def raise_libsbml_errors():
with pytest.raises(ImportError):
io.read_sbml_model('test')
with pytest.raises(ImportError):
io.write_sbml_model(None, 'test')
with pytest.raises(ImportError):
io.load_matlab_model('test')
with pytest.raises(ImportError):
io.write_legacy_sbml(None, 'test')
with pytest.raises(ImportError):
io.read_legacy_sbml(None, 'test')
def raise_libsbml_errors():
with pytest.raises(ImportError):
io.read_sbml_model('test')
with pytest.raises(ImportError):
io.write_sbml_model(None, 'test')
with pytest.raises(ImportError):
io.load_matlab_model('test')
with pytest.raises(ImportError):
io.write_legacy_sbml(None, 'test')
with pytest.raises(ImportError):
io.read_legacy_sbml(None, 'test')
def create_cobra_model_from_vmh_recon2(validate=False):
""" Create a COBRA model from the current Recon2 model.
Parameters
----------
validate : bool, optional
When True, perform validity checks on COBRA model
Returns
-------
cobra.Model
COBRA model created from Matlab representation of Recon2 model
"""
# Download the zip file that contains the Matlab file and extract it.
response = requests.get('{0}{1}_.zip'.format(vmh_url, recon2_file_name), stream=True)
if response.status_code != requests.codes.OK:
response.raise_for_status()
zipfile.ZipFile(io.BytesIO(response.content), 'r').extract(recon2_file_name, gettempdir())
# Convert to a cobra.Model object.
temp_file = join(gettempdir(), recon2_file_name)
model = load_matlab_model(temp_file)
unlink(temp_file)
# If requested, validate the COBRA model.
if validate:
warn('Coming soon')
return model
def load_model_from_file(filename):
""" Load a model from a file based on the extension of the file name.
Parameters
----------
filename : str
Path to model file
Returns
-------
cobra.core.Model
Model object loaded from file
Raises
------
IOError
If model file extension is not supported.
"""
(root, ext) = splitext(filename)
if ext == '.mat':
model = load_matlab_model(filename)
elif ext == '.xml' or ext == '.sbml':
model = read_sbml_model(filename)
elif ext == '.json':
model = load_json_model(filename)
else:
raise IOError(
'Model file extension not supported for {0}'.format(filename))
return model
def test_mat_read_write(self):
test_output_filename = "test_mat_write.mat"
io.save_matlab_model(self.model, test_output_filename)
reread = io.load_matlab_model(test_output_filename)
self.assertEqual(len(self.model.reactions), len(reread.reactions))
self.assertEqual(len(self.model.metabolites), len(reread.metabolites))
for i in range(len(self.model.reactions)):
self.assertEqual(len(self.model.reactions[i]._metabolites), len(reread.reactions[i]._metabolites))
self.assertEqual(self.model.reactions[i].id, reread.reactions[i].id)
unlink(test_output_filename)
def test_nnz(self):
S = load_matlab_model(testing_models_filepath, "ecoli_core").to_array_based_model().S
self.assertEqual(nnz(S), 309) # lil sparse matrix
self.assertEqual(nnz(S.tocsc()), 309) # csc sparse matrix
self.assertEqual(nnz(S.todok()), 309) # dok sparse matrix
self.assertEqual(nnz(S.todense()), 309) # matrix
self.assertEqual(nnz(array(S.todense())), 309) # ndarray
self.assertEqual(nnz(array([0])), 0)
self.assertEqual(nnz(array([0, 1])), 1)
self.assertEqual(nnz(array([[0, 0], [0, 0]])), 0)
self.assertEqual(nnz(array([[0, 1], [0, 0]])), 1)
self.assertEqual(nnz(array([[1, 0], [0, 1]])), 2)
def test_mat_read_write(self):
