If you use
pymatvizin your research, see how to cite. Check out 23 existing papers usingpymatvizfor inspiration!
See pyproject.toml for available extras like pip install 'pymatviz[brillouin]' to render 3d Brillouin zones.
See the /api page.
See the Jupyter notebooks under examples/ for how to use pymatviz. PRs with additional examples are welcome! š
See 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 and support displaying additional data on hover.
Warning
Version 0.16.0 of pymatviz dropped the matplotlib-based functions in ptable_matplotlib.py in #270. Please use the plotly-based functions shown below instead which have feature parity, interactivity and better test coverage.
ptable_heatmap_plotly()
See examples/mprester_ptable.ipynb.
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:
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()
On the roadmap but no ETA yet.
See pymatviz/structure/plotly.py.
See pymatviz/xrd.py.
See pymatviz/coordination/plotly.py.
See pymatviz/sunburst.py.
See pymatviz/treemap/chem_sys.py.
chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...]) chem_sys_treemap(["FeO", "Fe2O3", "LiPO4", ...], group_by="formula") chem_env_treemap(structures) chem_env_treemap(structures, max_cells_cn=3, max_cells_ce=4) py_pkg_treemap("pymatviz") py_pkg_treemap(["pymatviz", "torch_sim", "pymatgen"])
See pymatviz/sankey.py.
See pymatviz/bar.py.
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, ...)
See pymatviz/classify/confusion_matrix.py.
See pymatviz/classify/curves.py.
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
}
Sorted by number of citations, then year. Last updated 2025-05-07. Auto-generated from Google Scholar. Manual additions via PR welcome.
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