Implementation of the chirp-z transform in python. Can be used to evaluate creatively on the unit disk, or to zoom the FFT. Two implementations, one in numba and one in C++ with eigen that is stand-alone enough to be used in other C++ projects.

Two papers:

Rabiner, L. R., Schafer, R. W., & Rader, C. M. (1969). The Chirp z-Transform Algorithm and Its Application. Bell System Technical Journal, 48(5), 1249–1292. http://doi.org/10.1002/j.1538-7305.1969.tb04268.x

Rabiner, L. R., & Schafer, R. W. (1969). The Chirp z-Transform Algorithm. IEEE Transactions on Audio and Electroacoustics, 17(2), 86–92. http://doi.org/10.1109/TAU.1969.1162034

A basic example can be seen in this 1D tutorial.

Make sure you have both FFTW and Boost installed. On ubuntu-based linux this can be done via

apt-get install  libfftw3-dev  libboost-dev

Note you will probably want a bleeding-edge eigency

pip install git+https://github.com/wouterboomsma/eigency

On OSX, make sure you have all the relevant build tools installed and install FFTW and Boost. A recommended way is using Brew:

brew install boost fftw

Note you will probably want a bleeding-edge eigency

pip install git+https://github.com/wouterboomsma/eigency

The Chirp-Z transform lets you evaluate any evenly-spaced set of frequencies along the unit circle (or even along an arc inside the unit circle, but we'll ignore that right now). Imagine you have a 256-element-long vector, and you'd like to compute the DFT at a more finely-spaced set of samples, but over a narrow range (the so-called "zoomed FFT". The chirp-z transform can help. Normally we'd just pad the FFT and then extract the region of interest in the output, but this can result in us doing really large FFTs.

The speed-up can be dramatic. Below shows our input signal length (N), the number of points in the output that we use (M), and the equivalent FFT padding size, for a 2D fft with complex inputs. All of the numbers below are the speed gains relative to simply padding.