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")