"""

Stefania Fresca, MOX Laboratory, Politecnico di Milano

February 2019

"""

import os

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import numpy as np

import scipy.io as sio

import h5py

def read_data(mat):

    data = sio.loadmat(mat)

    S = data['S'].squeeze()

    S = np.transpose(S)

    return S

def read_large_data(mat):

    file = h5py.File(mat, 'r')

    S = file['S'][:]

    return S

def read_params(mat):

    params = sio.loadmat(mat)

    params = params['I'].squeeze()

    return params

def max_min(S_train, n_train):

    S_max = np.max(np.max(S_train[:n_train], axis = 1), axis = 0)

    S_min = np.min(np.min(S_train[:n_train], axis = 1), axis = 0)

    return S_max, S_min

def scaling(S, S_max, S_min):

    S[ : ] = (S - S_min)/(S_max - S_min)

def inverse_scaling(S, S_max, S_min):

    S[ : ] = (S_max - S_min) * S + S_min

def zero_pad(S, n):

    paddings = np.zeros((S.shape[0], n))

    S = np.hstack((S, paddings))

    return S

def safe_mkdir(path):

    try:

        os.mkdir(path)

    except OSError:

        pass