Add argument for pass function to define emission probabilities

lshmm/api.py

@@ -223,23 +223,34 @@ def forwards(
     )
 
     if ploidy == 1:
-        forward_function = forwards_ls_hap
+        (
+            forward_array,
+            normalisation_factor_from_forward,
+            log_lik,
+        ) = forwards_ls_hap(
+            num_ref_haps,
+            num_sites,
+            ref_panel_checked,
+            query_checked,
+            emission_matrix,
+            prob_recombination,
+            norm=normalise,
+            emission_func=core.get_emission_probability_haploid,
+        )
     else:
-        forward_function = forward_ls_dip_loop
-
-    (
-        forward_array,
-        normalisation_factor_from_forward,
-        log_lik,
-    ) = forward_function(
-        num_ref_haps,
-        num_sites,
-        ref_panel_checked,
-        query_checked,
-        emission_matrix,
-        prob_recombination,
-        norm=normalise,
-    )
+        (
+            forward_array,
+            normalisation_factor_from_forward,
+            log_lik,
+        ) = forward_ls_dip_loop(
+            num_ref_haps,
+            num_sites,
+            ref_panel_checked,
+            query_checked,
+            emission_matrix,
+            prob_recombination,
+            norm=normalise,
+        )
 
     return forward_array, normalisation_factor_from_forward, log_lik
 
@@ -267,19 +278,26 @@ def backwards(
     )
 
     if ploidy == 1:
-        backward_function = backwards_ls_hap
+        backwards_array = backwards_ls_hap(
+            num_ref_haps,
+            num_sites,
+            ref_panel_checked,
+            query_checked,
+            emission_matrix,
+            normalisation_factor_from_forward,
+            prob_recombination,
+            emission_func=core.get_emission_probability_haploid,
+        )
     else:
-        backward_function = backward_ls_dip_loop
-
-    backwards_array = backward_function(
-        num_ref_haps,
-        num_sites,
-        ref_panel_checked,
-        query_checked,
-        emission_matrix,
-        normalisation_factor_from_forward,
-        prob_recombination,
-    )
+        backwards_array = backward_ls_dip_loop(
+            num_ref_haps,
+            num_sites,
+            ref_panel_checked,
+            query_checked,
+            emission_matrix,
+            normalisation_factor_from_forward,
+            prob_recombination,
+        )
 
     return backwards_array
 
@@ -313,6 +331,7 @@ def viterbi(
             query_checked,
             emission_matrix,
             prob_recombination,
+            emission_func=core.get_emission_probability_haploid,
         )
         best_path = backwards_viterbi_hap(num_sites, V, P)
     else:
@@ -353,18 +372,25 @@ def path_loglik(
     )
 
     if ploidy == 1:
-        path_ll_function = path_ll_hap
+        log_lik = path_ll_hap(
+            num_ref_haps,
+            num_sites,
+            ref_panel_checked,
+            path,
+            query_checked,
+            emission_matrix,
+            prob_recombination,
+            emission_func=core.get_emission_probability_haploid,
+        )
     else:
-        path_ll_function = path_ll_dip
-
-    log_lik = path_ll_function(
-        num_ref_haps,
-        num_sites,
-        ref_panel_checked,
-        path,
-        query_checked,
-        emission_matrix,
-        prob_recombination,
-    )
+        log_lik = path_ll_dip(
+            num_ref_haps,
+            num_sites,
+            ref_panel_checked,
+            path,
+            query_checked,
+            emission_matrix,
+            prob_recombination,
+        )
 
     return log_lik

lshmm/fb_haploid.py

@@ -7,7 +7,9 @@
 
 
 @jit.numba_njit
-def forwards_ls_hap(n, m, H, s, e, r, norm=True):
+def forwards_ls_hap(
+    n, m, H, s, e, r, emission_func, *, norm=True,
+):
     """
     A matrix-based implementation using Numpy.
 
@@ -20,7 +22,7 @@ def forwards_ls_hap(n, m, H, s, e, r, norm=True):
     if norm:
         c = np.zeros(m)
         for i in range(n):
-            emission_prob = core.get_emission_probability_haploid(
+            emission_prob = emission_func(
                 ref_allele=H[0, i],
                 query_allele=s[0, 0],
                 site=0,
@@ -36,7 +38,7 @@ def forwards_ls_hap(n, m, H, s, e, r, norm=True):
         for l in range(1, m):
             for i in range(n):
                 F[l, i] = F[l - 1, i] * (1 - r[l]) + r_n[l]
-                emission_prob = core.get_emission_probability_haploid(
+                emission_prob = emission_func(
                     ref_allele=H[l, i],
                     query_allele=s[0, l],
                     site=l,
@@ -53,7 +55,7 @@ def forwards_ls_hap(n, m, H, s, e, r, norm=True):
     else:
         c = np.ones(m)
         for i in range(n):
-            emission_prob = core.get_emission_probability_haploid(
+            emission_prob = emission_func(
                 ref_allele=H[0, i],
                 query_allele=s[0, 0],
                 site=0,
@@ -65,7 +67,7 @@ def forwards_ls_hap(n, m, H, s, e, r, norm=True):
         for l in range(1, m):
             for i in range(n):
                 F[l, i] = F[l - 1, i] * (1 - r[l]) + np.sum(F[l - 1, :]) * r_n[l]
-                emission_prob = core.get_emission_probability_haploid(
+                emission_prob = emission_func(
                     ref_allele=H[l, i],
                     query_allele=s[0, l],
                     site=l,
@@ -79,7 +81,9 @@ def forwards_ls_hap(n, m, H, s, e, r, norm=True):
 
 
 @jit.numba_njit
-def backwards_ls_hap(n, m, H, s, e, c, r):
+def backwards_ls_hap(
+    n, m, H, s, e, c, r, *, emission_func,
+):
     """
     A matrix-based implementation using Numpy.
 
@@ -96,7 +100,7 @@ def backwards_ls_hap(n, m, H, s, e, c, r):
         tmp_B = np.zeros(n)
         tmp_B_sum = 0
         for i in range(n):
-            emission_prob = core.get_emission_probability_haploid(
+            emission_prob = emission_func(
                 ref_allele=H[l + 1, i],
                 query_allele=s[0, l + 1],
                 site=l + 1,