| Name | pymatviz JSON |
| Version |
0.17.1
JSON |
| download |
| home_page | None |
| Summary | A toolkit for visualizations in materials informatics |
| upload_time | 2025-07-29 07:26:22 |
| maintainer | None |
| docs_url | None |
| author | Janosh Riebesell |
| requires_python | >=3.11 |
| license | MIT License Copyright (c) 2021 Janosh Riebesell Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software. |
| keywords |
chemistry
crystallography
data analysis
data visualization
materials informatics
plotly
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="center">
<img src="https://github.com/janosh/pymatviz/raw/main/site/static/favicon.svg" alt="Logo" height="60px">
<br class="hide-in-docs">
pymatviz
</h1>
<h4 align="center" class="toc-exclude">
A toolkit for visualizations in materials informatics.
[](https://github.com/janosh/pymatviz/actions/workflows/test.yml)
[](https://python.org/downloads)
[](https://pypi.org/project/pymatviz)
[](https://codecov.io/gh/janosh/pymatviz)
[](https://pypistats.org/packages/pymatviz)
[](https://zenodo.org/records/10456384)
</h4>
[fig-icon]: https://api.iconify.design/lsicon:scatter-diagram-outline.svg?color=%234c8bf5&height=16 "View example code"
<slot name="how-to-cite">
> If you use `pymatviz` in your research, [see how to cite](#how-to-cite-pymatviz). Check out [23 existing papers using `pymatviz`](#papers-using-pymatviz) for inspiration!
</slot>
## Installation
```sh
pip install pymatviz
```
See `pyproject.toml` for available extras like `pip install 'pymatviz[brillouin]'` to render 3d Brillouin zones.
## API Docs
See the [/api][/api] page.
[/api]: https://janosh.github.io/pymatviz/api
## Usage
See the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz`. PRs with additional examples are welcome! š
| | | |
| ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |
| [matbench_dielectric_eda.ipynb](examples/matbench_dielectric_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [Launch Codespace][codespace url] |
| [mp_bimodal_e_form.ipynb](examples/mp_bimodal_e_form.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [Launch Codespace][codespace url] |
| [matbench_perovskites_eda.ipynb](examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [Launch Codespace][codespace url] |
| [mprester_ptable.ipynb](examples/mprester_ptable.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [Launch Codespace][codespace url] |
[Open in Google Colab]: https://colab.research.google.com/assets/colab-badge.svg
[codespace url]: https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=340898532
## Periodic Table
See [`pymatviz/ptable/plotly.py`](pymatviz/ptable/plotly.py). The module supports heatmaps, heatmap splits (multiple values per element), histograms, scatter plots and line plots. All visualizations are interactive through [Plotly](https://plotly.com) and support displaying additional data on hover.
> [!WARNING]
> Version 0.16.0 of `pymatviz` dropped the matplotlib-based functions in `ptable_matplotlib.py` in https://github.com/janosh/pymatviz/pull/270. Please use the `plotly`-based functions shown below instead which have feature parity, interactivity and better test coverage.
| [`ptable_heatmap_plotly(atomic_masses)`](pymatviz/ptable/plotly.py#L38) | [`ptable_heatmap_plotly(compositions, log=True)`](pymatviz/ptable/plotly.py#L38) [![fig-icon]](assets/scripts/ptable/ptable_heatmap_plotly.py) |
| :----------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------: |
| ![ptable-heatmap-plotly-more-hover-data] | ![ptable-heatmap-plotly-log] |
| [`ptable_hists_plotly(data)`](pymatviz/ptable/plotly.py#L398) [![fig-icon]](assets/scripts/ptable/ptable_hists_plotly.py) | [`ptable_scatter_plotly(data, mode="markers")`](pymatviz/ptable/plotly.py#L1359) [![fig-icon]](assets/scripts/ptable/ptable_scatter_plotly.py) |
| ![ptable-hists-plotly] | ![ptable-scatter-plotly-markers] |
| [`ptable_heatmap_splits_plotly(2_vals_per_elem)`](pymatviz/ptable/plotly.py#L778) [![fig-icon]](assets/scripts/ptable/ptable_heatmap_splits_plotly.py) | [`ptable_heatmap_splits_plotly(3_vals_per_elem)`](pymatviz/ptable/plotly.py#L778) |
| ![ptable-heatmap-splits-plotly-2] | ![ptable-heatmap-splits-plotly-3] |
[ptable-heatmap-plotly-log]: assets/svg/ptable-heatmap-plotly-log.svg
[ptable-heatmap-plotly-more-hover-data]: assets/svg/ptable-heatmap-plotly-more-hover-data.svg
[ptable-heatmap-splits-plotly-2]: assets/svg/ptable-heatmap-splits-plotly-2.svg
[ptable-heatmap-splits-plotly-3]: assets/svg/ptable-heatmap-splits-plotly-3.svg
[ptable-hists-plotly]: assets/svg/ptable-hists-plotly.svg
[ptable-scatter-plotly-markers]: assets/svg/ptable-scatter-plotly-markers.svg
### Dash app using `ptable_heatmap_plotly()`
See [`examples/mprester_ptable.ipynb`](examples/mprester_ptable.ipynb).
[https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4](https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4)
## Phonons
| [`phonon_bands(bands_dict)`](pymatviz/phonons/plotly.py#L35) [![fig-icon]](assets/scripts/phonons/phonon_bands.py) | [`phonon_dos(doses_dict)`](pymatviz/phonons/plotly.py#L381) [![fig-icon]](assets/scripts/phonons/phonon_dos.py) |
| :----------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
| ![phonon-bands] | ![phonon-dos] |
| [`phonon_bands_and_dos(bands_dict, doses_dict)`](pymatviz/phonons/plotly.py#L528) | [`phonon_bands_and_dos(single_bands, single_dos)`](pymatviz/phonons/plotly.py#L528) [![fig-icon]](assets/scripts/phonons/phonon_bands_and_dos.py) |
| ![phonon-bands-and-dos-mp-2758] | ![phonon-bands-and-dos-mp-23907] |
[phonon-bands]: assets/svg/phonon-bands-mp-2758.svg
[phonon-dos]: assets/svg/phonon-dos-mp-2758.svg
[phonon-bands-and-dos-mp-2758]: assets/svg/phonon-bands-and-dos-mp-2758.svg
[phonon-bands-and-dos-mp-23907]: assets/svg/phonon-bands-and-dos-mp-23907.svg
### Composition Clustering
| [`cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=2)`](pymatviz/cluster/composition/plot.py#L32) [![fig-icon]](assets/scripts/cluster/composition/cluster_compositions_matbench.py) | [`cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=3)`](pymatviz/cluster/composition/plot.py#L32) |
| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |
| ![matbench-perovskites-magpie-pca-2d] | ![matbench-perovskites-magpie-tsne-3d] |
[matbench-perovskites-magpie-pca-2d]: assets/svg/matbench-perovskites-magpie-pca-2d.svg
[matbench-perovskites-magpie-tsne-3d]: assets/svg/matbench-perovskites-magpie-tsne-3d.svg
Visualize 2D or 3D relationships between compositions and properties using multiple embedding and dimensionality reduction techniques:
Embedding methods: **One-hot** encoding of element fractions, **Magpie** features (elemental properties), **Matscholar** element embeddings, **MEGNet** element embeddings
Dimensionality reduction methods: **PCA** (linear), **t-SNE** (non-linear), **UMAP** (non-linear), **Isomap** (non-linear), **Kernel PCA** (non-linear)
Example usage:
```py
import pymatviz as pmv
from pymatgen.core import Composition
compositions = ("Fe2O3", "Al2O3", "SiO2", "TiO2")
# Create embeddings
embeddings = pmv.cluster.composition.one_hot_encode(compositions)
comp_emb_map = dict(zip(compositions, embeddings, strict=True))
# Plot with optional property coloring
fig = pmv.cluster_compositions(
compositions=comp_emb_map,
properties=[1.0, 2.0, 3.0, 4.0], # Optional property values
prop_name="Property", # Optional property label
embedding_method="one-hot", # or "magpie", "matscholar_el", "megnet_el", etc.
projection_method="pca", # or "tsne", "umap", "isomap", "kernel_pca", etc.
show_chem_sys="shape", # works best for small number of compositions; "color" | "shape" | "color+shape" | None
n_components=2, # or 3 for 3D plots
)
fig.show()
```
## Structure Clustering
On the roadmap but no ETA yet.
