def lris_standard(standards, xcorr=yes):
'''Extract standard stars and calculate sensitivity functions.'''
for ofile, nstd in standards:
shutil.copy(ofile, "%s.fits" % nstd)
# Extract standard
if get_head("%s.fits" % nstd, "SKYSUB"):
iraf.apall(nstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
else:
iraf.apall(nstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
if xcorr:
# Cross correlate to tweak wavelength
iraf.xcsao("%s.ms" % nstd, templates=XCTEMPLATE,
correlate="wavelength", logfiles="%s.xcsao" % ofile)
# If a solution was found, apply shift
try:
xcsaof = open("%s.xcsao" % ofile)
shift = float(xcsaof.readlines()[6].split()[14])
except:
shift = 0.0
iraf.specshift("%s.ms" % nstd, -shift)
# Create telluric
iraf.splot("%s.ms" % nstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % nstd, "/", "a%s.ms" % nstd, "telluric.%s.fits" % nstd)
iraf.imreplace("telluric.%s.fits" % nstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.%s.fits" % nstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % nstd, "s%s.ms" % nstd, 5.0)
iraf.sarith("%s.ms" % nstd, "/", "s%s.ms" % nstd, "ds%s.ms" % nstd)
# Apply telluric correction
iraf.telluric("ds%s.ms" % nstd, "tds%s.ms" % nstd, "telluric.%s.fits" % nstd,
xcorr=no, tweakrms=no, interactive=no,
sample='4000:4010,6850:6975,7150:7350,7575:7725,8050:8400,8900:9725')
# Define bandpasses for standard star calculation
obj=get_head("%s.fits" % nstd, "OBJECT")
iraf.standard("tds%s.ms" % nstd, "%s.std" % nstd,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=obj, airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % nstd, '%s.sens' % nstd,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
def mk24noiseimage(self):
os.chdir(self.imagepath24)
# remove temp images if they exist
os.system('rm temp*.fits')
# multiply image by exptime x gain x coverage map
scale=FLUXCONV*GAIN*EXPT
iraf.imarith(operand1=self.sex_image,op='*',operand2=scale,result='temp1.fits')
iraf.imarith(operand1='temp1.fits',op='*',operand2=self.cov_image,result='temp2.fits')
# smooth image using iraf.images.imfilter.gauss
iraf.gauss(input='temp2.fits',output='temp3.fits',sigma=2,nsigma=6)
# take sqrt
iraf.imfunction(input='temp3.fits',output='temp4.fits',function='sqrt')
# divide by exptime x gain x coverage map
iraf.imarith(operand1='temp4.fits',op='/',operand2=scale,result='temp5.fits')
# mutliply image and sigma image by 100
s=self.prefix+'-scalednoise.fits'
iraf.imarith(operand1='temp5.fits',op='*',operand2=100,result=s)
s=self.prefix+'-scaled24.fits'
iraf.imarith(operand1=self.sex_image,op='*',operand2=100,result=s)
def run_gauss(imageFilename, sigma): ''' using the image given, calls iraf's gauss method and returns the filename of the result, which is blurred using the sigma given parameter imageFilename - the original image's filename parameter sigma - the number of pixels to blur the image by returns - the blurred image's filename ''' newImageFilename = imageFilename[:-5] + "_gauss.fits" iraf.gauss(imageFilename, newImageFilename, sigma) return newImageFilename
def skyest(inputimg, segimg, gsigma):
"""
Rough estimate of local sky background.
"""
if len(glob.glob('segcutout*.fits')):
os.system('rm segcutout*.fits')
iraf.imexpr("a > 0 ? 100 : 0", "segcutout2.fits", a=segimg)
# grow mask by Gaussian wing
iraf.gauss(input="segcutout2.fits", output="segcutout3.fits", sigma=gsigma)
h1 = pyfits.open(inputimg)
h2 = pyfits.open('segcutout3.fits')
him1 = h1[0].data # image data
him2 = h2[0].data
skyim = np.compress(him2.ravel() < 10., him1.ravel())
bkgnd = np.median(skyim) # take the median of unmasked sky
#os.system('rm cutout.fits')
os.system('rm segcutout*.fits')
return bkgnd
def skyest(inputimg, segimg, gsigma):
#if len(glob.glob("cutout.fits")):
# os.system("rm cutout.fits")
#iraf.imcopy("%s[%d:%d,%d:%d]"%(inputimg,xmin,xmax,ymin,ymax),"cutout.fits")
#iraf.imcopy(segimg+"[%d:%d,%d:%d]"%(xmin,xmax,ymin,ymax),"segcutout.fits")
if len(glob.glob('segcutout*.fits')):
os.system('rm segcutout*.fits')
iraf.imexpr("a > 0 ? 100 : 0", "segcutout2.fits", a=segimg)
# grow mask by Gaussian wing
iraf.gauss(input="segcutout2.fits", output="segcutout3.fits", sigma=gsigma)
h1 = pyfits.open(inputimg)
h2 = pyfits.open('segcutout3.fits')
him1 = h1[0].data # image data
him2 = h2[0].data
skyim = compress(him2.ravel() < 10., him1.ravel())
bkgnd = median(skyim) # take the median of unmasked sky
#os.system('rm cutout.fits')
os.system('rm segcutout*.fits')
return bkgnd
def prep(df_image, hi_res_image):
'run SExtractor to get bright sources that are easily detected in the high resolution data'
##### DEB: Can choose to run sextractor more or less aggressively
subprocess.call('sex %s' % hi_res_image, shell=True)
'copy the segmentation map to a mask'
iraf.imcopy('seg.fits', '_mask.fits')
'replace the values in the segments in the segmentation map (i.e. stars) all to 1, all the background is still 0'
iraf.imreplace('_mask.fits', 1, lower=0.5)
'multiply the mask (with 1s at the stars) by the high res image to get the star flux back - now have the flux model'
iraf.imarith('_mask.fits', '*', '%s' % hi_res_image, '_fluxmod_cfht')
'smooth the flux model'
'increase size of mask, so more is subtracted'
'the "1.5" in the next line should be a user-defined parameter that is given'
'to the script; it controls how much of the low surface brightness'
'emission in the outskirts of galaxies is subtracted. This choice'
'depends on the science application'
iraf.gauss('_fluxmod_cfht', '_fluxmod_cfht_smoothed', 2.5) ### nsigma=4
'''
imreplace _fluxmod_cfht_rs -1 lower=0 upper=0
imreplace _fluxmod_cfht_rs 1 lower=-0.5
imreplace _fluxmod_cfht_rs 0 upper=-0.5
imarith _cfht_r * _fluxmod_cfht_rs _fluxmod_cfht_r_new
'''
'''
