voltcycle.functions_and_tests.test_calculations

Complexity

Total Complexity

5

Size/Duplication

Total Lines	74
Duplicated Lines	0 %

Importance

Changes

0

5 Functions

Rating	Name	Duplication	Size	Complexity
A	test_del_potential()	0	12	1
A	test_peak_values()	0	13	1
A	test_peak_ratio()	0	11	1
A	test_peak_heights()	0	12	1
A	test_half_wave_potential()	0	11	1

import numpy as np
import pandas as pd


import calculations

def test_peak_values():
    """This function tests peak_values() function."""
    potentials = [0.500, 0.499, 0.498, 0.497]
    currents = [7.040, 6.998, 8.256, 8.286]
    potentials_d = pd.DataFrame(potentials)
    currents_d = pd.DataFrame(currents)

    assert type(calculations.peak_values(potentials_d, currents_d)) == np.ndarray, "output is not an array"
    assert calculations.peak_values(potentials_d, currents_d)[0] == 0.498, "array value incorrect for data"
    assert calculations.peak_values(potentials_d, currents_d)[2] == 0.499, "array value incorrect for data"
    assert calculations.peak_values(potentials_d, currents_d)[1] == 8.256, "array value incorrect for data"
    assert calculations.peak_values(potentials_d, currents_d)[3] == 6.998, "array value incorrect for data"
    return


def test_del_potential():
    """This function tests the del_potential function."""
    potentials = [0.500, 0.498, 0.499, 0.497]
    currents = [7.040, 6.998, 8.256, 8.286]
    potentials_d = pd.DataFrame(potentials)
    currents_d = pd.DataFrame(currents)

    assert type(calculations.del_potential(potentials_d, currents_d)) == np.ndarray, "output is not an array"
    assert calculations.del_potential(potentials_d, currents_d).shape == (1,), "output shape incorrect"
    assert calculations.del_potential(potentials_d, currents_d).size == 1, "array size incorrect"
    np.testing.assert_almost_equal(calculations.del_potential(potentials_d, currents_d), 0.001, decimal=3), "value incorrect for data"
    return


def test_half_wave_potential():
    """This function tests half_wave_potential() function."""
    potentials = [0.500, 0.498, 0.499, 0.497]
    currents = [7.040, 6.998, 8.256, 8.286]
    potentials_d = pd.DataFrame(potentials)
    currents_d = pd.DataFrame(currents)

    assert type(calculations.half_wave_potential(potentials_d, currents_d)) == np.ndarray, "output is not an array"
    assert calculations.half_wave_potential(potentials_d, currents_d).size == 1, "out not correct size"
    np.testing.assert_almost_equal(calculations.half_wave_potential(potentials_d, currents_d), 0.0005, decimal=4), "value incorrect for data"
    return


def test_peak_heights():
    """This function tests peak_heights() function."""
    potentials = [0.500, 0.498, 0.499, 0.497]
    currents = [7.040, 6.998, 8.256, 8.286]
    potentials_d = pd.DataFrame(potentials)
    currents_d = pd.DataFrame(currents)

    assert type(calculations.peak_heights(potentials_d, currents_d)) == list, "output is not a list"
    assert len(calculations.peak_heights(potentials_d, currents_d)) == 2, "output list is not the correct length"
    np.testing.assert_almost_equal(calculations.peak_heights(potentials_d, currents_d)[0], 7.256, decimal=3), "max peak height incorrect for data"
    np.testing.assert_almost_equal(calculations.peak_heights(potentials_d, currents_d)[1], 4.998, decimal=3), "min peak height incorrect for data"
    return


def test_peak_ratio():
    """This function tests peak_ratio() function."""
    potentials = [0.500, 0.498, 0.499, 0.497]
    currents = [7.040, 6.998, 8.256, 8.286]
    potentials_d = pd.DataFrame(potentials)
    currents_d = pd.DataFrame(currents)

    assert type(calculations.peak_ratio(potentials_d, currents_d)) == np.ndarray, "output is not an array"
    assert len(calculations.peak_ratio(potentials_d, currents_d)) == 1, "output list is not the correct length"
    np.testing.assert_almost_equal(calculations.peak_ratio(potentials_d, currents_d), 1.451, decimal=3), "max peak height incorrect for data"
    return