def txdump_photometry_info(target_dir, data_dir_name):
"""Extract the results of photometry from files to save them to a text file.
This function search files containing the results of photometry
and using txdump extracts the coordinates and photometric measure
to save it to a text file.
Args:
target_dir: Directory that contains the files to process.
data_dir_name: Name for the directories with data.
"""
# Walk from current directory.
for path,dirs,files in os.walk(target_dir):
# Process only directories without subdirectories.
if len(dirs) == 0:
split_path = path.split(os.sep)
# Check if current directory is for data images.
if split_path[-2] == data_dir_name:
logging.debug("Found a directory for data: %s" % (path))
# Get the list of magnitude files.
mag_files = glob.glob(os.path.join(path, "*.%s" %
MAGNITUDE_FILE_EXT))
logging.debug("Found %d magnitude files" % (len(mag_files)))
# Reduce each data file one by one.
for mfile in mag_files:
# Get the name of the file where the magnitude data will
# be saved.
mag_dest_file_name = \
mfile.replace(".%s" %(MAGNITUDE_FILE_EXT),
"%s%s.%s" %
(FILE_NAME_PARTS_DELIM,
MAGNITUDE_FILE_EXT, CSV_FILE_EXT))
# Remove the destiny file if exists.
if os.path.exists(mag_dest_file_name):
os.remove(mag_dest_file_name)
try:
mag_dest_file = open(mag_dest_file_name, 'w' )
iraf.txdump(mfile, fields=TXDUMP_FIELDS, expr='yes', \
Stdout=mag_dest_file)
mag_dest_file.close()
except iraf.IrafError as exc:
logging.error("Error executing txdump to get: %s" %
(mag_dest_file_name))
logging.error("Iraf error is: %s" % (exc))
except IOError as ioe:
logging.error("Reading file: %s" % (mag_dest_file_name))
def Photometry(setn, flist, band):
# J-band
os.system("head -1 -q %s/xylist/%sf*pos | awk '{print $3}'> tmp" %
(setn, band))
ff = open("tmp")
fr = ff.readlines()
ff.close
r = []
for x in fr:
r.append(float(x))
radi = np.nanmean(r)
print s, band, radi
# aperture,annulus,dannulus
iraf.apphot.photpars.apertures = radi
iraf.apphot.fitskypars.annulus = radi + 4
iraf.apphot.fitskypars.dannulus = 10
for x in flist:
if x[0] in s:
infile = "%s/xylist/%sf%s.pos" % (setn, band, x[1])
outfile = "%s/photometry/%sf%s.mag" % (setn, band, x[1])
dumped = "%s/photometry/%sf%s.txt" % (setn, band, x[1])
fits = "%s/%sf%smasked.fits" % (setn, band, x[1])
print infile, fits
iraf.phot(fits, coords=infile, output=outfile, wcsin="physical")
iraf.txdump(outfile,
fields="xc,yc,mag,merr,flux,msky",
expr="yes",
Stdout=dumped)
def psffit2(img, fwhm, psfstars, hdr, _datamax, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile, calculate the magnitudes of strs in the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.photpars.zmag = zmag
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
iraf.delete("_als2,_psf.grp,_psf.nrj", verify=False)
iraf.group(img + '[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_als2', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als2", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def psffit2(img, fwhm, psfstars, hdr, _datamax=45000, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile, calculate the magnitudes of strs in the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.photpars.zmag = zmag
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
iraf.delete("_als2,_psf.grp,_psf.nrj", verify=False)
iraf.group(img + '[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_als2', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als2", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def transform(self, image: Image):
if not self.requirements_check(image):
raise ValueError(
"Missing required transformations on image. Need:" +
str(self.REQUIRES))
# INIT
from pyraf import iraf
iraf.noao.digiphot(_doprint=0)
iraf.noao.digiphot.daophot(_doprint=0)
capable.OF_GRAPHICS = False
# File handling
temp_final_out = CONFIG["FILE_DUMP"] + 'TempPhotOut.dat'
if os.path.exists(temp_final_out):
os.remove(temp_final_out)
base = image.fixed_parameters["path"].split('/')[-1]
dao_phot_out = CONFIG["FILE_DUMP"] + base + ".mag.dat"
phot_txt_out = CONFIG["FILE_DUMP"] + base + "PhoTxOut.dat"
# Setting pyraf phot parameters
photpars = iraf.photpars.getParList()
iraf.photpars.setParam('apertures', CONFIG["APERTURE"])
iraf.phot.setParam('image', image.fixed_parameters["path"])
iraf.phot.setParam('coords', self._get_coordinates_file(image))
iraf.phot.setParam('verify', 'no')
iraf.phot.setParam('output', dao_phot_out)
iraf.phot.setParam('interactive', 'no')
if os.path.exists(dao_phot_out):
os.remove(dao_phot_out)
# Running IRAF task
dump = iraf.phot(mode='h', Stdout=1)
if os.path.exists(phot_txt_out):
os.remove(phot_txt_out)
# Getting better formatted file with magnitudes
iraf.txdump(dao_phot_out,
'XCENTER,YCENTER,MAG,MERR',
'yes',
Stdout=phot_txt_out)
# Getting results from output file
with open(phot_txt_out) as output_file:
results = {}
i = 0
for lines in output_file:
parts = lines.split()
results[self.objects[i].fixed_parameters["id"]] = (
float(parts[2]) if parts[2] != "INDEF" else None,
float(parts[3]) if parts[3] != "INDEF" else None)
i += 1
image.processing_parameters["photometry"] = results
return image
def psffit2(img, fwhm, psfstars, hdr, _datamax=45000, psffun='gauss', fixaperture=False):
import agnkey
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = agnkey.util.readhdr(img+'.fits')
_pixelscale = agnkey.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
_center='no'
iraf.fitskypars.annulus = a4
iraf.fitskypars.dannulus = a4
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.wsannul = int(a4)
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
# iraf.photpars.apertures = '%d,%d,%d'%(a2,a3,a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = agnkey.util.readkey3(hdr, 'ron')
iraf.datapars.epadu = agnkey.util.readkey3(hdr, 'gain')
iraf.datapars.exposure = 'exptime' #agnkey.util.readkey3(hdr,'exptime')
iraf.datapars.airmass = 'airmass'
iraf.datapars.filter = 'filter2'
iraf.centerpars.calgori = 'gauss'
iraf.centerpars.cbox = 1
iraf.daopars.recenter = _center
iraf.photpars.zmag = zmag
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = int(a4)
iraf.daopars.wsannul = int(a4)
iraf.daopars.recenter = _center
iraf.daopars.varorder = varord
iraf.delete("_als,_psf.grp,_psf.nrj", verify=False)
iraf.group(img+'[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img+'[0]', '_psf.grp', img + '.psf', '_als', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def save_matched_coords(image, coordfile='',output='',box=50,**kwargs):
'''
Finds and stores co-ordinates of the objects on all the frames based on the reference co-ordinates
'''
im = filenames2list(image)
imlist = [im] if type(im)==str else im
if output!='':
output = filenames2list(output)
urefcoo = True
coordsprefix = kwargs.get('coordsprefix','')
if coordfile=='':
refim = kwargs.get('refimage', 0)
if type(refim) == int:
refim, irefim = imlist[refim], refim
else:
refim, irefim = refim, imlist.index(refim)
coordfile = get_coords_filename_from_prefix(image=refim, prefix=coordsprefix)
urefcoo = kwargs.get('update_refim_coords',True)
if type(im)==str:
if output=='':
output = get_coords_filename_from_prefix(image=im,prefix=coordsprefix)
if coordfile!=output and not urefcoo:
os.system(f'cp {coordfile} {output}')
if urefcoo:
task = ir.center
task.centerpars.calgorithm = 'centroid'
task.centerpars.cbox = box
task.centerpars.cmaxiter = 10
task.centerpars.maxshift = 2
task.plotfile = ''
task.interactive = 'no'
task.radplots = 'no'
task.verify = 'no'
task.update = 'no'
task.verbose = 'no'
if os.path.exists(j2s('__coo__')):
os.remove(j2s('__coo__'))
task(image=im, coords=coordfile, output=j2s('__coo__'))
ir.txdump(textfiles=j2s('__coo__'), fields='xcenter,ycenter', expr='yes', headers='no', parameters='no', Stdout=output)
return output
if type(output)==str and output!='':
warn('For multiple input images you need to give multiple output files either as a tuple or list of textfiles '
'or a comma separated string of textfilenames or a list of filenames. Output file names will be set '
'automatically for now.')
output = ''
oplist = []
for i,im in enumerate(imlist):
if output!='':
j = list(range(len(imlist))).remove(irefim)[i]
op = output[j]
else:
op = ''
oplist.append(save_matched_coords(im,coordfile,op,box,**kwargs))
return oplist
def psffit2(img, fwhm, psfstars, hdr, _datamax=45000, psffun='gauss', fixaperture=False):
import agnkey
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = agnkey.util.readhdr(img+'.fits')
_pixelscale = agnkey.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
_center='no'
iraf.fitskypars.annulus = a4
iraf.fitskypars.dannulus = a4
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.wsannul = int(a4)
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
# iraf.photpars.apertures = '%d,%d,%d'%(a2,a3,a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = agnkey.util.readkey3(hdr, 'ron')
iraf.datapars.epadu = agnkey.util.readkey3(hdr, 'gain')
iraf.datapars.exposure = 'exptime' #agnkey.util.readkey3(hdr,'exptime')
iraf.datapars.airmass = 'airmass'
iraf.datapars.filter = 'filter2'
iraf.centerpars.calgori = 'gauss'
iraf.centerpars.cbox = 1
iraf.daopars.recenter = _center
iraf.photpars.zmag = zmag
iraf.delete('_psf2.ma*', verify=False)
iraf.phot(img+'[0]', '_psf2.coo', '_psf2.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = int(a4)
iraf.daopars.wsannul = int(a4)
iraf.daopars.recenter = _center
iraf.daopars.varorder = varord
iraf.delete("_als,_psf.grp,_psf.nrj", verify=False)
iraf.group(img+'[0]', '_psf2.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img+'[0]', '_psf.grp', img + '.psf', '_als', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf2.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
fitmag = iraf.txdump("_als", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, fitmag
def get_phot_output(output, silent=False):
from pyraf import iraf as ir
# Now get the results using txdump
radStr = ir.txdump(output, 'RAPERT', 'yes', Stdout=1)
fluxStr = ir.txdump(output, 'FLUX', 'yes', Stdout=1)
magStr = ir.txdump(output, 'MAG', 'yes', Stdout=1)
merrStr = ir.txdump(output, 'MERR', 'yes', Stdout=1)
pierStr = ir.txdump(output, 'PIER', 'yes', Stdout=1)
radFields = radStr[0].split()
fluxFields = fluxStr[0].split()
magFields = magStr[0].split()
merrFields = merrStr[0].split()
pierFields = pierStr[0].split()
count = len(radFields)
radius = np.zeros(count, dtype=float)
flux = np.zeros(count, dtype=float)
mag = np.zeros(count, dtype=float)
merr = np.zeros(count, dtype=float)
for rr in range(count):
radius[rr] = float(radFields[rr])
if (int(pierFields[rr]) != 0 or magFields[rr] == 'INDEF'
or merrFields[rr] == 'INDEF'):
print('Problem in image: ' + output)
# Error
flux[rr] = 0
mag[rr] = 0
merr[rr] = 0
else:
flux[rr] = float(fluxFields[rr])
mag[rr] = float(magFields[rr])
merr[rr] = float(merrFields[rr])
if not silent:
print('%6s %10s %6s %6s' % ('Radius', 'Flux', 'Mag', 'MagErr'))
for ii in range(count):
print( '%8.1f %10d %6.3f %6.3f' % \
(radius[ii], flux[ii], mag[ii], merr[ii]))
return (radius, flux, mag, merr)
def get_phot_output(output, silent=False):
# Now get the results using txdump
radStr = ir.txdump(output, 'RAPERT', 'yes', Stdout=1)
fluxStr = ir.txdump(output, 'FLUX', 'yes', Stdout=1)
magStr = ir.txdump(output, 'MAG', 'yes', Stdout=1)
merrStr = ir.txdump(output, 'MERR', 'yes', Stdout=1)
pierStr = ir.txdump(output, 'PIER', 'yes', Stdout=1)
radFields = radStr[0].split()
fluxFields = fluxStr[0].split()
magFields = magStr[0].split()
merrFields = merrStr[0].split()
pierFields = pierStr[0].split()
count = len(radFields)
radius = np.zeros(count, dtype=float)
flux = np.zeros(count, dtype=float)
mag = np.zeros(count, dtype=float)
merr = np.zeros(count, dtype=float)
for rr in range(count):
radius[rr] = float(radFields[rr])
if (int(pierFields[rr]) != 0 or magFields[rr] == 'INDEF' or
merrFields[rr] == 'INDEF'):
print 'Problem in image: ' + output
# Error
flux[rr] = 0
mag[rr] = 0
merr[rr] = 0
else:
flux[rr] = float(fluxFields[rr])
mag[rr] = float(magFields[rr])
merr[rr] = float(merrFields[rr])
if not silent:
print '%6s %10s %6s %6s' % ('Radius', 'Flux', 'Mag', 'MagErr')
for ii in range(count):
print '%8.1f %10d %6.3f %6.3f' % \
(radius[ii], flux[ii], mag[ii], merr[ii])
return (radius, flux, mag, merr)
def calc_apcorr(file):
''' MEASURE APPERTURE CORRECTION GIVEN TWO MAGNITUDES'''
''' file -- daophot filename (expects only 2 circular aperture magnitude estimates in file)'''
# Save a trimmed daophot catalog with columns relevant to this study (xc,yc,mag1,mag2)
trimfile = file+'.trimmed'
file_query = os.access(trimfile, os.R_OK)
if file_query == True: os.remove(trimfile) # remove previous trimmed files
iraf.txdump(file,'xcenter,ycenter,mag', 'yes', Stdout=trimfile)
# Measure the aperture correction assuming a pt source
xc, yc, mag1,mag2 = np.loadtxt(trimfile, unpack=True)
deltamag=mag1-mag2
# Use somewhat hacky method to extract box in mag.vs.deltamag plot where we will estimate median deltamag
xgd=[21.8,25.0] # fixed--works well for both filters
#estimate y-range from a rough median estimate
tmp_median = np.median(deltamag[(mag1 >= xgd[0])&(mag1 <= xgd[1])&(np.abs(deltamag) < 1)])
ygd=[tmp_median-0.1, tmp_median+0.1]
f_deltamag=deltamag[(mag1 >= xgd[0])&(mag1 <= xgd[1])&(deltamag > ygd[0])&(deltamag <ygd[1])] # avoid saturated and faint sources
ac05 = np.median(f_deltamag)
# Plot the aperture correction on delta_mag vs. mag scatter diagram
pylab.scatter(mag1, deltamag,s=1.0,c='r',marker='o')
pylab.ylim(-0.5,1.0)
pylab.xlim(19.0,29.0)
pylab.xlabel('m3')
pylab.ylabel('m3 - m16')
pylab.axhline(0.0, c='k', ls='--', linewidth=3)
pylab.axhline(ac05)
pylab.vlines(xgd,ygd[0],ygd[1], colors='red',linestyles='dotted')
pylab.hlines(ygd,xgd[0],xgd[1], colors='red',linestyles='dotted')
pylab.annotate('ac05='+str(ac05),[19.3,ac05-0.07])
pylab.annotate('m_corr = m1 - ac05 - AC05',[19.3,-0.4])
fsplit=file.split('_')
pylab.annotate(fsplit[0], [20.0,-0.2],color='r')
pylab.savefig('deltamag_vs_mag_'+fsplit[0]+'.pdf')
pylab.clf()
return ac05
def textdump(self):
'''
Prepares photometry data for science
'''
countlist = glob.glob(root + '/*counts.fits')
photlist = glob.glob(root + '/*phot.dat')
for image, phot_file in zip(countlist, photlist):
saveout = sys.stdout
fsock = open(image[:-12] + '_txtdump_tmp.dat', 'w')
sys.stdout = fsock
iraf.txdump(textfiles=phot_file, \
fields='xcenter,ycenter,id,msky,area,flux,mag,merr', \
expr='yes', headers='yes', parameters='no')
sys.stdout = saveout
fsock.close()
sfile = open(image[:-12] + '_txtdump_tmp.dat')
rfile = open(image[:-12] + '_txtdump.dat', 'w')
for s in sfile:
rfile.write(s.replace('INDEF', ' 99.99 '))
sfile.close()
rfile.close()
os.remove(image[:-12] + '_txtdump_tmp.dat')
def _get_photometry(self, seeing=None, aperture=None):
""" Get the photometry for the target.
If the target is a standard star, aperture photometry will be performed. For the moment nothing is done with
the others, but in due time (TODO) photometry.py will be included here. """
if self.objtype is not "standards":
return None
basename = "standards"
fd, coords_file = tempfile.mkstemp(prefix=basename, suffix=".coords")
os.write(fd, "{0} {1} \n".format(self.RA, self.DEC))
os.close(fd)
# If aperture was not given by the user, try and use the seeing as a reference for default values
if not aperture:
try:
seeing = self.header.hdr[self.header.seeingk]
aperture = 3 * seeing
except ValueError: # keyword was not correctly guessed
pass
# If aperture exists, do the photometry using it
if aperture:
annulus = 2 * aperture
dannulus = max([aperture, 3]) # minimum of 3 pixels thickness for the sky annulus
fd, photfile_name = tempfile.mkstemp(".mag.1")
utilities.if_exists_remove(photfile_name)
kwargs = dict(output=photfile_name, coords=coords_file, salgorithm='median',
wcsin='world', fwhm=seeing, gain=self.header.gaink, exposure=self.header.exptimek,
airmass=self.header.airmassk, annulus=annulus, dannulus=dannulus,
apertures=aperture, verbose="no", verify="no", interac="no")
iraf.phot(self.header.im_name, **kwargs)
[counts] = iraf.txdump(photfile_name, 'FLUX', 'yes', Stdout=subprocess.PIPE)
else:
sys.exit("\n \n Sorry, no aperture was passed by you, and a seeing keyword was not "
"found in the header. \n\n ")
utilities.if_exists_remove(coords_file)
return float(counts)
def _getfrommagfile(magfile,get_t=True,get_apert=True,get_others=True):
res = []
if get_t:
ir.txdump(magfile, fields='otime', expr='yes', headers=False, parameters=False, Stdout=j2s('__obs__'))
try: t = np.loadtxt(j2s('__obs__'),)
except ValueError: t = np.loadtxt(j2s('__obs__'), usecols=0, dtype=object)
res.append(t)
os.remove(j2s('__obs__'))
if get_others:
print(magfile)
ir.txdump(magfile, fields='itime,stdev,nsky', expr='yes', headers=False, parameters=False, Stdout=j2s('__isn__'))
exptime, stdev, nsky = np.loadtxt(j2s('__isn__'), unpack=True)
os.remove(j2s('__isn__'))
ir.txdump(magfile, fields='flux', expr='yes', headers=False, parameters=False, Stdout=j2s('__flux__'))
flux = np.loadtxt(j2s('__flux__'), ndmin=2)
os.remove(j2s('__flux__'))
ir.txdump(magfile, fields='area', expr='yes', headers=False, parameters=False, Stdout=j2s('__area__'))
area = np.loadtxt(j2s('__area__'), ndmin=2)
os.remove(j2s('__area__'))
ir.txdump(magfile, fields='mag', expr='yes', headers=False, parameters=False, Stdout=j2s('__mag__'))
mag = np.loadtxt(j2s('__mag__'), ndmin=2)
os.remove(j2s('__mag__'))
res += [exptime, stdev, nsky,flux,area,mag]
if get_apert:
ir.txdump(magfile, fields='rapert', expr='yes', headers=False, parameters=False, Stdout=j2s('__apert__'))
apert = np.loadtxt(j2s('__apert__'))[0]
res.append(apert)
os.remove(j2s('__apert__'))
return res
def manu(dmag0, apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag,
truemag, magerr, centx, centy, z22, z11, midpt, size, fwhm0, img, x1,
y1, arterr):
from pyraf import iraf
import string, os, sys
from numpy import array
a1 = int(fwhm0 + .5)
a2 = int(2. * fwhm0 + .5)
a3 = int(3. * fwhm0 + .5)
a4 = int(4. * fwhm0 + .5)
a5 = int(5. * fwhm0 + .5)
ap = str(a1) + "," + str(a2) + "," + str(a3)
fdmag = 10**(-0.4 * float(dmag0))
iraf.delete(",_snfit.fit?,skyfit.fit?", ve='no')
iraf.imarith("snfit", "*", fdmag, "_snfit")
iraf.imarith("original", "-", "_snfit", "skyfit")
iraf.display("original",
1,
fill='yes',
xcen=.25,
ycen=.25,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z22,
z1=z11)
z01 = z11 - midpt
z02 = z22 - midpt
s1 = 1
s2 = -int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " ORIGINAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='yes',
mark="none",
inter='no',
label='yes',
txsize=2)
iraf.display("_snfit",
1,
erase='no',
fill='yes',
xcen=.25,
ycen=.75,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z02,
z1=z01)
iraf.delete("tmptbl", ve='no')
tmptbl = iraf.txdump(img + ".sn.als", "xcen,ycen", expr='yes', Stdout=1)
ff = open('tmptbl', 'w')
for i in tmptbl:
ff.write(i)
ff.close()
lra = int((2 * size * fwhm0) * 2)
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="circle",
number='yes',
nyoffset=lra,
radi=a2,
txsize=2,
inter='no')
s1 = 1
s2 = -int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " FITTED")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
iraf.display("skyfit",
1,
erase='no',
fill='yes',
xcen=.75,
ycen=.25,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z22,
z1=z11)
s1 = 1
s2 = -int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " RESIDUAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
# newmag=truemag
# for i in range(0,len(truemag)):
# try:
# newmag[i]=float(truemag[i])+float(dmag0)
# except:
# newmag[i]=float(dmag0)
newmag = list(array(truemag) + float(dmag0))
print "***************************************************************************"
print "#id x_ori y_ori x y ap_ori ap_bgsub fit_mag err_art err_fit"
for i in range(len(fitmag)):
print "SN", i, str(centx[i] + x1), str(centy[i] + y1), str(
centx[i]), str(centy[i]), " ", str(apori3[i]), " ", str(
apmag3[i]), " ", str(newmag[i]), " ", str(arterr), " ", str(
magerr[i])
print "**************************************************************************"
return apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag, truemag, magerr, centx, centy, newmag
if __name__ == "__main__":
iraf.imred()
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)s %(levelname)s: %(message)s')
logger = logging.getLogger(__name__)
#iraf.reset(use_new_imt="no")
root = sys.argv[1]
logger.info("Root: " + sys.argv[1])
fitsExt = ".fits"
cooFnRgx = "*.coo"
bfns = [os.path.splitext(os.path.relpath(os.path.join(dirpath, f), root))[0]
for dirpath, dirnames, files in os.walk(root)
for f in fnmatch.filter(files, cooFnRgx)]
bfns.sort()
for bfn in bfns:
logger.debug("Doing photometry on " + bfn)
phot(root + bfn, root + bfn + ".mag")
txdmpFnRgx = "*.txdmp"
txdmpFns = [os.path.relpath(os.path.join(dirpath, f), root)
for dirpath, dirnames, files in os.walk(root)
for f in fnmatch.filter(files, txdmpFnRgx)]
txdmpFns.sort()
for fn in txdmpFns:
#txdump *.mag.1 image,id,mag,otime yes > output.txt
iraf.txdump(textfiles = "@" + root + fn, fields= "id,mag,rapert,ifilter", expr = "yes", Stdout = root + fn + ".res")
def performPhotometry(task, logger):
#iraf.prcacheOff()
[iraf.unlearn(t) for t in ('phot','pstselect','psf','allstar')]
iraf.set(imtype="fits,noinherit") # set image output format
iraf.set(clobber="yes")
hdu=pyfits.open(task['images'])[0]
hdr = hdu.header
imdata = hdu.data
for key,value in task['fits'].iteritems():
task[key] = hdr.get(value,1)
#Sextractor to find stars; add an object for the force detect
logger.info('Running SExtractor on [%s]' % os.path.basename(task['images']))
sex = sextractor.SExtractor()
makeSexConfig(sex,task)
sex.run(task['images'])
catalog = sex.catalog()
#Set up image parameters
MIN_X = max(1,int(task['numpixx']*task['filtfactor']))
MIN_Y = max(1,int(task['numpixy']*task['filtfactor']))
MAX_X = int(task['numpixx']*(1-task['filtfactor']))
MAX_Y = int(task['numpixy']*(1-task['filtfactor']))
AREAXY = '[%s:%s,%s:%s]' % (MIN_X, MAX_X, MIN_Y, MAX_Y)
AREANO = '[%s:%s,%s:%s]' % (MIN_X, MAX_X-2*MIN_X, MIN_Y, MAX_Y-2*MIN_Y)
try:
task['pixscale'] = abs(hdr.get('CD1_1'))*3600.
