def update_model(self):
"""Update absorption model"""
from linetools.analysis import voigt as lav
if len(self.abssys_widg.all_abssys) == 0:
self.dla_model = None
self.spec_widg.model = None
return
# use finer wavelength array to resolve absorption features.
wa = self.full_model.wavelength
# Angstroms
# should really make this a constant velocity width array instead.
if not self.skip_wveval:
wa1 = np.arange(wa[0].value, wa[-1].value, self.dw) * wa.unit
else:
wa1 = wa
#all_tau_model = igmlls.tau_multi_lls(wa1,
# self.abssys_widg.all_abssys, skip_wveval=self.skip_wveval)
all_lines = []
for abssys in self.abssys_widg.all_abssys:
for iline in abssys.dla_lines:
all_lines.append(iline)
#QtCore.pyqtRemoveInputHook()
#import pdb; pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
tau_Lyman = lav.voigt_from_abslines(wa1,
all_lines,
ret='tau',
skip_wveval=self.skip_wveval)
'''
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = lav.voigt_from_abslines(wa1, forest.lines,
ret='tau', skip_wveval=self.skip_wveval)
all_tau_model += tau_Lyman
'''
all_tau_model = tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
if not self.skip_wveval:
mult = np.median(np.diff(wa.value)) / self.dw
flux = lsc.convolve_psf(flux, self.smooth * mult)
else:
flux = lsc.convolve_psf(flux, self.smooth)
if not self.skip_wveval:
self.dla_model = np.interp(wa.value, wa1.value, flux)
else:
self.dla_model = flux
# Finish
self.full_model.flux = self.dla_model * self.conti_dict['co']
# Over-absorbed
self.spec_widg.bad_model = np.where((self.dla_model < 0.7) & (
self.full_model.flux <
(self.spec_widg.spec.flux - self.spec_widg.spec.sig * 1.5)))[0]
# Model
self.spec_widg.model = self.full_model
def update_model(self):
"""Update absorption model"""
from linetools.analysis import voigt as lav
if len(self.abssys_widg.all_abssys) == 0:
self.dla_model = None
self.spec_widg.model = None
return
# use finer wavelength array to resolve absorption features.
wa = self.full_model.wavelength
# Angstroms
# should really make this a constant velocity width array instead.
if not self.skip_wveval:
wa1 = np.arange(wa[0].value, wa[-1].value, self.dw) * wa.unit
else:
wa1 = wa
#all_tau_model = igmlls.tau_multi_lls(wa1,
# self.abssys_widg.all_abssys, skip_wveval=self.skip_wveval)
all_lines = []
for abssys in self.abssys_widg.all_abssys:
for iline in abssys.dla_lines:
all_lines.append(iline)
#QtCore.pyqtRemoveInputHook()
#import pdb; pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
tau_Lyman = lav.voigt_from_abslines(wa1, all_lines,
ret='tau',
skip_wveval=self.skip_wveval)
'''
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = lav.voigt_from_abslines(wa1, forest.lines,
ret='tau', skip_wveval=self.skip_wveval)
all_tau_model += tau_Lyman
'''
all_tau_model = tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
if not self.skip_wveval:
mult = np.median(np.diff(wa.value)) / self.dw
flux = lsc.convolve_psf(flux, self.smooth * mult)
else:
flux = lsc.convolve_psf(flux, self.smooth)
if not self.skip_wveval:
self.dla_model = np.interp(wa.value, wa1.value, flux)
else:
self.dla_model = flux
# Finish
self.full_model.flux = self.dla_model * self.conti_dict['co']
# Over-absorbed
self.spec_widg.bad_model = np.where( (self.dla_model < 0.7) &
(self.full_model.flux < (self.spec_widg.spec.flux-
self.spec_widg.spec.sig*1.5)))[0]
# Model
self.spec_widg.model = self.full_model
def update_model(self):
'''Update absorption model '''
from linetools.analysis import voigt as lav
if len(self.abssys_widg.all_abssys) == 0:
self.lls_model = None
self.spec_widg.model = None
return
'''
# Regenerate (in case we have switched between multiple spectra)
if self.spec_widg.spec.nspec > 1:
self.full_model = XSpectrum1D.from_tuple((
self.spec_widg.spec.wavelength,np.ones(len(self.spec_widg.spec.wavelength))))
'''
