FONT_SIZE = 20 D_BINS = np.linspace(0, 12, 30) L = 0.725 TEMP_FILE_NAME = 'reciprocity_temp.npy' RECIPROCITY_FILE_NAME = 'reciprocity.npy' ############################################### if not os.path.isfile(TEMP_FILE_NAME): # load brain graph print('Loading brain graph...') 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)
ticksize = 10 legendsize = 8 def dist(x, y): return np.sqrt(np.sum((x - y)**2)) if __name__ == "__main__": # this isn't right... the third return object is a distance matrix # Get the connectome G, _, labels = binary_directed() nodes = G.nodes() new_labels = {nodes[i]: labels[i] for i in range(len(nodes))} distance_matrix = aux.load_brain_dist_matrix(new_labels) mds = manifold.MDS(n_components=3, max_iter=1000, eps=1e-10, dissimilarity='precomputed') centroids = mds.fit_transform(distance_matrix) inter_node_distances = [ dist(edge1, edge2) for edge1 in centroids for edge2 in centroids if not all(edge1 == edge2) ] G_sgpa, _, model_centroids = sgpa() model_distances = [ model_centroids[edge1][edge2] for edge1 in G_sgpa.nodes()
ticksize = 10 legendsize = 8 def dist(x, y): return np.sqrt(np.sum((x - y) ** 2)) if __name__ == "__main__": # this isn't right... the third return object is a distance matrix # Get the connectome G, _, labels = binary_directed() nodes = G.nodes() new_labels = {nodes[i]: labels[i] for i in range(len(nodes))} distance_matrix = aux.load_brain_dist_matrix(new_labels) mds = manifold.MDS(n_components=3, max_iter=1000, eps=1e-10, dissimilarity="precomputed") centroids = mds.fit_transform(distance_matrix) inter_node_distances = [dist(edge1, edge2) for edge1 in centroids for edge2 in centroids if not all(edge1 == edge2)] G_sgpa, _, model_centroids = sgpa() model_distances = [ model_centroids[edge1][edge2] for edge1 in G_sgpa.nodes() for edge2 in G_sgpa.nodes() if edge1 != edge2 ] fig, axs = plt.subplots(1, facecolor="white", figsize=(4, 2.8)) fig.subplots_adjust(bottom=0.15, left=0.15) bins = np.linspace(0, 13, 51)
D_BINS = np.linspace(0, 12, 30) L = 0.725 TEMP_FILE_NAME = 'reciprocity_temp.npy' RECIPROCITY_FILE_NAME = 'reciprocity.npy' ############################################### if not os.path.isfile(TEMP_FILE_NAME): # load brain graph print('Loading brain graph...') 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)