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"""This module tests the baseline function.""" |
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# import functions and modules |
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import numpy as np |
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from voltcycle import file_read |
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from voltcycle import baseline |
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#Test functions: |
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def test_split(): |
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""" |
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This function tests the split function. |
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The output of the function has to be np.array. |
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Split function splits the length of input vector |
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in two. So, len of output should equal to half len |
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of input. |
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""" |
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dict_1 = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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data = file_read.data_frame(dict_1, 1) |
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vec_x = data.Potential |
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a_val, b_val = baseline.split(vec_x) |
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assert isinstance(a_val == np.ndarray), "The output type is incorrect." |
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assert isinstance(b_val == np.ndarray), "The output type is incorrect." |
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#assert len(a) int(len(x)/2), "The output should be " |
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(np.testing.assert_almost_equal(len(a_val), (len(vec_x)/2), decimal=0), |
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"Output length is incorrect") |
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(np.testing.assert_almost_equal(len(b_val), (len(vec_x)/2), decimal=0), |
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"Output length is incorrect") |
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return "Test of split function passed!" |
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def test_critical_idx(): |
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""" |
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Critical_idx returns idx of the index of the intercepts of different moving average curves. |
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Test the output if it is a single index. |
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Test if the output is integer. |
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Test if the index exist in original input. |
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""" |
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dict_1 = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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data = file_read.data_frame(dict_1, 1) |
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col_x1, col_x2 = baseline.split(data.Potential) |
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col_y1, col_y2 = baseline.split(data.Current) |
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idx = baseline.critical_idx(col_x1, col_y1) |
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assert isinstance(idx == np.int64), ("Output should be integer, but" |
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"Function is returning {}".format(type(idx))) |
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assert idx.shape == (), "This function should return single idx" |
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assert 0 < idx < len(col_x1), "Output index is out of order" |
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return "Test of critical_idx function passed!" |
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def test_sum_mean(): |
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""" |
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Target function returns the mean and sum of the given vector. |
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Expect output to be a list, with length 2. |
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Can also test if the mean is correctly calculated. |
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""" |
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dict_1 = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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data = file_read.data_frame(dict_1, 1) |
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col_x1, col_x2 = baseline.split(data.Potential) |
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a_val = baseline.sum_mean(col_x1) |
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assert isinstance(a_val == list), ("Output should be list object," |
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" but fuction is returning{}".format(type(a_val))) |
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assert len(a_val) == 2, ("length of output should be 2," |
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"but, function is returning a list with length{}".format(len(a_val))) |
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(np.testing.assert_almost_equal(a_val[1], np.mean(col_x1), decimal=3), |
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"Mean is calculated incorrectly") |
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return "Test of sum_mean function passed!" |
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def test_multiplica(): |
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""" |
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Target function returns the sum of the multilica of two given vector. |
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Expect output as np.float object. |
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""" |
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dict_1 = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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data = file_read.data_frame(dict_1, 1) |
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col_x1, col_x2 = baseline.split(data.Potential) |
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col_y1, col_y2 = baseline.split(data.Current) |
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a_val = baseline.multiplica(col_x1, col_y1) |
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assert isinstance(a_val == np.float64), ("Output should be float object," |
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" but fuction is returning{}".format(type(a_val))) |
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b_val = np.multiply(col_x1, col_y1).sum() |
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(np.testing.assert_almost_equal(a_val, b_val, decimal=3), |
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"Calculation is incorrect") |
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return "Test Passed for multiplica function!" |
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def test_linear_coeff(): |
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""" |
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Target function returns the inclination coeffecient |
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and y axis interception coeffecient m and b. |
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T |
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""" |
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x_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) |
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y_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) |
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m_val, b_val = baseline.linear_coeff(x_arr, y_arr) |
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assert m_val == 1, "Inclination coeffecient is incorrect" |
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assert b_val == 0, "Interception is incorrect" |
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return "Test passed for linear_coeff function!" |
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def test_y_fitted_line(): |
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""" |
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Target function returns the fitted baseline y. |
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Should exam if the output is correct shape, |
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correct type, and correct value. |
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""" |
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x_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) |
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m_val = 1 |
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b_val = 0 |
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y_val = baseline.y_fitted_line(m_val, b_val, x_arr) |
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if len(y_val) != len(x_arr): |
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raise ValueError("Output must have same length as input x," |
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"but have lenth {}".format(len(y_val))) |
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assert isinstance(y_val == list), "Output should be list object" |
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if np.all(y_val != x_arr): |
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raise ValueError("Fitted line y values are calculated incorrectly") |
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return "Test passed for y_fitted_line function!" |
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def test_linear_background(): |
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""" |
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Target function is wrapping function which returns |
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linear fitted line.Should exam if the output is |
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correct shape, correct type, and correct value. |
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""" |
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dict_1 = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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data = file_read.data_frame(dict_1, 1) |
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col_x1, col_x2 = baseline.split(data.Potential) |
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col_y1, col_y2 = baseline.split(data.Current) |
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y_fit = baseline.linear_background(col_x1, col_y1) |
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assert isinstance(y_fit == list), "Output should be list object" |
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if len(y_fit) != len(col_x1): |
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raise ValueError("Output must have same length as input x," |
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"but have lenth {}".format(len(y_fit))) |
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if len(y_fit) != len(col_y1): |
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raise ValueError("Output must have same length as input y," |
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"but have lenth {}".format(len(y_fit))) |
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return "Test passed for linear_background function!" |
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sabiharustam /
voltcycle
