v0.1.4

What's new ?

• Fixed a bug where subtracting background had no effect
• Add the possibility to use a spectral model as the background
• Luminosity can be computed using either distance or redshift
• Prior predicting coverage is clearer

Full Changelog: v0.1.3...v0.1.4

v0.1.3

Just fix a silent issue in the previous release

v0.1.2

• Prior predictive coverage using the BayesianFitter
• Internal fixings
• Pretty representation for SpectralModel

v0.1.1

This is our first release after the article acceptance.

• All the numpyro's MCMC are now gathered within our MCMCFitter class, where the user can switch using the sampler parameter
• Priors can now be passed as a flat dictionary instead of the nested dictionary from haiku. We want it to be the main way to pass prior in the future releases.
• Changes in the inner workings of the fitter for future features.

v0.1.0

That's it, we are now entering the beta release of jaxspec! You can expect bugs and changing APIs as always, but now, the project starts to be mature enough so that anyone can use it. You are welcome to give it a try!

Full Changelog: v0.0.8...v0.1.0

v0.0.8

New features

• The best-fit model photon flux, energy flux and luminosity can be computed using the FitResult new methods
• Introduction of NestedSamplingFitter which is still in beta status
• Large data tables and others are now downloaded at runtime using the pooch package

Breaking changes

• Prior distribution must now be passed when building the fitter object instead of using the .fit method
• BayesianFitter is renamed to NUTSFitter

Full Changelog: v0.0.7...v0.0.8

v0.0.7

New features

• Users can now compute the photon flux, energy flux and luminosity using the related methods from the FitResult class. These values can be registered so that comparison with the model parameters can be shown in the corner plots.
• A subset of parameters can be plotted in the FitResult.plot_corner by passing the columns parameter, which should be the list of parameters to display
• The cstat is accessible using FitResult.c_stat
• The plot_ppc has been slightly reworked, more upgrades to come in the future

v0.0.6

Major bugfixes related to the import of APEC tables. jaxspec should now automatically release for each new vX.X.X tag

v0.0.5

The 0.0.5 release of jaxspec is out ! Many code refactoring and internal cleaning is happening, but there are also new features, including a direct fit method for your spectral models.

New features

• jaxspec.fit module now includes BayesianFitter and MinimizationFitter classes to perform either MCMC or direct minimization to find the best set of parameters. Note that MinimizationFitter is faster but more likely to fall in local minima.
• Both BayesianFitter and MinimizationFitter can fit the same model using multiple ObsConfiguration at once to obtain stronger constraints. Either pass a list[ObsConfiguration] or a dict[str, ObsConfiguration] to achieve this.
• Fit results are now plotable with any unit you want! FitResult.plot_ppc haw two new kwargs : x_unit and y_type. x_unit argument can be any unit of length, frequency, or energy and the plot will adapt. y_type can be any of "counts", "countrate", "photon_flux", "photon_flux_density" and will plot the equivalent curve.
• fakeit and fakeit_for_multiple_parameters can specify sparsify_matrix = True for large RMF.
• jaxspec.util.abundances now contains two tables. abundance_table contains the most used abundance tables in astrophysics, and element_table contains various data about elements, imported from the mendeleevpackage.
• jaxspec is now tested against the HEACIT dataset to ensure that it is performant at reading the many types of instrument and observation files produced by the OGIP standard.

Breaking changes

• ChainResult is renamed to FitResult
• ChainResult.chain is renamed to FitResult.to_chain
• ChainResult.sample has been split to FitResult.samples_haiku for a two level dictionary of posterior samples, e.g. samples['powerlaw_1']['alpha'], and to FitResult.samples_flat for a one level dictionary of posterior samples, e.g. samples['powerlaw_1_alpha'].
• BayesianModel is renamed to BayesianFitter.
• Instrument.from_ogip_file now takes arf_path as a kwarg since some instruments use a single .rmf file to carry both the redistribution matrix and effective area. Instrument.from_ogip_file("data/PN.rmf","data/PN.arf") should be changed to Instrument.from_ogip_file("data/PN.rmf", arf_path="data/PN.arf"). Even if the first syntax should work, be carefull to check that you did not intervert .arf and .rmf.

v0.0.4

This small update speeds up the data reading process