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pymatviz

A toolkit for visualizations in materials informatics.

Tests This project supports Python 3.9+ PyPI PyPI Downloads Zenodo

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! 🙏

matbench_dielectric_eda.ipynb Open in Google Colab Launch Codespace
mp_bimodal_e_form.ipynb Open in Google Colab Launch Codespace
matbench_perovskites_eda.ipynb Open in Google Colab Launch Codespace
mprester_ptable.ipynb Open in Google Colab Launch Codespace

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 ptable-heatmap-ratio
ptable_heatmap_plotly(atomic_masses) ptable_heatmap_plotly(compositions, log=True)
ptable-heatmap-plotly-more-hover-data ptable-heatmap-plotly-log
ptable_hists(data, colormap="coolwarm" ptable_scatters(data, colormap="coolwarm"
ptable-hists ptable-scatters

Phonons

See pymatviz/phonons.py.

plot_phonon_bands(bands_dict) plot_phonon_dos(doses_dict)
phonon-bands phonon-dos
plot_phonon_bands_and_dos(bands_dict, doses_dict)
phonon-bands-and-dos

Dash app using ptable_heatmap_plotly()

See examples/mprester_ptable.ipynb.

https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4

Sunburst

See pymatviz/sunburst.py.

spacegroup_sunburst([65, 134, 225, ...]) spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...])
spg-num-sunburst spg-symbol-sunburst

Sankey

See pymatviz/sankey.py.

sankey_from_2_df_cols(df_perovskites) sankey_from_2_df_cols(df_rand_ints)
sankey-spglib-vs-aflow-spacegroups sankey-from-2-df-cols-randints

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)
struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered struct-2d-mp-12712-Hf9Zr9Pd24-disordered

matbench-phonons-structures-2d

Histograms

See pymatviz/histograms.py.

spacegroup_hist([65, 134, 225, ...], backend="matplotlib") spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...], backend="matplotlib")
spg-num-hist-matplotlib spg-symbol-hist-matplotlib
spacegroup_hist([65, 134, 225, ...], backend="plotly") spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")
spg-num-hist-plotly spg-symbol-hist-plotly
elements_hist(compositions, log=True, bar_values='count')
elements-hist

Parity Plots

See pymatviz/parity.py.

density_scatter(xs, ys, ...) density_scatter_with_hist(xs, ys, ...)
density-scatter density-scatter-with-hist
density_hexbin(xs, ys, ...) density_hexbin_with_hist(xs, ys, ...)
density-hexbin density-hexbin-with-hist
scatter_with_err_bar(xs, ys, yerr, ...) residual_vs_actual(y_true, y_pred, ...)
scatter-with-err-bar residual-vs-actual

Uncertainty

See pymatviz/uncertainty.py.

qq_gaussian(y_true, y_pred, y_std) qq_gaussian(y_true, y_pred, y_std: dict)
normal-prob-plot normal-prob-plot-multiple
error_decay_with_uncert(y_true, y_pred, y_std) error_decay_with_uncert(y_true, y_pred, y_std: dict)
error-decay-with-uncert error-decay-with-uncert-multiple

Cumulative Metrics

See pymatviz/cumulative.py.

cumulative_error(preds, targets) cumulative_residual(preds, targets)
cumulative-error cumulative-residual

Classification

See pymatviz/relevance.py.

roc_curve(targets, proba_pos) precision_recall_curve(targets, proba_pos)
roc-curve precision-recall-curve

Correlation

See pymatviz/correlation.py.

marchenko_pastur(corr_mat, gamma=ncols/nrows) marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)
marchenko-pastur marchenko-pastur-significant-eval

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},
  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 = {0.7.1}, % replace with the version you use
}