def drizzle_images(label='macs0647-jd1', ra=101.9822125, dec=70.24326667, pixscale=0.06, size=10, wcs=None, pixfrac=0.8, kernel='square', theta=0, half_optical_pixscale=False, filters=['f160w','f814w', 'f140w','f125w','f105w','f110w','f098m','f850lp', 'f775w', 'f606w','f475w','f555w','f600lp', 'f390w', 'f350lp'], remove=True, rgb_params=RGB_PARAMS, master='grizli-jan2019', aws_bucket='s3://grizli/CutoutProducts/', scale_ab=21, thumb_height=2.0, sync_fits=True, subtract_median=True, include_saturated=True, include_ir_psf=False):
"""
label='cp561356'; ra=150.208875; dec=1.850241667; size=40; filters=['f160w','f814w', 'f140w','f125w','f105w','f606w','f475w']
"""
import glob
import copy
import os
import numpy as np
import astropy.io.fits as pyfits
from astropy.coordinates import SkyCoord
import astropy.units as u
from drizzlepac.adrizzle import do_driz
import boto3
from grizli import prep, utils
from grizli.pipeline import auto_script
if isinstance(ra, str):
coo = SkyCoord('{0} {1}'.format(ra, dec), unit=(u.hour, u.deg))
ra, dec = coo.ra.value, coo.dec.value
if label is None:
try:
import mastquery.utils
label = mastquery.utils.radec_to_targname(ra=ra, dec=dec, round_arcsec=(1/15, 1), targstr='j{rah}{ram}{ras}{sign}{ded}{dem}{des}')
except:
label = 'grizli-cutout'
#master = 'cosmos'
#master = 'grizli-jan2019'
if master == 'grizli-jan2019':
parent = 's3://grizli/MosaicTools/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket('grizli')
elif master == 'cosmos':
parent = 's3://grizli-preprocess/CosmosMosaic/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket('grizli-preprocess')
else:
# Run on local files, e.g., "Prep" directory
parent = None
remove = False
for ext in ['_visits.fits', '_visits.npy', '_filter_groups.npy'][-1:]:
if (not os.path.exists('{0}{1}'.format(master, ext))) & (parent is not None):
s3_path = parent.split('/')[-2]
s3_file = '{0}{1}'.format(master, ext)
print('{0}{1}'.format(parent, s3_file))
bkt.download_file(s3_path+'/'+s3_file, s3_file,
ExtraArgs={"RequestPayer": "requester"})
#os.system('aws s3 cp {0}{1}{2} ./'.format(parent, master, ext))
#tab = utils.read_catalog('{0}_visits.fits'.format(master))
#all_visits = np.load('{0}_visits.npy'.format(master))[0]
if parent is not None:
groups = np.load('{0}_filter_groups.npy'.format(master), allow_pickle=True)[0]
else:
# Reformat local visits.npy into a groups file
groups_files = glob.glob('*filter_groups.npy')
if len(groups_files) == 0:
visit_file = glob.glob('*visits.npy')[0]
visits, groups, info = np.load(visit_file)
visit_root = visit_file.split('_visits')[0]
visit_filters = np.array([v['product'].split('-')[-1] for v in visits])
groups = {}
for filt in np.unique(visit_filters):
groups[filt] = {}
groups[filt]['filter'] = filt
groups[filt]['files'] = []
groups[filt]['footprints'] = []
groups[filt]['awspath'] = None
ix = np.where(visit_filters == filt)[0]
for i in ix:
groups[filt]['files'].extend(visits[i]['files'])
groups[filt]['footprints'].extend(visits[i]['footprints'])
np.save('{0}_filter_groups.npy'.format(visit_root), [groups])
else:
groups = np.load(groups_files[0])[0]
#filters = ['f160w','f814w', 'f110w', 'f098m', 'f140w','f125w','f105w','f606w', 'f475w']
has_filts = []
for filt in filters:
if filt not in groups:
continue
visits = [copy.deepcopy(groups[filt])]
#visits[0]['reference'] = 'CarlosGG/ak03_j1000p0228/Prep/ak03_j1000p0228-f160w_drz_sci.fits'
visits[0]['product'] = label+'-'+filt
if wcs is None:
hdu = utils.make_wcsheader(ra=ra, dec=dec, size=size, pixscale=pixscale, get_hdu=True, theta=theta)
h = hdu.header
else:
h = utils.to_header(wcs)
if (filt[:2] in ['f0', 'f1', 'g1']) | (not half_optical_pixscale):
#data = hdu.data
pass
else:
for k in ['NAXIS1','NAXIS2','CRPIX1','CRPIX2']:
h[k] *= 2
h['CRPIX1'] -= 0.5
h['CRPIX2'] -= 0.5
for k in ['CD1_1', 'CD1_2', 'CD2_1', 'CD2_2']:
h[k] /= 2
#data = np.zeros((h['NAXIS2'], h['NAXIS1']), dtype=np.int16)
#pyfits.PrimaryHDU(header=h, data=data).writeto('ref.fits', overwrite=True, output_verify='fix')
#visits[0]['reference'] = 'ref.fits'
print('\n\n###\nMake filter: {0}'.format(filt))
if (filt.upper() in ['F105W','F125W','F140W','F160W']) & include_ir_psf:
clean_i = False
else:
clean_i = remove
status = utils.drizzle_from_visit(visits[0], h, pixfrac=pixfrac, kernel=kernel, clean=clean_i, include_saturated=include_saturated)
if status is not None:
sci, wht, outh = status
if subtract_median:
med = np.median(sci[sci != 0])
print('\n\nMedian {0} = {1:.3f}\n\n'.format(filt, med))
sci -= med
outh['IMGMED'] = (med, 'Median subtracted from the image')
else:
med = 0.
