def pick_up_intermediate_cone(internal_metabolites, network,
intermediate_cone_path):
with open(intermediate_cone_path) as file:
header = file.readline()
metab_ids_intermediate = header.split(',')
if metab_ids_intermediate[-1][-1:] == '\n':
metab_ids_intermediate[-1] = metab_ids_intermediate[-1][:-1]
R_transpose = np.genfromtxt(intermediate_cone_path,
dtype='str',
delimiter=',',
skip_header=1)
R = np.transpose(to_fractions(R_transpose))
new_netw_metabs = []
for metab_id in metab_ids_intermediate:
new_netw_metabs.append([
metab for metab in network.metabolites if metab.id == metab_id
][0])
network.metabolites = new_netw_metabs
internal_metabolites = [
ind for ind, metab in enumerate(network.metabolites)
if not metab.is_external
]
network.N = R
return R, internal_metabolites, network
if args.direct:
# Direct intersection method
R = network.N
external = np.asarray(network.external_metabolite_indices())
internal = np.setdiff1d(range(R.shape[0]), external)
T_intersected, ids = intersect_directly(
R,
internal,
network,
verbose=args.verbose,
sort_order=args.sort_order,
manual_override=args.manual_override,
intermediate_cone_path=args.intermediate_cone_path)
if external_cycles:
external_cycles_array = to_fractions(
np.zeros((T_intersected.shape[0], len(external_cycles))))
for ind, cycle in enumerate(external_cycles):
for cycle_metab in cycle:
metab_ind = [
ind for ind, metab in enumerate(ids)
if metab == cycle_metab
][0]
external_cycles_array[metab_ind, ind] = cycle[cycle_metab]
T_intersected = np.concatenate(
(T_intersected, external_cycles_array, -external_cycles_array),
axis=1)
cone = np.transpose(T_intersected)
else:
if args.iterative:
def unique(matrix):
unique_set = list(
{tuple(row)
for row in matrix if np.count_nonzero(row) > 0})
return np.vstack(unique_set) if len(unique_set) else to_fractions(
np.ndarray(shape=(0, matrix.shape[1])))