Add DuckTrack as initial annotation tool; Initial multimodal test

annotation/experiments/sleep_testing/calc_errors.py

@@ -0,0 +1,48 @@
+import csv
+import time
+
+import numpy as np
+from tqdm import tqdm
+
+
+def check_sleep(duration, sleep_function):
+    start = time.perf_counter()
+    sleep_function(duration)
+    end = time.perf_counter()
+    elapsed = end - start
+    return abs(elapsed - duration)
+
+def busy_sleep(duration):
+    end_time = time.perf_counter() + duration
+    while time.perf_counter() < end_time:
+        pass
+
+def measure_accuracy(sleep_function, durations, iterations=100):
+    average_errors = []
+    for duration in tqdm(durations):
+        errors = [check_sleep(duration, sleep_function) for _ in range(iterations)]
+        average_error = np.mean(errors)
+        average_errors.append(average_error)
+    return average_errors
+
+durations = np.arange(0.001, 0.101, 0.001)  # From 1ms to 100ms in 1ms increments
+iterations = 100
+
+sleep_errors = measure_accuracy(time.sleep, durations, iterations)
+busy_sleep_errors = measure_accuracy(busy_sleep, durations, iterations)
+
+def save_to_csv(filename, durations, sleep_errors, busy_sleep_errors):
+    with open(filename, 'w', newline='') as csvfile:
+        fieldnames = ['duration', 'sleep_error', 'busy_sleep_error']
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        
+        writer.writeheader()
+        for duration, sleep_error, busy_sleep_error in zip(durations, sleep_errors, busy_sleep_errors):
+            writer.writerow({
+                'duration': duration,
+                'sleep_error': sleep_error,
+                'busy_sleep_error': busy_sleep_error
+            })
+    print("Data saved to", filename)
+
+save_to_csv('sleep_data.csv', durations * 1000, np.array(sleep_errors) * 1000, np.array(busy_sleep_errors) * 1000)

annotation/experiments/sleep_testing/plot_errors.py

@@ -0,0 +1,33 @@
+import csv
+
+import matplotlib.pyplot as plt
+
+
+def plot_from_csv(filename, save_plot=False):
+    durations = []
+    sleep_errors = []
+    busy_sleep_errors = []
+    
+    with open(filename, 'r') as csvfile:
+        reader = csv.DictReader(csvfile)
+        for row in reader:
+            durations.append(float(row['duration']))
+            sleep_errors.append(float(row['sleep_error']))
+            busy_sleep_errors.append(float(row['busy_sleep_error']))
+    
+    plt.figure(figsize=(10, 5))
+    plt.plot(durations, sleep_errors, label='time.sleep()', marker='o')
+    plt.plot(durations, busy_sleep_errors, label='busy_sleep()', marker='x')
+    plt.xlabel('Desired Delay (ms)')
+    plt.ylabel('Average Error (ms)')
+    plt.title('Sleep Accuracy: time.sleep() vs Busy-Wait Loop (macOS)')
+    plt.legend()
+    plt.grid(True)
+    
+    if save_plot:
+        plt.savefig('sleep_accuracy_plot.png', dpi=300)
+        print("Plot saved as sleep_accuracy_plot.png")
+        
+    plt.show()
+
+plot_from_csv('sleep_data.csv', save_plot=True)