# -*- coding: utf-8 -*-
from __future__ import absolute_import
import types
import logging
from copy import copy, deepcopy
from functools import partial
from warnings import warn
import optlang
import six
import sympy
from six import iteritems, string_types
from sympy import S
from cobra.core.dictlist import DictList
from cobra.core.object import Object
from cobra.core.reaction import separate_forward_and_reverse_bounds, Reaction
from cobra.core.solution import get_solution
from cobra.solvers import optimize
from cobra.util.context import HistoryManager, resettable, get_context
from cobra.util.solver import (
SolverNotFound, get_solver_name, interface_to_str, set_objective, solvers,
add_cons_vars_to_problem, remove_cons_vars_from_problem, choose_solver,
check_solver_status, assert_optimal)
from cobra.util.util import AutoVivification, format_long_string
LOGGER = logging.getLogger(__name__)
[docs]class Model(Object):
"""Class representation for a cobra model
Parameters
----------
id_or_model : Model, string
Either an existing Model object in which case a new model object is
instantiated with the same properties as the original model,
or a the identifier to associate with the model as a string.
name : string
Human readable name for the model
Attributes
----------
reactions : DictList
A DictList where the key is the reaction identifier and the value a
Reaction
metabolites : DictList
A DictList where the key is the metabolite identifier and the value a
Metabolite
genes : DictList
A DictList where the key is the gene identifier and the value a
Gene
solution : Solution
The last obtained solution from optimizing the model.
"""
def __setstate__(self, state):
"""Make sure all cobra.Objects in the model point to the model.
"""
self.__dict__.update(state)
for y in ['reactions', 'genes', 'metabolites']:
for x in getattr(self, y):
x._model = self
if not hasattr(self, "name"):
self.name = None
def __getstate__(self):
"""Get state for serialization.
Ensures that the context stack is cleared prior to serialization,
since partial functions cannot be pickled reliably.
"""
odict = self.__dict__.copy()
odict['_contexts'] = []
return odict
def __init__(self, id_or_model=None, name=None):
if isinstance(id_or_model, Model):
Object.__init__(self, name=name)
self.__setstate__(id_or_model.__dict__)
if not hasattr(self, "name"):
self.name = None
self._solver = id_or_model.solver
else:
Object.__init__(self, id_or_model, name=name)
self._trimmed = False
self._trimmed_genes = []
self._trimmed_reactions = {}
self.genes = DictList()
self.reactions = DictList() # A list of cobra.Reactions
self.metabolites = DictList() # A list of cobra.Metabolites
# genes based on their ids {Gene.id: Gene}
self._compartments = dict()
self._contexts = []
# from cameo ...
# if not hasattr(self, '_solver'): # backwards compatibility
# with older cobrapy pickles?
interface = solvers[get_solver_name()]
self._solver = interface.Model()
self._solver.objective = interface.Objective(S.Zero)
self._populate_solver(self.reactions, self.metabolites)
@property
def solver(self):
"""Get or set the attached solver instance.
The associated the solver object, which manages the interaction with
the associated solver, e.g. glpk.
This property is useful for accessing the optimization problem
directly and to define additional non-metabolic constraints.
Examples
--------
>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> new = model.problem.Constraint(model.objective.expression,
>>> lb=0.99)
>>> model.solver.add(new)
"""
return self._solver
@solver.setter
@resettable
def solver(self, value):
not_valid_interface = SolverNotFound(
'%s is not a valid solver interface. Pick from %s, or specify an '
'optlang interface (e.g. optlang.glpk_interface).' % (
value, list(solvers.keys())))
if isinstance(value, six.string_types):
try:
interface = solvers[interface_to_str(value)]
except KeyError:
raise not_valid_interface
elif isinstance(value, types.ModuleType) and hasattr(value, 'Model'):
interface = value
elif isinstance(value, optlang.interface.Model):
interface = value.interface
else:
raise not_valid_interface
# Do nothing if the solver did not change
if self.problem == interface:
return
self._solver = interface.Model.clone(self._solver)
@property
def description(self):
warn("description deprecated", DeprecationWarning)
return self.name if self.name is not None else ""
@description.setter
def description(self, value):
self.name = value
warn("description deprecated", DeprecationWarning)
@property
def compartments(self):
return {met.compartment: self._compartments.get(met.compartment, '')
for met in self.metabolites if met.compartment is not None}
@compartments.setter
def compartments(self, value):
"""Get or set the dictionary of current compartment descriptions.
