thesis/RBN.py at master · riccardoscheda/thesis · GitHub

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 20 14:32:00 2020

@author: riccardo
"""


import numpy as np
import pylab as plt
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation # animation plot
import pandas as pd

import integration as inte


#number of genes
n = 50
threshold = 0
vmax = 4
nodes = np.zeros((n,n))
nodes[0] = np.random.randint(0,2,size=n)
#nodes[0][0] = 0
#nodes[0][1] = 1
for i in range(n):
    if nodes[0][i] == 0:
        nodes[0][i] = -1


#W = np.random.randint(-1,2,size=(n,n))

W = np.zeros((n,n))
for i in range(n):
    W[i][np.random.randint(0,n)] = +1
    #W[i][np.random.randint(0,n)] = +1
    #W[i][np.random.randint(0,n)] = +1
# W = np.array([[0,-1],
#               [-1,0]])

print(np.linalg.eig(W)[0])
fig, ax = plt.subplots(2,2)

upper, = ax[0,0].plot([],[], c="black",linestyle = "--",label = "p")
lower, = ax[0,0].plot([],[], c="black",linestyle = "--",label = "p")
upper2, = ax[1,0].plot([],[], c="black",linestyle = "--",label = "p")
lower2, = ax[1,0].plot([],[], c="black",linestyle = "--",label = "p")
trajectory = []
mean = []

for i in range(n):
    t, = ax[0,0].plot([],[])
    field, = ax[1,0].plot([],[])
    mean.append(field)
    trajectory.append(t)

#maxwelldist, = ax[1,0].step([],[],label = 'distribution')
#maxwellfit, = ax[1,0].plot([],[],label = "fit")
# ax[0,0].legend()
# ax[1,0].legend()
#phasespace, = ax[0,1].step([],[])
#entr, = ax[1,1].plot([],[], label = "S")
#shan, = ax[1,1].plot([],[], linestyle = "--", label = "S_infty")
#ax[1,1].legend()


t = {}
field = {}

for i in range(n):
    t[i] = []
    field[i] = []

def init():

  ax[0,0].set_xlim(-0.1,200)
  ax[0,0].set_ylim(-1.5,1.5)
  ax[0,0].set_title("Equazione stocastica")
  ax[0,0].set_xlabel("time")
  ax[0,0].set_ylabel("p")

  ax[0,1].imshow(nodes)
  # ax[0,1].set_xlim(-5,5)
  # ax[0,1].set_ylim(0,0.1)
  # ax[0,1].set_xlabel("x")
  # ax[0,1].set_ylabel("rho_x")
  # ax[0,1].set_title("Distribution of positions")

  ax[1,0].set_xlim(-0.1,200)
  ax[1,0].set_ylim(-1.5,1.5)
 # ax[1,0].set_xlabel("time")
  ax[1,0].set_ylabel("p")
  ax[1,0].set_title("Equazione di campo medio")

  ax[1,1].set_xlim(-0.1,200)
  ax[1,1].set_ylim(-1.5,1.5)
  ax[1,1].set_xlabel("time")
  # ax[1,1].set_ylabel("entropy")
  #ax[1,1].set_title("Media delle realizzazioni")

  x = np.linspace(0,200, num = 200)
  y = np.ones(200)
  upper.set_data(x,y)
  y = np.zeros(200)
  lower.set_data(x,y)
  x = np.linspace(0,200, num = 200)
  y = np.ones(200)
  upper2.set_data(x,y)
  y = np.zeros(200)
  lower2.set_data(x,y)

  return trajectory[0],


def realization(s,sigma,threshold):
    if s - threshold > 0 :
        return 1
    elif s - threshold < 0 :
        return 0
    else:
        return sigma

def evo(frames):

    for i in range(n):
        somma = 0
        for j in range(n):
            somma = somma + W[i][j]*nodes[frames][j]

        nodes[frames+1][i] = somma
        #nodes[frames+1][i] = realization(somma, nodes[frames][i], threshold)

    # for i in range(n):
    #     if nodes[frames+1][i] != 0:
    #         nodes[frames+1][i] = 1
    #     else:
    #         nodes[frames+1][i] = 0

    ax[0,1].imshow(nodes)

    extent = ax[0,1].get_window_extent().transformed(fig.dpi_scale_trans.inverted())
    fig.savefig('ax2_figure.png', bbox_inches=extent)
    #print(nodes[0][frames])
    # # Pad the saved area by 10% in the x-direction and 20% in the y-direction
    fig.savefig('ax2_figure_expanded.png', bbox_inches=extent.expanded(1.1, 1.2))
    return  trajectory[0]#,trajectory[1],trajectory[2]#,#ax[0,1]

#print((L.shape))
#print(np.linalg.eig(L)[0])


ani = FuncAnimation(fig, evo, frames = np.arange(0,200), interval = 50,init_func = init, blit = False)
#plt.tight_layout()
#ani.save('biblio/transition.gif', dpi=120, writer='imagemagick')