def test_read_weighted_network(self):
M_multiplex = network_construction.make_multiplex(self.imgdata,
timewindow=7,
overlap=2)
network_io.write_weighted_network(
M_multiplex, 'test_for_network_reading_WILL_BE_REMOVED.txt',
'Created by test_read_weighted_network')
M_multiplex_read = network_io.read_weighted_network(
'test_for_network_reading_WILL_BE_REMOVED.txt')
try:
self.assertEqual(self.round_edge_weights(M_multiplex),
self.round_edge_weights(M_multiplex_read))
finally:
os.remove('test_for_network_reading_WILL_BE_REMOVED.txt')
M_multilayer = network_construction.make_clustered_multilayer(
self.imgdata, timewindow=7, overlap=2, n_clusters=3)
network_io.write_weighted_network(
M_multilayer, 'test_for_network_reading_WILL_BE_REMOVED.txt',
'Created by test_read_weighted_network')
M_multilayer_read = network_io.read_weighted_network(
'test_for_network_reading_WILL_BE_REMOVED.txt')
try:
self.assertEqual(self.round_edge_weights(M_multilayer),
self.round_edge_weights(M_multilayer_read))
finally:
os.remove('test_for_network_reading_WILL_BE_REMOVED.txt')
def null_model_subgraphs_from_network_file(filename, intralayer_density,
interlayer_density,
null_model_function, nnodes,
nlayers, number_of_repeats):
M = network_io.read_weighted_network(filename)
M = network_construction.threshold_multilayer_network(
M, intralayer_density, interlayer_density)
all_repetitions_results = null_model_subgraphs_from_network(
M, null_model_function, nnodes, nlayers, number_of_repeats)
return all_repetitions_results
def isomorphism_classes_from_existing_network_files(network_folder,
subnets_savefolder,
subgraph_size_dict,
allowed_aspects=[0],
intralayer_density=0.05,
interlayer_density=0.05):
'''
Find isomorphism classes from previously constructed and saved networks.
Subgraph size dict can only contain one n_layers because saved nets have fixed n_layers.
Aggregated results and examples dict only.
'''
sorted_filenames = sorted(os.listdir(network_folder),
key=lambda s: [int(l) for l in s.split('_')])
aggregated_dicts_dict = dict()
examples_dicts_dict = dict()
for n_layers in subgraph_size_dict:
for n_nodes in subgraph_size_dict[n_layers]:
aggregated_dicts_dict[(n_nodes,
n_layers)] = collections.defaultdict(dict)
examples_dicts_dict[(n_nodes, n_layers)] = dict()
for filename in sorted_filenames:
full_path = network_folder + filename
M = network_io.read_weighted_network(full_path)
M = network_construction.threshold_multilayer_network(
M, intralayer_density, interlayer_density)
for nlayers in subgraph_size_dict:
for nnodes in subgraph_size_dict[nlayers]:
subgraph_classification.find_isomorphism_classes(
M,
nnodes,
nlayers,
filename='this_file_should_not_exist',
allowed_aspects=allowed_aspects,
aggregated_dict=aggregated_dicts_dict[(nnodes, nlayers)],
examples_dict=examples_dicts_dict[(nnodes, nlayers)])
for n_layers in subgraph_size_dict:
for n_nodes in subgraph_size_dict[n_layers]:
aggregated_dict_filename = subnets_savefolder + str(
n_nodes) + '_' + str(n_layers) + '_agg.pickle'
f = open(aggregated_dict_filename, 'w')
pickle.dump(aggregated_dicts_dict[(n_nodes, n_layers)], f)
f.close()
del (aggregated_dicts_dict[(n_nodes, n_layers)])
examples_dict_filename = subnets_savefolder + 'examples_' + str(
n_nodes) + '_' + str(n_layers) + '.pickle'
f = open(examples_dict_filename, 'w')
pickle.dump(examples_dicts_dict[(n_nodes, n_layers)], f)
f.close()
del (examples_dicts_dict[(n_nodes, n_layers)])