Often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automated by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library implements the latter option to help with matplotlib graphs.

This package provides a Python interface to the QDLDL LDL factorization routine for quasi-definite linear system.

SALib provides tools for global sensitivity analysis. It contains Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent and fractional factorial methods.

PyAMG is a Python library of Algebraic Multigrid (AMG) solvers. It features implementations of:

• Ruge-Stuben (RS) or Classical AMG

• AMG based on Smoothed Aggregation (SA)

• Adaptive Smoothed Aggregation (αSA)

• Compatible Relaxation (CR)

• Krylov methods such as CG, GMRES, FGMRES, BiCGStab, MINRES, etc.

Uproot is a Python library for reading and writing ROOT files. It uses NumPy and does not depend on C++ ROOT.

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.

Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts.

python-pydicom is a Python library for reading and writing DICOM medical imaging data. It can read, modify and write DICOM data.

unyt is a Python library working with data that has physical units. It defines the unyt.array.unyt_array and unyt.array.unyt_quantity classes (subclasses of NumPy’s ndarray class) for handling arrays and scalars with units,respectively

This package provides a storage plugin for downloading input files from HTTP(s) in Snakemake.

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

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.

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 library allows for contracting tensor networks or einsum expressions involving large numbers of tensors. It includes drop-in replacements for einsum and ncon, an explicit contraction tree object, a hyper optimizer that samples trees while tuning the generating meta-parameters, dynamic slicing for massive memory savings and parallelism, simulated annealing as an alternative strategy for optimizing and slicing, and integrations with other libraries such as numpy, opt_einsum, quimb, and others.

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.

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.

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

vedo is a fast and lightweight python module for scientific analysis and visualization. The package provides a wide range of functionalities for working with three-dimensional meshes and point clouds. It can also be used to generate high quality two-dimensional renderings such as scatter plots and histograms. vedo is based on vtk and numpy.

PyMCubes is an implementation of the marching cubes algorithm to extract iso-surfaces from volumetric data. The volumetric data can be given as a three-dimensional NumPy array or as a Python function f(x, y, z).

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

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

This package provides a software deployment plugin for Snakemake using environment modules.

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

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