Commits on Sep 4, 2023

1. Implement a two-locus C API for count statistics. …

   We also fully implement two-site statistics, mirroring the functionality
   of the LdCalculator.
   
   There are a number of tasks that need to be done before this is
   production ready, but it is a start. We've opened a number of issues in
   github for improving the work that was done for this PR and for
   implementing the python API.
   
   The general approach for the two-site statistics is to gather all of the
   samples below each mutation over the whole tree sequence (we will be
   implementing windows in the near future). To do this, we iterate over
   each tree in the tree sequence once and perform an in-order traversal
   starting at each mutation's node, storing the samples we encounter along
   the way. We then perform another step that examines the parentage of
   each mutation and removes samples that exist in child mutations. With
   the samples for each allele, we perform intersections to get haplotype
   weights, which are then passed to summary functions that fill out a
   stats matrix.
   
   We also implement a few happy-path tests that test the functionality of
   sample sets and multi-allelic sites. We also cover all of the non
   memory-related error cases in the C api. We plan to do more in-depth
   testing once the python API is implemented.
   
   This PR also introduces the concept of a "bit array" in the form of
   tsk_bit_array_t, which needs some further refinement. In essence, this
   data structure stores all of the possible samples in a bit set where one
   sample takes up one bit of information and storage space. This enables a
   few functions that are critical to this code: 1) it's a relatively
   memory efficient method of storing sample identities 2) we can
   efficiently intersect the arrays (I believe much of this is
   autovectorized by gcc) 3) we can use tricks like SWAR (SIMD within an
   array) to count the number of samples in a set. It's important to note
   that this is also where the algorithm spends ~50% of it's time. It can
   use more improvement in both the API design and the performance
   characteristics.

   

   lkirk authored and jeromekelleher committed Sep 4, 2023
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