| Name | pymatviz JSON |
| Version |
0.8.1
JSON |
| download |
| home_page | |
| Summary | A toolkit for visualizations in materials informatics |
| upload_time | 2024-02-11 17:37:06 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2021 Janosh Riebesell Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software. |
| keywords |
chemistry
data visualization
materials discovery
materials informatics
matplotlib
plotly
science
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="center">
<img src="https://github.com/janosh/pymatviz/raw/main/site/static/favicon.svg" alt="Logo" height="60px">
<br class="hide-in-docs">
pymatviz
</h1>
<h4 align="center" class="toc-exclude">
A toolkit for visualizations in materials informatics.
[](https://github.com/janosh/pymatviz/actions/workflows/test.yml)
[](https://python.org/downloads)
[](https://pypi.org/project/pymatviz)
[](https://pypistats.org/packages/pymatviz)
[](https://zenodo.org/records/10456384)
</h4>
<slot name="how-to-cite">
> If you use `pymatviz` in your research, [see how to cite](#--how-to-cite-pymatviz).
</slot>
## 🔨   Installation
```sh
pip install pymatviz
```
## 💡   API Docs
See the [/api] page.
[/api]: https://janosh.github.io/pymatviz/api
## 📙   Usage
See the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz`. PRs with additional examples are welcome! 🙏
| | | |
| ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ |
| [matbench_dielectric_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [![Launch Codespace]][codespace url] |
| [mp_bimodal_e_form.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [![Launch Codespace]][codespace url] |
| [matbench_perovskites_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Launch Codespace]][codespace url] |
| [mprester_ptable.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [![Launch Codespace]][codespace url] |
[Open in Google Colab]: https://colab.research.google.com/assets/colab-badge.svg
[Launch Codespace]: https://img.shields.io/badge/Launch-Codespace-darkblue?logo=github
[codespace url]: https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=340898532
## Periodic Table
See [`pymatviz/ptable.py`](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](https://plotly.com/dash).
| [`ptable_heatmap(compositions, log=True)`](pymatviz/ptable.py) | [`ptable_heatmap_ratio(comps_a, comps_b)`](pymatviz/ptable.py) |
| :------------------------------------------------------------: | :-------------------------------------------------------------------: |
| ![ptable-heatmap] | ![ptable-heatmap-ratio] |
| [`ptable_heatmap_plotly(atomic_masses)`](pymatviz/ptable.py) | [`ptable_heatmap_plotly(compositions, log=True)`](pymatviz/ptable.py) |
| ![ptable-heatmap-plotly-more-hover-data] | ![ptable-heatmap-plotly-log] |
## Nested Periodic Table
See [`pymatviz/ptable.py`](pymatviz/ptable.py). Plot histogram plots and scatter plots nested within periodic table.
| [`ptable_hists(data, colormap="coolwarm"`](pymatviz/ptable.py) | [`ptable_scatters(data, colormap="coolwarm"`](pymatviz/ptable.py) |
| :------------------------------------------------------------: | :---------------------------------------------------------------: |
<!-- prettier-ignore -->
| ![ptable-hists] | ![ptable-scatters] |
[ptable-hists]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-hists.svg
[ptable-scatters]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-scatters.svg
## Phonons
See [`pymatviz/phonons.py`](pymatviz/phonons.py).
| [`plot_phonon_bands(bands_dict)`](pymatviz/phonons.py) | [`plot_phonon_dos(doses_dict)`](pymatviz/phonons.py) |
| :------------------------------------------------------------------------: | :--------------------------------------------------: |
| ![phonon-bands] | ![phonon-dos] |
| [`plot_phonon_bands_and_dos(bands_dict, doses_dict)`](pymatviz/phonons.py) | |
| ![phonon-bands-and-dos] | |
[phonon-bands]: https://github.com/janosh/pymatviz/raw/main/assets/phonon-bands-mp-2758.svg
[phonon-dos]: https://github.com/janosh/pymatviz/raw/main/assets/phonon-dos-mp-2758.svg
[phonon-bands-and-dos]: https://github.com/janosh/pymatviz/raw/main/assets/phonon-bands-and-dos-mp-2758.svg
### Dash app using `ptable_heatmap_plotly()`
See [`examples/mprester_ptable.ipynb`](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb).
