def test_nist_database_to_pyteomics(self):
nist_database = nist_database_to_pyteomics(
os.path.join(self.path, "beamspy", "data", "nist_database.txt"))
self.assertEqual(nist_database["C"][0], (12.0, 1.0))
self.assertEqual(nist_database["H"][0], (1.00782503223, 1.0))
self.assertEqual(nist_database["N"][0], (14.00307400443, 1.0))
self.assertEqual(nist_database["O"][0], (15.99491461957, 1.0))
self.assertEqual(nist_database["P"][0], (30.97376199842, 1.0))
self.assertEqual(nist_database["S"][0], (31.9720711744, 1.0))
def read_compounds(filename,
separator="\t",
calculate=True,
lib_adducts=[],
filename_atoms=""):
if calculate:
path_nist_database = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'data',
'nist_database.txt')
nist_database = nist_database_to_pyteomics(path_nist_database)
df = read_csv(filename, sep=separator, float_precision="round_trip")
records = []
for index, row in df.iterrows():
record = collections.OrderedDict()
comp = pyteomics_mass.Composition(str(row.molecular_formula))
if comp:
record["composition"] = collections.OrderedDict(
(k, comp[k]) for k in order_composition_by_hill(comp.keys()))
sum_CHNOPS = sum(
[comp[e] for e in comp if e in ["C", "H", "N", "O", "P", "S"]])
record["CHNOPS"] = sum_CHNOPS == sum(list(comp.values()))
if calculate:
record["exact_mass"] = round(
pyteomics_mass.calculate_mass(formula=str(
str(row.molecular_formula)),
mass_data=nist_database), 6)
else:
record["exact_mass"] = float(row.exact_mass)
record["compound_id"] = row.compound_id
record["compound_name"] = row.compound_name
comp = pyteomics_mass.Composition(str(row.molecular_formula))
record["molecular_formula"] = composition_to_string(comp)
if "retention_time" in df.columns:
record["retention_time"] = row.retention_time
elif "rt" in df.columns:
record["retention_time"] = row.rt
if "adduct" in df.columns:
record["adduct"] = row.adduct
if lib_adducts and calculate:
record["exact_mass"] += lib_adducts.lib[row.adduct]["mass"]
records.append(record)
else:
Warning("{} Skipped".format(row))
return records
def read_molecular_formulae(filename,
separator="\t",
calculate=True,
filename_atoms=""):
if calculate:
path_nist_database = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'data',
'nist_database.txt')
nist_database = nist_database_to_pyteomics(path_nist_database)
df = read_csv(filename, sep=separator, float_precision="round_trip")
records = []
for index, row in df.iterrows():
record = collections.OrderedDict()
comp = pyteomics_mass.Composition(str(row.molecular_formula))
if comp:
record["composition"] = collections.OrderedDict(
(k, comp[k]) for k in order_composition_by_hill(comp.keys()))
sum_CHNOPS = sum(
[comp[e] for e in comp if e in ["C", "H", "N", "O", "P", "S"]])
record["CHNOPS"] = sum_CHNOPS == sum(list(comp.values()))
if calculate:
record["exact_mass"] = round(
pyteomics_mass.mass.calculate_mass(
formula=str(row.molecular_formula),
mass_data=nist_database), 6)
else:
record["exact_mass"] = float(row.exact_mass)
record.update(HC_HNOPS_rules(str(row.molecular_formula)))
record.update(lewis_senior_rules(str(row.molecular_formula)))
record["double_bond_equivalents"] = double_bond_equivalents(
record["composition"])
records.append(record)
else:
Warning("{} Skipped".format(row))
return records