/
auto_script.py
6590 lines (5144 loc) · 229 KB
/
auto_script.py
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"""
Automatic processing scripts for grizli
"""
import os
import inspect
import traceback
import glob
import time
import warnings
import gc
import yaml
import numpy as np
import astropy.io.fits as pyfits
import astropy.wcs as pywcs
from .. import prep, utils, GRIZLI_PATH
from .. import catalog as grizli_catalog
from .default_params import UV_N_FILTERS, UV_M_FILTERS, UV_W_FILTERS
from .default_params import OPT_N_FILTERS, OPT_M_FILTERS, OPT_W_FILTERS
from .default_params import IR_N_FILTERS, IR_M_FILTERS, IR_W_FILTERS
from .default_params import NIRISS_FILTERS, NIRCAM_SW_FILTERS, NIRCAM_LW_FILTERS
from .default_params import ALL_IMAGING_FILTERS, VALID_FILTERS
from .default_params import UV_GRISMS, OPT_GRISMS, IR_GRISMS, GRIS_REF_FILTERS
from .default_params import get_yml_parameters, write_params_to_yml
# needed for function definitions
args = get_yml_parameters()
if False:
np.seterr(divide='ignore', invalid='ignore', over='ignore', under='ignore')
# Only fetch F814W optical data for now
#ONLY_F814W = True
ONLY_F814W = False
def get_extra_data(root='j114936+222414', HOME_PATH='/Volumes/Pegasus/Grizli/Automatic', PERSIST_PATH=None, instruments=['WFC3'], filters=['F160W', 'F140W', 'F098M', 'F105W'], radius=2, run_fetch=True, from_mast=True, reprocess_parallel=True, s3_sync=False):
"""
Not used [2023]
"""
import os
import glob
import numpy as np
from hsaquery import query, fetch, fetch_mast
from hsaquery.fetch import DEFAULT_PRODUCTS
if PERSIST_PATH is None:
PERSIST_PATH = os.path.join(HOME_PATH, root, 'Persistence')
tab = utils.GTable.gread(os.path.join(HOME_PATH,
f'{root}_footprint.fits'))
# Fix CLEAR filter names
for i, filt_i in enumerate(tab['filter']):
if 'clear' in filt_i.lower():
spl = filt_i.lower().split(';')
if len(spl) > 1:
for s in spl:
if 'clear' not in s:
#print(filt_i, s)
filt_i = s.upper()
break
tab['filter'][i] = filt_i.upper()
ra, dec = tab.meta['RA'], tab.meta['DEC']
fp = np.load(os.path.join(HOME_PATH, '{0}_footprint.npy'.format(root)),
allow_pickle=True)[0]
radius = np.sqrt(fp.area*np.cos(dec/180*np.pi))*60/np.pi
xy = np.array(fp.boundary.convex_hull.boundary.xy)
dims = np.array([(xy[0].max()-xy[0].min())*np.cos(dec/180*np.pi),
xy[1].max()-xy[1].min()])*60
extra = query.run_query(box=[ra, dec, radius],
proposid=[],
instruments=instruments,
extensions=['FLT'],
filters=filters,
extra=query.DEFAULT_EXTRA)
# Fix CLEAR filter names
for i, filt_i in enumerate(extra['filter']):
if 'clear' in filt_i.lower():
spl = filt_i.lower().split(';')
if len(spl) > 1:
for s in spl:
if 'clear' not in s:
#print(filt_i, s)
filt_i = s.upper()
break
extra['filter'][i] = filt_i.upper()
for k in tab.meta:
extra.meta[k] = tab.meta[k]
extra.write(os.path.join(HOME_PATH, root, 'extra_data.fits'),
format='fits', overwrite=True)
CWD = os.getcwd()
os.chdir(os.path.join(HOME_PATH, root, 'RAW'))
if run_fetch:
if from_mast:
out = fetch_mast.get_from_MAST(extra,
