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.

The COde-independent Organized LEns STandard (COOLEST) defines a set of conventions to be shared across the strong lensing community, in order to consistently store, share and improve lens modeling analyses. In short, this project provides tools to manipulate lens models as a single, human-readable JSON template file alongside Python routines for visualizing and comparing lens models possibly obtained from different modeling codes.

iminuit is a Jupyter-friendly Python interface for the Minuit2 C++ library maintained by CERN's ROOT team.

Minuit was designed to optimize statistical cost functions, for maximum-likelihood and least-squares fits. It provides the best-fit parameters and error estimates from likelihood profile analysis.

Optionally, Iminuit supports SciPy minimizers as alternatives to Minuit's MIGRAD algorithm and Numba accelerated functions.

Libpass a password hashing library for Python 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. Libpass is a fork of passlib.

This is a backport of the standard library typing module to Python versions older than 3.5. Typing defines a standard notation for Python function and variable type annotations. The notation can be used for documenting code in a concise, standard format, and it has been designed to also be used by static and runtime type checkers, static analyzers, IDEs and other tools.

Vulture finds unused code in Python programs. This is useful for cleaning up and finding errors in large code bases. If you run Vulture on both your library and test suite you can find untested code. Due to Python's dynamic nature, static code analyzers like Vulture are likely to miss some dead code. Also, code that is only called implicitly may be reported as unused.

PyVista is...

• Pythonic VTK: a high-level API to the Visualization Toolkit (VTK);

• mesh data structures and filtering methods for spatial datasets;

• 3D plotting made simple and built for large/complex data geometries.

This package provides a Pythonic, well-documented interface exposing VTK's powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets.

This package provides CLI tool and Python library parallel file downloader using asyncio. parfive can handle downloading multiple files in parallel as well as downloading each file in a number of chunks.

asciicast demo of parfive parfive works by creating a downloader object, appending files to it and then running the download. parfive has a synchronous API, but uses asyncio to parallelise downloading the files.

msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consistent with a given demographic model) under a range of different models and evolutionary processes. Msprime can also simulate mutations on a given ancestral history (which can be produced by msprime or other programs supporting tskit) under a variety of genome sequence evolution models.

Plaster is a loader interface around multiple config file formats. It exists to define a common API for applications to use when they wish to load configuration. The library itself does not aim to handle anything except a basic API that applications may use to find and load configuration settings. Any specific constraints should be implemented in a pluggable loader which can be registered via an entrypoint.

This package provides a Python/C(++) implementation of Hartigan & Hartigan's dip test for unimodality.

The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference. Other than unimodality, it makes no further assumptions about the form of the null distribution.

Acoular is a Python module for acoustic beamforming that is distributed under the BSD 3-clause license.

It is aimed at (but not limited to) applications in acoustic testing. Multichannel data recorded by microphone arrays can be processed and analyzed to generate mappings of sound source distributions. The maps (acoustic photographs) can then be used to locate sources of interest and to characterize them using their spectra.

Pympler is a development tool to measure, monitor and analyze the memory behavior of Python objects in a running Python application.

By pympling a Python application, detailed insight in the size and the lifetime of Python objects can be obtained. Undesirable or unexpected runtime behavior like memory bloat and other pymples can easily be identified.

A web profiling frontend exposes process statistics, garbage visualisation and class tracker statistics.

This Python package implements Ted Dunning's t-digest data structure, which is designed around computing accurate estimates such as percentiles, quantiles and trimmed means from streaming or distributed data. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10 kB, instead of storing the entire list of data.

This package provides Kullback-Leibler projections for Bayesian model selection. Variable selection refers to the process of identifying the most relevant variables in a model from a larger set of predictors. When performing this process, we usually assume that variables contribute unevenly to the outcome, and we want to identify the most important ones. Sometimes we also care about the order in which variables are included in the model.

distfit is a Python package for probability density fitting of univariate distributions for random variables. The distfit library can determine the best fit for over 90 theoretical distributions. The goodness-of-fit test is used to score for the best fit and after finding the best-fitted theoretical distribution, the loc, scale, and arg parameters are returned. It can be used for parametric, non-parametric, and discrete distributions.

This package provides some extra widgets for PyQt/PySide:

• Multi-handle slider for float values

• Spinbox with arbitrarily large integers

• Magnitude combined with unit dropdown

• Label that will elide text

• Searchable ComboBox populated from Enum

• Searchable List

• Searchable Tree

• Color ComboBox

• Colormap ComboBox

• Toggle switch

• Collapsible widget to hide and unhide child widgets

• Flow layout

CoolBox is a toolkit for visual analysis of genomics data. It aims to be highly compatible with the Python ecosystem, easy to use and highly customizable with a well-designed user interface. It can be used in various visualization situations, for example, to produce high-quality genome track plots or fetch common used genomic data files with a Python script or command line, interactively explore genomic data within Jupyter environment or web browser.