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using BinaryBuilder, Pkg name = "SPIRV_LLVM_Translator" repo = "https://github.com/KhronosGroup/SPIRV-LLVM-Translator.git" # These are the platforms we will build for by default, unless further # platforms are passed in on the command line platforms = expand_cxxstring_abis(supported_platforms()) # Bash recipe for building across all platforms script = raw""" cd SPIRV-LLVM-Translator install_license LICENSE.TXT CMAKE_FLAGS=() # Release build for best performance CMAKE_FLAGS+=(-DCMAKE_BUILD_TYPE=Release) # Install things into $prefix CMAKE_FLAGS+=(-DCMAKE_INSTALL_PREFIX=${prefix}) # Explicitly use our cmake toolchain file and tell CMake we're cross-compiling CMAKE_FLAGS+=(-DCMAKE_TOOLCHAIN_FILE=${CMAKE_TARGET_TOOLCHAIN}) CMAKE_FLAGS+=(-DCMAKE_CROSSCOMPILING:BOOL=ON) # Tell CMake where LLVM is CMAKE_FLAGS+=(-DLLVM_DIR="${prefix}/lib/cmake/llvm") # Build the library CMAKE_FLAGS+=(-DBUILD_SHARED_LIBS=ON) cmake -B build -S . -GNinja ${CMAKE_FLAGS[@]} ninja -C build -j ${nproc} llvm-spirv install install -Dm755 build/tools/llvm-spirv/llvm-spirv${exeext} -t ${bindir} """ # The products that we will ensure are always built products = Product[ LibraryProduct(["libLLVMSPIRVLib", "LLVMSPIRVLib"], :libLLVMSPIRV), ExecutableProduct("llvm-spirv", :llvm_spirv), ]
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<reponame>tmigot/MPCC.jl using Documenter using MPCC makedocs( sitename = "MPCC.jl", format = Documenter.HTML(assets = ["assets/style.css"], prettyurls = get(ENV, "CI", nothing) == "true"), modules = [MPCC], pages = [ "Home" => "index.md", "API" => "api.md", "Examples and tutorials" => "tutorial.md", ] ) # Documenter can also automatically deploy documentation to gh-pages. # See "Hosting Documentation" and deploydocs() in the Documenter manual # for more information. deploydocs(repo = "github.com/tmigot/MPCC.jl")# #https://juliadocs.github.io/Documenter.jl/stable/man/hosting/ ?
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# MadNLP.jl. # Created by <NAME> (<EMAIL>) mutable struct MonolevelPartition g::Graph nparts::Int part::Vector{Int} end mutable struct MonolevelStruc V::Vector{Int} new_nbr::Vector{Int} end mutable struct BilevelPartition g_lower::Graph nparts_lower::Int part_lower::Vector{Int} V_lower::Vector{Vector{Int}} g_upper::Graph nparts_upper::Int part_upper::Vector{Int} end mutable struct BilevelStruc V_lower::Vector{Int} V_upper::Vector{Int} new_nbr_upper::Vector{Int} end mutable struct TwoStagePartition nparts::Int part::Vector{Int} end get_current_V(mls::MonolevelStruc) = mls.V get_current_size(mls::MonolevelStruc) = length(mls.V) get_full_size(mlp::MonolevelPartition) = nv(mlp.g) function MonolevelPartition(csc::SparseMatrixCSC,part,nparts;max_size=0.) g = Graph(csc) isempty(part) && (part=partition(g,nparts,alg=:KWAY)) return MonolevelPartition(g,nparts,part) end function MonolevelStruc(mlp::MonolevelPartition,k;max_size=0.) V = findall(mlp.part.==k) new_nbr = expand!(V,mlp.g,max_size) return MonolevelStruc(V,new_nbr) end function expand!(mls::MonolevelStruc,mlp::MonolevelPartition,max_size) mls.new_nbr = expand!(mls.V,mlp.g,max_size,new_nbr=mls.new_nbr) return end get_current_V(bls::BilevelStruc) = bls.V_lower get_current_size(bls::BilevelStruc) = length(bls.V_upper) get_full_size(blp::BilevelPartition) = blp.nparts_lower function BilevelPartition(csc,part_lower,nparts_lower,part_upper,nparts_upper;max_size=0.) g_lower = Graph(csc) isempty(part_lower) && (part_lower= partition(g_lower,nparts_lower,alg=:KWAY)) V_lower = Vector{Vector{Int}}(undef,nparts_lower) @blas_safe_threads for k=1:nparts_lower V_lower[k] = findall(part_lower.==k) end g_upper = Graph(nparts_lower) for e in edges(g_lower) add_edge!(g_upper,part_lower[src(e)],part_lower[dst(e)]) end isempty(part_upper) && (part_upper = partition(g_upper,nparts_upper,alg=:KWAY)) return BilevelPartition(g_lower,nparts_lower,part_lower,V_lower,g_upper,nparts_upper,part_upper) end function BilevelStruc(blp::BilevelPartition,k;max_size=0.) V_upper = findall(blp.part_upper.==k) new_nbr_upper = expand!(V_upper,blp.g_upper,max_size) V_lower = vcat(blp.V_lower[V_upper]...) return BilevelStruc(V_lower,V_upper,new_nbr_upper) end function TwoStagePartition(csc::SparseMatrixCSC,part,nparts) if isempty(part) || findfirst(x->x==0.,part) == nothing g = Graph(csc) isempty(part) && (part = partition(g,nparts,alg=:KWAY)) mark_boundary!(g,part) end return TwoStagePartition(nparts,part) end Graph(csc::SparseMatrixCSC) = Graph(getelistcsc(csc.colptr,csc.rowval)) getelistcsc(colptr,rowval) = [Edge(i,Int(j)) for i=1:length(colptr)-1 for j in @view rowval[colptr[i]:colptr[i+1]-1]] function expand!(bls::BilevelStruc,blp::BilevelPartition,max_size) orig_size = length(bls.V_upper) bls.new_nbr_upper = expand!(bls.V_upper,blp.g_upper,max_size,new_nbr=bls.new_nbr_upper) bls.V_lower = vcat(blp.V_lower[bls.V_upper]...) return end function expand!(V_om,g::Graph,max_size; new_nbr=[]) if isempty(new_nbr) new_nbr = Int[] for v in V_om append!(new_nbr,neighbors(g,v)) end unique!(new_nbr) setdiff!(new_nbr,V_om) end old_nbr = V_om while (length(V_om) + length(new_nbr) < max_size) && length(V_om) < nv(g) && !isempty(new_nbr) append!(V_om,new_nbr) old_old_nbr = old_nbr old_nbr=new_nbr new_nbr = Int[] for v in old_nbr append!(new_nbr,neighbors(g,v)) end unique!(new_nbr) setdiff!(new_nbr,old_old_nbr) setdiff!(new_nbr,old_nbr) end return new_nbr end function mark_boundary!(g,part) for e in edges(g) (part[src(e)]!=part[dst(e)] && part[src(e)]!= 0 && part[dst(e)] != 0) && (part[src(e)] = 0; part[dst(e)] = 0) end end
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2.019861
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function spiral!(r) a = 1 s = Nord while !ismarker(r) for i in 1:a if ismarker(r) break end move!(r, s) end s = rotate_right(s) for i in 1:a if ismarker(r) break end move!(r, s) end if ismarker(r) break end s = rotate_right(s) a += 1 end end
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using Test using PowerModels using NetDecOPF using DualDecomposition using Ipopt const DD = DualDecomposition sub_optimizer = optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0, "warm_start_init_point" => "yes") optimizer = optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0, "warm_start_init_point" => "yes") file = "../examples/case5.m" data = parse_file(file) # Partition network partitions = metis_cluster(data, 2) dn_model = decompose(data, partitions, ACRPowerModel, NetDecOPF.build_acopf_with_free_lines) # dn_model = decompose(data, partitions, W_ACRModel, NetDecOPF.build_acopf_with_free_lines) algo = init_DD_algo(dn_model) set_subnet_optimizer!(dn_model, sub_optimizer) # Change parameters (for example) params = BM.Parameters() BM.set_parameter(params, "maxiter", 3000) BM.set_parameter(params, "ϵ_s", 1.e-5) # Lagrange master method LM = DD.BundleMaster(BM.ProximalMethod, optimizer, params) DD.run!(algo, LM) @test isapprox(DD.dual_objective_value(algo), 17551, rtol = 0.1) # @show DD.dual_solution(algo)
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2.567237
409
using DistributionsAD using Turing, Random, Test, LinearAlgebra using Turing: Variational using Turing.Variational: TruncatedADAGrad, DecayedADAGrad, AdvancedVI include("../test_utils/AllUtils.jl") @testset "advi.jl" begin @turing_testset "advi constructor" begin Random.seed!(0) N = 500 s1 = ADVI() q = vi(gdemo_default, s1) c1 = rand(q, N) end @numerical_testset "advi inference" begin @testset for opt in [TruncatedADAGrad(), DecayedADAGrad()] Random.seed!(1) N = 500 alg = ADVI(10, 5000) q = vi(gdemo_default, alg; optimizer = opt) samples = transpose(rand(q, N)) chn = Chains(reshape(samples, size(samples)..., 1), ["s", "m"]) # TODO: uhmm, seems like a large `eps` here... check_gdemo(chn, atol = 0.5) end end @turing_testset "advi different interfaces" begin Random.seed!(1234) target = MvNormal(ones(2)) logπ(z) = logpdf(target, z) advi = ADVI(10, 1000) # Using a function z ↦ q(⋅∣z) getq(θ) = TuringDiagMvNormal(θ[1:2], exp.(θ[3:4])) q = vi(logπ, advi, getq, randn(4)) xs = rand(target, 10) @test mean(abs2, logpdf(q, xs) - logpdf(target, xs)) ≤ 0.07 # OR: implement `update` and pass a `Distribution` function AdvancedVI.update(d::TuringDiagMvNormal, θ::AbstractArray{<:Real}) return TuringDiagMvNormal(θ[1:length(q)], exp.(θ[length(q) + 1:end])) end q0 = TuringDiagMvNormal(zeros(2), ones(2)) q = vi(logπ, advi, q0, randn(4)) xs = rand(target, 10) @test mean(abs2, logpdf(q, xs) - logpdf(target, xs)) ≤ 0.05 end end
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1.977477
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module SubmodularMaximization # # Hack in python imports for now # using PyCall const tikzplotlib = PyNULL() const ag = PyNULL() function __init__() try copy!(tikzplotlib, pyimport("tikzplotlib")) catch e println("Could not import tikzplotlib, trying matploblib2tikz instead") copy!(tikzplotlib, pyimport("matplotlib2tikz")) end copy!(ag, pyimport("mpl_toolkits.axes_grid1")) end # # End hack # using Base.Iterators import Base.