Roger Flores wrote:

I've read through the docs but all the filter examples seem to affect one value at a time. How do I write something like a wavelet that transforms multiple values at the the same time?

I'm hoping for a Python version (as opposed to a C function) for portability and easy exploration.

These algorithms can be done in Python as you might do them in C except that the looping is implied. Assuming 'y' is an array of length N. The Haar transform can be written as: s = (y[1::2] + y[0::2])/2. d = (y[1::2] - y[0::2]) where s is the average (smoothed) values and d is the difference (detailed) values. In the lifting scheme (i.e. using filter banks), it is: y[1::2] = y[1::2] - y[0::2] y[0::2] = y[0::2] + y[1::2]/2. Note that y[1::2] is the difference and y[0::2] is the average. And that the order is important, since the operation is being done in-place. For higher order wavelets, boundary effects must be taken into account. For example using a cubic B-spline wavelet, the algorithm is: s = (y[0:-4] + 4*y[1:-3] + 6*y[2:-2] + 4*y[3:-1] + y[4:])/16. d = y[2:-2] - s Note that this expression does not calculate the first and last two values. These must be done as special boundary cases, e.g. s[0] = (6*y[2:-2] + 4*y[3:-1] + y[4:])/11. s[1] = (4*y[1:-3] + 6*y[2:-2] + 4*y[3:-1] + y[4:])/15. s[-2] = (y[0:-4] + 4*y[1:-3] + 6*y[2:-2] + 4*y[3:-1])/15. s[-1] = (y[0:-4] + 4*y[1:-3] + 6*y[2:-2])/11. d[0] = y[0] - s[:3] d[1] = y[1] - s[:4] d[-2] = y[-2] - s[-4:] d[-1] = y[-1] - s[-3:] or by padding the array. I hope this helps. -- Paul -- Paul Barrett, PhD Space Telescope Science Institute Phone: 410-338-4475 ESS/Science Software Branch FAX: 410-338-4767 Baltimore, MD 21218