Refactor interpolate_allele_probabilities

python/tests/beagle_numba.py

@@ -292,24 +292,13 @@ def interpolate_allele_probabilities(
     assert m == len(genotyped_pos)
     x = len(imputed_pos)
     assert (x, h) == ref_h.shape
-    genotyped_cm = convert_to_genetic_map_position(genotyped_pos)
-    imputed_cm = convert_to_genetic_map_position(imputed_pos)
     weights, marker_interval_start = get_weights(genotyped_pos, imputed_pos)
     assert x == len(weights) == len(marker_interval_start)
     p = np.zeros((x, len(alleles)), dtype=np.float64)
-    # Compute allele probabilities as per Equation 1 in BB2016.
     for a in alleles:
         for i in np.arange(x):
             # Identify the genotyped markers k and k + 1 between the imputed marker i
-            # The indices k should mirror those in `get_weights`.
-            if imputed_cm[i] < genotyped_cm[0]:
-                # Special case: imputed marker is before the first genotyped marker.
-                k = 0
-            elif imputed_cm[i] > genotyped_cm[-1]:
-                # Special case: imputed marker is after the last genotyped marker.
-                k = m - 1
-            else:
-                k = np.max(np.where(imputed_cm[i] > genotyped_cm)[0])
+            k = marker_interval_start[i]
             # Sum over the reference haplotypes where the allele is a at marker i.
             has_a = ref_h[i, :] == a
             p[i, a] += np.sum(weights[i] * sm[k, has_a])