def update_obslog(obstype='science', program='DARK', expid=None, dateobs=None,
tileid=-1, ra=None, dec=None):
"""
Update obslog with a new exposure
obstype : 'arc', 'flat', 'bias', 'test', 'science', ...
program : 'DARK', 'GRAY', 'BRIGHT', 'CALIB'
expid : integer exposure ID, default from get_next_expid()
dateobs : time.struct_time tuple; default time.localtime()
tileid : integer TileID, default -1, i.e. not a DESI tile
ra, dec : float (ra, dec) coordinates, default tile ra,dec or (0,0)
returns tuple (expid, dateobs)
TODO: normalize obstype vs. program; see desisim issue #97
"""
#- Connect to sqlite database file and create DB if needed
dbdir = io.simdir() + '/etc'
if not os.path.exists(dbdir):
os.makedirs(dbdir)
dbfile = dbdir+'/obslog.sqlite'
with sqlite3.connect(dbfile, isolation_level="EXCLUSIVE") as db:
db.execute("""\
CREATE TABLE IF NOT EXISTS obslog (
expid INTEGER PRIMARY KEY,
dateobs DATETIME, -- seconds since Unix Epoch (1970)
night TEXT, -- YEARMMDD
obstype TEXT DEFAULT "science",
program TEXT DEFAULT "DARK",
tileid INTEGER DEFAULT -1,
ra REAL DEFAULT 0.0,
dec REAL DEFAULT 0.0
)
""")
#- Fill in defaults
if expid is None:
expid = get_next_expid()
if dateobs is None:
dateobs = time.localtime()
if ra is None:
assert (dec is None)
if tileid < 0:
ra, dec = (0.0, 0.0)
else:
ra, dec = io.get_tile_radec(tileid)
night = get_night(utc=dateobs)
insert = """\
INSERT OR REPLACE INTO obslog(expid,dateobs,night,obstype,program,tileid,ra,dec)
VALUES (?,?,?,?,?,?,?,?)
"""
db.execute(insert, (int(expid), time.mktime(dateobs), str(night), str(obstype.upper()), str(program.upper()), int(tileid), float(ra), float(dec)))
db.commit()
return expid, dateobs
def update_obslog(obstype='science', program='DARK', expid=None, dateobs=None,
tileid=-1, ra=None, dec=None):
"""
Update obslog with a new exposure
obstype : 'arc', 'flat', 'bias', 'test', 'science', ...
program : 'DARK', 'GRAY', 'BRIGHT', 'CALIB'
expid : integer exposure ID, default from get_next_expid()
dateobs : time.struct_time tuple; default time.localtime()
tileid : integer TileID, default -1, i.e. not a DESI tile
ra, dec : float (ra, dec) coordinates, default tile ra,dec or (0,0)
returns tuple (expid, dateobs)
TODO: normalize obstype vs. program; see desisim issue #97
"""
#- Connect to sqlite database file and create DB if needed
dbdir = io.simdir() + '/etc'
if not os.path.exists(dbdir):
os.makedirs(dbdir)
dbfile = dbdir+'/obslog.sqlite'
with sqlite3.connect(dbfile, isolation_level="EXCLUSIVE") as db:
db.execute("""\
CREATE TABLE IF NOT EXISTS obslog (
expid INTEGER PRIMARY KEY,
dateobs DATETIME, -- seconds since Unix Epoch (1970)
night TEXT, -- YEARMMDD
obstype TEXT DEFAULT "science",
program TEXT DEFAULT "DARK",
tileid INTEGER DEFAULT -1,
ra REAL DEFAULT 0.0,
dec REAL DEFAULT 0.0
)
""")
#- Fill in defaults
if expid is None:
expid = get_next_expid()
if dateobs is None:
dateobs = time.localtime()
if ra is None:
assert (dec is None)
if tileid < 0:
ra, dec = (0.0, 0.0)
else:
ra, dec = io.get_tile_radec(tileid)
night = get_night(utc=dateobs)
insert = """\
INSERT OR REPLACE INTO obslog(expid,dateobs,night,obstype,program,tileid,ra,dec)
VALUES (?,?,?,?,?,?,?,?)
"""
db.execute(insert, (int(expid), time.mktime(dateobs), str(night), str(obstype.upper()), str(program.upper()), int(tileid), float(ra), float(dec)))
db.commit()
return expid, dateobs
def update_obslog(obstype='science', expid=None, dateobs=None,
tileid=-1, ra=None, dec=None):
"""
Update obslog with a new exposure
obstype : 'science', 'arc', 'flat', 'bias', 'dark', or 'test'
expid : integer exposure ID, default from get_next_expid()
dateobs : time.struct_time tuple; default time.localtime()
tileid : integer TileID, default -1, i.e. not a DESI tile
ra, dec : float (ra, dec) coordinates, default tile ra,dec or (0,0)
returns tuple (expid, dateobs)
"""
#- Connect to sqlite database file and create DB if needed
dbdir = io.simdir() + '/etc'
if not os.path.exists(dbdir):
os.makedirs(dbdir)
dbfile = dbdir+'/obslog.sqlite'
db = sqlite3.connect(dbfile)
db.execute("""\
CREATE TABLE IF NOT EXISTS obslog (
expid INTEGER PRIMARY KEY,
dateobs DATETIME, -- seconds since Unix Epoch (1970)
night TEXT, -- YEARMMDD
obstype TEXT DEFAULT "science",
tileid INTEGER DEFAULT -1,
ra REAL DEFAULT 0.0,
dec REAL DEFAULT 0.0
)
""")
#- Fill in defaults
if expid is None:
expid = get_next_expid()
if dateobs is None:
dateobs = time.localtime()
if ra is None:
assert (dec is None)
if tileid < 0:
ra, dec = (0.0, 0.0)
else:
ra, dec = io.get_tile_radec(tileid)
night = get_night(utc=dateobs)
insert = """\
INSERT OR REPLACE INTO obslog(expid,dateobs,night,obstype,tileid,ra,dec)
VALUES (?,?,?,?,?,?,?)
"""
db.execute(insert, (expid, time.mktime(dateobs), night, obstype, tileid, ra, dec))
db.commit()
return expid, dateobs
def update_obslog(obstype='science',
expid=None,
dateobs=None,
tileid=-1,
ra=None,
dec=None):
"""
Update obslog with a new exposure
obstype : 'science', 'arc', 'flat', 'bias', 'dark', or 'test'
expid : integer exposure ID, default from get_next_expid()
dateobs : time.struct_time tuple; default time.localtime()
tileid : integer TileID, default -1, i.e. not a DESI tile
ra, dec : float (ra, dec) coordinates, default tile ra,dec or (0,0)
returns tuple (expid, dateobs)
"""
#- Connect to sqlite database file and create DB if needed
dbdir = io.simdir() + '/etc'
if not os.path.exists(dbdir):
os.makedirs(dbdir)
dbfile = dbdir + '/obslog.sqlite'
db = sqlite3.connect(dbfile)
db.execute("""\
CREATE TABLE IF NOT EXISTS obslog (
expid INTEGER PRIMARY KEY,
dateobs DATETIME, -- seconds since Unix Epoch (1970)
night TEXT, -- YEARMMDD
obstype TEXT DEFAULT "science",
tileid INTEGER DEFAULT -1,
ra REAL DEFAULT 0.0,
dec REAL DEFAULT 0.0
)
""")
#- Fill in defaults
if expid is None:
expid = get_next_expid()
if dateobs is None:
dateobs = time.localtime()
if ra is None:
assert (dec is None)
if tileid < 0:
ra, dec = (0.0, 0.0)
else:
ra, dec = io.get_tile_radec(tileid)
night = get_night(utc=dateobs)
insert = """\
INSERT OR REPLACE INTO obslog(expid,dateobs,night,obstype,tileid,ra,dec)
VALUES (?,?,?,?,?,?,?)
"""
db.execute(insert,
(expid, time.mktime(dateobs), night, obstype, tileid, ra, dec))
db.commit()
return expid, dateobs
def new_exposure(flavor, nspec=5000, night=None, expid=None, tileid=None, \
airmass=1.0, exptime=None):
"""
Create a new exposure and output input simulation files.
Does not generate pixel-level simulations or noisy spectra.