except TypeError:
task['pixscale'] = abs(hdr.get('CDELT1'))*3600.
task['seeing'] = np.median( sorted([i['FWHM_IMAGE'] for i in catalog])[:int(-len(catalog)*0.5)] ) #Take the median of the "bottom" 50% of objects
logger.info('--> %s SExtractor detected bright objects in the field' % (len(catalog),) )
logger.info('--> %0.2f median FWHM of bright objects in the field, in arcsec' % (task['seeing']*task['pixscale'],))
task['objects'] = [(i['ALPHA_J2000'],i['DELTA_J2000']) for i in catalog]
task['objects'].append(task['objwcs'])
task['objectlist'] = open(os.path.join(task['output_directory'],'objectlist'),'w')
task['objectlist'].write('\n'.join([' %s %s' % (i[0],i[1]) for i in task['objects']]))
task['objectlist'].close()
logger.info('Running iraf.imstat')
irafoutput = iraf.imstat(images=task['images']+AREANO,fields='midpt,min,max,stddev', format=0, Stdout=1)
task['nimgs'] = hdr.get('NIMGS',1)
task['gain'] *= task['nimgs']*2/3.
task['ron'] *= np.sqrt(task['nimgs'])/task['nimgs']*constants.INTERPSM[task['band']]
task['datamean'], task['datamin'], task['datamax'], task['datastdev'] = map(float, irafoutput[0].split())
irafoutput = iraf.imstat(images=task['images'],fields='stddev,midpt',nclip=25,format=0,cache='yes',Stdout=1)
task['skynoise'], task['datamean'] = map(float, irafoutput[0].split() )
task['skynoise'] *= constants.INTERPSM[task['band']]
task['airmass'] = hdr.get('AIRMASS',1)
task['zmag'] -= (float(task['airmass'])-1.0)*constants.extinction_coefficients[task['band']]
task['match_proximity'] = 2.5 * task['seeing']
logger.info('--> %5.2f counts: Sky noise, corrected for drizzle imcombine' % task['skynoise'])
logger.info('--> %5.2f Median count value, after background subtraction' % task['datamean'])
logger.info('--> %5.2f Airmass' % task['airmass'])
#prepare temp files that iraf will use
for filename in ('photfile','pstfile','psfimg','opstfile','groupfile','allstarfile','rejfile','subimage'):
task[filename] = open(os.path.join(task['output_directory'],filename),'w')
task[filename].close()
#iraf.phot to get APP magnitudes
logger.info('Running iraf.apphot.phot')
#apsizes = [i*task['faperture']*task['seeing'] for i in (0.4,0.5,0.6,0.8,1.0,1.2,1.5,2.0,2.5,3.0)]
#irafapsizes = ','.join(['%.2f' % i for i in apsizes])
irafapsizes = '%0.2f' % (task['faperture']*task['seeing'])
kwargs = dict(image=task['images'],coords=task['objectlist'].name,
output=task['photfile'].name,
interac='no',scale=1,
fwhmpsf=task['seeing'],
wcsin='world', wcsout='physical',
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
aperture= irafapsizes,
zmag=task['zmag'],
annulus=task['fannulus']*task['seeing'],
dannulus=task['fdannulus']*task['seeing'],
calgorithm='gauss',
cbox = 1.5*task['seeing'],
maxshift=2.0*task['seeing'],
mode="h",Stdout=1,verify=0)
iraf.phot(**kwargs)
if task['band'] not in constants.infrared:
#iraf.pstselect to choose objects for PSF modelling
logger.info('Running iraf.daophot.pstselect')
kwargs = dict(image=task['images'],
photfile=task['photfile'].name,pstfile=task['pstfile'].name,
maxnpsf=task['pstnumber'],
wcsin='physical',
wcsout='physical',
interac="no",verify='no',scale=1,
fwhmpsf=task['seeing'],
datamin=0,
datamax=task['datamax'],
psfrad=3.0*task['seeing'],
fitrad=1.0*task['seeing'],
recente='yes',
nclean=task['nclean'],
mode="h",Stdout=1)
iraf.pstselect(**kwargs)
#iraf.psf to model PSF
logger.info('Running iraf.daophot.psf')
kwargs = dict( image=task['images'],
photfile=task['photfile'].name,
pstfile=task['pstfile'].name,
psfimage=task['psfimg'].name,
opstfile=task['opstfile'].name,
groupfile=task['groupfile'].name,
wcsin='physical',wcsout='physical',
interac="no",verify="no",scale=1,
fwhmpsf=task['seeing'],
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
function=task['func'],
varorder=task['varorder'],
saturat='no',
psfrad=3.0*task['seeing'],
fitrad=1.*task['faperture']*task['seeing'],
nclean=task['nclean'],
mergerad=1.5*task['seeing'],
mode='h',Stdout=1)
iraf.psf(**kwargs)
logger.info('Running iraf.daophot.allstar')
#iraf.allstars to compute PSF photometry; recenter with recenter='yes', mergerad=<value> to avoid duplicate detection
kwargs = dict(image=task['images'],
photfile=task['photfile'].name,
wcsin='physical',
wcsout='physical',
psfimage=task['psfimg'].name,
allstarf=task['allstarfile'].name,
rejfile=task['rejfile'].name,
subimage=task['subimage'].name,
verbose=1,verify='no',scale=1,
fwhmpsf=task['seeing'],
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
function=task['func'],
varorder=task['varorder'],
psfrad=3.*task['seeing'],
fitrad=1.*task['faperture']*task['seeing'],
recenter='yes',
mergerad=1.5*task['seeing'],
mode='h',Stdout=1)
iraf.allstar(**kwargs)
#Parse both photometry, convert to RA,DEC,MAG,MAGERR
logger.info('iraf tasks complete. Parsing results and calibrating')
photometry = {}
photometry['APP'] = iraf.txdump(textfiles=task['photfile'].name,
fields='XCENTER,YCENTER,MAG,MERR',expr='yes',
headers='no',Stdout=1)
if task['band'] not in constants.infrared:
photometry['PSF'] = iraf.txdump(textfiles=task['allstarfile'].name,
fields='XCENTER,YCENTER,MAG,MERR',expr='yes',
headers='no',Stdout=1)
for phototype in photometry:
kwargs = dict(input='STDIN',
output='STDOUT',
insystem='%s physical' % task['images'],
outsystem='%s world' % task['images'],
ilatuni='physical',
ilnguni='physical',
olnguni='degrees',
olatuni='degrees',
ilngfor='%10.7f',
ilatfor='%10.7f',
olngfor='%10.5f',
olatfor='%10.5f',
Stdin=photometry[phototype],Stdout=1)
photometry[phototype] = [i.split() for i in iraf.skyctran(**kwargs) if i and not i.startswith('#') and 'INDEF' not in i]
photometry[phototype] = [map(float,(i[4],i[5],i[2],i[3])) for i in photometry[phototype] ] #Now we have [(ra,dec,'mag','mageerr'),...]
results = calibrate((task['objwcs'][0],task['objwcs'][1]),task,photometry,logger)
# if 'PSF' not in results:
return results
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2, plotdir=".", box=15):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
# Read values from .ec file
ecfile= image+".ec"
filter_value=''.join(ecfile).split('.',1)[0]
fwhm_value = fwhm
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
try:
with open(''.join(ecfile),'r') as f:
for line in f:
if "airmass" in line:
airmass_value = line.split('=',1)[1]
else:
airmass_value = 1
if "FWHM" in line:
print line
fwhm_value = line.split('FWHM=',1)[1]
fwhm_value = fwhm_value.rsplit("aperture")[0]
except:
pass
print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*2) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*5
print aperture_rad, sky_rad
if (not plot_only):
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
# Check if files in list, otherwise exit
if not ecfile:
print "No .ec files in directory, exiting"
sys.exit()
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 15. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filters" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("ph_mag","<f4"), ("stdev","<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(4)
dimY = int(np.ceil(len(m)*1./4))
outerrad = sky_rad+10
cutrad = outerrad + 15
plt.suptitle("FWHM="+str(fwhm_value))
k = 0
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( k < len(m)):
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except:
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
continue
else:
continue
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=5.)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+10)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
k = k+1
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
import numpy as np
import astropy.io.fits as pyfits
from pyraf import iraf
from iraf import noao, digiphot, apphot, phot, txdump
import matplotlib
from matplotlib import pyplot as plt
iraf.noao.digiphot.apphot.phot.interac = 'no'
iraf.noao.digiphot.apphot.phot.centerp.calgori = 'none'
iraf.noao.digiphot.apphot.phot.photpar.apertur = '3.9'
iraf.noao.digiphot.apphot.phot.fitskypars.salgori = 'constant'
iraf.noao.digiphot.apphot.phot.cache = 'yes'
iraf.noao.digiphot.apphot.phot.salgori = 'constant'
iraf.phot(image='hff_abells1063-hffpar_f140w_wht.fits',
interactive='no',
calgorithm='none',
salgori='constant',
coords='box_coords_f140w.coo',
output='PHOT_WHT_hff_abells1063_par_f140w.txt',
verify='no')
iraf.txdump(textfile='PHOT_WHT_hff_abells1063_par_f140w.txt',
fields='XCEN, YCEN, SUM',
expr='yes',
Stdout='PHOT_WHT_hff_abells1063_par_f140w_OUTFILE.txt')
def build(f):
### is this an MEF file
current_ext = 0
NEXTEND = 0
EXTEND = f[0].header["EXTEND"]
if EXTEND == "T":
NEXTEND = f[0].header["NEXTEND"]
current_ext = 1
### create the name of the output MEF psf file
if not f[0].header.has_key("FILENAME"):
os.unlink(opt.filename)
sys.exit("The fits file " + opt.filename + " has no EXPNUM keyword\n")
sexp = f[0].header["FILENAME"]
mef_psf = sexp + "p_psf_iraf.fits"
### create an MEF file that will contian the PSF(s)
import pyfits
fitsobj = pyfits.HDUList()
prihdu = pyfits.PrimaryHDU()
import re
prihdu.header.update("FILENAME", sexp, comment="CFHT Exposure Numebr")
prihdu.header.update("NEXTEND", NEXTEND, comment="number of extensions")
version = re.match(r"\$Rev.*: (\d*.\d*) \$", __Version__).group(1)
prihdu.header.update("MKPSF_V", float(version), comment="Version number of mkpsf")
fitsobj.append(prihdu)
if os.access(mef_psf, os.F_OK):
os.unlink(mef_psf)
fitsobj.writeto(mef_psf)
fitsobj.close()
outfits = pyfits.open(mef_psf, "append")
prihdr = outfits[0].header
import jjkmode
### Get my python routines
from pyraf import iraf
from pyraf.irafpar import IrafParList
### keep all the parameters locally cached.
iraf.set(uparm="./")
### Load the required IRAF packages
iraf.digiphot()
iraf.apphot()
iraf.daophot()
### temp file name hash.
tfile = {}
while current_ext <= NEXTEND:
### this is a psf SCRIPT so the showplots and interactive are off by force
print "Working on image section " + str(current_ext)
iraf.findpars.sharplo = 0
iraf.findpars.sharphi = 0.7
iraf.findpars.roundlo = -0.7
iraf.findpars.roundhi = 0.7
iraf.datapars.datamax = 20000
iraf.datapars.airmass = "AIRMASS"
iraf.datapars.filter = "FILTER"
iraf.datapars.obstime = "TIME-OBS"
iraf.datapars.exposure = "EXPTIME"
iraf.datapars.gain = "GAIN"
iraf.datapars.ccdread = "RDNOISE"
iraf.datapars.fwhmpsf = opt.fwhm
iraf.daopars.nclean = 2
iraf.daopars.psfrad = 5.0 * opt.fwhm
iraf.daopars.fitrad = 0.85 * opt.fwhm
iraf.daopars.function = "gauss"
iraf.centerpars.calgorithm = "centroid"
zero_mag = 26.19
iraf.photpars.zmag = zero_mag
iraf.photpars.apertures = int(0.85 * opt.fwhm)
iraf.fitskypars.annulus = 2 + int(opt.fwhm * 4.00)
iraf.fitskypars.dannulus = int(opt.fwhm * 2.0)
iraf.daophot.verbose = no
iraf.daophot.verify = no
iraf.daophot.update = no
iraf.psf.interactive = no
iraf.pstselect.interactive = no
iraf.datapars.saveParList()
iraf.fitskypars.saveParList()
iraf.centerpars.saveParList()
iraf.findpars.saveParList()
iraf.photpars.saveParList()
tfiles = [
"coo_bright",
"coo_ok",
"coo_faint",
"mag_all",
"mag_bright",
"mag_ok",
"mag_good",
"mag_best",
"pst_in",
"pst_out",
"pst_out2",
"prf",
"psg_org",
"psg",
"psf_1.fits",
"psf_2.fits",
"psf_final.fits",
"psf_3.fits",
"psf_4.fits",
"mag_pst",
"coo_pst",
"nst",
"nrj",
"seepsf.fits",
"sub.fits",
"fwhm",
"apcor",
]
for file in tfiles:
extname = "chip" + str(f[current_ext].header.get("IMAGEID", str(current_ext))).zfill(2)
tfile[file] = sexp + "_" + extname + "." + file
if os.access(tfile[file], os.F_OK):
os.unlink(tfile[file])
if EXTEND == "T":
this_image = opt.filename + "[" + extname + "]"
else:
this_image = opt.filename
gain = f[current_ext].header.get("GAIN", 1.0)
#### set sky/sigma parameters specific to this frame.
(skyvalue, sigma) = jjkmode.stats(f[current_ext].data)
import math
sigma = math.sqrt(skyvalue / gain)
datamin = float(skyvalue) - 8.0 * float(sigma)
print "Determined sky level to be " + str(skyvalue) + " +/-" + str(sigma)
iraf.datapars.datamin = datamin
iraf.datapars.sigma = float(sigma)
iraf.datapars.saveParList()
iraf.fitskypars.skyvalue = skyvalue
iraf.fitskypars.saveParList()
### find the bright stars in the image.
print "sextracting for stars in " + this_image
###iraf.daophot.daofind(image=this_image,
### output=tfile['coo_bright'],threshold=4.0)
os.system(
"sex -c /home/cadc/kavelaar/12kproc/config/default.sex -SATUR_LEVEL 25000 -CATALOG_NAME "
+ tfile["coo_bright"]
+ " "
+ this_image
)
### print "finding stellar locus in sround/ground space"
print "clipping using star_class > 0.85 "
fcoo = open(tfile["coo_bright"], "r")
lines = fcoo.readlines()
fcoo.close()
import numarray, math
fout = open(tfile["coo_ok"], "w")
for line in lines:
if re.match(r"^#", line) or re.search(r"INDEF", line):
continue
values = line.split()
star_class = float(values[2])
if star_class > 0.75:
fout.write(line)
fout.close()
print "Measuring photometry for psf candidate stars in " + tfile["coo_ok"]
iraf.daophot.phot(image=this_image, coords=tfile["coo_ok"], output=tfile["mag_bright"])
### do this selection in 2 steps because of the way IRAF handles INDEFs
print "Selecting stars that have good centroids and magnitudes "
iraf.pselect(
tfile["mag_bright"], tfile["mag_ok"], "(CIER==0)&&(PIER==0)&&(SIER==0)&&(MSKY>0)&&(MSKY<2e5)&&(MSKY!=INDEF)"
)
print "Selecting stars that have normal sky levels"
condition = "(abs(MSKY -" + str(skyvalue) + ") < 5.0*" + str(sigma) + ")"
iraf.pselect(tfile["mag_ok"], tfile["mag_good"], condition)
a = iraf.txdump(tfile["mag_good"], "SSKEW", iraf.yes, Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean ** 2)
limit = mean + 2 * stddev
os.unlink(tfile["mag_good"])
condition = condition + " && SSKEW < " + str(limit)
iraf.pselect(tfile["mag_ok"], tfile["mag_good"], condition)
print "Choosing the psf stars"
iraf.pstselect(image=this_image, photfile=tfile["mag_good"], pstfile=tfile["pst_in"], maxnpsf=25)
## construct an initial PSF image
print "computing psf with neighbor stars based on complete star list"
iraf.psf.mode = "a"
iraf.psf(
image=this_image,
photfile=tfile["mag_bright"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_1.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg_org"],
varorder=0,
)
try:
print "subtracting the psf neighbors and placing the results in " + tfile["sub.fits"]
iraf.daophot.nstar(
image=this_image,
groupfile=tfile["psg_org"],
psfimage=tfile["psf_1.fits"],
nstarfile=tfile["nst"],
rejfile=tfile["nrj"],
)
iraf.daophot.substar(
image=this_image,
photfile=tfile["nst"],
exfile=tfile["pst_in"],
psfimage=tfile["psf_1.fits"],
subimage=tfile["sub.fits"],
)
a = iraf.daophot.txdump(tfile["nst"], "chi", "yes", Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean ** 2)
limit = mean + 2.5 * stddev
print "Selecting those psf stars with CHI^2 <" + str(limit) + " after fitting with trial psf"
iraf.pselect(tfile["nst"], tfile["mag_best"], "CHI < " + str(limit))
os.unlink(tfile["pst_out"])
## os.unlink(tfile['psg'])
## rebuild the PSF file with the psf stars that fit well..
## using the neighbor subtracted image
print "Rebuilding the PSF"
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_2.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=0,
)
print "re-subtracting with rebuilt psf"
os.unlink(tfile["nst"])
os.unlink(tfile["nrj"])
iraf.daophot.nstar(
image=this_image,
groupfile=tfile["psg"],
psfimage=tfile["psf_2.fits"],
nstarfile=tfile["nst"],
rejfile=tfile["nrj"],
)
os.unlink(tfile["sub.fits"])
iraf.daophot.substar(
image=this_image,
photfile=tfile["nst"],
exfile=tfile["pst_in"],
psfimage=tfile["psf_2.fits"],
subimage=tfile["sub.fits"],
)
os.unlink(tfile["psg"])
os.unlink(tfile["pst_out"])
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_3.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=0,
)
os.unlink(tfile["nrj"])
os.unlink(tfile["nst"])
iraf.daophot.nstar(
image=this_image,
groupfile=tfile["psg"],
psfimage=tfile["psf_3.fits"],
nstarfile=tfile["nst"],
rejfile=tfile["nrj"],
)
a = iraf.daophot.txdump(tfile["nst"], "chi", "yes", Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean ** 2)
limit = mean + 2 * stddev
limit = 2.0
# print "Selecting those psf stars with CHI^2 < "+str(limit)+" after fit with GOOD psf"
os.unlink(tfile["mag_best"])
iraf.pselect(tfile["nst"], tfile["mag_best"], "CHI < " + str(limit))
print "Building final PSF.... "
os.unlink(tfile["sub.fits"])
iraf.daophot.substar(
image=this_image,
photfile=tfile["nst"],
exfile=tfile["pst_in"],
psfimage=tfile["psf_3.fits"],
subimage=tfile["sub.fits"],
)
os.unlink(tfile["psg"])
os.unlink(tfile["pst_out"])
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_final.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=0,
)
print "building an analytic psf for the FWHM calculations"
os.unlink(tfile["pst_out"])
os.unlink(tfile["psg"])
iraf.daophot.psf(
image=tfile["sub.fits"],
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_4.fits"],
opstfile=tfile["pst_out"],
groupfile=tfile["psg"],
varorder=-1,
)
except:
print sys.exc_info()[1]
print "ERROR: Reverting to first pass psf"
tfile["psf_final.fits"] = tfile["psf_1.fits"]
iraf.daophot.psf(
image=this_image,
photfile=tfile["mag_best"],
pstfile=tfile["pst_in"],
psfimage=tfile["psf_4.fits"],
opstfile=tfile["pst_out2"],
groupfile=tfile["psg"],
varorder=-1,
)
psf_ap = iraf.photpars.apertures
ap1 = int(psf_ap)
ap2 = int(4.0 * opt.fwhm)
apcor = "INDEF"
aperr = "INDEF"
if 0:
# try
### now that we have the psf use the output list of psf stars
### to compute the aperature correction
lines = iraf.txdump(tfile["pst_out"], "xcen,ycen,mag,id", iraf.yes, Stdout=tfile["coo_pst"])
## set the lower ap value for the COG (normally set to 2)
if ap1 < 3:
smallap = 1
else:
smallap = 2 - ap1 + 1
ap1 = 2
ap2 = int(math.floor(4.0 * opt.fwhm))
naperts = ap2 - ap1 + 1
iraf.photpars.apertures = str(ap1) + ":" + str(ap2) + ":1"
iraf.photpars.saveParList()
iraf.daophot.phot(image=this_image, coords=tfile["coo_pst"], output=tfile["mag_pst"])
iraf.photcal()
iraf.photcal.mkapfile(
tfile["mag_pst"],
naperts=naperts,
apercors=tfile["apcor"],
smallap=smallap,
verify="no",
gcommands="",
interactive=0,
)
fin = open(tfile["apcor"], "r")
lines = fin.readlines()
values = lines[2].split()
apcor = values[1]
aperr = values[2]
# except:
## compute the FWHM of the PSF image using the analytic PSF (VarOrd=-1)
psf_file = pyfits.open(tfile["psf_4.fits"])
fwhm = psf_file[0].header.get("PAR1", 99.0) + psf_file[0].header.get("PAR2", 99.0)
psf_file.close()
# ## Open the psf.fits
infits = pyfits.open(tfile["psf_final.fits"])
hdu = infits[0]
inhdu = hdu.header
inhdu.update("XTENSION", "IMAGE", before="SIMPLE")
inhdu.update("PCOUNT", 0, after="NAXIS2")
inhdu.update("GCOUNT", 1, after="PCOUNT")
del hdu.header["SIMPLE"]
del hdu.header["EXTEND"]
inhdu.update("EXTNAME", extname, comment="image extension identifier")
# inhdu.update("SLOW",slow,comment="SROUND low cutoff")
# inhdu.update("SIGH",sigh,comment="SROUND high cutoff")
inhdu.update("PFWHM", fwhm, comment="FWHM of stars based on PSF fitting")
inhdu.update("ZMAG", zero_mag, comment="ZMAG of PSF ")
inhdu.update("BCKG", skyvalue, comment="Mean sky level in counts")
inhdu.update("BCKG_STD", sigma, comment="standard deviation of sky in counts")
inhdu.update("AP1", psf_ap, comment="Apperture used for PSF flux")
inhdu.update("AP2", ap2, comment="Full Flux aperture")
inhdu.update("APCOR", apcor, comment="Apperture correction (ap1->ap2)")
inhdu.update("APERR", apcor, comment="Uncertainty in APCOR")
# ### append this psf to the output images....
print "Sticking this PSF onto the output file"
f[current_ext].header.update("PFWHM", fwhm, comment="FWHM of stars based on PSF fitting")
f[current_ext].header.update("BCKG", skyvalue, comment="Mean sky level in counts")
f[current_ext].header.update("BCKG_STD", sigma, comment="Standard deviation of sky in counts")
f.flush()
outfits.append(hdu)
outfits.flush()
infits.close()
### remove the temp file we used for this computation.
for tf in tfile.keys():
if os.access(tfile[tf], os.F_OK):
os.unlink(tfile[tf])
current_ext = current_ext + 1
outfits.close()
return mef_psf
def organizeFiles():
