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using Pkg Pkg.add(PackageSpec(; url="https://github.com/StructuralEquationModels/StructuralEquationModels.jl", rev = "devel"))
[ 3500, 350, 10025, 198, 198, 47, 10025, 13, 2860, 7, 27813, 22882, 7, 26, 19016, 2625, 5450, 1378, 12567, 13, 785, 14, 44909, 1523, 23588, 341, 5841, 1424, 14, 44909, 1523, 23588, 341, 5841, 1424, 13, 20362, 1600, 2710, 796, 366, 2934, 626, 48774 ]
2.886364
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""" What element(s) is this material suitable as a standard for? """ struct DBStandardFor database::SQLite.DB element::Element material::Material end function Base.write(db::SQLite.DB, ::Type{DBStandardFor}, elm::Element, mat::Union{String, Material, Integer}) if mat isa String matkey = find(db, Material, mat) mat = read(db, Material, matkey) elseif mat isa Material matkey = write(db, mat) else matkey = mat mat = read(db, Material, matkey) end @assert haskey(mat, elm) "The material $mat does not contain the element $elm." stmt1 = SQLite.Stmt(db, "SELECT * FROM STANDARDFOR WHERE ELEMENT=? AND MATKEY=?;") q1 = DBInterface.execute(stmt1, ( z(elm), matkey, ) ) if SQLite.done(q1) stmt1 = SQLite.Stmt(db, "INSERT INTO STANDARDFOR ( ELEMENT, MATKEY ) VALUES ( ?, ? );") DBInterface.execute(stmt1, ( z(elm), matkey, ) ) end return lastrowid(q1) end function Base.findall(db::SQLite.DB, ::Type{DBStandardFor}, elm::Element)::Array{DBMaterial} stmt1 = SQLite.Stmt(db, "SELECT * FROM STANDARDFOR WHERE ELEMENT=?;") q1 = DBInterface.execute(stmt1, ( z(elm), ) ) return [ read(db, r[:MATKEY]) for r in q1 ] end
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print("Enter an upper bound: ") lower = 0 input = readline() upper = parse(Int, input) if upper < 1 throw(DomainError) end attempts = 1 print("Think of a number, ", lower, "--", upper, ", then press ENTER.") readline() const maxattempts = round(Int, ceil(-log(1 / (upper - lower)) / log(2))) println("I will need at most ", maxattempts, " attempts ", "(⌈-log(1 / (", upper, " - ", lower, ")) / log(2)⌉ = ", maxattempts, ").\n") previous = -1 guess = -1 while true previous = guess guess = lower + round(Int, (upper - lower) / 2, RoundNearestTiesUp) if guess == previous || attempts > maxattempts println("\nThis is impossible; did you forget your number?") exit() end print("I guess ", guess, ".\n[l]ower, [h]igher, or [c]orrect? ") input = chomp(readline()) while input ∉ ["c", "l", "h"] print("Please enter one of \"c\", \"l\", or \"h\". ") input = chomp(readline()) end if input == "l" upper = guess elseif input == "h" lower = guess else break end attempts += 1 end println("\nI win after ", attempts, attempts == 1 ? " attempt." : " attempts.")
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function heatbath_SU2!(U,NC,temps,β,Dim=4) temp1 = temps[1] temp2 = temps[2] V = temps[3] ITERATION_MAX = 10^5 temps2 = Array{Matrix{ComplexF64},1}(undef,5) for i=1:5 temps2[i] = zeros(ComplexF64,2,2) end mapfunc!(A,B) = SU2update_KP!(A,B,β,NC,temps2,ITERATION_MAX) for μ=1:Dim loops = loops_staple[(Dim,μ)] iseven = true evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven) map_U!(U[μ],mapfunc!,V,iseven) iseven = false evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven) map_U!(U[μ],mapfunc!,V,iseven) end end function heatbath_SU3!(U,NC,temps,β,Dim=4) temp1 = temps[1] temp2 = temps[2] V = temps[3] ITERATION_MAX = 10^5 temps2 = Array{Matrix{ComplexF64},1}(undef,5) temps3 = Array{Matrix{ComplexF64},1}(undef,5) for i=1:5 temps2[i] = zeros(ComplexF64,2,2) temps3[i] = zeros(ComplexF64,3,3) end mapfunc!(A,B) = SU3update_matrix!(A,B,β,NC,temps2,temps3,ITERATION_MAX) for μ=1:Dim loops = loops_staple[(Dim,μ)] iseven = true evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven) map_U!(U[μ],mapfunc!,V,iseven) iseven = false evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven) map_U!(U[μ],mapfunc!,V,iseven) end end function heatbath_SUN!(U,NC,temps,β,Dim = 4) #Dim = 4 temp1 = temps[1] temp2 = temps[2] V = temps[3] ITERATION_MAX = 10^5 temps2 = Array{Matrix{ComplexF64},1}(undef,5) temps3 = Array{Matrix{ComplexF64},1}(undef,5) for i=1:5 temps2[i] = zeros(ComplexF64,2,2) temps3[i] = zeros(ComplexF64,NC,NC) end mapfunc!(A,B) = SUNupdate_matrix!(A,B,β,NC,temps2,temps3,ITERATION_MAX) for μ=1:Dim loops = loops_staple[(Dim,μ)] iseven = true evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven) map_U!(U[μ],mapfunc!,V,iseven) iseven = false evaluate_gaugelinks_evenodd!(V,loops,U,[temp1,temp2],iseven) map_U!(U[μ],mapfunc!,V,iseven) end end function heatbathtest_4D(NX,NY,NZ,NT,β,NC) Dim = 4 Nwing = 1 #= u1 = IdentityGauges(NC,Nwing,NX,NY,NZ,NT) U = Array{typeof(u1),1}(undef,Dim) U[1] = u1 for μ=2:Dim U[μ] = IdentityGauges(NC,Nwing,NX,NY,NZ,NT) end =# U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold") h = Heatbath(U,β) #= gauge_action = GaugeAction(U) plaqloop = make_loops_fromname("plaquette",Dim=Dim) append!(plaqloop,plaqloop') βinp = β/2 push!(gauge_action,βinp,plaqloop) =# temp1 = similar(U[1]) temp2 = similar(U[1]) temp3 = similar(U[1]) comb = 6 factor = 1/(comb*U[1].NV*U[1].NC) @time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor println("plaq_t = $plaq_t") poly = calculate_Polyakov_loop(U,temp1,temp2) println("polyakov loop = $(real(poly)) $(imag(poly))") #hnew = Heatbath_update(U,gauge_action) plaq_ave = 0.0 numhb = 200 for itrj = 1:numhb #heatbath!(U,hnew) heatbath!(U,h) #= if NC == 2 heatbath_SU2!(U,NC,[temp1,temp2,temp3],β) elseif NC == 3 heatbath_SU3!(U,NC,[temp1,temp2,temp3],β) else heatbath_SUN!(U,NC,[temp1,temp2,temp3],β) end =# plaq_t = calculate_Plaquette(U,temp1,temp2)*factor plaq_ave += plaq_t if itrj % 40 == 0 #@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor println("$itrj plaq_t = $plaq_t average: $(plaq_ave/itrj)") #println("$itrj plaq_t = $plaq_t") poly = calculate_Polyakov_loop(U,temp1,temp2) println("$itrj polyakov loop = $(real(poly)) $(imag(poly))") end #= if itrj % 40 == 0 @time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor println("$itrj plaq_t = $plaq_t") poly = calculate_Polyakov_loop(U,temp1,temp2) println("$itrj polyakov loop = $(real(poly)) $(imag(poly))") end =# end return plaq_ave/numhb end function heatbathtest_2D(NX,NT,β,NC) Dim = 2 Nwing = 1 #= u1 = RandomGauges(NC,Nwing,NX,NT) U = Array{typeof(u1),1}(undef,Dim) U[1] = u1 for μ=2:Dim U[μ] = RandomGauges(NC,Nwing,NX,NT) end =# U = Initialize_Gaugefields(NC,Nwing,NX,NT,condition = "hot",randomnumber="Reproducible") temp1 = similar(U[1]) temp2 = similar(U[1]) temp3 = similar(U[1]) #comb = 6 if Dim == 4 comb = 6 #4*3/2 elseif Dim == 3 comb = 3 elseif Dim == 2 comb = 1 else error("dimension $Dim is not supported") end factor = 1/(comb*U[1].NV*U[1].NC) @time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor println("plaq_t = $plaq_t") poly = calculate_Polyakov_loop(U,temp1,temp2) println("polyakov loop = $(real(poly)) $(imag(poly))") #= gauge_action = GaugeAction(U) plaqloop = make_loops_fromname("plaquette",Dim=Dim) append!(plaqloop,plaqloop') βinp = β/2 push!(gauge_action,βinp,plaqloop) hnew = Heatbath_update(U,gauge_action) =# numhb = 200 plaq_ave = 0.0 for itrj = 1:numhb #heatbath!(U,hnew) heatbath!(U,[temp1,temp2,temp3],β) #= if NC == 2 heatbath_SU2!(U,NC,[temp1,temp2,temp3],β,Dim) elseif NC == 3 heatbath_SU3!(U,NC,[temp1,temp2,temp3],β,Dim) else heatbath_SUN!(U,NC,[temp1,temp2,temp3],β,Dim) end =# plaq_t = calculate_Plaquette(U,temp1,temp2)*factor plaq_ave += plaq_t if itrj % 40 == 0 #@time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor println("$itrj plaq_t = $plaq_t average: $(plaq_ave/itrj)") #println("$itrj plaq_t = $plaq_t") poly = calculate_Polyakov_loop(U,temp1,temp2) println("$itrj polyakov loop = $(real(poly)) $(imag(poly))") end #= if itrj % 40 == 0 @time plaq_t = calculate_Plaquette(U,temp1,temp2)*factor println("$itrj plaq_t = $plaq_t") poly = calculate_Polyakov_loop(U,temp1,temp2) println("$itrj polyakov loop = $(real(poly)) $(imag(poly))") end =# end return plaq_ave/numhb end #eps = 1e-1 println("4D system") @testset "4D" begin NX = 4 NY = 4 NZ = 4 NT = 4 Nwing = 1 @testset "NC=2" begin β = 2.3 NC = 2 println("NC = $NC") #val =0.6414596466929057 val = 0.6042874193905048 #@time plaq_t = heatbathtest_2D(NX,NT,β,NC) @time plaq_ave = heatbathtest_4D(NX,NY,NZ,NT,β,NC) @test abs(plaq_ave-val)/abs(val) < eps end @testset "NC=3" begin β = 5.7 NC = 3 println("NC = $NC") #val = 0.5779454661484242 val = 0.5616169101071591 #@time plaq_t = heatbathtest_2D(NX,NT,β,NC) @time plaq_ave = heatbathtest_4D(NX,NY,NZ,NT,β,NC) @test abs(plaq_ave-val)/abs(val) < eps end @testset "NC=4" begin β = 5.7 NC = 4 println("NC = $NC") #val =0.19127260002797497 val = 0.1967198548214144 #@time plaq_t = heatbathtest_2D(NX,NT,β,NC) @time plaq_ave = heatbathtest_4D(NX,NY,NZ,NT,β,NC) @test abs(plaq_ave-val)/abs(val) < eps end end println("2D system") @testset "2D" begin NX = 4 #NY = 4 #NZ = 4 NT = 4 Nwing = 1 @testset "NC=2" begin β = 2.3 NC = 2 println("NC = $NC") val = 0.47007878197368624 #@time plaq_t = heatbathtest_2D(NX,NT,β,NC) @time plaq_ave = heatbathtest_2D(NX,NT,β,NC) @test abs(plaq_ave-val)/abs(val) < eps end @testset "NC=3" begin β = 5.7 NC = 3 println("NC = $NC") val = 0.40073421896125794 #@time plaq_t = heatbathtest_2D(NX,NT,β,NC) @time plaq_ave = heatbathtest_2D(NX,NT,β,NC) @test abs(plaq_ave-val)/abs(val) < eps end @testset "NC=4" begin β = 5.7 NC = 4 println("NC = $NC") val = 0.17476796328668975 #@time plaq_t = heatbathtest_2D(NX,NT,β,NC) @time plaq_ave = heatbathtest_2D(NX,NT,β,NC) @test abs(plaq_ave-val)/abs(val) < eps #@test abs(plaq_t-val)/abs(val) < eps end end
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1.786351
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struct Angle i :: Int64 j :: Int64 k :: Int64 ktheta :: Float64 theta0 :: Float64 kub :: Float64 s0 :: Float64 end
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2.19697
66
using Configurations import TOML import Pluto export SliderServerSettings, ExportSettings, PlutoDeploySettings, get_configuration using TerminalLoggers: TerminalLogger using Logging: global_logger using FromFile @from "./ConfigurationDocs.jl" import @extract_docs, get_kwdocs, list_options_md @extract_docs @option struct SliderServerSettings enabled::Bool = true "List of notebook files to skip. Provide paths relative to `start_dir`. *If `Export.enabled` is `true` (default), then only paths in `SliderServer_exclude ∩ Export_exclude` will be skipped, paths in `setdiff(SliderServer_exclude, Export_exclude)` will be shut down after exporting.*" exclude::Vector = String[] "Port to run the HTTP server on." port::Integer = 2345 """Often set to `"0.0.0.0"` on a server.""" host::Any = "127.0.0.1" "Watch the input directory for file changes, and update the slider server sessions automatically. Only takes effect when running the slider server. More info in the README." watch_dir::Bool = true "Besides handling slider server request, should we also run a static file server of the export output folder? Set to `false` if you are serving the HTML files in another way, e.g. using GitHub Pages, and, for some reason, you do not want to *also* serve the HTML files using this serve." serve_static_export_folder::Bool = true simulated_lag::Real = 0 end @extract_docs @option struct ExportSettings "Generate static HTML files? This setting can only be `false` if you are also running a slider server." enabled::Bool = true "Folder to write generated HTML files to (will create directories to preserve the input folder structure). The behaviour of the default value depends on whether you are running the slider server, or just exporting. If running the slider server, we use a temporary directory; otherwise, we use `start_dir` (i.e. we generate each HTML file in the same folder as the notebook file)." output_dir::Union{Nothing,String} = nothing "List of notebook files to skip. Provide paths relative to `start_dir`." exclude::Vector{String} = String[] "List of notebook files that should always re-run, skipping the `cache_dir` system. Provide paths relative to `start_dir`." ignore_cache::Vector = String[] "base64-encode the state object and write it inside the .html file. If `false`, a separate `.plutostate` file is generated. A separate statefile allows us to show a loading bar in pluto while the statefile is loading, but it can complicate setup in some environments." baked_state::Bool = true baked_notebookfile::Bool = true "Hide all buttons and toolbars in Pluto to make it look like an article." disable_ui::Bool = true """Show a "Run on Binder" button on the notebooks.""" offer_binder::Bool = true """If 1) you are using this setup to export HTML files for notebooks, AND 2) you are running the slider server **on another setup/computer**, then this setting should be the URL pointing to the slider server, e.g. `"https://sliderserver.mycoolproject.org/"`. For example, you need this if you use GitHub Actions and GitHub Pages to generate HTML files, with a slider server on DigitalOcean. === If you only have *one* server for both the static exports and the slider server, and people will read notebooks directly on your server, then the default value `nothing` will work: it will automatically make the HTML files use their current domain for the slider server.""" slider_server_url::Union{Nothing,String} = nothing "If provided, use this directory to read and write cached notebook states. Caches will be indexed by the hash of the notebook file, but you need to take care to invalidate the cache when Pluto or this export script updates. Useful in combination with https://github.com/actions/cache, see https://github.com/JuliaPluto/static-export-template for an example." cache_dir::Union{Nothing,String} = nothing "Automatically generate an `index.html` file, listing all the exported notebooks (only if no `index.jl` or `index.html` file exists already)." create_index::Bool = true """ADVANCED: URL of the binder repository to load when you click the "Run on binder" button in the top right, this will be set automatically if you leave it at the default value. This setting is quite advanced, and only makes sense if you have a fork of `https://github.com/fonsp/pluto-on-binder/` (because you want to control the binder launch, or because you are using your own fork of Pluto). If so, the setting should be of the form `"https://mybinder.org/v2/gh/fonsp/pluto-on-binder/v0.17.2"`, where `fonsp/pluto-on-binder` is the name of your repository, and `v0.17.2` is a tag or commit hash.""" binder_url::Union{Nothing,String} = nothing pluto_cdn_root::Union{Nothing,String} = nothing end @option struct PlutoDeploySettings SliderServer::SliderServerSettings = SliderServerSettings() Export::ExportSettings = ExportSettings() Pluto::Pluto.Configuration.Options = Pluto.Configuration.Options() end function get_configuration( toml_path::Union{Nothing,String}=nothing; kwargs..., )::PlutoDeploySettings # we set `Pluto_server_notebook_path_suggestion=joinpath(homedir(),"")` to because the default value for this setting changes when the pwd changes. This causes our run_git_directory to exit... Not necessary for Pluto 0.17.3 and up. if !isnothing(toml_path) && isfile(toml_path) Configurations.from_toml( PlutoDeploySettings, toml_path; Pluto_server_notebook_path_suggestion=joinpath(homedir(), ""), kwargs..., ) else Configurations.from_kwargs( PlutoDeploySettings; Pluto_server_notebook_path_suggestion=joinpath(homedir(), ""), kwargs..., ) end end merge_recursive(a::AbstractDict, b::AbstractDict) = mergewith(merge_recursive, a, b) merge_recursive(a, b) = b
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3.315031
1,803
# Residual equation for linear systems linear_residual!(R, A, x, b) = R .= A * x - b ## Fully-simultaneous Newton method #==========================================================================================# # Coupled aero-structural-load-factor residuals function coupled_residuals!(R, all_horsies, Γs, U, Ω, speed, stiffness_matrix, δs, fem_loads, weight, load_factor, L) # Get residual vector views R_A = R.aerodynamics R_S = R.structures R_W = @view R[end] # Aerodynamic residuals @timeit "Aerodynamic Residuals" solve_nonlinear!(R_A, all_horsies, Γs / speed, U / speed, Ω / speed) # Structural residuals @timeit "Structural Residuals" linear_residual!(R_S, stiffness_matrix, δs, fem_loads) # Weight residual linear_residual!(R_W, weight, load_factor, L) nothing end # Residual setup for multiple aerostructural surfaces and multiple aerodynamic surfaces function solve_coupled_residual!(R, x, speed, β, ρ, Ω, syms :: Vector{Symbol}, chord_meshes, camber_meshes, fem_meshes, other_horsies, stiffness_matrix, weight, load_factor) # Unpack aerodynamic and structural variables Γs = x.aerodynamics δs = x.structures α = x.load_factor # Compute velocity with new angle of attack U = freestream_to_cartesian(-speed, α, β) # Compute displacements Δs = map((key, fem_mesh) -> reshape(δs[key][7:end], 6, length(fem_mesh)), valkeys(δs), fem_meshes) # @timeit "Get Translations" dxs = # @timeit "Get Rotations" Ts = # New VLM variables @timeit "New Horseshoes" new_horsies = @. new_horseshoes(mesh_translation(Δs), mesh_rotation(Δs), chord_meshes, camber_meshes, fem_meshes) # Compute component forces for structural residual @timeit "Get Circulations" new_Γs = getindex.(Ref(Γs), syms) @timeit "Get Aerodynamic Centers" new_acs = map(horsies -> bound_leg_center.(horsies), new_horsies) @timeit "All Horseshoes" all_horsies = [ mapreduce(vec, vcat, new_horsies); vec(other_horsies) ] @timeit "New Forces" new_forces = map((Γ_comp, hs_comp) -> surface_forces(hs_comp, Γ_comp, all_horsies, Γs, U, Ω, ρ), new_Γs, new_horsies) # Compute other forces for load factor residual @timeit "Get Symbols" other_syms = filter(sym -> !(sym ∈ syms), keys(Γs)) @timeit "Get Other Circulations" other_Γs = mapreduce(sym -> vec(getindex(Γs, sym)), vcat, other_syms) @timeit "Other Forces" other_forces = surface_forces(vec(other_horsies), other_Γs, all_horsies, Γs, U, Ω, ρ) # Compute lift @timeit "All Forces" vlm_forces = [ mapreduce(vec, vcat, new_forces); vec(other_forces) ] @timeit "Transform Summed Forces" D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β) # Build force vector with constraint for structures @timeit "FEM Loads" fem_loads = mapreduce(vec ∘ fem_load_vector, vcat, new_acs, new_forces, fem_meshes) # Compute residuals @timeit "Compute Residuals" coupled_residuals!(R, all_horsies, Γs, U, Ω, speed, stiffness_matrix, δs, fem_loads, weight, load_factor, L * cos(α)) return R end # Residual setup for single aerostructural surface function solve_coupled_residual!(R, x, speed, β, ρ, Ω, chord_mesh, camber_mesh, fem_mesh, stiffness_matrix, weight, load_factor) # Unpack aerodynamic and structural variables Γ = x.aerodynamics δ = x.structures α = x.load_factor # Compute velocity with new angle of attack U = freestream_to_cartesian(-speed, α, β) # Compute displacements Δs = δ.displacement dxs = mesh_translation(Δs) Ts = mesh_rotation(Δs) # New VLM variables @timeit "New Horseshoes" new_horsies = new_horseshoes(dxs, Ts, chord_mesh, camber_mesh, fem_mesh) # Compute aerodynamic forces @timeit "Surface Forces" vlm_forces = surface_forces(new_horsies, Γ, U, Ω, ρ) # Compute structural residual and loads @timeit "FEM Loads" fem_loads = fem_load_vector(bound_leg_center.(new_horsies), vlm_forces, fem_mesh) # Compute lift for load factor residual D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β) # Compute residuals coupled_residuals!(R, new_horsies, Γ, U, Ω, speed, stiffness_matrix, δ, fem_loads, weight, load_factor, L * cos(α)) return R end # # Residual setup for single aerostructural surface and multiple aerodynamic surfaces # function solve_coupled_residual!(R, x, speed, β, ρ, Ω, chord_mesh, camber_mesh, other_horsies, fem_mesh, stiffness_matrix, weight, load_factor) # # Unpack aerodynamic and structural variables # Γ = x.aerodynamics # δ = x.structures # α = x.load_factor # # Get residual vector views # R_A = R.aerodynamics # R_S = R.structures # R_W = @view R[end] # # Compute velocity with new angle of attack # U = freestream_to_cartesian(-speed, α, β) # # Compute displacements # δs = @views reshape(δ[7:end], 6, length(fem_mesh)) # dxs = mesh_translation(Δs) # Ts = mesh_rotation(Δs) # # New VLM variables # new_horsies = new_horseshoes(dxs, Ts, chord_mesh, camber_mesh, fem_mesh) # @timeit "Combine Horseshoes" all_horsies = [ vec(new_horsies); other_horsies ] # # Compute aerodynamic residual and loads # @timeit "Surface Forces" vlm_forces = surface_forces(Γ, all_horsies, U, Ω, ρ) # @timeit "FEM Forces" new_forces = @views reshape(vlm_forces[1:length(new_horsies)], size(new_horsies)) # # Compute structural residual and loads # @timeit "FEM Loads" fem_loads = fem_load_vector(bound_leg_center.(new_horsies), new_forces, fem_mesh) # # Compute lift for load factor residual # D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β) # # Compute residuals # coupled_residuals!(R, all_horsies, Γ, U, Ω, speed, stiffness_matrix, δ, fem_loads, weight, load_factor, L * cos(α)) # return R # end # Residual setup for multiple aerostructural surfaces (NEED TO REDUCE REDUNDANCIES) # function solve_coupled_residual!(R, x, speed, β, ρ, Ω, syms :: Vector{Symbol}, chord_meshes, camber_meshes, fem_meshes, stiffness_matrix, weight, load_factor) # # Unpack aerodynamic and structural variables # Γs = x.aerodynamics # δs = x.structures # α = x.load_factor # # Compute velocity with new angle of attack # U = freestream_to_cartesian(-speed, α, β) # # Compute displacements # Δs = map((key, n) -> reshape(δs[key][7:end], 6, n), valkeys(δs), length.(fem_meshes)) # dxs = mesh_translation.(Δs) # Ts = mesh_rotation.(Δs) # # New VLM variables # new_horsies = new_horseshoes.(dxs, Ts, chord_meshes, camber_meshes, fem_meshes) # all_horsies = mapreduce(vec, vcat, new_horsies) # # Compute component forces for structural residual # new_Γs = getindex.(Ref(Γs), syms) # new_acs = map(horsies -> bound_leg_center.(horsies), new_horsies) # @timeit "New Forces" new_forces = surface_forces.(new_Γs, new_horsies, Ref(Γs), Ref(all_horsies), Ref(U), Ref(Ω), Ref(ρ)) # # Compute lift # D, Y, L = geometry_to_wind_axes(sum(mapreduce(vec, vcat, new_forces)), α, β) # # Build force vector with constraint for structures # @timeit "FEM Loads" fem_loads = mapreduce(fem_load_vector, vcat, new_acs, new_forces, fem_meshes) # # Compute residuals # coupled_residuals!(R, all_horsies, Γs, U, Ω, speed, stiffness_matrix, δs, fem_loads, weight, load_factor, L * cos(α)) # return R # end ## Nonlinear block Gauss-Seidel #==========================================================================================# function aerostruct_gauss_seidel(x0, speed, β, ρ, Ω, chord_mesh, camber_mesh, fem_mesh, stiffness_matrix, weight, load_factor; max_iters = 50, tol = 1e-9) x = deepcopy(x0) ε = 1e5 for i = 1:max_iters xp = deepcopy(x) @show xp Γ = @views x.aerodynamics δ = @views x.structures α = @views x.load_factor # Compute velocity with new angle of attack U = freestream_to_cartesian(-speed, α, β) # Compute displacements δs = δ.displacement dxs = mesh_translation(δs) Ts = mesh_rotation(δs) # New geometric variables new_horsies = new_horseshoes(dxs, Ts, chord_mesh, camber_mesh, fem_mesh) # Solve circulations Γ = reshape(influence_matrix(vec(new_horsies), -U / speed) \ boundary_condition(quasi_steady_freestream(vec(new_horsies), U, Ω), horseshoe_normal.(vec(new_horsies))), size(new_horsies)) x.aerodynamics = Γ @show x # Compute VLM forces vlm_forces = surface_forces(Γ, new_horsies, U, Ω, ρ) # Compute FEM loads fem_loads = fem_load_vector(bound_leg_center.(new_horsies), vlm_forces, fem_mesh) # Solve displacements δ = reshape(stiffness_matrix \ vec(fem_loads), size(fem_loads)) x.structures = δ @show x # Compute lift D, Y, L = geometry_to_wind_axes(sum(vlm_forces), α, β) # Weight residual α = acos(abs(weight * load_factor - L) / (weight * load_factor)) x.load_factor = α @show L ε = LinearAlgebra.norm(x - xp) @show (i, ε) if ε <= tol return x end # Needs NAN checks and everything like NLsolve end return x end
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#using PolyhedralOmega, LinearAlgebra, Test using LinearAlgebra, Test, PolyhedralOmega include("../src/SymbolicCone.jl") @testset "Symbolic Cone Test Set" begin @test true end
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using Plots, VortexDistributions @load "./examples/one_frame.jld2" heatmap(x,y,abs2.(ψ1')) psi = Torus(ψ1,x,y) vortices = findvortices(psi) # zoom window x0,y0 = 70,200 Lx,Ly = 100,100 # simple cartesian mask xm = x[@. abs(x-x0)<Lx/2] ym = y[@. abs(y-y0)<Ly/2] psim = @. ψ1[abs(x-x0)<Lx/2,abs(y-y0)<Ly/2] heatmap(xm,ym,angle.(psim),transpose=true) psi2 = Torus(psim,xm,ym) vort = findvortices(psi2) scatter!([vort[1].xv],[vort[1].yv],alpha=0.4) scatter!([vort[2].xv],[vort[2].yv],alpha=0.4)
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""" Embedding operator that randomly samples between parent's value and the nearest parameter boundary to get the new valid value if target's parameter is out-of-bounds. """ struct RandomBound{S<:RectSearchSpace} <: EmbeddingOperator search_space::S RandomBound(search_space::S) where {S<:RectSearchSpace} = new{S}(search_space) end search_space(rb::RandomBound) = rb.search_space function apply!(eo::RandomBound, target::AbstractIndividual, ref::AbstractIndividual) length(target) == length(ref) == numdims(eo.search_space) || throw(ArgumentError("Dimensions of problem/individuals do not match")) ss = search_space(eo) ssmins = dimmin(ss) ssmaxs = dimmax(ss) @inbounds for i in eachindex(target) l, u = ssmins[i], ssmaxs[i] if target[i] < l target[i] = l + rand() * (ref[i]-l) elseif target[i] > u target[i] = u + rand() * (ref[i]-u) else # continuous range doesn't need further checks (ss isa MixedPrecisionRectSearchSpace) || continue end if (ss isa MixedPrecisionRectSearchSpace) && (dimdigits(ss, i) >= 0) target[i] = round(target[i], digits=dimdigits(ss, i)) end @assert l <= target[i] <= u "target[$i]=$(target[i]) is out of [$l, $u]" end return target end apply!(eo::RandomBound, target::AbstractIndividual, pop, refIndex::Int) = apply!(eo, target, viewer(pop, refIndex)) function apply!(eo::RandomBound, target::AbstractIndividual, pop, parentIndices::AbstractVector{Int}) @assert length(parentIndices) == 1 apply!(eo, target, pop, parentIndices[1]) end
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export Image mutable struct Image mHandle::vk.VkImage mType::vk.VkImageViewType mCreateInfo::vk.VkImageCreateInfo mUnowned::Bool mDevice::Device mMemory::MemoryChunk function Image(device::Device, createInfo::vk.VkImageCreateInfo, type::vk.VkImageViewType) this = new() this.mDevice = device this.mType = type this.mCreateInfo = createInfo this.mUnowned = false newImage = Ref{vk.VkImage}() infoRef = Ref(createInfo) GC.@preserve infoRef begin if (vk.vkCreateImage(getLogicalDevice(device), infoRef, C_NULL, newImage) != vk.VK_SUCCESS) error("Failed to create image!") end end this.mHandle = newImage[] return this end # Assumes that the Image is managed somewhere else (e.g. in a swapchain) # The ImageCreateInfo will not be used to actually create an image, but to # derive properties of the image (format, extend, ...) function Image(device::Device, createInfo::vk.VkImageCreateInfo, unowned::vk.VkImage, type::vk.VkImageViewType) this = new() this.mDevice = device this.mType = type this.mCreateInfo = createInfo this.mHandle = unowned this.mUnowned = true return this end end @class Image [:handle, :destroy] function destroy(this::Image) if (this.mUnowned) return end vk.vkDestroyImage(getLogicalDevice(this.mDevice), this.mHandle, C_NULL) destroy(this.mMemory) end function usageToFeatures(usage::vk.VkImageUsageFlags)::vk.VkFormatFeatureFlags result::vk.VkFormatFeatureFlags = 0 if (usage & vk.VK_IMAGE_USAGE_COLOR_ATTACHMENT_BIT != 0) result |= vk.VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BIT | vk.VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BLEND_BIT end if (usage & vk.VK_IMAGE_USAGE_DEPTH_STENCIL_ATTACHMENT_BIT != 0) result |= vk.VK_FORMAT_FEATURE_DEPTH_STENCIL_ATTACHMENT_BIT end if (usage & vk.VK_IMAGE_USAGE_INPUT_ATTACHMENT_BIT != 0) result |= vk.VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BIT end if (usage & vk.VK_IMAGE_USAGE_SAMPLED_BIT != 0) result |= vk.VK_FORMAT_FEATURE_SAMPLED_IMAGE_BIT end if (usage & vk.VK_IMAGE_USAGE_STORAGE_BIT != 0) result |= vk.VK_FORMAT_FEATURE_STORAGE_IMAGE_BIT end if (usage & vk.VK_IMAGE_USAGE_TRANSFER_DST_BIT != 0) # not sure if I should use this, because it's hidden behind an # extension # result |= vk.VK_FORMAT_FEATURE_TRANSFER_DST_BIT_KHR end if (usage & vk.VK_IMAGE_USAGE_TRANSFER_SRC_BIT != 0) # not sure if I should use this, because it's hidden behind an # extension # result |= vk.VK_FORMAT_FEATURE_TRANSFER_SRC_BIT_KHR end return result; end function createImage(createInfo::ImageCreateInfo, device::Device)::Image return Image(device, handleRef(createInfo)[], createInfo.mViewType) end function createView(this::Image, range::vk.VkImageSubresourceRange = vk.VkImageSubresourceRange(0, 0, 0, 0, 0)) return createView(this, this.mType, range) end function handle(this::Image)::vk.VkImage return this.mHandle end function getLogicalDeviceOf(this::Image)::vk.VkDevice return getLogicalDevice(this.mDevice) end function realizeAttachment(this::Image) req = VkExt.getImageMemoryRequirements(getLogicalDevice(this.mDevice), this.mHandle) this.mMemory = allocateDedicated(this.mDevice.mSuballocator, req, VRAM) bindToImage(this.mMemory, this.mHandle) end function getWidth(this::Image)::UInt32 return this.mCreateInfo.extent.width end function getHeight(this::Image)::UInt32 return this.mCreateInfo.extent.height end # TODO # setDataVRAM # setDataRAM # getData # realizeVRAM # realizeRAM # copyFrom # copyTo # blitTo # changeLayout # changeOwner # generateMipmaps # createView # changeOwnerImpl # levelPixels # levelBytes # prepStagingBuffer
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include("layout.jl") # layout include(joinpath("api", "endpoint.jl")) # Endpoint include("wasm.jl") # WASM include("front.jl") # Front if PROGRAM_FILE == basename(@__FILE__) using Bukdu import Bukdu.Actions: index, show, new, edit, create, delete, update using .Front # WelcomeController using .Endpoint # CustomerController import .WASM: WasmController, hello_js, hello_wast using Sockets pipeline(:api) do conn::Conn end routes(:api) do resources("/customers", CustomerController) end routes(:wasm) do get("/wasm", WasmController, index) get("/hello.js", WasmController, hello_js) get("/hello.wast", WasmController, hello_wast) plug(Plug.Static, at="/", from=normpath(@__DIR__, "public")) end routes(:front) do get("/", WelcomeController, index) end if haskey(ENV, "ON_HEROKU") Bukdu.start(parse(Int, ENV["PORT"]); host=Sockets.IPAddr(0,0,0,0)) else Bukdu.start(8080) end Router.call(get, "/") # # CLI.routes() Base.JLOptions().isinteractive==0 && wait() # Bukdu.stop() end # if
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# %% Interactive bifurcations for 1D energy balance using DrWatson @quickactivate "NonlinearDynamicsTextbook" include(srcdir("colorscheme.jl")) using DynamicalSystems, OrdinaryDiffEq, Random using InteractiveDynamics: record_interaction import GLMakie D = 25 omega_vec = randn(D) ds = Systems.kuramoto(D) diffeq = (alg = Tsit5(), adaptive = false, dt = 0.05) integ = integrator(ds; diffeq...) fig = GLMakie.Figure() display(fig) cmap = :curl axku = GLMakie.Axis(fig[1,1]) # Other static elements GLMakie.hidedecorations!(axku) axku.aspect = GLMakie.DataAspect() GLMakie.lines!(axku, cos.(0:0.001:2π+0.002), sin.(0:0.001:2π+0.002);color = :black) # Plot balls phases = GLMakie.Observable(copy(integ.u)) balls = GLMakie.lift(u -> [GLMakie.Point2f0(cos(φ), sin(φ)) for φ in u], phases) GLMakie.scatter!(axku, balls; markersize = 12, color = 1:D, colormap = cmap, strokewidth=2, strokecolor = :black ) # Plot arrows arrows_end = GLMakie.lift(b -> 0.8b, balls) arrows_start = [GLMakie.Point2f0(0,0) for i in 1:D] GLMakie.arrows!(axku, arrows_start, arrows_end; color = 1:D, colormap = (cmap, 0.5), arrowsize = 10, linewidth = 4, ) # Plot mean field Vector # add sliders and buttons fig[2, 1] = controllayout = GLMakie.GridLayout(tellwidth = false) run = controllayout[1, 1] = GLMakie.Button(fig; label = "run") Ks = 0.0:0.1:10.0 kslider = GLMakie.labelslider!(fig, "K =", Ks; sliderkw = Dict(:startvalue => ds.p.K)) controllayout[1, 2] = kslider.layout # run button functionality isrunning = GLMakie.Observable(false) GLMakie.on(run.clicks) do clicks; isrunning[] = !isrunning[]; end GLMakie.on(run.clicks) do clicks @async while isrunning[] GLMakie.isopen(fig.scene) || break # ensures computations stop if closed window step!(integ) phases[] = integ.u yield() sleep(0.001) # or `yield()` instead end end GLMakie.on(kslider.slider.value) do val integ.p.K = val u_modified!(integ, true) end # %% record_interaction(fig, string(@__FILE__)[1:end-2]*"mp4"; total_time = 15, sleep_time = 2)
