A Julia framework for implementing FFTs
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Updated Last
11 Months Ago
Started In
May 2017


A general framework for fast Fourier transforms (FFTs) in Julia.

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This package is mainly not intended to be used directly. Instead, developers of packages that implement FFTs (such as FFTW.jl or FastTransforms.jl) extend the types/functions defined in AbstractFFTs. This allows multiple FFT packages to co-exist with the same underlying fft(x) and plan_fft(x) interface.

Developer information

To define a new FFT implementation in your own module, you should

  • Define a new subtype (e.g. MyPlan) of AbstractFFTs.Plan{T} for FFTs and related transforms on arrays of T. This must have a pinv::Plan field, initially undefined when a MyPlan is created, that is used for caching the inverse plan.

  • Define a new method AbstractFFTs.plan_fft(x, region; kws...) that returns a MyPlan for at least some types of x and some set of dimensions region. The region (or a copy thereof) should be accessible via fftdims(p::MyPlan) (which defaults to p.region).

  • Define a method of LinearAlgebra.mul!(y, p::MyPlan, x) (or A_mul_B!(y, p::MyPlan, x) on Julia prior to 0.7.0-DEV.3204) that computes the transform p of x and stores the result in y.

  • Define a method of *(p::MyPlan, x), which can simply call your mul! (or A_mul_B!) method. This is not defined generically in this package due to subtleties that arise for in-place and real-input FFTs.

  • If the inverse transform is implemented, you should also define plan_inv(p::MyPlan), which should construct the inverse plan to p, and plan_bfft(x, region; kws...) for an unnormalized inverse ("backwards") transform of x.

  • You can also define similar methods of plan_rfft and plan_brfft for real-input FFTs.

The normalization convention for your FFT should be that it computes $y_k = \sum_j \exp(-2 \pi i \cdot \frac{j k}{n}) x_j$ for a transform of length $n$, and the "backwards" (unnormalized inverse) transform computes the same thing but with $\exp(+2 \pi i \cdot \frac{j k}{n})$.