internal_ids = []
for metab in network.metabolites:
if not metab.is_external:
id_ind = [ind for ind, id in enumerate(ids) if id == metab.id]
if len(id_ind):
internal_ids.append(id_ind[0])
ids = list(np.delete(ids, internal_ids))
cone = np.delete(cone, internal_ids, axis=1)
if mpi_wrapper.is_first_process():
try:
np.savetxt(args.out_path,
cone,
delimiter=',',
header=','.join(ids),
comments='')
except OverflowError:
normalised = np.transpose(normalize_columns(np.transpose(cone)))
np.savetxt(args.out_path,
normalised,
delimiter=',',
header=','.join(ids),
comments='')
if args.print_conversions:
print_ecms_direct(np.transpose(cone), ids)
end = time()
mp_print('Ran in %f seconds' % (end - start))
def calc_ECMs(file_path, print_results=False, input_file_path=''):
"""
Calculates ECMs using ECMtool
:return ecms: np.array
This array contains the ECMs as columns and the metabolites as rows
:param file_path: string
String with path to the SBML-file.
:param reactions_to_tag: list with strings
List with reaction-IDs of reactions that need to be tagged
:param print_results: Boolean
:param hide_metabs: indices of metabolites that should be ignored
"""
# Stap 1: netwerk bouwen
network = extract_sbml_stoichiometry(file_path, determine_inputs_outputs=True)
external_inds = [ind for ind, metab in enumerate(network.metabolites) if metab.is_external]
"""The following are just for checking the inputs to this program."""
metab_info_ext = [(ind, metab.id, metab.name, metab.direction) for ind, metab in
enumerate(network.metabolites) if metab.is_external]
"""I extract some information about the external metabolites for checking"""
metab_info_ext_df = pd.DataFrame(metab_info_ext, columns=['metab_ind', 'metab_id', 'metab_name', 'Direction'])
"""You can choose to save this information, by uncommenting this line"""
# metab_info_ext_df.to_csv(path_or_buf='external_info_iJR904.csv', index=False)
"""If an input file is supplied, we set in input, output, and hide metabolites from this"""
if input_file_path: # If no input file is supplied, standard detection of ecmtool is used
info_metabs_df = pd.read_csv(input_file_path)
info_metabs_input = info_metabs_df[info_metabs_df.Input == 1]
info_metabs_output = info_metabs_df[info_metabs_df.Output == 1]
info_metabs_hidden = info_metabs_df[info_metabs_df.Hidden == 1]
# Get the indices that correspond to the metabolites that are inputs, outputs, or hidden.
input_inds = list(info_metabs_input.Index.values)
output_inds = list(info_metabs_output.Index.values) + [ind for ind, metab in enumerate(network.metabolites) if
metab.id == 'objective']
hide_inds = list(info_metabs_hidden.Index.values)
prohibit_inds = [ind for ind, metab in enumerate(network.metabolites) if
(metab.is_external) & (not ind in input_inds + output_inds + hide_inds) & (
not metab.id == 'objective')]
both_inds = [ind for ind in range(len(network.metabolites)) if (ind in input_inds) and (ind in output_inds)]
# Use input information to set input, output, hidden, and prohibited metabolites
network.set_inputs(input_inds)
network.set_outputs(output_inds)
network.set_both(both_inds)
network.prohibit(prohibit_inds)
network.hide(hide_inds)
# Print comma-separated lists of input information. These lists can be used for running the same computation
# via command line, for example to use other arguments
print(','.join(map(str, input_inds)))
print(','.join(map(str, output_inds)))
print(','.join(map(str, hide_inds)))
print(','.join(map(str, prohibit_inds)))
"""Keep a copy of the full network before compression. This can be nice for later."""
full_network = copy.deepcopy(network)
orig_N = network.N
""""Split in and out metabolites, to facilitate ECM computation"""
network.split_in_out(only_rays=False)
"""Stap 2: compress network"""
network.compress(verbose=True)
"""Stap 3: Ecms enumereren"""
# In this script, indirect intersection is used. Use command line options to use direct intersection
cone = get_conversion_cone(network.N, network.external_metabolite_indices(), network.reversible_reaction_indices(),
network.input_metabolite_indices(), network.output_metabolite_indices(), only_rays=False,
verbose=True)
cone_transpose, ids = unsplit_metabolites(np.transpose(cone), network)
cone = np.transpose(cone_transpose)
if print_results:
print_ecms_direct(np.transpose(cone), ids)
cone = cone.transpose() # columns will be the different ECMs, rows are metabolites
return cone, ids, full_network