This is a Python package to compute statistical test and add statistical annotations on an existing boxplots and barplots generated by seaborn.

This package provides a Python interface for the SCS (Splitting conic solver) library.

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. This package provides the core modules of Vaex.

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.

Einops provides a set of tensor operations for NumPy and multiple deep learning frameworks.

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.

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

Plotnine is a Python implementation of the Grammar of Graphics. It is a powerful graphics concept for creating plots and visualizations in a structured and declarative manner. It is inspired by the R package ggplot2 and aims to provide a similar API and functionality in Python.

Histoprint uses a mix of terminal color codes and Unicode trickery (i.e. combining characters) to plot overlaying histograms.

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

PyZX is a Python tool implementing the theory of ZX-calculus for the creation, visualisation, and automated rewriting of large-scale quantum circuits. PyZX currently allows you to:

• Read in quantum circuits in the file format of QASM, Quipper or Quantomatic;

• Rewrite circuits into a pseudo-normal form using the ZX-calculus;

• Extract new simplified circuits from these reduced graphs;

• Visualise the ZX-graphs and rewrites using either Matplotlib, Quantomatic or as a TikZ file for use in LaTeX documents;

• Output the optimised circuits in QASM, QC or QUIPPER format.

A Snakemake executor plugin for running srun jobs inside of SLURM jobs (meant for internal use by python-snakemake-executor-plugin-slurm).

This package provides miscellaneous tools for data analysis and scientific computing.

AlgoPy provides a functionality to differentiate functions implemented as computer programs by using Algorithmic Differentiation (AD) techniques in the forward and reverse mode.

The forward mode propagates univariate Taylor polynomials of arbitrary order. Hence it is also possible to use AlgoPy to evaluate higher-order derivative tensors. The reverse mode is also known as backpropagation and can be found in similar form in tools like PyTorch. Speciality of AlgoPy is the possibility to differentiate functions that contain matrix functions as +,-,*,/, dot, solve, qr, eigh, cholesky.

This package provides Numba-accelerated implementations of common SciPy probability distributions and others used in particle physics.

The supported distributions are:

• Uniform

• (Truncated) Normal

• Log-normal

• Poisson

• Binomial

• (Truncated) Exponential

• Student's t

• Voigtian

• Crystal Ball

• Generalised double-sided Crystal Ball

• Tsallis-Hagedorn, a model for the minimum bias pT distribution

• Q-Gaussian

• Bernstein density (not normalized to unity)

• Cruijff density (not normalized to unity)

• CMS-Shape

• Generalized Argus

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

Modin uses Ray or Dask to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code.

This is a package for image processing with Dask arrays. Features:

• Provides support for loading image files.

• Implements commonly used N-D filters.

• Includes a few N-D Fourier filters.

• Provides some functions for working with N-D label images.

• Supports a few N-D morphological operators.

hepunits collects the most commonly used units and constants in the HEP System of Units, as derived from the basic units originally defined by the CLHEP project.

This package lets you generate a multiscale, chunked, multi-dimensional spatial image data structure that can serialized to OME-NGFF. Each scale is a scientific Python Xarray spatial-image Dataset, organized into nodes of an Xarray Datatree.

This package implements a functionality to solve automatic numerical differentiation problems in one or more variables. Finite differences are used in an adaptive manner, coupled with a Richardson extrapolation methodology to provide a maximally accurate result. The user can configure many options like; changing the order of the method or the extrapolation, even allowing the user to specify whether complex-step, central, forward or backward differences are used.

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

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.

pyjanitor provides a set of data cleaning routines for pandas DataFrames. These routines extend the method chaining API defined by pandas for a subset of its methods. Originally, this package was a port of the R package by the same name and it is inspired by the ease-of-use and expressiveness of the dplyr package.