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
import matplotlib.pyplot as plt
import numpy as np
import os
import subprocess
import psql
import targets
import plots
#load from \o psql dump
m_gal= psql.load_ofile('../truth_table_results/deep2_f2_matched_galaxy.txt',str_kws=['type'])
m_star= psql.load_ofile('../truth_table_results/deep2_f2_matched_star.txt',str_kws=['type'])
un_gal= psql.load_ofile('../truth_table_results/deep2_f2_unmatched_galaxy.txt')
un_star= psql.load_ofile('../truth_table_results/deep2_f2_unmatched_star.txt')
#matched have dr2 tractor catalog data, color cut
targets.get_targets(m_gal)
targets.get_targets(m_star)
#base name for pngs
base= 'truth_v_dr2_'
#locations of matched, unmatched objects
kwargs=dict(s=50,facecolors='none',linewidths=2.)
plt.scatter(m_gal['dr2_ra'],m_gal['dr2_dec'],edgecolors='b',label='m_gal',marker='o',**kwargs)
plt.scatter(m_star['dr2_ra'],m_star['dr2_dec'],edgecolors='m',label='m_star',marker='*',**kwargs)
plt.scatter(un_gal['ra'],un_gal['dec'],edgecolors='g',label='un_gal',marker='o',**kwargs)
plt.scatter(un_star['ra'],un_star['dec'],edgecolors='y',label='un_star',marker='*',**kwargs)
plt.legend(loc=(1.01,0),scatterpoints=1)
plt.savefig('./'+base+'radec.png')
plt.close()
#r-z vs. g-r plot
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
import subprocess
import psql
import targets
import plots
#load from \o psql dump
m_gal = psql.load_ofile('../truth_table_results/deep2_f2_matched_galaxy.txt',
str_kws=['type'])
m_star = psql.load_ofile('../truth_table_results/deep2_f2_matched_star.txt',
str_kws=['type'])
un_gal = psql.load_ofile(
'../truth_table_results/deep2_f2_unmatched_galaxy.txt')
un_star = psql.load_ofile('../truth_table_results/deep2_f2_unmatched_star.txt')
#matched have dr2 tractor catalog data, color cut
targets.get_targets(m_gal)
targets.get_targets(m_star)
#base name for pngs
base = 'truth_v_dr2_'
#locations of matched, unmatched objects
kwargs = dict(s=50, facecolors='none', linewidths=2.)
plt.scatter(m_gal['dr2_ra'],
m_gal['dr2_dec'],
edgecolors='b',
label='m_gal',
marker='o',
**kwargs)
plt.scatter(m_star['dr2_ra'],
m_star['dr2_dec'],
edgecolors='m',
