#!/usr/bin/env python3

import glob
from datetime import datetime

import matplotlib.pyplot as plt
import numpy as np

def load(path):
    data = []
    for file in glob.glob(path):
        with open(file, 'r') as f:
            inp = f.readlines()
            data.append((datetime.fromtimestamp(float(file[6:-4])), float(inp[1][10:-1])))
            # data[datetime.fromtimestamp(float(file[6:-4]))] = float(inp[1][10:-1])

    return sorted(data, key=lambda x: x[0])

def read(path):
    data = []
    with open(path, 'r') as f:
        data.extend(map(float, f.readlines()))

    return data

# ranks = [0.25, 0.5, 0.75]
# iterations = 5

# for rank in ranks:
#     for iteration in range(iterations):
#         count, bins, ignored = plt.hist(read(f'./{rank}_{iteration+1}_sr.txt'), 30)
#         plt.xlabel('SensorRank')
#         plt.ylabel('Peers')
#         s = 's'
#         e = ''
#         plt.title(f'SensorRank distribution after {iteration+1} iteration{s if iteration+1 > 1 else e} with initial rank {rank}')
#         plt.savefig(f'./{rank:.2f}_{iteration+1}_sr.png')
#         plt.clf()
#         plt.cla()


# count, bins, ignored = plt.hist(data, 30)
# # plt.show()
# # plt.imsave("", data)
# plt.xlabel('PageRank')
# plt.ylabel('peers')
# plt.title()
# plt.savefig('./0.25_5_sr.png')

def main():
    data = load('./inp/*')
    N = len(data)
    # for x in data:
    #     print(x)
    # exit(1)
    fig, ax = plt.subplots(figsize=(8, 4))
    ax.plot([v[0] for v in data], [v[1] for v in data])
    # ax.plot([v[0] for v in data], [v[1] for v in data], 'o-')
    ax.set_title('Average outgoing edges per peer per hour')
    ax.set_ylim(ymin=0)
    fig.autofmt_xdate()

    # plt.show()
    plt.savefig('./avg_out_edges_no_dot.png')

if __name__ == "__main__":
    main()