# this is the key new step! we're registering the CFHT image
# to a frame that is 4x finer sampled than the Dragonfly image.
# this avoids all the pixelation effects we had before.
# (4x seems enough; but we could have it as a free parameter - need
# to be careful as it occurs elsewhere in the scripts too)
blkrep _df_g _df_g4 4 4
'''
'register the flux model onto the same pixel scale as the dragonfly image'
iraf.wregister('_fluxmod_cfht_smoothed',
'%s' % df_image,
'_fluxmod_dragonfly',
interpo='linear',
fluxcon='yes')
return None
def prep(df_image,hi_res_image,width_mask=1.5,unmaskgal=False,galvalues = None):
print "\n************ Running the preparation steps ************\n"
iraf.imdel('_mask.fits')
iraf.imdel('_fluxmod_cfht*.fits')
iraf.imdel('_df_4*.fits')
'run SExtractor to get bright sources that are easily detected in the high resolution data'
subprocess.call('sex %s'%hi_res_image,shell=True) ##### Add in option to change sextractor threshold detect_thresh and analysis_thresh 2/3
'copy the segmentation map to a mask'
iraf.imcopy('seg.fits','_mask.fits')
##### Add step to get rid of diffraction spikes
if unmaskgal:
'Run SExtractor to get values for the central galaxy'
print '\nDoing a second sextractor run to unmask central galaxy. \n'
analysis_thresh_lg=2;back_size_lg=128;detect_thresh_lg=2;detect_minarea_lg=60
segname = run_SExtractor(hi_res_image,detect_thresh=detect_thresh_lg,analysis_thresh=analysis_thresh_lg,back_size=back_size_lg,detect_minarea=detect_minarea_lg)
print '\nSegmentation map is named: '+segname
'Open up the data'
seg2data = fits.getdata(segname)
segdata,segheader = fits.getdata('_mask.fits',header=True)
'Detect sources from the segmentation map'
from photutils import detect_sources
segrefdata = detect_sources(seg2data, 3, npixels=5)#, filter_kernel=kernel)
segrefdata = segrefdata.data
segrefname = re.sub('.fits','_ds.fits',segname)
writeFITS(segrefdata,segheader,segrefname)
print '\nSource separated segmentation map is named: '+segrefname
'Use detected source seg map to mask galaxies' #galvalues = [3531,5444,5496]
print 'Unmask the galaxies in the mask from the original segmap. \n'
segdatanew = unmaskgalaxy(segdata,'_mask.fits',segrefname=segrefname,segref=segrefdata,galvalues=galvalues)
writeFITS(segdatanew,segheader,'_mask.fits')
if verbose:
print_verbose_string('Carrying on')
'replace the values in the segments in the segmentation map (i.e. stars) all to 1, all the background is still 0'
iraf.imreplace('_mask.fits',1,lower=0.5)
'multiply the mask (with 1s at the stars) by the high res image to get the star flux back - now have the flux model'
iraf.imarith('_mask.fits','*','%s'%hi_res_image,'_fluxmod_cfht')
'smooth the flux model'
'increase size of mask, so more is subtracted'
'the "1.5" in the next line should be a user-defined parameter that is given'
'to the script; it controls how much of the low surface brightness'
'emission in the outskirts of galaxies is subtracted. This choice'
'depends on the science application'
iraf.gauss('_fluxmod_cfht','_fluxmod_cfht_smoothed',width_mask,nsigma=4.)
iraf.imreplace('_fluxmod_cfht_smoothed', -1, lower=0, upper=0)
iraf.imreplace('_fluxmod_cfht_smoothed', 1, lower=-0.5)
iraf.imreplace('_fluxmod_cfht_smoothed', 0, upper=-0.5)
iraf.imarith('%s'%hi_res_image, '*','_fluxmod_cfht_smoothed', '_fluxmod_cfht_new')
' this is the key new step! we"re registering the CFHT image'
' to a frame that is 4x finer sampled than the Dragonfly image.'
' this avoids all the pixelation effects we had before.'
