\\n }\\n \\n fa = copy.deepcopy(filter_attributes)\\n\\n for a in filter_attributes:\\n \\n if not a in filterable_attributes:\\n raise Exception(\\\"QuickReport() doesn't know how to filter on\\\"+\\n \\\" %s. Please use one of:\\\\n%s\\\" % \\n (a, filterable_attributes))\\n \\n #yes, we're being permissive\\n if isinstance(filter_attributes[a],(int,float,long,str)):\\n \\n fa[a]=[filter_attributes[a]]\\n\\n elif isinstance(filter_attributes[a],(list,tuple)):\\n \\n fa[a]=filter_attributes[a]\\n\\n else:\\n \\n raise Exception(\\\"filter_attributes items must be lists,\\\" + \\\\\\n \\\"tuples, or stand-alone values\\\")\\n\\n filtered_lines = [qbo_session.ledgerize(\\\"_\\\", headers=True)]\\n\\n for ledger_line in qbo_session.ledger_lines():\\n\\n #now let's apply the filter, white-list style\\n\\n for a in fa:\\n\\n white_list = fa[a]\\n\\n #sometimes a Line will just HAVE the attribute\\n #e.g. a JournalEntry line will always have an account\\n #othertimes, we'll have to look it up with a cross reference\\n #e.g. an Invoice line will NOT have an account, it'll have\\n #an item, so we need to look up the account in the item\\n\\n #so we're breaking that functionality out into it's own function\\n\\n i = filterable_attributes[a]\\n\\n if ledger_line[i] in white_list:\\n\\n filtered_lines.append(ledger_line)\\n\\n return filtered_lines\",\n \"def create_equity_curve_dataframe(self):\\n # returns the cumulative product for percent change over every timestamp in the index\\n curve = pd.DataFrame(self.all_holdings)\\n curve.set_index('datetime', inplace=True)\\n curve['returns'] = curve['total'].pct_change()\\n #curve['equity_curve'] = (1.0+curve['returns']).cumprod()\\n curve['equity_curve'] = curve['returns']\\n curve['equity_curve'] += 1\\n curve['equity_curve'] = curve['equity_curve'].cumprod()\\n self.equity_curve = curve\",\n \"def pnl(qbo_session, period = \\\"YEARLY\\\", start_date=\\\"first\\\", end_date=\\\"last\\\",\\n **kwargs):\\n\\n pnl_account_types = [\\n \\n \\\"Income\\\", \\\"Other Income\\\",\\n \\\"Expense\\\", \\\"Other Expense\\\", \\\"Cost of Goods Sold\\\"\\n \\n ]\\n\\n \\n\\n # go through the accounts, collecting a list of those that are \\n # pnl accounts\\n\\n relevant_accounts = []\\n\\n coa = qbo_session.chart_of_accounts()\\n\\n AccountType_i = coa[0].index(\\\"AccountType\\\")\\n fqa_i = coa[0].index(\\\"FullyQualifiedName\\\")\\n\\n for a in coa:\\n\\n AccountType = a[AccountType_i]\\n\\n if AccountType in pnl_account_types:\\n\\n relevant_accounts.append(a[fqa_i])\\n \\n # now collect the ledger_lines that are even relevant to the time\\n # period and pnl accounts (and we'll handle presentation last)\\n\\n relevant_activity = {} #{account:[relevant lines]}\\n\\n all_ledger_lines = qbo_session.ledger_lines(None, None, None, True,\\n **kwargs)\\n\\n headers = all_ledger_lines[0]\\n\\n account_i = headers.index(\\\"account\\\") \\n amount_i = headers.index(\\\"amount\\\")\\n date_i = headers.index(\\\"TxnDate\\\")\\n \\n earliest_date = datetime(2100,1,1)\\n latest_date = datetime(1900,1,1)\\n\\n for line in all_ledger_lines[1:]:\\n\\n account = line[account_i]\\n line_date = line[date_i]\\n\\n #first apply the date filter!\\n if not start_date == \\\"first\\\" and line_date < start_date:\\n continue\\n \\n if not end_date == \\\"last\\\" and line_date > end_date:\\n continue\\n \\n #if it's made the cut, we can update the report date bounds\\n earliest_date = min(line_date,earliest_date)\\n latest_date = max(line_date,latest_date)\\n\\n #then apply the account filter!\\n\\n if not account in relevant_activity:\\n #then let's confirm that its account type is a pnl one\\n \\n if not account in relevant_accounts:\\n \\n continue\\n\\n else:\\n relevant_activity[account] = []\\n\\n relevant_activity[account].append(line)\\n\\n #now let's do presentation\\n #TODO -- incorporate pandas tables...do only minimal work on it until then\\n\\n pnl_lines = []\\n\\n if period == \\\"YEARLY\\\":\\n\\n report_start_date = datetime(earliest_date.year,1,1)\\n report_end_date = datetime(latest_date.year,12,31)\\n\\n period_start_dates = list(rrule(YEARLY, bymonth=1, bymonthday=1,\\n dtstart=report_start_date,\\n until=report_end_date))\\n\\n period_end_dates = list(rrule(YEARLY, bymonth=12, bymonthday=-1,\\n dtstart=report_start_date,\\n until=report_end_date))\\n\\n elif period == \\\"MONTHLY\\\":\\n\\n report_start_date = datetime(earliest_date.year,\\n earliest_date.month,\\n 1)\\n report_end_date = datetime(latest_date.year,\\n latest_date.month,\\n calendar.monthrange(latest_date.year,\\n latest_date.month)[1])\\n\\n period_start_dates = list(rrule(MONTHLY, bymonthday=1,\\n dtstart=report_start_date,\\n until=report_end_date))\\n\\n period_end_dates = list(rrule(YEARLY, bymonthday=-1,\\n dtstart=report_start_date,\\n until=report_end_date)) \\n\\n header_1 = [\\\"\\\", \\\"Period Start -->\\\"] + period_start_dates\\n header_2 = [\\\"Account\\\", \\\"Period End -->\\\"] + period_end_dates\\n\\n pnl_lines.append(header_1)\\n pnl_lines.append(header_2)\\n\\n \\\"\\\"\\\"Clearly, there's a way to do this with only one pass of the data...\\n let's get that right in the first re-write...probably with pandas\\\"\\\"\\\"\\n\\n #now let's fill up the pnl_lines with what we know to be the relevant data\\n #for now, we'll rely on the knowledge that the data is coming to us in\\n #date order, but that should be fixed too...\\n\\n for account in relevant_activity:\\n\\n account_row = [account, \\\"\\\"] #one value per period \\n\\n current_period_index = 0 #primitive counter, yes!\\n this_period_total = 0 #this will be this period's total\\n\\n for line in relevant_activity[account]:\\n \\n line_amount = line[amount_i]\\n line_date = line[date_i] \\n\\n if line_date > period_end_dates[current_period_index]:\\n\\n account_row.append(this_period_total)\\n this_period_total = line_amount\\n current_period_index +=1\\n\\n else:\\n \\n this_period_total = round(this_period_total +\\n line_amount, 2)\\n\\n \\\"\\\"\\\"super sloppy...\\\"\\\"\\\"\\n account_row.append(this_period_total) #for the last period\\n current_period_index +=1\\n\\n while current_period_index < len(period_end_dates):\\n account_row.append(0)\\n current_period_index +=1\\n\\n pnl_lines.append(account_row)\\n\\n return pnl_lines\",\n \"def action_generate_lines_txt(self):\\n rp_obj = self.env['res.partner']\\n voucher_obj = self.env['account.wh.iva']\\n txt_iva_obj = self.env['txt.iva.line']\\n vouchers = []\\n txt_brw = self.browse(self._ids)[0]\\n txt_ids = txt_iva_obj.search([('txt_id', '=', txt_brw.id)])\\n if txt_ids:\\n txt_ids.unlink()\\n\\n if txt_brw.type:\\n vouchers = voucher_obj.search([\\n ('date_ret', '>=', txt_brw.date_start),\\n ('date_ret', '<=', txt_brw.date_end),\\n ('period_id', '=', txt_brw.period_id.id),\\n ('state', '=', 'done'),\\n ('type', 'in', ['in_invoice', 'in_refund'])])\\n else:\\n vouchers = voucher_obj.search([\\n ('date_ret', '>=', txt_brw.date_start),\\n ('date_ret', '<=', txt_brw.date_end),\\n ('period_id', '=', txt_brw.period_id.id),\\n ('state', '=', 'done'),\\n ('type', 'in', ['out_invoice', 'out_refund'])])\\n\\n for voucher in vouchers:\\n acc_part_id = rp_obj._find_accounting_partner(voucher.partner_id)\\n for voucher_lines in voucher.wh_lines:\\n if voucher_lines.invoice_id.state not in ['open', 'paid']:\\n continue\\n for voucher_tax_line in voucher_lines.tax_line:\\n txt_iva_obj.create(\\n {'partner_id': acc_part_id.id,\\n 'voucher_id': voucher.id,\\n 'invoice_id': voucher_lines.invoice_id.id,\\n 'txt_id': txt_brw.id,\\n 'untaxed': voucher_tax_line.base,\\n 'amount_withheld': voucher_tax_line.amount_ret,\\n 'tax_wh_iva_id': voucher_tax_line.id,\\n })\\n return True\",\n \"def invoices(self):\\r\\n return inv.Invoices(self)\",\n \"def features_orders(df_customers, df_receipts):\\n df_customers.sort_values(by=['customerId2'], ascending=[True], inplace=True)\\n # total amount of all the orders of a cusrtomer\\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')['price'].sum(), 'sum_orders')\\n # the min amount paid in one receipt by a customer\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)[\\n 'price'].sum().groupby('customerId2').min()['price'], 'min_order')\\n # the mean amount paid per receipt by a customer\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\\n .groupby('customerId2').mean()['price'], 'mean_order')\\n # the max amount paid per receipt by a customer\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\\n .groupby('customerId2').max()['price'], 'max_order')\\n # the number of orders\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\\n .groupby('customerId2').count()['price'], 'count_orders')\\n\\n # the total amount of items bought by a user\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby('customerId2')['itemQty'].sum(), 'sum_itemQty')\\n # the min amount of items bought by a user in a receipt\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\\n .groupby('customerId2').min()['itemQty'], 'min_itemQty')\\n # the mean amount of items bought by a user in a receipt\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\\n .groupby('customerId2').mean()['itemQty'], 'mean_itemQty')\\n # the max amount of items bought by a user in a receipt\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\\n .groupby('customerId2').max()['itemQty'], 'max_itemQty')\\n # from which dividion type a user buys\\n for i in DIVISIONID_VALS:\\n k = 'divisionId_' + str(i)\\n df_receipts[k] = np.where(df_receipts['divisionId'] == i, 1, 0)\\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\\n # which source type a user uses to pay\\n for i in SOURCEID_VALS:\\n k = 'sourceId_' + str(i)\\n df_receipts[k] = np.where(df_receipts['sourceId'] == i, 1, 0)\\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\\n\\n logger.info(\\\"Features from the returns table: {shape} {dtypes}\\\"\\n .format(shape=df_customers.shape, dtypes=df_customers.dtypes))\\n return df_customers\",\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 df(client_ids, start, end):\\n obj = search(client_ids, start, end)\\n df = DataFrame.from_dict(obj).T\\n\\n if df.empty:\\n return df\\n\\n df.index.name = 'client_id'\\n df = df.rename(columns={ 0: 'inactive', 1: 'active' })\\n df['total'] = df.sum(axis=1)\\n df = df.fillna(0).astype('int64')\\n\\n return df\",\n \"def _prepare_invoice(self):\\n self.ensure_one()\\n # journal_id = self.env['account.invoice'].with_context(force_company=self.env.user.company_id.id).default_get(['journal_id'])['journal_id']\\n journal_id = self.company_id.journal_id.id\\n if not journal_id:\\n raise UserError(_('Please define an accounting sales journal for this company.'))\\n invoice_vals = {\\n 'name': self.client_order_ref or '',\\n 'origin': self.name,\\n 'type': 'out_invoice',\\n 'account_id': self.partner_invoice_id.property_account_receivable_id.id,\\n 'partner_id': self.partner_invoice_id.id,\\n 'partner_shipping_id': self.partner_shipping_id.id,\\n 'journal_id': journal_id,\\n 'currency_id': self.pricelist_id.currency_id.id,\\n 'comment': self.note,\\n 'payment_term_id': self.payment_term_id.id,\\n 'fiscal_position_id': self.fiscal_position_id.id or self.partner_invoice_id.property_account_position_id.id,\\n 'company_id': self.company_id.id,\\n 'user_id': self.user_id and self.user_id.id,\\n 'team_id': self.team_id.id\\n }\\n return invoice_vals\",\n \"def sub_transfer_pricing(manager, df_fields, seq_recs, seq_reservas):\\n # df_hotel = manager.get_dataframe(tables[\\\"dwc_bok_t_canco_hotel\\\"])\\n # df_circuit = manager.get_dataframe(tables[\\\"dwc_bok_t_canco_hotel_circuit\\\"])\\n # df_other = manager.get_dataframe(tables[\\\"dwc_bok_t_canco_other\\\"])\\n # df_transfer = manager.get_dataframe(tables[\\\"dwc_bok_t_canco_transfer\\\"])\\n # df_endow = manager.get_dataframe(tables[\\\"dwc_bok_t_canco_endowments\\\"])\\n # df_extra = manager.get_dataframe(tables[\\\"dwc_bok_t_canco_extra\\\"])\\n\\n df_aux = df_fields.select(\\\"operative_incoming\\\", \\\"booking_id\\\", \\\"invoicing_company\\\",\\n \\\"creation_date\\\", \\\"booking_currency\\\")\\n\\n df_hotel = sub_transfer_pricing_aux(manager, df_hotelt, seq_recs, seq_reservas, df_aux)\\n df_circuit = sub_transfer_pricing_aux(manager, df_circuitt, seq_recs, seq_reservas, df_aux)\\n df_other = sub_transfer_pricing_aux(manager, df_othert, seq_recs, seq_reservas, df_aux)\\n df_transfer = sub_transfer_pricing_aux(manager, df_transfert, seq_recs, seq_reservas, df_aux)\\n df_endow = sub_transfer_pricing_aux(manager, df_endowt, seq_recs, seq_reservas, df_aux)\\n df_extra = sub_transfer_pricing_aux_extra(manager, df_extrat, seq_recs, seq_reservas, df_aux)\\n\\n df_impuesto_canco = df_hotel.union(df_circuit).union(df_other).union(df_transfer).union(df_endow).union(\\n df_extra)\\n\\n df_impuesto_canco = df_impuesto_canco.groupBy(\\\"operative_incoming\\\", \\\"booking_id\\\") \\\\\\n .agg({'impuesto_canco': 'sum'}).withColumnRenamed(\\\"SUM(impuesto_canco)\\\", \\\"impuesto_canco\\\")\\n\\n df_impuesto_canco = df_impuesto_canco.withColumnRenamed(\\\"operative_incoming\\\", \\\"seq_rec\\\") \\\\\\n .withColumnRenamed(\\\"booking_id\\\", \\\"seq_res\\\")\\n\\n # add impuesto_canco\\n df_fields = df_fields.join(df_impuesto_canco, [df_fields.operative_incoming == df_impuesto_canco.seq_rec,\\n df_fields.booking_id == df_impuesto_canco.seq_res],\\n 'left_outer').drop(\\\"seq_rec\\\", \\\"seq_res\\\")\\n\\n df_addcanco = sub_transfer_pricing_aux_add_canco(manager, df_fields, seq_recs, seq_reservas)\\n\\n df_addcanco = df_addcanco.withColumnRenamed(\\\"operative_incoming\\\", \\\"seq_rec\\\") \\\\\\n .withColumnRenamed(\\\"booking_id\\\", \\\"seq_res\\\")\\n\\n # add add_impuesto_canco\\n df_fields = df_fields.join(df_addcanco, [df_fields.operative_incoming == df_addcanco.seq_rec,\\n df_fields.booking_id == df_addcanco.seq_res],\\n \\\"left_outer\\\").drop(df_addcanco.seq_rec).drop(df_addcanco.seq_res)\\n\\n df_fields = df_fields.na.fill({'impuesto_canco': 0, 'add_impuesto_canco': 0})\\n\\n df_fields = df_fields.withColumn(\\\"Transfer_pricing\\\", df_fields.impuesto_canco + df_fields.add_impuesto_canco) \\\\\\n .drop(\\\"impuesto_canco\\\", \\\"add_impuesto_canco\\\")\\n\\n df_fields = df_fields.withColumn(\\\"Transfer_pricing\\\", udf_round_ccy(df_fields.Transfer_pricing,\\n df_fields.booking_currency))\\n\\n del df_hotel, df_circuit, df_other, df_transfer, df_endow, df_extra, df_impuesto_canco, df_addcanco, df_aux\\n\\n return df_fields\",\n \"def _create_payments(self, invoice):\\n self.ensure_one()\\n if self.schedule_id and self.schedule_id.occurences > 0:\\n # TODO: make more intelligent price cut\\n amount = invoice.amount_total\\n amount_per_occurence = amount / self.schedule_id.occurences\\n for day in self.schedule_id.day_ids:\\n payment = self.env['account.payment'].new({\\n 'payment_type': 'inbound',\\n 'partner_type': 'customer',\\n 'partner_id': self.member_id.partner_id.id,\\n 'amount': amount_per_occurence,\\n 'payment_date': day.day,\\n 'journal_id': self.journal_id.id,\\n })\\n payment._onchange_journal()\\n payment_values = dict(payment._cache)\\n payment = self.env['account.payment'].create(payment_values)\\n payment.invoice_ids = [(4, invoice.id, False)]\",\n \"def _getdata(self, data):\\n lines = []\\n start_date = str(data['form']['start_date'])\\n end_date = str(data['form']['end_date'])\\n department_ids = data['form']['department_ids']\\n\\n vehicles_ids = self.pool.get('fleet.vehicle').search(self.cr, self.uid,\\\\\\n [('department_id', 'in', department_ids)], context=self.context)\\n\\n fuel_qty_line_obj = self.pool.get('fuel.qty.line')\\n\\n sdate = datetime.strptime(start_date, \\\"%Y-%m-%d\\\")\\n syear = sdate.year\\n smonth = sdate.month\\n edate = datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n eyear = edate.year\\n emonth = edate.month\\n\\n fuel_qty_line_ids = fuel_qty_line_obj.search(self.cr, self.uid,\\\\\\n [('vehicles_id', 'in', vehicles_ids)], context=self.context)\\n\\n\\n\\n counter = 1\\n for qty_line in fuel_qty_line_obj.browse(self.cr, self.uid, \\\\\\n fuel_qty_line_ids, context=self.context):\\n current_m = int(qty_line.month)\\n current_y = int(qty_line.year)\\n start = current_m >= smonth and current_y >= syear\\n end = current_m <= emonth and current_y <= eyear\\n if start and end:\\n line = {'type':str(counter)+\\\" : \\\"+\\\\\\n qty_line.vehicles_id.type.name}\\n line['vehicle_no'] = qty_line.vehicles_id.vin_sn\\n line['spent'] = qty_line.spent_qty\\n line['counter_no'] = str(qty_line.vehicles_id.odometer)+\\\" \\\"+\\\\\\n qty_line.vehicles_id.odometer_unit\\n line['date'] = qty_line.month+\\\"/\\\"+qty_line.year\\n lines.append(line)\\n counter += 1\\n return lines\",\n \"def invoice_onsettled(invoice):\\n\\n db = current.db\\n s3db = current.s3db\\n\\n # Look up claim, invoice number, program and billing\\n btable = s3db.fin_voucher_billing\\n ctable = s3db.fin_voucher_claim\\n itable = s3db.fin_voucher_invoice\\n ptable = s3db.fin_voucher_program\\n join = [ptable.on(ptable.id == ctable.program_id),\\n btable.on(btable.id == ctable.billing_id),\\n itable.on(itable.id == ctable.invoice_id),\\n ]\\n query = (ctable.invoice_id == invoice.id) & \\\\\\n (ctable.deleted == False)\\n row = db(query).select(ctable.id,\\n ctable.program_id,\\n ctable.billing_id,\\n ctable.pe_id,\\n btable.date,\\n itable.invoice_no,\\n ptable.name,\\n ptable.organisation_id,\\n join = join,\\n limitby = (0, 1),\\n ).first()\\n if not row:\\n return\\n program = row.fin_voucher_program\\n billing = row.fin_voucher_billing\\n claim = row.fin_voucher_claim\\n invoice_no = row.fin_voucher_invoice.invoice_no\\n\\n error = None\\n\\n # Look up the provider organisation\\n pe_id = claim.pe_id\\n otable = s3db.org_organisation\\n provider = db(otable.pe_id == pe_id).select(otable.id,\\n otable.name,\\n limitby = (0, 1),\\n ).first()\\n\\n from .helpers import get_role_emails\\n provider_accountants = get_role_emails(\\\"PROVIDER_ACCOUNTANT\\\", pe_id)\\n if not provider_accountants:\\n error = \\\"No provider accountant found\\\"\\n\\n if not error:\\n # Lookup the template variables\\n base_url = current.deployment_settings.get_base_public_url()\\n appname = current.request.application\\n data = {\\\"program\\\": program.name,\\n \\\"date\\\": btable.date.represent(billing.date),\\n \\\"invoice\\\": invoice_no,\\n \\\"organisation\\\": provider.name,\\n \\\"url\\\": \\\"%s/%s/fin/voucher_claim/%s\\\" % (base_url, appname, claim.id),\\n }\\n\\n # Send the email notification\\n from .notifications import CMSNotifications\\n error = CMSNotifications.send(provider_accountants,\\n \\\"InvoiceSettled\\\",\\n data,\\n module = \\\"fin\\\",\\n resource = \\\"voucher_invoice\\\",\\n )\\n if error:\\n msg = \\\"%s could not be notified about invoice settlement: %s\\\"\\n current.log.error(msg % (provider.name, error))\\n else:\\n msg = \\\"%s notified about invoice settlement\\\"\\n current.log.debug(msg % provider.name)\",\n \"def generate_agreement_orders(self, start_date, end_date):\\n self.ensure_one()\\n if not self.active:\\n return\\n lines_to_order = {}\\n exp_date = fields.Date.from_string(self.next_expiration_date)\\n if exp_date < end_date and self.prolong != 'unlimited':\\n end_date = exp_date\\n for line in self.agreement_line:\\n # Check if there is any agreement line to order\\n if not line.active_chk:\\n continue\\n # Check future orders for this line until end_date\\n next_order_date = self._get_next_order_date(line, start_date)\\n while next_order_date <= end_date:\\n # Add to a list to order all lines together\\n if not lines_to_order.get(next_order_date):\\n lines_to_order[next_order_date] = self.env[\\n 'sale.recurring_orders.agreement.line']\\n lines_to_order[next_order_date] |= line\\n next_order_date = self._get_next_order_date(\\n line, next_order_date)\\n # Order all pending lines\\n dates = lines_to_order.keys()\\n dates.sort()\\n for date in dates:\\n # Check if an order exists for that date\\n order = self.order_line.filtered(\\n lambda x: (\\n fields.Date.to_string(\\n fields.Datetime.from_string(x.date_order)) ==\\n fields.Date.to_string(date)))\\n if not order:\\n # create it if not exists\\n self.create_order(\\n fields.Date.to_string(date), lines_to_order[date])\",\n \"def get_invoices(self, since, until):\\n return self._request('getInvoices', data={\\n 'date_from': since.strftime('%d/%m/%Y'),\\n 'date_to': until.strftime('%d/%m/%Y')\\n })\",\n \"def duplicate_invoice(invoice):\\n from invoicer.models import Invoice\\n from invoicer.models import LineItem\\n\\n # copy main attributes\\n new_invoice = Invoice(\\n company=invoice.company,\\n invoice_date=datetime.now(),\\n client=invoice.client,\\n location=invoice.location,\\n tax_rate=invoice.tax_rate,\\n left_address=invoice.left_address,\\n right_address=invoice.right_address,\\n terms=invoice.terms,\\n footer=invoice.footer\\n )\\n new_invoice.save()\\n\\n # now line items\\n for line_item in invoice.line_items.all():\\n new_invoice.line_items.add(LineItem(\\n name=line_item.name,\\n description=line_item.description,\\n price=line_item.price,\\n taxable=line_item.taxable,\\n item=line_item.item,\\n quantity=line_item.quantity\\n ))\\n\\n return new_invoice\",\n \"def _prepare_invoice_line(self, cr, uid, group, picking, move_line, invoice_id,\\n invoice_vals, context=None):\\n result = super(stock_picking, self)._prepare_invoice_line(cr, uid, group, picking, move_line, invoice_id,\\n invoice_vals, context=None)\\n \\n result['discount'] = self._get_discount_invoice(cr, uid, move_line)\\n result['discount2'] = self._get_discount2_invoice(cr, uid, move_line)\\n result['price_unit'] = self._get_price_unit_invoice(cr, uid, move_line, invoice_vals['type'], context=None)\\n return result\",\n \"def invoices_in_route(self):\\n from invoicing.models import Invoice\\n invoices = Invoice.objects.filter(\\n route=self.number, print_date__range=(date.today() - timedelta(6), date.today()),\\n canceled=False).count()\\n return invoices\",\n \"def get_historic_rates(cls, client, product, start_date, end_date,\\n granularity=1):\\n startDate = dt.datetime.strptime(start_date, \\\"%Y-%m-%d\\\")\\n startDateTimestamp = startDate.timestamp()\\n endDate = dt.datetime.strptime(end_date, \\\"%Y-%m-%d\\\")\\n endDateTimestamp = endDate.timestamp()\\n\\n # List of time divisions for retrieving data.\\n timeRange = range(int(startDateTimestamp), int(endDateTimestamp),\\n 200 * granularity)\\n timeRange = list(timeRange) + [endDateTimestamp]\\n\\n # New DataFrame.\\n columns = ['time', 'low', 'high', 'open', 'close', 'volume']\\n data = pd.DataFrame(columns=columns)\\n\\n # Populating dataframe.\\n for i in tqdm(range(len(timeRange) - 1)):\\n try:\\n data = cls.append_data(client, data, product, columns,\\n timeRange[i], timeRange[i + 1])\\n except ValueError:\\n time.sleep(3)\\n data = cls.append_data(data, columns, product,\\n timeRange[i], timeRange[i + 1])\\n\\n # Reindexing dataframe.\\n data['time'] = data.time.apply(dt.datetime.fromtimestamp)\\n data.set_index('time', inplace=True)\\n\\n # Using data points where the price has changed.\\n data = data.where(data.close != data.close.shift()).dropna().sort_index()\\n\\n return data\",\n \"def execute(filters=None):\\n\\tcolumns, data = [\\n\\t\\t\\\"Accident:Link/Accident:150\\\",\\n\\t\\t\\\"Victim:Data:200\\\",\\n\\t\\t\\\"Nature of Accident:Data:200\\\",\\n\\t\\t\\\"Fire Extinguisher Used:Link/Fire Extinguisher:150\\\",\\n\\t\\t\\\"Fire Extinguisher Location: Data:150\\\",\\n\\t\\t\\\"Reported By:Data:100\\\",\\n\\t\\t\\\"Reported Date:Data:100\\\"\\n\\t], []\\n\\n\\tconditions = \\\"tabAcc.fire_related = 1\\\" # Only Fire related accidents\\n\\n\\tif filters.get('modified_from') and filters.get('modified_to'):\\n\\t\\tconditions += ' and tabAcc.prepared_date BETWEEN \\\\'{0}\\\\' and \\\\'{1}\\\\''.format(filters.get('modified_from'),filters.get('modified_to'))\\n\\tif filters.get('fire_extinguisher'):\\n\\t\\tconditions += ' and tabAcc.fire_extinguisher_used = \\\\'{0}\\\\''.format(filters.get('fire_extinguisher'))\\n\\tif filters.get('nature_of_accident'):\\n\\t\\tconditions += ' and tabAcc.type_of_accident = \\\\'{0}\\\\''.format(filters.get('nature_of_accident'))\\n\\tif filters.get('reported_by'):\\n\\t\\tconditions += ' and tabAcc.prepared_by = \\\\'{0}\\\\''.format(filters.get('reported_by'))\\n\\n\\tsql = '''select tabAcc.name accident,tabAcc.employee_name victim, tabAcc.type_of_accident nature_of_accident , tabAcc.fire_extinguisher_used fire_extinguisher_used,\\n\\ttabFire.location fire_extinguisher_used, tabAcc.prepared_by reported_by,tabAcc.prepared_date reported_date from `tabFire Extinguisher` tabFire LEFT JOIN `tabAccident` tabAcc\\n\\tON (tabFire.name = tabAcc.fire_extinguisher_used) WHERE {0}'''\\n\\n\\tfrappe.errprint(sql.format(conditions))\\n\\tdata = frappe.db.sql(sql.format(conditions))\\n\\treturn columns,data\",\n \"def generate_orders(self, cr, uid, ids, context=None):\\n voucher_pool = self.pool.get('account.voucher')\\n payment_term_obj = self.pool.get('account.payment.term')\\n account_budget_confirmation_obj = self.pool.get('account.budget.confirmation')\\n period_obj = self.pool.get('account.period')\\n if context is None:\\n context = {}\\n for order in self.browse(cr, uid, ids, context=context):\\n #################################to remind\\n total_fixed = total_percent = 0\\n for line in order.payment_term.line_ids:\\n if line.value == 'fixed':\\n total_fixed += line.value_amount\\n if line.value == 'procent':\\n total_percent += line.value_amount\\n total_fixed = (total_fixed * 100) / (order.amount or 1.0)\\n if (total_fixed + total_percent) > 100:\\n raise orm.except_orm(_('Error!'), _(\\\"Can not create the payments !\\\\n\\\\\\n The related payment term is probably miss configured as it gives a computed amount greater than the total permanent payment amount. \\\\\\n The latest line of your payment term must be of type 'balance' to avoid rounding issues.\\\"))\\n # create one move line for the total and possibly adjust the other lines amount\\n totlines1 = []\\n for o in order.line_ids:\\n totlines1 += payment_term_obj.compute(cr, uid, order.payment_term.id, o.amount, order.date or False, context=context)\\n \\n d = {}\\n for k, v in totlines1:\\n d.setdefault(k, [k]).append(v)\\n totlines = map(tuple, d.values())\\n\\n for t in totlines :\\n #to substract date from the interval number \\n order_date = t[0]\\n entered_date = datetime.datetime.strptime(order_date, '%Y-%m-%d')\\n entered_date = entered_date.date()\\n account_id = (order.partner_id.property_account_payable and order.partner_id.property_account_payable.id) or \\\\\\n (order.journal_id.default_credit_account_id and order.journal_id.default_credit_account_id.id)\\n period_id = period_obj.find(cr, uid, t[0], context=context)[0]\\n\\n list_confirm = [conf.id for conf in o.confirmation_ids]\\n confirmations = account_budget_confirmation_obj.search(cr, uid, [('id','in', list_confirm),('period_id','=', period_id)], context=context) #('date','=',t[0]),\\n\\n for confirm in confirmations:\\n confirm_id = confirm\\n\\n voucher_lines = [(0, 0, {'name':ol.name, 'account_id':ol.account_id.id, 'type':'dr',\\n 'amount':t[count + 1], 'account_analytic_id':ol.account_analytic_id.id, 'budget_confirm_id': confirm_id })\\n for count, ol in enumerate(order.line_ids)]\\n res = voucher_pool.onchange_price(cr, uid, 0, voucher_lines, [], partner_id=order.partner_id.id, context=context).get(\\\"value\\\", {})\\n voucher_dict = {\\n 'partner_id' : order.partner_id.id,\\n 'account_id': account_id,\\n 'company_id' : order.company_id.id,\\n 'journal_id' : order.journal_id.id,\\n 'period_id': order.period_id.id,\\n 'type':'purchase',\\n 'date' : t[0],\\n 'reference': order.name,\\n 'payment_permanent_voucher_id': order.id,\\n 'line_ids':voucher_lines,\\n 'amount':res.get(\\\"amount\\\", 0.0)\\n }\\n voucher_pool.create(cr, uid, voucher_dict, context=context)\\n return self.write(cr, uid, ids, {'state':'done'}, context=context)\",\n \"def process_ticket_data():\\r\\n c = conn.cursor()\\r\\n c.execute('Select Count(*) from raw_ticket_data')\\r\\n totalleft = c.fetchone()[0]\\r\\n print('{} total rows required'.format(totalleft))\\r\\n np.random.seed(1)\\r\\n df_total = pd.read_sql_query('Select Ticketnumber, TickIssueDate, TickIssueTime, ViolationDesc, '\\r\\n ' VehMake, TickRPPlate, TickStreetNo, TickMeter, Agency, TickBadgeIssued, '\\r\\n 'TickStreetName , TotalPaid, TotalAmtDue from raw_ticket_data ', conn)\\r\\n columnlist = df_total.columns.tolist()\\r\\n df_total.sort_values(by = 'TickIssueDate', inplace = True)\\r\\n n = 500000\\r\\n totalsize = df_total.shape[0]\\r\\n indexes = [i for i in range(0,totalsize, n)]\\r\\n columnlist = df_total.columns.tolist()\\r\\n columnlist.append('address')\\r\\n tqdm.pandas()\\r\\n j = 1\\r\\n for i in indexes:\\r\\n df = df_total[i:i+n]\\r\\n print('Iteration {} started at {}. {} records left'.format(j, dt.datetime.now().strftime(\\\"%H:%M\\\"), totalsize))\\r\\n df['TickStreetNo'] = df['TickStreetNo'].apply(return_num)\\r\\n df['ViolationDesc'] = df['ViolationDesc'].apply(lambda x: x.replace('METER DTN','MTR OUT DT'))\\r\\n df['TickStreetName'] = df['TickStreetName'].apply(replace_street)\\r\\n df['TickStreetName'] = df['TickStreetName'].apply(return_street)\\r\\n df['TotalPaid'] = df['TotalPaid'].apply(return_cost)\\r\\n df['TotalAmtDue'] = df['TotalAmtDue'].apply(lambda x: re.sub('[^1-9]', '', str(x)))\\r\\n df['TickRPPlate'] = df['TickRPPlate'].apply(lambda x: 'None' if len(re.findall('[\\\\w+]', str(x))) == 0 else str(x).replace('[^\\\\w+]', ''))\\r\\n df['Tdelt'] = df['TickIssueTime'].apply(return_time_delta)\\r\\n\\r\\n\\r\\n df_1 = df.merge(single_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\\r\\n df_2 = df.merge(double_address, left_on = ['TickStreetNo', 'TickStreetName'], right_on = ['number', 'streetname'])\\r\\n\\r\\n df_2 = df_2.merge(df_1, how = 'left', left_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'], right_on = ['TickIssueDate', 'TickBadgeIssued', 'nhood'])\\r\\n df_3 = df_2[pd.isnull(df_2['Tdelt_y'])]\\r\\n df_2.dropna(subset = ['Tdelt_y'], inplace = True)\\r\\n df_2['timedelta'] = df_2.apply(lambda x: np.abs(x['Tdelt_y'] - x['Tdelt_x']), axis = 1)\\r\\n df_2.sort_values(by = 'timedelta', inplace = True)\\r\\n\\r\\n df_2.columns = [col.replace('_x', '') for col in df_2.columns]\\r\\n df_3.columns = [col.replace('_x', '') for col in df_3.columns]\\r\\n df_2.drop_duplicates(subset = 'TicketNumber', inplace = True)\\r\\n print(\\\"Searching for unmatchable addresses\\\")\\r\\n df_3['address'] = df_3.progress_apply(return_address, axis = 1)\\r\\n\\r\\n df = df_1.append(df_2)\\r\\n df = df.append(df_3)\\r\\n df['TickIssueDate'] = df.apply(Time, axis = 1)\\r\\n df = df[columnlist]\\r\\n\\r\\n if i == 0:\\r\\n df.to_sql('ticket_data', if_exists = 'replace',con = conn)\\r\\n else:\\r\\n df.to_sql('ticket_data', if_exists = 'append',con = conn)\\r\\n\\r\\n totalsize -= n\\r\\n j+=1\\r\\n\\r\\n del c\\r\\n\\r\\n return\",\n \"def compute_counterpart_lines(self):\\n for item in self:\\n move_debit_lines = []\\n move_credit_lines = []\\n\\n # list of all the move lines of the payment's move\\n line_list = []\\n for entry in item.journal_entry_ids:\\n for line in entry.line_ids:\\n if line.account_id.treasury_planning:\\n line_list.append(line)\\n\\n # for each line above collect all the reconciled counterpart lines\\n for line in line_list:\\n if line.credit > 0 and line.debit == 0:\\n for match in line.matched_debit_ids:\\n move_debit_lines.append(match.debit_move_id.id)\\n\\n if line.credit == 0 and line.debit > 0:\\n for match in line.matched_credit_ids:\\n move_credit_lines.append(match.credit_move_id.id)\\n\\n if move_credit_lines:\\n counterpart_move_ids = move_credit_lines\\n else:\\n counterpart_move_ids = move_debit_lines\\n\\n # bank move share is transformed to dictionary\\n bank_move_dict = (ast.literal_eval(item.cf_share) if\\n item.cf_share else {})\\n\\n # the share of each counterpart line is \\\"merged or added\\\"\\n # in a weighted manner to the bank line share\\n for cpt in counterpart_move_ids:\\n dest_move_line = self.env['account.move.line'].browse(cpt)\\n weight = round(dest_move_line.balance / item.amount, 2)\\n # counterpart share is transformed into dictionary\\n move_line_dict = ast.literal_eval(dest_move_line.cf_share)\\n\\n # each key is finally added to the bank line share\\n for key, value in move_line_dict.iteritems():\\n draft_dictionary = dictop.sum_dictionary(\\n bank_move_dict.get(key, {}), 1,\\n move_line_dict.get(key, {}), weight)\\n bank_move_dict[key] = dictop.check_dict_total(\\n draft_dictionary, 1)\\n\\n # the dictionary is transformed into string and assigned\\n item.cf_share = json.dumps(bank_move_dict)\",\n \"def _prepare_analytic_line(self, cr, uid, obj_line, context=None):\\n return {'name': obj_line.name,\\n 'date': obj_line.date,\\n 'account_id': obj_line.analytic_account_id.id,\\n 'unit_amount': obj_line.quantity,\\n 'product_id': obj_line.product_id and obj_line.product_id.id or False,\\n 'product_uom_id': obj_line.product_uom_id and obj_line.product_uom_id.id or False,\\n 'amount': (obj_line.credit or 0.0) - (obj_line.debit or 0.0),\\n 'general_account_id': obj_line.account_id.id,\\n 'journal_id': obj_line.journal_id.analytic_journal_id.id,\\n 'ref': obj_line.ref,\\n 'move_id': obj_line.id,\\n 'user_id': uid,\\n }\",\n \"def _prepare_order_line_invoice_line(self, cr, uid, line, account_id=False, context=None):\\n res = super(sale_order_line, self)._prepare_order_line_invoice_line(cr, uid, line, account_id=account_id, context=context)\\n \\n res.update({'part_number': line.part_number, 'internal_part_number' : line.internal_part_number})\\n return res\",\n \"def _prepare_invoice_line(self, qty):\\n self.ensure_one()\\n res = super(SaleOrderLine, self)._prepare_invoice_line(qty)\\n\\n res.update({\\n 'cost_center_id': self.cost_center_id and self.cost_center_id.id or False\\n })\\n return res\",\n \"def billing_history(cls, user=None):\\n invoices = Invoice.query.filter(Invoice.user_id == user.id) \\\\\\n .order_by(Invoice.created_on.desc()).limit(12)\\n\\n return invoices\",\n \"def _initialize_df(self, df):\\n df['values'] = (self.tc.instrument_returns['cumulative'] *\\n self.tc.starting_cash).mul(self.target_weights, axis=1).values * (1 - self.tc.commission)\\n df['allocations'] = self.df['values'].div(df['values'].sum(axis=1), axis=0)\\n df['returns'] = (df['values'].sum(axis=1)).pct_change(1).fillna(0)\",\n \"def generate_pr_table(start_ref, end_ref):\\r\\n header = \\\"|| Merged By || Author || Title || PR || JIRA || Verified? ||\\\"\\r\\n pr_link = \\\"[#{num}|https://github.com/edx/edx-platform/pull/{num}]\\\"\\r\\n user_link = \\\"[@{user}|https://github.com/{user}]\\\"\\r\\n rows = [header]\\r\\n prbe = prs_by_email(start_ref, end_ref)\\r\\n for email, pull_requests in prbe.items():\\r\\n for i, pull_request in enumerate(pull_requests):\\r\\n try:\\r\\n pr_info = get_pr_info(pull_request)\\r\\n title = pr_info[\\\"title\\\"] or \\\"\\\"\\r\\n body = pr_info[\\\"body\\\"] or \\\"\\\"\\r\\n author = pr_info[\\\"user\\\"][\\\"login\\\"]\\r\\n except requests.exceptions.RequestException as e:\\r\\n message = (\\r\\n \\\"Warning: could not fetch data for #{num}: \\\"\\r\\n \\\"{message}\\\".format(num=pull_request, message=e.message)\\r\\n )\\r\\n print(colorize(\\\"red\\\", message), file=sys.stderr)\\r\\n title = \\\"?\\\"\\r\\n body = \\\"?\\\"\\r\\n author = \\\"\\\"\\r\\n rows.append(\\\"| {merged_by} | {author} | {title} | {pull_request} | {jira} | {verified} |\\\".format(\\r\\n merged_by=email if i == 0 else \\\"\\\",\\r\\n author=user_link.format(user=author) if author else \\\"\\\",\\r\\n title=title.replace(\\\"|\\\", \\\"\\\\|\\\"),\\r\\n pull_request=pr_link.format(num=pull_request),\\r\\n jira=\\\", \\\".join(parse_ticket_references(body)),\\r\\n verified=\\\"\\\",\\r\\n ))\\r\\n return \\\"\\\\n\\\".join(rows)\",\n \"def _get_report_data(self, request, queryset):\\n first_item = queryset[0]\\n data = {\\n 'id': str(slugify(first_item.invoice_no)),\\n 'property_of_id': (\\n first_item.property_of.id\\n if first_item.property_of else None\\n ),\\n 'model': queryset.model._meta.model_name,\\n 'base_info': {\\n 'invoice_no': first_item.invoice_no,\\n 'invoice_date': first_item.invoice_date,\\n 'provider': first_item.provider,\\n 'datetime': datetime.datetime.now().strftime(\\n self._invoice_report_datetime_format\\n ),\\n },\\n 'items': list(map(self._parse_item, queryset)),\\n 'sum_price': str(\\n queryset.aggregate(\\n Sum(self._price_field)\\n ).get('{}__sum'.format(self._price_field))\\n )\\n }\\n logger.info('Invoice report data: {}'.format(data))\\n return data\",\n \"def add_invoice() -> str:\\r\\n invoice_details = []\\r\\n #Catching values user has entered in UI\\r\\n invoice_number = request.args.get(\\\"invoice_number\\\")\\r\\n invoice_details.append(invoice_number)\\r\\n customer = request.args.get(\\\"customer\\\")\\r\\n invoice_details.append(customer)\\r\\n date_required = request.args.get(\\\"date_required\\\")\\r\\n invoice_details.append(date_required)\\r\\n recipe = request.args.get(\\\"recipe\\\")\\r\\n invoice_details.append(recipe)\\r\\n gyle_number = request.args.get(\\\"gyle_number\\\")\\r\\n invoice_details.append(gyle_number)\\r\\n quantity_ordered = request.args.get(\\\"quantity_ordered\\\")\\r\\n invoice_details.append(quantity_ordered)\\r\\n #Passing list to function which writes list to CSV file\\r\\n data_add(invoice_details)\\r\\n invoice_message = \\\"INVOICE ADDED\\\"\\r\\n return render_template(\\\"singular_message.html\\\",\\r\\n user_display=invoice_message)\",\n \"def create_or_find_b2b_invoices_and_process_ept(self, row, sale_order, invoice_date, tax):\\n\\n vat_number = row.get('Buyer Tax Registration', False)\\n invoice_number = row.get('VAT Invoice Number', False)\\n\\n invoices = sale_order.invoice_ids.filtered(\\n lambda x: x.type == 'out_invoice' and x.state != 'cancel')\\n if not invoices:\\n lines = sale_order.order_line.filtered(lambda line: line.qty_to_invoice > 0)\\n if not lines:\\n return False\\n invoices = sale_order._create_invoices()\\n self.write({'invoice_ids': [(4, invoices and invoices.id)]})\\n\\n for invoice in invoices:\\n if not invoice.partner_id.vat:\\n invoice.partner_id.vat = vat_number\\n\\n payments_lines = []\\n if invoice.invoice_payments_widget != 'false':\\n payments_dict = json.loads(invoice.invoice_payments_widget)\\n payments_content = payments_dict.get('content', [])\\n for line in payments_content:\\n payments_lines.append(line.get('payment_id', False))\\n\\n invoice_line = invoice.mapped('invoice_line_ids').filtered(\\\\\\n lambda line: line.tax_ids != tax)\\n if invoice_line:\\n invoice.button_draft()\\n invoice.write({'ref': invoice_number, 'date': invoice_date})\\n\\n if len(invoice_line) > 1:\\n for line in invoice_line:\\n line.with_context({'check_move_validity': False}).write( \\\\\\n {'tax_ids': [(6, 0, [tax.id])]})\\n else:\\n invoice_line.with_context({'check_move_validity': False}).write( \\\\\\n {'tax_ids': [(6, 0, [tax.id])]})\\n\\n invoice.with_context({'check_move_validity': False})._recompute_tax_lines( \\\\\\n recompute_tax_base_amount=True)\\n invoice.action_post()\\n for line in payments_lines:\\n invoice.js_assign_outstanding_line(line)\\n\\n return True\",\n \"def get_invoice(self):\\n\\n # Check if unclosed invoice for the client exists\\n old_inv = connection.Kinko.find_one({'cl': self.cl, 'tid': None,\\n 'typ': TYPE_MAP[self.tab_type]})\\n\\n inv_num = None\\n # If it does, update its values and update packages\\n if old_inv:\\n old_inv.dt = datetime.datetime.today()\\n old_inv.range.lt = self.q_dict[\\\"cs.sd\\\"].get(\\\"$lt\\\", None)\\n old_inv.save()\\n\\n inv_num = old_inv.num\\n\\n else:\\n #kinko dict to be updated in Kinko Collection.\\n kdict = {\\n \\\"amt\\\": 0.0,\\n \\\"cl\\\": unicode(self.cl),\\n \\\"dt\\\": datetime.datetime.today(),\\n \\\"typ\\\": TYPE_MAP[self.tab_type],\\n \\\"range\\\": {\\\"lt\\\": self.q_dict[\\\"cs.sd\\\"].get(\\\"$lt\\\", None),\\n \\\"gt\\\": self.q_dict[\\\"cs.sd\\\"].get(\\\"$gte\\\", None),\\n }\\n }\\n\\n k = Kinko(kdict)\\n\\n k_count = 1\\n\\n #the get num method of Kinko model generates the unique no for new kinko\\n k[\\\"num\\\"] = self.get_knum(1)\\n while connection.Kinko.collection.find({\\\"num\\\": k.num}).count() > 0:\\n k[\\\"num\\\"] = self.get_knum(k_count+1)\\n k_count += k_count\\n\\n connection.Kinko(k).save()\\n\\n inv_num = k['num']\\n\\n if inv_num:\\n #after creating a new document in Kinko all packages are updated.\\n connection.Package.collection.update(self.q_dict, {'$set': {'inv.num': inv_num}}, safe=True, multi=True)\\n \\n #Aggrigation of remitted amount for requested client\\n non_invoiced = kinko_map_reduce(inv_num, TYPE_MAP[self.tab_type])\\n\\n if len(non_invoiced) == 0:\\n return False\\n else:\\n inv = connection.Kinko.find_one({'num': inv_num})\\n if inv:\\n inv.amt = non_invoiced[0]['value']['amt']\\n inv.save()\\n return inv\\n else:\\n return False\\n else:\\n return False\",\n \"async def daily(self, ctx):\\r\\n # TODO: Asssess whether this can be cleaned up. \\r\\n # As it stands, very similar to inv()\\r\\n author = ctx.author\\r\\n with DB() as db:\\r\\n company = await self.get_active_company(ctx, db, author)\\r\\n stock = self.iex.get_held_stocks(db, company.id)\\r\\n inventory = []\\r\\n for s in stock:\\r\\n close = await self.get_latest_close(ctx, db, s.symbol)\\r\\n inventory.append([s.symbol, s.quantity, s.purchase_price, close.close, s.quantity*close.close - s.quantity*s.purchase_price ]) \\r\\n inv_df = pd.DataFrame(inventory, columns=['Symbol', 'Quantity', 'Purchase Price', 'Close', 'Current Value'])\\r\\n inv_df['sign'] = np.where(inv_df['Current Value']>=0, '+', '-')\\r\\n inv_df['%'] = abs(((inv_df['Close'] - inv_df['Purchase Price']) / inv_df['Purchase Price']) * 100)\\r\\n inv_df['%'] = inv_df['%'].round(1)\\r\\n inv_df = inv_df.sort_values(['Symbol'])\\r\\n inv_df = inv_df[['sign', '%', 'Symbol', 'Quantity', 'Purchase Price', 'Close', 'Current Value']]\\r\\n aggregated = tabulate(inv_df.values.tolist(), headers=['Δ', '%', 'Symbol', 'Quantity', 'Purchase Price', 'Close', 'Current Value'])\\r\\n await ctx.send(f'```diff\\\\n{aggregated}```')\",\n \"def records(self, variables=None, **kwargs):\\n # Get necessary data for the variables\\n all_df = self._data.records_all().set_index(self.DATE)\\n variables = self._convert_variables(variables, all_df.columns.tolist())\\n df = all_df.loc[:, variables]\\n # Figure\\n if self._data.complemented:\\n title = f\\\"{self.area}: Cases over time\\\\nwith {self._data.complemented}\\\"\\n else:\\n title = f\\\"{self.area}: Cases over time\\\"\\n self.line_plot(df=df, title=title, y_integer=True, **kwargs)\\n return df.reset_index()\",\n \"def action_invoice_create(self, cr, uid, ids, context=None):\\n res = False\\n\\n journal_obj = self.pool.get('account.journal')\\n inv_obj = self.pool.get('account.invoice')\\n inv_line_obj = self.pool.get('account.invoice.line')\\n fiscal_obj = self.pool.get('account.fiscal.position')\\n property_obj = self.pool.get('ir.property')\\n\\n for order in self.browse(cr, uid, ids, context=context):\\n pay_acc_id = order.partner_id.property_account_payable.id\\n journal_ids = journal_obj.search(cr, uid, [('type', '=','purchase'),('company_id', '=', order.company_id.id)], limit=1)\\n if not journal_ids:\\n raise osv.except_osv(_('Error !'),\\n _('There is no purchase journal defined for this company: \\\"%s\\\" (id:%d)') % (order.company_id.name, order.company_id.id))\\n\\n # generate invoice line correspond to PO line and link that to created invoice (inv_id) and PO line\\n inv_lines = []\\n for po_line in order.order_line:\\n if po_line.product_id:\\n acc_id = po_line.product_id.product_tmpl_id.property_account_expense.id\\n if not acc_id:\\n acc_id = po_line.product_id.categ_id.property_account_expense_categ.id\\n if not acc_id:\\n raise osv.except_osv(_('Error !'), _('There is no expense account defined for this product: \\\"%s\\\" (id:%d)') % (po_line.product_id.name, po_line.product_id.id,))\\n else:\\n acc_id = property_obj.get(cr, uid, 'property_account_expense_categ', 'product.category').id\\n fpos = order.fiscal_position or False\\n acc_id = fiscal_obj.map_account(cr, uid, fpos, acc_id)\\n\\n inv_line_data = self._prepare_inv_line(cr, uid, acc_id, po_line, context=context)\\n inv_line_id = inv_line_obj.create(cr, uid, inv_line_data, context=context)\\n inv_lines.append(inv_line_id)\\n\\n po_line.write({'invoiced':True, 'invoice_lines': [(4, inv_line_id)]}, context=context)\\n\\n # get invoice data and create invoice\\n inv_data = {\\n 'name': order.partner_ref or order.name,\\n 'reference': order.partner_ref or order.name,\\n 'account_id': pay_acc_id,\\n 'type': 'in_invoice',\\n 'partner_id': order.partner_id.id,\\n 'currency_id': order.pricelist_id.currency_id.id,\\n 'address_invoice_id': order.partner_address_id.id,\\n 'address_contact_id': order.partner_address_id.id,\\n 'journal_id': len(journal_ids) and journal_ids[0] or False,\\n 'invoice_line': [(6, 0, inv_lines)], \\n 'origin': order.name,\\n 'fiscal_position': order.fiscal_position.id or order.partner_id.property_account_position.id,\\n 'payment_term': order.partner_id.property_payment_term and order.partner_id.property_payment_term.id or False,\\n 'company_id': order.company_id.id,\\n 'add_disc': order.add_disc or 0.0\\n }\\n inv_id = inv_obj.create(cr, uid, inv_data, context=context)\\n\\n # compute the invoice\\n inv_obj.button_compute(cr, uid, [inv_id], context=context, set_total=True)\\n\\n # Link this new invoice to related purchase order\\n order.write({'invoice_ids': [(4, inv_id)]}, context=context)\\n res = inv_id\\n return res\",\n \"def GenerateVersion(self):\\n pol_obj = self.env['account.analytic.account']\\n hist_obj = self.env['account.analytic.account']\\n inv_obj = self.env['account.invoice']\\n values = self.GeneratePolicy()\\n # logger.info('\\\\n === values = %s' % values)\\n policies = values.get('policy', [])\\n res = []\\n i = 0\\n for policy in pol_obj.browse(policies):\\n i += 1\\n logger.info('version %s -> %s (%s / %s)' % (policy.name, policy.id, i, len(policies)))\\n # search invoice\\n inv_ids = inv_obj.search([('pol_numpol', '=', policy.name), ('id','in', values.get('invoice'))], order='prm_datedeb')\\n # logger.info('=== inv_ids = %s' % inv_ids.mapped('prm_datedeb'))\\n inv_len = len(inv_ids)\\n c = 0\\n for inv_id in inv_ids:\\n c += 1\\n # logger.info('inv_len = %s ?= %s c' % (inv_len,c))\\n hist_buf = {\\n 'type': 'contract',\\n 'is_insurance': True,\\n 'partner_id': policy.partner_id.id,\\n 'property_account_position': policy.property_account_position.id,\\n 'insured_id': policy.insured_id.id,\\n 'manager_id': policy.manager_id.id,\\n 'branch_id': policy.branch_id.id,\\n 'ins_product_id': policy.ins_product_id.id,\\n 'fraction_id': policy.fraction_id.id or False,\\n #'name': policy.name + '_' + str(c).zfill(4) + 'AA',\\n 'name': policy.name + '_' + str(policy.next_sequence).zfill(4) + 'AA',\\n 'parent_id': policy.id,\\n 'date_start': inv_id.prm_datedeb,\\n 'date': inv_id.prm_datefin,\\n 'agency_id': inv_id.journal_id.agency_id.id or False,\\n 'invoice_id': inv_id.id,\\n 'stage_id': self.env.ref('insurance_management.avenant').id\\n }\\n if c == inv_len:\\n hist_buf['is_last_situation'] = True\\n hist_ids = hist_obj.search([('name','=', hist_buf.get('name'))])\\n if not hist_ids:\\n # logger.info('===> create history')\\n res.append(hist_obj.create(hist_buf).id)\\n next_sequence = policy.next_sequence + 1\\n policy.write({'next_sequence': next_sequence})\\n else:\\n hist_ids.update(hist_buf)\\n res += hist_ids.ids\\n return res\",\n \"def income_model_user_defined_returns(num_of_years=30, trials=100, method='normal'):\\n\\n print(\\\"Running method income_model_user_defined_returns()\\\")\\n\\n sim_fia_cv = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_base_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_base_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n sim_port_total = pd.DataFrame(index=range(num_of_years + 1))\\n sim_port_income = pd.DataFrame(index=range(num_of_years + 1))\\n\\n read_income_inputs = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_model_inputs',\\n index_col=[0])\\n\\n read_returns_est = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='income_assets_returns_estimates',\\n index_col=[0])\\n clean_names = list(read_returns_est.index)\\n clean_names = [s.split(' ')[0] for s in clean_names]\\n read_returns_est.loc[:, 'names'] = clean_names\\n read_returns_est.set_index('names', drop=True, inplace=True)\\n read_returns_est = read_returns_est[:-1]\\n read_returns_est.rename(index={'SBMMTB3': 'Cash', read_returns_est.index[-1]: 'FIA'}, inplace=True)\\n\\n # ---------------Returns DataFrame based on the user input------------------------------------\\n ann_ret = np.full((num_of_years + 1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\\n read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years + 1), columns=read_returns_est.index)\\n # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\\n user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\\n read_normal.loc[:, 'FIA'] = user_est_fia_return\\n\\n read_asset_weights = pd.read_excel(src + \\\"portfolio_information.xlsx\\\", sheet_name='asset_weights',\\n index_col=[0])\\n\\n # read_asset_weights.drop(read_asset_weights.index[-1], axis=0, inplace=True)\\n\\n # read random returns for simulation\\n read_normal = pd.read_csv(src + 'median_returns_unsorted.csv', index_col=[0], parse_dates=True)\\n cols = [read_normal.columns[c].split('_')[1] for c in np.arange(len(read_normal.columns))]\\n read_normal.rename(columns=dict(zip(list(read_normal.columns), cols)), inplace=True)\\n\\n # ann_ret = np.full((num_of_years+1, len(read_returns_est)), read_returns_est.loc[:, 'Annualized Returns'])\\n # read_normal = pd.DataFrame(ann_ret, index=np.arange(num_of_years+1), columns=read_returns_est.index)\\n # # read_normal.rename(columns={read_normal.columns[-1]: 'FIA'}, inplace=True)\\n # user_est_fia_return = float(read_income_inputs.loc['fia_forecast', 'inputs'])\\n # read_normal.loc[:, 'FIA'] = user_est_fia_return\\n\\n assets_col_names = list(read_normal.columns)\\n\\n tickers = list(read_asset_weights.index)\\n wts = np.array(read_asset_weights.loc[:, 'base'])\\n\\n def asset_median_returns(data, ticker):\\n return data.filter(regex=ticker).median(axis=1)\\n\\n # dataframe for unsorted returns (normal)\\n median_returns_normal = read_normal.copy()\\n median_returns_normal.loc[:, 'portfolio_return'] = median_returns_normal.dot(wts)\\n median_normal_fia = pd.DataFrame({'FIA': asset_median_returns(read_normal, 'FIA')})\\n\\n years = list(range(0, num_of_years + 1))\\n income_cols = ['year', 'strategy_term', 'index_returns', 'term_ret', 'term_ret_with_par', 'term_annualize',\\n 'ann_net_spread', 'term_ret_netspr', 'high_inc_benefit_base', 'rider_fee', 'eoy_income',\\n 'contract_value']\\n\\n term = int(read_income_inputs.loc['term', 'inputs'])\\n fia_ret = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Returns']\\n fia_risk = read_returns_est.loc[read_returns_est.index[-1], 'Annualized Risk']\\n par_rate = float(read_income_inputs.loc['par_rate', 'inputs'])\\n spread = float(read_income_inputs.loc['spread', 'inputs'])\\n bonus_term = int(read_income_inputs.loc['bonus_term', 'inputs'])\\n premium = float(read_income_inputs.loc['premium', 'inputs'])\\n income_bonus = float(read_income_inputs.loc['income_bonus', 'inputs'])\\n\\n income_starts = int(read_income_inputs.loc['start_income_years', 'inputs'])\\n income_growth = float(read_income_inputs.loc['income_growth', 'inputs'])\\n rider_fee = float(read_income_inputs.loc['rider_fee', 'inputs'])\\n inc_payout_factor = float(read_income_inputs.loc['income_payout_factor', 'inputs'])\\n contract_bonus = float(read_income_inputs.loc['contract_bonus', 'inputs'])\\n social = float(read_income_inputs.loc['social', 'inputs'])\\n inflation = float(read_income_inputs.loc['inflation', 'inputs'])\\n wtd_cpn_yield = float(read_income_inputs.loc['wtd_coupon_yld', 'inputs'])\\n life_expectancy = int(read_income_inputs.loc['life_expectancy_age', 'inputs'])\\n clients_age = int(read_income_inputs.loc['clients_age', 'inputs'])\\n\\n # ---------------INCOME MODEL--------------------------------------------\\n runs = 0\\n returns_dict = {}\\n asset_dict = {}\\n fia_dict = {}\\n\\n income_df = pd.DataFrame(index=years, columns=income_cols)\\n income_df.loc[:, 'year'] = years\\n income_df.loc[:, 'strategy_term'] = income_df.loc[:, 'year'] % term\\n income_df.loc[:, 'strategy_term'] = income_df['strategy_term'].apply(lambda x: 1 if x == 0 else 0)\\n income_df.loc[:, 'index_returns'] = read_normal.loc[:, 'FIA']\\n\\n # income_df.loc[:, 'index_returns'] = np.random.normal(fia_ret, fia_risk, size=(len(years), 1))\\n cumprod = (1. + income_df['index_returns']).rolling(window=term).agg(lambda x: x.prod()) - 1\\n income_df.loc[:, 'term_ret'] = np.where(income_df.loc[:, 'strategy_term'] == 1, cumprod, 0)\\n income_df.loc[:, 'term_ret_with_par'] = income_df.loc[:, 'term_ret'] * par_rate\\n income_df.loc[:, 'term_annualize'] = income_df.loc[:, 'term_ret_with_par'].apply(\\n lambda x: (1 + x) ** (1 / term) - 1)\\n income_df.loc[:, 'ann_net_spread'] = income_df.loc[:, 'term_annualize'] - spread\\n income_df.loc[:, 'ann_net_spread'] = np.where(income_df.loc[:, 'strategy_term'] == 1,\\n income_df.loc[:, 'ann_net_spread'], 0)\\n income_df.loc[:, 'term_ret_netspr'] = income_df.loc[:, 'ann_net_spread'].apply(lambda x: (1 + x) ** term - 1)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'high_inc_benefit_base'] = premium * (1 + income_bonus)\\n\\n elif counter <= min(bonus_term, income_starts):\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base'] * \\\\\\n (1 + income_growth)\\n else:\\n income_df.loc[counter, 'high_inc_benefit_base'] = income_df.loc[counter - 1, 'high_inc_benefit_base']\\n\\n income_df.loc[:, 'rider_fee'] = income_df.loc[:, 'high_inc_benefit_base'] * rider_fee\\n income_df.loc[:, 'eoy_income'] = np.where(income_df.loc[:, 'year'] > income_starts,\\n income_df.loc[:, 'high_inc_benefit_base'] * inc_payout_factor, 0)\\n\\n for counter in years:\\n if counter == 0:\\n income_df.loc[counter, 'contract_value'] = premium * (1 + contract_bonus)\\n\\n elif income_df.loc[counter, 'strategy_term'] == 1:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee']\\n x2 = (x1 * (1 + income_df.loc[counter, 'term_ret_netspr'])) - income_df.loc[counter, 'eoy_income']\\n income_df.loc[counter, 'contract_value'] = x2\\n\\n else:\\n x1 = income_df.loc[counter - 1, 'contract_value'] - income_df.loc[counter, 'rider_fee'] - \\\\\\n income_df.loc[counter, 'eoy_income']\\n\\n income_df.loc[counter, 'contract_value'] = x1\\n\\n # variable stores the income number that is used in the base and fia portfolio calcs.\\n\\n income_from_fia = income_df.loc[income_df.index[-1], 'eoy_income']\\n\\n income_df.loc[:, 'contract_value'] = income_df.loc[:, 'contract_value'].apply(lambda x: 0 if x <= 0 else x)\\n\\n sim_fia_cv.loc[:, str(runs)] = income_df.loc[:, 'contract_value']\\n\\n # --------------------BASE MODEL---------------------------------------------\\n base_wts = read_asset_weights.loc[:, 'base']\\n base_assets = list(base_wts.index)\\n base_weights = list(base_wts.values)\\n base_returns = list(read_returns_est.loc[:, 'Annualized Returns'].values)\\n base_std = list(read_returns_est.loc[:, 'Annualized Risk'].values)\\n\\n base_investment = float(read_income_inputs.loc['risky_assets', 'Base'])\\n adv_fees = float(read_income_inputs.loc['advisor_fees', 'Base'])\\n\\n # -------------------required income----------------------------------\\n req_annual_income = float(read_income_inputs.loc['annual_income', 'inputs'])\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n cpn_income_base = base_investment * wtd_cpn_yield\\n\\n # ----------------------RANDOM RETURNS--------------------------\\n r_cols = base_assets\\n boy_value = ['bv_{}'.format(name) for name in base_assets]\\n eoy_value = ['ev_{}'.format(name) for name in base_assets]\\n\\n random_returns = pd.DataFrame(index=income_df.index, columns=r_cols)\\n\\n for c in range(len(r_cols)):\\n ret = np.random.normal(base_returns[c], base_std[c], size=(len(random_returns.index), 1))\\n\\n random_returns = read_normal.copy()\\n\\n base_df = random_returns.copy()\\n fia_portfolio_df = random_returns.copy()\\n port_investment = float(read_income_inputs.loc['risky_assets', 'FIA'])\\n cpn_income_port = port_investment * wtd_cpn_yield\\n\\n # -------------BASE PORTFOLIO----------------------------\\n for name in boy_value:\\n base_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment for c in range(len(boy_value))]\\n\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'total_net_fees'] = 0.0\\n base_df.loc[counter, 'income'] = 0.0\\n base_investment = base_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - base_df.loc[\\n counter, 'adv_fees']\\n\\n # --coupon payment is invested back into the risky portfolio until the income is withdrawn----\\n base_investment = base_df.loc[counter, 'total_net_fees'] + cpn_income_base\\n\\n else:\\n\\n base_df.loc[counter, boy_value] = [base_weights[c] * base_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n base_df.loc[counter, 'total'] = base_df.loc[counter, boy_value].sum()\\n base_df.loc[counter, 'adv_fees'] = base_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n base_df.loc[counter, 'income'] = income_needed - cpn_income_base\\n income_needed = income_needed + social\\n\\n base_df.loc[counter, 'total_net_fees'] = base_df.loc[counter, 'total'] - \\\\\\n base_df.loc[counter, 'adv_fees'] - \\\\\\n base_df.loc[counter, 'income']\\n\\n base_investment = base_df.loc[counter, 'total_net_fees']\\n\\n base_df.loc[:, 'adj_total'] = base_df.loc[:, 'total_net_fees'].apply(lambda x: x if x > 0 else 0)\\n sim_base_total.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'total_net_fees']\\n sim_base_income.loc[:, 's_{}'.format(str(runs))] = base_df.loc[:, 'income']\\n\\n # ----------------------------FIA PORTFOLIO----------------------------------------------\\n for name in boy_value:\\n fia_portfolio_df.loc[:, name] = 0.0\\n\\n for counter in years:\\n period_returns = list(random_returns.loc[counter, :])\\n if counter == 0:\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = 0.0\\n fia_portfolio_df.loc[counter, 'income'] = 0.0\\n port_investment = fia_portfolio_df.loc[counter, boy_value].sum()\\n\\n elif (counter > 0) and (counter < income_starts):\\n\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees'] + cpn_income_port\\n\\n else:\\n fia_portfolio_df.loc[counter, boy_value] = [base_weights[c] * port_investment * (1 + period_returns[c])\\n for c in range(len(boy_value))]\\n fia_portfolio_df.loc[counter, 'total'] = fia_portfolio_df.loc[counter, boy_value].sum()\\n fia_portfolio_df.loc[counter, 'adv_fees'] = fia_portfolio_df.loc[counter, 'total'] * adv_fees\\n\\n # ---req. income is adjusted for inflation from the second year of withdrawal. Reinvestment of coupon\\n # stops from the year income starts. Req. income is reduced by the coupon payments\\n\\n if counter == income_starts:\\n\\n income_needed = req_annual_income - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = req_annual_income\\n\\n else:\\n income_needed = income_needed * (1 + inflation) - social\\n income_net_fia_income = max(0, income_needed - income_from_fia)\\n fia_portfolio_df.loc[counter, 'income'] = max(0, income_net_fia_income - cpn_income_port)\\n income_needed = income_needed + social\\n\\n fia_portfolio_df.loc[counter, 'total_net_fees'] = fia_portfolio_df.loc[counter, 'total'] - \\\\\\n fia_portfolio_df.loc[counter, 'adv_fees'] - \\\\\\n fia_portfolio_df.loc[counter, 'income']\\n\\n port_investment = fia_portfolio_df.loc[counter, 'total_net_fees']\\n\\n sim_port_total.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'total_net_fees'] + \\\\\\n income_df.loc[:, 'contract_value']\\n\\n sim_port_income.loc[:, 's_{}'.format(str(runs))] = fia_portfolio_df.loc[:, 'income']\\n\\n fia_portfolio_df.loc[:, 'adj_total'] = fia_portfolio_df.loc[:, 'total_net_fees'].apply(\\n lambda x: x if x > 0 else 0)\\n\\n # ---------income breakdown for Base portfolio----------------------------------\\n base_df.to_csv(src + 'base_port_detail.csv')\\n sim_base_total.to_csv(src + 'base_ending_values.csv')\\n income_breakdown_base = pd.DataFrame(sim_base_total.quantile(0.5, axis=1))\\n income_breakdown_base.loc[:, 'income_from_portfolio'] = sim_base_income.quantile(0.5, axis=1)\\n income_breakdown_base.loc[:, 'fia_income'] = 0.0\\n income_breakdown_base.loc[:, 'social_security_income'] = social\\n income_breakdown_base.loc[:, 'coupon_income'] = cpn_income_base\\n\\n income_breakdown_base.rename(columns={income_breakdown_base.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_base.loc[:, 'income_from_portfolio'][\\n income_breakdown_base.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_base.loc[:, 'total_income'] = income_breakdown_base.loc[:, income_breakdown_base.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n\\n # ---------income breakdown for FIA portfolio----------------------------------\\n fia_portfolio_df.to_csv(src + 'fia_port_detail.csv')\\n sim_port_total.to_csv(src + 'fiaport_ending_values.csv')\\n\\n income_breakdown_port = pd.DataFrame(sim_port_total.quantile(0.5, axis=1))\\n income_breakdown_port.loc[:, 'income_from_portfolio'] = sim_port_income.quantile(0.5, axis=1)\\n income_breakdown_port.loc[:, 'fia_income'] = income_from_fia\\n income_breakdown_port.loc[:, 'social_security_income'] = social\\n income_breakdown_port.loc[:, 'coupon_income'] = cpn_income_port\\n\\n income_breakdown_port.rename(columns={income_breakdown_port.columns[0]: 'portfolio_ending_value'}, inplace=True)\\n income_breakdown_port.loc[:, 'income_from_portfolio'][\\n income_breakdown_port.loc[:, 'portfolio_ending_value'] <= 0] = 0\\n income_breakdown_port.loc[:, 'total_income'] = income_breakdown_port.loc[:, income_breakdown_port.columns[1:]].sum(\\n axis=1)\\n\\n # ------------Block Ends-------------------------------------------------------------\\n q_cut = [0.0, 0.05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n sim_base_income[sim_base_total < income_needed] = 0.0\\n\\n sim_port_income[sim_port_total < income_net_fia_income] = 0\\n\\n sim_port_income = sim_port_income + income_from_fia\\n\\n # base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n # port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile([0.05, 0.25, 0.50, 0.75, 0.90])\\n\\n base_quantile = sim_base_total.loc[sim_base_total.index[-1]].quantile(q_cut)\\n\\n port_quantile = sim_port_total.loc[sim_port_total.index[-1]].quantile(q_cut)\\n\\n # q_cut = [0.0, .05, 0.25, 0.5, 0.75, 0.95, 1.0]\\n cols = ['Min', '5th', '25th', '50th', '75th', '90th', 'Max']\\n\\n # ----drop year 0--------\\n sim_base_total = sim_base_total[1:]\\n sim_port_total = sim_port_total[1:]\\n\\n # ----------------quantile analysis for base terminal value--------------------------\\n base_qcut = pd.DataFrame(index=sim_base_total.index, columns=cols)\\n for c in range(len(cols)):\\n base_qcut.loc[:, cols[c]] = sim_base_total.quantile(q_cut[c], axis=1)\\n\\n base_qcut.clip(lower=0, inplace=True)\\n\\n # ----------------------quantile analysis for base income----------------------------\\n base_income_qcut = pd.DataFrame(index=sim_base_income.index, columns=cols)\\n for c in range(len(cols)):\\n base_income_qcut.loc[:, cols[c]] = sim_base_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # base_income_qcut = base_income_qcut.loc[income_starts:]\\n\\n # -------------quantile analysis for portfolio terminal value ----------------\\n port_qcut = pd.DataFrame(index=sim_port_total.index, columns=cols)\\n for c in range(len(cols)):\\n port_qcut.loc[:, cols[c]] = sim_port_total.quantile(q_cut[c], axis=1)\\n\\n port_qcut.clip(lower=0, inplace=True)\\n\\n # ---------------quantile analysis for portfolio income----------------------------\\n port_income_qcut = pd.DataFrame(index=sim_port_income.index, columns=cols)\\n for c in range(len(cols)):\\n port_income_qcut.loc[:, cols[c]] = sim_port_income.quantile(q_cut[c], axis=1)\\n\\n # ----Remove NaN's prior to the income start years------------\\n # port_income_qcut = port_income_qcut.loc[income_starts:]\\n\\n # ----------probability ending value will be less than 0 at the end of the horizon -----------------------\\n base_legacy_risk = (sim_base_total.loc[sim_base_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n port_legacy_risk = (sim_port_total.loc[sim_port_total.index[life_expectancy - clients_age]] < 0).sum() / (\\n trials + 1)\\n\\n legacy_risk = pd.DataFrame([base_legacy_risk, port_legacy_risk,\\n 'Prob. of portfolio value less than 0 at the end of the expected life'],\\n index=['base', 'fia_portfolio', 'Notes'],\\n columns=['Ruin Probability'])\\n\\n # -----------Year-wise probability of ending value greater than 0 -----------------\\n base_psuccess = sim_base_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n port_psuccess = sim_port_total.apply(lambda x: x > 0).sum(axis=1) / (trials + 1)\\n\\n # -----------------------WRITING FILES TO EXCEL ---------------------------\\n col_names = ['50th', 'age', 'comment']\\n writer = pd.ExcelWriter(src + method + '_simulated_income_summary_custom.xlsx', engine='xlsxwriter')\\n read_income_inputs.to_excel(writer, sheet_name='inputs_for_income')\\n\\n read_returns_est.to_excel(writer, sheet_name='asset_returns_estimates')\\n\\n age_index = list(range(clients_age + 1, clients_age + len(base_qcut) + 1))\\n base_qcut.loc[:, 'age'] = age_index\\n base_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_ending_value_quantiles')\\n\\n base_income_qcut = base_income_qcut.loc[1:, :]\\n base_income_qcut.loc[:, 'age'] = age_index\\n base_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n base_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='base_income_quantiles')\\n\\n port_qcut.loc[:, 'age'] = age_index\\n port_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_ending_value_quantiles')\\n\\n port_income_qcut = port_income_qcut.loc[1:, :]\\n port_income_qcut.loc[:, 'age'] = age_index\\n port_income_qcut.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n port_income_qcut.loc[income_starts:, col_names].to_excel(writer, sheet_name='fia_port_income_quantiles')\\n\\n income_breakdown_base = income_breakdown_base.loc[1:, :]\\n income_breakdown_base.loc[:, 'age'] = age_index\\n income_breakdown_base.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_base.loc[income_starts:, :].to_excel(writer, sheet_name='base_income_breakdown_median')\\n\\n income_breakdown_port = income_breakdown_port.loc[1:, :]\\n income_breakdown_port.loc[:, 'age'] = age_index\\n income_breakdown_port.loc[life_expectancy - clients_age, 'comment'] = 'expected_life'\\n income_breakdown_port.loc[:, 'ending_contract_value'] = income_df.loc[:, 'contract_value']\\n income_breakdown_port.loc[income_starts:, :].to_excel(writer, sheet_name='fia_income_breakdown_median')\\n\\n median_returns_normal.to_excel(writer, sheet_name='gr_port_median_normal')\\n\\n terminal_val = pd.read_csv(src + 'terminal_values.csv', index_col=[0])\\n ending_val = pd.read_csv(src + 'ending_values.csv', index_col=[0])\\n ending_val_ror = pd.read_csv(src + 'ending_values_ror.csv', index_col=[0])\\n\\n terminal_val.to_excel(writer, sheet_name='terminal_values')\\n ending_val.to_excel(writer, sheet_name='port_ending_values')\\n ending_val_ror.to_excel(writer, sheet_name='port_annual_growth')\\n\\n writer.save()\\n\\n # -----------------Plotting charts--------------------------------------------\\n base_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - Base Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_base.png\\\")\\n plt.close('all')\\n\\n base_income_qcut.plot(grid=True, title='Quantile Income - Base Portfolio')\\n plt.savefig(src + \\\"quantile_income_base.png\\\")\\n plt.close('all')\\n\\n base_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - Base Portfolio')\\n plt.savefig(src + \\\"success_probabilty_base.png\\\")\\n plt.close('all')\\n\\n (1 - base_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - Base Portfolio')\\n plt.savefig(src + \\\"ruin_probability_base.png\\\")\\n plt.close('all')\\n\\n port_qcut.loc[income_starts:].plot(grid=True, title='Quantile Terminal Value - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_terminal_fia.png\\\")\\n plt.close('all')\\n\\n port_income_qcut.plot(grid=True, title='Quantile Income - FIA Portfolio')\\n plt.savefig(src + \\\"quantile_income_fia.png\\\")\\n plt.close('all')\\n\\n port_psuccess.plot(grid=True, title='Probability of Success (Portfolio Ending Value > 0) - FIA Portfolio')\\n plt.savefig(src + \\\"success_probabilty_fia.png\\\")\\n plt.close('all')\\n\\n (1 - port_psuccess).plot(grid=True, title='Probability of Ruin (Portfolio Ending Value < 0) - FIA Portfolio')\\n plt.savefig(src + \\\"ruin_probability_fia.png\\\")\\n plt.close('all')\\n\\n print(\\\"simulation completed for {}\\\".format(method))\",\n \"def prepare_invoice(self):\\n journal_id = self.env['account.invoice'].default_get(['journal_id'])['journal_id']\\n if not journal_id:\\n raise UserError(_('Please define sales journal for this company: \\\"%s\\\" (id:%d).') % (self.company_id.name, self.company_id.id))\\n invoice_vals = {\\n 'order_id': self.id,\\n 'name': self.order_no,\\n 'origin': self.order_no,\\n 'type': 'out_invoice',\\n 'reference': self.patient_id.name + ':' + self.name,\\n 'account_id': self.patient_id.partner_id.property_account_receivable_id.id,\\n 'partner_id': self.patient_id.partner_id.id,\\n 'journal_id': journal_id,\\n 'comment': self.note,\\n 'doctor_id': self.doctor_id.id,\\n 'payment_term': False,\\n 'user_id': False,\\n }\\n return invoice_vals\",\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 \"def __init__(self, numQueues, rate, start_hour, end_hour, appt_low, appt_high):\\n\\n self.rate = rate\\n self.numQueues = numQueues\\n self.start = datetime.datetime.combine(datetime.date.today(), datetime.time(start_hour,0,0))\\n self.end = datetime.datetime.combine(datetime.date.today(), datetime.time(end_hour,0,0))\\n self.appt_low = appt_low\\n self.appt_high = appt_high\\n minutes_for_new_items = (end_hour-start_hour)*60 #new patients seen between 9AM and 4PM\\n time_between_items = rate #exponential dist. time parameter\\n self.expected_count = int(np.ceil(stats.poisson.ppf(.9999, minutes_for_new_items/time_between_items)))\\n self.ques = [datetime.datetime.combine(datetime.datetime.today(), datetime.time(start_hour,0,0)) for i in range(0, self.numQueues)]\\n cols = ['simulation', 'num_items', 'wait_count', 'avg_wait_time', 'close_time']\\n self.results = pd.DataFrame(columns = cols)\\n return\",\n \"def run(params, conn, outputfile):\\n date_begin = parse(params['date_begin'] + ' 00:00:00 +0700')\\n date_end = parse(params['date_end'] + ' 23:59:59 +0700')\\n domain_id = params['domain_id']\\n authority_ids = params['authority_ids']\\n\\n covid_report_type_id = fetch_report_type_id(conn, 'surveillance-covid-19', domain_id)\\n main_data = fetch_data(conn, date_begin, date_end, authority_ids, domain_id, covid_report_type_id)\\n\\n covid_report_type_id = fetch_report_type_id(conn, 'surveillance-covid-19-followup', domain_id)\\n follow_data = fetch_data(conn, date_begin, date_end, authority_ids, domain_id, covid_report_type_id)\\n\\n line_list = join(main_data, follow_data)\\n tabular(line_list)\\n\\n if len(line_list) == 0:\\n return False\\n\\n df = pandas.DataFrame(line_list)\\n df['date'] = df['date'].dt.tz_convert(tz)\\n df['date'] = df['date'].dt.strftime('%d/%m/%Y %H:%M')\\n writer = pandas.ExcelWriter(outputfile)\\n df.to_excel(writer, 'covid-19', columns=['report_id', 'name', 'gender', 'age',\\n 'village_no', 'village', 'tumbols', 'amphurs',\\n 'arrival_date_village', 'mobile_phone',\\n 'risk_factor', 'symptom_check', 'symptom_covid',\\n 'date', 'latitude', 'longitude',\\n '01', '02', '03', '04', '05', '06',\\n '07', '08', '09', '10', '11', '12', '13', '14'], index=False)\\n ldf = pandas.DataFrame(flat(main_data))\\n ldf['date'] = ldf['date'].dt.tz_convert(tz)\\n ldf.sort_values(by=['date'], inplace=True)\\n ldf['date'] = ldf['date'].dt.strftime('%d/%m/%Y %H:%M')\\n\\n def is_followup(row):\\n return row['report_id'] != row['group_id']\\n\\n ldf['followup'] = ldf.apply(is_followup, axis=1)\\n ldf.to_excel(writer,\\n 'all',\\n columns=['report_id', 'group_id', 'followup', 'name', 'gender', 'age',\\n 'village_no', 'village', 'tumbols', 'amphurs',\\n 'arrival_date_village', 'mobile_phone',\\n 'risk_factor', 'symptom_check', 'symptom_covid',\\n 'total_times', 'activity_other',\\n 'date', 'latitude', 'longitude'],\\n index=False)\\n writer.save()\\n return True\",\n \"def action_invoice_create(self, cr, uid, ids, grouped=False, states=None, date_invoice=False, context=None):\\n order = self.browse(cr, uid, ids[0], context=context)\\n inv_obj = self.pool.get('account.invoice')\\n # create the invoice\\n inv_id = super(sale_order, self).action_invoice_create(cr, uid, ids, grouped, states, date_invoice, context=context)\\n # modify the invoice\\n inv_obj.write(cr, uid, [inv_id], {'past_doc': order.past_doc})\\n return inv_id\",\n \"def get_rows(self):\\n rows = []\\n common_fields = self.get_common_data()\\n\\n p_identities = self.data['participantIdentities']\\n p_data = self.data['participants']\\n winning_team = self.get_winning_team()\\n items_purchased = self.get_items_purchased()\\n team_champions = self.get_team_champions()\\n teams = set(team_champions.keys())\\n gold_per_player = self.get_gold_per_player()\\n xp_per_player = self.get_xp_per_player()\\n gold_diff = self.get_gold_diff()\\n xp_diff = self.get_xp_diff()\\n dmg_taken_diff = self.get_dmg_taken_diff()\\n\\n for p in p_identities:\\n p_id = int(p['participantId'])\\n p_idx = p_id - 1\\n team_id = p_data[p_idx]['teamId']\\n opposing_team_id = (teams - {team_id}).pop()\\n player_purchases = items_purchased[p_idx]\\n purchase_list = [item_pair[0] for item_pair in player_purchases]\\n items_10min = {\\n item_pair[0] for item_pair in player_purchases\\n if item_pair[1] < 1000*60*10\\n }\\n cur_row = {\\n 'summonerId': p['player']['summonerId'],\\n\\n 'allyChampions': team_champions[team_id],\\n 'championId': p_data[p_idx]['championId'],\\n 'gold5': gold_per_player[p_idx].get(5, None),\\n 'gold10': gold_per_player[p_idx].get(10, None),\\n 'gold15': gold_per_player[p_idx].get(15, None),\\n 'gold20': gold_per_player[p_idx].get(20, None),\\n 'xp5': xp_per_player[p_idx].get(5, None),\\n 'xp10': xp_per_player[p_idx].get(10, None),\\n 'xp15': xp_per_player[p_idx].get(15, None),\\n 'xp20': xp_per_player[p_idx].get(20, None),\\n 'items10': list(items_10min),\\n 'opponentChampions': team_champions[opposing_team_id],\\n 'purchases': purchase_list,\\n 'lane': p_data[p_idx]['timeline']['lane'],\\n 'role': p_data[p_idx]['timeline']['role'],\\n 'xpDiff10': xp_diff[p_idx],\\n 'damageTakenDiff10': dmg_taken_diff[p_idx],\\n 'gdPerMin10': gold_diff[p_idx],\\n 'winner': (team_id == winning_team),\\n }\\n\\n cur_row.update(common_fields)\\n rows.append(cur_row)\\n\\n return rows\",\n \"def show_outstanding_invoices(self, show_outstanding_invoices):\\n\\n self._show_outstanding_invoices = show_outstanding_invoices\",\n \"def create_data():\\n data_set = pd.DataFrame()\\n customer_id = list()\\n for i in range(1, 10001):\\n customer_id.append(i)\\n data_set = pd.DataFrame()\\n data_set.loc[:, 'customer_id'] = np.array(customer_id)\\n product_name = ('dining chair', 'dining table', 'bed', 'dining set',\\n 'stool', 'couch', 'occasional table',\\n 'recliner')\\n product_name_random = random.choices(product_name, k=10000)\\n data_set.loc[:, 'product_name'] = np.array(product_name_random)\\n quantity_rented = (1, 2, 3, 4)\\n quantity_rented_random = random.choices(quantity_rented, k=10000)\\n data_set.loc[:, 'quantity_rented'] = np.array(quantity_rented_random)\\n unit_rental_price_monthly = list()\\n for i in range(0, 10000):\\n unit_rental_price_monthly.append(random.uniform(1.5, 25))\\n data_set.loc[:, 'unit_rental_price'] = np.array(unit_rental_price_monthly)\\n rental_period_months = list()\\n for i in range(0, 10000):\\n rental_period_months.append(randint(6, 60))\\n data_set.loc[:, 'rental_period_months'] = np.array(rental_period_months)\\n return data_set\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58933586","0.58273345","0.5787141","0.5730418","0.57022715","0.56592727","0.5587117","0.5582993","0.5557562","0.55526274","0.55505604","0.55257595","0.5520605","0.54978883","0.5488627","0.5473216","0.5465187","0.54585433","0.54323196","0.53706306","0.5359521","0.5348314","0.53445005","0.5337536","0.5308676","0.52830005","0.52545595","0.5254459","0.5245022","0.5233254","0.52227414","0.52169895","0.5212123","0.52061456","0.51900005","0.5183848","0.51801354","0.51608694","0.5129148","0.5118703","0.51106626","0.51043624","0.51041967","0.5069794","0.5069502","0.5053597","0.50529736","0.5030675","0.50295323","0.5025132","0.50219774","0.5016678","0.50110257","0.50099194","0.50089276","0.5001617","0.49978667","0.49924296","0.49906254","0.4988517","0.49851596","0.4981629","0.49691814","0.49354625","0.4933138","0.49310076","0.49308822","0.49284595","0.49177936","0.49116883","0.49013725","0.48992148","0.4895135","0.48943272","0.4893423","0.48932913","0.48863798","0.4880579","0.48781058","0.48766106","0.48757657","0.4875047","0.48737803","0.48716915","0.48674923","0.48625204","0.4858591","0.4856049","0.48459306","0.48428443","0.48425218","0.481653","0.48075104","0.48051313","0.48012373","0.4795892","0.47882003","0.47876215","0.47791627","0.47791484"],"string":"[\n \"0.58933586\",\n \"0.58273345\",\n \"0.5787141\",\n \"0.5730418\",\n \"0.57022715\",\n \"0.56592727\",\n \"0.5587117\",\n \"0.5582993\",\n \"0.5557562\",\n \"0.55526274\",\n \"0.55505604\",\n \"0.55257595\",\n \"0.5520605\",\n \"0.54978883\",\n \"0.5488627\",\n \"0.5473216\",\n \"0.5465187\",\n \"0.54585433\",\n \"0.54323196\",\n \"0.53706306\",\n \"0.5359521\",\n \"0.5348314\",\n \"0.53445005\",\n \"0.5337536\",\n \"0.5308676\",\n \"0.52830005\",\n \"0.52545595\",\n \"0.5254459\",\n \"0.5245022\",\n \"0.5233254\",\n \"0.52227414\",\n \"0.52169895\",\n \"0.5212123\",\n \"0.52061456\",\n \"0.51900005\",\n \"0.5183848\",\n \"0.51801354\",\n \"0.51608694\",\n \"0.5129148\",\n \"0.5118703\",\n \"0.51106626\",\n \"0.51043624\",\n \"0.51041967\",\n \"0.5069794\",\n \"0.5069502\",\n \"0.5053597\",\n \"0.50529736\",\n \"0.5030675\",\n \"0.50295323\",\n \"0.5025132\",\n \"0.50219774\",\n \"0.5016678\",\n \"0.50110257\",\n \"0.50099194\",\n \"0.50089276\",\n \"0.5001617\",\n \"0.49978667\",\n 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customerID given as parameter."},"document":{"kind":"string","value":"def get_customer_history_df_invoice_line(self, customerID):\n df_invoice_line \\\n = self._df_invoice_original[self._df_invoice_original.CustomerID \\\n == customerID]\n return df_invoice_line"},"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_customer_df_invoice_line(self, customerID, list_stockCode\\\n , list_quantity, invoiceDate):\n \n dict_invoice = dict()\n\n dict_invoice['Quantity'] = list_quantity\n dict_invoice['StockCode'] = list_stockCode\n\n #------------------------------------------------------------------------\n # Build invoiceDate from local current time\n #------------------------------------------------------------------------\n if invoiceDate is None:\n time_struct = time.localtime()\n invoiceDate = str(time_struct.tm_year)+'-'+str(time_struct.tm_mon)\\\n +'-'+str(time_struct.tm_mday)\n invoiceDate +=' '\n invoiceDate +=str(time_struct.tm_hour)+':'+str(time_struct.tm_min)\\\n +':'+str(time_struct.tm_sec)\n invoiceDate = pd.Timestamp(invoiceDate)\n else:\n pass\n\n\n #------------------------------------------------------------------------\n # Lists initialization\n #------------------------------------------------------------------------\n list_customerID = list()\n list_invoiceNo = list()\n list_invoiceDate = list()\n list_invoice_line_index = list()\n \n #------------------------------------------------------------------------\n # Increase Invoice number\n #------------------------------------------------------------------------\n invoiceNo = max(self._df_invoice_original.InvoiceNo)\n invoiceNo += 1\n\n #------------------------------------------------------------------------\n # Get latest invoice line index value\n #------------------------------------------------------------------------\n invoice_line_index = max(self._df_invoice_original.index)\n\n #------------------------------------------------------------------------\n # Build lists for CustomerID, InvoiceNo, InvoiceDate\n # A list of incremented indexes is built for new rows.\n #------------------------------------------------------------------------\n for quantity in list_quantity:\n list_customerID.append(customerID)\n list_invoiceNo.append(invoiceNo)\n list_invoiceDate.append(invoiceDate)\n invoice_line_index += 1\n list_invoice_line_index.append(invoice_line_index) \n\n \n dict_invoice['CustomerID'] = list_customerID\n dict_invoice['InvoiceNo'] = list_invoiceNo\n dict_invoice['InvoiceDate'] = list_invoiceDate\n\n #------------------------------------------------------------------------\n # Get description list from list of stock codes.\n #------------------------------------------------------------------------\n list_description = self.getDescriptionList(list_stockCode)\n \n dict_invoice['Description'] = list_description\n\n #------------------------------------------------------------------------\n # Get unit price list from list of stock codes.\n #------------------------------------------------------------------------\n list_unitPrice = self.getUnitPriceList(list_stockCode)\n \n dict_invoice['UnitPrice'] = list_unitPrice\n\n #------------------------------------------------------------------------\n # Dataframe with new invoices lines is created.\n #------------------------------------------------------------------------\n df_invoice_line \\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys()\\\n , index=list_invoice_line_index)\n \n return df_invoice_line","def invoice(customer_id):\n encoder = request.url_rule.endpoint\n template = \"{{ encoder }}#{{ customer_id|%s }}\" % encoder\n return render_template_string(template, **locals())","def get_dataframe() -> pandas.DataFrame:\n database_connection = processing.establish_connection(database_path)\n dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)\n database_connection.close()\n dataframe = processing.get_invoice_date_fixed(dataframe)\n analyze_dataframe = dataframe.copy()\n total_sum_dataframe = processing.get_column_sum(analyze_dataframe)\n\n customer_count_dataframe = processing.drop_duplicates(analyze_dataframe)\n customer_count_dataframe = processing.get_column_count(customer_count_dataframe)\n return customer_count_dataframe.merge(total_sum_dataframe, how='inner', on='InvoiceDate')","def order_process(self, customerID, list_stockCode, list_quantity\\\n , orderDate=None):\n\n segmentID = -1\n\n #-------------------------------------------------------------------------\n # A new customer is created and inserted into data-set.\n #-------------------------------------------------------------------------\n if customerID is None:\n customerID = int(self.createCustomerID())\n else:\n pass\n \n #-------------------------------------------------------------------------\n # A new dataframe with new invoice lines are created.\n #-------------------------------------------------------------------------\n df_invoice_line = self.create_customer_df_invoice_line(customerID\\\n , list_stockCode, list_quantity, orderDate)\n \n #-------------------------------------------------------------------------\n # Original dataframe is updated with customer invoices lines.\n #-------------------------------------------------------------------------\n print(\"order_process : shape before concat= \"+str(self._df_invoice_original.shape))\n self._df_invoice_original \\\n = pd.concat([self._df_invoice_original, df_invoice_line], axis=0)\n print(\"order_process : shape after concat= \"+str(self._df_invoice_original.shape))\n \n #-------------------------------------------------------------------------\n # All invoices lines (including new one) related to customer is retrieved \n # from original dataframe.\n #-------------------------------------------------------------------------\n df_invoice_line_customer \\\n = self.get_customer_history_df_invoice_line(customerID)\n\n #-------------------------------------------------------------------------\n # When calling get_customer_marketSegment(), df_invoice_line_customer is\n # concatened to the original dataframe.\n #------------------------------------------------------------------------- \n segmentID = self.get_customer_marketSegment(df_invoice_line_customer)\n \n return segmentID, customerID","def get_rows(contract_address: str, file_name: str, receipts_filename: str) -> pd.DataFrame():\n receipts_df = pd.read_csv(receipts_filename)\n receipts_df = receipts_df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\n df = pd.read_csv(file_name)\n df = df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\n\n df = df.loc[receipts_df['status'] == 1] \n df = df.loc[df[\"to_address\"] == contract_address.lower()]\n df = df.reset_index()\n df = df.drop(columns='index')\n return df","def invoices(self,org_id=None,invoice_id=''):\n if org_id is None:\n org_id = self.org_id\n return self.get('{}/orgs/{}/invoices/{}'.format(ApiVersion.A1.value,org_id,invoice_id))","def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\n , UnitPrice ):\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\n dict_invoice['CustomerID'] = CustomerID\n dict_invoice['InvoiceNo'] = InvoiceNo\n df_invoice_line \\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])\n \n self.data_transform(df_invoice_line)\n\n #self.feature_rfm_encode()\n\n self.feature_scale()\n\n self.list_feature_drop()\n\n self.feature_description_nlp()\n return","def return_customer_orders(customer_id):\n with MY_CONNECTION as connection:\n cursor = connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT id_order, id_customer, id_product, quantity, total_price,\n payment_status, send_status, order_date, location\n FROM Orders\n Where id_customer=?\n \"\"\",\n (customer_id,))\n return cursor.fetchall()","def invoice(self, id):\r\n return Invoice(self, id)","def get_all_customer_ids():\n table = data_manager.get_table_from_file(\"sales/sales.csv\")\n return get_all_customer_ids_from_table(table)","def get_all_sales_ids_for_customer_ids():\n\n # your code","def invoices(self):\r\n return Invoices(self)","def invoices(self):\r\n return inv.Invoices(self)","def invoice(self,context,params):\n url = f\"https://api.freshbooks.com/accounting/account/{params['account_id']}/invoices/invoices/{params['invoice_id']}\"\n result = json.loads(util.rest(\"GET\", url, {}, context[\"headers\"][\"access_token\"]).text)\n invoice = result[\"response\"][\"result\"][\"invoice\"]\n invoice_obj = FreshbooksInvoice(\n account_id=invoice['accountid'],\n customerid=invoice['customerid'], \n invoice_id=invoice['invoiceid'],\n currency_code=invoice['currency_code'],\n language=invoice['language'],\n terms=invoice['terms'],\n discount_value=invoice['discount_value'],\n discount_amount=invoice['discount_total']['amount'],\n invoice_number=invoice['invoice_number'],\n po_number=invoice['po_number'],\n amount=invoice['amount']['amount'],\n code=invoice['amount']['code'],\n create_date=invoice['create_date']\n )\n return invoice_obj.