# use finer wavelength array to resolve absorption features.
wa = self.full_model.wavelength
# Angstroms
# should really make this a constant velocity width array instead.
if not self.skip_wveval:
wa1 = np.arange(wa[0].value, wa[-1].value, self.dw) * wa.unit
else:
wa1 = wa
all_tau_model = igmlls.tau_multi_lls(wa1,
self.abssys_widg.all_abssys,
skip_wveval=self.skip_wveval)
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = lav.voigt_from_abslines(wa1,
forest.lines,
ret='tau',
skip_wveval=self.skip_wveval)
all_tau_model += tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
if not self.skip_wveval:
mult = np.median(np.diff(wa.value)) / self.dw
flux = lsc.convolve_psf(flux, self.smooth * mult)
else:
flux = lsc.convolve_psf(flux, self.smooth)
if not self.skip_wveval:
self.lls_model = np.interp(wa.value, wa1.value, flux)
else:
self.lls_model = flux
# Finish
self.full_model.flux = self.lls_model * self.continuum.flux
# Over-absorbed
try:
self.spec_widg.bad_model = np.where((self.lls_model < 0.7) & (
self.full_model.flux <
(self.spec_widg.spec.flux - self.spec_widg.spec.sig * 1.5)))[0]
except:
pass
# Model
self.spec_widg.model = self.full_model
def update_model(self):
'''Update absorption model '''
if len(self.abssys_widg.all_abssys) == 0:
self.lls_model = None
self.spec_widg.model = None
return
#
all_tau_model = xialu.tau_multi_lls(self.full_model.dispersion,
self.abssys_widg.all_abssys)
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = xsv.voigt_model(self.full_model.dispersion,
forest.lines,
flg_ret=2)
all_tau_model += tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
self.lls_model = lsc.convolve_psf(flux, self.smooth)
else:
self.lls_model = flux
# Finish
self.full_model.flux = self.lls_model * self.continuum.flux
self.spec_widg.model = self.full_model
def update_model(self):
'''Update absorption model '''
if len(self.abssys_widg.all_abssys) == 0:
self.lls_model = None
self.spec_widg.model = None
return
#
all_tau_model = xialu.tau_multi_lls(self.full_model.dispersion,
self.abssys_widg.all_abssys)
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = xsv.voigt_model(self.full_model.dispersion,
forest.lines, flg_ret=2)
all_tau_model += tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
self.lls_model = lsc.convolve_psf(flux, self.smooth)
else:
self.lls_model = flux
# Finish
self.full_model.flux = self.lls_model * self.continuum.flux
self.spec_widg.model = self.full_model
def gauss_smooth(self, fwhm, **kwargs):
""" Smooth a spectrum with a Gaussian
Note that the uncertainty array is not smoothed.
Parameters
----------
fwhm : float
FWHM of the Gaussian in pixels (unitless)
Returns
-------
A new XSpectrum1D instance of the smoothed spectrum
"""
# Import
from linetools.spectra import convolve as lsc
# Apply to flux
new_fx = lsc.convolve_psf(
self.flux.value, fwhm, **kwargs) * self.flux.unit
# Get the right sigma
if self.sig_is_set:
new_sig = self.sig.value
else:
new_sig = None
# Return
return XSpectrum1D.from_tuple(
(self.wavelength, new_fx, new_sig), meta=self.meta.copy())
def update_model(self):
'''Update absorption model
'''
if len(self.abssys_widg.all_abssys) == 0:
self.lls_model = None
self.spec_widg.model = None
return
#
all_tau_model = np.zeros(len(self.full_model.flux))
# Loop on LLS
for lls in self.abssys_widg.all_abssys:
# LL
wv_rest = self.full_model.dispersion / (lls.zabs + 1)
energy = wv_rest.to(u.eV, equivalencies=u.spectral())
# Get photo_cross and calculate tau
tau_LL = (10.**lls.NHI / u.cm**2) * xatomi.photo_cross(
1, 1, energy)
# Lyman
tau_Lyman = xsv.voigt_model(self.full_model.dispersion,
lls.lls_lines,
flg_ret=2)
tau_model = tau_LL + tau_Lyman
