A toolkit for visualizations in materials informatics

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Project description

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.

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)`

matbench-phonons-structures-2d

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.)

Project details

These details have not been verified by PyPI

Project links

Homepage

Release history Release notifications | RSS feed

0.13.2

Nov 3, 2024

0.13.1

Oct 18, 2024

0.13.0

Oct 17, 2024

0.12.0

Oct 7, 2024

0.11.0

Sep 1, 2024

0.10.1

Aug 18, 2024

0.10.0

Jul 31, 2024

0.9.3

Jul 18, 2024

0.9.2

Jul 7, 2024

0.9.1

Jul 5, 2024

0.9.0

Jun 22, 2024

0.8.3

May 30, 2024

0.8.2

May 11, 2024

0.8.1

Feb 11, 2024

0.8.0

Dec 15, 2023

0.7.3

Nov 5, 2023

0.7.2

Oct 30, 2023

0.7.1

Oct 22, 2023

0.7.0

Oct 10, 2023

0.6.3

Jul 24, 2023

This version

0.6.2

Apr 29, 2023

0.6.1

Mar 21, 2023

0.6.0

Feb 21, 2023

0.5.3

Feb 21, 2023

0.5.2

Jan 14, 2023

0.5.1

Oct 8, 2022

0.5.0

Sep 21, 2022

0.4.4

Jul 9, 2022

0.4.3

Jun 2, 2022

0.4.2

May 16, 2022

0.4.1

Mar 20, 2022

0.4.0

Mar 1, 2022

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