def decam_sky(overwrite=False):
''' read in decam sky data
'''
fpickle = os.path.join(UT.dat_dir(), 'decam_sky.p')
if not os.path.isfile(fpickle) or overwrite:
fdecam = fits.open(os.path.join(UT.dat_dir(), 'decalsobs-zpt-dr3-allv2.fits'))
_decam = fdecam[1].data
keep = ((_decam['AIRMASS'] != 0.0) &
(_decam['TRANSP'] > .75) &
(_decam['TRANSP'] < 1.3))
decam = {}
for k in _decam.names:
decam[k.lower()] = _decam[k][keep]
# calculate moon altitude and moon separation
time = astropy.time.Time(decam['date'], format='jd')
location = astropy.coordinates.EarthLocation.from_geodetic(
lat='-30d10m10.78s', lon='-70d48m23.49s', height=2241.4*u.m)
moon_position = astropy.coordinates.get_moon(time, location)
moon_ra = moon_position.ra.value
moon_dec = moon_position.dec.value
moon_position_altaz = moon_position.transform_to(astropy.coordinates.AltAz(obstime=time, location=location))
decam['moon_alt'] = moon_position_altaz.alt.value
sun_position = astropy.coordinates.get_sun(time)
decam['moon_sun_sep'] = sun_position.separation(moon_position).deg
pickle.dump(decam, open(fpickle, 'wb'))
else:
decam = pickle.load(open(fpickle, 'rb'))
return decam
def _twilight_coeffs():
''' save twilight coefficients from Parker
'''
import pandas as pd
f = os.path.join(UT.code_dir(), 'dat', 'sky', 'MoonResults.csv')
coeffs = pd.DataFrame.from_csv(f)
coeffs.columns = [
'wl', 'model', 'data_var', 'unexplained_var',' X2', 'rX2',
'c0', 'c_am', 'tau', 'tau2', 'c_zodi', 'c_isl', 'sol', 'I',
't0', 't1', 't2', 't3', 't4', 'm0', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6',
'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec',
'c2', 'c3', 'c4', 'c5', 'c6']
# keep moon models
twi_coeffs = coeffs[coeffs['model'] == 'twilight']
coeffs = coeffs[coeffs['model'] == 'moon']
# order based on wavelengths for convenience
wave_sort = np.argsort(np.array(coeffs['wl']))
twi = {}
twi['wave'] = np.array(coeffs['wl'])[wave_sort]
for k in ['t0', 't1', 't2', 't3', 't4', 'c0']:
twi[k] = np.array(twi_coeffs[k])[wave_sort]
# save to file
ftwi = os.path.join(UT.dat_dir(), 'sky', 'twilight_coeffs.p')
pickle.dump(twi, open(ftwi, 'wb'))
return None
def _Noll_sky_ESO():
''' try to reproduce plots in Noll et al. (2012) using the ESO sky model
'''
# Noll+(2012) Figure 1 generated from parameters in Table 1
dic = {
'airmass': 1.0366676717, # skysim.zodiacal.airmass_zodi(90 - 85.1) (based on alitutde)
'moon_sun_sep': 77.9, # separation of sun and moon
'moon_target_sep': 51.3, # separation of moon and target
'moon_alt': 41.3, # altitude of the moon above the horizon
'moon_earth_dist': 1., # relative distance to the moon
'ecl_lon': -124.5, # heliocentric eclipitic longitude
'ecl_lat': -31.6, # heliocentric eclipitic latitude
'msolflux': 205.5, # monthly-averaged solar radio flux at 10.7 cm
'pwv_mode': 'season', # pwv or season
'season': 4,
'time': 3,
'vacair': 'air',
'wmin': 300.,
'wmax': 4200.,
'wdelta': 5.,
'observatory': '2640'}
skyModel = skycalc.SkyModel()
skyModel.callwith(dic)
ftmp = os.path.join(UT.dat_dir(), 'sky', '_tmp.fits')
skyModel.write(ftmp) # the clunkiest way to deal with this ever.
