README.md

widget-bandsplot: Jupyter Widget to Plot Band Structure and Density of States

A Jupyter widget to plot band structures and density of states. The widget is using the mc-react-bands Javascript package and is turned into a Jupyter widget with anywidget.

Installation

pip install widget-bandsplot

Usage

Minimal usage example of the widget is the following:

widget = BandsPlotWidget(
    bands = [bands_data],
    dos = dos_data,
    energy_range = [-10.0, 10.0],
    format_settings = {
        "showFermi": True,
        "showLegend": True,
    }
)
display(widget)

where bands_data and dos_data are contain the band structure and density of states data, respectively. The format for these data objects is the following:

• Band structure data follows the AiiDA CLI export format that can be generated from an AiiDA BandsData node with the following command:

  verdi data band export <PK> --format=json

• The density of states data uses a custom format, with a a valid example being:

  dos_data = {
      "fermi_energy": -7.0,
      "dos": [
          {
              "label": "Total DOS",          # required
              "x": [0.0, 0.1, 0.2],          # required
              "y": [1.2, 3.2, 0.0],          # required
              "lineStyle": "dash",           # optional
              "borderColor": "#41e2b3",      # optional
              "backgroundColor": "#51258b",  # optional
          },
          {
              "label": "Co",
              "x": [0.0, 0.1, 0.2],
              "y": [1.2, 3.2, 0.0],
              "lineStyle": "solid",
              "borderColor": "#43ee8b",
              "backgroundColor": "#59595c",
          },
      ],
  }

For more detailed usage, see example/example.ipynb and for more example input files see example/data.

Development

Install the python code:

pip install -e .[dev]

You then need to install the JavaScript dependencies and run the development server.

npm install
npm run dev

Open examples/example.ipynb in Jupyter notebook or lab to start developing. Changes made in js/ will be reflected in the notebook.

Releasing and publishing a new version

In order to make a new release of the library and publish to PYPI, run

bumpver update --major/--minor/--patch

This will

• update version numbers, make a corresponding git commit and a git tag;
• push this commit and tag to Github, which triggers the Github Action that makes a new Github Release and publishes the package to PYPI.

Github workflow testing

The screenshot comparison test will generate images of the widget using selenium and chrome-driver, and compares them to the reference image in test/widget-sample.png.

To update the reference image: download the generated image from the Github Workflow step called "Upload screenshots" and replace test/widget-sample.png.

How to cite

When using the content of this repository, please cite the following two articles:

1. D. Du, T. J. Baird, S. Bonella and G. Pizzi, OSSCAR, an open platform for collaborative development of computational tools for education in science, Computer Physics Communications, 282, 108546 (2023). https://doi.org/10.1016/j.cpc.2022.108546

2. D. Du, T. J. Baird, K. Eimre, S. Bonella, G. Pizzi, Jupyter widgets and extensions for education and research in computational physics and chemistry, Computer Physics Communications, 305, 109353 (2024). https://doi.org/10.1016/j.cpc.2024.109353

Acknowledgements

We acknowledge support from the EPFL Open Science Fund via the OSSCAR project.