def __init__(self, specprod_dir=None, **kwargs):
""" Class to organize and execute QA for a DESI production
Args:
specprod_dir(str): Path containing the exposures/ directory to use. If the value
is None, then the value of :func:`specprod_root` is used instead.
Notes:
Attributes:
qa_exps : list
List of QA_Exposure classes, one per exposure in production
data : dict
"""
if specprod_dir is None:
specprod_dir = specprod_root()
self.specprod_dir = specprod_dir
# Init
QA_MultiExp.__init__(self, specprod_dir=specprod_dir, **kwargs)
# Load up exposures for the full production
nights = get_nights(specprod_dir=self.specprod_dir)
for night in nights:
self.mexp_dict[night] = {}
'''
for exposure in get_exposures(night, specprod_dir = self.specprod_dir):
# Object only??
frames_dict = get_files(filetype = str('frame'), night = night,
expid = exposure, specprod_dir = self.specprod_dir)
self.mexp_dict[night][exposure] = frames_dict
'''
# Output file names
self.qaexp_outroot = self.qaprod_dir+'/'+self.prod_name+'_qa'
# Nights list
self.qa_nights = []
def __init__(self, specprod_dir=None, qaprod_dir=None):
""" Class to organize and execute QA for a DESI production
Args:
specprod_dir(str): Path containing the exposures/ directory to use. If the value
is None, then the value of :func:`specprod_root` is used instead.
qaprod_dir(str): Path containing the root path for QA output
Notes:
Attributes:
qa_exps : list
List of QA_Exposure classes, one per exposure in production
data : dict
"""
# Init
if specprod_dir is None:
specprod_dir = specprod_root()
if qaprod_dir is None:
qaprod_dir = qaprod_root()
#
self.specprod_dir = specprod_dir
self.qaprod_dir = qaprod_dir
tmp = specprod_dir.split('/')
self.prod_name = tmp[-1] if (len(tmp[-1]) > 0) else tmp[-2]
# Exposure dict stored as [night][exposure]
self.mexp_dict = {}
# QA Exposure objects
self.qa_exps = []
# dict to hold QA data
# Data Model : key1 = Night(s); key2 = Expids
self.data = {}
#
self.qaexp_outroot = None
def __init__(self, specprod_dir=None, qaprod_dir=None):
""" Class to organize and execute QA for a DESI production
Args:
specprod_dir(str): Path containing the exposures/ directory to use. If the value
is None, then the value of :func:`specprod_root` is used instead.
qaprod_dir(str): Path containing the root path for QA output
Notes:
Attributes:
qa_exps : list
List of QA_Exposure classes, one per exposure in production
data : dict
"""
# Init
if specprod_dir is None:
specprod_dir = specprod_root()
if qaprod_dir is None:
qaprod_dir = qaprod_root()
#
self.specprod_dir = specprod_dir
self.qaprod_dir = qaprod_dir
tmp = specprod_dir.split('/')
self.prod_name = tmp[-1] if (len(tmp[-1]) > 0) else tmp[-2]
# Exposure dict stored as [night][exposure]
self.mexp_dict = {}
# QA Exposure objects
self.qa_exps = []
# dict to hold QA data
# Data Model : key1 = Night(s); key2 = Expids
self.data = {}
#
self.qaexp_outroot = None
def __init__(self, specprod_dir=None, **kwargs):
""" Class to organize and execute QA for a DESI production
Args:
specprod_dir(str): Path containing the exposures/ directory to use. If the value
is None, then the value of :func:`specprod_root` is used instead.
Notes:
Attributes:
qa_exps : list
List of QA_Exposure classes, one per exposure in production
data : dict
"""
if specprod_dir is None:
specprod_dir = specprod_root()
self.specprod_dir = specprod_dir
# Init
QA_MultiExp.__init__(self, specprod_dir=specprod_dir, **kwargs)
# Load up exposures for the full production
nights = get_nights(specprod_dir=self.specprod_dir)
for night in nights:
self.mexp_dict[night] = {}
for exposure in get_exposures(night,
specprod_dir=self.specprod_dir):
# Object only??
frames_dict = get_files(filetype=str('frame'),
night=night,
expid=exposure,
specprod_dir=self.specprod_dir)
self.mexp_dict[night][exposure] = frames_dict
# Output file names
self.qaexp_outroot = self.qaprod_dir + '/' + self.prod_name + '_qa'
# Nights list
self.qa_nights = []
def main(args, comm=None):
t1 = datetime.datetime.now()
log = get_logger()
rank = 0
nproc = 1
if comm is not None:
rank = comm.rank
nproc = comm.size
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
if rank == 0:
log.info("starting at {}".format(time.asctime()))
log.info("using raw dir {}".format(rawdir))
log.info("using spectro production dir {}".format(proddir))
# run it!
pipe.run_steps(args.first,
args.last,
spectrographs=args.spectrographs,
nightstr=args.nights,
comm=comm)
t2 = datetime.datetime.now()
if rank == 0:
if "STARTTIME" in os.environ:
try:
t0 = datetime.datetime.strptime(os.getenv("STARTTIME"),
"%Y%m%d-%H:%M:%S")
dt = t1 - t0
minutes, seconds = dt.seconds // 60, dt.seconds % 60
log.info("Python startup time: {} min {} sec".format(
minutes, seconds))
except ValueError:
log.error("unable to parse $STARTTIME={}".format(
os.getenv("STARTTIME")))
else:
log.info("python startup time unknown since $STARTTIME not set")
dt = t2 - t1
minutes, seconds = dt.seconds // 60, dt.seconds % 60
log.info("Run time: {} min {} sec".format(minutes, seconds))
def setUpClass(cls):
cls.nspec = 6
cls.nwave = 20
id = 1
cls.night = '20160101'
cls.expid = 1
# Paths
os.environ['DESI_SPECTRO_REDUX'] = os.environ['HOME']
os.environ['SPECPROD'] = 'desi_test_qa'
# Files
cls.testDir = specprod_root()
cls.qafile_b0 = findfile('qa_data',
night=cls.night,
expid=cls.expid,
specprod_dir=cls.testDir,
camera='b0')
cls.qafile_b1 = findfile('qa_data',
night=cls.night,
expid=cls.expid,
specprod_dir=cls.testDir,
camera='b1')
cls.qafile_exp = cls.testDir + '/exposures/' + cls.night + '/{:08d}/qa-{:08d}'.format(
id, id)
cls.qafile_brick = cls.testDir + '/brick/3582m005/qa-3582m005.yaml'
cls.flux_pdf = cls.testDir + '/exposures/' + cls.night + '/{:08d}/qa-flux-{:08d}.pdf'.format(
id, id)
# Files for exposure fibermap QA figure
cls.frame_b0 = findfile('frame',
night=cls.night,
expid=cls.expid,
specprod_dir=cls.testDir,
camera='b0')
cls.frame_b1 = findfile('frame',
night=cls.night,
expid=cls.expid,
specprod_dir=cls.testDir,
camera='b1')
cls.fflat_b0 = findfile('fiberflat',
night=cls.night,
expid=cls.expid,
specprod_dir=cls.testDir,
camera='b0')
cls.fflat_b1 = findfile('fiberflat',
night=cls.night,
expid=cls.expid,
specprod_dir=cls.testDir,
camera='b1')
cls.exp_fmap_plot = cls.testDir + '/test_exp_fibermap_plot.png'
def setUpClass(cls):
cls.nspec = 6
cls.nwave = 20
cls.id = 1
# Run
cls.nights = ['20160101']*2 + ['20160102']*2
cls.expids = [1,2,3,4]
cls.cameras = ['b0','b1']
# Files
cls.files_written = []
# Paths
os.environ['DESI_SPECTRO_REDUX'] = os.environ['HOME']
os.environ['SPECPROD'] = 'desi_test_qa'
cls.testDir = specprod_root()
# Files
cls.qafile_brick = cls.testDir+'/brick/3582m005/qa-3582m005.yaml'
cls.flux_pdf = cls.testDir+'/exposures/'+cls.nights[0]+'/{:08d}/qa-flux-{:08d}.pdf'.format(cls.id,cls.id)
cls.frame_pdf = cls.testDir+'/exposures/'+cls.nights[0]+'/{:08d}/qa-frame-{:08d}.pdf'.format(cls.id,cls.id)
# Files for exposure fibermap QA figure
cls.exp_fmap_plot = cls.testDir+'/test_exp_fibermap_plot.png'
def setUpClass(cls):
cls.nspec = 6
cls.nwave = 20
cls.id = 1
# Run
cls.nights = ['20160101']*2 + ['20160102']*2
cls.expids = [1,2,3,4]
cls.cameras = ['b0','b1']
# Files
cls.files_written = []
# Paths
os.environ['DESI_SPECTRO_REDUX'] = os.environ['HOME']
os.environ['SPECPROD'] = 'desi_test_qa'
cls.testDir = specprod_root()
# Files
cls.qafile_brick = cls.testDir+'/brick/3582m005/qa-3582m005.yaml'
cls.flux_pdf = cls.testDir+'/exposures/'+cls.nights[0]+'/{:08d}/qa-flux-{:08d}.pdf'.format(cls.id,cls.id)
cls.frame_pdf = cls.testDir+'/exposures/'+cls.nights[0]+'/{:08d}/qa-frame-{:08d}.pdf'.format(cls.id,cls.id)
# Files for exposure fibermap QA figure
cls.exp_fmap_plot = cls.testDir+'/test_exp_fibermap_plot.png'
def __init__(self, specprod_dir=None):
""" Class to organize and execute QA for a DESI production
Args:
specprod_dir(str): Path containing the exposures/ directory to use. If the value
is None, then the value of :func:`specprod_root` is used instead.
Notes:
Attributes:
qa_exps : list
List of QA_Exposure classes, one per exposure in production
data : dict
"""
if specprod_dir is None:
specprod_dir = specprod_root()
self.specprod_dir = specprod_dir
tmp = specprod_dir.split('/')
self.prod_name = tmp[-1] if (len(tmp[-1]) > 0) else tmp[-2]
self.qa_exps = []
#
self.data = {}
def compute_skymag(night, expid, specprod_dir=None):
"""
Computes the sky magnitude for a given exposure. Uses the sky model
and apply a fixed calibration for which the fiber aperture loss
is well understood.
