prog-team-proj/utilsv2/stats.py

import time
from datetime import datetime

import numpy as np


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


def pprint(v):
    return f"\tMédia: {v[0]} \u00b1 {v[1]}; 1o Quartil: {v[3]}; Mediana: {v[2]}; 3o Quartil: {v[4]}; Máximo: {v[5]}; Mínimo: {v[6]}"


def stats(data):
    _stats = f"===Estatística==\nNúmero total de eventos: {len(data)}\n"
    aux = []
    currMonth: datetime = data[0]["DateTime"]
    idx = 0
    while idx < len(data):
        if data[idx]["DateTime"].month == currMonth.month:
            aux.append(data[idx])
            idx += 1
        else:
            m = calc_mag(aux)
            d = calc_depth(aux)
            aux = []
            _stats += f"{currMonth.strftime('%Y-%m')}:\n\tMagnitude: {pprint(m)}\n\tProfundidade: {pprint(d)}\n\n"
            currMonth = data[idx]["DateTime"]

    m = calc_mag(aux)
    d = calc_depth(aux)
    _stats += f"{currMonth.strftime('%Y-%m')}:\nMagnitude: {pprint(m)}\nProfundidade: {pprint(d)}\n"

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

    # print(_stats)


def calc_depth(data):
    if len(data) == 0:
        return 0
    depths = np.array([v["Depth"] for v in data], dtype=float)
    quantile = np.quantile(depths, [0.25, 0.75])
    return list(
        map(
            float,
            (
                round(np.average(depths), 3),
                round(np.std(depths), 3),
                round(np.median(depths), 3),
                round(quantile[0], 3),
                round(quantile[1], 3),
                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)
    quantile = np.quantile(mags, [0.25, 0.75])
    return list(
        map(
            float,
            (
                round(np.average(mags), 3),
                round(np.std(mags), 3),
                round(np.median(mags), 3),
                round(quantile[0], 3),
                round(quantile[1], 3),
                np.min(mags),
                np.max(mags),
            ),
        )
    )