def makenoiselessimage(self, band, galfile, magz, psffile, save=0, gain=1.0):
"""
Creates a noiseless convolved image
"""
assert os.path.exists(psffile), "PSF image %s does not exist." % psffile
broot = self.root + '_%s' % band
outfile = broot + '_sim.fits'
outfile_nonoise = broot + '_sim_noiseless.fits'
# print outfile,xmax,ymax,galfile,magz,gain
iraf.unlearn('artdata')
iraf.unlearn('mkobjects')
iraf.artdata.dynrange=1.e5
print "Running iraf.mkobject for %s..." % band
iraf.mkobjects(outfile_nonoise, output="", title="", ncols=self.xmax,
nlines=self.ymax, header="", background=0.0, objects=galfile,
xoffset=0., yoffset=0., star="gaussian", radius=0.1,
beta=2.5, ar=1., pa=0., distance=1., exptime=1.,
magzero=magz, gain=gain, rdnoise=0., poisson=0,
seed=2, comments=1)
print "Convolving with PSF..."
if _hconvolve:
imhconvolve(outfile_nonoise, psffile, outfile, overwrite=True)
else:
print "No hconvolve..."
outimage = pyfits.getdata(outfile_nonoise)
psfimage = pyfits.getdata(psffile)
outimage2 = fftconvolve(outimage, psfimage, mode='same')
h = pyfits.open(outfile, mode='update')
h[0].data = outimage2
h.flush()
h.close()
self.noiselessimages[band] = outfile
os.remove(outfile_nonoise)
def _make_const_image(self, value):
"""
Creates a constant image
"""
# unlearn some iraf tasks
iraf.unlearn('mkpattern')
# get a random filename
tmpfile1 = get_random_filename('t', '.fits')
iraf.mkpattern(input=tmpfile1,
output="",
pattern="constant",
option="replace",
v1=value,
v2=0.0,
size=1,
title="",
pixtype="real",
ndim=2,
ncols=self.dimension[1],
nlines=self.dimension[0],
n3=1,
n4=1,
n5=1,
n6=1,
n7=1,
header="")
return tmpfile1
def trim_img(img,x1,x2,y1,y2):
"""
Trim a stacked image based on the coordinates given. The image is trimmed
using ``imcopy`` through pyraf, so the x and y pixel ranges should be given
in the correct ``imcopy`` format. ``[x1:x2,y1:y2]``
Parameters
---------
img : str
String containing name of the image currently in use
x1 : int
Pixel coordinate of x1
x2 : int
Pixel coordinate of x2
y1 : int
Pixel coordinate of y1
y2 : int
Pixel coordinate of y2
Returns
-------
img : str
The new image is given the extension ``.trim.fits``.
"""
x1,x2 = x1,x2
y1,y2 = y1,y2
input = img.nofits()+'['+repr(x1)+':'+repr(x2)+','+repr(y1)+':'+repr(y2)+']'
output = img.nofits()+'.trim.fits'
if not os.path.isfile(output):
print 'Trimming image: ' ,img
iraf.unlearn(iraf.imcopy)
iraf.imcopy(input = input,output = output,verbose='no',mode='h')
def mkbiasINTWFC(filelist, type='median'):
"""
Creates a master bias from given list of bias frames.
Saves each extension to a different FITS file.
Reads readnoise and gain from the FITS header, as each WFC chip has
different values.
:note: This function has been written specifically for INT WFC.
:param filelist:
:type filelist:
:param type: combining parameter (default = median)
:type type: string
:return: None
"""
input1 = ''
for line in filelist:
input1 += line[0] + '[1],'
input2 = input1.replace('[1]', '[2]')
input3 = input1.replace('[1]', '[3]')
input4 = input1.replace('[1]', '[4]')
inputs = [input1, input2, input3, input4]
#note there are four SCI extensions
for ext, input in enumerate(inputs):
iraf.unlearn('zerocombine')
iraf.zerocombine(input=input[:-1],
output='BIAS%i.fits' % (ext+1),
combine=type,
rdnoise='READNOIS',
gain='GAIN')
def execute(self):
"""Execute pyraf task: gmosaic"""
log.debug("GmosaicETI.execute()")
# Populate object lists
xcldict = copy(self.clparam_dict)
for fil in self.file_objs:
xcldict.update(fil.get_parameter())
for par in self.param_objs:
xcldict.update(par.get_parameter())
iraf.unlearn(iraf.gmos.gmosaic)
# Use setParam to list the parameters in the logfile
for par in xcldict:
#Stderr and Stdout are not recognized by setParam
if par != "Stderr" and par != "Stdout":
gemini.gmos.gmosaic.setParam(par, xcldict[par])
log.fullinfo("\nGMOSAIC PARAMETERS:\n")
iraf.lpar(iraf.gmos.gmosaic, Stderr=xcldict["Stderr"], \
Stdout=xcldict["Stdout"])
# Execute the task using the same dict as setParam
# (but this time with Stderr and Stdout)
#from pprint import pprint
#pprint(xcldict)
try:
gemini.gmos.gmosaic(**xcldict)
except:
# catch hard crash
raise RuntimeError("The IRAF task gmos.gmosaic failed")
if gemini.gmos.gmosaic.status:
# catch graceful exit upon error
raise RuntimeError("The IRAF task gmos.gmosaic failed")
else:
log.fullinfo("The IRAF task gmos.gmosaic completed successfully")
def execute(self):
"""Execute pyraf task: gsappwave"""
log.debug("GsappwaveETI.execute()")
# Populate object lists
xcldict = copy(self.clparam_dict)
for fil in self.file_objs:
xcldict.update(fil.get_parameter())
for par in self.param_objs:
xcldict.update(par.get_parameter())
iraf.unlearn(iraf.gmos.gsappwave)
# Use setParam to list the parameters in the logfile
for par in xcldict:
# Stderr and Stdout are not recognized by setParam
if par != "Stderr" and par != "Stdout":
gemini.gmos.gsappwave.setParam(par, xcldict[par])
log.fullinfo("\nGSAPPWAVE PARAMETERS:\n")
iraf.lpar(iraf.gmos.gsappwave, Stderr=xcldict["Stderr"], Stdout=xcldict["Stdout"])
# Execute the task using the same dict as setParam
# (but this time with Stderr and Stdout)
# from pprint import pprint
# pprint(xcldict)
gemini.gmos.gsappwave(**xcldict)
if gemini.gmos.gsappwave.status:
raise Errors.OutputError("The IRAF task gmos.gsappwave failed")
else:
log.fullinfo("The IRAF task gmos.gsappwave completed successfully")
def bootstrapZP(image,cat='zp.cat',refcat='ref.cat',refzp=1.0):
(ra,dec,mag,star)=np.loadtxt(cat,usecols=(0,1,2,3),unpack=True,
dtype=np.dtype([('ra','<f10'),('dec','<f10'),('mag','<f10'),
('star','<f10')]))
(rra,rdec,rmag,rstar)=np.loadtxt(refcat,usecols=(0,1,2,3),unpack=True,
dtype=np.dtype([('rra','<f10'),('rdec','<f10'),('rmag','<f10'),
('rstar','<f10')]))
#Grab only stars from the reference catalog
refgood=np.where((rstar >= 0.98) & (rmag != 99.0) & (rmag > 17.0) & (rmag < 22.5))
refcat=SkyCoord(ra=rra[refgood]*u.degree,dec=rdec[refgood]*u.degree)
#Sort through and remove anything that is not a star and is not isolated
#from other sources in the input catalog
catgood=np.where((star >= 0.98) & (mag != 99.0))
cat=SkyCoord(ra=ra[catgood]*u.degree,dec=dec[catgood]*u.degree)
idx,d2d,_=cat.match_to_catalog_sky(refcat)
_,d2d2,_=cat.match_to_catalog_sky(cat,2)
final=np.where((d2d.arcsec < 0.5) & (d2d2.arcsec >= 5.0))
diff=rmag[refgood][idx][final]-mag[catgood][final]
imgZP=np.mean(diff)
print '\n\tUsing '+str(len(diff))+' stars to calculate ZP...'
print '\tMean ZP: '+str(round(imgZP,3))+' mag\n'
scaleFactor=10.0**(0.4*(refzp-imgZP))
iraf.unlearn('imcalc')
iraf.imcalc(image,image[:-5]+'_scaled.fits','im1*'+str(scaleFactor))
def _compute_err_ext(self, sci_ext):
"""
Compute the error image for a science image
For a image in [e], the module computes the associated
error image assuming a simple noise model with photon shot noise
and readout noise.