outh['IMGMED'] = (med, 'Median subtracted from the image')
pyfits.writeto('{0}-{1}_drz_sci.fits'.format(label, filt),
data=sci, header=outh, overwrite=True,
output_verify='fix')
pyfits.writeto('{0}-{1}_drz_wht.fits'.format(label, filt),
data=wht, header=outh, overwrite=True,
output_verify='fix')
has_filts.append(filt)
if (filt.upper() in ['F105W','F125W','F140W','F160W']) & include_ir_psf:
from grizli.galfit.psf import DrizzlePSF
hdu = pyfits.open('{0}-{1}_drz_sci.fits'.format(label, filt),
mode='update')
flt_files = [] #visits[0]['files']
for i in range(1, 10000):
key = 'FLT{0:05d}'.format(i)
if key not in hdu[0].header:
break
flt_files.append(hdu[0].header[key])
dp = DrizzlePSF(flt_files=flt_files, driz_hdu=hdu[0])
psf = dp.get_psf(ra=dp.driz_wcs.wcs.crval[0],
dec=dp.driz_wcs.wcs.crval[1],
filter=filt.upper(),
pixfrac=dp.driz_header['PIXFRAC'],
kernel=dp.driz_header['KERNEL'],
wcs_slice=dp.driz_wcs, get_extended=True,
verbose=False, get_weight=False)
psf[1].header['EXTNAME'] = 'PSF'
#psf[1].header['EXTVER'] = filt
hdu.append(psf[1])
hdu.flush()
#psf.writeto('{0}-{1}_drz_sci.fits'.format(label, filt),
# overwrite=True, output_verify='fix')
#status = prep.drizzle_overlaps(visits, parse_visits=False, check_overlaps=True, pixfrac=pixfrac, skysub=False, final_wcs=True, final_wht_type='IVM', static=True, max_files=260, fix_wcs_system=True)
#
# if len(glob.glob('{0}-{1}*sci.fits'.format(label, filt))):
# has_filts.append(filt)
if remove:
os.system('rm *_fl*fits')
if len(has_filts) == 0:
return []
if rgb_params:
#auto_script.field_rgb(root=label, HOME_PATH=None, filters=has_filts, **rgb_params)
show_all_thumbnails(label=label, thumb_height=thumb_height, scale_ab=scale_ab, close=True, rgb_params=rgb_params)
if aws_bucket:
#aws_bucket = 's3://grizli-cosmos/CutoutProducts/'
#aws_bucket = 's3://grizli/CutoutProducts/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket(aws_bucket.split("/")[2])
aws_path = '/'.join(aws_bucket.split("/")[3:])
if sync_fits:
files = glob.glob('{0}*'.format(label))
else:
files = glob.glob('{0}*png'.format(label))
for file in files:
print('{0} -> {1}'.format(file, aws_bucket))
bkt.upload_file(file, '{0}/{1}'.format(aws_path, file).replace('//','/'), ExtraArgs={'ACL': 'public-read'})
#os.system('aws s3 sync --exclude "*" --include "{0}*" ./ {1} --acl public-read'.format(label, aws_bucket))
#os.system("""echo "<pre>" > index.html; aws s3 ls AWSBUCKETX --human-readable | sort -k 1 -k 2 | grep -v index | awk '{printf("%s %s",$1, $2); printf(" %6s %s ", $3, $4); print "<a href="$5">"$5"</a>"}'>> index.html; aws s3 cp index.html AWSBUCKETX --acl public-read""".replace('AWSBUCKETX', aws_bucket))
return has_filts
def drizzle_images(label='macs0647-jd1', ra=101.9822125, dec=70.24326667, pixscale=0.1, size=10, wcs=None, pixfrac=0.33, kernel='square', theta=0, half_optical_pixscale=True, filters=['f160w', 'f140w', 'f125w', 'f105w', 'f110w', 'f098m', 'f850lp', 'f814w', 'f775w', 'f606w', 'f475w', 'f555w', 'f600lp', 'f390w', 'f350lp'], skip=None, remove=True, rgb_params=RGB_PARAMS, master='grizli-jan2019', aws_bucket='s3://grizli/CutoutProducts/', scale_ab=21, thumb_height=2.0, sync_fits=True, subtract_median=True, include_saturated=True, include_ir_psf=False, show_filters=['visb', 'visr', 'y', 'j', 'h'], combine_similar_filters=True, single_output=True, aws_prep_dir=None, make_segmentation_figure=False, get_dict=False, dryrun=False, **kwargs):
"""
label='cp561356'; ra=150.208875; dec=1.850241667; size=40; filters=['f160w','f814w', 'f140w','f125w','f105w','f606w','f475w']
master: These are sets of large lists of available exposures
'cosmos': deprecated
'grizli-cosmos-v2': All imaging covering the COSMOS field
'candels-july2019': CANDELS fields other than COSMOS
'grizli-v1': First processing of the Grizli CHArGE dataset
'grizli-v1-19.12.04': Updated CHArGE fields
"""
import glob
import copy
import os
import numpy as np
import astropy.io.fits as pyfits
from astropy.coordinates import SkyCoord
import astropy.units as u
from drizzlepac.adrizzle import do_driz
import boto3
from grizli import prep, utils
from grizli.pipeline import auto_script
# Function arguments
if get_dict:
frame = inspect.currentframe()
args = inspect.getargvalues(frame).locals
pop_args = ['get_dict', 'frame', 'kwargs']
pop_classes = (np.__class__, do_driz.__class__, SkyCoord.__class__)
for k in kwargs:
args[k] = kwargs[k]
for k in args:
if isinstance(args[k], pop_classes):
pop_args.append(k)
for k in pop_args:
if k in args:
args.pop(k)
return args
# Boto objects
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
if isinstance(ra, str):
coo = SkyCoord('{0} {1}'.format(ra, dec), unit=(u.hour, u.deg))
ra, dec = coo.ra.value, coo.dec.value
if label is None:
try:
import mastquery.utils
label = mastquery.utils.radec_to_targname(ra=ra, dec=dec, round_arcsec=(1/15, 1), targstr='j{rah}{ram}{ras}{sign}{ded}{dem}{des}')
except:
label = 'grizli-cutout'
#master = 'cosmos'
#master = 'grizli-jan2019'
if master == 'grizli-jan2019':
parent = 's3://grizli/MosaicTools/'
bkt = s3.Bucket('grizli')
elif master == 'cosmos':