Args:
night: int, YYYYMMDD
expid: int, exposure id
specprod_dir: str, optional, specify the production directory.
default is $DESI_SPECTRO_REDUX/$SPECPROD
returns (gmag,rmag,zmag) AB magnitudes per arcsec2, tuple with 3 float values
"""
log = get_logger()
# AR/DK DESI spectra wavelengths
wmin, wmax, wdelta = 3600, 9824, 0.8
fullwave = np.round(np.arange(wmin, wmax + wdelta, wdelta), 1)
# AR (wmin,wmax) to "stitch" all three cameras
wstitch = {"b": (wmin, 5790), "r": (5790, 7570), "z": (7570, 9824)}
istitch = {}
for camera in ["b", "r", "z"]:
ii = np.where((fullwave >= wstitch[camera][0])
& (fullwave < wstitch[camera][1]))[0]
istitch[camera] = (ii[0], ii[-1] + 1
) # begin (included), end (excluded)
if specprod_dir is None:
specprod_dir = specprod_root()
# AR looking for a petal with brz sky and ivar>0
sky_spectra = []
for spec in range(10):
sky = np.zeros(fullwave.shape)
ok = True
for camera in ["b", "r", "z"]:
camspec = "{}{}".format(camera, spec)
filename = findfile("sky",
night=night,
expid=expid,
camera=camspec,
specprod_dir=specprod_dir)
if not os.path.isfile(filename):
log.warning("skipping {}-{:08d}-{} : missing {}".format(
night, expid, spec, filename))
ok = False
break
fiber = 0
skyivar = fitsio.read(filename, "IVAR")[fiber]
if np.all(skyivar == 0):
log.warning("skipping {}-{:08d}-{} : ivar=0 for {}".format(
night, expid, spec, filename))
ok = False
break
skyflux = fitsio.read(filename, 0)[fiber]
skywave = fitsio.read(filename, "WAVELENGTH")
header = fitsio.read_header(filename)
exptime = header["EXPTIME"]
# for now we use a fixed calibration as used in DESI-6043 for which we know what was the fiber aperture loss
cal_filename = "{}/spec/fluxcalib/fluxcalibnight-{}-20201216.fits".format(
os.environ["DESI_SPECTRO_CALIB"], camera)
# apply the correction from
fiber_acceptance_for_point_sources = 0.60 # see DESI-6043
mean_fiber_diameter_arcsec = 1.52 # see DESI-6043
fiber_area_arcsec = np.pi * (mean_fiber_diameter_arcsec / 2)**2
acal = _get_average_calibration(cal_filename)
begin, end = istitch[camera]
flux = np.interp(fullwave[begin:end], skywave, skyflux)
acal_val = np.interp(fullwave[begin:end], acal.wave, acal.value())
sky[begin:
end] = flux / exptime / acal_val * fiber_acceptance_for_point_sources / fiber_area_arcsec * 1e-17 # ergs/s/cm2/A/arcsec2
if not ok: continue # to next spectrograph
sky_spectra.append(sky)
if len(sky_spectra) == 0: return (99., 99., 99.)
if len(sky_spectra) == 1:
sky = sky_spectra[0]
else:
sky = np.mean(np.array(sky_spectra),
axis=0) # mean over petals/spectrographs
# AR integrate over the DECam grz-bands
filts = _get_decam_filters()
# AR zero-padding spectrum so that it covers the DECam grz passbands
# AR looping through filters while waiting issue to be solved (https://github.com/desihub/speclite/issues/64)
sky_pad, fullwave_pad = sky.copy(), fullwave.copy()
for i in range(len(filts)):
sky_pad, fullwave_pad = filts[i].pad_spectrum(sky_pad,
fullwave_pad,
method="zero")
mags = filts.get_ab_magnitudes(
sky_pad * units.erg / (units.cm**2 * units.s * units.angstrom),
fullwave_pad * units.angstrom).as_array()[0]
return mags # AB mags for flux per arcsec2
def integration_test(night=None, nspec=5, clobber=False):
"""Run an integration test from raw data simulations through redshifts
Args:
night (str, optional): YEARMMDD, defaults to current night
nspec (int, optional): number of spectra to include
clobber (bool, optional): rerun steps even if outputs already exist
Raises:
RuntimeError if any script fails
"""
import argparse
parser = argparse.ArgumentParser(usage = "{prog} [options]")
# parser.add_argument("-i", "--input", type=str, help="input data")
# parser.add_argument("-o", "--output", type=str, help="output data")
parser.add_argument("--skip-psf", action="store_true", help="Skip PSF fitting step")
args = parser.parse_args()
log = logging.get_logger()
# YEARMMDD string, rolls over at noon not midnight
if night is None:
night = "20160726"
# check for required environment variables
check_env()
# simulate inputs
sim(night, nspec=nspec, clobber=clobber)
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
# create production
if clobber and os.path.isdir(proddir):
shutil.rmtree(proddir)
dbfile = io.get_pipe_database()
if not os.path.exists(dbfile):
com = "desi_pipe create --db-sqlite"
log.info('Running {}'.format(com))
sp.check_call(com, shell=True)
else:
log.info("Using pre-existing production database {}".format(dbfile))
# Modify options file to restrict the spectral range
optpath = os.path.join(proddir, "run", "options.yaml")
opts = pipe.prod.yaml_read(optpath)
opts['extract']['specmin'] = 0
opts['extract']['nspec'] = nspec
opts['psf']['specmin'] = 0
opts['psf']['nspec'] = nspec
opts['traceshift']['nfibers'] = nspec
pipe.prod.yaml_write(optpath, opts)
if args.skip_psf:
#- Copy desimodel psf into this production instead of fitting psf
import shutil
for channel in ['b', 'r', 'z']:
refpsf = '{}/data/specpsf/psf-{}.fits'.format(
os.getenv('DESIMODEL'), channel)
nightpsf = io.findfile('psfnight', night, camera=channel+'0')
shutil.copy(refpsf, nightpsf)
for expid in [0,1,2]:
exppsf = io.findfile('psf', night, expid, camera=channel+'0')
shutil.copy(refpsf, exppsf)
#- Resync database to current state
dbpath = io.get_pipe_database()
db = pipe.load_db(dbpath, mode="w")
db.sync(night)
# Run the pipeline tasks in order
from desispec.pipeline.tasks.base import default_task_chain
for tasktype in default_task_chain:
#- if we skip psf/psfnight/traceshift, update state prior to extractions
if tasktype == 'traceshift' and args.skip_psf:
db.getready()
run_pipeline_step(tasktype)
# #-----
# #- Did it work?
# #- (this combination of fibermap, simspec, and zbest is a pain)
expid = 2
fmfile = io.findfile('fibermap', night=night, expid=expid)
fibermap = io.read_fibermap(fmfile)
simdir = os.path.dirname(fmfile)
simspec = '{}/simspec-{:08d}.fits'.format(simdir, expid)
siminfo = fits.getdata(simspec, 'TRUTH')
try:
elginfo = fits.getdata(simspec, 'TRUTH_ELG')
except:
elginfo = None
from desimodel.footprint import radec2pix
nside=64
pixels = np.unique(radec2pix(nside, fibermap['TARGET_RA'], fibermap['TARGET_DEC']))
num_missing = 0
for pix in pixels:
zfile = io.findfile('zbest', groupname=pix)
if not os.path.exists(zfile):
log.error('Missing {}'.format(zfile))
num_missing += 1
if num_missing > 0:
log.critical('{} zbest files missing'.format(num_missing))
sys.exit(1)
print()
print("--------------------------------------------------")
print("Pixel True z -> Class z zwarn")
# print("3338p190 SKY 0.00000 -> QSO 1.60853 12 - ok")
for pix in pixels:
zfile = io.findfile('zbest', groupname=pix)
if not os.path.exists(zfile):
log.error('Missing {}'.format(zfile))
continue
zfx = fits.open(zfile, memmap=False)
zbest = zfx['ZBEST'].data
for i in range(len(zbest['Z'])):
objtype = zbest['SPECTYPE'][i]
z, zwarn = zbest['Z'][i], zbest['ZWARN'][i]
j = np.where(fibermap['TARGETID'] == zbest['TARGETID'][i])[0][0]
truetype = siminfo['OBJTYPE'][j]
oiiflux = 0.0
if truetype == 'ELG':
k = np.where(elginfo['TARGETID'] == zbest['TARGETID'][i])[0][0]
oiiflux = elginfo['OIIFLUX'][k]
truez = siminfo['REDSHIFT'][j]
dv = C_LIGHT*(z-truez)/(1+truez)
status = None
if truetype == 'SKY' and zwarn > 0:
status = 'ok'
elif truetype == 'ELG' and zwarn > 0 and oiiflux < 8e-17:
status = 'ok ([OII] flux {:.2g})'.format(oiiflux)
elif zwarn == 0:
if truetype == 'LRG' and objtype == 'GALAXY' and abs(dv) < 150:
status = 'ok'
elif truetype == 'ELG' and objtype == 'GALAXY':
if abs(dv) < 150:
status = 'ok'
elif oiiflux < 8e-17:
status = 'ok ([OII] flux {:.2g})'.format(oiiflux)
else:
status = 'OOPS ([OII] flux {:.2g})'.format(oiiflux)
elif truetype == 'QSO' and objtype == 'QSO' and abs(dv) < 750:
status = 'ok'
elif truetype in ('STD', 'FSTD') and objtype == 'STAR':
status = 'ok'
else:
status = 'OOPS'
else:
status = 'OOPS'
print('{0:<8d} {1:4s} {2:8.5f} -> {3:5s} {4:8.5f} {5:4d} - {6}'.format(
pix, truetype, truez, objtype, z, zwarn, status))
print("--------------------------------------------------")
def integration_test(night=None, nspec=5, clobber=False):
"""Run an integration test from raw data simulations through redshifts
Args:
night (str, optional): YEARMMDD, defaults to current night
nspec (int, optional): number of spectra to include
clobber (bool, optional): rerun steps even if outputs already exist
Raises:
RuntimeError if any script fails
"""
log = logging.get_logger()
log.setLevel(logging.DEBUG)
# YEARMMDD string, rolls over at noon not midnight
# TODO: fix usage of night to be something other than today
if night is None:
#night = time.strftime('%Y%m%d', time.localtime(time.time()-12*3600))
night = "20160726"
# check for required environment variables
check_env()
# simulate inputs
sim(night, nspec=nspec, clobber=clobber)
# create production
# FIXME: someday run PSF estimation too...
### com = "desi_pipe --env env.txt --spectrographs 0 --fakeboot --fakepsf"
rawdir = os.path.join(os.getenv('DESI_SPECTRO_SIM'), os.getenv('PIXPROD'))
com = "desi_pipe --spectrographs 0 --fakeboot --fakepsf --raw {}".format(rawdir)
sp.check_call(com, shell=True)
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
# Modify options file to restrict the spectral range
optpath = os.path.join(proddir, "run", "options.yaml")
opts = pipe.read_options(optpath)
opts['extract']['specmin'] = 0
opts['extract']['nspec'] = nspec
pipe.write_options(optpath, opts)
# run the generated shell scripts
# FIXME: someday run PSF estimation too...
# print("Running bootcalib script...")
# com = os.path.join(proddir, "run", "scripts", "bootcalib_all.sh")
# sp.check_call(["bash", com])
# print("Running specex script...")
# com = os.path.join(proddir, "run", "scripts", "specex_all.sh")
# sp.check_call(["bash", com])
# print("Running psfcombine script...")
# com = os.path.join(proddir, "run", "scripts", "psfcombine_all.sh")
# sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "extract_all.sh")
print("Running extraction script "+com)
sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "fiberflat-procexp_all.sh")
print("Running calibration script "+com)
sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "bricks.sh")
print("Running makebricks script "+com)
sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "zfind_all.sh")
print("Running zfind script "+com)
sp.check_call(["bash", com])