# Create files that are usually generated by mkimset.
# We have more information about how to properly group our
# files together.
imsetsFilename = 'standards.imageSets'
obsparFilename = 'standards.obsParams'
shiftsFilename = 'standards.shifts'
iMagniFilename = 'standards.instMag'
imsets = open(imsetsFilename, 'w')
obspar = open(obsparFilename, 'w')
shifts = open(shiftsFilename, 'w')
for star in stars:
roots = []
for filter in filters:
dir = star.lower() + '_' + filter.lower()
if filter == 'Lp':
dir += '2'
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = []
for ii in images:
fileRoot = ii.split('/')[-1]
imageRoots.append(fileRoot.replace('.fits', ''))
roots.append(imageRoots)
print star, filter, len(imageRoots)
longCount = np.array([len(r) for r in roots]).max()
print 'Long Count for %s = %d' % (star, longCount)
for ii in range(longCount):
imsets.write('%5s : ' % star)
for ff in range(len(roots)):
if ii < len(roots[ff]):
# Write obsParams
ir.txdump(roots[ff][ii] + '.phot.mag',
'IMAGE,IFILTER,ITIME,XAIRMASS,OTIME',
'yes',
Stdout=obspar)
# Write shifts
posInfo = ir.txdump(roots[ff][ii] + '.phot.mag',
'IMAGE,XCENTER,YCENTER',
'yes',
Stdout=1)
posFields = posInfo[0].split()
shiftX = -1.0 * float(posFields[1])
shiftY = -1.0 * float(posFields[2])
shifts.write('%-10s %7.2f %7.2f\n' %
(posFields[0], shiftX, shiftY))
# Write imageSet
imsets.write('%-10s ' % (roots[ff][ii] + '.fits'))
else:
imsets.write('%-10s ' % ('INDEF'))
imsets.write('\n')
imsets.close()
obspar.close()
shifts.close()
# Now run mknobsfile
ir.delete(iMagniFilename)
ir.digiphot()
ir.photcal()
ir.unlearn('mknobsfile')
ir.mknobsfile.obsparams = obsparFilename
ir.mknobsfile.shifts = shiftsFilename
ir.mknobsfile('*.phot.mag',
','.join(filters),
imsetsFilename,
iMagniFilename,
verbose='no',
wrap='no')
# Run mkconfig
ir.delete('standards.config')
ir.unlearn('mkconfig')
ir.mkconfig('standards.config',
'fphotcal_ukirt_faint.dat',
'fstandards.instMag.dat',
'tphotcal_ukirt_faint.dat',
check='no',
edit='no')
def iraf_phot(f,x,y,zmag=26.5,apin=10,skyin=15,skywidth=10):
"""Compute the magnitude of the star at location x/y"""
import pyfits
import re
infits=pyfits.open(f,'update')
f=re.sub(r'.fits$','',f)
### Get my python routines
from pyraf import iraf
from pyraf.irafpar import IrafParList
### keep all the parameters locally cached.
iraf.set(uparm="./")
iraf.set(imtype="fits")
### Load the required IRAF packages
iraf.digiphot()
iraf.apphot()
iraf.daophot()
### temp file name hash.
tfile={}
iraf.datapars.datamax=60000
iraf.datapars.datamin=-1000
iraf.datapars.airmass='AIRMASS'
iraf.datapars.filter='FILTER'
iraf.datapars.obstime='TIME-OBS'
iraf.datapars.exposure='EXPTIME'
iraf.datapars.gain='GAIN'
iraf.datapars.ccdread='RDNOISE'
iraf.datapars.fwhmpsf=5.0
iraf.centerpars.calgorithm='centroid'
iraf.photpars.zmag=zmag
iraf.photpars.apertures=apin
iraf.fitskypars.annulus=skyin
iraf.fitskypars.dannulus=skywidth
iraf.daophot.verbose=iraf.no
iraf.daophot.verify=iraf.no
iraf.daophot.update=iraf.no
iraf.psf.interactive=iraf.no
iraf.pstselect.interactive=iraf.no
iraf.datapars.saveParList()
iraf.fitskypars.saveParList()
iraf.centerpars.saveParList()
iraf.findpars.saveParList()
iraf.photpars.saveParList()
tfiles = ['coo','mag']
for file in tfiles:
extname=f
tfile[file]=extname+"."+file
if ( os.access(tfile[file],os.F_OK) ):
os.unlink(tfile[file])
this_image=f
fd = open(tfile['coo'],'w')
fd.write('%f %f\n' % ( x, y) )
fd.close()
print "Measuring photometry psf star in "+tfile['coo']
iraf.daophot.phot(image=this_image,
coords=tfile['coo'],
output=tfile['mag'])
import string
a=iraf.txdump(tfile['mag'],"MAG,XCEN,YCEN",iraf.yes,Stdout=1)
(mag,x,y)=string.split(a[0])
inhdu=infits[0].header
inhdu.update("PSFMAG",float(mag),comment="PSF Magnitude")
inhdu.update("PSF_X",float(x),comment="PSF Magnitude")
inhdu.update("PSF_Y",float(y),comment="PSF Magnitude")
inhdu.update("ZMAG",zmag,comment="ZMAG of PSF ")
## now measure using a smaller aperture to get aper correction
iraf.photpars.apertures=apin*3.0
os.unlink(tfile['mag'])
iraf.daophot.phot(image=this_image,
coords=tfile['coo'],
output=tfile['mag'])
a=iraf.txdump(tfile['mag'],"MAG,XCEN,YCEN",iraf.yes,Stdout=1)
(magout,x,y)=string.split(a[0])
inhdu.update("APCOR",float(magout)-float(mag),comment="AP_OUT - AP_IN")
inhdu.update("AP_IN",apin*3.0,comment="Small aperature")
inhdu.update("AP_OUT",apin,comment="Large aperture")
# ### append this psf to the output images....
infits.close()
### remove the temp file we used for this computation.
#for tf in tfile.keys():
# if os.access(tfile[tf],os.F_OK):
# os.unlink(tfile[tf])
return 0
def Main( obj, make_plots=True ):
"""
Does absolute photometry for one or more stars given a list of images.
Output is generated in the form of two types of file:
1. starX_absphot.dat for X=0,1,2,... files contain columns with the
more detailed output for each of the stars, with each line
corresponding to a different image.
2. absolute.phot file containing the important numerical columns for
each of the stars; it's supposed to be the most convenient output
for use with numpy and for generating relative photometry.
Summary plots are also generated by default:
Figure 1:
** Top left = traces of xy drift for each of the stars
** Bottom left = airmass versus time
** Top right = absolute flux versus time for each star
** Bottom right = sky annulus value as a function of time for each star
Figure 2:
?? Plots image number versus measured scatter divided by the calculated Poisson noise ??
"""
# Get the list of reduced images in the data directory:
red_images = np.loadtxt( obj.red_image_list, dtype='str' )
if obj.datapars[ 'obstime' ]!='':
obstimes = np.zeros( len(red_images) )
# Read the headers and put the observation times into an array:
for i in range( len(red_images) ):
h = pyfits.getheader( red_images[i] )
obstimes[i] = h[ obj.datapars['obstime'] ]
ixs = np.argsort( obstimes )
red_images = red_images[ ixs ]
if obj.nimages_total==None:
obj.nimages_total = len( red_images )
if obj.goodbad_flags==None:
obj.goodbad_flags = np.ones( obj.nimages_total )
obj.nimages_good = int( np.sum( obj.goodbad_flags ) )
# Set the parameters for the various IRAF routines:
set_iraf_params( obj )
# Work out how many stars we're doing photometry for:
if np.rank( obj.coords_input_files )>0:
obj.nstars = len( obj.coords_input_files )
else:
obj.coords_input_files = [ obj.coords_input_files ]
obj.nstars = 1
obj.ncomparisons = obj.nstars-1
# Determine whether we're using the coordinates from the first frame only then updating
# as we go along, or if we have a list of coordinates for each of the frames already:
obj.dat_files = []
if obj.coords_input_type=='init':
coords_temps = []
elif obj.coords_input_type=='list':
coords_lists = []
# Open the various dat files that will receive the output from iraf.phot and give them
# a header line recording the aperture parameters used for the photometry:
open_dat_files = []
ap_string = '# APERTURE RADIUS = %.2f pixels \n# ANNULUS RADIUS = %.2f pixels \n# DELTA ANNULUS = %.2f pixels\n\n' % \
( float( iraf.photpars.apertures ), float( iraf.fitskypars.annulus ), float( iraf.fitskypars.dannulus ) )
# Refer to the IRAF Phot handbook for further information on the following:
# flux = sky-subtracted ADU counts due to star (i.e. sum-sky)
# sum = total ADU counts within aperture (i.e. star+sky)
# area = number of pixels within aperture
# msky = mean sky value ADU counts per pixel (i.e. sky=area*msky)
# stdev = standard deviation of the sky values per pixel in ADU counts
# We will extract the following fields for the output:
txdump_fields = [ 'otime', 'xairmass', 'xcenter', 'ycenter', 'flux', 'itime', 'area', 'msky', 'stdev', \
'cerror', 'serror', 'perror', 'image' ]
# Set up a dictionary to hold the field name and its corresponding
# column index in the txdump output file:
txdump_field_ixs = {}
for field in txdump_fields:
txdump_field_ixs[ field ] = i
# Make a header string containing the column names for the txdump output:
txdump_fields_str = '# '
for i in range( len( field_items ) ):
for j in range( len( field_items ) ):
if field_items[ j ][ 1 ]==i:
txdump_fields_str += field_items[ j ][ 0 ]
if i<len( field_items )-1:
txdump_fields_str += ', '
else:
continue
# Write the column headings to the files that will hold the photometry output:
for j in range( obj.nstars ):
dat_file = ( obj.analysis_dir+'/star%i_absphot.dat' % j).replace( '//' , '/' )
obj.dat_files = obj.dat_files + [ dat_file ]
if os.path.isfile( dat_file ): os.remove( dat_file )
open_dat_files = open_dat_files + [ open( dat_file,'w' ) ]
open_dat_files[ j ].write( ap_string )
# Replace some of the txdump keywords for the column header that gets written to file:
columns_header = str( txdump_fields_str ).replace( 'xairmass', 'airmass' )
columns_header = str( columns_header ).replace( 'area' , 'ap_area' )
columns_header = str( columns_header ).replace( 'msky' , 'skyval' )
columns_header = str( columns_header ).replace( 'stdev' , 'skystdv' )
columns_header = columns_header + '\n'
open_dat_files[j].write( columns_header )
if obj.coords_input_type=='init':
# Copy the star coordinate files to temporary files that will be overwritten below at
# the start of each new image to account for shifts in the pointing:
coords_temps = coords_temps + [ ( obj.analysis_dir+'/star%i_coords.temp' % j ).replace( '//' , '/' ) ]
shutil.copyfile( obj.coords_input_files[ j ], coords_temps[ j ] )
elif obj.coords_input_type=='list':
# Otherwise, we must already have a list of coordinates for each of the images, and we
# can read these in as a list of xy-arrays, one for each star:
coords_list = np.loadtxt( obj.coords_input_files[ j ] )
coords_lists = coords_lists + [ coords_list ]
# Now go through each of the images, and do aperture photometry for each of the stars:
for i in range( len( red_images ) ):
print 'Doing photometry for image %i of %i' % ( i+1, len( red_images ) )
if obj.goodbad_flags!=None:
if obj.goodbad_flags[ i ]==0:
print '( ... current image flagged as bad )'
for j in range( obj.nstars ):
coords_temp_filename = ( obj.analysis_dir + '/star%i_coords.temp' % j ).replace( '//', '/' )
if obj.coords_input_type=='list':
# If we're using a list of coordinates that has been determined ahead of time,
# extract the coordinates for the current star on the current image and put them
# into the temporary coordinates file that IRAF will use:
coords_string = '%.2f %.2f \n' % ( coords_lists[ j ][ i, 0 ], coords_lists[ j ][ i, 1 ] )
coords_temp_file = open( coords_temp_filename, 'w' )
coords_temp_file.write( coords_string )
coords_temp_file.close()
# The output of the iraf.phot task will be saved in the temporary mag file:
mag_temp_filename = ( obj.analysis_dir + '/star%i_mag.temp' % j ).replace( '//', '/' )
if os.path.isfile( mag_temp_filename ):
os.remove( mag_temp_filename )
# Do the photometry for the current star on the current image:
iraf.phot( image=red_images[ i ], coords=coords_temp_filename, output=mag_temp_filename )
# Since the mag output file generated by iraf.phot is not very easy to read, we use the
# iraf.txdump routine to extract the fields we're interested in and append these to the
# relevant dat file in nice columns:
iraf.txdump( mag_temp_filename, fields=txdump_fields_str, expr='yes', Stdout=open_dat_files[ j ] )
# The iraf.phot routine will have refined our estimate for the source coordinates, so we can
# use these as our initial guess for the next image, assuming the pointing change between images
# is not too severe:
os.remove( coords_temp_filename )
coords_temp_file = open( coords_temp_filename, 'w' )
iraf.txdump( mag_temp_filename, fields='xcenter,ycenter', expr='yes', Stdout=coords_temp_file )
coords_temp_file.close()
# Save the dat files and remove the temporary mag and coords files:
for j in range( obj.nstars ):
open_dat_files[ j ].close()
mag_temp_filename = obj.analysis_dir + '/star%i_mag.temp' % j
os.remove( mag_temp_filename )
coords_temp_filename = obj.analysis_dir + '/star%i_coords.temp' % j
os.remove( coords_temp_filename )
# If we did not have a list of coordinates for all the images to start with, we can one now using the
# output from above:
if obj.coords_input_type=='init':
coords_init2list( obj, txdump_field_ixs )
# We can also use the information stored in the dat file to generate a reformatted file that is nicer
# to use with numpy, having only float entries:
dats2absphot( obj, txdump_field_ixs )
# Plot some of the output:
if make_plots==True:
plot( obj )
return None
def makeillumination(lista, flatfield): #,outputfile,illum_frame):
import os, glob, string, re
from astropy.io import fits as pyfits
import ntt
from ntt.util import readhdr, readkey3, delete, display_image, defsex, name_duplicate, correctcard
from numpy import compress, array, argmax, argmin, min, argsort, float32
import datetime
MJDtoday = 55927 + (datetime.date.today() -
datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
illum_frame = name_duplicate(
lista[0], 'illum_' + _date + '_' + _filter + '_' + str(MJDtoday), '')
from pyraf import iraf
iraf.images(_doprint=0, Stdout=0)
iraf.imutil(_doprint=0, Stdout=0)
iraf.utilities(_doprint=0, Stdout=0)
iraf.noao(_doprint=0, Stdout=0)
iraf.imred(_doprint=0, Stdout=0)
iraf.ccdred(_doprint=0, Stdout=0)
iraf.digiphot(_doprint=0, Stdout=0)
iraf.daophot(_doprint=0, Stdout=0)
iraf.generic(_doprint=0, Stdout=0)
toforget = [
'digiphot.daophot', 'imutil.imarith', 'image', 'utilities.surfit'
]
for t in toforget:
iraf.unlearn(t)
n = len(lista)
# start loop to read image names from the input file
lista1 = []
iraf.ccdred.verbose = 'no'
ff = open('templist.lst', 'w')
for i in range(0, len(lista)):
ff.write('C' + lista[i] + '\n')
delete('C' + lista[i])
delete('C' + re.sub('.fits', '_sub.fits', lista[i]))
ntt.sofiphotredudef.crosstalk(lista[i], 'C' + lista[i])
iraf.noao.imred.ccdred.ccdproc('C' + lista[i],
output='',
overscan="no",
trim="yes",
ccdtype='',
darkcor='no',
fixpix='no',
zerocor="no",
flatcor='yes',
illumco='no',
trimsec='[1:1024,1:1007]',
biassec='',
flat=flatfield,
illum='')
correctcard('C' + lista[i])
lista1.append('C' + lista[i])
ff.close()
print '\n### prereducing STD frames to compute illumination correction ........'
lista2, skyfile = ntt.sofiphotredudef.skysub(
lista1, readkey3(readhdr(lista1[0]), 'ron'),
readkey3(readhdr(lista1[0]), 'gain'), True)
lista2 = ntt.sofiphotredudef.sortbyJD(lista2)
print '\n### use x on the star and q to continue....'
display_image(lista2[0], 2, '', '', False)
delete('tmpone.coo')
iraf.image.tv.imexamine(lista2[0],
2,
logfile='tmpone.coo',
keeplog='yes',
xformat='',
yformat='',
wcs='logical')
iraf.tvmark(2,
'tmpone.coo',
mark="circle",
number='yes',
label='no',
radii=8,
nxoffse=5,
nyoffse=5,
color=204,
txsize=2)
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x0, y0 = string.split(xycoo[0])
x0 = float(x0)
y0 = float(y0)
xcum0 = readkey3(readhdr(lista2[0]), 'xcum')
ycum0 = readkey3(readhdr(lista2[0]), 'ycum')
iraf.digiphot(_doprint=0, Stdout=0)
iraf.daophot(_doprint=0, Stdout=0)
iraf.noao.digiphot.daophot.datapars.datamin = -1000
iraf.noao.digiphot.daophot.datapars.datamax = 60000
iraf.noao.digiphot.daophot.daopars.function = 'gauss'
iraf.noao.digiphot.daophot.photpars.zmag = 0
namesex = defsex('default.sex')
for i in range(0, len(lista2)):
j = i + 1
xcum = readkey3(readhdr(lista2[i]), 'xcum')
ycum = readkey3(readhdr(lista2[i]), 'ycum')
xx = x0 - xcum0 + xcum
yy = y0 - ycum0 + ycum
# sex objects
os.system('sex ' + lista2[i] + ' -c ' + namesex + '> _logsex')
delete('_logsex')
xpix = iraf.proto.fields('detections.cat', fields='2', Stdout=1)
ypix = iraf.proto.fields('detections.cat', fields='3', Stdout=1)
cm = iraf.proto.fields('detections.cat', fields='4', Stdout=1)
cm = compress((array(xpix) != ''), array(cm, float))
ypix = compress((array(xpix) != ''), array(ypix, float))
xpix = compress((array(xpix) != ''), array(xpix, float))
if len(xpix) > 300:
num = 300
else:
num = len(xpix) - 1
xpix = xpix[argsort(cm)][0:num]
ypix = ypix[argsort(cm)][0:num]
distance = (ypix - yy)**2 + (xpix - xx)**2
xx1, yy1 = xpix[argmin(distance)], ypix[argmin(distance)]
f = open('tmpone.coo', 'w')
f.write(str(xx1) + ' ' + str(yy1) + '\n')
f.close()
display_image(lista2[i], 1, '', '', False)
iraf.tvmark(1,
'tmpone.coo',
mark="circle",
number='yes',
label='no',
radii=8,
nxoffse=5,
nyoffse=5,
color=204,
txsize=2)
answ = 'n'
while answ != 'y':
answ = raw_input('selected the right one [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['y', 'YES', 'yes', 'Y']:
print lista2[i]
delete('pippo.' + str(j) + '.mag')
gggg = iraf.digiphot.daophot.phot(lista2[i],
"tmpone.coo",
output="pippo." + str(j) +
".mag",
verify='no',
interac='no',
Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
else:
print '\n### select the std star'
display_image(lista2[i], 1, '', '', False)
iraf.image.tv.imexamine(lista2[i],
1,
logfile='tmpone.coo',
keeplog='yes',
xformat='',
yformat='',
wcs='logical')
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x2, y2 = string.split(xycoo[0])
f = open('tmpone.coo', 'w')
f.write(str(x2) + ' ' + str(y2) + '\n')
f.close()
delete('pippo.' + str(j) + '.mag')
print '###### new selection ' + str(x2), str(y2)
gggg = iraf.digiphot.daophot.phot(lista2[i],
"tmpone.coo",
output='pippo.' + str(j) +
'.mag',
verify='no',
interac='no',
Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
os.system('ls pippo.*.mag > tempmag.lst')
tmptbl0 = iraf.txdump(textfile="@tempmag.lst",
fields="XCENTER,YCENTER,FLUX",
expr='yes',
Stdout=1)
ff = open('magnitudini', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
# delete the temporary images and files
delete("temp*.fits")
delete('temp*.lst')
delete(illum_frame)
print '\n### fitting the illumination surface...'
