#!/usr/bin/env python3

from collections import defaultdict
from datetime import datetime
from node_ranking import (
    parse_csv,
    csv_loader,
    build_graph,
    Node,
)

def load_data(path):
    data = defaultdict(list)
    with open(path, 'r') as f:
        for line in f.readlines():
            when = datetime.strptime(line.split(',')[0]+'00', '%Y-%m-%d %H:%M:%S%z')
            data[when].append(
                parse_csv(line, source_ip_index=1, source_port_index=2, dest_ip_index=3, dest_port_index=4)
            )

    return data


def main():
    data = load_data('./part-dist-edges.csv')
    print(f'loaded data. {len(data.keys())} buckets')
    for bucket, edges in data.items():
        edges = list(edges)
        print(f'bucket: {bucket}')
        print(f'edges: {len(edges)}')
        g = build_graph(edges)
        count_map = {}
        for node in g:
            count_map[node.node] = len(list(g.successors(node)))

        sum_out = 0
        known = Node('34.204.196.211', 9796)
        for v in count_map.values():
            sum_out += v

        min_out = min(count_map.items(), key=lambda kv: kv[1])
        max_out = max(count_map.items(), key=lambda kv: kv[1])

        avg_out = float(sum_out) / len(count_map.keys())
        known_out = count_map[known]
        print(f'\tavg_out: {avg_out}')
        print(f'\tmin_out: {min_out}')
        print(f'\tmax_out: {max_out}')
        print(f'\tknown_out: {known} {known_out}')
        with open(f'./avg_out/{bucket.timestamp()}.txt', 'w') as out:
            out.write(f'bucket: {bucket}\n')
            out.write(f'\tavg_out: {avg_out}\n')
            out.write(f'\tmin_out: {min_out}\n')
            out.write(f'\tmax_out: {max_out}\n')
            out.write(f'\tknown_out: {known} {known_out}\n')



if __name__ == "__main__":
    main()