| |
| """ |
| IPT-SENSOR-Φ — Invisible Pressure Sensor device model. |
| |
| Sensing modalities: |
| photonic (432-528nm coherence) · bio-signal (HRV/GSR/EEG collective) · |
| information field (sentiment entropy/narrative velocity) · social |
| (network topology shift) · environmental (ion density/geomagnetic) · |
| quantum (entanglement coherence fluctuation) |
| |
| Outputs: Pressure Index (0.0-1.0) · Gradient Vector · Tipping Point ETA · |
| Resonance Frequency Signature |
| |
| Form factors: IPT-MINI (desktop) · IPT-GRID (mesh) · IPT-PULSE (wearable) |
| IPT-CORAL (embedded in drones/bio-chips) |
| """ |
|
|
| from __future__ import annotations |
|
|
| import time |
| from typing import Any, Dict, List, Optional |
|
|
| import numpy as np |
|
|
| from pec5d.constants import CARRIER_HZ, PHI |
|
|
|
|
| class IPTSensor: |
| """IPT-SENSOR-Φ — pressure field monitoring device.""" |
|
|
| MODALITIES = [ |
| "photonic_resonance", |
| "bio_signal", |
| "information_field", |
| "social_topology", |
| "environmental", |
| "quantum_coherence", |
| ] |
| FORM_FACTORS = { |
| "mini": "IPT-MINI — USB desktop ambient monitor", |
| "grid": "IPT-GRID — mesh network, city-scale", |
| "pulse": "IPT-PULSE — wearable personal field", |
| "coral": "IPT-CORAL — embedded in drones & bio-chips", |
| } |
|
|
| def __init__(self, form_factor: str = "mini"): |
| if form_factor not in self.FORM_FACTORS: |
| raise ValueError(f"unknown form factor '{form_factor}'") |
| self.form_factor = form_factor |
| self.calibration_hz = CARRIER_HZ |
| self.readings: List[Dict[str, Any]] = [] |
| self.active = False |
|
|
| def initialize(self) -> "IPTSensor": |
| """Calibrate and activate the sensor.""" |
| print(f"📡 IPT-SENSOR-Φ initializing — {self.FORM_FACTORS[self.form_factor]}") |
| print(f" Calibration: {self.calibration_hz} Hz · Φ-aligned sampling") |
| self.active = True |
| return self |
|
|
| def read(self) -> Dict[str, Any]: |
| """One pressure field reading.""" |
| rng = np.random.default_rng() |
| modalities = { |
| m: round(float(rng.random()), 4) for m in self.MODALITIES |
| } |
| pressure_index = round(float(np.mean(list(modalities.values()))), 4) |
| reading = { |
| "pressure_index": pressure_index, |
| "gradient_vector": [round(float(v), 4) for v in rng.standard_normal(5)], |
| "tipping_point_eta_days": int(rng.integers(3, 180)), |
| "resonance_signature": round(CARRIER_HZ * PHI, 2), |
| "modalities": modalities, |
| "form_factor": self.form_factor, |
| "timestamp": time.time(), |
| } |
| self.readings.append(reading) |
| return reading |
|
|
| def calibrate(self) -> Dict[str, Any]: |
| """Continuous self-calibration via quantum reference.""" |
| return { |
| "baseline_hz": self.calibration_hz, |
| "phi_window": PHI, |
| "self_calibrated": True, |
| } |
|
|
| def get_state(self) -> Dict[str, Any]: |
| return { |
| "active": self.active, |
| "form_factor": self.form_factor, |
| "form_factor_desc": self.FORM_FACTORS[self.form_factor], |
| "modalities": self.MODALITIES, |
| "readings": len(self.readings), |
| "last_pressure_index": ( |
| self.readings[-1]["pressure_index"] if self.readings else None |
| ), |
| } |
|
|
