def setup_class(cls): cls.fragment = Fragment('Glucose', 'C6H12O6') cls.frag_key = 'glucose_C6H12O6' cls.label_dict = {'C13': 4} cls.label_key = 'C13_4' cls.intensities = numpy.array([2345, 5673, 456, 567.3]) cls.intensity_err = [2345, 5673, 456, 567.3]
def setup_class(cls): cls.fragment = Fragment('Glucose', 'C5H10NO2S', label_dict={ 'C13': 5, 'N15': 1 }) cls.fragment_isotope_none = Fragment('Glucose', 'C5H10NO2S', label_dict={'C13': 0}) cls.fragment_natural_isotope = Fragment('Glucose', 'C5H10NO2S', label_dict={ 'C13': 0, 'N14': 1 }) cls.fragment_mass = Fragment('Glucose', 'C6H12O6', isotracer='C13', isotope_mass=181, molecular_mass=180) cls.fragment_mode = Fragment('Glucose', 'C6H12O6', isotracer='C13', isotope_mass=181, mode='pos') cls.fragment_unlabel_nat = Fragment('Glucose', 'C6H12O6', label_dict={'C12': 5})
def test_fragmentdict_model(): cit_frag = Fragment('Citruline', 'C6H13N3O3', label_dict={'C13': 0}) fragments_dict = { 'Citruline_C13_0_N15_0': Infopacket(frag=cit_frag, data={'sample_1': np.array([0.3624])}, unlabeled=True, name='Acetic') } lab_sam_dict = {(0, 0): {'sample_1': 0.3624}} assert algo.fragmentdict_model(['C13', 'N15'], fragments_dict, lab_sam_dict) == { 'Citruline_C13_0_N15_0': Infopacket(frag=cit_frag, data={'sample_1': 0.3624}, unlabeled=True, name='Acetic') }
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 Name': {0: 'sample_1', 1: 'sample_1', 2: 'sample_1'}}) correc_inten_dict = {'sample_1': {(0, 1): np.array([ 0.0619]), (0, 0): np.array([ 0.2456]), \ (3, 0): np.array([ 0.0003]), (3, 1): np.array([ 0.0001]), (2, 1): np.array([ 0.0015]), \ (2, 0): np.array([ 0.0071]), (5, 0): np.array([ 0.]), (5, 1): np.array([ 0.60045]), \ (1, 0): np.array([ 0.06665]), (4, 1): np.array([ 0.]), (1, 1): np.array([ 0.0164]), \ (4, 0): np.array([ 0.])}} acetic_frag = Fragment('Acetic', 'H4C2O2', label_dict={'C13': 0}) fragments_dict = { 'Acetic_C13_0': iso.Infopacket(frag=acetic_frag, data={'sample_1': np.array([0.3624])}, unlabeled=True, name='Acetic') } label_list = [(0, 1), (0, 0), (3, 0), (3, 1), (2, 1), (2, 0), (5, 0), \ (5, 1), (1, 0), (4, 1), (1, 1), (4, 0)] def test_corr_matrix(): iso_tracer = 'C' with pytest.raises(KeyError):
def output_constants(): nest_dict = { out.OutKey(name='L-Methionine', formula='C5H10NO2S'): { 'N15_0_C13_5': { 'sample_1': 3.156529191428407e-08 }, 'N15_0_C13_4': { 'sample_1': -4.6457232333485042e-06 }, 'N15_0_C13_1': { 'sample_1': 0.055358607526132128 }, 'N15_0_C13_0': { 'sample_1': 0.26081351241574013 }, 'N15_0_C13_3': { 'sample_1': 0.00010623115401093528 }, 'N15_0_C13_2': { 'sample_1': 0.0045440397693722904 }, 'N15_1_C13_0': { 'sample_1': 0.064545716018271096 }, 'N15_1_C13_1': { 'sample_1': 0.013283380798059349 }, 'N15_1_C13_2': { 'sample_1': 0.00084379489120955248 }, 'N15_1_C13_3': { 'sample_1': 6.1161350126415117e-05 }, 'N15_1_C13_4': { 'sample_1': -1.8408545189878132e-06 }, 'N15_1_C13_5': { 'sample_1': 0.60114017085422033 } } } acetic_frag = Fragment('Acetic', 'H4C2O2', label_dict={'C13': 1, 'C13': 2}) fragment_dict = { 'Acetic_C13_1': Infopacket(frag='H4C2O2', data={'sample_1': 1}, unlabeled=False, name='Acetic'), 'Acetic_C13_2': Infopacket(frag='H4C2O2', data={'sample_1': 0}, unlabeled=False, name='Acetic') } metabolite_dict = {('Acetic', 'H4C2O2'): fragment_dict} metabolite_dict = { ('L-Methionine', 'C5H10NO2S'): { 'C13_1': { 'sample_1': 3.18407678e-07 }, 'C13_0': { 'sample_1': 0.48557866 } } } df = pd.DataFrame({ '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' }, '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' }, 'Intensity': { 0: 3.156529191428407e-08, 1: -4.6457232333485042e-06, 2: 0.055358607526132128, 3: 0.26081351241574013, 4: 0.00010623115401093528, 5: 0.0045440397693722904, 6: 0.064545716018271096, 7: 0.013283380798059349, 8: 0.00084379489120955248, 9: 6.1161350126415117e-05, 10: -1.8408545189878132e-06, 11: 0.60114017085422033 }, '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' }, 'Label': { 0: 'N15_0_C13_5', 1: 'N15_0_C13_4', 2: 'N15_0_C13_1', 3: 'N15_0_C13_0', 4: 'N15_0_C13_3', 5: 'N15_0_C13_2', 6: 'N15_1_C13_0', 7: 'N15_1_C13_1', 8: 'N15_1_C13_2', 9: 'N15_1_C13_3', 10: 'N15_1_C13_4', 11: 'N15_1_C13_5' } }) return nest_dict, df