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.

PyMCubes is an implementation of the marching cubes algorithm to extract iso-surfaces from volumetric data. The volumetric data can be given as a three-dimensional NumPy array or as a Python function f(x, y, z).

flox mainly provides strategies for fast GroupBy reductions with dask.array. It uses the MapReduce paradigm (or a "tree reduction") to run the GroupBy operation in a parallel-native way totally avoiding a sort or shuffle operation. flox can use either dask or cubed as its backend.

Deepdish is a Python library to load and save HDF5 files. The primary feature of deepdish is its ability to save and load all kinds of data as HDF5. It can save any Python data structure, offering the same ease of use as pickling or numpy.save, but with the language interoperability offered by HDF5.

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 simplified scipy.signal.spectral module to do spectral analysis in Python.

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

Particle provides a pythonic interface to the Particle Data Group (PDG) particle data tables and particle identification codes, with extended particle information and extra goodies.

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 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 utilities for exploratory analysis of large scale genetic variation data.

Hist is an analyst-friendly front-end for boost-histogram.

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.

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

Scikit-image is a collection of algorithms for image processing.

This package provides a Python library for building and analyzing recommender systems that deal with explicit rating data. It was designed with the following purposes in mind:

• Provide tools to handle downloaded or user-provided datasets.

• Provide ready-to-use prediction algorithms and similarity measures.

• Provide a base for creating custom algorithms.

• Provide tools to evaluate, analyse and compare algorithm performance.

• Provide documentation with precise details regarding library algorithms.

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.

Often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automated by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library implements the latter option to help with matplotlib graphs.

vedo is a fast and lightweight python module for scientific analysis and visualization. The package provides a wide range of functionalities for working with three-dimensional meshes and point clouds. It can also be used to generate high quality two-dimensional renderings such as scatter plots and histograms. vedo is based on vtk and numpy.

The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.

This package implements schema validation for Xarray objects.

python-pydicom is a Python library for reading and writing DICOM medical imaging data. It can read, modify and write DICOM data.

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.

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.