## Structure
See [`pymatviz/structure/plotly.py`](pymatviz/structure/plotly.py).
| [`structure_3d(hea_structure)`](pymatviz/structure/plotly.py#L318) | [`structure_3d(lco_supercell)`](pymatviz/structure/plotly.py#L318) [![fig-icon]](assets/scripts/structure/structure_3d.py) |
| :---------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------: |
| ![hea-structure-3d] | ![lco-structure-3d] |
| [`structure_2d(six_structs)`](pymatviz/structure/plotly.py#L42) [![fig-icon]](assets/scripts/structure/structure_2d.py) | [`structure_3d(six_structs)`](pymatviz/structure/plotly.py#L318) [![fig-icon]](assets/scripts/structure/structure_3d.py) |
| ![matbench-phonons-structures-2d] | ![matbench-phonons-structures-3d] |
[matbench-phonons-structures-2d]: assets/svg/matbench-phonons-structures-2d.svg
[matbench-phonons-structures-3d]: assets/svg/matbench-phonons-structures-3d.svg
[hea-structure-3d]: assets/svg/hea-structure-3d.svg
[lco-structure-3d]: assets/svg/lco-structure-3d.svg
## Interactive Widgets
See [`pymatviz/widgets`](pymatviz/widgets). Interactive 3D structure, molecular dynamics trajectory and composition visualization widgets for [Jupyter](https://jupyter.org), [Marimo](https://marimo.io), and VSCode notebooks, powered by [anywidget](https://anywidget.dev) and [MatterViz](https://matterviz.janosh.dev) (<https://github.com/janosh/matterviz>). Supports pymatgen `Structure`, ASE `Atoms`, and `PhonopyAtoms`, as well as ASE, `pymatgen` and plain Python trajectory formats.
```py
from pymatviz import StructureWidget, CompositionWidget, TrajectoryWidget
from pymatgen.core import Structure, Composition
# Interactive 3D structure visualization
structure = Structure.from_file("structure.cif")
struct_widget = StructureWidget(structure=structure)
# Interactive composition visualization
composition = Composition("Fe2O3")
comp_widget = CompositionWidget(composition=composition)
# Interactive trajectory visualization
trajectory1 = [struct1, struct2, struct3] # List of structures
traj_widget1 = TrajectoryWidget(trajectory=trajectory1)
trajectory2 = [{"structure": struct1, "energy": 1.0}, {"structure": struct2, "energy": 2.0}, {"structure": struct3, "energy": 3.0}] # dicts with "structure" and property values
traj_widget2 = TrajectoryWidget(trajectory=trajectory2)
```
**Examples:**
- [Jupyter notebook demo](examples/widgets/jupyter_demo.ipynb)
- [Marimo demo](examples/widgets/marimo_demo.py)
- [VSCode interactive demo](examples/widgets/vscode_interactive_demo.py)
> [!TIP]
> Checkout the **ā
MatterViz VSCode extension** for using the same viewers directly in VSCode/Cursor editor tabs for rendering local and remote files: [marketplace.visualstudio.com/items?itemName=janosh.matterviz](https://marketplace.visualstudio.com/items?itemName=janosh.matterviz)
Importing `pymatviz` auto-registers all widgets for their respective sets of supported objects via `register_matterviz_widgets()`. To customize the registration, use [`set_renderer()`](pymatviz/notebook.py).
## Brillouin Zone
See [`pymatviz/brillouin.py`](pymatviz/brillouin.py).
| [`brillouin_zone_3d(cubic_struct)`](pymatviz/brillouin.py#L15) [![fig-icon]](assets/scripts/brillouin/brillouin_zone_3d.py) | [`brillouin_zone_3d(hexagonal_struct)`](pymatviz/brillouin.py#L15) |
| :------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------: |
| ![brillouin-cubic-mp-10018] | ![brillouin-hexagonal-mp-862690] |
| [`brillouin_zone_3d(monoclinic_struct)`](pymatviz/brillouin.py#L15) [![fig-icon]](assets/scripts/brillouin/brillouin_zone_3d.py) | [`brillouin_zone_3d(orthorhombic_struct)`](pymatviz/brillouin.py#L15) |
| ![brillouin-monoclinic-mp-1183089] | ![brillouin-volumes-3-cols] |
[brillouin-cubic-mp-10018]: assets/svg/brillouin-cubic-mp-10018.svg
[brillouin-hexagonal-mp-862690]: assets/svg/brillouin-hexagonal-mp-862690.svg
[brillouin-monoclinic-mp-1183089]: assets/svg/brillouin-monoclinic-mp-1183089.svg
[brillouin-orthorhombic-mp-1183085]: assets/svg/brillouin-orthorhombic-mp-1183085.svg
[brillouin-volumes-3-cols]: assets/svg/brillouin-volumes-3-cols.svg
## X-Ray Diffraction
See [`pymatviz/xrd.py`](pymatviz/xrd.py).
| [`xrd_pattern(pattern)`](pymatviz/xrd.py#L42) [![fig-icon]](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern({key1: patt1, key2: patt2})`](pymatviz/xrd.py#L42) |
| :--------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------: |
| ![xrd-pattern] | ![xrd-pattern-multiple] |
| [`xrd_pattern(struct_dict, stack="horizontal")`](pymatviz/xrd.py#L42) [![fig-icon]](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern(struct_dict, stack="vertical")`](pymatviz/xrd.py#L42) |
| ![xrd-pattern-horizontal-stack] | ![xrd-pattern-vertical-stack] |
[xrd-pattern]: assets/svg/xrd-pattern.svg
[xrd-pattern-multiple]: assets/svg/xrd-pattern-multiple.svg
[xrd-pattern-horizontal-stack]: assets/svg/xrd-pattern-horizontal-stack.svg
[xrd-pattern-vertical-stack]: assets/svg/xrd-pattern-vertical-stack.svg
## Radial Distribution Functions
See [`pymatviz/rdf/plotly.py`](pymatviz/rdf/plotly.py).
| [`element_pair_rdfs(pmg_struct)`](pymatviz/rdf/plotly.py#L33) | [`element_pair_rdfs({"A": struct1, "B": struct2})`](pymatviz/rdf/plotly.py#L33) [![fig-icon]](assets/scripts/rdf/element_pair_rdfs.py) |
| :-----------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------: |
| ![element-pair-rdfs-Na8Nb8O24] | ![element-pair-rdfs-crystal-vs-amorphous] |
[element-pair-rdfs-Na8Nb8O24]: assets/svg/element-pair-rdfs-Na8Nb8O24.svg
[element-pair-rdfs-crystal-vs-amorphous]: assets/svg/element-pair-rdfs-crystal-vs-amorphous.svg
## Coordination
See [`pymatviz/coordination/plotly.py`](pymatviz/coordination/plotly.py).
| [`coordination_hist(struct_dict)`](pymatviz/coordination/plotly.py#L34) | [`coordination_hist(struct_dict, by_element=True)`](pymatviz/coordination/plotly.py#L34) [![fig-icon]](assets/scripts/coordination/coordination_hist.py) |
| :-----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![coordination-hist-single] | ![coordination-hist-by-structure-and-element] |
| [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py#L337) | [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py#L337) [![fig-icon]](assets/scripts/coordination/coordination_vs_cutoff_line.py#L52) |
| ![coordination-vs-cutoff-single] | ![coordination-vs-cutoff-multiple] |
[coordination-hist-single]: assets/svg/coordination-hist-single.svg
[coordination-hist-by-structure-and-element]: assets/svg/coordination-hist-by-structure-and-element.svg
[coordination-vs-cutoff-single]: assets/svg/coordination-vs-cutoff-single.svg
[coordination-vs-cutoff-multiple]: assets/svg/coordination-vs-cutoff-multiple.svg
## Sunburst
See [`pymatviz/sunburst.py`](pymatviz/sunburst.py).
