Example 3

Advanced Usage - Interact with Daophot and astropy

While the original intent for the imexam module was to replicate the realtime interaction of the old IRAF imexamine interface with data, there are other posibilities for data analysis which this module can support. One such example, performing more advanced interaction which can be scripted, is outlined below.

If you have a list of source identifications, perhaps prepared by SExtractor, DAOFind, Starfind or a similar program, you can use imexam to display the science image and overlay apertures for all their locations. From there you can do some visual examination and cleaning up of the list with a combination of region manipulation and useful imexam methods.

Here’s our example image to work with, which is a subsection of a larger image:

subsection of image being examined

I’ll use the IRAF DAOFind to find objects in my field:

from pyraf import iraf
from iraf import noao,digiphot,daophot
from astropy.io import fits

image='iabf01bzq_flt.fits'

fits.info('iabf01bzq_flt.fits')

    Filename: iabf01bzq_flt.fits
    No.    Name         Type      Cards   Dimensions   Format
    0    PRIMARY     PrimaryHDU     210   ()           int16
    1    SCI         ImageHDU        81   (1014, 1014)   float32
    2    ERR         ImageHDU        43   (1014, 1014)   float32
    3    DQ          ImageHDU        35   (1014, 1014)   int16
    4    SAMP        ImageHDU        30   ()           int16
    5    TIME        ImageHDU        30   ()           float32



#set up some finding parameters, you can make this more explicit
iraf.daophot.findpars.threshold=3.0 #3sigma detections only
iraf.daophot.findpars.nsigma=1.5 #width of convolution kernal in sigma
iraf.daophot.findpars.ratio=1.0 #ratio of gaussian axes
iraf.daophot.findpars.theta=0.
iraf.daophot.findpars.sharplo=0.2 #lower bound on feature
iraf.daophot.findpars.sharphi=1.0 #upper bound on feature
iraf.daophot.findpars.roundlo=-1.0 #lower bound on roundness
iraf.daophot.findpars.roundhi=1.0 #upper bound on roundness
iraf.daophot.findpars.mkdetections="no"

    In [84]: iraf.lpar(iraf.daophot.datapars)
           (scale = 1.0)            Image scale in units per pixel
         (fwhmpsf = 2.5)            FWHM of the PSF in scale units
        (emission = yes)            Features are positive?
           (sigma = 1.0)            Standard deviation of background in counts
         (datamin = 0.0)            Minimum good data value
         (datamax = INDEF)          Maximum good data value
           (noise = "poisson")      Noise model
         (ccdread = "")             CCD readout noise image header keyword
            (gain = "ccdgain")      CCD gain image header keyword
       (readnoise = 2.0)            CCD readout noise in electrons
           (epadu = 1.0)            Gain in electrons per count
        (exposure = "exptime")      Exposure time image header keyword
         (airmass = "")             Airmass image header keyword
          (filter = "")             Filter image header keyword
         (obstime = "")             Time of observation image header keyword
           (itime = 1.0)            Exposure time
        (xairmass = INDEF)          Airmass
         (ifilter = "INDEF")        Filter
           (otime = "INDEF")        Time of observation
            (mode = "ql")

iraf.daophot.datapars.datamin=0.
iraf.daophot.datapars.gain="ccdgain"
iraf.daophot.datapars.exposure="exptime"
iraf.daophot.datapars.sigma=105.



#assume the science extension and find some stars
sci="[SCI,1]"
output_locations='iabf01bzq_stars.dat'
iraf.daofind(image=image+sci,output=output_locations,interactive="no",verify="no",verbose="no")

#This is just the top of the file that daofind produced:

    In [24]: more iabf01bzq_stars.dat
    #K IRAF       = NOAO/IRAFV2.16          version    %-23s
    #K USER       = sosey                   name       %-23s
    #K HOST       = intimachay.stsci.edu    computer   %-23s
    #K DATE       = 2014-03-28              yyyy-mm-dd %-23s
    #K TIME       = 15:34:56                hh:mm:ss   %-23s
    #K PACKAGE    = apphot                  name       %-23s
    #K TASK       = daofind                 name       %-23s
    #
    #K SCALE      = 1.                      units      %-23.7g
    #K FWHMPSF    = 2.5                     scaleunit  %-23.7g
    #K EMISSION   = yes                     switch     %-23b
    #K DATAMIN    = 0.                      counts     %-23.7g
    #K DATAMAX    = INDEF                   counts     %-23.7g
    #K EXPOSURE   = exptime                 keyword    %-23s
    #K AIRMASS    = ""                      keyword    %-23s
    #K FILTER     = ""                      keyword    %-23s
    #K OBSTIME    = ""                      keyword    %-23s
    #
    #K NOISE      = poisson                 model      %-23s
    #K SIGMA      = 105.                    counts     %-23.7g
    #K GAIN       = ccdgain                 keyword    %-23s
    #K EPADU      = 2.5                     e-/adu     %-23.7g
    #K CCDREAD    = ""                      keyword    %-23s
    #K READNOISE  = 0.                      e-         %-23.7g
    #
    #K IMAGE      = iabf01bzq_flt.fits[SCI, imagename  %-23s
    #K FWHMPSF    = 2.5                     scaleunit  %-23.7g
    #K THRESHOLD  = 3.                      sigma      %-23.7g
    #K NSIGMA     = 2.                      sigma      %-23.7g
    #K RATIO      = 1.                      number     %-23.7g
    #K THETA      = 0.                      degrees    %-23.7g
    #
    #K SHARPLO    = 0.2                     number     %-23.7g
    #K SHARPHI    = 1.                      number     %-23.7g
    #K ROUNDLO    = -1.                     number     %-23.7g
    #K ROUNDHI    = 1.                      number     %-23.7g
    #
    #N XCENTER   YCENTER   MAG      SHARPNESS   SROUND      GROUND      ID         \
    #U pixels    pixels    #        #           #           #           #          \
    #F %-13.3f   %-10.3f   %-9.3f   %-12.3f     %-12.3f     %-12.3f     %-6d       \
    #
       194.694   2.357     -3.335   0.919       0.141       -0.004      1
       232.659   2.889     -1.208   0.768       0.572       -0.289      2
       237.782   2.925     -1.182   0.669       0.789       -0.971      3
       265.715   2.797     -1.395   0.976       -0.450      -0.669      4
       419.792   2.902     -3.045   0.925       -0.990      0.213       5
       424.566   3.081     -1.202   0.923       0.513       -0.555      6
       534.758   2.856     -1.341   0.659       -0.676      -0.302      7
       580.964   2.485     -1.326   0.821       -0.489      -0.752      8
       587.521   3.568     -1.282   0.911       -0.537      -0.119      9
       725.016   3.999     -1.103   0.714       -0.653      -0.490      10
       736.495   2.808     -1.345   0.710       -0.996      -0.730      11
       746.529   3.200     -0.868   0.303       -0.376      -0.682      12
       757.672   3.172     -1.527   0.420       0.271       0.211       13
       768.768   2.830     -1.321   0.741       -0.842      -0.252      14
       799.199   2.696     -2.096   0.926       0.476       -0.511      15
       807.575   2.445     -4.136   0.745       0.171       -0.131      16
       836.661   2.790     -1.482   0.709       0.205       0.636       17
       879.390   3.069     -1.018   0.549       -0.479      -0.495      18
       912.820   2.806     -1.414   0.576       0.504       0.109       19
       938.794   3.448     -1.731   0.997       -0.239      0.100       20
       17.713    2.731     -1.896   0.286       -0.947      -0.359      21
       48.757    2.755     -1.172   0.586       0.646       -0.543      22
       105.894   3.030     -1.700   0.321       -0.233      -0.006      23

Now we want to read in the file that Daofind produced and save the x,y and ID information. I’m going to read the results using astropy.io.ascii

reader=ascii.Daophot()
photfile=reader.read(output_locations)

#some quick information on what we have now
photfile.colnames

    ['XCENTER', 'YCENTER', 'MAG', 'SHARPNESS', 'SROUND', 'GROUND', 'ID']

photfile.print()

    In [103]: photfile.pprint()
       XCENTER     YCENTER      MAG     SHARPNESS      SROUND       GROUND      ID
    ------------- ---------- --------- ------------ ------------ ------------ ------
    194.694       2.357      -3.335    0.919        0.141        -0.004       1
    232.659       2.889      -1.208    0.768        0.572        -0.289       2
    237.782       2.925      -1.182    0.669        0.789        -0.971       3
    265.715       2.797      -1.395    0.976        -0.450       -0.669       4
    419.792       2.902      -3.045    0.925        -0.990       0.213        5
    424.566       3.081      -1.202    0.923        0.513        -0.555       6
    534.758       2.856      -1.341    0.659        -0.676       -0.302       7
    580.964       2.485      -1.326    0.821        -0.489       -0.752       8
    587.521       3.568      -1.282    0.911        -0.537       -0.119       9
    725.016       3.999      -1.103    0.714        -0.653       -0.490       10
    736.495       2.808      -1.345    0.710        -0.996       -0.730       11
    746.529       3.200      -0.868    0.303        -0.376       -0.682       12
    757.672       3.172      -1.527    0.420        0.271        0.211        13
    768.768       2.830      -1.321    0.741        -0.842       -0.252       14
    799.199       2.696      -2.096    0.926        0.476        -0.511       15
    807.575       2.445      -4.136    0.745        0.171        -0.131       16

You can even pop this up in your web browser if that’s a good format for you: photfile.show_in_browser(). imexam has several functions to help display regions on the DS9 window. Since we have this data loaded into memory, the one we will use here is mark_region_from_array(). Let’s make an array that the method will accept, namely a list of tuples which contain the (x,y,comment) that we want marked to the display. It will also accept a single tuple, or a string containing “x,y,comment”.

