#cobra.core.ArrayBasedModel.py
#Dresses a cobra.Model with arrays and vectors so that linear algebra operations
#can be carried out on
from numpy import array, empty, sign, repeat, vstack, hstack
from numpy import ones, nonzero, zeros
from scipy import sparse
from scipy.sparse import lil_matrix, dok_matrix
from copy import deepcopy
from .Model import Model
from warnings import warn
#######################
#BEGIN Class ArrayBasedModel
#
[docs]class ArrayBasedModel(Model):
"""ArrayBasedModel is a class that adds arrays and vectors to
a cobra.Model to make it easier to perform linear algebra operations.
S : :class:`scipy.sparse.lil_matrix` (in CPython)
Stoichiometric matrix of the model
lower_bounds
upper_bounds
objective_coefficients
b
constraint_sense
"""
def __setstate__(self, state):
"""Make sure all cobra.Objects in the model point to the model
TODO: Make sure that the genes and metabolites referenced by
the reactions.(genes|metabolites) point to the model's genes
and metabolites.
"""
self.__dict__.update(state)
[[setattr(x, '_model', self)
for x in self.__dict__[y]]
for y in ['reactions', 'genes', 'metabolites']]
def __init__(self, description=None, deepcopy_model=False):
"""
description: None | String | cobra.Model
deepcopy_model: Boolean. If True and description is
a cobra.Model then make a deepcopy of the Model before
creating the ArrayBasedModel.
"""
if deepcopy_model and isinstance(description, Model):
description = description.copy()
Model.__init__(self, description)
self.S = None
self.lower_bounds = None
self.upper_bounds = None
self.objective_coefficients = None
self.b = None
self.constraint_sense = None
self.update()
def __add__(self, other_model):
"""Adds two models. +
The issue of reactions being able to exists in multiple Models now arises, the same
for metabolites and such. This might be a little difficult as a reaction with the
same name / id in two models might have different coefficients for their metabolites
due to pH and whatnot making them different reactions.
"""
new_model = Model.__add__(self, other_model)
return new_model
def __iadd__(self, other_model):
"""Adds a Model to this model +=
The issue of reactions being able to exists in multiple Models now arises, the same
for metabolites and such. This might be a little difficult as a reaction with the
same name / id in two models might have different coefficients for their metabolites
due to pH and whatnot making them different reactions.
"""
Model.__iadd__(self, other_model)
return self
[docs] def copy(self, print_time=False):
"""Provides a partial 'deepcopy' of the Model. All of the Metabolite, Gene,
and Reaction objects are created anew but in a faster fashion than deepcopy
print_time: Boolean used for debugging
"""
the_copy = Model.copy(self, print_time)
the_copy.update()
return the_copy
def _update_reaction(self, reaction):
"""Updates everything associated with the reaction.id of reaction.
reaction: A cobra.Reaction object, or a list of these objects.
WARNING: This function is only used after the Model has been
converted to matrices. It is typically faster to access the objects
in the Model directly. This function will eventually moved to another
module for advanced users due to the potential for mistakes.
"""
if not hasattr(reaction, '__iter__'):
reaction = [reaction]
Model._update_reaction(reaction)
for the_reaction in reaction:
if the_reaction.id not in self.reactions:
print the_reaction.id + ' is not in the model\n'
continue
#zero reaction stoichiometry column
the_column = self.S[:, reaction_index]
for nonzero_index in the_column.nonzero()[0]:
the_column[nonzero_index, 0] = 0
self.lower_bounds[reaction_index] = the_reaction.lower_bound
self.upper_bounds[reaction_index] = the_reaction.upper_bound
self.objective_coefficients[reaction_index] = the_reaction.objective_coefficient
self.add_metabolites(the_reaction._metabolites)
#Make sure that the metabolites are the ones contained in the model
the_reaction._metabolites = [self.metabolite.get_by_id(x.id)
for x in the_reaction._metabolites]
#Update the stoichiometric matrix
metabolite_indices = map(self.metabolites.index, the_reaction._metabolites)
for (index, metabolite_index) in enumerate(metabolite_indices):
self.S[metabolite_index, reaction_index] = the_reaction.stoichiometric_coefficients[index]
[docs] def add_reactions(self, reaction_list, update_matrices=False):
"""Will add a cobra.Reaction object to the model, if
reaction.id is not in self.reactions.
reaction_list: A :class:`~cobra.core.Reaction` object or a list of them
update_matrices: Boolean. If true populate / update matrices
S, lower_bounds, upper_bounds, .... Note this is slow to run
for very large models and using this option with repeated calls
will degrade performance. Better to call self.update() after
adding all reactions.