| [`spacegroup_sunburst([65, 134, 225, ...])`](pymatviz/sunburst.py#L111) [![fig-icon]](assets/scripts/sunburst/spacegroup_sunburst.py) | [`chem_sys_sunburst(["FeO", "Fe2O3", "LiPO4", ...])`](pymatviz/sunburst.py#L206) [![fig-icon]](assets/scripts/sunburst/chem_sys_sunburst.py) |
| :-----------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------: |
| ![spg-num-sunburst] | ![chem-sys-sunburst-ward-bmg] |
| [`chem_env_sunburst(single_struct)`](pymatviz/sunburst.py#L351) | [`chem_env_sunburst(multiple_structs)`](pymatviz/sunburst.py#L351) |
| ![chem-env-sunburst-basic] | ![chem-env-sunburst-mp-carbon] |
[spg-num-sunburst]: assets/svg/spg-num-sunburst.svg
[chem-sys-sunburst-ward-bmg]: assets/svg/chem-sys-sunburst-ward-bmg.svg
[chem-env-sunburst-basic]: assets/svg/chem-env-sunburst-basic.svg
[chem-env-sunburst-mp-carbon]: assets/svg/chem-env-sunburst-mp-carbon.svg
## Treemap
See [`pymatviz/treemap/chem_sys.py`](pymatviz/treemap/chem_sys.py).
| [`chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...])`](pymatviz/treemap/chem_sys.py#L36) [![fig-icon]](assets/scripts/treemap/chem_sys_treemap.py) | [`chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...], group_by="formula")`](pymatviz/treemap/chem_sys.py#L36) |
| :----------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: |
| ![chem-sys-treemap-formula] | ![chem-sys-treemap-ward-bmg] |
| [`chem_env_treemap(structures)`](pymatviz/treemap/chem_env.py#L36) [![fig-icon]](assets/scripts/treemap/chem_env_treemap.py) | [`chem_env_treemap(structures, max_cells_cn=3, max_cells_ce=4)`](pymatviz/treemap/chem_env.py#L36) |
| ![chem-env-treemap-basic] | ![chem-env-treemap-large-dataset] |
| [`py_pkg_treemap("pymatviz")`](pymatviz/treemap/py_pkg.py#L36) [![fig-icon]](assets/scripts/treemap/py_pkg_treemap.py) | [`py_pkg_treemap(["pymatviz", "torch_sim", "pymatgen"])`](pymatviz/treemap/py_pkg.py#L36) |
| ![py-pkg-treemap-pymatviz] | ![py-pkg-treemap-multiple] |
[chem-sys-treemap-formula]: assets/svg/chem-sys-treemap-formula.svg
[chem-sys-treemap-ward-bmg]: assets/svg/chem-sys-treemap-ward-bmg.svg
[py-pkg-treemap-pymatviz]: assets/svg/py-pkg-treemap-pymatviz.svg
[py-pkg-treemap-multiple]: assets/svg/py-pkg-treemap-multiple.svg
[chem-env-treemap-large-dataset]: assets/svg/chem-env-treemap-large-dataset.svg
[chem-env-treemap-basic]: assets/svg/chem-env-treemap-basic.svg
## Rainclouds
See [`pymatviz/rainclouds.py`](pymatviz/rainclouds.py).
| [`rainclouds(two_key_dict)`](pymatviz/rainclouds.py#L20) [![fig-icon]](assets/scripts/rainclouds/rainclouds.py) | [`rainclouds(three_key_dict)`](pymatviz/rainclouds.py#L20) |
| :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------: |
| ![rainclouds-bimodal] | ![rainclouds-trimodal] |
[rainclouds-bimodal]: assets/svg/rainclouds-bimodal.svg
[rainclouds-trimodal]: assets/svg/rainclouds-trimodal.svg
## Sankey
See [`pymatviz/sankey.py`](pymatviz/sankey.py).
| [`sankey_from_2_df_cols(df_perovskites)`](pymatviz/sankey.py#L16) [![fig-icon]](assets/scripts/sankey/sankey_from_2_df_cols.py) | [`sankey_from_2_df_cols(df_space_groups)`](pymatviz/sankey.py#L16) |
| :-----------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------: |
| ![sankey-spglib-vs-aflow-spacegroups] | ![sankey-crystal-sys-to-spg-symbol] |
[sankey-spglib-vs-aflow-spacegroups]: assets/svg/sankey-spglib-vs-aflow-spacegroups.svg
[sankey-crystal-sys-to-spg-symbol]: assets/svg/sankey-crystal-sys-to-spg-symbol.svg
## Bar Plots
See [`pymatviz/bar.py`](pymatviz/bar.py).
| [`spacegroup_bar([65, 134, 225, ...])`](pymatviz/bar.py#L29) [![fig-icon]](assets/scripts/bar/spacegroup_bar.py) | [`spacegroup_bar(["C2/m", "P-43m", "Fm-3m", ...])`](pymatviz/bar.py#L29) |
| :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------: |
| ![spg-num-hist-plotly] | ![spg-symbol-hist-plotly] |
[spg-symbol-hist-plotly]: assets/svg/spg-symbol-hist-plotly.svg
[spg-num-hist-plotly]: assets/svg/spg-num-hist-plotly.svg
## Histograms
See [`pymatviz/histogram.py`](pymatviz/histogram.py).
| [`elements_hist(compositions, log=True, bar_values='count')`](pymatviz/histogram.py#L37) [![fig-icon]](assets/scripts/histogram/elements_hist.py) | [`histogram({'key1': values1, 'key2': values2})`](pymatviz/histogram.py#L108) [![fig-icon]](assets/scripts/histogram/histogram.py) |
| :-----------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
| ![elements-hist] | ![histogram-ecdf] |
[histogram-ecdf]: assets/svg/histogram-ecdf.svg
## Scatter Plots
See [`pymatviz/scatter.py`](pymatviz/scatter.py).
| [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py#L149) | [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py#L149) [![fig-icon]](assets/scripts/scatter/density_scatter_plotly.py) |
| :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |
| ![density-scatter-plotly] | ![density-scatter-plotly-blobs] |
| [`density_scatter(xs, ys, ...)`](pymatviz/scatter.py#L71) [![fig-icon]](assets/scripts/scatter/density_scatter.py) | [`density_scatter_with_hist(xs, ys, ...)`](pymatviz/scatter.py#L570) [![fig-icon]](assets/scripts/scatter/density_scatter.py) |
| ![density-scatter] | ![density-scatter-with-hist] |
| [`density_hexbin(xs, ys, ...)`](pymatviz/scatter.py#L493) [![fig-icon]](assets/scripts/scatter/density_hexbin.py) | [`density_hexbin_with_hist(xs, ys, ...)`](pymatviz/scatter.py#L587) [![fig-icon]](assets/scripts/scatter/density_hexbin.py) |
| ![density-hexbin] | ![density-hexbin-with-hist] |
[density-scatter-plotly]: assets/svg/density-scatter-plotly.svg
[density-scatter-plotly-blobs]: assets/svg/density-scatter-plotly-blobs.svg
[density-hexbin-with-hist]: assets/svg/density-hexbin-with-hist.svg
[density-hexbin]: assets/svg/density-hexbin.svg
[density-scatter-with-hist]: assets/svg/density-scatter-with-hist.svg
[density-scatter]: assets/svg/density-scatter.svg
## Uncertainty
See [`pymatviz/uncertainty.py`](pymatviz/uncertainty.py).
| [`qq_gaussian(y_true, y_pred, y_std)`](pymatviz/uncertainty.py#L27) [![fig-icon]](assets/scripts/uncertainty/qq_gaussian.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py#L27) |
| :---------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: |
| ![qq-gaussian] | ![qq-gaussian-multiple] |
| [`error_decay_with_uncert(y_true, y_pred, y_std)`](pymatviz/uncertainty.py#L208) [![fig-icon]](assets/scripts/uncertainty/error_decay_with_uncert.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py#L208) |
| ![error-decay-with-uncert] | ![error-decay-with-uncert-multiple] |
## Classification
See [`pymatviz/classify/confusion_matrix.py`](pymatviz/classify/confusion_matrix.py).