__dict__","def invoices(self, account_id):\n from pureport_client.commands.accounts.invoices import Command\n return Command(self.client, account_id)","def invoice_items(self,org_id=None,query={}):\n if org_id is None:\n org_id = self.org_id\n query_end_date = datetime.strptime(query['endDate'],'%Y-%m-%dT%H:%M:%SZ')\n # Given a 'query_end_date' to find the invoice containing the\n # line items for that date we need to find the invoice which \n # has 'endDate' equal to the end of the month of the `query_end_date`\n query_first_next_month = query_end_date + relativedelta(months=+1) - relativedelta(days=(query_end_date.day-1))\n target_invoices = []\n invoices = self.invoices(org_id)\n if self.verbose:\n print('Searching invoices org_id={}'.format(org_id))\n print('query={} query_end_date={}'.format(query,query_end_date))\n print('Result keys: {}'.format( invoices['content'].keys() ))\n print('Total result count: {}'.format( invoices['content']['totalCount'] ))\n for invoice in invoices['content']['results']:\n #pprint.pprint(invoice)\n end_date = datetime.strptime(invoice['endDate'],'%Y-%m-%dT%H:%M:%SZ')\n if self.verbose: \n print('invoice({})[\\'endDate\\']={} end_date={}'.format(invoice['id'],invoice['endDate'],end_date))\n if end_date == query_first_next_month:\n target_invoices.append(invoice)\n \n if self.verbose: \n print('Target invoices: {}'.format(target_invoices))\n \n\n target_line_items = []\n for invoice in target_invoices:\n invoice_details = self.invoices(org_id,invoice['id']) \n print('invoice_details: {}'.format(invoice_details))\n for item in invoice_details['content']['lineItems']:\n end_date = datetime.strptime(item['endDate'],'%Y-%m-%dT%H:%M:%SZ')\n if end_date == query_end_date:\n target_line_items.append(item)\n if self.verbose:\n print('target_line_items: {}'.format(target_line_items)) \n return target_line_items","def invoice(self, invoice_number):\r\n return inv.Invoice(self, invoice_number)","def get_all_customer_ids():\n\n # your code","def invoices(self):\r\n return inv.AccountInvoices(self)","def invoices_in_route(self):\n from invoicing.models import Invoice\n invoices = Invoice.objects.filter(\n route=self.number, print_date__range=(date.today() - timedelta(6), date.today()),\n canceled=False).count()\n return invoices","def _prepare_invoice(self, cr, uid, order, lines, context=None):\n if context is None:\n context = {}\n journal_ids = self.pool.get('account.journal').search(cr, uid,\n [('type', '=', 'sale'), ('company_id', '=', order.company_id.id)],\n limit=1)\n if not journal_ids:\n raise osv.except_osv(_('Error !'),\n _('There is no sales journal defined for this company: \"%s\" (id:%d)') % (order.company_id.name, order.company_id.id))\n\n invoice_vals = {\n 'name': order.client_order_ref or '',\n 'origin': order.name,\n 'type': 'out_invoice',\n 'reference': order.client_order_ref or order.name,\n 'account_id': order.partner_id.property_account_receivable.id,\n 'journal_id': order.partner_id.property_default_sale_invoice_journal.id,\n 'partner_id': order.partner_id.id,\n 'address_invoice_id': order.partner_invoice_id.id,\n #'address_contact_id': order.partner_order_id.id,\n 'invoice_line': [(6, 0, lines)],\n 'currency_id': order.pricelist_id.currency_id.id,\n 'comment': order.note,\n 'payment_term': order.payment_term and order.payment_term.id or False,\n 'fiscal_position': order.fiscal_position.id or order.partner_id.property_account_position.id,\n 'date_invoice': context.get('date_invoice', False),\n 'company_id': order.company_id.id,\n 'user_id': order.user_id and order.user_id.id or False\n }\n\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\n\n return invoice_vals","def get_customer_orders(customerId):\n data = user_obj.get_customer_orders(customerId)\n return data","def ListInvoices(self, **kwargs):\n return self._stub.ListInvoices(ln.ListInvoiceRequest(**kwargs))","def get_invoices(self, since, until):\n return self._request('getInvoices', data={\n 'date_from': since.strftime('%d/%m/%Y'),\n 'date_to': until.strftime('%d/%m/%Y')\n })","def get_all_customer_ids_from_table(table):\n\n # your code","def _get_invoices_for_payment(cls, account_id: int) -> List[InvoiceModel]:\n valid_statuses = (InvoiceStatus.APPROVED.value, InvoiceStatus.REFUND_REQUESTED.value)\n invoice_ref_subquery = db.session.query(InvoiceReferenceModel.invoice_id). \\\n filter(InvoiceReferenceModel.status_code.in_((InvoiceReferenceStatus.ACTIVE.value,)))\n\n invoices: List[InvoiceModel] = db.session.query(InvoiceModel) \\\n .filter(InvoiceModel.invoice_status_code.in_(valid_statuses)) \\\n .filter(InvoiceModel.payment_method_code == PaymentMethod.EJV.value) \\\n .filter(InvoiceModel.payment_account_id == account_id) \\\n .filter(InvoiceModel.id.notin_(invoice_ref_subquery)) \\\n .all()\n return invoices","def _prepare_invoice(self, cr, uid, order, lines, context=None):\n if context is None:\n context = {}\n journal_id = self.pool['account.invoice'].default_get(cr, uid, ['journal_id'], context=context)['journal_id']\n if not journal_id:\n raise osv.except_osv(_('Error!'),\n _('Please define sales journal for this company: \"%s\" (id:%d).') % (order.company_id.name, order.company_id.id))\n invoice_vals = {\n 'name': order.client_order_ref or '',\n 'origin': order.name,\n 'type': 'out_invoice',\n 'reference': order.client_order_ref or order.name,\n 'account_id': order.partner_invoice_id.property_account_receivable.id,\n 'partner_id': order.partner_invoice_id.id,\n 'journal_id': journal_id,\n 'invoice_line': [(6, 0, lines)],\n 'currency_id': order.pricelist_id.currency_id.id,\n 'comment': order.note,\n 'payment_term': order.payment_term and order.payment_term.id or False,\n 'fiscal_position': order.fiscal_position.id or order.partner_invoice_id.property_account_position.id,\n 'date_invoice': context.get('date_invoice', False),\n 'company_id': order.company_id.id,\n 'user_id': order.user_id and order.user_id.id or False,\n 'section_id' : order.section_id.id,\n 'test_1' :order.test\n }\n\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\n return invoice_vals","def _create_invoice(self):\n self.ensure_one()\n partner = self.member_id.partner_id\n invoice = self.env['account.invoice'].create({\n 'partner_id': partner.id,\n 'account_id': partner.property_account_receivable_id.id,\n 'fiscal_position_id': partner.property_account_position_id.id\n })\n for line in self.line_ids:\n product = line.activity_id.product_id\n # Handling of invoice lines : needs cache record for onchange, then\n # real writing...\n invoice_line = self.env['account.invoice.line'].new({\n 'product_id': product.id,\n 'invoice_id': invoice.id\n })\n invoice_line._onchange_product_id()\n line_values = dict(invoice_line._cache)\n line_values['price_unit'] = line.price\n invoice_line = self.env['account.invoice.line'].create(line_values)\n invoice.compute_taxes()\n line.registration_id.invoice_line_id = invoice_line.id\n return invoice","def _prepare_invoice_line(self, inv_id):\n res = {}\n account_id = self.product_id.property_account_income_id.id or self.product_id.categ_id.property_account_income_categ_id.id\n if not account_id:\n raise UserError(_('Please define income account for this product: \"%s\" (id:%d).') % \\\n (self.product_id.name, self.product_id.id,))\n price_unit = self.product_id.lst_price\n res = {\n 'invoice_id': inv_id.id,\n 'name': self.name,\n 'origin': self.order_id.name,\n 'account_id': account_id,\n 'uom_id': self.product_uom_id.id,\n 'quantity': self.product_uom_qty,\n 'price_unit': price_unit,\n 'product_id': self.product_id.id,\n 'invoice_line_tax_id': False,\n 'order_line_id': self.id\n }\n return res","def get_listCustomer_out_sample(self, customerCount=10):\n \n if customerCount is None :\n listCustomer= list(self._df_invoice_line_out_sample.CustomerID.unique())\n else:\n if customerCount <= 0 :\n listCustomer \\\n = list(self._df_invoice_line_out_sample.CustomerID.unique())\n else:\n listCustomer \\\n = list(self._df_invoice_line_out_sample.CustomerID.unique()[:customerCount])\n return listCustomer","def _get_lines(self, cr, uid, ids, context=None):\n List=[]\n if ids:\n line = self.pool.get('payment.enrich.lines').browse(cr, uid, ids[0], context=context)\n \n record = line.enrich_id\n val = 0.0\n for line in record.enrich_lines:\n if line.state == 'done' :\n val += line.cost\n res = {\n 'paid_amount':val,\n 'residual_amount':record.amount - val,\n }\n record.write(res)\n return List","def single_customer(customer_name, invoice_file):\n def customer_rental(rental_items):\n \"\"\"Closure to add single customer details\"\"\"\n with open(rental_items, 'r', newline='') as rentals:\n reader = csv.reader(rentals)\n add_invoice_items = partial(add_furniture, invoice_file, customer_name)\n for row in reader:\n add_invoice_items(item_code=row[0],\n item_description=row[1],\n item_monthly_price=row[2])\n return customer_rental","def invoice_lines(self, invoice_lines):\n if self.local_vars_configuration.client_side_validation and invoice_lines is None: # noqa: E501\n raise ValueError(\"Invalid value for `invoice_lines`, must not be `None`\") # noqa: E501\n\n self._invoice_lines = invoice_lines","def get_customer_marketSegment(self, df_invoice_line_customer):\n #-------------------------------------------------------------------------\n # Building data model \n #-------------------------------------------------------------------------\n self.data_transform(df_invoice_line_customer)\n\n #-------------------------------------------------------------------------\n # Customer features are built thanks to transformers.\n #-------------------------------------------------------------------------\n self.df_customers_features_build()\n \n #-------------------------------------------------------------------------\n # Customer market segment is predicted\n #-------------------------------------------------------------------------\n X_test = self._df_customers.values\n y_pred = self._classifier_model.predict(X_test)\n segmentID = y_pred[0]\n \n return segmentID","def billing_history(cls, user=None):\n invoices = Invoice.query.filter(Invoice.user_id == user.id) \\\n .order_by(Invoice.created_on.desc()).limit(12)\n\n return invoices","def customerReport(self):\n self._setFormat()\n for cust in self.getCustomerAccountData():\n self.output.write(self.form_line(cust))","def features_orders(df_customers, df_receipts):\n df_customers.sort_values(by=['customerId2'], ascending=[True], inplace=True)\n # total amount of all the orders of a cusrtomer\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')['price'].sum(), 'sum_orders')\n # the min amount paid in one receipt by a customer\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)[\n 'price'].sum().groupby('customerId2').min()['price'], 'min_order')\n # the mean amount paid per receipt by a customer\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\n .groupby('customerId2').mean()['price'], 'mean_order')\n # the max amount paid per receipt by a customer\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\n .groupby('customerId2').max()['price'], 'max_order')\n # the number of orders\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\n .groupby('customerId2').count()['price'], 'count_orders')\n\n # the total amount of items bought by a user\n df_customers = _add_column(df_customers,\n df_receipts.groupby('customerId2')['itemQty'].sum(), 'sum_itemQty')\n # the min amount of items bought by a user in a receipt\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\n .groupby('customerId2').min()['itemQty'], 'min_itemQty')\n # the mean amount of items bought by a user in a receipt\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\n .groupby('customerId2').mean()['itemQty'], 'mean_itemQty')\n # the max amount of items bought by a user in a receipt\n df_customers = _add_column(df_customers,\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\n .groupby('customerId2').max()['itemQty'], 'max_itemQty')\n # from which dividion type a user buys\n for i in DIVISIONID_VALS:\n k = 'divisionId_' + str(i)\n df_receipts[k] = np.where(df_receipts['divisionId'] == i, 1, 0)\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\n # which source type a user uses to pay\n for i in SOURCEID_VALS:\n k = 'sourceId_' + str(i)\n df_receipts[k] = np.where(df_receipts['sourceId'] == i, 1, 0)\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\n\n logger.info(\"Features from the returns table: {shape} {dtypes}\"\n .format(shape=df_customers.shape, dtypes=df_customers.dtypes))\n return df_customers","def get_invoices(self, limit=50, closed=False, get_all=False):\n mask = \"mask[invoiceTotalAmount, itemCount]\"\n _filter = {\n 'invoices': {\n 'createDate': {\n 'operation': 'orderBy',\n 'options': [{\n 'name': 'sort',\n 'value': ['DESC']\n }]\n },\n 'statusCode': {'operation': 'OPEN'},\n }\n }\n if closed:\n del _filter['invoices']['statusCode']\n\n return self.client.call('Account', 'getInvoices', mask=mask, filter=_filter, iter=get_all, limit=limit)","def generate_customer_info(self):\n consecutive = check_consecutive(self.customerIds)\n print(\"consecutive\" + str(consecutive))\n if consecutive:\n for customer_id in self.customerIds:\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\n insured = np.random.choice(\n self.distributions[\"insurance_status\"],\n 1,\n p=self.distributions[\"insurance_distribution\"],\n )[0]\n dob = self.dobs[customer_id - 1]\n customer = pd.DataFrame(\n {\n \"customer_id\": [customer_id],\n \"dob\": str(dob),\n \"insurance\": insured,\n \"experiment_id\": random.randint(1, 2),\n }\n )\n self.Customers = self.Customers.append(customer)\n else:\n print(\"Error generating customer info: customerIds aren't consecutive\")\n return True","def df(client_ids, start, end):\n obj = search(client_ids, start, end)\n df = DataFrame.from_dict(obj).T\n\n if df.empty:\n return df\n\n df.index.name = 'client_id'\n df = df.rename(columns={ 0: 'inactive', 1: 'active' })\n df['total'] = df.sum(axis=1)\n df = df.fillna(0).astype('int64')\n\n return df","def open_invoices(self):\n return {\n 'domain': \"[('id', 'in', \" + str(self.invoice_ids.ids) + \" )]\",\n 'name': 'Invoices',\n 'view_mode': 'tree,form',\n 'res_model': 'account.move',\n 'type': 'ir.actions.act_window',\n }","def get_invoiced_lot_values(self):\n self.ensure_one()\n\n if self.state == 'draft':\n return []\n\n sale_orders = self.mapped('invoice_line_ids.sale_line_ids.order_id')\n stock_move_lines = sale_orders.mapped('picking_ids.move_lines.move_line_ids')\n\n # Get the other customer invoices and refunds.\n ordered_invoice_ids = sale_orders.mapped('invoice_ids') \\\n .filtered(lambda i: i.state not in ['draft', 'cancel']) \\\n .sorted(lambda i: (i.invoice_date, i.id))\n\n # Get the position of self in other customer invoices and refunds.\n self_index = None\n i = 0\n for invoice in ordered_invoice_ids:\n if invoice.id == self.id:\n self_index = i\n break\n i += 1\n\n # Get the previous invoice if any.\n previous_invoices = ordered_invoice_ids[:self_index]\n last_invoice = previous_invoices[-1] if len(previous_invoices) else None\n\n # Get the incoming and outgoing sml between self.invoice_date and the previous invoice (if any).\n write_dates = [wd for wd in self.invoice_line_ids.mapped('write_date') if wd]\n self_datetime = max(write_dates) if write_dates else None\n last_write_dates = last_invoice and [wd for wd in last_invoice.invoice_line_ids.mapped('write_date') if wd]\n last_invoice_datetime = max(last_write_dates) if last_write_dates else None\n\n def _filter_incoming_sml(ml):\n if ml.state == 'done' and ml.location_id.usage == 'customer' and ml.lot_id:\n if last_invoice_datetime:\n return last_invoice_datetime <= ml.date <= self_datetime\n else:\n return ml.date <= self_datetime\n return False\n\n def _filter_outgoing_sml(ml):\n if ml.state == 'done' and ml.location_dest_id.usage == 'customer' and ml.lot_id:\n if last_invoice_datetime:\n return last_invoice_datetime <= ml.date <= self_datetime\n else:\n return ml.date <= self_datetime\n return False\n\n incoming_sml = stock_move_lines.filtered(_filter_incoming_sml)\n outgoing_sml = stock_move_lines.filtered(_filter_outgoing_sml)\n\n # Prepare and return lot_values\n qties_per_lot = defaultdict(lambda: 0)\n if self.type == 'out_refund':\n for ml in outgoing_sml:\n qties_per_lot[ml.lot_id] -= ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\n for ml in incoming_sml:\n qties_per_lot[ml.lot_id] += ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\n else:\n for ml in outgoing_sml:\n qties_per_lot[ml.lot_id] += ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\n for ml in incoming_sml:\n qties_per_lot[ml.lot_id] -= ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\n lot_values = []\n for lot_id, qty in qties_per_lot.items():\n if float_is_zero(qty, precision_rounding=lot_id.product_id.uom_id.rounding):\n continue\n lot_values.append({\n 'product_name': lot_id.product_id.display_name,\n 'product_color': lot_id.x_studio_color.x_name,\n 'quantity': qty,\n 'uom_name': lot_id.product_uom_id.name,\n 'lot_name': lot_id.name\n })\n #AQUI ORDENO TODOS LOS LOTES QUE ME QUEDAN EN lot_values POR EL COLOR\n lot_values.sort(key=lambda r: r['product_color'], reverse=False)\n return lot_values","def invoiceitem(self, id):\r\n return InvoiceItem(self, id)","def create_invoices(self, cr, uid, ids, context=None):\n invoice_list = []\n po_obj = self.pool.get('purchase.order')\n inv_line_obj = self.pool.get('account.invoice.line')\n inv_obj = self.pool.get('account.invoice')\n addr_obj = self.pool.get('res.partner')\n journal_obj = self.pool.get('account.journal')\n if context is None:\n context = {}\n\n for purchase_adv_obj in self.browse(cr, uid, ids, context=context):\n for purchase_order in po_obj.browse(cr, uid, context.get('active_ids', []), context=context):\n inv_line_ids = []\n invoice_ids = []\n val = inv_line_obj.product_id_change(cr, uid, [], purchase_adv_obj.product_id.id,\n uom_id=False, partner_id=purchase_order.partner_id.id, fposition_id=purchase_order.fiscal_position.id)\n line_id = inv_line_obj.create(cr, uid, {\n 'name': val['value']['name'],\n 'account_id': val['value']['account_id'],\n 'price_unit': purchase_adv_obj.amount,\n 'quantity': purchase_adv_obj.qtty,\n 'discount': False,\n 'uos_id': val['value']['uos_id'],\n 'product_id': purchase_adv_obj.product_id.id,\n 'invoice_line_tax_id': [(6, 0, val['value']['invoice_line_tax_id'])],\n })\n inv_line_ids.append(line_id)\n addr = addr_obj.address_get(cr, uid, [purchase_order.partner_id.id], ['invoice'])\n journal_ids = journal_obj.search(cr, uid, [('type', '=', 'purchase')])\n context.update({'type':'in_invoice','journal_type':'purchase'})\n inv_vals = {\n 'name': purchase_order.partner_ref or purchase_order.name,\n 'origin': purchase_order.name,\n 'type': 'in_invoice',\n 'reference': False,\n 'account_id': purchase_order.partner_id.property_account_payable.id,\n 'journal_id':journal_ids and journal_ids[0] or False,\n 'partner_id': purchase_order.partner_id.id,\n 'address_invoice_id': addr['invoice'],\n 'invoice_line': [(6, 0, inv_line_ids)],\n 'currency_id': purchase_order.pricelist_id.currency_id.id,\n 'comment': '',\n 'payment_term': purchase_order.payment_term_id and purchase_order.payment_term_id.id or False,\n 'fiscal_position': purchase_order.fiscal_position.id or purchase_order.partner_id.property_account_position.id,\n 'prepaid': True\n }\n\n inv_id = inv_obj.create(cr, uid, inv_vals, context=context)\n inv_obj.button_reset_taxes(cr, uid, [inv_id], context=context)\n for invoice in purchase_order.invoice_ids:\n invoice_ids.append(invoice.id)\n invoice_ids.append(inv_id)\n po_obj.write(cr, uid, purchase_order.id, {'invoice_ids': [(6, 0, invoice_ids)]})\n invoice_list.append(inv_id)\n\n if purchase_order.invoice_method in ('picking','order'):\n self.pool.get('purchase.order.line').create(cr, uid, {\n 'order_id': purchase_order.id,\n 'name': val['value']['name'],\n 'date_planned':purchase_order.date_order,\n 'price_unit': -purchase_adv_obj.amount,\n 'product_uom_qty': purchase_adv_obj.qtty,\n 'product_uos': val['value']['uos_id'],\n 'product_uom': val['value']['uos_id'],\n 'product_id': purchase_adv_obj.product_id.id,\n 'adavance_product':True,\n 'discount': False,\n 'taxes_id': [(6, 0, val['value']['invoice_line_tax_id'])],\n }, context=context)\n\n\n context.update({'invoice_id':invoice_list})\n return {\n 'name': 'Open Invoice',\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'purchase.open.invoice',\n 'type': 'ir.actions.act_window',\n 'target': 'new',\n 'context': context\n }","def invoice_line_create(self, invoice_id, qty):\n invoice_lines = self.env['account.invoice.line']\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\n for line in self:\n if not float_is_zero(qty, precision_digits=precision):\n vals = line._prepare_invoice_line(qty=qty)\n vals.update({'invoice_id': invoice_id, 'purchase_line_id': line.id})\n invoice_lines |= self.env['account.invoice.line'].create(vals)\n return invoice_lines","def create_invoice(self):\n for line in self:\n # if not line.account_id:\n # raise UserError(_('Please Add the incoming Account !!'))\n self.ensure_one()\n journal_id = self.env['account.journal'].search([\n ('type', '=', 'sale')], limit=1)\n inv_line_main = {\n 'name': line.description.name,\n 'price_unit': line.amount or 0.00,\n 'quantity': 1,\n 'discount': line.discount,\n 'account_id': line.description.property_account_income_id.id or line.description.categ_id.property_account_income_categ_id.id or False,\n }\n inv_values = {\n 'partner_id': line.patient_id.partner_id.id,\n 'patient_id': line.patient_id.id,\n 'dentist': line.dentist.id,\n 'move_type': 'out_invoice',\n 'invoice_date': datetime.now().strftime(DF) or False,\n 'journal_id': journal_id and journal_id.id or False,\n 'teeth_id': line.patient_id and line.patient_id.id or False,\n }\n acc_id = self.env['account.move'].create(inv_values)\n acc_id.write({'invoice_line_ids': [(0, 0, inv_line_main)]})\n\n self.write({'invc_id': acc_id.id, 'inv': True})\n context = dict(self._context or {})\n wiz_form_id = self.env['ir.model.data'].get_object_reference(\n 'account', 'view_move_form')[1]\n\n return {\n 'view_type': 'form',\n 'view_id': wiz_form_id,\n 'view_mode': 'form',\n 'res_model': 'account.move',\n 'res_id': self.invc_id.id,\n 'type': 'ir.actions.act_window',\n 'target': 'current',\n 'context': context,\n }","def filter_by_customer(table, customer):\n transactions = []\n for record in table:\n if record[CUSTOMER_ID] == customer:\n transactions.append(record)\n return transactions","def customer(self, customer_id=None):\r\n return customers.Customer(self, customer_id)","def single_customer(customer_name, invoice_file):\n def customer_rental(rental_items):\n \"\"\"\n Loop through rental_items file and append each row to curried invoice_file with same\n customer_name\n \"\"\"\n customer = partial(add_furniture, invoice_file=invoice_file, customer_name=customer_name)\n with open(rental_items, \"r\") as rental_csv:\n for row in csv.reader(rental_csv):\n customer(item_code=row[0], item_description=row[1], item_monthly_price=row[2])\n return customer_rental","def df_customers_fileRead(self):\n \n #-------------------------------------------------------------------------\n # RFM features are restored\n #-------------------------------------------------------------------------\n df_customers_rfm \\\n = p5_util.object_load(self.df_customers_rfm_fileName)\n self.strprint(\"RFM features : \"+str(df_customers_rfm.shape))\n \n #-------------------------------------------------------------------------\n # Time features are restored\n #-------------------------------------------------------------------------\n df_customers_timeFeature \\\n = p5_util.object_load(self._df_customers_timeFeature_fileName)\n self.strprint(\"Time features : \"+str(df_customers_timeFeature.shape))\n \n #-------------------------------------------------------------------------\n # NLP features are restored\n #-------------------------------------------------------------------------\n df_customers_nlp = p5_util.object_load(self._df_customers_nlp_fileName)\n self.strprint(\"NLP features : \"+str(df_customers_nlp.shape))\n\n if False:\n df_customers_rfm = self._df_customers_rfm.copy()\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\n df_customers_nlp = self._df_customers_pca_nlp.copy()\n\n #-------------------------------------------------------------------------\n # Dataframe are aggregated; note that indexes are customerID.\n #-------------------------------------------------------------------------\n df_customers = pd.DataFrame()\n\n df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)\n\n df_customers = pd.concat([df_customers,df_customers_timeFeature]\\\n , join='inner', axis=1)\n\n df_customers = pd.concat([df_customers,df_customers_nlp]\\\n , join='inner', axis=1)\n\n self.strprint(\"All features : \"+str(df_customers.shape))\n\n #----------------------------------------------------------------------\n # Dataframe is dumped into a file\n #----------------------------------------------------------------------\n p5_util.object_dump(df_customers, self._df_customers_fileName)\n if False:\n #----------------------------------------------------------------------\n # Dataframe is copied as an attribute\n #----------------------------------------------------------------------\n self._df_customers = df_customers.copy()\n \n return","def bc_records_dataframe(self, run_idxs):\n\n return pd.DataFrame(self.bc_records(run_idxs))","def get(self):\n user = get_authenticated_user()\n if not user.stripe_id:\n raise NotFound()\n\n return get_invoices(user.stripe_id)","def get_customer_segments(self, date):\n date = current_date_to_day().isoformat() if date is None else date\n self.products = pd.merge(self.products,\n self.cs.fetch(start_date=convert_dt_to_day_str(date))[['client', 'segments']],\n on='client', how='left')","def getInvoice(self):\n return self.base.get(\"invoice\", [])","def single_customer(customer_name, invoice_file):\n def single_customer_rentals(rental_items):\n add_item = partial(add_furniture, customer_name=customer_name,\n invoice_file=invoice_file)\n with open(rental_items, \"r\") as file:\n for row in csv.reader(file):\n add_item(item_code=row[0], item_description=row[1],\n item_monthly_price=row[2])\n return single_customer_rentals","def show_all_customers():\n return cr.show_all_customers()","def extract_table_customer(last_id, db_engine):\n\n if last_id == None:\n last_id = -1\n\n query = \"SELECT * FROM customer WHERE customer_id > {} LIMIT 100000\".format(\n last_id)\n return pd.read_sql(query, db_engine)","def invoiceitems(self):\r\n return InvoiceItems(self)","def test_single_customer(_full_invoice):\n\n test_invoice = \"../data/test-invoice.csv\"\n items_to_insert = \"../data/items.csv\"\n csv_contents = []\n\n function = l.single_customer(\"Kyouma Hououin\", test_invoice)\n function(items_to_insert)\n\n with open(test_invoice, \"r\") as csv_file:\n contents = reader(csv_file, delimiter=',')\n for line in contents:\n if line != []:\n csv_contents += [line]\n\n csv_contents += contents\n\n assert _full_invoice == csv_contents","def draw_header(canvas, invoice):\n\n canvas.setLineWidth(2)\n canvas.line(2 * cm, -4 * cm, 19 * cm, -4 * cm)\n \"\"\" Draws the business address \"\"\"\n business_details = settings.BUSINESS_DETAIL\n business_data = []\n for line in business_details:\n business_data.append([line])\n\n table = Table(business_data, colWidths=[17 * cm], rowHeights=[15, 17, 11, 11, 11, 11, 11])\n table.setStyle([\n ('FONT', (0, 0), (-1, -1), 'Helvetica-Oblique'),\n ('FONTSIZE', (0, 0), (0, 0), 14),\n ('FONTSIZE', (0, 1), (0, -1), 6),\n ('TEXTCOLOR', (0, 0), (-1, -1), (0.2, 0.2, 0.2)),\n ('ALIGN', (0, 0), (-1, -1), 'CENTER'),\n ('BACKGROUND', (0, 0), (-1, -1), (0.95, 0.95,0.95)),\n ])\n tw, th, = table.wrapOn(canvas, 2 * cm, 19 * cm)\n table.drawOn(canvas, 2 * cm, -4 * cm)","def get_invoices(\n page_number=None,\n limit=10,\n filter_by=None,\n filter_value=None,\n order_by=None\n):\n where_filter = \"\"\n \n valid_fields = {\n 'month': 'ReferenceMonth',\n 'year': 'ReferenceYear',\n 'document': 'Document'\n }\n\n ob_fields = []\n for ob in order_by:\n field = valid_fields.get(ob)\n if field:\n ob_fields.append(field)\n\n order_by = ob_fields\n\n if not order_by:\n order_by = [\"CreatedAt\"]\n\n order_by = [ob + \" ASC\" for ob in order_by]\n order_by = \", \".join(order_by)\n\n if page_number:\n pagination_filter = f\"\"\"\n id NOT IN (\n SELECT\n id\n FROM\n invoice\n ORDER BY\n {order_by} LIMIT {limit*(page_number-1)} )\n \"\"\"\n where_filter += \"AND \" + pagination_filter\n order_by += f\" LIMIT {limit}\"\n \n if filter_by and filter_value:\n if filter_by == 'document':\n filter_value = f'\"{filter_value}\"'\n\n where_filter += f\"AND {filter_by} = {filter_value}\"\n \n query = f\"\"\"\n SELECT\n id,\n ReferenceMonth AS month,\n ReferenceYear AS year,\n Document AS document,\n Description AS description,\n Amount AS amount\n FROM\n invoice\n WHERE\n IsActive = 1\n {where_filter}\n ORDER BY\n {order_by}\n ;\n \"\"\"\n\n try:\n conn = sqlite3.connect(DATABASE)\n conn.row_factory = dict_factory\n cursor = conn.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n return result, True\n except:\n return [], False","def _prepare_invoice_lines(self, exchange_line, order_line):\n invoice_type = {\n 'sale.order.line': {\n 'higher': 'out_invoice', 'lower': 'out_refund',\n 'type': 'sale', 'field': 'exchange_sale_line_id'\n },\n 'purchase.order.line': {\n 'higher': 'in_invoice', 'lower': 'in_refund',\n 'type': 'purchase', 'field': 'exchange_purchase_line_id'\n },\n }\n product = exchange_line.exchange_product_id or exchange_line.product_id\n data = {\n 'invoice_type': False,\n 'values': {\n 'product_id': product.id,\n 'quantity': exchange_line.quantity,\n 'name': 'Exchange for [%s]' % exchange_line.product_id.display_name,\n }\n }\n if exchange_line.exchange_product_id or \\\n exchange_line.price_subtotal > order_line.price_subtotal:\n data['invoice_type'] = invoice_type[order_line._name]['higher']\n elif exchange_line.price_subtotal < order_line.price_subtotal:\n data['invoice_type'] = invoice_type[order_line._name]['lower']\n else:\n return {}\n data[invoice_type[order_line._name]['type']] = order_line.order_id\n data['values'][invoice_type[order_line._name]['field']] = order_line.id\n data['values']['price_unit'] = exchange_line.price_unit\n # TODO i think we should take the different between prices NOT the all price\n # abs(exchange_line.price_unit - order_line.price_unit)\n return data","def print_customers(self):\n output = ''\n for i in range(len(self.customers)):\n output += f'Customer no. {self.customers[i].id} is in {self.customers[i].state[0]} section\\n'\n #print(output)\n with open('oneday.csv','a') as outfile:\n for i in range(len(self.customers)):\n outfile.write(f'{self.get_time()};{self.customers[i].id};{self.customers[i].state[0]}\\n')","def create_data():\n data_set = pd.DataFrame()\n customer_id = list()\n for i in range(1, 10001):\n customer_id.append(i)\n data_set = pd.DataFrame()\n data_set.loc[:, 'customer_id'] = np.array(customer_id)\n product_name = ('dining chair', 'dining table', 'bed', 'dining set',\n 'stool', 'couch', 'occasional table',\n 'recliner')\n product_name_random = random.choices(product_name, k=10000)\n data_set.loc[:, 'product_name'] = np.array(product_name_random)\n quantity_rented = (1, 2, 3, 4)\n quantity_rented_random = random.choices(quantity_rented, k=10000)\n data_set.loc[:, 'quantity_rented'] = np.array(quantity_rented_random)\n unit_rental_price_monthly = list()\n for i in range(0, 10000):\n unit_rental_price_monthly.append(random.uniform(1.5, 25))\n data_set.loc[:, 'unit_rental_price'] = np.array(unit_rental_price_monthly)\n rental_period_months = list()\n for i in range(0, 10000):\n rental_period_months.append(randint(6, 60))\n data_set.loc[:, 'rental_period_months'] = np.array(rental_period_months)\n return data_set","def df_customers_features_build(self):\n\n df_customers_rfm = self._df_customers_rfm.copy()\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\n df_customers_nlp = self._df_customers_pca_nlp.copy()\n\n #-------------------------------------------------------------------------\n # Dataframe are aggregated; note that indexes are customerID.\n #-------------------------------------------------------------------------\n df_customers = pd.DataFrame()\n\n df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)\n\n df_customers = pd.concat([df_customers,df_customers_timeFeature]\\\n , join='inner', axis=1)\n\n df_customers = pd.concat([df_customers,df_customers_nlp]\\\n , join='inner', axis=1)\n \n self.strprint(\"All features : \"+str(df_customers.shape))\n self._df_customers = df_customers.copy()\n return","def get_customers(filters):\n\treturn frappe.db.sql(\"\"\"\n\t\tSELECT\n\n\t\t\tpar.debtor_creditor_number as 'Konto',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Company' THEN cus.customer_name\n\t\t\t\tELSE null\n\t\t\t\tEND as 'Name (Adressatentyp Unternehmen)',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Individual' THEN TRIM(SUBSTR(cus.customer_name, LOCATE(' ', cus.customer_name)))\n\t\t\t\tELSE null\n\t\t\t\tEND as 'Name (Adressatentyp natürl. Person)',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Individual' THEN SUBSTRING_INDEX(SUBSTRING_INDEX(cus.customer_name, ' ', 1), ' ', -1)\n\t\t\t\tELSE null\n\t\t\t\tEND as 'Vorname (Adressatentyp natürl. Person)',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Individual' THEN '1'\n\t\t\t\tWHEN 'Company' THEN '2'\n\t\t\t\tELSE '0'\n\t\t\t\tEND as 'Adressatentyp',\n\t\t\tadr.address_line1 as 'Straße',\n\t\t\tadr.pincode as 'Postleitzahl',\n\t\t\tadr.city as 'Ort',\n\t\t\tUPPER(country.code) as 'Land',\n\t\t\tadr.address_line2 as 'Adresszusatz',\n\t\t\tadr.email_id as 'E-Mail',\n\t\t\tadr.phone as 'Telefon',\n\t\t\tadr.fax as 'Fax',\n\t\t\tcus.website as 'Internet',\n\t\t\tcus.tax_id as 'Steuernummer'\n\n\t\tFROM `tabCustomer` cus\n\n\t\t\tleft join `tabParty Account` par\n\t\t\ton par.parent = cus.name\n\t\t\tand par.parenttype = 'Customer'\n\t\t\tand par.company = %(company)s\n\n\t\t\tleft join `tabDynamic Link` dyn_adr\n\t\t\ton dyn_adr.link_name = cus.name\n\t\t\tand dyn_adr.link_doctype = 'Customer'\n\t\t\tand dyn_adr.parenttype = 'Address'\n\n\t\t\tleft join `tabAddress` adr\n\t\t\ton adr.name = dyn_adr.parent\n\t\t\tand adr.is_primary_address = '1'\n\n\t\t\tleft join `tabCountry` country\n\t\t\ton country.name = adr.country\n\n\t\tWHERE adr.is_primary_address = '1'\n\t\t\"\"\", filters, as_dict=1)","def fetch_customer_info_identities(self, client_id):\n\n try:\n return self._make_private_api_request(\n method=PyttributionIo.GET_REQUEST,\n endpoint='customers',\n subject_id=client_id,\n show_identities='true'\n ).get('customer')\n except RequestException as e:\n logger.error('Pyttribution.io: Retrieval of customer identities failed with HTTP status {exception}'.format(\n exception=e))","def get_customer_info(self, customer_id):\n #requested_customer_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))\n # Get predicted ratings\n customers = self.__get_customers(customer_id).collect()\n\n return customers","def get_invoiceitems(self):\n from invoicing.models import InvoiceItem\n\n invoiceitem_list = []\n # First we get all the product invoiceitems\n for product in self.products: # TODO: SOLVE BUNDLED PRODUCTS!\n item = InvoiceItem()\n # Get the copies for this product, when used on with_copies\n item.copies = product[1]\n # Add the amount of frequency if necessary\n frequency_extra = (\n _(\" {} months\".format(self.frequency)) if self.frequency > 1 else \"\"\n )\n item.description = product[0].name + frequency_extra\n item.price = product[0].price * self.frequency\n item.amount = item.price * item.copies\n item.product = product[0]\n item.subscription = self\n # TODO: Service from, service to\n invoiceitem_list.append(item)\n\n # Next, we append all discount invoiceitems\n for discount in self.get_discounts():\n discount_item = InvoiceItem()\n # Add the amount of frequency if necessary\n frequency_extra = (\n _(\" {} months\".format(self.frequency)) if self.frequency > 1 else \"\"\n )\n discount_item.description = discount[\"description\"] + frequency_extra\n discount_item.amount = discount[\"amount\"] * self.frequency\n discount_item.type_dr = discount[\"type_dr\"]\n discount_item.type = discount[\"type\"]\n discount_item.subscription = self\n invoiceitem_list.append(discount_item)\n\n return invoiceitem_list","def show_table(table, has_customer_id=True):\n titles = [\"ID\", \"Title\", \"Price\", \"Date\"]\n if has_customer_id:\n titles.append(\"Customer ID\")\n output_table = [[row[ID], row[TITLE], row[PRICE],\n '/'.join((str(row[YEAR]), str(row[MONTH]), str(row[DAY]))), row[CUSTOMER_ID]] for row in table]\n else:\n output_table = [[row[ID], row[TITLE], row[PRICE],\n '/'.join((str(row[YEAR]), str(row[MONTH]), str(row[DAY])))] for row in table]\n\n ui.clear_scr()\n ui.print_table(output_table, titles, TITLE)","def merge_purchase_invoice(self):\r\n active_id = self.env['purchase.order'].browse(self.env['purchase.order']._context.get('active_ids'))\r\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')]) \r\n active_id_count = 0\r\n active_count = 0\r\n exist_vendor = []; invoice = [];exist_vendors = [];ctx = ();invoice_id = []\r\n for rec in active_id : \r\n po_reference = self.env['account.invoice'].search([('origin', 'like', rec.name)])\r\n active_count = len(active_id)\r\n if rec.picking_count >= 1 and rec.picking_count != rec.invoice_count:\r\n len_name = [] \r\n for inv in po_reference: \r\n len_name = inv.origin.split(\":\") \r\n if rec.name in len_name:\r\n if po_reference.state == 'draft':\r\n for record in po_reference.invoice_line_ids:\r\n print (record.line_id)\r\n for res in rec.order_line:\r\n if res.id == record.line_id: \r\n record.write({'quantity':res.qty_received})\r\n res.write({'qty_invoiced':record.quantity})\r\n \r\n else:\r\n \r\n po_list = [];line_values = {};lines = {};purchase = []\r\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\r\n purchase.append(rec.id)\r\n active_id_count = len(purchase)\r\n if rec.partner_id.id in exist_vendor:\r\n for inv in invoice:\r\n if inv['partner_id'] == rec.partner_id.id:\r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received \r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received ,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id ,\r\n 'line_id':recc.id\r\n }) \r\n inv['invoice_line_ids'].append(line_values)\r\n inv['origin'] = inv['origin'] + ':' + rec.name\r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendors.append(rec.partner_id.id) \r\n else: \r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received\r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id,\r\n 'line_id':recc.id\r\n }) \r\n print (rec.id)\r\n po_list.append(line_values) \r\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':datetime.today()}) \r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendor.append(rec.partner_id.id) \r\n \r\n else:\r\n po_list = [];line_values = {};lines = {};purchase = []\r\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\r\n purchase.append(rec.id)\r\n active_id_count = len(purchase)\r\n if rec.partner_id.id in exist_vendor:\r\n for inv in invoice:\r\n if inv['partner_id'] == rec.partner_id.id:\r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received\r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received ,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id ,\r\n 'line_id':recc.id\r\n }) \r\n inv['invoice_line_ids'].append(line_values)\r\n inv['origin'] = inv['origin'] + ':' + rec.name\r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendors.append(rec.partner_id.id) \r\n else: \r\n for recc in rec.order_line:\r\n if rec.picking_count > 1 and rec.invoice_count >= 1:\r\n qty_received = recc.qty_received - recc.qty_invoiced \r\n else:\r\n qty_received = recc.qty_received\r\n line_values = (0, 0, {'product_id': recc.product_id.id,\r\n 'quantity': qty_received,\r\n 'price_unit': recc.price_unit,\r\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\r\n 'price_subtotal': recc.price_subtotal,\r\n 'product_uom': recc.product_uom.id,\r\n 'name': recc.name,\r\n 'account_id': journal_id.default_debit_account_id.id,\r\n 'line_id':recc.id\r\n }) \r\n print (rec.id)\r\n po_list.append(line_values) \r\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':date.today()}) \r\n if rec.partner_id.id not in exist_vendor:\r\n exist_vendor.append(rec.partner_id.id) \r\n \r\n invoices = []\r\n invoice_counts = 0\r\n for record in invoice:\r\n invoice_id = self.env['account.invoice'].create(record)\r\n invoices.append(invoice_id.id)\r\n invoice_counts = len(invoices)\r\n if active_id_count == 1:\r\n if invoice_counts == 1:\r\n form_view = self.env.ref('purchase.view_invoice_supplier_purchase_form').id\r\n tree_view = self.env.ref('account.invoice_tree').id \r\n return{\r\n 'name': _('Invoice'),\r\n 'type':'ir.actions.act_window',\r\n 'view_type':'form',\r\n 'view_mode':'form,tree',\r\n 'res_model':'account.invoice',\r\n 'res_id':invoices[0],\r\n 'views_id':False,\r\n 'views':[(form_view , 'form'), (tree_view , 'tree')],\r\n 'domain':[('id', 'in', invoices)],\r\n 'target': 'current',\r\n } \r\n else: \r\n form_view = self.env.ref('account.invoice_supplier_form').id\r\n tree_view = self.env.ref('account.invoice_supplier_tree').id \r\n return{\r\n 'name': _('Invoice'),\r\n 'type':'ir.actions.act_window',\r\n 'view_type':'form',\r\n 'view_mode':'form,tree',\r\n 'res_model':'account.invoice',\r\n 'views_id':True,\r\n 'views':[(tree_view , 'tree'), (form_view , 'form')],\r\n 'domain':[('id', 'in', invoices)],\r\n 'target': 'current',\r\n }","def top_ten_customers(data_frame):\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\n * data_frame['quantity_rented']\n * data_frame['rental_period_months'])\n data_set = data_frame.groupby(['customer_id']).agg({'total_payment': 'sum'})\n data_set = data_set.nlargest(10, 'total_payment')\n return data_set","def get_customer_data(self, customer_id):\n customer_url = urllib.parse.urljoin(self.base_url,\n \"/api/v1/pgecustomers/{}\".format(customer_id))\n response = requests.get(customer_url, headers=self.headers)\n\n if response.status_code == 200:\n return response.json()\n else:\n raise ValueError(response)","def display_customers_list():\n selected_path = request.url_rule\n selected_customer_list = get_selected_path(selected_path)\n page_number = 0\n if 'page' in request.args:\n page_number = int(request.args.get('page'))\n customers, pagination = generate_pagination(page_number, selected_customer_list)\n total_number_of_records = get_number_of_records(selected_customer_list)\n return render_template(\"clients.html\", domain_name= DOMAIN_NAME, selected_customer_list=selected_customer_list, customers=customers, pagination=pagination, url_path=selected_path, total_number_of_records=total_number_of_records, phone_error=None)","def _generate_invoice_report(self, request, queryset):\n logger.info('Generating invoice report for model {}'.format(\n queryset.model\n ))\n data = self._get_report_data(request, queryset)\n content = self._get_pdf_content(data)\n file_name = '{}-{}.pdf'.format(\n self._invoice_report_name, data['id'],\n )\n return generate_pdf_response(content, file_name)","def get_mall_data(): \n filename = 'mall_customers.csv'\n \n if os.path.isfile(filename):\n return pd.read_csv(filename, index_col=0)\n else: \n df = pd.read_sql(\"\"\"select * from customers\"\"\", get_connection('mall_customers'))\n df.to_csv(filename)\n return df","def get_customer_viewmodel(self, customer_id):\n credit_card_list = []\n customers = self._customer_repo.get_customer_list()\n credit_cards = self._customer_repo.get_credit_card_list()\n for customer in customers:\n if customer.get_customer_id() == customer_id:\n customer_first_name = customer.get_first_name()\n customer_last_name = customer.get_last_name()\n country = customer.get_country()\n for credit_card in credit_cards:\n if credit_card.get_customer_id() == customer_id:\n credit_card_list.append(credit_card.get_card_number())\n customer_to_view = CustomerViewModel(customer_id, customer_first_name,\n customer_last_name, country, credit_card_list)\n return customer_to_view","def get_billing_items(self, identifier):\n\n mask = \"\"\"mask[\n id, description, hostName, domainName, oneTimeAfterTaxAmount, recurringAfterTaxAmount, createDate,\n categoryCode,\n category[name],\n location[name],\n children[id, category[name], description, oneTimeAfterTaxAmount, recurringAfterTaxAmount]\n ]\"\"\"\n return self.client.call(\n 'Billing_Invoice',\n 'getInvoiceTopLevelItems',\n id=identifier,\n mask=mask,\n iter=True,\n limit=100\n )","def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\n \n # remove customers with more than 20 purchases\n if 'frequency' in customer_df.columns:\n customer_df = customer_df[customer_df.frequency < 20]\n \n return customer_df","def from_invoice_and_line_item(cls, invoice: InvoiceModel, line_item: LineItemModel, line_number: int,\n distribution: str):\n # Note the invoice_date should be the payment_date in the future.\n return cls(total=line_item.total, invoice_number=invoice.id,\n line_number=line_number,\n is_reversal=invoice.invoice_status_code in\n [InvoiceStatus.REFUNDED.value, InvoiceStatus.REFUND_REQUESTED.value],\n distribution=distribution)","def load_customers(dir):\n customSchema = StructType([ \\\n StructField(\"customerId2\", IntegerType(), True), \\\n StructField(\"churnlabel\", IntegerType(), True), \\\n StructField(\"gender\", StringType(), True), \\\n StructField(\"shippingCountry\", StringType(), True), \\\n StructField(\"dateCreated\", StringType(), True), \\\n StructField(\"yearOfBirth\", IntegerType(), True), \\\n StructField(\"premier\", IntegerType(), True)])\n\n df = sqlContext.read.format('com.databricks.spark.csv') \\\n .options(header='false', delimiter='\\t', nullValue='\\\\N') \\\n .load(get_dir_customers(dir) + '/*', schema=customSchema)\n\n return df","def _prepare_invoice(self, cr, uid, order, lines, context=None):\n invoice_vals = super(my_sale_order, self)._prepare_invoice(cr, uid, order,\n lines, context)\n\n invoice_vals.update({\n 'partner_shipping_id': order.partner_shipping_id.id,\n })\n\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\n\n return invoice_vals","def landing(request):\n\n data = Invoice.objects.all().order_by(\"-number\")[:10]\n\n return render(request,\n \"invoice/invoice_landing.html\",\n {\n \"invoices\": data\n })","def get_flat_file_data(kind: str, server: str='PROD', ID: str='42') -> DataFrame:\r\n k = {\r\n 'c': 'customer_data_{0}_{1}_.csv',\r\n 'b': 'vendor_data_{0}_{1}_.csv'\r\n }\r\n f = k[kind].format(server, ID)\r\n df = pd.read_csv(f'{BASE_DIR}/{f}', encoding='UTF-8')\r\n df = prepare_input_df(df)\r\n return df","def stripe_invoices(self, stripe_invoices):\n\n self._stripe_invoices = stripe_invoices","def upcoming(cls, customer_id):\n invoice = PaymentInvoice.upcoming(customer_id)\n\n return Invoice.parse_from_api(invoice)","def return_orders():\n with MY_CONNECTION as connection:\n cursor = connection.cursor()\n cursor.execute(\n \"\"\"\n SELECT id_order, id_customer, id_product, quantity, total_price,\n payment_status, send_status, order_date, location\n FROM Orders\n \"\"\")\n records = cursor.fetchall()\n return records","def run_contig_records_dataframe(self, run_idxs, run_record_key):\n records = self.run_contig_records(run_idxs, run_record_key)\n return pd.DataFrame(records)","def search_customer(cust_id):\n query = (Customer\n .select(Customer.customer_id, Customer.name, Customer.lastname,\n Customer.email, Customer.phone_number,\n Customer.home_address, Customer.status,\n Customer.credit_limit)\n .where(Customer.customer_id == cust_id))\n result = {}\n for person in query:\n result[\"customer_id\"] = person.customer_id\n result[\"name\"] = person.name\n result[\"lastname\"] = person.lastname\n result[\"email\"] = person.email\n result[\"phone_number\"] = person.phone_number\n result[\"home_address\"] = person.home_address\n result[\"status\"] = person.status\n result[\"credit_limit\"] = person.credit_limit\n return result","def view_budget_lines(self, cr, uid, ids, context=None):\n ctx = context.copy()\n ctx['default_line_id'] = ids[0]\n ctx['allow_create'] = True\n # Return view with budget lines\n return {\n 'name': _('Budget lines'),\n 'domain': \"[('line_id', 'in', %s)]\" % ids,\n 'view_type': 'form',\n 'view_mode': 'tree',\n 'res_model': 'account.balance.reporting.template.line.budget',\n 'context': ctx,\n 'type': 'ir.actions.act_window',\n }","def get_all_customer_ids_from_table(table):\n return {row[CUSTOMER_ID] for row in table}","def get_latest_invoice(self) -> CreditorInvoice:\n\n LOGGER.info(\"Getting latest invoice from EON Romania\")\n\n session = http.create_session()\n\n response = session.get(self._login_page_url)\n if response.status_code != 200:\n raise self.Error(\"Login page is not functioning\")\n\n soup = bs4.BeautifulSoup(response.content, \"html.parser\")\n csrf_token_elem = soup.find(\"input\", {\"name\": \"_csrf_token\"})\n if not csrf_token_elem:\n raise self.Error(\"Could not extract CSRF token\")\n\n login_data = {\n \"_username\": self._email,\n \"_password\": self._password,\n \"_csrf_token\": csrf_token_elem.get(\"value\"),\n }\n\n if session.post(self._login_url, login_data).status_code != 200:\n raise self.AuthError()\n\n response = session.get(self._invoices_url)\n soup = bs4.BeautifulSoup(response.content, \"html.parser\")\n\n if not soup.select_one(self._selectors.sidebar):\n raise self.AuthError()\n\n invoice_date_elem = soup.select_one(self._selectors.invoice_date)\n if not invoice_date_elem:\n raise self.Error(\"Failed to get invoice date\")\n\n invoice_due_date_elem = soup.select_one(self._selectors.invoice_due_date)\n if not invoice_due_date_elem:\n raise self.Error(\"Failed to get invoice due date\")\n\n invoice_payment_status_elem = soup.select_one(\n self._selectors.invoice_payment_status\n )\n if not invoice_payment_status_elem:\n raise self.Error(\"Failed to get invoice payment status\")\n\n invoice_amount_elem = soup.select_one(self._selectors.invoice_amount)\n if not invoice_amount_elem:\n raise self.Error(\"Failed to get invoice amount\")\n\n invoice_date = invoice_date_elem.contents[-1]\n invoice_due_date = invoice_due_date_elem.contents[-1]\n invoice_payment_status = invoice_payment_status_elem.contents[-1]\n invoice_amount = invoice_amount_elem.contents[-1]\n\n invoice = CreditorInvoice(\n float(invoice_amount.replace(\",\", \".\")),\n Currency.RON,\n datetime.strptime(invoice_date, \"%d.%m.%Y\"),\n datetime.strptime(invoice_due_date, \"%d.%m.%Y\"),\n PaymentStatus.PAID_CONFIRMED\n if invoice_payment_status == \"0.00\"\n else PaymentStatus.UNPAID,\n )\n\n LOGGER.info(\"Found latest EON Romania invoice\", invoice=invoice)\n return invoice","def invoice(self, reference_no=None, with_vat=True):\n\n return self.invoice_class(apiobj=self, reference_no=reference_no)","def customer(self, id):\r\n return Customer(self, id)","def _prepare_invoice_line(self, qty):\n self.ensure_one()\n res = {\n 'name': self.name,\n 'sequence': self.sequence,\n 'origin': self.order_id.name,\n 'account_id': self.product_id.product_tmpl_id._get_product_accounts()['stock_input'].id,\n 'price_unit': self.price_unit,\n 'quantity': qty,\n 'uom_id': self.product_uom.id,\n 'product_id': self.product_id.id or False,\n 'invoice_line_tax_ids': [(6, 0, self.taxes_id.ids)],\n 'account_analytic_id': self.account_analytic_id.id,\n 'analytic_tag_ids': [(6, 0, self.analytic_tag_ids.ids)],\n }\n return res","def transactions_df():\n return pd.DataFrame(\n {\n \"user_id\": [1, 1, 1, 2, 2, 2, 3, 3, 3],\n \"item_id\": [11, 22, 22, 11, 22, 33, 33, 33, 44],\n \"amount\": [10, 20, 30, 40, 50, 60, 70, 80, 90],\n }\n )","def build_invoice(payment_object):\n # Fill html template with the domain orders and user profile info\n html_template = get_template('billing/billing_invoice.html')\n rendered_html = html_template.render({\n 'payment': payment_object,\n 'user_profile': payment_object.owner.profile,\n })\n # Create pdf file from a html file\n pdfkit.from_string(rendered_html, '/tmp/out.pdf')\n with open(\"/tmp/out.pdf\", \"rb\") as pdf_file:\n pdf_raw = pdf_file.read()\n os.remove(\"/tmp/out.pdf\")\n return {\n 'body': pdf_raw,\n 'filename': 'invoice_{}.pdf'.format(payment_object.transaction_id),\n }","def get_ARNA_flight_log_as_df():\n flight_nums = [\n # 216,\n 217, 218, 219, 220, 221, 222, 223, 224, 225\n ]\n flight_IDs = ['C{}'.format(i) for i in flight_nums]\n dfs = []\n for flight_ID in flight_IDs:\n dfs += [get_summary4flight(flight_ID=flight_ID)]\n # Combine and return as a single dataframe sorted by time\n df = pd.concat(dfs)\n df = df.sort_index()\n return df","def get_customer_count(self):\n return self._df_invoice_original.CustomerID.unique().shape[0]","def predict_segment(self, df_invoice_line=None):\n if df_invoice_line is not None:\n self.data_transform(df_invoice_line) \n self.df_customers_features_build() \n else:\n pass\n X_test = self._df_customers.values\n y_pred = self._classifier_model.predict(X_test)\n return y_pred[0]"],"string":"[\n \"def create_customer_df_invoice_line(self, customerID, list_stockCode\\\\\\n , list_quantity, invoiceDate):\\n \\n dict_invoice = dict()\\n\\n dict_invoice['Quantity'] = list_quantity\\n dict_invoice['StockCode'] = list_stockCode\\n\\n #------------------------------------------------------------------------\\n # Build invoiceDate from local current time\\n #------------------------------------------------------------------------\\n if invoiceDate is None:\\n time_struct = time.localtime()\\n invoiceDate = str(time_struct.tm_year)+'-'+str(time_struct.tm_mon)\\\\\\n +'-'+str(time_struct.tm_mday)\\n invoiceDate +=' '\\n invoiceDate +=str(time_struct.tm_hour)+':'+str(time_struct.tm_min)\\\\\\n +':'+str(time_struct.tm_sec)\\n invoiceDate = pd.Timestamp(invoiceDate)\\n else:\\n pass\\n\\n\\n #------------------------------------------------------------------------\\n # Lists initialization\\n #------------------------------------------------------------------------\\n list_customerID = list()\\n list_invoiceNo = list()\\n list_invoiceDate = list()\\n list_invoice_line_index = list()\\n \\n #------------------------------------------------------------------------\\n # Increase Invoice number\\n #------------------------------------------------------------------------\\n invoiceNo = max(self._df_invoice_original.InvoiceNo)\\n invoiceNo += 1\\n\\n #------------------------------------------------------------------------\\n # Get latest invoice line index value\\n #------------------------------------------------------------------------\\n invoice_line_index = max(self._df_invoice_original.index)\\n\\n #------------------------------------------------------------------------\\n # Build lists for CustomerID, InvoiceNo, InvoiceDate\\n # A list of incremented indexes is built for new rows.\\n #------------------------------------------------------------------------\\n for quantity in list_quantity:\\n list_customerID.append(customerID)\\n list_invoiceNo.append(invoiceNo)\\n list_invoiceDate.append(invoiceDate)\\n invoice_line_index += 1\\n list_invoice_line_index.append(invoice_line_index) \\n\\n \\n dict_invoice['CustomerID'] = list_customerID\\n dict_invoice['InvoiceNo'] = list_invoiceNo\\n dict_invoice['InvoiceDate'] = list_invoiceDate\\n\\n #------------------------------------------------------------------------\\n # Get description list from list of stock codes.\\n #------------------------------------------------------------------------\\n list_description = self.getDescriptionList(list_stockCode)\\n \\n dict_invoice['Description'] = list_description\\n\\n #------------------------------------------------------------------------\\n # Get unit price list from list of stock codes.\\n #------------------------------------------------------------------------\\n list_unitPrice = self.getUnitPriceList(list_stockCode)\\n \\n dict_invoice['UnitPrice'] = list_unitPrice\\n\\n #------------------------------------------------------------------------\\n # Dataframe with new invoices lines is created.\\n #------------------------------------------------------------------------\\n df_invoice_line \\\\\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys()\\\\\\n , index=list_invoice_line_index)\\n \\n return df_invoice_line\",\n \"def invoice(customer_id):\\n encoder = request.url_rule.endpoint\\n template = \\\"{{ encoder }}#{{ customer_id|%s }}\\\" % encoder\\n return render_template_string(template, **locals())\",\n \"def get_dataframe() -> pandas.DataFrame:\\n database_connection = processing.establish_connection(database_path)\\n dataframe = database_connection.to_dataframe(['CustomerId', 'InvoiceDate', 'Total'], table_name)\\n database_connection.close()\\n dataframe = processing.get_invoice_date_fixed(dataframe)\\n analyze_dataframe = dataframe.copy()\\n total_sum_dataframe = processing.get_column_sum(analyze_dataframe)\\n\\n customer_count_dataframe = processing.drop_duplicates(analyze_dataframe)\\n customer_count_dataframe = processing.get_column_count(customer_count_dataframe)\\n return customer_count_dataframe.merge(total_sum_dataframe, how='inner', on='InvoiceDate')\",\n \"def order_process(self, customerID, list_stockCode, list_quantity\\\\\\n , orderDate=None):\\n\\n segmentID = -1\\n\\n #-------------------------------------------------------------------------\\n # A new customer is created and inserted into data-set.\\n #-------------------------------------------------------------------------\\n if customerID is None:\\n customerID = int(self.createCustomerID())\\n else:\\n pass\\n \\n #-------------------------------------------------------------------------\\n # A new dataframe with new invoice lines are created.\\n #-------------------------------------------------------------------------\\n df_invoice_line = self.create_customer_df_invoice_line(customerID\\\\\\n , list_stockCode, list_quantity, orderDate)\\n \\n #-------------------------------------------------------------------------\\n # Original dataframe is updated with customer invoices lines.\\n #-------------------------------------------------------------------------\\n print(\\\"order_process : shape before concat= \\\"+str(self._df_invoice_original.shape))\\n self._df_invoice_original \\\\\\n = pd.concat([self._df_invoice_original, df_invoice_line], axis=0)\\n print(\\\"order_process : shape after concat= \\\"+str(self._df_invoice_original.shape))\\n \\n #-------------------------------------------------------------------------\\n # All invoices lines (including new one) related to customer is retrieved \\n # from original dataframe.\\n #-------------------------------------------------------------------------\\n df_invoice_line_customer \\\\\\n = self.get_customer_history_df_invoice_line(customerID)\\n\\n #-------------------------------------------------------------------------\\n # When calling get_customer_marketSegment(), df_invoice_line_customer is\\n # concatened to the original dataframe.\\n #------------------------------------------------------------------------- \\n segmentID = self.get_customer_marketSegment(df_invoice_line_customer)\\n \\n return segmentID, customerID\",\n \"def get_rows(contract_address: str, file_name: str, receipts_filename: str) -> pd.DataFrame():\\n receipts_df = pd.read_csv(receipts_filename)\\n receipts_df = receipts_df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\\n df = pd.read_csv(file_name)\\n df = df.sort_values(['block_number', 'transaction_index'], ignore_index=True)\\n\\n df = df.loc[receipts_df['status'] == 1] \\n df = df.loc[df[\\\"to_address\\\"] == contract_address.lower()]\\n df = df.reset_index()\\n df = df.drop(columns='index')\\n return df\",\n \"def invoices(self,org_id=None,invoice_id=''):\\n if org_id is None:\\n org_id = self.org_id\\n return self.get('{}/orgs/{}/invoices/{}'.format(ApiVersion.A1.value,org_id,invoice_id))\",\n \"def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\\\\n , UnitPrice ):\\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\\n dict_invoice['CustomerID'] = CustomerID\\n dict_invoice['InvoiceNo'] = InvoiceNo\\n df_invoice_line \\\\\\n = pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])\\n \\n self.data_transform(df_invoice_line)\\n\\n #self.feature_rfm_encode()\\n\\n self.feature_scale()\\n\\n self.list_feature_drop()\\n\\n self.feature_description_nlp()\\n return\",\n \"def return_customer_orders(customer_id):\\n with MY_CONNECTION as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT id_order, id_customer, id_product, quantity, total_price,\\n payment_status, send_status, order_date, location\\n FROM Orders\\n Where id_customer=?\\n \\\"\\\"\\\",\\n (customer_id,))\\n return cursor.fetchall()\",\n \"def invoice(self, id):\\r\\n return Invoice(self, id)\",\n \"def get_all_customer_ids():\\n table = data_manager.get_table_from_file(\\\"sales/sales.csv\\\")\\n return get_all_customer_ids_from_table(table)\",\n \"def get_all_sales_ids_for_customer_ids():\\n\\n # your code\",\n \"def invoices(self):\\r\\n return Invoices(self)\",\n \"def invoices(self):\\r\\n return inv.Invoices(self)\",\n \"def invoice(self,context,params):\\n url = f\\\"https://api.freshbooks.com/accounting/account/{params['account_id']}/invoices/invoices/{params['invoice_id']}\\\"\\n result = json.loads(util.rest(\\\"GET\\\", url, {}, context[\\\"headers\\\"][\\\"access_token\\\"]).text)\\n invoice = result[\\\"response\\\"][\\\"result\\\"][\\\"invoice\\\"]\\n invoice_obj = FreshbooksInvoice(\\n account_id=invoice['accountid'],\\n customerid=invoice['customerid'], \\n invoice_id=invoice['invoiceid'],\\n currency_code=invoice['currency_code'],\\n language=invoice['language'],\\n terms=invoice['terms'],\\n discount_value=invoice['discount_value'],\\n discount_amount=invoice['discount_total']['amount'],\\n invoice_number=invoice['invoice_number'],\\n po_number=invoice['po_number'],\\n amount=invoice['amount']['amount'],\\n code=invoice['amount']['code'],\\n create_date=invoice['create_date']\\n )\\n return invoice_obj.__dict__\",\n \"def invoices(self, account_id):\\n from pureport_client.commands.accounts.invoices import Command\\n return Command(self.client, account_id)\",\n \"def invoice_items(self,org_id=None,query={}):\\n if org_id is None:\\n org_id = self.org_id\\n query_end_date = datetime.strptime(query['endDate'],'%Y-%m-%dT%H:%M:%SZ')\\n # Given a 'query_end_date' to find the invoice containing the\\n # line items for that date we need to find the invoice which \\n # has 'endDate' equal to the end of the month of the `query_end_date`\\n query_first_next_month = query_end_date + relativedelta(months=+1) - relativedelta(days=(query_end_date.day-1))\\n target_invoices = []\\n invoices = self.invoices(org_id)\\n if self.verbose:\\n print('Searching invoices org_id={}'.format(org_id))\\n print('query={} query_end_date={}'.format(query,query_end_date))\\n print('Result keys: {}'.format( invoices['content'].keys() ))\\n print('Total result count: {}'.format( invoices['content']['totalCount'] ))\\n for invoice in invoices['content']['results']:\\n #pprint.pprint(invoice)\\n end_date = datetime.strptime(invoice['endDate'],'%Y-%m-%dT%H:%M:%SZ')\\n if self.verbose: \\n print('invoice({})[\\\\'endDate\\\\']={} end_date={}'.format(invoice['id'],invoice['endDate'],end_date))\\n if end_date == query_first_next_month:\\n target_invoices.append(invoice)\\n \\n if self.verbose: \\n print('Target invoices: {}'.format(target_invoices))\\n \\n\\n target_line_items = []\\n for invoice in target_invoices:\\n invoice_details = self.invoices(org_id,invoice['id']) \\n print('invoice_details: {}'.format(invoice_details))\\n for item in invoice_details['content']['lineItems']:\\n end_date = datetime.strptime(item['endDate'],'%Y-%m-%dT%H:%M:%SZ')\\n if end_date == query_end_date:\\n target_line_items.append(item)\\n if self.verbose:\\n print('target_line_items: {}'.format(target_line_items)) \\n return target_line_items\",\n \"def invoice(self, invoice_number):\\r\\n return inv.Invoice(self, invoice_number)\",\n \"def get_all_customer_ids():\\n\\n # your code\",\n \"def invoices(self):\\r\\n return inv.AccountInvoices(self)\",\n \"def invoices_in_route(self):\\n from invoicing.models import Invoice\\n invoices = Invoice.objects.filter(\\n route=self.number, print_date__range=(date.today() - timedelta(6), date.today()),\\n canceled=False).count()\\n return invoices\",\n \"def _prepare_invoice(self, cr, uid, order, lines, context=None):\\n if context is None:\\n context = {}\\n journal_ids = self.pool.get('account.journal').search(cr, uid,\\n [('type', '=', 'sale'), ('company_id', '=', order.company_id.id)],\\n limit=1)\\n if not journal_ids:\\n raise osv.except_osv(_('Error !'),\\n _('There is no sales journal defined for this company: \\\"%s\\\" (id:%d)') % (order.company_id.name, order.company_id.id))\\n\\n invoice_vals = {\\n 'name': order.client_order_ref or '',\\n 'origin': order.name,\\n 'type': 'out_invoice',\\n 'reference': order.client_order_ref or order.name,\\n 'account_id': order.partner_id.property_account_receivable.id,\\n 'journal_id': order.partner_id.property_default_sale_invoice_journal.id,\\n 'partner_id': order.partner_id.id,\\n 'address_invoice_id': order.partner_invoice_id.id,\\n #'address_contact_id': order.partner_order_id.id,\\n 'invoice_line': [(6, 0, lines)],\\n 'currency_id': order.pricelist_id.currency_id.id,\\n 'comment': order.note,\\n 'payment_term': order.payment_term and order.payment_term.id or False,\\n 'fiscal_position': order.fiscal_position.id or order.partner_id.property_account_position.id,\\n 'date_invoice': context.get('date_invoice', False),\\n 'company_id': order.company_id.id,\\n 'user_id': order.user_id and order.user_id.id or False\\n }\\n\\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\\n\\n return invoice_vals\",\n \"def get_customer_orders(customerId):\\n data = user_obj.get_customer_orders(customerId)\\n return data\",\n \"def ListInvoices(self, **kwargs):\\n return self._stub.ListInvoices(ln.ListInvoiceRequest(**kwargs))\",\n \"def get_invoices(self, since, until):\\n return self._request('getInvoices', data={\\n 'date_from': since.strftime('%d/%m/%Y'),\\n 'date_to': until.strftime('%d/%m/%Y')\\n })\",\n \"def get_all_customer_ids_from_table(table):\\n\\n # your code\",\n \"def _get_invoices_for_payment(cls, account_id: int) -> List[InvoiceModel]:\\n valid_statuses = (InvoiceStatus.APPROVED.value, InvoiceStatus.REFUND_REQUESTED.value)\\n invoice_ref_subquery = db.session.query(InvoiceReferenceModel.invoice_id). \\\\\\n filter(InvoiceReferenceModel.status_code.in_((InvoiceReferenceStatus.ACTIVE.value,)))\\n\\n invoices: List[InvoiceModel] = db.session.query(InvoiceModel) \\\\\\n .filter(InvoiceModel.invoice_status_code.in_(valid_statuses)) \\\\\\n .filter(InvoiceModel.payment_method_code == PaymentMethod.EJV.value) \\\\\\n .filter(InvoiceModel.payment_account_id == account_id) \\\\\\n .filter(InvoiceModel.id.notin_(invoice_ref_subquery)) \\\\\\n .all()\\n return invoices\",\n \"def _prepare_invoice(self, cr, uid, order, lines, context=None):\\n if context is None:\\n context = {}\\n journal_id = self.pool['account.invoice'].default_get(cr, uid, ['journal_id'], context=context)['journal_id']\\n if not journal_id:\\n raise osv.except_osv(_('Error!'),\\n _('Please define sales journal for this company: \\\"%s\\\" (id:%d).') % (order.company_id.name, order.company_id.id))\\n invoice_vals = {\\n 'name': order.client_order_ref or '',\\n 'origin': order.name,\\n 'type': 'out_invoice',\\n 'reference': order.client_order_ref or order.name,\\n 'account_id': order.partner_invoice_id.property_account_receivable.id,\\n 'partner_id': order.partner_invoice_id.id,\\n 'journal_id': journal_id,\\n 'invoice_line': [(6, 0, lines)],\\n 'currency_id': order.pricelist_id.currency_id.id,\\n 'comment': order.note,\\n 'payment_term': order.payment_term and order.payment_term.id or False,\\n 'fiscal_position': order.fiscal_position.id or order.partner_invoice_id.property_account_position.id,\\n 'date_invoice': context.get('date_invoice', False),\\n 'company_id': order.company_id.id,\\n 'user_id': order.user_id and order.user_id.id or False,\\n 'section_id' : order.section_id.id,\\n 'test_1' :order.test\\n }\\n\\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\\n return invoice_vals\",\n \"def _create_invoice(self):\\n self.ensure_one()\\n partner = self.member_id.partner_id\\n invoice = self.env['account.invoice'].create({\\n 'partner_id': partner.id,\\n 'account_id': partner.property_account_receivable_id.id,\\n 'fiscal_position_id': partner.property_account_position_id.id\\n })\\n for line in self.line_ids:\\n product = line.activity_id.product_id\\n # Handling of invoice lines : needs cache record for onchange, then\\n # real writing...\\n invoice_line = self.env['account.invoice.line'].new({\\n 'product_id': product.id,\\n 'invoice_id': invoice.id\\n })\\n invoice_line._onchange_product_id()\\n line_values = dict(invoice_line._cache)\\n line_values['price_unit'] = line.price\\n invoice_line = self.env['account.invoice.line'].create(line_values)\\n invoice.compute_taxes()\\n line.registration_id.invoice_line_id = invoice_line.id\\n return invoice\",\n \"def _prepare_invoice_line(self, inv_id):\\n res = {}\\n account_id = self.product_id.property_account_income_id.id or self.product_id.categ_id.property_account_income_categ_id.id\\n if not account_id:\\n raise UserError(_('Please define income account for this product: \\\"%s\\\" (id:%d).') % \\\\\\n (self.product_id.name, self.product_id.id,))\\n price_unit = self.product_id.lst_price\\n res = {\\n 'invoice_id': inv_id.id,\\n 'name': self.name,\\n 'origin': self.order_id.name,\\n 'account_id': account_id,\\n 'uom_id': self.product_uom_id.id,\\n 'quantity': self.product_uom_qty,\\n 'price_unit': price_unit,\\n 'product_id': self.product_id.id,\\n 'invoice_line_tax_id': False,\\n 'order_line_id': self.id\\n }\\n return res\",\n \"def get_listCustomer_out_sample(self, customerCount=10):\\n \\n if customerCount is None :\\n listCustomer= list(self._df_invoice_line_out_sample.CustomerID.unique())\\n else:\\n if customerCount <= 0 :\\n listCustomer \\\\\\n = list(self._df_invoice_line_out_sample.CustomerID.unique())\\n else:\\n listCustomer \\\\\\n = list(self._df_invoice_line_out_sample.CustomerID.unique()[:customerCount])\\n return listCustomer\",\n \"def _get_lines(self, cr, uid, ids, context=None):\\n List=[]\\n if ids:\\n line = self.pool.get('payment.enrich.lines').browse(cr, uid, ids[0], context=context)\\n \\n record = line.enrich_id\\n val = 0.0\\n for line in record.enrich_lines:\\n if line.state == 'done' :\\n val += line.cost\\n res = {\\n 'paid_amount':val,\\n 'residual_amount':record.amount - val,\\n }\\n record.write(res)\\n return List\",\n \"def single_customer(customer_name, invoice_file):\\n def customer_rental(rental_items):\\n \\\"\\\"\\\"Closure to add single customer details\\\"\\\"\\\"\\n with open(rental_items, 'r', newline='') as rentals:\\n reader = csv.reader(rentals)\\n add_invoice_items = partial(add_furniture, invoice_file, customer_name)\\n for row in reader:\\n add_invoice_items(item_code=row[0],\\n item_description=row[1],\\n item_monthly_price=row[2])\\n return customer_rental\",\n \"def invoice_lines(self, invoice_lines):\\n if self.local_vars_configuration.client_side_validation and invoice_lines is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `invoice_lines`, must not be `None`\\\") # noqa: E501\\n\\n self._invoice_lines = invoice_lines\",\n \"def get_customer_marketSegment(self, df_invoice_line_customer):\\n #-------------------------------------------------------------------------\\n # Building data model \\n #-------------------------------------------------------------------------\\n self.data_transform(df_invoice_line_customer)\\n\\n #-------------------------------------------------------------------------\\n # Customer features are built thanks to transformers.\\n #-------------------------------------------------------------------------\\n self.df_customers_features_build()\\n \\n #-------------------------------------------------------------------------\\n # Customer market segment is predicted\\n #-------------------------------------------------------------------------\\n X_test = self._df_customers.values\\n y_pred = self._classifier_model.predict(X_test)\\n segmentID = y_pred[0]\\n \\n return segmentID\",\n \"def billing_history(cls, user=None):\\n invoices = Invoice.query.filter(Invoice.user_id == user.id) \\\\\\n .order_by(Invoice.created_on.desc()).limit(12)\\n\\n return invoices\",\n \"def customerReport(self):\\n self._setFormat()\\n for cust in self.getCustomerAccountData():\\n self.output.write(self.form_line(cust))\",\n \"def features_orders(df_customers, df_receipts):\\n df_customers.sort_values(by=['customerId2'], ascending=[True], inplace=True)\\n # total amount of all the orders of a cusrtomer\\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')['price'].sum(), 'sum_orders')\\n # the min amount paid in one receipt by a customer\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)[\\n 'price'].sum().groupby('customerId2').min()['price'], 'min_order')\\n # the mean amount paid per receipt by a customer\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\\n .groupby('customerId2').mean()['price'], 'mean_order')\\n # the max amount paid per receipt by a customer\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\\n .groupby('customerId2').max()['price'], 'max_order')\\n # the number of orders\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['price'].sum()\\n .groupby('customerId2').count()['price'], 'count_orders')\\n\\n # the total amount of items bought by a user\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby('customerId2')['itemQty'].sum(), 'sum_itemQty')\\n # the min amount of items bought by a user in a receipt\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\\n .groupby('customerId2').min()['itemQty'], 'min_itemQty')\\n # the mean amount of items bought by a user in a receipt\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\\n .groupby('customerId2').mean()['itemQty'], 'mean_itemQty')\\n # the max amount of items bought by a user in a receipt\\n df_customers = _add_column(df_customers,\\n df_receipts.groupby(['customerId2', 'receiptId'], as_index=False)['itemQty'].sum()\\n .groupby('customerId2').max()['itemQty'], 'max_itemQty')\\n # from which dividion type a user buys\\n for i in DIVISIONID_VALS:\\n k = 'divisionId_' + str(i)\\n df_receipts[k] = np.where(df_receipts['divisionId'] == i, 1, 0)\\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\\n # which source type a user uses to pay\\n for i in SOURCEID_VALS:\\n k = 'sourceId_' + str(i)\\n df_receipts[k] = np.where(df_receipts['sourceId'] == i, 1, 0)\\n df_customers = _add_column(df_customers, df_receipts.groupby('customerId2')[k].sum(), k)\\n\\n logger.info(\\\"Features from the returns table: {shape} {dtypes}\\\"\\n .format(shape=df_customers.shape, dtypes=df_customers.dtypes))\\n return df_customers\",\n \"def get_invoices(self, limit=50, closed=False, get_all=False):\\n mask = \\\"mask[invoiceTotalAmount, itemCount]\\\"\\n _filter = {\\n 'invoices': {\\n 'createDate': {\\n 'operation': 'orderBy',\\n 'options': [{\\n 'name': 'sort',\\n 'value': ['DESC']\\n }]\\n },\\n 'statusCode': {'operation': 'OPEN'},\\n }\\n }\\n if closed:\\n del _filter['invoices']['statusCode']\\n\\n return self.client.call('Account', 'getInvoices', mask=mask, filter=_filter, iter=get_all, limit=limit)\",\n \"def generate_customer_info(self):\\n consecutive = check_consecutive(self.customerIds)\\n print(\\\"consecutive\\\" + str(consecutive))\\n if consecutive:\\n for customer_id in self.customerIds:\\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\\n insured = np.random.choice(\\n self.distributions[\\\"insurance_status\\\"],\\n 1,\\n p=self.distributions[\\\"insurance_distribution\\\"],\\n )[0]\\n dob = self.dobs[customer_id - 1]\\n customer = pd.DataFrame(\\n {\\n \\\"customer_id\\\": [customer_id],\\n \\\"dob\\\": str(dob),\\n \\\"insurance\\\": insured,\\n \\\"experiment_id\\\": random.randint(1, 2),\\n }\\n )\\n self.Customers = self.Customers.append(customer)\\n else:\\n print(\\\"Error generating customer info: customerIds aren't consecutive\\\")\\n return True\",\n \"def df(client_ids, start, end):\\n obj = search(client_ids, start, end)\\n df = DataFrame.from_dict(obj).T\\n\\n if df.empty:\\n return df\\n\\n df.index.name = 'client_id'\\n df = df.rename(columns={ 0: 'inactive', 1: 'active' })\\n df['total'] = df.sum(axis=1)\\n df = df.fillna(0).astype('int64')\\n\\n return df\",\n \"def open_invoices(self):\\n return {\\n 'domain': \\\"[('id', 'in', \\\" + str(self.invoice_ids.ids) + \\\" )]\\\",\\n 'name': 'Invoices',\\n 'view_mode': 'tree,form',\\n 'res_model': 'account.move',\\n 'type': 'ir.actions.act_window',\\n }\",\n \"def get_invoiced_lot_values(self):\\n self.ensure_one()\\n\\n if self.state == 'draft':\\n return []\\n\\n sale_orders = self.mapped('invoice_line_ids.sale_line_ids.order_id')\\n stock_move_lines = sale_orders.mapped('picking_ids.move_lines.move_line_ids')\\n\\n # Get the other customer invoices and refunds.\\n ordered_invoice_ids = sale_orders.mapped('invoice_ids') \\\\\\n .filtered(lambda i: i.state not in ['draft', 'cancel']) \\\\\\n .sorted(lambda i: (i.invoice_date, i.id))\\n\\n # Get the position of self in other customer invoices and refunds.\\n self_index = None\\n i = 0\\n for invoice in ordered_invoice_ids:\\n if invoice.id == self.id:\\n self_index = i\\n break\\n i += 1\\n\\n # Get the previous invoice if any.\\n previous_invoices = ordered_invoice_ids[:self_index]\\n last_invoice = previous_invoices[-1] if len(previous_invoices) else None\\n\\n # Get the incoming and outgoing sml between self.invoice_date and the previous invoice (if any).\\n write_dates = [wd for wd in self.invoice_line_ids.mapped('write_date') if wd]\\n self_datetime = max(write_dates) if write_dates else None\\n last_write_dates = last_invoice and [wd for wd in last_invoice.invoice_line_ids.mapped('write_date') if wd]\\n last_invoice_datetime = max(last_write_dates) if last_write_dates else None\\n\\n def _filter_incoming_sml(ml):\\n if ml.state == 'done' and ml.location_id.usage == 'customer' and ml.lot_id:\\n if last_invoice_datetime:\\n return last_invoice_datetime <= ml.date <= self_datetime\\n else:\\n return ml.date <= self_datetime\\n return False\\n\\n def _filter_outgoing_sml(ml):\\n if ml.state == 'done' and ml.location_dest_id.usage == 'customer' and ml.lot_id:\\n if last_invoice_datetime:\\n return last_invoice_datetime <= ml.date <= self_datetime\\n else:\\n return ml.date <= self_datetime\\n return False\\n\\n incoming_sml = stock_move_lines.filtered(_filter_incoming_sml)\\n outgoing_sml = stock_move_lines.filtered(_filter_outgoing_sml)\\n\\n # Prepare and return lot_values\\n qties_per_lot = defaultdict(lambda: 0)\\n if self.type == 'out_refund':\\n for ml in outgoing_sml:\\n qties_per_lot[ml.lot_id] -= ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\\n for ml in incoming_sml:\\n qties_per_lot[ml.lot_id] += ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\\n else:\\n for ml in outgoing_sml:\\n qties_per_lot[ml.lot_id] += ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\\n for ml in incoming_sml:\\n qties_per_lot[ml.lot_id] -= ml.product_uom_id._compute_quantity(ml.qty_done, ml.product_id.uom_id)\\n lot_values = []\\n for lot_id, qty in qties_per_lot.items():\\n if float_is_zero(qty, precision_rounding=lot_id.product_id.uom_id.rounding):\\n continue\\n lot_values.append({\\n 'product_name': lot_id.product_id.display_name,\\n 'product_color': lot_id.x_studio_color.x_name,\\n 'quantity': qty,\\n 'uom_name': lot_id.product_uom_id.name,\\n 'lot_name': lot_id.name\\n })\\n #AQUI ORDENO TODOS LOS LOTES QUE ME QUEDAN EN lot_values POR EL COLOR\\n lot_values.sort(key=lambda r: r['product_color'], reverse=False)\\n return lot_values\",\n \"def invoiceitem(self, id):\\r\\n return InvoiceItem(self, id)\",\n \"def create_invoices(self, cr, uid, ids, context=None):\\n invoice_list = []\\n po_obj = self.pool.get('purchase.order')\\n inv_line_obj = self.pool.get('account.invoice.line')\\n inv_obj = self.pool.get('account.invoice')\\n addr_obj = self.pool.get('res.partner')\\n journal_obj = self.pool.get('account.journal')\\n if context is None:\\n context = {}\\n\\n for purchase_adv_obj in self.browse(cr, uid, ids, context=context):\\n for purchase_order in po_obj.browse(cr, uid, context.get('active_ids', []), context=context):\\n inv_line_ids = []\\n invoice_ids = []\\n val = inv_line_obj.product_id_change(cr, uid, [], purchase_adv_obj.product_id.id,\\n uom_id=False, partner_id=purchase_order.partner_id.id, fposition_id=purchase_order.fiscal_position.id)\\n line_id = inv_line_obj.create(cr, uid, {\\n 'name': val['value']['name'],\\n 'account_id': val['value']['account_id'],\\n 'price_unit': purchase_adv_obj.amount,\\n 'quantity': purchase_adv_obj.qtty,\\n 'discount': False,\\n 'uos_id': val['value']['uos_id'],\\n 'product_id': purchase_adv_obj.product_id.id,\\n 'invoice_line_tax_id': [(6, 0, val['value']['invoice_line_tax_id'])],\\n })\\n inv_line_ids.append(line_id)\\n addr = addr_obj.address_get(cr, uid, [purchase_order.partner_id.id], ['invoice'])\\n journal_ids = journal_obj.search(cr, uid, [('type', '=', 'purchase')])\\n context.update({'type':'in_invoice','journal_type':'purchase'})\\n inv_vals = {\\n 'name': purchase_order.partner_ref or purchase_order.name,\\n 'origin': purchase_order.name,\\n 'type': 'in_invoice',\\n 'reference': False,\\n 'account_id': purchase_order.partner_id.property_account_payable.id,\\n 'journal_id':journal_ids and journal_ids[0] or False,\\n 'partner_id': purchase_order.partner_id.id,\\n 'address_invoice_id': addr['invoice'],\\n 'invoice_line': [(6, 0, inv_line_ids)],\\n 'currency_id': purchase_order.pricelist_id.currency_id.id,\\n 'comment': '',\\n 'payment_term': purchase_order.payment_term_id and purchase_order.payment_term_id.id or False,\\n 'fiscal_position': purchase_order.fiscal_position.id or purchase_order.partner_id.property_account_position.id,\\n 'prepaid': True\\n }\\n\\n inv_id = inv_obj.create(cr, uid, inv_vals, context=context)\\n inv_obj.button_reset_taxes(cr, uid, [inv_id], context=context)\\n for invoice in purchase_order.invoice_ids:\\n invoice_ids.append(invoice.id)\\n invoice_ids.append(inv_id)\\n po_obj.write(cr, uid, purchase_order.id, {'invoice_ids': [(6, 0, invoice_ids)]})\\n invoice_list.append(inv_id)\\n\\n if purchase_order.invoice_method in ('picking','order'):\\n self.pool.get('purchase.order.line').create(cr, uid, {\\n 'order_id': purchase_order.id,\\n 'name': val['value']['name'],\\n 'date_planned':purchase_order.date_order,\\n 'price_unit': -purchase_adv_obj.amount,\\n 'product_uom_qty': purchase_adv_obj.qtty,\\n 'product_uos': val['value']['uos_id'],\\n 'product_uom': val['value']['uos_id'],\\n 'product_id': purchase_adv_obj.product_id.id,\\n 'adavance_product':True,\\n 'discount': False,\\n 'taxes_id': [(6, 0, val['value']['invoice_line_tax_id'])],\\n }, context=context)\\n\\n\\n context.update({'invoice_id':invoice_list})\\n return {\\n 'name': 'Open Invoice',\\n 'view_type': 'form',\\n 'view_mode': 'form',\\n 'res_model': 'purchase.open.invoice',\\n 'type': 'ir.actions.act_window',\\n 'target': 'new',\\n 'context': context\\n }\",\n \"def invoice_line_create(self, invoice_id, qty):\\n invoice_lines = self.env['account.invoice.line']\\n precision = self.env['decimal.precision'].precision_get('Product Unit of Measure')\\n for line in self:\\n if not float_is_zero(qty, precision_digits=precision):\\n vals = line._prepare_invoice_line(qty=qty)\\n vals.update({'invoice_id': invoice_id, 'purchase_line_id': line.id})\\n invoice_lines |= self.env['account.invoice.line'].create(vals)\\n return invoice_lines\",\n \"def create_invoice(self):\\n for line in self:\\n # if not line.account_id:\\n # raise UserError(_('Please Add the incoming Account !!'))\\n self.ensure_one()\\n journal_id = self.env['account.journal'].search([\\n ('type', '=', 'sale')], limit=1)\\n inv_line_main = {\\n 'name': line.description.name,\\n 'price_unit': line.amount or 0.00,\\n 'quantity': 1,\\n 'discount': line.discount,\\n 'account_id': line.description.property_account_income_id.id or line.description.categ_id.property_account_income_categ_id.id or False,\\n }\\n inv_values = {\\n 'partner_id': line.patient_id.partner_id.id,\\n 'patient_id': line.patient_id.id,\\n 'dentist': line.dentist.id,\\n 'move_type': 'out_invoice',\\n 'invoice_date': datetime.now().strftime(DF) or False,\\n 'journal_id': journal_id and journal_id.id or False,\\n 'teeth_id': line.patient_id and line.patient_id.id or False,\\n }\\n acc_id = self.env['account.move'].create(inv_values)\\n acc_id.write({'invoice_line_ids': [(0, 0, inv_line_main)]})\\n\\n self.write({'invc_id': acc_id.id, 'inv': True})\\n context = dict(self._context or {})\\n wiz_form_id = self.env['ir.model.data'].get_object_reference(\\n 'account', 'view_move_form')[1]\\n\\n return {\\n 'view_type': 'form',\\n 'view_id': wiz_form_id,\\n 'view_mode': 'form',\\n 'res_model': 'account.move',\\n 'res_id': self.invc_id.id,\\n 'type': 'ir.actions.act_window',\\n 'target': 'current',\\n 'context': context,\\n }\",\n \"def filter_by_customer(table, customer):\\n transactions = []\\n for record in table:\\n if record[CUSTOMER_ID] == customer:\\n transactions.append(record)\\n return transactions\",\n \"def customer(self, customer_id=None):\\r\\n return customers.Customer(self, customer_id)\",\n \"def single_customer(customer_name, invoice_file):\\n def customer_rental(rental_items):\\n \\\"\\\"\\\"\\n Loop through rental_items file and append each row to curried invoice_file with same\\n customer_name\\n \\\"\\\"\\\"\\n customer = partial(add_furniture, invoice_file=invoice_file, customer_name=customer_name)\\n with open(rental_items, \\\"r\\\") as rental_csv:\\n for row in csv.reader(rental_csv):\\n customer(item_code=row[0], item_description=row[1], item_monthly_price=row[2])\\n return customer_rental\",\n \"def df_customers_fileRead(self):\\n \\n #-------------------------------------------------------------------------\\n # RFM features are restored\\n #-------------------------------------------------------------------------\\n df_customers_rfm \\\\\\n = p5_util.object_load(self.df_customers_rfm_fileName)\\n self.strprint(\\\"RFM features : \\\"+str(df_customers_rfm.shape))\\n \\n #-------------------------------------------------------------------------\\n # Time features are restored\\n #-------------------------------------------------------------------------\\n df_customers_timeFeature \\\\\\n = p5_util.object_load(self._df_customers_timeFeature_fileName)\\n self.strprint(\\\"Time features : \\\"+str(df_customers_timeFeature.shape))\\n \\n #-------------------------------------------------------------------------\\n # NLP features are restored\\n #-------------------------------------------------------------------------\\n df_customers_nlp = p5_util.object_load(self._df_customers_nlp_fileName)\\n self.strprint(\\\"NLP features : \\\"+str(df_customers_nlp.shape))\\n\\n if False:\\n df_customers_rfm = self._df_customers_rfm.copy()\\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\\n df_customers_nlp = self._df_customers_pca_nlp.copy()\\n\\n #-------------------------------------------------------------------------\\n # Dataframe are aggregated; note that indexes are customerID.