How to use all_filtered method in hypothesis

Best Python code snippet using hypothesis

business_logic.py

Source:business_logic.py

1import pandas as pd2import numpy as np3import datetime4import helper_methods as helpers5from dateutil import relativedelta6from custom_exceptions import NoLeftOperandInPercentage7from chatbot.models import MONTH_NAMES8# class for storing all complex business logic9# All methods should be class method and  a data frame as parameter10class BusinessLogic:11	def __init__(self, dimensions, fact_condition, date_condition, dim_filters):12		self.dimensions = dimensions13		self.fact_condition = fact_condition14		self.agg = fact_condition['aggregation']15		self.dim_filters = dim_filters16		self.date_condition = date_condition17		self.start_date = datetime.datetime.today()18		self.end_date = datetime.datetime.strptime('2000-01-01', '%Y-%m-%d')19		for cond in date_condition:20			# Use max date in date_condition for end date and min for start date21			date = datetime.datetime.strptime(cond['CalendarDate'], '%Y-%m-%d')22			if date < self.start_date:23				self.start_date = date24			if date > self.end_date:25				self.end_date = date26	# self.curr_month = self.date.month27	# self.curr_year = self.date.year28	# self.curr_quarter = (self.curr_month - 1) // 3 + 129	# self.last_month = 12 if self.curr_month - 1 == 0 else self.curr_month - 130	# self.last_quarter = 4 if self.curr_quarter - 1 == 0 else self.curr_quarter - 131	# self.last_year = self.curr_year - 132	# self.last_month_year = self.last_year if self.curr_month - 1 == 0 else self.curr_year33	# self.last_qtr_year = self.last_year if self.curr_quarter - 1 == 0 else self.curr_year34	# # Append Q to quarter after creating last qtr and last qtr year35	# self.curr_quarter = "Q" + str(self.curr_quarter)36	# self.last_quarter = "Q" + str(self.last_quarter)37	def mom_facts(self, df):38		if self.dim_filters:39			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)40		final_df = pd.DataFrame()41		s_date = self.start_date42		e_date = self.end_date43		if e_date.strftime('%Y-%m-%d') > df['CalendarDate'].max():44			e_date = datetime.datetime.strptime(df['CalendarDate'].max(), '%Y-%m-%d')45		r = relativedelta.relativedelta(e_date, s_date)46		n_months = r.years * 12 + r.months47		# if there is no date or same date then make mom for last 6 months48		if n_months < 1:49			n_months = 650			e_date = datetime.datetime.now()51			s_date = e_date - relativedelta.relativedelta(months=6)52		# show 1 extra month for current month53		for month in range(n_months + 1):54			curr_month_year = MONTH_NAMES[s_date.month]['ShortMonthName'] + '-' + str(s_date.year)55			prev_month_year = helpers.get_prev_month_year(curr_month_year)56			curr_month_facts = helpers.safe_groupby(df[df['MonthYear'] == curr_month_year], self.dimensions + [57				'MonthYear'], self.agg)58			last_month_facts = helpers.safe_groupby(df[df['MonthYear'] == prev_month_year], self.dimensions + [59				'MonthYear'], self.agg)60			if not last_month_facts.empty:61				last_month_facts.index = curr_month_facts.index62			mom_facts = (curr_month_facts / last_month_facts).fillna(63				0) if last_month_facts.empty else 100 * curr_month_facts / last_month_facts64			mom_facts = mom_facts.add_prefix("% MOM ")65			final_df = final_df.append(pd.concat([curr_month_facts, mom_facts], axis=1, sort=False))66			s_date = s_date + relativedelta.relativedelta(months=1)67		# curr_month_facts = curr_month_facts.add_prefix(MONTH_NAMES[self.curr_month] + '-' + str(self.curr_year) + " ")68		# last_month_facts = last_month_facts.add_prefix(MONTH_NAMES[self.last_month] + '-' + str(self.last_month_year) + " ")69		# return pd.concat([curr_month_facts, last_month_facts, mom_facts], axis=1, sort=False)70		return final_df71	def qoq_facts(self, df):72		if self.dim_filters:73			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)74		final_df = pd.DataFrame()75		s_date = self.start_date76		e_date = self.end_date77		n_qtr = helpers.get_n_quarters(e_date, s_date)78		# if there is no date or same date then make qoq for last 4 quarters79		if n_qtr < 1:80			n_qtr = 481			e_date = datetime.datetime.now()82			s_date = e_date - relativedelta.relativedelta(months=n_qtr * 3)83		# use 1 extra quarter to show current quarter facts alse84		for qtr in range(n_qtr + 1):85			curr_qtr_year = MONTH_NAMES[s_date.month]['QtrName'] + '-' + str(s_date.year)86			prev_qtr_year = helpers.get_prev_qtr_year(curr_qtr_year)87			curr_qtr_facts = helpers.safe_groupby(df[df['QuarterYear'] == curr_qtr_year], self.dimensions + [88				'QuarterYear'], self.agg)89			last_qtr_facts = helpers.safe_groupby(df[df['QuarterYear'] == prev_qtr_year], self.dimensions + [90				'QuarterYear'], self.agg)91			if not last_qtr_facts.empty:92				last_qtr_facts.index = curr_qtr_facts.index93			qoq_facts = (curr_qtr_facts / last_qtr_facts).fillna(94				0) if last_qtr_facts.empty else 100 * curr_qtr_facts / last_qtr_facts95			qoq_facts = qoq_facts.add_prefix("% QOQ ")96			final_df = final_df.append(pd.concat([curr_qtr_facts, qoq_facts], axis=1, sort=False))97			s_date = s_date + relativedelta.relativedelta(months=3)98		return final_df99	def yoy_fact(self, df):100		if self.dim_filters:101			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)102		final_df = pd.DataFrame()103		s_date = self.start_date104		e_date = self.end_date105		n_year = relativedelta.relativedelta(e_date, s_date).years106		# if there is no date or same date then make yoy for last 3 years107		if n_year < 1:108			n_year = 3109			e_date = datetime.datetime.now()110			s_date = e_date - relativedelta.relativedelta(years=3)111		# add 1 to show current year sales also112		for year in range(n_year + 1):113			curr_year = s_date.year114			prev_year = curr_year - 1115			curr_year_facts = helpers.safe_groupby(df[df['Year'] == curr_year], self.dimensions + [116				'Year'], self.agg)117			last_year_facts = helpers.safe_groupby(df[df['Year'] == prev_year], self.dimensions + [118				'Year'], self.agg)119			if not last_year_facts.empty:120				last_year_facts.index = curr_year_facts.index121			yoy_facts = (curr_year_facts / last_year_facts).fillna(122				0) if last_year_facts.empty else 100 * curr_year_facts / last_year_facts123			yoy_facts = yoy_facts.add_prefix("% YoY ")124			final_df = final_df.append(pd.concat([curr_year_facts, yoy_facts], axis=1, sort=False))125			s_date = s_date + relativedelta.relativedelta(years=1)126		return final_df127	def mtd(self, df):128		if self.dim_filters:129			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)130		# get max date from the user query for mtd131		date = self.end_date if self.end_date > self.start_date else self.start_date132		curr_month_year = MONTH_NAMES[date.month]['ShortMonthName'] + '-' + str(date.year)133		mtd_facts = helpers.safe_groupby(df[(df['MonthYear'] == curr_month_year) & (134				df['CalendarDate'] <= date.strftime("%Y-%m-%d"))], self.dimensions, self.agg)135		mtd_facts = mtd_facts.add_prefix(136			date.replace(day=1).strftime("%Y-%m-%d") + ' to ' + date.strftime("%Y-%m-%d") + " ")137		return mtd_facts138	def qtd(self, df):139		if self.dim_filters:140			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)141		# get max date from the user query for qtd142		date = self.end_date if self.end_date > self.start_date else self.start_date143		curr_qtr_year = MONTH_NAMES[date.month]['QtrName'] + '-' + str(date.year)144		qtd_facts = helpers.safe_groupby(df[(df['QuarterYear'] == curr_qtr_year) & (145				df['CalendarDate'] <= date.strftime("%Y-%m-%d"))], self.dimensions, self.agg)146		qtr_first_date = datetime.datetime(date.year, (3 * int(MONTH_NAMES[date.month]['QtrName'][1:]) - 2), 1)147		qtd_facts = qtd_facts.add_prefix(148			qtr_first_date.strftime("%Y-%m-%d") + ' to ' + date.strftime("%Y-%m-%d") + " ")149		return qtd_facts150	def ytd(self, df):151		if self.dim_filters:152			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)153		# get max date from the user query for ytd154		date = self.end_date if self.end_date > self.start_date else self.start_date155		ytd_facts = helpers.safe_groupby(df[(df['Year'] == date.year) & (156				df['CalendarDate'] <= date.strftime("%Y-%m-%d"))], self.dimensions, self.agg)157		ytd_facts = ytd_facts.add_prefix(158			date.replace(day=1, month=1).strftime("%Y-%m-%d") + ' to ' + date.strftime("%Y-%m-%d") + " ")159		return ytd_facts160	def target_achievement(self, df):161		fact_condition = self.fact_condition.copy()162		if self.dim_filters:163			df = helpers.apply_dim_filters(df, dim_filters=self.dim_filters)164		if self.date_condition:165			df = helpers.apply_date_condition(df, self.date_condition)166		# add Sales in Aggregation167		if 'SalesAmount' not in fact_condition['aggregation']:168			fact_condition['aggregation']['SalesAmount'] = ['sum']169			fact_condition['conditions'] += [170				{'fact_name': 'SalesAmount sum', 'conditions': np.nan, 'fact_value': np.nan}]171		# add Target Amount in Aggregation172		if 'TargetAmount' not in fact_condition['aggregation']:173			