This package implements a reader for CORSIKA binary output files using NumPy.

CalcMySky is a software package that simulates scattering of light by the atmosphere to render daytime and twilight skies (without stars). Its primary purpose is to enable realistic view of the sky in applications such as planetaria. Secondary objective is to make it possible to explore atmospheric effects such as glories, fogbows etc., as well as simulate unusual environments such as on Mars or an exoplanet orbiting a star with a non-solar spectrum of radiation.

This package consists of three parts:

• calcmysky utility that does the precomputation of the atmosphere model to enable rendering.

• libShowMySky library that lets the applications render the atmosphere model.

• ShowMySky preview GUI that makes it possible to preview the rendering of the atmosphere model and examine its properties.

Radio Beam is a simple toolkit for reading beam information from FITS headers and manipulating beams. Some example applications include:

• Convolution and deconvolution

• Unit conversion (Jy to/from K)

• Handle sets of beams for spectral cubes with varying resolution between channels

• Find the smallest common beam from a set of beams

• Add the beam shape to a matplotlib plot

This package provides an astronomical image processing tool - SIRIL. It is specially tailored for noise reduction and improving the signal/noise ratio of an image from multiple captures, as required in astronomy. SIRIL can align automatically or manually, stack and enhance pictures from various file formats, even image sequence files (films and SER files). It works well with limited system resources, like in embedded platforms, but is also very fast when run on more powerful computers and provides conversion to FITS from a large number of image formats.

This package consists of Python replacements for functions that are part of the IDL built-in library or part of astronomical IDL libraries. The emphasis is on reproducing results of the astronomical library functions. Only the bare minimum of IDL built-in functions are implemented to support this.

WCSTools is a set of software utilities, written in C, which create, display and manipulate the world coordinate system of a FITS or IRAF image, using specific keywords in the image header which relate pixel position within the image to position on the sky. Auxiliary programs search star catalogs and manipulate images.

Baseband is a package for reading and writing VLBI and other radio baseband files, with the aim of simplifying and streamlining data conversion and standardization.

The FITS "World Coordinate System" (WCS) standard defines keywords and usage that provide for the description of astronomical coordinate systems in a FITS (Flexible Image Transport System) image header.

CAMB is a cosmology code for calculating cosmological observables, including CMB, lensing, source count and 21cm angular power spectra, matter power spectra, transfer functions and background evolution. The code is in Python, with numerical code implemented in fast modern Fortran.

This package provides build downsampled previews of Space Telescope products.

Astropy is a single core package for Astronomy in Python. It contains much of the core functionality and some common tools needed for performing astronomy and astrophysics.

The spherical_geometry library is a Python package for handling spherical polygons that represent arbitrary regions of the sky.

This package provides ASDF schemas for validating FITS tags.

This package provides schema definitions for the Data Formats For Gamma-Ray Astronomy.

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

This package implements a functionality to produce map based simulations for the Simons Observatory or other CMB experiments. It creates simulated maps in HEALPix and CAR pixelization based on:

• foreground models included in PySM

• custom foregrounds models from the so_pysm_models package

• precomputed Cosmic Microwave Background simulations

• noise simulations based on expected performance and simulated hitmaps

• effect of gaussian beam convolution

Astropy is a single core package for Astronomy in Python. It contains much of the core functionality and some common tools needed for performing astronomy and astrophysics.

PHD2 is the enhanced,second generation version of the PHD guiding software from Stark Labs.

Sunwait calculates sunrise or sunset times with civil, nautical, astronomical and custom twilights. The sun's position is calculated using time, and position - latitude and longitude should be specified on the command line.

Features:

• calculates sunrise and sunset for given coordinates

• can wait for sunrise/sunset, or return DAY or NIGHT codes

• works with Windows Task Scheduler (or cron)

• supports custom twilight angles

• used to automate domestic lighting with Arduino transmitter and radio controlled sockets

This package implements a functionality to match sky on image mosaic.

This package provides a wrapper around casacore, the radio astronomy library.

MARTINI is a modular package for the creation of synthetic resolved HI line observations (data cubes) of smoothed-particle hydrodynamics simulations of galaxies. The various aspects of the mock-observing process are divided logically into sub-modules handling the data cube, source, beam, noise,spectral model and SPH kernel. MARTINI is object-oriented: each sub-module provides a class (or classes) which can be configured as desired. For most sub-modules, base classes are provided to allow for straightforward customization. Instances of each sub-module class are given as parameters to the Martini class; a mock observation is then constructed by calling a handful of functions to execute the desired steps in the mock-observing process.

Stackistry implements the lucky imaging principle of astronomical imaging: creating a high-quality still image out of a series of many (possibly thousands) low quality ones (blurred, deformed, noisy). The resulting image stack typically requires post-processing, including sharpening (e.g. via deconvolution). Such post-processing is not performed by Stackistry.

This package provides astronomical interstellar dust extinction curves implemented using the astropy.modeling framework.