pySSV 0.7.1

Leverage the power of shaders for scientific visualisation in Jupyter

Shaders For Scientific Visualisation

Author: Thomas Mathieson
Student ID: 2576219m
Supervisor: Dr. John Williamson

A python library for advanced interactive data visualisation on the GPU. This project is being developed for my Level 4 Individual Project at the University of Glasgow for my Computing Science degree.

The dissertation and accompanying notes and planning documents can be found here.

[!NOTE]
FEEDBACK WANTED
To evaluate the utility of this project for my dissertation, I would appreciate if you could spare some time to fill out this survey:

https://forms.gle/rX7uPxaxQ1xcNVQB8

Gallery

Installation

You can install using pip:

pip install pySSV

Quick Start

Try the example notebook

Create a suitable python environment (optional if you already have a python environment with jupyterlab setup) and install the package using pip:

conda create -n pySSV -c conda-forge python jupyterlab
conda activate pySSV

pip install pySSV

Download the example notebook from github (other example notebooks available here):

curl -o introduction.ipynb https://github.com/space928/Shaders-For-Scientific-Visualisation/raw/main/examples/introduction.ipynb

Start JupyterLab and play around with the notebook:

jupyter lab .\introduction.ipynb

Building From Source

Create a 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 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 lab, this is done by the command:

jupyter labextension develop --overwrite .
jlpm run build

How to see your changes

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 run 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.

Acknowledgements

Thanks to Dr. John H. Williamson for his support throughout the project.

This project was made using the fabulous Jupyter Widget template: https://github.com/jupyter-widgets/widget-ts-cookiecutter