GitLab

Adding missing index to recover old behavior for single VT calibration

Previous commit accidentally allowed tox to fail, but we want to allow
mypy to fail instead.

Type checker has been neglected for some time, and thus all CI jobs
have been marked as failed.  Now a failed type checker no longer
indicates a failed job.

The shifting and scaling was not being applied consistently to the
beta distribution's PDF, and values outside the supported range were being
extrapolated.  This had no impact for distributions on the standard
[0, 1] scale, as no shifting or scaling was needed, and in the case of
spin distributions, values outside the range were never passed in.

We now correctly apply the shifting and scaling, and perform a check
to ensure values are outside the range give a PDF of zero.  This will
impact performance, as extra operations are added, but will ensure the
output is always correct.  In the future we may want to add an option
to disable the bounds check for models using the [0, 1] scale, as it
isn't necessary there.

Broadcasting was broken for the beta and truncnorm models when the
parameters' dimensions exceed 1.  This occurs when `where=True`, as
`x[True].shape == (1,)+x.shape`, whereas the desired behavior is to
maintain the original shape.  To fix this, we simply broadcast the
`where` array to `parameters.shape`.

- Added a general framework for defining bins for arbitrary uses.
- Added a routine for computing rates restricted to said arbitrary bins.
- Added an executable for computing binned rates for the pl+g model

Added a truncated Gaussian prior, as well as a KDE prior.  KDE prior
loads samples from a specified file, and can optionally be
interpolated to reduce computational cost.  Neither is tested yet, so
no guarantees anything works.

When combining `--unique` and `--max-samples`, the potentially reduced
number of samples from limiting to unique samples causes the random
selection to satisfy `--max-samples` to potentially attempt to index
outside the array of remaining samples.

Now we step through the samples in order, terminating once enough
samples have posterior support to initialize all walkers.  This has
the side effect of removing the shuffling that was previously done.
If your input samples are not independent, be sure to shuffle them
ahead of time, e.g., with `pop_models_h5_cleaned_samples post_cleaned.hdf5 copy`
and `--max-samples` set less than the total.