Intel update after shenron complete integration. into main pipeline.

intel/radar/metrology_suite/Auto_MCAP_SHENRON.md

@@ -13,48 +13,44 @@ Every time a simulation is triggered (e.g., via `run.bat`), the system follows a
 - **Output:** `data/<session>/lidar/*.npy`, `camera/*.png`, and `frames.jsonl`.
 - **Key Feature:** LiDAR is saved with **Semantic Tags** and **Radial Velocity** calculations, which are essential inputs for the physics-based radar model.
 
-### Stage 2: Shenron Synthesis (`scripts/generate_shenron.py`)
-- **Objective:** Convert LiDAR point clouds into high-fidelity radar signals.
-- **Logic:** Calls `ShenronRadarModel` to perform FMCW signal synthesis.
+### Stage 2: Multi-Radar Synthesis (`scripts/generate_shenron.py`)
+- **Objective:** Convert LiDAR point clouds into high-fidelity radar signals for multiple hardware profiles.
+- **Logic:** Calls `ShenronRadarModel` concurrently for **awrl1432** and **radarbook**.
 - **Improved Output:**
-    - `shenron_radar/*.npy`: 3D Radar Point Cloud (x, y, z, vel, mag).
-    - `metrology/rd/*.npy`: Range-Doppler Heatmaps.
-    - `metrology/ra/*.npy`: Range-Azimuth Heatmaps.
-    - `metrology/cfar/*.npy`: CFAR threshold masks.
-    - `range_axis.npy` & `angle_axis.npy`: Physical axis definitions for visualization.
-- **Physics Constraint:** As of Iteration 26, all artificial normalizations (e.g. 1/1000) have been removed. Signals follow the pure **1/R⁴ Radar Range Equation**.
+    - `awrl1432/` & `radarbook/`: Dedicated synthesis folders per radar type.
+    - `{r_type}/metrology/`: Nested diagnostic heatmaps (RD, RA, CFAR).
+    - `{r_type}/metrology/metrics.jsonl`: Automated **Telemetry** records (SNR, Noise Floor).
+- **Physics Constraint:** All signals follow the pure **1/R⁴ Radar Range Equation** with hardware-specific gain offsets.
 
 ### Stage 3: MCAP Conversion (`scripts/data_to_mcap.py`)
-- **Objective:** Package all disparate files into a single Foxglove-compatible `.mcap` log.
-- **Visual Enhancement:** Implements **Polar Scan Conversion** for the RA map, projecting the rectangular $(R, \theta)$ data onto a forward-facing BEV sector.
-- **Final Output:** `data/<session>/<session>.mcap`.
+- **Objective:** Package all hardware profiles and metrics into a unified Foxglove log.
+- **Topic Alignment:** Follows the standardized `test_shenron` namespace:
+    - `/radar/native`: Raw CARLA radar.
+    - `/radar/awrl1432/...`: TI-style radar data and metrology.
+    - `/radar/radarbook/...`: Logic-style radar data and metrology.
+- **Visual Enhancement:** Implements **Polar Scan Conversion** for RA maps and publishes **Telemetry** curves for live signal monitoring.
 
 ---
 
-## 🛰️ 2. Core Metrology Algorithms
+## 🛰️ 2. Core Metrology & Telemetry
 
-The current system implements several state-of-the-art radar processing fixes to ensure simulation fidelity matches real-world hardware.
+### A. Signal Quality Metrics (Telemetry)
+The simulation now tracks real-time signal health using the `foxglove.Telemetry` schema.
+- **Peak SNR (dB):** Tracks the separation between the strongest reflection and the noise floor.
+- **Avg Noise Floor:** Monitors ambient thermal and phase noise in the RD spectrum.
+- **Active Bins:** Counts detections surviving the CFAR threshold.
 
-### A. Doppler-Slice RA Accumulation
+### B. Doppler-Slice RA Accumulation
 To prevent angular smearing, the Range-Azimuth map is generated by looping over Doppler bins.
 1. Each bin is processed via a **Windowed Angle FFT**.
 2. **Phase Preservation:** Magnitude is never taken before the FFT. Complex inputs ensure beamforming works.
 3. Power is summed **Incoherently** across Doppler bins *after* the FFT.
 
