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77 lines
2.4 KiB
77 lines
2.4 KiB
import numpy as np
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import os
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import sys
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from pathlib import Path
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import matplotlib
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matplotlib.use('Agg') # Headless
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import matplotlib.pyplot as plt
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# Add project paths
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project_root = Path(__file__).parent.parent
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sys.path.append(str(project_root / 'scripts' / 'ISOLATE'))
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try:
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from model_wrapper import ShenronRadarModel
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except ImportError as e:
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print(f"Error: {e}")
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sys.exit(1)
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def run_sensitivity_sweep(frame_idx=207):
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lidar_path = project_root / 'Shenron_debug' / 'logs' / 'lidar' / f"frame_{frame_idx:06d}.npy"
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if not lidar_path.exists():
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print(f"[ERROR] Frame {lidar_path} not found.")
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return
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data = np.load(lidar_path)
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if data.shape[1] == 6:
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padded = np.zeros((data.shape[0], 7), dtype=np.float32)
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padded[:, 0:3] = data[:, 0:3]
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padded[:, 4:7] = data[:, 3:6]
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data = padded
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model = ShenronRadarModel(radar_type='awrl1432')
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# Thresholds to sweep for Frame 207
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thresholds = [0.0, 1e-5, 5e-5, 1e-4, 2e-4, 5e-4, 0.001]
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results = []
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artifacts_dir = Path(r"C:\Users\rakadu1\.gemini\antigravity\brain\67913a3c-cbc2-4fba-87e3-88fbea20f043\artifacts")
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print(f"\n🧪 Starting Sensitivity Sweep (Frame {frame_idx})...")
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for i, thresh in enumerate(thresholds):
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print(f" -> Testing Threshold: {thresh}")
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model.radar_obj.sensitivity_floor = thresh
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# Process frame
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rich_pcd = model.process(data)
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count = rich_pcd.shape[0]
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mean_mag = np.mean(rich_pcd[:, 4]) if count > 0 else 0
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results.append({
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'threshold': thresh,
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'count': count,
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'mean_mag': mean_mag
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})
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# Plotting
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plt.figure(figsize=(10, 8))
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if count > 0:
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plt.scatter(rich_pcd[:, 1], rich_pcd[:, 0], c=rich_pcd[:, 4], cmap='viridis', s=12, vmin=0, vmax=100)
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plt.colorbar(label='Magnitude')
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plt.title(f"Threshold: {thresh} | Pts: {count} | Avg Mag: {mean_mag:.2f}")
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plt.xlabel("Side (Y)")
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plt.ylabel("Forward (X)")
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plt.xlim([-30, 30])
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plt.ylim([0, 100])
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plt.grid(True, alpha=0.3)
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save_path = artifacts_dir / f"sweep_207_thresh_{i}.png"
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plt.savefig(save_path)
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plt.close()
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print(f" [DONE] Result counts: {count} pts")
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if __name__ == "__main__":
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run_sensitivity_sweep(207)
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