我写了一个代码,用于绘制随机游走。生成了traj
个不同的随机漫步,每个步行都包含n
个步骤。我想动画他们的动作。我怎么能这样做?
我的代码如下:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def random_walk_2D(n, traj = 1):
for i in range(traj):
skoki = np.array([[0, 1], [1, 0], [-1, 0], [0, -1]])
losy = np.random.randint(4, size = n)
temp = skoki[losy, :]
x = np.array([[0, 0]])
temp1 = np.concatenate((x, temp), axis = 0)
traj = np.cumsum(temp1, axis = 0)
plt.plot(traj[:, 0], traj[:, 1])
plt.plot(traj[-1][0], traj[-1][1], 'ro') #the last point
plt.show()
答案 0 :(得分:2)
现在,您可以一次性生成traj。我的意思是MOJO
中的traj
已经包含所有"故事"从开始到结束。如果你想创建一个实时"的动画,你不应该一次性生成traj = np.cumsum(temp1, axis = 0)
,而是迭代地绘制新的步骤。怎么样:
traj
import numpy as np
import matplotlib.pyplot as plt
def real_time_random_walk_2D_NT(
nb_steps, nb_trajs, with_dots=False, save_trajs=False, tpause=.01
):
"""
Parameters
----------
nb_steps : integer
number of steps
nb_trajs : integer
number of trajectories
save_trajs : boolean (optional)
If True, entire trajectories are saved rather than
saving only the last steps needed for plotting.
False by default.
with_dots : boolean (optional)
If True, dots representative of random-walking entities
are displayed. Has precedence over `save_trajs`.
False by default.
tpause : float (optional)
Pausing time between 2 steps. .01 secondes by default.
"""
skoki = np.array([[0, 1], [1, 0], [-1, 0], [0, -1]])
trajs = np.zeros((nb_trajs, 1, 2))
for i in range(nb_steps):
_steps = []
for j in range(nb_trajs):
traj = trajs[j,:,:]
losy = np.random.randint(4, size = 1)
temp = skoki[losy, :]
traj = np.concatenate((traj, temp), axis = 0)
traj[-1,:] += traj[-2,:]
_steps.append(traj)
if save_trajs or with_dots:
trajs = np.array(_steps)
if with_dots:
plt.cla()
plt.plot(trajs[:,i, 0].T, trajs[:,i, 1].T, 'ro') ## There are leeway in avoiding these costly transpositions
plt.plot(trajs[:,:i+1, 0].T, trajs[:,:i+1, 1].T)
else:
plt.plot(trajs[:,-1+i:i+1, 0].T, trajs[:,-1+i:i+1, 1].T)
else:
trajs = np.array(_steps)[:,-2:,:]
plt.plot(trajs[:,:, 0].T, trajs[:,:, 1].T)
plt.pause(tpause)
real_time_random_walk_2D_NT(50, 6, with_dots=True)