scipy-stubs

Type annotations for SciPy

41 个版本 Python >=3.12
Joren Hammudoglu <Joren Hammudoglu <jhammudoglu@gmail.com>>
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安装
pip install scipy-stubs
poetry add scipy-stubs
pipenv install scipy-stubs
conda install scipy-stubs
描述

scipy-stubs

The official type stubs for SciPy.

GitHub License PyPI Version Conda Version Python Versions PyPI Downloads

Used by Pandas, JAX, Apache Spark, Astropy, Ultralytics, Altair, and many other projects.

Features

  • Complete coverage: the entire public SciPy API is annotated.
  • Shape- and dtype-aware: array shapes and dtypes are tracked where applicable.
  • Generics: subscriptable types for sparse arrays, distributions, splines, and linear operators.
  • No configuration: works on install, affects type checking only, with no runtime impact.
  • Broad checker support: tested against mypy, pyright, basedpyright, pyrefly, and ty.
  • Thoroughly tested: stubtest against the runtime, assert_type tests for inference, and mypy_primer for regressions.

Examples

Accurate type hints for shapes and dtypes

precise type inference demo

Prevent mistakes with precise type hints

bug prevention demo

Installation

The source code is hosted on GitHub at github.com/scipy/scipy-stubs.

Binary distributions are available on PyPI and conda-forge.

Using pip (PyPI)

To install from the PyPI, run:

pip install scipy-stubs

In case you haven't installed scipy yet, both can be installed with:

pip install scipy-stubs[scipy]

Using conda (conda-forge)

To install using Conda from the conda-forge channel, run:

conda install conda-forge::scipy-stubs

It's also possible to install both scipy and scipy-stubs together through the bundled scipy-typed package:

conda install conda-forge::scipy-typed

Why is scipy-stubs a separate package?

Here are some of the key reasons why scipy-stubs is maintained as a separate package rather than being integrated into SciPy itself.

Separation of concerns

If scipy-stubs were part of SciPy, every contributor would need to be familiar with type stubs. Some areas of scipy-stubs get intricate, so keeping them in good shape goes more smoothly when you’re comfortable with how the typing pieces interact (example). Requiring all SciPy developers to become typing experts would likely reduce overall willingness to contribute. By keeping stubs separate, SciPy contributors can focus on scientific functionality while typing specialists handle the stubs, with each group reviewing changes in its domain of expertise.

Opt-in by design

A significant portion of SciPy users don’t use type checking. Without scipy-stubs installed, type checkers won’t analyze SciPy-related code. Some patterns may be technically type-unsafe while still working fine in practice, and installing scipy-stubs would flag these cases. By keeping stubs separate, users retain full control over whether to enable type checking for their SciPy code.

Independent release cycles

The scipy-stubs versioning scheme uses four numbers: {scipy_version}.{stubs_version}. The first three match the SciPy semver version, while the fourth is specific to stub releases. Since stub releases occur more frequently than SciPy releases, this independence allows typing improvements and bug fixes to ship without waiting for the next SciPy version.

CI efficiency

The SciPy CI pipeline is large and takes over 30 minutes to run. The scipy-stubs CI is lightweight and finishes in under 4 minutes. This difference has a real impact on development workflow, enabling quicker iteration and faster delivery of changes. The speed gap exists mainly because scipy-stubs doesn’t require compilation - it can rely on SciPy’s pre-built wheels.

Frequently Asked Questions

Q: What static type-checkers are supported?

A: scipy-stubs is tested against the most recent stable versions of

Q: Do I need to change my existing code?

A: No! scipy-stubs works with your existing code without any modifications. Just install it and your type checker and IDE will automatically use the type information.

Q: How much of SciPy is covered?

A: All of it! If you find any missing or incorrect type annotations, please open an issue on GitHub.

Q: Can I use this with Jupyter notebooks?

A: Yes! Most modern Jupyter environments (JupyterLab, VSCode notebooks) support type checking and will benefit from scipy-stubs.