| [`confusion_matrix(conf_mat, ...)`](pymatviz/classify/confusion_matrix.py#L10) | [`confusion_matrix(y_true, y_pred, ...)`](pymatviz/classify/confusion_matrix.py#L10) [![fig-icon]](assets/scripts/classify/confusion_matrix.py) |
| :----------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |
| ![stability-confusion-matrix] | ![crystal-system-confusion-matrix] |
See [`pymatviz/classify/curves.py`](pymatviz/classify/curves.py).
| [`roc_curve_plotly(targets, probs_positive)`](pymatviz/classify/curves.py#L76) [![fig-icon]](assets/scripts/classify/roc_curve.py) | [`precision_recall_curve_plotly(targets, probs_positive)`](pymatviz/classify/curves.py#L176) [![fig-icon]](assets/scripts/classify/precision_recall_curve.py) |
| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ![roc-curve-plotly-multiple] | ![precision-recall-curve-plotly-multiple] |
[roc-curve-plotly-multiple]: assets/svg/roc-curve-plotly-multiple.svg
[precision-recall-curve-plotly-multiple]: assets/svg/precision-recall-curve-plotly-multiple.svg
[stability-confusion-matrix]: assets/svg/stability-confusion-matrix.svg
[crystal-system-confusion-matrix]: assets/svg/crystal-system-confusion-matrix.svg
[error-decay-with-uncert-multiple]: assets/svg/error-decay-with-uncert-multiple.svg
[error-decay-with-uncert]: assets/svg/error-decay-with-uncert.svg
[elements-hist]: assets/svg/elements-hist.svg
[qq-gaussian-multiple]: assets/svg/qq-gaussian-multiple.svg
[qq-gaussian]: assets/svg/qq-gaussian.svg
## How to cite `pymatviz`
See [`citation.cff`](citation.cff) or cite the [Zenodo record](https://zenodo.org/badge/latestdoi/340898532) using the following BibTeX entry:
```bib
@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
}
```
## Papers using `pymatviz`
Sorted by number of citations, then year. Last updated 2025-05-07. Auto-generated [from Google Scholar](https://scholar.google.com/scholar?q=pymatviz). Manual additions [via PR](https://github.com/janosh/pymatviz/edit/main/readme.md) welcome.
1. C Zeni, R Pinsler, D ZĆ¼gner et al. (2023). [Mattergen: a generative model for inorganic materials design](https://arxiv.org/abs/2312.03687) (cited by 134)
1. J Riebesell, REA Goodall, P Benner et al. (2023). [Matbench Discovery--A framework to evaluate machine learning crystal stability predictions](https://arxiv.org/abs/2308.14920) (cited by 53)
1. L Barroso-Luque, M Shuaibi, X Fu et al. (2024). [Open materials 2024 (omat24) inorganic materials dataset and models](https://www.rivista.ai/wp-content/uploads/2024/10/2410.12771v1.pdf) (cited by 48)
1. C Chen, DT Nguyen, SJ Lee et al. (2024). [Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation](https://pubs.acs.org/doi/abs/10.1021/jacs.4c03849) (cited by 43)
1. M Giantomassi, G Materzanini (2024). [Systematic assessment of various universal machineālearning interatomic potentials](https://onlinelibrary.wiley.com/doi/abs/10.1002/mgea.58) (cited by 22)
1. AA Naik, C Ertural, P Benner et al. (2023). [A quantum-chemical bonding database for solid-state materials](https://www.nature.com/articles/s41597-023-02477-5) (cited by 15)
1. K Li, AN Rubungo, X Lei et al. (2025). [Probing out-of-distribution generalization in machine learning for materials](https://www.nature.com/articles/s43246-024-00731-w) (cited by 9)
1. A Kapeliukha, RA Mayo (2025). [MOSAEC-DB: a comprehensive database of experimental metalāorganic frameworks with verified chemical accuracy suitable for molecular simulations](https://pubs.rsc.org/en/content/articlehtml/2025/sc/d4sc07438f) (cited by 3)
1. N Tuchinda, CA Schuh (2025). [Grain Boundary Segregation and Embrittlement of Aluminum Binary Alloys from First Principles](https://arxiv.org/abs/2502.01579) (cited by 2)
1. A Onwuli, KT Butler, A Walsh (2024). [Ionic species representations for materials informatics](https://pubs.aip.org/aip/aml/article/2/3/036112/3313198) (cited by 2)
1. A Peng, MY Guo (2025). [The OpenLAM Challenges](https://arxiv.org/abs/2501.16358) (cited by 1)
1. F Therrien, JA Haibeh (2025). [OBELiX: A curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes](https://arxiv.org/abs/2502.14234) (cited by 1)
1. Aaron D. Kaplan, Runze Liu, Ji Qi et al. (2025). [A Foundational Potential Energy Surface Dataset for Materials](http://arxiv.org/abs/2503.04070)
1. Fei Shuang, Zixiong Wei, Kai Liu et al. (2025). [Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys](http://arxiv.org/abs/2502.03578)
1. Yingheng Tang, Wenbin Xu, Jie Cao et al. (2025). [MatterChat: A Multi-Modal LLM for Material Science](http://arxiv.org/abs/2502.13107)
1. Liming Wu, Wenbing Huang, Rui Jiao et al. (2025). [Siamese Foundation Models for Crystal Structure Prediction](http://arxiv.org/abs/2503.10471)
1. K Yan, M Bohde, A Kryvenko (2025). [A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures](https://arxiv.org/abs/2503.05771)
1. N Tuchinda, CA Schuh (2025). [A Grain Boundary Embrittlement Genome for Substitutional Cubic Alloys](https://arxiv.org/abs/2502.06531)
1. Daniel W. Davies, Keith T. Butler, Adam J. Jackson et al. (2024). [SMACT: Semiconducting Materials by Analogy and Chemical Theory](https://github.com/WMD-group/SMACT)
1. Hui Zheng, Eric Sivonxay, Rasmus Christensen et al. (2024). [The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity](https://www.nature.com/articles/s41524-024-01469-2)
1. HH Li, Q Chen, G Ceder (2024). [Voltage Mining for (De) lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage](https://pubs.acs.org/doi/abs/10.1021/acsami.4c15742)
1. Janosh Riebesell, Ilyes Batatia, Philipp Benner et al. (2023). [A foundation model for atomistic materials chemistry](https://arxiv.org/abs/2401.00096v1)
1. Jack Douglas Sundberg (2022). [A New Framework for Material Informatics and Its Application Toward Electride-Halide Material Systems](https://cdr.lib.unc.edu/concern/dissertations/r494vw405)
Raw data
{
"_id": null,
"home_page": null,
"name": "pymatviz",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "chemistry, crystallography, data analysis, data visualization, materials informatics, plotly",
"author": "Janosh Riebesell",