Args:
nspec (optional): integer number of spectra to simulate
night (optional): YEARMMDD string
expid (optional): positive integer exposure ID
tileid (optional): tile ID
airmass (optional): airmass, default 1.0
Writes:
$DESI_SPECTRO_SIM/$PIXPROD/{night}/fibermap-{expid}.fits
$DESI_SPECTRO_SIM/$PIXPROD/{night}/simspec-{expid}.fits
Returns:
fibermap numpy structured array
truth dictionary
"""
if expid is None:
expid = get_next_expid()
if tileid is None:
tileid = get_next_tileid()
if night is None:
#- simulation obs time = now, even if sun is up
dateobs = time.gmtime()
night = get_night(utc=dateobs)
else:
#- 10pm on night YEARMMDD
dateobs = time.strptime(night + ':22', '%Y%m%d:%H')
params = desimodel.io.load_desiparams()
if flavor == 'arc':
infile = os.getenv(
'DESI_ROOT'
) + '/spectro/templates/calib/v0.2/arc-lines-average.fits'
d = fits.getdata(infile, 1)
wave = d['AIRWAVE']
phot = d['ELECTRONS']
truth = dict(WAVE=wave)
meta = None
fibermap = desispec.io.fibermap.empty_fibermap(nspec)
for channel in ('B', 'R', 'Z'):
thru = desimodel.io.load_throughput(channel)
ii = np.where((thru.wavemin <= wave) & (wave <= thru.wavemax))[0]
truth['WAVE_' + channel] = wave[ii]
truth['PHOT_' + channel] = np.tile(phot[ii],
nspec).reshape(nspec, len(ii))
elif flavor == 'flat':
infile = os.getenv(
'DESI_ROOT'
) + '/spectro/templates/calib/v0.2/flat-3100K-quartz-iodine.fits'
flux = fits.getdata(infile, 0)
hdr = fits.getheader(infile, 0)
wave = desispec.io.util.header2wave(hdr)
#- resample to 0.2 A grid
dw = 0.2
ww = np.arange(wave[0], wave[-1] + dw / 2, dw)
flux = resample_flux(ww, wave, flux)
wave = ww
#- Convert to 2D for projection
flux = np.tile(flux, nspec).reshape(nspec, len(wave))
truth = dict(WAVE=wave, FLUX=flux)
meta = None
fibermap = desispec.io.fibermap.empty_fibermap(nspec)
for channel in ('B', 'R', 'Z'):
thru = desimodel.io.load_throughput(channel)
ii = (thru.wavemin <= wave) & (wave <= thru.wavemax)
phot = thru.photons(wave[ii],
flux[:, ii],
units=hdr['BUNIT'],
objtype='CALIB',
exptime=10)
truth['WAVE_' + channel] = wave[ii]
truth['PHOT_' + channel] = phot
elif flavor == 'science':
fibermap, truth = get_targets(nspec, tileid=tileid)
flux = truth['FLUX']
wave = truth['WAVE']
nwave = len(wave)
if exptime is None:
exptime = params['exptime']
#- Load sky [Magic knowledge of units 1e-17 erg/s/cm2/A/arcsec2]
skyfile = os.getenv('DESIMODEL') + '/data/spectra/spec-sky.dat'
skywave, skyflux = np.loadtxt(skyfile, unpack=True)
skyflux = np.interp(wave, skywave, skyflux)
truth['SKYFLUX'] = skyflux
for channel in ('B', 'R', 'Z'):
thru = desimodel.io.load_throughput(channel)
ii = np.where((thru.wavemin <= wave) & (wave <= thru.wavemax))[0]
#- Project flux to photons
phot = thru.photons(wave[ii],
flux[:, ii],
units=truth['UNITS'],
objtype=truth['OBJTYPE'],
exptime=exptime,
airmass=airmass)
truth['PHOT_' + channel] = phot
truth['WAVE_' + channel] = wave[ii]
#- Project sky flux to photons
skyphot = thru.photons(wave[ii],
skyflux[ii] * airmass,
units='1e-17 erg/s/cm2/A/arcsec2',
objtype='SKY',
exptime=exptime,
airmass=airmass)
#- 2D version
### truth['SKYPHOT_'+channel] = np.tile(skyphot, nspec).reshape((nspec, len(ii)))
#- 1D version
truth['SKYPHOT_' + channel] = skyphot.astype(np.float32)
#- NOTE: someday skyflux and skyphot may be 2D instead of 1D
#- Extract the metadata part of the truth dictionary into a table
columns = (
'OBJTYPE',
'REDSHIFT',
'TEMPLATEID',
'D4000',
'OIIFLUX',
'VDISP',
)
meta = {key: truth[key] for key in columns}
#- (end indentation for arc/flat/science flavors)
#- Override $DESI_SPECTRO_DATA in order to write to simulation area
datadir_orig = os.getenv('DESI_SPECTRO_DATA')
simbase = os.path.join(os.getenv('DESI_SPECTRO_SIM'), os.getenv('PIXPROD'))
os.environ['DESI_SPECTRO_DATA'] = simbase
#- Write fibermap
telera, teledec = io.get_tile_radec(tileid)
hdr = dict(
NIGHT=(night, 'Night of observation YEARMMDD'),
EXPID=(expid, 'DESI exposure ID'),
TILEID=(tileid, 'DESI tile ID'),
FLAVOR=(flavor, 'Flavor [arc, flat, science, ...]'),
TELRA=(telera, 'Telescope pointing RA [degrees]'),
TELDEC=(teledec, 'Telescope pointing dec [degrees]'),
)
#- ISO 8601 DATE-OBS year-mm-ddThh:mm:ss
fiberfile = desispec.io.findfile('fibermap', night, expid)
desispec.io.write_fibermap(fiberfile, fibermap, header=hdr)
print fiberfile
#- Write simspec; expand fibermap header
hdr['AIRMASS'] = (airmass, 'Airmass at middle of exposure')
hdr['EXPTIME'] = (exptime, 'Exposure time [sec]')
hdr['DATE-OBS'] = (time.strftime('%FT%T', dateobs), 'Start of exposure')
simfile = io.write_simspec(meta, truth, expid, night, header=hdr)
print(simfile)
#- Update obslog that we succeeded with this exposure
update_obslog(flavor, expid, dateobs, tileid)
#- Restore $DESI_SPECTRO_DATA
if datadir_orig is not None:
os.environ['DESI_SPECTRO_DATA'] = datadir_orig
else:
del os.environ['DESI_SPECTRO_DATA']
return fibermap, truth
def new_exposure(flavor, nspec=5000, night=None, expid=None, tileid=None, airmass=1.0, \
exptime=None):
"""
Create a new exposure and output input simulation files.
Does not generate pixel-level simulations or noisy spectra.
