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drizzle_stamp.py
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drizzle_stamp.py
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"""
Try "drizzling" a region of grism FLTs at a specific wavelength to an output stamp
"""
import os
import glob
import numpy as np
import astropy.io.fits as pyfits
from drizzlepac import astrodrizzle
import stwcs
def diff(xarr):
"""
Like np diff but make same size as input array filling first element
with diff[0]
"""
import numpy as np
d = np.diff(xarr)
return np.append(d[0], d)
def get_drizzled_cutouts():
### Optinally display products to ds9 with, e.g., pysao.ds9
try:
import pysao
ds9 = pysao.ds9()
ds9.set('scale limits -0.08 8')
ds9.set('lock colorbar')
ds9.set('frame lock wcs')
ds9.set('tile')
for f in range(6):
ds9.frame(f+1)
except:
ds9 = None
### Full mosaic, also provides a reference WCS
im_mosaic = pyfits.open('../../MACS1149/Catalog/MACS1149-F160W_drz_sci.fits')
wcs_mosaic = stwcs.wcsutil.HSTWCS(im_mosaic, ext=0)
### Get cutout of NX pixels around the center position from the reference mosaic
NX, pix_scale = 50, 0.065
wcs_mosaic.updatePscale(pix_scale)
line_wavelengths = {'O2':3727, 'Ne3':3869, 'Hb':4861, 'O3':5007, 'Ha':6563., 'S2':6724}
### Object
stats = {'id':404, 'z':1.40707, 'lines':{'G141':['O3','Ha','S2'], 'G102':['O2']}}
stats = {'id':1422, 'z':2.27763, 'lines':{'G141':['O2','Hb','O3']}}
stats = {'id':1917, 'z':1.89105, 'lines':{'G141':['Hb','O3'], 'G102':['O2']}}
stats = {'id':2315, 'z':1.8936, 'lines':{'G141':['Hb','O3'], 'G102':['O2']}}
stats = {'id':2389, 'z':1.8936, 'lines':{'G141':['Hb','O3'], 'G102':['O2']}}
stats = {'id':3746, 'z':1.2477, 'lines':{'G141':['O3','Ha','S2'], 'G102':['O2','Hb','O3']}}
import collections
drizzled = collections.OrderedDict()
for grism in stats['lines'].keys():
files=glob.glob('MACS1149-???-%s_%05d*2D.fits' %(grism, stats['id']))
for line in stats['lines'][grism]:
lam = line_wavelengths[line]*(1+stats['z'])
drizzled['%s_%s' %(grism, line)] = driz_from_twod(files, lam=lam, pixfrac=0.5, wcs_mosaic=wcs_mosaic, ds9=ds9)
### Show all
keys = drizzled.keys()
for i, key in enumerate(keys):
print 'Frame %d: %s' %(i+1, key)
ds9.frame(i+1)
ds9.view(drizzled[key][1], header=drizzled[key][2])
def driz_from_twod(files, lam=1.4e4, pixfrac=0.5, wcs_mosaic=None, ds9=None):
"""
Drizzle from 2D cutout spectra
"""
import os
### Drizzle 2D spectra!
for i, file in enumerate(files):
### Open the 2D spectrum
twod = pyfits.open(file)
shg = twod['SCI'].data.shape
shd = twod['DSCI'].data.shape
### If "unicorn" scripts available for computing 2D models
### will still oversubtract continuum if there are bright lines
try:
import unicorn
gris = unicorn.reduce.Interlace2D(file)
gris.compute_mode()
twod['MODEL'].data = gris.model*1.
