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

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.

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`

.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 yₖ = ∑ⱼ xⱼ exp(-2πi jk/n) for a transform of length n, and the "backwards" (unnormalized inverse) transform computes the same thing but with exp(+2πi jk/n).

05/25/2017

6 days ago

1226 commits