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query.py documentation

ztfquery.query.py is module to access ZTF data [IRSA]((https://irsa.ipac.caltech.edu/)

Requirements

Remark

  • you can have access to the public ZTF data, see DR3
  • reminder to set the global variable $ZTFQUERY (see installation.

Accessing ZTF data.

Finding the ztf data requires two step:

  1. do the query to know which data are accessible: this uses the load_metadata() method
  2. do the actual download of the accessible data: this uses the download_data() method.

The metadata structure is detailed here: ztf_api

Generic SQL, no coordinate in particular.

In this example, we are going to query any thing that have been observed with a seeing lower than 2arcsec between the 1st of May 2018 and the 1st of June 2018.

from ztfquery import query
zquery = query.ZTFQuery()
# Check what are the Julian Dates of 1st of May 2018 and 1st of June 2018
from astropy import time
jdstart = time.Time("2018-05-01").jd
jdend = time.Time("2018-05-15").jd 
# Do the Query to see what exists
zquery.load_metadata(sql_query=f"seeing<2 and obsjd BETWEEN {jdstart} AND {jdend}") # this may take some time.

The information is save as Pandas DataFrame undern metatable

zquery.metatable # it contains about 50 000 entries...

Show the observed fields, limiting it to the main (or primary) grid for visibility (say grid="secondary" to see this rest):

zquery.show_gri_fields(title="1stMay2018< time <1stJune2018 \n seeing<2", grid="main")

Query data for a given coordinate with filter and time constraints.

In this second example, we will want to access the I-band filter (filter #3) observations within 0.01 degree around RA=276.107960 Dec=+44.130398 since the 14th of May 2018.

from ztfquery import query
zquery = query.ZTFQuery()
# Print what are the Julian Dates of 14th of May 2018
from astropy import time
starttime = time.Time("2018-05-14").jd
# Do the Query to see what exists
zquery.load_metadata(radec=[276.107960,+44.130398], size=0.01, sql_query=f"fid=3 and obsjd>{starttime}") # takes a few seconds
# As of when the README has being written, this had 8 entries:
zquery.metatable
"""
	obsjd	ccdid	filtercode
0	2.458268e+06	1	zi
1	2.458268e+06	15	zi
2	2.458256e+06	15	zi
3	2.458255e+06	1	zi
4	2.458262e+06	1	zi
5	2.458273e+06	1	zi
6	2.458262e+06	15	zi
7	2.458273e+06	15	zi
"""

Getting reference image for a given coordinate

Let's imagine you want have a target at a given coordinate RA=276.107960 Dec=+44.130398. You want the reference image associated to it.

To get the reference image metadata simply do:

from ztfquery import query
zquery = query.ZTFQuery()
zquery.load_metadata(kind="ref",radec=[276.107960, +44.130398], size=0.0001)
zquery.metatable[["field","filtercode", "ccdid","qid"]]
"""
	field	filtercode	ccdid	qid
0	764		zg	1	3
1	726		zr	15	2
2	726		zg	15	2
3	726		zi	15	2
4	764		zr	1	3
5	764		zi	1	3
"""

If you only want reference images of for "g" filter:

zquery.load_metadata(kind="ref",radec=[276.107960, +44.130398], size=0.0001,  sql_query="fid=1")

or, instead of sql_query="fid=1", you could use sql_query="filtercode='zg'" but be careful with the quotes around zg

You can then simply download the refence image by doing zquery.download_data() as detailed below.

Downloading the Data

The actual data download is made possible after you did the load_metadata() (see above)

Dowloading data associated to a given position in the sky

In this example, we will want to access All observations within 0.01 degree around RA=276.107960 Dec+44.130398 since the 14th of May 2018 with a seeing lower than 2arcsec.

from ztfquery import query
zquery = query.ZTFQuery()

# Step 1, load the meta data (NB: Julian Date of 14th of May 2018 is 2458252.
zquery.load_metadata(radec=[276.107960,+44.130398], size=0.01, sql_query="seeing<2 and obsjd>2458252.5")
# As of when the README was being written, this had 42 entries (only partnership data)
zquery.metatable[["obsjd", "seeing", "filtercode"]]
"""
	obsjd	seeing	filtercode
0	2.458277e+06	1.83882	zr
1	2.458277e+06	1.84859	zr
2	2.458268e+06	1.74317	zi
3	2.458269e+06	1.65564	zr
4	2.458267e+06	1.90791	zr
...
35	2.458253e+06	1.84952	zr
36	2.458273e+06	1.77137	zr
37	2.458270e+06	1.71865	zr
38	2.458274e+06	1.84936	zg
39	2.458270e+06	1.60568	zr
40	2.458253e+06	1.98775	zr
41	2.458275e+06	1.99942	zg
"""

# Downloading the Data
zquery.download_data("psfcat.fits", show_progress=False)

You can download in multiprocessing simply by adding the keywork nprocess=X where X is the number of parallel process you want. The show_progress option will then show the overall progress. For example:

zquery.download_data("psfcat.fits", show_progress=True, notebook=True, 
                     nprocess=4, verbose=True, overwrite=True)

In the above example, overwrite=True enables to re-download existing file. By default overwrite is False, which means that the code checks if you already have the file you want to download where you want to download it and if so, skips it. verbose prints additional information like the name of files been downloaded.

You can download simply a part of the data (starting version >1.4.2): download_data() have an indexes options. Simply provide the indexes of the metatable you want to download, only these will be downloaded.

zquery.download_data("psfcat.fits", indexes=[4,6,12,40])

What is happening inside download_data()?

For each observation made with ZTF (that you have queried using load_metadata()) there are plenty of data product made available. Here is the list for the science exposure (default of load_metadata(), details here):

  • sciimg.fits (primary science image)
  • mskimg.fits (bit-mask image)
  • psfcat.fits (PSF-fit photometry catalog)
  • sexcat.fits (nested-aperture photometry catalog)
  • sciimgdao.psf (spatially varying PSF estimate in DAOPhot's lookup table format)
  • sciimgdaopsfcent.fits (PSF estimate at science image center as a FITS image)
  • sciimlog.txt (log output from instrumental calibration pipeline)
  • scimrefdiffimg.fits.fz (difference image: science minus reference; fpack-compressed)
  • diffimgpsf.fits (PSF estimate for difference image as a FITS image)
  • diffimlog.txt (log output from image subtraction and extraction pipeline)
  • log.txt (overall system summary log from realtime pipeline)

In the above example, we have selected the catalog generated by PSF-fit photometry "psfcat.fits" ; the download of all the 42 catalogs took a couple of minutes ; images would be much slower.

Where are the downloaded data ?

The data are saved following IRSA structure (default, see download_data option if you do not want that).

Retreive the local data:

To retrieve data you downloaded (once you have done the load_metatable()):

zquery.get_local_data("psfcat.fits")

If you need to get them again later on, after you closed the session, you will need to redo the load_metadata() query to find back the structure of the database, otherwise get_local_data() will not know what to do.

Get the Data from a filename

from ztfquery import io
io.download_from_filename(ztfdata_filename):