def mean_templ_zi(zimag, debug=False, i_wind=0.1, z_wind=0.05,
boss_pca_fil=None):
'''
Generate 'mean' templates at given z,i
Parameters
----------
zimag: list of tuples
Redshift, imag pairs for the templates
i_wind: float (0.1 mag)
Window for smoothing imag
z_wind: float (0.05 mag)
Window for smoothing redshift
'''
# PCA values
if boss_pca_fil is None:
boss_pca_fil = 'BOSS_DR10Lya_PCA_values_nocut.fits.gz'
hdu = fits.open(boss_pca_fil)
pca_coeff = hdu[1].data
# BOSS Eigenvectors
eigen, eigen_wave = fbq.read_qso_eigen()
npix = len(eigen_wave)
# Open the BOSS catalog file
boss_cat_fil = os.environ.get('BOSSPATH')+'/DR10/BOSSLyaDR10_cat_v2.1.fits.gz'
bcat_hdu = fits.open(boss_cat_fil)
t_boss = bcat_hdu[1].data
zQSO = t_boss['z_pipe']
tmp = t_boss['PSFMAG']
imag = tmp[:,3] # i-band mag
# Output array
ntempl = len(zimag)
out_spec = np.zeros( (ntempl, npix) )
# Iterate on z,imag
for izi in zimag:
tt = zimag.index(izi)
# Find matches
idx = np.where( (np.fabs(imag-izi[1]) < i_wind) &
(np.fabs(zQSO-izi[0]) < z_wind))[0]
if len(idx) < 50:
raise ValueError('mean_templ_zi: Not enough QSOs! {:d}'.format(len(idx)))
# Calculate median PCA values
PCA0 = np.median(pca_coeff['PCA0'][idx])
PCA1 = np.median(pca_coeff['PCA1'][idx])
PCA2 = np.median(pca_coeff['PCA2'][idx])
PCA3 = np.median(pca_coeff['PCA3'][idx])
acoeff = np.array( [PCA0, PCA1, PCA2, PCA3] )
# Make the template
out_spec[tt,:] = np.dot(eigen.T,acoeff)
if debug is True:
xdb.xplot(eigen_wave*(1.+izi[0]), out_spec[tt,:])
xdb.set_trace()
# Return
return out_spec
def fig_boss_eigen(outfil=None, boss_fil=None, flg=0):
'''
flg = 0: Redshift
flg = 1: imag
'''
# Todo
# Include NHI on the label
# Imports
# Read
eigen, eigen_wave = fbq.read_qso_eigen()
# Initialize
ymnx = ((0.0, 10), (-1, 1), (-1, 1), (-1, 1))
# Start the plot
if outfil != None:
pp = PdfPages(outfil)
plt.figure(figsize=(8, 5))
plt.clf()
gs = gridspec.GridSpec(4, 1)
#xdb.set_trace()
# Looping
for ii in range(4):
# Axis
ax = plt.subplot(gs[ii])
#ax = plt.subplot(gs[ii//2,ii%2])
ylbl = str('Eigen' + str(ii))
# Labels
if ii == 3:
ax.set_xlabel('Rest Wavelength')
else:
ax.get_xaxis().set_ticks([])
ax.set_ylabel(ylbl)
ax.set_xlim((400., 8000))
# Data
# Values
ax.plot(eigen_wave,
eigen[ii, :],
'k-',
drawstyle='steps-mid',
linewidth=0.5)
# Layout and save
plt.tight_layout(pad=0.2, h_pad=0.0, w_pad=0.25)
if outfil != None:
pp.savefig(bbox_inches='tight')
pp.close()
else:
plt.show()
def fig_boss_eigen(outfil=None, boss_fil=None, flg=0):
'''
flg = 0: Redshift
flg = 1: imag
'''
# Todo
# Include NHI on the label
# Imports
# Read
eigen, eigen_wave = fbq.read_qso_eigen()
# Initialize
ymnx = ((0.0, 10),
(-1, 1),
(-1, 1),
(-1, 1))
# Start the plot
if outfil != None:
pp = PdfPages(outfil)
plt.figure(figsize=(8, 5))
plt.clf()
gs = gridspec.GridSpec(4, 1)
#xdb.set_trace()
# Looping
for ii in range(4):
# Axis
ax = plt.subplot(gs[ii])
#ax = plt.subplot(gs[ii//2,ii%2])
ylbl=str('Eigen'+str(ii))
# Labels
if ii == 3:
ax.set_xlabel('Rest Wavelength')
else:
ax.get_xaxis().set_ticks([])
ax.set_ylabel(ylbl)
ax.set_xlim((400., 8000))
# Data
# Values
ax.plot( eigen_wave, eigen[ii,:], 'k-',drawstyle='steps-mid', linewidth=0.5)
# Layout and save
plt.tight_layout(pad=0.2,h_pad=0.0,w_pad=0.25)
if outfil != None:
pp.savefig(bbox_inches='tight')
pp.close()
else:
plt.show()
def fig_desi_templ_z_i(outfil=None, boss_fil=None, flg=0):
'''
flg = 0: Redshift
flg = 1: imag
'''
# Todo
# Include NHI on the label
# Imports
import matplotlib as mpl
mpl.rcParams['font.family'] = 'stixgeneral'
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
# Eigen (for wavelengths)
eigen, eigen_wave = fbq.read_qso_eigen()
all_zi = [ [ (2.3, 18.5), (2.3, 19.5), (2.3, 20.5), (2.3, 21.5) ],
[ (2.5, 18.5), (2.5, 19.5), (2.5, 20.5), (2.5, 21.5) ],
[ (2.7, 19.5), (2.7, 20.5), (2.7, 21.5) ],
[ (3.2, 19.5), (3.2, 20.5), (3.2, 21.5) ] ]
xmnx = (3600., 9000.)
