def get_match_criteria(self):
"""Set the general matching criteria for GNIRS. Copied from NIRES"""
match_criteria = {}
for key in framematch.FrameTypeBitMask().keys():
match_criteria[key] = {}
match_criteria['standard']['match'] = {}
# match_criteria['standard']['match']['naxis0'] = '=0'
# match_criteria['standard']['match']['naxis1'] = '=0'
match_criteria['bias']['match'] = {}
# match_criteria['bias']['match']['naxis0'] = '=0'
# match_criteria['bias']['match']['naxis1'] = '=0'
match_criteria['pixelflat']['match'] = {}
# match_criteria['pixelflat']['match']['naxis0'] = '=0'
# match_criteria['pixelflat']['match']['naxis1'] = '=0'
match_criteria['trace']['match'] = {}
# match_criteria['trace']['match']['naxis0'] = '=0'
# match_criteria['trace']['match']['naxis1'] = '=0'
match_criteria['arc']['match'] = {}
# match_criteria['arc']['match']['naxis0'] = '=0'
# match_criteria['arc']['match']['naxis1'] = '=0'
return match_criteria
def get_match_criteria(self):
match_criteria = {}
for key in framematch.FrameTypeBitMask().keys():
match_criteria[key] = {}
#
match_criteria['standard']['match'] = {}
match_criteria['standard']['match']['naxis0'] = '=0'
match_criteria['standard']['match']['naxis1'] = '=0'
# Bias
match_criteria['bias']['match'] = {}
match_criteria['bias']['match']['naxis0'] = '=0'
match_criteria['bias']['match']['naxis1'] = '=0'
# Pixelflat
match_criteria['pixelflat']['match']['naxis0'] = '=0'
match_criteria['pixelflat']['match']['naxis1'] = '=0'
match_criteria['pixelflat']['match']['dispname'] = ''
# Traceflat
match_criteria['trace']['match'] = match_criteria['pixelflat'][
'match'].copy()
# Arc
match_criteria['arc']['match'] = match_criteria['pixelflat'][
'match'].copy()
# Return
return match_criteria
def build_group_dict(fitstbl, setupIDs, all_sci_idx, all_sci_ID):
"""
Build a dictionary with the list of exposures of each type for a
given instrumental setup.
Args:
fitstbl (:class:`pypeit.metadata.PypeItMetaData`):
The metadata table for the fits files to reduce.
setupIDs (:obj:`list`):
The list of setups.
all_sci_idx (:obj:`list`):
The indices of the science frames in the data table.
TODO: Why is this needed?
all_sci_ID (:obj:`list`):
The ID number assigned to each science frame.
Returns:
dict: A dictionary with the list of file names associated with
each instrument configuration. It also provides the list of
science and standard star targets. TODO: How are the latter
used, if at all?
"""
type_keys = framematch.FrameTypeBitMask().keys() + ['None']
group_dict = {}
for sc,setupID in enumerate(setupIDs):
#scidx = sciexp[sc]._idx_sci[0]
scidx = all_sci_idx[sc]
sci_ID = all_sci_ID[sc]
# Set group_key
config_key = setupID[0]
# Plan init
if config_key not in group_dict.keys():
group_dict[config_key] = {}
for key in type_keys:
# if key not in ['None', 'dark']:
# group_dict[config_key][key] = []
group_dict[config_key][key] = []
group_dict[config_key]['sciobj'] = []
group_dict[config_key]['stdobj'] = []
# Fill group_dict too
for key in type_keys:
# if key in ['None', 'dark']:
# continue
indices = np.where(fitstbl.find_frames(key))[0] if key in ['None', 'dark'] \
else np.where(fitstbl.find_frames(key, sci_ID=sci_ID))[0]
for idx in indices:
# Only add if new
if fitstbl['filename'][idx] not in group_dict[config_key][key]:
group_dict[config_key][key].append(fitstbl['filename'][idx])
# TODO: How is this used?
if key == 'standard' and 'target' in fitstbl.keys(): # Add target name
group_dict[config_key]['stdobj'].append(fitstbl['target'][idx])
