# Haar nD [Haar][2] transform and it's inverse. ## Introduction I wanted to play around with wavelet based blending and multi-scale representations in the data I work with, which is often a 3d-scalar field. I also wrote a [z-order][1] transform thinking it would be fun to implement the haar transform in a cache oblivious way. The z-ordering gives the data a hierarchical structure in memory that helps coallesce memory access in a recursive algorithm, like the one commonly used to implement wavelet transforms. I haven't gotten around to actually use the z ordering. There's been a gap of time between when I wrote the code and wrote this README. I think the algorithms are possibly in place (this may be untested?), with a little bit of extra memory used for book-keeping. I believe the dimensions of the input must be powers of two, but the dimensions don't all need to be the same. You can also pass in, or output to, a subvolume of an nd-array. The next thing I want to do is do a GPU implementation, maybe do some other wavelets, and then actually use the code for something (good, not evil). [1]: http://en.wikipedia.org/wiki/z-order_curve [2]: http://en.wikipedia.org/wiki/Haar_wavelet