Args:
nspec (optional): integer number of spectra to simulate
night (optional): YEARMMDD string
expid (optional): positive integer exposure ID
tileid (optional): tile ID
airmass (optional): airmass, default 1.0
Writes:
$DESI_SPECTRO_SIM/$PIXPROD/{night}/fibermap-{expid}.fits
$DESI_SPECTRO_SIM/$PIXPROD/{night}/simspec-{expid}.fits
Returns:
fibermap numpy structured array
truth dictionary
"""
if expid is None:
expid = get_next_expid()
if tileid is None:
tileid = get_next_tileid()
if night is None:
#- simulation obs time = now, even if sun is up
dateobs = time.gmtime()
night = get_night(utc=dateobs)
else:
#- 10pm on night YEARMMDD
dateobs = time.strptime(night+':22', '%Y%m%d:%H')
params = desimodel.io.load_desiparams()
if flavor == 'arc':
infile = os.getenv('DESI_ROOT')+'/spectro/templates/calib/v0.1/arc-lines-average.fits'
d = fits.getdata(infile, 1)
wave = d['AIRWAVE']
phot = d['ELECTRONS']
truth = dict(WAVE=wave)
meta = None
fibermap = desispec.io.fibermap.empty_fibermap(nspec)
for channel in ('B', 'R', 'Z'):
thru = desimodel.io.load_throughput(channel)
ii = np.where( (thru.wavemin <= wave) & (wave <= thru.wavemax) )[0]
truth['WAVE_'+channel] = wave[ii]
truth['PHOT_'+channel] = np.tile(phot[ii], nspec).reshape(nspec, len(ii))
elif flavor == 'flat':
infile = os.getenv('DESI_ROOT')+'/spectro/templates/calib/v0.1/flat-3100K-quartz-iodine.fits'
flux = fits.getdata(infile, 0)
hdr = fits.getheader(infile, 0)
wave = desispec.io.util.header2wave(hdr)
#- resample to 0.2 A grid
dw = 0.2
ww = np.arange(wave[0], wave[-1]+dw/2, dw)
flux = resample_flux(ww, wave, flux)
wave = ww
#- Convert to 2D for projection
flux = np.tile(flux, nspec).reshape(nspec, len(wave))
truth = dict(WAVE=wave, FLUX=flux)
meta = None
fibermap = desispec.io.fibermap.empty_fibermap(nspec)
for channel in ('B', 'R', 'Z'):
psf = desimodel.io.load_psf(channel)
thru = desimodel.io.load_throughput(channel)
ii = (psf.wmin <= wave) & (wave <= psf.wmax)
phot = thru.photons(wave[ii], flux[:,ii], units=hdr['BUNIT'], objtype='CALIB')
truth['WAVE_'+channel] = wave[ii]
truth['PHOT_'+channel] = phot
elif flavor == 'science':
fibermap, truth = get_targets(nspec, tileid=tileid)
flux = truth['FLUX']
wave = truth['WAVE']
nwave = len(wave)
if exptime is None:
exptime = params['exptime']
#- Load sky [Magic knowledge of units 1e-17 erg/s/cm2/A/arcsec2]
skyfile = os.getenv('DESIMODEL')+'/data/spectra/spec-sky.dat'
skywave, skyflux = np.loadtxt(skyfile, unpack=True)
skyflux = np.interp(wave, skywave, skyflux)
truth['SKYFLUX'] = skyflux
for channel in ('B', 'R', 'Z'):
thru = desimodel.io.load_throughput(channel)
ii = np.where( (thru.wavemin <= wave) & (wave <= thru.wavemax) )[0]
#- Project flux to photons
phot = thru.photons(wave[ii], flux[:,ii], units='1e-17 erg/s/cm2/A',
objtype=truth['OBJTYPE'], exptime=exptime,
airmass=airmass)
truth['PHOT_'+channel] = phot
truth['WAVE_'+channel] = wave[ii]
#- Project sky flux to photons
skyphot = thru.photons(wave[ii], skyflux[ii]*airmass,
units='1e-17 erg/s/cm2/A/arcsec2',
objtype='SKY', exptime=exptime, airmass=airmass)
#- 2D version
### truth['SKYPHOT_'+channel] = np.tile(skyphot, nspec).reshape((nspec, len(ii)))
#- 1D version
truth['SKYPHOT_'+channel] = skyphot.astype(np.float32)
#- NOTE: someday skyflux and skyphot may be 2D instead of 1D
#- Extract the metadata part of the truth dictionary into a table
columns = (
'OBJTYPE',
'REDSHIFT',
'TEMPLATEID',
'O2FLUX',
)
meta = _dict2ndarray(truth, columns)
#- (end indentation for arc/flat/science flavors)
#- Write fibermap
telera, teledec = io.get_tile_radec(tileid)
hdr = dict(
NIGHT = (night, 'Night of observation YEARMMDD'),
EXPID = (expid, 'DESI exposure ID'),
TILEID = (tileid, 'DESI tile ID'),
FLAVOR = (flavor, 'Flavor [arc, flat, science, ...]'),
TELERA = (telera, 'Telescope pointing RA [degrees]'),
TELEDEC = (teledec, 'Telescope pointing dec [degrees]'),
)
fiberfile = desispec.io.findfile('fibermap', night, expid)
desispec.io.write_fibermap(fiberfile, fibermap, header=hdr)
print fiberfile
#- Write simfile
hdr = dict(
AIRMASS=(airmass, 'Airmass at middle of exposure'),
EXPTIME=(exptime, 'Exposure time [sec]'),
FLAVOR=(flavor, 'exposure flavor [arc, flat, science]'),
)
simfile = io.write_simspec(meta, truth, expid, night, header=hdr)
print simfile
#- Update obslog that we succeeded with this exposure
update_obslog(flavor, expid, dateobs, tileid)
return fibermap, truth
def new_exposure(program,
nspec=5000,
night=None,
expid=None,
tileid=None,
nproc=None,
seed=None,
obsconditions=None,
specify_targets=dict(),
testslit=False,
exptime=None,
arc_lines_filename=None,
flat_spectrum_filename=None,
outdir=None,
config='desi',
telescope=None,
overwrite=False):
"""
Create a new exposure and output input simulation files.
Does not generate pixel-level simulations or noisy spectra.
Args:
program (str): 'arc', 'flat', 'bright', 'dark', 'bgs', 'mws', ...
nspec (int, optional): number of spectra to simulate
night (str, optional): YEARMMDD string
expid (int, optional): positive integer exposure ID
tileid (int, optional): integer tile ID
nproc (object, optional): What does this do?
seed (int, optional): random seed
obsconditions (str or dict-like, optional): see options below
specify_targets (dict of dicts, optional): Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
testslit (bool, optional): simulate test slit if True, default False; only for arc/flat
exptime (float, optional): exposure time [seconds], overrides obsconditions['EXPTIME']
arc_lines_filename (str, optional): use alternate arc lines filename (used if program="arc")
flat_spectrum_filename (str, optional): use alternate flat spectrum filename (used if program="flat")
outdir (str, optional): output directory
config (str, optional): the yaml configuration to load
telescope (str, optional): the telescope used (i.e. 1m, 160mm)
overwrite (bool, optional): optionally clobber existing files
Returns:
science: sim, fibermap, meta, obsconditions, objmeta
Writes to outdir or $DESI_SPECTRO_SIM/$PIXPROD/{night}/
* fibermap-{expid}.fits
* simspec-{expid}.fits
input obsconditions can be a string 'dark', 'gray', 'bright', or dict-like
observation metadata with keys SEEING (arcsec), EXPTIME (sec), AIRMASS,
MOONFRAC (0-1), MOONALT (deg), MOONSEP (deg). Output obsconditions is
is expanded dict-like structure.
program is used to pick the sky brightness, and is propagated to
desisim.targets.sample_objtype() to get the correct distribution of
targets for a given program, e.g. ELGs, LRGs, QSOs for program='dark'.
if program is 'arc' or 'flat', then `sim` is truth table with keys
FLUX and WAVE; and meta=None and obsconditions=None.
Also see simexp.simarc(), .simflat(), and .simscience(), the last of
which simulates a science exposure given surveysim obsconditions input,
fiber assignments, and pre-generated mock target spectra.
