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