Since average filtering with a 3-by-3 kernel is equivalent to filtering first with a 3-by-1 kernel and then a 1-by-3 kernel, you can get (3+3)/(3*3) = ~30% speed improvement by consecutive convolution with two 1-d kernels (this obviously gets better as the kernel gets larger).
# calculate the smoothed moving average weights = np.repeat(1.0, windowSize) / windowSize yMA = np.convolve(y[0, :], weights, 'valid') Last but not least we are going to plot the results.
numpy.convolve¶ numpy.convolve (a, v, mode='full') [source] ¶ Returns the discrete, linear convolution of two one-dimensional sequences. You may still be … Should have the same number of dimensions as in1.. mode str {‘full’, ‘valid’, ‘same’}, optional import numpy def smooth (x, window_len = 11, window = 'hanning'): """smooth the data using a window with requested size. scipy.signal.convolve¶ scipy.signal.convolve (in1, in2, mode = 'full', method = 'auto') [source] ¶ Convolve two N-dimensional arrays.
The following are code examples for showing how to use numpy.convolve().They are from open source Python projects. import numpy as np N = 10 w = np.ones(N) / N data = [1,2,3,4,5,5,5,5,5,5,5,5,5,5,5] data_ma = np.convolve(data, w, mode='valid') Instead of using the convolve function, you can use a generator to sum over the sliding window (first pad the data with zeros to implement linear convolution instead of circular convolution): You can vote up the examples you like or vote down the ones you don't like. Second input. Convolve in1 and in2, with the output size determined by the mode argument.. Parameters in1 array_like. First input. This method is based on the convolution of a scaled window with the signal. Secondly we convolve the time-series with this filter. For other variations of moving averages have a look at the Outlook section below. bottleneck has move_mean which is a simple moving average: import numpy as np import bottleneck as bn a = np.arange(10) + np.random.random(10) mva = bn.move_mean(a, window=2, min_count=1) min_count is a handy parameter that will basically take the moving in2 array_like.