< # Abstract interface export PartitionProblem, PartitionElement, ElementArray, Solution, get_num_agents, solve_block, objective, evaluate_solution, empty # Explicit interface export ExplicitPartitionProblem, get_element, get_agent, get_center # General methods that probably need updates export marginal_gain, compute_weight, compute_weight_matrix, mean_weight, total_weight, extract_triangle, visualize_solution # Agent specification for coverage problems export Agent, generate_agents, generate_colors, visualize_agents, get_element_indices, independent # Solvers export solve_optimal, solve_worst, solve_myopic, solve_random, solve_sequential # Max now uses isless to exclude types that do not have a total order. We would # like to have something like this defined for solutions but probably should not # break things. This is a good compromise partial_max(a, b) = ifelse(b < a, a, b) partial_min(a, b) = ifelse(b > a, a, b) # Interface # get_block(Agent) = <array of objects associated with agents' block of the # partition matroid> # get_center(Agent) = <agent center> # (Note really a general property but currently defined for all agents) # plot_element(x) = <Nothing> # make_agent(agent_specification) = < agent >::AgentType struct Agent{T} center::Array{Float64, 1} radius::Float64 sensors::Array{T, 1} end get_block(agent::Agent) = agent.sensors get_center(agent::Agent) = agent.center function generate_agents(agent_specification, num_agents) [make_agent(agent_specification) for agent in 1:num_agents] end # # # Abstract partition matroid problems # # This interface is suitable for cases where the matroid is defined implicitly # and solved suboptimally # # Interface # abstract type PartitionProblem{PartitionElement} end # Define dependent types PartitionElement(::PartitionProblem{T}) where T = T # Subtypes should redefine this method PartitionElement(::Type{<:PartitionProblem{T}}) where T = T # Defines the structure of a solution for the given matroid or its type ElementArray(::T) where T = ElementArray(T) ElementArray(::Type{T}) where T = Vector{PartitionElement(T)} # Solutions consist of their value and a set of solution elements struct Solution{PartitionElement} value::Float64 elements::Vector{PartitionElement} end objective(p::PartitionProblem, X) = error("Objective not defined for ", typeof(p)) # Define the solution for individual solution elements objective(p::PartitionProblem{T}, X::T) where T = objective(p, [X]) evaluate_solution(p::PartitionProblem, X) = Solution(objective(p, X), X) # Compare solutions by value <(a::Solution, b::Solution) = a.value < b.value # empty array (vector) of solution elements empty(::T) where T <: PartitionProblem = ElementArray(T)() empty(::Type{T}) where T <: PartitionProblem = ElementArray(T)() # By default, store the representation of the matroid in something like a vector # vector get_num_agents(p::PartitionProblem) = length(p.partition_matroid) # This should be an optimal or subotimal solver that outputs the appropriate # PartitionElement for a block given prior selections solve_block(p::PartitionProblem, block::Integer, selections::Vector) = error("Single agent (block) solver not defined for ", typeof(p)) # # Concrete partition matroid problems # # The objective provides f({x} | Y) # as objective(x, Y) # # The inputs are elements of the blocks # # The partition matroid is a Vector of the blocks of the partition matroid. # The blocks contain whatever the elements of the ground set correspond to. # For example, the blocks may contain specifications of coverage regions. # Solution elemnts for concrete partition matroids are indices within the array # of blocks # # (agent_index, block_index) const ExplicitSolutionElement = Tuple{Int64,Int64} struct ExplicitPartitionProblem <: PartitionProblem{ExplicitSolutionElement} objective::Function partition_matroid::Vector end get_element(problem::ExplicitPartitionProblem, x) = get_element(problem.partition_matroid, x) get_element(partition_matroid, x) = get_block(partition_matroid[x[1]])[x[2]] get_agent(problem::ExplicitPartitionProblem, x) = get_agent(problem.partition_matroid, x) get_agent(partition_matroid::Vector, x) = partition_matroid[x] get_center(p::PartitionProblem, x) = get_center(get_agent(p, x)) objective(p::ExplicitPartitionProblem, X::Vector{ExplicitSolutionElement}) = p.objective(map(x->get_element(p.partition_matroid, x), X)) marginal_gain(f, x, Y) = f(vcat([x], Y)) - f(Y) compute_weight(f, x, y) = f([x]) - marginal_gain(f, x, [y]) compute_weight(f, X::Array, Y::Array) = maximum([compute_weight(f, x, y) for x in X, y in Y]) # Solve explicit partition matroids by iteration over blocks given a set of # prior selections function solve_block(p::ExplicitPartitionProblem, block_index::Integer, selections::Vector) block::ElementArray(p) = get_element_indices(p, block_index) _, index = findmax(map(x->objective(p, [selections; x]), block)) block[index] end function compute_weight_matrix(p::PartitionProblem) indices = get_element_indices(p.partition_matroid) n = length(p.partition_matroid) weights = zeros(n, n) f(x) = objective(p, x) for ii in 2:n, jj in 1:ii-1 w = compute_weight(f, indices[ii], indices[jj]) weights[ii, jj] = w weights[jj, ii] = w end weights end function mean_weight(W::Array) n = size(W,1) twice_edges = n * (n - 1) sum(W) / twice_edges end total_weight(W::Array) = sum(W) / 2 mean_weight(p::PartitionProblem) = mean_weight(compute_weight_matrix(p)) total_weight(p::PartitionProblem) = total_weight(compute_weight_matrix(p)) function extract_triangle(A) values = Float64[] for ii in 2:size(A, 1), jj in 1:ii-1 push!(values, A[ii, jj]) end values end # indexing and solver tools # Construct index set for ease in manipulation of the matroid # # The resulting index is an array of arrays whereas each array corresponds # to indices for an element in a block of the partition matroid get_element_indices(p::ExplicitPartitionProblem, xs...) = get_element_indices(p.partition_matroid, xs...) function get_element_indices(agents::Vector, block::Integer) map(1:length(get_block(agents[block]))) do block_index (block, block_index) end end function get_element_indices(agents::Vector) map(agents, 1:length(agents)) do agent, agent_index get_element_indices(agents, agent_index) end end # A set is independent if it contains at most one assignment to each agent # Tuple consist of agent and agent-index independent(x) = length(x) == length(Set(map(first, x))) # Returns true if any element of Y can be added to X can_augment(x, Y::Vector) = any(y->independent(vcat(x, y)), Y) # Can agent index add solution elements to x in a partition matroid can_augment(x, index::Int64) = !in(index, map(first, x)) # Can a given solution be added to x can_augment(x, e::ExplicitSolutionElement) = can_augment(x, first(e)) include("src/utils.jl") include("src/visualization.jl") include("src/normal_lookup_table.jl") include("src/solvers.jl") # Support for solvers with range-based communication graphs export make_adjacency_matrix, plot_adjacency, plot_shortest_path, make_hop_adjacency, neighbors, shortest_path, path_distance, is_connected, generate_connected_problem, solve_multi_hop export communication_span, communication_messages, communication_volume export MultiHopSolver, SequentialCommunicationSolver, AuctionSolver, LocalAuctionSolver include("src/communications_graphs_solvers.jl") include("src/coverage/coverage.jl") include("src/coverage/probabilistic_coverage.jl") include("src/target_tracking/target_tracking.jl") include("src/target_tracking/filtering.jl") include("src/target_tracking/information.jl") include("src/target_tracking/target_coverage.jl") include("src/target_tracking/single_robot_solver.jl") include("src/target_tracking/multi_robot_solvers.jl") include("src/target_tracking/multi_robot_target_tracking_problem.jl") include("src/target_tracking/multi_robot_target_coverage_problem.jl") include("src/target_tracking/pairwise_weights.jl") include("src/target_tracking/experiment_tools.jl") include("src/target_tracking/visualization.jl") end
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function integral(A::AbstractInterpolation, t::Number) bw, fw = samples(A) idx = max(1+bw, min(searchsortedlast(A.t, t), length(A.t) - fw)) _integral(A, idx, t) end function integral(A::AbstractInterpolation, t1::Number, t2::Number) bw, fw = samples(A) # the index less than or equal to t1 idx1 = max(1+bw, min(searchsortedlast(A.t, t1), length(A.t) - fw)) # the index less than t2 idx2 = max(2+bw, min(searchsortedlast(A.t, t2), length(A.t) - fw)) if A.t[idx2] == t2 idx2-=1 end total = zero(eltype(A)) for idx in idx1:idx2 lt1 = idx == idx1 ? t1 : A.t[idx] lt2 = idx == idx2 ? t2 : A.t[idx+1] total += _integral(A, idx, lt2)-_integral(A, idx, lt1) end total end samples(A::LinearInterpolation{<:AbstractVector}) = (0, 1) function _integral(A::LinearInterpolation{<:AbstractVector{<:Number}}, idx::Number, t::Number) t1 = A.t[idx] t2 = A.t[idx+1] u1 = A.u[idx] u2 = A.u[idx+1] t^2*(u1 - u2)/(2*t1 - 2*t2) + t*(t1*u2 - t2*u1)/(t1 - t2) end samples(A::ConstantInterpolation{<:AbstractVector}) = (0, 1) function _integral(A::ConstantInterpolation{<:AbstractVector}, idx::Number, t::Number) if A.dir === :left # :left means that value to the left is used for interpolation return A.u[idx]*t else # :right means that value to the right is used for interpolation return A.u[idx+1]*t end end samples(A::QuadraticInterpolation{<:AbstractVector}) = (0, 2) function _integral(A::QuadraticInterpolation{<:AbstractVector{<:Number}}, idx::Number, t::Number) t1 = A.t[idx] t2 = A.t[idx+1] t3 = A.t[idx+2] u1 = A.u[idx] u2 = A.u[idx+1] u3 = A.u[idx+2] (t^3*(-t1*u2 + t1*u3 + t2*u1 - t2*u3 - t3*u1 + t3*u2)/ (3*t1^2*t2 - 3*t1^2*t3 - 3*t1*t2^2 + 3*t1*t3^2 + 3*t2^2*t3 - 3*t2*t3^2) + t^2*(t1^2*u2 - t1^2*u3 - t2^2*u1 + t2^2*u3 + t3^2*u1 - t3^2*u2)/ (2*t1^2*t2 - 2*t1^2*t3 - 2*t1*t2^2 + 2*t1*t3^2 + 2*t2^2*t3 - 2*t2*t3^2) + t*(t1^2*t2*u3 - t1^2*t3*u2 - t1*t2^2*u3 + t1*t3^2*u2 + t2^2*t3*u1 - t2*t3^2*u1)/ (t1^2*t2 - t1^2*t3 - t1*t2^2 + t1*t3^2 + t2^2*t3 - t2*t3^2)) end samples(A::QuadraticSpline{<:AbstractVector{<:Number}}) = (0, 1) function _integral(A::QuadraticSpline{<:AbstractVector{<:Number}}, idx::Number, t::Number) t1 = A.t[idx] t2 = A.t[idx+1] u1 = A.u[idx] z1 = A.z[idx] z2 = A.z[idx+1] t^3*(z1 - z2)/(6*t1 - 6*t2) + t^2*(t1*z2 - t2*z1)/(2*t1 - 2*t2) + t*(-t1^2*z1 - t1^2*z2 + 2*t1*t2*z1 + 2*t1*u1 - 2*t2*u1)/(2*t1 - 2*t2) end samples(A::CubicSpline{<:AbstractVector{<:Number}}) = (0, 1) function _integral(A::CubicSpline{<:AbstractVector{<:Number}}, idx::Number, t::Number) t1 = A.t[idx] t2 = A.t[idx+1] u1 = A.u[idx] u2 = A.u[idx+1] z1 = A.z[idx] z2 = A.z[idx+1] h2 = A.h[idx+1] (t^4*(-z1 + z2)/(24*h2) + t^3*(-t1*z2 + t2*z1)/(6*h2) + t^2*(h2^2*z1 - h2^2*z2 + 3*t1^2*z2 - 3*t2^2*z1 - 6*u1 + 6*u2)/(12*h2) + t*(h2^2*t1*z2 - h2^2*t2*z1 - t1^3*z2 - 6*t1*u2 + t2^3*z1 + 6*t2*u1)/(6*h2)) end
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<filename>docs/make.jl using Documenter, Interpolations makedocs( sitename="Interpolations.jl", modules=[Interpolations], format=Documenter.HTML(prettyurls = get(ENV, "CI", nothing)=="true"), pages=["Home" => "index.md", "General usage" => "interpolations.md", "Interpolation algorithms" => "control.md", "Extrapolation" => "extrapolation.md", "Convenience Constructors" => "convenience-construction.md", "Library" => "api.md"] ) deploydocs(repo="github.com/JuliaMath/Interpolations.jl")