parent = 's3://grizli-preprocess/CosmosMosaic/'
bkt = s3.Bucket('grizli-preprocess')
elif master == 'grizli-cosmos-v2':
parent = 's3://grizli-cosmos-v2/Mosaics/'
bkt = s3.Bucket('grizli-cosmos-v2')
elif master == 'candels-july2019':
parent = 's3://grizli-v1/Mosaics/'
bkt = s3.Bucket('grizli-v1')
elif master == 'grizli-v1-19.12.04':
parent = 's3://grizli-v1/Mosaics/'
bkt = s3.Bucket('grizli-v1')
elif master == 'grizli-v1-19.12.05':
parent = 's3://grizli-v1/Mosaics/'
bkt = s3.Bucket('grizli-v1')
else:
# Run on local files, e.g., "Prep" directory
parent = None
bkt = None
#remove = False
# Download summary files from S3
for ext in ['_visits.fits', '_visits.npy', '_filter_groups.npy'][-1:]:
newfile = '{0}{1}'.format(master, ext)
if (not os.path.exists(newfile)) & (parent is not None):
s3_path = parent.split('/')[-2]
s3_file = '{0}{1}'.format(master, ext)
print('{0}{1}'.format(parent, s3_file))
bkt.download_file(s3_path+'/'+s3_file, s3_file,
ExtraArgs={"RequestPayer": "requester"})
#os.system('aws s3 cp {0}{1}{2} ./'.format(parent, master, ext))
#tab = utils.read_catalog('{0}_visits.fits'.format(master))
#all_visits = np.load('{0}_visits.npy'.format(master))[0]
if parent is not None:
groups = np.load('{0}_filter_groups.npy'.format(master), allow_pickle=True)[0]
else:
if aws_prep_dir is not None:
spl = aws_prep_dir.replace('s3://', '').split('/')
prep_bucket = spl[0]
prep_root = spl[2]
prep_bkt = s3.Bucket(prep_bucket)
s3_prep_path = 'Pipeline/{0}/Prep/'.format(prep_root)
s3_full_path = '{0}/{1}'.format(prep_bucket, s3_prep_path)
s3_file = '{0}_visits.npy'.format(prep_root)
# Make output path Prep/../Thumbnails/
if aws_bucket is not None:
aws_bucket = ('s3://' +
s3_full_path.replace('/Prep/', '/Thumbnails/'))
print('{0}{1}'.format(s3_prep_path, s3_file))
if not os.path.exists(s3_file):
prep_bkt.download_file(os.path.join(s3_prep_path, s3_file),
s3_file, ExtraArgs={"RequestPayer": "requester"})
groups_files = glob.glob('{0}_filter_groups.npy'.format(prep_root))
visit_query = prep_root+'_'
else:
groups_files = glob.glob('*filter_groups.npy')
visit_query = '*'
# Reformat local visits.npy into a groups file
if (len(groups_files) == 0):
visit_file = glob.glob(visit_query+'visits.npy')[0]
visits, groups, info = np.load(visit_file, allow_pickle=True)
visit_root = visit_file.split('_visits')[0]
visit_filters = np.array([v['product'].split('-')[-1] for v in visits])
groups = {}
for filt in np.unique(visit_filters):
groups[filt] = {}
groups[filt]['filter'] = filt
groups[filt]['files'] = []
groups[filt]['footprints'] = []
groups[filt]['awspath'] = []
ix = np.where(visit_filters == filt)[0]
for i in ix:
groups[filt]['files'].extend(visits[i]['files'])
groups[filt]['footprints'].extend(visits[i]['footprints'])
Nf = len(groups[filt]['files'])
print('{0:>6}: {1:>3} exposures'.format(filt, Nf))
if aws_prep_dir is not None:
groups[filt]['awspath'] = [s3_full_path
for file in range(Nf)]
np.save('{0}_filter_groups.npy'.format(visit_root), [groups])
else:
print('Use groups file: {0}'.format(groups_files[0]))
groups = np.load(groups_files[0], allow_pickle=True)[0]
#filters = ['f160w','f814w', 'f110w', 'f098m', 'f140w','f125w','f105w','f606w', 'f475w']
filt_dict = FilterDict()
filt_dict.meta['label'] = label
filt_dict.meta['ra'] = ra
filt_dict.meta['dec'] = dec
filt_dict.meta['size'] = size
filt_dict.meta['master'] = master
filt_dict.meta['parent'] = parent
if filters is None:
filters = list(groups.keys())
has_filts = []
lower_filters = [f.lower() for f in filters]
for filt in lower_filters:
if filt not in groups:
continue
visits = [copy.deepcopy(groups[filt])]
#visits[0]['reference'] = 'CarlosGG/ak03_j1000p0228/Prep/ak03_j1000p0228-f160w_drz_sci.fits'
visits[0]['product'] = label+'-'+filt
if wcs is None:
hdu = utils.make_wcsheader(ra=ra, dec=dec, size=size, pixscale=pixscale, get_hdu=True, theta=theta)
h = hdu.header
else:
h = utils.to_header(wcs)
if (filt[:2] in ['f0', 'f1', 'g1']) | (not half_optical_pixscale):
#data = hdu.data
pass
else:
for k in ['NAXIS1', 'NAXIS2', 'CRPIX1', 'CRPIX2']:
h[k] *= 2
h['CRPIX1'] -= 0.5
h['CRPIX2'] -= 0.5
for k in ['CD1_1', 'CD1_2', 'CD2_1', 'CD2_2']:
if k in h:
h[k] /= 2
#data = np.zeros((h['NAXIS2'], h['NAXIS1']), dtype=np.int16)
#pyfits.PrimaryHDU(header=h, data=data).writeto('ref.fits', overwrite=True, output_verify='fix')
#visits[0]['reference'] = 'ref.fits'
print('\n\n###\nMake filter: {0}'.format(filt))
if (filt.upper() in ['F105W', 'F110W', 'F125W', 'F140W', 'F160W']) & include_ir_psf:
clean_i = False
else:
clean_i = remove
status = utils.drizzle_from_visit(visits[0], h, pixfrac=pixfrac, kernel=kernel, clean=clean_i, include_saturated=include_saturated, skip=skip, dryrun=dryrun)
if dryrun:
filt_dict[filt] = status
continue
elif status is not None:
sci, wht, outh, filt_dict[filt] = status
if subtract_median:
#med = np.median(sci[sci != 0])
try:
un_data = np.unique(sci[(sci != 0) & np.isfinite(sci)])
med = utils.mode_statistic(un_data)
except:
med = 0.
if not np.isfinite(med):
med = 0.
print('\n\nMedian {0} = {1:.3f}\n\n'.format(filt, med))
outh['IMGMED'] = (med, 'Median subtracted from the image')
else:
med = 0.