except:
pass
#### (distorted) WCS of the cutout
wcs_grism = stwcs.wcsutil.HSTWCS(twod, ext=1)
### Initialize if first spectrum
if i==0:
### Output WCS
xy = np.array(wcs_mosaic.all_world2pix([twod[0].header['RA']], [twod[0].header['DEC']], 0)).flatten()
xy0 = np.cast[int](xy)
if wcs_mosaic is None:
wcs_mosaic = wcs_grism.copy()
slx_mosaic = slice(xy0[0]-NX,xy0[0]+NX)
sly_mosaic = slice(xy0[1]-NX,xy0[1]+NX)
wcs_mosaic_slice = wcs_mosaic.slice((sly_mosaic, slx_mosaic))
out_head = wcs_mosaic_slice.to_header()
for ii in [1,2]:
for jj in [1,2]:
if 'PC%d_%d' %(ii,jj) in out_head:
out_head['CD%d_%d' %(ii,jj)] = out_head['PC%d_%d' %(ii,jj)]
out_head.remove('PC%d_%d' %(ii,jj))
### Initialize drizzle products
dsci = np.zeros((2*NX, 2*NX), dtype=np.float32)
dwht = np.zeros((2*NX, 2*NX), dtype=np.float32)
dcon = np.zeros((2*NX, 2*NX), dtype=np.int32)
gsci = np.zeros((2*NX, 2*NX), dtype=np.float32)
gwht = np.zeros((2*NX, 2*NX), dtype=np.float32)
gcon = np.zeros((2*NX, 2*NX), dtype=np.int32)
### Drizzle direct thumbnail
astrodrizzle.adrizzle.do_driz(twod['DINTER'].data, wcs_grism, twod['DWHT'].data, wcs_mosaic_slice, dsci, dwht, dcon, 1., 'cps', 1, wcslin_pscale=1, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=np.nan, stepsize=10, wcsmap=None)
#############
### Drizzle 2D spectrum
### Center pixel along the trace at the desired wavelength
x0 = np.interp(lam, twod['WAVE'].data, np.arange(shg[1])) - shd[1]/2
y0 = np.interp(lam, twod['WAVE'].data, twod['YTRACE'].data) - shd[0]/2
### Drizzle the contamination & model-subtracted spectrum. Here, the ['MODEL'] extension is likely only
### a rough approxtwodation, since it won't include strong emission lines
clean = twod['SCI'].data-twod['CONTAM'].data-twod['MODEL'].data
### Units of line flux (1e-17 erg/s/cm2)
clean_flux = clean / twod['SENS'].data * diff(twod['WAVE'].data)
### Shift the 2D spectrum so that it should line up on top of the direct thumbnail, where the WCS is defined
gris_thumb = np.roll(np.roll(clean_flux, -int(np.floor(x0)), axis=1), -int(np.floor(y0)), axis=0)
### Need the shifted uncertainty extension as well, which will indicate the masked pixels
wht_thumb = np.cast[np.float32](1/np.roll(np.roll(twod['WHT'].data, -int(np.floor(x0)), axis=1), -int(np.floor(y0)), axis=0)**2)
wht_thumb[~np.isfinite(wht_thumb)] = 0
## Slightly shifted WCS to account for pixel centering
hdu = wcs_grism.to_fits(relax=True)
hdu[0].data = gris_thumb*1
hdu[0].header['CRPIX1'] += x0-np.floor(x0)
hdu[0].header['CRPIX2'] += y0-np.floor(y0)
wcs_grism_spec = stwcs.wcsutil.HSTWCS(hdu, ext=0)
## Drizzle it
astrodrizzle.adrizzle.do_driz(gris_thumb, wcs_grism_spec, wht_thumb, wcs_mosaic_slice, gsci, gwht, gcon, 1., 'cps', 1, wcslin_pscale=1, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=np.nan, stepsize=10, wcsmap=None)
if ds9:
ds9.frame(i+1)
ds9.view(twod['DINTER'].data, header=twod['DINTER'].header)
ds9.frame(5)
ds9.view(dsci/wcs_grism.pscale**2, header=out_head)
ds9.frame(6)
ds9.view(gsci/wcs_grism_spec.pscale**2, header=out_head)
#time.sleep(1)
dsci /= wcs_grism.pscale**2
gsci /= wcs_grism_spec.pscale**2
return dsci, gsci, out_head