Assigning a dictionary to this property updates the model's
dictionary of compartment descriptions with the new values.
Parameters
----------
value : dict
Dictionary mapping compartments abbreviations to full names.
Examples
--------
>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> model.compartments = {'c': 'the cytosol'}
{'c': 'the cytosol', 'e': 'extracellular'}
"""
self._compartments.update(value)
@property
def medium(self):
def is_active(reaction):
"""Determine if a boundary reaction permits flux towards creating
metabolites
"""
return ((bool(reaction.products) and (reaction.upper_bound > 0)) or
(bool(reaction.reactants) and (reaction.lower_bound < 0)))
def get_active_bound(reaction):
"""For an active boundary reaction, return the relevant bound"""
if reaction.reactants:
return -reaction.lower_bound
elif reaction.products:
return reaction.upper_bound
return {rxn.id: get_active_bound(rxn) for rxn in self.exchanges
if is_active(rxn)}
@medium.setter
def medium(self, medium):
"""Get or set the constraints on the model exchanges.
`model.medium` returns a dictionary of the bounds for each of the
boundary reactions, in the form of `{rxn_id: bound}`, where `bound`
specifies the absolute value of the bound in direction of metabolite
creation (i.e., lower_bound for `met <--`, upper_bound for `met -->`)
Parameters
----------
medium: dictionary-like
The medium to initialize. medium should be a dictionary defining
`{rxn_id: bound}` pairs.
"""
def set_active_bound(reaction, bound):
if reaction.reactants:
reaction.lower_bound = -bound
elif reaction.products:
reaction.upper_bound = bound
# Set the given media bounds
media_rxns = list()
for rxn_id, bound in iteritems(medium):
rxn = self.reactions.get_by_id(rxn_id)
media_rxns.append(rxn)
set_active_bound(rxn, bound)
boundary_rxns = set(self.exchanges)
media_rxns = set(media_rxns)
# Turn off reactions not present in media
for rxn in (boundary_rxns - media_rxns):
set_active_bound(rxn, 0)
def __add__(self, other_model):
"""Add the content of another model to this model (+).
The model is copied as a new object, with a new model identifier,
and copies of all the reactions in the other model are added to this
model. The objective is the sum of the objective expressions for the
two models.
"""
warn('use model.merge instead', DeprecationWarning)
return self.merge(other_model, objective='sum', inplace=False)
def __iadd__(self, other_model):
"""Incrementally add the content of another model to this model (+=).
Copies of all the reactions in the other model are added to this
model. The objective is the sum of the objective expressions for the
two models.
