Merge pull request #3913 from JuliaLang/sf/dspdocs

Add documentation for conv2()/plan_brfft()

Author: JeffBezanson

base/dsp.jl

@@ -98,14 +98,14 @@ conv{T<:Integer}(u::Vector{T}, v::Vector{T}) = conv(float(u), float(v))
 conv{T<:Integer, S<:LinAlg.BlasFloat}(u::Vector{T}, v::Vector{S}) = conv(float(u), v)
 conv{T<:Integer, S<:LinAlg.BlasFloat}(u::Vector{S}, v::Vector{T}) = conv(u, float(v))
 
-function conv2{T}(y::Vector{T}, x::Vector{T}, A::Matrix{T})
-    m = length(y)+size(A,1)-1
-    n = length(x)+size(A,2)-1
+function conv2{T}(u::Vector{T}, v::Vector{T}, A::Matrix{T})
+    m = length(u)+size(A,1)-1
+    n = length(v)+size(A,2)-1
     B = zeros(T, m, n)
     B[1:size(A,1),1:size(A,2)] = A
-    y = fft([y;zeros(T,m-length(y))])
-    x = fft([x;zeros(T,n-length(x))])
-    C = ifft(fft(B) .* (y * x.'))
+    u = fft([u;zeros(T,m-length(u))])
+    v = fft([v;zeros(T,n-length(v))])
+    C = ifft(fft(B) .* (u * v.'))
     if T <: Real
         return real(C)
     end

doc/stdlib/base.rst

@@ -2877,6 +2877,13 @@ FFT functions in Julia are largely implemented by calling functions from `FFTW <
    arguments, and the size of the transformed result, are the same as
    for :func:`rfft`.
 
+.. function:: plan_brfft(A, d [, dims [, flags [, timelimit]]])
+
+   Pre-plan an optimized real-input unnormalized transform, similar to
+   :func:`plan_rfft` except for :func:`brfft` instead of :func:`rfft`.
+   The first two arguments and the size of the transformed result, are
+   the same as for :func:`brfft`.
+
 .. function:: plan_irfft(A, d [, dims [, flags [, timelimit]]])
 
    Pre-plan an optimized inverse real-input FFT, similar to :func:`plan_rfft`
@@ -2958,6 +2965,15 @@ FFT functions in Julia are largely implemented by calling functions from `FFTW <
 
    Convolution of two vectors. Uses FFT algorithm.
 
+.. function:: conv2(u,v,A)
+
+   2-D convolution of the matrix ``A`` with the 2-D separable kernel generated by
+   the vectors ``u`` and ``v``.  Uses 2-D FFT algorithm
+
+.. function:: conv2(B,A)
+
+   2-D convolution of the matrix ``B`` with the matrix ``A``.  Uses 2-D FFT algorithm
+
 .. function:: xcorr(u,v)
 
    Compute the cross-correlation of two vectors.