aaa = iraf.utilities.surfit('magnitudini',
image=illum_frame,
function="polynomial",
xorder=2,
yorder=2,
xterms="full",
ncols=1024,
nlines=1024,
Stdout=1)
iraf.noao.imred.generic.normalize(illum_frame)
correctcard(lista[0])
data, hdr = pyfits.getdata(illum_frame, 0, header=True)
data0, hdr0 = pyfits.getdata(lista[0], 0, header=True)
delete(illum_frame)
pyfits.writeto(illum_frame, float32(data), hdr0)
flatfield0 = string.split(flatfield, '/')[-1]
ntt.util.updateheader(illum_frame, 0,
{'MKILLUM': [flatfield0, 'flat field']})
display_image(illum_frame, 1, '', '', False)
for i in range(0, len(lista)): # in lista:
img = lista[i]
delete('pippo.' + str(i) + '.mag')
delete('C' + img)
delete('C' + re.sub('.fits', '_sky.fits', img))
# delete('C*.fits.mag.1')
# iraf.hedit(illum_frame,'MKILLUM','Illum. corr. created '+flatfield,add='yes',update='yes',verify='no')
return illum_frame
daophot.pselect(infiles=image + '.pst.' + str(j),
outfiles=image + '.pst.' + str(j + 1),
expr='ID!=' + str(rmStar))
daophot.psf(image=image,
photfile='default',
pstfile='default',
psfimage='default',
opstfile='default',
groupfil='default',
verify='no',
interactive='no')
iraf.txdump(textfile=image + '.pst.' + str(j + 1),
fields='ID',
expr='yes',
Stdout=os.path.join(outdir, image + '_psf_stars.txt'))
psfList = np.genfromtxt(os.path.join(outdir, image + '_psf_stars.txt'),
dtype='int')
for k in psfList:
ax1.errorbar(co[k - 1, 0],
co[k - 1, 1],
fmt='*',
mfc='none',
markeredgecolor='lime',
markeredgewidth=2,
markersize=30,
label='Used in PSF fit')
for file2del in files2del:
os.remove(file2del)
try:
sexcatname=imgroot+'_sex.cat'
sexcommand='sex '+image+' -c '+sys.argv[2]+' -CATALOG_NAME '+sexcatname+' -PARAMETERS_NAME '+sys.argv[3]+' -DETECT_THRESH 100 -VERBOSE_TYPE QUIET'
os.system(sexcommand)
iraf.datapars.setParam('scale','1')
iraf.datapars.setParam('fwhmpsf','2.8')
iraf.datapars.setParam('datamin','-200')
iraf.datapars.setParam('gain','GAIN')
iraf.datapars.setParam('exposure','EXPTIME')
iraf.centerpars.setParam('cbox','10.0')
iraf.fitskypars.setParam('annulus','30')
iraf.fitskypars.setParam('dannulus','10')
iraf.photpars.setParam('zmag','17.5')
iraf.photpars.setParam('apertures','20')
iraf.daophot.phot(image=image,coords=sexcatname,output=imgroot+'_superphot.mags',interactive=0,verify=0,update=0,verbose=0)
iraf.txdump(textfiles=imgroot+'_superphot.mags',fields='IMAGE,XCENTER,YCENTER,MSKY,STDEV,NSKY,RAPERT,AREA,FLUX,MAG,MERR',expr='yes',headers=0,parameters=0,Stdout=aperall)
except Exception, e:
exstr = traceback.format_exc()
print >> logerror, image, '\n', exstr
if len(open(errlogname).read().strip()) == 0:
os.remove(errlogname)
print ' Finished !\n'
def fitsn(_recenter, img, imgpsf, fwhm0, apco0, z22, z11, midpt, size, nor,
_values, DM):
from pyraf import iraf
import string, os, sys
from numpy import log10
a1 = int(fwhm0 + .5)
a2 = int(2. * fwhm0 + .5)
a3 = int(3. * fwhm0 + .5)
a4 = int(4. * fwhm0 + .5)
a5 = int(5. * fwhm0 + .5)
ap = str(a1) + "," + str(a2) + "," + str(a3)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
from iraf import digiphot
from iraf import daophot
from iraf import ptools
zmag = 0
iraf.noao.digiphot.daophot.photpars.zmag = zmag
iraf.delete("apori", verify='no')
iraf.delete(img + ".sn.mag", verify='no')
iraf.noao.digiphot.daophot.phot("original",
img + ".sn.coo",
"apori",
veri='no')
iraf.noao.digiphot.daophot.phot("sn",
img + ".sn.coo",
img + ".sn.mag",
veri='no')
iraf.noao.digiphot.daophot.daopars.psfrad = a4
iraf.noao.digiphot.daophot.daopars.fitrad = fwhm0
iraf.noao.digiphot.daophot.daopars.fitsky = 'no'
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
if _recenter:
answ = raw_input(">>> recentering for targets [yes/no] ? [yes] ")
if not answ: answ = 'yes'
else:
answ = 'yes'
iraf.noao.digiphot.daophot.daopars.recenter = answ
iraf.noao.digiphot.daophot.daopars.fitsky = 'yes'
iraf.delete(img + ".sn.als", verify='no')
iraf.allstar("sn",
img + ".sn.mag",
imgpsf,
img + ".sn.als",
"",
"residual",
veri='no',
verb='no')
iraf.delete("snfit.fits", verify='no')
iraf.imarith("sn", "-", "residual", "snfit")
iraf.delete("skyfit.fits", verify='no')
iraf.imarith("original", "-", "snfit", "skyfit")
iraf.txsort(img + ".sn.als", "ID")
tmptbl = iraf.txdump(img + ".sn.als",
"mag,merr,xcenter,ycenter",
expr='yes',
Stdout=1)
magerr, fitmag, centx, centy = [], [], [], []
for i in tmptbl:
try:
fitmag.append(float(string.split(i)[0])) #+2.5*log10(nor))
except:
fitmag.append('INDEF')
try:
magerr.append(float(string.split(i)[1]))
except:
magerr.append('INDEF')
centx.append(float(string.split(i)[2]))
centy.append(float(string.split(i)[3]))
tmptbl = iraf.txdump("apori", "mag", expr='yes', Stdout=1)
apori1, apori2, apori3 = [], [], []
for i in tmptbl:
try:
apori1.append(
float(string.split(i)[0]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apori1.append('INDEF')
try:
apori2.append(
float(string.split(i)[1]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apori2.append('INDEF')
try:
apori3.append(
float(string.split(i)[2]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apori3.append('INDEF')
iraf.txsort(img + ".sn.mag", "YCENTER")
tmptbl = iraf.txdump(img + ".sn.mag", "mag,magerr", expr='yes', Stdout=1)
print "********************************************************************"
print "ID <apmag on original> <apmag on bgsubt> fitmag truemag err_fit"
print " ", a1, " ", a2, " ", a3, " ", a1, " ", a2, " ", a3
apmag1, apmag2, apmag3, truemag = [], [], [], []
for i in range(len(tmptbl)):
try:
apmag1.append(
float(string.split(tmptbl[i])[0]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apmag1.append('INDEF')
try:
apmag2.append(
float(string.split(tmptbl[i])[1]) - float(_values) -
float(DM)) #+2.5*log10(nor)
except:
apmag2.append('INDEF')
try:
apmag3.append(
float(string.split(tmptbl[i])[2]) - float(_values) -
float(DM)) #+2.5*log10(no)
except:
apmag3.append('INDEF')
try:
truemag.append(
float(fitmag[i]) + float(apco0) + 2.5 * log10(nor) -
float(_values) - float(DM))
except:
truemag.append('INDEF')
print i, apori1[i], apori2[i], apori3[i], apmag1[i], apmag2[i], apmag3[
i], fitmag[i], truemag[i], magerr[i]
print "********************************************************************"
iraf.display("original",
1,
fill='yes',
xcen=.25,
ycen=.25,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z22,
z1=z11)
z01 = z11 - midpt
z02 = z22 - midpt
s1 = 1
s2 = -int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " ORIGINAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
iraf.display("snfit",
1,
erase='no',
fill='yes',
xcen=.25,
ycen=.75,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z02,
z1=z01)
iraf.delete("tmptbl", ve='no')
tmptbl0 = iraf.txdump(img + ".sn.als", "xcen,ycen", expr='yes', Stdout=1)
ff = open('tmptbl', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
lra = int((2 * float(size) * float(fwhm0)) * 2)
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="circle",
number='yes',
nyoffset=lra,
radi=a2,
txsize=2,
inter='no')
s1 = 1
s2 = -1 * int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " FITTED")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
iraf.display("skyfit",
1,
erase='no',
fill='yes',
xcen=.75,
ycen=.25,
xsize=.3,
ysize=.3,
zscal='no',
zrang='no',
z2=z22,
z1=z11)
s1 = 1
s2 = -1 * int(fwhm0)
iraf.delete("tmptbl", ve='no')
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " RESIDUAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
print '###'
print '### magnitude scaled to target exposure time using: ' + str(
2.5 * log10(nor))
print '### fit magnitude corrected with aperture correction: ' + str(
float(apco0))
print '### magnitude scaled to target using DM: ' + str(DM)
print '###'
return apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag, truemag, magerr, centx, centy
sigma, sky = findsky(FitsFileName,10)
print 'Skyvalue: ',sky
print 'Stdv_sky: ',sigma
iraf.datapars.setParam('sigma',sigma)
iraf.fitskypars.setParam('skyvalu',sky)
pars['gain'] = gain
pars['rdnoise'] = rdnoise
pars['sigma'] = sigma
pars['sky'] = sky
iraf.datapars.setParam('fwhmpsf',4.0)
iraf.daofind.setParam('image',FitsFileName)
iraf.daofind.setParam('output',base+'im'+ext+'.guess.coo')
iraf.daofind(mode='h',Stdout=1)
iraf.txdump(textfile=base+'im'+ext+'.guess.coo',fields='xcenter,ycenter,sharpness',expr='sharpness!=INDEF',
Stdout=base+'daofindtmp')
daofind = np.loadtxt(base+'daofindtmp')
if os.path.exists(base+'im'+ext+'.guess.coo') == True: os.remove(base+'im'+ext+'.guess.coo')
i = (daofind[:,2] < np.median(daofind[:,2]) + 0.12) & (daofind[:,2] > np.median(daofind[:,2]) - 0.12)
np.savetxt(base+'im'+ext+'.guess.coo',daofind[i,0:2],fmt=['%-10.3f','%-10.3f'])
iraf.fitpsf.setParam('image',FitsFileName)
iraf.fitpsf.setParam('coords',base+'im'+ext+'.guess.coo')
val = linspace(2.,6.,11.)
best = 0.0
res = 99999.99
for i in val:
iraf.datapars.setParam('fwhmpsf',i)
iraf.fitpsf.setParam('output',base+'fitpsf')
iraf.fitpsf(mode='h',Stdout=1)
asd = iraf.txdump(textfile=base+'fitpsf',fields='rsigma',expr='rsigma!=INDEF && rsigma < 7.0',Stdout=1)
def txdump_photometry_info(target_dir, data_dir_name):
"""Extract the results of photometry from files to save them to a text file.
This function search files containing the results of photometry
and using txdump extracts the coordinates and photometric measure
to save it to a text file.
Args:
target_dir: Directory that contains the files to process.
data_dir_name: Name for the directories with data.
"""
# Walk from current directory.
for path, dirs, files in os.walk(target_dir):
# Process only directories without subdirectories.
if len(dirs) == 0:
split_path = path.split(os.sep)
# Check if current directory is for data images.
if split_path[-2] == data_dir_name:
logging.debug("Found a directory for data: %s" % (path))
# Get the list of magnitude files.
mag_files = glob.glob(
os.path.join(path, "*.%s" % MAGNITUDE_FILE_EXT))
logging.debug("Found %d magnitude files" % (len(mag_files)))
# Reduce each data file one by one.
for mfile in mag_files:
# Get the name of the file where the magnitude data will
# be saved.
mag_dest_file_name = \
mfile.replace(".%s" %(MAGNITUDE_FILE_EXT),
"%s%s.%s" %
(FILE_NAME_PARTS_DELIM,
MAGNITUDE_FILE_EXT, CSV_FILE_EXT))
# Remove the destiny file if exists.
if os.path.exists(mag_dest_file_name):
os.remove(mag_dest_file_name)
try:
mag_dest_file = open(mag_dest_file_name, 'w')
iraf.txdump(mfile, fields=TXDUMP_FIELDS, expr='yes', \
Stdout=mag_dest_file)
mag_dest_file.close()
except iraf.IrafError as exc:
logging.error("Error executing txdump to get: %s" %
(mag_dest_file_name))
logging.error("Iraf error is: %s" % (exc))
except IOError as ioe:
logging.error("Reading file: %s" %
(mag_dest_file_name))
def phot(im,coords,radii,
useOtherData=None,
aperCorrs=[],
refImDir='refImages',
cent='centroid',
xxx='',
linearInterp=False,
psfInterp=False):
"""Aperture photometry of WFC3 ir/uv1,2 images.
This code uses iraf's phot package to perform
simple aperture photometry.
It then returns a list of arrays, where each entry
is the magnitudes and errors for every coordinate pair
given.
It can do three different versions. The most
basic (and default) is photometry where
all pixels with imquality flags>0 are
ignored. This is the fastest, but typically
produces faintward biased answers.
Another technique is the use of linear interpolation
using the nearest four good pixels in a cross pattern
that bound the bad pixel. This produces less biased
answers the the simple verion. WARNING: not very well
tested! This is set with linearInterp=True
The last and most reliable technique (also the slowest)
uses tinytim psfs to perform the interpolation. All
good pixels within the aperture are used to match the
psf flux to that observed. The bad pixels are sampled
off the psf and scaled to the correct flux to produce
the corrected image from which the photometry is
acquired. This is set with psfInterp=True
Inputs:
im - the image filename without the '.fits'.
coords - an array of coordinates in form [x,y]. These
are in iraf coordinates ie. (+1,+1) from pyfits
coordinates!
radii - an array of radii [r1,r2,r3,..] float acceptable.
aperCorrs - an array of aperture corrections to apply.
refImDir='refImages' - the directory with reference images.
This will actually be ../refImages
cent='centroid' - perform an iraf centroid as default.
set to 'none' if no centroid desired.
if centroiding, returns IRAF type
coordinates.
xxx='' - a string to add to the corrected image filename.
"""
if psfInterp:
import WFC3_PSFFits as P
import sys
from pyraf import iraf
###for pyraf
yes=1
no=0
#import the necessary packages
iraf.digiphot()
iraf.daophot()
#set local parameters
iraf.set(uparm="./")
iraf.daophot.verbose=no
iraf.daophot.verify=no
iraf.daophot.update=no
iraf.centerpars.calgori=cent
iraf.centerpars.maxshift=2.
iraf.centerpars.minsnra=1.0
#iraf.daopars.fitrad=1.0 #enough to leave set
iraf.photpars.apertur=2
iraf.photpars.zmag=0.0
iraf.datapars.fwhmpsf=2.
iraf.datapars.sigma=0.0
iraf.datapars.datamin=-1000
iraf.datapars.datamax=45000
iraf.datapars.ccdread=""
iraf.datapars.gain=""
iraf.datapars.readnoi=0.0
iraf.datapars.epadu=1.0
iraf.datapars.exposure=""
iraf.datapars.itime=1.0
iraf.datapars.airmass=""
iraf.datapars.filter=""
iraf.datapars.obstime=""
iraf.fitskypars.salgori='mode'
iraf.fitskypars.annulus=max(radii)*1.2
iraf.fitskypars.dannulu=8
#save the new parameters
iraf.datapars.saveParList()
iraf.photpars.saveParList()
#iraf.daopars.saveParList()
iraf.centerpars.saveParList()
iraf.daophot.saveParList()
iraf.fitskypars.saveParList()
if '.fits' in im: fn=im
else: fn=im+'.fits'
#get the image data
ha=pyfits.open(fn)
header0=ha[0].header
header1=ha[1].header
data=ha[1].data.astype('f')
error=ha[2].data
qual=ha[3].data
if useOtherData<>None: data=useOtherData
(a,b)=data.shape
DETECTOR=header0['DETECTOR']
FILTER=string.strip(header0['FILTER'])
GAIN=float(header0['CCDGAIN'])
EXPTIME=float(header0['EXPTIME'])
if DETECTOR=="UVIS":
PHOTZPT=float(header1['PHOTZPT'])
PHOTFLAM=float(header1['PHOTFLAM'])
data/=EXPTIME
error/=EXPTIME
else:
PHOTZPT=float(header0['PHOTZPT'])
PHOTFLAM=float(header0['PHOTFLAM'])
XCEN=int(float(header1['CENTERA1']))
YCEN=int(float(header1['CENTERA2']))
XSIZE=int(float(header1['SIZAXIS1']))
YSIZE=int(float(header1['SIZAXIS2']))
#headers are f****d in some of the ir images. This is a
#cluge to fix that
if XSIZE==522: XSIZE=512
if YSIZE==522: YSIZE=512
###get the flat
#flathan=pyfits.open('flat_'+FILTER+'.fits')
#flatdat=flathan[1].data
#flathan.close()
#get the pixel area map
if DETECTOR=='UVIS':
if YCEN<2051:
#chip 1
name='UVIS1wfc3_map.fits'
detector='uv1'
else:
#chip 2
name='UVIS1wfc3_map.fits'
detector='uv2'
else:
name='ir_wfc3_map.fits'
detector='ir'
PAMhan=pyfits.open(refImDir+'/'+name)
(A,B)=PAMhan[1].data.shape
if (a==YSIZE or a==A):
my=0
My=a
else:
if detector=='uv1' or detector=='uv2':
my=YCEN-YSIZE/2-2051-1
My=my+YSIZE+1
else:
my=YCEN-YSIZE/2-1
My=my+YSIZE+1
if (b==XSIZE or b==B):
mx=0
Mx=b
else:
mx=XCEN-XSIZE/2-1
Mx=mx+XSIZE+1
PAMdata=PAMhan[1].data.astype('f')[my:My,mx:Mx]
data*=PAMdata
error*=PAMdata
#data/=flatdat
#get the hand-selected bad-pixel map
badHan=open(im+'.fits.badPix')
dunk=badHan.readlines()
badHan.close()
badPixManual=[]
for ii in range(len(dunk)):
s=dunk[ii].split()
xbp=int(float(s[0]))
ybp=int(float(s[1]))
for yy in range(-1,2):
for xx in range(-1,2):
badPixManual.append([xbp-1+xx,ybp-1+yy])
#fill in all the bad pixels using the median bg value around the images
#edges (4 pixels wide) and with a linear interpolation within the
#central area near each individual input coordinate
#ldata=data.reshape(a*b)*1.0
#ldata.sort()
#median=ldata[a*b/2]
median=num.median(data)
noise=num.std(data)
#at least mark each manually identified bac pixel with the median of the image
for ii in range(len(badPixManual)):
data[badPixManual[ii][1],badPixManual[ii][0]]=median
"""
for i in range(a):
for j in range(4):
if qual[i][j]>0:
data[i][j]=median
if qual[i][b-j-1]>0:
data[i][b-j-1]=median
for i in range(b):
for j in range(4):
if qual[j][i]>0:
data[j][i]=median
if qual[a-j-1][i]>0:
data[a-j-1][i]=median
"""
if psfInterp:
for kk in range(len(coords)):
xp=int(coords[kk][0]-1+0.5)
yp=int(coords[kk][1]-1+0.5)
R=int(max(radii))+2
x=[]
y=[]
gx=[]
gy=[]
for i in range(yp-R,yp+R):
for j in range(xp-R,xp+R):
if (j<0 or j>b or i<0 or i>a): continue
if qual[i][j]>0:# or ([j,i] in badPixManual):
x.append(j)
y.append(i)
else:
gx.append(j)
gy.append(i)
bad_x=num.array(x)
bad_y=num.array(y)
good_x=num.array(gx)
good_y=num.array(gy)
if len(x)==0: continue
[d,f]=im.split('/')
os.chdir(d)
FILTER=FILTER.replace('W','w').replace('M','m')
obj=P.PSFFit(f+'.fits',1,useMemory='n')
obj.initObject(0,coords[kk][0]-1,coords[kk][1]-1,1,3.5)
try: os.remove('genImage.fits')
except: pass
try: os.remove('genImagePSF.fits')
except: pass
obj.produceImage(write='y')
psfHan=pyfits.open('genImage.fits')
psfData=(psfHan[0].data/EXPTIME)*PAMdata
psfHan.close()
try: os.remove('genImage.fits')
except: pass
try: os.remove('genImagePSF.fits')
except: pass
os.chdir('../')
xmin=max(0,xp-R)
xmax=min(b,xp+R)
ymin=max(0,yp-R)
ymax=min(a,yp+R)
dcut=data[good_y,good_x]
psfCut=psfData[good_y,good_x]
w=num.where(dcut>median+median**0.5)[0]
multi=num.sum(dcut[w]/psfCut[w])/len(w)
data[bad_y,bad_x]=psfData[bad_y,bad_x]*multi+median
if linearInterp: #probably will fail if the peak of the psf is on a bad pixel!
for kk in range(len(coords)):
xp=int(coords[kk][0]-1+0.5)
yp=int(coords[kk][1]-1+0.5)
R=int(max(radii))+2
for i in range(yp-R,yp+R):
for j in range(xp-R,xp+R):
if (j<0 or j>b or i<0 or i>a): continue
if qual[i][j]>0 or ([j,i] in badPix):
x=[]
y=[]
z=[]
val=-1
c=-1
while val==-1:
if qual[i+c][j]==0:
val=data[i+c][j]
else:
c-=1
x.append(j)
y.append(i+c)
z.append(val)
val=-1
c=1
while val==-1:
if qual[i+c][j]==0:
val=data[i+c][j]
else:
c+=1
x.append(j)
y.append(i+c)
z.append(val)
val=-1
c=-1
while val==-1:
if qual[i][j+c]==0:
val=data[i][j+c]
else:
c-=1
x.append(j+c)
y.append(i)
z.append(val)
val=-1
c=1
while val==-1:
if qual[i][j+c]==0:
val=data[i][j+c]
else:
c+=1
x.append(j+c)
y.append(i)
z.append(val)
inte=interp.bisplrep(x,y,z,kx=1,ky=1)
value=interp.bisplev([i],[j],inte)
data[i][j]=value
#print "BORKED! Something about linear interpolation produces negative pixel values. This has not been checked to see if it's OK. It could just be noise. Though it might not be!!!"
#write the coordinates file
han=open('spot','w+')
for ii in range(len(coords)):
print >>han,coords[ii][0],coords[ii][1]
han.close()
#setup the necessary keywords
iraf.datapars.gain='CCDGAIN'
iraf.datapars.itime=1.0
iraf.datapars.saveParList()
iraf.photpars.zmag=PHOTZPT
#write out the interpolated image for photometry
#get centroid
#only works on the non-phot-flam-ed image
if cent=='centroid':
try:
os.remove('junk'+xxx+'_cent.fits')
os.remove('junk'+xxx+'_cent.fits1.ctr.1')
except:
pass
for ii in range(a):
ha[1].data[ii,:]=data[ii,:]
ha.writeto('junk'+xxx+'_cent.fits')
iraf.photpars.apertur=radii[0]
iraf.photpars.saveParList()
iraf.center(image='junk'+xxx+'_cent.fits[1]',coords='spot',verify=no,interactive=no)
Zing= iraf.txdump(textfiles = 'junk'+xxx+'_cent.fits1.ctr.1', fields = 'XCENTER,YCENTER', expr=iraf.yes, Stdout=1)
try:
os.remove('junk'+xxx+'_cent.fits1.ctr.1')
except:
pass
XC=[]
YC=[]
ZZ=Zing[0].split()
for ii in range(len(ZZ)/2):
if 'INDEF' not in ZZ[0]:
XC.append(float(ZZ[0]))
YC.append(float(ZZ[1]))
else:
XC.append(-32768.)
YC.append(-32768.)
#write the new centroided coordinates file
han=open('spot','w+')
for ii in range(len(coords)):
print >>han,XC[ii],YC[ii]
han.close()
iraf.centerpars.calgori=cent
iraf.centerpars.saveParList()
else:
XC=[]
YC=[]
for ii in range(len(coords)):
XC.append(coords[ii][0])
YC.append(coords[ii][1])
data*=PHOTFLAM
error*=PHOTFLAM
for ii in range(a):
ha[1].data[ii,:]=data[ii,:]
try:
os.remove('junk'+xxx+'.fits')
except:
pass
ha.writeto('junk'+xxx+'.fits')
ha.close()
#get the quadrature sum photometric error approximately
repFac=1
E=num.repeat(num.repeat(error,repFac,axis=0),repFac,axis=1)/float(repFac*repFac)
xcoord=num.repeat(num.array([num.arange(b*repFac)+1.]),(a)*repFac,axis=0)/float(repFac)
ycoord=num.repeat(num.transpose(num.array([num.arange(a*repFac)+1.])),b*repFac,axis=1)/float(repFac)
ES=[]
for i in range(len(coords)):
ES.append([])
for kk in range(len(radii)):
EEr=E*1.0
dist=((xcoord-XC[i])**2+(ycoord-YC[i])**2)**0.5
w=num.where(dist>radii[kk])
EEr[w]*=0.0
ES[i].append(num.sqrt(num.sum(EEr**2)))
MS=[]
MES=[]
for i in range(len(coords)):
MS.append([])
MES.append([])
for kk in range(len(radii)):
iraf.photpars.apertur=radii[kk]
iraf.photpars.saveParList()
try:
os.remove('spottedm')
except:
pass
iraf.daophot.phot(image='junk'+xxx+'.fits[1]', coords='spot', output= 'spottedm', interactive=no, verbose=no)
####magnitude equation is
#### m = Z-2.5*LOG(f_DN*PHOTFLAM)
####magnitude error is then
#### dm=1.09/sqrt(f_DN*EXPTIME*GAIN)
#### Remember that to get f back in ADU we need to divide by PHOTFLAM
#### 0.95 included to consider approximate qe effect
Zing= iraf.txdump(textfiles = 'spottedm', fields = 'MAG,FLUX,SUM', expr=iraf.yes, Stdout=1)
for i in range(len(MS)):
ZZ=Zing[i].split()
#print ZZ
if 'INDEF' not in ZZ[0]:
magVal=float(ZZ[0])
if aperCorrs<>[]: magVal-=aperCorrs[i]
MS[i].append(magVal)
ES[i][kk]*=2.5/num.log(10)
ES[i][kk]/=float(ZZ[1])
flux=float(ZZ[1])/PHOTFLAM
dflux=num.sqrt(float(ZZ[2])/PHOTFLAM)
#print flux,dflux
MES[i].append(1.09*dflux/(flux*num.sqrt(GAIN*EXPTIME/0.95)))
else:
MS[i].append(-32768)
MES[i].append(-32768)
try:
os.remove('junk'+xxx+'_cent.fits')
except:
pass
try:
os.remove('junk'+xxx+'.fits')
except:
pass
if cent<>'none':
return (MS,ES,median,XC,YC)
else:
return (MS,ES,median)
def organizeFiles():
