def to_array_based_model(self, deepcopy_model=False, **kwargs):
"""Makes a `cobra.core.ArrayBasedModel` from a cobra.Model
which may be used to perform linear algebra operations with the
stoichiometric matrix.
Deprecated (0.6). Use `cobra.util.array.create_stoichiometric_matrix`
instead.
Parameters
----------
deepcopy_model : bool
If False then the ArrayBasedModel points to the Model
"""
warn("to_array_based_model is deprecated. "
"use cobra.util.array.create_stoichiometric_matrix instead",
DeprecationWarning)
from cobra.core.arraybasedmodel import ArrayBasedModel
return ArrayBasedModel(self, deepcopy_model=deepcopy_model, **kwargs)
def compute_nullmatrix(label_model,
remove_inactive_reactions=True,
fname=None):
reaction_n_dict = {}
n_reaction_dict = {}
reaction0_list = []
model = copy.deepcopy(label_model.constrained_model)
if remove_inactive_reactions:
fva = flux_variability_analysis(model, fraction_of_optimum=0)
reactions_to_remove = []
print "The following reactions will be omitted as they cannot carry flux:"
for x in fva: #Remove inactive reactions
if fva[x]["minimum"] == 0.0 and 0.0 == fva[x]["maximum"]:
print x, fva[x]
reactions_to_remove.append(model.reactions.get_by_id(x))
reaction0_list.append(x)
for r in reactions_to_remove:
r.remove_from_model()
metabolites2remove = []
for x in model.metabolites:
if len(x.reactions) == 0:
metabolites2remove.append(x)
for x in metabolites2remove:
print x.id + " removed"
x.remove_from_model()
rgroup_dict = {}
list_group_metabolites = []
n = 0
for reaction in model.reactions: # sorted([x.id for x in model.reactions ]):
#print str(n)+reaction_id
#reaction=model.reactions.get_by_id(reaction_id)
if "LABEL_RGROUP_" in reaction.id:
group_dict = {}
group_metabolite = reaction.metabolites.keys()[
0] #Group reactions only have one metabolite
#Get the coefficients of reactions in the list
for group_member_reaction in group_metabolite.reactions:
if reaction != group_member_reaction:
group_dict[group_member_reaction.
id] = group_member_reaction.metabolites[
group_metabolite]
list_group_metabolites.append(group_metabolite)
rgroup_dict[reaction.id] = group_dict
else:
reaction_n_dict[reaction.id] = n
n_reaction_dict[n] = reaction.id
n += 1
print rgroup_dict
if fname == None:
array_based = ArrayBasedModel(model)
sm = array_based.S.toarray()
print len(sm), len(sm[0])
sm_list = []
for n_row, metabolite in enumerate(
model.metabolites
): #in enumerate(sorted([x.id for x in model.metabolites])):
#metabolite=model.metabolites.get_by_id(metabolite_id)
if metabolite in list_group_metabolites:
continue
for n_col, reaction in enumerate(model.reactions):
if n_col not in n_reaction_dict:
continue
coef = sm[n_row, n_col]
if coef.is_integer():
sm_list.append(int(coef))
else:
sm_list.append(sympy.Rational(coef))
smm = sympy.Matrix(
len(model.metabolites) - len(list_group_metabolites),
len(reaction_n_dict), sm_list)
#print len(smm),len(smm[0])
print "computing null matrix..."
nullms = nullspace(smm, True)
#nullms=smm.nullspace(True) #Simplify True
nullmnp = np.transpose(np.array(nullms, dtype=np.float64))
#original_nullmnp=copy.deepcopy(nullmnp)
#Adding group reactions:
else:
nullmnp = np.loadtxt(fname)
for group_reaction in sorted(rgroup_dict.keys()):
n = len(n_reaction_dict)
reaction_n_dict[group_reaction] = n
n_reaction_dict[n] = group_reaction
if fname != None: #If it has been loaded this is not necessary
continue
new_row = np.zeros((1, len(nullmnp[0])))
for grouped_reaction in rgroup_dict[group_reaction]:
print rgroup_dict[group_reaction]
grouped_reaction_n = reaction_n_dict[grouped_reaction]
grouped_reaction_coef = rgroup_dict[group_reaction][
grouped_reaction]
for n_coef, null_coef in enumerate(nullmnp[grouped_reaction_n]):
new_row[0][n_coef] += null_coef * grouped_reaction_coef
nullmnp = np.append(nullmnp, new_row, axis=0)
for reaction0 in reaction0_list:
n = len(n_reaction_dict)
reaction_n_dict[reaction0] = n
n_reaction_dict[n] = reaction0
if fname != None: #If it has been loaded this is not necessary
continue
new_row = np.zeros((1, len(nullmnp[0])))
nullmnp = np.append(nullmnp, new_row, axis=0)
#Create empty numpy array to append
label_model.flux_solver_nullmnp = nullmnp
label_model.flux_solver_n_reaction_dict = n_reaction_dict
label_model.flux_solver_reaction_n_dict = reaction_n_dict
label_model.compute_fluxes = get_compute_fluxes_function(label_model)
return label_model.flux_solver_nullmnp