Developer install
To install a developer version of pySSV, you will first need to clone the repository:
git clone https://github.com/space928/Shaders-For-Scientific-Visualisation
cd Shaders-For-Scientific-Visualisation
You can optionally create a new Python dev environment:
conda create -n pySSV-dev -c conda-forge nodejs yarn python jupyterlab=4
conda activate pySSV-dev
Install the python package. This will also build the TS package:
pip install -e ".[test, examples]"
The jlpm
command is JupyterLab’s pinned version of [yarn](https://yarnpkg.com/) that is installed with JupyterLab.
You may use yarn
or npm
in lieu of jlpm
below. Using jlpm
and yarn
sometimes breaks the package
cache if this happens, just delete the yarn.lock
file and the .yarn
folder and rerun jlpm install
.
When developing your extensions, you need to manually enable your extensions with the notebook / lab frontend. For Jupyter Notebook, this is done with the command:
jupyter nbextension install [–sys-prefix / –user / –system] –symlink –py pySSV jupyter nbextension enable [–sys-prefix / –user / –system] –py pySSV
with the appropriate flag.
Or, if you are using Jupyterlab:
jupyter labextension develop --overwrite .
Build the frontend of the plugin:
jlpm run build
If you plan on making changes to the plugin frontend (any of the typescript), then run the watch task to automatically rebuild the plugin when there are changes. Changes to the python are automatically reloaded when the Jupyter Kernel is restarted:
jlpm run watch
If you’re building the documentation, you’ll also need to have the libclang binaries installed. On Windows these can be installed with chocolatey:
choco install llvm