inst_products=DEFAULT_PRODUCTS,
direct=True,
path=os.path.join(HOME_PATH, root, 'RAW'),
skip_existing=True)
else:
curl = fetch.make_curl_script(extra,
level=None,
script_name='extra.sh',
inst_products={'WFC3/UVIS': ['FLC'],
'WFPC2/PC': ['C0M', 'C1M'],
'WFC3/IR': ['RAW'],
'ACS/WFC': ['FLC']},
skip_existing=True,
output_path=os.path.join(HOME_PATH, root, 'RAW'),
s3_sync=s3_sync)
os.system('sh extra.sh')
files = glob.glob('*raw.fits.gz')
files.extend(glob.glob('*fl?.fits.gz'))
for file in files:
print('gunzip '+file)
os.system('gunzip {0}'.format(file))
else:
return extra
remove_bad_expflag(field_root=root, HOME_PATH=HOME_PATH, min_bad=2)
# Reprocess the RAWs into FLTs
status = os.system("python -c 'from grizli.pipeline import reprocess; reprocess.reprocess_wfc3ir(parallel={0})'".format(reprocess_parallel))
if status != 0:
from grizli.pipeline import reprocess
reprocess.reprocess_wfc3ir(parallel=False)
# Persistence products
os.chdir(PERSIST_PATH)
persist_files = fetch.persistence_products(extra)
for file in persist_files:
if not os.path.exists(os.path.basename(file)):
print(file)
os.system('curl -O {0}'.format(file))
for file in persist_files:
root = os.path.basename(file).split('.tar.gz')[0]
if os.path.exists(root):
print('Skip', root)
continue
if not os.path.exists(file):
print('Persistence tar file {0} not found'.format(file))
continue
# Ugly callout to shell
os.system('tar xzvf {0}.tar.gz'.format(root))
# Clean unneeded files
clean_files = glob.glob('{0}/*extper.fits'.format(root))
clean_files += glob.glob('{0}/*flt_cor.fits'.format(root))
for f in clean_files:
os.remove(f)
# Symlink to ./
pfiles = glob.glob('{0}/*persist.fits'.format(root))
if len(pfiles) > 0:
for f in pfiles:
if not os.path.exists(os.path.basename(f)):
os.system('ln -sf {0} ./'.format(f))
os.chdir(CWD)
def create_path_dict(root='j142724+334246', home='$PWD', raw=None, prep=None, extract=None, persist=None, thumbs=None, paths={}):
"""
Generate path dict.
Default:
{home}
{home}/{root}
{home}/{root}/RAW
{home}/{root}/Prep
{home}/{root}/Persistence
{home}/{root}/Extractions
{home}/{root}/Thumbnails
If ``home`` specified as '$PWD', then will be calculated from
`os.getcwd`.
Only generates values for keys not already specified in `paths`.
"""
import copy
if home == '$PWD':
home = os.getcwd()
base = os.path.join(home, root)
if raw is None:
raw = os.path.join(home, root, 'RAW')
if prep is None:
prep = os.path.join(home, root, 'Prep')
if persist is None:
persist = os.path.join(home, root, 'Persistence')
if extract is None:
extract = os.path.join(home, root, 'Extractions')
if thumbs is None:
thumbs = os.path.join(home, root, 'Thumbnaails')
xpaths = copy.deepcopy(paths)
for k in xpaths:
if xpaths[k] is None:
_ = xpaths.pop(k)
if 'home' not in xpaths:
xpaths['home'] = home
if 'base' not in xpaths:
xpaths['base'] = base
if 'raw' not in xpaths:
xpaths['raw'] = raw
if 'prep' not in xpaths:
xpaths['prep'] = prep
if 'persist' not in xpaths:
xpaths['persist'] = persist
if 'extract' not in xpaths:
xpaths['extract'] = extract
if 'thumbs' not in xpaths:
xpaths['thumbs'] = extract
return xpaths
MIRI_FLAT_ROUND = {'F560W' : 8,
'F770W':4,
'F1000W': 5,
'F1280W': 5,
'F1500W': 8,
'F1800W': 5,
'F2100W': 4,}
def get_miri_flat_by_date(file, tsplit=MIRI_FLAT_ROUND, verbose=True):
"""
MIRI flats computed by date
Flat-files defined by
>>> tsplit = {'F560W' : 8,
'F770W':4,
'F1000W': 5,
'F1280W': 5,
'F1500W': 8,
'F1800W': 5,
'F2100W': 4 }
>>> tkey = np.round(t_min/tsplit[FILTER])
>>> file = f'miri-{FILTER}-{tkey}_skyflat.fits'
Parameters
----------
file : str
MIRI exposure filename, with header keywords EXPSTART and FILTER
tsplit : dict
Date rounding factors by filter
Returns
-------
flat_file : str
Path to the flat file, ``None`` if not found
"""
# try:
# from .. import GRIZLI_PATH
# except ImportError:
# from grizli import utils, GRIZLI_PATH
tsplit = {'F770W':4,
'F1800W': 5,
'F560W' : 8,
'F1280W': 5,
'F1500W': 8,
'F1000W': 5,
'F2100W': 4,
}
with pyfits.open(file) as im:
t_min = im[0].header['EXPSTART']
filt = im[0].header['FILTER']
tkey = '{0:.0f}'.format(np.round(t_min/tsplit[filt]))
flat_key = f'miri-{filt.lower()}-{tkey}_skyflat.fits'
flat_file = None
for path in [os.path.join(GRIZLI_PATH,'CONF'),
'/mnt/efs/fs1/telescopes/MiriDateFlats']:
flat_file_i = os.path.join(path, flat_key)
if os.path.exists(flat_file_i):
flat_file = flat_file_i
break
msg = f'get_miri_flat_by_date: {file} {flat_key} - {flat_file}'
utils.log_comment(utils.LOGFILE, msg, show_date=False,
verbose=verbose)
return flat_file
def go(root='j010311+131615',
HOME_PATH='$PWD',
RAW_PATH=None, PREP_PATH=None, PERSIST_PATH=None, EXTRACT_PATH=None,
CRDS_CONTEXT=None,
filters=args['filters'],
fetch_files_args=args['fetch_files_args'],
global_miri_skyflat=False,
inspect_ramps=False,
is_dash=False, run_prepare_dash=True,
run_parse_visits=True,
is_parallel_field=False,
parse_visits_args=args['parse_visits_args'],
manual_alignment=False,
manual_alignment_args=args['manual_alignment_args'],
min_gaia_count=128,
gaia_mag_limits=[16,20.5,0.05],
preprocess_args=args['preprocess_args'],
visit_prep_args=args['visit_prep_args'],
persistence_args=args['persistence_args'],
redo_persistence_mask=False,
run_fine_alignment=True,
fine_backup=True,
fine_alignment_args=args['fine_alignment_args'],
make_mosaics=True,
mosaic_args=args['mosaic_args'],
mosaic_drizzle_args=args['mosaic_drizzle_args'],
mask_spikes=False,
mosaic_driz_cr_type=0,
make_phot=True,
multiband_catalog_args=args['multiband_catalog_args'],
only_preprocess=False,
overwrite_fit_params=False,
grism_prep_args=args['grism_prep_args'],
refine_with_fits=True,
run_extractions=False,
include_photometry_in_fit=False,
extract_args=args['extract_args'],
make_thumbnails=True,
thumbnail_args=args['thumbnail_args'],
make_final_report=True,
get_dict=False,
kill='',
use_jwst_crds=False,
**kwargs
):
"""
Run the full pipeline for a given target
Parameters
----------
root : str
Rootname of the `mastquery` file.
extract_maglim : [min, max]
Magnitude limits of objects to extract and fit.
