pymatviz 0.7.0

A toolkit for visualizations in materials informatics

pymatviz

A toolkit for visualizations in materials informatics.

Note: This project is not endorsed by pymatgen, but aims to complement it with additional plotting functionality.

Installation

pip install pymatviz

API Docs

See the /api page.

Usage

See the Jupyter notebooks under examples/ for how to use pymatviz.

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

Periodic Table

See pymatviz/ptable.py. Heat maps 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)

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, ...])	spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...])
residual_hist(y_true, y_pred)	hist_elemental_prevalence(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)

Glossary

1. Residual y_res = y_true - y_pred: The difference between ground truth target and model prediction.
2. Error y_err = abs(y_true - y_pred): Absolute error between target and model prediction.
3. Uncertainty y_std: The model's estimate for its error, i.e. how much the model thinks its prediction can be trusted (std for standard deviation).