<https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4>
## Sunburst
See [`pymatviz/sunburst.py`](pymatviz/sunburst.py).
| [`spacegroup_sunburst([65, 134, 225, ...])`](pymatviz/sunburst.py) | [`spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...])`](pymatviz/sunburst.py) |
| :----------------------------------------------------------------: | :----------------------------------------------------------------------------: |
| ![spg-num-sunburst] | ![spg-symbol-sunburst] |
## Sankey
See [`pymatviz/sankey.py`](pymatviz/sankey.py).
| [`sankey_from_2_df_cols(df_perovskites)`](pymatviz/sankey.py) | [`sankey_from_2_df_cols(df_rand_ints)`](pymatviz/sankey.py) |
| :-----------------------------------------------------------: | :---------------------------------------------------------: |
| ![sankey-spglib-vs-aflow-spacegroups] | ![sankey-from-2-df-cols-randints] |
## Structure
See [`pymatviz/structure_viz.py`](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)`](pymatviz/structure_viz.py) | [`plot_structure_2d(mp_12712)`](pymatviz/structure_viz.py) |
| :--------------------------------------------------------: | :--------------------------------------------------------: |
| ![struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered] | ![struct-2d-mp-12712-Hf9Zr9Pd24-disordered] |
![matbench-phonons-structures-2d]
## Histograms
See [`pymatviz/histograms.py`](pymatviz/histograms.py).
| [`spacegroup_hist([65, 134, 225, ...], backend="matplotlib")`](pymatviz/histograms.py) | [`spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...], backend="matplotlib")`](pymatviz/histograms.py) |
| :------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: |
| ![spg-num-hist-matplotlib] | ![spg-symbol-hist-matplotlib] |
| [`spacegroup_hist([65, 134, 225, ...], backend="plotly")`](pymatviz/histograms.py) | [`spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")`](pymatviz/histograms.py) |
| ![spg-num-hist-plotly] | ![spg-symbol-hist-plotly] |
| [`elements_hist(compositions, log=True, bar_values='count')`](pymatviz/ptable.py) | |
| ![elements-hist] | |
[spg-symbol-hist-plotly]: https://github.com/janosh/pymatviz/raw/main/assets/spg-symbol-hist-plotly.svg
[spg-num-hist-plotly]: https://github.com/janosh/pymatviz/raw/main/assets/spg-num-hist-plotly.svg
[spg-num-hist-matplotlib]: https://github.com/janosh/pymatviz/raw/main/assets/spg-num-hist-matplotlib.svg
[spg-symbol-hist-matplotlib]: https://github.com/janosh/pymatviz/raw/main/assets/spg-symbol-hist-matplotlib.svg
## Parity Plots
See [`pymatviz/parity.py`](pymatviz/parity.py).
| [`density_scatter(xs, ys, ...)`](pymatviz/parity.py) | [`density_scatter_with_hist(xs, ys, ...)`](pymatviz/parity.py) |
| :-------------------------------------------------------------: | :-------------------------------------------------------------: |
| ![density-scatter] | ![density-scatter-with-hist] |
| [`density_hexbin(xs, ys, ...)`](pymatviz/parity.py) | [`density_hexbin_with_hist(xs, ys, ...)`](pymatviz/parity.py) |
| ![density-hexbin] | ![density-hexbin-with-hist] |
| [`scatter_with_err_bar(xs, ys, yerr, ...)`](pymatviz/parity.py) | [`residual_vs_actual(y_true, y_pred, ...)`](pymatviz/parity.py) |
| ![scatter-with-err-bar] | ![residual-vs-actual] |
## Uncertainty Calibration
See [`pymatviz/uncertainty.py`](pymatviz/uncertainty.py).
