https://github.com/JuliaLang/julia
Tip revision: a865ca2777a883d4d67a77e71e5aa3ed59fe8f6d authored by Jeff Bezanson on 15 August 2014, 03:12:32 UTC
more scheme portability fixes
more scheme portability fixes
Tip revision: a865ca2
dsp.jl
module DSP
importall Base.FFTW
import Base.FFTW.normalization
import Base.trailingsize
export FFTW, filt, filt!, deconv, conv, conv2, xcorr, fftshift, ifftshift,
dct, idct, dct!, idct!, plan_dct, plan_idct, plan_dct!, plan_idct!,
# the rest are defined imported from FFTW:
fft, bfft, ifft, rfft, brfft, irfft,
plan_fft, plan_bfft, plan_ifft, plan_rfft, plan_brfft, plan_irfft,
fft!, bfft!, ifft!, plan_fft!, plan_bfft!, plan_ifft!
_zerosi(b,a,T) = zeros(promote_type(eltype(b), eltype(a), T), max(length(a), length(b))-1)
function filt{T,S}(b::Union(AbstractVector, Number), a::Union(AbstractVector, Number),
x::AbstractArray{T}, si::AbstractArray{S}=_zerosi(b,a,T))
filt!(Array(promote_type(eltype(b), eltype(a), T, S), size(x)), b, a, x, si)
end
# in-place filtering: returns results in the out argument, which may shadow x
# (and does so by default)
function filt!{T,S,N}(out::AbstractArray, b::Union(AbstractVector, Number), a::Union(AbstractVector, Number),
x::AbstractArray{T}, si::AbstractArray{S,N}=_zerosi(b,a,T))
isempty(b) && error("b must be non-empty")
isempty(a) && error("a must be non-empty")
a[1] == 0 && error("a[1] must be nonzero")
size(x) != size(out) && error("out size must match x")
as = length(a)
bs = length(b)
sz = max(as, bs)
silen = sz - 1
ncols = trailingsize(x,2)
size(si, 1) != silen && error("si must have max(length(a),length(b))-1 rows")
N > 1 && trailingsize(si,2) != ncols && error("si must either be a vector or have the same number of columns as x")
size(x,1) == 0 && return out
sz == 1 && return scale!(out, x, b[1]/a[1]) # Simple scaling without memory
# Filter coefficient normalization
if a[1] != 1
norml = a[1]
a ./= norml
b ./= norml
end
# Pad the coefficients with zeros if needed
bs<sz && (b = copy!(zeros(eltype(b), sz), b))
1<as<sz && (a = copy!(zeros(eltype(a), sz), a))
initial_si = si
for col = 1:ncols
# Reset the filter state
si = initial_si[:, N > 1 ? col : 1]
if as > 1
_filt_iir!(out, b, a, x, si, col)
else
_filt_fir!(out, b, x, si, col)
end
end
return out
end
function _filt_iir!(out, b, a, x, si, col)
silen = length(si)
@inbounds for i=1:size(x, 1)
xi = x[i,col]
val = si[1] + b[1]*xi
for j=1:(silen-1)
si[j] = si[j+1] + b[j+1]*xi - a[j+1]*val
end
si[silen] = b[silen+1]*xi - a[silen+1]*val
out[i,col] = val
end
end
function _filt_fir!(out, b, x, si, col)
silen = length(si)
@inbounds for i=1:size(x, 1)
xi = x[i,col]
val = si[1] + b[1]*xi
for j=1:(silen-1)
si[j] = si[j+1] + b[j+1]*xi
end
si[silen] = b[silen+1]*xi
out[i,col] = val
end
end
function deconv{T}(b::StridedVector{T}, a::StridedVector{T})
lb = size(b,1)
la = size(a,1)
if lb < la
return [zero(T)]
end
lx = lb-la+1
x = zeros(T, lx)
x[1] = 1
filt(b, a, x)
end
function conv{T<:Base.LinAlg.BlasFloat}(u::StridedVector{T}, v::StridedVector{T})
nu = length(u)
nv = length(v)
n = nu + nv - 1
np2 = n > 1024 ? nextprod([2,3,5], n) : nextpow2(n)
upad = [u, zeros(T, np2 - nu)]
vpad = [v, zeros(T, np2 - nv)]
if T <: Real
p = plan_rfft(upad)
y = irfft(p(upad).*p(vpad), np2)
else
p = plan_fft!(upad)
y = ifft!(p(upad).*p(vpad))
end
return y[1:n]
end
conv{T<:Integer}(u::StridedVector{T}, v::StridedVector{T}) = int(conv(float(u), float(v)))
conv{T<:Integer, S<:Base.LinAlg.BlasFloat}(u::StridedVector{T}, v::StridedVector{S}) = conv(float(u), v)
conv{T<:Integer, S<:Base.LinAlg.BlasFloat}(u::StridedVector{S}, v::StridedVector{T}) = conv(u, float(v))
function conv2{T}(u::StridedVector{T}, v::StridedVector{T}, A::StridedMatrix{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
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
return C
end
function conv2{T}(A::StridedMatrix{T}, B::StridedMatrix{T})
sa, sb = size(A), size(B)
At = zeros(T, sa[1]+sb[1]-1, sa[2]+sb[2]-1)
Bt = zeros(T, sa[1]+sb[1]-1, sa[2]+sb[2]-1)
At[1:sa[1], 1:sa[2]] = A
Bt[1:sb[1], 1:sb[2]] = B
p = plan_fft(At)
C = ifft(p(At).*p(Bt))
if T <: Real
return real(C)
end
return C
end
conv2{T<:Integer}(A::StridedMatrix{T}, B::StridedMatrix{T}) = int(conv2(float(A), float(B)))
conv2{T<:Integer}(u::StridedVector{T}, v::StridedVector{T}, A::StridedMatrix{T}) = int(conv2(float(u), float(v), float(A)))
function xcorr(u, v)
su = size(u,1); sv = size(v,1)
if su < sv
u = [u;zeros(eltype(u),sv-su)]
elseif sv < su
v = [v;zeros(eltype(v),su-sv)]
end
flipud(conv(flipud(u), v))
end
fftshift(x) = circshift(x, div([size(x)...],2))
function fftshift(x,dim)
s = zeros(Int,ndims(x))
s[dim] = div(size(x,dim),2)
circshift(x, s)
end
ifftshift(x) = circshift(x, div([size(x)...],-2))
function ifftshift(x,dim)
s = zeros(Int,ndims(x))
s[dim] = -div(size(x,dim),2)
circshift(x, s)
