widget-bandsplot 0.5.0

Package for bandsplot widget

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 bands-widget Javascript package, which is developed by Materials Cloud group.

Installation

To install use pip:

$ pip install widget_bandsplot

Usage

1. Plot both the band structure and the density of states (DOS) side by side

w = BandsPlotWidget(bands=[banddata1, banddata2], dos=dosdata, plot_fermilevel = True, show_legend = True, energy_range = {"ymin": -10.0, "ymax": 10.0})
display(w)

In order to plot the band structure and density of states, one needs to provide bands data and DOS data as JSON-files. The examples of the input JSON-files are provided in the examples/data folder. The JSON-files for the band structure can be exported with the AiiDA command line interface (CLI) verdi as demonstrated in the code below:

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

One can plot several band structure input files together with the widget. The format of the DOS input dict is validate by the PDOS Schema, the exampla valid input is:

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
            "borderColor": "#41e2b3",   # optional
            "backgroundColor": "#51258b",   # optional
            "backgroundAlpha": "52%",  #optional: A string with integer between 0-100 and '%' in end.
            "lineStyle": "dash",    # optional
        },
        {
            "label": "Co (s↑)",
            "x": [0.0, 0.1, 0.2],
            "y": [1.2, 3.2, 0.0],
            "lineStyle": "solid",
            "borderColor": "#43ee8b",
            "backgroundColor": "#59595c",
        },
        {
            "label": "Co (s↓)",
            "x": [0.0, 0.1, 0.2],
            "y": [1.2, 3.2, 0.0],
            "lineStyle": "solid",
            "borderColor": "#403bae",
            "backgroundColor": "#a16c5e",
        },
    ],
}

2. Plot only the band structure

w = BandsPlotWidget(bands=[banddata1, banddata2], plot_fermilevel = True, show_legend = True, energy_range = {"ymin": -10.0, "ymax": 10.0})
display(w)

3. Plot only the density of states (DOS)

w = BandsPlotWidget(dos=dosdata, plot_fermilevel = True, show_legend = True, energy_range = {"ymin": -10.0, "ymax": 10.0})
display(w)

When only plotting the density of states, the plot will be shown in horizontal format.

For developers

For a development installation (requires Node.js and Yarn version 1),

$ git clone https://github.com/osscar/widget-bandsplot.git
$ cd widget-bandsplot
$ pip install -e .
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix widget_bandsplot
$ jupyter nbextension enable --py --sys-prefix widget_bandsplot

When actively developing your extension for JupyterLab, run the command:

$ jupyter labextension develop --overwrite widget_bandsplot

Then you need to rebuild the JS when you make a code change:

$ cd js
$ yarn run build

You then need to refresh the JupyterLab page when your javascript changes.

For maintainers

To create a new release, clone the repository, install development dependencies with pip install -e '.[dev]', and then execute bumpver update [--major|--minor|--patch] [--tag [alpha|beta|rc]]. This will:

1. Create a tagged release with bumped version and push it to the repository.
2. Trigger a GitHub actions workflow that creates a GitHub release and publishes it on PyPI.

Additional notes:

• Use the --dry option to preview the release change.
• The release tag (e.g. a/b/rc) is determined from the last release. Use the --tag option to switch the release tag.
• This packages follows semantic versioning.

Acknowledgements

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