' (4x seems enough; but we could have it as a free parameter - need'
' to be careful as it occurs elsewhere in the scripts too) '
iraf.blkrep('%s'%df_image,'_df_4',4,4)
'register the flux model onto the same pixel scale as the dragonfly image'
iraf.wregister('_fluxmod_cfht_new','_df_4','_fluxmod_dragonfly',interpo='linear',fluxcon='yes')
return None
def lris_standard(standards, xcorr=yes):
'''Extract standard stars and calculate sensitivity functions.'''
for ofile, nstd in standards:
shutil.copy(ofile, "%s.fits" % nstd)
# Extract standard
if get_head("%s.fits" % nstd, "SKYSUB"):
iraf.apall(nstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
else:
iraf.apall(nstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
if xcorr:
# Cross correlate to tweak wavelength
iraf.xcsao("%s.ms" % nstd,
templates=XCTEMPLATE,
correlate="wavelength",
logfiles="%s.xcsao" % ofile)
# If a solution was found, apply shift
try:
xcsaof = open("%s.xcsao" % ofile)
shift = float(xcsaof.readlines()[6].split()[14])
except:
shift = 0.0
iraf.specshift("%s.ms" % nstd, -shift)
# Create telluric
iraf.splot("%s.ms" %
nstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % nstd, "/", "a%s.ms" % nstd,
"telluric.%s.fits" % nstd)
iraf.imreplace("telluric.%s.fits" % nstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.%s.fits" %
nstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % nstd, "s%s.ms" % nstd, 5.0)
iraf.sarith("%s.ms" % nstd, "/", "s%s.ms" % nstd, "ds%s.ms" % nstd)
# Apply telluric correction
iraf.telluric(
"ds%s.ms" % nstd,
"tds%s.ms" % nstd,
"telluric.%s.fits" % nstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='4000:4010,6850:6975,7150:7350,7575:7725,8050:8400,8900:9725'
)
# Define bandpasses for standard star calculation
obj = get_head("%s.fits" % nstd, "OBJECT")
iraf.standard("tds%s.ms" % nstd,
"%s.std" % nstd,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=obj,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % nstd,
'%s.sens' % nstd,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
return
def deimos_standard(standard):
'''Extract standard star and calculate sensitivit function.'''
bstd = "%s_01_B" % standard; rstd = "%s_01_R" % standard
# Extract standard
if get_head("%s.fits" % bstd, "SKYSUB"):
iraf.apall(bstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
iraf.apall(rstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="none")
else:
iraf.apall(bstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
iraf.apall(rstd, output="", inter=yes, find=yes, recenter=yes, resize=yes,
edit=yes, trace=yes, fittrace=yes, extract=yes, extras=yes,
review=no, background="fit")
# Fit the continuum
#iraf.continuum("%s.ms" % bstd, output="s%s.ms" % bstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
#iraf.continuum("%s.ms" % rstd, output="s%s.ms" % rstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
# Create telluric
iraf.splot("%s.ms" % bstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.%s.fits" % bstd)
iraf.imreplace("telluric.B.%s.fits" % bstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.B.%s.fits" % bstd) # Remove stellar features and resave
iraf.splot("%s.ms" % rstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.%s.fits" % rstd)
iraf.imreplace("telluric.R.%s.fits" % rstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.R.%s.fits" % rstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % bstd, "s%s.ms" % bstd, 5.0)
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.gauss("a%s.ms[*,1,1]" % rstd, "s%s.ms" % rstd, 5.0)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Divide through by smoothed standard
#iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
#iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Create and apply telluric correction
#iraf.splot("%s.ms" % bstd) # Remove telluric, save as a%s.ms
#iraf.splot("%s.ms" % rstd) # Remove telluric, save as a%s.ms
#iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.fits")
#iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.fits")
#iraf.imreplace("telluric.B.fits", 1.0, lower=0.0, upper=0.0)
#iraf.imreplace("telluric.R.fits", 1.0, lower=0.0, upper=0.0)
iraf.telluric("ds%s.ms" % bstd, "tds%s.ms" % bstd, "telluric.B.%s.fits" % bstd,
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd, "tds%s.ms" % rstd, "telluric.R.%s.fits" % rstd,
xcorr=no, tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard("tds%s.ms" % bstd, "%s.B.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=standard, airmass='', exptime='')
iraf.standard("tds%s.ms" % rstd, "%s.R.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/', observatory='Keck', interac=yes,
star_name=standard, airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.B.std' % standard, '%s.B.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
iraf.sensfunc('%s.R.std' % standard, '%s.R.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat', observatory='Keck',
function='legendre', order=4, interactive=yes)
return
def convolve_images():
for i in range(nfiles):
convolved_image = 'g'+files[i]
gauss(input=files[i],output=convolved_image,sigma=sigma_filter[i])
def gauss2(fn):
iraf.images()
iraf.imfilter()
for i in (fn):
iraf.gauss(i, i, '2')
def gauss2(setup, filelist):
iraf.images()
iraf.imfilter()
for i in (filelist):
name = setup + '/' + i
iraf.gauss(name, name, '2')
def fitone(self, objectid, psffile=None, magzpt=None, exptime=1.0):
"""
A driver function that fits one object, given its ID.
Most of the work is done here.
Provide a PSF file and magnitude zero point for each object!
