def imgShift(self, fn_in, fn_out, dxdy):
try:
print("imshift %s %s\n" % (fn_in, fn_out))
iraf.imshift(fn_in, fn_out, dxdy.x, dxdy.y, shifts_file="", \
interp_type="linear", boundary_typ="nearest")
history_str = "Imshifted (%f, %f)" % (dxdy.x, dxdy.y)
self.addFitsHistory(fn_out, history_str)
except:
sys.stderr.write("Error in imshift: %s\n" % (fn_in))
def imshift(self, file, output, dx, dy, overwrite=True):
"""Shifts a given fits file with given dx and dy"""
try:
iraf.imshift.unlearn()
if self.fop.is_file(output) and overwrite:
self.logger.warning("Over Writing file({})".format(output))
self.fop.rm(output)
iraf.imshift(input=file, output=output, x=dx, y=dy)
return True
except Exception as e:
self.logger.error(e)
return False
def image_shift(targetdir):
"""
Shift all B-A images by 48 pixels in y-direction
"""
print 'Target directory is ' + targetdir
print 'Shifting images...'
innames, outnames = [], []
for n in os.listdir(targetdir):
if (n.endswith('.fit')) & (n.startswith('dfcr')):
outnames.append(os.path.join(targetdir,'s' + n ) )
innames.append(os.path.join(targetdir,n) )
if os.path.exists( os.path.join(targetdir,'s' + n) ):
os.remove(os.path.join(targetdir,'s' + n))
print 'Removing file ' + os.path.join(targetdir,'s' + n)
with open(os.path.join(targetdir,'input.list'), 'w') as f:
for name in innames:
print name.replace(targetdir + '/','')
f.write( name + '\n' )
with open(os.path.join(targetdir,'output.list'), 'w') as f:
for name in outnames:
f.write( name + '\n' )
names = np.array([name.replace('dfcr','r') + '[1]' for name in innames])
decoff = np.asarray([iraf.hselect(f , 'DECOFF', 'yes', Stdout=1)[0] for f in names],dtype='float')
with open(os.path.join(targetdir,'shifts.lst'),'w') as f:
for d in decoff:
if d >= -1.0:
yshift = '0.0'
else:
yshift = '-48.0'
f.write('0.0 ' + yshift + '\n')
iraf.images(_doprint=0)
iraf.imgeom(_doprint=0)
iraf.imshift.setParam('input', '@' + os.path.join(targetdir, 'input.list'))
iraf.imshift.setParam('output', '@' + os.path.join(targetdir, 'output.list'))
iraf.imshift.setParam('shifts_file', os.path.join(targetdir,'shifts.lst') )
iraf.imshift()
return None
def shift_align(filenames):
if len(filenames) == 0:
return ''
fno = 1
for i in filenames:
iraf.display(image = i, frame = fno)
fno += 1
print 'The fits number is', len(filenames)
if len(filenames) > 1:
print 'Please check the fits pic and determine whether you need to shift the pic'
print 'imexamine...'
iraf.imexamine()
valget = raw_input('Are you need to shift the fits picture?(y)')
if valget == 'y' or valget == 'Y':
nfilenames = [filenames[0]]
for i2 in filenames:
print 'implot ' + i2
iraf.implot(image = i2)
fno = 1
iraf.immatch()
while fno < len(filenames):
valget = raw_input('Please input ' + filenames[fno] + ' xshift, yshift: ')
valget = valget.split()
fxshift = float(valget[0])
fyshift = float(valget[1])
ims_key = 1
if os.path.isfile('shift_' + filenames[fno]):
valget = raw_input('shift_' + filenames[fno] + ' is already exist, are you want to delete the old one?(y):')
if valget == 'y' or valget == 'Y' or valget == 'yes' or valget == 'Yes':
os.remove('shift_' + filenames[fno])
else:
ims_key = 0
if ims_key == 1:
print 'what happend????????????????'