# Create files that are usually generated by mkimset.
# We have more information about how to properly group our
# files together.
imsetsFilename = 'standards.imageSets'
obsparFilename = 'standards.obsParams'
shiftsFilename = 'standards.shifts'
iMagniFilename = 'standards.instMag'
imsets = open(imsetsFilename, 'w')
obspar = open(obsparFilename, 'w')
shifts = open(shiftsFilename, 'w')
for star in stars:
roots = []
for filter in filters:
dir = star.lower() + '_' + filter.lower()
if filter == 'Lp':
dir += '2'
images = glob.glob(dataDir + dir + '/c????.fits')
imageRoots = []
for ii in images:
fileRoot = ii.split('/')[-1]
imageRoots.append(fileRoot.replace('.fits', ''))
roots.append(imageRoots)
print star, filter, len(imageRoots)
longCount = np.array([len(r) for r in roots]).max()
print 'Long Count for %s = %d' % (star, longCount)
for ii in range(longCount):
imsets.write('%5s : ' % star)
for ff in range(len(roots)):
if ii < len(roots[ff]):
# Write obsParams
ir.txdump(roots[ff][ii] + '.phot.mag',
'IMAGE,IFILTER,ITIME,XAIRMASS,OTIME',
'yes', Stdout=obspar)
# Write shifts
posInfo = ir.txdump(roots[ff][ii] + '.phot.mag',
'IMAGE,XCENTER,YCENTER',
'yes', Stdout=1)
posFields = posInfo[0].split()
shiftX = -1.0 * float(posFields[1])
shiftY = -1.0 * float(posFields[2])
shifts.write('%-10s %7.2f %7.2f\n' %
(posFields[0], shiftX, shiftY))
# Write imageSet
imsets.write('%-10s ' % (roots[ff][ii] + '.fits'))
else:
imsets.write('%-10s ' % ('INDEF'))
imsets.write('\n')
imsets.close()
obspar.close()
shifts.close()
# Now run mknobsfile
ir.delete(iMagniFilename)
ir.digiphot()
ir.photcal()
ir.unlearn('mknobsfile')
ir.mknobsfile.obsparams = obsparFilename
ir.mknobsfile.shifts = shiftsFilename
ir.mknobsfile('*.phot.mag', ','.join(filters), imsetsFilename,
iMagniFilename, verbose='no', wrap='no')
# Run mkconfig
ir.delete('standards.config')
ir.unlearn('mkconfig')
ir.mkconfig('standards.config', 'fphotcal_ukirt_faint.dat',
'fstandards.instMag.dat', 'tphotcal_ukirt_faint.dat',
check='no', edit='no')
def execute():
################NGC1023####################################################
## use GALFIT to fit and to create a model disk vs bulge##
##galfit.feedme##
iraf.images()
# path to fits files:
total = config.iraf_input_dir+config.totalfits
bulge = config.iraf_input_dir+config.bulgefits
fraction = config.iraf_tmp_dir+config.fractionfits
input_dir = config.iraf_input_dir
tmp_dir = config.iraf_tmp_dir
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
if os.path.exists(fraction):
iraf.imdelete(fraction)
print fraction
print bulge
# Makes the fraction image by dividing the bulge by the total:
iraf.imarith(bulge, "/", total, fraction)
""" LLT: I don't think we need this anymore:
if os.path.exists(tmp_dir+"snb.fits"):
iraf.module.imdelete(tmp_dir+"snb.fits")
if os.path.exists(tmp_dir+"tnb.fits"):
iraf.module.imdelete(tmp_dir+"tnb.fits")
iraf.module.imcopy(bulge, tmp_dir+"snb.fits")
iraf.module.imcopy(total, tmp_dir+"tnb.fits")
"""
##########################################################################
##Note: the coordinate in pixel as obtained from RA and Dec differ from the
##pixels one because of distorsion corrections as done with astrometry==>
##RA and Dec coordinate in arcsec are the correct one and don't need do be
##transfer in pixels
############################################################################
##Consistency check
#displ(image="../fits_inputs/galaxy.fits",frame=1)
#tvmark(frame=1,coords="Kall.dat",color=205,lab-,num+)
#displ(image="f.fits",zrange=no,zscale=no,ztrans="linear",z1=0,z2=1,frame=3)
#tvmark(frame=3,coords="Kall.dat",color=201,mark="rectangle",lengths="6",lab-,num-)
###########################################################################
##Use program overplot.sm to create both Kall.dat & compagna.dat
##in the RA & Dec of PNS there is a rotation of 90deg respect to the pixel
## x-->-x
##after long reflexion I decide the right PA is 90-47.11=42.89 (just turning the PNe as observed on the R image)
##########################################################################
############################################################################
## to measure the relative flux use phot (daophot) ##with centerpars=none!!!###
##I used sigma=stdv(skys)=0 ===> no bg
##fwhmpsf=2.27 as in galfit psf2.fits (imexam A enclosed)
##skyvalue=0 ===> no bg
##no centerpars
##apertures=3.0 pixels (=1.8 arcsec)
##zmag=25
##snb.fits & tnb.fits without bg (when I made the model)
# filenames
silentremove(config.bulgemag)
silentremove(config.bulgetxt)
silentremove(config.totalmag)
silentremove(config.totaltxt)
silentremove(config.fractionmag)
silentremove(config.fractiontxt)
iraf.noao(_doprint=False)
iraf.digiphot(_doprint=False)
iraf.daophot(_doprint=False)
iraf.phot(bulge,config.position_file, config.bulgemag, skyfile="", datapars="",scale=1.,fwhmpsf=2.27,emissio="yes",sigma=0.0,datamin=-100, datamax=1e5,noise="poisson", centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1., cmaxiter=0,maxshift=0,clean="no",rclean=0.,rclip=0.,kclean=0., mkcenter="no", fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3., shireject=3.,khist=3.,binsize=0.1,smooth="no",rgrow=0., mksky="no", photpars="",weighting="constant",apertures=3,zmag=0.,mkapert="no", interactive="no",radplots="no",verify="no",update="no",verbose="no", graphics="stdgraph",display="stdimage",icommands="",gcommands="")
# LLT: Corrected to pyraf, use stdout = instead of >>
iraf.txdump(config.bulgemag, "XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID", "yes", headers="no",paramet="no", Stdout=config.bulgetxt)
iraf.phot(total, config.position_file, config.totalmag, skyfile="", datapars="",scale=1.,fwhmpsf=2.27,emissio="yes",sigma=0.0,datamin=-100, datamax=1e5,noise="poisson", centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1., cmaxiter=0,maxshift=0.,clean="no",rclean=0.,rclip=0.,kclean=0., mkcenter="no", fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3., shireject=3.,khist=3.,binsize=0.1,smooth="no",rgrow=0., mksky="no", photpars="",weighting="constant",apertures=3,zmag=25.,mkapert="no", interactive="no",radplots="no",verify="no",update="no",verbose="no", graphics="stdgraph",display="stdimage",icommands="",gcommands="")
iraf.txdump(config.totalmag, "XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID", "yes", headers="no",paramet="no", Stdout=config.totaltxt)
iraf.phot(fraction,config.position_file, config.fractionmag, skyfile="", datapars="", scale=1., fwhmpsf=2.27, emissio="no", sigma=0.0, datamin=-100, datamax=1e5, noise="poisson", centerpars="", calgorithm="none", cbox=0, cthreshold=0., minsnratio=1., cmaxiter=0, maxshift=0., clean="no", rclean=0., rclip=0., kclean=0., mkcenter="no", fitskypars="", salgorithm="constant", annulus=10, dannulus=10, skyvalue=0., smaxiter=10, sloclip=0., shiclip=0., snreject=50, sloreject=3., shireject=3., khist=3., binsize=0.1, smooth="no", rgrow=0., mksky="no", photpars="", weighting="constant", apertures=3, zmag=25., mkapert="no", interactive="no", radplots="no", verify="no", update="no", verbose="no", graphics="stdgraph", display="stdimage", icommands="", gcommands="")
iraf.txdump(config.fractionmag, "XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID", "yes", headers="no",paramet="no", Stdout=config.fractiontxt)
def psffit(img, fwhm, psfstars, hdr, interactive, _datamax=45000, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile compute the psf using the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.photpars.zmag = zmag
iraf.delete('_psf.ma*,' + img + '.psf.fit?,_psf.ps*,_psf.gr?,_psf.n*,_psf.sub.fit?', verify=False)
iraf.phot(img+'[0]', '_psf.coo', '_psf.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
if interactive:
shutil.copyfile('_psf.mag', '_psf.pst')
print '_' * 80
print '>>> Mark good stars with "a" or "d"-elete. Then "f"-it,' + \
' "w"-write and "q"-uit (cursor on ds9)'
print '-' * 80
else:
iraf.pstselect(img+'[0]', '_psf.mag', '_psf.pst', psfstars, interac=False, verify=False)
iraf.psf(img + '[0]', '_psf.mag', '_psf.pst', img + '.psf', '_psf.psto', '_psf.psg', interac=interactive,
verify=False, verbose=False)
iraf.group(img + '[0]', '_psf.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_psf.nst', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
pst = iraf.txdump("_psf.pst", 'xcenter,ycenter,id', expr='yes', Stdout=1)
fitmag = iraf.txdump("_psf.nst", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, pst, fitmag
else:
print 'NO OBJECTS MATCHED SHARPNESS/ROUNDNESS CRITERIA.'
pdb.set_trace()
else:
sxx = np.array([sharp])
rxx = np.array([round])
gxx = np.array([ground])
#if find_name[ff] == 'iaaug1jlq_flt.clean_cnts.fits0.coo.1': pdb.set_trace()
find_sharp.append(sxx[0])
find_round.append(rxx[0])
find_ground.append(gxx[0])
back, backrms = run_daophot(cnts_name[ff], coordfile=find_name[ff], outfile=phot_name[ff], calgorithm='gauss', cbox=10.,backmethod=backmeth)
replace_filevalue(phot_name[ff], 'INDEF',-9999.0)
iraf.txdump(phot_name[ff],'xcenter,ycenter,flux, mag', 'yes',Stdout=phot_name[ff]+'.trimmed')
xc,yc,f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f15,f16,f17,f18,f19,f20,f21,f22,f23,f24,f25,f26, \
m1,m2,m3,m4,m5,m6,m7,m8,m9,m10,m11,m12,m13,m14,m15,m16,m17,m18,m19,m20,m21,m22,m23,m24,m25,m26=np.loadtxt(phot_name[ff]+'.trimmed',unpack=True)
# record various properties of observation (chip,amp,mjd, etc.)
dd=pyfits.open(file)
hdr0=dd[0].header
im = dd[0].data
dd.close()
amps = hdr0['CCDAMP']
filter = hdr0['FILTER']
LTV1 = hdr0['LTV1']
LTV2 = hdr0['LTV2']
chip = hdr0['CCDCHIP']
shut = hdr0['SHUTRPOS']
expt = hdr0['EXPTIME']
iraf.datapars.setParam('exposure','EXPTIME')
iraf.centerpars.setParam('cbox','5.0')
iraf.fitskypars.setParam('annulus','50')
iraf.fitskypars.setParam('dannulus','10')
iraf.photpars.setParam('zmag','17.5')
clipcat=np.loadtxt(imgroot+'_psfms.clip')
clipdemen = len(clipcat.shape)
if clipdemen==1:
fwhm = clipcat[3]
napertures = 22
apertures ="%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % tuple(np.array([0.5,0.7,1,1.2,1.5,2,2.5,3,4,5,6,7,8,9,10,12,14,16,18,20,22,25])*fwhm)
iraf.photpars.setParam('apertures',apertures)
iraf.daophot.phot(image=image,coords=imgroot+'_psfms.clip',output=imgroot+'_superphot.mags',interactive=0,verify=0,update=0,verbose=0)
iraf.txdump(textfiles=imgroot+'_superphot.mags',fields='XCENTER,YCENTER,RAPERT,FLUX,MAG,MERR',expr='yes',headers=0,parameters=0,Stdout=imgroot+'_photdump')
#iraf.txdump(textfiles=imgroot+'_superphot.mags',fields='IMAGE,XCENTER,YCENTER,MSKY,STDEV,NSKY,RAPERT,AREA,FLUX,MAG,MERR',expr='yes',headers=0,parameters=0,Stdout=aperall)
if clipdemen==2:
for i in range(clipcat.shape[0]):
fwhm = clipcat[i,3]
apertures ="%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % tuple(np.array([0.5,0.7,1,1.2,1.5,2,2.5,3,4,5,6,7,8,9,10,12,14,16,18,20,22,25])*fwhm)
iraf.photpars.setParam('apertures',apertures)
iraf.daophot.phot(image=image,coords=imgroot+'_psfms.clip',output=imgroot+'_s1'+'_superphot.mags',interactive=0,verify=0,update=0,verbose=0)
iraf.txdump(textfiles=imgroot+'_superphot.mags',fields='XCENTER,YCENTER,RAPERT,FLUX,MAG,MERR',expr='yes',headers=0,parameters=0,Stdout=imgroot+'_photdump')
#iraf.txdump(textfiles=imgroot+'_s1'+'_superphot.mags',fields='IMAGE,XCENTER,YCENTER,MSKY,STDEV,NSKY,RAPERT,AREA,FLUX,MAG,MERR',expr='yes',headers=0,parameters=0,Stdout=aperall)
growth = open(imgroot+'_growth.dat','w')
dumpdata = np.loadtxt(imgroot+'_photdump')
growthdata = np.transpose(dumpdata[2:(3+napertures*4)].reshape((4,napertures)))
growthhead = 'RAPERT FLUX MAG MERR'
np.savetxt(imgroot+'_growth.dat',growthdata,header=growthhead)
def psffit(img, fwhm, psfstars, hdr, interactive, _datamax=45000, psffun='gauss', fixaperture=False):
import agnkey
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = agnkey.util.readhdr(img+'.fits')
_pixelscale = agnkey.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
# a1, a2, a3, a4, = int(5), int(8), int(10), int(12)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
_center='no'
iraf.fitskypars.annulus = a4
iraf.fitskypars.dannulus = a4
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.wsannul = int(a4)
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
# iraf.photpars.apertures = '%d,%d,%d'%(a2,a3,a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = agnkey.util.readkey3(hdr, 'ron')
iraf.datapars.epadu = agnkey.util.readkey3(hdr, 'gain')
iraf.datapars.exposure = 'exptime' #agnkey.util.readkey3(hdr,'exptime')
iraf.datapars.airmass = 'airmass'
iraf.datapars.filter = 'filter2'
iraf.centerpars.calgori = 'gauss'
iraf.centerpars.cbox = 1
iraf.daopars.recenter = _center
iraf.photpars.zmag = zmag
iraf.delete('_psf.ma*,' + img + '.psf.fit?,_psf.ps*,_psf.gr?,_psf.n*,_psf.sub.fit?', verify=False)
iraf.phot(img+'[0]', '_psf.coo', '_psf.mag', interac=False, verify=False, verbose=False)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = int(a4)
iraf.daopars.wsannul = int(a4)
iraf.daopars.recenter = _center
iraf.daopars.varorder = varord
if interactive:
shutil.copyfile('_psf.mag', '_psf.pst')
print '_' * 80
print '>>> Mark good stars with "a" or "d"-elete. Then "f"-it,' + \
' "w"-write and "q"-uit (cursor on ds9)'
print '-' * 80
else:
iraf.pstselect(img+'.fits[0]', '_psf.mag', '_psf.pst', psfstars, interac=False, verify=False)
iraf.psf(img+'.fits[0]', '_psf.mag', '_psf.pst', img + '.psf', '_psf.psto', '_psf.psg', interac=interactive,
verify=False, verbose=False)
# if os.path.isfile(img + '.psf.fits'):
# print 'file there'
iraf.group(img+'.fits[0]', '_psf.mag', img + '.psf.fits', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img+'.fits[0]', '_psf.grp', img + '.psf.fits', '_psf.nst', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
pst = iraf.txdump("_psf.pst", 'xcenter,ycenter,id', expr='yes', Stdout=1)
fitmag = iraf.txdump("_psf.nst", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, pst, fitmag
iraf.noao.digiphot.apphot.phot.fitskypars.salgori = 'constant'
iraf.noao.digiphot.apphot.phot.cache = 'yes'
iraf.noao.digiphot.apphot.phot.salgori = 'constant'
iraf.phot(image='hff_abells1063-hffpar_f814w_microJy_backsub.fits',
interactive='no',
calgorithm='none',
salgori='constant',
coords='coords.coo',
apertures='radii.txt',
output='test.txt',
verify='no')
iraf.txdump.textfile = 'test.txt'
iraf.txdump.expr = 'yes'
iraf.txdump(textfile='test.txt', fields='SUM', expr='yes', Stdout='stuff.txt')
def tidy_txdump(filename, band):
radii = np.loadtxt("radii.txt")
data = np.loadtxt(filename)
a = np.array([data])
b = a.T
b = b.reshape(radii_pts, 1)
radii = np.array([radii])
radii = radii.reshape(radii_pts, 1)
c = np.hstack((radii, b / np.max(b)))
np.savetxt("out_" + band + ".txt", c)
d = b / np.max(b)
def build(f):
### is this an MEF file
current_ext = 0
NEXTEND = 0
EXTEND = f[0].header['EXTEND']
if (EXTEND == "T"):
NEXTEND = f[0].header['NEXTEND']
current_ext = 1
### create the name of the output MEF psf file
if not f[0].header.has_key('FILENAME'):
os.unlink(opt.filename)
sys.exit('The fits file ' + opt.filename + ' has no EXPNUM keyword\n')
sexp = f[0].header['FILENAME']
mef_psf = sexp + "p_psf_iraf.fits"
### create an MEF file that will contian the PSF(s)
import pyfits
fitsobj = pyfits.HDUList()
prihdu = pyfits.PrimaryHDU()
import re
prihdu.header.update('FILENAME', sexp, comment='CFHT Exposure Numebr')
prihdu.header.update('NEXTEND', NEXTEND, comment='number of extensions')
version = re.match(r'\$Rev.*: (\d*.\d*) \$', __Version__).group(1)
prihdu.header.update('MKPSF_V',
float(version),
comment="Version number of mkpsf")
fitsobj.append(prihdu)
if os.access(mef_psf, os.F_OK):
os.unlink(mef_psf)
fitsobj.writeto(mef_psf)
fitsobj.close()
outfits = pyfits.open(mef_psf, "append")
prihdr = outfits[0].header
import jjkmode
### Get my python routines
from pyraf import iraf
from pyraf.irafpar import IrafParList
### keep all the parameters locally cached.
iraf.set(uparm="./")
### Load the required IRAF packages
iraf.digiphot()
iraf.apphot()
iraf.daophot()
### temp file name hash.
tfile = {}
while (current_ext <= NEXTEND):
### this is a psf SCRIPT so the showplots and interactive are off by force
print "Working on image section " + str(current_ext)
iraf.findpars.sharplo = 0
iraf.findpars.sharphi = 0.7
iraf.findpars.roundlo = -0.7
iraf.findpars.roundhi = 0.7
iraf.datapars.datamax = 20000
iraf.datapars.airmass = 'AIRMASS'
iraf.datapars.filter = 'FILTER'
iraf.datapars.obstime = 'TIME-OBS'
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.gain = 'GAIN'
iraf.datapars.ccdread = 'RDNOISE'
iraf.datapars.fwhmpsf = opt.fwhm
iraf.daopars.nclean = 2
iraf.daopars.psfrad = 5.0 * opt.fwhm
iraf.daopars.fitrad = 0.85 * opt.fwhm
iraf.daopars.function = "gauss"
iraf.centerpars.calgorithm = 'centroid'
zero_mag = 26.19
iraf.photpars.zmag = zero_mag
iraf.photpars.apertures = int(0.85 * opt.fwhm)
iraf.fitskypars.annulus = 2 + int(opt.fwhm * 4.00)
iraf.fitskypars.dannulus = int(opt.fwhm * 2.0)
iraf.daophot.verbose = no
iraf.daophot.verify = no
iraf.daophot.update = no
iraf.psf.interactive = no
iraf.pstselect.interactive = no
iraf.datapars.saveParList()
iraf.fitskypars.saveParList()
iraf.centerpars.saveParList()
iraf.findpars.saveParList()
iraf.photpars.saveParList()
tfiles = [
'coo_bright', 'coo_ok', 'coo_faint', 'mag_all', 'mag_bright',
'mag_ok', 'mag_good', 'mag_best', 'pst_in', 'pst_out', 'pst_out2',
'prf', 'psg_org', 'psg', 'psf_1.fits', 'psf_2.fits',
'psf_final.fits', 'psf_3.fits', 'psf_4.fits', 'mag_pst', 'coo_pst',
'nst', 'nrj', 'seepsf.fits', 'sub.fits', 'fwhm', 'apcor'
]
for file in tfiles:
extname = "chip" + str(f[current_ext].header.get(
'IMAGEID', str(current_ext))).zfill(2)
tfile[file] = sexp + "_" + extname + "." + file
if (os.access(tfile[file], os.F_OK)):
os.unlink(tfile[file])
if (EXTEND == "T"):
this_image = opt.filename + "[" + extname + "]"
else:
this_image = opt.filename
gain = f[current_ext].header.get('GAIN', 1.0)
#### set sky/sigma parameters specific to this frame.
(skyvalue, sigma) = jjkmode.stats(f[current_ext].data)
import math
sigma = math.sqrt(skyvalue / gain)
datamin = float(skyvalue) - 8.0 * float(sigma)
print "Determined sky level to be " + str(skyvalue) + " +/-" + str(
sigma)
iraf.datapars.datamin = datamin
iraf.datapars.sigma = float(sigma)
iraf.datapars.saveParList()
iraf.fitskypars.skyvalue = skyvalue
iraf.fitskypars.saveParList()
### find the bright stars in the image.
print "sextracting for stars in " + this_image
###iraf.daophot.daofind(image=this_image,
### output=tfile['coo_bright'],threshold=4.0)
os.system(
"sex -c /home/cadc/kavelaar/12kproc/config/default.sex -SATUR_LEVEL 25000 -CATALOG_NAME "
+ tfile['coo_bright'] + " " + this_image)
### print "finding stellar locus in sround/ground space"
print "clipping using star_class > 0.85 "
fcoo = open(tfile['coo_bright'], 'r')
lines = fcoo.readlines()
fcoo.close()
import numarray, math
fout = open(tfile['coo_ok'], 'w')
for line in lines:
if re.match(r'^#', line) or re.search(r'INDEF', line):
continue
values = line.split()
star_class = float(values[2])
if star_class > 0.75:
fout.write(line)
fout.close()
print "Measuring photometry for psf candidate stars in " + tfile[
'coo_ok']
iraf.daophot.phot(image=this_image,
coords=tfile['coo_ok'],
output=tfile['mag_bright'])
### do this selection in 2 steps because of the way IRAF handles INDEFs
print "Selecting stars that have good centroids and magnitudes "
iraf.pselect(
tfile['mag_bright'], tfile['mag_ok'],
"(CIER==0)&&(PIER==0)&&(SIER==0)&&(MSKY>0)&&(MSKY<2e5)&&(MSKY!=INDEF)"
)
print "Selecting stars that have normal sky levels"
condition = "(abs(MSKY -" + str(skyvalue) + ") < 5.0*" + str(
sigma) + ")"
iraf.pselect(tfile['mag_ok'], tfile['mag_good'], condition)
a = iraf.txdump(tfile['mag_good'], "SSKEW", iraf.yes, Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean**2)
limit = mean + 2 * stddev
os.unlink(tfile['mag_good'])
condition = condition + " && SSKEW < " + str(limit)
iraf.pselect(tfile['mag_ok'], tfile['mag_good'], condition)
print "Choosing the psf stars"
iraf.pstselect(image=this_image,
photfile=tfile['mag_good'],
pstfile=tfile['pst_in'],
maxnpsf=25)
## construct an initial PSF image
print "computing psf with neighbor stars based on complete star list"
iraf.psf.mode = 'a'
iraf.psf(image=this_image,
photfile=tfile['mag_bright'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_1.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg_org'],
varorder=0)
try:
print "subtracting the psf neighbors and placing the results in " + tfile[
'sub.fits']
iraf.daophot.nstar(image=this_image,
groupfile=tfile['psg_org'],
psfimage=tfile['psf_1.fits'],
nstarfile=tfile['nst'],
rejfile=tfile['nrj'])
iraf.daophot.substar(image=this_image,
photfile=tfile['nst'],
exfile=tfile['pst_in'],
psfimage=tfile['psf_1.fits'],
subimage=tfile['sub.fits'])
a = iraf.daophot.txdump(tfile['nst'], 'chi', 'yes', Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean**2)
limit = mean + 2.5 * stddev
print "Selecting those psf stars with CHI^2 <" + str(
limit) + " after fitting with trial psf"
iraf.pselect(tfile['nst'], tfile['mag_best'],
"CHI < " + str(limit))
os.unlink(tfile['pst_out'])
