def get_atom_vector(self):
"""
Returns a NumPy row array describing the number of atoms
from each element (the column index is the atomic number
of that element).
The first column (index=0) contains the number of electrons.
"""
atom_bag = self.get_atom_bag()
if not atom_bag:
return None
atom_vector = np.zeros((Molecule.GetNumberOfElements() + 1),
dtype='int')
for elem, count in atom_bag.iteritems():
if elem in ['R', 'X']:
return None # wildcard compound!
an = Molecule.GetAtomicNum(elem)
if not an:
logging.warning("Unsupported element in (C%05d): %s",
(self.cid, elem))
return None
atom_vector[an] = count
atom_vector[0] = self.get_num_electrons()
return atom_vector
def get_num_electrons(self):
"""Return the putative number of electrons in the molecule."""
mol = self.GetMolecule()
if mol:
return mol.GetNumElectrons()
# if there is no InChI assume that self.formula is correct and that
# the charge is 0.
atom_bag = self.get_atom_bag()
if not atom_bag:
return None
n_protons = 0
for elem, count in atom_bag.iteritems():
n_protons += count * Molecule.GetAtomicNum(elem)
return n_protons
def ConvertFormation2Reaction(self, output_fname):
logging.info("Converting all formation energies to reactions")
output_csv = csv.writer(open(output_fname, 'w'))
# keep the format used for TECRDB
output_csv.writerow(
('ref', 'ID', 'method', 'eval', 'EC', 'name', 'kegg_reaction',
'reaction', 'dG0\'', 'T', 'I', 'pH', 'pMg'))
atom2cid = {}
for atom, (name, stoich) in KeggObservation.ATOM2ELEMENT.iteritems():
cid, _, _ = self.kegg.name2cid(name, 0)
if cid is None:
raise Exception(
"Cannot find the element %s in the KEGG database" % name)
atom2cid[atom] = (cid, stoich)
#output_csv.writerow(('element',
# 'C%05d' % cid, 'formation', 'A', '',
# 'formation of %s' % self.kegg.cid2name(cid),
# "C%05d" % cid,
# name, 0, self.T, self.I, self.pH, self.pMg))
for label in ['training', 'testing']:
ptable = PsuedoisomerTableThermodynamics.FromCsvFile(
self.FormationEnergyFileName, label=label)
for cid in ptable.get_all_cids():
pmatrix = ptable.cid2PseudoisomerMap(cid).ToMatrix()
if len(pmatrix) != 1:
raise Exception("multiple training species for C%05d" %
cid)
nH, _charge, nMg, dG0 = pmatrix[0]
diss_table = dissociation.GetDissociationTable(cid, False)
if diss_table is None:
continue
diss_table.SetFormationEnergyByNumHydrogens(dG0, nH, nMg)
dG0_prime = diss_table.Transform(pH=self.pH,
I=self.I,
pMg=self.pMg,
T=self.T)
ref = ptable.cid2SourceString(cid)
atom_bag = self.kegg.cid2atom_bag(cid)
if not atom_bag:
continue
ne = self.kegg.cid2num_electrons(cid)
elem_ne = 0
sparse = {cid: 1}
for elem, count in atom_bag.iteritems():
if elem == 'H':
continue
elem_ne += count * Molecule.GetAtomicNum(elem)
elem_cid, elem_coeff = atom2cid[elem]
sparse.setdefault(elem_cid, 0)
sparse[elem_cid] += -count * elem_coeff
# use the H element to balance the electrons in the formation
# reactions (we don't need to balance protons since this is
# a biochemical reaction, so H+ are 'free').
H_cid, H_coeff = atom2cid['H']
sparse[H_cid] = (elem_ne - ne) * H_coeff
reaction = Reaction(
"formation of %s" % self.kegg.cid2name(cid), sparse)
output_csv.writerow(
(ref, 'C%05d' % cid, 'formation', 'A', '',
'formation of %s' % self.kegg.cid2name(cid),
reaction.FullReactionString(),
reaction.FullReactionString(show_cids=False),
'%.2f' % dG0_prime, self.T, self.I, self.pH, self.pMg))
