Simulating fish swarming behavior with the model by Iain Couzin et al.
Simulate fish swarming behavior with the model by Iain Couzin *et al.*.
from couzinswarm import Swarm swarm = Swarm() r, v = swarm.simulate(1000)
pip install couzinswarm
import numpy as np import matplotlib.pyplot as pl from mpl_toolkits.mplot3d import Axes3D from couzinswarm import Swarm # note: the dimension of space is measure in fish length, # such that r = 1 means a length of one fish swarm = Swarm( number_of_fish=20, repulsion_radius=1, orientation_width=10, attraction_width=10, # this angle is given in radians # up to np.pi (not 360 degrees as in # the paper) angle_of_perception=np.pi, # radians per unit of time turning_rate=0.1, # fish lengths per unit of time speed=0.1, # in units of fish length noise_sigma=0.1, dt=0.1, # geometry of box box_lengths=[100,100,100], # boundary conditions reflect_at_boundary = [True, True, True], verbose=False, ) fig = pl.figure() ax = fig.add_subplot(111, projection='3d') N_t = 1000 t = np.arange(N_t+1) # Note that r.shape = v.shape = ( N_fish, N_t+1, 3 ) positions, directions = swarm.simulate(N_t) r, v = positions, directions for i in range(swarm.number_of_fish): ax.plot(r[i,:,0], r[i,:,1], r[i,:,2]) pl.show()
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