"""
#isJWST = visit_prep_args['isJWST']
# Function defaults
if get_dict:
if get_dict <= 2:
# Default function arguments (different value to avoid recursion)
default_args = go(get_dict=10)
frame = inspect.currentframe()
args = inspect.getargvalues(frame).locals
for k in ['root', 'HOME_PATH', 'frame', 'get_dict']:
if k in args:
args.pop(k)
if get_dict == 2:
# Print keywords summary
if len(kwargs) > 0:
print('\n*** Extra args ***\n')
for k in kwargs:
if k not in default_args:
print('\'{0}\':{1},'.format(k, kwargs[k]))
print('\n*** User args ***\n')
for k in args:
if k in default_args:
if args[k] != default_args[k]:
print('\'{0}\':{1},'.format(k, args[k]))
print('\n*** Default args ***\n')
for k in args:
if k in default_args:
print('\'{0}\':{1},'.format(k, args[k]))
return args
else:
return args
# import os
# import glob
# import traceback
#
#
try:
from .. import multifit
from . import auto_script
except:
from grizli import multifit
from grizli.pipeline import auto_script
# #import grizli.utils
import matplotlib.pyplot as plt
# Silence numpy and astropy warnings
utils.set_warnings()
PATHS = create_path_dict(root=root, home=HOME_PATH,
raw=RAW_PATH, prep=PREP_PATH,
persist=PERSIST_PATH, extract=EXTRACT_PATH)
fpfile = os.path.join(PATHS['home'], '{0}_footprint.fits'.format(root))
exptab = utils.GTable.gread(fpfile)
# Fix CLEAR filter names
for i, filt_i in enumerate(exptab['filter']):
if 'clear' in filt_i.lower():
spl = filt_i.lower().split(';')
if len(spl) > 1:
for s in spl:
if 'clear' not in s:
#print(filt_i, s)
filt_i = s.upper()
break
exptab['filter'][i] = filt_i.upper()
utils.LOGFILE = os.path.join(PATHS['home'], f'{root}.auto_script.log.txt')
utils.log_comment(utils.LOGFILE, '### Pipeline start', show_date=True)
if CRDS_CONTEXT is not None:
utils.log_comment(utils.LOGFILE,
f"# export CRDS_CONTEXT='{CRDS_CONTEXT}'",
show_date=True)
os.environ['CRDS_CONTEXT'] = CRDS_CONTEXT
# Working directory
os.chdir(PATHS['home'])
######################
# Preliminary gaia catalog
# Get gaia catalog
try:
gaia = grizli_catalog.gaia_catalog_for_assoc(assoc_name=root)
msg = f"{root} : found {gaia['valid'].sum()} GAIA sources"
utils.log_comment(utils.LOGFILE, msg, show_date=False,
verbose=True)
gaia.write(f'{root}.gaia.fits', overwrite=True)
prep.table_to_radec(gaia[gaia['valid']], f'{root}.gaia.radec')
prep.table_to_regions(gaia[gaia['valid']], f'{root}.gaia.reg')
if gaia['valid'].sum() > min_gaia_count:
preprocess_args['master_radec'] = os.path.join(PATHS['home'],
f'{root}.gaia.radec')
visit_prep_args['align_mag_limits'] = gaia_mag_limits
msg = f"{root} : Use {root}.gaia.radec as master_radec"
msg += f"\n{root} : Set align_mag_limits={gaia_mag_limits}"
utils.log_comment(utils.LOGFILE, msg, show_date=False,
verbose=True)
except:
utils.log_exception(utils.LOGFILE, traceback)
pass
######################
# Download data
if fetch_files_args is not None:
fetch_files_args['reprocess_clean_darks'] &= (not is_dash)
auto_script.fetch_files(field_root=root, HOME_PATH=HOME_PATH,
paths=PATHS,
filters=filters, **fetch_files_args)
else:
os.chdir(PATHS['prep'])
if global_miri_skyflat:
os.chdir(PATHS['raw'])
files = glob.glob('*mirimage*rate.fits')
if len(files) > 0:
files.sort()
skyfile = get_miri_flat_by_date(files[0])
if skyfile is None:
skyfile = os.path.join(PATHS['prep'], f'{root}_skyflat.fits')
msg = f"{root} : global_miri_skyflat N={len(files)}"
utils.log_comment(utils.LOGFILE, msg, show_date=False,
verbose=True)
os.chdir(PATHS['prep'])
# Don't redo if file found
if not os.path.exists(skyfile):
for file in files:
prep.fresh_flt_file(file, use_skyflats=False)
prep.make_visit_average_flat({'product':root,
'files':files},
dilate=1,
threshold=5,
clip_max=np.minimum(len(files)//2,4),
apply=False)
visit_prep_args['miri_skyflat'] = False
visit_prep_args['use_skyflats'] = False
visit_prep_args['miri_skyfile'] = skyfile
if is_dash & run_prepare_dash:
from wfc3dash import process_raw
os.chdir(PATHS['raw'])
process_raw.run_all()
files = glob.glob(os.path.join(PATHS['raw'], '*_fl*fits'))
files += glob.glob(os.path.join(PATHS['raw'], '*_c[01]m.fits'))
files += glob.glob(os.path.join(PATHS['raw'], '*_rate.fits'))
files += glob.glob(os.path.join(PATHS['raw'], '*_cal.fits'))
if len(files) == 0:
print('No FL[TC] files found!')
