#!/usr/bin/env python3

from collections import defaultdict
from typing import Dict
import matplotlib.pyplot as plt
import time
from datetime import datetime

def load_log(path: str) -> Dict[datetime, str]:
    time_crawler = {}
    with open(path, 'r') as f:
        for line in f:
            unix_nanos, crawler, _ = line.split(' , ')
            when = datetime.utcfromtimestamp(int(unix_nanos) / 1000000000)
            time_crawler[when] = crawler

    return time_crawler



def plot_deriv(data: Dict[datetime, str]):
    diffs = []
    per_crawler = defaultdict(list)
    sor = list(sorted(data.items(), key=lambda kv: kv[0]))
    for prev, next in zip(sor, sor[1:]):
        diffs.append(abs(2.5 - (next[0].timestamp() - prev[0].timestamp())))
        per_crawler[prev[1]].append(prev[0])


    # expected = [2.5] * len(diffs)
    # x = list(range(len(diffs)))
    # x = []
    x = [2.5 * x for x in range(len(diffs))]
    fig, ax = plt.subplots()
    ax.set_title('Timedelta between crawl events in seconds')
    # ax.set_ylabel()
    ax.set_xlabel('Time passed in seconds')
    ax.set_ylabel('Deviation in seconds')
    # ax.plot(x, expected, label='Expected difference')
    ax.plot(x, diffs, label='Deviation from the expected value')
    fig.legend()
    # plt.show()
    plt.savefig('./time_deriv.png')
    plt.close()

    for c in per_crawler.keys():
        t = per_crawler[c]
        devi = []
        for pre, nex in zip(t, t[1:]):
            devi.append(abs(10 - (nex.timestamp() - pre.timestamp())))
        x = [10 * x for x in range(len(devi))]
        fig, ax = plt.subplots()
        ax.plot(x, devi)
        ax.set_title(f'Timedeviation for {c}')
        ax.set_xlabel('Time passed in seconds')
        ax.set_ylabel('Deviation in seconds')
        plt.savefig(f'./time_deriv_{c}.png')
        plt.close()
        # for ts in per_crawler[c]:




def main():
    data = load_log('./dummy.log')
    plot_deriv(data)


if __name__ == '__main__':
    main()