The catsHTM package is a tool for fast accessing and cross-matching large astronomical catalogs, originally written in Matlab by Eran O. Ofek. Here we present the Python version.

>>> import catsHTM
>>> catsHTM.cone_search('FIRST',0,0,500)

The HDF5/HTM format, designed to store and provide fast access for large astronomical catalogs (with >10^6 rows) is described in the preliminary documentation, together with the Matlab version.

The catsHTM package is also described in a paper by Soumagnac & Ofek 2018.

If you are using one of the large catalogs, or this tool, please give the specific reference and acknowledgments to the catalogs you used (see list) and add the following acknowledgment:

The XXX catalog we use was formatted into the HDF5/HTM large catalog format as described in Soumagnac & Ofek (2018) and was developed as part of the Matlab Astronomy & Astrophysics Toolbox (Ofek 2014; ascl.soft 07005).

Bibtex entry for Soumagnac & Ofek 2018

The catalogs are available from:

• Web download is now available. See instructions here
• If you have any question or encounter any prblem while trying to download, email eran dot ofek at weizmann dot ac dot il or maayane dot soumagnac at weizmann dot ac dot il .

These instruction are for installing the catsHTM code, i.e. do not include the installation of the catalogs in HDF5 format. In order to download the catalogs, see section on 'How to obtain the formatted catalogs'.

pip install catsHTM

• python 2: higher than 2.7.10
• python 3 (required for the cross-matcher)

• math
• numpy
• scipy
• h5py

First, you need to specify the path to the directory where the HDF5 formatted catalogs where downloaded (default is ./data). This will only work if you have previously downloaded the catalogs in HDF5 format (in order to download them, see section on 'How to obtain the formatted catalogs'):

>>> import catsHTM
>>> path='path/to/directory'

You can then call the cone_search function. For example, to look for the sources in a cone of 100 arcsec centered on RA=0 rad and DEC=0 rad, in the FIRST catalog, type:

>>> cat,colcell, colunits=catsHTM.cone_search('FIRST',0,0,100,catalogs_dir=path)

The catalog lines corresponding to the sources within the cone are stored in the numpy array cat:

>>> print cat
[[  6.28300408e+00  -7.24311612e-05   1.68128476e-01   7.59999990e-01
    6.70588255e-01   9.23669338e-02   4.30000019e+00   0.00000000e+00
    6.20000000e+01   7.13999987e+00   4.32999992e+00   5.32000008e+01
    2.45002071e+06   2.45247496e+06]]

If you set the optionnal argument verbose to be true, you will get a line with a summary of your search:

>>> cat,colcell, colunits=catsHTM.cone_search('FIRST',0,0,100,catalogs_dir=path, verbose=True)
>>> print cat
*************
Catalog: FIRST; cone radius: 100 arcsec; cone center: (RA,DEC)=(0,0)
*************
[[  6.28300408e+00  -7.24311612e-05   1.68128476e-01   7.59999990e-01
    6.70588255e-01   9.23669338e-02   4.30000019e+00   0.00000000e+00
    6.20000000e+01   7.13999987e+00   4.32999992e+00   5.32000008e+01
    2.45002071e+06   2.45247496e+06]]

The names of the catalog columns are stored in the numpy array colcell

>>> print colcell
['RA' 'Dec' 'SideProb' 'Fpeak' 'Fint' 'rms' 'Major' 'Minor' 'PosAng'
 'FitMajor' 'FitMinor' 'FitPosAng' 'StartMJD' 'StopMJD']

The units of the catalog columns are stored in the numpy array colunits

>>> print colunits
['rad' 'rad' ' ' 'mJy' 'mJy' 'mJy' 'arcsec' 'arcsec' 'deg'
 'arcsec' 'arcsec' 'deg' 'MJD' 'MJD']

First, you need to specify the path to the directory where the HDF5 formatted catalogs where downloaded (default is ./data). This will only work if you have previously downloaded the catalogs in HDF5 format (in order to download them, see section on 'How to obtain the formatted catalogs'):

>>> import catsHTM
>>> path='path/to/directory'

You then need to call the xmatch_2cats function. For example, to look for overlaps between the FIRST and NVSS catalogs:

>>> catsHTM.xmatch_2cats('FIRST','NVSS',catalogs_dir=path)

Catalog_1 is FIRST (43688 trixels)
Catalog_2 is NVSS (43688 trixels)
************** I am building all the trixels relevant to our search **************
The number of trixels in the highest level, for FIRST is 32768
The number of trixels in the highest level, for NVSS is 32768
************** I am looking for overlapping trixels **************
I am looking for Catalog_2 (NVSS) trixels overlapping with the non-empty trixel #10921 of Catalog_1 (FIRST)
...