-### B. Incoherent RD Integration
-The Range-Doppler map uses **Incoherent Power Summation** across receiver antenna channels ($|FFT|^2$). This preserves signal energy from moving targets that would otherwise cancel out due to phase misalignment if simple averaging was used.
-
-### C. 120° Symmetric Sector Scan-Conversion
-The RA visualizer uses a **Radial Projection** to create a true Birds-Eye View (BEV) fan.
-- **Symmetry-Lock:** As of Iteration 21, the FFT indices are perfectly centered to ensure the fan is symmetric around the boresight (0°).
-- **Aesthetic:** Uses the **Viridis (Radar Blue)** colormap with **Global Normalization**. 
-- **Physics Fidelity:** No per-range-bin normalization is allowed. Brightness must decay with distance ($M \propto 1/R^2$) to represent real-world sensor behavior.
-
 ---
 
 ## 📁 3. Data Structure Reference
 
-A successful automated run will produce the following structure:
+A successful automated run will produce the following nested structure:
 
 ```text
 data/
@@ -62,14 +58,15 @@ data/
     ├── frames.jsonl          # Metadata & Synchronization
     ├── lidar/                # Semantic LiDAR [N, 7]
     ├── camera/               # Dashcam imagery
-    ├── shenron_radar/        # 3D Radar Point Cloud [M, 5]
-    ├── metrology/            # Diagnostic Heatmaps
-    │   ├── rd/               # Range-Doppler
-    │   ├── ra/               # Range-Azimuth
-    │   ├── cfar/             # CFAR mask
-    │   ├── range_axis.npy    # Physical Range Axis (metres)
-    │   └── angle_axis.npy    # Physical Azimuth Axis (radians)
-    └── scenario_timestamp.mcap # Final Foxglove Package
+    ├── awrl1432/             # TI 1432 Radar PCD & Metrology
+    │   └── metrology/
+    │       ├── metrics.jsonl # Telemetry logs
+    │       ├── rd/           # RD Heatmaps
+    │       └── ...
+    ├── radarbook/            # RadarBook PCD & Metrology
+    │   └── metrology/
+    │       └── ...
+    └── scenario_timestamp.mcap # Final Unified Log
 ```
 
 ---
@@ -78,10 +75,10 @@ data/
 
 When evaluating a session, AI agents should look for the following "Success Indicators":
 
-1.  **Azimuth Variance:** In the RA heatmap, energy should be non-uniform across the angle axis. Objects should appear as discrete blobs.
-2.  **Target Alignment:** The "Orange Blobs" in the RA Sector plot must spatially align with the "White Points" in the 3D LiDAR point cloud.
-3.  **V=0 Vertical:** In the RD heatmap, static geometry (ground/buildings) must form a sharp vertical line at the 0 m/s mark.
-4.  **Inverse Square Decay:** Target intensity in heatmaps should naturally decrease as range increases ($1/R^2$ in voltage, $1/R^4$ in power). Near-field objects must dominate the energy profile.
+1.  **Hardware Parity:** Compare `/radar/awrl1432` vs `/radar/radarbook`. Differences in range/velocity resolution should match the physical hardware specs.
+2.  **Telemetry Health:** In Foxglove, the **Telemetry** panel should show a stable SNR curve. Sudden drops suggest occlusion or sensor saturation.
+3.  **Target Alignment:** The "Jet Spectrum" blobs in the RA Sector plot must spatially align with the "White Points" in the 3D LiDAR point cloud.
+4.  **V=0 Vertical:** Static geometry in the RD heatmap must form a sharp vertical line at the 0 m/s mark, confirming clutter removal is working.
 
 ---
-*Documented by Antigravity | Project: Fox CARLA ADAS | 2026-04-01*
+*Documented by Antigravity | Project: Fox CARLA ADAS | 2026-04-09*

intel/radar/metrology_suite/README.md

@@ -57,6 +57,11 @@ Current state: Performs 2D FFT and CA-CFAR detection.
 - [x] Inspect raw `.npy` files for precision integrity.
 - [x] Verify Foxglove visual alignment.
 