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<reponame>lukketotte/EpdTest.jl using EpdTest using Test @testset "EpdTest.jl" begin μ,σ,p = 0., 1., 1. y = [0.2, -1., 2.2, 3.1] @test EpdTest.epdTest(y, μ, σ, p) === -1.4366494480275978 end
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# The branching factors for the various INode levels, as determined by # _config_for_size(N). A NodeConfig remains valid until the number of dictionary # entries N reaches next_config_size - changing configurations is handled by # insert!(dict,h,k,v). struct NodeConfig inode_capacity::Vector{Int} next_config_size::Int end function _config_for_size2(N::Int)::NodeConfig if N < 16 return NodeConfig(Int64[], 16) elseif N < 65536 # For small dictionaries, use inode_capacity=4, and height increasing with N. num_bits = ceil(log2(N+1)) leaf_bits = min(num_bits,4) num_inodes = max(1, convert(Int, ceil((num_bits-leaf_bits)/2))) inode_capacity = [4 for i in 1:num_inodes] next_config_size = 2^(4+2*(num_inodes)) else # For large dictionaries, we use height 7, and inode capacity increasing with # N. We partition ceil(log2(N)) bits among 7 levels, and to grow capacity as # N increases we add a single bit to one level. # We will have 1/7 of the bits at the leaf node, so 6/7 of the bits will be # partitioned among the inodes. We start by giving all levels the same number # of bits, then take care of the remainder by putting one extra bit at deeper # levels. num_bits = ceil(log2(N+1)) leaf_bits = max(4, convert(Int, floor(num_bits/7))) inode_bits = num_bits - leaf_bits m = convert(Int, floor(inode_bits/6)) inode_capacity = [2^m for i in 1:6] have_bits = m*6 for k=6:-1:1 if have_bits < inode_bits inode_capacity[k] *= 2 @dassert1 inode_capacity[k] <= 256 # N <= 2^56 have_bits += 1 else break end end next_config_size = convert(Int, 2^(floor(log2(N))+1)) end shifts = [0 for i in 1:6] return NodeConfig(inode_capacity, next_config_size) end const config1 = _config_for_size2(1) const config16 = _config_for_size2(16) const config64 = _config_for_size2(64) const config256 = _config_for_size2(256) function _config_for_size(N::Int)::NodeConfig # For N < 1024, use one of the pre-allocated instances config1/config16/ # config64/config256, to reduce space use by small dictionaries. if N < 64 if N < 16 @dassert1 config1.next_config_size == 16 return config1 else @dassert1 config16.next_config_size == 64 return config16 end elseif N < 1024 if N < 256 @dassert1 config64.next_config_size == 256 return config64 else @dassert1 config256.next_config_size == 1024 return config256 end else return _config_for_size2(N) end end
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<reponame>simonbyrne/CUDA.jl<filename>deps/discovery.jl function resolve(path) if islink(path) dir = dirname(path) resolve(joinpath(dir, readlink(path))) else path end end # return a list of valid directories, resolving symlinks and pruning duplicates function valid_dirs(dirs) map!(resolve, dirs, dirs) filter(isdir, unique(dirs)) end ## generic discovery routines function library_versioned_names(name::String, version::Union{Nothing,VersionNumber,String}=nothing) names = String[] if Sys.iswindows() # Windows encodes the version in the filename if version isa VersionNumber append!(names, ["$(name)$(Sys.WORD_SIZE)_$(version.major)$(version.minor).$(Libdl.dlext)", "$(name)$(Sys.WORD_SIZE)_$(version.major).$(Libdl.dlext)"]) elseif version isa String push!(names, "$(name)$(Sys.WORD_SIZE)_$(version).$(Libdl.dlext)") elseif version === nothing push!(names, "$(name)$(Sys.WORD_SIZE).$(Libdl.dlext)") end # some libraries (e.g. CUTENSOR) are shipped without the word size-prefix if version isa VersionNumber append!(names, ["$(name)_$(version.major)$(version.minor).$(Libdl.dlext)", "$(name)_$(version.major).$(Libdl.dlext)"]) elseif version isa String push!(names, "$(name)_$(version).$(Libdl.dlext)") elseif version === nothing push!(names, "$(name).$(Libdl.dlext)") end elseif Sys.isapple() # macOS puts the version number before the dylib extension if version isa VersionNumber append!(names, ["lib$(name).$(version.major).$(version.minor).$(Libdl.dlext)", "lib$(name).$(version.major).$(Libdl.dlext)"]) elseif version isa String push!(names, "lib$(name).$(version).$(Libdl.dlext)") elseif version === nothing push!(names, "lib$(name).$(Libdl.dlext)") end elseif Sys.isunix() # most UNIX distributions ship versioned libraries (also see JuliaLang/julia#22828) if version isa VersionNumber append!(names, ["lib$(name).$(Libdl.dlext).$(version.major).$(version.minor).$(version.patch)", "lib$(name).$(Libdl.dlext).$(version.major).$(version.minor)", "lib$(name).$(Libdl.dlext).$(version.major)"]) elseif version isa String push!(names, "lib$(name).$(Libdl.dlext).$(version)") elseif version === nothing push!(names, "lib$(name).$(Libdl.dlext)") end elseif version === nothing push!(names, "lib$name.$(Libdl.dlext)") end return names end """ find_library(name, version; locations=String[]) Wrapper for Libdl.find_library, performing a more exhaustive search: - variants of the library name (including version numbers, platform-specific tags, etc); - various subdirectories of the `locations` list, and finally system library directories. Returns the full path to the library. """ function find_library(name::String, version::Union{Nothing,VersionNumber,String}=nothing; locations::Vector{String}=String[]) @debug "Request to look for library $name $version" locations # figure out names all_names = library_versioned_names(name, version) # figure out locations all_locations = String[] for location in locations push!(all_locations, location) push!(all_locations, joinpath(location, "lib")) if Sys.WORD_SIZE == 64 push!(all_locations, joinpath(location, "lib64")) push!(all_locations, joinpath(location, "libx64")) end if Sys.iswindows() push!(all_locations, joinpath(location, "bin")) push!(all_locations, joinpath(location, "bin", Sys.WORD_SIZE==64 ? "x64" : "Win32")) end end @debug "Looking for library $(join(all_names, ", "))" locations=all_locations name_found = Libdl.find_library(all_names, all_locations) if isempty(name_found) return nothing end # find the full path of the library (which Libdl.find_library doesn't guarantee to return) path = Libdl.dlpath(name_found) @debug "Found library $(basename(path)) at $(dirname(path))" return path end """ find_binary(name; locations=String[]) Similar to `find_library`, performs an exhaustive search for a binary in various subdirectories of `locations`, and finally PATH by using `Sys.which`. """ function find_binary(name::String; locations::Vector{String}=String[]) @debug "Request to look for binary $name" locations # figure out locations all_locations = String[] for location in locations push!(all_locations, location) push!(all_locations, joinpath(location, "bin")) end # we look in PATH too by using `Sys.which` with unadorned names @debug "Looking for binary $name" locations=all_locations all_paths = [name; [joinpath(location, name) for location in all_locations]] for path in all_paths try program_path = Sys.which(path) if program_path !== nothing @debug "Found binary $path at $program_path" return program_path end catch # some system disallow `stat` on certain paths end end return nothing end ## CUDA-specific discovery routines const cuda_releases = [v"1.0", v"1.1", v"2.0", v"2.1", v"2.2", v"3.0", v"3.1", v"3.2", v"4.0", v"4.1", v"4.2", v"5.0", v"5.5", v"6.0", v"6.5", v"7.0", v"7.5", v"8.0", v"9.0", v"9.1", v"9.2", v"10.0", v"10.1", v"10.2", v"11.0", v"11.1", v"11.2", v"11.3", v"11.4"] const cuda_library_versions = Dict( v"11.0.1" => Dict( # NOTE: encountered this version in a Docker container; not sure where it came from. "cupti" => "2020.1.0", # wtf "nvtx" => v"11.0.167", "cublas" => v"11.0.0", #.191 "cufft" => v"10.1.3", #.191 "curand" => v"10.2.0", #.191 "cusolver" => v"10.4.0", #.191 "cusparse" => v"11.0.0", #.191 ), v"11.0.2" => Dict( "cupti" => "2020.1.0", # wtf "nvtx" => v"11.0.167", "cublas" => v"11.0.0", #.191 "cufft" => v"10.1.3", #.191 "curand" => v"10.2.0", #.191 "cusolver" => v"10.4.0", #.191 "cusparse" => v"11.0.0", #.191 ), v"11.0.3" => Dict( "cupti" => "2020.1.1", # docs mention 11.0.221 "nvtx" => v"11.0.167", "cublas" => v"11.2.0", #.252 "cufft" => v"10.2.1", #.245 "curand" => v"10.2.1", #.245 "cusolver" => v"10.6.0", #.245 "cusparse" => v"11.1.1", #.245 ), v"11.1.0" => Dict( "cupti" => "2020.2.0", # docs mention 11.1.69 "nvtx" => v"11.1.74", "cublas" => v"11.2.1", #.74 "cufft" => v"10.3.0", #.74 "curand" => v"10.2.2", #.74 "cusolver" => v"11.0.0", #.74 "cusparse" => v"11.2.0", #.275 ), v"11.1.1" => Dict( "cupti" => "2020.2.1", # docs mention 11.1.105 "nvtx" => v"11.1.74", "cublas" => v"11.3.0", #.106 "cufft" => v"10.3.0", #.105 "curand" => v"10.2.2", #.105 "cusolver" => v"11.0.1", #.105 "cusparse" => v"11.3.0", #.10 ), v"11.2.0" => Dict( "cupti" => "2020.3.0", # docs mention 11.2.67 "nvtx" => v"11.2.67", "cublas" => v"11.3.1", #.68 "cufft" => v"10.4.0", #.72 "curand" => v"10.2.3", #.68 "cusolver" => v"11.0.2", #.68 "cusparse" => v"11.3.1", #.68 ), v"11.2.1" => Dict( "cupti" => "2020.3.1", # docs mention 11.2.135 "nvtx" => v"11.2.67", "cublas" => v"11.4.1", #.1026 "cufft" => v"10.4.0", #.135 "curand" => v"10.2.3", #.135 "cusolver" => v"11.1.0", #.135 "cusparse" => v"11.4.0", #.135 ), v"11.2.2" => Dict( "cupti" => "2020.3.1", # docs mention 11.2.152 "nvtx" => v"11.2.152", "cublas" => v"11.4.1", #.1043 "cufft" => v"10.4.1", #.152 "curand" => v"10.2.3", #.152 "cusolver" => v"11.1.0", #.152 "cusparse" => v"11.4.1", #.1152 ), v"11.3.0" => Dict( "cupti" => "2021.1.0", # docs mention 11.3.58 "nvtx" => v"11.3.58", "cublas" => v"11.4.2", #.10064 "cufft" => v"10.4.2", #.58 "curand" => v"10.2.4", #.58 "cusolver" => v"11.1.1", #.58 "cusparse" => v"11.5.0", #.58 ), v"11.3.1" => Dict( "cupti" => "2021.1.1", # docs mention 11.3.111 "nvtx" => v"11.3.109", "cublas" => v"11.5.1", #.109 "cufft" => v"10.4.2", #.109 "curand" => v"10.2.4", #.109 "cusolver" => v"11.1.2", #.109 "cusparse" => v"11.6.0", #.109 ), v"11.4.0" => Dict( "cupti" => "2021.2.0", # docs mention 11.4.65 "nvtx" => v"11.4.43", "cublas" => v"11.5.2", #.43 "cufft" => v"10.5.0", #.43 "curand" => v"10.2.5", #.43 "cusolver" => v"11.2.0", #.43 "cusparse" => v"11.6.0", #.43 ), ) function cuda_library_version(library, toolkit_version) if library == "nvtx" v"1" elseif toolkit_version >= v"11" # starting