iraf.imshift(input = filenames[fno]
, output = 'shift_' + filenames[fno], xshift = fxshift
, yshift = fyshift, shifts_file = '', interp_type = 'linear'
, boundary_type = 'nearest', constant = 0.0)
nfilenames.append('shift_' + filenames[fno])
fno += 1
for i2 in xrange(len(nfilenames)):
iraf.display(image = nfilenames[i2], frame = i2 + 1)
iraf.imexamine()
return cos_clear(nfilenames)
return cos_clear(filenames)
def combine(doreduce=True, doshifts=True):
if doreduce:
ims = glob('oc01020[1-4]*_raw.fits')
ims += glob('oc016*_raw.fits')
# for each image
for im in ims:
print im
stistools.basic2d.basic2d(im, im.replace('raw', 'flt'))
im = im.replace('raw', 'flt')
print im
# Update the target position at 0.0
for i in range(4):
pyfits.setval(im, 'POSTARG2', value=0.0, ext=i)
# reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im, 'APERTAB', value='oref$y2r1559to_apt.fits')
# Reset the wcs to have CRPIX2 along the trace
# Run x2d on the flt frame
stistools.x2d.x2d(input=im, output=im.replace('flt', 'x2d'))
h = pyfits.open(im.replace('flt', 'x2d'), mode='update')
# Replace all of the bad pixels in the image by -10000 based on the DQ array
# save them to a new file
# Throw away bad reference file pixels and saturated pixels. None of the other error codes
# were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(
bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[1].data[badpix] = -10000
h.flush()
# Trim the image
for i in range(1, 4):
h[i].data = h[i].data[100:-100, 120:-100]
h[i].header['CRPIX1'] -= 120
h[i].header['CRPIX2'] -= 100
h.close()
ims = glob('oc01020[1-4]*_x2d.fits')
ims += glob('oc01610[1-2]*_x2d.fits')
if doshifts:
init_guesses = [501, 542, 522, 523, 541, 524]
centroids = []
for i, im in enumerate(ims):
print(im)
h = pyfits.open(im)
d = average(h[1].data[:, 915:925], axis=1)
popt, _pcov = curve_fit(gauss,
arange(len(d)),
d,
p0=[10, init_guesses[i], 1.5, 0])
centroids.append(popt[1])
shift = centroids[0] - popt[1]
from matplotlib import pyplot as pl
pl.ion()
pl.clf()
pl.plot(arange(len(d)), d)
pl.plot(arange(len(d)),
gauss(arange(len(d)), popt[0], popt[1], popt[2], popt[3]))
_w = raw_input('Press return to continue')
# watch the sign convention
# This gives what you need to shift the input image by to get to the reference image
iraf.unlearn(iraf.imshift)
iraf.imshift(im + '[1]',
im[:-8] + 'shift1.fits',
0.0,
shift,
interp_type='drizzle')
# Run imcombine on the rectified (but not flux scaled) images with crreject
iraf.unlearn(iraf.imcombine)
imlist = ''
for im in ims:
imlist += im[:-8] + 'shift1.fits,'
# drop the last comma
imlist = imlist[:-1]
iraf.imcombine(input=imlist,
output='13dh_uv_com.fits',
reject='none',
lthreshold=-20,
hthreshold=300)
# run apall on the combined flux scaled image
iraf.unlearn(iraf.apall)
iraf.apall(input='13dh_uv_com.fits',
output='13dh_uv',
review='no',
line=1024,
nsum=-50,
b_order=2,
b_function='legendre',
b_niterate=30,
b_naverage=-21,
nfind=1,
t_order=2,
background='fit',
weights='variance')
def p60sub(refimage, inlist, ra=-1, dec=-1, dx=-1, dy=-1, \
nsx=5, nsy=5, sx=1, sy=1, sat1=60000, sat2=60000, min=5, ms=130, dback=1, \
dspace=2, hms=9, hss=15, thresh=20, seeing=5.0, scale=0.0, tol=0.005, \
matchlist="", goodmatch=2.0, aorder=2, nstars=30, outim="", MAX=900, \
order=3, clobber=globclob):
""" subtraction pipeline for one object/filter P60 data """
# Parse inputs
infiles = iraffiles(inlist)
# Find location (in pixels) of central object if specified
[nx, ny] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
if not ((ra == -1) and (dec == -1)):
[xcenpix, ycenpix] = imwcs2pix(refimage, ra, dec)
else:
xcenpix = (nx / 2.0)
ycenpix = (ny / 2.0)
[ra, dec] = impix2wcs(refimage, xcenpix, ycenpix)
# Trim the reference image
if (dx == -1):
xl = 1
xu = nx
else:
xl = int(xcenpix - (dx / 2.0))
xu = int(xcenpix + (dx / 2.0))
if (dy == -1):
yl = 1
yu = ny
else:
yl = int(ycenpix - (dy / 2.0))
yu = int(ycenpix + (dy / 2.0))
check_exist('t' + refimage, 'w', clobber)
iraf.imcopy(refimage + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
't' + refimage)
# Big Loop
for image in infiles:
# Separate image rootname from extension
imageroot = image.split('.')[0]
# Check to make sure the WCS fit was successful
if not check_head(image, 'IQWCS'):
print 'Skipping image %s: IQWCS not successful' % image
infiles.remove(image)
continue
# If so, then shift all the images appropriately
else:
xsh = xcenpix - imwcs2pix(image, ra, dec)[0]
ysh = ycenpix - imwcs2pix(image, ra, dec)[1]
check_exist('s' + image, 'w', clobber)
iraf.imshift(image, 's' + image, xsh, ysh)
# Make sure sky subtraction is not done
[skysub, skybkg] = get_head(image, ['SKYSUB', 'SKYBKG'])
if (skysub == 1):