+### Phase 5: Pipeline Alignment (COMPLETED)
+- [X] Integrated Multi-Radar support (`awrl1432` + `radarbook`).
+- [X] Standardized Topic Naming (`/radar/native`, `/radar/{type}/metrics`).
+- [X] Synchronized `run.bat` logic for Dashboard-ready automation.
+
 ---
 
 ## 🧭 4. Pointers for Future Agents

intel/radar/metrology_suite/walkthrough.md

@@ -1,76 +1,73 @@
-# 🪐 Walkthrough: Radar Metrology Suite (Iteration 18)
+# 🪐 Walkthrough: Simulation Pipeline Alignment (Iteration 19)
 
-This walkthrough documents the full implementation and verification of the **Radar Metrology Suite**, a major architectural upgrade that exposes the internal signal processing states of the C-SHENRON radar engine for high-fidelity ADAS debugging.
+This walkthrough documents the full implementation of the **Simulation Pipeline Alignment**, which synchronizes the automated `run.bat` workflow with the high-fidelity diagnostic standards of the radar metrology suite.
 
 ---
 
 ## 🚀 1. Overview of Achievements
 
-The Metrology Suite has successfully transitioned the simulation from generating "Points" to generating a complete **Signal Spectrum**. 
+The simulation pipeline now behaves as a production-grade ADAS testbench, generating multi-radar data and telemetry in a single automated pass.
 
-- **Phase 1 (Engine Extraction):** Modified the CA-CFAR and RadarProcessor to capture raw energy matrices.
-- **Phase 2 (Visual Pipeline):** Implemented real-time image colormapping (Viridis/Magma/Plasma) and MCAP streaming.
-- **Phase 3 (Persistence):** Established a structured hierarchy for raw `.npy` and `.jsonl` telemetry.
-- **Phase 4 (Verification):** Completed a 250-frame simulation sweep (Iteration 18) proving system stability.
+- **Multi-Radar Support:** Concurrent synthesis of `awrl1432` and `radarbook` hardware profiles.
+- **Protocol Alignment:** Topics and folder structures now match the `test_shenron` testbench identically.
+- **Telemetry Integration:** Automatic extraction of SNR, Noise Floor, and Detection Bins.
+- **Seamless MCAP:** Single-pass packaging of all raw point clouds, metrology heatmaps, and telemetry curves.
 
 ---
 
-## 📡 2. Visual Diagnostic Capabilities
+## 📡 2. Unified Hardware Namespace
 
-We have introduced three new specialized heatmaps, now available as live topics in Foxglove:
+Visualization topics in Foxglove are now organized by hardware profile for easy comparison:
 
-| Topic | Channel | Colormap | Purpose |
+| Topic Namespace | Component | Colormap | Purpose |
 | :--- | :--- | :--- | :--- |
-| `/radar/heatmaps/range_doppler` | RD Heatmap | **Viridis** | High-contrast view of targets in Velocity vs. Range space. |
-| `/radar/heatmaps/range_azimuth` | RA Heatmap | **Magma** | Top-down spatial energy distribution (Bird's Eye View). |
-| `/radar/heatmaps/cfar_mask` | Detection Gate| **Plasma** | Visualizes the "Detection Wall" (Noise $\times$ Threshold). |
+| `/radar/native` | RAW CARLA | -- | Baseline physics points. |
+| `/radar/{type}` | Shenron PCD | -- | High-fidelity rich point cloud. |
+| `/radar/{type}/metrics` | Telemetry | Plot | SNR and Noise monitoring. |
+| `/radar/{type}/heatmaps/ra`| RA Heatmap | **Jet** | Top-down spatial energy (BEV). |
+| `/radar/{type}/heatmaps/rd`| RD Heatmap | **Viridis** | Range-Doppler spectrum analysis. |
 
 ---
 
 ## 📊 3. Deep Telemetry & Data Structure
 
-For every frame processed, the system now persists bit-perfect data for offline engineering analysis.
+The system persists data in a hardware-centric hierarchy inside each session folder.
 
-### Raw Signal Dumps (`metrology/`)
-Saved in `Shenron_debug/iterations/<iter_name>/<radar_type>/metrology/`:
-- **`rd/*.npy`**: 256x64 Raw Power floating-point matrices.
-- **`ra/*.npy`**: 256x256 Angular energy polar-coordinates.
-- **`cfar/*.npy`**: The adaptive threshold baseline for that specific frame.
+### Raw Signal Dumps
+Saved in `data/<session>/<radar_type>/metrology/`:
+- **`rd/*.npy`**: Log-scaled power matrices.
+- **`ra/*.npy`**: Polar-to-Cartesian scan-converted BEV maps.
+- **`metrics.jsonl`**: Standardized telemetry records for Foxglove Telemetry panels.
 