# Start the plot
if outfil != None:
pp = PdfPages(outfil)
plt.figure(figsize=(8, 5))
plt.clf()
gs = gridspec.GridSpec(4, 1)
#xdb.set_trace()
# Looping
for ii in range(4):
# Get the templates
ztempl = all_zi[ii][0][0]
spec = mean_templ_zi(all_zi[ii])
# Axis
ax = plt.subplot(gs[ii])
#ax = plt.subplot(gs[ii//2,ii%2])
# Labels
if ii == 3:
ax.set_xlabel('Wavelength')
else:
ax.get_xaxis().set_ticks([])
ax.set_ylabel('Flux')
ax.set_xlim(xmnx)
# Data
# Values
for jj in range(len(all_zi[ii])):
ax.plot( eigen_wave*(1.+ ztempl),
spec[jj,:], '-',drawstyle='steps-mid', linewidth=0.5)
if jj == 0:
ymx = 1.05*np.max(spec[jj,:])
ax.set_ylim((0., ymx))
# Label
zlbl = 'z={:g}'.format(ztempl)
ax.text(7000., ymx*0.7, zlbl)
# Layout and save
plt.tight_layout(pad=0.2,h_pad=0.0,w_pad=0.25)
if outfil != None:
pp.savefig(bbox_inches='tight')
pp.close()
else:
plt.show()
def desi_qso_templates(z_wind=0.2, zmnx=(0.4,4.), outfil=None, Ntempl=500,
boss_pca_fil=None, wvmnx=(3500., 10000.),
sdss_pca_fil=None, no_write=False):
'''
Generate 9000 templates for DESI from z=0.4 to 4
Parameters
----------
z_wind: float (0.2)
Window for sampling
zmnx: tuple ( (0.5,4) )
Min/max for generation
Ntempl: int (500)
Number of draws per redshift window
'''
# Cosmology
from astropy import cosmology
cosmo = cosmology.core.FlatLambdaCDM(70., 0.3)
# PCA values
if boss_pca_fil is None:
boss_pca_fil = 'BOSS_DR10Lya_PCA_values_nocut.fits.gz'
hdu = fits.open(boss_pca_fil)
boss_pca_coeff = hdu[1].data
if sdss_pca_fil is None:
sdss_pca_fil = 'SDSS_DR7Lya_PCA_values_nocut.fits.gz'
hdu2 = fits.open(sdss_pca_fil)
sdss_pca_coeff = hdu2[1].data
# Eigenvectors
eigen, eigen_wave = fbq.read_qso_eigen()
npix = len(eigen_wave)
chkpix = np.where((eigen_wave > 900.) & (eigen_wave < 5000.) )[0]
lambda_912 = 911.76
pix912 = np.argmin( np.abs(eigen_wave-lambda_912) )
# Open the BOSS catalog file
boss_cat_fil = os.environ.get('BOSSPATH')+'/DR10/BOSSLyaDR10_cat_v2.1.fits.gz'
bcat_hdu = fits.open(boss_cat_fil)
t_boss = bcat_hdu[1].data
boss_zQSO = t_boss['z_pipe']
# Open the SDSS catalog file
sdss_cat_fil = os.environ.get('SDSSPATH')+'/DR7_QSO/dr7_qso.fits.gz'
scat_hdu = fits.open(sdss_cat_fil)
t_sdss = scat_hdu[1].data
sdss_zQSO = t_sdss['z']
if len(sdss_pca_coeff) != len(sdss_zQSO):
print('Need to finish running the SDSS models!')
sdss_zQSO = sdss_zQSO[0:len(sdss_pca_coeff)]
# Outfil
if outfil is None:
outfil = 'DESI_QSO_Templates_v1.1.fits'
# Loop on redshift
z0 = np.arange(zmnx[0],zmnx[1],z_wind)
z1 = z0 + z_wind
pca_list = ['PCA0', 'PCA1', 'PCA2', 'PCA3']
PCA_mean = np.zeros(4)
PCA_sig = np.zeros(4)
PCA_rand = np.zeros( (4,Ntempl*2) )
final_spec = np.zeros( (npix, Ntempl * len(z0)) )
final_wave = np.zeros( (npix, Ntempl * len(z0)) )
final_z = np.zeros( Ntempl * len(z0) )
seed = -1422
for ii in range(len(z0)):