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# Level-2 LinearAlgebra.BLAS interface. # # TODO: support gbmv!, sbmv! and hbmv!. # hpmv! and spmv! is not possible within current frame. # Level2 BLAS size checker. bli_check_lv2(trans::BliTrans, m ::Integer, n ::Integer, n_::Integer, m_::Integer) = begin ((trans.enum & BLIS_TRANS_BIT) != 0) && ((m, n) = (n, m)) (m == m_) || throw(DimensionMismatch("Target buffer size mismatch.")) (n == n_) || throw(DimensionMismatch("Contracted size mismatch.")) nothing end macro blis_interface_linalg_lv2_gemv(Tc1, T1, T2, Tc2, T3, targetfunc, bliname) # Get method for the object API backend. blifuncname = Symbol("bli_", bliname) blifunc = getproperty(ObjectBackend, blifuncname) return quote $(esc(targetfunc))(tA::AbstractChar, α::$Tc1, A::StridedMatrix{<:$T1}, x::StridedVector{<:$T2}, β::$Tc2, y::StridedVector{<:$T3}) = begin bli_tA = char_to_trans[tA] bli_check_lv2(bli_tA, size(A)..., length(x), length(y)) oα = BliObj(α) oA = BliObj(A) ox = BliObj(x) oβ = BliObj(β) oy = BliObj(y) ObjectBackend.bli_obj_set_onlytrans!(bli_tA, oA.obj) $blifunc(oα, oA, ox, oβ, oy) y end end end @doc """ gemv!(tA, α, A, x, β, y) BLIS-based GEMV with strides support & mixed-precision. """ gemv! @blis_interface_linalg_lv2_gemv(BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, gemv!, gemv!) @blis_interface_linalg_lv2_gemv Float32 Float32 Float32 Float32 Float32 gemv! gemv! @blis_interface_linalg_lv2_gemv Float64 Float64 Float64 Float64 Float64 gemv! gemv! @blis_interface_linalg_lv2_gemv ComplexF32 ComplexF32 ComplexF32 ComplexF32 ComplexF32 gemv! gemv! @blis_interface_linalg_lv2_gemv ComplexF64 ComplexF64 ComplexF64 ComplexF64 ComplexF64 gemv! gemv! macro blis_interface_linalg_lv2_hemv(Tc1, T1, T2, Tc2, T3, targetfunc, bliname, bli_struc) # Get method for the object API backend. blifuncname = Symbol("bli_", bliname) blifunc = getproperty(ObjectBackend, blifuncname) return quote $(esc(targetfunc))(ul::AbstractChar, α::$Tc1, A::StridedMatrix{<:$T1}, x::StridedVector{<:$T2}, β::$Tc2, y::StridedVector{<:$T3}) = begin bli_ul = char_to_uplo[ul] bli_check_lv2(BLIS_NO_TRANSPOSE, size(A)..., length(x), length(y)) oα = BliObj(α) oA = BliObj(A) ox = BliObj(x) oβ = BliObj(β) oy = BliObj(y) ObjectBackend.bli_obj_set_uplo!(bli_ul, oA) ObjectBackend.bli_obj_set_struc!($bli_struc, oA.obj) $blifunc(oα, oA, ox, oβ, oy) y end end end @doc """ hemv!(ul, α, A, x, β, y) BLIS-based HEMV with strides support & mixed-precision. """ hemv! @blis_interface_linalg_lv2_hemv(BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, hemv!, hemv!, BLIS_HERMITIAN) @blis_interface_linalg_lv2_hemv Float32 Float32 Float32 Float32 Float32 hemv! hemv! BLIS_HERMITIAN @blis_interface_linalg_lv2_hemv Float64 Float64 Float64 Float64 Float64 hemv! hemv! BLIS_HERMITIAN @blis_interface_linalg_lv2_hemv ComplexF32 ComplexF32 ComplexF32 ComplexF32 ComplexF32 hemv! hemv! BLIS_HERMITIAN @blis_interface_linalg_lv2_hemv ComplexF64 ComplexF64 ComplexF64 ComplexF64 ComplexF64 hemv! hemv! BLIS_HERMITIAN @doc """ symv!(ul, α, A, x, β, y) BLIS-based SYMV with strides support & mixed-precision. """ symv! @blis_interface_linalg_lv2_hemv(BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, symv!, symv!, BLIS_SYMMETRIC) @blis_interface_linalg_lv2_hemv Float32 Float32 Float32 Float32 Float32 symv! symv! BLIS_SYMMETRIC @blis_interface_linalg_lv2_hemv Float64 Float64 Float64 Float64 Float64 symv! symv! BLIS_SYMMETRIC @blis_interface_linalg_lv2_hemv ComplexF32 ComplexF32 ComplexF32 ComplexF32 ComplexF32 symv! symv! BLIS_SYMMETRIC @blis_interface_linalg_lv2_hemv ComplexF64 ComplexF64 ComplexF64 ComplexF64 ComplexF64 symv! symv! BLIS_SYMMETRIC macro blis_interface_linalg_lv2_trmv(T1, T2, targetfunc, bliname) # Get method for the object API backend. blifuncname = Symbol("bli_", bliname) blifunc = getproperty(ObjectBackend, blifuncname) return quote $(esc(targetfunc))(ul::AbstractChar, tA::AbstractChar, dA::AbstractChar, A::StridedMatrix{<:$T1}, b::StridedVector{<:$T2}) = begin bli_ul = char_to_uplo[ul] bli_dA = char_to_diag[dA] bli_tA = char_to_trans[tA] bli_check_lv2(bli_tA, size(A)..., length(b), length(b)) oα = BliObj($T1(1.0)) oA = BliObj(A) ob = BliObj(b) ObjectBackend.bli_obj_set_uplo!(bli_ul, oA) ObjectBackend.bli_obj_set_diag!(bli_dA, oA) ObjectBackend.bli_obj_set_onlytrans!(bli_tA, oA) $blifunc(oα, oA, ob) b end end end @doc """ trmv!(ul, tA, dA, A, b) BLIS-based TRMV with strides support & mixed-precision. """ @blis_interface_linalg_lv2_trmv(BliCompatibleType, BliCompatibleType, trmv!, trmv!) @blis_interface_linalg_lv2_trmv Float32 Float32 trmv! trmv! @blis_interface_linalg_lv2_trmv Float64 Float64 trmv! trmv! @blis_interface_linalg_lv2_trmv ComplexF32 ComplexF32 trmv! trmv! @blis_interface_linalg_lv2_trmv ComplexF64 ComplexF64 trmv! trmv! @doc """ trsv!(ul, tA, dA, A, b) BLIS-based TRSV with strides support & mixed-precision. """ @blis_interface_linalg_lv2_trmv(BliCompatibleType, BliCompatibleType, trsv!, trsv!) @blis_interface_linalg_lv2_trmv Float32 Float32 trsv! trsv! @blis_interface_linalg_lv2_trmv Float64 Float64 trsv! trsv! @blis_interface_linalg_lv2_trmv ComplexF32 ComplexF32 trsv! trsv! @blis_interface_linalg_lv2_trmv ComplexF64 ComplexF64 trsv! trsv! macro blis_interface_linalg_lv2_ger(Tc1, T1, T2, T3, targetfunc, bliname) # Get method for the object API backend. blifuncname = Symbol("bli_", bliname) blifunc = getproperty(ObjectBackend, blifuncname) return quote $(esc(targetfunc))(α::$Tc1, x::StridedVector{<:$T1}, y::StridedVector{<:$T2}, A::StridedMatrix{<:$T3}) = begin bli_check_lv2(BLIS_NO_TRANSPOSE, size(A)..., length(y), length(x)) oα = BliObj(α) ox = BliObj(x) oy = BliObj(y) oA = BliObj(A) $blifunc(oα, ox, oy, oA) A end end end @doc """ ger!(α, x, y, A) BLIS-based GER with strides support & mixed-precision. """ ger! @blis_interface_linalg_lv2_ger(BliCompatibleType, BliCompatibleType, BliCompatibleType, BliCompatibleType, ger!, ger!) @blis_interface_linalg_lv2_ger Float32 Float32 Float32 Float32 ger! ger! @blis_interface_linalg_lv2_ger Float64 Float64 Float64 Float64 ger! ger! @blis_interface_linalg_lv2_ger ComplexF32 ComplexF32 ComplexF32 ComplexF32 ger! ger! @blis_interface_linalg_lv2_ger ComplexF64 ComplexF64 ComplexF64 ComplexF64 ger! ger! macro blis_interface_linalg_lv2_her(Tc1, T1, T2, targetfunc, bliname, bli_struc) # Get method for the object API backend. blifuncname = Symbol("bli_", bliname) blifunc = getproperty(ObjectBackend, blifuncname) return quote $(esc(targetfunc))(ul::AbstractChar, α::$Tc1, x::StridedVector{<:$T1}, A::StridedMatrix{<:$T2}) = begin bli_ul = char_to_uplo[ul] bli_check_lv2(BLIS_NO_TRANSPOSE, size(A)..., length(x), length(x)) oα = BliObj(α) ox = BliObj(x) oA = BliObj(A) ObjectBackend.bli_obj_set_uplo!(bli_ul, oA) ObjectBackend.bli_obj_set_struc!($bli_struc, oA.obj) $blifunc(oα, ox, oA) A end end end @doc """ her!(uplo, α, x, A) BLIS-based HER with strides support & mixed-precision. """ her! @blis_interface_linalg_lv2_her(BliCompatibleType, BliCompatibleType, BliCompatibleType, her!, her!, BLIS_HERMITIAN) @blis_interface_linalg_lv2_her Float32 Float32 Float32 her! her! BLIS_HERMITIAN @blis_interface_linalg_lv2_her Float64 Float64 Float64 her! her! BLIS_HERMITIAN @blis_interface_linalg_lv2_her Float32 ComplexF32 ComplexF32 her! her! BLIS_HERMITIAN @blis_interface_linalg_lv2_her Float64 ComplexF64 ComplexF64 her! her! BLIS_HERMITIAN @doc """ syr!(uplo, α, x, A) BLIS-based SYR with strides support & mixed-precision. """ syr! @blis_interface_linalg_lv2_her(BliCompatibleType, BliCompatibleType, BliCompatibleType, syr!, syr!, BLIS_SYMMETRIC) @blis_interface_linalg_lv2_her Float32 Float32 Float32 syr! syr! BLIS_SYMMETRIC @blis_interface_linalg_lv2_her Float64 Float64 Float64 syr! syr! BLIS_SYMMETRIC @blis_interface_linalg_lv2_her ComplexF32 ComplexF32 ComplexF32 syr! syr! BLIS_SYMMETRIC @blis_interface_linalg_lv2_her ComplexF64 ComplexF64 ComplexF64 syr! syr! BLIS_SYMMETRIC
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1.725754
6,465
@testset "shapes" begin inputsize = 20 poollength = 10 datalength = 100 x = rand(Float32, inputsize, poollength, 1, datalength) m = LSTnet(inputsize, 2, 3, poollength, 20) @test size(m(x)) == (1, datalength) if Flux.CUDA.functional() @test size(gpu(m)(gpu(x))) == (1, datalength) end FluxArchitectures.initialize_bias!(m) @test all(m.RecurLayer.chain.cell.b .== 1) @test all(m.RecurSkipLayer.cell.b .== 1) end @testset "ReluGRU" begin inputsize = 20 datalength = 100 x = rand(Float32, inputsize, datalength) m = FluxArchitectures.ReluGRU(inputsize, 10) @test size(m(x)) == (10, datalength) if Flux.CUDA.functional() @test size(gpu(m)(gpu(x))) == (10, datalength) end end @testset "misc" begin @test repr(LSTnet(10, 2, 3, 10, 20)) == "LSTnet(10, 2, 3, 10, 20)" @test repr(FluxArchitectures.ReluGRU(20, 10)) == "Recur(ReluGRUCell(20, 10))" end
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2.147126
435
using Test using .Stubs using DandelionWebSockets @testset "InProcessIO" begin @testset "Write 1-5 on endpoint 1; Can read 1-5 on endpoint 2" begin iopair = InProcessIOPair() readvalues = UInt8[] @sync begin @async begin for i = 1:5 write(iopair.endpoint1, UInt8(i)) end end @async begin for i = 1:5 x = read(iopair.endpoint2, UInt8) push!(readvalues, x) end end end @test readvalues == UInt8[1, 2, 3, 4, 5] end @testset "Write 1-5 on endpoint 1, and 100-110 on endpoint 2; Values can be read independently" begin iopair = InProcessIOPair() readon1 = UInt8[] readon2 = UInt8[] @sync begin @async begin for i = 1:5 write(iopair.endpoint1, UInt8(i)) end close(iopair.endpoint1) end @async begin for i = 100:110 write(iopair.endpoint2, UInt8(i)) end close(iopair.endpoint2) end @async begin try while true x = read(iopair.endpoint1, UInt8) push!(readon1, x) end catch ex if typeof(ex) != EOFError rethrow() end end end @async begin try while true x = read(iopair.endpoint2, UInt8) push!(readon2, x) end catch ex if typeof(ex) != EOFError rethrow() end end end end @test readon1 == UInt8[100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110] @test readon2 == UInt8[1, 2, 3, 4, 5] end @testset "Send 5 values, and then close endpoint 1; EOF is reported on endpoint 2 after 5 values" begin iopair = InProcessIOPair() readvalues = [] for i = 1:5 write(iopair.endpoint1, UInt8(i)) end close(iopair.endpoint1) for i = 1:5 x = read(iopair.endpoint2, UInt8) push!(readvalues, x) end @test readvalues == UInt8[1, 2, 3, 4, 5] @test eof(iopair.endpoint2) end @testset "Send 5 values, and then close endpoint 1; EOF is not reported on endpoint 2 until after 5 values have been read" begin iopair = InProcessIOPair() for i = 1:5 write(iopair.endpoint1, UInt8(1)) end close(iopair.endpoint1) for i = 1:5 @test !eof(iopair.endpoint2) read(iopair.endpoint2, UInt8) end end @testset "Send a frame on endpoint 1; Received a frame on endpoint 2" begin iopair = InProcessIOPair() sentframe = Stubs.createserverframe(DandelionWebSockets.OPCODE_TEXT, b"Hello") write(iopair.endpoint1, sentframe) receivedframe = read(iopair.endpoint2, DandelionWebSockets.Frame) @test sentframe.payload == receivedframe.payload end @testset "Read from empty stream on endpoint 1 and close endpoint 2; Read throws exception" begin eofthrown = false iopair = InProcessIOPair() @sync begin @async begin try read(iopair.endpoint1, UInt64) catch ex if typeof(ex) == EOFError eofthrown = true else rethrow() end end end @async begin sleep(0.5) close(iopair.endpoint2) end end @test eofthrown end end
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198, 437 ]
1.711205
2,365
#MDDatasets DataRS (Recursive Sweep) defninitions #------------------------------------------------------------------------------- #==Main types ===============================================================================# #Linked-list representation of multi-dimensional datasets: #------------------------------------------------------------------------------- mutable struct DataRS{T} <: DataMD sweep::PSweep elem::Vector{T} function DataRS{T}(sweep::PSweep, elem::Vector{T}) where {T} if !elemallowed(DataRS, eltype(elem)) msg = "Can only create DataRS{T} for T ∈ {DataRS, DataF1, DataFloat, DataInt, DataComplex}" throw(ArgumentError(msg)) elseif length(sweep) != length(elem) throw(ArgumentError("sweep length does not match number of elem")) end return new(sweep, elem) end end #Shorthand (because default (non-parameterized) constructor was overwritten): DataRS(sweep::PSweep, elem::Vector{T}) where T = DataRS{T}(sweep, elem) elemallowed(::Type{DataRS}, t::Type{T}) where T = elemallowed(DataMD, t) #Allow basic types elemallowed(::Type{DataRS}, ::Type{DataRS}) = true #Also allow recursive structures #Generate empty DataRS structure: (::Type{DataRS{T}})(sweep::PSweep) where T = DataRS{T}(sweep, Array{T}(undef, length(sweep))) #==Type promotions ===============================================================================# Base.promote_rule(::Type{T1}, ::Type{T2}) where {T1<:DataRS, T2<:Number} = DataRS #==Accessor functions ===============================================================================# Base.eltype(d::DataRS{T}) where T = T Base.length(d::DataRS) = length(d.elem) #==Help with construction ===============================================================================# #Implement "fill(DataRS, ...) do sweepval" syntax: function Base.fill!(fn::Function, d::DataRS) for i in 1:length(d.sweep) d.elem[i] = fn(d.sweep.v[i]) end return d end Base.fill(fn::Function, ::Type{DataRS{T}}, sweep::PSweep) where T = fill!(fn, DataRS(sweep, Array{T}(undef, length(sweep)))) Base.fill(fn::Function, ::Type{DataRS}, sweep::PSweep) = fill(fn, DataRS{DataRS}, sweep) #==Data generation ===============================================================================# function _ensuresweepunique(d::DataRS, sweepid::String) if sweepid == d.sweep.id msg = "Sweep occurs multiple times in DataRS: $sweepid" throw(ArgumentError(msg)) end end #Define "parameter". #(Generates a DataRS object containing the value of a given swept parameter) #------------------------------------------------------------------------------- #Deal with non-leaf elements, once the sweep value is found: function _parameter(d::DataRS{DataRS}, sweepid::String, sweepval::T) where T _ensuresweepunique(d, sweepid) elem = DataRS[_parameter(d.elem[i], sweepid, sweepval) for i in 1:length(d.sweep)] return DataRS(d.sweep, elem) end #Deal with leaf elements, once the sweep value is found: function _parameter(d::DataRS, sweepid::String, sweepval::T) where T _ensuresweepunique(d, sweepid) elem = T[sweepval for i in 1:length(d.sweep)] return DataRS(d.sweep, elem) end #Main "parameter" algorithm (non-leaf elements): function parameter(d::DataRS{DataRS}, sweepid::String) if sweepid == d.sweep.id #Sweep found elem = DataRS[_parameter(d.elem[i], sweepid, d.sweep.v[i]) for i in 1:length(d.sweep)] else elem = DataRS[parameter(d.elem[i], sweepid) for i in 1:length(d.sweep)] end return DataRS(d.sweep, elem) end #Main "parameter" algorithm (leaf elements): function parameter(d::DataRS, sweepid::String) T = eltype(d.sweep.v) if sweepid == d.sweep.id #Sweep found return DataRS(d.sweep, d.sweep.v) else msg = "Sweep not found in DataRS: $sweepid" throw(ArgumentError(msg)) end end #Generate DataRS from DataHR. #------------------------------------------------------------------------------- function _buildDataRS(d::DataHR, firstinds::Vector{Int}) curidx = length(firstinds) + 1 sweep = d.sweeps[curidx] if curidx < length(d.sweeps) result = DataRS{DataRS}(sweep) for i in 1:length(sweep.v) result.elem[i] = _buildDataRS(d, vcat(firstinds, i)) end else #Last index. Copy data over: result = DataRS{eltype(d.elem)}(sweep) for i in 1:length(sweep.v) result.elem[i] = d.elem[firstinds..., i] end end return result end function DataRS(d::DataHR) return _buildDataRS(d, Int[]) end #==User-friendly show functions ===============================================================================# #Print leaf element: function printDataRSelem(io::IO, ds::DataRS, idx::Int, indent::String) if isassigned(ds.elem, idx) println(io, ds.elem[idx]) else println(io, indent, "UNDEFINED") end end #Print next level of recursive DataRS: function printDataRSelem(io::IO, ds::DataRS{T}, idx::Int, indent::String) where T<:DataRS println(io) if isassigned(ds.elem, idx) printDataRS(io, ds.elem[idx], indent) else println(io, indent, "UNDEFINED") end end #Print DataRS structure: function printDataRS(io::IO, ds::DataRS, indent::String) for i in 1:length(ds.elem) print(io, "$indent", ds.sweep.id, "=", ds.sweep.v[i], ": ") printDataRSelem(io, ds, i, "$indent ") end end function Base.show(io::IO, ds::DataRS) print(io, "DataRS[\n") printDataRS(io, ds, " ") print(io, "]\n") end
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""" reduce_axes(a, dims) Returns the appropriate axes for a measure that reduces dimensions along the dimensions `dims`. ## Example ```jldoctest julia> reduce_axes((Index{:a}(1:4), Index{:b}(1:4)), 2) (Index{a}(1:4 => Base.OneTo(4)), Index{b}(1:1 => Base.OneTo(1))) julia> reduce_axes((Index{:a}(1:4), Index{:b}(1:4)), :a) (Index{a}(1:1 => Base.OneTo(1)), Index{b}(1:4 => Base.OneTo(4))) ``` """ reduce_axes(x::AbstractArray, dims) = reduce_axes(axes(x), dims) reduce_axes(x::Tuple, dims) = _reduce_axes(x, to_dims(x, dims)) reduce_axes(x::Tuple, dims::Colon) = () _reduce_axes(x::Tuple{Vararg{Any,D}}, dims::Int) where {D} = _reduce_axes(x, (dims,)) function _reduce_axes(x::Tuple{Vararg{Any,D}}, dims::Tuple{Vararg{Int}}) where {D} Tuple(map(i -> ifelse(in(i, dims), reduce_axis(x[i]), x[i]), 1:D)) end """ reduce_axis(a) Reduces axis `a` to single value. Allows custom index types to have custom behavior throughout reduction methods (e.g., sum, prod, etc.) See also: [`reduce_axes`](@ref) ## Example ```jldoctest julia> reduce_axis(Index{:a}(1:4)) Index{a}(1:1 => Base.OneTo(1)) julia> reduce_axis(1:4) 1:1 ``` """ function reduce_axis(x::AbstractIndex) if isempty(x) error("Cannot reduce empty index.") else return unsafe_reindex(x, 1:1) end end reduce_axis(x::OneTo{T}) where {T} = OneTo(one(T)) reduce_axis(x::OneToSRange{T}) where {T} = OneToSRange(one(T)) reduce_axis(x::OneToMRange{T}) where {T} = OneToMRange(one(T)) reduce_axis(x::UnitRange{T}) where {T} = UnitRange{T}(one(T), one(T)) reduce_axis(x::UnitSRange{T}) where {T} = UnitSRange{T}(one(T), one(T)) reduce_axis(x::UnitMRange{T}) where {T} = UnitMRange{T}(one(T), one(T))
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@testset "Postprocessing" begin @test LatexSVG._adjust_web_svg_dim("16.180339pt") == "1.618034rem" @test begin svg_test = """ <?xml version="1.0" encoding="utf-8"?> <svg width="21.923673pt" height="8.966376pt" viewBox="67.695921 70.081196 21.923673 8.966376"> <defs/> </svg> """ svg_adjusted = LatexSVG._adjust_web_svg_display(svg_test) svg_adjusted == """ <?xml version="1.0" encoding="UTF-8"?> <svg height="0.896638rem" viewBox="67.695921 70.081196 21.923673 8.966376" class="latexsvg-display" style="display: block; margin: auto; max-width: 80%"> <defs/> </svg> """ end @test begin svg_test = """ <?xml version="1.0" encoding="utf-8"?> <svg width="21.923673pt" height="8.966376pt" viewBox="67.695921 70.081196 21.923673 8.966376"> <defs/> </svg> """ svg_adjusted = LatexSVG._adjust_web_svg_inline(svg_test) svg_adjusted == """ <?xml version="1.0" encoding="UTF-8"?> <svg height="0.896638rem" viewBox="67.695921 70.081196 21.923673 8.966376" class="latexsvg-inline" style="vertical-align: middle"> <defs/> </svg> """ end end
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## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-information-about-the-users-current-playback """ player_get_state(;additional_types::String="track", market::String="US") **Summary**: Get information about the user’s current playback state, including track or episode, progress, and active device. # Optional keywords - `additional_types::String` : "track" (default) or "episode" - `market::String` : An ISO 3166-1 alpha-2 country code. If a country code is specified, only episodes that are available in that market will be returned. Default is set to "US". # Example ```julia-repl julia> Spotify.player_get_state()[1] [ Info: We try the request without checking if current grant includes scope user-read-playback-state. JSON3.Object{Base.CodeUnits{UInt8, String}, Vector{UInt64}} with 10 entries: :device => {… :shuffle_state => false :repeat_state => "off" :timestamp => 1636493367689 :context => {… :progress_ms => 66454 :item => {… :currently_playing_type => "track" :actions => {… :is_playing => true ``` """ function player_get_state(;additional_types::String="track", market::String="US") url = "me/player?additional_types=$additional_types&market=$market" return Spotify.spotify_request(url; scope = "user-read-playback-state") end ## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-a-users-available-devices """ player_get_devices() **Summary**: Get information about a user’s available devices. # Example ```julia-repl julia> Spotify.player_get_devices()[1]["devices"] 2-element JSON3.Array{JSON3.Object, Base.CodeUnits{UInt8, String}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}}: { "id": "your_device_id", "is_active": false, "is_private_session": false, "is_restricted": false, "name": "Web Player (Chrome)", "type": "Computer", "volume_percent": 100 } ``` """ function player_get_devices() return Spotify.spotify_request("me/player/devices"; scope = "user-read-playback-state") end ## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-the-users-currently-playing-track """ player_get_current_track(;additional_types::String="track", market::String="US") **Summary**: Get the object currently being played on the user's Spotify account. # Optional keywords - `additional_types::String` : "track" (default) or "episode" - `market::String` : An ISO 3166-1 alpha-2 country code. If a country code is specified, only episodes that are available in that market will be returned. Default is set to "US". # Example ```julia-repl julia> Spotify.player_get_current_track()[1] [ Info: We try the request without checking if current grant includes scope user-read-playback-state. JSON3.Object{Base.CodeUnits{UInt8, String}, Vector{UInt64}} with 7 entries: :timestamp => 1636491068506 :context => {… :progress_ms => 5265 :item => {… :currently_playing_type => "track" :actions => {… :is_playing => true ``` """ function player_get_current_track(;additional_types::String="track", market::String="US") url = "me/player/currently-playing?additional_types=$additional_types&market=$market" return Spotify.spotify_request(url; scope = "user-read-playback-state") end ## https://developer.spotify.com/documentation/web-api/reference/#/operations/get-recently-played """ player_get_recent_tracks(;duration::Int64=1, limit::Int64=20) **Summary**: Get current user's recently played tracks. # Optional keywords - `duration::Int64` : Number of days to look in the past, default is set to 1 - `limit::Int64` : Maximum number of items to return, default is set to 20 # Example ```julia-repl julia> Spotify.player_get_recent_tracks()[1] [ Info: We try the request without checking if current grant includes scope user-read-recently-played. JSON3.Object{Base.CodeUnits{UInt8, String}, Vector{UInt64}} with 5 entries: :items => JSON3.Object[{… :next => "https://api.spotify.com/v1/me/player/recently-played?after=1636123644988&limit=20" :cursors => {… :limit => 20 :href => "https://api.spotify.com/v1/me/player/recently-played?after=1636410050&limit=20" ``` """ function player_get_recent_tracks(;duration::Int64=1, limit::Int64=20) # Subtract duration from current date and convert to Int64 starting_date = Dates.datetime2unix(Dates.now() - Dates.Day(duration)) after = round(Int64, starting_date) url = "me/player/recently-played?after=$after&limit=$limit" return Spotify.spotify_request(url; scope = "user-read-recently-played") end
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using Test using Covers cc=Cover(4,8) cc(1,1,true) cc(2,2,true) @test all(cc .^ Bool[1 1 1 1 1 1 1 1; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0]) @test all(ncovered(cc) .== [1 2 1 1 1 1 1 1; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0]) @test all(multicovered(cc) .== Bool[0 1 0 0 0 0 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0]) @test cc[9] && (!cc[10]) cc[9] =false @test all(cc .^ Bool[0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0]) cc[10] =true @test all(cc .^ Bool[0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0]) cc[9] =true @test all(cc .^ Bool[0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0]) cc[10] =false @test all(cc .^ Bool[0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0]) cc=Cover(4,8) cc(1,1,true) cc(2,2,true) @test cc[1,3] && (!cc[2,3]) cc[1,3] =false @test all(cc .^ Bool[0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0; 0 1 0 0 0 0 0 0]) cc[2,3] =true @test all(cc .^ Bool[0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0; 0 1 1 0 0 0 0 0]) cc.=false @test all(cc .^ Bool[0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 0])
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abstract type AbstractProblem end struct CellProblemAdvecTemp{MeshType,TbfType,TypeK,TypeS,VType,BcondType,LaplacianStruct,AdvectionStruct} <: AbstractProblem Tc::Vector{Float64} k::TypeK s::TypeS ρC::Float64 u::VType bcond::BcondType mesh::MeshType laplacian!::LaplacianStruct advection!::AdvectionStruct ∇Tc::Vector{Vec2D{Float64}} Tbf::TbfType end function CellProblemAdvecTemp(Tc,mesh,d) bcond = boundary_conditions(d) k = ConstVec{d[:conductivity]}() s = ConstVec{d[:source]}() ρC = d[Symbol("rho*C")] uvec = ConstVec{d[:velocity]}() ∇Tc = zeros(Vec2D{Float64},length(Tc)) Tbf = FieldAtBoundary(Tc,mesh,bcond) advection = get_advection_method(d) laplacian = get_laplacian_method(Tc,bcond,k,mesh,d) types = typeof.((mesh,Tbf,k,s,uvec,bcond,laplacian,advection)) return CellProblemAdvecTemp{types...}(Tc,k,s,ρC,uvec,bcond,mesh,laplacian,advection,∇Tc,Tbf) end @inline needs_gradient_calculation(p) = (typeof(p.advection!) === UpWind2ndOrder) && (typeof(p.laplacian!) === MethodB) && (typeof(p.u) <: ConstVec && p.u[1] == zero(eltype(p.u))) function calculate_rhs!(rhs,p::CellProblemAdvecTemp) fill!(rhs,zero(eltype(rhs))) is_implicit(p.laplacian!) || p.laplacian!(rhs,p) needs_gradient_calculation(p) && gradient!(p.∇Tc,FaceSimpleInterpolation(p.Tc,p.mesh),p.Tbf,p.mesh.f2cloops) if !(typeof(p.u) <: ConstVec && p.u[1] == zero(eltype(p.u))) p.advection!(rhs,p) end if !(typeof(p.s) <: ConstVec && p.s[1] == zero(eltype(p.s))) add_source!(rhs,p) end is_implicit(p.laplacian!) && p.laplacian!(rhs,p) return nothing end function add_source!(rhs,p) s = p.s @inbounds @simd for i=1:length(rhs) rhs[i] += s[i] end end struct StokesProblem{UfType,UbfType,MeshType,uBcondType,LaplacianStruct,Ma,PMa,PMap} <: AbstractProblem u::Vec2DArray{Float64} u_old::Vec2DArray{Float64} uf::UfType ubf::UbfType δu::Vec2DArray{Float64} ru::Vec2DArray{Float64} p::Vector{Float64} δp::Vector{Float64} ν::ConstVec{Float64} ρ::ConstVec{Float64} bcond::uBcondType mesh::MeshType laplacian!::LaplacianStruct ∇u::Vector{FiniteVolumeMesh.Ten2D{Float64}} A::Ma pcgA::PMa s::Vector{Float64} Ap::PoissonP{MeshType} pcgAp::PMap dt::Float64 end function StokesProblem(u,mesh,d) u_old = similar(u) uf = FaceSimpleInterpolation(u,mesh) bcond = uboundary_conditions(d) ubf = FieldAtBoundary(u,mesh,bcond) δu = similar(u) ru = similar(u) ν = ConstVec(d[:viscosity]) ρ = ConstVec(d[:density]) ∇u = zeros(Ten2D{Float64},length(u)) p = zeros(length(u)) δp = zeros(length(u)) laplacian = get_laplacian_method(u,bcond,ν,mesh,d) A = aIpDbG(d,mesh) pcgA = PCG(δu) pcgAp = PCG(δp) types = typeof.((uf,ubf,mesh,bcond,laplacian,A,pcgA,pcgAp)) s = zeros(length(mesh.cells)) Ap = PoissonP(d,mesh) dt = d[:dt] return StokesProblem{types...}(u,u_old,uf,ubf,δu,ru,p,δp,ν,ρ,bcond,mesh,laplacian,∇u,A,pcgA,s,Ap,pcgAp,dt) end struct NSProblem{UfType,UbfType,MeshType,uBcondType,LaplacianStruct,AdvecStruct,Ma,PMa,PMap,TBcondType,TLaplacianStruct,TAdvectionStruct,TbfType,MaT,PMaT} <: AbstractProblem u::Vec2DArray{Float64} uf::UfType ubf::UbfType δu::Vec2DArray{Float64} ru::Vec2DArray{Float64} p::Vector{Float64} δp::Vector{Float64} ν::ConstVec{Float64} ρ::ConstVec{Float64} bcond::uBcondType mesh::MeshType laplacian!::LaplacianStruct uadvection!::AdvecStruct ∇u::Vector{FiniteVolumeMesh.Ten2D{Float64}} A::Ma pcgA::PMa sp::Vector{Float64} Ap::PoissonP{MeshType} pcgAp::PMap dt::Float64 Tc::Vector{Float64} ∇Tc::Vec2DArray{Float64} rT::Vector{Float64} δT::Vector{Float64} k::ConstVec{Float64} s::ConstVec{Float64} ρC::Float64 Tbcond::TBcondType Tlaplacian!::TLaplacianStruct Tadvection!::TAdvectionStruct Tbf::TbfType AT::MaT pcgAT::PMaT end function NSProblem(u,mesh,d) uf = FaceSimpleInterpolation(u,mesh) bcond = uboundary_conditions(d) ubf = FieldAtBoundary(u,mesh,bcond) δu = Vec2DArray{Float64}(length(u)) ru = Vec2DArray{Float64}(length(u)) ν = ConstVec(d[:viscosity]) ρ = ConstVec(d[:density]) ∇u = zeros(Ten2D{Float64},length(u)) p = zeros(length(u)) δp = zeros(length(u)) laplacian = get_laplacian_method(u,bcond,ConstVec{Float64}(0.5*ν[1]),mesh,d) A = aIpDbG(ConstVec(d[:density]/d[:dt]),-0.5*ν[1],d,mesh) pcgA = PCG(δu,KrylovCtrl("vPCG.set")) pcgAp = PCG(δp,KrylovCtrl("pPCG.set")) uadvection = UpWindAdvection(u,ubf,u,ubf,mesh) sp = zeros(length(mesh.cells)) Ap = PoissonP(d,mesh) dt = d[:dt] Tc = zeros(Float64,length(mesh.cells)) ∇Tc = Vec2DArray{Float64}(length(u)) rT = zeros(Float64,length(mesh.cells)) dT = zeros(Float64,length(mesh.cells)) Tbcond = boundary_conditions(d) k = ConstVec{Float64}(d[:conductivity]) s = ConstVec{Float64}(d[:source]) ρC = d[Symbol("rho*C")] Tbf = FieldAtBoundary(Tc,mesh,Tbcond) Tadvection = UpWindAdvection(Tc,Tbf,u,ubf,mesh) Tlaplacian = get_laplacian_method(Tc,Tbcond,ConstVec{Float64}(0.5*k[1]),mesh,d) AT = aIpDbG(ConstVec{Float64}(ρC/dt),-0.5*k[1],d,mesh) pcgAT = PCG(dT,KrylovCtrl("tPCG.set")) types = typeof.((uf,ubf,mesh,bcond,laplacian,uadvection,A,pcgA,pcgAp,Tbcond,Tlaplacian,Tadvection,Tbf,AT,pcgAT)) return NSProblem{types...}(u,uf,ubf,δu,ru,p,δp,ν,ρ,bcond,mesh,laplacian,uadvection,∇u,A,pcgA,sp,Ap,pcgAp,dt,Tc,∇Tc,rT,dT,k,s,ρC,Tbcond,Tlaplacian,Tadvection,Tbf,AT,pcgAT) end
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1.912357