iraf.imarith('s' + image, '+', skybkg, 's' + image)
# Trim the images
check_exist('ts' + image, 'w', clobber)
iraf.imcopy('s' + image + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
'ts' + image)
# Cosmic Ray Cleaning
iraf.lacos_im('ts%s' % image, 'cts%s' % image, 'ts%s_crmask' % \
imageroot, gain=2.2, readn=5.3, niter=3)
# Register the images
os.system(
'$REDUCTION/imregister_new.pl t%s cts%s -sat %i -thresh %i -s %f -scale %f -tol %f %s -o %i -g %i -n %i -out cts%s.shift.fits -max %i'
% (refimage, image, sat1, thresh, seeing, scale, tol, matchlist,
order, goodmatch, nstars, imageroot, MAX))
# Write the configuration parameters for subtraction
pfile = open('default_config', 'w+')
pfile.write('nstamps_x %i\n' % nsx)
pfile.write('nstamps_y %i\n' % nsy)
pfile.write('sub_x %i\n' % sx)
pfile.write('sub_y %i\n' % sy)
pfile.write('half_mesh_size %i\n' % hms)
pfile.write('half_stamp_size %i\n' % hss)
pfile.write('deg_bg %i\n' % dback)
pfile.write('saturation1 %f\n' % sat1)
pfile.write('saturation2 %f\n' % sat2)
pfile.write('pix_min %i\n' % min)
pfile.write('min_stamp_center %i\n' % ms)
pfile.write('ngauss 3\n')
pfile.write('deg_gauss1 6\n')
pfile.write('deg_gauss2 4\n')
pfile.write('deg_gauss3 2\n')
pfile.write('sigma_gauss1 0.7\n')
pfile.write('sigma_gauss2 1.5\n')
pfile.write('sigma_gauss3 2.0\n')
pfile.write('deg_spatial %i\n' % dspace)
pfile.write('reverse 0\n')
pfile.write('stampsbyxy 0\n')
pfile.close()
# Run the subtraction
os.system("$REDUCTION/ISIS/bin/mrj_phot t%s cts%s.shift.fits" \
% (refimage, imageroot))
# Clean up files
os.system("mv conv.fits cts%s.shift.sub.fits" % imageroot)
os.system("mv conv0.fits cts%s.shift.conv.fits" % imageroot)
# Return
print 'Exiting successfully'
return
def psf_construct(img,
x,
y,
halosize,
norm=None,
magnify=1,
pixel_scale=0.168,
sub_indiv_bkg=False,
show_figure=False,
verbose=False):
from pyraf import iraf
from iraf import stsdas
from iraf import analysis
from iraf import isophote
from iraf import bmodel
from sep import Background
from .image import save_to_fits, extract_obj, seg_remove_cen_obj
if not os.path.isdir('./temp/'):
os.mkdir('./temp/')
# Delete `fits` file first
iraf.imdel('./temp/_halo*.fits')
x_int = x.astype(np.int)
y_int = y.astype(np.int)
dx = -1.0 * (x - x_int)
dy = -1.0 * (y - y_int)
halosize = int(halosize)
# Note that stars near the boundary will have problems when making cutout. So we pad the image here.
padsize = 40
ny, nx = img.shape
im_padded = np.zeros((ny + 2 * padsize, nx + 2 * padsize))
# Making the left edge empty
im_padded[padsize:ny + padsize, padsize:nx + padsize] = img
halo_i = (im_padded[y_int + padsize - halosize:y_int + padsize + halosize +
1, x_int + padsize - halosize:x_int + padsize +
halosize + 1])
if verbose:
print('### Cuting out the star and masking out contaminations ###')
# Build mask to move out contaminations
psf_raw = halo_i.byteswap().newbyteorder()
from astropy.convolution import convolve, Box2DKernel
psf_blur = convolve(abs(psf_raw), Box2DKernel(1.5))
psf_objects, psf_segmap = extract_obj(abs(psf_blur),
b=5,
f=4,
sigma=5.5,
minarea=2,
pixel_scale=pixel_scale,
deblend_nthresh=32,
deblend_cont=0.0001,
sky_subtract=False,
show_fig=show_figure,
verbose=verbose)
# remove central object
psf_segmap = seg_remove_cen_obj(psf_segmap)
psf_mask = (psf_segmap != 0).astype(float)
save_to_fits(psf_mask, './temp/_halo_mask.fits')
# Here I subtract local sky, so that the final image will not be too negative.
if sub_indiv_bkg is True:
# Evaluate local sky backgroud within `halo_i`
# Actually this should be estimated in larger cutuouts.
# So make another cutout (larger)!
psf_raw = psf_raw.byteswap().newbyteorder()
bk = Background(psf_raw, psf_segmap != 0)
glbbck = bk.globalback
if verbose:
print('# Global background: ', glbbck)
save_to_fits(halo_i - glbbck, './temp/_halo_img.fits') #
else:
save_to_fits(halo_i, './temp/_halo_img.fits') #
# Shift halo_i to integer grid
iraf.imshift('./temp/_halo_img.fits',
'./temp/_halo_img_shift.fits',
dx,
dy,
interp_type='poly3',
boundary='nearest')
iraf.imshift('./temp/_halo_mask.fits',
'./temp/_halo_mask_shift.fits',
dx,
dy,
interp_type='poly3',
boundary='nearest')
if not isinstance(magnify, int):
raise TypeError('# "magnify" must be positive integer less than 10!')
elif magnify != 1 and magnify > 10:
raise TypeError('# "magnify" must be positive integer less than 10!')
elif magnify == 1:
iraf.imcopy('./temp/_halo_img_shift.fits',
'./temp/_halo_i_shift.fits',
verbose=verbose)
iraf.imcopy('./temp/_halo_mask_shift.fits',
'./temp/_halo_m_shift.fits',
verbose=verbose)
else:
iraf.magnify('./temp/_halo_img_shift.fits',
'./temp/_halo_i_shift.fits',
magnify,
magnify,
interpo='poly3',
bound='refl',
const=0.)