# Kludge around the limit
pix_LL = np.argmin(np.fabs(wv_rest - 911.3 * u.AA))
pix_kludge = np.where((wv_rest > 911.5 * u.AA)
& (wv_rest < 912.8 * u.AA))[0]
tau_model[pix_kludge] = tau_model[pix_LL]
# Add
all_tau_model += tau_model
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = xsv.voigt_model(self.full_model.dispersion,
forest.lines,
flg_ret=2)
all_tau_model += tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
self.lls_model = lsc.convolve_psf(flux, self.smooth)
else:
self.lls_model = flux
# Finish
self.full_model.flux = self.lls_model * self.continuum.flux
self.spec_widg.model = self.full_model
def lls_model(wave, all_lls, smooth=0.):
'''Generate an absorption model '''
from linetools.spectra import convolve as lsc
from xastropy.igm.abs_sys import lls_utils as xialu
# Tau from LLS
all_tau_model = xialu.tau_multi_lls(wave, all_lls)
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if smooth > 0:
lls_model = lsc.convolve_psf(flux, smooth)
else:
lls_model = flux
# Finish
norm_flux = lls_model
# Return
return norm_flux
def update_model(self):
'''Update absorption model
'''
if len(self.abssys_widg.all_abssys) == 0:
self.lls_model = None
self.spec_widg.model = None
return
#
all_tau_model = np.zeros(len(self.full_model.flux))
# Loop on LLS
for lls in self.abssys_widg.all_abssys:
# LL
wv_rest = self.full_model.dispersion / (lls.zabs+1)
energy = wv_rest.to(u.eV, equivalencies=u.spectral())
# Get photo_cross and calculate tau
tau_LL = (10.**lls.NHI / u.cm**2) * xatomi.photo_cross(1,1,energy)
# Lyman
tau_Lyman = xsv.voigt_model(self.full_model.dispersion,
lls.lls_lines, flg_ret=2)
tau_model = tau_LL + tau_Lyman
# Kludge around the limit
pix_LL = np.argmin( np.fabs( wv_rest- 911.3*u.AA ) )
pix_kludge = np.where( (wv_rest > 911.5*u.AA) & (wv_rest < 912.8*u.AA) )[0]
tau_model[pix_kludge] = tau_model[pix_LL]
# Add
all_tau_model += tau_model
# Loop on forest lines
for forest in self.all_forest:
tau_Lyman = xsv.voigt_model(self.full_model.dispersion,
forest.lines, flg_ret=2)
all_tau_model += tau_Lyman
# Flux and smooth
flux = np.exp(-1. * all_tau_model)
if self.smooth > 0:
self.lls_model = lsc.convolve_psf(flux, self.smooth)
else:
self.lls_model = flux
# Finish
self.full_model.flux = self.lls_model * self.continuum.flux
self.spec_widg.model = self.full_model
def gauss_smooth(self, fwhm, **kwargs):
""" Smooth a spectrum with a Gaussian
Need to consider smoothing the uncertainty array
Parameters
----------
fwhm: float
FWHM of the Gaussian in pixels (unitless)
Returns:
--------
XSpectrum1D of the smoothed spectrum
Returns:
"""
# Import
from linetools.spectra import convolve as lsc
# Apply to flux
new_fx = lsc.convolve_psf(self.flux.value, fwhm, **kwargs) * self.flux.unit
# Return
return XSpectrum1D.from_array(self.dispersion, new_fx, meta=self.meta.copy(), uncertainty=self.uncertainty)
def auto_plls(self,x,y):
'''Automatically fit a pLLS
Parameters:
----------
x,y: floats
x,y values in the GUI
'''
spec = self.spec_widg.spec # For convenience
if len(self.abssys_widg.all_abssys) > 0:
conti= self.full_model
else:
conti= self.continuum
# Generate toy LLS from click
ximn = np.argmin(np.abs(spec.dispersion.value-x))
NHI = 17.29 + np.log10(-1.*np.log(y/conti.flux.value[ximn]))
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
#print('NHI={:g}'.format(NHI))
z = x/(911.7)-1
plls = LLSSystem((0*u.deg,0*u.deg),z,[-300.,300]*u.km/u.s,NHI=NHI)
plls.bval = 20*u.km/u.s
plls.fill_lls_lines(bval=20*u.km/u.s, do_analysis=0)
# wrest, Tau model, flux
wrest = spec.dispersion/(1+plls.zabs)
tau = igmlls.tau_multi_lls(spec.dispersion,[plls])
emtau = np.exp(-1. * tau)
lls_flux = lsc.convolve_psf(emtau, 3.)