from warnings import catch_warnings
test_output_filename = join(gettempdir(), "test_mat_write.mat")
io.save_matlab_model(self.model, test_output_filename)
with catch_warnings(record=True) as w:
reread = io.load_matlab_model(test_output_filename)
self.assertEqual(len(self.model.reactions), len(reread.reactions))
self.assertEqual(len(self.model.metabolites), len(reread.metabolites))
for i in range(len(self.model.reactions)):
self.assertEqual(len(self.model.reactions[i]._metabolites), \
len(reread.reactions[i]._metabolites))
self.assertEqual(self.model.reactions[i].id, reread.reactions[i].id)
unlink(test_output_filename)
def test_nnz(self):
S = load_matlab_model(testing_models_filepath,
"ecoli_core").to_array_based_model().S
self.assertEqual(nnz(S), 309) # lil sparse matrix
self.assertEqual(nnz(S.tocsc()), 309) # csc sparse matrix
self.assertEqual(nnz(S.todok()), 309) # dok sparse matrix
self.assertEqual(nnz(S.todense()), 309) # matrix
self.assertEqual(nnz(array(S.todense())), 309) # ndarray
self.assertEqual(nnz(array([0])), 0)
self.assertEqual(nnz(array([0, 1])), 1)
self.assertEqual(nnz(array([[0, 0], [0, 0]])), 0)
self.assertEqual(nnz(array([[0, 1], [0, 0]])), 1)
self.assertEqual(nnz(array([[1, 0], [0, 1]])), 2)
def test_mat_read_write(self):
"""read and write COBRA toolbox models in .mat files"""
from warnings import catch_warnings
test_output_filename = join(gettempdir(), "test_mat_write.mat")
io.save_matlab_model(self.model, test_output_filename)
with catch_warnings(record=True) as w:
reread = io.load_matlab_model(test_output_filename)
self.assertEqual(len(self.model.reactions), len(reread.reactions))
self.assertEqual(len(self.model.metabolites), len(reread.metabolites))
for i in range(len(self.model.reactions)):
self.assertEqual(len(self.model.reactions[i]._metabolites), \
len(reread.reactions[i]._metabolites))
self.assertEqual(self.model.reactions[i].id,
reread.reactions[i].id)
unlink(test_output_filename)
def pub_model(request):
# get a specific model. This fixture and all the tests that use it will run
# for every model in the model_files list.
model_file = request.param
model_path = join(settings.model_directory, model_file)
# load the file
start = time()
try:
if model_path.endswith('.xml'):
pub_model = read_sbml_model(model_path)
elif model_path.endswith('.mat'):
pub_model = load_matlab_model(model_path)
else:
raise Exception('Unrecongnized extension for model %s' %
model_file)
except IOError:
raise Exception('Could not find model %s' % model_path)
print("Loaded %s in %.2f sec" % (model_file, time() - start))
return pub_model
def model_loader(gem_file_path, gem_file_type):
"""Consolidated function to load a GEM using COBRApy. Specify the file type being loaded.
Args:
gem_file_path (str): Path to model file
gem_file_type (str): GEM model type - ``sbml`` (or ``xml``), ``mat``, or ``json`` format
Returns:
COBRApy Model object.
"""
if gem_file_type.lower() == 'xml' or gem_file_type.lower() == 'sbml':
model = read_sbml_model(gem_file_path)
elif gem_file_type.lower() == 'mat':
model = load_matlab_model(gem_file_path)
elif gem_file_type.lower() == 'json':
model = load_json_model(gem_file_path)
else:
raise ValueError('File type must be "sbml", "xml", "mat", or "json".')
return model
def _load_model_from_file(path, handle):
"""Try to parse a model from a file handle using different encodings.
Adapted from cameo.