iraf.magnify('./temp/_halo_mask_shift.fits',
'./temp/_halo_m_shift.fits',
magnify,
magnify,
interpo='poly3',
bound='refl',
const=0.)
return psf_raw, psf_mask #, x_int, y_int, dx, dy
def mkcombmask(combimg,
inlist,
sspref='sdfr',
outpref='mc',
minpix=250,
hsigma=3,
lsigma=10,
conv='block 3 3',
bpm='none',
sigmap='none'):
# load IRAF package
iraf.nproto()
# chceck combined image
if not os.access(combimg, os.R_OK):
print >> sys.stderr, 'cannot open combined image (%s)' % combimg
return 1
# open input list and check if it exists
inimg_arr = check_input(inlist, sspref)
if isinstance(inimg_arr, int):
return 1
# check output images
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
if lsigma <= 0:
print >> sy.stderr, 'invalid lsigma value (%f)' % lsigma
return 1
if hsigma <= 0:
print >> sys.stderr, 'invalid hsigma value (%f)' % hsigma
return 1
# check convolution kernel name
ret = 1
param = conv.split()
if len(param) == 1:
if os.access(param[0], os.R_OK):
conv = '@' + conv
ret = 0
else:
print >> sys.stderr, 'convolution kernel file (%s) does not exist' % param[
0]
elif param[0] == 'block' and len(param) == 3:
if param[1].isdigit() and param[2].isdigit():
ret = 0
elif param[0] == 'bilinear' and len(param) == 3:
if param[1].isdigit() and param[2].isdigit() and int(
param[1]) > 0 and int(param[2]) > 0:
ret = 0
elif param[0] == 'gauss' and len(param) == 5:
if param[1].isdigit() and param[2].isdigit() and int(
param[1]) > 0 and int(param[2]) > 0:
if isfloat(param[3]) and isfloat(
param[4]) and float(param[3]) > 0 and float(param[4]) > 0:
ret = 0
if ret > 0:
print >> sys.stderr, 'invalid convolve parameter (%s)' % conv
return 1
# check bad pixel mask
if bpm.lower() != 'none':
bpmex = 1
if os.access(bpm, os.R_OK) == False:
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
else:
bpmex = 0
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# check sigma map
if sigmap.lower() == 'none':
tmp_sigma = ''
tmp_mask = ''
else:
if os.access(sigmap, os.R_OK) == False:
print >> sys.stderr, 'cannot read sigma map image (%s)' % sigmap
return 1
else:
tmp_mask = tmp_prefix + 'mask' + os.path.basename(sigmap)
tmp_sigma = tmp_prefix + 'sigma' + os.path.basename(sigmap)
iraf.imcopy(sigmap, tmp_sigma, verbose='no')
ret = iraf.imstat(sigmap,
nclip=50,
field='mode',
format='no',
Stdout=1)
sigma_mode = float(ret[0])
iraf.imreplace(tmp_sigma, sigma_mode, upper=0)
iraf.imarith(sigmap, '*', -1, tmp_mask, verbose='no')
iraf.imreplace(tmp_mask, 1, lower=0)
iraf.imreplace(tmp_mask, 0, upper=0)
iraf.imarith(tmp_mask, '*', -1, tmp_mask)
iraf.imarith(tmp_mask, '+', 1, tmp_mask)
#
tmp_precombmask = tmp_prefix + 'premask' + os.path.basename(combimg)
tmp_combmask = tmp_prefix + 'mask' + os.path.basename(combimg)
iraf.unlearn('objmasks')
try:
iraf.objmasks(combimg,
tmp_precombmask,
omtype='boolean',
masks='',
convolve=conv,
lsigma=lsigma,
hsigma=hsigma,
hdetect='yes',
ldetect='yes',
minpix=minpix,
sigmas=tmp_sigma)
iraf.objmasks(combimg,
tmp_combmask,
omtype='boolean',
masks=tmp_precombmask + '[pl]',
convolve=conv,
lsigma=lsigma,
hsigma=hsigma,
hdetect='yes',
ldetect='yes',
minpix=minpix,
sigmas=tmp_sigma)
os.remove(tmp_precombmask)
except:
print >> sys.stderr, 'failed to execute iraf objmasks task for %s' % combimg
remove_temp_all(tmp_prefix)
return 1
# remove the area with no sigma value
tmp_combmask2 = tmp_prefix + 'mask2' + os.path.basename(combimg)
if sigmap.lower() == 'none':
iraf.imcopy(tmp_combmask + '[pl]', tmp_combmask2, verbose='no')
else:
iraf.imarith(tmp_combmask + '[pl]',
'*',
tmp_mask,
tmp_combmask2,
verbose='no')
os.remove(tmp_combmask)
# check if input images exist and fix bad pixel if requested
iraf.unlearn('imcopy')
iraf.unlearn('imshift')
for i in range(len(inimg_arr)):
# get offset values from header
im = pyfits.open(inimg_arr[i])
try:
dx = float(im[0].header['dx'])
dy = float(im[0].header['dy'])
dxc = float(im[0].header['dxc'])
dyc = float(im[0].header['dyc'])
nx = int(im[0].header['NAXIS1'])
ny = int(im[0].header['NAXIS2'])
