Add DuckTrack as initial annotation tool; Initial multimodal test

annotation/experiments/delays/delay.py

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+import glob
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from scipy.stats import sem, t
+
+
+def calculate_confidence_interval(data, confidence=0.95):
+    n = len(data)
+    m = np.mean(data)
+    std_err = sem(data)
+    h = std_err * t.ppf((1 + confidence) / 2, n - 1)
+    return m, m-h, m+h
+
+runs = glob.glob("run*.txt")
+TOTAL_EVENTS = 22509
+percent_delays = []
+all_delays = []
+
+for run in runs:
+    with open(run, "r") as f:
+        delays = [float(line.split()[3]) for line in f if float(line.split()[3]) > 0]  # consider only positive delays
+        percent_delays.append((len(delays) / TOTAL_EVENTS) * 100)
+        all_delays.extend(delays)
+
+average_percent_delays = np.mean(percent_delays)
+confidence_interval_percent_delays = calculate_confidence_interval(percent_delays)
+print(f"Average percentage of delayed events across all runs: {average_percent_delays:.2f}%")
+print(f"95% Confidence interval: ({confidence_interval_percent_delays[1]:.2f}%, {confidence_interval_percent_delays[2]:.2f}%)")
+
+if all_delays:
+    mean_delay = np.mean(all_delays)
+    confidence_interval_delays = calculate_confidence_interval(all_delays)
+    print(f"Mean delay time: {mean_delay:.2f}")
+    print(f"95% Confidence interval for delay time: ({confidence_interval_delays[1]:.2f}, {confidence_interval_delays[2]:.2f})")
+else:
+    print("No delay data available for calculation.")
+
+sns.histplot(all_delays, bins=30, kde=False)
+plt.xlabel('Delay Time (ms)')
+plt.ylabel('Frequency')
+plt.yscale('log')
+plt.title('Histogram of Delay Times (macOS)')
+
+plt.savefig('delays.png', dpi=300)
+
+plt.show()