#ds9.frame(6)
#ds9.view(im_mosaic[0].data[sly_mosaic, slx_mosaic], header=out_h)
########## This all testing below
def test_driz_from_twod():
import os
import astropy.io.fits as pyfits
from drizzlepac import astrodrizzle
import stwcs
### Checking
flt = pyfits.open('../PREP_Apr15/ica521naq_flt.fits')
inter = pyfits.open('../PREP_Apr15/MACS1149-032-F160W_inter.fits')
h_grow = unicorn.reduce.scale_header_wcs(flt[1].header.copy(), factor=2, growx=2, growy=2, pad=60, NGROWX=200, NGROWY=50)
out = inter[1].data*1
# ### Try dummy interlaced
# flt_grow = np.zeros((2028,2028))
# for i in [0,1]:
# for j in [0,1]:
# flt_grow[i::2,j::2] += flt[1].data
#
# flt_wcs = stwcs.wcsutil.HSTWCS(flt, 1)
# flt_wcs_grow = stwcs.wcsutil.HSTWCS(flt, 1)
# h_grow = unicorn.reduce.scale_header_wcs(flt[1].header.copy(), factor=1, growx=2, growy=2, pad=0, NGROWX=0, NGROWY=0)
### try just CDELT
#h_grow = flt[1].header.copy()
#h_grow['CDELT1'] /= 2
#h_grow['CDELT2'] /= 2
hdu_grow = pyfits.HDUList([pyfits.ImageHDU(data=out, header=h_grow)])
grow_wcs = stwcs.wcsutil.HSTWCS(hdu_grow, ext=0)
sk = 50
yp, xp = np.indices((1014/sk, 1014/sk))
rd = flt_wcs.all_pix2world(xp.flatten()*sk, yp.flatten()*sk, 0)
xp_grow, yp_grow = grow_wcs.all_world2pix(rd[0], rd[1], 0)
ds9.frame(1)
ds9.view(flt[1].data, header=flt[1].header)
ds9.frame(2)
ds9.view(inter[1].data, header=inter[1].header)
#ds9.frame(4)
ds9.view(inter[1].data, header=header)
files=glob.glob('*model.fits')
models = []
for file in files:
im = pyfits.open(file.replace('_model',''))
grism = im[0].header['FILTER']
direct = {'G102':'F105W', 'G141':'F160W'}[grism]
model = unicorn.reduce.process_GrismModel(root=file.split('-%s' %(grism))[0], grism=grism, direct=direct)
#### Fix headers with updated "scale_header_wcs"
root = file.split('_')[0]
asn = threedhst.utils.ASNFile('../PREP_Apr15/%s_asn.fits' %(root))
flt = pyfits.open('../PREP_Apr15/%s_flt.fits' %(asn.exposures[0]))
h = model.gris[1].header
growx, growy, pad = h['GROWX'], h['GROWY'], h['PAD']
ngrowx, ngrowy = h['NGROWX'], h['NGROWY']
header = unicorn.reduce.scale_header_wcs(flt[1].header.copy(), factor=1, growx=growx, growy=growy, pad=pad, NGROWX=ngrowx, NGROWY=ngrowy)
model.gris[1].header.update(header)
model.gris_wcs = stwcs.wcsutil.HSTWCS(model.gris, ext=1)
models.append(model)
# id=3746
# lam = 1.1275e4
#
# im_mosaic = pyfits.open('../MACS1149/Catalog/MACS1149-F160W_drz_sci.fits')
# wcs_mosaic = stwcs.wcsutil.HSTWCS(im_mosaic, ext=0)
#
# N = 10
#
# ### re-extract 2D
if len(glob.glob('*%05d.2D.fits' %(id))) < 100:
for model in models:
model.twod_spectrum(id, miny=-80, refine=False)
files=glob.glob('MACS1149-???-%s_%05d*2D.fits' %(grism, id))
#files=glob.glob('MACS1149-???-G102_%05d*2D.fits' %(id))
for i, file in enumerate(files):
im = pyfits.open(file)
shg = im['SCI'].data.shape
shd = im['DSCI'].data.shape
if i==0:
NX = 50
xy = np.array(wcs_mosaic.all_world2pix([im[0].header['RA']], [im[0].header['DEC']], 0)).flatten()
xy0 = np.cast[int](xy)
slx_mosaic = slice(xy0[0]-NX,xy0[0]+NX)
sly_mosaic = slice(xy0[1]-NX,xy0[1]+NX)
wcs_mosaic_slice = wcs_mosaic.slice((sly_mosaic, slx_mosaic))