# #-----
# #- Did it work?
# #- (this combination of fibermap, simspec, and zbest is a pain)
expid = 2
fmfile = io.findfile('fibermap', night=night, expid=expid)
fibermap = io.read_fibermap(fmfile)
simdir = os.path.dirname(fmfile)
simspec = '{}/simspec-{:08d}.fits'.format(simdir, expid)
siminfo = fits.getdata(simspec, 'METADATA')
brickdirs = glob.glob(os.path.join(proddir, "bricks", "*"))
bricks = [ os.path.basename(x) for x in brickdirs ]
print()
print("--------------------------------------------------")
print("Brick True z -> Class z zwarn")
# print("3338p190 SKY 0.00000 -> QSO 1.60853 12 - ok")
for b in bricks:
zbest = io.read_zbest(io.findfile('zbest', brickname=b))
for i in range(len(zbest.z)):
if zbest.spectype[i] == 'ssp_em_galaxy':
objtype = 'GAL'
elif zbest.spectype[i] == 'spEigenStar':
objtype = 'STAR'
else:
objtype = zbest.spectype[i]
z, zwarn = zbest.z[i], zbest.zwarn[i]
j = np.where(fibermap['TARGETID'] == zbest.targetid[i])[0][0]
truetype = siminfo['OBJTYPE'][j]
truez = siminfo['REDSHIFT'][j]
dv = 3e5*(z-truez)/(1+truez)
if truetype == 'SKY' and zwarn > 0:
status = 'ok'
elif zwarn == 0:
if truetype == 'LRG' and objtype == 'GAL' and abs(dv) < 150:
status = 'ok'
elif truetype == 'ELG' and objtype == 'GAL' and abs(dv) < 150:
status = 'ok'
elif truetype == 'QSO' and objtype == 'QSO' and abs(dv) < 750:
status = 'ok'
elif truetype == 'STD' and objtype == 'STAR':
status = 'ok'
else:
status = 'OOPS'
else:
status = 'OOPS'
print('{0} {1:4s} {2:8.5f} -> {3:5s} {4:8.5f} {5:4d} - {6}'.format(
b, truetype, truez, objtype, z, zwarn, status))
print("--------------------------------------------------")
def integration_test(night=None, nspec=5, clobber=False):
"""Run an integration test from raw data simulations through redshifts
Args:
night (str, optional): YEARMMDD, defaults to current night
nspec (int, optional): number of spectra to include
clobber (bool, optional): rerun steps even if outputs already exist
Raises:
RuntimeError if any script fails
"""
import argparse
parser = argparse.ArgumentParser(usage="{prog} [options]")
# parser.add_argument("-i", "--input", type=str, help="input data")
# parser.add_argument("-o", "--output", type=str, help="output data")
parser.add_argument("--skip-psf",
action="store_true",
help="Skip PSF fitting step")
args = parser.parse_args()
log = logging.get_logger()
# YEARMMDD string, rolls over at noon not midnight
if night is None:
night = "20160726"
# check for required environment variables
check_env()
# simulate inputs
sim(night, nspec=nspec, clobber=clobber)
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
# create production
if clobber and os.path.isdir(proddir):
shutil.rmtree(proddir)
dbfile = io.get_pipe_database()
if not os.path.exists(dbfile):
com = "desi_pipe create --db-sqlite"
log.info('Running {}'.format(com))
sp.check_call(com, shell=True)
else:
log.info("Using pre-existing production database {}".format(dbfile))
# Modify options file to restrict the spectral range
optpath = os.path.join(proddir, "run", "options.yaml")
opts = pipe.prod.yaml_read(optpath)
opts['extract']['specmin'] = 0
opts['extract']['nspec'] = nspec
opts['psf']['specmin'] = 0
opts['psf']['nspec'] = nspec
opts['traceshift']['nfibers'] = nspec
pipe.prod.yaml_write(optpath, opts)
if args.skip_psf:
#- Copy desimodel psf into this production instead of fitting psf
import shutil
for channel in ['b', 'r', 'z']:
refpsf = '{}/data/specpsf/psf-{}.fits'.format(
os.getenv('DESIMODEL'), channel)
nightpsf = io.findfile('psfnight', night, camera=channel + '0')
shutil.copy(refpsf, nightpsf)
for expid in [0, 1, 2]:
exppsf = io.findfile('psf', night, expid, camera=channel + '0')
shutil.copy(refpsf, exppsf)
#- Resync database to current state
dbpath = io.get_pipe_database()
db = pipe.load_db(dbpath, mode="w")
db.sync(night)
# Run the pipeline tasks in order
from desispec.pipeline.tasks.base import default_task_chain
for tasktype in default_task_chain:
#- if we skip psf/psfnight/traceshift, update state prior to extractions
if tasktype == 'traceshift' and args.skip_psf:
db.getready()
run_pipeline_step(tasktype)
# #-----
# #- Did it work?
# #- (this combination of fibermap, simspec, and zbest is a pain)
expid = 2
fmfile = io.findfile('fibermap', night=night, expid=expid)
fibermap = io.read_fibermap(fmfile)
simdir = os.path.dirname(fmfile)
simspec = '{}/simspec-{:08d}.fits'.format(simdir, expid)
siminfo = fits.getdata(simspec, 'TRUTH')
try:
elginfo = fits.getdata(simspec, 'TRUTH_ELG')
except:
elginfo = None
from desimodel.footprint import radec2pix
nside = 64
pixels = np.unique(
radec2pix(nside, fibermap['TARGET_RA'], fibermap['TARGET_DEC']))
num_missing = 0
for pix in pixels:
zfile = io.findfile('zbest', groupname=pix)
if not os.path.exists(zfile):
log.error('Missing {}'.format(zfile))
num_missing += 1
if num_missing > 0:
log.critical('{} zbest files missing'.format(num_missing))
sys.exit(1)
print()
print("--------------------------------------------------")
print("Pixel True z -> Class z zwarn")
# print("3338p190 SKY 0.00000 -> QSO 1.60853 12 - ok")
for pix in pixels:
zfile = io.findfile('zbest', groupname=pix)
if not os.path.exists(zfile):
log.error('Missing {}'.format(zfile))
continue
zfx = fits.open(zfile, memmap=False)
zbest = zfx['ZBEST'].data
for i in range(len(zbest['Z'])):
objtype = zbest['SPECTYPE'][i]
z, zwarn = zbest['Z'][i], zbest['ZWARN'][i]
j = np.where(fibermap['TARGETID'] == zbest['TARGETID'][i])[0][0]
truetype = siminfo['OBJTYPE'][j]
oiiflux = 0.0
if truetype == 'ELG':
k = np.where(elginfo['TARGETID'] == zbest['TARGETID'][i])[0][0]
oiiflux = elginfo['OIIFLUX'][k]
truez = siminfo['REDSHIFT'][j]
dv = C_LIGHT * (z - truez) / (1 + truez)
status = None
if truetype == 'SKY' and zwarn > 0:
status = 'ok'
elif truetype == 'ELG' and zwarn > 0 and oiiflux < 8e-17:
status = 'ok ([OII] flux {:.2g})'.format(oiiflux)
elif zwarn == 0:
if truetype == 'LRG' and objtype == 'GALAXY' and abs(dv) < 150:
status = 'ok'
elif truetype == 'ELG' and objtype == 'GALAXY':
if abs(dv) < 150:
status = 'ok'
elif oiiflux < 8e-17:
status = 'ok ([OII] flux {:.2g})'.format(oiiflux)
else:
status = 'OOPS ([OII] flux {:.2g})'.format(oiiflux)
elif truetype == 'QSO' and objtype == 'QSO' and abs(dv) < 750:
status = 'ok'
elif truetype in ('STD', 'FSTD') and objtype == 'STAR':
status = 'ok'
else:
status = 'OOPS'
else:
status = 'OOPS'
print('{0:<8d} {1:4s} {2:8.5f} -> {3:5s} {4:8.5f} {5:4d} - {6}'.
format(pix, truetype, truez, objtype, z, zwarn, status))
print("--------------------------------------------------")
def integration_test(night=None, nspec=5, clobber=False):
"""Run an integration test from raw data simulations through redshifts
Args:
night (str, optional): YEARMMDD, defaults to current night
nspec (int, optional): number of spectra to include
clobber (bool, optional): rerun steps even if outputs already exist
Raises:
RuntimeError if any script fails
"""
log = logging.get_logger()
log.setLevel(logging.DEBUG)
# YEARMMDD string, rolls over at noon not midnight
# TODO: fix usage of night to be something other than today
if night is None:
#night = time.strftime('%Y%m%d', time.localtime(time.time()-12*3600))
night = "20160726"
# check for required environment variables
check_env()
# simulate inputs
sim(night, nspec=nspec, clobber=clobber)
# create production
# FIXME: someday run PSF estimation too...
### com = "desi_pipe --spectrographs 0 --fakeboot --fakepsf"
com = "desi_pipe --spectrographs 0 --fakeboot --fakepsf"
sp.check_call(com, shell=True)
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
# Modify options file to restrict the spectral range
optpath = os.path.join(proddir, "run", "options.yaml")
opts = pipe.yaml_read(optpath)
opts['extract']['specmin'] = 0
opts['extract']['nspec'] = nspec
pipe.yaml_write(optpath, opts)
# run the generated shell scripts
# FIXME: someday run PSF estimation too...
# print("Running bootcalib script...")
# com = os.path.join(proddir, "run", "scripts", "bootcalib_all.sh")
# sp.check_call(["bash", com])
# print("Running specex script...")
# com = os.path.join(proddir, "run", "scripts", "specex_all.sh")
# sp.check_call(["bash", com])
# print("Running psfcombine script...")
# com = os.path.join(proddir, "run", "scripts", "psfcombine_all.sh")
# sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "run_shell.sh")
print("Running extraction through calibration: " + com)
sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "spectra.sh")
print("Running spectral regrouping: " + com)
sp.check_call(["bash", com])
com = os.path.join(proddir, "run", "scripts", "redshift.sh")
print("Running redshift script " + com)
sp.check_call(["bash", com])
# #-----
# #- Did it work?
# #- (this combination of fibermap, simspec, and zbest is a pain)
expid = 2
fmfile = io.findfile('fibermap', night=night, expid=expid)
fibermap = io.read_fibermap(fmfile)
simdir = os.path.dirname(fmfile)
simspec = '{}/simspec-{:08d}.fits'.format(simdir, expid)
siminfo = fits.getdata(simspec, 'METADATA')
brickdirs = glob.glob(os.path.join(proddir, "bricks", "*"))
bricks = [os.path.basename(x) for x in brickdirs]
print()
print("--------------------------------------------------")
print("Brick True z -> Class z zwarn")
# print("3338p190 SKY 0.00000 -> QSO 1.60853 12 - ok")
for b in bricks:
zbest = io.read_zbest(io.findfile('zbest', brickname=b))
for i in range(len(zbest.z)):
if zbest.spectype[i] == 'ssp_em_galaxy':
objtype = 'GAL'
elif zbest.spectype[i] == 'spEigenStar':
objtype = 'STAR'
else:
objtype = zbest.spectype[i]
z, zwarn = zbest.z[i], zbest.zwarn[i]
j = np.where(fibermap['TARGETID'] == zbest.targetid[i])[0][0]
truetype = siminfo['OBJTYPE'][j]
oiiflux = siminfo['OIIFLUX'][j]
truez = siminfo['REDSHIFT'][j]
dv = 3e5 * (z - truez) / (1 + truez)
if truetype == 'SKY' and zwarn > 0:
status = 'ok'
elif truetype == 'ELG' and zwarn > 0 and oiiflux < 8e-17:
status = 'ok ([OII] flux {:.2g})'.format(oiiflux)
elif zwarn == 0:
if truetype == 'LRG' and objtype == 'GAL' and abs(dv) < 150:
status = 'ok'
elif truetype == 'ELG' and objtype == 'GAL':
if abs(dv) < 150 or oiiflux < 8e-17:
status = 'ok ([OII] flux {:.2g})'.format(oiiflux)
else:
status = 'OOPS ([OII] flux {:.2g})'.format(oiiflux)
elif truetype == 'QSO' and objtype == 'QSO' and abs(dv) < 750:
status = 'ok'
elif truetype in ('STD', 'FSTD') and objtype == 'STAR':
status = 'ok'
else:
status = 'OOPS'
else:
status = 'OOPS'
print('{0} {1:4s} {2:8.5f} -> {3:5s} {4:8.5f} {5:4d} - {6}'.