\\n #-------------------------------------------------------------------------\\n df_customers = pd.DataFrame()\\n\\n df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)\\n\\n df_customers = pd.concat([df_customers,df_customers_timeFeature]\\\\\\n , join='inner', axis=1)\\n\\n df_customers = pd.concat([df_customers,df_customers_nlp]\\\\\\n , join='inner', axis=1)\\n\\n self.strprint(\\\"All features : \\\"+str(df_customers.shape))\\n\\n #----------------------------------------------------------------------\\n # Dataframe is dumped into a file\\n #----------------------------------------------------------------------\\n p5_util.object_dump(df_customers, self._df_customers_fileName)\\n if False:\\n #----------------------------------------------------------------------\\n # Dataframe is copied as an attribute\\n #----------------------------------------------------------------------\\n self._df_customers = df_customers.copy()\\n \\n return\",\n \"def bc_records_dataframe(self, run_idxs):\\n\\n return pd.DataFrame(self.bc_records(run_idxs))\",\n \"def get(self):\\n user = get_authenticated_user()\\n if not user.stripe_id:\\n raise NotFound()\\n\\n return get_invoices(user.stripe_id)\",\n \"def get_customer_segments(self, date):\\n date = current_date_to_day().isoformat() if date is None else date\\n self.products = pd.merge(self.products,\\n self.cs.fetch(start_date=convert_dt_to_day_str(date))[['client', 'segments']],\\n on='client', how='left')\",\n \"def getInvoice(self):\\n return self.base.get(\\\"invoice\\\", [])\",\n \"def single_customer(customer_name, invoice_file):\\n def single_customer_rentals(rental_items):\\n add_item = partial(add_furniture, customer_name=customer_name,\\n invoice_file=invoice_file)\\n with open(rental_items, \\\"r\\\") as file:\\n for row in csv.reader(file):\\n add_item(item_code=row[0], item_description=row[1],\\n item_monthly_price=row[2])\\n return single_customer_rentals\",\n \"def show_all_customers():\\n return cr.show_all_customers()\",\n \"def extract_table_customer(last_id, db_engine):\\n\\n if last_id == None:\\n last_id = -1\\n\\n query = \\\"SELECT * FROM customer WHERE customer_id > {} LIMIT 100000\\\".format(\\n last_id)\\n return pd.read_sql(query, db_engine)\",\n \"def invoiceitems(self):\\r\\n return InvoiceItems(self)\",\n \"def test_single_customer(_full_invoice):\\n\\n test_invoice = \\\"../data/test-invoice.csv\\\"\\n items_to_insert = \\\"../data/items.csv\\\"\\n csv_contents = []\\n\\n function = l.single_customer(\\\"Kyouma Hououin\\\", test_invoice)\\n function(items_to_insert)\\n\\n with open(test_invoice, \\\"r\\\") as csv_file:\\n contents = reader(csv_file, delimiter=',')\\n for line in contents:\\n if line != []:\\n csv_contents += [line]\\n\\n csv_contents += contents\\n\\n assert _full_invoice == csv_contents\",\n \"def draw_header(canvas, invoice):\\n\\n canvas.setLineWidth(2)\\n canvas.line(2 * cm, -4 * cm, 19 * cm, -4 * cm)\\n \\\"\\\"\\\" Draws the business address \\\"\\\"\\\"\\n business_details = settings.BUSINESS_DETAIL\\n business_data = []\\n for line in business_details:\\n business_data.append([line])\\n\\n table = Table(business_data, colWidths=[17 * cm], rowHeights=[15, 17, 11, 11, 11, 11, 11])\\n table.setStyle([\\n ('FONT', (0, 0), (-1, -1), 'Helvetica-Oblique'),\\n ('FONTSIZE', (0, 0), (0, 0), 14),\\n ('FONTSIZE', (0, 1), (0, -1), 6),\\n ('TEXTCOLOR', (0, 0), (-1, -1), (0.2, 0.2, 0.2)),\\n ('ALIGN', (0, 0), (-1, -1), 'CENTER'),\\n ('BACKGROUND', (0, 0), (-1, -1), (0.95, 0.95,0.95)),\\n ])\\n tw, th, = table.wrapOn(canvas, 2 * cm, 19 * cm)\\n table.drawOn(canvas, 2 * cm, -4 * cm)\",\n \"def get_invoices(\\n page_number=None,\\n limit=10,\\n filter_by=None,\\n filter_value=None,\\n order_by=None\\n):\\n where_filter = \\\"\\\"\\n \\n valid_fields = {\\n 'month': 'ReferenceMonth',\\n 'year': 'ReferenceYear',\\n 'document': 'Document'\\n }\\n\\n ob_fields = []\\n for ob in order_by:\\n field = valid_fields.get(ob)\\n if field:\\n ob_fields.append(field)\\n\\n order_by = ob_fields\\n\\n if not order_by:\\n order_by = [\\\"CreatedAt\\\"]\\n\\n order_by = [ob + \\\" ASC\\\" for ob in order_by]\\n order_by = \\\", \\\".join(order_by)\\n\\n if page_number:\\n pagination_filter = f\\\"\\\"\\\"\\n id NOT IN (\\n SELECT\\n id\\n FROM\\n invoice\\n ORDER BY\\n {order_by} LIMIT {limit*(page_number-1)} )\\n \\\"\\\"\\\"\\n where_filter += \\\"AND \\\" + pagination_filter\\n order_by += f\\\" LIMIT {limit}\\\"\\n \\n if filter_by and filter_value:\\n if filter_by == 'document':\\n filter_value = f'\\\"{filter_value}\\\"'\\n\\n where_filter += f\\\"AND {filter_by} = {filter_value}\\\"\\n \\n query = f\\\"\\\"\\\"\\n SELECT\\n id,\\n ReferenceMonth AS month,\\n ReferenceYear AS year,\\n Document AS document,\\n Description AS description,\\n Amount AS amount\\n FROM\\n invoice\\n WHERE\\n IsActive = 1\\n {where_filter}\\n ORDER BY\\n {order_by}\\n ;\\n \\\"\\\"\\\"\\n\\n try:\\n conn = sqlite3.connect(DATABASE)\\n conn.row_factory = dict_factory\\n cursor = conn.cursor()\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result, True\\n except:\\n return [], False\",\n \"def _prepare_invoice_lines(self, exchange_line, order_line):\\n invoice_type = {\\n 'sale.order.line': {\\n 'higher': 'out_invoice', 'lower': 'out_refund',\\n 'type': 'sale', 'field': 'exchange_sale_line_id'\\n },\\n 'purchase.order.line': {\\n 'higher': 'in_invoice', 'lower': 'in_refund',\\n 'type': 'purchase', 'field': 'exchange_purchase_line_id'\\n },\\n }\\n product = exchange_line.exchange_product_id or exchange_line.product_id\\n data = {\\n 'invoice_type': False,\\n 'values': {\\n 'product_id': product.id,\\n 'quantity': exchange_line.quantity,\\n 'name': 'Exchange for [%s]' % exchange_line.product_id.display_name,\\n }\\n }\\n if exchange_line.exchange_product_id or \\\\\\n exchange_line.price_subtotal > order_line.price_subtotal:\\n data['invoice_type'] = invoice_type[order_line._name]['higher']\\n elif exchange_line.price_subtotal < order_line.price_subtotal:\\n data['invoice_type'] = invoice_type[order_line._name]['lower']\\n else:\\n return {}\\n data[invoice_type[order_line._name]['type']] = order_line.order_id\\n data['values'][invoice_type[order_line._name]['field']] = order_line.id\\n data['values']['price_unit'] = exchange_line.price_unit\\n # TODO i think we should take the different between prices NOT the all price\\n # abs(exchange_line.price_unit - order_line.price_unit)\\n return data\",\n \"def print_customers(self):\\n output = ''\\n for i in range(len(self.customers)):\\n output += f'Customer no. {self.customers[i].id} is in {self.customers[i].state[0]} section\\\\n'\\n #print(output)\\n with open('oneday.csv','a') as outfile:\\n for i in range(len(self.customers)):\\n outfile.write(f'{self.get_time()};{self.customers[i].id};{self.customers[i].state[0]}\\\\n')\",\n \"def create_data():\\n data_set = pd.DataFrame()\\n customer_id = list()\\n for i in range(1, 10001):\\n customer_id.append(i)\\n data_set = pd.DataFrame()\\n data_set.loc[:, 'customer_id'] = np.array(customer_id)\\n product_name = ('dining chair', 'dining table', 'bed', 'dining set',\\n 'stool', 'couch', 'occasional table',\\n 'recliner')\\n product_name_random = random.choices(product_name, k=10000)\\n data_set.loc[:, 'product_name'] = np.array(product_name_random)\\n quantity_rented = (1, 2, 3, 4)\\n quantity_rented_random = random.choices(quantity_rented, k=10000)\\n data_set.loc[:, 'quantity_rented'] = np.array(quantity_rented_random)\\n unit_rental_price_monthly = list()\\n for i in range(0, 10000):\\n unit_rental_price_monthly.append(random.uniform(1.5, 25))\\n data_set.loc[:, 'unit_rental_price'] = np.array(unit_rental_price_monthly)\\n rental_period_months = list()\\n for i in range(0, 10000):\\n rental_period_months.append(randint(6, 60))\\n data_set.loc[:, 'rental_period_months'] = np.array(rental_period_months)\\n return data_set\",\n \"def df_customers_features_build(self):\\n\\n df_customers_rfm = self._df_customers_rfm.copy()\\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\\n df_customers_nlp = self._df_customers_pca_nlp.copy()\\n\\n #-------------------------------------------------------------------------\\n # Dataframe are aggregated; note that indexes are customerID.\\n #-------------------------------------------------------------------------\\n df_customers = pd.DataFrame()\\n\\n df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)\\n\\n df_customers = pd.concat([df_customers,df_customers_timeFeature]\\\\\\n , join='inner', axis=1)\\n\\n df_customers = pd.concat([df_customers,df_customers_nlp]\\\\\\n , join='inner', axis=1)\\n \\n self.strprint(\\\"All features : \\\"+str(df_customers.shape))\\n self._df_customers = df_customers.copy()\\n return\",\n \"def get_customers(filters):\\n\\treturn frappe.db.sql(\\\"\\\"\\\"\\n\\t\\tSELECT\\n\\n\\t\\t\\tpar.debtor_creditor_number as 'Konto',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Company' THEN cus.customer_name\\n\\t\\t\\t\\tELSE null\\n\\t\\t\\t\\tEND as 'Name (Adressatentyp Unternehmen)',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Individual' THEN TRIM(SUBSTR(cus.customer_name, LOCATE(' ', cus.customer_name)))\\n\\t\\t\\t\\tELSE null\\n\\t\\t\\t\\tEND as 'Name (Adressatentyp natürl. Person)',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Individual' THEN SUBSTRING_INDEX(SUBSTRING_INDEX(cus.customer_name, ' ', 1), ' ', -1)\\n\\t\\t\\t\\tELSE null\\n\\t\\t\\t\\tEND as 'Vorname (Adressatentyp natürl. Person)',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Individual' THEN '1'\\n\\t\\t\\t\\tWHEN 'Company' THEN '2'\\n\\t\\t\\t\\tELSE '0'\\n\\t\\t\\t\\tEND as 'Adressatentyp',\\n\\t\\t\\tadr.address_line1 as 'Straße',\\n\\t\\t\\tadr.pincode as 'Postleitzahl',\\n\\t\\t\\tadr.city as 'Ort',\\n\\t\\t\\tUPPER(country.code) as 'Land',\\n\\t\\t\\tadr.address_line2 as 'Adresszusatz',\\n\\t\\t\\tadr.email_id as 'E-Mail',\\n\\t\\t\\tadr.phone as 'Telefon',\\n\\t\\t\\tadr.fax as 'Fax',\\n\\t\\t\\tcus.website as 'Internet',\\n\\t\\t\\tcus.tax_id as 'Steuernummer'\\n\\n\\t\\tFROM `tabCustomer` cus\\n\\n\\t\\t\\tleft join `tabParty Account` par\\n\\t\\t\\ton par.parent = cus.name\\n\\t\\t\\tand par.parenttype = 'Customer'\\n\\t\\t\\tand par.company = %(company)s\\n\\n\\t\\t\\tleft join `tabDynamic Link` dyn_adr\\n\\t\\t\\ton dyn_adr.link_name = cus.name\\n\\t\\t\\tand dyn_adr.link_doctype = 'Customer'\\n\\t\\t\\tand dyn_adr.parenttype = 'Address'\\n\\n\\t\\t\\tleft join `tabAddress` adr\\n\\t\\t\\ton adr.name = dyn_adr.parent\\n\\t\\t\\tand adr.is_primary_address = '1'\\n\\n\\t\\t\\tleft join `tabCountry` country\\n\\t\\t\\ton country.name = adr.country\\n\\n\\t\\tWHERE adr.is_primary_address = '1'\\n\\t\\t\\\"\\\"\\\", filters, as_dict=1)\",\n \"def fetch_customer_info_identities(self, client_id):\\n\\n try:\\n return self._make_private_api_request(\\n method=PyttributionIo.GET_REQUEST,\\n endpoint='customers',\\n subject_id=client_id,\\n show_identities='true'\\n ).get('customer')\\n except RequestException as e:\\n logger.error('Pyttribution.io: Retrieval of customer identities failed with HTTP status {exception}'.format(\\n exception=e))\",\n \"def get_customer_info(self, customer_id):\\n #requested_customer_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))\\n # Get predicted ratings\\n customers = self.__get_customers(customer_id).collect()\\n\\n return customers\",\n \"def get_invoiceitems(self):\\n from invoicing.models import InvoiceItem\\n\\n invoiceitem_list = []\\n # First we get all the product invoiceitems\\n for product in self.products: # TODO: SOLVE BUNDLED PRODUCTS!\\n item = InvoiceItem()\\n # Get the copies for this product, when used on with_copies\\n item.copies = product[1]\\n # Add the amount of frequency if necessary\\n frequency_extra = (\\n _(\\\" {} months\\\".format(self.frequency)) if self.frequency > 1 else \\\"\\\"\\n )\\n item.description = product[0].name + frequency_extra\\n item.price = product[0].price * self.frequency\\n item.amount = item.price * item.copies\\n item.product = product[0]\\n item.subscription = self\\n # TODO: Service from, service to\\n invoiceitem_list.append(item)\\n\\n # Next, we append all discount invoiceitems\\n for discount in self.get_discounts():\\n discount_item = InvoiceItem()\\n # Add the amount of frequency if necessary\\n frequency_extra = (\\n _(\\\" {} months\\\".format(self.frequency)) if self.frequency > 1 else \\\"\\\"\\n )\\n discount_item.description = discount[\\\"description\\\"] + frequency_extra\\n discount_item.amount = discount[\\\"amount\\\"] * self.frequency\\n discount_item.type_dr = discount[\\\"type_dr\\\"]\\n discount_item.type = discount[\\\"type\\\"]\\n discount_item.subscription = self\\n invoiceitem_list.append(discount_item)\\n\\n return invoiceitem_list\",\n \"def show_table(table, has_customer_id=True):\\n titles = [\\\"ID\\\", \\\"Title\\\", \\\"Price\\\", \\\"Date\\\"]\\n if has_customer_id:\\n titles.append(\\\"Customer ID\\\")\\n output_table = [[row[ID], row[TITLE], row[PRICE],\\n '/'.join((str(row[YEAR]), str(row[MONTH]), str(row[DAY]))), row[CUSTOMER_ID]] for row in table]\\n else:\\n output_table = [[row[ID], row[TITLE], row[PRICE],\\n '/'.join((str(row[YEAR]), str(row[MONTH]), str(row[DAY])))] for row in table]\\n\\n ui.clear_scr()\\n ui.print_table(output_table, titles, TITLE)\",\n \"def merge_purchase_invoice(self):\\r\\n active_id = self.env['purchase.order'].browse(self.env['purchase.order']._context.get('active_ids'))\\r\\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')]) \\r\\n active_id_count = 0\\r\\n active_count = 0\\r\\n exist_vendor = []; invoice = [];exist_vendors = [];ctx = ();invoice_id = []\\r\\n for rec in active_id : \\r\\n po_reference = self.env['account.invoice'].search([('origin', 'like', rec.name)])\\r\\n active_count = len(active_id)\\r\\n if rec.picking_count >= 1 and rec.picking_count != rec.invoice_count:\\r\\n len_name = [] \\r\\n for inv in po_reference: \\r\\n len_name = inv.origin.split(\\\":\\\") \\r\\n if rec.name in len_name:\\r\\n if po_reference.state == 'draft':\\r\\n for record in po_reference.invoice_line_ids:\\r\\n print (record.line_id)\\r\\n for res in rec.order_line:\\r\\n if res.id == record.line_id: \\r\\n record.write({'quantity':res.qty_received})\\r\\n res.write({'qty_invoiced':record.quantity})\\r\\n \\r\\n else:\\r\\n \\r\\n po_list = [];line_values = {};lines = {};purchase = []\\r\\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\\r\\n purchase.append(rec.id)\\r\\n active_id_count = len(purchase)\\r\\n if rec.partner_id.id in exist_vendor:\\r\\n for inv in invoice:\\r\\n if inv['partner_id'] == rec.partner_id.id:\\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received \\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received ,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id ,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n inv['invoice_line_ids'].append(line_values)\\r\\n inv['origin'] = inv['origin'] + ':' + rec.name\\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendors.append(rec.partner_id.id) \\r\\n else: \\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received\\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n print (rec.id)\\r\\n po_list.append(line_values) \\r\\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':datetime.today()}) \\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendor.append(rec.partner_id.id) \\r\\n \\r\\n else:\\r\\n po_list = [];line_values = {};lines = {};purchase = []\\r\\n if rec.state in 'purchase' and rec.invoice_status in 'to invoice':\\r\\n purchase.append(rec.id)\\r\\n active_id_count = len(purchase)\\r\\n if rec.partner_id.id in exist_vendor:\\r\\n for inv in invoice:\\r\\n if inv['partner_id'] == rec.partner_id.id:\\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received\\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received ,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids) or False] ,\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id ,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n inv['invoice_line_ids'].append(line_values)\\r\\n inv['origin'] = inv['origin'] + ':' + rec.name\\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendors.append(rec.partner_id.id) \\r\\n else: \\r\\n for recc in rec.order_line:\\r\\n if rec.picking_count > 1 and rec.invoice_count >= 1:\\r\\n qty_received = recc.qty_received - recc.qty_invoiced \\r\\n else:\\r\\n qty_received = recc.qty_received\\r\\n line_values = (0, 0, {'product_id': recc.product_id.id,\\r\\n 'quantity': qty_received,\\r\\n 'price_unit': recc.price_unit,\\r\\n 'invoice_line_tax_ids': [(6, 0, recc.taxes_id and recc.taxes_id.ids)or False],\\r\\n 'price_subtotal': recc.price_subtotal,\\r\\n 'product_uom': recc.product_uom.id,\\r\\n 'name': recc.name,\\r\\n 'account_id': journal_id.default_debit_account_id.id,\\r\\n 'line_id':recc.id\\r\\n }) \\r\\n print (rec.id)\\r\\n po_list.append(line_values) \\r\\n invoice.append({'origin':rec.name, 'partner_id': rec.partner_id.id, 'invoice_line_ids':po_list, 'account_id': rec.partner_id.property_account_payable_id.id, 'type': 'in_invoice', 'journal_id':journal_id.id,'date_invoice':date.today()}) \\r\\n if rec.partner_id.id not in exist_vendor:\\r\\n exist_vendor.append(rec.partner_id.id) \\r\\n \\r\\n invoices = []\\r\\n invoice_counts = 0\\r\\n for record in invoice:\\r\\n invoice_id = self.env['account.invoice'].create(record)\\r\\n invoices.append(invoice_id.id)\\r\\n invoice_counts = len(invoices)\\r\\n if active_id_count == 1:\\r\\n if invoice_counts == 1:\\r\\n form_view = self.env.ref('purchase.view_invoice_supplier_purchase_form').id\\r\\n tree_view = self.env.ref('account.invoice_tree').id \\r\\n return{\\r\\n 'name': _('Invoice'),\\r\\n 'type':'ir.actions.act_window',\\r\\n 'view_type':'form',\\r\\n 'view_mode':'form,tree',\\r\\n 'res_model':'account.invoice',\\r\\n 'res_id':invoices[0],\\r\\n 'views_id':False,\\r\\n 'views':[(form_view , 'form'), (tree_view , 'tree')],\\r\\n 'domain':[('id', 'in', invoices)],\\r\\n 'target': 'current',\\r\\n } \\r\\n else: \\r\\n form_view = self.env.ref('account.invoice_supplier_form').id\\r\\n tree_view = self.env.ref('account.invoice_supplier_tree').id \\r\\n return{\\r\\n 'name': _('Invoice'),\\r\\n 'type':'ir.actions.act_window',\\r\\n 'view_type':'form',\\r\\n 'view_mode':'form,tree',\\r\\n 'res_model':'account.invoice',\\r\\n 'views_id':True,\\r\\n 'views':[(tree_view , 'tree'), (form_view , 'form')],\\r\\n 'domain':[('id', 'in', invoices)],\\r\\n 'target': 'current',\\r\\n }\",\n \"def top_ten_customers(data_frame):\\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\\n * data_frame['quantity_rented']\\n * data_frame['rental_period_months'])\\n data_set = data_frame.groupby(['customer_id']).agg({'total_payment': 'sum'})\\n data_set = data_set.nlargest(10, 'total_payment')\\n return data_set\",\n \"def get_customer_data(self, customer_id):\\n customer_url = urllib.parse.urljoin(self.base_url,\\n \\\"/api/v1/pgecustomers/{}\\\".format(customer_id))\\n response = requests.get(customer_url, headers=self.headers)\\n\\n if response.status_code == 200:\\n return response.json()\\n else:\\n raise ValueError(response)\",\n \"def display_customers_list():\\n selected_path = request.url_rule\\n selected_customer_list = get_selected_path(selected_path)\\n page_number = 0\\n if 'page' in request.args:\\n page_number = int(request.args.get('page'))\\n customers, pagination = generate_pagination(page_number, selected_customer_list)\\n total_number_of_records = get_number_of_records(selected_customer_list)\\n return render_template(\\\"clients.html\\\", domain_name= DOMAIN_NAME, selected_customer_list=selected_customer_list, customers=customers, pagination=pagination, url_path=selected_path, total_number_of_records=total_number_of_records, phone_error=None)\",\n \"def _generate_invoice_report(self, request, queryset):\\n logger.info('Generating invoice report for model {}'.format(\\n queryset.model\\n ))\\n data = self._get_report_data(request, queryset)\\n content = self._get_pdf_content(data)\\n file_name = '{}-{}.pdf'.format(\\n self._invoice_report_name, data['id'],\\n )\\n return generate_pdf_response(content, file_name)\",\n \"def get_mall_data(): \\n filename = 'mall_customers.csv'\\n \\n if os.path.isfile(filename):\\n return pd.read_csv(filename, index_col=0)\\n else: \\n df = pd.read_sql(\\\"\\\"\\\"select * from customers\\\"\\\"\\\", get_connection('mall_customers'))\\n df.to_csv(filename)\\n return df\",\n \"def get_customer_viewmodel(self, customer_id):\\n credit_card_list = []\\n customers = self._customer_repo.get_customer_list()\\n credit_cards = self._customer_repo.get_credit_card_list()\\n for customer in customers:\\n if customer.get_customer_id() == customer_id:\\n customer_first_name = customer.get_first_name()\\n customer_last_name = customer.get_last_name()\\n country = customer.get_country()\\n for credit_card in credit_cards:\\n if credit_card.get_customer_id() == customer_id:\\n credit_card_list.append(credit_card.get_card_number())\\n customer_to_view = CustomerViewModel(customer_id, customer_first_name,\\n customer_last_name, country, credit_card_list)\\n return customer_to_view\",\n \"def get_billing_items(self, identifier):\\n\\n mask = \\\"\\\"\\\"mask[\\n id, description, hostName, domainName, oneTimeAfterTaxAmount, recurringAfterTaxAmount, createDate,\\n categoryCode,\\n category[name],\\n location[name],\\n children[id, category[name], description, oneTimeAfterTaxAmount, recurringAfterTaxAmount]\\n ]\\\"\\\"\\\"\\n return self.client.call(\\n 'Billing_Invoice',\\n 'getInvoiceTopLevelItems',\\n id=identifier,\\n mask=mask,\\n iter=True,\\n limit=100\\n )\",\n \"def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\\n \\n # remove customers with more than 20 purchases\\n if 'frequency' in customer_df.columns:\\n customer_df = customer_df[customer_df.frequency < 20]\\n \\n return customer_df\",\n \"def from_invoice_and_line_item(cls, invoice: InvoiceModel, line_item: LineItemModel, line_number: int,\\n distribution: str):\\n # Note the invoice_date should be the payment_date in the future.\\n return cls(total=line_item.total, invoice_number=invoice.id,\\n line_number=line_number,\\n is_reversal=invoice.invoice_status_code in\\n [InvoiceStatus.REFUNDED.value, InvoiceStatus.REFUND_REQUESTED.value],\\n distribution=distribution)\",\n \"def load_customers(dir):\\n customSchema = StructType([ \\\\\\n StructField(\\\"customerId2\\\", IntegerType(), True), \\\\\\n StructField(\\\"churnlabel\\\", IntegerType(), True), \\\\\\n StructField(\\\"gender\\\", StringType(), True), \\\\\\n StructField(\\\"shippingCountry\\\", StringType(), True), \\\\\\n StructField(\\\"dateCreated\\\", StringType(), True), \\\\\\n StructField(\\\"yearOfBirth\\\", IntegerType(), True), \\\\\\n StructField(\\\"premier\\\", IntegerType(), True)])\\n\\n df = sqlContext.read.format('com.databricks.spark.csv') \\\\\\n .options(header='false', delimiter='\\\\t', nullValue='\\\\\\\\N') \\\\\\n .load(get_dir_customers(dir) + '/*', schema=customSchema)\\n\\n return df\",\n \"def _prepare_invoice(self, cr, uid, order, lines, context=None):\\n invoice_vals = super(my_sale_order, self)._prepare_invoice(cr, uid, order,\\n lines, context)\\n\\n invoice_vals.update({\\n 'partner_shipping_id': order.partner_shipping_id.id,\\n })\\n\\n # Care for deprecated _inv_get() hook - FIXME: to be removed after 6.1\\n invoice_vals.update(self._inv_get(cr, uid, order, context=context))\\n\\n return invoice_vals\",\n \"def landing(request):\\n\\n data = Invoice.objects.all().order_by(\\\"-number\\\")[:10]\\n\\n return render(request,\\n \\\"invoice/invoice_landing.html\\\",\\n {\\n \\\"invoices\\\": data\\n })\",\n \"def get_flat_file_data(kind: str, server: str='PROD', ID: str='42') -> DataFrame:\\r\\n k = {\\r\\n 'c': 'customer_data_{0}_{1}_.csv',\\r\\n 'b': 'vendor_data_{0}_{1}_.csv'\\r\\n }\\r\\n f = k[kind].format(server, ID)\\r\\n df = pd.read_csv(f'{BASE_DIR}/{f}', encoding='UTF-8')\\r\\n df = prepare_input_df(df)\\r\\n return df\",\n \"def stripe_invoices(self, stripe_invoices):\\n\\n self._stripe_invoices = stripe_invoices\",\n \"def upcoming(cls, customer_id):\\n invoice = PaymentInvoice.upcoming(customer_id)\\n\\n return Invoice.parse_from_api(invoice)\",\n \"def return_orders():\\n with MY_CONNECTION as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\n \\\"\\\"\\\"\\n SELECT id_order, id_customer, id_product, quantity, total_price,\\n payment_status, send_status, order_date, location\\n FROM Orders\\n \\\"\\\"\\\")\\n records = cursor.fetchall()\\n return records\",\n \"def run_contig_records_dataframe(self, run_idxs, run_record_key):\\n records = self.run_contig_records(run_idxs, run_record_key)\\n return pd.DataFrame(records)\",\n \"def search_customer(cust_id):\\n query = (Customer\\n .select(Customer.customer_id, Customer.name, Customer.lastname,\\n Customer.email, Customer.phone_number,\\n Customer.home_address, Customer.status,\\n Customer.credit_limit)\\n .where(Customer.customer_id == cust_id))\\n result = {}\\n for person in query:\\n result[\\\"customer_id\\\"] = person.customer_id\\n result[\\\"name\\\"] = person.name\\n result[\\\"lastname\\\"] = person.lastname\\n result[\\\"email\\\"] = person.email\\n result[\\\"phone_number\\\"] = person.phone_number\\n result[\\\"home_address\\\"] = person.home_address\\n result[\\\"status\\\"] = person.status\\n result[\\\"credit_limit\\\"] = person.credit_limit\\n return result\",\n \"def view_budget_lines(self, cr, uid, ids, context=None):\\n ctx = context.copy()\\n ctx['default_line_id'] = ids[0]\\n ctx['allow_create'] = True\\n # Return view with budget lines\\n return {\\n 'name': _('Budget lines'),\\n 'domain': \\\"[('line_id', 'in', %s)]\\\" % ids,\\n 'view_type': 'form',\\n 'view_mode': 'tree',\\n 'res_model': 'account.balance.reporting.template.line.budget',\\n 'context': ctx,\\n 'type': 'ir.actions.act_window',\\n }\",\n \"def get_all_customer_ids_from_table(table):\\n return {row[CUSTOMER_ID] for row in table}\",\n \"def get_latest_invoice(self) -> CreditorInvoice:\\n\\n LOGGER.info(\\\"Getting latest invoice from EON Romania\\\")\\n\\n session = http.create_session()\\n\\n response = session.get(self._login_page_url)\\n if response.status_code != 200:\\n raise self.Error(\\\"Login page is not functioning\\\")\\n\\n soup = bs4.BeautifulSoup(response.content, \\\"html.parser\\\")\\n csrf_token_elem = soup.find(\\\"input\\\", {\\\"name\\\": \\\"_csrf_token\\\"})\\n if not csrf_token_elem:\\n raise self.Error(\\\"Could not extract CSRF token\\\")\\n\\n login_data = {\\n \\\"_username\\\": self._email,\\n \\\"_password\\\": self._password,\\n \\\"_csrf_token\\\": csrf_token_elem.get(\\\"value\\\"),\\n }\\n\\n if session.post(self._login_url, login_data).status_code != 200:\\n raise self.AuthError()\\n\\n response = session.get(self._invoices_url)\\n soup = bs4.BeautifulSoup(response.content, \\\"html.parser\\\")\\n\\n if not soup.select_one(self._selectors.sidebar):\\n raise self.AuthError()\\n\\n invoice_date_elem = soup.select_one(self._selectors.invoice_date)\\n if not invoice_date_elem:\\n raise self.Error(\\\"Failed to get invoice date\\\")\\n\\n invoice_due_date_elem = soup.select_one(self._selectors.invoice_due_date)\\n if not invoice_due_date_elem:\\n raise self.Error(\\\"Failed to get invoice due date\\\")\\n\\n invoice_payment_status_elem = soup.select_one(\\n self._selectors.invoice_payment_status\\n )\\n if not invoice_payment_status_elem:\\n raise self.Error(\\\"Failed to get invoice payment status\\\")\\n\\n invoice_amount_elem = soup.select_one(self._selectors.invoice_amount)\\n if not invoice_amount_elem:\\n raise self.Error(\\\"Failed to get invoice amount\\\")\\n\\n invoice_date = invoice_date_elem.contents[-1]\\n invoice_due_date = invoice_due_date_elem.contents[-1]\\n invoice_payment_status = invoice_payment_status_elem.contents[-1]\\n invoice_amount = invoice_amount_elem.contents[-1]\\n\\n invoice = CreditorInvoice(\\n float(invoice_amount.replace(\\\",\\\", \\\".\\\")),\\n Currency.RON,\\n datetime.strptime(invoice_date, \\\"%d.%m.%Y\\\"),\\n datetime.strptime(invoice_due_date, \\\"%d.%m.%Y\\\"),\\n PaymentStatus.PAID_CONFIRMED\\n if invoice_payment_status == \\\"0.00\\\"\\n else PaymentStatus.UNPAID,\\n )\\n\\n LOGGER.info(\\\"Found latest EON Romania invoice\\\", invoice=invoice)\\n return invoice\",\n \"def invoice(self, reference_no=None, with_vat=True):\\n\\n return self.invoice_class(apiobj=self, reference_no=reference_no)\",\n \"def customer(self, id):\\r\\n return Customer(self, id)\",\n \"def _prepare_invoice_line(self, qty):\\n self.ensure_one()\\n res = {\\n 'name': self.name,\\n 'sequence': self.sequence,\\n 'origin': self.order_id.name,\\n 'account_id': self.product_id.product_tmpl_id._get_product_accounts()['stock_input'].id,\\n 'price_unit': self.price_unit,\\n 'quantity': qty,\\n 'uom_id': self.product_uom.id,\\n 'product_id': self.product_id.id or False,\\n 'invoice_line_tax_ids': [(6, 0, self.taxes_id.ids)],\\n 'account_analytic_id': self.account_analytic_id.id,\\n 'analytic_tag_ids': [(6, 0, self.analytic_tag_ids.ids)],\\n }\\n return res\",\n \"def transactions_df():\\n return pd.DataFrame(\\n {\\n \\\"user_id\\\": [1, 1, 1, 2, 2, 2, 3, 3, 3],\\n \\\"item_id\\\": [11, 22, 22, 11, 22, 33, 33, 33, 44],\\n \\\"amount\\\": [10, 20, 30, 40, 50, 60, 70, 80, 90],\\n }\\n )\",\n \"def build_invoice(payment_object):\\n # Fill html template with the domain orders and user profile info\\n html_template = get_template('billing/billing_invoice.html')\\n rendered_html = html_template.render({\\n 'payment': payment_object,\\n 'user_profile': payment_object.owner.profile,\\n })\\n # Create pdf file from a html file\\n pdfkit.from_string(rendered_html, '/tmp/out.pdf')\\n with open(\\\"/tmp/out.pdf\\\", \\\"rb\\\") as pdf_file:\\n pdf_raw = pdf_file.read()\\n os.remove(\\\"/tmp/out.pdf\\\")\\n return {\\n 'body': pdf_raw,\\n 'filename': 'invoice_{}.pdf'.format(payment_object.transaction_id),\\n }\",\n \"def get_ARNA_flight_log_as_df():\\n flight_nums = [\\n # 216,\\n 217, 218, 219, 220, 221, 222, 223, 224, 225\\n ]\\n flight_IDs = ['C{}'.format(i) for i in flight_nums]\\n dfs = []\\n for flight_ID in flight_IDs:\\n dfs += [get_summary4flight(flight_ID=flight_ID)]\\n # Combine and return as a single dataframe sorted by time\\n df = pd.concat(dfs)\\n df = df.sort_index()\\n return df\",\n \"def get_customer_count(self):\\n return self._df_invoice_original.CustomerID.unique().shape[0]\",\n \"def predict_segment(self, df_invoice_line=None):\\n if df_invoice_line is not None:\\n self.data_transform(df_invoice_line) \\n self.df_customers_features_build() \\n else:\\n pass\\n X_test = self._df_customers.values\\n y_pred = self._classifier_model.predict(X_test)\\n return y_pred[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6706535","0.637564","0.6069902","0.59014344","0.58117783","0.5807765","0.57650644","0.56994027","0.5648026","0.5595924","0.5531552","0.55192786","0.54935724","0.54334897","0.5429923","0.5344283","0.53376174","0.5330216","0.532311","0.5300056","0.5274764","0.52722394","0.5269863","0.5219844","0.521598","0.52118987","0.5197928","0.5197842","0.51852095","0.5170273","0.516928","0.5154288","0.5143536","0.5134455","0.5126698","0.51252824","0.51150334","0.503439","0.50236344","0.5015868","0.4992553","0.4989618","0.49772316","0.49752775","0.49654034","0.49631137","0.49570814","0.49528533","0.49473453","0.49449715","0.49375638","0.49341756","0.49199423","0.49108192","0.48983532","0.4893141","0.48883817","0.48743278","0.4873027","0.48677635","0.48666134","0.48421705","0.48345634","0.48243216","0.48005515","0.47952944","0.47876883","0.47873965","0.4780031","0.47785765","0.47675258","0.47567818","0.47465554","0.47415647","0.47356957","0.47343823","0.4732808","0.47262654","0.47257543","0.47123232","0.4710476","0.4701827","0.46935883","0.46840206","0.46823874","0.46802357","0.46786046","0.4677307","0.4667701","0.46603394","0.46563148","0.4650109","0.464094","0.46402997","0.46371892","0.46364066","0.46287006","0.462498","0.45975658","0.45913565"],"string":"[\n \"0.6706535\",\n \"0.637564\",\n \"0.6069902\",\n \"0.59014344\",\n \"0.58117783\",\n \"0.5807765\",\n \"0.57650644\",\n \"0.56994027\",\n \"0.5648026\",\n \"0.5595924\",\n \"0.5531552\",\n \"0.55192786\",\n \"0.54935724\",\n \"0.54334897\",\n \"0.5429923\",\n \"0.5344283\",\n \"0.53376174\",\n \"0.5330216\",\n \"0.532311\",\n \"0.5300056\",\n \"0.5274764\",\n \"0.52722394\",\n \"0.5269863\",\n \"0.5219844\",\n \"0.521598\",\n \"0.52118987\",\n \"0.5197928\",\n \"0.5197842\",\n \"0.51852095\",\n \"0.5170273\",\n \"0.516928\",\n \"0.5154288\",\n \"0.5143536\",\n \"0.5134455\",\n \"0.5126698\",\n \"0.51252824\",\n \"0.51150334\",\n \"0.503439\",\n \"0.50236344\",\n \"0.5015868\",\n \"0.4992553\",\n \"0.4989618\",\n \"0.49772316\",\n \"0.49752775\",\n \"0.49654034\",\n \"0.49631137\",\n \"0.49570814\",\n \"0.49528533\",\n \"0.49473453\",\n \"0.49449715\",\n \"0.49375638\",\n \"0.49341756\",\n \"0.49199423\",\n \"0.49108192\",\n \"0.48983532\",\n \"0.4893141\",\n \"0.48883817\",\n \"0.48743278\",\n \"0.4873027\",\n \"0.48677635\",\n \"0.48666134\",\n \"0.48421705\",\n \"0.48345634\",\n \"0.48243216\",\n \"0.48005515\",\n \"0.47952944\",\n \"0.47876883\",\n \"0.47873965\",\n \"0.4780031\",\n \"0.47785765\",\n \"0.47675258\",\n \"0.47567818\",\n \"0.47465554\",\n \"0.47415647\",\n \"0.47356957\",\n \"0.47343823\",\n \"0.4732808\",\n \"0.47262654\",\n \"0.47257543\",\n \"0.47123232\",\n \"0.4710476\",\n \"0.4701827\",\n \"0.46935883\",\n \"0.46840206\",\n \"0.46823874\",\n \"0.46802357\",\n \"0.46786046\",\n \"0.4677307\",\n \"0.4667701\",\n \"0.46603394\",\n \"0.46563148\",\n \"0.4650109\",\n \"0.464094\",\n \"0.46402997\",\n \"0.46371892\",\n \"0.46364066\",\n \"0.46287006\",\n \"0.462498\",\n \"0.45975658\",\n \"0.45913565\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76564085"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":1893,"cells":{"query":{"kind":"string","value":"Returns a list of customers that have been excluded of data sampling used for building model. By default, 10 customers identifier is returned. If customerCount value is None, or <= 0, then list of all customers that have been excluded of data sampling is returned."},"document":{"kind":"string","value":"def get_listCustomer_out_sample(self, customerCount=10):\n \n if customerCount is None :\n listCustomer= list(self._df_invoice_line_out_sample.CustomerID.unique())\n else:\n if customerCount <= 0 :\n listCustomer \\\n = list(self._df_invoice_line_out_sample.CustomerID.unique())\n else:\n listCustomer \\\n = list(self._df_invoice_line_out_sample.CustomerID.unique()[:customerCount])\n return listCustomer"},"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_all_customers_count():\n data = user_obj.get_all_customers(\"1\")\n return data","def pdelements_num_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(4), ctypes.c_int32(0))","def get_all_customer_ids():\n table = data_manager.get_table_from_file(\"sales/sales.csv\")\n return get_all_customer_ids_from_table(table)","def get_all_customers():\n data = user_obj.get_all_customers()\n return data","def get_all_customer_ids():\n\n # your code","def pdelements_total_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(5), ctypes.c_int32(0))","def show_all_customers():\n return cr.show_all_customers()","def get_all_customers_not_purchasing_next_quarter(self) -> list:\n\n #Check if predictions can be made\n if self._load_model:\n y_labels = self._predict_labels()\n return y_labels[y_labels == 0].index.tolist()\n\n else:\n raise NoTrainedModelError('There is no trained model to make predictions with, please call initialize_purchase_predictor() first or set load_existing_model to True.')","def get_customers(self):\n self.navigate_to_page()\n customer_list=[]\n while True:\n page_customer = [{\n 'name': self.get_name(customer), \n 'parent':self.get_parent(customer),\n 'active':self.get_active(customer),\n 'link':self.get_details_link(customer)\n } for customer in self.get_page_customers()]\n customer_list = page_customer + customer_list\n if not CustomerGroupsPage.have_next_page(self):\n break\n self.navigate_to_page()\n return customer_list","def get_customer_list(self):\n return self._customer_repo.get_customer_list()","def get(self):\n return get_all_customers()","def get_all_sales_ids_for_customer_ids():\n\n # your code","def get_page_customers(self):\n return self.driver.find_elements(*CustomerGroupsPage.CUSTOMER_GROUP)","def users_excludeds(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"users_excludeds\")","def get_excluded_observations(self):\n\n return copy.deepcopy(self._excluded_observations)","def list_active_customers():\n return Customer.select().where(Customer.is_active).count()","def get_customer_count(self):\n return self._df_invoice_original.CustomerID.unique().shape[0]","def get_unselected_benefits(cls, excluded_benefits):\n benefits = cls.query.filter(cls.id.notin_(excluded_benefits))\n return [benefit.serialize() for benefit in benefits]","def excluded(cls):\n return []","def list_active_customers():\n count_query = (Customer\n .select(Customer, fn.COUNT(Customer.name)\n .alias('cust_count'))\n .where(Customer.status == 'active'))\n for count in count_query:\n return count.cust_count","def generateCustomers(self):\r\n\r\n # Counters\r\n shoppers = 0\r\n models = 0\r\n oldl = 0\r\n oldf = 0\r\n doctor = 0\r\n nudist = 0\r\n hippie = 0\r\n nerd = 0\r\n\r\n for i in range(self.num_of_customers):\r\n\r\n # With these weights, our store has plenty of youngs and olds, but few mids\r\n # Most grocery shoppers come in the evening\r\n # Young people have equal distribution between morning and evening\r\n # etc\r\n age1 = random.randint(18, 28)\r\n age2 = random.randint(28, 50)\r\n age3 = random.randint(50, 85)\r\n weighted_ages = [(age1, 10), (age2, 2), (age3, 15)]\r\n randomAge = [val for val, cnt in weighted_ages for a in range(cnt)]\r\n\r\n hour1 = random.randint(8, 13)\r\n hour2 = random.randint(13, 18)\r\n hour3 = random.randint(18, 22)\r\n weighted_hours = [(hour1, 10), (hour2, 3), (hour3, 20)]\r\n randomHour = [val for val, cnt in weighted_hours for b in range(cnt)]\r\n\r\n age = random.choice(randomAge)\r\n hour = random.choice(randomHour)\r\n gender = random.choice(['M', 'M', 'M', 'M', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F'])\r\n\r\n # Base chances, 100 total\r\n gs, sm, hp, ol, nrd, of, sd, nud = 20, 5, 5, 5, 5, 5, 10, 10\r\n\r\n customerID = random.randint(0, self.num_of_customers*2)\r\n while customerID in self.all_customers:\r\n customerID = random.randint(0, self.num_of_customers*2)\r\n\r\n # Weights\r\n if 18 < age < 22:\r\n if gender == 'M':\r\n if 8 <= hour <= 12:\r\n ol, sm, nrd, hp = 2, 2, 35, 20\r\n elif 13 <= hour <= 17:\r\n ol, sm, nrd, hp, gs = 2, 2, 15, 30, 5\r\n elif 18 <= hour <= 22:\r\n ol, sm, gs = 2, 2, 50\r\n\r\n elif gender == 'F':\r\n if 8 <= hour <= 12:\r\n ol, sm, nrd = 5, 35, 15\r\n elif 13 <= hour <= 17:\r\n ol, sm, hp = 5, 30, 30\r\n elif 18 <= hour <= 22:\r\n ol, sm, gs, = 5, 25, 50\r\n\r\n elif gender == 'M' and 22 < age < 29:\r\n if 8 <= hour <= 12:\r\n ol, sm, nrd, hp = 5, 5, 35, 25\r\n elif 13 <= hour <= 17:\r\n ol, sm, nrd, hp = 5, 5, 35, 40\r\n elif 18 <= hour <= 22:\r\n ol, sm, nrd, hp, gs = 5, 5, 20, 20, 50\r\n\r\n elif gender == 'M' and 29 < age < 50:\r\n if 8 <= hour <= 12:\r\n ol, sm, nrd, gs = 5, 5, 40, 30\r\n elif 13 <= hour <= 17:\r\n ol, sm, nrd = 5, 5, 30\r\n elif 18 <= hour <= 22:\r\n ol, sm, gs = 5, 5, 70\r\n\r\n elif gender == 'M' and age > 50:\r\n if 8 <= hour <= 12:\r\n ol, sm, gs, of, hp = 5, 5, 30, 60, 20\r\n elif 13 <= hour <= 17:\r\n ol, sm, gs, of, hp = 5, 5, 15, 70, 20\r\n elif 18 <= hour <= 22:\r\n ol, sm, gs, of, hp = 5, 5, 50, 25, 20\r\n\r\n elif gender == 'F' and 22 < age < 35:\r\n if 8 <= hour <= 12:\r\n ol, sm, hp, gs = 5, 30, 30, 30\r\n elif 13 <= hour <= 17:\r\n ol, sm, hp, gs = 5, 30, 30, 15\r\n elif 18 <= hour <= 22:\r\n ol, sm, hp, gs = 5, 15, 25, 60\r\n\r\n elif gender == 'F' and 35 < age < 55:\r\n if 8 <= hour <= 12:\r\n ol, sm, hp, gs = 5, 5, 5, 40\r\n elif 13 <= hour <= 17:\r\n ol, sm, hp, gs = 25, 5, 5, 25\r\n elif 18 <= hour <= 22:\r\n ol, sm, hp, gs = 30, 5, 5, 40\r\n\r\n elif gender == 'F' and age > 55:\r\n if 8 <= hour <= 12:\r\n ol, sm, of, gs = 20, 5, 15, 30\r\n elif 13 <= hour <= 17:\r\n ol, sm, of, gs = 60, 5, 30, 15\r\n elif 18 <= hour <= 22:\r\n ol, sm, of, gs = 40, 5, 20, 40\r\n\r\n weighted_choices = [('Grocery Shopper', gs), ('Supermodel', sm), ('Hippie', hp), ('Old Lady', ol), ('Nerd', nrd), ('Self Doctor', sd), ('Nudist', nud), ('Old Fart', of)]\r\n randomType = [val for val, cnt in weighted_choices for n in range(cnt)]\r\n\r\n customer = random.choice(randomType)\r\n\r\n if customer == 'Grocery Shopper':\r\n shoppers += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 18\r\n medicalChance = 3\r\n electronicsChance = 1\r\n outdoorsChance = 1\r\n clothingChance = 1\r\n beautyChance = 2\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Supermodel':\r\n models += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 0\r\n medicalChance = 5\r\n electronicsChance = 0\r\n outdoorsChance = 0\r\n clothingChance = 10\r\n beautyChance = 13\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Hippie':\r\n hippie += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 6\r\n medicalChance = 2\r\n electronicsChance = 1\r\n outdoorsChance = 14\r\n clothingChance = 7\r\n beautyChance = 1\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Old Lady':\r\n oldl += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 6\r\n medicalChance = 8\r\n electronicsChance = 0\r\n outdoorsChance = 0\r\n clothingChance = 3\r\n beautyChance = 10\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Nerd':\r\n nerd += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 4\r\n medicalChance = 3\r\n electronicsChance = 14\r\n outdoorsChance = 0\r\n clothingChance = 2\r\n beautyChance = 1\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Self Doctor':\r\n doctor += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 5\r\n medicalChance = 32\r\n electronicsChance = 4\r\n outdoorsChance = 1\r\n clothingChance = 2\r\n beautyChance = 1\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Nudist':\r\n nudist += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 10\r\n medicalChance = 5\r\n electronicsChance = 0\r\n outdoorsChance = 14\r\n clothingChance = 0\r\n beautyChance = 0\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n elif customer == 'Old Fart':\r\n oldf += 1\r\n num_of_purchases = random.randint(0, 20)\r\n foodChance = 10\r\n medicalChance = 18\r\n electronicsChance = 5\r\n outdoorsChance = 3\r\n clothingChance = 3\r\n beautyChance = 0\r\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\r\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\r\n\r\n itemsBought = (\", \".join(repr(e) for e in self.customer_purchases))\r\n self.c.execute(\"INSERT INTO Customer (CustomerID, Hour, Age, Gender, Items) VALUES (?, ?, ?, ?, ?)\", (customerID, hour, age, gender, itemsBought))\r\n self.conn.commit()\r\n\r\n if self.print_counters:\r\n print(\"\\nShoppers:\", shoppers)\r\n print(\"Models:\", models)\r\n print(\"Old Ladies:\", oldl)\r\n print(\"Old Farts:\", oldf)\r\n print(\"Self doctors:\", doctor)\r\n print(\"Nerds:\", nerd)\r\n print(\"Hippies:\", hippie)\r\n print(\"Nudists:\", nudist)\r\n\r\n if self.print_customers:\r\n print(\"\\nRaw Customer Data: \")\r\n print(self.all_customers)","def list_active_customers():\n active_customers = 0\n for customer in cm.Customers:\n if customer.status == \"Active \":\n active_customers += 1\n return active_customers","def getCustomer(self):\n return self.base.get(\"customer\", [])","def remove_existing_customers(self):\n\n for i in range(len(self.customers)):\n if self.customers[i].is_active() == False:\n self.customers[i]= 'out'\n self.customers = [item for item in self.customers if item!='out' ]","def display_customers_list():\n selected_path = request.url_rule\n selected_customer_list = get_selected_path(selected_path)\n page_number = 0\n if 'page' in request.args:\n page_number = int(request.args.get('page'))\n customers, pagination = generate_pagination(page_number, selected_customer_list)\n total_number_of_records = get_number_of_records(selected_customer_list)\n return render_template(\"clients.html\", domain_name= DOMAIN_NAME, selected_customer_list=selected_customer_list, customers=customers, pagination=pagination, url_path=selected_path, total_number_of_records=total_number_of_records, phone_error=None)","def list_active_customers():\n init_database()\n return Customer.select().where(Customer.active_status).count()","def list_active_customers():\n try:\n active_customer_count = 0\n for _ in Customer.select().where(Customer.status == 'Active'):\n active_customer_count += 1\n logger.info(\n f\"Successfully counted active customers {active_customer_count}\"\n )\n return active_customer_count\n except Exception as unknown_error:\n logger.error(f\"Error. Failed to count customers. {unknown_error}\")\n print(\n f'Error. Not able to count number of active customers.'\n ' {unknown_error}'\n )","def get_num_of_sales_per_customer_ids():\n\n # your code","def customer_agents(self):\n return self.get(\"customer_agents\")","def getCustomersInfo(self):\n rval = None\n tries = 0\n while not rval and tries < 5:\n if tries > 0:\n time.sleep(30)\n rval = self._getService(self.ns_customer,\n self.soapCustomer % (self.ns_customer, \"\",\n \"100\", \"Advertiser\"),\n \"GetCustomersInfo\", '', '',\n self.cwsdl, self.chost)\n tries += 1\n return rval","def list_active_customers():\n with cm.DATABASE.transaction():\n # .select() has a .where() method to specify criteria for searching\n active_customers = cm.Customer.select().where(\n cm.Customer.status == \"Active\").count()\n LOGGER.info(\"Active customers: %s\", active_customers)\n return active_customers","def get_all_nodes_without_provider(self):\n\n no_provider_nodes = []\n # create list of all nodes without provider and more than tier1_threshold customers\n for node in self.nodes():\n tier1 = True\n\n # check that node is not a customer of any node\n if not self.has_providers(node):\n no_provider_nodes.append(node)\n\n return no_provider_nodes","def list_active_customers():\n customer_active = Customer.select().where(Customer.status == 'Active')\n print('{} Active Customers'.format(len(customer_active)))\n return len(customer_active)","def list_active_customer():\n active_customer = Customer.select().where(Customer.is_active).count()\n LOGGER.info('Number of active customers retrieved.')\n return active_customer","def excluded_from_scan(self):\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/excludedFromScan/')))","def list_active_customers():\n db_customers = Customers.select()\n LOGGER.debug(\"Calculating number of active customers\")\n # Technically used this in Lesson 03, but it is a comprehension. Another method added below.\n number_active = sum([int(x.status) for x in db_customers])\n LOGGER.info(\"There are %d active customers\", number_active)\n\n return number_active","def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\n \n # remove customers with more than 20 purchases\n if 'frequency' in customer_df.columns:\n customer_df = customer_df[customer_df.frequency < 20]\n \n return customer_df","def get_customer_info(self, customer_id):\n #requested_customer_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))\n # Get predicted ratings\n customers = self.__get_customers(customer_id).collect()\n\n return customers","def filter_sent_to_customer(self, queryset, name, value):\n if str2bool(value):\n return queryset.exclude(customer=None)\n else:\n return queryset.filter(customer=None)","def generatePurchases(self, num_of_purchases, food, medical, electronics, outdoors, clothing, beauty, customer):\r\n\r\n # Empty purchases\r\n self.customer_purchases = []\r\n\r\n # Customer is *likely* to buy from some categories, but anything can happen\r\n weighted_categories = [('Food', food), ('Medical', medical), ('Electronics', electronics), ('Outdoors', outdoors), ('Clothing', clothing), ('Beauty', beauty)]\r\n randomCategory = [val for val, cnt in weighted_categories for i in range(cnt)]\r\n\r\n # Buy items\r\n for i in range(num_of_purchases):\r\n choice = random.choice(randomCategory)\r\n if choice == 'Food':\r\n tempIndex = random.randint(self.num_of_items, self.num_of_items*2-1)\r\n self.customer_purchases.append(tempIndex)\r\n elif choice == 'Medical':\r\n tempIndex = random.randint(self.num_of_items*2, self.num_of_items*3-1)\r\n self.customer_purchases.append(tempIndex)\r\n elif choice == 'Electronics':\r\n tempIndex = random.randint(3*self.num_of_items, 4*self.num_of_items-1)\r\n self.customer_purchases.append(tempIndex)\r\n elif choice == 'Outdoors':\r\n tempIndex = random.randint(4*self.num_of_items, 5*self.num_of_items-1)\r\n self.customer_purchases.append(tempIndex)\r\n elif choice == 'Clothing':\r\n tempIndex = random.randint(5*self.num_of_items, 6*self.num_of_items-1)\r\n self.customer_purchases.append(tempIndex)\r\n elif choice == 'Beauty':\r\n tempIndex = random.randint(6*self.num_of_items, 7*self.num_of_items-1)\r\n self.customer_purchases.append(tempIndex)\r\n\r\n if self.print_customers:\r\n print(self.customer_purchases)","def generate_customer_info(self):\n consecutive = check_consecutive(self.customerIds)\n print(\"consecutive\" + str(consecutive))\n if consecutive:\n for customer_id in self.customerIds:\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\n insured = np.random.choice(\n self.distributions[\"insurance_status\"],\n 1,\n p=self.distributions[\"insurance_distribution\"],\n )[0]\n dob = self.dobs[customer_id - 1]\n customer = pd.DataFrame(\n {\n \"customer_id\": [customer_id],\n \"dob\": str(dob),\n \"insurance\": insured,\n \"experiment_id\": random.randint(1, 2),\n }\n )\n self.Customers = self.Customers.append(customer)\n else:\n print(\"Error generating customer info: customerIds aren't consecutive\")\n return True","def customers(self):\r\n return Customers(self)","def list_customers():\n customers = db_helper.get_all_customers()\n return jsonify({\"customers\": customers})","def list_active_customers():\n try:\n active_customer_count = 0\n for _ in Customer.select().where(Customer.status == 'Active'):\n active_customer_count += 1\n return active_customer_count\n except Exception as unknown_error:\n print(f'Error. Not able to count number of active customers. {unknown_error}')","def get_absent_client_names(self, clients):\n return list(set(self.get_all_client_names()) - set(clients))","def is_customer_out_sample(self, customerID):\n listCustomer = list(self._df_invoice_line_out_sample.CustomerID.unique())\n is_flag = customerID in listCustomer\n return is_flag","def users_excludeds(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"users_excludeds\")","def users_excludeds(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"users_excludeds\")","def search_all_customers(customer_ids):\n return (search_customer(cid) for cid in customer_ids)","def get_seated_customers(restaurant: Restaurant):\n customers = Reservation.query.filter(Reservation.restaurant == restaurant).filter(Reservation.status == ReservationState.SEATED).all()\n \n return reduce(lambda acc,rsv: acc + rsv.seats, customers, 0)","def customers(self):\r\n return customers.Customers(self)","def get_contacts(self, count=-1, excluded_guid=None):\n current_len = len(self._contacts)\n if current_len == 0 or count == 0:\n return []\n\n if count < 0:\n count = current_len\n else:\n count = min(count, current_len)\n\n if excluded_guid is None:\n # Get the last `count` contacts.\n contact_list = self._contacts[-count:]\n else:\n contact_list = []\n for contact in reversed(self._contacts):\n if contact.guid == excluded_guid:\n continue\n contact_list.append(contact)\n if len(contact_list) >= count:\n break\n return contact_list","def get_customers(filters):\n\treturn frappe.db.sql(\"\"\"\n\t\tSELECT\n\n\t\t\tpar.debtor_creditor_number as 'Konto',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Company' THEN cus.customer_name\n\t\t\t\tELSE null\n\t\t\t\tEND as 'Name (Adressatentyp Unternehmen)',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Individual' THEN TRIM(SUBSTR(cus.customer_name, LOCATE(' ', cus.customer_name)))\n\t\t\t\tELSE null\n\t\t\t\tEND as 'Name (Adressatentyp natürl. Person)',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Individual' THEN SUBSTRING_INDEX(SUBSTRING_INDEX(cus.customer_name, ' ', 1), ' ', -1)\n\t\t\t\tELSE null\n\t\t\t\tEND as 'Vorname (Adressatentyp natürl. Person)',\n\t\t\tCASE cus.customer_type\n\t\t\t\tWHEN 'Individual' THEN '1'\n\t\t\t\tWHEN 'Company' THEN '2'\n\t\t\t\tELSE '0'\n\t\t\t\tEND as 'Adressatentyp',\n\t\t\tadr.address_line1 as 'Straße',\n\t\t\tadr.pincode as 'Postleitzahl',\n\t\t\tadr.city as 'Ort',\n\t\t\tUPPER(country.code) as 'Land',\n\t\t\tadr.address_line2 as 'Adresszusatz',\n\t\t\tadr.email_id as 'E-Mail',\n\t\t\tadr.phone as 'Telefon',\n\t\t\tadr.fax as 'Fax',\n\t\t\tcus.website as 'Internet',\n\t\t\tcus.tax_id as 'Steuernummer'\n\n\t\tFROM `tabCustomer` cus\n\n\t\t\tleft join `tabParty Account` par\n\t\t\ton par.parent = cus.name\n\t\t\tand par.parenttype = 'Customer'\n\t\t\tand par.company = %(company)s\n\n\t\t\tleft join `tabDynamic Link` dyn_adr\n\t\t\ton dyn_adr.link_name = cus.name\n\t\t\tand dyn_adr.link_doctype = 'Customer'\n\t\t\tand dyn_adr.parenttype = 'Address'\n\n\t\t\tleft join `tabAddress` adr\n\t\t\ton adr.name = dyn_adr.parent\n\t\t\tand adr.is_primary_address = '1'\n\n\t\t\tleft join `tabCountry` country\n\t\t\ton country.name = adr.country\n\n\t\tWHERE adr.is_primary_address = '1'\n\t\t\"\"\", filters, as_dict=1)","def get_all_customers(connection):\n connection.command_path = \"customers\"\n extra_headers = {connection.header_key: connection.token}\n url = connection.build_url()\n verify_ssl = connection.verify_ssl\n res = requests.get(url=url, headers=extra_headers, verify=verify_ssl)\n if res.status_code > 210:\n return\n body = res.content\n return customers.parse_all_customers(body)","def get_targeted_campaign_ids(client, customer_id, resource_name):\n ga_service = client.get_service(\"GoogleAdsService\")\n\n query = \"\"\"\n SELECT\n campaign.id,\n campaign_extension_setting.extension_feed_items\n FROM campaign_extension_setting\n WHERE\n campaign_extension_setting.extension_type = 'PROMOTION'\n AND campaign.status != 'REMOVED'\"\"\"\n\n stream = ga_service.search_stream(customer_id=customer_id, query=query)\n\n campaign_ids = []\n\n for batch in stream:\n for row in batch.results:\n feed_items = row.campaign_extension_setting.extension_feed_items\n if resource_name in feed_items:\n print(f\"Found matching campaign with ID: '{row.campaign.id}'\")\n campaign_ids.append(row.campaign.id)\n\n return campaign_ids","def list_active_customers():\n with database.transaction():\n query = (Customer\n .select(fn.COUNT(Customer.status).alias('count'))\n .where(Customer.status == 'Active'))\n LOGGER.info(query)\n\n customer_count = [item.count for item in query]\n LOGGER.info('Number of active customers: %s', customer_count[0])\n\n return customer_count[0]","def filter_by_customer(table, customer):\n transactions = []\n for record in table:\n if record[CUSTOMER_ID] == customer:\n transactions.append(record)\n return transactions","def customers_presence(self):\n return self._customers_presence","def get_customers(cls, api, id='', **params):\n return api.get_customers(id, **params)","def getCustomerAccount(self):\n self.logger.debug(\"\")\n for cust in self.getCustomerAccountData():\n accounts = len(cust['accounts'])\n self.logger.debug(\"%d accounts in %s\", accounts, cust['CustomerId'])\n ii = 1\n for acct in cust['accounts']:\n self.logger.debug(\"yield %s, %s\", cust['CustomerId'], acct['Id'])\n yield cust['CustomerId'], acct['Id'], ii, accounts\n ii += 1","def get_customers(cls, agent_id):\n customer_numbers = [str(customer.customer_number)\n for customer in cls.query.filter_by(agent_id=agent_id).all()]\n customer_data = {'individual':[], 'organization':[]}\n for customer_number in customer_numbers:\n customer_type = helper.get_customer_type(customer_number)\n if customer_type == 'IN':\n customer_details = IndividualCustomer.query.filter_by(\n customer_number=customer_number).first()\n customer_data['individual'].append(customer_details.serialize())\n else:\n customer_details = OrganizationCustomer.query.filter_by(\n customer_number=customer_number).first()\n customer_data['organization'].append(customer_details.serialize())\n\n return customer_data","def _exclude_indices(self):\n idx = self._next_idx\n exclude = np.arange(idx - 1, idx + self.obs_len) % self._maxsize\n return exclude","def fetch_customer_info_identities(self, client_id):\n\n try:\n return self._make_private_api_request(\n method=PyttributionIo.GET_REQUEST,\n endpoint='customers',\n subject_id=client_id,\n show_identities='true'\n ).get('customer')\n except RequestException as e:\n logger.error('Pyttribution.io: Retrieval of customer identities failed with HTTP status {exception}'.format(\n exception=e))","def test_list_active_users(_list_active_customers):\n for customer in _list_active_customers:\n bo.add_customer(\n customer[0],\n customer[1],\n customer[2],\n customer[3],\n customer[4],\n customer[5],\n customer[6],\n customer[7]\n )\n\n assert bo.list_active_customers() == 4\n\n for customer in _list_active_customers:\n bo.delete_customer(customer[0])\n \n assert bo.list_active_customers() == 0","def get_vendor_bills(self, count: int = 10) -> list:\n return list(\n itertools.islice(self.client.vendor_bills.get_all_generator(), count)\n )","def bus_total_customers(self) -> int:\n return self.dss_obj.BUSI(4, 0)","def download_latest_customers(self):\n file_id, data = self._gdrive.get_last_customer_data_file()\n self._datastore.force_data_storage('customer', data)","def excludes(self):\r\n\r\n return self._excludes","def get_customer_columns():\n return cr.get_columns()","def get_customer_statistics(self, n):\n trusted = []\n useful = []\n\n trust_dict = {}\n self.cursor.execute(\"\"\"select otherLoginID, COUNT(loginID) as score_trusted\n FROM trusts GROUP BY otherLoginID, trustStatus HAVING trustStatus='TRUSTED'\"\"\")\n for cust in self.cursor.fetchall():\n trust_dict[cust[0]] = cust[1]\n self.cursor.execute(\"\"\"SELECT otherLoginID, COUNT(loginID) as score_trusted FROM trusts\n GROUP BY otherLoginID, trustStatus HAVING trustStatus='UNTRUSTED'\"\"\")\n for cust in self.cursor.fetchall():\n if cust[0] in trust_dict:\n trust_dict[cust[0]] = trust_dict[cust[0]] - cust[1]\n else:\n trust_dict[cust[0]] = -cust[1]\n m = 0\n n_temp = n\n while n_temp > m and len(trust_dict):\n loginID = max(trust_dict.items(), key=operator.itemgetter(1))[0]\n self.cursor.execute(\"\"\"SELECT firstName, lastName FROM customercredentials WHERE loginID=%s\"\"\", (loginID,))\n name = self.cursor.fetchone()\n trusted.append([loginID, name[0], name[1], trust_dict[loginID]])\n del trust_dict[loginID]\n n_temp = n_temp - 1\n\n self.cursor.execute(\"\"\"SELECT C.loginID, firstName, lastName, AVG(avg_usefulness) as total_avg\n FROM comment C, customercredentials CR WHERE C.loginID = CR.loginID GROUP BY C.loginID\n ORDER BY total_avg DESC LIMIT %s\"\"\", (n,))\n for cust in self.cursor.fetchall():\n useful.append(cust)\n return trusted, useful","def get_customer_orders_count(customerId):\n data = user_obj.get_customer_orders(customerId,\"1\")\n return data","def load_known_customer_data(customers_filename: str) -> Tuple[List[Customer], Dict[Tuple[str, str], float]]:\n\n if not customers_filename or not os.path.exists(customers_filename):\n # empty list of customers, empty dict of distances\n return [], {}\n\n else:\n all_cust_data = load_json(customers_filename)\n custs = [Customer(**customer) for customer in all_cust_data['customers']]\n dists = dist_dict_from_json(all_cust_data['distances'])\n return custs, dists","def test_get_customer_list(self):\n customer = self._create_customers(\"Alex\")\n customer.create()\n customer = self._create_customers(\"Sally\")\n customer.create()\n customer = self._create_customers(\"John\")\n customer.create()\n resp = self.app.get(\"/customers\")\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n data = resp.get_json()\n self.assertEqual(len(data), 3)","def test_search_no_customer(self):\n set_up_db()\n self.assertEqual({}, search_customer(1))","def movable_intraroute_customers(route, customers):\n mcust = []\n for c in range(route.ncustomers):\n if len(factible_route_positions(route.customers[c+1],\n route,customers)) > 1:\n mcust.append(c)\n return mcust","def test_list_active_customers(self):\n set_up_db()\n add_customer(*self.test_customer)\n add_customer(customer_id=2, name=\"Clark\", last_name=\"Kent\", home_address=None,\n phone_number=\"228-626-7899\", email=\"ckent@dailyplanet.com\",\n status=True, credit_limit=200.00)\n add_customer(customer_id=3, name=\"Diana\", last_name=\"Prince\", home_address=None,\n phone_number=\"587-8423\", email=\"ww@justiceleague.net\",\n status=False, credit_limit=100.00)\n self.assertEqual(2, list_active_customers())","def get_users_with_missing_data() -> Set[str]:\n users_data = {user[\"_source\"][\"VENDOR_UUID\"] for user in Handlers.elastic_handler.get_all_today_data(\n _type=\"status\",\n date_start=dt.date.today() + dt.timedelta(days=1),\n date_end=dt.date.today() + dt.timedelta(days=7),\n )}\n\n all_tokens = Handlers.token_handler.get_all_today_data(_type=\"token\")\n to_dict = {dict_[\"_source\"][\"VENDOR_UUID\"]: dict_[\"_source\"][\"TOKEN\"] for dict_ in all_tokens}\n\n return set(dict(filter(lambda item_tup: item_tup[0] not in users_data, to_dict.items())).values())","def get(self, customer=None, count=None, offset=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def get(self, customer=None, count=None, offset=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def get(self, customer=None, count=None, offset=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def get(self, customer=None, count=None, offset=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def get_dataset_names(self, include = ['*'], exclude = []):\n \n raise NotImplementedError('get_dataset_names')","def test_no_counterfactuals_found(self):\n threshold = 4.0\n self._config['Regression threshold'] = str(threshold)\n self._example = {'x_1': 1.0, 'x_2': 1.0}\n output = self._gen.generate(\n example=self._example,\n model=self._model,\n dataset=self._dataset,\n config=self._config)\n self.assertEmpty(output)","def get_all_user_data(request):\n \n try:\n customers = Customer.objects.all()\n serializer = CustomerSerializer(customers, many=True)\n \n return Response(serializer.data)\n except Exception as e:\n return Response({\"Error\":str(e)})","def fetch_customer_info_full(self, client_id):\n\n try:\n return self._make_private_api_request(\n method=PyttributionIo.GET_REQUEST,\n endpoint='customers',\n subject_id=client_id,\n show_all='true'\n ).get('customer')\n except RequestException as e:\n logger.error('Pyttribution.io: Retrieval of full customer info failed with HTTP status {exception}'.format(\n exception=e))","def filter_out_non_helpful_records(data_records):\n for r in data_records:\n if r.get_cruciality() is None:\n continue\n # We only want records that got exactly 3 votes\n if len(r.cruciality) != 3:\n continue\n else:\n yield r","def get_dependent_accounts(client):\n\n PAGE_SIZE = 500\n managed_customer_service = client.GetService(\n 'ManagedCustomerService', version=settings.API_VERSION)\n\n offset = 0\n selector = {\n 'fields': ['CustomerId', 'Name'],\n 'predicates': {\n 'field': 'CanManageClients',\n 'operator': 'EQUALS',\n 'values': 'False'\n },\n 'paging': {\n 'startIndex': str(offset),\n 'numberResults': str(PAGE_SIZE)\n }\n }\n more_pages = True\n accounts = {}\n while more_pages:\n page = managed_customer_service.get(selector)\n\n if 'entries' in page and page['entries']:\n for account in page['entries']:\n accounts[account['customerId']] = str(account['name']) \\\n if hasattr(account, 'name') else 'None'\n\n offset += PAGE_SIZE\n selector['paging']['startIndex'] = str(offset)\n more_pages = offset < int(page['totalNumEntries'])\n\n return accounts","def get_random_index(self, excludes: Set[int]) -> int:\n index = random.randint(0, self.space.categorical_size() - 1)\n while index in excludes:\n index = random.randint(0, self.space.categorical_size() - 1)\n return index","def crew_needing_reports(self):\n reports = self.ccreport_set.all().values_list('crew_chief', flat=True)\n return self.ccinstances.exclude(crew_chief__in=reports)","def get_customers_by_name(name: str = '') -> List[Customer]:\n customers = []\n for c in get_market().customers.values():\n if not name or c.name == name:\n customers.append(c)\n return customers","def not_applicable_device_count(self):\n if \"notApplicableDeviceCount\" in self._prop_dict:\n return self._prop_dict[\"notApplicableDeviceCount\"]\n else:\n return None","def to_exclude(self):\n midnight = now().replace(hour=0, minute=0, microsecond=0)\n return CenterOpen.objects.filter(\n creation_date__gte=midnight,\n ).values_list('phone_number', flat=True)","def prune(self):\n target_user_ids = self.get_queryset().values_list('id', flat=True)\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\n drip=self.drip_model,\n user__id__in=target_user_ids)\\\n .values_list('user_id', flat=True)\n self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)","def remove_existing_customers(self):\n # remove the customers which are not active (.is_active )\n self.to_move = False\n #for cust in self.customers:\n # print(cust.state)\n self.customers = [cust for cust in self.customers if cust.state != 'checkout']\n #if cust.to_move():\n # self.to_move = True","def GetListDoctors(self):\n\t\treturn self.ClientsMap.values()","def get_users(self, customer_id='my_customer'):\n try:\n paged_results = self.repository.users.list(customer=customer_id,\n viewType='admin_view')\n flattened_results = api_helpers.flatten_list_results(\n paged_results, 'users')\n LOGGER.debug('Getting all the users for customer_id = %s,'\n ' flattened_results = %s',\n customer_id, flattened_results)\n return flattened_results\n except RefreshError as e:\n # Authentication failed, log before raise.\n LOGGER.exception(GSUITE_AUTH_FAILURE_MESSAGE)\n raise e\n except (errors.HttpError, HttpLib2Error) as e:\n raise api_errors.ApiExecutionError('users', e)","def get_all_except(id_list, order_by_field=DATA_SORTING_FIELDS):\n return Data.get_all_except(order_by_field, id_list)","def _random_customer(cust_dtls) -> tuple:\n return choices(cust_dtls)[0]","def fetch_data(excludeNonPollutants):\n if excludeNonPollutants:\n return AirMeasurement().select().where(~AirMeasurement.uploaded,\n AirMeasurement.type != 'Temperature',\n AirMeasurement.type != 'Altitude',\n AirMeasurement.type != 'Pressure',\n AirMeasurement.type != 'Humidity',\n AirMeasurement.type != 'PM1.0').limit(500)\n\n return AirMeasurement().select().where(~(AirMeasurement.uploaded)).limit(500)","def getExcludedAtoms(self):\n excludedAtomsIdList = self.getFlagData('EXCLUDED_ATOMS_LIST')\n numberExcludedAtoms = self.getFlagData('NUMBER_EXCLUDED_ATOMS')\n atoms = self.atoms\n interval = 0\n excludedAtomsList = []\n for number in numberExcludedAtoms:\n temp = excludedAtomsIdList[interval:interval + number]\n if temp == [0]:\n excludedAtomsList.append([])\n else:\n excludedAtomsList.append([atoms[a-1] for a in temp])\n interval += number\n self.excludedAtoms = excludedAtomsList\n self.printDebug(\"getExcludedAtoms\")"],"string":"[\n \"def get_all_customers_count():\\n data = user_obj.get_all_customers(\\\"1\\\")\\n return data\",\n \"def pdelements_num_customers(self) -> int:\\n return self.dss_obj.PDElementsI(ctypes.c_int32(4), ctypes.c_int32(0))\",\n \"def get_all_customer_ids():\\n table = data_manager.get_table_from_file(\\\"sales/sales.csv\\\")\\n return get_all_customer_ids_from_table(table)\",\n \"def get_all_customers():\\n data = user_obj.get_all_customers()\\n return data\",\n \"def get_all_customer_ids():\\n\\n # your code\",\n \"def pdelements_total_customers(self) -> int:\\n return self.dss_obj.PDElementsI(ctypes.c_int32(5), ctypes.c_int32(0))\",\n \"def show_all_customers():\\n return cr.show_all_customers()\",\n \"def get_all_customers_not_purchasing_next_quarter(self) -> list:\\n\\n #Check if predictions can be made\\n if self._load_model:\\n y_labels = self._predict_labels()\\n return y_labels[y_labels == 0].index.tolist()\\n\\n else:\\n raise NoTrainedModelError('There is no trained model to make predictions with, please call initialize_purchase_predictor() first or set load_existing_model to True.')\",\n \"def get_customers(self):\\n self.navigate_to_page()\\n customer_list=[]\\n while True:\\n page_customer = [{\\n 'name': self.get_name(customer), \\n 'parent':self.get_parent(customer),\\n 'active':self.get_active(customer),\\n 'link':self.get_details_link(customer)\\n } for customer in self.get_page_customers()]\\n customer_list = page_customer + customer_list\\n if not CustomerGroupsPage.have_next_page(self):\\n break\\n self.navigate_to_page()\\n return customer_list\",\n \"def get_customer_list(self):\\n return self._customer_repo.get_customer_list()\",\n \"def get(self):\\n return get_all_customers()\",\n \"def get_all_sales_ids_for_customer_ids():\\n\\n # your code\",\n \"def get_page_customers(self):\\n return self.driver.find_elements(*CustomerGroupsPage.CUSTOMER_GROUP)\",\n \"def users_excludeds(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"users_excludeds\\\")\",\n \"def get_excluded_observations(self):\\n\\n return copy.deepcopy(self._excluded_observations)\",\n \"def list_active_customers():\\n return Customer.select().where(Customer.is_active).count()\",\n \"def get_customer_count(self):\\n return self._df_invoice_original.CustomerID.unique().shape[0]\",\n \"def get_unselected_benefits(cls, excluded_benefits):\\n benefits = cls.query.filter(cls.id.notin_(excluded_benefits))\\n return [benefit.serialize() for benefit in benefits]\",\n \"def excluded(cls):\\n return []\",\n \"def list_active_customers():\\n count_query = (Customer\\n .select(Customer, fn.COUNT(Customer.name)\\n .alias('cust_count'))\\n .where(Customer.status == 'active'))\\n for count in count_query:\\n return count.cust_count\",\n \"def generateCustomers(self):\\r\\n\\r\\n # Counters\\r\\n shoppers = 0\\r\\n models = 0\\r\\n oldl = 0\\r\\n oldf = 0\\r\\n doctor = 0\\r\\n nudist = 0\\r\\n hippie = 0\\r\\n nerd = 0\\r\\n\\r\\n for i in range(self.num_of_customers):\\r\\n\\r\\n # With these weights, our store has plenty of youngs and olds, but few mids\\r\\n # Most grocery shoppers come in the evening\\r\\n # Young people have equal distribution between morning and evening\\r\\n # etc\\r\\n age1 = random.randint(18, 28)\\r\\n age2 = random.randint(28, 50)\\r\\n age3 = random.randint(50, 85)\\r\\n weighted_ages = [(age1, 10), (age2, 2), (age3, 15)]\\r\\n randomAge = [val for val, cnt in weighted_ages for a in range(cnt)]\\r\\n\\r\\n hour1 = random.randint(8, 13)\\r\\n hour2 = random.randint(13, 18)\\r\\n hour3 = random.randint(18, 22)\\r\\n weighted_hours = [(hour1, 10), (hour2, 3), (hour3, 20)]\\r\\n randomHour = [val for val, cnt in weighted_hours for b in range(cnt)]\\r\\n\\r\\n age = random.choice(randomAge)\\r\\n hour = random.choice(randomHour)\\r\\n gender = random.choice(['M', 'M', 'M', 'M', 'F', 'F', 'F', 'F', 'F', 'F', 'F', 'F'])\\r\\n\\r\\n # Base chances, 100 total\\r\\n gs, sm, hp, ol, nrd, of, sd, nud = 20, 5, 5, 5, 5, 5, 10, 10\\r\\n\\r\\n customerID = random.randint(0, self.num_of_customers*2)\\r\\n while customerID in self.all_customers:\\r\\n customerID = random.randint(0, self.num_of_customers*2)\\r\\n\\r\\n # Weights\\r\\n if 18 < age < 22:\\r\\n if gender == 'M':\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, nrd, hp = 2, 2, 35, 20\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, nrd, hp, gs = 2, 2, 15, 30, 5\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, gs = 2, 2, 50\\r\\n\\r\\n elif gender == 'F':\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, nrd = 5, 35, 15\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, hp = 5, 30, 30\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, gs, = 5, 25, 50\\r\\n\\r\\n elif gender == 'M' and 22 < age < 29:\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, nrd, hp = 5, 5, 35, 25\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, nrd, hp = 5, 5, 35, 40\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, nrd, hp, gs = 5, 5, 20, 20, 50\\r\\n\\r\\n elif gender == 'M' and 29 < age < 50:\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, nrd, gs = 5, 5, 40, 30\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, nrd = 5, 5, 30\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, gs = 5, 5, 70\\r\\n\\r\\n elif gender == 'M' and age > 50:\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, gs, of, hp = 5, 5, 30, 60, 20\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, gs, of, hp = 5, 5, 15, 70, 20\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, gs, of, hp = 5, 5, 50, 25, 20\\r\\n\\r\\n elif gender == 'F' and 22 < age < 35:\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, hp, gs = 5, 30, 30, 30\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, hp, gs = 5, 30, 30, 15\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, hp, gs = 5, 15, 25, 60\\r\\n\\r\\n elif gender == 'F' and 35 < age < 55:\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, hp, gs = 5, 5, 5, 40\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, hp, gs = 25, 5, 5, 25\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, hp, gs = 30, 5, 5, 40\\r\\n\\r\\n elif gender == 'F' and age > 55:\\r\\n if 8 <= hour <= 12:\\r\\n ol, sm, of, gs = 20, 5, 15, 30\\r\\n elif 13 <= hour <= 17:\\r\\n ol, sm, of, gs = 60, 5, 30, 15\\r\\n elif 18 <= hour <= 22:\\r\\n ol, sm, of, gs = 40, 5, 20, 40\\r\\n\\r\\n weighted_choices = [('Grocery Shopper', gs), ('Supermodel', sm), ('Hippie', hp), ('Old Lady', ol), ('Nerd', nrd), ('Self Doctor', sd), ('Nudist', nud), ('Old Fart', of)]\\r\\n randomType = [val for val, cnt in weighted_choices for n in range(cnt)]\\r\\n\\r\\n customer = random.choice(randomType)\\r\\n\\r\\n if customer == 'Grocery Shopper':\\r\\n shoppers += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 18\\r\\n medicalChance = 3\\r\\n electronicsChance = 1\\r\\n outdoorsChance = 1\\r\\n clothingChance = 1\\r\\n beautyChance = 2\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Supermodel':\\r\\n models += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 0\\r\\n medicalChance = 5\\r\\n electronicsChance = 0\\r\\n outdoorsChance = 0\\r\\n clothingChance = 10\\r\\n beautyChance = 13\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Hippie':\\r\\n hippie += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 6\\r\\n medicalChance = 2\\r\\n electronicsChance = 1\\r\\n outdoorsChance = 14\\r\\n clothingChance = 7\\r\\n beautyChance = 1\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Old Lady':\\r\\n oldl += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 6\\r\\n medicalChance = 8\\r\\n electronicsChance = 0\\r\\n outdoorsChance = 0\\r\\n clothingChance = 3\\r\\n beautyChance = 10\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Nerd':\\r\\n nerd += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 4\\r\\n medicalChance = 3\\r\\n electronicsChance = 14\\r\\n outdoorsChance = 0\\r\\n clothingChance = 2\\r\\n beautyChance = 1\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Self Doctor':\\r\\n doctor += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 5\\r\\n medicalChance = 32\\r\\n electronicsChance = 4\\r\\n outdoorsChance = 1\\r\\n clothingChance = 2\\r\\n beautyChance = 1\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Nudist':\\r\\n nudist += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 10\\r\\n medicalChance = 5\\r\\n electronicsChance = 0\\r\\n outdoorsChance = 14\\r\\n clothingChance = 0\\r\\n beautyChance = 0\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n elif customer == 'Old Fart':\\r\\n oldf += 1\\r\\n num_of_purchases = random.randint(0, 20)\\r\\n foodChance = 10\\r\\n medicalChance = 18\\r\\n electronicsChance = 5\\r\\n outdoorsChance = 3\\r\\n clothingChance = 3\\r\\n beautyChance = 0\\r\\n self.generatePurchases(num_of_purchases, foodChance, medicalChance, electronicsChance, outdoorsChance, clothingChance, beautyChance, customer)\\r\\n self.all_customers[customerID] = [age, gender, hour, customer, self.customer_purchases]\\r\\n\\r\\n itemsBought = (\\\", \\\".join(repr(e) for e in self.customer_purchases))\\r\\n self.c.execute(\\\"INSERT INTO Customer (CustomerID, Hour, Age, Gender, Items) VALUES (?, ?, ?, ?, ?)\\\", (customerID, hour, age, gender, itemsBought))\\r\\n self.conn.commit()\\r\\n\\r\\n if self.print_counters:\\r\\n print(\\\"\\\\nShoppers:\\\", shoppers)\\r\\n print(\\\"Models:\\\", models)\\r\\n print(\\\"Old Ladies:\\\", oldl)\\r\\n print(\\\"Old Farts:\\\", oldf)\\r\\n print(\\\"Self doctors:\\\", doctor)\\r\\n print(\\\"Nerds:\\\", nerd)\\r\\n print(\\\"Hippies:\\\", hippie)\\r\\n print(\\\"Nudists:\\\", nudist)\\r\\n\\r\\n if self.print_customers:\\r\\n print(\\\"\\\\nRaw Customer Data: \\\")\\r\\n print(self.all_customers)\",\n \"def list_active_customers():\\n active_customers = 0\\n for customer in cm.Customers:\\n if customer.status == \\\"Active \\\":\\n active_customers += 1\\n return active_customers\",\n \"def getCustomer(self):\\n return self.base.get(\\\"customer\\\", [])\",\n \"def remove_existing_customers(self):\\n\\n for i in range(len(self.customers)):\\n if self.customers[i].is_active() == False:\\n self.customers[i]= 'out'\\n self.customers = [item for item in self.customers if item!='out' ]\",\n \"def display_customers_list():\\n selected_path = request.url_rule\\n selected_customer_list = get_selected_path(selected_path)\\n page_number = 0\\n if 'page' in request.args:\\n page_number = int(request.args.get('page'))\\n customers, pagination = generate_pagination(page_number, selected_customer_list)\\n total_number_of_records = get_number_of_records(selected_customer_list)\\n return render_template(\\\"clients.html\\\", domain_name= DOMAIN_NAME, selected_customer_list=selected_customer_list, customers=customers, pagination=pagination, url_path=selected_path, total_number_of_records=total_number_of_records, phone_error=None)\",\n \"def list_active_customers():\\n init_database()\\n return Customer.select().where(Customer.active_status).count()\",\n \"def list_active_customers():\\n try:\\n active_customer_count = 0\\n for _ in Customer.select().where(Customer.status == 'Active'):\\n active_customer_count += 1\\n logger.info(\\n f\\\"Successfully counted active customers {active_customer_count}\\\"\\n )\\n return active_customer_count\\n except Exception as unknown_error:\\n logger.error(f\\\"Error. Failed to count customers. {unknown_error}\\\")\\n print(\\n f'Error. Not able to count number of active customers.'\\n ' {unknown_error}'\\n )\",\n \"def get_num_of_sales_per_customer_ids():\\n\\n # your code\",\n \"def customer_agents(self):\\n return self.get(\\\"customer_agents\\\")\",\n \"def getCustomersInfo(self):\\n rval = None\\n tries = 0\\n while not rval and tries < 5:\\n if tries > 0:\\n time.sleep(30)\\n rval = self._getService(self.ns_customer,\\n self.soapCustomer % (self.ns_customer, \\\"\\\",\\n \\\"100\\\", \\\"Advertiser\\\"),\\n \\\"GetCustomersInfo\\\", '', '',\\n self.cwsdl, self.chost)\\n tries += 1\\n return rval\",\n \"def list_active_customers():\\n with cm.DATABASE.transaction():\\n # .select() has a .where() method to specify criteria for searching\\n active_customers = cm.Customer.select().where(\\n cm.Customer.status == \\\"Active\\\").count()\\n LOGGER.info(\\\"Active customers: %s\\\", active_customers)\\n return active_customers\",\n \"def get_all_nodes_without_provider(self):\\n\\n no_provider_nodes = []\\n # create list of all nodes without provider and more than tier1_threshold customers\\n for node in self.nodes():\\n tier1 = True\\n\\n # check that node is not a customer of any node\\n if not self.has_providers(node):\\n no_provider_nodes.append(node)\\n\\n return no_provider_nodes\",\n \"def list_active_customers():\\n customer_active = Customer.select().where(Customer.status == 'Active')\\n print('{} Active Customers'.format(len(customer_active)))\\n return len(customer_active)\",\n \"def list_active_customer():\\n active_customer = Customer.select().where(Customer.is_active).count()\\n LOGGER.info('Number of active customers retrieved.')\\n return active_customer\",\n \"def excluded_from_scan(self):\\n return six.next(six.itervalues(self.zap._request(self.zap.base + 'spider/view/excludedFromScan/')))\",\n \"def list_active_customers():\\n db_customers = Customers.select()\\n LOGGER.debug(\\\"Calculating number of active customers\\\")\\n # Technically used this in Lesson 03, but it is a comprehension. Another method added below.\\n number_active = sum([int(x.status) for x in db_customers])\\n LOGGER.info(\\\"There are %d active customers\\\", number_active)\\n\\n return number_active\",\n \"def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\\n \\n # remove customers with more than 20 purchases\\n if 'frequency' in customer_df.columns:\\n customer_df = customer_df[customer_df.frequency < 20]\\n \\n return customer_df\",\n \"def get_customer_info(self, customer_id):\\n #requested_customer_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))\\n # Get predicted ratings\\n customers = self.__get_customers(customer_id).collect()\\n\\n return customers\",\n \"def filter_sent_to_customer(self, queryset, name, value):\\n if str2bool(value):\\n return queryset.exclude(customer=None)\\n else:\\n return queryset.filter(customer=None)\",\n \"def generatePurchases(self, num_of_purchases, food, medical, electronics, outdoors, clothing, beauty, customer):\\r\\n\\r\\n # Empty purchases\\r\\n self.customer_purchases = []\\r\\n\\r\\n # Customer is *likely* to buy from some categories, but anything can happen\\r\\n weighted_categories = [('Food', food), ('Medical', medical), ('Electronics', electronics), ('Outdoors', outdoors), ('Clothing', clothing), ('Beauty', beauty)]\\r\\n randomCategory = [val for val, cnt in weighted_categories for i in range(cnt)]\\r\\n\\r\\n # Buy items\\r\\n for i in range(num_of_purchases):\\r\\n choice = random.choice(randomCategory)\\r\\n if choice == 'Food':\\r\\n tempIndex = random.randint(self.num_of_items, self.num_of_items*2-1)\\r\\n self.customer_purchases.append(tempIndex)\\r\\n elif choice == 'Medical':\\r\\n tempIndex = random.randint(self.num_of_items*2, self.num_of_items*3-1)\\r\\n self.customer_purchases.append(tempIndex)\\r\\n elif choice == 'Electronics':\\r\\n tempIndex = random.randint(3*self.num_of_items, 4*self.num_of_items-1)\\r\\n self.customer_purchases.append(tempIndex)\\r\\n elif choice == 'Outdoors':\\r\\n tempIndex = random.randint(4*self.num_of_items, 5*self.num_of_items-1)\\r\\n self.customer_purchases.append(tempIndex)\\r\\n elif choice == 'Clothing':\\r\\n tempIndex = random.randint(5*self.num_of_items, 6*self.num_of_items-1)\\r\\n self.customer_purchases.append(tempIndex)\\r\\n elif choice == 'Beauty':\\r\\n tempIndex = random.randint(6*self.num_of_items, 7*self.num_of_items-1)\\r\\n self.customer_purchases.append(tempIndex)\\r\\n\\r\\n if self.print_customers:\\r\\n print(self.customer_purchases)\",\n \"def generate_customer_info(self):\\n consecutive = check_consecutive(self.customerIds)\\n print(\\\"consecutive\\\" + str(consecutive))\\n if consecutive:\\n for customer_id in self.customerIds:\\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\\n insured = np.random.choice(\\n self.distributions[\\\"insurance_status\\\"],\\n 1,\\n p=self.distributions[\\\"insurance_distribution\\\"],\\n )[0]\\n dob = self.dobs[customer_id - 1]\\n customer = pd.DataFrame(\\n {\\n \\\"customer_id\\\": [customer_id],\\n \\\"dob\\\": str(dob),\\n \\\"insurance\\\": insured,\\n \\\"experiment_id\\\": random.randint(1, 2),\\n }\\n )\\n self.Customers = self.Customers.append(customer)\\n else:\\n print(\\\"Error generating customer info: customerIds aren't consecutive\\\")\\n return True\",\n \"def customers(self):\\r\\n return Customers(self)\",\n \"def list_customers():\\n customers = db_helper.get_all_customers()\\n return jsonify({\\\"customers\\\": customers})\",\n \"def list_active_customers():\\n try:\\n active_customer_count = 0\\n for _ in Customer.select().where(Customer.status == 'Active'):\\n active_customer_count += 1\\n return active_customer_count\\n except Exception as unknown_error:\\n print(f'Error. Not able to count number of active customers. {unknown_error}')\",\n \"def get_absent_client_names(self, clients):\\n return list(set(self.get_all_client_names()) - set(clients))\",\n \"def is_customer_out_sample(self, customerID):\\n listCustomer = list(self._df_invoice_line_out_sample.CustomerID.unique())\\n is_flag = customerID in listCustomer\\n return is_flag\",\n \"def users_excludeds(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"users_excludeds\\\")\",\n \"def users_excludeds(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"users_excludeds\\\")\",\n \"def search_all_customers(customer_ids):\\n return (search_customer(cid) for cid in customer_ids)\",\n \"def get_seated_customers(restaurant: Restaurant):\\n customers = Reservation.query.filter(Reservation.restaurant == restaurant).filter(Reservation.status == ReservationState.SEATED).all()\\n \\n return reduce(lambda acc,rsv: acc + rsv.seats, customers, 0)\",\n \"def customers(self):\\r\\n return customers.Customers(self)\",\n \"def get_contacts(self, count=-1, excluded_guid=None):\\n current_len = len(self._contacts)\\n if current_len == 0 or count == 0:\\n return []\\n\\n if count < 0:\\n count = current_len\\n else:\\n count = min(count, current_len)\\n\\n if excluded_guid is None:\\n # Get the last `count` contacts.