# Path: Echobase/Common/errors.py
# def check_dims(arr, nd):
# def check_type(obj, typ):
# def check_function(obj):
# def check_path(path):
# def make_path(path):
# def check_path_overwrite(path):
# def check_has_key(dictionary, key_ref):


import numpy as np
from ..Common import errors

    <AAFTsur.m>, v 1.0 2010/02/11 22:09:14  Kugiumtzis & Tsimpiris
    This is part of the MATS-Toolkit http://eeganalysis.web.auth.gr/

    Copyright (C) 2010 by Dimitris Kugiumtzis and Alkiviadis Tsimpiris
                           <dkugiu@gen.auth.gr>

    Reference : D. Kugiumtzis and A. Tsimpiris, "Measures of Analysis of Time
                Series (MATS): A Matlab  Toolkit for Computation of Multiple
                Measures on Time Series Data Bases", Journal of Statistical
                Software, 2010

    The original python author was: https://github.com/lneisenman/aaft

    Parameters
    ----------
        xV: ndarray, shape (N,)
            A univariate time-series with N samples

        rand_phase: ndarray, shape (N//2 - 1,)
            Sequence of randomized phase offsets

    Returns
    -------
        zM: ndarray, shape (N,)
            A univariate surrogate of the input time-series
    """

    # Standard param checks
    errors.check_type(xV, np.ndarray)

# Path: Echobase/Common/errors.py
# def check_dims(arr, nd):
# def check_type(obj, typ):
# def check_function(obj):
# def check_path(path):
# def make_path(path):
# def check_path_overwrite(path):
# def check_has_key(dictionary, key_ref):


# Path: Echobase/Sigproc/ts_surr.py
import numpy as np
from ..Common import errors


    <AAFTsur.m>, v 1.0 2010/02/11 22:09:14  Kugiumtzis & Tsimpiris
    This is part of the MATS-Toolkit http://eeganalysis.web.auth.gr/

    Copyright (C) 2010 by Dimitris Kugiumtzis and Alkiviadis Tsimpiris
                           <dkugiu@gen.auth.gr>

    Reference : D. Kugiumtzis and A. Tsimpiris, "Measures of Analysis of Time
                Series (MATS): A Matlab  Toolkit for Computation of Multiple
                Measures on Time Series Data Bases", Journal of Statistical
                Software, 2010

    The original python author was: https://github.com/lneisenman/aaft

    Parameters
    ----------
        xV: ndarray, shape (N,)
            A univariate time-series with N samples

        rand_phase: ndarray, shape (N//2 - 1,)
            Sequence of randomized phase offsets

    Returns
    -------
        zM: ndarray, shape (N,)
            A univariate surrogate of the input time-series
    """

    # Standard param checks
    errors.check_type(xV, np.ndarray)

    errors.check_dims(xV, 1)