with CUDA 11, libraries are versioned independently if !haskey(cuda_library_versions, toolkit_version) error("CUDA.jl does not yet support CUDA $toolkit_version; please file an issue.") end # HACK: generalize this? if library == "cusolverMg" library = "cusolver" end if library == "cublasLt" library = "cublas" end cuda_library_versions[toolkit_version][library] else toolkit_version end end const cuda_library_names = Dict( "nvtx" => "nvToolsExt" ) # only for nvdisasm, to discover the CUDA toolkit version const cuda_binary_versions = Dict( v"11.0.1" => Dict( # NOTE: encountered this version in a Docker container; not sure where it came from. "nvdisasm" => v"11.0.167" ), v"11.0.2" => Dict( "nvdisasm" => v"11.0.194" ), v"11.0.3" => Dict( "nvdisasm" => v"11.0.221" ), v"11.1.0" => Dict( "nvdisasm" => v"11.1.74" ), v"11.1.1" => Dict( "nvdisasm" => v"11.1.74" # ambiguous! ), v"11.2.0" => Dict( "nvdisasm" => v"11.2.67" ), v"11.2.1" => Dict( "nvdisasm" => v"11.2.135" ), v"11.2.2" => Dict( "nvdisasm" => v"11.2.152" ), v"11.3.0" => Dict( "nvdisasm" => v"11.3.58", "ptxas" => v"11.3.58" ), v"11.3.1" => Dict( "nvdisasm" => v"11.3.58", # ambiguous! "ptxas" => v"11.3.109" ), v"11.3.1" => Dict( "nvdisasm" => v"11.4.43" ), ) # simplified find_library/find_binary entry-points, # looking up name aliases and known version numbers # and passing the (optional) toolkit dirs as locations. function find_cuda_library(library::String, toolkit_dirs::Vector{String}, toolkit_version::VersionNumber) toolkit_release = VersionNumber(toolkit_version.major, toolkit_version.minor) # figure out the location locations = toolkit_dirs ## CUPTI is in the "extras" directory of the toolkit if library == "cupti" toolkit_extras_dirs = filter(dir->isdir(joinpath(dir, "extras")), toolkit_dirs) cupti_dirs = map(dir->joinpath(dir, "extras", "CUPTI"), toolkit_extras_dirs) append!(locations, cupti_dirs) end ## NVTX is located in an entirely different location on Windows if library == "nvtx" && Sys.iswindows() if haskey(ENV, "NVTOOLSEXT_PATH") dir = ENV["NVTOOLSEXT_PATH"] @debug "Looking for NVTX library via environment variable" dir else program_files = ENV[Sys.WORD_SIZE == 64 ? "ProgramFiles" : "ProgramFiles(x86)"] dir = joinpath(program_files, "NVIDIA Corporation", "NvToolsExt") @debug "Looking for NVTX library in the default directory" dir end isdir(dir) && push!(locations, dir) end version = cuda_library_version(library, toolkit_version) name = get(cuda_library_names, library, library) find_library(name, version; locations=locations) end find_cuda_binary(name::String, toolkit_dirs::Vector{String}=String[]) = find_binary(name; locations=toolkit_dirs) """ find_toolkit()::Vector{String} Look for directories where (parts of) the CUDA toolkit might be installed. This returns a (possibly empty) list of paths that can be used as an argument to other discovery functions. The behavior of this function can be overridden by defining the `CUDA_PATH`, `CUDA_HOME` or `CUDA_ROOT` environment variables, which should point to the root of the CUDA toolkit. """ function find_toolkit() dirs = String[] # look for environment variables to override discovery envvars = ["CUDA_PATH", "CUDA_HOME", "CUDA_ROOT"] filter!(var -> haskey(ENV, var) && ispath(ENV[var]), envvars) if !isempty(envvars) paths = unique(map(var->ENV[var], envvars)) if length(paths) > 1 @warn "Multiple CUDA environment variables set to different values: $(join(paths, ", "))" end @debug "Looking for CUDA toolkit via environment variables $(join(envvars, ", "))" append!(dirs, paths) return dirs end # look for the compiler binary (in the case PATH points to the installation) ptxas_path = find_binary("ptxas") if ptxas_path !== nothing ptxas_dir = dirname(ptxas_path) if occursin(r"^bin(32|64)?$", basename(ptxas_dir)) ptxas_dir = dirname(ptxas_dir) end @debug "Looking for CUDA toolkit via ptxas binary" path=ptxas_path dir=ptxas_dir push!(dirs, ptxas_dir) end # look for the runtime library (in the case LD_LIBRARY_PATH points to the installation) libcudart_path = find_library("cudart") if libcudart_path !== nothing libcudart_dir = dirname(libcudart_path) if occursin(r"^(lib|bin)(32|64)?$", basename(libcudart_dir)) libcudart_dir = dirname(libcudart_dir) end @debug "Looking for CUDA toolkit via CUDA runtime library" path=libcudart_path dir=libcudart_dir push!(dirs, libcudart_dir) end # look in default installation directories default_dirs = String[] if Sys.iswindows() # CUDA versions are installed in separate directories under a single base dir program_files = ENV[Sys.WORD_SIZE == 64 ? "ProgramFiles" : "ProgramFiles(x86)"] basedir = joinpath(program_files, "NVIDIA GPU Computing Toolkit", "CUDA") if isdir(basedir) entries = map(dir -> joinpath(basedir, dir), readdir(basedir)) append!(default_dirs, entries) end else # CUDA versions are installed in unversioned dirs, or suffixed with the version basedirs = ["/usr/local/cuda", "/opt/cuda"] for ver in cuda_releases, dir in basedirs push!(default_dirs, "$dir-$(ver.major).$(ver.minor)") end append!(default_dirs, basedirs) push!(default_dirs, "/usr/lib/nvidia-cuda-toolkit") push!(default_dirs, "/usr/share/cuda") end reverse!(default_dirs) # we want to search starting from the newest CUDA version default_dirs = valid_dirs(default_dirs) if !isempty(default_dirs) @debug "Looking for CUDA toolkit via default installation directories" dirs=default_dirs append!(dirs, default_dirs) end # filter dirs = valid_dirs(dirs) @debug "Found CUDA toolkit at $(join(dirs, ", "))" return dirs end # figure out the CUDA toolkit version (by looking at the output of a tool like `nvdisasm`) function parse_toolkit_version(tool, tool_path::String) # parse the version string verstr = withenv("LANG"=>"C") do read(`$tool_path --version`, String) end m = match(r"\bV(?<major>\d+).(?<minor>\d+).(?<patch>\d+)\b", verstr) if m === nothing @error "Could not parse CUDA version info (\"$verstr\"); please file an issue." return nothing end version = VersionNumber(parse(Int, m[:major]), parse(Int, m[:minor]), parse(Int, m[:patch])) if version >= v"11" # starting with CUDA 11, binaries are versioned independently # NOTE: we can't always tell, e.g. nvdisasm is the same in CUDA 11.1.0 and 11.1.1. # return the lowest version to ensure compatibility. for toolkit_version in sort(collect(keys(cuda_binary_versions))) if haskey(cuda_binary_versions[toolkit_version], tool) && cuda_binary_versions[toolkit_version][tool] == version @debug "CUDA toolkit identified as $toolkit_version (providing $tool $version)" return toolkit_version end end @error "CUDA.jl does not yet support CUDA with $tool $version; please file an issue." return nothing else @debug "CUDA toolkit identified as $version" return version end end """ find_libdevice(toolkit_dirs::Vector{String}) Look for the CUDA device library supporting `targets` in any of the CUDA toolkit directories `toolkit_dirs`. On CUDA >= 9.0, a single library unified library is discovered and returned as a string. On older toolkits, individual libraries for each of the targets are returned as a vector of strings. """ function find_libdevice(toolkit_dirs) @debug "Request to look for libdevice" locations=toolkit_dirs # figure out locations dirs = String[] for toolkit_dir in toolkit_dirs push!(dirs, toolkit_dir) push!(dirs, joinpath(toolkit_dir, "libdevice")) push!(dirs, joinpath(toolkit_dir, "nvvm", "libdevice")) end # filter dirs = valid_dirs(dirs) @debug "Look for libdevice" locations=dirs for dir in dirs path = joinpath(dir, "libdevice.10.bc") if isfile(path) @debug "Found unified device library at $path" return path end end return nothing end """ find_libcudadevrt(toolkit_dirs::Vector{String}) Look for the CUDA device runtime library in any of the CUDA toolkit directories `toolkit_dirs`. """ function find_libcudadevrt(toolkit_dirs) locations = toolkit_dirs @debug "Request to look for libcudadevrt " locations name = nothing if Sys.isunix() name = "libcudadevrt.a" elseif Sys.iswindows() name = "cudadevrt.lib" else error("No support for discovering the CUDA device runtime library on your platform, please file an issue.") end # figure out locations all_locations = String[] for location in locations push!(all_locations, location) if Sys.iswindows() if Sys.WORD_SIZE == 64 push!(all_locations, joinpath(location, "lib", "x64")) elseif Sys.WORD_SIZE == 32 push!(all_locations, joinpath(location, "lib", "Win32")) end else push!(all_locations, joinpath(location, "lib")) if Sys.WORD_SIZE == 64 push!(all_locations, joinpath(location, "lib64")) end end end @debug "Looking for CUDA device runtime library $name" locations=all_locations paths = filter(isfile, map(location->joinpath(location, name), all_locations)) if isempty(paths) return nothing else path = first(paths) @debug "Found CUDA device runtime library $(basename(path)) at $(dirname(path))" return path end end
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2.077262
9,772
<reponame>JuliaPOMDP/SARSOP.jl<filename>test/cancer.jl<gh_stars>1-10 S = [:healthy, :in_situ, :invasive, :death] A = [:wait, :test, :treat] O = [:pos, :neg] γ = 0.99 s0 = Deterministic(:healthy) term = Set([:death]) function T(s, a, sp) if s == :healthy if sp == :in_situ return 0.02 elseif sp == s return 0.98 else return 0.0 end elseif s == :in_situ if a == :treat if sp == :healthy return 0.60 elseif sp == s return 0.40 else return 0.0 end else #a == :test || a == :wait if sp == :invasive return 0.10 elseif sp == s return 0.90 else return 0.0 end end elseif s == :invasive if a == :treat if sp == :healthy return 0.20 elseif sp == :death return 0.20 elseif sp == s return 0.60 else return 0.0 end else if sp == :death return 0.60 elseif sp == s return 0.40 else return 0.0 end end else # s == :death return 0.25 end end function Z(a, sp, o) if a == :test if sp == :healthy if o == :pos return 0.05 else return 0.95 end elseif sp == :in_situ if o == :pos return 0.80 else return 0.20 end else if o == :pos return 1.0 else return 0.0 end end elseif a == :treat if sp == :in_situ || sp == :invasive if o == :pos return 1.0 else return 0.0 end else if o == :pos return 0.0 else return 1.0 end end else #a == :wait if o == :pos return 0.0 else return 1.0 end end end function R(s, a) if s == :death return 0.0 elseif a == :wait return 1.0 elseif a == :test return 0.80 else return 0.10 end end c = DiscreteExplicitPOMDP(S, A, O, T, Z, R, γ, s0, terminals=term); function evalSolver(m) results = Dict{String, Float64}() for (key, solver) in ["SARSOP"=>SARSOPSolver()] policy = solve(solver, m) N = 10000 rsum = 0.0 for i in 1:N rsum += simulate(RolloutSimulator(max_steps=500), m, policy) end results[key] = rsum/N end; return results end results_c = evalSolver(c); @show results_c["SARSOP"] @test results_c["SARSOP"] >= 62.5
