Adds reproducible tooling to test whether genuinely-independent rtl_433 test
captures can relieve the statistical classifier's minority-class starvation
(train: Weather 12, Security 2). Local Flipper corpora are byte-identical
mirrors, so rtl_433 is the only independent on-disk source.
- rtl433_iq_demod.py: .cu8 (interleaved uint8 IQ) -> Flipper-style signed µs
pulse train via amplitude/OOK demod. A quality-gate self-rejects FSK/degenerate
captures. Recovered pulse widths match rtl_433's own -A analysis.
- experiment_rtl433_augment.py: conservative folder->category map + an HONEST
evaluation — train on Flipper (group-disjoint) +/- rtl_433, score on held-out
FLIPPER (regression guardrail) and held-out rtl_433 (new capability).
RESULT: adding rtl_433 to training DEGRADES the Flipper gate metric
(balanced 0.625 -> 0.412), collapsing Remote Control (20/26 -> 7-10/26) as
cross-domain samples bleed into Flipper's remote region and their volume swamps
it. Small capped adds keep Flipper within seed-noise while gaining large
rtl_433-domain Weather recognition — a coverage-vs-precision tradeoff, not a
clean win. Per gate-metric discipline the production model is NOT regenerated;
the tooling is kept to re-measure in-domain once real Flipper weather/security
captures arrive via the platform.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Trains a PyTorch 1D CNN on encoded RAW pulse arrays as the Phase 3B leg
of the designed ensemble, scored on the SAME group-disjoint held-out test
set as the heuristic and statistical models (GroupShuffleSplit by device
sub-folder, no near-duplicate leakage).
RESULT — CNN is data-starved and loses decisively:
CNN balanced 0.338, top-1 0.795 (garage-inflated)
statistical balanced 0.625, top-1 0.868
heuristic top-1 0.300
Only 489 train samples with severe class imbalance (Garage 565, Security 2,
Weather 12). A blend sweep confirmed every non-zero CNN weight degrades the
statistical model (0.625 -> 0.613 at 15% CNN, worse beyond), so the CNN is
NOT wired into decode(). Production ensemble stays heuristic + statistical,
both already live.
Committing the trainer + shared pulse_encoder (trainer/inference parity) +
metrics.json to document the reproducible negative result. The benched
.pt/.onnx binaries are intentionally NOT committed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Trains sklearn RandomForest + GradientBoosting on 14 timing/statistical
features from RAW-only UberGuidoZ captures, with a group-aware split
(GroupShuffleSplit by device sub-folder) so near-duplicate captures never
leak across train/test. The heuristic CategoryRouter is scored on the exact
same held-out files for a fair comparison.
Best model (gradient_boost): 86.8% top-1 accuracy, 62.5% balanced accuracy
vs heuristic 30.0% top-1 / 55.9% routed. Raw accuracy is inflated by the
70%-Garage RAW class balance; balanced accuracy (~2x the heuristic) is the
honest number and meets the Phase 3A 60-75% target. Not yet wired into the
decoder ensemble.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
KEY .sub files (~35% of real captures) returned zero identification
because every path bailed on `not has_raw_data`, despite the file
already carrying a decoded Protocol name. Route those by protocol name
instead:
- category_router: add route_by_protocol() — device-specific brands
(CAME/Nice/KeeLoq/Security+/Honeywell) map to one confident category;
generic shared encoders (Princeton/EV1527/Holtek/Intertechno) map to a
broad allowed family set, since the same silicon spans
remote/doorbell/fan/gate.
- pattern_decoder.decode: emit a decoded_key DeviceMatch (details carry
predicted_category) for KEY files instead of [].
- device_identifier.identify: only bail when there is neither RAW data
nor a protocol name; gate statistical scoring on has_raw_data.
Adds scripts/benchmark_realworld.py — validates the real pipeline
against the real UberGuidoZ corpus (folder = ground-truth device type),
reporting top-1, routed (truth in allowed set), and coverage.
Measured on n=344 (seed 42): coverage 58%→93%, routed 59.3%→65.2%;
generalizes on seed 7 (93%/63.6%). RAW path byte-identical (no
regression); synthetic phase-0 gate still passes (top-3 67%).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Adds a rules-based device category router that classifies a signal by
frequency band + timing ratio + pulse count BEFORE the per-protocol
scoring loop, restricting the candidate set. This fixes the "everything
matches a weather sensor with 69-76% false confidence" problem.