utils.LOGFILE = '/tmp/grizli.log'
return False
if kill == 'fetch_files':
print('kill=\'fetch_files\'')
return True
if inspect_ramps:
# Inspect for CR trails
os.chdir(PATHS['raw'])
status = os.system("python -c 'from grizli.pipeline.reprocess import inspect; inspect()'")
######################
# Parse visit associations
os.chdir(PATHS['prep'])
visit_file = auto_script.find_visit_file(root=root)
if (visit_file is None) | run_parse_visits:
# Parsing for parallel fields, where time-adjacent exposures
# may have different visit IDs and should be combined
if 'combine_same_pa' in parse_visits_args:
if (parse_visits_args['combine_same_pa'] == -1):
if is_parallel_field:
parse_visits_args['combine_same_pa'] = True
parse_visits_args['max_dt'] = 4./24
else:
parse_visits_args['combine_same_pa'] = False
parse_visits_args['max_dt'] = 1.
else:
parse_visits_args['combine_same_pa'] = is_parallel_field
parsed = auto_script.parse_visits(field_root=root,
RAW_PATH=PATHS['raw'],
filters=filters, is_dash=is_dash,
**parse_visits_args)
else:
#parsed = np.load(f'{root}_visits.npy', allow_pickle=True)
parsed = load_visit_info(root, verbose=False)
if kill == 'parse_visits':
print('kill=\'parse_visits\'')
return True
visits, all_groups, info = parsed
run_has_grism = np.in1d(info['FILTER'], ['G141', 'G102', 'G800L', 'GR150C', 'GR150R']).sum() > 0
# is PUPIL in info?
run_has_grism |= np.in1d(info['PUPIL'], ['GRISMR', 'GRISMC']).sum() > 0
# Alignment catalogs
#catalogs = ['PS1','SDSS','GAIA','WISE']
#######################
# Manual alignment
if manual_alignment:
os.chdir(PATHS['prep'])
auto_script.manual_alignment(field_root=root, HOME_PATH=PATHS['home'],
**manual_alignment_args)
if kill == 'manual_alignment':
print('kill=\'manual_alignment\'')
return True
#####################
# Initial visit footprints
visit_file = find_visit_file(root=root)
print(f'Initial exposure footprints in {visit_file}')
check_paths = ['./', PATHS['raw'], '../RAW']
get_visit_exposure_footprints(root=root, check_paths=check_paths)
#####################
# Alignment & mosaics
os.chdir(PATHS['prep'])
tweak_max_dist = (5 if is_parallel_field else 1)
if 'tweak_max_dist' not in visit_prep_args:
visit_prep_args['tweak_max_dist'] = tweak_max_dist
if 'use_self_catalog' not in visit_prep_args:
visit_prep_args['use_self_catalog'] = is_parallel_field
auto_script.preprocess(field_root=root, HOME_PATH=PATHS['home'],
PERSIST_PATH=PATHS['persist'],
visit_prep_args=visit_prep_args,
persistence_args=persistence_args,
**preprocess_args)
if kill == 'preprocess':
print('kill=\'preprocess\'')
visit_file = find_visit_file(root=root)
print(f'Update exposure footprints in {visit_file}')
check_paths = ['./', PATHS['raw'], '../RAW']
get_visit_exposure_footprints(root=root, check_paths=check_paths)
return True
if redo_persistence_mask:
comment = '# Redo persistence masking: {0}'.format(persistence_args)
print(comment)
utils.log_comment(utils.LOGFILE, comment)
all_flt_files = glob.glob('*_flt.fits')
all_flt_files.sort()
for file in all_flt_files:
print(file)
pfile = os.path.join(PATHS['persist'],
file.replace('_flt', '_persist'))
if os.path.exists(pfile):
prep.apply_persistence_mask(file, path=PATHS['persist'],
**persistence_args)
##########
# Fine alignment
fine_files = glob.glob('{0}*fine.png'.format(root))
if (run_fine_alignment == 2) & (len(fine_files) > 0) & (len(visits) > 1):
msg = '\n\n### Redo visit-level mosaics and catalogs for fine alignment\n\n'