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<filename>test/adapt.jl using DimensionalData, Test, Unitful, Adapt struct CustomArray{T,N} <: AbstractArray{T,N} arr::Array end CustomArray(x::Array{T,N}) where {T,N} = CustomArray{T,N}(x) Adapt.adapt_storage(::Type{<:CustomArray}, xs::Array) = CustomArray(xs) Base.size(x::CustomArray, y...) = size(x.arr, y...) Base.getindex(x::CustomArray, y...) = getindex(x.arr, y...) Base.count(x::CustomArray) = count(x.arr) @testset "Metadata" begin @test adapt(CustomArray, Metadata(:a=>"1", :b=>"2")) == NoMetadata() end @testset "Dimension" begin d = X([1:10...]; metadata=Metadata(:a=>"1", :b=>"2")) d1 = adapt(CustomArray, d) @test val(d1) isa CustomArray @test val(d1).arr == [1:10...] @test metadata(d1) == NoMetadata() end @testset "DimArray" begin A = rand(4, 5) da = DimArray(A, (X, Y)) da1 = adapt(CustomArray, da) @test parent(da1) isa CustomArray @test parent(da1).arr == A @test metadata(da1) == NoMetadata() end @testset "DimStack" begin A = rand(4, 5) B = rand(4, 5) ds = DimStack((a=A, b=B), (X, Y)) ds1 = adapt(CustomArray, ds) @test parent(ds1[:a]) isa CustomArray @test parent(ds1[:a]).arr == A @test parent(ds1[:b]) isa CustomArray @test parent(ds1[:b]).arr == B @test metadata(ds) == NoMetadata() end
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<gh_stars>0 using Klara function plogtarget(x::Vector{Float64}) s2 = exp(x[1]) nx = length(x)-1 -0.5*((x[1]-0.0)^2/9.0+dot(x[2:end], x[2:end])/s2+nx*log(s2)) end p = BasicContMuvParameter(:p, logtarget=plogtarget) model = likelihood_model(p, false) sampler = SliceSampler(1., 5) mcrange = BasicMCRange(nsteps=100000, burnin=10000) v0 = Dict(:p=>[10.; zeros(4)]) job = BasicMCJob(model, sampler, mcrange, v0) run(job) chain = output(job) mean(chain)
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#Define Simulation state that also contains the control variables using the DEDataVector #interface from the DifferentialEquations package import DifferentialEquations: ODEProblem mutable struct SimState{Float64} <: DEDataVector{Float64} x::Vector{Float64} #State variables u::Vector{Float64} #Control Variables F::Type{<:AbstractFrame} #FrameType end #SimState SimState(x::Vector{Float64}, u::Vector{Float64}) = SimState(x, u, ICRF) # Create New ODE interface that uses x_dot = f(x,u,t), instead of x_dot = f(x,t) #Also, make sure that integration is done in ICRF reference frame function ODEProblem(f::Function, x0::Vector{Float64}, u0::Vector{Float64}, t0::Float64, tend::Float64) y0 = SimState(x0,u0) return ODEProblem(f,y0,(t0,tend)) end
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<filename>src/blocksizes.jl ############## # BlockSizes # ############## # Keeps track of the sizes of all the blocks in the `BlockArray` immutable BlockSizes{N} sizes::NTuple{N, Vector{Int}} end Base.:(==)(a::BlockSizes, b::BlockSizes) = a.sizes == b.sizes BlockSizes{N}(sizes::Vararg{Vector{Int}, N}) = BlockSizes(sizes) Base.getindex(block_sizes::BlockSizes, i) = block_sizes.sizes[i] Base.getindex(block_sizes::BlockSizes, i, j) = block_sizes.sizes[i][j] function blocksize{N}(block_sizes::BlockSizes{N}, i::NTuple{N, Int}) return ntuple(k->block_sizes[k, i[k]], Val{N}) end function Base.show{N}(io::IO, block_sizes::BlockSizes{N}) if N == 0 print(io, "[]") else print(io, block_sizes.sizes[1]) for i in 2:N print(io, "×", block_sizes.sizes[i]) end end end nblocks{N}(block_sizes::BlockSizes{N}) = ntuple(i -> length(block_sizes[i]), Val{N}) nblocks(block_sizes::BlockSizes, i::Int) = length(block_sizes[i]) Base.copy{N}(block_sizes::BlockSizes{N}) = BlockSizes(ntuple(i -> copy(block_sizes[i]), Val{N})) # Computes the global range of an Array that corresponds to a given block_index @generated function globalrange{N}(block_sizes::BlockSizes, block_index::NTuple{N, Int}) start_indices_ex = Expr(:tuple, [:(1 + _cumsum(block_sizes[$i], block_index[$i]-1)) for i=1:N]...) indices_ex = Expr(:tuple, [:(start_indices[$i]:start_indices[$i] + block_sizes[$i, block_index[$i]] - 1) for i = 1:N]...) return quote @inbounds start_indices = $start_indices_ex @inbounds indices = $indices_ex return indices end end
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""" MultiSampler A sampler that makes it easy to sample from a vector of independent samplers. ##### Fields - `samplers::T` : the independent samplers. """ struct MultiSampler{T<:AbstractVector{<:Sampleable}} <: Sampleable{Multivariate, Discrete} samplers::T end """ Base.show(io::IO, multisampler::MultiSampler) -> nothing Show a `MultiSampler` in a human-friendly manner. """ function Base.show(io::IO, multisampler::MultiSampler) println(io, typeof(multisampler)) println(io, " independent samplers: ") for sampler in multisampler.samplers println(io, " ", sampler) end return nothing end """ MultiSampler(graph::AbstractGraph, nsamplers::Integer) -> MultiSampler Construct and return a `MultiSampler` with `nsamplers` `NeighborSampler`s defined w.r.t. `graph`. ##### Complexity - Time complexity: `O(nsamplers)`. """ function MultiSampler(graph::AbstractGraph, nsamplers::Integer) return MultiSampler([NeighborSampler(graph, rand(1:nv(graph))) for _ in 1:nsamplers]) end """ Base.rand(rng::AbstractRNG, multisampler::MultiSampler{<:AbstractVector{<:NeighborSampler}}) -> ( Vector{Int}) Draw from each `NeighborSampler` independently and return a vector of the samples. ##### Complexity - Time complexity: `O(nsamplers)`. Use this for e.g., simulating multiple-agent random walks on a graph. """ function Base.rand(rng::AbstractRNG, multisampler::MultiSampler{<:AbstractVector{<:NeighborSampler}}) return [rand(sampler) for sampler in multisampler.samplers] end """ Base.rand(rng::AbstractRNG, multisampler::MultiSampler{<:AbstractVector{<:Sampleable}}) -> Vector{Float64} Draw from each `sampler` independently and return a vector of the samples. ##### Complexity - Time complexity: `O(nsamplers(multisampler))`. Use this for e.g., simulating multiple-agent random walks on an integer lattice. """ function Base.rand(rng::AbstractRNG, multisampler::MultiSampler{<:AbstractVector{<:Sampleable}}) # convert to float for type stability return [float(rand(sampler)) for sampler in multisampler.samplers] end """ nsamplers(multisampler::MultiSampler) Return the number of samplers. ##### Complexity - Time complexity: `O(1)`. """ function nsamplers(multisampler::MultiSampler) return length(multisampler.samplers) end
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<gh_stars>1-10 #= Find local bindings Downstreams: completions.jl, goto.jl, datatip.jl, refactor.jl =# struct LocalBinding name::String verbatim::String span::UnitRange{Int64} line::Int expr::EXPR end struct LocalScope name::String verbatim::String span::UnitRange{Int64} line::Int children::Vector{Union{LocalBinding,LocalScope}} expr::EXPR end const LocalBS = Union{LocalBinding,LocalScope} struct ActualLocalBinding name::String verbatim::String root::String line::Int locality::Float64 expr::EXPR end function ActualLocalBinding(bs::LocalBS, root::String, line::Int, byteoffset::Int) locality = distance(line, byteoffset, bs.line, bs.span) return ActualLocalBinding(bs.name, bs.verbatim, root, bs.line, locality, bs.expr) end """ locals(text::String, line::Int, col::Int)::Vector{ActualLocalBinding} Returns local bindings in `text`, while computing localities based on `line` and `col`. """ function locals(text::String, line::Int, col::Int)::Vector{ActualLocalBinding} expr = CSTParser.parse(text, true) traverse_expr!(expr) bindings = localbindings(expr, text) actual_localbindings(bindings, line, byteoffset(text, line, col)) end function localbindings(expr, text, bindings = LocalBS[], pos = 1, line = 1) # binding bind = bindingof(expr) hs = hasscope(expr) if bind !== nothing && !hs verbatim = str_value_verbatim(bind, text, pos) range = pos:pos+expr.span push!(bindings, LocalBinding(bind.name, verbatim, range, line, expr)) end if hs typof(expr) === CSTParser.Kw && return bindings # destructure multiple returns if ismultiplereturn(expr) for arg in expr # don't update `pos` & `line`, i.e.: treat all the multiple returns as same localbindings(arg, text, bindings, pos, line) end # properly detect the parameters of a method with where clause: https://github.com/JunoLab/Juno.jl/issues/404 elseif iswhereclause(expr) for arg in expr localbindings(arg, text, bindings, pos, line) line += countlines(arg, text, pos) pos += arg.fullspan end else # find local binds in a scope # calculate fields for `LocalScope` first verbatim = str_value_verbatim(expr, text, pos) range = pos:pos+expr.span name = bind === nothing ? "" : bind.name children = LocalBS[] for arg in expr localbindings(arg, text, children, pos, line) line += countlines(arg, text, pos) pos += arg.fullspan end push!(bindings, LocalScope(name, verbatim, range, line, children, expr)) end return bindings end # look for more local bindings if exists for arg in expr localbindings(arg, text, bindings, pos, line) line += countlines(arg, text, pos) pos += arg.fullspan end return bindings end function byteoffset(text, line, col) byteoffset = 1 current_line = 1 current_char = 0 for c in text if line == current_line current_char += 1 c === '\n' && break end current_char == col && break byteoffset += @static VERSION >= v"1.1" ? ncodeunits(c) : ncodeunits(string(c)) c === '\n' && (current_line += 1) end byteoffset end function actual_localbindings(bindings, line, byteoffset) actual_bindings = _actual_localbindings(bindings, line, byteoffset) filter!(b -> !isempty(b.name), actual_bindings) sort!(actual_bindings, lt = (b1, b2) -> b1.locality < b2.locality) return @static VERSION ≥ v"1.1" ? unique!(b->b.name, actual_bindings) : unique(b->b.name, actual_bindings) end function _actual_localbindings(bindings, line, byteoffset, root = "", actual_bindings = ActualLocalBinding[]) for bind in bindings push!(actual_bindings, ActualLocalBinding(bind, root, line, byteoffset)) if bind isa LocalScope && byteoffset in bind.span _actual_localbindings(bind.children, line, byteoffset, bind.name, actual_bindings) end end return actual_bindings end function distance(line, byteoffset, defline, defspan) abslinediff = abs(line - defline) absbytediff = abs(byteoffset - defspan[1]) # tiebreaker for bindings on the same line diff = if byteoffset in defspan Inf elseif line < defline (defline - line)*10 # bindings defined *after* the current line have a lower priority else line - defline end diff + absbytediff*1e-6 end
