A toolkit for visualizations in materials informatics

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  • License: MIT License (MIT License Copyright (c) 2021 Janosh Riebesell Permission is hereby granted, free of charge, to a...)
  • Author: Janosh Riebesell
  • Tags chemistry, data visualization, materials discovery, materials informatics, matplotlib, plotly, science
  • Requires: Python >=3.10
  • Provides-Extra: auto-text-pos, df-pdf-export, df-svg-export, export-figs, gh-pages, make-assets, test
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Project description

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pymatviz

A toolkit for visualizations in materials informatics.

Tests This project supports Python 3.10+ PyPI PyPI Downloads Zenodo

If you use pymatviz in 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! 🙏
mlff_phonons.ipynb Open in Google Colab Launch Codespace
matbench_dielectric_eda.ipynb Open in Google Colab Launch Codespace
mp_bimodal_e_form.ipynb Open in Google Colab Launch Codespace
matbench_perovskites_eda.ipynb Open in Google Colab Launch Codespace
mprester_ptable.ipynb Open in Google Colab Launch Codespace

Periodic Table

See pymatviz/ptable/ptable_matplotlib.py and pymatviz/ptable/ptable_plotly.py. matplotlib supports heatmaps, heatmap ratios, heatmap splits (multiple values per element), histograms, scatter plots and line plots. plotly currently only supports heatmaps (PRs to port over other matplotlib ptable variants to plotly are very welcome!). The plotly heatmap 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 ptable-heatmap-ratio
ptable_heatmap_plotly(atomic_masses) ptable_heatmap_plotly(compositions, log=True)
ptable-heatmap-plotly-more-hover-data ptable-heatmap-plotly-log
ptable_hists(data, colormap="coolwarm") ptable_lines(data)
ptable-hists ptable-lines
ptable_scatters(data, colormap="coolwarm") ptable_scatters(data, colormap="coolwarm")
ptable-scatters-parity ptable-scatters-parabola
ptable_heatmap_splits(2_vals_per_elem, colormap="coolwarm", start_angle=135) ptable_heatmap_splits(3_vals_per_elem, colormap="coolwarm", start_angle=90)
ptable-heatmap-splits-2 ptable-heatmap-splits-3

Phonons

See examples/mlff_phonons.ipynb for usage example.

phonon_bands(bands_dict) phonon_dos(doses_dict)
phonon-bands phonon-dos
phonon_bands_and_dos(bands_dict, doses_dict) phonon_bands_and_dos(single_bands, single_dos)
phonon-bands-and-dos-mp-2758 phonon-bands-and-dos-mp-23907

Dash app using ptable_heatmap_plotly()

See examples/mprester_ptable.ipynb.

https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4

Structure

See pymatviz/structure_viz/(mpl|plotly).py. Currently structure plotting is only supported with matplotlib in 2d. 3d interactive plots (probably with plotly) are on the road map.

structure_2d(mp_19017) structure_2d(mp_12712)
struct-2d-mp-19017-Li4Mn0.8Fe1.6P4C1.6O16-disordered struct-2d-mp-12712-Hf9Zr9Pd24-disordered
structure_2d_plotly(six_structs) structure_3d_plotly(six_structs)
matbench-phonons-structures-2d-plotly matbench-phonons-structures-3d-plotly

X-Ray Diffraction

See pymatviz/xrd.py.

xrd_pattern(pattern) xrd_pattern({key1: patt1, key2: patt2})
xrd-pattern xrd-pattern-multiple
xrd_pattern(struct_dict, stack="horizontal") xrd_pattern(struct_dict, stack="vertical", title="Custom Title")
xrd-pattern-horizontal-stack xrd-pattern-vertical-stack

Radial Distribution Functions

See pymatviz/rdf/plotly.py.

rdf_plot(rdf) rdf_plot(rdf, rdf2)
element-pair-rdfs-Na8Nb8O24 element-pair-rdfs-crystal-vs-amorphous

Coordination

See pymatviz/coordination.py.

coordination_hist(struct_dict) coordination_hist(struct_dict, by_element=True)
coordination-hist-single coordination-hist-by-structure-and-element
coordination_vs_cutoff_line(struct_dict, strategy=None) coordination_vs_cutoff_line(struct_dict, strategy=None)
coordination-vs-cutoff-single coordination-vs-cutoff-multiple

Sunburst

See pymatviz/sunburst.py.

spacegroup_sunburst([65, 134, 225, ...]) spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...])
spg-num-sunburst spg-symbol-sunburst

Rainclouds

See pymatviz/rainclouds.py.

rainclouds(two_key_dict) rainclouds(three_key_dict)
rainclouds-bimodal rainclouds-trimodal

Sankey

See pymatviz/sankey.py.

sankey_from_2_df_cols(df_perovskites) sankey_from_2_df_cols(df_space_groups)
sankey-spglib-vs-aflow-spacegroups sankey-crystal-sys-to-spg-symbol

Histograms

See pymatviz/histogram.py.

spacegroup_bar([65, 134, 225, ...], backend="matplotlib") spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="matplotlib")
spg-num-hist-matplotlib spg-symbol-hist-matplotlib
spacegroup_bar([65, 134, 225, ...], backend="plotly") spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...], backend="plotly")
spg-num-hist-plotly spg-symbol-hist-plotly
elements_hist(compositions, log=True, bar_values='count') histogram({'key1': values1, 'key2': values2})
elements-hist histogram-ecdf

Scatter Plots

See pymatviz/scatter.py.

density_scatter_plotly(df, x=x_col, y=y_col, ...) density_scatter_plotly(df, x=x_col, y=y_col, ...)
density-scatter-plotly density-scatter-plotly-blobs
density_scatter(xs, ys, ...) density_scatter_with_hist(xs, ys, ...)
density-scatter density-scatter-with-hist
density_hexbin(xs, ys, ...) density_hexbin_with_hist(xs, ys, ...)
density-hexbin density-hexbin-with-hist

Uncertainty

See pymatviz/uncertainty.py.

qq_gaussian(y_true, y_pred, y_std) qq_gaussian(y_true, y_pred, y_std: dict)
normal-prob-plot normal-prob-plot-multiple
error_decay_with_uncert(y_true, y_pred, y_std) error_decay_with_uncert(y_true, y_pred, y_std: dict)
error-decay-with-uncert error-decay-with-uncert-multiple

Cumulative Metrics

See pymatviz/cumulative.py.

cumulative_error(preds, targets) cumulative_residual(preds, targets)
cumulative-error cumulative-residual

Classification

See pymatviz/relevance.py.

roc_curve(targets, proba_pos) precision_recall_curve(targets, proba_pos)
roc-curve precision-recall-curve

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
}

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
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Avatar for janosh from gravatar.com janosh

Unverified details

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Meta
  • License: MIT License (MIT License Copyright (c) 2021 Janosh Riebesell Permission is hereby granted, free of charge, to a...)
  • Author: Janosh Riebesell
  • Tags chemistry, data visualization, materials discovery, materials informatics, matplotlib, plotly, science
  • Requires: Python >=3.10
  • Provides-Extra: auto-text-pos, df-pdf-export, df-svg-export, export-figs, gh-pages, make-assets, test
Classifiers

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