This Python module uses matplotlib to visualize multidimensional samples using a scatterplot matrix. In these visualizations, each one- and two-dimensional projection of the sample is plotted to reveal covariances. corner was originally conceived to display the results of Markov Chain Monte Carlo simulations and the defaults are chosen with this application in mind but it can be used for displaying many qualitatively different samples.

Snakemake aims to reduce the complexity of creating workflows by providing a clean and modern domain specific specification language (DSL) in Python style, together with a fast and comfortable execution environment.

This package provides a Python library for working with NeuroML descriptions of neuronal models

This package provides a domain-specific language for modeling convex optimization problems in Python.

Scikit-build-core is a build backend for Python that uses CMake to build extension modules. It has a simple yet powerful static configuration system in pyproject.toml, and supports almost unlimited flexibility via CMake. It was initially developed to support the demanding needs of scientific users, but can build any sort of package that uses CMake.

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. This package provides the core modules of Vaex.

Dvc data is DVC's data management subsystem.

PyAMG is a Python library of Algebraic Multigrid (AMG) solvers. It features implementations of:

• Ruge-Stuben (RS) or Classical AMG

• AMG based on Smoothed Aggregation (SA)

• Adaptive Smoothed Aggregation (αSA)

• Compatible Relaxation (CR)

• Krylov methods such as CG, GMRES, FGMRES, BiCGStab, MINRES, etc.

Marsilea is a Python library for creating composable visualizations in a declarative way. It is built on top of Matplotlib and provides a high-level API for you to puzzle different visualizations together like logo.

paramz is a lightweight parameterization framework for parameterized model creation and handling. Its features include:

• Easy model creation with parameters.

• Fast optimized access of parameters for optimization routines.

• Memory efficient storage of parameters (only one copy in memory).

• Renaming of parameters.

• Intuitive printing of models and parameters.

• Gradient saving directly inside parameters.

• Gradient checking of parameters.

• Optimization of parameters.

• Jupyter notebook integration.

• Efficient storage of models, for reloading.

• Efficient caching.

nibabel is a library that provides read and write access to common neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2 and later), GIFTI, NIfTI1, NIfTI2, CIFTI-2, MINC1, MINC2, AFNI BRIK/HEAD, ECAT and Philips PAR/REC. In addition, NiBabel also supports FreeSurfer’s MGH, geometry, annotation and morphometry files, and provides some limited support for DICOM.

This package implements sparse arrays of arbitrary dimension on top of numpy and scipy.sparse. Sparse array is a matrix in which most of the elements are zero. python-sparse generalizes the scipy.sparse.coo_matrix and scipy.sparse.dok_matrix layouts, but extends beyond just rows and columns to an arbitrary number of dimensions. Additionally, this project maintains compatibility with the numpy.ndarray interface rather than the numpy.matrix interface used in scipy.sparse. These differences make this project useful in certain situations where scipy.sparse matrices are not well suited, but it should not be considered a full replacement. It lacks layouts that are not easily generalized like compressed sparse row/column(CSR/CSC) and depends on scipy.sparse for some computations.

This package provides an extremely lightweight compatibility layer between dataframe libraries.

• full API support: cuDF, Modin, pandas, Polars, PyArrow

• lazy-only support: Dask, DuckDB, Ibis, PySpark, SQLFrame

Optimized einsum can significantly reduce the overall execution time of einsum-like expressions by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API. See the documentation for more information.

Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.

Modin uses Ray or Dask to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code.

This is a Python implementation of UpSet plots by Lex et al. UpSet plots are used to visualize set overlaps; like Venn diagrams but more readable.

PyQtGraph is a Pure-python graphics library for PyQt5, PyQt6, PySide2 and PySide6. It is intended for use in mathematics, scientific or engineering applications.

The OSQP (Operator Splitting Quadratic Program) solver is a numerical optimization package.

Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C.

This package provides a Python package for calculating tissue-specificity metrics for gene expression.

Baycomp is a library for Bayesian comparison of classifiers. Functions in the library compare two classifiers on one or on multiple data sets. They compute three probabilities: the probability that the first classifier has higher scores than the second, the probability that differences are within the region of practical equivalence (rope), or that the second classifier has higher scores.

This package provides encoding and decoding routines that enable the serialization and deserialization of numerical and array data types provided by numpy using the highly efficient msgpack format. Serialization of Python's native complex data types is also supported.

This is a rewrite of Dask DataFrame that includes query optimization and generally improved organization.