def test_fromula_obj(self):
fstr = 'C6H12O6'
f = Formula.from_str(fstr)
self.assertIsNotNone(f)
self.assertEqual(f['C'], 6)
self.assertEqual(f['H'], 12)
self.assertEqual(f['O'], 6)
self.assertEqual(str(f), fstr)
f.remove('C5H3O12')
self.assertEqual(str(f), 'CH9')
d = {'C': 148, 'H': 122}
f.add(d)
self.assertEqual(str(f), 'C149H131')
f2 = Formula(d)
f.remove(f2)
self.assertEqual(str(f), 'CH9')
self.assertRaises(TypeError, f.add, ['C', 12, 'H', 32])
f3 = Formula.from_str('C4H12')
f4 = Formula(f3)
self.assertIsNot(f3, f4)
self.assertEqual(str(f4), 'C4H12')
f5 = f3.remove('CH', new_obj=True)
self.assertEqual(str(f3), 'C4H12')
self.assertIsNot(f5, f3)
self.assertEqual(str(f5), 'C3H11')
# test + -
i = Formula.from_str('C4H4O4')
i += 'C4H4O4'
self.assertEqual(str(i), 'C8H8O8')
j = i + 'C2H2O2'
self.assertIsNot(j, i)
self.assertEqual(str(j), 'C10H10O10')
self.assertEqual(str(i), 'C8H8O8')
def parse_metabolite_card(args):
"""
@return:
"""
filepath = args[0]
emass_path = args[1]
context = cElementTree.iterparse(filepath, events=('end', ))
metab = list()
try:
for action, elem in context:
if elem.tag == 'metabolite' and action == 'end':
f = elem.find('chemical_formula').text
metab.append(elem.find('accession').text)
metab.append(elem.find('name').text)
metab.append(f)
metab.append(elem.find('inchi').text)
metab.append(elem.find('inchikey').text[9:])
try:
metab.append(
float(elem.find('monisotopic_moleculate_weight').text))
except (ValueError, TypeError):
logging.info(
"Failed to parse monisotopic_moleculate_weight in {0}".
format(filepath))
metab.append(0.0)
try:
metab.append(
float(elem.find('average_molecular_weight').text))
except (ValueError, TypeError):
logging.info(
"Failed to parse average_molecular_weight in {0}".
format(filepath))
metab.append(0.0)
metab.append(elem.find('description').text)
# float(elem.find('description').text)
metab.append("")
# float(elem.find('description').text)
metab.append("")
# float(elem.find('description').text)
# isotopic pattern elem.find('kegg_id').text
metab.append(elem.find('kegg_id').text)
formula = Formula.from_str(f)
# add one H to have positive
f1 = formula.add('H', new_obj=True)
metab.append(get_theo_ip(emass_path, str(f1), min_rel_int=1.0))
#
f2 = formula.remove('H', new_obj=True)
metab.append(get_theo_ip(emass_path, str(f2), min_rel_int=1.0))
except (WindowsError, IOError, ValueError, TypeError) as e:
logging.warn(
"Error parsing metabolite card : {0} with following exception : \n {1}"
.format(filepath, e))
return None
return tuple(metab)
def parse_metabolite_card(args):
"""
@param filepath: str
@return:
"""
filepath = args[0]
emass_path = args[1]
context = cElementTree.iterparse(filepath, events=('end',))
metab = list()
try:
for action, elem in context:
if elem.tag == 'metabolite' and action == 'end':
f = elem.find('chemical_formula').text
metab.append(elem.find('accession').text)
metab.append(elem.find('name').text)
metab.append(f)
metab.append(elem.find('inchi').text)
metab.append(elem.find('inchikey').text[9:])
try:
metab.append(float(elem.find('monisotopic_moleculate_weight').text))
except (ValueError, TypeError):
logging.info("Failed to parse monisotopic_moleculate_weight in {}".format(filepath))
metab.append(0.0)
try:
metab.append(float(elem.find('average_molecular_weight').text))
except (ValueError, TypeError):
logging.info("Failed to parse average_molecular_weight in {}".format(filepath))
metab.append(0.0)
metab.append(elem.find('description').text)
#float(elem.find('description').text)
metab.append("")
#float(elem.find('description').text)
metab.append("")
#float(elem.find('description').text)
# isotopic pattern elem.find('kegg_id').text
metab.append(elem.find('kegg_id').text)
formula = Formula.from_str(f)
# # add one H to have positive
f1 = formula.add('H', new_obj=True)
metab.append(get_theo_ip(emass_path, str(f1), min_rel_int=1.0))
#
f2 = formula.remove('H', new_obj=True)
metab.append(get_theo_ip(emass_path, str(f2), min_rel_int=1.0))
except (WindowsError, IOError, ValueError, TypeError) as e:
logging.warn("Error parsing metabolite card : {} with following exception : \n {}".format(filepath, e))
return None
return tuple(metab)
def test_theo_ip(self):
fstr = 'C6H12O6'
f = Formula.from_str(fstr)
r = f.get_theo_ip()
self.assertIsNotNone(r)
def test_moz_bounds(self):
f2 = Peakel(260.029718526 + 1.007276, 0.0, 0.0, 260.029718526)
m, m_min, m_max = get_moz_bounds(f2, Formula.from_str('H1'), 10)
self.assertAlmostEqual(m, 260.029718526, places=2)
def assign_formula(self, features, for_adducts, with_tol_ppm=10.0):
"""
assign molecular formula to features using multiprocessing module
:param for_adducts: string adducts list
:param features: list or set ? of features
:param with_tol_ppm: mz tolerance in order to perform the look up
:return: dictionary with key: feature, value: list of metabolites
"""
m_count, not_found = 0, 0
for for_adduct in for_adducts:
formula = Formula.from_str(for_adduct)
logging.info("searching for adducts: {0}".format(str(formula)))
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
metabolites = []
if self.bank == 'hmdb':
args = [(self.HMDB_FILE, f, formula, with_tol_ppm)
for f in features]
metabolites = pool.map(search_metabolites_for,
args,
chunksize=20)
elif self.bank == 'lmsd':
args = [(self.LMSD_FILE, f, formula, with_tol_ppm)
for f in features]
metabolites = pool.map(search_lipids_for, args, chunksize=20)
elif self.bank in {'lmsd + hmdb', 'hmdb + lmsd'}:
logging.info('Searching in LMSD...')
args_lmsd = [(self.LMSD_FILE, f, formula, with_tol_ppm)
for f in features]
metabs_lmsd = pool.map(search_lipids_for,
args_lmsd,
chunksize=20)
logging.info('Searching in HMDB...')
args_hmdb = [(self.HMDB_FILE, f, formula, with_tol_ppm)
for f in features]
metabs_hmdb = pool.map(search_metabolites_for,
args_hmdb,
chunksize=20)
# merge the 2 results set
for lmsd_met, hmdb_met in izip(metabs_lmsd, metabs_hmdb):
metabolites.append(lmsd_met + hmdb_met)
# create Annotation objects
for f, metabs in izip(features, metabolites):
if not metabs:
not_found += 1
else:
m_count += len(metabs)
for_adducts_str = '[M{0}{1}]='.format(
'-' if f.polarity > 0 else '+', formula)
f.annotations += [
Annotation(m, for_adducts_str) for m in metabs
]
pool.close()
try:
pool.terminate()
except OSError:
# seen error on windows OS
pass
return m_count, not_found