-### Global Metrics (`metrics.jsonl`)
-A frame-by-frame log of signal health, including:
-- **`peak_snr_db`**: Signal-to-Noise ratio of the strongest target.
-- **`avg_noise_floor`**: Estimated environment noise level.
-- **`active_bins`**: Number of cells currently "breaking through" the CFAR gate.
+### Performance Indicators
+- **Real-time Streaming:** `PYTHONUNBUFFERED` logging enables instant SSE console feedback in the Dashboard.
+- **GPU Acceleration:** Multi-radar synthesis is distributed across available CUDA cores.
 
 ---
 
-## 🏁 4. Verification Results (Iteration 18)
+## 🏁 4. Verification Results
 
-The initial full-scale run of the suite confirmed **Optimal Signal Integrity**:
-- **Peak SNR:** ~15.3 dB in peak frames (Confirmed by `metrics.jsonl`).
-- **Processing Performance:** 16.63 Frames/Sec (Streaming images + PCD logic).
-- **GPU Acceleration:** Confirmed active on `cuda:0:0`.
+Recent full-scale runs confirm **Protocol Parity**:
+- **Topic Integrity:** MCAP files are now 1:1 compatible with testbench dashboards.
+- **Metric Accuracy:** SNR extraction is verified against the RD spectrum across all active hardware profiles.
 
 ---
 
-## 🛠️ 5. How to Use & Debug
+## 🛠️ 5. How to Launch
 
-### Launching the Metrology Suite
-To run a new iteration with full metrology extraction:
-```powershell
-cmd /c "C:\ProgramData\miniconda3\Scripts\activate.bat carla312 && python scripts/test_shenron.py --iter <your_name>"
-```
+The pipeline is now optimized for "One-Click" execution via the Dashboard or CLI:
 
-### Signal Verification Utility
-Use the specialized verification tool to check a single frame's signal chain:
+### Scenario CLI
 ```powershell
-cmd /c "C:\ProgramData\miniconda3\Scripts\activate.bat carla312 && python scripts/analysis/verify_metrology_logic.py"
+./run.bat braking --frames 200
 ```
 
+### Dashboard GUI
+Simply launch `dashboard.bat` and select your scenario. The post-processing (Shenron + MCAP) will trigger automatically upon completion.
+
 ---
 
 > [!IMPORTANT]
-> **Engineering Note:** The RA Heatmap uses a 1D Angle-FFT reduction across all Doppler bins. This provides a spatial "snapshot" that ignores velocity, making it ideal for checking multipath and ghosting against the ground-truth LiDAR map.
+> **Engineering Note:** The RA Heatmap has transitioned to the **Jet (Radar Spectrum)** colormap and includes a safety clip to prevent numerical overflow in the log-conversion stage.
 
-*Verified by Antigravity | Date: 2026-04-08*
+*Verified by Antigravity | Date: 2026-04-09*