@param sci_ext: the input image
@type sci_extsci_ext: string
"""
# unlearn some iraf tasks
iraf.unlearn('imexpr')
# get a random filename
tmpfile1 = get_random_filename('t', '.fits')
# compute the error array
expression = "sqrt(a + b*b)"
iraf.imexpr(expr=expression,
output=tmpfile1,
a=sci_ext,
b=self.rdnoise,
Stdout=1)
return tmpfile1
def wavelength_solution(WORK_DIR, skip_exist=False):
print '\n + Finding the wavelength solution\n'
# calibrate wavelength
# This step will require you to calibrate first arc by hand. The rest will be done automatically.
# after l (automatic find other lines) change x and y order to 4
# x - choose plots of the fit (per order, pixel, ...)
iraf.unlearn('ecreidentify')
iraf.unlearn('ecidentify')
if observations[WORK_DIR]['REF_ARC']:
try:
print 'shutil.copy'
shutil.copy(observations[WORK_DIR]['REF_ARC'], 'database/' + observations[WORK_DIR]['REF_ARC'].split('/')[-1])
except:
pass
for cal in observations[WORK_DIR]['calibs']:
print 'iraf.ecreident'
iraf.ecreident(images=cal+'.ec', referenc=observations[WORK_DIR]['REF_ARC'].replace('/ec', '/').split('/')[-1], refit='yes', shift=0, cradius=5, thresho=10)
pass
for cal in observations[WORK_DIR]['calibs']:
print 'iraf.ecident'
iraf.ecident(images=cal+'.ec', coordli='linelists$thar.dat', match=1, maxfeat=1800, ftype='emission', fwidth=4, cradius=5, thresho=2, minsep=2, functio='chebyshev', xorder=3, yorder=3, niterat=5, lowreje=3, highreje=3, autowri='yes')
def makedark(files, output):
"""
Make dark image for NIRC2 data. Makes a calib/ directory
and stores all output there. All output and temporary files
will be created in a darks/ subdirectory.
files: integer list of the files. Does not require padded zeros.
output: output file name. Include the .fits extension.
"""
redDir = os.getcwd() + '/' # Reduce directory.
curDir = redDir + 'calib/'
darkDir = util.trimdir(curDir + 'darks/')
rawDir = util.trimdir(os.path.abspath(redDir + '../raw') + '/')
util.mkdir(curDir)
util.mkdir(darkDir)
_out = darkDir + output
_outlis = darkDir + 'dark.lis'
util.rmall([_out, _outlis])
darks = [rawDir + 'n' + str(i).zfill(4) + '.fits' for i in files]
f_on = open(_outlis, 'w')
f_on.write('\n'.join(darks) + '\n')
f_on.close()
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'sigclip'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine('@' + _outlis, _out)
def setup_drizzle(imgsize):
"""Setup drizzle parameters for NIRC2 data.
@param imgsize: The size (in pixels) of the final drizzle image.
This assumes that the image will be square.
@type imgsize: int
@param type: str
"""
print 'Setting up drizzle'
# Setup the drizzle parameters we will use
ir.module.load('stsdas', doprint=0, hush=1)
ir.module.load('analysis', doprint=0, hush=1)
ir.module.load('dither', doprint=0, hush=1)
ir.unlearn('drizzle')
ir.drizzle.outweig = ''
ir.drizzle.in_mask = ''
ir.drizzle.wt_scl = 1
ir.drizzle.outnx = imgsize
ir.drizzle.outny = imgsize
ir.drizzle.pixfrac = 1
ir.drizzle.kernel = 'lanczos3'
ir.drizzle.scale = 1
ir.drizzle.coeffs = distCoef
ir.drizzle.xgeoim = distXgeoim
ir.drizzle.ygeoim = distYgeoim
ir.drizzle.shft_un = 'input'
ir.drizzle.shft_fr = 'output'
ir.drizzle.align = 'center'
ir.drizzle.expkey = 'ITIME'
ir.drizzle.in_un = 'counts'
ir.drizzle.out_un = 'counts'
def rotate_2005_lgsao():
# Need to rotate an image.
ir.unlearn('rotate')
ir.rotate.boundary = 'constant'
ir.rotate.constant = 0
ir.rotate.interpolant = 'spline3'
ir.rotate.ncols = 1040
ir.rotate.nlines = 1040
ir.rotate.xin = 528
ir.rotate.xout = 540
ir.rotate.yin = 645
ir.rotate.yout = 410
ir.rotate(workdir + 'mag05jullgs_kp.fits', workdir + 'mag05jullgs_kp_rot.fits', 190)
ir.rotate.xin = 535
ir.rotate.xout = 540
ir.rotate.yin = 655
ir.rotate.yout = 410
ir.rotate(workdir + 'mag05jullgs_h.fits', workdir + 'mag05jullgs_h_rot.fits', 190)
ir.rotate.xin = 572
ir.rotate.xout = 540
ir.rotate.yin = 694
ir.rotate.yout = 410
ir.rotate(workdir + 'mag05jullgs_lp.fits', workdir + 'mag05jullgs_lp_rot.fits', 190)
ir.rotate.xin = 483
ir.rotate.xout = 483
ir.rotate.yin = 612
ir.rotate.yout = 612
ir.rotate(workdir + 'mag04jul.fits', workdir + 'mag04jul_rot.fits', 1)
def stack_images(refimg):
from astropy.io import fits
from pyraf import iraf
print refimg
fitsref = fits.open(refimg)
hduref = fitsref[0]
objname = hduref.header['object']
filter_name = hduref.header['filter']
sky_med = odi.find_new_bg(refimg, filter_name)
# sky_med = hduref.header['skybg']
output = objname+'_'+filter_name+'.fits'
output_bpm = objname+'_'+filter_name+'_bpm.pl'
iraf.unlearn(iraf.immatch.imcombine, iraf.imutil.imarith)
iraf.immatch.imcombine(odi.scaledpath+'*'+filter_name+'*.fits', 'temp', combine='average', reject='none', offsets='wcs', masktype='goodvalue', maskval=0, blank=-999, scale='none', zero='none', lthresh=-900, hthresh=60000)
# iraf.imutil.imarith.setParam('operand1','temp')
# iraf.imutil.imarith.setParam('op','+')
# iraf.imutil.imarith.setParam('operand2',sky_med)
# iraf.imutil.imarith.setParam('result',output)
# iraf.imutil.imarith.setParam('verbose','yes')
# iraf.imutil.imarith(mode='h')
iraf.imutil.imexpr('(a != -999) ? a + b : -999',output,'temp.fits',sky_med)
iraf.imutil.imexpr('a < 0',output_bpm, output)
iraf.imutil.imdelete('temp', verify='no')
iraf.unlearn(iraf.imutil.hedit)
iraf.imutil.hedit.setParam('images',output)
iraf.imutil.hedit.setParam('fields','BPM')
iraf.imutil.hedit.setParam('value',output_bpm)
iraf.imutil.hedit.setParam('add','yes')
iraf.imutil.hedit.setParam('addonly','no')
iraf.imutil.hedit.setParam('verify','no')
iraf.imutil.hedit.setParam('update','yes')
iraf.imutil.hedit(show='no', mode='h')
