A toolkit for visualizations in materials informatics
Project description
pymatviz
A toolkit for visualizations in materials informatics.
If you use
pymatvizin your research, see how to cite.
Installation
pip install pymatviz
API Docs
See the /api page.
Usage
See the Jupyter notebooks under examples/ for how to use pymatviz. PRs with additional examples are welcome! 🙏
| 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 | |
|
Periodic Table
See pymatviz/ptable.py. Heatmaps of the periodic table can be plotted both with matplotlib and plotly. plotly supports displaying additional data on hover or full interactivity through Dash.
Phonons
See pymatviz/phonons.py.
plot_phonon_bands(bands_dict) |
plot_phonon_dos(doses_dict) |
|---|---|
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plot_phonon_bands_and_dos(bands_dict, doses_dict) |
plot_phonon_bands_and_dos(single_bands, single_dos) |
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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
Parity Plots
See pymatviz/parity.py.
Uncertainty
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 Metrics
cumulative_error(preds, targets) |
cumulative_residual(preds, targets) |
|---|---|
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Classification
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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How to cite pymatviz
See citation.cff or cite the Zenodo record using the following BibTeX entry:
@software{riebesell_pymatviz_2022,
title = {Pymatviz: visualization toolkit for materials informatics},
author = {Riebesell, Janosh and Yang, Haoyu and Goodall, Rhys and Baird, Sterling G.},
date = {2022-10-01},
year = {2022},
doi = {10.5281/zenodo.7486816},
url = {https://github.com/janosh/pymatviz},
note = {10.5281/zenodo.7486816 - https://github.com/janosh/pymatviz},
urldate = {2023-01-01}, % optional, replace with your date of access
version = {0.8.2}, % replace with the version you use
}
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