"""
warn('use model.merge instead', DeprecationWarning)
return self.merge(other_model, objective='sum', inplace=True)
[docs] def copy(self):
"""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
"""
new = self.__class__()
do_not_copy_by_ref = {"metabolites", "reactions", "genes", "notes",
"annotation"}
for attr in self.__dict__:
if attr not in do_not_copy_by_ref:
new.__dict__[attr] = self.__dict__[attr]
new.notes = deepcopy(self.notes)
new.annotation = deepcopy(self.annotation)
new.metabolites = DictList()
do_not_copy_by_ref = {"_reaction", "_model"}
for metabolite in self.metabolites:
new_met = metabolite.__class__()
for attr, value in iteritems(metabolite.__dict__):
if attr not in do_not_copy_by_ref:
new_met.__dict__[attr] = copy(
value) if attr == "formula" else value
new_met._model = new
new.metabolites.append(new_met)
new.genes = DictList()
for gene in self.genes:
new_gene = gene.__class__(None)
for attr, value in iteritems(gene.__dict__):
if attr not in do_not_copy_by_ref:
new_gene.__dict__[attr] = copy(
value) if attr == "formula" else value
new_gene._model = new
new.genes.append(new_gene)
new.reactions = DictList()
do_not_copy_by_ref = {"_model", "_metabolites", "_genes"}
for reaction in self.reactions:
new_reaction = reaction.__class__()
for attr, value in iteritems(reaction.__dict__):
if attr not in do_not_copy_by_ref:
new_reaction.__dict__[attr] = copy(value)
new_reaction._model = new
new.reactions.append(new_reaction)
# update awareness
for metabolite, stoic in iteritems(reaction._metabolites):
new_met = new.metabolites.get_by_id(metabolite.id)
new_reaction._metabolites[new_met] = stoic
new_met._reaction.add(new_reaction)
for gene in reaction._genes:
new_gene = new.genes.get_by_id(gene.id)
new_reaction._genes.add(new_gene)
new_gene._reaction.add(new_reaction)
try:
new._solver = deepcopy(self.solver)
# Cplex has an issue with deep copies
except Exception: # pragma: no cover
new._solver = copy(self.solver) # pragma: no cover
# it doesn't make sense to retain the context of a copied model so
# assign a new empty context
new._contexts = list()
return new
[docs] def add_reaction(self, reaction):
"""Will add a cobra.Reaction object to the model, if
reaction.id is not in self.reactions.
Parameters
----------
reaction : cobra.Reaction
The reaction to add
Deprecated (0.6). Use `~cobra.Model.add_reactions` instead
"""
warn("add_reaction deprecated. Use add_reactions instead",
DeprecationWarning)
self.add_reactions([reaction])
[docs] def add_boundary(self, metabolite, type="exchange", reaction_id=None,
lb=None, ub=1000.0):
"""Add a boundary reaction for a given metabolite.
There are three different types of pre-defined boundary reactions:
exchange, demand, and sink reactions.
An exchange reaction is a reversible, imbalanced reaction that adds
to or removes an extracellular metabolite from the extracellular
compartment.
A demand reaction is an irreversible reaction that consumes an
intracellular metabolite.
A sink is similar to an exchange but specifically for intracellular
metabolites.
If you set the reaction `type` to something else, you must specify the
desired identifier of the created reaction along with its upper and
lower bound. The name will be given by the metabolite name and the
given `type`.
Parameters
----------
metabolite : cobra.Metabolite
Any given metabolite. The compartment is not checked but you are
encouraged to stick to the definition of exchanges and sinks.
type : str, {"exchange", "demand", "sink"}
Using one of the pre-defined reaction types is easiest. If you
want to create your own kind of boundary reaction choose
any other string, e.g., 'my-boundary'.
reaction_id : str, optional
The ID of the resulting reaction. Only used for custom reactions.
lb : float, optional
The lower bound of the resulting reaction. Only used for custom
reactions.
ub : float, optional
The upper bound of the resulting reaction. For the pre-defined
reactions this default value determines all bounds.
Returns
-------
cobra.Reaction
The created boundary reaction.
Examples
--------
>>> import cobra.test
>>> model = cobra.test.create_test_model("textbook")
>>> demand = model.add_boundary(model.metabolites.atp_c, type="demand")
>>> demand.id
'DM_atp_c'
>>> demand.name
'ATP demand'
>>> demand.bounds
(0, 1000.0)
>>> demand.build_reaction_string()
'atp_c --> '
"""
types = dict(exchange=("EX", -ub, ub), demand=("DM", 0, ub),
sink=("SK", -ub, ub))
if type in types:
prefix, lb, ub = types[type]
reaction_id = "{}_{}".format(prefix, metabolite.id)
if reaction_id in self.reactions:
raise ValueError('boundary %s already exists' % reaction_id)
name = "{} {}".format(metabolite.name, type)
rxn = Reaction(id=reaction_id, name=name, lower_bound=lb,
upper_bound=ub)
rxn.add_metabolites({metabolite: -1})
self.add_reactions([rxn])
return rxn
[docs] def add_reactions(self, reaction_list):
"""Add reactions to the model.
Reactions with identifiers identical to a reaction already in the
model are ignored.
The change is reverted upon exit when using the model as a context.