"""
try:
model = pickle.load(handle)
except (TypeError, pickle.UnpicklingError):
try:
model = load_json_model(path)
except ValueError:
try:
model = load_matlab_model(path)
except ValueError:
try:
model = read_sbml_model(path)
except AttributeError:
click.ClickException(
"cobrapy doesn't raise a proper exception"
" if a file does not contain an SBML model"
).show()
except Exception as e:
click.ClickException(e).show()
return model
def test_load_mat(db_model):
if db_model.id.startswith('iCHO') or db_model.id == 'iJB785':
# remove when this is solved: https://github.com/opencobra/cobrapy/issues/919
return
model = load_matlab_model(join(static_model_dir, db_model.id + '.mat'))
assert model.id == db_model.id
from pytfa.io import load_thermoDB, \
read_lexicon, annotate_from_lexicon, \
read_compartment_data, apply_compartment_data
from pytfa.optim.variables import DeltaG, DeltaGstd, ThermoDisplacement
from pytfa.analysis import variability_analysis, \
apply_reaction_variability, \
apply_generic_variability, \
apply_directionality
CPLEX = 'optlang-cplex'
GUROBI = 'optlang-gurobi'
GLPK = 'optlang-glpk'
# Load the cobra_model
cobra_model = load_matlab_model('../models/small_ecoli.mat')
# Load reaction DB
thermo_data = load_thermoDB('../data/thermo_data.thermodb')
lexicon = read_lexicon('../models/small_ecoli/lexicon.csv')
compartment_data = read_compartment_data(
'../models/small_ecoli/compartment_data.json')
# Initialize the cobra_model
tmodel = pytfa.ThermoModel(thermo_data, cobra_model)
tmodel.name = 'tutorial'
# Annotate the cobra_model
annotate_from_lexicon(tmodel, lexicon)
apply_compartment_data(tmodel, compartment_data)
double_reaction_deletion)
from cobra.manipulation import remove_genes
from cobra.manipulation import *
from cobra.flux_analysis.moma import moma
sys.path.append(r"/Users/luho/PycharmProjects/model/cobrapy/code")
sys.path.append(r"/Users/luho/PycharmProjects/model/strain_design/code")
os.chdir('/Users/luho/PycharmProjects/model/strain_design/code')
# import self function
from mainFunction import *
from in_silico_strain_design_scripts import *
# load the ecYeast model
ecYeast = load_matlab_model("../data/ec3HP.mat")
"""knock-out"""
# pFBA method is used
# run the gene deletion with pFBA method, minimization of proteins pool
# in this simulation, 3HP production is as the objective function
# qs is unconstrait
def getModelWithRemoveGene(model0, gene_remove0):
model1 = model0.copy()
remove_genes(model1, gene_remove0,
remove_reactions=True) # this script can't be used
return model1
def solveEcModel(model, obj_id, target_id, glucose_uptake='r_1714_REV'):
def setUp(self):
self.model = load_matlab_model(testing_models_filepath, "ecoli_core")
self.S = self.model.to_array_based_model().S
def test_load_matlab_model(data_directory, mini_model, raven_model):
"""Test the reading of MAT model."""
mini_mat_model = io.load_matlab_model(join(data_directory, "mini.mat"))
raven_mat_model = io.load_matlab_model(join(data_directory, "raven.mat"))
assert compare_models(mini_model, mini_mat_model) is None
assert compare_models(raven_model, raven_mat_model) is None
def test_null(self):
S = load_matlab_model(testing_models_filepath,
"ecoli_core").to_array_based_model().S.todense()
N = null(S)
self.assertEqual(N.shape, (84, 23))
self.assertAlmostEqual(abs(S * N).max(), 0)
g.name = tg.name
g.annotation = tg.annotation
mini.reactions.sort()
mini.genes.sort()
mini.metabolites.sort()
# output to various formats
with open("mini.pickle", "wb") as outfile:
dump(mini, outfile, protocol=2)
save_matlab_model(mini, "mini.mat")
save_json_model(mini, "mini.json", pretty=True)
write_sbml_model(mini, "mini_fbc2.xml")
write_sbml_model(mini, "mini_fbc2.xml.bz2")
write_sbml_model(mini, "mini_fbc2.xml.gz")
write_sbml2(mini, "mini_fbc1.xml", use_fbc_package=True)
write_sbml_model(mini, "mini_cobra.xml", use_fbc_package=False)
raven = load_matlab_model("raven.mat")
with open("raven.pickle", "wb") as outfile:
dump(raven, outfile, protocol=2)
# fva results
fva_result = cobra.flux_analysis.flux_variability_analysis(textbook)
clean_result = OrderedDict()
for key in sorted(fva_result):
clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()}
with open("textbook_fva.json", "w") as outfile:
json_dump(clean_result, outfile)