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__precompile__() module UnitFloat import Base: +, -, *, bits, convert, exponent, isfinite, isinf, isless, one, show, signbit, significand, typemax, typemin, zero export UFloat """ UFloat The 64-bit type storing the *fraction* and the *exponent* of the unit-interval floating point number. ``` |-----------------------------------------|----------------------| | Exponent (41 bits) | Fraction (23 bits) | |-----------------------------------------|----------------------| bit no |63 23|22 0| ``` """ primitive type UFloat 64 end # TODO can we extend AbstractFloat, like BigFloat? """ _UFloat Construct a `UFloat` given the exponent and the fraction. The exponent must be strictly negative and >= `-2^41`. Only the last 23 bits of the given fraction are used. """ @inline function _UFloat(exponent::Int64, fraction::Float32) bits = UInt64(reinterpret(UInt32, fraction)) & 0x00000000007fffff # ~UInt(0) >> 41 bits |= ~reinterpret(UInt64, -exponent) << 23 reinterpret(UFloat, bits) end @inline _is_zero(x) = x == zero(typeof(x)) @inline _is_one(x) = x == one(typeof(x)) @inline zero(::Type{UFloat}) = reinterpret(UFloat, 0x0000000000000000) @inline one(::Type{UFloat}) = reinterpret(UFloat, 0xffffffffff800000) zero(::UFloat) = zero(UFloat) one(::UFloat) = one(UFloat) typemin(::Type{UFloat}) = zero(UFloat) typemax(::Type{UFloat}) = one(UFloat) typemin(::UFloat) = zero(UFloat) typemax(::UFloat) = one(UFloat) isinf(::UFloat) = false isfinite(::UFloat) = true bits(u::UFloat) = bits(reinterpret(UInt64, u)) signbit(u::UFloat) = false @inline function _exponent_unsafe(u::UFloat) # gives incorrect results for zero(UFloat) and one(UFloat) -reinterpret(Int64, ~reinterpret(UInt64, u) >> 23) end @inline function _exponent_unsafe(f::Float32) Int64((reinterpret(UInt32, f) & 0x7f800000) >> 23) - 127 end @inline function _exponent_unsafe(f::Float64) Int64((reinterpret(UInt64, f) & 0x7ff0000000000000) >> 52) - 1023 end function exponent(u::UFloat) _is_zero(u) && throw(DomainError()) _exponent_unsafe(u) end @inline function _significand_unsafe(u::UFloat) # incorrect for zero(UFloat) and one(UFloat) bits = UInt32((reinterpret(UInt64, u) & 0x00000000007fffff)) # zero first 41 bits bits |= 0x3f800000 # add zero exponent to Float32 reinterpret(Float32, bits) end @inline function _significand_unsafe(f::Float32) reinterpret(Float32, (reinterpret(UInt32, f) & 0x007fffff) | 0x3f800000) end @inline function _significand_unsafe(f::Float64) _significand_unsafe(Float32(f)) end function significand(u::UFloat) _is_zero(u) && return 0.0f0 _is_one(u) && return 1.0f0 _significand_unsafe(u) end function convert(::Type{UFloat}, f::F) where {F<:AbstractFloat} f < zero(F) && throw(InexactError()) f == zero(F) && return zero(UFloat) f == one(F) && return one(UFloat) # one special case because exponent==0 exp, frac = exponent(f), significand(f) exp >= 0 && throw(InexactError()) _UFloat(exp, Float32(frac)) end function convert(::Type{F}, u::UFloat) where {F<:AbstractFloat} _is_zero(u) && return zero(F) _is_one(u) && return one(F) frac = _significand_unsafe(u) exp = _exponent_unsafe(u) ldexp(F(frac), exp) end convert(::Type{UFloat}, b::Bool) = b ? one(UFloat) : zero(UFloat) convert(::Type{Bool}, u::UFloat) = u == one(UFloat) ############################################################################### function _multiply(a, b)::UFloat sa = _significand_unsafe(a) sb = _significand_unsafe(b) ea = _exponent_unsafe(a) eb = _exponent_unsafe(b) sn = sa * sb en = ea + eb + _exponent_unsafe(sn) # cannot be <= 1<<41 en > -2199023255552 ? _UFloat(en, sn) : zero(UFloat) end @inline function _add(a, b)::UFloat _is_zero(a) && return UFloat(b) _is_zero(b) && return UFloat(a) sa = _significand_unsafe(a) sb = _significand_unsafe(b) ea = _exponent_unsafe(a) eb = _exponent_unsafe(b) ed = ea - eb sn = sb + ldexp(sa, ed) en = eb + _exponent_unsafe(sn) en >= 0 ? one(UFloat) : _UFloat(en, sn) end +(a::UFloat, b::UFloat) = _add(a, b) *(a::UFloat, b::UFloat) = _multiply(a, b) for T in [Float32, Float64], (op, fn) in [(:+, :_add), (:*, :_multiply)] @eval begin ($op)(a::UFloat, b::$T) = ($fn)(a, b) ($op)(a::$T, b::UFloat) = ($fn)(a, b) end end ############################################################################### @inline function _isless(a, b)::Bool a == one(typeof(a)) && return false b == one(typeof(b)) && return true # assumed a ≠ 1.0 b == zero(typeof(b)) && return false a == zero(typeof(a)) && return true # assumed b ≠ 0.0 ea = _exponent_unsafe(a) eb = _exponent_unsafe(b) ea < eb && return true ea > eb && return false # ea == eb sa = _significand_unsafe(a) sb = _significand_unsafe(b) sa < sb end isless(a::UFloat, b::UFloat) = _isless(a, b) begin local T = [UFloat, Float32, Float64] for T1 in T, T2 in T if T1 != T2 @eval begin isless(a::$T1, b::$T2) = _isless(a, b) end end end end ############################################################################### function show(io::IO, u::UFloat) if _is_zero(u) print(io, "0.0uf") elseif _is_one(u) print(io, "1.0uf") else # represent the number as a×10^b, with b int e2 = exponent(u) e10 = e2 * log10(2.0) e10i = round(e10) e10r = e10 - e10i s = Float32(significand(u) * exp10(e10r)) while round(s, 5) >= 10.0f0 s /= 10.0f0; e10i += 1 end while round(s, 5) < 1.0f0 s *= 10.0f0; e10i -= 1 end @printf(io, "%.5fuf%d", s, e10i) end end end # module
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2.241405
2,676
using Test, PlotReferenceImages @testset "PlotReferenceImages" begin @test !isdir(reference_path(:notabackend, v"1")) @test isfile(reference_file(:gr, 1, v"1")) end
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struct DisjointPartitioner{Mtd, Mtd′} mtd::Mtd mtd′::Mtd′ end function partition_plaid(A::SparseMatrixCSC, K, method::DisjointPartitioner; kwargs...) Φ = partition_stripe(A, K, method.mtd; kwargs...) Π = partition_stripe(PermutedDimsArray(A, (2, 1)), K, method.mtd′, Φ; kwargs...) return (Π, Φ) end struct AlternatingPartitioner{Mtds} mtds::Mtds end AlternatingPartitioner(mtds...) = AlternatingPartitioner{typeof(mtds)}(mtds) function partition_plaid(A::SparseMatrixCSC, K, method::AlternatingPartitioner; adj_A = nothing, kwargs...) if adj_A === nothing adj_A = adjointpattern(A) end Φ = partition_stripe(A, K, method.mtds[1]; adj_A=adj_A, kwargs...) Π = partition_stripe(adj_A, K, method.mtds[2], Φ; adj_A=A, kwargs...) for (i, mtd) in enumerate(method.mtds[3:end]) if isodd(i) Φ = partition_stripe(A, K, mtd, Π; adj_A=adj_A, kwargs...) else Π = partition_stripe(adj_A, K, mtd, Φ; adj_A=A, kwargs...) end end return (Π, Φ) end struct AlternatingNetPartitioner{Hint, Mtds} hint::Hint mtds::Mtds end AlternatingNetPartitioner(mtds...) = AlternatingNetPartitioner{NoHint, typeof(mtds)}(NoHint(), mtds) AlternatingNetPartitioner(hint::Hint, mtds...) where {Hint<:AbstractHint} = AlternatingNetPartitioner{Hint, typeof(mtds)}(hint, mtds) function partition_plaid(A::SparseMatrixCSC, K, method::AlternatingNetPartitioner; adj_A = nothing, net = nothing, kwargs...) if adj_A === nothing adj_A = adjointpattern(A) end if net === nothing net = netcount(method.hint, A; kwargs...) end Φ = partition_stripe(A, K, method.mtds[1]; net=net, adj_A=adj_A, kwargs...) Π = partition_stripe(adj_A, K, method.mtds[2], Φ; adj_A=A, kwargs...) for (i, mtd) in enumerate(method.mtds[3:end]) if isodd(i) Φ = partition_stripe(A, K, mtd, Π; net=net, adj_A=adj_A, kwargs...) else Π = partition_stripe(adj_A, K, mtd, Φ; adj_A=A, adj_net = net, kwargs...) end end return (Π, Φ) end struct SymmetricPartitioner{Mtds} mtds::Mtds function SymmetricPartitioner{Mtds}(mtds::Mtds) where {Mtds} return new{Mtds}(mtds) end end SymmetricPartitioner(mtd) = SymmetricPartitioner{Tuple{typeof(mtd)}}((mtd,)) SymmetricPartitioner(mtds...) = SymmetricPartitioner{typeof(mtds)}(mtds) function partition_plaid(A::SparseMatrixCSC, K, method::SymmetricPartitioner; adj_A = nothing, kwargs...) if length(method.mtds) > 1 if adj_A === nothing adj_A = adjointpattern(A) end Π = partition_stripe(A, K, method.mtds[1]; adj_A=adj_A, kwargs...) for (i, mtd) in enumerate(method.mtds[2:end]) if isodd(i) Π = partition_stripe(A, K, mtd, Π; adj_A=adj_A, kwargs...) else Π = partition_stripe(adj_A, K, mtd, Π; adj_A=A, kwargs...) end end else Π = partition_stripe(A, K, method.mtds[1]; kwargs...) end return (Π, Π) end
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using CSV, DataFrames, Plots, StatsPlots export plot_trade_union_membership, plot_GDP_per_capita, plot_maddison_GDP_data export plot_GDP export north_vs_south_countries, anglo_germanic_countries export plot_relative_GDP ENV["GKS_ENCODING"]="utf8" # Fix some annoying warning when plotting # Find names of colors here: http://juliagraphics.github.io/Colors.jl/stable/namedcolors/ function plot_trade_union_membership() path = joinpath(datadir, "trade-union-membership.csv") unions = CSV.File(path) |> DataFrame unions = select(unions, :Country, :Time, :Value) country_unions = groupby(unions, :Country) country_names = ["Norway", "Sweden", "Denmark", "Finland", "United Kingdom", "United States", "Canada"] colors = [:purple, :blue, :cyan, :green, :orange, :red, :brown] plot() for (i, cname) in enumerate(country_names) country = country_unions[(Country=cname,)] @df country plot!(:Time, :Value, label=cname, lw=2, seriescolor=colors[i]) end plot!(minorgrid=true, # turn on minor grid lines gridalpha=0.3, # Make major grid lines a bit more visible legend=:outertopleft # Place box with legends outside of plot on top left ) end function plot_GDP_per_capita() gdp = load_GDP_per_capita() p = @df gdp plot( :year, [:norway, :sweden, :denmark, :finland, :uk, :usa], lw=2, minorgrid=true, gridalpha=0.3, legend=:bottomright, label=["norway" "sweden" "denmark" "finland" "uk" "usa"], seriescolor=[:magenta4 :mediumblue :deeppink2 :purple4 :lime :green] # seriescolor=[:aqua :teal :dodgerblue :blue :gold :orange :orangered3] ) # p = plot(gdp.year, # x-axis # [gdp.norway, gdp.sweden, gdp.finland, gdp.denmark, gdp.uk, gdp.usa, gdp.canada], # lw=2, # line width # legend=:bottomright, # where to put legend # label=["norway" "sweden" "finland" "denmark" "uk" "usa" "canada"] # labels in legend # ) # savefig(p, "nordic-GDP-growth-from-60s.svg") end function plot_maddison_GDP_data() gdp = load_maddison_GDP_data() country_gdp = groupby(gdp, :Country) plot_GDP(country_gdp) end # This is a smarter way of doing the plotting function simpler_maddison_GDP_plot() gdp = load_maddison_GDP_data() countries = DataFrame(Country=["Norway", "Sweden", "Denmark", "Finland", "United Kingdom", "United States"]) # Like a set intersection. Match on :Country column sample = innerjoin(gdp, countries, on=:Country) # Filter out irrelevant years sample = filter(row -> 1950 < row.Year < 2020, sample) # Creates a separate series for every unique value in value provided to # group property. Every row with that group value gets assigned to the same series plot(sample.Year, sample.GDP, group=sample.Country) # You can also write this as: @df sample plot(:Year, :GDP, group=:Country) end const north_vs_south_countries = [ "Norway" => :purple, "Sweden" => :cyan, "Finland" => :blue, "Portugal" => :goldenrod2, "Argentina" => :orange, "Spain" => :brown, "Italy" => :red, "Venezuela" => :orangered3, ] const anglo_germanic_countries = [ "Denmark" => :purple, "Germany" => :cyan, "Austria" => :blue, "United Kingdom" => :goldenrod2, "United States" => :orange, "Canada" => :brown, "Italy" => :red, "France" => :orangered3, ] """ plot_GDP(country_gdp::GroupedDataFrame, range) Plots GDP data for the year `range` given. """ function plot_GDP(country_gdp::GroupedDataFrame, range = 1800:2020, countries = north_vs_south_countries) plot() for (cname, color) in countries country = country_gdp[(Country=cname,)] country = filter(row -> row.Year in range, country) @df country plot!(:Year, :GDP, label=cname, lw=2, seriescolor=color) # @df country plot!(:Year, :GDP, label=cname, lw=1) end plot!(minorgrid=true, # turn on minor grid lines gridalpha=0.3, # Make major grid lines a bit more visible # legend=:outertopleft # Place box with legends outside of plot on top left legend=:topleft ) end function plot_relative_GDP(base_country::DataFrame, country_gdp::GroupedDataFrame, range = 1900:2020, countries = anglo_germanic_countries) bcountry = base_country = filter(row -> row.Year in range, base_country) plot(ylims = (40, 130)) for (cname, color) in countries country = country_gdp[(Country=cname,)] country = filter(row -> row.Year in range, country) country.rGDP = 100*(country.GDP ./ bcountry.GDP) @df country plot!(:Year, :rGDP, label=cname, lw=2, seriescolor=color) # @df country plot!(:Year, :GDP, label=cname, lw=1) end plot!(minorgrid=true, # turn on minor grid lines gridalpha=0.3, # Make major grid lines a bit more visible # legend=:outertopleft # Place box with legends outside of plot on top left legend=:topleft ) end function plot_cultural_diff() path = joinpath(datadir, "geerthofstede-2015-08-16.csv") cultural = CSV.File(path) |> DataFrame cultures = select(cultural, :country, :pdi => :hierarchy, :idv => :individualism, :mas => :masculinity, :uai => :uncertainty, :ltowvs => :long_term, :ivr => :indulgence) # Countries we want to filter on by performing an innerjoin # countries = DataFrame(country=["Norway", "Sweden", "Denmark", "Finland", "U.S.A."]) countries = DataFrame(country=["Norway", "Sweden", "Denmark", "Japan", "China"]) # Pick cultures where the :country column matches in both DataFrame objects sample = innerjoin(cultures, countries, on = :country) # Stack turns column names into row values. E.g. if you got the row: # country hierarchy individualism # China 80 20 # # then this becomes: # country variable value # China hierarchy 80 # China individualism 20 # # So every column entry gets a separate row with the name of that column data = stack(sample, 2:7) # Why is this useful? Because the `group` attribute allows you to bundle values # having identical column value such as "hierarchy" into a seprate series. # Here each series is one country. The x-axis values for each country is the # cultural dimensions under the `variable` column groupedbar(data.variable, data.value, group=data.country) # Turn into CSV viewable in Numbers # clipboard(repr(MIME("text/csv"), sample)) end
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2.328428
2,990
mutable struct Rake stop_words::Vector{String} min_char_length::Int max_words_length::Int min_keyword_frequency::Int min_words_length_adj::Int max_words_length_adj::Int min_phrase_freq_adj::Int end struct Score rank::Float64 frequency::Int end function Rake(stop_words; min_char_length=3, max_words_length=3, min_keyword_frequency=10, min_words_length_adj=1, max_words_length_adj=1, min_phrase_freq_adj=2) Rake(stop_words, min_char_length, max_words_length, min_keyword_frequency, min_words_length_adj, max_words_length_adj, min_phrase_freq_adj) end import Base.run function run(self::Rake, text) # Convert all multiple whitespace to single whitespace cleaned_text = remove_redundant_whitespace(text) # Convert to lower case cleaned_text = lowercase(cleaned_text) # Split on punctuation split_on = ['.', '?', '!', '\n', ';', ',', ':', '\u2019', '\u2013'] sentences = split(cleaned_text, split_on) # Split on stopwords phrases = split_at_stopwords(sentences, Rake.stop_words) # Find all possible keywords all_possible_keyphrases = find_all_possible_keyphrases(phrases) # Score all possible keywords keyphrase_with_score = calculate_keyphrase_scores(all_possible_keyphrases) # Function to compare our (keyword, score) tuples #tuple_compare(x,y) = x[2].rank > y[2].rank #sorted_keywords = sort(keyword_candidates, lt=tuple_compare) return keyphrase_with_score end
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2.519263
597
using CairoMakie, Makie.GeometryBasics n = 20 function ngonShape(h, k, r, n) Polygon([Point2f(h .+ r*sin.(m*2π/n),k .+ r*cos.(m*2π/n)) for m in 1:n]) end polysCentric = [ngonShape(0, 0, 3/i^1.5, i) for i in 3:n] polysCircular = [ngonShape(√2/2*sin(θ), √2/2*cos(θ), 0.15/√idx, idx + 2) for (idx, θ) in enumerate(LinRange(0,2π*(1 -1/(n-2)), n-2))] cmap = to_colormap(:linear_protanopic_deuteranopic_kbw_5_98_c40_n256)[3:end] with_theme(theme_black()) do fig, ax, = poly(polysCentric; color = 1:n-2, colormap = cmap, axis = (;aspect = DataAspect()), figure = (;resolution = (600,400))) poly!(polysCircular; color = 1:n-2, colormap = cmap) hidedecorations!(ax; grid = false) hidespines!(ax) save("polygons.svg", fig) display(fig) end
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1.979487
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import Dagger: DArray, ArrayDomain, Thunk, chunks, domainchunks # Domain munging conveniences struct ChunkDomain domain::AbstractVector{Int} chunks::Vector{<:AbstractVector{Int}} end ArrayDomain(d::ChunkDomain...) = ArrayDomain(map(d -> d.domain, d)...) mapcart(f, xs) = f.(xs) mapcart(f, xs, ys) = f.(xs, reshape(xs, 1, :)) domprod(ds...) = mapcart(tuple, ds...) chunkinds(i::ChunkDomain...) = domprod(map(i -> i.chunks, i)...) chunkinds(xs::DArray, dim::Integer) = map(d -> d.indexes[dim], domainchunks(xs)[ntuple(i -> i == dim ? (:) : 1 , ndims(xs))...]) domain(xs::DArray, i::Integer) = ChunkDomain(indices(xs, i), chunkinds(xs, i)) # Constructors DArray{T,N}(dom::NTuple{N,ChunkDomain}, chunks::AbstractArray{<:Any,N}) where {T,N} = DArray(T, ArrayDomain(dom...), ArrayDomain.(chunkinds(dom...)), chunks) DArray{T,N}(f, dom::NTuple{N,ChunkDomain}) where {T,N} = DArray{T,N}(dom, f.(chunkinds(dom...))) # Views using Dagger: lookup_parts function catchunks(chs) for i = 1:ndims(chs) chs = mapslices(xs -> [cat(i, xs...)], chs, i) end return chs[1] end function chslice(xs::DArray, d::ArrayDomain) subchunks, subdomains = lookup_parts(chunks(xs), domainchunks(xs), d) chsize = size(subdomains) subchunks Thunk(subchunks...) do subchunks... catchunks(reshape(collect(subchunks), chsize)) end end chslice(xs::DArray, i...) = chslice(xs, ArrayDomain(i))
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2.408547
585
export Catalog """ Catalog( data, ssm) Data type to store analogs and succesors observations from the Space State model - [Example Catalog](@ref) """ struct Catalog nt::Int64 nv::Int64 data::Array{Float64,2} analogs::Array{Float64,2} successors::Array{Float64,2} sources::StateSpaceModel function Catalog(data::Array{Float64,2}, ssm::StateSpaceModel) nv, nt = size(data) analogs = data[:, 1:end-ssm.dt_states] successors = data[:, ssm.dt_states+1:end] new(nt, nv, data, analogs, successors, ssm) end end
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2.410788
241
input = read("2020/data/day_8.txt", String) test_input = """ nop +0 acc +1 jmp +4 acc +3 jmp -3 acc -99 acc +1 jmp -4 acc +6 """ mutable struct State total::Int index::Int used_codes::Array{Int,1} end function apply_rule!(state::State, rule::AbstractString) push!(state.used_codes, state.index) key, str_val = split(rule) val = parse(Int, str_val) if key == "nop" state.index += 1 elseif key == "jmp" state.index += val elseif key == "acc" state.total += val state.index += 1 end # @info state end function run_program(rules) state = State(0, 1, []) for unused in 1:length(rules) # theoretical max iterations if state.index ∈ state.used_codes return (state, "ERROR: Infinite Loop") elseif state.index == length(rules) + 1 return state, "SUCCESS" else rule = rules[state.index] apply_rule!(state, rule) end end end function part_1(input) rules = split(strip(input), "\n") state, exit_code = run_program(rules) @assert exit_code == "ERROR: Infinite Loop" state end @assert part_1(test_input).total == 5 @info part_1(input) function part_2(input) state = State(0, 1, []) rules = split(strip(input), "\n") for i in 1:length(rules) if occursin("nop", rules[i]) | occursin("jmp", rules[i]) new_rules = copy(rules) if occursin("nop", rules[i]) new_rules[i] = replace(rules[i], "nop" => "jmp") else new_rules[i] = replace(rules[i], "jmp" => "nop") end state, exit_code = run_program(new_rules) if exit_code == "SUCCESS" return state, i end end end end @info part_2(input)
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2.092997
871
using PerfChecker using BenchmarkTools using PatternFolds target = PatternFolds function bench() # 0.2.x # Intervals itv = Interval{Open,Closed}(0.0, 1.0) i = IntervalsFold(itv, 2.0, 1000) unfold(i) collect(i) reverse(collect(i)) # rand(i, 1000) # Vectors vf = make_vector_fold([0, 1], 2, 1000) # @info "Checking VectorFold" vf pattern(vf) gap(vf) folds(vf) length(vf) unfold(vf) collect(vf) reverse(collect(vf)) rand(vf, 1000) return nothing end # function bench() # 0.1.1-0.1.5 # # Intervals # i = IntervalsFold(Interval((0.0, true), (1.0, false)), 2.0, 1000) # unfold(i) # collect(i) # reverse(collect(i)) # # rand(i, 1000) # # Vectors # vf = VectorFold([0, 1], 2, 1000) # # @info "Checking VectorFold" vf pattern(vf) gap(vf) folds(vf) length(vf) # unfold(vf) # collect(vf) # reverse(collect(vf)) # map(_ -> rand(vf), 1:1000) # return nothing # end t = @benchmark bench() evals = 1 samples = 1000 seconds = 3600 # Actual call to PerfChecker store_benchmark(t, target; path=@__DIR__)
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2.117202
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using Base.Test import ArchGDAL; const AG = ArchGDAL @testset "Test methods for dataset" begin AG.registerdrivers() do AG.read("data/utmsmall.tif") do dataset AG.createcopy(dataset, "tmp/utmcopy.tif","GTiff") do copydataset @test AG.ngcp(copydataset) == 0 @test AG.noverview(AG.getband(copydataset,1)) == 0 AG.buildoverviews!(copydataset, Cint[2,4,8]) @test AG.noverview(AG.getband(copydataset,1)) == 3 AG.copywholeraster(dataset, copydataset, progressfunc=GDAL.C.GDALTermProgress) end AG.copyfiles("GTiff", "tmp/utmcopy2.tif", "tmp/utmcopy.tif") AG.update("tmp/utmcopy2.tif") do copydataset AG.copywholeraster(dataset, copydataset, ["COMPRESS=LZW"]) end end end rm("tmp/utmcopy.tif") rm("tmp/utmcopy2.tif") end # untested: AG.deletelayer!(copydataset, 0)
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module PkgTemplates using Dates using InteractiveUtils using LibGit2 using Mustache using Pkg using REPL.TerminalMenus using URIParser export # Template/package generation. Template, generate, interactive_template, generate_interactive, # Licenses. show_license, available_licenses, # Plugins. GitHubPages, GitLabPages, AppVeyor, TravisCI, GitLabCI, CirrusCI, Codecov, Coveralls, Citation """ A plugin to be added to a [`Template`](@ref), which adds some functionality or integration. New plugins should almost always extend [`GenericPlugin`](@ref) or [`CustomPlugin`](@ref). """ abstract type Plugin end include("licenses.jl") include("template.jl") include("generate.jl") include("plugin.jl") include(joinpath("plugins", "documenter.jl")) include(joinpath("plugins", "coveralls.jl")) include(joinpath("plugins", "appveyor.jl")) include(joinpath("plugins", "codecov.jl")) include(joinpath("plugins", "travisci.jl")) include(joinpath("plugins", "gitlabci.jl")) include(joinpath("plugins", "cirrusci.jl")) include(joinpath("plugins", "githubpages.jl")) include(joinpath("plugins", "gitlabpages.jl")) include(joinpath("plugins", "citation.jl")) const DEFAULTS_DIR = normpath(joinpath(@__DIR__, "..", "defaults")) const BADGE_ORDER = [GitHubPages, GitLabPages, TravisCI, AppVeyor, GitLabCI, Codecov, Coveralls] end
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include("boundconstraint_enum.jl") include("misc.jl") include("abstract_bc_quadsolver_conf.jl") include("abstract_bc_quadsolver_result.jl") include("bc_quadsolver_interface.jl") include("Kunisch-Rendl.jl")
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2.64557
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using OCToolbox using LinearAlgebra using QuantumInformation i2 = Matrix{Complex{Float64}}(I, 2, 2) # we define our initial and final states Ψ = [1+0.0im, 0.0] ρ = [0+0.0im, 1.0] N_ensemble = 10 ΔRange = 10 * 2π H_drift = collect(range(-ΔRange, ΔRange, length = N_ensemble)) .* [sz] H_ctrl = [π * sx, π * sy] # we set up a functional for a robust pulse function fn(controls) controls = complex.(real.(controls)) err = 0 for i = 1:N_ensemble U = pw_full_evolution(H_drift[i], H_ctrl, controls, Δt, i2) err += C2(ρ, U * Ψ) end err end K = 2 N = 20 T = 10 Δt = T/N control_guess = rand(K, N).*0.001 fn(control_guess) using Zygote Zygote.gradient(fn, control_guess) o = GRAPE(fn, control_guess, K, N) fn(o.minimizer)
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module CcallMacros export @ccall, @cdef, @disable_sigint, @nonzero_systemerror """ `calltoccall` is an implementation detail of @ccall and @cdef takes and expression like :(printf("%d"::Cstring, value::Cuint)::Cvoid) returns: a tuple of (function_name, return_type, arg_types, args The above input outputs this: (:printf, :Cvoid, :((Cstring, Cuint)), ["%d", :value]) Note that the args are in an array, not a quote block and have to be appended to the ccall in a separate step. """ function calltoccall(expr) expr.head != :(::) && error("@ccall needs a function signature with a return type") rettype = expr.args[2] call = expr.args[1] call.head != :call && error("@ccall has to be a function call") if (f = call.args[1]) isa Expr lib = f.args[1] fname = f.args[2] func = :(($fname, $lib)) else func = QuoteNode(f) end argtypes = :(()) args = [] for arg in call.args[2:end] varargs = false if arg.head == :... varargs = true arg = arg.args[1] end arg.head != :(::) && error("args in @ccall must be annotated") value = arg.args[1] type_ = arg.args[2] # This currently doesn't work. if varargs value = :($value...) type_ = :($type_...) end push!(args, value) push!(argtypes.args, type_) end func, rettype, argtypes, args end """ convert a julia-style function definition to a ccall: `@ccall printf("%d"::Cstring, 10::Cint)::Cvoid` same as: `ccall(:printf, Cvoid, (Cstring, Cint), "%d", 10)` """ macro ccall(expr) func, rettype, argtypes, args = calltoccall(expr) output = :(ccall($func, $rettype, $argtypes)) append!(output.args, args) esc(output) end """ define a _very_ thin wrapper function on a ccall. Mostly for wrapping libraries quickly as a foundation for a higher-level interface. @cdef mkfifo(path::Cstring, mode::Cuint)::Cint becomes: mkfifo(path, mode) = ccall(:mkfifo, Cint, (Cstring, Cuint), path, mode) """ macro cdef(expr) func, rettype, argtypes, args = calltoccall(expr) call = :(ccall($func, $rettype, $argtypes)) append!(call.args, args) name = func isa QuoteNode ? func.value : func.args[1].value definition = :($name()) append!(definition.args, args) esc(:($definition = $call)) end """ disable SIGINT while expr is being executed. Mostly useful for calling C functions that call back into Julia in a concurrent context because memory corruption can occur and crash the whole program. """ macro disable_sigint(expr) out = quote disable_sigint() do $expr end end esc(out) end const comment = r"#=.*?=# " """ throw a system error if the expression returns a non-zero exit status. """ macro nonzero_systemerror(expr) str = replace(string(expr), comment => "") out = quote err = $expr systemerror($str, err != 0) err end esc(out) end end # module
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__precompile__(false) module Buildkite import HTTP import JSON export BuildkiteAPI abstract type AbstractBuildkiteAPI end Base.@kwdef struct BuildkiteAPI <: AbstractBuildkiteAPI base_url::HTTP.URI = HTTP.URI("https://api.buildkite.com/v2/") access_token::String = "" end function buildkite_api_uri(api::BuildkiteAPI, path) HTTP.URIs.merge(api.base_url, path = api.base_url.path * path) end function buildkite_request(api::BuildkiteAPI, request_method, endpoint; handle_error = true, headers = Dict(), params = Dict(), allowredirects::Bool = true, idle_timeout = 20, status_exception = false) api_endpoint = buildkite_api_uri(api, endpoint) request_headers = convert(Dict{String, String}, headers) if !haskey(request_headers, "Authorization") request_headers["Authorization"] = "Bearer $(api.access_token)" end if !haskey(request_headers, "User-Agent") request_headers["User-Agent"] = "Buildkite-jl" end if request_method == HTTP.get api_uri = HTTP.URIs.merge(api_endpoint, query = params) println("DEBUG: ", api_uri) r = request_method(api_uri, request_headers, redirect = allowredirects, status_exception = false, idle_timeout=idle_timeout) else api_uri = string(api_uri) r = request_method(api_uri, request_headers, JSON.json(params), redirect=allowredirects, status_exception=status_exception, idle_timeout=idle_timeout) end if handle_error #handle_response_error(r) end return r end # REST primitives function buildkite_get(api::BuildkiteAPI, endpoint = ""; options...) buildkite_request(api, HTTP.get, endpoint; options...) end function buildkite_post(api::BuildkiteAPI, endpoint = ""; options...) buildkite_request(api, HTTP.post, endpoint; options...) end function buildkite_put(api::BuildkiteAPI, endpoint = ""; options...) buildkite_request(api, HTTP.put, endpoint; options...) end function buildkite_delete(api::BuildkiteAPI, endpoint = ""; options...) buildkite_request(api, HTTP.delete, endpoint; options...) end function buildkite_patch(api::BuildkiteAPI, endpoint = ""; options...) buildkite_request(api, HTTP.patch, endpoint; options...) end function buildkite_get_json(api::BuildkiteAPI, endpoint = ""; options...) JSON.parse(HTTP.payload(buildkite_get(api, endpoint; options...), String)) end function buildkite_post_json(api::BuildkiteAPI, endpoint = ""; options...) JSON.parse(HTTP.payload(buildkite_post(api, endpoint; options...), String)) end function buildkite_put_json(api::BuildkiteAPI, endpoint = ""; options...) JSON.parse(HTTP.payload(buildkite_put(api, endpoint; options...), String)) end function buildkite_delete_json(api::BuildkiteAPI, endpoint = ""; options...) JSON.parse(HTTP.payload(buildkite_delete(api, endpoint; options...), String)) end function buildkite_patch_json(api::BuildkiteAPI, endpoint = ""; options...) JSON.parse(HTTP.payload(buildkite_patch(api, endpoint; options...), String)) end function hello_world() base_url = HTTP.URI("https://api.buildkite.com") r = buildkite_get_json(BuildkiteAPI(base_url=base_url), "") return r["response"] end # organization api struct Organization api::BuildkiteAPI data::Dict end function organization(api::BuildkiteAPI, name) return Organization(api, buildkite_get_json(api, "organizations/$(lowercase(name))")) end # pipelines api struct Pipeline api::BuildkiteAPI data::Dict end function pipelines(api::BuildkiteAPI, organization; page=0, pagination=false) query_params = Dict("page" => page) endpoint = "organizations/$(lowercase(organization))/pipelines" return [Pipeline(api, p) for p in buildkite_get_json(api, endpoint; params = query_params)] end function pipelines(org::Buildkite.Organization) return pipelines(org.api, org.data["name"]) end # build api struct Build api::BuildkiteAPI data::Dict end function builds(api::BuildkiteAPI; state=nothing) query_params = Dict("state" => state) endpoint = "builds" return [Build(api, b) for b in buildkite_get_json(api, endpoint; params = query_params)] end build_state(b::Build) = b.data["state"] end
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function count_nucleotides(strand::AbstractString) a = 0 c = 0 g = 0 t = 0 for i in strand if i == 'A' a += 1 elseif i == 'C' c += 1 elseif i == 'G' g += 1 elseif i == 'T' t += 1 else return throw(DomainError()) end end return Dict( 'A' => a, 'C' => c, 'G' => g, 'T' => t) end count_nucleotides("GAGAGACGTT")
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261
using AutonomousMerging using Random using AutomotiveDrivingModels using POMDPs using POMDPPolicies using POMDPSimulators using Test function test_state(mdp::GenerativeMergingMDP, s,v=0.0, acc=0.0) ego = Vehicle(vehicle_state(35.0, merge_lane(mdp.env), 5.0, mdp.env.roadway), VehicleDef(), EGO_ID) veh1 = Vehicle(vehicle_state(s, main_lane(mdp.env), 4.9, mdp.env.roadway), VehicleDef(), EGO_ID + 1) scene = Scene() push!(scene, ego) push!(scene, veh1) return AugScene(scene, (acc=acc,)) end @testset "Environment" begin env = MergingEnvironment(main_lane_angle = 0.0, merge_lane_angle = pi/12) main = main_lane(env) merge = merge_lane(env) @test main == env.roadway[LaneTag(MAIN_LANE_ID, 1)] @test main.curve[end].s == env.main_lane_length + env.after_merge_length @test merge.curve[end].s == env.merge_lane_length end @testset "GenerativeMDP" begin rng = MersenneTwister(1) mdp = GenerativeMergingMDP(max_cars=12, min_cars=10, driver_type = :random, observe_cooperation = true, initial_ego_velocity=0.0) s0 = initialstate(mdp, rng) policy = RandomPolicy(mdp, rng=rng) hr = HistoryRecorder(rng = rng, max_steps=100) hist = POMDPSimulators.simulate(hr, mdp, policy, s0) s = s0 svec = convert_s(Vector{Float64}, s, mdp) srec = convert_s(AugScene, svec, mdp) @test get_by_id(srec.scene, EGO_ID).state.posG ≈ get_by_id(s.scene, EGO_ID).state.posG s = state_hist(hist)[end] svec = convert_s(Vector{Float64}, s, mdp) srec = convert_s(AugScene, svec, mdp) @test get_by_id(srec.scene, EGO_ID).state.posG ≈ get_by_id(s.scene, EGO_ID).state.posG end @testset "CooperativeIDM" begin rng = MersenneTwister(1) mdp = GenerativeMergingMDP(random_n_cars=true, dt=0.5) mdp.driver_models[2] = CooperativeIDM(c=1.0) set_desired_speed!(mdp.driver_models[2], 5.0) s0 = test_state(mdp, 85.0) policy = FunctionPolicy(s->7) hr = HistoryRecorder(rng = rng, max_steps=40) hist = POMDPSimulators.simulate(hr, mdp, policy, s0) end
