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"""Test functionalities of pFBA."""
import warnings
from typing import Callable, List
import pytest
from cobra.core import Model
from cobra.exceptions import Infeasible
from cobra.flux_analysis.parsimonious import add_pfba, pfba
def test_pfba_benchmark(
large_model: Model, benchmark: Callable, all_solvers: List[str]
) -> None:
"""Benchmark pFBA functionality."""
large_model.solver = all_solvers
benchmark(pfba, large_model)
def test_pfba(model: Model, all_solvers: List[str]) -> None:
"""Test pFBA functionality."""
model.solver = all_solvers
with model:
add_pfba(model)
with pytest.raises(ValueError):
add_pfba(model)
expression = model.objective.expression
n_constraints = len(model.constraints)
solution = pfba(model)
assert solution.status == "optimal"
assert solution.fluxes["Biomass_Ecoli_core"] == pytest.approx(
0.8739, abs=1e-4, rel=0.0
)
assert solution.fluxes.abs().sum() == pytest.approx(518.4221, abs=1e-4, rel=0.0)
# test changes to model reverted
assert expression == model.objective.expression
assert len(model.constraints) == n_constraints
# needed?
# Test desired_objective_value
# desired_objective = 0.8
# pfba(model, solver=solver,
# desired_objective_value=desired_objective)
# abs_x = [abs(i) for i in model.solution.x]
# assert model.solution.status == "optimal"
# assert abs(model.solution.f - desired_objective) < 0.001
# assert abs(sum(abs_x) - 476.1594) < 0.001
# TODO: parametrize fraction (DRY it up)
# Test fraction_of_optimum
solution = pfba(model, fraction_of_optimum=0.95)
assert solution.status == "optimal"
assert solution.fluxes["Biomass_Ecoli_core"] == pytest.approx(
0.95 * 0.8739, abs=1e-4, rel=0.0
)
abs_x = [abs(i) for i in solution.fluxes.values]
assert sum(abs_x) == pytest.approx(493.4400, abs=1e-4, rel=0.0)
# Infeasible solution
model.reactions.ATPM.lower_bound = 500
with warnings.catch_warnings():
warnings.simplefilter("error", UserWarning)
with pytest.raises((UserWarning, Infeasible, ValueError)):
pfba(model)
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