A toolkit for visualizations in materials informatics
Project description
pymatviz
A toolkit for visualizations in materials informatics.
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_plotly(atomic_masses) |
ptable_heatmap_plotly(compositions) |
Sunburst
See pymatviz/sunburst.py
.
spacegroup_sunburst([65, 134, 225, ...]) |
spacegroup_sunburst(["C2/m", "P-43m", "Fm-3m", ...]) |
---|---|
Structure
plot_structure_2d(pmg_struct) |
plot_structure_2d(pmg_struct, show_unit_cell=False, site_labels=False) |
---|---|
Histograms
spacegroup_hist([65, 134, 225, ...]) |
spacegroup_hist(["C2/m", "P-43m", "Fm-3m", ...]) |
---|---|
residual_hist(y_true, y_pred) |
hist_elemental_prevalence(compositions, log=True, bar_values='count') |
Parity Plots
See pymatviz/parity.py
.
Uncertainty Calibration
See pymatviz/quantile.py
.
qq_gaussian(y_true, y_pred, y_std) |
qq_gaussian(y_true, y_pred, y_std: dict) |
---|---|
Ranking
See pymatviz/ranking.py
.
err_decay(y_true, y_pred, y_std) |
err_decay(y_true, y_pred, y_std: dict) |
---|---|
Cumulative Error and Residual
cum_err(preds, targets) |
cum_res(preds, targets) |
---|---|
Classification Metrics
roc_curve(targets, proba_pos) |
precision_recall_curve(targets, proba_pos) |
---|---|
Correlation
marchenko_pastur(corr_mat, gamma=ncols/nrows) |
marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows) |
---|---|
Glossary
- Residual
y_res = y_true - y_pred
: The difference between ground truth target and model prediction. - Error
y_err = abs(y_true - y_pred)
: Absolute error between target and model prediction. - 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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pymatviz-0.4.2.tar.gz
(37.1 kB
view hashes)