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@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+LIVING_ROOM_SCENE3_place_the_ketchup_bottle_upside_down_in_the_wooden_tray_demo_1752513943.hdf5 filter=lfs diff=lfs merge=lfs -text

LIVING_ROOM_SCENE3_place_the_ketchup_bottle_upside_down_in_the_wooden_tray_demo_1752513943.hdf5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a3441b8dad0253843c7b63a24ed5aec27080d56518063c53c54582810881c9fc
+size 1768073482

stat.py

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+# + Group: data/demo_8
+#   - Dataset: data/demo_8/actions, shape: (79, 7), dtype: float64
+#   - Dataset: data/demo_8/dones, shape: (79,), dtype: uint8
+#   + Group: data/demo_8/obs
+#     - Dataset: data/demo_8/obs/agentview_rgb, shape: (79, 128, 128, 3), dtype: uint8
+#     - Dataset: data/demo_8/obs/ee_ori, shape: (79, 3), dtype: float64
+#     - Dataset: data/demo_8/obs/ee_pos, shape: (79, 3), dtype: float64
+#     - Dataset: data/demo_8/obs/ee_states, shape: (79, 6), dtype: float64
+#     - Dataset: data/demo_8/obs/eye_in_hand_rgb, shape: (79, 128, 128, 3), dtype: uint8
+#     - Dataset: data/demo_8/obs/gripper_states, shape: (79, 2), dtype: float64
+#     - Dataset: data/demo_8/obs/joint_states, shape: (79, 7), dtype: float64
+#   - Dataset: data/demo_8/rewards, shape: (79,), dtype: uint8
+#   - Dataset: data/demo_8/robot_states, shape: (79, 9), dtype: float64
+#   - Dataset: data/demo_8/states, shape: (79, 92), dtype: float64
+# + Group: data/demo_9
+#   - Dataset: data/demo_9/actions, shape: (89, 7), dtype: float64
+#   - Dataset: data/demo_9/dones, shape: (89,), dtype: uint8
+#   + Group: data/demo_9/obs
+#     - Dataset: data/demo_9/obs/agentview_rgb, shape: (89, 128, 128, 3), dtype: uint8
+#     - Dataset: data/demo_9/obs/ee_ori, shape: (89, 3), dtype: float64
+#     - Dataset: data/demo_9/obs/ee_pos, shape: (89, 3), dtype: float64
+#     - Dataset: data/demo_9/obs/ee_states, shape: (89, 6), dtype: float64
+#     - Dataset: data/demo_9/obs/eye_in_hand_rgb, shape: (89, 128, 128, 3), dtype: uint8
+#     - Dataset: data/demo_9/obs/gripper_states, shape: (89, 2), dtype: float64
+#     - Dataset: data/demo_9/obs/joint_states, shape: (89, 7), dtype: float64
+#   - Dataset: data/demo_9/rewards, shape: (89,), dtype: uint8
+#   - Dataset: data/demo_9/robot_states, shape: (89, 9), dtype: float64
+#   - Dataset: data/demo_9/states, shape: (89, 92), dtype: float64
+
+# The above is the structure of the HDF5 file. Read all the HDF5 files in the directory, and calculate the mean, std, min, max, q01, q99 of the actions, obs/ee_states, gripper_states, joint_states of all the files.
+
+import h5py
+import numpy as np
+import os
+
+def calculate_statistics(hdf5_path):
+    actions = []
+    ee_states = []
+    gripper_states = []
+    joint_states = []
+
+    with h5py.File(hdf5_path, 'r') as f:
+        for demo in f['data']:
+            actions.append(f[f'data/{demo}/actions'][:])
+            ee_states.append(f[f'data/{demo}/obs/ee_states'][:])
+            gripper_states.append(f[f'data/{demo}/obs/gripper_states'][:])
+            joint_states.append(f[f'data/{demo}/obs/joint_states'][:])
+    actions = np.concatenate(actions, axis=0)
+    ee_states = np.concatenate(ee_states, axis=0)
+    gripper_states = np.concatenate(gripper_states, axis=0)
+    joint_states = np.concatenate(joint_states, axis=0)
+    stats = {
+        'actions': {
+            'mean': np.mean(actions, axis=0),
+            'std': np.std(actions, axis=0),
+            'min': np.min(actions, axis=0),
+            'max': np.max(actions, axis=0),
+            'q01': np.percentile(actions, 1, axis=0),
+            'q99': np.percentile(actions, 99, axis=0)
+        },
+        'ee_states': {
+            'mean': np.mean(ee_states, axis=0),
+            'std': np.std(ee_states, axis=0),
+            'min': np.min(ee_states, axis=0),
+            'max': np.max(ee_states, axis=0),
+            'q01': np.percentile(ee_states, 1, axis=0),
+            'q99': np.percentile(ee_states, 99, axis=0)
+        },
+        'gripper_states': {
+            'mean': np.mean(gripper_states, axis=0),
+            'std': np.std(gripper_states, axis=0),
+            'min': np.min(gripper_states, axis=0),
+            'max': np.max(gripper_states, axis=0),
+            'q01': np.percentile(gripper_states, 1, axis=0),
+            'q99': np.percentile(gripper_states, 99, axis=0)
+        },
+        'joint_states': {
+            'mean': np.mean(joint_states, axis=0),
+            'std': np.std(joint_states, axis=0),
+            'min': np.min(joint_states, axis=0),
+            'max': np.max(joint_states, axis=0),
+            'q01': np.percentile(joint_states, 1, axis=0),
+            'q99': np.percentile(joint_states, 99, axis=0)
+        }
+    }
+    return stats    
+
+def process_directory(directory):
+    all_stats = {
+        'actions': [],
+        'ee_states': [],
+        'gripper_states': [],
+        'joint_states': []
+    }
+    
+    for filename in os.listdir(directory):
+        if filename.endswith('.hdf5'):
+            hdf5_path = os.path.join(directory, filename)
+            stats = calculate_statistics(hdf5_path)
+            for key in all_stats:
+                all_stats[key].append(stats[key])
+    
+    # Calculate overall statistics
+    overall_stats = {}
+    for key, values in all_stats.items():
+        # values: a list of dictionaries
+        means = np.array([v['mean'] for v in values])
+        stds = np.array([v['std'] for v in values])
+        mins = np.array([v['min'] for v in values])
+        maxs = np.array([v['max'] for v in values])
+        q01s = np.array([v['q01'] for v in values])
+        q99s = np.array([v['q99'] for v in values])
+        overall_stats[key] = {
+            'mean': np.mean(means, axis=0),
+            'std': np.mean(stds, axis=0),
+            'min': np.min(mins, axis=0),
+            'max': np.max(maxs, axis=0),
+            'q01': np.mean(q01s, axis=0),
+            'q99': np.mean(q99s, axis=0)
+        }
+
+        
+    return overall_stats
+
+if __name__ == "__main__":
+    directory = '/home2/czhang/datasets/LIBERO/libero_spatial'
+    stats = process_directory(directory)
+    for key, value in stats.items():
+        print(f"{key}:")
+        for stat_name, stat_value in value.items():
+            print(f"  {stat_name}: {stat_value}")
+    print("Statistics calculated successfully.")