Extract objects from images

The following script saves one example FITS image to your MySpace (in the sextractor directory) and uses SExtractor to extract objects. If you have Aladin running it will send the image and catalogue to it for display using PLASTIC.

#!/usr/bin/python
"""
Run SExtractor on an image.

Demonstrates how to remotely run a task and retrieve the results.

"""
__id__='$Id$'
import time, urllib
from astrogrid import acr
from astrogrid import Applications, MySpace

print ''
print 'Start Aladin if you want to see the extracted catalogue on top of the image...'
print ''

# We need to login.
acr.login()

# Image
image='http://help.astrogrid.org/raw-attachment/wiki/Help/IntroScripting/AstrogridPython/image.fits'

# We define some configuration files here

config="""
#-------------------------------- Catalog ------------------------------------

CATALOG_NAME    test.cat        # name of the output catalog
CATALOG_TYPE    ASCII_HEAD      # "NONE","ASCII_HEAD","ASCII","FITS_1.0"
                                # "FITS_LDAC" or "ASCII_SKYCAT"

PARAMETERS_NAME int-wfs.param   
                                # name of the file containing catalog contents

#------------------------------- Extraction ----------------------------------

DETECT_TYPE     CCD             # "CCD" or "PHOTO" (*)
#FLAG_IMAGE     flag            # filename for an input FLAG-image
DETECT_MINAREA  3               # minimum number of pixels above threshold
DETECT_THRESH   5.0             # <sigmas> or <threshold>,<ZP> in mag.arcsec-2
ANALYSIS_THRESH 5.0             # <sigmas> or <threshold>,<ZP> in mag.arcsec-2

FILTER          Y               # apply filter for detection ("Y" or "N")?
FILTER_NAME     gauss_4.0_7x7.conv      
                                # name of the file containing the filter

DEBLEND_NTHRESH 32              # Number of deblending sub-thresholds
DEBLEND_MINCONT 0.005           # Minimum contrast parameter for deblending

CLEAN           Y               # Clean spurious detections? (Y or N)?
CLEAN_PARAM     1.0             # Cleaning efficiency

MASK_TYPE       CORRECT         # type of detection MASKing: can be one of
                                # "NONE", "BLANK" or "CORRECT"

#------------------------------ Photometry -----------------------------------

PHOT_APERTURES  3.5, 7.0, 9.9, 14.0, 19.8       # MAG_APER aperture diameter(s) in pixels
PHOT_AUTOPARAMS 2.5, 3.5        # MAG_AUTO parameters: <Kron_fact>,<min_radius>

SATUR_LEVEL     50000.0         # level (in ADUs) at which arises saturation

MAG_ZEROPOINT   0.0             # magnitude zero-point
MAG_GAMMA       4.0             # gamma of emulsion (for photographic scans)
GAIN            1.0             # detector gain in e-/ADU.
PIXEL_SCALE     0.333           # size of pixel in arcsec (0=use FITS WCS info).

#------------------------- Star/Galaxy Separation ----------------------------

SEEING_FWHM     1.2             # stellar FWHM in arcsec
STARNNW_NAME    default.nnw     
                                # Neural-Network_Weight table filename

#------------------------------ Background -----------------------------------

BACK_SIZE       64              # Background mesh: <size> or <width>,<height>
BACK_FILTERSIZE 3               # Background filter: <size> or <width>,<height>

BACKPHOTO_TYPE  GLOBAL          # can be "GLOBAL" or "LOCAL" (*)
BACKPHOTO_THICK 24              # thickness of the background LOCAL annulus (*)

#------------------------------ Check Image ----------------------------------

CHECKIMAGE_TYPE NONE            # can be one of "NONE", "BACKGROUND",
                                # "MINIBACKGROUND", "-BACKGROUND", "OBJECTS",
                                # "-OBJECTS", "SEGMENTATION", "APERTURES",
                                # or "FILTERED" (*)
#CHECKIMAGE_NAME        check   # Filename for the check-image (*)

#--------------------- Memory (change with caution!) -------------------------

MEMORY_OBJSTACK 2000            # number of objects in stack
MEMORY_PIXSTACK 100000          # number of pixels in stack
MEMORY_BUFSIZE  1024            # number of lines in buffer

#----------------------------- Miscellaneous ---------------------------------

VERBOSE_TYPE    NORMAL          # can be "QUIET", "NORMAL" or "FULL" (*)
"""

params="""NUMBER
X_WORLD
Y_WORLD
X_IMAGE
Y_IMAGE
MAG_ISO
MAGERR_ISO
MAG_AUTO
MAGERR_AUTO
CLASS_STAR
"""

filter="""CONV NORM
# 3x3 ``all-ground'' convolution mask with FWHM = 2 pixels.
1 2 1
2 4 2
1 2 1
"""

# We write the image to MySpace
m = MySpace()
m.savefile(image, '#sextractor/image.fits')

# Application ID. This is the name of the application we are going to run.
# It can be obtained from a registry search.
id = 'ivo://org.astrogrid/SExtractor'

# We fill in the application parameters
app = Applications(id)

app.inputs['ANALYSIS_THRESH']['value']=1.5
app.inputs['IMAGE_BAND']['value']='R'
app.inputs['MAG_ZEROPOINT']['value']=25.0
app.inputs['SEEING_FWHM']['value']=1.2

app.inputs['PARAMETERS_NAME']['value']=params
app.inputs['FILTER_NAME']['value']=filter
app.inputs['config_file']['value']=config

app.inputs['DetectionImage']['value'] = '#sextractor/image.fits'
app.inputs['PhotoImage']['value'] = '#sextractor/image.fits'

app.outputs['CATALOG_NAME']['value'] = '#sextractor/image_cat.fits'

# and submit the application
task=app.submit()

# Wait until completed
time.sleep(10)
while task.status() <> 'COMPLETED':
        time.sleep(10)
        print 'Status: ', task.status()
        
print task.status()
print task.results()
print 'Catalogue written to\n%s' % task.results()

# Now with Aladin running we can send the image and catalogue
acr.startplastic()
acr.plastic.broadcast('#sextractor/image.fits')
acr.plastic.broadcast('#sextractor/image_cat.fits')