pymatviz

A toolkit for visualizations in materials informatics.

If you use pymatviz in your research, see how to cite.

Installation

pip install pymatviz

API Docs

See the /api page.

Usage

See the Jupyter notebooks under examples/ for how to use pymatviz. PRs with additional examples are welcome! 🙏


mlff_phonons.ipynb
matbench_dielectric_eda.ipynb
mp_bimodal_e_form.ipynb
matbench_perovskites_eda.ipynb
mprester_ptable.ipynb

Periodic Table

See pymatviz/ptable.py. Heatmaps of the periodic table can be plotted both with matplotlib and plotly. plotly supports displaying additional data on hover or full interactivity through Dash.

`ptable_heatmap(compositions, log=True)`	`ptable_heatmap_ratio(comps_a, comps_b)`

`ptable_heatmap_plotly(atomic_masses)`	`ptable_heatmap_plotly(compositions, log=True)`

`ptable_hists(data, colormap="coolwarm")`	`ptable_lines(data)`

`ptable_heatmap_splits(2_vals_per_elem, colormap="coolwarm", start_angle=135)`	`ptable_heatmap_splits(3_vals_per_elem, colormap="coolwarm", start_angle=90)`

Phonons

See examples/mlff_phonons.ipynb for usage example.

`plot_phonon_bands(bands_dict)`	`plot_phonon_dos(doses_dict)`

`plot_phonon_bands_and_dos(bands_dict, doses_dict)`	`plot_phonon_bands_and_dos(single_bands, single_dos)`

Dash app using `ptable_heatmap_plotly()`

See examples/mprester_ptable.ipynb.

2022-07-28-ptable_heatmap_plotly-dash-example.mp4

Sunburst

See pymatviz/sunburst.py.

`spacegroup_sunburst([65, 134, 225, ...])`	`spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...])`

Sankey

See pymatviz/sankey.py.

`sankey_from_2_df_cols(df_perovskites)`	`sankey_from_2_df_cols(df_rand_ints)`

Structure

See pymatviz/structure_viz.py. Currently structure plotting is only supported with matplotlib in 2d. 3d interactive plots (probably with plotly) are on the road map.

`plot_structure_2d(mp_19017)`	`plot_structure_2d(mp_12712)`

Histograms

See pymatviz/histograms.py.

`spacegroup_hist([65, 134, 225, ...], backend="matplotlib")`	`spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...], backend="matplotlib")`

`spacegroup_hist([65, 134, 225, ...], backend="plotly")`	`spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")`

`elements_hist(compositions, log=True, bar_values='count')`

Parity Plots

See pymatviz/parity.py.

`density_scatter(xs, ys, ...)`	`density_scatter_with_hist(xs, ys, ...)`

`density_hexbin(xs, ys, ...)`	`density_hexbin_with_hist(xs, ys, ...)`

`scatter_with_err_bar(xs, ys, yerr, ...)`	`residual_vs_actual(y_true, y_pred, ...)`

Uncertainty

See pymatviz/uncertainty.py.

`qq_gaussian(y_true, y_pred, y_std)`	`qq_gaussian(y_true, y_pred, y_std: dict)`

`error_decay_with_uncert(y_true, y_pred, y_std)`	`error_decay_with_uncert(y_true, y_pred, y_std: dict)`

Cumulative Metrics

See pymatviz/cumulative.py.

`cumulative_error(preds, targets)`	`cumulative_residual(preds, targets)`

Classification

See pymatviz/relevance.py.

`roc_curve(targets, proba_pos)`	`precision_recall_curve(targets, proba_pos)`

Correlation

See pymatviz/correlation.py.

`marchenko_pastur(corr_mat, gamma=ncols/nrows)`	`marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)`

How to cite `pymatviz`

See citation.cff or cite the Zenodo record using the following BibTeX entry:

@software{riebesell_pymatviz_2022,
  title = {Pymatviz: visualization toolkit for materials informatics},
  author = {Riebesell, Janosh and Yang, Haoyu and Goodall, Rhys and Baird, Sterling G.},
  date = {2022-10-01},
  year = {2022},
  doi = {10.5281/zenodo.7486816},
  url = {https://github.com/janosh/pymatviz},
  note = {10.5281/zenodo.7486816 - https://github.com/janosh/pymatviz},
  urldate = {2023-01-01}, % optional, replace with your date of access
  version = {0.8.2}, % replace with the version you use
}

fastflair/pymatviz