prev_runduration = runduration prev_runvelocity = runvelocity prev_memory = gradient_memory POS = np.zeros(2) #The World with cells scattered about at random locations pos = ct.initial_state(size) POS = pos data = np.zeros(5) prev_tumble = np.random.random_integers(360) init_concentration = ct.gradient_equation(attractant_source, attractant_concentration, gtau, pos) prev_concentration = init_concentration #Simulate Tumble-Sense-Run sequence while t < simtime + init_runduration: #TUMBLE if turn_direction == 'random': turn = ct.random_tumble(pos, size, prev_tumble) if turn_direction == 'skewed': turn = ct.skewed_tumble(pos, size, prev_tumble) prev_tumble = turn #SENSE elapsed_time = prev_runduration
@author: Sriram """ import chemotaxis as ct import numpy as np import matplotlib.pyplot as plt size = 200 fig = plt.figure(figsize=[12, 10]) plt.rc('xtick', labelsize=18) plt.rc('ytick', labelsize=18) axis = fig.add_axes([.1, .1, .8, .8]) axis.set_xlim(0, size) axis.set_ylim(0, size) axis.set_xticks([]) axis.set_yticks([]) gradient = np.zeros((size, size)) i = 0 while i < size: ii = 0 while ii < size: gradient[ii, i] = ct.gradient_equation([size / 2, size / 2], 800, 50, [ii, i]) ii += 1 i += 1 plt.pcolor(gradient, vmin=0, vmax=800, cmap='Greys') #plt.colorbar() plt.show()