def calibrate(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='ca',
extinction=extpath,
caldir=stdpath)
for fitname in namelst:
outname = 'mark_' + fitname
if os.path.isfile(outname):
print('remove file ' + outname)
os.remove(outname)
iraf.calibrate(input=fitname,
output=outname,
extinct='yes',
flux='yes',
extinction=extpath,
ignoreaps='yes',
sensitivity='Sens',
fnu='no')
iraf.splot(images=outname)
iraf.flpr()
def fixpix(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('nrm/sci*nrm*.fits')
if len(fs) == 0:
print "WARNING: No rectified images to fix."
iraf.cd('..')
return
if not os.path.exists('fix'):
os.mkdir('fix')
for f in fs:
outname = f.replace('nrm', 'fix')
# Copy the file to the fix directory
shutil.copy(f, outname)
# Set all of the BPM pixels = 0
h = pyfits.open(outname, mode='update')
h['SCI'].data[h['BPM'].data == 1] = 0
# Grab the CRM extension from the lax file
laxhdu = pyfits.open(f.replace('nrm', 'lax'))
h.append(pyfits.ImageHDU(data=laxhdu['CRM'].data.copy(),
header=laxhdu['CRM'].header.copy(),
name='CRM'))
h.flush()
h.close()
laxhdu.close()
# Run iraf's fixpix on the cosmic rays, not ideal,
# but better than nothing because apall doesn't take a bad pixel mask
iraf.unlearn(iraf.fixpix)
iraf.flpr()
iraf.fixpix(outname + '[SCI]', outname + '[CRM]', mode='hl')
iraf.cd('..')
def combine_flat(ftblst):
iraf.flatcombine(input='tb//@' + ftblst,
output='flat',
combine='average',
reject='avsigclip',
ccdtype='',
process=False,
subsets=False,
delete=False,
clobber=False,
scale='mode',
statsec='',
nlow=1,
nhigh=1,
nkeep=1,
mclip=True,
lsigma=3.0,
hsigma=3.0,
rdnoise='CCDRON',
gain='CCDGAIN',
snoise=0.0,
pclip=-0.5,
blank=1.0)
iraf.flpr()
print '<<<<<combine flat successfully>>>>>'
def fixpix(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('nrm/sci*nrm*.fits')
if len(fs) == 0:
print "WARNING: No rectified images to fix."
iraf.cd('..')
return
if not os.path.exists('fix'):
os.mkdir('fix')
for f in fs:
outname = f.replace('nrm', 'fix')
# Copy the file to the fix directory
shutil.copy(f, outname)
# Set all of the BPM pixels = 0
h = pyfits.open(outname, mode='update')
h['SCI'].data[h['BPM'].data == 1] = 0
# Grab the CRM extension from the lax file
laxhdu = pyfits.open(f.replace('nrm', 'lax'))
h.append(pyfits.ImageHDU(data=laxhdu['CRM'].data.copy(),
header=laxhdu['CRM'].header.copy(),
name='CRM'))
h.flush()
h.close()
laxhdu.close()
# Run iraf's fixpix on the cosmic rays, not ideal,
# but better than nothing because apall doesn't take a bad pixel mask
iraf.unlearn(iraf.fixpix)
iraf.flpr()
iraf.fixpix(outname + '[SCI]', outname + '[CRM]', mode='hl')
iraf.cd('..')
def cl_bye(verb=None):
from pyraf import iraf
if verb:
print(" Clearing iraf --> iraf.flpr()", file=sys.stderr)
iraf.flpr()
iraf.flpr()
return
def identify2d(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('mos/arc*mos*.fits')
if len(fs) == 0:
print "WARNING: No mosaiced (2D) specidentify."
# Change directories to fail gracefully
iraf.cd('..')
return
arcfs, arcgas = get_ims(fs, 'arc')
if not os.path.exists('id2'):
os.mkdir('id2')
lampfiles = {
'Th Ar': 'ThAr.salt',
'Xe': 'Xe.salt',
'Ne': 'NeAr.salt',
'Cu Ar': 'CuAr.salt',
'Ar': 'Argon_hires.salt',
'Hg Ar': 'HgAr.salt'
}
for i, f in enumerate(arcfs):
ga = arcgas[i]
# find lamp and corresponding linelist
lamp = pyfits.getval(f, 'LAMPID')
lampfn = lampfiles[lamp]
if pyfits.getval(f, 'GRATING') == 'PG0300' and lamp == 'Ar':
lampfn = 'Argon_lores.swj'
ccdsum = int(pyfits.getval(f, 'CCDSUM').split()[1])
# linelistpath is a global variable defined in beginning, path to
# where the line lists are.
lamplines = pysaltpath + '/data/linelists/' + lampfn
print(lamplines)
# img num should be right before the .fits
imgnum = f[-9:-5]
# run pysalt specidentify
idfile = 'id2/arc%05.2fid2%04i' % (float(ga), int(imgnum)) + '.db'
iraf.unlearn(iraf.specidentify)
iraf.flpr()
iraf.specidentify(
images=f,
linelist=lamplines,
outfile=idfile,
guesstype='rss',
inter=True, # automethod='FitXcor',
rstep=600 / ccdsum,
rstart=200 / ccdsum,
startext=1,
clobber='yes',
#startext=1, clobber='yes',
verbose='no',
mode='hl',
logfile='salt.log',
mdiff=2,
function='legendre')
iraf.cd('..')
def coroverbiastrim(lstfile):
iraf.noao()
iraf.imred()
iraf.ccdred()
x1,x2,y1,y2 = get_trim_sec()
iraf.ccdproc(images = '@' + lstfile + '//[1]'
, output = '%bo%bo%@' + lstfile
, ccdtype = '', max_cache = 0, noproc = False
, fixpix = False, overscan = True, trim = False
, zerocor = True, darkcor = False, flatcor = False
, illumcor = False, fringecor = False, readcor = False
, scancor = False, readaxis = 'line', fixfile = ''
, biassec = '[5:45,%s:%s]'%(y1,y2), trimsec = '[%s:%s,%s:%s]'%(x1,x2,y1,y2)
, zero = 'Zero', dark = '', flat = '', illum = '', fringe = ''
, minreplace = 1.0, scantype = 'shortscan', nscan = 1
, interactive = False, function = 'chebyshev', order = 1
, sample = '*', naverage = 1, niterate = 1
, low_reject = 3.0, high_reject = 3.0, grow = 1.0)
iraf.ccdproc(images = '%bo%bo%@' + lstfile
, output = '%tbo%tbo%@' + lstfile
, ccdtype = '', max_cache = 0, noproc = False
, fixpix = False, overscan = False, trim = True
, zerocor = False, darkcor = False, flatcor = False
, illumcor = False, fringecor = False, readcor = False
, scancor = False, readaxis = 'line', fixfile = ''
, biassec = '[5:45,%s:%s]'%(y1,y2), trimsec = '[%s:%s,%s:%s]'%(x1,x2,y1,y2)
, zero = 'Zero', dark = '', flat = '', illum = '', fringe = ''
, minreplace = 1.0, scantype = 'shortscan', nscan = 1
, interactive = False, function = 'chebyshev', order = 1
, sample = '*', naverage = 1, niterate = 1
, low_reject = 3.0, high_reject = 3.0, grow = 1.0)
iraf.flpr()
def main():
if os.path.isdir('bias'):
if os.path.isfile(os.getcwd() + os.sep + 'bias' + os.sep +
'spec_bias.lst'):
os.chdir('bias')
clear()
coroverscan('spec_bias.lst')
combinebias('spec_bias.lst')
dirname, filename = os.path.split(os.getcwd())
os.chdir(dirname)
iraf.flpr()
path = os.getcwd()
dirname = os.listdir(path)
dirname = [
tmp for tmp in dirname if os.path.isdir(tmp)
and 'bias' not in tmp and 'other' not in tmp
]
for dirg in dirname:
print('copy Zero to ' + path + os.sep + dirg)
shutil.copyfile(path + os.sep + 'bias' + os.sep + 'Zero.fits',
path + os.sep + dirg + os.sep + 'Zero.fits')
else:
print('no spec_bias.lst in ' + os.getcwd())
else:
print('no bias dir in ' + os.getcwd())
def reidentify():
iraf.twodspec()
iraf.longslit()
iraf.reidentify(reference='Lamp',
images='Lamp',
interactive='no',
section='column',
newaps='yes',
override='yes',
refit='yes',
trace='no',
step=10,
nsum=10,
shift=0.0,
search=0.0,
nlost=5,
cradius=7.0,
threshold=0.0,
addfeatures='no',
coordlist=cdherb_file,
match=-3.0,
maxfeatures=50,
minsep=2.0,
database='database')
iraf.flpr()
def identify():
iraf.twodspec()
iraf.longslit()
iraf.identify(images='Lamp.fits',
section='middle column',
database='database',
coordlist=cdherb_file,
nsum=10,
match=-3.0,
maxfeatures=50,
zwidth=100.0,
ftype='emission',
fwidth=20.0,
cradius=7.0,
threshold=0.0,
minsep=2.0,
function='chebyshev',
order=6,
sample='*',
niterate=0,
low_reject=3.0,
high_reject=3.0,
grow=0.0,
autowrite='no')
iraf.flpr()
def fluxcal(stdsfolder='./', fs=None):
iraf.cd('work')
if fs is None:
fs = glob('x1d/sci*x1d*c*.fits')
if len(fs) == 0:
print "WARNING: No science chip spectra to flux calibrate."
iraf.cd('..')
return
if not os.path.exists('flx'):
os.mkdir('flx')
extfile = pysaltpath + '/data/site/suth_extinct.dat'
stdfiles = glob(stdsfolder + '/std/*sens*c?.dat')
print(stdfiles)
for f in fs:
outfile = f.replace('x1d', 'flx')
chip = outfile[-6]
hdu = pyfits.open(f)
ga = f.split('/')[1][3:8]
# Get the standard sensfunc with the same grating angle
stdfile = None
for stdf in stdfiles:
if np.isclose(float(ga),
float(stdf.split('/')[stdf.count('/')][3:8]),
rtol=1e-2):
# Get the right chip number
if chip == stdf[-5]:
stdfile = stdf
break
if stdfile is None:
print('No standard star with grating-angle %s' % ga)
continue
# for each extracted aperture
for i in range(hdu[0].data.shape[1]):
# create an ascii file that pysalt can read
asciiname = 'flx/sciflx.dat'
outtmpname = 'flx/scical.dat'
spectoascii(f, asciiname, i)
# Run pysalt.speccal
iraf.unlearn(iraf.speccal)
iraf.flpr()
iraf.speccal(asciiname,
outtmpname,
stdfile,
extfile,
airmass=pyfits.getval(f, 'AIRMASS'),
exptime=pyfits.getval(f, 'EXPTIME'),
clobber=True,
mode='h')
# read in the flux calibrated ascii file and copy its
# contents into a fits file
flxcal = np.genfromtxt(outtmpname).transpose()
hdu[0].data[0, i] = flxcal[1]
hdu[0].data[2, i] = flxcal[2]
# delete the ascii file
os.remove(asciiname)
os.remove(outtmpname)
hdu.writeto(outfile, clobber=True)
iraf.cd('..')
def combinelamp(lst):
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.imcombine(input='%ftb%ftb%@' + lst,
output='Lamp',
combine='sum',
reject='none')
print('<<<<<combine the lamp & generate the Lamp.fits>>>>>')
iraf.flpr()
def check_bias():
biaspath = os.getcwd() + os.sep + 'bias'
if os.path.isfile(biaspath + os.sep + 'spec_bias.lst'):
os.chdir('bias')
os.system('gedit spec_bias.lst &')
iraf.imexamine(input = '@spec_bias.lst[1]', frame = 1)
dirname, filename = os.path.split(os.getcwd())
os.chdir(dirname)
iraf.flpr()
else:
print('no bias dir in ' + os.getcwd())
def check_bias():
biaspath = os.getcwd() + os.sep + 'bias'
if os.path.isfile(biaspath + os.sep + 'spec_bias.lst'):
os.chdir('bias')
webbrowser.open('spec_bias.lst')
iraf.imexamine(input='@spec_bias.lst[1]', frame=1)
dirname = os.path.dirname(os.getcwd())
os.chdir(dirname)
iraf.flpr()
else:
print('no bias dir in ' + os.getcwd())
def fluxcal(stdsfolder='./', fs=None):
iraf.cd('work')
if fs is None:
fs = glob('x1d/sci*x1d*c*.fits')
if len(fs) == 0:
print "WARNING: No science chip spectra to flux calibrate."
iraf.cd('..')
return
if not os.path.exists('flx'):
os.mkdir('flx')
extfile = pysaltpath + '/data/site/suth_extinct.dat'
stdfiles = glob(stdsfolder + '/std/*sens*c?.dat')
print(stdfiles)
for f in fs:
outfile = f.replace('x1d', 'flx')
chip = outfile[-6]
hdu = pyfits.open(f)
ga = f.split('/')[1][3:8]
# Get the standard sensfunc with the same grating angle
stdfile = None
for stdf in stdfiles:
if ga in stdf:
# Get the right chip number
if chip == stdf[-5]:
stdfile = stdf
break
if stdfile is None:
print('No standard star with grating-angle %s' % ga)
continue
# for each extracted aperture
for i in range(hdu[0].data.shape[1]):
# create an ascii file that pysalt can read
asciiname = 'flx/sciflx.dat'
outtmpname = 'flx/scical.dat'
spectoascii(f, asciiname, i)
# Run pysalt.speccal
iraf.unlearn(iraf.speccal)
iraf.flpr()
iraf.speccal(asciiname, outtmpname, stdfile, extfile,
airmass=pyfits.getval(f, 'AIRMASS'),
exptime=pyfits.getval(f, 'EXPTIME'),
clobber=True, mode='h')
# read in the flux calibrated ascii file and copy its
# contents into a fits file
flxcal = np.genfromtxt(outtmpname).transpose()
hdu[0].data[0, i] = flxcal[1]
hdu[0].data[2, i] = flxcal[2]
# delete the ascii file
os.remove(asciiname)
os.remove(outtmpname)
hdu.writeto(outfile, clobber=True)
iraf.cd('..')
