myabs.nH.max = 1000
mypow.norm.min = 1e-10
mypow.norm.max = 10
mypow.PhoIndex.min = 1
mypow.PhoIndex.max = 3
# add ancillary parameter for NH, so we can work in nice logarithmic units
from sherpa.models.parameter import Parameter
lognH = Parameter(modelname='myabs', name='logNH', val=22, min=20, max=24)
myabs.nH = 10**(lognH - 22)
# define prior
parameters = [lognH, mypow.norm, mypow.PhoIndex]
priors = [
# jeffreys prior for nH -- is uniform in lognH
bxa.create_uniform_prior_for(lognH),
# jeffreys prior for scale variable
bxa.create_jeffreys_prior_for(mypow.norm),
# custom gaussian prior function for photon index
bxa.create_gaussian_prior_for(mypow.PhoIndex, 1.9, 0.15),
# and possibly many more
]
prior = bxa.create_prior_function(priors=priors)
# send it off!
bxa.nested_run(
prior=prior,
parameters=parameters,
outputfiles_basename=outputfiles_basename,
verbose=True, # show a bit of progress
resume=True, # MultiNest supports resuming a crashed/aborted run
srclevel2 = Parameter('src2', 'level', numpy.log10(sphere2.norm.val), -8, 3,
-8, 3)
srcnh = Parameter('src', 'nh', numpy.log10(sphere.nh.val) + 22, 20, 26, 20, 26)
galnh = galabso.nH.val
sphere.norm = 10**srclevel
sphere2.norm = 10**srclevel2
sphere.nh = 10**(srcnh - 22)
sphere2.nh = 10**(srcnh - 22)
galabso.nH = 10**(galnh - 22)
priors = []
parameters = [srclevel, sphere.phoindex, srcnh]
import bxa.sherpa as bxa
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_gaussian_prior_for(sphere.phoindex, 1.95, 0.15)]
priors += [bxa.create_uniform_prior_for(srcnh)]
if id2:
priors += [bxa.create_uniform_prior_for(srclevel2)]
parameters.append(srclevel2)
priors += [bxa.create_uniform_prior_for(sphere2.phoindex)]
parameters.append(sphere2.phoindex)
if id2:
otherids = (id2, )
else:
otherids = tuple()
if redshift_uncertain:
priors.append(limited_0_10)
parameters.append(redshift)
priorfunction = bxa.create_prior_function(priors=priors)
import invgauss
f = invgauss.get_invgauss_func(numpy.log10(props['nhgal']), 0.15)
def limited_19_24(x):
v = f(x)
if v <= 19:
v = 19
if v >= 24:
v = 24
return v
priors = []
parameters = [srclevel, src.PhoIndex, srcnh, galnh]
import bxa.sherpa as bxa
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_uniform_prior_for(src.PhoIndex)]
priors += [bxa.create_uniform_prior_for(srcnh)]
priors += [limited_19_24] # galnh
priorfunction = bxa.create_prior_function(priors = priors)
print('running BXA ...')
bxa.nested_run(id, prior=priorfunction, parameters = parameters,
resume = True, verbose=True,
outputfiles_basename = 'superfit_')
exit()
f = invgauss.get_invgauss_func(numpy.log10(props['nhgal']), 0.15)
def limited_19_24(x):
v = f(x)
if v <= 19:
v = 19
if v >= 24:
v = 24
return v
priors = []
parameters = [srclevel, src.PhoIndex, srcnh, galnh]
import bxa.sherpa as bxa
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_uniform_prior_for(src.PhoIndex)]
priors += [bxa.create_uniform_prior_for(srcnh)]
priors += [limited_19_24] # galnh
priorfunction = bxa.create_prior_function(priors=priors)
print 'running BXA ...'