## os.unlink(tfile['psg'])
## rebuild the PSF file with the psf stars that fit well..
## using the neighbor subtracted image
print "Rebuilding the PSF"
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_2.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=0)
print "re-subtracting with rebuilt psf"
os.unlink(tfile['nst'])
os.unlink(tfile['nrj'])
iraf.daophot.nstar(image=this_image,
groupfile=tfile['psg'],
psfimage=tfile['psf_2.fits'],
nstarfile=tfile['nst'],
rejfile=tfile['nrj'])
os.unlink(tfile['sub.fits'])
iraf.daophot.substar(image=this_image,
photfile=tfile['nst'],
exfile=tfile['pst_in'],
psfimage=tfile['psf_2.fits'],
subimage=tfile['sub.fits'])
os.unlink(tfile['psg'])
os.unlink(tfile['pst_out'])
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_3.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=0)
os.unlink(tfile['nrj'])
os.unlink(tfile['nst'])
iraf.daophot.nstar(image=this_image,
groupfile=tfile['psg'],
psfimage=tfile['psf_3.fits'],
nstarfile=tfile['nst'],
rejfile=tfile['nrj'])
a = iraf.daophot.txdump(tfile['nst'], 'chi', 'yes', Stdout=1)
aa = []
for v in a:
aa.append(float(v))
a = numarray.array(aa)
mean = a.mean()
aa = a * a
stddev = math.sqrt(aa.sum() / len(aa) - mean**2)
limit = mean + 2 * stddev
limit = 2.0
#print "Selecting those psf stars with CHI^2 < "+str(limit)+" after fit with GOOD psf"
os.unlink(tfile['mag_best'])
iraf.pselect(tfile['nst'], tfile['mag_best'],
"CHI < " + str(limit))
print "Building final PSF.... "
os.unlink(tfile['sub.fits'])
iraf.daophot.substar(image=this_image,
photfile=tfile['nst'],
exfile=tfile['pst_in'],
psfimage=tfile['psf_3.fits'],
subimage=tfile['sub.fits'])
os.unlink(tfile['psg'])
os.unlink(tfile['pst_out'])
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_final.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=0)
print "building an analytic psf for the FWHM calculations"
os.unlink(tfile['pst_out'])
os.unlink(tfile['psg'])
iraf.daophot.psf(image=tfile['sub.fits'],
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_4.fits'],
opstfile=tfile['pst_out'],
groupfile=tfile['psg'],
varorder=-1)
except:
print sys.exc_info()[1]
print "ERROR: Reverting to first pass psf"
tfile['psf_final.fits'] = tfile['psf_1.fits']
iraf.daophot.psf(image=this_image,
photfile=tfile['mag_best'],
pstfile=tfile['pst_in'],
psfimage=tfile['psf_4.fits'],
opstfile=tfile['pst_out2'],
groupfile=tfile['psg'],
varorder=-1)
psf_ap = iraf.photpars.apertures
ap1 = int(psf_ap)
ap2 = int(4.0 * opt.fwhm)
apcor = "INDEF"
aperr = "INDEF"
if (0):
#try
### now that we have the psf use the output list of psf stars
### to compute the aperature correction
lines = iraf.txdump(tfile['pst_out'],
'xcen,ycen,mag,id',
iraf.yes,
Stdout=tfile['coo_pst'])
## set the lower ap value for the COG (normally set to 2)
if (ap1 < 3):
smallap = 1
else:
smallap = 2 - ap1 + 1
ap1 = 2
ap2 = int(math.floor(4.0 * opt.fwhm))
naperts = ap2 - ap1 + 1
iraf.photpars.apertures = str(ap1) + ":" + str(ap2) + ":1"
iraf.photpars.saveParList()
iraf.daophot.phot(image=this_image,
coords=tfile['coo_pst'],
output=tfile['mag_pst'])
iraf.photcal()
iraf.photcal.mkapfile(tfile['mag_pst'],
naperts=naperts,
apercors=tfile['apcor'],
smallap=smallap,
verify='no',
gcommands='',
interactive=0)
fin = open(tfile['apcor'], 'r')
lines = fin.readlines()
values = lines[2].split()
apcor = values[1]
aperr = values[2]
#except:
## compute the FWHM of the PSF image using the analytic PSF (VarOrd=-1)
psf_file = pyfits.open(tfile['psf_4.fits'])
fwhm = (psf_file[0].header.get('PAR1', 99.0) +
psf_file[0].header.get('PAR2', 99.0))
psf_file.close()
# ## Open the psf.fits
infits = pyfits.open(tfile['psf_final.fits'])
hdu = infits[0]
inhdu = hdu.header
inhdu.update('XTENSION', 'IMAGE', before='SIMPLE')
inhdu.update('PCOUNT', 0, after='NAXIS2')
inhdu.update('GCOUNT', 1, after='PCOUNT')
del hdu.header['SIMPLE']
del hdu.header['EXTEND']
inhdu.update("EXTNAME", extname, comment="image extension identifier")
#inhdu.update("SLOW",slow,comment="SROUND low cutoff")
#inhdu.update("SIGH",sigh,comment="SROUND high cutoff")
inhdu.update("PFWHM",
fwhm,
comment="FWHM of stars based on PSF fitting")
inhdu.update("ZMAG", zero_mag, comment="ZMAG of PSF ")
inhdu.update("BCKG", skyvalue, comment="Mean sky level in counts")
inhdu.update("BCKG_STD",
sigma,
comment="standard deviation of sky in counts")
inhdu.update("AP1", psf_ap, comment="Apperture used for PSF flux")
inhdu.update("AP2", ap2, comment="Full Flux aperture")
inhdu.update("APCOR", apcor, comment="Apperture correction (ap1->ap2)")
inhdu.update("APERR", apcor, comment="Uncertainty in APCOR")
# ### append this psf to the output images....
print "Sticking this PSF onto the output file"
f[current_ext].header.update(
"PFWHM", fwhm, comment="FWHM of stars based on PSF fitting")
f[current_ext].header.update("BCKG",
skyvalue,
comment="Mean sky level in counts")
f[current_ext].header.update(
"BCKG_STD", sigma, comment="Standard deviation of sky in counts")
f.flush()
outfits.append(hdu)
outfits.flush()
infits.close()
### remove the temp file we used for this computation.
for tf in tfile.keys():
if os.access(tfile[tf], os.F_OK):
os.unlink(tfile[tf])
current_ext = current_ext + 1
outfits.close()
return mef_psf
recen = raw_input('\n> Adjust recentering radius? [n] ')
if not recen: recen = 'n'
if recen != 'n':
recen_rad = raw_input('\n> Enter radius [' + str(recen_rad_1) +
'] ')
if not recen_rad: recen_rad = recen_rad_1
recen_rad = int(recen_rad)
iraf.centerpars.cbox = recen_rad
recen_rad_1 = recen_rad
j += 1
iraf.txdump(textfile=image + '.mag.' + str(j),
fields='MAG,MERR',
expr='yes',
Stdout=os.path.join(outdir, image + '_SN_ap.txt'))
iraf.txdump(textfile=image + '.als.' + str(j),
fields='MAG,MERR',
expr='yes',
Stdout=os.path.join(outdir, image + '_SN_dao.txt'))
apmag = np.genfromtxt(os.path.join(outdir, image + '_SN_ap.txt'))
SNap = apmag[0]
errSNap = apmag[1]
daomag = np.genfromtxt(os.path.join(outdir, image + '_SN_dao.txt'))
try:
def get_app_phot_target(image, ra=None, dec=None, plot=True, store=True, wcsin="logical", fwhm=None, box=15, arcsecpix=0.394, app=2):
'''
coords: files:
wcsin: can be "world", "logic"
fwhm: in arcsec
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
print "Assuming ra=%.5f, dec=%.5f"%(ra, dec)
pra, pdec = get_xy_coords(image, ra, dec)
else:
if("logic" in wcsin):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as wcs module does not seem to be working well.
try:
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#print "Retrieved the pixel number"
pra, pdec = get_xy_coords(image, ra, dec)
except IndexError:
print "Error with astrometry.net. trying the rudimentary method."
pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
if (not fwhm is None):
fwhm_value = fwhm
elif (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')
else:
#Put some default value for Palomar
fwhm_value=1.5
if (wcsin == 'logical'):
fwhm_value = fwhm_value / arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
if (not fitsutils.has_par(image, "EXPTIME")):
if (fitsutils.has_par(image, "ITIME") and fitsutils.has_par(image, "COADDS")):
exptime = fitsutils.get_par(image, "ITIME")*fitsutils.get_par(image, "COADDS")
fitsutils.update_par(image, "EXPTIME", exptime)
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*app) # Set aperture radius to two times the PSF radius
sky_rad= math.ceil(aperture_rad*app*2)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), \
("stdev","<f4"), ("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT") and fitsutils.has_par(image, "ZEROPTU")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
else:
mag = 0
magerr = 0
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
#zmin, zmax = zscale.zscale(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50])
#zmin, zmax = zscale.zscale(impf[0].data)
#im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
impf[0].data[np.isnan(impf[0].data)] = np.nanmedian(impf[0].data)
print np.percentile(impf[0].data, 5), np.percentile(impf[0].data, 95)
im = plt.imshow(impf[0].data, vmin=np.percentile(impf[0].data, 5), vmax=np.percentile(impf[0].data, 95), origin="bottom")
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"), dpi=200)
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
def psffit(img,
fwhm,
psfstars,
hdr,
interactive,
_datamin,
_datamax,
psffun='gauss',
fixaperture=False):
'''
giving an image, a psffile compute the psf using the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning gain not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img + '.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(
5. / _pixelscale), float(8. / _pixelscale), float(10. /
_pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 +
0.5), int(fwhm * 3 +
0.5), int(fwhm * 4 +
0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = _datamin
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.photpars.zmag = zmag
psfout = img.replace('.zogypsf', '') + '.psf.fits'
iraf.delete('_psf.ma*,_psf.ps*,_psf.gr?,_psf.n*,_psf.sub.fit?,' + psfout,
verify=False)
iraf.phot(img + '[0]',
'_psf.coo',
'_psf.mag',
interac=False,
verify=False,
verbose=False)
# removes saturated stars from the list (IRAF just issues a warning)
with open('_psf.mag') as f:
text = f.read()
text = re.sub('(.*\n){6}.*BadPixels\* \n', '', text)
with open('_psf.mag', 'w') as f:
f.write(text)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
if interactive: # not possible to run pstselect or psf interactively on 64-bit linux (Error 851)
os.system('cp _psf.mag _psf.pst')
print '_' * 80
print '>>> Mark good stars with "a" or "d"-elete. Then "f"-it,' + \
' "w"-write and "q"-uit (cursor on ds9)'
print '-' * 80
else:
iraf.pstselect(img + '[0]',
'_psf.mag',
'_psf.pst',
psfstars,
interac=False,
verify=False)
iraf.psf(img + '[0]',
'_psf.mag',
'_psf.pst',
psfout,
'_psf.psto',
'_psf.psg',
interac=interactive,
verify=False,
verbose=False)
iraf.group(img + '[0]',
'_psf.mag',
psfout,
'_psf.grp',
verify=False,
verbose=False)
iraf.nstar(img + '[0]',
'_psf.grp',
psfout,
'_psf.nst',
'_psf.nrj',
verify=False,
verbose=False)
photmag = iraf.txdump("_psf.mag",
'xcenter,ycenter,id,mag,merr',
expr='yes',
Stdout=1)
pst = iraf.txdump("_psf.pst", 'xcenter,ycenter,id', expr='yes', Stdout=1)
fitmag = iraf.txdump("_psf.nst",
'xcenter,ycenter,id,mag,merr',
expr='yes',
Stdout=1)
return photmag, pst, fitmag
iraf.phot.setParam('image',raw_filenames[i])
iraf.phot.setParam('coords','../coo_'+exam_date+'/')
iraf.phot.setParam('output','../mag_'+exam_date+'/'+raw_filenames[i]+'.mag.1')
iraf.phot.setParam('verify','no')
photstring=iraf.phot(mode='h',Stdout=1)
lenphotstring=len(photstring)
print lenphotstring
print photstring[0].split()[0]
print photstring[0].split()[1]
print photstring[0].split()[2]
print photstring[0].split()[3]
print photstring[0].split()[4]
print photstring[0].split()[5]
iraf.txdump('../mag_'+exam_date+'/'+raw_filenames[i]+'.mag.1',
"xcenter,ycenter,xerr,yerr,mag,flux,area",'yes',
Stdout='../mag_'+exam_date+'/'+raw_filenames[i]+'.mag.1.txt')
txstr=iraf.txdump('../mag_'+exam_date+'/'+raw_filenames[i]+'.mag.1',
"xcenter,ycenter,xerr,yerr,mag,flux,area",'yes',
Stdout=1)
lentxdump=len(txstr)
print txstr[1]
print len(txstr[1])
for spot in range(1,lentxdump-1):
xcoord=float(txstr[spot].split()[0])
ycoord=float(txstr[spot].split()[1])
index_check=where((abs(x-xcoord) < 11) &
(abs(y-ycoord) < 11))
if index_check[0].nelements()==0:
def psffit(img, fwhm, psfstars, hdr, interactive, _datamax, psffun='gauss', fixaperture=False):
'''
giving an image, a psffile compute the psf using the file _psf.coo
'''
import lsc
_ron = lsc.util.readkey3(hdr, 'ron')
_gain = lsc.util.readkey3(hdr, 'gain')
if not _ron:
_ron = 1
print 'warning ron not defined'
if not _gain:
_gain = 1
print 'warning ron not defined'
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
zmag = 0.
varord = 0 # -1 analitic 0 - numeric
if fixaperture:
print 'use fix aperture 5 8 10'
hdr = lsc.util.readhdr(img+'.fits')
_pixelscale = lsc.util.readkey3(hdr, 'PIXSCALE')
a1, a2, a3, a4, = float(5. / _pixelscale), float(5. / _pixelscale), float(8. / _pixelscale), float(
10. / _pixelscale)
else:
a1, a2, a3, a4, = int(fwhm + 0.5), int(fwhm * 2 + 0.5), int(fwhm * 3 + 0.5), int(fwhm * 4 + 0.5)
iraf.fitskypars.annulus = a4
iraf.fitskypars.salgori = 'mean' #mode,mean,gaussian
iraf.photpars.apertures = '%d,%d,%d' % (a2, a3, a4)
iraf.datapars.datamin = -100
iraf.datapars.datamax = _datamax
iraf.datapars.readnoise = _ron
iraf.datapars.epadu = _gain
iraf.datapars.exposure = 'EXPTIME'
iraf.datapars.airmass = ''
iraf.datapars.filter = ''
iraf.centerpars.calgori = 'centroid'
iraf.centerpars.cbox = a2
iraf.daopars.recenter = 'yes'
iraf.photpars.zmag = zmag
iraf.delete('_psf.ma*,' + img + '.psf.fit?,_psf.ps*,_psf.gr?,_psf.n*,_psf.sub.fit?', verify=False)
iraf.phot(img+'[0]', '_psf.coo', '_psf.mag', interac=False, verify=False, verbose=False)
# removes saturated stars from the list (IRAF just issues a warning)
with open('_psf.mag') as f:
text = f.read()
text = re.sub('(.*\n){6}.*BadPixels\* \n', '', text)
with open('_psf.mag', 'w') as f:
f.write(text)
iraf.daopars.psfrad = a4
iraf.daopars.functio = psffun
iraf.daopars.fitrad = a1
iraf.daopars.fitsky = 'yes'
iraf.daopars.sannulus = a4
iraf.daopars.recenter = 'yes'
iraf.daopars.varorder = varord
if interactive: # not possible to run pstselect or psf interactively on 64-bit linux (Error 851)
shutil.copyfile('_psf.mag', '_psf.pst')
print '_' * 80
print '>>> Mark good stars with "a" or "d"-elete. Then "f"-it,' + \
' "w"-write and "q"-uit (cursor on ds9)'
print '-' * 80
else:
iraf.pstselect(img+'[0]', '_psf.mag', '_psf.pst', psfstars, interac=False, verify=False)
iraf.psf(img + '[0]', '_psf.mag', '_psf.pst', img + '.psf', '_psf.psto', '_psf.psg', interac=interactive,
verify=False, verbose=False)
iraf.group(img + '[0]', '_psf.mag', img + '.psf', '_psf.grp', verify=False, verbose=False)
iraf.nstar(img + '[0]', '_psf.grp', img + '.psf', '_psf.nst', '_psf.nrj', verify=False, verbose=False)
photmag = iraf.txdump("_psf.mag", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
pst = iraf.txdump("_psf.pst", 'xcenter,ycenter,id', expr='yes', Stdout=1)
fitmag = iraf.txdump("_psf.nst", 'xcenter,ycenter,id,mag,merr', expr='yes', Stdout=1)
return photmag, pst, fitmag
iraf.unlearn('daophot')
iraf.setimpars('int','yes','no')
FWHM = raw_input('What is the stellar FWHM in pixels?\n')
skysigma = raw_input('What is the sky sigma in the first image?\n')
iraf.datapars.scale = 1/float(FWHM)
iraf.datapars.sigma = float(skysigma)
# Finding calibration stars
os.system('rm *.coo.*')
iraf.daofind('../SCIENCE/' + image0,'default',verify='no',verbose='no')
os.system('rm *.mag.*')
iraf.phot('../SCIENCE/' + image0,'default','default',verify='no',verbose='no')
try:
os.remove('calibcoords')
except: True
iraf.txdump(image0 + '.mag.1','xcenter,ycenter',\
"cerror='NoError' && perror='NoError' && serror='NoError' && merr<0.005 && mag>0",Stdout = 'calibcoords')
coordarray = []
f = open('calibcoords')
calibcoords = f.readlines()
f.close()
for i in calibcoords:
coordarray.append(i)
# Calculate mean calibration star magnitude for each imagine
os.system('rm *.mag.*')
iraf.phot('@imagelist','calibcoords','default',verify='no',verbose='no')
os.system('rm magfiles')
iraf.files(path + 'lightcurve/al*mag.1',Stdout = 'magfiles')
magarray = []
# Open files to store data that imalign will need later on
obj_list = open(current_object + ".lis", "w")
shifts_list = open(current_object + ".shifts", "w")
coords_list = open(current_object + ".coords","w")
output_list = open(current_object + ".out", "w")
# All images of this object. Read fiirst as reference
whr = np.where(list_images["objname"] == current_object)[0]
ref_im = list_images["filename"][whr[0]]
ref_catalog = utilities.replace_extension(ref_im, ".cat")
# Txdump writes to a temporary file, then we read it
iraf.ptools(_doprint=0)
if os.path.isfile("temp.txt"):
os.remove("temp.txt")
iraf.txdump(ref_catalog, "xcenter, ycenter, mag", "yes", Stdout="temp.txt")
x_ref=y_ref=mag_ref=np.array([],dtype=np.float16)
with open("temp.txt") as f:
for line in f:
x_ref = np.append(x_ref,float(line.split()[0]))
y_ref = np.append(y_ref,float(line.split()[1]))
mag_ref = np.append(mag_ref, float(line.split()[2]))
brightest_stars = np.argsort(mag_ref)[:nstars]
x_ref = x_ref[brightest_stars]
y_ref = y_ref[brightest_stars]
#Write to a file, which imalign will need later
for ii,jj in zip(x_ref,y_ref):
coords_list.write( str(ii) + " " + str(jj) + "\n")
coords_list.close()
def errore(size, truemag, fwhm0, leng0):
from pyraf import iraf
import string, os, sys
dartf = 100
while dartf >= size - 1:
artfac0 = raw_input(
'>>> Dispersion of artificial star positions (in units of FWHM) [1] '
)
if not artfac0: artfac0 = 1
try:
artfac0 = float(artfac0)
if float(artfac0) >= size - 1:
print '!!! WARNING: ' + str(
artfac0) + ' too large (max ' + str(size) + '- 1)'
print 'try again....'
else:
dartf = artfac0
except:
print '#### WARNING: ' + str(artfac0) + ' should be a number !!!!'
print 'try again....'
iraf.delete("tmpar?", ve='no')
i = 0
tmpart = []
while i <= 8:
iraf.delete(
"reserr.fit?,artbg.fit?,artstar.fit?,artres.fit?,artfit.fit?,artskyfit.fit?",
ve='no')
artrad = fwhm0 / 2.
#artseed = artseed+1234
artx = int(i / 3.) - 1
if i <= 2: arty = artx + i
if 3 <= i <= 5: arty = artx - 1 + i - 3
if i >= 6: arty = artx - 2 + i - 6
ff = open(img + ".sn.coo", 'r')
ss = ff.readline()
ff.close()
xbb = float(string.split(ss)[0])
ybb = float(string.split(ss)[1])
xbb = xbb + artx * fwhm0 * artfac0
ybb = ybb + arty * fwhm0 * artfac0
print xbb, ybb
iraf.delete("artlist.coo", ve='no')
ff = open("artlist.coo", 'w')
ff.write(str(xbb) + ' ' + str(ybb) + ' ' + str(truemag[0]) + " 1")
ff.close()
xb1 = int(float(xbb) - fwhm0 * float(leng0) / 2)
xb2 = int(float(xbb) + fwhm0 * float(leng0) / 2)
yb1 = int(float(ybb) - fwhm0 * float(leng0) / 2)
yb2 = int(float(ybb) + fwhm0 * float(leng0) / 2)
sec = "1 " + str(xb1) + " 1 " + str(nay) + '\n'
sec = sec + str(xb2) + ' ' + str(nax) + " 1 " + str(nay) + '\n'
sec = sec + str(xb1) + ' ' + str(xb2) + " 1 " + str(yb1) + '\n'
sec = sec + str(xb1) + ' ' + str(xb2) + ' ' + str(yb2) + ' ' + str(
nay) + '\n'
ff = open('sec', 'w')
ff.write(sec)
ff.close()
iraf.delete("reserr.art", ve='no')
iraf.delete("artlist.mag", ve='no')
iraf.delete("artlist.als", ve='no')
iraf.addstar("skyfit",
"artlist.coo",
imgpsf,
"reserr",
nstar=1,
veri='no',
simple='yes')
iraf.imsurfit("reserr",
"artbg",
xorder=xbgord0,
yorder=ybgord0,
regions="section",
section="sec")
iraf.imarith("reserr", "-", "artbg", "artstar")
iraf.phot("artstar", "artlist.coo", "artlist.mag", veri='no')
iraf.allstar("artstar",
"artlist.mag",
imgpsf,
"artlist.als",
"",
"artres",
veri='no',
verb='no')
iraf.imarith("artstar", "-", "artres", "artfit")
iraf.imarith("reserr", "-", "artfit", "artskyfit")
if i == 0: era = 'yes'
else: era = 'no'
artx = .5 + .25 * artx
arty = .5 + .25 * arty
iraf.display("artskyfit",
1,
fill='yes',
erase=era,
xcen=artx,
ycen=arty,
xsize=.25,
ysize=.25)
try:
tmpart.append(
float(
iraf.txdump("artlist.als", "mag", expr='yes',
Stdout=1)[0]))
except:
pass
i = i + 1
for i in tmpart:
print i
print " ########## "
try:
media = mean(array(tmpart))
arterr = std(array(tmpart))
arterr2 = std(
compress((average(tmpart) - std(tmpart) < array(tmpart)) &
(array(tmpart) < average(tmpart) + std(tmpart)),
array(tmpart)))
except:
media = 0
arterr = 0
arterr2 = 0
print '### average = %6.6s \t arterr= %6.6s ' % (str(media), str(arterr))
print '### %6.6s \t (error at 1 sigma rejection) ' % (str(arterr2))
iraf.delete(
"reserr.fit?,artbg.fit?,artstar.fit?,artres.fit?,artfit.fit?,artskyfit.fit?",
ve='no')
iraf.delete("reserr.art", ve='no')
iraf.delete("artlist.*", ve='no')
return arterr2, arterr
def makeillumination(lista,flatfield):#,outputfile,illum_frame):
import os,glob,string,re
from astropy.io import fits as pyfits
import ntt
from ntt.util import readhdr, readkey3, delete, display_image, defsex, name_duplicate, correctcard
from numpy import compress, array, argmax, argmin, min, argsort, float32
import datetime
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
illum_frame = name_duplicate(
lista[0], 'illum_' + _date + '_' + _filter + '_' + str(MJDtoday), '')
from pyraf import iraf
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
iraf.utilities(_doprint=0)
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.generic(_doprint=0)
toforget = ['digiphot.daophot', 'imutil.imarith',
'image', 'utilities.surfit']
for t in toforget:
iraf.unlearn(t)
n = len(lista)
# start loop to read image names from the input file
lista1 = []
iraf.ccdred.verbose = 'no'
ff = open('templist.lst', 'w')
for i in range(0, len(lista)):
ff.write('C' + lista[i] + '\n')
delete('C' + lista[i])
delete('C' + re.sub('.fits', '_sub.fits', lista[i]))
ntt.sofiphotredudef.crosstalk(lista[i], 'C' + lista[i])
iraf.noao.imred.ccdred.ccdproc('C' + lista[i], output='', overscan="no", trim="yes", ccdtype='', darkcor='no', fixpix='no', zerocor="no", flatcor='yes',
illumco='no', trimsec='[1:1024,1:1007]', biassec='', flat=flatfield, illum='')
correctcard('C' + lista[i])
lista1.append('C' + lista[i])
ff.close()
print '\n### prereducing STD frames to compute illumination correction ........'
