def test_pop():
# Check if the length of the dataframe is as expected
assert len(generate_virtual_pop(100, "Height", np.random.normal, 0,
1)) == 100
assert len(generate_virtual_pop(200, "Height", np.random.poisson,
10)) == 200
assert len(generate_virtual_pop(50, "Height", np.random.gamma, 2, 2)) == 50
def test_draw_samples():
"""
This function tests the draw_samples function.
Returns:
--------
None
All tests should pass and no asserts or errors should be raised.
"""
# Create helper data
pop = generate_virtual_pop(10, "Height", np.random.normal, 0, 1)
pop1 = generate_virtual_pop(100000, "Height", np.random.exponential, 0)
pop2 = generate_virtual_pop(100, "Height", np.random.normal, 0, 1)
pop3 = generate_virtual_pop(10, "Height", np.random.poisson, 0)
pop4 = generate_virtual_pop(100, 42, np.random.normal, 0, 1)
# Check that column names are as expected
assert (all(
draw_samples(pop1, 1, [1]).columns.values == (
["replicate", "Height", "size", "rep_size"])))
# Check that data frame has correct row and column length
assert (len(draw_samples(pop2, 2, [2, 3, 4])) == 18)
assert (len(draw_samples(pop3, 2, [2, 3]).columns) == 4)
# Check that non valid data frame will raise TypeError
with raises(TypeError, match="Population input is not a valid data frame"):
draw_samples(np.array([1, 2, 3]), 1, [2, 3])
# Check that if pop input is not string TypeError will be raised
with raises(TypeError,
match="Population input name "
"is not a valid string"):
draw_samples(pop4, 2, [5, 10, 15, 20])
# Check that if rep input is non-integer TypeError is raised
with raises(TypeError,
match="Number of replications "
"input must be an integer value"):
draw_samples(pop, 1.5, [2, 3])
# Check that if rep input is 0 or less ValueError is raised
with raises(ValueError,
match="Number of replications "
"must be greater than 0"):
draw_samples(pop, 0, [2, 3])
# Check if not all sample array values are integers raise TypeError
with raises(TypeError,
match="At least one value in sample "
"size array is not an integer value"):
draw_samples(pop, 3, [2, 3, 4.5])
def test_not_func():
# Check if the not exist function will give a AttributeError
with pytest.raises(AttributeError):
generate_virtual_pop(100, "Height", np.random.nomal, 0, 1)
with pytest.raises(AttributeError):
generate_virtual_pop(100, "Height", np.random.norma, 0, 1)
with pytest.raises(AttributeError):
generate_virtual_pop(200, "Height", np.random.ormal, 0, 1)
def test_neg_size():
# Check if the negative size will give a ValueError
with pytest.raises(ValueError):
generate_virtual_pop(-100, "Height", np.random.normal, 0, 1)
with pytest.raises(ValueError):
generate_virtual_pop(-10, "Height", np.random.normal, 0, 1)
with pytest.raises(ValueError):
generate_virtual_pop(-1, "Height", np.random.normal, 0, 1)
def test_not_int():
# Check if the float number size will give a ValueError
with pytest.raises(TypeError,
match='Size of population must be a positive integer'):
generate_virtual_pop(10.5, "Height", np.random.normal, 0, 1)
with pytest.raises(TypeError,
match='Size of population must be a positive integer'):
generate_virtual_pop(100.5, "Height", np.random.normal, 0, 1)
with pytest.raises(TypeError,
match='Size of population must be a positive integer'):
generate_virtual_pop(20.5, "Height", np.random.normal, 0, 1)
def test_not_right_number():
# Check if the incorrect number of parameters will give a TypeError
with pytest.raises(TypeError,
match='Please enter a valid distribution '
'function with correct number of parameters for '
'the distribution function'):
generate_virtual_pop(100, "Height", np.random.normal, 0, 1, 2)
with pytest.raises(TypeError,
match='Please enter a valid distribution '
'function with correct number of parameters for '
'the distribution function'):
generate_virtual_pop(200, "Height", np.random.poisson, 10, 10)
with pytest.raises(TypeError,
match='Please enter a valid distribution '
'function with correct number of parameters for '
'the distribution function'):
generate_virtual_pop(50, "Height", np.random.gamma, 2, 2, 3)
import numpy as np
from samplingsimulatorpy.generate_virtual_pop import generate_virtual_pop
from samplingsimulatorpy.draw_samples import draw_samples
from samplingsimulatorpy.stat_summary import stat_summary
import pytest
import pandas as pd
# Get the helper data
pop1 = generate_virtual_pop(100, "Height", np.random.normal, 0, 1)
pop2 = generate_virtual_pop(10, "Height", np.random.poisson, 0)
samples1 = draw_samples(pop1, 2, [2, 3, 4])
samples2 = draw_samples(pop2, 2, [2, 3])
def test_pop():
# Test if empty dataframes will get a TypeError
with pytest.raises(TypeError,
match="Population input is not a valid data frame"):
stat_summary([], [], [np.mean, np.std])
# Test if a empty dataframe will get a TypeError
with pytest.raises(TypeError,
match="Samples input is not a valid data frame"):
stat_summary(pop1, [], [np.mean, np.std])
# Test if not a wrong function will get a TypeError
with pytest.raises(TypeError, match="Parameter input is not a valid list"):
stat_summary(pop1, samples1, 'np.mean')
def test_value():
# Test if the output of the dataframe is as expected
assert (len(stat_summary(pop1, samples1, [np.mean, np.std])) == 2)