f = fits.open(ftmp)
fdata = f[1].data
wave = fdata['lam'] # wavelength in Ang
radiance = fdata['flux'] # photons/s/m^2/microm/arcsec^2 (radiance -- f**k)
print fdata.names
fig = plt.figure()
sub = fig.add_subplot(111)
sub.plot(wave, np.log10(radiance), c='k', lw=0.5, label='composite')
for k, lbl, clr in zip(['flux_sml', 'flux_ssl', 'flux_zl', 'flux_tie', 'flux_tme', 'flux_ael', 'flux_arc'],
['moon', 'stars', 'zodiacal', 'telescope', 'lower atmos.', 'airglow lines', 'resid. cont.'], ['b', 'C0', 'g', 'r', 'y', 'm', 'cyan']):
sub.plot(wave, np.log10(fdata[k]), lw=0.5, c=clr, label=lbl)
sub.legend(loc='lower right', frameon=True, fontsize=15)
sub.set_xlabel(r'Wavelength [$\mu m$]', fontsize=20)
sub.set_xlim(0.2, 4.2)
sub.set_ylabel(r'Radiance [dex]', fontsize=20)
sub.set_ylim(-1.5, 7.5)
fig.savefig(os.path.join(UT.code_dir(), 'figs', '_Nollfig1_sky_ESO.png'), bbox_inches='tight')
fig = plt.figure()
sub = fig.add_subplot(111)
w0p5 = np.abs(wave - 0.5).argmin()
sub.plot(wave, (fdata['flux_sml']/fdata['flux_sml'][w0p5]), lw=0.5, c='b', label='Moon')
sub.plot(wave, (fdata['flux_ssl']/fdata['flux_ssl'][w0p5]), lw=0.5, c='g', ls='--', label='stars')
sub.plot(wave, (fdata['flux_zl']/fdata['flux_zl'][w0p5]), lw=0.5, c='r', ls='-.', label='zodiacal light')
sub.legend(loc='lower right', fontsize=15)
sub.set_xlabel(r'Wavelength [$\mu m$]', fontsize=20)
sub.set_xlim(0.36, 0.885)
sub.set_ylabel(r'rel.radiance', fontsize=20)
sub.set_ylim(0.,1.8)
fig.savefig(os.path.join(UT.code_dir(), 'figs', '_Nollfig6_sky_ESO.png'), bbox_inches='tight')
return None
def boss_sky():
''' read in BOSS sky data -- fluxes and meta-data
:notes:
BOSS sky fiber treats the sky as a point source and therefore corrects for
fiber-loss accordingly. Therefore to do it correctly would involve backing
out the uncorrected sky flux, which is complicated.
'''
fboss = os.path.join(UT.dat_dir(), 'sky', 'Bright_BOSS_Sky_blue.fits')
boss = aTable.read(fboss)
return boss
def sky_ESO(airmass, moon_sun_sep, moonalt, moonsep, observatory='2400'):
''' calculate sky brightness using ESO sky calc
:param airmass:
airmass ranging from [1., 3.]
:param moon_sun_sep:
Separation in deg of Sun and Moon as seen from Earth
:param moonalt:
Moon Altitude over Horizon in deg
:param moonsep:
Moon-Target Separation in deg
:references:
- https://www.eso.org/observing/etc/bin/gen/form?INS.MODE=swspectr+INS.NAME=SKYCALC
- https://www.eso.org/observing/etc/doc/skycalc/helpskycalccli.html
'''
airmassp = 1./(1./airmass + 0.025*np.exp(-11./airmass)) # correct sec(z) airmass to Rozenberg (1966) airmass
dic = {'airmass': round(airmass,5),
'incl_moon': 'Y',
'moon_sun_sep': moon_sun_sep,
'moon_target_sep': moonsep,
'moon_alt': moonalt,
'wmin': 355.,
'wmax': 985.,
'observatory': observatory}
skyModel = skycalc.SkyModel()
skyModel.callwith(dic)
ftmp = os.path.join(UT.dat_dir(), 'sky', '_tmp.fits')
skyModel.write(ftmp) # the clunkiest way to deal with this ever.
