chore: black formatting

Author: shloknatarajan

src/citations/line_citation_generator.py

@@ -413,8 +413,8 @@ def add_citations_to_annotations(
             )
 
             # Get p-value citations for this annotation
-            p_value_citations_candidates = self._get_top_p_value_citations_for_annotation(
-                annotation, top_k=3
+            p_value_citations_candidates = (
+                self._get_top_p_value_citations_for_annotation(annotation, top_k=3)
             )
 
             # Filter out duplicate citations within this annotation
@@ -487,8 +487,8 @@ def add_citations_to_annotations(
             # Final fallback for p-value citations: if still no unique p-value citations, use lower similarity threshold
             if len(unique_p_value_citations) == 0:
                 # Try with a lower similarity threshold
-                fallback_p_value_candidates = self._get_top_p_value_citations_for_annotation(
-                    annotation, top_k=5
+                fallback_p_value_candidates = (
+                    self._get_top_p_value_citations_for_annotation(annotation, top_k=5)
                 )
                 for citation in fallback_p_value_candidates:
                     is_duplicate = any(
@@ -511,10 +511,15 @@ def add_citations_to_annotations(
                     p_value_mentions = [
                         s
                         for s in self.sentences
-                        if any(keyword in s.lower() for keyword in ["p-value", "p<", "p =", "significant"])
+                        if any(
+                            keyword in s.lower()
+                            for keyword in ["p-value", "p<", "p =", "significant"]
+                        )
                     ]
                     if p_value_mentions:
-                        unique_p_value_citations = p_value_mentions[:2]  # Take top 2 p-value citations
+                        unique_p_value_citations = p_value_mentions[
+                            :2
+                        ]  # Take top 2 p-value citations
 
             # Create new annotation with unique citations
             updated_annotation = AnnotationRelationship(
@@ -523,7 +528,9 @@ def add_citations_to_annotations(
                 relationship_effect=annotation.relationship_effect,
                 p_value=annotation.p_value,
                 citations=unique_citations[:3],  # Take top 3 unique citations
-                p_value_citations=unique_p_value_citations[:2],  # Take top 2 unique p-value citations
+                p_value_citations=unique_p_value_citations[
+                    :2
+                ],  # Take top 2 unique p-value citations
             )
 
             updated_relationships.append(updated_annotation)
@@ -858,14 +865,15 @@ def _score_sentence_for_p_value(
 
         # Check for numerical patterns that might be p-values
         import re
+
         p_value_patterns = [
             r"p\s*[<>=≤≥]\s*0\.\d+",
             r"p\s*=\s*\d+\.\d+",
             r"p\s*<\s*0\.05",
             r"p\s*<\s*0\.01",
             r"p\s*<\s*0\.001",
         ]
-        
+
         for pattern in p_value_patterns:
             if re.search(pattern, sentence_lower):
                 score += 3
@@ -1254,7 +1262,9 @@ def main():
         print()
 
     # Get p-value citations for the annotation
-    p_value_citations = generator._get_top_p_value_citations_for_annotation(test_annotation, top_k=2)
+    p_value_citations = generator._get_top_p_value_citations_for_annotation(
+        test_annotation, top_k=2
+    )
 
     print(f"Found {len(p_value_citations)} p-value citations:")
     for i, citation in enumerate(p_value_citations, 1):

src/ontology_module/drug_ontology.py

@@ -1,31 +1,28 @@
-
-
 from typing import Optional
 import logging
 from .variant_ontology import BaseNormalizer, NormalizationResult
 
 import requests
 
-# how to use, you have thew following, 
+# how to use, you have thew following,
 
 
 logger = logging.getLogger(__name__)
 
+
 class DrugNormalizer(BaseNormalizer):
     """Normalizes drug names, and connect to common ID's per use."""
 
     def __init__(self):
         super().__init__()
-        
-        self.register_handler(self.lookup_drug_pubchem)
-        
-
 
-        #TODO: insert logic to handle base generic instead of what we have 
+        self.register_handler(self.lookup_drug_pubchem)
 