"""
if psffile == None: psffile = self.psffile
if magzpt == None: magzpt = self.magzpt
idcrit = (self.id_array == objectid)
ra = self.ra_array[idcrit][0]
dec = self.dec_array[idcrit][0]
mag = self.mag_array[idcrit][0]
Re = self.Re_array[idcrit][0]
axratio = self.axratio_array[idcrit][0]
theta = self.theta_array[idcrit][0]
isoarea = self.isoarea_array[idcrit][0]
# Now figure out image coordinates
x = self.x_array[idcrit][0]
y = self.y_array[idcrit][0]
print "x, y =", x, y
OUT1 = open(self.crashes, "ab")
OUT2 = open(self.fitted, "ab")
# now create arrays that will be included in this GALFIT run
id_fit = [objectid]
mag_fit = [mag]
Re_fit = [Re]
x_fit = [x]
y_fit = [y]
theta_fit = [theta]
axratio_fit = [axratio]
# step 1: determine temporary image size
# Experimental image size
majx = abs(N.sqrt(isoarea) * 4. *
N.sin(theta / 180. * N.pi)) # convert degree into radian
majy = abs(N.sqrt(isoarea) * 4. * N.cos(theta / 180. * N.pi))
minx = axratio * N.sqrt(isoarea) * 4. * abs(
N.sin((theta + N.pi / 2.) / 180. * N.pi))
miny = axratio * N.sqrt(isoarea) * 4. * abs(
N.sin((theta + N.pi / 2.) / 180. * N.pi))
#Re_max = 20.
Re_max = 60
if Re > Re_max:
Re_max = Re
xsize = majx
# determine the offset of this cutout relative to the entire mosaic
if minx >= majx:
xsize = minx
if xsize <= 30.:
xsize = 30.
ysize = majy
if miny >= majy:
ysize = miny
if ysize <= 30.:
ysize = 30.
xmin = int(x - xsize)
if xmin <= 0:
xmin = 1
xmax = int(x + xsize)
ymin = int(y - ysize)
if ymin <= 0:
ymin = 1
ymax = int(y + ysize)
# construct postage stamp name
outname = self.root + '/obj%d_out.fits' % objectid
# step 2: determine which objects to be included
for jj in range(self.nobj_all): # iterate through other objects
if self.id_array[jj] != objectid:
dx = x - self.x_array[jj]
dy = y - self.y_array[jj]
dist = N.sqrt(dx**2 + dy**2)
# Fit all objects that fall within a certain
# radius of the central object:
radck = N.sqrt(self.isoarea_array[jj])
angle = N.arctan2(dy, dx)
phi = N.pi / 2. - (self.theta_array[jj] / 180. * N.pi +
N.pi / 2. - angle)
thetap = N.arctan2(self.axratio_array[jj] * N.tan(phi), 1.)
isorad = radck * N.sqrt((self.axratio_array[jj] *
N.cos(thetap))**2 + N.sin(thetap)**2)
# If object is within the fitted image region...
if (((self.x_array[jj] >= xmin) and
(self.x_array[jj] <= xmax) and
(self.y_array[jj] >= ymin) and
(self.y_array[jj] <= ymax) and
# but is greater than 2 Re away, and is not more than 3 mag fainter, or
((dist > 2 * Re and self.mag_array[jj] - 3. <= mag) or
# is less than 2 Re away and not more than 5 mag fainter, or
(dist < 2. * Re and self.mag_array[jj] - 5. <= mag))) or
# is outside image, but is about 3 Re away and 1 mag brighter
((self.x_array[jj] >= (xmin - isorad) and
(self.x_array[jj] <= (xmax + isorad) and
(self.y_array[jj] >=
(ymin - isorad)) and (self.y_array[jj] <=
(ymax + isorad)) and
(self.mag_array[jj] + 1.) <= mag)))):
id_fit += [self.id_array[jj]]
x_fit += [self.x_array[jj]]
y_fit += [self.y_array[jj]]
mag_fit += [self.mag_array[jj]]
Re_fit += [self.Re_array[jj]]
axratio_fit += [self.axratio_array[jj]]
theta_fit += [self.theta_array[jj]]
if self.Re_array[jj] > Re_max:
Re_max = self.Re_array[jj]
# step 3: determine fitting size & copy images
# Determine image fitting size and convolution box info
# into the galfit template file
Refrac = 0.7
#Refrac = 2.0
if xmin >= min(x_fit) - Refrac * Re_max:
xmin = min(x_fit) - Refrac * Re_max
if xmin < 1:
xmin = 1
if ymin >= min(y_fit) - Refrac * Re_max:
ymin = min(y_fit) - Refrac * Re_max
if ymin < 1:
ymin = 1
if xmax <= max(x_fit) + Refrac * Re_max:
xmax = max(x_fit) + Refrac * Re_max
if ymax <= max(y_fit) + Refrac * Re_max:
ymax = max(y_fit) + Refrac * Re_max
#cbox = 60
imgxsize = xmax - xmin + 1
imgysize = ymax - ymin + 1
if os.path.exists(outname):
os.remove(outname)
# copy drizzled images
if os.path.exists(self.root + '/obj%d_drz.fits' % objectid):
os.system('rm %s/obj%d_drz.fits' % (self.root, objectid))
drzimg = self.sci_image + '[%d:%d,%d:%d]' % (xmin, xmax, ymin, ymax)
iraf.imcopy(drzimg, self.root + '/obj%d_drz.fits' % objectid)
h = pyfits.open(self.root + '/obj%d_drz.fits' % objectid, 'update')
h[0].header.update('xmin', xmin)
h[0].header.update('xmax', xmax)
h[0].header.update('ymin', ymin)
h[0].header.update('ymax', ymax)
h[0].header.update('exptime', exptime)
h.flush()
h.close()
# copy sigma image
if os.path.exists(self.root + '/obj%d_rms.fits' % objectid):
os.system('rm %s/obj%d_rms.fits' % (self.root, objectid))
iraf.imcopy(
self.rms_image + '[%d:%d,%d:%d]' % (xmin, xmax, ymin, ymax),
self.root + '/obj%d_rms.fits' % objectid)
# copy segmentation images
if os.path.exists(self.root + '/obj%d_seg.fits' % objectid):
os.system('rm %s/obj%d_seg.fits' % (self.root, objectid))
iraf.imcopy(
self.seg_image + '[%d:%d,%d:%d]' % (xmin, xmax, ymin, ymax),
self.root + '/obj%d_seg.fits' % objectid)
# make mask image -- zero-out the included components in the mask image
if os.path.exists("mask2.fits"):
iraf.imdelete("mask2.fits")
if os.path.exists("mask3.fits"):
iraf.imdelete("mask3.fits")
maskexpr = ""
maskname = self.root + "/obj%d_seg.fits" % objectid
print "id_fit", id_fit
for j in range(len(id_fit)):
inid = id_fit[j]
maskexpr = maskexpr + "(a==%d)" % inid
if j != len(id_fit) - 1:
maskexpr = maskexpr + "||"
print maskexpr
# Now make the masks
# In GALFIT, good pixels have mask value 0, while bad pixels have mask values > 0
iraf.imexpr(maskexpr + " ? 0 : a", "mask2.fits", a=maskname)
