import time

import numpy as np
from pymongo import MongoClient


def print_filters(filters):
    _res = ""
    for k, v in filters.items():
        _res += f"{k}: {v}\n"


def calculate_stats(data: list, filters):
    _stats_txt = "Estatísticas\n"
    _stats_txt += f"Número de eventos: {len(data)}\n"
    _res = calc_mag(data)
    _stats_txt += f"Magnitudes:\n\tMédia: {_res[0]} \u00b1 {_res[1]}\n\tMediana: {_res[2]}\n\tValor Mínimo: {_res[3]}\n\tValor Máximo: {_res[4]}\n"
    _res = calc_depth(data)
    _stats_txt += f"\nProfundidade:\n\tMédia: {_res[0]} \u00b1 {_res[1]}\n\tMediana: {_res[2]}\n\tValor Mínimo: {_res[3]}\n\tValor Máximo: {_res[4]}\n"

    fname = f"stats-{time.time_ns()}.txt"
    with open(fname, "wb") as fp:
        fp.write(_stats_txt.encode("utf-8"))

    print(_stats_txt)


def get_data(client: MongoClient):
    pass


def calc_depth(data):
    if len(data) == 0:
        return 0
    depths = np.array([v["Depth"] for v in data], dtype=float)
    return (
        np.average(depths),
        np.std(depths),
        np.median(depths),
        np.min(depths),
        np.max(depths),
    )


def calc_mag(data):
    if len(data) == 0:
        return 0
    mags = np.array([v["Magnitudes"]["L"]["Magnitude"] for v in data], dtype=float)
    return (np.average(mags), np.std(mags), np.median(mags), np.min(mags), np.max(mags))