fact_condition['aggregation']['TargetAmount'] = fact_condition['aggregation']['SalesAmount']174			fact_condition['conditions'] += [175				{'fact_name': 'TargetAmount sum', 'conditions': np.nan, 'fact_value': np.nan}]176		# add sum to sales Amount177		if 'sum' not in fact_condition['aggregation']['SalesAmount']:178			fact_condition['aggregation']['SalesAmount'] += ['sum']179			fact_condition['conditions'] += [180				{'fact_name': 'SalesAmount sum', 'conditions': np.nan, 'fact_value': np.nan}]181		# Apply fact condition182		df = helpers.apply_fact_condition(df, self.dimensions + list(self.dim_filters.keys()), fact_condition)183		sale_fact_names = [c['fact_name'] for c in fact_condition['conditions'] if c['fact_name'][0:5] == 'Sales']184		target_fact_names = [c['fact_name'] for c in fact_condition['conditions'] if c['fact_name'][0:6] == 'Target']185		# target facts for 0th name186		target_facts = pd.DataFrame(100 * df[sale_fact_names[0]] / df[target_fact_names[0]])187		target_facts.columns = ["% Target Achievement"]188		# return pd.concat([df, target_facts], axis=1, sort=False)189		return target_facts190	# TODO Need to implement again for more dynamic191	def contribution(self, df):192		# Raise Error if there is no filters193		if not self.dim_filters and not self.date_condition and not self.dimensions:194			raise NoLeftOperandInPercentage(195				"Could not apply percentage without a filter! Either apply filter in dimension or date")196		all_filtered = df.copy()197		less_filtered = df.copy()198		contribution = pd.DataFrame({'Error': ["Could not applied percentage"]})199		if (self.dim_filters or self.dimensions) and self.date_condition:200			# Do not filter lowest level dimension201			all_filtered = helpers.apply_dim_filters(all_filtered, dim_filters=self.dim_filters)202			all_filtered = helpers.apply_date_condition(all_filtered, self.date_condition)203			less_filtered = helpers.apply_date_condition(less_filtered, self.date_condition)204			all_filtered = helpers.apply_fact_condition(all_filtered, self.dimensions, self.fact_condition)205			less_filtered = helpers.apply_fact_condition(less_filtered, self.dimensions[1:], self.fact_condition)206			contribution = 100 * all_filtered / less_filtered.iloc[0]207			contribution = contribution.add_prefix("% ")208		elif len(self.dim_filters.keys()) == 1 or self.dimensions:209			# Do not filter lowest level dimension210			less_dim_filters = {k: v for k, v in self.dim_filters.items() if k != list(self.dim_filters.keys())[0]}211			all_filtered = helpers.apply_dim_filters(all_filtered, dim_filters=self.dim_filters)212			less_filtered = helpers.apply_dim_filters(less_filtered, dim_filters=less_dim_filters)213			all_filtered = helpers.apply_fact_condition(all_filtered, self.dimensions, self.fact_condition)214			less_filtered = helpers.apply_fact_condition(less_filtered, self.dimensions[1:], self.fact_condition)215			contribution = 100 * all_filtered / less_filtered.iloc[0]216			contribution = contribution.add_prefix("% ")217		elif len(self.dim_filters.keys()) > 1:218			# Do not filter lowest level dimension219			less_dim_filters = {k: v for k, v in self.dim_filters.items() if k != list(self.dim_filters.keys())[0]}220			all_filtered = helpers.apply_dim_filters(all_filtered, dim_filters=self.dim_filters)221			less_filtered = helpers.apply_dim_filters(less_filtered, dim_filters=less_dim_filters)222			all_filtered = helpers.apply_fact_condition(all_filtered, self.dimensions, self.fact_condition)223			less_filtered = helpers.apply_fact_condition(less_filtered, self.dimensions[1:], self.fact_condition)224			contribution = 100 * all_filtered / less_filtered225			contribution = contribution.add_prefix("% ")226		elif self.date_condition:227			# Do not filter first level date228			less_date_filters = self.date_condition[1:]229			more_date_filters = self.date_condition[0:1]230			if not len(self.date_condition) % 2:231				less_date_filters = self.date_condition[2:]232				more_date_filters = self.date_condition[0:2]233			all_filtered = helpers.apply_date_condition(all_filtered, more_date_filters)234			less_filtered = helpers.apply_date_condition(less_filtered, less_date_filters)235			all_filtered = helpers.apply_fact_condition(all_filtered, self.dimensions, self.fact_condition)236			less_filtered = helpers.apply_fact_condition(less_filtered, self.dimensions[1:], self.fact_condition)237			contribution = 100 * all_filtered / less_filtered238			contribution = contribution.add_prefix("% ")...