f = fits.open(ftmp)
fdata = f[1].data
wave = fdata['lam'] * 1e4 # wavelength in Ang
radiance = fdata['flux'] # photons/s/m^2/microm/arcsec^2 (radiance -- f**k)
radiance *= 1e-8 # photons/s/cm^2/Ang/arcsec^2
# photons/s/cm^2/Ang/arcsec^2 * h * c / lambda
Isky = 1.99 * 1e-8 * radiance / wave * 1e17 # 10^-17 erg/s/cm^2/Ang/arcsec^2
return wave, Isky
def cI_twi(alpha, delta, airmass):
''' twilight contribution
:param alpha:
:param delta:
:param airmass:
:return twi:
'''
ftwi = os.path.join(UT.dat_dir(), 'sky', 'twilight_coeffs.p')
twi_coeffs = pickle.load(open(ftwi, 'rb'))
twi = (
twi_coeffs['t0'] * np.abs(alpha) + # CT2
twi_coeffs['t1'] * np.abs(alpha)**2 + # CT1
twi_coeffs['t2'] * np.abs(delta)**2 + # CT3
twi_coeffs['t3'] * np.abs(delta) # CT4
) * np.exp(-twi_coeffs['t4'] * airmass) + twi_coeffs['c0']
return twi_coeffs['wave'], np.array(twi)
def _sky_ESOvsKSvband():
'''
'''
# get default ESO moon scatterlight brightness
dic = {
'airmass': 1.0366676717, # skysim.zodiacal.airmass_zodi(90 - 85.1) (based on alitutde)
'moon_sun_sep': 77.9, # separation of sun and moon
'moon_target_sep': 51.3, # separation of moon and target
'moon_alt': 41.3, # altitude of the moon above the horizon
'moon_earth_dist': 1., # relative distance to the moon
'ecl_lon': -124.5, # heliocentric eclipitic longitude
'ecl_lat': -31.6, # heliocentric eclipitic latitude
'msolflux': 205.5, # monthly-averaged solar radio flux at 10.7 cm
'pwv_mode': 'season', # pwv or season
'season': 4,
'time': 3,
'vacair': 'air',
'wmin': 300.,
'wmax': 4200.,
'wdelta': 5.,
'observatory': '2640'}
skyModel = skycalc.SkyModel()
skyModel.callwith(dic)
ftmp = os.path.join(UT.dat_dir(), 'sky', '_tmp.fits')
skyModel.write(ftmp) # the clunkiest way to deal with this ever.
f = fits.open(ftmp)
fdata = f[1].data
wave_eso = fdata['lam'] # wavelength in Ang
radiance = fdata['flux_sml'] # photons/s/m^2/microm/arcsec^2 (radiance -- f**k)
radiance *= 1e-8 # photons/s/cm^2/Ang/arcsec^2
Im_eso = 1.99 * 1e-8 * radiance / (wave_eso * 1e4) * 1e17 # 10^-17 erg/s/cm^2/Ang/arcsec^2
# get moon brightness where some moon spectra is caled by KS V-band
specsim_sky = Sky.specsim_initialize('desi')
specsim_wave = specsim_sky._wavelength # Ang
specsim_sky.airmass = 1.0366676717
specsim_sky.moon.moon_phase = 77.9/180. #np.arccos(2.*moonill - 1)/np.pi
specsim_sky.moon.moon_zenith = (90. - 41.3) * u.deg
specsim_sky.moon.separation_angle = 51.3 * u.deg
# updated KS coefficients
specsim_sky.moon.KS_CR = 10**5.70
specsim_sky.moon.KS_CM0 = 7.15
specsim_sky.moon.KS_CM1 = 40.
Im_ks = specsim_sky.moon.surface_brightness.value
rho = specsim_sky.moon.separation_angle.to(u.deg).value
fR = 10**5.36*(1.06 + np.cos(np.radians(rho))**2)
fM = 10 ** (6.15 - rho/ 40.)
fRp = 10**5.70*(1.06 + np.cos(np.radians(rho))**2)
fMp = 10 ** (7.15 - rho/ 40.)
fRn = 10**5.66*(1.06 + np.cos(np.radians(rho))**2)
fMn = 10 ** (5.54 - rho/ 178.)
tRS = np.interp(specsim_wave/1e4, wave_eso, fdata['trans_rs'])
tMS = np.interp(specsim_wave/1e4, wave_eso, fdata['trans_ms'])
tall = np.interp(specsim_wave/1e4, wave_eso, fdata['trans'])
Xo = (1 - 0.96 * np.sin(specsim_sky.moon.obs_zenith)**2)**(-0.5)
Xm = (1 - 0.96 * np.sin(specsim_sky.moon.moon_zenith)**2)**(-0.5)
tkso = 10 ** (-0.4 * (specsim_sky.moon.vband_extinction * Xo))
tksm = 10 ** (-0.4 * (specsim_sky.moon.vband_extinction * Xm))
fcorr = ((fRp*(1.-tRS**Xo) + fMp*(1.-tMS**Xo))*tall**Xm)/((fRp+fMp) * (1-tkso) * tksm)
f_eso_ks = ((fRp*(1.-tRS**Xo) + fMp*(1.-tMS**Xo))*tall**Xm)/((fR+fM) * (1-tkso) * tksm)
f_nks_ks = (fRn + fMn)/(fR + fM)
f_peso_ks = (fRp + fMp)/(fR + fM)
fig = plt.figure(figsize=(10,5))
sub = fig.add_subplot(111)
sub.plot(wave_eso, Im_eso, c='k', label='ESO refit KS')
sub.plot(specsim_wave/1e4, fcorr*Im_ks * 1e17, c='C1', label='V-band scale ESO coeff.')
