def test():
import threedhst.prep_flt_astrodrizzle as prep
asn_file = 'HOPS183B-10-140-G141_asn.fits'
#asn_file = 'HOPS183B-10-140-F160W_asn.fits'
threedhst.process_grism.fresh_flt_files(asn_file, from_path='../RAW/', preserve_dq=False)
asn = threedhst.utils.ASNFile(asn_file)
for exp in asn.exposures:
prep.subtract_fixed_background(flt_file='%s_flt.fits' %(exp), path_to_raw='../RAW/')
def m82():
import glob
import os
import threedhst
import threedhst.prep_flt_astrodrizzle as init
from threedhst import catIO
import unicorn
import research.hawkiff
import stsci.convolve
import research.pab.align as align
### Make ACS associations
unicorn.candels.make_asn_files(uniquename=True, translate={'-ROT': ''})
threedhst.options['FLT_PERSISTENCE_PATH'] = '../Persistence/'
#### IR
files = glob.glob('*-F1*asn.fits')
radec = None
for asn_file in files:
#init.prep_direct_grism_pair(direct_asn=asn_file, grism_asn=None, radec=radec, ACS=False, align_threshold=5, raw_path='../RAW/', order=0)
threedhst.process_grism.fresh_flt_files(asn_file)
asn = threedhst.utils.ASNFile(asn_file)
for exp in asn.exposures:
init.subtract_fixed_background(flt_file='%s_flt.fits' % (exp))
#drizzlepac.astrodrizzle.AstroDrizzle(asn_file, static=False, skysub=False, driz_separate=True, driz_sep_wcs=False, median=True, blot=True, driz_cr=True, driz_combine=True, final_wht_type='IVM', clean=True, final_wcs=True, final_rot=0, final_scale=0.128, final_pixfrac=1, context=False, resetbits=0, final_bits=64, preserve=False)
#
ims = []
hdrs = []
for asn_file in files:
asn = threedhst.utils.ASNFile(asn_file)
### cross correlation
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' % (exp))
slx, sly = slice(400, 600), slice(400, 600)
#slx, sly = slice(450,550), slice(450,550)
ims.append((flt[1].data * (flt[3].data == 0))[sly, slx])
hdrs.append(flt[1].header)
shifts = [[0, 0]]
for i in range(1, len(ims)):
ccorr = stsci.convolve.correlate2d(ims[0], ims[i], fft=1)
from astropy.modeling import models, fitting
poly = models.Polynomial2D(degree=4)
lsq = fitting.LevMarLSQFitter()
yp, xp = np.indices(ccorr.shape)
pfit = lsq(poly, xp, yp, ccorr)
#ix = np.unravel_index(np.argmax(ccorr-pfit(xp, yp)), ccorr.shape)
sh = ccorr.shape
ix = [sh[0] / 2, sh[1] / 2]
ccorr[ix[0], ix[1]] = np.nan
ds9.view(ccorr - pfit(xp, yp))
status = raw_input('Mark center of ccorr peak')
xcf = np.cast[float](ds9.get('pan image').split())
xc = np.cast[int](np.round(xcf))
N = 10
sly2, slx2 = slice(xc[1] - N - 1,
xc[1] + N - 1), slice(xc[0] - N - 1, xc[0] + N - 1)
sub = (ccorr - pfit(xp, yp))[sly2, slx2]
flat = models.Polynomial2D(degree=1)
gauss = models.Gaussian2D(100000,
xcf[0],
xcf[1],
1,
1,
0,
bounds={
'x_mean': [xcf[0] - 5, xcf[0] + 5],
'y_mean': [xcf[1] - 5, xcf[1] + 5]
})
ok = np.isfinite(sub)
