pymatviz 0.12.0

A toolkit for visualizations in materials informatics

pymatviz

A toolkit for visualizations in materials informatics.

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
matbench_dielectric_eda.ipynb
mp_bimodal_e_form.ipynb
matbench_perovskites_eda.ipynb
mprester_ptable.ipynb

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_plotly(atomic_masses)	ptable_heatmap_plotly(compositions, log=True)
ptable_hists(data, colormap="coolwarm")	ptable_lines(data)
ptable_scatters(data, colormap="coolwarm")	ptable_scatters(data, colormap="coolwarm")
ptable_heatmap_splits(2_vals_per_elem, colormap="coolwarm", start_angle=135)	ptable_heatmap_splits(3_vals_per_elem, colormap="coolwarm", start_angle=90)

Phonons

See examples/mlff_phonons.ipynb for usage example.

phonon_bands(bands_dict)	phonon_dos(doses_dict)
phonon_bands_and_dos(bands_dict, doses_dict)	phonon_bands_and_dos(single_bands, single_dos)

Dash app using ptable_heatmap_plotly()

See examples/mprester_ptable.ipynb.

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

Sunburst

See pymatviz/sunburst.py.

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

Sankey

See pymatviz/sankey.py.

sankey_from_2_df_cols(df_perovskites)	sankey_from_2_df_cols(df_rand_ints)

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)
structure_2d_plotly(six_structs)	structure_3d_plotly(six_structs)

Histograms

See pymatviz/histogram.py.

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

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(xs, ys, ...)	density_scatter_with_hist(xs, ys, ...)
density_hexbin(xs, ys, ...)	density_hexbin_with_hist(xs, ys, ...)

X-Ray Diffraction

See pymatviz/xrd.py.

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

Radial Distribution Functions

See pymatviz/rdf.py.

rdf_plot(rdf)	rdf_plot(rdf, rdf2)

Uncertainty

See pymatviz/uncertainty.py.

qq_gaussian(y_true, y_pred, y_std)	qq_gaussian(y_true, y_pred, y_std: dict)
error_decay_with_uncert(y_true, y_pred, y_std)	error_decay_with_uncert(y_true, y_pred, y_std: dict)

Cumulative Metrics

See pymatviz/cumulative.py.

cumulative_error(preds, targets)	cumulative_residual(preds, targets)

Classification

See pymatviz/relevance.py.

roc_curve(targets, proba_pos)	precision_recall_curve(targets, proba_pos)

Correlation

See pymatviz/correlation.py.

marchenko_pastur(corr_mat, gamma=ncols/nrows)	marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)

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
}