if redshift_uncertain:
priors.append(limited_0_10)
parameters.append(redshift)
priorfunction = bxa.create_prior_function(priors=priors)
assert not numpy.isnan(
calc_stat(id)), 'NaN on calc_stat, probably a bad RMF/ARF file for PC'
if id2:
assert not numpy.isnan(
calc_stat(id2)), 'NaN on calc_stat, probably a bad RMF/ARF file for WT'
print('running BXA ...')
outputfiles_basename = 'spherefit3_'
if not os.path.exists(outputfiles_basename + 'params.json'):
bxa.nested_run(id,
otherids=otherids,
prior=priorfunction,
parameters=parameters,
resume=True,
verbose=True,
n_live_points=400,
outputfiles_basename=outputfiles_basename)
if os.environ.get('INTERACTIVE', '0') == '1':
print('setting to best fit ...')
bxa.set_best_fit(id,
otherids=otherids,
parameters=parameters,
outputfiles_basename=outputfiles_basename)
no_exit = True
else:
import pymultinest
a = pymultinest.analyse.Analyzer(n_params=len(parameters),
outputfiles_basename=outputfiles_basename)
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()
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
)
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()
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)
assert not numpy.isnan(calc_stat(id)), 'NaN on calc_stat, probably a bad RMF/ARF file for PC'
if id2:
assert not numpy.isnan(calc_stat(id2)), 'NaN on calc_stat, probably a bad RMF/ARF file for WT'
print('running BXA ...')
outputfiles_basename = 'spherefit3_'
if not os.path.exists(outputfiles_basename + 'params.json'):
bxa.nested_run(id, otherids=otherids, prior=priorfunction, parameters = parameters,
resume = True, verbose=True, n_live_points=400,
outputfiles_basename = outputfiles_basename)
if os.environ.get('INTERACTIVE', '0') == '1':
print('setting to best fit ...')
bxa.set_best_fit(id, otherids=otherids, parameters = parameters, outputfiles_basename = outputfiles_basename)
no_exit = True
else:
import pymultinest
a = pymultinest.analyse.Analyzer(n_params = len(parameters), outputfiles_basename = outputfiles_basename)
if not os.path.exists('%sfit.json' % outputfiles_basename):
print('collecting fit plot data')
set_analysis(id, 'ener', 'counts')
group_counts(id, 40)
set_stat('chi2gehrels')
pl = get_fit_plot(id)
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()
p = bkg_model.pars[0]
p.max = p.val + 2
p.min = p.val - 2
parameters.append(p)
priors += [bxa.create_uniform_prior_for(p)]
#################
# BXA run
priorfunction = bxa.create_prior_function(priors=priors)
print('running BXA ...')
bxa.nested_run(id=ids[0],
otherids=tuple(ids[1:]),
prior=priorfunction,
parameters=parameters,
resume=True,
verbose=True,
outputfiles_basename=prefix,
n_live_points=os.environ.get('NLIVEPOINTS', 400),
importance_nested_sampling=False)
try:
from mpi4py import MPI
if MPI.COMM_WORLD.Get_rank() > 0:
sys.exit(0)
except Exception as e:
pass
outputfiles_basename = prefix
print('getting best-fit ...')
bxa.set_best_fit(parameters=parameters,
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,
outputfiles_basename = 'wabs_noz_',
importance_nested_sampling = False)
m = get_bkg_fit_plot(id)
numpy.savetxt('test_bkg.txt', numpy.transpose([m.dataplot.x, m.dataplot.y, m.modelplot.x, m.modelplot.y]))
m = get_fit_plot(id)
numpy.savetxt('test_src.txt', numpy.transpose([m.dataplot.x, m.dataplot.y, m.modelplot.x, m.modelplot.y]))
if id2:
m = get_fit_plot(id2)
numpy.savetxt('test_src2.txt', numpy.transpose([m.dataplot.x, m.dataplot.y, m.modelplot.x, m.modelplot.y]))
exit()