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# this should exist? """ filename(path) Sring -> String Extract core name of the file from path E.g. filename("/some/path/to/file_name.ext") -> "file_name" """ filename(path) = splitext(basename(path))[1] # this should be in PropDicts? """ propdict(json_file) AbstractString -> PropDict Construct a PropDict based on given <json_file>. 1) Why can't I name it function PropDict()? 2) Why doesn't this already exist in PropDicts? I find the PropDicts.read(PropDict, String) format kinda cumbersome """ function propdict(json_file::AbstractString) PropDicts.read(PropDict, json_file) end """ remove_negative(value) Real -> Real Replace negative values by zeros. Used to remove a glitch in SSD pulses as a quick fix while the glitch is being debugged. """ function remove_negative(value::Real) value < 0 ? 0 : value end """ preamp_gain(preamp, noise_model) GenericPreAmp, NoiseFromData -> Real Calculate gain of <preamp> based on its max energy and the offset observed in data baselines contained in <noise_model> In principle we should not do this in this simulation, and user has to provide the precise parameters of the electronics chain used in producing data the baselines from which are contained in <noise_model>. """ function preamp_gain(preamp::PreAmp, noise_model::NoiseFromData) # average offset in baselines offset = mean(mean.(noise_model.baseline_catalog.waveform.value)) # calculate gain based on offset in data baselines (typemax(UInt16) - offset) * germanium_ionization_energy / uconvert(u"eV", preamp.max_e) end """ preamp_gain(preamp, ::NoiseFromSim) GenericPreAmp -> Float64 Do nothing and return original value of gain parameter in <preamp>, since when NoiseFromSim model is used, gain is provided by the user (or calculated based on user given max energy) """ function preamp_gain(preamp::GenericPreAmp, ::NoiseFromSim) preamp.gain end # """ # trigger_threshold(trigger, noise_model, preamp) # Trigger, NoiseFromSim, GenericPreAmp -> Real # Calculate trigger threshold based on whether # the user provided it in the simulation settings # """ # function trigger_threshold(trigger::Trigger, noise_model::NoiseFromSim, preamp::PreAmp) # # if trigger threshold not given, take noise level as reference # threshold = trigger.threshold_keV == 0u"keV" ? noise_model.noise_σ * 3 : trigger.threshold_keV # uconvert(u"eV", threshold) / germanium_ionization_energy * preamp.gain # end """ trigger_threshold(trigger, preamp, ::NoiseFromSim) Trigger, GenericPreAmp -> Real In NoiseFromSim setting, non-zero trigger threshold in keV MUST be given by the user. Calculate final threshold based the value contained in <trigger>, and <preamp> gain. """ function trigger_threshold(trigger::Trigger, preamp::GenericPreAmp, ::NoiseFromSim) uconvert(u"eV", trigger.threshold_keV) / germanium_ionization_energy * preamp.gain # threshold = trigger.threshold_keV == 0u"keV" ? noise_model.noise_σ * 3 : trigger.threshold_keV # uconvert(u"eV", threshold) / germanium_ionization_energy * preamp.gain end """ trigger_threshold(trigger, preamp, noise_model) Trigger, GenericPreAmp, NoiseFromData -> Real In NoiseFromData setting, if trigger threshold in keV is not provided, it is calculated based on noise levels in the baselines contained in <noise_model>. Otherwise the final threshold is calculated based on <preamp> gain. """ function trigger_threshold(trigger::Trigger, preamp::GenericPreAmp, noise_model::NoiseFromData) trigger.threshold_keV == 0u"keV" ? std(noise_model.baseline_catalog.waveform[1].value) * 3 : uconvert(u"eV", trigger.threshold_keV) / germanium_ionization_energy * preamp.gain end
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""" abstract type AbstractHyperRectangle{N,T} <: AbstractElement{ReferenceHyperCube{N},SVector{N,T}} Axis-aligned hyperrectangle in `N` dimensions with coordinates of type `SVector{N,T}`. """ abstract type AbstractHyperRectangle{N,T} <: AbstractElement{ReferenceHyperCube{N},SVector{N,T}} end Base.eltype(::AbstractHyperRectangle{N,T}) where {N,T} = T ambient_dimension(::AbstractHyperRectangle{N}) where {N} = N geometric_dimension(::AbstractHyperRectangle{N}) where {N} = N low_corner(r::AbstractHyperRectangle) = abstractmethod(r) high_corner(r::AbstractHyperRectangle) = abstractmethod(r) diameter(r::AbstractHyperRectangle) = abstractmethod(r) center(r::AbstractHyperRectangle) = abstractmethod(r) radius(r::AbstractHyperRectangle) = abstractmethod(r) vertices(r::AbstractHyperRectangle) = abstractmethod(r) Base.in(point,h::AbstractHyperRectangle) = all(low_corner(h) .<= point .<= high_corner(h)) function vertices(rec::AbstractHyperRectangle{2}) lc = low_corner(rec) hc = high_corner(rec) return SVector( SVector(lc[1],lc[2]), SVector(hc[1],lc[2]), SVector(hc[1],hc[2]), SVector(lc[1],hc[2]) ) end function vertices(rec::AbstractHyperRectangle{3}) lc = low_corner(rec) hc = high_corner(rec) return SVector( # lower face SVector(lc[1],lc[2],lc[3]), SVector(hc[1],lc[2],lc[3]), SVector(hc[1],hc[2],lc[3]), SVector(lc[1],hc[2],lc[3]), # upper face SVector(lc[1],lc[2],hc[3]), SVector(hc[1],lc[2],hc[3]), SVector(hc[1],hc[2],hc[3]), SVector(lc[1],hc[2],hc[3]) ) end ## implement the AbstractElement interface for convenience function (el::AbstractHyperRectangle)(u) @assert u in domain(el) lc = low_corner(el) hc = high_corner(el) # map from reference domain to v = @. lc + (hc - lc)*u return v end function jacobian(el::AbstractHyperRectangle,u) @assert u in domain(el) lc = low_corner(el) hc = high_corner(el) return SDiagonal(hc - lc) end """ distance(r1::AbstractHyperRectangle,r2::AbstractHyperRectangle) The (minimal) Euclidean distance between a point `x ∈ r1` and `y ∈ r2`. """ function distance(rec1::AbstractHyperRectangle{N},rec2::AbstractHyperRectangle{N}) where {N} d2 = 0 rec1_low_corner = low_corner(rec1) rec1_high_corner = high_corner(rec1) rec2_low_corner = low_corner(rec2) rec2_high_corner = high_corner(rec2) for i=1:N d2 += max(0,rec1_low_corner[i] - rec2_high_corner[i])^2 + max(0,rec2_low_corner[i] - rec1_high_corner[i])^2 end return sqrt(d2) end """ distance(x::SVector,r::HyperRectangle) The (minimal) Euclidean distance between the point `x` and any point `y ∈ r`. """ function distance(pt::SVector{N},rec::AbstractHyperRectangle{N}) where {N} d2 = 0 rec_low_corner = low_corner(rec) rec_high_corner = high_corner(rec) for i=1:N d2 += max(0,pt[i] - rec_high_corner[i])^2 + max(0,rec_low_corner[i] - pt[i])^2 end return sqrt(d2) end distance(rec::AbstractHyperRectangle{N},pt::SVector{N}) where {N} = distance(pt,rec) ###### # HyperRectangle ###### """ struct HyperRectangle{N,T} Axis-aligned hyperrectangle in `N` dimensions given by `low_corner::SVector{N,T}` and `high_corner::SVector{N,T}`. """ struct HyperRectangle{N,T} <: AbstractHyperRectangle{N,T} low_corner::SVector{N,T} high_corner::SVector{N,T} end HyperRectangle(l::Tuple,h::Tuple) = HyperRectangle(SVector(l),SVector(h)) HyperRectangle(l::SVector,h::SVector) = HyperRectangle(promote(l,h)...) # 1d case HyperRectangle(a::Number,b::Number) = HyperRectangle(SVector(a),SVector(b)) low_corner(r::HyperRectangle) = r.low_corner high_corner(r::HyperRectangle) = r.high_corner center(r::HyperRectangle) = (low_corner(r) + high_corner(r)) / 2 diameter(r::HyperRectangle) = norm(high_corner(r) .- low_corner(r),2) radius(r::HyperRectangle) = diameter(r) / 2 Base.isapprox(h1::HyperRectangle,h2::HyperRectangle;kwargs...) = isapprox(h1.low_corner,h2.low_corner;kwargs...) && isapprox(h1.high_corner,h2.high_corner;kwargs...) ###### # HyperCube ###### """ struct HyperCube{N,T} Axis-aligned hypercube in `N` dimensions given by `low_corner::SVector{N,T}` and `side::T`. """ struct HyperCube{N,T} <: AbstractHyperRectangle{N,T} low_corner::SVector{N,T} side::T end # 1d case HyperCube(low_corner::Number,side::Number) = HyperCube(SVector(low_corner),side) side(r::HyperCube) = r.side low_corner(r::HyperCube) = r.low_corner high_corner(r::HyperCube) = low_corner(r) .+ side(r) center(r::HyperCube) = low_corner(r) .+ side(r)/2 diameter(r::HyperCube{N}) where N = side(r)*sqrt(N) radius(r::HyperCube) = diameter(r) / 2 Base.isapprox(h1::HyperCube,h2::HyperCube;kwargs...) = isapprox(h1.low_corner,h2.low_corner;kwargs...) && isapprox(h1.side,h2.side;kwargs...) ###### # Utils ###### function HyperRectangle(els,cube=false) isempty(els) && (error("data cannot be empty") ) lb = first(els) |> coords ub = first(els) |> coords for el in els pt = coords(el) lb = min.(lb,pt) ub = max.(ub,pt) end if cube # fit a square/cube instead w = maximum(ub-lb) xc = (ub + lb) / 2 lb = xc .- w/2 ub = xc .+ w/2 # TODO: return HyperCube instead end return HyperRectangle(lb,ub) end """ split(rec::AbstractHyperRectangle,[axis]::Int,[place]) Split a hyperrectangle in two along the `axis` direction at the position `place`. Returns a tuple with the two resulting hyperrectangles. When no `place` is given, defaults to splitting in the middle of the axis. When no axis and no place is given, defaults to splitting along the largest axis. """ function Base.split(rec::AbstractHyperRectangle,axis,place) rec_low_corner = low_corner(rec) rec_high_corner = high_corner(rec) N = ambient_dimension(rec) high_corner1 = svector(n-> n==axis ? place : rec_high_corner[n], N) low_corner2 = svector(n-> n==axis ? place : rec_low_corner[n], N) rec1 = HyperRectangle(rec_low_corner, high_corner1) rec2 = HyperRectangle(low_corner2,rec_high_corner) return (rec1, rec2) end function Base.split(rec::AbstractHyperRectangle,axis) place = (high_corner(rec)[axis] + low_corner(rec)[axis])/2 return split(rec,axis,place) end function Base.split(rec::AbstractHyperRectangle) axis = argmax(high_corner(rec) .- low_corner(rec)) return split(rec,axis) end
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function _add_forces!( f::Tuple, p, idx::Integer, jdx::Integer, fp::AbstractFloat, rp::AbstractFloat, ) @inbounds for (k, c) in zip(p, f) c[idx] += (fp * k) / rp c[jdx] -= (fp * k) / rp end end function _compute_distance( x::SubArray, y::SubArray, z::SubArray, i::Integer, j::Integer, boxl::Real, ) xij = x[i] - x[j] yij = y[i] - y[j] zij = z[i] - z[j] xij -= boxl * round(xij / boxl) yij -= boxl * round(yij / boxl) zij -= boxl * round(zij / boxl) rij2 = xij * xij + yij * yij + zij * zij st_positions = @SVector [xij, yij, zij] return st_positions, rij2 end function _compressibilityz( pos::StaticVector, fp::AbstractFloat, rij::AbstractFloat, zfactor::ZFactor, ) tmp_sum = @. (pos^2.0 * -fp) / rij zfactor.zval += sum(tmp_sum) end function _compute_energy!( positions::AbstractArray, forces::AbstractArray, params::NamedTuple, pot::PairwisePotential; rdfobj = nothing, zfactor = nothing, ) # Initialize necessary variables energy = zero(params.rc2) x = view(positions, :, 1) y = view(positions, :, 2) z = view(positions, :, 3) fx = view(forces, :, 1) fy = view(forces, :, 2) fz = view(forces, :, 3) for i = 1:params.N-1 @inbounds @fastmath for j = (i+1):params.N (static_positions, rij2) = _compute_distance(x, y, z, i, j, params.boxl) if rij2 < params.rc2 rij2 = √rij2 u_pair, f_pair = apply!(pot, rij2) _add_forces!((fx, fy, fz), static_positions, i, j, f_pair, rij2) energy += u_pair if !isnothing(rdfobj) simple_rdf!(rdfobj, rij2) end if !isnothing(zfactor) _compressibilityz(static_positions, f_pair, rij2, zfactor) end end end end return energy / params.N end function energy_force!( positions::AbstractArray, forces::AbstractArray, params::NamedTuple, pot::PairwisePotential; gofr = nothing, zfactor = nothing, ) # Retrieve data from the system fill!(forces, zero(params.boxl)) energy = _compute_energy!(positions, forces, params, pot; rdfobj = gofr, zfactor = zfactor) end
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module TestDocumentation using Base.Test using DataFrames using TimeData println("\n Running documentation tests\n") using WorldBankDataTd ## single indicator, single country gnp = WorldBankDataTd.wdi("NY.GNP.PCAP.CD", "BR") ## single indicator, multiple countries gnp = WorldBankDataTd.wdi("NY.GNP.PCAP.CD", ["BR", "US", "DE"]) ## multiple indicators, single country data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], "BR") ## multiple indicators, multiple countries data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], ["BR", "US", "DE"]) ## same, but as DataFrame data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], ["BR", "US", "DE"], format = DataFrame) ## multiple indicators, multiple countries, additional information data = WorldBankDataTd.wdi(["NY.GNP.PCAP.CD", "SP.DYN.LE00.IN"], ["BR", "US", "DE"], extra = true) data[1:5, :] countryData = getWBMeta("countries") countryData[1:5, :] names(countryData) size(countryData) indicatorData = getWBMeta("indicators") indicatorData[1:5, :] names(indicatorData) size(indicatorData) WorldBankDataTd.country_cache[1:5, :] WorldBankDataTd.indicator_cache[1:5, :] res = search_wdi("countries", :name, r"united"i) res res = search_wdi("indicators", :description, r"gross national expenditure"i) res[:name] search_wdi("countries", :iso2c, r"TZ"i) search_wdi("countries", :income, r"upper middle"i) search_wdi("countries", :region, r"Latin America"i) search_wdi("countries", :capital, r"^Ka"i) search_wdi("countries", :lending, r"IBRD"i) search_wdi("indicators", :name, r"gross national expenditure"i) search_wdi("indicators", :description, r"gross national expenditure"i) search_wdi("indicators", :source_database, r"Sustainable"i) search_wdi("indicators", :source_organization, r"Global Partnership"i)[1:5, :] data = wdi("NY.GNP.PCAP.CD", ["US","BR"], 1980, 2012, extra = true) usData = chkDates(x-> x[:iso2c] .== "US", eachdate(data)) |> x -> asArr(x, Bool, false) |> x -> data[x[:], :] usData dfAS = wdi("EN.ATM.CO2E.KT", "AS") end
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2.294792
960
module TexAssistant import ArgParse import TOML include("utils.jl") include("regexs.jl") include("split_comment.jl") include("expand_input.jl") include("extract_sections_to_files.jl") include("settings.jl") include("extractors.jl") include("tex_match.jl") function __init__() _fill_regexs!() end end
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2.409396
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# Julia program to Left Rotate a number by a specific bits. # Left Rotate 'cnt' number of bits of the given number 'n' function left_rotate_bits(n, cnt) cnt = cnt % 31 while(cnt > 0) # Store the current MSB in a temporary variable msb = (n >> 31) & 1; # Left rotate the given number by one bit n = (n<<1); # Set the dropped MSB as the new LSB n = n | msb; # Decrement cnt cnt = cnt - 1 end return n end print("Enter the number? ") num = readline() num = parse(Int, num) print("How many bits do you want to rotate? ") cnt = readline() cnt = parse(Int, cnt) left = left_rotate_bits(num, cnt) print("The Left-rotated number is: $left") """ Time Complexity: O(n) Space Complexity: O(1) SAMPLE INPUT AND OUTPUT Enter the number? 39 How many bits do you want to rotate? 17 The Left-rotated number is: 5111808 """
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2.452055
365
include("differencial.jl") include("RungeKutta.jl") """ `dynamics(q::SatelliteToolbox.Quaternion,ω::Vector,T::Vector,I::Matrix,dt)` トルク`T`を与えられた剛体の回転を刻み時間`dt`秒ぶん計算する関数 ## Arguments - `q::SatelliteToolbox.Quaternion` :`t`秒時点の姿勢を表す単位クォータニオン 衛星軌道面座標系の基底をボディ座標系の基底に変換する - `ω::Vector` :`t`秒時点の角速度ベクトル ボディ座標系基準 - `T::Vector` :剛体に与えられるトルク ボディ座標系基準 - `I::Matrix` :慣性テンソル - `dt::Number` :刻み時間 ## Returns - `(qk/norm(qk), ωk)::Tuple{SatelliteToolbox.Quaternion,Vector}` :`(t+dt)`秒時点の姿勢を表す単位クォータニオンと角速度ベクトルの組 ## Example `qk,ωk=dynamics(q,ω,T,I,dt)` """ function dynamics(q::SatelliteToolbox.Quaternion,ω::Vector,T::Vector,I::Matrix,dt::Number) qk,ωk=RK4((q,ω),(T,I),dt) return qk/norm(qk),ωk end
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1.304577
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Dict{Float32,String}( 19=>"North Europe", 8=>"Central Europe", 15=>"South Europe", 33=>"West South America", 2=>"Amazon", 18=>"Northeast Brazil", 26=>"Southeastern South America", 5=>"Central America/Mexico", 1=>"Alaska/N.W. Canada", 13=>"East North America", 32=>"West North America", 10=>"Central North America", 9=>"Canada/Greenland/Iceland" , 11=>"East Africa", 30=>"West Asia" , 29=>"West Africa" , 22=>"Sahara", 21=>"Southern Africa", 7=>"Central Asia", 12=>"East Asia", 28=>"Tibetan Plateau", 16=>"North Asia", 25=>"Southeast Asia", 23=>"South Asia", 17=>"Northern Australia", 24=>"Southern Australia")
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2.772727
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using Metal @show devices() dev = MtlDevice(1) #lib = MTL.LibraryWithFile(d, "default.metallib") src = read(dirname(pathof(Metal))*"/Metal/kernels/add.metal", String) bufferSize = 128 bufferA = MtlArray{Float32,1}(undef, tuple(bufferSize), storage=Shared) bufferB = MtlArray{Float32,1}(undef, tuple(bufferSize), storage=Shared) bufferC = MtlArray{Float32,1}(undef, tuple(bufferSize), storage=Shared) vecA = unsafe_wrap(Vector{Float32}, bufferA.buffer, tuple(bufferSize)) vecB = unsafe_wrap(Vector{Float32}, bufferB.buffer, tuple(bufferSize)) vecC = unsafe_wrap(Vector{Float32}, bufferC.buffer, tuple(bufferSize)) using Random rand!.([vecA, vecB]) ## Setup opts = MtlCompileOptions() lib = MtlLibrary(dev, src, opts) fun = MtlFunction(lib, "add_arrays") pip_addfun = MtlComputePipelineState(dev, fun) queue = global_queue(dev) #MtlCommandQueue(dev) ## vecA .= 0.0; vecB .= 0.0; vecC .= 0.0; cmd = MTL.commit!(queue) do cmdbuf MtlComputeCommandEncoder(cmdbuf) do enc MTL.set_function!(enc, pip_addfun) MTL.set_buffer!(enc, bufferA.buffer, 0, 1) MTL.set_buffer!(enc, bufferB.buffer, 0, 2) MTL.set_buffer!(enc, bufferC.buffer, 0, 3) #MTL.set_buffers!(enc, # [bufferA.buffer, bufferB.buffer, bufferC.buffer], # [0,0,0], 1:3) gridSize = MtSize(length(vecA), 1, 1) threadGroupSize = min(length(vecA), pip_addfun.maxTotalThreadsPerThreadgroup) threadGroupSize = MTL.MtSize(threadGroupSize, 1, 1) @info threadGroupSize MTL.append_current_function!(enc, gridSize, threadGroupSize) end end # Execute wait(cmd) @show vecC
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2.264228
738
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Description # ============================================================================== # # Structures related to the orbit propagators. # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # export OrbitPropagator """ OrbitPropagator{T} Abstract type of the orbit propagator. Every propagator structure must be a subtype of this type and must implement the following API functions: propagate!(orbp, t::Number) propagate!(orbp, t::AbstractVector) propagate_to_epoch!(orbp, JD::Number) propagate_to_epoch!(orbp, JD::AbstractVector) step!(orbp, Δt::Number) """ abstract type OrbitPropagator{T} end # Two Body Orbit Propagator # ============================================================================== export TwoBody_Structure, OrbitPropagatorTwoBody """ TwoBody_Structure{T} Low level Two Body orbit propagator structure. """ @with_kw mutable struct TwoBody_Structure{T} # Initial mean orbital elements # ========================================================================== epoch::T # ......................... Epoch of the initial mean elements [JD] a_0::T # ..................... Initial mean normalized semi-major axis [m] n_0::T # ..................................... Initial mean motion [rad/s] e_0::T # .................................. Initial mean eccentricity [ ] i_0::T # .................................. Initial mean inclination [rad] Ω_0::T # ......................................... Initial mean RAAN [rad] ω_0::T # .......................... Initial mean argument of perigee [rad] f_0::T # ................................. Initial mean true anomaly [rad] M_0::T # ................................. Initial mean mean anomaly [rad] μ::T # .. Standard gravitational parameter of the central body [m^3/s^2] # Current mean orbital elements # ========================================================================== Δt::T # .............. Timespan from the epoch of the initial elements [s] f_k::T # .................................. Current mean true anomaly [rad] M_k::T # .................................. Current mean mean anomaly [rad] end """ OrbitPropagatorTwoBody{T} <: OrbitPropagator{T} Structure that holds the information related to the Two Body orbit propagator. # Fields * `orb`: Mean orbital elements (see `Orbit`). * `tbd`: Structure that stores the Two Body orbit propagator data (see `TwoBody_Structure`). """ struct OrbitPropagatorTwoBody{T} <: OrbitPropagator{T} tbd::TwoBody_Structure{T} end # J2 Orbit Propagator # ============================================================================== export J2_GravCte, J2_Structure, OrbitPropagatorJ2 """ J2_GravCte{T} Gravitational constants for J2 orbit propagator. # Fields * `R0`: Earth equatorial radius [m]. * `μm`: √GM [er/s]^(3/2). * `J2`: The second gravitational zonal harmonic of the Earth. """ @with_kw struct J2_GravCte{T} R0::T μm::T J2::T end """ J2_Structure{T} Low level J2 orbit propagator structure. """ @with_kw mutable struct J2_Structure{T} # Initial mean orbital elements # ========================================================================== epoch::T # ............ Epoch of the initial mean elements [JD] al_0::T # ....... Initial mean normalized semi-major axis [er] n_0::T # ........................ Initial mean motion [rad/s] e_0::T # ..................... Initial mean eccentricity [ ] i_0::T # ..................... Initial mean inclination [rad] Ω_0::T # ............................ Initial mean RAAN [rad] ω_0::T # ............. Initial mean argument of perigee [rad] f_0::T # .................... Initial mean true anomaly [rad] M_0::T # .................... Initial mean mean anomaly [rad] dn_o2::T # ...... First time derivative of mean motion [rad/s²] ddn_o6::T # ..... Second time derivative of mean motion [rad/s³] j2_gc::J2_GravCte{T} # ........ J2 orbit propagator gravitational constants # Current mean orbital elements # ========================================================================== Δt::T # .............. Timespan from the epoch of the initial elements [s] al_k::T # ..................... Current mean normalized semi-major axis [er] e_k::T # ................................... Current mean eccentricity [ ] i_k::T # ................................... Current mean inclination [rad] Ω_k::T # .......................................... Current mean RAAN [rad] ω_k::T # ........................... Current mean argument of perigee [rad] f_k::T # .................................. Current mean true anomaly [rad] M_k::T # .................................. Current mean mean anomaly [rad] # Auxiliary variables # ========================================================================== δa::T # ........................... Semi-major axis time derivative [er/s] δe::T # ............................... Eccentricity time derivative [1/s] δΩ::T # ..................................... RAAN time derivative [rad/s] δω::T # ...................... Argument of perigee time derivative [rad/s] δM_0::T # ............................. Mean anomaly time derivative [rad/s] end """ OrbitPropagatorJ2{T} <: OrbitPropagator{T} Structure that holds the information related to the J2 orbit propagator. # Fields * `j2d`: Structure that stores the J2 orbit propagator data (see `J2_Structure`). """ struct OrbitPropagatorJ2{T} <: OrbitPropagator{T} j2d::J2_Structure{T} end # J2 osculating orbit propagator # ============================================================================== export J2osc_Strutcture, OrbitPropagatorJ2osc """ J2osc_Structure{T} Low level J2 osculating orbit propagator structure. """ @with_kw mutable struct J2osc_Structure{T} # J2 orbit propagator to propagate the mean elements. j2d::J2_Structure{T} # Propagation time from epoch. Δt::T # Current osculating Keplerian elements # ========================================================================== a_k::T # ................................... Osculating semi-major axis [er] e_k::T # ....................................... Osculating eccentricity [ ] i_k::T # ...................................... Osculating inclination [rad] Ω_k::T # ............................................. Osculating RAAN [rad] ω_k::T # .............................. Osculating argument of perigee [rad] f_k::T # ..................................... Osculating true anomaly [rad] M_k::T # ..................................... Osculating mean anomaly [rad] end """ OrbitPropagatorJ2osc{T} <: OrbitPropagator{T} Structure that holds the information related to the J2 osculating orbit propagator. # Fields * `j2oscd`: Structure that stores the J2 osculating orbit propagator data (see `J2osc_Structure`). """ struct OrbitPropagatorJ2osc{T} <: OrbitPropagator{T} j2oscd::J2osc_Structure{T} end # J4 orbit propagator # ============================================================================== export J4_GravCte, J4_Structure, OrbitPropagatorJ4 """ J4_GravCte{T} Gravitational constants for J4 orbit propagator. # Fields * `R0`: Earth equatorial radius [m]. * `μm`: √GM [er/s]^(3/2). * `J2`: The second gravitational zonal harmonic of the Earth. * `J4`: The fourth gravitational zonal harmonic of the Earth. """ @with_kw struct J4_GravCte{T} R0::T μm::T J2::T J4::T end """ J4_Structure{T} Low level J4 orbit propagator structure. """ @with_kw mutable struct J4_Structure{T} # Initial mean orbital elements # ========================================================================== epoch::T # ............ Epoch of the initial mean elements [JD] al_0::T # ....... Initial mean normalized semi-major axis [er] n_0::T # ........................ Initial mean motion [rad/s] e_0::T # ..................... Initial mean eccentricity [ ] i_0::T # ..................... Initial mean inclination [rad] Ω_0::T # ............................ Initial mean RAAN [rad] ω_0::T # ............. Initial mean argument of perigee [rad] f_0::T # .................... Initial mean true anomaly [rad] M_0::T # .................... Initial mean mean anomaly [rad] dn_o2::T # ...... First time derivative of mean motion [rad/s²] ddn_o6::T # ..... Second time derivative of mean motion [rad/s³] j4_gc::J4_GravCte{T} # ........ J4 orbit propagator gravitational constants # Current mean orbital elements # ========================================================================== Δt::T # .............. Timespan from the epoch of the initial elements [s] al_k::T # ..................... Current mean normalized semi-major axis [er] e_k::T # ................................... Current mean eccentricity [ ] i_k::T # ................................... Current mean inclination [rad] Ω_k::T # .......................................... Current mean RAAN [rad] ω_k::T # ........................... Current mean argument of perigee [rad] f_k::T # .................................. Current mean true anomaly [rad] M_k::T # .................................. Current mean mean anomaly [rad] # Auxiliary variables # ========================================================================== δa::T # ........................... Semi-major axis time derivative [er/s] δe::T # ............................... Eccentricity time derivative [1/s] δΩ::T # ..................................... RAAN time derivative [rad/s] δω::T # ...................... Argument of perigee time derivative [rad/s] δM_0::T # ............................. Mean anomaly time derivative [rad/s] end """ OrbitPropagatorJ4{T} <: OrbitPropagator{T} Structure that holds the information related to the J4 orbit propagator. # Fields * `j4d`: Structure that stores the J4 orbit propagator data (see `J4_Structure`). """ struct OrbitPropagatorJ4{T} <: OrbitPropagator{T} j4d::J4_Structure{T} end # SGP4 # ============================================================================== export OrbitPropagatorSGP4 """ OrbitPropagatorSGP4{T} <: OrbitPropagator{T} Structure that holds the information related to the SGP4 propagator. # Fields * `sgp4d`: Structure that stores the SGP4 data (see `SGP4_Structure`). """ struct OrbitPropagatorSGP4{T} <: OrbitPropagator{T} sgp4d::SGP4_Structure{T} end
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1612, 32362, 4847, 198, 220, 220, 220, 1303, 38093, 2559, 28, 198, 220, 220, 220, 37455, 83, 3712, 51, 220, 220, 1303, 44912, 3782, 6839, 422, 262, 36835, 286, 262, 4238, 4847, 685, 82, 60, 198, 220, 220, 220, 435, 62, 74, 3712, 51, 1303, 44713, 12359, 9236, 1612, 39279, 10663, 12, 22478, 16488, 685, 263, 60, 198, 220, 220, 220, 304, 62, 74, 3712, 51, 220, 1303, 220, 8864, 986, 9236, 1612, 29303, 414, 685, 220, 2361, 198, 220, 220, 220, 1312, 62, 74, 3712, 51, 220, 1303, 220, 8864, 986, 9236, 1612, 36793, 685, 6335, 60, 198, 220, 220, 220, 7377, 102, 62, 74, 3712, 51, 220, 1303, 220, 8864, 2109, 492, 9236, 1612, 17926, 1565, 685, 6335, 60, 198, 220, 220, 220, 18074, 231, 62, 74, 3712, 51, 220, 1303, 220, 27754, 986, 9236, 1612, 4578, 286, 583, 328, 1453, 685, 6335, 60, 198, 220, 220, 220, 277, 62, 74, 3712, 51, 220, 1303, 220, 8864, 492, 9236, 1612, 2081, 32172, 685, 6335, 60, 198, 220, 220, 220, 337, 62, 74, 3712, 51, 220, 1303, 220, 8864, 492, 9236, 1612, 1612, 32172, 685, 6335, 60, 628, 220, 220, 220, 1303, 47105, 28129, 9633, 198, 220, 220, 220, 1303, 38093, 2559, 28, 198, 220, 220, 220, 7377, 112, 64, 3712, 51, 220, 220, 1303, 220, 27754, 986, 35525, 12, 22478, 16488, 640, 27255, 685, 263, 14, 82, 60, 198, 220, 220, 220, 7377, 112, 68, 3712, 51, 220, 220, 1303, 220, 27754, 25780, 38308, 22317, 414, 640, 27255, 685, 16, 14, 82, 60, 198, 220, 220, 220, 7377, 112, 138, 102, 3712, 51, 220, 220, 1303, 220, 8864, 12359, 17926, 1565, 640, 27255, 685, 6335, 14, 82, 60, 198, 220, 220, 220, 7377, 112, 49535, 3712, 51, 220, 220, 1303, 44713, 16317, 45751, 286, 583, 328, 1453, 640, 27255, 685, 6335, 14, 82, 60, 198, 220, 220, 220, 7377, 112, 44, 62, 15, 3712, 51, 1303, 220, 27754, 12359, 22728, 32172, 640, 27255, 685, 6335, 14, 82, 60, 198, 437, 198, 198, 37811, 198, 220, 220, 220, 38161, 24331, 363, 1352, 41, 17, 90, 51, 92, 1279, 25, 38161, 24331, 363, 1352, 90, 51, 92, 198, 198, 1273, 5620, 326, 6622, 262, 1321, 3519, 284, 262, 449, 17, 13066, 8928, 1352, 13, 198, 198, 2, 23948, 198, 198, 9, 4600, 73, 17, 67, 63, 25, 32522, 326, 7000, 262, 449, 17, 13066, 8928, 1352, 1366, 357, 3826, 198, 220, 220, 220, 220, 220, 220, 220, 220, 4600, 41, 17, 62, 1273, 5620, 63, 737, 198, 198, 37811, 198, 7249, 38161, 24331, 363, 1352, 41, 17, 90, 51, 92, 1279, 25, 38161, 24331, 363, 1352, 90, 51, 92, 198, 220, 220, 220, 474, 17, 67, 3712, 41, 17, 62, 1273, 5620, 90, 51, 92, 198, 437, 198, 198, 2, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 449, 17, 267, 1416, 8306, 13066, 8928, 1352, 198, 2, 38093, 25609, 28, 198, 198, 39344, 449, 17, 17500, 62, 13290, 315, 48715, 11, 38161, 24331, 363, 1352, 41, 17, 17500, 198, 198, 37811, 198, 220, 220, 220, 449, 17, 17500, 62, 1273, 5620, 90, 51, 92, 198, 198, 20535, 1241, 449, 17, 267, 1416, 8306, 13066, 8928, 1352, 4645, 13, 198, 198, 37811, 198, 31, 4480, 62, 46265, 4517, 540, 2878, 449, 17, 17500, 62, 1273, 5620, 90, 51, 92, 198, 220, 220, 220, 1303, 449, 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20768, 1612, 29303, 414, 685, 220, 2361, 198, 220, 220, 220, 1312, 62, 15, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 44713, 12359, 20768, 1612, 36793, 685, 6335, 60, 198, 220, 220, 220, 7377, 102, 62, 15, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 220, 27754, 1106, 20768, 1612, 17926, 1565, 685, 6335, 60, 198, 220, 220, 220, 18074, 231, 62, 15, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 20004, 12359, 20768, 1612, 4578, 286, 583, 328, 1453, 685, 6335, 60, 198, 220, 220, 220, 277, 62, 15, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 44713, 1106, 20768, 1612, 2081, 32172, 685, 6335, 60, 198, 220, 220, 220, 337, 62, 15, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 44713, 1106, 20768, 1612, 1612, 32172, 685, 6335, 60, 198, 220, 220, 220, 288, 77, 62, 78, 17, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 47082, 3274, 640, 27255, 286, 1612, 6268, 685, 6335, 14, 82, 31185, 60, 198, 220, 220, 220, 288, 32656, 62, 78, 21, 3712, 51, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1303, 11485, 986, 5498, 640, 27255, 286, 1612, 6268, 685, 6335, 14, 82, 126, 111, 60, 198, 220, 220, 220, 474, 19, 62, 36484, 3712, 41, 19, 62, 38, 4108, 34, 660, 90, 51, 92, 220, 1303, 20004, 449, 19, 13066, 8928, 1352, 29973, 38491, 628, 220, 220, 220, 1303, 9236, 1612, 32362, 4847, 198, 220, 220, 220, 1303, 38093, 2559, 28, 198, 220, 220, 220, 37455, 83, 3712, 51, 220, 220, 1303, 44912, 3782, 6839, 422, 262, 36835, 286, 262, 4238, 4847, 685, 82, 60, 198, 220, 220, 220, 435, 62, 74, 3712, 51, 1303, 44713, 12359, 9236, 1612, 39279, 10663, 12, 22478, 16488, 685, 263, 60, 198, 220, 220, 220, 304, 62, 74, 3712, 51, 220, 1303, 220, 8864, 986, 9236, 1612, 29303, 414, 685, 220, 2361, 198, 220, 220, 220, 1312, 62, 74, 3712, 51, 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2.980428