utils.log_comment(utils.LOGFILE, msg, show_date=True, verbose=True)
keep_visits = []
for visit in visits:
visit_files = glob.glob(visit['product']+'*.cat.*')
visit_files += glob.glob(visit['product']+'_dr*')
visit_files += glob.glob(visit['product']+'*seg.fits*')
if len(visit_files) > 0:
keep_visits.append(visit)
for file in visit_files:
os.remove(file)
# Redrizzle visit-level mosaics and remake catalogs
prep.drizzle_overlaps(keep_visits, check_overlaps=False, skysub=False,
static=False, pixfrac=0.5, scale=None,
final_wcs=False, fetch_flats=False,
final_rot=None,
include_saturated=True)
# Make new catalogs
for visit in keep_visits:
if len(visit['files']) == 0:
continue
visit_filter = visit['product'].split('-')[-1]
is_single = len(visit['files']) == 1
isACS = '_flc' in visit['files'][0]
isWFPC2 = '_c0' in visit['files'][0]
if visit_filter in ['g102', 'g141', 'g800l', 'g280']:
print('# Skip grism visit: {0}'.format(visit['product']))
continue
# New catalog
if visit_prep_args['align_thresh'] is None:
thresh = 2.5
else:
thresh = visit_prep_args['align_thresh']
cat = prep.make_SEP_catalog(root=visit['product'],
threshold=thresh)
# New region file
prep.table_to_regions(cat, '{0}.cat.reg'.format(visit['product']))
# New radec
if not ((isACS | isWFPC2) & is_single):
# 140 brightest or mag range
clip = (cat['MAG_AUTO'] > 18) & (cat['MAG_AUTO'] < 23)
clip &= cat['MAGERR_AUTO'] < 0.05
clip &= utils.catalog_mask(cat,
max_err_percentile=visit_prep_args['max_err_percentile'],
pad=visit_prep_args['catalog_mask_pad'],
pad_is_absolute=False, min_flux_radius=1.)
NMAX = 140
so = np.argsort(cat['MAG_AUTO'][clip])
if clip.sum() > NMAX:
so = so[:NMAX]
prep.table_to_radec(cat[clip][so],
'{0}.cat.radec'.format(visit['product']))
for file in fine_files:
print('rm {0}'.format(file))
os.remove(file)
fine_files = []
if (len(fine_files) == 0) & (run_fine_alignment > 0) & (len(visits) > 1):
fine_catalogs = ['GAIA', 'PS1', 'DES', 'SDSS', 'WISE']
try:
out = auto_script.fine_alignment(field_root=root,
HOME_PATH=PATHS['home'],
**fine_alignment_args)
plt.close()
# Update WCS headers with fine alignment
auto_script.update_wcs_headers_with_fine(root, backup=fine_backup)
except:
utils.log_exception(utils.LOGFILE, traceback)
utils.log_comment(utils.LOGFILE, "# !! Fine alignment failed")
# Update the visits file with the new exposure footprints
visit_file = auto_script.find_visit_file(root=root)
print('Update exposure footprints in {0}'.format(visit_file))
check_paths = ['./', PATHS['raw'], '../RAW']
get_visit_exposure_footprints(root=root, check_paths=check_paths)
# Make combined mosaics
no_mosaics_found = len(glob.glob(f'{root}-ir_dr?_sci.fits')) == 0
if no_mosaics_found & make_mosaics:
skip_single = preprocess_args['skip_single_optical_visits']
if 'fix_stars' in visit_prep_args:
fix_stars = visit_prep_args['fix_stars']
else:
fix_stars = False
# For running at the command line
# if False:
# mos_args = {'mosaic_args': kwargs['mosaic_args'],
# 'fix_stars': kwargs['visit_prep_args']['fix_stars'],
# 'mask_spikes': kwargs['mask_spikes'], 'skip_single_optical_visits': kwargs['preprocess_args']['skip_single_optical_visits']}
# auto_script.make_combined_mosaics(root, **mos_args)
make_combined_mosaics(root, mosaic_args=mosaic_args,
fix_stars=fix_stars, mask_spikes=mask_spikes,
skip_single_optical_visits=skip_single,
mosaic_driz_cr_type=mosaic_driz_cr_type,
mosaic_drizzle_args=mosaic_drizzle_args)