# iraf.immatch.imcombine(reprojpath+'*.fits', 'test', expm='exp.pl', combine='average', reject='none', offsets='wcs', masktype='goodvalue', maskval=0, blank=-999, scale='none', zero='none', lthresh=-900, hthresh=60000)
return output
def reproject_ota(img, ota, rad, decd):
from pyraf import iraf
image = odi.illcorpath+'illcor_'+ota+'.'+str(img[16:])
imout = odi.reprojpath+'reproj_'+ota+'.'+str(img[16:])
iraf.mscred(_doprint=0)
iraf.clobber='no'
iraf.unlearn(iraf.mscred.mscimage)
iraf.mscred.mscimage.format='image'
iraf.mscred.mscimage.pixmask='yes'
iraf.mscred.mscimage.verbose='yes'
iraf.mscred.mscimage.wcssour='parameters'
iraf.mscred.mscimage.ref=''
iraf.mscred.mscimage.ra=rad
iraf.mscred.mscimage.dec=decd
iraf.mscred.mscimage.scale=0.11
iraf.mscred.mscimage.rotation=0.0
iraf.mscred.mscimage.blank=-999
iraf.mscred.mscimage.interpo='poly5'
iraf.mscred.mscimage.minterp='poly5'
iraf.mscred.mscimage.nxbl=4096
iraf.mscred.mscimage.nybl=4096
iraf.mscred.mscimage.fluxcon='yes'
iraf.mscred.mscimage(image,imout)
return
def do_axecore(filters,
basepath,
back='yes',
extrfwhm=4.0,
drzfwhm=0.0,
backfwhm=10.0,
orient='no',
slitless_geom='no',
cont_model='gauss',
sampling='drizzle',
np=5,
interp=1):
olddir = os.getcwd()
os.chdir(basepath)
for filter in filters:
if filter[0].lower() == 'g': #grism, continue
listfile = os.path.join(basepath, filter + '.lis')
conffile = "WFC3.IR." + filter + ".V2.5.conf"
iraf.unlearn('axecore')
iraf.axecore(listfile,
conffile,
extrfwhm=extrfwhm,
drzfwhm=drzfwhm,
back=back,
backfwhm=backfwhm,
orient=orient,
slitless_geom=slitless_geom,
cont_model=cont_model,
sampling=sampling,
np=np,
interp=interp)
os.chdir(olddir)
def trim_img(img, x1, x2, y1, y2):
"""
Trim a stacked image based on the coordinates given. The image is trimmed
using ``imcopy`` through pyraf, so the x and y pixel ranges should be given
in the correct ``imcopy`` format. ``[x1:x2,y1:y2]``
Parameters
---------
img : str
String containing name of the image currently in use
x1 : int
Pixel coordinate of x1
x2 : int
Pixel coordinate of x2
y1 : int
Pixel coordinate of y1
y2 : int
Pixel coordinate of y2
Returns
-------
img : str
The new image is given the extension ``.trim.fits``.
"""
x1, x2 = x1, x2
y1, y2 = y1, y2
input = img.nofits() + '[' + repr(x1) + ':' + repr(x2) + ',' + repr(
y1) + ':' + repr(y2) + ']'
output = img.nofits() + '.trim.fits'
if not os.path.isfile(output):
print 'Trimming image: ', img
iraf.unlearn(iraf.imcopy)
iraf.imcopy(input=input, output=output, verbose='no', mode='h')
def mkflatINTWFC(data, combine='median', reject='avsigclip'):
"""
Creates a master flat from a given list of flat frames.
Reads readnoise and gain from the FITS header, as each WFC chip has
different values.
:note: ccdproc
"""
for filter in data:
input1 = ''
for file in data[filter]:
input1 += file + '[1],'
input2 = input1.replace('[1]', '[2]')
input3 = input1.replace('[1]', '[3]')
input4 = input1.replace('[1]', '[4]')
inputs = [input1, input2, input3, input4]
for ext, input in enumerate(inputs):
print input
iraf.unlearn('flatcombine')
iraf.flatcombine(input=input[:-1],
output='FLAT_{0:>s}_{1:d}.fits'.format(
filter, ext + 1),
combine=combine,
reject=reject,
rdnoise='READNOIS',
gain='GAIN')
def mkflatINTWFC(data, combine='median', reject='avsigclip'):
"""
Creates a master flat from a given list of flat frames.
Reads readnoise and gain from the FITS header, as each WFC chip has
different values.
:note: ccdproc
"""
for filter in data:
input1 = ''
for file in data[filter]:
input1 += file + '[1],'
input2 = input1.replace('[1]', '[2]')
input3 = input1.replace('[1]', '[3]')
input4 = input1.replace('[1]', '[4]')
inputs = [input1, input2, input3, input4]
for ext, input in enumerate(inputs):
print input
iraf.unlearn('flatcombine')
iraf.flatcombine(input=input[:-1],
output='FLAT_{0:>s}_{1:d}.fits'.format(filter, ext + 1),
combine=combine,
reject=reject,
rdnoise='READNOIS',
gain='GAIN')
def subtract_bias(self):
"""Subtracting Bias Frame"""
iraf.unlearn(iraf.ccdproc)
iraf.ccdproc(self.data.iraf.modatfile(),
ccdtype="", fixpix="no", overscan="no", trim ="no", zerocor="yes", darkcor="no", flatcor ="no",
zero=self.bias.iin("Bias"))
self.data.idone()
def subtract_dark(self):
"""Subtracting Dark Frame"""
iraf.unlearn(iraf.ccdproc)
iraf.ccdproc(self.data.iraf.modatfile(),
ccdtype="", fixpix="no", overscan="no", trim ="no", zerocor="no", darkcor="yes", flatcor ="no",
dark=self.dark.iin("Dark"))
self.data.idone()
def stdsensfunc(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('x1d/sci*x1d*c?.fits')
if len(fs) == 0:
print "WARNING: No extracted spectra to create sensfuncs from."
iraf.cd('..')
return
if not os.path.exists('std'):
os.mkdir('std')
for f in fs:
# Put the file in the std directory, but last 3 letters of sens
outfile = 'std/' + f.split('/')[1]
outfile = outfile.replace('x1d', 'sens').replace('sci', 'std')
outfile = outfile.replace('.fits', '.dat')
# if the object name is in the list of standard stars from pysalt
if isstdstar(f):
# We use pysalt here because standard requires a
# dispersion correction which was already taken care of above
# Write out an ascii file that pysalt.specsens can read
asciispec = 'std/std.ascii.dat'
spectoascii(f, asciispec)
# run specsens
stdfile = pysaltpath + '/data/standards/spectroscopic/m%s.dat' % pyfits.getval(f, 'OBJECT').lower().replace('-','_')
extfile = pysaltpath + '/data/site/suth_extinct.dat'
iraf.unlearn(iraf.specsens)
iraf.specsens(asciispec, outfile, stdfile, extfile,
airmass=pyfits.getval(f, 'AIRMASS'),
exptime=pyfits.getval(f, 'EXPTIME'), function='poly',
order=11, clobber=True, mode='h', thresh=1e10)
# delete the ascii file
os.remove(asciispec)
iraf.cd('..')
