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 '''
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
# use finer wavelength array to resolve absorption features.
wa = self.full_model.dispersion
# 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)
#QtCore.pyqtRemoveInputHook()
#import pdb; pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
# 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
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]
# 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
# use finer wavelength array to resolve absorption features.
wa = self.full_model.dispersion
# 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)
#QtCore.pyqtRemoveInputHook()
#import pdb; pdb.set_trace()
#QtCore.pyqtRestoreInputHook()
# 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
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]
# Model
self.spec_widg.model = self.full_model
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 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!')