gaussian_filter_factory.py

Source:gaussian_filter_factory.py

1'''2Created on Jun 19, 20133@author: Simon4'''5from algorithms.AbstractAlgorithmFactory import AbstractAlgorithmFactory6from algorithms.image.gaussian_filter import GaussianFilter7class GaussianFilterFactory(AbstractAlgorithmFactory):8    '''9    Factory class for Linear Regression.10    Provides the functionalities specified by the AbstractAlgorithmClass.11    '''12    def __init__(self, sigma):13        '''14        Constructor15        '''16        self.sigma = sigma17    18    def get_instance(self):19        '''20        Create a Gaussian filter21        :return: Object implementing AbstractAlgorithm22        '''23        gaussian_filter = GaussianFilter(self.sigma)24        return gaussian_filter25    def aggregate(self, algs):26        '''27        Aggregates a list of GaussianFilter instances.28        :param algs list of algorithm instances29        :return same as input30        '''31        all_filtered = []32        for f in algs:33            all_filtered.extend(f.filtered)34        gaussian_filter = GaussianFilter(self.sigma)35        gaussian_filter.filtered = all_filtered36        return gaussian_filter37    38    def encode(self, alg_instance):39        return alg_instance.filtered40    41    def decode(self, encoded):42        deserialized = []43        for f in encoded:44            # create new algorithm object45            gauss_filter = GaussianFilter(self.sigma)46            gauss_filter.set_params(f)47            # append to list of algorithm objetcs48            deserialized.append(gauss_filter)...

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