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 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 stdsensfunc(fs=None):
iraf.cd('work')
if fs is None:
fs = glob('x1d/sci*x1d*c?.fits')
if len(fs) == 0:
print "WARNING: No extracted spectra to create sensfuncs from."
iraf.cd('..')
return
if not os.path.exists('std'):
os.mkdir('std')
for f in fs:
# Put the file in the std directory, but last 3 letters of sens
outfile = 'std/' + f.split('/')[1]
outfile = outfile.replace('x1d', 'sens').replace('sci', 'std')
outfile = outfile.replace('.fits', '.dat')
# if the object name is in the list of standard stars from pysalt
if isstdstar(f):
# We use pysalt here because standard requires a
# dispersion correction which was already taken care of above
# Write out an ascii file that pysalt.specsens can read
asciispec = 'std/std.ascii.dat'
spectoascii(f, asciispec)
# run specsens
stdfile = pysaltpath + '/data/standards/spectroscopic/m%s.dat' % pyfits.getval(f, 'OBJECT').lower().replace('-','_')
extfile = pysaltpath + '/data/site/suth_extinct.dat'
iraf.unlearn(iraf.specsens)
iraf.specsens(asciispec, outfile, stdfile, extfile,
airmass=pyfits.getval(f, 'AIRMASS'),
exptime=pyfits.getval(f, 'EXPTIME'), function='poly',
order=11, clobber=True, mode='h', thresh=1e10)
# delete the ascii file
os.remove(asciispec)
iraf.cd('..')
def execute(self):
"""Execute pyraf task: gmosaic"""
log.debug("GmosaicETI.execute()")
# Populate object lists
xcldict = copy(self.clparam_dict)
for fil in self.file_objs:
xcldict.update(fil.get_parameter())
for par in self.param_objs:
xcldict.update(par.get_parameter())
iraf.unlearn(iraf.gmos.gmosaic)
# Use setParam to list the parameters in the logfile
for par in xcldict:
#Stderr and Stdout are not recognized by setParam
if par != "Stderr" and par != "Stdout":
gemini.gmos.gmosaic.setParam(par, xcldict[par])
log.fullinfo("\nGMOSAIC PARAMETERS:\n")
iraf.lpar(iraf.gmos.gmosaic, Stderr=xcldict["Stderr"], \
Stdout=xcldict["Stdout"])
# Execute the task using the same dict as setParam
# (but this time with Stderr and Stdout)
#from pprint import pprint
#pprint(xcldict)
try:
gemini.gmos.gmosaic(**xcldict)
except:
# catch hard crash
raise RuntimeError("The IRAF task gmos.gmosaic failed")
if gemini.gmos.gmosaic.status:
# catch graceful exit upon error
raise RuntimeError("The IRAF task gmos.gmosaic failed")
else:
log.fullinfo("The IRAF task gmos.gmosaic completed successfully")
def tpv2tan_hdr(img, ota):
image = odi.reprojpath+'reproj_'+ota+'.'+img.stem()
# change the CTYPENs to be TANs if they aren't already
tqdm.write('TPV -> TAN in {:s}'.format(image))
iraf.imutil.hedit.setParam('images',image)
iraf.imutil.hedit.setParam('fields','CTYPE1')
iraf.imutil.hedit.setParam('value','RA---TAN')
iraf.imutil.hedit.setParam('add','yes')
iraf.imutil.hedit.setParam('addonly','no')
iraf.imutil.hedit.setParam('verify','no')
iraf.imutil.hedit.setParam('update','yes')
iraf.imutil.hedit(show='no', mode='h')
iraf.imutil.hedit.setParam('images',image)
iraf.imutil.hedit.setParam('fields','CTYPE2')
iraf.imutil.hedit.setParam('value','DEC--TAN')
iraf.imutil.hedit.setParam('add','yes')
iraf.imutil.hedit.setParam('addonly','no')
iraf.imutil.hedit.setParam('verify','no')
iraf.imutil.hedit.setParam('update','yes')
iraf.imutil.hedit(show='no', mode='h')
# delete any PV keywords
# leaving them in will give you trouble with the img wcs
iraf.unlearn(iraf.imutil.hedit)
iraf.imutil.hedit.setParam('images',image)
iraf.imutil.hedit.setParam('fields','PV*')
iraf.imutil.hedit.setParam('delete','yes')
iraf.imutil.hedit.setParam('verify','no')
iraf.imutil.hedit.setParam('update','yes')
iraf.imutil.hedit(show='no', mode='h')
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 make_bpms(images):
for i in range(len(images)):
for key in OTA_dictionary:
mask,masked_array = mask_ota(images[i],OTA_dictionary[key])
hdu = fits.PrimaryHDU(mask.astype(float))
# create string for mask fits name
mask_name = bppath+'mask_'+OTA_dictionary[key]+'.'+str(images[i][-27:49])
BPM = mask_name.replace('fits','pl')
if not os.path.isfile(mask_name):
hdu.writeto(mask_name,clobber=True)
if not os.path.isfile(mask_name.replace('fits','pl')):
iraf.unlearn(iraf.imutil.imcopy)
iraf.imutil.imcopy.setParam('input',mask_name)
iraf.imutil.imcopy.setParam('output',mask_name.replace('fits','pl'))
iraf.imutil.imcopy.setParam('verbose','no')
iraf.imutil.imcopy(mode='h')
iraf.unlearn(iraf.imutil.hedit)
iraf.imutil.hedit.setParam('images',images[i]+'['+str(key)+']')
iraf.imutil.hedit.setParam('fields','BPM')
iraf.imutil.hedit.setParam('value',BPM)
iraf.imutil.hedit.setParam('add','no')
iraf.imutil.hedit.setParam('addonly','no')
iraf.imutil.hedit.setParam('verify','no')
iraf.imutil.hedit.setParam('update','yes')
iraf.imutil.hedit(mode='h')
return
def _medFilterMaskedWgtIm(self, imname, medfiltsize=5):
""" Method to median filter an image, intended to be the
detection weight image. Default filter size is 5 pix.
It first makes a mask from the input image so that no input
zero pixels (which are specially flagged as having no weight)
become nonzero after filtering.
"""
tmpMed = '_tmpMED.fits'
tmpMask = '_tmpMask.fits'
iraf.flpr()
iraf.flpr(iraf.median)
iraf.flpr(iraf.imcalc)
iraf.unlearn(iraf.median)
iraf.median.input = imname
iraf.median.output = tmpMed
iraf.median.xwindow = medfiltsize
iraf.median.ywindow = medfiltsize
iraf.median.mode = 'h'
iraf.median()
iraf.unlearn(iraf.imcalc)
iraf.imcalc(imname, tmpMask, "if im1 .eq. 0 then 0 else 1")
_instring = tmpMed+','+tmpMask
iraf.imcalc(_instring, tmpMed, "im1 * im2")
os.rename(tmpMed,imname)
os.remove(tmpMask)
iraf.flpr(iraf.median)
iraf.flpr(iraf.imcalc)
iraf.flpr()
return
def execute(self):
"""Execute pyraf task: gemcombine"""
log.debug("GemcombineETI.execute()")
# Populate object lists
xcldict = copy(self.clparam_dict)
for fil in self.file_objs:
xcldict.update(fil.get_parameter())
for par in self.param_objs:
xcldict.update(par.get_parameter())
iraf.unlearn(iraf.gemcombine)
# Use setParam to list the parameters in the logfile
for par in xcldict:
#Stderr and Stdout are not recognized by setParam
if par != "Stderr" and par !="Stdout":
gemini.gemcombine.setParam(par,xcldict[par])
log.fullinfo("\nGEMCOMBINE PARAMETERS:\n")
iraf.lpar(iraf.gemcombine, Stderr=xcldict["Stderr"], \
Stdout=xcldict["Stdout"])
# Execute the task using the same dict as setParam
# (but this time with Stderr and Stdout)
try:
gemini.gemcombine(**xcldict)
except:
# catch hard crash of the primitive
raise Errors.OutputError("The IRAF task gemcombine failed")
if gemini.gemcombine.status:
# catch graceful exit on error
raise Errors.OutputError("The IRAF task gemcombine failed")
else:
log.fullinfo("The IRAF task gemcombine completed successfully")
def rotate_2005_lgsao():
