def getInferredReaction(self, reaction_operations): """ Create an inferred reaction from reaction_operations. An inferred reaction is a linear combination of reactions. :param list-ReactionOperation reaction_operations: :return SimplifiedReaction: inferred_reaction """ INFERRED_REACTION = "Inferred Reaction" stoichiometry_df = self.getOperationStoichiometryMatrix(reaction_operations) reaction_index = [op.reaction for op in reaction_operations] operation_series = pd.Series([val.operation for val in reaction_operations], index=reaction_index) resulting_reaction = stoichiometry_df.dot(operation_series) # Create a simplified reaction based on the resulting_reaction reactants = [] products = [] for ms in resulting_reaction.iteritems(): if abs(ms[1]) < TOLERANCE: continue elif ms[1] > 0: products.append(MoleculeStoichiometry(molecule=self.mesgraph.simple.getMolecule(ms[0]), stoichiometry=ms[1])) else: reactants.append(MoleculeStoichiometry(molecule=self.mesgraph.simple.getMolecule(ms[0]), stoichiometry=abs(ms[1]))) inferred_reaction = SimplifiedReaction(reactants, products, NULL_STR, self.mesgraph) inferred_reaction.reduceBySOMs() return inferred_reaction
def reduceBySOMs(self):
"""
Cancel reactants and products by SOMs
SOMs is given by existing MESGraph.
Return a new identifier.
:return str:
"""
reactant_soms = {
self.mesgraph.getNode(r.molecule)
for r in self.reactants
}
product_soms = {
self.mesgraph.getNode(p.molecule)
for p in self.products
}
common_soms = list(reactant_soms.intersection(product_soms))
if common_soms:
for som in common_soms:
reactants_in = collections.deque([
ms for ms in self.reactants
if self.mesgraph.getNode(ms.molecule) == som
])
reactants_out = [
ms for ms in self.reactants
if self.mesgraph.getNode(ms.molecule) != som
]
products_in = collections.deque([
ms for ms in self.products
if self.mesgraph.getNode(ms.molecule) == som
])
products_out = [
ms for ms in self.products
if self.mesgraph.getNode(ms.molecule) != som
]
#
while reactants_in and products_in:
reactant = reactants_in[0]
product = products_in[0]
if reactant.stoichiometry > product.stoichiometry:
reactants_in[0] = MoleculeStoichiometry(
reactant.molecule,
reactant.stoichiometry - product.stoichiometry)
products_in.popleft()
elif reactant.stoichiometry < product.stoichiometry:
products_in[0] = MoleculeStoichiometry(
product.molecule,
product.stoichiometry - reactant.stoichiometry)
reactants_in.popleft()
else:
reactants_in.popleft()
products_in.popleft()
reactants = list(reactants_in) + reactants_out
products = list(products_in) + products_out
#
if (len(self.reactants) > len(reactants)) | \
(len(self.products) > len(products)):
self.reactants = reactants
self.products = products
self.identifier = self.makeIdentifier()
return self.identifier
def testDifference(self):
if IGNORE_TEST:
return
df = self.comparator.difference()
self.assertLess(df.loc[MOIETY_NAME1].tolist()[0], 0)
#
comparator = MoietyComparator(self.molecules1,
self.molecules2,
implicits=[MOIETY_NAME1])
df = comparator.difference()
self.assertFalse(MOIETY_NAME1 in df.index)
#
config._config_dict[cn.CFG_PROCESS_BOUNDARY_REACTIONS] = False
molecules1 = [
MoleculeStoichiometry(Molecule(n), 0)
for n in MOLECULE_NAME_SET[:3]
]
comparator = MoietyComparator(molecules1, self.molecules2)
df = comparator.difference()
self.assertEqual(df[df.columns[0]].sum(), 0)
#
config._config_dict[cn.CFG_PROCESS_BOUNDARY_REACTIONS] = True
molecules1 = [
MoleculeStoichiometry(Molecule(n), 0)
for n in MOLECULE_NAME_SET[:3]
]
comparator = MoietyComparator(molecules1, self.molecules2)
df = comparator.difference()
self.assertNotEqual(df[df.columns[0]].sum(), 0)
def testGetMolecules(self):
if IGNORE_TEST:
return
names = ["a", "b", "c"]
full_names = list(names)
full_names.extend(names)
m_ss = [MoleculeStoichiometry(Molecule(n), NUM1) for n in full_names]
molecules = MoleculeStoichiometry.getMolecules(m_ss)
self.assertEqual(len(molecules), len(names))
def testCountMoietys(self):
if IGNORE_TEST:
return
moiety_stoich = MoietyStoichiometry(Molecule(MOIETY_NAME1), NUM1)
mole_stoich = MoleculeStoichiometry(Molecule(str(moiety_stoich)), NUM2)
df = mole_stoich.countMoietys()
self.assertEqual(df.columns.tolist(), [cn.VALUE])
expected = NUM1 * NUM2
trues = [expected == n for n in df[cn.VALUE]]
def testCountMoietysInCollection(self):
if IGNORE_TEST:
return
m_ss = [
MoleculeStoichiometry(Molecule(n), NUM1)
for n in MOLECULE_NAME_SET[:3]
]
df = MoleculeStoichiometry.countMoietysInCollection(m_ss)
indexes = df.index.tolist()
self.assertEqual(len(indexes), len(set(indexes)))
def _getMolecules(self):
"""
:return dict: key is species name, value is species object
"""
molecules = []
for reaction in self.reactions:
molecules.extend(