except:
print >> sys.stderr, 'failed to get offset values from the header of %s' % inimg_arr[
i]
remove_temp_all(tmp_prefix)
return 1
im.close()
# shift combined mask
tmp_shiftimg = tmp_prefix + os.path.basename(outimg_arr[i])
iraf.imshift(tmp_combmask2,
tmp_shiftimg,
dxc,
dyc,
boundary_typ='constant',
constant=0.0)
tmp_shiftimg_reg = '%s[1:%d,1:%d]' % (tmp_shiftimg, nx, ny)
#iraf.imcopy(tmp_shiftimg_reg, outimg_arr[i]+'[pl]',verbose='no')
iraf.imcopy(tmp_shiftimg_reg, outimg_arr[i], verbose='no')
if bpmex == 1:
iraf.imarith(outimg_arr[i], '+', bpm, outimg_arr[i], verbose='no')
convert_maskfits_int(outimg_arr[i], outimg_arr[i])
os.remove(tmp_shiftimg)
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def combine(doreduce=True, doshifts=True):
if doreduce:
ims = glob('oc01020[1-4]*_raw.fits')
ims += glob('oc016*_raw.fits')
# for each image
for im in ims:
print im
stistools.basic2d.basic2d(im, im.replace('raw','flt'))
im = im.replace('raw', 'flt')
print im
# Update the target position at 0.0
for i in range(4):
pyfits.setval(im, 'POSTARG2', value=0.0, ext=i)
# reset the aperture table to the newer file (we maybe should check this)
pyfits.setval(im, 'APERTAB', value='oref$y2r1559to_apt.fits')
# Reset the wcs to have CRPIX2 along the trace
# Run x2d on the flt frame
stistools.x2d.x2d(input=im, output=im.replace('flt','x2d') )
h = pyfits.open(im.replace('flt','x2d'), mode='update')
# Replace all of the bad pixels in the image by -10000 based on the DQ array
# save them to a new file
# Throw away bad reference file pixels and saturated pixels. None of the other error codes
# were in the first file so I haven't included them here, but we might want to
d = h[3].data
badpix = logical_and(bitwise_and(d, 256) == 256,
bitwise_and(d, 512) == 512)
h[1].data[badpix] = -10000
h.flush()
# Trim the image
for i in range(1,4):
h[i].data = h[i].data[100:-100, 120:-100]
h[i].header['CRPIX1'] -= 120
h[i].header['CRPIX2'] -= 100
h.close()
ims = glob('oc01020[1-4]*_x2d.fits')
ims += glob('oc01610[1-2]*_x2d.fits')
if doshifts:
init_guesses = [501, 542, 522, 523, 541, 524]
centroids = []
for i, im in enumerate(ims):
print(im)
h = pyfits.open(im)
d = average(h[1].data[:, 915:925], axis=1)
popt, _pcov = curve_fit(gauss, arange(len(d)), d, p0=[10, init_guesses[i], 1.5, 0])
centroids.append(popt[1])
shift = centroids[0] - popt[1]
from matplotlib import pyplot as pl
pl.ion()
pl.clf()
pl.plot(arange(len(d)), d)
pl.plot(arange(len(d)), gauss(arange(len(d)), popt[0], popt[1], popt[2], popt[3]))
_w = raw_input('Press return to continue')
# watch the sign convention
# This gives what you need to shift the input image by to get to the reference image
iraf.unlearn(iraf.imshift)
iraf.imshift(im + '[1]', im[:-8] + 'shift1.fits', 0.0, shift,
interp_type='drizzle')
# Run imcombine on the rectified (but not flux scaled) images with crreject
iraf.unlearn(iraf.imcombine)
imlist = ''
for im in ims:
imlist += im[:-8] + 'shift1.fits,'
# drop the last comma
imlist = imlist[:-1]
iraf.imcombine(input=imlist, output='13dh_uv_com.fits', reject='none',
lthreshold=-20, hthreshold=300)
# run apall on the combined flux scaled image
iraf.unlearn(iraf.apall)
iraf.apall(input='13dh_uv_com.fits', output='13dh_uv', review='no',
line=1024, nsum=-50, b_order=2, b_function='legendre',
b_niterate=30, b_naverage=-21, nfind=1, t_order=2,
background='fit', weights='variance')
def subract(df_image,shifts=99.99,width_cfhtsm=0.,upperlim=0.04,medphotosc=True,numsources=100,sigmaclip=False,cutout=False):
print "\n************ Running the subtraction steps ************\n"
#iraf.imdel('_psf*.fits')
#iraf.imdel('_model*.fits')
#iraf.imdel('_df_sub')
iraf.imdel('_res*.fits')
iraf.imdel('cc_images')
iraf.imdel('_model_sh')
iraf.imdel('_model_sc')
iraf.imdel('_model_mask')
iraf.imdel('_model_maskb')
'shift the images so they have the same physical coordinates'
if shifts[0]==99.99:
print('\n**** Calculating shifts... ****\n')
iraf.stsdas.analysis.dither.crossdriz('_df_sub.fits','_model.fits','cc_images',dinp='no',dref='no')
