_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


python-ruptures 1.1.10
Propagated dependencies: python-numpy@2.3.1 python-scipy@1.16.3
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://centre-borelli.github.io/ruptures-docs/
Licenses: FreeBSD
Build system: pyproject
Synopsis: Change point detection for signals in Python
Description:

ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.

python-foldedtensor 0.4.0
Propagated dependencies: python-numpy@2.3.1 python-pytorch@2.10.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/aphp/foldedtensor
Licenses: Modified BSD
Build system: pyproject
Synopsis: PyTorch extension for handling deeply nested sequences of variable length
Description:

PyTorch extension for handling deeply nested sequences of variable length.

python-datasets 4.5.0
Propagated dependencies: python-dill@0.4.0 python-filelock@3.16.1 python-fsspec@2026.1.0 python-httpx@0.28.1 python-huggingface-hub@0.31.4 python-multiprocess@0.70.18 python-numpy@2.3.1 python-packaging@25.0 python-pandas@2.3.3 python-pyarrow@23.0.1 python-pyyaml@6.0.2 python-requests@2.32.5 python-tqdm@4.67.1 python-xxhash@3.5.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://huggingface.co/docs/datasets/
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Datasets and manipulation tools for AI models
Description:

Datasets is a lightweight library providing access to major public datasets (image, audio, text, etc.), as well as enabling efficient data preparation for inspection and ML model evaluation and training.

python-pythresh 1.0.2
Propagated dependencies: python-joblib@1.5.2 python-numpy@2.3.1 python-pandas@2.3.3 python-pyod@2.0.6 python-pytorch@2.10.0 python-ruptures@1.1.10 python-scikit-learn@1.7.2 python-scikit-lego@0.9.5 python-scipy@1.16.3 python-tqdm@4.67.1 python-xgboost@1.7.6
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://pythresh.readthedocs.io/
Licenses: Modified BSD
Build system: pyproject
Synopsis: Outlier detection thresholding in Python
Description:

PyThresh is a comprehensive and scalable Python toolkit for thresholding outlier detection likelihood scores in univariate/multivariate data. It has been written to work in tandem with PyOD and has similar syntax and data structures. However, it is not limited to this single library.

PyThresh is meant to threshold likelihood scores generated by an outlier detector. It thresholds these likelihood scores and replaces the need to set a contamination level or have the user guess the amount of outliers that may exist in the dataset beforehand. These non-parametric methods were written to reduce the user's input/guess work and rather rely on statistics instead to threshold outlier likelihood scores. For thresholding to be applied correctly, the outlier detection likelihood scores must follow this rule: the higher the score, the higher the probability that it is an outlier in the dataset. All threshold functions return a binary array where inliers and outliers are represented by a 0 and 1 respectively.

PyThresh includes more than 30 thresholding algorithms. These algorithms range from using simple statistical analysis like the Z-score to more complex mathematical methods that involve graph theory and topology.

python-accelerate 1.12.0
Propagated dependencies: python-huggingface-hub@0.31.4 python-numpy@2.3.1 python-packaging@25.0 python-psutil@7.0.0 python-pytorch@2.10.0 python-pyyaml@6.0.2 python-safetensors@0.4.3
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://huggingface.co/docs/accelerate/
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Launch, train and use PyTorch models on any configuration
Description:

Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16. It abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.

python-pydmd 2025.08.01
Propagated dependencies: python-h5netcdf@1.3.0 python-matplotlib@3.10.8 python-numpy@2.3.1 python-scikit-learn@1.7.2 python-scipy@1.16.3 python-typing-extensions@4.15.0 python-xarray@2025.12.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://pydmd.github.io/PyDMD
Licenses: Expat
Build system: pyproject
Synopsis: Python Dynamic Mode Decomposition
Description:

PyDMD is a Python package designed for Dynamic Mode Decomposition (DMD), a data-driven method used for analyzing and extracting spatiotemporal coherent structures from time-varying datasets. It provides a comprehensive and user-friendly interface for performing DMD analysis, making it a valuable tool for researchers, engineers, and data scientists working in various fields.

python-flax 0.8.0
Propagated dependencies: python-einops@0.8.1 python-jax@0.4.28 python-optax@0.1.5 python-orbax-checkpoint@0.4.5 python-msgpack@1.1.2 python-numpy@2.3.1 python-pyyaml@6.0.2 python-rich@14.2.0 python-tensorstore@0.1.67 python-typing-extensions@4.15.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/google/flax
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Neural network library for JAX designed for flexibility
Description:

Flax is a neural network library for JAX that is designed for flexibility.

