def construct_graph_list_und(graphs_to_const):
"""Construct and return a list of graphs so graph construction is easily
repeatable.
Can handle: Random, Small-world, Scale-free, SGPA, SGPA-random"""
graph_list = []
# Always construct and add Allen Institute mouse brain to list
G_brain = brain_graph.binary_undirected()[0]
graph_list.append(G_brain)
# Calculate degree & clustering coefficient distribution
n_nodes = G_brain.order()
brain_degree = nx.degree(G_brain).values()
brain_degree_mean = np.mean(brain_degree)
# Construct degree controlled random
if 'Random' in graphs_to_const:
G_RAND = und_graphs.random_simple_deg_seq(sequence=brain_degree,
brain_size=BRAIN_SIZE,
tries=1000)[0]
graph_list.append(G_RAND)
# Construct small-world graph
if 'Small-world' in graphs_to_const:
graph_list.append(
nx.watts_strogatz_graph(n_nodes, int(round(brain_degree_mean)),
SW_REWIRE_PROB))
# Construct scale-free graph
if 'Scale-free' in graphs_to_const:
graph_list.append(
nx.barabasi_albert_graph(n_nodes,
int(round(brain_degree_mean / 2.))))
# Construct SGPA graph
if 'SGPA' in graphs_to_const:
G_SGPA = source_growth(bc.num_brain_nodes,
bc.num_brain_edges_directed,
L=LENGTH_SCALE)[0]
graph_list.append(G_SGPA.to_undirected())
# Construct degree-controlled SGPA graph
if 'SGPA-random' in graphs_to_const:
SGPA_degree = nx.degree(G_SGPA).values()
G_SGPA_RAND = und_graphs.random_simple_deg_seq(
sequence=SGPA_degree, brain_size=BRAIN_SIZE, tries=1000)[0]
graph_list.append(G_SGPA_RAND)
# Error check that we created correct number of graphs
if len(graph_list) != len(graphs_to_const):
raise RuntimeError('Graph list/names don\'t match')
return graph_list
def construct_graph_list_und(graphs_to_const):
"""Construct and return a list of graphs so graph construction is easily
repeatable.
Can handle: Random, Small-world, Scale-free, SGPA, SGPA-random"""
graph_list = []
# Always construct and add Allen Institute mouse brain to list
G_brain = brain_graph.binary_undirected()[0]
graph_list.append(G_brain)
# Calculate degree & clustering coefficient distribution
n_nodes = G_brain.order()
brain_degree = nx.degree(G_brain).values()
brain_degree_mean = np.mean(brain_degree)
# Construct degree controlled random
if 'Random' in graphs_to_const:
G_RAND = und_graphs.random_simple_deg_seq(
sequence=brain_degree, brain_size=BRAIN_SIZE, tries=1000)[0]
graph_list.append(G_RAND)
# Construct small-world graph
if 'Small-world' in graphs_to_const:
graph_list.append(nx.watts_strogatz_graph(
n_nodes, int(round(brain_degree_mean)), SW_REWIRE_PROB))
# Construct scale-free graph
if 'Scale-free' in graphs_to_const:
graph_list.append(nx.barabasi_albert_graph(
n_nodes, int(round(brain_degree_mean / 2.))))
# Construct SGPA graph
if 'SGPA' in graphs_to_const:
G_SGPA = source_growth(bc.num_brain_nodes, bc.num_brain_edges_directed,
L=LENGTH_SCALE)[0]
graph_list.append(G_SGPA.to_undirected())
# Construct degree-controlled SGPA graph
if 'SGPA-random' in graphs_to_const:
SGPA_degree = nx.degree(G_SGPA).values()
G_SGPA_RAND = und_graphs.random_simple_deg_seq(
sequence=SGPA_degree, brain_size=BRAIN_SIZE, tries=1000)[0]
graph_list.append(G_SGPA_RAND)
# Error check that we created correct number of graphs
if len(graph_list) != len(graphs_to_const):
raise RuntimeError('Graph list/names don\'t match')
return graph_list
def construct_graph_list_und(graphs_to_const):
"""Construct and return a list of graphs so graph construction is easily
repeatable"""
graph_check = ['Random', 'Small-world', 'Scale-free', 'SGPA']
graph_list = []
# Always construct and add Allen Institute mouse brain to list
G_brain, _, _ = brain_graph.binary_undirected()
graph_list.append(G_brain)
# Calculate degree & clustering coefficient distribution
n_nodes = G_brain.order()
brain_degree = nx.degree(G_brain).values()
brain_degree_mean = np.mean(brain_degree)
# Construct degree controlled random
if graph_check[0] in graphs_to_const:
G_RAND, _, _ = und_graphs.random_simple_deg_seq(
sequence=brain_degree, brain_size=brain_size, tries=100)
graph_list.append(G_RAND)
# Construct small-world graph
if graph_check[1] in graphs_to_const:
graph_list.append(nx.watts_strogatz_graph(
n_nodes, int(round(brain_degree_mean)), 0.159))
# Construct scale-free graph
if graph_check[2] in graphs_to_const:
graph_list.append(nx.barabasi_albert_graph(
n_nodes, int(round(brain_degree_mean / 2.))))
