feat: Add commodity sensing, proof bundle, Three.js viz, mock isolation
Commodity Sensing Module (ADR-013): - sensing/rssi_collector.py: Real Linux WiFi RSSI collection from /proc/net/wireless and iw commands, with SimulatedCollector for testing - sensing/feature_extractor.py: FFT-based spectral analysis, CUSUM change-point detection, breathing/motion band power extraction - sensing/classifier.py: Rule-based presence/motion classification with confidence scoring and multi-receiver agreement - sensing/backend.py: Common SensingBackend protocol with honest capability reporting (PRESENCE + MOTION only for commodity) Proof of Reality Bundle (ADR-011): - data/proof/generate_reference_signal.py: Deterministic synthetic CSI with known breathing (0.3 Hz) and walking (1.2 Hz) signals - data/proof/sample_csi_data.json: Generated reference signal - data/proof/verify.py: One-command pipeline verification with SHA-256 - data/proof/expected_features.sha256: Expected output hash Three.js Visualization: - ui/components/scene.js: 3D scene setup with OrbitControls Mock Isolation: - testing/mock_pose_generator.py: Mock pose generation moved out of production pose_service.py - services/pose_service.py: Cleaned mock paths https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
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Claude
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v1/data/proof/verify.py
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v1/data/proof/verify.py
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#!/usr/bin/env python3
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"""
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Proof-of-Reality Verification Script for WiFi-DensePose Pipeline.
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This script verifies that the signal processing pipeline produces
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DETERMINISTIC, REPRODUCIBLE output from a known reference signal.
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Steps:
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1. Load the synthetic reference CSI signal from sample_csi_data.json
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2. Feed each frame through the actual CSI processor feature extraction
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3. Collect all feature outputs into a canonical byte representation
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4. Compute SHA-256 hash of the full feature output
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5. Compare against the expected hash in expected_features.sha256
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6. Print PASS or FAIL
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The reference signal is SYNTHETIC (generated by generate_reference_signal.py)
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and is used purely for pipeline determinism verification.
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Usage:
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python verify.py # Run verification against stored hash
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python verify.py --generate-hash # Generate and print the expected hash
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"""
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import hashlib
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import json
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import os
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import struct
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import sys
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import argparse
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from datetime import datetime, timezone
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import numpy as np
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# Add the v1 directory to sys.path so we can import the actual modules
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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V1_DIR = os.path.abspath(os.path.join(SCRIPT_DIR, "..", "..")) # v1/data/proof -> v1/
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if V1_DIR not in sys.path:
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sys.path.insert(0, V1_DIR)
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# Import the actual pipeline modules
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from src.hardware.csi_extractor import CSIData
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from src.core.csi_processor import CSIProcessor, CSIFeatures
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# -- Configuration for the CSI processor (matches production defaults) --
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PROCESSOR_CONFIG = {
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"sampling_rate": 100,
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"window_size": 56,
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"overlap": 0.5,
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"noise_threshold": -60,
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"human_detection_threshold": 0.8,
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"smoothing_factor": 0.9,
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"max_history_size": 500,
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"enable_preprocessing": True,
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"enable_feature_extraction": True,
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"enable_human_detection": True,
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}
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# Number of frames to process for the feature hash
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# We process a representative subset to keep verification fast while
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# still covering temporal dynamics (Doppler requires history)
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VERIFICATION_FRAME_COUNT = 100 # First 100 frames = 1 second
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def load_reference_signal(data_path):
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"""Load the reference CSI signal from JSON.
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Args:
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data_path: Path to sample_csi_data.json.
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Returns:
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dict: Parsed JSON data.
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Raises:
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FileNotFoundError: If the data file doesn't exist.
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json.JSONDecodeError: If the data is malformed.
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"""
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with open(data_path, "r") as f:
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data = json.load(f)
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return data
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def frame_to_csi_data(frame, signal_meta):
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"""Convert a JSON frame dict into a CSIData dataclass instance.
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Args:
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frame: Dict with 'amplitude', 'phase', 'timestamp_s', 'frame_index'.
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signal_meta: Top-level signal metadata (num_antennas, frequency, etc).
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Returns:
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CSIData instance.
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"""
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amplitude = np.array(frame["amplitude"], dtype=np.float64)
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phase = np.array(frame["phase"], dtype=np.float64)
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timestamp = datetime.fromtimestamp(frame["timestamp_s"], tz=timezone.utc)
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return CSIData(
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timestamp=timestamp,
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amplitude=amplitude,
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phase=phase,
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frequency=signal_meta["frequency_hz"],
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bandwidth=signal_meta["bandwidth_hz"],
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num_subcarriers=signal_meta["num_subcarriers"],
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num_antennas=signal_meta["num_antennas"],
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snr=15.0, # Fixed SNR for synthetic signal
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metadata={
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"source": "synthetic_reference",
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"frame_index": frame["frame_index"],
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},
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)
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def features_to_bytes(features):
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"""Convert CSIFeatures to a deterministic byte representation.