python-scikit-lego 0.9.5
Propagated dependencies: python-importlib-resources@6.5.2 python-narwhals@2.15.0 python-pandas@2.3.3 python-scikit-learn@1.7.2 python-sklearn-compat@0.1.4
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://koaning.github.io/scikit-lego/
Licenses: Expat
Build system: pyproject
Synopsis: Extra blocks for scikit-learn pipelines
Description:

This package provides a set of custom transformers, metrics and models complementing scikit-learn, which results from a collaboration between multiple companies in the Netherlands.

python-keopscore 2.3
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://www.kernel-operations.io/
Licenses: Expat
Build system: pyproject
Synopsis: Core package for kernel operations (KeOps)
Description:

keopscore is the KeOps meta programming engine. This python module should be used through a binder (e.g. pykeops or rkeops).

python-keras 3.13.1
Propagated dependencies: python-absl-py@2.3.1 python-h5py@3.15.1 python-ml-dtypes@0.5.3 python-namex@0.0.7 python-numpy@2.3.1 python-optree@0.14.0 python-packaging@25.0 python-rich@14.2.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/keras-team/keras
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Deep learning API
Description:

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation and providing a delightful developer experience.

python-skorch 1.3.0
Propagated dependencies: python-numpy@2.3.1 python-pytorch@2.10.0 python-safetensors@0.4.3 python-scikit-learn@1.7.2 python-scipy@1.16.3 python-tabulate@0.9.0 python-tqdm@4.67.1
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://skorch.readthedocs.io/
Licenses: Modified BSD
Build system: pyproject
Synopsis: Scikit-learn compatible neural network library for PyTorch
Description:

This package provides a neural network library for PyTorch compatible with the scikit-learn API.

python-melissa-core 2.3.0
Dependencies: coreutils-minimal@9.1
Propagated dependencies: python-cloudpickle@3.1.0 python-iterative-stats@0.1.1 python-jsonschema@4.23.0 python-mpi4py@4.1.0 python-numpy@2.3.1 python-plotext@5.2.8 python-pyzmq@27.0.1 python-rapidjson@1.10 python-requests@2.32.5 python-scipy@1.16.3
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://gitlab.inria.fr/melissa/melissa
Licenses: Modified BSD
Build system: pyproject
Synopsis: Python front-end server and launcher for Melissa
Description:

Python front-end in charge of orchestrating the execution a Melissa based study. It automatically handles large-scale scheduler interactions in OpenMPI and with common cluster schedulers (e.g. slurm or OAR).

fenics-basix 0.10.0.post0
Dependencies: openblas@0.3.30
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: cmake
Synopsis: Finite element basis evaluation library
Description:
Basix is a finite element definition and tabulation runtime library. Basix allows users to: @itemize @item evaluate finite element basis functions and their derivatives at a set of points; @item access geometric and topological information about reference cells; @item apply push forward and pull back operations to map data between a reference cell and a physical cell; @item permute and transform DOFs to allow higher-order elements to be use on arbitrary meshes; @item interpolate into and between finite element spaces. Basix includes a range of built-in elements, and also allows the user to define their own custom elements. This package provides the C++ library for Basix.
fenics-dolfinx 0.10.0.post5
Propagated dependencies: adios2@2.11.0 boost@1.89.0 fenics-basix@0.10.0.post0 fenics-ffcx@0.10.1.post0 hdf5-parallel-openmpi@1.14.6 openmpi@4.1.6 petsc-openmpi@3.24.0 pt-scotch32@7.0.7 pugixml@1.12.1 slepc-openmpi@3.24.0 spdlog@1.15.3
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: cmake
Synopsis: FEniCS problem solving environment in C++
Description:

DOLFINx is the computational environment of FEniCSx and implements the FEniCS Problem Solving Environment in C++ and Python.

This package provides the C++ interface.

python-fenics-dolfinx 0.10.0.post5
Dependencies: fenics-dolfinx@0.10.0.post5
Propagated dependencies: python-cffi@1.17.1 python-fenics-basix@0.10.0.post0 python-fenics-ffcx@0.10.1.post0 python-fenics-ufl@2025.2.1 python-mpi4py@4.1.0 python-numba@0.62.1 python-numpy@2.3.1 python-petsc4py@3.24.0 python-pyamg@5.3.0
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: pyproject
Synopsis: FEniCS problem solving environment in Python
Description:

DOLFINx is the computational environment of FEniCSx and implements the FEniCS Problem Solving Environment in C++ and Python.

