def test_flattenExternalModelDefinition():
sbml_path = data.DFBA_EMD_SBML
print(sbml_path)
doc = sbmlio.read_sbml(sbml_path)
# test that resource could be read
assert doc is not None
# test that model in document
assert doc.getModel() is not None
print(doc)
print(doc.getModel().getId())
# check that model exists
doc_no_emd = flattenExternalModelDefinitions(doc, validate=True)
assert doc_no_emd is not None
# check that there are no external model definitions
comp_doc_no_emd = doc_no_emd.getPlugin("comp")
assert 0 == comp_doc_no_emd.getNumExternalModelDefinitions()
# check that all model definitions are still there
assert 3 == comp_doc_no_emd.getNumModelDefinitions()
# check model consistency
Nall, Nerr, Nwarn = validation.check_doc(doc_no_emd)
def test_flattenExternalModelDefinition():
sbml_path = data.DFBA_EMD_SBML
print(sbml_path)
doc = sbmlio.read_sbml(sbml_path)
# test that resource could be read
assert doc is not None
# test that model in document
assert doc.getModel() is not None
print(doc)
print(doc.getModel().getId())
# check that model exists
doc_no_emd = flattenExternalModelDefinitions(doc, validate=True)
assert doc_no_emd is not None
# check that there are no external model definitions
comp_doc_no_emd = doc_no_emd.getPlugin("comp")
assert 0 == comp_doc_no_emd.getNumExternalModelDefinitions()
# check that all model definitions are still there
assert 3 == comp_doc_no_emd.getNumModelDefinitions()
# check model consistency
Nall, Nerr, Nwarn = validation.check_doc(doc_no_emd)
def check_model_dict(d):
"""Check that no errors."""
# create model and print SBML
core_model = CoreModel.from_dict(model_dict=d)
core_model.create_sbml()
assert core_model.doc is not None
[Nall, Nerr, Nwar] = check_doc(core_model.doc, units_consistency=False)
assert Nerr == 0
def test_biomodel_merge():
""" Test model merging.
Using the pytest tmpdir fixture
:param tmpdir:
:return:
"""
manipulation_dir = os.path.join(data_dir, 'manipulation')
# dictionary of ids & paths of models which should be combined
# here we just bring together the first Biomodels
model_ids = ["BIOMD000000000{}".format(k) for k in range(1, 5)]
model_paths = dict(zip(model_ids,
[os.path.join(manipulation_dir, "{}.xml".format(mid)) for mid in model_ids])
)
print(model_paths)
# merge model
out_dir = os.path.join(manipulation_dir, 'output')
if not os.path.exists(out_dir):
os.mkdir(out_dir)
print('out_dir:', out_dir)
doc = manipulation.merge_models(model_paths, out_dir=out_dir, validate=False)
assert doc is not None
Nall, Nerr, Nwarn = validation.check_doc(doc, ucheck=False)
assert Nerr == 0
assert Nwarn == 0
assert Nall == 0
# flatten the model
doc_flat = comp.flattenSBMLDocument(doc)
assert doc_flat is not None
libsbml.writeSBMLToFile(doc_flat, os.path.join(out_dir, "merged_flat.xml"))
Nall, Nerr, Nwarn = validation.check_doc(doc_flat, ucheck=False)
assert Nerr == 0
assert Nwarn in [0, 74]
assert Nall in [0, 74]
# dictionary of ids & paths of models which should be combined
# here we just bring together the first Biomodels
model_ids = ["BIOMD000000000{}".format(k) for k in range(1, 5)]
model_paths = dict(
zip(model_ids,
[os.path.join(merge_dir, "{}.xml".format(mid)) for mid in model_ids]))
pprint(model_paths)
# create merged model
output_dir = os.path.join(merge_dir, 'output')
doc = manipulation.merge_models(model_paths,
out_dir=output_dir,
validate=False)