outh['IMGMED'] = (0., 'Median subtracted from the image')
pyfits.writeto('{0}-{1}_drz_sci.fits'.format(label, filt),
data=sci, header=outh, overwrite=True,
output_verify='fix')
pyfits.writeto('{0}-{1}_drz_wht.fits'.format(label, filt),
data=wht, header=outh, overwrite=True,
output_verify='fix')
has_filts.append(filt)
if (filt.upper() in ['F105W', 'F110W', 'F125W', 'F140W', 'F160W']) & include_ir_psf:
from grizli.galfit.psf import DrizzlePSF
hdu = pyfits.open('{0}-{1}_drz_sci.fits'.format(label, filt),
mode='update')
flt_files = [] # visits[0]['files']
for i in range(1, 10000):
key = 'FLT{0:05d}'.format(i)
if key not in hdu[0].header:
break
flt_files.append(hdu[0].header[key])
try:
dp = DrizzlePSF(flt_files=flt_files, driz_hdu=hdu[0])
psf = dp.get_psf(ra=dp.driz_wcs.wcs.crval[0],
dec=dp.driz_wcs.wcs.crval[1],
filter=filt.upper(),
pixfrac=dp.driz_header['PIXFRAC'],
kernel=dp.driz_header['KERNEL'],
wcs_slice=dp.driz_wcs, get_extended=True,
verbose=False, get_weight=False)
psf[1].header['EXTNAME'] = 'PSF'
#psf[1].header['EXTVER'] = filt
hdu.append(psf[1])
hdu.flush()
except:
pass
if remove:
os.system('rm *_fl*fits')
# Dry run, just return dictionary of the found exposure files
if dryrun:
return filt_dict
# Nothing found
if len(has_filts) == 0:
return []
if combine_similar_filters:
combine_filters(label=label)
if rgb_params:
#auto_script.field_rgb(root=label, HOME_PATH=None, filters=has_filts, **rgb_params)
show_all_thumbnails(label=label, thumb_height=thumb_height, scale_ab=scale_ab, close=True, rgb_params=rgb_params, filters=show_filters)
if (single_output != 0):
# Concatenate into a single FITS file
files = glob.glob('{0}-f*_dr[cz]_sci.fits'.format(label))
files.sort()
if combine_similar_filters:
comb_files = glob.glob('{0}-[a-eg-z]*_dr[cz]_sci.fits'.format(label))
comb_files.sort()
files += comb_files
hdul = None
for file in files:
hdu_i = pyfits.open(file)
hdu_i[0].header['EXTNAME'] = 'SCI'
if 'NCOMBINE' in hdu_i[0].header:
if hdu_i[0].header['NCOMBINE'] <= single_output:
continue
filt_i = file.split('-')[-1].split('_dr')[0]
else:
filt_i = utils.get_hst_filter(hdu_i[0].header)
for h in hdu_i:
h.header['EXTVER'] = filt_i
if hdul is None:
hdul = pyfits.HDUList([h])
else:
hdul.append(h)
print('Add to {0}.thumb.fits: {1}'.format(label, file))
# Weight
hdu_i = pyfits.open(file.replace('_sci', '_wht'))
hdu_i[0].header['EXTNAME'] = 'WHT'
for h in hdu_i:
h.header['EXTVER'] = filt_i
if hdul is None:
hdul = pyfits.HDUList([h])
else:
hdul.append(h)
hdul.writeto('{0}.thumb.fits'.format(label), overwrite=True,
output_verify='fix')
for file in files:
for f in [file, file.replace('_sci', '_wht')]:
if os.path.exists(f):
print('Remove {0}'.format(f))
os.remove(f)
# Segmentation figure
thumb_file = '{0}.thumb.fits'.format(label)
if (make_segmentation_figure) & (os.path.exists(thumb_file)) & (aws_prep_dir is not None):
print('Make segmentation figure')
# Fetch segmentation image and catalog
s3_prep_path = 'Pipeline/{0}/Prep/'.format(prep_root)
s3_full_path = '{0}/{1}'.format(prep_bucket, s3_prep_path)
s3_file = '{0}_visits.npy'.format(prep_root)
has_seg_files = True
seg_files = ['{0}-ir_seg.fits.gz'.format(prep_root),
'{0}_phot.fits'.format(prep_root)]
for s3_file in seg_files:
if not os.path.exists(s3_file):
remote_file = os.path.join(s3_prep_path, s3_file)
try:
print('Fetch {0}'.format(remote_file))
prep_bkt.download_file(remote_file, s3_file,
ExtraArgs={"RequestPayer": "requester"})
except:
has_seg_files = False
print('Make segmentation figure failed: {0}'.format(remote_file))
break
if has_seg_files:
s3_cat = utils.read_catalog(seg_files[1])
segmentation_figure(label, s3_cat, seg_files[0])
if aws_bucket:
#aws_bucket = 's3://grizli-cosmos/CutoutProducts/'
#aws_bucket = 's3://grizli/CutoutProducts/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket(aws_bucket.split("/")[2])
aws_path = '/'.join(aws_bucket.split("/")[3:])
if sync_fits:
files = glob.glob('{0}*'.format(label))
else:
files = glob.glob('{0}*png'.format(label))
for file in files:
print('{0} -> {1}'.format(file, aws_bucket))
bkt.upload_file(file, '{0}/{1}'.format(aws_path, file).replace('//', '/'), ExtraArgs={'ACL': 'public-read'})
#os.system('aws s3 sync --exclude "*" --include "{0}*" ./ {1} --acl public-read'.format(label, aws_bucket))
#os.system("""echo "<pre>" > index.html; aws s3 ls AWSBUCKETX --human-readable | sort -k 1 -k 2 | grep -v index | awk '{printf("%s %s",$1, $2); printf(" %6s %s ", $3, $4); print "<a href="$5">"$5"</a>"}'>> index.html; aws s3 cp index.html AWSBUCKETX --acl public-read""".replace('AWSBUCKETX', aws_bucket))
return has_filts
def resample_array(img,
wht=None,
pixratio=2,
slice_if_int=True,
int_tol=1.e-3,
method='drizzle',
drizzle_kwargs=DRIZZLE_KWARGS,
rescale_kwargs=RESCALE_KWARGS,
scale_by_area=False,
verbose=False,
blot_stepsize=-1,
**kwargs):
"""
Resample an image to a new grid. If pixratio is an integer, just return a
slice of the input `img`. Otherwise resample with `~drizzlepac` or `~resample`.
"""
from grizli.utils import (make_wcsheader, drizzle_array_groups,
blot_nearest_exact)
from skimage.transform import rescale, resize, downscale_local_mean
is_int = np.isclose(pixratio, np.round(pixratio), atol=int_tol)
if is_int & (pixratio > 1):
# Integer scaling
step = int(np.round(pixratio))
if method.lower() == 'drizzle':
_, win = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=1.,
get_hdu=False,
theta=0.)
_, wout = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=pixratio,
get_hdu=False,
theta=0.)
if wht is None:
wht = np.ones_like(img)
_drz = drizzle_array_groups([img], [wht], [win],
outputwcs=wout,
**drizzle_kwargs)
res = _drz[0]
res_wht = _drz[1]
method_used = 'drizzle'
elif slice_if_int:
# Simple slice
res = img[step // 2::step, step // 2::step] * 1
res_wht = np.ones_like(res)
method_used = 'slice'
else:
# skimage downscale with averaging
res = downscale_local_mean(img, (step, step), cval=0, clip=True)
res_wht = np.ones_like(res)
method_used = 'downscale'
else:
if method.lower() == 'drizzle':
# Drizzle
_, win = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=1.,
get_hdu=False,
theta=0.)