If the stoichiometric matrix is initially empty then initialize a 1x1
sparse matrix and add more rows as needed in the self.add_metabolites
function
"""
Model.add_reactions(reaction_list)
if update_matrices:
self._update_matrices(reaction_list)
def _construct_matrices():
"""Large sparse matrices take time to construct and to read / write.
This function allows one to let the model exists without cobra_model.S
and then generate it at needed.
"""
self._update_matrices() #This does basic construction as well.
def _update_reaction_vectors(self):
"""regenerates the lower_bounds, upper_bounds,
and objective_coefficients vectors.
WARNING: This function is only used after the Model has been
converted to matrices. It is typically faster to access the objects
in the Model directly. This function will eventually moved to another
module for advanced users due to the potential for mistakes.
"""
lower_bounds, upper_bounds = [], []
objective_coefficients = []
[(lower_bounds.append(x.lower_bound),
upper_bounds.append(x.upper_bound),
objective_coefficients.append(x.objective_coefficient))
for x in self.reactions]
self.lower_bounds = array(lower_bounds)
self.upper_bounds = array(upper_bounds)
self.objective_coefficients = array(objective_coefficients)
def _update_metabolite_vectors(self):
"""regenerates _b and _constraint_sense
WARNING: This function is only used after the Model has been
converted to matrices. It is typically faster to access the objects
in the Model directly. This function will eventually moved to another
module for advanced users due to the potential for mistakes.
"""
_b, _constraint_sense = [], []
[(_b.append(x._bound),
_constraint_sense.append(x._constraint_sense))
for x in self.metabolites]
self.b = array(_b)
self.constraint_sense = _constraint_sense
def _update_matrices(self, reaction_list=None):
"""
reaction_list: None or a list of cobra.Reaction objects that are in
self.reactions. If None then reconstruct the whole matrix.
NOTE: reaction_list is assumed to be at the end of self.reactions.
In the future, we'll be able to use reactions from anywhere in the
list
WARNING: This function is only used after the Model has been
converted to matrices. It is typically faster to access the objects
in the Model directly. This function will eventually moved to another
module for advanced users due to the potential for mistakes.
"""
#Pretty much all of these things are unnecessary to use the objects and
#interact with the optimization solvers. It might be best to move them
#to linear algebra modules.
#If no reactions are present in the Model, initialize the arrays
if not self.S or reaction_list is None:
reaction_list = self.reactions
self.S = dok_matrix((len(self.metabolites),
len(self.reactions)))
self.lower_bounds = array([reaction.lower_bound
for reaction in reaction_list])
self.upper_bounds = array([reaction.upper_bound
for reaction in reaction_list])
self.objective_coefficients = array([reaction.objective_coefficient
for reaction in reaction_list])
else: #Expand the arrays to accomodate the new reaction
self.S = self.S.todok()
self.S.resize((len(self.metabolites),
len(self.reactions)))
lower_bounds = array([x.lower_bound
for x in reaction_list])
upper_bounds = array([x.upper_bound
for x in reaction_list])
objective_coefficients = array([x.objective_coefficient
for x in reaction_list])
self.lower_bounds = hstack((self.lower_bounds,
lower_bounds))
self.upper_bounds = hstack((self.upper_bounds,
upper_bounds))
self.objective_coefficients = hstack((self.objective_coefficients,
objective_coefficients))
#Use dok format to speed up additions.
coefficient_dictionary = {}
#SPEED this up. This is the slow part. Can probably use a dict to index.
for the_reaction in reaction_list:
reaction_index = self.reactions.index(the_reaction.id)
for the_key, the_value in the_reaction._metabolites.items():
coefficient_dictionary[(self.metabolites.index(the_key.id),
reaction_index)] = the_value
if not self.S.getformat() == 'dok':
self.S = self.S.todok()
self.S.update(coefficient_dictionary)
self.S = self.S.tolil()
[docs] def update(self):
"""Regenerates the stoichiometric matrix and vectors
"""
self._update_matrices()
self._update_metabolite_vectors()
#
#END Class ArrayBasedModel
#####################