test_models.py

"""Tests for statistics functions within the Model layer."""

import numpy as np
import numpy.testing as npt
import pytest

def test_daily_mean_zeros():
    """Test that mean function works for an array of zeros."""
    from inflammation.models import daily_mean

    test_input = np.array([[0, 0],
                           [0, 0],
                           [0, 0]])
    test_result = np.array([0, 0])

    # Need to use Numpy testing functions to compare arrays
    npt.assert_array_equal(daily_mean(test_input), test_result)


def test_daily_mean_integers():
    """Test that mean function works for an array of positive integers."""
    from inflammation.models import daily_mean

    test_input = np.array([[1, 2],
                           [3, 4],
                           [5, 6]])
    test_result = np.array([3, 4])

    # Need to use Numpy testing functions to compare arrays
    npt.assert_array_equal(daily_mean(test_input), test_result)

@pytest.mark.parametrize('data, expected_standard_deviation', [
    ([0, 0, 0], 0.0),
    ([1.0, 1.0, 1.0], 0),
    ([0.0, 2.0], 1.0)
])
def test_daily_standard_deviation(data, expected_standard_deviation):
    from inflammation.models import s_dev
    result_data = s_dev(data)['standard deviation']
    npt.assert_approx_equal(result_data, expected_standard_deviation)


def test_daily_standard_deviation_on_np_array():
    from inflammation.models import s_dev
    data = np.array([[0.0]])
    expected_std_devs = [[0.0]]
    out = s_dev(data)['standard deviation']
    npt.assert_array_almost_equal(data, expected_std_devs)