To save the results, you need to specify save_results==True!

By default, this will create a directory ./cross-matching_results, where it will save three files:

1. cross-matching_result_[name of catalog 1].txt: the catalog entries of catalog 1 (e.g. FIRST) for which one or mors counterparts were found in catalog 2 (e.g. NVSS), within the search radius.
2. cross-matching_result_[name of catalog 2].txt: the catalog entries corresponding to the closest counterpart found in catalog 2 (e.g. NVSS)
3. cross-matching_result_full.txt: a file where the two above files were merged.

The header of all these files specify the catalog columns. Examples of such files, obtained when running the code for FIRST and NVSS as in the commands above can be found in the directory cross-matching_results_test/.

You can modify the location of the output files with the output keyword:

>>> catsHTM.xmatch_2cats('FIRST','NVSS',catalogs_dir=path)

You can speed-up the run by leaving save_results to its default value (False), e.g., if you do not need to save the results and would rather use the output of the cross-matching algorythm in your own function.

You can also choose to only save the two separate files (1. and 2. in the list above), by setting the save_in_one_file keyword to False, or save only the large file (3. in the list above) by setting the save_in_separate_files keyword to False. E.g.:

>>> catsHTM.xmatch_2cats('FIRST','NVSS',catalogs_dir=path,save_in_one_file=False)

You can modify the search radius (default is 2 arcsec) with the Search_radius keyword. E.g.

>>> catsHTM.xmatch_2cats('FIRST','NVSS',Search_radius=5)

The QueryAllFun and QueryAllFunPar keywords allow you to define a function to be ran on the outputs of the cross-matching algorythm. QueryAllFun takes the following input arguments:

1. Cat1: the content of a trixel of catalog 1.
2. Cat2: the content of a trixel of catalog 2 overlapping with Cat1.
3. Ind: a list of dictionnaries, with one dictionnary per Cat1's object having one or more counterparts in Cat2:

• Ind[i]["IndRef"]: the index of the i-th Cat1's source having one or more counterpart in Cat2
• Ind[i]["IndCat"]: after sorting Cat2 by declination (cat2=Cat2[Cat2[:, 1].argsort(),] if DEC is the second column of the catalog), Ind[i]["IndCat"] is the list of indices of the counterparts of the matched Cat1's source.
• Ind[i]["Dist"]: a vector of angular distances (radians) between the i-th Cat1's source and its counterparts in Cat2.

4. IndCatMinDist: a vector, with as many elements as lines in Cat1, with 'nan' at lines where there is no counterpart in Cat2, and at line where there is, the index - in Cat2 - of the closest counterpart.

You can write a QueryAllFun function e.g. to save or use one or all of the above informations.

An example of such a function, Example_QueryAllFun, is built in the code. Simply structure yours in the same way:

def Your_QueryAllFun(Cat1,Ind,Cat2,IndCatMinDist,i,additionnal_args=[1,2,'hi']):
    return [your output]

The QueryAllFunPar keyword, if not None, must be a tuple which will be passed to the additional_args keyword of Your_QueryAllFun.

For example, runing the code with QueryAllFun=Example_QueryAllFun and QueryAllFunPar=['test'] allows you to print Cat1, Cat2, Ind and IndCatMinDist and save the content of Cat1 in a directory called test:

>>> import catsHTM
>>> from catsHTM import Example_QueryAllFun
>>> catsHTM.xmatch_2cats('FIRST','APASS',catalogs_dir=path,QueryAllFun=Example_QueryAllFun,QueryAllFunPar=['test'])

Other default parameters, such as the files and datasets naming format, can be edited in the python file params.py (type pip show catsHTM in the comand line to see where params.py is stored).