# textbook solution
# TODO: this needs a reference solution rather than circular solution checking!
solution = cobra.flux_analysis.parsimonious.optimize_minimal_flux(textbook)
with open('textbook_solution.pickle', 'wb') as f:
savemat(round_filename % (k, now()),
{"fluxes": dok_matrix(fluxes)}, oned_as="column")
# if no overall improvement occured in this round, we are done
if nnz(fluxes) == prevNum:
break
# save the final result
savemat(final_filename % now(),
{
"fluxes": dok_matrix(fluxes),
"history": array(improvement_tracker),
"nnz_log": array(nnz_log)},
oned_as="column")
# done!
return fluxes
if __name__ == "__main__":
from cobra.io import load_matlab_model
from time import time
model = load_matlab_model("testing_models.mat", "ecoli_core")
S = model.to_array_based_model().S
start = time()
solved_fluxes = minspan(model, cores=1, verbose=True)
print "solved in %.2f seconds" % (time() - start)
print "nnz", nnz(solved_fluxes)
print "rank", matrix_rank(solved_fluxes)
print "max(S * v) =", abs(S * solved_fluxes).max()
#from IPython import embed; embed()
def test_load_matlab_model(data_directory, mini_model, raven_model):
"""Test the reading of MAT model."""
mini_mat_model = io.load_matlab_model(join(data_directory, "mini.mat"))
raven_mat_model = io.load_matlab_model(join(data_directory, "raven.mat"))
assert compare_models(mini_model, mini_mat_model) is None
assert compare_models(raven_model, raven_mat_model) is None
savemat(round_filename % (k, now()), {"fluxes": dok_matrix(fluxes)},
oned_as="column")
# if no overall improvement occured in this round, we are done
if nnz(fluxes) == prevNum:
break
# save the final result
savemat(final_filename % now(), {
"fluxes": dok_matrix(fluxes),
"history": array(improvement_tracker),
"nnz_log": array(nnz_log)
},
oned_as="column")
# done!
return fluxes
if __name__ == "__main__":
from cobra.io import load_matlab_model
from time import time
model = load_matlab_model("testing_models.mat", "ecoli_core")
S = model.to_array_based_model().S
start = time()
solved_fluxes = minspan(model, cores=1, verbose=True)
print "solved in %.2f seconds" % (time() - start)
print "nnz", nnz(solved_fluxes)
print "rank", matrix_rank(solved_fluxes)
print "max(S * v) =", abs(S * solved_fluxes).max()
#from IPython import embed; embed()
def test_load_mat(db_model):
model = load_matlab_model(join(static_model_dir, db_model.id + '.mat'))
assert model.id == db_model.id
def test_null(self):
S = load_matlab_model(testing_models_filepath, "ecoli_core").to_array_based_model().S.todense()
N = null(S)
self.assertEqual(N.shape, (84, 23))
self.assertAlmostEqual(abs(S * N).max(), 0)
def setUp(self):
self.model = load_matlab_model(testing_models_filepath, "ecoli_core")
self.S = self.model.to_array_based_model().S
def raise_scipy_errors():
with pytest.raises(ImportError):
io.save_matlab_model(None, 'test')
with pytest.raises(ImportError):
io.load_matlab_model('test')
def raise_scipy_errors():
with pytest.raises(ImportError):
io.save_matlab_model(None, 'test')
with pytest.raises(ImportError):
io.load_matlab_model('test')
def stop_engines(mpi=False, verbose=False):
profile = profile_minspan_mpi_dir if mpi else profile_minspan_dir
silence = "" if verbose else "2>/dev/null"
os.system('ipcluster stop --profile-dir="%s" %s' % (profile, silence))
if __name__ == "__main__":
stop_engines()
c, v = start_engines()
print "%d engines connected" % (len(c.ids))
from minspan import *
from cobra.io import load_matlab_model
from time import time
model = load_matlab_model("testing_models.mat", "inj661")
S = model.to_array_based_model().S
start = time()
solved_fluxes = minspan(model,
cores=1,
verbose=True,
mapper=v.map_sync,
starting_fluxes="auto")
print "solved in %.2f seconds" % (time() - start)
print "nnz", nnz(solved_fluxes)
print "rank", matrix_rank(solved_fluxes)
print "max(S * v) =", abs(S * solved_fluxes).max()
#from IPython import embed; embed()
stop_engines()
g.name = tg.name
g.annotation = tg.annotation
mini.reactions.sort()
mini.genes.sort()
mini.metabolites.sort()
# output to various formats
with open("mini.pickle", "wb") as outfile:
dump(mini, outfile, protocol=2)
save_matlab_model(mini, "mini.mat")
save_json_model(mini, "mini.json", pretty=True)
write_sbml_model(mini, "mini_fbc2.xml")
write_sbml_model(mini, "mini_fbc2.xml.bz2")
write_sbml_model(mini, "mini_fbc2.xml.gz")
write_sbml2(mini, "mini_fbc1.xml", use_fbc_package=True)
write_sbml_model(mini, "mini_cobra.xml", use_fbc_package=False)
raven = load_matlab_model("raven.mat")
with open("raven.pickle", "wb") as outfile:
dump(raven, outfile, protocol=2)