3,781
function clean(phone_number) end
[ 8818, 3424, 7, 4862, 62, 17618, 8, 198, 220, 220, 220, 220, 198, 437, 198 ]
2.533333
15
@testset "Ensembles" begin d = PointSet(rand(2,100)) r = (z=[1:100 for i in 1:10],) s = Ensemble(d, r) @test domain(s) == d @test s[:z] == r[:z] for i in 1:10 @test s[i] == georef((z=1:100,), d) end s1 = Ensemble(d, r) s2 = Ensemble(d, r) @test s1 == s2 @test sprint(show, s) == "2D Ensemble" @test sprint(show, MIME"text/plain"(), s) == "2D Ensemble\n domain: 100 PointSet{2,Float64}\n variables: z\n N° reals: 10" d = CartesianGrid(10,10) r = (z=[1:100 for i in 1:10],) s = Ensemble(d, r) @test domain(s) == d @test s[:z] == r[:z] for i in 1:10 @test s[i] == georef((z=1:100,), d) end @test sprint(show, s) == "2D Ensemble" @test sprint(show, MIME"text/plain"(), s) == "2D Ensemble\n domain: 10×10 CartesianGrid{2,Float64}\n variables: z\n N° reals: 10" if visualtests @test_reference "data/ensemble.png" plot(s,size=(800,300)) end grid = CartesianGrid(3,3) reals = (z=[i*ones(nelements(grid)) for i in 1:3],) ensemble = Ensemble(grid, reals) # mean mean2D = mean(ensemble) @test mean2D.z == 2.0*ones(nelements(mean2D)) @test domain(mean2D) == ensemble.domain # variance var2D = var(ensemble) @test var2D.z == 1.0*ones(nelements(var2D)) @test domain(var2D) == ensemble.domain # quantile (scalar) p = 0.5 quant2D = quantile(ensemble, p) @test quant2D.z == 2.0*ones(nelements(quant2D)) @test domain(quant2D) == ensemble.domain # quantile (vector) ps = [0.0, 0.5, 1.0] quants2D = quantile(ensemble, ps) @test quants2D[2].z == quant2D.z end
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2.20255
706
"" function get_solution_tf(pm::GenericPowerModel, sol::Dict{String,Any}) PowerModels.add_bus_voltage_setpoint(sol, pm) PowerModels.add_generator_power_setpoint(sol, pm) PowerModels.add_branch_flow_setpoint(sol, pm) PowerModels.add_dcline_flow_setpoint(sol, pm) add_risk(sol, pm) add_branch_shift_setpoint(sol, pm) add_branch_tap_setpoint(sol, pm) add_load_power_setpoint(sol, pm) add_nodal_power_setpoint(sol, pm) add_pdelta(sol, pm) add_qdelta(sol, pm) return sol end "" function add_branch_shift_setpoint(sol, pm::GenericPowerModel) PowerModels.add_setpoint(sol, pm, "branch", "shiftf", :va_shift; extract_var = (var,idx,item) -> var[(idx, item["f_bus"], item["t_bus"])], default_value = (item) -> 0) PowerModels.add_setpoint(sol, pm, "branch", "shiftt", :va_shift; extract_var = (var,idx,item) -> var[(idx, item["t_bus"], item["f_bus"])], default_value = (item) -> 0) end "" function add_branch_tap_setpoint(sol, pm::GenericPowerModel) dict_name = "branch" index_name = "index" if pm.data["multinetwork"] == false sol_dict = get(sol, dict_name, Dict{String,Any}()) if length(pm.data[dict_name]) > 0 sol[dict_name] = sol_dict end for (i,item) in pm.data[dict_name] idx = Int(item[index_name]) fbus = Int(item["f_bus"]) tbus = Int(item["t_bus"]) sol_item = sol_dict[i] = get(sol_dict, i, Dict{String,Any}()) sol_item["tapf"] = 1 sol_item["tapt"] = 1 try extract_vtap_fr = (var,idx,item) -> var[(idx, item["f_bus"], item["t_bus"])] extract_vtap_to = (var,idx,item) -> var[(idx, item["t_bus"], item["f_bus"])] vtap_fr = getvalue(extract_vtap_fr(PowerModels.var(pm, :vm_tap), idx, item)) vtap_to = getvalue(extract_vtap_to(PowerModels.var(pm, :vm_tap), idx, item)) vf = getvalue(PowerModels.var(pm,:vm))[fbus] vt = getvalue(PowerModels.var(pm,:vm))[tbus] sol_item["tapf"] = vf / vtap_fr sol_item["tapt"] = vt / vtap_to catch end end else for (n,nw_data) in pm.data["nw"] sol_dict = get(sol, dict_name, Dict{String,Any}()) if length(pm.data["nw"][n][dict_name]) > 0 sol[dict_name] = sol_dict end for (i,item) in pm.data["nw"][n][dict_name] idx = Int(item[index_name]) fbus = Int(item["f_bus"]) tbus = Int(item["t_bus"]) sol_item = sol_dict[i] = get(sol_dict, i, Dict{String,Any}()) sol_item["tapf"] = 1 sol_item["tapt"] = 1 try extract_vtap_fr = (var,idx,item) -> var[(idx, item["f_bus"], item["t_bus"])] extract_vtap_to = (var,idx,item) -> var[(idx, item["t_bus"], item["f_bus"])] vtap_fr = getvalue(extract_vtap_fr(PowerModels.var(pm, :vm_tap), idx, item)) vtap_to = getvalue(extract_vtap_to(PowerModels.var(pm, :vm_tap), idx, item)) vf = getvalue(PowerModels.var(pm,:vm))[fbus] vt = getvalue(PowerModels.var(pm,:vm))[tbus] sol_item["tapf"] = vf / vtap_fr sol_item["tapt"] = vt / vtap_to catch end end end end end "" function add_load_power_setpoint(sol, pm::GenericPowerModel) mva_base = pm.data["baseMVA"] PowerModels.add_setpoint(sol, pm, "load", "pl", :pl) PowerModels.add_setpoint(sol, pm, "load", "ql", :ql) end "" function add_nodal_power_setpoint(sol, pm::GenericPowerModel) mva_base = pm.data["baseMVA"] PowerModels.add_setpoint(sol, pm, "bus", "pnode", :pnode) PowerModels.add_setpoint(sol, pm, "bus", "qnode", :qnode) end "" function add_pdelta(sol, pm::GenericPowerModel) mva_base = pm.data["baseMVA"] PowerModels.add_setpoint(sol, pm, "load", "pdelta", :pl_delta) PowerModels.add_setpoint(sol, pm, "gen", "pdelta", :pg_delta) end "" function add_qdelta(sol, pm::GenericPowerModel) mva_base = pm.data["baseMVA"] PowerModels.add_setpoint(sol, pm, "load", "qdelta", :ql_delta) PowerModels.add_setpoint(sol, pm, "gen", "qdelta", :qg_delta) end "" function add_risk(sol, pm::GenericPowerModel) add_case_level_setpoint(sol, pm, "first_stage_cost", :first_stage_cost) add_case_level_setpoint(sol, pm, "second_stage_risk", :second_stage_risk) PowerModels.add_setpoint(sol, pm, "contingencies", "dispatch_cost", :dispatch_cost) PowerModels.add_setpoint(sol, pm, "contingencies", "redispatch_cost", :redispatch_cost) PowerModels.add_setpoint(sol, pm, "contingencies", "loadshedding_cost", :loadshedding_cost) # add_cont_level_setpoint(sol, pm, "contingencies", "dispatch_cost", :dispatch_cost) # add_cont_level_setpoint(sol, pm, "contingencies", "redispatch_cost", :redispatch_cost) # add_cont_level_setpoint(sol, pm, "contingencies", "loadshedding_cost", :loadshedding_cost) end "" function add_case_level_setpoint(sol, pm::GenericPowerModel, param_name, variable_symbol; scale = (x) -> x, extract_var = var -> var) if !haskey(sol, param_name) sol[param_name] = Dict() end # print(pm.var[:nw][1][:cnd][pm.ccnd][variable_symbol]) # print(sol) variable = extract_var(pm.var[:nw][1][:cnd][pm.ccnd][variable_symbol]) sol[param_name] = scale(getvalue(variable)) # catch # end end "" function add_cont_level_setpoint(sol, pm::GenericPowerModel, dict_name, param_name, variable_symbol; scale = (x) -> x, extract_var = var -> var) if !haskey(sol, dict_name) sol[dict_name] = Dict() end sol[dict_name][param_name] = Dict() sol[dict_name][param_name] = getvalue(extract_var(pm.var[:nw][1][:cnd][pm.ccnd][variable_symbol])) end
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2.024872
2,935
using Random using LightGraphs mutable struct Agent id::Int32 values::AbstractArray vote::Int8 energy::Float32 end function initNetwork(N) #Initialize agents global nodes = [] for i in 1:N push!(nodes,Agent(i,rand(0:10,(5)),rand(1:10),0)) end #= #Generate graph with N A = bitrand((N,N)) #Adjacency matrix #TODO:Make sparse for i in 1:N A[i,i] = 0 #Make sure nodes are not be connected to themselves end global Network = DiGraph(A) #Generate graph A = nothing #Clear A #We are now implementing a Barabasi-Albert graph, instead of a random one =# #Generate Barabasi graph with N nodes, 3 conntections each, 10 initial nodes global Network = barabasi_albert(N, 10, 3, seed=1, is_directed=true) #global Network = erdos_renyi(N, 4, is_directed=true) #Initialize energy for i in 1:N dE(i) end computeEnergy() #Count initial preferences trackPreference() end
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2.437653
409
using Plots using Random Random.seed!(123) # Solver options tf = 5.0 N = 101 integration = :rk4 opts = SolverOptions() opts.verbose = true opts.cost_tolerance = 1e-8 opts.cost_tolerance_intermediate = 1e-8 opts.constraint_tolerance = 1e-8 opts.constraint_tolerance_intermediate = 1e-8 opts.gradient_norm_tolerance = 1e-12 opts.gradient_norm_tolerance_intermediate = 1e-12 opts.use_gradient_aula = true opts.active_constraint_tolerance = 0.0 opts.penalty_scaling = 10.0 opts.penalty_initial = 1e-3 opts.penalty_update_frequency = 1 opts.constraint_decrease_ratio = .25 opts.iterations = 1000 opts.iterations_outerloop = 50 opts.iterations_innerloop = 300 opts.outer_loop_update_type = :individual # Set up model, objective, solver model, = TrajectoryOptimization.Dynamics.quadrotor n = model.n m = model.m # -initial state x0 = zeros(n) x0[1:3] = [0.; 0.; 0.] q0 = [1.;0.;0.;0.] x0[4:7] = q0 # -final state xf = copy(x0) xf[1:3] = [0.;40.;0.] # xyz position xf[4:7] = q0 # -control limits u_min = 0.0 u_max = 10.0 # Q = (1e-1)*Matrix(I,n,n) # Q[4,4] = 1.0 # Q[5,5] = 1.0 # Q[6,6] = 1.0 # Q[7,7] = 1.0 # R = (1e-1)*Matrix(I,m,m) Q = (1e-1)*Matrix(I,n,n) R = (1e-1)*Matrix(I,m,m) Qf = (1000.0)*Matrix(I,n,n) # obstacles constraint r_quad = 3.0 r_sphere = 3.0 spheres = ((0.,10.,0.,r_sphere),(0.,20.,0.,r_sphere),(0.,30.,0.,r_sphere)) n_spheres = 3 function cI(c,x,u) for i = 1:n_spheres c[i] = sphere_constraint(x,spheres[i][1],spheres[i][2],spheres[i][3],spheres[i][4]+r_quad) end c end # unit quaternion constraint function cE(c,x,u) c = sqrt(x[4]^2 + x[5]^2 + x[6]^2 + x[7]^2) - 1.0 end obj_uncon = LQRObjective(Q, R, Qf, tf, x0, xf) obj_con = TrajectoryOptimization.ConstrainedObjective(obj_uncon,u_min=u_min,u_max=u_max,cI=cI,cE=cE) solver_uncon = Solver(model,obj_uncon,integration=integration,N=N,opts=opts) solver_con = Solver(model,obj_con,integration=integration,N=N,opts=opts) U0 = zeros(solver_uncon.model.m, solver_uncon.N-1) @time results_uncon, stats_uncon = solve(solver_uncon,U0) plot(to_array(results_uncon.X)[1:3,:]') solver_con = Solver(model,obj_con,integration=integration,N=N,opts=opts) results_con.μ @time results_con, stats_con = solve(solver_con,U0) plot(to_array(results_con.U)[:,1:solver_con.N-1]') plot(to_array(results_con.X)[1:3,:]') plot(to_array(results_con.λ[1:N-1])') max_violation(results_con) total_time(solver_con,results_con) plot!(log.(stats_con["max_condition_number"])) plot(log.(stats_con["c_max"]).+15.5) plot(log.(stats_con["cost"]))
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<html> <head> <style type="text/css"> body { margin: 2em; } h1 { text-align: center; } table { border-spacing: 0; box-shadow: 0 0 0.25em #888; margin: auto; } table, tr, th, td { border-collapse: collapse; } th { color: white; background-color: rgb(43, 53, 59); } th, td { padding: 0.5em; } table tr:nth-child(even) td { background-color: rgba(218, 224, 229, 0.850); } </style> </head> <body> <h1>csv2html Example</h1> <table> <tr> <th>Character</th> <th>Speech</th> </tr> <tr> <td>The multitude</td> <td>The messiah! Show us the messiah!</td> </tr> <tr> <td>Brians mother</td> <td>Now you listen here! He's not the messiah; he's a very naughty boy! Now go away!</td> </tr> <tr> <td>The multitude</td> <td>Who are you?</td> </tr> <tr> <td>Brians mother</td> <td>I'm his mother; that's who!</td> </tr> <tr> <td>The multitude</td> <td>Behold his mother! Behold his mother!</td> </tr> </table> </body> </html>
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# length(pdp::VkPhysicalDeviceProperties) = 1 # iterate(pdp::VkPhysicalDeviceProperties) = (pdp, nothing)
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# Test Linear System Optimizaiton using SwitchTimeOpt using MathOptInterface using LinearAlgebra const MOI = MathOptInterface using Ipopt # Define test options objtol = 1e-04 primaltol = 1e-03 # Define Solver options maxiter = 50 maxtime = 100.0 verbose = 0 tolerance = 1e-06 #Define solver solver = Ipopt.Optimizer() MOI.set(solver, MOI.RawOptimizerAttribute("tol"), tolerance) MOI.set(solver, MOI.RawOptimizerAttribute("print_level"), verbose) MOI.set(solver, MOI.RawOptimizerAttribute("max_cpu_time"), maxtime) MOI.set(solver, MOI.RawOptimizerAttribute("max_iter"), maxiter) # Size of the state space nx = 2; # Cost function Matrix Q = 1.0 * Matrix(I, nx, nx) # Initial State x0 = [1.0; 1.0] # Define initial and final time t0 = 0.0 tf = 1.0 ### Define System Dynamics A = zeros(nx, nx, 2) A[:, :, 1] = [-1 0; 1 2] A[:, :, 2] = [1 1; 1 -2] m = stoproblem(x0, A, solver=solver) solve!(m) @testset "Test optimal switching time" begin @test isapprox(gettau(m)[1], 0.26486646235103123, atol=primaltol) end @testset "Test status and optimal objective value" begin @test isapprox(getobjval(m), 5.2545429449272145, atol=objtol) @test string(getstat(m)) == "LOCALLY_SOLVED" end
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## ScalarAffineFunction-in-Set ## ScalarQuadraticFunction-in-Set function _build_constraint( model::Optimizer, f::F, s::MOI.GreaterThan{T}, ) where { T <: Real, F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}}, } return cpo_java_ge(model.inner, _parse(model, f), s.lower) end function _build_constraint( model::Optimizer, f::F, s::MOI.LessThan{T}, ) where { T <: Real, F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}}, } return cpo_java_le(model.inner, _parse(model, f), s.upper) end function _build_constraint( model::Optimizer, f::F, s::MOI.EqualTo{T}, ) where { T <: Real, F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}}, } return cpo_java_eq(model.inner, _parse(model, f), s.value) end function _build_constraint( model::Optimizer, f::F, s::MOI.EqualTo{T}, ) where { T <: Integer, F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}}, } return cpo_java_eq(model.inner, _parse(model, f), Int32(s.value)) end function _build_constraint( model::Optimizer, f::F, s::MOI.Interval{T}, ) where { T <: Real, F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}}, } if s.lower == Inf return _build_constraint(model, f, MOI.LessThan(s.upper)) elseif s.upper == Inf return _build_constraint(model, f, MOI.GreaterThan(s.lower)) elseif s.lower == s.upper return _build_constraint(model, f, MOI.EqualTo(s.lower)) end return cpo_java_range(model.inner, s.lower, _parse(model, f), s.upper) end # No vector of constraints, there is no more efficient way to do it. function MOI.delete( model::Optimizer, c::MOI.ConstraintIndex{F, S}, ) where { T <: Real, F <: Union{MOI.ScalarAffineFunction{T}, MOI.ScalarQuadraticFunction{T}}, S <: Union{MOI.GreaterThan{T}, MOI.LessThan{T}, MOI.EqualTo{T}, MOI.Interval{T}}, } cpo_java_remove(model.inner, _info(model, c).constraint) delete!(model.constraint_info, c) return end # TODO: function MOI.set(model::Optimizer, ::MOI.ConstraintSet, c::MOI.ConstraintIndex{MOI.ScalarAffineFunction{Float64}, S}, s::S) where {S} ## VectorOfVariables-in-SOS{I|II} # Not available. Bridge it? ## VectorOfVariables-in-SecondOrderCone function _build_constraint( model::Optimizer, f::MOI.VectorOfVariables, s::MOI.SecondOrderCone, ) if length(f.variables) != s.dimension error("Dimension of $(s) does not match number of terms in $(f)") end # First, check the lower bound on t. t_info = _info(model, f.variables[1]) if !_has_lb(model, t_info.index) || _get_lb(model, t_info.index) < 0.0 if _get_lb(model, t_info.index) < 0.0 t_info.old_lb = _get_lb(model, t_info.index) end t_info.n_socs += 1 _set_lb(model, t_info.index, 0.0) end # Then, add the quadratic constraint. expr = _parse(model, f, s) return cpo_java_gt(model, expr, 0) end function MOI.delete( model::Optimizer, c::MOI.ConstraintIndex{MOI.VectorOfVariables, MOI.SecondOrderCone}, ) # Remove the constraint. cpo_java_remove(model, _info(model, c).constraint) # Maybe restore the old bound on t. t_info = _info(model, f.variables[1]) t_info.n_socs -= 1 if t_info.n_socs == 0 _set_lb(model, t_info.index, t_info.old_lb, typeof(t_info.old_lb)) t_info.old_lb = nothing end return end
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2.172414
1,624
module InterfaceTesting using Base.Test export partial_method_exists, test_iterator_interface, test_index_interface, test_abstractarray_interface """ Checks if a method exist. The first parameter is the function. The second parameter is a list of types, or wild cards. THe `:` is used as a wildcard. Wild cards match any type (Not just `Any`) """ function partial_method_exists(f, types) meths = methods(f) for mm in meths @show mm sig_types = mm.sig.types[2:end] length(types) != length(sig_types) && continue for (spec, sig) in zip(types, sig_types) spec==(:) && continue spec <: sig || @goto next_meth end return true @label next_meth end return false end """ Checks that all the method expected of an Interator are defined. This is the most formal and common of informal interfaced. It is practically useless to not implement it properly. """ function test_iterator_interface(itertype; testset_type=Test.DefaultTestSet) @testset testset_type "$(itertype)" begin #Basic Methods @test method_exists(start, (itertype,)) @test partial_method_exists(next, (itertype,:)) @test partial_method_exists(done, (itertype,:)) #Iterators size @test which(Base.iteratorsize, (itertype,)) == which(Base.iteratorsize, (Any,))#Method for Instance of type should always be the fall back -- never set directly if Base.iteratorsize(itertype)==Base.HasShape() @test method_exists(size, (itertype,)) @test method_exists(length, (itertype,)) elseif Base.iteratorsize(itertype)==Base.HasLength() @test method_exists(length, (itertype,)) else @test Base.iteratorsize(itertype) in (Base.SizeUnknown(), Base.IsInfinite()) end #Iterator Eltype if Base.iteratoreltype(itertype) == Base.HasEltype() @test method_exists(eltype, (itertype,)) end end end """ Checks Indexing Interface implemented. This is the least formal of informal interfaces. There a plenty of useful partial implementatons of it. Eg a read-only things """ function test_index_interface(kind; testset_type=Test.DefaultTestSet) @testset testset_type "$(kind)" begin if method_exists(getindex, (kind, Vararg)) @test partial_method_exists(size, (kind, :)) #otherwise `end` won't work in multidimentional index expressions else #Assume 1D indexing only has been defined @test partial_method_exists(getindex, (kind,:)) end @test partial_method_exists(setindex!, (kind,:,:)) #Got to have something. 1D will do. Might have more, kinda complex @test method_exists(endof, (kind,)) #If not defined then `end` won't work end end """ Checks AbstractArray Interface implemented. This is required for all subtypes of AbstractArray. Filling this will mean automatically furfilling `test_index_interface` """ function test_abstractarray_interface(kind; testset_type=Test.DefaultTestSet) @testset testset_type "$(kind)" begin @test method_exists(size, (kind,)) @test method_exists(getindex, (kind, Vararg{Int})) @test partial_method_exists(setindex!, (kind, :, :)) end end end # module
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2.61326
1,267
###################################################################### # Suite of macros to automatically generate getters and setters for # object properties along with the accompanying `hasproperty` and # `propertynames` specializations. # ----- # Licensed under MIT License module GenerateProperties const Optional{T} = Union{T, Nothing} const GetterSetterBody = Tuple{Optional{LineNumberNode}, Expr} struct StructProp{S, F} end struct StructField{S, F} end assign(inst, field::Symbol, value) = assign(StructProp{typeof(inst), field}, inst, value) assign( ::Type{StructProp{ S, F}}, inst::S, value) where {S, F} = assign(StructField{S, F}, inst, value) assign(T::Type{StructField{S, F}}, inst::S, value) where {S, F} = assign(getfieldtype(T), inst, F, value) assign(::Type{T}, inst, field::Symbol, value) where {T} = setfield!(inst, field, convert(T, value)) assign(::Type{T1}, inst, field::Symbol, value::T2) where {T1, T2<:T1} = setfield!(inst, field, value) assign(::Type{T1}, inst, field::Symbol, value::T2) where {T1<:Number, T2<:Number} = setfield!(inst, field, T1(value)) retrieve(inst, field::Symbol) = retrieve(StructProp{typeof(inst), field}, inst) retrieve(::Type{StructProp{ S, F}}, inst::S) where {S, F} = retrieve(StructField{S, F}, inst) retrieve(::Type{StructField{S, F}}, inst::S) where {S, F} = getfield(inst, F) getparambody(x) = x getparambody(unionall::UnionAll) = getparambody(unionall.body) getfieldtype(S::Type, F::Symbol) = getfieldtype(StructField{S, F}) @generated function getfieldtype(::Type{StructField{S, F}}) where {S, F} @assert !isa(S, UnionAll) "Cannot reliably use getfieldtype with UnionAll" idx = findfirst(field->field==F, fieldnames(S)) @assert idx !== nothing "Field $F not found in type $S" T = S.types[idx] :($T) end export @generate_properties macro generate_properties(T, block) if !isa(block, Expr) || block.head != :block throw(ArgumentError("Second argument to @generate_properties must be a block")) end result = Expr(:block) props = Set{Symbol}() symget = Symbol("@get") symset = Symbol("@set") symeq = Symbol("=") lastlinenumber = nothing for expr ∈ block.args if isa(expr, LineNumberNode) lastlinenumber = expr else if expr.head != :macrocall || expr.args[1] ∉ (symget, symset) throw(ArgumentError("Every line must be a call to either @get or @set")) end args = filterlinenumbers(expr.args) if args[2].head != symeq throw(ArgumentError("Getter/Setter not an assignment")) end prop, body = filterlinenumbers(args[2].args) body = replace_self(T, body) push!(props, prop) if expr.args[1] == symget push!(result.args, generate_getter(T, prop, lastlinenumber, body)) elseif expr.args[1] == symset push!(result.args, generate_setter(T, prop, lastlinenumber, body)) end lastlinenumber = nothing end end # Generate propertynames push!(result.args, quote @generated function Base.propertynames(::$T) res = tuple(union($props, fieldnames($T))...) :($res) end end) push!(result.args, :(Base.getproperty(self::$T, prop::Symbol) = GenerateProperties.retrieve(self, prop))) push!(result.args, :(Base.setproperty!(self::$T, prop::Symbol, value) = GenerateProperties.assign(self, prop, value))) esc(result) end export @get, @set macro get(args...) end macro set(args...) end filterlinenumbers(exprs) = filter(expr->!isa(expr, LineNumberNode), exprs) replace_self(_, expr) = expr function replace_self(T::Union{Symbol, Expr}, expr::Expr) @assert isa(T, Symbol) || T.head == :curly if expr.head == :(=) lhs, rhs = expr.args if isa(lhs, Expr) && lhs.head == :. && lhs.args[1] == :self prop = lhs.args[2]::QuoteNode expr = :(GenerateProperties.assign(self, $rhs)) insert!(expr.args, 2, structfieldexpr(prop)) end elseif expr.head == :. if expr.args[1] == :self prop = expr.args[2]::QuoteNode expr = :(GenerateProperties.retrieve(self)) insert!(expr.args, 2, structfieldexpr(prop)) end end expr.args = map(sub->replace_self(T, sub), expr.args) expr end function generate_getter(T, prop::Symbol, linenumber::Optional{LineNumberNode}, body) block = Expr(:block) if linenumber !== nothing push!(block.args, linenumber) end push!(block.args, body) fnexpr = :(GenerateProperties.retrieve(self::$T) = $block) insert!(fnexpr.args[1].args, 2, argsubtypeexpr(structpropexpr(T, prop))) fnexpr end function generate_setter(T, prop::Symbol, linenumber::Optional{LineNumberNode}, body) block = Expr(:block) if linenumber !== nothing push!(block.args, linenumber) end push!(block.args, body) fnexpr = :(GenerateProperties.assign(self::$T, value) = $block) insert!(fnexpr.args[1].args, 2, argsubtypeexpr(structpropexpr(T, prop))) fnexpr end structpropexpr(T, prop::QuoteNode) = Expr(:curly, :(GenerateProperties.StructProp), :(<:$T), prop) structpropexpr(T, prop::Symbol) = structpropexpr(T, QuoteNode(prop)) structfieldexpr(prop::QuoteNode) = Expr(:curly, :(GenerateProperties.StructField), :(typeof(self)), prop) structfieldexpr(prop::Symbol) = structfieldexpr(QuoteNode(prop)) function argtypeexpr(type::Expr) Expr(:(::), Expr(:curly, :Type, type)) end function argsubtypeexpr(type::Expr) Expr(:(::), Expr(:curly, :Type, Expr(:<:, type))) end end # module GenerateProperties
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import Base: +, -, *, /, length, iterate, zero, convert, Vector, Array """ This correspond to coordinate of atom. This contain 3 fields like below. - x <: Real - y <: Real - z <: Real Some operators and functions were overloaded to make Coordinate objects broadcastable. """ mutable struct Coordinate{RealT <: Real} # This class is for make vector operation broadcastable. x::RealT y::RealT z::RealT function Coordinate(vector::VectorT ) where VectorT <: AbstractArray{RealT} where RealT <: Real if length(vector) != 3 ArgumentError("Argument for Coordinate constructor must be array of length 3") end new{RealT}(vector[1], vector[2], vector[3]) end function Coordinate(x::RealT, y::RealT, z::RealT) where RealT <: Real new{RealT}(x, y, z) end end function +(arg1::Coordinate{T}, arg2::Coordinate{U}) where T <: Real where U <: Real Coordinate(arg1.x + arg2.x, arg1.y + arg2.y, arg1.z + arg2.z) end function -(arg1::Coordinate{T}, arg2::Coordinate{U}) where T <: Real where U <: Real Coordinate(arg1.x - arg2.x, arg1.y - arg2.y, arg1.z - arg2.z) end function *(arg1::Coordinate{T}, arg2::U) where T <: Real where U <: Real Coordinate(arg1.x * arg2, arg1.y * arg2, arg1.z * arg2) end function *(arg1::U, arg2::Coordinate{T}) where T <: Real where U <: Real Coordinate(arg1 * arg2.x, arg1 * arg2.y, arg1 * arg2.z) end function *(arg1::Coordinate{T}, arg2::Coordinate{U}) where T <: Real where U <: Real arg1.x * arg2.x + arg1.y * arg2.y + arg1.z * arg2.z end function /(arg1::Coordinate{T}, arg2::U) where T <: Real where U <: Real Coordinate(arg1.x / arg2, arg1.y / arg2, arg1.z / arg2) end # for operator broadcast function length(arg::Coordinate{T}) where T <: Real 1 end function iterate(arg::Coordinate{T}) where T <: Real (arg, nothing) end function iterate(arg::Coordinate{T}, nothing) where T <: Real nothing end function zero(arg::Coordinate{T}) where T <: Real Coordinate(T(0.0), T(0.0), T(0.0)) end function convert(::Type{Coordinate{RealT}}, coord::Coordinate{<:Real}) where RealT <: Real return Coordinate(RealT(coord.x), RealT(coord.y), RealT(coord.z)) end function Vector(arg::Coordinate{RealT}) where RealT <: Real [arg.x, arg.y, arg.z] end function Array(arg::Coordinate{RealT}) where RealT <: Real [arg.x, arg.y, arg.z] end function norm(arg::Coordinate{T}) where T <: Real sqrt(abs2(arg.x) + abs2(arg.y) + abs2(arg.z)) end function distance(first_coord::Coordinate{T}, second_coord::Coordinate{T}) where T <: Real norm(first_coord - second_coord) end
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const Orbits = [(1,0), (2,0), (0,1), (1,1), (0,2)] """ flatten(pdm::Dict{Int, <:PersistenceDiagram}) -> PersistenceDiagram Flatten a multi-dimensional pd into a barcode. """ flatten(pdm::Dict{Int, PD}) where {T<:Real, PD<:PersistenceDiagram{T}} = (collect∘Iterators.flatten∘values)(pdm) """ maxsize(pds) -> Int Return the number of bars in the largest barcode from the collection of barcodes `pds` """ maxsize(pds::AbstractVector{PD}) where {T<:Real, PD<:PersistenceDiagram{T}} = maximum(map(length, pds)) tropicdim(n) = n+(div(n*(n+1),2)) # codomain dimension function tropicstats(pds::AbstractVector{PD}) where {T<:Real, PD<:PersistenceDiagram{T}} n = ComputationalHomology.maxsize(pds) d = ComputationalHomology.tropicdim(n) maxval = filter!(!isinf, map(i->max(birth(i),death(i)), Iterators.flatten(pds))) |> maximum m = map(i->birth(i)/-ComputationalHomology.birthx(i), Iterators.flatten(pds)) |> maximum (d, n, ceil(Int, m), maxval) end """ Caluclate 2-symmetric tropical rational polynomial for the persistence diagram """ function tropicp(pd::PersistenceDiagram{T},m,l,p; maxval=T(Inf)) where {T<:Real} B = Tuple{T,T}[] for i in pd d = min(maxval, death(i)) b = birth(i) push!(B, (min(m*(d-b), b), d-b)) end # pad zeros while length(B) < l+p push!(B, (zero(T), zero(T))) end n = length(B) if l == 0 && p == 0 0 elseif l == 0 sort!(map(sum, B), rev=true)[1:p] |> sum elseif p == 0 sort!(map(last,B), rev=true)[1:l] |> sum else res = 0 L = hcat(combinations(1:n,l)...)' for i in 1:length(L) ii = L[i,:] e0 = sum(last, B[ii]) K = hcat(combinations(setdiff(1:n, ii),p)...)' for j = 1:length(K) jj = K[j,:] e1 = sum(map(first, B[jj]) .+ map(last, B[jj])) res = max(res, e0 + e1) end end res end end """ tropic(pd) Calculate a tropical coordinates for a persistent diagram `pd` Paper: "Tropical Sufficient Statistics for Persistent Homology" by A.Monod et al. Ref: https://arxiv.org/abs/1709.02647 """ function tropic(pds::AbstractVector{PD}) where {T<:Real, PD<:PersistenceDiagram{T}} d,n,m,mv = ComputationalHomology.tropicstats(pds) [tropicp(pd,m,i,j,maxval=mv) for (i,j) in ComputationalHomology.Orbits, pd in pds ] end tropic(pd::PersistenceDiagram{T}) where {T<:Real} = tropic([pd]) |> vec tropic(pdd::Dict{Int, PD}) where {T<:Real, PD<:PersistenceDiagram{T}} = tropic(flatten(pdd))
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2.113729
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using ImageFiltering using Statistics using StatsBase function badpixremoval_clump(frame::AbstractMatrix{T}; max_niter=15, kwargs...) where T # create bad pixel map by sigma clipping outliers bad_pixels = badpixel_clip(frame; kwargs...) bad_pixels_cumul = copy(bad_pixels) total = sum(bad_pixels) # iterate replacing bad pixels with sigma clipping out = copy(frame) it = 0 while total > 0 && it < max_niter it += 1 out = sigma_filter!(out, bad_pixels) bad_pixels = badpixel_clip(out) bad_pixels_cumul .|= bad_pixels total = sum(bad_pixels) end return out, bad_pixels_cumul end function badpixel_clip(frame; sigma=4, box_size=9) med = mapwindow(median, frame, (box_size, box_size); border="reflect") std = mapwindow(mad, frame, (box_size, box_size); border="reflect") return @. abs(frame - med) > sigma * std end function sigma_filter!(frame, bad_pixels; box_size=9) bpm = copy(bad_pixels) min_neigh = sum(3:2:box_size) half_box = box_size ÷ 2 while sum(bpm) > 0 pad_bpm = BorderArray(bpm, Pad(:reflect, half_box, half_box)) pad_im = BorderArray(frame, Pad(:reflect, half_box, half_box)) for idx in findall(bpm) ax1 = idx.I[1] - half_box:idx.I[1] + half_box ax2 = idx.I[2] - half_box:idx.I[2] + half_box block_bpm = @view pad_bpm[ax1, ax2] # ensure enough "good" neighbors for imputation if sum(block_bpm) < min_neigh block_im = @view pad_im[ax1, ax2] frame[idx] = median(block_im) bpm[idx] = false end end end return frame end
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2.134253
797
