move head-tracking files accross computers

head-tracking/main.py

@@ -0,0 +1,59 @@
+import os
+import matplotlib.pyplot as plt
+
+def hex_to_base10(hex_string):
+    hex_values = hex_string.split()
+    base10_values = [int(hex_value, 16) for hex_value in hex_values]
+    return base10_values
+
+def convert_file(input_filepath):
+    data = []
+    with open(input_filepath, 'r') as infile:
+        for line in infile:
+            base10_line = hex_to_base10(line.strip())
+            data.append(base10_line)
+    return data
+
+def plot_data(data, title, save_path):
+    # Transpose the data to get columns
+    columns = list(zip(*data))
+    # Filter out columns that stay the same across all rows
+    filtered_columns = [column for column in columns if len(set(column)) > 1]
+    # Generate time values for the x-axis
+    time_values = [i * 35 for i in range(len(data))]
+    
+    # Plot in sets of 4 columns
+    for start in range(0, len(filtered_columns), 4):
+        end = start + 4
+        subset_columns = filtered_columns[start:end]
+        num_columns = len(subset_columns)
+        fig, axs = plt.subplots(num_columns, 1, figsize=(10, 2 * num_columns), sharex=True)
+        
+        if num_columns == 1:
+            axs = [axs]  # Ensure axs is iterable when there's only one subplot
+        
+        for i, column in enumerate(subset_columns):
+            axs[i].plot(time_values, column, label=f'Column {start + i + 1}')
+            axs[i].set_ylabel('Value')
+            axs[i].legend()
+        
+        plt.xlabel('Time (ms)')
+        plt.suptitle(f'{title} (Columns {start + 1} to {end})')
+        
+        # Save the plot as a PNG file
+        plot_filename = f'{title.replace(".txt","")} - {start + 1} to {end}.png'
+        plt.savefig(os.path.join(save_path, plot_filename))
+        plt.close()
+
+def process_files(directory):
+    for filename in os.listdir(directory):
+        if filename.endswith('.txt'):
+            input_filepath = os.path.join(directory, filename)
+            data = convert_file(input_filepath)
+            plot_data(data, f'{filename}', directory)
+
+# Directory containing the files
+directory = '/Users/kavish/AirPodsLikeNormal/training data/Extracted Data/'
+
+# Process each file in the directory
+process_files(directory)