added txt, data and scripts

Data/scripts/plot_distances.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+def load_csv_with_standard_decimal(file_path):
+    with open(file_path, 'r') as f:
+        scene_line = f.readline().strip()
+        scene_name = scene_line.split(': ')[1].split('_')[0]
+    df = pd.read_csv(file_path, skiprows=1)
+    return df, scene_name
+
+def process_position_data(data_dir):
+    # Group by scene
+    scene_data = {}
+    csv_files = list(Path(data_dir).glob('**/P*_PositionData*.csv'))
+    for file_path in csv_files:
+        df, scene_name = load_csv_with_standard_decimal(str(file_path))
+        if scene_name not in scene_data:
+            scene_data[scene_name] = []
+        scene_data[scene_name].append(df)
+    return scene_data
+
+def plot_relative_positions(scene_data):
+    scenes = list(scene_data.keys())
+    # Create one figure with 4 subplots
+    fig, axes = plt.subplots(2, 2, figsize=(16, 14))
+    axes = axes.flatten()
+    for idx, (scene, recordings) in enumerate(scene_data.items()):
+        all_rel_x = []
+        all_rel_z = []
+        avg_obj_xs = []
+        avg_obj_zs = []
+        for df in recordings:
+            obj_x = df['TrackedObject_X'].values
+            obj_z = df['TrackedObject_Z'].values
+            avg_obj_x = np.mean(obj_x)
+            avg_obj_z = np.mean(obj_z)
+            avg_obj_xs.append(avg_obj_x)
+            avg_obj_zs.append(avg_obj_z)
+            rel_x = df['XR_Origin_X'].values - avg_obj_x
+            rel_z = df['XR_Origin_Z'].values - avg_obj_z
+            all_rel_x.extend(rel_x)
+            all_rel_z.extend(rel_z)
+        ax = axes[idx]
+        ax.scatter(all_rel_x, all_rel_z, alpha=0.2, c='blue', label='Player Positions (relative)')
+        ax.scatter(0, 0, c='red', s=800, marker='*', label='Agent', edgecolor='black', linewidths=2, zorder=10)
+        ax.set_title(f'Scene: {scene}')
+        ax.set_xlabel('X Position (relative to Agent)')
+        ax.set_ylabel('Z Position (relative to Agent)')
+        ax.legend()
+        ax.axis('equal')
+        ax.grid(True, linestyle='--', alpha=0.7)
+        # Also save individual images
+        plt.figure(figsize=(8, 8))
+        plt.scatter(all_rel_x, all_rel_z, alpha=0.2, c='blue', label='Player Positions (relative)')
+        plt.scatter(0, 0, c='red', s=800, marker='*', label='Agent', edgecolor='black', linewidths=2, zorder=10)
+        plt.title(f'Player Positions Relative to Agent\nScene: {scene}')
+        plt.xlabel('X Position (relative to Agent)')
+        plt.ylabel('Z Position (relative to Agent)')
+        plt.legend()
+        plt.axis('equal')
+        plt.grid(True, linestyle='--', alpha=0.7)
+        plt.tight_layout()
+        plt.savefig(f'room_layout_{scene}.png', dpi=300)
+        plt.close()
+    plt.tight_layout()
+    plt.savefig('room_layout_all_scenes.png', dpi=300)
+    plt.close()
+
+def main():
+    scene_data = process_position_data('Recordings')
+    plot_relative_positions(scene_data)
+
+if __name__ == "__main__":
+    main()