A toolkit for visualizations in materials informatics

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for janosh from gravatar.com janosh

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Janosh Riebesell
  • Tags materials informatics, materials discovery, data visualization, plotly, matplotlib
  • Requires: Python >=3.8
Classifiers

Project description

pymatviz

A toolkit for visualizations in materials informatics.

Tests pre-commit.ci status This project supports Python 3.8+ PyPI PyPI Downloads

Note: This project is not associated with or endorsed by pymatgen, but aims to complement it by adding additional plotting functionality.

Installation

pip install pymatviz

Usage

Check out the Jupyter notebooks under examples/ to learn how to use pymatviz.

Elements

See pymatviz/elements.py. Heat maps of the periodic table can be plotted both with matplotlib and plotly. Latter 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)
ptable-heatmap-plotly-more-hover-data ptable-heatmap-plotly-percent-labels

Sunburst

See pymatviz/sunburst.py.

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

Sankey

See pymatviz/sankey.py.

sankey_from_2_df_cols(df_perovskites) sankey_from_2_df_cols(df_rand_ints)
sankey-spglib-vs-aflow-spacegroups sankey-from-2-df-cols-randints

Structure

See pymatviz/struct_vis.py.

plot_structure_2d(pmg_struct) plot_structure_2d(pmg_struct, show_unit_cell=False, site_labels=False)
struct-2d-mp-19017-disordered struct-2d-mp-12712

![mp_structures_2d]

Histograms

See pymatviz/histograms.py.

spacegroup_hist([65, 134, 225, ...]) spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...])
spg-num-hist spg-symbol-hist
residual_hist(y_true, y_pred) hist_elemental_prevalence(compositions, log=True, bar_values='count')
residual-hist hist-elemental-prevalence

Parity Plots

See pymatviz/parity.py.

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
scatter_with_err_bar(xs, ys, yerr, ...) residual_vs_actual(y_true, y_pred, ...)
scatter-with-err-bar residual-vs-actual

Uncertainty Calibration

See pymatviz/quantile.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

Ranking

See pymatviz/ranking.py.

err_decay(y_true, y_pred, y_std) err_decay(y_true, y_pred, y_std: dict)
err-decay err-decay-multiple

Cumulative Error and Residual

See pymatviz/cumulative.py.

cum_err(preds, targets) cum_res(preds, targets)
cumulative-error cumulative-residual

Classification Metrics

See pymatviz/relevance.py.

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

Correlation

See pymatviz/correlation.py.

marchenko_pastur(corr_mat, gamma=ncols/nrows) marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)
marchenko-pastur marchenko-pastur-significant-eval

Glossary

  1. Residual y_res = y_true - y_pred: The difference between ground truth target and model prediction.
  2. Error y_err = abs(y_true - y_pred): Absolute error between target and model prediction.
  3. Uncertainty y_std: The model's estimate for its error, i.e. how much the model thinks its prediction can be trusted. (std for standard deviation.)

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for janosh from gravatar.com janosh

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Janosh Riebesell
  • Tags materials informatics, materials discovery, data visualization, plotly, matplotlib
  • Requires: Python >=3.8
Classifiers

Release history Release notifications | RSS feed

0.13.2

0.13.1

0.13.0

0.12.0

0.11.0

0.10.1

0.10.0

0.9.3

0.9.2

0.9.1

0.9.0

0.8.3

0.8.2

0.8.1

0.8.0

0.7.3

0.7.2

0.7.1

0.7.0

0.6.3

0.6.2

0.6.1

0.6.0

0.5.3

0.5.2

0.5.1

0.5.0

0.4.4

This version

0.4.3

0.4.2

0.4.1

0.4.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pymatviz-0.4.3.tar.gz (39.2 kB view details)

Uploaded Source

File details

Details for the file pymatviz-0.4.3.tar.gz.

File metadata

  • Download URL: pymatviz-0.4.3.tar.gz
  • Upload date:
  • Size: 39.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.8.12

File hashes

Hashes for pymatviz-0.4.3.tar.gz
Algorithm Hash digest
SHA256 27e191fd303354d8abf7760ee660799dfcf0791b6a835268a406dffb8aa37164
MD5 1479192b3c8dc8402ae8318f332ecd9a
BLAKE2b-256 26c097f27260618545299c4bfbebbd0d23932b0138bc46b94f5749b5bee4213d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page