Astronify is a Python package for sonifying astronomical data - turning telescope observations into sound.

PyIRI is a Python implementation of the International Reference Ionosphere model. It provides fast, global, and altitude‑dependent evaluation of ionospheric parameters using a new spherical harmonics (SH) architecture. The model supports multiple coordinate systems and efficiently evaluates all grid points and time frames simultaneously.

This package provides a Glue plugin for geospatial imagery.

This package provides pydantic typing support for astropy unit types. Can be used to de(serialize) astropy quantities and units.

Types:

• AstroPydanticUnit: an overlay for astropy.units.UnitBase (all unit types).

• AstroPydanticQuantity: an overlay for astropy.units.Quantity, including array types.

• AstroPydanticTime: an overlay for astropy.time.Time.

• AstroPydanticICRS: an overlay for astropy.coordinates.ICRS and astropy.coordinates.SkyCoord.

uranimator is a CLI tool that works with your existing (code uraniborg) install to create animations. See how the sky evolves over a million years or what traveling to a star 100 light years away looks like.

PyAVM is a module to represent, read, and write metadata following the AVM standard provided by vamp project.

Python read-only implementation of the EventIO file format.

The glue-astronomy plugin for glue provides a collection of astronomy-specific functionality

This package provides a plugin for SiriL, see the installation guide on the project's Wiki.

SiriLic (SiriL's Interactif Companion) is a software for preparing acquisition files (raw, Biases, Flat and Dark) for processing with SiriL software.

Features:

• structuring the SiriL working directory into sub-folders

• convert Raw, Biases , Dark or Flat files into SiriL sequence

• automatically generate the SiriL script according to the files present and the options

• batch process multiple channel and sessions

Generalized World Coordinate System (GWCS) is an Astropy affiliated package providing tools for managing the World Coordinate System of astronomical data.

GWCS takes a general approach to the problem of expressing transformations between pixel and world coordinates. It supports a data model which includes the entire transformation pipeline from input coordinates (detector by default) to world coordinates.

Tempo analyzes pulsar timing data. Pulse times of arrival (TOAs), pulsar model parameters, and coded instructions are read from one or more input files. The TOAs are fitted by a pulse timing model incorporating transformation to the solar-system barycenter, pulsar rotation and spin-down and, where necessary, one of several binary models. Program output includes parameter values and uncertainties, residual pulse arrival times, chi-squared statistics, and the covariance matrix of the model. In prediction mode,ephemerides of pulse phase behavior (in the form of polynomial expansions) are calculated from input timing models.

SExtractor is a program that builds a catalogue of objects from an astronomical image. Although it is particularly oriented towards reduction of large scale galaxy-survey data, it can perform reasonably well on moderately crowded star fields.

High-level Data Reduction Library provides instrument-independent, high-level scientific functions for ESO data reduction pipelines. It aims to concentrate cross-pipeline algorithms in a single place, verify and homogenize algorithms, implement error propagation, and is extensively tested. Pipeline developers and instrument consortia can use HDRL so that bug-fixes and improvements benefit all pipelines that use it.

This package includes the HDRL library (built on top of the ESO Common Pipeline Library, unit tests, and documentation. The library covers functionalities such as: overscan, bias, dark, flat, bad-pixel handling, Strehl, fringing, catalogue operations, efficiency and response computation, refraction and airmass, pattern noise, resampling, limiting magnitude, and barycentric correction.

PypeIt is a Python package for semi-automated reduction of astronomical spectroscopic data. Its algorithms build on decades-long development of previous data reduction pipelines by the developers.

It is designed to be used by both advanced spectroscopists with prior data reduction expertise and astronomers with no prior experience of data reduction. It is highly configurable and designed to be applied to any standard slit-imaging spectrograph, including long-slit, multi-slit, as well as cross-dispersed echelle spectra.

This package provides an way to compute dendrograms of observed or simulated Astronomical data in Python.

The Python-based Space Physics Environment Data Analysis Software (PySPEDAS) framework supports multi-mission, multi-instrument retrieval, analysis, and visualization of heliophysics time series data.

Fitsverify is a computer program that rigorously checks whether a FITS data file conforms to the requirements defined in Version 3.0 of the FITS Standard document.

This package provides ASDF schemas for validating coordinates tags. Users should not need to install this directly; instead, install an implementation package such as asdf-astropy.

Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions. Cesium has two main components - a Python library, and a web application platform that allows interactive exploration of machine learning pipelines. Take control over the workflow in a Python terminal or Jupyter notebook with the Cesium library, or upload your time-series files, select your machine learning model, and watch Cesium do feature extraction and evaluation right in your browser with the web application.

fiasco provides a Python interface to CHIANTI, an atomic database for astrophysical spectroscopy. In addition to several high-level abstractions of the atomic data, fiasco also provides many common atomic physics calculations.

radiospectra provides support for some type of radio spectra in solar physics.

This package supports the creation of a combined header for a FITS file based on the contents of the headers of a set of input FITS images. A rules file defines what keywords will be present in the combined output header as well as how the output value will be determined from the set of values from all the input image headers.

Python package of utility functions that are useful in analyzing various datasets, in particular, catalogs of particles or galaxies/halos from cosmological simulations.

Portable library and tools for manipulating extended attributes.