Update cognitive_mapping_probe/orchestrator_seismograph.py

cognitive_mapping_probe/orchestrator_seismograph.py

@@ -7,7 +7,7 @@ from .llm_iface import get_or_load_model, LLM, release_model
 from .resonance_seismograph import run_cogitation_loop, run_silent_cogitation_seismic
 from .concepts import get_concept_vector
 from .introspection import generate_introspective_report
-from .signal_analysis import analyze_cognitive_signal, get_spectrum_for_plotting
+from .signal_analysis import analyze_cognitive_signal, get_power_spectrum_for_plotting
 from .utils import dbg
 
 def run_seismic_analysis(
@@ -42,21 +42,21 @@ def run_seismic_analysis(
             llm=llm, prompt_type=prompt_type, num_steps=num_steps, temperature=0.1,
             injection_vector=injection_vector, injection_strength=injection_strength
         )
-        
+
         stats: Dict[str, Any] = {}
         results: Dict[str, Any] = {}
         verdict = "### ⚠️ Analysis Warning\nNo state changes recorded."
 
         if state_deltas:
             deltas_np = np.array(state_deltas)
-            stats = { "mean_delta": float(np.mean(deltas_np)), "std_delta": float(np.std(deltas_np)), 
+            stats = { "mean_delta": float(np.mean(deltas_np)), "std_delta": float(np.std(deltas_np)),
                       "max_delta": float(np.max(deltas_np)), "min_delta": float(np.min(deltas_np)) }
-            
+
             signal_metrics = analyze_cognitive_signal(deltas_np)
             stats.update(signal_metrics)
 
-            periods, power = get_spectrum_for_plotting(deltas_np)
-            results["power_spectrum"] = {"periods": periods.tolist(), "power": power.tolist()}
+            freqs, power = get_power_spectrum_for_plotting(deltas_np)
+            results["power_spectrum"] = {"frequencies": freqs.tolist(), "power": power.tolist()}
 
             verdict = f"### ✅ Seismic Analysis Complete"
             if injection_vector is not None:
@@ -94,8 +94,8 @@ def run_triangulation_probe(
             llm=llm, context_prompt_type=prompt_type,
             introspection_prompt_type="describe_dynamics_structured", num_steps=num_steps
         )
-        
-        stats = {}
+
+        stats: Dict[str, Any] = {}
         verdict = "### ⚠️ Triangulation Warning"
         if state_deltas:
             deltas_np = np.array(state_deltas)
@@ -139,10 +139,10 @@ def run_causal_surgery_probe(
             llm=llm, context_prompt_type=dest_prompt_type,
             introspection_prompt_type="describe_dynamics_structured", num_steps=num_steps
         )
-        
+
         deltas_np = np.array(patched_run_results["state_deltas"])
         stats = { "mean_delta": float(np.mean(deltas_np)), "std_delta": float(np.std(deltas_np)), "max_delta": float(np.max(deltas_np)) }
-        
+
         results = {
             "verdict": "### ✅ Causal Surgery Probe Complete",
             "stats": stats, "state_deltas": patched_run_results["state_deltas"],
@@ -168,7 +168,7 @@ def run_act_titration_probe(
             temperature=0.1, record_states=True
         )
         state_history = source_results["state_history"]
-        
+
         titration_results = []
         for step in patch_steps:
             if step >= len(state_history): continue
@@ -178,7 +178,7 @@ def run_act_titration_probe(
                 llm=llm, prompt_type=dest_prompt_type, num_steps=num_steps,
                 temperature=0.1, patch_step=step, patch_state_source=patch_state
             )
-            
+
             deltas = patched_run_results["state_deltas"]
             buffer = 10
             post_patch_deltas = deltas[step + buffer:]
@@ -189,4 +189,4 @@ def run_act_titration_probe(
 
         return { "verdict": "### ✅ ACT Titration Complete", "titration_data": titration_results }
     finally:
-        release_model(llm)
+        release_model(llm)