Added chi_eff functions

Resolves #10.

This adopts several of the functions from Iwaya et al. in DCC P2400395. Coded up the equations in that paper in a way that can be handled with jax.

data_collection.py:

• We now read in "chi_eff" and "chi_p" from the PE files.
• We now include "chi_eff" and "chi_p" from the injections if these parameters are in args.parameters
• If both "chi_eff" and "chi_p" are in args.parameters do this using the joint chi_eff-chi-p jacobian. This is for both the PE samples and the injections.

main.py

• if "spin" is a vt-parameter then check if chi-eff and chi_p are the specified spin parameters. If not, use the magnitude and tilt models.

analytic_spin_prior.py

• The analytic integrals (I_1, I_2, J_3, I_4) are implemented in a jax-agnostic way under the function prior_chieff_chip_isotropic, which is @maybe_jitted
• These functions call the Dilogarithm/Spence function which is defined under Li2. If jax is not the active backend, then it uses the scipy spence function. Otherwise, I wrote a jaxspence implementation of spence which is used, as the jax.scipy version of spence does not work with complex inputs

There are probably some improvements that can be made, but I think this is in a form that we can start testing. @colm.talbot