convolve#
- scipy.signal.convolve(in1, in2, mode='full', method='auto')[source]#
- Convolve two N-dimensional arrays. - Convolve in1 and in2, with the output size determined by the mode argument. - Parameters:
- in1array_like
- First input. 
- in2array_like
- Second input. Should have the same number of dimensions as in1. 
- modestr {‘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. 
 
- methodstr {‘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. - Added in version 0.19.0. 
 
 
- Returns:
- convolvearray
- An N-dimensional array containing a subset of the discrete linear convolution of in1 with in2. 
 
- Warns:
- RuntimeWarning
- Use of the FFT convolution on input containing NAN or INF will lead to the entire output being NAN or INF. Use method=’direct’ when your input contains NAN or INF values. 
 
 - 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, - convolveand- correlateuse- method='auto', which calls- choose_conv_methodto choose the fastest method using pre-computed values (- choose_conv_methodcan also measure real-world timing with a keyword argument). Because- fftconvolverelies on floating point numbers, there are certain constraints that may force- method='direct'(more detail in- choose_conv_methoddocstring).- Examples - Smooth a square pulse using a Hann window: - >>> import numpy as np >>> 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() 