gfit = lsq(flat + gauss, xp[sly2, slx2][ok], yp[sly2, slx2][ok],
sub[ok])
shifts.append([ix[1] - gfit.x_mean_1, ix[0] - gfit.y_mean_1])
print shifts[-1]
wcs = pywcs.WCS(hdrs[0])
crpix = [[hdrs[0]['CRPIX1'], hdrs[0]['CRPIX2']]]
for i in range(1, len(ims)):
wcs = pywcs.WCS(hdrs[i])
xy = wcs.all_world2pix([hdrs[0]['CRVAL1']], [hdrs[0]['CRVAL2']], 1)
crpix.append([xy[0][0], xy[1][0]])
print crpix[-1]
dx = np.array(shifts) - (np.array(crpix) - crpix[0])
### M82
dx = np.array([[0., 0.], [0.1692337, -0.03445068],
[0.11051269, 0.26955819], [0.04652894, 0.30199132]])
i0 = 0
for asn_file in files:
asn = threedhst.utils.ASNFile(asn_file)
fp = open(asn_file.replace('_asn.fits', '_shifts.txt'), 'w')
fp.write("""# frame: output
# refimage: %s_flt.fits[1]
# form: delta
# units: pixels
""" % (asn.exposures[0]))
for exp in asn.exposures:
fp.write('%s_flt.fits %.4f %.4f 0, 1.0 0.1 0.1\n' %
(exp, dx[i0][0], dx[i0][1]))
flt = pyfits.open('%s_flt.fits' % (exp), mode='update')
flt[1].header['CRPIX1'] += dx[i0][0]
flt[1].header['CRPIX2'] += dx[i0][1]
flt.flush()
i0 += 1
fp.close()
for asn_file in files:
drizzlepac.astrodrizzle.AstroDrizzle(asn_file,
static=False,
skysub=True,
driz_separate=True,
driz_sep_wcs=False,
median=True,
blot=True,
driz_cr=True,
driz_combine=True,
final_wht_type='IVM',
clean=True,
final_wcs=True,
final_rot=0,
final_scale=0.1,
final_pixfrac=1,
context=False,
resetbits=4096,
final_bits=64,
preserve=False)
for asn_file in files:
align.coarse_align(image=asn_file.replace('_asn.fits',
'_drz_sci.fits'),
ref='h_m82_v_s20_drz_sci.fits')
def m82():
import glob
import os
import threedhst
import threedhst.prep_flt_astrodrizzle as init
from threedhst import catIO
import unicorn
import research.hawkiff
import stsci.convolve
import research.pab.align as align
### Make ACS associations
unicorn.candels.make_asn_files(uniquename=True, translate={'-ROT': ''})
threedhst.options['FLT_PERSISTENCE_PATH'] = '../Persistence/'
#### IR
files = glob.glob('*-F1*asn.fits')
radec = None
for asn_file in files:
#init.prep_direct_grism_pair(direct_asn=asn_file, grism_asn=None, radec=radec, ACS=False, align_threshold=5, raw_path='../RAW/', order=0)
threedhst.process_grism.fresh_flt_files(asn_file)
asn = threedhst.utils.ASNFile(asn_file)
for exp in asn.exposures:
init.subtract_fixed_background(flt_file='%s_flt.fits' % (exp))
#drizzlepac.astrodrizzle.AstroDrizzle(asn_file, static=False, skysub=False, driz_separate=True, driz_sep_wcs=False, median=True, blot=True, driz_cr=True, driz_combine=True, final_wht_type='IVM', clean=True, final_wcs=True, final_rot=0, final_scale=0.128, final_pixfrac=1, context=False, resetbits=0, final_bits=64, preserve=False)
#
ims = []
hdrs = []
for asn_file in files:
asn = threedhst.utils.ASNFile(asn_file)