# BOSS or SDSS?
if z0[ii] > 1.99:
zQSO = boss_zQSO
pca_coeff = boss_pca_coeff
else:
zQSO = sdss_zQSO
pca_coeff = sdss_pca_coeff
# Random z values and wavelengths
zrand = np.random.uniform( z0[ii], z1[ii], Ntempl*2)
wave = np.outer(eigen_wave, 1+zrand)
# MFP (Worseck+14)
mfp = 37. * ( (1+zrand)/5. )**(-5.4) # Physical Mpc
# Grab PCA mean + sigma
idx = np.where( (zQSO >= z0[ii]) & (zQSO < z1[ii]) )[0]
print('Making z=({:g},{:g}) with {:d} input quasars'.format(z0[ii],z1[ii],len(idx)))
# Get PCA stats and random values
for ipca in pca_list:
jj = pca_list.index(ipca)
if jj == 0: # Use bounds for PCA0 [avoids negative values]
xmnx = xstat_b.perc( pca_coeff[ipca][idx], per=0.95 )
PCA_rand[jj,:] = np.random.uniform( xmnx[0], xmnx[1], Ntempl*2)
else:
PCA_mean[jj] = np.mean(pca_coeff[ipca][idx])
PCA_sig[jj] = np.std(pca_coeff[ipca][idx])
# Draws
PCA_rand[jj,:] = np.random.uniform( PCA_mean[jj] - 2*PCA_sig[jj],
PCA_mean[jj] + 2*PCA_sig[jj], Ntempl*2)
# Generate the templates (2*Ntempl)
spec = np.dot(eigen.T,PCA_rand)
# Take first good Ntempl
# Truncate, MFP, Fill
ngd = 0
for kk in range(2*Ntempl):
# Any zero values?
mn = np.min(spec[chkpix,kk])
if mn < 0.:
continue
# MFP
if z0[ii] > 2.39:
z912 = wave[0:pix912,kk]/lambda_912 - 1.
phys_dist = np.fabs( cosmo.lookback_distance(z912) -
cosmo.lookback_distance(zrand[kk]) ) # Mpc
spec[0:pix912,kk] = spec[0:pix912,kk] * np.exp(-phys_dist.value/mfp[kk])
# Write
final_spec[:, ii*Ntempl+ngd] = spec[:,kk]
final_wave[:, ii*Ntempl+ngd] = wave[:,kk]
final_z[ii*Ntempl+ngd] = zrand[kk]
ngd += 1
if ngd == Ntempl:
break
if ngd != Ntempl:
print('Did not make enough!')
xdb.set_trace()
if no_write is True: # Mainly for plotting
return final_wave, final_spec, final_z
# Rebin
light = 2.99792458e5 # [km/s]
velpixsize = 10. # [km/s]
pixsize = velpixsize/light/np.log(10) # [pixel size in log-10 A]
minwave = np.log10(wvmnx[0]) # minimum wavelength [log10-A]
maxwave = np.log10(wvmnx[1]) # maximum wavelength [log10-A]
r_npix = np.round((maxwave-minwave)/pixsize+1)
log_wave = minwave+np.arange(r_npix)*pixsize # constant log-10 spacing
totN = Ntempl * len(z0)
rebin_spec = np.zeros((r_npix, totN))
from scipy.interpolate import interp1d
for ii in range(totN):
# Interpolate (in log space)
f1d = interp1d(np.log10(final_wave[:,ii]), final_spec[:,ii])
rebin_spec[:,ii] = f1d(log_wave)
#xdb.xplot(final_wave[:,ii], final_spec[:,ii], xtwo=10.**log_wave, ytwo=rebin_spec[:,ii])
#xdb.set_trace()
# Transpose for consistency
out_spec = np.array(rebin_spec.T, dtype='float32')
# Write
hdu = fits.PrimaryHDU(out_spec)
hdu.header.set('PROJECT', 'DESI QSO TEMPLATES')
hdu.header.set('VERSION', '1.1')
hdu.header.set('OBJTYPE', 'QSO')
hdu.header.set('DISPAXIS', 1, 'dispersion axis')
hdu.header.set('CRPIX1', 1, 'reference pixel number')
hdu.header.set('CRVAL1', minwave, 'reference log10(Ang)')
hdu.header.set('CDELT1', pixsize, 'delta log10(Ang)')
hdu.header.set('LOGLAM', 1, 'log10 spaced wavelengths?')
hdu.header.set('AIRORVAC', 'vac', ' wavelengths in vacuum (vac) or air')
hdu.header.set('VELSCALE', velpixsize, ' pixel size in km/s')
hdu.header.set('WAVEUNIT', 'Angstrom', ' wavelength units')
hdu.header.set('BUNIT', '1e-17 erg/s/cm2/A', ' flux unit')
idval = range(totN)
col0 = fits.Column(name='TEMPLATEID',format='K', array=idval)
col1 = fits.Column(name='Z',format='E',array=final_z)
cols = fits.ColDefs([col0, col1])
tbhdu = fits.BinTableHDU.from_columns(cols)
tbhdu.header.set('EXTNAME','METADATA')
hdulist = fits.HDUList([hdu, tbhdu])
hdulist.writeto(outfil, clobber=True)
return final_wave, final_spec, final_z