MoleculeStoichiometry.getMolecules(reaction.reactants))
molecules.extend(
MoleculeStoichiometry.getMolecules(reaction.products))
return util.uniqueify(molecules)
def testCountMoietys2(self):
if IGNORE_TEST:
return
m_ss = [
MoleculeStoichiometry(Molecule("A_P_P_P"), 1),
MoleculeStoichiometry(Molecule("A__P_3"), 1),
]
dfs = []
for m_s in m_ss:
dfs.append(m_s.countMoietys())
self.assertTrue(dfs[0].equals(dfs[1]))
def reduceReaction(self, reaction):
"""
Reduce the given reaction
:param Reaction reaction:
:return False/Reaction reaction:
"""
if reaction.category != cn.REACTION_n_n:
return False
# Reduces the reaction by examining for each SOM
for som in list(self.nodes):
reactants_in = collections.deque([
mole_stoich for mole_stoich in reaction.reactants
if self.getNode(mole_stoich.molecule) == som
])
reactants_out = [
mole_stoich for mole_stoich in reaction.reactants
if self.getNode(mole_stoich.molecule) != som
]
products_in = collections.deque([
mole_stoich for mole_stoich in reaction.products
if self.getNode(mole_stoich.molecule) == som
])
products_out = [
mole_stoich for mole_stoich in reaction.products
if self.getNode(mole_stoich.molecule) != som
]
#
while reactants_in and products_in:
reactant = reactants_in[0]
product = products_in[0]
if reactant.stoichiometry > product.stoichiometry:
reactants_in[0] = MoleculeStoichiometry(
reactant.molecule,
reactant.stoichiometry - product.stoichiometry)
products_in.popleft()
elif reactant.stoichiometry < product.stoichiometry:
products_in[0] = MoleculeStoichiometry(
product.molecule,
product.stoichiometry - reactant.stoichiometry)
reactants_in.popleft()
else:
reactants_in.popleft()
products_in.popleft()
reactants = list(reactants_in) + reactants_out
products = list(products_in) + products_out
#
if (len(reaction.reactants) > len(reactants)) | \
(len(reaction.products) > len(products)):
reaction.reactants = reactants
reaction.products = products
reaction.identifier = reaction.makeIdentifier()
reaction.category = reaction.getCategory()
# reduced_reaction = cn.ReactionComponents(label = reaction.label, reactants=reactants, products=products)
return reaction
def setUp(self):
self.molecules1 = [
MoleculeStoichiometry(Molecule(n), NUM1)
for n in MOLECULE_NAME_SET[:3]
]
self.molecules2 = [
MoleculeStoichiometry(Molecule(n), NUM2)
for n in MOLECULE_NAME_SET[3:]
]
self.comparator = MoietyComparator(self.molecules1, self.molecules2)
self.config_dict = copy.deepcopy(config._config_dict)
def testConstructor(self):
if IGNORE_TEST:
return
molecule = Molecule(MOLECULE_NAME)
m_s = MoleculeStoichiometry(molecule, NUM1)
self.assertTrue(m_s.molecule.isEqual(molecule))
self.assertEqual(m_s.stoichiometry, NUM1)
def makeMoleculeStoichiometrys(self, func_get_one, func_get_num):
"""
Creates a list of MoleculeStoichiometry
:param Function func_get_one: get one element by index
:param Function func_get_num: get number of elements
:return list-MoleculeStoichiometry:
"""
result = []
collection = [func_get_one(n) for n in range(func_get_num())]
for s_r in collection:
molecule = Molecule(s_r.species)
stoich = s_r.getStoichiometry()
result.append(MoleculeStoichiometry(molecule, stoich))
return result
def _makeDFS(self):
dfs = []
for collection in self.molecule_stoichiometry_collections:
if len(collection) > 0:
df = MoleculeStoichiometry.countMoietysInCollection(collection)
dfs.append(df)
if len(dfs) == 0:
dfs = [pd.DataFrame(), pd.DataFrame()]
elif len(dfs) == 1:
col = dfs[0].columns.tolist()[0]
df = dfs[0].copy()
df[col] = 0
dfs.append(df)
return dfs
def getMolecules(libsbml_reaction, func):
"""
Constructs molecules for the species returned by function.
:param Function func: gets libsbml.SpeciesReference
:return list-MoleculeStoichiometry:
"""
species = func(libsbml_reaction)
molecule_stoichiometrys = []
for spc in species:
molecule = Molecule.getMolecule(spc.getSpecies())
if molecule is None:
molecule = Molecule(spc.getSpecies())
molecule_stoichiometrys.append(
MoleculeStoichiometry(molecule, spc.getStoichiometry()))
return molecule_stoichiometrys
def _makeDFS(self):
"""
Constructs a list of dataframes containing counts of moieties.
:return list-pd.DataFrame: columns in DataFrame
cn.VALUE
"""
dfs = []
for collection in self.molecule_stoichiometry_collections:
if len(collection) > 0:
df = MoleculeStoichiometry.countMoietysInCollection(collection)
dfs.append(df)
if len(dfs) == 0:
dfs = [pd.DataFrame(), pd.DataFrame()]
elif len(dfs) == 1:
col = dfs[0].columns.tolist()[0]
df = dfs[0].copy()
df[col] = 0
dfs.append(df)
return dfs
def testExtractMoietyStoichiometrys(self):
if IGNORE_TEST:
return
moiety_stoich = MoietyStoichiometry(Molecule(MOIETY_NAME1), NUM1)
mole_stoich = MoleculeStoichiometry(Molecule(str(moiety_stoich)), NUM2)