intel/radar/shenron_implementation_guide.md

@@ -55,20 +55,21 @@ In `src/sensors.py`, you must change the LiDAR blueprint to the **semantic** ver
 ```
 
 ### Step 2: Implement the Batch Processor
-Create `scripts/generate_shenron.py`. This script acts as a wrapper for the `ISOLATE` package. It iterates through your `data/` directory, looks for `lidar/` folders, and generates a corresponding `shenron_radar/` folder.
+Create `scripts/generate_shenron.py`. This script acts as a wrapper for the `ISOLATE` package. It iterates through your `data/` directory, looks for `lidar/` folders, and generates corresponding hardware folders (e.g. `awrl1432/`, `radarbook/`).
 
 > [!TIP]
 > Use the **`heatmap_gen_fast.py`** logic within the processor to leverage CUDA/GPU acceleration for the FMCW signal generation.
 
 ### Step 3: Update the MCAP Writer
-Modify `scripts/data_to_mcap.py` to recognize the new `shenron_radar/` directory.
+Modify `scripts/data_to_mcap.py` to recognize the new hardware directories.
 
 **File:** [`scripts/data_to_mcap.py`](file:///d:/Work/Repo/C-Shenron/scripts/data_to_mcap.py)
 ```python
-# Add a check for the shenron folder
-shenron_path = session_dir / "shenron_radar"
-if shenron_path.exists():
-    # Write to Foxglove topic: "/radar/shenron"
+# Loop over hardware profiles
+for r_type in ['awrl1432', 'radarbook']:
+    shenron_path = session_dir / r_type
+    if shenron_path.exists():
+        # Write to Foxglove topic: "/radar/{r_type}"
 ```
 
 ---

intel/radar/shenron_integration.md

@@ -27,27 +27,28 @@ Modify `convert_to_pcd()` to preserve the magnitude of the detected peaks.
 - **New**: Returns `[x, y, z, velocity, magnitude]`.
 
 ### [NEW] `ISOLATE/model_wrapper.py`
-The wrapper will now handle the "Rich" data synthesis:
-1. Call `Sceneset` to get the ground-truth physical power (RCS).
-2. Call `HeatmapGen` to generate the noisy ADC stream.
+The wrapper handles "Rich" data synthesis and telemetry:
+1. Call `Sceneset` for ground-truth physical power (RCS).
+2. Call `HeatmapGen` for the noisy ADC stream.
 3. Call `RadarProcessor` to extract detected peaks.
-4. Merge the detected peaks with the underlying RCS data to provide a complete "Semantic Radar" point cloud.
+4. **Telemetry**: New `get_signal_metrics()` method exports SNR and noise floor data.
 
 ---
 
 ## Phase 3: Modular Pipeline Integration
-... (Previously defined)
-
----
 
-## Verification Plan
+The automated pipeline now supports concurrent hardware simulation.
 
-### Automated Verification
-1. **API Integrity**: Run a script that initializes the `ShenronRadarModel` and passes a dummy [N, 7] array to ensure it executes and returns the correct [N, 4] shape.
-2. **Batch Run**: Execute `generate_shenron.py` on an existing dataset.
+### [MODIFY] `scripts/generate_shenron.py`
+Loop through multiple hardware profiles:
+- **AWRL1432**: Short-range, high-resolution.
+- **RadarBook**: Long-range standard.
 
-### Manual Verification
-1. Compare the MCAP output with vs. without the Shenron layer to ensure topic alignment.
+### [MODIFY] `scripts/data_to_mcap.py`
+UnifiedTopic Management:
+- `/radar/native`
+- `/radar/awrl1432`
+- `/radar/radarbook`
 
 ---
 
@@ -56,9 +57,9 @@ The wrapper will now handle the "Rich" data synthesis:
 ### Automated Verification
 1. **Pilot Run**: Record a 100-frame scenario with semantic LiDAR.
 2. **Post-Process**: Run `python scripts/generate_shenron.py`.
-3. **Check Output**: Verify `data/<session>/shenron_radar/` contains 100 `.npy` files.
+3. **Check Output**: Verify `data/<session>/awrl1432/` and `radarbook/` contain 100 `.npy` files each.
 
 ### Manual Verification
 1. Run `python scripts/data_to_mcap.py`.
-2. Open the MCAP in Foxglove and verify that the `/radar/shenron` topic perfectly overlays with the `/lidar` data but shows radar-typical noise and sparsity.
+2. Open the MCAP in Foxglove and verify that the `/radar/radarbook/metrics` topic shows stable telemetry curves.
 

intel/scenarios/dashboard.md

@@ -45,6 +45,8 @@ When the `/api/run` endpoint is hit:
    Flask builds the command array. A payload of `{"scenario": "showcase", "frames": 50}` translates to the equivalent of typing:
    `cmd.exe /c run.bat showcase --frames 50`
 
+   *Note: `run.bat` supports **Smart Argument Detection**. It intelligently determines if CLI flags (like `-s` or `--scenario`) are already present in the command array to prevent redundant prefixing.*
+
 2. **Subprocess Spawning:**
    Flask leverages Python's `subprocess.Popen` to spawn a detached child process running the constructed command.
    - `cwd=PROJECT_ROOT`: Ensures `run.bat` is executed contextually from the root workspace so relative imports inside `src/main.py` do not break.