def mosaic(fs=None):
iraf.cd('work')
# If the file list is not given, grab the default files
if fs is None:
fs = glob('flts/*.fits')
# Abort if there are no files
if len(fs) == 0:
print "WARNING: No flat-fielded images to mosaic."
iraf.cd('..')
return
if not os.path.exists('mos'):
os.mkdir('mos')
# Get the images to work with
ims, gas = get_scis_and_arcs(fs)
for i, f in enumerate(ims):
ga = gas[i]
fname = f.split('/')[1]
typestr = fname[:3]
# by our naming convention, imnum should be the last 4 characters
# before the '.fits'
imnum = fname[-9:-5]
outname = 'mos/' + typestr
outname += '%05.2fmos%04i.fits' % (float(ga), int(imnum))
# prepare to run saltmosaic
iraf.unlearn(iraf.saltmosaic)
iraf.flpr()
iraf.saltmosaic(images=f,
outimages=outname,
outpref='',
geomfile=pysaltpath + '/data/rss/RSSgeom.dat',
clobber=True,
mode='h')
# Make a bad pixel mask marking where there is no data.
h = pyfits.open(outname, 'update')
maskim = h[1].data.copy()
maskim[:, :] = 0.0
maskim[abs(h[1].data) < 1e-5] = 1
imhdu = pyfits.ImageHDU(maskim)
h.append(imhdu)
h[1].header['BPMEXT'] = 2
h[2].header['EXTNAME'] = 'BPM'
h[2].header['CD2_2'] = 1
h.flush()
h.close()
iraf.cd('..')
def standard(namelst):
iraf.noao()
iraf.twodspec()
iraf.longslit(dispaxis=2,
nsum=1,
observatory='ca',
extinction=extpath,
caldir=stdpath)
std_fitsname = namelst[0]
stdname, stdmag, stdmagband = standard_star_info(std_fitsname)
wid, sep = get_band_width_sep(stdname)
print('<<<<<the standard star is ', stdname, '>>>>>')
print std_fitsname
if os.path.isfile('Std'):
print('remove file Std')
os.remove('Std')
iraf.standard(
input=std_fitsname,
output='Std',
samestar=True,
beam_switch=False,
apertures='',
bandwidth=wid,
bandsep=sep, # 30.0 20.0
fnuzero=3.6800000000000E-20,
extinction=extpath,
caldir=stdpath,
observatory='ca',
interact=True,
graphics='stdgraph',
cursor='',
star_name=stdname,
airmass='',
exptime='',
mag=stdmag,
magband=stdmagband,
teff='',
answer='yes')
if os.path.isfile('Sens.fits'):
print('remove file Sens.fits')
os.remove('Sens.fits')
iraf.sensfunc(standards='Std',
sensitivity='Sens',
extinction=extpath,
function='spline3',
order=15)
iraf.splot('Sens')
iraf.flpr()
def check_other(path):
if os.path.isdir(path):
os.chdir(path)
lstlst = ['halogen.lst', 'lamp.lst', 'cor_lamp.lst','std.lst','cor_std.lst']
for i in lstlst:
if os.path.isfile(i):
os.system('gedit %s &' % i)
iraf.imexamine(input = '@%s[1]'%i, frame = 1)
dirname, filename = os.path.split(os.getcwd())
os.chdir(dirname)
iraf.flpr()
else:
print('no dir ' + path + ' in ' + os.getcwd())
def main():
dirname = os.listdir(os.getcwd())
dirname = [tmp for tmp in dirname if os.path.isdir(tmp) and \
'bias' not in tmp and 'other' not in tmp]
path = os.getcwd()
for i in dirname:
os.chdir(path + os.sep + i)
print('current dir : ' + os.getcwd())
iraf.flpr()
cor_ftbo.main()
wcal2d.main()
re_apall.main()
re_corflux.main()
os.chdir(path)
def mosaic(fs=None):
iraf.cd('work')
# If the file list is not given, grab the default files
if fs is None:
fs = glob('flts/*.fits')
# Abort if there are no files
if len(fs) == 0:
print "WARNING: No flat-fielded images to mosaic."
iraf.cd('..')
return
if not os.path.exists('mos'):
os.mkdir('mos')
# Get the images to work with
ims, gas = get_scis_and_arcs(fs)
for i, f in enumerate(ims):
ga = gas[i]
fname = f.split('/')[1]
typestr = fname[:3]
# by our naming convention, imnum should be the last 4 characters
# before the '.fits'
imnum = fname[-9:-5]
outname = 'mos/' + typestr
outname += '%05.2fmos%04i.fits' % (float(ga), int(imnum))
# prepare to run saltmosaic
iraf.unlearn(iraf.saltmosaic)
iraf.flpr()
iraf.saltmosaic(images=f, outimages=outname, outpref='',
geomfile=pysaltpath + '/data/rss/RSSgeom.dat',
clobber=True, mode='h')
# Make a bad pixel mask marking where there is no data.
h = pyfits.open(outname, 'update')
maskim = h[1].data.copy()
maskim[:, :] = 0.0
maskim[abs(h[1].data) < 1e-5] = 1
imhdu = pyfits.ImageHDU(maskim)
h.append(imhdu)
h[1].header['BPMEXT'] = 2
h[2].header['EXTNAME'] = 'BPM'
h[2].header['CD2_2'] = 1
h.flush()
h.close()
iraf.cd('..')
def check_other(path):
if os.path.isdir(path):
os.chdir(path)
lstlst = [
'halogen.lst', 'lamp.lst', 'cor_lamp.lst', 'std.lst', 'cor_std.lst'
]
for i in lstlst:
if os.path.isfile(i):
webbrowser.open(i)
iraf.imexamine(input='@%s[1]' % i, frame=1)
dirname, filename = os.path.split(os.getcwd())
os.chdir(dirname)
iraf.flpr()
else:
print('no dir ' + path + ' in ' + os.getcwd())
def correct_overscan(filename): iraf.ccdproc(images = '@' + filename + '//[1]' , output = '%o%o%@' + filename , ccdtype = '', max_cache = 0, noproc = False , fixpix = False, overscan = True, trim = False , zerocor = False, darkcor = False, flatcor = False , illumcor = False, fringecor = False, readcor = False , scancor = False, readaxis = 'line', fixfile = '' , biassec = '[5:45,1:4612]', trimsec = '', zero = '' , dark = '', flat = '', illum = '', fringe = '' , minreplace = 1.0, scantype = 'shortscan', nscan = 1 , interactive = False, function = 'legendre', order = 1 , sample = '*', naverage = 1, niterate = 1 , low_reject = 3.0, high_reject = 3.0, grow = 0.0) iraf.flpr()
def correct_bias(filename1, filename2):
iraf.ccdproc(images = 'o//@' + filename1
, output = 'bo//@' + filename1
, ccdtype = '', max_cache = 0, noproc = False
, fixpix = False, overscan = False, trim = False
, zerocor = True, darkcor = False, flatcor = False
, illumcor = False, fringecor = False, readcor = False
, scancor = False, readaxis = 'line', fixfile = ''
, biassec = '', trimsec = '', zero = filename2
, dark = '', flat = '', illum = '', fringe = ''
, minreplace = 1.0, scantype = 'shortscan', nscan = 1
, interactive = False, function = 'chebyshev', order = 1
, sample = '*', naverage = 1, niterate = 1
, low_reject = 3.0, high_reject = 3.0, grow = 0.0)
iraf.flpr()
def HandleEllipseTask(cutimage, xcntr, ycntr, SizeX, SizeY, sky, out):
"""Running the ellipse task. SizeX, SizeY are the total size"""
manual_profile = 0
try:
raise ImportError() #Temporarily kill this loop as the new flagging does not work with pyraf-functions, yet
from pyraf import iraf
from fitellifunc import run_elli
use_pyraf = 1
except ImportError:
use_pyraf = 0
print 'No pyraf installed!'
WriteError('Cannot find pyraf installation! Trying manual 1d ' + \
'profile finder\n')
if use_pyraf:
if out:
ell_mask_file = 'OEM_' + c.fstring + '.fits'
ell_out = 'OE_' + c.fstring + '.txt'
else:
ell_mask_file = 'EM_' + c.fstring + '.fits'
ell_out = 'E_' + c.fstring + '.txt'
plfile = 'GalEllFit.fits.pl'
CleanEllipse(ell_out, 0)
try:
iraf.imcopy(ell_mask_file, plfile, verbose='no')
iraf.flpr()
except:
pass
try:
run_elli(cutimage, ell_out, xcntr, ycntr, c.eg, \
c.pos_ang, c.major_axis, sky)
CleanEllipse(ell_out, 1)
try:
iraf.flpr()
except:
pass
if exists(ell_out):
pass
else:
manual_profile = 1
except:
manual_profile = 1
WriteError('Error in ellipse task. Trying manual profile finder\n')
try:
c.Flag = SetFlag(c.Flag, GetFlag('ELLIPSE_FAIL'))
except badflag:
pass
if use_pyraf == 0 or manual_profile:
FitEllipseManual(cutimage, xcntr, ycntr, SizeX, SizeY, sky, out)
def apall(lstfile):
iraf.noao()
iraf.twodspec()
iraf.apextract(dispaxis = 2, database = 'database')
f = open(lstfile)
l = f.readlines()
f.close()
l = [tmp.split('\n')[0] for tmp in l]
path = os.getcwd()
for i in l:
infile = 'wftbo' + i
outfile = 'awftbo' + i
while True:
if os.path.isfile(outfile):
print('remove ' + outfile)
os.remove(outfile)
delfile = path + os.sep + 'database/ap' + infile[0:-5]
if os.path.isfile(delfile):
print('remove ' + delfile)
os.remove(delfile)
iraf.apall(input = infile
, output = outfile, apertures = 2, format = 'multispec'
, references = '', profiles = '', interactive = True
, find = True, recenter = True, resize = False
, edit = True, trace = True, fittrace = True
, extract = True, extras = True, review = True
, line = 'INDEF', nsum = 10, lower = -15.0
, upper = 15.0, apidtable = '', b_function = 'chebyshev'
, b_order = 2, b_sample = '-50:-26,26:50', b_naverage = -25
, b_niterate = 1, b_low_reject = 3.0, b_high_reject = 3.0
, b_grow = 0.0, width = 5.0, radius = 10.0
, threshold = 0.0, nfind = 2, minsep = 5.0
, maxsep = 100000.0, order = 'increasing', aprecenter = ''
, npeaks = 'INDEF', shift = True, llimit ='INDEF'
, ulimit = 'INDEF', ylevel = 0.1, peak = True
, bkg = True, r_grow = 0.0, avglimits = False
, t_nsum = 20, t_step = 10, t_nlost = 3, t_function = 'legendre'
, t_order = 7, t_sample = '*', t_naverage = 1
, t_niterate = 1, t_low_reject = 3.0, t_high_reject = 3.0
, t_grow = 0.0, background = 'fit', skybox = 1
, weights = 'variance', pfit = 'fit1d', clean = True
, saturation = 'INDEF', readnoise = 9.4, gain = 0.35
, lsigma = 4.0, usigma = 4.0, nsubaps = 1)
iraf.flpr()
sspecplot.sspecplot(outfile)
getval = raw_input('Are you need repeat apall,may be clean should be close(r/n)')
if getval != 'r':
break
def correct_flat(filename, flatname):
# flatname = 'per' + filename.replace('_no_flat.lst', '_flat.fits')
iraf.ccdproc(images = 'tbo//@' + filename
, output = 'ftbo//@' + filename
, ccdtype = '', max_cache = 0, noproc = False
, fixpix = False, overscan = False, trim = False
, zerocor = False, darkcor = False, flatcor = True
, illumcor = False, fringecor = False, readcor = False
, scancor = False, readaxis = 'line', fixfile = ''
, biassec = '', trimsec = '', zero = ''
, dark = '', flat = flatname, illum = '', fringe = ''
, minreplace = 1.0, scantype = 'shortscan', nscan = 1
, interactive = False, function = 'legendre', order = 1
, sample = '*', naverage = 1, niterate = 1
, low_reject = 3.0, high_reject = 3.0, grow = 0.0)
iraf.flpr()
def coroverscan(lstfn):
"""
call iraf command ccdproc, overscan correct.
lstfn : lst file name
type : string
output file : oYF*.fits
"""
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdproc(images='@' + lstfn + '//[1]',
output='%o%o%@' + lstfn,
ccdtype='',
max_cache=0,
noproc=False,
fixpix=False,
overscan=True,
trim=False,
zerocor=False,
darkcor=False,
flatcor=False,
illumcor=False,
fringecor=False,
readcor=False,
scancor=False,
readaxis='line',
fixfile='',
biassec='[5:45,1:4612]',
trimsec='',
zero='',
dark='',
flat='',
illum='',
fringe='',
minreplace=1.0,
scantype='shortscan',
nscan=1,
interactive=False,
function='chebyshev',
order=1,
sample='*',
naverage=1,
niterate=1,
low_reject=3.0,
high_reject=3.0,
grow=1.0)
iraf.flpr()
def trim(tblst, trimsec):
iraf.ccdproc(images='b//@' + tblst,
output='tb//@' + tblst,
ccdtype='',
noproc=False,
overscan=False,
trim=True,
zerocor=False,
flatcor=False,
readaxis='line',
biassec='',
trimsec=trimsec,
interactive=False,
function='legendre',
order=1)
iraf.flpr()
print '<<<<<trim section successfully>>>>>'
def identify2d(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('mos/arc*mos*.fits')
if len(fs) == 0:
print "WARNING: No mosaiced (2D) specidentify."