# After this step, the only non-zero pixels will be those belonging to the objects that are NOT
# included in the list id_fit.
# Now flatten the mask image and convolve with a Gaussian, in order to grow the masked region a little bit
# to account for wings of objects.
iraf.imexpr("a>0 ? 100. : 0.", "mask3.fits", a="mask2.fits")
iraf.gauss(input="mask3.fits", output="mask3.fits", sigma=5)
if os.path.exists(self.root + '/mask_obj%d.fits' % objectid):
os.remove(self.root + '/mask_obj%d.fits' % objectid)
iraf.imexpr("a<=10. ? 0. : a",
self.root + "/mask_obj%d.fits" % objectid,
a="mask3.fits")
os.remove("mask2.fits")
os.remove("mask3.fits")
# step 4: write GALFIT input file
parfile = "obj%d" % objectid
f = open(parfile, 'w')
# Write image parameters
print >> f, "==============================================================================="
print >> f, "# IMAGE PARAMETERS"
print >> f, "A) %s/obj%d_drz.fits # Input Data image (FITS file) " % (
self.root, objectid)
print >> f, "B) %s/obj%d_out.fits # Output data image block " % (
self.root, objectid)
print >> f, 'C) %s/obj%d_rms.fits # Noise image name (made from data if blank or "none") ' % (
self.root, objectid)
print >> f, "D) %s # Input PSF image for convolution (FITS file) " % psffile
print >> f, "E) 1 # PSF oversampling factor relative to data "
print >> f, "F) %s/mask_obj%d.fits # Bad pixel mask (FITS image or ASCII coord list)" % (
self.root, objectid)
print >> f, "G) %s # Parameter constraint file " % self.constraint_file
print >> f, "H) %d %d %d %d # Image region to fit " % (1, imgxsize, 1,
imgysize)
print >> f, "I) %d %d # Size of the convolution box (x y)" % (
self.cbox, self.cbox)
print >> f, "J) %f # Magnitude photometric zeropoint " % magzpt
print >> f, "K) %s %s # Plate scale (dx dy). " % (self.xscale,
self.yscale)
print >> f, "O) regular # Display type (regular, curses, both) "
print >> f, "P) 0 # Create ouput only? (1=yes; 0=optimize) "
print >> f, "S) 0 # Modify/create objects interactively? "
print >> f, ""
print >> f, "# INITIAL FITTING PARAMETERS "
print >> f, ""
print >> f, "# For object type, allowed functions are: sersic, nuker, "
print >> f, "# expdisk, devauc, moffat, gaussian. "
print >> f, ""
print >> f, "# Objtype: Fit? Parameters "
print >> f, ""
# Write individual component
for j in range(len(id_fit)):
self.component(f, j + 1, x_fit[j] - xmin + 1, y_fit[j] - ymin + 1,
mag_fit[j], Re_fit[j], axratio_fit[j], theta_fit[j])
# Make sky component
#bkgnd = skyest('images/obj%d_drz.fits'%objectid,'images/obj%d_seg.fits'%objectid,5.)
bkgnd = 0.
self.sky(f, len(id_fit) + 1, bkgnd)
f.close()
# step 5: run GALFIT
t1 = time.time()
galfitrun = subprocess.Popen(["galfit", parfile], stdout=sys.stdout)
# runs GALFIT
# ======================================================= #
# Try to time GALFIT and kill it after spending more than
# 10 min on one object
# ======================================================= #
timelimit = 10. * 60. # time limit: 10 minutes
timeint = 10. # check every 10 seconds
maxcheck = int(timelimit / timeint)
kill = 1
for j in range(maxcheck):
time.sleep(timeint) # sleep for check time interval
if galfitrun.poll() == None: # if it's still running:
pass # haven't reached time limit; let it run
else:
kill = 0 # finished before time limit; don't need to kill
break # jump out of process checking
if kill: # if reached time limit and still running:
os.kill(galfitrun.pid, signal.SIGKILL) # kill GALFIT
print "Killed GALFIT; GALFIT froze"
retcode = galfitrun.returncode
t2 = time.time()
try:
print >> OUT1, "returned code is %d" % retcode
except:
print >> OUT1, "returned code is ", retcode
dt = t2 - t1
if retcode != 7: # not sure about the behavior when GALFIT crashes...!!!
