"""

Make images and catalogs for extracting UDF spectra

"""

import os

import threedhst

import unicorn

import threedhst.prep_flt_files

from threedhst.prep_flt_files import process_3dhst_pair as pair

import threedhst.catIO as catIO

os.chdir(unicorn.GRISM_HOME+'UDF/PREP_FLT')

ALIGN = '../XDF/xdfh_sci.fits'

ALIGN_EXT=0

info = catIO.Readfile('files.info')

#### Make ASN files for different grisms / orientations

asn = threedhst.utils.ASNFile(glob.glob('../RAW/i*asn.fits')[0])

## 10-26

for d in ['10-26','11-01']:

match = (info.targname == 'PRIMO') & (info.filter == 'G141') & (info.date_obs == '2010-%s' %(d))

asn.exposures = []

for exp in info.file[match]:

asn.exposures.append(exp.split('_flt')[0])

#

asn.product = 'PRIMO-%s-G141' %(d.replace('-',''))

asn.write(asn.product+'_asn.fits', clobber=True)

#

match = (info.targname == 'PRIMO') & (info.filter != 'G141') & (info.date_obs == '2010-%s' %(d))

filt = info.filter[match][0]

asn.exposures = []

for exp in info.file[match]:

asn.exposures.append(exp.split('_flt')[0])

#

asn.product = 'PRIMO-%s-%s' %(d.replace('-',''), filt)

asn.write(asn.product+'_asn.fits', clobber=True)

for pointing in [34,36,37,38]:

for filt in ['F140W','G141']:

match = (info.targname == 'GOODS-SOUTH-%d' %(pointing)) & (info.filter == filt)

asn.exposures = []

for exp in info.file[match]:

asn.exposures.append(exp.split('_flt')[0])

#

asn.product = 'GOODS-SOUTH-%d-%s' %(pointing, filt)

asn.write(asn.product+'_asn.fits', clobber=True)

##### Run background subtraction on all images

direct = glob.glob('*[0-9]-F*asn.fits')

grism = glob.glob('*[0-9]-G141_asn.fits')

for i in range(len(direct)):

if not os.path.exists(grism[i].replace('asn','drz')):

pair(direct[i], grism[i], adjust_targname=False, ALIGN_IMAGE = ALIGN, ALIGN_EXTENSION=ALIGN_EXT, SKIP_GRISM=False, GET_SHIFT=True, SKIP_DIRECT=False, align_geometry='rotate,shift')

### Fix offsets for 1026 since aperture combination was different

files=['ibfup1myq_flt.fits','ibfup1n1q_flt.fits']

for file in files:

im = pyfits.open(file, mode='update')

im[0].header.update('POSTARG1', im[0].header['POSTARG1']+8.814150)

im[0].header.update('POSTARG2', im[0].header['POSTARG2']+0.025025)

im.flush()

#### Interlaced combinations

## Need to fake a combination for the interlaced direct image for PRIMO

## The first image is the direct image and the rest are G141 exposures

## to fill a 2x2 interlaced array

##

## Give them "F140W" filenames to work with interlacing code

for d, f in zip(['1026', '1101'], ['F160W', 'F125W']):

asn_im = threedhst.utils.ASNFile('PRIMO-'+d+'-%s_asn.fits' %(f))

asn = threedhst.utils.ASNFile('PRIMO-'+d+'-G141_asn.fits')

sf = threedhst.shifts.ShiftFile('PRIMO-'+d+'-G141_shifts.txt')

#

asn.exposures[0] = asn_im.exposures[0]

sf.images[0] = asn.exposures[0]+'_flt.fits'

#

### Enough images to fill 2x2 grid: (Even-Even, Odd-Odd, OE, EO)

# xo, yo = unicorn.reduce.get_interlace_offsets('PRIMO-'+d+'-G141_asn.fits', verbose=1, path_to_flt='./')

keep = [0,1,3,5]

for i in range(len(asn.exposures))[::-1]:

if i not in keep:

p = asn.exposures.pop(i)

p = sf.images.pop(i)

p = sf.xshift.pop(i)

p = sf.yshift.pop(i)

p = sf.scale.pop(i)

p = sf.rotate.pop(i)

