WIP: Jax backend

Implements a different backend in gwdistributions.backends.numerics so that users can rely on jax when using gw-distribution's objects. This should allow users to smoothly support HMC sampling with numpyro.

fixes #23 (closed)

To Do

• implement backend for jax
• remove backend for theano.tensor
• change all calls to be.vector and be.matrix to just be.array
• remove all instances where we set specific indecies of arrays (not supported with trivial syntax in jax)
  • we just have to live with array assignment during sampling? or use lists and then cast?
  • we can eat the little bit of extra cost when instantiating interpolators?
• run full test suite with each backend
  • numpy
  • jax.numpy
  • cupy (NOT TESTED)

Note: it appears that the following numpy opperations are ordered in this way from fastest to slowest

• x += y
• x = x + y
• x[:] = x + y

not sure about the memory cost, though.