A toolkit for visualizations in materials informatics
Project description
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 |  |
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| mp_bimodal_e_form.ipynb | |
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| matbench_perovskites_eda.ipynb | |
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| mprester_ptable.ipynb | |
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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) |
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ptable_heatmap_plotly(atomic_masses) |
ptable_heatmap_plotly(compositions, log=True) |
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Dash app using ptable_heatmap_plotly()
See examples/mprester_ptable.ipynb.
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/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) |
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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
qq_gaussian(y_true, y_pred, y_std) |
qq_gaussian(y_true, y_pred, y_std: dict) |
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error_decay_with_uncert(y_true, y_pred, y_std) |
error_decay_with_uncert(y_true, y_pred, y_std: dict) |
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Cumulative Error & Residual
cumulative_error(preds, targets) |
cumulative_residual(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).
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