refact(call): move call_alleles_with_gmm procedure to function

strkit/call/call_locus.py

@@ -240,12 +240,48 @@ def calculate_read_distance(
     return distance_matrix
 
 
+def call_alleles_with_gmm(
+    params: CallParams,
+    n_alleles: int,
+    read_dict: dict[str, ReadDict],
+    assign_method: str,
+    # ---
+    rng: np.random.Generator,
+    # ---
+    logger_: logging.Logger,
+    locus_log_str: str,
+) -> CallDict | dict:
+    # Dicts are ordered in Python; very nice :)
+    rdvs = tuple(read_dict.values())
+    read_cns = np.fromiter(map(cn_getter, rdvs), dtype=np.int_)
+    read_weights = np.fromiter(map(weight_getter, rdvs), dtype=np.float_)
+    read_weights /= read_weights.sum()  # Normalize to probabilities
+
+    logger_.debug(f"{locus_log_str} - assigning alleles using {assign_method} method with {read_cns.shape[0]} reads")
+
+    return call_alleles(
+        read_cns, (),
+        read_weights, (),
+        params=params,
+        min_reads=params.min_reads,
+        n_alleles=n_alleles,
+        separate_strands=False,
+        read_bias_corr_min=0,  # TODO: parametrize
+        gm_filter_factor=3,  # TODO: parametrize
+        seed=get_new_seed(rng),
+        logger_=logger_,
+        debug_str=locus_log_str,
+    ) or {}  # Still false-y
+
+
 def call_alleles_with_haplotags(
     params: CallParams,
     haplotags: list[int],
     ps_id: int,
     read_dict_items: tuple[tuple[str, ReadDict], ...],  # We could derive this again, but we already have before...
+    # ---
     rng: np.random.Generator,
+    # ---
     logger_: logging.Logger,
     locus_log_str: str,
 ) -> Optional[dict]:
@@ -266,7 +302,7 @@ def call_alleles_with_haplotags(
 
         # Calculate weights array
         ws = np.fromiter(map(weight_getter, crs), dtype=np.float_)
-        c_ws.append(ws / np.sum(ws))
+        c_ws.append(ws / ws.sum())
 
         hp_reads.append(crs)
 
@@ -710,7 +746,7 @@ def call_alleles_with_incorporated_snvs(
 
         # TODO: Readjust peak weights when combining or don't include
         # Make peak weights sum to 1
-        "peak_weights": peak_weights_pre_adj / np.sum(peak_weights_pre_adj),
+        "peak_weights": peak_weights_pre_adj / peak_weights_pre_adj.sum(),
 
         "peak_stdevs": np.concatenate(tuple(cc["peak_stdevs"] for cc in cdd_ordered), axis=0),
         "modal_n_peaks": n_alleles,  # n. of alleles = n. of peaks always -- if we phased using SNVs
@@ -1382,28 +1418,7 @@ def call_locus(
     single_or_dist_assign: bool = assign_method in ("single", "dist")
 
     if single_or_dist_assign:  # Didn't use SNVs, so call the 'old-fashioned' way - using only copy number
-        # Dicts are ordered in Python; very nice :)
-        rdvs = tuple(read_dict.values())
-        rcns = tuple(map(cn_getter, rdvs))
-        read_cns = np.fromiter(rcns, dtype=np.int_)
-        read_weights = np.fromiter(map(weight_getter, rdvs), dtype=np.float_)
-        read_weights = read_weights / np.sum(read_weights)  # Normalize to probabilities
-
-        logger_.debug(f"{locus_log_str} - assigning alleles using {assign_method} method with {len(rcns)} reads")
-
-        call_data = call_alleles(
-            read_cns, (),
-            read_weights, (),
-            params=params,
-            min_reads=params.min_reads,
-            n_alleles=n_alleles,
-            separate_strands=False,
-            read_bias_corr_min=0,  # TODO: parametrize
-            gm_filter_factor=3,  # TODO: parametrize
-            seed=get_new_seed(rng),
-            logger_=logger_,
-            debug_str=locus_log_str,
-        ) or {}  # Still false-y
+        call_data = call_alleles_with_gmm(params, n_alleles, read_dict, assign_method, rng, logger_, locus_log_str)
 
     allele_time = (datetime.now() - allele_start_time).total_seconds()