pymatviz

A toolkit for visualizations in materials informatics.

Note: This project is not associated with or 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.

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, ...])`	`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

Residual y_res = y_true - y_pred: The difference between ground truth target and model prediction.
Error y_err = abs(y_true - y_pred): Absolute error between target and model prediction.
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.)

JaGeo/pymatviz