Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort.

Pandas 0.23 added a simple API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by

• adding support for registering methods as well

• making each of these functions backwards compatible with older versions of Pandas

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 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.

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.

This is a package meant primarily for documenting histogram indexing and the PlottableHistogram Protocol and any future cross-library standards. It also contains the code for the PlottableHistogram Protocol, to be used in type checking libraries wanting to conform to the protocol. It is not usually a runtime dependency, but only a type checking, testing, and/or docs dependency in support of other libraries.

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 contains public type stubs for python-pandas, following the convention of providing stubs in a separate package, as specified in PEP 561. The stubs cover the most typical use cases of python-pandas. In general, these stubs are narrower than what is possibly allowed by python-pandas, but follow a convention of suggesting best recommended practices for using python-pandas.

This package provides a Python package for time series classification.

A LEMS simulator written in Python which can be used to run NeuroML2 models.

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

climin is a Python package for optimization, heavily biased to machine learning scenarios. It works on top of numpy and (partially) gnumpy.

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

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

geosketch is a Python package that implements the geometric sketching algorithm described by Brian Hie, Hyunghoon Cho, Benjamin DeMeo, Bryan Bryson, and Bonnie Berger in "Geometric sketching compactly summarizes the single-cell transcriptomic landscape", Cell Systems (2019). This package provides an example implementation of the algorithm as well as scripts necessary for reproducing the experiments in the paper.

This package provides fast computations for principal component analysis (PCA), SVD, and eigendecompositions via randomized methods

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

Numpoly is a generic library for creating, manipulating and evaluating arrays of polynomials based on numpy.ndarray objects.

fgivenx is a Python package for plotting posteriors of functions. It is currently used in astronomy, but will be of use to any scientists performing Bayesian analyses which have predictive posteriors that are functions.

This package allows one to plot a predictive posterior of a function, dependent on sampled parameters. It assumes one has a Bayesian posterior Post(theta|D,M) described by a set of posterior samples theta_i~Post. If there is a function parameterised by theta y=f(x;theta), then this script will produce a contour plot of the conditional posterior P(y|x,D,M) in the (x,y) plane.

This package provides optimized tools for group-indexing operations: aggregated sum and more.

This package provides a simple and easy-to-use PID controller.

Vector is a Python library for 2D and 3D spatial vectors, as well as 4D space-time vectors. It is especially intended for performing geometric calculations on arrays of vectors, rather than one vector at a time in a Python for loop.

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.

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.