Feb-12-2019, 02:06 PM (This post was last modified: Feb-12-2019, 02:08 PM by buran. Edited 1 time in total.)

if anyone could help me improve the efficiency of this code it would be greatly appreciated. I would like it to only loop over the newly contaminated cells at each time step instead of looping over the whole grid. Any other ideas welcome.

Please, use proper tags when post code, traceback, output, etc. This time I have added tags for you.

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import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib import colors import random import os print("=== Cellular Automata for Radioactive Waste Disposal ===") # ======= User Inputs L = 20.0 # km numCells = 100 # This needs to remain the same for! totalTime = (40 * 12) # number of time steps (in months) print_every_n_steps = 480 # don't make this too small! print_time_every = 200 source_x = 0.2 # x - coordinate location of source of radio active waste source_y = 0.2 # y - coordinate location of course of radio active waste city_x = 12 city_y = 16 cell_city_x = int(numCells * (city_x / L)) cell_city_y = int(numCells * (city_y / L)) verbosity = 0 #Permeability Maximum Variable # *** Model Developement 3 *** Kmax = 3.5#Permeability Maximum Variable #K = np.loadtxt("perm.txt", dtype='f', delimiter=' ') Ktmp = np.loadtxt("perm.txt") K=Ktmp.astype(np.float) cmap = mpl.cm.jet norm = mpl.colors.Normalize(vmin=0, vmax = 1) fig, ax = plt.subplots() ax.imshow(K, cmap=cmap, norm=norm) ax.grid(which='major', axis='both', linestyle='-', color='k', linewidth=2) ax.axes.get_xaxis().set_visible(False) ax.axes.get_yaxis().set_visible(False) results_dir = os.path.join(os.getcwd(),'solutions/') if not os.path.isdir(results_dir): os.makedirs(results_dir) plt.savefig(results_dir + "perm.png") cell_source_x = int(numCells * (source_x / L)) cell_source_y = int(numCells * (source_y / L)) count = 0 for n in range(0, 20): C = np.zeros((numCells ,numCells )) C[cell_source_x, cell_source_y] = 1; #1 is contaminated iteration = str(n+1) print("Iteration:" + iteration) #C[cell_city_x][cell_city_y] = 1 #will show city for t in range(0,totalTime+1): if (t % print_time_every == 0) : print("Time = " + str(t)) Cnew = np.zeros((numCells ,numCells )) for i in range(0,numCells): for j in range(0,numCells): if (C[i][j] == 0) :#and i+1 j+1 ==0 etc if(verbosity > 0): print("Cell " + str(i) + " , " + str(j)) # *** Model Developement 1 ** #sij = contaminated neighbours sij = 0 #using 'if' and 'elif' to assess the 9 different cases for a given cell (i,j) if(i!=0 and i!=numCells-1 and j!=0 and j!=numCells-1): sij= C[i][j+1] + C[i][j-1] + C[i-1][j] + C[i-1][j-1] + C[i-1][j+1] + C[i+1][j] + C[i+1][j-1] + C[i+1][j+1] elif(i==0 and j!=0 and j!=numCells-1):#mid left sij= C[i][j+1] + C[i][j-1] + C[i+1][j+1] + C[i+1][j] + C[i+1][j-1] elif(i==numCells-1 and j!=0 and j!=numCells-1):#mid right sij= C[i][j+1] + C[i][j-1] + C[i-1][j+1] + C[i-1][j] + C[numCells-1][numCells-1] elif(i!=0 and i!=numCells-1 and j==numCells-1):#mid top sij= C[i-1][j] + C[i-1][j-1] + C[i][j-1] + C[i+1][j-1] + C[i+1][j] elif(i!=0 and i!=numCells-1 and j==0):#mid bottom sij= C[i-1][j] + C[i-1][j+1] + C[i][j+1] + C[i+1][j+1] + C[i+1][j] elif(i == 0 and j==0) : #bottom left sij = C[0][1] + C[1][0] + C[1][1] elif(i==0 and j==numCells-1):#top left sij = C[1][numCells-1] + C[0][numCells-2] + C[1][numCells-2] elif(i==numCells-1 and j==0):#bottom right sij = C[numCells-1][1] + C[numCells-2][0] + C[numCells-2][1] else:#top right sij = C[numCells-1][numCells-2] + C[numCells-2][numCells-1] + C[numCells-2][numCells-2] # *** Model Developement 2 *** p = (sij/8)*((K[i][j])/Kmax) #gives probability n = random.uniform(0,1) if p>n: Cnew[i][j]=1#make this =p to give range of intensity? else : Cnew[i][j] = 0 C = Cnew; # Set new contamination Matrix to old C. #this is how it goes through, adding on each time # Plot grid and save to file if (t % print_every_n_steps == 0) : cmap = mpl.cm.jet norm = mpl.colors.Normalize(vmin=0, vmax = 1) fig, ax = plt.subplots() ax.imshow(C, cmap=cmap, norm=norm) ax.grid(which='major', axis='both', linestyle='-', color='k', linewidth=2) ax.axes.get_xaxis().set_visible(False) ax.axes.get_yaxis().set_visible(False) results_dir = os.path.join(os.getcwd(),'solutions/') if not os.path.isdir(results_dir): os.makedirs(results_dir) plt.savefig(results_dir + "Run:" + (iteration) + "_solution_time_" + str(t) + ".png") city = C[cell_city_x][cell_city_y] if city == 1: print("City Dead") count += 1 else: print("City Alive") print(count)

**buran**wrote Feb-12-2019, 02:08 PM:Please, use proper tags when post code, traceback, output, etc. This time I have added tags for you.

See BBcode help for more info.

Also, better use 4 spaces per indentation level - it's easier to read