| [`qq_gaussian(y_true, y_pred, y_std)`](pymatviz/uncertainty.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py) |
| :-------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |
| ![normal-prob-plot] | ![normal-prob-plot-multiple] |
| [`error_decay_with_uncert(y_true, y_pred, y_std)`](pymatviz/uncertainty.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py) |
| ![error-decay-with-uncert] | ![error-decay-with-uncert-multiple] |
## Cumulative Error & Residual
See [`pymatviz/cumulative.py`](pymatviz/cumulative.py).
| [`cumulative_error(preds, targets)`](pymatviz/cumulative.py) | [`cumulative_residual(preds, targets)`](pymatviz/cumulative.py) |
| :----------------------------------------------------------: | :-------------------------------------------------------------: |
| ![cumulative-error] | ![cumulative-residual] |
## Classification Metrics
See [`pymatviz/relevance.py`](pymatviz/relevance.py).
| [`roc_curve(targets, proba_pos)`](pymatviz/relevance.py) | [`precision_recall_curve(targets, proba_pos)`](pymatviz/relevance.py) |
| :------------------------------------------------------: | :-------------------------------------------------------------------: |
| ![roc-curve] | ![precision-recall-curve] |
## Correlation
See [`pymatviz/correlation.py`](pymatviz/correlation.py).
| [`marchenko_pastur(corr_mat, gamma=ncols/nrows)`](pymatviz/correlation.py) | [`marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)`](pymatviz/correlation.py) |
| :------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: |
| ![marchenko-pastur] | ![marchenko-pastur-significant-eval] |
[cumulative-error]: https://github.com/janosh/pymatviz/raw/main/assets/cumulative-error.svg
[cumulative-residual]: https://github.com/janosh/pymatviz/raw/main/assets/cumulative-residual.svg
[density-hexbin-with-hist]: https://github.com/janosh/pymatviz/raw/main/assets/density-hexbin-with-hist.svg
[density-hexbin]: https://github.com/janosh/pymatviz/raw/main/assets/density-hexbin.svg
[density-scatter-with-hist]: https://github.com/janosh/pymatviz/raw/main/assets/density-scatter-with-hist.svg
[density-scatter]: https://github.com/janosh/pymatviz/raw/main/assets/density-scatter.svg
[error-decay-with-uncert-multiple]: https://github.com/janosh/pymatviz/raw/main/assets/error-decay-with-uncert-multiple.svg
[error-decay-with-uncert]: https://github.com/janosh/pymatviz/raw/main/assets/error-decay-with-uncert.svg
[elements-hist]: https://github.com/janosh/pymatviz/raw/main/assets/elements-hist.svg
[marchenko-pastur-significant-eval]: https://github.com/janosh/pymatviz/raw/main/assets/marchenko-pastur-significant-eval.svg
[marchenko-pastur]: https://github.com/janosh/pymatviz/raw/main/assets/marchenko-pastur.svg
[matbench-phonons-structures-2d]: https://github.com/janosh/pymatviz/raw/main/assets/matbench-phonons-structures-2d.svg
[normal-prob-plot-multiple]: https://github.com/janosh/pymatviz/raw/main/assets/normal-prob-plot-multiple.svg
[normal-prob-plot]: https://github.com/janosh/pymatviz/raw/main/assets/normal-prob-plot.svg
[precision-recall-curve]: https://github.com/janosh/pymatviz/raw/main/assets/precision-recall-curve.svg
[ptable-heatmap-plotly-log]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap-plotly-log.svg
[ptable-heatmap-plotly-more-hover-data]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap-plotly-more-hover-data.svg
[ptable-heatmap-ratio]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap-ratio.svg
[ptable-heatmap]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap.svg
[residual-vs-actual]: https://github.com/janosh/pymatviz/raw/main/assets/residual-vs-actual.svg
[roc-curve]: https://github.com/janosh/pymatviz/raw/main/assets/roc-curve.svg
