def create_flux_chain(analyzer, transformations, spectrum, erange = "2.0 10.0"):
"""
For each posterior sample, computes the flux in the given energy range.
The so-created chain can be combined with redshift information to propagate
the uncertainty. This is especially important if redshift is a variable
parameter in the fit (with some prior).
Returns erg/cm^2 energy flux (first column) and photon flux (second column)
for each posterior sample.
"""
posterior = analyzer.get_equal_weighted_posterior()
prefix = analyzer.outputfiles_basename
modelnames = set([t['model'].name for t in transformations])
oldchatter = Xset.chatter, Xset.logChatter
Xset.chatter, Xset.logChatter = 0, 0
# plot models
flux = []
pbar = progressbar.ProgressBar(
widgets=[progressbar.Percentage(), progressbar.Counter('%5d'),
progressbar.Bar(), progressbar.ETA()],
maxval=len(posterior)).start()
for k, row in enumerate(posterior):
set_parameters(parameters=row[:-1], transformations=transformations)
AllModels.calcFlux(erange)
f = spectrum.flux
# compute flux in current energies
f.append([f[0], f[3]])
pbar.update(k)
pbar.finish()
Xset.chatter, Xset.logChatter = oldchatter
return numpy.array(flux)
def create_flux_chain(analyzer, transformations, spectrum, erange = "2.0 10.0"): """ For each posterior sample, computes the flux in the given energy range. The so-created chain can be combined with redshift information to propagate the uncertainty. This is especially important if redshift is a variable parameter in the fit (with some prior). Returns erg/cm^2 energy flux (first column) and photon flux (second column) for each posterior sample. """ posterior = analyzer.get_equal_weighted_posterior() #prefix = analyzer.outputfiles_basename #modelnames = set([t['model'].name for t in transformations]) oldchatter = Xset.chatter, Xset.logChatter Xset.chatter, Xset.logChatter = 0, 0 # plot models flux = [] for k, row in enumerate(tqdm(posterior, disable=None)): set_parameters(values=row[:-1], transformations=transformations) AllModels.calcFlux(erange) f = spectrum.flux # compute flux in current energies flux.append([f[0], f[3]]) Xset.chatter, Xset.logChatter = oldchatter return numpy.array(flux)
def create_flux_chain(analyzer, transformations, spectrum, erange = "2.0 10.0"): """ For each posterior sample, computes the flux in the given energy range. The so-created chain can be combined with redshift information to propagate the uncertainty. This is especially important if redshift is a variable parameter in the fit (with some prior). Returns erg/cm^2 energy flux (first column) and photon flux (second column) for each posterior sample. """ posterior = analyzer.get_equal_weighted_posterior() #prefix = analyzer.outputfiles_basename #modelnames = set([t['model'].name for t in transformations]) oldchatter = Xset.chatter, Xset.logChatter Xset.chatter, Xset.logChatter = 0, 0 # plot models flux = [] for k, row in enumerate(tqdm(posterior, disable=None)): set_parameters(values=row[:-1], transformations=transformations) AllModels.calcFlux(erange) f = spectrum.flux # compute flux in current energies flux.append([f[0], f[3]]) Xset.chatter, Xset.logChatter = oldchatter return numpy.array(flux)
def create_flux_chain(self, spectrum, erange="2.0 10.0", nsamples=None):
"""
For each posterior sample, computes the flux in the given energy range.
The so-created chain can be combined with redshift information to propagate
the uncertainty. This is especially important if redshift is a variable
parameter in the fit (with some prior).
Returns erg/cm^2 energy flux (first column) and photon flux (second column)
for each posterior sample.
"""
#prefix = analyzer.outputfiles_basename
#modelnames = set([t['model'].name for t in transformations])
with XSilence():
# plot models
flux = []
for k, row in enumerate(
tqdm(self.posterior[:nsamples], disable=None)):
set_parameters(values=row,
transformations=self.transformations)
AllModels.calcFlux(erange)
f = spectrum.flux
# compute flux in current energies
flux.append([f[0], f[3]])
return numpy.array(flux)
def set_parameters(transformations, values):
"""
Set current parameters.
"""
pars = []
for i, t in enumerate(transformations):
v = t['aftertransform'](values[i])
assert not isnan(v) and not isinf(v), 'ERROR: parameter %d (index %d, %s) to be set to %f' % (
i, t['index'], t['name'], v)
pars += [t['model'], {t['index']:v}]
AllModels.setPars(*pars)
def set_parameters(transformations, values):
"""
Set current parameters.
"""
pars = []
for i, t in enumerate(transformations):
v = t['aftertransform'](values[i])
assert not isnan(v) and not isinf(v), 'ERROR: parameter %d (index %d, %s) to be set to %f' % (
i, t['index'], t['name'], v)
pars += [t['model'], {t['index']:v}]
AllModels.setPars(*pars)
def create_flux_chain(analyzer, transformations, spectrum, erange="2.0 10.0"):
"""
For each posterior sample, computes the flux in the given energy range.
The so-created chain can be combined with redshift information to propagate
the uncertainty. This is especially important if redshift is a variable
parameter in the fit (with some prior).
Returns erg/cm^2 energy flux (first column) and photon flux (second column)
for each posterior sample.
"""
posterior = analyzer.get_equal_weighted_posterior()
prefix = analyzer.outputfiles_basename
modelnames = set([t['model'].name for t in transformations])
oldchatter = Xset.chatter, Xset.logChatter
Xset.chatter, Xset.logChatter = 0, 0
# plot models
flux = []
pbar = progressbar.ProgressBar(widgets=[
progressbar.Percentage(),
progressbar.Counter('%5d'),
progressbar.Bar(),
progressbar.ETA()
],
maxval=len(posterior)).start()
for k, row in enumerate(posterior):
set_parameters(parameters=row[:-1], transformations=transformations)
AllModels.calcFlux(erange)
f = spectrum.flux
# compute flux in current energies
f.append([f[0], f[3]])
pbar.update(k)
pbar.finish()
Xset.chatter, Xset.logChatter = oldchatter
return numpy.array(flux)
