使用matplotlib对人群运动进行建模 [英] modelling crowd movement with matplotlib
问题描述
我想用python建立基本的人群运动模型.我想展示一个动画.我已经制作了以下程序来使用matplotlib对其进行测试:
I would like to model basic crowd movement with python. I want to show an animation. I have made the following program to test it with matplotlib :
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
#size of the crowd
N = 100
def gen_data():
""" init position and speed of each people """
x = y = np.zeros(N)
theta = np.random.random(N) * 360 / (2 * np.pi)
v0 = 0.1
vx, vy = v0 * np.cos(theta), v0 * np.sin(theta)
return np.array([x, y, vx, vy]).T
def init():
for line in lines:
line.set_data([],[])
return line,
def update_lines(i, lines, data):
for d, line in zip(data, lines):
d[0:2] += d[2:4]
if abs(d[0]) > 5: d[2] *= -1
if abs(d[1]) > 5: d[3] *= -1
line.set_data(d[0] ,d[1])
return lines
fig = plt.figure()
ax = plt.axes(xlim=(-5,5),ylim=(-5,5))
lines = [plt.plot([],[], 'ko')[0] for i in range(N)]
data = gen_data()
anim = animation.FuncAnimation(fig, update_lines, init_func=init, fargs=(lines, data), interval=10, blit=True)
plt.show()
即使对于N = 100,动画也很慢...我可以使用mathplotlib加快速度吗? matplotlib是用python制作稀薄动画的最佳图形工具吗?如果没有,那会是什么?
Even for N=100, the animation is slow... Is there something I can do to speed it up with mathplotlib ? Is matplotlib the best graphic tool to make thins kind of animation with python ? If no, what would it be ?
推荐答案
您可以执行以下3项操作来使动画更快:
Here are 3 things you can do to make the animation faster:
- 将
N
呼叫替换为plt.plot
,将一个呼叫替换为plt.scatter
. -
用赋值一次替换
data
的整个切片的赋值替换update
中的for-loop
:
- Replace the
N
calls toplt.plot
with one call toplt.scatter
. Replace the
for-loop
inupdate
with assignments which modify whole slices ofdata
at once:
data[:, 0:2] += data[:, 2:4]
data[:, 2] = np.where(np.abs(data[:, 0]) > 5, -data[:, 2], data[:, 2])
data[:, 3] = np.where(np.abs(data[:, 1]) > 5, -data[:, 3], data[:, 3])
将interval=10
减小为interval=0
.
Reduce interval=10
to interval=0
.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import animation
# size of the crowd
N = 100
def gen_data():
""" init position and speed of each people """
x = y = np.zeros(N)
theta = np.random.random(N) * 360 / (2 * np.pi)
v0 = 0.1
vx, vy = v0 * np.cos(theta), v0 * np.sin(theta)
return np.column_stack([x, y, vx, vy])
def init():
pathcol.set_offsets([[], []])
return pathcol,
def update(i, pathcol, data):
data[:, 0:2] += data[:, 2:4]
data[:, 2] = np.where(np.abs(data[:, 0]) > 5, -data[:, 2], data[:, 2])
data[:, 3] = np.where(np.abs(data[:, 1]) > 5, -data[:, 3], data[:, 3])
pathcol.set_offsets(data[:, 0:2])
return [pathcol]
fig = plt.figure()
ax = plt.axes(xlim=(-5, 5), ylim=(-5, 5))
pathcol = plt.scatter([], [])
data = gen_data()
anim = animation.FuncAnimation(fig, update, init_func=init,
fargs=(pathcol, data), interval=0, blit=True)
plt.show()
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