\\n contact_list = self._contacts[-count:]\\n else:\\n contact_list = []\\n for contact in reversed(self._contacts):\\n if contact.guid == excluded_guid:\\n continue\\n contact_list.append(contact)\\n if len(contact_list) >= count:\\n break\\n return contact_list\",\n \"def get_customers(filters):\\n\\treturn frappe.db.sql(\\\"\\\"\\\"\\n\\t\\tSELECT\\n\\n\\t\\t\\tpar.debtor_creditor_number as 'Konto',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Company' THEN cus.customer_name\\n\\t\\t\\t\\tELSE null\\n\\t\\t\\t\\tEND as 'Name (Adressatentyp Unternehmen)',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Individual' THEN TRIM(SUBSTR(cus.customer_name, LOCATE(' ', cus.customer_name)))\\n\\t\\t\\t\\tELSE null\\n\\t\\t\\t\\tEND as 'Name (Adressatentyp natürl. Person)',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Individual' THEN SUBSTRING_INDEX(SUBSTRING_INDEX(cus.customer_name, ' ', 1), ' ', -1)\\n\\t\\t\\t\\tELSE null\\n\\t\\t\\t\\tEND as 'Vorname (Adressatentyp natürl. Person)',\\n\\t\\t\\tCASE cus.customer_type\\n\\t\\t\\t\\tWHEN 'Individual' THEN '1'\\n\\t\\t\\t\\tWHEN 'Company' THEN '2'\\n\\t\\t\\t\\tELSE '0'\\n\\t\\t\\t\\tEND as 'Adressatentyp',\\n\\t\\t\\tadr.address_line1 as 'Straße',\\n\\t\\t\\tadr.pincode as 'Postleitzahl',\\n\\t\\t\\tadr.city as 'Ort',\\n\\t\\t\\tUPPER(country.code) as 'Land',\\n\\t\\t\\tadr.address_line2 as 'Adresszusatz',\\n\\t\\t\\tadr.email_id as 'E-Mail',\\n\\t\\t\\tadr.phone as 'Telefon',\\n\\t\\t\\tadr.fax as 'Fax',\\n\\t\\t\\tcus.website as 'Internet',\\n\\t\\t\\tcus.tax_id as 'Steuernummer'\\n\\n\\t\\tFROM `tabCustomer` cus\\n\\n\\t\\t\\tleft join `tabParty Account` par\\n\\t\\t\\ton par.parent = cus.name\\n\\t\\t\\tand par.parenttype = 'Customer'\\n\\t\\t\\tand par.company = %(company)s\\n\\n\\t\\t\\tleft join `tabDynamic Link` dyn_adr\\n\\t\\t\\ton dyn_adr.link_name = cus.name\\n\\t\\t\\tand dyn_adr.link_doctype = 'Customer'\\n\\t\\t\\tand dyn_adr.parenttype = 'Address'\\n\\n\\t\\t\\tleft join `tabAddress` adr\\n\\t\\t\\ton adr.name = dyn_adr.parent\\n\\t\\t\\tand adr.is_primary_address = '1'\\n\\n\\t\\t\\tleft join `tabCountry` country\\n\\t\\t\\ton country.name = adr.country\\n\\n\\t\\tWHERE adr.is_primary_address = '1'\\n\\t\\t\\\"\\\"\\\", filters, as_dict=1)\",\n \"def get_all_customers(connection):\\n connection.command_path = \\\"customers\\\"\\n extra_headers = {connection.header_key: connection.token}\\n url = connection.build_url()\\n verify_ssl = connection.verify_ssl\\n res = requests.get(url=url, headers=extra_headers, verify=verify_ssl)\\n if res.status_code > 210:\\n return\\n body = res.content\\n return customers.parse_all_customers(body)\",\n \"def get_targeted_campaign_ids(client, customer_id, resource_name):\\n ga_service = client.get_service(\\\"GoogleAdsService\\\")\\n\\n query = \\\"\\\"\\\"\\n SELECT\\n campaign.id,\\n campaign_extension_setting.extension_feed_items\\n FROM campaign_extension_setting\\n WHERE\\n campaign_extension_setting.extension_type = 'PROMOTION'\\n AND campaign.status != 'REMOVED'\\\"\\\"\\\"\\n\\n stream = ga_service.search_stream(customer_id=customer_id, query=query)\\n\\n campaign_ids = []\\n\\n for batch in stream:\\n for row in batch.results:\\n feed_items = row.campaign_extension_setting.extension_feed_items\\n if resource_name in feed_items:\\n print(f\\\"Found matching campaign with ID: '{row.campaign.id}'\\\")\\n campaign_ids.append(row.campaign.id)\\n\\n return campaign_ids\",\n \"def list_active_customers():\\n with database.transaction():\\n query = (Customer\\n .select(fn.COUNT(Customer.status).alias('count'))\\n .where(Customer.status == 'Active'))\\n LOGGER.info(query)\\n\\n customer_count = [item.count for item in query]\\n LOGGER.info('Number of active customers: %s', customer_count[0])\\n\\n return customer_count[0]\",\n \"def filter_by_customer(table, customer):\\n transactions = []\\n for record in table:\\n if record[CUSTOMER_ID] == customer:\\n transactions.append(record)\\n return transactions\",\n \"def customers_presence(self):\\n return self._customers_presence\",\n \"def get_customers(cls, api, id='', **params):\\n return api.get_customers(id, **params)\",\n \"def getCustomerAccount(self):\\n self.logger.debug(\\\"\\\")\\n for cust in self.getCustomerAccountData():\\n accounts = len(cust['accounts'])\\n self.logger.debug(\\\"%d accounts in %s\\\", accounts, cust['CustomerId'])\\n ii = 1\\n for acct in cust['accounts']:\\n self.logger.debug(\\\"yield %s, %s\\\", cust['CustomerId'], acct['Id'])\\n yield cust['CustomerId'], acct['Id'], ii, accounts\\n ii += 1\",\n \"def get_customers(cls, agent_id):\\n customer_numbers = [str(customer.customer_number)\\n for customer in cls.query.filter_by(agent_id=agent_id).all()]\\n customer_data = {'individual':[], 'organization':[]}\\n for customer_number in customer_numbers:\\n customer_type = helper.get_customer_type(customer_number)\\n if customer_type == 'IN':\\n customer_details = IndividualCustomer.query.filter_by(\\n customer_number=customer_number).first()\\n customer_data['individual'].append(customer_details.serialize())\\n else:\\n customer_details = OrganizationCustomer.query.filter_by(\\n customer_number=customer_number).first()\\n customer_data['organization'].append(customer_details.serialize())\\n\\n return customer_data\",\n \"def _exclude_indices(self):\\n idx = self._next_idx\\n exclude = np.arange(idx - 1, idx + self.obs_len) % self._maxsize\\n return exclude\",\n \"def fetch_customer_info_identities(self, client_id):\\n\\n try:\\n return self._make_private_api_request(\\n method=PyttributionIo.GET_REQUEST,\\n endpoint='customers',\\n subject_id=client_id,\\n show_identities='true'\\n ).get('customer')\\n except RequestException as e:\\n logger.error('Pyttribution.io: Retrieval of customer identities failed with HTTP status {exception}'.format(\\n exception=e))\",\n \"def test_list_active_users(_list_active_customers):\\n for customer in _list_active_customers:\\n bo.add_customer(\\n customer[0],\\n customer[1],\\n customer[2],\\n customer[3],\\n customer[4],\\n customer[5],\\n customer[6],\\n customer[7]\\n )\\n\\n assert bo.list_active_customers() == 4\\n\\n for customer in _list_active_customers:\\n bo.delete_customer(customer[0])\\n \\n assert bo.list_active_customers() == 0\",\n \"def get_vendor_bills(self, count: int = 10) -> list:\\n return list(\\n itertools.islice(self.client.vendor_bills.get_all_generator(), count)\\n )\",\n \"def bus_total_customers(self) -> int:\\n return self.dss_obj.BUSI(4, 0)\",\n \"def download_latest_customers(self):\\n file_id, data = self._gdrive.get_last_customer_data_file()\\n self._datastore.force_data_storage('customer', data)\",\n \"def excludes(self):\\r\\n\\r\\n return self._excludes\",\n \"def get_customer_columns():\\n return cr.get_columns()\",\n \"def get_customer_statistics(self, n):\\n trusted = []\\n useful = []\\n\\n trust_dict = {}\\n self.cursor.execute(\\\"\\\"\\\"select otherLoginID, COUNT(loginID) as score_trusted\\n FROM trusts GROUP BY otherLoginID, trustStatus HAVING trustStatus='TRUSTED'\\\"\\\"\\\")\\n for cust in self.cursor.fetchall():\\n trust_dict[cust[0]] = cust[1]\\n self.cursor.execute(\\\"\\\"\\\"SELECT otherLoginID, COUNT(loginID) as score_trusted FROM trusts\\n GROUP BY otherLoginID, trustStatus HAVING trustStatus='UNTRUSTED'\\\"\\\"\\\")\\n for cust in self.cursor.fetchall():\\n if cust[0] in trust_dict:\\n trust_dict[cust[0]] = trust_dict[cust[0]] - cust[1]\\n else:\\n trust_dict[cust[0]] = -cust[1]\\n m = 0\\n n_temp = n\\n while n_temp > m and len(trust_dict):\\n loginID = max(trust_dict.items(), key=operator.itemgetter(1))[0]\\n self.cursor.execute(\\\"\\\"\\\"SELECT firstName, lastName FROM customercredentials WHERE loginID=%s\\\"\\\"\\\", (loginID,))\\n name = self.cursor.fetchone()\\n trusted.append([loginID, name[0], name[1], trust_dict[loginID]])\\n del trust_dict[loginID]\\n n_temp = n_temp - 1\\n\\n self.cursor.execute(\\\"\\\"\\\"SELECT C.loginID, firstName, lastName, AVG(avg_usefulness) as total_avg\\n FROM comment C, customercredentials CR WHERE C.loginID = CR.loginID GROUP BY C.loginID\\n ORDER BY total_avg DESC LIMIT %s\\\"\\\"\\\", (n,))\\n for cust in self.cursor.fetchall():\\n useful.append(cust)\\n return trusted, useful\",\n \"def get_customer_orders_count(customerId):\\n data = user_obj.get_customer_orders(customerId,\\\"1\\\")\\n return data\",\n \"def load_known_customer_data(customers_filename: str) -> Tuple[List[Customer], Dict[Tuple[str, str], float]]:\\n\\n if not customers_filename or not os.path.exists(customers_filename):\\n # empty list of customers, empty dict of distances\\n return [], {}\\n\\n else:\\n all_cust_data = load_json(customers_filename)\\n custs = [Customer(**customer) for customer in all_cust_data['customers']]\\n dists = dist_dict_from_json(all_cust_data['distances'])\\n return custs, dists\",\n \"def test_get_customer_list(self):\\n customer = self._create_customers(\\\"Alex\\\")\\n customer.create()\\n customer = self._create_customers(\\\"Sally\\\")\\n customer.create()\\n customer = self._create_customers(\\\"John\\\")\\n customer.create()\\n resp = self.app.get(\\\"/customers\\\")\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n data = resp.get_json()\\n self.assertEqual(len(data), 3)\",\n \"def test_search_no_customer(self):\\n set_up_db()\\n self.assertEqual({}, search_customer(1))\",\n \"def movable_intraroute_customers(route, customers):\\n mcust = []\\n for c in range(route.ncustomers):\\n if len(factible_route_positions(route.customers[c+1],\\n route,customers)) > 1:\\n mcust.append(c)\\n return mcust\",\n \"def test_list_active_customers(self):\\n set_up_db()\\n add_customer(*self.test_customer)\\n add_customer(customer_id=2, name=\\\"Clark\\\", last_name=\\\"Kent\\\", home_address=None,\\n phone_number=\\\"228-626-7899\\\", email=\\\"ckent@dailyplanet.com\\\",\\n status=True, credit_limit=200.00)\\n add_customer(customer_id=3, name=\\\"Diana\\\", last_name=\\\"Prince\\\", home_address=None,\\n phone_number=\\\"587-8423\\\", email=\\\"ww@justiceleague.net\\\",\\n status=False, credit_limit=100.00)\\n self.assertEqual(2, list_active_customers())\",\n \"def get_users_with_missing_data() -> Set[str]:\\n users_data = {user[\\\"_source\\\"][\\\"VENDOR_UUID\\\"] for user in Handlers.elastic_handler.get_all_today_data(\\n _type=\\\"status\\\",\\n date_start=dt.date.today() + dt.timedelta(days=1),\\n date_end=dt.date.today() + dt.timedelta(days=7),\\n )}\\n\\n all_tokens = Handlers.token_handler.get_all_today_data(_type=\\\"token\\\")\\n to_dict = {dict_[\\\"_source\\\"][\\\"VENDOR_UUID\\\"]: dict_[\\\"_source\\\"][\\\"TOKEN\\\"] for dict_ in all_tokens}\\n\\n return set(dict(filter(lambda item_tup: item_tup[0] not in users_data, to_dict.items())).values())\",\n \"def get(self, customer=None, count=None, offset=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self, customer=None, count=None, offset=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self, customer=None, count=None, offset=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get(self, customer=None, count=None, offset=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def get_dataset_names(self, include = ['*'], exclude = []):\\n \\n raise NotImplementedError('get_dataset_names')\",\n \"def test_no_counterfactuals_found(self):\\n threshold = 4.0\\n self._config['Regression threshold'] = str(threshold)\\n self._example = {'x_1': 1.0, 'x_2': 1.0}\\n output = self._gen.generate(\\n example=self._example,\\n model=self._model,\\n dataset=self._dataset,\\n config=self._config)\\n self.assertEmpty(output)\",\n \"def get_all_user_data(request):\\n \\n try:\\n customers = Customer.objects.all()\\n serializer = CustomerSerializer(customers, many=True)\\n \\n return Response(serializer.data)\\n except Exception as e:\\n return Response({\\\"Error\\\":str(e)})\",\n \"def fetch_customer_info_full(self, client_id):\\n\\n try:\\n return self._make_private_api_request(\\n method=PyttributionIo.GET_REQUEST,\\n endpoint='customers',\\n subject_id=client_id,\\n show_all='true'\\n ).get('customer')\\n except RequestException as e:\\n logger.error('Pyttribution.io: Retrieval of full customer info failed with HTTP status {exception}'.format(\\n exception=e))\",\n \"def filter_out_non_helpful_records(data_records):\\n for r in data_records:\\n if r.get_cruciality() is None:\\n continue\\n # We only want records that got exactly 3 votes\\n if len(r.cruciality) != 3:\\n continue\\n else:\\n yield r\",\n \"def get_dependent_accounts(client):\\n\\n PAGE_SIZE = 500\\n managed_customer_service = client.GetService(\\n 'ManagedCustomerService', version=settings.API_VERSION)\\n\\n offset = 0\\n selector = {\\n 'fields': ['CustomerId', 'Name'],\\n 'predicates': {\\n 'field': 'CanManageClients',\\n 'operator': 'EQUALS',\\n 'values': 'False'\\n },\\n 'paging': {\\n 'startIndex': str(offset),\\n 'numberResults': str(PAGE_SIZE)\\n }\\n }\\n more_pages = True\\n accounts = {}\\n while more_pages:\\n page = managed_customer_service.get(selector)\\n\\n if 'entries' in page and page['entries']:\\n for account in page['entries']:\\n accounts[account['customerId']] = str(account['name']) \\\\\\n if hasattr(account, 'name') else 'None'\\n\\n offset += PAGE_SIZE\\n selector['paging']['startIndex'] = str(offset)\\n more_pages = offset < int(page['totalNumEntries'])\\n\\n return accounts\",\n \"def get_random_index(self, excludes: Set[int]) -> int:\\n index = random.randint(0, self.space.categorical_size() - 1)\\n while index in excludes:\\n index = random.randint(0, self.space.categorical_size() - 1)\\n return index\",\n \"def crew_needing_reports(self):\\n reports = self.ccreport_set.all().values_list('crew_chief', flat=True)\\n return self.ccinstances.exclude(crew_chief__in=reports)\",\n \"def get_customers_by_name(name: str = '') -> List[Customer]:\\n customers = []\\n for c in get_market().customers.values():\\n if not name or c.name == name:\\n customers.append(c)\\n return customers\",\n \"def not_applicable_device_count(self):\\n if \\\"notApplicableDeviceCount\\\" in self._prop_dict:\\n return self._prop_dict[\\\"notApplicableDeviceCount\\\"]\\n else:\\n return None\",\n \"def to_exclude(self):\\n midnight = now().replace(hour=0, minute=0, microsecond=0)\\n return CenterOpen.objects.filter(\\n creation_date__gte=midnight,\\n ).values_list('phone_number', flat=True)\",\n \"def prune(self):\\n target_user_ids = self.get_queryset().values_list('id', flat=True)\\n exclude_user_ids = SentDrip.objects.filter(date__lt=conditional_now(),\\n drip=self.drip_model,\\n user__id__in=target_user_ids)\\\\\\n .values_list('user_id', flat=True)\\n self._queryset = self.get_queryset().exclude(id__in=exclude_user_ids)\",\n \"def remove_existing_customers(self):\\n # remove the customers which are not active (.is_active )\\n self.to_move = False\\n #for cust in self.customers:\\n # print(cust.state)\\n self.customers = [cust for cust in self.customers if cust.state != 'checkout']\\n #if cust.to_move():\\n # self.to_move = True\",\n \"def GetListDoctors(self):\\n\\t\\treturn self.ClientsMap.values()\",\n \"def get_users(self, customer_id='my_customer'):\\n try:\\n paged_results = self.repository.users.list(customer=customer_id,\\n viewType='admin_view')\\n flattened_results = api_helpers.flatten_list_results(\\n paged_results, 'users')\\n LOGGER.debug('Getting all the users for customer_id = %s,'\\n ' flattened_results = %s',\\n customer_id, flattened_results)\\n return flattened_results\\n except RefreshError as e:\\n # Authentication failed, log before raise.\\n LOGGER.exception(GSUITE_AUTH_FAILURE_MESSAGE)\\n raise e\\n except (errors.HttpError, HttpLib2Error) as e:\\n raise api_errors.ApiExecutionError('users', e)\",\n \"def get_all_except(id_list, order_by_field=DATA_SORTING_FIELDS):\\n return Data.get_all_except(order_by_field, id_list)\",\n \"def _random_customer(cust_dtls) -> tuple:\\n return choices(cust_dtls)[0]\",\n \"def fetch_data(excludeNonPollutants):\\n if excludeNonPollutants:\\n return AirMeasurement().select().where(~AirMeasurement.uploaded,\\n AirMeasurement.type != 'Temperature',\\n AirMeasurement.type != 'Altitude',\\n AirMeasurement.type != 'Pressure',\\n AirMeasurement.type != 'Humidity',\\n AirMeasurement.type != 'PM1.0').limit(500)\\n\\n return AirMeasurement().select().where(~(AirMeasurement.uploaded)).limit(500)\",\n \"def getExcludedAtoms(self):\\n excludedAtomsIdList = self.getFlagData('EXCLUDED_ATOMS_LIST')\\n numberExcludedAtoms = self.getFlagData('NUMBER_EXCLUDED_ATOMS')\\n atoms = self.atoms\\n interval = 0\\n excludedAtomsList = []\\n for number in numberExcludedAtoms:\\n temp = excludedAtomsIdList[interval:interval + number]\\n if temp == [0]:\\n excludedAtomsList.append([])\\n else:\\n excludedAtomsList.append([atoms[a-1] for a in temp])\\n interval += number\\n self.excludedAtoms = excludedAtomsList\\n self.printDebug(\\\"getExcludedAtoms\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5755343","0.5630526","0.5468172","0.5455088","0.5447088","0.53655386","0.5348006","0.5255881","0.52160037","0.5184504","0.5118855","0.51016146","0.50954854","0.5094486","0.50568765","0.50267607","0.49917355","0.49625525","0.49529928","0.49519303","0.49320677","0.49264386","0.49157014","0.4894947","0.48897344","0.4889139","0.48599762","0.48556495","0.48475817","0.48341656","0.4827593","0.4824127","0.48215884","0.48111558","0.48075563","0.4803708","0.4793977","0.4738182","0.4727644","0.47134954","0.47122827","0.47013596","0.469767","0.4694221","0.46777287","0.46562466","0.46538055","0.46538055","0.46522975","0.46454203","0.464386","0.46349066","0.46322682","0.4624423","0.46088406","0.4608093","0.45889562","0.45762601","0.4570697","0.45690352","0.45425043","0.451929","0.45174477","0.4508532","0.45010126","0.44999465","0.44910756","0.44861954","0.4478877","0.44775662","0.44412106","0.44368094","0.44321892","0.441941","0.43984798","0.4394206","0.439287","0.43892136","0.43892136","0.43892136","0.43892136","0.4383296","0.43615562","0.43568084","0.43567845","0.43558758","0.43447962","0.43443578","0.4342788","0.43326914","0.43266642","0.43235832","0.43188372","0.4304886","0.43013406","0.42984247","0.42974642","0.42893043","0.4289288","0.42832902"],"string":"[\n \"0.5755343\",\n \"0.5630526\",\n \"0.5468172\",\n \"0.5455088\",\n \"0.5447088\",\n \"0.53655386\",\n \"0.5348006\",\n \"0.5255881\",\n \"0.52160037\",\n \"0.5184504\",\n \"0.5118855\",\n \"0.51016146\",\n \"0.50954854\",\n \"0.5094486\",\n \"0.50568765\",\n \"0.50267607\",\n \"0.49917355\",\n \"0.49625525\",\n \"0.49529928\",\n \"0.49519303\",\n \"0.49320677\",\n \"0.49264386\",\n \"0.49157014\",\n \"0.4894947\",\n \"0.48897344\",\n \"0.4889139\",\n \"0.48599762\",\n \"0.48556495\",\n \"0.48475817\",\n \"0.48341656\",\n \"0.4827593\",\n \"0.4824127\",\n \"0.48215884\",\n \"0.48111558\",\n \"0.48075563\",\n \"0.4803708\",\n \"0.4793977\",\n \"0.4738182\",\n \"0.4727644\",\n \"0.47134954\",\n \"0.47122827\",\n \"0.47013596\",\n \"0.469767\",\n \"0.4694221\",\n \"0.46777287\",\n \"0.46562466\",\n \"0.46538055\",\n \"0.46538055\",\n \"0.46522975\",\n \"0.46454203\",\n \"0.464386\",\n \"0.46349066\",\n \"0.46322682\",\n \"0.4624423\",\n \"0.46088406\",\n \"0.4608093\",\n \"0.45889562\",\n \"0.45762601\",\n \"0.4570697\",\n \"0.45690352\",\n \"0.45425043\",\n \"0.451929\",\n \"0.45174477\",\n \"0.4508532\",\n \"0.45010126\",\n \"0.44999465\",\n \"0.44910756\",\n \"0.44861954\",\n \"0.4478877\",\n \"0.44775662\",\n \"0.44412106\",\n \"0.44368094\",\n \"0.44321892\",\n \"0.441941\",\n \"0.43984798\",\n \"0.4394206\",\n \"0.439287\",\n \"0.43892136\",\n \"0.43892136\",\n \"0.43892136\",\n \"0.43892136\",\n \"0.4383296\",\n \"0.43615562\",\n \"0.43568084\",\n \"0.43567845\",\n \"0.43558758\",\n \"0.43447962\",\n \"0.43443578\",\n \"0.4342788\",\n \"0.43326914\",\n \"0.43266642\",\n \"0.43235832\",\n \"0.43188372\",\n \"0.4304886\",\n \"0.43013406\",\n \"0.42984247\",\n \"0.42974642\",\n \"0.42893043\",\n \"0.4289288\",\n \"0.42832902\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7302814"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":1894,"cells":{"query":{"kind":"string","value":"Returns a True if a customer identifier does not belongs to dataframe used to build classifier model."},"document":{"kind":"string","value":"def is_customer_out_sample(self, customerID):\n listCustomer = list(self._df_invoice_line_out_sample.CustomerID.unique())\n is_flag = customerID in listCustomer\n return is_flag"},"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 is_customer(self) -> bool:\n return self.customer_id is not None","def has_customer(self):\n return self.customer is not None","def is_customer(self):\n return self.user_type == 'C'","def is_label_dataframe(label, df):\n\n setdiff = set(label) - set(df.columns.tolist())\n\n if len(setdiff) == 0:\n return True\n else:\n return False","def is_customer(self):\n return self.rol == ProfileRoles.CUSTOMER","def check_data(dataframe):\n if dataframe.iloc[0, 0] == 'No data available':\n return False\n else:\n return True","def is_velas_df(df):\n empty_df = get_empty_df()\n \n if sorted(list(empty_df.columns)) != sorted(list(df.columns)):\n return False\n if empty_df.index.name != df.index.name:\n return False\n return True","def is_dataset(self):\n return self._dataset is not None","def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\n \n # remove customers with more than 20 purchases\n if 'frequency' in customer_df.columns:\n customer_df = customer_df[customer_df.frequency < 20]\n \n return customer_df","def test_case_user_not_yet_customer(self):\n pass","def generate_customer_info(self):\n consecutive = check_consecutive(self.customerIds)\n print(\"consecutive\" + str(consecutive))\n if consecutive:\n for customer_id in self.customerIds:\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\n insured = np.random.choice(\n self.distributions[\"insurance_status\"],\n 1,\n p=self.distributions[\"insurance_distribution\"],\n )[0]\n dob = self.dobs[customer_id - 1]\n customer = pd.DataFrame(\n {\n \"customer_id\": [customer_id],\n \"dob\": str(dob),\n \"insurance\": insured,\n \"experiment_id\": random.randint(1, 2),\n }\n )\n self.Customers = self.Customers.append(customer)\n else:\n print(\"Error generating customer info: customerIds aren't consecutive\")\n return True","def is_appropriate_data_instance(self, df) -> bool:\n return isinstance(df, pl.DataFrame) or isinstance(df, pl.LazyFrame)","def customer_wants_condiments(self):\n return True","def has_data(self):\n return ([0] != self.__contexts) and ([0] != self.__weights)","def is_specific(self) -> bool:\n return False","def is_customer_id_exist(customer_id) -> bool:\n with MY_CONNECTION as connection:\n cursor = connection.cursor()\n cursor.execute(\"SELECT exists(SELECT 1 FROM Customers WHERE id_customer=?)\", (customer_id,))\n return cursor.fetchone()[0] == 1","def check_user(self):\n try:\n if self.get_customer()[0][0] == self.dni:\n return True\n else:\n return False\n except:\n return False","def has_cid(self):\n return hasattr(self, 'cid')","def _isIndexedDataframe(self, dataframe):\n return len(dataframe.index.names) > 1 or not dataframe.index.names[0] is None","def __is_nan(self):\n return _VirtualBooleanColumn(\n df_name=self.thisptr[\"df_name_\"],\n operator=\"is_nan\",\n operand1=self,\n operand2=None\n )","def is_new_data(df: pd.DataFrame) -> bool:\n import io\n\n with io.StringIO() as s:\n save_as_data_table(df, s)\n new_data = s.getvalue()\n\n existing_data = read_data_table(as_text=True)\n\n return new_data != existing_data","def check_sparkdf_not_nulls(sparkdf,columns):\n\n\tfor column in columns:\n\n\t\tempties = sparkdf.select(col(column)).where(col(column).isNull())\n\t\tif len(empties.head(1)) > 0:\n\t\t\tprint(\"Checking DataFrame. I found null values in column\", column)\n\t\t\treturn False\n\t\telse:\n\t\t\tprint(\"Checking DataFrame. No null values found in column\", column)\n\n\treturn True","def is_false(self):\n return _VirtualBooleanColumn(\n df_name=self.thisptr[\"df_name_\"],\n operator=\"not\",\n operand1=self,\n operand2=None\n )","def is_non_reducing(self):\n return bool(set(self.kind) & set(\"ABC\"))","def single_records(df,\n key_cols=['report_date', 'plant_id_eia', 'generator_id']):\n len_1 = len(df)\n len_2 = len(df.drop_duplicates(subset=key_cols))\n return bool(len_1 == len_2)","def to_drop(self):\n return self.id is None","def __bool__(self):\n return self.fam.c_nonzero(self)","def has_customers(self, asn):\n for neighbor in nx.all_neighbors(self, asn):\n edge_data = self.get_edge_data(asn, neighbor)\n\n # node is a provider of neighbor\n if edge_data[\"relationship\"] == -1 and edge_data[\"as1\"] == asn:\n return True\n return False","def bool(self) -> bool:\n if isinstance(self, ps.DataFrame):\n df = self\n elif isinstance(self, ps.Series):\n df = self.to_dataframe()\n return df.head(2)._to_internal_pandas().bool()","def __is_inf(self):\n return _VirtualBooleanColumn(\n df_name=self.thisptr[\"df_name_\"],\n operator=\"is_inf\",\n operand1=self,\n operand2=None\n )","def is_valid(self, dataset):\n pass","def noyable(self):\n return False","def is_relevant(self):\n return self.metadata.is_relevant","def have_cdc() -> bool:","def train_column_is_all_null(self, column):\n return self.df_column_is_all_null(self.train, column)","def test_import_customer_without_data(self):\n\n customer = self.import_customer.create_customer_object(\"cust002010\", {})\n self.assertIsInstance(customer, Customer)","def test_not_contains_user(self):\n print('(' + self.test_not_contains_user.__name__+')',\n self.test_not_contains_user.__doc__)\n # non existing doctor, it could be patient as well\n self.assertFalse(self.connection.contains_user(\n NON_EXIST_DOCTOR_USERNAME))","def test_single_missing_column():\n imp = MissingnessClassifier()\n imp.fit_predict(dfs.df_mis_classifier)\n imp.fit_predict_proba(dfs.df_mis_classifier)","def _has_data(cls):\n return User.objects.count() > 0","def is_isolated_cds(gene, gene_info, idx):\n\n if len(gene_info.vertex_succ_list[idx]) > 0:\n return False\n\n return np.sum(gene.splicegraph.edges[:, idx]) == 0","def is_absent(self, tokenized_record):\n\n return bool(set(tokenized_record).intersection(self.absent_markers))","def has_stockrecords(self):\n try:\n a=self.stockrecords.pk\n return True\n except:\n return False","def isUnConditional(self) -> bool:\n ...","def customers_presence(self):\n return self._customers_presence","def _exists(self) -> bool:\n client = MlflowClient()\n all_metrics = client._tracking_client.store.get_all_metrics(\n run_uuid=self.run_id\n )\n return any(self._is_dataset_metric(x) for x in all_metrics)","def has_no_uses(self) -> bool:\n\n return len(self.users_) == 0","def verify_pandas(self):\n self.check_dataset_duplicate_ids(self.vertices)\n # self.check_dataset_children_ids()\n self.check_dataset_litter_ids()\n self.check_dataset_dates()","def ref_known_flag(self):\n if CredentialApplication.objects.filter(\n reference_email__iexact=self.reference_email,\n reference_contact_datetime__isnull=False).exclude(\n reference_email=''):\n return True\n elif LegacyCredential.objects.filter(\n reference_email__iexact=self.reference_email).exclude(\n reference_email=''):\n return True\n else:\n return False","def cols_valid(self,\n df: pd.DataFrame,\n req_cols: set) -> bool:\n missing_cols = req_cols.difference(df.columns)\n\n if len(missing_cols) > 0:\n logging.error(f\"{missing_cols} columns required but missing\")\n return False\n\n return True","def __contains__(self, item):\n return item in self.default_dataset","def _check_notnull(self):\n candnull = self.df_test_resampled[self.candidate_col_name].isnull().all()\n refnull = self.df_test_resampled[self.reference_col_name].isnull().all()\n if candnull or refnull:\n return 1, 'No data for selected time frame'\n else:\n return 0, 'No error occurred'","def validate(\n self, feature_set: FeatureSet, dataframe: DataFrame, spark_client: SparkClient\n ) -> Any:","def _is_dataclass_instance(obj):\n return hasattr(type(obj), '__dataclass_fields__')","def does_usage_charges_grid_have_no_records(self):\n return self.is_element_present(self.usage_charges_grid_no_record_found_message_locator)","def is_identity(self):\n\n if self.rows != self.columns:\n return False\n\n for i in range(self.rows):\n row = self.row(i + 1)\n for j in range(self.columns):\n if i == j and row[j] != 1:\n return False\n\n if i != j and row[j] != 0:\n return False\n\n return True;","def is_allc(self):\n g = self.get_gene().get_seq()\n for i in range(1, len(g)):\n if g[i] is 'd':\n return False\n return True","def is_indicator():\n return True","def is_id_only(self):\n for key, value in self.items():\n if key not in {'names', 'labels', 'roles'} and value:\n return False\n if self.names or self.labels:\n return True\n return False","def has_carbon(self):\n return len(self.c_indices) > 0","def isEmptyLandmarkset(self):\n return self.subsetpointcloud is None","def _is_categorical(df, field):\n return df[field].dtype.name == 'category'","def is_dataclass_instance(obj: Any) -> bool:\n return dataclasses.is_dataclass(obj) and not isinstance(obj, type)","def is_ode_noad_link(self):\n if self.project_name in IDENTIFIERS:\n return True\n else:\n return False","def is_yummy(self):\n return False","def is_customers_room(room, customer_id):\n room_last_10 = room.title[-10:]\n customer_id_last10 = customer_id[-10:]\n if room_last_10 == customer_id_last10:\n return True\n logging.log(logging.INFO, \"NO MATCH for %s %s \" % (room_last_10, customer_id_last10))\n return False","def is_emptiable(self) -> bool:\n raise NotImplementedError()","def __ne__(self, other):\n if not isinstance(other, BusinessInvoiceAnalysisRequest):\n return True\n\n return self.to_dict() != other.to_dict()","def __nonzero__(self):\n return True","def check_if_sectors_are_naics(df_load, crosswalk_list, column_headers):\n\n # create a df of non-sectors to export\n non_sectors_df = []\n # create a df of just the non-sectors column\n non_sectors_list = []\n # loop through the df headers and determine if value\n # is not in crosswalk list\n for c in column_headers:\n # create df where sectors do not exist in master crosswalk\n non_sectors = df_load[~df_load[c].isin(crosswalk_list)]\n # drop rows where c is empty\n non_sectors = non_sectors[non_sectors[c] != '']\n # subset to just the sector column\n if len(non_sectors) != 0:\n sectors = non_sectors[[c]].rename(columns={c: 'NonSectors'})\n non_sectors_df.append(non_sectors)\n non_sectors_list.append(sectors)\n\n if len(non_sectors_df) != 0:\n # concat the df and the df of sectors\n ns_list = pd.concat(non_sectors_list, sort=False, ignore_index=True)\n # print the NonSectors\n non_sectors = ns_list['NonSectors'].drop_duplicates().tolist()\n vLog.debug('There are sectors that are not NAICS 2012 Codes')\n vLog.debug(non_sectors)\n else:\n vLog.debug('All sectors are NAICS 2012 Codes')\n\n return non_sectors","def isInternal(self):\n if self.data.depend_er_job == self.data.depend_on_job:\n return True\n return False","def is_duplicate(self, **kwargs):\n return len(list(self.c.select(**kwargs))) > 0","def _isNewTxn(self, identifier, reply, txnId) -> bool:\n return (identifier not in self.processedRequests or\n reply.reqId not in self.processedRequests[identifier]) and \\\n txnId is not None","def __ne__(self, other):\n if not isinstance(other, CreditSupportAnnex):\n return True\n\n return self.to_dict() != other.to_dict()","def exists(cls, customer_id):\n customer_id = int(customer_id)\n cust = DB_CUSTOMER_TABLE.get(doc_id=customer_id)\n if not cust:\n raise ValueError(f\"unknown customer '{customer_id}'\")\n return customer_id","def _check_primary_key(df: \"pd.DataFrame\", primary_key_name: str):\n if primary_key_name in df.columns and primary_key_name == df.index.name:\n raise primary_key.Ambiguous(\n f\"Index {primary_key_name} has the same name as column {primary_key_name}\"\n )\n elif primary_key_name not in df.columns and primary_key_name != df.index.name:\n raise primary_key.NotFound(\n f\"Primary key: {primary_key_name} is not DataFrame index name: {df.index.name} or in\"\n f\" DataFrame column names: {df.columns}\"\n )","def is_distributed_model(model):\n try:\n get_tenant_field(model)\n return True\n except ValueError:\n return False","def is_dnf(self):\n return False","def is_data(i):\n keys = ['_id', '_time']\n return all(i != k for k in keys)","def check_for_null_values(self):\r\n print(self.dataframe_name)\r\n print(self.data_frame.isna().sum())","def invariant(self):\n\t\treturn ((self.tenant != \"\") and (self.loginUser != \"\"))","def validateModelCol(self):\n \n ret = False\n \n dc = self.__args['datacolumn'].upper() \n if \"MODEL\" in dc or dc == 'ALL':\n ret = True\n\n return ret","def invariant(self):\n\t\treturn ((self.name != \"\") and (self.locationId != \"\"))","def checkfornan(chosen_df):\n if not chosen_df.isnull().values.any():\n raise ValueError('NaN in DataFrame')","def tie_exists(self):\n return len(self.marks) == 9","def __eq__(self, other):\n if not isinstance(other, Customer):\n return False\n\n return self.__dict__ == other.__dict__","def __eq__(self, other):\n if not isinstance(other, CustomersCustomerDetails):\n return False\n\n return self.__dict__ == other.__dict__","def is_alld(self):\n g = self.get_gene().get_seq()\n for i in range(1, len(g)):\n if g[i] is 'c':\n return False\n return True","def chcek_exist(df, Point):\n\n exist = (df.OperatingPoint == Point)\n exist = exist.sum()\n if exist == 0:\n return False\n else:\n return True","def unmatching_driver_id(df):\n\ttemp = df[df['driver_id_bkg'].notnull()]\n\torder_ids = temp[temp['driver_id_bkg'] != temp['driver_id_pnt']]['order_id'].values\n\treturn df[~df['order_id'].isin(order_ids)]","def get_customer_count(self):\n return self._df_invoice_original.CustomerID.unique().shape[0]","def delete_customer(customer_id):\n del_query = Customer.get(Customer.customer_id == customer_id)\n return bool(del_query.delete_instance())","def _check_features_df(df, features):\n # check columns\n if not set(features).issubset(df.columns):\n raise ValueError(\"The dataframe does not seem to have the right \"\n \"features. {0} instead of {1}\"\n .format(df.columns, features))\n\n return","def is_internal(self):\n return bool(self.is_reducing() and self.is_non_reducing())","def is_indeed(self) -> bool:\n return self.mukluk > 5","def IgnorePersistedDecision(self) -> bool:","def is_inequality(self): \n return False","def __bool__(self):\n return self.taxonomy.exists","def is_person_identifier_used(person_id):\n try:\n conn = sqlite3.connect(settings.database_name)\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n c.execute(\"PRAGMA foreign_keys = ON\")\n c.execute(\"SELECT personid FROM person WHERE personid =?\", (person_id,))\n person_identifier = \"\"\n is_used = True\n for row in c:\n person_identifier = row[\"personid\"]\n conn.close()\n if len(person_identifier) == 0:\n is_used = False\n if len(person_identifier) > 0:\n is_used = True\n return is_used\n except:\n return False","def is_trainable(self):\n return False","def isDeleted(self):\n return self.air is None"],"string":"[\n \"def is_customer(self) -> bool:\\n return self.customer_id is not None\",\n \"def has_customer(self):\\n return self.customer is not None\",\n \"def is_customer(self):\\n return self.user_type == 'C'\",\n \"def is_label_dataframe(label, df):\\n\\n setdiff = set(label) - set(df.columns.tolist())\\n\\n if len(setdiff) == 0:\\n return True\\n else:\\n return False\",\n \"def is_customer(self):\\n return self.rol == ProfileRoles.CUSTOMER\",\n \"def check_data(dataframe):\\n if dataframe.iloc[0, 0] == 'No data available':\\n return False\\n else:\\n return True\",\n \"def is_velas_df(df):\\n empty_df = get_empty_df()\\n \\n if sorted(list(empty_df.columns)) != sorted(list(df.columns)):\\n return False\\n if empty_df.index.name != df.index.name:\\n return False\\n return True\",\n \"def is_dataset(self):\\n return self._dataset is not None\",\n \"def clean_customer_df(customer_df: pd.DataFrame) -> pd.DataFrame:\\n \\n # remove customers with more than 20 purchases\\n if 'frequency' in customer_df.columns:\\n customer_df = customer_df[customer_df.frequency < 20]\\n \\n return customer_df\",\n \"def test_case_user_not_yet_customer(self):\\n pass\",\n \"def generate_customer_info(self):\\n consecutive = check_consecutive(self.customerIds)\\n print(\\\"consecutive\\\" + str(consecutive))\\n if consecutive:\\n for customer_id in self.customerIds:\\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\\n insured = np.random.choice(\\n self.distributions[\\\"insurance_status\\\"],\\n 1,\\n p=self.distributions[\\\"insurance_distribution\\\"],\\n )[0]\\n dob = self.dobs[customer_id - 1]\\n customer = pd.DataFrame(\\n {\\n \\\"customer_id\\\": [customer_id],\\n \\\"dob\\\": str(dob),\\n \\\"insurance\\\": insured,\\n \\\"experiment_id\\\": random.randint(1, 2),\\n }\\n )\\n self.Customers = self.Customers.append(customer)\\n else:\\n print(\\\"Error generating customer info: customerIds aren't consecutive\\\")\\n return True\",\n \"def is_appropriate_data_instance(self, df) -> bool:\\n return isinstance(df, pl.DataFrame) or isinstance(df, pl.LazyFrame)\",\n \"def customer_wants_condiments(self):\\n return True\",\n \"def has_data(self):\\n return ([0] != self.__contexts) and ([0] != self.__weights)\",\n \"def is_specific(self) -> bool:\\n return False\",\n \"def is_customer_id_exist(customer_id) -> bool:\\n with MY_CONNECTION as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT exists(SELECT 1 FROM Customers WHERE id_customer=?)\\\", (customer_id,))\\n return cursor.fetchone()[0] == 1\",\n \"def check_user(self):\\n try:\\n if self.get_customer()[0][0] == self.dni:\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def has_cid(self):\\n return hasattr(self, 'cid')\",\n \"def _isIndexedDataframe(self, dataframe):\\n return len(dataframe.index.names) > 1 or not dataframe.index.names[0] is None\",\n \"def __is_nan(self):\\n return _VirtualBooleanColumn(\\n df_name=self.thisptr[\\\"df_name_\\\"],\\n operator=\\\"is_nan\\\",\\n operand1=self,\\n operand2=None\\n )\",\n \"def is_new_data(df: pd.DataFrame) -> bool:\\n import io\\n\\n with io.StringIO() as s:\\n save_as_data_table(df, s)\\n new_data = s.getvalue()\\n\\n existing_data = read_data_table(as_text=True)\\n\\n return new_data != existing_data\",\n \"def check_sparkdf_not_nulls(sparkdf,columns):\\n\\n\\tfor column in columns:\\n\\n\\t\\tempties = sparkdf.select(col(column)).where(col(column).isNull())\\n\\t\\tif len(empties.head(1)) > 0:\\n\\t\\t\\tprint(\\\"Checking DataFrame. I found null values in column\\\", column)\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"Checking DataFrame. No null values found in column\\\", column)\\n\\n\\treturn True\",\n \"def is_false(self):\\n return _VirtualBooleanColumn(\\n df_name=self.thisptr[\\\"df_name_\\\"],\\n operator=\\\"not\\\",\\n operand1=self,\\n operand2=None\\n )\",\n \"def is_non_reducing(self):\\n return bool(set(self.kind) & set(\\\"ABC\\\"))\",\n \"def single_records(df,\\n key_cols=['report_date', 'plant_id_eia', 'generator_id']):\\n len_1 = len(df)\\n len_2 = len(df.drop_duplicates(subset=key_cols))\\n return bool(len_1 == len_2)\",\n \"def to_drop(self):\\n return self.id is None\",\n \"def __bool__(self):\\n return self.fam.c_nonzero(self)\",\n \"def has_customers(self, asn):\\n for neighbor in nx.all_neighbors(self, asn):\\n edge_data = self.get_edge_data(asn, neighbor)\\n\\n # node is a provider of neighbor\\n if edge_data[\\\"relationship\\\"] == -1 and edge_data[\\\"as1\\\"] == asn:\\n return True\\n return False\",\n \"def bool(self) -> bool:\\n if isinstance(self, ps.DataFrame):\\n df = self\\n elif isinstance(self, ps.Series):\\n df = self.to_dataframe()\\n return df.head(2)._to_internal_pandas().bool()\",\n \"def __is_inf(self):\\n return _VirtualBooleanColumn(\\n df_name=self.thisptr[\\\"df_name_\\\"],\\n operator=\\\"is_inf\\\",\\n operand1=self,\\n operand2=None\\n )\",\n \"def is_valid(self, dataset):\\n pass\",\n \"def noyable(self):\\n return False\",\n \"def is_relevant(self):\\n return self.metadata.is_relevant\",\n \"def have_cdc() -> bool:\",\n \"def train_column_is_all_null(self, column):\\n return self.df_column_is_all_null(self.train, column)\",\n \"def test_import_customer_without_data(self):\\n\\n customer = self.import_customer.create_customer_object(\\\"cust002010\\\", {})\\n self.assertIsInstance(customer, Customer)\",\n \"def test_not_contains_user(self):\\n print('(' + self.test_not_contains_user.__name__+')',\\n self.test_not_contains_user.__doc__)\\n # non existing doctor, it could be patient as well\\n self.assertFalse(self.connection.contains_user(\\n NON_EXIST_DOCTOR_USERNAME))\",\n \"def test_single_missing_column():\\n imp = MissingnessClassifier()\\n imp.fit_predict(dfs.df_mis_classifier)\\n imp.fit_predict_proba(dfs.df_mis_classifier)\",\n \"def _has_data(cls):\\n return User.objects.count() > 0\",\n \"def is_isolated_cds(gene, gene_info, idx):\\n\\n if len(gene_info.vertex_succ_list[idx]) > 0:\\n return False\\n\\n return np.sum(gene.splicegraph.edges[:, idx]) == 0\",\n \"def is_absent(self, tokenized_record):\\n\\n return bool(set(tokenized_record).intersection(self.absent_markers))\",\n \"def has_stockrecords(self):\\n try:\\n a=self.stockrecords.pk\\n return True\\n except:\\n return False\",\n \"def isUnConditional(self) -> bool:\\n ...\",\n \"def customers_presence(self):\\n return self._customers_presence\",\n \"def _exists(self) -> bool:\\n client = MlflowClient()\\n all_metrics = client._tracking_client.store.get_all_metrics(\\n run_uuid=self.run_id\\n )\\n return any(self._is_dataset_metric(x) for x in all_metrics)\",\n \"def has_no_uses(self) -> bool:\\n\\n return len(self.users_) == 0\",\n \"def verify_pandas(self):\\n self.check_dataset_duplicate_ids(self.vertices)\\n # self.check_dataset_children_ids()\\n self.check_dataset_litter_ids()\\n self.check_dataset_dates()\",\n \"def ref_known_flag(self):\\n if CredentialApplication.objects.filter(\\n reference_email__iexact=self.reference_email,\\n reference_contact_datetime__isnull=False).exclude(\\n reference_email=''):\\n return True\\n elif LegacyCredential.objects.filter(\\n reference_email__iexact=self.reference_email).exclude(\\n reference_email=''):\\n return True\\n else:\\n return False\",\n \"def cols_valid(self,\\n df: pd.DataFrame,\\n req_cols: set) -> bool:\\n missing_cols = req_cols.difference(df.columns)\\n\\n if len(missing_cols) > 0:\\n logging.error(f\\\"{missing_cols} columns required but missing\\\")\\n return False\\n\\n return True\",\n \"def __contains__(self, item):\\n return item in self.default_dataset\",\n \"def _check_notnull(self):\\n candnull = self.df_test_resampled[self.candidate_col_name].isnull().all()\\n refnull = self.df_test_resampled[self.reference_col_name].isnull().all()\\n if candnull or refnull:\\n return 1, 'No data for selected time frame'\\n else:\\n return 0, 'No error occurred'\",\n \"def validate(\\n self, feature_set: FeatureSet, dataframe: DataFrame, spark_client: SparkClient\\n ) -> Any:\",\n \"def _is_dataclass_instance(obj):\\n return hasattr(type(obj), '__dataclass_fields__')\",\n \"def does_usage_charges_grid_have_no_records(self):\\n return self.is_element_present(self.usage_charges_grid_no_record_found_message_locator)\",\n \"def is_identity(self):\\n\\n if self.rows != self.columns:\\n return False\\n\\n for i in range(self.rows):\\n row = self.row(i + 1)\\n for j in range(self.columns):\\n if i == j and row[j] != 1:\\n return False\\n\\n if i != j and row[j] != 0:\\n return False\\n\\n return True;\",\n \"def is_allc(self):\\n g = self.get_gene().get_seq()\\n for i in range(1, len(g)):\\n if g[i] is 'd':\\n return False\\n return True\",\n \"def is_indicator():\\n return True\",\n \"def is_id_only(self):\\n for key, value in self.items():\\n if key not in {'names', 'labels', 'roles'} and value:\\n return False\\n if self.names or self.labels:\\n return True\\n return False\",\n \"def has_carbon(self):\\n return len(self.c_indices) > 0\",\n \"def isEmptyLandmarkset(self):\\n return self.subsetpointcloud is None\",\n \"def _is_categorical(df, field):\\n return df[field].dtype.name == 'category'\",\n \"def is_dataclass_instance(obj: Any) -> bool:\\n return dataclasses.is_dataclass(obj) and not isinstance(obj, type)\",\n \"def is_ode_noad_link(self):\\n if self.project_name in IDENTIFIERS:\\n return True\\n else:\\n return False\",\n \"def is_yummy(self):\\n return False\",\n \"def is_customers_room(room, customer_id):\\n room_last_10 = room.title[-10:]\\n customer_id_last10 = customer_id[-10:]\\n if room_last_10 == customer_id_last10:\\n return True\\n logging.log(logging.INFO, \\\"NO MATCH for %s %s \\\" % (room_last_10, customer_id_last10))\\n return False\",\n \"def is_emptiable(self) -> bool:\\n raise NotImplementedError()\",\n \"def __ne__(self, other):\\n if not isinstance(other, BusinessInvoiceAnalysisRequest):\\n return True\\n\\n return self.to_dict() != other.to_dict()\",\n \"def __nonzero__(self):\\n return True\",\n \"def check_if_sectors_are_naics(df_load, crosswalk_list, column_headers):\\n\\n # create a df of non-sectors to export\\n non_sectors_df = []\\n # create a df of just the non-sectors column\\n non_sectors_list = []\\n # loop through the df headers and determine if value\\n # is not in crosswalk list\\n for c in column_headers:\\n # create df where sectors do not exist in master crosswalk\\n non_sectors = df_load[~df_load[c].isin(crosswalk_list)]\\n # drop rows where c is empty\\n non_sectors = non_sectors[non_sectors[c] != '']\\n # subset to just the sector column\\n if len(non_sectors) != 0:\\n sectors = non_sectors[[c]].rename(columns={c: 'NonSectors'})\\n non_sectors_df.append(non_sectors)\\n non_sectors_list.append(sectors)\\n\\n if len(non_sectors_df) != 0:\\n # concat the df and the df of sectors\\n ns_list = pd.concat(non_sectors_list, sort=False, ignore_index=True)\\n # print the NonSectors\\n non_sectors = ns_list['NonSectors'].drop_duplicates().tolist()\\n vLog.debug('There are sectors that are not NAICS 2012 Codes')\\n vLog.debug(non_sectors)\\n else:\\n vLog.debug('All sectors are NAICS 2012 Codes')\\n\\n return non_sectors\",\n \"def isInternal(self):\\n if self.data.depend_er_job == self.data.depend_on_job:\\n return True\\n return False\",\n \"def is_duplicate(self, **kwargs):\\n return len(list(self.c.select(**kwargs))) > 0\",\n \"def _isNewTxn(self, identifier, reply, txnId) -> bool:\\n return (identifier not in self.processedRequests or\\n reply.reqId not in self.processedRequests[identifier]) and \\\\\\n txnId is not None\",\n \"def __ne__(self, other):\\n if not isinstance(other, CreditSupportAnnex):\\n return True\\n\\n return self.to_dict() != other.to_dict()\",\n \"def exists(cls, customer_id):\\n customer_id = int(customer_id)\\n cust = DB_CUSTOMER_TABLE.get(doc_id=customer_id)\\n if not cust:\\n raise ValueError(f\\\"unknown customer '{customer_id}'\\\")\\n return customer_id\",\n \"def _check_primary_key(df: \\\"pd.DataFrame\\\", primary_key_name: str):\\n if primary_key_name in df.columns and primary_key_name == df.index.name:\\n raise primary_key.Ambiguous(\\n f\\\"Index {primary_key_name} has the same name as column {primary_key_name}\\\"\\n )\\n elif primary_key_name not in df.columns and primary_key_name != df.index.name:\\n raise primary_key.NotFound(\\n f\\\"Primary key: {primary_key_name} is not DataFrame index name: {df.index.name} or in\\\"\\n f\\\" DataFrame column names: {df.columns}\\\"\\n )\",\n \"def is_distributed_model(model):\\n try:\\n get_tenant_field(model)\\n return True\\n except ValueError:\\n return False\",\n \"def is_dnf(self):\\n return False\",\n \"def is_data(i):\\n keys = ['_id', '_time']\\n return all(i != k for k in keys)\",\n \"def check_for_null_values(self):\\r\\n print(self.dataframe_name)\\r\\n print(self.data_frame.isna().sum())\",\n \"def invariant(self):\\n\\t\\treturn ((self.tenant != \\\"\\\") and (self.loginUser != \\\"\\\"))\",\n \"def validateModelCol(self):\\n \\n ret = False\\n \\n dc = self.__args['datacolumn'].upper() \\n if \\\"MODEL\\\" in dc or dc == 'ALL':\\n ret = True\\n\\n return ret\",\n \"def invariant(self):\\n\\t\\treturn ((self.name != \\\"\\\") and (self.locationId != \\\"\\\"))\",\n \"def checkfornan(chosen_df):\\n if not chosen_df.isnull().values.any():\\n raise ValueError('NaN in DataFrame')\",\n \"def tie_exists(self):\\n return len(self.marks) == 9\",\n \"def __eq__(self, other):\\n if not isinstance(other, Customer):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n if not isinstance(other, CustomersCustomerDetails):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"def is_alld(self):\\n g = self.get_gene().get_seq()\\n for i in range(1, len(g)):\\n if g[i] is 'c':\\n return False\\n return True\",\n \"def chcek_exist(df, Point):\\n\\n exist = (df.OperatingPoint == Point)\\n exist = exist.sum()\\n if exist == 0:\\n return False\\n else:\\n return True\",\n \"def unmatching_driver_id(df):\\n\\ttemp = df[df['driver_id_bkg'].notnull()]\\n\\torder_ids = temp[temp['driver_id_bkg'] != temp['driver_id_pnt']]['order_id'].values\\n\\treturn df[~df['order_id'].isin(order_ids)]\",\n \"def get_customer_count(self):\\n return self._df_invoice_original.CustomerID.unique().shape[0]\",\n \"def delete_customer(customer_id):\\n del_query = Customer.get(Customer.customer_id == customer_id)\\n return bool(del_query.delete_instance())\",\n \"def _check_features_df(df, features):\\n # check columns\\n if not set(features).issubset(df.columns):\\n raise ValueError(\\\"The dataframe does not seem to have the right \\\"\\n \\\"features. {0} instead of {1}\\\"\\n .format(df.columns, features))\\n\\n return\",\n \"def is_internal(self):\\n return bool(self.is_reducing() and self.is_non_reducing())\",\n \"def is_indeed(self) -> bool:\\n return self.mukluk > 5\",\n \"def IgnorePersistedDecision(self) -> bool:\",\n \"def is_inequality(self): \\n return False\",\n \"def __bool__(self):\\n return self.taxonomy.exists\",\n \"def is_person_identifier_used(person_id):\\n try:\\n conn = sqlite3.connect(settings.database_name)\\n conn.row_factory = sqlite3.Row\\n c = conn.cursor()\\n c.execute(\\\"PRAGMA foreign_keys = ON\\\")\\n c.execute(\\\"SELECT personid FROM person WHERE personid =?\\\", (person_id,))\\n person_identifier = \\\"\\\"\\n is_used = True\\n for row in c:\\n person_identifier = row[\\\"personid\\\"]\\n conn.close()\\n if len(person_identifier) == 0:\\n is_used = False\\n if len(person_identifier) > 0:\\n is_used = True\\n return is_used\\n except:\\n return False\",\n \"def is_trainable(self):\\n return False\",\n \"def isDeleted(self):\\n return self.air is None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.661681","0.6462838","0.59257627","0.5741564","0.5628499","0.55574435","0.5537199","0.5509296","0.5507439","0.5461328","0.5405652","0.53825015","0.5370098","0.5365358","0.5328702","0.53143424","0.529039","0.52698815","0.52663475","0.52634853","0.52370775","0.52231854","0.5220413","0.51524615","0.5137677","0.51331055","0.5102486","0.5101493","0.5096956","0.5080815","0.50726473","0.50654197","0.50583327","0.50484157","0.5031694","0.50285304","0.50268906","0.5021727","0.50192124","0.49972594","0.49842697","0.49818462","0.49800837","0.4974319","0.49574834","0.49545977","0.49542335","0.49349925","0.4929173","0.49284396","0.49217662","0.4920269","0.49192423","0.49140215","0.49076545","0.4907331","0.49031","0.48979303","0.4897661","0.48917526","0.4886968","0.48839128","0.48584569","0.48571783","0.4854011","0.48536837","0.48520273","0.4849794","0.4846591","0.4843272","0.48382357","0.48360077","0.48343554","0.48331633","0.48315686","0.48314548","0.4828786","0.48276383","0.4825132","0.48237842","0.48231232","0.48219508","0.4821141","0.48177683","0.48146838","0.48142496","0.4813486","0.48127973","0.48126787","0.481166","0.4810733","0.48083964","0.48059034","0.48043257","0.4796356","0.47944203","0.47888148","0.47876745","0.47837427","0.47829413"],"string":"[\n \"0.661681\",\n \"0.6462838\",\n \"0.59257627\",\n \"0.5741564\",\n \"0.5628499\",\n \"0.55574435\",\n \"0.5537199\",\n \"0.5509296\",\n \"0.5507439\",\n \"0.5461328\",\n \"0.5405652\",\n \"0.53825015\",\n \"0.5370098\",\n \"0.5365358\",\n \"0.5328702\",\n \"0.53143424\",\n \"0.529039\",\n \"0.52698815\",\n \"0.52663475\",\n \"0.52634853\",\n \"0.52370775\",\n \"0.52231854\",\n \"0.5220413\",\n \"0.51524615\",\n \"0.5137677\",\n \"0.51331055\",\n \"0.5102486\",\n \"0.5101493\",\n \"0.5096956\",\n \"0.5080815\",\n \"0.50726473\",\n \"0.50654197\",\n \"0.50583327\",\n \"0.50484157\",\n \"0.5031694\",\n \"0.50285304\",\n \"0.50268906\",\n \"0.5021727\",\n \"0.50192124\",\n \"0.49972594\",\n \"0.49842697\",\n \"0.49818462\",\n \"0.49800837\",\n \"0.4974319\",\n \"0.49574834\",\n \"0.49545977\",\n \"0.49542335\",\n \"0.49349925\",\n \"0.4929173\",\n \"0.49284396\",\n \"0.49217662\",\n \"0.4920269\",\n \"0.49192423\",\n \"0.49140215\",\n \"0.49076545\",\n \"0.4907331\",\n \"0.49031\",\n \"0.48979303\",\n \"0.4897661\",\n \"0.48917526\",\n \"0.4886968\",\n \"0.48839128\",\n \"0.48584569\",\n \"0.48571783\",\n \"0.4854011\",\n \"0.48536837\",\n \"0.48520273\",\n \"0.4849794\",\n \"0.4846591\",\n \"0.4843272\",\n \"0.48382357\",\n \"0.48360077\",\n \"0.48343554\",\n \"0.48331633\",\n \"0.48315686\",\n \"0.48314548\",\n \"0.4828786\",\n \"0.48276383\",\n \"0.4825132\",\n \"0.48237842\",\n \"0.48231232\",\n \"0.48219508\",\n \"0.4821141\",\n \"0.48177683\",\n \"0.48146838\",\n \"0.48142496\",\n \"0.4813486\",\n \"0.48127973\",\n \"0.48126787\",\n \"0.481166\",\n \"0.4810733\",\n \"0.48083964\",\n \"0.48059034\",\n \"0.48043257\",\n \"0.4796356\",\n \"0.47944203\",\n \"0.47888148\",\n \"0.47876745\",\n \"0.47837427\",\n \"0.47829413\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63775516"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":1895,"cells":{"query":{"kind":"string","value":"Returns number of invoices from original dataset."},"document":{"kind":"string","value":"def get_invoice_count(self):\n return self._df_invoice_original.InvoiceNo.unique().shape[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 _compute_no_of_invoices(self):\n for record in self:\n record.invoice_count = len(record.invoice_ids)","def get_invl_count(self):\n return self._df_invoice_original.index.unique().shape[0]","def invoices(self):\r\n return inv.Invoices(self)","def get_customer_count(self):\n return self._df_invoice_original.CustomerID.unique().shape[0]","def expired_invoices_count(self):\n return self.get_expired_invoices().count()","def getNumOfInvoice(self,id,start,finish):\n self.calls += 1\n invoice = self.getResponse(self.buildParams(id,start,finish))\n if not self.isNumeric(invoice):\n middle = self.diveDates(start,finish)\n plusMiddle = middle + timedelta(days = 1)\n middle = self.removeHours(middle)\n plusMiddle = self.removeHours(plusMiddle)\n invoice = self.getNumOfInvoice(id,start,middle)+\\\n self.getNumOfInvoice(id,plusMiddle,finish)\n return invoice","def invoices(self):\r\n return inv.AccountInvoices(self)","def countAndGetCallInvoice(self,id,start,finish):\n self.calls = 0\n return self.getNumOfInvoice(id,start,finish)","def get_num_of_sales_per_customer_ids():\n\n # your code","def invoices_in_route(self):\n from invoicing.models import Invoice\n invoices = Invoice.objects.filter(\n route=self.number, print_date__range=(date.today() - timedelta(6), date.today()),\n canceled=False).count()\n return invoices","def test_total_invoices(self):\n sale = SaleFactory(total_value=1000)\n InvoiceFactory(sale=sale, total_value=50)\n InvoiceFactory(sale=sale, total_value=500)\n self.assertEqual(sale.total_invoices, 550)","def invoices(self):\r\n return Invoices(self)","def test_total_invoices_in_cero(self):\n sale = SaleFactory(total_value=100)\n self.assertEqual(sale.total_invoices, 0)","def _compute_count(self):\n for orders in self:\n orders.count = self.env['account.move'].search_count(\n [('invoice_origin', '=', self.name)])","def getNoOfRows(self):\n return _patchExtractor.patchExtractor_getNoOfRows(self)","def num_items(self):\n num_items = 0\n for line in self.lines.all():\n num_items += line.quantity\n return num_items","def getNumRows(self) -> int:\n ...","def getInvoice(self):\n return self.base.get(\"invoice\", [])","def get_entity_contracts_count():\n url = 'http://www.base.gov.pt/base2/rest/contratos?adjudicatariaid=%d' \\\n '&sort(-id)' % entity.base_id\n\n response = requests.get(url, headers={'Range': 'items=0-24'})\n\n results_range = response.headers['content-range']\n _, count = results_range.split('/')\n\n return int(count)","def row_count(data):\n return int(arcpy.GetCount_management(data).getOutput(0))","def get_num_rows(self, data, omit_metric=False):\n if omit_metric:\n num_rows = int((len(data.keys())-1)/4)\n else:\n num_rows = int(len(data.keys())/4)\n if len(data.keys())%4 != 0:\n num_rows += 1\n return num_rows","def invoices(self,org_id=None,invoice_id=''):\n if org_id is None:\n org_id = self.org_id\n return self.get('{}/orgs/{}/invoices/{}'.format(ApiVersion.A1.value,org_id,invoice_id))","def get_invoices(self, limit=50, closed=False, get_all=False):\n mask = \"mask[invoiceTotalAmount, itemCount]\"\n _filter = {\n 'invoices': {\n 'createDate': {\n 'operation': 'orderBy',\n 'options': [{\n 'name': 'sort',\n 'value': ['DESC']\n }]\n },\n 'statusCode': {'operation': 'OPEN'},\n }\n }\n if closed:\n del _filter['invoices']['statusCode']\n\n return self.client.call('Account', 'getInvoices', mask=mask, filter=_filter, iter=get_all, limit=limit)","def get_total_item_size(dataset):\n total_items = 0\n for element in dataset:\n total_items += 1\n return total_items","def invoices(self):\n if self.__invoices_manager is None:\n self.__invoices_manager = TaxRetunsManager(\"/invoices\", self._client)\n return self.__invoices_manager","def count_elements_in_dataset(dataset):\n return dataset.count()","def get_invoices(self, since, until):\n return self._request('getInvoices', data={\n 'date_from': since.strftime('%d/%m/%Y'),\n 'date_to': until.strftime('%d/%m/%Y')\n })","def count(self):\n return self.data_container.count","def get_data_count(self, collection):\n # Use 'data_count' attribute when available. It is created in the\n # BaseCollectionViewSet class.\n return (\n collection.data_count\n if hasattr(collection, \"data_count\")\n else collection.data.count()\n )","def data_count(self):\n return(len(self.data))","def get_num_countries():\n num_countries = np.zeros(shape=(len(annual_files), 1))\n \n for year in annual_files:\n df = get_runners_data(year)\n country_count = df['countryCode'].value_counts()\n num_countries[annual_files.index(\n year)] = len(country_count.index)\n return num_countries","def count_indications(self) -> int:\n return self._count_model(Indication)","def count_data(self):\n try:\n ndata = len(self.x)\n logger.info(\"Number of data points: {0}\".format(ndata))\n except AttributeError:\n logger.error(\"Data object has not been defined\")\n ndata = 0\n return ndata","def __len__(self):\n return int(self.total)","def prepare_count_incidents(self, object):\n roles = object.actorrole_set.all()\n return Incident.objects.filter(actors_role__in=roles).count()","def get_num_records(self):\n return self.__num_records","def count_items(self):\n count = 0\n for o in self.order_lst:\n count += o.count()\n \n return count","def get_medicines_count(visit):\r\n return visit.medicines.all().count()","def summarize_invoice(line_items):\n sku_summary = AtlasClient.summarize_invoice_items_by_sku(line_items)\n total = 0\n for item in sku_summary:\n total += sku_summary[item]['totalPriceCents']\n\n return total","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def get_total_instruments(self):\n\n total = 0\n for exchange in self.exchanges:\n total += len(exchange.symbols)\n return total","def gettingNumberVaccines(tx, query, personId):\n return tx.run(query, personId=personId).data()","def total_exs(dataset):\n total = 0\n for article in dataset['data']:\n for para in article['paragraphs']:\n total += len(para['qas'])\n return total","def get_data_ninstances(self):\n return self.data_ninstances","def dataCount(self, collectionName):\n count = collectionName.find().count()\n return count","def numnems(self):\n count = 0\n for o in self._objs.values():\n count += len(o.netifs())\n return count","def get_number_of_persons(self):\n self.__load_persons_from_file_into_memory()\n return super().get_number_of_persons()","def getNumRows(self):\n return self.__rows","def count(self):\r\n return self.data_array.size","def get_total_number_of_documents(self):\n return self.total_number_of_documents","def employees_count(self, obj):\n return obj.employees_count()","def get_contracts_count():\n url = 'http://www.base.gov.pt/base2/rest/contratos'\n\n response = requests.get(url, headers={'Range': 'items=0-24'})\n\n # should be \"items 0-%d/%d\", we want the second %d that represents the\n # total\n results_range = response.headers['content-range']\n _, count = results_range.split('/')\n\n return int(count)","def __len__(self):\r\n if self.is_superset:\r\n length = 0\r\n for ds in self.data:\r\n length += len(ds)\r\n return length\r\n else:\r\n return len(self.data)","def getRowCount(self) -> int:\n ...","def getNumberOfReviews(self):\n try:\n count = 0\n with open(self.metadata_path, \"r\", newline='') as metadata:\n mdata = csv.reader(metadata, delimiter=' ', quotechar='|')\n for review_data in mdata:\n count += 1\n return count\n except Exception:\n print(\"Cant load metadata file\")\n traceback.print_exc()","def get_count(self, denom: CashDenomination) -> int:\n if self.__open:\n raise RuntimeError(\"Cash drawer must be closed to count.\")\n return self.__contents[denom]","def fit(stat, invoices):\n total_entries = sum(invoice.total_entries for invoice in invoices.values())\n total_number = sum(invoice.total_number for invoice in invoices.values())\n\n return stat[\"загальна кількість записів\"] == total_entries and stat[\"сума кількостей\"] == total_number","def getTotalIndividualCount(self):\r\n return self._n","def get_invoice(self):\n\n # Check if unclosed invoice for the client exists\n old_inv = connection.Kinko.find_one({'cl': self.cl, 'tid': None,\n 'typ': TYPE_MAP[self.tab_type]})\n\n inv_num = None\n # If it does, update its values and update packages\n if old_inv:\n old_inv.dt = datetime.datetime.today()\n old_inv.range.lt = self.q_dict[\"cs.sd\"].get(\"$lt\", None)\n old_inv.save()\n\n inv_num = old_inv.num\n\n else:\n #kinko dict to be updated in Kinko Collection.\n kdict = {\n \"amt\": 0.0,\n \"cl\": unicode(self.cl),\n \"dt\": datetime.datetime.today(),\n \"typ\": TYPE_MAP[self.tab_type],\n \"range\": {\"lt\": self.q_dict[\"cs.sd\"].get(\"$lt\", None),\n \"gt\": self.q_dict[\"cs.sd\"].get(\"$gte\", None),\n }\n }\n\n k = Kinko(kdict)\n\n k_count = 1\n\n #the get num method of Kinko model generates the unique no for new kinko\n k[\"num\"] = self.get_knum(1)\n while connection.Kinko.collection.find({\"num\": k.num}).count() > 0:\n k[\"num\"] = self.get_knum(k_count+1)\n k_count += k_count\n\n connection.Kinko(k).save()\n\n inv_num = k['num']\n\n if inv_num:\n #after creating a new document in Kinko all packages are updated.\n connection.Package.collection.update(self.q_dict, {'$set': {'inv.num': inv_num}}, safe=True, multi=True)\n \n #Aggrigation of remitted amount for requested client\n non_invoiced = kinko_map_reduce(inv_num, TYPE_MAP[self.tab_type])\n\n if len(non_invoiced) == 0:\n return False\n else:\n inv = connection.Kinko.find_one({'num': inv_num})\n if inv:\n inv.amt = non_invoiced[0]['value']['amt']\n inv.save()\n return inv\n else:\n return False\n else:\n return False","def getNumData(self):\n return len(self.data)","def _compute_total_invoices_amount(self):\n payment_currency = self.currency_id or self.register_id.journal_id.currency_id or self.register_id.journal_id.company_id.currency_id or self.env.user.company_id.currency_id\n if self.invoice_id.company_currency_id != payment_currency:\n total = self.invoice_id.company_currency_id.with_context(date=self.register_id.payment_date).compute(self.invoice_id.residual_company_signed, payment_currency)\n else:\n total = self.invoice_id.residual_company_signed\n return abs(total)","def __len__(self):\n return sum(item['cantidad'] for item in self.carro.values())","def count(self):\n\n raise NotImplementedError","def get_total_line_counts(self):\n return get_total_line_counts(self.files.all())","def get_order_detail_count(orderid): \n data = order_obj.get_order_detail(orderid,\"1\")\n return data","def data_count(self):\r\n\r\n shp = self.df.shape\r\n row_count = shp[0]\r\n return row_count","def ListInvoices(self, **kwargs):\n return self._stub.ListInvoices(ln.ListInvoiceRequest(**kwargs))","def getDataSetCount(self):\n\t\treturn int(self.numberOfImages / self.slicesPerTimepoint)","def pdelements_total_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(5), ctypes.c_int32(0))","def size(self):\n ret = 0\n for ii in self.__data:\n ret += int(ii.get_size())\n return ret","def cpp_getInRowCount(self, patchNo, rowsInPatch):\n return _patchExtractor.patchExtractor_cpp_getInRowCount(self, patchNo, rowsInPatch)","def get_table_nb_lines(self, table):\n sql = \"SELECT COUNT(*) FROM \" + table + \";\"\n cur = self._connection.cursor()\n cur.execute(sql)\n res = cur.fetchall()\n cur.close()\n return res[0][0]","def totallines(self):\n return self._totallines","def customer_acccounting(customer_orders):","def get_all_orders_count(): \n data = order_obj.get_all_orders(\"1\")\n return data","def document_count(self):\n raise NotImplementedError","def count(self):\n\t\treturn sum(read.copy for read in self.__iter__())","def _count(self):\n if self._count_valid:\n return self._total_results\n\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\n results = result.get(\"results\", [])\n\n self._total_results = len(results)\n self._count_valid = True\n\n return self._total_results","def __len__(self):\n\n return int(self._rows)","def count_data_items(fileids, train=True):\n sizes = 28000 if train else 22500\n return len(fileids) * sizes","def size(self) -> int:\n return sum(ob.size for ob in self.objects.ravel())","def get_inventory_count(self):\n resp = self.app.get('/inventories')\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n data = json.loads(resp.data)\n return len(data)","def ingested_record_count(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"ingested_record_count\")","def numPostings(years):\n\tcount = []\n\tfor year in years:\n\t\tfilename = \"SmartEnergy\" +str(year) +\".xlsx\"\n\t\tDB = pd.read_excel(filename, sheet_name = 'Filters')\n\t\tcount.append(DB.iloc[10][1])\n\treturn count","def office_count(self):\n return self.candidate_set.values(\"office_id\").distinct().count()","def GetNumberOfElements(self, assoc):\n result = 0\n for dataset in self:\n result += dataset.GetNumberOfElements(assoc)\n return int(result)","def number_of_employees(self) -> object:\n return self._number_of_employees","def __len__(self): \r\n length = len(self.data) - 2* self.skip_window\r\n #print ('length', length)\r\n return length\r\n #raise NotImplementedError('Implement the __len__ method of the dataset')\r","def len(self, table):\n return self.get_table_nb_lines(table)","def test_data_source_soaps_id_dynamic_datas_count_get(self):\n pass","def datacounts(self):\n return self._properties[\"datacounts\"]","def _grand_total(self):\n count = 0\n for product in self.products:\n count += product.price\n return count","def _get_as_dict_count(self):\n counter = Counter()\n for product in self.products:\n counter[product.id] += 1\n return counter","def test_data_source_soaps_count_get(self):\n pass","def getNrEntries(self):\n return len(self.data)","def __len__(self):\n return sum(item[\"quantity\"] for item in self.carro.values())","def price_count(self):\n return self.price_set.count()"],"string":"[\n \"def _compute_no_of_invoices(self):\\n for record in self:\\n record.invoice_count = len(record.invoice_ids)\",\n \"def get_invl_count(self):\\n return self._df_invoice_original.index.unique().shape[0]\",\n \"def invoices(self):\\r\\n return inv.Invoices(self)\",\n \"def get_customer_count(self):\\n return self._df_invoice_original.CustomerID.unique().shape[0]\",\n \"def expired_invoices_count(self):\\n return self.get_expired_invoices().count()\",\n \"def getNumOfInvoice(self,id,start,finish):\\n self.calls += 1\\n invoice = self.getResponse(self.buildParams(id,start,finish))\\n if not self.isNumeric(invoice):\\n middle = self.diveDates(start,finish)\\n plusMiddle = middle + timedelta(days = 1)\\n middle = self.removeHours(middle)\\n plusMiddle = self.removeHours(plusMiddle)\\n invoice = self.getNumOfInvoice(id,start,middle)+\\\\\\n self.getNumOfInvoice(id,plusMiddle,finish)\\n return invoice\",\n \"def invoices(self):\\r\\n return inv.AccountInvoices(self)\",\n \"def countAndGetCallInvoice(self,id,start,finish):\\n self.calls = 0\\n return self.getNumOfInvoice(id,start,finish)\",\n \"def get_num_of_sales_per_customer_ids():\\n\\n # your code\",\n \"def invoices_in_route(self):\\n from invoicing.models import Invoice\\n invoices = Invoice.objects.filter(\\n route=self.number, print_date__range=(date.today() - timedelta(6), date.today()),\\n canceled=False).count()\\n return invoices\",\n \"def test_total_invoices(self):\\n sale = SaleFactory(total_value=1000)\\n InvoiceFactory(sale=sale, total_value=50)\\n InvoiceFactory(sale=sale, total_value=500)\\n self.assertEqual(sale.total_invoices, 550)\",\n \"def invoices(self):\\r\\n return Invoices(self)\",\n \"def test_total_invoices_in_cero(self):\\n sale = SaleFactory(total_value=100)\\n self.assertEqual(sale.total_invoices, 0)\",\n \"def _compute_count(self):\\n for orders in self:\\n orders.count = self.env['account.move'].search_count(\\n [('invoice_origin', '=', self.name)])\",\n \"def getNoOfRows(self):\\n return _patchExtractor.patchExtractor_getNoOfRows(self)\",\n \"def num_items(self):\\n num_items = 0\\n for line in self.lines.all():\\n num_items += line.quantity\\n return num_items\",\n \"def getNumRows(self) -> int:\\n ...\",\n \"def getInvoice(self):\\n return self.base.get(\\\"invoice\\\", [])\",\n \"def get_entity_contracts_count():\\n url = 'http://www.base.gov.pt/base2/rest/contratos?adjudicatariaid=%d' \\\\\\n '&sort(-id)' % entity.base_id\\n\\n response = requests.get(url, headers={'Range': 'items=0-24'})\\n\\n results_range = response.headers['content-range']\\n _, count = results_range.split('/')\\n\\n return int(count)\",\n \"def row_count(data):\\n return int(arcpy.GetCount_management(data).getOutput(0))\",\n \"def get_num_rows(self, data, omit_metric=False):\\n if omit_metric:\\n num_rows = int((len(data.keys())-1)/4)\\n else:\\n num_rows = int(len(data.keys())/4)\\n if len(data.keys())%4 != 0:\\n num_rows += 1\\n return num_rows\",\n \"def invoices(self,org_id=None,invoice_id=''):\\n if org_id is None:\\n org_id = self.org_id\\n return self.get('{}/orgs/{}/invoices/{}'.format(ApiVersion.A1.value,org_id,invoice_id))\",\n \"def get_invoices(self, limit=50, closed=False, get_all=False):\\n mask = \\\"mask[invoiceTotalAmount, itemCount]\\\"\\n _filter = {\\n 'invoices': {\\n 'createDate': {\\n 'operation': 'orderBy',\\n 'options': [{\\n 'name': 'sort',\\n 'value': ['DESC']\\n }]\\n },\\n 'statusCode': {'operation': 'OPEN'},\\n }\\n }\\n if closed:\\n del _filter['invoices']['statusCode']\\n\\n return self.client.call('Account', 'getInvoices', mask=mask, filter=_filter, iter=get_all, limit=limit)\",\n \"def get_total_item_size(dataset):\\n total_items = 0\\n for element in dataset:\\n total_items += 1\\n return total_items\",\n \"def invoices(self):\\n if self.__invoices_manager is None:\\n self.__invoices_manager = TaxRetunsManager(\\\"/invoices\\\", self._client)\\n return self.__invoices_manager\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def get_invoices(self, since, until):\\n return self._request('getInvoices', data={\\n 'date_from': since.strftime('%d/%m/%Y'),\\n 'date_to': until.strftime('%d/%m/%Y')\\n })\",\n \"def count(self):\\n return self.data_container.count\",\n \"def get_data_count(self, collection):\\n # Use 'data_count' attribute when available. It is created in the\\n # BaseCollectionViewSet class.\\n return (\\n collection.data_count\\n if hasattr(collection, \\\"data_count\\\")\\n else collection.data.count()\\n )\",\n \"def data_count(self):\\n return(len(self.data))\",\n \"def get_num_countries():\\n num_countries = np.zeros(shape=(len(annual_files), 1))\\n \\n for year in annual_files:\\n df = get_runners_data(year)\\n country_count = df['countryCode'].value_counts()\\n num_countries[annual_files.index(\\n year)] = len(country_count.index)\\n return num_countries\",\n \"def count_indications(self) -> int:\\n return self._count_model(Indication)\",\n \"def count_data(self):\\n try:\\n ndata = len(self.x)\\n logger.info(\\\"Number of data points: {0}\\\".format(ndata))\\n except AttributeError:\\n logger.error(\\\"Data object has not been defined\\\")\\n ndata = 0\\n return ndata\",\n \"def __len__(self):\\n return int(self.total)\",\n \"def prepare_count_incidents(self, object):\\n roles = object.actorrole_set.all()\\n return Incident.objects.filter(actors_role__in=roles).count()\",\n \"def get_num_records(self):\\n return self.__num_records\",\n \"def count_items(self):\\n count = 0\\n for o in self.order_lst:\\n count += o.count()\\n \\n return count\",\n \"def get_medicines_count(visit):\\r\\n return visit.medicines.all().count()\",\n \"def summarize_invoice(line_items):\\n sku_summary = AtlasClient.summarize_invoice_items_by_sku(line_items)\\n total = 0\\n for item in sku_summary:\\n total += sku_summary[item]['totalPriceCents']\\n\\n return total\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def get_total_instruments(self):\\n\\n total = 0\\n for exchange in self.exchanges:\\n total += len(exchange.symbols)\\n return total\",\n \"def gettingNumberVaccines(tx, query, personId):\\n return tx.run(query, personId=personId).data()\",\n \"def total_exs(dataset):\\n total = 0\\n for article in dataset['data']:\\n for para in article['paragraphs']:\\n total += len(para['qas'])\\n return total\",\n \"def get_data_ninstances(self):\\n return self.data_ninstances\",\n \"def dataCount(self, collectionName):\\n count = collectionName.find().count()\\n return count\",\n \"def numnems(self):\\n count = 0\\n for o in self._objs.values():\\n count += len(o.netifs())\\n return count\",\n \"def get_number_of_persons(self):\\n self.__load_persons_from_file_into_memory()\\n return super().get_number_of_persons()\",\n \"def getNumRows(self):\\n return self.__rows\",\n \"def count(self):\\r\\n return self.data_array.size\",\n \"def get_total_number_of_documents(self):\\n return self.total_number_of_documents\",\n \"def employees_count(self, obj):\\n return obj.employees_count()\",\n \"def get_contracts_count():\\n url = 'http://www.base.gov.pt/base2/rest/contratos'\\n\\n response = requests.get(url, headers={'Range': 'items=0-24'})\\n\\n # should be \\\"items 0-%d/%d\\\", we want the second %d that represents the\\n # total\\n results_range = response.headers['content-range']\\n _, count = results_range.split('/')\\n\\n return int(count)\",\n \"def __len__(self):\\r\\n if self.is_superset:\\r\\n length = 0\\r\\n for ds in self.data:\\r\\n length += len(ds)\\r\\n return length\\r\\n else:\\r\\n return len(self.data)\",\n \"def getRowCount(self) -> int:\\n ...\",\n \"def getNumberOfReviews(self):\\n try:\\n count = 0\\n with open(self.metadata_path, \\\"r\\\", newline='') as metadata:\\n mdata = csv.reader(metadata, delimiter=' ', quotechar='|')\\n for review_data in mdata:\\n count += 1\\n return count\\n except Exception:\\n print(\\\"Cant load metadata file\\\")\\n traceback.print_exc()\",\n \"def get_count(self, denom: CashDenomination) -> int:\\n if self.__open:\\n raise RuntimeError(\\\"Cash drawer must be closed to count.\\\")\\n return self.__contents[denom]\",\n \"def fit(stat, invoices):\\n total_entries = sum(invoice.total_entries for invoice in invoices.values())\\n total_number = sum(invoice.total_number for invoice in invoices.values())\\n\\n return stat[\\\"загальна кількість записів\\\"] == total_entries and stat[\\\"сума кількостей\\\"] == total_number\",\n \"def getTotalIndividualCount(self):\\r\\n return self._n\",\n \"def get_invoice(self):\\n\\n # Check if unclosed invoice for the client exists\\n old_inv = connection.Kinko.find_one({'cl': self.cl, 'tid': None,\\n 'typ': TYPE_MAP[self.tab_type]})\\n\\n inv_num = None\\n # If it does, update its values and update packages\\n if old_inv:\\n old_inv.dt = datetime.datetime.today()\\n old_inv.range.lt = self.q_dict[\\\"cs.sd\\\"].get(\\\"$lt\\\", None)\\n old_inv.save()\\n\\n inv_num = old_inv.num\\n\\n else:\\n #kinko dict to be updated in Kinko Collection.\\n kdict = {\\n \\\"amt\\\": 0.0,\\n \\\"cl\\\": unicode(self.cl),\\n \\\"dt\\\": datetime.datetime.today(),\\n \\\"typ\\\": TYPE_MAP[self.tab_type],\\n \\\"range\\\": {\\\"lt\\\": self.q_dict[\\\"cs.sd\\\"].get(\\\"$lt\\\", None),\\n \\\"gt\\\": self.q_dict[\\\"cs.sd\\\"].get(\\\"$gte\\\", None),\\n }\\n }\\n\\n k = Kinko(kdict)\\n\\n k_count = 1\\n\\n #the get num method of Kinko model generates the unique no for new kinko\\n k[\\\"num\\\"] = self.get_knum(1)\\n while connection.Kinko.collection.find({\\\"num\\\": k.num}).count() > 0:\\n k[\\\"num\\\"] = self.get_knum(k_count+1)\\n k_count += k_count\\n\\n connection.Kinko(k).save()\\n\\n inv_num = k['num']\\n\\n if inv_num:\\n #after creating a new document in Kinko all packages are updated.\\n connection.Package.collection.update(self.q_dict, {'$set': {'inv.num': inv_num}}, safe=True, multi=True)\\n \\n #Aggrigation of remitted amount for requested client\\n non_invoiced = kinko_map_reduce(inv_num, TYPE_MAP[self.tab_type])\\n\\n if len(non_invoiced) == 0:\\n return False\\n else:\\n inv = connection.Kinko.find_one({'num': inv_num})\\n if inv:\\n inv.amt = non_invoiced[0]['value']['amt']\\n inv.save()\\n return inv\\n else:\\n return False\\n else:\\n return False\",\n \"def getNumData(self):\\n return len(self.data)\",\n \"def _compute_total_invoices_amount(self):\\n payment_currency = self.currency_id or self.register_id.journal_id.currency_id or self.register_id.journal_id.company_id.currency_id or self.env.user.company_id.currency_id\\n if self.invoice_id.company_currency_id != payment_currency:\\n total = self.invoice_id.company_currency_id.with_context(date=self.register_id.payment_date).compute(self.invoice_id.residual_company_signed, payment_currency)\\n else:\\n total = self.invoice_id.residual_company_signed\\n return abs(total)\",\n \"def __len__(self):\\n return sum(item['cantidad'] for item in self.carro.values())\",\n \"def count(self):\\n\\n raise NotImplementedError\",\n \"def get_total_line_counts(self):\\n return get_total_line_counts(self.files.all())\",\n \"def get_order_detail_count(orderid): \\n data = order_obj.get_order_detail(orderid,\\\"1\\\")\\n return data\",\n \"def data_count(self):\\r\\n\\r\\n shp = self.df.shape\\r\\n row_count = shp[0]\\r\\n return row_count\",\n \"def ListInvoices(self, **kwargs):\\n return self._stub.ListInvoices(ln.ListInvoiceRequest(**kwargs))\",\n \"def getDataSetCount(self):\\n\\t\\treturn int(self.numberOfImages / self.slicesPerTimepoint)\",\n \"def pdelements_total_customers(self) -> int:\\n return self.dss_obj.PDElementsI(ctypes.c_int32(5), ctypes.c_int32(0))\",\n \"def size(self):\\n ret = 0\\n for ii in self.__data:\\n ret += int(ii.get_size())\\n return ret\",\n \"def cpp_getInRowCount(self, patchNo, rowsInPatch):\\n return _patchExtractor.patchExtractor_cpp_getInRowCount(self, patchNo, rowsInPatch)\",\n \"def get_table_nb_lines(self, table):\\n sql = \\\"SELECT COUNT(*) FROM \\\" + table + \\\";\\\"\\n cur = self._connection.cursor()\\n cur.execute(sql)\\n res = cur.fetchall()\\n cur.close()\\n return res[0][0]\",\n \"def totallines(self):\\n return self._totallines\",\n \"def customer_acccounting(customer_orders):\",\n \"def get_all_orders_count(): \\n data = order_obj.get_all_orders(\\\"1\\\")\\n return data\",\n \"def document_count(self):\\n raise NotImplementedError\",\n \"def count(self):\\n\\t\\treturn sum(read.copy for read in self.__iter__())\",\n \"def _count(self):\\n if self._count_valid:\\n return self._total_results\\n\\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\\n results = result.get(\\\"results\\\", [])\\n\\n self._total_results = len(results)\\n self._count_valid = True\\n\\n return self._total_results\",\n \"def __len__(self):\\n\\n return int(self._rows)\",\n \"def count_data_items(fileids, train=True):\\n sizes = 28000 if train else 22500\\n return len(fileids) * sizes\",\n \"def size(self) -> int:\\n return sum(ob.size for ob in self.objects.ravel())\",\n \"def get_inventory_count(self):\\n resp = self.app.get('/inventories')\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n data = json.loads(resp.data)\\n return len(data)\",\n \"def ingested_record_count(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"ingested_record_count\\\")\",\n \"def numPostings(years):\\n\\tcount = []\\n\\tfor year in years:\\n\\t\\tfilename = \\\"SmartEnergy\\\" +str(year) +\\\".xlsx\\\"\\n\\t\\tDB = pd.read_excel(filename, sheet_name = 'Filters')\\n\\t\\tcount.append(DB.iloc[10][1])\\n\\treturn count\",\n \"def office_count(self):\\n return self.candidate_set.values(\\\"office_id\\\").distinct().count()\",\n \"def GetNumberOfElements(self, assoc):\\n result = 0\\n for dataset in self:\\n result += dataset.GetNumberOfElements(assoc)\\n return int(result)\",\n \"def number_of_employees(self) -> object:\\n return self._number_of_employees\",\n \"def __len__(self): \\r\\n length = len(self.data) - 2* self.skip_window\\r\\n #print ('length', length)\\r\\n return length\\r\\n #raise NotImplementedError('Implement the __len__ method of the dataset')\\r\",\n \"def len(self, table):\\n return self.get_table_nb_lines(table)\",\n \"def test_data_source_soaps_id_dynamic_datas_count_get(self):\\n pass\",\n \"def datacounts(self):\\n return self._properties[\\\"datacounts\\\"]\",\n \"def _grand_total(self):\\n count = 0\\n for product in self.products:\\n count += product.price\\n return count\",\n \"def _get_as_dict_count(self):\\n counter = Counter()\\n for product in self.products:\\n counter[product.id] += 1\\n return counter\",\n \"def test_data_source_soaps_count_get(self):\\n pass\",\n \"def getNrEntries(self):\\n return len(self.data)\",\n \"def __len__(self):\\n return sum(item[\\\"quantity\\\"] for item in self.carro.values())\",\n \"def price_count(self):\\n return self.price_set.count()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7744239","0.73818606","0.63354856","0.6231307","0.62079424","0.6154787","0.6135494","0.6101816","0.606694","0.6054052","0.60302943","0.5938585","0.5850497","0.58089167","0.57880515","0.56934583","0.568253","0.56357646","0.55606055","0.55459076","0.54968005","0.5485106","0.5460834","0.54239196","0.5421888","0.54191077","0.5399579","0.5378061","0.5360827","0.53525543","0.5338459","0.5330316","0.5321197","0.5318589","0.5308966","0.530786","0.5287273","0.5272226","0.5268587","0.52672607","0.52672607","0.52672607","0.52672607","0.5266757","0.5259575","0.5243879","0.5241024","0.52335805","0.52264345","0.5222952","0.5210757","0.52049506","0.5203487","0.5190994","0.51863927","0.518001","0.5174888","0.5173043","0.5160422","0.5160228","0.5149745","0.51456","0.51399404","0.51374507","0.51307994","0.51275206","0.5119661","0.5118743","0.5116079","0.5110573","0.51034147","0.51033324","0.5101613","0.5095857","0.50953174","0.50916654","0.50865495","0.50830024","0.50828624","0.50781906","0.5077424","0.50771844","0.5074266","0.5065841","0.5055209","0.5051975","0.50486887","0.5045918","0.50433725","0.50429696","0.50414944","0.50311434","0.50276893","0.5025031","0.5021916","0.5020568","0.5020076","0.50197095","0.5019685","0.50187796"],"string":"[\n \"0.7744239\",\n \"0.73818606\",\n \"0.63354856\",\n \"0.6231307\",\n \"0.62079424\",\n \"0.6154787\",\n \"0.6135494\",\n \"0.6101816\",\n \"0.606694\",\n \"0.6054052\",\n \"0.60302943\",\n \"0.5938585\",\n \"0.5850497\",\n \"0.58089167\",\n \"0.57880515\",\n \"0.56934583\",\n \"0.568253\",\n \"0.56357646\",\n \"0.55606055\",\n \"0.55459076\",\n \"0.54968005\",\n \"0.5485106\",\n \"0.5460834\",\n \"0.54239196\",\n \"0.5421888\",\n \"0.54191077\",\n \"0.5399579\",\n \"0.5378061\",\n \"0.5360827\",\n \"0.53525543\",\n \"0.5338459\",\n \"0.5330316\",\n \"0.5321197\",\n \"0.5318589\",\n \"0.5308966\",\n \"0.530786\",\n \"0.5287273\",\n \"0.5272226\",\n \"0.5268587\",\n \"0.52672607\",\n \"0.52672607\",\n \"0.52672607\",\n \"0.52672607\",\n \"0.5266757\",\n \"0.5259575\",\n \"0.5243879\",\n \"0.5241024\",\n \"0.52335805\",\n \"0.52264345\",\n \"0.5222952\",\n \"0.5210757\",\n \"0.52049506\",\n \"0.5203487\",\n \"0.5190994\",\n \"0.51863927\",\n \"0.518001\",\n \"0.5174888\",\n \"0.5173043\",\n \"0.5160422\",\n \"0.5160228\",\n \"0.5149745\",\n \"0.51456\",\n \"0.51399404\",\n \"0.51374507\",\n \"0.51307994\",\n \"0.51275206\",\n \"0.5119661\",\n \"0.5118743\",\n \"0.5116079\",\n \"0.5110573\",\n \"0.51034147\",\n \"0.51033324\",\n \"0.5101613\",\n \"0.5095857\",\n \"0.50953174\",\n \"0.50916654\",\n \"0.50865495\",\n \"0.50830024\",\n \"0.50828624\",\n \"0.50781906\",\n \"0.5077424\",\n \"0.50771844\",\n \"0.5074266\",\n \"0.5065841\",\n \"0.5055209\",\n \"0.5051975\",\n \"0.50486887\",\n \"0.5045918\",\n \"0.50433725\",\n \"0.50429696\",\n \"0.50414944\",\n \"0.50311434\",\n \"0.50276893\",\n \"0.5025031\",\n \"0.5021916\",\n \"0.5020568\",\n \"0.5020076\",\n \"0.50197095\",\n \"0.5019685\",\n \"0.50187796\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7667302"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":1896,"cells":{"query":{"kind":"string","value":"Returns number of customers from original dataset."},"document":{"kind":"string","value":"def get_customer_count(self):\n return self._df_invoice_original.CustomerID.unique().shape[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 get_num_of_sales_per_customer_ids():\n\n # your code","def get_all_customers_count():\n data = user_obj.get_all_customers(\"1\")\n return data","def customer_acccounting(customer_orders):","def pdelements_num_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(4), ctypes.c_int32(0))","def pdelements_total_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(5), ctypes.c_int32(0))","def get_customer_orders_count(customerId):\n data = user_obj.get_customer_orders(customerId,\"1\")\n return data","def bus_total_customers(self) -> int:\n return self.dss_obj.BUSI(4, 0)","def get_total_trans(all_customers_data, trans_column):\n return all_customers_data.select(trans_column).distinct().count()","def list_active_customers():\n try:\n active_customer_count = 0\n for _ in Customer.select().where(Customer.status == 'Active'):\n active_customer_count += 1\n logger.info(\n f\"Successfully counted active customers {active_customer_count}\"\n )\n return active_customer_count\n except Exception as unknown_error:\n logger.error(f\"Error. Failed to count customers. {unknown_error}\")\n print(\n f'Error. Not able to count number of active customers.'\n ' {unknown_error}'\n )","def list_active_customers():\n try:\n active_customer_count = 0\n for _ in Customer.select().where(Customer.status == 'Active'):\n active_customer_count += 1\n return active_customer_count\n except Exception as unknown_error:\n print(f'Error. Not able to count number of active customers. {unknown_error}')","def get_invoice_count(self):\n return self._df_invoice_original.InvoiceNo.unique().shape[0]","def list_active_customers():\n db_customers = Customers.select()\n LOGGER.debug(\"Calculating number of active customers\")\n # Technically used this in Lesson 03, but it is a comprehension. Another method added below.