#

### Image is combination of G141, F140W but need Multidrizzle outputs

asn.product = 'PRIMO-'+d+'-F140W'

sf.nrows = 4

asn.write('%s_asn.fits' %(asn.product), clobber=True)

sf.write('%s_shifts.txt' %(asn.product))

threedhst.prep_flt_files.startMultidrizzle(asn.product + '_asn.fits',

use_shiftfile=True, skysub=False,

final_scale=0.06, pixfrac=0.8, driz_cr=False,

updatewcs=False, clean=True, median=False)

#####################

#### Deep F160W reference for interlaced reductions

#####################

wht = pyfits.open('xdfh_wht.fits')

sci = pyfits.open('xdfh_sci.fits')

texp = 3.e3

f = 10**(-0.4*(33.4549980163574-25.96))

#wht[0].data = wht[0].data*1000.-

wht[0].data = (1.e5*wht[0].data**2+1./((sci[0].data*f+0.5)/texp))*f**2

wht.writeto('xdfh_VAR.fits', clobber=True)

wht[0].data = 1./np.sqrt(wht[0].data)

wht.writeto('xdfh_SIG.fits', clobber=True)

sci[0].data *= f

sci.writeto('xdfh_sci_scaled.fits')

## Make catalog

os.chdir("/research/HST/GRISM/3DHST/UDF/XDF")

se = threedhst.sex.SExtractor()

se.aXeParams()

se.copyConvFile()

se.overwrite = True

se.options['CATALOG_NAME'] = 'xdf.cat'

se.options['CHECKIMAGE_NAME'] = 'xdf_seg.fits'

se.options['CHECKIMAGE_TYPE'] = 'SEGMENTATION'

se.options['WEIGHT_TYPE'] = 'MAP_WEIGHT'

se.options['WEIGHT_IMAGE'] = 'xdfh_VAR.fits'

se.options['FILTER'] = 'Y'

se.options['DETECT_THRESH'] = '1.5'

se.options['ANALYSIS_THRESH'] = '1.5'

se.options['MAG_ZEROPOINT'] = '33.45499801'

se.options['DEBLEND_NTHRESH'] = '64'

se.options['DEBLEND_MINCONT'] = '0.00005'

status = se.sextractImage('xdfh_sci.fits')

threedhst.sex.sexcatRegions('xdf.cat', 'xdf.reg', format=2)

## Prep blot drizzle images

REF_ROOT = 'XDF-F160W'

CATALOG = '../XDF/xdf.cat'

unicorn.reduce.prepare_blot_reference(REF_ROOT=REF_ROOT, filter='F160W', REFERENCE = '../XDF/xdfh_sci_scaled.fits', SEGM = '../XDF/xdf_seg.fits', sci_extension=0)

REF_ROOT = 'HUDF12-F160W'

CATALOG = '../HUDF12/hudf12.cat'

unicorn.reduce.prepare_blot_reference(REF_ROOT=REF_ROOT, filter='F160W', REFERENCE = '../HUDF12/hlsp_hudf12_hst_wfc3ir_udfmain_f160w_v1.0_drz.fits', SEGM = '../HUDF12/hudf12_seg.fits', sci_extension=0)

## Use new F140W image

REF_ROOT = 'HUDF12-F140W'

CATALOG = '../F140W/HUDF12-F140W.cat'

unicorn.reduce.prepare_blot_reference(REF_ROOT=REF_ROOT, filter='F140W', REFERENCE = '../F140W/HUDF12-F140W_drz_sci.fits', SEGM = '../F140W/HUDF12-F140W_seg.fits', sci_extension=0)

### Generate DRZ images

files=glob.glob('*F*asn.fits')

for file in files:

threedhst.prep_flt_files.startMultidrizzle(file,

use_shiftfile=True, skysub=False,

final_scale=0.06, pixfrac=0.8, driz_cr=False,

updatewcs=False, clean=True, median=False)

NGROW=125

ROOT = 'PRIMO-1101'

ROOT = 'GOODS-SOUTH-34'

for p in [34, 36, 37, 38]:

ROOT = 'GOODS-SOUTH-%d' %(p)

unicorn.reduce.blot_from_reference(REF_ROOT=REF_ROOT, DRZ_ROOT = ROOT+'-F140W', NGROW=NGROW, verbose=True)

unicorn.reduce.interlace_combine_blot(root=ROOT+'-F140W', view=False, pad=60+200*(ROOT=='PRIMO-1026'), REF_ROOT=REF_ROOT, CATALOG=CATALOG, NGROW=NGROW, verbose=True, auto_offsets=True, NSEGPIX=3)

# seg = pyfits.open(ROOT+'_inter_seg.fits', mode='update')

# seg[0].data[seg[0].data < 0] = 0

# seg.flush()

#

unicorn.reduce.interlace_combine(root=ROOT+'-G141', view=False, pad=60+200*(ROOT=='PRIMO-1026'), NGROW=NGROW, auto_offsets=True)

unicorn.reduce.interlace_combine(root=ROOT+'-F140W', view=False, pad=60+200*(ROOT=='PRIMO-1026'), NGROW=NGROW, auto_offsets=True)

#

ref = pyfits.open(ROOT+'_ref_inter.fits', mode='update')

ref[0].header['FILTER'] = 'F140W'

ref.flush()

### Shift 1026-G141 image right by 130 pixels

#xo, yo = unicorn.reduce.get_interlace_offsets('PRIMO-1026-F140W_asn.fits', verbose=1, path_to_flt='./')

im = pyfits.open('PRIMO-1026-G141_inter.fits', mode='update')

fill = im[1].data*0

fill[:,480:2580] = im[1].data[:,350:2450]

im[1].data = fill*1

fill = im[2].data*0

fill[:,480:2580] = im[2].data[:,350:2450]

im[2].data = fill*1

im.flush()

#### Shift 1101-G141 image down by 1 pixel

im = pyfits.open('PRIMO-1101-G141_inter.fits', mode='update')

fill = im[1].data*0

fill[100:-100,:] = im[1].data[101:-99,:]

im[1].data = fill*1

fill = im[2].data*0

fill[100:-100,:] = im[2].data[101:-99,:]

im[2].data = fill*1

im.flush()

if ROOT.startswith('PRIMO'):

ref = pyfits.open(ROOT+'_ref_inter.fits')

im140 = pyfits.open(ROOT+'-F140W_inter.fits', mode='update')

im140[0].header['FILTER'] = 'F140W'

im140[1].data = ref[1].data #/ 10**(-0.4*(26.46-25.96))

#im140[2].data = im140[2].data # / 10**(-0.4*(26.46-25.96))

im140.flush()

#

files = glob.glob('*ref_inter.fits')

for file in files:

ROOT=file.split('_ref_inter')[0]

model = unicorn.reduce.process_GrismModel(root=ROOT, MAG_LIMIT=28, REFINE_MAG_LIMIT=23, make_zeroth_model=False, grism='G141')

model.make_wcs_region_file()

#model = unicorn.reduce.GrismModel(root=ROOT, MAG_LIMIT=30, grism='G141')

### Force use F160W as detection image

#if ROOT.startswith('PRIMO'):

#model.direct[1].data = model.im[1].data*1./10**(-0.4*(26.46-25.96))

model.get_corrected_wcs()

model.make_wcs_region_file()

#for p in [34,36,37,38]:

# ROOT = 'GOODS-SOUTH-%d' %(p)

##### Extract all objects

c = threedhst.sex.mySexCat('../F140W/HUDF12-F140W.cat')

c.write(c.filename.replace('.cat','.reform.cat'), reformat_header=True)

c = catIO.Readfile('../F140W/HUDF12-F140W.reform.cat')

ok = c.mag_auto < 26

hudf.extract_all(c.number[ok], miny=-80)

### FOr some reason, some 2D files weren't extracted with 80 pix. Redo those

bad = []

files=glob.glob('*2D.fits')

for

for i in range(ok.sum()):

id = c.number[ok][i]

twod = glob.glob('[GP]*_%05d.2D.fits' %(id))

