#!/usr/bin/env python3

import matplotlib.pyplot as plt
import rank_with_crawlers
import statistics
import multiprocessing
from random import sample
import glob
import reduce_edges
import rank_with_churn
from datetime import datetime
import json
from node_ranking import (
    # rank as rank_nr,
    page_rank,
    sensor_rank,
    find_rank,
    parse_csv,
    csv_loader,
    build_graph,
    Node,
    RankedNode,
)


def analyze(g):
    known = rank_with_crawlers.find_known(g, rank_with_churn.KNOWN)
    # avg_r = rank_with_churn.avg_without_known(g)
    # avg_in = rank_with_churn.avg_in(g)
    # kn_in = known_in(g, known)
    # kn_out = known_out(g, known)
    d = list(map(lambda node: node.rank, g.nodes()))
    mean = statistics.mean(d)
    stddev = statistics.stdev(d)
    return {
        'known_rank': known.rank,
        # 'known_in': kn_in,
        # 'known_out': kn_out,
        # 'avg_rank': avg_r,
        # 'avg_in': avg_in,
        'mean': mean,
        'stdev': stddev,
    }


def perform(path):
    when = datetime.fromtimestamp(float(path.split('/')[-1][:-4]))
    print(f'{when=}, {path=}')
    edges = reduce_edges.load_data(path)
    g = build_graph(edges)
    g_sr = sensor_rank(sensor_rank(g))
    g_pr = page_rank(page_rank(g))
    res_sr = analyze(g_sr)
    res_pr = analyze(g_pr)
    path = f'./mean_and_deriv/{when.timestamp()}.json'
    return {
        path: {
        'sr': res_sr,
        'pr': res_pr,
        }
    }

def load_json(path):
    with open(path, 'r') as f:
        return json.load(f)

# def plot(path):
#     data = load_json(path)
#     when = datetime.fromtimestamp(float(path.split('/')[-1][:-5]))


def main_plot():
    data = {}
    for file in glob.glob('./mean_and_deriv/*.json'):
        when = datetime.fromtimestamp(float(file.split('/')[-1][:-5]))
        data[when] = load_json(file)
    times = []
    means_sr = []
    stder_sr = []
    for t, d in sorted(data.items(), key=lambda kv: kv[0]):
        times.append(t)
        means_sr.append(d['sr']['mean'])
        stder_sr.append(d['sr']['stdev'])

    plt.errorbar(times, means_sr, stder_sr)
    plt.show()



def main():
    params = []
    for file in glob.glob('./edges/*.txt'):
        params.append(file)

    with multiprocessing.Pool(processes=8) as pool:
        l_path_data = pool.map(perform, params)
        for path_data in l_path_data:
            for path, data in path_data.items():
                with open(path, 'w') as f:
                    json.dump(data, f)


if __name__ == '__main__':
    main_plot()