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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.
To enable the ipympl
backend, simply use the matplotlib
Jupyter
magic:
%matplotlib widget
See the documentation at: https://matplotlib.org/ipympl/
See the example notebook for more!
conda install -c conda-forge ipympl
pip install ipympl
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
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 |
---|---|---|---|
0.9.5-7 | 0.11.5-7 | >=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 |
Create a dev environment that has nodejs installed. The instructions here use mamba but you can also use conda.
mamba env create --file dev-environment.yml
conda activate ipympl-dev
Install the Python Packge
pip install -e .
When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For lab, this is done by the command:
jupyter labextension develop --overwrite .
jlpm build
For classic notebook, you need to run:
jupyter nbextension install --py --symlink --sys-prefix --overwrite ipympl
jupyter nbextension enable --py --sys-prefix ipympl
Typescript:
If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the widget.
# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab
After a change wait for the build to finish and then refresh your browser and the changes should take effect.
Python:
If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
FAQs
Matplotlib Jupyter Extension
We found that ipympl demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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