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Added multi-threading via the ThreadsX package #3

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Jun 1, 2022
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Added multi-threading via the ThreadsX package #3

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4 changes: 3 additions & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ keywords = ["AeroAcoustics"]
license = "MIT"
desc = "A package for post-processing of microphone array measurements"
authors = ["Oliver Lylloff <[email protected]>"]
version = "0.2.1"
version = "0.2.2"

[deps]
DSP = "717857b8-e6f2-59f4-9121-6e50c889abd2"
Expand All @@ -15,6 +15,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
ThreadsX = "ac1d9e8a-700a-412c-b207-f0111f4b6c0d"

[compat]
DSP = "0.6.7"
Expand All @@ -23,6 +24,7 @@ FFTW = "1.1"
LazyArrays = "0.16"
NLsolve = "4.4"
Parameters = "0.12.3"
ThreadsX = "0.1.10"
julia = "1.6"

[extras]
Expand Down
3 changes: 2 additions & 1 deletion docs/src/index.md
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Expand Up @@ -50,7 +50,8 @@ E = Environment(
Nx = 21,
Ny = 21,
xlim=(-0.5,0.5),
ylim=(-0.5,0.5)
ylim=(-0.5,0.5),
multi_thread = true # multi-threading can be enabled globally like this
)
```
Where the measurement distance `z0`, the microphone geometry `micgeom`, and the csm `CSM` are required variables.
Expand Down
81 changes: 31 additions & 50 deletions examples/Quick_start.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,18 +2,9 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Warning: PyPlot is using tkagg backend, which is known to cause crashes on MacOS (#410); use the MPLBACKEND environment variable to request a different backend.\n",
"└ @ PyPlot /Users/oliver/.julia/packages/PyPlot/XaELc/src/init.jl:198\n"
]
}
],
"outputs": [],
"source": [
"using HDF5, AeroAcoustics, PyPlot"
]
Expand All @@ -27,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 4,
"metadata": {},
"outputs": [
{
Expand All @@ -36,7 +27,7 @@
"1.6"
]
},
"execution_count": 2,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -53,7 +44,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -62,7 +53,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -74,13 +65,14 @@
" Nx = 21,\n",
" Ny = 21,\n",
" xlim=(-0.5,0.5),\n",
" ylim=(-0.5,0.5)\n",
" ylim=(-0.5,0.5),\n",
" multi_thread = true, # multi-threading can be enabled globally like this\n",
" );"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -89,7 +81,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -98,7 +90,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"outputs": [
{
Expand All @@ -125,7 +117,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"outputs": [
{
Expand All @@ -134,7 +126,7 @@
"(0.1, -0.05)"
]
},
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -153,14 +145,14 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 4.742135 seconds (6.09 M allocations: 1.055 GiB, 5.72% gc time, 56.05% compilation time)\n"
" 4.762169 seconds (6.85 M allocations: 1.066 GiB, 6.01% gc time, 58.86% compilation time)\n"
]
}
],
Expand All @@ -170,7 +162,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -204,14 +196,14 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2.603157 seconds (1.39 M allocations: 1.265 GiB, 11.42% gc time, 24.62% compilation time)\n"
" 2.377261 seconds (1.35 M allocations: 1.255 GiB, 12.46% gc time, 24.53% compilation time)\n"
]
}
],
Expand All @@ -221,7 +213,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 14,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -255,18 +247,14 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"breaking at iter = 42...\n",
"breaking at iter = 47...\n",
"breaking at iter = 71...\n",
"breaking at iter = 55...\n",
" 2.753007 seconds (5.40 M allocations: 345.352 MiB, 3.79% gc time)\n"
" 2.979043 seconds (5.47 M allocations: 328.361 MiB, 3.92% gc time, 98.86% compilation time)\n"
]
}
],
Expand All @@ -277,7 +265,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 16,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -311,7 +299,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -325,7 +313,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 18,
"metadata": {},
"outputs": [
{
Expand All @@ -339,7 +327,7 @@
