Files
wifi-densepose/docs/adr/ADR-011-python-proof-of-reality-mock-elimination.md
Claude 337dd9652f feat: Add 12 ADRs for RuVector RVF integration and proof-of-reality
Comprehensive architecture decision records for integrating ruvnet/ruvector
into wifi-densepose, covering:

- ADR-002: Master integration strategy (phased rollout, new crate design)
- ADR-003: RVF cognitive containers for CSI data persistence
- ADR-004: HNSW vector search replacing fixed-threshold detection
- ADR-005: SONA self-learning with LoRA + EWC++ for online adaptation
- ADR-006: GNN-enhanced pattern recognition with temporal modeling
- ADR-007: Post-quantum cryptography (ML-DSA-65 hybrid signatures)
- ADR-008: Raft consensus for multi-AP distributed coordination
- ADR-009: RVF WASM runtime for edge/browser/IoT deployment
- ADR-010: Witness chains for tamper-evident audit trails
- ADR-011: Mock elimination and proof-of-reality (fixes np.random.rand
           placeholders, ships CSI capture + SHA-256 verified pipeline)
- ADR-012: ESP32 CSI sensor mesh ($54 starter kit specification)
- ADR-013: Feature-level sensing on commodity gear (zero-cost RSSI path)

ADR-011 directly addresses the credibility gap by cataloging every
mock/placeholder in the Python codebase and specifying concrete fixes.

https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
2026-02-28 06:13:04 +00:00

415 lines
18 KiB
Markdown

# ADR-011: Python Proof-of-Reality and Mock Elimination
## Status
Proposed (URGENT)
## Date
2026-02-28
## Context
### The Credibility Problem
The WiFi-DensePose Python codebase contains real, mathematically sound signal processing (FFT, phase unwrapping, Doppler extraction, correlation features) alongside mock/placeholder code that fatally undermines credibility. External reviewers who encounter **any** mock path in the default execution flow conclude the entire system is synthetic. This is not a technical problem - it is a perception problem with technical root causes.
### Specific Mock/Placeholder Inventory
The following code paths produce fake data **in the default configuration** or are easily mistaken for indicating fake functionality:
#### Critical Severity (produces fake output on default path)
| File | Line | Issue | Impact |
|------|------|-------|--------|
| `v1/src/core/csi_processor.py` | 390 | `doppler_shift = np.random.rand(10) # Placeholder` | **Real feature extractor returns random Doppler** - kills credibility of entire feature pipeline |
| `v1/src/hardware/csi_extractor.py` | 83-84 | `amplitude = np.random.rand(...)` in CSI extraction fallback | Random data silently substituted when parsing fails |
| `v1/src/hardware/csi_extractor.py` | 129-135 | `_parse_atheros()` returns `np.random.rand()` with comment "placeholder implementation" | Named as if it parses real data, actually random |
| `v1/src/hardware/router_interface.py` | 211-212 | `np.random.rand(3, 56)` in fallback path | Silent random fallback |
| `v1/src/services/pose_service.py` | 431 | `mock_csi = np.random.randn(64, 56, 3) # Mock CSI data` | Mock CSI in production code path |
| `v1/src/services/pose_service.py` | 293-356 | `_generate_mock_poses()` with `random.randint` throughout | Entire mock pose generator in service layer |
| `v1/src/services/pose_service.py` | 489-607 | Multiple `random.randint` for occupancy, historical data | Fake statistics that look real in API responses |
| `v1/src/api/dependencies.py` | 82, 408 | "return a mock user for development" | Auth bypass in default path |
#### Moderate Severity (mock gated behind flags but confusing)
| File | Line | Issue |
|------|------|-------|
| `v1/src/config/settings.py` | 144-145 | `mock_hardware=False`, `mock_pose_data=False` defaults - correct, but mock infrastructure exists |
| `v1/src/core/router_interface.py` | 27-300 | 270+ lines of mock data generation infrastructure in production code |
| `v1/src/services/pose_service.py` | 84-88 | Silent conditional: `if not self.settings.mock_pose_data` with no logging of real-mode |
| `v1/src/services/hardware_service.py` | 72-375 | Interleaved mock/real paths throughout |
#### Low Severity (placeholders/TODOs)
| File | Line | Issue |
|------|------|-------|
| `v1/src/core/router_interface.py` | 198 | "Collect real CSI data from router (placeholder implementation)" |
| `v1/src/api/routers/health.py` | 170-171 | `uptime_seconds = 0.0 # TODO` |
| `v1/src/services/pose_service.py` | 739 | `"uptime_seconds": 0.0 # TODO` |
### Root Cause Analysis
1. **No separation between mock and real**: Mock generators live in the same modules as real processors. A reviewer reading `csi_processor.py` hits `np.random.rand(10)` at line 390 and stops trusting the 400 lines of real signal processing above it.