iraf.stsdas.analysis.dither.shiftfind('cc_images.fits','shift_values')
x_shift=0
y_shift=0
with open('shift_values','r') as datafile:
line = datafile.read().split()
x_shift = float(line[2])
y_shift = float(line[4])
print('Shifting model image by x and y: '+str(x_shift)+','+str(y_shift))
iraf.imshift('_model','_model_sh',0-x_shift,0-y_shift)
else:
print('Shifting model image by x and y: '+str(shifts[0])+','+str(shifts[1]))
iraf.imshift('_model','_model_sh',shifts[0],shifts[1])
'scale the model so that roughly the same as the _df_sub image'
# if usemodelpsf:
# iraf.imarith('_model_sh','/',16.,'_model_sc')
# else:
'the photometric step, matching the images to each other. '
'in principle this comes from the headers - both datasets are calibrated,'
'so this multiplication should be something like 10^((ZP_DF - ZP_CFHT)/-2.5)'
'(perhaps with a correction for the difference in pixel size - depending'
'on what wregister does - so another factor (PIX_SIZE_DF)^2/(PIX_SIZE_CFHT)^2'
avgphotosc,medianphotosc = getphotosc('_model_sh.fits','_df_sub.fits',numsources=numsources,sigmaclip=sigmaclip,cutout=cutout)
if medphotosc:
photosc = medianphotosc
print 'Using median calculated photosc: %s'%photosc
else:
photosc = avgphotosc
print 'Using average calculated photosc: %s'%photosc
iraf.imarith('_model_sh','*',photosc,'_model_sc')
iraf.imdel('_df_ga.fits')
'correction for the smoothing that was applied to the CFHT'
if width_cfhtsm != 0:
iraf.gauss('_df_sub','_df_ga',width_cfhtsm) # iraf.gauss('%s'%df_image,'_df_ga',width_cfhtsm)
else:
print 'WARNING: No smoothing applied to the dragonfly image before subtracting the model.'
iraf.imcopy('_df_sub.fits','_df_ga.fits')
'subtract the model from the dragonfly cutout'
iraf.imarith('_df_ga','-','_model_sc','_res')
'duplicate the residual so that we can redo the masking step later'
iraf.imcopy('_res.fits','_res_org.fits')
'mask the image to get block out stuff that didnt subtract nicely'
mask('_res',upperlim=upperlim)
return None
if i == 0:
x0 = g[0][0]
y0 = g[0][1]
f.write('%d %d\n' % (round(x0-g[0][0],0),round(y0-g[0][1],0)))
if i != len(images)-1:
iraf.disp(images[-1][0:-1],1)
os.system('rm tmpcoords')
iraf.imexam(Stdout = 'tmpcoords')
g = numpy.loadtxt('tmpcoords')
f.write('%d %d\n' % (round(x0-g[0][0],0),round(y0-g[0][1],0)))
f.close()
os.system('rm tmpcoords')
print '\n Aligning... \n'
# Shift the images
os.system('rm aligned_list')
f = open('aligned_list','a')
for i in range(0,len(images)):
indx = images[i][::-1].index('/')
image = images[i][-indx:-1]
f.write('al_%s\n' % image)
f.close()
os.system('rm al_red*')
iraf.imshift('@imagelist','@aligned_list',shifts='shifts')
os.system('mv al_red_* ../SCIENCE/')
print(' X offset : {0:.3f} pix, Y offset : {1:.3f} pix'.format(offset_X, offset_Y))
f.write("{0:.3f} {1:.3f} \n".format(offset_X, offset_Y))
f.close()
# ----- Shifting & combining Ha sum images ----- #
# Shifting w/ IRAF/imshift task
iraf.images()
iraf.imgeom()
iraf.unlearn('imshift')
os.system('ls -1 Ha_sum-*.fits > input_Ha_sum.lis')
os.system('rm -rfv al1_Ha_sum*.fits')
iraf.imshift(input='@input_Ha_sum.lis', output='al1_//@input_Ha_sum.lis', shifts_file='offset.txt',
interp_type='linear', boundary_type='nearest')
al1_list = sorted(glob.glob('al1_Ha_sum*.fits'))
f1 = open('al1_comb1.lis', 'w')
f2 = open('al1_comb2.lis', 'w')
for i in np.arange(len(ic.cube_list)):
cb = ic.cube_list[i].split('/')[-1].split('cstxeqxbrg')[-1].split('_3D.fits')[0]
# cb = ic.cube_list[i].split('_3D.fits')[0].split(ic.dir_redux+'cstxeqxbrg')[1]
if (cb not in ic.cube_spa_off):
f1.write(al1_list[i]+'\n')
else:
f2.write(al1_list[i]+'\n')
f1.close()
f2.close()
def p60sub(refimage, inlist, ra=-1, dec=-1, dx=-1, dy=-1, \
nsx=5, nsy=5, sx=1, sy=1, sat1=60000, sat2=60000, min=5, ms=130, dback=1, \
dspace=2, hms=9, hss=15, thresh=20, seeing=5.0, scale=0.0, tol=0.005, \
matchlist="", goodmatch=2.0, aorder=2, nstars=30, outim="", MAX=900, \
order=3, clobber=globclob):