### cross correlation
for exp in asn.exposures:
flt = pyfits.open('%s_flt.fits' % (exp))
slx, sly = slice(400, 600), slice(400, 600)
#slx, sly = slice(450,550), slice(450,550)
ims.append((flt[1].data * (flt[3].data == 0))[sly, slx])
hdrs.append(flt[1].header)
shifts = [[0, 0]]
for i in range(1, len(ims)):
ccorr = stsci.convolve.correlate2d(ims[0], ims[i], fft=1)
from astropy.modeling import models, fitting
poly = models.Polynomial2D(degree=4)
lsq = fitting.LevMarLSQFitter()
yp, xp = np.indices(ccorr.shape)
pfit = lsq(poly, xp, yp, ccorr)
#ix = np.unravel_index(np.argmax(ccorr-pfit(xp, yp)), ccorr.shape)
sh = ccorr.shape
ix = [sh[0] / 2, sh[1] / 2]
ccorr[ix[0], ix[1]] = np.nan
ds9.view(ccorr - pfit(xp, yp))
status = raw_input('Mark center of ccorr peak')
xcf = np.cast[float](ds9.get('pan image').split())
xc = np.cast[int](np.round(xcf))
N = 10
sly2, slx2 = slice(xc[1] - N - 1, xc[1] + N - 1), slice(
xc[0] - N - 1, xc[0] + N - 1)
sub = (ccorr - pfit(xp, yp))[sly2, slx2]
flat = models.Polynomial2D(degree=1)
gauss = models.Gaussian2D(
100000,
xcf[0],
xcf[1],
1,
1,
0,
bounds={
'x_mean': [xcf[0] - 5, xcf[0] + 5],
'y_mean': [xcf[1] - 5, xcf[1] + 5]
})
ok = np.isfinite(sub)
gfit = lsq(flat + gauss, xp[sly2, slx2][ok], yp[sly2, slx2][ok],
sub[ok])
shifts.append([ix[1] - gfit.x_mean_1, ix[0] - gfit.y_mean_1])
print shifts[-1]
wcs = pywcs.WCS(hdrs[0])
crpix = [[hdrs[0]['CRPIX1'], hdrs[0]['CRPIX2']]]
for i in range(1, len(ims)):
wcs = pywcs.WCS(hdrs[i])
xy = wcs.all_world2pix([hdrs[0]['CRVAL1']], [hdrs[0]['CRVAL2']], 1)
crpix.append([xy[0][0], xy[1][0]])
print crpix[-1]
dx = np.array(shifts) - (np.array(crpix) - crpix[0])
### M82
dx = np.array([[0., 0.], [0.1692337, -0.03445068],
[0.11051269, 0.26955819], [0.04652894, 0.30199132]])
i0 = 0
for asn_file in files:
asn = threedhst.utils.ASNFile(asn_file)
fp = open(asn_file.replace('_asn.fits', '_shifts.txt'), 'w')
fp.write("""# frame: output
# refimage: %s_flt.fits[1]
# form: delta
# units: pixels
""" % (asn.exposures[0]))
for exp in asn.exposures:
fp.write('%s_flt.fits %.4f %.4f 0, 1.0 0.1 0.1\n' %
(exp, dx[i0][0], dx[i0][1]))
flt = pyfits.open('%s_flt.fits' % (exp), mode='update')
flt[1].header['CRPIX1'] += dx[i0][0]
flt[1].header['CRPIX2'] += dx[i0][1]
flt.flush()
i0 += 1
fp.close()
for asn_file in files:
drizzlepac.astrodrizzle.AstroDrizzle(
asn_file,
static=False,
skysub=True,
driz_separate=True,
driz_sep_wcs=False,
median=True,
blot=True,
driz_cr=True,
driz_combine=True,
final_wht_type='IVM',
clean=True,
final_wcs=True,
final_rot=0,
final_scale=0.1,
final_pixfrac=1,
context=False,
resetbits=4096,
final_bits=64,
preserve=False)
for asn_file in files:
align.coarse_align(
image=asn_file.replace('_asn.fits', '_drz_sci.fits'),
ref='h_m82_v_s20_drz_sci.fits')