[sankey-from-2-df-cols-randints]: https://github.com/janosh/pymatviz/raw/main/assets/sankey-from-2-df-cols-randints.svg
[sankey-spglib-vs-aflow-spacegroups]: https://github.com/janosh/pymatviz/raw/main/assets/sankey-spglib-vs-aflow-spacegroups.svg
[scatter-with-err-bar]: https://github.com/janosh/pymatviz/raw/main/assets/scatter-with-err-bar.svg
[spg-num-sunburst]: https://github.com/janosh/pymatviz/raw/main/assets/spg-num-sunburst.svg
[spg-symbol-sunburst]: https://github.com/janosh/pymatviz/raw/main/assets/spg-symbol-sunburst.svg
[struct-2d-mp-12712-Hf9Zr9Pd24-disordered]: https://github.com/janosh/pymatviz/raw/main/assets/struct-2d-mp-12712-Hf9Zr9Pd24-disordered.svg
[struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered]: https://github.com/janosh/pymatviz/raw/main/assets/struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered.svg
## 📖   How to cite `pymatviz`
You can cite the [Zenodo record](https://zenodo.org/badge/latestdoi/340898532) using the following BibTeX entry:
```bib
@software{riebesell_pymatviz_2022,
title = {Pymatviz: visualization toolkit for materials informatics},
author = {Riebesell, Janosh},
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 = {0.7.1}, % replace with the version you use
}
```
Raw data
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"home_page": "",
"name": "pymatviz",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "",
"keywords": "chemistry,data visualization,materials discovery,materials informatics,matplotlib,plotly,science",
"author": "",
"author_email": "Janosh Riebesell <janosh.riebesell@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/bc/fb/9e47f25899f07ceeb66ccfd62955aba81fa415222c2c9ed581b0ac788537/pymatviz-0.8.1.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">\n<img src=\"https://github.com/janosh/pymatviz/raw/main/site/static/favicon.svg\" alt=\"Logo\" height=\"60px\">\n<br class=\"hide-in-docs\">\npymatviz\n</h1>\n\n<h4 align=\"center\" class=\"toc-exclude\">\n\nA toolkit for visualizations in materials informatics.\n\n[](https://github.com/janosh/pymatviz/actions/workflows/test.yml)\n[](https://python.org/downloads)\n[](https://pypi.org/project/pymatviz)\n[](https://pypistats.org/packages/pymatviz)\n[](https://zenodo.org/records/10456384)\n\n</h4>\n\n<slot name=\"how-to-cite\">\n\n> If you use `pymatviz` in your research, [see how to cite](#--how-to-cite-pymatviz).\n\n</slot>\n\n## \ud83d\udd28   Installation\n\n```sh\npip install pymatviz\n```\n\n## \ud83d\udca1   API Docs\n\nSee the [/api] page.\n\n[/api]: https://janosh.github.io/pymatviz/api\n\n## \ud83d\udcd9   Usage\n\nSee the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz`. PRs with additional examples are welcome! \ud83d\ude4f\n\n| | | |\n| ---------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ |\n| [matbench_dielectric_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [![Launch Codespace]][codespace url] |\n| [mp_bimodal_e_form.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [![Launch Codespace]][codespace url] |\n| [matbench_perovskites_eda.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [![Launch Codespace]][codespace url] |\n| [mprester_ptable.ipynb](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [![Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [![Launch Codespace]][codespace url] |\n\n[Open in Google Colab]: https://colab.research.google.com/assets/colab-badge.svg\n[Launch Codespace]: https://img.shields.io/badge/Launch-Codespace-darkblue?logo=github\n[codespace url]: https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=340898532\n\n## Periodic Table\n\nSee [`pymatviz/ptable.py`](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](https://plotly.com/dash).