File size: 3,415 Bytes
547efd7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | # pec5d/ipt_sensor.py
"""
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
),
}
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