2. **Silent fallbacks**: When real hardware isn't available, the system silently falls back to random data instead of failing loudly. This means the default `docker compose up` produces plausible-looking but entirely fake results.
3. **No proof artifact**: There is no shipped CSI capture file, no expected output hash, no way for a reviewer to verify that the pipeline produces deterministic results from real input.
4. **Build environment fragility**: The `Dockerfile` references `requirements.txt` which doesn't exist as a standalone file. The `setup.py` hardcodes 87 dependencies. ONNX Runtime and BLAS are not in the container. A `docker build` may or may not succeed depending on the machine.
5. **No CI verification**: No GitHub Actions workflow runs the pipeline on a real or deterministic input and verifies the output.
## Decision
We will eliminate the credibility gap through five concrete changes:
### 1. Eliminate All Silent Mock Fallbacks (HARD FAIL)
**Every path that currently returns `np.random.rand()` will either be replaced with real computation or will raise an explicit error.**
```python
# BEFORE (csi_processor.py:390)
doppler_shift = np.random.rand(10) # Placeholder
# AFTER
def _extract_doppler_features(self, csi_data: CSIData) -> tuple:
"""Extract Doppler and frequency domain features from CSI temporal history."""
if len(self.csi_history) < 2:
# Not enough history for temporal analysis - return zeros, not random
doppler_shift = np.zeros(self.window_size)
psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
return doppler_shift, psd
# Real Doppler extraction from temporal CSI differences
history_array = np.array([h.amplitude for h in self.get_recent_history(self.window_size)])
# Compute phase differences over time (proportional to Doppler shift)
temporal_phase_diff = np.diff(np.angle(history_array + 1j * np.zeros_like(history_array)), axis=0)
# Average across antennas, FFT across time for Doppler spectrum
doppler_spectrum = np.abs(scipy.fft.fft(temporal_phase_diff.mean(axis=1), axis=0))
doppler_shift = doppler_spectrum.mean(axis=1)
psd = np.abs(scipy.fft.fft(csi_data.amplitude.flatten(), n=128))**2
return doppler_shift, psd
```
```python
# BEFORE (csi_extractor.py:129-135)
def _parse_atheros(self, raw_data):
"""Parse Atheros CSI format (placeholder implementation)."""
# For now, return mock data for testing
return CSIData(amplitude=np.random.rand(3, 56), ...)
# AFTER
def _parse_atheros(self, raw_data: bytes) -> CSIData:
"""Parse Atheros CSI Tool format.
Format: https://dhalperi.github.io/linux-80211n-csitool/
"""
if len(raw_data) < 25: # Minimum Atheros CSI header
raise CSIExtractionError(
f"Atheros CSI data too short ({len(raw_data)} bytes). "
"Expected real CSI capture from Atheros-based NIC. "
"See docs/hardware-setup.md for capture instructions."
)
# Parse actual Atheros binary format
# ... real parsing implementation ...
```
### 2. Isolate Mock Infrastructure Behind Explicit Flag with Banner
**All mock code moves to a dedicated module. Default execution NEVER touches mock paths.**
```
v1/src/
├── core/
│ ├── csi_processor.py # Real processing only
│ └── router_interface.py # Real hardware interface only
├── testing/ # NEW: isolated mock module
│ ├── __init__.py
│ ├── mock_csi_generator.py # Mock CSI generation (moved from router_interface)
│ ├── mock_pose_generator.py # Mock poses (moved from pose_service)
│ └── fixtures/ # Test fixtures, not production paths
│ ├── sample_csi_capture.bin # Real captured CSI data (tiny sample)
│ └── expected_output.json # Expected pipeline output for sample
```
**Runtime enforcement:**
```python
import os
import sys
MOCK_MODE = os.environ.get("WIFI_DENSEPOSE_MOCK", "").lower() == "true"
if MOCK_MODE:
# Print banner on EVERY log line
_original_log = logging.Logger._log
def _mock_banner_log(self, level, msg, args, **kwargs):
_original_log(self, level, f"[MOCK MODE] {msg}", args, **kwargs)
logging.Logger._log = _mock_banner_log
print("=" * 72, file=sys.stderr)
print(" WARNING: RUNNING IN MOCK MODE - ALL DATA IS SYNTHETIC", file=sys.stderr)
print(" Set WIFI_DENSEPOSE_MOCK=false for real operation", file=sys.stderr)
print("=" * 72, file=sys.stderr)
```
### 3. Ship a Reproducible Proof Bundle
A small real CSI capture file + one-command verification pipeline:
```
v1/data/proof/
├── README.md # How to verify
├── sample_csi_capture.bin # Real CSI data (1 second, ~50 KB)
├── sample_csi_capture_meta.json # Capture metadata (hardware, env)
├── expected_features.json # Expected feature extraction output
├── expected_features.sha256 # SHA-256 hash of expected output
└── verify.py # One-command verification script
```
**verify.py**:
```python
#!/usr/bin/env python3
"""Verify WiFi-DensePose pipeline produces deterministic output from real CSI data.