using Dates function hfun_datetext(date=locvar("rss_pubdate")) datetext = "$(monthname(date)) $(day(date)), $(year(date))" return datetext end # Based on https://github.com/JuliaLang/www.julialang.org/blob/master/utils.jl function hfun_blogposts() curyear = year(Dates.today()) io = IOBuffer() for year in curyear:-1:2020 ys = "$year" for month in 12:-1:1 ms = "0"^(month < 10) * "$month" base = joinpath("blog", ys, ms) isdir(base) || continue posts = filter!(p -> endswith(p, ".md"), readdir(base)) days = zeros(Int, length(posts)) lines = Vector{String}(undef, length(posts)) for (i, post) in enumerate(posts) ps = splitext(post)[1] url = "/blog/$ys/$ms/$ps/" surl = strip(url, '/') title = pagevar(surl, :title) rss = pagevar(surl, :rss) date = pagevar(surl, :rss_pubdate) datetext = hfun_datetext(date) days[i] = day(date) lines[i] = """ ~~~ <div class="card border-dark mb-4"> <div class="card-body"> <h3 class="card-title"> <a class="text-dark" href="$url">$title</a> </h3> <p class="card-text">$rss</p> <p class="card-text text-muted">$datetext</p> </div> </div> ~~~ """ end # Sort by day foreach(line -> write(io, line), lines[sortperm(days, rev=true)]) end end # markdown conversion adds `<p>` beginning and end but # we want to avoid this to avoid an empty separator r = Franklin.fd2html(String(take!(io)), internal=true) return r end
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1.769369
1,110
""" `PrincipalAxes` is an abstract type to be used as the argument of a function. """ abstract PrincipalAxes
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3.483871
31
using BenchmarkTools import ParticleSwarmOptimizer pso = ParticleSwarmOptimizer pso.get_localbest([0.5, 0.5, 0.5, 0.4, 0.5], [2, 4, 5], <) @benchmark pso.get_localbest([0.5, 0.5, 0.5, 0.4, 0.5], [2, 4, 5], <) @benchmark pso.get_localbest([0.5, 0.5, 0.5, 0.4, 0.5], [2], <)
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1.923077
143
""" loglike_beta(nij, A, cij) Profile likelihood for the frequency response vectors. *Reference*: Based on Equation (9) of Stompor et al., MNRAS, 392, 216 (2009) # Arguments - `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands. - `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky. - E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components. - `cij::Array{<:AbstractFloat,2}`: symmetric covariance matrix with the dimention of `(nν, nν)`. """ function loglike_beta( nij::Array{T,2}, A::Array{T,2}, cij::Array{T,2}, ) where {T<:AbstractFloat} if size(nij)[1] ≠ size(nij)[2] || size(cij)[1] ≠ size(cij)[2] throw(DimensionMismatch("covariance matrix must be a square matrix")) elseif size(nij)[1] ≠ size(A)[1] || size(nij)[1] ≠ size(cij)[1] throw(DimensionMismatch("dimensions of input matrices do not match")) else N = Symmetric(nij) M = Symmetric(cij) end Ninv = inv(N) lnlike = 0.5 * tr(Ninv * A * inv(A' * Ninv * A) * A' * Ninv * M) end """ loglike_beta(nij, A, d) Profile likelihood for the frequency response vectors. *Reference*: Equation (9) of Stompor et al., MNRAS, 392, 216 (2009) # Arguments - `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands. - `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky. - E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components. - `d::Array{<:AbstractFloat,1}`: data vector for a given pixel (or any other appropriate domain). The number of elements is `nν`. """ function loglike_beta( nij::Array{T,2}, A::Array{T,2}, d::Array{T,1}, ) where {T<:AbstractFloat} if size(nij)[1] ≠ size(nij)[2] throw(DimensionMismatch("covariance matrix must be a square matrix")) elseif size(nij)[1] ≠ size(A)[1] || size(nij)[1] ≠ length(d) throw(DimensionMismatch("dimensions of matrices/vector do not match")) else M = Symmetric(nij) end x = M \ d s = inv(A' * inv(M) * A) * A'x lnlike = 0.5 * x'A * s end """ loglike_beta_grad(nij, A, dAdβ, d) Derivative of the profile likelihood for the frequency response vectors with respect to a foreground parameter *Reference*: Equation (A1) of Errard et al., PRD, 84, 063005 (2011) # Arguments - `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands. - `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky. - E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components. - `dAdβ::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the derivative of the frequency response with respect to a foreground parameter. - E.g., ``dAdβ = [zeros(nν) dsynch/dβs zeros(nν)]`` where `zeros(nν)` for CMB and dust because they do not depend on the synchrotron index `βs`. - `d::Array{<:AbstractFloat,1}`: data vector for a given pixel (or any other appropriate domain). The number of elements is `nν`. """ function loglike_beta_grad( nij::Array{T,2}, A::Array{T,2}, dAdβ::Array{T,2}, d::Array{T,1}, ) where {T<:AbstractFloat} if size(nij)[1] ≠ size(nij)[2] throw(DimensionMismatch("covariance matrix must be a square matrix")) elseif size(nij)[1] ≠ size(A)[1] || size(nij)[1] ≠ length(d) || size(A)[1] ≠ size(dAdβ)[1] || size(A)[2] ≠ size(dAdβ)[2] throw(DimensionMismatch("dimensions of matrices/vector do not match")) else M = Symmetric(nij) end x = M \ d s = inv(A' * inv(M) * A) * A'x lnlike_grad = s' * dAdβ' * (x - M \ (A * s)) end """ loglike_beta_hess(nij, A, dAdβI, dAdβJ, d) Hessian of the profile likelihood for the frequency response vectors with respect to foreground parameters *Reference*: Equation (5) of Errard et al., PRD, 84, 063005 (2011) # Arguments - `nij::Array{<:AbstractFloat,2}`: symmetric noise covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands. - `A::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the frequency response, for `nc` components in sky. - E.g., ``A = [a B]`` where `a = ones(nν)` for CMB and `B` is a `nν`-by-`nc-1` matrix for the frequency response of foreground components. - `dAdβI::Array{<:AbstractFloat,2}` and `dAdβJ::Array{<:AbstractFloat,2}`: `nν`-by-`nc` matrix of the derivative of the frequency response with respect to a foreground parameter. - E.g., ``dAdβI = [zeros(nν) dsynch/dβs zeros(nν)]`` where `zeros(nν)` for CMB and dust because they do not depend on the synchrotron index `βs`. - `d::Array{<:AbstractFloat,1}`: data vector for a given pixel (or any other appropriate domain). The number of elements is `nν`. """ function loglike_beta_hess( nij::Array{T,2}, A::Array{T,2}, dAdβI::Array{T,2}, dAdβJ::Array{T,2}, d::Array{T,1}, ) where {T<:AbstractFloat} if size(nij)[1] ≠ size(nij)[2] throw(DimensionMismatch("covariance matrix must be a square matrix")) elseif size(nij)[1] ≠ size(A)[1] || size(nij)[1] ≠ length(d) || size(A)[1] ≠ size(dAdβI)[1] || size(A)[2] ≠ size(dAdβI)[2] || size(A)[1] ≠ size(dAdβJ)[1] || size(A)[2] ≠ size(dAdβJ)[2] throw(DimensionMismatch("dimensions of matrices/vector do not match")) else M = Symmetric(nij) end x = M \ d AtMinvAinv = inv(A' * inv(M) * A) s = AtMinvAinv * A'x u = M \ (dAdβJ * s) v = M \ (A * AtMinvAinv * A'u) lnlike_hess = tr(dAdβI' * (v - u) * s') end
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# Main file for solving the J-SAA and J+-SAA problems using Distributions, StatsFuns, StatsBase, JuMP, Gurobi, GLMNet const gurobi_env = Gurobi.Env() # read these as inputs from the corresponding "instance file" modRepNum = parse(ARGS[1]) degNum = parse(ARGS[2]) dataRepNum = parse(ARGS[3]) sampSizeNum = parse(ARGS[4]) #*========= MODEL INFORMATION ========== const caseNum = 2 const paramsFile = "params_case" * string(caseNum) * "_jsaa.jl" #*====================================== #*========= INCLUDE FILES ==================== include("maxParameters.jl") include("randomSeeds.jl") include(paramsFile) # the next two data files are defined in paramsFile include(modelFile) include(dataFile) include("regressionMethods.jl") include("genJSAAScenarios.jl") include("solveModel.jl") include("estimateSolnCost.jl") include("evaluateSoln.jl") #*====================================== # set seed for reproducibility srand(startingSeed) # directory name for storing results const baseDirName = "case" * string(caseNum) * "_jsaa/" * "mod_" * string(modRepNum) * "/" * "deg_" * string(degNum) * "/" const subDirName = baseDirName * "rep_" * string(dataRepNum) * "/" const subDirName2 = subDirName * regressionMethod * "/" * "samp_" * string(sampSizeNum) * "/" mkpath(subDirName2) #*========= PRINT OPTIONS ==================== const storeResults = true const infoFile = "ddsp.txt" const modelDataFile = "model_data.txt" const numSampFile = "num_samples_" * regressionMethod * ".txt" const jsaaObjFile = "jsaa_obj.txt" const jsaaDDObjFile = "jsaa_ddobj.txt" const jpsaaObjFile = "jpsaa_obj.txt" const jpsaaDDObjFile = "jpsaa_ddobj.txt" const jsaaTimeFile = "jsaa_time.txt" const covRealFile = "covariate_obs.txt" #*====================================== #*========= GENERATE MODEL PARAMETERS ==================== srand(randomSeeds_models[modRepNum]) covariate_mean, covariate_covMat, coeff_true = generateBaseDemandModel(numCovariates) #*========= STORE RESULTS ==================== if(storeResults) # write details to text file, including some key details about the test instance details_file = baseDirName * infoFile open(details_file, "w") do f write(f,"case number: $caseNum \n") write(f,"numResources: $numResources \n") write(f,"numCustomers: $numCustomers \n") write(f,"model replicate number: $modRepNum \n") write(f,"degree: $(degree[degNum]) \n") write(f,"sample sizes: $numDataSamples \n") write(f,"demand_errors_scaling: $demand_errors_scaling \n") write(f,"regressionMethod: $regressionMethod \n\n") write(f,"randomSeeds_MC: $randomSeeds_MC \n") write(f,"randomSeeds_models: $randomSeeds_models \n") write(f,"randomSeeds_data: $randomSeeds_data \n") end mod_data_file = baseDirName * modelDataFile open(mod_data_file, "w") do f write(f,"covariate_mean = $covariate_mean \n") write(f,"covariate_covMat = $covariate_covMat \n") write(f,"coeff_true = $coeff_true \n") end samp_size_file = baseDirName * numSampFile open(samp_size_file, "w") do f for i = 1:length(numDataSamples) write(f,"$(numDataSamples[i]) \n") end end end #*============================================ #*========= GENERATE DATA REPLICATE ==================== srand(randomSeeds_data[dataRepNum]) covariate_obs = generateCovariateReal(numCovariates,covariate_mean,covariate_covMat) demand_data, covariate_data = generateDemandData(numCovariates,numDataSamples[sampSizeNum],degree[degNum],covariate_mean,covariate_covMat,coeff_true) #*========= STORE RESULTS ==================== if(storeResults) cov_real_file = subDirName * covRealFile open(cov_real_file, "w") do f write(f,"covariate_obs = $covariate_obs") end end #*============================================ #*========= SOLVE THE J-SAA and J+-SAA MODELS ==================== tic() demand_scen_jsaa, demand_scen_jpsaa = generateJSAAScenarios(demand_data,covariate_data,covariate_obs,regressionMethod) z_soln_jsaa, objDDJSAA = solveSAAModel(demand_scen_jsaa) jsaaObjEstimates = estimateSolnQuality(z_soln_jsaa,covariate_obs,degree[degNum],numMCScenarios,numMCReplicates,coeff_true) z_soln_jpsaa, objDDJpSAA = solveSAAModel(demand_scen_jpsaa) jpsaaObjEstimates = estimateSolnQuality(z_soln_jpsaa,covariate_obs,degree[degNum],numMCScenarios,numMCReplicates,coeff_true) jsaaTime = toq() #*========= STORE RESULTS ==================== if(storeResults) # write data to text file obj_est_file1 = subDirName2 * jsaaObjFile open(obj_est_file1, "w") do f for i = 1:length(jsaaObjEstimates) write(f,"$(jsaaObjEstimates[i]) \n") end end obj_est_file2 = subDirName2 * jpsaaObjFile open(obj_est_file2, "w") do f for i = 1:length(jpsaaObjEstimates) write(f,"$(jpsaaObjEstimates[i]) \n") end end ddobj_est_file1 = subDirName2 * jsaaDDObjFile open(ddobj_est_file1, "w") do f write(f,"$objDDJSAA") end ddobj_est_file2 = subDirName2 * jpsaaDDObjFile open(ddobj_est_file2, "w") do f write(f,"$objDDJpSAA") end time_obj_file = subDirName2 * jsaaTimeFile open(time_obj_file, "w") do f write(f,"$jsaaTime") end end #*============================================
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2.446633
2,183
function py2ju_obj(py_obj) py_attr = keys(py_obj) n_attr = length(py_attr) ju_obj = Dict() for i = 1:n_attr # skip base class attributes if !contains(String(py_attr[i]), "__") if py_obj[py_attr[i]] != nothing error("Cannot convert Python object with non-None attribute value $(prop[i]).") end attr_name = String(py_attr[i]) ju_obj[attr_name] = nothing end end return ju_obj end
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2.004082
245
""" """ struct ConditionalGaussian <: SciMLBase.AbstractLinearAlgorithm end struct LinearGaussian <: SciMLBase.AbstractLinearAlgorithm end
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3.564103
39
export MtlArgumentEncoder, set_buffer!, set_buffers!, set_constant! const MTLArgumentEncoder = Ptr{MtArgumentEncoder} mutable struct MtlArgumentEncoder handle::MTLArgumentEncoder end function MtlArgumentEncoder(fun::MtlFunction, entry::Integer) handle = mtNewArgumentEncoderWithBufferIndexFromFunction(fun, entry-1) obj = MtlArgumentEncoder(handle) finalizer(unsafe_destroy!, obj) return obj end function unsafe_destroy!(enc::MtlArgumentEncoder) mtRelease(enc.handle) end Base.unsafe_convert(::Type{MTLArgumentEncoder}, enc::MtlArgumentEncoder) = enc.handle Base.:(==)(a::MtlArgumentEncoder, b::MtlArgumentEncoder) = a.handle == b.handle Base.hash(fun::MtlArgumentEncoder, h::UInt) = hash(mod.handle, h) ## properties Base.propertynames(o::MtlArgumentEncoder) = ( # identification #=:device, :label,=# # creation :encodedLength, # alignment :alignment, ) function Base.getproperty(o::MtlArgumentEncoder, f::Symbol) if f === :encodedLength mtArgumentEncoderLength(o) elseif f === :alignment mtArgumentEncoderAlignment(o) else getfield(o, f) end end Base.sizeof(a::MtlArgumentEncoder) = Int(mtArgumentEncoderLength(a)) ## operations # NOTE: indices aren't 1-based here, because they map onto exact IDs in the metadata function assign_argument_buffer!(enc::MtlArgumentEncoder, buf::MtlBuffer, offset::Integer=0) mtArgumentEncoderSetArgumentBufferWithOffset(enc, buf, offset) end function assign_argument_buffer!(enc::MtlArgumentEncoder, buf::MtlBuffer, offset::Integer, element::Integer) mtArgumentEncoderSetArgumentBufferWithOffsetForElement(enc, buf, offset, element) end set_buffer!(enc::MtlArgumentEncoder, buf::MtlBuffer, offset::Integer, index::Integer) = mtArgumentEncoderSetBufferOffsetAtIndex(enc, buf, offset, index) set_buffers!(enc::MtlArgumentEncoder, bufs::Vector{<:MtlBuffer}, offsets::Vector{Int}, indices::UnitRange{Int}) = mtArgumentEncoderSetBuffersOffsetsWithRange(enc, handle_array(bufs), offsets, indices) function set_constant!(enc::MtlArgumentEncoder, val, index::Integer) dst = Base.bitcast(Ptr{typeof(val)}, mtArgumentEncoderConstantDataAtIndex(enc, index)) Base.unsafe_store!(dst, val, 1) return end
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2.69395
843
# Thanks @tlienart using Franklin, JSON using Markdown, Dates include("youtube_videos.jl") const DATEFMT = dateformat"yyyy-mm-dd HH:MMp" const TZ = "America/New_York" function hfun_doc(params) fname = join(params[1:max(1, length(params)-2)], " ") head = params[end-1] type = params[end] doc = eval(Meta.parse("@doc $fname")) txt = Markdown.plain(doc) # possibly further processing here body = Franklin.fd2html(txt, internal=true) return """ <div class="docstring"> <h2 class="doc-header" id="$head"> <a href="#$head">$head</a> <div class="doc-type">$type</div></h2> <div class="doc-content">$body</div> </div> """ end function hfun_youtube(params) id = params[1] return """ <iframe id="$id" width="100%" height="360" src="https://www.youtube.com/embed/$(get(videos, id, id))" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> """ end function hfun_showtime(params) id = params[1] str = locvar(id) if isnothing(str) @warn "Unknown datetime variable $str" return "" end try DateTime(str, DATEFMT) catch err @warn "There was an error parsing date $str, the format is yyyy-mm-dd HH:MMp (see ?DateFormat)" rethrow(err) end end function parse_duration(str) str = replace(str, r"^PT"=>"") hrex, mrex, srex = Regex.(string.("^([0-9]+)", ["H","M","S"])) t = 0 hmatch = match(hrex, str) if !isnothing(hmatch) h = parse(Int, hmatch[1]) t += 60*60*h str = replace(str, hrex=>"") end mmatch = match(mrex, str) if !isnothing(mmatch) m = parse(Int, mmatch[1]) t += 60*m str = replace(str, mrex=>"") end smatch = match(srex, str) if !isnothing(smatch) s = parse(Int, smatch[1]) t += s str = replace(str, srex=>"") end t end function hfun_go_live() seq = locvar("sequence") airtime = locvar("airtime") if isnothing(seq) @warn "airtime set, but no `sequence` variable not defined." * "sequence is an array of video IDs to play in order on this page" end vid_ids = [get(videos, s, s) for s in seq] f = tempname() # Get the duration of each video download("https://www.googleapis.com/youtube/v3/videos?id=$(join(vid_ids, ","))&part=contentDetails&key=AIzaSyDZhbWHc2PTEFTx173MaTgddnWCGPqdbB8", f) dict = JSON.parse(String(read(f))) durations = [parse_duration(video["contentDetails"]["duration"]) for video in dict["items"]] jrepr(x) = sprint(io->JSON.print(io, x)) """ <script src="/assets/moment.min.js"></script> <script src="/assets/moment-timezone.js"></script> <script src="/assets/live-player.js"></script> <script> play_live($(jrepr(string(DateTime(airtime, DATEFMT)))), $(jrepr(TZ)), $(jrepr(seq)), $(jrepr(durations))) </script> """ end
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2.239146
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struct BitsIntChar a::Int b::Char end struct BitsCharBitsIntChar a::Char b::BitsIntChar end @testset "Bits types with custom fields" begin @test begin b = JlrsReflect.reflect([BitsCharBitsIntChar]) sb = JlrsReflect.StringWrappers(b) sb[BitsIntChar] === """#[repr(C)] #[derive(Clone, Debug, Unbox, ValidLayout, Typecheck, IntoJulia)] #[jlrs(julia_type = "Main.BitsIntChar")] pub struct BitsIntChar { pub a: i64, pub b: ::jlrs::wrappers::inline::char::Char, }""" sb[BitsCharBitsIntChar] === """#[repr(C)] #[derive(Clone, Debug, Unbox, ValidLayout, Typecheck, IntoJulia)] #[jlrs(julia_type = "Main.BitsCharBitsIntChar")] pub struct BitsCharBitsIntChar { pub a: ::jlrs::wrappers::inline::char::Char, pub b: BitsIntChar, }""" end end
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2.038462
442
using MatrixFactorizations, LinearAlgebra, Random, ArrayLayouts, Test using LinearAlgebra: BlasComplex, BlasFloat, BlasReal, rmul!, lmul!, require_one_based_indexing, checksquare n = 10 # Split n into 2 parts for tests needing two matrices n1 = div(n, 2) n2 = 2*n1 Random.seed!(1234321) areal = randn(n,n)/2 aimg = randn(n,n)/2 a2real = randn(n,n)/2 a2img = randn(n,n)/2 breal = randn(n,2)/2 bimg = randn(n,2)/2 using MatrixFactorizations: RQPackedQ using MatrixFactorizations: rq, rq! const Our=MatrixFactorizations @testset "RQ" begin @testset "LAPACK $elty" for elty in (Float32,Float64,ComplexF32,ComplexF64) @testset "Compare with LAPACK (square $elty)" begin n = 10 A = randn(n,n) R, Q = @inferred rq(A) Ṟ, Q̄ = Our._rqfactUnblocked!(copy(A)) fla,τla = LinearAlgebra.LAPACK.gerqf!(copy(A)) @test Q.factors ≈ fla @test Q.τ ≈ τla @test Q.factors ≈ Q̄.factors @test Q.τ ≈ Q̄.τ @test R ≈ Ṟ @test Q ≈ Q̄ end @testset "Compare with LAPACK (rectangular $elty)" begin n = 10 A = randn(n,n+2) R, Q = rq(A) Ṟ, Q̄ = Our._rqfactUnblocked!(copy(A)) fla,τla = LinearAlgebra.LAPACK.gerqf!(copy(A)) @test Q.factors ≈ fla @test Q.τ ≈ τla @test Q.factors ≈ Q̄.factors @test Q.τ ≈ Q̄.τ @test R ≈ Ṟ @test Q ≈ Q̄ A = randn(n+2,n) R, Q = rq(A) Ṟ, Q̄ = Our._rqfactUnblocked!(copy(A)) fla,τla = LinearAlgebra.LAPACK.gerqf!(copy(A)) @test Q.factors ≈ fla @test Q.τ ≈ τla @test Q.factors ≈ Q̄.factors @test Q.τ ≈ Q̄.τ @test R ≈ Ṟ @test Q ≈ Q̄ end end @testset for eltya in (Float32, Float64, ComplexF32, ComplexF64, BigFloat, Complex{BigFloat}, Int) # @testset for eltya in (Float32,ComplexF32) raw_a = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(areal, aimg) : areal) raw_a2 = eltya == Int ? rand(1:7, n, n) : convert(Matrix{eltya}, eltya <: Complex ? complex.(a2real, a2img) : a2real) asym = raw_a' + raw_a # symmetric indefinite apd = raw_a' * raw_a # symmetric positive-definite ε = εa = eps(abs(float(one(eltya)))) @testset for eltyb in (Float32, Float64, ComplexF32, ComplexF64, Int) # @info "stubbed eltyb loop" # @testset for eltyb in (eltya, ) raw_b = eltyb == Int ? rand(1:5, n, 2) : convert(Matrix{eltyb}, eltyb <: Complex ? complex.(breal, bimg) : breal) εb = eps(abs(float(one(eltyb)))) ε = max(εa, εb) tab = promote_type(eltya, eltyb) @testset "RQ decomposition of a Number" begin α = rand(eltyb) aα = fill(α, 1, 1) @test rq(α).R * rq(α).Q ≈ rq(aα).R * rq(aα).Q @test abs(rq(α).Q[1,1]) ≈ one(eltyb) end for (a, b) in ((raw_a, raw_b), (view(raw_a, 1:n-1, 1:n-1), view(raw_b, 1:n-1, 1))) a_1 = size(a, 1) @testset "RQ decomposition" begin rqa = @inferred rq(a) @inferred rq(a) q, r = rqa.Q, rqa.R @test_throws ErrorException rqa.Z @test q'*q ≈ Matrix(I, a_1, a_1) @test q*q' ≈ Matrix(I, a_1, a_1) @test q'*Matrix(1.0I, a_1, a_1)' ≈ q' @test q'q ≈ Matrix(I, a_1, a_1) @test Matrix(1.0I, a_1, a_1)'q' ≈ q' @test r*q ≈ a @test a*(rqa\b) ≈ b atol=5000ε @test Array(rqa) ≈ a sq = size(q.factors, 2) @test *(Matrix{eltyb}(I, sq, sq), adjoint(q)) * q ≈ Matrix(I, sq, sq) atol=5000ε if eltya != Int @test Matrix{eltyb}(I, a_1, a_1)*q ≈ convert(AbstractMatrix{tab}, q) ac = copy(a) # would need rectangular ldiv! method @test_throws DimensionMismatch rq!(a[:, 1:5])\b == rq!(view(ac, :, 1:5))\b end rqstring = sprint((t, s) -> show(t, "text/plain", s), rqa) rstring = sprint((t, s) -> show(t, "text/plain", s), r) qstring = sprint((t, s) -> show(t, "text/plain", s), q) @test rqstring == "$(summary(rqa))\nR factor:\n$rstring\nQ factor:\n$qstring" end end if eltya != Int @testset "Matmul with RQ factorizations" begin a = raw_a rqa = rq(a[:,1:n1]) q, r = rqa.Q, rqa.R @test rmul!(copy(q'), q) ≈ Matrix(I, n1, n1) @test_throws DimensionMismatch rmul!(Matrix{eltya}(I, n1+1, n1+1),q) @test rmul!(copy(q), adjoint(q)) ≈ Matrix(I, n1, n1) @test_throws DimensionMismatch rmul!(Matrix{eltya}(I, n1+1, n1+1),adjoint(q)) @test_throws ErrorException size(q,-1) @test_throws DimensionMismatch q * Matrix{Int8}(I, n+4, n+4) end end end @testset "Wide RQ" begin m = n-2 A = raw_a[1:m,1:n] R,Q = rq(A) @test Q'*Q ≈ Matrix(I, n, n) @test istriu(R) @test hcat(zeros(m,n-m),R)*Q ≈ A # test the perverse padded product @test R*Q ≈ A Qm = Matrix(Q) @test Qm' * Qm ≈ Matrix(I, n, n) @test hcat(zeros(m,n-m),R)*Qm ≈ A end @testset "Tall RQ" begin p = n-2 A = raw_a[1:n,1:p] R,Q = rq(A) @test Q'*Q ≈ Matrix(I, p, p) @test istriu(R,p-n) @test R*Q ≈ A Qm = Matrix(Q) @test Qm' * Qm ≈ Matrix(I, p, p) @test R*Qm ≈ A end end @testset "transpose errors" begin @test_throws MethodError transpose(rq(randn(3,3))) @test_throws MethodError adjoint(rq(randn(3,3))) @test_throws MethodError transpose(rq(big.(randn(3,3)))) @test_throws MethodError adjoint(rq(big.(randn(3,3)))) end @testset "Issue 7304" begin A = [-√.5 -√.5; -√.5 √.5] Q = rq(A).Q @test norm(A+Q) < eps() end @testset "rq on AbstractVector" begin vl = [3.0, 4.0] for Tl in (Float32, Float64) for T in (Tl, Complex{Tl}) v = convert(Vector{T}, vl) nv, nm = rq(v) @test nv*nm ≈ v end end end @testset "Issue 24589. Promotion of rational matrices" begin A = rand(1//1:5//5, 4,3) @test first(rq(A)) == first(rq(float(A))) end # omit "Issue Test Factorization fallbacks for rectangular problems" @testset "lmul!/rmul! $elty" for elty in (:real, :cplx) s = elty == :real ? 0.0 : 0.25im A = randn(100,100) .+ s R,Q = rq(A) x = randn(100) .+ s b = randn(100,2) .+ s @test lmul!(Q, copy(x)) ≈ Matrix(Q)*x @test lmul!(Q, copy(b)) ≈ Matrix(Q)*b @test lmul!(Q', copy(x)) ≈ Matrix(Q)'*x @test lmul!(Q', copy(b)) ≈ Matrix(Q)'*b c = randn(2,100) .+ s @test rmul!(copy(c), Q) ≈ c*Matrix(Q) @test rmul!(copy(c), Q') ≈ c*Matrix(Q') A = randn(103,100) .+ s R,Q = rq(A) x = randn(100) .+ s b = randn(100,2) .+ s @test lmul!(Q, copy(x)) ≈ Matrix(Q)*x @test lmul!(Q, copy(b)) ≈ Matrix(Q)*b @test lmul!(Q', copy(x)) ≈ Matrix(Q)'*x @test lmul!(Q', copy(b)) ≈ Matrix(Q)'*b c = randn(2,100) .+ s @test rmul!(copy(c), Q) ≈ c*Matrix(Q) @test rmul!(copy(c), Q') ≈ c*Matrix(Q') end end
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1.640429
4,942
#------------------------------------------------------------------- #* EMSO Model Library (EML) Copyright (C) 2004 - 2007 ALSOC. #* #* This LIBRARY is free software; you can distribute it and/or modify #* it under the therms of the ALSOC FREE LICENSE as available at #* http://www.enq.ufrgs.br/alsoc. #* #* EMSO Copyright (C) 2004 - 2007 ALSOC, original code #* from http://www.rps.eng.br Copyright (C) 2002-2004. #* All rights reserved. #* #* EMSO is distributed under the therms of the ALSOC LICENSE as #* available at http://www.enq.ufrgs.br/alsoc. #* #*---------------------------------------------------------------------- #* Authors: Rafael de Pelegrini Soares #* Andrey Copat, Estefane S. Horn, Marcos L. Alencastro #* $Id$ #*-------------------------------------------------------------------- #Needs to be reformulated type HidraulicTurbine HidraulicTurbine()=begin NComp=outers.NComp PP=outers.PP new( DanaInteger(Dict{Symbol,Any}( :Brief=>"Number of chemical components", :Lower=>1 )), DanaPlugin(Dict{Symbol,Any}( :Brief=>"External Physical Properties", :Type=>"PP" )), fill(molweight(Dict{Symbol,Any}( :Brief=>"Molar Weight" )),(NComp)), efficiency(Dict{Symbol,Any}( :Brief=>"Turbine efficiency" )), efficiency(Dict{Symbol,Any}( :Brief=>"Brake efficiency" )), positive(Dict{Symbol,Any}( :Brief=>"Volumetric expansivity", :Unit=>"1/K" )), head(Dict{Symbol,Any}( :Brief=>"Head Developed" )), power(Dict{Symbol,Any}( :Brief=>"Fluid Power" )), power(Dict{Symbol,Any}( :Brief=>"Brake Power" )), positive(Dict{Symbol,Any}( :Brief=>"Pressure Ratio" )), press_delta(Dict{Symbol,Any}( :Brief=>"Pressure Drop", :DisplayUnit=>"kPa", :Symbol=>"\\Delta P" )), molweight(Dict{Symbol,Any}( :Brief=>"Mixture Molar Weight" )), dens_mass(Dict{Symbol,Any}( :Brief=>"Specific Mass" )), cp_mol(Dict{Symbol,Any}( :Brief=>"Heat Capacity" )), stream(Dict{Symbol,Any}( :Brief=>"Inlet stream", :PosX=>0.05, :PosY=>0.0, :Symbol=>"_{in}" )), stream(Dict{Symbol,Any}( :Brief=>"Outlet stream", :PosX=>0.65, :PosY=>1, :Symbol=>"_{out}" )), power(Dict{Symbol,Any}( :Brief=>"Work Outlet", :PosX=>1, :PosY=>0.46 )), [ :(Mwm = sum(Mw*Inlet.z)), :(rho = PP.LiquidDensity(Inlet.T,Inlet.P,Inlet.z)), :(Outlet.v = PP.VapourFraction(Outlet.T, Outlet.P, Outlet.z)), :(Cp = PP.LiquidCp(Inlet.T,Inlet.P,Inlet.z)), :(Outlet.P = Inlet.P * Pratio), :(Outlet.P = Inlet.P - Pdrop), :(FPower * rho = -Pdrop * Inlet.F * Mwm), :(BPower = FPower * Eff), :(BPower = WorkOut), :((Outlet.T - Inlet.T) * rho * Cp = (Outlet.h - Inlet.h) * rho + Pdrop * Mwm * (1-Beta*Inlet.T)), :((Outlet.h - Inlet.h) * rho = -Pdrop * Mwm), :(Outlet.F = Inlet.F), :(Outlet.z = Inlet.z), :(Head = Outlet.h - Inlet.h), ], [ "Calculate Mwm for Inlet Mixture","Calculate rho using a External Physical Properties Routine","Calculate Outlet Vapour Fraction","Calculate Cp Using a External Physical Properties Routine","Pressure Ratio","Pressure Drop","Calculate Fluid Power","Calculate Brake Power","","Calculate Outlet Temperature","Calculate Outlet Enthalpy","Molar Balance","Calculate Outlet Composition","Calculate Head", ], [:NComp,:PP,:Mw,], [:Eff,:Meff,:Beta,:Head,:FPower,:BPower,:Pratio,:Pdrop,:Mwm,:rho,:Cp,:Inlet,:Outlet,:WorkOut,] ) end NComp::DanaInteger PP::DanaPlugin Mw::Array{molweight} Eff::efficiency Meff::efficiency Beta::positive Head::head FPower::power BPower::power Pratio::positive Pdrop::press_delta Mwm::molweight rho::dens_mass Cp::cp_mol Inlet::stream Outlet::stream WorkOut::power equations::Array{Expr,1} equationNames::Array{String,1} parameters::Array{Symbol,1} variables::Array{Symbol,1} attributes::Dict{Symbol,Any} end export HidraulicTurbine function set(in::HidraulicTurbine) Mw = PP.MolecularWeight() end function setEquationFlow(in::HidraulicTurbine) #Mixtures Properties addEquation(1) addEquation(2) addEquation(3) addEquation(4) addEquation(5) addEquation(6) addEquation(7) addEquation(8) addEquation(9) addEquation(10) addEquation(11) addEquation(12) addEquation(13) addEquation(14) end function atributes(in::HidraulicTurbine,_::Dict{Symbol,Any}) fields::Dict{Symbol,Any}=Dict{Symbol,Any}() fields[:Pallete]=true fields[:Icon]="icon/HidraulicTurbine" fields[:Brief]="Testing Model of a Hidraulic Turbine." drive!(fields,_) return fields end HidraulicTurbine(_::Dict{Symbol,Any})=begin newModel=HidraulicTurbine() newModel.attributes=atributes(newModel,_) newModel end
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module TestExpressionsTVL import Test: @testset, @test import Volcanito: tvl, @tvl @testset "TVL || truth table" begin @test @tvl(true || true) === true @test @tvl(true || false) === true @test @tvl(true || missing) === true @test @tvl(false || true) === true @test @tvl(false || false) === false @test @tvl(false || missing) === missing @test @tvl(missing || true) === true @test @tvl(missing || false) === missing @test @tvl(missing || missing) === missing end @testset "TVL && truth table" begin @test @tvl(true && true) === true @test @tvl(true && false) === false @test @tvl(true && missing) === missing @test @tvl(false && true) === false @test @tvl(false && false) === false @test @tvl(false && missing) === false @test @tvl(missing && true) === missing @test @tvl(missing && false) === false @test @tvl(missing && missing) === missing end # We define a function that prints out a unique ID for each argument # to a Boolean operator. By wrapping all Boolean values in calls to this # function, we're able to check that the order of evaluation and # side-effects of the short-circuiting operators are retained by our # macro rewrites. function f(io, i, x) print(io, i) x end @testset "Order of evaluation for tvl" begin for x in (true, false) for y in (true, false) for z in (true, false) io = IOBuffer() a = f(io, 1, x) && f(io, 2, y) || f(io, 3, z) order_a = String(take!(io)) b = @tvl f(io, 1, x) && f(io, 2, y) || f(io, 3, z) order_b = String(take!(io)) @test a === b @test order_a === order_b a = f(io, 1, x) || f(io, 2, y) && f(io, 3, z) order_a = String(take!(io)) b = @tvl f(io, 1, x) || f(io, 2, y) && f(io, 3, z) order_b = String(take!(io)) @test a === b @test order_a === order_b end end end end @testset "@tvl with local variables" begin let x = 1, y = 2 @test (@tvl x > 1 || y < 2) === false end end end
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module DoublyPeriodicTaylorGreen using Printf using Oceananigans, Oceananigans.OutputWriters # Advected vortex: ψ(x, y, t) = exp(-2t) * cos(x - U*t) * cos(y) u(x, y, t, U=1) = U + exp(-2t) * cos(x - U*t) * sin(y) v(x, y, t, U=1) = - exp(-2t) * sin(x - U*t) * cos(y) ##### ##### x, y ##### function setup_simulation(; Nx, Δt, stop_iteration, U=1, architecture=CPU(), dir="data") grid = RegularCartesianGrid(size=(Nx, Nx, 1), x=(0, 2π), y=(0, 2π), z=(0, 1), topology=(Periodic, Periodic, Bounded)) model = IncompressibleModel(architecture = architecture, grid = grid, coriolis = nothing, buoyancy = nothing, tracers = nothing, closure = ConstantIsotropicDiffusivity(ν=1)) set!(model, u = (x, y, z) -> u(x, y, 0, U), v = (x, y, z) -> v(x, y, 0, U)) simulation = Simulation(model, Δt=Δt, stop_iteration=stop_iteration, progress_frequency=stop_iteration) simulation.output_writers[:fields] = JLD2OutputWriter(model, FieldOutputs(model.velocities); dir = dir, force = true, prefix = @sprintf("taylor_green_Nx%d_Δt%.1e", Nx, Δt), interval = stop_iteration * Δt / 10) return simulation end function setup_and_run(; setup...) simulation = setup_simulation(; setup...) println(""" Running decaying Taylor-Green vortex simulation in x, y with Nx = $(setup[:Nx]) Δt = $(setup[:Δt]) """) @time run!(simulation) return nothing end end # module