_, wout = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=pixratio,
get_hdu=False,
theta=0.)
if wht is None:
wht = np.ones_like(img)
_drz = drizzle_array_groups([img], [wht], [win],
outputwcs=wout,
**drizzle_kwargs)
res = _drz[0]
res_wht = _drz[1]
method_used = 'drizzle'
elif method.lower() == 'blot':
# Blot exact values
_, win = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=1.,
get_hdu=False,
theta=0.)
_, wout = make_wcsheader(ra=90,
dec=0,
size=img.shape,
pixscale=pixratio,
get_hdu=False,
theta=0.)
# Ones for behaviour around zeros
res = blot_nearest_exact(img + 1,
win,
wout,
verbose=False,
stepsize=blot_stepsize,
scale_by_pixel_area=False,
wcs_mask=False,
fill_value=0) - 1
res_wht = np.ones_like(res)
method_used = 'blot'
elif method.lower() == 'rescale':
res = rescale(img, 1. / pixratio, **rescale_kwargs)
res_wht = np.ones_like(res)
method_used = 'rescale'
else:
raise ValueError("method must be 'drizzle', 'blot' or 'rescale'.")
if scale_by_area:
scale = 1. / pixratio**2
else:
scale = 1
if verbose:
msg = 'resample_array x {4:.1f}: {0} > {1}, method={2}, scale={3:.2f}'
print(msg.format(img.shape, res.shape, method_used, scale, pixratio))
if not np.isclose(scale, 1, 1.e-4):
res = res * scale
res_wht = res_wht / scale**2
#print(res_wht, res_wht.dtype, scale, res_wht.shape)
#res_wht /= scale**2
return res, res_wht
def irac_mosaics(root='j000308m3303', home='/GrizliImaging/', pixfrac=0.2, kernel='square', initial_pix=1.0, final_pix=0.5, pulldown_mag=15.2, sync_xbcd=True, skip_fetch=False, radec=None, mosaic_pad=2.5, drizzle_ref_file='', run_alignment=True, assume_close=True, bucket='grizli-v1', aor_query='r*', mips_ext='[_e]bcd.fits', channels=['ch1','ch2','ch3','ch4','mips1'], drz_query='r*', sync_results=True, ref_seg=None, min_frame={'irac':5, 'mips':1.0}, med_max_size=500e6, stop_at='', make_psf=True, **kwargs):
"""
stop_at: preprocess, make_compact
"""
from grizli import utils
from . import irac
from .utils import get_wcslist, fetch_irac
PATH = os.path.join(home, root)
try:
os.mkdir(PATH)
except:
pass
os.chdir(PATH)
if not skip_fetch:
# Fetch IRAC bcds
if not os.path.exists(f'{root}_ipac.fits'):
os.system(f'wget https://s3.amazonaws.com/{bucket}/IRAC/{root}_ipac.fits')
res = fetch_irac(root=root, path='./', channels=channels)
if res in [False, None]:
# Nothing to do
make_html(root, bucket=bucket)
print(f'### Done: \n https://s3.amazonaws.com/{bucket}/Pipeline/{root}/IRAC/{root}.irac.html')
utils.log_comment(f'/tmp/{root}.success', 'Done!',
verbose=True, show_date=True)
return True
# Sync CHArGE HST images
os.system(f'aws s3 sync s3://{bucket}/Pipeline/{root}/Prep/ ./ '
f' --exclude "*" --include "{root}*seg.fits*"'
f' --include "{root}-ir_drz*fits*"'
f' --include "{root}*psf.fits*"'
f' --include "{root}-f[01]*_drz*fits.gz"'
f' --include "{root}*phot.fits"')
# Drizzle properties of the preliminary mosaic
#pixfrac, pix, kernel = 0.2, 1.0, 'square'
# Define an output WCS aligned in pixel phase to the HST mosaic ()
if not os.path.exists('ref_hdu.fits'):
wcslist = get_wcslist(skip=-500)
out_hdu = utils.make_maximal_wcs(wcslist, pixel_scale=initial_pix, theta=0, pad=5, get_hdu=True, verbose=True)
# Make sure pixels align
ref_file = glob.glob('{0}-f[01]*_drz_sci.fits*'.format(root))
if len(ref_file) == 0:
os.system(f'aws s3 sync s3://{bucket}/Pipeline/{root}/Prep/ ./ '
f' --exclude "*"'
f' --include "{root}-f[678]*_dr*fits.gz"')
ref_file = glob.glob('{0}-f[678]*_dr*_sci.fits*'.format(root))
ref_file = ref_file[-1]
print(f'\nHST reference image: {ref_file}\n')
ref_hdu = pyfits.open(ref_file)[0].header
ref_filter = utils.get_hst_filter(ref_hdu).lower()
ref_wcs = pywcs.WCS(ref_hdu)
ref_rd = ref_wcs.all_pix2world(np.array([[-0.5, -0.5]]), 0).flatten()
target_phase = np.array([0.5, 0.5])#/(pix/0.1)
for k in ['RADESYS', 'LATPOLE', 'LONPOLE']:
out_hdu.header[k] = ref_hdu[k]
# Shift CRVAL to same tangent point
out_wcs = pywcs.WCS(out_hdu.header)
out_xy = out_wcs.all_world2pix(np.array([ref_wcs.wcs.crval]), 1).flatten()
out_hdu.header['CRVAL1'], out_hdu.header['CRVAL2'] = tuple(ref_wcs.wcs.crval)
out_hdu.header['CRPIX1'], out_hdu.header['CRPIX2'] = tuple(out_xy)
# Align integer pixel phase
out_wcs = pywcs.WCS(out_hdu.header)
out_xy = out_wcs.all_world2pix(np.array([ref_rd]), 0).flatten()
xy_phase = out_xy - np.floor(out_xy)
new_crpix = out_wcs.wcs.crpix - (xy_phase - target_phase)
out_hdu.header['CRPIX1'], out_hdu.header['CRPIX2'] = tuple(new_crpix)
out_wcs = pywcs.WCS(out_hdu.header)
out_hdu.writeto('ref_hdu.fits', output_verify='Fix')
else:
out_hdu = pyfits.open('ref_hdu.fits')[1]
########
files = []
for ch in channels:
if 'mips' in ch:
mc = ch.replace('mips','ch')
files += glob.glob(f'{aor_query}/{mc}/bcd/SPITZER_M*{mips_ext}')
files += glob.glob(f'{aor_query}/{mc}/bcd/SPITZER_M*xbcd.fits.gz')
else:
files += glob.glob(f'{aor_query}/{ch}/bcd/SPITZER_I*cbcd.fits')
files += glob.glob(f'{aor_query}/{ch}/bcd/SPITZER_I*xbcd.fits.gz')
files.sort()
roots = np.array([file.split('/')[0] for file in files])
with_channels = np.array([file.split('_')[1] for file in files])
all_roots = np.array(['{0}-{1}'.format(r, c.replace('I','ch').replace('M', 'mips')) for r, c in zip(roots, with_channels)])
tab = {'aor':[], 'N':[], 'channel':[]}
for r in np.unique(all_roots):
tab['aor'].append(r.split('-')[0])
tab['N'].append((all_roots == r).sum())
tab['channel'].append(r.split('-')[1])
aors = utils.GTable(tab)
print(aors)