\n number_active = sum([int(x.status) for x in db_customers])\n LOGGER.info(\"There are %d active customers\", number_active)\n\n return number_active","def bus_interruptions_total_customers(self) -> float:\n return self.dss_obj.BUSF(9, 0)","def bottom_twenty_customers(data_frame):\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\n * data_frame['quantity_rented']\n * data_frame['rental_period_months'])\n data_set = data_frame.groupby(['customer_id']).agg({'total_payment': 'sum'})\n data_set = data_set.nsmallest(20, 'total_payment')\n return data_set","def cluster_obs_count(self):\n return(self.merged_data.groupby(\n 'labels').count().transpose().iloc[0, :])","def count_elements_in_dataset(dataset):\n return dataset.count()","def get_invl_count(self):\n return self._df_invoice_original.index.unique().shape[0]","def top_ten_customers(data_frame):\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\n * data_frame['quantity_rented']\n * data_frame['rental_period_months'])\n data_set = data_frame.groupby(['customer_id']).agg({'total_payment': 'sum'})\n data_set = data_set.nlargest(10, 'total_payment')\n return data_set","def data_center_count(self) -> int:\n return pulumi.get(self, \"data_center_count\")","def list_active_customers():\n active_customers = 0\n for customer in cm.Customers:\n if customer.status == \"Active \":\n active_customers += 1\n return active_customers","def all_client_number():\n\n url = CMX_URL + '/api/location/v2/clients/count'\n header = {'content-type': 'application/json', 'accept': 'application/json'}\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\n response_json = response.json()\n clients_number = response_json['count']\n return clients_number","def count_target_class_data(data, target_class):\n count = 0\n for row in data:\n if row[0] == target_class:\n count += 1\n\n return count","def data_count(self):\r\n\r\n shp = self.df.shape\r\n row_count = shp[0]\r\n return row_count","def get_num_countries():\n num_countries = np.zeros(shape=(len(annual_files), 1))\n \n for year in annual_files:\n df = get_runners_data(year)\n country_count = df['countryCode'].value_counts()\n num_countries[annual_files.index(\n year)] = len(country_count.index)\n return num_countries","def get_data_count(self, collection):\n # Use 'data_count' attribute when available. It is created in the\n # BaseCollectionViewSet class.\n return (\n collection.data_count\n if hasattr(collection, \"data_count\")\n else collection.data.count()\n )","def list_active_customers():\n with database.transaction():\n query = (Customer\n .select(fn.COUNT(Customer.status).alias('count'))\n .where(Customer.status == 'Active'))\n LOGGER.info(query)\n\n customer_count = [item.count for item in query]\n LOGGER.info('Number of active customers: %s', customer_count[0])\n\n return customer_count[0]","def list_active_customers():\n count_query = (Customer\n .select(Customer, fn.COUNT(Customer.name)\n .alias('cust_count'))\n .where(Customer.status == 'active'))\n for count in count_query:\n return count.cust_count","def carn_count(self):\n return len(self.carnivores)","def coauthor_count(self):\n return self._json.get('coauthor-count', '0')","def get_count(cls):\n total = 0\n for counter in SimpleCounterShard.objects.all():\n total += counter.count\n return total","def get_all_customer_ids():\n table = data_manager.get_table_from_file(\"sales/sales.csv\")\n return get_all_customer_ids_from_table(table)","def count(self):\n return self.data_container.count","def row_count(data):\n return int(arcpy.GetCount_management(data).getOutput(0))","def count_data(self):\n try:\n ndata = len(self.x)\n logger.info(\"Number of data points: {0}\".format(ndata))\n except AttributeError:\n logger.error(\"Data object has not been defined\")\n ndata = 0\n return ndata","def createCustomerID(self):\n\n customerID = self._df_invoice_original.CustomerID.max()\n customerID += 1\n return int(customerID)","def count_codon(self, codon):\n return sum([1 for c in self if c == codon])","def list_active_customers():\n customer_active = Customer.select().where(Customer.status == 'Active')\n print('{} Active Customers'.format(len(customer_active)))\n return len(customer_active)","def dataCountBy(self, collectionName, catagory, data):\n count = collectionName.find({catagory: data}).count()\n return count","def data_count(self):\n return(len(self.data))","def getDataCount(self, filter: t.Mapping[t.Text, t.Any] = {}\n ) -> DatasetCount:\n aggregate_data = self.getAggregateData(\n pipeline={\"count\": {\"$sum\": 1}},\n filter=filter,\n )\n count = first(aggregate_data.data)[\"count\"]\n return DatasetCount(count=count)","def count_codon_all(self):\n return Counter(list(self))","def _count_parties(data_set): #DEMOCRATS, THEN REPUBLICANS\r\n reps = 0\r\n dems = 0\r\n for data_point in data_set:\r\n if data_point.dat_party == \"R\": reps+=1\r\n if data_point.dat_party == \"D\": dems+=1\r\n\r\n return (dems, reps)","def getNumberOfReviews(self):\n try:\n count = 0\n with open(self.metadata_path, \"r\", newline='') as metadata:\n mdata = csv.reader(metadata, delimiter=' ', quotechar='|')\n for review_data in mdata:\n count += 1\n return count\n except Exception:\n print(\"Cant load metadata file\")\n traceback.print_exc()","def n_points(self):\n\n if self.data_reduced:\n return len(self.data_reduced[0])\n else:\n return 0","def get_count(username):\n return get_contributor(username)[\"count\"]","def get_all_customers():\n data = user_obj.get_all_customers()\n return data","def features_customers(df_customers):\n for i in PREMIER_VALS:\n k = 'premier_' + str(i)\n df_customers[k] = np.where(df_customers['premier'] == i, 1, 0)\n\n df_customers['age'] = datetime.now().date().year - df_customers['yearOfBirth']\n df_customers['male'] = np.where(df_customers['gender'] == 'M', 1, 0)\n df_customers['female'] = np.where(df_customers['gender'] == 'F', 1, 0)\n df_customers['days_in_asos'] = (datetime.now().date() - df_customers['dateCreated']).dt.days\n\n logger.info(\"Features from the customers table: {shape} {dtypes}\"\n .format(shape=df_customers.shape, dtypes=df_customers.dtypes))\n return df_customers","def dataCount(self, collectionName):\n count = collectionName.find().count()\n return count","def top_five_customers(data_frame):\n data_set = data_frame.groupby(['customer_id']).agg({'quantity_rented': 'sum'})\n data_set = data_set.nlargest(5, 'quantity_rented')\n return data_set","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def Count(self) -> int:","def getCustomersInfo(self):\n rval = None\n tries = 0\n while not rval and tries < 5:\n if tries > 0:\n time.sleep(30)\n rval = self._getService(self.ns_customer,\n self.soapCustomer % (self.ns_customer, \"\",\n \"100\", \"Advertiser\"),\n \"GetCustomersInfo\", '', '',\n self.cwsdl, self.chost)\n tries += 1\n return rval","def reducer(self, customer, visit_counts):\n total =0\n total = sum(i for i in visit_counts)\n yield customer,total","def get_all_customer_ids():\n\n # your code","def test_data_when_import_customer_with_data(self):\n\n customer = self.customers[0]\n self.assertEqual(\"Jimena\", customer.get_first_name())\n self.assertEqual(\"Sanabria\", customer.get_last_name())\n self.assertEqual(\"21-08-1980\", customer.get_date_of_birth())\n self.assertEqual([\"Nueva Granada #1837\"], customer.get_addresses())\n self.assertEqual([4244270,70759942], customer.get_phones())\n self.assertEqual(\"giovi_times@hotmail.com\", customer.get_email())\n self.assertEqual(\"Gold\", customer.get_membership())\n self.assertEqual(\"Active\", customer.get_status())","def number_of_crew(self):\n return self._number_of_crew","def count(self):\r\n return self.data_array.size","def get_total_item_size(dataset):\n total_items = 0\n for element in dataset:\n total_items += 1\n return total_items","def get_customer_info(self, customer_id):\n #requested_customer_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))\n # Get predicted ratings\n customers = self.__get_customers(customer_id).collect()\n\n return customers","def get_num_countries(self):\n return len(self.countries)","def import_customers(input_data):\n error_count = 0\n insert_count = 0\n LOGGER.info('Starting Customer import')\n for onecust in input_data:\n try:\n Customer(onecust['user_id'], onecust['first_name'], onecust['last_name'],\n onecust['address'], onecust['phone_number'], onecust['email'])\\\n .save(full_clean=True, force_insert=True)\n insert_count += 1\n except ValidationError as valerror:\n LOGGER.exception(\"Error importing data from csv: %s \", valerror.message)\n error_count += 1\n except (OperationError, DuplicateKeyError) as operror:\n LOGGER.exception(\"Error importing data from csv: %s \", operror)\n error_count += 1\n\n return insert_count, error_count","def getNumRows(self) -> int:\n ...","def count():","def test_get_customer_list(self):\n customer = self._create_customers(\"Alex\")\n customer.create()\n customer = self._create_customers(\"Sally\")\n customer.create()\n customer = self._create_customers(\"John\")\n customer.create()\n resp = self.app.get(\"/customers\")\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n data = resp.get_json()\n self.assertEqual(len(data), 3)","def _compute_count(self):\n for orders in self:\n orders.count = self.env['account.move'].search_count(\n [('invoice_origin', '=', self.name)])","def test_addsNewCustomersFromAPIs(self):\n DataManagerUnitTest.dm.reload()\n result = DataManagerUnitTest.dm.onlineStoreDatabase.getCustomers()\n customers = set([key['user']['email'] for key in DataManagerUnitTest.dm.getAllOrders()])\n self.assertEqual(len(customers), len(result))","def count_records(batches: List[Batch]) -> int:\n return sum(b.current_size for b in batches)","def _compute_no_of_invoices(self):\n for record in self:\n record.invoice_count = len(record.invoice_ids)","def number_of_trips(filename): \r\n \r\n with open(filename, 'r') as f_in:\r\n # set up csv reader object\r\n trip_reader = csv.DictReader(f_in)\r\n \r\n # initialize count variables\r\n n_subscribers = 0\r\n n_customers = 0\r\n \r\n # tally up ride types\r\n for row in trip_reader:\r\n if row['user_type'] == 'Subscriber':\r\n n_subscribers += 1\r\n else:\r\n n_customers += 1\r\n \r\n # compute total number of rides\r\n n_total = n_subscribers + n_customers\r\n \r\n # return tallies as a tuple\r\n return(n_subscribers, n_customers, n_total)","def N(self):\n return len(self.cavity_grid.cavities) + 1","def count_subscribers(self):\n return self.request(\"count:Contact\", [ None ])","def get_seated_customers(restaurant: Restaurant):\n customers = Reservation.query.filter(Reservation.restaurant == restaurant).filter(Reservation.status == ReservationState.SEATED).all()\n \n return reduce(lambda acc,rsv: acc + rsv.seats, customers, 0)","def num_carns(self):\n return self._num_carns","def customers(self):\r\n return customers.Customers(self)","def count_comentions_csv(csv_reader):\n header = next(csv_reader)\n index_of = {col: index for index, col in enumerate(header)}\n comention_counter = Counter()\n for line in csv_reader:\n body = line[index_of['body']]\n for isbn1, isbn2 in get_comentions(body):\n comention_counter[(isbn1, isbn2)] += 1\n return comention_counter","def customers(self):\r\n return Customers(self)","def total_demand(self) -> float:\n return self.inputs.num_customers * self.operations.m3_per_customer()","def _get_cus_info(self):\n label_enc = LabelEncoder()\n customer_info = self._inv.drop_duplicates(['customer_code'], keep='last')\n customer_info = customer_info[['customer_code', 'customer_name', 'sales_cen_code',\n 'sales_cen_name', 'sales_region_name', 'province',\n 'city', 'district', 'customer_type', 'is_usable', 'channel_level']]\n customer_info['customer_id'] = label_enc.fit_transform(customer_info['customer_code'])\n customer_info['sales_cen_id'] = label_enc.fit_transform(customer_info['sales_cen_code'])\n customer_info['sales_region_id'] = label_enc.fit_transform(customer_info['sales_region_name'])\n customer_info['province_id'] = label_enc.fit_transform(customer_info['province'])\n customer_info['city_id'] = label_enc.fit_transform(customer_info['city'])\n customer_info['district_id'] = label_enc.fit_transform(customer_info['district'])\n customer_info['customer_type'] = label_enc.fit_transform(customer_info['customer_type'])\n customer_info['is_usable'] = label_enc.fit_transform(customer_info['is_usable'])\n customer_info['channel_level'] = label_enc.fit_transform(customer_info['channel_level'])\n customer_info_encoded = customer_info.drop(\n columns=['customer_name', 'sales_cen_code', 'sales_cen_name',\n 'sales_region_name', 'province', 'city', 'district']\n ).set_index('customer_code')\n customer_info.set_index('customer_code', inplace=True)\n customer_info_encoded = customer_info_encoded.reindex(self._index.get_level_values(0))\n return customer_info, customer_info_encoded","def getNoOfRows(self):\n return _patchExtractor.patchExtractor_getNoOfRows(self)","def citation_count(self):\n return self._json['coredata'].get('citation-count', '0')","def get_num_datasets(self, data):\n dsets = set()\n for items in data:\n dsetid = items[3]\n dsets.add(dsetid)\n return len(dsets)","def generate_customer_info(self):\n consecutive = check_consecutive(self.customerIds)\n print(\"consecutive\" + str(consecutive))\n if consecutive:\n for customer_id in self.customerIds:\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\n insured = np.random.choice(\n self.distributions[\"insurance_status\"],\n 1,\n p=self.distributions[\"insurance_distribution\"],\n )[0]\n dob = self.dobs[customer_id - 1]\n customer = pd.DataFrame(\n {\n \"customer_id\": [customer_id],\n \"dob\": str(dob),\n \"insurance\": insured,\n \"experiment_id\": random.randint(1, 2),\n }\n )\n self.Customers = self.Customers.append(customer)\n else:\n print(\"Error generating customer info: customerIds aren't consecutive\")\n return True","def test_get_customers(self):\n get_customers_url = reverse(\"customer_list\")\n response = self.client.get(get_customers_url)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n # get data from db\n customers = Customer.objects.all()\n serializer = CustomerSerializer(customers, many=True)\n self.assertEqual(response.data, serializer.data)\n\n self.assertEqual(len(response.data), 4)","def get_entity_contracts_count():\n url = 'http://www.base.gov.pt/base2/rest/contratos?adjudicatariaid=%d' \\\n '&sort(-id)' % entity.base_id\n\n response = requests.get(url, headers={'Range': 'items=0-24'})\n\n results_range = response.headers['content-range']\n _, count = results_range.split('/')\n\n return int(count)","def customer_id(uncapped_flatprice, uncapped_flatprice_finalizer, team_multisig) -> int:\n customer_id = int(uuid.uuid4().hex, 16) # Customer ids are 128-bit UUID v4\n return customer_id","def get_listCustomer_out_sample(self, customerCount=10):\n \n if customerCount is None :\n listCustomer= list(self._df_invoice_line_out_sample.CustomerID.unique())\n else:\n if customerCount <= 0 :\n listCustomer \\\n = list(self._df_invoice_line_out_sample.CustomerID.unique())\n else:\n listCustomer \\\n = list(self._df_invoice_line_out_sample.CustomerID.unique()[:customerCount])\n return listCustomer","def prepare_count_incidents(self, object):\n roles = object.actorrole_set.all()\n return Incident.objects.filter(actors_role__in=roles).count()","def count_children(self, element_name, customers):\n count = 0\n childlist = [customer for customer in customers if customer.get('parent') == element_name]\n count = len(childlist)\n # Call the function recursively for each child in the list (Customer group childs and descendents)\n for child in childlist:\n count = count + CustomerGroupsPage.count_children(self, child.get('name'), customers)\n return count","def getCustomer(self):\n return self.base.get(\"customer\", [])","def count(self):\n return self._reduce_for_stat_function(F.count, only_numeric=False)","def list_active_customer():\n active_customer = Customer.select().where(Customer.is_active).count()\n LOGGER.info('Number of active customers retrieved.')\n return active_customer","def get_num_records(self):\n return self.__num_records","def get_sum_of_sales_per_customer_from_table(table):\n summed_sales_per_customer = {}\n for customer in {line[CUSTOMER_ID] for line in table}:\n sum_of_sales = common.szum_list([line[PRICE] for line in table if line[CUSTOMER_ID] == customer])\n summed_sales_per_customer[customer] = sum_of_sales\n return summed_sales_per_customer","def count(self):\n\n if self.cluster:\n return self.cluster.count()\n\n return super().count()","def get_country_count():\n numbers=country_populations.split('\\n')\n count_numbers= len(numbers)-1\n return count_numbers","def cluster_count(self) -> int:\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\n return self.distribution.scale_cluster_count(cluster_count)","def getTotalIndividualCount(self):\r\n return self._n","def get_total_dim(client_template):\n return sum(np.prod(x.shape) for x in client_template)"],"string":"[\n \"def get_num_of_sales_per_customer_ids():\\n\\n # your code\",\n \"def get_all_customers_count():\\n data = user_obj.get_all_customers(\\\"1\\\")\\n return data\",\n \"def customer_acccounting(customer_orders):\",\n \"def pdelements_num_customers(self) -> int:\\n return self.dss_obj.PDElementsI(ctypes.c_int32(4), ctypes.c_int32(0))\",\n \"def pdelements_total_customers(self) -> int:\\n return self.dss_obj.PDElementsI(ctypes.c_int32(5), ctypes.c_int32(0))\",\n \"def get_customer_orders_count(customerId):\\n data = user_obj.get_customer_orders(customerId,\\\"1\\\")\\n return data\",\n \"def bus_total_customers(self) -> int:\\n return self.dss_obj.BUSI(4, 0)\",\n \"def get_total_trans(all_customers_data, trans_column):\\n return all_customers_data.select(trans_column).distinct().count()\",\n \"def list_active_customers():\\n try:\\n active_customer_count = 0\\n for _ in Customer.select().where(Customer.status == 'Active'):\\n active_customer_count += 1\\n logger.info(\\n f\\\"Successfully counted active customers {active_customer_count}\\\"\\n )\\n return active_customer_count\\n except Exception as unknown_error:\\n logger.error(f\\\"Error. Failed to count customers. {unknown_error}\\\")\\n print(\\n f'Error. Not able to count number of active customers.'\\n ' {unknown_error}'\\n )\",\n \"def list_active_customers():\\n try:\\n active_customer_count = 0\\n for _ in Customer.select().where(Customer.status == 'Active'):\\n active_customer_count += 1\\n return active_customer_count\\n except Exception as unknown_error:\\n print(f'Error. Not able to count number of active customers. {unknown_error}')\",\n \"def get_invoice_count(self):\\n return self._df_invoice_original.InvoiceNo.unique().shape[0]\",\n \"def list_active_customers():\\n db_customers = Customers.select()\\n LOGGER.debug(\\\"Calculating number of active customers\\\")\\n # Technically used this in Lesson 03, but it is a comprehension. Another method added below.\\n number_active = sum([int(x.status) for x in db_customers])\\n LOGGER.info(\\\"There are %d active customers\\\", number_active)\\n\\n return number_active\",\n \"def bus_interruptions_total_customers(self) -> float:\\n return self.dss_obj.BUSF(9, 0)\",\n \"def bottom_twenty_customers(data_frame):\\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\\n * data_frame['quantity_rented']\\n * data_frame['rental_period_months'])\\n data_set = data_frame.groupby(['customer_id']).agg({'total_payment': 'sum'})\\n data_set = data_set.nsmallest(20, 'total_payment')\\n return data_set\",\n \"def cluster_obs_count(self):\\n return(self.merged_data.groupby(\\n 'labels').count().transpose().iloc[0, :])\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def get_invl_count(self):\\n return self._df_invoice_original.index.unique().shape[0]\",\n \"def top_ten_customers(data_frame):\\n data_frame.loc[:, 'total_payment'] = (data_frame['unit_rental_price']\\n * data_frame['quantity_rented']\\n * data_frame['rental_period_months'])\\n data_set = data_frame.groupby(['customer_id']).agg({'total_payment': 'sum'})\\n data_set = data_set.nlargest(10, 'total_payment')\\n return data_set\",\n \"def data_center_count(self) -> int:\\n return pulumi.get(self, \\\"data_center_count\\\")\",\n \"def list_active_customers():\\n active_customers = 0\\n for customer in cm.Customers:\\n if customer.status == \\\"Active \\\":\\n active_customers += 1\\n return active_customers\",\n \"def all_client_number():\\n\\n url = CMX_URL + '/api/location/v2/clients/count'\\n header = {'content-type': 'application/json', 'accept': 'application/json'}\\n response = requests.get(url, headers=header, auth=CMX_AUTH, verify=False)\\n response_json = response.json()\\n clients_number = response_json['count']\\n return clients_number\",\n \"def count_target_class_data(data, target_class):\\n count = 0\\n for row in data:\\n if row[0] == target_class:\\n count += 1\\n\\n return count\",\n \"def data_count(self):\\r\\n\\r\\n shp = self.df.shape\\r\\n row_count = shp[0]\\r\\n return row_count\",\n \"def get_num_countries():\\n num_countries = np.zeros(shape=(len(annual_files), 1))\\n \\n for year in annual_files:\\n df = get_runners_data(year)\\n country_count = df['countryCode'].value_counts()\\n num_countries[annual_files.index(\\n year)] = len(country_count.index)\\n return num_countries\",\n \"def get_data_count(self, collection):\\n # Use 'data_count' attribute when available. It is created in the\\n # BaseCollectionViewSet class.\\n return (\\n collection.data_count\\n if hasattr(collection, \\\"data_count\\\")\\n else collection.data.count()\\n )\",\n \"def list_active_customers():\\n with database.transaction():\\n query = (Customer\\n .select(fn.COUNT(Customer.status).alias('count'))\\n .where(Customer.status == 'Active'))\\n LOGGER.info(query)\\n\\n customer_count = [item.count for item in query]\\n LOGGER.info('Number of active customers: %s', customer_count[0])\\n\\n return customer_count[0]\",\n \"def list_active_customers():\\n count_query = (Customer\\n .select(Customer, fn.COUNT(Customer.name)\\n .alias('cust_count'))\\n .where(Customer.status == 'active'))\\n for count in count_query:\\n return count.cust_count\",\n \"def carn_count(self):\\n return len(self.carnivores)\",\n \"def coauthor_count(self):\\n return self._json.get('coauthor-count', '0')\",\n \"def get_count(cls):\\n total = 0\\n for counter in SimpleCounterShard.objects.all():\\n total += counter.count\\n return total\",\n \"def get_all_customer_ids():\\n table = data_manager.get_table_from_file(\\\"sales/sales.csv\\\")\\n return get_all_customer_ids_from_table(table)\",\n \"def count(self):\\n return self.data_container.count\",\n \"def row_count(data):\\n return int(arcpy.GetCount_management(data).getOutput(0))\",\n \"def count_data(self):\\n try:\\n ndata = len(self.x)\\n logger.info(\\\"Number of data points: {0}\\\".format(ndata))\\n except AttributeError:\\n logger.error(\\\"Data object has not been defined\\\")\\n ndata = 0\\n return ndata\",\n \"def createCustomerID(self):\\n\\n customerID = self._df_invoice_original.CustomerID.max()\\n customerID += 1\\n return int(customerID)\",\n \"def count_codon(self, codon):\\n return sum([1 for c in self if c == codon])\",\n \"def list_active_customers():\\n customer_active = Customer.select().where(Customer.status == 'Active')\\n print('{} Active Customers'.format(len(customer_active)))\\n return len(customer_active)\",\n \"def dataCountBy(self, collectionName, catagory, data):\\n count = collectionName.find({catagory: data}).count()\\n return count\",\n \"def data_count(self):\\n return(len(self.data))\",\n \"def getDataCount(self, filter: t.Mapping[t.Text, t.Any] = {}\\n ) -> DatasetCount:\\n aggregate_data = self.getAggregateData(\\n pipeline={\\\"count\\\": {\\\"$sum\\\": 1}},\\n filter=filter,\\n )\\n count = first(aggregate_data.data)[\\\"count\\\"]\\n return DatasetCount(count=count)\",\n \"def count_codon_all(self):\\n return Counter(list(self))\",\n \"def _count_parties(data_set): #DEMOCRATS, THEN REPUBLICANS\\r\\n reps = 0\\r\\n dems = 0\\r\\n for data_point in data_set:\\r\\n if data_point.dat_party == \\\"R\\\": reps+=1\\r\\n if data_point.dat_party == \\\"D\\\": dems+=1\\r\\n\\r\\n return (dems, reps)\",\n \"def getNumberOfReviews(self):\\n try:\\n count = 0\\n with open(self.metadata_path, \\\"r\\\", newline='') as metadata:\\n mdata = csv.reader(metadata, delimiter=' ', quotechar='|')\\n for review_data in mdata:\\n count += 1\\n return count\\n except Exception:\\n print(\\\"Cant load metadata file\\\")\\n traceback.print_exc()\",\n \"def n_points(self):\\n\\n if self.data_reduced:\\n return len(self.data_reduced[0])\\n else:\\n return 0\",\n \"def get_count(username):\\n return get_contributor(username)[\\\"count\\\"]\",\n \"def get_all_customers():\\n data = user_obj.get_all_customers()\\n return data\",\n \"def features_customers(df_customers):\\n for i in PREMIER_VALS:\\n k = 'premier_' + str(i)\\n df_customers[k] = np.where(df_customers['premier'] == i, 1, 0)\\n\\n df_customers['age'] = datetime.now().date().year - df_customers['yearOfBirth']\\n df_customers['male'] = np.where(df_customers['gender'] == 'M', 1, 0)\\n df_customers['female'] = np.where(df_customers['gender'] == 'F', 1, 0)\\n df_customers['days_in_asos'] = (datetime.now().date() - df_customers['dateCreated']).dt.days\\n\\n logger.info(\\\"Features from the customers table: {shape} {dtypes}\\\"\\n .format(shape=df_customers.shape, dtypes=df_customers.dtypes))\\n return df_customers\",\n \"def dataCount(self, collectionName):\\n count = collectionName.find().count()\\n return count\",\n \"def top_five_customers(data_frame):\\n data_set = data_frame.groupby(['customer_id']).agg({'quantity_rented': 'sum'})\\n data_set = data_set.nlargest(5, 'quantity_rented')\\n return data_set\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def Count(self) -> int:\",\n \"def getCustomersInfo(self):\\n rval = None\\n tries = 0\\n while not rval and tries < 5:\\n if tries > 0:\\n time.sleep(30)\\n rval = self._getService(self.ns_customer,\\n self.soapCustomer % (self.ns_customer, \\\"\\\",\\n \\\"100\\\", \\\"Advertiser\\\"),\\n \\\"GetCustomersInfo\\\", '', '',\\n self.cwsdl, self.chost)\\n tries += 1\\n return rval\",\n \"def reducer(self, customer, visit_counts):\\n total =0\\n total = sum(i for i in visit_counts)\\n yield customer,total\",\n \"def get_all_customer_ids():\\n\\n # your code\",\n \"def test_data_when_import_customer_with_data(self):\\n\\n customer = self.customers[0]\\n self.assertEqual(\\\"Jimena\\\", customer.get_first_name())\\n self.assertEqual(\\\"Sanabria\\\", customer.get_last_name())\\n self.assertEqual(\\\"21-08-1980\\\", customer.get_date_of_birth())\\n self.assertEqual([\\\"Nueva Granada #1837\\\"], customer.get_addresses())\\n self.assertEqual([4244270,70759942], customer.get_phones())\\n self.assertEqual(\\\"giovi_times@hotmail.com\\\", customer.get_email())\\n self.assertEqual(\\\"Gold\\\", customer.get_membership())\\n self.assertEqual(\\\"Active\\\", customer.get_status())\",\n \"def number_of_crew(self):\\n return self._number_of_crew\",\n \"def count(self):\\r\\n return self.data_array.size\",\n \"def get_total_item_size(dataset):\\n total_items = 0\\n for element in dataset:\\n total_items += 1\\n return total_items\",\n \"def get_customer_info(self, customer_id):\\n #requested_customer_RDD = self.sc.parallelize(movie_ids).map(lambda x: (user_id, x))\\n # Get predicted ratings\\n customers = self.__get_customers(customer_id).collect()\\n\\n return customers\",\n \"def get_num_countries(self):\\n return len(self.countries)\",\n \"def import_customers(input_data):\\n error_count = 0\\n insert_count = 0\\n LOGGER.info('Starting Customer import')\\n for onecust in input_data:\\n try:\\n Customer(onecust['user_id'], onecust['first_name'], onecust['last_name'],\\n onecust['address'], onecust['phone_number'], onecust['email'])\\\\\\n .save(full_clean=True, force_insert=True)\\n insert_count += 1\\n except ValidationError as valerror:\\n LOGGER.exception(\\\"Error importing data from csv: %s \\\", valerror.message)\\n error_count += 1\\n except (OperationError, DuplicateKeyError) as operror:\\n LOGGER.exception(\\\"Error importing data from csv: %s \\\", operror)\\n error_count += 1\\n\\n return insert_count, error_count\",\n \"def getNumRows(self) -> int:\\n ...\",\n \"def count():\",\n \"def test_get_customer_list(self):\\n customer = self._create_customers(\\\"Alex\\\")\\n customer.create()\\n customer = self._create_customers(\\\"Sally\\\")\\n customer.create()\\n customer = self._create_customers(\\\"John\\\")\\n customer.create()\\n resp = self.app.get(\\\"/customers\\\")\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n data = resp.get_json()\\n self.assertEqual(len(data), 3)\",\n \"def _compute_count(self):\\n for orders in self:\\n orders.count = self.env['account.move'].search_count(\\n [('invoice_origin', '=', self.name)])\",\n \"def test_addsNewCustomersFromAPIs(self):\\n DataManagerUnitTest.dm.reload()\\n result = DataManagerUnitTest.dm.onlineStoreDatabase.getCustomers()\\n customers = set([key['user']['email'] for key in DataManagerUnitTest.dm.getAllOrders()])\\n self.assertEqual(len(customers), len(result))\",\n \"def count_records(batches: List[Batch]) -> int:\\n return sum(b.current_size for b in batches)\",\n \"def _compute_no_of_invoices(self):\\n for record in self:\\n record.invoice_count = len(record.invoice_ids)\",\n \"def number_of_trips(filename): \\r\\n \\r\\n with open(filename, 'r') as f_in:\\r\\n # set up csv reader object\\r\\n trip_reader = csv.DictReader(f_in)\\r\\n \\r\\n # initialize count variables\\r\\n n_subscribers = 0\\r\\n n_customers = 0\\r\\n \\r\\n # tally up ride types\\r\\n for row in trip_reader:\\r\\n if row['user_type'] == 'Subscriber':\\r\\n n_subscribers += 1\\r\\n else:\\r\\n n_customers += 1\\r\\n \\r\\n # compute total number of rides\\r\\n n_total = n_subscribers + n_customers\\r\\n \\r\\n # return tallies as a tuple\\r\\n return(n_subscribers, n_customers, n_total)\",\n \"def N(self):\\n return len(self.cavity_grid.cavities) + 1\",\n \"def count_subscribers(self):\\n return self.request(\\\"count:Contact\\\", [ None ])\",\n \"def get_seated_customers(restaurant: Restaurant):\\n customers = Reservation.query.filter(Reservation.restaurant == restaurant).filter(Reservation.status == ReservationState.SEATED).all()\\n \\n return reduce(lambda acc,rsv: acc + rsv.seats, customers, 0)\",\n \"def num_carns(self):\\n return self._num_carns\",\n \"def customers(self):\\r\\n return customers.Customers(self)\",\n \"def count_comentions_csv(csv_reader):\\n header = next(csv_reader)\\n index_of = {col: index for index, col in enumerate(header)}\\n comention_counter = Counter()\\n for line in csv_reader:\\n body = line[index_of['body']]\\n for isbn1, isbn2 in get_comentions(body):\\n comention_counter[(isbn1, isbn2)] += 1\\n return comention_counter\",\n \"def customers(self):\\r\\n return Customers(self)\",\n \"def total_demand(self) -> float:\\n return self.inputs.num_customers * self.operations.m3_per_customer()\",\n \"def _get_cus_info(self):\\n label_enc = LabelEncoder()\\n customer_info = self._inv.drop_duplicates(['customer_code'], keep='last')\\n customer_info = customer_info[['customer_code', 'customer_name', 'sales_cen_code',\\n 'sales_cen_name', 'sales_region_name', 'province',\\n 'city', 'district', 'customer_type', 'is_usable', 'channel_level']]\\n customer_info['customer_id'] = label_enc.fit_transform(customer_info['customer_code'])\\n customer_info['sales_cen_id'] = label_enc.fit_transform(customer_info['sales_cen_code'])\\n customer_info['sales_region_id'] = label_enc.fit_transform(customer_info['sales_region_name'])\\n customer_info['province_id'] = label_enc.fit_transform(customer_info['province'])\\n customer_info['city_id'] = label_enc.fit_transform(customer_info['city'])\\n customer_info['district_id'] = label_enc.fit_transform(customer_info['district'])\\n customer_info['customer_type'] = label_enc.fit_transform(customer_info['customer_type'])\\n customer_info['is_usable'] = label_enc.fit_transform(customer_info['is_usable'])\\n customer_info['channel_level'] = label_enc.fit_transform(customer_info['channel_level'])\\n customer_info_encoded = customer_info.drop(\\n columns=['customer_name', 'sales_cen_code', 'sales_cen_name',\\n 'sales_region_name', 'province', 'city', 'district']\\n ).set_index('customer_code')\\n customer_info.set_index('customer_code', inplace=True)\\n customer_info_encoded = customer_info_encoded.reindex(self._index.get_level_values(0))\\n return customer_info, customer_info_encoded\",\n \"def getNoOfRows(self):\\n return _patchExtractor.patchExtractor_getNoOfRows(self)\",\n \"def citation_count(self):\\n return self._json['coredata'].get('citation-count', '0')\",\n \"def get_num_datasets(self, data):\\n dsets = set()\\n for items in data:\\n dsetid = items[3]\\n dsets.add(dsetid)\\n return len(dsets)\",\n \"def generate_customer_info(self):\\n consecutive = check_consecutive(self.customerIds)\\n print(\\\"consecutive\\\" + str(consecutive))\\n if consecutive:\\n for customer_id in self.customerIds:\\n # next we need to 1) decide if the customer has insurance, and if yes, generate the EOB that gets sent to the customer\\n insured = np.random.choice(\\n self.distributions[\\\"insurance_status\\\"],\\n 1,\\n p=self.distributions[\\\"insurance_distribution\\\"],\\n )[0]\\n dob = self.dobs[customer_id - 1]\\n customer = pd.DataFrame(\\n {\\n \\\"customer_id\\\": [customer_id],\\n \\\"dob\\\": str(dob),\\n \\\"insurance\\\": insured,\\n \\\"experiment_id\\\": random.randint(1, 2),\\n }\\n )\\n self.Customers = self.Customers.append(customer)\\n else:\\n print(\\\"Error generating customer info: customerIds aren't consecutive\\\")\\n return True\",\n \"def test_get_customers(self):\\n get_customers_url = reverse(\\\"customer_list\\\")\\n response = self.client.get(get_customers_url)\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n\\n # get data from db\\n customers = Customer.objects.all()\\n serializer = CustomerSerializer(customers, many=True)\\n self.assertEqual(response.data, serializer.data)\\n\\n self.assertEqual(len(response.data), 4)\",\n \"def get_entity_contracts_count():\\n url = 'http://www.base.gov.pt/base2/rest/contratos?adjudicatariaid=%d' \\\\\\n '&sort(-id)' % entity.base_id\\n\\n response = requests.get(url, headers={'Range': 'items=0-24'})\\n\\n results_range = response.headers['content-range']\\n _, count = results_range.split('/')\\n\\n return int(count)\",\n \"def customer_id(uncapped_flatprice, uncapped_flatprice_finalizer, team_multisig) -> int:\\n customer_id = int(uuid.uuid4().hex, 16) # Customer ids are 128-bit UUID v4\\n return customer_id\",\n \"def get_listCustomer_out_sample(self, customerCount=10):\\n \\n if customerCount is None :\\n listCustomer= list(self._df_invoice_line_out_sample.CustomerID.unique())\\n else:\\n if customerCount <= 0 :\\n listCustomer \\\\\\n = list(self._df_invoice_line_out_sample.CustomerID.unique())\\n else:\\n listCustomer \\\\\\n = list(self._df_invoice_line_out_sample.CustomerID.unique()[:customerCount])\\n return listCustomer\",\n \"def prepare_count_incidents(self, object):\\n roles = object.actorrole_set.all()\\n return Incident.objects.filter(actors_role__in=roles).count()\",\n \"def count_children(self, element_name, customers):\\n count = 0\\n childlist = [customer for customer in customers if customer.get('parent') == element_name]\\n count = len(childlist)\\n # Call the function recursively for each child in the list (Customer group childs and descendents)\\n for child in childlist:\\n count = count + CustomerGroupsPage.count_children(self, child.get('name'), customers)\\n return count\",\n \"def getCustomer(self):\\n return self.base.get(\\\"customer\\\", [])\",\n \"def count(self):\\n return self._reduce_for_stat_function(F.count, only_numeric=False)\",\n \"def list_active_customer():\\n active_customer = Customer.select().where(Customer.is_active).count()\\n LOGGER.info('Number of active customers retrieved.')\\n return active_customer\",\n \"def get_num_records(self):\\n return self.__num_records\",\n \"def get_sum_of_sales_per_customer_from_table(table):\\n summed_sales_per_customer = {}\\n for customer in {line[CUSTOMER_ID] for line in table}:\\n sum_of_sales = common.szum_list([line[PRICE] for line in table if line[CUSTOMER_ID] == customer])\\n summed_sales_per_customer[customer] = sum_of_sales\\n return summed_sales_per_customer\",\n \"def count(self):\\n\\n if self.cluster:\\n return self.cluster.count()\\n\\n return super().count()\",\n \"def get_country_count():\\n numbers=country_populations.split('\\\\n')\\n count_numbers= len(numbers)-1\\n return count_numbers\",\n \"def cluster_count(self) -> int:\\n cluster_count = max(1, round(16**3 * (self.vein.purity / 100.0) / self.cluster_size))\\n return self.distribution.scale_cluster_count(cluster_count)\",\n \"def getTotalIndividualCount(self):\\r\\n return self._n\",\n \"def get_total_dim(client_template):\\n return sum(np.prod(x.shape) for x in client_template)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7528541","0.741451","0.7031075","0.6915427","0.68836576","0.6863681","0.6667784","0.616603","0.61287034","0.6101881","0.59332806","0.5910718","0.5855194","0.58472735","0.58410436","0.5774803","0.57345396","0.57238173","0.572342","0.5717885","0.56658965","0.56646013","0.56542027","0.56331044","0.55945414","0.5580822","0.5579558","0.55575264","0.55567914","0.5548149","0.5531757","0.5524814","0.5483556","0.5476428","0.5473485","0.5468041","0.5449823","0.5448093","0.54428476","0.5432522","0.5418882","0.54042727","0.53786004","0.5363866","0.53576905","0.5340247","0.53341746","0.5333146","0.53305423","0.5326809","0.5326809","0.5326809","0.5326809","0.5320343","0.53171074","0.53091294","0.5303633","0.5302074","0.5300916","0.52999884","0.52960175","0.52945805","0.52919424","0.5277712","0.5276107","0.5271022","0.5263932","0.5252489","0.52481586","0.5246491","0.52381486","0.5234534","0.5220975","0.52105397","0.5204587","0.51881856","0.51857144","0.5181162","0.51715326","0.51567143","0.5147119","0.5145199","0.5139283","0.5136319","0.51305956","0.51303196","0.5127429","0.51229084","0.51198864","0.51190037","0.5118167","0.5115953","0.51130235","0.51099783","0.51086617","0.5103266","0.51019204","0.50885737","0.50816697","0.5076146"],"string":"[\n \"0.7528541\",\n \"0.741451\",\n \"0.7031075\",\n \"0.6915427\",\n \"0.68836576\",\n \"0.6863681\",\n \"0.6667784\",\n \"0.616603\",\n \"0.61287034\",\n \"0.6101881\",\n \"0.59332806\",\n \"0.5910718\",\n \"0.5855194\",\n \"0.58472735\",\n \"0.58410436\",\n \"0.5774803\",\n \"0.57345396\",\n \"0.57238173\",\n \"0.572342\",\n \"0.5717885\",\n \"0.56658965\",\n \"0.56646013\",\n \"0.56542027\",\n \"0.56331044\",\n \"0.55945414\",\n \"0.5580822\",\n \"0.5579558\",\n \"0.55575264\",\n \"0.55567914\",\n \"0.5548149\",\n \"0.5531757\",\n \"0.5524814\",\n \"0.5483556\",\n \"0.5476428\",\n \"0.5473485\",\n \"0.5468041\",\n \"0.5449823\",\n \"0.5448093\",\n \"0.54428476\",\n \"0.5432522\",\n \"0.5418882\",\n \"0.54042727\",\n \"0.53786004\",\n \"0.5363866\",\n \"0.53576905\",\n \"0.5340247\",\n \"0.53341746\",\n \"0.5333146\",\n \"0.53305423\",\n \"0.5326809\",\n \"0.5326809\",\n \"0.5326809\",\n \"0.5326809\",\n \"0.5320343\",\n \"0.53171074\",\n \"0.53091294\",\n \"0.5303633\",\n \"0.5302074\",\n \"0.5300916\",\n \"0.52999884\",\n \"0.52960175\",\n \"0.52945805\",\n \"0.52919424\",\n \"0.5277712\",\n \"0.5276107\",\n \"0.5271022\",\n \"0.5263932\",\n \"0.5252489\",\n \"0.52481586\",\n \"0.5246491\",\n \"0.52381486\",\n \"0.5234534\",\n \"0.5220975\",\n \"0.52105397\",\n \"0.5204587\",\n \"0.51881856\",\n \"0.51857144\",\n \"0.5181162\",\n \"0.51715326\",\n \"0.51567143\",\n \"0.5147119\",\n \"0.5145199\",\n \"0.5139283\",\n \"0.5136319\",\n \"0.51305956\",\n \"0.51303196\",\n \"0.5127429\",\n \"0.51229084\",\n \"0.51198864\",\n \"0.51190037\",\n \"0.5118167\",\n \"0.5115953\",\n \"0.51130235\",\n \"0.51099783\",\n \"0.51086617\",\n \"0.5103266\",\n \"0.51019204\",\n \"0.50885737\",\n \"0.50816697\",\n \"0.5076146\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8029719"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":1897,"cells":{"query":{"kind":"string","value":"Returns number of invoice lines (number of rows) from original dataset."},"document":{"kind":"string","value":"def get_invl_count(self):\n return self._df_invoice_original.index.unique().shape[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 get_invoice_count(self):\n return self._df_invoice_original.InvoiceNo.unique().shape[0]","def getNumRows(self) -> int:\n ...","def _compute_no_of_invoices(self):\n for record in self:\n record.invoice_count = len(record.invoice_ids)","def getNoOfRows(self):\n return _patchExtractor.patchExtractor_getNoOfRows(self)","def num_items(self):\n num_items = 0\n for line in self.lines.all():\n num_items += line.quantity\n return num_items","def row_count(data):\n return int(arcpy.GetCount_management(data).getOutput(0))","def __len__(self):\n nlines = self.get_endline() - self.get_startline() + 1\n if nlines < 0:\n nlines = 0\n return nlines","def getNumRows(self):\n return self.__rows","def get_table_nb_lines(self, table):\n sql = \"SELECT COUNT(*) FROM \" + table + \";\"\n cur = self._connection.cursor()\n cur.execute(sql)\n res = cur.fetchall()\n cur.close()\n return res[0][0]","def get_line_count(blob):\n return len(blob.split('\\n'))","def getRowCount(self) -> int:\n ...","def n_lines(self):\n try: \n return self._n_lines\n except AttributeError:\n self._n_lines = len(self.lines())\n return self._n_lines","def len(self, table):\n return self.get_table_nb_lines(table)","def get_num_rows(self, data, omit_metric=False):\n if omit_metric:\n num_rows = int((len(data.keys())-1)/4)\n else:\n num_rows = int(len(data.keys())/4)\n if len(data.keys())%4 != 0:\n num_rows += 1\n return num_rows","def get_customer_count(self):\n return self._df_invoice_original.CustomerID.unique().shape[0]","def get_total_line_counts(self):\n return get_total_line_counts(self.files.all())","def getNbRows(self):\n return self.data.shape[1]","def num_lines(self, snapshot: Bug, filepath: str) -> int:\n return len(self._line_offsets(snapshot, filepath))","def data_count(self):\r\n\r\n shp = self.df.shape\r\n row_count = shp[0]\r\n return row_count","def cpp_getInRowCount(self, patchNo, rowsInPatch):\n return _patchExtractor.patchExtractor_cpp_getInRowCount(self, patchNo, rowsInPatch)","def get_row_number(self):\n return int(len(self.data_items)/12)","def get_rows(self) -> int:\r\n return 1 + self.display.get_rows() + 1","def NumberOfRows(self):\n return _table.DSTable_NumberOfRows(self)","def data_len(self):\n Nrows_data = 0\n with self._compression_safe_file_opener(self.input_fname, \"r\") as f:\n for i, l in enumerate(f):\n if (l[0 : len(self.header_char)] != self.header_char) and (l != \"\\n\"):\n Nrows_data += 1\n return Nrows_data","def _get_line_no_(obj, line):\n \n iNo = 0\n for item in obj.order_line:\n iNo += 1\n if (item.id == line.id):\n break\n \n return iNo","def countLines(file_name, start, end):\r\n\r\n with open(file_name, \"r\") as file:\r\n counter_lines = 0\r\n\r\n for line in islice(file, start, end):\r\n counter_lines += 1\r\n\r\n return counter_lines","def linecounter(x):\n return linecount(x) + longlines(x)","def get_number_lines(running_reward_file, running_loss_file, action_count_file):\n if Path(running_reward_file).exists():\n data = np.loadtxt(running_reward_file).reshape(-1,2)\n return data.shape[0]\n if Path(running_loss_file).exists():\n data = np.loadtxt(running_loss_file).reshape(-1,2)\n return data.shape[0]\n if Path(action_count_file).exists():\n data = np.loadtxt(action_count_file).reshape(-1,2)\n return data.shape[0]\n raise NameError(\"No files to count lines\")","def linecount(x):\n return sum(1 for char in x if char == \"\\n\")","def num_data_lines(filepath):\n\tif not file_exists(filepath):\n\t\treturn -1\n\tcount = 0\n\twith open(filepath, 'r') as f:\n\t\twhile read_floats(f):\n\t\t\tcount += 1\n\tf.close()\n\treturn count","def get_nrows(self):\n return self.nrows","def get_nrows(self):\n return self.nrows","def _get_num_lines_from_csv(self, filename):\n\n _file = open(get_full_path(filename))\n _reader = csv.reader(_file)\n\n return len(list(_reader))","def num_rows(self) -> str:\n return pulumi.get(self, \"num_rows\")","def n_rows(self) -> int:\n\n return len(self.plaincolumns[0].values)","def count_lines(file_uri):\n\n with open(file_uri) as file_obj:\n for i, line in enumerate(file_obj):\n pass\n num_lines = i + 1\n return num_lines","def get_linecount(self):\n self._update_linetab(len(self.input))\n lcount = len(self.__linepos)\n return lcount - (self.input.endswith('\\n'))","def count_lines(file_obj):\n for idx, line in enumerate(file_obj):\n pass\n file_obj.seek(0)\n return idx + 1","def nrows(self):\n return len(self.__data)","def number_of_rows(self):\n if self.number_of_columns():\n return self._number_of_rows\n else:\n return 0","def get_total_item_size(dataset):\n total_items = 0\n for element in dataset:\n total_items += 1\n return total_items","def _chunklines(self):\r\n text = self.textwnd.toPlainText()\r\n lines_in_chunk = len(text.split(\"\\n\"))\r\n logger.debug(\"Lines in chunk: {}\".format(lines_in_chunk))\r\n return lines_in_chunk","def _get_line_number(vcf):\n with open(vcf) as vcf_input_file:\n i = -1\n for line in vcf_input_file:\n i += 1\n return i","def rows_count(self):\n return len(self.value)","def __len__(self):\n\n return int(self._rows)","def no_of_lines():\n number_of_lines = len(open(FILE_NAME).readlines())\n return number_of_lines","def calculate_line_number(text):\n return len([line for line in text.split(\"\\n\") if line.strip() != \"\"])","def n(self):\n return nrow(self._array)","def row_count(self) -> int:\n return len(self.rows)","def row_count(self) -> int:\n return len(self.rows)","def _calc_nrows(self, len_data, target_pop):\n return target_pop - len_data","def read_num_lines(data_socket):\r\n size_bytes = b''\r\n for i in range(0, 4):\r\n size_bytes += next_byte(data_socket)\r\n return int.from_bytes(size_bytes, 'big')","def num_rows(self):\n return (len(self.rows))","def number_of_lines(filename=\"\"):\n with open(filename, encoding='UTF8') as a_file:\n\n lineNum = 0\n\n for eachLine in a_file:\n lineNum += 1\n return lineNum","def GetNumRows(self):\n return _hypre.HypreParMatrix_GetNumRows(self)","def row_count(self):\n return self.well_count // self.col_count","def get_nrows(self):\n return self._nrows","def num_rows(self):\n return len(self.rows)","def get_num_records(self):\n return self.__num_records","def rowCount(self, parent):\r\n return len(self.arraydata)","def get_number_rows(si_settings, ship_height, alien_height):\r\n available_space_y = (si_settings.screen_height - alien_height - ship_height)/2\r\n number_rows = int(available_space_y / (2 * alien_height))\r\n return number_rows","def num_lines(file_name):\n with open(file_name) as file:\n for i, line in enumerate(file):\n pass\n return i + 1","def count_elements_in_dataset(dataset):\n return dataset.count()","def data_count(self):\n return(len(self.data))","def num_rows(self):\n return len(self[0])","def _calc_nrows(self, len_data, growth_rate):\n return int(round(len_data * growth_rate))","def __len__(self):\n return self._nrows","def get_line_nr(view, point):\n return view.rowcol(point)[0] + 1","def lineNumber(self):\n if self.__lineNumber is None:\n self.__lineNumber = self.__source.count(\"\\n\", 0, self.__offset) + 1\n\n return self.__lineNumber","def _getNewCodeLength(self):\n nb_lines = 0\n for line in self.body.splitlines():\n if not line.startswith(\"-\"):\n nb_lines += 1\n return nb_lines","def row_counter(self) -> int:\n return self.writer.row_counter","def num_cells_for_rows(self, rows):\r\n return (rows * rows + rows) // 2","def totallines(self):\n return self._totallines","def count_lines(filename):\r\n with open(filename, 'rb') as f:\r\n return sum(1 for line in f)","def nrows(self):\n return self.__nrows","def rowCount(self, index):\n return len(self._data)","def get_num_rows(self):\n return self._num_rows","def row_count(self):\n return self.__row_count","def test_line_count(self):\n\t\tself.assertEqual(analyse_text(self.filename)[0], 4)","def nrows(self):\n if self.ncolumns() == 0:\n return 0\n nrows = self.table_column(0).nrows()\n for i in range(1, self.ncolumns()):\n nrows = min(self.table_column(i).nrows(), nrows)\n return nrows","def rowcount(self):\n return self._count","def number_of_lines(filename=\"\"):\n\n number_lines = 0\n with open(filename) as file_opened:\n for line in file_opened:\n number_lines += 1\n return number_lines","def get_num_of_sales_per_customer_ids():\n\n # your code","def count_lines(filename):\n with open(filename, 'rb') as f:\n return sum(1 for line in f)","def get_rows_num(file_name):\n # By default add the first datetime\n row_num = 1\n with open(file_name, \"r\") as file:\n for line in file:\n if line == \"\\n\":\n break\n else:\n row_num += 1\n\n return row_num","def count_lines(stream):\n return len(stream.readlines())","def rowcount(self):\n\t\treturn self._count","def num_rows(self):\n if self._is_vertex_frame():\n return self.__graph__.summary()['num_vertices']\n elif self._is_edge_frame():\n return self.__graph__.summary()['num_edges']","def total_rows(self):\n self._fetch_if_needed()\n # reduce case, count number of lines\n if self._total_rows is None:\n return self.count()\n return self._total_rows","def checkEachLineCount(mat):\n n = sum(mat[0])\n \n assert all(sum(line) == n for line in mat[1:]), \"Line count != %d (n value).\" % n\n return n","def number_of_lines(filename=\"\"):\n with open(filename, encoding='utf-8') as myFile:\n return sum([1 for line in myFile])","def get_roi_line_len(self):\n return len(self.line_list)","def getNumOfInvoice(self,id,start,finish):\n self.calls += 1\n invoice = self.getResponse(self.buildParams(id,start,finish))\n if not self.isNumeric(invoice):\n middle = self.diveDates(start,finish)\n plusMiddle = middle + timedelta(days = 1)\n middle = self.removeHours(middle)\n plusMiddle = self.removeHours(plusMiddle)\n invoice = self.getNumOfInvoice(id,start,middle)+\\\n self.getNumOfInvoice(id,plusMiddle,finish)\n return invoice","def checkEachLineCount(mat):\n n = sum(mat[0])\n\n assert all(sum(line) == n for line in mat[1:]), \"Line count != %d (n value).\" % n\n return n","def num_lineages(self, t):\n return self._ll_tree.get_num_lineages(t)","def leafNumberOfRows(self):\r\n\r\n shape = self.data_source.shape\r\n if shape == None:\r\n # Node is not a Leaf or there was problems getting the shape\r\n nrows = 0\r\n elif shape == ():\r\n # Node is a rank 0 array (e.g. numpy.array(5))\r\n nrows = 1\r\n elif isinstance(self.data_source, tables.EArray):\r\n # Warning: the number of rows of an EArray, ea, can be different\r\n # from the number of rows of the numpy array ea.read()\r\n nrows = self.data_source.shape[0]\r\n else:\r\n nrows = self.data_source.nrows\r\n\r\n return numpy.array(nrows, dtype=numpy.int64)","def row_count(self):\n return self._row_count","def nrows(filename):\n with fopen(filename, 'rb') as fdsc:\n bufgen = takewhile(lambda x: x, (fdsc.read(1024*1024) for _ in repeat(None)))\n return sum([buf.count(b'\\n') for buf in bufgen])","def rows(self):\n return self.prov[PROV_ROWS]","def getNumData(self):\n return len(self.data)"],"string":"[\n \"def get_invoice_count(self):\\n return self._df_invoice_original.InvoiceNo.unique().shape[0]\",\n \"def getNumRows(self) -> int:\\n ...\",\n \"def _compute_no_of_invoices(self):\\n for record in self:\\n record.invoice_count = len(record.invoice_ids)\",\n \"def getNoOfRows(self):\\n return _patchExtractor.patchExtractor_getNoOfRows(self)\",\n \"def num_items(self):\\n num_items = 0\\n for line in self.lines.all():\\n num_items += line.quantity\\n return num_items\",\n \"def row_count(data):\\n return int(arcpy.GetCount_management(data).getOutput(0))\",\n \"def __len__(self):\\n nlines = self.get_endline() - self.get_startline() + 1\\n if nlines < 0:\\n nlines = 0\\n return nlines\",\n \"def getNumRows(self):\\n return self.__rows\",\n \"def get_table_nb_lines(self, table):\\n sql = \\\"SELECT COUNT(*) FROM \\\" + table + \\\";\\\"\\n cur = self._connection.cursor()\\n cur.execute(sql)\\n res = cur.fetchall()\\n cur.close()\\n return res[0][0]\",\n \"def get_line_count(blob):\\n return len(blob.split('\\\\n'))\",\n \"def getRowCount(self) -> int:\\n ...\",\n \"def n_lines(self):\\n try: \\n return self._n_lines\\n except AttributeError:\\n self._n_lines = len(self.lines())\\n return self._n_lines\",\n \"def len(self, table):\\n return self.get_table_nb_lines(table)\",\n \"def get_num_rows(self, data, omit_metric=False):\\n if omit_metric:\\n num_rows = int((len(data.keys())-1)/4)\\n else:\\n num_rows = int(len(data.keys())/4)\\n if len(data.keys())%4 != 0:\\n num_rows += 1\\n return num_rows\",\n \"def get_customer_count(self):\\n return self._df_invoice_original.CustomerID.unique().shape[0]\",\n \"def get_total_line_counts(self):\\n return get_total_line_counts(self.files.all())\",\n \"def getNbRows(self):\\n return self.data.shape[1]\",\n \"def num_lines(self, snapshot: Bug, filepath: str) -> int:\\n return len(self._line_offsets(snapshot, filepath))\",\n \"def data_count(self):\\r\\n\\r\\n shp = self.df.shape\\r\\n row_count = shp[0]\\r\\n return row_count\",\n \"def cpp_getInRowCount(self, patchNo, rowsInPatch):\\n return _patchExtractor.patchExtractor_cpp_getInRowCount(self, patchNo, rowsInPatch)\",\n \"def get_row_number(self):\\n return int(len(self.data_items)/12)\",\n \"def get_rows(self) -> int:\\r\\n return 1 + self.display.get_rows() + 1\",\n \"def NumberOfRows(self):\\n return _table.DSTable_NumberOfRows(self)\",\n \"def data_len(self):\\n Nrows_data = 0\\n with self._compression_safe_file_opener(self.input_fname, \\\"r\\\") as f:\\n for i, l in enumerate(f):\\n if (l[0 : len(self.header_char)] != self.header_char) and (l != \\\"\\\\n\\\"):\\n Nrows_data += 1\\n return Nrows_data\",\n \"def _get_line_no_(obj, line):\\n \\n iNo = 0\\n for item in obj.order_line:\\n iNo += 1\\n if (item.id == line.id):\\n break\\n \\n return iNo\",\n \"def countLines(file_name, start, end):\\r\\n\\r\\n with open(file_name, \\\"r\\\") as file:\\r\\n counter_lines = 0\\r\\n\\r\\n for line in islice(file, start, end):\\r\\n counter_lines += 1\\r\\n\\r\\n return counter_lines\",\n \"def linecounter(x):\\n return linecount(x) + longlines(x)\",\n \"def get_number_lines(running_reward_file, running_loss_file, action_count_file):\\n if Path(running_reward_file).exists():\\n data = np.loadtxt(running_reward_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(running_loss_file).exists():\\n data = np.loadtxt(running_loss_file).reshape(-1,2)\\n return data.shape[0]\\n if Path(action_count_file).exists():\\n data = np.loadtxt(action_count_file).reshape(-1,2)\\n return data.shape[0]\\n raise NameError(\\\"No files to count lines\\\")\",\n \"def linecount(x):\\n return sum(1 for char in x if char == \\\"\\\\n\\\")\",\n \"def num_data_lines(filepath):\\n\\tif not file_exists(filepath):\\n\\t\\treturn -1\\n\\tcount = 0\\n\\twith open(filepath, 'r') as f:\\n\\t\\twhile read_floats(f):\\n\\t\\t\\tcount += 1\\n\\tf.close()\\n\\treturn count\",\n \"def get_nrows(self):\\n return self.nrows\",\n \"def get_nrows(self):\\n return self.nrows\",\n \"def _get_num_lines_from_csv(self, filename):\\n\\n _file = open(get_full_path(filename))\\n _reader = csv.reader(_file)\\n\\n return len(list(_reader))\",\n \"def num_rows(self) -> str:\\n return pulumi.get(self, \\\"num_rows\\\")\",\n \"def n_rows(self) -> int:\\n\\n return len(self.plaincolumns[0].values)\",\n \"def count_lines(file_uri):\\n\\n with open(file_uri) as file_obj:\\n for i, line in enumerate(file_obj):\\n pass\\n num_lines = i + 1\\n return num_lines\",\n \"def get_linecount(self):\\n self._update_linetab(len(self.input))\\n lcount = len(self.__linepos)\\n return lcount - (self.input.endswith('\\\\n'))\",\n \"def count_lines(file_obj):\\n for idx, line in enumerate(file_obj):\\n pass\\n file_obj.seek(0)\\n return idx + 1\",\n \"def nrows(self):\\n return len(self.__data)\",\n \"def number_of_rows(self):\\n if self.number_of_columns():\\n return self._number_of_rows\\n else:\\n return 0\",\n \"def get_total_item_size(dataset):\\n total_items = 0\\n for element in dataset:\\n total_items += 1\\n return total_items\",\n \"def _chunklines(self):\\r\\n text = self.textwnd.toPlainText()\\r\\n lines_in_chunk = len(text.split(\\\"\\\\n\\\"))\\r\\n logger.debug(\\\"Lines in chunk: {}\\\".format(lines_in_chunk))\\r\\n return lines_in_chunk\",\n \"def _get_line_number(vcf):\\n with open(vcf) as vcf_input_file:\\n i = -1\\n for line in vcf_input_file:\\n i += 1\\n return i\",\n \"def rows_count(self):\\n return len(self.value)\",\n \"def __len__(self):\\n\\n return int(self._rows)\",\n \"def no_of_lines():\\n number_of_lines = len(open(FILE_NAME).readlines())\\n return number_of_lines\",\n \"def calculate_line_number(text):\\n return len([line for line in text.split(\\\"\\\\n\\\") if line.strip() != \\\"\\\"])\",\n \"def n(self):\\n return nrow(self._array)\",\n \"def row_count(self) -> int:\\n return len(self.rows)\",\n \"def row_count(self) -> int:\\n return len(self.rows)\",\n \"def _calc_nrows(self, len_data, target_pop):\\n return target_pop - len_data\",\n \"def read_num_lines(data_socket):\\r\\n size_bytes = b''\\r\\n for i in range(0, 4):\\r\\n size_bytes += next_byte(data_socket)\\r\\n return int.from_bytes(size_bytes, 'big')\",\n \"def num_rows(self):\\n return (len(self.rows))\",\n \"def number_of_lines(filename=\\\"\\\"):\\n with open(filename, encoding='UTF8') as a_file:\\n\\n lineNum = 0\\n\\n for eachLine in a_file:\\n lineNum += 1\\n return lineNum\",\n \"def GetNumRows(self):\\n return _hypre.HypreParMatrix_GetNumRows(self)\",\n \"def row_count(self):\\n return self.well_count // self.col_count\",\n \"def get_nrows(self):\\n return self._nrows\",\n \"def num_rows(self):\\n return len(self.rows)\",\n \"def get_num_records(self):\\n return self.__num_records\",\n \"def rowCount(self, parent):\\r\\n return len(self.arraydata)\",\n \"def get_number_rows(si_settings, ship_height, alien_height):\\r\\n available_space_y = (si_settings.screen_height - alien_height - ship_height)/2\\r\\n number_rows = int(available_space_y / (2 * alien_height))\\r\\n return number_rows\",\n \"def num_lines(file_name):\\n with open(file_name) as file:\\n for i, line in enumerate(file):\\n pass\\n return i + 1\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def data_count(self):\\n return(len(self.data))\",\n \"def num_rows(self):\\n return len(self[0])\",\n \"def _calc_nrows(self, len_data, growth_rate):\\n return int(round(len_data * growth_rate))\",\n \"def __len__(self):\\n return self._nrows\",\n \"def get_line_nr(view, point):\\n return view.rowcol(point)[0] + 1\",\n \"def lineNumber(self):\\n if self.__lineNumber is None:\\n self.__lineNumber = self.__source.count(\\\"\\\\n\\\", 0, self.__offset) + 1\\n\\n return self.__lineNumber\",\n \"def _getNewCodeLength(self):\\n nb_lines = 0\\n for line in self.body.splitlines():\\n if not line.startswith(\\\"-\\\"):\\n nb_lines += 1\\n return nb_lines\",\n \"def row_counter(self) -> int:\\n return self.writer.row_counter\",\n \"def num_cells_for_rows(self, rows):\\r\\n return (rows * rows + rows) // 2\",\n \"def totallines(self):\\n return self._totallines\",\n \"def count_lines(filename):\\r\\n with open(filename, 'rb') as f:\\r\\n return sum(1 for line in f)\",\n \"def nrows(self):\\n return self.__nrows\",\n \"def rowCount(self, index):\\n return len(self._data)\",\n \"def get_num_rows(self):\\n return self._num_rows\",\n \"def row_count(self):\\n return self.__row_count\",\n \"def test_line_count(self):\\n\\t\\tself.assertEqual(analyse_text(self.filename)[0], 4)\",\n \"def nrows(self):\\n if self.ncolumns() == 0:\\n return 0\\n nrows = self.table_column(0).nrows()\\n for i in range(1, self.ncolumns()):\\n nrows = min(self.table_column(i).nrows(), nrows)\\n return nrows\",\n \"def rowcount(self):\\n return self._count\",\n \"def number_of_lines(filename=\\\"\\\"):\\n\\n number_lines = 0\\n with open(filename) as file_opened:\\n for line in file_opened:\\n number_lines += 1\\n return number_lines\",\n \"def get_num_of_sales_per_customer_ids():\\n\\n # your code\",\n \"def count_lines(filename):\\n with open(filename, 'rb') as f:\\n return sum(1 for line in f)\",\n \"def get_rows_num(file_name):\\n # By default add the first datetime\\n row_num = 1\\n with open(file_name, \\\"r\\\") as file:\\n for line in file:\\n if line == \\\"\\\\n\\\":\\n break\\n else:\\n row_num += 1\\n\\n return row_num\",\n \"def count_lines(stream):\\n return len(stream.readlines())\",\n \"def rowcount(self):\\n\\t\\treturn self._count\",\n \"def num_rows(self):\\n if self._is_vertex_frame():\\n return self.__graph__.summary()['num_vertices']\\n elif self._is_edge_frame():\\n return self.__graph__.summary()['num_edges']\",\n \"def total_rows(self):\\n self._fetch_if_needed()\\n # reduce case, count number of lines\\n if self._total_rows is None:\\n return self.count()\\n return self._total_rows\",\n \"def checkEachLineCount(mat):\\n n = sum(mat[0])\\n \\n assert all(sum(line) == n for line in mat[1:]), \\\"Line count != %d (n value).\\\" % n\\n return n\",\n \"def number_of_lines(filename=\\\"\\\"):\\n with open(filename, encoding='utf-8') as myFile:\\n return sum([1 for line in myFile])\",\n \"def get_roi_line_len(self):\\n return len(self.line_list)\",\n \"def getNumOfInvoice(self,id,start,finish):\\n self.calls += 1\\n invoice = self.getResponse(self.buildParams(id,start,finish))\\n if not self.isNumeric(invoice):\\n middle = self.diveDates(start,finish)\\n plusMiddle = middle + timedelta(days = 1)\\n middle = self.removeHours(middle)\\n plusMiddle = self.removeHours(plusMiddle)\\n invoice = self.getNumOfInvoice(id,start,middle)+\\\\\\n self.getNumOfInvoice(id,plusMiddle,finish)\\n return invoice\",\n \"def checkEachLineCount(mat):\\n n = sum(mat[0])\\n\\n assert all(sum(line) == n for line in mat[1:]), \\\"Line count != %d (n value).\\\" % n\\n return n\",\n \"def num_lineages(self, t):\\n return self._ll_tree.get_num_lineages(t)\",\n \"def leafNumberOfRows(self):\\r\\n\\r\\n shape = self.data_source.shape\\r\\n if shape == None:\\r\\n # Node is not a Leaf or there was problems getting the shape\\r\\n nrows = 0\\r\\n elif shape == ():\\r\\n # Node is a rank 0 array (e.g. numpy.array(5))\\r\\n nrows = 1\\r\\n elif isinstance(self.data_source, tables.EArray):\\r\\n # Warning: the number of rows of an EArray, ea, can be different\\r\\n # from the number of rows of the numpy array ea.read()\\r\\n nrows = self.data_source.shape[0]\\r\\n else:\\r\\n nrows = self.data_source.nrows\\r\\n\\r\\n return numpy.array(nrows, dtype=numpy.int64)\",\n \"def row_count(self):\\n return self._row_count\",\n \"def nrows(filename):\\n with fopen(filename, 'rb') as fdsc:\\n bufgen = takewhile(lambda x: x, (fdsc.read(1024*1024) for _ in repeat(None)))\\n return sum([buf.count(b'\\\\n') for buf in bufgen])\",\n \"def rows(self):\\n return self.prov[PROV_ROWS]\",\n \"def getNumData(self):\\n return len(self.data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7554764","0.69679993","0.69573605","0.6792377","0.6672587","0.6513209","0.649585","0.6461261","0.63839567","0.63666666","0.62937206","0.62934756","0.62633395","0.6255323","0.62531394","0.6229491","0.62243587","0.62149376","0.6199522","0.61816734","0.6143403","0.6119764","0.6068898","0.6065976","0.60635984","0.6019333","0.60095745","0.6004078","0.598484","0.59634006","0.5942197","0.5942197","0.59370714","0.59357464","0.59351766","0.59282154","0.5921665","0.5913103","0.59015846","0.5879758","0.5877579","0.58711284","0.586797","0.5866093","0.58623797","0.58475757","0.5846488","0.58416605","0.5833643","0.5833643","0.5832923","0.5809249","0.5798335","0.5795151","0.57939434","0.57912433","0.57664037","0.57591206","0.5758961","0.5754973","0.57529634","0.5747646","0.5746591","0.573754","0.57304007","0.57299453","0.5726847","0.5724813","0.57241255","0.5713855","0.5695984","0.5692786","0.56895894","0.567945","0.56770504","0.56734014","0.5668757","0.5665486","0.5662684","0.56584096","0.5647418","0.5640751","0.56395733","0.5638604","0.5634486","0.5631732","0.5626726","0.56230414","0.5622943","0.56229043","0.5619804","0.56185436","0.56090724","0.5607252","0.5607041","0.55957663","0.5593497","0.55890036","0.55882055","0.55865383"],"string":"[\n \"0.7554764\",\n \"0.69679993\",\n \"0.69573605\",\n \"0.6792377\",\n \"0.6672587\",\n \"0.6513209\",\n \"0.649585\",\n \"0.6461261\",\n \"0.63839567\",\n \"0.63666666\",\n \"0.62937206\",\n \"0.62934756\",\n \"0.62633395\",\n \"0.6255323\",\n \"0.62531394\",\n \"0.6229491\",\n \"0.62243587\",\n \"0.62149376\",\n \"0.6199522\",\n \"0.61816734\",\n \"0.6143403\",\n \"0.6119764\",\n \"0.6068898\",\n \"0.6065976\",\n \"0.60635984\",\n \"0.6019333\",\n \"0.60095745\",\n \"0.6004078\",\n \"0.598484\",\n \"0.59634006\",\n \"0.5942197\",\n \"0.5942197\",\n \"0.59370714\",\n \"0.59357464\",\n \"0.59351766\",\n \"0.59282154\",\n \"0.5921665\",\n \"0.5913103\",\n \"0.59015846\",\n \"0.5879758\",\n \"0.5877579\",\n \"0.58711284\",\n \"0.586797\",\n \"0.5866093\",\n \"0.58623797\",\n \"0.58475757\",\n \"0.5846488\",\n \"0.58416605\",\n \"0.5833643\",\n \"0.5833643\",\n \"0.5832923\",\n \"0.5809249\",\n \"0.5798335\",\n \"0.5795151\",\n \"0.57939434\",\n \"0.57912433\",\n \"0.57664037\",\n \"0.57591206\",\n \"0.5758961\",\n \"0.5754973\",\n \"0.57529634\",\n \"0.5747646\",\n \"0.5746591\",\n \"0.573754\",\n \"0.57304007\",\n \"0.57299453\",\n \"0.5726847\",\n \"0.5724813\",\n \"0.57241255\",\n \"0.5713855\",\n \"0.5695984\",\n \"0.5692786\",\n \"0.56895894\",\n \"0.567945\",\n \"0.56770504\",\n \"0.56734014\",\n \"0.5668757\",\n \"0.5665486\",\n \"0.5662684\",\n \"0.56584096\",\n \"0.5647418\",\n \"0.5640751\",\n \"0.56395733\",\n \"0.5638604\",\n \"0.5634486\",\n \"0.5631732\",\n \"0.5626726\",\n \"0.56230414\",\n \"0.5622943\",\n \"0.56229043\",\n \"0.5619804\",\n \"0.56185436\",\n \"0.56090724\",\n \"0.5607252\",\n \"0.5607041\",\n \"0.55957663\",\n \"0.5593497\",\n \"0.55890036\",\n \"0.55882055\",\n \"0.55865383\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7293856"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":1898,"cells":{"query":{"kind":"string","value":"Returns a json sructure built from given parameters. {"},"document":{"kind":"string","value":"def json_all_builder(self, customer_count, invoice_count, invl_count ):\n json_result = '{\\n'\n json_result += '\\t \"_results\":[\\n'\n json_result += '\\t\\t{ \"customer_count\": \"' + str(customer_count)\n json_result += ', \"invoice_count\": \"' + str(invoice_count)\n json_result += ', \"invl_count\": \"' + str(invl_count)\n json_result += '}\\n'\n json_result += '\\n\\t]\\n}'\n return json_result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_json_string(self, **kwargs):\n ...","def format_data(self, params):\n return json.dumps(params)","def json(self):\n robot_dict = self.robot_dict()\n target_dict = self.target_dict()\n json_str = '{'\n json_str = json_str + '\"robot_obj\" : ' + json.dumps(robot_dict) + \",\\n\"\n json_str = json_str + '\"target_obj\" : ' + json.dumps(target_dict) + \"\\n\"\n json_str = json_str + '}'\n return(json_str)","def json_friendly(self):","def json(self):\n return self.kwargs","def json(self):\n class ExtendedJSONEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\n encoded_object = obj.isoformat()\n else:\n encoded_object = json.JSONEncoder.default(self, obj)\n return encoded_object\n\n obj = {\n 'operation': self.operation,\n 'version': self.version,\n 'language': self.language,\n 'identifiers': self.identifiers,\n 'store_execute': self.store_execute,\n 'status': self.status,\n 'lineage': self.lineage,\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\n 'outputs': self.outputs,\n 'raw': self.raw\n }\n\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)","def to_json(self) -> dict:\n return {\n 'script': base64.b64encode(self.script).decode('utf-8'),\n 'parameters': list(map(lambda index: {'name': self.parameter_names[index],\n 'type': self.parameter_list[index].PascalCase()\n },\n range(len(self.parameter_list)))),\n 'deployed': self.deployed\n }","def format(self, *args):\n\t\tweb.header('Content-Type', 'application/json; charset=utf-8')\n\t\treturn json.dumps(self.content)","def create_json(sensor):\n json_object = {'building': sensor.get('building'),\n 'name': sensor.get('name'),\n 'tags': sensor.get('tags'),\n 'metadata': sensor.get('metadata'),\n 'source_identifier': sensor.get('source_identifier'),\n 'source_name': sensor.get('source_name')\n }\n return json_object","def json(self):\n return {'id': self.id, 'name': self.name, 'description': self.description}","def serialize(self):\n\t\treturn { 'type': self.type, 'parameters' : self.parameters}","def json(self):\r\n return {\"id\": self.id, \"code\": self.code, \"description\": self.description, \"xCoor\": self.x_coor, \"yCoor\": self.y_coor, \"latitude\": self.latitude,\r\n \"longitude\": self.longitude, \"waterschapId\": self.waterschap_id, \"watertypeId\": self.watertype_id, \"watertypeKrwId\": self.watertype_krw_id}","def get_json(self):\n json_item = {\"id: \": self.id,\n \"question: \": self.question,\n \"documents: \": self.documents,\n \"document_ids: \": self.document_ids,\n \"gold answers: \": self.gold}\n return json_item","def GetJSON(self):\n return json.dumps(self.GetDict())","def get_json(self):\n return {\n \"power\": self.get_power(), \n \"timestamp\": self.get_timestamp(), \n \"shortage\": self.get_shortage()\n }","def json_api():\n if 'category' in request.args:\n sqlsession = SQLSESSION()\n category = sqlsession.query(Category)\\\n .filter_by(name=request.args['category']).first()\n items = sqlsession.query(Item).filter_by(category_id=category.id)\\\n .all()\n return json.dumps({'category_id': category.id,\n 'category_name': category.name,\n 'items': [item.serialize() for item in items]})\n elif 'item' in request.args:\n sqlsession = SQLSESSION()\n items = sqlsession.query(Item).filter_by(name=request.args['item'])\\\n .all()\n return json.dumps([item.serialize() for item in items])\n sqlsession = SQLSESSION()\n categories = sqlsession.query(Category).all()\n items = sqlsession.query(Item).all()\n return json.dumps(\n {'categories': [cat.serialize() for cat in categories],\n 'items': [item.serialize() for item in items]})","def bridge_create_json():\n return {\n \"base_stations\": {\n \"id\": 98765,\n \"name\": \"New Bridge\",\n \"mode\": \"home\",\n \"hardware_id\": \"0x1234567890abcdef\",\n \"hardware_revision\": 4,\n \"firmware_version\": {\n \"wifi\": \"0.121.0\",\n \"wifi_app\": \"3.3.0\",\n \"silabs\": \"1.0.1\",\n },\n \"missing_at\": None,\n \"created_at\": \"2019-04-30T01:43:50.497Z\",\n \"updated_at\": \"2019-04-30T01:44:43.749Z\",\n \"system_id\": 12345,\n \"firmware\": {\"wifi\": \"0.121.0\", \"wifi_app\": \"3.3.0\", \"silabs\": \"1.0.1\"},\n \"links\": {\"system\": 12345},\n }\n }","def json(self):\n return {\n \"qualified_name\": self.qualified_name,\n \"description\": self.description,\n \"data\": self.data,\n }","def _json_spec(request):\n return make_param_spec(JSON_PARAMETER_SKELETON, request.param)","def getJSON(self):\n text = super().getJSON() + f', \"exchange\": \"{self.__exchange}\"'\n text += f', \"market pair\": \"{self.__market_pairs}\"'\n text += f', \"interval\": \"{self.__interval}\"}}'\n return text","def as_json(self):","def getJSONfromUI(self):\n newParams = JSONObject()\n try:\n if not self.name.getText().isEmpty():\n newParams.put(\"name\", self.name.getText())\n newParams.put(\"strategy\", self.strategy.getSelectedItem().__str__())\n newParams.put(\"metagameStrategy\", self.metagameStrategy.getSelectedItem().__str__())\n newParams.put(\"stateMachine\", self.stateMachine.getSelectedItem().__str__())\n newParams.put(\"cacheStateMachine\", self.cacheStateMachine.isSelected())\n newParams.put(\"maxPlys\", self.maxPlys.getModel().getValue())\n newParams.put(\"heuristicFocus\", self.heuristicFocus.getModel().getValue())\n newParams.put(\"heuristicMobility\", self.heuristicMobility.getModel().getValue())\n newParams.put(\"heuristicOpponentFocus\", self.heuristicOpponentFocus.getModel().getValue())\n newParams.put(\"heuristicOpponentMobility\", self.heuristicOpponentMobility.getModel().getValue())\n newParams.put(\"mcDecayRate\", self.mcDecayRate.getModel().getValue())\n except JSONException as je:\n je.printStackTrace()\n return newParams","def json(self):\n return {\n 'id': self.id,\n 'name': self.name\n }","def data_json(request):\n json_data = []\n for resource in Resource.objects.all():\n record = {} \n record['title'] = resource.name\n record['description'] = resource.description\n record['keyword'] = resource.csw_keywords.split(',')\n record['modified'] = resource.last_updated\n record['publisher'] = resource.organization\n record['contactPoint'] = resource.metadata_contact\n record['mbox'] = resource.contact_email\n record['identifier'] = resource.csw_identifier\n if resource.is_published:\n record['accessLevel'] = 'public'\n else:\n record['accessLevel'] = 'non-public'\n\n json_data.append(record)\n\n return HttpResponse(json.dumps(json_data), 'application/json')","def json(self) -> dict:\n return {\n 'id': self.id,\n 'requestType': self.request_type.name,\n 'isProcessed': self.is_processed,\n 'serviceName': self.service_name.name,\n 'isAdmin': self.is_admin,\n 'creationDate': LegislationDatetime.as_legislation_timezone(self.creation_date).isoformat()\n }","def construct_json(self):\n\n if 'message' not in self.data:\n self.data['message'] = self.message\n\n if self.status == 200:\n self.data['status'] = 'OK'\n else:\n self.data['status'] = 'Not OK'\n\n return json.dumps(self.data)","def json(self):\n raise NotImplementedError(\"JSON not specified!\")","def generate(self, sorted=False):\n json_result = json.dumps(self.generate_dict(), sort_keys=sorted)\n return json_result","def get_params(self):\n return {}","def create_gen_json(self, out_file):\n\n params = self.create_package_dict()\n with open(out_file, 'w') as fp:\n json.dump(params, fp)","def _build_data_string(self, params):\n return \"{}={}\".format(self.KLAVIYO_DATA_VARIABLE, quote(json.dumps(params)))","def generate_hyperoptimize_json(parameters: Dict[str, List[Any]], path_json: str):\n if not path_json.endswith(\".json\"):\n path_json += \".json\"\n with open(path_json, \"w\") as file:\n json.dump(parameters, file, indent=2)\n\n print(\"\\n======= JSON string of hyperparameters =======\\n\")\n json_formatted_str = json.dumps(parameters, indent=2)\n print(json_formatted_str)\n print(\"\\n======= End JSON string =======\\n\")\n print(f\"Saved hyperoptimize json file at: {path_json}\")","def dict(self):\n\t\treturn self.json","def serialize(self) -> dict:\n return {\n \"parameters\": self.parameters,\n \"results\": self.results,\n }","def json_market_builder(self, customerID, marketID) :\n json_result = '{\\n'\n json_result += '\\t \"_results\":[\\n'\n json_result += '\\t\\t{ \"customerID\": \"' + str(customerID)\n json_result += ', \"marketID\": \"' + str(marketID)\n json_result += '}\\n'\n json_result += '\\n\\t]\\n}'\n return json_result","def toJson(p):\n data = {p.name: {'Memory': p.memory, 'Camera': p.camera, 'Battery': p.battery, 'Ram': p.ram, 'Price': p.price,\n 'Image url': p.image}}\n return data","def jsonizable_object(self):\n obj = {\n 'title': self.title,\n 'url': self.url,\n 'abstract': self.abstract\n }\n if self.metadata:\n obj['metadata'] = self.metadata\n return obj","def create_json_from_model(self):\n json = {\n \"enableAutoReply\": self.enable_auto_reply,\n \"responseSubject\": self.response_subject,\n \"responseBodyPlainText\": self.response_body_plain_text,\n \"responseBodyHtml\": self.response_body_html,\n \"restrictToContacts\": self.restrict_to_contacts,\n \"restrictToDomain\": self.restrict_to_domain,\n \"startTime\": self.start_time,\n \"endTime\": self.end_time\n }\n return json","def to_init_json(self) -> JSON:\n pass","def params_helper(self,**kwargs):\n\n dic = {'output' : 'json, xml, kml',\n 'maxresults' : 'limit on max number of results returned ; Default is limited to 100',\n 'countrycode' : 'GB, US etc ISO Country Code ==> Only 2 caracters !',\n 'latitude' : 'latitude reference for distance calculation',\n 'distance' : 'return results based on specified distance from specified latitude/longitude',\n 'distanceunit' : 'Miles or km',\n 'operatorid' : 'exact match on a given EVSE operator id (comma separated list)',\n 'connectiontypeid' : ' exact match on a given connection type id (comma separated list)',\n 'countryid' : 'exact match on a given country id (comma separated list)',\n 'levelid' : 'exact match on a given charging level (1-3) id (comma separated list)',\n 'minpowerkw' : 'minimum output power in kW (this information is not known for many locations)',\n 'usagetypeid' : 'exact match on a given usage type id (comma separated list) ',\n 'statustypeid' : ' exact match on a given status type id (comma separated list)',\n 'dataproviderid ' : 'exact match on a given data provider id id (comma separated list). Use opendata=true for only OCM provided (\"Open\") data.',\n 'modifiedsince' : 'POIs modified since the given date (UTC) e.g. 2016-09-15T09:30',\n 'opendata' : ' true or false. Set to true to include only Open Data licensed content, false to return only non-open licensed data. By default all available data is returned.',\n 'includecomments' : ' true or false. Set to true to also include user comments and media items (photos) per charging location. Default = false.',\n 'verbose ' : ' true or false. Set to false to get a smaller result set with null items removed. Default = true.',\n 'compact ' : 'true or false. Set to true to remove reference data objects from output (just returns IDs for common reference data such as DataProvider etc). Default = false.',\n 'camelcase' : 'true or false. Set to true to get a property names in camelCase format. Default = false',\n 'callback' : 'specify the name of the JSONP callback (if required), JSON response type only.'\n }\n\n if len(kwargs)==0 :\n\n for key in dic.keys() :\n print(key)\n\n else :\n \n for k in kwargs: \n print(dic.get(k))","def data(self, **kw):\n return dict(params=kw)","def data(self, **kw):\n return dict(params=kw)","def json(self):\n return {\n 'id': self.id,\n 'id_bank_data': self.id_bank_data,\n 'national_id_document': self.national_id_document,\n 'country': self.country,\n 'name': self.name,\n 'surname': self.surname,\n 'mail': self.mail,\n 'google_token': self.google_token,\n 'role': self.role\n }","def json(self):\n\n json = {}\n json['type'] = self.type\n json['value'] = self.value\n json['status'] = self.status\n json['tags'] = list(set(self.tags))\n json['relationships'] = list(set(self.relationships))\n json['whitelisted'] = self.whitelisted\n json['path'] = self.path\n\n return json","def get_json(self):\n return {'name': self.name, \n 'path': self.path, \n 'enabled': self.enabled}","def gen_python_api(json_data, model_name,model_version,endpoint=\"http://127.0.0.1:8400\"):\n\n code_template = \"\"\"#!/usr/bin/env python\n\n import requests\n\n def main():\n #endpoint = \"http://127.0.0.1:8000\"\n endpoint = {{endpoint}}\n param={\"model_name\": \"{{ model_name }}\", \"model_version\": \"{{ model_version }}\"}\n json_data = {{json_data}}\n result = requests.post(endpoint, param=param,json=json_data)\n print(result.text)\n\n if __name__ == \"__main__\":\n main()\n \"\"\"\n\n generated_tensor_data_string = json.dumps(json_data)\n template = Template(code_template)\n generate_code = template.render(\n model_name=model_name, model_version=model_version,json_data=generated_tensor_data_string,endpoint=endpoint)\n logging.debug(\"Generate the code in Python:\\n{}\".format(generate_code))\n return generate_code","def __json__(self) -> dict[Any, Any]:\n return self.dict(\n include={\n **{k: ... for k in self.dict().keys() if k != \"input\"},\n \"input\": {\n \"dataset\": {\"id\"},\n \"asset\": {\"id\"},\n },\n },\n exclude={\n \"steps\": {\"__all__\": {\"id\"}},\n },\n )","def __json_encode__(self) -> Dict[str, Any]:\n return {\"figure\": self.figure, \"name\": self.name, \"metadata\": self.metadata}","def build(self, data: dict):","def generate_json_query(self):\n if not self.properties:\n print(\"ERROR: no properties given to generate JSON query.\")\n raise ValueError\n\n if self.data_type == DataType.ENTRY:\n q_str = \"entry_ids\"\n elif \"entit\" in self.data_type.value:\n if \"instance\" in self.data_type.value:\n q_str = \"instance_ids\"\n else:\n q_str = \"entity_ids\"\n elif self.data_type == DataType.ASSEMBLY:\n q_str = \"assembly_ids\"\n elif self.data_type == DataType.CHEMICAL_COMPONENT:\n q_str = \"comp_ids\"\n\n data_str = f\"{self.data_type.value}({q_str}: [\" + \",\".join(f\"\\\"{w}\\\"\" for w in self.id) + \"])\"\n\n props_string = \"\"\n for key, val in self.properties.items():\n if len(val) == 0:\n props_string += f\"{key},\"\n else:\n props_string += f\"{key} {{\" + \",\".join(val) + \"}\"\n\n self.json_query = {'query': \"{\" + data_str + \"{\" + props_string + \"}}\"}","def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:","def to_api_repr(self):\n value = self.value\n converter = _SCALAR_VALUE_TO_JSON_PARAM.get(self.type_)\n if converter is not None:\n value = converter(value)\n resource = {\n \"parameterType\": {\"type\": self.type_},\n \"parameterValue\": {\"value\": value},\n }\n if self.name is not None:\n resource[\"name\"] = self.name\n return resource","def json(self) -> Dict[str, Any]:\n return {\n \"product_id\": self.product_id,\n \"detection_index\": self.detection_index,\n \"product_name\": self.class_name,\n \"confidence\": self.conf,\n \"bounding_box\": [int(coord)\n for coord in self.scale_coordinates.round()],\n \"top_k_product_names\": self.top_k_names,\n \"top_k_confidences\": self.top_k_confidences,\n \"top_k_product_ids\": self.top_k_product_ids,\n \"top_k_detection_indices\": self.top_k_indices\n }","def render_dictionary(self): \n asset_json = {\n 'name': self.name,\n 'product_name': self.product_name,\n 'product_vendor': self.product_vendor,\n 'configuration': self.configuration,\n 'description': self.description,\n 'primary_users': self.primary_users,\n 'primary_voting': self.primary_voting,\n 'secondary_users': self.secondary_users,\n 'secondary_voting': self.secondary_voting,\n 'tags': self.tags,\n 'type': self.asset_type,\n 'action_whitelist': self.action_whitelist\n }\n\n if self.ingest_container_label:\n asset_json['ingest'] = {\n 'container_label': self.ingest_container_label,\n 'interval_mins': self.ingest_interval_mins,\n 'poll': self.ingest_poll,\n 'start_time_epoch_utc': self.ingest_start_time\n }\n\n return asset_json","def parameters(self):\n return {}","def gen_output(json_dct, *args):\n keys_to_add = ('job_title', 'location', 'date', 'company', 'num_stars')\n for arg, key in zip(args, keys_to_add): \n if arg: \n json_dct[key] = arg\n\n return json_dct","def json_response(self, request, *args, **kwargs):\n\n return HttpResponse(self.construct_json(),\n content_type='application/json',\n mimetype='application/json', status=self.status)","def AssembleProjectParametersJson(KratosWindowManager,root_path):\n\n ##Create the dict.\n projectparameters_dict = {}\n\n projectparameters_dict[\"problem_data\"] = pp.problem_data_dict\n if KratosWindowManager.is2D:\n projectparameters_dict[\"problem_data\"][\"domain_size\"]=2\n \n for key in KratosWindowManager.SSsave:\n pp.solver_settings[key] = KratosWindowManager.SSsave[key] \n\n projectparameters_dict[\"solver_settings\"] = pp.solver_settings\n \n projectparameters_dict[\"solver_settings\"][\"model_import_settings\"][\"input_filename\"]=root_path[\"mdpa_name\"]\n\n\n\n\n\n projectparameters_dict[\"output_configuration\"] = pp.output_configuration_value\n\n projectparameters_dict[\"list_other_processes\"] = []\n projectparameters_dict[\"contact_process_list\"] = []\n\n\n projectparameters_dict[\"loads_process_list\"]=[]\n projectparameters_dict[\"constraints_process_list\"]=[]\n for boundarycondition in KratosWindowManager.boundaryConditionEditor:\n if boundarycondition.load_process_list:\n projectparameters_dict[\"solver_settings\"][\"processes_sub_model_part_list\"][1]=boundarycondition.name\n projectparameters_dict[\"loads_process_list\"].append(boundarycondition.load_process_list)\n \n if boundarycondition.constrain_process_list:\n projectparameters_dict[\"solver_settings\"][\"processes_sub_model_part_list\"][0]=boundarycondition.name\n projectparameters_dict[\"constraints_process_list\"].append(boundarycondition.constrain_process_list)\n if boundarycondition.entityType=='Element':## if element, it is the domain and get its name\n projectparameters_dict[\"solver_settings\"][\"problem_domain_sub_model_part_list\"][0]=boundarycondition.name\n if(DEBUG): \n print(projectparameters_dict)\n return pp.WriteProjectToJson(projectparameters_dict)","def create() -> TJsonResponse:\n if request.headers['Content-Type'] == 'application/json':\n url = request.json.get('url')\n else:\n url = request.form.get('url')\n if not url:\n return jsonify(error='bad request'), 400\n result = scrape.scrape_meta_for_url(url)\n inserted_id, tags = result.get()\n url_hash = encode(inserted_id)\n response_body: Dict[str, Any] = jsonify(hash=url_hash, short_url=f'https://fanlens.io/@{url_hash}', tags=tags)\n return response_body","def get_json(box=None, \n out = 'dict', \n language = 'en'):\n \n table_url = box.children[3].value\n variables = get_variables(full_url = table_url)\n nvars = len(box.children[2].children)\n var_list = list(range(nvars))\n query_element = {}\n \n # create a dict of strings, one for each variable that specifies \n # the json-stat that selects the variables/values\n \n for x in var_list:\n value_list = str(list(box.children[2].children[x].value))\n query_element[x] = '{{\"code\": \"{code}\", \"selection\": {{\"filter\": \"item\", \"values\": {values} }}}}'.format(\n code = variables[x]['code'], \n values = value_list)\n query_element[x] = query_element[x].replace(\"\\'\", '\"')\n \n all_elements = str(list(query_element.values()))\n all_elements = all_elements.replace(\"\\'\", \"\")\n\n query = '{{\"query\": {all_elements} , \"response\": {{\"format\": \"json-stat\" }}}}'.format(all_elements = all_elements)\n \n if out == 'dict':\n query = ast.literal_eval(query)\n \n \n # todo: build it as a dictionary to start with (and not a string that is made into a dict as now)\n # todo: add error message if required variables are not selected\n # todo: avoid repeat downloading of same information \n # eg. get_variables is sometimes used three times before a table is downloaded\n \n return query","def json(self, **kwargs):\n return json.loads(self.content, **kwargs)","def json(data):\n if isinstance(data, dict):\n data = ujson.encode(data)\n uid = str(uuid.uuid4())\n display(HTML('<div id=\"{0}\" style=\"height: 600px; width:100%;\"></div>'.format(uid)))\n display(Javascript(\"\"\"\n require([\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\"], function() {\n document.getElementById('%s').appendChild(renderjson(%s))\n });\n \"\"\" % (uid, data)))","def _build_payload(self, query):\n\n def _normalize_payload(payload):\n if isinstance(payload, ClientObject) or isinstance(payload, ClientValue):\n return payload.to_json(self._default_json_format)\n elif isinstance(payload, dict):\n return {k: _normalize_payload(v) for k, v in payload.items() if v is not None}\n elif isinstance(payload, list):\n return [_normalize_payload(item) for item in payload]\n return payload\n\n json = _normalize_payload(query.parameters_type)\n if isinstance(query, ServiceOperationQuery) and query.parameters_name is not None:\n json = {query.parameters_name: json}\n return json","def __json_init__(cls, **kwargs):\n return cls(**kwargs)","def to_json(self):\n params = {\n 'id': self.id,\n 'team_1_id': self.team_1_id,\n 'team_2_id': self.team_2_id,\n 'table': self.table,\n 'score_1': self.score_1,\n 'score_2': self.score_2,\n 'start_date': self.start_date.isoformat() if self.start_date else None,\n 'end_date': self.end_date.isoformat() if self.end_date else None,\n 'version': self.version,\n 'created_on': self.created_on.isoformat() if self.created_on else None\n }\n\n return params","def create_custom():\n # Extract initialisation parameters\n alpha = request.args.get('alpha')\n alpha = float(alpha)\n generations = request.args.get('generations')\n generations = int(generations)\n beta = request.args.get('beta')\n beta = float(beta)\n pec = request.args.get('pec')\n pec = float(pec)\n q = request.args.get('q')\n q = float(q)\n\n # Extract the custom coordinates and create a list of nodes\n coords = request.args.get('custom_coords')\n coords = str(coords)\n nodes = custom_nodes(coords)\n\n # Initialise instance\n i = Instance(nodes, alpha, beta, pec, q)\n\n return jsonify(nodes=i.nodes, alpha=i.alpha, beta=i.beta, decay=i.decay,\n min_pheromone=i.min_pheromone, q=i.q,\n local_deposit=i.local_deposit, distances=i.distances,\n pheromones=i.pheromones, ants=i.ants, shortest_path=i.shortest_path,\n min_distance=i.min_distance, message=\"Instance Initialised\")","def to_json(self, *args, **kwargs):\n return json.dumps(self.serialize(), *args, **kwargs)","def json(self):\n return {\n '_id' : self._id,\n 'name' : self.name,\n 'description' : self.description,\n }","def create_json(self, request, qs):\n\n j = Work.objects.get_dict(qs)\n\n response = JsonResponse(j, json_dumps_params={'indent': 4})\n name = '{}{}'.format(\n settings.PUBLISHER_CODE, datetime.now().toordinal())\n cd = 'attachment; filename=\"{}.json\"'.format(name)\n response['Content-Disposition'] = cd\n return response","def format(cls):\n return \"json\"","def serialize(self):\n return{\n 'name':self.name,\n 'id' :self.id,\n }","def to_json(self):\n return {'name': self.