plt.ion()
NTRIALS = 500
# PLOT PARAMETERS
FIGSIZE = (11, 4)
FACECOLOR = 'k'
BRAINCOLOR = 'm'
MODELCOLOR = 'orange'
ERCOLOR = 'r'
FONTSIZE = 16
NBINS = 15
# load brain graph
Gbrain, Abrain, Dbrain, labels = brain_graph(p_th=.05)
# create model graph
G, A, D = biophysical_model(N=bc.num_brain_nodes,
N_edges=bc.num_brain_edges_directed,
L=.75,
gamma=1.)
# create random graph
Grandom, Arandom, Drandom = ER_distance(bc.num_brain_nodes,
p=bc.p_brain_edge_directed)
# calculate random swap cost distribution for brain and model
brain_cost = swapped_cost_distr(Abrain, Dbrain, n_trials=NTRIALS)
model_cost = swapped_cost_distr(A, D, n_trials=NTRIALS)
random_cost = swapped_cost_distr(Arandom, Drandom, n_trials=NTRIALS)
for j,L in enumerate(Ls):
for n in range(N):
G, A, labels = biophysical_model(N=num_brain_nodes,
N_edges=num_brain_edges_directed,
L=L,
gamma=1.)
recip = reciprocity(A)
modelReciprocity[j,n] = recip
print "Reciprocity: " + str(np.mean(modelReciprocity[:,n])) + "(" + str(np.std(modelReciprocity[:,n])) + ")"
meanReciprocity = np.mean(modelReciprocity,axis=1)
G,A,labels = brain_graph()
brainReciprocity = reciprocity(A)
configReciprocity = np.zeros([20,1])
nodes = G.nodes()
inDeg = G.in_degree(); inDeg = [inDeg[node] for node in nodes]
outDeg = G.out_degree(); outDeg = [outDeg[node] for node in nodes]
for j in range(20):
G_config = nx.directed_configuration_model(inDeg,outDeg)
A_config = nx.adjacency_matrix(G_config)
A_configInt = np.zeros(A_config.shape,dtype=int)
A_configInt[A_config < 0.5] = 0
A_configInt[A_config > 0.5] = 1
configReciprocity[j] = reciprocity(A_configInt)
from numpy import concatenate as cc
from extract.brain_graph import binary_directed as brain_graph
from random_graph.binary_directed import biophysical_reverse_outdegree_reciprocal as model_graph
from metrics.binary_directed import reciprocity
from network_plot.change_settings import set_all_text_fontsizes as set_fontsize
from brain_constants import *
# PARAMETERS
NER = 100 # number of ER-directed graphs to generate
NMODEL = 100
# load brain graph, adjacency matrix, and labels
Gbrain, Abrain, labels = brain_graph()
reciprocity_brain = reciprocity(G)
# calculate reciprocity for several ER-directed graphs
reciprocity_ER = np.zeros((NER,), dtype=float)
for ctr in range(NER):
GER = nx.erdos_renyi_graph(num_brain_nodes, p_brain_edge_directed,
directed=True)
reciprocity_ER[ctr] = reciprocity(GER)
# calculate reciprocity for several model graphs
reciprocity_model = np.zeros((NMODEL,), dtype=float)
for ctr in range(NMODEL):
print ctr
Gmodel = model_graph(N=num_brain_nodes,
N_edges=num_brain_edges_directed,
""" Created on Fri Jan 23 13:11:36 2015 @author: rkp Plot the in- vs. outdegree distribution for the Allen Brain mouse connectome. """ import numpy as np import matplotlib.pyplot as plt import networkx as nx from extract.brain_graph import binary_directed as brain_graph from graph_tools.auxiliary import swap_node_positions as swap plt.ion() # PLOT PARAMETERS FACECOLOR = 'black' MARKERCOLOR='m' FONTSIZE = 24 NBINS = 15 # load brain graph, adjacency matrix, and labels G, A, D, labels = brain_graph(p_th=.01) brain_avg_shortest_path = nx.average_shortest_path_length(G, weight='distance') # randomly swap two nodes Gswapped, Dswapped = swap(G, D) swapped_avg_shortest_path = nx.average_shortest_path_length(Gswapped, weight='distance')
from numpy import concatenate as cc
from extract.brain_graph import binary_directed as brain_graph
from random_graph.binary_directed import biophysical_reverse_outdegree as biophysical_model
from network_plot.change_settings import set_all_text_fontsizes as set_fontsize
import aux_random_graphs
from brain_constants import *
if __name__ == "__main__":
# load brain graph, adjacency matrix, and labels
G_brain, A_brain, labels_brain = brain_graph()
G_model, A_model, labels_model = biophysical_model(N=num_brain_nodes,
N_edges=num_brain_edges_directed,
L=.76,
gamma=1.)