"""
if expid is None:
expid = get_next_expid()
if tileid is None:
tileid = get_next_tileid()
if night is None:
#- simulation obs time = now, even if sun is up
dateobs = time.gmtime()
night = get_night(utc=dateobs)
else:
#- 10pm on night YEARMMDD
night = str(night) #- just in case we got an integer instead of string
dateobs = time.strptime(night + ':22', '%Y%m%d:%H')
outsimspec = desisim.io.findfile('simspec', night, expid)
outfibermap = desisim.io.findfile('simfibermap', night, expid)
if outdir is not None:
outsimspec = os.path.join(outdir, os.path.basename(outsimspec))
outfibermap = os.path.join(outdir, os.path.basename(outfibermap))
program = program.lower()
log.debug('Generating {} targets'.format(nspec))
header = dict(NIGHT=night, EXPID=expid, PROGRAM=program)
if program in ('arc', 'flat'):
header['FLAVOR'] = program
else:
header['FLAVOR'] = 'science'
#- ISO 8601 DATE-OBS year-mm-ddThh:mm:ss
header['DATE-OBS'] = time.strftime('%FT%T', dateobs)
if program == 'arc':
if arc_lines_filename is None:
infile = os.getenv(
'DESI_ROOT'
) + '/spectro/templates/calib/v0.4/arc-lines-average-in-vacuum-from-winlight-20170118.fits'
else:
infile = arc_lines_filename
arcdata = fits.getdata(infile, 1)
if exptime is None:
exptime = 5
wave, phot, fibermap = desisim.simexp.simarc(arcdata,
nspec=nspec,
testslit=testslit)
header['EXPTIME'] = exptime
desisim.io.write_simspec_arc(outsimspec,
wave,
phot,
header,
fibermap=fibermap,
overwrite=overwrite)
fibermap.meta['NIGHT'] = night
fibermap.meta['EXPID'] = expid
desispec.io.write_fibermap(outfibermap, fibermap)
truth = dict(WAVE=wave, PHOT=phot, UNITS='photon')
return truth, fibermap, None, None, None
elif program == 'flat':
if flat_spectrum_filename is None:
infile = os.getenv(
'DESI_ROOT'
) + '/spectro/templates/calib/v0.4/flat-3100K-quartz-iodine.fits'
else:
infile = flat_spectrum_filename
if exptime is None:
exptime = 10
sim, fibermap = desisim.simexp.simflat(infile,
nspec=nspec,
exptime=exptime,
testslit=testslit,
psfconvolve=False)
header['EXPTIME'] = exptime
header['FLAVOR'] = 'flat'
desisim.io.write_simspec(sim,
truth=None,
fibermap=fibermap,
obs=None,
expid=expid,
night=night,
header=header,
filename=outsimspec,
overwrite=overwrite)
fibermap.meta['NIGHT'] = night
fibermap.meta['EXPID'] = expid
desispec.io.write_fibermap(outfibermap, fibermap)
# fluxunits = 1e-17 * u.erg / (u.s * u.cm**2 * u.Angstrom)
fluxunits = '1e-17 erg/(s * cm2 * Angstrom)'
flux = sim.simulated['source_flux'].to(fluxunits)
wave = sim.simulated['wavelength'].to('Angstrom')
truth = dict(WAVE=wave, FLUX=flux, UNITS=str(fluxunits))
return truth, fibermap, None, None, None
#- all other programs
fibermap, (flux, wave, meta,
objmeta) = get_targets_parallel(nspec,
program,
tileid=tileid,
nproc=nproc,
seed=seed,
specify_targets=specify_targets,
config=config,
telescope=telescope)
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
obsconditions = desisim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = desisim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = desisim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = desisim.simexp.reference_conditions[
obsconditions.upper()]
except KeyError:
raise ValueError('obsconditions {} not in {}'.format(
obsconditions.upper(),
list(desisim.simexp.reference_conditions.keys())))
if exptime is not None:
obsconditions['EXPTIME'] = exptime
desiparams = load_desiparams(config=config, telescope=telescope)
sim = simulate_spectra(wave,
flux,
fibermap=fibermap,
obsconditions=obsconditions,
psfconvolve=False,
specsim_config_file=config,
params=desiparams)
#- Write fibermap
telera, teledec = io.get_tile_radec(tileid)
hdr = dict(
NIGHT=(night, 'Night of observation YEARMMDD'),
EXPID=(expid, 'DESI exposure ID'),
TILEID=(tileid, 'DESI tile ID'),
PROGRAM=(program, 'program [dark, bright, ...]'),
FLAVOR=('science', 'Flavor [arc, flat, science, zero, ...]'),
TELRA=(telera, 'Telescope pointing RA [degrees]'),
TELDEC=(teledec, 'Telescope pointing dec [degrees]'),
AIRMASS=(obsconditions['AIRMASS'], 'Airmass at middle of exposure'),
EXPTIME=(obsconditions['EXPTIME'], 'Exposure time [sec]'),
SEEING=(obsconditions['SEEING'], 'Seeing FWHM [arcsec]'),
MOONFRAC=(obsconditions['MOONFRAC'],
'Moon illumination fraction 0-1; 1=full'),
MOONALT=(obsconditions['MOONALT'], 'Moon altitude [degrees]'),
MOONSEP=(obsconditions['MOONSEP'],
'Moon:tile separation angle [degrees]'),
)
hdr['DATE-OBS'] = (time.strftime('%FT%T', dateobs), 'Start of exposure')
simfile = io.write_simspec(sim,
meta,
fibermap,
obsconditions,
expid,
night,
objmeta=objmeta,
header=hdr,
filename=outsimspec,
overwrite=overwrite)
if not isinstance(fibermap, table.Table):
fibermap = table.Table(fibermap)
fibermap.meta.update(hdr)
desispec.io.write_fibermap(outfibermap, fibermap)
log.info('Wrote ' + outfibermap)
update_obslog(obstype='science',
program=program,
expid=expid,
dateobs=dateobs,
tileid=tileid)
return sim, fibermap, meta, obsconditions, objmeta
def get_targets(nspec, program, tileid=None, seed=None, specify_targets=dict(), specmin=0):
"""
Generates a set of targets for the requested program
Args:
nspec: (int) number of targets to generate
program: (str) program name DARK, BRIGHT, GRAY, MWS, BGS, LRG, ELG, ...
Options:
* tileid: (int) tileid, used for setting RA,dec
* seed: (int) random number seed
* specify_targets: (dict of dicts) Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
* specmin: (int) first spectrum number (0-indexed)
Returns:
* fibermap
* targets as tuple of (flux, wave, meta)
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
program = program.upper()
log.debug('Using random seed {}'.format(seed))
np.random.seed(seed)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec, program)
#- Get DESI wavelength coverage
try:
params = desimodel.io.load_desiparams()
wavemin = params['ccd']['b']['wavemin']
wavemax = params['ccd']['z']['wavemax']
except KeyError:
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
flux = np.zeros( (nspec, len(wave)) )
meta, _ = empty_metatable(nmodel=nspec, objtype='SKY')
objmeta = dict()
fibermap = empty_fibermap(nspec)
targetid = np.random.randint(sys.maxsize, size=nspec).astype(np.int64)
meta['TARGETID'] = targetid
fibermap['TARGETID'] = targetid
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
if objtype in specify_targets.keys():
obj_kwargs = specify_targets[objtype]
else:
obj_kwargs = dict()
# Simulate spectra
if objtype == 'SKY':
fibermap['DESI_TARGET'][ii] = desi_mask.SKY
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta1, objmeta1 = elg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.ELG
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta1, objmeta1 = lrg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.LRG
elif objtype == 'BGS':
from desisim.templates import BGS
bgs = BGS(wave=wave)
simflux, wave1, meta1, objmeta1 = bgs.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.BGS_ANY
fibermap['BGS_TARGET'][ii] = bgs_mask.BGS_BRIGHT
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta1, objmeta1 = qso.make_templates(nmodel=nobj, seed=seed, lyaforest=False, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
# Note by @moustakas: we can now do this using desisim/#150, but we are
# going to need 'noisy' photometry (because the QSO color-cuts
# explicitly avoid the stellar locus).
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
#from desitarget.cuts import isQSO
#star = STAR(wave=wave, colorcuts_function=isQSO)
star = STAR(wave=wave)
simflux, wave1, meta1, objmeta1 = star.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
elif objtype == 'STD':
from desisim.templates import STD
std = STD(wave=wave)
simflux, wave1, meta1, objmeta1 = std.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
#- Loop options for forwards/backwards compatibility
for name in ['STD_FAINT', 'STD_FSTAR', 'STD']:
if name in desi_mask.names():
fibermap['DESI_TARGET'][ii] |= desi_mask[name]
break
elif objtype == 'MWS_STAR':
from desisim.templates import MWS_STAR
mwsstar = MWS_STAR(wave=wave)
# TODO: mag ranges for different programs of STAR targets should be in desimodel
if 'magrange' not in obj_kwargs.keys():
obj_kwargs['magrange'] = (15.0,20.0)
simflux, wave1, meta1, objmeta1 = mwsstar.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] |= desi_mask.MWS_ANY
#- MWS bit names changed after desitarget 0.6.0 so use number
#- instead of name for now (bit 0 = mask 1 = MWS_MAIN currently)
fibermap['MWS_TARGET'][ii] = 1
else:
raise ValueError('Unable to simulate OBJTYPE={}'.format(objtype))
# Assign targetid
meta1['TARGETID'] = targetid[ii]
if hasattr(objmeta1, 'data'): # simqso.sqgrids.QsoSimPoints object
objmeta1.data['TARGETID'] = targetid[ii]
else:
if len(objmeta1) > 0:
objmeta1['TARGETID'] = targetid[ii]
# We want the dict key tied to the "true" object type (e.g., STAR),
# not, e.g., QSO_BAD.
objmeta[meta1['OBJTYPE'][0]] = objmeta1
flux[ii] = simflux
meta[ii] = meta1
for band in ['G', 'R', 'Z', 'W1', 'W2']:
key = 'FLUX_'+band
fibermap[key][ii] = meta[key][ii]
#- TODO: FLUX_IVAR
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][specmin:specmin+nspec]
y = fiberpos['Y'][specmin:specmin+nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][specmin:specmin+nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][specmin:specmin+nspec]
fibermap['TARGETCAT'] = np.zeros(nspec, dtype=(str, 20))
fibermap['LAMBDA_REF'] = np.ones(nspec, dtype=np.float32)*5400
fibermap['TARGET_RA'] = ra
fibermap['TARGET_DEC'] = dec
fibermap['FIBERASSIGN_X'] = x
fibermap['FIBERASSIGN_Y'] = y
fibermap['FIBER_RA'] = fibermap['TARGET_RA']
fibermap['FIBER_DEC'] = fibermap['TARGET_DEC']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, (flux, wave, meta, objmeta)
def get_targets(nspec, tileid=None):
"""
Returns:
fibermap
truth table
TODO: document this better
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec)
#- Get DESI wavelength coverage
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
truth = dict()
truth['FLUX'] = np.zeros( (nspec, len(wave)) )
truth['REDSHIFT'] = np.zeros(nspec, dtype='f4')
truth['TEMPLATEID'] = np.zeros(nspec, dtype='i4')
truth['O2FLUX'] = np.zeros(nspec, dtype='f4')
truth['OBJTYPE'] = np.zeros(nspec, dtype='S10')
#- Note: unlike other elements, first index of WAVE isn't spectrum index
truth['WAVE'] = wave
fibermap = empty_fibermap(nspec)
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
fibermap['OBJTYPE'][ii] = target_objtype[ii]
truth['OBJTYPE'][ii] = true_objtype[ii]
if objtype == 'SKY':
continue
try:
simflux, meta = io.read_templates(wave, objtype, len(ii))
except ValueError, err:
print err
continue
truth['FLUX'][ii] = simflux
#- STD don't have redshift Z; others do
#- In principle we should also have redshifts (radial velocities)
#- for standards as well.