__class__.__name__, 'args': self.args}","def to_json(self) -> Dict:\n return {\"function\": self.function.__name__, \"kwargs\": self.kwargs_to_json()}","def json(self) -> typing.Dict[str, typing.Any]:\n if self.getboolean(\"JSON\", \"compact\", fallback=False):\n kwargs = {\"separators\": (\",\", \":\")}\n else:\n kwargs = {\"indent\": 4}\n kwargs[\"ensure_ascii\"] = self.getboolean(\"JSON\", \"ensure ascii\", fallback=True)\n kwargs[\"sort_keys\"] = self.getboolean(\"JSON\", \"sort keys\", fallback=False)\n return kwargs","def _to_json(self):\n const_list = [\n {'key': key, 'value': value}\n for key, value in self.constants.items()\n ]\n constr_list = [\n {'key': key, 'value': value}\n for key, value in self.constraints.items()\n ]\n\n json_task = {\n 'name': self._name,\n 'profile': self._profile,\n 'pooluuid': self._pooluuid,\n 'constants': const_list,\n 'constraints': constr_list\n }\n\n if self._shortname is not None:\n json_task['shortname'] = self._shortname\n\n alldisk = all(isinstance(x, Disk) for x in self._resource_objects)\n allbucket = all(isinstance(x, Bucket) for x in self._resource_objects)\n\n if alldisk or allbucket:\n self._resource_objects_ids = [x.uuid for x in self._resource_objects]\n else:\n raise ValueError(\"Can't mix Buckets and Disks as resources\")\n if allbucket:\n self._resource_type = Bucket\n json_task['resourceBuckets'] = self._resource_objects_ids\n if alldisk:\n self._resource_type = Disk\n json_task['resourceDisks'] = self._resource_objects_ids\n\n if self._result_object is not None:\n self._result_type = type(self._result_object)\n if isinstance(self._result_object, Bucket):\n json_task['resultBucket'] = self._result_object.uuid\n elif isinstance(self._result_object, Disk):\n json_task['resultDisk'] = self._result_object.uuid\n\n if self._advanced_range is not None:\n json_task['advancedRanges'] = self._advanced_range\n else:\n json_task['instanceCount'] = self._instancecount\n\n json_task[\"tags\"] = self._tags\n\n if self._snapshot_whitelist is not None:\n json_task['snapshotWhitelist'] = self._snapshot_whitelist\n if self._snapshot_blacklist is not None:\n json_task['snapshotBlacklist'] = self._snapshot_blacklist\n if self._results_whitelist is not None:\n json_task['resultsWhitelist'] = self._results_whitelist\n if self._results_blacklist is not None:\n json_task['resultsBlacklist'] = self._results_blacklist\n return json_task","def __json_encode__(self):\r\n return self.config()","def encode_params(params, **kwargs):\n cleaned = clean_params(params, **kwargs)\n return json.dumps(cleaned)","def getJsonObject(self):\n jsonObject = {\n 'id': self.id,\n 'name': self.name,\n 'cards': [self.cards[card].getJsonObject() for card in self.cards] if self.cards else [\"\"]\n }\n return jsonObject","def to_json(self, *args, **kwargs):\n data = self.to_dict()\n\n return json_util.dumps(data)","def as_json(self, args=None):\n seg_data = _dict()\n seg_data[u'id'] = int(self.id)\n seg_data[u'parentID'] = int(self.parent_id)\n if self.biological_annotation is not None:\n seg_data[u'biologicalAnnotation'] = self.biological_annotation.as_json(args=args)\n if self.complexes_and_macromolecules:\n complexes = list()\n for _complex in self.complexes_and_macromolecules.complexes:\n complexes.append(_complex)\n macromolecules = list()\n for macromolecule in self.complexes_and_macromolecules.macromolecules:\n macromolecules.append(macromolecule)\n seg_data[u'complexesAndMacromolecules'] = {\n u'complexes': complexes,\n u'macromolecules': macromolecules,\n }\n seg_data.update(self.colour.as_json(args=args))\n # seg_data[u'colour'] = tuple(map(float, self.colour.value))\n if self.meshes:\n seg_data[u'meshList'] = len(self.meshes)\n if self.shapes:\n seg_data[u'shapePrimitiveList'] = len(self.shapes)\n return seg_data","def json(self, value):\n\n self.operation = value['operation']\n self.version = value['version']\n self.language = value['language']\n self.identifiers = value['identifiers']\n self.store_execute = value['store_execute']\n self.status = value['status']\n self.lineage = value['lineage']\n self.outputs = value['outputs']\n self.raw = value['raw']\n self.inputs = {}\n\n for identifier in value['inputs']:\n inpt = None\n inpt_defs = value['inputs'][identifier]\n\n for inpt_def in inpt_defs:\n\n if inpt_def['type'] == 'complex':\n inpt = ComplexInput(\n identifier=inpt_def['identifier'],\n title=inpt_def.get('title'),\n abstract=inpt_def.get('abstract'),\n workdir=inpt_def.get('workdir'),\n data_format=Format(\n schema=inpt_def['data_format'].get('schema'),\n extension=inpt_def['data_format'].get('extension'),\n mime_type=inpt_def['data_format']['mime_type'],\n encoding=inpt_def['data_format'].get('encoding')\n ),\n supported_formats=[\n Format(\n schema=infrmt.get('schema'),\n extension=infrmt.get('extension'),\n mime_type=infrmt['mime_type'],\n encoding=infrmt.get('encoding')\n ) for infrmt in inpt_def['supported_formats']\n ],\n mode=MODE.NONE\n )\n inpt.file = inpt_def['file']\n elif inpt_def['type'] == 'literal':\n\n allowed_values = []\n for allowed_value in inpt_def['allowed_values']:\n if allowed_value['type'] == 'anyvalue':\n allowed_values.append(AnyValue())\n elif allowed_value['type'] == 'novalue':\n allowed_values.append(NoValue())\n elif allowed_value['type'] == 'valuesreference':\n allowed_values.append(ValuesReference())\n elif allowed_value['type'] == 'allowedvalue':\n allowed_values.append(AllowedValue(\n allowed_type=allowed_value['allowed_type'],\n value=allowed_value['value'],\n minval=allowed_value['minval'],\n maxval=allowed_value['maxval'],\n spacing=allowed_value['spacing'],\n range_closure=allowed_value['range_closure']\n ))\n\n inpt = LiteralInput(\n identifier=inpt_def['identifier'],\n title=inpt_def.get('title'),\n abstract=inpt_def.get('abstract'),\n data_type=inpt_def.get('data_type'),\n workdir=inpt_def.get('workdir'),\n allowed_values=AnyValue,\n uoms=inpt_def.get('uoms'),\n mode=inpt_def.get('mode')\n )\n inpt.uom = inpt_def.get('uom')\n inpt.data = inpt_def.get('data')\n\n elif inpt_def['type'] == 'bbox':\n inpt = BBoxInput(\n identifier=inpt_def['identifier'],\n title=inpt_def['title'],\n abstract=inpt_def['abstract'],\n crss=inpt_def['crs'],\n dimensions=inpt_def['dimensions'],\n workdir=inpt_def['workdir'],\n mode=inpt_def['mode']\n )\n inpt.ll = inpt_def['bbox'][0]\n inpt.ur = inpt_def['bbox'][1]\n\n if identifier in self.inputs:\n self.inputs[identifier].append(inpt)\n else:\n self.inputs[identifier] = [inpt]","def __init__( self, parameters={} ):\n self.params = {}","def create_params(self):\n\n params = {'time_step':\n DesignParameter('time_step',\n unit='s',\n description='Time step with which the component model will be discretized'),\n 'horizon':\n DesignParameter('horizon',\n unit='s',\n description='Horizon of the optimization problem'),\n 'lines': DesignParameter('lines',\n unit='-',\n description='List of names of the lines that can be found in the network, e.g. '\n '\\'supply\\' and \\'return\\'',\n val=['supply', 'return'])\n }\n return params","def json_data(self):\n self.check_proof()\n return {\n \"vars\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\n \"proof\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\n for item in self.prf.items], []),\n \"report\": self.rpt.json_data(),\n \"method_sig\": self.get_method_sig()\n }","def json_meta_obj(location, from_date, to_date, item):\n return '{\"meta\":{\"location\":\"' + location \\\n + '\",\"from\":\"' + from_date \\\n + '\",\"to\":\"' + to_date \\\n + '\",\"item\":\"' + item \\\n + '\"},\\n'","def readjson():\n uuid = request.query.get('uuid','')\n if(uuid == \"\"):\n result = { \"code\":\"fail\", \"message\":\"empty uuid\"}\n return result\n else:\n zenodo = ZenodoRequest(uuid)\n return {'data':zenodo.saveInDatabase()}","def __json__(self, request=None):\n # start = self.start.isoformat() if self.start else None\n # end = self.end.isoformat() if self.end else None\n return dict(\n timeref_type=\"daterange\",\n interval=self.interval,\n start=self.start.isoformat(),\n end=self.end.isoformat(),\n )","def get_json(self, cid, **kwargs):\n\t\treturn self.cat(cid, decoder='json', **kwargs)","def format_json(self,query_results):\n results=query_results.data\n factory=factory_json()\n dump=factory.dumps(results)\n print(dump)\n # TODO return output for this\n return \"\"","def json(self):\n d = OrderedDict()\n if self.id is not None:\n d[\"id\"] = str(self.id)\n d[\"status\"] = self.http_status\n d[\"title\"] = self.title\n if self.about:\n d[\"links\"] = OrderedDict()\n d[\"links\"][\"about\"] = self.about\n if self.code:\n d[\"code\"] = self.code\n if self.detail:\n d[\"detail\"] = self.detail\n if self.source_pointer or self.source_parameter:\n d[\"source\"] = OrderedDict()\n if self.source_pointer:\n d[\"source\"][\"pointer\"] = self.source_pointer\n if self.source_parameter:\n d[\"source\"][\"parameter\"] = self.source_parameter\n if self.meta:\n d[\"meta\"] = meta\n return d","def make():\n data = {}\n data.update({'earth' : {'description': 'Planet with 20% O2 with 75% of surface covered by H2O. Humans inhabitants enjoy both of these aspects.',\n 'order' : 1,\n 'type': 'planet',\n }})\n \n return data","def toJSON(self):\n return {\n 'id': self.id,\n 'name': self.name,\n 'dependencies': self.dependencies\n }","def request_body(self):\n return json.dumps({\n \"schemaId\": self.schemaId,\n \"associationIds\": self.associationIds,\n \"name\": self.name,\n \"value\": self.value\n })","def to_json(self, *args, **kwargs):\n return json.dumps(self.serialize(primitive=True), *args, **kwargs)","def generate(self):\n return {\n 'json_class': 'Pact::ArrayLike',\n 'contents': from_term(self.matcher),\n 'min': self.minimum}","def minimal_json43():\n return {\n 'identifiers': [{\n 'identifierType': 'DOI',\n 'identifier': '10.1234/foo.bar',\n }],\n 'creators': [\n {'name': 'Nielsen, Lars Holm'},\n ],\n 'titles': [\n {'title': 'Minimal Test Case'}\n ],\n 'publisher': 'Invenio Software',\n 'publicationYear': '2016',\n 'types': {\n 'resourceType': '',\n 'resourceTypeGeneral': 'Software'\n },\n 'schemaVersion': 'http://datacite.org/schema/kernel-4'\n }","def json(self):\n ar_min_date, ar_max_date = self.get_ar_dates(\n (self.last_ar_year if self.last_ar_year else self.founding_date.year) + 1\n )\n d = {\n 'foundingDate': self.founding_date.isoformat(),\n 'identifier': self.identifier,\n 'lastModified': self.last_modified.isoformat(),\n 'lastAnnualReport': datetime.date(self.last_ar_date).isoformat() if self.last_ar_date else '',\n 'nextAnnualReport': LegislationDatetime.as_legislation_timezone_from_date(\n self.next_anniversary\n ).astimezone(timezone.utc).isoformat(),\n 'lastAnnualGeneralMeetingDate': datetime.date(self.last_agm_date).isoformat() if self.last_agm_date else '',\n 'lastLedgerTimestamp': self.last_ledger_timestamp.isoformat(),\n 'legalName': self.legal_name,\n 'legalType': self.legal_type,\n 'hasRestrictions': self.restriction_ind,\n 'goodStanding': self.good_standing,\n 'arMinDate': ar_min_date.isoformat(),\n 'arMaxDate': ar_max_date.isoformat()\n }\n # if self.last_remote_ledger_timestamp:\n # # this is not a typo, we want the external facing view object ledger timestamp to be the remote one\n # d['last_ledger_timestamp'] = self.last_remote_ledger_timestamp.isoformat()\n # else:\n # d['last_ledger_timestamp'] = None\n\n if self.dissolution_date:\n d['dissolutionDate'] = datetime.date(self.dissolution_date).isoformat()\n if self.fiscal_year_end_date:\n d['fiscalYearEndDate'] = datetime.date(self.fiscal_year_end_date).isoformat()\n if self.tax_id:\n d['taxId'] = self.tax_id\n\n return d","def jasonizar(self, todosPermiso, listaPermisoEnRol, enRol):\n p='' \n pre=\"{\\\"totalpages\\\": \\\"\"+str(self.totalPages) + \"\\\",\\\"currpage\\\" : \\\"\" + str(self.currPage) + \"\\\",\\\"totalrecords\\\" : \\\"\" \n pre= pre + str(self.totalRecords) + \" \\\",\\\"invdata\\\" : [\" \n SIoNO='no'\n if(enRol=='Todos'):\n for permiso in todosPermiso:\n if permiso in listaPermisoEnRol:\n SIoNO='Si'\n else:\n SIoNO='No'\n \n p=p+\"{\\\"nombre\\\": \\\"\"+permiso.nombre+\"\\\",\\\"descripcion\\\": \\\"\"+permiso.descripcion+\"\\\", \\\"enRol\\\": \\\"\"+SIoNO+\"\\\",\\\"idPermiso\\\": \\\"\"+str(permiso.id)+\"\\\"},\"\n elif enRol=='Si':\n SIoNO='Si'\n for permiso in listaPermisoEnRol:\n p=p+\"{\\\"nombre\\\": \\\"\"+permiso.nombre+\"\\\",\\\"descripcion\\\": \\\"\"+permiso.descripcion+\"\\\", \\\"enRol\\\": \\\"\"+SIoNO+\"\\\",\\\"idPermiso\\\": \\\"\"+str(permiso.id)+\"\\\"},\"\n elif enRol=='No':\n SIoNO='No'\n for permiso in todosPermiso:\n if permiso not in listaPermisoEnRol:\n p=p+\"{\\\"nombre\\\": \\\"\"+permiso.nombre+\"\\\",\\\"descripcion\\\": \\\"\"+permiso.descripcion+\"\\\", \\\"enRol\\\": \\\"\"+SIoNO+\"\\\",\\\"idPermiso\\\": \\\"\"+str(permiso.id)+\"\\\"},\"\n \n \n p=p[0:len(p)-1] \n p=p+\"]}\" \n p=pre+p\n return p","def to_api_repr(self):\n s_types = {}\n values = {}\n for name, value in self.struct_values.items():\n type_ = self.struct_types[name]\n if type_ in (\"STRUCT\", \"ARRAY\"):\n repr_ = value.to_api_repr()\n s_types[name] = {\"name\": name, \"type\": repr_[\"parameterType\"]}\n values[name] = repr_[\"parameterValue\"]\n else:\n s_types[name] = {\"name\": name, \"type\": {\"type\": type_}}\n converter = _SCALAR_VALUE_TO_JSON_PARAM.get(type_)\n if converter is not None:\n value = converter(value)\n values[name] = {\"value\": value}\n\n resource = {\n \"parameterType\": {\n \"type\": \"STRUCT\",\n \"structTypes\": [s_types[key] for key in self.struct_types],\n },\n \"parameterValue\": {\"structValues\": values},\n }\n if self.name is not None:\n resource[\"name\"] = self.name\n return resource","def test_06_CreateJson(self):\n l_buttons = self.m_api.read_all_buttons_xml(self.m_pyhouse_obj, self.m_xml.button_sect, self.m_version)\n # print('ButtonsS: {0:}'.format(l_buttons))\n # print('Button 0: {0:}'.format(vars(l_buttons[0])))\n l_json = json_tools.encode_json(l_buttons)\n # print('JSON: {0:}'.format(l_json))"],"string":"[\n \"def get_json_string(self, **kwargs):\\n ...\",\n \"def format_data(self, params):\\n return json.dumps(params)\",\n \"def json(self):\\n robot_dict = self.robot_dict()\\n target_dict = self.target_dict()\\n json_str = '{'\\n json_str = json_str + '\\\"robot_obj\\\" : ' + json.dumps(robot_dict) + \\\",\\\\n\\\"\\n json_str = json_str + '\\\"target_obj\\\" : ' + json.dumps(target_dict) + \\\"\\\\n\\\"\\n json_str = json_str + '}'\\n return(json_str)\",\n \"def json_friendly(self):\",\n \"def json(self):\\n return self.kwargs\",\n \"def json(self):\\n class ExtendedJSONEncoder(json.JSONEncoder):\\n def default(self, obj):\\n if isinstance(obj, datetime.date) or isinstance(obj, datetime.time):\\n encoded_object = obj.isoformat()\\n else:\\n encoded_object = json.JSONEncoder.default(self, obj)\\n return encoded_object\\n\\n obj = {\\n 'operation': self.operation,\\n 'version': self.version,\\n 'language': self.language,\\n 'identifiers': self.identifiers,\\n 'store_execute': self.store_execute,\\n 'status': self.status,\\n 'lineage': self.lineage,\\n 'inputs': dict((i, [inpt.json for inpt in self.inputs[i]]) for i in self.inputs),\\n 'outputs': self.outputs,\\n 'raw': self.raw\\n }\\n\\n return json.dumps(obj, allow_nan=False, cls=ExtendedJSONEncoder)\",\n \"def to_json(self) -> dict:\\n return {\\n 'script': base64.b64encode(self.script).decode('utf-8'),\\n 'parameters': list(map(lambda index: {'name': self.parameter_names[index],\\n 'type': self.parameter_list[index].PascalCase()\\n },\\n range(len(self.parameter_list)))),\\n 'deployed': self.deployed\\n }\",\n \"def format(self, *args):\\n\\t\\tweb.header('Content-Type', 'application/json; charset=utf-8')\\n\\t\\treturn json.dumps(self.content)\",\n \"def create_json(sensor):\\n json_object = {'building': sensor.get('building'),\\n 'name': sensor.get('name'),\\n 'tags': sensor.get('tags'),\\n 'metadata': sensor.get('metadata'),\\n 'source_identifier': sensor.get('source_identifier'),\\n 'source_name': sensor.get('source_name')\\n }\\n return json_object\",\n \"def json(self):\\n return {'id': self.id, 'name': self.name, 'description': self.description}\",\n \"def serialize(self):\\n\\t\\treturn { 'type': self.type, 'parameters' : self.parameters}\",\n \"def json(self):\\r\\n return {\\\"id\\\": self.id, \\\"code\\\": self.code, \\\"description\\\": self.description, \\\"xCoor\\\": self.x_coor, \\\"yCoor\\\": self.y_coor, \\\"latitude\\\": self.latitude,\\r\\n \\\"longitude\\\": self.longitude, \\\"waterschapId\\\": self.waterschap_id, \\\"watertypeId\\\": self.watertype_id, \\\"watertypeKrwId\\\": self.watertype_krw_id}\",\n \"def get_json(self):\\n json_item = {\\\"id: \\\": self.id,\\n \\\"question: \\\": self.question,\\n \\\"documents: \\\": self.documents,\\n \\\"document_ids: \\\": self.document_ids,\\n \\\"gold answers: \\\": self.gold}\\n return json_item\",\n \"def GetJSON(self):\\n return json.dumps(self.GetDict())\",\n \"def get_json(self):\\n return {\\n \\\"power\\\": self.get_power(), \\n \\\"timestamp\\\": self.get_timestamp(), \\n \\\"shortage\\\": self.get_shortage()\\n }\",\n \"def json_api():\\n if 'category' in request.args:\\n sqlsession = SQLSESSION()\\n category = sqlsession.query(Category)\\\\\\n .filter_by(name=request.args['category']).first()\\n items = sqlsession.query(Item).filter_by(category_id=category.id)\\\\\\n .all()\\n return json.dumps({'category_id': category.id,\\n 'category_name': category.name,\\n 'items': [item.serialize() for item in items]})\\n elif 'item' in request.args:\\n sqlsession = SQLSESSION()\\n items = sqlsession.query(Item).filter_by(name=request.args['item'])\\\\\\n .all()\\n return json.dumps([item.serialize() for item in items])\\n sqlsession = SQLSESSION()\\n categories = sqlsession.query(Category).all()\\n items = sqlsession.query(Item).all()\\n return json.dumps(\\n {'categories': [cat.serialize() for cat in categories],\\n 'items': [item.serialize() for item in items]})\",\n \"def bridge_create_json():\\n return {\\n \\\"base_stations\\\": {\\n \\\"id\\\": 98765,\\n \\\"name\\\": \\\"New Bridge\\\",\\n \\\"mode\\\": \\\"home\\\",\\n \\\"hardware_id\\\": \\\"0x1234567890abcdef\\\",\\n \\\"hardware_revision\\\": 4,\\n \\\"firmware_version\\\": {\\n \\\"wifi\\\": \\\"0.121.0\\\",\\n \\\"wifi_app\\\": \\\"3.3.0\\\",\\n \\\"silabs\\\": \\\"1.0.1\\\",\\n },\\n \\\"missing_at\\\": None,\\n \\\"created_at\\\": \\\"2019-04-30T01:43:50.497Z\\\",\\n \\\"updated_at\\\": \\\"2019-04-30T01:44:43.749Z\\\",\\n \\\"system_id\\\": 12345,\\n \\\"firmware\\\": {\\\"wifi\\\": \\\"0.121.0\\\", \\\"wifi_app\\\": \\\"3.3.0\\\", \\\"silabs\\\": \\\"1.0.1\\\"},\\n \\\"links\\\": {\\\"system\\\": 12345},\\n }\\n }\",\n \"def json(self):\\n return {\\n \\\"qualified_name\\\": self.qualified_name,\\n \\\"description\\\": self.description,\\n \\\"data\\\": self.data,\\n }\",\n \"def _json_spec(request):\\n return make_param_spec(JSON_PARAMETER_SKELETON, request.param)\",\n \"def getJSON(self):\\n text = super().getJSON() + f', \\\"exchange\\\": \\\"{self.__exchange}\\\"'\\n text += f', \\\"market pair\\\": \\\"{self.__market_pairs}\\\"'\\n text += f', \\\"interval\\\": \\\"{self.__interval}\\\"}}'\\n return text\",\n \"def as_json(self):\",\n \"def getJSONfromUI(self):\\n newParams = JSONObject()\\n try:\\n if not self.name.getText().isEmpty():\\n newParams.put(\\\"name\\\", self.name.getText())\\n newParams.put(\\\"strategy\\\", self.strategy.getSelectedItem().__str__())\\n newParams.put(\\\"metagameStrategy\\\", self.metagameStrategy.getSelectedItem().__str__())\\n newParams.put(\\\"stateMachine\\\", self.stateMachine.getSelectedItem().__str__())\\n newParams.put(\\\"cacheStateMachine\\\", self.cacheStateMachine.isSelected())\\n newParams.put(\\\"maxPlys\\\", self.maxPlys.getModel().getValue())\\n newParams.put(\\\"heuristicFocus\\\", self.heuristicFocus.getModel().getValue())\\n newParams.put(\\\"heuristicMobility\\\", self.heuristicMobility.getModel().getValue())\\n newParams.put(\\\"heuristicOpponentFocus\\\", self.heuristicOpponentFocus.getModel().getValue())\\n newParams.put(\\\"heuristicOpponentMobility\\\", self.heuristicOpponentMobility.getModel().getValue())\\n newParams.put(\\\"mcDecayRate\\\", self.mcDecayRate.getModel().getValue())\\n except JSONException as je:\\n je.printStackTrace()\\n return newParams\",\n \"def json(self):\\n return {\\n 'id': self.id,\\n 'name': self.name\\n }\",\n \"def data_json(request):\\n json_data = []\\n for resource in Resource.objects.all():\\n record = {} \\n record['title'] = resource.name\\n record['description'] = resource.description\\n record['keyword'] = resource.csw_keywords.split(',')\\n record['modified'] = resource.last_updated\\n record['publisher'] = resource.organization\\n record['contactPoint'] = resource.metadata_contact\\n record['mbox'] = resource.contact_email\\n record['identifier'] = resource.csw_identifier\\n if resource.is_published:\\n record['accessLevel'] = 'public'\\n else:\\n record['accessLevel'] = 'non-public'\\n\\n json_data.append(record)\\n\\n return HttpResponse(json.dumps(json_data), 'application/json')\",\n \"def json(self) -> dict:\\n return {\\n 'id': self.id,\\n 'requestType': self.request_type.name,\\n 'isProcessed': self.is_processed,\\n 'serviceName': self.service_name.name,\\n 'isAdmin': self.is_admin,\\n 'creationDate': LegislationDatetime.as_legislation_timezone(self.creation_date).isoformat()\\n }\",\n \"def construct_json(self):\\n\\n if 'message' not in self.data:\\n self.data['message'] = self.message\\n\\n if self.status == 200:\\n self.data['status'] = 'OK'\\n else:\\n self.data['status'] = 'Not OK'\\n\\n return json.dumps(self.data)\",\n \"def json(self):\\n raise NotImplementedError(\\\"JSON not specified!\\\")\",\n \"def generate(self, sorted=False):\\n json_result = json.dumps(self.generate_dict(), sort_keys=sorted)\\n return json_result\",\n \"def get_params(self):\\n return {}\",\n \"def create_gen_json(self, out_file):\\n\\n params = self.create_package_dict()\\n with open(out_file, 'w') as fp:\\n json.dump(params, fp)\",\n \"def _build_data_string(self, params):\\n return \\\"{}={}\\\".format(self.KLAVIYO_DATA_VARIABLE, quote(json.dumps(params)))\",\n \"def generate_hyperoptimize_json(parameters: Dict[str, List[Any]], path_json: str):\\n if not path_json.endswith(\\\".json\\\"):\\n path_json += \\\".json\\\"\\n with open(path_json, \\\"w\\\") as file:\\n json.dump(parameters, file, indent=2)\\n\\n print(\\\"\\\\n======= JSON string of hyperparameters =======\\\\n\\\")\\n json_formatted_str = json.dumps(parameters, indent=2)\\n print(json_formatted_str)\\n print(\\\"\\\\n======= End JSON string =======\\\\n\\\")\\n print(f\\\"Saved hyperoptimize json file at: {path_json}\\\")\",\n \"def dict(self):\\n\\t\\treturn self.json\",\n \"def serialize(self) -> dict:\\n return {\\n \\\"parameters\\\": self.parameters,\\n \\\"results\\\": self.results,\\n }\",\n \"def json_market_builder(self, customerID, marketID) :\\n json_result = '{\\\\n'\\n json_result += '\\\\t \\\"_results\\\":[\\\\n'\\n json_result += '\\\\t\\\\t{ \\\"customerID\\\": \\\"' + str(customerID)\\n json_result += ', \\\"marketID\\\": \\\"' + str(marketID)\\n json_result += '}\\\\n'\\n json_result += '\\\\n\\\\t]\\\\n}'\\n return json_result\",\n \"def toJson(p):\\n data = {p.name: {'Memory': p.memory, 'Camera': p.camera, 'Battery': p.battery, 'Ram': p.ram, 'Price': p.price,\\n 'Image url': p.image}}\\n return data\",\n \"def jsonizable_object(self):\\n obj = {\\n 'title': self.title,\\n 'url': self.url,\\n 'abstract': self.abstract\\n }\\n if self.metadata:\\n obj['metadata'] = self.metadata\\n return obj\",\n \"def create_json_from_model(self):\\n json = {\\n \\\"enableAutoReply\\\": self.enable_auto_reply,\\n \\\"responseSubject\\\": self.response_subject,\\n \\\"responseBodyPlainText\\\": self.response_body_plain_text,\\n \\\"responseBodyHtml\\\": self.response_body_html,\\n \\\"restrictToContacts\\\": self.restrict_to_contacts,\\n \\\"restrictToDomain\\\": self.restrict_to_domain,\\n \\\"startTime\\\": self.start_time,\\n \\\"endTime\\\": self.end_time\\n }\\n return json\",\n \"def to_init_json(self) -> JSON:\\n pass\",\n \"def params_helper(self,**kwargs):\\n\\n dic = {'output' : 'json, xml, kml',\\n 'maxresults' : 'limit on max number of results returned ; Default is limited to 100',\\n 'countrycode' : 'GB, US etc ISO Country Code ==> Only 2 caracters !',\\n 'latitude' : 'latitude reference for distance calculation',\\n 'distance' : 'return results based on specified distance from specified latitude/longitude',\\n 'distanceunit' : 'Miles or km',\\n 'operatorid' : 'exact match on a given EVSE operator id (comma separated list)',\\n 'connectiontypeid' : ' exact match on a given connection type id (comma separated list)',\\n 'countryid' : 'exact match on a given country id (comma separated list)',\\n 'levelid' : 'exact match on a given charging level (1-3) id (comma separated list)',\\n 'minpowerkw' : 'minimum output power in kW (this information is not known for many locations)',\\n 'usagetypeid' : 'exact match on a given usage type id (comma separated list) ',\\n 'statustypeid' : ' exact match on a given status type id (comma separated list)',\\n 'dataproviderid ' : 'exact match on a given data provider id id (comma separated list). Use opendata=true for only OCM provided (\\\"Open\\\") data.',\\n 'modifiedsince' : 'POIs modified since the given date (UTC) e.g. 2016-09-15T09:30',\\n 'opendata' : ' true or false. Set to true to include only Open Data licensed content, false to return only non-open licensed data. By default all available data is returned.',\\n 'includecomments' : ' true or false. Set to true to also include user comments and media items (photos) per charging location. Default = false.',\\n 'verbose ' : ' true or false. Set to false to get a smaller result set with null items removed. Default = true.',\\n 'compact ' : 'true or false. Set to true to remove reference data objects from output (just returns IDs for common reference data such as DataProvider etc). Default = false.',\\n 'camelcase' : 'true or false. Set to true to get a property names in camelCase format. Default = false',\\n 'callback' : 'specify the name of the JSONP callback (if required), JSON response type only.'\\n }\\n\\n if len(kwargs)==0 :\\n\\n for key in dic.keys() :\\n print(key)\\n\\n else :\\n \\n for k in kwargs: \\n print(dic.get(k))\",\n \"def data(self, **kw):\\n return dict(params=kw)\",\n \"def data(self, **kw):\\n return dict(params=kw)\",\n \"def json(self):\\n return {\\n 'id': self.id,\\n 'id_bank_data': self.id_bank_data,\\n 'national_id_document': self.national_id_document,\\n 'country': self.country,\\n 'name': self.name,\\n 'surname': self.surname,\\n 'mail': self.mail,\\n 'google_token': self.google_token,\\n 'role': self.role\\n }\",\n \"def json(self):\\n\\n json = {}\\n json['type'] = self.type\\n json['value'] = self.value\\n json['status'] = self.status\\n json['tags'] = list(set(self.tags))\\n json['relationships'] = list(set(self.relationships))\\n json['whitelisted'] = self.whitelisted\\n json['path'] = self.path\\n\\n return json\",\n \"def get_json(self):\\n return {'name': self.name, \\n 'path': self.path, \\n 'enabled': self.enabled}\",\n \"def gen_python_api(json_data, model_name,model_version,endpoint=\\\"http://127.0.0.1:8400\\\"):\\n\\n code_template = \\\"\\\"\\\"#!/usr/bin/env python\\n\\n import requests\\n\\n def main():\\n #endpoint = \\\"http://127.0.0.1:8000\\\"\\n endpoint = {{endpoint}}\\n param={\\\"model_name\\\": \\\"{{ model_name }}\\\", \\\"model_version\\\": \\\"{{ model_version }}\\\"}\\n json_data = {{json_data}}\\n result = requests.post(endpoint, param=param,json=json_data)\\n print(result.text)\\n\\n if __name__ == \\\"__main__\\\":\\n main()\\n \\\"\\\"\\\"\\n\\n generated_tensor_data_string = json.dumps(json_data)\\n template = Template(code_template)\\n generate_code = template.render(\\n model_name=model_name, model_version=model_version,json_data=generated_tensor_data_string,endpoint=endpoint)\\n logging.debug(\\\"Generate the code in Python:\\\\n{}\\\".format(generate_code))\\n return generate_code\",\n \"def __json__(self) -> dict[Any, Any]:\\n return self.dict(\\n include={\\n **{k: ... for k in self.dict().keys() if k != \\\"input\\\"},\\n \\\"input\\\": {\\n \\\"dataset\\\": {\\\"id\\\"},\\n \\\"asset\\\": {\\\"id\\\"},\\n },\\n },\\n exclude={\\n \\\"steps\\\": {\\\"__all__\\\": {\\\"id\\\"}},\\n },\\n )\",\n \"def __json_encode__(self) -> Dict[str, Any]:\\n return {\\\"figure\\\": self.figure, \\\"name\\\": self.name, \\\"metadata\\\": self.metadata}\",\n \"def build(self, data: dict):\",\n \"def generate_json_query(self):\\n if not self.properties:\\n print(\\\"ERROR: no properties given to generate JSON query.\\\")\\n raise ValueError\\n\\n if self.data_type == DataType.ENTRY:\\n q_str = \\\"entry_ids\\\"\\n elif \\\"entit\\\" in self.data_type.value:\\n if \\\"instance\\\" in self.data_type.value:\\n q_str = \\\"instance_ids\\\"\\n else:\\n q_str = \\\"entity_ids\\\"\\n elif self.data_type == DataType.ASSEMBLY:\\n q_str = \\\"assembly_ids\\\"\\n elif self.data_type == DataType.CHEMICAL_COMPONENT:\\n q_str = \\\"comp_ids\\\"\\n\\n data_str = f\\\"{self.data_type.value}({q_str}: [\\\" + \\\",\\\".join(f\\\"\\\\\\\"{w}\\\\\\\"\\\" for w in self.id) + \\\"])\\\"\\n\\n props_string = \\\"\\\"\\n for key, val in self.properties.items():\\n if len(val) == 0:\\n props_string += f\\\"{key},\\\"\\n else:\\n props_string += f\\\"{key} {{\\\" + \\\",\\\".join(val) + \\\"}\\\"\\n\\n self.json_query = {'query': \\\"{\\\" + data_str + \\\"{\\\" + props_string + \\\"}}\\\"}\",\n \"def json(self) -> Dict[str, Union[List, Dict, str, int, float]]:\",\n \"def to_api_repr(self):\\n value = self.value\\n converter = _SCALAR_VALUE_TO_JSON_PARAM.get(self.type_)\\n if converter is not None:\\n value = converter(value)\\n resource = {\\n \\\"parameterType\\\": {\\\"type\\\": self.type_},\\n \\\"parameterValue\\\": {\\\"value\\\": value},\\n }\\n if self.name is not None:\\n resource[\\\"name\\\"] = self.name\\n return resource\",\n \"def json(self) -> Dict[str, Any]:\\n return {\\n \\\"product_id\\\": self.product_id,\\n \\\"detection_index\\\": self.detection_index,\\n \\\"product_name\\\": self.class_name,\\n \\\"confidence\\\": self.conf,\\n \\\"bounding_box\\\": [int(coord)\\n for coord in self.scale_coordinates.round()],\\n \\\"top_k_product_names\\\": self.top_k_names,\\n \\\"top_k_confidences\\\": self.top_k_confidences,\\n \\\"top_k_product_ids\\\": self.top_k_product_ids,\\n \\\"top_k_detection_indices\\\": self.top_k_indices\\n }\",\n \"def render_dictionary(self): \\n asset_json = {\\n 'name': self.name,\\n 'product_name': self.product_name,\\n 'product_vendor': self.product_vendor,\\n 'configuration': self.configuration,\\n 'description': self.description,\\n 'primary_users': self.primary_users,\\n 'primary_voting': self.primary_voting,\\n 'secondary_users': self.secondary_users,\\n 'secondary_voting': self.secondary_voting,\\n 'tags': self.tags,\\n 'type': self.asset_type,\\n 'action_whitelist': self.action_whitelist\\n }\\n\\n if self.ingest_container_label:\\n asset_json['ingest'] = {\\n 'container_label': self.ingest_container_label,\\n 'interval_mins': self.ingest_interval_mins,\\n 'poll': self.ingest_poll,\\n 'start_time_epoch_utc': self.ingest_start_time\\n }\\n\\n return asset_json\",\n \"def parameters(self):\\n return {}\",\n \"def gen_output(json_dct, *args):\\n keys_to_add = ('job_title', 'location', 'date', 'company', 'num_stars')\\n for arg, key in zip(args, keys_to_add): \\n if arg: \\n json_dct[key] = arg\\n\\n return json_dct\",\n \"def json_response(self, request, *args, **kwargs):\\n\\n return HttpResponse(self.construct_json(),\\n content_type='application/json',\\n mimetype='application/json', status=self.status)\",\n \"def AssembleProjectParametersJson(KratosWindowManager,root_path):\\n\\n ##Create the dict.\\n projectparameters_dict = {}\\n\\n projectparameters_dict[\\\"problem_data\\\"] = pp.problem_data_dict\\n if KratosWindowManager.is2D:\\n projectparameters_dict[\\\"problem_data\\\"][\\\"domain_size\\\"]=2\\n \\n for key in KratosWindowManager.SSsave:\\n pp.solver_settings[key] = KratosWindowManager.SSsave[key] \\n\\n projectparameters_dict[\\\"solver_settings\\\"] = pp.solver_settings\\n \\n projectparameters_dict[\\\"solver_settings\\\"][\\\"model_import_settings\\\"][\\\"input_filename\\\"]=root_path[\\\"mdpa_name\\\"]\\n\\n\\n\\n\\n\\n projectparameters_dict[\\\"output_configuration\\\"] = pp.output_configuration_value\\n\\n projectparameters_dict[\\\"list_other_processes\\\"] = []\\n projectparameters_dict[\\\"contact_process_list\\\"] = []\\n\\n\\n projectparameters_dict[\\\"loads_process_list\\\"]=[]\\n projectparameters_dict[\\\"constraints_process_list\\\"]=[]\\n for boundarycondition in KratosWindowManager.boundaryConditionEditor:\\n if boundarycondition.load_process_list:\\n projectparameters_dict[\\\"solver_settings\\\"][\\\"processes_sub_model_part_list\\\"][1]=boundarycondition.name\\n projectparameters_dict[\\\"loads_process_list\\\"].append(boundarycondition.load_process_list)\\n \\n if boundarycondition.constrain_process_list:\\n projectparameters_dict[\\\"solver_settings\\\"][\\\"processes_sub_model_part_list\\\"][0]=boundarycondition.name\\n projectparameters_dict[\\\"constraints_process_list\\\"].append(boundarycondition.constrain_process_list)\\n if boundarycondition.entityType=='Element':## if element, it is the domain and get its name\\n projectparameters_dict[\\\"solver_settings\\\"][\\\"problem_domain_sub_model_part_list\\\"][0]=boundarycondition.name\\n if(DEBUG): \\n print(projectparameters_dict)\\n return pp.WriteProjectToJson(projectparameters_dict)\",\n \"def create() -> TJsonResponse:\\n if request.headers['Content-Type'] == 'application/json':\\n url = request.json.get('url')\\n else:\\n url = request.form.get('url')\\n if not url:\\n return jsonify(error='bad request'), 400\\n result = scrape.scrape_meta_for_url(url)\\n inserted_id, tags = result.get()\\n url_hash = encode(inserted_id)\\n response_body: Dict[str, Any] = jsonify(hash=url_hash, short_url=f'https://fanlens.io/@{url_hash}', tags=tags)\\n return response_body\",\n \"def get_json(box=None, \\n out = 'dict', \\n language = 'en'):\\n \\n table_url = box.children[3].value\\n variables = get_variables(full_url = table_url)\\n nvars = len(box.children[2].children)\\n var_list = list(range(nvars))\\n query_element = {}\\n \\n # create a dict of strings, one for each variable that specifies \\n # the json-stat that selects the variables/values\\n \\n for x in var_list:\\n value_list = str(list(box.children[2].children[x].value))\\n query_element[x] = '{{\\\"code\\\": \\\"{code}\\\", \\\"selection\\\": {{\\\"filter\\\": \\\"item\\\", \\\"values\\\": {values} }}}}'.format(\\n code = variables[x]['code'], \\n values = value_list)\\n query_element[x] = query_element[x].replace(\\\"\\\\'\\\", '\\\"')\\n \\n all_elements = str(list(query_element.values()))\\n all_elements = all_elements.replace(\\\"\\\\'\\\", \\\"\\\")\\n\\n query = '{{\\\"query\\\": {all_elements} , \\\"response\\\": {{\\\"format\\\": \\\"json-stat\\\" }}}}'.format(all_elements = all_elements)\\n \\n if out == 'dict':\\n query = ast.literal_eval(query)\\n \\n \\n # todo: build it as a dictionary to start with (and not a string that is made into a dict as now)\\n # todo: add error message if required variables are not selected\\n # todo: avoid repeat downloading of same information \\n # eg. get_variables is sometimes used three times before a table is downloaded\\n \\n return query\",\n \"def json(self, **kwargs):\\n return json.loads(self.content, **kwargs)\",\n \"def json(data):\\n if isinstance(data, dict):\\n data = ujson.encode(data)\\n uid = str(uuid.uuid4())\\n display(HTML('<div id=\\\"{0}\\\" style=\\\"height: 600px; width:100%;\\\"></div>'.format(uid)))\\n display(Javascript(\\\"\\\"\\\"\\n require([\\\"https://rawgit.com/caldwell/renderjson/master/renderjson.js\\\"], function() {\\n document.getElementById('%s').appendChild(renderjson(%s))\\n });\\n \\\"\\\"\\\" % (uid, data)))\",\n \"def _build_payload(self, query):\\n\\n def _normalize_payload(payload):\\n if isinstance(payload, ClientObject) or isinstance(payload, ClientValue):\\n return payload.to_json(self._default_json_format)\\n elif isinstance(payload, dict):\\n return {k: _normalize_payload(v) for k, v in payload.items() if v is not None}\\n elif isinstance(payload, list):\\n return [_normalize_payload(item) for item in payload]\\n return payload\\n\\n json = _normalize_payload(query.parameters_type)\\n if isinstance(query, ServiceOperationQuery) and query.parameters_name is not None:\\n json = {query.parameters_name: json}\\n return json\",\n \"def __json_init__(cls, **kwargs):\\n return cls(**kwargs)\",\n \"def to_json(self):\\n params = {\\n 'id': self.id,\\n 'team_1_id': self.team_1_id,\\n 'team_2_id': self.team_2_id,\\n 'table': self.table,\\n 'score_1': self.score_1,\\n 'score_2': self.score_2,\\n 'start_date': self.start_date.isoformat() if self.start_date else None,\\n 'end_date': self.end_date.isoformat() if self.end_date else None,\\n 'version': self.version,\\n 'created_on': self.created_on.isoformat() if self.created_on else None\\n }\\n\\n return params\",\n \"def create_custom():\\n # Extract initialisation parameters\\n alpha = request.args.get('alpha')\\n alpha = float(alpha)\\n generations = request.args.get('generations')\\n generations = int(generations)\\n beta = request.args.get('beta')\\n beta = float(beta)\\n pec = request.args.get('pec')\\n pec = float(pec)\\n q = request.args.get('q')\\n q = float(q)\\n\\n # Extract the custom coordinates and create a list of nodes\\n coords = request.args.get('custom_coords')\\n coords = str(coords)\\n nodes = custom_nodes(coords)\\n\\n # Initialise instance\\n i = Instance(nodes, alpha, beta, pec, q)\\n\\n return jsonify(nodes=i.nodes, alpha=i.alpha, beta=i.beta, decay=i.decay,\\n min_pheromone=i.min_pheromone, q=i.q,\\n local_deposit=i.local_deposit, distances=i.distances,\\n pheromones=i.pheromones, ants=i.ants, shortest_path=i.shortest_path,\\n min_distance=i.min_distance, message=\\\"Instance Initialised\\\")\",\n \"def to_json(self, *args, **kwargs):\\n return json.dumps(self.serialize(), *args, **kwargs)\",\n \"def json(self):\\n return {\\n '_id' : self._id,\\n 'name' : self.name,\\n 'description' : self.description,\\n }\",\n \"def create_json(self, request, qs):\\n\\n j = Work.objects.get_dict(qs)\\n\\n response = JsonResponse(j, json_dumps_params={'indent': 4})\\n name = '{}{}'.format(\\n settings.PUBLISHER_CODE, datetime.now().toordinal())\\n cd = 'attachment; filename=\\\"{}.json\\\"'.format(name)\\n response['Content-Disposition'] = cd\\n return response\",\n \"def format(cls):\\n return \\\"json\\\"\",\n \"def serialize(self):\\n return{\\n 'name':self.name,\\n 'id' :self.id,\\n }\",\n \"def to_json(self):\\n return {'name': self.__class__.__name__, 'args': self.args}\",\n \"def to_json(self) -> Dict:\\n return {\\\"function\\\": self.function.__name__, \\\"kwargs\\\": self.kwargs_to_json()}\",\n \"def json(self) -> typing.Dict[str, typing.Any]:\\n if self.getboolean(\\\"JSON\\\", \\\"compact\\\", fallback=False):\\n kwargs = {\\\"separators\\\": (\\\",\\\", \\\":\\\")}\\n else:\\n kwargs = {\\\"indent\\\": 4}\\n kwargs[\\\"ensure_ascii\\\"] = self.getboolean(\\\"JSON\\\", \\\"ensure ascii\\\", fallback=True)\\n kwargs[\\\"sort_keys\\\"] = self.getboolean(\\\"JSON\\\", \\\"sort keys\\\", fallback=False)\\n return kwargs\",\n \"def _to_json(self):\\n const_list = [\\n {'key': key, 'value': value}\\n for key, value in self.constants.items()\\n ]\\n constr_list = [\\n {'key': key, 'value': value}\\n for key, value in self.constraints.items()\\n ]\\n\\n json_task = {\\n 'name': self._name,\\n 'profile': self._profile,\\n 'pooluuid': self._pooluuid,\\n 'constants': const_list,\\n 'constraints': constr_list\\n }\\n\\n if self._shortname is not None:\\n json_task['shortname'] = self._shortname\\n\\n alldisk = all(isinstance(x, Disk) for x in self._resource_objects)\\n allbucket = all(isinstance(x, Bucket) for x in self._resource_objects)\\n\\n if alldisk or allbucket:\\n self._resource_objects_ids = [x.uuid for x in self._resource_objects]\\n else:\\n raise ValueError(\\\"Can't mix Buckets and Disks as resources\\\")\\n if allbucket:\\n self._resource_type = Bucket\\n json_task['resourceBuckets'] = self._resource_objects_ids\\n if alldisk:\\n self._resource_type = Disk\\n json_task['resourceDisks'] = self._resource_objects_ids\\n\\n if self._result_object is not None:\\n self._result_type = type(self._result_object)\\n if isinstance(self._result_object, Bucket):\\n json_task['resultBucket'] = self._result_object.uuid\\n elif isinstance(self._result_object, Disk):\\n json_task['resultDisk'] = self._result_object.uuid\\n\\n if self._advanced_range is not None:\\n json_task['advancedRanges'] = self._advanced_range\\n else:\\n json_task['instanceCount'] = self._instancecount\\n\\n json_task[\\\"tags\\\"] = self._tags\\n\\n if self._snapshot_whitelist is not None:\\n json_task['snapshotWhitelist'] = self._snapshot_whitelist\\n if self._snapshot_blacklist is not None:\\n json_task['snapshotBlacklist'] = self._snapshot_blacklist\\n if self._results_whitelist is not None:\\n json_task['resultsWhitelist'] = self._results_whitelist\\n if self._results_blacklist is not None:\\n json_task['resultsBlacklist'] = self._results_blacklist\\n return json_task\",\n \"def __json_encode__(self):\\r\\n return self.config()\",\n \"def encode_params(params, **kwargs):\\n cleaned = clean_params(params, **kwargs)\\n return json.dumps(cleaned)\",\n \"def getJsonObject(self):\\n jsonObject = {\\n 'id': self.id,\\n 'name': self.name,\\n 'cards': [self.cards[card].getJsonObject() for card in self.cards] if self.cards else [\\\"\\\"]\\n }\\n return jsonObject\",\n \"def to_json(self, *args, **kwargs):\\n data = self.to_dict()\\n\\n return json_util.dumps(data)\",\n \"def as_json(self, args=None):\\n seg_data = _dict()\\n seg_data[u'id'] = int(self.id)\\n seg_data[u'parentID'] = int(self.parent_id)\\n if self.biological_annotation is not None:\\n seg_data[u'biologicalAnnotation'] = self.biological_annotation.as_json(args=args)\\n if self.complexes_and_macromolecules:\\n complexes = list()\\n for _complex in self.complexes_and_macromolecules.complexes:\\n complexes.append(_complex)\\n macromolecules = list()\\n for macromolecule in self.complexes_and_macromolecules.macromolecules:\\n macromolecules.append(macromolecule)\\n seg_data[u'complexesAndMacromolecules'] = {\\n u'complexes': complexes,\\n u'macromolecules': macromolecules,\\n }\\n seg_data.update(self.colour.as_json(args=args))\\n # seg_data[u'colour'] = tuple(map(float, self.colour.value))\\n if self.meshes:\\n seg_data[u'meshList'] = len(self.meshes)\\n if self.shapes:\\n seg_data[u'shapePrimitiveList'] = len(self.shapes)\\n return seg_data\",\n \"def json(self, value):\\n\\n self.operation = value['operation']\\n self.version = value['version']\\n self.language = value['language']\\n self.identifiers = value['identifiers']\\n self.store_execute = value['store_execute']\\n self.status = value['status']\\n self.lineage = value['lineage']\\n self.outputs = value['outputs']\\n self.raw = value['raw']\\n self.inputs = {}\\n\\n for identifier in value['inputs']:\\n inpt = None\\n inpt_defs = value['inputs'][identifier]\\n\\n for inpt_def in inpt_defs:\\n\\n if inpt_def['type'] == 'complex':\\n inpt = ComplexInput(\\n identifier=inpt_def['identifier'],\\n title=inpt_def.get('title'),\\n abstract=inpt_def.get('abstract'),\\n workdir=inpt_def.get('workdir'),\\n data_format=Format(\\n schema=inpt_def['data_format'].get('schema'),\\n extension=inpt_def['data_format'].get('extension'),\\n mime_type=inpt_def['data_format']['mime_type'],\\n encoding=inpt_def['data_format'].get('encoding')\\n ),\\n supported_formats=[\\n Format(\\n schema=infrmt.get('schema'),\\n extension=infrmt.get('extension'),\\n mime_type=infrmt['mime_type'],\\n encoding=infrmt.get('encoding')\\n ) for infrmt in inpt_def['supported_formats']\\n ],\\n mode=MODE.NONE\\n )\\n inpt.file = inpt_def['file']\\n elif inpt_def['type'] == 'literal':\\n\\n allowed_values = []\\n for allowed_value in inpt_def['allowed_values']:\\n if allowed_value['type'] == 'anyvalue':\\n allowed_values.append(AnyValue())\\n elif allowed_value['type'] == 'novalue':\\n allowed_values.append(NoValue())\\n elif allowed_value['type'] == 'valuesreference':\\n allowed_values.append(ValuesReference())\\n elif allowed_value['type'] == 'allowedvalue':\\n allowed_values.append(AllowedValue(\\n allowed_type=allowed_value['allowed_type'],\\n value=allowed_value['value'],\\n minval=allowed_value['minval'],\\n maxval=allowed_value['maxval'],\\n spacing=allowed_value['spacing'],\\n range_closure=allowed_value['range_closure']\\n ))\\n\\n inpt = LiteralInput(\\n identifier=inpt_def['identifier'],\\n title=inpt_def.get('title'),\\n abstract=inpt_def.get('abstract'),\\n data_type=inpt_def.get('data_type'),\\n workdir=inpt_def.get('workdir'),\\n allowed_values=AnyValue,\\n uoms=inpt_def.get('uoms'),\\n mode=inpt_def.get('mode')\\n )\\n inpt.uom = inpt_def.get('uom')\\n inpt.data = inpt_def.get('data')\\n\\n elif inpt_def['type'] == 'bbox':\\n inpt = BBoxInput(\\n identifier=inpt_def['identifier'],\\n title=inpt_def['title'],\\n abstract=inpt_def['abstract'],\\n crss=inpt_def['crs'],\\n dimensions=inpt_def['dimensions'],\\n workdir=inpt_def['workdir'],\\n mode=inpt_def['mode']\\n )\\n inpt.ll = inpt_def['bbox'][0]\\n inpt.ur = inpt_def['bbox'][1]\\n\\n if identifier in self.inputs:\\n self.inputs[identifier].append(inpt)\\n else:\\n self.inputs[identifier] = [inpt]\",\n \"def __init__( self, parameters={} ):\\n self.params = {}\",\n \"def create_params(self):\\n\\n params = {'time_step':\\n DesignParameter('time_step',\\n unit='s',\\n description='Time step with which the component model will be discretized'),\\n 'horizon':\\n DesignParameter('horizon',\\n unit='s',\\n description='Horizon of the optimization problem'),\\n 'lines': DesignParameter('lines',\\n unit='-',\\n description='List of names of the lines that can be found in the network, e.g. '\\n '\\\\'supply\\\\' and \\\\'return\\\\'',\\n val=['supply', 'return'])\\n }\\n return params\",\n \"def json_data(self):\\n self.check_proof()\\n return {\\n \\\"vars\\\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\\n \\\"proof\\\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\\n for item in self.prf.items], []),\\n \\\"report\\\": self.rpt.json_data(),\\n \\\"method_sig\\\": self.get_method_sig()\\n }\",\n \"def json_meta_obj(location, from_date, to_date, item):\\n return '{\\\"meta\\\":{\\\"location\\\":\\\"' + location \\\\\\n + '\\\",\\\"from\\\":\\\"' + from_date \\\\\\n + '\\\",\\\"to\\\":\\\"' + to_date \\\\\\n + '\\\",\\\"item\\\":\\\"' + item \\\\\\n + '\\\"},\\\\n'\",\n \"def readjson():\\n uuid = request.query.get('uuid','')\\n if(uuid == \\\"\\\"):\\n result = { \\\"code\\\":\\\"fail\\\", \\\"message\\\":\\\"empty uuid\\\"}\\n return result\\n else:\\n zenodo = ZenodoRequest(uuid)\\n return {'data':zenodo.saveInDatabase()}\",\n \"def __json__(self, request=None):\\n # start = self.start.isoformat() if self.start else None\\n # end = self.end.isoformat() if self.end else None\\n return dict(\\n timeref_type=\\\"daterange\\\",\\n interval=self.interval,\\n start=self.start.isoformat(),\\n end=self.end.isoformat(),\\n )\",\n \"def get_json(self, cid, **kwargs):\\n\\t\\treturn self.cat(cid, decoder='json', **kwargs)\",\n \"def format_json(self,query_results):\\n results=query_results.data\\n factory=factory_json()\\n dump=factory.dumps(results)\\n print(dump)\\n # TODO return output for this\\n return \\\"\\\"\",\n \"def json(self):\\n d = OrderedDict()\\n if self.id is not None:\\n d[\\\"id\\\"] = str(self.id)\\n d[\\\"status\\\"] = self.http_status\\n d[\\\"title\\\"] = self.title\\n if self.about:\\n d[\\\"links\\\"] = OrderedDict()\\n d[\\\"links\\\"][\\\"about\\\"] = self.about\\n if self.code:\\n d[\\\"code\\\"] = self.code\\n if self.detail:\\n d[\\\"detail\\\"] = self.detail\\n if self.source_pointer or self.source_parameter:\\n d[\\\"source\\\"] = OrderedDict()\\n if self.source_pointer:\\n d[\\\"source\\\"][\\\"pointer\\\"] = self.source_pointer\\n if self.source_parameter:\\n d[\\\"source\\\"][\\\"parameter\\\"] = self.source_parameter\\n if self.meta:\\n d[\\\"meta\\\"] = meta\\n return d\",\n \"def make():\\n data = {}\\n data.update({'earth' : {'description': 'Planet with 20% O2 with 75% of surface covered by H2O. Humans inhabitants enjoy both of these aspects.',\\n 'order' : 1,\\n 'type': 'planet',\\n }})\\n \\n return data\",\n \"def toJSON(self):\\n return {\\n 'id': self.id,\\n 'name': self.name,\\n 'dependencies': self.dependencies\\n }\",\n \"def request_body(self):\\n return json.dumps({\\n \\\"schemaId\\\": self.schemaId,\\n \\\"associationIds\\\": self.associationIds,\\n \\\"name\\\": self.name,\\n \\\"value\\\": self.value\\n })\",\n \"def to_json(self, *args, **kwargs):\\n return json.dumps(self.serialize(primitive=True), *args, **kwargs)\",\n \"def generate(self):\\n return {\\n 'json_class': 'Pact::ArrayLike',\\n 'contents': from_term(self.matcher),\\n 'min': self.minimum}\",\n \"def minimal_json43():\\n return {\\n 'identifiers': [{\\n 'identifierType': 'DOI',\\n 'identifier': '10.1234/foo.bar',\\n }],\\n 'creators': [\\n {'name': 'Nielsen, Lars Holm'},\\n ],\\n 'titles': [\\n {'title': 'Minimal Test Case'}\\n ],\\n 'publisher': 'Invenio Software',\\n 'publicationYear': '2016',\\n 'types': {\\n 'resourceType': '',\\n 'resourceTypeGeneral': 'Software'\\n },\\n 'schemaVersion': 'http://datacite.org/schema/kernel-4'\\n }\",\n \"def json(self):\\n ar_min_date, ar_max_date = self.get_ar_dates(\\n (self.last_ar_year if self.last_ar_year else self.founding_date.year) + 1\\n )\\n d = {\\n 'foundingDate': self.founding_date.isoformat(),\\n 'identifier': self.identifier,\\n 'lastModified': self.last_modified.isoformat(),\\n 'lastAnnualReport': datetime.date(self.last_ar_date).isoformat() if self.last_ar_date else '',\\n 'nextAnnualReport': LegislationDatetime.as_legislation_timezone_from_date(\\n self.next_anniversary\\n ).astimezone(timezone.utc).isoformat(),\\n 'lastAnnualGeneralMeetingDate': datetime.date(self.last_agm_date).isoformat() if self.last_agm_date else '',\\n 'lastLedgerTimestamp': self.last_ledger_timestamp.isoformat(),\\n 'legalName': self.legal_name,\\n 'legalType': self.legal_type,\\n 'hasRestrictions': self.restriction_ind,\\n 'goodStanding': self.good_standing,\\n 'arMinDate': ar_min_date.isoformat(),\\n 'arMaxDate': ar_max_date.isoformat()\\n }\\n # if self.last_remote_ledger_timestamp:\\n # # this is not a typo, we want the external facing view object ledger timestamp to be the remote one\\n # d['last_ledger_timestamp'] = self.last_remote_ledger_timestamp.isoformat()\\n # else:\\n # d['last_ledger_timestamp'] = None\\n\\n if self.dissolution_date:\\n d['dissolutionDate'] = datetime.date(self.dissolution_date).isoformat()\\n if self.fiscal_year_end_date:\\n d['fiscalYearEndDate'] = datetime.date(self.fiscal_year_end_date).isoformat()\\n if self.tax_id:\\n d['taxId'] = self.tax_id\\n\\n return d\",\n \"def jasonizar(self, todosPermiso, listaPermisoEnRol, enRol):\\n p='' \\n pre=\\\"{\\\\\\\"totalpages\\\\\\\": \\\\\\\"\\\"+str(self.totalPages) + \\\"\\\\\\\",\\\\\\\"currpage\\\\\\\" : \\\\\\\"\\\" + str(self.currPage) + \\\"\\\\\\\",\\\\\\\"totalrecords\\\\\\\" : \\\\\\\"\\\" \\n pre= pre + str(self.totalRecords) + \\\" \\\\\\\",\\\\\\\"invdata\\\\\\\" : [\\\" \\n SIoNO='no'\\n if(enRol=='Todos'):\\n for permiso in todosPermiso:\\n if permiso in listaPermisoEnRol:\\n SIoNO='Si'\\n else:\\n SIoNO='No'\\n \\n p=p+\\\"{\\\\\\\"nombre\\\\\\\": \\\\\\\"\\\"+permiso.nombre+\\\"\\\\\\\",\\\\\\\"descripcion\\\\\\\": \\\\\\\"\\\"+permiso.descripcion+\\\"\\\\\\\", \\\\\\\"enRol\\\\\\\": \\\\\\\"\\\"+SIoNO+\\\"\\\\\\\",\\\\\\\"idPermiso\\\\\\\": \\\\\\\"\\\"+str(permiso.id)+\\\"\\\\\\\"},\\\"\\n elif enRol=='Si':\\n SIoNO='Si'\\n for permiso in listaPermisoEnRol:\\n p=p+\\\"{\\\\\\\"nombre\\\\\\\": \\\\\\\"\\\"+permiso.nombre+\\\"\\\\\\\",\\\\\\\"descripcion\\\\\\\": \\\\\\\"\\\"+permiso.descripcion+\\\"\\\\\\\", \\\\\\\"enRol\\\\\\\": \\\\\\\"\\\"+SIoNO+\\\"\\\\\\\",\\\\\\\"idPermiso\\\\\\\": \\\\\\\"\\\"+str(permiso.id)+\\\"\\\\\\\"},\\\"\\n elif enRol=='No':\\n SIoNO='No'\\n for permiso in todosPermiso:\\n if permiso not in listaPermisoEnRol:\\n p=p+\\\"{\\\\\\\"nombre\\\\\\\": \\\\\\\"\\\"+permiso.nombre+\\\"\\\\\\\",\\\\\\\"descripcion\\\\\\\": \\\\\\\"\\\"+permiso.descripcion+\\\"\\\\\\\", \\\\\\\"enRol\\\\\\\": \\\\\\\"\\\"+SIoNO+\\\"\\\\\\\",\\\\\\\"idPermiso\\\\\\\": \\\\\\\"\\\"+str(permiso.id)+\\\"\\\\\\\"},\\\"\\n \\n \\n p=p[0:len(p)-1] \\n p=p+\\\"]}\\\" \\n p=pre+p\\n return p\",\n \"def to_api_repr(self):\\n s_types = {}\\n values = {}\\n for name, value in self.struct_values.items():\\n type_ = self.struct_types[name]\\n if type_ in (\\\"STRUCT\\\", \\\"ARRAY\\\"):\\n repr_ = value.to_api_repr()\\n s_types[name] = {\\\"name\\\": name, \\\"type\\\": repr_[\\\"parameterType\\\"]}\\n values[name] = repr_[\\\"parameterValue\\\"]\\n else:\\n s_types[name] = {\\\"name\\\": name, \\\"type\\\": {\\\"type\\\": type_}}\\n converter = _SCALAR_VALUE_TO_JSON_PARAM.get(type_)\\n if converter is not None:\\n value = converter(value)\\n values[name] = {\\\"value\\\": value}\\n\\n resource = {\\n \\\"parameterType\\\": {\\n \\\"type\\\": \\\"STRUCT\\\",\\n \\\"structTypes\\\": [s_types[key] for key in self.struct_types],\\n },\\n \\\"parameterValue\\\": {\\\"structValues\\\": values},\\n }\\n if self.name is not None:\\n resource[\\\"name\\\"] = self.name\\n return resource\",\n \"def test_06_CreateJson(self):\\n l_buttons = self.m_api.read_all_buttons_xml(self.m_pyhouse_obj, self.m_xml.button_sect, self.m_version)\\n # print('ButtonsS: {0:}'.format(l_buttons))\\n # print('Button 0: {0:}'.format(vars(l_buttons[0])))\\n l_json = json_tools.encode_json(l_buttons)\\n # print('JSON: {0:}'.format(l_json))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6857647","0.6645711","0.6554232","0.65235794","0.6426044","0.6354805","0.6256294","0.62538326","0.62132627","0.62050444","0.6196949","0.6165802","0.6154298","0.6132933","0.6096993","0.6083696","0.6067892","0.6067397","0.606541","0.60606605","0.6041364","0.6040328","0.60323185","0.5976703","0.5960331","0.59419984","0.5930116","0.59277177","0.58699954","0.5869324","0.5866438","0.5860992","0.5845905","0.5828563","0.58105284","0.58041316","0.5790436","0.57802415","0.57717127","0.5769771","0.5760462","0.5760462","0.57572633","0.57350427","0.5732893","0.5714532","0.57103425","0.56945586","0.5677327","0.5674568","0.5667098","0.56668895","0.5663047","0.56608295","0.56458735","0.5643502","0.5637894","0.5630685","0.5625672","0.56246126","0.56188744","0.561275","0.561225","0.560185","0.5597176","0.55953723","0.5592145","0.5590307","0.558896","0.55815107","0.5568854","0.55682606","0.5562299","0.55616444","0.55608773","0.55454445","0.5530052","0.5520436","0.5519693","0.5498833","0.5491575","0.5481645","0.5478158","0.5477973","0.5472057","0.54654306","0.546486","0.5463099","0.5456311","0.5442346","0.54358476","0.5434536","0.5433218","0.542951","0.54277164","0.54217744","0.5419493","0.5418262","0.54163504","0.5415879"],"string":"[\n \"0.6857647\",\n \"0.6645711\",\n \"0.6554232\",\n \"0.65235794\",\n \"0.6426044\",\n \"0.6354805\",\n \"0.6256294\",\n \"0.62538326\",\n \"0.62132627\",\n \"0.62050444\",\n \"0.6196949\",\n \"0.6165802\",\n \"0.6154298\",\n \"0.6132933\",\n \"0.6096993\",\n \"0.6083696\",\n \"0.6067892\",\n \"0.6067397\",\n \"0.606541\",\n \"0.60606605\",\n \"0.6041364\",\n \"0.6040328\",\n \"0.60323185\",\n \"0.5976703\",\n \"0.5960331\",\n \"0.59419984\",\n \"0.5930116\",\n \"0.59277177\",\n \"0.58699954\",\n \"0.5869324\",\n \"0.5866438\",\n \"0.5860992\",\n \"0.5845905\",\n \"0.5828563\",\n \"0.58105284\",\n \"0.58041316\",\n \"0.5790436\",\n \"0.57802415\",\n \"0.57717127\",\n \"0.5769771\",\n \"0.5760462\",\n \"0.5760462\",\n \"0.57572633\",\n \"0.57350427\",\n \"0.5732893\",\n \"0.5714532\",\n \"0.57103425\",\n \"0.56945586\",\n \"0.5677327\",\n \"0.5674568\",\n \"0.5667098\",\n \"0.56668895\",\n \"0.5663047\",\n \"0.56608295\",\n \"0.56458735\",\n \"0.5643502\",\n \"0.5637894\",\n \"0.5630685\",\n \"0.5625672\",\n \"0.56246126\",\n \"0.56188744\",\n \"0.561275\",\n \"0.561225\",\n \"0.560185\",\n \"0.5597176\",\n \"0.55953723\",\n \"0.5592145\",\n \"0.5590307\",\n \"0.558896\",\n \"0.55815107\",\n \"0.5568854\",\n \"0.55682606\",\n \"0.5562299\",\n \"0.55616444\",\n \"0.55608773\",\n \"0.55454445\",\n \"0.5530052\",\n \"0.5520436\",\n \"0.5519693\",\n \"0.5498833\",\n \"0.5491575\",\n \"0.5481645\",\n \"0.5478158\",\n \"0.5477973\",\n \"0.5472057\",\n \"0.54654306\",\n \"0.546486\",\n \"0.5463099\",\n \"0.5456311\",\n \"0.5442346\",\n \"0.54358476\",\n \"0.5434536\",\n \"0.5433218\",\n \"0.542951\",\n \"0.54277164\",\n \"0.54217744\",\n \"0.5419493\",\n \"0.5418262\",\n \"0.54163504\",\n \"0.5415879\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5603465"},"document_rank":{"kind":"string","value":"63"}}},{"rowIdx":1899,"cells":{"query":{"kind":"string","value":"Returns JSON structure issued form dataframe content given as parameter ."},"document":{"kind":"string","value":"def json_df_builder(self, df, marketID, RFM=None):\n \n #-------------------------------------------------------------------------\n # Extract from dataframe content to be returned\n #-------------------------------------------------------------------------\n str_customerID = str(df.CustomerID.unique()[0])\n \n invoice_count = len(df.InvoiceNo.unique())\n item_count = df.Quantity.sum()\n invl_count = df.shape[0]\n \n ser_incomes = df.UnitPrice * df.Quantity\n incomes = ser_incomes.sum()\n str_incomes = \"{0:1.2F}\".format(incomes)\n \n mean_unit_price = incomes/item_count\n str_mean_unit_price = \"{0:1.2F}\".format(mean_unit_price)\n \n serInvoiceDate = df.InvoiceDate\n str_old_date = serInvoiceDate.map(str).min()\n str_new_date = serInvoiceDate.map(str).max()\n \n #-------------------------------------------------------------------------\n # Build JSON structure form content\n #-------------------------------------------------------------------------\n json_result = '{\\n'\n json_result += '\\t \"_results\":[\\n'\n json_result += \"{\\n\"\n json_result += \"\\t\\t\"+\" \\\"customerID\\\":\"+str_customerID+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"marketID\\\":\"+str(marketID)+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"invoice_count\\\":\"+str(invoice_count)+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"item_count\\\":\"+str(item_count)+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"invl_count\\\":\"+str(invl_count)+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"mean_unit_price\\\":\"+str_mean_unit_price+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"incomes\\\":\"+str_incomes+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"old_date\\\":\"+str_old_date+\"\\n\"\n json_result += \"\\t\\t\"+\",\\\"new_date\\\":\"+str_new_date+\"\\n\"\n \n if RFM is not None:\n json_result += \"\\t\\t\"+\",\\\"RFM\\\":\"+RFM+\"\\n\"\n else:\n pass\n\n json_result += \"}\\n\"\n json_result += '\\n\\t]\\n}'\n return json_result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def to_json(self):\n\t\treturn self._dataframe.reset_index().to_json(orient=\"records\")","def Mydata():\n\n stmt = db.session.query(Appsdata).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n \n return jsonify(df.to_dict())","def get_data(dataframe,index=None):\n dflen = len(dataframe)\n if index==None or index <0 or index >= dflen:\n index = randint(0,dflen)\n return dataframe.iloc[index].to_json()","def json_temp_ges(df):\n\n json_str = (\n df.groupby(\n [\n \"sensor_id\",\n \"measure_name\",\n \"run_id\",\n \"ventilation_rate\",\n \"num_dehumidifiers\",\n \"lighting_shift\",\n \"scenario_type\",\n ],\n as_index=True,\n )\n .apply(\n lambda x: x[\n [\n \"prediction_value\",\n \"prediction_index\",\n \"run_id\",\n \"time\",\n \"timestamp\",\n ]\n ].to_dict(orient=\"records\")\n )\n .reset_index()\n .rename(columns={0: \"Values\"})\n .to_json(orient=\"records\")\n )\n return json_str","def receive_json_ids(dataframe, jsondata, just_headers = False):\n\n dict_data = ast.literal_eval(jsondata)\n jsondict = {1: 'Kingdom', 2: 'Phylum', 3: 'Class', 4: 'Order', 5: 'Family', 6: 'Genus', 7: 'Species', 8 : 'Strain'}\n # this checks how long the jsondata is and from this it selects the correct Letter out of the jsondict #\n suffix = jsondict[len(dict_data)]\n\n # This selects the data which has the same name as the recieved jsondata\n fw_subset = dataframe[(dataframe[\"fw_\"+ suffix] == dict_data[-1])] \n rv_subset = dataframe[(dataframe[\"rv_\"+suffix] == dict_data[-1])]\n\n # This is only used so that the columns can be easily renamed in something more generic so the append will merge the correct columns\n columns_rename = pd.DataFrame(columns=[\"bitscore\", \"identity\", \"length\"])\n\n # Get the specified data\n fw_sideDf = fw_subset[[\"fw_bit\", \"fw_id\", \"fw_coverage_length\"]]\n rv_sideDf = rv_subset[[\"rv_bit\", \"rv_id\", \"rv_coverage_length\"]]\n\n # Get headers\n fw_headers = fw_subset.index.values.tolist()\n rv_headers = rv_subset.index.values.tolist()\n\n if just_headers:\n return fw_headers, rv_headers\n \n # Rename the columns\n fw_sideDf.columns = columns_rename.columns\n rv_sideDf.columns = columns_rename.columns\n # Combine the two dataframes in one since they have the same column names it will merge completly\n sideDf = fw_sideDf.append(rv_sideDf)\n # Count and group the different entries also convert them into a json\n count_id = sideDf.round(0).groupby(['identity']).size().to_json(orient='table')\n count_bit = sideDf.round(0).groupby(['bitscore']).size().to_json(orient='table')\n count_length = sideDf.round(0).groupby(['length']).size().to_json(orient='table')\n fw_seqs = fw_subset[\"fw_seq\"].tolist()\n rv_seqs = rv_subset[\"rv_seq\"].tolist()\n\n # Get taxonomy id's\n tax_ids = set([*fw_subset.fw_accession.tolist(), *rv_subset.rv_accession.tolist()])\n tax_len = len(tax_ids)\n if tax_len == 0:\n tax_id = \"None\"\n elif tax_len == 1:\n tax_id = list(tax_ids)[0]\n else:\n tax_id = \"More\"\n\n response = {\n \"count_id\":count_id,\n \"count_bit\": count_bit,\n \"count_length\": count_length,\n \"node_name\": dict_data[-1],\n \"tax_id\": str(tax_id),\n \"fw_headers\": fw_headers,\n \"rv_headers\": rv_headers,\n \"fw_seqs\": fw_seqs,\n \"rv_seqs\": rv_seqs\n }\n return jsonify(response)","def _data_frame(content):\n response = loads(content)\n key = [x for x in response.keys() if x in c.response_data][0]\n frame = DataFrame(response[key])\n final_frame = _convert(frame)\n return final_frame","def write_jason(df):\n\n\t# set Country as index of dataframe\n\tdf = df.set_index('Country')\n\n\t# write datafram to jason file \n\tdf = df.to_json('eda.json', orient='index')","async def full_report():\n return DF.to_dict(orient=\"records\")","def convert_to_json(dataframe):\n dataframe = dataframe.set_index('YYYYMMDD').to_json('schiphol_windstoten.json', orient = 'index')","def get_data(self)->pd.DataFrame:\n pass","def _dict(content):\n response = _data_frame(content).to_dict(orient='records')\n return response","def __parse_json(df):\n\t\tcol_names = ['genres', 'production_companies', 'production_countries', 'cast', 'crew', 'spoken_languages',\n\t\t\t\t\t 'Keywords']\n\t\tvalue_names = ['name', 'name', 'iso_3166_1', 'name', 'name', 'name', 'name']\n\t\tfor col_name, value_name in zip(col_names, value_names):\n\t\t\t# df[col_name] = df[col_name].fillna(\"{}\")\n\t\t\tdf[col_name] = df[col_name].apply(literal_eval_error_handling)\n\t\t\tdf[col_name] = df[col_name].apply(lambda x: [i[value_name] for i in x])\n\t\treturn df","def toDataFrame(self):\r\n if self.output_format in ('json', 'jsonExtended'):\r\n return json.dumps(self.result)\r\n \r\n elif self.output_format in ('tab2', 'extendedTab2'):\r\n return StringIO('\\t'.join(self.headers) + self.result)","def pandas_to_njson(df):\n \n #df['secao'] = df['secao'].astype(int)\n \n records = df.to_dict(orient='records')\n json_list = [json.dumps(add_process_date(record), ensure_ascii=False) for record in records]\n njson = '\\n'.join(json_list)\n \n return njson","def names():\n\n df = pd.read_sql_query(f\"SELECT * FROM olympics_raw\", con = engine)\n print(df.head())\n \n\n # return jsonify(all_olympians)\n return jsonify(df.to_dict(orient='records'))","def display_raw_data(df):\n raw_data_lenght=df.shape[0]\n #loop through from 0 to number of rows in steps of 5\n for i in range(0,raw_data_lenght,5):\n response=input('\\n Do you want examin a perticular user data? Type \\'yes \\'or \\'no \\'\\n')\n if response.lower()!='yes':\n break\n \n data=df.iloc[i: i+5].to_json(orient='records',lines=True).split('\\n')\n for row in data:\n passed=json.loads(row)\n j_row=json.dumps(passed,indent=3)\n print(j_row)","def create_data_frame(input_filepath):\n df = pd.read_json(input_filepath)\n logger = logging.getLogger(__name__)\n logger.info('Imported dataframe:')\n logger.info(df.info())\n logger.info(df.describe())\n logger.info(df.head())\n return df","def to_dataframe(self):\n return df_util.to_dataframe(requests.get(self.__url).json())","def json2dataframe(data):\n # Load and parse the available streamflow data\n siteId = data['value']['timeSeries'][0]['sourceInfo']['siteCode'][0]['value']\n df = pd.DataFrame(data['value']['timeSeries'][0]['values'][0]['value'])\n df = df.set_index(df['dateTime'], drop=True)\n df['value'] = df['value'].astype('float32')\n df.index = pd.to_datetime(df.index)\n last_available_date = df.index[-1].strftime(\"%Y-%m-%d\")\n return df, siteId, last_available_date","def converttojson(edge_df):\n\tedge_df_str = edge_df.copy()\n\tfor idx, col in enumerate(edge_df.columns):\n\t\tfirst_row_element = edge_df.iloc[0, idx]\n\t\tif isinstance(first_row_element, list) or isinstance(first_row_element, dict):\n\t\t\tedge_df_str[col] = edge_df[col].apply(json.dumps)\n\t\t\tprint('Field \"{}\" of class {} converted to json string'.format(col, type(first_row_element)))\n\t\t#else:\n\t\t#\tprint(col,type(edge_df[col][0]))\n\treturn edge_df_str","def json(self) -> CellJson:\n\n return {\"id\": self.id, \"content\": self.content, \"data\": self.data}","def insert_df_json(conn, table_name: str, df: pd.DataFrame):\n insert_json(conn=conn, table_name=table_name, data=df.reset_index().to_json(orient='records', lines=True))","def createDataframe(httpData):\n jsonData = json.loads(httpData)\n return pd.json_normalize(jsonData['data'])","def generate_df(js_dict, naming, value=\"value\"):\n\n values = []\n dimensions, dim_names = get_dimensions(js_dict, naming)\n values = get_values(js_dict, value=value)\n output = pd.DataFrame([category + [values[i]]\n for i, category in\n enumerate(get_df_row(dimensions, naming))])\n output.columns = dim_names + [value]\n output.index = range(0, len(values))\n return output","def input_fn(request_body, request_content_type):\n if request_content_type == \"application/json\":\n json_load = json.loads(request_body)\n data=get_dataframe_from_dict(json_load)\n csv_data=data.to_csv(index=False,header=None)\n data=csv_data.replace(\"\\n\",\"\")\n s = StringIO(data)\n data = pd.read_csv(s, header=None)\n\n return data\n else:\n # Handle other content-types here or raise an Exception\n # if the content type is not supported.\n pass","def json(path):\n try:\n # TODO: Check a better way to handle this Spark.instance.spark. Very verbose.\n df = Spark.instance.spark.read.json(path)\n except IOError as error:\n logging.error(error)\n raise\n return df","def dataframe(self):\n\t\treturn self._dataframe","def df():\n fs.df()","def to_df(self) -> pd.DataFrame:\n data = []\n for action in self.actions:\n data.append(action.to_df())\n df = pd.read_json(json.dumps(data), orient=\"list\")\n return df[self.fields]","def save_json(df):\n dict = {}\n for row in df.iterrows():\n dict[row[1]['Country']] = {'Region' : row[1]['Region'],\n 'Pop. Density (per sq. mi.)' : row[1]['Pop. Density (per sq. mi.)'],\n 'Infant mortality (per 1000 births)' : row[1]['Infant mortality (per 1000 births)'],\n 'GDP ($ per capita) dollars' : row[1]['GDP ($ per capita) dollars']\n }\n\n with open('data.json', 'w', encoding='utf8') as outfile:\n data = json.dumps(dict, indent=4, sort_keys=False, separators=(',', ': '), ensure_ascii=False)\n outfile.write(data)","def getDataFrame(self):\n return self.df","def as_json(self):","def df_to_json(complete_dataset, output_path, static_columns):\n megajson = {}\n\n static_columns = [\"continent\", \"location\"] + list(static_columns)\n\n complete_dataset = complete_dataset.dropna(axis=\"rows\", subset=[\"iso_code\"])\n\n for _, row in complete_dataset.iterrows():\n\n row_iso = row[\"iso_code\"]\n row_dict_static = row.drop(\"iso_code\")[static_columns].dropna().to_dict()\n row_dict_dynamic = row.drop(\"iso_code\").drop(static_columns).dropna().to_dict()\n\n if row_iso not in megajson:\n megajson[row_iso] = row_dict_static\n megajson[row_iso][\"data\"] = [row_dict_dynamic]\n else:\n megajson[row_iso][\"data\"].append(row_dict_dynamic)\n\n with open(output_path, \"w\") as file:\n file.write(json.dumps(megajson, indent=4))","def get_data_rows(self, as_json=False, pretty_print=False):\n if self.has_error():\n return None\n\n # Read the table\n try:\n if self.is_excel:\n msgt('Excel!')\n\n df = pd.read_excel(self.filepath)\n #error_bad_lines=False)\n else:\n df = pd.read_table(self.filepath)\n except Exception as ex_obj:\n msg(ex_obj)\n msgt('Failed to open file via pandas!')\n temp_file_helper.make_sure_file_deleted(self.filepath)\n if self.is_excel:\n self.add_error('Failed to open Excel file via pandas. [%s]' % ex_obj)\n else:\n self.add_error('<b>Probably not a tabular file!</b> Failed to open file via pandas. [%s]' % ex_obj)\n return None\n\n self.describe_as_html = df.describe().to_html()\n json_string = df.describe().to_json()\n self.describe_as_dict =json.loads(json_string, object_pairs_hook=OrderedDict)\n\n # Retrieve the columns\n self.column_names = df.columns.tolist()\n\n # Retrieve the rows\n self.data_rows = df[:self.num_preview_rows].values.tolist()\n\n #print 'rows', json.dumps(rows)\n\n # Format the response\n info_dict = OrderedDict()\n\n info_dict['total_row_count'] = len(df.index)\n info_dict['preview_row_count'] = len(self.data_rows)\n info_dict['column_names'] = self.column_names\n info_dict['rows'] = self.data_rows\n info_dict['describe_as_html'] = self.describe_as_html\n info_dict['describe_as_dict'] = self.describe_as_dict\n\n if as_json:\n if pretty_print:\n return json.dumps(info_dict, indent=4)\n return json.dumps(info_dict)\n\n return info_dict","def process_data(self):\n structure_data = self.parse_root(self.root)\n\n dict_data = {}\n for d in structure_data:\n dict_data = {**dict_data, **d}\n df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T\n\n return df","def addJSON(file: str, df, creat: bool):\n if creat is False :\n with open(file) as train_file:\n dict_train = json.load(train_file)\n data = pd.read_json(dict_train, orient=\"records\")\n df = pd.concat([data, df])\n \n js = df.to_json(orient='records').replace(\n \"[\\\\\\\"[\", '').replace(\"]\\\\\\\"]\", '')\n \n with open(file, 'w', encoding='utf8') as outfile:\n json.dump(js, outfile, ensure_ascii=False, indent=4)","def dataframe(self):\n return self.generator.dataframe","def df_to_json(tokenList):\n # If df's ouput format changes, we'll be in trouble, of course.\n # the 0 token is the name of the filesystem\n # the 1 token is the size of the filesystem in 1K blocks\n # the 2 token is the amount used of the filesystem\n # the 5 token is the mount point\n offset = len(tokenList) - 9\n result = {}\n fsName = tokenList[0+offset]\n fsSize = tokenList[1+offset]\n fsUsed = tokenList[2+offset]\n fsMountPoint = tokenList[5+offset]\n result[\"filesystem\"] = {}\n result[\"filesystem\"][\"name\"] = fsName\n result[\"filesystem\"][\"size\"] = float(fsSize)/1000\n result[\"filesystem\"][\"used\"] = float(fsUsed)/1000\n result[\"filesystem\"][\"mount_point\"] = fsMountPoint\n if result[\"filesystem\"][\"size\"] > 0 :\n result[\"filesystem\"][\"percentage\"] = result[\"filesystem\"][\"used\"]/result[\"filesystem\"][\"size\"]\n return result","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 list_data_frames():\n\n cmd = dict()\n cmd[\"type_\"] = \"list_data_frames\"\n cmd[\"name_\"] = \"\"\n \n s = comm.send_and_receive_socket(cmd)\n\n msg = comm.recv_string(s)\n\n if msg != \"Success!\":\n raise Exception(msg)\n \n json_str = comm.recv_string(s) \n \n s.close() \n\n return json.loads(json_str)","def create_cris_data_dict(df, filename, outdir):\n json_dict = {}\n for key in df.keys():\n if key != 'participant_id':\n json_dict[key] = {'Units': key.split()[-1]}\n else:\n json_dict[key] = {'Description': 'OpenNeuro ID of the subject.'}\n with open(outdir.joinpath(filename + '.json'), \"w\") as f:\n json.dump(json_dict, f, indent=4)","def json_view(self, recursive=False):\n\n context = self.context.aq_inner\n data = self.export(context, recursive=recursive)\n pretty = json.dumps(data, sort_keys=True, indent=4)\n self.request.response.setHeader(\"Content-type\", \"application/json\")\n return pretty","def _get_frame_data(self):\n\n def _text_formatting(bs4_tag):\n \"\"\"Simple formatting that will be called a lot\"\"\"\n return bs4_tag.get_text().replace('\\n', '')\n\n tables = Character._get_main_page(self._fd).find_all('table', {'class': 'wikitable'})\n result = {}\n for table in tables:\n rows = table.find_all('tr')\n for row in rows:\n move = row.find('th')\n\n if move is not None:\n move_small = move.find('small')\n else:\n continue\n\n if move_small is not None:\n move_name = move.get_text().replace(move_small.get_text(), ' ' + move_small.get_text()).replace(\n '\\n', '')\n else:\n move_name = _text_formatting(move)\n\n move_name = move_name.replace('+', '') # B+C fix\n fd = row.find_all('td')\n\n try:\n result.update({\n move_name: {\n \"Damage\": _text_formatting(fd[0]),\n \"Cancel\": _text_formatting(fd[1]),\n \"Attribute\": _text_formatting(fd[4]),\n \"Guard\": _text_formatting(fd[5]),\n \"Startup\": _text_formatting(fd[6]),\n \"Active\": _text_formatting(fd[7]),\n \"Recovery\": _text_formatting(fd[8]),\n \"Frame Adv\": _text_formatting(fd[9]),\n \"Blockstun\": _text_formatting(fd[11]),\n \"Invul/GP\": _text_formatting(fd[19])\n }\n })\n except (AttributeError, IndexError):\n continue\n\n return result","def download_data_df(main=False) -> pd.DataFrame:\n temp_file: str = './temp.csv'\n json_text: str = download_json_data(main)\n result: pd.DataFrame = json_to_csv(json_text, temp_file)\n os.remove(temp_file)\n return result","def toJSON(self):\r\n\r\n jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n if len(self.slctData) > 100:\r\n self.getSimMatSummary(100)\r\n jsonToRet.append(self.summaryOrdering)\r\n for i in range(0,len(self.simMatSmm)):\r\n for n in self.simMatSmm[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n\r\n jsonToRet.append(self.patchOrdering)\r\n # jsonToRet = []\r\n rowJson = []\r\n matrixJson = []\r\n\r\n for i in range(0,len(self.simMat)):\r\n for n in self.simMat[i]:\r\n rowJson.append(n)\r\n matrixJson.append(rowJson)\r\n rowJson = []\r\n jsonToRet.append(matrixJson)\r\n return jsonToRet","def dataframe_to_list(df: pandas.DataFrame) -> list:\n return json.loads(df.to_json(orient=\"records\"))","def format(df, format, features):\n\n def mapDocFormat(row):\n if format == \"csv\":\n return (row.id.encode(\"utf-8\", errors='ignore') + \",\" + \" \".join(\n [x.encode(\"utf-8\", errors='ignore') for x in [row.comments, row.caption, row.tags]])).lower()\n if format == \"tsv\":\n return (row.id.encode(\"utf-8\") + \"\\t\" + \" \".join(\n [x.encode(\"utf-8\", errors='ignore') for x in [row.comments, row.caption, row.tags]])).lower()\n if format == \"json\":\n return pyspark.sql.Row(doc=row.id.encode(\"utf-8\", errors='ignore').lower(),\n text=(''.join([x.encode(\"utf-8\", errors='ignore') for x in row]))).lower()\n\n if features:\n df = df.rdd.map(mapDocFormat)\n if format == \"json\":\n return df.toDF().toJSON()\n else:\n return df\n else:\n if format == \"csv\":\n return df.rdd.map(lambda row: (','.join(\n [x.encode(\"utf-8\", errors='ignore') for x in [row.id, row.url, row.comments, row.caption, row.tags]])).lower())\n if format == \"tsv\":\n return df.rdd.map(lambda row: ('\\t'.join(\n [x.encode(\"utf-8\", errors='ignore') for x in [row.id, row.url, row.comments, row.caption, row.tags]])).lower())\n if format == \"json\":\n return df.toJSON()","def parse_jsons(self):\n # store all data in a pandas DataFrame\n pandas_df = pandas.DataFrame(self.__input_jsons)\n return pandas_df","def display_data(df):\n row_length = df.shape[0]\n # iterate from 0 to the number of rows in steps of 5\n for i in range(0, row_length, 5):\n decision = input('\\nWould you like to examine the particular user trip data? Type \\'yes\\' or \\'no\\'\\n> ')\n if decision.lower() != 'yes':\n break\n # retrieve and convert data to json format\n # split each json row data \n # retrieving rows by iloc method\n #And use to_json which takes (orient = ‘records’ that list like [{column -> value}, … , {column -> value}]\n row_data = df.iloc[i: i + 5].to_json(orient='records', lines=True).split('\\n')\n for row in row_data:\n # pretty print each user data\n parsed_row = json.loads(row)\n #converts a Python object into a json string.\n json_row = json.dumps(parsed_row, indent=2)\n print(json_row)","def get_data(config_path):\n config = read_params(config_path)\n data_path = config[\"data_source\"][\"s3_source\"]\n df = pd.read_json(data_path, lines=True, orient='str')\n return df","def glass_pandas(self):\n # pandas.set_option('display.width', 120)\n # TODO timeit (git_implementation) vs (my_implementation)\n # * df = pd.DataFrame(json.loads(r.text))\n # * df = df.set_index('t')\n # * df.index = pd.to_datetime(df.index, unit='s')\n # * df = df.sort_index()\n # * s = df.v\n # * s.name = '_'.join(url.split('/')[-2:])\n # * return s\n # for elem in self.loaded:\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\n # try:\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\n # framed = pandas.DataFrame(\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\n # for k in frame_keys} for iters in range(len(_data))],\n # columns=frame_keys)\n # except KeyError:\n # framed = pandas.DataFrame(_data)\n # framed.set_index('t', inplace=True)\n # framed.index = pandas.to_datetime(\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\n # framed.sort_index(inplace=True)\n # framed.name = _metric\n # print(framed.name)\n # print(framed)","def json_data(self):\n self.check_proof()\n return {\n \"vars\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\n \"proof\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\n for item in self.prf.items], []),\n \"report\": self.rpt.json_data(),\n \"method_sig\": self.get_method_sig()\n }","def __data_row_to_json(self, row):\n raw_data = {}\n raw_data[\"body\"] = row.body\n raw_data[\"score_hidden\"] = row.score_hidden\n raw_data[\"archived\"] = row.archived\n raw_data[\"name\"] = row.name\n raw_data[\"author\"] = row.author\n raw_data[\"author_flair_text\"] = row.author_flair_text\n raw_data[\"downs\"] = row.downs\n raw_data[\"created_utc\"] = row.created_utc\n raw_data[\"subreddit_id\"] = row.subreddit_id\n raw_data[\"link_id\"] = row.link_id\n raw_data[\"parent_id\"] = row.parent_id\n raw_data[\"score\"] = row.score\n raw_data[\"retrieved_on\"] = row.retrieved_on\n raw_data[\"controversiality\"] = row.controversiality\n raw_data[\"gilded\"] = row.gilded\n raw_data[\"id\"] = row.id\n raw_data[\"subreddit\"] = row.subreddit\n raw_data[\"ups\"] = row.ups\n raw_data[\"distinguished\"] = row.distinguished\n raw_data[\"author_flair_css_class\"] = row.author_flair_css_class\n\n return json.dumps(raw_data)","def get_data(self):\n return self.data.to_json()","def get_data(self):\n\n # Import json data into a pandas dataframe\n df = pd.read_json(\"./nutrients.json\", lines=True)\n\n return df","def pandas_convert(self):\n data = {}\n\n for names in self.data[0]:\n col_values = []\n\n if names in objects:\n for items in self.data[0][names]:\n col_values = []\n\n col_name = names + \"_\" + items\n\n for i in range(len(self.data)):\n col_values.append(self.data[i][names][items])\n\n data[col_name] = col_values\n else:\n for i in range(len(self.data)):\n col_values.append(self.data[i][names])\n \n data[names] = col_values\n\n self.pandas_df = pd.DataFrame(data=data)\n self.__clean_df()\n\n return self.pandas_df","def get_time_series_data():\r\n # Grab the requested years and columns from the query arguments\r\n ls_year = [int(year) for year in request.args.getlist(\"n\")]\r\n ls_col = request.args.getlist(\"m\")\r\n\r\n # Generate a list of all the months we need to get\r\n all_years = [str(year) for year in range(min(ls_year), max(ls_year) + 1)]\r\n\r\n # Grab all of the wanted months by filtering for the ones we want\r\n wanted_months = reduce(\r\n lambda a, b: a | b, (app.df[\"month\"].str.contains(year) for year in all_years)\r\n )\r\n\r\n # Create a new dataframe from the one that\r\n df_new = app.df[wanted_months][[\"month\"] + ls_col]\r\n\r\n # Convert all string dates into datetime objects and then sort them\r\n df_new[\"month\"] = pd.to_datetime(df_new[\"month\"])\r\n df_new = df_new.sort_values(by=[\"month\"])\r\n\r\n # Return the dataframe as json\r\n return df_new.to_json(), 200","def format_bgc_metadata(df,float_id):\n mdf = df[bgc_metadata_columns]\n bgc_metadata_dict = {}\n for col in list(mdf):\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\"'\",'\"'))\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \n bgc_metadata_df = pd.DataFrame({\"float_id\": [float_id], \"Metadata_Dict\": [bgc_metadata_dict]})\n return bgc_metadata_df","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 dataFrame(self):\n\n memory_file = StringIO(initial_value=self.sparql_result.decode('utf-8'), newline='\\n')\n reader = DictReader(memory_file)\n\n schema = StructType(\n list(map(lambda f: StructField(f, StringType()), reader.fieldnames))\n )\n\n data = list(map(lambda d: [d[f] for f in reader.fieldnames], list(reader)))\n\n return self.spark.createDataFrame(data, schema)","def get_datalist_fr_json(self):\n raw_data = json.load(open(self.saved_json_file, 'r'))\n for indivdual_set in raw_data['query']['results']['stats']:\n temp_dict_data = {}\n if type(indivdual_set) == str:\n #for single data\n continue # temp do not use\n for parameters in indivdual_set.keys():\n if type(indivdual_set[parameters]) == str:\n temp_dict_data[parameters] = indivdual_set[parameters]#for symbol\n elif type(indivdual_set[parameters]) == dict:\n if indivdual_set[parameters].has_key('content'):\n temp_dict_data[parameters] = indivdual_set[parameters]['content']\n\n ## append to list\n self.com_data_allstock_list.append(temp_dict_data)","def df(self):\n return self._df","def _jsonify(self):\n return self.experiment_record.to_ddb_record()","def precipitation():\n\n return jsonify(prcp_df)","def json_temp_arima(df_arima):\n # The shift_hours=1 accounts for df_arima starting its indexing from 1\n # instead of 0.\n df_arima = add_time_columns(df_arima, shift_hours=1)\n if len(df_arima) < 1:\n return \"{}\"\n json_str = (\n df_arima.groupby([\"sensor_id\", \"measure_name\", \"run_id\"], as_index=True)\n .apply(\n lambda x: x[\n [\n \"prediction_value\",\n \"prediction_index\",\n \"run_id\",\n \"time\",\n \"timestamp\",\n ]\n ].to_dict(orient=\"records\")\n )\n .reset_index()\n .rename(columns={0: \"Values\"})\n .to_json(orient=\"records\")\n )\n return json_str","def dataframe(self):\n if not self.all_records:\n print('No rows cached.')