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Nearby galaxies database for use with multiwavelength image analysis.
This project forked from akleroy/galbase
Nearby galaxies database for use with multiwavelength image analysis.
------------------------ Nearby Galaxies Database ------------------------ A code base to provide infrastructure for working with ISM, structure, and star formation in nearby galaxies. ################################################################ HOW TO ACCESS THE DATABASE ################################################################ IN IDL "gal_data" is your interface. It returns a structure containing distance, size, orientation, etc. s = gal_data("ngc1234") Use, e.g., "help, s, /str" to see the contents. From there you are on your own. The program should be able to handle a vector of galaxies and return a vector of structures. IN PYTHON "gal_data" is also your interface here. Right now it will return a FITS record read using astropy. ################################################################ HOW TO ADD NEW INFORMATION FOR INDIVIDUAL SYSTEMS ################################################################ We use a system of "override" tables to change the values for any parameter in the structure. The idea is that override_###.txt will be read in, comments (#) ignored, and then lines interpretted as: SOURCE FIELD VALUE Where FIELD for SOURCE will be replaced with VALUE. Don't forget to override the references if needed. These are folded in at creation of the database (make_gal_base), so you need to rerun that to fold these in. But you don't need to do anything more upstream than that. ################################################################ HOW TO REBUILD THE DATABASE ################################################################ The core of the database mostly reskins LEDA with organization and value added, particularly NED distances and user-supplied overrides. This is evolving with time, with a main goal being SFR, Mstar, and structural parameters. Right now the process of creating the database runs in IDL and is summarized in master_script At a high level the process goes like this: - [Optional] Compile the list of surveys that we are interested in. - Build LEDA SQL queries, feed these in to hyperleda, and make text files. To do this, go to http://leda.univ-lyon1.fr/leda/fullsql.html . Choose | as a delimiter and NaN as the undefined string. - Compile the LEDA text files into FITS files. - Run the NED query to make sure that we have a distance from NED for each target. - Run the galaxy database constructor (make_gal_base) to put everything together. - Re-run this constructor to incorporate the latest override values as needed. +++ HISTORY - Started as "things_galaxies" c. 2008 - Revised to include HERACLES+KINGFISH c. 2010 - Revised to full nearby volume spring 2014 - Python accessor, refactor, upgrades 2016. - Issues and history now captured in github, this section closed.
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