+        # TODO: insert logic to handle base generic instead of what we have
 
         self.register_handler(self.lookup_drug_pharmgkb)
-        # register the pubchem first before I register the other. 
+        # register the pubchem first before I register the other.
+
     def name(self):
         return "Drug Normalizer"
 
@@ -68,8 +65,8 @@ def lookup_drug_pubchem(self, raw: str) -> Optional[NormalizationResult]:
                 metadata={
                     "cid": cid,
                     "molecular_formula": props.get("MolecularFormula"),
-                    "canonical_smiles": props.get("CanonicalSMILES")
-                }
+                    "canonical_smiles": props.get("CanonicalSMILES"),
+                },
             )
 
         except requests.RequestException as exc:
@@ -78,6 +75,7 @@ def lookup_drug_pubchem(self, raw: str) -> Optional[NormalizationResult]:
             logger.warning("Unexpected error for '%s': %s", raw, exc)
 
         return None
+
     def get_generic_from_brand_pubchem(self, raw: str) -> Optional[str]:
         """
         Resolves a brand name to a generic (IUPAC) name using PubChem.
@@ -88,13 +86,12 @@ def get_generic_from_brand_pubchem(self, raw: str) -> Optional[str]:
             return result.normalized_output
         return None
 
-
     def lookup_drug_pharmgkb(self, raw: str) -> Optional[NormalizationResult]:
         """
         Lookup drug info from PharmGKB using its REST API.
         Returns all available metadata without filtering.
         """
-        query = raw.strip().lower()  
+        query = raw.strip().lower()
         if not query:
             logger.debug("Empty drug input for PharmGKB lookup.")
             return None
@@ -121,15 +118,18 @@ def lookup_drug_pharmgkb(self, raw: str) -> Optional[NormalizationResult]:
                 normalized_output=entry.get("name", raw),
                 entity_type="drug",
                 source="PharmGKB",
-                metadata=entry  # Store the entire returned dictionary
+                metadata=entry,  # Store the entire returned dictionary
             )
 
         except requests.RequestException as exc:
             logger.warning("PharmGKB request failed for '%s': %s", raw, exc)
         except Exception as exc:
-            logger.warning("Unexpected error during PharmGKB lookup for '%s': %s", raw, exc)
+            logger.warning(
+                "Unexpected error during PharmGKB lookup for '%s': %s", raw, exc
+            )
 
         return None
+
     def lookup_drug_rxnorm(self, raw: str) -> Optional[NormalizationResult]:
         """
         Resolves a drug name (brand or generic) using the RxNorm API.
@@ -153,7 +153,9 @@ def lookup_drug_rxnorm(self, raw: str) -> Optional[NormalizationResult]:
             rxcui = rxcui_list[0]
 
             # Step 2: Get related ingredient (generic) names from RxCUI
-            related_url = f"https://rxnav.nlm.nih.gov/REST/rxcui/{rxcui}/related.json?tty=IN"
+            related_url = (
+                f"https://rxnav.nlm.nih.gov/REST/rxcui/{rxcui}/related.json?tty=IN"
+            )
             related_resp = requests.get(related_url, timeout=5)
             related_resp.raise_for_status()
             related_data = related_resp.json()
@@ -174,10 +176,7 @@ def lookup_drug_rxnorm(self, raw: str) -> Optional[NormalizationResult]:
                 normalized_output=ingredients[0],  # first generic match
                 entity_type="drug",
                 source="RxNorm",
-                metadata={
-                    "rxcui": rxcui,
-                    "generic_candidates": ingredients
-                }
+                metadata={"rxcui": rxcui, "generic_candidates": ingredients},
             )
 
         except requests.RequestException as exc:
@@ -188,8 +187,6 @@ def lookup_drug_rxnorm(self, raw: str) -> Optional[NormalizationResult]:
         return None
 
 
-
-
 def test_lookup_pubchem():
     normalizer = DrugNormalizer()
     drug = "Imatinib"
@@ -239,9 +236,8 @@ def test_lookup_pharmgkb():
 
 if __name__ == "__main__":
     test_lookup_pubchem()
-    
+
     test_lookup_pharmgkb()
     normalizer = DrugNormalizer()
     result = normalizer.lookup_drug_rxnorm("Gleevec")
     print(result.normalized_output)  # → "imatinib"
-