### temporary

# if (len(twod) < 2) | os.path.exists('UDF_%05d_stack.png' %(id)):

# continue

# #

#### Redo background fits

if len(twod) > 0:

try:

hudf.stack(id, dy=40, inverse=True)

hudf.fix_2d_background('UDF_%05d' %(id), force=True)

hudf.stack(id, dy=16, inverse=True)

if c.mag_auto[ok][i] > 24:

hudf.fix_2d_background('UDF_%05d' %(id), force=False)

except:

pass

#

# twod = glob.glob('[GP]*_%05d.2D.fits' %(id))

# ### Get just center of the trace

# if len(twod) > 0:

# try:

# hudf.stack(id, dy=12, inverse=True)

# except:

# pass

######### Subtract background on smaller cutout

for id in c.number[ok]:

twod = glob.glob('[GP]*_%05d.2D.fits' %(id))

if len(twod) > 0:

try:

hudf.stack(id, dy=18, inverse=True)

hudf.fix_2d_background('UDF_%05d' %(id), force=False)

except:

pass

for id in c.number[ok][::-1]:

if (os.path.exists('UDF_%05d.new_zfit.png' %(id))) | (not os.path.exists('UDF_%05d.bg.png' %(id))):

continue

#

try:

unicorn.analysis.FORCE_GOODSS = False

self = test.SimultaneousFit('UDF_%05d' %(id), RELEASE=False, p_flat=1.e-8, lowz_thresh=0.)

if self.dr > 0.5:

unicorn.analysis.FORCE_GOODSS = True

self = test.SimultaneousFit('UDF_%05d' %(id), RELEASE=False, p_flat=1.e-8, lowz_thresh=0.)

#

self.read_master_templates()

self.new_fit_constrained(faint_limit=25)

#os.system('open UDF_%05d.new_zfit.png' %(id))

#self.new_fit_free_emlines()

except:

"""

UDF

The fitting code saves a FITS file with p(z) but doesn't print the

results. Pull it out and make a catalog

"""

import unicorn.interlace_test as test

import unicorn.catalogs2 as cat2

c = catIO.Readfile('../F140W/HUDF12-F140W.reform.cat')

zsp = cat2.SpeczCatalog()

dr, idx = zsp.match_list(c.x_world, c.y_world)

c.z_spec = zsp.zspec[idx] #[dr < 0.3]

c.z_spec[dr > 0.3] = -1

ok = c.mag_auto < 26

original_path = os.getcwd()

fp = open('udf_redshifts.dat','w')

fp.write('# id mag flag z_max z_peak l68 u68 l95 u95 z_spec z_source\n')

fp.write('# %s' %(PATH))

os.chdir(PATH)

for i in range(ok.sum()):

id = c.number[ok][i]

z_spec = c.z_spec[ok][i]

if z_spec > 0:

z_source = zsp.source[idx][ok][i]

else:

z_source = '-'

#

logstr = 'UDS_%05d %.2f ' %(id, c.mag_auto[ok][i])

if os.path.exists('UDF_%05d.new_zfit.pz.fits' %(id)):

self = test.SimultaneousFit('UDF_%05d' %(id))

self.read_master_templates()

status = self.new_load_fits()

if status:

logstr += ' 1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %s' %(self.z_max_spec, self.z_peak_spec, self.c68[0], self.c68[1], self.c95[0], self.c95[1], z_spec, z_source)

else:

logstr += '-1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %s' %(-1, -1, -1, -1, -1, -1, -1, '-')

else:

logstr += ' 0 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %s' %(-1, -1, -1, -1, -1, -1, -1, '-')

print logstr

fp.write(logstr+'\n')

fp.close()

"""

UDF

I had fixed a bug in the UDF stacking script that caused the thumbnails

to be filled with NaNs if one of the four exposures had no valid pixels

in it. Go through and find them and extract them again

"""

files = glob.glob('UDF*2D.fits')

for file in files:

im = pyfits.open(file)

if np.isfinite(im['DSCI'].data).sum() == 0:

print file

id=int(file.split('_')[1].split('.')[0])

"""

