An animation engine for explanatory math videos
Manim is an animation engine for explanatory math videos. It's used to create precise animations programmatically, as demonstrated in the videos of 3Blue1Brown.
NOTE: This repository is maintained by the Manim Community, and is not associated with Grant Sanderson or 3Blue1Brown in any way (although we are definitely indebted to him for providing his work to the world). If you would like to study how Grant makes his videos, head over to his repository (3b1b/manim). This fork is updated more frequently than his, and it's recommended to use this fork if you'd like to use Manim for your own projects.
Manim requires a few dependencies that must be installed prior to using it. If you want to try it out first before installing it locally, you can do so in our online Jupyter environment.
For the local installation, please visit the Documentation and follow the appropriate instructions for your operating system.
Once the dependencies have been installed, run the following in a terminal window:
pip install manimManim is an extremely versatile package. The following is an example Scene you can construct:
from manim import *
class SquareToCircle(Scene):
def construct(self):
circle = Circle()
square = Square()
square.flip(RIGHT)
square.rotate(-3 * TAU / 8)
circle.set_fill(PINK, opacity=0.5)
self.play(Create(square))
self.play(Transform(square, circle))
self.play(FadeOut(square))In order to view the output of this scene, save the code in a file called example.py. Then, run the following in a terminal window:
manim -p -ql example.py SquareToCircle You should see your native video player program pop up and play a simple scene in which a square is transformed into a circle. You may find some more simple examples within this GitHub repository. You can also visit the official gallery for more advanced examples.
Manim also ships with a %%manim IPython magic which allows to use it conveniently in JupyterLab (as well as classic Jupyter) notebooks. See the
corresponding documentation for some guidance and
try it out online.
The general usage of Manim is as follows:
The -p flag in the command above is for previewing, meaning the video file will automatically open when it is done rendering. The -ql flag is for a faster rendering at a lower quality.
Some other useful flags include:
-sto skip to the end and just show the final frame.-n <number>to skip ahead to then'th animation of a scene.-fshow the file in the file browser.
For a thorough list of command line arguments, visit the documentation.
Documentation is in progress at ReadTheDocs.
The community also maintains a docker image (manimcommunity/manim), which can be found on DockerHub. The following tags are supported:
latest-- the most recent version corresponding to the master branchstable-- the latest released version (according to the releases page)vX.Y.Z-- any particular released version (according to the releases page)
To render a scene CircleToSquare in a file test_scenes.py contained in your current working directory while preserving your user and group ID, use
docker run --rm -it --user="$(id -u):$(id -g)" -v "$(pwd)":/manim manimcommunity/manim manim test_scenes.py CircleToSquare -qm
Instead of using the "throwaway container" approach sketched above, you can also create a named container that you can also modify to your liking. First, run
docker run -it --name my-manim-container -v "$(pwd):/manim" manimcommunity/manim /bin/bash
to obtain an interactive shell inside your container allowing you to, e.g., install further dependencies (like texlive packages using tlmgr). Exit the container as soon as you are satisfied. Then, before using it, start the container by running
docker start my-manim-container
Then, to render a scene CircleToSquare in a file test_scenes.py, call
docker exec -it --user="$(id -u):$(id -g)" my-manim-container manim test.py CircleToSquare -qm
Another alternative is to use the docker image to spin up a local webserver running
JupyterLab in whose Python kernel manim is installed and can be accessed via the %%manim cell magic.
To use JupyterLab, run
docker run -it -p 8888:8888 manimcommunity/manim jupyter lab --ip=0.0.0.0
and then follow the instructions in the terminal.
When executing manim within a Docker container, several command line flags (in particular -p (preview file) and -f (show output file in the file browser)) are not supported.
If you need help installing or using Manim, feel free to reach out to our Discord Server or Reddit Community. If you would like to submit bug report or feature request, please open an issue.
Contributions to Manim are always welcome. In particular, there is a dire need for tests and documentation. For contribution guidelines, please see the documentation.
Most developers on the project use Poetry for management. You'll want to have poetry installed and available in your environment. You can learn more poetry and how to use it at its documentation.
Our full code of conduct, and how we enforce it, can be read on our website.
The software is double-licensed under the MIT license, with copyright by 3blue1brown LLC (see LICENSE), and copyright by Manim Community Developers (see LICENSE.community).