我在网上发现了一些关于在matplotlib动画崩溃Python时该怎么做的片状资源。
这是代码。我知道dt变量的点数或小数的某些变化可能会导致一些压倒性的计算，但我真的可以使用一些帮助。总的来说，它表现得非常缓慢，我需要让它顺利运行。代码用于物理类，它不是优化问题 - 项目的目标不是优化matplotlib的动画设施，而是在相空间中建模系统。我可以使用任何帮助。
import matplotlib
#matplotlib.use("TkAgg")
from matplotlib import animation
import matplotlib.pyplot as plt
import matplotlib.pylab as pl
import numpy as np
import math
# Create the window and the plot.
fig = plt.figure()
fig.canvas.set_window_title("Modelling Non-Linear Chaotic Systems in Phase ")
ax = fig.add_subplot(111)
##### CONSTANTS #####
NUM_POINTS = 40 # The number of points to discretize.
AUTOSCALE = False # Sets autoscale to true or false. If true, there will be odd (expected to a degree) behavior as time goes on.
NO_TRAIL = False # Adds a trail.
##### CONSTANTS #####
##### Cool Settings #####
# Double tail swirl.
# k = .2
# alpha = .4
# dt = .1
##### #####
##### VARIABLES #####
m = 1 # mass constant
k = 5 # spring constant
alpha = .3 # stretch/deformation constant
dt = .01 # change in time
interval = 100 # interval in milliseconds between each animation
##### VARIABLES #####
class Point():
def x():
return self.x
def y():
return self.y
def pos(xy):
self.x=xy[0]
self.y=xy[1]
# initial_pos is a tuple
def __init__(self, initial_pos):
self.initial_position = initial_pos
self._x = initial_pos[0]
self._y = initial_pos[1]
self.x = self._x
self.y = self._y
def evolve(x_map, y_map, l):
return [Point([x_map(el), y_map(el)]) for el in l]
"""
Lagrangian
L = T - V = .5dq^2 - .5kq^2 - .333alpha*q^3
p = mdq; dx = p/m
Hamiltonian
H = p^2/m - L
H = p^2/m - p^2/2m + .5kq^2 + .333alpha*q^3
dp = -partial(H)/partial(q) = -kq - alpha*q^2
dx = partial(H)/partial(p) = p/m
"""
def time_dependent_funcs(name, dt):
### This is the adjustment of p over time. ###
def p_map(point):
p = point.y
q = point.x
try:
dp = -1*k*q-alpha*q**2
ddp = -k*p/m-alpha*q*p/m
return p+dp*dt+ddp*dt**2
except:
return p
### End adjustment of p. ###
### This is the adjustment of q over time. ###
def q_map(point):
q = point.x
p = point.y
try:
dq = p/m
dp = -1*k*q-alpha*q**2
ddp = -k*p/m-alpha*q*p/m
return q+dq*dt+ddp*dt**2/m
except:
return q
### End adjustment of q. ###
if name == "p_map":
return p_map
elif name == "q_map":
return q_map
# Used this post to help create this function: http://stackoverflow.com/questions/14619876/how-do-i-generate-pair-of-random-points-in-a-circle-using-matlab
def circle(r, n):
l = []
for i in range(n):
x=r+1 # necessary to enter loop
y=r+1 # necessary to enter loop
while (x**2 + y**2) > r**2:
x = 2*r*np.random.randn()
y = 2*r*np.random.randn()
l.append(Point([x,y]))
return l
### DOESN'T CURRENTLY WORK
def square(h, n):
l = []
for i in range(n):
x=h+1 # necessary to enter loop
y=h+1 # necessary to enter loop
while x < h and y < h:
x = np.random.randn()
y = np.random.randn()
l.append(Point([x,y]))
return l
def rand(n):
return [Point([np.random.randn(),np.random.randn()]) for i in range(n)]
def x(l):
return [el.x for el in l]
def y(l):
return [el.y for el in l]
#list_of_points = rand(NUM_POINTS) # works
#list_of_points = square(.2, 400) # doesn't currently work
list_of_points = circle(20,NUM_POINTS) # need this to be a global
def init():
global list_of_points, dt
scat = ax.scatter(x(list_of_points), y(list_of_points),color="blue", animated=NO_TRAIL)
if NO_TRAIL:
return scat,
def animate(i):
global list_of_points, dt
map_p = time_dependent_funcs("p_map", dt)
map_q = time_dependent_funcs("q_map", dt)
### evolve the points ###
list_of_points = evolve(map_q, map_p, list_of_points)
scat = ax.scatter(x(list_of_points), y(list_of_points), color="blue",lw=2, animated=NO_TRAIL)
if NO_TRAIL:
return scat,
### Animates the figure fig by executing init_func init (initially) followed by animation function
### animate for 10 frames at an interval of 100ms all the while erasing the last animation (blit=True).
ani = animation.FuncAnimation(fig, animate, init_func=init, frames = 100, interval=interval,blit=NO_TRAIL,save_count=0)
if not AUTOSCALE:
plt.autoscale(enable=False)
plt.show()