def test_all_random_graphs_yield_correct_number_of_nodes_and_edges(self):
G, A, D = random_graph.target_attraction(N=426, N_edges=2000)
self.assertEqual(len(G.nodes()), 426)
self.assertEqual(len(G.edges()), 2000)
G, A, D = random_graph.source_growth(N=426, N_edges=2000)
self.assertEqual(len(G.nodes()), 426)
self.assertEqual(len(G.edges()), 2000)
def test_all_random_graphs_yield_correct_number_of_nodes_and_edges(self):
G, A, D = random_graph.target_attraction(N=426, N_edges=2000)
self.assertEqual(len(G.nodes()), 426)
self.assertEqual(len(G.edges()), 2000)
G, A, D = random_graph.source_growth(N=426, N_edges=2000)
self.assertEqual(len(G.nodes()), 426)
self.assertEqual(len(G.edges()), 2000)
print('Making example models...')
g_sg_l_inf, a_sg_l_inf, d_sg_l_inf = source_growth(
N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=np.inf,
gamma=1,
brain_size=BRAIN_SIZE)
g_sg_l_0725, a_sg_l_0725, d_sg_l_0725 = source_growth(
N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=L,
gamma=1,
brain_size=BRAIN_SIZE)
g_ta_l_inf, a_ta_l_inf, d_ta_l_inf = target_attraction(
N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=np.inf,
gamma=1,
brain_size=BRAIN_SIZE)
g_ta_l_0725, a_ta_l_0725, d_ta_l_0725 = target_attraction(
N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=L,
gamma=1,
brain_size=BRAIN_SIZE)
# make graphs and calculate and save reciprocities if not done yet
if not os.path.isfile(RECIPROCITY_FILE_NAME):
print('Looping through construction of models for reciprocity...')
algos = {'sg': source_growth, 'ta': target_attraction}
rs = {'LS': LS}
import in_out_plot_config as cf
MARKERSIZE = 25.
FONTSIZE = 12.
ALPHA = 0.5
L = 0.725
BRAIN_SIZE = [7., 7., 7.]
######################################
# Create graphs and calculate metrics
######################################
# create attachment and growth models
G_attachment = target_attraction(N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed, L=L,
gamma=1., brain_size=BRAIN_SIZE)[0]
G_growth = source_growth(N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed, L=L, gamma=1.,
brain_size=BRAIN_SIZE)[0]
# Get in- & out-degree
indeg_attachment = np.array([G_attachment.in_degree()[node]
for node in G_attachment])
outdeg_attachment = np.array([G_attachment.out_degree()[node]
for node in G_attachment])
deg_attachment = indeg_attachment + outdeg_attachment
indeg_growth = np.array([G_growth.in_degree()[node] for node in G_growth])
outdeg_growth = np.array([G_growth.out_degree()[node] for node in G_growth])
import config
import in_out_plot_config as cf
MARKERSIZE = 25.
FONTSIZE = 13.
ALPHA = 0.5
L = np.inf
BRAIN_SIZE = [7., 7., 7.]
# create attachment and growth models
Gattachment, _, _ = target_attraction(
N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=L,
gamma=1.,
brain_size=BRAIN_SIZE,
)
Ggrowth, _, _ = source_growth(
N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=L,
gamma=1.,
brain_size=BRAIN_SIZE,
)
# Get in- & out-degree
indeg_attachment = np.array(
[Gattachment.in_degree()[node] for node in Gattachment])
g_brain, a_brain, labels = brain_graph.binary_directed()
brain_in_deg = g_brain.in_degree().values()
brain_out_deg = g_brain.out_degree().values()
# load distance matrix
d_brain = load_brain_dist_matrix(labels, in_mm=True)
# make two SG graphs and two TA graphs (each one with either L=0.725 or 0)
print('Making example models...')
g_sg_l_inf, a_sg_l_inf, d_sg_l_inf = source_growth(
N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed, L=np.inf,
gamma=1, brain_size=BRAIN_SIZE)
g_sg_l_0725, a_sg_l_0725, d_sg_l_0725 = source_growth(
N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed, L=L,
gamma=1, brain_size=BRAIN_SIZE)
g_ta_l_inf, a_ta_l_inf, d_ta_l_inf = target_attraction(
N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed, L=np.inf,
gamma=1, brain_size=BRAIN_SIZE)
g_ta_l_0725, a_ta_l_0725, d_ta_l_0725 = target_attraction(
N=bc.num_brain_nodes, N_edges=bc.num_brain_edges_directed, L=L,
gamma=1, brain_size=BRAIN_SIZE)
# make graphs and calculate and save reciprocities if not done yet
if not os.path.isfile(RECIPROCITY_FILE_NAME):
print('Looping through construction of models for reciprocity...')
algos = {'sg': source_growth, 'ta': target_attraction}
rs = {'LS': LS}
for key, algo in algos.items():
print(key)
rs[key] = np.nan * np.zeros((len(LS), N_REPEATS), dtype=float)