""" subtraction pipeline for one object/filter P60 data """
# Parse inputs
infiles = iraffiles(inlist)
# Find location (in pixels) of central object if specified
[nx, ny] = get_head(refimage, ['NAXIS1', 'NAXIS2'])
if not ((ra == -1) and (dec == -1)):
[xcenpix, ycenpix] = imwcs2pix(refimage, ra, dec)
else:
xcenpix = (nx / 2.0)
ycenpix = (ny / 2.0)
[ra, dec] = impix2wcs(refimage, xcenpix, ycenpix)
# Trim the reference image
if (dx == -1):
xl = 1
xu = nx
else:
xl = int(xcenpix - (dx / 2.0))
xu = int(xcenpix + (dx / 2.0))
if (dy == -1):
yl = 1
yu = ny
else:
yl = int(ycenpix - (dy / 2.0))
yu = int(ycenpix + (dy / 2.0))
check_exist('t' + refimage, 'w', clobber)
iraf.imcopy(refimage + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
't' + refimage)
# Big Loop
for image in infiles:
# Separate image rootname from extension
imageroot = image.split('.')[0]
# Check to make sure the WCS fit was successful
if not check_head(image, 'IQWCS'):
print 'Skipping image %s: IQWCS not successful' % image
infiles.remove(image)
continue
# If so, then shift all the images appropriately
else:
xsh = xcenpix - imwcs2pix(image, ra, dec)[0]
ysh = ycenpix - imwcs2pix(image, ra, dec)[1]
check_exist('s' + image, 'w', clobber)
iraf.imshift(image, 's' + image, xsh, ysh)
# Make sure sky subtraction is not done
[skysub, skybkg] = get_head(image, ['SKYSUB', 'SKYBKG'])
if (skysub == 1):
iraf.imarith('s'+image,'+',skybkg,'s'+image)
# Trim the images
check_exist('ts' + image, 'w', clobber)
iraf.imcopy('s' + image + '[%i:%i,%i:%i]' % (xl, xu, yl, yu), \
'ts' + image)
# Cosmic Ray Cleaning
iraf.lacos_im('ts%s' % image, 'cts%s' % image, 'ts%s_crmask' % \
imageroot, gain=2.2, readn=5.3, niter=3)
# Register the images
os.system('$REDUCTION/imregister_new.pl t%s cts%s -sat %i -thresh %i -s %f -scale %f -tol %f %s -o %i -g %i -n %i -out cts%s.shift.fits -max %i' % (refimage, image, sat1, thresh, seeing, scale, tol, matchlist, order, goodmatch, nstars, imageroot, MAX))
# Write the configuration parameters for subtraction
pfile = open('default_config', 'w+')
pfile.write('nstamps_x %i\n' % nsx)
pfile.write('nstamps_y %i\n' % nsy)
pfile.write('sub_x %i\n' % sx)
pfile.write('sub_y %i\n' % sy)
pfile.write('half_mesh_size %i\n' % hms)
pfile.write('half_stamp_size %i\n' % hss)
pfile.write('deg_bg %i\n' % dback)
pfile.write('saturation1 %f\n' % sat1)
pfile.write('saturation2 %f\n' % sat2)
pfile.write('pix_min %i\n' % min)
pfile.write('min_stamp_center %i\n' % ms)
pfile.write('ngauss 3\n')
pfile.write('deg_gauss1 6\n')
pfile.write('deg_gauss2 4\n')
pfile.write('deg_gauss3 2\n')
pfile.write('sigma_gauss1 0.7\n')
pfile.write('sigma_gauss2 1.5\n')
pfile.write('sigma_gauss3 2.0\n')
pfile.write('deg_spatial %i\n' % dspace)
pfile.write('reverse 0\n')
pfile.write('stampsbyxy 0\n')
pfile.close()
# Run the subtraction
os.system("$REDUCTION/ISIS/bin/mrj_phot t%s cts%s.shift.fits" \
% (refimage, imageroot))
# Clean up files
os.system("mv conv.fits cts%s.shift.sub.fits" % imageroot)
os.system("mv conv0.fits cts%s.shift.conv.fits" % imageroot)
# Return
print 'Exiting successfully'
return
list_temp = []
list_total = []
iraf.cd(path + galname + '/shifts')
for index in range(len(shift_fin)):
arr1 = shift_fin[index]
for index2 in range(len(arr1)):
i_nam = arr1[index2][0] # Initial name for imshift
xs = arr1[index2][1]
ys = arr1[index2][2]
posa = arr1[index2][3]
list_in = i_nam.replace('.fits', '.fits[1,overwrite+]')
list_ot = list_in.replace('x2d', 'shift')
list_out = list_ot.replace('.fits[1,overwrite+]', '.fits')
list_temp.append(list_out)
if not os.path.exists(list_out):
iraf.imshift(input=list_in, output=list_out, xshift=xs, yshift=ys)
print('File ' + str(i_nam) + ' changed to ' + str(list_out) + '!')
list_total.append(list_temp)
list_temp = []
#
# Now we have to combine the images as long as they have not been created yet
# We use the IRAF task imcombine
# The rejection algorithm would be ccdclip, with weights for combining (TEXPTIME in headers), readnoise = 4.32 and gain = 1.
