The package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the term time-series is interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.

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.

Asyncer is a small library built on top of AnyIO.

The main goal of Asyncer is to improve developer experience by providing better support for autocompletion and inline errors in the editor, and more certainty that the code is bug-free by providing better support for type checking tools like mypy.

zconfig is a configuration library intended for general use. It supports a hierarchical schema-driven configuration model that allows a schema to specify data conversion routines written in Python. Its model is very different from the model supported by the configparser module found in Python's standard library, and is more suitable to configuration-intensive applications.

The rencode module is a data structure serialization library, similar to bencode from the BitTorrent project. For complex, heterogeneous data structures with many small elements, r-encoding stake up significantly less space than b-encodings. This version of rencode is a complete rewrite in Cython to attempt to increase the performance over the pure Python module.

The ufoLib2 Python library is meant to be a thin representation of the Unified Font Object (UFO) version 3 data model, intended for programmatic manipulation and fast batch processing of UFOs. It resembles the defcon library, but does without notifications, the layout engine and other support classes. Where useful and possible, ufoLib2 tries to be API-compatible with defcon.

Whisper is one of three components within the Graphite project. Whisper is a fixed-size database, similar in design and purpose to RRD (round-robin-database). It provides fast, reliable storage of numeric data over time. Whisper allows for higher resolution (seconds per point) of recent data to degrade into lower resolutions for long-term retention of historical data.

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.

TOML Kit is a 1.0.0rc1-compliant TOML library. It includes a parser that preserves all comments, indentations, whitespace and internal element ordering, and makes them accessible and editable via an intuitive API. It can also create new TOML documents from scratch using the provided helpers. Part of the implementation has been adapted, improved, and fixed from Molten.

API to query the distutils metadata written in PKG-INFO inside a source distriubtion (an sdist) or a binary distribution (e.g., created by running bdist_egg). It can also query the EGG-INFO directory of an installed distribution, and the *.egg-info stored in a "development checkout" (e.g, created by running python setup.py develop).

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays are accelerated and use less memory than doing the same calculation in Python. In addition, its multi-threaded capabilities can make use of all your cores, which may accelerate computations, most specially if they are not memory-bounded (e.g. those using transcendental functions).

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.

It features:

• object oriented projection definitions

• point, line, polygon and image transformations between projections

• integration to expose advanced mapping in Matplotlib with a simple and intuitive interface

• powerful vector data handling by integrating shapefile reading with Shapely capabilities

Passlib is a password hashing library for Python 2 & 3, which provides cross-platform implementations of over 30 password hashing algorithms, as well as a framework for managing existing password hashes. It's designed to be useful for a wide range of tasks, from verifying a hash found in /etc/shadow, to providing full-strength password hashing for multi-user application.

This package provides a Python implementation of the CANopen standard for CANs. The aim of the project is to support the most common parts of the CiA 301 standard in a simple Pythonic interface. It is mainly targeted for testing and automation tasks rather than a standard compliant master implementation.

msgspec is a fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML. It includes the following features:

• High performance encoders/decoders for common protocols.

• Support for a wide variety of Python types.

• Zero-cost schema validation using familiar Python type annotations.

• A speedy Struct type for representing structured data.

Execnet provides a share-nothing model with channel-send/receive communication for distributing execution across many Python interpreters across version, platform and network barriers. It has a minimal and fast API targeting the following uses:

1. distribute tasks to (many) local or remote CPUs

2. write and deploy hybrid multi-process applications

3. write scripts to administer multiple environments

python-pymonad implements data structures typically available in purely functional or functional first programming languages such as Haskell and F#. Included are

• Monad and Monoid data types with several common monads such as Maybe and State

• Useful tools such as the @curry decorator for defining curried functions

• Type annotations to help ensure correct usage

msgspec is a fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML. It includes the following features:

• High performance encoders/decoders for common protocols.

• Support for a wide variety of Python types.

• Zero-cost schema validation using familiar Python type annotations.

• A speedy Struct type for representing structured data.

This package provides a Python package to calculate gravitational-wave sensitivity curves for pulsar timing arrays.

Features:

• pulsar transmission functions

• inverse-noise-weighted transmission functions

• individual pulsar sensitivity curves

• pulsar timing array sensitivity curves as characteristic strain, strain sensitivity or energy density

• power-law integrated sensitivity curves

• sensitivity sky maps for pulsar timing arrays

Serpent provides ast.literal_eval()-compatible object tree serialization. It serializes an object tree into bytes (an utf-8 encoded string) that can be decoded and then passed as-is to ast.literal_eval() to rebuild the original object tree.

Because only safe literals are encoded, it is safe to send serpent data to other machines, such as over the network.

This library implements a simple lossless compression scheme adapted to time-dependent high-frequency, high-dimensional signals. It is being developed within the International Brain Laboratory with the aim of being the compression library used for all large-scale electrophysiological recordings based on Neuropixels. The signals are typically recorded at 30 kHz and 10 bit depth, and contain several hundreds of channels.