end
# Discrete cosine and sine transforms via FFTW's r2r transforms;
# we follow the Matlab convention and adopt a unitary normalization here.
# Unlike Matlab we compute the multidimensional transform by default,
# similar to the Julia fft functions.
fftwcopy{T<:fftwNumber}(X::StridedArray{T}) = copy(X)
fftwcopy{T<:Real}(X::StridedArray{T}) = float(X)
fftwcopy{T<:Complex}(X::StridedArray{T}) = complex128(X)
for (f, fr2r, Y, Tx) in ((:dct, :r2r, :Y, :Number),
(:dct!, :r2r!, :X, :fftwNumber))
plan_f = symbol(string("plan_",f))
plan_fr2r = symbol(string("plan_",fr2r))
fi = symbol(string("i",f))
plan_fi = symbol(string("plan_",fi))
Ycopy = Y == :X ? 0 : :(Y = fftwcopy(X))
@eval begin
function $f{T<:$Tx}(X::StridedArray{T}, region)
$Y = $fr2r(X, REDFT10, region)
scale!($Y, sqrt(0.5^length(region) * normalization(X,region)))
sqrthalf = sqrt(0.5)
r = map(n -> 1:n, [size(X)...])
for d in region
r[d] = 1:1
$Y[r...] *= sqrthalf
r[d] = 1:size(X,d)
end
return $Y
end
function $plan_f{T<:$Tx}(X::StridedArray{T}, region,
flags::Unsigned, timelimit::Real)
p = $plan_fr2r(X, REDFT10, region, flags, timelimit)
sqrthalf = sqrt(0.5)
r = map(n -> 1:n, [size(X)...])
nrm = sqrt(0.5^length(region) * normalization(X,region))
return X::StridedArray{T} -> begin
$Y = p(X)
scale!($Y, nrm)
for d in region
r[d] = 1:1
$Y[r...] *= sqrthalf
r[d] = 1:size(X,d)
end
return $Y
end
end
function $fi{T<:$Tx}(X::StridedArray{T}, region)
$Ycopy
scale!($Y, sqrt(0.5^length(region) * normalization(X, region)))
sqrt2 = sqrt(2)
r = map(n -> 1:n, [size(X)...])
for d in region
r[d] = 1:1
$Y[r...] *= sqrt2
r[d] = 1:size(X,d)
end
return r2r!($Y, REDFT01, region)
end
function $plan_fi{T<:$Tx}(X::StridedArray{T}, region,
flags::Unsigned, timelimit::Real)
p = $plan_fr2r(X, REDFT01, region, flags, timelimit)
sqrt2 = sqrt(2)
r = map(n -> 1:n, [size(X)...])
nrm = sqrt(0.5^length(region) * normalization(X,region))
return X::StridedArray{T} -> begin
$Ycopy
scale!($Y, nrm)
for d in region
r[d] = 1:1
$Y[r...] *= sqrt2
r[d] = 1:size(X,d)
end
return p($Y)
end
end
end
for (g,plan_g) in ((f,plan_f), (fi, plan_fi))
@eval begin
$g{T<:$Tx}(X::StridedArray{T}) = $g(X, 1:ndims(X))
$plan_g(X, region, flags::Unsigned) =
$plan_g(X, region, flags, NO_TIMELIMIT)
$plan_g(X, region) =
$plan_g(X, region, ESTIMATE, NO_TIMELIMIT)
$plan_g{T<:$Tx}(X::StridedArray{T}) =
$plan_g(X, 1:ndims(X), ESTIMATE, NO_TIMELIMIT)
end
end
end
# DCT of scalar is just the identity:
dct(x::Number, dims) = length(dims) == 0 || dims[1] == 1 ? x : throw(BoundsError())
idct(x::Number, dims) = dct(x, dims)
dct(x::Number) = x
idct(x::Number) = x
plan_dct(x::Number, dims, flags, tlim) = length(dims) == 0 || dims[1] == 1 ? y::Number -> y : throw(BoundsError())
plan_idct(x::Number, dims, flags, tlim) = plan_dct(x, dims)
plan_dct(x::Number) = y::Number -> y
plan_idct(x::Number) = y::Number -> y
end # module