- src/matcher/category_router.py: frequency-band + timing routing
- pattern_decoder.py: category filter, category-mismatch penalty,
post-match spread penalty (surfaces low-discrimination cases)
- protocol_database.py: garage door / doorbell / fan controller entries
- scripts/benchmark_phase0.py: real-world-shaped benchmark
Benchmark gate: top-3 accuracy 0% -> 67% (target >=30%). 52/52 unit
tests pass. NOTE: benchmark .sub files are synthetic-from-DB-params,
so 67% is an upper bound pending real Flipper capture validation.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
## Phase 1: Code Cleanup (~1,859 lines removed)
**Deleted Redundant Matchers:**
- ❌ strategies_orm.py (356 lines) - Old ORM-based strategies
- ❌ simple_matcher.py (351 lines) - Replaced by strategies.py
- ❌ rtl433_matcher.py (352 lines) - Replaced by strategies.py
**Archived Old Scripts:**
- Moved 11 one-time analysis/import scripts to scripts/archive/
- Scripts: analyze_flipper_signatures, analyze_tembed_files, identify_tembed_devices,
import_flipper_sqlite, import_tembed_signatures, match_tembed_with_db,
match_with_flipper_db, rematch_captures, test_gps_extraction,
test_tembed_matching, test_wardriving_import
**Consolidated API:**
- Renamed main.py → main_orm_legacy.py (archived old ORM-based API)
- main_simple.py is now the primary production API
## Phase 2: Pattern Decoder Integration ✅
**CRITICAL FIX: Pattern decoder now integrated into production API!**
**Changes:**
1. Updated main_simple.py to use unified SignatureMatcher
2. Added 6 strategies to matcher pipeline:
- ExactMatcher (protocol + frequency)
- FrequencyMatcher (frequency-based)
- BitPatternMatcher (data patterns)
- TimingMatcher (timing-based)
- RTL433DecoderStrategy (RTL_433 decoder)
- PatternBasedStrategy (NEW! Pattern decoder for short captures)
3. Created MockDB class for simplified mode (no real database)
4. Replaced old get_matcher() with get_matcher_engine()
5. Updated upload handler to use MatchResult format
6. All matches now include confidence scores and match methods
**Result:**
- Pattern decoder is NOW ACTIVE in production 🎉
- Unified matching pipeline with 6 strategies
- Cleaner codebase (-1,859 lines)
- Single source of truth for matching logic
🎉 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Created RF_TEST_DATABASES.md with 6 primary databases (30,000+ files)
- Added RTL433_TESTING_GUIDE.md explaining 0% decode rate
- Created download_rf_test_datasets.sh to fetch all test data
- Documented web search results from Google dorking
- Listed repositories: rtl_433_tests, FlipperZero-Subghz-DB, UberGuidoZ, etc
- Included testing strategy and expected success rates
Major Features:
- Multi-format GPS data import (Wigle CSV, GPS JSON, filename GPS)
- Data source tracking (test/mock/production)
- Database cleanup tools for managing test data
- JSON file persistence for simplified server
- UI improvements for map controls
Backend:
- Added wardriving_importer.py with WigleCSVImporter, GPSJSONImporter, BatchImporter
- Added data_source and session_id tracking to captures
- New API endpoints: DELETE /api/v1/admin/cleanup
- Auto-save/load functionality for captures_simple.json
- Updated stats endpoint to show data source breakdown
CLI Tools:
- scripts/import_wardriving_data.py - Batch import with --data-source flag
- scripts/cleanup_database.py - Clean by source, session, or all
- scripts/create_test_dataset.py - Generate GPS-tagged test data
- scripts/test_wardriving_import.py - Test suite for importers
Frontend:
- Fixed map controls z-index and positioning issues
- Moved controls to top-right to avoid zoom button overlap
- Fixed Leaflet zoom controls rendering over header
- Changed controls to position:fixed for persistent visibility
- Export map object to window.map for proper invalidateSize
Documentation:
- docs/WARDRIVING_IMPORT.md - Complete import guide
- docs/DATA_CLEANUP_GUIDE.md - Cleanup system documentation
- docs/TEST_LOCATIONS.md - Test GPS coordinates reference
- TEST_RESULTS_SUMMARY.md - Format testing results
Test Data:
- Created test_dataset_gps with 15 captures across 5 US cities
- All test data marked with data_source="test" for easy cleanup
Testing:
- Verified Wigle CSV import (1 capture from West LA)
- Verified GPS filename import (3 captures from UCLA area)
- Verified GPS JSON companion files (15 captures, 5 cities)
- Verified cleanup functionality (deleted 15 test captures)
- Verified data persistence across server restarts
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
Major milestone: GPS coordinates now auto-extract from filenames and
uploads appear on map with full end-to-end workflow functional!
✨ GPS Auto-Extraction Features:
- JavaScript GPS extractor class matching Python patterns
- Supports 3 filename formats:
* N/S/E/W: 34.0478N_118.2349W_filename.sub
* lat/lon prefix: lat34.0478lon-118.2348_filename.sub
* Signed decimal: -34.0478_118.2348_filename.sub
- Auto-populates latitude/longitude form fields on file drop
- Green notification toast shows detected coordinates
- File list shows GPS badge for files with coordinates
🗺️ Web Interface Improvements:
- Upload endpoint now stores captures in-memory
- Query endpoint returns uploaded captures for map display
- Stats endpoint shows real-time upload counts
- Map displays uploaded captures as markers
- Color-coded by frequency band
📁 Updated Files:
- static/js/upload.js: GPS extraction + auto-population
- src/api/main_simple.py: In-memory storage + endpoints
- src/parser/gps_extractor.py: Backend GPS extraction (Python)
- scripts/test_gps_extraction.py: Python test suite
- test_gps_extraction.html: Browser test suite
📊 T-Embed Files Updated:
- 34.0478N_118.2348W_1637_raw_8.sub: 315 MHz Princeton
- 34.0478N_118.2349W_1351_raw_10.sub: 433.92 MHz Princeton
- 34.0478N_118.2349W_1650_test_raw.sub: 433.92 MHz RAW
- All now have proper Flipper SubGhz headers
✅ Tested Features:
- GPS extraction from filename: 34.0478N_118.2349W → 34.0478, -118.2349
- Auto-population of GPS fields in upload form
- File upload with GPS validation
- Capture appears on map after upload
- Statistics update in real-time
- Frequency distribution calculated correctly
🎯 End-to-End Flow Working:
1. User drops .sub file with GPS in filename
2. GPS auto-detected and form fields populate
3. User clicks Upload
4. Server parses RF data + GPS coordinates
5. Capture stored in memory
6. Map refreshes and displays new marker
7. Stats update with new counts
🚀 Demo: http://localhost:8000
Upload 34.0478N_118.2349W_1351_raw_10.sub and watch it appear on map!
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
leetcrypt
Trilltechnician