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An enhanced interactive environment for the computer algebra system Maxima, based on CL-Jupyter, a Jupyter kernel for Common Lisp, by Frederic Peschanski. Thanks, Frederic!

This file describes the installation and usage of Maxima-Jupyter on a local machine, but you can try out Maxima-Jupyter without installing anything by clicking on the Binder badge above.


These examples make use of nbviewer. You can submit a link to your own notebook to tell nbviewer to render it.

Note that the Github notebook renderer (i.e., what you see if you click on a notebook file in the Github file browser) is currently (November 2018) somewhat suboptimal (bug report); it renders math as plain text, not as typeset formulas.


Maxima-Jupyter may be installed on a machine using a local installation, a repo2docker installation, or via a Docker image.

Local Installation


To try Maxima-Jupyter you need :

  • a Maxima executable

    • built with a Common Lisp implementation which has native threads

      • SBCL works for sure

      • Clozure CL works for sure

      • Other implementations which support the Bordeaux Threads package might work. The Bordeaux Threads project description says "Supports all major Common Lisp implementations: SBCL, CCL, Lispworks, Allegro, ABCL, ECL, Clisp." Aside from SBCL and CCL (i.e. Clozure CL) which are known to work, the others in that list are untested with maxima-jupyter.

      • Note also that ECL might theoretically work, since it is supported by Bordeaux Threads. However, nobody (neither Maxima-Jupyter developers nor users) has been able to get ECL to work, therefore you should assume ECL does not work with Maxima-Jupyter. SBCL and Clozure CL are known to work, try those instead.

      • Note specifically that GCL is not supported by Bordeaux Threads, and therefore GCL cannot work with maxima-jupyter.

    • You might or might not need to build Maxima. (A) If you have available a Maxima binary package compiled with a compatible Lisp implementation (i.e. SBCL, Clozure CL, Lispworks, etc. as enumerated above), then you do not need to build Maxima. (B) Otherwise, you must install a compatible Lisp implementation and compile Maxima yourself.

  • Quicklisp

    • When you load Maxima-Jupyter into Maxima for the first time, Quicklisp will download some dependencies automatically. Good luck.
  • Python 3.2 or above

  • Jupyter, or IPython 3.x

  • If the build aborts because the file zmq.h is missing, you may need to install the development files for the high-level C binding for ZeroMQ. On debian-based systems, you can satisfy this requirement by installing the package libczmq-dev.

Installing Maxima-Jupyter

The following installation methods are still relatively new. If you experience any issues than please use the "Old Methods" detailed later in the document.

First you must install Jupyter, then you can install Maxima-Jupyter. If you plan on using JupyterLab then you must install with the --user option.

 python3 -m pip --user install jupyter jupyterlab

Once Jupyter is installed you can either install from the source files of this repository, or you can install via the AUR if you are using Arch Linux.

Method 1. Source Based Installation

To install from the current source files first download the source files and then start a shell in the source directory. Then start Maxima and load the initialization script.

$ maxima
Maxima 5.43.0
using Lisp SBCL 1.5.5
Distributed under the GNU Public License. See the file COPYING.
Dedicated to the memory of William Schelter.
The function bug_report() provides bug reporting information.
(%i1) load("load-maxima-jupyter.lisp");

After the install script has loaded then install using one of the kernel types.

  1. User specific Quicklisp kernel: jupyter_install();
  2. User specific binary image kernel: jupyter_install_image();
  3. System-wide Quicklisp bundled kernel: jupyter_system_install(true, "pkg/");

After the installation is complete then exit Maxima. For the System-wide installation copy the files in pkg to the system root, i.e. sudo cp -r pkg/* / on Linux.

Method 2. Installation on Arch/Manjaro

The package for Arch Linux is maxima-jupyter-git. Building and installing (including dependencies) can be accomplished with:

yaourt -Sy maxima-jupyter-git

Alternatively use makepkg:

curl -L -O
tar -xvf maxima-jupyter-git.tar.gz
cd maxima-jupyter-git
makepkg -Csri

Please consult the Arch Wiki for more information regarding installing packages from the AUR.

Installing Maxima-Jupyter (Old Method)

First you must install Jupyter, then you can install Maxima-Jupyter.

I installed Jupyter via:

 python3 -m pip install jupyter

For Maxima-Jupyter, there are two kernel installation methods. In both methods, the effect of the installation command is to create a file named kernel.json which tells Jupyter where to find Maxima-Jupyter. Note that Maxima-Jupyter installation DOES NOT copy any Maxima-Jupyter files; it only creates kernel.json which points to the location of Maxima-Jupyter in your file system.