" 60.905 54.7118 54.8137 54.5308"
]
},
"execution_count": 21,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -357,7 +345,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -366,7 +354,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 20,
"metadata": {},
"outputs": [
{
Expand All @@ -380,7 +368,7 @@
" 60.905 54.1891 54.7118 54.8137 54.5308"
]
},
"execution_count": 23,
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -395,26 +383,19 @@
"source": [
"The four methods `SPI`, `Clean-SC`, `DAMAS`, and `FISTA` now return very similar results of the source power for the 4 frequency bins defined in the environment (3000-4000 Hz)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.6.2",
"display_name": "Julia 1.7.2",
"language": "julia",
"name": "julia-1.6"
"name": "julia-1.7"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.6.2"
"version": "1.7.2"
}
},
"nbformat": 4,
Expand Down
8 changes: 7 additions & 1 deletion src/AeroAcoustics.jl
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Expand Up @@ -7,6 +7,7 @@ using Parameters
using LazyArrays
using FFTW
import DSP
using ThreadsX

import Base.length,
Base.push!,
Expand Down Expand Up @@ -104,4 +105,9 @@ function DR(CSM::FreqArray)
return FreqArray(CSMd,CSM.fc)
end

end
function check_multithread(multi_thread)
_foreach = multi_thread ? ThreadsX.foreach : Base.foreach
return _foreach
end

end # Module
5 changes: 3 additions & 2 deletions src/beamforming.jl
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Expand Up @@ -25,10 +25,11 @@ end
Calculate frequency-domain beamforming using cross-spectral matrix `csm` and steering vector `v`
stored in Environment struct
"""
function beamforming(E::Environment)
function beamforming(E::Environment;multi_thread=E.multi_thread)
_foreach = AeroAcoustics.check_multithread(multi_thread)
@unpack CSM_s,w,steeringvec,M,N,Nf,fn = E
b = Array{Float64}(undef, N, Nf)
@views @inbounds for j in 1:Nf
@views @inbounds _foreach(1:Nf) do j
st = E.steeringvec.arr[:,:,j]
@. st = complex(w[:,j].*real(st), imag(st))
bf_col!(b[:,j],st,selectdim(CSM_s.arr, 3, j))
Expand Down
5 changes: 3 additions & 2 deletions src/cleansc.jl
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Expand Up @@ -8,7 +8,8 @@ CLEAN-SC algorithm for source identification and quantification optionally setti

With inspiration from https://github.com/acoular/acoular/blob/66cba3cffb3bc72602c869f99347be76798f4ac1/acoular/fbeamform.py#L1496
"""
function cleanSC(E;maxiter=50,ϕ=0.5,stopn=10,peak_removal=false,trust_region=nothing)
function cleanSC(E;maxiter=50,ϕ=0.5,stopn=10,peak_removal=false,trust_region=nothing,multi_thread=E.multi_thread)
_foreach = AeroAcoustics.check_multithread(multi_thread)
@unpack CSM_s,steeringvec,M,N,Nf,fn = E
x = zeros(N,Nf)

Expand All @@ -20,7 +21,7 @@ function cleanSC(E;maxiter=50,ϕ=0.5,stopn=10,peak_removal=false,trust_region=no
trust_indices = LinearIndices((E.Nx,E.Ny))
end

@views @inbounds for j = 1:Nf
@views @inbounds _foreach(1:Nf) do j
_cleanSC!(x[:,j],steeringvec.arr[:,:,j],CSM_s.arr[:,:,j],maxiter,ϕ,stopn,peak_removal,trust_indices)
end
return FreqArray(x,E.fn)
Expand Down
2 changes: 1 addition & 1 deletion src/fista.jl
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Expand Up @@ -50,7 +50,7 @@ function _fista!(x,b,psf,gam;maxit=1000,tol=1e-6)
x .= max.(real.(y),0.0)
# stopping criterion
if norm(x_ex-x)/gam <= tol*(1+norm(x))
println("breaking at iter = $(it)...")
#println("breaking at iter = $(it)...")
break
end
end
Expand Down
5 changes: 3 additions & 2 deletions src/psf.jl
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Expand Up @@ -4,10 +4,11 @@
Calculate frequency-domain point spread function using the Environment struct to access
steeringvectors. Optionally, supply the index where the psf is centered, default is (N/2)+1.
"""
function psf(E::Environment,cent::Int64=floor(Int,E.N/2)+1)
function psf(E::Environment,cent::Int64=floor(Int,E.N/2)+1;multi_thread=false)
_foreach = AeroAcoustics.check_multithread(multi_thread)
@unpack steeringvec,M,N,Nf,fn = E
p = Array{Float64, 2}(undef, N, Nf)
@views @inbounds for j in 1:Nf
@views @inbounds _foreach(1:Nf) do j
AeroAcoustics.psf_col!(p[:,j],steeringvec.arr[:,:,j],cent)
end
return FreqArray(p,fn)
Expand Down
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