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 possibilities for data analysis which this module can support.One such example, performing more advanced interaction which can be scripted, is outlined below, using familiar IRAF tasks.
Note
You can see a similar photometry example which uses photutils
and it’s implementation of DAOPhot aperture photometry instead of IRAF in the imexam_ds9_photometry example jupyter notebook.
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:

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 any iterator containing a tuple of (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
viewer=imexam.connect()
viewer.load_fits('iabf01bzq_flt.fits')
viewer.scale() #scale to DS9 zscale by default
viewer.mark_region_from_array(locations)

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.

You can save these new regions to a DS9
style region file, either through DS9
or imexam
viewer.save_regions('badstars.reg')
Note
A future version of the imexam
package will make use of the region interpreter currently being developed with astropy for smoother creation and use of parsable regions files
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 viewer.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
viewer=imexam.connect('82a7e75f:53892')
#A little unsure this is the correct window? Let's check by asking what image is loaded. The image I'm working with is iabf01bzq_flt.fits
viewer.get_data_filename()
'/Users/sosey/ssb/sosey/testme/iabf01bzq_flt.fits' <-- notice it returned the full pathname to the file
viewer.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 conjunction with viewer.readcursor()
I’ll also mark the object we’re interested in on the display for reference
viewer.zoom(8)
for object in locations[100:110]:
viewer.panto_image(object[0],object[1])
viewer.mark_region_from_array(object)
viewer.imexam()