# TODO: How is this used?
if key == 'science' and 'target' in fitstbl.keys(): # Add target name
group_dict[config_key]['sciobj'].append(fitstbl['target'][scidx])
return group_dict
def get_match_criteria(self):
match_criteria = {}
for key in framematch.FrameTypeBitMask().keys():
match_criteria[key] = {}
# Standard
# Can be over-ruled by flux calibrate = False
match_criteria['standard']['match'] = {}
match_criteria['standard']['match']['decker'] = ''
match_criteria['standard']['match']['binning'] = ''
match_criteria['standard']['match']['filter1'] = ''
# Bias
match_criteria['bias']['match'] = {}
match_criteria['bias']['match']['binning'] = ''
# Pixelflat
match_criteria['pixelflat']['match'] = match_criteria['standard'][
'match'].copy()
# Traceflat
match_criteria['trace']['match'] = match_criteria['standard'][
'match'].copy()
# Arc
match_criteria['arc']['match'] = match_criteria['standard'][
'match'].copy()
# Return
return match_criteria
def main(args):
import os
import sys
import numpy as np
from IPython import embed
from pypeit import pypeit
from pypeit import pypeitsetup
from pypeit.core import framematch
from pypeit import msgs
# Setup
data_files = [os.path.join(args.full_rawpath, args.fileA),
os.path.join(args.full_rawpath,args.fileB)]
ps = pypeitsetup.PypeItSetup(data_files, path='./', spectrograph_name='keck_nires')
ps.build_fitstbl()
# TODO -- Get the type_bits from 'science'
bm = framematch.FrameTypeBitMask()
file_bits = np.zeros(2, dtype=bm.minimum_dtype())
file_bits[0] = bm.turn_on(file_bits[0], ['arc', 'science', 'tilt'])
file_bits[1] = bm.turn_on(file_bits[0], ['arc', 'science', 'tilt'])
ps.fitstbl.set_frame_types(file_bits)
ps.fitstbl.set_combination_groups()
# Extras
ps.fitstbl['setup'] = 'A'
# A-B
ps.fitstbl['bkg_id'] = [2,1]
# Calibrations
master_dir = os.getenv('NIRES_MASTERS')
if master_dir is None:
msgs.error('You need to set an Environmental variable NIRES_MASTERS that points at the '
'Master Calibs')
# Config the run
cfg_lines = ['[rdx]']
cfg_lines += [' spectrograph = {0}'.format('keck_nires')]
cfg_lines += [' redux_path = {0}'.format(os.path.join(os.getcwd(),'keck_nires_A'))]
# Calibrations
cfg_lines += ['[baseprocess]']
cfg_lines += [' use_biasimage = False']
cfg_lines += [' use_overscan = False']
cfg_lines += [' use_pixelflat = False']
cfg_lines += ['[calibrations]']
cfg_lines += [' master_dir = {0}'.format(master_dir)]
cfg_lines += [' raise_chk_error = False']
cfg_lines += ['[scienceframe]']
cfg_lines += [' [[process]]']
cfg_lines += [' mask_cr = False']
cfg_lines += ['[reduce]']
cfg_lines += [' [[extraction]]']
cfg_lines += [' skip_optimal = True']
if args.box_radius is not None: # Boxcar radius
cfg_lines += [' boxcar_radius = {0}'.format(args.box_radius)]
cfg_lines += [' [[findobj]]']
cfg_lines += [' skip_second_find = True']
# Write
ofiles = ps.fitstbl.write_pypeit(configs='A', write_bkg_pairs=True, cfg_lines=cfg_lines)
if len(ofiles) > 1:
msgs.error("Bad things happened..")
