Using Matplotlib.animate To Animate A Contour Plot In Python

I have a 3D array of data (2 spatial dimensions and 1 time dimension) and I'm trying to produce an animated contour plot using matplotlib.animate. I'm using this link as a basis:

Solution 1:

Felix Schneider is correct about the animation becoming very slow. His solution of setting ax.collections = [] removes all old (and superseded) "artist"s. A more surgical approach is to only remove the artists involved in the drawing the contours:

for c in cont.collections:
    c.remove()

which is useful in more complicated cases, in lieu of reconstructing the entire figure for each frame. This also works in Rehman Ali's example; instead of clearing the entire figure with clf() the value returned by contourf() is saved and used in the next iteration.

Here is an example code similar to Luke's from Jun 7 '13, demonstrating removing the contours only:

import pylab as plt
import numpy
import matplotlib.animation as animation
#plt.rcParams['animation.ffmpeg_path'] = r"C:\some_path\ffmpeg.exe"   # if necessary

# Generate data for plotting
Lx = Ly = 3
Nx = Ny = 11
Nt = 20
x = numpy.linspace(0, Lx, Nx)
y = numpy.linspace(0, Ly, Ny)
x,y = numpy.meshgrid(x,y)
z0 = numpy.exp(-(x-Lx/2)**2-(y-Ly/2)**2)   # 2 dimensional Gaussian

def some_data(i):   # function returns a 2D data array
    return z0 * (i/Nt)

fig = plt.figure()
ax = plt.axes(xlim=(0, Lx), ylim=(0, Ly), xlabel='x', ylabel='y')

cvals = numpy.linspace(0,1,Nt+1)      # set contour values 
cont = plt.contourf(x, y, some_data(0), cvals)    # first image on screen
plt.colorbar()

# animation function
def animate(i):
    global cont
    z = some_data(i)
    for c in cont.collections:
        c.remove()  # removes only the contours, leaves the rest intact
    cont = plt.contourf(x, y, z, cvals)
    plt.title('t = %i:  %.2f' % (i,z[5,5]))
    return cont

anim = animation.FuncAnimation(fig, animate, frames=Nt, repeat=False)
anim.save('animation.mp4', writer=animation.FFMpegWriter())

Solution 2:

This is what I got to work:

# Generate grid for plotting
x = linspace(0, Lx, Nx)
y = linspace(0, Ly, Ny)
x,y = meshgrid(x,y)

fig = plt.figure()
ax = plt.axes(xlim=(0, Lx), ylim=(0, Ly))  
plt.xlabel(r'x')
plt.ylabel(r'y')

# animation function
def animate(i): 
    z = var[i,:,0,:].T
    cont = plt.contourf(x, y, z, 25)
    if (tslice == 0):
        plt.title(r't = %1.2e' % t[i] )
    else:
        plt.title(r't = %i' % i)

    return cont  

anim = animation.FuncAnimation(fig, animate, frames=Nt)

anim.save('animation.mp4')

I found that removing the blit=0 argument in the FuncAnimation call also helped...

Solution 3:

This is the line:

cont, = ax.contourf([], [], [], 500)

change to:

 x = linspace(0, 200, Nx)
 y = linspace(0, 100, Ny)
 x, y = meshgrid(x, y)
 z = n[i,:,0,:].T
 cont, = ax.contourf(x, y, z, 500)

You need to intilize with sized arrays.

Solution 4:

Here is another way of doing the same thing if matplotlib.animation don't work for you. If you want to continuously update the colorbar and everything else in the figure, use plt.ion() at the very beginning to enable interactive plotting and use a combo of plt.draw() and plt.clf() to continuously update the plot.

import matplotlib.pyplot as plt
import numpy as np

plt.ion(); plt.figure(1);
for k in range(10):
    plt.clf(); plt.subplot(121);
    plt.contourf(np.random.randn(10,10)); plt.colorbar();
    plt.subplot(122,polar=True)
    plt.contourf(np.random.randn(10,10)); plt.colorbar();
    plt.draw();

Note that this works with figures containing different subplots and various types of plots (i.e. polar or cartesian)

Solution 5:

I used Lukes approach (from Jun 7 '13 at 8:08 ), but added

ax.collections = [] 

right before

cont = plt.contourf(x, y, z, 25).

Otherwise I experienced that creating the animation will become very slow for large frame numbers.