bxa.nested_run(id,
prior=priorfunction,
parameters=parameters,
resume=True,
verbose=True,
outputfiles_basename='superfit_')
exit()
outputfiles_basename = 'simplest-' set_model(xspowerlaw.mypow) # set model parameters ranges mypow.norm.min = 1e-10 mypow.norm.max = 10 mypow.PhoIndex.min = 1 mypow.PhoIndex.max = 3 # define prior parameters = [mypow.PhoIndex, mypow.norm] priors = [ # uniform prior for Photon Index (see other example for # something more advanced) bxa.create_uniform_prior_for(mypow.PhoIndex), # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(mypow.norm), # and possibly many more parameters here ] prior = bxa.create_prior_function(priors=priors) # send it off! bxa.nested_run(prior = prior, parameters = parameters, outputfiles_basename = outputfiles_basename, verbose=True, # show a bit of progress resume=True, # MultiNest supports resuming a crashed/aborted run ) exit()
srclevel = Parameter('src', 'level', numpy.log10(sphere.norm.val), -8, 3, -8, 3)
srclevel2 = Parameter('src2', 'level', numpy.log10(sphere2.norm.val), -8, 3, -8, 3)
srcnh = Parameter('src', 'nh', numpy.log10(sphere.nh.val)+22, 20, 26, 20, 26)
galnh = galabso.nH.val
sphere.norm = 10**srclevel
sphere2.norm = 10**srclevel2
sphere.nh = 10**(srcnh - 22)
sphere2.nh = 10**(srcnh - 22)
galabso.nH = 10**(galnh - 22)
priors = []
parameters = [srclevel, sphere.phoindex, srcnh]
import bxa.sherpa as bxa
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_gaussian_prior_for(sphere.phoindex, 1.95, 0.15)]
priors += [bxa.create_uniform_prior_for(srcnh)]
if id2:
priors += [bxa.create_uniform_prior_for(srclevel2)]
parameters.append(srclevel2)
priors += [bxa.create_uniform_prior_for(sphere2.phoindex)]
parameters.append(sphere2.phoindex)
if id2:
otherids = (id2,)
else:
otherids = tuple()
if redshift_uncertain:
priors.append(limited_0_10)
parameters.append(redshift)
priorfunction = bxa.create_prior_function(priors = priors)
redshift = Parameter('src', 'z', 1, 0, 10, 0, 10)
torus.redshift = redshift
scat.redshift = redshift
scat.phoindex = torus.phoindex
scat.ecut = torus.ecut
scat.theta_inc = torus.theta_inc
scat.torsigma = torus.torsigma
scat.ctkcover = torus.ctkcover
softscatnorm = Parameter('src', 'softscatnorm', -2, -7, -1, -7, -1)
scat.norm = 10**(srclevel + softscatnorm)
print('creating priors')
priors = []
parameters = [srclevel, torus.phoindex, srcnh, softscatnorm]
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_gaussian_prior_for(torus.phoindex, 1.95, 0.15)]
priors += [bxa.create_uniform_prior_for(srcnh)]
priors += [bxa.create_uniform_prior_for(softscatnorm)]
#
# apec with L(2-10keV) = 1e42 erg/s
# z norm 10keV norm 2keV
# 0.1 40e-6 150e-6
# 0.5 2e-6 6e-6
# 1 0.7e-6 2e-6
# 3 0.15e-6 0.5e-6
# ================================
# z --> 0.5e-6/ z**2 2e-6/z**2
#
if os.environ.get('WITHAPEC', '1') == '1':
