#!/usr/bin/python


import numpy as np
from libs.consts import NOISE_FLOOR


def touch(filepath):
    try:
        open(filepath, 'a').close()
        return True
    except BaseException:
        pass
    return False


def convert_mat_to_vector(
    mat: np.ndarray,
    block_size: float = 0.1
):
    '''
    '''
    rx_locs = []
    rx_rsses = []
    width, length = mat.shape
    for i in range(width):
        for j in range(length):
            if mat[i, j] <= NOISE_FLOOR + 5:
                continue
            rx_locs.append([(i + 0.5) * block_size, (j + 0.5) * block_size])
            rx_rsses.append(mat[i, j])
    return np.array(rx_locs), np.array(rx_rsses)


def convert_vector_to_mat(
    rx_locs: np.ndarray,
    rx_rsses: np.ndarray,
    shape: tuple
):
    '''
    '''
    result = np.ones(shape) * -85.0
    for i in range(rx_locs.shape[0]):
        # if offset is None:
        #     x, y = rx_locs[i, :] / block_size
        # else:
        #     x, y = (rx_locs[i, :] + offset) / block_size
        # x, y = int(round(x)), int(round(y))
        result[i % shape[0], int(i / shape[0])] = rx_rsses[i]
    return result


def stats(data):
    '''
    '''
    data = data.reshape(-1,)
    return {
        "5perc": np.percentile(data, 5),
        "25perc": np.percentile(data, 25),
        "median": np.median(data),
        "75perc": np.percentile(data, 75),
        "95perc": np.percentile(data, 95),
        "mean": np.mean(data),
        "std": np.std(data),
    }