convolve(in1, in2, mode='full', method='auto')
Convolve :None:None:`in1` and in2
, with the output size determined by the :None:None:`mode` argument.
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 :None:None:`method=direct` (more detail in choose_conv_method
docstring).
First input.
Second input. Should have the same number of dimensions as :None:None:`in1`.
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 :None:None:`in1` or in2
must be at least as large as the other in every dimension.
same
The output is the same size as :None:None:`in1`, centered with respect to the 'full' output.
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.
An N-dimensional array containing a subset of the discrete linear convolution of :None:None:`in1` with in2
.
Convolve two N-dimensional arrays.
choose_conv_method
chooses the fastest appropriate convolution method
fftconvolve
Always uses the FFT method.
numpy.polymul
performs polynomial multiplication (same operation, but also accepts poly1d objects)
oaconvolve
Uses the overlap-add method to do convolution, which is generally faster when the input arrays are large and significantly different in size.
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 pltSee :
... 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.signal._signaltools.deconvolve
scipy.signal._signaltools.fftconvolve
scipy.signal._signaltools.choose_conv_method
scipy.signal._signaltools.convolve
scipy.signal._signaltools.oaconvolve
scipy.signal._signaltools.correlate
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