Implement nagel-schreckenberg

main.py

+from random import uniform, shuffle
+import matplotlib.pyplot as plt
 import numpy as np
-import scipy as sp
+ 
+L = 500 # number of cells in row
+num_iters = 500 # number of iterations
+density = 0.48 # how many positives
+vmax = 2
+p = 0.2
+
+cars_num = int(density * L)
+initial = [0] * cars_num + [-1] * (L - cars_num)
+shuffle(initial)
+
+iterations = [initial]
+
+for i in range(num_iters):
+	prev,curr = iterations[-1],[-1] * L
+
+	for x in range(L):
+		if prev[x] > -1:
+			vi = prev[x]
+			d = 1
+			while prev[(x + d) % L] < 0:
+				d += 1
+
+			vtemp = min(vi+1, d - 1, vmax) # increse speed up to max speed, but don't move further than next car
+			v = max(vtemp - 1, 0) if uniform(0,1) < p else vtemp # with probability p hit the brakes, otherwise sustain velocity
+			curr[(x + v) % L] = v # perform the move
+			#print(x,v)
+
+	iterations.append(curr)
+
+
+a = np.zeros(shape=(num_iters,L))
+for i in range(L):
+	for j in range(num_iters):
+		a[j,i] = 1 if iterations[j][i] > -1 else 0
+ 
+# showing image
+plt.imshow(a, cmap="Greys", interpolation="nearest")
+plt.show()