"author_email": "Janosh Riebesell <janosh.riebesell@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/e1/8a/e42640e6c17cc06a5ff1d65ff16edbba49cd2e3c0c6f68d75f2edc9da35e/pymatviz-0.17.1.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">\n<img src=\"https://github.com/janosh/pymatviz/raw/main/site/static/favicon.svg\" alt=\"Logo\" height=\"60px\">\n<br class=\"hide-in-docs\">\npymatviz\n</h1>\n\n<h4 align=\"center\" class=\"toc-exclude\">\n\nA toolkit for visualizations in materials informatics.\n\n[](https://github.com/janosh/pymatviz/actions/workflows/test.yml)\n[](https://python.org/downloads)\n[](https://pypi.org/project/pymatviz)\n[](https://codecov.io/gh/janosh/pymatviz)\n[](https://pypistats.org/packages/pymatviz)\n[](https://zenodo.org/records/10456384)\n\n</h4>\n\n[fig-icon]: https://api.iconify.design/lsicon:scatter-diagram-outline.svg?color=%234c8bf5&height=16 \"View example code\"\n\n<slot name=\"how-to-cite\">\n\n> If you use `pymatviz` in your research, [see how to cite](#how-to-cite-pymatviz). Check out [23 existing papers using `pymatviz`](#papers-using-pymatviz) for inspiration!\n\n</slot>\n\n## Installation\n\n```sh\npip install pymatviz\n```\n\nSee `pyproject.toml` for available extras like `pip install 'pymatviz[brillouin]'` to render 3d Brillouin zones.\n\n## API Docs\n\nSee the [/api][/api] page.\n\n[/api]: https://janosh.github.io/pymatviz/api\n\n## Usage\n\nSee the Jupyter notebooks under [`examples/`](examples) for how to use `pymatviz`. PRs with additional examples are welcome! \ud83d\ude4f\n\n| | | |\n| ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |\n| [matbench_dielectric_eda.ipynb](examples/matbench_dielectric_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_dielectric_eda.ipynb) | [Launch Codespace][codespace url] |\n| [mp_bimodal_e_form.ipynb](examples/mp_bimodal_e_form.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mp_bimodal_e_form.ipynb) | [Launch Codespace][codespace url] |\n| [matbench_perovskites_eda.ipynb](examples/matbench_perovskites_eda.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/matbench_perovskites_eda.ipynb) | [Launch Codespace][codespace url] |\n| [mprester_ptable.ipynb](examples/mprester_ptable.ipynb) | [![Open in Google Colab][Open in Google Colab]](https://colab.research.google.com/github/janosh/pymatviz/blob/main/examples/mprester_ptable.ipynb) | [Launch Codespace][codespace url] |\n\n[Open in Google Colab]: https://colab.research.google.com/assets/colab-badge.svg\n[codespace url]: https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=340898532\n\n## Periodic Table\n\nSee [`pymatviz/ptable/plotly.py`](pymatviz/ptable/plotly.py). The module supports heatmaps, heatmap splits (multiple values per element), histograms, scatter plots and line plots. All visualizations are interactive through [Plotly](https://plotly.com) and support displaying additional data on hover.\n\n> [!WARNING]\n> Version 0.16.0 of `pymatviz` dropped the matplotlib-based functions in `ptable_matplotlib.py` in https://github.com/janosh/pymatviz/pull/270. Please use the `plotly`-based functions shown below instead which have feature parity, interactivity and better test coverage.\n\n| [`ptable_heatmap_plotly(atomic_masses)`](pymatviz/ptable/plotly.py#L38) | [`ptable_heatmap_plotly(compositions, log=True)`](pymatviz/ptable/plotly.py#L38) [![fig-icon]](assets/scripts/ptable/ptable_heatmap_plotly.py) |\n| :----------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![ptable-heatmap-plotly-more-hover-data] | ![ptable-heatmap-plotly-log] |\n| [`ptable_hists_plotly(data)`](pymatviz/ptable/plotly.py#L398) [![fig-icon]](assets/scripts/ptable/ptable_hists_plotly.py) | [`ptable_scatter_plotly(data, mode=\"markers\")`](pymatviz/ptable/plotly.py#L1359) [![fig-icon]](assets/scripts/ptable/ptable_scatter_plotly.py) |\n| ![ptable-hists-plotly] | ![ptable-scatter-plotly-markers] |\n| [`ptable_heatmap_splits_plotly(2_vals_per_elem)`](pymatviz/ptable/plotly.py#L778) [![fig-icon]](assets/scripts/ptable/ptable_heatmap_splits_plotly.py) | [`ptable_heatmap_splits_plotly(3_vals_per_elem)`](pymatviz/ptable/plotly.py#L778) |\n| ![ptable-heatmap-splits-plotly-2] | ![ptable-heatmap-splits-plotly-3] |\n\n[ptable-heatmap-plotly-log]: assets/svg/ptable-heatmap-plotly-log.svg\n[ptable-heatmap-plotly-more-hover-data]: assets/svg/ptable-heatmap-plotly-more-hover-data.svg\n[ptable-heatmap-splits-plotly-2]: assets/svg/ptable-heatmap-splits-plotly-2.svg\n[ptable-heatmap-splits-plotly-3]: assets/svg/ptable-heatmap-splits-plotly-3.svg\n[ptable-hists-plotly]: assets/svg/ptable-hists-plotly.svg\n[ptable-scatter-plotly-markers]: assets/svg/ptable-scatter-plotly-markers.svg\n\n### Dash app using `ptable_heatmap_plotly()`\n\nSee [`examples/mprester_ptable.ipynb`](examples/mprester_ptable.ipynb).\n\n[https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4](https://user-images.githubusercontent.com/30958850/181644052-b330f0a2-70fc-451c-8230-20d45d3af72f.mp4)\n\n## Phonons\n\n| [`phonon_bands(bands_dict)`](pymatviz/phonons/plotly.py#L35) [![fig-icon]](assets/scripts/phonons/phonon_bands.py) | [`phonon_dos(doses_dict)`](pymatviz/phonons/plotly.py#L381) [![fig-icon]](assets/scripts/phonons/phonon_dos.py) |\n| :----------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![phonon-bands] | ![phonon-dos] |\n| [`phonon_bands_and_dos(bands_dict, doses_dict)`](pymatviz/phonons/plotly.py#L528) | [`phonon_bands_and_dos(single_bands, single_dos)`](pymatviz/phonons/plotly.py#L528) [![fig-icon]](assets/scripts/phonons/phonon_bands_and_dos.py) |\n| ![phonon-bands-and-dos-mp-2758] | ![phonon-bands-and-dos-mp-23907] |\n\n[phonon-bands]: assets/svg/phonon-bands-mp-2758.svg\n[phonon-dos]: assets/svg/phonon-dos-mp-2758.svg\n[phonon-bands-and-dos-mp-2758]: assets/svg/phonon-bands-and-dos-mp-2758.svg\n[phonon-bands-and-dos-mp-23907]: assets/svg/phonon-bands-and-dos-mp-23907.svg\n\n### Composition Clustering\n\n| [`cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=2)`](pymatviz/cluster/composition/plot.py#L32) [![fig-icon]](assets/scripts/cluster/composition/cluster_compositions_matbench.py) | [`cluster_compositions(compositions, properties, embedding_method, projection_method, n_components=3)`](pymatviz/cluster/composition/plot.py#L32) |\n| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![matbench-perovskites-magpie-pca-2d] | ![matbench-perovskites-magpie-tsne-3d] |\n\n[matbench-perovskites-magpie-pca-2d]: assets/svg/matbench-perovskites-magpie-pca-2d.svg\n[matbench-perovskites-magpie-tsne-3d]: assets/svg/matbench-perovskites-magpie-tsne-3d.svg\n\nVisualize 2D or 3D relationships between compositions and properties using multiple embedding and dimensionality reduction techniques:\n\nEmbedding methods: **One-hot** encoding of element fractions, **Magpie** features (elemental properties), **Matscholar** element embeddings, **MEGNet** element embeddings\n\nDimensionality reduction methods: **PCA** (linear), **t-SNE** (non-linear), **UMAP** (non-linear), **Isomap** (non-linear), **Kernel PCA** (non-linear)\n\nExample usage:\n\n```py\nimport pymatviz as pmv\nfrom pymatgen.core import Composition\n\ncompositions = (\"Fe2O3\", \"Al2O3\", \"SiO2\", \"TiO2\")\n\n# Create embeddings\nembeddings = pmv.cluster.composition.one_hot_encode(compositions)\ncomp_emb_map = dict(zip(compositions, embeddings, strict=True))\n\n# Plot with optional property coloring\nfig = pmv.cluster_compositions(\n compositions=comp_emb_map,\n properties=[1.0, 2.0, 3.0, 4.0], # Optional property values\n prop_name=\"Property\", # Optional property label\n embedding_method=\"one-hot\", # or \"magpie\", \"matscholar_el\", \"megnet_el\", etc.\n projection_method=\"pca\", # or \"tsne\", \"umap\", \"isomap\", \"kernel_pca\", etc.