Q: What if I don't use type hints in my code?

A: You'll still benefit! Your IDE will provide better autocompletion and error detection even without explicit type annotations in your code.

Q: How do I know if it's working?

A: You should see improved autocompletion in your IDE and more precise type information. You can also run mypy, pyright or another type checker on your code to see type checking in action.

Q: Is this vibe-coded?

A: No.

Versioning and requirements

The versioning scheme of scipy-stubs includes the compatible scipy version as {scipy_version}.{stubs_version}. Even though scipy-stubs doesn't enforce an upper bound on the scipy version, later scipy versions aren't guaranteed to be fully compatible.

There are no additional restrictions enforced by scipy-stubs on the numpy requirements. For scipy-stubs==1.18.* that is numpy >= 2.0.0.

Currently, scipy-stubs has one required dependency: optype. This is essential for scipy-stubs to work properly, as it relies heavily on it for annotating (shaped) array-likes, scalar-likes, shape-typing in general, and much more. At the moment, scipy-stubs requires the latest version optype.

The exact version requirements are specified in the pyproject.toml.

Generics

scipy-stubs provides many generic classes that enable precise type checking for SciPy's complex APIs.

All generic type parameters are optional and can be omitted if not needed.

Note that not all classes are subscriptable at runtime, as that requires the __class_getitem__ method to be implemented in scipy. This can be worked around with from __future__ import annotations or by manually stringifying the generic annotations. We are working on improving this in future versions of SciPy. See the scipy columns below for which classes are subscriptable at runtime.

Browse all generic types by module

scipy.integrate

generic type scipy-stubs scipy
BDF[T: f64 | c128] >=1.14.0.1 >=1.17 docs
DOP853[T: f64 | c128] >=1.14.0.1 >=1.17 docs
RK23[T: f64 | c128] >=1.14.0.1 >=1.17 docs
RK45[T: f64 | c128] >=1.14.0.1 >=1.17 docs
OdeSolver[T: f64 | c128] >=1.16.2.1 >=1.17 docs
DenseOutput[T: inexact] >=1.16.2.1 >=1.17 docs
ode[T: f64 | c128, *ArgTs] >=1.16.0.3 >=1.17 docs
complex_ode[*ArgTs] >=1.14.0.0 >=1.17 docs

scipy.interpolate

generic type scipy-stubs scipy
AAA[T: inexact] >=1.15.0.0 >=1.17 docs
BarycentricInterpolator[T: f64 | c128] >=1.16.0.1 >=1.17 docs
BPoly[T: f64 | c128] >=1.14.1.4 >=1.17 docs
BSpline[T: f64 | c128] >=1.14.1.6 >=1.17 docs
CubicHermiteSpline[T: f64 | c128] >=1.14.1.4 >=1.17 docs
CubicSpline[T: f64 | c128] >=1.14.1.4 >=1.17 docs
FloaterHormannInterpolator[T: f64 | c128] >=1.15.0.0 >=1.17 docs
KroghInterpolator[T: f64 | c128, S: (int, ...)] >=1.16.0.1 >=1.17 docs
LinearNDInterpolator[T: f64 | c128] >=1.15.0.0 >=1.17 docs
NdBSpline[T: f64 | c128] >=1.15.2.1 >=1.17 docs
NdPPoly[T: f64 | c128] >=1.14.1.4 >=1.17 docs
NearestNDInterpolator[T: f64 | c128] >=1.14.1.6 >=1.17 docs
PPoly[T: f64 | c128] >=1.14.1.4 >=1.17 docs
RBFInterpolator[T: f64 | c128, S: (int, ...)] >=1.16.0.1 >=1.17 docs
RegularGridInterpolator[T: f64 | c128] >=1.14.1.6 >=1.17 docs