# Make PSFs. Always set get_line_maps=False since PSFs now
# provided for each object.
mosaic_files = glob.glob('{0}-f*sci.fits'.format(root))
if (not is_dash) & (len(mosaic_files) > 0):
print('Make field PSFs')
auto_script.field_psf(root=root, PREP_PATH=PATHS['prep'],
RAW_PATH=PATHS['raw'],
EXTRACT_PATH=PATHS['extract'],
get_line_maps=False, skip=False)
# Are there full-field mosaics?
mosaic_files = glob.glob(f'{root}-f*sci.fits')
# Photometric catalog
has_phot_file = os.path.exists(f'{root}_phot.fits')
if (not has_phot_file) & make_phot & (len(mosaic_files) > 0):
try:
tab = auto_script.multiband_catalog(field_root=root,
**multiband_catalog_args)
try:
# Add columns indicating objects that fall in grism exposures
phot = utils.read_catalog(f'{root}_phot.fits')
out = count_grism_exposures(phot, all_groups,
grisms=['g800l', 'g102', 'g141', 'gr150c', 'gr150r'],
verbose=True)
phot.write(f'{root}_phot.fits', overwrite=True)
except:
pass
except:
utils.log_exception(utils.LOGFILE, traceback)
utils.log_comment(utils.LOGFILE,
'# Run `multiband_catalog` with `detection_background=True`')
multiband_catalog_args['detection_background'] = True
tab = auto_script.multiband_catalog(field_root=root,
**multiband_catalog_args)
#tab = auto_script.multiband_catalog(field_root=root, threshold=threshold, detection_background=True, photometry_background=True, get_all_filters=False)
# Make exposure json / html report
auto_script.exposure_report(root, log=True)
# Stop if only want to run pre-processing
if (only_preprocess | (len(all_groups) == 0)):
if make_thumbnails:
print('#####\n# Make RGB thumbnails\n#####')
if thumbnail_args['drizzler_args'] is None:
thumbnail_args['drizzler_args'] = DRIZZLER_ARGS.copy()
os.chdir(PATHS['prep'])
#print('XXX ', thumbnail_args)
auto_script.make_rgb_thumbnails(root=root, **thumbnail_args)
if not os.path.exists(PATHS['thumbs']):
os.mkdir(PATHS['thumbs'])
os.system('mv {0}_[0-9]*.png {0}_[0-9]*.fits {1}'.format(root,
PATHS['thumbs']))
if make_final_report:
make_report(root, make_rgb=True)
utils.LOGFILE = '/tmp/grizli.log'
return True
######################
# Grism prep
files = glob.glob(os.path.join(PATHS['prep'], '*GrismFLT.fits'))
files += glob.glob(os.path.join(PATHS['extract'], '*GrismFLT.fits'))
if len(files) == 0:
os.chdir(PATHS['prep'])
grp = auto_script.grism_prep(field_root=root, PREP_PATH=PATHS['prep'],
EXTRACT_PATH=PATHS['extract'],
**grism_prep_args)
del(grp)
######################
# Grism extractions
os.chdir(PATHS['extract'])
#####################
# Update the contam model with the "full.fits"
# files in the working directory
if (len(glob.glob('*full.fits')) > 0) & (refine_with_fits):
auto_script.refine_model_with_fits(field_root=root, clean=True,
grp=None, master_files=None,
spectrum='continuum', max_chinu=5)
# Drizzled grp objects
# All files
if len(glob.glob(f'{root}*_grism*fits*')) == 0:
grism_files = glob.glob('*GrismFLT.fits')
grism_files.sort()
catalog = glob.glob(f'{root}-*.cat.fits')[0]
try:
seg_file = glob.glob(f'{root}-*_seg.fits')[0]
