Module « scipy.signal »

Fonction fftconvolve - module scipy.signal

Signature de la fonction fftconvolve

def fftconvolve(in1, in2, mode='full', axes=None) 

Description

fftconvolve.__doc__

Convolve two N-dimensional arrays using FFT.

    Convolve `in1` and `in2` using the fast Fourier transform method, with
    the output size determined by the `mode` argument.

    This is generally much faster than `convolve` for large arrays (n > ~500),
    but can be slower when only a few output values are needed, and can only
    output float arrays (int or object array inputs will be cast to float).

    As of v0.19, `convolve` automatically chooses this method or the direct
    method based on an estimation of which is faster.

    Parameters
    ----------
    in1 : array_like
        First input.
    in2 : array_like
        Second input. Should have the same number of dimensions as `in1`.
    mode : str {'full', 'valid', 'same'}, optional
        A string indicating the size of the output:

        ``full``
           The output is the full discrete linear convolution
           of the inputs. (Default)
        ``valid``
           The output consists only of those elements that do not
           rely on the zero-padding. In 'valid' mode, either `in1` or `in2`
           must be at least as large as the other in every dimension.
        ``same``
           The output is the same size as `in1`, centered
           with respect to the 'full' output.
    axes : int or array_like of ints or None, optional
        Axes over which to compute the convolution.
        The default is over all axes.

    Returns
    -------
    out : array
        An N-dimensional array containing a subset of the discrete linear
        convolution of `in1` with `in2`.

    See Also
    --------
    convolve : Uses the direct convolution or FFT convolution algorithm
               depending on which is faster.
    oaconvolve : Uses the overlap-add method to do convolution, which is
                 generally faster when the input arrays are large and
                 significantly different in size.

    Examples
    --------
    Autocorrelation of white noise is an impulse.

    >>> from scipy import signal
    >>> rng = np.random.default_rng()
    >>> sig = rng.standard_normal(1000)
    >>> autocorr = signal.fftconvolve(sig, sig[::-1], mode='full')

    >>> import matplotlib.pyplot as plt
    >>> fig, (ax_orig, ax_mag) = plt.subplots(2, 1)
    >>> ax_orig.plot(sig)
    >>> ax_orig.set_title('White noise')
    >>> ax_mag.plot(np.arange(-len(sig)+1,len(sig)), autocorr)
    >>> ax_mag.set_title('Autocorrelation')
    >>> fig.tight_layout()
    >>> fig.show()

    Gaussian blur implemented using FFT convolution.  Notice the dark borders
    around the image, due to the zero-padding beyond its boundaries.
    The `convolve2d` function allows for other types of image boundaries,
    but is far slower.

    >>> from scipy import misc
    >>> face = misc.face(gray=True)
    >>> kernel = np.outer(signal.windows.gaussian(70, 8),
    ...                   signal.windows.gaussian(70, 8))
    >>> blurred = signal.fftconvolve(face, kernel, mode='same')

    >>> fig, (ax_orig, ax_kernel, ax_blurred) = plt.subplots(3, 1,
    ...                                                      figsize=(6, 15))
    >>> ax_orig.imshow(face, cmap='gray')
    >>> ax_orig.set_title('Original')
    >>> ax_orig.set_axis_off()
    >>> ax_kernel.imshow(kernel, cmap='gray')
    >>> ax_kernel.set_title('Gaussian kernel')
    >>> ax_kernel.set_axis_off()
    >>> ax_blurred.imshow(blurred, cmap='gray')
    >>> ax_blurred.set_title('Blurred')
    >>> ax_blurred.set_axis_off()
    >>> fig.show()