\n\n| [`ptable_heatmap(compositions, log=True)`](pymatviz/ptable.py) | [`ptable_heatmap_ratio(comps_a, comps_b)`](pymatviz/ptable.py) |\n| :------------------------------------------------------------: | :-------------------------------------------------------------------: |\n| ![ptable-heatmap] | ![ptable-heatmap-ratio] |\n| [`ptable_heatmap_plotly(atomic_masses)`](pymatviz/ptable.py) | [`ptable_heatmap_plotly(compositions, log=True)`](pymatviz/ptable.py) |\n| ![ptable-heatmap-plotly-more-hover-data] | ![ptable-heatmap-plotly-log] |\n\n## Nested Periodic Table\n\nSee [`pymatviz/ptable.py`](pymatviz/ptable.py). Plot histogram plots and scatter plots nested within periodic table.\n| [`ptable_hists(data, colormap=\"coolwarm\"`](pymatviz/ptable.py) | [`ptable_scatters(data, colormap=\"coolwarm\"`](pymatviz/ptable.py) |\n| :------------------------------------------------------------: | :---------------------------------------------------------------: |\n\n<!-- prettier-ignore -->\n| ![ptable-hists] | ![ptable-scatters] |\n\n[ptable-hists]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-hists.svg\n[ptable-scatters]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-scatters.svg\n\n## Phonons\n\nSee [`pymatviz/phonons.py`](pymatviz/phonons.py).\n\n| [`plot_phonon_bands(bands_dict)`](pymatviz/phonons.py) | [`plot_phonon_dos(doses_dict)`](pymatviz/phonons.py) |\n| :------------------------------------------------------------------------: | :--------------------------------------------------: |\n| ![phonon-bands] | ![phonon-dos] |\n| [`plot_phonon_bands_and_dos(bands_dict, doses_dict)`](pymatviz/phonons.py) | |\n| ![phonon-bands-and-dos] | |\n\n[phonon-bands]: https://github.com/janosh/pymatviz/raw/main/assets/phonon-bands-mp-2758.svg\n[phonon-dos]: https://github.com/janosh/pymatviz/raw/main/assets/phonon-dos-mp-2758.svg\n[phonon-bands-and-dos]: https://github.com/janosh/pymatviz/raw/main/assets/phonon-bands-and-dos-mp-2758.svg\n\n### Dash app using `ptable_heatmap_plotly()`\n\nSee [`examples/mprester_ptable.ipynb`](https://github.com/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb).\n\n<https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4>\n\n## Sunburst\n\nSee [`pymatviz/sunburst.py`](pymatviz/sunburst.py).\n\n| [`spacegroup_sunburst([65, 134, 225, ...])`](pymatviz/sunburst.py) | [`spacegroup_sunburst([\"C2/m\", \"P-43m\", \"Fm-3m\", ...])`](pymatviz/sunburst.py) |\n| :----------------------------------------------------------------: | :----------------------------------------------------------------------------: |\n| ![spg-num-sunburst] | ![spg-symbol-sunburst] |\n\n## Sankey\n\nSee [`pymatviz/sankey.py`](pymatviz/sankey.py).\n\n| [`sankey_from_2_df_cols(df_perovskites)`](pymatviz/sankey.py) | [`sankey_from_2_df_cols(df_rand_ints)`](pymatviz/sankey.py) |\n| :-----------------------------------------------------------: | :---------------------------------------------------------: |\n| ![sankey-spglib-vs-aflow-spacegroups] | ![sankey-from-2-df-cols-randints] |\n\n## Structure\n\nSee [`pymatviz/structure_viz.py`](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.\n\n| [`plot_structure_2d(mp_19017)`](pymatviz/structure_viz.py) | [`plot_structure_2d(mp_12712)`](pymatviz/structure_viz.py) |\n| :--------------------------------------------------------: | :--------------------------------------------------------: |\n| ![struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered] | ![struct-2d-mp-12712-Hf9Zr9Pd24-disordered] |\n\n![matbench-phonons-structures-2d]\n\n## Histograms\n\nSee [`pymatviz/histograms.py`](pymatviz/histograms.py).