Function correlate

numpy/ma/core.py:8371–8427

Cross-correlation of two 1-dimensional sequences. Parameters ---------- a, v : array_like Input sequences. mode : {'valid', 'same', 'full'}, optional Refer to the `np.convolve` docstring. Note that the default is 'valid', unlike `convolve`, which uses '

(a, v, mode='valid', propagate_mask=True)

def correlate(a, v, mode='valid', propagate_mask=True):
    """
    Cross-correlation of two 1-dimensional sequences.

    Parameters
    ----------
    a, v : array_like
        Input sequences.
    mode : {'valid', 'same', 'full'}, optional
        Refer to the `np.convolve` docstring.  Note that the default
        is 'valid', unlike `convolve`, which uses 'full'.
    propagate_mask : bool
        If True, then a result element is masked if any masked element contributes
        towards it. If False, then a result element is only masked if no non-masked
        element contribute towards it

    Returns
    -------
    out : MaskedArray
        Discrete cross-correlation of `a` and `v`.

    See Also
    --------
    numpy.correlate : Equivalent function in the top-level NumPy module.

    Examples
    --------
    Basic correlation:

    >>> a = np.ma.array([1, 2, 3])
    >>> v = np.ma.array([0, 1, 0])
    >>> np.ma.correlate(a, v, mode='valid')
    masked_array(data=[2],
                 mask=[False],
           fill_value=999999)

    Correlation with masked elements:

    >>> a = np.ma.array([1, 2, 3], mask=[False, True, False])
    >>> v = np.ma.array([0, 1, 0])
    >>> np.ma.correlate(a, v, mode='valid', propagate_mask=True)
    masked_array(data=[--],
                 mask=[ True],
           fill_value=999999,
                dtype=int64)

    Correlation with different modes and mixed array types:

    >>> a = np.ma.array([1, 2, 3])
    >>> v = np.ma.array([0, 1, 0])
    >>> np.ma.correlate(a, v, mode='full')
    masked_array(data=[0, 1, 2, 3, 0],
                 mask=[False, False, False, False, False],
           fill_value=999999)

    """
    return _convolve_or_correlate(np.correlate, a, v, mode, propagate_mask)