# Change directories to fail gracefully
iraf.cd('..')
return
arcfs, arcgas = get_ims(fs, 'arc')
if not os.path.exists('id2'):
os.mkdir('id2')
lampfiles = {'Th Ar': 'ThAr.salt', 'Xe': 'Xe.salt', 'Ne': 'NeAr.salt',
'Cu Ar': 'CuAr.salt', 'Ar': 'Argon_hires.salt',
'Hg Ar': 'HgAr.salt'}
for i, f in enumerate(arcfs):
ga = arcgas[i]
# find lamp and corresponding linelist
lamp = pyfits.getval(f, 'LAMPID')
lampfn = lampfiles[lamp]
if pyfits.getval(f,'GRATING') == 'PG0300' and lamp == 'Ar':
lampfn = 'Argon_lores.swj'
ccdsum = int(pyfits.getval(f, 'CCDSUM').split()[1])
# linelistpath is a global variable defined in beginning, path to
# where the line lists are.
lamplines = pysaltpath + '/data/linelists/' + lampfn
print(lamplines)
# img num should be right before the .fits
imgnum = f[-9:-5]
# run pysalt specidentify
idfile = 'id2/arc%05.2fid2%04i' % (float(ga), int(imgnum)) + '.db'
iraf.unlearn(iraf.specidentify)
iraf.flpr()
iraf.specidentify(images=f, linelist=lamplines, outfile=idfile,
guesstype='rss', inter=True, # automethod='FitXcor',
rstep=600 / ccdsum,
rstart=200 / ccdsum, startext=1, clobber='yes',
#startext=1, clobber='yes',
verbose='no', mode='hl', logfile='salt.log',
mdiff=2, function='legendre')
iraf.cd('..')
def corhalogen(lstfile):
namelst = glob.glob('ftbo*.fits')
for name in namelst:
print 'remove ', name
os.remove(name)
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdproc(images='tbo@' + lstfile,
output='%ftbo%ftbo%@' + lstfile,
ccdtype='',
max_cache=0,
noproc=False,
fixpix=False,
overscan=False,
trim=False,
zerocor=False,
darkcor=False,
flatcor=True,
illumcor=False,
fringecor=False,
readcor=False,
scancor=False,
readaxis='line',
fixfile='',
biassec='',
trimsec='',
zero='Zero',
dark='',
flat='Resp',
illum='',
fringe='',
minreplace=1.0,
scantype='shortscan',
nscan=1,
interactive=False,
function='chebyshev',
order=1,
sample='*',
naverage=1,
niterate=1,
low_reject=3.0,
high_reject=3.0,
grow=1.0)
iraf.flpr()
def corhalogen(lstfile):
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdproc(images = 'tbo@' + lstfile
, output = '%ftbo%ftbo%@' + lstfile
, ccdtype = '', max_cache = 0, noproc = False
, fixpix = False, overscan = False, trim = False
, zerocor = False, darkcor = False, flatcor = True
, illumcor = False, fringecor = False, readcor = False
, scancor = False, readaxis = 'line', fixfile = ''
, biassec = '', trimsec = ''
, zero = 'Zero', dark = '', flat = 'Resp', illum = '', fringe = ''
, minreplace = 1.0, scantype = 'shortscan', nscan = 1
, interactive = False, function = 'chebyshev', order = 1
, sample = '*', naverage = 1, niterate = 1
, low_reject = 3.0, high_reject = 3.0, grow = 1.0)
iraf.flpr()
def __init__(self,
shortparlists,
parlists,
FitsDir,
logfile,
verbose=1,
clean_up=1,
skyKey='ALIGNSKY',
hdrGain=0,
crlower=None,
imNsci=1):
self.modName = string.split(string.split(str(self))[0], '.')[0][1:]
self.shortparlists = shortparlists
self.parlists = parlists
self.Fits = FitsDir
self.verbose = verbose
self.crmasks = {} # cosmic ray masks names
self.removeList = []
self.clean_up = clean_up
self.skyKey = skyKey
self.hdrGain = hdrGain
self.crlower = crlower
if imNsci < 1:
raise ValueError, 'Error: pyblot got imNsci = ' + imNsci
self.imNsci = imNsci
self.logfile = logfile
print self.modName, 'version', __version__
self.logfile.write('Instantiating ' + self.modName + ' version ' +
__version__)
# make sure these packages are loaded
iraf.stsdas()
iraf.toolbox()
iraf.imgtool()
iraf.fourier()
iraf.fitting()
iraf.ttools()
iraf.analysis()
iraf.dither()
# flush the cash! twice!
iraf.flpr()
iraf.flpr()
iraf.reset(imtype='fits') # seems to make deriv task a bit happier
iraf.set(tmp='./')
def apall(ilst, olst):
iraf.noao()
iraf.twodspec()
iraf.apextract(dispaxis=1, database='database')
for i, infile in enumerate(ilst):
outfile = olst[i]
laper, raper, back_samp = -7, 7, '-30:-15,15:30'
while True:
if os.path.isfile(outfile):
print('remove ' + outfile)
os.remove(outfile)
delfile = os.getcwd()+os.sep+'database/ap'+infile[0:-5]
if os.path.isfile(delfile):
print('remove ' + delfile)
os.remove(delfile)
iraf.apall(input=infile, output=outfile, apertures=2,
format='multispec', references='', profiles='',
interactive=True, find=True, recenter=True,
resize=False, edit=True, trace=True, fittrace=True,
extract=True, extras=True, review=True, line='INDEF',
nsum=10, lower=laper, upper=raper, apidtable='',
b_function='chebyshev', b_order=2, b_sample=back_samp,
b_naverage=-25, b_niterate=1, b_low_reject=3.0,
b_high_reject=3.0, b_grow=0.0, width=5.0, radius=10.0,
threshold=0.0, nfind=2, minsep=5.0, maxsep=100000.0,
order='increasing', aprecenter='', npeaks='INDEF',
shift=True, llimit='INDEF', ulimit='INDEF', ylevel=0.1,
peak=True, bkg=True, r_grow=0.0, avglimits=False,
t_nsum=20, t_step=10, t_nlost=3, t_function='legendre',
t_order=12, t_sample='*', t_naverage=1, t_niterate=1,
t_low_reject=3.0, t_high_reject=3.0, t_grow=0.0,
background='median', skybox=1, weights='none',
pfit='fit1d', clean=True, saturation='INDEF',
readnoise='CCDRON', gain='CCDGAIN', lsigma=4.0, usigma=4.0,
nsubaps=1)
iraf.flpr()
getval = raw_input(('Are you need repeat apall,'
'may be clean should be close(r/n)'))
if getval != 'r':
break
def correct_trim(filename, trimsec):
# print 'Please check the fits image and determine the size to trim...'
# os.popen('gedit ' + filename +' &')
# iraf.imexamine(input = 'bo//@' + filename, frame = 1)
# xy = raw_input("Please input x1, x2, y1, y2 to trim: ")
# xy = xy.split()
# trimsec = '[' + xy[0] + ':' + xy[1] + ',' + xy[2] + ':' + xy[3] +']'
iraf.ccdproc(images = 'bo//@' + filename
, output = 'tbo//@' + filename
, ccdtype = '', max_cache = 0, noproc = False
, fixpix = False, overscan = False, trim = True
, zerocor = False, darkcor = False, flatcor = False
, illumcor = False, fringecor = False, readcor = False
, scancor = False, readaxis = 'line', fixfile = ''
, biassec = '', trimsec = trimsec, zero = ''
, dark = '', flat = '', illum = '', fringe = ''
, minreplace = 1.0, scantype = 'shortscan', nscan = 1
, interactive = False, function = 'legendre', order = 1
, sample = '*', naverage = 1, niterate = 1
, low_reject = 3.0, high_reject = 3.0, grow = 0.0)
iraf.flpr()
def cor_flat(corftblst):
print 1
iraf.ccdproc(images='tb//@' + corftblst,
output='ftb//@' + corftblst,
ccdtype='',
max_cache=0,
noproc=False,
fixpix=False,
overscan=False,
trim=False,
zerocor=False,
darkcor=False,
flatcor=True,
illumcor=False,
fringecor=False,
readcor=False,
scancor=False,
readaxis='line',
fixfile='',
biassec='',
trimsec='',
zero='',
dark='',
flat='flat',
illum='',
fringe='',
minreplace=1.0,
scantype='shortscan',
nscan=1,
interactive=False,
function='chebyshev',
order=1,
sample='*',
naverage=1,
niterate=1,
low_reject=3.0,
high_reject=3.0,
grow=1.0)
iraf.flpr()
print '<<<<<correct flat successfully>>>>>'
def main():
if os.path.isdir('bias'):
if os.path.isfile(os.getcwd() + os.sep + 'bias' + os.sep + 'spec_bias.lst'):
os.chdir('bias')
clear()
coroverscan('spec_bias.lst')
combinebias('spec_bias.lst')
dirname, filename = os.path.split(os.getcwd())
os.chdir(dirname)
iraf.flpr()
path = os.getcwd()
dirname = os.listdir(path)
dirname = [tmp for tmp in dirname if os.path.isdir(tmp) \
and 'bias' not in tmp and 'other' not in tmp]
for dirg in dirname:
print('copy Zero to ' + path + os.sep + dirg)
shutil.copyfile(path + os.sep + 'bias' + os.sep + 'Zero.fits' \
, path + os.sep + dirg + os.sep + 'Zero.fits')
else:
print('no spec_bias.lst in ' + os.getcwd())
else:
print('no bias dir in ' + os.getcwd())
def iraf_flpr():
### Reset the IRAF cache.
### Obviously this needs to be done three times
### becuase IRAF is such a sweet and intuitive piece of
### software.
iraf.flpr()
iraf.flpr()
iraf.flpr()
def combine_bias(blst):
iraf.zerocombine(input='@' + blst,
output='Zero',
combine='average',
reject='minmax',
ccdtype='',
process=False,
delete=False,
clobber=False,
scale='none',
statsec='',
nlow=0,
nhigh=1,
nkeep=1,
mclip=True,
lsigma=3.0,
hsigma=3.0,
rdnoise='CCDRON',
gain='CCDGAIN',
snoise=0.0,
pclip=-0.5,
blank=0.0)
iraf.flpr()
print '<<<<<combine bias successfully>>>>>'
def run_tweakshifts(asn_direct, verbose=False, clean=True):
"""
run_tweakshifts(asn_direct)
asn_direct - filename of ASN table of direct images [...]_asn.fits
This routine only uses dither.tweakshifts to compute the relative shifts of
the direct images
"""
from pyraf import iraf
from iraf import stsdas, dither
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
root = asn_direct.split('_asn.fits')[0] #.lower()
try:
os.remove(root + '_tweak.fits')
except:
pass
iraf.flpr()
iraf.flpr()
iraf.flpr()
if clean:
clean = iraf.yes
else:
clean = iraf.no
iraf.unlearn('tweakshifts')
status = iraf.tweakshifts(input=asn_direct, shiftfile='',
reference=root+'_tweak.fits',
output = root+'_shifts.txt', findmode = 'catalog',
gencatalog = 'daofind', sextractpars = '',
undistort = yes, computesig = yes, idckey = 'idctab', \
clean = clean, verbose = no, catfile = '', xcol = 1, ycol = 2, \
fluxcol = 3, fluxmax = INDEF, fluxmin = INDEF, fluxunits = 'counts', \
nbright = INDEF, refcat = '', refxcol = 1, refycol = 2, rfluxcol = 3, \
rfluxmax = INDEF, rfluxmin = INDEF, rfluxunits = 'counts', \
refnbright = INDEF, minobj = 15, nmatch = 30, matching = 'tolerance', \
xyxin = INDEF, xyyin = INDEF, tolerance = 4.0, fwhmpsf = 1.5, \
sigma = 0.0, datamin = INDEF, datamax = INDEF, threshold = 4.0, \
nsigma = 1.5, fitgeometry = 'shift', function = 'polynomial', \
maxiter = 3, reject = 3.0, crossref = '', margin = 50, tapersz = 50, \
pad = no, fwhm = 7.0, ellip = 0.05, pa = 45.0, fitbox = 7, \
Stdout=1)
if verbose:
for line in status:
print line
return status
def runApphot(imfile, coofile=None, magfile=None):
"""
use iraf.digiphot.apphot
to collect aperture photometry
"""
import pyraf
from pyraf import iraf
import os
import pyfits
if not coofile :
coofile = imfile.replace('.fits', '.coo')
if not magfile :
magfile = imfile.replace('.fits', '.mag')
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
if os.path.exists( magfile ) :
os.remove( magfile )
hdr = pyfits.getheader( imfile )
if 'FILTER' in hdr:
filt=hdr['FILTER']
filtkey='FILTER'
elif 'FILTER1' in hdr :
filt=hdr['FILTER1']
filtkey='FILTER1'
if filt.startswith('CLEAR'):
filt = hdr['FILTER2']
filtkey='FILTER2'
if filt not in APCOR.keys() :
return( [] )
# iraf.digiphot.apphot.datapars :
iraf.datapars.scale = 1.0
iraf.datapars.fwhmpsf = 2.5
iraf.datapars.emission = True
iraf.datapars.sigma = 'INDEF'
iraf.datapars.datamin = 'INDEF'
iraf.datapars.datamax = 'INDEF'
iraf.datapars.noise = 'constant'
iraf.datapars.ccdread = ''
iraf.datapars.gain = ''
iraf.datapars.readnoise = 0.0
iraf.datapars.epadu = 1.0
iraf.datapars.exposure = ''
iraf.datapars.airmass = ''
iraf.datapars.filter = filt
iraf.datapars.obstime = ''
iraf.datapars.itime = 1.0
iraf.datapars.xairmass = 'INDEF'
iraf.datapars.ifilter = 'INDEF'
iraf.datapars.otime = 'INDEF'
# iraf.digiphot.apphot.centerpars :
iraf.unlearn( iraf.centerpars )
iraf.centerpars.calgorithm = 'centroid'
iraf.centerpars.cbox = 3.0
iraf.centerpars.cthreshold = 0.0
iraf.centerpars.minsnratio = 1.0
iraf.centerpars.cmaxiter = 10.0
iraf.centerpars.maxshift = 1.0
iraf.centerpars.clean = False
iraf.centerpars.rclean = 1.0
iraf.centerpars.rclip = 2.0
iraf.centerpars.kclean = 3.0
iraf.centerpars.mkcenter = False
# iraf.digiphot.apphot.fitskypars :
iraf.unlearn( iraf.fitskypars )
iraf.fitskypars.salgorithm = 'median'
iraf.fitskypars.annulus = 25.0
iraf.fitskypars.dannulus = 40.0
iraf.fitskypars.skyvalue = 0.0
iraf.fitskypars.smaxiter = 10.0
iraf.fitskypars.sloclip = 0.0
iraf.fitskypars.shiclip = 0.0
iraf.fitskypars.snreject = 50.0
iraf.fitskypars.sloreject = 3.0
iraf.fitskypars.shireject = 3.0
iraf.fitskypars.khist = 3.0
iraf.fitskypars.binsize = 0.1
iraf.fitskypars.smooth = False
iraf.fitskypars.rgrow = 0.0
iraf.fitskypars.mksky = False
# iraf.digiphot.apphot.photpars :
iraf.unlearn(iraf.photpars)
iraf.photpars.weighting = 'constant'
iraf.photpars.apertures = '2,3,4,5'
iraf.photpars.zmag = ZPT[filt]
iraf.photpars.mkapert = False
iraf.unlearn(iraf.phot)
iraf.gflush(); iraf.gflush()
iraf.flpr(); iraf.flpr(); iraf.flpr()
photparams={
'interac':False,
'radplot':False,
}
outputstuff = iraf.phot(image=imfile, skyfile='',coords=coofile, output=magfile,
verify=False, verbose=True, Stdout=1,**photparams)
sourcelist = apphotOutput( magfile )
return( sourcelist )
def plot_shifts(
ROOT_DIRECT,
ALIGN_IMAGE,
clean=True,
verbose=True,
ALIGN_EXTENSION=0,
toler=3,
skip_swarp=False,
threshold=7,
force=False,
drz=True,
WEIGHT_IMAGE=None,
):
"""
Run SExtractor on two images and match the objects to plot the shifts between them.
