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dnf2d.py
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dnf2d.py
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from __future__ import division
import numpy as np
import math
from matplotlib import cm
import matplotlib.pyplot as plt
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.mplot3d import Axes3D
import scipy.signal as sp
import cv2
nn=101
dx=2*math.pi/nn
dy=2*math.pi/nn
sig=2*math.pi/15
sig1=sig*4#mexican hat
C=0.09
pat=np.zeros((nn,nn))
h=10.0 # Just now it is set to 0.0 later I will give its some input
tau_inv=0.1
print 'hello world'
X,Y=np.mgrid[-nn/2:nn/2,-nn/2:nn/2]
def weights(sig):
f=np.zeros((nn,nn))
f=-(((dx*X)**2/2*sig**2)+((dy*Y)**2/2*sig**2))
#f=f/2*sig**2
f=np.exp(f)
f=f*(1./(np.sqrt(2*math.pi)*sig))
return f
#sig=sig**2
#f=f*dx
#f=-(f**2)
#f=f/(2*(sig**2))
#f=sp.norm.pdf(f,0,sig)
#f=f*dx
#f=1./(np.sqrt(2*math.pi)*sig)*np.exp(-(f**2/(2*sig**2)))
#print f.shape
#f=np.exp(f)
#f=np.dot(f,f.transpose())
#f=np.dot(f,f)
#return f
w0=1./(np.sqrt(2*math.pi)*sig)*(np.exp((-(((dx*X)**2)+((dy*Y)**2)))/(2*sig**2)))
#w=4*(w-C)
w1=1./(np.sqrt(2*math.pi)*sig1)*(np.exp((-(((dx*X)**2)+((dy*Y)**2)))/(2*sig1**2)))
#w0=weights(sig)
#w1=weights(sig1)
w=100*((w0-w1)-C)
def plot(figno,u_history):
fig = plt.figure(figno)
ax = fig.gca(projection='3d')
X, Y = np.meshgrid(np.arange(nn),np.arange(nn))
surf = ax.plot_surface(X, Y,u_history)#,cmap=cm.jet,
#linewidth=0, antialiased=True)
ax.w_zaxis.set_major_locator(LinearLocator(10))
ax.w_zaxis.set_major_formatter(FormatStrFormatter('%.3f'))
ax.set_zlabel("Excitation")
ax.set_xlabel("Node X")
ax.set_ylabel("Node Y")
#fig.colorbar(surf, shrink=0.5, aspect=5)
def update(u,I):
r=1/(1+np.exp(-u))
#print u.shape
t=sp.convolve2d(r,w,'same','wrap')
#t=np.dot(r,w)
sum1=t*dx*dx
u=u+tau_inv*((-u+sum1+50*I)+h)
return u
def gauss_pbc(locx,locy,sig):
z=np.zeros((nn,nn))
for i in range(nn):
for j in range(nn):
d=min([abs(i*dx-locx) , 2*math.pi-abs(i*dx-locx)])
d2=min([abs(j*dy-locy), 2*math.pi-abs(j*dy-locy)])
z[j][i]=1./(np.sqrt(2*math.pi)*sig)*np.exp(-(d**2/(2*sig**2)+(d2**2/(2*sig**2))))
return z
#Update With Localised Input
'''
I_ext=np.zeros((nn,nn))
for k in range(int(nn/2-np.floor(nn/20)),int(nn/2+np.floor(nn/20))+1):
I_ext[k]=1
'''
I_ext=np.zeros((nn,nn))
input_image=cv2.imread('/home/rohan/Documents/dnf_python/test1.jpg',0)
input_image=cv2.resize(input_image,(101,101))
for i in range (101):
for j in range (101):
if input_image[i][j]>140:
input_image[i][j]=0
else:
input_image[i][j]=1
I_ext=input_image#/np.max(input_image)
#cv2.imshow('test',input_image)
#I_ext[int(nn/2-np.floor(nn/20)):int(nn/2+np.floor(nn/20))+1,
# int(nn/2-np.floor(nn/20)):int(nn/2+np.floor(nn/20))+1] = 1
#I_ext[50:60,50:60]=1
#I_ext=gauss_pbc(3*math.pi/2,3*math.pi/2,sig)
#!print u.shape
#I_ext[10:20,10:20] = 1
time=20
u_history=np.zeros((nn,nn))
u=np.zeros((nn,nn))
for k in range(time):
u=update(u,I_ext)
r=1/(1+np.exp(-u))
u_history=r
plot(1,u_history)
plot(7,I_ext)
u_history=np.zeros((nn,nn))
I_ext=np.zeros((nn,nn))
#I_ext[10:20,10:20] = 1
time=30
#h=-15.0
for k in range(time):
u=update(u,I_ext)
r=1/(1+np.exp(-u))
u_history=r
plot(2,u_history)
u_history=np.zeros((nn,nn))
I_ext=np.zeros((nn,nn))
I_ext[10:20,10:20] = 1
time=30
h=-15.0
for k in range(time):
u=update(u,I_ext)
r=1/(1+np.exp(-u))
u_history=r
plot(3,u_history)
'''
I_ext[10:20,10:20] = 1
time=40
u_history=np.zeros((nn,nn))
#h=5
for k in range(time):
u=update(u,I_ext)
r=1/(1+np.exp(-u))
u_history=r
plot(3,u_history)
'''
'''
I_ext=np.zeros((nn,nn))
time=20
for k in range(time):
u=update(u,I_ext)
r=1/(1+np.exp(-u))
u_history=r
plot(4,u_history)
'''
plot(6,w)
plt.show()