def test_ppm_nacorrection():
df = pd.DataFrame({'Name': {0: 'L-Methionine', 1: 'L-Methionine'},
'Label': {0: 'C12 PARENT', 1: 'C13-label-1'},
'Intensity': {0: 0.203405, 1: 0.050069999999999996},
'Formula': {0: 'C5H10NO2S', 1: 'C5H10NO2S'},
'Sample': {0: 'sample_1', 1: 'sample_1'}})
eleme_corr = {}
na_corr_dict, corr_dict = na_correction(df, ['C13', 'N15'], 40, na_dict, eleme_corr, intensity_col=INTENSITY_COL,
autodetect=True)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [-0.0053063306434838163, 0.32858944654474742]
assert list(na_corr_df['NA corrected']) == output_list
assert corr_dict == {'L-Methionine': {'C': [], 'N': ['O17', 'O18']}}
def test_na_corr_multi_trac_indist():
df = pd.DataFrame({'Name': {0: 'L-Methionine', 1: 'L-Methionine'},
'Label': {0: 'C12 PARENT', 1: 'C13-label-1'},
'Intensity': {0: 0.203405, 1: 0.050069999999999996},
'Formula': {0: 'C5H10NO2S', 1: 'C5H10NO2S'},
'Sample': {0: 'sample_1', 1: 'sample_1'}})
eleme_corr = {'C': ['H']}
na_corr_dict, corr_dict = na_correction(df, ['C13', 'N15'],'',
na_dict, eleme_corr,
intensity_col=INTENSITY_COL,autodetect=False)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [-0.045478760757226011, 0.40215440095785504]
assert na_corr_df['NA corrected'].tolist() == output_list
def test_na_corr_single_tracer():
df = pd.DataFrame({'Name': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'},
'Parent': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'},
'Label': {0: 'C12 PARENT', 1: 'C13-label-1', 2: 'C13-label-2'},
'Intensity': {0: 0.3624, 1: 0.040349999999999997, 2: 0.59724999999999995},
'Formula': {0: 'H4C2O2', 1: 'H4C2O2', 2: 'H4C2O2'},
'info2': {0: 'culture_1', 1: 'culture_1', 2: 'culture_1'},
'Sample': {0: 'sample_1', 1: 'sample_1', 2: 'sample_1'}})
eleme_corr = {}
na_corr_dict, corr_dct = na_correction(df, ['C13'], '', na_dict, eleme_corr, intensity_col=INTENSITY_COL,
autodetect=False)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [0.59613019390581734, 0.0023185595567865968, 0.4015512465373961]
assert na_corr_df['NA corrected'].tolist() == output_list
def test_na_corr_single_trac_indist():
df = pd.DataFrame({'Name': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'}, \
'Parent': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'}, \
'Label': {0: 'C12 PARENT', 1: 'C13-label-1', 2: 'C13-label-2'}, \
'Intensity': {0: 0.2274, 1: 0.4361, 2: 0.25405}, \
'Formula': {0: 'H4C2O2', 1: 'H4C2O2', 2: 'H4C2O2'}, \
'info2': {0: 'culture_1', 1: 'culture_1', 2: 'culture_1'}, \
'Sample': {0: 'sample_1', 1: 'sample_1', 2: 'sample_1'}})
eleme_corr = {'C': ['H', 'O']}
na_corr_dict = na_correction(df, single_tracers, eleme_corr, na_dict)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [0.2783720710600131, 0.52118757080316813, 0.3026855833807266]
assert na_corr_df['NA corrected'].tolist() == output_list
def test_na_corr_single_trac_indist():
df = pd.DataFrame({'Name': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'}, \
'Parent': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'}, \
'Label': {0: 'C12 PARENT', 1: 'C13-label-1', 2: 'C13-label-2'}, \
'Intensity': {0: 0.2274, 1: 0.4361, 2: 0.25405}, \
'Formula': {0: 'H4C2O2', 1: 'H4C2O2', 2: 'H4C2O2'}, \
'info2': {0: 'culture_1', 1: 'culture_1', 2: 'culture_1'}, \
'Sample': {0: 'sample_1', 1: 'sample_1', 2: 'sample_1'}})
eleme_corr = {'C': ['H', 'O']}
na_corr_dict, corr_dict = na_correction(df, ['C13'], '', na_dict, eleme_corr, intensity_col=INTENSITY_COL,
autodetect=False)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [0.1961957568543734, 0.48572975053068485, 0.3035536244690365]
assert na_corr_df['NA corrected'].tolist() == output_list
def test_na_corr_single_tracer():
df = pd.DataFrame({'Name': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'}, \
'Parent': {0: 'Acetic', 1: 'Acetic', 2: 'Acetic'}, \
'Label': {0: 'C12 PARENT', 1: 'C13-label-1', 2: 'C13-label-2'}, \
'Intensity': {0: 0.3624, 1: 0.040349999999999997, 2: 0.59724999999999995}, \
'Formula': {0: 'H4C2O2', 1: 'H4C2O2', 2: 'H4C2O2'}, \
'info2': {0: 'culture_1', 1: 'culture_1', 2: 'culture_1'}, \
'Sample': {0: 'sample_1', 1: 'sample_1', 2: 'sample_1'}})
eleme_corr = {}