\n return\n dict_list = [row.as_dict() for row in self.all_records]\n columns = self.all_records[0].keys\n dataframe = pd.DataFrame(dict_list, columns=columns)\n return dataframe","def make_df_from_json(json_files, out_file):\n table = [[\"name\", \n \"cik\", \n \"city\",\n \"state\",\n \"street1\",\n \"street2\",\n \"zip_code\",\n \"year_of_incorp\", \n \"min_inv\", \n \"tot_off\", \n \"tot_sold\", \n \"tot_rem\", \n \"ind_group_type\", \n \"has_non_accred\", \n \"num_non_accred\", \n \"tot_num_inv\"\n ]] \n\n for json_dict in json_files:\n\n with open(json_dict, \"rb\") as f:\n data = json.load(f)\n print(json_dict)\n\n for i, key in enumerate(data):\n # if i % 1000 == 0:\n # print(i)\n entry = data[key] \n if entry == {}:\n #print(\"missing entry {0}\".format(i))\n continue\n row = []\n\n primary_issuer = entry[\"Primary Issuer\"]\n cik = primary_issuer[\"cik\"]\n name = primary_issuer[\"entity_name\"]\n phone = primary_issuer[\"phone\"]\n year_of_incorp = primary_issuer[\"year_of_incorp\"]\n address = primary_issuer[\"address\"]\n city = address[\"city\"]\n state = address[\"state\"]\n street1 = address[\"street1\"]\n street2 = address[\"street2\"]\n zip_code = address[\"zip_code\"]\n\n secondary_issuers = entry[\"Secondary Issuers\"]\n related_people = entry[\"Related People\"]\n \n offering_data = entry[\"Offering Data\"]\n min_inv = offering_data[\"min_investment_accepted\"]\n tot_off = offering_data[\"total_offering_amount\"]\n tot_sold = offering_data[\"total_amount_sold\"]\n tot_rem = offering_data[\"total_remaining\"]\n ind_group_type = offering_data[\"ind_group_type\"]\n has_non_accred = offering_data[\"has_non_accred\"]\n num_non_accred = offering_data[\"num_non_accred\"]\n tot_num_inv = offering_data[\"tot_num_inv\"] \n\n row = [name, \n cik, \n city,\n state,\n street1,\n street2,\n zip_code,\n year_of_incorp,\n min_inv,\n tot_off,\n tot_sold,\n tot_rem,\n ind_group_type,\n has_non_accred,\n num_non_accred,\n tot_num_inv\n ]\n\n table.append(row)\n\n df = pd.DataFrame(table)\n df.to_csv(out_file)\n\n return 0","def _get_data_as_json(self, data):\n data = self._get_data_as_df(data)\n data = data.to_json(orient=\"records\")\n char_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)\n return list(map(''.join, zip(*[iter(data)] * char_per_line)))","def dataframe(self):\n return self._df","def data(self):\n return self.as_named_DataFrame()","def format_data(self, params):\n return json.dumps(params)","def convert_data(df):\n print(\"Converting history...\")\n return [ dict(row) for i, row in df.iterrows() ]","def expected_df():\n return pd.DataFrame(\n {\n \"growth\": [0.873922, 0.814298, 0.0],\n \"gene\": [\"b2935\", \"b0723\", \"b0451\"],\n \"status\": [\"optimal\", \"optimal\", \"optimal\"],\n }\n )","async def get_all_record():\n # X_new = item.to_df()\n # item_str = item.to_string()\n # project_code = int(item_str[item_str.find('=')+1:])\n pg = PostgreSQL()\n return_json = pg.fetch_all_records()\n return return_json","def cNames():\n a = pd.DataFrame(df['Country Name'].unique(), columns=['cname']).to_json()\n r = Response(response=a,\n status=200,\n mimetype=\"application/json\")\n r.headers[\"Content-Type\"] = \"text/json; charset=utf-8\"\n return r","def __str__(self) -> str:\n if self.data is not None:\n list_of_params = []\n for key, data_dict in self.data.to_dict(orient=\"index\").items():\n data_dict[\"index\"] = key\n list_of_params.append(data_dict)\n formated_list_of_params = self.format_params(list_of_params)\n return f\"\\n{tabulate(formated_list_of_params, headers='keys', tablefmt='fancy_grid')}\"\n else:\n return \"Empty DataFrame\"","def lines_():\n query = f\"\"\"\nSELECT script_l, `name`, episode\nFROM script\nINNER JOIN characters\nON characters.char_id = script.characters_char_id\nINNER JOIN episodes\nON episodes.ep_id = script.episodes_ep_id\n\"\"\"\n data = pd.read_sql_query(query, engine)\n return data.to_json(orient=\"records\")","def dx(dataframe, sampled=None):\n # All the metadata keys that we'll apply for just the data explorer media\n # type, `application/vnd.dataresource+json`\n scoped_metadata = {}\n\n if sampled:\n scoped_metadata[\"sampled\"] = sampled\n\n metadata = {\"application/vnd.dataresource+json\": scoped_metadata}\n\n with pd.option_context('display.html.table_schema', True):\n display(dataframe, metadata=metadata)","def to_df(self) -> Dict[str, Any]:\n return {key: self.render_df(key) for key, value in self.__dict__.items()}","def format(self, row):\n return json.dumps(row.print_fields)","def frame_data(self) -> str:\n pass","def dataframe(self):\n return self.get_target().dataframe()","def FetchQueryResultToDF(data, col_name: List[str]) -> pd.DataFrame:\r\n result = []\r\n for row in data:\r\n to_be_append = []\r\n for col in row:\r\n to_be_append.append(col)\r\n result.append(to_be_append)\r\n df = pd.DataFrame(result, columns=col_name)\r\n print(df)\r\n return df","def __create_data_frame(self, soup):\n self.__data_frame = pd.read_html(str(soup))[0]\n timestamp = self.__navigate_rows(soup)\n # rename dataframe columns by columns name in sqlite\n self.__data_frame = self.__data_frame.rename(\n columns=self.__columns_name)\n self.__data_frame['time'] = pd.Series(timestamp)\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\n str.replace('%', '')\n self.__data_frame['created_date'] = datetime.now()\n # save_file(self.__name_file, self.__data_frame.to_string())","def transform(self, dataframe: DataFrame) -> DataFrame:","def get_updated_dataframe():\n # pylint: disable=import-outside-toplevel\n from sotaque_brasileiro.io import fetch_paginated_data\n records = fetch_paginated_data(constants.API_RECORDS_ENDPOINT.value)\n df = parse_records_to_dataframe(records) # pylint: disable=invalid-name\n return df","def _get_data_as_flattened_dataframe(self, json_lines):\n if isinstance(json_lines, pd.DataFrame):\n return json_lines\n payload_data = None\n if isinstance(json_lines, dict):\n # Glean Payload Data\n found_payload_key = None\n payloads = {}\n for payload_key in self._payload_keys:\n if payload_key in json_lines.keys():\n payload_data = json_lines[payload_key]\n if isinstance(payload_data, dict):\n payload_data = self._find_data(payload_data)\n payload_data = self._coalesce_dicts(payload_data)\n payload_data, original_df_dtypes = data_utils.json_to_dataframe(\n json_lines=payload_data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n for column in payload_data.columns:\n payload_data.rename(\n columns={column: payload_key + self._key_separator + str(column)},\n inplace=True)\n payloads[payload_key] = payload_data\n \n max_payload_length = 0\n for payload in payloads:\n if len(payloads[payload]) > max_payload_length:\n payload_data = payloads[payload]\n max_payload_length = len(payloads[payload])\n found_payload_key = payload\n \n # Get the non-payload data\n flattened_json = []\n for key in json_lines:\n if key != found_payload_key:\n flattened_json = flattened_json + self._find_data(json_lines[key], path=key)\n\n # Coalesce the data together\n json_lines = self._coalesce_dicts(flattened_json)\n\n data, original_df_dtypes = data_utils.json_to_dataframe(\n json_lines=json_lines,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n self._original_df_dtypes = original_df_dtypes\n\n if payload_data is not None:\n self._metadata = data\n data = payload_data\n\n return data","def dataframe(self, *args, **kwargs):\n\n try:\n return self.url.generator.dataframe(*args, **kwargs)\n except AttributeError:\n pass\n\n try:\n return self.url.dataframe(*args, **kwargs)\n except AttributeError:\n pass\n\n raise NotImplementedError(\"Url '{}' of type '{}' can't generate a dataframe \".format(self.url, type(self.url)))","def to_dict_records(df):\r\n return df.to_dict('records')","def data():\n df = gen_sliced_df()\n df = df[[\"x\", \"z_categ\", \"y\", \"residual\"]]\n new_df = df.iloc[[1, 100, 150, 200, 250, 300, 305, 400, 405, 500, 550, 609]].copy()\n return {\"df\": df, \"new_df\": new_df}","def _get_data_as_df(self, data):\n if isinstance(data, pd.DataFrame):\n return data\n if isinstance(data, dict):\n data = [data]\n data, original_df_dtypes = data_utils.json_to_dataframe(\n json_lines=data,\n selected_columns=self.selected_keys,\n read_in_string=False\n )\n self._original_df_dtypes = original_df_dtypes\n return data","def to_df(self):\r\n return pd.DataFrame([dict(self)])","def _getvXXXXAsOneString(self,vXXXX=None,start=0,end=-1,dropColList=None,filterColList=None,mapFunc={},sortList=None,ascending=True,roundDct=None,fmtFunc={},index=True,header=True):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n\r\n dfContentAsOneString=None\r\n\r\n df=self.dataFrames[vXXXX]\r\n\r\n # select rows\r\n if end == -1:\r\n df=df[start:]\r\n else:\r\n df=df[start:end]\r\n\r\n # select cols \r\n colList=df.columns.values.tolist()\r\n if isinstance(dropColList,list):\r\n colListOut=[col for col in colList if col not in dropColList]\r\n else:\r\n colListOut=colList\r\n df=df.loc[:,colListOut]\r\n if filterColList!=None:\r\n df=df.filter(items=filterColList)\r\n\r\n # map cols\r\n for col,func in mapFunc.items(): \r\n if col not in df.columns:\r\n pass\r\n else:\r\n df[col]=df[col].map(func)\r\n\r\n # sort \r\n if isinstance(sortList,list):\r\n df=df.sort_values(sortList,ascending=ascending) \r\n\r\n # round \r\n if isinstance(roundDct,dict):\r\n df=df.round(roundDct) \r\n\r\n try: \r\n dfContentAsOneString=df.to_string(formatters=fmtFunc,index=index,header=header,justify='right') \r\n except MxError:\r\n raise \r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n logger.error(logStrFinal) \r\n raise XmError(logStrFinal) \r\n finally:\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.')) \r\n return dfContentAsOneString","def _json_export(self, exppath):\n # TODO: Settle on JSON format for colortable\n pass","def get_json_data():\n return None","def linedata():\n get_values = request.args\n pc = get_values.get('pc') is not None # Per Capita\n gr = get_values.get('gr') is not None # Growth Rate\n place_args, _ = get_place_args(get_values)\n plot_data, _ = datachart_handler.get_plot_data(place_args, pc, gr)\n return json.dumps(plot_data)","def listings_data():\n\n stmt = db.session.query(nyc).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n df[\"latitude\"] = pd.to_numeric(df[\"latitude\"])\n df[\"longitude\"] = pd.to_numeric(df[\"longitude\"])\n df[\"accommodates\"] = pd.to_numeric(df[\"accommodates\"])\n\n data = df.to_dict(orient='index')\n # Create a dictionary entry for each row of metadata information\n # data = {}\n # for result in results:\n #\n # data[\"ID\"] = result[0]\n # data[\"LISTING_URL\"] = result[1]\n # data[\"NAME\"] = result[2]\n # data[\"HOST_ID\"] = result[3]\n # data[\"NEIGHBORHOOD\"] = result[4]\n # data[\"NEIGHBORHOOD_GROUP\"] = result[5]\n # data[\"CITY\"] = result[6]\n # data[\"ZIPCODE\"] = result[7]\n # data[\"LAT\"] = float(result[8])\n # data[\"LON\"] = float(result[9])\n #\n # print(data)\n\n return jsonify(data)","def bubblechart(department, position):\n # Use Pandas to perform the sql query\n stmt = db.session.query(oc_salary_db).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n # print(dept.upper())\n dept_total = df.loc[(df[\"department\"] == department.upper()) & (df[\"position\"] == position.upper()), [department, position, \"2014\",\"2015\",\"2016\",\"2017\",\"2018\"]]\n # Format the data to send as json\n data = {\n \"2014\": dept_total[\"2014\"].tolist(),\n \"2015\": dept_total[\"2015\"].tolist(),\n \"2016\": dept_total[\"2016\"].tolist(),\n \"2017\": dept_total[\"2017\"].tolist(),\n \"2018\": dept_total[\"2018\"].tolist()\n }\n return jsonify(data)","def get_file_df(filepath):\n dd = [json.loads(f) for f in open(filepath).readlines()]\n return pd.DataFrame(dd)","def my_feature_xxx(df: pd.DataFrame):\n\n # CODE HERE\n\n return df"],"string":"[\n \"def to_json(self):\\n\\t\\treturn self._dataframe.reset_index().to_json(orient=\\\"records\\\")\",\n \"def Mydata():\\n\\n stmt = db.session.query(Appsdata).statement\\n df = pd.read_sql_query(stmt, db.session.bind)\\n \\n return jsonify(df.to_dict())\",\n \"def get_data(dataframe,index=None):\\n dflen = len(dataframe)\\n if index==None or index <0 or index >= dflen:\\n index = randint(0,dflen)\\n return dataframe.iloc[index].to_json()\",\n \"def json_temp_ges(df):\\n\\n json_str = (\\n df.groupby(\\n [\\n \\\"sensor_id\\\",\\n \\\"measure_name\\\",\\n \\\"run_id\\\",\\n \\\"ventilation_rate\\\",\\n \\\"num_dehumidifiers\\\",\\n \\\"lighting_shift\\\",\\n \\\"scenario_type\\\",\\n ],\\n as_index=True,\\n )\\n .apply(\\n lambda x: x[\\n [\\n \\\"prediction_value\\\",\\n \\\"prediction_index\\\",\\n \\\"run_id\\\",\\n \\\"time\\\",\\n \\\"timestamp\\\",\\n ]\\n ].to_dict(orient=\\\"records\\\")\\n )\\n .reset_index()\\n .rename(columns={0: \\\"Values\\\"})\\n .to_json(orient=\\\"records\\\")\\n )\\n return json_str\",\n \"def receive_json_ids(dataframe, jsondata, just_headers = False):\\n\\n dict_data = ast.literal_eval(jsondata)\\n jsondict = {1: 'Kingdom', 2: 'Phylum', 3: 'Class', 4: 'Order', 5: 'Family', 6: 'Genus', 7: 'Species', 8 : 'Strain'}\\n # this checks how long the jsondata is and from this it selects the correct Letter out of the jsondict #\\n suffix = jsondict[len(dict_data)]\\n\\n # This selects the data which has the same name as the recieved jsondata\\n fw_subset = dataframe[(dataframe[\\\"fw_\\\"+ suffix] == dict_data[-1])] \\n rv_subset = dataframe[(dataframe[\\\"rv_\\\"+suffix] == dict_data[-1])]\\n\\n # This is only used so that the columns can be easily renamed in something more generic so the append will merge the correct columns\\n columns_rename = pd.DataFrame(columns=[\\\"bitscore\\\", \\\"identity\\\", \\\"length\\\"])\\n\\n # Get the specified data\\n fw_sideDf = fw_subset[[\\\"fw_bit\\\", \\\"fw_id\\\", \\\"fw_coverage_length\\\"]]\\n rv_sideDf = rv_subset[[\\\"rv_bit\\\", \\\"rv_id\\\", \\\"rv_coverage_length\\\"]]\\n\\n # Get headers\\n fw_headers = fw_subset.index.values.tolist()\\n rv_headers = rv_subset.index.values.tolist()\\n\\n if just_headers:\\n return fw_headers, rv_headers\\n \\n # Rename the columns\\n fw_sideDf.columns = columns_rename.columns\\n rv_sideDf.columns = columns_rename.columns\\n # Combine the two dataframes in one since they have the same column names it will merge completly\\n sideDf = fw_sideDf.append(rv_sideDf)\\n # Count and group the different entries also convert them into a json\\n count_id = sideDf.round(0).groupby(['identity']).size().to_json(orient='table')\\n count_bit = sideDf.round(0).groupby(['bitscore']).size().to_json(orient='table')\\n count_length = sideDf.round(0).groupby(['length']).size().to_json(orient='table')\\n fw_seqs = fw_subset[\\\"fw_seq\\\"].tolist()\\n rv_seqs = rv_subset[\\\"rv_seq\\\"].tolist()\\n\\n # Get taxonomy id's\\n tax_ids = set([*fw_subset.fw_accession.tolist(), *rv_subset.rv_accession.tolist()])\\n tax_len = len(tax_ids)\\n if tax_len == 0:\\n tax_id = \\\"None\\\"\\n elif tax_len == 1:\\n tax_id = list(tax_ids)[0]\\n else:\\n tax_id = \\\"More\\\"\\n\\n response = {\\n \\\"count_id\\\":count_id,\\n \\\"count_bit\\\": count_bit,\\n \\\"count_length\\\": count_length,\\n \\\"node_name\\\": dict_data[-1],\\n \\\"tax_id\\\": str(tax_id),\\n \\\"fw_headers\\\": fw_headers,\\n \\\"rv_headers\\\": rv_headers,\\n \\\"fw_seqs\\\": fw_seqs,\\n \\\"rv_seqs\\\": rv_seqs\\n }\\n return jsonify(response)\",\n \"def _data_frame(content):\\n response = loads(content)\\n key = [x for x in response.keys() if x in c.response_data][0]\\n frame = DataFrame(response[key])\\n final_frame = _convert(frame)\\n return final_frame\",\n \"def write_jason(df):\\n\\n\\t# set Country as index of dataframe\\n\\tdf = df.set_index('Country')\\n\\n\\t# write datafram to jason file \\n\\tdf = df.to_json('eda.json', orient='index')\",\n \"async def full_report():\\n return DF.to_dict(orient=\\\"records\\\")\",\n \"def convert_to_json(dataframe):\\n dataframe = dataframe.set_index('YYYYMMDD').to_json('schiphol_windstoten.json', orient = 'index')\",\n \"def get_data(self)->pd.DataFrame:\\n pass\",\n \"def _dict(content):\\n response = _data_frame(content).to_dict(orient='records')\\n return response\",\n \"def __parse_json(df):\\n\\t\\tcol_names = ['genres', 'production_companies', 'production_countries', 'cast', 'crew', 'spoken_languages',\\n\\t\\t\\t\\t\\t 'Keywords']\\n\\t\\tvalue_names = ['name', 'name', 'iso_3166_1', 'name', 'name', 'name', 'name']\\n\\t\\tfor col_name, value_name in zip(col_names, value_names):\\n\\t\\t\\t# df[col_name] = df[col_name].fillna(\\\"{}\\\")\\n\\t\\t\\tdf[col_name] = df[col_name].apply(literal_eval_error_handling)\\n\\t\\t\\tdf[col_name] = df[col_name].apply(lambda x: [i[value_name] for i in x])\\n\\t\\treturn df\",\n \"def toDataFrame(self):\\r\\n if self.output_format in ('json', 'jsonExtended'):\\r\\n return json.dumps(self.result)\\r\\n \\r\\n elif self.output_format in ('tab2', 'extendedTab2'):\\r\\n return StringIO('\\\\t'.join(self.headers) + self.result)\",\n \"def pandas_to_njson(df):\\n \\n #df['secao'] = df['secao'].astype(int)\\n \\n records = df.to_dict(orient='records')\\n json_list = [json.dumps(add_process_date(record), ensure_ascii=False) for record in records]\\n njson = '\\\\n'.join(json_list)\\n \\n return njson\",\n \"def names():\\n\\n df = pd.read_sql_query(f\\\"SELECT * FROM olympics_raw\\\", con = engine)\\n print(df.head())\\n \\n\\n # return jsonify(all_olympians)\\n return jsonify(df.to_dict(orient='records'))\",\n \"def display_raw_data(df):\\n raw_data_lenght=df.shape[0]\\n #loop through from 0 to number of rows in steps of 5\\n for i in range(0,raw_data_lenght,5):\\n response=input('\\\\n Do you want examin a perticular user data? Type \\\\'yes \\\\'or \\\\'no \\\\'\\\\n')\\n if response.lower()!='yes':\\n break\\n \\n data=df.iloc[i: i+5].to_json(orient='records',lines=True).split('\\\\n')\\n for row in data:\\n passed=json.loads(row)\\n j_row=json.dumps(passed,indent=3)\\n print(j_row)\",\n \"def create_data_frame(input_filepath):\\n df = pd.read_json(input_filepath)\\n logger = logging.getLogger(__name__)\\n logger.info('Imported dataframe:')\\n logger.info(df.info())\\n logger.info(df.describe())\\n logger.info(df.head())\\n return df\",\n \"def to_dataframe(self):\\n return df_util.to_dataframe(requests.get(self.__url).json())\",\n \"def json2dataframe(data):\\n # Load and parse the available streamflow data\\n siteId = data['value']['timeSeries'][0]['sourceInfo']['siteCode'][0]['value']\\n df = pd.DataFrame(data['value']['timeSeries'][0]['values'][0]['value'])\\n df = df.set_index(df['dateTime'], drop=True)\\n df['value'] = df['value'].astype('float32')\\n df.index = pd.to_datetime(df.index)\\n last_available_date = df.index[-1].strftime(\\\"%Y-%m-%d\\\")\\n return df, siteId, last_available_date\",\n \"def converttojson(edge_df):\\n\\tedge_df_str = edge_df.copy()\\n\\tfor idx, col in enumerate(edge_df.columns):\\n\\t\\tfirst_row_element = edge_df.iloc[0, idx]\\n\\t\\tif isinstance(first_row_element, list) or isinstance(first_row_element, dict):\\n\\t\\t\\tedge_df_str[col] = edge_df[col].apply(json.dumps)\\n\\t\\t\\tprint('Field \\\"{}\\\" of class {} converted to json string'.format(col, type(first_row_element)))\\n\\t\\t#else:\\n\\t\\t#\\tprint(col,type(edge_df[col][0]))\\n\\treturn edge_df_str\",\n \"def json(self) -> CellJson:\\n\\n return {\\\"id\\\": self.id, \\\"content\\\": self.content, \\\"data\\\": self.data}\",\n \"def insert_df_json(conn, table_name: str, df: pd.DataFrame):\\n insert_json(conn=conn, table_name=table_name, data=df.reset_index().to_json(orient='records', lines=True))\",\n \"def createDataframe(httpData):\\n jsonData = json.loads(httpData)\\n return pd.json_normalize(jsonData['data'])\",\n \"def generate_df(js_dict, naming, value=\\\"value\\\"):\\n\\n values = []\\n dimensions, dim_names = get_dimensions(js_dict, naming)\\n values = get_values(js_dict, value=value)\\n output = pd.DataFrame([category + [values[i]]\\n for i, category in\\n enumerate(get_df_row(dimensions, naming))])\\n output.columns = dim_names + [value]\\n output.index = range(0, len(values))\\n return output\",\n \"def input_fn(request_body, request_content_type):\\n if request_content_type == \\\"application/json\\\":\\n json_load = json.loads(request_body)\\n data=get_dataframe_from_dict(json_load)\\n csv_data=data.to_csv(index=False,header=None)\\n data=csv_data.replace(\\\"\\\\n\\\",\\\"\\\")\\n s = StringIO(data)\\n data = pd.read_csv(s, header=None)\\n\\n return data\\n else:\\n # Handle other content-types here or raise an Exception\\n # if the content type is not supported.\\n pass\",\n \"def json(path):\\n try:\\n # TODO: Check a better way to handle this Spark.instance.spark. Very verbose.\\n df = Spark.instance.spark.read.json(path)\\n except IOError as error:\\n logging.error(error)\\n raise\\n return df\",\n \"def dataframe(self):\\n\\t\\treturn self._dataframe\",\n \"def df():\\n fs.df()\",\n \"def to_df(self) -> pd.DataFrame:\\n data = []\\n for action in self.actions:\\n data.append(action.to_df())\\n df = pd.read_json(json.dumps(data), orient=\\\"list\\\")\\n return df[self.fields]\",\n \"def save_json(df):\\n dict = {}\\n for row in df.iterrows():\\n dict[row[1]['Country']] = {'Region' : row[1]['Region'],\\n 'Pop. Density (per sq. mi.)' : row[1]['Pop. Density (per sq. mi.)'],\\n 'Infant mortality (per 1000 births)' : row[1]['Infant mortality (per 1000 births)'],\\n 'GDP ($ per capita) dollars' : row[1]['GDP ($ per capita) dollars']\\n }\\n\\n with open('data.json', 'w', encoding='utf8') as outfile:\\n data = json.dumps(dict, indent=4, sort_keys=False, separators=(',', ': '), ensure_ascii=False)\\n outfile.write(data)\",\n \"def getDataFrame(self):\\n return self.df\",\n \"def as_json(self):\",\n \"def df_to_json(complete_dataset, output_path, static_columns):\\n megajson = {}\\n\\n static_columns = [\\\"continent\\\", \\\"location\\\"] + list(static_columns)\\n\\n complete_dataset = complete_dataset.dropna(axis=\\\"rows\\\", subset=[\\\"iso_code\\\"])\\n\\n for _, row in complete_dataset.iterrows():\\n\\n row_iso = row[\\\"iso_code\\\"]\\n row_dict_static = row.drop(\\\"iso_code\\\")[static_columns].dropna().to_dict()\\n row_dict_dynamic = row.drop(\\\"iso_code\\\").drop(static_columns).dropna().to_dict()\\n\\n if row_iso not in megajson:\\n megajson[row_iso] = row_dict_static\\n megajson[row_iso][\\\"data\\\"] = [row_dict_dynamic]\\n else:\\n megajson[row_iso][\\\"data\\\"].append(row_dict_dynamic)\\n\\n with open(output_path, \\\"w\\\") as file:\\n file.write(json.dumps(megajson, indent=4))\",\n \"def get_data_rows(self, as_json=False, pretty_print=False):\\n if self.has_error():\\n return None\\n\\n # Read the table\\n try:\\n if self.is_excel:\\n msgt('Excel!')\\n\\n df = pd.read_excel(self.filepath)\\n #error_bad_lines=False)\\n else:\\n df = pd.read_table(self.filepath)\\n except Exception as ex_obj:\\n msg(ex_obj)\\n msgt('Failed to open file via pandas!')\\n temp_file_helper.make_sure_file_deleted(self.filepath)\\n if self.is_excel:\\n self.add_error('Failed to open Excel file via pandas. [%s]' % ex_obj)\\n else:\\n self.add_error('<b>Probably not a tabular file!</b> Failed to open file via pandas. [%s]' % ex_obj)\\n return None\\n\\n self.describe_as_html = df.describe().to_html()\\n json_string = df.describe().to_json()\\n self.describe_as_dict =json.loads(json_string, object_pairs_hook=OrderedDict)\\n\\n # Retrieve the columns\\n self.column_names = df.columns.tolist()\\n\\n # Retrieve the rows\\n self.data_rows = df[:self.num_preview_rows].values.tolist()\\n\\n #print 'rows', json.dumps(rows)\\n\\n # Format the response\\n info_dict = OrderedDict()\\n\\n info_dict['total_row_count'] = len(df.index)\\n info_dict['preview_row_count'] = len(self.data_rows)\\n info_dict['column_names'] = self.column_names\\n info_dict['rows'] = self.data_rows\\n info_dict['describe_as_html'] = self.describe_as_html\\n info_dict['describe_as_dict'] = self.describe_as_dict\\n\\n if as_json:\\n if pretty_print:\\n return json.dumps(info_dict, indent=4)\\n return json.dumps(info_dict)\\n\\n return info_dict\",\n \"def process_data(self):\\n structure_data = self.parse_root(self.root)\\n\\n dict_data = {}\\n for d in structure_data:\\n dict_data = {**dict_data, **d}\\n df = pd.DataFrame(data=list(dict_data.values()), index=dict_data.keys()).T\\n\\n return df\",\n \"def addJSON(file: str, df, creat: bool):\\n if creat is False :\\n with open(file) as train_file:\\n dict_train = json.load(train_file)\\n data = pd.read_json(dict_train, orient=\\\"records\\\")\\n df = pd.concat([data, df])\\n \\n js = df.to_json(orient='records').replace(\\n \\\"[\\\\\\\\\\\\\\\"[\\\", '').replace(\\\"]\\\\\\\\\\\\\\\"]\\\", '')\\n \\n with open(file, 'w', encoding='utf8') as outfile:\\n json.dump(js, outfile, ensure_ascii=False, indent=4)\",\n \"def dataframe(self):\\n return self.generator.dataframe\",\n \"def df_to_json(tokenList):\\n # If df's ouput format changes, we'll be in trouble, of course.\\n # the 0 token is the name of the filesystem\\n # the 1 token is the size of the filesystem in 1K blocks\\n # the 2 token is the amount used of the filesystem\\n # the 5 token is the mount point\\n offset = len(tokenList) - 9\\n result = {}\\n fsName = tokenList[0+offset]\\n fsSize = tokenList[1+offset]\\n fsUsed = tokenList[2+offset]\\n fsMountPoint = tokenList[5+offset]\\n result[\\\"filesystem\\\"] = {}\\n result[\\\"filesystem\\\"][\\\"name\\\"] = fsName\\n result[\\\"filesystem\\\"][\\\"size\\\"] = float(fsSize)/1000\\n result[\\\"filesystem\\\"][\\\"used\\\"] = float(fsUsed)/1000\\n result[\\\"filesystem\\\"][\\\"mount_point\\\"] = fsMountPoint\\n if result[\\\"filesystem\\\"][\\\"size\\\"] > 0 :\\n result[\\\"filesystem\\\"][\\\"percentage\\\"] = result[\\\"filesystem\\\"][\\\"used\\\"]/result[\\\"filesystem\\\"][\\\"size\\\"]\\n return result\",\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 list_data_frames():\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"list_data_frames\\\"\\n cmd[\\\"name_\\\"] = \\\"\\\"\\n \\n s = comm.send_and_receive_socket(cmd)\\n\\n msg = comm.recv_string(s)\\n\\n if msg != \\\"Success!\\\":\\n raise Exception(msg)\\n \\n json_str = comm.recv_string(s) \\n \\n s.close() \\n\\n return json.loads(json_str)\",\n \"def create_cris_data_dict(df, filename, outdir):\\n json_dict = {}\\n for key in df.keys():\\n if key != 'participant_id':\\n json_dict[key] = {'Units': key.split()[-1]}\\n else:\\n json_dict[key] = {'Description': 'OpenNeuro ID of the subject.'}\\n with open(outdir.joinpath(filename + '.json'), \\\"w\\\") as f:\\n json.dump(json_dict, f, indent=4)\",\n \"def json_view(self, recursive=False):\\n\\n context = self.context.aq_inner\\n data = self.export(context, recursive=recursive)\\n pretty = json.dumps(data, sort_keys=True, indent=4)\\n self.request.response.setHeader(\\\"Content-type\\\", \\\"application/json\\\")\\n return pretty\",\n \"def _get_frame_data(self):\\n\\n def _text_formatting(bs4_tag):\\n \\\"\\\"\\\"Simple formatting that will be called a lot\\\"\\\"\\\"\\n return bs4_tag.get_text().replace('\\\\n', '')\\n\\n tables = Character._get_main_page(self._fd).find_all('table', {'class': 'wikitable'})\\n result = {}\\n for table in tables:\\n rows = table.find_all('tr')\\n for row in rows:\\n move = row.find('th')\\n\\n if move is not None:\\n move_small = move.find('small')\\n else:\\n continue\\n\\n if move_small is not None:\\n move_name = move.get_text().replace(move_small.get_text(), ' ' + move_small.get_text()).replace(\\n '\\\\n', '')\\n else:\\n move_name = _text_formatting(move)\\n\\n move_name = move_name.replace('+', '') # B+C fix\\n fd = row.find_all('td')\\n\\n try:\\n result.update({\\n move_name: {\\n \\\"Damage\\\": _text_formatting(fd[0]),\\n \\\"Cancel\\\": _text_formatting(fd[1]),\\n \\\"Attribute\\\": _text_formatting(fd[4]),\\n \\\"Guard\\\": _text_formatting(fd[5]),\\n \\\"Startup\\\": _text_formatting(fd[6]),\\n \\\"Active\\\": _text_formatting(fd[7]),\\n \\\"Recovery\\\": _text_formatting(fd[8]),\\n \\\"Frame Adv\\\": _text_formatting(fd[9]),\\n \\\"Blockstun\\\": _text_formatting(fd[11]),\\n \\\"Invul/GP\\\": _text_formatting(fd[19])\\n }\\n })\\n except (AttributeError, IndexError):\\n continue\\n\\n return result\",\n \"def download_data_df(main=False) -> pd.DataFrame:\\n temp_file: str = './temp.csv'\\n json_text: str = download_json_data(main)\\n result: pd.DataFrame = json_to_csv(json_text, temp_file)\\n os.remove(temp_file)\\n return result\",\n \"def toJSON(self):\\r\\n\\r\\n jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n if len(self.slctData) > 100:\\r\\n self.getSimMatSummary(100)\\r\\n jsonToRet.append(self.summaryOrdering)\\r\\n for i in range(0,len(self.simMatSmm)):\\r\\n for n in self.simMatSmm[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n\\r\\n jsonToRet.append(self.patchOrdering)\\r\\n # jsonToRet = []\\r\\n rowJson = []\\r\\n matrixJson = []\\r\\n\\r\\n for i in range(0,len(self.simMat)):\\r\\n for n in self.simMat[i]:\\r\\n rowJson.append(n)\\r\\n matrixJson.append(rowJson)\\r\\n rowJson = []\\r\\n jsonToRet.append(matrixJson)\\r\\n return jsonToRet\",\n \"def dataframe_to_list(df: pandas.DataFrame) -> list:\\n return json.loads(df.to_json(orient=\\\"records\\\"))\",\n \"def format(df, format, features):\\n\\n def mapDocFormat(row):\\n if format == \\\"csv\\\":\\n return (row.id.encode(\\\"utf-8\\\", errors='ignore') + \\\",\\\" + \\\" \\\".join(\\n [x.encode(\\\"utf-8\\\", errors='ignore') for x in [row.comments, row.caption, row.tags]])).lower()\\n if format == \\\"tsv\\\":\\n return (row.id.encode(\\\"utf-8\\\") + \\\"\\\\t\\\" + \\\" \\\".join(\\n [x.encode(\\\"utf-8\\\", errors='ignore') for x in [row.comments, row.caption, row.tags]])).lower()\\n if format == \\\"json\\\":\\n return pyspark.sql.Row(doc=row.id.encode(\\\"utf-8\\\", errors='ignore').lower(),\\n text=(''.join([x.encode(\\\"utf-8\\\", errors='ignore') for x in row]))).lower()\\n\\n if features:\\n df = df.rdd.map(mapDocFormat)\\n if format == \\\"json\\\":\\n return df.toDF().toJSON()\\n else:\\n return df\\n else:\\n if format == \\\"csv\\\":\\n return df.rdd.map(lambda row: (','.join(\\n [x.encode(\\\"utf-8\\\", errors='ignore') for x in [row.id, row.url, row.comments, row.caption, row.tags]])).lower())\\n if format == \\\"tsv\\\":\\n return df.rdd.map(lambda row: ('\\\\t'.join(\\n [x.encode(\\\"utf-8\\\", errors='ignore') for x in [row.id, row.url, row.comments, row.caption, row.tags]])).lower())\\n if format == \\\"json\\\":\\n return df.toJSON()\",\n \"def parse_jsons(self):\\n # store all data in a pandas DataFrame\\n pandas_df = pandas.DataFrame(self.__input_jsons)\\n return pandas_df\",\n \"def display_data(df):\\n row_length = df.shape[0]\\n # iterate from 0 to the number of rows in steps of 5\\n for i in range(0, row_length, 5):\\n decision = input('\\\\nWould you like to examine the particular user trip data? Type \\\\'yes\\\\' or \\\\'no\\\\'\\\\n> ')\\n if decision.lower() != 'yes':\\n break\\n # retrieve and convert data to json format\\n # split each json row data \\n # retrieving rows by iloc method\\n #And use to_json which takes (orient = ‘records’ that list like [{column -> value}, … , {column -> value}]\\n row_data = df.iloc[i: i + 5].to_json(orient='records', lines=True).split('\\\\n')\\n for row in row_data:\\n # pretty print each user data\\n parsed_row = json.loads(row)\\n #converts a Python object into a json string.\\n json_row = json.dumps(parsed_row, indent=2)\\n print(json_row)\",\n \"def get_data(config_path):\\n config = read_params(config_path)\\n data_path = config[\\\"data_source\\\"][\\\"s3_source\\\"]\\n df = pd.read_json(data_path, lines=True, orient='str')\\n return df\",\n \"def glass_pandas(self):\\n # pandas.set_option('display.width', 120)\\n # TODO timeit (git_implementation) vs (my_implementation)\\n # * df = pd.DataFrame(json.loads(r.text))\\n # * df = df.set_index('t')\\n # * df.index = pd.to_datetime(df.index, unit='s')\\n # * df = df.sort_index()\\n # * s = df.v\\n # * s.name = '_'.join(url.split('/')[-2:])\\n # * return s\\n # for elem in self.loaded:\\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\\n # try:\\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\\n # framed = pandas.DataFrame(\\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\\n # for k in frame_keys} for iters in range(len(_data))],\\n # columns=frame_keys)\\n # except KeyError:\\n # framed = pandas.DataFrame(_data)\\n # framed.set_index('t', inplace=True)\\n # framed.index = pandas.to_datetime(\\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\\n # framed.sort_index(inplace=True)\\n # framed.name = _metric\\n # print(framed.name)\\n # print(framed)\",\n \"def json_data(self):\\n self.check_proof()\\n return {\\n \\\"vars\\\": [{'name': v.name, 'T': str(v.T)} for v in self.vars],\\n \\\"proof\\\": sum([printer.export_proof_item(self.thy, item, unicode=True, highlight=True)\\n for item in self.prf.items], []),\\n \\\"report\\\": self.rpt.json_data(),\\n \\\"method_sig\\\": self.get_method_sig()\\n }\",\n \"def __data_row_to_json(self, row):\\n raw_data = {}\\n raw_data[\\\"body\\\"] = row.body\\n raw_data[\\\"score_hidden\\\"] = row.score_hidden\\n raw_data[\\\"archived\\\"] = row.archived\\n raw_data[\\\"name\\\"] = row.name\\n raw_data[\\\"author\\\"] = row.author\\n raw_data[\\\"author_flair_text\\\"] = row.author_flair_text\\n raw_data[\\\"downs\\\"] = row.downs\\n raw_data[\\\"created_utc\\\"] = row.created_utc\\n raw_data[\\\"subreddit_id\\\"] = row.subreddit_id\\n raw_data[\\\"link_id\\\"] = row.link_id\\n raw_data[\\\"parent_id\\\"] = row.parent_id\\n raw_data[\\\"score\\\"] = row.score\\n raw_data[\\\"retrieved_on\\\"] = row.retrieved_on\\n raw_data[\\\"controversiality\\\"] = row.controversiality\\n raw_data[\\\"gilded\\\"] = row.gilded\\n raw_data[\\\"id\\\"] = row.id\\n raw_data[\\\"subreddit\\\"] = row.subreddit\\n raw_data[\\\"ups\\\"] = row.ups\\n raw_data[\\\"distinguished\\\"] = row.distinguished\\n raw_data[\\\"author_flair_css_class\\\"] = row.author_flair_css_class\\n\\n return json.dumps(raw_data)\",\n \"def get_data(self):\\n return self.data.to_json()\",\n \"def get_data(self):\\n\\n # Import json data into a pandas dataframe\\n df = pd.read_json(\\\"./nutrients.json\\\", lines=True)\\n\\n return df\",\n \"def pandas_convert(self):\\n data = {}\\n\\n for names in self.data[0]:\\n col_values = []\\n\\n if names in objects:\\n for items in self.data[0][names]:\\n col_values = []\\n\\n col_name = names + \\\"_\\\" + items\\n\\n for i in range(len(self.data)):\\n col_values.append(self.data[i][names][items])\\n\\n data[col_name] = col_values\\n else:\\n for i in range(len(self.data)):\\n col_values.append(self.data[i][names])\\n \\n data[names] = col_values\\n\\n self.pandas_df = pd.DataFrame(data=data)\\n self.__clean_df()\\n\\n return self.pandas_df\",\n \"def get_time_series_data():\\r\\n # Grab the requested years and columns from the query arguments\\r\\n ls_year = [int(year) for year in request.args.getlist(\\\"n\\\")]\\r\\n ls_col = request.args.getlist(\\\"m\\\")\\r\\n\\r\\n # Generate a list of all the months we need to get\\r\\n all_years = [str(year) for year in range(min(ls_year), max(ls_year) + 1)]\\r\\n\\r\\n # Grab all of the wanted months by filtering for the ones we want\\r\\n wanted_months = reduce(\\r\\n lambda a, b: a | b, (app.df[\\\"month\\\"].str.contains(year) for year in all_years)\\r\\n )\\r\\n\\r\\n # Create a new dataframe from the one that\\r\\n df_new = app.df[wanted_months][[\\\"month\\\"] + ls_col]\\r\\n\\r\\n # Convert all string dates into datetime objects and then sort them\\r\\n df_new[\\\"month\\\"] = pd.to_datetime(df_new[\\\"month\\\"])\\r\\n df_new = df_new.sort_values(by=[\\\"month\\\"])\\r\\n\\r\\n # Return the dataframe as json\\r\\n return df_new.to_json(), 200\",\n \"def format_bgc_metadata(df,float_id):\\n mdf = df[bgc_metadata_columns]\\n bgc_metadata_dict = {}\\n for col in list(mdf):\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip())\\n bgc_metadata_dict[col] = list(pd.Series(mdf[col].unique()).astype(str).str.strip().replace(\\\"'\\\",'\\\"'))\\n bgc_metadata_dict = json.dumps(bgc_metadata_dict) \\n bgc_metadata_df = pd.DataFrame({\\\"float_id\\\": [float_id], \\\"Metadata_Dict\\\": [bgc_metadata_dict]})\\n return bgc_metadata_df\",\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 dataFrame(self):\\n\\n memory_file = StringIO(initial_value=self.sparql_result.decode('utf-8'), newline='\\\\n')\\n reader = DictReader(memory_file)\\n\\n schema = StructType(\\n list(map(lambda f: StructField(f, StringType()), reader.fieldnames))\\n )\\n\\n data = list(map(lambda d: [d[f] for f in reader.fieldnames], list(reader)))\\n\\n return self.spark.createDataFrame(data, schema)\",\n \"def get_datalist_fr_json(self):\\n raw_data = json.load(open(self.saved_json_file, 'r'))\\n for indivdual_set in raw_data['query']['results']['stats']:\\n temp_dict_data = {}\\n if type(indivdual_set) == str:\\n #for single data\\n continue # temp do not use\\n for parameters in indivdual_set.keys():\\n if type(indivdual_set[parameters]) == str:\\n temp_dict_data[parameters] = indivdual_set[parameters]#for symbol\\n elif type(indivdual_set[parameters]) == dict:\\n if indivdual_set[parameters].has_key('content'):\\n temp_dict_data[parameters] = indivdual_set[parameters]['content']\\n\\n ## append to list\\n self.com_data_allstock_list.append(temp_dict_data)\",\n \"def df(self):\\n return self._df\",\n \"def _jsonify(self):\\n return self.experiment_record.to_ddb_record()\",\n \"def precipitation():\\n\\n return jsonify(prcp_df)\",\n \"def json_temp_arima(df_arima):\\n # The shift_hours=1 accounts for df_arima starting its indexing from 1\\n # instead of 0.\\n df_arima = add_time_columns(df_arima, shift_hours=1)\\n if len(df_arima) < 1:\\n return \\\"{}\\\"\\n json_str = (\\n df_arima.groupby([\\\"sensor_id\\\", \\\"measure_name\\\", \\\"run_id\\\"], as_index=True)\\n .apply(\\n lambda x: x[\\n [\\n \\\"prediction_value\\\",\\n \\\"prediction_index\\\",\\n \\\"run_id\\\",\\n \\\"time\\\",\\n \\\"timestamp\\\",\\n ]\\n ].to_dict(orient=\\\"records\\\")\\n )\\n .reset_index()\\n .rename(columns={0: \\\"Values\\\"})\\n .to_json(orient=\\\"records\\\")\\n )\\n return json_str\",\n \"def dataframe(self):\\n if not self.all_records:\\n print('No rows cached.')\\n return\\n dict_list = [row.as_dict() for row in self.all_records]\\n columns = self.all_records[0].keys\\n dataframe = pd.DataFrame(dict_list, columns=columns)\\n return dataframe\",\n \"def make_df_from_json(json_files, out_file):\\n table = [[\\\"name\\\", \\n \\\"cik\\\", \\n \\\"city\\\",\\n \\\"state\\\",\\n \\\"street1\\\",\\n \\\"street2\\\",\\n \\\"zip_code\\\",\\n \\\"year_of_incorp\\\", \\n \\\"min_inv\\\", \\n \\\"tot_off\\\", \\n \\\"tot_sold\\\", \\n \\\"tot_rem\\\", \\n \\\"ind_group_type\\\", \\n \\\"has_non_accred\\\", \\n \\\"num_non_accred\\\", \\n \\\"tot_num_inv\\\"\\n ]] \\n\\n for json_dict in json_files:\\n\\n with open(json_dict, \\\"rb\\\") as f:\\n data = json.load(f)\\n print(json_dict)\\n\\n for i, key in enumerate(data):\\n # if i % 1000 == 0:\\n # print(i)\\n entry = data[key] \\n if entry == {}:\\n #print(\\\"missing entry {0}\\\".format(i))\\n continue\\n row = []\\n\\n primary_issuer = entry[\\\"Primary Issuer\\\"]\\n cik = primary_issuer[\\\"cik\\\"]\\n name = primary_issuer[\\\"entity_name\\\"]\\n phone = primary_issuer[\\\"phone\\\"]\\n year_of_incorp = primary_issuer[\\\"year_of_incorp\\\"]\\n address = primary_issuer[\\\"address\\\"]\\n city = address[\\\"city\\\"]\\n state = address[\\\"state\\\"]\\n street1 = address[\\\"street1\\\"]\\n street2 = address[\\\"street2\\\"]\\n zip_code = address[\\\"zip_code\\\"]\\n\\n secondary_issuers = entry[\\\"Secondary Issuers\\\"]\\n related_people = entry[\\\"Related People\\\"]\\n \\n offering_data = entry[\\\"Offering Data\\\"]\\n min_inv = offering_data[\\\"min_investment_accepted\\\"]\\n tot_off = offering_data[\\\"total_offering_amount\\\"]\\n tot_sold = offering_data[\\\"total_amount_sold\\\"]\\n tot_rem = offering_data[\\\"total_remaining\\\"]\\n ind_group_type = offering_data[\\\"ind_group_type\\\"]\\n has_non_accred = offering_data[\\\"has_non_accred\\\"]\\n num_non_accred = offering_data[\\\"num_non_accred\\\"]\\n tot_num_inv = offering_data[\\\"tot_num_inv\\\"] \\n\\n row = [name, \\n cik, \\n city,\\n state,\\n street1,\\n street2,\\n zip_code,\\n year_of_incorp,\\n min_inv,\\n tot_off,\\n tot_sold,\\n tot_rem,\\n ind_group_type,\\n has_non_accred,\\n num_non_accred,\\n tot_num_inv\\n ]\\n\\n table.append(row)\\n\\n df = pd.DataFrame(table)\\n df.to_csv(out_file)\\n\\n return 0\",\n \"def _get_data_as_json(self, data):\\n data = self._get_data_as_df(data)\\n data = data.to_json(orient=\\\"records\\\")\\n char_per_line = min(len(data), self.SAMPLES_PER_LINE_DEFAULT)\\n return list(map(''.join, zip(*[iter(data)] * char_per_line)))\",\n \"def dataframe(self):\\n return self._df\",\n \"def data(self):\\n return self.as_named_DataFrame()\",\n \"def format_data(self, params):\\n return json.dumps(params)\",\n \"def convert_data(df):\\n print(\\\"Converting history...\\\")\\n return [ dict(row) for i, row in df.iterrows() ]\",\n \"def expected_df():\\n return pd.DataFrame(\\n {\\n \\\"growth\\\": [0.873922, 0.814298, 0.0],\\n \\\"gene\\\": [\\\"b2935\\\", \\\"b0723\\\", \\\"b0451\\\"],\\n \\\"status\\\": [\\\"optimal\\\", \\\"optimal\\\", \\\"optimal\\\"],\\n }\\n )\",\n \"async def get_all_record():\\n # X_new = item.to_df()\\n # item_str = item.to_string()\\n # project_code = int(item_str[item_str.find('=')+1:])\\n pg = PostgreSQL()\\n return_json = pg.fetch_all_records()\\n return return_json\",\n \"def cNames():\\n a = pd.DataFrame(df['Country Name'].unique(), columns=['cname']).to_json()\\n r = Response(response=a,\\n status=200,\\n mimetype=\\\"application/json\\\")\\n r.headers[\\\"Content-Type\\\"] = \\\"text/json; charset=utf-8\\\"\\n return r\",\n \"def __str__(self) -> str:\\n if self.data is not None:\\n list_of_params = []\\n for key, data_dict in self.data.to_dict(orient=\\\"index\\\").items():\\n data_dict[\\\"index\\\"] = key\\n list_of_params.append(data_dict)\\n formated_list_of_params = self.format_params(list_of_params)\\n return f\\\"\\\\n{tabulate(formated_list_of_params, headers='keys', tablefmt='fancy_grid')}\\\"\\n else:\\n return \\\"Empty DataFrame\\\"\",\n \"def lines_():\\n query = f\\\"\\\"\\\"\\nSELECT script_l, `name`, episode\\nFROM script\\nINNER JOIN characters\\nON characters.char_id = script.characters_char_id\\nINNER JOIN episodes\\nON episodes.ep_id = script.episodes_ep_id\\n\\\"\\\"\\\"\\n data = pd.read_sql_query(query, engine)\\n return data.to_json(orient=\\\"records\\\")\",\n \"def dx(dataframe, sampled=None):\\n # All the metadata keys that we'll apply for just the data explorer media\\n # type, `application/vnd.dataresource+json`\\n scoped_metadata = {}\\n\\n if sampled:\\n scoped_metadata[\\\"sampled\\\"] = sampled\\n\\n metadata = {\\\"application/vnd.dataresource+json\\\": scoped_metadata}\\n\\n with pd.option_context('display.html.table_schema', True):\\n display(dataframe, metadata=metadata)\",\n \"def to_df(self) -> Dict[str, Any]:\\n return {key: self.render_df(key) for key, value in self.__dict__.items()}\",\n \"def format(self, row):\\n return json.dumps(row.print_fields)\",\n \"def frame_data(self) -> str:\\n pass\",\n \"def dataframe(self):\\n return self.get_target().dataframe()\",\n \"def FetchQueryResultToDF(data, col_name: List[str]) -> pd.DataFrame:\\r\\n result = []\\r\\n for row in data:\\r\\n to_be_append = []\\r\\n for col in row:\\r\\n to_be_append.append(col)\\r\\n result.append(to_be_append)\\r\\n df = pd.DataFrame(result, columns=col_name)\\r\\n print(df)\\r\\n return df\",\n \"def __create_data_frame(self, soup):\\n self.__data_frame = pd.read_html(str(soup))[0]\\n timestamp = self.__navigate_rows(soup)\\n # rename dataframe columns by columns name in sqlite\\n self.__data_frame = self.__data_frame.rename(\\n columns=self.__columns_name)\\n self.__data_frame['time'] = pd.Series(timestamp)\\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\\\\n str.replace('%', '')\\n self.__data_frame['created_date'] = datetime.now()\\n # save_file(self.__name_file, self.__data_frame.to_string())\",\n \"def transform(self, dataframe: DataFrame) -> DataFrame:\",\n \"def get_updated_dataframe():\\n # pylint: disable=import-outside-toplevel\\n from sotaque_brasileiro.io import fetch_paginated_data\\n records = fetch_paginated_data(constants.API_RECORDS_ENDPOINT.value)\\n df = parse_records_to_dataframe(records) # pylint: disable=invalid-name\\n return df\",\n \"def _get_data_as_flattened_dataframe(self, json_lines):\\n if isinstance(json_lines, pd.DataFrame):\\n return json_lines\\n payload_data = None\\n if isinstance(json_lines, dict):\\n # Glean Payload Data\\n found_payload_key = None\\n payloads = {}\\n for payload_key in self._payload_keys:\\n if payload_key in json_lines.keys():\\n payload_data = json_lines[payload_key]\\n if isinstance(payload_data, dict):\\n payload_data = self._find_data(payload_data)\\n payload_data = self._coalesce_dicts(payload_data)\\n payload_data, original_df_dtypes = data_utils.json_to_dataframe(\\n json_lines=payload_data,\\n selected_columns=self.selected_keys,\\n read_in_string=False\\n )\\n for column in payload_data.columns:\\n payload_data.rename(\\n columns={column: payload_key + self._key_separator + str(column)},\\n inplace=True)\\n payloads[payload_key] = payload_data\\n \\n max_payload_length = 0\\n for payload in payloads:\\n if len(payloads[payload]) > max_payload_length:\\n payload_data = payloads[payload]\\n max_payload_length = len(payloads[payload])\\n found_payload_key = payload\\n \\n # Get the non-payload data\\n flattened_json = []\\n for key in json_lines:\\n if key != found_payload_key:\\n flattened_json = flattened_json + self._find_data(json_lines[key], path=key)\\n\\n # Coalesce the data together\\n json_lines = self._coalesce_dicts(flattened_json)\\n\\n data, original_df_dtypes = data_utils.json_to_dataframe(\\n json_lines=json_lines,\\n selected_columns=self.selected_keys,\\n read_in_string=False\\n )\\n self._original_df_dtypes = original_df_dtypes\\n\\n if payload_data is not None:\\n self._metadata = data\\n data = payload_data\\n\\n return data\",\n \"def dataframe(self, *args, **kwargs):\\n\\n try:\\n return self.url.generator.dataframe(*args, **kwargs)\\n except AttributeError:\\n pass\\n\\n try:\\n return self.url.dataframe(*args, **kwargs)\\n except AttributeError:\\n pass\\n\\n raise NotImplementedError(\\\"Url '{}' of type '{}' can't generate a dataframe \\\".format(self.url, type(self.url)))\",\n \"def to_dict_records(df):\\r\\n return df.to_dict('records')\",\n \"def data():\\n df = gen_sliced_df()\\n df = df[[\\\"x\\\", \\\"z_categ\\\", \\\"y\\\", \\\"residual\\\"]]\\n new_df = df.iloc[[1, 100, 150, 200, 250, 300, 305, 400, 405, 500, 550, 609]].copy()\\n return {\\\"df\\\": df, \\\"new_df\\\": new_df}\",\n \"def _get_data_as_df(self, data):\\n if isinstance(data, pd.DataFrame):\\n return data\\n if isinstance(data, dict):\\n data = [data]\\n data, original_df_dtypes = data_utils.json_to_dataframe(\\n json_lines=data,\\n selected_columns=self.selected_keys,\\n read_in_string=False\\n )\\n self._original_df_dtypes = original_df_dtypes\\n return data\",\n \"def to_df(self):\\r\\n return pd.DataFrame([dict(self)])\",\n \"def _getvXXXXAsOneString(self,vXXXX=None,start=0,end=-1,dropColList=None,filterColList=None,mapFunc={},sortList=None,ascending=True,roundDct=None,fmtFunc={},index=True,header=True):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n\\r\\n dfContentAsOneString=None\\r\\n\\r\\n df=self.dataFrames[vXXXX]\\r\\n\\r\\n # select rows\\r\\n if end == -1:\\r\\n df=df[start:]\\r\\n else:\\r\\n df=df[start:end]\\r\\n\\r\\n # select cols \\r\\n colList=df.columns.values.tolist()\\r\\n if isinstance(dropColList,list):\\r\\n colListOut=[col for col in colList if col not in dropColList]\\r\\n else:\\r\\n colListOut=colList\\r\\n df=df.loc[:,colListOut]\\r\\n if filterColList!=None:\\r\\n df=df.filter(items=filterColList)\\r\\n\\r\\n # map cols\\r\\n for col,func in mapFunc.items(): \\r\\n if col not in df.columns:\\r\\n pass\\r\\n else:\\r\\n df[col]=df[col].map(func)\\r\\n\\r\\n # sort \\r\\n if isinstance(sortList,list):\\r\\n df=df.sort_values(sortList,ascending=ascending) \\r\\n\\r\\n # round \\r\\n if isinstance(roundDct,dict):\\r\\n df=df.round(roundDct) \\r\\n\\r\\n try: \\r\\n dfContentAsOneString=df.to_string(formatters=fmtFunc,index=index,header=header,justify='right') \\r\\n except MxError:\\r\\n raise \\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n logger.error(logStrFinal) \\r\\n raise XmError(logStrFinal) \\r\\n finally:\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.')) \\r\\n return dfContentAsOneString\",\n \"def _json_export(self, exppath):\\n # TODO: Settle on JSON format for colortable\\n pass\",\n \"def get_json_data():\\n return None\",\n \"def linedata():\\n get_values = request.args\\n pc = get_values.get('pc') is not None # Per Capita\\n gr = get_values.get('gr') is not None # Growth Rate\\n place_args, _ = get_place_args(get_values)\\n plot_data, _ = datachart_handler.get_plot_data(place_args, pc, gr)\\n return json.dumps(plot_data)\",\n \"def listings_data():\\n\\n stmt = db.session.query(nyc).statement\\n df = pd.read_sql_query(stmt, db.session.bind)\\n df[\\\"latitude\\\"] = pd.to_numeric(df[\\\"latitude\\\"])\\n df[\\\"longitude\\\"] = pd.to_numeric(df[\\\"longitude\\\"])\\n df[\\\"accommodates\\\"] = pd.to_numeric(df[\\\"accommodates\\\"])\\n\\n data = df.to_dict(orient='index')\\n # Create a dictionary entry for each row of metadata information\\n # data = {}\\n # for result in results:\\n #\\n # data[\\\"ID\\\"] = result[0]\\n # data[\\\"LISTING_URL\\\"] = result[1]\\n # data[\\\"NAME\\\"] = result[2]\\n # data[\\\"HOST_ID\\\"] = result[3]\\n # data[\\\"NEIGHBORHOOD\\\"] = result[4]\\n # data[\\\"NEIGHBORHOOD_GROUP\\\"] = result[5]\\n # data[\\\"CITY\\\"] = result[6]\\n # data[\\\"ZIPCODE\\\"] = result[7]\\n # data[\\\"LAT\\\"] = float(result[8])\\n # data[\\\"LON\\\"] = float(result[9])\\n #\\n # print(data)\\n\\n return jsonify(data)\",\n \"def bubblechart(department, position):\\n # Use Pandas to perform the sql query\\n stmt = db.session.query(oc_salary_db).statement\\n df = pd.read_sql_query(stmt, db.session.bind)\\n # print(dept.upper())\\n dept_total = df.loc[(df[\\\"department\\\"] == department.upper()) & (df[\\\"position\\\"] == position.upper()), [department, position, \\\"2014\\\",\\\"2015\\\",\\\"2016\\\",\\\"2017\\\",\\\"2018\\\"]]\\n # Format the data to send as json\\n data = {\\n \\\"2014\\\": dept_total[\\\"2014\\\"].tolist(),\\n \\\"2015\\\": dept_total[\\\"2015\\\"].tolist(),\\n \\\"2016\\\": dept_total[\\\"2016\\\"].tolist(),\\n \\\"2017\\\": dept_total[\\\"2017\\\"].tolist(),\\n \\\"2018\\\": dept_total[\\\"2018\\\"].tolist()\\n }\\n return jsonify(data)\",\n \"def get_file_df(filepath):\\n dd = [json.loads(f) for f in open(filepath).readlines()]\\n return pd.DataFrame(dd)\",\n \"def my_feature_xxx(df: pd.DataFrame):\\n\\n # CODE HERE\\n\\n return df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64720726","0.63688964","0.62659526","0.62423277","0.61731887","0.61728024","0.6165461","0.610696","0.608971","0.608501","0.60734797","0.6067765","0.60032827","0.5975856","0.59611905","0.5942278","0.5940992","0.59368914","0.5936283","0.5933788","0.5910125","0.58526474","0.57914597","0.578663","0.57680786","0.57611877","0.5734831","0.5733008","0.5713259","0.5712131","0.57013756","0.5667361","0.5653684","0.56121963","0.5609037","0.56043917","0.5593471","0.5587373","0.55801827","0.5572345","0.55614245","0.55541474","0.55497986","0.55477005","0.5536616","0.5533723","0.5523065","0.5517754","0.55160123","0.55133444","0.55055434","0.54977125","0.549696","0.54903024","0.5482618","0.54810697","0.54809046","0.5480375","0.5475449","0.5472704","0.547248","0.5464888","0.54643726","0.5454899","0.5446767","0.5443123","0.54307973","0.54279673","0.54041535","0.54010785","0.5390844","0.5385566","0.53795356","0.5378529","0.5377424","0.537345","0.5371914","0.53675467","0.53625745","0.53588146","0.535555","0.53510463","0.5349599","0.5341289","0.53411806","0.5337197","0.5336624","0.53314006","0.53307575","0.5322711","0.5322071","0.53197044","0.53190225","0.53115416","0.53079593","0.5303784","0.5301862","0.5301385","0.5300864","0.52915525"],"string":"[\n \"0.64720726\",\n \"0.63688964\",\n \"0.62659526\",\n \"0.62423277\",\n \"0.61731887\",\n \"0.61728024\",\n \"0.6165461\",\n \"0.610696\",\n \"0.608971\",\n \"0.608501\",\n \"0.60734797\",\n \"0.6067765\",\n \"0.60032827\",\n \"0.5975856\",\n \"0.59611905\",\n \"0.5942278\",\n \"0.5940992\",\n \"0.59368914\",\n \"0.5936283\",\n \"0.5933788\",\n \"0.5910125\",\n \"0.58526474\",\n \"0.57914597\",\n \"0.578663\",\n \"0.57680786\",\n \"0.57611877\",\n \"0.5734831\",\n \"0.5733008\",\n \"0.5713259\",\n \"0.5712131\",\n \"0.57013756\",\n \"0.5667361\",\n \"0.5653684\",\n \"0.56121963\",\n \"0.5609037\",\n \"0.56043917\",\n \"0.5593471\",\n \"0.5587373\",\n \"0.55801827\",\n \"0.5572345\",\n \"0.55614245\",\n \"0.55541474\",\n \"0.55497986\",\n \"0.55477005\",\n \"0.5536616\",\n \"0.5533723\",\n \"0.5523065\",\n \"0.5517754\",\n \"0.55160123\",\n \"0.55133444\",\n \"0.55055434\",\n \"0.54977125\",\n \"0.549696\",\n \"0.54903024\",\n \"0.5482618\",\n \"0.54810697\",\n \"0.54809046\",\n \"0.5480375\",\n \"0.5475449\",\n \"0.5472704\",\n \"0.547248\",\n \"0.5464888\",\n \"0.54643726\",\n \"0.5454899\",\n \"0.5446767\",\n \"0.5443123\",\n \"0.54307973\",\n \"0.54279673\",\n \"0.54041535\",\n \"0.54010785\",\n \"0.5390844\",\n \"0.5385566\",\n \"0.53795356\",\n \"0.5378529\",\n \"0.5377424\",\n \"0.537345\",\n \"0.5371914\",\n \"0.53675467\",\n \"0.53625745\",\n \"0.53588146\",\n \"0.535555\",\n \"0.53510463\",\n \"0.5349599\",\n \"0.5341289\",\n \"0.53411806\",\n \"0.5337197\",\n \"0.5336624\",\n \"0.53314006\",\n \"0.53307575\",\n \"0.5322711\",\n \"0.5322071\",\n \"0.53197044\",\n \"0.53190225\",\n \"0.53115416\",\n \"0.53079593\",\n \"0.5303784\",\n \"0.5301862\",\n \"0.5301385\",\n \"0.5300864\",\n \"0.52915525\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6871927"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":18,"numItemsPerPage":100,"numTotalItems":95772,"offset":1800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzYyMTE0Niwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM2MjQ3NDYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.HG6CYATj8wZubs9QjxdKBm6v2nI1plrLT-GyuidifXR-Meff77ml7HDMcV_D_ae25cqTWYlCx9PUDXS9O6svCg","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 |
|---|---|---|---|---|---|---|
takes csv files, parses with panda and returns result | def import_data(csv_file):
# skips bad lines
data = pd.read_csv(csv_file, error_bad_lines=False)
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_csv():",
"def csv_loader(csv_file):\n df = pd.read_csv(csv_file, sep=';', parse_dates=['Data_Alteraçao'])\n pd.set_option('display.float_format', '{:.0f}'.format)\n\n df = df.fillna(0)\n df = df.drop(columns=['Cod. Pareamento', 'Cod. UF', 'Sigla UF', 'Cod. Subarea',\n ... | [
"0.6957248",
"0.6680294",
"0.66800976",
"0.6676007",
"0.6644503",
"0.65280634",
"0.64709973",
"0.6345747",
"0.63367206",
"0.63359916",
"0.6311614",
"0.63049614",
"0.630401",
"0.62790114",
"0.62762284",
"0.6265179",
"0.6254957",
"0.6253625",
"0.62445545",
"0.6242823",
"0.62291... | 0.61578256 | 23 |
cleans data by lowers cases and removing accentuated chars then extracts word tokens of at least 2 chars | def cleanData(s):
# extract only word tokens of at least 2 chars
re.compile(r"\b\w\w + \b", re.U).findall(s)
# xml_dict = {';': '', '<': '<', '&': '&', '>': '>', '"': '"',
# '&apos': '\''}
# for key, value in xml_dict.iteritems():
# s = s.replace(key, value)
s.translate(maketrans('?!,.', ' '))
with open('stopwords.txt') as stop_words:
stop_words = {line.strip().lower() for line in stop_words if line!='\n'}
return s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processes_and_tokenize(raw_document):\n\ttokenizer = RegexpTokenizer(r'\\w+')\n\ttokens = tokenizer.tokenize(raw_document.lower())\t\t# tokens = nltk.word_tokenize(corpus.lower()) # without removing punctiation\n\n\t#remove stop words\n\tstop_words = set(nltk.corpus.stopwords.words('english'))\n\t#stop_words =... | [
"0.6861259",
"0.6852118",
"0.67767626",
"0.6744147",
"0.67203104",
"0.6711012",
"0.6704925",
"0.66891634",
"0.66849273",
"0.6602998",
"0.6546478",
"0.65269834",
"0.65201867",
"0.65182596",
"0.64966595",
"0.6496125",
"0.64865",
"0.6471367",
"0.6440876",
"0.64231676",
"0.640726... | 0.60811347 | 61 |
Support the following DHCP DeviceManager calls. | def create_dhcp_port(self, port):
LOG.debug("create_dhcp_port: %s", port)
port['port']['id'] = port['port']['network_id']
# The following MAC address will be assigned to the Linux dummy
# interface that
# networking_calico.agent.linux.interface.RoutedInterfaceDriver
# creates. Therefore it will never actually be used or involved in the
# sending or receiving of any real data. Hence it should not matter
# that we use a hardcoded value here, and the same value on every
# networking-calico compute host. The '2' bit of the first byte means
# 'locally administered', which makes sense for a hardcoded value like
# this and distinguishes it from the space of managed MAC addresses.
port['port']['mac_address'] = '02:00:00:00:00:00'
port['port']['device_owner'] = constants.DEVICE_OWNER_DHCP
return dhcp.DictModel(port['port']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_dhcp_env(device):\n raise NotImplementedError",
"def _RunDHCPCD(self, **kwargs):\n del kwargs\n clear_ifconfig_command = 'ifconfig {interface} 0.0.0.0'.format(\n interface=self.interface)\n # -K: Don't receive link messages for carrier status. You should\n # only have to ... | [
"0.6585499",
"0.6445782",
"0.63871115",
"0.62862945",
"0.6191916",
"0.61801624",
"0.61206764",
"0.60355914",
"0.59601206",
"0.5855561",
"0.57844305",
"0.5700581",
"0.56996197",
"0.56953347",
"0.5656746",
"0.5649535",
"0.5625262",
"0.5623599",
"0.5604455",
"0.5591835",
"0.5564... | 0.0 | -1 |
Support the following DHCP DeviceManager calls. self.plugin.release_dhcp_port(network.id, self.get_device_id(network)) | def release_dhcp_port(self, network_id, device_id):
LOG.debug("release_dhcp_port: %s %s", network_id, device_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def release_dhcp_port(self, network_id, device_id):\n return self.call(self.context,\n self.make_msg('release_dhcp_port',\n network_id=network_id,\n device_id=device_id,\n ... | [
"0.8054723",
"0.71377176",
"0.6566923",
"0.6155076",
"0.5985578",
"0.59473217",
"0.58076227",
"0.5672177",
"0.5659102",
"0.5577378",
"0.5557",
"0.55073285",
"0.5491355",
"0.54843926",
"0.5454663",
"0.5447936",
"0.5415151",
"0.54091704",
"0.5385533",
"0.53768826",
"0.53333277"... | 0.84403765 | 0 |
Construct and return an empty network model. | def empty_network(network_id=NETWORK_ID):
return make_net_model({"id": network_id,
"subnets": [],
"ports": [],
"tenant_id": "calico",
"mtu": neutron_constants.DEFAULT_NETWORK_MTU}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_model():\n\n class Net(nn.Cell):\n def construct(self, x, y):\n return x\n\n net = Net()\n model_simple = Model(net)\n\n return model_simple",
"def create_network(self):\n\n print ('Creating network, changing data will have no effect beyond this point.')\n n... | [
"0.703458",
"0.6730677",
"0.6650045",
"0.66165984",
"0.65974486",
"0.65902346",
"0.6558993",
"0.645672",
"0.6456341",
"0.6397473",
"0.6397053",
"0.63881767",
"0.6345971",
"0.6332582",
"0.63210064",
"0.62721264",
"0.62575936",
"0.6252461",
"0.62491995",
"0.6246717",
"0.6237882... | 0.749267 | 0 |
Construct and return a copy of an existing network model. | def copy_network(source_net):
return make_net_model({"id": source_net.id,
"subnets": source_net.subnets,
"ports": source_net.ports,
"tenant_id": source_net.tenant_id,
"mtu": source_net.mtu}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy(self):\n new_model = Model(\n name=self.name,\n functions=copy.deepcopy(self.functions),\n domain=self.domain.copy(),\n density=self.density.copy(),\n )\n new_model.update()\n\n return new_model",
"def clone(self):\n return _... | [
"0.7690615",
"0.7457661",
"0.7384238",
"0.7347668",
"0.69865394",
"0.6961353",
"0.6951828",
"0.6855561",
"0.6832441",
"0.6813686",
"0.6811424",
"0.6808021",
"0.66637117",
"0.66387904",
"0.66021425",
"0.6570278",
"0.65329057",
"0.65179616",
"0.6502068",
"0.64940655",
"0.644854... | 0.68326706 | 8 |
Handler for endpoint creations and updates. | def on_endpoint_set(self, response, name):
try:
hostname, orchestrator, workload_id, endpoint_id = \
split_endpoint_name(name)
except ValueError:
# For some reason this endpoint's name does not have the expected
# form. Ignore it.
LOG.warning("Unexpected form for endpoint name: %s", name)
return
if hostname != self.hostname:
LOG.info("Endpoint not on this node: %s", name)
return
# Get the endpoint spec.
endpoint = etcdutils.safe_decode_json(response.value, 'endpoint')
if not (isinstance(endpoint, dict) and
'spec' in endpoint and
isinstance(endpoint['spec'], dict) and
'interfaceName' in endpoint['spec'] and
'ipNetworks' in endpoint['spec'] and
'mac' in endpoint['spec']):
# Endpoint data is invalid; treat as deletion.
LOG.warning("Invalid endpoint data: %s => %s",
response.value, endpoint)
self.on_endpoint_delete(None, name)
return
annotations = endpoint.get('metadata', {}).get('annotations', {})
endpoint = endpoint['spec']
# If the endpoint has no ipNetworks, treat as deletion. This happens
# when a resync from the mechanism driver overlaps with a port/VM being
# deleted.
if not endpoint['ipNetworks']:
LOG.info("Endpoint has no ipNetworks: %s", endpoint)
self.on_endpoint_delete(None, name)
return
# Construct NetModel port equivalent of Calico's endpoint data.
fixed_ips = []
dns_assignments = []
fqdn = annotations.get(datamodel_v3.ANN_KEY_FQDN)
network_id = annotations.get(datamodel_v3.ANN_KEY_NETWORK_ID)
allowedIps = [e.split('/')[0] for e in endpoint.get('allowedIps', [])]
for addrm in endpoint['ipNetworks']:
ip_addr = addrm.split('/')[0]
if ip_addr in allowedIps:
continue
subnet_id = self.subnet_watcher.get_subnet_id_for_addr(
ip_addr,
network_id
) or self.v1_subnet_watcher.get_subnet_id_for_addr(
ip_addr,
network_id
)
if subnet_id is None:
LOG.warning("Missing subnet data for one of port's IPs")
continue
fixed_ips.append({'subnet_id': subnet_id,
'ip_address': ip_addr})
if fqdn:
dns_assignments.append({'hostname': fqdn.split('.')[0],
'ip_address': ip_addr,
'fqdn': fqdn})
if not fixed_ips:
LOG.warning("Endpoint has no DHCP-served IPs: %s", endpoint)
return
extra_dhcp_opts = []
mtu = self.mtu_watcher.get_mtu(endpoint['interfaceName'])
self.mtu_watcher.watch_port(endpoint_id, endpoint['interfaceName'])
if mtu:
extra_dhcp_opts.append(self.get_mtu_option(mtu))
port = {'id': endpoint_id,
'device_owner': 'calico',
'device_id': endpoint['interfaceName'],
'fixed_ips': fixed_ips,
'mac_address': endpoint['mac'],
# FIXME: Calico currently does not handle extra DHCP
# options, other than MTU, but there might be use cases
# where it should handle further options.
# https://bugs.launchpad.net/networking-calico/+bug/1553348
'extra_dhcp_opts': extra_dhcp_opts}
if fqdn:
port['dns_assignment'] = dns_assignments
# Ensure that the cache includes the network and subnets for this port,
# and set the port's network ID correctly.
try:
port['network_id'] = self._ensure_net_and_subnets(port)
except SubnetIDNotFound:
LOG.warning("Missing data for one of port's subnets")
return
# Report this at INFO level if it is a new port. Note, we
# come through this code periodically for existing ports also,
# because of how we watch the etcd DB for changes.
if endpoint_id not in self.local_endpoint_ids:
LOG.info("New port: %s", port)
self.local_endpoint_ids.add(endpoint_id)
else:
LOG.debug("Refresh already known port: %s", port)
# Add this port into the NetModel.
self.agent.cache.put_port(dhcp.DictModel(port))
# If we have seen the TAP interface, schedule updating Dnsmasq;
# otherwise wait until we do see the TAP interface, whereupon
# _update_dnsmasq will be called again. Dnsmasq updates can
# take a little time, and they run in series, so it's best to
# wait if we don't have the information we need yet, to avoid
# delaying the correct Dnsmasq update that we really want.
if mtu:
self._update_dnsmasq(port['network_id']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_handler(event, context):\n return update_endpoint(event)",
"def endpoint_create(self, endpoint_name=None, config=None):\n if config is None:\n raise Exception(\"Config required!\")\n if endpoint_name is None:\n self.request('/v1.1/endpoint', 'PUT', body=config)\n... | [
"0.73057497",
"0.68444353",
"0.633986",
"0.6060341",
"0.6045354",
"0.59219885",
"0.5871254",
"0.5845486",
"0.58109283",
"0.5770383",
"0.5706339",
"0.5677897",
"0.5664306",
"0.5596975",
"0.55959713",
"0.5579243",
"0.5576328",
"0.554373",
"0.553638",
"0.55146235",
"0.5514128",
... | 0.6030018 | 5 |
Ensure that the cache has a NetModel and subnets for PORT. | def _ensure_net_and_subnets(self, port):
# Gather the subnet IDs that we need for this port, and get the
# NetModel if we already have it in the cache.
needed_subnet_ids = set()
net = None
for fixed_ip in port['fixed_ips']:
subnet_id = fixed_ip.get('subnet_id')
if subnet_id:
needed_subnet_ids.add(subnet_id)
if not net:
net = self.agent.cache.get_network_by_subnet_id(subnet_id)
LOG.debug("Needed subnet IDs: %s", needed_subnet_ids)
LOG.debug("Existing network model by subnet ID: %s", net)
# For each subnet that we need, get its data from SubnetWatcher and
# hold for adding into the cache.
new_subnets = {}
for subnet_id in needed_subnet_ids:
# Get data for this subnet from the SubnetWatchers.
subnet = (self.subnet_watcher.get_subnet(subnet_id) or
self.v1_subnet_watcher.get_subnet(subnet_id))
if subnet is None:
LOG.warning("No data for subnet %s", subnet_id)
raise SubnetIDNotFound()
new_subnets[subnet_id] = subnet
if not net:
# We don't already have a NetModel, so look for a cached NetModel
# with the right network ID. (In this case we must have new
# subnets to add into the cache, and the cached NetModel must have
# subnets other than the ones that we're adding in this iteration;
# otherwise we would have already found it when searching by
# subnet_id above.)
assert new_subnets
network_id = list(new_subnets.values())[0]['network_id']
net = self.agent.cache.get_network_by_id(network_id)
LOG.debug("Existing network model by network ID: %s", net)
if not net:
# We still have no NetModel for the relevant network ID, so create
# a new one. In this case we _must_ be adding new subnets.
assert new_subnets
net = empty_network(network_id)
LOG.debug("New network %s", net)
elif new_subnets:
# We have a NetModel that was already in the cache and are about to
# modify it. Cache replacement only works if the new NetModel is a
# distinct object from the existing one, so make a copy here.
net = copy_network(net)
LOG.debug("Copied network %s", net)
if new_subnets:
# Add the new subnets into the NetModel.
assert net
net.subnets = [s for s in net.subnets
if s.id not in new_subnets]
net.subnets += list(new_subnets.values())
# Add (or update) the NetModel in the cache.
LOG.debug("Net: %s", net)
_fix_network_cache_port_lookup(self.agent, net.id)
self.agent.cache.put(net)
return net.id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fix_network_cache_port_lookup(agent, network_id):\n\n # If there is an existing NetModel for this network ID, ensure that all\n # its ports are in the port_lookup dict.\n if network_id in agent.cache.cache:\n for port in agent.cache.cache[network_id].ports:\n agent.cache.port_lookup... | [
"0.5711651",
"0.5685266",
"0.5486034",
"0.5470297",
"0.53793174",
"0.53505814",
"0.52206916",
"0.520188",
"0.50624466",
"0.50617975",
"0.5060805",
"0.5031961",
"0.5029634",
"0.50271976",
"0.5011506",
"0.5004499",
"0.5002426",
"0.49931684",
"0.49833202",
"0.4974392",
"0.496706... | 0.7904054 | 0 |
Start/stop/restart Dnsmasq for NETWORK_ID. | def _update_dnsmasq(self, network_id):
# Check whether we should really do the following processing.
if self.suppress_dnsmasq_updates:
LOG.debug("Don't update dnsmasq yet;"
" must be processing a snapshot")
self.dirty_networks.add(network_id)
return
self.dnsmasq_updater.update_network(network_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startServices():\n # dnsmasq\n out_dnsmasq = subprocess.run([\"systemctl\", \"restart\", \"dnsmasq\"], stdout=subprocess.PIPE)\n if out_dnsmasq.returncode == 0:\n logging.info(\"dnsmasq service started/restarted successfully\")\n else:\n logging.error(\"dnsmasq service start restart e... | [
"0.57506925",
"0.5736145",
"0.5705236",
"0.5628683",
"0.56053996",
"0.55890465",
"0.5588804",
"0.5274913",
"0.5227108",
"0.5199843",
"0.5196082",
"0.5161446",
"0.51592255",
"0.51375973",
"0.51361805",
"0.51020324",
"0.50229216",
"0.49852252",
"0.49827528",
"0.49735984",
"0.49... | 0.6410348 | 0 |
Handler for endpoint deletion. | def on_endpoint_delete(self, response_ignored, name):
try:
hostname, orchestrator, workload_id, endpoint_id = \
split_endpoint_name(name)
except ValueError:
# For some reason this endpoint's name does not have the expected
# form. Ignore it.
LOG.warning("Unexpected form for endpoint name: %s", name)
return
# Remove endpoint ID from our cache. Note, it might not be
# there because we haven't checked whether the endpoint just
# deleted is a local one; hence 'discard' instead of 'remove'.
self.local_endpoint_ids.discard(endpoint_id)
# Find the corresponding port in the DHCP agent's cache.
port = self.agent.cache.get_port_by_id(endpoint_id)
if port:
LOG.debug("deleted port: %s", port)
self.mtu_watcher.unwatch_port(endpoint_id, port.device_id)
self.agent.cache.remove_port(port)
self._update_dnsmasq(port.network_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_handler(event, context):\n delete_endpoint_config(event)",
"def delete_endpoint(self):\n logger.warning(f\"Deleting hosting endpoint '{self.endpoint_name}'...\")\n self._realtime_predictor.delete_endpoint()",
"def delete_endpoint(EndpointName=None):\n pass",
"def delete_endpoin... | [
"0.8157214",
"0.76216006",
"0.74637145",
"0.7360159",
"0.73364997",
"0.7239041",
"0.70955694",
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"0.6783907",
"0.6775918",
"0.6722421",
"0.6706916",
"0.6706916",
"0.66402143",
"0.6639... | 0.76158285 | 2 |
Called when a new snapshot is about to be read from etcdv3. | def _pre_snapshot_hook(self):
# Add all current networks to the dirty set, so that we will stop their
# Dnsmasqs if no longer needed. Also remove all port and subnet
# information.
LOG.debug("Reset cache for new snapshot")
for network_id in list(self.agent.cache.get_network_ids()):
self.dirty_networks.add(network_id)
_fix_network_cache_port_lookup(self.agent, network_id)
self.agent.cache.put(empty_network(network_id))
# Suppress Dnsmasq updates until we've processed the whole snapshot.
self.suppress_dnsmasq_updates = True
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_snapshot(self):\r\n assert self.snapshot is not None\r\n self.name = self.snapshot[0]\r\n self.size = self.snapshot[1]\r\n self.cells = self.snapshot[2]\r\n self.bucket_array.load_snapshot()",
"def load_snapshot(self):\r\n assert self.snapshot is not None\r\n ... | [
"0.61946166",
"0.6155114",
"0.57125986",
"0.56532395",
"0.5642027",
"0.5526328",
"0.55234843",
"0.552134",
"0.5452202",
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"0.5240661",
"0.52345574",
"0.520435",
"0.5192152",
"0.51732963",
"0.508970... | 0.49107116 | 29 |
Fix NetworkCache before removing or replacing a network. neutron.agent.dhcp.agent is bugged in that it adds the DHCP port into the cache without updating the cache's port_lookup dict, but then NetworkCache.remove() barfs if there is a port in network.ports but not in that dict... NetworkCache.put() implicitly does a remove() first if there is already a NetModel in the cache with the same ID. So a put() to update or replace a network also hits this problem. This method avoids that problem by ensuring that all of a network's ports are in the port_lookup dict. A caller should call this immediately before a remove() or a put(). | def _fix_network_cache_port_lookup(agent, network_id):
# If there is an existing NetModel for this network ID, ensure that all
# its ports are in the port_lookup dict.
if network_id in agent.cache.cache:
for port in agent.cache.cache[network_id].ports:
agent.cache.port_lookup[port.id] = network_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _ensure_net_and_subnets(self, port):\n\n # Gather the subnet IDs that we need for this port, and get the\n # NetModel if we already have it in the cache.\n needed_subnet_ids = set()\n net = None\n for fixed_ip in port['fixed_ips']:\n subnet_id = fixed_ip.get('subne... | [
"0.6243899",
"0.5750837",
"0.55582297",
"0.5493871",
"0.54649335",
"0.53545606",
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"0.50081223",
"0.50078607",
"0.49672914",
"0.4941016",
"0.49056178",
"0.4... | 0.7541457 | 0 |
Handler for subnet creations and updates. | def on_subnet_set(self, response, subnet_id):
LOG.debug("Subnet %s created or updated", subnet_id)
subnet_data = etcdutils.safe_decode_json(response.value, 'subnet')
if subnet_data is None:
LOG.warning("Invalid subnet data %s", response.value)
return
if not (isinstance(subnet_data, dict) and
'cidr' in subnet_data and
'gateway_ip' in subnet_data):
LOG.warning("Invalid subnet data: %s", subnet_data)
return
self.subnets_by_id[subnet_id] = subnet_data
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subnet_create_event(self, subnet_info):\n\n subnet = subnet_info.get('subnet')\n if subnet:\n self.create_subnet(subnet)\n else:\n # Check whether request is for subnets.\n subnets = subnet_info.get('subnets')\n if subnets:\n for s... | [
"0.7963921",
"0.7852703",
"0.77121514",
"0.74027985",
"0.72448415",
"0.7210939",
"0.7181357",
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"0.6573212",
"0.65678436",
"0.6544619",
"0.6485936",
"0.6472504",
"0.64677... | 0.68601817 | 9 |
Handler for subnet deletions. | def on_subnet_del(self, response, subnet_id):
LOG.info("Subnet %s deleted", subnet_id)
if subnet_id in self.subnets_by_id:
del self.subnets_by_id[subnet_id]
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post_delete_subnet(self, sender, instance, **kwargs):\n RecurseNetworks.delete_entries(subnet=str(instance.ip_network), net_name=instance.name)",
"def subnet_delete_end(self, payload):\n subnet_id = payload['subnet_id']\n network = self.cache.get_network_by_subnet_id(subnet_id)\n ... | [
"0.7702621",
"0.75508094",
"0.7533499",
"0.7260703",
"0.72326857",
"0.70582956",
"0.6918789",
"0.6917834",
"0.6906911",
"0.66798496",
"0.6626956",
"0.6558707",
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"0.63931185",
"0.6186472",
"0.60645396",
"0.6064328",
"0.5995911",
"0.59661895",
"0.59549713",
"0.59370... | 0.73326564 | 3 |
Get data for the specified subnet. | def get_subnet(self, subnet_id):
LOG.debug("Get subnet %s", subnet_id)
if subnet_id not in self.subnets_by_id:
return None
data = self.subnets_by_id[subnet_id]
LOG.debug("Subnet data: %s", data)
# Convert to form expected by NetModel.
ip_version = 6 if ':' in data['cidr'] else 4
subnet = {'enable_dhcp': True,
'ip_version': ip_version,
'cidr': data['cidr'],
'dns_nameservers': data.get('dns_servers') or [],
'id': subnet_id,
'gateway_ip': data['gateway_ip'],
'host_routes': data.get('host_routes', []),
'network_id': data.get('network_id', NETWORK_ID)}
if ip_version == 6:
subnet['ipv6_address_mode'] = DHCPV6_STATEFUL
subnet['ipv6_ra_mode'] = DHCPV6_STATEFUL
return dhcp.DictModel(subnet) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_subnet(self, subnet, **_params):\r\n return self.get(self.subnet_path % (subnet), params=_params)",
"def get_subnet_details(self, subnet_name=\"dummy_subnet\", subnet_id=None):\n _url = \"http://\" + self.host_ip + \":9696/v2.0/subnets\"\n _headers = {'x-auth-token': self.project_in... | [
"0.65944624",
"0.6546326",
"0.64507025",
"0.61743736",
"0.6011078",
"0.6010956",
"0.58433086",
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"0.57784945",
"0.57641536",
"0.5734289",
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"0.556625",
"0.556153",
"0.5557205",
"0.54509723",
"0.54425985",
"0.5385783",
"0.5367... | 0.6255019 | 3 |
Run the EtcdWatcher loop. | def run(self):
self.etcd.start() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n watcher = self._watcher(self.on_recv)\n watcher.loop()",
"def run(self):\n logging.basicConfig(level=logging.INFO,\n format='%(asctime)s - %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S')\n\n event_handler = PatternMat... | [
"0.60920537",
"0.60423386",
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"0.59458005",
"0.5935371",
"0.5914737",
"0.5896926",
"0.58915985",
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"0.5884968",
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"0.5844063",
"0.5844063",
"0.58165354",
"0.58041525",
"0.58041525",
"0.5799248",
"0.57745546",
"0.57614356",
"0.5740617",
"0.57... | 0.6893604 | 0 |
starting_position (x,y) tuple representing current_position | def init(starting_position, steering_noise, distance_noise, sonar_noise,
measurement_noise, speed, turning_speed, gps_delay, execution_cpu_time_limit):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_start_loc(self) -> Tuple[int, int]:\n assert self.pos_marker\n return self.pos_marker.working_loc",
"def set_starting_pos(self):\n if self.start and self.is_unoccupied(*self.start):\n self.current_pos = self.start[:]\n else:\n self.set_random_pos('startin... | [
"0.7147887",
"0.7056669",
"0.69363606",
"0.6935041",
"0.69009805",
"0.69009805",
"0.69009805",
"0.6888912",
"0.6874065",
"0.68649364",
"0.6857859",
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"0.67153525",
"0.67080766",
"0.6668199",
"0.66649705",
"0.66494197",
"0.6641967",
"0.66... | 0.0 | -1 |
React to sensory data | def on_sense_sonar(self, dist):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_data():\n pass",
"def data_changed(self):\n return",
"def update_data(self):\n # Just set data_changed, the component should do the rest.\n self.data_changed = True",
"def update(self):",
"def update(self):",
"def update(self):",
"def manage_info():",
"def updat... | [
"0.5340235",
"0.5297468",
"0.5195286",
"0.51405054",
"0.51405054",
"0.51405054",
"0.50970787",
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"0.485973",
"0.485973",
"0.485973",
"0.485973",
"0.485973",
"0.... | 0.0 | -1 |
React to sensory data | def on_sense_field(self, field_type, field_parameter):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_data():\n pass",
"def data_changed(self):\n return",
"def update_data(self):\n # Just set data_changed, the component should do the rest.\n self.data_changed = True",
"def update(self):",
"def update(self):",
"def update(self):",
"def manage_info():",
"def updat... | [
"0.5340235",
"0.5297468",
"0.5195286",
"0.51405054",
"0.51405054",
"0.51405054",
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"0.485973",
"0.485973",
"0.485973",
"0.485973",
"0.485973",
"0.... | 0.0 | -1 |
React to sensory data | def on_sense_gps(self, x, y):
raise NotImplementedError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_data():\n pass",
"def data_changed(self):\n return",
"def update_data(self):\n # Just set data_changed, the component should do the rest.\n self.data_changed = True",
"def update(self):",
"def update(self):",
"def update(self):",
"def manage_info():",
"def updat... | [
"0.5340235",
"0.5297468",
"0.5195286",
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"0.51405054",
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"0.485973",
"0.485973",
"0.485973",
"0.485973",
"0.485973",
"0.... | 0.0 | -1 |
Compiles robot from given file and returns class object | def compile_robot(file_name, module_name = "contestant_module"):
global counter_module
module_name += str(counter_module)
counter_module += 1
mod = importCode(file_name, module_name)
compiled_class = None
for symbol in dir(mod):
if hasattr(getattr(mod, symbol), "act") and getattr(mod, symbol).__name__ != "RobotController":
compiled_class = getattr(mod, symbol)
print compiled_class
globals()[compiled_class.__name__] = compiled_class
if compiled_class is None:
raise KrakrobotException("Not found class with act() function named different than RobotController in provided .py")
return compiled_class, mod | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepareRobot(self):\n f = StringIO.StringIO()\n f.write(self.zipfile)\n\n zip = zipfile.ZipFile(f)\n \n #modules of the form {\"robot\" : \"from sr import *...\", ...}\n modules = dict([(os.path.splitext(z.filename)[0], zip.open(z.filename).read())\n ... | [
"0.5830989",
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"0.522042",
"0.5217083",
"0.52124226",
"0.52053887",
"0.5201384",
"0.5178366",
"0.517518... | 0.7441334 | 0 |
Returns the associated driver with some custom settings. | def _get_selenium_browser(navigator, fLOG=noLOG):
with warnings.catch_warnings():
warnings.simplefilter("ignore", ImportWarning)
from selenium import webdriver
from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
fLOG("[webshot] navigator=", navigator)
if navigator == "firefox":
firefox_capabilities = DesiredCapabilities.FIREFOX.copy()
firefox_capabilities['marionette'] = True
firefox_capabilities[
'binary'] = r"C:\Program Files (x86)\Mozilla Firefox\firefox.exe"
browser = webdriver.Firefox(capabilities=firefox_capabilities)
elif navigator == "chrome":
if sys.platform.startswith("win"):
chromed = where_in_path("chromedriver.exe")
if chromed is None:
install_chromedriver(fLOG=fLOG)
chromed = where_in_path("chromedriver.exe")
if chromed is None:
raise FileNotFoundError(
"unable to install 'chromedriver.exe'")
else:
fLOG("[_get_selenium_browser] found chromedriver:", chromed)
else:
chromed = 'chromedriver'
start_navi = True
if start_navi:
fLOG("[_get_selenium_browser] start", navigator)
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--headless')
chrome_options.add_argument('--no-sandbox')
chrome_options.add_argument('--verbose')
browser = webdriver.Chrome(executable_path=chromed,
chrome_options=chrome_options)
else:
with warnings.catch_warnings():
warnings.simplefilter("ignore", ImportWarning)
import selenium.webdriver.chrome.service as wservice
fLOG("[_get_selenium_browser] create service")
service = wservice.Service(chromed)
fLOG("[_get_selenium_browser] start service")
service.start()
fLOG("[_get_selenium_browser] declare remote")
capabilities = {'chrome.binary': chromed}
browser = webdriver.Remote(service.service_url, capabilities)
elif navigator == "ie":
browser = webdriver.Ie()
elif navigator == "opera":
if sys.platform.startswith("win"):
chromed = where_in_path("operadriver.exe")
if chromed is None:
install_operadriver(fLOG=fLOG)
chromed = where_in_path("operadriver.exe")
if chromed is None:
raise FileNotFoundError(
"unable to install operadriver.exe")
else:
fLOG("[_get_selenium_browser] found chromedriver:", chromed)
else:
chromed = 'operadriver'
browser = webdriver.Opera(chromed) # pylint: disable=E1101
elif navigator == "edge":
browser = webdriver.Edge()
else:
raise RuntimeError(
f"unable to interpret the navigator '{navigator}'")
fLOG("[_get_selenium_browser] navigator is started")
return browser | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_driver(drv):\n return GenericDriver.get_driver(drv)",
"def get_driver(self):\n\t\treturn self.driver",
"def get_driver(self):\n return self.driver",
"def driver(self):\n \n return self.__driver",
"def _get_driver(self, driver_name):\n driver = lb_const.SERVICE_TYPE + driv... | [
"0.7748907",
"0.7700252",
"0.75033057",
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"0.62124... | 0.0 | -1 |
>>> isinstance(lab7_q1(), str) True | def lab7_q1():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def is_str(x):\n return isinstance(x, str)",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def isString(x):\n if type(x) == str:\n return True\n else:\n return False",
"def test_is_str... | [
"0.69384867",
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"0.64524394"... | 0.0 | -1 |
>>> isinstance(lab7_q2(), str) True | def lab7_q2():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)",
"def _is_string(arg):\n return isi... | [
"0.68337935",
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"0.635... | 0.0 | -1 |
>>> isinstance(lab7_q3(), str) True | def lab7_q3():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def is_str(x):\n return isinstance(x, str)",
"def isString(x):\n if type(x) == str:\n return True\n else:\n return Fal... | [
"0.6728935",
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"0.6293086... | 0.0 | -1 |
>>> isinstance(lab7_q4(), str) True | def lab7_q4():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str(x):\n return isinstance(x, str)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat... | [
"0.6849578",
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"0.64056... | 0.0 | -1 |
>>> isinstance(lab8_q2(), str) True | def lab8_q2():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str ( self, s ):\r\n\t\treturn i... | [
"0.69167274",
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"0.62652... | 0.0 | -1 |
>>> isinstance(lab8_q3(), str) True | def lab8_q3():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardu... | [
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"0.6251587",
"0.6250445",
... | 0.0 | -1 |
>>> isinstance(lab8_q4(), str) True | def lab8_q4():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)",
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def is_string(obj):\n return isi... | [
"0.69331145",
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"0.631172",
"0.630439... | 0.0 | -1 |
>>> isinstance(lab8_q5(), str) True | def lab8_q5():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_string(data):\n return len(data) and isinstance(_to_ndarray(data).flat[0], str)",
"def is_string(value):\n return isinstance(value, basestring)",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def test_is_str_rep_string(self):\n self.assertIsInstance(card... | [
"0.69726",
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"0.63950634... | 0.0 | -1 |
>>> isinstance(lab9_q2(), str) True | def lab9_q2():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def is_str(x):\n return isinstance(x, str)",
"def _is_... | [
"0.69377685",
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"0.637876... | 0.0 | -1 |
>>> isinstance(lab9_q3(), str) True | def lab9_q3():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_str(x):\n return isinstance(x, str)",
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def isString(x):\n if type(x) == str:\n return True\n else:\n return False",
"def _is_string(data):\n return len(data) and isinstanc... | [
"0.67149335",
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"0.63617307",
"0.63562024",
"0.6313202",
"0.62957275",
"0.6292955",
"0.62... | 0.0 | -1 |
>>> isinstance(lab9_q4(), str) True | def lab9_q4():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str(x):\n return isinstance(x, str)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def isString(x):\n if type(x) == str:\n return True\n else:\n ... | [
"0.6942101",
"0.6876074",
"0.67846864",
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"0.63935167",
"0.63... | 0.0 | -1 |
>>> isinstance(lab9_q5(), str) True | def lab9_q5():
return """
YOUR EXPLANATION HERE
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_string(value):\n return isinstance(value, basestring)",
"def is_str(x):\n return isinstance(x, str)",
"def isString(x):\n if type(x) == str:\n return True\n else:\n ... | [
"0.6877922",
"0.6834238",
"0.68263847",
"0.6825494",
"0.68185055",
"0.6800018",
"0.6793659",
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"0.64687836",
"0.64158064",
"0.6410573",
"0.639751... | 0.0 | -1 |
>>> isinstance(lab10_q2(), str) True | def lab10_q1():
return """
Since there has to be at least two, len(self.fruits) >=2 and at least one cup, self.cups (number == True)
Decrease cups by 1 because we use one to create mixed juice.
first pop gives the 0 index fruit, second pop gives the next 0 index which is originally at 1 index.
len(item) to show number of letters then -1 because of hyphen.
