Calibration marginalization using a discrete set of realizations

Implementation of the calibration-marginalized likelihood outlined in LIGO DCC document P2000294.

The code also includes a slight change to the distance marginalization function allowing for arrays of both optimal and matched filter signal-to-noise ratios.

An example of likelihood reweighting with the calibration-marginalized likelihood is included.

Note that this MR relies on !855 (merged) and !856 (merged) to work. Those must be merged first.

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Thanks @ethan.payne I wonder if it makes sense to split this into two MRs, one with the calibration specific changes and one with the modification to the reweighting?

mentioned in merge request !855 (merged)

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Thanks for the comments @colm.talbot! As per your suggestion, I've opened a MR for the reweighting changes (!856 (merged)) though I'm yet to remove them from this MR. It'd be best to merge the reweighting changes first anyway. I've also made a MR for the pyTables import to ensure that the CI pipeline doesn't fail.

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