82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
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#!/usr/bin/env python3
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from datetime import datetime
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import numpy as np
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import glob
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import json
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import matplotlib.pyplot as plt
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def load_json(path):
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with open(path, 'r') as f:
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return json.load(f)
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# foo/0.5/10/1618959600.0.json
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def plot(path):
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data = {}
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# edges = 0
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# initial_rank = 0.0
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for file in glob.glob(path):
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path_split = file.split('/')
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initial_rank = float(path_split[1])
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edges = float(path_split[2])
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when = datetime.fromtimestamp(float(path_split[-1][:-5]))
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data[when] = load_json(file)
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x = []
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sr_avg = []
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sr_avg_filtered = []
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sr_known = []
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sr_perc_50 = []
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pr_avg = []
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pr_avg_filtered = []
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pr_known = []
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pr_perc_50 = []
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pr_known = []
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for when, d in sorted(data.items(), key=lambda kv: kv[0]):
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x.append(when)
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sr_avg.append(d['sr_avg'])
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sr_avg_filtered.append(d['sr_avg_filtered'])
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sr_known.append(d['sr_known'])
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sr_perc_50.append(d['sr_perc_50'])
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pr_avg.append(d['pr_avg'])
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pr_avg_filtered.append(d['pr_avg_filtered'])
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pr_known.append(d['pr_known'])
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pr_perc_50.append(d['pr_perc_50'])
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fig, ax = plt.subplots()
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plt.yticks(np.arange(0, 0.5, step=0.05))
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ax.plot(x, sr_avg_filtered, label='Average SR')
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ax.plot(x, sr_known, label='SR of Sensor')
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# ax.plot(x, sr_perc_50, label='sr_perc_50')
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ax.set_title(f'SensorRank after adding {edges * 100}% edges')
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fig.autofmt_xdate()
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fig.legend()
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plt.savefig(f'perc.bak/{initial_rank}/{edges}/sr.png')
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fig, ax = plt.subplots()
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plt.yticks(np.arange(0, 0.5, step=0.05))
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ax.plot(x, pr_avg_filtered, label='Average PR')
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ax.plot(x, pr_known, label='PR of Sensor')
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# ax.plot(x, pr_perc_50, label='pr_perc_50')
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ax.set_title(f'PageRank after adding {edges * 100}% edges')
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fig.autofmt_xdate()
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fig.legend()
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plt.savefig(f'perc.bak/{initial_rank}/{edges}/pr.png')
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def main():
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# for perc in [0.75, 0.9, 1.0, 1.5, 2.0, 2.5]:
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# for perc in [0.5, 0.75, 0.8, 1.0, 1.5, 2.0, 2.5]:
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for perc in [0.75, 0.9, 1.0, 1.5, 2.0, 2.5]:
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plot(f'perc.bak/0.5/{perc}/*.json')
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if __name__ == '__main__':
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main()
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