Jaxspec

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