The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

ufo2ft (UFO to FontTools) is a fork of ufo2fdk intended to leverage FontTools (a Python library) rather than the Adobe Font Development Kit for OpenType (AFDKO), a set of C libraries/utilities so that it can be more easily extended. Like ufo2fdk, its primary purpose is to generate OpenType font binaries from Unified Font Objects (UFOs).

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool.

This package provides tools to easily search and download French data from INSEE and IGN APIs. This data includes more than 150 000 macroeconomic series, a dozen datasets of local french data, numerous sources available on insee.fr, geographical limits of administrative areas taken from IGN as well as key metadata and SIRENE database containing data on all French compagnies.

Peewee is a simple and small ORM (object-relation mapping) tool. Peewee handles converting between pythonic values and those used by databases, so you can use Python types in your code without having to worry. It has built-in support for sqlite, mysql and postgresql. If you already have a database, you can autogenerate peewee models using pwiz, a model generator.

The goal of pygmsh is to combine the power of Gmsh with the versatility of Python. The package generalises many of the methods and functions that comprise the Gmsh Python API. In this way the meshing of complex geometries using high-level abstractions is made possible. The package provides a Python library together with a command-line utility for mesh optimisation.

SNData provides an access to data releases published by a variety of supernova (SN) surveys. It is designed to support the development of scalable analysis pipelines that translate with minimal effort between and across data sets. A summary of accessible data is provided below. Access to additional surveys is added upon request or as needed for individual research projects undertaken by the developers.

SPISEA is an python package that generates single-age, single-metallicity populations (i.e. star clusters). It gives the user control over many parameters:

• cluster characteristics (age, metallicity, mass, distance)

• total extinction, differential extinction, and extinction law

• stellar evolution and atmosphere models

• stellar multiplicity and Initial Mass Function

• initial-Final Mass Relation

• photometric filters

There are various file formats available for representing unstructured meshes and mesh data. The meshio package is able to read and write mesh files in many formats and to convert files from one format to another. Formats such as cgns, h5m, gmsh, xdmf and vtk are supported. The package provides command-line tools and a collection of Python modules for programmatic use.

NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, useful linear algebra, Fourier transform, and random number capabilities. Version 1.8 is the last one to contain the numpy.oldnumeric API that includes the compatibility layer numpy.oldnumeric with NumPy's predecessor Numeric.

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.

JAXopt provides hardware accelerated, batchable and differentiable optimizers in JAX.

1. Hardware accelerated: the implementations run on GPU and TPU, in addition to CPU.

2. Batchable: multiple instances of the same optimization problem can be automatically vectorized using JAX’s vmap.

3. Differentiable: optimization problem solutions can be differentiated with respect to their inputs either implicitly or via autodiff of unrolled algorithm iterations.

Xarray (formerly xray) makes working with labelled multi-dimensional arrays simple, efficient, and fun!

Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures.

This package provides Python modules that abstract many formats of Debian-related files, such as:

• Debtags information;

• debian/changelog files;

• packages files, pdiffs;

• control files of single or multiple RFC822-style paragraphs---e.g. debian/control, .changes, .dsc;

• Raw .deb and .ar files, with (read-only) access to contained files and meta-information.

LibCST parses Python source code as a CST tree that keeps all formatting details (comments, whitespaces, parentheses, etc). It's useful for building automated refactoring (codemod) applications and linters. LibCST creates a compromise between an Abstract Syntax Tree (AST) and a traditional Concrete Syntax Tree (CST). By carefully reorganizing and naming node types and fields, LibCST creates a lossless CST that looks and feels like an AST.

tablib is a format-agnostic tabular dataset library, written in Python. Supported output formats are Excel (Sets + Books), JSON (Sets + Books), YAML (Sets + Books), HTML (Sets), Jira (Sets), TSV (Sets), ODS (Sets), CSV (Sets), and DBF (Sets).

tablib also supports Pandas DataFrames (Sets). Anyhow, since pandas is quite huge, this Guix package doesn't depend on pandas. In case, just also install python-pandas.

This package provides a Python CDF reader toolkit. It provides the following functionality:

• Ability to read variables and attributes from CDF files

• Writes CDF version 3 files

• Can convert between CDF time types (EPOCH/EPOCH16/TT2000) to other common time formats

• Can convert CDF files into XArray Dataset objects and vice versa, attempting to maintain ISTP compliance

The Python pyperf module is a toolkit for writing, running and analyzing benchmarks. It features a simple API that can:

• automatically calibrate a benchmark for a time budget;

• spawn multiple worker processes;

• compute the mean and standard deviation;

• detect if a benchmark result seems unstable;

• store benchmark results in JSON format;

• support multiple units: seconds, bytes and integer.

nbdime provides tools for diffing and merging of Jupyter Notebooks. It includes the following commands:

nbdiff compare notebooks in a terminal-friendly way

nbmerge three-way merge of notebooks with automatic conflict resolution

nbdiff-web rich rendered diff of notebooks

nbmerge-web web-based three-way merge tool for notebooks

nbshow present a single notebook in a terminal-friendly way

psutil (Python system and process utilities) is a library for retrieving information on running processes and system utilization (CPU, memory, disks, network) in Python. It is useful mainly for system monitoring, profiling and limiting process resources and management of running processes. It implements many functionalities offered by command line tools such as: ps, top, lsof, netstat, ifconfig, who, df, kill, free, nice, ionice, iostat, iotop, uptime, pidof, tty, taskset, pmap.

This package provides a Python library for reading from and writing to FITS files using the CFITSIO library. Among other things, it can

• read and write image, binary, and ascii table extensions;

• read arbitrary subsets of tables in a lazy manner;

• query the rows and columns of a table;

• read and write header keywords;

• read and write Gzip files.

This is a Python library that aims to provide functions to handle, parse and validate standard numbers. The module supports more than 100 different number formats amongst which a great number of VAT and other tax numbers, personal identity and company identification codes, international standard numbers (ISBN, IBAN, EAN, etc.) and various other formats. The module also includes implementations of the Verhoeff, Luhn and family of ISO/IEC 7064 check digit algorithms.

Pylint is a Python source code analyzer which looks for programming errors, helps enforcing a coding standard and sniffs for some code smells (as defined in Martin Fowler's Refactoring book).

Pylint has many rules enabled by default, way too much to silence them all on a minimally sized program. It's highly configurable and handle pragmas to control it from within your code. Additionally, it is possible to write plugins to add your own checks.