# Instantiate the main pipeline reduction object
pypeIt = pypeit.PypeIt(ofiles[0], verbosity=2,
reuse_masters=True, overwrite=True,
logname='nires_proc_AB.log', show=False)
# Run
pypeIt.reduce_all()
msgs.info('Data reduction complete')
# QA HTML
msgs.info('Generating QA HTML')
pypeIt.build_qa()
return 0
def main(pargs):
import os
import numpy as np
from IPython import embed
from pypeit import pypeit
from pypeit import pypeitsetup
from pypeit.core import framematch
spec = pargs.spectrograph
# Config the run
cfg_lines = ['[rdx]']
cfg_lines += [' spectrograph = {0}'.format(spec)]
cfg_lines += [
' redux_path = {0}_A'.format(os.path.join(os.getcwd(), spec))
]
cfg_lines += [' detnum = {0}'.format(pargs.det)]
if pargs.ignore_headers:
cfg_lines += [' ignore_bad_headers = True']
cfg_lines += ['[scienceframe]']
cfg_lines += [' [[process]]']
cfg_lines += [' cr_reject = False']
if pargs.user_pixflat is not None:
cfg_lines += ['[calibrations]']
cfg_lines += [' [[flatfield]]']
cfg_lines += [' frame = {0}'.format(pargs.user_pixflat)]
cfg_lines += ['[reduce]']
cfg_lines += [' [[extraction]]']
cfg_lines += [' skip_optimal = True']
if pargs.box_radius is not None: # Boxcar radius
cfg_lines += [' boxcar_radius = {0}'.format(pargs.box_radius)]
cfg_lines += [' [[findobj]]']
cfg_lines += [' skip_second_find = True']
# Data files
data_files = [
os.path.join(pargs.full_rawpath, pargs.arc),
os.path.join(pargs.full_rawpath, pargs.flat),
os.path.join(pargs.full_rawpath, pargs.science)
]
# Setup
ps = pypeitsetup.PypeItSetup(data_files,
path='./',
spectrograph_name=spec,
cfg_lines=cfg_lines)
ps.build_fitstbl()
# TODO -- Get the type_bits from 'science'
bm = framematch.FrameTypeBitMask()
file_bits = np.zeros(3, dtype=bm.minimum_dtype())
file_bits[0] = bm.turn_on(file_bits[0], ['arc', 'tilt'])
file_bits[1] = bm.turn_on(
file_bits[1],
['pixelflat', 'trace'] if pargs.user_pixflat is None else 'trace')
file_bits[2] = bm.turn_on(file_bits[2], 'science')
# PypeItSetup sorts according to MJD
# Deal with this
asrt = []
for ifile in data_files:
bfile = os.path.basename(ifile)
idx = ps.fitstbl['filename'].data.tolist().index(bfile)
asrt.append(idx)
asrt = np.array(asrt)
# Set bits
ps.fitstbl.set_frame_types(file_bits[asrt])
ps.fitstbl.set_combination_groups()
# Extras
ps.fitstbl['setup'] = 'A'
# Write
ofiles = ps.fitstbl.write_pypeit('',
configs=['A'],
write_bkg_pairs=True,
cfg_lines=cfg_lines)
if len(ofiles) > 1:
msgs.error("Bad things happened..")
# Instantiate the main pipeline reduction object
pypeIt = pypeit.PypeIt(ofiles[0],
verbosity=2,
reuse_masters=True,
overwrite=True,
logname='mos.log',
show=False)
# Run
pypeIt.reduce_all()
msgs.info('Data reduction complete')
# QA HTML
msgs.info('Generating QA HTML')
pypeIt.build_qa()
return 0
def main(args: argparse.Namespace):
t = time.perf_counter()
# need an arc frame and a flat frame
root = args.fname.rstrip('0123456789.fits')
paths = glob.glob(f'{root}*.fits')
spectrograph = 'p200_dbsp_red' if 'red' in os.path.basename(
args.fname) else 'p200_dbsp_blue'
arm = spectrograph.split('_')[-1]
CFG_LINES = get_cfg_lines(spectrograph)
flatimg = ""
arcimg = ""
sciimg = args.fname
calib_only = not os.path.isfile(sciimg)
if calib_only:
for path in paths:
with fits.open(path) as hdul:
if not flatimg:
if hdul[0].header['OBJECT'] == 'flat' or hdul[0].header[
'IMGTYPE'] == 'flat':
flatimg = path
if not arcimg:
if hdul[0].header['OBJECT'] == 'arcs' or hdul[0].header[
'IMGTYPE'] == 'cal':
arcimg = path
if flatimg and arcimg:
break
if not (flatimg and arcimg):
raise Exception(
f"Could not find a flat and an arc frame in the same directory as {root}!"
