pymatviz 0.8.1

A toolkit for visualizations in materials informatics

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! 🙏

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)

Nested Periodic Table

See pymatviz/ptable.py. Plot histogram plots and scatter plots nested within periodic table.

ptable_hists(data, colormap="coolwarm"	ptable_scatters(data, colormap="coolwarm"

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Phonons

See pymatviz/phonons.py.

plot_phonon_bands(bands_dict)	plot_phonon_dos(doses_dict)
plot_phonon_bands_and_dos(bands_dict, doses_dict)

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", ...])

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 Calibration

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 Error & Residual

See pymatviz/cumulative.py.

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

Classification Metrics

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

You can 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
}