ALIGN_IMAGE is a string that may contain wildcards, and the function will use
`align_img_list` to find ALIGN_IMAGEs
"""
import glob
from pyraf import iraf
from iraf import stsdas, dither
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
if os.path.exists(ROOT_DIRECT + "_align.fits") & (not force):
if verbose:
print "Image %s_align.fits exists. Skipping SWarp." % (ROOT_DIRECT)
skip_swarp = True
if not skip_swarp:
if drz:
align_img_list = find_align_images_that_overlap(
ROOT_DIRECT + "_drz.fits", ALIGN_IMAGE, ALIGN_EXTENSION=ALIGN_EXTENSION
)
else:
align_img_list = glob.glob(ALIGN_IMAGE)
if not align_img_list:
print "threedhst.shifts.align_to_reference: no alignment images overlap."
return 0, 0
#
try:
os.remove(ROOT_DIRECT + "_align.fits")
except:
pass
if drz:
matchImagePixels(
input=align_img_list,
matchImage=ROOT_DIRECT + "_drz.fits",
output=ROOT_DIRECT + "_align.fits",
match_extension=1,
input_extension=ALIGN_EXTENSION,
)
ALIGN_FITS = ROOT_DIRECT + "_align.fits"
else:
ALIGN_FITS = os.path.basename(ROOT_DIRECT.split(".fits")[0]) + "_align.fits"
matchImagePixels(
input=align_img_list,
matchImage=ROOT_DIRECT,
output=ALIGN_FITS,
match_extension=0,
input_extension=ALIGN_EXTENSION,
)
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options["CHECKIMAGE_TYPE"] = "NONE"
se.options["WEIGHT_TYPE"] = "MAP_WEIGHT"
if drz:
se.options["WEIGHT_IMAGE"] = ROOT_DIRECT + "_drz.fits[1]"
else:
if WEIGHT_IMAGE:
se.options["WEIGHT_IMAGE"] = WEIGHT_IMAGE
else:
se.options["WEIGHT_TYPE"] = "NONE"
se.options["WEIGHT_IMAGE"] = "NONE"
se.options["FILTER"] = "Y"
## Detect thresholds (default = 1.5)
se.options["DETECT_THRESH"] = "%f" % (threshold)
se.options["ANALYSIS_THRESH"] = "%f" % (threshold)
se.options["MAG_ZEROPOINT"] = str(threedhst.options["MAG_ZEROPOINT"])
#### Run SExtractor on direct and alignment images
## direct image
se.options["CATALOG_NAME"] = "direct.cat"
if drz:
status = se.sextractImage(ROOT_DIRECT + "_drz.fits[0]")
INPUT_IMAGE = ROOT_DIRECT + "_drz.fits"
else:
status = se.sextractImage(ROOT_DIRECT)
INPUT_IMAGE = ROOT_DIRECT
## alignment image
se.options["CATALOG_NAME"] = "align.cat"
se.options["WEIGHT_TYPE"] = "NONE"
status = se.sextractImage(ALIGN_FITS)
## Read the catalogs
directCat = threedhst.sex.mySexCat("direct.cat")
alignCat = threedhst.sex.mySexCat("align.cat")
xshift = 0
yshift = 0
rot = 0
scale = 1.0
xrms = 2
yrms = 2
NITER = 5
IT = 0
while IT < NITER:
IT = IT + 1
#### Get x,y coordinates of detected objects
## direct image
fp = open("direct.xy", "w")
for i in range(len(directCat.X_IMAGE)):
fp.write("%s %s\n" % (directCat.X_IMAGE[i], directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open("align.xy", "w")
for i in range(len(alignCat.X_IMAGE)):
fp.write("%s %s\n" % (np.float(alignCat.X_IMAGE[i]) + xshift, np.float(alignCat.Y_IMAGE[i]) + yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler * 1.0
try:
os.remove("align.match")
except:
pass
status1 = iraf.xyxymatch(
input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2 ** pow,
separation=0,
verbose=yes,
Stdout=1,
)
while status1[-1].startswith("0"):
pow += 1
os.remove("align.match")
status1 = iraf.xyxymatch(
input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2 ** pow,
separation=0,
verbose=yes,
Stdout=1,
)
#### Images are aligned, plot the offsets
dx, dy, ax, ay, di, ai = np.loadtxt("align.match", unpack=True)
ddx, ddy = dx - ax, dy - ay
keep = (np.abs(ddx) < 15) & (np.abs(ddy) < 15)
for i in range(5):
sx, sy = threedhst.utils.biweight(ddx[keep], both=True), threedhst.utils.biweight(ddy[keep], both=True)
keep = keep & (np.abs(ddx - sx[0]) < 5 * sx[1]) & (np.abs(ddy - sy[0]) < 5 * sy[1])
if USE_PLOT_GUI:
fig = plt.figure(figsize=[8, 4], dpi=100)
else:
fig = Figure(figsize=[8, 4], dpi=100)
fig.subplots_adjust(wspace=0.28, hspace=0.0, left=0.08, bottom=0.14, right=0.98, top=0.98)
ax = fig.add_subplot(121)
ax.plot(ddx, ddy, marker="o", linestyle="None", color="black", alpha=0.4, ms=2, zorder=-1)
ax.errorbar(sx[0], sy[0], sx[1], sy[1], marker="o", ms=0.1, color="white", linewidth=3, zorder=100)
ax.errorbar(sx[0], sy[0], sx[1], sy[1], marker="o", ms=0.1, color="red", linewidth=2, zorder=500)
ax.grid(alpha=0.5)
dwin = 5 * np.max([sx[1], sy[1]])
ax.set_xlim(sx[0] - dwin, sx[0] + dwin)
ax.set_ylim(sy[0] - dwin, sy[0] + dwin)
ax.set_xlabel(r"$\Delta x$ [pix]")
ax.set_ylabel(r"$\Delta y$ [pix]")
ax.text(0.5, 0.95, os.path.basename(INPUT_IMAGE), fontsize=9, horizontalalignment="center", transform=ax.transAxes)
ax.text(0.5, 0.9, os.path.basename(ALIGN_IMAGE), fontsize=9, horizontalalignment="center", transform=ax.transAxes)
ax.text(
0.5,
0.1,
r"$\Delta x, \Delta y = %.2f \pm %.2f, %.2f \pm %.2f)$" % (sx[0], sx[1], sy[0], sy[1]),
fontsize=11,
horizontalalignment="center",
transform=ax.transAxes,
)
ax = fig.add_subplot(122)
ax.plot(dx[keep], dy[keep], marker="o", ms=1, linestyle="None", color="black", alpha=0.1)
ax.quiver(
dx[keep],
dy[keep],
ddx[keep],
ddy[keep],
alpha=0.5,
angles="xy",
headlength=1,
headwidth=1,
scale=30.0 / (dx.max() - dx.min()),
units="x",
minlength=1,
)
aa = np.array([1, 1])
ax.quiver(
dx[keep].mean() * aa,
dy[keep].max() * 0.95 * aa,
1 * aa,
0 * aa,
alpha=0.9,
angles="xy",
headlength=0,
headwidth=1,
scale=30.0 / (dx.max() - dx.min()),
units="x",
color="red",
)
ax.set_xlabel(r"$x$ [pix]")
ax.set_ylabel(r"$y$ [pix]")
if USE_PLOT_GUI:
fig.savefig(os.path.basename(ROOT_DIRECT.split(".fits")[0]) + "_align.pdf", dpi=100, transparent=False)
else:
canvas = FigureCanvasAgg(fig)
canvas.print_figure(os.path.basename(ROOT_DIRECT.split(".fits")[0]) + "_align.pdf", dpi=100, transparent=False)
if clean:
rmfiles = [
"SCI.fits",
"WHT.fits",
"align.cat",
"align.map",
"align.match",
"align.reg",
"align.xy",
"direct.cat",
"direct.reg",
"direct.xy",
"drz_sci.fits",
"drz_wht.fits",
"bg.fits",
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
def run_tweakshifts(asn_direct, verbose=False, clean=True):
"""
run_tweakshifts(asn_direct)
asn_direct - filename of ASN table of direct images [...]_asn.fits
This routine only uses dither.tweakshifts to compute the relative shifts of
the direct images
"""
from pyraf import iraf
from iraf import stsdas, dither
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
root = asn_direct.split("_asn.fits")[0] # .lower()
try:
os.remove(root + "_tweak.fits")
except:
pass
iraf.flpr()
iraf.flpr()
iraf.flpr()
if clean:
clean = iraf.yes
else:
clean = iraf.no
iraf.unlearn("tweakshifts")
status = iraf.tweakshifts(
input=asn_direct,
shiftfile="",
reference=root + "_tweak.fits",
output=root + "_shifts.txt",
findmode="catalog",
gencatalog="daofind",
sextractpars="",
undistort=yes,
computesig=yes,
idckey="idctab",
clean=clean,
verbose=no,
catfile="",
xcol=1,
ycol=2,
fluxcol=3,
fluxmax=INDEF,
fluxmin=INDEF,
fluxunits="counts",
nbright=INDEF,
refcat="",
refxcol=1,
refycol=2,
rfluxcol=3,
rfluxmax=INDEF,
rfluxmin=INDEF,
rfluxunits="counts",
refnbright=INDEF,
minobj=15,
nmatch=30,
matching="tolerance",
xyxin=INDEF,
xyyin=INDEF,
tolerance=4.0,
fwhmpsf=1.5,
sigma=0.0,
datamin=INDEF,
datamax=INDEF,
threshold=4.0,
nsigma=1.5,
fitgeometry="shift",
function="polynomial",
maxiter=3,
reject=3.0,
crossref="",
margin=50,
tapersz=50,
pad=no,
fwhm=7.0,
ellip=0.05,
pa=45.0,
fitbox=7,
Stdout=1,
)
if verbose:
for line in status:
print line
return status
def rectify(ids=None, fs=None):
iraf.cd('work')
if ids is None:
ids = np.array(glob('id2/arc*id2*.db'))
if fs is None:
fs = glob('mos/*mos*.fits')
if len(ids) == 0:
print "WARNING: No wavelength solutions for rectification."
iraf.cd('..')
return
if len(fs) == 0:
print "WARNING: No images for rectification."
iraf.cd('..')
