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# import functions and modules |
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import file_read |
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import baseline |
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import numpy as np |
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import pandas as pd |
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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,n_cycle = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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df = file_read.data_frame(dict_1, 1) |
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x = df.Potential |
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a,b = baseline.split(x) |
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assert type(a) == np.ndarray, "The output type is incorrect." |
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assert type(b) == 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),(len(x)/2), decimal=0), "Output length is incorrect" |
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np.testing.assert_almost_equal(len(b),(len(x)/2), decimal=0), "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,n_cycle = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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df = file_read.data_frame(dict_1, 1) |
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x1,x2 = baseline.split(df.Potential) |
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y1,y2 = baseline.split(df.Current) |
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idx = baseline.critical_idx(x1,y1) |
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assert type(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(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,n_cycle = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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df = file_read.data_frame(dict_1, 1) |
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x1,x2 = baseline.split(df.Potential) |
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y1,y2 = baseline.split(df.Current) |
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a = baseline.sum_mean(x1) |
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assert type(a) == list, ("Output should be list object," |
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" but fuction is returning{}".format(type(a))) |
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assert len(a) == 2, ("length of output should be 2," |
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"but, function is returning a list with length{}".format(len(a))) |
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np.testing.assert_almost_equal(a[1],np.mean(x1), decimal=3), "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,n_cycle = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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df = file_read.data_frame(dict_1, 1) |
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x1,x2 = baseline.split(df.Potential) |
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y1,y2 = baseline.split(df.Current) |
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a = baseline.multiplica(x1,y1) |
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assert type(a) == np.float64, ("Output should be float object," |
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" but fuction is returning{}".format(type(a))) |
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b = np.multiply(x1,y1).sum() |
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(np.testing.assert_almost_equal(a,b, 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 = np.array([1,2,3,4,5,6,7,8,9]) |
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y = np.array([1,2,3,4,5,6,7,8,9]) |
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m,b = baseline.linear_coeff(x,y) |
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assert m == 1, "Inclination coeffecient is incorrect" |
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assert b == 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 = np.array([1,2,3,4,5,6,7,8,9]) |
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m = 1 |
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b = 0 |
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y = baseline.y_fitted_line(m,b,x) |
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if len(y) != len(x): |
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raise ValueError("Output must have same length as input x," |
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"but have lenth {}".format(len(y))) |
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assert type(y) == list, "Output should be list object" |
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if np.all(y != x): |
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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,n_cycle = file_read.read_file('../../data/10mM_2,7-AQDS_1M_KOH_25mVs_0.5step_2.txt') |
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df = file_read.data_frame(dict_1, 1) |
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x1,x2 = baseline.split(df.Potential) |
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y1,y2 = baseline.split(df.Current) |
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y_fit = baseline.linear_background(x1,y1) |
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assert type(y_fit) == list, "Output should be list object" |
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if len(y_fit) != len(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(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!" |
sabiharustam /
voltcycle
