""" Created on Wed Aug 27 23:16:17 2014 @author: rkp Test creation of random weighted undirected graphs. """ import numpy as np import extract.brain_graph import random_graph.weighted_undirected as rg # Load weight matrix from mouse connectivity to get model parameters G_brain, W_brain, _ = extract.brain_graph.weighted_undirected() N_brain = W_brain.shape[0] N_edges_brain = (W_brain > 0).sum()/2 L = 1. gamma = 1.5 # Create random biophysical graph with properly sampled weights brain_size = np.array([10.,10.,10.]) G,W,D = rg.biophysical_sample_weights(N=N_brain, N_edges=N_edges_brain, L=L, gamma=gamma, brain_size=brain_size, use_brain_weights=True)
""" Created on Wed Aug 27 23:16:17 2014 @author: rkp Test creation of random weighted undirected graphs. """ import numpy as np import extract.brain_graph import random_graph.weighted_undirected as rg # Load weight matrix from mouse connectivity to get model parameters G_brain, W_brain, _ = extract.brain_graph.weighted_undirected() N_brain = W_brain.shape[0] N_edges_brain = (W_brain > 0).sum() / 2 L = 1. gamma = 1.5 # Create random biophysical graph with properly sampled weights brain_size = np.array([10., 10., 10.]) G, W, D = rg.biophysical_sample_weights(N=N_brain, N_edges=N_edges_brain, L=L, gamma=gamma, brain_size=brain_size, use_brain_weights=True)
# Get mouse connectivity graph & distance G_brain, W_brain, _ = extract.brain_graph.weighted_undirected() D_brain, _ = extract.brain_graph.distance_matrix() N_brain = W_brain.shape[0] N_edges_brain = (W_brain > 0).sum()/2 L = 1.3 GAMMA = 1.7 BRAIN_SIZE = [9., 9, 9] # Calculate swapped-cost distribution for graph cost_changes = metrics.weighted_undirected.swapped_cost_distr(W_brain, D_brain) positive_cost_changes = float((cost_changes > 0).sum()) / len(cost_changes) # Create random biophysical graph with properly sampled weights G,W,D = rg.biophysical_sample_weights(N=N_brain, N_edges=N_edges_brain, L=L, gamma=GAMMA, brain_size=BRAIN_SIZE, use_brain_weights=True) # Calculate swapped-cost distribution for graph cost_changes_random = metrics.weighted_undirected.swapped_cost_distr(W, D) positive_cost_changes_random = float((cost_changes_random > 0).sum()) / \ len(cost_changes_random) fig, axs = plt.subplots(2, 1, facecolor=FACECOLOR) axs[0].hist(cost_changes, bins=20, normed=True) axs[1].hist(cost_changes_random, bins=20, normed=True) for ax_idx, ax in enumerate(axs): if ax_idx == 1: ax.set_xlabel('Change in cost (per cent)') ax.set_xlim(-1,10)
G_brain, W_brain, _ = extract.brain_graph.weighted_undirected() D_brain, _ = extract.brain_graph.distance_matrix() N_brain = W_brain.shape[0] N_edges_brain = (W_brain > 0).sum() / 2 L = 1.3 GAMMA = 1.7 BRAIN_SIZE = [9., 9, 9] # Calculate swapped-cost distribution for graph cost_changes = metrics.weighted_undirected.swapped_cost_distr(W_brain, D_brain) positive_cost_changes = float((cost_changes > 0).sum()) / len(cost_changes) # Create random biophysical graph with properly sampled weights G, W, D = rg.biophysical_sample_weights(N=N_brain, N_edges=N_edges_brain, L=L, gamma=GAMMA, brain_size=BRAIN_SIZE, use_brain_weights=True) # Calculate swapped-cost distribution for graph cost_changes_random = metrics.weighted_undirected.swapped_cost_distr(W, D) positive_cost_changes_random = float((cost_changes_random > 0).sum()) / \ len(cost_changes_random) fig, axs = plt.subplots(2, 1, facecolor=FACECOLOR) axs[0].hist(cost_changes, bins=20, normed=True) axs[1].hist(cost_changes_random, bins=20, normed=True) for ax_idx, ax in enumerate(axs): if ax_idx == 1: ax.set_xlabel('Change in cost (per cent)')