Use AbstractFFTs instead of FFTW

JuliaStats · Oct 3, 2023 · e2ae064 · e2ae064
1 parent ae70f46
commit e2ae064
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Showing 6 changed files with 18 additions and 5 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,25 +1,26 @@
 name = "KernelDensity"
 uuid = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 authors = ["Simon Byrne and various contributors"]
-version = "0.6.7"
+version = "0.7.0"
 
 [deps]
+AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
-FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
+AbstractFFTs = "1"
 Distributions = "0.23, 0.24, 0.25"
 DocStringExtensions = "0.8, 0.9"
-FFTW = "1"
 Interpolations = "0.9, 0.10, 0.11, 0.12, 0.13, 0.14"
 StatsBase = "0.33, 0.34"
 julia = "1"
 
 [extras]
+FFTW = "7a1cc6ca-52ef-59f5-83cd-3a7055c09341"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test"]
+test = ["FFTW", "Test"]
diff --git a/README.md b/README.md
@@ -7,6 +7,15 @@ Kernel density estimators for Julia.
 
 ## Usage
 
+### Prerequisite
+
+The kernel density estimates are computed with fast Fourier transforms (FFTs) internally using the [AbstractFFTs.jl](https://github.com/JuliaMath/AbstractFFTs.jl) interface.
+You have to load a backend such as [FFTW.jl](https://github.com/JuliaMath/FFTW.jl) that implements this interface:
+
+```julia
+using FFTW
+```
+
 ### Univariate
 The main accessor function is `kde`:
 

diff --git a/src/KernelDensity.jl b/src/KernelDensity.jl
@@ -6,7 +6,7 @@ using Distributions
 using Interpolations
 
 import Distributions: twoπ, pdf
-import FFTW: rfft, irfft
+using AbstractFFTs: rfft, irfft
 
 export kde, kde_lscv, UnivariateKDE, BivariateKDE, InterpKDE, pdf
 

diff --git a/test/bivariate.jl b/test/bivariate.jl
@@ -1,6 +1,7 @@
 using Test
 using Distributions
 using KernelDensity
+using FFTW
 
 import KernelDensity: kernel_dist, default_bandwidth, kde_boundary, kde_range, tabulate
 

diff --git a/test/interp.jl b/test/interp.jl
@@ -1,5 +1,6 @@
 using Test
 using KernelDensity
+using FFTW
 
 X = randn(100)
 Y = randn(100)

diff --git a/test/univariate.jl b/test/univariate.jl
@@ -1,6 +1,7 @@
 using Test
 using Distributions
 using KernelDensity
+using FFTW
 
 import KernelDensity: kernel_dist, default_bandwidth, kde_boundary, kde_range, tabulate