scipy.optimize

generic type scipy-stubs scipy
BroydenFirst[T: inexact] >=1.15.2.0 >=1.17 docs
InverseJacobian[T: inexact] >=1.15.2.0 >=1.17 docs
KrylovJacobian[T: inexact] >=1.15.2.0 >=1.17 docs
Bounds[S: (int, int, ...), T: scalar] >=1.16.0.1 >=1.17 docs

scipy.signal

generic type scipy-stubs scipy
ShortTimeFFT[T: inexact] >=1.16.0.0 >=1.17 docs
ShortTimeFFT[T: inexact, I: f64 | c128] >=1.18.0.0 >=1.17 docs
StateSpace[Z: inexact, P: floating, D: scalar] >=1.15.2.0 >=1.17 docs
TransferFunction[P: floating, D: scalar] >=1.15.2.0 >=1.17 docs
ZerosPolesGain[Z: inexact, P: floating, D: scalar] >=1.15.2.0 >=1.17 docs
lti[Z: inexact, P: floating] >=1.15.2.0 >=1.17 docs
dlti[Z: inexact, P: floating, D: scalar] >=1.15.2.0 >=1.17 docs

scipy.sparse

generic type scipy-stubs scipy
bsr_array[T: scalar] >=1.14.1.6 >=1.16 docs
bsr_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
coo_array[T: scalar, S: (int, ...)] >=1.14.1.6 >=1.16 docs
coo_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
csc_array[T: scalar] >=1.14.1.6 >=1.16 docs
csc_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
csr_array[T: scalar, S: (int,) | (int, int)] >=1.14.1.6 >=1.16 docs
csr_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
dia_array[T: scalar] >=1.14.1.6 >=1.16 docs
dia_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
dok_array[T: scalar, S: (int,) | (int, int)] >=1.14.1.6 >=1.16 docs
dok_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
lil_array[T: scalar] >=1.14.1.6 >=1.16 docs
lil_matrix[T: scalar] >=1.14.1.6 >=1.16 docs
sparray[T: scalar, S: (int, ...)] >=1.15.2.0 >=1.16 docs
spmatrix[T: scalar] >=1.14.1.6 >=1.16 docs

scipy.sparse.linalg

generic type scipy-stubs scipy
LaplacianNd[T: real] >=1.14.1.6 >=1.17 docs
LinearOperator[T: scalar] >=1.14.1.6 >=1.17 docs
dopc[T: scalar, S: (int, int, ...)] >=1.18.0.0 >=1.18 docs
SuperLU[T: inexact] >=1.16.0.1 >=1.17 docs

scipy.spatial.transform

generic type scipy-stubs scipy
Rotation[S: (int, ...)] >=1.17.1.1 >=1.18 docs
RigidTransform[S: (int, ...)] >=1.17.1.1 >=1.18 docs

scipy.stats

generic type scipy-stubs scipy
Covariance[T: real] >=1.14.0.0 >=1.17 docs
Uniform[S: (int, ...), T: floating] >=1.15.0.0 >=1.17 docs
Normal[S: (int, ...), T: floating] >=1.15.0.0 >=1.17 docs
Binomial[S: (int, ...), T: floating] >=1.16.0.0 >=1.17 docs
Mixture[T: floating] >=1.15.0.0 >=1.17 docs
rv_frozen[D: rv_generic, T: scalar or array] >=1.14.0.0 >=1.17
multi_rv_frozen[D: rv_generic] >=1.14.0.0 >=1.17
CensoredData[X: f64 | c128, L: f64 | c128, R: f64 | c128, I: f64 | c128] >=1.17.0.2 >=1.18 docs
gaussian_kde[T: floating] >=1.17.0.2 >=1.18 docs
FitResult[F: (float, *) -> f64] >=1.15.0.0 docs
DunnettResult[T: f64 | ld] >=1.17.1.4 >=1.18 docs
PearsonRResult[T: floating scalar/array, P: f64 scalar/array] >=1.14.1.0 docs
TtestResult[T: floating scalar/array] >=1.14.1.0 docs

scipy.stats.contingency

generic type scipy-stubs scipy
Chi2ContingencyResult[S: (int, ...)] >=1.17.1.2 * docs

