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standard_graphs_xy.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Aug 27 23:16:17 2014
@author: sid
"""
import networkx as nx
import plot_net
import matplotlib.pyplot as plt
import numpy as np
import network_gen
import aux_random_graphs
# Set parameters
p_th = .01 # P-value threshold
w_th = 0 # Weight-value threshold
# Set relative directory path
#dir_name = '../friday-harbor/linear_model'
dir_name = '../Data/linear_model'
# Load weights & p-values
W,P,row_labels,col_labels = network_gen.load_weights(dir_name)
# Threshold weights according to weights & p-values
W_net,mask = network_gen.threshold(W,P,p_th=p_th,w_th=w_th)
# Set weights to zero if they don't satisfy threshold criteria
W_net[W_net==-1] = 0.
# Set diagonal weights to zero
np.fill_diagonal(W_net,0)
# Put everything in a dictionary
W_net_dict = {'row_labels':row_labels,'col_labels':col_labels,
'data':W_net}
# Convert to networkx graph object
G = network_gen.import_weights_to_graph(W_net_dict)
N = len(G.nodes())
# These are the new params, derived from adjusting the proverbial knobs
G_ER = nx.erdos_renyi_graph(N,0.085)
G_BA = nx.barabasi_albert_graph(N,19)
G_WS = nx.watts_strogatz_graph(N,36,0.159)
G_BA_cc = nx.powerlaw_cluster_graph(N,19,1)
#=======
# G_BA_cc = aux_random_graphs.scale_free_cc_graph(n=N,m=25,k0=20,p=np.array([1]),fp=np.array([1]))
G_PWC = nx.powerlaw_cluster_graph(426,10,p=.9)
# Biophysical graph
print 'Generating biophysical graph...'
G_BIO,A,D = aux_random_graphs.biophysical_graph(N=426,N_edges=7804,L=1.,power=1.5,mode=0)
#>>>>>>> upstream/master
# Here you can specify which plotting function you want to run.
#x It needs to take a single graph as input!
plotfunction = plot_net.plot_edge_btwn
myrange = np.linspace(0,0.002,20)
MyX = nx.betweenness_centrality
x_ER = MyX(G_ER).values()
y_ER = nx.clustering(G_ER).values()
#y_ER = G_ER.degree().values()
x_BA = MyX(G_BA).values()
#y_BA = G_BA.degree().values()
y_BA = nx.clustering(G_BA).values()
x_WS = MyX(G_WS).values()
#y_WS = G_WS.degree().values()
y_WS = nx.clustering(G_WS).values()
#<<<<<<< HEAD
x_BA_cc = MyX(G_BA_cc).values()
#y_BA_cc = G_BA_cc.degree().values()
y_BA_cc = nx.clustering(G_BA_cc).values()
x = MyX(G).values()
#y = G.degree().values()
y = nx.clustering(G).values()
#=======
x_PWC = MyX(G_PWC).values()
y_PWC = G_PWC.degree().values()
x_BIO = MyX(G_BIO).values()
y_BIO = G_BIO.degree().values()
x = MyX(G).values()
y = G.degree().values()
Fig,axs = plt.subplots(nrows=2,ncols=3,sharex=False, sharey=False, facecolor='White')
ax = axs[0,0]; ax_ER = axs[0,1]; ax_WS = axs[0,2];
ax_BA = axs[1,0]; ax_PWC = axs[1,1]; ax_BIO = axs[1,2]
ax.scatter(x,y)
ax_ER.scatter(x_ER,y_ER)
ax_BA.scatter(x_BA,y_BA)
#>>>>>>> upstream/master
Fig,axs = plt.subplots(nrows=2,ncols=2,sharex=False, sharey=False, facecolor='White')
