def DR_histogram(X,Y,Xr,Yr,distance,th_min,th_max,theta_bins,cat_number=3):
n_points=len(X)
#Xmin=1.0*np.floor( np.amin(X) )
#Xmax=1.0*np.ceil( np.amax(X) )
#Ymin=1.0*np.floor( np.amin(Y) )
#Ymax=1.0*np.ceil( np.amax(Y) )
#Xr= Xmin + ( Xmax - Xmin )*np.random.random_sample(n_points*cat_number)
#Yr=Ymin + ( Ymax - Ymin )*np.random.random_sample(n_points*cat_number)
d_arr=[]
print "number of LAEs=" + str(n_points)
for m in range(cat_number):
for i in range(n_points):
if(i%100==0):
print "LAE_"+str(i)
for j in range(n_points):
d=( X[i] - Xr[j + n_points*m] )*( X[i] - Xr[j + n_points*m] ) + ( Y[i] - Yr[j + n_points*m] )*( Y[i] - Yr[j + n_points*m] )
d=np.sqrt(d)/distance
d_arr=np.append(d_arr,d)
theta=206265*d_arr
DR,bins=np.histogram(theta,bins=theta_bins, range=(th_min,th_max))
N=1.0*n_points*(n_points)
N=(N*cat_number)
DR=DR/N
return DR,bins
def RR_histogram(X,Y,Xr,Yr,distance,th_min,th_max,theta_bins,cat_number=3):
n_points=len(X)
#Xmin=1.0*np.floor( np.amin(X) )
#Xmax=1.0*np.ceil( np.amax(X) )
#Ymin=1.0*np.floor( np.amin(Y) )
#Ymax=1.0*np.ceil( np.amax(Y) )
#Xr= Xmin + ( Xmax - Xmin )*np.random.random_sample(n_points*cat_number)
#Yr=Ymin + ( Ymax - Ymin )*np.random.random_sample(n_points*cat_number)
d_arr=[]
for m in range(cat_number):
print "random cat=",m
for i in range(n_points):
for j in range(i+1,n_points):
d=(Xr[i + n_points*m ] - Xr[j + n_points*m ])*(Xr[i + n_points*m ] - Xr[j + n_points*m ]) + (Yr[i + n_points*m ] - Yr[j + n_points*m ])*(Yr[i + n_points*m ] - Yr[j + n_points*m ])
d=np.sqrt(d)/distance
d_arr=np.append(d_arr,d)
theta=206265*d_arr
RR,bins=np.histogram(theta,bins=theta_bins, range=(th_min,th_max))
N=1.0*n_points*(1.0*n_points-1.0)/2.0
N=N*cat_number
RR=RR/N
return RR,bins
def DD_histogram(X,Y,distance,th_min,th_max,theta_bins):
n_points=len(X)
d_arr=[]
print "number of LAEs=" + str(n_points)
for i in range(n_points):
if(i%100==0):
print "LAE_"+str(i)
for j in range(i+1,n_points):
d=(X[i] - X[j])*(X[i] - X[j]) + (Y[i] - Y[j])*(Y[i] - Y[j])
d=np.sqrt(d)/distance
d_arr=np.append(d_arr,d)
theta=206265.0*d_arr
DD,bins=np.histogram(theta,bins=theta_bins, range=(th_min,th_max))
N=1.0*n_points*(1.0*n_points-1.0)/2.0
DD=DD/N
return DD,bins
#plt.figure()
#p = plt.imshow(board2.T, cmap = plt.cm.gist_yarg_r, vmin=0, vmax=n, origin = 'lower', extent = [0, phi, 0, Z], aspect='auto')
#fig = plt.gcf()
#plt.clim()
#plt.colorbar()
##plt.title('GiantComponent Heatmap2 - %d, %d, %d, %f, %f, %f, %d, %d, %d, %d' %(n, Z, Z_min, rho, phi, sigma, MAX_Steps_to_cascade, res, resPhi, NO_realization_at_each_meandegree_step))
#plt.title('GiantComponent Heatmap')
#plt.ylabel('Mean degree')
#plt.xlabel('Mean threshold')
#plt.savefig(figure_path + Filename_str+ 'GiantComponent-Homophily.pdf')
plt.figure()
hist, bins = np.histogram(node_Phi_data, bins = 50)
width = 0.7*(bins[1]-bins[0])
center = (bins[:-1] + bins[1:])/2
plt.title('Threshold distribution after homophily update')
plt.xlabel('Threshold value')
plt.ylabel('Frequency')
plt.bar(center, hist, align = 'center', width = width)
plt.savefig(figure_path + Filename_str+ 'Histogram-Homophily.pdf')