# Need to rotate an image.
ir.unlearn('rotate')
ir.rotate.boundary = 'constant'
ir.rotate.constant = 0
ir.rotate.interpolant = 'spline3'
ir.rotate.ncols = 1040
ir.rotate.nlines = 1040
ir.rotate.xin = 528
ir.rotate.xout = 540
ir.rotate.yin = 645
ir.rotate.yout = 410
ir.rotate(workdir + 'mag05jullgs_kp.fits',
workdir + 'mag05jullgs_kp_rot.fits', 190)
ir.rotate.xin = 535
ir.rotate.xout = 540
ir.rotate.yin = 655
ir.rotate.yout = 410
ir.rotate(workdir + 'mag05jullgs_h.fits',
workdir + 'mag05jullgs_h_rot.fits', 190)
ir.rotate.xin = 572
ir.rotate.xout = 540
ir.rotate.yin = 694
ir.rotate.yout = 410
ir.rotate(workdir + 'mag05jullgs_lp.fits',
workdir + 'mag05jullgs_lp_rot.fits', 190)
ir.rotate.xin = 483
ir.rotate.xout = 483
ir.rotate.yin = 612
ir.rotate.yout = 612
ir.rotate(workdir + 'mag04jul.fits', workdir + 'mag04jul_rot.fits', 1)
def makedark(files, output):
"""
Make dark image for NIRC2 data. Makes a calib/ directory
and stores all output there. All output and temporary files
will be created in a darks/ subdirectory.
files: integer list of the files. Does not require padded zeros.
output: output file name. Include the .fits extension.
"""
redDir = os.getcwd() + '/' # Reduce directory.
curDir = redDir + 'calib/'
darkDir = util.trimdir(curDir + 'darks/')
rawDir = util.trimdir(os.path.abspath(redDir + '../raw') + '/')
util.mkdir(curDir)
util.mkdir(darkDir)
_out = darkDir + output
_outlis = darkDir + 'dark.lis'
util.rmall([_out, _outlis])
darks = [rawDir + 'n' + str(i).zfill(4) + '.fits' for i in files]
f_on = open(_outlis, 'w')
f_on.write('\n'.join(darks) + '\n')
f_on.close()
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'sigclip'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine('@' + _outlis, _out)
def make_bpms(img, ota):
# for i in range(len(images)):
# for key in OTA_dictionary:
# create string for mask fits name
mask_name = odi.bppath+'mask_'+ota+'.'+str(img[16:])
BPM = mask_name.replace('fits','pl')
if not os.path.isfile(BPM):
mask,gaps = odi.mask_ota(img,ota)
hdu = odi.fits.PrimaryHDU(mask.astype(float))
if not os.path.isfile(mask_name):
hdu.writeto(mask_name,clobber=True)
#if not os.path.isfile(mask_name.replace('fits','pl')):
iraf.unlearn(iraf.imutil.imcopy)
iraf.imutil.imcopy.setParam('input',mask_name)
iraf.imutil.imcopy.setParam('output',mask_name.replace('fits','pl'))
iraf.imutil.imcopy.setParam('verbose','no')
iraf.imutil.imcopy(mode='h')
iraf.unlearn(iraf.imutil.hedit)
iraf.imutil.hedit.setParam('images',img+'['+ota+']')
iraf.imutil.hedit.setParam('fields','BPM')
iraf.imutil.hedit.setParam('value',BPM)
iraf.imutil.hedit.setParam('add','yes')
iraf.imutil.hedit.setParam('addonly','no')
iraf.imutil.hedit.setParam('verify','no')
iraf.imutil.hedit.setParam('update','yes')
iraf.imutil.hedit(show='no', mode='h')
if os.path.isfile(mask_name):
os.remove(mask_name)
return
def make_bpms(img, ota):
# for i in range(len(images)):
# for key in OTA_dictionary:
# create string for mask fits name
mask_name = odi.bppath + 'mask_' + ota + '.' + img.stem()
BPM = mask_name.replace('fits', 'pl')
if not os.path.isfile(BPM):
mask, gaps = odi.mask_ota(img, ota)
hdu = odi.fits.PrimaryHDU(mask.astype(float))
if not os.path.isfile(mask_name):
hdu.writeto(mask_name, clobber=True)
#if not os.path.isfile(mask_name.replace('fits','pl')):
iraf.unlearn(iraf.imutil.imcopy)
iraf.imutil.imcopy.setParam('input', mask_name)
iraf.imutil.imcopy.setParam('output',
mask_name.replace('fits', 'pl'))
iraf.imutil.imcopy.setParam('verbose', 'no')
iraf.imutil.imcopy(mode='h')
iraf.unlearn(iraf.imutil.hedit)
iraf.imutil.hedit.setParam('images', img.f + '[' + ota + ']')
iraf.imutil.hedit.setParam('fields', 'BPM')
iraf.imutil.hedit.setParam('value', BPM)
iraf.imutil.hedit.setParam('add', 'yes')
iraf.imutil.hedit.setParam('addonly', 'no')
iraf.imutil.hedit.setParam('verify', 'no')
iraf.imutil.hedit.setParam('update', 'yes')
iraf.imutil.hedit(show='no', mode='h')
if os.path.isfile(mask_name):
os.remove(mask_name)
return
def mkbiasINTWFC(filelist, type='median'):
"""
Creates a master bias from given list of bias frames.
Saves each extension to a different FITS file.
Reads readnoise and gain from the FITS header, as each WFC chip has
different values.
:note: This function has been written specifically for INT WFC.
:param filelist:
:type filelist:
:param type: combining parameter (default = median)
:type type: string
:return: None
"""
input1 = ''
for line in filelist:
input1 += line[0] + '[1],'
input2 = input1.replace('[1]', '[2]')
input3 = input1.replace('[1]', '[3]')
input4 = input1.replace('[1]', '[4]')
inputs = [input1, input2, input3, input4]
#note there are four SCI extensions
for ext, input in enumerate(inputs):
iraf.unlearn('zerocombine')
iraf.zerocombine(input=input[:-1],
output='BIAS%i.fits' % (ext + 1),
combine=type,
rdnoise='READNOIS',
gain='GAIN')
def makedark(files, output):
"""
Make dark image for NIRC2 data. Makes a calib/ directory
and stores all output there. All output and temporary files
will be created in a darks/ subdirectory.
files: integer list of the files. Does not require padded zeros.
output: output file name. Include the .fits extension.
"""
redDir = os.getcwd() + "/" # Reduce directory.
curDir = redDir + "calib/"
darkDir = util.trimdir(curDir + "darks/")
rawDir = util.trimdir(os.path.abspath(redDir + "../raw") + "/")
util.mkdir(curDir)
util.mkdir(darkDir)
_out = darkDir + output
_outlis = darkDir + "dark.lis"
util.rmall([_out, _outlis])
darks = [rawDir + "n" + str(i).zfill(4) + ".fits" for i in files]
f_on = open(_outlis, "w")
f_on.write("\n".join(darks) + "\n")
f_on.close()
ir.unlearn("imcombine")
ir.imcombine.combine = "median"
ir.imcombine.reject = "sigclip"
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine("@" + _outlis, _out)
def setup_drizzle(imgsize):
"""Setup drizzle parameters for NIRC2 data.
@param imgsize: The size (in pixels) of the final drizzle image.
This assumes that the image will be square.