sub.legend(loc='upper right', fontsize=20)
sub.set_xlabel(r'Wavelength [$\mu m$]', fontsize=20)
sub.set_xlim(0.3, 1.)
sub.set_ylabel(r'Moon Brightness', fontsize=20)
fig.savefig(os.path.join(UT.code_dir(), 'figs', '_sky_ESOvsKSvband.png'), bbox_inches='tight')
fig = plt.figure(figsize=(10,5))
sub = fig.add_subplot(111)
sub.plot(specsim_wave, f_eso_ks, c='k', label='ESO')
sub.plot(specsim_wave, np.repeat(f_nks_ks, len(specsim_wave)), c='C0', label='refit KS')
sub.plot(specsim_wave, np.repeat(f_peso_ks, len(specsim_wave)), c='C1', label='pseudo ESO')
sub.legend(loc='upper right', fontsize=20)
sub.set_xlabel(r'Wavelength [$A$]', fontsize=20)
sub.set_xlim(3.4e3, 9.8e3)
sub.set_ylabel(r'(moon model)/(KS moon)', fontsize=20)
fig.savefig(os.path.join(UT.code_dir(), 'figs', '_sky_ESOoverKS.png'), bbox_inches='tight')
fig = plt.figure(figsize=(10,5))
sub = fig.add_subplot(111)
sub.plot(wave_eso, fdata['trans_rs'], c='k', label='Rayleigh')
sub.plot(wave_eso, fdata['trans_ms'], c='C1', label='Mie')
sub.plot(wave_eso, np.repeat(10**(-0.4*specsim_sky.moon.vband_extinction*dic['airmass']), len(wave_eso)))
sub.legend(loc='upper right', fontsize=20)
sub.set_xlabel(r'Wavelength [$\mu m$]', fontsize=20)
sub.set_xlim(0.3, 1.)
sub.set_ylabel(r'transmission', fontsize=20)
fig.savefig(os.path.join(UT.code_dir(), 'figs', '_sky_ESOvsKSvband.trans.png'), bbox_inches='tight')
return None
def Obvs_HODLHD_test(theta,
halo,
mneut=0.0,
nreal=1,
nzbin=4,
seed=1,
obvs='plk',
Nmesh=360):
''' Build the observables `obvs` for {theta_test} given halo catalog
'''
folder = ''.join([
UT.dat_dir(), 'lhd/',
str(mneut), 'eV_',
str(nreal), '_z',
str(nzbin), '_',
str(samples), 'samples/', 'HOD', method, '_seed',
str(seed_hod), '_',
str(i_p), '/'
])
# F(theta) --- i.e. the galaxy catalog generated
# from the halo catalog
p_hod = {
'logMmin': theta[0],
'sigma_logM': theta[1],
'logM0': theta[2],
'logM1': theta[3],
'alpha': theta[4]
}
g = FM.Galaxies(halos, p_hod, seed=seed)
g['RSDPosition'] = FM.RSD(g, LOS=[0, 0, 1]) # impose RSD
# measure P(k) from F(theta) in real and z space
for rsd in [False, True]:
if obvs == 'plk': # power spectrum multipole
plk = FM.Observables(gals, observable='plk', rsd=rsd, Nmesh=Nmesh)
# write to file
if rsd: str_rsd = '.zspace'
else: str_rsd = '.rspace'
fname = ''.join([
folder, 'pk.theta_test.menut',
str(mneut), '.nreal',
str(nreal), '.nzbin',
str(nzbin), str_rsd, '.',
str(Nmesh), '.nbkt.dat'
])
# save to file
f = open(fname, 'w')
f.write("### header ### \n")
f.write("# shotnoise %f \n" % plk['shotnoise'])
f.write("# columns : k , P0, P2, P4 \n")
f.write('### header ### \n')
for ik in range(len(plk['k'])):
f.write("%f \t %f \t %f \t %f" %
(plk['k'][ik], plk['p0k'][ik], plk['p2k'][ik],
plk['p4k'][ik]))
f.write("\n")
f.close()
obvs = plk
else:
raise NotImplementedError('only Plk implemented')
return None