if 'Z' in meta.dtype.names:
truth['REDSHIFT'][ii] = meta['Z']
elif objtype != 'STD':
print "No redshifts for", objtype, len(ii)
#- Only ELGs have [OII] flux
if objtype == 'ELG':
truth['O2FLUX'][ii] = meta['OII_3727']
#- Everyone had a templateid
truth['TEMPLATEID'][ii] = meta['TEMPLATEID']
#- Extract magnitudes from colors
#- TODO: make this more consistent at the input level
#- Standard Stars have SDSS magnitudes
if objtype == 'STD':
magr = meta['SDSS_R']
magi = magr - meta['SDSS_RI']
magz = magi - meta['SDSS_IZ']
magg = magr - meta['SDSS_GR'] #- R-G, not G-R ?
magu = magg - meta['SDSS_UG']
mags = np.vstack( [magu, magg, magr, magi, magz] ).T
filters = ['SDSS_U', 'SDSS_G', 'SDSS_R', 'SDSS_I', 'SDSS_Z']
fibermap['MAG'][ii] = mags
fibermap['FILTER'][ii] = filters
#- LRGs
elif objtype == 'LRG':
magz = meta['DECAM_Z']
magr = magz - meta['DECAM_RZ']
magw = magr - meta['DECAM_RW1']
fibermap['MAG'][ii, 0:3] = np.vstack( [magr, magz, magw] ).T
fibermap['FILTER'][ii, 0:3] = ['DECAM_R', 'DECAM_Z', 'WISE_W1']
#- ELGs
elif objtype == 'ELG':
magr = meta['DECAM_R']
magg = magr - meta['DECAM_GR']
magz = magr - meta['DECAM_RZ']
fibermap['MAG'][ii, 0:3] = np.vstack( [magg, magr, magz] ).T
fibermap['FILTER'][ii, 0:3] = ['DECAM_G', 'DECAM_R', 'DECAM_Z']
elif objtype == 'QSO':
#- QSO templates don't have magnitudes yet
pass
def get_targets(nspec, tileid=None):
"""
Returns:
fibermap
truth table
TODO (@moustakas): Deal with the random seed correctly.
TODO: document this better
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec)
#- Get DESI wavelength coverage
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
truth = dict()
truth['FLUX'] = np.zeros((nspec, len(wave)))
truth['REDSHIFT'] = np.zeros(nspec, dtype='f4')
truth['TEMPLATEID'] = np.zeros(nspec, dtype='i4')
truth['OIIFLUX'] = np.zeros(nspec, dtype='f4')
truth['D4000'] = np.zeros(nspec, dtype='f4')
truth['VDISP'] = np.zeros(nspec, dtype='f4')
truth['OBJTYPE'] = np.zeros(nspec, dtype='S10')
#- Note: unlike other elements, first index of WAVE isn't spectrum index
truth['WAVE'] = wave
fibermap = empty_fibermap(nspec)
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
truth['OBJTYPE'][ii] = true_objtype[ii]
# Simulate spectra
if objtype == 'SKY':
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta = elg.make_templates(nmodel=nobj)
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta = lrg.make_templates(nmodel=nobj)
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta = qso.make_templates(nmodel=nobj)
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
star = STAR(wave=wave)
simflux, wave1, meta = star.make_templates(nmodel=nobj)
elif objtype == 'STD':
from desisim.templates import STAR
star = STAR(wave=wave, FSTD=True)
simflux, wave1, meta = star.make_templates(nmodel=nobj)
truth['FLUX'][ii] = 1e17 * simflux
truth['UNITS'] = '1e-17 erg/s/cm2/A'
truth['TEMPLATEID'][ii] = meta['TEMPLATEID']
truth['REDSHIFT'][ii] = meta['REDSHIFT']
# Pack in the photometry. TODO: Include WISE.
magg = meta['GMAG']
magr = meta['RMAG']
magz = meta['ZMAG']
fibermap['MAG'][ii, 0:3] = np.vstack([magg, magr, magz]).T
fibermap['FILTER'][ii, 0:3] = ['DECAM_G', 'DECAM_R', 'DECAM_Z']
if objtype == 'ELG':
truth['OIIFLUX'][ii] = meta['OIIFLUX']
truth['D4000'][ii] = meta['D4000']
truth['VDISP'][ii] = meta['VDISP']
if objtype == 'LRG':
truth['D4000'][ii] = meta['D4000']
truth['VDISP'][ii] = meta['VDISP']
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][0:nspec]
y = fiberpos['Y'][0:nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][0:nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][0:nspec]
fibermap['TARGETID'] = np.random.randint(sys.maxint, size=nspec)
fibermap['TARGETCAT'] = np.zeros(nspec, dtype='|S20')
fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
fibermap['TARGET_MASK0'] = np.zeros(nspec, dtype='i8')
fibermap['RA_TARGET'] = ra
fibermap['DEC_TARGET'] = dec
fibermap['X_TARGET'] = x
fibermap['Y_TARGET'] = y
fibermap['X_FVCOBS'] = fibermap['X_TARGET']
fibermap['Y_FVCOBS'] = fibermap['Y_TARGET']
fibermap['X_FVCERR'] = np.zeros(nspec, dtype=np.float32)
fibermap['Y_FVCERR'] = np.zeros(nspec, dtype=np.float32)
fibermap['RA_OBS'] = fibermap['RA_TARGET']
fibermap['DEC_OBS'] = fibermap['DEC_TARGET']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, truth
def get_targets(nspec, tileid=None):
"""
Returns:
fibermap
truth table
TODO (@moustakas): Deal with the random seed correctly.
TODO: document this better
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
# - Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec)
# - Get DESI wavelength coverage
wavemin = desimodel.io.load_throughput("b").wavemin
wavemax = desimodel.io.load_throughput("z").wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
truth = dict()
truth["FLUX"] = np.zeros((nspec, len(wave)))
truth["REDSHIFT"] = np.zeros(nspec, dtype="f4")
truth["TEMPLATEID"] = np.zeros(nspec, dtype="i4")
truth["OIIFLUX"] = np.zeros(nspec, dtype="f4")
truth["D4000"] = np.zeros(nspec, dtype="f4")
truth["OBJTYPE"] = np.zeros(nspec, dtype="S10")
# - Note: unlike other elements, first index of WAVE isn't spectrum index
truth["WAVE"] = wave
fibermap = empty_fibermap(nspec)
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap["OBJTYPE"][ii] = target_objtype[ii]
truth["OBJTYPE"][ii] = true_objtype[ii]
# Simulate spectra
if objtype == "SKY":
continue
elif objtype == "ELG":
from desisim.templates import ELG
elg = ELG(nmodel=nobj, wave=wave)
simflux, wave1, meta = elg.make_templates()
elif objtype == "LRG":
from desisim.templates import LRG
lrg = LRG(nmodel=nobj, wave=wave)
simflux, wave1, meta = lrg.make_templates()
elif objtype == "QSO":
from desisim.templates import QSO
qso = QSO(nmodel=nobj, wave=wave)
simflux, wave1, meta = qso.make_templates()
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
elif objtype == "QSO_BAD":
from desisim.templates import STAR
star = STAR(nmodel=nobj, wave=wave)
simflux, wave1, meta = star.make_templates()
elif objtype == "STD":
from desisim.templates import STAR
star = STAR(nmodel=nobj, wave=wave, FSTD=True)
simflux, wave1, meta = star.make_templates()
truth["FLUX"][ii] = simflux
truth["TEMPLATEID"][ii] = meta["TEMPLATEID"]
truth["REDSHIFT"][ii] = meta["REDSHIFT"]