@type imgsize: int
@param type: str
"""
print 'Setting up drizzle'
# Setup the drizzle parameters we will use
ir.module.load('stsdas', doprint=0, hush=1)
ir.module.load('analysis', doprint=0, hush=1)
ir.module.load('dither', doprint=0, hush=1)
ir.unlearn('drizzle')
ir.drizzle.outweig = ''
ir.drizzle.in_mask = ''
ir.drizzle.wt_scl = 1
ir.drizzle.outnx = imgsize
ir.drizzle.outny = imgsize
ir.drizzle.pixfrac = 1
ir.drizzle.kernel = 'lanczos3'
ir.drizzle.scale = 1
ir.drizzle.coeffs = distCoef
ir.drizzle.xgeoim = distXgeoim
ir.drizzle.ygeoim = distYgeoim
ir.drizzle.shft_un = 'input'
ir.drizzle.shft_fr = 'output'
ir.drizzle.align = 'center'
ir.drizzle.expkey = 'ITIME'
ir.drizzle.in_un = 'counts'
ir.drizzle.out_un = 'counts'
def scireduce(scifiles, rawpath):
for f in scifiles:
setupname = getsetupname(f)
# gsreduce subtracts bias and mosaics detectors
iraf.unlearn(iraf.gsreduce)
iraf.gsreduce('@' + f, outimages=f[:-4]+'.mef', rawpath=rawpath, bias="bias",
fl_over=dooverscan, fl_fixpix='no', fl_flat=False,
fl_gmosaic=False, fl_cut=False, fl_gsappwave=False, fl_oversize=False)
if is_GS:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=setupname + '.arc.arc.fits',
corrimages=setupname +'.qe.fits', verbose=True)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4] +'.fits')
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4] +'.fits')
# Flat field the image
hdu = pyfits.open(f[:-4]+'.fits', mode='update')
hdu['SCI'].data /= pyfits.getdata(setupname+'.flat.fits', extname='SCI')
hdu.flush()
hdu.close()
# Transform the data based on the arc wavelength solution
iraf.unlearn(iraf.gstransform)
iraf.gstransform(f[:-4], wavtran=setupname + '.arc')
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 mkScaleImage(image):
hdr=pf.open(image)[0].header
(mult,add)=(hdr['MSCSCALE'],hdr['MSCZERO'])
iraf.unlearn('imcalc')
if os.path.exists(image[:-5]+'_scale.fits'):
os.remove(image[:-5]+'_scale.fits')
iraf.imcalc(image,image[:-5]+'_scale.fits','(im1+'+str(add)+')*'+str(mult))
def divide_flat(self):
"""Dividing by Flat Frame"""
iraf.unlearn(iraf.ccdproc)
iraf.ccdproc(self.data.iraf.inatfile(),
output=self.data.iraf.outatfile(append="-Flat"),
flat=self.flat.iin("Flat"),
ccdtype="", fixpix="no", overscan="no", trim ="no", zerocor="no", flatcor="yes", darkcor ="no")
self.data.iraf.done()
def __init__(self):
"""
Initializes the class
"""
from pyraf import iraf
from iraf import stsdas, analysis, dither
# unlearn the task
iraf.unlearn('drizzle')
def mkWeightMap(image,search=''):
if search != '':
imname=glob.glob(search)[0]
else:
imname=image
iraf.unlearn('imcalc')
iraf.imcalc(imname,'weight.fits','if im1 .eq. 0 then 0.0 else 1.0')
def trim_img(img):
x1,x2 = 2508,15798
y1,y2 = 2216,15506
input = img[:-5]+'['+repr(x1)+':'+repr(x2)+','+repr(y1)+':'+repr(y2)+']'
output = img[:-5]+'.trim.fits'
if not os.path.isfile(output):
print 'Trimming image: ' ,img
iraf.unlearn(iraf.imcopy)
iraf.imcopy(input = input,output = output,verbose='no',mode='h')
def skysub(scifiles, rawpath):
for f in scifiles:
# sky subtraction
# output has an s prefixed on the front
# This step is currently quite slow for Gemini-South data
iraf.unlearn(iraf.gsskysub)
iraf.gsskysub('t' + f[:-4], long_sample='*', fl_inter='no', fl_vardq=dodq,
naverage=-10, order=1, low_reject=2.0, high_reject=2.0,
niterate=10, mode='h')
def skysub(scifiles, rawpath):
for f in scifiles:
# sky subtraction
# output has an s prefixed on the front
# This step is currently quite slow for Gemini-South data
iraf.unlearn(iraf.gsskysub)
iraf.gsskysub('t' + f[:-4], long_sample='*', fl_inter='no',
naverage=-10, order=1, low_reject=2.0, high_reject=2.0,
niterate=10, mode='h')
def convolve_images_psf(images_with_headers):
print("Convolving images (not implemented yet)")
for i in range(0, len(images_with_headers)):
original_filename = os.path.basename(images_with_headers[i][2])
original_directory = os.path.dirname(images_with_headers[i][2])
new_directory = original_directory + "/convolved/"
#artificial_filename = new_directory + original_filename + "_pixelgrid.fits"
#registered_filename = new_directory + original_filename + "_registered.fits"
input_directory = original_directory + "/registered/"
input_filename = input_directory + original_filename + "_registered.fits"
print("Artificial filename: " + artificial_filename)
print("Registered filename: " + registered_filename)
print("Input filename: " + input_filename)
if not os.path.exists(new_directory):
os.makedirs(new_directory)
#reading the science image:
science_image = fits.getdata(input_filename)
# if using a kernel image, then we first regrid the kernel to the same as in the science image, and we re-center the kernel:
# create a fake image "apixel_kernel.fits"
# the original kernel has a grid of 3645*3645 pixels and centered at (1822, 1822)
# ncols = nlines = initial_number_of_rows * initial_pixelsize_of_the_kernel / science_image_pixelsize
# in the current case: 3645* 0.25 (arcsecs per pixel) / 2 (arcsecs per pixel) = 455.62
artdata.mkpattern(input="apixel_kernel.fits", output="apixel_kernel.fits", pattern="constant", option="replace",v1=0., v2=1., size=1, title="", pixtype="real", ndim=2, ncols=455,nlines=455,n3=1, n4=1, n5=1, n6=1, n7=1, header="")
#Then, tag the desired WCS in this fake image:
#
# unlearn some iraf tasks
iraf.unlearn('ccsetwcs')
#xref = yref = ncols/2 = nlines/2
#xmag, ymag = pixel scale of science image
iraf.ccsetwcs(images="apixel_kernel.fits", database="", solution="", xref=227.5, yref=227.5, xmag=2, ymag=2, xrotati=0.,yrotati=0.,lngref=0, latref=0, lngunit="hours", latunit="degrees", transpo="no", project="tan", coosyst="j2000", update="yes", pixsyst="logical", verbose="yes")
# Then, register the fits file of interest to the WCS of the fake fits file
#
# unlearn some iraf tasks
iraf.unlearn('wregister')
iraf.wregister(input="Kernel_HiRes_PACS_70_to_SPIRE_500.fits", reference="apixel_kernel.fits", output="Kernel_P70_2_S500.fits", fluxconserve="yes")
# then we get the data from the kernel
kernel_image = pyfits.getdata('Kernel_P70_2_S500.fits')
#several ways to do the convolution, but is best to use number 3 or 4:
#3.
result3 = astropy.nddata.convolution.convolve.convolve(science_image, kernel_image) # got a segmentation fault - it needs an odd number of columns/rows for the kernel
pyfits.writeto('science_image_convolved_3.fits',result3)
#4.