format(b, truetype, truez, objtype, z, zwarn, status))
print("--------------------------------------------------")
def main(args, comm=None):
t1 = datetime.datetime.now()
log = get_logger()
rank = 0
nproc = 1
if comm is not None:
rank = comm.rank
nproc = comm.size
# Check start up time.
if rank == 0:
if "STARTTIME" in os.environ:
try:
t0 = datetime.datetime.strptime(os.getenv("STARTTIME"), "%Y%m%d-%H%M%S")
dt = t1 - t0
minutes, seconds = dt.seconds//60, dt.seconds%60
log.info("Python startup time: {} min {} sec".format(minutes, seconds))
except ValueError:
log.error("unable to parse $STARTTIME={}".format(os.getenv("STARTTIME")))
else:
log.info("Python startup time unknown since $STARTTIME not set")
sys.stdout.flush()
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
if rank == 0:
log.info("Starting at {}".format(time.asctime()))
log.info(" Using raw dir {}".format(rawdir))
log.info(" Using spectro production dir {}".format(proddir))
sys.stdout.flush()
# Get task list from disk or from STDIN
tasklist = None
if args.taskfile is not None:
# One process reads the file and broadcasts
if rank == 0:
tasklist = pipe.prod.task_read(args.taskfile)
if comm is not None:
tasklist = comm.bcast(tasklist, root=0)
else:
# Every process has the same STDIN contents.
tasklist = list()
for line in sys.stdin:
tasklist.append(line.rstrip())
# Do we actually have any tasks?
if len(tasklist) == 0:
warnings.warn("Task list is empty", RuntimeWarning)
# run it!
(db, opts) = pipe.load_prod("w")
ntask = len(tasklist)
ready = None
done = None
failed = None
if args.nodb:
ready, done, failed = pipe.run_task_list(args.tasktype, tasklist, opts,
comm=comm, db=None)
else:
ready, done, failed = pipe.run_task_list(args.tasktype, tasklist, opts,
comm=comm, db=db)
t2 = datetime.datetime.now()
if rank == 0:
log.info(" {} tasks already done, {} tasks were ready, and {} failed".format(done, ready, failed))
dt = t2 - t1
minutes, seconds = dt.seconds//60, dt.seconds%60
log.info("Run time: {} min {} sec".format(minutes, seconds))
sys.stdout.flush()
if comm is not None:
comm.barrier()
# Did we have any ready tasks that were not already done?
# Note: if there were no ready tasks, but some were already
# done, then we want to exit with a "0" error code. This will
# allow the calling script to continue with other pipeline steps
# and / or allow other dependent jobs run.
if done == 0:
# nothing is done
if ready == 0:
if rank == 0:
warnings.warn("No tasks were ready or done", RuntimeWarning)
sys.exit(1)
if (failed == ready) and (failed > 1) :
# all tasks failed (and there are more than one)
if rank == 0:
warnings.warn("All tasks that were run failed", RuntimeWarning)
sys.exit(1)
else:
# At least some tasks were done- we return zero so that future
# jobs can run.
if (ready > 0) and (failed == ready):
if rank == 0:
warnings.warn("All tasks that were run failed", RuntimeWarning)
sys.exit(1)
return
def main(args, comm=None):
t1 = datetime.datetime.now()
log = get_logger()
rank = 0
nproc = 1
if comm is not None:
rank = comm.rank
nproc = comm.size
# Check start up time.
if rank == 0:
if "STARTTIME" in os.environ:
try:
t0 = datetime.datetime.strptime(os.getenv("STARTTIME"),
"%Y%m%d-%H%M%S")
dt = t1 - t0
minutes, seconds = dt.seconds // 60, dt.seconds % 60
log.info("Python startup time: {} min {} sec".format(
minutes, seconds))
except ValueError:
log.error("unable to parse $STARTTIME={}".format(
os.getenv("STARTTIME")))
else:
log.info("Python startup time unknown since $STARTTIME not set")
sys.stdout.flush()
# raw and production locations
rawdir = os.path.abspath(io.rawdata_root())
proddir = os.path.abspath(io.specprod_root())
if rank == 0:
log.info("Starting at {}".format(time.asctime()))
log.info(" Using raw dir {}".format(rawdir))
log.info(" Using spectro production dir {}".format(proddir))
sys.stdout.flush()
# Get task list from disk or from STDIN
tasklist = None
if args.taskfile is not None:
# One process reads the file and broadcasts
if rank == 0:
tasklist = pipe.prod.task_read(args.taskfile)
if comm is not None:
tasklist = comm.bcast(tasklist, root=0)
else:
# Every process has the same STDIN contents.
tasklist = list()
for line in sys.stdin:
tasklist.append(line.rstrip())
# Do we actually have any tasks?
if len(tasklist) == 0:
warnings.warn("Task list is empty", RuntimeWarning)
# run it!
(db, opts) = pipe.load_prod("w")
ntask = len(tasklist)
ready = None
done = None
failed = None
if args.nodb:
ready, done, failed = pipe.run_task_list(args.tasktype,
tasklist,
opts,
comm=comm,
db=None)
else:
ready, done, failed = pipe.run_task_list(args.tasktype,
tasklist,
opts,
comm=comm,
db=db)
t2 = datetime.datetime.now()
if rank == 0:
log.info(" {} tasks already done, {} tasks were ready, and {} failed".
format(done, ready, failed))
dt = t2 - t1
minutes, seconds = dt.seconds // 60, dt.seconds % 60
log.info("Run time: {} min {} sec".format(minutes, seconds))
sys.stdout.flush()
if comm is not None:
comm.barrier()
# Did we have any ready tasks that were not already done?
# Note: if there were no ready tasks, but some were already
# done, then we want to exit with a "0" error code. This will
# allow the calling script to continue with other pipeline steps
# and / or allow other dependent jobs run.
if done == 0:
# nothing is done
if ready == 0:
if rank == 0:
warnings.warn("No tasks were ready or done", RuntimeWarning)
sys.exit(1)
if failed == ready:
# all tasks failed
if rank == 0:
warnings.warn("All tasks that were run failed", RuntimeWarning)
sys.exit(1)
else:
# At least some tasks were done- we return zero so that future
# jobs can run.
if (ready > 0) and (failed == ready):
if rank == 0:
warnings.warn("All tasks that were run failed", RuntimeWarning)
sys.exit(1)
return
def main(args):
# Set up the logger
if args.verbose:
log = get_logger(DEBUG)
else:
log = get_logger()
# Make sure all necessary environment variables are set
DESI_SPECTRO_REDUX_DIR="./quickGen"
if 'DESI_SPECTRO_REDUX' not in os.environ:
log.info('DESI_SPECTRO_REDUX environment is not set.')
else:
DESI_SPECTRO_REDUX_DIR=os.environ['DESI_SPECTRO_REDUX']
if os.path.exists(DESI_SPECTRO_REDUX_DIR):
if not os.path.isdir(DESI_SPECTRO_REDUX_DIR):
raise RuntimeError("Path %s Not a directory"%DESI_SPECTRO_REDUX_DIR)
else:
try:
os.makedirs(DESI_SPECTRO_REDUX_DIR)
except:
raise
SPECPROD_DIR='specprod'
if 'SPECPROD' not in os.environ:
log.info('SPECPROD environment is not set.')
else:
SPECPROD_DIR=os.environ['SPECPROD']
prod_Dir=specprod_root()
if os.path.exists(prod_Dir):
if not os.path.isdir(prod_Dir):
raise RuntimeError("Path %s Not a directory"%prod_Dir)
else:
try:
os.makedirs(prod_Dir)
except:
raise
# Initialize random number generator to use.
np.random.seed(args.seed)
random_state = np.random.RandomState(args.seed)
# Derive spectrograph number from nstart if needed
if args.spectrograph is None:
args.spectrograph = args.nstart / 500
# Read fibermapfile to get object type, night and expid
if args.fibermap:
log.info("Reading fibermap file {}".format(args.fibermap))
fibermap=read_fibermap(args.fibermap)
objtype = get_source_types(fibermap)
stdindx=np.where(objtype=='STD') # match STD with STAR
mwsindx=np.where(objtype=='MWS_STAR') # match MWS_STAR with STAR
bgsindx=np.where(objtype=='BGS') # match BGS with LRG
objtype[stdindx]='STAR'
objtype[mwsindx]='STAR'
objtype[bgsindx]='LRG'
NIGHT=fibermap.meta['NIGHT']
EXPID=fibermap.meta['EXPID']
else:
# Create a blank fake fibermap
fibermap = empty_fibermap(args.nspec)
targetids = random_state.randint(2**62, size=args.nspec)
fibermap['TARGETID'] = targetids
night = get_night()
expid = 0
log.info("Initializing SpecSim with config {}".format(args.config))
desiparams = load_desiparams()
qsim = get_simulator(args.config, num_fibers=1)
if args.simspec:
# Read the input file
log.info('Reading input file {}'.format(args.simspec))
simspec = desisim.io.read_simspec(args.simspec)
nspec = simspec.nspec
if simspec.flavor == 'arc':
log.warning("quickgen doesn't generate flavor=arc outputs")
return
else:
wavelengths = simspec.wave
spectra = simspec.flux
if nspec < args.nspec:
log.info("Only {} spectra in input file".format(nspec))
args.nspec = nspec
else:
# Initialize the output truth table.
spectra = []
wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value
npix = len(wavelengths)
truth = dict()
meta = Table()
truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10))
truth['FLUX'] = np.zeros((args.nspec, npix))
truth['WAVE'] = wavelengths
jj = list()
for thisobj in set(true_objtype):
ii = np.where(true_objtype == thisobj)[0]
nobj = len(ii)
truth['OBJTYPE'][ii] = thisobj
log.info('Generating {} template'.format(thisobj))
# Generate the templates
if thisobj == 'ELG':
elg = desisim.templates.ELG(wave=wavelengths, add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = elg.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_elg,sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'LRG':
lrg = desisim.templates.LRG(wave=wavelengths, add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = lrg.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_lrg,sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'QSO':
qso = desisim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = desisim.templates.BGS(wave=wavelengths, add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = bgs.make_templates(nmodel=nobj, seed=args.seed, zrange=args.zrange_bgs,rmagrange=args.rmagrange_bgs,sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj =='STD':
std = desisim.templates.STD(wave=wavelengths)
flux, tmpwave, meta1 = std.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = desisim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = desisim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'WD':
wd = desisim.templates.WD(wave=wavelengths)
flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj, seed=args.seed)
elif thisobj == 'SKY':
flux = np.zeros((nobj, npix))
meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32)))
elif thisobj == 'TEST':
flux = np.zeros((args.nspec, npix))
indx = np.where(wave>5800.0-1E-6)[0][0]
ref_integrated_flux = 1E-10
ref_cst_flux_density = 1E-17
single_line = (np.arange(args.nspec)%2 == 0).astype(np.float32)
continuum = (np.arange(args.nspec)%2 == 1).astype(np.float32)
for spec in range(args.nspec) :
flux[spec,indx] = single_line[spec]*ref_integrated_flux/np.gradient(wavelengths)[indx] # single line
flux[spec] += continuum[spec]*ref_cst_flux_density # flat continuum
meta1 = Table(dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32),
LINE=wave[indx]*np.ones(args.nspec, dtype=np.float32),
LINEFLUX=single_line*ref_integrated_flux,
CONSTFLUXDENSITY=continuum*ref_cst_flux_density))
else:
log.fatal('Unknown object type {}'.format(thisobj))
sys.exit(1)
# Pack it in.
truth['FLUX'][ii] = flux
meta = vstack([meta, meta1])
jj.append(ii.tolist())
