Updated readme and fix axes.

intel/radar/metrology_suite/README.md

@@ -2,6 +2,9 @@
 
 This directory serves as the source of truth for the **C-SHENRON Radar Metrology Suite**. The goal of this suite is to transform the physics-based radar simulation from a "Black Box" into a transparent "Radar Lab" environment.
 
+> [!TIP]
+> **New Feature:** For a detailed step-by-step guide on using these new tools, see the official [**Walkthrough Guide**](./walkthrough.md).
+
 ---
 
 ## 🎯 1. The Core Objective
@@ -26,28 +29,33 @@ Current state: Successfully converts LiDAR points into complex ADC time-series.
 
 ### B. Signal Processing (`radar_processor.py` & `cfar_detector.py`)
 Current state: Performs 2D FFT and CA-CFAR detection.
-*   **Modification Plan:** 
-    -   Retain the **3D FFT Cube** (Range x Doppler x Angle).
-    -   Extract the **Threshold Matrix** from `CA_CFAR.__call__`.
-    -   Compute global **Range-Azimuth** energy maps via mean-Doppler reduction.
+*   **Implementation Status:** 
+    -   Exposes the **3D FFT Cube** (Range x Doppler x Angle).
+    -   Extracts the **Detection Gate** from `CA_CFAR.__call__`.
+    -   Computes global **Range-Azimuth** energy maps via mean-Doppler reduction.
 
 ---
 
-## 🗺️ 3. Active Implementation Roadmap
+## 🗺️ 3. Implementation Roadmap
+
+### Phase 1: Engine Heatmap Extraction (COMPLETED)
+- [x] Modify `cfar_detector.py` to return the `detection_gate` (Threshold Baseline).
+- [x] Update `radar_processor.py` to capture RD and compute global RA heatmaps.
+- [x] Update `model_wrapper.py` to expose heatmaps and signal telemetry.
 
-### Phase 1: Engine Heatmap Extraction (IN PROGRESS)
-- [ ] Modify `cfar_detector.py` to return the `rd_avg_noise_power` (Threshold Baseline).
-- [ ] Update `radar_processor.py` to capture RD and compute global RA heatmaps.
-- [ ] Update `model_wrapper.py` to expose heatmaps and signal telemetry.
+### Phase 2: Signal-to-Visual Pipeline (COMPLETED)
+- [x] Implement 8-bit normalization and Viridis colormapping for radar image topics.
+- [x] Update `test_shenron.py` to register new `/heatmaps/` image channels in MCAP.
+- [x] Add JSON telemetry packaging for SNR/Noise metrics.
 
-### Phase 2: Signal-to-Visual Pipeline (PENDING)
-- [ ] Implement 8-bit normalization and Viridis colormapping for radar image topics.
-- [ ] Update `test_shenron.py` to register new `/heatmaps/` image channels in MCAP.
-- [ ] Add JSON telemetry packaging for SNR/Noise metrics.
+### Phase 3: Raw Data Persistence (COMPLETED)
+- [x] Create `metrology/rd`, `metrology/ra`, and `metrology/cfar` directory structure.
+- [x] Implement `.npy` serialization for every frame during the simulation loop.
 
-### Phase 3: Raw Data Persistence (PENDING)
-- [ ] Create `metrology/rd`, `metrology/ra`, and `metrology/cfar` directory structure.
-- [ ] Implement `.npy` serialization for every frame during the simulation loop.
+### Phase 4: Verification (COMPLETED)
+- [x] Run Iteration 18 (250-frame simulation sweep).
+- [x] Inspect raw `.npy` files for precision integrity.
+- [x] Verify Foxglove visual alignment.
 
 ---
 
@@ -58,4 +66,4 @@ Current state: Performs 2D FFT and CA-CFAR detection.
 *   **Performance:** Keep the `signal.convolve2d` calls optimized; we must maintain at least 1.0 FPS for UX.
 
 ---
-*Created by Antigravity | Project: Fox CARLA ADAS | 2026-04-07*
+*Created by Antigravity | Project: Fox CARLA ADAS | 2026-04-08*

scripts/test_shenron.py

@@ -337,21 +337,24 @@ def run_testbench(iter_name):
                     
                     if rd_p.exists():
                         rd_data = np.load(rd_p)
-                        b64 = render_heatmap(np.log10(rd_data + 1e-9), cmap='viridis')
+                        # Flip UD so Range 0 (ego) is at the bottom
+                        b64 = render_heatmap(np.log10(np.flipud(rd_data) + 1e-9), cmap='viridis')
                         if b64:
                             msg = {"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "format": "png", "data": b64}
                             writer.add_message(metrology_channels[r_type]["rd"], log_time=ts_ns, data=json.dumps(msg).encode(), publish_time=ts_ns)
 
                     if ra_p.exists():
                         ra_data = np.load(ra_p)
-                        b64 = render_heatmap(np.log10(ra_data + 1e-9), cmap='magma') # Magma for top-down contrast
+                        # Flip UD so Range 0 (ego) is at the bottom
+                        b64 = render_heatmap(np.log10(np.flipud(ra_data) + 1e-9), cmap='magma') # Magma for top-down contrast
                         if b64:
                             msg = {"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "format": "png", "data": b64}
                             writer.add_message(metrology_channels[r_type]["ra"], log_time=ts_ns, data=json.dumps(msg).encode(), publish_time=ts_ns)
                     
                     if cf_p.exists():
                         cf_data = np.load(cf_p)
-                        b64 = render_heatmap(np.log10(cf_data + 1e-9), cmap='plasma') # Plasma for threshold mask
+                        # Flip UD so Range 0 (ego) is at the bottom
+                        b64 = render_heatmap(np.log10(np.flipud(cf_data) + 1e-9), cmap='plasma') # Plasma for threshold mask
                         if b64:
                             msg = {"timestamp": {"sec": ts_sec, "nsec": ts_nsec}, "frame_id": "ego_vehicle", "format": "png", "data": b64}
                             writer.add_message(metrology_channels[r_type]["cfar"], log_time=ts_ns, data=json.dumps(msg).encode(), publish_time=ts_ns)