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2.412093
1,968
export Surgery module Surgery using CSV using ..Empirikos: BinomialSample const DATA = joinpath(@__DIR__, "surgery.csv") function load_table() CSV.File(DATA) end function ebayes_samples() tbl = load_table() Zs = BinomialSample.(tbl.s, tbl.n) end end
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2.481481
108
<filename>src/wrappers/x86_64-linux-gnu-cxx11.jl # Autogenerated wrapper script for SCIP_jll for x86_64-linux-gnu-cxx11 export libscip using bliss_jll using GMP_jll using CompilerSupportLibraries_jll using Ipopt_jll using Zlib_jll JLLWrappers.@generate_wrapper_header("SCIP") JLLWrappers.@declare_library_product(libscip, "libscip.so.7.0") function __init__() JLLWrappers.@generate_init_header(bliss_jll, GMP_jll, CompilerSupportLibraries_jll, Ipopt_jll, Zlib_jll) JLLWrappers.@init_library_product( libscip, "lib/libscip.so", RTLD_LAZY | RTLD_DEEPBIND, ) JLLWrappers.@generate_init_footer() end # __init__()
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2.202703
296
export proxLLR!, normLLR """ proxLLR!(x::Vector{T}, λ::Float64=1e-6; kargs...) where T proximal map for LLR regularization using singular-value-thresholding # Arguments * `x::Vector{T}` - Vector to apply proximal map to * `λ::Float64` - regularization parameter * `shape::Tuple{Int}=[]` - dimensions of the image * `blockSize::Tuple{Int}=[2;2]` - size of patches to perform singluar value thresholding on * `randshift::Bool=true` - randomly shifts the patches to ensure translation invariance """ function proxLLR!(x::Vector{T}, λ; shape::NTuple{N,TI}=error(), blockSize::NTuple{N,TI}=ntuple(_-> 2, N), randshift::Bool=true) where {T, N,TI <: Integer} x = reshape(x, tuple(shape..., length(x) ÷ prod(shape))) block_idx = CartesianIndices(blockSize) K = size(x)[end] if randshift # Random.seed!(1234) shift_idx = (Tuple(rand(block_idx))..., 0) xs = circshift(x, shift_idx) else xs = x end ext = mod.(shape,blockSize) pad = mod.(blockSize .- ext, blockSize) if any(pad .!= 0) xp = zeros(T, (shape .+ pad)..., K) xp[CartesianIndices(x)] .= xs else xp = xs end xᴸᴸᴿ = Array{T}(undef, prod(blockSize), K) for i ∈ CartesianIndices(StepRange.(0, blockSize, shape .- 1)) @views xᴸᴸᴿ .= reshape(xp[i .+ block_idx,:], :, K) # threshold singular values SVDec = svd!(xᴸᴸᴿ) proxL1!(SVDec.S,λ) xp[i .+ block_idx,:] .= reshape(SVDec.U * Diagonal(SVDec.S) * SVDec.Vt, blockSize..., :) end if any(pad .!= 0) xs .= xp[CartesianIndices(xs)] end if randshift x .= circshift(xs, -1 .* shift_idx) end x = vec(x) return x end """ normLLR(x::Vector{T}, λ::Float64; kargs...) where T returns the value of the LLR-regularization term. Arguments are the same is in `proxLLR!` """ function normLLR(x::Vector{T}, λ::Float64; shape::NTuple{N,TI}, L=1, blockSize::NTuple{N,TI}=ntuple(_-> 2, N), randshift::Bool=true, kargs...) where {N, T, TI <: Integer} Nvoxel = prod(shape) K = floor(Int,length(x)/(Nvoxel*L)) normᴸᴸᴿ = 0. for i = 1:L normᴸᴸᴿ += blockNuclearNorm(x[(i-1)*Nvoxel*K+1:i*Nvoxel*K], shape; blockSize=blockSize, randshift=randshift, kargs...) end return λ*normᴸᴸᴿ end function blockNuclearNorm(x::Vector{T}, shape::NTuple{N,TI}; blockSize::NTuple{N,TI}=ntuple(_-> 2, N), randshift::Bool=true, kargs...) where {N, T, TI <: Integer} x = reshape( x, tuple( shape...,floor(Int64, length(x)/prod(shape)) ) ) Wy = blockSize[1] Wz = blockSize[2] if randshift srand(1234) shift_idx = [rand(1:Wy) rand(1:Wz) 0] x = circshift(x, shift_idx) end ny, nz, K = size(x) # reshape into patches L = floor(Int,ny*nz/Wy/Wz) # number of patches, assumes that image dimensions are divisble by the blocksizes xᴸᴸᴿ = zeros(T,Wy*Wz,L,K) for i=1:K xᴸᴸᴿ[:,:,i] = im2colDistinct(x[:,:,i], (Wy,Wz)) end xᴸᴸᴿ = permutedims(xᴸᴸᴿ,[1 3 2]) # L1-norm of singular values normᴸᴸᴿ = 0. for i = 1:L SVDec = svd(xᴸᴸᴿ[:,:,i]) normᴸᴸᴿ += norm(SVDec.S,1) end return normᴸᴸᴿ end
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function importSR(SIMurl::String,LOCurl::String) #1.. Import Data #Load Data with colnames: #mol_ID|loc_ID|framenumber|pos_x|pos_y #[Int64,Int64,Int64,Float64,Float64,Float64,Float64,Float64,Float64,Float64] #DataSim = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/Simulationen_01-24-19/sim_Tet.csv", #DataSim = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/ORI_SIM/locs_gamma_conf.csv", # print("importing DataSim\n") # DataSim = CSV.read(SIMurl, # types=[Int64,Int64,Int64,Float64,Float64], # header=["label_ID","oligomer_ID","framenum","x_nm","y_nm"], datarow=2, DataFrame) print("importing DataSim\n") DataSim = CSV.read(SIMurl, types=[Int64,Int64,Float64,Float64,Int64], header=["label_ID","framenum","x_nm","y_nm","oligomer_ID"], datarow=2, DataFrame) #RealLoc = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/Simulationen_01-24-19/sim_Tet_locs.csv", #RealLoc = CSV.read("/Users/Patrick/Documents/GitHub/BayesianEstimation/STORM/ORI_SIM/labels_gamma.csv", print("importing RealLoc\n") RealLoc = CSV.read(LOCurl, types=[Int64,Float64,Float64,Int64], #header=["label_ID","x_nm","y_nm"], datarow=2, DataFrame) header=["label_ID","x_nm","y_nm","oligomer_ID"], datarow=2, DataFrame) return DataSim, RealLoc end export importSR
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<gh_stars>0 """ load(file::String, vari::String; poly = Array{Float64}([]), start_date::Tuple, end_date::Tuple, data_units::String = "") Returns a ClimGrid type with the data in **file** of variable **vari** inside the polygon **poly**. Metadata is built-in the ClimGrid type, from the netCDF attributes. Inside the ClimgGrid type, the data is stored into an AxisArray data type, with time, longitude/x and latitude/y dimensions. The polygon provided should be in the -180, +180 longitude format. If the polygon crosses the International Date Line, the polygon should be splitted in multiple parts (i.e. multi-polygons). Options for data_units are for precipitation : "mm", which converts the usual "kg m-2 s-1" unit found in netCDF files. For temperature : "Celsius", which converts the usual "Kelvin" unit. Temporal subsetting can be done by providing start_date and end-date Tuples of length 1 (year), length 3 (year, month, day) or 6 (hour, minute, second). **Note:** load uses [CF conventions](http://cfconventions.org/). If you are unable to read the netCDF file with load, the user will need to read it with low-level functions available in [NetCDF.jl package](https://github.com/JuliaGeo/NetCDF.jl) or [NCDatasets.jl](https://github.com/Alexander-Barth/NCDatasets.jl) or re-create standartized netCDF files. """ function load(file::String, vari::String; poly = ([]), start_date::Tuple=(Inf,), end_date::Tuple=(Inf,), data_units::String = "") # TODO this file is a complete mess, but it works. Clean it up! # Get attributes ds = NCDatasets.Dataset(file) attribs_dataset = ds.attrib attribs = Dict(attribs_dataset) # The following attributes should be set for netCDF files that follows CF conventions. # project, institute, model, experiment, frequency project = ClimateTools.project_id(attribs_dataset) institute = ClimateTools.institute_id(attribs_dataset) model = ClimateTools.model_id(attribs_dataset) experiment = ClimateTools.experiment_id(attribs_dataset) frequency = ClimateTools.frequency_var(attribs_dataset) runsim = ClimateTools.runsim_id(attribs_dataset) grid_mapping = ClimateTools.get_mapping(keys(ds)) # Get dimensions names latname, latstatus = getdim_lat(ds) lonname, lonstatus = getdim_lon(ds) dataunits = ds[vari].attrib["units"] latunits = ds[latname].attrib["units"] lonunits = ds[lonname].attrib["units"] caltype = "" try caltype = ds["time"].attrib["calendar"] catch caltype = "standard" end # Create dict with latname and lonname dimension_dict = Dict(["lon" => lonname, "lat" => latname]) # lat_raw = NetCDF.ncread(file, latname) lat_raw = nomissing(ds[latname][:], NaN) # lon_raw = NetCDF.ncread(file, lonname) lon_raw = nomissing(ds[lonname][:], NaN) # Get variable attributes varattrib = Dict(ds[vari].attrib) if latstatus # means we don't have a "regular" grid # Get names of grid latgrid_name = latgridname(ds) longrid_name = longridname(ds) # latgrid = NetCDF.ncread(file, latgrid_name) latgrid = nomissing(ds[latgrid_name][:], NaN) # longrid = NetCDF.ncread(file, longrid_name) longrid = nomissing(ds[longrid_name][:], NaN) # Ensure we have a grid if ndims(latgrid) == 1 && ndims(longrid) == 1 longrid, latgrid = ndgrid(lon_raw, lat_raw) map_attrib = Dict(["grid_mapping" => "Regular_longitude_latitude"]) end map_attrib = build_grid_mapping(ds, grid_mapping) varattrib["grid_mapping"] = grid_mapping else # if no grid provided, create one longrid, latgrid = ndgrid(lon_raw, lat_raw) map_attrib = Dict(["grid_mapping" => grid_mapping]) varattrib["grid_mapping"] = grid_mapping end # ===================== # TIME # Get time resolution timeV = ds["time"][:] if frequency == "N/A" || !ClimateTools.@isdefined frequency try try frequency = string(diff(timeV)[2]) catch frequency = string(diff(timeV)[1]) end catch frequency = "N/A" end end timeattrib = Dict(ds["time"].attrib) T = typeof(timeV[1]) idxtimebeg, idxtimeend = timeindex(timeV, start_date, end_date, T) timeV = timeV[idxtimebeg:idxtimeend] # ================== # Spatial shift if grid is 0-360. rotatedgrid = false if sum(longrid .> 180) >= 1 rotatedgrid = true # Shift 360 degrees grid to -180, +180 degrees longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid) # Shift 360 degrees vector to -180, +180 degrees lon_raw_flip = ClimateTools.shiftvector_180_west_east(lon_raw) else longrid_flip = longrid # grid is already "flipped" by design end # =================== # GET DATA data_pointer = ds[vari] if !isempty(poly) # Test to see if the polygon crosses the meridian meridian = ClimateTools.meridian_check(poly) # Build mask based on provided polygon msk = inpolygrid(longrid_flip, latgrid, poly) if sum(isnan.