# Pack in the photometry. TODO: Include WISE.
magg = meta["GMAG"]
magr = meta["RMAG"]
magz = meta["ZMAG"]
fibermap["MAG"][ii, 0:3] = np.vstack([magg, magr, magz]).T
fibermap["FILTER"][ii, 0:3] = ["DECAM_G", "DECAM_R", "DECAM_Z"]
if objtype == "ELG":
truth["OIIFLUX"][ii] = meta["OIIFLUX"]
truth["D4000"][ii] = meta["D4000"]
if objtype == "LRG":
truth["D4000"][ii] = meta["D4000"]
# - Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
# - Where are these targets? Centered on positioners for now.
x = fiberpos["X"][0:nspec]
y = fiberpos["Y"][0:nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
# - Fill in the rest of the fibermap structure
fibermap["FIBER"] = np.arange(nspec, dtype="i4")
fibermap["POSITIONER"] = fiberpos["POSITIONER"][0:nspec]
fibermap["SPECTROID"] = fiberpos["SPECTROGRAPH"][0:nspec]
fibermap["TARGETID"] = np.random.randint(sys.maxint, size=nspec)
fibermap["TARGETCAT"] = np.zeros(nspec, dtype="|S20")
fibermap["LAMBDAREF"] = np.ones(nspec, dtype=np.float32) * 5400
fibermap["TARGET_MASK0"] = np.zeros(nspec, dtype="i8")
fibermap["RA_TARGET"] = ra
fibermap["DEC_TARGET"] = dec
fibermap["X_TARGET"] = x
fibermap["Y_TARGET"] = y
fibermap["X_FVCOBS"] = fibermap["X_TARGET"]
fibermap["Y_FVCOBS"] = fibermap["Y_TARGET"]
fibermap["X_FVCERR"] = np.zeros(nspec, dtype=np.float32)
fibermap["Y_FVCERR"] = np.zeros(nspec, dtype=np.float32)
fibermap["RA_OBS"] = fibermap["RA_TARGET"]
fibermap["DEC_OBS"] = fibermap["DEC_TARGET"]
fibermap["BRICKNAME"] = brick.brickname(ra, dec)
return fibermap, truth
def get_targets(nspec,
program,
tileid=None,
seed=None,
specify_targets=dict(),
specmin=0):
"""
Generates a set of targets for the requested program
Args:
nspec: (int) number of targets to generate
program: (str) program name DARK, BRIGHT, GRAY, MWS, BGS, LRG, ELG, ...
Options:
* tileid: (int) tileid, used for setting RA,dec
* seed: (int) random number seed
* specify_targets: (dict of dicts) Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
* specmin: (int) first spectrum number (0-indexed)
Returns:
* fibermap
* targets as tuple of (flux, wave, meta)
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
program = program.upper()
log.debug('Using random seed {}'.format(seed))
np.random.seed(seed)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec, program)
#- Get DESI wavelength coverage
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
flux = np.zeros((nspec, len(wave)))
meta = empty_metatable(nmodel=nspec, objtype='SKY')
fibermap = empty_fibermap(nspec)
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
if objtype in specify_targets.keys():
obj_kwargs = specify_targets[objtype]
else:
obj_kwargs = dict()
# Simulate spectra
if objtype == 'SKY':
fibermap['DESI_TARGET'][ii] = desi_mask.SKY
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta1 = elg.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.ELG
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta1 = lrg.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.LRG
elif objtype == 'BGS':
from desisim.templates import BGS
bgs = BGS(wave=wave)
simflux, wave1, meta1 = bgs.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.BGS_ANY
fibermap['BGS_TARGET'][ii] = bgs_mask.BGS_BRIGHT
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta1 = qso.make_templates(nmodel=nobj,
seed=seed,
lyaforest=False,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
# Note by @moustakas: we can now do this using desisim/#150, but we are
# going to need 'noisy' photometry (because the QSO color-cuts
# explicitly avoid the stellar locus).
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
#from desitarget.cuts import isQSO
#star = STAR(wave=wave, colorcuts_function=isQSO)
star = STAR(wave=wave)
simflux, wave1, meta1 = star.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
elif objtype == 'STD':
from desisim.templates import FSTD
fstd = FSTD(wave=wave)
simflux, wave1, meta1 = fstd.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.STD_FSTAR
elif objtype == 'MWS_STAR':
from desisim.templates import MWS_STAR
mwsstar = MWS_STAR(wave=wave)
# todo: mag ranges for different programs of STAR targets should be in desimodel
if 'rmagrange' not in obj_kwargs.keys():
obj_kwargs['rmagrange'] = (15.0, 20.0)
simflux, wave1, meta1 = mwsstar.make_templates(nmodel=nobj,
seed=seed,
**obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.MWS_ANY
#- MWS bit names changed after desitarget 0.6.0 so use number
#- instead of name for now (bit 0 = mask 1 = MWS_MAIN currently)
fibermap['MWS_TARGET'][ii] = 1
else:
raise ValueError('Unable to simulate OBJTYPE={}'.format(objtype))
flux[ii] = simflux
meta[ii] = meta1
fibermap['FILTER'][ii, :6] = [
'DECAM_G', 'DECAM_R', 'DECAM_Z', 'WISE_W1', 'WISE_W2'
]
fibermap['MAG'][ii, 0] = 22.5 - 2.5 * np.log10(meta['FLUX_G'][ii])
fibermap['MAG'][ii, 1] = 22.5 - 2.5 * np.log10(meta['FLUX_R'][ii])
fibermap['MAG'][ii, 2] = 22.5 - 2.5 * np.log10(meta['FLUX_Z'][ii])
fibermap['MAG'][ii, 3] = 22.5 - 2.5 * np.log10(meta['FLUX_W1'][ii])
fibermap['MAG'][ii, 4] = 22.5 - 2.5 * np.log10(meta['FLUX_W2'][ii])
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][specmin:specmin + nspec]
y = fiberpos['Y'][specmin:specmin + nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][specmin:specmin + nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][specmin:specmin + nspec]
fibermap['TARGETID'] = np.random.randint(sys.maxsize, size=nspec)
fibermap['TARGETCAT'] = np.zeros(nspec, dtype=(str, 20))
fibermap['LAMBDAREF'] = np.ones(nspec, dtype=np.float32) * 5400
fibermap['RA_TARGET'] = ra
fibermap['DEC_TARGET'] = dec
fibermap['X_TARGET'] = x
fibermap['Y_TARGET'] = y
fibermap['X_FVCOBS'] = fibermap['X_TARGET']
fibermap['Y_FVCOBS'] = fibermap['Y_TARGET']
fibermap['X_FVCERR'] = np.zeros(nspec, dtype=np.float32)
fibermap['Y_FVCERR'] = np.zeros(nspec, dtype=np.float32)
fibermap['RA_OBS'] = fibermap['RA_TARGET']
fibermap['DEC_OBS'] = fibermap['DEC_TARGET']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, (flux, wave, meta)
def get_targets(nspec, tileid=None):
"""
Returns:
fibermap
truth table
TODO: document this better
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec)
#- Get DESI wavelength coverage
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
truth = dict()
truth['FLUX'] = np.zeros((nspec, len(wave)))
truth['REDSHIFT'] = np.zeros(nspec, dtype='f4')
truth['TEMPLATEID'] = np.zeros(nspec, dtype='i4')
truth['O2FLUX'] = np.zeros(nspec, dtype='f4')
truth['OBJTYPE'] = np.zeros(nspec, dtype='S10')
#- Note: unlike other elements, first index of WAVE isn't spectrum index
truth['WAVE'] = wave
fibermap = empty_fibermap(nspec)
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
fibermap['OBJTYPE'][ii] = target_objtype[ii]
truth['OBJTYPE'][ii] = true_objtype[ii]
if objtype == 'SKY':
continue
try:
simflux, meta = io.read_templates(wave, objtype, len(ii))
except ValueError, err:
print err
continue
truth['FLUX'][ii] = simflux
#- STD don't have redshift Z; others do
#- In principle we should also have redshifts (radial velocities)
#- for standards as well.
if 'Z' in meta.dtype.names:
truth['REDSHIFT'][ii] = meta['Z']
elif objtype != 'STD':
print "No redshifts for", objtype, len(ii)
#- Only ELGs have [OII] flux
if objtype == 'ELG':
truth['O2FLUX'][ii] = meta['OII_3727']
#- Everyone had a templateid
truth['TEMPLATEID'][ii] = meta['TEMPLATEID']
#- Extract magnitudes from colors
#- TODO: make this more consistent at the input level
#- Standard Stars have SDSS magnitudes
if objtype == 'STD':
magr = meta['SDSS_R']
magi = magr - meta['SDSS_RI']
magz = magi - meta['SDSS_IZ']
magg = magr - meta['SDSS_GR'] #- R-G, not G-R ?