result4 = astropy.nddata.convolution.convolve.convolve_fft(science_image,kernel_image) # worked OK - was the fastest thus far
pyfits.writeto('science_image_convolved_4.fits',result4)
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 run_elli(input, output, xcntr, ycntr, eg, pa, sma):#,radd,background):
"""The function resposible for fit ellipse"""
iraf.stsdas(_doprint=0)
iraf.tables(_doprint=0)
iraf.stsdas.analysis(_doprint=0)
iraf.stsdas.analysis.isophote(_doprint=0)
image_exist = 1
# if(str(input)[:3] == 'ima'):
# output = 'elli_' + input[6:-4] + 'txt'
# if(str(input)[:3] == 'out'):
# output = 'out_elli_' + str(input)[4:-7] + 'txt'
# else:
# output = 'elli_' + input[:-5] + '.txt'
#unlearn geompar controlpar samplepar magpar ellipse
iraf.unlearn('geompar')
iraf.geompar.x0=xcntr
iraf.geompar.y0=ycntr
iraf.geompar.ellip0=eg
iraf.geompar.pa0=pa
iraf.geompar.sma0=10
iraf.geompar.minsma=0.1
iraf.geompar.maxsma=sma*5.0
iraf.geompar.step=0.1
iraf.geompar.recente="no"
iraf.geompar.xylearn="no"
iraf.unlearn('controlpar')
iraf.controlpar.conver=0.05
iraf.controlpar.minit=10
iraf.controlpar.maxit=50
iraf.controlpar.hcenter="no"
iraf.controlpar.hellip="no"
iraf.controlpar.hpa="no"
iraf.controlpar.wander="INDEF"
iraf.controlpar.maxgerr=0.5
iraf.controlpar.olthres=1
iraf.controlpar.soft="no"
iraf.samplepar.integrm="bi-linear"
iraf.samplepar.usclip=3
iraf.samplepar.lsclip=3
iraf.samplepar.nclip=0
iraf.samplepar.fflag=0.5
iraf.magpar.mag0=c.mag_zero
iraf.magpar.refer=1
iraf.magpar.zerolev=0
iraf.ellipse("".join(input), output="test", interac="no",Stdout="ellip", \
Stderr="err")
iraf.tprint("test.tab", prparam="no", prdata="yes", pwidth=80, plength=0, \
showrow="no", orig_row="no", showhdr="no", showunits="no", \
columns="SMA, INTENS, INT_ERR, MAG, MAG_LERR, MAG_UERR, \
TFLUX_E", rows="-", \
option="plain", align="yes", sp_col="", lgroup=0, Stdout=output)
for myfile in ['ellip','err','test.tab']:
if os.access(myfile,os.F_OK):
os.remove(myfile)
def scale_ota(img, ota, scale):
image = odi.bgsubpath + 'bgsub_' + ota + '.' + img.stem()
imout = odi.scaledpath + 'scaled_' + ota + '.' + img.stem()
iraf.unlearn(iraf.imutil.imarith)
iraf.imutil.imarith.setParam('operand1', image)
iraf.imutil.imarith.setParam('op', '/')
iraf.imutil.imarith.setParam('operand2', scale)
iraf.imutil.imarith.setParam('result', imout)
iraf.imutil.imarith.setParam('verbose', 'yes')
iraf.imutil.imarith(mode='h')
return
def calibrate(scifiles, extfile, observatory):
for f in scifiles:
redorblue = getredorblue(f)
iraf.unlearn(iraf.gscalibrate)
iraf.gscalibrate('et' + f[:-4] + '.fits',
sfunc='sens' + redorblue + '.fits', fl_ext=True, fl_vardq=dodq,
extinction=extfile, observatory=observatory)
if os.path.exists('cet' + f[:-4] + '.fits'):
iraf.unlearn(iraf.splot)
iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def calibrate(scifiles, extfile, observatory):
for f in scifiles:
redorblue = getredorblue(f)
iraf.unlearn(iraf.gscalibrate)
iraf.gscalibrate('et' + f[:-4] + '.fits',
sfunc='sens' + redorblue + '.fits', fl_ext=True,
extinction=extfile, observatory=observatory)
if os.path.exists('cet' + f[:-4] + '.fits'):
iraf.unlearn(iraf.splot)
iraf.splot('cet' + f.replace('.txt', '.fits') + '[sci]') # just to check
def get_gaps_rep(img, ota):
"""
Create a numpy array mask of the gaps in a reprojected ota.
Parameters
----------
img : str
Name of image
ota : str
Name of OTA
Returns
-------
gaps_mask : numpy array
A numpy array of the gap location on the ota.
"""
image = odi.reprojpath + 'reproj_' + ota + '.' + img.stem()
hdulist = odi.fits.open(image)
hdu = hdulist[0]
# plt.imshow(hdu.data, origin='lower', cmap='Greys_r', vmin=-10., vmax=500.)
# plt.show()
gaps_mask1 = (hdu.data < 1.0).astype(int)
selem = np.ones((5, 5)) # dilate using a 25x25 box
gaps_mask = odi.binary_dilation(gaps_mask1, selem)
hdulist.close()
# also update the bad pixel mask for the image to make sure the cell gaps are masked
# this is necessary for the final imcombine
mask_name = odi.bppath + 'reproj_mask_' + ota + '.' + img.stem()
BPM = mask_name.replace('fits', 'pl')
if not os.path.isfile(BPM):
# mask,gaps = mask_ota(img,ota)
hdu = odi.fits.PrimaryHDU(gaps_mask.astype(float))
if not os.path.isfile(mask_name):
hdu.writeto(mask_name, clobber=True)
if not os.path.isfile(mask_name.replace('fits', 'pl')):
iraf.unlearn(iraf.imutil.imcopy)
iraf.imutil.imcopy.setParam('input', mask_name)
iraf.imutil.imcopy.setParam('output', mask_name.replace('fits', 'pl'))
iraf.imutil.imcopy.setParam('verbose', 'no')
iraf.imutil.imcopy(mode='h')
# if os.path.isfile(mask_name): # we don't need to keep the reproj fits mask, it takes up a ton of space
# iraf.imutil.imdelete(mask_name, verify='no', mode='h')
iraf.unlearn(iraf.imutil.hedit)
iraf.imutil.hedit.setParam('images', image)
iraf.imutil.hedit.setParam('fields', 'BPM')
iraf.imutil.hedit.setParam('value', BPM)
iraf.imutil.hedit.setParam('add', 'yes')
iraf.imutil.hedit.setParam('addonly', 'no')
iraf.imutil.hedit.setParam('verify', 'no')
iraf.imutil.hedit.setParam('update', 'yes')
iraf.imutil.hedit(mode='h')
return gaps_mask
def set_findpars():
iraf.unlearn(iraf.findpars)
iraf.findpars.setParam("threshold",5.0,check=1,exact=1)
iraf.findpars.setParam("nsigma",2.0,check=1,exact=1)
iraf.findpars.setParam("ratio",1.0,check=1,exact=1)
iraf.findpars.setParam("theta",0.0,check=1,exact=1)
iraf.findpars.setParam("sharplo",0.0,check=1,exact=1)
iraf.findpars.setParam("sharphi",1.0,check=1,exact=1)
iraf.findpars.setParam("roundlo",-1.0,check=1,exact=1)
iraf.findpars.setParam("roundhi",1.0,check=1,exact=1)
iraf.findpars.setParam("mkdetections",0,check=1,exact=1)
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 speccombine(fs, outfile):
nsteps = 8001
lamgrid = np.linspace(3000.0, 11000.0, nsteps)
nfs = len(fs)
# for each aperture
# get all of the science images
specs = np.zeros((nfs, nsteps))
specerrs = np.zeros((nfs, nsteps))
for i, f in enumerate(fs):
hdu = fits.open(f)
lam = fitshdr_to_wave(hdu[0].header.copy())
# interpolate each spectrum onto a common wavelength scale
specs[i] = np.interp(lamgrid, lam, hdu[0].data,
left=0.0, right=0.0)