out_h = wcs_mosaic_slice.to_header()
outsci = np.zeros((2*NX, 2*NX), dtype=np.float32)
outwht = np.zeros((2*NX, 2*NX), dtype=np.float32)
outcon = np.zeros((2*NX, 2*NX), dtype=np.int32)
gsci = np.zeros((2*NX, 2*NX), dtype=np.float32)
gwht = np.zeros((2*NX, 2*NX), dtype=np.float32)
gcon = np.zeros((2*NX, 2*NX), dtype=np.int32)
root = file.split('_')[0]
inter_grism = pyfits.open('../PREP_Apr15/%s_inter.fits' %(root))
inter_ref_grism = pyfits.open('../PREP_Apr15/%s_ref_inter.fits' %(root))
#wcs_grism = stwcs.wcsutil.HSTWCS(inter_grism, ext=1)
#### Drizzle individual FLTs
#asn = threedhst.utils.ASNFile('../PREP_Apr15/%s-F160W_asn.fits' %(root.split('-G1')[0]))
# for exp in asn.exposures:
# print exp
# flt = pyfits.open('../PREP_Apr15/%s_flt.fits' %(exp))
# wcs_flt = stwcs.wcsutil.HSTWCS(flt, ext=1)
# wht = 1/flt['ERR'].data**2
# wht[flt['DQ'].data > 0] = 0
# wht[flt['SCI'].data/flt['ERR'].data < -3] = 0
#
# astrodrizzle.adrizzle.do_driz(flt['SCI'].data, wcs_flt, wht, wcs_mosaic_slice, outsci, outwht, outcon, 1., 'cps', 1, wcslin_pscale=1.0, uniqid=1, pixfrac=0.5, kernel='square', fillval=0, stepsize=10, wcsmap=None)
# ds9.view(outsci)
#### Get fixed interlaced header
asn = threedhst.utils.ASNFile('../PREP_Apr15/%s_asn.fits' %(root))
flt = pyfits.open('../PREP_Apr15/%s_flt.fits' %(asn.exposures[0]))
print asn.exposures[0]
header = unicorn.reduce.scale_header_wcs(flt[1].header.copy(), factor=1, growx=2, growy=2, pad=60, NGROWX=200, NGROWY=50)
inter_grism[1].header = header.copy()
wcs_grism = stwcs.wcsutil.HSTWCS(inter_grism, ext=1)
h = im[0].header
slx = slice(h['XDIRECT0'], h['XDIRECT0']+shd[1])
sly = slice(h['YDIRECT0'], h['YDIRECT0']+shd[0])
wcs_grism_slice = wcs_grism.slice((sly, slx))
grism_cutout = inter_ref_grism[1].data[sly, slx]*1.
#ds9.view(grism_cutout, header=wcs_grism_slice.to_header(relax=True))
ds9.frame(i+1)
ds9.view(im['DINTER'].data, header=wcs_grism_slice.to_header(relax=True))
#wcs_grism_slice.wcs.crpix += dx #np.array(shd)/2.-dx
wcs_grism_slice.naxis1 = im['DINTER'].header['NAXIS1']
wcs_grism_slice.naxis2 = im['DINTER'].header['NAXIS2']
hdu = wcs_grism_slice.to_fits(relax=True)
hdu[0].data = im['DINTER'].data*1
wcs_grism_slice2 = stwcs.wcsutil.HSTWCS(hdu, ext=0)
#astrodrizzle.adrizzle.do_driz(im['DINTER'].data, wcs_grism_slice, im['DWHT'].data, wcs_mosaic_slice, outsci, outwht, outcon, 1., 'cps', 1, wcslin_pscale=1, uniqid=1, pixfrac=0.1, kernel='square', fillval=np.nan, stepsize=10, wcsmap=wcs_functions.WCSMap)
astrodrizzle.adrizzle.do_driz(hdu[0].data, wcs_grism_slice2, im['DWHT'].data, wcs_mosaic_slice, outsci, outwht, outcon, 1., 'cps', 1, wcslin_pscale=1, uniqid=1, pixfrac=0.1, kernel='square', fillval=np.nan, stepsize=10, wcsmap=wcs_functions.WCSMap)
#astrodrizzle.adrizzle.do_driz(inter_ref_grism[1].data*1, wcs_grism, np.ones_like(inter_ref_grism[1].data, dtype=np.float32), wcs_mosaic_slice, outsci, outwht, outcon, 1., 'cps', 1, wcslin_pscale=1, uniqid=1, pixfrac=0., kernel='square', fillval=np.nan, stepsize=10, wcsmap=None)
### Drizzle the spectrum
x0 = np.interp(lam, im['WAVE'].data, np.arange(shg[1]))-shd[1]/2
