(input, sigma, order=0, output=None,
                  mode="reflect", cval=0.0, truncate=4.0)
return return_value
Multidimensional Gaussian filter.

    Parameters
    ----------
    %(input)s
    sigma : scalar or sequence of scalars
        Standard deviation for Gaussian kernel. The standard
        deviations of the Gaussian filter are given for each axis as a
        sequence, or as a single number, in which case it is equal for
        all axes.
    order : {0, 1, 2, 3} or sequence from same set, optional
        The order of the filter along each axis is given as a sequence
        of integers, or as a single number.  An order of 0 corresponds
        to convolution with a Gaussian kernel. An order of 1, 2, or 3
        corresponds to convolution with the first, second or third
        derivatives of a Gaussian. Higher order derivatives are not
        implemented
    %(output)s
    %(mode)s
    %(cval)s
    truncate : float
        Truncate the filter at this many standard deviations.
        Default is 4.0.

    Returns
    -------
    gaussian_filter : ndarray
        Returned array of same shape as `input`.

    Notes
    -----
    The multidimensional filter is implemented as a sequence of
    one-dimensional convolution filters. The intermediate arrays are
    stored in the same data type as the output. Therefore, for output
    types with a limited precision, the results may be imprecise
    because intermediate results may be stored with insufficient
    precision.

    Examples
    --------
    >>> from scipy.ndimage import gaussian_filter
    >>> a = np.arange(50, step=2).reshape((5,5))
    >>> a
    array([[ 0,  2,  4,  6,  8],
           [10, 12, 14, 16, 18],
           [20, 22, 24, 26, 28],
           [30, 32, 34, 36, 38],
           [40, 42, 44, 46, 48]])
    >>> gaussian_filter(a, sigma=1)
    array([[ 4,  6,  8,  9, 11],
           [10, 12, 14, 15, 17],
           [20, 22, 24, 25, 27],
           [29, 31, 33, 34, 36],
           [35, 37, 39, 40, 42]])
Multidimensional Gaussian filter.
Multidimensional Gaussian filter .
def gaussian_filter(input, sigma, order=0, output=None,
                  mode="reflect", cval=0.0, truncate=4.0):
    """Multidimensional Gaussian filter.

    Parameters
    ----------
    %(input)s
    sigma : scalar or sequence of scalars
        Standard deviation for Gaussian kernel. The standard
        deviations of the Gaussian filter are given for each axis as a
        sequence, or as a single number, in which case it is equal for
        all axes.
    order : {0, 1, 2, 3} or sequence from same set, optional
        The order of the filter along each axis is given as a sequence
        of integers, or as a single number.  An order of 0 corresponds
        to convolution with a Gaussian kernel. An order of 1, 2, or 3
        corresponds to convolution with the first, second or third
        derivatives of a Gaussian. Higher order derivatives are not
        implemented
    %(output)s
    %(mode)s
    %(cval)s
    truncate : float
        Truncate the filter at this many standard deviations.
        Default is 4.0.

    Returns
    -------
    gaussian_filter : ndarray
        Returned array of same shape as `input`.

    Notes
    -----
    The multidimensional filter is implemented as a sequence of
    one-dimensional convolution filters. The intermediate arrays are
    stored in the same data type as the output. Therefore, for output
    types with a limited precision, the results may be imprecise
    because intermediate results may be stored with insufficient
    precision.

    Examples
    --------
    >>> from scipy.ndimage import gaussian_filter
    >>> a = np.arange(50, step=2).reshape((5,5))
    >>> a
    array([[ 0,  2,  4,  6,  8],
           [10, 12, 14, 16, 18],
           [20, 22, 24, 26, 28],
           [30, 32, 34, 36, 38],
           [40, 42, 44, 46, 48]])
    >>> gaussian_filter(a, sigma=1)
    array([[ 4,  6,  8,  9, 11],
           [10, 12, 14, 15, 17],
           [20, 22, 24, 25, 27],
           [29, 31, 33, 34, 36],
           [35, 37, 39, 40, 42]])

    """
    input = numpy.asarray(input)
    output, return_value = _ni_support._get_output(output, input)
    orders = _ni_support._normalize_sequence(order, input.ndim)
    if not set(orders).issubset(set(range(4))):
        raise ValueError('Order outside 0..4 not implemented')
    sigmas = _ni_support._normalize_sequence(sigma, input.ndim)
    axes = list(range(input.ndim))
    axes = [(axes[ii], sigmas[ii], orders[ii])
                        for ii in range(len(axes)) if sigmas[ii] > 1e-15]
    if len(axes) > 0:
        for axis, sigma, order in axes:
            gaussian_filter1d(input, sigma, axis, order, output,
                              mode, cval, truncate)
            input = output
    else:
        output[...] = input[...]
    return return_value
def gaussian_filter ( input , sigma , order = 0 , output = None , mode = "reflect" , cval = 0.0 , truncate = 4.0 ) : input = numpy . asarray ( input ) output , return_value = _ni_support . _get_output ( output , input ) orders = _ni_support . _normalize_sequence ( order , input . ndim ) if not set ( orders ) . issubset ( set ( range ( 4 ) ) ) : raise ValueError ( 'Order outside 0..4 not implemented' ) sigmas = _ni_support . _normalize_sequence ( sigma , input . ndim ) axes = list ( range ( input . ndim ) ) axes = [ ( axes [ ii ] , sigmas [ ii ] , orders [ ii ] ) for ii in range ( len ( axes ) ) if sigmas [ ii ] > 1e-15 ] if len ( axes ) > 0 : for axis , sigma , order in axes : gaussian_filter1d ( input , sigma , axis , order , output , mode , cval , truncate ) input = output else : output [ ... ] = input [ ... ] return return_value