UDF

Plot the automatically-determined backgrounds as a function of position

in the UDF frame

"""

c = catIO.Readfile('../F140W/HUDF12-F140W.reform.cat')

ok = c.mag_auto < 27.5

fp = open('udf_backgrounds.dat','w')

fp.write('# id c0 cx cy x y mag\n')

for i in np.arange(c.N)[ok]:

id = c.number[i]

bgfile = 'UDF_FIT/UDF_%05d.bg.dat' %(id)

if os.path.exists(bgfile):

line = open(bgfile).readlines()[1][:-1]

fp.write('%s %7.1f %7.1f %.2f\n' %(line, c.x_image[i], c.y_image[i], c.mag_auto[i]))

fp.close()

bg = catIO.Readfile('udf_backgrounds.dat')

ok = (bg.mag > 24) & (bg.c0+0.003 > 0) & (bg.c0+0.003 < 0.004)

plt.scatter(bg.x[ok], bg.y[ok], c=bg.c0[ok], s=30, vmin=-0.004, vmax=0.002)

#### Try 2D as in sciypy example (need newer version)

# from scipy.interpolate import griddata

#

# points = [bg.x[ok], bg.y[ok]]

# values = bg.c0[ok]

#

# grid_z2 = griddata(points, values, (bg.x, bg.y), method='cubic')

from scipy import interpolate

bg_spline = interpolate.SmoothBivariateSpline(bg.x[ok], bg.y[ok], bg.c0[ok], kx=4, ky=4)

test = bg.c0*0.

for i in range(bg.N):

test[i] = bg_spline(bg.x[i], bg.y[i])

#

plt.scatter(bg.x+3600, bg.y, c=test, s=30, vmin=-0.004, vmax=0.002)

plt.text(2000,3800,'Data (mag > 24)', ha='center', va='center')

plt.text(5600,3800,'Interpolated', ha='center', va='center')

plt.savefig('background_spline.pdf')

import cPickle as pickle

fp = open('background_spline.pkl','wb')

pickle.dump(bg_spline, fp)

"""

UDF

Take out the old best-fit backgrounds that were applied automatically

and subtract the interpolated version determined above

"""

import unicorn.hudf as hudf

import cPickle as pickle

fp = open('background_spline.pkl','rb')

bg_spline = pickle.load(fp)

fp.close()

files = glob.glob('UDF*2D.fits')

for file in files:

im = pyfits.open(file)

header = im[0].header

if 'BG_C0' in header.keys():

hudf.fix_2d_background(object=file.split('.2D')[0], force=False, clip=100, remove=False)

#

new_bg = bg_spline(header['X_PIX'], header['Y_PIX'])

im = pyfits.open(file, mode='update')

im[0].header.update('BG_C0', new_bg)

im[0].header.update('BG_CX', 0)

im[0].header.update('BG_CY', 0)

im['SCI'].data -= new_bg

#### UDF browser, note "f" parameter for deeper UDF images

scales = [10**(-0.4*(25.96-25.96)), 10**(-0.4*(26.25-25.96)), 10**(-0.4*(25.94-25.96))*1.5]

f140 = pyfits.open('HUDF12-F140W_drz_sci.fits')

match = ['HUDF12_F160W.fits', 'HUDF12_F125W.fits', 'UDF_ACS_i.fits']

f125 = pyfits.open('HUDF12_F125W.fits')

mask = f125[0].data == 0

#### Fill empty HUDF12 images with wide-F140W

for i in range(3):

print match[i]

im = pyfits.open(match[i], mode='update')

scale = 10**(-0.4*(26.46-25.96))/scales[i]

im[0].data[mask] = f140[0].data[mask]*scale

im.flush()

#

f = 6

rgb1 = 'HUDF12_F160W.fits[0]*%.3f, HUDF12_F125W.fits[0]*%.3f, UDF_ACS_i.fits[0]*%.3f' %(scales[0]*f, scales[1]*f*1., scales[2]*f*1.)

f = 20

rgb2 = 'HUDF12_F160W.fits[0]*%.3f, HUDF12_F125W.fits[0]*%.3f, UDF_ACS_i.fits[0]*%.3f' %(scales[0]*f, scales[1]*f*1., scales[2]*f*1.)