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def _is_string(arg):\n return isinstance(arg, types.StringTypes)",
"def is_str(x):\n return isinstance(x, str)",
"def _is_... | [
"0.70104426",
"0.6694115",
"0.66540843",
"0.6631845",
"0.65846175",
"0.6546801",
"0.6527376",
"0.6501472",
"0.6483732",
"0.6467406",
"0.64658016",
"0.64406013",
"0.64081883",
"0.6402971",
"0.6375239",
"0.63414836",
"0.6304244",
"0.62933546",
"0.62872994",
"0.6280994",
"0.6278... | 0.0 | -1 |
>>> isinstance(lab10_q3(), str) True | def lab10_q2():
return """
Make a 'total' to add up the total revenue as while loop plays.
item = qvm.dispense, makes it so item is the juice.
While item: because if there is a juice this while loop will continue. If there aren't two fruits, there wont be a juice so while loop stops.
total += qvm.collect_money(item). (this should be += not = right?) to add the revenue from that 'item'
revalue item to the next juice.
return total
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str(x):\n return isinstance(x, str)",
"def isString(x):\n if type(x) == str:\n return True\n else:\n return False",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, typ... | [
"0.6767156",
"0.662008",
"0.65915257",
"0.65897584",
"0.6570212",
"0.6540568",
"0.6512019",
"0.6466866",
"0.6387287",
"0.6369226",
"0.634048",
"0.6340179",
"0.6338254",
"0.633478",
"0.6307848",
"0.6269958",
"0.6269275",
"0.6240808",
"0.6229523",
"0.62286043",
"0.62249273",
... | 0.0 | -1 |
>>> isinstance(lab10_q4(), str) True | def lab10_q3():
return """
Use list comprehension max(lst_of_qvm, key=lambda qvm : total_revenue(qvm))
This makes each element of the list go through the key which gives total_revenue for each one. Then just get the max in that list
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_str(x):\n return isinstance(x, str)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def isString(x):\n if type(x) == str:\n return True\n else:\n ... | [
"0.7007634",
"0.6753712",
"0.6680644",
"0.6641429",
"0.66254205",
"0.66071725",
"0.6594754",
"0.65537024",
"0.6541653",
"0.6496038",
"0.6468674",
"0.6443504",
"0.6440572",
"0.6406669",
"0.6398117",
"0.6318649",
"0.63153744",
"0.6313979",
"0.63125205",
"0.6301631",
"0.6300007"... | 0.0 | -1 |
>>> isinstance(lab10_q5(), str) True | def lab10_q4():
return """
Use 'for i in range(len(seq)//2):' to give i be the indexing for the first half of the lst.
Then make sure it is equal to the opposite indexing which is [-i-1] or [len(seq)-1-i]
if it is not equal return false
if the for loop is done without fail it means they are all equal so return True
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_string(value):\n return isinstance(value, basestring)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def isString(x):\n if type(x) == str:\n retur... | [
"0.69396424",
"0.6750825",
"0.67340475",
"0.6716429",
"0.6714517",
"0.66726655",
"0.665966",
"0.66213614",
"0.6544444",
"0.64893436",
"0.64751285",
"0.64728814",
"0.64611757",
"0.6450565",
"0.6434197",
"0.6419371",
"0.6414928",
"0.638308",
"0.6381245",
"0.6368124",
"0.6322138... | 0.0 | -1 |
>>> isinstance(lab10_q5(), str) True | def lab10_q5():
return """
'assert type(c) is int' to make sure c is a number/integer
make a helper function to solve this with the same parameters
first base case is if the count is 0 which returns Link.empty if true
Then is when lst is Link.empty, which should just Link.empty as well
recursion for these base cases where you just link the first with the helper(rest, c) :Link(lst.first, helper(lst.rest, count))
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_string(value):\n return isinstance(value, basestring)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def isString(x):\n if type(x) == str:\n retur... | [
"0.69393235",
"0.67513615",
"0.6735242",
"0.67170364",
"0.67146415",
"0.6673512",
"0.6659964",
"0.6621408",
"0.6545047",
"0.6489594",
"0.64740264",
"0.64732605",
"0.64615583",
"0.6448504",
"0.64349174",
"0.6421704",
"0.64157605",
"0.63823795",
"0.6382036",
"0.6368499",
"0.632... | 0.0 | -1 |
>>> isinstance(lab10_q5(), str) True | def lab10_q6():
return """
Use try: return dictionary[key] so that if there is a key and value the value will be the output.
make except Keyerror where it prints "Avoid Exception" and the new value for this key will be 'no value' next time it is called. It will output 'no value'
""" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_is_str_rep_string(self):\n self.assertIsInstance(cardutils.Card(10,1).__str__(), str)",
"def is_string(value):\n return isinstance(value, basestring)",
"def is_str ( self, s ):\r\n\t\treturn isinstance ( s, type( str () ) )",
"def isString(x):\n if type(x) == str:\n retur... | [
"0.69393235",
"0.67513615",
"0.6735242",
"0.67170364",
"0.67146415",
"0.6673512",
"0.6659964",
"0.6621408",
"0.6545047",
"0.6489594",
"0.64740264",
"0.64732605",
"0.64615583",
"0.6448504",
"0.64349174",
"0.6421704",
"0.64157605",
"0.63823795",
"0.6382036",
"0.6368499",
"0.632... | 0.0 | -1 |
Function to read the file | def read_file(file="input4.txt"):
res = []
with open(file, encoding='utf-8') as f:
for i in f.readlines():
res.append(i.split())
for i in res:
t = i.pop(1).split(':')
i.insert(1, t[0])
return res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(path):",
"def read(self, filename):\n pass",
"def read(self, filename):\n pass",
"def read_from_file(self, filename: str) -> None:",
"def read_file(path_to_file):\n 8",
"def read(self, filename):\n raise NotImplementedError",
"def read_file():\n with open(FILE_NAME) as... | [
"0.8188726",
"0.8140984",
"0.8140984",
"0.7953263",
"0.78393626",
"0.7722133",
"0.7643032",
"0.761069",
"0.75103563",
"0.7457068",
"0.735805",
"0.73315775",
"0.7328963",
"0.7327442",
"0.73023856",
"0.72954583",
"0.72721654",
"0.72382605",
"0.7223386",
"0.7222797",
"0.7218205"... | 0.0 | -1 |
Function to create a dict | def create_dict(info):
"""
dict = {ip: {counter:*}, {weekdays: []}, {hours: []}}
"""
dict_info = dict()
for i in info:
ip = i[0]
hours = i[1]
weekdays = i[2]
if ip not in dict_info:
dict_info[ip] = {}
dict_info[ip]['counter'] = 0
dict_info[ip]['hours'] = []
dict_info[ip]['weekdays'] = []
dict_info[ip]['counter'] += 1
dict_info[ip]['hours'].append(hours)
dict_info[ip]['weekdays'].append(weekdays)
return dict_info | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_dict(cls, *args: Any, **kwargs: Any) -> Dict[str, Any]:\n return _DictMaker(struct_class=cls, positional_args=args, keyword_args=kwargs).make_dict()",
"def create_dict(*args):\n output = {}\n idx = 0\n while idx < len(args):\n output[args[idx + 1]] = args[idx]\n idx += 2\n\... | [
"0.71769756",
"0.70463216",
"0.69759154",
"0.68891",
"0.68752956",
"0.6867047",
"0.67607605",
"0.6733505",
"0.66315436",
"0.6606104",
"0.65966946",
"0.6580841",
"0.6546656",
"0.65415686",
"0.64865774",
"0.64681804",
"0.64519143",
"0.6376904",
"0.63741493",
"0.6370084",
"0.634... | 0.6324139 | 23 |
Function to write to file | def write_to_file(info, mode='w', file="output4.txt"):
with open(file, mode, encoding='utf-8') as f:
for line in info:
f.write(' '.join(map(str, line)) + '\n') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_to_file(self, filename: str) -> None:",
"def write(self, filename):\n pass",
"def write(self, filename):\n pass",
"def write(self, fname):\n pass",
"def file_write(stuff, file_path):\n with open(file_path, \"wt\") as fo:\n fo.write(stuff)",
"def write_file(*args, **kwar... | [
"0.8413077",
"0.80287683",
"0.80287683",
"0.80265",
"0.79803306",
"0.7701467",
"0.7610143",
"0.75384223",
"0.7487648",
"0.7396836",
"0.73585635",
"0.730112",
"0.72875077",
"0.7278125",
"0.7264448",
"0.72381777",
"0.7235967",
"0.7231617",
"0.7231545",
"0.7180415",
"0.7160275",... | 0.0 | -1 |
Function to create the resulting list | def result_info(file="input4.txt"):
dict_info = create_dict(read_file())
result = []
dict_of_hours = dict()
for ip, info in dict_info.items(): # I go through the dictionary with information
for i in info['hours']: # I consider for each hour the number of visits
if i not in dict_of_hours:
dict_of_hours[i] = 0
dict_of_hours[i] += 1
most_frequent_day = 0 # counter for the most frequent for for each ip
most_frequent_word = info['weekdays'][0]
for i in info['weekdays']:
if most_frequent_day < info['weekdays'].count(i):
most_frequent_day = info['weekdays'].count(i)
most_frequent_word = i
result.append([ip, info['counter'], most_frequent_word])
max_number = 0
most_frequent_hour = 0
for hour, number in dict_of_hours.items(): # I go through the dict with the hours to find the most popular
if number > max_number:
max_number = number
most_frequent_hour = hour
result.append(['Самый популярный час на сайте:', most_frequent_hour])
write_to_file(result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _make_result_list(self,res):\n res_list = []\n for r in res:\n res_list.append(r)\n\n return res_list",
"def X(self)->list:",
"def make_list(unused_s, unused_l, toks):\n result = []\n for item in toks:\n result.append(item.asList())\n return r... | [
"0.72328603",
"0.6897152",
"0.67518175",
"0.6660444",
"0.6660444",
"0.6498758",
"0.6368195",
"0.63266546",
"0.63058394",
"0.62481105",
"0.62481105",
"0.6236731",
"0.62217695",
"0.6189998",
"0.617926",
"0.6137528",
"0.6121866",
"0.61136776",
"0.6106905",
"0.60869616",
"0.60864... | 0.0 | -1 |
Decode serialized example into image and segmentation label. | def decode(value):
keys_to_features = {
'image/encoded':
tf.FixedLenFeature((), tf.string, default_value=''),
'image/height':
tf.FixedLenFeature((), tf.int64, default_value=0),
'image/width':
tf.FixedLenFeature((), tf.int64, default_value=0),
'image/segmentation/class/encoded':
tf.FixedLenFeature((), tf.string, default_value='')
}
data = tf.parse_single_example(value, keys_to_features)
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode(self, serialized_example):\n parsed_tensors = tf.io.parse_single_example(\n serialized_example, self.KEYS_TO_FEATURES\n )\n for k in parsed_tensors:\n if isinstance(parsed_tensors[k], tf.SparseTensor):\n if parsed_tensors[k].dtype == tf.string:\n... | [
"0.66928875",
"0.65845627",
"0.6560088",
"0.65355873",
"0.64627737",
"0.62374485",
"0.6098066",
"0.6090909",
"0.59154075",
"0.5877989",
"0.58274025",
"0.5801115",
"0.5764262",
"0.574245",
"0.5734998",
"0.57296777",
"0.57066524",
"0.56981283",
"0.56740636",
"0.5664909",
"0.565... | 0.62265766 | 6 |
Initializes parameters for parsing annotations in the dataset. | def __init__(self,
output_size,
resize_eval=False,
ignore_label=255,
aug_rand_hflip=False,
aug_scale_min=1.0,
aug_scale_max=1.0,
aug_policy='',
use_bfloat16=True,
mode=None):
self._mode = mode
self._is_training = (mode == ModeKeys.TRAIN)
self._output_size = output_size
self._resize_eval = resize_eval
self._ignore_label = ignore_label
# Data augmentation.
self._aug_rand_hflip = aug_rand_hflip
self._aug_scale_min = aug_scale_min
self._aug_scale_max = aug_scale_max
self._aug_policy = aug_policy
# Device.
self._use_bfloat16 = use_bfloat16
# Data is parsed depending on the model Modekey.
if mode == ModeKeys.TRAIN:
self._parse_fn = self._parse_train_data
elif mode == ModeKeys.EVAL:
self._parse_fn = self._parse_eval_data
elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
self._parse_fn = self._parse_predict_data
else:
raise ValueError('mode is not defined.') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_all_params(self):\n self.annotations_timestamp = 0\n # self.annotations_offset = 0\n # self.annotation_offset_text.configure(text='Current: %d' % self.annotations_offset)\n self.annotations_timestamp_text.configure(text='Annotation timestamp:\\n %d' % self.annotations_timestamp... | [
"0.6673975",
"0.65937763",
"0.6560803",
"0.65485716",
"0.65461046",
"0.64888483",
"0.6485457",
"0.64349765",
"0.63921803",
"0.6385465",
"0.63169533",
"0.62924635",
"0.6280703",
"0.6174638",
"0.61208785",
"0.60870314",
"0.6060363",
"0.6031667",
"0.60257536",
"0.60226005",
"0.6... | 0.0 | -1 |
Parses data to an image and associated training labels. | def __call__(self, value):
with tf.name_scope('parser'):
data = decode(value)
return self._parse_fn(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_train_data(self, data):\n image, label = self._prepare_image_and_label(data)\n\n # Flips image randomly during training.\n if self._aug_rand_hflip:\n image, label = input_utils.random_horizontal_flip(image, masks=label)\n\n # Resizes and crops image.\n image, image_info = input_utils... | [
"0.7921838",
"0.75767523",
"0.7083806",
"0.70367414",
"0.6984986",
"0.6949761",
"0.68525404",
"0.6838453",
"0.68272054",
"0.6822626",
"0.67811656",
"0.67373544",
"0.6718452",
"0.66978455",
"0.6692323",
"0.66836244",
"0.66790134",
"0.66592354",
"0.6651176",
"0.6622316",
"0.661... | 0.0 | -1 |
Prepare normalized image and label. | def _prepare_image_and_label(self, data):
image = tf.io.decode_image(data['image/encoded'], channels=3)
label = tf.io.decode_image(data['image/segmentation/class/encoded'],
channels=1)
height = data['image/height']
width = data['image/width']
image = tf.reshape(image, (height, width, 3))
label = tf.reshape(label, (1, height, width))
label = tf.cast(label, tf.float32)
# Normalizes image with mean and std pixel values.
image = input_utils.normalize_image(image)
return image, label | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize(image, label):\n image -= settings.DATASET_MEAN\n image /= settings.DATASET_STD\n\n return image, label",
"def img_normalize(image, label):\n mean, std = ds_stats\n image -= tf.constant(mean, shape=[1, 1, num_channels], dtype=image.dtype)\n image /= tf.constant(std, shape=[1, 1, n... | [
"0.70676106",
"0.68583",
"0.66364515",
"0.65736663",
"0.6464647",
"0.64554685",
"0.6352765",
"0.6297984",
"0.625139",
"0.62383085",
"0.62295324",
"0.62290204",
"0.62271994",
"0.6218579",
"0.61705977",
"0.6143832",
"0.61042327",
"0.60809606",
"0.60745084",
"0.60027677",
"0.598... | 0.72255284 | 0 |
Parses data for training and evaluation. | def _parse_train_data(self, data):
image, label = self._prepare_image_and_label(data)
# Flips image randomly during training.
if self._aug_rand_hflip:
image, label = input_utils.random_horizontal_flip(image, masks=label)
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image,
self._output_size,
self._output_size,
aug_scale_min=self._aug_scale_min,
aug_scale_max=self._aug_scale_max)
# Resizes and crops boxes.
image_scale = image_info[2, :]
offset = image_info[3, :]
# Pad label and make sure the padded region assigned to the ignore label.
# The label is first offset by +1 and then padded with 0.
label += 1
label = tf.expand_dims(label, axis=3)
label = input_utils.resize_and_crop_masks(
label, image_scale, self._output_size, offset)
label -= 1
label = tf.where(tf.equal(label, -1),
self._ignore_label * tf.ones_like(label), label)
label = tf.squeeze(label, axis=0)
valid_mask = tf.not_equal(label, self._ignore_label)
labels = {
'masks': label,
'valid_masks': valid_mask
}
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
return image, labels | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_data():\n n_train, n_test = 15000, 4996\n n_features = 1355191\n\n print('- parsing train data')\n X_train = sp.lil_matrix((n_train, n_features))\n y_train = np.zeros(n_train)\n with open('/Users/kitazawa/data/news20.train') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.... | [
"0.7268114",
"0.6815508",
"0.6703304",
"0.6685302",
"0.65728796",
"0.64867747",
"0.6361533",
"0.63356423",
"0.6325312",
"0.6323176",
"0.631201",
"0.63008344",
"0.6293298",
"0.6284289",
"0.62585795",
"0.62380934",
"0.6225368",
"0.61996716",
"0.6173986",
"0.6165433",
"0.6161794... | 0.60057056 | 45 |
Parses data for training and evaluation. | def _parse_eval_data(self, data):
image, label = self._prepare_image_and_label(data)
# The label is first offset by +1 and then padded with 0.
label += 1
label = tf.expand_dims(label, axis=3)
if self._resize_eval:
# Resizes and crops image.
image, image_info = input_utils.resize_and_crop_image(
image, self._output_size, self._output_size)
# Resizes and crops mask.
image_scale = image_info[2, :]
offset = image_info[3, :]
label = input_utils.resize_and_crop_masks(label, image_scale,
self._output_size, offset)
else:
# Pads image and mask to output size.
image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
self._output_size[1])
label = tf.image.pad_to_bounding_box(label, 0, 0, self._output_size[0],
self._output_size[1])
label -= 1
label = tf.where(tf.equal(label, -1),
self._ignore_label * tf.ones_like(label), label)
label = tf.squeeze(label, axis=0)
valid_mask = tf.not_equal(label, self._ignore_label)
labels = {
'masks': label,
'valid_masks': valid_mask
}
# If bfloat16 is used, casts input image to tf.bfloat16.
if self._use_bfloat16:
image = tf.cast(image, dtype=tf.bfloat16)
return image, labels | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_data():\n n_train, n_test = 15000, 4996\n n_features = 1355191\n\n print('- parsing train data')\n X_train = sp.lil_matrix((n_train, n_features))\n y_train = np.zeros(n_train)\n with open('/Users/kitazawa/data/news20.train') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.... | [
"0.7268114",
"0.6815508",
"0.6703304",
"0.6685302",
"0.65728796",
"0.64867747",
"0.6361533",
"0.63356423",
"0.6325312",
"0.6323176",
"0.631201",
"0.63008344",
"0.6293298",
"0.6284289",
"0.62585795",
"0.62380934",
"0.6225368",
"0.61996716",
"0.6173986",
"0.6165433",
"0.6161794... | 0.0 | -1 |
Parses data for prediction. | def _parse_predict_data(self, data):
image, labels = self._parse_eval_data(data)
return {
'images': image,
'labels': labels
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_dataset(self, data):\n pass",
"def predict(self, datafile):",
"def predict(self, data: List):",
"def predict(self, data):\n\t\traise NotImplementedError",
"def postprocess(self, data):\n all_predictions, all_nbest_json, scores_diff_json = predictions(self._dev_dataset,\n ... | [
"0.70179003",
"0.68369985",
"0.67741996",
"0.6700655",
"0.665754",
"0.6550395",
"0.6539271",
"0.6422481",
"0.6383678",
"0.63100535",
"0.6299874",
"0.62833273",
"0.6269634",
"0.6264631",
"0.6253759",
"0.62460303",
"0.62442476",
"0.621106",
"0.6204033",
"0.6197395",
"0.61885244... | 0.7601515 | 0 |
If an iteriter_op is given an iterator as input, no exception should be thrown, and we should return the wrapped function's output. | def test_iteriter_op_1():
@ops.iteriter_op
def f(x):
return iter([4, 5, 6])
result = f(iter([1, 2, 3])) # Passing in an iterator, as expected
assert(isinstance(result, collections.abc.Iterator)), f"{result}"
assert(list(result) == [4, 5, 6]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_iteriter_op_3():\n\n @ops.iteriter_op\n def f(x):\n return [4, 5, 6] # Returning a list instead of an iterator\n\n with pytest.raises(ValueError):\n result = f(iter([1, 2, 3]))",
"def test_iteriter_op_2():\n\n @ops.iteriter_op\n def f(x):\n return iter([4, 5, 6])\n\n... | [
"0.734651",
"0.7288327",
"0.6928596",
"0.6799886",
"0.66857606",
"0.6591529",
"0.6551025",
"0.6511721",
"0.63353145",
"0.59754914",
"0.5973058",
"0.59268594",
"0.5852119",
"0.58467436",
"0.5843037",
"0.583689",
"0.57858115",
"0.5740564",
"0.5712443",
"0.56730723",
"0.5633042"... | 0.74068475 | 0 |
If an iteriter_op is given something besides an iterator as input, raise a ValueError. | def test_iteriter_op_2():
@ops.iteriter_op
def f(x):
return iter([4, 5, 6])
with pytest.raises(ValueError):
f([1, 2, 3]) # Passing in a list instead of an iterator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_iteriter_op_3():\n\n @ops.iteriter_op\n def f(x):\n return [4, 5, 6] # Returning a list instead of an iterator\n\n with pytest.raises(ValueError):\n result = f(iter([1, 2, 3]))",
"def test_listiter_op_2():\n\n @ops.listiter_op\n def f(x):\n return iter([4, 5, 6])\n\n... | [
"0.7217243",
"0.7193178",
"0.6957775",
"0.68198645",
"0.6498388",
"0.6485671",
"0.63076574",
"0.6105469",
"0.58576494",
"0.5822482",
"0.57532716",
"0.57530195",
"0.5725821",
"0.56677824",
"0.5644975",
"0.5638798",
"0.5572163",
"0.5566992",
"0.5561028",
"0.5558914",
"0.5509184... | 0.75408363 | 0 |
If an iteriter_op returns something besides an iterator as output, raise a ValueError. | def test_iteriter_op_3():
@ops.iteriter_op
def f(x):
return [4, 5, 6] # Returning a list instead of an iterator
with pytest.raises(ValueError):
result = f(iter([1, 2, 3])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_iteriter_op_2():\n\n @ops.iteriter_op\n def f(x):\n return iter([4, 5, 6])\n\n with pytest.raises(ValueError):\n f([1, 2, 3]) # Passing in a list instead of an iterator",
"def test_listiter_op_2():\n\n @ops.listiter_op\n def f(x):\n return iter([4, 5, 6])\n\n with... | [
"0.76426315",
"0.73116994",
"0.7197692",
"0.69156325",
"0.6845044",
"0.6789451",
"0.63753885",
"0.6361283",
"0.62555766",
"0.6095181",
"0.6085253",
"0.59735376",
"0.5929287",
"0.5857033",
"0.58470154",
"0.58267",
"0.57885355",
"0.57856303",
"0.57807213",
"0.576038",
"0.573720... | 0.7515847 | 1 |
If a listlist_op is given a list as input, no exception should be thrown, and we should return the wrapped function's output. | def test_listlist_op_1():
@ops.listlist_op
def f(x):
return [4, 5, 6]
result = f([1, 2, 3]) # Passing in a list, as expected
assert(isinstance(result, list)), f"{result}"
assert(result == [4, 5, 6]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listlist_op_3():\n\n @ops.listlist_op\n def f(x):\n return iter([4, 5, 6]) # Returning an iterator instead of an list\n\n with pytest.raises(ValueError):\n result = f([1, 2, 3])",
"def test_listlist_op_2():\n\n @ops.listlist_op\n def f(x):\n return [4, 5, 6]\n\n w... | [
"0.7166146",
"0.71624446",
"0.7014189",
"0.6786135",
"0.6684333",
"0.6671589",
"0.6536824",
"0.6533272",
"0.6519165",
"0.64344203",
"0.6429467",
"0.64098346",
"0.6329758",
"0.6261592",
"0.6214731",
"0.61999166",
"0.6112252",
"0.6028815",
"0.6003266",
"0.5961093",
"0.5949775",... | 0.77922696 | 0 |
If a listlist_op is given something besides a list as input, raise a ValueError. | def test_listlist_op_2():
@ops.listlist_op
def f(x):
return [4, 5, 6]
with pytest.raises(ValueError):
f(iter([1, 2, 3])) # Passing in an iterator instead of an list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listlist_op_3():\n\n @ops.listlist_op\n def f(x):\n return iter([4, 5, 6]) # Returning an iterator instead of an list\n\n with pytest.raises(ValueError):\n result = f([1, 2, 3])",
"def is_listing(op):\n return isinstance(op, (list, tuple))",
"def test_neg_operate_list_invalid_... | [
"0.6896146",
"0.6843723",
"0.6687192",
"0.66674805",
"0.6584861",
"0.650589",
"0.64047855",
"0.6385669",
"0.6318132",
"0.62996775",
"0.6293289",
"0.62533414",
"0.6251247",
"0.62425464",
"0.62239265",
"0.62199765",
"0.6166311",
"0.61608076",
"0.6119108",
"0.6056942",
"0.600924... | 0.71428686 | 0 |
If a listlist_op returns something besides a list as output, raise a ValueError. | def test_listlist_op_3():
@ops.listlist_op
def f(x):
return iter([4, 5, 6]) # Returning an iterator instead of an list
with pytest.raises(ValueError):
result = f([1, 2, 3]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listlist_op_2():\n\n @ops.listlist_op\n def f(x):\n return [4, 5, 6]\n\n with pytest.raises(ValueError):\n f(iter([1, 2, 3])) # Passing in an iterator instead of an list",
"def test_listlist_op_1():\n\n @ops.listlist_op\n def f(x):\n return [4, 5, 6]\n\n result = ... | [
"0.73349303",
"0.71511084",
"0.6736014",
"0.66537863",
"0.6652018",
"0.6627136",
"0.65927994",
"0.6493474",
"0.6485327",
"0.6485104",
"0.64288867",
"0.6416885",
"0.6406958",
"0.63908684",
"0.63792086",
"0.6378879",
"0.63132125",
"0.6302996",
"0.6289423",
"0.62221825",
"0.6194... | 0.72641724 | 1 |
If a listiter_op is given a list as input, no exception should be thrown, and we should return the wrapped function's output. | def test_listiter_op_1():
@ops.listiter_op
def f(x):
return iter([4, 5, 6])
result = f([1, 2, 3]) # Passing in a list, as expected
assert(isinstance(result, collections.abc.Iterator)), f"{result}"
assert(list(result) == [4, 5, 6]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_iterlist_op_1():\n\n @ops.iterlist_op\n def f(x):\n return [4, 5, 6]\n\n result = f(iter([1, 2, 3])) # Passing in an iterator, as expected\n\n assert(isinstance(result, list)), f\"{result}\"\n assert(result == [4, 5, 6])",
"def test_listiter_op_3():\n\n @ops.listiter_op\n de... | [
"0.7756567",
"0.75442225",
"0.75318354",
"0.75228345",
"0.747424",
"0.74187565",
"0.7351864",
"0.71246946",
"0.6990763",
"0.6881843",
"0.68182045",
"0.66678816",
"0.65725154",
"0.65193516",
"0.62121695",
"0.6203566",
"0.59676445",
"0.5919752",
"0.5807262",
"0.5753957",
"0.565... | 0.76254886 | 1 |
If a listiter_op is given something besides a list as input, raise a ValueError. | def test_listiter_op_2():
@ops.listiter_op
def f(x):
return iter([4, 5, 6])
with pytest.raises(ValueError):
f(iter([1, 2, 3])) # Passing in an iterator instead of a list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listlist_op_2():\n\n @ops.listlist_op\n def f(x):\n return [4, 5, 6]\n\n with pytest.raises(ValueError):\n f(iter([1, 2, 3])) # Passing in an iterator instead of an list",
"def test_iterlist_op_2():\n\n @ops.iterlist_op\n def f(x):\n return [4, 5, 6]\n\n with pyte... | [
"0.76405627",
"0.7451966",
"0.7407579",
"0.73711157",
"0.7272287",
"0.6842472",
"0.67784035",
"0.6713179",
"0.65655285",
"0.6331",
"0.62677497",
"0.62505025",
"0.6126729",
"0.6060513",
"0.60524344",
"0.60413057",
"0.5994478",
"0.5923151",
"0.5910163",
"0.5815476",
"0.5803793"... | 0.75528175 | 1 |
If a listiter_op returns something besides an iterator as output, raise a ValueError. | def test_listiter_op_3():
@ops.listiter_op
def f(x):
return [4, 5, 6] # Returning a list instead of an iterator
with pytest.raises(ValueError):
result = f([1, 2, 3]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listiter_op_2():\n\n @ops.listiter_op\n def f(x):\n return iter([4, 5, 6])\n\n with pytest.raises(ValueError):\n f(iter([1, 2, 3])) # Passing in an iterator instead of a list",
"def test_iterlist_op_3():\n\n @ops.iterlist_op\n def f(x):\n return iter([4, 5, 6]) # Re... | [
"0.800803",
"0.7905361",
"0.77610767",
"0.77083707",
"0.77032125",
"0.75699127",
"0.7554352",
"0.70976245",
"0.70190036",
"0.67172563",
"0.6027455",
"0.58875996",
"0.5732145",
"0.57207274",
"0.57120436",
"0.57120436",
"0.5675813",
"0.5638414",
"0.5630779",
"0.5580102",
"0.555... | 0.77557844 | 3 |
If an iterlist_op is given an iterator as input, no exception should be thrown, and we should return the wrapped function's output. | def test_iterlist_op_1():
@ops.iterlist_op
def f(x):
return [4, 5, 6]
result = f(iter([1, 2, 3])) # Passing in an iterator, as expected
assert(isinstance(result, list)), f"{result}"
assert(result == [4, 5, 6]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listiter_op_2():\n\n @ops.listiter_op\n def f(x):\n return iter([4, 5, 6])\n\n with pytest.raises(ValueError):\n f(iter([1, 2, 3])) # Passing in an iterator instead of a list",
"def test_listiter_op_1():\n\n @ops.listiter_op\n def f(x):\n return iter([4, 5, 6])\n\n ... | [
"0.76466507",
"0.76008874",
"0.7570353",
"0.7508546",
"0.74234086",
"0.7396815",
"0.7382202",
"0.73407215",
"0.7291801",
"0.72297966",
"0.6824224",
"0.666192",
"0.6128793",
"0.60040385",
"0.5923186",
"0.58794075",
"0.58676404",
"0.57020146",
"0.56718487",
"0.5595165",
"0.5567... | 0.76173055 | 1 |
If an iterlist_op is given something besides an iterator as input, raise a ValueError. | def test_iterlist_op_2():
@ops.iterlist_op
def f(x):
return [4, 5, 6]
with pytest.raises(ValueError):
f([1, 2, 3]) # Passing in a list instead of an iterator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listiter_op_2():\n\n @ops.listiter_op\n def f(x):\n return iter([4, 5, 6])\n\n with pytest.raises(ValueError):\n f(iter([1, 2, 3])) # Passing in an iterator instead of a list",
"def test_iterlist_op_3():\n\n @ops.iterlist_op\n def f(x):\n return iter([4, 5, 6]) # Re... | [
"0.78835404",
"0.7705365",
"0.7613191",
"0.7498913",
"0.7423901",
"0.74038625",
"0.73198646",
"0.67817676",
"0.6730599",
"0.6505109",
"0.5887717",
"0.5857515",
"0.58052135",
"0.5753452",
"0.5708452",
"0.5689192",
"0.56823623",
"0.5681861",
"0.56544685",
"0.56081665",
"0.56028... | 0.7661832 | 2 |
If an iterlist_op returns something besides a list as output, raise a ValueError. | def test_iterlist_op_3():
@ops.iterlist_op
def f(x):
return iter([4, 5, 6]) # Returning an iterator instead of a list
with pytest.raises(ValueError):
result = f(iter([1, 2, 3])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_listlist_op_2():\n\n @ops.listlist_op\n def f(x):\n return [4, 5, 6]\n\n with pytest.raises(ValueError):\n f(iter([1, 2, 3])) # Passing in an iterator instead of an list",
"def test_listlist_op_3():\n\n @ops.listlist_op\n def f(x):\n return iter([4, 5, 6]) # Returni... | [
"0.77509636",
"0.76935655",
"0.76132554",
"0.7517712",
"0.7484861",
"0.7235555",
"0.70362914",
"0.7030836",
"0.6869668",
"0.66613793",
"0.6354757",
"0.630842",
"0.6297937",
"0.61403257",
"0.60431194",
"0.6029395",
"0.6019079",
"0.6019079",
"0.59903765",
"0.5965533",
"0.595312... | 0.75992316 | 3 |
Constant evaluation should ignore the existing fitness function and set the fitness of all individuals to the same value. | def test_const_evaluate():
pop = test_population
pop = ops.const_evaluate(pop, value=123456789.0)
for ind in pop:
assert(pytest.approx(123456789.0) == ind.fitness) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _recompute_fitness(self):\n for cur in self.population:\n if cur['fitness'] is None:\n cur['fitness'] = self.op.fitness(cur['individual'])",
"def fitness(self):\n pass",
"def evaluate(self, fitness):\n self.fitness = fitness(self.phenotype)",
"def fitness_sh... | [
"0.74931896",
"0.7079468",
"0.69477665",
"0.6825639",
"0.67214495",
"0.6705714",
"0.6655073",
"0.6554352",
"0.6519272",
"0.6481715",
"0.6447769",
"0.62911665",
"0.61991894",
"0.6193287",
"0.6174193",
"0.6151497",
"0.6150842",
"0.6150842",
"0.610781",
"0.60786945",
"0.607092",... | 0.6333001 | 11 |
If a pool of size 3 is used, the first 3 individuals in the input iterator should be collected into a list. | def test_pool():
pop = iter([ 'a', 'b', 'c', 'd', 'e' ])
pop = ops.pool(pop, size=3)
assert(len(pop) == 3)
assert(pop == [ 'a', 'b', 'c' ]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lazy_groups_of(iterator: Iterator[A], group_size: int) -> Iterator[List[A]]:\n return iter(lambda: list(islice(iterator, 0, group_size)), [])",
"def construct(self, x):\n results = []\n x = self.pool0(x)\n results.append(x)\n x = self.pool1(x)\n results.append(x)\n ... | [
"0.640328",
"0.6058334",
"0.58099174",
"0.5747291",
"0.57104117",
"0.5681089",
"0.56514186",
"0.5616282",
"0.56008047",
"0.55808794",
"0.5538672",
"0.5503756",
"0.5503756",
"0.54849136",
"0.5432563",
"0.54290795",
"0.54290795",
"0.54163194",
"0.54163116",
"0.5399655",
"0.5391... | 0.7016338 | 0 |
Compute the color of a given pixel. | def start_up(t, coord, ii, n_pixels, value):
global position
x, y, z = coord
# print(coord)
# print("position")
# print(int(position))
if (ii == 0):
r = value[int(position)]
g = value[int(position)]
b = value[int(position)]
elif (ii == 1 or ii == 29 or ii == 28):
r = value[int(position)] * .7
g = value[int(position)] * .5
b = value[int(position)] * .5
else:
r = 0
g = 0
b = 0
position += .01
if (position > 499):
position = 0
# padXData = touchOSC.padXData
# padYData = int(touchOSC.padYData * .65)
# print padYData
# print touchOSC.padYData
# r,g,b = colorOSC
# if x == padXData and z == padYData:
# r,g,b = draw.circle(padXData,padYData, x, z,colorOSC)
# draw.circle(5,5, x, z)
return (r, g, b) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_color(im_obj):\n #im = Image.open(path, 'r')\n x, y = im_obj.size\n\n r, g, b = 0, 0, 0\n for i in xrange(x):\n for j in xrange(y):\n color_px = im_obj.getpixel((i, j))\n #print color_px\n r += color_px[0]\n g += color_px[1]\n b += c... | [
"0.700929",
"0.69985324",
"0.6966529",
"0.68848133",
"0.6798888",
"0.6789337",
"0.6751443",
"0.67474806",
"0.6740431",
"0.65576667",
"0.6495866",
"0.6494163",
"0.6480991",
"0.64658093",
"0.64402866",
"0.64176357",
"0.64166456",
"0.64017946",
"0.63647753",
"0.63250977",
"0.631... | 0.0 | -1 |
Converts a wave to a vector of prosodic features. offset (in ms) determines where the signal will be sampled. window_len is ignored. | def wav_to_prosodic(path, sr=16000, offset=10):
sound = parselmouth.Sound(path)
pitch = sound.to_pitch() #timestep, pitch_floor, pitch_ceiling
intensity = sound.to_intensity()
features = []
max_time = sound.get_total_duration()
for time in np.arange(0, max_time, 0.001):
f0 = pitch.get_value_at_time(time)
f0_nan = 0
if np.isnan(f0):
f0 = 0
f0_nan = 1
int_db = intensity.get_value(time)
if np.isnan(int_db):
int_db = 0
features.append([f0, f0_nan, int_db])
array_feats = np.array(features).T
print("SHAPE OF THE FEATURES:", array_feats.shape)
assert(not np.any(np.isnan(array_feats)))
return array_feats, max_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _choose_wavelength_slice(self, offset):\n if 'WAVE' not in self.axes_wcs.wcs.ctype:\n raise cu.CubeError(2, \"Spectral dimension not present\")\n if self.data.ndim == 4:\n raise cu.CubeError(4, \"Can only work with 3D cubes\")\n\n axis = -2 if self.axes_wcs.wcs.ctype[... | [
"0.5480287",
"0.5404212",
"0.5282063",
"0.50624555",
"0.50224143",
"0.50093454",
"0.4989605",
"0.49353826",
"0.486388",
"0.48465458",
"0.4828349",
"0.47970074",
"0.47777593",
"0.47630838",
"0.4727671",
"0.47226426",
"0.4674579",
"0.4674468",
"0.46724492",
"0.46629953",
"0.464... | 0.6025671 | 0 |
Converts a wave to a vector of prosodic features. offset (in ms) determines where the signal will be sampled. window_len is ignored. | def wav_to_intensity(path, sr=16000, offset=10):
sound = parselmouth.Sound(path)
intensity = sound.to_intensity()
features = []
max_time = sound.get_total_duration()
for time in np.arange(0, max_time, 0.001):
int_db = intensity.get_value(time)
if np.isnan(int_db):
int_db = 0
features.append([int_db])
array_feats = np.array(features).T
print("SHAPE OF THE FEATURES:", array_feats.shape)
assert(not np.any(np.isnan(array_feats)))
return array_feats, max_time | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wav_to_prosodic(path, sr=16000, offset=10):\n sound = parselmouth.Sound(path)\n pitch = sound.to_pitch() #timestep, pitch_floor, pitch_ceiling\n intensity = sound.to_intensity()\n\n features = []\n\n max_time = sound.get_total_duration()\n\n for time in np.arange(0, max_time, 0.001):\n ... | [
"0.6025386",
"0.54781526",
"0.5399577",
"0.5278359",
"0.50593245",
"0.50171155",
"0.50058156",
"0.49881226",
"0.49332815",
"0.4863905",
"0.4842912",
"0.48232105",
"0.47933024",
"0.47808012",
"0.4761141",
"0.47247434",
"0.47176567",
"0.46723312",
"0.46707693",
"0.4661695",
"0.... | 0.46706223 | 19 |
Load dumped object handled by file_name. If file_name is None, then default file name is used. | def model_load(file_name=None):
if file_name is None :
file_name = "./data/_oP5_SegmentClassifier.dump"
else:
pass
return p5_util.object_load(file_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadFromFile(file_name=\"saved_object.pickle\"):\n\n try:\n with open(file_name, \"rb\") as inputToLoad:\n loaded_object = pickle.load(inputToLoad)\n return loaded_object\n except IOError:\n raise InvalidFilesPath\n except ImportError as e:\n raise InvalidFile(\n... | [
"0.71736634",
"0.7085416",
"0.7077506",
"0.7077506",
"0.6953144",
"0.6927678",
"0.6907037",
"0.68987334",
"0.680562",
"0.67826605",
"0.67519313",
"0.6732998",
"0.67002213",
"0.6668907",
"0.66492176",
"0.6630586",
"0.6611128",
"0.6594634",
"0.6578726",
"0.65453917",
"0.6457661... | 0.6612186 | 16 |
Print percentage of rows that have been processed. | def _print_stat_rows(title,rows_before,rows_after):
self.strprint(str(title)+" : Percent of processed rows = %1.2F"\
%(np.abs(rows_before-rows_after)*100/rows_before)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printProgress(self, percentage):\n #print '%s\\r' % ' '*20, # clean up row\n #print '%3d%% ' % percentage, # ending with comma prevents newline from being appended\n sys.stdout.flush()",
"def PrintProgress(self):\n print ' Examined %d nodes, found %d unique...' % (\n ... | [
"0.7295432",
"0.690456",
"0.6810523",
"0.65595317",
"0.64016294",
"0.63650185",
"0.63531333",
"0.6352935",
"0.6349985",
"0.6310579",
"0.6283685",
"0.6247981",
"0.6222485",
"0.6173231",
"0.61398053",
"0.61388904",
"0.61365205",
"0.6132179",
"0.61136687",
"0.61033106",
"0.60934... | 0.74636936 | 0 |
Print percentage of rows that have been processed. | def print_stat_rows(title,rows_before,rows_after):
_print_stat_rows(title,rows_before,rows_after) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _print_stat_rows(title,rows_before,rows_after):\n self.strprint(str(title)+\" : Percent of processed rows = %1.2F\"\\\n %(np.abs(rows_before-rows_after)*100/rows_before))",
"def printProgress(self, percentage):\n #print '%s\\r' % ' '*20, # clean up row\n #print '%3d%% ' % percentage,... | [
"0.74636936",
"0.7295432",
"0.690456",
"0.6810523",
"0.65595317",
"0.64016294",
"0.63650185",
"0.63531333",
"0.6352935",
"0.6349985",
"0.6310579",
"0.6283685",
"0.6247981",
"0.6222485",
"0.6173231",
"0.61398053",
"0.61388904",
"0.61365205",
"0.6132179",
"0.61136687",
"0.61033... | 0.0 | -1 |
Encapsulation of print function. If flag is_verbose is fixed to True, then print takes place. | def strprint(self, mystr):
if self.is_verbose is True:
print(mystr)
else:
pass
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _verbose(self,text):\n if self.verbose:\n print(text)",
"def _print(self, text):\n\t\tif self.verbose:\n\t\t\tprint text",
"def verbose_print(verbose, print_function=None):\n\n if verbose:\n return print_function or print\n else:\n def vprint(*args, **kwars):\n ... | [
"0.7844468",
"0.7715159",
"0.76063657",
"0.75858647",
"0.74731535",
"0.7468149",
"0.74547803",
"0.7313309",
"0.72769666",
"0.72654116",
"0.7237131",
"0.7225605",
"0.7189013",
"0.7179139",
"0.7146172",
"0.70976067",
"0.70916754",
"0.70261127",
"0.6895416",
"0.68448365",
"0.682... | 0.68400043 | 20 |
Copy all attributes from a given object P5_SegmentClassifier object into self. | def copy(self, other_object):
#-------------------------------------------------------------------------
# Debug parameters
#-------------------------------------------------------------------------
self._path_to_model = other_object.path_to_model
#-------------------------------------------------------------------------
# Data-model parameters
#-------------------------------------------------------------------------
self.is_verbose = other_object.is_verbose
self._df_invoice_line = other_object._df_invoice_line.copy()
self._total_outliers = other_object._total_outliers
self._df_invoice_ref = other_object._df_invoice_ref.copy()
self._list_quant_feature = other_object._list_quant_feature.copy()
self._list_feature_to_drop = other_object._list_feature_to_drop.copy()
self._df_invoice_original = other_object._df_invoice_original.copy()
if other_object._arr_sample_customerID is not None:
self._arr_sample_customerID = other_object._arr_sample_customerID.copy()
else :
self._arr_sample_customerID = None
self._df_invoice_line_out_sample \
= other_object._df_invoice_line_out_sample.copy()
#-------------------------------------------------------------------------
# RFM features
#-------------------------------------------------------------------------
self._is_rfm_encode = other_object._is_rfm_encode
self._encoder_rfm = other_object._encoder_rfm
self._df_customers_rfm = other_object._df_customers_rfm.copy()
self.df_customers_rfm_fileName = other_object.df_customers_rfm_fileName
self.df_RFM_quantiles = other_object.df_RFM_quantiles
self._day_now = other_object._day_now
self._is_transform_rfm = other_object._is_transform_rfm
#-------------------------------------------------------------------------
# Time features
#-------------------------------------------------------------------------
self._list_new_feature = other_object._list_new_feature
self._pca_timeFeature = other_object._pca_timeFeature
self._std_scaler_timeFeature = other_object._std_scaler_timeFeature
self._df_customers_timeFeature_fileName \
= other_object._df_customers_timeFeature_fileName
if other_object._dict_timeFeature_encoder is not None:
self._dict_timeFeature_encoder \
= other_object._dict_timeFeature_encoder.copy()
else:
self._dict_timeFeature_encoder = other_object._dict_timeFeature_encoder
if other_object._df_customers_timeFeature is not None:
self._df_customers_timeFeature \
= other_object._df_customers_timeFeature.copy()
else:
self._df_customers_timeFeature = other_object._df_customers_timeFeature
self._is_transform_timeFeature = other_object._is_transform_timeFeature
#-------------------------------------------------------------------------
# NLP features
#-------------------------------------------------------------------------
self._vectorizer_nlp = other_object._vectorizer_nlp
self._matrix_weights_nlp = other_object._matrix_weights_nlp
self._df_customers_nlp_fileName = other_object._df_customers_nlp_fileName
self._pca_nlp = other_object._pca_nlp
self._df_customers_pca_nlp = other_object._df_customers_pca_nlp.copy()
self._nlp_pca_ndim = other_object._nlp_pca_ndim
self._is_transform_nlp = other_object._is_transform_nlp
#-------------------------------------------------------------------------
# All features
#-------------------------------------------------------------------------
self._df_customers_fileName = other_object._df_customers_fileName
self._df_customers = other_object._df_customers.copy()
#-------------------------------------------------------------------------
# Classifier
#-------------------------------------------------------------------------
if other_object._y_clusters is not None:
self._y_clusters = other_object._y_clusters.copy()
else:
self._y_clusters = other_object._y_clusters
self._dict_classifier_param = other_object._dict_classifier_param.copy()
self._classifier_name = other_object._classifier_name
self._classifier_model = other_object._classifier_model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy(self, threshold):\n self.indicator = threshold['indicator']\n self.stage = threshold['stage']\n self.begin = threshold['begin']\n self.end = threshold['end']\n self.quality = threshold['quality']\n self.weight = threshold['weight']\n return self",
"def po... | [
"0.5413871",
"0.51940006",
"0.5167523",
"0.5163446",
"0.5083279",
"0.49784464",
"0.49112073",
"0.48626807",
"0.48342088",
"0.48338348",
"0.482826",
"0.48198396",
"0.48187032",
"0.4814801",
"0.48118797",
"0.4783213",
"0.4774069",
"0.47707164",
"0.47706905",
"0.47557005",
"0.47... | 0.47427928 | 20 |
Remove raws with countries other then 'United Kingdom' then remove Country feature. | def _feature_country_process(self):
if 'Country' not in self._df_invoice_line.columns:
return
list_countries_keep = ['United Kingdom']
rows_before = self._df_invoice_line.shape[0]
df_invoice_line_new = pd.DataFrame()
for country in list_countries_keep :
df_invoice_line_new = df_invoice_line_new.append(\
self._df_invoice_line[self._df_invoice_line['Country']==country]\
, ignore_index=True)
self.df_invoice_line = df_invoice_line_new
del(df_invoice_line_new)
rows_after = self._df_invoice_line.shape[0]
_print_stat_rows("Countries filtering : ",rows_before, rows_after)
#-------------------------------------------------------------------------
# Due to the fact only one country is used, then this feature is dropped
#-------------------------------------------------------------------------
list_col_to_keep = [col for col in self._df_invoice_line.columns \
if col not in 'Country']
self._df_invoice_line = self._df_invoice_line[list_col_to_keep]
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply_feature_filter(self):\n self.features = set()\n for language in self.data.values():\n features_in_data = set(language.keys())\n features_to_keep = features_in_data & self.feature_filter\n self.features |= features_to_keep\n features_to_remove = fe... | [
"0.59036386",
"0.5863824",
"0.5825038",
"0.5797917",
"0.5704074",
"0.5514831",
"0.55131525",
"0.5411934",
"0.5399952",
"0.53786814",
"0.53708786",
"0.53605676",
"0.5328933",
"0.53094655",
"0.51719415",
"0.5159425",
"0.51442134",
"0.5126341",
"0.51245403",
"0.50806504",
"0.506... | 0.70100856 | 0 |
Builds features issued from InvoiceDate. A dataframe is built per new feature and dumped into a file. Each one of the dataframe have encoded features issues from InvoiceDate. | def data_transform_timeFeature(self):
#-------------------------------------------------------------------------
# All new features are built into separate dataframes
# and each of them are dumped into a separate file.
#-------------------------------------------------------------------------
self.strprint("self.df_invoice_line : "+str(self.df_invoice_line.shape))
self._dict_timeFeature_encoder, df_customers_timeFeature \
= p5_util.time_list_feature_build(self.df_invoice_line\
, self._list_new_feature, dict_encoder = self._dict_timeFeature_encoder\
,is_verbose=self.is_verbose)
#-------------------------------------------------------------------------
# New time features are aggregated into a single dataframe.
# Values are scaled.
#-------------------------------------------------------------------------
df_customers_timeFeature, self._std_scaler_timeFeature \
= p5_util.time_list_feature_restore(self._list_new_feature \
, std_scale = self._std_scaler_timeFeature\
, df_timeFeature = df_customers_timeFeature, is_verbose = self.is_verbose)
self.strprint("df_customers_timeFeature : "+str(df_customers_timeFeature.shape))
#-------------------------------------------------------------------------
# Dimension reduction thanks to PCA
#-------------------------------------------------------------------------
n_dim=30
root_name = 'time_pca_'
# Column CustomerID is used into df_pca_reduce
df_customers_timeFeature['CustomerID'] = df_customers_timeFeature.index
df_customers_timeFeature, pca_timeFeature \
= p5_util.df_pca_reduce(df_customers_timeFeature, n_dim, root_name\
, p_is_scale=False, pca = self._pca_timeFeature)
self.strprint(df_customers_timeFeature.shape)
if self._pca_timeFeature is None:
#----------------------------------------------------------------------
# Data-model is in built process with part of data-set.
#----------------------------------------------------------------------
self._pca_timeFeature = pca_timeFeature
p5_util.object_dump(df_customers_timeFeature\
, self._df_customers_timeFeature_fileName)
else:
#----------------------------------------------------------------------
# Data-model is already built and this method is called
# for a customer classification.
#----------------------------------------------------------------------
self._df_customers_timeFeature = df_customers_timeFeature.copy()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_date_features(df = None, date = None):\n #TODO",
"def data_transform_rfm(self) :\n \n is_built_step = False\n if self._encoder_rfm is None:\n is_built_step = True \n \n #-------------------------------------------------------------------------\n # RFM feature ... | [
"0.6386668",
"0.6208587",
"0.62022436",
"0.60953605",
"0.60796374",
"0.60608006",
"0.6022764",
"0.5993953",
"0.5985361",
"0.59586924",
"0.5820426",
"0.5809583",
"0.58089864",
"0.58070034",
"0.5787359",
"0.575435",
"0.57058054",
"0.56917906",
"0.56418794",
"0.56213087",
"0.560... | 0.62781596 | 1 |
Builds for each customer, RFM scores and encode scores. When this method is called during building data_model step, then dataframe handling new RFM features is dumped into a file. | def data_transform_rfm(self) :
is_built_step = False
if self._encoder_rfm is None:
is_built_step = True
#-------------------------------------------------------------------------
# RFM feature is built
#-------------------------------------------------------------------------
ser_invoice_date = self._df_invoice_line.InvoiceDate
self.df_invoice_line, df_RFM, self.df_RFM_quantiles, self._day_now \
= p5_util.p5_df_rfm_build(self.df_invoice_line, day_now = self._day_now\
, df_RFM_threshold=self.df_RFM_quantiles)
self._df_invoice_line.InvoiceDate = ser_invoice_date
#-------------------------------------------------------------------------
# RFM score is added to dataframe
#-------------------------------------------------------------------------
df_merged = pd.merge(self.df_invoice_line\
, df_RFM[['CustomerID','RFM']], how='left', on=['CustomerID'])
self._df_invoice_line \
= pd.DataFrame(df_merged.values, index = self._df_invoice_line.index\
, columns=df_merged.columns)
#self._df_invoice_line \
#= pd.concat([ self.df_invoice_line,df_RFM[['CustomerID','RFM']] ], axis=1\
#,join='inner')
#-------------------------------------------------------------------------
# RFM encoding
#-------------------------------------------------------------------------
self._encoder_rfm, df_RFM_encoded \
= p5_util.df_rfm_one_hot_encode(df_RFM,'RFM', encoder=self._encoder_rfm)
#-------------------------------------------------------------------------
# Encoded RFM features are renamed
#-------------------------------------------------------------------------
df_customers_rfm, list_col_unchanged \
= p5_util.df_rename_columns(df_RFM_encoded, df_RFM_encoded.columns\
, 'w_rfm_')
self.strprint("df_customers_rfm =" +str(df_customers_rfm.shape))
#-------------------------------------------------------------------------
# dataframe with RFM encoded values per customer is dumped
#-------------------------------------------------------------------------
if is_built_step is True:
p5_util.object_dump(df_customers_rfm, self.df_customers_rfm_fileName)
else :
self._df_customers_rfm = df_customers_rfm.copy()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def df_customers_fileRead(self):\n \n #-------------------------------------------------------------------------\n # RFM features are restored\n #-------------------------------------------------------------------------\n df_customers_rfm \\\n = p5_util.object_load(self.df_customer... | [
"0.70747465",
"0.7067084",
"0.62919354",
"0.6007114",
"0.58264214",
"0.5718458",
"0.560313",
"0.56008935",
"0.55872756",
"0.55600125",
"0.5532584",
"0.5520967",
"0.55154175",
"0.5512787",
"0.54379237",
"0.53890693",
"0.53649795",
"0.5364341",
"0.53604376",
"0.53556436",
"0.53... | 0.724281 | 0 |
Creates new features from Description feature thanks to NLTK, a NLP package. NLP features are handled into a dataframe. A PCA reduction is applied on this dataframe. Features from dataframe are renamed with root ane w_nlp. When this method is called during building data_model step, then dataframe handling new NLP feature is dumped into a file. | def data_transform_nlp(self):
df_invoice_line = None
is_build_step = False
if self._vectorizer_nlp is None:
is_build_step = True
list_no_words=['SET','PACK']
df_invoice_line, csr_matrix_weights, self._vectorizer_nlp \
= p5_util.nlp_process(self.df_invoice_line\
, 'Description' , vectorizer= self._vectorizer_nlp\
, list_no_words=list_no_words, is_verbose= self.is_verbose)
if df_invoice_line is None:
self.strprint("***ERROR : NLP process interrupted!")
return
#-------------------------------------------------------------------------
# NLP weights are cumulated (sumerized) per customer
#-------------------------------------------------------------------------
if csr_matrix_weights is None:
csr_matrix_weights \
= p5_util.object_load('./data/matrix_weights_NLP.dump')
else:
pass
self.strprint("df_invoice_line : "+str(df_invoice_line.shape))
self.dbg_df = df_invoice_line.copy()
root_name = 'w_nlp_'
self._df_w_nlp = p5_util.df_nlp_sum_per_customer(df_invoice_line\
, csr_matrix_weights, root_name)
del(csr_matrix_weights)
#-------------------------------------------------------------------------
# Dimension reduction thanks to PCA
#-------------------------------------------------------------------------
self.strprint("self._df_w_nlp : "+str(self._df_w_nlp.shape))
root_name_pca = 'nlp_pca_'
n_dim = self._nlp_pca_ndim
df_customers_pca_nlp, self._pca_nlp \
= p5_util.df_pca_reduce(self._df_w_nlp, n_dim, root_name_pca\
, p_is_scale=False, pca=self._pca_nlp)
self.strprint("df_customers_pca_nlp : " +str(df_customers_pca_nlp.shape))
#-------------------------------------------------------------------------
# Backup of NLP features per customer
#-------------------------------------------------------------------------
if is_build_step is True:
p5_util.object_dump(df_customers_pca_nlp\
, self._df_customers_nlp_fileName)
else:
self._df_customers_pca_nlp = df_customers_pca_nlp.copy()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def feature_description_nlp(self):\n \n #-------------------------------------------------------------------------\n # Returned dataframe is aggregated with weights from self.vectorizer\n #-------------------------------------------------------------------------\n list_no_words=['SET','PAC... | [
"0.7509502",
"0.63895476",
"0.63655084",
"0.6190819",
"0.61304533",
"0.60974437",
"0.6039494",
"0.6013443",
"0.6001541",
"0.59564126",
"0.59309864",
"0.584246",
"0.58098847",
"0.5809157",
"0.5786593",
"0.57630605",
"0.57536405",
"0.57465136",
"0.57437116",
"0.5743342",
"0.574... | 0.6648716 | 1 |
Proceed to data transformation in order to deliver computable data model. | def data_transform(self, df) :
#-------------------------------------------------------------------------
# Copy of given dataframe to be transformed
#-------------------------------------------------------------------------
self.df_invoice_line = df
#-------------------------------------------------------------------------
# Features issued from InvoiceDate are created
#-------------------------------------------------------------------------
if self.is_transform_timeFeature is True:
self.strprint("\n*** Time features transformation ***")
self.data_transform_timeFeature()
#-------------------------------------------------------------------------
# RFM is computed and encoded
#-------------------------------------------------------------------------
if self.is_transform_rfm is True:
self.strprint("\n*** RFM transformation ***")
self.data_transform_rfm()
#-------------------------------------------------------------------------
# NLP features issued from Description are created
#-------------------------------------------------------------------------
if self.is_transform_nlp is True:
self.strprint("\n*** NLP transformation ***")
self.data_transform_nlp()
return self.df_invoice_line | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _apply_transform(self):\n pass",
"def transform(self, data):",
"def _transform(self, dataset):\n raise NotImplementedError()",
"def _transform_data(self, *args, **kwargs) -> None:\n raise NotImplementedError",
"def _process(self):\n # choose the correct transform model befor... | [
"0.71496886",
"0.7044139",
"0.7000777",
"0.6858309",
"0.6785153",
"0.6729821",
"0.67230743",
"0.65904725",
"0.65476114",
"0.6538246",
"0.6538246",
"0.6501432",
"0.6452522",
"0.6446105",
"0.6395718",
"0.639384",
"0.639384",
"0.639384",
"0.639384",
"0.639384",
"0.639384",
"0.... | 0.56914866 | 67 |
Build dataframe df_customers from transformed data. Transformed data are issued from NLP, Time and RFM features. See data_transform(). These data are stored as dataframes attributes. | def df_customers_features_build(self):
df_customers_rfm = self._df_customers_rfm.copy()
df_customers_timeFeature = self._df_customers_timeFeature.copy()
df_customers_nlp = self._df_customers_pca_nlp.copy()
#-------------------------------------------------------------------------
# Dataframe are aggregated; note that indexes are customerID.
#-------------------------------------------------------------------------
df_customers = pd.DataFrame()
df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)
df_customers = pd.concat([df_customers,df_customers_timeFeature]\
, join='inner', axis=1)
df_customers = pd.concat([df_customers,df_customers_nlp]\
, join='inner', axis=1)
self.strprint("All features : "+str(df_customers.shape))
self._df_customers = df_customers.copy()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def features_customers(df_customers):\n for i in PREMIER_VALS:\n k = 'premier_' + str(i)\n df_customers[k] = np.where(df_customers['premier'] == i, 1, 0)\n\n df_customers['age'] = datetime.now().date().year - df_customers['yearOfBirth']\n df_customers['male'] = np.where(df_customers['gender'... | [
"0.70902693",
"0.6727287",
"0.6064118",
"0.60265195",
"0.6024846",
"0.5994544",
"0.596741",
"0.58626324",
"0.584423",
"0.5838772",
"0.5787832",
"0.5785175",
"0.5773647",
"0.5766124",
"0.5743439",
"0.57312465",
"0.57225657",
"0.5721255",
"0.5717279",
"0.5693215",
"0.5649832",
... | 0.80397373 | 0 |
Build dataframe df_customers from transformed data. Transformed data are loaded from dumped files issued from NLP, Time and RFM features. See data_transform() | def df_customers_fileRead(self):
#-------------------------------------------------------------------------
# RFM features are restored
#-------------------------------------------------------------------------
df_customers_rfm \
= p5_util.object_load(self.df_customers_rfm_fileName)
self.strprint("RFM features : "+str(df_customers_rfm.shape))
#-------------------------------------------------------------------------
# Time features are restored
#-------------------------------------------------------------------------
df_customers_timeFeature \
= p5_util.object_load(self._df_customers_timeFeature_fileName)
self.strprint("Time features : "+str(df_customers_timeFeature.shape))
#-------------------------------------------------------------------------
# NLP features are restored
#-------------------------------------------------------------------------
df_customers_nlp = p5_util.object_load(self._df_customers_nlp_fileName)
self.strprint("NLP features : "+str(df_customers_nlp.shape))
if False:
df_customers_rfm = self._df_customers_rfm.copy()
df_customers_timeFeature = self._df_customers_timeFeature.copy()
df_customers_nlp = self._df_customers_pca_nlp.copy()
#-------------------------------------------------------------------------
# Dataframe are aggregated; note that indexes are customerID.
#-------------------------------------------------------------------------
df_customers = pd.DataFrame()
df_customers = pd.concat([df_customers,df_customers_rfm], axis=1)
df_customers = pd.concat([df_customers,df_customers_timeFeature]\
, join='inner', axis=1)
df_customers = pd.concat([df_customers,df_customers_nlp]\
, join='inner', axis=1)
self.strprint("All features : "+str(df_customers.shape))
#----------------------------------------------------------------------
# Dataframe is dumped into a file
#----------------------------------------------------------------------
p5_util.object_dump(df_customers, self._df_customers_fileName)
if False:
#----------------------------------------------------------------------
# Dataframe is copied as an attribute
#----------------------------------------------------------------------
self._df_customers = df_customers.copy()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def df_customers_features_build(self):\n\n df_customers_rfm = self._df_customers_rfm.copy()\n df_customers_timeFeature = self._df_customers_timeFeature.copy()\n df_customers_nlp = self._df_customers_pca_nlp.copy()\n\n #-------------------------------------------------------------------------\n ... | [
"0.78012073",
"0.70509386",
"0.6482284",
"0.6199711",
"0.5975251",
"0.5896229",
"0.58942175",
"0.5884299",
"0.5793099",
"0.57307404",
"0.5701118",
"0.568415",
"0.56560767",
"0.5655341",
"0.56509733",
"0.5625573",
"0.5603968",
"0.55976146",
"0.55959636",
"0.5586056",
"0.557069... | 0.7298348 | 1 |
Creates a new customer identifier from existing dataset. | def createCustomerID(self):
customerID = self._df_invoice_original.CustomerID.max()
customerID += 1
return int(customerID) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def customer_id(uncapped_flatprice, uncapped_flatprice_finalizer, team_multisig) -> int:\n customer_id = int(uuid.uuid4().hex, 16) # Customer ids are 128-bit UUID v4\n return customer_id",
"def create_customer(cls, api, **data):\n return api.create_customer(**data)",
"def create_customer(data):\n... | [
"0.6462454",
"0.63013685",
"0.6199043",
"0.6019253",
"0.60129994",
"0.60011494",
"0.59338015",
"0.59171224",
"0.5879934",
"0.58377534",
"0.5820006",
"0.5756114",
"0.5745354",
"0.57360977",
"0.57085544",
"0.5678208",
"0.5672292",
"0.56669915",
"0.56526893",
"0.5649953",
"0.564... | 0.6483069 | 0 |
Drop from df_invoice_line dataframe features in list given as parameter. All elements from list are checked to be into dataframe columns. | def list_feature_drop(self):
list_to_drop = list()
list_not_in_df = list()
#-------------------------------------------------------------------------
# Columns are checked to be into df_invoice_line dataframe
#-------------------------------------------------------------------------
for col in self._list_feature_to_drop:
if col in self.df_invoice_line.columns:
list_to_drop.append(col)
else:
list_not_in_df.append(col)
if 0 == len(list_to_drop):
self.strprint("\n*** ERROR : no element in list belonging to dataframe!")
else:
if len(self._list_feature_to_drop) != len(list_to_drop):
self.strprint("\n*** WARNING : followings features do not belong to \
dataframe : {}".format(list_not_in_df))
else:
pass
list_col_keep \
= [col for col in self.df_invoice_line.columns \
if col not in list_to_drop]
s
self.df_invoice_line = self.df_invoice_line[list_col_keep]
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drop_dfcol(self, drop_list):\n self.data = self.df\n for lbl in drop_list:\n self.data = self.data.drop(lbl, axis=1)\n self.n_features = np.shape(self.data)[1]",
"def drop(self,df, column_list):\n df.drop(columns = column_list, inplace = True)\n return df",
"de... | [
"0.6483683",
"0.61852366",
"0.61701894",
"0.61167115",
"0.5998328",
"0.5879889",
"0.5828969",
"0.57773757",
"0.57595223",
"0.56772375",
"0.55653924",
"0.55105036",
"0.5499361",
"0.5488699",
"0.5475562",
"0.54356617",
"0.5433665",
"0.5431893",
"0.54176056",
"0.5401929",
"0.537... | 0.7785573 | 0 |
Process df_invoice_line.Description with NLTK package. | def feature_description_nlp(self):
#-------------------------------------------------------------------------
# Returned dataframe is aggregated with weights from self.vectorizer
#-------------------------------------------------------------------------
list_no_words=['SET','PACK']
self.df_invoice_line, vectorizer, matrix_weights \
= p5_util.nlp_process(self.df_invoice_line,'Description'\
, vectorizer=self.vectorizer, list_no_words=list_no_words)
#-------------------------------------------------------------------------
# Each vectorized column 'x' is renamed w_nlp_i
#-------------------------------------------------------------------------
dict_matching_name = dict()
for col in self.df_invoice_line.columns:
if str(col).isdigit() is True:
new_col_name = "w_nlp_"+str(col)
dict_matching_name[col] = new_col_name
self.df_invoice_line.rename(columns=dict_matching_name,inplace=True)
#-------------------------------------------------------------------------
# Description is droped from columns
#-------------------------------------------------------------------------
del(self.df_invoice_line['Description']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\\\n , UnitPrice ):\n dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\\\n , 'Quantity':Quantity, 'UnitPrice':UnitPrice}\n dict_invoice['CustomerID'] = CustomerID\n dict_invoice['Inv... | [
"0.5984977",
"0.5817351",
"0.5476201",
"0.5464979",
"0.54381603",
"0.5437341",
"0.539142",
"0.5348691",
"0.53211063",
"0.5294485",
"0.5236899",
"0.51855206",
"0.51376784",
"0.51221997",
"0.50945246",
"0.50891274",
"0.5085302",
"0.50443596",
"0.5012782",
"0.5011681",
"0.493943... | 0.7215489 | 0 |
Standardize quantitatives features. Standardizer is stored as object attribute. It will be copied into P5_SegmentClassifier object. | def feature_scale(self):
#-------------------------------------------------------------------------
# List of quantitative features to be standardized
#-------------------------------------------------------------------------
list_quant_feature = ['Quantity','UnitPrice']
self._list_quant_feature = list_quant_feature.copy()
#-------------------------------------------------------------------------
# Standardization is applied over quantitative features in list.
#-------------------------------------------------------------------------
X_std = self.std_scale.transform(self.df_invoice_line[self.list_quant_feature])
df_quant_std = pd.DataFrame(X_std, index=self.df_invoice_line.index)
#-------------------------------------------------------------------------
# Columns from standardized dataframe are renamed
#-------------------------------------------------------------------------
df_quant_std.rename(columns={0:'STD_Quantity',1:'STD_UnitPrice'}\
,inplace=True)
#-------------------------------------------------------------------------
# Standardized values dataframe is aggregated to df_invoice_line
#-------------------------------------------------------------------------
list_col_drop = ['Quantity','UnitPrice']
list_col_keep = \
[col for col in self.df_invoice_line.columns if col not in list_col_drop ]
self.df_invoice_line = self.df_invoice_line[list_col_keep]
self.df_invoice_line \
= pd.concat([self.df_invoice_line,df_quant_std], axis=1)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def standardiser(self):\n # Select only numeric features first\n\n #self.X = self.data.loc[:, self.data.columns != self.target].values\n numeric_columns = []\n for col in self.X.columns:\n if self.X[col].dtype!='object':\n numeric_columns.append(col)\n s... | [
"0.66549325",
"0.5813407",
"0.57823",
"0.57652164",
"0.57482",
"0.5628171",
"0.56227326",
"0.54852945",
"0.54692763",
"0.5413241",
"0.5376666",
"0.53717995",
"0.5229775",
"0.51795113",
"0.5171377",
"0.51348805",
"0.51237977",
"0.5088582",
"0.5076588",
"0.50762415",
"0.5056169... | 0.64846426 | 1 |
Returns market segment ID related to a customer thanks to customer invoices lines given as parameter. Features transformations are applied on data included into invoice lines. Once done, a machine learning algorithm is invocated in order to predict customer market segment. | def get_customer_marketSegment(self, df_invoice_line_customer):
#-------------------------------------------------------------------------
# Building data model
#-------------------------------------------------------------------------
self.data_transform(df_invoice_line_customer)
#-------------------------------------------------------------------------
# Customer features are built thanks to transformers.
#-------------------------------------------------------------------------
self.df_customers_features_build()
#-------------------------------------------------------------------------
# Customer market segment is predicted
#-------------------------------------------------------------------------
X_test = self._df_customers.values
y_pred = self._classifier_model.predict(X_test)
segmentID = y_pred[0]
return segmentID | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_segment(self, df_invoice_line=None):\n if df_invoice_line is not None:\n self.data_transform(df_invoice_line) \n self.df_customers_features_build() \n else:\n pass\n X_test = self._df_customers.values\n y_pred = self._classifier_model.predict(X_test)\n... | [
"0.7056929",
"0.62926954",
"0.61490464",
"0.6014562",
"0.59545195",
"0.5511189",
"0.54317766",
"0.5338872",
"0.53378683",
"0.53256035",
"0.5227046",
"0.5198791",
"0.5182063",
"0.51410466",
"0.5138717",
"0.51151025",
"0.51103526",
"0.51066583",
"0.5078583",
"0.5048637",
"0.502... | 0.81526893 | 0 |
This function creates an invoice compounding invoices lines from data given as parameters. Once done, this function computes market segment customer belongs to. If customerID is None, then a new customer identifier is created before order process to take place. | def order_process(self, customerID, list_stockCode, list_quantity\
, orderDate=None):
segmentID = -1
#-------------------------------------------------------------------------
# A new customer is created and inserted into data-set.
#-------------------------------------------------------------------------
if customerID is None:
customerID = int(self.createCustomerID())
else:
pass
#-------------------------------------------------------------------------
# A new dataframe with new invoice lines are created.
#-------------------------------------------------------------------------
df_invoice_line = self.create_customer_df_invoice_line(customerID\
, list_stockCode, list_quantity, orderDate)
#-------------------------------------------------------------------------
# Original dataframe is updated with customer invoices lines.
#-------------------------------------------------------------------------
print("order_process : shape before concat= "+str(self._df_invoice_original.shape))
self._df_invoice_original \
= pd.concat([self._df_invoice_original, df_invoice_line], axis=0)
print("order_process : shape after concat= "+str(self._df_invoice_original.shape))
#-------------------------------------------------------------------------
# All invoices lines (including new one) related to customer is retrieved
# from original dataframe.
#-------------------------------------------------------------------------
df_invoice_line_customer \
= self.get_customer_history_df_invoice_line(customerID)
#-------------------------------------------------------------------------
# When calling get_customer_marketSegment(), df_invoice_line_customer is
# concatened to the original dataframe.
#-------------------------------------------------------------------------
segmentID = self.get_customer_marketSegment(df_invoice_line_customer)
return segmentID, customerID | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_customer_df_invoice_line(self, customerID, list_stockCode\\\n , list_quantity, invoiceDate):\n \n dict_invoice = dict()\n\n dict_invoice['Quantity'] = list_quantity\n dict_invoice['StockCode'] = list_stockCode\n\n #--------------------------------------------------------------... | [
"0.65959746",
"0.6386151",
"0.6226632",
"0.5918294",
"0.58827716",
"0.5865145",
"0.5850917",
"0.58120507",
"0.57048744",
"0.57040673",
"0.563682",
"0.56352204",
"0.5545573",
"0.55451196",
"0.5510552",
"0.5419621",
"0.54099417",
"0.5400348",
"0.53597164",
"0.5323142",
"0.53154... | 0.68115205 | 0 |
Performs data processing in order data to feed prediction algorithm. | def data_process_deprecated(self, CustomerID, InvoiceDate, InvoiceNo, Description, Quantity\
, UnitPrice ):
dict_invoice = {'InvoiceDate':InvoiceDate, 'Description':Description\
, 'Quantity':Quantity, 'UnitPrice':UnitPrice}
dict_invoice['CustomerID'] = CustomerID
dict_invoice['InvoiceNo'] = InvoiceNo
df_invoice_line \
= pd.DataFrame(dict_invoice, columns=dict_invoice.keys(), index=[0])
self.data_transform(df_invoice_line)
#self.feature_rfm_encode()
self.feature_scale()
self.list_feature_drop()
self.feature_description_nlp()
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, data):\n return self.estimator.predict(data)",
"def process(self, data_batch: Any, predictions: Sequence[dict]) -> None:\n self.results.extend(_to_cpu(predictions))",
"def run_prediction(self):\r\n self.get_prediction_indices()\r\n self.walk_forward_prediction()",
... | [
"0.6911158",
"0.68740946",
"0.6579429",
"0.65545535",
"0.65444416",
"0.6536913",
"0.64836574",
"0.6466419",
"0.6466419",
"0.6391505",
"0.63673496",
"0.6256279",
"0.6249069",
"0.6240798",
"0.62286144",
"0.6211028",
"0.6206762",
"0.61867833",
"0.6183594",
"0.6156628",
"0.614365... | 0.0 | -1 |
Return the segment identifier a customers is predicted to belongs to. | def predict_segment(self, df_invoice_line=None):
if df_invoice_line is not None:
self.data_transform(df_invoice_line)
self.df_customers_features_build()
else:
pass
X_test = self._df_customers.values
y_pred = self._classifier_model.predict(X_test)
return y_pred[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_customer_marketSegment(self, df_invoice_line_customer):\n #-------------------------------------------------------------------------\n # Building data model \n #-------------------------------------------------------------------------\n self.data_transform(df_invoice_line_customer)\n\n ... | [
"0.768172",
"0.6201237",
"0.61484486",
"0.5937784",
"0.5902539",
"0.5902539",
"0.58263624",
"0.57347125",
"0.5691365",
"0.56732774",
"0.5623136",
"0.55613863",
"0.5555856",
"0.5535719",
"0.5528222",
"0.55247766",
"0.5446849",
"0.54316",
"0.5398863",
"0.5389348",
"0.52877736",... | 0.63881826 | 1 |
Returns list of stock codes from list of items descriptions. | def getStockCodeList(self, list_description=None):
list_stockCode = list()
df = self._df_invoice_original
if list_description is None:
list_stockCode = list(df.StockCode.unique())
else :
for description in list_description:
stockCode = df[df.Description==description].StockCode.unique()[0]
list_stockCode.append(stockCode)
return list_stockCode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDescriptionList(self, list_stockCode=None):\n df = self._df_invoice_original\n\n list_description = list()\n if list_stockCode is None :\n list_description = list(df.Description.unique())\n else:\n for stockCode in list_stockCode:\n description = df[df.StockCod... | [
"0.63454723",
"0.5953065",
"0.58789575",
"0.5782579",
"0.5730202",
"0.551616",
"0.5417973",
"0.5402704",
"0.5288644",
"0.5257909",
"0.5255299",
"0.5200416",
"0.5199842",
"0.51931006",
"0.5064142",
"0.50508547",
"0.50235844",
"0.49738976",
"0.4950196",
"0.49323055",
"0.4927703... | 0.7344879 | 0 |
Returns list of imtes unit price from list of stock codes. | def getUnitPriceList(self, list_stockCode):
df = self._df_invoice_original
list_unitPrice = list()
for stockCode in list_stockCode:
unitPrice = df[df.StockCode==stockCode].UnitPrice.unique()[0]
list_unitPrice.append(unitPrice)
return list_unitPrice | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getStockCodeList(self, list_description=None):\n list_stockCode = list()\n df = self._df_invoice_original\n \n if list_description is None:\n list_stockCode = list(df.StockCode.unique())\n else :\n for description in list_description:\n stockCode = df[df.Desc... | [
"0.6023552",
"0.5827989",
"0.5810261",
"0.57817996",
"0.57182974",
"0.5632579",
"0.55539757",
"0.5493873",
"0.5484011",
"0.54804665",
"0.54708654",
"0.5443625",
"0.5422661",
"0.5405019",
"0.5299616",
"0.5295281",
"0.527327",
"0.5272383",
"0.52600485",
"0.52459085",
"0.5221727... | 0.8246834 | 0 |
Returns list of items descriptions from list of stock codes. | def getDescriptionList(self, list_stockCode=None):
df = self._df_invoice_original
list_description = list()
if list_stockCode is None :
list_description = list(df.Description.unique())
else:
for stockCode in list_stockCode:
description = df[df.StockCode==stockCode].Description.unique()[0]
list_description.append(description)
return list_description | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getStockCodeList(self, list_description=None):\n list_stockCode = list()\n df = self._df_invoice_original\n \n if list_description is None:\n list_stockCode = list(df.StockCode.unique())\n else :\n for description in list_description:\n stockCode = df[df.Desc... | [
"0.65685076",
"0.566973",
"0.5336407",
"0.5295758",
"0.51715976",
"0.51711005",
"0.5148782",
"0.5139008",
"0.508993",
"0.5061929",
"0.5032067",
"0.49966037",
"0.49956048",
"0.4957243",
"0.4957215",
"0.4956653",
"0.49447757",
"0.49301252",
"0.4907753",
"0.49050713",
"0.4902797... | 0.7398652 | 0 |
Creates new dataframe with invoices lines issued from given parameters. Once done, the new dataframe is aggregated with original one. | def create_customer_df_invoice_line(self, customerID, list_stockCode\
, list_quantity, invoiceDate):
dict_invoice = dict()
dict_invoice['Quantity'] = list_quantity
dict_invoice['StockCode'] = list_stockCode
#------------------------------------------------------------------------
# Build invoiceDate from local current time
#------------------------------------------------------------------------
if invoiceDate is None:
time_struct = time.localtime()
invoiceDate = str(time_struct.tm_year)+'-'+str(time_struct.tm_mon)\
+'-'+str(time_struct.tm_mday)
invoiceDate +=' '
invoiceDate +=str(time_struct.tm_hour)+':'+str(time_struct.tm_min)\
+':'+str(time_struct.tm_sec)
invoiceDate = pd.Timestamp(invoiceDate)
else:
pass
#------------------------------------------------------------------------
# Lists initialization
#------------------------------------------------------------------------
list_customerID = list()
list_invoiceNo = list()
list_invoiceDate = list()
list_invoice_line_index = list()
#------------------------------------------------------------------------
# Increase Invoice number
#------------------------------------------------------------------------
invoiceNo = max(self._df_invoice_original.InvoiceNo)
invoiceNo += 1
#------------------------------------------------------------------------
# Get latest invoice line index value
#------------------------------------------------------------------------
invoice_line_index = max(self._df_invoice_original.index)
#------------------------------------------------------------------------
# Build lists for CustomerID, InvoiceNo, InvoiceDate
# A list of incremented indexes is built for new rows.
#------------------------------------------------------------------------
for quantity in list_quantity:
list_customerID.append(customerID)
list_invoiceNo.append(invoiceNo)
list_invoiceDate.append(invoiceDate)
invoice_line_index += 1
list_invoice_line_index.append(invoice_line_index)
dict_invoice['CustomerID'] = list_customerID
dict_invoice['InvoiceNo'] = list_invoiceNo
dict_invoice['InvoiceDate'] = list_invoiceDate
#------------------------------------------------------------------------
# Get description list from list of stock codes.
#------------------------------------------------------------------------
list_description = self.getDescriptionList(list_stockCode)
dict_invoice['Description'] = list_description
#------------------------------------------------------------------------
# Get unit price list from list of stock codes.
#------------------------------------------------------------------------
list_unitPrice = self.getUnitPriceList(list_stockCode)
dict_invoice['UnitPrice'] = list_unitPrice
#------------------------------------------------------------------------
# Dataframe with new invoices lines is created.
#------------------------------------------------------------------------
df_invoice_line \
= pd.DataFrame(dict_invoice, columns=dict_invoice.keys()\
, index=list_invoice_line_index)
return df_invoice_line | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge_purchase_invoice(self):\r\n active_id = self.env['purchase.order'].browse(self.env['purchase.order']._context.get('active_ids'))\r\n journal_id = self.env['account.journal'].search([('type', '=', 'purchase')]) \r\n active_id_count = 0\r\n active_count = 0\r\n exist_vend... | [
"0.58933586",
"0.58273345",
"0.5787141",
"0.5730418",
"0.57022715",
"0.56592727",
"0.5587117",
"0.5582993",
"0.5557562",
"0.55526274",
"0.55505604",
"0.55257595",
"0.5520605",
"0.54978883",
"0.5488627",
"0.5473216",
"0.5465187",
"0.54585433",
"0.54323196",
"0.53706306",
"0.53... | 0.61920017 | 0 |
Returns a dataframe with all invoice lines from customerID given as parameter. | def get_customer_history_df_invoice_line(self, customerID):
df_invoice_line \
= self._df_invoice_original[self._df_invoice_original.CustomerID \
== customerID]
return df_invoice_line | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_customer_df_invoice_line(self, customerID, list_stockCode\\\n , list_quantity, invoiceDate):\n \n dict_invoice = dict()\n\n dict_invoice['Quantity'] = list_quantity\n dict_invoice['StockCode'] = list_stockCode\n\n #--------------------------------------------------------------... | [
"0.6706535",
"0.637564",
"0.6069902",
"0.59014344",
"0.58117783",
"0.5807765",
"0.57650644",
"0.56994027",
"0.5648026",
"0.5595924",
"0.5531552",
"0.55192786",
"0.54935724",
"0.54334897",
"0.5429923",
"0.5344283",
"0.53376174",
"0.5330216",
"0.532311",
"0.5300056",
"0.5274764... | 0.76564085 | 0 |
Returns a list of customers that have been excluded of data sampling used for building model. By default, 10 customers identifier is returned. If customerCount value is None, or <= 0, then list of all customers that have been excluded of data sampling is returned. | def get_listCustomer_out_sample(self, customerCount=10):
if customerCount is None :
listCustomer= list(self._df_invoice_line_out_sample.CustomerID.unique())
else:
if customerCount <= 0 :
listCustomer \
= list(self._df_invoice_line_out_sample.CustomerID.unique())
else:
listCustomer \
= list(self._df_invoice_line_out_sample.CustomerID.unique()[:customerCount])
return listCustomer | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_customers_count():\n data = user_obj.get_all_customers(\"1\")\n return data",
"def pdelements_num_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(4), ctypes.c_int32(0))",
"def get_all_customer_ids():\n table = data_manager.get_table_from_file(\"sales/sales.c... | [
"0.5755343",
"0.5630526",
"0.5468172",
"0.5455088",
"0.5447088",
"0.53655386",
"0.5348006",
"0.5255881",
"0.52160037",
"0.5184504",
"0.5118855",
"0.51016146",
"0.50954854",
"0.5094486",
"0.50568765",
"0.50267607",
"0.49917355",
"0.49625525",
"0.49529928",
"0.49519303",
"0.493... | 0.7302814 | 0 |
Returns a True if a customer identifier does not belongs to dataframe used to build classifier model. | def is_customer_out_sample(self, customerID):
listCustomer = list(self._df_invoice_line_out_sample.CustomerID.unique())
is_flag = customerID in listCustomer
return is_flag | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_customer(self) -> bool:\n return self.customer_id is not None",
"def has_customer(self):\n return self.customer is not None",
"def is_customer(self):\n return self.user_type == 'C'",
"def is_label_dataframe(label, df):\n\n setdiff = set(label) - set(df.columns.tolist())\n\n ... | [
"0.661681",
"0.6462838",
"0.59257627",
"0.5741564",
"0.5628499",
"0.55574435",
"0.5537199",
"0.5509296",
"0.5507439",
"0.5461328",
"0.5405652",
"0.53825015",
"0.5370098",
"0.5365358",
"0.5328702",
"0.53143424",
"0.529039",
"0.52698815",
"0.52663475",
"0.52634853",
"0.52370775... | 0.63775516 | 2 |
Returns number of invoices from original dataset. | def get_invoice_count(self):
return self._df_invoice_original.InvoiceNo.unique().shape[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_no_of_invoices(self):\n for record in self:\n record.invoice_count = len(record.invoice_ids)",
"def get_invl_count(self):\n return self._df_invoice_original.index.unique().shape[0]",
"def invoices(self):\r\n return inv.Invoices(self)",
"def get_customer_count(self):... | [
"0.7744239",
"0.73818606",
"0.63354856",
"0.6231307",
"0.62079424",
"0.6154787",
"0.6135494",
"0.6101816",
"0.606694",
"0.6054052",
"0.60302943",
"0.5938585",
"0.5850497",
"0.58089167",
"0.57880515",
"0.56934583",
"0.568253",
"0.56357646",
"0.55606055",
"0.55459076",
"0.54968... | 0.7667302 | 1 |
Returns number of customers from original dataset. | def get_customer_count(self):
return self._df_invoice_original.CustomerID.unique().shape[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_num_of_sales_per_customer_ids():\n\n # your code",
"def get_all_customers_count():\n data = user_obj.get_all_customers(\"1\")\n return data",
"def customer_acccounting(customer_orders):",
"def pdelements_num_customers(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(4), ... | [
"0.7528541",
"0.741451",
"0.7031075",
"0.6915427",
"0.68836576",
"0.6863681",
"0.6667784",
"0.616603",
"0.61287034",
"0.6101881",
"0.59332806",
"0.5910718",
"0.5855194",
"0.58472735",
"0.58410436",
"0.5774803",
"0.57345396",
"0.57238173",
"0.572342",
"0.5717885",
"0.56658965"... | 0.8029719 | 0 |
Returns number of invoice lines (number of rows) from original dataset. | def get_invl_count(self):
return self._df_invoice_original.index.unique().shape[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_invoice_count(self):\n return self._df_invoice_original.InvoiceNo.unique().shape[0]",
"def getNumRows(self) -> int:\n ...",
"def _compute_no_of_invoices(self):\n for record in self:\n record.invoice_count = len(record.invoice_ids)",
"def getNoOfRows(self):\n retur... | [
"0.7554764",
"0.69679993",
"0.69573605",
"0.6792377",
"0.6672587",
"0.6513209",
"0.649585",
"0.6461261",
"0.63839567",
"0.63666666",
"0.62937206",
"0.62934756",
"0.62633395",
"0.6255323",
"0.62531394",
"0.6229491",
"0.62243587",
"0.62149376",
"0.6199522",
"0.61816734",
"0.614... | 0.7293856 | 1 |
Returns a json sructure built from given parameters. { | def json_all_builder(self, customer_count, invoice_count, invl_count ):
json_result = '{\n'
json_result += '\t "_results":[\n'
json_result += '\t\t{ "customer_count": "' + str(customer_count)
json_result += ', "invoice_count": "' + str(invoice_count)
json_result += ', "invl_count": "' + str(invl_count)
json_result += '}\n'
json_result += '\n\t]\n}'
return json_result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_json_string(self, **kwargs):\n ...",
"def format_data(self, params):\n return json.dumps(params)",
"def json(self):\n robot_dict = self.robot_dict()\n target_dict = self.target_dict()\n json_str = '{'\n json_str = json_str + '\"robot_obj\" : ' + json.dumps(robo... | [
"0.6857647",
"0.6645711",
"0.6554232",
"0.65235794",
"0.6426044",
"0.6354805",
"0.6256294",
"0.62538326",
"0.62132627",
"0.62050444",
"0.6196949",
"0.6165802",
"0.6154298",
"0.6132933",
"0.6096993",
"0.6083696",
"0.6067892",
"0.6067397",
"0.606541",
"0.60606605",
"0.6041364",... | 0.5603465 | 63 |
Returns JSON structure issued form dataframe content given as parameter . | def json_df_builder(self, df, marketID, RFM=None):
#-------------------------------------------------------------------------
# Extract from dataframe content to be returned
#-------------------------------------------------------------------------
str_customerID = str(df.CustomerID.unique()[0])
invoice_count = len(df.InvoiceNo.unique())
item_count = df.Quantity.sum()
invl_count = df.shape[0]
ser_incomes = df.UnitPrice * df.Quantity
incomes = ser_incomes.sum()
str_incomes = "{0:1.2F}".format(incomes)
mean_unit_price = incomes/item_count
str_mean_unit_price = "{0:1.2F}".format(mean_unit_price)
serInvoiceDate = df.InvoiceDate
str_old_date = serInvoiceDate.map(str).min()
str_new_date = serInvoiceDate.map(str).max()
#-------------------------------------------------------------------------
# Build JSON structure form content
#-------------------------------------------------------------------------
json_result = '{\n'
json_result += '\t "_results":[\n'
json_result += "{\n"
json_result += "\t\t"+" \"customerID\":"+str_customerID+"\n"
json_result += "\t\t"+",\"marketID\":"+str(marketID)+"\n"
json_result += "\t\t"+",\"invoice_count\":"+str(invoice_count)+"\n"
json_result += "\t\t"+",\"item_count\":"+str(item_count)+"\n"
json_result += "\t\t"+",\"invl_count\":"+str(invl_count)+"\n"
json_result += "\t\t"+",\"mean_unit_price\":"+str_mean_unit_price+"\n"
json_result += "\t\t"+",\"incomes\":"+str_incomes+"\n"
json_result += "\t\t"+",\"old_date\":"+str_old_date+"\n"
json_result += "\t\t"+",\"new_date\":"+str_new_date+"\n"
if RFM is not None:
json_result += "\t\t"+",\"RFM\":"+RFM+"\n"
else:
pass
json_result += "}\n"
json_result += '\n\t]\n}'
return json_result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_json(self):\n\t\treturn self._dataframe.reset_index().to_json(orient=\"records\")",
"def Mydata():\n\n stmt = db.session.query(Appsdata).statement\n df = pd.read_sql_query(stmt, db.session.bind)\n \n return jsonify(df.to_dict())",
"def get_data(dataframe,index=None):\n dflen = len(datafra... | [
"0.64720726",
"0.63688964",
"0.62659526",
"0.62423277",
"0.61731887",
"0.61728024",
"0.6165461",
"0.610696",
"0.608971",
"0.608501",
"0.60734797",
"0.6067765",
"0.60032827",
"0.5975856",
"0.59611905",
"0.5942278",
"0.5940992",
"0.59368914",
"0.5936283",
"0.5933788",
"0.591012... | 0.6871927 | 0 |
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