Navigating a QR image

Opening the fits file

odi-tools utilizes the fits.io module in the astropy package to open the multi-extension QR fits files:

>>> from astropy.io import fits
>>> img = '20140406T214040.2_GCPair-F1_odi_g.6183.fits'
>>> hdulist = fits.open(img)

Accessing the extensions

hdulist is now an astropy HDUlist where each element cooresponds to an extension of the fits file.

>>> print hdulist.info()
Filename: 20140406T214040.2_GCPair-F1_odi_g.6183.fits
No.    Name         Type      Cards   Dimensions     Format
0    PRIMARY     PrimaryHDU     294   ()
1    OTA33.SCI   ImageHDU       285   (4096, 4096)   float32
2    OTA34.SCI   ImageHDU       285   (4096, 4096)   float32
3    OTA44.SCI   ImageHDU       285   (4096, 4096)   float32
4    OTA43.SCI   ImageHDU       285   (4096, 4096)   float32
5    OTA42.SCI   ImageHDU       285   (4096, 4096)   float32
6    OTA32.SCI   ImageHDU       285   (4096, 4096)   float32
7    OTA22.SCI   ImageHDU       285   (4096, 4096)   float32
8    OTA23.SCI   ImageHDU       285   (4096, 4096)   float32
9    OTA24.SCI   ImageHDU       321   (4096, 4096)   float32
10   CAT.2MASS   BinTableHDU     15   680R x 2C    [D, D]
11   CAT.ODI     BinTableHDU    138   562R x 33C   [D, D, E, E, E, E, E, I, I, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]
12   CAT.ODI+2MASS  BinTableHDU  31   165R x 5C    [D, D, D, D, E]
13   CAT.PHOTCALIB  BinTableHDU 172   251R x 36C   [D, D, D, D, E, E, E, E, E, E, E, E, E, E, D, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E, E]
14   SKYLEVEL    BinTableHDU     50   1663R x 7C   [D, D, D, D, D, D, I]
15   ASSOCIATIONS  BinTableHDU   17   16R x 3C     [25A, 375A, 100A]

This particular image is from podi meaning there are a total of 9 OTAs in the focal plane. In the hdulist the extensions for each of the OTAs are given in rows 1-9 and have names with the following convention OTAxy.SCI. For a 5x6 ODI image, there would be 30 of these extensions.

The hdulist allows us to access each of the extensions by the its name give in the Name column, or by its number. If, for example, we wanted to only look at OTA33 we can do the following:

>>> hdu_ota = hdulist['OTA33.SCI']
# or using the number
>>> hdu_ota = hdulist[1]

Now that we have isolated this single OTA we can pick out its individual header and data:

>>> ota_header = hdu_ota.header
>>> ota_data = hdu_ota.data

ota_header and ota_data are now easily accessible to use in other scripts and modules. It is also easy to access header keywords:

>>> print ota_header['CRVAL1']
198.750082573
>>> print ota_header['CRVAL2']
18.4113084341

It is also convenient to pass individual OTAs to pyraf tasks based on the name of the extension. If we wanted to run daofind on OTA33 we could do the following:

>>> from pyraf import iraf
>>> img = '20140406T214040.2_GCPair-F1_odi_g.6183.fits'
>>> iraf.unlearn(iraf.apphot.daofind)
>>> iraf.datapars.setParam('fwhmpsf',fwhm,check=1)
>>> iraf.datapars.setParam('datamin',-900,check=1)
>>> iraf.datapars.setParam('datamax',60000,check=1)
>>> iraf.datapars.setParam('sigma',25.,check=1)
>>> iraf.findpars.setParam('threshold',2.5)
>>> iraf.apphot.daofind.setParam('output',output.txt)
>>> iraf.apphot.daofind(image=img+'['+'OTA33.SCI'+']', verbose="no", verify='no')

The odi-tools scheme

The modules in odi-tools are designed to work over lists of images while stepping through each of the OTA extensions in a given image. This will discussed in further detail in other parts of the documentation. An pseudo code of this scheme would be:

>>> imglist = ['img1.fits','img2.fits','img3.fits']
>>> ota_dictionary = {1:'OTA33.SCI',2: 'OTA34.SCI',3 :'OTA44.SCI',
...                   4:'OTA43.SCI',5:'OTA42.SCI', 6:'OTA32.SCI',
...                   7:'OTA22.SCI' ,8:'OTA23.SCI',9:'OTA24.SCI'}
>>> for img in imglist:
...     for key in ota_dictionary:
...         ota = ota_dictionary[key]
...         perform tasks on img[ota]

The other extensions

In addition to the extensions for each OTA, the hdulist also contains extensions linking to fits tables with useful information. They are CAT.2MASS, CAT.ODI, CAT.ODI+2MASS, CAT.PHOTCATLIB, SKYLEVEL, ASSOCIATIONS. The header and data in each of these tables are easily accessed.

>>> photcat_data = hdulist['CAT.PHOTCATLIB'].data
>>> photcat_header = hdulist['CAT.PHOTCATLIB'].header

Some of the information in these tables is used by odi-tools during the image processing.