With the --user option in Method 1 or Method 2, the kernel.json file is created in a directory somewhere under your home directory. Otherwise, kernel.json is created in a system directory. You might need superuser privilege (via sudo for example) to execute a system installation, if the directory into which kernel.json is copied is not user-writable.

Note that jupyter --paths lists file system paths used by Jupyter; kernels are sought in the paths under data. Also, jupyter kernelspec list tells the kernels which are known to Jupyter.

For the record, on my system, a system installation copies kernel.json into /usr/local/share/jupyter/kernels/maxima/kernel.json and a user installation copies kernel.json into /home/robert/.local/share/jupyter/kernels/maxima/kernel.json.

Method 1. Maxima-Jupyter binary executable installation (Old Method)

The first installation method is to create a binary executable image, as detailed in make-maxima-jupyter-recipe.txt. After creating that image, execute one of these two commands to tell Jupyter about it.

For a system installation,

python3 ./ --exec=path/to/maxima-jupyter-image

For a user installation,

python3 ./ --exec=path/to/maxima-jupyter-image --user

Method 2. Maxima-Jupyter loadable source installation (Old Method)

The second installation method executes Maxima and then loads Maxima-Jupyter into Maxima. The advantange to this method is that the normal initialization behavior of Maxima, such as loading maxima-init.mac, is preserved.

Note that in order for this method to work, Quicklisp needs be loaded by default in every Maxima session. See Quicklisp documentation for details.

For a system installation,

python3 ./ --root=`pwd`

where the shell command pwd emits the current working directory (which must be the Maxima-Jupyter top-level directory, since it contains

For a user installation,

python3 ./ --root=`pwd` --user

The option --maxima may also be used to specify the location of the Maxima executable. If not specified, the command which launches Maxima is just maxima, therefore the first instance of maxima in the PATH environment variable is the one which is executed.

Code Highlighting Installation

Highlighting Maxima code is handled by CodeMirror in the notebook and Pygments in HTML export.

The CodeMirror mode for Maxima is maxima.js. To install it, find the CodeMirror mode installation directory, create a directory named maxima there, copy maxima.js to the maxima directory, and update codemirror/mode/meta.js as shown in codemirror-mode-meta-patch. Yes, this is pretty painful, sorry about that.

The Pygments lexer for Maxima is To install it, find the Pygments installation directory, copy to the lexers directory, and update lexers/ as shown in pygments-mapping-patch. Yes, this is pretty painful too.

Running Maxima-Jupyter

Maxima-Jupyter may be run from a local installation in console mode by the following.

jupyter console --kernel=maxima

Notebook mode is initiated by the following.

jupyter notebook

When you enter stuff to be evaluated, you must include the usual trailing semicolon or dollar sign:

In [1]: 2*21;
Out[1]: 42

In [2]:

repo2docker Usage

Maxima-Jupyter may be run as a Docker image managed by repo2docker which will fetch the current code from GitHub and handle all the details of running the Jupyter Notebook server.

First you need to install repo2docker (sudo may be required)

pip install jupyter-repo2docker

Once repo2docker is installed then the following will build and start the server. Directions on accessing the server will be displayed once the image is built.

jupyter-repo2docker --user-id=1000 --user-name=mj

Docker Image

A Docker image of Maxima-Jupyter may be built using the following command (sudo may be required). This image is based on the docker image archlinux/base.

docker build --tag=maxima-jupyter .

If you'd like to build with a different user than the default (mj), you may override it with the following:

docker build --build-arg NB_USER=alice --tag=maxima-jupyter .

After the image is built the container may be run with:

docker run -it maxima-jupyter

The Dockerfile makes use of the ENTRYPOINT command; the default behaviour executes the jupyter binary with the arguments console --kernel=maxima.

If you'd like to run using Juypter's notebook web server, you may do the following to override the default use of console:

docker run -it \
    -v `pwd`/notebooks:/home/USER/maxima-jupyter/examples \
    -p 8888:8888 \
    maxima-jupyter \
    notebook --ip= --port=8888

where the last line is the set of arguments to jupyter that cause it to run in the notebook server mode.

To run the Bash shell on the container, just override the entry point:

docker run -it --entrypoint=bash maxima-jupyter

If you cannot build the Docker image, you may use a pre-built one by subsituting the Docker image name maxima-jupyter in the above docker commands with calyau/maxima-jupyter. Note that the default user on the calyau image is not mj, but is rather oubiwann.

Additional examples of notebooks created using this mode have been created here (taken from the Maxima tutorial):

Have fun and keep me posted. Feel free to send pull requests, comments, etc.

Robert Dodier robert-dodier @ github


A Maxima kernel for Jupyter, based on CL-Jupyter (Common Lisp kernel)




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