pykdtree is a kd-tree implementation for fast nearest neighbour search in Python.

Formulaic is a high-performance implementation of Wilkinson formulas for Python.

This is a Python implementation of the APTED algorithm,which supersedes the RTED algorithm for computing the tree edit distance.

openPMD is an open meta-data schema that provides meaning and self-description for data sets in science and engineering. See the openPMD standard for details of this schema.

This library provides a reference API for openPMD data handling. Since openPMD is a schema (or markup) on top of portable, hierarchical file formats, this library implements various backends such as HDF5, ADIOS2 and JSON. Writing & reading through those backends and their associated files are supported for serial and MPI-parallel workflows.

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

Anndata is a package for simple (functional) high-level APIs for data analysis pipelines. In this context, it provides an efficient, scalable way of keeping track of data together with learned annotations and reduces the code overhead typically encountered when using a mostly object-oriented library such as scikit-learn.

Scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.

pynrrd is a Python module for reading and writing NRRD files (format designed to support scientific visualization and image processing involving N-dimensional raster data) into and from numpy arrays.

This package provides Python bindings for the Boost::Histogram library, one of the fastest libraries for histogramming.

This package provides a framework for building scientific applications. It aims to bring state of the art software design practices to scientific computing, with the goal of providing a strong skeleton on which to build scientific codes by steering the implementation towards usability and maintainability.

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.

Uproot is a Python library for reading and writing ROOT files. It uses NumPy and does not depend on C++ ROOT.

This package provides a Python library for manipulating indices of ndarrays.

pynetdicom is a Python package that implements the DICOM networking protocol. It allows the easy creation of DICOM SCUs and SCPs.

scikit-fem is a library for performing finite element assembly. Its main purpose is the transformation of bilinear forms into sparse matrices and linear forms into vectors.

This package provides functionality to make it easy to make scatter density maps, both for interactive and non-interactive use.

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

This package implements a functionality to create and manipulate plot legends for matplotlib.

Pingouin is a statistical package written in Python 3 and based mostly on Pandas and NumPy. Its features include

• ANOVAs: N-ways, repeated measures, mixed, ancova

• Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations

• Robust, partial, distance and repeated measures correlations

• Linear/logistic regression and mediation analysis

• Bayes Factors

• Multivariate tests

• Reliability and consistency

• Effect sizes and power analysis

• Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient

• Circular statistics

• Chi-squared tests

• Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation, and more

This package is an implementation of the Promises/A+ specification and test suite in Python.

This library allows for contracting tensor networks or einsum expressions involving large numbers of tensors. It includes drop-in replacements for einsum and ncon, an explicit contraction tree object, a hyper optimizer that samples trees while tuning the generating meta-parameters, dynamic slicing for massive memory savings and parallelism, simulated annealing as an alternative strategy for optimizing and slicing, and integrations with other libraries such as numpy, opt_einsum, quimb, and others.

The goal of this package is to provide a reference implementation of trait types for common data structures used in the scipy stack such as numpy arrays or pandas and xarray data structures. These are out of the scope of the main traitlets project but are a common requirement to build applications with traitlets in combination with the scipy stack.

This package provides a stable interface for interactions between Snakemake and its executor plugins.

This package provides a Python interface to the QDLDL LDL factorization routine for quasi-definite linear system.