A toolkit for visualizations in materials informatics
Project description
pymatviz
A toolkit for visualizations in materials informatics.
Note: This project is not endorsed by pymatgen
, but aims to complement it with additional plotting functionality.
Installation
pip install pymatviz
API Docs
See the /api page.
Usage
See the Jupyter notebooks under examples/
for how to use pymatviz
.
matbench_dielectric_eda.ipynb | |||
mp_bimodal_e_form.ipynb | |||
matbench_perovskites_eda.ipynb | |||
mprester_ptable.ipynb |
Periodic Table
See pymatviz/ptable.py
. Heat maps of the periodic table can be plotted both with matplotlib
and plotly
. plotly
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) |
Dash app using ptable_heatmap_plotly()
See examples/mprester_ptable.ipynb
.
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.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) |
plot_structure_2d(mp_12712) |
---|---|
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
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 Error & Residual
cumulative_error(preds, targets) |
cumulative_residual(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
Built Distribution
Hashes for pymatviz-0.7.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8c6ad693cc0e64adcd515e39d1b885cec60d4c2a82de1f9ea5a8ea58dfcccb72 |
|
MD5 | a38a5668e8d62063e2a5497e5fd468db |
|
BLAKE2b-256 | 4dbcd9ce5cb0fe41525abb547e006e28b45daae2d8e259e715d8f01dfe73fbab |