#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright (c) 2018, Cabral, Juan; Luczywo, Nadia; Zanazi Jose Luis
# All rights reserved.

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# modification, are permitted provided that the following conditions are met:

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# =============================================================================
#
# =============================================================================

"""Wrappers around some test to check if some data is comming from a
Normal distribution methods

"""


# =============================================================================
# IMPORTS
# =============================================================================

import numpy as np
from scipy import stats


# =============================================================================
# FUNCTIONS
# =============================================================================

def shapiro(x, axis=None):
    if axis is None:
        results = stats.shapiro(x)
        return results[0], results[1]
    rsize = np.shape(x)[axis]
    ntst, pvals = np.empty(rsize), np.empty(rsize)
    for idx, values in enumerate(np.rollaxis(x, axis)):
        results = stats.shapiro(values)
        ntst[idx], pvals[idx] = results[0], results[1]
    return ntst, pvals


def kstest(x, axis=None, *args, **kwargs):
    if axis is None:
        results = stats.kstest(x, cdf="norm", *args, **kwargs)
        return results.statistic, results.pvalue
    rsize = np.shape(x)[axis]
    ntst, pvals = np.empty(rsize), np.empty(rsize)
    for idx, values in enumerate(np.rollaxis(x, axis)):
        results = stats.kstest(values, cdf="norm", *args, **kwargs)
        ntst[idx], pvals[idx] = results.statistic, results.pvalue
    return ntst, pvals
