Package: pygaia
PyGaiaPython toolkit for Gaia science performance simulation and astrometric catalogue data manipulation.DescriptionPyGaia provides python modules for the simulation of Gaia data and their uncertainties, as well modules for the
manipulation of the Gaia catalogue data. In particular transformations between astrometric observables and phase space
variables are provided as well as transformations between sky coordinate systems. Only (very) basic functionality is
provided. Full blown simulations of Gaia data in all their gory detail requires the Java tools developed by the Gaia
Data Processing and Analysis Consortium (DPAC) in particular its Coordination Unit 2 (CU2).This toolkit is basically an implementation of the performance models for Gaia which are publicly available at:http://www.cosmos.esa.int/web/gaia/science-performance. In
addition much of the material in chapter 4 of the bookAstrometry for Astrophysics: Methods, Models, and
Applications (2012, van Altena et al.)is implemented.The code in this package isnot intended for accurate astrometry applications, such as predicting in detail
astrometric paths of stars on the sky.Epoch transformationisprovided, including the transformation of the astrometric covariance matrix to different
epochs.Astropy astrometry modules versus pygaia.astrometryIt is recommended to use theAstropyfacilities for handling astrometric data, including transformations from (Cartesian) phase space coordinates to astrometric observables and vice versa. See theastropy.coordinatespackage. Compared to thepygaia.astrometrypackage this gives you more functionality, the use of units, and much better maintained code.The only functionality not (yet) provided in Astropy is the propagation of the covariance matrix of the astrometric observables to to a different epoch. This is implemented in the classpygaia.astrometry.coordinates.EpochPropagation. Epoch propagation as such is implemented in Astropy as theapply_space_motion()function of theSkyCoordclass.DocumentationAll classes and methods/functions are documented so use the python help() function to find out more.InstallationTo install from source.gitclonehttps://github.com/agabrown/PyGaia.gitcdPyGaia
python-mpipinstall.From PyPI:pipinstallpygaiaDependenciesThis package is intended for Python3.The following python packages are required:numpyscipypandasUltraJSONFor the plotting tools:matplotlibAcknowledgementsPyGaia is based on the effort by Jos de Bruijne to create and maintain the Gaia Science Performance pages (with support from David Katz, Paola Sartoretti, Francesca De Angeli, Dafydd Evans,Marco Riello, and Josep Manel Carrasco), and benefits from the suggestions and contributions byMorgan FouesneauandTom Callingham.The photometric uncertainties code in PyGaia is based on the tool provided by Gaia DPAC (https://www.cosmos.esa.int/web/gaia/dr3-software-tools) to reproduce (E)DR3 Gaia photometric uncertainties described in theGAIA-C5-TN-UB-JMC-031technical note using data inRiello et al (2021).AttributionPlease acknowledge the Gaia Project Scientist Support Team and the Gaia Data Processing and Analysis Consortium
(DPAC) if you used this code in your research.LicenseCopyright (c) 2012-2023 Anthony Brown, Leiden University, Gaia Data Processing and Analysis Consortium