This package provides the Python interface.

fabulous 1.1.4
Dependencies: openblas@0.3.30
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://gitlab.inria.fr/solverstack/fabulous
Licenses: CeCILL-C
Build system: cmake
Synopsis: Fast Accurate Block Linear Krylov Solver
Description:

Library implementing Block-GMres with Inexact Breakdown and Deflated Restarting, Breakdown Free Block Conjudate Gradiant, Block General Conjugate Residual and Block General Conjugate Residual with Inner Orthogonalization and with inexact breakdown and deflated restarting.

grace 5.1.25
Dependencies: fftw@3.3.10 libjpeg-turbo@2.1.4 libpng@1.6.39 motif@2.3.8-1.0f556b0 netcdf@4.9.2 t1lib@5.1.2 xbae@4.60.4
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://plasma-gate.weizmann.ac.il/Grace/
Licenses: GPL 2+
Build system: gnu
Synopsis: 2D plotting tool for the X Window System
Description:

Grace is a 2D plotting tool for the X Window System. It has a Motif-based GUI and a scripting language that includes curve fitting, analysis, and export capabilities.

python-fenics-ffcx 0.10.1.post0
Propagated dependencies: python-cffi@1.17.1 python-fenics-basix@0.10.0.post0 python-fenics-ufl@2025.2.1 python-numba@0.62.1 python-numpy@2.3.1 python-pygraphviz@1.14
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: pyproject
Synopsis: FEniCS Form Compiler for finite element forms
Description:

FFCx is a compiler for finite element variational forms.

From a high-level description of the form in the UFL, it generates efficient low-level C code that can be used to assemble the corresponding discrete operator (tensor). In particular, a bilinear form may be assembled into a matrix and a linear form may be assembled into a vector.

This package provides the CLI and Python library.

fenics-ffcx 0.10.1.post0
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: cmake
Synopsis: UFCx interface header for finite element kernels
Description:

FFCx is a compiler for finite element variational forms.

From a high-level description of the form in the UFL, it generates efficient low-level C code that can be used to assemble the corresponding discrete operator (tensor). In particular, a bilinear form may be assembled into a matrix and a linear form may be assembled into a vector.

This package provides the UFCx interface header.

python-fenics-ufl 2025.2.1
Propagated dependencies: python-numpy@2.3.1
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: pyproject
Synopsis: Unified Form Language for FEniCS
Description:

The Unified Form Language (UFL) is a domain specific language for declaration of finite element discretizations of variational forms. More precisely, it defines a flexible interface for choosing finite element spaces and defining expressions for weak forms in a notation close to mathematical notation.

blaspp 2025.05.28
Dependencies: openblas@0.3.30
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://github.com/icl-utk-edu/blaspp
Licenses: Modified BSD
Build system: cmake
Synopsis: C++ API for the Basic Linear Algebra Subroutines
Description:

The Basic Linear Algebra Subprograms (BLAS) have been around for many decades and serve as the de facto standard for performance-portable and numerically robust implementation of essential linear algebra functionality. The objective of BLAS++ is to provide a convenient, performance oriented API for development in the C++ language, that, for the most part, preserves established conventions, while, at the same time, takes advantages of modern C++ features, such as: namespaces, templates, exceptions, etc.

lapackpp 2025.05.28
Dependencies: blaspp@2025.05.28 openblas@0.3.30
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://github.com/icl-utk-edu/lapackpp
Licenses: Modified BSD
Build system: cmake
Synopsis: C++ API for the Linear Algebra PACKage
Description:

The Linear Algebra PACKage (LAPACK) is a standard software library for numerical linear algebra. The objective of LAPACK++ is to provide a convenient, performance oriented API for development in the C++ language, that, for the most part, preserves established conventions, while, at the same time, takes advantages of modern C++ features, such as: namespaces, templates, exceptions, etc.

dbcsr 2.9.1
Dependencies: openmpi@4.1.6 lapack@3.12.1
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://cp2k.github.io/dbcsr/
Licenses: GPL 2
Build system: cmake
Synopsis: Distributed Block Compressed Sparse Row matrix library
Description:

DBCSR is a library designed to efficiently perform sparse matrix-matrix multiplication, among other operations. It is MPI and OpenMP parallel and can exploit Nvidia and AMD GPUs via CUDA and HIP.

python-fenics-basix 0.10.0.post0
Dependencies: fenics-basix@0.10.0.post0
Propagated dependencies: python-numba@0.62.1 python-numpy@2.3.1
Channel: guix-science
Location: guix-science/packages/maths.scm (guix-science packages maths)
Home page: https://fenicsproject.org/
Licenses: LGPL 3+
Build system: pyproject
Synopsis: Python wrapper for fenics-basix
Description:

Basix is a finite element definition and tabulation runtime library.

Basix allows users to:

  • evaluate finite element basis functions and their derivatives at a set of points;

  • access geometric and topological information about reference cells;

  • apply push forward and pull back operations to map data between a reference cell and a physical cell;

  • permute and transform DOFs to allow higher-order elements to be use on arbitrary meshes;

  • interpolate into and between finite element spaces.

Basix includes a range of built-in elements, and also allows the user to define their own custom elements.

This package provides the Python wrapper for Basix.

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