src/spectrum/mtm.py
def _crosscorr(x, y, **kwargs):
    """
    Returns the crosscorrelation sequence between two ndarrays.
    This is performed by calling fftconvolve on x, y[::-1]

    Parameters


    x: ndarray
    y: ndarray
    axis: time axis
    all_lags: {True/False}
    whether to return all nonzero lags, or to clip the length of r_xy
    to be the length of x and y. If False, then the zero lag correlation
    is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

    Notes


    cross correlation is defined as
    rxy[k] := E{X[t]*Y[t+k]}/(E{X*X}E{Y*Y})**.5,
    where X,Y are zero mean random processes. It is the noramlized cross
    covariance.
    """
    sxy = _crosscov(x, y, **kwargs)
    # estimate sigma_x, sigma_y to normalize
    sx = np.std(x)
    sy = np.std(y)
    return sxy/(sx*sy)
def _crosscorr(x, y, **kwargs):
    """
    Returns the crosscorrelation sequence between two ndarrays.
    This is performed by calling fftconvolve on x, y[::-1]

    Parameters


    x: ndarray
    y: ndarray
    axis: time axis
    all_lags: {True/False}
    whether to return all nonzero lags, or to clip the length of r_xy
    to be the length of x and y. If False, then the zero lag correlation
    is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

    Notes


    cross correlation is defined as
    rxy[k] := E{X[t]*Y[t+k]}/(E{X*X}E{Y*Y})**.5,
    where X,Y are zero mean random processes. It is the noramlized cross
    covariance.
    """
    sxy = _crosscov(x, y, **kwargs)
    # estimate sigma_x, sigma_y to normalize
    sx = np.std(x)
    sy = np.std(y)
    return sxy/(sx*sy)
def _crosscorr ( x , y , * * kwargs ) : sxy = _crosscov ( x , y , * * kwargs ) # estimate sigma_x, sigma_y to normalize sx = np . std ( x ) sy = np . std ( y ) return sxy / ( sx * sy )
Returns the crosscorrelation sequence between two ndarrays.
    This is performed by calling fftconvolve on x, y[::-1]

    Parameters


    x: ndarray
    y: ndarray
    axis: time axis
    all_lags: {True/False}
    whether to return all nonzero lags, or to clip the length of r_xy
    to be the length of x and y. If False, then the zero lag correlation
    is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2

    Notes


    cross correlation is defined as
    rxy[k] := E{X[t]*Y[t+k]}/(E{X*X}E{Y*Y})**.5,
    where X,Y are zero mean random processes. It is the noramlized cross
    covariance.
Returns the crosscorrelation sequence between two ndarrays . This is performed by calling fftconvolve on x y [ :: - 1 ]