我写了一个代码来解释波的叠加。我粘贴我的代码和输出如下。但问题是我只是创建静态图....如果我可以设置波动(在我的代码中:subplot(211)
)和subplot(212)
中的correspoding结果,事情会变得更有趣。但到目前为止,我只能在没有子图的情况下制作动画......当我在互联网上研究“使用matplotlib
在子图中制作动画”时,我发现这些结果对我来说不太可理解,也不同于我在这种情况下的代码。
请问有谁可以在这方面帮助我?如果动画基于我的以下代码结构会更好(当然,对于子图动画的必要更改是值得赞赏的)。谢谢你们。
我的守则
#Composition of Waves
import matplotlib as mpl
mpl.rc('text', usetex = True)
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
plt.gca().set_aspect('equal', adjustable='box')
plt.style.use(['ggplot','dark_background'])
title = 'Composition of Waves'
#Parameters:
#a=Amplitude; w=Angular Frequency; phi = Phase Angle.
#Definition of the function:
def f(t,a,w,phi):
y = a*np.sin(w*t + phi)
return y
t = np.arange(0,4*np.pi,0.001)
def create_plot(ptype):
y1 = f(t,1,1,1)
y2 = f(t,2,2,2)
y = y1 + y2
if ptype == 'waves':
plt.plot(t, y1, label='$y=f_1(t)$')
plt.plot(t, y2, label='$y=f_2(t)$')
elif ptype == 'composition':
plt.plot(t, y, label='$Composition$', color= 'm')
plt.figure(1)
plt.subplot(211)
create_plot('waves')
plt.legend()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
#plt.xlabel('$x$')
plt.ylabel('$y$')
plt.title(title)
plt.subplot(212)
create_plot('composition')
plt.legend()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xlabel('$t$')
plt.ylabel('$y$')
# Tweak spacing between subplots to prevent labels
from overlapping
plt.subplots_adjust(hspace=0.5)
plt.savefig('composition_Waves.eps', format='eps', dpi=1000,bbox_inches='tight')
plt.show()
在这里,我想用不同的w
和phi
制作波浪的动画。
无论是否有子图,创建动画都没有区别。唯一重要的是保持Artist
对象的引用(在这种情况下,Line2D
返回的plt.plot()
对象能够在动画函数中修改它们的属性(数据)。
import matplotlib as mpl
mpl.rc('text', usetex = False)
mpl.rc('font', family = 'serif')
import matplotlib.pyplot as plt
import numpy as np
plt.gca().set_aspect('equal', adjustable='box')
plt.style.use(['ggplot','dark_background'])
title = 'Composition of Waves'
#Parameters:
#a=Amplitude; w=Angular Frequency; phi = Phase Angle.
#Definition of the function:
def f(t,a,w,phi):
y = a*np.sin(w*t + phi)
return y
t = np.arange(0,4*np.pi,0.001)
def create_plot(ptype):
y1 = f(t,1,1,1)
y2 = f(t,2,2,2)
y = y1 + y2
arts = []
if ptype == 'waves':
l1, = plt.plot(t, y1, label='$y=f_1(t)$')
l2, = plt.plot(t, y2, label='$y=f_2(t)$')
arts = [l1, l2]
elif ptype == 'composition':
l3, = plt.plot(t, y, label='$Composition$', color= 'm')
arts = [l3]
return arts ## return the artists created by `plt.plot()`
my_lines = [] ## array to keep track of the Line2D artists
fig = plt.figure(1)
plt.subplot(211)
l = create_plot('waves')
my_lines += l ## add artists to array
plt.legend()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
#plt.xlabel('$x$')
plt.ylabel('$y$')
plt.title(title)
plt.subplot(212)
l = create_plot('composition')
my_lines += l
plt.legend()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xlabel('$t$')
plt.ylabel('$y$')
# Tweak spacing between subplots to prevent labels from overlapping
plt.subplots_adjust(hspace=0.5)
print(my_lines)
def animate(i):
## in this examples, i takes values 0-10 by steps of 0.01 (the frames in the animation call)
## and will represent the frequency of the 2nd wave in the top subplot
y1 = f(t,1,1,1)
y2 = f(t,2,i,2)
y = y1 + y2
# update the content of the Line2D objects
my_lines[1].set_ydata(y2)
my_lines[2].set_ydata(y)
return my_lines ## return updated artists
ani = animation.FuncAnimation(fig, animate, frames=np.linspace(0,10,100))
plt.show()