#
if os.path.exists('*comb.fits'):
print(
'Combined images present in this folder. Imcombine will not be applied. Please, delete them if needed! '
)
if not os.path.exists('*comb.fits'):
for index3 in range(len(list_total)):
def subract(df_image, psf):
iraf.imdel('_model*.fits')
iraf.imdel('_res*.fits')
iraf.imdel('_df_sub')
'subtract the sky value from the dragonfly image header'
df_backval = fits.getheader(df_image)['BACKVAL']
iraf.imarith('%s' % df_image, '-', '%s' % df_backval, '_df_sub')
'convolve the model with the Dragonfly PSF'
if usemodelpsf:
makeallisonspsf()
psf = './psf/psf_static_fullframe.fits'
### XXX resample the PSF by a factor of 4
if verbose:
print 'VERBOSE: Using %s for the psf convolution.' % psf
iraf.stsdas.analysis.fourier.fconvolve('_fluxmod_dragonfly', '%s' % psf,
'_model')
'shift the images so they have the same physical coordinates'
iraf.stsdas.analysis.dither.crossdriz('_df_sub.fits',
'_model.fits',
'cc_images',
dinp='no',
dref='no')
iraf.stsdas.analysis.dither.shiftfind('cc_images.fits', 'shift_values')
x_shift = 0
y_shift = 0
with open('shift_values', 'r') as datafile:
line = datafile.read().split()
x_shift = float(line[2])
y_shift = float(line[4])
print('The shift in x and y are: ' + str(x_shift) + ',' + str(y_shift))
iraf.imshift('_model', '_model_sh', 0 - x_shift, 0 - y_shift)
'scale the model so that roughly the same as the _df_sub image'
if usemodelpsf:
iraf.imarith(
'_model_sh', '/', 16., '_model_sc'
) ## just trying to match it to the original (below) approximately
else:
iraf.imarith(
'_model_sh', '/', 2422535.2, '_model_sc'
) ## difference between the flux of the star used to make the psf in both frames
iraf.imarith('_model_sc', '*', 1.5, '_model_sc')
'subtract the model frm the dragonfly cutout'
iraf.imarith('_df_sub', '-', '_model_sc', '_res')
'????'
iraf.imcopy('_model_sc', '_model_mask')
iraf.imreplace('_model_mask.fits', 0, upper=50)
iraf.imreplace('_model_mask.fits', 1, lower=0.01)
iraf.boxcar('_model_mask', '_model_maskb', 5, 5)
iraf.imreplace('_model_maskb.fits', 1, lower=0.1)
iraf.imreplace('_model_maskb.fits', 0, upper=0.9)
iraf.imarith(1, '-', '_model_maskb', '_model_maskb')
iraf.imarith('_model_maskb', '*', '_res', '_res_final')
return None
shimlst22.close()
xave = sum(x) / len(x)
yave = sum(y) / len(y)
#print x,y sum(x)/len(x), sum(y)/len(y), xave,yave
# shifting of images
print "> shifting images"
iraf.lintran("imexamine.irafl",
x1=xave,
y1=yave,
angle=180,
Stdout="shiftlis.irafl") # angle set for SKinakas IR
iraf.imshift("@images_to_shift.irafl",
output="@shifted_images.irafl",
shifts_file="shiftlis.irafl",
boundary_type="constant",
constant=0.0)
# creating the final image
print "> creating the final combined image,", rname + '_comb.fits'
iraf.imcombine("@shifted_images.irafl",
output=rname + '_comb.fit',
combine="median")
# cleaning ...
rmvoption = raw_input(
'Would you like to remove all intermediate steps? [press "n" for no, else "enter"]\t'
)
if rmvoption == "n" or rmvoption == "N":
print "\n Enjoy! ;)\n"
os.chdir(working_dir)
# Stop point #1
sys.exit('Split images generated')
# Shifting cubes with offset.txt (IRAF/imshift task)
os.chdir(split_dir)
iraf.unlearn('imshift')
for j in np.arange(np.shape(d_sci_cut)[0]):
print('Shifting frame {0:04d}'.format(j+1))
os.system('rm -rfv al1_N*_2D_{0:04d}.fits'.format(j+1))
os.system('ls -1 N*_2D_{0:04d}.fits > split_{1:04d}.lis'.format(j+1, j+1))
iraf.sleep(1.0)
iraf.imshift(input='@split_{0:04d}.lis'.format(j+1), output='al1_//@split_{0:04d}.lis'.format(j+1),
shifts_file='../offset.txt', interp_type='linear', boundary_type='nearest')
os.system('rm -rfv al1_N*_VAR_{0:04d}.fits'.format(j+1))
os.system('ls -1 N*_VAR_{0:04d}.fits > variance_{1:04d}.lis'.format(j+1, j+1))
iraf.sleep(1.0)
iraf.imshift(input='@variance_{0:04d}.lis'.format(j+1), output='al1_//@variance_{0:04d}.lis'.format(j+1),
shifts_file='../offset.txt', interp_type='linear', boundary_type='nearest')
os.chdir(working_dir)
# Stop point #2
sys.exit('Shifted images generated.')
