# calculate nodal efficiency for brain
G_brain.efficiency_matrix = metrics_bd.efficiency_matrix(G_brain)
G_brain.nodal_efficiency = np.sum(G_brain.efficiency_matrix, axis=1) /\
(len(G_brain.nodes()) - 1)
print('global eff brain: {}'.format(np.mean(G_brain.nodal_efficiency)))
# calculate power-law fits for each graph type and brain
power_law_fits = {}
fits_r_squared = {}
for key, graphs in graphss.items():
gammas = []
r_squareds = []
for graph in graphs:
fit = metrics_bd.power_law_fit_deg_cc(graph)
gammas.append(fit[0])
r_squareds.append(fit[2]**2)
power_law_fits[key] = np.array(gammas)
fits_r_squared[key] = np.array(r_squareds)
power_law_fit_brain = metrics_bd.power_law_fit_deg_cc(G_brain)
power_law_fits['brain'] = power_law_fit_brain[0]
fits_r_squared['brain'] = power_law_fit_brain[2]**2
##############################################################################
# plot clustering vs. degree and nodal_efficiencies for brain and three models
##############################################################################
fig, axs = plt.subplots(1,
2,
# calculate nodal efficiency for brain
G_brain.efficiency_matrix = metrics_bd.efficiency_matrix(G_brain)
G_brain.nodal_efficiency = np.sum(G_brain.efficiency_matrix, axis=1) /\
(len(G_brain.nodes()) - 1)
print('global eff brain: {}'.format(np.mean(G_brain.nodal_efficiency)))
# calculate power-law fits for each graph type and brain
power_law_fits = {}
fits_r_squared = {}
for key, graphs in graphss.items():
gammas = []
r_squareds = []
for graph in graphs:
fit = metrics_bd.power_law_fit_deg_cc(graph)
gammas.append(fit[0])
r_squareds.append(fit[2] ** 2)
power_law_fits[key] = np.array(gammas)
fits_r_squared[key] = np.array(r_squareds)
power_law_fit_brain = metrics_bd.power_law_fit_deg_cc(G_brain)
power_law_fits['brain'] = power_law_fit_brain[0]
fits_r_squared['brain'] = power_law_fit_brain[2] ** 2
##############################################################################
# plot clustering vs. degree and nodal_efficiencies for brain and three models
##############################################################################
fig, axs = plt.subplots(1, 2, facecolor=FACE_COLOR, figsize=FIG_SIZE,
tight_layout=True)
def test_power_law_fit_deg_cc(self):
G = nx.erdos_renyi_graph(100, .2)
fit = metrics_bd.power_law_fit_deg_cc(G)
self.assertEqual(len(fit), 5)