#xdb.xplot(wrest, lls_flux)
# zmin (next highest LLS or zem)
if len(self.abssys_widg.all_abssys) != 0:
zlls = [lls.zabs for lls in self.abssys_widg.all_abssys if lls.zabs > plls.zabs]
if len(zlls) == 0:
zmin = self.zqso+0.01
else:
zmin = np.min(np.array(zlls)) - 0.01
else:
zmin = self.zqso+0.01
# Pixels for analysis and rolling
# NEED TO CUT ON X-Shooter ARM
apix = np.where( (wrest > 914*u.AA) & #(spec.dispersion<5600*u.AA) &
(spec.dispersion<(1+zmin)*1026.*u.AA))[0] # Might go to Lyb
nroll = (np.argmin(np.abs(spec.dispersion-(911.7*u.AA*(1+zmin))))- # Extra 0.01 for bad z
np.argmin(np.abs(spec.dispersion-(911.7*u.AA*(1+plls.zabs)))))
# Require nroll does not exceed length of spectrum
if np.max(apix)+nroll > len(spec.dispersion):
nroll = len(spec.dispersion) - np.max(apix) - 1
gdpix = np.arange(np.min(apix)-nroll,np.max(apix)+nroll+1)
roll_flux = np.concatenate([np.ones(nroll),lls_flux[apix], np.ones(nroll)])
roll_msk = roll_flux < 0.7
# Generate data arrays
wave_pad = spec.dispersion[gdpix]
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
flux_pad = spec.flux[gdpix]
sig_pad = spec.sig[gdpix]
if len(self.abssys_widg.all_abssys) > 0:
conti_pad = conti.flux[gdpix]
else:
conti_pad = conti.flux[gdpix]
# Generate matricies
flux_matrix = np.zeros((len(roll_flux),nroll))
sig_matrix = np.zeros((len(roll_flux),nroll))
conti_matrix = np.zeros((len(roll_flux),nroll))
roll_matrix = np.zeros((len(roll_flux),nroll))
mask_matrix = np.zeros((len(roll_flux),nroll))
for kk in range(nroll):
roll_matrix[:,kk] = np.roll(roll_flux,kk)
mask_matrix[:,kk] = np.roll(roll_msk,kk)
flux_matrix[:,kk] = flux_pad
conti_matrix[:,kk] = conti_pad
sig_matrix[:,kk] = sig_pad
# Model -- Multiply by continuum
model = roll_matrix * conti_matrix
# Condition
idx = np.where( (model < (flux_matrix-sig_matrix*1.5)) & (mask_matrix==True))
bad_matrix = np.zeros((len(roll_flux),nroll))
bad_matrix[idx] = 1
# Sum on offsets and get redshift
bad = np.sum(bad_matrix,0)
ibest = np.argmin(bad)
zbest = spec.dispersion[ibest+ximn]/(911.7*u.AA)-1 # Quantity
# Add pLLS?
if bad[ibest] < 10:
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
self.add_LLS(zbest.value, bval=20.*u.km/u.s, NHI=NHI)
else:
print('No viable pLLS found with our criteria!')
def evaluate(wave,logN,b,z,wrest,f,gamma,fwhm):
tau = voigt_tau(wave/1e8, [logN,z,b*1e5,wrest/1e8,f,gamma])
fx = np.exp(-1*tau)
if fwhm > 0.:
fx = lsc.convolve_psf(fx, fwhm)
return fx
def auto_plls(self, x, y):
"""Automatically fit a pLLS
Parameters:
----------
x,y: floats
x,y values in the GUI
"""
spec = self.spec_widg.spec # For convenience
if len(self.abssys_widg.all_abssys) > 0:
conti = self.full_model
else:
conti = self.continuum
# Generate toy LLS from click
ximn = np.argmin(np.abs(spec.wavelength.value - x))
if y > conti.flux.value[ximn]:
print("Type F below the continuum fool!")