Parameters
----------
reaction_list : list
A list of `cobra.Reaction` objects
"""
try:
reaction_list = DictList(reaction_list)
except TypeError:
reaction_list = DictList([reaction_list])
# First check whether the metabolites exist in the model
existing = [rxn for rxn in reaction_list if rxn.id in self.reactions]
for rxn in existing:
LOGGER.info('skip adding reaction %s as already existing', rxn.id)
reaction_list = [rxn for rxn in reaction_list
if rxn.id not in existing]
context = get_context(self)
# Add reactions. Also take care of genes and metabolites in the loop
for reaction in reaction_list:
reaction._model = self # the reaction now points to the model
# keys() is necessary because the dict will be modified during
# the loop
for metabolite in list(reaction._metabolites.keys()):
# if the metabolite is not in the model, add it
# should we be adding a copy instead.
if metabolite not in self.metabolites:
self.add_metabolites(metabolite)
# A copy of the metabolite exists in the model, the reaction
# needs to point to the metabolite in the model.
else:
stoichiometry = reaction._metabolites.pop(metabolite)
model_metabolite = self.metabolites.get_by_id(
metabolite.id)
reaction._metabolites[model_metabolite] = stoichiometry
model_metabolite._reaction.add(reaction)
if context:
context(partial(
model_metabolite._reaction.remove, reaction))
for gene in list(reaction._genes):
# If the gene is not in the model, add it
if not self.genes.has_id(gene.id):
self.genes += [gene]
gene._model = self
if context:
# Remove the gene later
context(partial(self.genes.__isub__, [gene]))
context(partial(setattr, gene, '_model', None))
# Otherwise, make the gene point to the one in the model
else:
model_gene = self.genes.get_by_id(gene.id)
if model_gene is not gene:
reaction._dissociate_gene(gene)
reaction._associate_gene(model_gene)
self.reactions += reaction_list
if context:
context(partial(self.reactions.__isub__, reaction_list))
# from cameo ...
self._populate_solver(reaction_list)
[docs] def remove_reactions(self, reactions, remove_orphans=False):
"""Remove reactions from the model.
The change is reverted upon exit when using the model as a context.
Parameters
----------
reactions : list
A list with reactions (`cobra.Reaction`), or their id's, to remove
remove_orphans : bool
Remove orphaned genes and metabolites from the model as well
"""
if isinstance(reactions, string_types) or hasattr(reactions, "id"):
warn("need to pass in a list")
reactions = [reactions]
context = get_context(self)
for reaction in reactions:
# Make sure the reaction is in the model
try:
reaction = self.reactions[self.reactions.index(reaction)]
except ValueError:
warn('%s not in %s' % (reaction, self))
else:
forward = reaction.forward_variable
reverse = reaction.reverse_variable
self.remove_cons_vars([forward, reverse])
self.reactions.remove(reaction)
reaction._model = None
if context:
context(partial(setattr, reaction, '_model', self))
context(partial(self.reactions.add, reaction))
for met in reaction._metabolites:
if reaction in met._reaction:
met._reaction.remove(reaction)
if context:
context(partial(met._reaction.add, reaction))
if remove_orphans and len(met._reaction) == 0:
self.remove_metabolites(met)
for gene in reaction._genes:
if reaction in gene._reaction:
gene._reaction.remove(reaction)
if context:
context(partial(gene._reaction.add, reaction))
if remove_orphans and len(gene._reaction) == 0:
self.genes.remove(gene)
if context:
context(partial(self.genes.add, gene))
[docs] def add_cons_vars(self, what, **kwargs):
"""Add constraints and variables to the model's mathematical problem.
Useful for variables and constraints that can not be expressed with
reactions and simple lower and upper bounds.
Additions are reversed upon exit if the model itself is used as
context.
Parameters
----------
what : list or tuple of optlang variables or constraints.
The variables or constraints to add to the model. Must be of
class `optlang.interface.Variable` or
`optlang.interface.Constraint`.
**kwargs : keyword arguments
Passed to solver.add()
"""
add_cons_vars_to_problem(self, what, **kwargs)
[docs] def remove_cons_vars(self, what):
"""Remove variables and constraints from the model's mathematical
problem.
