title: Wavelet transform in R or python using a finite dataset
body: <p>I am trying to implement a wavelet transform similar to that of Bardshaw and Spies (1992) in R or Python (...or even Matlab if needs be!).  I am currently using the Passage2 software package (<a href="http://www.passagesoftware.net/" rel="nofollow">http://www.passagesoftware.net/</a>) but would like to recreate this in one of the above packages so as to have more flexibility over output and analysis.</p>
  
  <p>In more detail, I would like to perform a (I think continuous) wavelet transform using the mexican hat wavelet over a set of observations with a <b>finite</b> length from which I would like to calculate the scale-variance.  The packages I have tried in R (wmtsa, waveslim, rwave) and Python (scipy.signal.cwt) assume the data is circular (I think this is the right term) and therefore the output does not taper as wavelet scale increases.</p>
  
  <p>Even when assuming the data is circular the output from Passage2 and R are VERY different.</p>
  
  <p>The idea of wavelets is very new to me but I am willing to learn! Any help most gratefully received.</p>
  
  <p>Thanks, Phil</p>
  
  <p>Bradshaw, GA &amp; TA Spies (1992). Characterizing canopy gap structure in forests using wavelet analysis. Journal of Ecology, 80(2), pp.205–215.</p>