return
NHI = 17.29 + np.log10(-1. * np.log(y / conti.flux.value[ximn]))
#QtCore.pyqtRemoveInputHook()
#pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
#print('NHI={:g}'.format(NHI))
z = x / (911.7) - 1
plls = LLSSystem((0 * u.deg, 0 * u.deg),
z, [-300., 300] * u.km / u.s,
NHI=NHI)
plls.bval = 20 * u.km / u.s
plls.fill_lls_lines(bval=20 * u.km / u.s, do_analysis=0)
# wrest, Tau model, flux
wrest = spec.wavelength / (1 + plls.zabs)
tau = igmlls.tau_multi_lls(spec.wavelength, [plls])
emtau = np.exp(-1. * tau)
lls_flux = lsc.convolve_psf(emtau, 3.)
#xdb.xplot(wrest, lls_flux)
# zmin (next highest LLS or zem)
if len(self.abssys_widg.all_abssys) != 0:
zlls = [
lls.zabs for lls in self.abssys_widg.all_abssys
if lls.zabs > plls.zabs
]
if len(zlls) == 0:
zmin = self.zqso + 0.05
else:
zmin = np.min(np.array(zlls)) - 0.01
else:
zmin = self.zqso + 0.05
# Pixels for analysis and rolling
# NEED TO CUT ON X-Shooter ARM
apix = np.where((wrest > 914 * u.AA) & #(spec.wavelength<5600*u.AA) &
(spec.wavelength < (1 + zmin) * 1026. * u.AA))[
0] # Might go to Lyb
nroll = (
np.argmin(np.abs(spec.wavelength -
(911.7 * u.AA *
(1 + zmin)))) - # Extra 0.01 for bad z
np.argmin(
np.abs(spec.wavelength - (911.7 * u.AA * (1 + plls.zabs)))))
# Require nroll does not exceed length of spectrum
if np.max(apix) + nroll > len(spec.wavelength):
nroll = len(spec.wavelength) - np.max(apix) - 1
gdpix = np.arange(np.min(apix) - nroll, np.max(apix) + nroll + 1)
roll_flux = np.concatenate(
[np.ones(nroll), lls_flux[apix],
np.ones(nroll)])
roll_msk = roll_flux < 0.7
# Generate data arrays
wave_pad = spec.wavelength[gdpix]
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
flux_pad = spec.flux[gdpix]
sig_pad = spec.sig[gdpix]
if len(self.abssys_widg.all_abssys) > 0:
conti_pad = conti.flux[gdpix]
else:
conti_pad = conti.flux[gdpix]
# Generate matricies
flux_matrix = np.zeros((len(roll_flux), nroll))
sig_matrix = np.zeros((len(roll_flux), nroll))
conti_matrix = np.zeros((len(roll_flux), nroll))
roll_matrix = np.zeros((len(roll_flux), nroll))
mask_matrix = np.zeros((len(roll_flux), nroll))
for kk in range(nroll):
roll_matrix[:, kk] = np.roll(roll_flux, kk)
mask_matrix[:, kk] = np.roll(roll_msk, kk)
flux_matrix[:, kk] = flux_pad
conti_matrix[:, kk] = conti_pad
sig_matrix[:, kk] = sig_pad
# Model -- Multiply by continuum
model = roll_matrix * conti_matrix
# Condition
idx = np.where((model < (flux_matrix - sig_matrix * 1.5))
& (mask_matrix == True))
bad_matrix = np.zeros((len(roll_flux), nroll))
bad_matrix[idx] = 1
# Sum on offsets and get redshift
bad = np.sum(bad_matrix, 0)
ibest = np.argmin(bad)
zbest = spec.wavelength[ibest + ximn] / (911.7 * u.AA) - 1 # Quantity
# Add pLLS?
if bad[ibest] < 10:
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
self.add_LLS(zbest.value, bval=20. * u.km / u.s, NHI=NHI)
else:
print('No viable pLLS found with our criteria!')
def evaluate(wave, logN, b, z, wrest, f, gamma, fwhm):
tau = voigt_tau(wave / 1e8, [logN, z, b * 1e5, wrest / 1e8, f, gamma])
fx = np.exp(-1 * tau)
if fwhm > 0.:
fx = lsc.convolve_psf(fx, fwhm)
return fx