return
# Get the grating angles of the solution files
idgas = []
for i, thisid in enumerate(ids):
f = open(thisid)
idlines = np.array(f.readlines(), dtype=str)
f.close()
idgaline = idlines[np.char.startswith(idlines, '#graang')][0]
idgas.append(float(idgaline.split('=')[1]))
ims, gas = get_scis_and_arcs(fs)
if not os.path.exists('rec'):
os.mkdir('rec')
for i, f in enumerate(ims):
fname = f.split('/')[1]
typestr = fname[:3]
ga, imgnum = gas[i], fname[-9:-5]
outfile = 'rec/' + typestr + '%05.2frec' % (ga) + imgnum + '.fits'
iraf.unlearn(iraf.specrectify)
iraf.flpr()
idfile = ids[np.array(idgas) == ga][0]
iraf.specrectify(images=f, outimages=outfile, solfile=idfile,
outpref='', function='legendre', order=3,
inttype='interp', conserve='yes', clobber='yes',
verbose='yes')
# Update the BPM to mask any blank regions
h = pyfits.open(outfile, 'update')
# Cover the chip gaps. The background task etc do better if the chip
# gaps are straight
# To deal with this we just throw away the min and max of each side of
# the curved chip gap
chipgaps = get_chipgaps(h)
# Chip 1
h[2].data[:, chipgaps[0][0]:chipgaps[0][1]] = 1
# Chip 2
h[2].data[:, chipgaps[1][0]:chipgaps[1][1]] = 1
# edge of chip 3
h[2].data[:, chipgaps[2][0]:chipgaps[2][1]] = 1
# Cover the other blank regions
h[2].data[[h[1].data == 0]] = 1
# Set all of the data to zero in the BPM
h[1].data[h[2].data == 1] = 0.0
h.flush()
h.close()
iraf.cd('..')
def align_to_reference(
ROOT_DIRECT,
ALIGN_IMAGE,
fitgeometry="shift",
clean=True,
verbose=False,
ALIGN_EXTENSION=0,
toler=3,
skip_swarp=False,
align_sdss_ds9=False,
catalog=None,
):
"""
xshift, yshift, rot, scale, xrms, yrms = align_to_reference()
"""
import os
import glob
import shutil
from pyraf import iraf
from iraf import stsdas, dither
import threedhst
from threedhst import catIO
no = iraf.no
yes = iraf.yes
INDEF = iraf.INDEF
#### Clean slate
rmfiles = [
"SCI.fits",
"WHT.fits",
"align.cat",
"direct.cat" "align.map",
"align.match",
"align.reg",
"align.xy",
"direct.reg",
"direct.xy",
"ds9_align.tsv",
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
if catalog is not None:
align_sdss_ds9 = True
#### Get only images that overlap from the ALIGN_IMAGE list
if not align_sdss_ds9:
align_img_list = find_align_images_that_overlap(
ROOT_DIRECT + "_drz.fits", ALIGN_IMAGE, ALIGN_EXTENSION=ALIGN_EXTENSION
)
if not align_img_list:
print "threedhst.shifts.align_to_reference: no alignment images overlap."
return 0, 0
#### Use swarp to combine the alignment images to the same image
#### dimensions as the direct mosaic
if (not skip_swarp) & (not align_sdss_ds9):
try:
os.remove(ROOT_DIRECT + "_align.fits")
except:
pass
matchImagePixels(
input=align_img_list,
matchImage=ROOT_DIRECT + "_drz.fits",
output=ROOT_DIRECT + "_align.fits",
match_extension=1,
input_extension=ALIGN_EXTENSION,
)
#### Run SExtractor on the direct image, with the WHT
#### extension as a weight image
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options["CHECKIMAGE_TYPE"] = "NONE"
se.options["WEIGHT_TYPE"] = "MAP_WEIGHT"
se.options["WEIGHT_IMAGE"] = "WHT.fits"
se.options["FILTER"] = "Y"
## Detect thresholds (default = 1.5)
THRESH = 10
if align_sdss_ds9:
if "Vizier" not in REFERENCE_CATALOG:
THRESH = 20
se.options["DETECT_THRESH"] = "%d" % (THRESH)
se.options["ANALYSIS_THRESH"] = "%d" % (THRESH)
se.options["MAG_ZEROPOINT"] = str(threedhst.options["MAG_ZEROPOINT"])
#### Run SExtractor on direct and alignment images
## direct image
se.options["CATALOG_NAME"] = "direct.cat"
iraf.imcopy(ROOT_DIRECT + "_drz.fits[1]", "SCI.fits")
iraf.imcopy(ROOT_DIRECT + "_drz.fits[2]", "WHT.fits")
status = se.sextractImage("SCI.fits")
## Read the catalog
directCat = threedhst.sex.mySexCat("direct.cat")
if align_sdss_ds9:
### Use ds9 SDSS catalog to refine alignment
import threedhst.dq
import pywcs
import threedhst.catIO as catIO
wcs = pywcs.WCS(pyfits.getheader("SCI.fits", 0))
# wcs = pywcs.WCS(pyfits.getheader('Q0821+3107-F140W_drz.fits', 1))
if "Vizier" in REFERENCE_CATALOG:
#### Use (unstable) astroquery Vizier search
#### CFHTLS-Deep: 'Vizier.II/317'
VIZIER_CAT = REFERENCE_CATALOG.split("Vizier.")[1]
print "Align to Vizier catalog: http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=%s" % (VIZIER_CAT)
import astroquery
if astroquery.__version__ < "0.0.dev1078":
from astroquery import vizier
query = {}
query["-source"] = VIZIER_CAT
# query["-out"] = ["_r", "CFHTLS", "rmag"]
query["-out"] = ["_RAJ2000", "_DEJ2000"] ### Just RA/Dec.
#### Center position and query radius
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1 / 2.0, wcs.naxis2 / 2.0]], 1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = (
np.sqrt((r0 - rll) ** 2 * np.cos(d0 / 360.0 * 2 * np.pi) ** 2 + (d0 - dll) ** 2) * 60.0 * 1.5
)
h = query["-c"] = "%.6f %.6f" % (r0, d0)
query["-c.rm"] = "%.3f" % (corner_radius) ### xxx check image size
#### Run the query
vt = vizier.vizquery(query)
else:
#### Newer astroquery
from astroquery.vizier import Vizier
import astropy.coordinates as coord
import astropy.units as u
Vizier.ROW_LIMIT = -1
r0, d0 = wcs.wcs_pix2sky([[wcs.naxis1 / 2.0, wcs.naxis2 / 2.0]], 1)[0]
rll, dll = wcs.wcs_pix2sky([[0, 0]], 1)[0]
corner_radius = (
np.sqrt((r0 - rll) ** 2 * np.cos(d0 / 360.0 * 2 * np.pi) ** 2 + (d0 - dll) ** 2) * 60.0 * 1.5
)
#
c = coord.ICRSCoordinates(ra=r0, dec=d0, unit=(u.deg, u.deg))
#### something with astropy.coordinates
# c.icrs.ra.degree = c.icrs.ra.degrees
# c.icrs.dec.degree = c.icrs.dec.degrees
#
vt = Vizier.query_region(c, width=u.Quantity(corner_radius, u.arcminute), catalog=[VIZIER_CAT])[0]
#### Make a region file
ra_list, dec_list = vt["RAJ2000"], vt["DEJ2000"]
print "Vizier, found %d objects." % (len(ra_list))
fp = open("%s.vizier.reg" % (ROOT_DIRECT), "w")
fp.write("# %s, r=%.1f'\nfk5\n" % (VIZIER_CAT, corner_radius))
for ra, dec in zip(ra_list, dec_list):
fp.write('circle(%.6f, %.6f, 0.5")\n' % (ra, dec))
#
fp.close()
else:
#### Use DS9 catalog
ds9 = threedhst.dq.myDS9()
ds9.set("file SCI.fits")
# ds9.set('file Q0821+3107-F140W_drz.fits')
ds9.set("catalog %s" % (REFERENCE_CATALOG))
### Can't find XPA access point for "copy to regions"
ds9.set("catalog export tsv ds9_align.tsv")
lines = open("ds9_align.tsv").readlines()
ra_list, dec_list = [], []
for line in lines[1:]:
spl = line.split()
ra, dec = float(spl[0]), float(spl[1])
ra_list.append(ra)
dec_list.append(dec)
#
del (ds9)
x_image, y_image = [], []
for ra, dec in zip(ra_list, dec_list):
x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
x_image.append(x)
y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat["X_IMAGE"] = np.array(x_image)
alignCat["Y_IMAGE"] = np.array(y_image)
else:
## alignment image
se.options["CATALOG_NAME"] = "align.cat"
status = se.sextractImage(ROOT_DIRECT + "_align.fits")
alignCat = threedhst.sex.mySexCat("align.cat")
xshift = 0
yshift = 0
rot = 0
scale = 1.0
xrms = 2
yrms = 2
NITER = 5
IT = 0
while IT < NITER:
IT = IT + 1
#### Get x,y coordinates of detected objects
## direct image
fp = open("direct.xy", "w")
for i in range(len(directCat.X_IMAGE)):
fp.write("%s %s\n" % (directCat.X_IMAGE[i], directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open("align.xy", "w")
for i in range(len(alignCat.X_IMAGE)):
fp.write("%s %s\n" % (np.float(alignCat.X_IMAGE[i]) + xshift, np.float(alignCat.Y_IMAGE[i]) + yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler * 1.0
try:
os.remove("align.match")
except:
pass
status1 = iraf.xyxymatch(
input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2 ** pow,
separation=0,
verbose=yes,
Stdout=1,
)
nmatch = 0
while status1[-1].startswith("0") | (nmatch < 10):
pow += 1
os.remove("align.match")
status1 = iraf.xyxymatch(
input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2 ** pow,
separation=0,
verbose=yes,
Stdout=1,
)
#
nmatch = 0
for line in open("align.match").xreadlines():
nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(
input="align.match",
database="align.map",
fitgeometry=fitgeometry,
interactive=no,
xmin=INDEF,
xmax=INDEF,
ymin=INDEF,
ymax=INDEF,
maxiter=10,
reject=2.0,
Stdout=1,
)
if verbose:
for line in status2:
print line
# fp = open(root+'.iraf.log','a')
# fp.writelines(status1)
# fp.writelines(status2)
# fp.close()
#### Parse geomap.output
fp = open("align.map", "r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith("xshift"):
xshift += float(spl[1])
if spl[0].startswith("yshift"):
yshift += float(spl[1])
if spl[0].startswith("xrotation"):
rot = float(spl[1])
if spl[0].startswith("xmag"):
scale = float(spl[1])
if spl[0].startswith("xrms"):
xrms = float(spl[1])
if spl[0].startswith("yrms"):
yrms = float(spl[1])
fp.close()
# os.system('wc align.match')
print "Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)" % (
IT,
xshift,
yshift,
rot,
scale,
xrms,
yrms,
)
im = pyfits.open("SCI.fits")
shutil.copy("align.map", ROOT_DIRECT + "_align.map")
shutil.copy("align.match", ROOT_DIRECT + "_align.match")
#### Cleanup
if clean:
rmfiles = [
"SCI.fits",
"WHT.fits",
"align.cat",
"align.map",
"align.match",
"align.reg",
"align.xy",
"direct.cat",
"direct.reg",
"direct.xy",
"drz_sci.fits",
"drz_wht.fits",
"bg.fits",
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
return xshift, yshift, rot, scale, xrms, yrms
def drztranback( drzfile, x=0, y=0, fltlist=None,
xylistfile='', verbose=True ):
"""
convert a set of coordinates from a drz image into
the coordinate space of each of the contributing _flt files
returns a list of tuples with (fltfile,x,y)
"""
import os
import pyfits
from pyraf import iraf
from iraf import stsdas
from numpy import loadtxt, iterable
stsdas.analysis()
stsdas.dither()
# get output (i.e. drizzled) image size
nxout = pyfits.getval( drzfile, 'NAXIS1')
nyout = pyfits.getval( drzfile, 'NAXIS2')
scaleout = 3600*( abs(pyfits.getval(drzfile,'CD1_1')) +
abs(pyfits.getval(drzfile,'CD2_2')) )
xscaleout = 7200*abs(pyfits.getval(drzfile,'CD1_1'))
yscaleout = 7200*abs(pyfits.getval(drzfile,'CD2_2'))
# if needed, get a list of contributing flt files and/or coeff files
if not fltlist :
fltlist = getfltlist( drzfile )
# build the list of coordinates, starting with the original drzfile x,y coords
returnlist = []
if xylistfile :
xlist,ylist = loadtxt( xylistfile, unpack=True )
elif iterable(x) :
xlist, ylist = x, y
else :
xlist,ylist = [float(x)],[float(y)]
for xx,yy in zip(x,y) :
returnlist.append( (drzfile,1,xx,yy) )
# write out a list of drz-frame x,y positions into a text file.
# this is used as input to wtranback for all flt files
xylistfile = util.naming.chsuffix( os.path.basename(drzfile), '_fake.xylist')
fout = open(xylistfile, 'w')
for xx,yy in zip(xlist,ylist) :
print >> fout, "%15.5f %15.5f"%(xx,yy)
fout.close()
# translate the x,y coords back to _flt coordinates
# TODO : probably need to allow for up to 2 coeff files
# for every flt file, for UVIS and ACS
# for fltfile,coefffile in zip(fltlist,coefflist) :
for fltfile in fltlist:
if verbose: print("translating %s to %s coords"%(drzfile,fltfile))
# find all the sci extensions
flthdulist = pyfits.open( fltfile )
extlist = [ i for i in range(len(flthdulist)) if flthdulist[i].name.lower().startswith('sci') ]
for ext in extlist :
nxin = pyfits.getval( fltfile, 'NAXIS1', ext=ext)
nyin = pyfits.getval( fltfile, 'NAXIS2', ext=ext)
scalein = 3600*( abs(pyfits.getval(fltfile,'CD1_1',ext=ext)) +
abs(pyfits.getval(fltfile,'CD2_2',ext=ext)) )
xscalein = 7200*abs(pyfits.getval(fltfile,'CD1_1',ext=ext))
yscalein = 7200*abs(pyfits.getval(fltfile,'CD2_2',ext=ext))
# slimmed down wtranback call from LS: 2011.04.28
#
# NOTE: we use the xylistfile as input even when
# there is only one pair of coordinates
iraf.wtranback.unlearn()
iraf.flpr(); iraf.flpr()
iraf.gflush(); iraf.gflush()
#coeffile = os.path.join(snworkdir,fltfile[:-5] + '_coeffs1.dat')
coeffile = fltfile[:-5] + '_coeffs1.dat'
output = iraf.wtranback( 0, 0, nxin=nxin, nxout=nxout,
nyin=nyin, nyout=nyout, xylist=xylistfile,
coeffs=coeffile,
geomode='wcs', refim=drzfile,
inimage=fltfile+'[%i]'%ext, Stdout=1 )
for line in output:
if line.startswith(' Xin,Yin:') :
xin,yin = map(float,line.split()[1:3])
if xin>0 and xin<nxin and yin>0 and yin<nyin :
returnlist.append( (fltfile,ext,float(xin),float(yin)) )
return( returnlist )
def runblots(self,
doblots=1,
doderivs=1,
doCRrej=1,
domedian=1,
useMedMask=1):
"""Blot drizzled images from mulitple assocations back to
the positions of the input images from the ASN table, looping
over the parlists attribute of drizzleImage object.