print >> OUT1, "%d %s" % (objectid, parfile)
else:
print >> OUT2, "%s-wmask-out.fits %.1f seconds" % (outname, dt)
OUT1.close()
OUT2.close()
print "Finishes GALFIT fitting"
def blue_standard(image,
arcs,
flats,
object=None,
biassec1=BLUEBIAS1,
trimsec1=BLUETRIM1,
biassec2=BLUEBIAS2,
trimsec2=BLUETRIM2,
outflat='Flat-Blue.fits',
gain=BLUEGAIN,
rdnoise=BLUERDNOISE,
arc='Arc-Blue.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with blue CCD'''
# Bias subtract everything first
bluebias(image,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
bluebias(arcs,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
bluebias(flats,
biassec1=biassec1,
trimsec1=trimsec1,
biassec2=biassec2,
trimsec2=trimsec2)
# Create and apply flat-field
for i in range(len(flats)):
flats[i] = 'j%s' % flats[i]
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc('j%s' % image[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
iraf.ccdproc('j%s' % arcs[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
# Extract spectrum of standard
if object == None:
object = get_head(image, 'OBJECT')
iraf.apall('j%s' % image[0],
output=object,
references='',
interactive=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background='fit',
weights='variance',
pfit='fit1d',
readnoise=rdnoise,
gain=gain)
# Extract arc and fit wavelength solution
reference_arc('j%s' % arcs[0], arc, 'j%s' % image[0])
# Apply wavelength solution to standard
iraf.refspec(object,
references=arc,
sort="",
group="",
override=yes,
confirm=no,
assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Define bandpasses for standard star calculation
iraf.standard('%s.s' % object,
'%s.std' % object,
extinction='onedstds$kpnoextinct.dat',
caldir=caldir,
observatory='Lick',
interact=yes,
star_name=object,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object,
'%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat',
observatory='Lick',
function='legendre',
order=3,
interactive=yes)
return
def red_standard(image, arcs, flats, object=None, biassec=REDBIAS,
trimsec=REDTRIM, outflat='Flat-Red.fits', gain=REDGAIN,
rdnoise=REDRDNOISE, arc='Arc-Red.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with red CCD'''
# Bias subtract everything first
redbias(image, biassec=biassec, trimsec=trimsec)
redbias(arcs, biassec=biassec, trimsec=trimsec)
redbias(flats, biassec=biassec, trimsec=trimsec)
# Create and apply flat-field
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc(image[0], ccdtype='', noproc=no, fixpix=no, overscan=no,
trim=no, zerocor=no, darkcor=no, flatcor=yes, illumcor=no,
fringecor=no, readcor=no, scancor=no, flat=outflat)
arcimages=','.join(arcs)
iraf.ccdproc(arcs, ccdtype='', noproc=no, fixpix=no, overscan=no,
trim=no, zerocor=no, darkcor=no, flatcor=yes, illumcor=no,
fringecor=no, readcor=no, scancor=no, flat=outflat)
# Extract spectrum of standard
if object==None:
object=get_head(image, 'OBJECT')
iraf.apall(image[0], output=object, references='', interactive=yes,
find=yes, recenter=yes, resize=yes, edit=yes, trace=yes,
fittrace=yes, extract=yes, extras=yes, review=no,
background='fit', weights='variance', pfit='fit1d',
readnoise=rdnoise, gain=gain)
# Extract arc and fit wavelength solution
iraf.imarith(arcs[0], '+', arcs[1], 'Arc-Sum.fits')
reference_arc('Arc-Sum.fits', arc, image[0])
# Apply wavelength solution to standard
iraf.refspec(object, references=arc, sort="", group="", override=yes,
confirm=no, assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Create and apply telluric correction
iraf.sarith('%s.w' % object, '/', 'temp1', 'telluric')
iraf.splot('telluric', 1, 1)
iraf.telluric('%s.s' % object, '%s.t' % object, 'telluric', xcorr=no,
tweakrms=no, interactive=no, sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard('%s.t' % object, '%s.std' % object,
extinction='onedstds$kpnoextinct.dat', caldir=caldir,
observatory='Lick', interact=yes, star_name=object,
airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object, '%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat', observatory='Lick',
function='legendre', order=3, interactive=yes)
return
def convolve_images():
for i in range(nfiles):
convolved_image = 'g' + files[i]
gauss(input=files[i], output=convolved_image, sigma=sigma_filter[i])
def deimos_standard(standard):
'''Extract standard star and calculate sensitivit function.'''