magu = magg - meta['SDSS_UG']
mags = np.vstack([magu, magg, magr, magi, magz]).T
filters = ['SDSS_U', 'SDSS_G', 'SDSS_R', 'SDSS_I', 'SDSS_Z']
fibermap['MAG'][ii] = mags
fibermap['FILTER'][ii] = filters
#- LRGs
elif objtype == 'LRG':
magz = meta['DECAM_Z']
magr = magz - meta['DECAM_RZ']
magw = magr - meta['DECAM_RW1']
fibermap['MAG'][ii, 0:3] = np.vstack([magr, magz, magw]).T
fibermap['FILTER'][ii, 0:3] = ['DECAM_R', 'DECAM_Z', 'WISE_W1']
#- ELGs
elif objtype == 'ELG':
magr = meta['DECAM_R']
magg = magr - meta['DECAM_GR']
magz = magr - meta['DECAM_RZ']
fibermap['MAG'][ii, 0:3] = np.vstack([magg, magr, magz]).T
fibermap['FILTER'][ii, 0:3] = ['DECAM_G', 'DECAM_R', 'DECAM_Z']
elif objtype == 'QSO':
#- QSO templates don't have magnitudes yet
pass
def get_targets(nspec, program, tileid=None, seed=None, specify_targets=dict(), specmin=0):
"""
Generates a set of targets for the requested program
Args:
nspec: (int) number of targets to generate
program: (str) program name DARK, BRIGHT, GRAY, MWS, BGS, LRG, ELG, ...
Options:
* tileid: (int) tileid, used for setting RA,dec
* seed: (int) random number seed
* specify_targets: (dict of dicts) Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
* specmin: (int) first spectrum number (0-indexed)
Returns:
* fibermap
* targets as tuple of (flux, wave, meta)
"""
if tileid is None:
tile_ra, tile_dec = 0.0, 0.0
else:
tile_ra, tile_dec = io.get_tile_radec(tileid)
program = program.upper()
log.debug('Using random seed {}'.format(seed))
np.random.seed(seed)
#- Get distribution of target types
true_objtype, target_objtype = sample_objtype(nspec, program)
#- Get DESI wavelength coverage
try:
params = desimodel.io.load_desiparams()
wavemin = params['ccd']['b']['wavemin']
wavemax = params['ccd']['z']['wavemax']
except KeyError:
wavemin = desimodel.io.load_throughput('b').wavemin
wavemax = desimodel.io.load_throughput('z').wavemax
dw = 0.2
wave = np.arange(round(wavemin, 1), wavemax, dw)
nwave = len(wave)
flux = np.zeros( (nspec, len(wave)) )
meta, _ = empty_metatable(nmodel=nspec, objtype='SKY')
objmeta = dict()
fibermap = empty_fibermap(nspec)
targetid = np.random.randint(sys.maxsize, size=nspec).astype(np.int64)
meta['TARGETID'] = targetid
fibermap['TARGETID'] = targetid
for objtype in set(true_objtype):
ii = np.where(true_objtype == objtype)[0]
nobj = len(ii)
fibermap['OBJTYPE'][ii] = target_objtype[ii]
if objtype in specify_targets.keys():
obj_kwargs = specify_targets[objtype]
else:
obj_kwargs = dict()
# Simulate spectra
if objtype == 'SKY':
fibermap['DESI_TARGET'][ii] = desi_mask.SKY
continue
elif objtype == 'ELG':
from desisim.templates import ELG
elg = ELG(wave=wave)
simflux, wave1, meta1, objmeta1 = elg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.ELG
elif objtype == 'LRG':
from desisim.templates import LRG
lrg = LRG(wave=wave)
simflux, wave1, meta1, objmeta1 = lrg.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.LRG
elif objtype == 'BGS':
from desisim.templates import BGS
bgs = BGS(wave=wave)
simflux, wave1, meta1, objmeta1 = bgs.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.BGS_ANY
fibermap['BGS_TARGET'][ii] = bgs_mask.BGS_BRIGHT
elif objtype == 'QSO':
from desisim.templates import QSO
qso = QSO(wave=wave)
simflux, wave1, meta1, objmeta1 = qso.make_templates(nmodel=nobj, seed=seed, lyaforest=False, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
# For a "bad" QSO simulate a normal star without color cuts, which isn't
# right. We need to apply the QSO color-cuts to the normal stars to pull
# out the correct population of contaminating stars.
# Note by @moustakas: we can now do this using desisim/#150, but we are
# going to need 'noisy' photometry (because the QSO color-cuts
# explicitly avoid the stellar locus).
elif objtype == 'QSO_BAD':
from desisim.templates import STAR
#from desitarget.cuts import isQSO
#star = STAR(wave=wave, colorcuts_function=isQSO)
star = STAR(wave=wave)
simflux, wave1, meta1, objmeta1 = star.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] = desi_mask.QSO
elif objtype == 'STD':
from desisim.templates import STD
std = STD(wave=wave)
simflux, wave1, meta1, objmeta1 = std.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
#- Loop options for forwards/backwards compatibility
for name in ['STD_FAINT', 'STD_FSTAR', 'STD']:
if name in desi_mask.names():
fibermap['DESI_TARGET'][ii] |= desi_mask[name]
break
elif objtype == 'MWS_STAR':
from desisim.templates import MWS_STAR
mwsstar = MWS_STAR(wave=wave)
# TODO: mag ranges for different programs of STAR targets should be in desimodel
if 'magrange' not in obj_kwargs.keys():
obj_kwargs['magrange'] = (15.0,20.0)
simflux, wave1, meta1, objmeta1 = mwsstar.make_templates(nmodel=nobj, seed=seed, **obj_kwargs)
fibermap['DESI_TARGET'][ii] |= desi_mask.MWS_ANY
#- MWS bit names changed after desitarget 0.6.0 so use number
#- instead of name for now (bit 0 = mask 1 = MWS_MAIN currently)
fibermap['MWS_TARGET'][ii] = 1
else:
raise ValueError('Unable to simulate OBJTYPE={}'.format(objtype))
# Assign targetid
meta1['TARGETID'] = targetid[ii]
if hasattr(objmeta1, 'data'): # simqso.sqgrids.QsoSimPoints object
objmeta1.data['TARGETID'] = targetid[ii]
else:
if len(objmeta1) > 0:
objmeta1['TARGETID'] = targetid[ii]
# We want the dict key tied to the "true" object type (e.g., STAR),
# not, e.g., QSO_BAD.
objmeta[meta1['OBJTYPE'][0]] = objmeta1
flux[ii] = simflux
meta[ii] = meta1
for band in ['G', 'R', 'Z', 'W1', 'W2']:
key = 'FLUX_'+band
fibermap[key][ii] = meta[key][ii]
#- TODO: FLUX_IVAR
#- Load fiber -> positioner mapping and tile information
fiberpos = desimodel.io.load_fiberpos()
#- Where are these targets? Centered on positioners for now.
x = fiberpos['X'][specmin:specmin+nspec]
y = fiberpos['Y'][specmin:specmin+nspec]
fp = FocalPlane(tile_ra, tile_dec)
ra = np.zeros(nspec)
dec = np.zeros(nspec)
for i in range(nspec):
ra[i], dec[i] = fp.xy2radec(x[i], y[i])
#- Fill in the rest of the fibermap structure
fibermap['FIBER'] = np.arange(nspec, dtype='i4')
fibermap['POSITIONER'] = fiberpos['POSITIONER'][specmin:specmin+nspec]
fibermap['SPECTROID'] = fiberpos['SPECTROGRAPH'][specmin:specmin+nspec]
fibermap['TARGETCAT'] = np.zeros(nspec, dtype=(str, 20))
fibermap['LAMBDA_REF'] = np.ones(nspec, dtype=np.float32)*5400
fibermap['TARGET_RA'] = ra
fibermap['TARGET_DEC'] = dec
fibermap['DESIGN_X'] = x
fibermap['DESIGN_Y'] = y
fibermap['FIBER_RA'] = fibermap['TARGET_RA']
fibermap['FIBER_DEC'] = fibermap['TARGET_DEC']
fibermap['BRICKNAME'] = brick.brickname(ra, dec)
return fibermap, (flux, wave, meta, objmeta)
def new_exposure(program, nspec=5000, night=None, expid=None, tileid=None,
nproc=None, seed=None, obsconditions=None,
specify_targets=dict(), testslit=False, exptime=None,
arc_lines_filename=None, flat_spectrum_filename=None,
outdir=None, overwrite=False):
"""
Create a new exposure and output input simulation files.