except:
seg_file = None
grp = multifit.GroupFLT(grism_files=grism_files, direct_files=[],
ref_file=None, seg_file=seg_file,
catalog=catalog, cpu_count=-1, sci_extn=1,
pad=(64,256),
use_jwst_crds=use_jwst_crds)
# Make drizzle model images
grp.drizzle_grism_models(root=root, kernel='point', scale=0.15)
# Free grp object
del(grp)
if is_parallel_field:
pline = auto_script.PARALLEL_PLINE.copy()
else:
pline = auto_script.DITHERED_PLINE.copy()
# Make script for parallel processing
args_file = f'{root}_fit_args.npy'
if (not os.path.exists(args_file)) | (overwrite_fit_params):
msg = '# generate_fit_params: ' + args_file
utils.log_comment(utils.LOGFILE, msg, verbose=True, show_date=True)
pline['pixscale'] = mosaic_args['wcs_params']['pixel_scale']
pline['pixfrac'] = mosaic_args['mosaic_pixfrac']
if pline['pixfrac'] > 0:
pline['kernel'] = 'square'
else:
pline['kernel'] = 'point'
has_g800l = utils.column_string_operation(info['FILTER'], ['G800L'],
'count', 'or').sum()
if has_g800l > 0:
min_sens = 0.
fit_trace_shift = True
else:
min_sens = 0.001
fit_trace_shift = True
try:
auto_script.generate_fit_params(field_root=root, prior=None, MW_EBV=exptab.meta['MW_EBV'], pline=pline, fit_only_beams=True, run_fit=True, poly_order=7, fsps=True, min_sens=min_sens, sys_err=0.03, fcontam=0.2, zr=[0.05, 3.4], save_file=args_file, fit_trace_shift=fit_trace_shift, include_photometry=True, use_phot_obj=include_photometry_in_fit)
except:
# include_photometry failed?
auto_script.generate_fit_params(field_root=root, prior=None, MW_EBV=exptab.meta['MW_EBV'], pline=pline, fit_only_beams=True, run_fit=True, poly_order=7, fsps=True, min_sens=min_sens, sys_err=0.03, fcontam=0.2, zr=[0.05, 3.4], save_file=args_file, fit_trace_shift=fit_trace_shift, include_photometry=False, use_phot_obj=False)
# Copy for now
os.system(f'cp {args_file} fit_args.npy')
# Done?
if (not run_extractions) | (run_has_grism == 0):
# Make RGB thumbnails
if make_thumbnails:
print('#####\n# Make RGB thumbnails\n#####')
if thumbnail_args['drizzler_args'] is None:
thumbnail_args['drizzler_args'] = DRIZZLER_ARGS.copy()
os.chdir(PATHS['prep'])
auto_script.make_rgb_thumbnails(root=root, **thumbnail_args)
if not os.path.exists(PATHS['thumbs']):
os.mkdir(PATHS['thumbs'])
os.system('mv {0}_[0-9]*.png {0}_[0-9]*.fits {1}'.format(root,
PATHS['thumbs']))
utils.LOGFILE = '/tmp/grizli.log'
return True
# Run extractions (and fits)
auto_script.extract(field_root=root, **extract_args)
# Make RGB thumbnails
if make_thumbnails:
print('#####\n# Make RGB thumbnails\n#####')
if thumbnail_args['drizzler_args'] is None:
thumbnail_args['drizzler_args'] = DRIZZLER_ARGS.copy()
os.chdir(PATHS['prep'])
auto_script.make_rgb_thumbnails(root=root, **thumbnail_args)
if not os.path.exists(PATHS['thumbs']):
os.mkdir(PATHS['thumbs'])
os.system('mv {0}_[0-9]*.png {0}_[0-9]*.fits {1}'.format(root,
PATHS['thumbs']))
if extract_args['run_fit']:
os.chdir(PATHS['extract'])
# Redrizzle grism models
grism_files = glob.glob('*GrismFLT.fits')
grism_files.sort()