gstools-cython
Cython backend for GSTools.
Decision gist · record as of 2026-08-14
Yes, as a dependency of its parent package. If you are using the parent package for geostatistical modeling, gstools-cython will be pulled in automatically and will transparently accelerate computations. The package is stable with no known vulnerabilities and supports current Python versions. The aging maintenance status (231 days since last release) is not a blocker for a stable backend component.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy >= 1.20.0 and Python >= 3.10.
- This package is not meant to be installed directly but as a backend component.
- Medium install friction due to compiled wheels across multiple platforms and Python versions (3.10–3.14).
License · maintenance · safety
LGPL-3.0-or-later (copyleft) — Licensed under LGPL-3.0-or-later (copyleft). Derivative works and modifications must be distributed under compatible terms; proprietary use requires careful license review.
last release 2025-12-26 (231 days) · last repo commit 2025-12-26 · 2 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 130,780 downloads/mo, #11,626 on PyPI
Alternatives
Verify before relying
# gstools-cython is installed as a backend dependency
pip install gstools-cython
import numpy
# Backend is used transparently by parent package- Specific performance improvements or benchmarks compared to non-Cython implementations.
- Whether this package is automatically selected or requires explicit configuration when used.
- Direct usage patterns and API surface beyond backend acceleration role.
What it is and what it does
gstools-cython is a compiled backend component containing Cython-optimized implementations of core spatial statistical algorithms. It is not intended for direct installation but is pulled in as a dependency by its parent package. The package depends only on numpy and supports Python 3.10 through 3.14 across Linux, macOS, and Windows platforms.
Pre-built wheels are available for common architectures (x86_64, ARM64, and others), so installation typically requires no compilation on the user's end. Because it functions as a backend component, interaction occurs indirectly through the parent package's public API rather than through direct imports.
Use it for
- Accelerating spatial statistical computations in geostatistical workflows.
- Speeding up numerical operations in large-scale spatial modeling.
- Enabling faster simulations in hydrology and hydrogeology applications.
- Improving performance of covariance and interpolation calculations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, as a dependency of its parent package.
If you are using the parent package for geostatistical modeling, gstools-cython will be pulled in automatically and will transparently accelerate computations. The package is stable with no known vulnerabilities and supports current Python versions. The aging maintenance status (231 days since last release) is not a blocker for a stable backend component.
Install
gstools-cython on PyPI
Before you install
Medium install friction due to compiled wheels across multiple platforms and Python versions (3.10–3.14). Last release was 231 days ago; maintenance status is aging but the repository remains active and marked Production/Stable.
Requires numpy >= 1.20.0 and Python >= 3.10. This package is not meant to be installed directly but as a backend component.
License in practice
Licensed under LGPL-3.0-or-later (copyleft). Derivative works and modifications must be distributed under compatible terms; proprietary use requires careful license review.
Quickstart
# gstools-cython is installed as a backend dependency
pip install gstools-cython
import numpy
# Backend is used transparently by parent packageVerify before relying
- Specific performance improvements or benchmarks compared to non-Cython implementations.
- Whether this package is automatically selected or requires explicit configuration when used.
- Direct usage patterns and API surface beyond backend acceleration role.
Package facts
| License | LGPL-3.0-or-later copyleft |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Aging 231 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 130,780 / month, #11,626 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: End Users/DesktopIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: MacOSOperating System :: MicrosoftOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: GISTopic :: Scientific/Engineering :: HydrologyTopic :: Scientific/Engineering :: MathematicsTopic :: Scientific/Engineering :: PhysicsTopic :: Utilities |
Evidence: gstools_cython-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl; gstools_cython-1.2.0-cp310-cp310-macosx_11_0_arm64.whl; gstools_cython-1.2.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; gstools_cython-1.2.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; gstools_cython-1.2.0-cp310-cp310-musllinux_1_2_aarch64.whl; gstools_cython-1.2.0-cp310-cp310-musllinux_1_2_x86_64.whl; gstools_cython-1.2.0-cp310-cp310-win32.whl; gstools_cython-1.2.0-cp310-cp310-win_amd64.whl; gstools_cython-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl; gstools_cython-1.2.0-cp311-cp311-macosx_11_0_arm64.whl; gstools_cython-1.2.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; gstools_cython-1.2.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; gstools_cython-1.2.0-cp311-cp311-musllinux_1_2_aarch64.whl; gstools_cython-1.2.0-cp311-cp311-musllinux_1_2_x86_64.whl; gstools_cython-1.2.0-cp311-cp311-win32.whl; gstools_cython-1.2.0-cp311-cp311-win_amd64.whl; gstools_cython-1.2.0-cp311-cp311-win_arm64.whl; gstools_cython-1.2.0-cp312-cp312-macosx_10_13_x86_64.whl; gstools_cython-1.2.0-cp312-cp312-macosx_11_0_arm64.whl; gstools_cython-1.2.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “geostatistics cython backend”
- gstools-cythonProvides optimized Cython implementations of geostatistical…
- gstoolsGSTools provides geostatistical tools for spatial random field…
- hatch-cythonA Hatch build hook that integrates Cython compilation into the wheel…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also gstools · dask-geopandas · access · esda · geopandas · pointpats · spreg · giddy · xtgeoviz · PyOpenGL-accelerate