# validate
Nall, Nerr, Nwarn = validation.check_doc(doc, units_consistency=False)
assert Nerr == 0
assert Nwarn == 0
assert Nall == 0
# write the merged model
print(libsbml.writeSBMLToString(doc))
libsbml.writeSBMLToFile(doc, os.path.join(output_dir, "merged.xml"))
# flatten the merged model
doc_flat = comp.flattenSBMLDocument(doc)
Nall, Nerr, Nwarn = validation.check_doc(doc_flat, units_consistency=False)
libsbml.writeSBMLToFile(doc_flat, os.path.join(output_dir, "merged_flat.xml"))
# In[ ]:
# success probability of Geometric-1
up_mean_geo1 = up.createUncertParameter() # type: libsbml.UncertParameter
up_mean_geo1.setType(libsbml.DISTRIB_UNCERTTYPE_EXTERNALPARAMETER)
up_mean_geo1.setName("success probability of Geometric 1")
up_mean_geo1.setValue(0.4)
up_mean_geo1.setDefinitionURL("http://www.probonto.org/ontology#PROB_k0000789")
return doc
if __name__ == "__main__":
functions = [
# distrib_normal,
# distrib_all,
uncertainty,
]
for f_creator in functions:
name = f_creator.__name__
print(name)
# distrib_example1()
doc = f_creator()
sbml = libsbml.writeSBMLToString(doc)
print("-" * 80)
print(sbml)
print("-" * 80)
sbml_path = "./{}.xml".format(name)
libsbml.writeSBMLToFile(doc, sbml_path)
validation.check_doc(doc)
def flattenExternalModelDefinitions(doc, validate=False):
""" Converts all ExternalModelDefinitions to ModelDefinitions.
I.e. the definition of models in external files are read
and directly included in the top model. The resulting
comp model consists than only of a single file.
The model refs in the submodel do not change in the process,
so no need to update the submodels.
:param doc: SBMLDocument
:return: SBMLDocument with ExternalModelDefinitions replaced
"""
logging.debug('* flattenExternalModelDefinitions')
# FIXME: handle multiple levels of hierarchies. Recursively to handle the ExternalModelDefinitions of submodels
warnings.warn("flattenExternalModelDefinitions does not work recursively!")
warnings.warn(
"flattenExternalModelDefinitions: THIS DOES NOT WORK - ONLY USE IF YOU KNOW WHAT YOU ARE DOING"
)
comp_doc = doc.getPlugin("comp")
if comp_doc is None:
logging.warning(
"Model is not a comp model, no ExternalModelDefinitions")
return doc
emd_list = comp_doc.getListOfExternalModelDefinitions()
if (emd_list is None) or (len(emd_list) == 0):
# no ExternalModelDefinitions
logging.warning("Model does not contain any ExternalModelDefinitions")
return doc
else:
model = doc.getModel()
comp_model = model.getPlugin("comp")
emd_ids = []
for emd in emd_list:
logging.debug(emd)
emd_ids.append(emd.getId())
# get the model definition from the model
ref_model = emd.getReferencedModel()
ref_doc = ref_model.getSBMLDocument()
# print(ref_model)
for k in range(ref_doc.getNumPlugins()):
plugin = ref_doc.getPlugin(k)
# print(k, plugin)
# enable the package on the main SBMLDocument
uri = plugin.getURI()
prefix = plugin.getPrefix()
name = plugin.getPackageName()
doc.enablePackage(uri, prefix, True)
# print(k, plugin)
# print(uri, prefix)
# print("\n")
# add model definition for model
md = libsbml.ModelDefinition(ref_model)
comp_doc.addModelDefinition(md)
# remove the emds afterwards
for emd_id in emd_ids:
# remove the emd from the model
comp_doc.removeExternalModelDefinition(emd_id)
# validate
if validate:
validation.check_doc(doc)
return doc
def flattenExternalModelDefinitions(doc, validate=False):
""" Converts all ExternalModelDefinitions to ModelDefinitions.