# Also calculate the errors. Right now we assume that the variances
# interpolate linearly. This is not strictly correct but it should be
# close. Also we don't include terms in the variance for the
# uncertainty in the wavelength solution.
specerrs[i] = 0.1 * specs[i]
# minimize the chi^2 given free parameters are multiplicative factors
# We could use linear or quadratic, but for now assume constant
# Assume 3 chips for now
p0 = np.ones(nfs / 3)
results = optimize.minimize(combine_spec_chi2, p0,
args=(lamgrid, specs, specerrs),
method='Nelder-Mead',
options={'maxfev': 1e5, 'maxiter': 1e5, 'ftol':1e-5})
# write the best fit parameters into the headers of the files
# Dump the list of spectra into a string that iraf can handle
iraf_filelist = str(fs).replace('[', '').replace(']', '').replace("'", '') #.replace(',', '[SCI],')
#iraf_filelist += '[SCI]'
# write the best fit results into a file
lines = []
for p in np.repeat(results['x'], 3):
lines.append('%f\n' % (1.0 / p))
f = open('scales.dat', 'w')
f.writelines(lines)
f.close()
# run scombine after multiplying the spectra by the best fit parameters
if os.path.exists(outfile):
os.remove(outfile)
iraf.unlearn(iraf.scombine)
iraf.scombine(iraf_filelist, outfile, scale='@scales.dat',
reject='avsigclip', lthreshold='INDEF', w1=bluecut)
def background(fs=None):
iraf.cd('work')
# Get rectified science images
if fs is None:
fs = glob('nrm/sci*nrm*.fits')
if len(fs) == 0:
print "WARNING: No rectified images for background-subtraction."
iraf.cd('..')
return
if not os.path.exists('bkg'):
os.mkdir('bkg')
for f in fs:
print("Subtracting background for %s" % f)
# Make sure dispaxis is set correctly
pyfits.setval(f, 'DISPAXIS', value=1)
# the outfile name is very similar, just change folder prefix and
# 3-char stage substring
outfile = f.replace('nrm','bkg')
# We are going to use fit1d instead of the background task
# Go look at the code for the background task: it is literally a wrapper for 1D
# but it removes the BPM option. Annoying.
iraf.unlearn(iraf.fit1d)
iraf.fit1d(input=f + '[SCI]', output='tmpbkg.fits', bpm=f + '[BPM]',
type='difference', sample='52:949', axis=2,
interactive='no', naverage='1', function='legendre',
order=5, low_reject=1.0, high_reject=1.0, niterate=5,
grow=0.0, mode='hl')
# Copy the background subtracted frame into the rectified image
# structure.
# Save the sky spectrum as extension 3
hdutmp = pyfits.open('tmpbkg.fits')
hdu = pyfits.open(f)
skydata = hdu[1].data - hdutmp[0].data
hdu[1].data[:, :] = hdutmp[0].data[:, :]
hdu.append(pyfits.ImageHDU(skydata))
hdu[3].header['EXTNAME'] = 'SKY'
hdu[3].data[hdu['BPM'] == 1] = 0.0
# Add back in the median sky level for things like apall and lacosmicx
hdu[1].data[:, :] += np.median(skydata)
hdu[1].data[hdu['BPM'] == 1] = 0.0
hdutmp.close()
hdu.writeto(outfile, clobber=True) # saving the updated file
# (data changed)
os.remove('tmpbkg.fits')
iraf.cd('..')
def makemasterflat(flatfiles, rawpath, plot=True):
# normalize the flat fields
for f in flatfiles:
binning = get_binning(f, rawpath)
# Use IRAF to get put the data in the right format and subtract the
# bias
# This will currently break if multiple flats are used for a single setting
iraf.unlearn(iraf.gsreduce)
if dobias:
biasfile = "bias{binning}".format(binning=binning)
else:
biasfile = ''
iraf.gsreduce('@' + f, outimages = f[:-4]+'.mef.fits',rawpath=rawpath, fl_bias=dobias,
bias=biasfile, fl_over=dooverscan, fl_flat=False, fl_gmosaic=False,
fl_fixpix=False, fl_gsappwave=False, fl_cut=False, fl_title=False,
fl_oversize=False, fl_vardq=dodq)
if do_qecorr:
# Renormalize the chips to remove the discrete jump in the
# sensitivity due to differences in the QE for different chips
iraf.unlearn(iraf.gqecorr)
iraf.gqecorr(f[:-4]+'.mef', outimages=f[:-4]+'.qe.fits', fl_keep=True, fl_correct=True,
refimages=f[:-4].replace('flat', 'arc.arc.fits'),
corrimages=f[:-9] +'.qe.fits', verbose=True, fl_vardq=dodq)
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.qe.fits', outimages=f[:-4]+'.mos.fits', fl_vardq=dodq, fl_clean=False)
else:
iraf.unlearn(iraf.gmosaic)
iraf.gmosaic(f[:-4]+'.mef.fits', outimages=f[:-4]+'.mos.fits', fl_vardq=dodq, fl_clean=False)
flat_hdu = fits.open(f[:-4] + '.mos.fits')
data = np.median(flat_hdu['SCI'].data, axis=0)
chip_edges = get_chipedges(data)
x = np.arange(len(data), dtype=np.float)
x /= x.max()
y = data / np.median(data)
fitme_x = x[chip_edges[0][0]:chip_edges[0][1]]
fitme_x = np.append(fitme_x, x[chip_edges[1][0]:chip_edges[1][1]])
fitme_x = np.append(fitme_x, x[chip_edges[2][0]:chip_edges[2][1]])
fitme_y = y[chip_edges[0][0]:chip_edges[0][1]]
fitme_y = np.append(fitme_y, y[chip_edges[1][0]:chip_edges[1][1]])
fitme_y = np.append(fitme_y, y[chip_edges[2][0]:chip_edges[2][1]])
fit = pfm.pffit(fitme_x, fitme_y, 21, 7, robust=True,
M=sm.robust.norms.AndrewWave())
if plot:
pyplot.ion()
pyplot.clf()
pyplot.plot(x, y)
pyplot.plot(x, pfm.pfcalc(fit, x))
_junk = raw_input('Press enter to continue')
flat_hdu['SCI'].data /= pfm.pfcalc(fit, x) * np.median(data)
flat_hdu.writeto(f[:-4] + '.fits')
def wavelength_calibration(targetdir):
"""
Does wavelength calibration.
Writes every fit to database so make sure it's using the correct one.
This needs to be run in object directory for database
"""
print 'Target directory is ' + targetdir
print 'Doing wavelength calibration...'
if os.getcwd() != targetdir:
print 'Warning: current working directory must be target directory!'
return None
iraf.noao(_doprint=0)
iraf.onedspec(_doprint=0)
iraf.unlearn('identify')
iraf.identify.setParam('images','aimcomb.fits')
iraf.identify.setParam('coordli','/home/lc585/Dropbox/IoA/WHT_Proposal_2015a/argon+xenon.dat')
iraf.identify.setParam('niterat',1)
iraf.identify.setParam('function','spline3')
iraf.identify.setParam('order',3)
iraf.identify.setParam('zwidth',200.0) # Zoom graph width in user units
iraf.identify.setParam('database','database')
iraf.identify()
# Update fits header
print '\n' '\n' '\n'
print 'Updating fits header...'
iraf.hedit.setParam('images','imcomb.ms.fits')
iraf.hedit.setParam('fields','REFSPEC1')
iraf.hedit.setParam('value','aimcomb.fits') # should be wavelength calibrated?
iraf.hedit.setParam('add','yes')
iraf.hedit.setParam('verify','yes')
iraf.hedit.setParam('show','yes')
iraf.hedit()
return None
def run_fitparams(weight=True):
if weight:
weighting = 'photometric'
else:
weighting = 'uniform'
ir.delete('standards.transParams')
ir.digiphot()
ir.photcal()
ir.unlearn('fitparams')
ir.fitparams('standards.instMag',
'photcal_ukirt_faint.dat',
'standards.config',
'standards.transParams',
weighting=weighting)
