Working with matplotlib¶
Defining custom animations¶
MoviePy allows you to produce custom animations by defining a function that returns a frame at a given time of the animation in the form of a numpy array.
An example of this workflow is below:
from moviepy.editor import VideoClip
def make_frame(t):
"""Returns an image of the frame for time t."""
# ... create the frame with any library here ...
return frame_for_time_t # (Height x Width x 3) Numpy array
animation = VideoClip(make_frame, duration=3) # 3-second clip
This animation can then be exported by the usual MoviePy means:
# export as a video file
animation.write_videofile("my_animation.mp4", fps=24)
# export as a GIF
animation.write_gif("my_animation.gif", fps=24) # usually slower
Simple matplotlib example¶
An example of an animation using matplotlib can then be as follows:
import matplotlib.pyplot as plt
import numpy as np
from moviepy.editor import VideoClip
from moviepy.video.io.bindings import mplfig_to_npimage
x = np.linspace(-2, 2, 200)
duration = 2
fig, ax = plt.subplots()
def make_frame(t):
ax.clear()
ax.plot(x, np.sinc(x**2) + np.sin(x + 2*np.pi/duration * t), lw=3)
ax.set_ylim(-1.5, 2.5)
return mplfig_to_npimage(fig)
animation = VideoClip(make_frame, duration=duration)
animation.write_gif('matplotlib.gif', fps=20)
Working in the Jupyter Notebook¶
If you are working inside a Jupyter Notebook, you can take advantage of the fact that VideoClips can be embedded in the output cells of the notebook with the ipython_display method. The above example then becomes:
import matplotlib.pyplot as plt
import numpy as np
from moviepy.editor import VideoClip
from moviepy.video.io.bindings import mplfig_to_npimage
x = np.linspace(-2, 2, 200)
duration = 2
fig, ax = plt.subplots()
def make_frame(t):
ax.clear()
ax.plot(x, np.sinc(x**2) + np.sin(x + 2*np.pi/duration * t), lw=3)
ax.set_ylim(-1.5, 2.5)
return mplfig_to_npimage(fig)
animation = VideoClip(make_frame, duration=duration)
animation.ipython_display(fps=20, loop=True, autoplay=True)