########
SKIP = True # Don't regenerate finished files
delete_group = False # Delete intermediate products from memory
zip_outputs = False # GZip intermediate products
aors_ch = {}
########
# Process mosaics by AOR
# Process in groups, helps for fields like HFF with dozens/hundreds of AORs!
for ch in channels:
aor = aors[(aors['channel'] == ch) & (aors['N'] > 5)]
if len(aor) == 0:
continue
#aors_ch[ch] = []
if ch in ['ch1','ch2']:
NPER, instrument = 500, 'irac'
if ch in ['ch3','ch4']:
NPER, instrument = 500, 'irac'
elif ch in ['mips1']:
NPER, instrument = 400, 'mips'
min_frametime = min_frame[instrument]
nsort = np.cumsum(aor['N']/NPER)
NGROUP = int(np.ceil(nsort.max()))
count = 0
for g in range(NGROUP):
root_i = root+'-{0:02d}'.format(g)
gsel = (nsort > g) & (nsort <= g+1)
aor_ids = list(aor['aor'][gsel])
print('{0}-{1} N_AOR = {2:>2d} N_EXP = {3:>4d}'.format(root_i, ch, len(aor_ids), aor['N'][gsel].sum()))
count += gsel.sum()
files = glob.glob('{0}-{1}*'.format(root_i, ch))
if (len(files) > 0) & (SKIP):
print('Skip {0}-{1}'.format(root_i, ch))
continue
with open('{0}-{1}.log'.format(root_i, ch),'w') as fp:
fp.write(time.ctime())
# Do internal alignment to GAIA.
# Otherwise, set `radec` to the name of a file that has two columns with
# reference ra/dec.
#radec = None
# Pipeline
if instrument == 'mips':
aors_ch[ch] = irac.process_all(channel=ch.replace('mips','ch'), output_root=root_i, driz_scale=initial_pix, kernel=kernel, pixfrac=pixfrac, wcslist=None, pad=0, out_hdu=out_hdu, aor_ids=aor_ids, flat_background=False, two_pass=True, min_frametime=min_frametime, instrument=instrument, align_threshold=0.15, radec=radec, run_alignment=False, mips_ext=mips_ext, ref_seg=ref_seg, global_mask=root+'_mask.reg')
else:
aors_ch[ch] = irac.process_all(channel=ch, output_root=root_i, driz_scale=initial_pix, kernel=kernel, pixfrac=pixfrac, wcslist=None, pad=0, out_hdu=out_hdu, aor_ids=aor_ids, flat_background=False, two_pass=True, min_frametime=min_frametime, instrument=instrument, radec=radec, run_alignment=run_alignment, assume_close=assume_close, ref_seg=ref_seg, global_mask=root+'_mask.reg', med_max_size=med_max_size)
if len(aors_ch[ch]) == 0:
continue
# PSFs
plt.ioff()
if (instrument != 'mips') & make_psf:
ch_num = int(ch[-1])
segmask=True
# psf_size=20
# for p in [0.1, final_pix]:
# irac.mosaic_psf(output_root=root_i, target_pix=p, channel=ch_num, aors=aors_ch[ch], kernel=kernel, pixfrac=pixfrac, size=psf_size, native_orientation=False, instrument=instrument, subtract_background=False, segmentation_mask=segmask, max_R=10)
# plt.close('all')
psf_size=30
p = 0.1
irac.mosaic_psf(output_root=root_i, target_pix=p, channel=ch_num, aors=aors_ch[ch], kernel=kernel, pixfrac=pixfrac, size=psf_size, native_orientation=True, subtract_background=False, segmentation_mask=segmask, max_R=10)
plt.close('all')
if delete_group:
del(aors_ch[ch])
print('Done {0}-{1}, gzip products'.format(root_i, ch))
if zip_outputs:
os.system('gzip {0}*-{1}_drz*fits'.format(root_i, ch))
# PSFs
if (instrument != 'mips') & make_psf:
# Average PSF
p = 0.1
files = glob.glob('*{0}-{1:.1f}*psfr.fits'.format(ch, p))
if len(files) == 0:
continue
files.sort()
avg = None
for file in files:
im = pyfits.open(file)
if avg is None:
wht = im[0].data != 0
avg = im[0].data*wht
else:
wht_i = im[0].data != 0
avg += im[0].data*wht_i
wht += wht_i
im.close()
avg = avg/wht
avg[wht == 0] = 0
# Window
from photutils import (HanningWindow, TukeyWindow,
CosineBellWindow,
SplitCosineBellWindow, TopHatWindow)
coswindow = CosineBellWindow(alpha=1)
avg *= coswindow(avg.shape)**0.05
avg /= avg.sum()
pyfits.writeto('{0}-{1}-{2:0.1f}.psfr_avg.fits'.format(root, ch, p), data=avg, header=im[0].header, overwrite=True)
####
## Show the initial product
plt.ioff()
for i in range(10):
files = glob.glob(f'{root}-{i:02d}-ch*sci.fits')
if len(files) > 0:
break
files.sort()
if len(files) == 1:
subs = 1,1
fs = [7,7]
elif len(files) == 2:
subs = 1,2
fs = [14,7]
elif len(files) == 3:
subs = 2,2
fs = [14,14]
else:
subs = 2,2
fs = [14,14]
fig = plt.figure(figsize=fs)
for i, file in enumerate(files[:4]):
im = pyfits.open(file)
print('{0} {1} {2:.1f} s'.format(file, im[0].header['FILTER'], im[0].header['EXPTIME']))
ax = fig.add_subplot(subs[0], subs[1], 1+i)
ax.imshow(im[0].data, vmin=-0.1, vmax=1, cmap='gray_r', origin='lower')
ax.text(0.05, 0.95, file, ha='left', va='top', color='k',
transform=ax.transAxes)
im.close()
if len(files) > 1:
fig.axes[1].set_yticklabels([])
if len(files) > 2:
fig.axes[0].set_xticklabels([])
fig.axes[1].set_xticklabels([])
if len(files) > 3:
fig.axes[3].set_yticklabels([])
fig.tight_layout(pad=0.5)
fig.savefig(f'{root}.init.png')
plt.close('all')
if stop_at == 'preprocess':
return True
#######
# Make more compact individual exposures and clean directories
wfiles = []
for ch in channels:
if 'mips' in ch:
chq = ch.replace('mips','ch')
wfiles += glob.glob(f'{aor_query}/{chq}/bcd/SPITZER_M*wcs.fits')
else:
wfiles += glob.glob(f'{aor_query}/{ch}/bcd/SPITZER_I*wcs.fits')
#wfiles = glob.glob('r*/*/bcd/*_I[1-4]_*wcs.fits')
#wfiles += glob.glob('r*/*/bcd/*_M[1-4]_*wcs.fits')
wfiles.sort()
for wcsfile in wfiles:
outfile = wcsfile.replace('_wcs.fits', '_xbcd.fits.gz')
if os.path.exists(outfile):
print(outfile)
else:
irac.combine_products(wcsfile)
print('Run: ', outfile)
if os.path.exists(outfile):
remove_files = glob.glob('{0}*fits'.format(wcsfile.split('_wcs')[0]))
for f in remove_files:
print(' rm ', f)
os.remove(f)
if stop_at == 'make_compact':