# Sanity check on units; templates currently return ergs, not 1e-17 ergs...
# assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6)
# Sort the metadata table.
jj = sum(jj,[])
meta_new = Table()
for k in range(args.nspec):
index = int(np.where(np.array(jj) == k)[0])
meta_new = vstack([meta_new, meta[index]])
meta = meta_new
# Add TARGETID and the true OBJTYPE to the metadata table.
meta.add_column(Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE'))
meta.add_column(Column(targetids, name='TARGETID'))
# Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z
meta.rename_column('REDSHIFT', 'TRUEZ')
# explicitly set location on focal plane if needed to support airmass
# variations when using specsim v0.5
if qsim.source.focal_xy is None:
qsim.source.focal_xy = (u.Quantity(0, 'mm'), u.Quantity(100, 'mm'))
# Set simulation parameters from the simspec header or desiparams
bright_objects = ['bgs','mws','bright','BGS','MWS','BRIGHT_MIX']
gray_objects = ['gray','grey']
if args.simspec is None:
object_type = objtype
flavor = None
elif simspec.flavor == 'science':
object_type = None
flavor = simspec.header['PROGRAM']
else:
object_type = None
flavor = simspec.flavor
log.warning('Maybe using an outdated simspec file with flavor={}'.format(flavor))
# Set airmass
if args.airmass is not None:
qsim.atmosphere.airmass = args.airmass
elif args.simspec and 'AIRMASS' in simspec.header:
qsim.atmosphere.airmass = simspec.header['AIRMASS']
else:
qsim.atmosphere.airmass = 1.25 # Science Req. Doc L3.3.2
# Set exptime
if args.exptime is not None:
qsim.observation.exposure_time = args.exptime * u.s
elif args.simspec and 'EXPTIME' in simspec.header:
qsim.observation.exposure_time = simspec.header['EXPTIME'] * u.s
elif objtype in bright_objects:
qsim.observation.exposure_time = desiparams['exptime_bright'] * u.s
else:
qsim.observation.exposure_time = desiparams['exptime_dark'] * u.s
# Set Moon Phase
if args.moon_phase is not None:
qsim.atmosphere.moon.moon_phase = args.moon_phase
elif args.simspec and 'MOONFRAC' in simspec.header:
qsim.atmosphere.moon.moon_phase = simspec.header['MOONFRAC']
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_phase = 0.7
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_phase = 0.1
else:
qsim.atmosphere.moon.moon_phase = 0.5
# Set Moon Zenith
if args.moon_zenith is not None:
qsim.atmosphere.moon.moon_zenith = args.moon_zenith * u.deg
elif args.simspec and 'MOONALT' in simspec.header:
qsim.atmosphere.moon.moon_zenith = simspec.header['MOONALT'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_zenith = 30 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_zenith = 80 * u.deg
else:
qsim.atmosphere.moon.moon_zenith = 100 * u.deg
# Set Moon - Object Angle
if args.moon_angle is not None:
qsim.atmosphere.moon.separation_angle = args.moon_angle * u.deg
elif args.simspec and 'MOONSEP' in simspec.header:
qsim.atmosphere.moon.separation_angle = simspec.header['MOONSEP'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.separation_angle = 50 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
else:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
# Initialize per-camera output arrays that will be saved
waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {}
maxbin = 0
nmax= args.nspec
for camera in qsim.instrument.cameras:
# Lookup this camera's resolution matrix and convert to the sparse
# format used in desispec.
R = Resolution(camera.get_output_resolution_matrix())
resolution[camera.name] = np.tile(R.to_fits_array(), [args.nspec, 1, 1])
waves[camera.name] = (camera.output_wavelength.to(u.Angstrom).value.astype(np.float32))
nwave = len(waves[camera.name])
maxbin = max(maxbin, len(waves[camera.name]))
nobj = np.zeros((nmax,3,maxbin)) # object photons
nsky = np.zeros((nmax,3,maxbin)) # sky photons
nivar = np.zeros((nmax,3,maxbin)) # inverse variance (object+sky)
cframe_observedflux = np.zeros((nmax,3,maxbin)) # calibrated object flux
cframe_ivar = np.zeros((nmax,3,maxbin)) # inverse variance of calibrated object flux
cframe_rand_noise = np.zeros((nmax,3,maxbin)) # random Gaussian noise to calibrated flux
sky_ivar = np.zeros((nmax,3,maxbin)) # inverse variance of sky
sky_rand_noise = np.zeros((nmax,3,maxbin)) # random Gaussian noise to sky only
frame_rand_noise = np.zeros((nmax,3,maxbin)) # random Gaussian noise to nobj+nsky
trueflux[camera.name] = np.empty((args.nspec, nwave)) # calibrated flux
noisyflux[camera.name] = np.empty((args.nspec, nwave)) # observed flux with noise
obsivar[camera.name] = np.empty((args.nspec, nwave)) # inverse variance of flux
if args.simspec:
for i in range(10):
cn = camera.name + str(i)
if cn in simspec.cameras:
dw = np.gradient(simspec.cameras[cn].wave)
break
else:
raise RuntimeError('Unable to find a {} camera in input simspec'.format(camera))
else:
sflux = np.empty((args.nspec, npix))
#- Check if input simspec is for a continuum flat lamp instead of science
#- This does not convolve to per-fiber resolution
if args.simspec:
if simspec.flavor == 'flat':
log.info("Simulating flat lamp exposure")
for i,camera in enumerate(qsim.instrument.cameras):
channel = camera.name #- from simspec, b/r/z not b0/r1/z9
assert camera.output_wavelength.unit == u.Angstrom
num_pixels = len(waves[channel])
phot = list()
for j in range(10):
cn = camera.name + str(j)
if cn in simspec.cameras:
camwave = simspec.cameras[cn].wave
dw = np.gradient(camwave)
phot.append(simspec.cameras[cn].phot)
if len(phot) == 0:
raise RuntimeError('Unable to find a {} camera in input simspec'.format(camera))
else:
phot = np.vstack(phot)
meanspec = resample_flux(
waves[channel], camwave, np.average(phot/dw, axis=0))
fiberflat = random_state.normal(loc=1.0,
scale=1.0 / np.sqrt(meanspec), size=(nspec, num_pixels))
ivar = np.tile(meanspec, [nspec, 1])
mask = np.zeros((simspec.nspec, num_pixels), dtype=np.uint32)
for kk in range((args.nspec+args.nstart-1)//500+1):
camera = channel+str(kk)
outfile = desispec.io.findfile('fiberflat', NIGHT, EXPID, camera)
start=max(500*kk,args.nstart)
end=min(500*(kk+1),nmax)
if (args.spectrograph <= kk):
log.info("Writing files for channel:{}, spectrograph:{}, spectra:{} to {}".format(channel,kk,start,end))
ff = FiberFlat(
waves[channel], fiberflat[start:end,:],
ivar[start:end,:], mask[start:end,:], meanspec,
header=dict(CAMERA=camera))
write_fiberflat(outfile, ff)
filePath=desispec.io.findfile("fiberflat",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(filePath))
sys.exit(0)
# Repeat the simulation for all spectra
fluxunits = 1e-17 * u.erg / (u.s * u.cm ** 2 * u.Angstrom)
for j in range(args.nspec):
thisobjtype = objtype[j]
sys.stdout.flush()
if flavor == 'arc':
qsim.source.update_in(
'Quickgen source {0}'.format, 'perfect',
wavelengths * u.Angstrom, spectra * fluxunits)
else:
qsim.source.update_in(
'Quickgen source {0}'.format(j), thisobjtype.lower(),
wavelengths * u.Angstrom, spectra[j, :] * fluxunits)
qsim.source.update_out()
qsim.simulate()
qsim.generate_random_noise(random_state)
for i, output in enumerate(qsim.camera_output):
assert output['observed_flux'].unit == 1e17 * fluxunits
# Extract the simulation results needed to create our uncalibrated
# frame output file.
num_pixels = len(output)
nobj[j, i, :num_pixels] = output['num_source_electrons'][:,0]
nsky[j, i, :num_pixels] = output['num_sky_electrons'][:,0]
nivar[j, i, :num_pixels] = 1.0 / output['variance_electrons'][:,0]
# Get results for our flux-calibrated output file.
cframe_observedflux[j, i, :num_pixels] = 1e17 * output['observed_flux'][:,0]
cframe_ivar[j, i, :num_pixels] = 1e-34 * output['flux_inverse_variance'][:,0]
# Fill brick arrays from the results.
camera = output.meta['name']
trueflux[camera][j][:] = 1e17 * output['observed_flux'][:,0]
noisyflux[camera][j][:] = 1e17 * (output['observed_flux'][:,0] +
output['flux_calibration'][:,0] * output['random_noise_electrons'][:,0])
obsivar[camera][j][:] = 1e-34 * output['flux_inverse_variance'][:,0]
# Use the same noise realization in the cframe and frame, without any
# additional noise from sky subtraction for now.
frame_rand_noise[j, i, :num_pixels] = output['random_noise_electrons'][:,0]
cframe_rand_noise[j, i, :num_pixels] = 1e17 * (
output['flux_calibration'][:,0] * output['random_noise_electrons'][:,0])
