pyomovi 0.18.0

A Custom Jupyter Widget Library

pyomovi

A Custom Jupyter Widget Library

Installation

You can install using pip:

pip install pyomovi

If you are using Jupyter Notebook 5.2 or earlier, you may also need to enable the nbextension:

jupyter nbextension enable --py [--sys-prefix|--user|--system] pyomovi

To create a visualizer window use the following code

import pyomovi
visualizer = pyomovi.Visualizer()
visualizer

Then in another cell (there is a bug, so the above must be executed 500ms before the next code) run e.g.

import numpy as np
positions = 100 * np.random.rand(1000000, 3)
visualizer.particle_positions = positions

to visualize 1 million atoms at random positions. If you want custom coloring and radii, you can

import numpy as np
visualizer.particle_positions = np.asarray([[0, 0, 0], [0,2,0], [0,2,2]])
visualizer.particle_colors = np.asarray([[255, 0, 0], [255, 255, 0], [255,255,255]]) # RGB
visualizer.particle_radii = np.asarray([1.0, 2.0, 3.0])

You can also set colors and radii using short names of atoms

import numpy as np
visualizer.particle_positions = np.asarray([[0, 0, 0], [0,2,0], [0,2,2]])
visualizer.set_atom_types(['O', 'H', 'H'])

Development Installation

Create a dev environment:

conda create -n pyomovi-dev -c conda-forge nodejs yarn python jupyterlab
conda activate pyomovi-dev

Install the python. This will also build the TS package.

pip install -e ".[test, examples]"

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 .
yarn run build

For classic notebook, you need to run:

jupyter nbextension install --sys-prefix --symlink --overwrite --py pyomovi
jupyter nbextension enable --sys-prefix --py pyomovi

Note that the --symlink flag doesn't work on Windows, so you will here have to run the install command every time that you rebuild your extension. For certain installations you might also need another flag instead of --sys-prefix, but we won't cover the meaning of those flags here.

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
yarn 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.

Updating the version

To update the version, install tbump and use it to bump the version. By default it will also create a tag.

pip install tbump
tbump <new-version>