diff --git a/tensorboard_data_03_csv_03.py b/tensorboard_data_03_csv_03.py
new file mode 100644
index 00000000..1f299909
--- /dev/null
+++ b/tensorboard_data_03_csv_03.py
@@ -0,0 +1,92 @@
+import os
+import json
+import numpy as np
+import pandas as pd
+
+
+def smooth(scalars, weight):  # Weight between 0 and 1
+    last = scalars[0]  # First value in the plot (first timestep)
+    smoothed = list()
+    for point in scalars:
+        smoothed_val = last * weight + (1 - weight) * point  # Calculate smoothed value
+        smoothed.append(smoothed_val)                        # Save it
+        last = smoothed_val                                  # Anchor the last smoothed value
+
+    return smoothed
+
+
+if __name__ == "__main__":
+
+    # Source directory where files are located
+    source_dir = os.path.join(os.getcwd(), "tensorboard_data_02_json_results")
+
+    # Destination directory where files will be copied
+    destination_dir = os.path.join(os.getcwd(), "tensorboard_data_03_csv_results")
+
+    # Find all files in the source directory starting with "events", choose 3 version to be draw on image
+    files = []
+    for root, dirs, filenames in os.walk(source_dir):
+        normal_root = os.path.normpath(root)
+        for filename in filenames:
+            version = os.path.split(normal_root)[-2]
+            if filename.startswith('events') and any(sub in version for sub in ('v7.0.0', 'v5.0.1')):
+                files.append(os.path.join(root, filename))
+    
+    # Legend labels of tensorboard_data_03_csv.py
+    legend_labels = {'v5.0.1': 'No motion', 'v7.0.0': 'Uniformly ramdom motion'}
+
+    # Create an empty DataFrame with three columns
+    df = pd.DataFrame(columns=['Step', 'Value', 'Legend'])
+    print(df)
+
+    # Loop through each file found
+    for file_path in files:
+        # Get the directory where the file is located relative to the source directory
+        relative_dir = os.path.relpath(os.path.dirname(file_path), source_dir)
+
+        # Split the directory specifying the version number, va.b.c-d
+        first_level_dir = os.path.split(relative_dir.rstrip(os.sep))[0]
+
+        # Split the version number to get va.b.c
+        version_number = first_level_dir.split('-')[0]
+
+        # Load the JSON file
+        with open(file_path, 'r') as f:
+            json_data = json.load(f)
+
+        # Extract 'step' and 'value' from the list of dictionaries
+        steps = np.array([d['step'] for d in json_data])
+        values = np.array([d['value'] for d in json_data])
+
+        # Find smoothed values
+        smoothed_values = smooth(values, 0.95)
+
+        # Concatenate the two arrays along the second axis (columns)
+        concatenated_array = np.stack((steps, smoothed_values), axis=1)
+        print(concatenated_array.shape)
+
+        # Create a DataFrame from the numpy array
+        df2 = pd.DataFrame(concatenated_array, columns=['Step', 'Value'])
+
+        # Concatenate the two DataFrames
+        df = pd.concat([df, df2], ignore_index=True)
+
+        # Assign the string legend_labels[version_number] to the last rows of the Legend column
+        df.loc[len(df) - len(df2):, 'Legend'] = legend_labels[version_number]
+
+        # Print out the coverted files
+        print(f"Covert {file_path} to DataFrame")
+
+    # Create the destination directory
+    os.makedirs(destination_dir, exist_ok=True)
+
+    # Define the new filename with '.csv'
+    csv_filename = 'result_03.csv'
+
+    # Define the new path of .csv file
+    csv_filepath = os.path.join(destination_dir, csv_filename)
+
+    # Write DataFrame to CSV file
+    df.to_csv(csv_filepath, index=False)  # Set index=False to exclude the index column in the CSV file
+
+    print("Convert operation completed.")
diff --git a/tensorboard_data_03_csv_04.py b/tensorboard_data_03_csv_04.py
new file mode 100644
index 00000000..9381f227
--- /dev/null
+++ b/tensorboard_data_03_csv_04.py
@@ -0,0 +1,92 @@
+import os
+import json
+import numpy as np
+import pandas as pd
+
+
+def smooth(scalars, weight):  # Weight between 0 and 1
+    last = scalars[0]  # First value in the plot (first timestep)
+    smoothed = list()
+    for point in scalars:
+        smoothed_val = last * weight + (1 - weight) * point  # Calculate smoothed value
+        smoothed.append(smoothed_val)                        # Save it
+        last = smoothed_val                                  # Anchor the last smoothed value
+
+    return smoothed
+
+
+if __name__ == "__main__":
+
+    # Source directory where files are located
+    source_dir = os.path.join(os.getcwd(), "tensorboard_data_02_json_results")
+
+    # Destination directory where files will be copied
+    destination_dir = os.path.join(os.getcwd(), "tensorboard_data_03_csv_results")
+
+    # Find all files in the source directory starting with "events", choose 3 version to be draw on image
+    files = []
+    for root, dirs, filenames in os.walk(source_dir):
+        normal_root = os.path.normpath(root)
+        for filename in filenames:
+            version = os.path.split(normal_root)[-2]
+            if filename.startswith('events') and any(sub in version for sub in ('v8.0.0', 'v5.0.1')):
+                files.append(os.path.join(root, filename))
+    
+    # Legend labels of tensorboard_data_03_csv.py
+    legend_labels = {'v5.0.1': 'Decision Range', 'v8.0.0': 'No Decision Range'}
+
+    # Create an empty DataFrame with three columns
+    df = pd.DataFrame(columns=['Step', 'Value', 'Legend'])
+    print(df)
+
+    # Loop through each file found
+    for file_path in files:
+        # Get the directory where the file is located relative to the source directory
+        relative_dir = os.path.relpath(os.path.dirname(file_path), source_dir)
+
+        # Split the directory specifying the version number, va.b.c-d
+        first_level_dir = os.path.split(relative_dir.rstrip(os.sep))[0]
+
+        # Split the version number to get va.b.c
+        version_number = first_level_dir.split('-')[0]
+
+        # Load the JSON file
+        with open(file_path, 'r') as f:
+            json_data = json.load(f)
+
+        # Extract 'step' and 'value' from the list of dictionaries
+        steps = np.array([d['step'] for d in json_data])
+        values = np.array([d['value'] for d in json_data])
+
+        # Find smoothed values
+        smoothed_values = smooth(values, 0.95)
+
+        # Concatenate the two arrays along the second axis (columns)
+        concatenated_array = np.stack((steps, smoothed_values), axis=1)
+        print(concatenated_array.shape)
+
+        # Create a DataFrame from the numpy array
+        df2 = pd.DataFrame(concatenated_array, columns=['Step', 'Value'])
+
+        # Concatenate the two DataFrames
+        df = pd.concat([df, df2], ignore_index=True)
+
+        # Assign the string legend_labels[version_number] to the last rows of the Legend column
+        df.loc[len(df) - len(df2):, 'Legend'] = legend_labels[version_number]
+
+        # Print out the coverted files
+        print(f"Covert {file_path} to DataFrame")
+
+    # Create the destination directory
+    os.makedirs(destination_dir, exist_ok=True)
+
+    # Define the new filename with '.csv'
+    csv_filename = 'result_04.csv'
+
+    # Define the new path of .csv file
+    csv_filepath = os.path.join(destination_dir, csv_filename)
+
+    # Write DataFrame to CSV file
+    df.to_csv(csv_filepath, index=False)  # Set index=False to exclude the index column in the CSV file
+
+    print("Convert operation completed.")