Add debug logging to weighted distances

workflow/scripts/weighted_distances.py

@@ -87,11 +87,14 @@ def build_cache(variant_table: pd.DataFrame, reference: Seq):
             if sample_name not in cache["hz"]:
                 cache["hz"][sample_name] = {}
             cache["freq"][sample_name][position] = get_frequencies_in_position(variant_table, sample_name, position, reference)
+            logging.debug(f"Frequencies for '{sample_name}':{position} = {cache['freq'][sample_name][position]}")
             cache["hz"][sample_name][position] = heterozygosity(cache["freq"][sample_name][position])
+            logging.debug(f"Heterozygosity for '{sample_name}':{position} = {cache['hz'][sample_name][position]}")
     return cache
 
 
 def calc_heterozygosities(sample1_name: str, sample2_name: str, pos: int, cache: dict):
+    logging.debug(f"Calculating heterozygosities at position {pos} for '{sample1_name}' and '{sample2_name}'")
     # Retrieve pre-computed values
     freqs1 = cache["freq"][sample1_name][pos]
     freqs2 = cache["freq"][sample2_name][pos]
@@ -129,21 +132,28 @@ def calculate_distance_matrix(variant_table: pd.DataFrame, samples: List[str], r
 
 def main():
 
-    logging.basicConfig(filename=snakemake.log[0], format=snakemake.config["LOG_PY_FMT"], level=logging.INFO)
+    logging.basicConfig(filename=snakemake.log[0], format=snakemake.config["LOG_PY_FMT"], level=logging.DEBUG)
 
     logging.info("Reading input FASTA files")
     ancestor = read_monofasta(snakemake.input.ancestor)
+    logging.debug(f"Ancestor: '{ancestor.description}', length={len(ancestor.seq)}")
     reference = read_monofasta(snakemake.input.reference)
+    logging.debug(f"Reference: '{reference.description}', length={len(reference.seq)}")
 
     logging.info("Reading input tables")
     masked_positions = read_masked_sites(snakemake.input.vcf, snakemake.params.mask_class)
+    logging.debug(f"Read {len(masked_positions)} masked positions")
     input_table = pd.read_table(snakemake.input.tsv, sep="\t")
+    logging.debug(f"Read {len(input_table)} rows in input TSV")
     ancestor_table = build_ancestor_variant_table(ancestor.seq, reference.seq, reference.id, masked_positions)
+    logging.debug(f"Ancestor has {len(ancestor_table)} variants")
     variant_table = pd.concat([input_table, ancestor_table], ignore_index=True)
+    logging.debug(f"Combined table has {len(ancestor_table)} variants")
 
     logging.info(f"Calculating distance matrix")
     sample_names = snakemake.params.samples + [reference.id]
     distances = calculate_distance_matrix(variant_table, sample_names, ancestor.seq)
+    logging.debug(f"Distance matrix has shape: {distances.shape}")
 
     logging.info("Writing results")
     distances.to_csv(snakemake.output.distances)