\n show_chem_sys=\"shape\", # works best for small number of compositions; \"color\" | \"shape\" | \"color+shape\" | None\n n_components=2, # or 3 for 3D plots\n)\nfig.show()\n```\n\n## Structure Clustering\n\nOn the roadmap but no ETA yet.\n\n## Structure\n\nSee [`pymatviz/structure/plotly.py`](pymatviz/structure/plotly.py).\n\n| [`structure_3d(hea_structure)`](pymatviz/structure/plotly.py#L318) | [`structure_3d(lco_supercell)`](pymatviz/structure/plotly.py#L318) [![fig-icon]](assets/scripts/structure/structure_3d.py) |\n| :---------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------: |\n| ![hea-structure-3d] | ![lco-structure-3d] |\n| [`structure_2d(six_structs)`](pymatviz/structure/plotly.py#L42) [![fig-icon]](assets/scripts/structure/structure_2d.py) | [`structure_3d(six_structs)`](pymatviz/structure/plotly.py#L318) [![fig-icon]](assets/scripts/structure/structure_3d.py) |\n| ![matbench-phonons-structures-2d] | ![matbench-phonons-structures-3d] |\n\n[matbench-phonons-structures-2d]: assets/svg/matbench-phonons-structures-2d.svg\n[matbench-phonons-structures-3d]: assets/svg/matbench-phonons-structures-3d.svg\n[hea-structure-3d]: assets/svg/hea-structure-3d.svg\n[lco-structure-3d]: assets/svg/lco-structure-3d.svg\n\n## Interactive Widgets\n\nSee [`pymatviz/widgets`](pymatviz/widgets). Interactive 3D structure, molecular dynamics trajectory and composition visualization widgets for [Jupyter](https://jupyter.org), [Marimo](https://marimo.io), and VSCode notebooks, powered by [anywidget](https://anywidget.dev) and [MatterViz](https://matterviz.janosh.dev) (<https://github.com/janosh/matterviz>). Supports pymatgen `Structure`, ASE `Atoms`, and `PhonopyAtoms`, as well as ASE, `pymatgen` and plain Python trajectory formats.\n\n```py\nfrom pymatviz import StructureWidget, CompositionWidget, TrajectoryWidget\nfrom pymatgen.core import Structure, Composition\n\n# Interactive 3D structure visualization\nstructure = Structure.from_file(\"structure.cif\")\nstruct_widget = StructureWidget(structure=structure)\n\n# Interactive composition visualization\ncomposition = Composition(\"Fe2O3\")\ncomp_widget = CompositionWidget(composition=composition)\n\n# Interactive trajectory visualization\ntrajectory1 = [struct1, struct2, struct3] # List of structures\ntraj_widget1 = TrajectoryWidget(trajectory=trajectory1)\n\ntrajectory2 = [{\"structure\": struct1, \"energy\": 1.0}, {\"structure\": struct2, \"energy\": 2.0}, {\"structure\": struct3, \"energy\": 3.0}] # dicts with \"structure\" and property values\ntraj_widget2 = TrajectoryWidget(trajectory=trajectory2)\n```\n\n**Examples:**\n\n- [Jupyter notebook demo](examples/widgets/jupyter_demo.ipynb)\n- [Marimo demo](examples/widgets/marimo_demo.py)\n- [VSCode interactive demo](examples/widgets/vscode_interactive_demo.py)\n\n> [!TIP]\n> Checkout the **\u2705 MatterViz VSCode extension** for using the same viewers directly in VSCode/Cursor editor tabs for rendering local and remote files: [marketplace.visualstudio.com/items?itemName=janosh.matterviz](https://marketplace.visualstudio.com/items?itemName=janosh.matterviz)\n\nImporting `pymatviz` auto-registers all widgets for their respective sets of supported objects via `register_matterviz_widgets()`. To customize the registration, use [`set_renderer()`](pymatviz/notebook.py).\n\n## Brillouin Zone\n\nSee [`pymatviz/brillouin.py`](pymatviz/brillouin.py).\n\n| [`brillouin_zone_3d(cubic_struct)`](pymatviz/brillouin.py#L15) [![fig-icon]](assets/scripts/brillouin/brillouin_zone_3d.py) | [`brillouin_zone_3d(hexagonal_struct)`](pymatviz/brillouin.py#L15) |\n| :------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------: |\n| ![brillouin-cubic-mp-10018] | ![brillouin-hexagonal-mp-862690] |\n| [`brillouin_zone_3d(monoclinic_struct)`](pymatviz/brillouin.py#L15) [![fig-icon]](assets/scripts/brillouin/brillouin_zone_3d.py) | [`brillouin_zone_3d(orthorhombic_struct)`](pymatviz/brillouin.py#L15) |\n| ![brillouin-monoclinic-mp-1183089] | ![brillouin-volumes-3-cols] |\n\n[brillouin-cubic-mp-10018]: assets/svg/brillouin-cubic-mp-10018.svg\n[brillouin-hexagonal-mp-862690]: assets/svg/brillouin-hexagonal-mp-862690.svg\n[brillouin-monoclinic-mp-1183089]: assets/svg/brillouin-monoclinic-mp-1183089.svg\n[brillouin-orthorhombic-mp-1183085]: assets/svg/brillouin-orthorhombic-mp-1183085.svg\n[brillouin-volumes-3-cols]: assets/svg/brillouin-volumes-3-cols.svg\n\n## X-Ray Diffraction\n\nSee [`pymatviz/xrd.py`](pymatviz/xrd.py).\n\n| [`xrd_pattern(pattern)`](pymatviz/xrd.py#L42) [![fig-icon]](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern({key1: patt1, key2: patt2})`](pymatviz/xrd.py#L42) |\n| :--------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------: |\n| ![xrd-pattern] | ![xrd-pattern-multiple] |\n| [`xrd_pattern(struct_dict, stack=\"horizontal\")`](pymatviz/xrd.py#L42) [![fig-icon]](assets/scripts/xrd/xrd_pattern.py) | [`xrd_pattern(struct_dict, stack=\"vertical\")`](pymatviz/xrd.py#L42) |\n| ![xrd-pattern-horizontal-stack] | ![xrd-pattern-vertical-stack] |\n\n[xrd-pattern]: assets/svg/xrd-pattern.svg\n[xrd-pattern-multiple]: assets/svg/xrd-pattern-multiple.svg\n[xrd-pattern-horizontal-stack]: assets/svg/xrd-pattern-horizontal-stack.svg\n[xrd-pattern-vertical-stack]: assets/svg/xrd-pattern-vertical-stack.svg\n\n## Radial Distribution Functions\n\nSee [`pymatviz/rdf/plotly.py`](pymatviz/rdf/plotly.py).\n\n| [`element_pair_rdfs(pmg_struct)`](pymatviz/rdf/plotly.py#L33) | [`element_pair_rdfs({\"A\": struct1, \"B\": struct2})`](pymatviz/rdf/plotly.py#L33) [![fig-icon]](assets/scripts/rdf/element_pair_rdfs.py) |\n| :-----------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------: |\n| ![element-pair-rdfs-Na8Nb8O24] | ![element-pair-rdfs-crystal-vs-amorphous] |\n\n[element-pair-rdfs-Na8Nb8O24]: assets/svg/element-pair-rdfs-Na8Nb8O24.svg\n[element-pair-rdfs-crystal-vs-amorphous]: assets/svg/element-pair-rdfs-crystal-vs-amorphous.svg\n\n## Coordination\n\nSee [`pymatviz/coordination/plotly.py`](pymatviz/coordination/plotly.py).\n\n| [`coordination_hist(struct_dict)`](pymatviz/coordination/plotly.py#L34) | [`coordination_hist(struct_dict, by_element=True)`](pymatviz/coordination/plotly.py#L34) [![fig-icon]](assets/scripts/coordination/coordination_hist.py) |\n| :-----------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![coordination-hist-single] | ![coordination-hist-by-structure-and-element] |\n| [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py#L337) | [`coordination_vs_cutoff_line(struct_dict, strategy=None)`](pymatviz/coordination/plotly.py#L337) [![fig-icon]](assets/scripts/coordination/coordination_vs_cutoff_line.py#L52) |\n| ![coordination-vs-cutoff-single] | ![coordination-vs-cutoff-multiple] |\n\n[coordination-hist-single]: assets/svg/coordination-hist-single.svg\n[coordination-hist-by-structure-and-element]: assets/svg/coordination-hist-by-structure-and-element.svg\n[coordination-vs-cutoff-single]: assets/svg/coordination-vs-cutoff-single.svg\n[coordination-vs-cutoff-multiple]: assets/svg/coordination-vs-cutoff-multiple.svg\n\n## Sunburst\n\nSee [`pymatviz/sunburst.py`](pymatviz/sunburst.py).