return True
#############
# Drizzle final mosaics
# Make final mosaic a bit bigger than the HST image
pad = mosaic_pad
# Pixel scale of final mosaic.
# Don't make too small if not many dithers available as in this example.
# But for well-sampled mosaics like RELICS / HFF, can push this to perhaps 0.3" / pix
pixscale = final_pix #0.5
# Again, if have many dithers maybe can use more aggressive drizzle parameters,
# like a 'point' kernel or smaller pixfrac (a 'point' kernel is pixfrac=0)
#kernel, pixfrac = 'square', 0.2
# Correction for bad columns near bright stars
#pulldown_mag = 15.2
##############
# Dilation for CR rejection
dil = np.ones((3,3))
driz_cr = [7, 4]
blot_interp = 'poly5'
bright_fmax = 0.5
### Drizzle
for ch in channels: #[:2]:
###########
# Files and reference image for extra CR rejection
if ch == 'mips1':
files = glob.glob('{0}/ch1/bcd/SPITZER_M1_*xbcd.fits*'.format(drz_query, ch))
files.sort()
pulldown_mag = -10
pixscale = 1.
kernel = 'point'
else:
files = glob.glob('{0}/{1}/bcd/*_I?_*xbcd.fits*'.format(drz_query, ch))
files.sort()
#ref = pyfits.open('{0}-00-{1}_drz_sci.fits'.format(root, ch))
#ref_data = ref[0].data.astype(np.float32)
ref_files = glob.glob(f'{root}-??-{ch}*sci.fits')
if len(ref_files) == 0:
continue
num = None
for ref_file in ref_files:
ref = pyfits.open(ref_file)
wht = pyfits.open(ref_file.replace('_sci.fits', '_wht.fits'))
if num is None:
num = ref[0].data*wht[0].data
den = wht[0].data
else:
num += ref[0].data*wht[0].data
den += wht[0].data
ref_data = (num/den).astype(np.float32)
ref_data[den <= 0] = 0
ref_wcs = pywcs.WCS(ref[0].header, relax=True)
ref_wcs.pscale = utils.get_wcs_pscale(ref_wcs)
if (not hasattr(ref_wcs, '_naxis1')) & hasattr(ref_wcs, '_naxis'):
ref_wcs._naxis1, ref_wcs._naxis2 = ref_wcs._naxis
##############
# Output WCS based on HST footprint
if drizzle_ref_file == '':
try:
hst_im = pyfits.open(glob.glob('{0}-f[01]*_drz_sci.fits*'.format(root))[-1])
except:
hst_im = pyfits.open(glob.glob('{0}-f[578]*_dr*sci.fits*'.format(root))[-1])
hst_wcs = pywcs.WCS(hst_im[0])
hst_wcs.pscale = utils.get_wcs_pscale(hst_wcs)
try:
size = (np.round(np.array([hst_wcs._naxis1, hst_wcs._naxis2])*hst_wcs.pscale*pad/pixscale)*pixscale)
except:
size = (np.round(np.array([hst_wcs._naxis[0], hst_wcs._naxis[1]])*hst_wcs.pscale*pad/pixscale)*pixscale)
hst_rd = hst_wcs.calc_footprint().mean(axis=0)
_x = utils.make_wcsheader(ra=hst_rd[0], dec=hst_rd[1],
size=size,
pixscale=pixscale,
get_hdu=False, theta=0)
out_header, out_wcs = _x
else:
driz_ref_im = pyfits.open(drizzle_ref_file)
out_wcs = pywcs.WCS(driz_ref_im[0].header, relax=True)
out_wcs.pscale = utils.get_wcs_pscale(out_wcs)
out_header = utils.to_header(out_wcs)
if (not hasattr(out_wcs, '_naxis1')) & hasattr(out_wcs, '_naxis'):
out_wcs._naxis1, out_wcs._naxis2 = out_wcs._naxis
##############
# Bright stars for pulldown correction
cat_file = glob.glob(f'{root}-[0-9][0-9]-{ch}.cat.fits')[0]
ph = utils.read_catalog(cat_file)
bright = (ph['mag_auto'] < pulldown_mag) # & (ph['flux_radius'] < 3)
ph = ph[bright]
##############
# Now do the drizzling
yp, xp = np.indices((256, 256))
orig_files = []
out_header['DRIZ_CR0'] = driz_cr[0]
out_header['DRIZ_CR1'] = driz_cr[1]
out_header['KERNEL'] = kernel
out_header['PIXFRAC'] = pixfrac
out_header['NDRIZIM'] = 0
out_header['EXPTIME'] = 0
out_header['BUNIT'] = 'microJy'
out_header['FILTER'] = ch
med_root = 'xxx'
N = len(files)
for i, file in enumerate(files):#[:100]):
print('{0}/{1} {2}'.format(i, N, file))
if file in orig_files:
continue
im = pyfits.open(file)
ivar = 1/im['CBUNC'].data**2
msk = (~np.isfinite(ivar)) | (~np.isfinite(im['CBCD'].data))
im['CBCD'].data[msk] = 0
ivar[msk] = 0
wcs = pywcs.WCS(im['WCS'].header, relax=True)
wcs.pscale = utils.get_wcs_pscale(wcs)
if (not hasattr(wcs, '_naxis1')) & hasattr(wcs, '_naxis'):
wcs._naxis1, wcs._naxis2 = wcs._naxis
fp = Path(wcs.calc_footprint())
med_root_i = im.filename().split('/')[0]
if med_root != med_root_i:
print('\n Read {0}-{1}_med.fits \n'.format(med_root_i, ch))
med = pyfits.open('{0}-{1}_med.fits'.format(med_root_i, ch))
med_data = med[0].data.astype(np.float32)
med_root = med_root_i
med.close()
try:
gaia_rd = utils.read_catalog('{0}-{1}_gaia.radec'.format(med_root_i, ch))
ii, rr = gaia_rd.match_to_catalog_sky(ph)
gaia_rd = gaia_rd[ii][rr.value < 2]
gaia_pts = np.array([gaia_rd['ra'].data,
gaia_rd['dec'].data]).T
except:
gaia_rd = []