I.e. the definition of models in external files are read
and directly included in the top model. The resulting
comp model consists than only of a single file.
The model refs in the submodel do not change in the process,
so no need to update the submodels.
:param doc: SBMLDocument
:return: SBMLDocument with ExternalModelDefinitions replaced
"""
logging.debug('* flattenExternalModelDefinitions')
# FIXME: handle multiple levels of hierarchies. Recursively to handle the ExternalModelDefinitions of submodels
warnings.warn("flattenExternalModelDefinitions does not work recursively!")
warnings.warn("flattenExternalModelDefinitions: THIS DOES NOT WORK - ONLY USE IF YOU KNOW WHAT YOU ARE DOING")
comp_doc = doc.getPlugin("comp")
if comp_doc is None:
logging.warning("Model is not a comp model, no ExternalModelDefinitions")
return doc
emd_list = comp_doc.getListOfExternalModelDefinitions()
if (emd_list is None) or (len(emd_list) == 0):
# no ExternalModelDefinitions
logging.warning("Model does not contain any ExternalModelDefinitions")
return doc
else:
model = doc.getModel()
comp_model = model.getPlugin("comp")
emd_ids = []
for emd in emd_list:
logging.debug(emd)
emd_ids.append(emd.getId())
# get the model definition from the model
ref_model = emd.getReferencedModel()
ref_doc = ref_model.getSBMLDocument()
# print(ref_model)
for k in range(ref_doc.getNumPlugins()):
plugin = ref_doc.getPlugin(k)
# print(k, plugin)
# enable the package on the main SBMLDocument
uri = plugin.getURI()
prefix = plugin.getPrefix()
name = plugin.getPackageName()
doc.enablePackage(uri, prefix, True)
# print(k, plugin)
# print(uri, prefix)
# print("\n")
# add model definition for model
md = libsbml.ModelDefinition(ref_model)
comp_doc.addModelDefinition(md)
# remove the emds afterwards
for emd_id in emd_ids:
# remove the emd from the model
comp_doc.removeExternalModelDefinition(emd_id)
# validate
if validate:
validation.check_doc(doc)
return doc
up_mean_geo1 = up.createUncertParameter() # type: libsbml.UncertParameter
up_mean_geo1.setType(libsbml.DISTRIB_UNCERTTYPE_EXTERNALPARAMETER)
up_mean_geo1.setName("success probability of Geometric 1")
up_mean_geo1.setValue(0.4)
up_mean_geo1.setDefinitionURL(
"http://www.probonto.org/ontology#PROB_k0000789")
return doc
if __name__ == "__main__":
functions = [
# distrib_normal,
# distrib_all,
uncertainty,
]
for f_creator in functions:
name = f_creator.__name__
print(name)
# distrib_example1()
doc = f_creator()
sbml = libsbml.writeSBMLToString(doc)
print("-" * 80)
print(sbml)
print("-" * 80)
sbml_path = "./{}.xml".format(name)
libsbml.writeSBMLToFile(doc, sbml_path)
validation.check_doc(doc)
length=UNIT_m,
area=UNIT_m2,
volume=UNIT_KIND_LITRE),
'units': [UNIT_hr, UNIT_m, UNIT_m2, UNIT_mM],
'parameters': [Parameter(sid="p1", value=0.0, unit=UNIT_mM)],
'assignments': [
InitialAssignment('p1', 'normal(0 mM, 1 mM)'),
]
}
# create model and print SBML
core_model = CoreModel.from_dict(model_dict=model_dict)
print_xml(core_model.get_sbml())
# validate model
check_doc(core_model.doc, units_consistency=False)
# ### Using a normal distribution
# In this example, the initial value of y is set as a draw from the normal distribution `normal(z,10)`:
# In[4]:
model_dict = {
'mid': 'normal',
'packages': ['distrib'],
'parameters': [
Parameter('y', value=1.0),
Parameter('z', value=1.0),
],
'assignments': [
InitialAssignment('y', 'normal(z, 10)'),