#threedhst.gmap.makeImageMap([rgb1,rgb2], aper_list=[15, 16], tileroot=['HJY','deep'], extension=0, path='./HTML/', zmin=-0.05, zmax=1)

threedhst.gmap.makeImageMap([rgb1,rgb2], aper_list=[15,16,17], tileroot=['iJH', 'deep'], extension=0, path='./HTML/', zmin=-0.05, zmax=1)

#### iJH

scales = [10**(-0.4*(25.96-25.96)), 10**(-0.4*(26.25-25.96)), 10**(-0.4*(25.94-25.96))*1.5]

#### UDS

#acs_f814w = '/3DHST/Ancillary/UDS/CANDELS/public/hlsp_candels_hst_acs_uds-tot_f814w_v1.0_drz.fits'

wfc3_f125w = '/3DHST/Ancillary/UDS/CANDELS/ASTRODRIZZLE/uds-f125w-astrodrizzle-v0.1_drz_sci.fits'

wfc3_f160w = '/3DHST/Ancillary/UDS/CANDELS/ASTRODRIZZLE/uds-f160w-astrodrizzle-v0.1_drz_sci.fits'

acs_f814w = '/3DHST/Photometry/Work/UDS/v3/images/UDS_F814W_sci.fits'

x0, y0, N = 30720/2, 12800/2, 500 # UDS

x0, y0, N = 20480/2, 20480/2, 500 # GOODS-N

x0, y0, N = 23000/2, 20000/2, 500 # GOODS-S

iraf.imcopy('%s[0][%d:%d,%d:%d]' %(wfc3_f160w, x0-N, x0+N-1, y0-N, y0+N-1), 'sub_f160w.fits')

iraf.imcopy('%s[0][%d:%d,%d:%d]' %(wfc3_f125w, x0-N, x0+N-1, y0-N, y0+N-1), 'sub_f125w.fits')

iraf.imcopy('%s[0][%d:%d,%d:%d]' %( acs_f814w, x0-N, x0+N-1, y0-N, y0+N-1), 'sub_f814w.fits')

#

# wfc3_f125w, wfc3_f160w, acs_f814w = 'sub_f125w.fits', 'sub_f160w.fits', 'sub_f814w.fits'

f = 2

#scales[2] = 10**(-0.4*(25.94-25.96))*1.5/2

rgb = '%s[0]*%.3f, %s[0]*%.3f, %s[0]*%.3f' %(wfc3_f160w, scales[0]*f, wfc3_f125w, scales[1]*f, acs_f814w, scales[2]*f)

threedhst.gmap.makeImageMap([rgb], aper_list=[14,15,16], tileroot=['iJH'], extension=0, path='./HTML/', zmin=-0.05, zmax=1)

#### GOODS-S

wfc3_f125w = '/3DHST/Ancillary/GOODS-S/CANDELS/ASTRODRIZZLE/goods-s-f125w-astrodrizzle-v0.1_drz_sci.fits'

wfc3_f160w = '/3DHST/Ancillary/GOODS-S/CANDELS/ASTRODRIZZLE/goods-s-f160w-astrodrizzle-v0.1_drz_sci.fits'

acs_f814w = '/3DHST/Photometry/Work/GOODS-S/v3/images/GOODS-S_candels_f814w_sci.fits'

threedhst.shifts.matchImagePixels(input=glob.glob(acs_f814w), matchImage=wfc3_f160w, output='GOODS-S_F814W_match3.0.fits', input_extension=0, match_extension=0)

acs_f814w = 'GOODS-S_F814W_match3.0.fits'

f = 2

#scales[2] = 10**(-0.4*(25.94-25.96))*1.5/2

rgb = '%s[0]*%.3f, %s[0]*%.3f, %s[0]*%.3f' %(wfc3_f160w, scales[0]*f, wfc3_f125w, scales[1]*f, acs_f814w, scales[2]*f)

threedhst.gmap.makeImageMap([rgb], aper_list=[14,15,16], tileroot=['iJH'], extension=0, path='./HTML/', zmin=-0.05, zmax=1)