# The sky output file represents a model fit to ~40 sky fibers.
# We reduce the variance by a factor of 25 to account for this and
# give the sky an independent (Gaussian) noise realization.
sky_ivar[j, i, :num_pixels] = 25.0 / (
output['variance_electrons'][:,0] - output['num_source_electrons'][:,0])
sky_rand_noise[j, i, :num_pixels] = random_state.normal(
scale=1.0 / np.sqrt(sky_ivar[j,i,:num_pixels]),size=num_pixels)
armName={"b":0,"r":1,"z":2}
for channel in 'brz':
#Before writing, convert from counts/bin to counts/A (as in Pixsim output)
#Quicksim Default:
#FLUX - input spectrum resampled to this binning; no noise added [1e-17 erg/s/cm2/s/Ang]
#COUNTS_OBJ - object counts in 0.5 Ang bin
#COUNTS_SKY - sky counts in 0.5 Ang bin
num_pixels = len(waves[channel])
dwave=np.gradient(waves[channel])
nobj[:,armName[channel],:num_pixels]/=dwave
frame_rand_noise[:,armName[channel],:num_pixels]/=dwave
nivar[:,armName[channel],:num_pixels]*=dwave**2
nsky[:,armName[channel],:num_pixels]/=dwave
sky_rand_noise[:,armName[channel],:num_pixels]/=dwave
sky_ivar[:,armName[channel],:num_pixels]/=dwave**2
# Now write the outputs in DESI standard file system. None of the output file can have more than 500 spectra
# Looping over spectrograph
for ii in range((args.nspec+args.nstart-1)//500+1):
start=max(500*ii,args.nstart) # first spectrum for a given spectrograph
end=min(500*(ii+1),nmax) # last spectrum for the spectrograph
if (args.spectrograph <= ii):
camera = "{}{}".format(channel, ii)
log.info("Writing files for channel:{}, spectrograph:{}, spectra:{} to {}".format(channel,ii,start,end))
num_pixels = len(waves[channel])
# Write frame file
framefileName=desispec.io.findfile("frame",NIGHT,EXPID,camera)
frame_flux=nobj[start:end,armName[channel],:num_pixels]+ \
nsky[start:end,armName[channel],:num_pixels] + \
frame_rand_noise[start:end,armName[channel],:num_pixels]
frame_ivar=nivar[start:end,armName[channel],:num_pixels]
sh1=frame_flux.shape[0] # required for slicing the resolution metric, resolusion matrix has (nspec,ndiag,wave)
# for example if nstart =400, nspec=150: two spectrographs:
# 400-499=> 0 spectrograph, 500-549 => 1
if (args.nstart==start):
resol=resolution[channel][:sh1,:,:]
else:
resol=resolution[channel][-sh1:,:,:]
# must create desispec.Frame object
frame=Frame(waves[channel], frame_flux, frame_ivar,\
resolution_data=resol, spectrograph=ii, \
fibermap=fibermap[start:end], \
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) )
desispec.io.write_frame(framefileName, frame)
framefilePath=desispec.io.findfile("frame",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(framefilePath))
if args.frameonly or simspec.flavor == 'arc':
continue
# Write cframe file
cframeFileName=desispec.io.findfile("cframe",NIGHT,EXPID,camera)
cframeFlux=cframe_observedflux[start:end,armName[channel],:num_pixels]+cframe_rand_noise[start:end,armName[channel],:num_pixels]
cframeIvar=cframe_ivar[start:end,armName[channel],:num_pixels]
# must create desispec.Frame object
cframe = Frame(waves[channel], cframeFlux, cframeIvar, \
resolution_data=resol, spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) )
desispec.io.frame.write_frame(cframeFileName,cframe)
cframefilePath=desispec.io.findfile("cframe",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(cframefilePath))
# Write sky file
skyfileName=desispec.io.findfile("sky",NIGHT,EXPID,camera)
skyflux=nsky[start:end,armName[channel],:num_pixels] + \
sky_rand_noise[start:end,armName[channel],:num_pixels]
skyivar=sky_ivar[start:end,armName[channel],:num_pixels]
skymask=np.zeros(skyflux.shape, dtype=np.uint32)
# must create desispec.Sky object
skymodel = SkyModel(waves[channel], skyflux, skyivar, skymask,
header=dict(CAMERA=camera))
desispec.io.sky.write_sky(skyfileName, skymodel)
skyfilePath=desispec.io.findfile("sky",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(skyfilePath))
# Write calib file
calibVectorFile=desispec.io.findfile("calib",NIGHT,EXPID,camera)
flux = cframe_observedflux[start:end,armName[channel],:num_pixels]
phot = nobj[start:end,armName[channel],:num_pixels]
calibration = np.zeros_like(phot)
jj = (flux>0)
calibration[jj] = phot[jj] / flux[jj]
#- TODO: what should calibivar be?
#- For now, model it as the noise of combining ~10 spectra
calibivar=10/cframe_ivar[start:end,armName[channel],:num_pixels]
#mask=(1/calibivar>0).astype(int)??
mask=np.zeros(calibration.shape, dtype=np.uint32)
# write flux calibration
fluxcalib = FluxCalib(waves[channel], calibration, calibivar, mask)
write_flux_calibration(calibVectorFile, fluxcalib)
calibfilePath=desispec.io.findfile("calib",NIGHT,EXPID,camera)
log.info("Wrote file {}".format(calibfilePath))
def main(args):
# imports
import glob
from desispec.io import findfile
from desispec.io import get_exposures
from desispec.io import get_files, get_nights
from desispec.io import read_frame
from desispec.io import get_reduced_frames
from desispec.io.sky import read_sky
from desispec.io import specprod_root
from desispec.qa import utils as qa_utils
import copy
import pdb
# Log
log = get_logger()
log.info("starting")
# Path
if args.reduxdir is not None:
specprod_dir = args.reduxdir
else:
specprod_dir = specprod_root()
# Channels
if args.channels is not None:
channels = [iarg for iarg in args.channels.split(',')]
else:
channels = ['b', 'r', 'z']
# Sky dict
sky_dict = dict(wave=[], skyflux=[], res=[], count=0)
channel_dict = dict(
b=copy.deepcopy(sky_dict),
r=copy.deepcopy(sky_dict),
z=copy.deepcopy(sky_dict),
)
# Exposure plot?
if args.expid is not None:
# Nights
path_nights = glob.glob(specprod_dir + '/exposures/*')
if nights is None:
nights = get_nights()
nights.sort()
# Find the exposure
for night in nights:
if args.expid in get_exposures(night, specprod_dir=specprod_dir):
frames_dict = get_files(filetype=str('cframe'),
night=night,
expid=args.expid,
specprod_dir=specprod_dir)
# Loop on channel
#for channel in ['b','r','z']:
for channel in ['z']:
channel_dict[channel]['cameras'] = []
for camera, cframe_fil in frames_dict.items():
if channel in camera:
sky_file = findfile(str('sky'),
night=night,
camera=camera,
expid=args.expid,
specprod_dir=specprod_dir)
wave, flux, res, _ = qa_utils.get_skyres(
cframe_fil)
# Append
channel_dict[channel]['wave'].append(wave)
channel_dict[channel]['skyflux'].append(
np.log10(np.maximum(flux, 1e-1)))
channel_dict[channel]['res'].append(res)
channel_dict[channel]['cameras'].append(camera)
channel_dict[channel]['count'] += 1
if channel_dict[channel]['count'] > 0:
from desispec.qa.qa_plots import skysub_resid_series # Hidden to help with debugging
skysub_resid_series(
channel_dict[channel],
'wave',
outfile='QA_skyresid_wave_expid_{:d}{:s}.png'.
format(args.expid, channel))
skysub_resid_series(
channel_dict[channel],
'flux',
outfile='QA_skyresid_flux_expid_{:d}{:s}.png'.
format(args.expid, channel))
return
# Nights
if args.nights is not None:
nights = [iarg for iarg in args.nights.split(',')]
else:
nights = None
# Full Prod Plot?
if args.prod:
from desispec.qa.qa_plots import skysub_resid_dual
# Loop on channel
for channel in channels:
cframes = get_reduced_frames(nights=nights, channels=[channel])
if len(cframes) > 0:
log.info("Loading sky residuals for {:d} cframes".format(
len(cframes)))
sky_wave, sky_flux, sky_res, _ = qa_utils.get_skyres(cframes)
# Plot
outfile = 'QA/skyresid_prod_dual_{:s}.png'.format(channel)
log.info("Plotting to {:s}".format(outfile))
skysub_resid_dual(sky_wave, sky_flux, sky_res, outfile=outfile)
return
# Full Prod Plot?
if args.gauss:
from desispec.qa.qa_plots import skysub_gauss
# Loop on channel
for channel in channels:
cframes = get_reduced_frames(nights=nights, channels=[channel])
if len(cframes) > 0:
# Cut down for debugging
#cframes = [cframes[ii] for ii in range(15)]
#
log.info("Loading sky residuals for {:d} cframes".format(
len(cframes)))
sky_wave, sky_flux, sky_res, sky_ivar = qa_utils.get_skyres(
cframes)
# Plot
log.info("Plotting..")
skysub_gauss(
sky_wave,
sky_flux,
sky_res,
sky_ivar,
outfile='skyresid_prod_gauss_{:s}.png'.format(channel))
return
def main(args):
# Set up the logger
if args.verbose:
log = get_logger(DEBUG)
else:
log = get_logger()
# Make sure all necessary environment variables are set
DESI_SPECTRO_REDUX_DIR = "./quickGen"
if 'DESI_SPECTRO_REDUX' not in os.environ:
log.info('DESI_SPECTRO_REDUX environment is not set.')
else:
DESI_SPECTRO_REDUX_DIR = os.environ['DESI_SPECTRO_REDUX']
if os.path.exists(DESI_SPECTRO_REDUX_DIR):
if not os.path.isdir(DESI_SPECTRO_REDUX_DIR):
raise RuntimeError("Path %s Not a directory" %
DESI_SPECTRO_REDUX_DIR)
else:
try:
os.makedirs(DESI_SPECTRO_REDUX_DIR)
except:
raise
SPECPROD_DIR = 'specprod'
if 'SPECPROD' not in os.environ:
log.info('SPECPROD environment is not set.')
else:
SPECPROD_DIR = os.environ['SPECPROD']
prod_Dir = specprod_root()
if os.path.exists(prod_Dir):
if not os.path.isdir(prod_Dir):
raise RuntimeError("Path %s Not a directory" % prod_Dir)
else:
try:
os.makedirs(prod_Dir)
except:
raise
# Initialize random number generator to use.
np.random.seed(args.seed)
random_state = np.random.RandomState(args.seed)
# Derive spectrograph number from nstart if needed
if args.spectrograph is None:
args.spectrograph = args.nstart / 500
# Read fibermapfile to get object type, night and expid
if args.fibermap:
log.info("Reading fibermap file {}".format(args.fibermap))
fibermap = read_fibermap(args.fibermap)
objtype = get_source_types(fibermap)
stdindx = np.where(objtype == 'STD') # match STD with STAR
mwsindx = np.where(objtype == 'MWS_STAR') # match MWS_STAR with STAR
bgsindx = np.where(objtype == 'BGS') # match BGS with LRG
objtype[stdindx] = 'STAR'
objtype[mwsindx] = 'STAR'
objtype[bgsindx] = 'LRG'
NIGHT = fibermap.meta['NIGHT']
EXPID = fibermap.meta['EXPID']
else:
# Create a blank fake fibermap
fibermap = empty_fibermap(args.nspec)
targetids = random_state.randint(2**62, size=args.nspec)
fibermap['TARGETID'] = targetids
night = get_night()
expid = 0
log.info("Initializing SpecSim with config {}".format(args.config))
desiparams = load_desiparams()
qsim = get_simulator(args.config, num_fibers=1)
if args.simspec:
# Read the input file
log.info('Reading input file {}'.format(args.simspec))
simspec = desisim.io.read_simspec(args.simspec)
nspec = simspec.nspec
if simspec.flavor == 'arc':
log.warning("quickgen doesn't generate flavor=arc outputs")
return
else:
wavelengths = simspec.wave
spectra = simspec.flux
if nspec < args.nspec:
log.info("Only {} spectra in input file".format(nspec))
args.nspec = nspec
else:
# Initialize the output truth table.
spectra = []
wavelengths = qsim.source.wavelength_out.to(u.Angstrom).value
npix = len(wavelengths)
truth = dict()
meta = Table()
truth['OBJTYPE'] = np.zeros(args.nspec, dtype=(str, 10))
truth['FLUX'] = np.zeros((args.nspec, npix))
truth['WAVE'] = wavelengths
jj = list()
for thisobj in set(true_objtype):
ii = np.where(true_objtype == thisobj)[0]
nobj = len(ii)
truth['OBJTYPE'][ii] = thisobj
log.info('Generating {} template'.format(thisobj))
# Generate the templates
if thisobj == 'ELG':
elg = desisim.templates.ELG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = elg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_elg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'LRG':
lrg = desisim.templates.LRG(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = lrg.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_lrg,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'QSO':
qso = desisim.templates.QSO(wave=wavelengths)
flux, tmpwave, meta1 = qso.make_templates(
nmodel=nobj, seed=args.seed, zrange=args.zrange_qso)
elif thisobj == 'BGS':
bgs = desisim.templates.BGS(wave=wavelengths,
add_SNeIa=args.add_SNeIa)
flux, tmpwave, meta1 = bgs.make_templates(
nmodel=nobj,
seed=args.seed,
zrange=args.zrange_bgs,
rmagrange=args.rmagrange_bgs,
sne_rfluxratiorange=args.sne_rfluxratiorange)
elif thisobj == 'STD':
std = desisim.templates.STD(wave=wavelengths)
flux, tmpwave, meta1 = std.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'QSO_BAD': # use STAR template no color cuts
star = desisim.templates.STAR(wave=wavelengths)
flux, tmpwave, meta1 = star.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'MWS_STAR' or thisobj == 'MWS':
mwsstar = desisim.templates.MWS_STAR(wave=wavelengths)
flux, tmpwave, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'WD':
wd = desisim.templates.WD(wave=wavelengths)
flux, tmpwave, meta1 = wd.make_templates(nmodel=nobj,
seed=args.seed)
elif thisobj == 'SKY':
flux = np.zeros((nobj, npix))
meta1 = Table(dict(REDSHIFT=np.zeros(nobj, dtype=np.float32)))
elif thisobj == 'TEST':
flux = np.zeros((args.nspec, npix))
indx = np.where(wave > 5800.0 - 1E-6)[0][0]
ref_integrated_flux = 1E-10
ref_cst_flux_density = 1E-17
single_line = (np.arange(args.nspec) % 2 == 0).astype(
np.float32)
continuum = (np.arange(args.nspec) % 2 == 1).astype(np.float32)
for spec in range(args.nspec):
flux[spec, indx] = single_line[
spec] * ref_integrated_flux / np.gradient(wavelengths)[
indx] # single line
flux[spec] += continuum[
spec] * ref_cst_flux_density # flat continuum
meta1 = Table(
dict(REDSHIFT=np.zeros(args.nspec, dtype=np.float32),
LINE=wave[indx] *
np.ones(args.nspec, dtype=np.float32),
LINEFLUX=single_line * ref_integrated_flux,
CONSTFLUXDENSITY=continuum * ref_cst_flux_density))
else:
log.fatal('Unknown object type {}'.format(thisobj))
sys.exit(1)
# Pack it in.
truth['FLUX'][ii] = flux
meta = vstack([meta, meta1])
jj.append(ii.tolist())
# Sanity check on units; templates currently return ergs, not 1e-17 ergs...
# assert (thisobj == 'SKY') or (np.max(truth['FLUX']) < 1e-6)
# Sort the metadata table.
jj = sum(jj, [])
meta_new = Table()
for k in range(args.nspec):
index = int(np.where(np.array(jj) == k)[0])
meta_new = vstack([meta_new, meta[index]])
meta = meta_new
# Add TARGETID and the true OBJTYPE to the metadata table.
meta.add_column(
Column(true_objtype, dtype=(str, 10), name='TRUE_OBJTYPE'))
meta.add_column(Column(targetids, name='TARGETID'))
# Rename REDSHIFT -> TRUEZ anticipating later table joins with zbest.Z
meta.rename_column('REDSHIFT', 'TRUEZ')
# explicitly set location on focal plane if needed to support airmass
# variations when using specsim v0.5
if qsim.source.focal_xy is None:
qsim.source.focal_xy = (u.Quantity(0, 'mm'), u.Quantity(100, 'mm'))
# Set simulation parameters from the simspec header or desiparams
bright_objects = ['bgs', 'mws', 'bright', 'BGS', 'MWS', 'BRIGHT_MIX']
gray_objects = ['gray', 'grey']
if args.simspec is None:
object_type = objtype
flavor = None
elif simspec.flavor == 'science':
object_type = None
flavor = simspec.header['PROGRAM']
else:
object_type = None
flavor = simspec.flavor
log.warning(
'Maybe using an outdated simspec file with flavor={}'.format(
flavor))
# Set airmass
if args.airmass is not None:
qsim.atmosphere.airmass = args.airmass
elif args.simspec and 'AIRMASS' in simspec.header:
qsim.atmosphere.airmass = simspec.header['AIRMASS']
else:
qsim.atmosphere.airmass = 1.25 # Science Req. Doc L3.3.2
# Set exptime
if args.exptime is not None:
qsim.observation.exposure_time = args.exptime * u.s
elif args.simspec and 'EXPTIME' in simspec.header:
qsim.observation.exposure_time = simspec.header['EXPTIME'] * u.s
elif objtype in bright_objects:
qsim.observation.exposure_time = desiparams['exptime_bright'] * u.s
else:
qsim.observation.exposure_time = desiparams['exptime_dark'] * u.s
# Set Moon Phase
if args.moon_phase is not None:
qsim.atmosphere.moon.moon_phase = args.moon_phase
elif args.simspec and 'MOONFRAC' in simspec.header:
qsim.atmosphere.moon.moon_phase = simspec.header['MOONFRAC']
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_phase = 0.7
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_phase = 0.1
else:
qsim.atmosphere.moon.moon_phase = 0.5
# Set Moon Zenith
if args.moon_zenith is not None:
qsim.atmosphere.moon.moon_zenith = args.moon_zenith * u.deg
elif args.simspec and 'MOONALT' in simspec.header:
qsim.atmosphere.moon.moon_zenith = simspec.header['MOONALT'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.moon_zenith = 30 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.moon_zenith = 80 * u.deg
else:
qsim.atmosphere.moon.moon_zenith = 100 * u.deg
# Set Moon - Object Angle
if args.moon_angle is not None:
qsim.atmosphere.moon.separation_angle = args.moon_angle * u.deg
elif args.simspec and 'MOONSEP' in simspec.header:
qsim.atmosphere.moon.separation_angle = simspec.header[
'MOONSEP'] * u.deg
elif flavor in bright_objects or object_type in bright_objects:
qsim.atmosphere.moon.separation_angle = 50 * u.deg
elif flavor in gray_objects:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
else:
qsim.atmosphere.moon.separation_angle = 60 * u.deg
# Initialize per-camera output arrays that will be saved
waves, trueflux, noisyflux, obsivar, resolution, sflux = {}, {}, {}, {}, {}, {}
maxbin = 0
nmax = args.nspec
for camera in qsim.instrument.cameras:
# Lookup this camera's resolution matrix and convert to the sparse
# format used in desispec.
R = Resolution(camera.get_output_resolution_matrix())
resolution[camera.name] = np.tile(R.to_fits_array(),
[args.nspec, 1, 1])
waves[camera.name] = (camera.output_wavelength.to(
u.Angstrom).value.astype(np.float32))
nwave = len(waves[camera.name])
maxbin = max(maxbin, len(waves[camera.name]))
nobj = np.zeros((nmax, 3, maxbin)) # object photons
nsky = np.zeros((nmax, 3, maxbin)) # sky photons
nivar = np.zeros((nmax, 3, maxbin)) # inverse variance (object+sky)
cframe_observedflux = np.zeros(
(nmax, 3, maxbin)) # calibrated object flux
cframe_ivar = np.zeros(
(nmax, 3, maxbin)) # inverse variance of calibrated object flux
cframe_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to calibrated flux
sky_ivar = np.zeros((nmax, 3, maxbin)) # inverse variance of sky
sky_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to sky only
frame_rand_noise = np.zeros(
(nmax, 3, maxbin)) # random Gaussian noise to nobj+nsky
trueflux[camera.name] = np.empty(
(args.nspec, nwave)) # calibrated flux
noisyflux[camera.name] = np.empty(
(args.nspec, nwave)) # observed flux with noise
obsivar[camera.name] = np.empty(
(args.nspec, nwave)) # inverse variance of flux
if args.simspec:
for i in range(10):
cn = camera.name + str(i)
if cn in simspec.cameras:
dw = np.gradient(simspec.cameras[cn].wave)
break
else:
raise RuntimeError(
'Unable to find a {} camera in input simspec'.format(
camera))
else:
sflux = np.empty((args.nspec, npix))
#- Check if input simspec is for a continuum flat lamp instead of science
#- This does not convolve to per-fiber resolution
if args.simspec:
if simspec.flavor == 'flat':
log.info("Simulating flat lamp exposure")
for i, camera in enumerate(qsim.instrument.cameras):
channel = camera.name #- from simspec, b/r/z not b0/r1/z9
assert camera.output_wavelength.unit == u.Angstrom
num_pixels = len(waves[channel])
phot = list()
for j in range(10):
cn = camera.name + str(j)
if cn in simspec.cameras:
camwave = simspec.cameras[cn].wave
dw = np.gradient(camwave)
phot.append(simspec.cameras[cn].phot)
if len(phot) == 0:
raise RuntimeError(
'Unable to find a {} camera in input simspec'.format(
camera))
else:
phot = np.vstack(phot)
meanspec = resample_flux(waves[channel], camwave,
np.average(phot / dw, axis=0))
fiberflat = random_state.normal(loc=1.0,
scale=1.0 / np.sqrt(meanspec),
size=(nspec, num_pixels))
ivar = np.tile(meanspec, [nspec, 1])
mask = np.zeros((simspec.nspec, num_pixels), dtype=np.uint32)
for kk in range((args.nspec + args.nstart - 1) // 500 + 1):
camera = channel + str(kk)
outfile = desispec.io.findfile('fiberflat', NIGHT, EXPID,
camera)
start = max(500 * kk, args.nstart)
end = min(500 * (kk + 1), nmax)
if (args.spectrograph <= kk):
log.info(
"Writing files for channel:{}, spectrograph:{}, spectra:{} to {}"
.format(channel, kk, start, end))
ff = FiberFlat(waves[channel],
fiberflat[start:end, :],
ivar[start:end, :],
mask[start:end, :],
meanspec,
header=dict(CAMERA=camera))
write_fiberflat(outfile, ff)
filePath = desispec.io.findfile("fiberflat", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(filePath))
sys.exit(0)
# Repeat the simulation for all spectra
fluxunits = 1e-17 * u.erg / (u.s * u.cm**2 * u.Angstrom)
for j in range(args.nspec):
thisobjtype = objtype[j]
sys.stdout.flush()
if flavor == 'arc':
qsim.source.update_in('Quickgen source {0}'.format, 'perfect',
wavelengths * u.Angstrom,
spectra * fluxunits)
else:
qsim.source.update_in('Quickgen source {0}'.format(j),
thisobjtype.lower(),
wavelengths * u.Angstrom,
spectra[j, :] * fluxunits)
qsim.source.update_out()
qsim.simulate()
qsim.generate_random_noise(random_state)
for i, output in enumerate(qsim.camera_output):
assert output['observed_flux'].unit == 1e17 * fluxunits
# Extract the simulation results needed to create our uncalibrated
# frame output file.
num_pixels = len(output)
nobj[j, i, :num_pixels] = output['num_source_electrons'][:, 0]
nsky[j, i, :num_pixels] = output['num_sky_electrons'][:, 0]
nivar[j, i, :num_pixels] = 1.0 / output['variance_electrons'][:, 0]
# Get results for our flux-calibrated output file.
cframe_observedflux[
j, i, :num_pixels] = 1e17 * output['observed_flux'][:, 0]
cframe_ivar[
j,
i, :num_pixels] = 1e-34 * output['flux_inverse_variance'][:, 0]
# Fill brick arrays from the results.
camera = output.meta['name']
trueflux[camera][j][:] = 1e17 * output['observed_flux'][:, 0]
noisyflux[camera][j][:] = 1e17 * (
output['observed_flux'][:, 0] +
output['flux_calibration'][:, 0] *
output['random_noise_electrons'][:, 0])
obsivar[camera][j][:] = 1e-34 * output['flux_inverse_variance'][:,
0]
# Use the same noise realization in the cframe and frame, without any
# additional noise from sky subtraction for now.
frame_rand_noise[
j, i, :num_pixels] = output['random_noise_electrons'][:, 0]
cframe_rand_noise[j, i, :num_pixels] = 1e17 * (
output['flux_calibration'][:, 0] *
output['random_noise_electrons'][:, 0])
