def read_spec(ispec):
'''Parse spectrum out of the input
Parameters:
-----------
ispec: Spectrum1D, str, list of files (ordered blue to red),
or tuple of arrays
Returns:
-----------
spec: XSpectrum1D
spec_file: str
'''
from specutils.spectrum1d import Spectrum1D
from astropy.nddata import StdDevUncertainty
from linetools.spectra import xspectrum1d as lsx
from linetools.spectra import io as lsi
from xastropy.stats import basic as xsb
#
if isinstance(ispec,basestring):
spec_fil = ispec
spec = lsx.XSpectrum1D.from_file(spec_fil)
elif isinstance(ispec,Spectrum1D):
spec = ispec
spec_fil = spec.filename # Grab from Spectrum1D
elif isinstance(ispec,tuple):
spec = lsx.XSpectrum1D.from_tuple(ispec)
spec_fil = 'none'
elif isinstance(ispec,list): # Multiple file names
# Loop on the files
for kk,ispecf in enumerate(ispec):
jspec = lsx.XSpectrum1D.from_file(ispecf)
if kk == 0:
spec = jspec
xper1 = xsb.perc(spec.flux, per=0.9)
else:
# Scale flux for convenience of plotting (sig is not scaled)
xper2 = xsb.perc(jspec.flux, per=0.9)
scl = xper1[1]/xper2[1]
# Splice
spec = spec.splice(jspec,scale=scl)
# Filename
spec_fil = ispec[0]
spec.filename=spec_fil
else:
raise ValueError('Bad input to read_spec')
# Return
return spec, spec_fil
def xq100_example(outfil='fig_xq100_example.pdf'):
# Load spectrum
spec_fil = os.getenv('DROPBOX_DIR') + '/XQ-100/data/J0030-5159_uvb.fits'
spec = load_spectrum(spec_fil)
# Start the plot
if outfil is not None:
pp = PdfPages(outfil)
plt.figure(figsize=(8, 5))
plt.clf()
gs = gridspec.GridSpec(1, 1)
fsz = 17.
# Read and plot full spectrum
ax_full = plt.subplot(gs[0])
# Limits
wvmnx = [3500., 7000.]
gdpix = np.where(spec.dispersion < wvmnx[1] * u.AA)[0]
perc = xsb.perc(spec.flux[gdpix])
ax_full.set_xlim(wvmnx)
#ax_full.set_ylim(-0.05*perc[1], 1.1*perc[1])
ax_full.set_ylim(-1e-18, 7e-17)
# Plot
ax_full.plot(spec.dispersion, spec.flux, color='black', lw=1.0)
ax_full.plot(wvmnx, [0., 0.], '--', color='green')
# Label
ax_full.set_xlabel('Wavelength')
ax_full.set_ylabel('Relative Flux')
ax_full.text(0.05,
0.9,
'XQ100 J0030-5159',
transform=ax_full.transAxes,
color='black',
size='x-large',
ha='left',
va='center',
bbox={'facecolor': 'white'})
# Font size
xputils.set_fontsize(ax_full, fsz)
# Finish page
plt.tight_layout(pad=0.2, h_pad=0.3, w_pad=0.0)
pp.savefig()
plt.close()
print('Wrote: {:s}'.format(outfil))
# Finish
pp.close()
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
def xq100_plls_ex(outfil='fig_xq100_plls_ex.pdf'):
# Load spectrum
spec_fil = os.getenv('DROPBOX_DIR') + '/XQ-100/data/J0030-5159_uvb.fits'
spec = load_spectrum(spec_fil)
# Generate model
model_file = os.getenv(
'DROPBOX_DIR') + '/XQ-100/LLS/convg_J0030-5159_llsfit.json'
with open(model_file) as data_file:
lls_dict = json.load(data_file)
# Continuum
basec = Quantity(lls_dict['conti'])
telfer_spec = XSpectrum1D.from_tuple((spec.dispersion.value, basec.value))
# Start the plot
if outfil is not None:
pp = PdfPages(outfil)
plt.figure(figsize=(8, 4))
plt.clf()
gs = gridspec.GridSpec(1, 1)
fsz = 17.
# Read and plot full spectrum
ax_full = plt.subplot(gs[0])
# Limits
wvmnx = [3600., 5000.]
gdpix = np.where(spec.dispersion < wvmnx[1] * u.AA)[0]
perc = xsb.perc(spec.flux[gdpix])
ax_full.set_xlim(wvmnx)
#ax_full.set_ylim(-0.05*perc[1], 1.1*perc[1])
ax_full.set_ylim(-1e-18, 4e-17)
# Plot
ax_full.plot(spec.dispersion, spec.flux, color='black', lw=1.0)
ax_full.plot(wvmnx, [0., 0.], '--', color='green')
# Label
ax_full.set_xlabel('Wavelength')
ax_full.set_ylabel('Relative Flux')
ax_full.text(0.05,
0.9,
'XQ100 J0030-5159',
transform=ax_full.transAxes,
color='black',
size='x-large',
ha='left',
va='center',
bbox={'facecolor': 'white'})
all_ills = []
all_lls = []
all_zlls = []
for key in lls_dict['LLS'].keys():
new_sys = LLSSystem(NHI=lls_dict['LLS'][key]['NHI'])
new_sys.zabs = lls_dict['LLS'][key]['z']
new_sys.fill_lls_lines(bval=lls_dict['LLS'][key]['bval'] * u.km / u.s)
#
all_ills.append(new_sys)
all_lls.append(new_sys)
all_zlls.append(new_sys.zabs)
# Model
norm_flux = lls_model(spec.dispersion, all_ills, smooth=lls_dict['smooth'])
continuum = telfer_spec.flux * lls_dict['conti_model']['Norm'] * (
spec.dispersion.value /
lls_dict['conti_model']['piv_wv'])**lls_dict['conti_model']['tilt']
# Model
ax_full.plot(spec.dispersion, continuum * norm_flux)
# Font size
xputils.set_fontsize(ax_full, fsz)
# Finish page
plt.tight_layout(pad=0.2, h_pad=0.3, w_pad=0.0)
pp.savefig()
plt.close()
print('Wrote: {:s}'.format(outfil))
# Finish
pp.close()