71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
#!/usr/bin/env python3
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from collections import defaultdict
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from typing import Dict
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import matplotlib.pyplot as plt
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import time
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from datetime import datetime
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def load_log(path: str) -> Dict[datetime, str]:
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time_crawler = {}
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with open(path, 'r') as f:
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for line in f:
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unix_nanos, crawler, _ = line.split(' , ')
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when = datetime.utcfromtimestamp(int(unix_nanos) / 1000000000)
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time_crawler[when] = crawler
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return time_crawler
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def plot_deriv(data: Dict[datetime, str]):
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diffs = []
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per_crawler = defaultdict(list)
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sor = list(sorted(data.items(), key=lambda kv: kv[0]))
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for prev, next in zip(sor, sor[1:]):
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diffs.append(abs(2.5 - (next[0].timestamp() - prev[0].timestamp())))
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per_crawler[prev[1]].append(prev[0])
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# expected = [2.5] * len(diffs)
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# x = list(range(len(diffs)))
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# x = []
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x = [2.5 * x for x in range(len(diffs))]
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fig, ax = plt.subplots()
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ax.set_title('Timedelta between crawl events in seconds')
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# ax.set_ylabel()
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ax.set_xlabel('Time passed in seconds')
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ax.set_ylabel('Deviation in seconds')
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# ax.plot(x, expected, label='Expected difference')
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ax.plot(x, diffs, label='Deviation from the expected value')
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fig.legend()
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# plt.show()
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plt.savefig('./time_deriv.png')
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plt.close()
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for c in per_crawler.keys():
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t = per_crawler[c]
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devi = []
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for pre, nex in zip(t, t[1:]):
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devi.append(abs(10 - (nex.timestamp() - pre.timestamp())))
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x = [10 * x for x in range(len(devi))]
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fig, ax = plt.subplots()
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ax.plot(x, devi)
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ax.set_title(f'Timedeviation for {c}')
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ax.set_xlabel('Time passed in seconds')
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ax.set_ylabel('Deviation in seconds')
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plt.savefig(f'./time_deriv_{c}.png')
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plt.close()
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# for ts in per_crawler[c]:
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def main():
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data = load_log('./dummy.log')
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plot_deriv(data)
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if __name__ == '__main__':
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main()
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