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# scipy.signal.fftconvolve

scipy.signal.fftconvolve(in1, in2, mode='full', axes=None)

  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.

      >>> import numpy as np
      >>> 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 datasets
      >>> face = datasets.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()