#### GOODS-N

wfc3_f125w = '/3DHST/Ancillary/GOODS-N/CANDELS/ASTRODRIZZLE/goods-n-f125w-astrodrizzle-v0.1_drz_sci.fits'

wfc3_f160w = '/3DHST/Ancillary/GOODS-N/CANDELS/ASTRODRIZZLE/goods-n-f160w-astrodrizzle-v0.1_drz_sci.fits'

threedhst.shifts.matchImagePixels(input=glob.glob('/3DHST/Ancillary/GOODS-N/GOODS_ACS/h_ni_sect*_v2.0_drz_img.fits'), matchImage=wfc3_f160w, output='GOODS-N_F775W_match3.0.fits', input_extension=0, match_extension=0)

acs_f814w = 'GOODS-N_F775W_match3.0.fits'

scales = [10**(-0.4*(25.96-25.96)), 10**(-0.4*(26.25-25.96)), 10**(-0.4*(25.666-25.96))*1.5]

rgb = '%s[0]*%.3f, %s[0]*%.3f, %s[0]*%.3f' %(wfc3_f160w, scales[0]*f, wfc3_f125w, scales[1]*f, acs_f814w, scales[2]*f)

threedhst.gmap.makeImageMap([rgb], aper_list=[14,15,16], tileroot=['iJH'], extension=0, path='./HTML/', zmin=-0.05, zmax=1)

#### cosmos

wfc3_f125w = '/3DHST/Ancillary/COSMOS/CANDELS/ASTRODRIZZLE/cosmos-f125w-astrodrizzle-v0.1_drz_sci.fits'

wfc3_f160w = '/3DHST/Ancillary/COSMOS/CANDELS/ASTRODRIZZLE/cosmos-f160w-astrodrizzle-v0.1_drz_sci.fits'

threedhst.shifts.matchImagePixels(input=glob.glob('/3DHST/Ancillary//COSMOS/ACS/acs_I_030mas_*_sci.fits'), matchImage=wfc3_f160w, output='COSMOS_F814W_match3.0.fits', input_extension=0, match_extension=0)

acs_f814w = 'COSMOS_F814W_match3.0.fits'

scales = [10**(-0.4*(25.96-25.96)), 10**(-0.4*(26.25-25.96)), 10**(-0.4*(25.94-25.96))*1.5]

rgb = '%s[0]*%.3f, %s[0]*%.3f, %s[0]*%.3f' %(wfc3_f160w, scales[0]*f, wfc3_f125w, scales[1]*f, acs_f814w, scales[2]*f)

threedhst.gmap.makeImageMap([rgb], aper_list=[14,15,16], tileroot=['iJH'], extension=0, path='./HTML/', zmin=-0.05, zmax=1)

#### AEGIS

wfc3_f125w = '/3DHST/Ancillary/AEGIS/CANDELS/ASTRODRIZZLE/aegis-f125w-astrodrizzle-v0.1_drz_sci.fits'

wfc3_f160w = '/3DHST/Ancillary/AEGIS/CANDELS/ASTRODRIZZLE/aegis-f160w-astrodrizzle-v0.1_drz_sci.fits'

# swarp /3DHST/Ancillary/AEGIS/CANDELS/ASTRODRIZZLE/aegis-f160w-astrodrizzle-v0.1_drz_sci.fits[0] -c make_square.swarp

# mv coadd.fits AEGIS_F160W_v0.1_sq.fits; rm coadd.weight.fits

# swarp /3DHST/Ancillary/AEGIS/CANDELS/ASTRODRIZZLE/aegis-f125w-astrodrizzle-v0.1_drz_sci.fits[0] -c make_square.swarp

# mv coadd.fits AEGIS_F125W_v0.1_sq.fits; rm coadd.weight.fits

wfc3_f125w, wfc3_f160w = 'AEGIS_F125W_v0.1_sq.fits', 'AEGIS_F160W_v0.1_sq.fits'

threedhst.shifts.matchImagePixels(input = glob.glob('/3DHST/Ancillary/AEGIS/ACS/mos_i_scale1_section*[0-9]_drz.fits'), matchImage=wfc3_f160w, output='AEGIS_F814W_match3.0_sq.fits', input_extension=0, match_extension=0)

acs_f814w = 'AEGIS_F814W_match3.0_sq.fits'

scales = [10**(-0.4*(25.96-25.96)), 10**(-0.4*(26.25-25.96)), 10**(-0.4*(25.94-25.96))*1.5]

rgb = '%s[0]*%.3f, %s[0]*%.3f, %s[0]*%.3f' %(wfc3_f160w, scales[0]*f, wfc3_f125w, scales[1]*f, acs_f814w, scales[2]*f)