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#= Log-likelihood for ODEs, integrating along the tree using flow algorithm Ignacio Quintero Mächler t(-_-t) Created 23 10 2019 =# """ make_Et(Et::Function, p0::Array{Float64,1}, u0::Array{Float64,1}, ts::Array{Float64,1}, ti::Float64, Make Extinction through time, `E(t)`. """ function make_Et(Et::Function, p0::Array{Float64,1}, u0::Array{Float64,1}, ts::Array{Float64,1}, ti::Float64, tf::Float64) prob = ODEProblem(Et, u0, (ti,tf), p0) int = init(prob, Tsit5(), saveat = ts, save_everystep = false, calck = false, force_dtmin = true, save_start = true, initialize_save = false, maxiters = 100_000_000, verbose = false) function f(p::Array{Float64,1}) int.p = p reinit!(int) solve!(int).u::Array{Array{Float64,1},1} end return f end """ make_Gt(At::Function, p0::Array{Array{Float64,1},1}, u0::Array{Float64,2}, ts::Array{Float64,1}, ti::Float64, tf::Float64) Make flow equation for likelihoods through time, `G(t)`. """ function make_Gt(At::Function, p0::Array{Array{Float64,1},1}, u0::Array{Float64,2}, ts::Array{Float64,1}, ti::Float64, tf::Float64) prob = ODEProblem(At, u0, (ti,tf), p0) int = init(prob, Tsit5(), saveat = ts, save_everystep = false, calck = false, force_dtmin = true, save_start = true, initialize_save = false, maxiters = 100_000_000, verbose = false) function f(p::Array{Array{Float64,1},1}) int.p = p reinit!(int) solve!(int).u::Array{Array{Float64,2},1} end return f end """ make_loglik(Gt ::Function, Et ::Function, X ::Array{Array{Float64,1},1}, triads ::Array{Array{Int64,1},1}, lbts ::Array{Int64,2}, bts ::Array{Float64,1}, ns ::Int64, ned ::Int64, nets ::Int64, ntip ::Int64, λevent!::Function, rootll ::Function) Make log-likelihood function using the flow algorithm. """ function make_loglik(Gt ::Function, Et ::Function, X ::Array{Array{Float64,1},1}, triads ::Array{Array{Int64,1},1}, lbts ::Array{Int64,2}, bts ::Array{Float64,1}, ns ::Int64, ned ::Int64, nets ::Int64, λevent!::Function, rootll ::Function) # preallocate arrays X0 = Array{Float64,1}(undef,ns) Xp1 = Array{Float64,1}(undef,ns) Xp2 = Array{Float64,1}(undef,ns) Xr = Array{Float64,1}(undef,ns) wg = Array{Float64,1}(undef,ns) Er = Array{Float64,1}(undef,ns) rtime = bts[end] netsm1 = nets - 1 nbts = length(bts) # start ll function for parameters function f(p::Array{Float64,1}) @inbounds begin Ets = Et(p) # push parameters as the last vector in Ets push!(Ets, p) # estimate `Gts` according to `Ets` and `p` Gts = Gt(Ets) ll = 0.0 if rank(Gts[nbts]) == ns for i in Base.OneTo(nbts) if rank(Gts[i]) != ns @show Gts[nbts] @show Gts[i] end end for trio in triads pr, d1, d2 = trio ## first daughter X0 = Gts[lbts[d1,2]]\X[d1] mul!(Xp1, Gts[lbts[d1,1]], X0) check_negs(Xp1, ns) && return -Inf ## second daughter X0 = Gts[lbts[d2,2]]\X[d2] mul!(Xp2, Gts[lbts[d2,1]], X0) check_negs(Xp2, ns) && return -Inf λtime = bts[lbts[d1,1]] λevent!(λtime, X, Xp1, Xp2, p, pr) if pr != ned mdlus = sum(X[pr]) X[pr] /= mdlus ll += log(mdlus) end end else for trio in triads pr, d1, d2 = trio ## first daughter ldiv!(X0, qr!(Gts[lbts[d1,2]], Val(true)), X[d1]) mul!(Xp1, Gts[lbts[d1,1]], X0) check_negs(Xp1, ns) && return -Inf ## second daughter ldiv!(X0, qr!(Gts[lbts[d2,2]], Val(true)), X[d2]) mul!(Xp2, Gts[lbts[d2,1]], X0) check_negs(Xp2, ns) && return -Inf λtime = bts[lbts[d1,1]] λevent!(λtime, X, Xp1, Xp2, p, pr) if pr != ned mdlus = sum(X[pr]) X[pr] /= mdlus ll += log(mdlus) end end end # assign root likelihoods @simd for i in Base.OneTo(ns) Xr[i] = X[ned][i] end # assign root extinction @simd for i in Base.OneTo(ns) Er[i] = Ets[netsm1][i] end # estimate weights normbysum!(Xr, wg, ns) ll += log(rootll(rtime, Xr, Er, wg, p)::Float64) end return ll end return f end
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type BitsContainer <: RoaringContainer card::UInt data::Vector{UInt64} end function BitsContainer() Vector{UInt64}() return BitsContainer() end bitset_container_t *bitset_container_create(void) { bitset_container_t *bitset = (bitset_container_t *)calloc(1, sizeof(bitset_container_t)); if (!bitset) { return NULL; } // sizeof(__m256i) == 32 bitset->array = (uint64_t *) aligned_malloc(32, sizeof(uint64_t) * BITSET_CONTAINER_SIZE_IN_WORDS); if (! bitset->array) { free(bitset); return NULL; } bitset_container_clear(bitset); return bitset; }
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2.327206
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@diffusion_process SIRAux begin :parameters (α, β, σ1, σ2, t, T) --> Float64 (u, v) --> ℝ{2} :additional linear --> true constdiff --> true statespace -->BoundedStateSpace(((1,2), (0.0,0.0)), ((1,2), (1.0,1.0))) end function B(t, P::SIRAux) @SMatrix[ (P.α*(1 - P.v[1] - P.v[2]) - P.β) 0.0; P.β 0.0 ] end β(t, P::SIRAux) = ℝ{2}(0.0, 0.0) function σ(t, P::SIRAux) @SMatrix Float64[ (-P.σ1*(1 - v[1] - v[2])*v[1]) -P.σ2*v[1]; 0.0 P.σ2*v[1] ] end
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1.610942
329
# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # formats: jl:light,ipynb # text_representation: # extension: .jl # format_name: light # format_version: '1.5' # jupytext_version: 1.4.2 # kernelspec: # display_name: Julia 1.4.0 # language: julia # name: julia-1.4 # --- # + using Gen using Statistics include("DistributionsBacked.jl") using AdvancedHMC const my_normal = DistributionsBacked{Float64}((mu, sigma) -> Distributions.Normal(mu, sigma), [true, true], true) const my_unif = DistributionsBacked{Float64}((lo, hi) -> Distributions.Uniform(lo, hi), [true, true], true) ; # + @gen function corbiv_model() #correlated bivariate normal ρ ~ my_unif(-1., 1.) x ~ my_normal(0., 1.) y ~ my_normal(ρ*x, sqrt(1. - ρ^2)) end @gen function cormiv_model() #correlated bivariate normal ρ ~ my_unif(0., 1.) #correlation between x and y ρ2 ~ my_unif(0., ρ) #correlation between z and (x or y) zy_cond_zx = (1-ρ2^2)*ρ2 x ~ my_normal(0., 1.) y ~ my_normal(ρ*x, sqrt(1. - ρ^2)) z ~ my_normal(ρ2*x + zy_cond_zx*y, sqrt(1. - ρ2^2 - zy_cond_zx^2)) end ; # + function constrainρ(ρ::Float64) constraints = Gen.choicemap() constraints[:ρ] = ρ constraints end function constrainρ2(ρ::Float64, ρ2, z) constraints = Gen.choicemap() constraints[:ρ] = ρ constraints[:ρ2] = ρ2 constraints[:z] = z constraints end function mcmc_inference(ρ, num_iters, update, selection) observation = constrainρ(ρ) (trace, _) = generate(corbiv_model, (), observation) samples = Array{Float64}(undef,num_iters,2) for i=1:num_iters trace = update(trace, selection) ch = get_choices(trace) samples[i,1] = ch[:x] samples[i,2] = ch[:y] end samples end function mcmc_m_inference(ρ, ρ2, z, num_iters, update) observation = constrainρ2(ρ, ρ2, z) (trace, _) = generate(cormiv_model, (), observation) samples = Array{Float64}(undef,num_iters,2) for i=1:num_iters trace = update(trace) ch = get_choices(trace) samples[i,1] = ch[:x] samples[i,2] = ch[:y] end samples end function block_mh(tr, selection) (tr, _) = mh(tr, select(:x, :y)) tr end function simple_hmc(tr, selection) (tr, _) = hmc(tr, select(:x, :y)) tr end ; # - iters = 10_000 show = 5 ρ = -.5 samps = mcmc_inference(ρ, iters, block_mh, select(:x,:y)) samps[(iters-show+1):iters,:] iters = 100 show = 5 ρ = .8 samps = mcmc_inference(ρ, iters, simple_hmc) samps[(iters-show+1):iters,:] println(mean(samps)) println(cor(samps[:,1],samps[:,2])) # Disable AdvancedHMC's NUTS logging # + using Logging using LoggingExtras function ignore_sampling_filter(log_args) !(occursin("sampling steps",log_args.message) || occursin("adapation steps",log_args.message)) end logger = ActiveFilteredLogger(ignore_sampling_filter, global_logger()) if !(@isdefined old_logger) #do this only once old_logger = global_logger(logger) end # - function my_nuts(trace, selection, n_postadapt_steps = 2, n_adapts = 1, initial_ϵ_reduce_fac = 10) n_NUTS_steps = n_postadapt_steps + n_adapts filtered_choices = get_selected(get_choices(trace), selection) cur_xy = to_array(filtered_choices, Float64) dimension = length(cur_xy) metric = DiagEuclideanMetric(dimension) retval_grad = nothing #accepts_output_grad(get_gen_fn(trace)) ? zero(get_retval(trace)) : nothing function update_xy(val) extra_constraints = from_array(filtered_choices, val) update(trace, (), (NoChange(),), extra_constraints) end function val_to_lp_plus_c(val) (new_trace, weight, discard, retdiff) = update_xy(val) weight end function val_to_grad(val) (new_trace, weight, discard, retdiff) = update_xy(val) (retval_grad_out, values_trie, gradient_trie) = choice_gradients(new_trace, selection, retval_grad) grad = [gradient_trie[:x], gradient_trie[:y]] (weight, grad) end # Define a Hamiltonian system, using metric defined globally above hamiltonian = Hamiltonian(metric, val_to_lp_plus_c, val_to_grad) # Define a leapfrog solver, with initial step size chosen heuristically initial_ϵ = find_good_stepsize(hamiltonian, cur_xy) ./ initial_ϵ_reduce_fac integrator = Leapfrog(initial_ϵ) # Define an HMC sampler, with the following components # - multinomial sampling scheme, # - generalised No-U-Turn criteria, and # - windowed adaption for step-size and diagonal mass matrix proposal = NUTS{MultinomialTS, GeneralisedNoUTurn}(integrator) adaptor = StanHMCAdaptor(MassMatrixAdaptor(metric), StepSizeAdaptor(0.8, integrator)) # Run the sampler to draw samples from the specified Gaussian, where # - `samples` will store the samples # - `stats` will store diagnostic statistics for each sample samples, stats = sample(hamiltonian, proposal, cur_xy, n_NUTS_steps, adaptor, n_adapts; progress=false) #println(samples[3]) (new_trace, weight, discard, retdiff) = update_xy(samples[n_NUTS_steps]) new_trace end # + iters = 200 show = 5 ρ = .99 samps = mcmc_inference(ρ, iters, my_nuts, select(:x,:y)) samps[(iters-show+1):iters,:] # - println(cor(samps[1:iters-1,1],samps[2:iters,1])) #serial correlation; lower is better println(ρ^4) #for comparison, gibbs would be ρ² for each step; ρ⁴ for two steps positive
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2.243287
2,495
export rule @rule BIFMHelper(:out, Marginalisation) (q_in::Any, ) = ProdFinal(q_in)
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2.4
35
"A macro to make calling KNITRO's KN_* C API a little cleaner" macro kn_ccall(func, args...) f = Base.Meta.quot(Symbol("KN_$(func)")) args = [esc(a) for a in args] quote ccall(($f, libknitro), $(args...)) end end macro kn_get_values(function_name, type) name_singular = "KN_" * string(function_name) name_plural = "KN_" * string(function_name) * "s" fname = Symbol(name_plural) fnameshort = Symbol(name_singular) # Names of C function in knitro.h c_fname = Symbol(name_singular) c_fnames = Symbol(name_plural) c_fnames_all = Symbol(name_plural * "_all") n = if occursin("var", name_singular) :(KN_get_number_vars(kc)) elseif occursin("con", name_singular) :(KN_get_number_cons(kc)) end quote function $(esc(fname))(kc::Model) result = zeros($type, $n) $c_fnames_all(kc, result) return result end function $(esc(fname))(kc::Model, index::Vector{Cint}) result = zeros($type, length(index)) $c_fnames(kc, length(index), index, result) return result end function $(esc(fname))(kc::Model, index::Integer) result = zeros($type, 1) $c_fname(kc, index, result) return result[1] end $(esc(fnameshort))(kc::Model, index::Integer) = $(esc(fname))(kc, index) end end macro kn_get_attribute(function_name, type) fname = Symbol("KN_" * string(function_name)) quote function $(esc(fname))(m::Model) val = zeros($type, 1) ret = $fname(m, val) return val[1] end end end "Format output returned by KNITRO as proper Julia string." function _format_output(output::AbstractString) # remove trailing whitespace res = strip(output) # remove special characters return res = strip(res, '\0') end "Return the current KNITRO version." function get_release() len = 15 out = zeros(Cchar, len) KN_get_release(len, out) return String(strip(String(convert(Vector{UInt8}, out)), '\0')) end "Wrapper for KNITRO KN_context." mutable struct Env ptr_env::Ptr{Cvoid} function Env() ptrptr_env = Ref{Ptr{Cvoid}}() res = KN_new(ptrptr_env) if res != 0 error("Fail to retrieve a valid KNITRO KN_context. Error $res") end return new(ptrptr_env[]) end Env(ptr::Ptr{Cvoid}) = new(ptr) end Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, env::Env) = env.ptr_env::Ptr{Cvoid} is_valid(env::Env) = env.ptr_env != C_NULL """ Free all memory and release any Knitro license acquired by calling KN_new. """ function free_env(env::Env) if env.ptr_env != C_NULL ptrptr_env = Ref{Ptr{Cvoid}}(env.ptr_env) KN_free(ptrptr_env) env.ptr_env = C_NULL end return end """ Structure specifying the callback context. Each evaluation callbacks (for objective, gradient or hessian) is attached to a unique callback context. """ mutable struct CallbackContext context::Ptr{Cvoid} n::Int m::Int # Add a dictionnary to store user params. userparams::Any # Oracle's callbacks are context dependent, so store # them inside dedicated CallbackContext. eval_f::Function eval_g::Function eval_h::Function eval_rsd::Function eval_jac_rsd::Function function CallbackContext(ptr_cb::Ptr{Cvoid}) return new(ptr_cb, 0, 0, nothing) end end Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, cb::CallbackContext) = cb.context::Ptr{Cvoid} mutable struct Model # KNITRO context environment. env::Env # Keep reference to callbacks for garbage collector. callbacks::Vector{CallbackContext} # Some structures for userParams puts_user::Any multistart_user::Any mip_user::Any newpoint_user::Any # Solution values. # Optimization status. Equal to 1 if problem is unsolved. status::Cint obj_val::Cdouble x::Vector{Cdouble} mult::Vector{Cdouble} # Special callbacks (undefined by default). # (this functions do not depend on callback environments) ms_process::Function newpt_callback::Function mip_callback::Function user_callback::Function ms_initpt_callback::Function puts_callback::Function # Constructor. function Model() model = new( Env(), CallbackContext[], nothing, nothing, nothing, nothing, 1, Inf, Cdouble[], Cdouble[], ) # Add a destructor to properly delete model. finalizer(KN_free, model) return model end # Instantiate a new Knitro instance in current environment `env`. function Model(env::Env) return new( env, CallbackContext[], nothing, nothing, nothing, nothing, 1, Inf, Cdouble[], Cdouble[], ) end end Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, kn::Model) = kn.env.ptr_env::Ptr{Cvoid} "Free solver object." KN_free(m::Model) = free_env(m.env) "Create solver object." KN_new() = Model() is_valid(m::Model) = is_valid(m.env) has_callbacks(m::Model) = !isempty(m.callbacks) register_callback(model::Model, cb::CallbackContext) = push!(model.callbacks, cb) function Base.show(io::IO, m::Model) if is_valid(m) println(io, "$(get_release())") println(io, "-----------------------") println(io, "Problem Characteristics") println(io, "-----------------------") println(io, "Objective goal: Minimize") println(io, "Objective type: $(KN_get_obj_type(m))") println( io, "Number of variables: $(KN_get_number_vars(m))", ) println( io, "Number of constraints: $(KN_get_number_cons(m))", ) println( io, "Number of nonzeros in Jacobian: $(KN_get_jacobian_nnz(m))", ) println( io, "Number of nonzeros in Hessian: $(KN_get_hessian_nnz(m))", ) else println(io, "KNITRO Problem: NULL") end end #= LM license manager =# """ Type declaration for the Artelys License Manager context object. Applications must not modify any part of the context. """ mutable struct LMcontext ptr_lmcontext::Ptr{Cvoid} # Keep a pointer to instantiated models in order to free # memory properly. linked_models::Vector{Model} function LMcontext() ptrref = Ref{Ptr{Cvoid}}() res = KN_checkout_license(ptrref) if res != 0 error("KNITRO: Error checkout license") end lm = new(ptrref[], Model[]) finalizer(KN_release_license, lm) return lm end end Base.unsafe_convert(ptr::Type{Ptr{Cvoid}}, lm::LMcontext) = lm.ptr_lmcontext::Ptr{Cvoid} function Env(lm::LMcontext) ptrptr_env = Ref{Ptr{Cvoid}}() res = KN_new_lm(lm, ptrptr_env) if res != 0 error("Fail to retrieve a valid KNITRO KN_context. Error $res") end return Env(ptrptr_env[]) end function attach!(lm::LMcontext, model::Model) push!(lm.linked_models, model) return end # create Model with license manager function Model(lm::LMcontext) model = Model(Env(lm)) attach!(lm, model) return model end KN_new_lm(lm::LMcontext) = Model(lm) function KN_release_license(lm::LMcontext) # First, ensure that all linked models are properly freed # before releasing license manager! KN_free.(lm.linked_models) if lm.ptr_lmcontext != C_NULL refptr = Ref{Ptr{Cvoid}}(lm.ptr_lmcontext) KN_release_license(refptr) lm.ptr_lmcontext = C_NULL end end #= VARIABLES =# function KN_add_var(m::Model) index = Cint[0] KN_add_var(m, index) return index[1] end function KN_add_vars(m::Model, nvars::Int) indexes = zeros(Cint, nvars) KN_add_vars(m, nvars, indexes) return indexes end @kn_get_values get_var_lobnd Cdouble @kn_get_values get_var_upbnd Cdouble @kn_get_values get_var_eqbnd Cdouble @kn_get_values get_var_fxbnd Cdouble @kn_get_values get_var_primal_value Cdouble @kn_get_values get_var_dual_value Cdouble @kn_get_values get_var_type Cint #= OBJECTIVE =# function KN_add_obj_linear_struct( m::Model, objIndices::Vector{Cint}, objCoefs::Vector{Cdouble}, ) nnz = length(objIndices) @assert nnz == length(objCoefs) return KN_add_obj_linear_struct(m, nnz, objIndices, objCoefs) end function KN_add_obj_linear_struct(m::Model, objindex::Int, objCoefs::Cdouble) return KN_add_obj_linear_struct(m, Cint[objindex], [objCoefs]) end function KN_add_obj_quadratic_struct( m::Model, indexVars1::Vector{Cint}, indexVars2::Vector{Cint}, coefs::Vector{Cdouble}, ) nnz = length(indexVars1) @assert nnz == length(indexVars2) == length(coefs) return KN_add_obj_quadratic_struct(m, nnz, indexVars1, indexVars2, coefs) end #= CONSTRAINTS =# function KN_add_cons(m::Model, ncons::Integer) indexes = zeros(Cint, ncons) KN_add_cons(m, ncons, indexes) return indexes end function KN_add_con(m::Model) index = Cint[0] KN_add_con(m, index) return index[1] end @kn_get_values get_con_lobnd Cdouble @kn_get_values get_con_upbnd Cdouble @kn_get_values get_con_eqbnd Cdouble @kn_get_values get_con_dual_value Cdouble @kn_get_values get_con_value Cdouble function KN_add_con_linear_struct( m::Model, index_cons::Vector{Cint}, index_vars::Vector{Cint}, coefs::Vector{Cdouble}, ) @assert length(index_cons) == length(index_vars) == length(coefs) nnz = length(index_cons) return KN_add_con_linear_struct(m, nnz, index_cons, index_vars, coefs) end function KN_add_con_linear_struct( m::Model, index_con::Integer, index_vars::Vector{Cint}, coefs::Vector{Cdouble}, ) @assert length(index_vars) == length(coefs) nnz = length(index_vars) return KN_add_con_linear_struct_one(m, nnz, index_con, index_vars, coefs) end function KN_add_con_linear_struct( m::Model, index_con::Integer, index_var::Integer, coef::Cdouble, ) nnz = 1 return KN_add_con_linear_struct_one(m, nnz, index_con, [index_var], [coef]) end function KN_add_con_quadratic_struct( m::Model, index_cons::Vector{Cint}, index_vars1::Vector{Cint}, index_vars2::Vector{Cint}, coefs::Vector{Cdouble}, ) @assert length(index_cons) == length(index_vars1) == length(index_vars2) == length(coefs) nnz = length(index_cons) return KN_add_con_quadratic_struct(m, nnz, index_cons, index_vars1, index_vars2, coefs) end function KN_add_con_quadratic_struct( m::Model, index_con::Integer, index_vars1::Vector{Cint}, index_vars2::Vector{Cint}, coefs::Vector{Cdouble}, ) @assert length(index_vars1) == length(index_vars2) == length(coefs) nnz = length(index_vars1) return KN_add_con_quadratic_struct_one( m, nnz, index_con, index_vars1, index_vars2, coefs, ) end function KN_add_con_quadratic_struct( m::Model, index_con::Integer, index_var1::Integer, index_var2::Integer, coef::Cdouble, ) nnz = 1 return KN_add_con_quadratic_struct_one( m, nnz, index_con, Cint[index_var1], Cint[index_var2], [coef], ) end function KN_set_compcons( m::Model, ccTypes::Vector{Cint}, indexComps1::Vector{Cint}, indexComps2::Vector{Cint}, ) # get number of constraints nnc = length(ccTypes) @assert nnc == length(indexComps1) == length(indexComps2) return KN_set_compcons(m, nnc, ccTypes, indexComps1, indexComps2) end #= RESIDUALS =# function KN_add_rsds(m::Model, ncons::Integer) indexes = zeros(Cint, ncons) KN_add_rsds(m, ncons, indexes) return indexes end function KN_add_rsd(m::Model) index = Cint[0] KN_add_rsd(m, index) return index end function KN_add_rsd_linear_struct( m::Model, indexRsds::Vector{Cint}, indexVars::Vector{Cint}, coefs::Vector{Cdouble}, ) nnz = length(indexRsds) @assert nnz == length(indexVars) == length(coefs) return KN_add_rsd_linear_struct(m, nnz, indexRsds, indexVars, coefs) end function KN_add_rsd_linear_struct( m::Model, indexRsd::Integer, indexVar::Vector{Cint}, coefs::Vector{Cdouble}, ) nnz = length(indexVar) @assert nnz == length(coefs) return KN_add_rsd_linear_struct_one(m, nnz, indexRsd, indexVar, coefs) end #= SOLVE =# function KN_solve(m::Model) # Check sanity. If model has Julia callbacks, we need to ensure # that Knitro is not multithreaded. Otherwise, the code will segfault # as we have trouble calling Julia code from multithreaded C # code. See issue #93 on https://github.com/jump-dev/KNITRO.jl. if has_callbacks(m) if KNITRO_VERSION >= v"13.0" KN_set_param(m, KN_PARAM_MS_NUMTHREADS, 1) KN_set_param(m, KN_PARAM_NUMTHREADS, 1) KN_set_param(m, KN_PARAM_MIP_NUMTHREADS, 1) else KN_set_param(m, "par_numthreads", 1) KN_set_param(m, "par_msnumthreads", 1) end end # For KN_solve, we do not return an error if ret is different of 0. m.status = KN_solve(m.env) return m.status end #= GETTERS =# function KN_get_solution(m::Model) # we first check that the model is well defined to avoid segfault @assert m.env != C_NULL nx = KN_get_number_vars(m) nc = KN_get_number_cons(m) x = zeros(Cdouble, nx) lambda = zeros(Cdouble, nx + nc) status = Cint[0] obj = Cdouble[0.0] KN_get_solution(m, status, obj, x, lambda) # Keep solution in cache. m.status = status[1] m.x = x m.mult = lambda m.obj_val = obj[1] return status[1], obj[1], x, lambda end # some wrapper functions for MOI function get_status(m::Model) @assert m.env != C_NULL if m.status != 1 return m.status end status = Cint[0] obj = Cdouble[0.0] KN_get_solution(m, status, obj, C_NULL, C_NULL) # Keep status in cache. m.status = status[1] return status[1] end function get_objective(m::Model) @assert m.env != C_NULL if isfinite(m.obj_val) return m.obj_val end status = Cint[0] obj = Cdouble[0.0] KN_get_solution(m, status, obj, C_NULL, C_NULL) # Keep objective value in cache. m.obj_val = obj[1] return obj[1] end function get_solution(m::Model) # We first check that the model is well defined to avoid segfault. @assert m.env != C_NULL if !isempty(m.x) return m.x end nx = KN_get_number_vars(m) x = zeros(Cdouble, nx) status = Cint[0] obj = Cdouble[0.0] KN_get_solution(m, status, obj, x, C_NULL) # Keep solution in cache. m.x = x return x end get_solution(m::Model, ix::Int) = isempty(m.x) ? get_solution(m)[ix] : m.x[ix] function get_dual(m::Model) # we first check that the model is well defined to avoid segfault @assert m.env != C_NULL if !isempty(m.mult) return m.mult end nx = KN_get_number_vars(m) nc = KN_get_number_cons(m) lambda = zeros(Cdouble, nx + nc) status = Cint[0] obj = Cdouble[0.0] KN_get_solution(m, status, obj, C_NULL, lambda) # Keep multipliers in cache. m.mult = lambda return lambda end get_dual(m::Model, ix::Int) = isempty(m.mult) ? get_dual(m)[ix] : m.mult[ix] function KN_get_objgrad_values(m::Model) nnz = KN_get_objgrad_nnz(m) indexVars = zeros(Cint, nnz) objGrad = zeros(Cdouble, nnz) KN_get_objgrad_values(m, indexVars, objGrad) return indexVars, objGrad end function KN_get_jacobian_values(m::Model) nnz = KN_get_jacobian_nnz(m) jacvars = zeros(Cint, nnz) jaccons = zeros(Cint, nnz) jaccoef = zeros(Cdouble, nnz) KN_get_jacobian_values(m, jacvars, jaccons, jaccoef) return jacvars, jaccons, jaccoef end function KN_get_rsd_jacobian_values(m::Model) nnz = KN_get_rsd_jacobian_nnz(m) jacvars = zeros(Cint, nnz) jaccons = zeros(Cint, nnz) jaccoef = zeros(Cdouble, nnz) KN_get_rsd_jacobian_values(m, jacvars, jaccons, jaccoef) return jacvars, jaccons, jaccoef end function KN_get_hessian_values(m::Model) nnz = KN_get_hessian_nnz(m) indexVars1 = zeros(Cint, nnz) indexVars2 = zeros(Cint, nnz) hess = zeros(Cdouble, nnz) KN_get_hessian_values(m, indexVars1, indexVars2, hess) return indexVars1, indexVars2, hess end function KN_get_var_viols(kc::Model, index::Vector{Cint}) bndInfeas = zeros(Cint, length(index)) intInfeas = zeros(Cint, length(index)) viols = zeros(Cdouble, length(index)) KN_get_var_viols(kc, length(index), index, bndInfeas, intInfeas, viols) return bndInfeas, intInfeas, viols end function KN_get_con_viols(kc::Model, index::Vector{Cint}) infeas = zeros(Cint, length(index)) viols = zeros(Cdouble, length(index)) KN_get_con_viols(kc, length(index), index, infeas, viols) return infeas, viols end function KN_get_presolve_error(m::Model) @assert m.env != C_NULL component, index, error = Cint[0], Cint[0], Cint[0] viol = Cdouble[0.0] KN_get_presolve_error(m, component, index, error, viol) return convert(Bool, component[1]), convert(Int64, index[1]), convert(Int64, error[1]), convert(Float64, viol[1]) end @kn_get_attribute get_number_vars Cint @kn_get_attribute get_number_cons Cint @kn_get_attribute get_obj_value Cdouble @kn_get_attribute get_obj_type Cint @kn_get_attribute get_number_iters Cint @kn_get_attribute get_number_cg_iters Cint @kn_get_attribute get_abs_feas_error Cdouble @kn_get_attribute get_rel_feas_error Cdouble @kn_get_attribute get_abs_opt_error Cdouble @kn_get_attribute get_rel_opt_error Cdouble @kn_get_attribute get_objgrad_nnz Cint @kn_get_attribute get_jacobian_nnz KNLONG @kn_get_attribute get_rsd_jacobian_nnz KNLONG @kn_get_attribute get_hessian_nnz KNLONG @kn_get_attribute get_solve_time_cpu Cdouble @kn_get_attribute get_solve_time_real Cdouble @kn_get_attribute get_mip_number_nodes Cint @kn_get_attribute get_mip_number_solves Cint @kn_get_attribute get_mip_abs_gap Cdouble @kn_get_attribute get_mip_rel_gap Cdouble @kn_get_attribute get_mip_incumbent_obj Cdouble @kn_get_attribute get_mip_relaxation_bnd Cdouble @kn_get_attribute get_mip_lastnode_obj Cdouble #= PARAMS =# function KN_set_param(m::Model, id::Integer, value::Integer) return KN_set_int_param(m, id, value) end function KN_set_param(m::Model, param::AbstractString, value::Integer) return KN_set_int_param_by_name(m, param, value) end function KN_set_param(m::Model, id::Integer, value::Cdouble) return KN_set_double_param(m, id, value) end function KN_set_param(m::Model, param::AbstractString, value::Cdouble) return KN_set_double_param_by_name(m, param, value) end function KN_set_param(m::Model, id::Integer, value::AbstractString) return KN_set_char_param(m, id, value) end function KN_set_param(m::Model, param::AbstractString, value::AbstractString) return KN_set_char_param_by_name(m, param, value) end function KN_get_int_param(m::Model, id::Integer) res = Cint[0] KN_get_int_param(m, id, res) return res[1] end function KN_get_int_param(m::Model, param::AbstractString) res = Cint[0] KN_get_int_param_by_name(m, param, res) return res[1] end function KN_get_double_param(m::Model, id::Integer) res = Cdouble[0.0] KN_get_double_param(m, id, res) return res[1] end function KN_get_double_param(m::Model, param::AbstractString) res = Cdouble[0.0] KN_get_double_param_by_name(m, param, res) return res[1] end function KN_get_param_name(m::Model, id::Integer) output_size = 128 res = " "^output_size KN_get_param_name(m, id, res, output_size) return _format_output(res) end function KN_get_param_doc(m::Model, id::Integer) output_size = 128 res = " "^output_size KN_get_param_doc(m, id, res, output_size) return _format_output(res) end function KN_get_param_type(m::Model, id::Integer) res = Cint[0] KN_get_param_type(m, id, res) return res[1] end function KN_get_num_param_values(m::Model, id::Integer) res = Cint[0] KN_get_num_param_values(m, id, res) return res[1] end function KN_get_param_value_doc(m::Model, id::Integer, value_id::Integer) output_size = 128 res = " "^output_size KN_get_param_value_doc(m, id, value_id, res, output_size) return _format_output(res) end function KN_get_param_id(m::Model, name::AbstractString) res = Cint[0] KN_get_param_id(m, name, res) return res[1] end #= NAMES =# function KN_get_var_names(m::Model, max_length=1024) return String[ KN_get_var_names(m, Cint(id - 1), max_length) for id in 1:KN_get_number_vars(m) ] end function KN_get_var_names(m::Model, index::Vector{Cint}, max_length=1024) return String[KN_get_var_names(m, id, max_length) for id in index] end function KN_get_var_names(m::Model, index::Cint, max_length=1024) rawname = zeros(Cchar, max_length) ret = KN_get_var_name(m, index, max_length, rawname) return String(strip(String(convert(Vector{UInt8}, rawname)), '\0')) end function KN_get_con_names(m::Model, max_length=1024) return String[ KN_get_con_names(m, Cint(id - 1), max_length) for id in 1:KN_get_number_cons(m) ] end function KN_get_con_names(m::Model, index::Vector{Cint}, max_length=1024) return String[KN_get_con_names(m, id, max_length) for id in index] end function KN_get_con_names(m::Model, index::Cint, max_length=1024) rawname = zeros(Cchar, max_length) ret = KN_get_con_name(m, index, max_length, rawname) return String(strip(String(convert(Vector{UInt8}, rawname)), '\0')) end
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using CartesianFDM using Test @testset "1D identities" begin n = (5,) top = (periodic(),) ctx = cartesianfdmcontext(top, n) # assumes 1D ((δ⁻,), (δ⁺,)) = getproperty(ctx, :δ) ((σ⁻,), (σ⁺,)) = getproperty(ctx, :σ) ((τ⁻,), (τ⁺,)) = getproperty(ctx, :τ) Ψ, Φ = scalar(:Ψ, n), scalar(:Φ, n) @testset "2x identity 53" begin id = (σ⁺ * (δ⁻ * Φ)) .- (δ⁺ * (σ⁻ * Φ)) @test all(iszero, Symbolics.expand.(id)) id = (σ⁻ * (δ⁺ * Φ)) .- (δ⁻ * (σ⁺ * Φ)) @test all(iszero, Symbolics.expand.(id)) end @testset "2x identity 56" begin id = (σ⁺ * (Ψ .* (δ⁻ * Φ))) .+ (2Φ .* (δ⁺ * Ψ)) .- (δ⁺ * (Ψ .* (σ⁻ * Φ))) @test all(iszero, expand.(id)) id = (σ⁻ * (Ψ .* (δ⁺ * Φ))) .+ (2Φ .* (δ⁻ * Ψ)) .- (δ⁻ * (Ψ .* (σ⁺ * Φ))) @test all(iszero, expand.(id)) end @testset "2x identity 57" begin id = (Φ .* ((δ⁺ * (Ψ .* (σ⁻ * Φ))) .+ (σ⁺ * (Ψ .* (δ⁻ * Φ))))) .- (δ⁺ * (Ψ .* ((Φ .* (τ⁻ * Φ)) .+ ((τ⁻ * Φ) .* Φ)))) @test all(iszero, expand.(id)) id = (Φ .* ((δ⁻ * (Ψ .* (σ⁺ * Φ))) .+ (σ⁻ * (Ψ .* (δ⁺ * Φ))))) .- (δ⁻ * (Ψ .* ((Φ .* (τ⁺ * Φ)) .+ ((τ⁺ * Φ) .* Φ)))) @test all(iszero, expand.(id)) end @testset "2x identity 59" begin id = (((σ⁺ * Ψ) .* (δ⁺ * Φ)) .+ ((σ⁺ * Φ) .* (δ⁺ * Ψ))) - (2δ⁺ * (Ψ .* Φ)) @test all(iszero, expand.(id)) id = (((σ⁻ * Ψ) .* (δ⁻ * Φ)) .+ ((σ⁻ * Φ) .* (δ⁻ * Ψ))) - (2δ⁻ * (Ψ .* Φ)) @test all(iszero, expand.(id)) end end @testset "star stencil" begin using SparseArrays n = (3, 4, 5) top = (periodic(), nonperiodic(), periodic()) ctx = cartesianfdmcontext(top, n) (τ⁻, τ⁺) = getproperty(ctx, :τ) X = scalar(:X, n) ### F = rand(prod(n)) .* X for i in eachindex(top) F .+= τ⁻[i] * (rand(prod(n)) .* X) end for i in eachindex(top) F .+= τ⁺[i] * (rand(prod(n)) .* X) end ### sym = linearize(star, ctx, F, X) num = Dict{Int,Vector{Float64}}() for (key, val) in sym num[key] = eval(first(build_function(val)))() end A = spdiagm(num...) ### @test all(iszero, F .- A * X) end
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# This file was generated by the Julia Swagger Code Generator # Do not modify this file directly. Modify the swagger specification instead. module PolicyAssignmentsClient using Random using Dates using Swagger import Swagger: field_name, property_type, hasproperty, validate_property, SwaggerApi, SwaggerModel import Base: convert, propertynames include("modelincludes.jl") include("api_PolicyAssignmentsApi.jl") # export models export convert, CloudError export convert, ErrorAdditionalInfo export convert, ErrorResponse export convert, Identity export convert, ParameterValues export convert, ParameterValuesValue export convert, PolicyAssignment export convert, PolicyAssignmentListResult export convert, PolicyAssignmentProperties export convert, PolicySku # export operations export convert, PolicyAssignmentsApi export check_required, field_name, property_type, hasproperty, propertynames, validate_property, convert end
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###Cargar datos using TopicModelsVB import Distributions.sample #Arreglar direccion corp = readcorp(:mac) corp.docs = vcat([sample(filter(doc -> round(doc.stamp / 100) == y, corp.docs), 400, replace=false) for y in 1984:2005]...) fixcorp!(corp, abr=100, len=10) # Remove words which appear < 100 times and documents of length < 10. basemodel = LDA(corp, 9) train!(basemodel, iter=150, chkelbo=151) # training... model = DTM(corp, 9, 200, basemodel) train!(model, iter=10) # This will likely take about an hour on a personal computer. # Convergence for all other models is worst-case quadratic, # while DTM convergence is linear or at best super-linear. # training...