Gs = [G_brain,G_model]
As = [A_brain,A_model]
allLabels = [labels_brain,labels_model]
fig,axs = plt.subplots(1,ncols=2,facecolor='white')
for g,G in enumerate(Gs):
A = As[g]
labels = allLabels[g]
rowspan=cf.top_margin_rowspan,
colspan=cf.top_margin_colspan,
sharex=left_main_ax)
right_margin_ax = plt.subplot2grid(cf.subplot_divisions,
cf.right_margin_location,
rowspan=cf.right_margin_rowspan,
colspan=cf.right_margin_colspan,
sharey=left_main_ax)
# To make log axes we need to create another axes on top of our existing ones
top_dummy_ax = top_margin_ax.twinx()
right_dummy_ax = right_margin_ax.twiny()
# Extract information for the Allen mouse connectome
G, _, _ = brain_graph()
# Get in- & out-degree
indeg = np.array([G.in_degree()[node] for node in G])
outdeg = np.array([G.out_degree()[node] for node in G])
deg = indeg + outdeg
# Calculate proportion in degree
percent_indeg = indeg / deg.astype(float)
a1 = 1.0
# Left main plot (in vs out degree)
left_main_ax.scatter(indeg,
outdeg,
c=config.COLORS['brain'],
s=cf.MARKERSIZE,
rowspan=cf.top_margin_rowspan,
colspan=cf.top_margin_colspan,
sharex=left_main_ax)
right_margin_ax = plt.subplot2grid(cf.subplot_divisions,
cf.right_margin_location,
rowspan=cf.right_margin_rowspan,
colspan=cf.right_margin_colspan,
sharey=left_main_ax)
# To make log axes we need to create another axes on top of our existing ones
top_dummy_ax = top_margin_ax.twinx()
right_dummy_ax = right_margin_ax.twiny()
# Extract information for the Allen mouse connectome
G, _, _ = brain_graph()
# Get in- & out-degree
indeg = np.array([G.in_degree()[node] for node in G])
outdeg = np.array([G.out_degree()[node] for node in G])
deg = indeg + outdeg
# Calculate proportion in degree
percent_indeg = indeg / deg.astype(float)
a1 = 1.0
# Left main plot (in vs out degree)
left_main_ax.scatter(indeg, outdeg, c=config.COLORS['brain'], s=cf.MARKERSIZE,
lw=0, alpha=ALPHA)
left_main_ax.set_xlabel('In-degree')
Plot proportion in-degree of mouse connectome """ import numpy as np import matplotlib.pyplot as plt import os import sys sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) from extract.brain_graph import binary_directed as brain_graph from config import COLORS fontsize = 12 # Load connectome graph G = brain_graph()[0] # Calculate proportion in-degree outdeg = G.out_degree() indeg = G.in_degree() nodes = G.nodes() sumdeg = [float(outdeg[node] + indeg[node]) for node in nodes] prop_indeg = [indeg[node] / sumdeg[node] for node in nodes] # Plot in-degree fig, ax = plt.subplots(1, facecolor='w', figsize=(3.5, 2.75)) fig.subplots_adjust(bottom=0.2, left=0.2) bins = np.linspace(0, 1, 11) ax.hist(prop_indeg, bins, facecolor=COLORS['brain'])
from __future__ import division
import numpy as np
import networkx as nx
import os
from extract.brain_graph import binary_directed as brain_graph
from network_compute import reciprocity
G, A, labels = brain_graph()
edges = G.edges()
edges = {edge: 1 for edge in edges}
recip_counter = 0
for edge in edges:
complementary_edge = (edge[1], edge[0])
if edges.has_key(complementary_edge):
recip_counter += 1
recip_counter /= 2 # double-counted above
recip_from_loop = recip_counter / len(edges)
recip_from_function = reciprocity(A)
print("Reciprocity from loop: %.3f" % (recip_from_loop))
print("Reciprocity from function: %.3f" % (recip_from_function))