# The sky output file represents a model fit to ~40 sky fibers.
# We reduce the variance by a factor of 25 to account for this and
# give the sky an independent (Gaussian) noise realization.
sky_ivar[
j,
i, :num_pixels] = 25.0 / (output['variance_electrons'][:, 0] -
output['num_source_electrons'][:, 0])
sky_rand_noise[j, i, :num_pixels] = random_state.normal(
scale=1.0 / np.sqrt(sky_ivar[j, i, :num_pixels]),
size=num_pixels)
armName = {"b": 0, "r": 1, "z": 2}
for channel in 'brz':
#Before writing, convert from counts/bin to counts/A (as in Pixsim output)
#Quicksim Default:
#FLUX - input spectrum resampled to this binning; no noise added [1e-17 erg/s/cm2/s/Ang]
#COUNTS_OBJ - object counts in 0.5 Ang bin
#COUNTS_SKY - sky counts in 0.5 Ang bin
num_pixels = len(waves[channel])
dwave = np.gradient(waves[channel])
nobj[:, armName[channel], :num_pixels] /= dwave
frame_rand_noise[:, armName[channel], :num_pixels] /= dwave
nivar[:, armName[channel], :num_pixels] *= dwave**2
nsky[:, armName[channel], :num_pixels] /= dwave
sky_rand_noise[:, armName[channel], :num_pixels] /= dwave
sky_ivar[:, armName[channel], :num_pixels] /= dwave**2
# Now write the outputs in DESI standard file system. None of the output file can have more than 500 spectra
# Looping over spectrograph
for ii in range((args.nspec + args.nstart - 1) // 500 + 1):
start = max(500 * ii,
args.nstart) # first spectrum for a given spectrograph
end = min(500 * (ii + 1),
nmax) # last spectrum for the spectrograph
if (args.spectrograph <= ii):
camera = "{}{}".format(channel, ii)
log.info(
"Writing files for channel:{}, spectrograph:{}, spectra:{} to {}"
.format(channel, ii, start, end))
num_pixels = len(waves[channel])
# Write frame file
framefileName = desispec.io.findfile("frame", NIGHT, EXPID,
camera)
frame_flux=nobj[start:end,armName[channel],:num_pixels]+ \
nsky[start:end,armName[channel],:num_pixels] + \
frame_rand_noise[start:end,armName[channel],:num_pixels]
frame_ivar = nivar[start:end, armName[channel], :num_pixels]
sh1 = frame_flux.shape[
0] # required for slicing the resolution metric, resolusion matrix has (nspec,ndiag,wave)
# for example if nstart =400, nspec=150: two spectrographs:
# 400-499=> 0 spectrograph, 500-549 => 1
if (args.nstart == start):
resol = resolution[channel][:sh1, :, :]
else:
resol = resolution[channel][-sh1:, :, :]
# must create desispec.Frame object
frame=Frame(waves[channel], frame_flux, frame_ivar,\
resolution_data=resol, spectrograph=ii, \
fibermap=fibermap[start:end], \
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) )
desispec.io.write_frame(framefileName, frame)
framefilePath = desispec.io.findfile("frame", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(framefilePath))
if args.frameonly or simspec.flavor == 'arc':
continue
# Write cframe file
cframeFileName = desispec.io.findfile("cframe", NIGHT, EXPID,
camera)
cframeFlux = cframe_observedflux[
start:end,
armName[channel], :num_pixels] + cframe_rand_noise[
start:end, armName[channel], :num_pixels]
cframeIvar = cframe_ivar[start:end,
armName[channel], :num_pixels]
# must create desispec.Frame object
cframe = Frame(waves[channel], cframeFlux, cframeIvar, \
resolution_data=resol, spectrograph=ii,
fibermap=fibermap[start:end],
meta=dict(CAMERA=camera, FLAVOR=simspec.flavor) )
desispec.io.frame.write_frame(cframeFileName, cframe)
cframefilePath = desispec.io.findfile("cframe", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(cframefilePath))
# Write sky file
skyfileName = desispec.io.findfile("sky", NIGHT, EXPID, camera)
skyflux=nsky[start:end,armName[channel],:num_pixels] + \
sky_rand_noise[start:end,armName[channel],:num_pixels]
skyivar = sky_ivar[start:end, armName[channel], :num_pixels]
skymask = np.zeros(skyflux.shape, dtype=np.uint32)
# must create desispec.Sky object
skymodel = SkyModel(waves[channel],
skyflux,
skyivar,
skymask,
header=dict(CAMERA=camera))
desispec.io.sky.write_sky(skyfileName, skymodel)
skyfilePath = desispec.io.findfile("sky", NIGHT, EXPID, camera)
log.info("Wrote file {}".format(skyfilePath))
# Write calib file
calibVectorFile = desispec.io.findfile("calib", NIGHT, EXPID,
camera)
flux = cframe_observedflux[start:end,
armName[channel], :num_pixels]
phot = nobj[start:end, armName[channel], :num_pixels]
calibration = np.zeros_like(phot)
jj = (flux > 0)
calibration[jj] = phot[jj] / flux[jj]
#- TODO: what should calibivar be?
#- For now, model it as the noise of combining ~10 spectra
calibivar = 10 / cframe_ivar[start:end,
armName[channel], :num_pixels]
#mask=(1/calibivar>0).astype(int)??
mask = np.zeros(calibration.shape, dtype=np.uint32)
# write flux calibration
fluxcalib = FluxCalib(waves[channel], calibration, calibivar,
mask)
write_flux_calibration(calibVectorFile, fluxcalib)
calibfilePath = desispec.io.findfile("calib", NIGHT, EXPID,
camera)
log.info("Wrote file {}".format(calibfilePath))
def main(args) :
# imports
import glob
from desispec.io import findfile, makepath
from desispec.io import get_exposures
from desispec.io import get_files, get_nights
from desispec.io import get_reduced_frames
from desispec.io import specprod_root
from desispec.io import qaprod_root
from desispec.qa import utils as qa_utils
import copy
import pdb
# Init
specprod_dir = specprod_root()
# Log
log=get_logger()
log.info("starting")
# Path
if args.qaprod_dir is not None:
qaprod_dir = args.qaprod_dir
else:
qaprod_dir = qaprod_root()
# Channels
if args.channels is not None:
channels = [iarg for iarg in args.channels.split(',')]
else:
channels = ['b','r','z']
# Sky dict
sky_dict = dict(wave=[], skyflux=[], res=[], count=0)
channel_dict = dict(b=copy.deepcopy(sky_dict),
r=copy.deepcopy(sky_dict),
z=copy.deepcopy(sky_dict),
)
# Nights
if args.nights is not None:
nights = [iarg for iarg in args.nights.split(',')]
else:
nights = None
# Exposure plot?
if args.expid is not None:
# Nights
if nights is None:
nights = get_nights()
nights.sort()
# Find the exposure
for night in nights:
if args.expid in get_exposures(night, specprod_dir=specprod_dir):
frames_dict = get_files(filetype=str('cframe'), night=night,
expid=args.expid, specprod_dir=specprod_dir)
# Loop on channel
#for channel in ['b','r','z']:
for channel in ['z']:
channel_dict[channel]['cameras'] = []
for camera, cframe_fil in frames_dict.items():
if channel in camera:
sky_file = findfile(str('sky'), night=night, camera=camera,
expid=args.expid, specprod_dir=specprod_dir)
wave, flux, res, _ = qa_utils.get_skyres(cframe_fil)
# Append
channel_dict[channel]['wave'].append(wave)
channel_dict[channel]['skyflux'].append(np.log10(np.maximum(flux,1e-1)))
channel_dict[channel]['res'].append(res)
channel_dict[channel]['cameras'].append(camera)
channel_dict[channel]['count'] += 1
if channel_dict[channel]['count'] > 0:
from desispec.qa.qa_plots import skysub_resid_series # Hidden to help with debugging
skysub_resid_series(channel_dict[channel], 'wave',
outfile=qaprod_dir+'/QA_skyresid_wave_expid_{:d}{:s}.png'.format(args.expid, channel))
skysub_resid_series(channel_dict[channel], 'flux',
outfile=qaprod_dir+'/QA_skyresid_flux_expid_{:d}{:s}.png'.format(args.expid, channel))
return
# Skyline
if args.skyline:
from desispec.qa.qa_plots import skyline_resid
# Loop on channel
for channel in channels:
cframes = get_reduced_frames(nights=nights, channels=[channel])
if len(cframes) > 0:
log.info("Loading sky residuals for {:d} cframes".format(len(cframes)))
if len(cframes) == 1:
log.error('len(cframes)==1; starting debugging')
pdb.set_trace() # Need to call differently
else:
sky_wave, sky_flux, sky_res, sky_ivar = qa_utils.get_skyres(
cframes, flatten=False)
# Plot
outfile=args.outdir+'/skyline_{:s}.png'.format(channel)
log.info("Plotting to {:s}".format(outfile))
skyline_resid(channel, sky_wave, sky_flux, sky_res, sky_ivar,
outfile=outfile)
return
# Full Prod Plot?
if args.prod:
from desispec.qa.qa_plots import skysub_resid_dual
# Loop on channel
for channel in channels:
cframes = get_reduced_frames(nights=nights, channels=[channel])
if len(cframes) > 0:
log.info("Loading sky residuals for {:d} cframes".format(len(cframes)))
sky_wave, sky_flux, sky_res, _ = qa_utils.get_skyres(cframes)
# Plot
outfile=qaprod_dir+'/skyresid_prod_dual_{:s}.png'.format(channel)
makepath(outfile)
log.info("Plotting to {:s}".format(outfile))
skysub_resid_dual(sky_wave, sky_flux, sky_res, outfile=outfile)
return
# Test sky noise for Gaussianity
if args.gauss:
from desispec.qa.qa_plots import skysub_gauss
# Loop on channel
for channel in channels:
cframes = get_reduced_frames(nights=nights, channels=[channel])
if len(cframes) > 0:
# Cut down for debugging
#cframes = [cframes[ii] for ii in range(15)]
#
log.info("Loading sky residuals for {:d} cframes".format(len(cframes)))
sky_wave, sky_flux, sky_res, sky_ivar = qa_utils.get_skyres(cframes)
# Plot
log.info("Plotting..")
outfile=qaprod_dir+'/skyresid_prod_gauss_{:s}.png'.format(channel)
makepath(outfile)
skysub_gauss(sky_wave, sky_flux, sky_res, sky_ivar,
outfile=outfile)
return