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using GNSSTools using FFTW using AllanDeviations using Statistics # using LsqFit # using NNLS using PyPlot pygui(true) # function psd_at_f(h_parms, f, v_0) # h₋₂, h₋₁, h₀, h₁, h₂ = h_parms # return v_0^2 * (h₋₂*f^(-4) + h₋₁*f^(-3) + h₀*f^(-2) + h₁*f^(-1) + h₂*f^0) # end # function fit_h_parms_to_signal_PSD(psd_dBc, f_c=10e6, # freqs=[1,10,100,1e3,1e4,1e5]) # N = length(freqs) # @assert N == length(psd_dBc) # b = zeros(N-1) # A = zeros(N-1) # for i in 1:N-1 # j = N - i # A[i] = (psd_dBc[j] + psd_dBc[j+1])/2 + 10*log10(freqs[j+1] - freqs[j]) # if i == 1 # b[i] = 2*10^(A[i]/10) / (freqs[j+1] - freqs[j]) # b₀ # elseif i == 2 # b[i] = 2*10^(A[i]/10) / log(freqs[j+1] - freqs[j]) # b₋₁ # else # b[i] = 2*10^(A[i]/10) / ((2-i)*(freqs[j+1]^(2-i) - freqs[j]^(2-i))) # end # end # # order is h2, h1, h0, h-1, h-2 # h = b ./ f_c.^2 # # reverse order to h-2, h-1, h₀, h₁, h₂ # return reverse(h) # end function calc_Sy(x, f_s) N = length(x) X = fft(x)[1:floor(Int, N/2)+1] X = (1/(f_s*N)) .* abs2.(X) X[2:end-1] .= 2 .* X[2:end-1] X_log10 = 10 .* log10.(X) frequencies = Array(range(0, f_s/2, length=length(X))) return (X, X_log10, frequencies) end # f_s = 36e6 f_carrier1 = 16.3676e6 f_carrier2 = 10e6 f_s = 1*f_carrier1 t_length = 1 N = floor(Int, t_length*f_s) N_over_2 = floor(Int, N/2) t = Array(range(0, t_length, length=N)) frequencies = Array(range(0, f_s/2, length=N_over_2+1)) # carrier1 = cis.(2π.*f_carrier1.*t) # carrier2 = cis.(2π.*f_carrier2.*t) phase_noise = zeros(Complex{Float64}, N) # signal = zeros(Complex{Float64}, N) dBc_freq = [1., 1e1, 1e2, 1e3, 1e4] X = zeros(Complex{Float64}, N_over_2+1) x = zeros(Complex{Float64}, N) rakon_it5300B = Dict("h_parms"=>[1.9e-6, 2e-6, 9.7e-8, 9.1e-11, 9.9e-15], "dBc"=>[-57, -88, -112, -130, -140], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"TCXO, Rakon IT5300B", "f_0"=>f_carrier1) agilent_e4424b = Dict("h_parms"=>[2.8e-9, 1.8e-11, 1.2e-10, 1e-11, 1.8e-15], "dBc"=>[-88, -118, -128, -135, -147], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"OCXO, Agilent E4424B", "f_0"=>10e6) isotemp_91_1 = Dict("h_parms"=>[2.2e-9, 1.6e-10, 1.6e-10, 7.1e-13, 7.2e-16], "dBc"=>[-89, -120, -140, -151, -154], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"OCXO, ISOTEMP 91-1", "f_0"=>f_carrier2) oscilloquartz_8607 = Dict("h_parms"=>[3.9e-14, 8.7e-13, 2.2e-14, 3.2e-13, 6.4e-15], "dBc"=>[-122, -137, -143, -145, -145], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"OCXO, Oscilloquartz 8607", "f_0"=>f_carrier2) srs_prs10 = Dict("h_parms"=>[5.3e-11, 4.7e-11, 1.6e-15, 9.6e-14, 9.9e-16], "dBc"=>[-103, -135, -150, -152, -153], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"Rubidum, SRS PRS10", "f_0"=>f_carrier2) cesium = Dict("h_parms"=>[1.5e-12, 6.2e-11, 7.6e-23, 1.4e-14, 6.2e-19], "dBc"=>[-105, -135, -160, -170, -190], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"Cesium, Typical Values", "f_0"=>f_carrier2) mti_250l = Dict("h_parms"=>[1.7e-9, 1e-10, 1.6e-10, 3.5e-13, 1.8e-16], "dBc"=>[-90, -120, -140, -155, -160], "dBc_freq"=>[1., 1e1, 1e2, 1e3, 1e4], "name"=>"OCXO, MTI 250L",#, Low g-sensitivity", "f_0"=>f_carrier2) oscillators = [rakon_it5300B, agilent_e4424b, isotemp_91_1, oscilloquartz_8607, srs_prs10, cesium, mti_250l] # figsize = (6.5, 4) colorz = ["k", "b", "g", "r", "y", "m", "c", "gray"] fig = figure(figsize=(6.5,4)) ax1 = fig.add_subplot(1,1,1) legend_markers = [] marker_names = [] for i in 1:length(oscillators) # for i in 1:1 color = colorz[i] oscillator = oscillators[i] if oscillator["name"] == "TCXO, Rakon IT5300B" # carrier = carrier1 f_carrier = f_carrier1 else # carrier = carrier2 f_carrier = f_carrier2 end h_parms = oscillator["h_parms"] ./ oscillator["f_0"]^2 generate_phase_noise!(phase_noise, t_length, f_carrier, h_parms) # ϕ = real.(phase_noise) # signal = carrier .* cis.(ϕ) # signal .= carrier .* cis.(real.(phase_noise)) # x = signal x .= real.(phase_noise) .+ 0im # X, X_log, freqs = calc_Sy(x, f_s) fft!(x); X .= abs2.(x[1:N_over_2+1]) / (f_s*N); X[2:end-1] .= 2 .* X[2:end-1]; X .= 10 .* log10.(X); # X_max = maximum(real.(X)); # X = X .- X_max; # ax1.plot(frequencies[2:end] .- f_carrier, X[2:end], color, # label=string(oscillator["name"], " from h Parms")) ax1.plot(frequencies[2:end], real.(X[2:end]), color)#), # label=oscillator["name"]) # ax1.plot(frequencies[2:end], real.(X[2:end]), color, # label=string(oscillator["name"], " from h Parms")) ax1.scatter(dBc_freq, oscillator["dBc"].+(10*log10(2)), c=color)#, label=string(oscillator["name"], " Truth")) legend_marker = plt.Line2D((0,1),(0,0), color=color, marker="o", linestyle="-") push!(legend_markers, legend_marker) push!(marker_names, oscillator["name"]) end xscale("log") xlim([1, 1e6]) xlabel("Frequency (Hz)") ylabel("dBc/Hz") subplots_adjust(top=0.96, left=0.12, right=0.88, bottom=0.13) ax1.legend(legend_markers, marker_names) # savefig("figures/ch2_oscillator_psds.pdf", dpi=300)
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1.627456
3,562
""" ``` measurement(m::PoolModel{T}) where {T<:AbstractFloat} ``` Assign measurement equation for Dynamic Pools ``` p(y_t) = λ_t * p_1 + (1 - λ_t) * p_2 ``` where ``` λ_t = weight assigned to model 1 p_1 = predictive density according to model 1 p_2 = predictive density according to model 2 ``` Equal weights fixes λ = 1/2, and static weights computes the likelihood as if λ is time-invariant. For Bayesian Model Averaging, we compute the average according to weights computed according to BMA. The outputs are a function that computes the average of passed predictive densities and a degenerate measurement error Distribution. """ function measurement(m::PoolModel{T}) where {T<:AbstractFloat} obs = m.observables weight_type = get_setting(m, :weight_type) # Assumes λ to be weight on the first model F_u = DiscreteUniform(0,0) if weight_type == :dynamic Ψ_dynamic_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, x) return Ψ_dynamic_pm, F_u elseif weight_type == :equal equal_wt = m[:λ].value Ψ_equal_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, equal_wt .* ones(2)) return Ψ_equal_pm, F_u elseif weight_type == :static Ψ_static_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, x) return Ψ_static_pm, F_u elseif weight_type == :bma Ψ_bma_pm(x::Vector{Float64}, data::Vector{Float64}) = dot(data, x) return Ψ_bma_pm, F_u end end
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2.520478
586
using ExecutableSpecifications: @given, @when, @then, @expect @given "some precondition which does nothing" begin end @when "we perform a no-op step" begin end @then "the scenario as a whole succeeds" begin end @when "a step has more than one matching step definition" begin end @when "a step has more than one matching step definition" begin end @when "foo is set to value 42" begin context[:foo] = 42 end @when "foo is set to value -17" begin context[:foo] = -17 end @then "foo is greater than zero" begin @expect context[:foo] > 0 end @then "foo is less than zero" begin @expect context[:foo] < 0 end
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2.930556
216
using MakieLayout using Makie begin scene = Scene(resolution = (1000, 1000)); screen = display(scene) campixel!(scene); maingl = GridLayout(scene, 1, 3; addedcolgaps = Fixed(0), alignmode = Outside(30) ) maingl[1, 2] = LAxis(scene) maingl[1, 3] = LAxis(scene) guigl = maingl[1, 1] = GridLayout(1, 3; height=Auto(false)) # guigl[1, 1] = LButton(scene, 100, 30, "button") guigl[1, 1] = LText(scene, text="HelloWorld", halign=:left) guigl[1, 2] = LText(scene, text="Blablo", halign=:left) guigl[1, 3] = LButton(scene, width = 120, height = 30, label = "Bliblu") guigl[2, 1] = LText(scene, text="Mamama", halign=:left) guigl[2, 2] = LText(scene, text="Mimimi", halign=:left) guigl[2, 3] = LButton(scene, width = 120, height = 30, label = "Momomo") guigl[3, 1] = LText(scene, text="VeerrryLoong", halign=:left) guigl[3, 2] = LText(scene, text="Blablo", halign=:left) guigl[3, 3] = LButton(scene, width = 120, height = 30, label = "Short") end
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2.238095
462
module day11 import ..aoc19: run_program! using DelimitedFiles, UnicodePlots, REPL readInput() = parse.(Int, split(readline(joinpath(@__DIR__,"input.txt")), ',')) const UP = (1, 0) const RIGHT = (0, 1) const DOWN = (-1, 0) const LEFT = (0, -1) turnLeft(d) = d == UP ? LEFT : (d == RIGHT ? UP : (d == DOWN ? RIGHT : DOWN)) turnRight(d) = d == UP ? RIGHT : (d == RIGHT ? DOWN : (d == DOWN ? LEFT : UP)) function part1(data) pos = (0, 0) dir = UP painted = Dict{Tuple{Int, Int}, Int}() input = Channel{Int}(Inf) output = Channel{Int}(Inf) task = @async run_program!(data, input, output) while !istaskdone(task) put!(input, get(painted, pos, 0)) color = take!(output) turn = take!(output) painted[pos] = color # paint dir = turn == 0 ? turnLeft(dir) : turnRight(dir) # turn pos = pos .+ dir # move forward end length(painted) end function part2(data) pos = (0, 0) dir = UP painted = Dict((0, 0) => 1) input = Channel{Int}(Inf) output = Channel{Int}(Inf) task = @async run_program!(data, input, output) while !istaskdone(task) put!(input, get(painted, pos, 0)) color = take!(output) turn = take!(output) painted[pos] = color # paint dir = turn == 0 ? turnLeft(dir) : turnRight(dir) # turn pos = pos .+ dir # move forward end white = filter(p -> p.second == 1, painted) scatterplot(last.(keys(white)), first.(keys(white)), xlim=extrema(last.(keys(painted))), ylim=extrema(first.(keys(painted))), height = 5, width = 70, color = :blue, canvas = BlockCanvas) end end
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2.36
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using Documenter, GMT makedocs( modules = [GMT], format = :html, sitename = "GMT", pages = [ "Home" => "index.md", "Some examples" => "examples.md", "Draw rectangles examples" => "rectangles.md", "Draw frames examples" => "frames.md", "Manual" => [ "usage.md", "monolitic.md", "modules.md", ], "The GMT types" => "types.md" ], html_prettyurls = true, ) deploydocs( repo = "github.com/GenericMappingTools/GMT.jl.git", target = "build", julia = "0.7", deps = nothing, make = nothing )
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2.019355
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using Plots anim = @animate for i in 1:10 plot(t->sinpi(t+i/5), range(0, 2, length=100)) end gif(anim, "test_animation.gif", fps=40)
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2.225806
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mutable struct TrackedReal{T<:Real} data::T grad::Union{Nothing,T} children::Dict # this field is only need for printing the graph. you can safely remove it. name::String end track(x::Real,name="") = TrackedReal(x,nothing,Dict(),name) function Base.show(io::IO, x::TrackedReal) t = isempty(x.name) ? "(tracked)" : "(tracked $(x.name))" print(io, "$(x.data) $t") end function accum!(x::TrackedReal) if isnothing(x.grad) x.grad = sum(accum!(v)*w for (v,w) in x.children) end x.grad end function gradient(f, args::Real...) ts = track.(args) y = f(ts...) y.grad = 1.0 accum!.(ts) end ########## RULES ############################################################# function Base.:*(a::TrackedReal, b::TrackedReal) z = track(a.data * b.data, "*") a.children[z] = b.data # dz/da=b b.children[z] = a.data # dz/db=a z end function Base.:+(a::TrackedReal{T}, b::TrackedReal{T}) where T z = track(a.data + b.data, "+") a.children[z] = one(T) b.children[z] = one(T) z end function Base.sin(x::TrackedReal) z = track(sin(x.data), "sin") x.children[z] = cos(x.data) z end ########## Optimizion 2D function ############################################ using Plots g(x,y) = y*y + sin(x) cscheme = cgrad(:RdYlBu_5, rev=true) p1 = contour(-4:0.1:4, -2:0.1:2, g, fill=true, c=cscheme, xlabel="x", ylabel="y") display(p1) function descend(f::Function, λ::Real, args::Real...) Δargs = gradient(f, args...) args .- λ .* Δargs end function minimize(f::Function, args::T...; niters=20, λ=0.01) where T<:Real paths = ntuple(_->Vector{T}(undef,niters), length(args)) for i in 1:niters args = descend(f, λ, args...) @info f(args...) for j in 1:length(args) paths[j][i] = args[j] end end paths end xs1, ys1 = minimize(g, 1.5, -2.4, λ=0.2, niters=34) xs2, ys2 = minimize(g, 1.8, -2.4, λ=0.2, niters=16) scatter!(p1, [xs1[1]], [ys1[1]], markercolor=:black, marker=:star, ms=7, label="Minimum") scatter!(p1, [xs2[1]], [ys2[1]], markercolor=:black, marker=:star, ms=7, label=false) scatter!(p1, [-π/2], [0], markercolor=:red, marker=:star, ms=7, label="Initial Point") scatter!(p1, xs1[1:1], ys1[1:1], markercolor=:black, label="GD Path", xlims=(-4,4), ylims=(-2,2)) anim = @animate for i in 1:max(length(xs1), length(xs2)) if i <= length(xs1) scatter!(p1, xs1[1:i], ys1[1:i], mc=:black, lw=3, xlims=(-4,4), ylims=(-2,2), label=false) end if i <= length(xs2) scatter!(p1, xs2[1:i], ys2[1:i], mc=:black, lw=3, label=false) end p1 end gif(anim, "gd-path.gif", fps=15)
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struct ConfusionMatrix tp::Int fp::Int fn::Int tn::Int end function getConfusionMatrix(Δ::AbstractMatrix{T}, hΔ::AbstractMatrix{T}, thr=1e-4) where T p = size(Δ, 1) tp = fp = fn = tn = 0 for c=1:p for r=c:p if Δ[r, c] != zero(T) if abs(hΔ[r, c]) > thr # not zero estimated as not zero tp += 1 else # not zero estimated as zero fn += 1 end else if abs(hΔ[r, c]) > thr # zero estimated as not zero fp += 1 else # zero estimated as zero tn += 1 end end end end ConfusionMatrix(tp, fp, fn, tn) end function getConfusionMatrix(Δ::AbstractMatrix{T}, hΔ::SparseIterate{T}, thr=1e-4) where T p = size(Δ, 1) tp = fp = fn = tn = 0 for c=1:p for r=c:p if Δ[r, c] != zero(T) if abs(hΔ[sub2indLowerTriangular(p, r, c)]) > thr # not zero estimated as not zero tp += 1 else # not zero estimated as zero fn += 1 end else if abs(hΔ[sub2indLowerTriangular(p, r, c)]) > thr # zero estimated as not zero fp += 1 else # zero estimated as zero tn += 1 end end end end ConfusionMatrix(tp, fp, fn, tn) end function tpr(t::ConfusionMatrix) dn = t.tp + t.fn dn > 0 ? t.tp / dn : 0. end function fpr(t::ConfusionMatrix) dn = t.fp + t.tn dn > 0 ? t.fp / dn : 0. end function precision(t::ConfusionMatrix) dn = t.tp + t.fp dn > 0 ? t.tp / dn : 1. end recall(t::ConfusionMatrix) = tpr(t)
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1.884211
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using Clang using Clang.Generators cd(@__DIR__) # current version checked in is v2.2.9 include_dir = normpath(joinpath(@__DIR__, "RadeonProRenderSDK", "RadeonProRender", "inc")) # LIBCLANG_HEADERS are those headers to be wrapped. headers = joinpath.(include_dir, [ "RadeonProRender_v2.h", # "RadeonProRender_GL.h", # "RadeonProRender_VK.h" ]) # wrapper generator options options = load_options(joinpath(@__DIR__, "rpr.toml")) # add compiler flags, e.g. "-DXXXXXXXXX" args = get_default_args() push!(args, "-I$include_dir") ctx = create_context(headers, args, options) # run generator build!(ctx, BUILDSTAGE_NO_PRINTING) function rewrite!(e::Expr) if Meta.isexpr(e, :function) func_args = e.args[1].args last_arg = func_args[end] body = e.args[2] ccall_expr = body.args[end] type_tuple = ccall_expr.args[4] new_body = if startswith(string(last_arg), "out_") last_type_ptr = type_tuple.args[end] @assert Meta.isexpr(last_type_ptr, :curly) @assert last_type_ptr.args[1] == :Ptr last_type = last_type_ptr.args[2] pop!(func_args) # remove from input args :($(last_arg) = Ref{$(last_type)}(); check_error($(ccall_expr)); return $(last_arg)[]) elseif ccall_expr.args[3] == :rpr_status :(check_error($(ccall_expr))) else ccall_expr end e.args[2] = new_body end return e end function rewrite!(dag::ExprDAG) for node in get_nodes(dag) for expr in get_exprs(node) rewrite!(expr) end end end rewrite!(ctx.dag) quote function check_error(error_code) error_code == RPR_SUCCESS && return return error("Error code returned: $(error_code)") end end cd(@__DIR__) build!(ctx, BUILDSTAGE_PRINTING_ONLY)
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2.139723
866
using MathProgBase ######################################################## ## this code is based on ModelReader in NLPModels ## and KNITRO.jl ######################################################## export OnePhaseSolver #, OnePhaseMathProgModel, NonlinearModel mutable struct OnePhaseSolver <: MathProgBase.AbstractMathProgSolver options end OnePhaseSolver(;kwargs...) = OnePhaseSolver(kwargs) type OnePhaseProblem status::Symbol # Final status # For MathProgBase x::Vector{Float64} # Starting and final solution lambda::Vector{Float64} g::Vector{Float64} # Final constraint values obj_val::Float64 # (length 1) Final objective solve_time::Float64 # Custom attributes of the OnePhaseSolver iter::Class_iterate hist::Array{alg_history2,1} pars::Class_parameters function OnePhaseProblem() return new() end end ######################################################## ## BEGIN ModelReader CODE (with minor edits) ######################################################## mutable struct OnePhaseMathProgModel <: MathProgBase.AbstractMathProgModel options inner::OnePhaseProblem eval :: Union{MathProgBase.AbstractNLPEvaluator, Void} numVar :: Int numConstr :: Int x :: Vector{Float64} y :: Vector{Float64} lvar :: Vector{Float64} uvar :: Vector{Float64} lcon :: Vector{Float64} ucon :: Vector{Float64} sense :: Symbol status :: Symbol end MathProgBase.NonlinearModel(solver :: OnePhaseSolver) = OnePhaseMathProgModel(solver.options,OnePhaseProblem(),nothing, 0, 0, Float64[], Float64[], Float64[], Float64[], Float64[], Float64[], :Min, :Uninitialized) function MathProgBase.loadproblem!(m :: OnePhaseMathProgModel, numVar, numConstr, l, u, lb, ub, sense, eval :: MathProgBase.AbstractNLPEvaluator) # TODO: :JacVec is not yet available. # [:Grad, :Jac, :JacVec, :Hess, :HessVec, :ExprGraph] MathProgBase.initialize(eval, [:Grad, :Jac, :Hess, :HessVec, :ExprGraph]) m.numVar = numVar m.numConstr = numConstr m.x = zeros(numVar) m.y = zeros(numConstr) m.eval = eval m.lvar = l m.uvar = u m.lcon = lb m.ucon = ub m.sense = sense end MathProgBase.setwarmstart!(m :: OnePhaseMathProgModel, x) = (m.x = x) MathProgBase.status(m :: OnePhaseMathProgModel) = m.inner.status MathProgBase.getsolution(m :: OnePhaseMathProgModel) = m.inner.x MathProgBase.getobjval(m :: OnePhaseMathProgModel) = m.inner.iter.cache.fval #MathProgBase.eval_f(m.eval, m.x) mutable struct MathProgNLPModel <: AbstractNLPModel meta :: NLPModelMeta mpmodel :: OnePhaseMathProgModel counters :: Counters # Evaluation counters. jrows :: Vector{Int} # Jacobian sparsity pattern. jcols :: Vector{Int} jvals :: Vector{Float64} # Room for the constraints Jacobian. hrows :: Vector{Int} # Hessian sparsity pattern. hcols :: Vector{Int} hvals :: Vector{Float64} # Room for the Lagrangian Hessian. end "Construct a `MathProgNLPModel` from a `OnePhaseMathProgModel`." function MathProgNLPModel(mpmodel :: OnePhaseMathProgModel; name :: String="Generic") nvar = mpmodel.numVar lvar = mpmodel.lvar uvar = mpmodel.uvar nlin = length(mpmodel.eval.m.linconstr) # Number of linear constraints. nquad = length(mpmodel.eval.m.quadconstr) # Number of quadratic constraints. nnln = length(mpmodel.eval.m.nlpdata.nlconstr) # Number of nonlinear constraints. ncon = mpmodel.numConstr # Total number of constraints. lcon = mpmodel.lcon ucon = mpmodel.ucon jrows, jcols = MathProgBase.jac_structure(mpmodel.eval) hrows, hcols = MathProgBase.hesslag_structure(mpmodel.eval) nnzj = length(jrows) nnzh = length(hrows) meta = NLPModelMeta(nvar, x0=mpmodel.x, lvar=lvar, uvar=uvar, ncon=ncon, y0=zeros(ncon), lcon=lcon, ucon=ucon, nnzj=nnzj, nnzh=nnzh, lin=collect(1:nlin), # linear constraints appear first in MPB nln=collect(nlin+1:ncon), minimize=(mpmodel.sense == :Min), islp=MathProgBase.isobjlinear(mpmodel.eval) & (nlin == ncon), name=name, ) return MathProgNLPModel(meta, mpmodel, Counters(), jrows, jcols, zeros(nnzj), # jvals hrows, hcols, zeros(nnzh), # hvals ) end ## import Base.show show(nlp :: MathProgNLPModel) = show(nlp.mpmodel) function obj(nlp :: MathProgNLPModel, x :: Array{Float64}) NLPModels.increment!(nlp, :neval_obj) return MathProgBase.eval_f(nlp.mpmodel.eval, x) end function grad(nlp :: MathProgNLPModel, x :: Array{Float64}) g = zeros(nlp.meta.nvar) return grad!(nlp, x, g) end function grad!(nlp :: MathProgNLPModel, x :: Array{Float64}, g :: Array{Float64}) NLPModels.increment!(nlp, :neval_grad) MathProgBase.eval_grad_f(nlp.mpmodel.eval, g, x) return g end function cons(nlp :: MathProgNLPModel, x :: Array{Float64}) c = zeros(nlp.meta.ncon) return cons!(nlp, x, c) end function cons!(nlp :: MathProgNLPModel, x :: Array{Float64}, c :: Array{Float64}) NLPModels.increment!(nlp, :neval_cons) MathProgBase.eval_g(nlp.mpmodel.eval, c, x) return c end function jac_coord(nlp :: MathProgNLPModel, x :: Array{Float64}) NLPModels.increment!(nlp, :neval_jac) MathProgBase.eval_jac_g(nlp.mpmodel.eval, nlp.jvals, x) return (nlp.jrows, nlp.jcols, nlp.jvals) end function jac(nlp :: MathProgNLPModel, x :: Array{Float64}) return sparse(jac_coord(nlp, x)..., nlp.meta.ncon, nlp.meta.nvar) end function jprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}) Jv = zeros(nlp.meta.ncon) return jprod!(nlp, x, v, Jv) end function jprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, Jv :: Array{Float64}) nlp.counters.neval_jac -= 1 NLPModels.increment!(nlp, :neval_jprod) Jv[:] = jac(nlp, x) * v return Jv end function jtprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}) Jtv = zeros(nlp.meta.nvar) return jtprod!(nlp, x, v, Jtv) end function jtprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, Jtv :: Array{Float64}) nlp.counters.neval_jac -= 1 NLPModels.increment!(nlp, :neval_jtprod) Jtv[1:nlp.meta.nvar] = jac(nlp, x)' * v return Jtv end # Uncomment if/when :JacVec becomes available in MPB. # "Evaluate the Jacobian-vector product at `x`." # function jprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}) # jv = zeros(nlp.meta.ncon) # return jprod!(nlp, x, v, jv) # end # # "Evaluate the Jacobian-vector product at `x` in place." # function jprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, jv :: Array{Float64}) # NLPModels.increment!(nlp, :neval_jprod) # MathProgBase.eval_jac_prod(nlp.mpmodel.eval, jv, x, v) # return jv # end # # "Evaluate the transposed-Jacobian-vector product at `x`." # function jtprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}) # jtv = zeros(nlp.meta.nvar) # return jtprod!(nlp, x, v, jtv) # end # # "Evaluate the transposed-Jacobian-vector product at `x` in place." # function jtprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, jtv :: Array{Float64}) # NLPModels.increment!(nlp, :neval_jtprod) # MathProgBase.eval_jac_prod_t(nlp.mpmodel.eval, jtv, x, v) # return jtv # end function hess_coord(nlp :: MathProgNLPModel, x :: Array{Float64}; obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon)) NLPModels.increment!(nlp, :neval_hess) MathProgBase.eval_hesslag(nlp.mpmodel.eval, nlp.hvals, x, obj_weight, y) return (nlp.hrows, nlp.hcols, nlp.hvals) end function hess(nlp :: MathProgNLPModel, x :: Array{Float64}; obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon)) return sparse(hess_coord(nlp, x, y=y, obj_weight=obj_weight)..., nlp.meta.nvar, nlp.meta.nvar) end function hprod(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}; obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon)) hv = zeros(nlp.meta.nvar) return hprod!(nlp, x, v, hv, obj_weight=obj_weight, y=y) end function hprod!(nlp :: MathProgNLPModel, x :: Array{Float64}, v :: Array{Float64}, hv :: Array{Float64}; obj_weight :: Float64=1.0, y :: Array{Float64}=zeros(nlp.meta.ncon)) NLPModels.increment!(nlp, :neval_hprod) MathProgBase.eval_hesslag_prod(nlp.mpmodel.eval, hv, x, v, obj_weight, y) return hv end ############################ ## END ModelReader CODE ############################ function status_One_Phase_To_JuMP(status::Symbol) # since our status are not equal to JuMPs we need to do a conversion if status == :Optimal return :Optimal elseif status == :primal_infeasible return :Infeasible elseif status == :dual_infeasible return :Unbounded elseif status == :MAX_IT || status === :MAX_TIME return :UserLimit else return :Error end end function create_pars_JuMP(options ) pars = Class_parameters() for (param,value) in options what = split(String(param),"!") # we represent a parameter such as init.mu_scale as init!mu_scale because we cannot pass init.mu_scale as a parameter node = pars # root for i in 1:length(what) field = what[i] if i < length(what) node = getfield(node,Symbol(field)) else # at the leaf setfield!(node,Symbol(field),value) end end end return pars end function MathProgBase.optimize!(m :: OnePhaseMathProgModel) t = time() nlp = MathProgNLPModel(m) pars = create_pars_JuMP(m.options) iter, status, hist, t, err, timer = one_phase_solve(nlp,pars) m.inner.status = status_One_Phase_To_JuMP(status) m.inner.x = get_original_x(iter) m.inner.obj_val = iter.cache.fval m.inner.lambda = get_y(iter) m.inner.solve_time = time() - t # custom one phase features m.inner.pars = pars m.inner.iter = iter m.inner.hist = hist end MathProgBase.getconstrsolution(m::OnePhaseMathProgModel) = m.inner.g MathProgBase.getrawsolver(m::OnePhaseMathProgModel) = m.inner MathProgBase.getsolvetime(m::OnePhaseMathProgModel) = m.inner.solve_time function MathProgBase.getreducedcosts(m::OnePhaseMathProgModel) return get_reducedcosts(m.inner.iter) end function MathProgBase.getconstrduals(m::OnePhaseMathProgModel) return get_constrduals(m.inner.iter) end #setvartype!(m::OnePhaseMathProgModel, typ::Vector{Symbol}) = # (m.varType = map(t->rev_var_type_map[t], typ)) function MathProgBase.freemodel!(m::OnePhaseMathProgModel) # TO DO end
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23578, 9104, 33634, 42714, 198, 14468, 7804, 4242, 198, 198, 8818, 3722, 62, 3198, 62, 35645, 62, 2514, 62, 33018, 7378, 7, 13376, 3712, 13940, 23650, 8, 198, 220, 220, 220, 1303, 1201, 674, 3722, 389, 407, 4961, 284, 12585, 7378, 82, 356, 761, 284, 466, 257, 11315, 198, 220, 220, 220, 611, 3722, 6624, 1058, 27871, 4402, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 1058, 27871, 4402, 198, 220, 220, 220, 2073, 361, 3722, 6624, 1058, 1050, 4402, 62, 259, 5036, 292, 856, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 1058, 818, 5036, 292, 856, 198, 220, 220, 220, 2073, 361, 3722, 6624, 1058, 646, 282, 62, 259, 5036, 292, 856, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 1058, 3118, 65, 6302, 198, 220, 220, 220, 2073, 361, 3722, 6624, 1058, 22921, 62, 2043, 8614, 3722, 24844, 1058, 22921, 62, 34694, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 1058, 12982, 39184, 198, 220, 220, 220, 2073, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 1058, 12331, 198, 220, 220, 220, 886, 198, 437, 198, 198, 8818, 2251, 62, 79, 945, 62, 33018, 7378, 7, 25811, 1267, 198, 220, 220, 220, 13544, 796, 5016, 62, 17143, 7307, 3419, 198, 220, 220, 220, 329, 357, 17143, 11, 8367, 8, 287, 3689, 198, 220, 220, 220, 220, 220, 644, 796, 6626, 7, 10100, 7, 17143, 27267, 2474, 8, 1303, 356, 2380, 257, 11507, 884, 355, 2315, 13, 30300, 62, 9888, 355, 2315, 0, 30300, 62, 9888, 780, 356, 2314, 1208, 2315, 13, 30300, 62, 9888, 355, 257, 11507, 198, 220, 220, 220, 220, 220, 10139, 796, 13544, 1303, 6808, 198, 220, 220, 220, 220, 220, 329, 1312, 287, 352, 25, 13664, 7, 10919, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2214, 796, 644, 58, 72, 60, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 611, 1312, 1279, 4129, 7, 10919, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 10139, 796, 651, 3245, 7, 17440, 11, 13940, 23650, 7, 3245, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2073, 1303, 379, 262, 12835, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 900, 3245, 0, 7, 17440, 11, 13940, 23650, 7, 3245, 828, 8367, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 886, 628, 220, 220, 220, 1441, 13544, 198, 437, 198, 198, 8818, 16320, 2964, 70, 14881, 13, 40085, 1096, 0, 7, 76, 7904, 1881, 35645, 37372, 2964, 70, 17633, 8, 198, 220, 220, 220, 256, 796, 640, 3419, 198, 220, 220, 220, 299, 34431, 796, 16320, 2964, 70, 45, 19930, 17633, 7, 76, 8, 628, 220, 220, 220, 13544, 796, 2251, 62, 79, 945, 62, 33018, 7378, 7, 76, 13, 25811, 8, 628, 220, 220, 220, 11629, 11, 3722, 11, 1554, 11, 256, 11, 11454, 11, 19781, 796, 530, 62, 40715, 62, 82, 6442, 7, 21283, 79, 11, 79, 945, 8, 628, 220, 220, 220, 285, 13, 5083, 13, 13376, 796, 3722, 62, 3198, 62, 35645, 62, 2514, 62, 33018, 7378, 7, 13376, 8, 198, 220, 220, 220, 285, 13, 5083, 13, 87, 796, 651, 62, 14986, 62, 87, 7, 2676, 8, 198, 220, 220, 220, 285, 13, 5083, 13, 26801, 62, 2100, 796, 11629, 13, 23870, 13, 69, 2100, 198, 220, 220, 220, 285, 13, 5083, 13, 50033, 796, 651, 62, 88, 7, 2676, 8, 198, 220, 220, 220, 285, 13, 5083, 13, 82, 6442, 62, 2435, 796, 640, 3419, 532, 256, 628, 220, 220, 220, 1303, 2183, 530, 7108, 3033, 198, 220, 220, 220, 285, 13, 5083, 13, 79, 945, 796, 13544, 198, 220, 220, 220, 285, 13, 5083, 13, 2676, 796, 11629, 198, 220, 220, 220, 285, 13, 5083, 13, 10034, 796, 1554, 198, 437, 198, 198, 37372, 2964, 70, 14881, 13, 1136, 1102, 2536, 82, 2122, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 8, 796, 285, 13, 5083, 13, 70, 198, 37372, 2964, 70, 14881, 13, 1136, 1831, 82, 14375, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 8, 796, 285, 13, 5083, 198, 37372, 2964, 70, 14881, 13, 11407, 6442, 2435, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 8, 796, 285, 13, 5083, 13, 82, 6442, 62, 2435, 198, 198, 8818, 16320, 2964, 70, 14881, 13, 1136, 445, 19513, 15805, 82, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 8, 198, 220, 220, 220, 1441, 651, 62, 445, 19513, 15805, 82, 7, 76, 13, 5083, 13, 2676, 8, 198, 437, 198, 198, 8818, 16320, 2964, 70, 14881, 13, 1136, 1102, 2536, 646, 874, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 8, 198, 220, 220, 220, 1441, 651, 62, 1102, 2536, 646, 874, 7, 76, 13, 5083, 13, 2676, 8, 198, 437, 198, 198, 2, 2617, 85, 433, 2981, 0, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 11, 2170, 3712, 38469, 90, 13940, 23650, 30072, 796, 198, 2, 220, 220, 220, 357, 76, 13, 7785, 6030, 796, 3975, 7, 83, 3784, 18218, 62, 7785, 62, 4906, 62, 8899, 58, 83, 4357, 2170, 4008, 198, 198, 8818, 16320, 2964, 70, 14881, 13, 19503, 368, 375, 417, 0, 7, 76, 3712, 3198, 35645, 37372, 2964, 70, 17633, 8, 198, 220, 220, 220, 1303, 5390, 8410, 198, 437, 198 ]
2.087689
5,702
using Test using Provenance
[ 3500, 6208, 198, 3500, 1041, 574, 590, 198 ]
3.5
8