code: def lowpass(data,filterSize=None):
      
      if filterSize is None:
          filterSize=len(data)/10
      kernel=kernel_gaussian(size=filterSize)
      data=convolve(data,kernel) # do the convolution with padded edges
      return data
docs: minimal complexity low-pass filtering.
      Filter size is how "wide" the filter will be.
      Sigma will be 1/10 of this filter width.
      If filter size isn't given, it will be 1/10 of the data size.