# TODO:these need a reference solutions rather than circular solution checking!
# fva results
fva_result = cobra.flux_analysis.flux_variability_analysis(textbook)
clean_result = OrderedDict()
for key in sorted(fva_result):
clean_result[key] = {k: round(v, 5) for k, v in fva_result[key].items()}
with open("textbook_fva.json", "w") as outfile:
json_dump(clean_result, outfile)
# fva with pfba constraint
fva_result = cobra.flux_analysis.flux_variability_analysis(textbook,
v = c.direct_view()
except Exception, e:
print e
sleep(5)
return c, v
def stop_engines(mpi=False, verbose=False):
profile = profile_minspan_mpi_dir if mpi else profile_minspan_dir
silence = "" if verbose else "2>/dev/null"
os.system('ipcluster stop --profile-dir="%s" %s' % (profile, silence))
if __name__ == "__main__":
stop_engines()
c, v = start_engines()
print "%d engines connected" % (len(c.ids))
from minspan import *
from cobra.io import load_matlab_model
from time import time
model = load_matlab_model("testing_models.mat", "inj661")
S = model.to_array_based_model().S
start = time()
solved_fluxes = minspan(model, cores=1, verbose=True, mapper=v.map_sync, starting_fluxes="auto")
print "solved in %.2f seconds" % (time() - start)
print "nnz", nnz(solved_fluxes)
print "rank", matrix_rank(solved_fluxes)
print "max(S * v) =", abs(S * solved_fluxes).max()
#from IPython import embed; embed()
stop_engines()
def import_matlab_model(path, variable_name=None):
""" Convert at matlab cobra_model to a pyTFA cobra_model, with Thermodynamic values
:param variable_name:
:param string path: The path of the file to import
:returns: The converted cobra_model
:rtype: cobra.thermo.model.Model
"""
# We're going to import the Matlab cobra_model and convert it to COBApy
mat_data = loadmat(path)
if variable_name:
mat_model = mat_data[variable_name][0, 0]
else:
meta_vars = {"__globals__", "__header__", "__version__"}
possible_keys = sorted(i for i in mat_data if i not in meta_vars)
if len(possible_keys) == 1:
variable_name = possible_keys[0]
else:
raise Exception('Need to specify a variable name if several ' +
'variables are contained int the mat file')
mat_model = mat_data[variable_name][0, 0]
# cobra_model = Model(mat_model['description'][0])
cobra_model = load_matlab_model(path)
cobra_model.description = mat_model['description'][0]
cobra_model.name = mat_model['description'][0]
metabolites = cobra_model.metabolites
## METABOLITES
# In the Matlab cobra_model, the corresponding components are :
# * mets = Identifiers
# * metNames = Names
# * metFormulas = formulas
# * metCompSymbol = compartments
# def read_mat_model(mat_struct, field_name, index):
# try:
# return mat_struct[field_name][index,0][0]
# except IndexError:
# return None
# metabolites = [Metabolite( read_mat_model(mat_model,'mets',i),
# formula=read_mat_model(mat_model,'metFormulas',i),
# name=read_mat_model(mat_model,'metNames',i),
# compartment=read_mat_model(mat_model,'metCompSymbol',i))
# for i in range(len(mat_model['metNames']))]
# Get the metSEEDID
seed_id = mat_model['metSEEDID']
for i, _ in enumerate(metabolites):
metabolites[i].annotation = {"seed_id": seed_id[i, 0][0]}
# ## REACTIONS
# # In the Matlab cobra_model, the corresponding components are :
# # * rxns = Names
# # * rev = Reversibility (not used, see https://cobrapy.readthedocs.io/en/0.5.11/faq.html#How-do-I-change-the-reversibility-of-a-Reaction?)