\n\n| [`spacegroup_hist([65, 134, 225, ...], backend=\"matplotlib\")`](pymatviz/histograms.py) | [`spacegroup_hist([\"C2/m\", \"P-43m\", \"Fm-3m\", ...], backend=\"matplotlib\")`](pymatviz/histograms.py) |\n| :------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: |\n| ![spg-num-hist-matplotlib] | ![spg-symbol-hist-matplotlib] |\n| [`spacegroup_hist([65, 134, 225, ...], backend=\"plotly\")`](pymatviz/histograms.py) | [`spacegroup_hist([\"C2/m\", \"P-43m\", \"Fm-3m\", ...], backend=\"plotly\")`](pymatviz/histograms.py) |\n| ![spg-num-hist-plotly] | ![spg-symbol-hist-plotly] |\n| [`elements_hist(compositions, log=True, bar_values='count')`](pymatviz/ptable.py) | |\n| ![elements-hist] | |\n\n[spg-symbol-hist-plotly]: https://github.com/janosh/pymatviz/raw/main/assets/spg-symbol-hist-plotly.svg\n[spg-num-hist-plotly]: https://github.com/janosh/pymatviz/raw/main/assets/spg-num-hist-plotly.svg\n[spg-num-hist-matplotlib]: https://github.com/janosh/pymatviz/raw/main/assets/spg-num-hist-matplotlib.svg\n[spg-symbol-hist-matplotlib]: https://github.com/janosh/pymatviz/raw/main/assets/spg-symbol-hist-matplotlib.svg\n\n## Parity Plots\n\nSee [`pymatviz/parity.py`](pymatviz/parity.py).\n\n| [`density_scatter(xs, ys, ...)`](pymatviz/parity.py) | [`density_scatter_with_hist(xs, ys, ...)`](pymatviz/parity.py) |\n| :-------------------------------------------------------------: | :-------------------------------------------------------------: |\n| ![density-scatter] | ![density-scatter-with-hist] |\n| [`density_hexbin(xs, ys, ...)`](pymatviz/parity.py) | [`density_hexbin_with_hist(xs, ys, ...)`](pymatviz/parity.py) |\n| ![density-hexbin] | ![density-hexbin-with-hist] |\n| [`scatter_with_err_bar(xs, ys, yerr, ...)`](pymatviz/parity.py) | [`residual_vs_actual(y_true, y_pred, ...)`](pymatviz/parity.py) |\n| ![scatter-with-err-bar] | ![residual-vs-actual] |\n\n## Uncertainty Calibration\n\nSee [`pymatviz/uncertainty.py`](pymatviz/uncertainty.py).\n\n| [`qq_gaussian(y_true, y_pred, y_std)`](pymatviz/uncertainty.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py) |\n| :-------------------------------------------------------------------------: | :-------------------------------------------------------------------------------: |\n| ![normal-prob-plot] | ![normal-prob-plot-multiple] |\n| [`error_decay_with_uncert(y_true, y_pred, y_std)`](pymatviz/uncertainty.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py) |\n| ![error-decay-with-uncert] | ![error-decay-with-uncert-multiple] |\n\n## Cumulative Error & Residual\n\nSee [`pymatviz/cumulative.py`](pymatviz/cumulative.py).\n\n| [`cumulative_error(preds, targets)`](pymatviz/cumulative.py) | [`cumulative_residual(preds, targets)`](pymatviz/cumulative.py) |\n| :----------------------------------------------------------: | :-------------------------------------------------------------: |\n| ![cumulative-error] | ![cumulative-residual] |\n\n## Classification Metrics\n\nSee [`pymatviz/relevance.py`](pymatviz/relevance.py).\n\n| [`roc_curve(targets, proba_pos)`](pymatviz/relevance.py) | [`precision_recall_curve(targets, proba_pos)`](pymatviz/relevance.py) |\n| :------------------------------------------------------: | :-------------------------------------------------------------------: |\n| ![roc-curve] | ![precision-recall-curve] |\n\n## Correlation\n\nSee [`pymatviz/correlation.py`](pymatviz/correlation.py).