Does not generate pixel-level simulations or noisy spectra.
Args:
program (str): 'arc', 'flat', 'bright', 'dark', 'bgs', 'mws', ...
nspec (int, optional): number of spectra to simulate
night (str, optional): YEARMMDD string
expid (int, optional): positive integer exposure ID
tileid (int, optional): integer tile ID
nproc (object, optional): What does this do?
seed (int, optional): random seed
obsconditions (str or dict-like, optional): see options below
specify_targets (dict of dicts, optional): Define target properties like magnitude and redshift
for each target class. Each objtype has its own key,value pair
see simspec.templates.specify_galparams_dict()
or simsepc.templates.specify_starparams_dict()
testslit (bool, optional): simulate test slit if True, default False; only for arc/flat
exptime (float, optional): exposure time [seconds], overrides obsconditions['EXPTIME']
arc_lines_filename (str, optional): use alternate arc lines filename (used if program="arc")
flat_spectrum_filename (str, optional): use alternate flat spectrum filename (used if program="flat")
outdir (str, optional): output directory
overwrite (bool, optional): optionally clobber existing files
Returns:
science: sim, fibermap, meta, obsconditions, objmeta
Writes to outdir or $DESI_SPECTRO_SIM/$PIXPROD/{night}/
* fibermap-{expid}.fits
* simspec-{expid}.fits
input obsconditions can be a string 'dark', 'gray', 'bright', or dict-like
observation metadata with keys SEEING (arcsec), EXPTIME (sec), AIRMASS,
MOONFRAC (0-1), MOONALT (deg), MOONSEP (deg). Output obsconditions is
is expanded dict-like structure.
program is used to pick the sky brightness, and is propagated to
desisim.targets.sample_objtype() to get the correct distribution of
targets for a given program, e.g. ELGs, LRGs, QSOs for program='dark'.
if program is 'arc' or 'flat', then `sim` is truth table with keys
FLUX and WAVE; and meta=None and obsconditions=None.
Also see simexp.simarc(), .simflat(), and .simscience(), the last of
which simulates a science exposure given surveysim obsconditions input,
fiber assignments, and pre-generated mock target spectra.
"""
if expid is None:
expid = get_next_expid()
if tileid is None:
tileid = get_next_tileid()
if night is None:
#- simulation obs time = now, even if sun is up
dateobs = time.gmtime()
night = get_night(utc=dateobs)
else:
#- 10pm on night YEARMMDD
night = str(night) #- just in case we got an integer instead of string
dateobs = time.strptime(night+':22', '%Y%m%d:%H')
outsimspec = desisim.io.findfile('simspec', night, expid)
outfibermap = desisim.io.findfile('simfibermap', night, expid)
if outdir is not None:
outsimspec = os.path.join(outdir, os.path.basename(outsimspec))
outfibermap = os.path.join(outdir, os.path.basename(outfibermap))
program = program.lower()
log.debug('Generating {} targets'.format(nspec))
header = dict(NIGHT=night, EXPID=expid, PROGRAM=program)
if program in ('arc', 'flat'):
header['FLAVOR'] = program
else:
header['FLAVOR'] = 'science'
#- ISO 8601 DATE-OBS year-mm-ddThh:mm:ss
header['DATE-OBS'] = time.strftime('%FT%T', dateobs)
if program == 'arc':
if arc_lines_filename is None :
infile = os.getenv('DESI_ROOT')+'/spectro/templates/calib/v0.4/arc-lines-average-in-vacuum-from-winlight-20170118.fits'
else :
infile = arc_lines_filename
arcdata = fits.getdata(infile, 1)
if exptime is None:
exptime = 5
wave, phot, fibermap = desisim.simexp.simarc(arcdata, nspec=nspec, testslit=testslit)
header['EXPTIME'] = exptime
desisim.io.write_simspec_arc(outsimspec, wave, phot, header, fibermap=fibermap, overwrite=overwrite)
fibermap.meta['NIGHT'] = night
fibermap.meta['EXPID'] = expid
desispec.io.write_fibermap(outfibermap, fibermap)
truth = dict(WAVE=wave, PHOT=phot, UNITS='photon')
return truth, fibermap, None, None, None
elif program == 'flat':
if flat_spectrum_filename is None :
infile = os.getenv('DESI_ROOT')+'/spectro/templates/calib/v0.4/flat-3100K-quartz-iodine.fits'
else :
infile = flat_spectrum_filename
if exptime is None:
exptime = 10
sim, fibermap = desisim.simexp.simflat(infile, nspec=nspec,
exptime=exptime, testslit=testslit, psfconvolve=False)
header['EXPTIME'] = exptime
header['FLAVOR'] = 'flat'
desisim.io.write_simspec(sim, truth=None, fibermap=fibermap, obs=None,
expid=expid, night=night, header=header, filename=outsimspec, overwrite=overwrite)
fibermap.meta['NIGHT'] = night
fibermap.meta['EXPID'] = expid
desispec.io.write_fibermap(outfibermap, fibermap)
# fluxunits = 1e-17 * u.erg / (u.s * u.cm**2 * u.Angstrom)
fluxunits = '1e-17 erg/(s * cm2 * Angstrom)'
flux = sim.simulated['source_flux'].to(fluxunits)
wave = sim.simulated['wavelength'].to('Angstrom')
truth = dict(WAVE=wave, FLUX=flux, UNITS=str(fluxunits))
return truth, fibermap, None, None, None
#- all other programs
fibermap, (flux, wave, meta, objmeta) = get_targets_parallel(nspec, program,
tileid=tileid, nproc=nproc, seed=seed, specify_targets=specify_targets)
if obsconditions is None:
if program in ['dark', 'lrg', 'qso']:
obsconditions = desisim.simexp.reference_conditions['DARK']
elif program in ['elg', 'gray', 'grey']:
obsconditions = desisim.simexp.reference_conditions['GRAY']
elif program in ['mws', 'bgs', 'bright']:
obsconditions = desisim.simexp.reference_conditions['BRIGHT']
else:
raise ValueError('unknown program {}'.format(program))
elif isinstance(obsconditions, str):
try:
obsconditions = desisim.simexp.reference_conditions[obsconditions.upper()]
except KeyError:
raise ValueError('obsconditions {} not in {}'.format(
obsconditions.upper(),
list(desisim.simexp.reference_conditions.keys())))
if exptime is not None:
obsconditions['EXPTIME'] = exptime
sim = simulate_spectra(wave, flux, fibermap=fibermap,
obsconditions=obsconditions, psfconvolve=False)
#- Write fibermap
telera, teledec = io.get_tile_radec(tileid)
hdr = dict(
NIGHT = (night, 'Night of observation YEARMMDD'),
EXPID = (expid, 'DESI exposure ID'),
TILEID = (tileid, 'DESI tile ID'),
PROGRAM = (program, 'program [dark, bright, ...]'),
FLAVOR = ('science', 'Flavor [arc, flat, science, zero, ...]'),
TELRA = (telera, 'Telescope pointing RA [degrees]'),
TELDEC = (teledec, 'Telescope pointing dec [degrees]'),
AIRMASS = (obsconditions['AIRMASS'], 'Airmass at middle of exposure'),
EXPTIME = (obsconditions['EXPTIME'], 'Exposure time [sec]'),
SEEING = (obsconditions['SEEING'], 'Seeing FWHM [arcsec]'),
MOONFRAC = (obsconditions['MOONFRAC'], 'Moon illumination fraction 0-1; 1=full'),
MOONALT = (obsconditions['MOONALT'], 'Moon altitude [degrees]'),
MOONSEP = (obsconditions['MOONSEP'], 'Moon:tile separation angle [degrees]'),
)
hdr['DATE-OBS'] = (time.strftime('%FT%T', dateobs), 'Start of exposure')
simfile = io.write_simspec(sim, meta, fibermap, obsconditions,
expid, night, objmeta=objmeta, header=hdr,
filename=outsimspec, overwrite=overwrite)
if not isinstance(fibermap, table.Table):
fibermap = table.Table(fibermap)
fibermap.meta.update(hdr)
desispec.io.write_fibermap(outfibermap, fibermap)
log.info('Wrote '+outfibermap)
update_obslog(obstype='science', program=program, expid=expid, dateobs=dateobs, tileid=tileid)
return sim, fibermap, meta, obsconditions, objmeta