y0 = np.interp(lam, im['WAVE'].data, im['YTRACE'].data)-shd[0]/2-dy
### contam and model subtracted
clean = im['SCI'].data-im['CONTAM'].data-im['MODEL'].data
clean_flux = clean / im['SENS'].data * threedhst.utils.diff(im['WAVE'].data)
gris_thumb = np.roll(np.roll(clean_flux, -int(np.floor(x0)), axis=1), -int(np.floor(y0)), axis=0)
wht_thumb = np.cast[np.float32](1/np.roll(np.roll(im['WHT'].data, -int(np.floor(x0)), axis=1), -int(np.floor(y0)), axis=0)**2)
wht_thumb[~np.isfinite(wht_thumb)] = 0
hdu = wcs_grism_slice.to_fits(relax=True)
hdu[0].data = gris_thumb*1
hdu[0].header['CRPIX1'] += x0-np.floor(x0)
hdu[0].header['CRPIX2'] += y0-np.floor(y0)
wcs_grism_slice3 = stwcs.wcsutil.HSTWCS(hdu, ext=0)
astrodrizzle.adrizzle.do_driz(gris_thumb, wcs_grism_slice3, wht_thumb, wcs_mosaic_slice, gsci, gwht, gcon, 1., 'cps', 1, wcslin_pscale=1, uniqid=1, pixfrac=0.5, kernel='square', fillval=np.nan, stepsize=10, wcsmap=wcs_functions.WCSMap)
# out = wcs_functions.WCSMap(wcs_grism_slice, wcs_mosaic_slice)
# xpo, ypo = out.forward(xp, yp)
# plt.scatter(xpo, ypo, alpha=0.5)
ds9.frame(5)
ds9.view(gsci/wcs_grism_slice3.pscale**2, header=out_h)
#time.sleep(1)
gsci /= wcs_grism_slice3.pscale**2
outsci /= wcs_grism_slice2.pscale**2
ds9.frame(6)
ds9.view(im_mosaic[0].data[sly_mosaic, slx_mosaic], header=out_h)
def test():
import numpy as np
from drizzlepac import astrodrizzle
import stwcs
from threedhst import catIO
import research.hawki_driz
import unicorn
import unicorn.interlace_test
import os
import astropy.io.fits as pyfits
grism, filter = 'G102', 'F105W'
main_root = 'FIGS-GS1-G102'
#flt_files = glob.glob('icoi1*flt.fits')
id=28445 # two knots
id = 29649 # edge-on
id = 30691 # fuzzy
lrest = 6563.
z = None
size=3
pixscale=0.05
pixfrac = 0.4
#twod_files = glob.glob('FIGS-GS1-*-G102_%05d.2D.fits' %(id))
#twod = unicorn.reduce.Interlace2D(twod_files[0])
#wave = 10625.5
gris = unicorn.interlace_test.SimultaneousFit('%s_%05d' %(main_root, id), fast=True)
if z is None:
wave = lrest*(1+gris.z_max_spec)
else:
wave = lrest*(1+z)
print 'Continue? (wave=%.1f, lrest=%.1f)' %(wave, lrest)
var = raw_input()
if var != '':
print breakme
else:
print 'OK!'
ra, dec = gris.twod.im[0].header['RA'], gris.twod.im[0].header['DEC']
#ra, dec = 53.158163486, -27.7810907
hout, wcs_out = research.hawki_driz.get_wcsheader(ra=ra, dec=dec, size=size, pixscale=pixscale, get_hdu=False, theta=0)
sh = (int(hout['NAXIS2']), int(hout['NAXIS1']))
files = catIO.Table('flt_files')
flt_files = files['FILE'][files['FILTER'] == filter]
outsci = np.zeros(sh, dtype=np.float32)
outwht = np.zeros(sh, dtype=np.float32)
outcon = np.zeros(sh, dtype=np.float32)
ds9.frame(1)
for file in flt_files:
print file
flt = pyfits.open(file)
flt_wcs = stwcs.wcsutil.HSTWCS(flt, 1)
# xflt, yflt = flt_wcs.all_world2pix([ra], [dec], 0)
# x0, y0 = int(xflt[0]), int(yflt[0])
# sub_flt = flt[1].data[y0-11:y0+10, x0-30:x0+30]
# ds9.view(sub_flt)
sci = flt[1].data
wht = 1/flt['ERR'].data**2
wht = np.ones((1014,1014), dtype=np.float32)*np.median(wht)
dq = flt['DQ'].data.copy()
for bit in [64,512]:
dq -= dq & bit
msk = (dq > 0) | (~np.isfinite(wht))
wht[msk] = 0.