\n\n| [`marchenko_pastur(corr_mat, gamma=ncols/nrows)`](pymatviz/correlation.py) | [`marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)`](pymatviz/correlation.py) |\n| :------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------: |\n| ![marchenko-pastur] | ![marchenko-pastur-significant-eval] |\n\n[cumulative-error]: https://github.com/janosh/pymatviz/raw/main/assets/cumulative-error.svg\n[cumulative-residual]: https://github.com/janosh/pymatviz/raw/main/assets/cumulative-residual.svg\n[density-hexbin-with-hist]: https://github.com/janosh/pymatviz/raw/main/assets/density-hexbin-with-hist.svg\n[density-hexbin]: https://github.com/janosh/pymatviz/raw/main/assets/density-hexbin.svg\n[density-scatter-with-hist]: https://github.com/janosh/pymatviz/raw/main/assets/density-scatter-with-hist.svg\n[density-scatter]: https://github.com/janosh/pymatviz/raw/main/assets/density-scatter.svg\n[error-decay-with-uncert-multiple]: https://github.com/janosh/pymatviz/raw/main/assets/error-decay-with-uncert-multiple.svg\n[error-decay-with-uncert]: https://github.com/janosh/pymatviz/raw/main/assets/error-decay-with-uncert.svg\n[elements-hist]: https://github.com/janosh/pymatviz/raw/main/assets/elements-hist.svg\n[marchenko-pastur-significant-eval]: https://github.com/janosh/pymatviz/raw/main/assets/marchenko-pastur-significant-eval.svg\n[marchenko-pastur]: https://github.com/janosh/pymatviz/raw/main/assets/marchenko-pastur.svg\n[matbench-phonons-structures-2d]: https://github.com/janosh/pymatviz/raw/main/assets/matbench-phonons-structures-2d.svg\n[normal-prob-plot-multiple]: https://github.com/janosh/pymatviz/raw/main/assets/normal-prob-plot-multiple.svg\n[normal-prob-plot]: https://github.com/janosh/pymatviz/raw/main/assets/normal-prob-plot.svg\n[precision-recall-curve]: https://github.com/janosh/pymatviz/raw/main/assets/precision-recall-curve.svg\n[ptable-heatmap-plotly-log]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap-plotly-log.svg\n[ptable-heatmap-plotly-more-hover-data]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap-plotly-more-hover-data.svg\n[ptable-heatmap-ratio]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap-ratio.svg\n[ptable-heatmap]: https://github.com/janosh/pymatviz/raw/main/assets/ptable-heatmap.svg\n[residual-vs-actual]: https://github.com/janosh/pymatviz/raw/main/assets/residual-vs-actual.svg\n[roc-curve]: https://github.com/janosh/pymatviz/raw/main/assets/roc-curve.svg\n[sankey-from-2-df-cols-randints]: https://github.com/janosh/pymatviz/raw/main/assets/sankey-from-2-df-cols-randints.svg\n[sankey-spglib-vs-aflow-spacegroups]: https://github.com/janosh/pymatviz/raw/main/assets/sankey-spglib-vs-aflow-spacegroups.svg\n[scatter-with-err-bar]: https://github.com/janosh/pymatviz/raw/main/assets/scatter-with-err-bar.svg\n[spg-num-sunburst]: https://github.com/janosh/pymatviz/raw/main/assets/spg-num-sunburst.svg\n[spg-symbol-sunburst]: https://github.com/janosh/pymatviz/raw/main/assets/spg-symbol-sunburst.svg\n[struct-2d-mp-12712-Hf9Zr9Pd24-disordered]: https://github.com/janosh/pymatviz/raw/main/assets/struct-2d-mp-12712-Hf9Zr9Pd24-disordered.svg\n[struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered]: https://github.com/janosh/pymatviz/raw/main/assets/struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered.svg\n\n## \ud83d\udcd6   How to cite `pymatviz`\n\nYou can cite the [Zenodo record](https://zenodo.org/badge/latestdoi/340898532) using the following BibTeX entry:\n\n```bib\n@software{riebesell_pymatviz_2022,\n title = {Pymatviz: visualization toolkit for materials informatics},\n author = {Riebesell, Janosh},\n date = {2022-10-01},\n year = {2022},\n doi = {10.5281/zenodo.7486816},\n url = {https://github.com/janosh/pymatviz},\n note = {10.5281/zenodo.7486816 - https://github.com/janosh/pymatviz},\n urldate = {2023-01-01}, % optional, replace with your date of access\n version = {0.7.1}, % replace with the version you use\n}\n```\n",
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"license": "MIT License Copyright (c) 2021 Janosh Riebesell Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided \"as is\", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software. ",
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