ipympl 0.10.0

Matplotlib Jupyter Extension

ipympl

Leveraging the Jupyter interactive widgets framework, ipympl enables the interactive features of matplotlib in the Jupyter notebook and in JupyterLab.

Besides, the figure canvas element is a proper Jupyter interactive widget which can be positioned in interactive widget layouts.

Usage

To enable the ipympl backend, simply use the matplotlib Jupyter magic:

%matplotlib ipympl

Documentation

See the documentation at: https://matplotlib.org/ipympl/

Example

See the example notebook for more!

Installation

With conda

conda install -c conda-forge ipympl

With pip

pip install ipympl

Use in JupyterLab

If you want to use ipympl in JupyterLab, we recommend using JupyterLab >= 3.

If you use JupyterLab 2, you still need to install the labextension manually:

conda install -c conda-forge nodejs
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib

Install an old JupyterLab extension

If you are using JupyterLab 1 or 2, you will need to install the right jupyter-matplotlib version, according to the ipympl and jupyterlab versions you installed. For example, if you installed ipympl 0.5.1, you need to install jupyter-matplotlib 0.7.0, and this version is only compatible with JupyterLab 1.

conda install -c conda-forge ipympl==0.5.1
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib@0.7.0

Versions lookup table:

ipympl	jupyter-matplotlib	JupyterLab	Matplotlib
0.10.0	0.12.0	>=2,<5	>=3.5.0
0.9.5-8	0.11.5-8	>=2,<5	>=3.5.0
0.9.3-4	0.11.3-4	>=2,<5	3.4.0>=
0.9.0-2	0.11.0-2	>=2,<5	3.4.0>= <3.7
0.8.8	0.10.x	>=2,<5	3.3.1>= <3.7
0.8.0-7	0.10.x	>=2,<5	3.3.1>=, <3.6
0.7.0	0.9.0	>=2,<5	3.3.1>=
0.6.x	0.8.x	>=2,<5	3.3.1>=, <3.4
0.5.8	0.7.4	>=1,<3	3.3.1>=, <3.4
0.5.7	0.7.3	>=1,<3	3.2.*
...	...	...
0.5.3	0.7.2	>=1,<3
0.5.2	0.7.1	>=1,<2
0.5.1	0.7.0	>=1,<2
0.5.0	0.6.0	>=1,<2
0.4.0	0.5.0	>=1,<2
0.3.3	0.4.2	>=1,<2
0.3.2	0.4.1	>=1,<2
0.3.1	0.4.0	>=0<2

For a development installation

We recommend using pixi for development as it handles both Python and Node.js dependencies (matplotlib has compiled dependencies).

# Install dependencies and set up environment
pixi install

# Install the Python package in editable mode
pixi run pip install -e .

# Install JavaScript dependencies and build
pixi run jlpm install
pixi run jlpm build

# Set up JupyterLab extension in development mode
pixi run jupyter labextension develop --overwrite .

# Start development workflow (in separate terminals)
pixi run npm run watch    # Terminal 1: Auto-rebuild on changes
pixi run jupyter lab      # Terminal 2: Run JupyterLab

Alternative: Using conda/mamba

mamba env create --file dev-environment.yml
conda activate ipympl-dev

pip install -e .
jlpm install
jlpm build
jupyter labextension develop --overwrite .

# Start development workflow (in separate terminals)
npm run watch    # Terminal 1: Auto-rebuild on changes
jupyter lab      # Terminal 2: Run JupyterLab

How to see your changes

TypeScript/JavaScript: After a change, the watch command will automatically rebuild. Wait for the build to finish, then refresh your browser and the changes should take effect.

Python: If you make a change to the Python code, restart the notebook kernel to have it take effect.

Classic Jupyter Notebook

If you need to develop for classic Jupyter Notebook (not JupyterLab), also run:

# With pixi:
pixi run jupyter nbextension install --py --symlink --sys-prefix --overwrite ipympl
pixi run jupyter nbextension enable --py --sys-prefix ipympl

# Or with conda/mamba:
jupyter nbextension install --py --symlink --sys-prefix --overwrite ipympl
jupyter nbextension enable --py --sys-prefix ipympl