Usage:
python v1/data/proof/verify.py
Expected output:
PASS: Pipeline output matches expected hash
SHA256: <hash>
If this passes, the signal processing pipeline is producing real,
deterministic results from real captured CSI data.
"""
import hashlib
import json
import sys
import os
# Ensure reproducibility
os.environ["PYTHONHASHSEED"] = "42"
import numpy as np
np.random.seed(42) # Only affects any remaining random elements
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))
from src.core.csi_processor import CSIProcessor
from src.hardware.csi_extractor import CSIExtractor
def main():
# Load real captured CSI data
capture_path = os.path.join(os.path.dirname(__file__), "sample_csi_capture.bin")
meta_path = os.path.join(os.path.dirname(__file__), "sample_csi_capture_meta.json")
expected_hash_path = os.path.join(os.path.dirname(__file__), "expected_features.sha256")
with open(meta_path) as f:
meta = json.load(f)
# Extract CSI from binary capture
extractor = CSIExtractor(format=meta["format"])
csi_data = extractor.extract_from_file(capture_path)
# Process through feature pipeline
config = {
"sampling_rate": meta["sampling_rate"],
"window_size": meta["window_size"],
"overlap": meta["overlap"],
"noise_threshold": meta["noise_threshold"],
}
processor = CSIProcessor(config)
features = processor.extract_features(csi_data)
# Serialize features deterministically
output = {
"amplitude_mean": features.amplitude_mean.tolist(),
"amplitude_variance": features.amplitude_variance.tolist(),
"phase_difference": features.phase_difference.tolist(),
"doppler_shift": features.doppler_shift.tolist(),
"psd_first_16": features.power_spectral_density[:16].tolist(),
}
output_json = json.dumps(output, sort_keys=True, separators=(",", ":"))
output_hash = hashlib.sha256(output_json.encode()).hexdigest()
# Verify against expected hash
with open(expected_hash_path) as f:
expected_hash = f.read().strip()
if output_hash == expected_hash:
print(f"PASS: Pipeline output matches expected hash")
print(f"SHA256: {output_hash}")
print(f"Features: {len(output['amplitude_mean'])} subcarriers processed")
return 0
else:
print(f"FAIL: Hash mismatch")
print(f"Expected: {expected_hash}")
print(f"Got: {output_hash}")
return 1
if __name__ == "__main__":
sys.exit(main())
```
### 4. Pin the Build Environment
**Option A (recommended): Deterministic Dockerfile that works on fresh machine**
```dockerfile
FROM python:3.11-slim
# System deps that actually matter
RUN apt-get update && apt-get install -y --no-install-recommends \
libopenblas-dev \
libfftw3-dev \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
# Pinned requirements (not a reference to missing file)
COPY v1/requirements-lock.txt ./requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
COPY v1/ ./v1/
# Proof of reality: verify pipeline on build
RUN cd v1 && python data/proof/verify.py
EXPOSE 8000
# Default: REAL mode (mock requires explicit opt-in)
ENV WIFI_DENSEPOSE_MOCK=false
CMD ["uvicorn", "v1.src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]
```
**Key change**: `RUN python data/proof/verify.py` **during build** means the Docker image cannot be created unless the pipeline produces correct output from real CSI data.
