Fast Fourier transformation in Python is pretty simple in many circumstances already. Just use the Numpy FFT and you're all set, right? Well, that's true if you have data that is regularly sampled, e.g. you've repeatedly made some measurement exactly once every second. But what if your measurements are taken at irregular intervals? Or what if you need to do a lot of phase shifts because your data doesn't start at the origin of whatever coordinate system on which it is defined?
Well, these are both situations that I and others in my group face quite often, and we found that we were using similar pieces of code again and again in a variety of projects. So Henrik Junklewitz and I developed GFFT to make life a bit simpler. The features of GFFT include:
- Fast Fourier transformations of data defined on, and being transformed to, arbitrarily defined coordinate spaces including:
- regular to regular
- irregular to regular
- regular to irregular
- irregular to irregular
- Fast transformations, even when working on irregular spaces, thanks to the included gridding algorithm.
- The gridding code is implemented in C (via Cython), so it's fast.
- When working with irregular to regular space transforms, you can store irregularly sampled Hermitian symmetric data in an efficient way... just store half of the data and the symmetric parts will be generated internally.
- Automatically handles arbitrary phase shifts of the input and output data if they are not defined on axes that start at the origin.
Please note that as of now the package is in beta, and a major revision is already underway (see the rewrite branch in the git repository). The revision will clean up the code base significantly (it's almost embarrassingly sloppy right now, but you know how it goes when you develop code for scientific applications...) and will make the interface more intuitive. So if you start using GFFT immediately, just be aware that we are planning to fundamentally change the interface soon and future updates may break your code (we'll put in a deprecation warning first...). Things will stabilize soon. Until then, try it out and let us know what you think!