# Construct SGPA graph
if graph_check[3] in graphs_to_const:
G_SGPA, _, _ = pgpa_dir(bc.num_brain_nodes,
bc.num_brain_edges_directed,
L=LENGTH_SCALE)
graph_list.append(G_SGPA.to_undirected())
# Error check that we created correct number of graphs
assert len(graph_list) == len(graphs_to_const), (
'Graph list/names don\'t match')
return graph_list
for met_i, bm in enumerate(metrics):
met_arr[graph_names.index('Connectome'), :, met_i] = bm(G_brain)
print 'Running metric table with %d repeats\n' % repeats
for rep in np.arange(repeats):
# PGPA model
if 'PGPA' in graph_names:
G_PGPA, _, _ = pgpa_dir(bc.num_brain_nodes,
L=LENGTH_SCALE)
met_arr[graph_names.index('PGPA'), rep, :] = \
calc_metrics(G_PGPA.to_undirected(), metrics)
# Random Configuration model (random with fixed degree sequence)
if 'Random' in graph_names:
G_CM, _, _ = binary_undirected.random_simple_deg_seq(sequence=brain_degree,
brain_size=brain_size,
tries=100)
met_arr[graph_names.index('Random'), rep, :] = \
calc_metrics(G_CM, metrics)
# Small-world (Watts-Strogatz) model with standard reconnection prob
if 'Small-world' in graph_names:
G_SW = nx.watts_strogatz_graph(n_nodes, int(round(brain_degree_mean)),
0.159)
met_arr[graph_names.index('Small-world'), rep, :] = \
calc_metrics(G_SW, metrics)
# Scale-free (Barabasi-Albert) graph
if 'Scale-free' in graph_names:
G_SF = nx.barabasi_albert_graph(n_nodes,
int(round(brain_degree_mean / 2.)))
# Calculate metrics specially because no repeats can be done on brain
brain_metrics = calc_metrics(G_brain, metrics)
for met_i, bm in enumerate(metrics):
met_arr[graph_names.index('Connectome'), :, met_i] = bm(G_brain)
print 'Running metric table with %d repeats\n' % repeats
for rep in np.arange(repeats):
# SGPA model
if 'SGPA' in graph_names:
G_SGPA = source_growth(bc.num_brain_nodes, L=LENGTH_SCALE)[0]
met_arr[graph_names.index('SGPA'), rep, :] = \
calc_metrics(G_SGPA.to_undirected(), metrics)
# Random Configuration model (random with fixed degree sequence)
if 'Random' in graph_names:
G_CM = binary_undirected.random_simple_deg_seq(
sequence=brain_degree, brain_size=BRAIN_SIZE, tries=100)[0]
met_arr[graph_names.index('Random'), rep, :] = \
calc_metrics(G_CM, metrics)
# Small-world (Watts-Strogatz) model with standard reconnection prob
if 'Small-world' in graph_names:
G_SW = nx.watts_strogatz_graph(n_nodes, int(round(brain_degree_mean)),
SW_REWIRE_PROB)
met_arr[graph_names.index('Small-world'), rep, :] = \
calc_metrics(G_SW, metrics)
# Scale-free (Barabasi-Albert) graph
if 'Scale-free' in graph_names:
G_SF = nx.barabasi_albert_graph(n_nodes,
int(round(brain_degree_mean / 2.)))
met_arr[graph_names.index('Scale-free'), rep, :] = \
