def getNewEnvelope(self, atomCnt_str, jP, precDigits):
counts = []
isotope_masses = []
isotope_probs = []
atomCnt = self.formParser.parse(atomCnt_str)
for el, cnt in list(atomCnt.items()):
counts.append(cnt)
isotope_masses.append(self.isoMasses[el])
isotope_probs.append(self.isoProbs[el])
envelope = IsoSpecPy.IsoSpec(counts, isotope_masses, isotope_probs, jP)
masses, logprobs, _ = envelope.getConfsRaw()
masses = cdata2numpyarray(masses)
probs = np.exp(cdata2numpyarray(logprobs))
masses, probs = agg_spec_proper(masses, probs, precDigits)
# memoization
self.isotopicEnvelopes[(atomCnt_str, jP, precDigits)] = (masses, probs)
return masses.copy(), probs.copy()
def __getNewEnvelope(self, atomCnt_str, jP, prec_digits):
T0 = time()
counts = []
isotope_masses = []
isotope_probs = []
atomCnt = self.formParser.parse(atomCnt_str)
for el, cnt in atomCnt.items():
counts.append(cnt)
isotope_masses.append(self.iso_masses[el])
isotope_probs.append(self.iso_probs[el])
envelope = IsoSpecPy.IsoSpec(counts, isotope_masses, isotope_probs, jP)
masses, logprobs, _ = envelope.getConfsRaw()
masses = cdata2numpyarray(masses)
probs = np.exp(cdata2numpyarray(logprobs))
masses, probs = aggregate_envelopes(masses, probs, prec_digits)
# memoization#TODO get rid of it when IsoSpec using stats convolution
self.isotopicEnvelopes[(atomCnt_str, jP, prec_digits)] = (masses,
probs)
T1 = time()
self.stats['Envelopes Generation Total T'] += T1 - T0
return masses.copy(), probs.copy()
def main():
global m_proton
parser = GooeyParser(prog='IsoTool')
#parser = argparse.ArgumentParser()
parser.add_argument('Molecule', type=str, widget='FileChooser')
g = parser.add_argument_group()
isos = g.add_mutually_exclusive_group(required=True)
isos.add_argument('--terrestrial',
metavar='Terrestrial abundances',
default=os.path.join(data_folder,
'isotopes_terrestrial.csv'),
dest='isotopes')
isos.add_argument('--custom',
metavar='Custom isotope definitons',
dest='isotopes',
widget='FileChooser')
#parser.add_argument('-i', '--isotopes', metavar='Isotopes',
# help='Isotope masses and abundances', type=str, widget='FileChooser',
# default = os.path.join(folder,'isotopes_terrestrial.csv'))
parser.add_argument('-o',
'--output_folder',
metavar='Output Folder',
type=str,
widget='DirChooser',
default=folder)
parser.add_argument(
'-z',
'--charge',
metavar='Charge',
help='z, adds protons and recalculates m/z accordingly',
default=1,
dest='z',
type=int)
parser.add_argument('-r',
'--resolution',
metavar='Resolution',
help='m/z*1/FWHM, determines Gaussian peak width',
type=int,
default=40000)
parser.add_argument('-bs',
'--binsize',
help='for summing Gaussian peaks of isotopologues',
type=float,
default=0.002)
parser.add_argument('-ps',
'--points',
help='no. of points for Gaussian peak',
type=int,
default=250)
parser.add_argument('-dmz',
'--delta_mz',
metavar='Delta m/z',
help='two sided mass offset for Gaussian peaks',
type=float,
default=0.05)
parser.add_argument(
'-p',
'--proba',
metavar='Probability Cutoff',
help='Cumulative probability threshold for isotopologue calculations',
type=float,
default=0.9999)
args = parser.parse_args()
isotopes = pd.read_csv(args.isotopes, index_col='element_symbol').dropna()
mol = pd.read_csv(args.Molecule).set_index('element')
mol.loc['H', 'n'] += args.z
elements = [
e for e in mol.reset_index().element.values if mol.loc[e].n > 0
]
atoms = [n for n in mol.n.values if n > 0]
masses = []
probs = []
for e in elements:
try:
assert e in isotopes.index
temp_masses = tuple(isotopes.loc[e].atomic_mass.values)
masses.append(temp_masses)
temp_probs = tuple(isotopes.loc[e].abundance.values)
probs.append(temp_probs)
except:
raise ValueError('Element %s has no defined isotopes' % e)
pass
i = IsoSpecPy.IsoSpec(atoms, masses, probs, args.proba)
confs = i.getConfs()
confs = pd.DataFrame(confs).transpose()
confs.columns = ['mz', 'p', 'isotopologue']
confs.p = confs.p.astype(np.float64).apply(np.exp)
confs.mz = (confs.mz - m_electron) / args.z
confs = confs.sort_values('mz')
f = isotopes.loc[elements]
iso_cols = [
''.join([str(y), str(x)]) for (x, y) in zip(f.index, f.mass_number)
]
temp_df = pd.DataFrame.from_records(confs.isotopologue, columns=iso_cols)
confs = pd.concat(
[
confs[['mz', 'p']].reset_index(drop=True),
temp_df.reset_index(drop=True)
],
axis=1,
)
print(confs.head().to_string())
spectrum = peak_shaper(confs[['mz', 'p']].values,
args.resolution,
delta_mz=args.delta_mz,
binsize=args.binsize,
no_points=args.points,
normalize=True,
process_binning=True)
spectrum = pd.DataFrame(spectrum, columns=['mz', 'int'])
confs.to_csv(os.path.join(args.output_folder, 'centroids.csv'),
index=False)
spectrum.to_csv(os.path.join(args.output_folder, 'spectrum.csv'),
index=False)
print "Number of Protium atoms:", confs[0][2][0][0]
print "Number of Deuterium atoms", confs[0][2][0][1]
print "Number of O16 atoms:", confs[0][2][1][0]
print "Number of O17 atoms:", confs[0][2][1][1]
print "Number of O18 atoms:", confs[0][2][1][2]
print
print "Now what if both isotopes of hydrogen were equally probable, while prob. of O16 was 50%, O17 at 30% and O18 at 20%?"