(msk)) == length(msk) # no grid point inside polygon throw(error("No grid points found inside the provided polygon")) end if rotatedgrid # Regrid msk to original grid if the grid have been rotated to get proper index for data extraction msk = ClimateTools.shiftarray_east_west(msk, longrid_flip) end #Extract data based on mask data_ext = ClimateTools.extractdata(data_pointer, msk, idxtimebeg, idxtimeend) data_ext = nomissing(data_ext, NaN) #new mask (e.g. representing the region of the polygon) begin I = Base.findall(!isnan, msk) idlon, idlat = (getindex.(I, 1), getindex.(I, 2)) end minXgrid = minimum(idlon) maxXgrid = maximum(idlon) minYgrid = minimum(idlat) maxYgrid = maximum(idlat) msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid] data_mask = applymask(data_ext, msk) # needed when polygon is not rectangular if rotatedgrid # Regrid msk to shifted grid if the grid have been rotated to get final mask #shiftarray_west_east(msk, longrid) end # Get lon_raw and lat_raw for such region lon_raw = lon_raw[minXgrid:maxXgrid] lat_raw = lat_raw[minYgrid:maxYgrid] if map_attrib["grid_mapping"] == "Regular_longitude_latitude" lon_raw = ClimateTools.shiftvector_180_west_east(lon_raw) end # Idem for longrid and latgrid if meridian longrid = ClimateTools.shiftgrid_180_east_west(longrid)#grideast, gridwest) longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid] latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid] # Re-translate to -180, 180 if 0, 360 if rotatedgrid longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid) data = permute_west_east(data_mask, longrid)#idxwest, idxeast) msk = ClimateTools.permute_west_east(msk, longrid) # TODO Try to trim padding when meridian is crossed and model was on a 0-360 coords # idlon, idlat = findn(.!isnan.(msk)) # minXgrid = minimum(idlon) # maxXgrid = maximum(idlon) # minYgrid = minimum(idlat) # maxYgrid = maximum(idlat) # # msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid] # data = data[:, minXgrid:maxXgrid, minYgrid:maxYgrid] # lon_raw_flip = lon_raw[minXgrid:maxXgrid] # # longrid_flip = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid] # longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid_flip) # latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid] # data = applymask(data, msk) else data = data_mask end else if rotatedgrid # flip in original grid longrid = ClimateTools.shiftgrid_180_east_west(longrid) #grideast, gridwest) end longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid] latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid] if rotatedgrid longrid_flip = shiftgrid_180_west_east(longrid) lon_raw_flip = shiftvector_180_east_west(lon_raw) data = permute_west_east(data_mask, longrid)#idxwest, idxeast) msk = ClimateTools.permute_west_east(msk, longrid) else data = data_mask end end elseif isempty(poly) # no polygon clipping msk = Array{Float64}(ones((size(data_pointer, 1), size(data_pointer, 2)))) data_ext = ClimateTools.extractdata(data_pointer, msk, idxtimebeg, idxtimeend) # replace_missing!(data_ext) data_ext = nomissing(data_ext, NaN) # data_ext = convert(data_ext, Float32) if rotatedgrid # Flip data "west-east" data = ClimateTools.permute_west_east(data_ext, longrid) else data = data_ext end end if rotatedgrid longrid .= longrid_flip lon_raw .= lon_raw_flip end # Convert units of optional argument data_units is provided if data_units == "Celsius" && (vari == "tas" || vari == "tasmax" || vari == "tasmin") && dataunits == "K" data .-= 273.15 dataunits = "°C" varattrib["units"] = "Celsius" # @warn "Using Celsius can be problematic for arithmetic operations. Best practice is to keep Kelvin and only convert to Celsius at the end with the overloaded ClimateTools.uconvert function." end if data_units == "mm" && vari == "pr" && (dataunits == "kg m-2 s-1" || dataunits == "mm s-1") factor = timeresolution(ds["time"]) # factor = pr_timefactor(rez) data .*= factor.value dataunits = "mm" varattrib["standard_name"] = "precipitation" varattrib["units"] = "mm" end # Create AxisArray from variable "data" if ndims(data) == 3 # Convert data to AxisArray dataOut = AxisArray(data, Axis{Symbol(lonname)}(lon_raw), Axis{Symbol(latname)}(lat_raw), Axis{:time}(timeV)) elseif ndims(data) == 4 # this imply a 3D field (height component) # Get level vector plev = ds["plev"][:] # Convert data to AxisArray dataOut = AxisArray(data, Axis{Symbol(lonname)}(lon_raw), Axis{Symbol(latname)}(lat_raw), Axis{:plev}(plev), Axis{:time}(timeV)) else throw(error("load takes only 3D and 4D variables for the moment")) end C = ClimGrid(dataOut, longrid=longrid, latgrid=latgrid, msk=msk, grid_mapping=map_attrib, dimension_dict=dimension_dict, timeattrib=timeattrib, model=model, frequency=frequency, experiment=experiment, run=runsim, project=project, institute=institute, filename=file, dataunits=dataunits, latunits=latunits, lonunits=lonunits, variable=vari, typeofvar=vari, typeofcal=caltype, varattribs=varattrib, globalattribs=attribs) close(ds) # NetCDF.close(ncfile) return C end """ load(files::Array{String,1}, vari::String; poly = ([]), start_date::Date = Date(-4000), end_date::Date = Date(-4000), data_units::String = "") Loads and merge the files contained in the arrar files. """ function load(files::Array{String,1}, vari::String; poly=([]), start_date::Tuple=(Inf,), end_date::Tuple=(Inf,), data_units::String="") nfiles = length(files) C = Array{ClimGrid}(undef, nfiles) # initialize # TODO better initialization datesort = Array{Any}(undef, nfiles) Cout = [] p = Progress(nfiles*2, 3, "Loading files: ") for ifile = 1:nfiles C[ifile] = load(files[ifile], vari, poly = poly, start_date=start_date, end_date=end_date, data_units=data_units) datesort[ifile] = get_timevec(C[ifile])[1] next!(p) end # Sort files based on timevector to ensure that merge results in amonotone increase in time idx = sortperm(datesort) C = C[idx] for imod = 1:nfiles if imod == 1 Cout = C[imod] else Cout = merge(Cout, C[imod]) end next!(p) end return Cout end """ load2D(file::String, vari::String; poly=[], data_units::String="") Returns a 2D array. Should be used for *fixed* data, such as orography. """ function load2D(file::String, vari::String; poly=[], data_units::String="") # Get attributes ds = NCDatasets.Dataset(file) attribs_dataset = ds.attrib attribs = Dict(attribs_dataset) project = ClimateTools.project_id(attribs_dataset) institute = ClimateTools.institute_id(attribs_dataset) model = ClimateTools.model_id(attribs_dataset) experiment = ClimateTools.experiment_id(attribs_dataset) frequency = ClimateTools.frequency_var(attribs_dataset) runsim = ClimateTools.runsim_id(attribs_dataset) grid_mapping = ClimateTools.get_mapping(keys(ds)) # Get dimensions names latname, latstatus = getdim_lat(ds) lonname, lonstatus = getdim_lon(ds) dataunits = ds[vari].attrib["units"] latunits = ds[latname].attrib["units"] lonunits = ds[lonname].attrib["units"] # Create dict with latname and lonname dimension_dict = Dict(["lon" => lonname, "lat" => latname]) # lat_raw = NetCDF.ncread(file, latname) lat_raw = nomissing(ds[latname][:], NaN) # lon_raw = NetCDF.ncread(file, lonname) lon_raw = nomissing(ds[lonname][:], NaN) # Get variable attributes varattrib = Dict(ds[vari].attrib) if latstatus # means we don't have a "regular" grid # Get names of grid latgrid_name = latgridname(ds) longrid_name = longridname(ds) # latgrid = NetCDF.ncread(file, latgrid_name) latgrid = nomissing(ds[latgrid_name][:], NaN) # longrid = NetCDF.ncread(file, longrid_name) longrid = nomissing(ds[longrid_name][:], NaN) map_attrib = build_grid_mapping(ds, grid_mapping) varattrib["grid_mapping"] = grid_mapping else # if no grid provided, create one longrid, latgrid = ndgrid(lon_raw, lat_raw) map_attrib = Dict(["grid_mapping" => grid_mapping]) varattrib["grid_mapping"] = grid_mapping end # ================== # Spatial shift if grid is 0-360. rotatedgrid = false if sum(longrid .> 180) >= 1 rotatedgrid = true # Shift 360 degrees grid to -180, +180 degrees longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid) # Shift 360 degrees vector to -180, +180 degrees lon_raw_flip = ClimateTools.shiftvector_180_west_east(lon_raw) else longrid_flip = longrid # grid is already "flipped" by design end # =================== # GET DATA # data = ds[variable] data_pointer = ds[vari] if !isempty(poly) # Test to see if the polygon crosses the meridian meridian = ClimateTools.meridian_check(poly) # Build mask based on provided polygon msk = inpolygrid(longrid_flip, latgrid, poly) if sum(isnan.(msk)) == length(msk) # no grid point insode polygon throw(error("No grid points found inside the provided polygon")) end if rotatedgrid # Regrid msk to original grid if the grid have been rotated to get proper index for data extraction msk = ClimateTools.shiftarray_east_west(msk, longrid_flip) end #Extract data based on mask data_ext = ClimateTools.extractdata2D(data_pointer, msk) # replace_missing!(data_ext) data_ext = nomissing(data_ext, NaN) # data_ext = convert(data_ext, Float32) begin I = Base.findall(!isnan, msk) idlon, idlat = (getindex.(I, 1), getindex.(I, 2)) end minXgrid = minimum(idlon) maxXgrid = maximum(idlon) minYgrid = minimum(idlat) maxYgrid = maximum(idlat) msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid] data_mask = applymask(data_ext, msk) # needed when polygon is not rectangular if rotatedgrid # Regrid msk to shifted grid if the grid have been rotated to get final mask #shiftarray_west_east(msk, longrid) end # Get lon_raw and lat_raw for such region lon_raw = lon_raw[minXgrid:maxXgrid] lat_raw = lat_raw[minYgrid:maxYgrid] if map_attrib["grid_mapping"] == "Regular_longitude_latitude" lon_raw = ClimateTools.shiftvector_180_west_east(lon_raw) end # Idem for longrid and latgrid if meridian longrid = ClimateTools.shiftgrid_180_east_west(longrid)#grideast, gridwest) longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid] latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid] # Re-translate to -180, 180 if 0, 360 if rotatedgrid longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid) data = permute_west_east(data_mask, longrid)#idxwest, idxeast) msk = ClimateTools.permute_west_east(msk, longrid) # TODO Try to trim padding when meridian is crossed and model was on a 0-360 coords # idlon, idlat = findn(.!isnan.