68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
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#!/usr/bin/env python3
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import glob
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from datetime import datetime
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import matplotlib.pyplot as plt
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import numpy as np
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def load(path):
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data = []
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for file in glob.glob(path):
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with open(file, 'r') as f:
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inp = f.readlines()
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data.append((datetime.fromtimestamp(float(file[6:-4])), float(inp[1][10:-1])))
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# data[datetime.fromtimestamp(float(file[6:-4]))] = float(inp[1][10:-1])
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return sorted(data, key=lambda x: x[0])
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def read(path):
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data = []
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with open(path, 'r') as f:
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data.extend(map(float, f.readlines()))
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return data
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# ranks = [0.25, 0.5, 0.75]
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# iterations = 5
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# for rank in ranks:
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# for iteration in range(iterations):
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# count, bins, ignored = plt.hist(read(f'./{rank}_{iteration+1}_sr.txt'), 30)
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# plt.xlabel('SensorRank')
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# plt.ylabel('Peers')
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# s = 's'
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# e = ''
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# plt.title(f'SensorRank distribution after {iteration+1} iteration{s if iteration+1 > 1 else e} with initial rank {rank}')
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# plt.savefig(f'./{rank:.2f}_{iteration+1}_sr.png')
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# plt.clf()
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# plt.cla()
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# count, bins, ignored = plt.hist(data, 30)
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# # plt.show()
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# # plt.imsave("", data)
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# plt.xlabel('PageRank')
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# plt.ylabel('peers')
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# plt.title()
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# plt.savefig('./0.25_5_sr.png')
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def main():
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data = load('./inp/*')
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N = len(data)
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# for x in data:
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# print(x)
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# exit(1)
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fig, ax = plt.subplots(figsize=(8, 4))
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ax.plot([v[0] for v in data], [v[1] for v in data])
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# ax.plot([v[0] for v in data], [v[1] for v in data], 'o-')
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ax.set_title('Average outgoing edges per peer per hour')
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ax.set_ylim(ymin=0)
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fig.autofmt_xdate()
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# plt.show()
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plt.savefig('./avg_out_edges_no_dot.png')
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if __name__ == "__main__":
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main()
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