hydrogen_probs = (0.5, 0.5)
oxygen_probs = (0.5, 0.3, 0.2)
hydrogen_masses = (1.00782503207, 2.0141017778)
oxygen_masses = (15.99491461956, 16.99913170, 17.9991610)
atom_counts = (2, 1)
i = IsoSpecPy.IsoSpec(atom_counts, (hydrogen_masses, oxygen_masses),
(hydrogen_probs, oxygen_probs), 0.9)
print "The isotopologue set containing at least 0.9 probability has", len(
i), "element(s)"
confs = i.getConfs()
print "The first configuration has the following parameters:"
print "Mass:", confs[0][0]
print "log-prob:", confs[0][1]
print "probability:", exp(confs[0][1])
print "Number of Protium atoms:", confs[0][2][0][0]
print "Number of Deuterium atoms", confs[0][2][0][1]
print "Number of O16 atoms:", confs[0][2][1][0]
print "Number of O17 atoms:", confs[0][2][1][1]
print "Number of O18 atoms:", confs[0][2][1][2]
def _cal_isotopologues(self, formula_dict, min_abundance, current_abundance, ms_dynamic_range):
'''
primary function to look for isotopologues based on a monoisotopic molecular formula
INPUT {'C':10, 'H', 20, 'O', 2, etc} Atomic labels need to follow Atoms class atoms labels
This function needs to be expanded to include the calculation of resolving power
and plot the results.
* use this function at runtime during the molecular identification algorithm
only when a positive ID is observed to the monoisotopic ion
* use this function to simulate mass spectrum
(needs resolving power calculation to be fully operational)
last update on 05-26-2020, Yuri E. Corilo
* it might break when adding non-conventional atoms (not yet tested)
* it needs speed optimization; update: (Using IsoSpeccPy, a C Library (fast and accurate))
https://github.com/MatteoLacki/IsoSpec
'''
# updated it to reflect min possible mass peak abundance
cut_off_to_IsoSpeccPy = 1-(1/ms_dynamic_range)
#print("cut_off_to_IsoSpeccPy", cut_off_to_IsoSpeccPy, current_abundance, min_abundance, ms_dynamic_range)
#print(cut_off_to_IsoSpeccPy)
atoms_labels = (atom for atom in formula_dict.keys() if atom != Labels.ion_type and atom != 'H')
atoms_count = []
masses_list_tuples = []
props_list_tuples = []
all_atoms_list = []
for atom_label in atoms_labels:
if not len(Atoms.isotopes.get(atom_label))>1:
'This atom_label has no heavy isotope'
atoms_count.append(formula_dict.get(atom_label))
mass = Atoms.atomic_masses.get(atom_label)
prop = Atoms.isotopic_abundance.get(atom_label)
masses_list_tuples.append([mass])
props_list_tuples.append([prop])
all_atoms_list.append(atom_label)
else:
isotopes_label_list = Atoms.isotopes.get(atom_label)[1]
if len(isotopes_label_list) > 1:
'This atom_label has two or more heavy isotope'
isotopos_labels = [i for i in isotopes_label_list]
else:
'This atom_label only has one heavy isotope'
isotopos_labels = [isotopes_label_list[0]]
#all_atoms_list.extend(isotopos_labels)
isotopos_labels = [atom_label] + isotopos_labels
all_atoms_list.extend(isotopos_labels)
masses = [Atoms.atomic_masses.get(atom_label) for atom_label in isotopos_labels]
props = [Atoms.isotopic_abundance.get(atom_label) for atom_label in isotopos_labels]
atoms_count.append(formula_dict.get(atom_label))
masses_list_tuples.append(masses)
props_list_tuples.append(props)
iso = IsoSpecPy.IsoSpec(atoms_count,masses_list_tuples,props_list_tuples, cut_off_to_IsoSpeccPy)
conf = iso.getConfs()
masses = conf[0]
probs = exp(conf[1])
molecular_formulas = conf[2]
#print('conf', conf)
#print('probs', conf[1])
new_formulas = []
for isotopologue_index in range(len(iso)):
#skip_mono_isotopic
formula_list = molecular_formulas[isotopologue_index]
new_formula_dict = dict(zip(all_atoms_list, formula_list))
new_formula_dict[Labels.ion_type] = formula_dict.get(Labels.ion_type)
if formula_dict.get('H'):
new_formula_dict['H'] = formula_dict.get('H')
new_formulas.append({x:y for x,y in new_formula_dict.items() if y!=0})
# formula_dict in new_formulas check if monoisotopic is being returned
if new_formulas:# and formula_dict in new_formulas:
#print(conf)
#print(new_formulas)
#print(atoms_count)
#print(all_atoms_list)
#print(masses_list_tuples)
#print(props_list_tuples)
# find where monoisotopic is
index_mono = new_formulas.index(formula_dict)
# calculate ratio iso/mono
probs = list(probs/probs[index_mono])
# delete the monoisotopic
del probs[index_mono]
del new_formulas[index_mono]
#print('probs_exp', probs)
for formulas, prob in zip(new_formulas, probs):
theor_abundance = current_abundance* prob
if theor_abundance > min_abundance:
#print(prob, theor_abundance, current_abundance)
yield (formulas, prob)