A toolkit for visualizations in materials informatics
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 by adding additional plotting functionality.
Installation
pip install pymatviz
Usage
Check out the Jupyter notebooks under examples/ to learn how to use pymatviz.
Elements
See pymatviz/elements.py. Heat maps of the periodic table can be plotted both with matplotlib and plotly. Latter supports displaying additional data on hover or full interactivity through Dash.
ptable_heatmap(compositions, log=True) |
ptable_heatmap_ratio(comps_a, comps_b) |
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ptable_heatmap_plotly(atomic_masses) |
ptable_heatmap_plotly(compositions) |
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Sunburst
See pymatviz/sunburst.py.
spacegroup_sunburst([65, 134, 225, ...]) |
spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...]) |
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Sankey
See pymatviz/sankey.py.
sankey_from_2_df_cols(df_perovskites) |
sankey_from_2_df_cols(df_rand_ints) |
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Structure
See pymatviz/struct_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) |
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Histograms
spacegroup_hist([65, 134, 225, ...]) |
spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...]) |
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residual_hist(y_true, y_pred) |
hist_elemental_prevalence(compositions, log=True, bar_values='count') |
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Parity Plots
See pymatviz/parity.py.
Uncertainty Calibration
See pymatviz/quantile.py.
qq_gaussian(y_true, y_pred, y_std) |
qq_gaussian(y_true, y_pred, y_std: dict) |
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Ranking
See pymatviz/ranking.py.
err_decay(y_true, y_pred, y_std) |
err_decay(y_true, y_pred, y_std: dict) |
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Cumulative Error and Residual
cum_err(preds, targets) |
cum_res(preds, targets) |
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Classification Metrics
roc_curve(targets, proba_pos) |
precision_recall_curve(targets, proba_pos) |
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Correlation
marchenko_pastur(corr_mat, gamma=ncols/nrows) |
marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows) |
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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. (stdfor standard deviation.)
Usage on Colab
For the time being, Google Colab only supports Python 3.7. pymatviz uses Python 3.8 features like self-documenting f-strings. You may still be able to use pymatviz on Colab by cloning the repo and patching the source code in place as shown here.
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