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 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 update_model(self):
if self.parent is None:
return
all_comp = self.parent.comps_widg.all_comp #selected_components()
if len(all_comp) == 0:
self.model.flux[:] = 1.
return
# Setup lines
wvmin = np.min(self.spec.dispersion)
wvmax = np.max(self.spec.dispersion)
gdlin = []
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
for comp in all_comp:
for line in comp.lines:
wvobs = (1+line.attrib['z'])*line.wrest
if (wvobs>wvmin) & (wvobs<wvmax):
gdlin.append(line)
# Voigt
self.model = xsv.voigt_model(self.spec.dispersion,gdlin,
fwhm=self.fwhm)#,debug=True)
#Define arrays for plotting residuals
if self.plot_residuals:
self.residual_limit = self.spec.sig * self.residual_normalization_factor
self.residual = (self.spec.flux - self.model.flux) * self.residual_normalization_factor
def tau_multi_lls(wave, all_lls):
'''Calculate opacities on an input observed wavelength grid
Parameters:
-----------
wave: Quantity array
Wavelengths
all_lls: List of LLS Class
'''
from xastropy.atomic import ionization as xai
from xastropy.spec import voigt as xsv
#
all_tau_model = np.zeros(len(wave))
# Loop on LLS
for lls in all_lls:
# LL
wv_rest = wave / (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) * xai.photo_cross(1, 1, energy)
# Lyman
tau_Lyman = xsv.voigt_model(wave, 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
# Return
return all_tau_model
def tau_multi_lls(wave, all_lls):
'''Calculate opacities on an input observed wavelength grid
Parameters:
-----------
wave: Quantity array
Wavelengths
all_lls: List of LLS Class
'''
from xastropy.atomic import ionization as xai
from xastropy.spec import voigt as xsv
#
all_tau_model = np.zeros(len(wave))
# Loop on LLS
for lls in all_lls:
# LL
wv_rest = wave / (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) * xai.photo_cross(1,1,energy)
# Lyman
tau_Lyman = xsv.voigt_model(wave, 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
# Return
return all_tau_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 = 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 flux_model(self, spec, smooth=0):
"""
Generate a LLS model given an input spectrum
Parameters:
spec: Spectrum1D
smooth : (0) Number of pixels to smooth by
Returns:
Output model is passed back as a Spectrum
"""
reload(xsv)
# ########
# LLS first
# Energies in LLS rest-frame
wv_rest = spec.dispersion / (self.zabs + 1)
energy = wv_rest.to(u.eV, equivalencies=u.spectral())
# Get photo_cross and calculate tau
tau_LL = (10.**self.NHI / u.cm**2) * xatomi.photo_cross(1, 1, energy)
# ########
# Now the Lyman series
# Check for lines
if 'lls_lines' not in self.__dict__.keys():
self.fill_lls_lines()
#xdb.set_trace()
tau_Lyman = xsv.voigt_model(spec.dispersion, self.lls_lines, flg_ret=2)
# Combine
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]
# Fill in flux
model = copy.deepcopy(spec)
model.flux = np.exp(-1. * tau_model).value
# Smooth?
if smooth > 0:
model.gauss_smooth(smooth)
# Return
return model
def flux_model(self,spec,smooth=0):
"""
Generate a LLS model given an input spectrum
Parameters:
spec: Spectrum1D
smooth : (0) Number of pixels to smooth by
Returns:
Output model is passed back as a Spectrum
"""
reload(xsv)
# ########
# LLS first
# Energies in LLS rest-frame
wv_rest = spec.dispersion / (self.zabs+1)
energy = wv_rest.to(u.eV, equivalencies=u.spectral())
# Get photo_cross and calculate tau
tau_LL = (10.**self.NHI / u.cm**2) * xatomi.photo_cross(1,1,energy)
# ########
# Now the Lyman series
# Check for lines
if 'lls_lines' not in self.__dict__.keys():
self.fill_lls_lines()
#xdb.set_trace()
tau_Lyman = xsv.voigt_model(spec.dispersion, self.lls_lines, flg_ret=2)
# Combine
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]
# Fill in flux
model = copy.deepcopy(spec)
model.flux = np.exp(-1. * tau_model).value
# Smooth?
if smooth > 0:
model.gauss_smooth(smooth)
# Return
return model
def update_model(self):
if self.parent is None:
return
all_comp = self.parent.comps_widg.selected_components()
if len(all_comp) == 0:
self.model.flux[:] = 1.
return
# Setup lines
wvmin = np.min(self.spec.dispersion)
wvmax = np.max(self.spec.dispersion)
gdlin = []
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
for comp in all_comp:
for line in comp.lines:
wvobs = (1+line.attrib['z'])*line.wrest
if (wvobs>wvmin) & (wvobs<wvmax):
gdlin.append(line)
# Voigt
self.model = xsv.voigt_model(self.spec.dispersion,gdlin,
fwhm=self.fwhm)#,debug=True)
def update_model(self):
if self.parent is None:
return
all_comp = self.parent.comps_widg.selected_components()
if len(all_comp) == 0:
self.model.flux[:] = 1.
return
# Setup lines
wvmin = np.min(self.spec.dispersion)
wvmax = np.max(self.spec.dispersion)
gdlin = []
#QtCore.pyqtRemoveInputHook()
#xdb.set_trace()
#QtCore.pyqtRestoreInputHook()
for comp in all_comp:
for line in comp.lines:
wvobs = (1 + line.attrib['z']) * line.wrest
if (wvobs > wvmin) & (wvobs < wvmax):
gdlin.append(line)
# Voigt
self.model = xsv.voigt_model(self.spec.dispersion,
gdlin,
fwhm=self.fwhm) #,debug=True)