#<<<<<<< HEAD
axs[0,0].scatter(x,y)
#=======
ax_PWC.scatter(x_PWC,y_PWC)
ax_BIO.scatter(x_BIO,y_BIO)
#>>>>>>> upstream/master
axs[0,1].scatter(x_ER,y_ER)
axs[1,0].scatter(x_BA,y_BA)
axs[1,1].scatter(x_BA_cc,y_BA_cc)
#<<<<<<< HEAD
#=======
#xLims = [ax.get_xlim(), ax_ER.get_xlim(), ax_WS.get_xlim(), ax_BA.get_xlim()]
#yLims = [ax.get_ylim(), ax_ER.get_ylim(), ax_WS.get_ylim(), ax_BA.get_ylim()]
#
#xLims = [i for entry in xLims for i in entry]
#yLims = [i for entry in yLims for i in entry]
#MyLims = [0,max(xLims),0,max(yLims)]
#MyLims = [0,1,0,max(yLims)]
#
#ax.axis(MyLims)
#ax_ER.axis(MyLims)
#ax_WS.axis(MyLims)
#ax_BA.axis(MyLims)
for a in [ax,ax_ER,ax_BA,ax_WS,ax_PWC,ax_BIO]:
pass
#a.set_xlim(0,1)
#a.set_ylim(0,200)
# For clustering
#>>>>>>> upstream/master
XTicks = [0,0.25,0.5,0.75,1]
XTicks = [0,0.0125,0.025,0.0375,0.05]
YTicks = [0,50,100,150,200]
#<<<<<<< HEAD
for i in [0,1]:
for j in [0,1]:
#axs[i,j].set_xlim(-.01,0.05)
#axs[i,j].set_ylim(0,200)
axs[i,j].tick_params(labelsize=20)
#axs[i,j].set_xticks(XTicks)
#axs[i,j].set_yticks(YTicks)
# For clustering
TitleFontSize = 22
LabelFontSize = 20
axs[0,0].set_title('Allen Mouse Brain Atlas (LM)', fontsize=TitleFontSize)
axs[0,1].set_title('Watts-Strogatz small world network', fontsize=TitleFontSize)
axs[1,0].set_title('Symmetric Barabasi-Albert scale-free network', fontsize=TitleFontSize)
axs[1,1].set_title('Clustered scale-free network', fontsize=TitleFontSize)
ax_BA.set_xlabel('Clustering coefficient', fontsize=LabelFontSize)
ax_WS.set_xlabel('Clustering coefficient', fontsize=LabelFontSize)
#=======
ax.set_xticks(XTicks)
ax_ER.set_xticks(XTicks)
ax_BA.set_xticks(XTicks)
ax_WS.set_xticks(XTicks)
ax_PWC.set_xticks(XTicks)
ax_BIO.set_xticks(XTicks)
ax.set_yticks(YTicks)
ax_ER.set_yticks(YTicks)
ax_BA.set_yticks(YTicks)
ax_WS.set_yticks(YTicks)
ax_PWC.set_yticks(YTicks)
ax_BIO.set_yticks(YTicks)
ax_BA.tick_params(size=10,labelsize=16)
ax_WS.tick_params(size=10,labelsize=16)
ax.tick_params(size=10,labelsize=16)
ax_ER.tick_params(size=10,labelsize=16)
ax_PWC.tick_params(size=10,labelsize=16)
ax_BIO.tick_params(size=10,labelsize=16)
TitleFontSize = 22
LabelFontSize = 20
ax.set_title('Allen Mouse Brain', fontsize=TitleFontSize)
ax_ER.set_title('ER random', fontsize=TitleFontSize)
ax_BA.set_title('BA scale-free', fontsize=TitleFontSize)
ax_WS.set_title('WA small world network', fontsize=TitleFontSize)
ax_PWC.set_title('Power-law clustering',fontsize=TitleFontSize)
ax_BIO.set_title('Biophysical',fontsize=TitleFontSize)
ax_BIO.set_xlabel('Clustering coefficient', fontsize=LabelFontSize)
#>>>>>>> upstream/master
ax.set_ylabel('Degree', fontsize=LabelFontSize)
ax_BA.set_ylabel('Degree', fontsize=LabelFontSize)
ax_PWC.set_ylabel('Degree', fontsize=LabelFontSize)
plt.draw()