lista2, skyfile = ntt.sofiphotredudef.skysub(lista1, readkey3(
readhdr(lista1[0]), 'ron'), readkey3(readhdr(lista1[0]), 'gain'), True)
lista2 = ntt.sofiphotredudef.sortbyJD(lista2)
print '\n### use x on the star and q to continue....'
display_image(lista2[0], 2, '', '', False)
delete('tmpone.coo')
iraf.image.tv.imexamine(lista2[0], 2, logfile='tmpone.coo',
keeplog='yes', xformat='', yformat='', wcs='logical')
iraf.tvmark(2, 'tmpone.coo', mark="circle", number='yes',
label='no', radii=8, nxoffse=5, nyoffse=5, color=204, txsize=2)
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x0, y0 = string.split(xycoo[0])
x0 = float(x0)
y0 = float(y0)
xcum0 = readkey3(readhdr(lista2[0]), 'xcum')
ycum0 = readkey3(readhdr(lista2[0]), 'ycum')
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.noao.digiphot.daophot.datapars.datamin = -1000
iraf.noao.digiphot.daophot.datapars.datamax = 60000
iraf.noao.digiphot.daophot.daopars.function = 'gauss'
iraf.noao.digiphot.daophot.photpars.zmag = 0
namesex = defsex('default.sex')
for i in range(0, len(lista2)):
j = i + 1
xcum = readkey3(readhdr(lista2[i]), 'xcum')
ycum = readkey3(readhdr(lista2[i]), 'ycum')
xx = x0 - xcum0 + xcum
yy = y0 - ycum0 + ycum
# sex objects
os.system('sex ' + lista2[i] + ' -c ' + namesex + '> _logsex')
delete('_logsex')
xpix = iraf.proto.fields('detections.cat', fields='2', Stdout=1)
ypix = iraf.proto.fields('detections.cat', fields='3', Stdout=1)
cm = iraf.proto.fields('detections.cat', fields='4', Stdout=1)
cm = compress((array(xpix) != ''), array(cm, float))
ypix = compress((array(xpix) != ''), array(ypix, float))
xpix = compress((array(xpix) != ''), array(xpix, float))
if len(xpix) > 300:
num = 300
else:
num = len(xpix) - 1
xpix = xpix[argsort(cm)][0:num]
ypix = ypix[argsort(cm)][0:num]
distance = (ypix - yy)**2 + (xpix - xx)**2
xx1, yy1 = xpix[argmin(distance)], ypix[argmin(distance)]
f = open('tmpone.coo', 'w')
f.write(str(xx1) + ' ' + str(yy1) + '\n')
f.close()
display_image(lista2[i], 1, '', '', False)
iraf.tvmark(1, 'tmpone.coo', mark="circle", number='yes',
label='no', radii=8, nxoffse=5, nyoffse=5, color=204, txsize=2)
answ = 'n'
while answ != 'y':
answ = raw_input('selected the right one [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['y', 'YES', 'yes', 'Y']:
print lista2[i]
delete('pippo.' + str(j) + '.mag')
gggg = iraf.digiphot.daophot.phot(
lista2[i], "tmpone.coo", output="pippo." + str(j) + ".mag", verify='no', interac='no', Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
else:
print '\n### select the std star'
display_image(lista2[i], 1, '', '', False)
iraf.image.tv.imexamine(lista2[
i], 1, logfile='tmpone.coo', keeplog='yes', xformat='', yformat='', wcs='logical')
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x2, y2 = string.split(xycoo[0])
f = open('tmpone.coo', 'w')
f.write(str(x2) + ' ' + str(y2) + '\n')
f.close()
delete('pippo.' + str(j) + '.mag')
print '###### new selection ' + str(x2), str(y2)
gggg = iraf.digiphot.daophot.phot(
lista2[i], "tmpone.coo", output='pippo.' + str(j) + '.mag', verify='no', interac='no', Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
os.system('ls pippo.*.mag > tempmag.lst')
tmptbl0 = iraf.txdump(textfile="@tempmag.lst",
fields="XCENTER,YCENTER,FLUX", expr='yes', Stdout=1)
ff = open('magnitudini', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
# delete the temporary images and files
delete("temp*.fits")
delete('temp*.lst')
delete(illum_frame)
print '\n### fitting the illumination surface...'
aaa = iraf.utilities.surfit('magnitudini', image=illum_frame, function="polynomial",
xorder=2, yorder=2, xterms="full", ncols=1024, nlines=1024, Stdout=1)
iraf.noao.imred.generic.normalize(illum_frame)
correctcard(lista[0])
data, hdr = pyfits.getdata(illum_frame, 0, header=True)
data0, hdr0 = pyfits.getdata(lista[0], 0, header=True)
delete(illum_frame)
pyfits.writeto(illum_frame, float32(data), hdr0)
flatfield0 = string.split(flatfield, '/')[-1]
ntt.util.updateheader(
illum_frame, 0, {'MKILLUM': [flatfield0, 'flat field']})
display_image(illum_frame, 1, '', '', False)
for i in range(0, len(lista)): # in lista:
img = lista[i]
delete('pippo.' + str(i) + '.mag')
delete('C' + img)
delete('C' + re.sub('.fits', '_sky.fits', img))
# delete('C*.fits.mag.1')
# iraf.hedit(illum_frame,'MKILLUM','Illum. corr. created '+flatfield,add='yes',update='yes',verify='no')
return illum_frame
def manusn(img,imgpsf,dmag0,apori1,apori2,apori3,apmag1,apmag2,apmag3,fitmag,truemag,magerr,centx,centy,z11,z22,midpt,size,fwhm0,x1,y1,arterr):
from pyraf import iraf
import lsc
import string,re,sys,os
from numpy import zeros
a2 = int(2.*fwhm0+.5)
fdmag = 10**(-0.4*float(dmag0))
lsc.util.delete(",_snfit.fit?,skyfit.fit?,_snfit.ar?")
if truemag[0]=='INDEF':
print 'ACTUNG'
magerr[0]=0.0
lsc.util.delete('test.fits')
os.system('echo '+str(iraf.field(img+'.sn.coo', field='1,2', Stdout=1)[0])+' '+dmag0+' > dddd')
iraf.addstar("snfit","dddd",imgpsf,"_snfit",nstar=1,veri='no',simple='yes',verb='yes')
lsc.util.delete('dddd')
else:
iraf.imarith("snfit.fits","*",fdmag,"_snfit.fits")
iraf.imarith("original.fits","-","_snfit.fits","skyfit.fits")
_tmp1,_tmp2,goon=lsc.util.display_image('original.fits',1, z11, z22, False, _xcen=.25, _ycen=.25, _xsize=.3, _ysize=.3)
print z11,z22,midpt
z01 = float(z11)-float(midpt)
z02 = float(z22)-float(midpt)
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" ORIGINAL")
ff.close()
iraf.tvmark(1,"tmptbl",autol='yes',mark="none",inter='no',label='yes',txsize=2)
_tmp1,_tmp2,goon=lsc.util.display_image('_snfit.fits',1, z01, z02, False, _xcen=.25, _ycen=.75, _xsize=.3, _ysize=.3, _erase='no')
lsc.util.delete("tmptbl")
tmptbl=iraf.txdump(img+".sn.als","xcen,ycen",expr='yes', Stdout=1)
ff=open('tmptbl','w')
for i in tmptbl:
ff.write(i)
ff.close()
lra = int((2*size*fwhm0)*2)
iraf.tvmark(1,"tmptbl",autol='no',mark="circle", number='yes',nyoffset=lra,radi=a2,txsize=2,inter='no')
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" FITTED")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
_tmp1,_tmp2,goon=lsc.util.display_image('skyfit.fits',1, z11, z22, False, _xcen=.75, _ycen=.25, _xsize=.3, _ysize=.3, _erase='no')
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" RESIDUAL")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
newmag=list(zeros(len(truemag)))
for i in range(0,len(truemag)):
try:
newmag[i]=float(truemag[i])+float(dmag0)
except:
newmag[i]=float(dmag0)
magerr[i]=0.0
print truemag
print newmag
print "***************************************************************************"
print "#id x_ori y_ori x y ap_ori ap_bgsub fit_mag err_art err_fit"
for i in range(len(fitmag)):
print "SN",i,str(centx[i]+x1),str(centy[i]+y1),str(centx[i]),str(centy[i])," ",str(apori3[i])," ",str(apmag3[i])," ",str(newmag[i])," ",str(arterr)," ",str(magerr[i])
print "**************************************************************************"
return apori1,apori2,apori3,apmag1,apmag2,apmag3,fitmag,truemag,magerr,centx,centy,newmag
def execute():
#here we define the coords variable as a list
coords=[]
#indenting matters in python. Everything
#indented under the for loop is inside the loop
#pdb.set_trace() # break point for python debugger
for line in open(coofnamein):
if (line.startswith("#") or line.startswith("z1")):
continue
#this skips the lines that start with a #
line=map(float,line.split())
#this takes the line and splits it into a list of floats
if len(line) != 4:
continue
#skips the lines that don't have 4 floats
coords.append((line[0],line[1]))
#adds the most recent coords (the 0th and 1st elements
# to the list of coordinates
#print coords
coofnameout1=open(coofnameout, "w")
for ci in coords:
coofnameout1.write("%.4g\t%.4g\n" % ci)
#the strange syntax is the formatting for the
#output. It prints each coordinate with 4 decimal places
# a tab in between, and then a new line at the end
coofnameout1.close()
#sys.exit(1)
l=os.listdir(image_dir)
l=[li for li in l if li.endswith(endstr)]
l=[li for li in l if li.startswith(rootstr)]
l =[image_dir + li for li in l]
#this list takes all the files in the directory,
#then only keeps those with the correct beginning and ending
#l = l[:10]
#this line can tell the code to only run on the first 10 images
#useful for debugging
lc_out1 = open(lc_out,"w")
for li in l:
print li
#print coofnameout
#print phot_out
iraf.phot(li,coofnameout, phot_out, plotfile="", datapars="", centerpars="", fitskypars="", photpars="",interactive="no", radplots="no", icommands="", gcommands="", verify="no", weighting="constant", calgorithm="centroid", obstime=obstime, annulus=annulus,dannulus=dannulus,skyvalue=skyvalue,fwhmpsf=fwhmpsf,sigma=sigma)
#this actually does the photometry
#note all of the parameters that must be defined
standard_output = sys.stdout
#saves the current output -ie to the screen
txdumpfile = open(txdumpout, 'w')
sys.stdout = txdumpfile
#directs the output to the txdumpfile
iraf.txdump(phot_out, "otime,mag,merr", "yes", headers="no", parameters="no")
#does the txdump - might you want to output other information?
sys.stdout = standard_output # restores the stdout
txdumpfile.close() # close the file
os.remove(phot_out)
#deletes the file
phots =[]
for line in open(txdumpout):
#line=map(string,line.split())
line=str.split(line)
phots.append((line[1],line[2]))
#print phots
#print line[0]
#lc_out1.write(line[0] + "\t")
#print stringtohours(line[0])
lc_out1.write( "%10.7f \t" % (stringtohours(line[0])))
for photsi in phots:
lc_out1.write(photsi[0] + "\t")
lc_out1.write(photsi[1] + "\t")
lc_out1.write("\n")
os.remove(txdumpout)
def fitsn(img,imgpsf,coordlist,_recenter,fwhm0,original,sn,residual,_show,_interactive,dmax,dmin,z11='',z22='',midpt='',size=7,apco0=0):
import lsc
lsc.util.delete("apori")
lsc.util.delete(img+".sn.mag")
#################################
from pyraf import iraf
import string
iraf.imcoords(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
from iraf import digiphot
from iraf import daophot
from iraf import ptools
a1 = int(fwhm0)
a2 = int(2.*fwhm0+.5)
a3 = int(3.*fwhm0+.5)
a4 = int(4.*fwhm0+.5)
ap = str(a1)+","+str(a2)+","+str(a3)
########################################
if _recenter: answ='yes'
else: answ='no'
#########################################
hdr=lsc.util.readhdr(img+'.fits')
_gain=lsc.util.readkey3(hdr,'gain')
_ron=lsc.util.readkey3(hdr,'ron')
_exptime=lsc.util.readkey3(hdr,'exptime')
iraf.noao.digiphot.daophot.photpars.zmag = 0
iraf.noao.digiphot.daophot.datapars.readnoi = _gain
iraf.noao.digiphot.daophot.datapars.epadu = _ron
iraf.noao.digiphot.daophot.datapars.datamin = dmin
iraf.noao.digiphot.daophot.datapars.datamax = dmax
iraf.noao.daophot.fitskypars.annulus=a3
iraf.noao.daophot.photpars.apertures = ap
iraf.noao.digiphot.daophot.datapars.exposure = 'exptime'
iraf.noao.digiphot.daophot.datapars.airmass = 'airmass'
iraf.noao.digiphot.daophot.datapars.filter = 'filter2'
iraf.noao.digiphot.daophot.daopars.psfrad = a4
# modify fitrad to 3 fwhm to see if works better
iraf.noao.digiphot.daophot.daopars.fitrad = fwhm0 #* 3
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.recenter = answ
iraf.noao.digiphot.daophot.daopars.fitsky = 'yes'
# iraf.noao.digiphot.daophot.centerpars.cbox = 0
iraf.noao.digiphot.daophot.centerpars.cbox = 4
iraf.noao.digiphot.daophot.centerpars.calgori = 'gauss'
# fitskypars.salgorithm = "constant"
# fitskypars.skyvalue = 0
print '\n### recentering: '+str(answ)
if _show:
iraf.noao.digiphot.daophot.phot(original,coordlist,"apori",veri='no')
iraf.noao.digiphot.daophot.phot(sn,coordlist,img+".sn.mag",veri='no')
else:
iraf.noao.digiphot.daophot.phot(original,coordlist,"apori",veri='no',verb='no')
iraf.noao.digiphot.daophot.phot(sn,coordlist,img+".sn.mag",veri='no',verb='no')
lsc.util.delete(img+".sn.als")
print sn,imgpsf,img
iraf.allstar(sn,img+".sn.mag",imgpsf,img+".sn.als","",residual,veri='no',verb='no')
lsc.util.delete("snfit.fits")
iraf.imarith(sn+'.fits',"-",residual+'.fits',"snfit.fits")
lsc.util.delete("skyfit.fits")
iraf.imarith(original+'.fits',"-","snfit.fits","skyfit.fits")
iraf.txsort(img+".sn.als","ID")
tmptbl = iraf.txdump(img+".sn.als","mag,merr,xcenter,ycenter",expr='yes', Stdout=1)
magerr,fitmag,centx,centy=[],[],[],[]
for i in tmptbl:
try:
fitmag.append(float(string.split(i)[0]))#-2.5*log10(_exptime))
except:
fitmag.append(string.split(i)[0])
try:
magerr.append(float(string.split(i)[1]))
except:
magerr.append(string.split(i)[1])
centx.append(float(string.split(i)[2]))
centy.append(float(string.split(i)[3]))
tmptbl=iraf.txdump("apori","mag",expr='yes', Stdout=1)
apori1,apori2,apori3=[],[],[]
for i in tmptbl:
try:
apori1.append(float(string.split(i)[0]))#-2.5*log10(_exptime))
except:
apori1.append(string.split(i)[0])
try:
apori2.append(float(string.split(i)[1]))#-2.5*log10(_exptime))
except:
apori2.append(string.split(i)[1])
try:
apori3.append(float(string.split(i)[2]))#-2.5*log10(_exptime))
except:
apori3.append(string.split(i)[2])
iraf.txsort(img+".sn.mag","YCENTER")
tmptbl=iraf.txdump(img+".sn.mag","mag,magerr",expr='yes', Stdout=1)
if _show:
print "********************************************************************"
print "ID <apmag on original> <apmag on bgsubt> fitmag truemag err_fit"
print " ",a1," ",a2," ",a3," ",a1," ",a2," ",a3
apmag1,apmag2,apmag3,truemag=[],[],[],[]
for i in range(len(tmptbl)):
try:
apmag1.append(float(string.split(tmptbl[i])[0]))#-2.5*log10(_exptime))
except:
apmag1.append(9999)
try:
apmag2.append(float(string.split(tmptbl[i])[1]))#-2.5*log10(_exptime))
except:
apmag2.append(9999)
try:
apmag3.append(float(string.split(tmptbl[i])[2]))#-2.5*log10(_exptime))
except:
apmag3.append(9999)
try:
truemag.append(fitmag[i]+float(apco0))
except:
truemag.append('INDEF')
if _show:
print i,apori1[i],apori2[i],apori3[i],apmag1[i],apmag2[i],apmag3[i],fitmag[i],truemag[i],magerr[i]
if _show:
print "********************************************************************"
if _show:
print midpt,z11,z22
_tmp1,_tmp2,goon=lsc.util.display_image(original+'.fits',1, z11, z22, False, _xcen=.25, _ycen=.25, _xsize=.3, _ysize=.3)
z01 = float(z11)-float(midpt)
z02 = float(z22)-float(midpt)
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" ORIGINAL")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
_tmp1,_tmp2,goon=lsc.util.display_image('snfit.fits',1, z01, z02, False, _xcen=.25, _ycen=.75, _xsize=.3, _ysize=.3, _erase='no')
lsc.util.delete("tmptbl")
tmptbl0=iraf.txdump(img+".sn.als","xcen,ycen",expr='yes',Stdout=1)
ff=open('tmptbl','w')
for i in tmptbl0:
ff.write(i+'\n')
ff.close()
lra = int((2*float(size)*float(fwhm0))*2)
iraf.tvmark(1,"tmptbl",autol='no',mark="circle", number='yes',nyoffset=lra,radi=a2,txsize=2,inter='no')
s1 = 1
s2 = -1*int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" FITTED")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
_tmp1,_tmp2,goon=lsc.util.display_image('skyfit.fits',1, z11, z22, False, _xcen=.75, _ycen=.25, _xsize=.3, _ysize=.3, _erase='no')
s1 = 1
s2 = -1*int(fwhm0)
lsc.util.delete("tmptbl")
ff=open('tmptbl','w')
ff.write(str(s1)+' '+str(s2)+" RESIDUAL")
ff.close()
iraf.tvmark(1,"tmptbl",autol='no',mark="none",inter='no',label='yes',txsize=2)
return apori1,apori2,apori3,apmag1,apmag2,apmag3,fitmag,truemag,magerr,centx,centy
def get_app_phot_target(image, plot=False, store=True, wcsin="logical", fwhm=2, box=4, ra=None, dec=None):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
impf = pf.open(image)
wcs = pywcs.WCS(impf[0].header)
#Check that actually the object is within this frame.
if (ra is None or dec is None):
if (fitsutils.has_par(image, "OBJRA") and fitsutils.has_par(image, "OBJRA")):
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'OBJRA'), fitsutils.get_par(image, 'OBJDEC'))
else:
ra, dec = cc.hour2deg(fitsutils.get_par(image, 'RA'), fitsutils.get_par(image, 'DEC'))
pra, pdec = get_xy_coords(image, ra, dec)
else:
if(wcsin == "logical"):
pra, pdec = ra, dec
else:
#Using new method to derive the X, Y pixel coordinates, as pywcs does not seem to be working well.
pra, pdec = get_xy_coords(image, ra, dec)
#pra, pdec = wcs.wcs_sky2pix(ra, dec, 1)
#pra, pdec = wcs.wcs_sky2pix(np.array([ra, dec], ndmin=2), 1)[0]
shape = impf[0].data.shape
if (pra > 0) and (pra < shape[0]) and (pdec > 0) and (pdec < shape[1]):
pass
else:
print image, "ERROR! Object coordinates are outside this frame. Skipping any aperture photometry!!"
print pra, pdec, shape
return
imdir = os.path.dirname(image)
imname = os.path.basename(image)
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".objapp.mag")
fwhm_value = fwhm
nsrc, fwhm_value, ellip = sextractor.get_image_pars(image)
if np.isnan(fwhm_value):
fwhm_value=99
fitsutils.update_par(image, 'FWHM', fwhm_value)
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
#print "FWHM", fwhm_value
aperture_rad = math.ceil(float(fwhm_value)*3) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad*4)
#print aperture_rad, sky_rad
print "Saving coodinates for the object in pixels",pra,pdec
coords = "/tmp/coords.dat"
np.savetxt("/tmp/coords.dat", np.array([[pra, pdec]]), fmt="%.4f %.4f")
if (plot):
zmin, zmax = zscale.zscale(impf[0].data)
im = plt.imshow(impf[0].data, vmin=zmin, vmax=zmax, origin="bottom")
plt.scatter(pra, pdec, marker="o", s=100, facecolor="none")
plt.savefig(os.path.join(plotdir, imname+".png"))
plt.clf()
if os.path.isfile(out_name): os.remove(out_name)
if os.path.isfile(clean_name): os.remove(clean_name)
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 15. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime" ,\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = "logical",
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("ph_mag","<f4"), ("stdev","<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
if (ma.size==1):
ma = np.array([ma])
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
insmag = np.round(ma['fit_mag'][0] , 3)
insmagerr = np.round(ma['fiterr'][0], 3)
if (fitsutils.has_par(image, "ZEROPT")):
mag = insmag + float(fitsutils.get_par(image, "ZEROPT"))
magerr = np.sqrt(insmagerr**2+ float(fitsutils.get_par(image, "ZEROPTU"))**2)
if np.isnan(mag):
mag, magerr = 0, 0
insmag, insmagerr = 0,0
fitsutils.update_par(image, "INSMAG", "%.3f"%insmag )
fitsutils.update_par(image, "INSMAGER", "%.3f"%insmagerr)
fitsutils.update_par(image, "APPMAG", np.round(mag, 3) )
fitsutils.update_par(image, "APPMAGER", np.round(magerr, 3))
if (plot):
X = int(ma["X"][0])
Y = int(ma["Y"][0])
pra = int(pra)
pdec = int(pdec)
plt.scatter(X, Y, marker="o", s=100, facecolor="none", edgecolor="red")
plt.colorbar(im)
plt.savefig(os.path.join(plotdir, imname+".png"))
plt.clf()
zmin, zmax = zscale.zscale(impf[0].data.T[X-50:X+50,Y-50:Y+50].T)
im = plt.imshow(impf[0].data.T[pra-50:pra+50,pdec-50:pdec+50].T, vmin=zmin, vmax=zmax, interpolation="none", origin="bottom", extent=(-50,50,-50,50))
c1 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=aperture_rad, label="Initial position")
c11 = plt.Circle( (pra-X, pdec-Y), edgecolor="k", facecolor="none", radius=sky_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=aperture_rad, label="Adjusted centroid")
c22 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
plt.gca().add_artist(c1)
plt.gca().add_artist(c11)
plt.gca().add_artist(c2)
plt.gca().add_artist(c22)
plt.colorbar(im)
myhandles = []
markers = ["o", "o"]
labels = ["Initial position", "Adjusted centroid"]
cols = ["k", "orange"]
for i in np.arange(len(markers)):
myhandles.append(mlines.Line2D([], [], mec=cols[i], mfc="none", marker=markers[i], ls="None", markersize=10, label=labels[i]))
plt.legend(handles=myhandles, loc="lower left", labelspacing=0.3, fontsize=11, numpoints=1, frameon=False, ncol=5, bbox_to_anchor=(0.0, 0.00), fancybox=False, shadow=True)
plt.title("MIN: %.0f MAX: %.0f"%(np.nanmin(impf[0].data.T[X-50:X+50,Y-50:Y+50]), np.nanmax(impf[0].data.T[X-50:X+50,Y-50:Y+50])))
plt.savefig(os.path.join(plotdir, imname+"_zoom.png"))
plt.clf()
def manusn(img, imgpsf, dmag0, apori1, apori2, apori3, apmag1, apmag2, apmag3,
fitmag, truemag, magerr, centx, centy, z11, z22, midpt, size, fwhm0,
x1, y1, arterr):
from pyraf import iraf
import lsc
import string, re, sys, os
from numpy import zeros
a2 = int(2. * fwhm0 + .5)
fdmag = 10**(-0.4 * float(dmag0))
lsc.util.delete(",_snfit.fit?,skyfit.fit?,_snfit.ar?")
