def test_invalidate():
"""Test whether the invalidate function turns the value of the 'Valid' column to False in a given set of well ids, columns and rows."""
example_platemap = pd.read_csv(inval_platemap, index_col=[0]) # read in an example platemap with invalidated well ids, rows and columns
test_object = fa.read_in_envision(data_csv=plate_2_repeat, platemap_csv=plate_map_file, data_type='plate', size=384) # read in actual data and plate map
test_object.invalidate(wells=['A2', 'B3', 'E4'], rows=['C', 'G'], columns=[7,8,12,20]) # invalidate specific well ids, rows and columns
pd.testing.assert_frame_equal(test_object.plate_map, example_platemap, check_dtype=False) # compare the two dfs without checking the data types because the example df was not read in using the read_in_envision function
def test_background_correct():
"""Tests whether the background correction function performs correct calculations to get the background corrected values of p and s channel signal"""
with open(HsHis6_PEX5C_vs_HsPEX5C_p_s_corrected, 'rb') as file: # load the list with expexcted data frames from .pkl file
expected_list = pickle.load(file)
test_object = fa.read_in_envision(data_csv=HsHis6_PEX5C_vs_HsPEX5C, platemap_csv=Hs_His6_PEX5C_vs_HsPEX5C_platemap, data_type='plate', size=384) # execute the tested function
test_object.background_correct()
# assert the p_corrected and s_corrected data frames are the same as the reference data frames
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['p_corrected'], expected_list[0], atol=1E-6)
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['s_corrected'], expected_list[1], atol=1E-6)
def test_calculate_r_i():
"""Tests whether the calculate_r_I function performs correct calculations to get the raw and background corrected values of intensity and anisotropy"""
with open(HsHis6_PEX5C_vs_HsPEX5C_calc_r_I, 'rb') as file: # load the list with expexcted data frames from .pkl file
expected_list = pickle.load(file)
test_object = fa.read_in_envision(data_csv=HsHis6_PEX5C_vs_HsPEX5C, platemap_csv=Hs_His6_PEX5C_vs_HsPEX5C_platemap, data_type='plate', size=384)
test_object.background_correct()
test_object.calculate_r_i(correct=True, plot_i=False, thr=50)
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['i_raw'], expected_list[0], atol=1E-6)
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['r_raw'], expected_list[1], atol=1E-6)
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['i_corrected'], expected_list[2], atol=1E-6)
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['r_corrected'], expected_list[3], atol=1E-6)
pd.testing.assert_frame_equal(test_object.data_dict['repeat_1']['data']['i_percent'], expected_list[4], atol=1E-6)
def _get_testing_data(data_csv, platemap_csv, data_type, size, pkl_file):
with open(pkl_file, 'rb') as file: # load the list with expexcted data frames from .pkl file
expected_list = pickle.load(file)
actual_output = fa.read_in_envision(data_csv = data_csv, platemap_csv=platemap_csv, data_type=data_type, size=size) # execute the tested function
actual_g = actual_output.g_factor
actual_list = []
for repeat in actual_output.data_dict.values(): # unpack the dictionary with df from the tested function
metadata, data = repeat.values()
p_channel, s_channel = data.values()
actual_list.append(metadata)
actual_list.append(p_channel)
actual_list.append(s_channel)
return actual_list, expected_list, actual_g
def test_no_backg_subt():
"""Test for an error raised if the calculate_r_i function is called with the correct parameter as True prior to the background subtraction"""
test_object = fa.read_in_envision(data_csv=HsHis6_PEX5C_vs_HsPEX5C, platemap_csv=Hs_His6_PEX5C_vs_HsPEX5C_platemap, data_type='plate', size=384)
test_object.calculate_r_i(correct=True, plot_i=False, thr=80)
def test_invalidate_error():
"""Test whether the 'invalidate' function raises an error if no arguments are passed."""
test_object = fa.read_in_envision(data_csv=plate_2_repeat, platemap_csv=plate_map_file, data_type='plate', size=384)
test_object.invalidate() # execute the invalidate function without specifying well ids, rows or columns to be invalidated
def test_incorrect_data_type():
"""Test for error if data_type argument is neither plate nor list."""
test_object = fa.read_in_envision(data_csv=list_A, platemap_csv=plate_map_file, data_type='typo', size=384)
def test_incorrect_data_type_plate():
"""Test for error if data_type = plate but raw data file is in list format."""
test_object = fa.read_in_envision(data_csv=list_A, platemap_csv=plate_map_file, data_type='plate', size=384)
def test_incorrect_data_type_list():
"""Test for error if data_type = list but raw data file is in plate format."""
test_object = fa.read_in_envision(data_csv=plate_1, platemap_csv=plate_map_file, data_type='list', size=384)
def test_plate_size_error():
"""Test for error raised if size is not 384 or 96."""
test_object = fa.read_in_envision(data_csv=plate_1, platemap_csv=plate_map_file, data_type='plate', size=100)