def test_addition(self): test_data = CsvReader('Tests/Data/test_addition.csv').data for row in test_data: self.assertEqual( self.calculator.add(int(row['Value 1']), int(row['Value 2'])), int(row['Result'])) self.assertEqual(self.calculator.result, int(row['Result']))
def test_multiply(self): test_data = CsvReader('Tests/Data/test_multiplication.csv').data for row in test_data: self.assertEqual( self.calculator.multiply(row['Value 1'], row['Value 2']), int(row['Result'])) self.assertEqual(self.calculator.result, int(row['Result']))
def test_var_pop_proportion(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual( self.statistics.variance_pop_proportion(test_data), float(row['Var Population Prop']))
def test_median(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data pprint(test_data) for row in test_result: self.assertEqual(self.statistics.med(test_data), float(row['Median']))
def test_division_calculator(self): test_Data = CsvReader("/Tests/Data/UnitTestDivision.csv").data pprint(test_Data) for row in test_Data: self.assertEqual( self.calculator.divide(row["Value 1"], row["Value 2"]), float(row["Result"])) self.assertEqual(self.calculator.result, float(row["Result"]))
def pop_correlation_coefficient(data): x_data = CsvReader('Tests/Data/female_height.csv').data y_data = CsvReader('Tests/Data/male_height.csv').data x = pop_stand_dev(x_data) y = pop_stand_dev(y_data) divisor = multiplication(x, y) z = len(data) # Covariance calculation: a = subtraction(data, sampleMean) b = subtraction(data, population_mean) c = multiplication(a, b) covariance = division(z, (sum(c))) # Population Correlation Coefficient calculation: d = division(divisor, covariance) return d
def test_square_root(self): test_data = CsvReader('Tests/Data/test_square_root.csv').data for row in test_data: self.assertAlmostEqual(self.calculator.sqrt(int(row['Value 1'])), float(row['Result']), places=4) self.assertAlmostEqual(self.calculator.result, float(row['Result']), places=4)
def test_p_value(self): test_data = CsvNormalReader('Tests/Data/normal_dist.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data z_score_test_result = str( self.statistics.z_score( CsvReaderStats('Tests/Data/female_height.csv').data)) actual = test_result[0]['P Value'] for test_value in test_data: expected = test_value.split(",") if z_score_test_result == expected[0]: self.assertAlmostEqual(actual, expected[1])
def test_pop_correlation_coefficient(self): test_data_f = CsvReaderStats('Tests/Data/female_height.csv').data test_data_m = CsvReaderStats('Tests/Data/male_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual( self.statistics.pop_correlation_coefficient( test_data_f, test_data_m), float(row['Population ' 'Correlation ' 'Coefficient']))
def __init__(self, filepath): super().__init__() self.data = CsvReader(filepath)
def test_sample_var_prop(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual(self.statistics.var_sam_prop(test_data), float(row['Variance of Sample Proportion']))
def test_sample_st_dev(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual(self.statistics.sample_st_dev(test_data), float(row['Sample SD']))
def test_proportion(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual(self.statistics.proportion(test_data), float(row['Proportion']))
def test_population_stand_deviation(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual(self.statistics.population_st_dev(test_data), float(row['Population SD (Female)']))
def test_z_score(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual(self.statistics.z_score(test_data), float(row['ZScore']))
def test_population_variance(self): test_data = CsvReaderStats('Tests/Data/female_height.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_result: self.assertEqual(self.statistics.population_variance(test_data), float(row['Population Variance']))
def test_mode(self): test_data = CsvReader('Tests/Data/female_data.csv').data test_result = CsvReader('Tests/Data/Results_Statistics_Calc.csv').data for row in test_data: self.assertEqual(self.statistics.mod(), float(row['Mode'])) self.assertEqual(self.statistics.result, test_result(row['Mode']))
def test_add(self): test_data = CsvReader("Tests/Data/CSV_reader.csv").data for row in test_data: self.assertEqual(row['Value 1'], '580')
def test_square(self): test_data = CsvReader('Tests/Data/test_square.csv').data for row in test_data: self.assertEqual(self.calculator.squaring(int(row['Value 1'])), int(row['Result'])) self.assertEqual(self.calculator.result, int(row['Result']))
def test_vsp(self): test_data = CsvReader("Tests/Data/datapoints.csv") answers = CsvReader("Tests/Data/answers.csv").data values = Data(test_data, 'value') x = self.statistics.vsp(values) self.assertEqual(x, x)
def test_zscore(self): test_data = CsvReader("Tests/Data/datapoints.csv") answers = CsvReader("Tests/Data/answers.csv").data values = Data(test_data, "value")
def setUp(self) -> None: self.csv_reader = CsvReader('Tests/Data/female_height.csv')
def test_CSV(self): test_data = CsvReader("Tests/Data/CSV_reader.csv").data for row in test_data: self.assertEqual(row["Value 1"], "580")