Input parameters are:
doblots (0/1): do the blotting? [def: 1(yes)]
doderivs (0/1): produce deriv images? [def:1]
doCRrej (0/1): do CR rejection to produce masks? [def:1]
domedian (0/1): median stack separate drizzled images for CR comp [def:1]
useMedMask(0/1): use context images as masks in median stack [def:1]
"""
# clean out the crmasks dictionary if it has junk in it
while self.crmasks:
a = self.crmasks.keys()[-1]
del self.crmasks[a]
curdir = os.getcwd()
os.chdir(self.Fits)
iraf.flpr('imcombine')
iraf.flpr('blot')
iraf.flpr('deriv')
iraf.flpr('driz_cr')
self.runNum = 0
i = 0
for parList in self.parlists:
shortparList = self.shortparlists[i]
if self.clean_up:
self.removeList = []
self.runNum += 1
self._blot_asn(shortparList, parList, doblots=doblots, doderivs=doderivs, \
doCRrej=doCRrej,domedian=domedian,useMedMask=useMedMask)
if self.clean_up:
for file in self.removeList:
try:
os.remove(file)
except:
pass
i = i + 1
iraf.flpr('imcombine')
iraf.flpr('blot')
iraf.flpr('deriv')
iraf.flpr('driz_cr')
iraf.flpr()
os.chdir(curdir)
return self.crmasks
os.mkdir(_obsdate)
os.system('mv '+img+' '+_obsdate)
'''
i = 0
refpath = ''
refpath2 = ''
datelist = os.listdir(imgdir)
datelist.sort()
for _dir in datelist:
toforget = ['flatcombine','zerocombine','ccdproc','specred.apall','identify','reidentify','specred.standard','dispcor','refspectra','response','apsum','sensfunc','calibrate']
for t in toforget:
iraf.unlearn(t)
# iraf.set(use_new_imt='no')
iraf.flpr()
i = i + 1
print 'Working on data from %s' % (_dir)
os.chdir(imgdir+_dir)
imgsort()
os.chdir('CHIP1')
os.system('cp /dark/jsamuel/agn/extinction_lasilla.dat ./')
os.system('cp /dark/jsamuel/agn/Lines_HgCdHeNeAr600.dat ./')
objdict = {}
process()
extract()
if i <= 1:
idref = ident()
# refpath = imgdir+_dir+'/CHIP1/'+idref+'.fits'
refpath = imgdir+_dir+'/CHIP1/database/id'+idref
print idref
overwrite=True,
output_verify='fix')
# if skylevel is in the header, swarp with bkg subtraction has been applyed
if 'SKYLEVEL' in head_temp:
pssl_temp = head_temp['SKYLEVEL']
# create mask image for template
mask = np.abs(data_temp) < 1e-6
fits.writeto('tempmask.fits',
mask.astype('i'),
overwrite=True)
if args.fixpix:
try:
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.fixpix)
cwd = os.getcwd()
iraf.fixpix(os.path.join(cwd, imgtarg),
os.path.join(cwd, targmask),
verbose='yes')
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix(os.path.join(cwd, imgtemp),
os.path.join(cwd, tempmask),
verbose='yes')
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.fixpix)
def extract(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('fix/*fix*.fits')
if len(fs) == 0:
print "WARNING: No fixpixed images available for extraction."
iraf.cd('..')
return
if not os.path.exists('x1d'):
os.mkdir('x1d')
print "Note: No continuum? Make nsum small (~-5) with 'line' centered on an emission line."
for f in fs:
# Get the output filename without the ".fits"
outbase = f.replace('fix', 'x1d')[:-5]
# Get the readnoise, right now assume default value of 5 but we could
# get this from the header
readnoise = 5
# If interactive open the rectified, background subtracted image in ds9
ds9display(f.replace('fix', 'bkg'))
# set dispaxis = 1 just in case
pyfits.setval(f, 'DISPAXIS', extname='SCI', value=1)
iraf.unlearn(iraf.apall)
iraf.flpr()
iraf.apall(input=f + '[SCI]', output=outbase, interactive='yes',
review='no', line='INDEF', nsum=-1000, lower=-5, upper=5,
b_function='legendre', b_order=5,
b_sample='-400:-200,200:400', b_naverage=-10, b_niterate=5,
b_low_reject=3.0, b_high_reject=3.0, nfind=1, t_nsum=15,
t_step=15, t_nlost=100, t_function='legendre', t_order=5,
t_niterate=5, t_low_reject=3.0, t_high_reject=3.0,
background='fit', weights='variance', pfit='fit1d',
clean='no', readnoise=readnoise, gain=1.0, lsigma=4.0,
usigma=4.0, mode='hl')
# Copy the CCDSUM keyword into the 1d extraction
pyfits.setval(outbase + '.fits', 'CCDSUM',
value=pyfits.getval(f, 'CCDSUM'))
# Extract the corresponding arc
arcname = glob('nrm/arc' + f.split('/')[1][3:8] + '*.fits')[0]
# set dispaxis = 1 just in case
pyfits.setval(arcname, 'DISPAXIS', extname='SCI', value=1)
iraf.unlearn(iraf.apsum)
iraf.flpr()
iraf.apsum(input=arcname + '[SCI]', output='auxext_arc',
references=f[:-5] + '[SCI]', interactive='no', find='no',
edit='no', trace='no', fittrace='no', extras='no',
review='no', background='no', mode='hl')
# copy the arc into the 5 column of the data cube
arcfs = glob('auxext_arc*.fits')
for af in arcfs:
archdu = pyfits.open(af)
scihdu = pyfits.open(outbase + '.fits', mode='update')
d = scihdu[0].data.copy()
scihdu[0].data = np.zeros((5, d.shape[1], d.shape[2]))
scihdu[0].data[:-1, :, :] = d[:, :, :]
scihdu[0].data[-1::, :] = archdu[0].data.copy()
scihdu.flush()
scihdu.close()
archdu.close()
os.remove(af)
# Add the airmass, exptime, and other keywords back into the
# extracted spectrum header
kws = ['AIRMASS','EXPTIME',
'PROPID','PROPOSER','OBSERVER','OBSERVAT','SITELAT','SITELONG',
'INSTRUME','DETSWV','RA','PM-RA','DEC','PM-DEC','EQUINOX',
'EPOCH','DATE-OBS','TIME-OBS','UTC-OBS','TIMESYS','LST-OBS',
'JD','MOONANG','OBSMODE','DETMODE','SITEELEV','BLOCKID','PA',
'TELHA','TELRA','TELDEC','TELPA','TELAZ','TELALT','DECPANGL',
'TELTEM','PAYLTEM','MASKID','MASKTYP','GR-ANGLE','GRATING',
'FILTER']
for kw in kws:
pyfits.setval(outbase + '.fits', kw, value=pyfits.getval(f,kw))
iraf.cd('..')
pyfits.writeto(temp_file0+'_tempnoise3.fits', noiseimg, output_verify='fix', clobber=True)
else:
pssl_temp = 0
print 'variance image already there, do not create noise image'
# if skylevel is in the header, swarp with bkg subtraction has been applyed
if 'SKYLEVEL' in head_temp:
pssl_temp = head_temp['SKYLEVEL']
# create mask image for template
data_temp, head_temp
mask = np.abs(data_temp) < 1e-6
pyfits.writeto(temp_file0+'_tempmask3.fits',mask.astype('i'))
if _fixpix:
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix('./'+imgtarg, './'+targmask, verbose='no')
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
iraf.fixpix('./'+imgtemp, './'+tempmask, verbose='no')
iraf.flpr(); iraf.flpr()
iraf.unlearn(iraf.fixpix)
# hotpants parameters
iuthresh = str(sat_targ) # upper valid data count, image
iucthresh = str(0.95*sat_targ) # upper valid data count for kernel, image
tuthresh = str(sat_temp) # upper valid data count, template
tucthresh = str(0.95*sat_temp) # upper valid data count for kernel, template
rkernel = str(np.median([10, 2.*max_fwhm, 20])) # convolution kernel half width
radius = str(np.median([15, 3.0*max_fwhm, 25])) # HW substamp to extract around each centroid
sconv = '-sconv' # all regions convolved in same direction (0)
def rectify(ids=None, fs=None):
iraf.cd('work')
if ids is None:
ids = np.array(glob('id2/arc*id2*.db'))
if fs is None:
fs = glob('srt/*mos*.fits')
if len(ids) == 0:
print "WARNING: No wavelength solutions for rectification."
iraf.cd('..')
return
if len(fs) == 0:
print "WARNING: No images for rectification."
iraf.cd('..')
return
# Get the grating angles of the solution files
idgas = []
for i, thisid in enumerate(ids):
f = open(thisid)
idlines = np.array(f.readlines(), dtype=str)
f.close()
idgaline = idlines[np.char.startswith(idlines, '#graang')][0]
idgas.append(float(idgaline.split('=')[1]))
ims, gas = get_scis_and_arcs(fs)
if not os.path.exists('rec'):
os.mkdir('rec')
for i, f in enumerate(ims):
fname = f.split('/')[1]
typestr = fname[:3]
ga, imgnum = gas[i], fname[-9:-5]
outfile = 'rec/' + typestr + '%05.2frec' % (ga) + imgnum + '.fits'
iraf.unlearn(iraf.specrectify)
iraf.flpr()
print('_____idgas_____')
print (np.array(idgas))
print('_____ga_____')
print (ga)
idfile = ids[np.array(idgas) == ga][0]
iraf.specrectify(images=f, outimages=outfile, solfile=idfile,
outpref='', function='legendre', order=3,
inttype='interp', conserve='yes', clobber='yes',
verbose='yes')
# Update the BPM to mask any blank regions
h = pyfits.open(outfile, 'update')
# Cover the chip gaps. The background task etc do better if the chip
# gaps are straight
# To deal with this we just throw away the min and max of each side of
# the curved chip gap
chipgaps = get_chipgaps(h)
print(" -- chipgaps --")
print(chipgaps)
# Chip 1
h[2].data[:, chipgaps[0][0]:chipgaps[0][1]] = 1
# Chip 2
h[2].data[:, chipgaps[1][0]:chipgaps[1][1]] = 1
# edge of chip 3
h[2].data[:, chipgaps[2][0]:chipgaps[2][1]] = 1
# Cover the other blank regions
h[2].data[[h[1].data == 0]] = 1
# Set all of the data to zero in the BPM
h[1].data[h[2].data == 1] = 0.0
h.flush()
h.close()
iraf.cd('..')
