Update model.py

model.py

@@ -1,59 +1,37 @@
-# model.py
 import hashlib
 import time
-from typing import Dict, List
-from scipy import stats
 import numpy as np
+from scipy import stats
 
-class ChronoLock:
+class TemporalLock:
     """
-    Geophysical Time-Keyed Cryptographic Validation
-    :: Official Use Cases ::
+    Geophysical Cycle-Based Cryptographic Validation
+    Applications:
     - Secure log timestamping
     - Event chronology forensics
-    - Temporal anomaly forecasting
     """
     def __init__(self):
         self.cycles = {
             'schumann': 7.83,    # Schumann resonance (Hz)
-            'pluto': 248.0,      # Pluto orbital period (years)
-            'solar': 11.2        # Solar cycle (years)
+            'solar': 11.2         # Solar cycle (years)
         }
         
-    def stamp(self, data: str) -> Dict[str, float]:
-        """Generate temporally-anchored signature"""
+    def stamp(self, data: str) -> dict:
+        """Generate time-anchored signature"""
         t = time.time()
-        moduli = [
-            t % self.cycles['schumann'],
-            t % self.cycles['pluto'],
-            t % self.cycles['solar']
-        ]
+        moduli = [t % freq for freq in self.cycles.values()]
         return {
             'timestamp': t,
-            'data_hash': hashlib.blake3(
+            'hash': hashlib.blake3(
                 f"{data}_{t}_{moduli[0]}".encode()
             ).hexdigest(),
-            'cycle_alignments': moduli,
-            'forecast_window': self._predict_next_anomaly(t)
+            'cycle_alignments': moduli
         }
     
-    def _predict_next_anomaly(self, timestamp: float) -> float:
-        """Forecast next high-risk window (simplified example)"""
-        solar_phase = timestamp % self.cycles['solar']
-        return timestamp + (self.cycles['solar'] - solar_phase) * 86400
-    
-    def validate(self, events: List[Dict]) -> float:
+    def validate(self, events: list) -> float:
         """Calculate anomaly confidence (0.0-1.0)"""
-        err_count = sum(e['errors'] for e in events)
-        total_obs = sum(e['observations'] for e in events)
+        errors = sum(e['errors'] for e in events)
+        total = sum(e['observations'] for e in events)
         return min(0.99, 1 - stats.binomtest(
-            k=err_count,
-            n=total_obs,
-            p=0.05
-        ).pvalue)
-
-# Example usage
-if __name__ == "__main__":
-    cl = ChronoLock()
-    sig = cl.stamp("TEST_EVENT")
-    print(f"Temporal Signature: {sig}")
+            k=errors, n=total, p=0.05
+        ).pvalue)