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()