def drztranback(drzfile, x=0, y=0, xylistfile="", ext="SCI", verbose=False):
"""
convert a set of coordinates from a drz image into
the coordinate space of each of the contributing _flt files
returns a list of tuples with (fltfile,x,y)
"""
import os
import pyfits
from pyraf import iraf
from iraf import stsdas
from numpy import loadtxt
stsdas.analysis()
stsdas.dither()
# get output (i.e. drizzled) image size
nxout = pyfits.getval(drzfile, "NAXIS1", ext=ext)
nyout = pyfits.getval(drzfile, "NAXIS2", ext=ext)
scaleout = 3600 * (abs(pyfits.getval(drzfile, "CD1_1", ext=ext)) + abs(pyfits.getval(drzfile, "CD2_2", ext=ext)))
xscaleout = 7200 * abs(pyfits.getval(drzfile, "CD1_1", ext=ext))
yscaleout = 7200 * abs(pyfits.getval(drzfile, "CD2_2", ext=ext))
# get a list of contributing flt files
fltfilelist = getfltlist(drzfile)
scifile = drzfile[:-8] + "sci.fits"
# translate the x,y coords back to _flt coordinates
returnlist = []
if xylistfile:
xlist, ylist = loadtxt(xylistfile, unpack=True)
for x, y in zip(xlist, ylist):
returnlist.append((drzfile, x, y))
else:
returnlist.append((drzfile, x, y))
for fltfile in fltfilelist:
if verbose:
print ("translating %s to %s coords" % (drzfile, fltfile))
# import pdb; pdb.set_trace()
nxin = pyfits.getval(fltfile, "NAXIS1", ext="SCI")
nyin = pyfits.getval(fltfile, "NAXIS2", ext="SCI")
scalein = 3600 * (
abs(pyfits.getval(fltfile, "CD1_1", ext="SCI")) + abs(pyfits.getval(fltfile, "CD2_2", ext="SCI"))
)
xscalein = 7200 * abs(pyfits.getval(fltfile, "CD1_1", ext="SCI"))
yscalein = 7200 * abs(pyfits.getval(fltfile, "CD2_2", ext="SCI"))
coeffile = fltfile[:-5] + "_coeffs1.dat"
# wtranback is better than tranback
iraf.flpr()
iraf.flpr()
iraf.unlearn(iraf.wtranback)
if ext == "SCI":
extstr = "[1]"
else:
extstr = ""
output = iraf.wtranback(
x,
y,
nxin=nxin,
nxout=nxout,
nyin=nyin,
nyout=nyout,
xylist=xylistfile,
coeffs=coeffile,
geomode="wcs",
refim=drzfile + extstr,
inimage=fltfile + "[sci,1]",
raref=pyfits.getval(drzfile, "CRVAL1", ext=ext),
decref=pyfits.getval(drzfile, "CRVAL2", ext=ext),
xrefpix=pyfits.getval(drzfile, "CRPIX1", ext=ext),
yrefpix=pyfits.getval(drzfile, "CRPIX2", ext=ext),
orient=pyfits.getval(drzfile, "ORIENTAT", ext=ext),
Stdout=1,
)
for line in output:
if line.startswith(" Xin,Yin:"):
xin, yin = line.split()[1:3]
returnlist.append((fltfile, float(xin), float(yin)))
return returnlist
def get_align_to_subaru(sci='M0416_Ks_c1_mp_avg.fits', wht='M0416_Ks_c1_mp_exp.fits', field='', clean=True, toler=3, verbose=False, fitgeometry='shift', shift_max=20, rms_max=1.1, rot_max=2, rot_only=True, THRESH=2, align_data=None):
"""
Align HAWK-I images to the FF Subaru astrometric reference catalogs
"""
#sci='M0416_Ks_c1_mp_avg.fits'; wht='M0416_Ks_c1_mp_exp.fits'
### Make object catalog
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
if wht is None:
se.options['WEIGHT_TYPE'] = 'NONE'
else:
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = wht
se.options['FILTER'] = 'Y'
se.options['DETECT_THRESH'] = '%d' %(THRESH)
se.options['ANALYSIS_THRESH'] = '%d' %(THRESH)
se.options['MAG_ZEROPOINT'] = '26.0'
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
status = se.sextractImage(sci)
threedhst.sex.sexcatRegions('direct.cat', 'direct.reg', format=2)
directCat = threedhst.sex.mySexCat('direct.cat')
#### Get the X/Y coords of the reference catalog
#head = pyfits.getheader(sci, 0)
#wcs = pywcs.WCS(head)
if 'M0416' in sci:
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_macs0416-astrom_R_v1_cat.txt', unpack=True)
if ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/M0416/macs0416_f814w_radec.cat', unpack=True)
#
if 'M0717' in sci:
ra_list, dec_list, mag = np.loadtxt('subaru.radec', unpack=True)
if ('M1149' in sci) | (field == 'M1149'):
ra_list, dec_list, mag = np.loadtxt('/Users/brammer/Research/VLT/HAWKI/MACS1149/hlsp_frontier_subaru_suprimecam_macs1149-astrom_R_v1_cat.txt', unpack=True)
if 'A2744' in sci:
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_abell2744-astrom_i_v1_cat.txt', unpack=True)
if ('c1' in sci) | ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(os.getenv('HAWKI')+'/FrontierFields/HST/abell2744_f814w_radec.cat', unpack=True)
if align_data is not None:
ra_list, dec_list, mag = align_data
im = pyfits.open(sci)
print sci
sh = im[0].shape
head = im[0].header
head['CUNIT1'] = 'deg'; head['CUNIT2'] = 'deg'
wcs = pywcs.WCS(head)
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
try:
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
except:
x_image, y_image = wcs.wcs_world2pix(ra_list, dec_list, 1)
ok = (x_image > 0) & (y_image > 0) & (x_image < sh[1]) & (y_image < sh[1])
x_image, y_image = x_image[ok], y_image[ok]
fpr = open('align.reg','w')
fpr.write('image\n')
for i in range(ok.sum()): fpr.write('circle(%.6f, %.6f,0.3") # color=magenta\n' %(x_image[i], y_image[i]))
fpr.close()
# x_image, y_image = [], []
#
# for ra, dec in zip(ra_list, dec_list):
# x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
# if (x > 0) & (y > 0) & (x < sh[1]) & (y < sh[1]):
# x_image.append(x)
# y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat['X_IMAGE'] = np.array(x_image)
alignCat['Y_IMAGE'] = np.array(y_image)
xshift = 0
yshift = 0
rot = 0
scale = 1.
xrms = 2
yrms = 2
NITER = 5
IT = 0
while (IT < NITER):
IT = IT+1
#### Get x,y coordinates of detected objects
## direct image
fp = open('direct.xy','w')
for i in range(len(directCat.X_IMAGE)):
fp.write('%s %s\n' %(directCat.X_IMAGE[i],directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open('align.xy','w')
for i in range(len(alignCat.X_IMAGE)):
fp.write('%s %s\n' %(np.float(alignCat.X_IMAGE[i])+xshift,
np.float(alignCat.Y_IMAGE[i])+yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler*1.
try:
os.remove('align.match')
except:
pass
status1 = iraf.xyxymatch(input="direct.xy", reference="align.xy",
output="align.match",
tolerance=2**pow, separation=0, verbose=iraf.yes, Stdout=1)
nmatch = 0
while status1[-1].startswith('0') | (nmatch < 10) | (float(status1[-3].split()[1]) > 40):
pow+=1
os.remove('align.match')
status1 = iraf.xyxymatch(input="direct.xy", reference="align.xy",
output="align.match",
tolerance=2**pow, separation=0, verbose=iraf.yes, Stdout=1)
#
nmatch = 0
for line in open('align.match').xreadlines( ): nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(input="align.match", database="align.map",
fitgeometry=fitgeometry, interactive=iraf.no,
xmin=iraf.INDEF, xmax=iraf.INDEF, ymin=iraf.INDEF, ymax=iraf.INDEF,
maxiter = 10, reject = 2.0, Stdout=1)
if verbose:
for line in status2:
print line
#fp = open(root+'.iraf.log','a')
#fp.writelines(status1)
#fp.writelines(status2)
#fp.close()
#### Parse geomap.output
fp = open("align.map","r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
#os.system('wc align.match')
print 'Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' %(IT, xshift, yshift, rot, scale, xrms, yrms)
os.system('cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $1, ",", $2, ",4) # color=green"}\' > d.reg')
os.system('cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $3, ",", $4, ",4) # color=magenta"}\' > a.reg')
shutil.copy('align.map', sci.replace('.fits', '.align.map'))
shutil.copy('align.match', sci.replace('.fits', '.align.match'))
#### Cleanup
if clean:
rmfiles = ['align.cat', 'align.map','align.match','align.reg','align.xy', 'direct.cat','direct.reg','direct.xy']
for file in rmfiles:
try:
os.remove(file)
except:
pass
fp = open(sci.replace('.fits', '.align.info'), 'w')
fp.write('# image xshift yshift rot scale xrms yrms\n')
fp.write('%s %.3f %.3f %.4f %.4f %.3f %.3f\n' %(sci, xshift, yshift, rot, scale, xrms, yrms))
if (np.abs(xshift) > shift_max) | (np.abs(yshift) > shift_max) | (xrms > rms_max) | (yrms > rms_max):
print 'Shifts out of allowed range. Run again with increased shift_max to accept.'
#return xshift, yshift, rot, scale, xrms, yrms
## Add a small shift that should come out easily with another
## shift iteration
xshift, yshift, rot, scale, xrms, yrms = 2,2,0,1.0,-99,-99
for file in [sci, wht]:
if ('r' in fitgeometry) & rot_only:
xshift, yshift = 0, 0
#apply_offsets(file, [[xshift, yshift, rot, scale]])
from drizzlepac import updatehdr
updatehdr.updatewcs_with_shift(file, sci, wcsname='DRZWCS',
rot=rot,scale=scale,
xsh=xshift, ysh=yshift,
fit=None,
xrms=xrms, yrms = yrms,
verbose=False, force=True, sciext=0)
if '_dr' in sci:
im = pyfits.open(sci)
h = im[0].header
for i in range(h['NDRIZIM']):
flt_str = h['D%03dDATA' %(i+1)]
if 'sci,2' in flt_str:
continue
#
flt_im = flt_str.split('[')[0]
ext = int(flt_str.split('[')[1][:-1].split(',')[1])
updatehdr.updatewcs_with_shift(flt_im, sci, wcsname='GTWEAK', rot=rot, scale=scale, xsh=xshift, ysh=yshift,
fit=None, xrms=xrms, yrms = yrms, verbose=False, force=True, sciext='SCI')
# im = pyfits.open(file, mode='update')
# wcs = pywcs.WCS(im[0].header)
# wcs.rotateCD(-rot)
# wcs.wcs.cd /= scale
# #
# im[0].header['CRPIX1'] += xshift
# im[0].header['CRPIX2'] += yshift
# #
# for i in [0,1]:
# for j in [0,1]:
# im[0].header['CD%d_%d' %(i+1, j+1)] = wcs.wcs.cd[i,j]
# #
# im.flush()
return xshift, yshift, rot, scale, xrms, yrms
def _medDriz(self, parList, usemask=1):
"""median-combined separate drizzle images. there is an iraf dependant kludge
in here involving the inputString which the a string of input files for imcombine.
iraf cannot apparently handle a string over a certain size (guessing 512 char).
So, we now write out a temp file, imcombine_input, which is just a list of the
input files imcombine is use. We use the iraf idiom input = "@file" to get this
task to run. So far it seems to work.
"""
drizList = []
maskList = []
iraf.flpr('imcombine')
iraf.unlearn(iraf.imcalc)
iraf.imcalc.pixtype = 'short'
self.logfile.write("Entered _medDriz for asn number %d." %
(self.runNum))
for ii in range(len(parList)):
for jj in range(ii + 1, len(parList)):
if parList[ii]['outdata'] == parList[jj]['outdata']:
raise KeyError, "requested to median stack same images!"
drizList.append(parList[ii]['outdata'])
if usemask:
plmask = string.split(parList[ii]['outcontext'],
'.')[0] + '.pl'
try:
os.remove(plmask)
except:
pass
if self.verbose:
print 'making', plmask, 'from', parList[ii][
'outcontext'], '...'
iraf.imcalc(parList[ii]['outcontext'], plmask,
"if im1 .eq. 0 then 0 else 1")
maskList.append(plmask)
self.removeList.append(plmask)
# construct input list and add masks info to the headers
inputString = drizList[0]
# ***> NOTE! If list too big, imcombine crashes!
# ***> NOTE! define 76 as a safe maximum, or 80...
MAX_IM = 80
NumTot = len(drizList)
NumIm = min(NumTot, MAX_IM)
for ii in range(NumIm):
if ii > 0:
inputString = inputString + ',' + drizList[ii]
if usemask:
fUtil.fixHeader(drizList[ii], [('BPM', maskList[ii])])
if self.verbose:
print 'median stacking: ', inputString
if usemask: print ' with masks: ', maskList
#if that all checks out, go ahead and median
iraf.unlearn(iraf.imcombine)
# 15/Apr/2002, jpb: want to keep all the medriz's around for CR-rej debugging
outfile = 'medriz_' + str(self.runNum) + '.fits'
# self.removeList.append(outfile)
try:
os.remove(outfile)
except:
pass
# temp file for iraf input because the list might be too big.
filekludge = open("imcombine_input", "w")
newinputList = inputString.split(',')
if (NumIm != len(newinputList)):
errtxt = "ERROR! Error: NumIm != len(newinputList) in _medDriz ?!"
print errtxt
self.logfile.write(errtxt)
for item in newinputList:
filekludge.write(item + "\n")
filekludge.close()
#iraf.imcombine.input = inputString # this is what we used to do.
iraf.imcombine.input = "@imcombine_input"
iraf.imcombine.output = outfile
# iraf.imcombine.plfile = ''
iraf.imcombine.sigma = ''
iraf.imcombine.combine = 'median'
iraf.imcombine.reject = 'minmax'
iraf.imcombine.outtype = 'real'
iraf.imcombine.offsets = 'none'
if usemask:
iraf.imcombine.masktype = 'badvalue'
else:
iraf.imcombine.masktype = 'none'
iraf.imcombine.maskvalue = 0.
iraf.imcombine.scale = 'exposure'
iraf.imcombine.expname = 'EXPTIME'
iraf.imcombine.nkeep = 1
# paradoxically, this is not what we want
# NumIm = len(drizList)/self.imNsci
# imcombine considers the total number of images being
# stacked, not the number at any given point!
# NOTE: nhigh must be >= NumIm/2 if cr rejection to be done everywhere
if NumIm == 1: #1,2
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 0
elif NumIm == 2: # 2
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 1
elif NumIm == 3: # 3
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 2
elif NumIm == 4: # 4 HRC; 2 WFC
iraf.imcombine.nlow = 0
iraf.imcombine.nhigh = 3
elif NumIm == 5: # 5 HRC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 3
elif NumIm == 6: # 6 HRC; 3 WFC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 4
elif NumIm == 7: # 7 HRC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 5
elif NumIm < 10: # 8,9 HRC; 4 WFC
iraf.imcombine.nlow = 1
iraf.imcombine.nhigh = 6
elif NumIm < 12: # 10,11 HRC; 5 WFC
iraf.imcombine.nlow = 2
iraf.imcombine.nhigh = 7
# next added/changed
else:
iraf.imcombine.nlow = (NumIm + 2) / 4 - 1
iraf.imcombine.nhigh = 3 * NumIm / 4
iraf.imcombine.mode = 'h'
self.logfile.write(self.modName+' calling imcombine. come in imcombine. NumIm/nlo/nhi: '+\
str(NumIm)+' '+str(iraf.imcombine.nlow)+' '+str(iraf.imcombine.nhigh)+\
(' [NumTot=%d]'%(NumTot)))
iraf.imcombine()
if self.verbose:
print 'NumIm = %d nlow,high: %d %d' % (
NumIm, iraf.imcombine.nlow, iraf.imcombine.nhigh)
print 'median image', outfile, 'created'
self.logfile.write('median image ' + outfile +
' created. Removing imcombine_input temp file.')