sci[msk] = 0.
astrodrizzle.adrizzle.do_driz(sci, flt_wcs, wht, wcs_out, outsci, outwht, outcon, 1., 'cps', 1, wcslin_pscale=1.0, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=0, stepsize=10, wcsmap=None)
ds9.view(outsci, header=hout)
## Try drizzling spectra
flt_files = files['FILE'][files['FILTER'] == grism]
import mywfc3.grism
conf = mywfc3.grism.aXeConf('%s/CONF/%s.test27s.gbb.conf' %(os.getenv('THREEDHST'), grism))
goutsci = np.zeros(sh, dtype=np.float32)
goutwht = np.zeros(sh, dtype=np.float32)
goutcon = np.zeros(sh, dtype=np.float32)
xpix = np.arange(0,300)
ds9.frame(2)
for file in flt_files:
flt = pyfits.open(file)
flt_wcs = stwcs.wcsutil.HSTWCS(flt, 1)
sci = flt[1].data
wht = 1/flt['ERR'].data**2
wht = np.ones((1014,1014), dtype=np.float32)*np.median(wht)
dq = flt['DQ'].data.copy()
for bit in [64,512]:
dq -= dq & bit
#
msk = (dq > 0) | (~np.isfinite(wht))
wht[msk] = 0.
sci[msk] = 0.
### offset for wavelength
xflt, yflt = flt_wcs.all_world2pix([ra], [dec], 0)
ytrace, lam = conf.get_beam_trace(x=xflt[0], y=yflt[0], dx=xpix, beam='A')
dx = np.interp(wave, lam, xpix)
dy = np.interp(wave, lam, ytrace)
print '%s (%.1f,%.1f), (%.2f,%.2f)' %(file, xflt[0], yflt[0], dx, dy)
flt_wcs.wcs.crpix += np.array([dx, dy])
try:
flt_wcs.sip.crpix += np.array([dx, dy])
except:
pass
x0, y0 = int(xflt[0]+dx), int(yflt[0]+dy)
sub_flt = flt[1].data[y0-30:y0+30, x0-50:x0+30]
prof = np.median(sub_flt, axis=1)
yprof = np.zeros(1014)
yprof[y0-30:y0+30] += prof
sci = (sci.T-yprof).T
#ds9.view(sub_flt)
#dx = int(np.round(dx))
#dy = int(np.round(dy))
#sci =
astrodrizzle.adrizzle.do_driz(sci, flt_wcs, wht, wcs_out, goutsci, goutwht, goutcon, 1., 'cps', 1, wcslin_pscale=1.0, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=0, stepsize=10, wcsmap=None)
ds9.view(goutsci, header=hout)
### Get continuum extraction from the 2D files
import mywfc3.grism
conf = mywfc3.grism.aXeConf('%s/CONF/%s.test27s.gbb.conf' %(os.getenv('THREEDHST'), grism))
xpix = np.arange(0,300)
ds9.frame(3)
im = pyfits.open(os.getenv('iref')+'ir_wfc3_map.fits')
PAM = im[1].data
im.close()
drizzled_sci = {}
drizzled_wht = {}
drizzled_ctx = {}
drizzled_mod = {}
drizzled_contam = {}
twod_files=glob.glob('*-???-%s_%05d.2D.fits' %(grism, id))
for twod_file in twod_files:
twod = unicorn.reduce.Interlace2D(twod_file)
twod.compute_model()
root = twod_file.split('_')[0]
print 'Interlaced: %s' %(root)
inter = pyfits.open('%s_inter.fits' %(root))
sh2 = twod.model.shape
h = twod.im[0].header
sly = slice(h['YGRISM0'], h['YGRISM0']+sh2[0])
slx = slice(h['XGRISM0'], h['XGRISM0']+sh2[1])
### continuum subtraction
clean = inter[1].data*0
clean[sly, slx] += twod.model #+ twod.im['CONTAM'].data
contam = inter[1].data*0
contam[sly, slx] += twod.im['CONTAM'].data
### Get FLT components and "de-interlace" model
asn = threedhst.utils.ASNFile('%s_asn.fits' %(root))
if root.split('-G10')[0] in grism_ref_exp.keys():
ref_exp = grism_ref_exp[root.split('-G10')[0]]
else:
ref_exp = 0
dxs, dys = unicorn.reduce.wcs_interlace_offsets('%s_asn.fits' %(root), growx=2, growy=2, path_to_flt='./', verbose=False, ref_exp=ref_exp, raw=False, reference_pixel=None)
hi = inter[1].header
x0 = hi['PAD']/2 + hi['NGROWX']*hi['GROWX']
y0 = hi['PAD']/2 + hi['NGROWY']*hi['GROWY']
foutsci = np.zeros(sh, dtype=np.float32)
foutwht = np.zeros(sh, dtype=np.float32)
foutctx = np.zeros(sh, dtype=np.float32)
coutsci = np.zeros(sh, dtype=np.float32)
coutwht = np.zeros(sh, dtype=np.float32)
coutctx = np.zeros(sh, dtype=np.float32)
moutsci = np.zeros(sh, dtype=np.float32)
moutwht = np.zeros(sh, dtype=np.float32)
moutctx = np.zeros(sh, dtype=np.float32)
for i,exp in enumerate(asn.exposures):
#print exp
flt = pyfits.open(exp+'_flt.fits')
islx = slice(x0+dxs[i], x0+dxs[i]+1014*hi['GROWX'], hi['GROWX'])
isly = slice(y0+dys[i], y0+dys[i]+1014*hi['GROWY'], hi['GROWY'])
flt_wcs = stwcs.wcsutil.HSTWCS(flt, 1)
sci = flt[1].data #- cutout
cutout = clean[isly, islx]*(hi['GROWX']*hi['GROWY'])/PAM
contam_cutout = contam[isly, islx]*(hi['GROWX']*hi['GROWY'])
#wht = 1/flt['ERR'].data**2
#wht = np.ones((1014,1014), dtype=np.float32)*np.median(wht)
err = (np.median(flt['ERR'].data)**2 + contam_cutout**2*2)
wht = 1/err
dq = flt['DQ'].data.copy()
for bit in [64,512]:
dq -= dq & bit
#
msk = (dq > 0) | (~np.isfinite(wht))
wht[msk] = 0.
sci[msk] = 0.
### offset for wavelength
xflt, yflt = flt_wcs.all_world2pix([ra], [dec], 0)
ytrace, lam = conf.get_beam_trace(x=xflt[0], y=yflt[0], dx=xpix, beam='A')
dx = np.interp(wave, lam, xpix)
dy = np.interp(wave, lam, ytrace)
print '%s (%.1f,%.1f), (%.2f,%.2f)' %(exp, xflt[0], yflt[0], dx, dy)
flt_wcs.wcs.crpix += np.array([dx, dy])
try:
flt_wcs.sip.crpix += np.array([dx, dy])
except:
pass
astrodrizzle.adrizzle.do_driz(sci, flt_wcs, wht, wcs_out, foutsci, foutwht, foutctx, 1., 'cps', 1, wcslin_pscale=1.0, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=0, stepsize=10, wcsmap=None)
astrodrizzle.adrizzle.do_driz(contam_cutout, flt_wcs, wht, wcs_out, coutsci, coutwht, coutctx, 1., 'cps', 1, wcslin_pscale=1.0, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=0, stepsize=10, wcsmap=None)
astrodrizzle.adrizzle.do_driz(cutout, flt_wcs, wht, wcs_out, moutsci, moutwht, moutctx, 1., 'cps', 1, wcslin_pscale=1.0, uniqid=1, pixfrac=pixfrac, kernel='square', fillval=0, stepsize=10, wcsmap=None)
ds9.frame(3)
ds9.view(foutsci, header=hout)
ds9.frame(4)
ds9.view(foutsci-moutsci-coutsci, header=hout)
drizzled_sci[root] = foutsci
drizzled_wht[root] = foutwht
drizzled_ctx[root] = foutctx
drizzled_mod[root] = moutsci
drizzled_contam[root] = coutsci
sci = foutsci*0
wht = sci*0
csci = sci*0
for root in drizzled_sci.keys():
print root
wht += drizzled_wht[root]
sci += drizzled_sci[root]*drizzled_wht[root]
csci += (drizzled_mod[root]+drizzled_contam[root])*drizzled_wht[root]
sci /= wht
csci /= wht
ds9.frame(3)
ds9.view(sci, header=hout)
ds9.frame(4)
ds9.view(sci-csci, header=hout)
#wave = 10625.5