\n\n| [`spacegroup_sunburst([65, 134, 225, ...])`](pymatviz/sunburst.py#L111) [![fig-icon]](assets/scripts/sunburst/spacegroup_sunburst.py) | [`chem_sys_sunburst([\"FeO\", \"Fe2O3\", \"LiPO4\", ...])`](pymatviz/sunburst.py#L206) [![fig-icon]](assets/scripts/sunburst/chem_sys_sunburst.py) |\n| :-----------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![spg-num-sunburst] | ![chem-sys-sunburst-ward-bmg] |\n| [`chem_env_sunburst(single_struct)`](pymatviz/sunburst.py#L351) | [`chem_env_sunburst(multiple_structs)`](pymatviz/sunburst.py#L351) |\n| ![chem-env-sunburst-basic] | ![chem-env-sunburst-mp-carbon] |\n\n[spg-num-sunburst]: assets/svg/spg-num-sunburst.svg\n[chem-sys-sunburst-ward-bmg]: assets/svg/chem-sys-sunburst-ward-bmg.svg\n[chem-env-sunburst-basic]: assets/svg/chem-env-sunburst-basic.svg\n[chem-env-sunburst-mp-carbon]: assets/svg/chem-env-sunburst-mp-carbon.svg\n\n## Treemap\n\nSee [`pymatviz/treemap/chem_sys.py`](pymatviz/treemap/chem_sys.py).\n\n| [`chem_sys_treemap([\"FeO\", \"Fe2O3\", \"LiPO4\", ...])`](pymatviz/treemap/chem_sys.py#L36) [![fig-icon]](assets/scripts/treemap/chem_sys_treemap.py) | [`chem_sys_treemap([\"FeO\", \"Fe2O3\", \"LiPO4\", ...], group_by=\"formula\")`](pymatviz/treemap/chem_sys.py#L36) |\n| :----------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------: |\n| ![chem-sys-treemap-formula] | ![chem-sys-treemap-ward-bmg] |\n| [`chem_env_treemap(structures)`](pymatviz/treemap/chem_env.py#L36) [![fig-icon]](assets/scripts/treemap/chem_env_treemap.py) | [`chem_env_treemap(structures, max_cells_cn=3, max_cells_ce=4)`](pymatviz/treemap/chem_env.py#L36) |\n| ![chem-env-treemap-basic] | ![chem-env-treemap-large-dataset] |\n| [`py_pkg_treemap(\"pymatviz\")`](pymatviz/treemap/py_pkg.py#L36) [![fig-icon]](assets/scripts/treemap/py_pkg_treemap.py) | [`py_pkg_treemap([\"pymatviz\", \"torch_sim\", \"pymatgen\"])`](pymatviz/treemap/py_pkg.py#L36) |\n| ![py-pkg-treemap-pymatviz] | ![py-pkg-treemap-multiple] |\n\n[chem-sys-treemap-formula]: assets/svg/chem-sys-treemap-formula.svg\n[chem-sys-treemap-ward-bmg]: assets/svg/chem-sys-treemap-ward-bmg.svg\n[py-pkg-treemap-pymatviz]: assets/svg/py-pkg-treemap-pymatviz.svg\n[py-pkg-treemap-multiple]: assets/svg/py-pkg-treemap-multiple.svg\n[chem-env-treemap-large-dataset]: assets/svg/chem-env-treemap-large-dataset.svg\n[chem-env-treemap-basic]: assets/svg/chem-env-treemap-basic.svg\n\n## Rainclouds\n\nSee [`pymatviz/rainclouds.py`](pymatviz/rainclouds.py).\n\n| [`rainclouds(two_key_dict)`](pymatviz/rainclouds.py#L20) [![fig-icon]](assets/scripts/rainclouds/rainclouds.py) | [`rainclouds(three_key_dict)`](pymatviz/rainclouds.py#L20) |\n| :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------: |\n| ![rainclouds-bimodal] | ![rainclouds-trimodal] |\n\n[rainclouds-bimodal]: assets/svg/rainclouds-bimodal.svg\n[rainclouds-trimodal]: assets/svg/rainclouds-trimodal.svg\n\n## Sankey\n\nSee [`pymatviz/sankey.py`](pymatviz/sankey.py).\n\n| [`sankey_from_2_df_cols(df_perovskites)`](pymatviz/sankey.py#L16) [![fig-icon]](assets/scripts/sankey/sankey_from_2_df_cols.py) | [`sankey_from_2_df_cols(df_space_groups)`](pymatviz/sankey.py#L16) |\n| :-----------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------: |\n| ![sankey-spglib-vs-aflow-spacegroups] | ![sankey-crystal-sys-to-spg-symbol] |\n\n[sankey-spglib-vs-aflow-spacegroups]: assets/svg/sankey-spglib-vs-aflow-spacegroups.svg\n[sankey-crystal-sys-to-spg-symbol]: assets/svg/sankey-crystal-sys-to-spg-symbol.svg\n\n## Bar Plots\n\nSee [`pymatviz/bar.py`](pymatviz/bar.py).\n\n| [`spacegroup_bar([65, 134, 225, ...])`](pymatviz/bar.py#L29) [![fig-icon]](assets/scripts/bar/spacegroup_bar.py) | [`spacegroup_bar([\"C2/m\", \"P-43m\", \"Fm-3m\", ...])`](pymatviz/bar.py#L29) |\n| :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------: |\n| ![spg-num-hist-plotly] | ![spg-symbol-hist-plotly] |\n\n[spg-symbol-hist-plotly]: assets/svg/spg-symbol-hist-plotly.svg\n[spg-num-hist-plotly]: assets/svg/spg-num-hist-plotly.svg\n\n## Histograms\n\nSee [`pymatviz/histogram.py`](pymatviz/histogram.py).\n\n| [`elements_hist(compositions, log=True, bar_values='count')`](pymatviz/histogram.py#L37) [![fig-icon]](assets/scripts/histogram/elements_hist.py) | [`histogram({'key1': values1, 'key2': values2})`](pymatviz/histogram.py#L108) [![fig-icon]](assets/scripts/histogram/histogram.py) |\n| :-----------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |\n| ![elements-hist] | ![histogram-ecdf] |\n\n[histogram-ecdf]: assets/svg/histogram-ecdf.svg\n\n## Scatter Plots\n\nSee [`pymatviz/scatter.py`](pymatviz/scatter.py).\n\n| [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py#L149) | [`density_scatter_plotly(df, x=x_col, y=y_col, ...)`](pymatviz/scatter.py#L149) [![fig-icon]](assets/scripts/scatter/density_scatter_plotly.py) |\n| :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![density-scatter-plotly] | ![density-scatter-plotly-blobs] |\n| [`density_scatter(xs, ys, ...)`](pymatviz/scatter.py#L71) [![fig-icon]](assets/scripts/scatter/density_scatter.py) | [`density_scatter_with_hist(xs, ys, ...)`](pymatviz/scatter.py#L570) [![fig-icon]](assets/scripts/scatter/density_scatter.py) |\n| ![density-scatter] | ![density-scatter-with-hist] |\n| [`density_hexbin(xs, ys, ...)`](pymatviz/scatter.py#L493) [![fig-icon]](assets/scripts/scatter/density_hexbin.py) | [`density_hexbin_with_hist(xs, ys, ...)`](pymatviz/scatter.py#L587) [![fig-icon]](assets/scripts/scatter/density_hexbin.py) |\n| ![density-hexbin] | ![density-hexbin-with-hist] |\n\n[density-scatter-plotly]: assets/svg/density-scatter-plotly.svg\n[density-scatter-plotly-blobs]: assets/svg/density-scatter-plotly-blobs.svg\n[density-hexbin-with-hist]: assets/svg/density-hexbin-with-hist.svg\n[density-hexbin]: assets/svg/density-hexbin.svg\n[density-scatter-with-hist]: assets/svg/density-scatter-with-hist.svg\n[density-scatter]: assets/svg/density-scatter.svg\n\n## Uncertainty\n\nSee [`pymatviz/uncertainty.py`](pymatviz/uncertainty.py).\n\n| [`qq_gaussian(y_true, y_pred, y_std)`](pymatviz/uncertainty.py#L27) [![fig-icon]](assets/scripts/uncertainty/qq_gaussian.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py#L27) |\n| :---------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: |\n| ![qq-gaussian] | ![qq-gaussian-multiple] |\n| [`error_decay_with_uncert(y_true, y_pred, y_std)`](pymatviz/uncertainty.py#L208) [![fig-icon]](assets/scripts/uncertainty/error_decay_with_uncert.py) | [`error_decay_with_uncert(y_true, y_pred, y_std: dict)`](pymatviz/uncertainty.py#L208) |\n| ![error-decay-with-uncert] | ![error-decay-with-uncert-multiple] |\n\n## Classification\n\nSee [`pymatviz/classify/confusion_matrix.py`](pymatviz/classify/confusion_matrix.py).