def test_sparse_matrix(self, model):
sparse_types = ['dok', 'lil']
solution = model.optimize()
for sparse_type in sparse_types:
S = create_stoichiometric_array(model, array_type=sparse_type)
mass_balance = S.dot(solution.fluxes)
assert numpy.allclose(mass_balance, 0)
def test_dense_matrix(self, model):
S = create_stoichiometric_array(model, array_type='dense', dtype=int)
assert S.dtype == int
assert numpy.allclose(S.max(), [59])
S = create_stoichiometric_array(model,
array_type='data_frame',
dtype=int)
assert S.stoichiometry.dtype == int
assert numpy.allclose(S.stoichiometry.max(), [59])
S = create_stoichiometric_array(model, array_type='dense', dtype=float)
solution = model.optimize()
mass_balance = S.dot(solution.fluxes)
assert numpy.allclose(mass_balance, 0)
def test_dense_matrix(self, model):
S = create_stoichiometric_matrix(model, array_type='dense', dtype=int)
assert S.dtype == int
assert numpy.allclose(S.max(), [59])
S_df = create_stoichiometric_matrix(model,
array_type='DataFrame',
dtype=int)
assert S_df.values.dtype == int
assert numpy.all(S_df.columns == [r.id for r in model.reactions])
assert numpy.all(S_df.index == [m.id for m in model.metabolites])
assert numpy.allclose(S_df.values, S)
S = create_stoichiometric_matrix(model,
array_type='dense',
dtype=float)
solution = model.optimize()
mass_balance = S.dot(solution.fluxes)
assert numpy.allclose(mass_balance, 0)
