style: Fix Black formatting issues in 7 files

check_harmony.py

@@ -10,24 +10,23 @@
 from harmonizer.legacy_mapper import LegacyCodeMapper
 
 
-def check_harmony(
-    target_dir: str = ".", config_path: str = None, verbose: bool = False
-):
+def check_harmony(target_dir: str = ".", config_path: str = None, verbose: bool = False):
     print(f"Running LJPW Harmony Check on: {os.path.abspath(target_dir)}")
     print("=" * 60)
 
     # If analyzing a subdirectory, find project root for config
     # Otherwise use target_dir
     project_root = os.getcwd() if target_dir != "." else target_dir
-    
+
     # Create mapper - it will load config from project_root
     mapper = LegacyCodeMapper(target_dir, quiet=not verbose)
-    
+
     # If we're in project root, use config from there
     if os.path.exists(os.path.join(project_root, "pyproject.toml")):
         from harmonizer.config import ConfigLoader
+
         mapper.config = ConfigLoader.load(project_root)
-    
+
     mapper.analyze_codebase(show_progress=True)
 
     failures = []
@@ -81,13 +80,9 @@ def check_harmony(
 
 def main():
     parser = argparse.ArgumentParser(description="LJPW Harmony Check")
-    parser.add_argument(
-        "target", nargs="?", default=".", help="Target directory to analyze"
-    )
+    parser.add_argument("target", nargs="?", default=".", help="Target directory to analyze")
     parser.add_argument("--config", help="Path to configuration file (optional)")
-    parser.add_argument(
-        "-v", "--verbose", action="store_true", help="Enable verbose output"
-    )
+    parser.add_argument("-v", "--verbose", action="store_true", help="Enable verbose output")
 
     args = parser.parse_args()
 

harmonizer/ljpw_baselines.py

@@ -271,118 +271,109 @@ def interpret_composite_score(score: float) -> str:
             return "Excellent - high-performing, growth active"
         else:
             return "Elite - exceptional, Love multiplier engaged"
-    
+
     @staticmethod
     def validate_coupling_structure() -> Dict[str, bool]:
         """
         Validate that the coupling matrix exhibits expected relationship patterns.
-        
+
         This validates the "grammar of semantic interaction":
         - Love amplifies (κ_L→* > 1)
         - Power constrains (κ_P→* < 1)
         - Justice supports Wisdom (κ_JW > κ_JP)
         - Asymmetry is present (κ_ij ≠ κ_ji)
-        
+
         Returns:
             Dict with validation results for each pattern
         """
         cm = LJPWBaselines.COUPLING_MATRIX
-        
+
         # Check Love amplifies
-        love_amplifies = (
-            cm['LJ'] > 1.0 and
-            cm['LP'] > 1.0 and
-            cm['LW'] > 1.0
-        )
-
+        love_amplifies = cm["LJ"] > 1.0 and cm["LP"] > 1.0 and cm["LW"] > 1.0
+
         # Check Power constrains
-        power_constrains = (
-            cm['PL'] < 1.0 and
-            cm['PJ'] < 1.0 and
-            cm['PW'] < 1.0
-        )
-
+        power_constrains = cm["PL"] < 1.0 and cm["PJ"] < 1.0 and cm["PW"] < 1.0
+
         # Check Justice supports Wisdom more than Power
-        justice_wisdom = cm['JW'] > cm['JP']
-        
+        justice_wisdom = cm["JW"] > cm["JP"]
+
         # Check asymmetry (giving ≠ receiving)
         asymmetry = (
-            abs(cm['LJ'] - cm['JL']) > 0.1 and
-            abs(cm['LP'] - cm['PL']) > 0.1 and
-            abs(cm['PJ'] - cm['JP']) > 0.1
+            abs(cm["LJ"] - cm["JL"]) > 0.1
+            and abs(cm["LP"] - cm["PL"]) > 0.1
+            and abs(cm["PJ"] - cm["JP"]) > 0.1
         )
-        
+
         return {
-            'love_amplifies': love_amplifies,
-            'power_constrains': power_constrains,
-            'justice_supports_wisdom': justice_wisdom,
-            'asymmetry_present': asymmetry,
-            'all_patterns_valid': all([
-                love_amplifies,
-                power_constrains,
-                justice_wisdom,
-                asymmetry
-            ])
+            "love_amplifies": love_amplifies,
+            "power_constrains": power_constrains,
+            "justice_supports_wisdom": justice_wisdom,
+            "asymmetry_present": asymmetry,
+            "all_patterns_valid": all(
+                [love_amplifies, power_constrains, justice_wisdom, asymmetry]
+            ),
         }
-    
+
     @staticmethod
-    def check_proportions(L: float, J: float, P: float, W: float, tolerance: float = 0.3) -> Dict[str, any]:
+    def check_proportions(
+        L: float, J: float, P: float, W: float, tolerance: float = 0.3
+    ) -> Dict[str, any]:
         """
         Check if L:J:P:W proportions match Natural Equilibrium (scale-invariant check).
-        
+
         This validates the core insight: "relationships between constants matter more
         than the constants themselves." The same proportions define harmony at any scale.
-        
+
         Args:
             L, J, P, W: Current dimension values (any scale)
             tolerance: Allowed deviation from ideal ratios (default 0.3 = 30%)
-        
+
         Returns:
             Dict with proportion analysis
         """
         NE = ReferencePoints.NATURAL_EQUILIBRIUM
-        
+
         # Calculate current ratios (scale-invariant)
         if J <= 0:
-            return {
-                'proportions_healthy': False,
-                'error': 'Justice dimension cannot be zero'
-            }
-
+            return {"proportions_healthy": False, "error": "Justice dimension cannot be zero"}
+
         current_ratios = {
-            'L/J': L / J,
-            'P/J': P / J,
-            'W/J': W / J,
+            "L/J": L / J,
+            "P/J": P / J,
+            "W/J": W / J,
         }
-        
+
         # Expected ratios from Natural Equilibrium
         expected_ratios = {
-            'L/J': NE[0] / NE[1],  # 1.492
-            'P/J': NE[2] / NE[1],  # 1.734
-            'W/J': NE[3] / NE[1],  # 1.673
+            "L/J": NE[0] / NE[1],  # 1.492
+            "P/J": NE[2] / NE[1],  # 1.734
+            "W/J": NE[3] / NE[1],  # 1.673
         }
-        
+
         # Check deviations
         deviations = {}
         checks = {}
-        
+
         for key in expected_ratios:
             expected = expected_ratios[key]
             actual = current_ratios[key]
             deviation = abs(actual - expected) / expected
             deviations[key] = deviation
             checks[key] = deviation < tolerance
-        
+
         all_pass = all(checks.values())
-        
+
         return {
-            'proportions_healthy': all_pass,
-            'current_ratios': current_ratios,
-            'expected_ratios': expected_ratios,
-            'deviations': deviations,
-            'checks': checks,
-            'summary': 'Proportions match Natural Equilibrium (scale-invariant)' if all_pass 
-                      else f'Proportions deviate from Natural Equilibrium'
+            "proportions_healthy": all_pass,
+            "current_ratios": current_ratios,
+            "expected_ratios": expected_ratios,
+            "deviations": deviations,
+            "checks": checks,
+            "summary": (
+                "Proportions match Natural Equilibrium (scale-invariant)"
+                if all_pass
+                else f"Proportions deviate from Natural Equilibrium"
+            ),
         }