pec5d/ipt.py

# 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,
        }