print "removing imcombine_input temp file."
try:
os.remove("imcombine_input")
except:
pass
return outfile
def get_align_to_subaru(sci='M0416_Ks_c1_mp_avg.fits',
wht='M0416_Ks_c1_mp_exp.fits',
field='',
clean=True,
toler=3,
verbose=False,
fitgeometry='shift',
shift_max=20,
rms_max=1.1,
rot_max=2,
rot_only=True,
THRESH=2,
align_data=None):
"""
Align HAWK-I images to the FF Subaru astrometric reference catalogs
"""
#sci='M0416_Ks_c1_mp_avg.fits'; wht='M0416_Ks_c1_mp_exp.fits'
### Make object catalog
se = threedhst.sex.SExtractor()
se.aXeParams()
se.copyConvFile()
se.overwrite = True
se.options['CHECKIMAGE_TYPE'] = 'NONE'
if wht is None:
se.options['WEIGHT_TYPE'] = 'NONE'
else:
se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'
se.options['WEIGHT_IMAGE'] = wht
se.options['FILTER'] = 'Y'
se.options['DETECT_THRESH'] = '%d' % (THRESH)
se.options['ANALYSIS_THRESH'] = '%d' % (THRESH)
se.options['MAG_ZEROPOINT'] = '26.0'
#### Run SExtractor on direct and alignment images
## direct image
se.options['CATALOG_NAME'] = 'direct.cat'
status = se.sextractImage(sci)
threedhst.sex.sexcatRegions('direct.cat', 'direct.reg', format=2)
directCat = threedhst.sex.mySexCat('direct.cat')
#### Get the X/Y coords of the reference catalog
#head = pyfits.getheader(sci, 0)
#wcs = pywcs.WCS(head)
if 'M0416' in sci:
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_macs0416-astrom_R_v1_cat.txt',
unpack=True)
if ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/M0416/macs0416_f814w_radec.cat',
unpack=True)
#
if 'M0717' in sci:
ra_list, dec_list, mag = np.loadtxt('subaru.radec', unpack=True)
if ('M1149' in sci) | (field == 'M1149'):
ra_list, dec_list, mag = np.loadtxt(
'/Users/brammer/Research/VLT/HAWKI/MACS1149/hlsp_frontier_subaru_suprimecam_macs1149-astrom_R_v1_cat.txt',
unpack=True)
if 'A2744' in sci:
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/hlsp_frontier_subaru_suprimecam_abell2744-astrom_i_v1_cat.txt',
unpack=True)
if ('c1' in sci) | ('c4' in sci):
ra_list, dec_list, mag = np.loadtxt(
os.getenv('HAWKI') +
'/FrontierFields/HST/abell2744_f814w_radec.cat',
unpack=True)
if align_data is not None:
ra_list, dec_list, mag = align_data
im = pyfits.open(sci)
print sci
sh = im[0].shape
head = im[0].header
head['CUNIT1'] = 'deg'
head['CUNIT2'] = 'deg'
wcs = pywcs.WCS(head)
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
try:
x_image, y_image = wcs.wcs_sky2pix(ra_list, dec_list, 1)
except:
x_image, y_image = wcs.wcs_world2pix(ra_list, dec_list, 1)
ok = (x_image > 0) & (y_image > 0) & (x_image < sh[1]) & (y_image < sh[1])
x_image, y_image = x_image[ok], y_image[ok]
fpr = open('align.reg', 'w')
fpr.write('image\n')
for i in range(ok.sum()):
fpr.write('circle(%.6f, %.6f,0.3") # color=magenta\n' %
(x_image[i], y_image[i]))
fpr.close()
# x_image, y_image = [], []
#
# for ra, dec in zip(ra_list, dec_list):
# x, y = wcs.wcs_sky2pix([[ra, dec]], 1)[0]
# if (x > 0) & (y > 0) & (x < sh[1]) & (y < sh[1]):
# x_image.append(x)
# y_image.append(y)
alignCat = catIO.EmptyCat()
alignCat['X_IMAGE'] = np.array(x_image)
alignCat['Y_IMAGE'] = np.array(y_image)
xshift = 0
yshift = 0
rot = 0
scale = 1.
xrms = 2
yrms = 2
NITER = 5
IT = 0
while (IT < NITER):
IT = IT + 1
#### Get x,y coordinates of detected objects
## direct image
fp = open('direct.xy', 'w')
for i in range(len(directCat.X_IMAGE)):
fp.write('%s %s\n' % (directCat.X_IMAGE[i], directCat.Y_IMAGE[i]))
fp.close()
## alignment image
fp = open('align.xy', 'w')
for i in range(len(alignCat.X_IMAGE)):
fp.write('%s %s\n' % (np.float(alignCat.X_IMAGE[i]) + xshift,
np.float(alignCat.Y_IMAGE[i]) + yshift))
fp.close()
iraf.flpr()
iraf.flpr()
iraf.flpr()
#### iraf.xyxymatch to find matches between the two catalogs
pow = toler * 1.
try:
os.remove('align.match')
except:
pass
status1 = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**pow,
separation=0,
verbose=iraf.yes,
Stdout=1)
nmatch = 0
while status1[-1].startswith('0') | (nmatch < 10) | (float(
status1[-3].split()[1]) > 40):
pow += 1
os.remove('align.match')
status1 = iraf.xyxymatch(input="direct.xy",
reference="align.xy",
output="align.match",
tolerance=2**pow,
separation=0,
verbose=iraf.yes,
Stdout=1)
#
nmatch = 0
for line in open('align.match').xreadlines():
nmatch += 1
if verbose:
for line in status1:
print line
#### Compute shifts with iraf.geomap
iraf.flpr()
iraf.flpr()
iraf.flpr()
try:
os.remove("align.map")
except:
pass
status2 = iraf.geomap(input="align.match",
database="align.map",
fitgeometry=fitgeometry,
interactive=iraf.no,
xmin=iraf.INDEF,
xmax=iraf.INDEF,
ymin=iraf.INDEF,
ymax=iraf.INDEF,
maxiter=10,
reject=2.0,
Stdout=1)
if verbose:
for line in status2:
print line
#fp = open(root+'.iraf.log','a')
#fp.writelines(status1)
#fp.writelines(status2)
#fp.close()
#### Parse geomap.output
fp = open("align.map", "r")
for line in fp.readlines():
spl = line.split()
if spl[0].startswith('xshift'):
xshift += float(spl[1])
if spl[0].startswith('yshift'):
yshift += float(spl[1])
if spl[0].startswith('xrotation'):
rot = float(spl[1])
if spl[0].startswith('xmag'):
scale = float(spl[1])
if spl[0].startswith('xrms'):
xrms = float(spl[1])
if spl[0].startswith('yrms'):
yrms = float(spl[1])
fp.close()
#os.system('wc align.match')
print 'Shift iteration #%d, xshift=%f, yshift=%f, rot=%f, scl=%f (rms: %5.2f,%5.2f)' % (
IT, xshift, yshift, rot, scale, xrms, yrms)
os.system(
'cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $1, ",", $2, ",4) # color=green"}\' > d.reg'
)
os.system(
'cat align.match | grep -v "\#" | grep [0-9] | awk \'{print "circle(", $3, ",", $4, ",4) # color=magenta"}\' > a.reg'
)
shutil.copy('align.map', sci.replace('.fits', '.align.map'))
shutil.copy('align.match', sci.replace('.fits', '.align.match'))
#### Cleanup
if clean:
rmfiles = [
'align.cat', 'align.map', 'align.match', 'align.reg', 'align.xy',
'direct.cat', 'direct.reg', 'direct.xy'
]
for file in rmfiles:
try:
os.remove(file)
except:
pass
fp = open(sci.replace('.fits', '.align.info'), 'w')
fp.write('# image xshift yshift rot scale xrms yrms\n')
fp.write('%s %.3f %.3f %.4f %.4f %.3f %.3f\n' %
(sci, xshift, yshift, rot, scale, xrms, yrms))
if (np.abs(xshift) > shift_max) | (np.abs(yshift) > shift_max) | (
xrms > rms_max) | (yrms > rms_max):
print 'Shifts out of allowed range. Run again with increased shift_max to accept.'
#return xshift, yshift, rot, scale, xrms, yrms
## Add a small shift that should come out easily with another
## shift iteration
xshift, yshift, rot, scale, xrms, yrms = 2, 2, 0, 1.0, -99, -99
for file in [sci, wht]:
if ('r' in fitgeometry) & rot_only:
xshift, yshift = 0, 0
#apply_offsets(file, [[xshift, yshift, rot, scale]])
from drizzlepac import updatehdr
updatehdr.updatewcs_with_shift(file,
sci,
wcsname='DRZWCS',
rot=rot,
scale=scale,
xsh=xshift,
ysh=yshift,
fit=None,
xrms=xrms,
yrms=yrms,
verbose=False,
force=True,
sciext=0)
if '_dr' in sci:
im = pyfits.open(sci)
h = im[0].header
for i in range(h['NDRIZIM']):
flt_str = h['D%03dDATA' % (i + 1)]
if 'sci,2' in flt_str:
continue
#
flt_im = flt_str.split('[')[0]
ext = int(flt_str.split('[')[1][:-1].split(',')[1])
updatehdr.updatewcs_with_shift(flt_im,
sci,
wcsname='GTWEAK',
rot=rot,
scale=scale,
xsh=xshift,
ysh=yshift,
fit=None,
xrms=xrms,
yrms=yrms,
verbose=False,
force=True,
sciext='SCI')
# im = pyfits.open(file, mode='update')
# wcs = pywcs.WCS(im[0].header)
# wcs.rotateCD(-rot)
# wcs.wcs.cd /= scale
# #
# im[0].header['CRPIX1'] += xshift
# im[0].header['CRPIX2'] += yshift
# #
# for i in [0,1]:
# for j in [0,1]:
# im[0].header['CD%d_%d' %(i+1, j+1)] = wcs.wcs.cd[i,j]
# #
# im.flush()
return xshift, yshift, rot, scale, xrms, yrms
def extract(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('fix/*fix*.fits')
if len(fs) == 0:
print "WARNING: No fixpixed images available for extraction."
iraf.cd('..')
return
if not os.path.exists('x1d'):
os.mkdir('x1d')
print "Note: No continuum? Make nsum small (~-5) with 'line' centered on an emission line."
for f in fs:
# Get the output filename without the ".fits"
outbase = f.replace('fix', 'x1d')[:-5]
# Get the readnoise, right now assume default value of 5 but we could
# get this from the header
readnoise = 5
# If interactive open the rectified, background subtracted image in ds9
ds9display(f.replace('fix', 'bkg'))
# set dispaxis = 1 just in case
pyfits.setval(f, 'DISPAXIS', extname='SCI', value=1)
iraf.unlearn(iraf.apall)
iraf.flpr()
iraf.apall(input=f + '[SCI]', output=outbase, interactive='yes',
review='no', line='INDEF', nsum=-1000, lower=-5, upper=5,
b_function='legendre', b_order=5,
b_sample='-200:-100,100:200', b_naverage=-10, b_niterate=5,
b_low_reject=3.0, b_high_reject=3.0, nfind=1, t_nsum=15,
t_step=15, t_nlost=200, t_function='legendre', t_order=5,
t_niterate=5, t_low_reject=3.0, t_high_reject=3.0,
background='fit', weights='variance', pfit='fit2d',
clean='no', readnoise=readnoise, gain=1.0, lsigma=4.0,
usigma=4.0, mode='hl')
# Copy the CCDSUM keyword into the 1d extraction
pyfits.setval(outbase + '.fits', 'CCDSUM',
value=pyfits.getval(f, 'CCDSUM'))
# Extract the corresponding arc
arcname = glob('nrm/arc' + f.split('/')[1][3:8] + '*.fits')[0]
# set dispaxis = 1 just in case
pyfits.setval(arcname, 'DISPAXIS', extname='SCI', value=1)
iraf.unlearn(iraf.apsum)
iraf.flpr()
iraf.apsum(input=arcname + '[SCI]', output='auxext_arc',
references=f[:-5] + '[SCI]', interactive='no', find='no',
edit='no', trace='no', fittrace='no', extras='no',
review='no', background='no', mode='hl')
# copy the arc into the 5 column of the data cube
arcfs = glob('auxext_arc*.fits')
for af in arcfs:
archdu = pyfits.open(af)
scihdu = pyfits.open(outbase + '.fits', mode='update')
d = scihdu[0].data.copy()
scihdu[0].data = np.zeros((5, d.shape[1], d.shape[2]))
scihdu[0].data[:-1, :, :] = d[:, :, :]
scihdu[0].data[-1::, :] = archdu[0].data.copy()
scihdu.flush()
scihdu.close()
archdu.close()
os.remove(af)
# Add the airmass, exptime, and other keywords back into the
# extracted spectrum header
kws = ['AIRMASS','EXPTIME',
'PROPID','PROPOSER','OBSERVER','OBSERVAT','SITELAT','SITELONG',
'INSTRUME','DETSWV','RA','PM-RA','DEC','PM-DEC','EQUINOX',
'EPOCH','DATE-OBS','TIME-OBS','UTC-OBS','TIMESYS','LST-OBS',
'JD','MOONANG','OBSMODE','DETMODE','SITEELEV','BLOCKID','PA',
'TELHA','TELRA','TELDEC','TELPA','TELAZ','TELALT','DECPANGL',
'TELTEM','PAYLTEM','MASKID','MASKTYP','GR-ANGLE','GRATING',
'FILTER']
for kw in kws:
pyfits.setval(outbase + '.fits', kw, value=pyfits.getval(f,kw))
iraf.cd('..')