bstd = "%s_01_B" % standard
rstd = "%s_01_R" % standard
# Extract standard
if get_head("%s.fits" % bstd, "SKYSUB"):
iraf.apall(bstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
iraf.apall(rstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="none")
else:
iraf.apall(bstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
iraf.apall(rstd,
output="",
inter=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background="fit")
# Fit the continuum
#iraf.continuum("%s.ms" % bstd, output="s%s.ms" % bstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
#iraf.continuum("%s.ms" % rstd, output="s%s.ms" % rstd, lines=1, bands=1,
# type="fit", sample="*", naverage=1, function="chebyshev",
# order=15, low_reject=3.0, high_reject=5.0, niterate=10, grow=1.0,
# interac=yes)
# Create telluric
iraf.splot("%s.ms" %
bstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd,
"telluric.B.%s.fits" % bstd)
iraf.imreplace("telluric.B.%s.fits" % bstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.B.%s.fits" %
bstd) # Remove stellar features and resave
iraf.splot("%s.ms" %
rstd) # Remove telluric and absorption, save as a%s.ms
iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd,
"telluric.R.%s.fits" % rstd)
iraf.imreplace("telluric.R.%s.fits" % rstd, 1.0, lower=0.0, upper=0.0)
iraf.splot("telluric.R.%s.fits" %
rstd) # Remove stellar features and resave
# Create smoothed standard
iraf.gauss("a%s.ms[*,1,1]" % bstd, "s%s.ms" % bstd, 5.0)
iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
iraf.gauss("a%s.ms[*,1,1]" % rstd, "s%s.ms" % rstd, 5.0)
iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Divide through by smoothed standard
#iraf.sarith("%s.ms" % bstd, "/", "s%s.ms" % bstd, "ds%s.ms" % bstd)
#iraf.sarith("%s.ms" % rstd, "/", "s%s.ms" % rstd, "ds%s.ms" % rstd)
# Create and apply telluric correction
#iraf.splot("%s.ms" % bstd) # Remove telluric, save as a%s.ms
#iraf.splot("%s.ms" % rstd) # Remove telluric, save as a%s.ms
#iraf.sarith("%s.ms" % bstd, "/", "a%s.ms" % bstd, "telluric.B.fits")
#iraf.sarith("%s.ms" % rstd, "/", "a%s.ms" % rstd, "telluric.R.fits")
#iraf.imreplace("telluric.B.fits", 1.0, lower=0.0, upper=0.0)
#iraf.imreplace("telluric.R.fits", 1.0, lower=0.0, upper=0.0)
iraf.telluric("ds%s.ms" % bstd,
"tds%s.ms" % bstd,
"telluric.B.%s.fits" % bstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
iraf.telluric("ds%s.ms" % rstd,
"tds%s.ms" % rstd,
"telluric.R.%s.fits" % rstd,
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard("tds%s.ms" % bstd,
"%s.B.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=standard,
airmass='',
exptime='')
iraf.standard("tds%s.ms" % rstd,
"%s.R.std" % standard,
extinction='home$extinct/maunakeaextinct.dat',
caldir='home$standards/',
observatory='Keck',
interac=yes,
star_name=standard,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.B.std' % standard,
'%s.B.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
iraf.sensfunc('%s.R.std' % standard,
'%s.R.sens' % standard,
extinction='home$extinct/maunakeaextinct.dat',
newextinction='extinct.dat',
observatory='Keck',
function='legendre',
order=4,
interactive=yes)
return
def blue_standard(image, arcs, flats, object=None, biassec1=BLUEBIAS1,
trimsec1=BLUETRIM1, biassec2=BLUEBIAS2,
trimsec2=BLUETRIM2, outflat='Flat-Blue.fits', gain=BLUEGAIN,
rdnoise=BLUERDNOISE, arc='Arc-Blue.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with blue CCD'''
# Bias subtract everything first
bluebias(image, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
bluebias(arcs, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
bluebias(flats, biassec1=biassec1, trimsec1=trimsec1, biassec2=biassec2,
trimsec2=trimsec2)
# Create and apply flat-field
for i in range(len(flats)):
flats[i]='j%s' % flats[i]
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc('j%s' % image[0], ccdtype='', noproc=no, fixpix=no,
overscan=no, trim=no, zerocor=no, darkcor=no, flatcor=yes,
illumcor=no, fringecor=no, readcor=no, scancor=no,
flat=outflat)
iraf.ccdproc('j%s' % arcs[0], ccdtype='', noproc=no, fixpix=no,
overscan=no, trim=no, zerocor=no, darkcor=no, flatcor=yes,
illumcor=no, fringecor=no, readcor=no, scancor=no,
flat=outflat)
# Extract spectrum of standard
if object==None:
object=get_head(image, 'OBJECT')
iraf.apall('j%s' % image[0], output=object, references='', interactive=yes,
find=yes, recenter=yes, resize=yes, edit=yes, trace=yes,
fittrace=yes, extract=yes, extras=yes, review=no,
background='fit', weights='variance', pfit='fit1d',
readnoise=rdnoise, gain=gain)
# Extract arc and fit wavelength solution
reference_arc('j%s' % arcs[0], arc, 'j%s' % image[0])
# Apply wavelength solution to standard
iraf.refspec(object, references=arc, sort="", group="", override=yes,
confirm=no, assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Define bandpasses for standard star calculation
iraf.standard('%s.s' % object, '%s.std' % object,
extinction='onedstds$kpnoextinct.dat', caldir=caldir,
observatory='Lick', interact=yes, star_name=object,
airmass='', exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object, '%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat', observatory='Lick',
