Add fixed parameters in the GWSampler

[stephengreen]

This adds the parameters that have delta-function priors when sampling. It is needed so that the sky rotation transform and importance sampling works when holding some parameters fixed.

Corner plotting is also updated with a new flag for whether to include these fixed parameters. Default is to not include them.

This still needs to be tested.

[annalena-k]

Yes, this PR fixes running injections with fixed parameters.

I tested the PR on the tutorial example and the following code worked to generate an injection with fixed posterior parameters except for chirp mass and mass ratio:

    # Load the network
    pm = PosteriorModel(model_path, device="cpu")
    sampler = GWSampler(model=pm)

    # Generate an injection consistent with the data the model was trained on.
    injection = Injection.from_posterior_model_metadata(pm.metadata)
    injection.asd = ASDDataset(asd_path, ifos=["H1", "L1"])
    theta = injection.prior.sample()
    inj = injection.injection(theta)

    # Generate 10,000 samples from the normalizing flow.
    sampler.context = inj
    sampler.run_sampler(10000)

    result = sampler.to_result()

    # Run importance-sampling on the result.
    num_processes = 8
    result.importance_sample(num_processes=num_processes)

    # Make a corner plot and save the result.
    result.print_summary()

However, running dingo_pipe on a model with Delta priors does not work (the weights are nan?). I will do some more investigation to track down the bug.

[stephengreen]

Try it now. I'm excluding the fixed parameters when evaluating the prior.

[annalena-k]

I tested the new commit on dingo_pipe of the tutorial example with chirp mass and mass ratio:

• It works and runs through.
• The corner plot contains chirp mass, mass ratio, log prob, log prior, log likelihood and weights (as expected).

Example plot:

This should be sufficient for the tutorial. You can merge the branch.