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.

Scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.

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

The OSQP (Operator Splitting Quadratic Program) solver is a numerical optimization package.

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.

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 an efficient implementation of Friedman's SuperSmoother based in Python. It makes use of numpy for fast numerical computation.

Thi package implements a functionality for mean-preserving interpolation of 1D data (for example, time series) with splines.

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.

pandarallel allows any Pandas user to take advantage of their multi-core computer, while Pandas uses only one core. pandarallel also offers nice progress bars (available on Notebook and terminal) to get an rough idea of the remaining amount of computation to be done.

Dask is a flexible parallel computing library for analytics. It consists of two components: dynamic task scheduling optimized for computation, and large data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

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.

pyfma provides an implementation of fused multiply-add which computes (x*y) + z with a single rounding. This is useful for dot products, matrix multiplications, polynomial evaluations (e.g., with Horner's rule), Newton's method for evaluating functions, convolutions, artificial neural networks etc.

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 package provides a stable interface for interactions between Snakemake and its software deployment plugins.

This package provides an extremely lightweight compatibility layer between dataframe libraries.

• full API support: cuDF, Modin, pandas, Polars, PyArrow

• lazy-only support: Dask, DuckDB, Ibis, PySpark, SQLFrame

This package implements a functionality to work with Nested sampling, a popular numerical method for Bayesian computation, which simultaneously generates samples from the posterior distribution and an estimate of the Bayesian evidence for a given likelihood and prior. nestcheck provides Python utilities for analysing samples produced by nested sampling, and estimating uncertainties on nested sampling calculations (which have different statistical properties to calculations using other numerical methods).

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.

Dvc data is DVC's data management subsystem.

spin is a simple interface for common development tasks. It comes with a few common build commands out the box, but can easily be customized per project.

The impetus behind developing the tool was the mass migration of scientific Python libraries (SciPy, scikit-image, and NumPy, etc.) to Meson, after distutils was deprecated. When many of the build and installation commands changed, it made sense to abstract away the nuisance of having to re-learn them.

This package provides a simplified scipy.signal.spectral module to do spectral analysis in Python.

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

This package implements a functionality to tell whether two images look nearly identical. The image hash algorithms (average, perceptual, difference, wavelet) analyse the image structure on luminance (without color information). The color hash algorithm analyses the color distribution and black & gray fractions (without position information).

Features:

• average hashing

• perceptual hashing

• difference hashing

• wavelet hashing

• HSV color hashing (colorhash)

• crop-resistant hashing

This package provides utilities and tools for open data science including tools for accessing data sets in Python.