Jaxspec

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

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 `mendeleev`package.
- `jaxspec` is now tested against the [HEACIT dataset](https://github.com/HEACIT/curated-test-data) 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.

This small update speeds up the data reading process

The v0.0.3 of `jaxspec` includes a large rework of the internal data handling, enabling sparse representation until runtime. It also includes a larger test suite based of the [HEACIT's data](https://github.com/HEACIT/curated-test-data), with more reliable keyword and columns reading.

What's Changed
* Added Agauss, Zagauss and Zgauss by CamilleDiez in https://github.com/renecotyfanboy/jaxspec/pull/122
* Data upgrade by renecotyfanboy in https://github.com/renecotyfanboy/jaxspec/pull/137


**Full Changelog**: https://github.com/renecotyfanboy/jaxspec/compare/v0.0.2...v0.0.3

jaxspec` is now deployed on pypi! Anyone can download it simply with `pip install jaxspec`. This update comes with some new features and a better backend overall :

- Instrumental data is now backed with `xarray.Dataset`, allowing semantic dimension naming which (in my opinion) greatly improves the clarity of coding.
- Same with instrument, which is now a wrapping around the amazing `arviz.InferenceData`, easing the use of `arviz`for various Bayesian checks on the fits
- We reverted the ability to fit several observations at once, as it was implemented in an unmaintainable way. It will come back soon.
- Models are automatically transformed when including transformed prior distributions (e.g. LogUniform). It eases the use of NUTS to sample the posterior distributions

This is the first release of jaxspec. It is highly unstable and a lot of API changes are to be expected until the first stable release.

At this time, you can basically use jaxspec to :

- Load OGIP data
- Define a spectral model
- Fit this spectral model using MCMC
- Include a background model
- Plot the results