Currently, the following catalogs are available in this format (alphabetical order):

• 2MASS (input name: TMASS)
• 2MASSxsc (input name: TMASSxsc) - 2MASS extended source catalog
• AKARI (input name: AKARI)
• APASS (input name: APASS) - AAVSO All Sky Photometric Sky Survey (~5.5x10^7 sources)
• Cosmos (input name: Cosmos) - Sources in the Cosmos field
• DECaLS (input name: DECaLS) - DECaLS DR5 release
• FIRST (input name: FIRST) - (~9.5x10^5 sources)
• GAIA/DR1 (input name: GAIADR1) - (~1.1x10^9 sources).
• GAIA/DR2 (input name: GAIADR2) - NEW! (~1.6x10^9 sources)
• GALEX (input name: GALEX) - GALAEX/GR6Plus7 (~1.7x10^8 sources).
• HSC/v2 (input name: HSCv2)- Hubble source catalog
• IPHAS/DR2 (input name: IPHAS)
• NED redshifts (input name: NEDz)
• NVSS (input name: NVSS) - (~1.8x10^6 sources)
• PS1 (input name: PS1) - Pan-STARRS (~2.6x10^9 sources; A cleaned version of the PS1 stack catalog; some missing tiles below declination of zero [being corrected])
• PTFpc (input name: PTFpc) - PTF photometric catalog
• ROSATfsc (input name: ROSATfsc) - ROSAT faint source catalog
• SDSS/DR10 (input name: SDSSDR10)- Primary sources from SDSS/DR10 (last photometric release)
• Skymapper - will be added soon.
• SpecSDSS/DR14 (input name: SpecSDSS) - SDSS spectroscopic catalog
• Spitzer/SAGE (input name SAGE)
• Spitzer/IRAC (input name IRACgc) - Spitzer IRAC galactic center survey
• UCAC4 (input name: UCAC4) - (~1.1x10^8 sources)
• UKIDSS/DR10 (input name: UKIDSS)
• USNOB1 (not yet available)
• VISTA/Viking/DR3 (not yet available)
• VST/ATLAS/DR3 (input name: VSTatlas)
• VST/KiDS/DR3 (input name: VSTkids)
• WISE (input name: WISE) - ~5.6x10^8 sources
• XMM (input name: XMM)- 7.3x10^5 sources 3XMM-DR7 (Rosen et al. 2016; A&A 26, 590)

The catsHTM directory is very large and therefore available on request. The HDF5/HTM catalogs requires about 1.6TB of disk space.

Data is available from:

• Web download (link will be provided soon)
• Shared (and updated automatically) via Dropbox on request from eran dot ofek at weizmann dot ac dot il.

The catalog format is based on the HDF5 file format and HDF5 file access utilities, which are available on many platforms. The catalog format is designed to allow fast access for cone searches in the range of 1 arcsec to about 1 deg. For fast access, the sources are sorted into hierarchical triangular mesh (HTM).

The catchecker.py in scripts is used to perform contextual checking. To run this script, make sure:

1. You have download all the catalogs you want to use. Line 155 indicates which catalogs you are using.
2. Also, you have to change the rootpath of the directory storing all the catalogs in line 12.

To run the script, simply run

python catchecker.py <ra_in_deg> <dec_in_deg> <search_radius_in_arcsec> 

Example:

python catchecker.py 169.80039 31.02912 15


     catname          ra        dec     offset    otype
0       WISE  169.800444  31.029100   0.181770  Unknown
1        tns  169.800146  31.029003   0.863380    SN Ia
2   TMASSxsc  169.800385  31.028143   3.517034  Unknown
3      GLADE  169.800385  31.028143   3.517234   Galaxy
4     simbad  169.800123  31.028164   3.538789   Galaxy
5   SpecSDSS  169.800130  31.028162   3.540838  Unknown
6       NEDz  169.800130  31.028160   3.547851  Unknown
7     DECaLS  169.800130  31.028143   3.606518  Unknown
8        PS1  169.800164  31.028123   3.657916  Unknown
9      TMASS  169.800162  31.028028   3.993625  Unknown
10   GAIADR1  169.803354  31.032378  14.871874  Unknown
11   GAIADR2  169.803354  31.032378  14.872855  Unknown

This script does not include minor planet checking. Also, the searching radius for GLADE is hard-coded as 30 arcsecs.