pec5d: add pec5d/ipt.py

pec5d/ipt.py

@@ -0,0 +1,159 @@
+# pec5d/ipt.py
+"""
+Invisible Pressure Theory (IPT) — mathematical & process engine.
+
+Definition: Invisible Pressure is the aggregate of subtle, non-obvious,
+distributed forces — social, psychological, economic, environmental,
+informational, quantum-field — that shape decisions, behaviors, markets,
+ecosystems, and outcomes before they become visible as overt events.
+It is the gradient beneath the surface: the potential energy that
+precedes kinetic action.
+
+Mathematical formalism:
+    ∇P = ∂/∂xᵐ [ Φ · ρ(x) · f(432Hz) · S(t) · Q ]
+      Φ    = golden-ratio coupling constant (1.618...)
+      ρ(x) = density of signals/agents/biological matter
+      f    = harmonic resonance frequency baseline (432 Hz)
+      S(t) = social/cultural/information field tensor
+      Q    = quantum entanglement coherence term
+
+7 operational processes:
+  1. Signal Acquisition      2. Latent Field Mapping   3. Gradient Vector Calculation
+  4. Resonance Amplify/Damp  5. Pre-Event Forecasting  6. Influence Architecture
+  7. Feedback Loop
+
+Honesty note: IPT is a planning/analysis framework. The "pressure index"
+is a model output, not a physical measurement claim.
+"""
+
+from __future__ import annotations
+
+import time
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from pec5d.constants import PHI, CARRIER_HZ
+
+
+class InvisiblePressureTheory:
+    """IPT engine — pressure field computation + 7-process pipeline."""
+
+    PROCESSES = [
+        "signal_acquisition",
+        "latent_field_mapping",
+        "gradient_vector_calculation",
+        "resonance_amplification_damping",
+        "pre_event_forecasting",
+        "influence_architecture",
+        "feedback_loop",
+    ]
+
+    def __init__(self):
+        self.history: List[Dict[str, Any]] = []
+        self.active = False
+
+    def initialize(self) -> "InvisiblePressureTheory":
+        """Initialize the theory engine."""
+        print("🌫 Invisible Pressure Theory engine initializing")
+        print(f"  Φ = {PHI:.4f} · carrier = {CARRIER_HZ} Hz")
+        self.active = True
+        return self
+
+    # ─── Formalism ────────────────────────────────────────────────────────
+    def pressure_gradient(self, density: np.ndarray, social: np.ndarray,
+                          quantum: float = 0.999423) -> Dict[str, Any]:
+        """
+        Compute the pressure gradient ∇P over 5 dimensions.
+
+        ∇P = ∂/∂xᵐ [Φ · ρ(x) · f(432) · S(t) · Q]
+        """
+        f = CARRIER_HZ / 1000.0  # normalized carrier term
+        field = PHI * density * f * social * quantum
+        gradient = np.gradient(field)
+        magnitude = float(np.linalg.norm(gradient))
+        direction = (gradient / (magnitude or 1.0)).tolist()
+        return {
+            "phi": PHI,
+            "carrier_hz": CARRIER_HZ,
+            "quantum_term": quantum,
+            "magnitude": round(magnitude, 6),
+            "direction": [round(v, 4) for v in direction],
+            "field_mean": round(float(np.mean(field)), 6),
+        }
+
+    # ─── 7 processes ──────────────────────────────────────────────────────
+    def run_process(self, process: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """Run one of the 7 IPT processes."""
+        params = params or {}
+        handlers = {
+            "signal_acquisition": self._acquire,
+            "latent_field_mapping": self._map_field,
+            "gradient_vector_calculation": self._gradient,
+            "resonance_amplification_damping": self._resonate,
+            "pre_event_forecasting": self._forecast,
+            "influence_architecture": self._influence,
+            "feedback_loop": self._feedback,
+        }
+        handler = handlers.get(process)
+        if handler is None:
+            return {"error": f"unknown process '{process}'"}
+        result = handler(params)
+        result["process"] = process
+        self.history.append(result)
+        return result
+
+    def run_pipeline(self, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """Run the full 7-process pipeline."""
+        outputs = [self.run_process(p, params) for p in self.PROCESSES]
+        return {"pipeline": self.PROCESSES, "steps": len(outputs), "outputs": outputs}
+
+    def _acquire(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        return {
+            "sensors": p.get("sensors", ["biometric", "social_listening", "geophysical",
+                                          "blockchain", "atmospheric", "photonic"]),
+            "signals": int(p.get("signals", 1000)),
+        }
+
+    def _map_field(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        rng = np.random.default_rng()
+        return {"heatmap_resolution": [64, 64], "weak_signals": int(rng.integers(100, 500))}
+
+    def _gradient(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        rng = np.random.default_rng()
+        density = rng.random(5) + 0.1
+        social = rng.random(5) + 0.1
+        return self.pressure_gradient(density, social)
+
+    def _resonate(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        return {
+            "mode": "amplify" if p.get("mode", "amplify") == "amplify" else "damp",
+            "carrier_hz": CARRIER_HZ,
+            "phi_aligned": True,
+        }
+
+    def _forecast(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        return {
+            "horizon_days": int(p.get("horizon_days", 90)),
+            "tipping_point_eta_days": int(p.get("tipping_point_eta_days", 30)),
+            "confidence": 0.78,
+        }
+
+    def _influence(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        return {
+            "strategy": "432Hz-aligned resonant signals — guidance without visible coercion",
+            "channels": p.get("channels", ["information", "social", "biometric"]),
+        }
+
+    def _feedback(self, p: Dict[str, Any]) -> Dict[str, Any]:
+        return {"measure": "response delta", "recalibrate": True, "cycle": len(self.history) + 1}
+
+    # ─── Status ───────────────────────────────────────────────────────────
+    def get_state(self) -> Dict[str, Any]:
+        return {
+            "active": self.active,
+            "processes": self.PROCESSES,
+            "history_size": len(self.history),
+            "phi": PHI,
+            "carrier_hz": CARRIER_HZ,
+        }