Remove variables and constraints that were added directly to the
model's underlying mathematical problem. Removals are reversed
upon exit if the model itself is used as context.
Parameters
----------
what : list or tuple of optlang variables or constraints.
The variables or constraints to add to the model. Must be of
class `optlang.interface.Variable` or
`optlang.interface.Constraint`.
"""
remove_cons_vars_from_problem(self, what)
@property
def problem(self):
"""The interface to the model's underlying mathematical problem.
Solutions to cobra models are obtained by formulating a mathematical
problem and solving it. Cobrapy uses the optlang package to
accomplish that and with this property you can get access to the
problem interface directly.
Returns
-------
optlang.interface
The problem interface that defines methods for interacting with
the problem and associated solver directly.
"""
return self.solver.interface
@property
def variables(self):
"""The mathematical variables in the cobra model.
In a cobra model, most variables are reactions. However,
for specific use cases, it may also be useful to have other types of
variables. This property defines all variables currently associated
with the model's problem.
Returns
-------
optlang.container.Container
A container with all associated variables.
"""
return self.solver.variables
@property
def constraints(self):
"""The constraints in the cobra model.
In a cobra model, most constraints are metabolites and their
stoichiometries. However, for specific use cases, it may also be
useful to have other types of constraints. This property defines all
constraints currently associated with the model's problem.
Returns
-------
optlang.container.Container
A container with all associated constraints.
"""
return self.solver.constraints
@property
def exchanges(self):
"""Exchange reactions in model.
Reactions that either don't have products or substrates.
"""
return [rxn for rxn in self.reactions if rxn.boundary]
def _populate_solver(self, reaction_list, metabolite_list=None):
"""Populate attached solver with constraints and variables that
model the provided reactions.
"""
constraint_terms = AutoVivification()
to_add = []
if metabolite_list is not None:
for met in metabolite_list:
to_add += [self.problem.Constraint(
S.Zero, name=met.id, lb=0, ub=0)]
self.add_cons_vars(to_add)
for reaction in reaction_list:
reverse_lb, reverse_ub, forward_lb, forward_ub = \
separate_forward_and_reverse_bounds(*reaction.bounds)
forward_variable = self.problem.Variable(
reaction.id, lb=forward_lb, ub=forward_ub)
reverse_variable = self.problem.Variable(
reaction.reverse_id, lb=reverse_lb, ub=reverse_ub)
self.add_cons_vars([forward_variable, reverse_variable])
self.solver.update()
for metabolite, coeff in six.iteritems(reaction.metabolites):
if metabolite.id in self.constraints:
constraint = self.constraints[metabolite.id]
else:
constraint = self.problem.Constraint(
S.Zero,
name=metabolite.id,
lb=0, ub=0)
self.add_cons_vars(constraint, sloppy=True)
constraint_terms[constraint][forward_variable] = coeff
constraint_terms[constraint][reverse_variable] = -coeff
self.solver.update()
for constraint, terms in six.iteritems(constraint_terms):
constraint.set_linear_coefficients(terms)
[docs] 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)
[docs] def slim_optimize(self, error_value=float('nan'), message=None):
"""Optimize model without creating a solution object.
Creating a full solution object implies fetching shadow prices and
flux values for all reactions and metabolites from the solver
object. This necessarily takes some time and in cases where only one
or two values are of interest, it is recommended to instead use this
function which does not create a solution object returning only the
value of the objective. Note however that the `optimize()` function
uses efficient means to fetch values so if you need fluxes/shadow
prices for more than say 4 reactions/metabolites, then the total
speed increase of `slim_optimize` versus `optimize` is expected to
be small or even negative depending on how you fetch the values
after optimization.
Parameters
----------
error_value : float, None
The value to return if optimization failed due to e.g.
infeasibility. If None, raise `OptimizationError` if the
optimization fails.
message : string
Error message to use if the model optimization did not succeed.
Returns
-------
float
The objective value.