\n\n| [`confusion_matrix(conf_mat, ...)`](pymatviz/classify/confusion_matrix.py#L10) | [`confusion_matrix(y_true, y_pred, ...)`](pymatviz/classify/confusion_matrix.py#L10) [![fig-icon]](assets/scripts/classify/confusion_matrix.py) |\n| :----------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![stability-confusion-matrix] | ![crystal-system-confusion-matrix] |\n\nSee [`pymatviz/classify/curves.py`](pymatviz/classify/curves.py).\n\n| [`roc_curve_plotly(targets, probs_positive)`](pymatviz/classify/curves.py#L76) [![fig-icon]](assets/scripts/classify/roc_curve.py) | [`precision_recall_curve_plotly(targets, probs_positive)`](pymatviz/classify/curves.py#L176) [![fig-icon]](assets/scripts/classify/precision_recall_curve.py) |\n| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------: |\n| ![roc-curve-plotly-multiple] | ![precision-recall-curve-plotly-multiple] |\n\n[roc-curve-plotly-multiple]: assets/svg/roc-curve-plotly-multiple.svg\n[precision-recall-curve-plotly-multiple]: assets/svg/precision-recall-curve-plotly-multiple.svg\n[stability-confusion-matrix]: assets/svg/stability-confusion-matrix.svg\n[crystal-system-confusion-matrix]: assets/svg/crystal-system-confusion-matrix.svg\n[error-decay-with-uncert-multiple]: assets/svg/error-decay-with-uncert-multiple.svg\n[error-decay-with-uncert]: assets/svg/error-decay-with-uncert.svg\n[elements-hist]: assets/svg/elements-hist.svg\n[qq-gaussian-multiple]: assets/svg/qq-gaussian-multiple.svg\n[qq-gaussian]: assets/svg/qq-gaussian.svg\n\n## How to cite `pymatviz`\n\nSee [`citation.cff`](citation.cff) or cite the [Zenodo record](https://zenodo.org/badge/latestdoi/340898532) using the following BibTeX entry:\n\n```bib\n@software{riebesell_pymatviz_2022,\n title = {Pymatviz: visualization toolkit for materials informatics},\n author = {Riebesell, Janosh and Yang, Haoyu and Goodall, Rhys and Baird, Sterling G.},\n date = {2022-10-01},\n year = {2022},\n doi = {10.5281/zenodo.7486816},\n url = {https://github.com/janosh/pymatviz},\n note = {10.5281/zenodo.7486816 - https://github.com/janosh/pymatviz},\n urldate = {2023-01-01}, % optional, replace with your date of access\n version = {0.8.2}, % replace with the version you use\n}\n```\n\n## Papers using `pymatviz`\n\nSorted by number of citations, then year. Last updated 2025-05-07. Auto-generated [from Google Scholar](https://scholar.google.com/scholar?q=pymatviz). Manual additions [via PR](https://github.com/janosh/pymatviz/edit/main/readme.md) welcome.\n\n1. C Zeni, R Pinsler, D Z\u00fcgner et al. (2023). [Mattergen: a generative model for inorganic materials design](https://arxiv.org/abs/2312.03687) (cited by 134)\n1. J Riebesell, REA Goodall, P Benner et al. (2023). [Matbench Discovery--A framework to evaluate machine learning crystal stability predictions](https://arxiv.org/abs/2308.14920) (cited by 53)\n1. L Barroso-Luque, M Shuaibi, X Fu et al. (2024). [Open materials 2024 (omat24) inorganic materials dataset and models](https://www.rivista.ai/wp-content/uploads/2024/10/2410.12771v1.pdf) (cited by 48)\n1. C Chen, DT Nguyen, SJ Lee et al. (2024). [Accelerating computational materials discovery with machine learning and cloud high-performance computing: from large-scale screening to experimental validation](https://pubs.acs.org/doi/abs/10.1021/jacs.4c03849) (cited by 43)\n1. M Giantomassi, G Materzanini (2024). [Systematic assessment of various universal machine\u2010learning interatomic potentials](https://onlinelibrary.wiley.com/doi/abs/10.1002/mgea.58) (cited by 22)\n1. AA Naik, C Ertural, P Benner et al. (2023). [A quantum-chemical bonding database for solid-state materials](https://www.nature.com/articles/s41597-023-02477-5) (cited by 15)\n1. K Li, AN Rubungo, X Lei et al. (2025). [Probing out-of-distribution generalization in machine learning for materials](https://www.nature.com/articles/s43246-024-00731-w) (cited by 9)\n1. A Kapeliukha, RA Mayo (2025). [MOSAEC-DB: a comprehensive database of experimental metal\u2013organic frameworks with verified chemical accuracy suitable for molecular simulations](https://pubs.rsc.org/en/content/articlehtml/2025/sc/d4sc07438f) (cited by 3)\n1. N Tuchinda, CA Schuh (2025). [Grain Boundary Segregation and Embrittlement of Aluminum Binary Alloys from First Principles](https://arxiv.org/abs/2502.01579) (cited by 2)\n1. A Onwuli, KT Butler, A Walsh (2024). [Ionic species representations for materials informatics](https://pubs.aip.org/aip/aml/article/2/3/036112/3313198) (cited by 2)\n1. A Peng, MY Guo (2025). [The OpenLAM Challenges](https://arxiv.org/abs/2501.16358) (cited by 1)\n1. F Therrien, JA Haibeh (2025). [OBELiX: A curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes](https://arxiv.org/abs/2502.14234) (cited by 1)\n1. Aaron D. Kaplan, Runze Liu, Ji Qi et al. (2025). [A Foundational Potential Energy Surface Dataset for Materials](http://arxiv.org/abs/2503.04070)\n1. Fei Shuang, Zixiong Wei, Kai Liu et al. (2025). [Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys](http://arxiv.org/abs/2502.03578)\n1. Yingheng Tang, Wenbin Xu, Jie Cao et al. (2025). [MatterChat: A Multi-Modal LLM for Material Science](http://arxiv.org/abs/2502.13107)\n1. Liming Wu, Wenbing Huang, Rui Jiao et al. (2025). [Siamese Foundation Models for Crystal Structure Prediction](http://arxiv.org/abs/2503.10471)\n1. K Yan, M Bohde, A Kryvenko (2025). [A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures](https://arxiv.org/abs/2503.05771)\n1. N Tuchinda, CA Schuh (2025). [A Grain Boundary Embrittlement Genome for Substitutional Cubic Alloys](https://arxiv.org/abs/2502.06531)\n1. Daniel W. Davies, Keith T. Butler, Adam J. Jackson et al. (2024). [SMACT: Semiconducting Materials by Analogy and Chemical Theory](https://github.com/WMD-group/SMACT)\n1. Hui Zheng, Eric Sivonxay, Rasmus Christensen et al. (2024). [The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity](https://www.nature.com/articles/s41524-024-01469-2)\n1. HH Li, Q Chen, G Ceder (2024). [Voltage Mining for (De) lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage](https://pubs.acs.org/doi/abs/10.1021/acsami.4c15742)\n1. Janosh Riebesell, Ilyes Batatia, Philipp Benner et al. (2023). [A foundation model for atomistic materials chemistry](https://arxiv.org/abs/2401.00096v1)\n1. Jack Douglas Sundberg (2022). [A New Framework for Material Informatics and Its Application Toward Electride-Halide Material Systems](https://cdr.lib.unc.edu/concern/dissertations/r494vw405)\n",
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"license": "MIT License Copyright (c) 2021 Janosh Riebesell Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided \"as is\", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.",
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