na_corr_dict = na_correction(df, single_tracers, eleme_corr, na_dict)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [
0.59613019390581723, 0.0023185595567866008, 0.40155124653739621
]
assert na_corr_df['NA corrected'].tolist() == output_list
def test_na_corr_multi_trac():
df = pd.DataFrame({
'Name': {
0: 'L-Methionine',
1: 'L-Methionine',
2: 'L-Methionine',
3: 'L-Methionine',
4: 'L-Methionine',
5: 'L-Methionine',
6: 'L-Methionine',
7: 'L-Methionine',
8: 'L-Methionine',
9: 'L-Methionine',
10: 'L-Methionine',
11: 'L-Methionine'
},
'Parent': {
0: 'L-Methionine',
1: 'L-Methionine',
2: 'L-Methionine',
3: 'L-Methionine',
4: 'L-Methionine',
5: 'L-Methionine',
6: 'L-Methionine',
7: 'L-Methionine',
8: 'L-Methionine',
9: 'L-Methionine',
10: 'L-Methionine',
11: 'L-Methionine'
},
'Label': {
0: 'C12 PARENT',
1: 'C13-label-1',
2: 'C13-label-2',
3: 'C13-label-3',
4: 'C13-label-4',
5: 'C13-label-5',
6: 'N15-label-1',
7: 'C13N15-label-1-1',
8: 'C13N15-label-2-1',
9: 'C13N15-label-3-1',
10: 'C13N15-label-4-1',
11: 'C13N15-label-5-1'
},
'Intensity': {
0: 0.24560000000000001,
1: 0.066650000000000001,
2: 0.0071000000000000004,
3: 0.00029999999999999997,
4: 0.0,
5: 0.0,
6: 0.061899999999999997,
7: 0.016400000000000001,
8: 0.0015,
9: 0.0001,
10: 0.0,
11: 0.60045000000000004
},
'Formula': {
0: 'C5H10NO2S',
1: 'C5H10NO2S',
2: 'C5H10NO2S',
3: 'C5H10NO2S',
4: 'C5H10NO2S',
5: 'C5H10NO2S',
6: 'C5H10NO2S',
7: 'C5H10NO2S',
8: 'C5H10NO2S',
9: 'C5H10NO2S',
10: 'C5H10NO2S',
11: 'C5H10NO2S'
},
'Sample': {
0: 'sample_1',
1: 'sample_1',
2: 'sample_1',
3: 'sample_1',
4: 'sample_1',
5: 'sample_1',
6: 'sample_1',
7: 'sample_1',
8: 'sample_1',
9: 'sample_1',
10: 'sample_1',
11: 'sample_1'
}
})
eleme_corr = {}
na_corr_dict = na_correction(df, multi_tracers, eleme_corr, na_dict)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [
0.00064617771744998609, 0.3967531185142435, -9.8844997716120012e-05,
7.0170861504082293e-05, -0.00024013579424740682,
-0.00018698767698716368, 0.0030976144330255844,
-0.00048382556594077245, -2.6604348209106283e-06,
-4.9943479640610513e-06, 2.6016225533332996e-07, 0.60045010712919833
]
assert na_corr_df['NA corrected'].tolist() == output_list
def test_na_corr_multi_trac_indist():
df = pd.DataFrame({
'Name': {
0: 'L-Methionine',
1: 'L-Methionine',
2: 'L-Methionine',
3: 'L-Methionine',
4: 'L-Methionine',
5: 'L-Methionine',
6: 'L-Methionine',
7: 'L-Methionine',
8: 'L-Methionine',
9: 'L-Methionine',
10: 'L-Methionine',
11: 'L-Methionine'
},
'Parent': {
0: 'L-Methionine',
1: 'L-Methionine',
2: 'L-Methionine',
3: 'L-Methionine',
4: 'L-Methionine',
5: 'L-Methionine',
6: 'L-Methionine',
7: 'L-Methionine',
8: 'L-Methionine',
9: 'L-Methionine',
10: 'L-Methionine',
11: 'L-Methionine'
},
'Label': {
0: 'C12 PARENT',
1: 'C13-label-1',
2: 'C13-label-2',
3: 'C13-label-3',
4: 'C13-label-4',
5: 'C13-label-5',
6: 'N15-label-1',
7: 'C13N15-label-1-1',
8: 'C13N15-label-2-1',
9: 'C13N15-label-3-1',
10: 'C13N15-label-4-1',
11: 'C13N15-label-5-1'
},
'Intensity': {
0: 0.203405,
1: 0.050069999999999996,
2: 0.093910000000000007,
3: 0.02402,
4: 0.02051,
5: 0.0051600000000000005,
6: 0.0028300000000000001,
7: 0.00065499999999999998,
8: 0.00022499999999999999,
9: 8.4999999999999993e-05,
10: 5.0000000000000004e-06,
11: 0.48973500000000003
},
'Formula': {
0: 'C5H10NO2S',
1: 'C5H10NO2S',
2: 'C5H10NO2S',
3: 'C5H10NO2S',
4: 'C5H10NO2S',
5: 'C5H10NO2S',
6: 'C5H10NO2S',
7: 'C5H10NO2S',
8: 'C5H10NO2S',
9: 'C5H10NO2S',
10: 'C5H10NO2S',
11: 'C5H10NO2S'
},
'Sample': {
0: 'sample_1',
1: 'sample_1',
2: 'sample_1',
3: 'sample_1',
4: 'sample_1',
5: 'sample_1',
6: 'sample_1',
7: 'sample_1',
8: 'sample_1',
9: 'sample_1',
10: 'sample_1',
11: 'sample_1'
}
})
eleme_corr = {'C': ['H']}
na_corr_dict = na_correction(df, multi_tracers, eleme_corr, na_dict)
na_corr_df = convert_to_df(na_corr_dict, False, colname='NA corrected')
output_list = [
-0.075954704287110375, 0.40215442709008198, 0.016489750088414929,
-0.0032216908381774477, -0.031526293342053771, 0.15881269215932281,
-0.0064943180078375707, 0.0011640245763342482, 0.030179296768192233,
-0.0060388880912154832, 0.0063239074820523374, 0.59811258028877923
]
assert na_corr_df['NA corrected'].tolist() == output_list