function='legendre', order=3, interactive=yes)
return
def subract(df_image,shifts=99.99,width_cfhtsm=0.,upperlim=0.04,medphotosc=True,numsources=100,sigmaclip=False,cutout=False):
print "\n************ Running the subtraction steps ************\n"
#iraf.imdel('_psf*.fits')
#iraf.imdel('_model*.fits')
#iraf.imdel('_df_sub')
iraf.imdel('_res*.fits')
iraf.imdel('cc_images')
iraf.imdel('_model_sh')
iraf.imdel('_model_sc')
iraf.imdel('_model_mask')
iraf.imdel('_model_maskb')
'shift the images so they have the same physical coordinates'
if shifts[0]==99.99:
print('\n**** Calculating shifts... ****\n')
iraf.stsdas.analysis.dither.crossdriz('_df_sub.fits','_model.fits','cc_images',dinp='no',dref='no')
iraf.stsdas.analysis.dither.shiftfind('cc_images.fits','shift_values')
x_shift=0
y_shift=0
with open('shift_values','r') as datafile:
line = datafile.read().split()
x_shift = float(line[2])
y_shift = float(line[4])
print('Shifting model image by x and y: '+str(x_shift)+','+str(y_shift))
iraf.imshift('_model','_model_sh',0-x_shift,0-y_shift)
else:
print('Shifting model image by x and y: '+str(shifts[0])+','+str(shifts[1]))
iraf.imshift('_model','_model_sh',shifts[0],shifts[1])
'scale the model so that roughly the same as the _df_sub image'
# if usemodelpsf:
# iraf.imarith('_model_sh','/',16.,'_model_sc')
# else:
'the photometric step, matching the images to each other. '
'in principle this comes from the headers - both datasets are calibrated,'
'so this multiplication should be something like 10^((ZP_DF - ZP_CFHT)/-2.5)'
'(perhaps with a correction for the difference in pixel size - depending'
'on what wregister does - so another factor (PIX_SIZE_DF)^2/(PIX_SIZE_CFHT)^2'
avgphotosc,medianphotosc = getphotosc('_model_sh.fits','_df_sub.fits',numsources=numsources,sigmaclip=sigmaclip,cutout=cutout)
if medphotosc:
photosc = medianphotosc
print 'Using median calculated photosc: %s'%photosc
else:
photosc = avgphotosc
print 'Using average calculated photosc: %s'%photosc
iraf.imarith('_model_sh','*',photosc,'_model_sc')
iraf.imdel('_df_ga.fits')
'correction for the smoothing that was applied to the CFHT'
if width_cfhtsm != 0:
iraf.gauss('_df_sub','_df_ga',width_cfhtsm) # iraf.gauss('%s'%df_image,'_df_ga',width_cfhtsm)
else:
print 'WARNING: No smoothing applied to the dragonfly image before subtracting the model.'
iraf.imcopy('_df_sub.fits','_df_ga.fits')
'subtract the model from the dragonfly cutout'
iraf.imarith('_df_ga','-','_model_sc','_res')
'duplicate the residual so that we can redo the masking step later'
iraf.imcopy('_res.fits','_res_org.fits')
'mask the image to get block out stuff that didnt subtract nicely'
mask('_res',upperlim=upperlim)
return None
def red_standard(image,
arcs,
flats,
object=None,
biassec=REDBIAS,
trimsec=REDTRIM,
outflat='Flat-Red.fits',
gain=REDGAIN,
rdnoise=REDRDNOISE,
arc='Arc-Red.fits',
caldir='home$standards/'):
'''Reduce and calibrate standard star observation with red CCD'''
# Bias subtract everything first
redbias(image, biassec=biassec, trimsec=trimsec)
redbias(arcs, biassec=biassec, trimsec=trimsec)
redbias(flats, biassec=biassec, trimsec=trimsec)
# Create and apply flat-field
make_flat(flats, outflat, gain=gain, rdnoise=rdnoise)
iraf.ccdproc(image[0],
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
arcimages = ','.join(arcs)
iraf.ccdproc(arcs,
ccdtype='',
noproc=no,
fixpix=no,
overscan=no,
trim=no,
zerocor=no,
darkcor=no,
flatcor=yes,
illumcor=no,
fringecor=no,
readcor=no,
scancor=no,
flat=outflat)
# Extract spectrum of standard
if object == None:
object = get_head(image, 'OBJECT')
iraf.apall(image[0],
output=object,
references='',
interactive=yes,
find=yes,
recenter=yes,
resize=yes,
edit=yes,
trace=yes,
fittrace=yes,
extract=yes,
extras=yes,
review=no,
background='fit',
weights='variance',
pfit='fit1d',
readnoise=rdnoise,
gain=gain)
# Extract arc and fit wavelength solution
iraf.imarith(arcs[0], '+', arcs[1], 'Arc-Sum.fits')
reference_arc('Arc-Sum.fits', arc, image[0])
# Apply wavelength solution to standard
iraf.refspec(object,
references=arc,
sort="",
group="",
override=yes,
confirm=no,
assign=yes)
iraf.dispcor(object, '%s.w' % object, confirm=no, listonly=no)
# Remove absorption features and smooth
iraf.splot('%s.w' % object, 1, 1)
iraf.gauss('temp1[*,1,1]', '%s.smooth' % object, 3.0)
iraf.sarith('%s.w' % object, '/', '%s.smooth' % object, '%s.s' % object)
# Create and apply telluric correction
iraf.sarith('%s.w' % object, '/', 'temp1', 'telluric')
iraf.splot('telluric', 1, 1)
iraf.telluric('%s.s' % object,
'%s.t' % object,
'telluric',
xcorr=no,
tweakrms=no,
interactive=no,
sample='6850:6950,7575:7700')
# Define bandpasses for standard star calculation
iraf.standard('%s.t' % object,
'%s.std' % object,
extinction='onedstds$kpnoextinct.dat',
caldir=caldir,
observatory='Lick',
interact=yes,
star_name=object,
airmass='',
exptime='')
# Determine sensitivity function
iraf.sensfunc('%s.std' % object,
'%s.sens' % object,
extinction='onedstds$kpnoextinct.dat',
newextinction='extinct.dat',
observatory='Lick',
function='legendre',
order=3,
interactive=yes)
return