if truemag[0] == 'INDEF':
print 'ACTUNG'
magerr[0] = 0.0
lsc.util.delete('test.fits')
os.system('echo ' +
str(iraf.field(img + '.sn.coo', field='1,2', Stdout=1)[0]) +
' ' + dmag0 + ' > dddd')
iraf.addstar("snfit",
"dddd",
imgpsf,
"_snfit",
nstar=1,
veri='no',
simple='yes',
verb='yes')
lsc.util.delete('dddd')
else:
iraf.imarith("snfit.fits", "*", fdmag, "_snfit.fits")
iraf.imarith("original.fits", "-", "_snfit.fits", "skyfit.fits")
_tmp1, _tmp2, goon = lsc.util.display_image('original.fits',
1,
z11,
z22,
False,
_xcen=.25,
_ycen=.25,
_xsize=.3,
_ysize=.3)
print z11, z22, midpt
z01 = float(z11) - float(midpt)
z02 = float(z22) - float(midpt)
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " ORIGINAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='yes',
mark="none",
inter='no',
label='yes',
txsize=2)
_tmp1, _tmp2, goon = lsc.util.display_image('_snfit.fits',
1,
z01,
z02,
False,
_xcen=.25,
_ycen=.75,
_xsize=.3,
_ysize=.3,
_erase='no')
lsc.util.delete("tmptbl")
tmptbl = iraf.txdump(img + ".sn.als", "xcen,ycen", expr='yes', Stdout=1)
ff = open('tmptbl', 'w')
for i in tmptbl:
ff.write(i)
ff.close()
lra = int((2 * size * fwhm0) * 2)
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="circle",
number='yes',
nyoffset=lra,
radi=a2,
txsize=2,
inter='no')
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " FITTED")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
_tmp1, _tmp2, goon = lsc.util.display_image('skyfit.fits',
1,
z11,
z22,
False,
_xcen=.75,
_ycen=.25,
_xsize=.3,
_ysize=.3,
_erase='no')
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " RESIDUAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
newmag = list(zeros(len(truemag)))
for i in range(0, len(truemag)):
try:
newmag[i] = float(truemag[i]) + float(dmag0)
except:
newmag[i] = float(dmag0)
magerr[i] = 0.0
print truemag
print newmag
print "***************************************************************************"
print "#id x_ori y_ori x y ap_ori ap_bgsub fit_mag err_art err_fit"
for i in range(len(fitmag)):
print "SN", i, str(centx[i] + x1), str(centy[i] + y1), str(
centx[i]), str(centy[i]), " ", str(apori3[i]), " ", str(
apmag3[i]), " ", str(newmag[i]), " ", str(arterr), " ", str(
magerr[i])
print "**************************************************************************"
return apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag, truemag, magerr, centx, centy, newmag
rxx = np.array([round])
gxx = np.array([ground])
find_sharp.append(sxx[0])
find_round.append(rxx[0])
find_ground.append(gxx[0])
back, backrms = run_daophot(cnts_name[ff],
coordfile=find_name[ff],
outfile=phot_name[ff],
calgorithm='gauss',
backmethod=backmeth,
cbox=10.)
replace_filevalue(phot_name[ff], 'INDEF', -9999.0)
iraf.txdump(phot_name[ff],
'xcenter,ycenter,flux, mag',
'yes',
Stdout=phot_name[ff] + '.trimmed')
xc,yc,f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f15,f16,f17,f18,f19,f20,f21,f22,f23,f24,f25,f26, \
m1,m2,m3,m4,m5,m6,m7,m8,m9,m10,m11,m12,m13,m14,m15,m16,m17,m18,m19,m20,m21,m22,m23,m24,m25,m26=np.loadtxt(phot_name[ff]+'.trimmed',unpack=True)
# record various properties of observation (chip,amp,mjd, etc.)
dd = pyfits.open(file)
hdr0 = dd[0].header
hdr1 = dd[1].header
im = dd[1].data
dd.close()
amps = hdr0['CCDAMP']
biaslev = hdr0['BIASLEV' + amps]
filter = hdr0['FILTER']
LTV1 = hdr1['LTV1']
LTV2 = hdr1['LTV2']
def fitsn(img,
imgpsf,
coordlist,
_recenter,
fwhm0,
original,
sn,
residual,
_show,
_interactive,
z11='',
z22='',
midpt='',
size=7,
apco0=0,
dmax=51000,
dmin=-500):
import lsc
lsc.util.delete("apori")
lsc.util.delete(img + ".sn.mag")
#################################
from pyraf import iraf
import string
iraf.imcoords(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
from iraf import digiphot
from iraf import daophot
from iraf import ptools
a1 = int(fwhm0)
a2 = int(2. * fwhm0 + .5)
a3 = int(3. * fwhm0 + .5)
a4 = int(4. * fwhm0 + .5)
ap = str(a1) + "," + str(a2) + "," + str(a3)
########################################
if _recenter: answ = 'yes'
else: answ = 'no'
#########################################
hdr = lsc.util.readhdr(img + '.fits')
_gain = lsc.util.readkey3(hdr, 'gain')
_ron = lsc.util.readkey3(hdr, 'ron')
_exptime = lsc.util.readkey3(hdr, 'exptime')
_datamin = dmin
_datamax = dmax
iraf.noao.digiphot.daophot.photpars.zmag = 0
iraf.noao.digiphot.daophot.datapars.readnoi = _gain #1.4 #_ron
iraf.noao.digiphot.daophot.datapars.epadu = _ron # 13 #_gain
iraf.noao.digiphot.daophot.datapars.datamin = _datamin # -100 #_datamin
iraf.noao.digiphot.daophot.datapars.datamax = _datamax
iraf.noao.daophot.fitskypars.annulus = a3
iraf.noao.daophot.photpars.apertures = ap
iraf.noao.digiphot.daophot.datapars.exposure = 'exptime'
iraf.noao.digiphot.daophot.datapars.airmass = 'airmass'
iraf.noao.digiphot.daophot.datapars.filter = 'filter2'
iraf.noao.digiphot.daophot.daopars.psfrad = a4
# modify fitrad to 3 fwhm to see if works better
iraf.noao.digiphot.daophot.daopars.fitrad = fwhm0 #* 3
iraf.noao.digiphot.daophot.daopars.sannulus = int(a4)
iraf.noao.digiphot.daophot.daopars.recenter = answ
iraf.noao.digiphot.daophot.daopars.fitsky = 'yes'
# iraf.noao.digiphot.daophot.centerpars.cbox = 0
iraf.noao.digiphot.daophot.centerpars.cbox = 4
iraf.noao.digiphot.daophot.centerpars.calgori = 'gauss'
# fitskypars.salgorithm = "constant"
# fitskypars.skyvalue = 0
print '\n### recentering: ' + str(answ)
if _show:
iraf.noao.digiphot.daophot.phot(original,
coordlist,
"apori",
veri='no')
iraf.noao.digiphot.daophot.phot(sn,
coordlist,
img + ".sn.mag",
veri='no')
else:
iraf.noao.digiphot.daophot.phot(original,
coordlist,
"apori",
veri='no',
verb='no')
iraf.noao.digiphot.daophot.phot(sn,
coordlist,
img + ".sn.mag",
veri='no',
verb='no')
lsc.util.delete(img + ".sn.als")
print sn, imgpsf, img
iraf.allstar(sn,
img + ".sn.mag",
imgpsf,
img + ".sn.als",
"",
residual,
veri='no',
verb='no')
lsc.util.delete("snfit.fits")
iraf.imarith(sn + '.fits', "-", residual + '.fits', "snfit.fits")
lsc.util.delete("skyfit.fits")
iraf.imarith(original + '.fits', "-", "snfit.fits", "skyfit.fits")
iraf.txsort(img + ".sn.als", "ID")
tmptbl = iraf.txdump(img + ".sn.als",
"mag,merr,xcenter,ycenter",
expr='yes',
Stdout=1)
magerr, fitmag, centx, centy = [], [], [], []
for i in tmptbl:
try:
fitmag.append(float(string.split(i)[0])) #-2.5*log10(_exptime))
except:
fitmag.append(string.split(i)[0])
try:
magerr.append(float(string.split(i)[1]))
except:
magerr.append(string.split(i)[1])
centx.append(float(string.split(i)[2]))
centy.append(float(string.split(i)[3]))
tmptbl = iraf.txdump("apori", "mag", expr='yes', Stdout=1)
apori1, apori2, apori3 = [], [], []
for i in tmptbl:
try:
apori1.append(float(string.split(i)[0])) #-2.5*log10(_exptime))
except:
apori1.append(string.split(i)[0])
try:
apori2.append(float(string.split(i)[1])) #-2.5*log10(_exptime))
except:
apori2.append(string.split(i)[1])
try:
apori3.append(float(string.split(i)[2])) #-2.5*log10(_exptime))
except:
apori3.append(string.split(i)[2])
iraf.txsort(img + ".sn.mag", "YCENTER")
tmptbl = iraf.txdump(img + ".sn.mag", "mag,magerr", expr='yes', Stdout=1)
if _show:
print "********************************************************************"
print "ID <apmag on original> <apmag on bgsubt> fitmag truemag err_fit"
print " ", a1, " ", a2, " ", a3, " ", a1, " ", a2, " ", a3
apmag1, apmag2, apmag3, truemag = [], [], [], []
for i in range(len(tmptbl)):
try:
apmag1.append(float(string.split(
tmptbl[i])[0])) #-2.5*log10(_exptime))
except:
apmag1.append(9999)
try:
apmag2.append(float(string.split(
tmptbl[i])[1])) #-2.5*log10(_exptime))
except:
apmag2.append(9999)
try:
apmag3.append(float(string.split(
tmptbl[i])[2])) #-2.5*log10(_exptime))
except:
apmag3.append(9999)
try:
truemag.append(fitmag[i] + float(apco0))
except:
truemag.append('INDEF')
if _show:
print i, apori1[i], apori2[i], apori3[i], apmag1[i], apmag2[
i], apmag3[i], fitmag[i], truemag[i], magerr[i]
if _show:
print "********************************************************************"
if _show:
print midpt, z11, z22
_tmp1, _tmp2, goon = lsc.util.display_image(original + '.fits',
1,
z11,
z22,
False,
_xcen=.25,
_ycen=.25,
_xsize=.3,
_ysize=.3)
z01 = float(z11) - float(midpt)
z02 = float(z22) - float(midpt)
s1 = 1
s2 = -int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " ORIGINAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
_tmp1, _tmp2, goon = lsc.util.display_image('snfit.fits',
1,
z01,
z02,
False,
_xcen=.25,
_ycen=.75,
_xsize=.3,
_ysize=.3,
_erase='no')
lsc.util.delete("tmptbl")
tmptbl0 = iraf.txdump(img + ".sn.als",
"xcen,ycen",
expr='yes',
Stdout=1)
ff = open('tmptbl', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
lra = int((2 * float(size) * float(fwhm0)) * 2)
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="circle",
number='yes',
nyoffset=lra,
radi=a2,
txsize=2,
inter='no')
s1 = 1
s2 = -1 * int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " FITTED")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
_tmp1, _tmp2, goon = lsc.util.display_image('skyfit.fits',
1,
z11,
z22,
False,
_xcen=.75,
_ycen=.25,
_xsize=.3,
_ysize=.3,
_erase='no')
s1 = 1
s2 = -1 * int(fwhm0)
lsc.util.delete("tmptbl")
ff = open('tmptbl', 'w')
ff.write(str(s1) + ' ' + str(s2) + " RESIDUAL")
ff.close()
iraf.tvmark(1,
"tmptbl",
autol='no',
mark="none",
inter='no',
label='yes',
txsize=2)
return apori1, apori2, apori3, apmag1, apmag2, apmag3, fitmag, truemag, magerr, centx, centy
def get_app_phot(coords, image, plot_only=False, store=True, wcsin="world", fwhm=2.5, plotdir=None, box=15, arcsecpix=0.394):
'''
coords: files:
wcsin: can be "world", "logic"
'''
# Load packages; splot is in the onedspec package, which is in noao.
# The special keyword _doprint=0 turns off displaying the tasks
# when loading a package.
from pyraf import iraf
if (not plot_only):
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.unlearn("apphot")
imdir = os.path.dirname(image)
imname = os.path.basename(image)
if (plotdir is None):
plotdir = os.path.join(imdir, "photometry")
if not os.path.isdir(plotdir):
os.makedirs(plotdir)
out_name = os.path.join(plotdir, imname + ".seq.mag")
clean_name = os.path.join(plotdir, imname + ".app.mag")
print "Will create output files", out_name, clean_name
# Read values from .ec file
fwhm_value = fwhm/arcsecpix
if (fitsutils.has_par(image, 'FWHM')):
fwhm_value = fitsutils.get_par(image, 'FWHM')/arcsecpix
elif (fwhm is None):
fwhm_value=3.5/arcsecpix
if (fitsutils.has_par(image, 'AIRMASS')):
airmass_value = fitsutils.get_par(image, 'AIRMASS')
else:
airmass_value = 1.3
exptime = fitsutils.get_par(image, 'EXPTIME')
gain = fitsutils.get_par(image, 'GAIN')
noise = fitsutils.get_par(image, 'RDNOISE')
print "FWHM: %.1f pixels, %.1f arcsec"%(fwhm_value, fwhm_value*arcsecpix)
aperture_rad = math.ceil(float(fwhm_value)*1.5) # Set aperture radius to three times the PSF radius
sky_rad= math.ceil(aperture_rad)*4
if (not plot_only):
iraf.noao.digiphot.apphot.qphot(image = image,\
cbox = box ,\
annulus = sky_rad ,\
dannulus = 20. ,\
aperture = str(aperture_rad),\
coords = coords ,\
output = out_name ,\
plotfile = "" ,\
zmag = 0. ,\
exposure = "exptime",\
airmass = "airmass" ,\
filter = "filter" ,\
obstime = "DATE" ,\
#fwhm = fwhm_value,\
epadu = gain ,\
interactive = "no" ,\
radplots = "yes" ,\
verbose = "no" ,\
graphics = "stdgraph" ,\
display = "stdimage" ,\
icommands = "" ,\
wcsin = wcsin,
wcsout = "logical",
gcommands = "")
#iraf.noao.digiphot.apphot.phot(image=image, cbox=5., annulus=12.4, dannulus=10., salgori = "centroid", aperture=9.3,wcsin="world",wcsout="tv", interac = "no", coords=coords, output=out_name)
iraf.txdump(out_name, "id,image,xcenter,ycenter,xshift,yshift,fwhm,msky,stdev,cier,rapert,sum,area,nsky,flux,itime,mag,merr", "yes", Stdout=clean_name)
ma = np.genfromtxt(clean_name, comments="#", dtype=[("id","<f4"), ("image","|S20"), ("X","<f4"), ("Y","<f4"), ("Xshift","<f4"), ("Yshift","<f4"),("fwhm","<f4"), ("msky","<f4"), ("stdev","<f4"),\
("flags", np.int), ("rapert", "<f4"), ("sum", "<f4"), ("area", "<f4"), ("nsky","<f4") , ("flux", "<f4"), ("itime", "<f4"), ("fit_mag","<f4"), ("fiterr","<f4")])
if (ma.size > 0):
m = ma[~np.isnan(ma["fit_mag"])]
else:
print "Only one object found!"
m = np.array([ma])
hdulist = pf.open(image)
prihdr = hdulist[0].header
img = hdulist[0].data * 1.
nx, ny = img.shape
dimX = int(np.floor(np.sqrt(len(m))))
dimY = int(np.ceil(len(m)*1./dimX))
outerrad = sky_rad+10
cutrad = outerrad + 15
print "Cutrad %.1f"%cutrad
plt.suptitle("FWHM=%.2f arcsec. %d stars"%(fwhm_value*arcsecpix, len(m)))
for i in np.arange(dimX):
for j in np.arange(dimY):
if ( i*dimY + j < len(m)):
k = i*dimY + j
#print dimX, dimY, i, j, k
ax = plt.subplot2grid((dimX,dimY),(i, j))
y1, y2, x1, x2 = m[k]["X"]-cutrad, m[k]["X"]+cutrad, m[k]["Y"]-cutrad, m[k]["Y"]+cutrad
y1, y2, x1, x2 = int(y1), int(y2), int(x1), int(x2)
y1 = np.maximum(y1, 0); y2=np.maximum(y2, 0); x1=np.maximum(x1, 0); x2 = np.maximum(x2, 0)
try:
zmin, zmax = zscale.zscale(img[x1:x2,y1:y2], nsamples=1000, contrast=0.25)
except ValueError:
print y1, y2, x1, x2
print img[x1:x2,y1:y2]
sh= img[x1:x2,y1:y2].shape
if sh[0]>0 and sh[1]>0:
zmin = np.nanmin(img[x1:x2,y1:y2])
zmax = np.nanmax(img[x1:x2,y1:y2])
ax.imshow(img[x1:x2,y1:y2], aspect="equal", extent=(-cutrad, cutrad, -cutrad, cutrad), origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=zmin, vmax=zmax)
c1 = plt.Circle( (0, 0), edgecolor="r", facecolor="none", radius=aperture_rad)
c2 = plt.Circle( (0, 0), edgecolor="orange", facecolor="none", radius=sky_rad)
c3 = plt.Circle( (0, 0), edgecolor="yellow", facecolor="none", radius=sky_rad+20)
plt.gca().add_artist(c1)
plt.gca().add_artist(c2)
plt.gca().add_artist(c3)
ax.set_xticks([])
ax.set_yticks([])
plt.text(+5, +5, "%d"%m[k]["id"])
plt.text(-cutrad, -cutrad, "%.2f$\pm$%.2f"%(m[k]["fit_mag"], m[k]["fiterr"]), color="b")
plt.tight_layout()
plt.savefig(os.path.join(plotdir, imname + "plot.png"))
plt.clf()
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0) #loads some needed iraf libraries
iraf.daophot(_doprint=0)
iraf.ptools(_doprint=0)
#starting photometry
#iraf.phot(path+"snb.fits",path+"2MASS.GC.cat",path+'N'+gal+'B_GC.mag',skyfile="",datapars="",scale=1.,fwhmpsf=2.92,emissio='yes',sigma=0.0,datamin=-100, datamax=1e5,noise="poisson",centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1.,cmaxiter=0,maxshift=0,clean='no',rclean=0.,rclip=0.,kclean=0.,mkcenter='no',fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3.,shireject=3.,khist=3.,binsize=0.1,smooth='no',rgrow=0.,mksky='no',photpars="",weighting="constant",apertures=1.8,zmag=20.,mkapert='no',interactive='no',radplots='no',verify='no',update='no',verbose='no', graphics="stdgraph",display="stdimage",icommands="",gcommands="")
#iraf.phot(path+"tnb.fits",path+"2MASS.GC.cat",path+'N'+gal+'T_GC.mag',skyfile="",datapars="",scale=1.,fwhmpsf=2.92,emissio='yes',sigma=0.0,datamin=-100, datamax=1e5,noise="poisson",centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1.,cmaxiter=0,maxshift=0,clean='no',rclean=0.,rclip=0.,kclean=0.,mkcenter='no',fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3.,shireject=3.,khist=3.,binsize=0.1,smooth='no',rgrow=0.,mksky='no',photpars="",weighting="constant",apertures=1.8,zmag=20.,mkapert='no',interactive='no',radplots='no',verify='no',update='no',verbose='no', graphics="stdgraph",display="stdimage",icommands="",gcommands="")
iraf.phot(path+"f.fits",path+"2MASS.GC.cat",path+'N'+gal+'F_GC.mag',skyfile="",datapars="",scale=1.,fwhmpsf=2.92,emissio='yes',sigma=0.0,datamin=-100, datamax=1e5,noise="poisson",centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1.,cmaxiter=0,maxshift=0,clean='no',rclean=0.,rclip=0.,kclean=0.,mkcenter='no',fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3.,shireject=3.,khist=3.,binsize=0.1,smooth='no',rgrow=0.,mksky='no',photpars="",weighting="constant",apertures=1.8,zmag=20.,mkapert='no',interactive='no',radplots='no',verify='no',update='no',verbose='no', graphics="stdgraph",display="stdimage",icommands="",gcommands="")
#dump outputs from iraf in readable format
#iraf.txdump(path+'N'+gal+'B_GC.mag',"XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID",'yes', headers='no',paramet='no', Stdout=path+'N'+gal+'B_GC.txt')
#iraf.txdump(path+'N'+gal+'T_GC.mag',"XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID",'yes', headers='no',paramet='no', Stdout=path+'N'+gal+'T_GC.txt')
iraf.txdump(path+'N'+gal+'F_GC.mag',"XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID",'yes', headers='no',paramet='no', Stdout=path+'N'+gal+'F_GC.txt')
#run ffinder to create the inputs to prob_bulge_bd.f
#ffinder(path+'N'+gal+'B_GC.txt', path+'N'+gal+'T_GC.txt', path+'N'+gal+'F_GC.txt', path+'N'+gal+'_f.GC.dat')
ffinder(path+'N'+gal+'F_GC.txt', path+'N'+gal+'_f.GC.dat')
#plot the fmap if requested:
plot_histf(path+'N'+gal+'_f.GC.dat')
op = raw_input("Plot the fmap? Y/N \n")
if op == "y" or "yes":
plot_fmap(gal_x, gal_y)
plot_vmap(gal_x, gal_y)
import numpy as np
import astropy.io.fits as pyfits
from pyraf import iraf
from iraf import noao, digiphot, apphot, phot, txdump
import matplotlib
from matplotlib import pyplot as plt
iraf.noao.digiphot.apphot.phot.interac='no'
iraf.noao.digiphot.apphot.phot.centerp.calgori='none'
iraf.noao.digiphot.apphot.phot.fitskypars.salgori='constant'
iraf.noao.digiphot.apphot.phot.cache='yes'
iraf.noao.digiphot.apphot.phot.salgori='constant'
for band, aperture in zip(['f105w','f125w','f140w','f160w','f435w','f606w','f814w'], ['3.2','3.5','3.9','4.1','3.3','3.3','3.3']):
iraf.noao.digiphot.apphot.phot.photpar.apertur=aperture
iraf.phot(image='SEG_' + band + '_sex_hff_abells1063_par_CHECK.fits', interactive='no',calgorithm='none', salgori='constant',coords='box_coords_' + band + '.coo', output='PHOT_SEG_hff_abells1063_par_' + band + '.txt',verify='no')
iraf.txdump(textfile='PHOT_SEG_hff_abells1063_par_' + band + '.txt', fields='XCEN, YCEN, SUM', expr='yes', Stdout='PHOT_SEG_hff_abells1063_par_' + band + '_OUTFILE.txt')
'''Generate source catalogs'''
# catalogs for src detection & photometry within same image
for file, ff in zip(file_list,xrange(len(file_list))):
mk_counts_image(file, outfile=cnts_name[ff])
run_daofind(cnts_name[ff], outfile=find_name[ff], dthreshold=4.0)
run_daophot(cnts_name[ff], coordfile=find_name[ff], outfile=phot_name[ff])
replace_filevalue(phot_name[ff], 'INDEF',-9999.0)
ac05[ff] = calc_apcorr(phot_name[ff])
# color catalogs--src detection in one image, photometry in other (***KLUGE*** ASSUMES ONLY 2 INPUT IMAGES)
# f606w color cats
run_daophot(cnts_name[0], outfile=col_name[0], coordfile=find_name[1])
replace_filevalue(col_name[0], 'INDEF', -9999.0)
file_query = os.access(tcol_name[0], os.R_OK)
if file_query == True: os.remove(tcol_name[0]) # remove previous trimmed files
iraf.txdump(col_name[0],'xcenter,ycenter,mag', 'yes', Stdout=tcol_name[0])
#f814w color cats
run_daophot(cnts_name[1], outfile=col_name[1], coordfile=find_name[0])
replace_filevalue(col_name[1], 'INDEF', -9999.0)
file_query = os.access(tcol_name[1], os.R_OK)
if file_query == True: os.remove(tcol_name[1]) # remove previous trimmed files
iraf.txdump(col_name[1],'xcenter,ycenter,mag', 'yes', Stdout=tcol_name[1])
'''make CMD'''
# extract color mags (apply aperture corrections)
xca, yca, mag1a,mag2a = np.loadtxt(phot_name[0]+'.trimmed', unpack=True)
xcb, ycb, mag1b,mag2b = np.loadtxt(tcol_name[1], unpack=True)
tmaga = mag1a - ac05[0] - AC05
tmagb = mag1b - ac05[1] - AC05