"""
self.solver.optimize()
if self.solver.status == optlang.interface.OPTIMAL:
return self.solver.objective.value
elif error_value is not None:
return error_value
else:
assert_optimal(self, message)
[docs] def optimize(self, objective_sense=None, raise_error=False, **kwargs):
"""
Optimize the model using flux balance analysis.
Parameters
----------
objective_sense : {None, 'maximize' 'minimize'}, optional
Whether fluxes should be maximized or minimized. In case of None,
the previous direction is used.
raise_error : bool
If true, raise an OptimizationError if solver status is not
optimal.
solver : {None, 'glpk', 'cglpk', 'gurobi', 'cplex'}, optional
If unspecified will use the currently defined `self.solver`
otherwise it will use the given solver and update the attribute.
quadratic_component : {None, scipy.sparse.dok_matrix}, optional
The dimensions should be (n, n) where n is the number of
reactions. This sets the quadratic component (Q) of the
objective coefficient, adding :math:`\\frac{1}{2} v^T \cdot Q
\cdot v` to the objective. Ignored for optlang based solvers.
tolerance_feasibility : float
Solver tolerance for feasibility. Ignored for optlang based
solvers
tolerance_markowitz : float
Solver threshold during pivot. Ignored for optlang based solvers
time_limit : float
Maximum solver time (in seconds). Ignored for optlang based solvers
Notes
-----
Only the most commonly used parameters are presented here. Additional
parameters for cobra.solvers may be available and specified with the
appropriate keyword argument.
"""
legacy, solver = choose_solver(self, solver=kwargs.get("solver"))
original_direction = self.objective.direction
if legacy:
if objective_sense is None:
objective_sense = {
"max": "maximize", "min": "minimize"}[original_direction]
solution = optimize(self, objective_sense=objective_sense,
**kwargs)
check_solver_status(solution.status, raise_error=raise_error)
return solution
self.solver = solver
self.objective.direction = \
{"maximize": "max", "minimize": "min"}.get(
objective_sense, original_direction)
self.slim_optimize()
solution = get_solution(self, raise_error=raise_error)
self.objective.direction = original_direction
return solution
[docs] def repair(self, rebuild_index=True, rebuild_relationships=True):
"""Update all indexes and pointers in a model
Parameters
----------
rebuild_index : bool
rebuild the indices kept in reactions, metabolites and genes
rebuild_relationships : bool
reset all associations between genes, metabolites, model and
then re-add them.
"""
if rebuild_index: # DictList indexes
self.reactions._generate_index()
self.metabolites._generate_index()
self.genes._generate_index()
if rebuild_relationships:
for met in self.metabolites:
met._reaction.clear()
for gene in self.genes:
gene._reaction.clear()
for rxn in self.reactions:
for met in rxn._metabolites:
met._reaction.add(rxn)
for gene in rxn._genes:
gene._reaction.add(rxn)
# point _model to self
for l in (self.reactions, self.genes, self.metabolites):
for e in l:
e._model = self
@property
def objective(self):
"""Get or set the solver objective
Before introduction of the optlang based problems,
this function returned the objective reactions as a list. With
optlang, the objective is not limited a simple linear summation of
individual reaction fluxes, making that return value ambiguous.
Henceforth, use `cobra.util.solver.linear_reaction_coefficients` to
get a dictionary of reactions with their linear coefficients (empty
if there are none)
The set value can be dictionary (reactions as keys, linear
coefficients as values), string (reaction identifier), int (reaction
index), Reaction or problem.Objective or sympy expression
directly interpreted as objectives.
When using a `HistoryManager` context, this attribute can be set
temporarily, reversed when the exiting the context.
"""
return self.solver.objective
@objective.setter
def objective(self, value):
if isinstance(value, sympy.Basic):
value = self.problem.Objective(value, sloppy=False)
if not isinstance(value, (dict, optlang.interface.Objective)):
try:
reactions = self.reactions.get_by_any(value)
except KeyError:
raise ValueError('invalid objective')
value = {rxn: 1 for rxn in reactions}
set_objective(self, value, additive=False)
@property
def objective_direction(self):
"""
Get or set the objective direction.
When using a `HistoryManager` context, this attribute can be set
temporarily, reversed when exiting the context.
