# Simple CA simulator in Python
#
# *** Forest fire ***
#
# Copyright 2008-2012 Hiroki Sayama
# sayama@binghamton.edu

# Modified to run with Python 3

import matplotlib
matplotlib.use('TkAgg')

import pylab as PL
import random as RD
import scipy as SP

RD.seed()

width = 100
height = 100
initProb = 0.4
empty, tree, fire, char = range(4)
ignitation = 0.5

def init():
    global time, config, nextConfig

    time = 0

    config = SP.zeros([height, width])
    for x in range(width):
        for y in range(height):
            if RD.random() < initProb:
                state = tree
            else:
                state = empty
            config[y, x] = state
    config[height//2, width//2] = fire

    nextConfig = SP.zeros([height, width])

def draw():
    PL.cla()
    PL.pcolor(config, vmin = 0, vmax = 3, cmap = PL.cm.binary)
    PL.axis('image')
    PL.title('t = ' + str(time))

def step():
    global time, config, nextConfig

    time += 1

    for x in range(width):
        for y in range(height):
            state = config[y, x]
            if state == fire:
                state = char
            elif state == tree:
                for dx in range(-1, 2):
                    for dy in range(-1, 2):
                        if config[(y+dy)%height, (x+dx)%width] == fire:
                            state = fire
            nextConfig[y, x] = state

    config, nextConfig = nextConfig, config

# import pycxsimulator
# pycxsimulator.GUI().start(func=[init,draw,step])


import numpy as np

fire_prob = np.linspace(0,1,num = 20)

Time = []
Charred = []

for prob in fire_prob:
    Char = []
    times_list =[]
    initial_Prob = prob #checking different initial fire probility
    for j in range(10):
        init() #initiate
        for i in range(20):
            draw()
            step()
            if np.all(config == nextConfig):
                times_list.append(time) #append each
                break
            Char.append((config == 3).sum())
        Charred.append(np.mean(Char)/width/height)
        #calculate the mean density of the Charred cells
        #All fired tree will turn chars and stay chars, so
        #np.mean(Char)/width/height is to get the proportion of fires
        Time.append(np.mean(times_list)) #calculate the mean

ignite_prob = np.linspace(0,1,num = 10)
#generate different probabilities for ignitation fires

def init():
    global time, config, nextConfig

    time = 0

    config = SP.zeros([height, width])
    for x in range(width):
        for y in range(height):
            if RD.random() < initProb:
                state = tree
            else:
                state = empty
            config[y, x] = state
    config[height//2, width//2] = fire

    nextConfig = SP.zeros([height, width])

def draw():
    PL.cla()
    PL.pcolor(config, vmin = 0, vmax = 3, cmap = PL.cm.binary)
    PL.axis('image')
    PL.title('t = ' + str(time))

def step():
    global time, config, nextConfig

    time += 1

    for x in range(width):
        for y in range(height):
            state = config[y, x]
            if state == fire:
                state = char
            elif state == tree:
                for dx in range(-1, 2):
                    for dy in range(-1, 2):
                        if config[(y+dy)%height, (x+dx)%width] == fire and RD.random < RD.random(ignite_prob):
                            state = fire
            nextConfig[y, x] = state

    config, nextConfig = nextConfig, config