Contributing

We welcome contributions from the community! There are many ways to help improve scipy-stubs:

Ways to Contribute

  • Report issues: Found a bug or incorrect type annotation? Open an issue
  • Improve stubs: Fix or enhance .pyi files (see CONTRIBUTING.md)
  • Add tests: Help with type-testing (see #1099 and the README.md in tests/)
  • Documentation: Write guides, examples, or improve existing documentation
  • Spread the word: Help others discover scipy-stubs

Development Setup

See the CONTRIBUTING.md for detailed instructions.

Who's using scipy-stubs?

scipy-stubs is used by a number of major open-source projects, including:

Agents | Apache Spark (PySpark) | Artisan | Arviz | asammdf | Astropy | Chemotools | Colour (Colour, Checker Detection, Demosaicing, HDRI, Visuals) | CSAPS | cuPyNumeric | derwen.ai (kglab) | Dify | Dify-Plus | Dingo | Efax | Fast GraphRAG | foamlib | Freqtrade | graphix | Gurobi (gurobi-logtools) | JAX | LazySlide | LensKit | librosa | linearmodels | Lmo | Materialize | MaxText | Metta AI (mettagrid) | MolPipeline | Monad | MouseTracks | movement | Mozilla (mozanalysis) | MTEB | mypy_primer | Namer | NeuroGym | Numba | Optuna | pandapower | Pandas (pandas, pandas-stubs) | Pandera | ProgressiVis | ProLIF | Ptera Software | PyMC (PyTensor) | pysmo | python-hvac | radioactivedecay | scikit-fingerprints | scqubits | scverse (PyDESeq2, scanpy) | SeQUeNCe | Skore | Splipy | Theis Lab (anndata2ri) | TorchFX | TQEC | traccuracy | Ultralytics | Vega (Altair) | Voctomix | xarray

License

scipy-stubs is licensed under the BSD 3-Clause License, the same as SciPy itself.

分类
Development Status :: 5 - Production/Stable Intended Audience :: Developers Intended Audience :: Science/Research Operating System :: OS Independent Programming Language :: Python Programming Language :: Python :: 3 Programming Language :: Python :: 3.12 Programming Language :: Python :: 3.13 Programming Language :: Python :: 3.14 Typing :: Stubs Only
版本列表
1.18.0.0 2026-06-19
1.17.1.5 2026-05-25
1.17.1.4 2026-04-13
1.17.1.3 2026-03-22
1.17.1.2 2026-03-15
1.17.1.1 2026-03-08
1.17.1.0 2026-02-23
1.17.0.2 2026-01-22
1.17.0.1 2026-01-14
1.17.0.0 2025-12-31
1.16.3.3 2025-12-08
1.16.3.2 2025-11-30
1.16.3.1 2025-11-23
1.16.3.0 2025-10-28
1.16.2.4 2025-10-17
1.16.2.3 2025-10-08
1.16.2.2 2025-10-07
1.16.2.1 2025-10-07
1.16.2.0 2025-09-11
1.16.1.1 2025-08-21
1.16.1.0 2025-08-01
1.16.0.2 2025-07-01
1.16.0.1 2025-06-30
1.16.0.0 2025-06-18
1.15.3.0 2025-05-08
1.15.2.2 2025-04-07
1.15.2.1 2025-03-12
1.15.2.0 2025-02-17
1.15.1.0 2025-01-11
1.15.0.0 2025-01-01
1.14.1.6 2024-12-15
1.14.1.5 2024-12-03
1.14.1.4 2024-11-19
1.14.1.3 2024-11-08
1.14.1.2 2024-11-01
1.14.1.1 2024-10-22
1.14.1.0 2024-10-14
1.4.1.dev0 2024-08-22
1.4.1a3 2024-09-08
1.4.1a2 2024-09-07
1.4.1a1 2024-08-30