#lets make a list of our locations as a tuple of x,y,comment
#we'll cut the list to a smaller area and only include those points whose mag is < -4.
locations=list()
for point in range(0,len(photfile['XCENTER']),1):
    if photfile['MAG'][point] < -4:
        locations.append((photfile['XCENTER'][point],photfile['YCENTER'][point],photfile['ID'][point]))

#so the first item looks like:
In [91]: locations[0]
Out[91]: (807.57500000000005, 2.4449999999999998, 16)

Let’s open up a DS9 window (if you haven’t already) and display your image. This will let us display our source locations and play with them

ds9=imexam.connect()
ds9.load_fits('iabf01bzq_flt.fits')
ds9.scale() #scale to DS9 zscale by default
ds9.mark_region_from_array(locations)
subsection of image being examined

Now we can get rid of some of the stars by hand and save a new file of locations we like. I did this arbitrarily because I decided I didn’t like stars in this part of space. Click on the regions you don’t want and delete them from the screen. You can even add more regions of your own choosing.

subsection of image being examined

Now you can save these new regions to a DS9 style region file, either through DS9 or imexam

ds9.save_regions('badstars.reg')

Here is what the saved region file looks like, you can choose to import this file into any future DS9 display of the same image using the ds9.load_regions() method. You might also want to parse the file to save just the location and comment information in a separate text file.

In [7]: !head badstars.reg
# Region file format: DS9 version 4.1
# Filename: /Users/sosey/ssb/sosey/testme/iabf01bzq_flt.fits[SCI]
global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1
fk5
circle(0:22:38.709,-72:02:50.58,0.677464")
# text(0:22:39.097,-72:02:50.86) font="time 12 bold" text={ 16 }
circle(0:22:36.340,-72:02:58.27,0.677464")
# text(0:22:36.729,-72:02:58.55) font="time 12 bold" text={ 140 }
circle(0:22:29.068,-72:03:20.78,0.677464")
# text(0:22:29.457,-72:03:21.06) font="time 12 bold" text={ 225 }

            . . .

# text(0:22:56.855,-72:04:23.16) font="time 12 bold" text={ 21985 }
circle(0:22:42.791,-72:05:04.04,0.677464")
# text(0:22:43.180,-72:05:04.32) font="time 12 bold" text={ 22002 }
box(0:22:45.694,-72:04:19.19,14.593",13.1774",149.933) # color=red font="helvetica 16 normal roman" text={I DONT LIKE THE STARS HERE}

Advanced Usage II - Cycle through objects from a list

This example will step through a list of object locations and center that object in the DS9 window with a narrow zoom so that you can examine it further (think about PSF profile creation options here..)

If you haven’t already, start DS9 and load your image into the viewer. I’ll assume that you started DS9 outside of imexam and will need to connect to the window first.

import imexam
imexam.list_active_ds9()

    DS9 1396283378.28 gs 82a7e75f:53892 sosey

ds9=imexam.connect('82a7e75f:53892')

#A little unsure this is the corrent window? Let's check by asking what image is loaded. The image I'm working with is iabf01bzq_flt.fits

ds9.get_data_filename()

    '/Users/sosey/ssb/sosey/testme/iabf01bzq_flt.fits'  <-- notice it returned the full pathname to the file

ds9.zoomtofit()  <-- let's zoom out  to see the whole image, incase just a small section was loaded

Read in your list of object locations, I’ll use the same DAOphot targets from the previous example

from astropy.io import ascii
reader=ascii.Daophot()
output_locations='iabf01bzq_stars.dat'
photfile=reader.read(output_locations)

#make some cuts on the list

locations=list()
for point in range(0,len(photfile['XCENTER']),1):
    if photfile['MAG'][point] < -4:
        locations.append((photfile['XCENTER'][point],photfile['YCENTER'][point],photfile['ID'][point])) <-- appending tuple to the list

Take your list of locations and cycle through each one, displaying a zoomed in section on the DS9 window and starting imexam for each coordinate. I’m just going to go through 10 or so random stars. You can set this up however you like, including using a keystroke as your stopping condition in conjuection with the ds9.readcursor()

I’ll also mark the object we’re interested in on the display for reference

ds9.zoom(8)
for object in locations[100:110]:
    ds9.panto_image(object[0],object[1])
    ds9.mark_region_from_array(object)
    ds9.imexam()

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