# # * lb = Lower bounds
# # * ub = Upper bounds
# # * subSystems = subsystem names
# # * S : Reactions matrix
# # 1 line = 1 metabolite
# # 1 column = 1 reaction
# # * c : Objective coefficient
# # * genes : Genes names
# # * rules : Genes rules
# # Some utilities for gene generation rules (convert the IDs to the names of the genes)
# gene_pattern = re.compile(r'x\([0-9]+\)')
# def gene_id_to_name(match):
# id_ = int(match.group()[2:-1])
# # /!\ These are indexed from 1, while python indexes from 0
# return mat_model['genes'][id_ - 1, 0][0]
# # Add each reaction
# for i in range(mat_model['S'].shape[1]):
# # Name of the reaction
# reaction = Reaction(str(mat_model['rxns'][i, 0][0]))
# # Add the reaction to the cobra_model
# cobra_model.add_reactions([reaction])
# # NOTE : The str() conversion above is needed, otherwise the CPLEX solver
# # does not work : "Invalid matrix input type --"
# # Reaction description
# # reaction.name not set
# # Subsystem (only if set in the Matlab cobra_model)
# if (len(mat_model['subSystems'][i, 0])):
# reaction.subsystem = mat_model['subSystems'][i, 0][0]
# # Lower bound
# reaction.lower_bound = float(mat_model['lb'][i, 0])
# # Upper bound
# reaction.upper_bound = float(mat_model['ub'][i, 0])
# # Objective coefficient
# reaction.objective_coefficient = float(mat_model['c'][i, 0])
# # Metabolites
# react_mets = {}
# # Iterate over each metabolite and see if it is part of the reaction
# # (stoechiomectric coefficient not equal to 0)
# for j, _ in enumerate(metabolites):
# if (mat_model['S'][j, i] != 0):
# react_mets[metabolites[j]] = mat_model['S'][j, i]
# reaction.add_metabolites(react_mets)
# # Genes
# try:
# if len(mat_model['rules'][i, 0]):
# rule = mat_model['rules'][i, 0][0]
# # Call the regex magic to convert IDs to gene names
# rule = gene_pattern.sub(gene_id_to_name, rule)
# # Add the data to the reaction
# reaction.gene_reaction_rule = rule
# except ValueError:
# pass
# except IndexError:
# # The gene number is higher than the length of the gene list
# warn('The gene reaction rule {} appears to be misaligned with '
# 'the gene list'.format(rule))
Compartments = dict()
CompartmentDB = mat_model['CompartmentData'][0]
num_comp = len(CompartmentDB['pH'][0][0])
comps = [{} for i in range(num_comp)]
for i in range(num_comp):
comp = comps[i]
comp['pH'] = CompartmentDB['pH'][0][0][i]
comp['ionicStr'] = CompartmentDB['ionicStr'][0][0][i]
comp['symbol'] = CompartmentDB['compSymbolList'][0][0, i][0]
comp['name'] = CompartmentDB['compNameList'][0][0, i][0]
comp['c_max'] = CompartmentDB['compMaxConc'][0][0][i]
comp['c_min'] = CompartmentDB['compMinConc'][0][0][i]
# Register our compartment by its name
if comp['symbol'] in Compartments:
raise Exception("Duplicate compartment ID : " + comp['symbol'])
Compartments[comp['symbol']] = comp
# We need to iterate first once to set the names of the compartments, so that we have the keys for our dictionnary...
for i in range(num_comp):
comp = comps[i]
comp['membranePot'] = {}
for j in range(num_comp):
comp['membranePot'][comps[j]['symbol']] = \
CompartmentDB['membranePot'][0][i, j]
cobra_model.compartments = Compartments
return cobra_model