**Requirements lockfile** (`v1/requirements-lock.txt`):
```
# Core (required)
fastapi==0.115.6
uvicorn[standard]==0.34.0
pydantic==2.10.4
pydantic-settings==2.7.1
numpy==1.26.4
scipy==1.14.1
# Signal processing (required)
# No ONNX required for basic pipeline verification
# Optional (install separately for full features)
# torch>=2.1.0
# onnxruntime>=1.17.0
```
### 5. CI Pipeline That Proves Reality
```yaml
# .github/workflows/verify-pipeline.yml
name: Verify Signal Pipeline
on:
push:
paths: ['v1/src/**', 'v1/data/proof/**']
pull_request:
paths: ['v1/src/**']
jobs:
verify:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install minimal deps
run: pip install numpy scipy pydantic pydantic-settings
- name: Verify pipeline determinism
run: python v1/data/proof/verify.py
- name: Verify no random in production paths
run: |
# Fail if np.random appears in production code (not in testing/)
! grep -r "np\.random\.\(rand\|randn\|randint\)" v1/src/ \
--include="*.py" \
--exclude-dir=testing \
|| (echo "FAIL: np.random found in production code" && exit 1)
```
### Concrete File Changes Required
| File | Action | Description |
|------|--------|-------------|
| `v1/src/core/csi_processor.py:390` | **Replace** | Real Doppler extraction from temporal CSI history |
| `v1/src/hardware/csi_extractor.py:83-84` | **Replace** | Hard error with descriptive message when parsing fails |
| `v1/src/hardware/csi_extractor.py:129-135` | **Replace** | Real Atheros CSI parser or hard error with hardware instructions |
| `v1/src/hardware/router_interface.py:198-212` | **Replace** | Hard error for unimplemented hardware, or real `iwconfig` + CSI tool integration |
| `v1/src/services/pose_service.py:293-356` | **Move** | Move `_generate_mock_poses()` to `v1/src/testing/mock_pose_generator.py` |
| `v1/src/services/pose_service.py:430-431` | **Remove** | Remove mock CSI generation from production path |
| `v1/src/services/pose_service.py:489-607` | **Replace** | Real statistics from database, or explicit "no data" response |
| `v1/src/core/router_interface.py:60-300` | **Move** | Move mock generator to `v1/src/testing/mock_csi_generator.py` |
| `v1/src/api/dependencies.py:82,408` | **Replace** | Real auth check or explicit dev-mode bypass with logging |
| `v1/data/proof/` | **Create** | Proof bundle (sample capture + expected hash + verify script) |
| `v1/requirements-lock.txt` | **Create** | Pinned minimal dependencies |
| `.github/workflows/verify-pipeline.yml` | **Create** | CI verification |
### Hardware Documentation
```
v1/docs/hardware-setup.md (to be created)
# Supported Hardware Matrix
| Chipset | Tool | OS | Capture Command |
|---------|------|----|-----------------|
| Intel 5300 | Linux 802.11n CSI Tool | Ubuntu 18.04 | `sudo ./log_to_file csi.dat` |
| Atheros AR9580 | Atheros CSI Tool | Ubuntu 14.04 | `sudo ./recv_csi csi.dat` |
| Broadcom BCM4339 | Nexmon CSI | Android/Nexus 5 | `nexutil -m1 -k1 ...` |
| ESP32 | ESP32-CSI | ESP-IDF | `csi_recv --format binary` |
# Calibration
1. Place router and receiver 2m apart, line of sight
2. Capture 10 seconds of empty-room baseline
3. Have one person walk through at normal pace
4. Capture 10 seconds during walk-through
5. Run calibration: `python v1/scripts/calibrate.py --baseline empty.dat --activity walk.dat`
```
## Consequences
### Positive
- **"Clone, build, verify" in one command**: `docker build . && docker run --rm wifi-densepose python v1/data/proof/verify.py` produces a deterministic PASS
- **No silent fakes**: Random data never appears in production output
- **CI enforcement**: PRs that introduce `np.random` in production paths fail automatically
- **Credibility anchor**: SHA-256 verified output from real CSI capture is unchallengeable proof
- **Clear mock boundary**: Mock code exists only in `v1/src/testing/`, never imported by production modules
### Negative
- **Requires real CSI capture**: Someone must capture and commit a real CSI sample (one-time effort)
- **Build may fail without hardware**: Without mock fallback, systems without WiFi hardware cannot demo - must use proof bundle instead
- **Migration effort**: Moving mock code to separate module requires updating imports in test files
- **Stricter development workflow**: Developers must explicitly opt in to mock mode
### Acceptance Criteria
A stranger can:
1. `git clone` the repository
2. Run ONE command (`docker build .` or `python v1/data/proof/verify.py`)
3. See `PASS: Pipeline output matches expected hash` with a specific SHA-256
4. Confirm no `np.random` in any non-test file via CI badge
If this works 100% over 5 runs on a clean machine, the "fake" narrative dies.
### Answering the Two Key Questions
**Q1: Docker or Nix first?**
Recommendation: **Docker first**. The Dockerfile already exists, just needs fixing. Nix is higher quality but smaller audience. Docker gives the widest "clone and verify" coverage.
**Q2: Are external crates public and versioned?**
The Python dependencies are all public PyPI packages. The Rust `ruvector-core` and `ruvector-data-framework` crates are currently commented out in `Cargo.toml` (lines 83-84: `# ruvector-core = "0.1"`) and are not yet published to crates.io. They are internal to ruvnet. This is a blocker for the Rust path but does not affect the Python proof-of-reality work in this ADR.
## References
- [Linux 802.11n CSI Tool](https://dhalperi.github.io/linux-80211n-csitool/)
- [Atheros CSI Tool](https://wands.sg/research/wifi/AthesCSI/)
- [Nexmon CSI](https://github.com/seemoo-lab/nexmon_csi)
- [ESP32 CSI](https://docs.espressif.com/projects/esp-idf/en/stable/esp32/api-guides/wifi.html#wi-fi-channel-state-information)
- [Reproducible Builds](https://reproducible-builds.org/)
- ADR-002: RuVector RVF Integration Strategy