improved type hints for lb score function

phykit/services/tree/lb_score.py

@@ -1,5 +1,8 @@
 import sys
 import itertools
+from typing import Dict, List, Tuple
+
+from Bio.Phylo import Newick
 
 from .base import Tree
 
@@ -26,28 +29,28 @@ def run(self):
             stats = calculate_summary_statistics_from_arr(LBis)
             print_summary_statistics(stats)
 
-    def process_args(self, args):
+    def process_args(self, args) -> Dict[str, str]:
         return dict(tree_file_path=args.tree, verbose=args.verbose)
 
-    def calculate_average_distance_between_tips(self, tips: list, tree) -> float:
-        # determine pairwise combinations of tips
-        combos = list(itertools.combinations(tips, 2))
+    def calculate_average_distance_between_tips(
+        self,
+        tips: List[str],
+        tree: Newick.Tree,
+    ) -> float:
+        total_dist = sum(
+            tree.distance(tip1, tip2)
+            for tip1, tip2 in itertools.combinations(tips, 2)
+        )
 
-        # determine average distance between tips
-        # avg_dist is PDa
-        total_dist = float()
-        for combo in combos:
-            total_dist += tree.distance(combo[0], combo[1])
+        num_combos = len(tips) * (len(tips) - 1) // 2
 
-        return total_dist / len(combos)
+        return total_dist / num_combos if num_combos else 0
 
     def calculate_average_distance_of_taxon_to_other_taxa(
-        self, tips: list, tree
-    ) -> list:
-        """
-        calculate average distance of taxon to all other taxon or average PDi.
-        Save results to avg PDis list
-        """
+        self,
+        tips: List[str],
+        tree: Newick.Tree,
+    ) -> List[float]:
         avg_PDis = []
         for tip in tips:
             tips_minus_i = list(set(tips) - set(tip))
@@ -59,10 +62,11 @@ def calculate_average_distance_of_taxon_to_other_taxa(
 
         return avg_PDis
 
-    def calculate_lb_score_per_taxa(self, avg_PDis: list, avg_dist: float) -> list:
-        """
-        create a list with the lb scores for each taxon
-        """
+    def calculate_lb_score_per_taxa(
+        self,
+        avg_PDis: List[float],
+        avg_dist: float
+    ) -> List[float]:
         LBis = []
         for PDi in avg_PDis:
             try:
@@ -76,18 +80,17 @@ def calculate_lb_score_per_taxa(self, avg_PDis: list, avg_dist: float) -> list:
 
         return LBis
 
-    def calculate_lb_score(self, tree):
-        # get tree tips
+    def calculate_lb_score(
+        self,
+        tree: Newick.Tree
+    ) -> Tuple[List[str], List[float]]:
         tips = self.get_tip_names_from_tree(tree)
 
-        # get average distance between tips
         avg_dist = self.calculate_average_distance_between_tips(tips, tree)
 
-        # calculate average distance of taxon i to all other taxa
-        # or PDi and save each result to LBi
-        avg_PDis = self.calculate_average_distance_of_taxon_to_other_taxa(tips, tree)
+        avg_PDis = \
+            self.calculate_average_distance_of_taxon_to_other_taxa(tips, tree)
 
-        # use PDis and avgDist to calculate LB values for each taxon
         LBis = self.calculate_lb_score_per_taxa(avg_PDis, avg_dist)
 
         return tips, LBis

tests/integration/tree/test_lb_score_integration.py

@@ -166,4 +166,4 @@ def test_lb_score_zero_division_error(self, mocked_print):
 
         assert mocked_print.mock_calls == [
             call("Invalid tree. Tree should contain branch lengths"),
-        ]
+        ]