Module « scipy.signal »

Fonction convolve - module scipy.signal

Signature de la fonction convolve

def convolve(in1, in2, mode='full', method='auto') 

Description

convolve.__doc__

    Convolve two N-dimensional arrays.

    Convolve `in1` and `in2`, with the output size determined by the
    `mode` argument.

    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.
    method : str {'auto', 'direct', 'fft'}, optional
        A string indicating which method to use to calculate the convolution.

        ``direct``
           The convolution is determined directly from sums, the definition of
           convolution.
        ``fft``
           The Fourier Transform is used to perform the convolution by calling
           `fftconvolve`.
        ``auto``
           Automatically chooses direct or Fourier method based on an estimate
           of which is faster (default).  See Notes for more detail.

           .. versionadded:: 0.19.0

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

    See Also
    --------
    numpy.polymul : performs polynomial multiplication (same operation, but
                    also accepts poly1d objects)
    choose_conv_method : chooses the fastest appropriate convolution method
    fftconvolve : Always uses the FFT method.
    oaconvolve : Uses the overlap-add method to do convolution, which is
                 generally faster when the input arrays are large and
                 significantly different in size.

    Notes
    -----
    By default, `convolve` and `correlate` use ``method='auto'``, which calls
    `choose_conv_method` to choose the fastest method using pre-computed
    values (`choose_conv_method` can also measure real-world timing with a
    keyword argument). Because `fftconvolve` relies on floating point numbers,
    there are certain constraints that may force `method=direct` (more detail
    in `choose_conv_method` docstring).

    Examples
    --------
    Smooth a square pulse using a Hann window:

    >>> from scipy import signal
    >>> sig = np.repeat([0., 1., 0.], 100)
    >>> win = signal.windows.hann(50)
    >>> filtered = signal.convolve(sig, win, mode='same') / sum(win)

    >>> import matplotlib.pyplot as plt
    >>> fig, (ax_orig, ax_win, ax_filt) = plt.subplots(3, 1, sharex=True)
    >>> ax_orig.plot(sig)
    >>> ax_orig.set_title('Original pulse')
    >>> ax_orig.margins(0, 0.1)
    >>> ax_win.plot(win)
    >>> ax_win.set_title('Filter impulse response')
    >>> ax_win.margins(0, 0.1)
    >>> ax_filt.plot(filtered)
    >>> ax_filt.set_title('Filtered signal')
    >>> ax_filt.margins(0, 0.1)
    >>> fig.tight_layout()
    >>> fig.show()