def test_lump_coupled_reactions_NV(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 1, 'r3': 1, 'r4': 1, 'r6': 1}], self.r_id2i)
NV = np.dot(N, V)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
N_new, V_new, new_r_id2i, r_id2lr_id, _ = lump_coupled_reactions(N, V, coupled_r_id_groups, self.r_id2i)
NV_new = np.dot(N_new, V_new)
self.assertListEqual(list(NV.flatten()), list(NV_new.flatten()), 'NV should not have changed')
def test_lump_coupled_reactions_N_2(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 1, 'r3': 1, 'r4': 1, 'r6': 1}], self.r_id2i)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
N_new, V_new, new_r_id2i, r_id2lr_id, _ = lump_coupled_reactions(N, V, coupled_r_id_groups, self.r_id2i)
lr_id = next(r_id2lr_id.values())
st = N_new[self.s_id2i['m2_b'], new_r_id2i[lr_id]]
self.assertEqual(1, st, 'The lumped reaction was supposed to produce 1 m2_b, consumes %g instead' % -st)
def test_lump_coupled_reactions_V(self):
N = model2stoichiometric_matrix(self.model, self.s_id2i, self.r_id2i)
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 1, 'r3': 1, 'r4': 1, 'r6': 1}], self.r_id2i)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
N_new, V_new, new_r_id2i, r_id2lr_id, _ = lump_coupled_reactions(N, V, coupled_r_id_groups, self.r_id2i)
lr_id = next(r_id2lr_id.values())
r_i = V_new[new_r_id2i[lr_id], :]
self.assertListEqual([10, 1], list(r_i),
'The lumped reaction was supposed to participate in EFMs with coefficients [10, 1] not %s'
% r_i)
def lump_coupled_reactions(self):
coupled_r_id_groups = get_coupled_reactions(self.V, self.r_id2i)
lr_i = 0
for r_ids in coupled_r_id_groups:
lumped_r_id, lr_i = get_unique_id(self.r_id2i, 'coupled_r', lr_i)
self.coupled_rs.add(lumped_r_id)
self.r_id2gr_id.update({r_id: lumped_r_id for r_id in r_ids})
self.r_id2i[lumped_r_id] = self.V.shape[0]
sample_r_index = self.r_id2i[r_ids[0]]
sample_efm_index = np.nonzero(self.V[sample_r_index, :])[0][0]
sample_efm = self.V[:, sample_efm_index]
c = abs(sample_efm[sample_r_index]) * 1.0
sign = coefficient_to_binary(sample_efm[sample_r_index])
self.gr_id2r_id2c[lumped_r_id] = {r_id: sample_efm[self.r_id2i[r_id]] / c for r_id in r_ids}
lambdas = np.array([[self.gr_id2r_id2c[lumped_r_id][r_id]] for r_id in r_ids])
r_new = np.dot(self.N[:, tuple((self.r_id2i[r_id] for r_id in r_ids))], lambdas)
replace_zeros(r_new)
self.N = np.concatenate((self.N, r_new), axis=1)
self.V = np.concatenate((self.V, [self.V[sample_r_index, :] * sign]), axis=0)
self.r_ids -= set(r_ids)
self.r_ids.add(lumped_r_id)
def test_coupled_reactions_content(self):
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 10, 'r3': 10, 'r4': 10, 'r6': 10}], self.r_id2i)
coupled_r_id_groups = get_coupled_reactions(V, self.r_id2i)
self.assertIn(['r1', 'r3'], coupled_r_id_groups, 'Was supposed to find [r1, r3] coupled reaction group.')
def test_coupled_reactions_len(self):
V = get_efm_matrix([{'r1': 10, 'r2': 10, 'r3': 10}, {'r1': 10, 'r3': 10, 'r4': 10, 'r6': 10}], self.r_id2i)
c_r_id_lists = get_coupled_reactions(V, self.r_id2i)
self.assertEqual(1, len(c_r_id_lists),
'Was supposed to find one coupled reaction group, got %g instead.' % len(c_r_id_lists))