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We serialize each numpy array to bytes in a canonical order
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using little-endian float64 representation. This ensures the
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hash is platform-independent for IEEE 754 compliant systems.
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Args:
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features: CSIFeatures instance.
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Returns:
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bytes: Canonical byte representation.
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"""
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parts = []
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# Serialize each feature array in declaration order
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for array in [
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features.amplitude_mean,
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features.amplitude_variance,
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features.phase_difference,
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features.correlation_matrix,
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features.doppler_shift,
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features.power_spectral_density,
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]:
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flat = np.asarray(array, dtype=np.float64).ravel()
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# Pack as little-endian double (8 bytes each)
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parts.append(struct.pack(f"<{len(flat)}d", *flat))
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return b"".join(parts)
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def compute_pipeline_hash(data_path):
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"""Run the full pipeline and compute the SHA-256 hash of all features.
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Args:
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data_path: Path to sample_csi_data.json.
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Returns:
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str: Hex-encoded SHA-256 hash of the feature output.
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"""
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# Load reference signal
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signal_data = load_reference_signal(data_path)
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frames = signal_data["frames"][:VERIFICATION_FRAME_COUNT]
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# Create processor
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processor = CSIProcessor(PROCESSOR_CONFIG)
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# Process all frames and accumulate feature bytes
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hasher = hashlib.sha256()
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features_count = 0
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for frame in frames:
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csi_data = frame_to_csi_data(frame, signal_data)
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# Run through the actual pipeline: preprocess -> extract features
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preprocessed = processor.preprocess_csi_data(csi_data)
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features = processor.extract_features(preprocessed)
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if features is not None:
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feature_bytes = features_to_bytes(features)
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hasher.update(feature_bytes)
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features_count += 1
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# Add to history for Doppler computation in subsequent frames
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processor.add_to_history(csi_data)
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print(f" Processed {features_count} frames through pipeline")
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return hasher.hexdigest()
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def main():
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"""Main verification entry point."""
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parser = argparse.ArgumentParser(
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description="WiFi-DensePose pipeline verification"
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)
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parser.add_argument(
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"--generate-hash",
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action="store_true",
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help="Generate and print the expected hash (do not verify)",
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)
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args = parser.parse_args()
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print("=" * 70)
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print("WiFi-DensePose: Pipeline Verification")
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print("=" * 70)
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print()
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# Locate data file
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data_path = os.path.join(SCRIPT_DIR, "sample_csi_data.json")
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hash_path = os.path.join(SCRIPT_DIR, "expected_features.sha256")
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if not os.path.exists(data_path):
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print(f"FAIL: Reference data not found at {data_path}")
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print(" Run generate_reference_signal.py first.")
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sys.exit(1)
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# Compute hash
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print("[1/2] Processing reference signal through pipeline...")
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computed_hash = compute_pipeline_hash(data_path)
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print(f" SHA-256: {computed_hash}")
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print()
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if args.generate_hash:
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# Write the hash file
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with open(hash_path, "w") as f:
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f.write(computed_hash + "\n")
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print(f"[2/2] Wrote expected hash to {hash_path}")
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print()
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print("HASH GENERATED - run without --generate-hash to verify")
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print("=" * 70)
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return
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# Verify against expected hash
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print("[2/2] Verifying against expected hash...")
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if not os.path.exists(hash_path):
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print(f" WARNING: No expected hash file at {hash_path}")
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print(f" Computed hash: {computed_hash}")
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print()
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print(" Run with --generate-hash to create the expected hash file.")
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print()
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print("SKIP (no expected hash to compare against)")
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print("=" * 70)
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sys.exit(2)
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with open(hash_path, "r") as f:
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expected_hash = f.read().strip()
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print(f" Expected: {expected_hash}")
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print(f" Computed: {computed_hash}")
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print()
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if computed_hash == expected_hash:
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print("PASS - Pipeline output is deterministic and matches expected hash.")
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print("=" * 70)
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sys.exit(0)
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else:
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print("FAIL - Pipeline output does NOT match expected hash.")
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print()
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print("Possible causes:")
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print(" - Numpy/scipy version mismatch (check requirements-lock.txt)")
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print(" - Code change in CSI processor that alters numerical output")
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print(" - Platform floating-point differences (unlikely for IEEE 754)")
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print()
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print("To update the expected hash after intentional changes:")
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print(" python verify.py --generate-hash")
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print("=" * 70)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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