bxa.sherpa package¶
Subpackages¶
Submodules¶
bxa.sherpa.cachedmodel module¶
bxa.sherpa.galabs module¶
bxa.sherpa.invgauss module¶
bxa.sherpa.priors module¶
- bxa.sherpa.priors.create_uniform_prior_for(parameter)[source]¶
Use for location variables (position) The uniform prior gives equal weight in non-logarithmic scale.
- Parameters
parameter – Parameter to create a prior for. E.g. xspowerlaw.mypowerlaw.PhoIndex
- bxa.sherpa.priors.create_jeffreys_prior_for(parameter)[source]¶
deprecated name for create_loguniform_prior_for
- bxa.sherpa.priors.create_loguniform_prior_for(parameter)[source]¶
Use for scale variables (order of magnitude) The Jeffreys prior gives equal weight to each order of magnitude between the minimum and maximum value. Flat in logarithmic scale.
- Parameters
parameter – Parameter to create a prior for. E.g. xspowerlaw.mypowerlaw.norm
It is usually easier to create an ancillary parameter, and link the actual parameter, like so:
from sherpa.models.parameter import Parameter lognorm = Parameter(modelname='mycomponent', name='lognorm', val=-5, min=-4*2, max=0) powerlaw.norm = 10**lognorm
- bxa.sherpa.priors.create_gaussian_prior_for(parameter, mean, std)[source]¶
Use for informed variables. The Gaussian prior weights by a Gaussian in the parameter. If you would like the logarithm of the parameter to be weighted by a Gaussian, create a ancillary parameter (see create_jeffreys_prior_for).
- Parameters
parameter – Parameter to create a prior for. E.g. xspowerlaw.mypowerlaw.PhoIndex
mean – Mean of the Gaussian
std – Standard deviation of the Gaussian
- bxa.sherpa.priors.prior_from_file(filename, parameter)[source]¶
Read a custom prior distribution from a file. The file should have two columns: cumulative probability and value, in ascii format. The cumulative probability has to be equally spaced and should exclude 0 and 1.
Returns a sherpa parameter, a list with that parameter inside, and the prior function.
If the file only constains a single value, that value is returned along with two empty lists.
- bxa.sherpa.priors.create_prior_function(priors=[], parameters=None)[source]¶
Combine the prior transformations into a single function.
This assumes factorized (independent) priors.
- Parameters
priors – individual prior transforms to combine into one function. If priors is empty, uniform priors are used on all passed parameters
parameters – If priors is empty, specify the list of parameters. Uniform priors will be created for them.
bxa.sherpa.qq module¶
QQ plots and goodness of fit
- bxa.sherpa.qq.qq_export(id=None, bkg=False, outfile='qq.txt', elow=None, ehigh=None)[source]¶
Export Q-Q plot into a file for plotting.
- Parameters
id – spectrum id to use (see get_bkg_plot/get_data_plot)
bkg – whether to use get_bkg_plot or get_data_plot
outfile – filename to write results into
elow – low energy limit
ehigh – low energy limit
Example:
qq.qq_export('bg', outfile='my_bg_qq', elow=0.2, ehigh=10)
bxa.sherpa.rebinnedmodel module¶
bxa.sherpa.solver module¶
BXA (Bayesian X-ray Analysis) for Sherpa
Copyright: Johannes Buchner (C) 2013-2020
- class bxa.sherpa.solver.BXASolver(id=None, otherids=(), prior=None, parameters=None, outputfiles_basename='chains/', resume_from=None)[source]¶
Bases:
objectSet up Bayesian analysis with specified parameters+transformations.
- Parameters
id – See the sherpa documentation of calc_stat.
otherids – See the sherpa documentation of calc_stat.
prior – prior function created with create_prior_function.
parameters – List of parameters to analyse.
outputfiles_basename – prefix for output filenames.
resume_from – prefix for output filenames of a previous run with similar posterior from which to resume
If prior is None, uniform priors are used on the passed parameters. If parameters is also None, all thawed parameters are used.
- prior_transform(cube)[source]¶
unit cube transformation.
see https://johannesbuchner.github.io/UltraNest/priors.html#Dependent-priors
- run(sampler_kwargs={'resume': 'overwrite'}, run_kwargs={'Lepsilon': 0.1}, speed='safe', resume=None, n_live_points=None, frac_remain=None, Lepsilon=0.1, evidence_tolerance=None)[source]¶
Run nested sampling with ultranest.
- Sampler_kwargs
arguments passed to ReactiveNestedSampler (see ultranest documentation)
- Run_kwargs
arguments passed to ReactiveNestedSampler.run() (see ultranest documentation)
The following arguments are also available directly for backward compatibility:
- Parameters
resume – sets sampler_kwargs[‘resume’]=’resume’ if True, otherwise ‘overwrite’
n_live_points – sets run_kwargs[‘min_num_live_points’]
evidence_tolerance – sets run_kwargs[‘dlogz’]
Lepsilon – sets run_kwargs[‘Lepsilon’]
frac_remain – sets run_kwargs[‘frac_remain’]
- get_distribution_with_fluxes(elo=None, ehi=None)[source]¶
Computes flux posterior samples.
Returns an array of equally weighted posterior samples (parameter values) with two additional columns: the photon fluxes and the energy fluxes.
The values will be correctly distributed according to the analysis run before.
Module contents¶
BXA (Bayesian X-ray Analysis) for Sherpa
Copyright: Johannes Buchner (C) 2013-2019