def test_find_unique_metabolites(model):
"""
Expect there to be less metabolites when removing compartment tag.
Metabolites may be transported into different compartments, which means
that in a compartimentalized model the number of metabolites may be
much higher than in a model with no compartments. This test counts only
one occurrence of each metabolite and returns this as the number of unique
metabolites. The test expects that the model is compartimentalized, and
thus, that the number of unique metabolites is generally lower than the
total number of metabolites.
Implementation:
Reduce the list of metabolites to a unique set by removing the compartment
tag. The cobrapy SBML parser adds compartment tags to each metabolite ID.
"""
ann = test_find_unique_metabolites.annotation
ann["data"] = list(basic.find_unique_metabolites(model))
ann["metric"] = len(ann["data"]) / len(model.metabolites)
ann["message"] = wrapper.fill(
"""Not counting the same entities in other compartments, there is a
total of {} ({:.2%}) unique metabolites in the model: {}""".format(
len(ann["data"]), ann["metric"], truncate(ann["data"])))
assert len(ann["data"]) < len(model.metabolites), ann["message"]
def test_find_unique_metabolites(model):
"""
Expect there to be less metabolites when removing compartment tag.
Metabolites may be transported into different compartments, which means
that in a compartimentalized model the number of metabolites may be
much higher than in a model with no compartments. This test counts only
one occurrence of each metabolite and returns this as the number of unique
metabolites. The test expects that the model is compartimentalized, and
thus, that the number of unique metabolites is generally lower than the
total number of metabolites.
Implementation:
Reduce the list of metabolites to a unique set by removing the compartment
tag. The cobrapy SBML parser adds compartment tags to each metabolite ID.
"""
ann = test_find_unique_metabolites.annotation
ann["data"] = list(basic.find_unique_metabolites(model))
ann["metric"] = len(ann["data"]) / len(model.metabolites)
ann["message"] = wrapper.fill(
"""Not counting the same entities in other compartments, there is a
total of {} ({:.2%}) unique metabolites in the model: {}""".format(
len(ann["data"]), ann["metric"], truncate(ann["data"])))
assert len(ann["data"]) < len(model.metabolites), ann["message"]
def test_find_unique_metabolites(model, num):
"""Expect amount of metabolic reactions to be identified correctly."""
assert len(basic.find_unique_metabolites(model)) == num
def test_find_unique_metabolites(read_only_model, store):
"""Expect there to be less metabolites when removing compartment tag."""
store["num_unique_metabolites"] = len(
basic.find_unique_metabolites(read_only_model))
assert store["num_unique_metabolites"] < len(read_only_model.metabolites)