#data = im['CBCD'].data - aor_med[0].data
# Change output units to uJy / pix
if ch == 'mips1':
# un = 1*u.MJy/u.sr
# #to_ujy_px = un.to(u.uJy/u.arcsec**2).value*(out_wcs.pscale**2)
# to_ujy_px = un.to(u.uJy/u.arcsec**2).value*(native_scale**2)
to_ujy_px = 146.902690
else:
# native_scale = 1.223
# un = 1*u.MJy/u.sr
# #to_ujy_px = un.to(u.uJy/u.arcsec**2).value*(out_wcs.pscale**2)
# to_ujy_px = un.to(u.uJy/u.arcsec**2).value*(native_scale**2)
to_ujy_px = 35.17517196810
blot_data = ablot.do_blot(ref_data, ref_wcs, wcs, 1, coeffs=True,
interp=blot_interp,
sinscl=1.0, stepsize=10,
wcsmap=None)/to_ujy_px
# mask for bright stars
eblot = 1-np.clip(blot_data, 0, bright_fmax)/bright_fmax
# Initial CR
clean = im[0].data - med_data - im['WCS'].header['PEDESTAL']
dq = (clean - blot_data)*np.sqrt(ivar)*eblot > driz_cr[0]
# Adjacent CRs
dq_dil = binary_dilation(dq, selem=dil)
dq |= ((clean - blot_data)*np.sqrt(ivar)*eblot > driz_cr[1]) & (dq_dil)
# Very negative pixels
dq |= clean*np.sqrt(ivar) < -4
original_dq = im['WCS'].data - (im['WCS'].data & 1)
dq |= original_dq > 0
# Pulldown correction for bright stars
if len(gaia_rd) > 0:
mat = fp.contains_points(gaia_pts)
if mat.sum() > 0:
xg, yg = wcs.all_world2pix(gaia_rd['ra'][mat], gaia_rd['dec'][mat], 0)
sh = dq.shape
mat = (xg > 0) & (xg < sh[1]) & (yg > 0) & (yg < sh[0])
if mat.sum() > 0:
for xi, yi in zip(xg[mat], yg[mat]):
dq |= (np.abs(xp-xi) < 2) & (np.abs(yp-yi) > 10)
if i == 0:
res = utils.drizzle_array_groups([clean], [ivar*(dq == 0)], [wcs], outputwcs=out_wcs, kernel=kernel, pixfrac=pixfrac, data=None, verbose=False)
# Copy header keywords
wcs_header = utils.to_header(wcs)
for k in im[0].header:
if (k not in ['', 'HISTORY', 'COMMENT']) & (k not in out_header) & (k not in wcs_header):
out_header[k] = im[0].header[k]
else:
_ = utils.drizzle_array_groups([clean], [ivar*(dq == 0)], [wcs], outputwcs=out_wcs, kernel=kernel, pixfrac=pixfrac, data=res[:3], verbose=False)
out_header['NDRIZIM'] += 1
out_header['EXPTIME'] += im[0].header['EXPTIME']
im.close()
# Pixel scale factor for weights
wht_scale = (out_wcs.pscale/wcs.pscale)**-4
# Write final images
pyfits.writeto('{0}-{1}_drz_sci.fits'.format(root, ch), data=res[0]*to_ujy_px, header=out_header,
output_verify='fix', overwrite=True)
pyfits.writeto('{0}-{1}_drz_wht.fits'.format(root, ch), data=res[1]*wht_scale/to_ujy_px**2,
header=out_header, output_verify='fix', overwrite=True)
##########
## Show the final drizzled images
plt.ioff()
files = glob.glob(f'{root}-ch*sci.fits')
files.sort()
if len(files) == 1:
subs = 1,1
fs = [7,7]
elif len(files) == 2:
subs = 1,2
fs = [14,7]
elif len(files) == 3:
subs = 2,2
fs = [14,14]
else:
subs = 2,2
fs = [14,14]
fig = plt.figure(figsize=fs)
for i, file in enumerate(files[:4]):
im = pyfits.open(file)
print('{0} {1} {2:.1f} s'.format(file, im[0].header['FILTER'], im[0].header['EXPTIME']))
ax = fig.add_subplot(subs[0], subs[1], 1+i)
scl = (final_pix/initial_pix)**2
ax.imshow(im[0].data, vmin=-0.1*scl, vmax=1*scl, cmap='gray_r', origin='lower')
ax.text(0.05, 0.95, file, ha='left', va='top', color='k',
transform=ax.transAxes)
im.close()
if len(files) > 1:
fig.axes[1].set_yticklabels([])
if len(files) > 2:
fig.axes[0].set_xticklabels([])
fig.axes[1].set_xticklabels([])
if len(files) > 3:
fig.axes[3].set_yticklabels([])
fig.tight_layout(pad=0.5)
fig.savefig(f'{root}.final.png')
plt.close('all')
if sync_results:
print('gzip mosaics')
os.system(f'gzip -f {root}-ch*_drz*fits {root}-mips*_drz*fits')
######## Sync
## Sync
print(f's3://{bucket}/Pipeline/{root}/IRAC/')
make_html(root, bucket=bucket)
os.system(f'aws s3 sync ./ s3://{bucket}/Pipeline/{root}/IRAC/'
f' --exclude "*" --include "{root}-ch*drz*fits*"'
f' --include "{root}-mips*drz*fits*"'
f' --include "{root}.*png"'
' --include "*-ch*psf*" --include "*log.fits"'
' --include "*wcs.[lp]*"'
' --include "*html" --include "*fail*"'
' --acl public-read')
if sync_xbcd:
aor_files = glob.glob('r*-ch*med.fits')
for aor_file in aor_files:
aor = aor_file.split('-ch')[0]
os.system(f'aws s3 sync ./{aor}/ s3://{bucket}/IRAC/AORS/{aor}/ --exclude "*" --include "ch*/bcd/*xbcd.fits.gz" --acl public-read')
os.system(f'aws s3 cp {aor_file} s3://{bucket}/IRAC/AORS/ --acl public-read')
msg = f'### Done: \n https://s3.amazonaws.com/{bucket}/Pipeline/{root}/IRAC/{root}.irac.html'
utils.log_comment(f'/tmp/{root}.success', msg, verbose=True, show_date=True)