myabs.nH.max = 1000 mypow.norm.min = 1e-10 mypow.norm.max = 10 mypow.PhoIndex.min = 1 mypow.PhoIndex.max = 3 # add ancillary parameter for NH, so we can work in nice logarithmic units from sherpa.models.parameter import Parameter lognH = Parameter(modelname='myabs', name='logNH', val=22, min=20, max=24) myabs.nH = 10**(lognH - 22) # define prior parameters = [lognH, mypow.norm, mypow.PhoIndex] priors = [ # jeffreys prior for nH -- is uniform in lognH bxa.create_uniform_prior_for(lognH), # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(mypow.norm), # custom gaussian prior function for photon index bxa.create_gaussian_prior_for(mypow.PhoIndex, 1.9, 0.15), # and possibly many more ] prior = bxa.create_prior_function(priors=priors) # send it off! bxa.nested_run(prior = prior, parameters = parameters, outputfiles_basename = outputfiles_basename, verbose=True, # show a bit of progress resume=True, # MultiNest supports resuming a crashed/aborted run )
outputfiles_basename = 'simplest-' set_model(xspowerlaw.mypow) # set model parameters ranges mypow.norm.min = 1e-10 mypow.norm.max = 10 mypow.PhoIndex.min = 1 mypow.PhoIndex.max = 3 # define prior parameters = [mypow.PhoIndex, mypow.norm] priors = [ # uniform prior for Photon Index (see other example for # something more advanced) bxa.create_uniform_prior_for(mypow.PhoIndex), # jeffreys prior for scale variable bxa.create_jeffreys_prior_for(mypow.norm), # and possibly many more parameters here ] prior = bxa.create_prior_function(priors=priors) # send it off! solver = bxa.BXASolver(prior = prior, parameters = parameters, outputfiles_basename = outputfiles_basename) results = solver.run( resume=True, # UltraNest supports resuming a crashed/aborted run ) exit()
# creating ancillary parameters for logarithmic treatment
print('creating prior functions...')
from sherpa.models.parameter import Parameter
srclevel = Parameter('src', 'level', numpy.log10(src.norm.val), -8, 2, -8, 2)
srcnh = Parameter('src', 'nh', numpy.log10(abso.nH.val)+22, 19, 24, 19, 24)
galnh = Parameter('gal', 'nh', numpy.log10(galabso.nH.val)+22, 19, 24, 19, 24)
src.norm = 10**srclevel
abso.nH = 10**(srcnh - 22)
priors = []
parameters = [srclevel, src.PhoIndex, srcnh, src.redshift, bkg_model.pars[0]]
import bxa.sherpa as bxa
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_gaussian_prior_for(src.PhoIndex, 1.95, 0.15)]
priors += [bxa.create_uniform_prior_for(srcnh)]
priors += [bxa.create_uniform_prior_for(src.redshift)] # galnh
priors += [bxa.create_uniform_prior_for(bkg_model.pars[0])]
otherids = ()
if id2:
parameters.append(bkg_model2.pars[0])
priors += [bxa.create_uniform_prior_for(bkg_model2.pars[0])]
otherids = (id2,)
priorfunction = bxa.create_prior_function(priors = priors)
print('running BXA ...')
bxa.nested_run(id, otherids=otherids, prior=priorfunction, parameters = parameters,
resume = True, verbose=True,
2)
srcnh = Parameter('src', 'nh',
numpy.log10(abso.nH.val) + 22, 19, 24, 19, 24)
galnh = Parameter('gal', 'nh',
numpy.log10(galabso.nH.val) + 22, 19, 24, 19, 24)
src.norm = 10**srclevel
abso.nH = 10**(srcnh - 22)
priors = []
parameters = [
srclevel, src.PhoIndex, srcnh, src.redshift, bkg_model.pars[0]
]
import bxa.sherpa as bxa
priors += [bxa.create_uniform_prior_for(srclevel)]
priors += [bxa.create_gaussian_prior_for(src.PhoIndex, 1.95, 0.15)]
priors += [bxa.create_uniform_prior_for(srcnh)]
priors += [bxa.create_gaussian_prior_for(src.redshift, 0.3,
0.05)] # redshift uncertainty
priors += [bxa.create_uniform_prior_for(bkg_model.pars[0])]
otherids = ()
if id2:
parameters.append(bkg_model2.pars[0])
priors += [bxa.create_uniform_prior_for(bkg_model2.pars[0])]
otherids = (id2, )
priorfunction = bxa.create_prior_function(priors=priors)
print('running BXA ...')
solver = bxa.BXASolver(id,