"""
return self.solver.objective.direction
@objective_direction.setter
@resettable
def objective_direction(self, value):
value = value.lower()
if value.startswith("max"):
self.solver.objective.direction = "max"
elif value.startswith("min"):
self.solver.objective.direction = "min"
else:
raise ValueError("Unknown objective direction '{}'.".format(value))
[docs] def summary(self, solution=None, threshold=1E-8, fva=None, floatfmt='.3g'):
"""Print a summary of the input and output fluxes of the model. This
method requires the model to have been previously solved.
Parameters
----------
solution: cobra.core.Solution
A previously solved model solution to use for generating the
summary. If none provided (default), the summary method will
resolve the model. Note that the solution object must match the
model, i.e., changes to the model such as changed bounds,
added or removed reactions are not taken into account by this
method.
threshold : float
tolerance for determining if a flux is zero (not printed)
fva : int or None
Whether or not to calculate and report flux variability in the
output summary
floatfmt : string
format method for floats, passed to tabulate. Default is '.3g'.
"""
from cobra.flux_analysis.summary import model_summary
return model_summary(self, solution=solution, threshold=threshold,
fva=fva, floatfmt=floatfmt)
def __enter__(self):
"""Record all future changes to the model, undoing them when a call to
__exit__ is received"""
# Create a new context and add it to the stack
try:
self._contexts.append(HistoryManager())
except AttributeError:
self._contexts = [HistoryManager()]
return self
def __exit__(self, type, value, traceback):
"""Pop the top context manager and trigger the undo functions"""
context = self._contexts.pop()
context.reset()
[docs] def merge(self, right, prefix_existing=None, inplace=True,
objective='left'):
"""Merge two models to create a model with the reactions from both
models.
Custom constraints and variables from right models are also copied
to left model, however note that, constraints and variables are
assumed to be the same if they have the same name.
right : cobra.Model
The model to add reactions from
prefix_existing : string
Prefix the reaction identifier in the right that already exist
in the left model with this string.
inplace : bool
Add reactions from right directly to left model object.
Otherwise, create a new model leaving the left model untouched.
When done within the model as context, changes to the models are
reverted upon exit.
objective : string
One of 'left', 'right' or 'sum' for setting the objective of the
resulting model to that of the corresponding model or the sum of
both.
"""
if inplace:
new_model = self
else:
new_model = self.copy()
new_model.id = '{}_{}'.format(self.id, right.id)
new_reactions = deepcopy(right.reactions)
if prefix_existing is not None:
existing = new_reactions.query(
lambda rxn: rxn.id in self.reactions)
for reaction in existing:
reaction.id = '{}{}'.format(prefix_existing, reaction.id)
new_model.add_reactions(new_reactions)
interface = new_model.problem
new_vars = [interface.Variable.clone(v) for v in right.variables if
v.name not in new_model.variables]
new_model.add_cons_vars(new_vars)
new_cons = [interface.Constraint.clone(c, model=new_model.solver)
for c in right.constraints if
c.name not in new_model.constraints]
new_model.add_cons_vars(new_cons, sloppy=True)
new_model.objective = dict(
left=self.objective,
right=right.objective,
sum=self.objective.expression + right.objective.expression
)[objective]
return new_model
def _repr_html_(self):
return """
<table>
<tr>
<td><strong>Name</strong></td>
<td>{name}</td>
</tr><tr>
<td><strong>Memory address</strong></td>
<td>{address}</td>
</tr><tr>
<td><strong>Number of metabolites</strong></td>
<td>{num_metabolites}</td>
</tr><tr>
<td><strong>Number of reactions</strong></td>
<td>{num_reactions}</td>
</tr><tr>
<td><strong>Objective expression</strong></td>
<td>{objective}</td>
</tr><tr>
<td><strong>Compartments</strong></td>
<td>{compartments}</td>
</tr>
</table>""".format(
name=self.id,
address='0x0%x' % id(self),
num_metabolites=len(self.metabolites),
num_reactions=len(self.reactions),
objective=format_long_string(str(self.objective.expression), 100),
compartments=", ".join(
v if v else k for k, v in iteritems(self.compartments)
))