)
files = [arcimg, flatimg]
else:
files = [sciimg]
ps = PypeItSetup(files,
path="./",
spectrograph_name=spectrograph,
cfg_lines=CFG_LINES)
ps.build_fitstbl()
bm = framematch.FrameTypeBitMask()
file_bits = np.zeros(len(files), dtype=bm.minimum_dtype())
if calib_only:
file_bits[0] = bm.turn_on(file_bits[0], ['arc', 'tilt'])
file_bits[1] = bm.turn_on(file_bits[1],
['pixelflat', 'trace', 'illumflat'])
else:
file_bits[0] = bm.turn_on(file_bits[0], 'science')
asrt = np.array([
ps.fitstbl['filename'].data.tolist().index(os.path.basename(fname))
for fname in files
])
ps.fitstbl.set_frame_types(file_bits[asrt])
ps.fitstbl.set_combination_groups()
ps.fitstbl['setup'] = 'A'
ofiles = ps.fitstbl.write_pypeit(configs='A', cfg_lines=CFG_LINES)
pypeIt = pypeit.PypeIt(ofiles[0],
verbosity=0,
reuse_masters=True,
overwrite=True,
logname='dbsp_ql.log',
show=False,
calib_only=calib_only)
if calib_only:
pypeIt.calib_all()
else:
pypeIt.reduce_all()
pypeIt.build_qa()
output_spec2ds = list(filter(lambda f: os.path.isfile(os.path.join('Science', f)), [
pypeIt.spec_output_file(i, True) \
for i in range(len(pypeIt.fitstbl.table)) \
if pypeIt.fitstbl.table[i]['frametype'] in ['science']
]))
output_spec1ds = list(filter(lambda f: os.path.isfile(os.path.join('Science', f)), [
pypeIt.spec_output_file(i) \
for i in range(len(pypeIt.fitstbl.table)) \
if pypeIt.fitstbl.table[i]['frametype'] in ['science']
]))
if output_spec1ds and not calib_only:
sensfiles = [
resource_filename("dbsp_drp", f"data/sens_{arm}_archived.fits")
]
FxCalib = fluxcalibrate.FluxCalibrate.get_instance(
output_spec1ds, sensfiles, par=ps.par['fluxcalib'])
print(f"Time elapsed: {time.perf_counter() - t}s.")
if not calib_only and not args.no_show:
p1 = Process(target=show_spec2d_helper, args=(output_spec2ds[0], ))
p1.start()
if output_spec1ds:
with fits.open(output_spec1ds[0]) as hdul:
specs = len(hdul) - 2
parr = [None] * specs
for i in range(specs):
parr[i] = Process(target=show_spec1d_helper,
args=(str(i), output_spec1ds[0]))
parr[i].start()
def main(pargs):
import os
import numpy as np
from IPython import embed
from pypeit import pypeit
from pypeit import pypeitsetup
from pypeit.core import framematch
spec = 'keck_deimos'
# Setup
data_files = [
os.path.join(pargs.full_rawpath, pargs.arc),
os.path.join(pargs.full_rawpath, pargs.flat),
os.path.join(pargs.full_rawpath, pargs.science)
]
ps = pypeitsetup.PypeItSetup(data_files, path='./', spectrograph_name=spec)
ps.build_fitstbl()
# TODO -- Get the type_bits from 'science'
bm = framematch.FrameTypeBitMask()
bits = [
bm.bits[iftype]
for iftype in ['arc', 'pixelflat', 'trace', 'science', 'tilt']
]
ps.fitstbl.set_frame_types(
np.array(
[2**bits[0] + 2**bits[4], 2**bits[1] + 2**bits[2],
2**bits[3]])) # 1=arc, 16=pixelflat, 32=science, trace=128
ps.fitstbl.set_combination_groups()
# Extras
ps.fitstbl['setup'] = 'A'
# Config the run
rdx_path = '{0}_A'.format(os.path.join(os.getcwd(), spec))
cfg_lines = ['[rdx]']
cfg_lines += [' spectrograph = {0}'.format(spec)]
cfg_lines += [' redux_path = {0}'.format(rdx_path)]
cfg_lines += [' detnum = {0}'.format(pargs.det)]
cfg_lines += ['[calibrations]']
cfg_lines += [' [[scienceframe]]']
cfg_lines += [' [[process]]']
cfg_lines += [' cr_reject = False']
cfg_lines += ['[scienceimage]']
cfg_lines += [' boxcar_only = True']
cfg_lines += [' skip_second_find = True']
# Boxcar radius
if pargs.box_radius is not None:
cfg_lines += [' boxcar_radius = {0}'.format(pargs.box_radius)]
# Write
ofiles = ps.fitstbl.write_pypeit('',
configs=['A'],
write_bkg_pairs=True,
cfg_lines=cfg_lines)
if len(ofiles) > 1:
msgs.error("Bad things happened..")
# Instantiate the main pipeline reduction object
pypeIt = pypeit.PypeIt(ofiles[0],
verbosity=2,
reuse_masters=True,
overwrite=True,
logname='deimos.log',
show=False)
# Run
pypeIt.reduce_all()
msgs.info('Data reduction complete')
# QA HTML
msgs.info('Generating QA HTML')
pypeIt.build_qa()
return 0