(msk)) # minXgrid = minimum(idlon) # maxXgrid = maximum(idlon) # minYgrid = minimum(idlat) # maxYgrid = maximum(idlat) # # msk = msk[minXgrid:maxXgrid, minYgrid:maxYgrid] # data = data[:, minXgrid:maxXgrid, minYgrid:maxYgrid] # lon_raw_flip = lon_raw[minXgrid:maxXgrid] # # longrid_flip = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid] # longrid_flip = ClimateTools.shiftgrid_180_west_east(longrid_flip) # latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid] # data = applymask(data, msk) else data = data_mask end else if rotatedgrid # flip in original grid longrid = shiftgrid_180_east_west(longrid) #grideast, gridwest) end longrid = longrid[minXgrid:maxXgrid, minYgrid:maxYgrid] latgrid = latgrid[minXgrid:maxXgrid, minYgrid:maxYgrid] if rotatedgrid longrid_flip = shiftgrid_180_west_east(longrid) lon_raw_flip = shiftvector_180_east_west(lon_raw) data = permute_west_east(data_mask, longrid)#idxwest, idxeast) msk = ClimateTools.permute_west_east(msk, longrid) else data = data_mask end end elseif isempty(poly) # no polygon clipping msk = Array{Float64}(ones((size(data_pointer, 1), size(data_pointer, 2)))) data_ext = extractdata2D(data_pointer, msk) # replace_missing!(data_ext) data_ext = nomissing(data_ext, NaN) # data_ext = convert(data_ext, Float32) if rotatedgrid # Flip data "west-east" data = permute_west_east(data_ext, longrid) else data = data_ext end end if rotatedgrid longrid = longrid_flip lon_raw = lon_raw_flip end # Convert data to AxisArray dataOut = AxisArray(data, Axis{Symbol(lonname)}(lon_raw), Axis{Symbol(latname)}(lat_raw)) C = ClimGrid(dataOut, longrid=longrid, latgrid=latgrid, msk=msk, grid_mapping=map_attrib, dimension_dict=dimension_dict, model=model, frequency=frequency, experiment=experiment, run=runsim, project=project, institute=institute, filename=file, dataunits=dataunits, latunits=latunits, lonunits=lonunits, variable=vari, typeofvar=vari, typeofcal="fixed", varattribs=varattrib, globalattribs=attribs) close(ds) return C end model_id(attrib::NCDatasets.Attributes) = get(attrib,"model_id", get(attrib, "parent_source_id", get(attrib,"model","N/A"))) experiment_id(attrib::NCDatasets.Attributes) = get(attrib,"experiment_id",get(attrib,"experiment","N/A")) project_id(attrib::NCDatasets.Attributes) = get(attrib,"project_id", get(attrib, "mip_era", get(attrib,"project","N/A"))) institute_id(attrib::NCDatasets.Attributes) = get(attrib,"institute_id",get(attrib, "institution_id", get(attrib,"institute","N/A"))) frequency_var(attrib::NCDatasets.Attributes) = get(attrib,"frequency","N/A") runsim_id(attrib::NCDatasets.Attributes) = get(attrib, "parent_experiment_rip", get(attrib,"driving_model_ensemble_member","N/A")) """ getdim_lat(ds::NCDatasets.Dataset) Returns the name of the "latitude" dimension and the status related to a regular grid. The latitude dimension is usually "latitude", "lat", "y", "yc", "rlat". """ function getdim_lat(ds::NCDatasets.Dataset) if sum(keys(ds.dim) .== "rlat") == 1 return "rlat", true elseif sum(keys(ds.dim) .== "lat") == 1 return "lat", false elseif sum(keys(ds.dim) .== "latitude") == 1 return "latitude", false elseif sum(keys(ds.dim) .== "y") == 1 return "y", true elseif sum(keys(ds.dim) .== "yc") == 1 return "yc", true else error("Manually verify x/lat dimension name") end end """ getdim_lon(ds::NCDatasets.Dataset) Returns the name of the "longitude" dimension and the status related to a regular grid. The longitude dimension is usually "longitue", "lon", "x", "xc", "rlon". """ function getdim_lon(ds::NCDatasets.Dataset) if sum(keys(ds.dim) .== "rlon") == 1 return "rlon", true elseif sum(keys(ds.dim) .== "lon") == 1 return "lon", false elseif sum(keys(ds.dim) .== "longitude") == 1 return "longitude", false elseif sum(keys(ds.dim) .== "x") == 1 return "x", false elseif sum(keys(ds.dim) .== "xc") == 1 return "xc", false else error("Manually verify x/lat dimension name") end end """ latgridname(ds::NCDatasets.Dataset) Returns the name of the latitude grid when datasets is not on a rectangular grid. """ function latgridname(ds::NCDatasets.Dataset) if in("lat", keys(ds)) return "lat" elseif in("latitude", keys(ds)) return "latitude" else error("Variable name is not supported. File an issue on https://github.com/Balinus/ClimateTools.jl/issues") end end """ longridname(ds::NCDatasets.Dataset) Returns the name of the longitude grid when datasets is not on a rectangular grid. """ function longridname(ds::NCDatasets.Dataset) if in("lon", keys(ds)) return "lon" elseif in("longitude", keys(ds)) return "longitude" else error("Variable name is not supported. File an issue on https://github.com/Balinus/ClimateTools.jl/issues") end end """ extractdata(data, msk, idxtimebeg, idxtimeend) Returns the data contained in netCDF file, using the appropriate mask and time index. Used internally by `load`. """ function extractdata(data, msk, idxtimebeg, idxtimeend) # idlon, idlat = findn(.!isnan.(msk)) begin I = Base.findall(!isnan, msk) idlon, idlat = (getindex.(I, 1), getindex.(I, 2)) end minXgrid = minimum(idlon) maxXgrid = maximum(idlon) minYgrid = minimum(idlat) maxYgrid = maximum(idlat) if ndims(data) == 3 dataout = data[minXgrid:maxXgrid, minYgrid:maxYgrid, idxtimebeg:idxtimeend] # Permute dims # data = permutedims(data, [3, 1, 2]) elseif ndims(data) == 4 dataout = data[minXgrid:maxXgrid, minYgrid:maxYgrid, :, idxtimebeg:idxtimeend] # Permute dims # data = permutedims(data, [4, 1, 2, 3]) end return dataout end function extractdata2D(data, msk) # idlon, idlat = findn(.!isnan.(msk)) begin I = Base.findall(!isnan, msk) idlon, idlat = (getindex.(I, 1), getindex.(I, 2)) end minXgrid = minimum(idlon) maxXgrid = maximum(idlon) minYgrid = minimum(idlat) maxYgrid = maximum(idlat) data = data[minXgrid:maxXgrid, minYgrid:maxYgrid] return data end """ get_mapping(ds::Array{String,1}) Returns the grid_mapping of Dataset *ds* """ function get_mapping(K::Array{String,1}) if in("rotated_pole", K) return "rotated_pole" elseif in("lambert_conformal_conic", K) return "lambert_conformal_conic" elseif in("rotated_latitude_longitude", K) return "rotated_latitude_longitude" elseif in("rotated_mercator", K) return "rotated_mercator" elseif in("crs", K) return "crs" elseif in("polar_stereographic", K) return "polar_stereographic" else return "Regular_longitude_latitude" end end function build_grid_mapping(ds::NCDatasets.Dataset, grid_mapping::String) if ClimateTools.@isdefined grid_mapping # map_dim = varattrib[grid_mapping] map_attrib = Dict(ds[grid_mapping].attrib) map_attrib["grid_mapping"] = grid_mapping else error("File an issue on https://github.com/Balinus/ClimateTools.jl/issues to get the grid supported") end return map_attrib end
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<reponame>RockwallNest/RubyWithJulia using CSV using DataFrames using StatsPlots btc_df = CSV.File("./btc_jpy.csv") |> DataFrame @df btc_df plot(:time, [:best_bid :best_ask :ltp], legend = :bottomright, title="BTC_JPY,Bitflyer.com 12/31/2020")
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<reponame>josePereiro/Chemostat_Rath2017.jl using ProjAssistant @quickactivate "Chemostat_Rath2017" @time begin import DataFrames: DataFrame import MAT import CSV import Chemostat import Chemostat.MetNets const Ch = Chemostat import Chemostat_Rath2017: Human1, RathData const Rd = RathData const H1 = Human1 const HG = H1.HumanGEM end ## ------------------------------------------------------------------ # load model model = HG.load_humangem_raw_model() biomass_idx = MetNets.rxnindex(model, HG.HUMAN_BIOMASS_IDER) ## ------------------------------------------------------------------ # Biomass equation # I will modified the biomass equation (biomass_human) of Human1 model with data # derived from Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1. # I compute de relation between the total of each group reported in Niklas 2013 with the equivalent # group found in the model biomass, and then rescaled each group to match the reported total. # I do not touch the energetic part of the equation, atp + h20 -> adp + h2 + pi biomass = Dict() for met_idx in MetNets.rxn_mets(model, biomass_idx) met = model.mets[met_idx] biomass[met] = model.S[met_idx, biomass_idx] end ## ------------------------------------------------------------------ # Carbohydrates ch_ids = ["m03161c"] exp_ch_tot = 438.3 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1 println("experimental total ch: ", exp_ch_tot) model_ch_tot = abs.(sum([biomass[met] for met in ch_ids])) # The model did not include directly any carbohydrate println("model total ch: ", model_ch_tot) ch_factor = exp_ch_tot/model_ch_tot println("factor exp/model: ", ch_factor) ## ------------------------------------------------------------------ # RNA rna_ids = ["m02847c"] model_rna_tot = abs.(sum([biomass[met] for met in rna_ids])) println("model total rna: ", model_rna_tot) exp_rna_tot = 176.9 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1 println("experimental total rna: ", exp_rna_tot) rna_factor = exp_rna_tot/model_rna_tot println("factor exp/model: ", rna_factor) ## ------------------------------------------------------------------ # DNA dna_ids = ["m01721n"] model_dna_tot = abs.(sum([biomass[met] for met in dna_ids])) println("model total dna: ", model_dna_tot) exp_dna_tot = 45.3 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1 println("experimental total dna: ", exp_dna_tot) dna_factor = exp_dna_tot/model_dna_tot println("factor exp/model: ", dna_factor) ## ------------------------------------------------------------------ # Lipids lip_ids = ["m10014c"] model_lip_tot = abs.(sum([biomass[met] for met in lip_ids])) println("model total lipids:", model_lip_tot) # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1 exp_lip_tot = 202.9 * 1e-3 println("experimental total lipids:", exp_lip_tot) lip_factor = exp_lip_tot/model_lip_tot println("factor exp/model:", lip_factor) # Aminoacids aa_ids = ["m10013c"] model_prot_tot = abs.(sum([biomass[met] for met in aa_ids])) println("model total protein: ", model_prot_tot) exp_prot_tot = 7462.6 * 1e-3 # Niklas (2013): 103–114. https://doi.org/10.1016/j.ymben.2013.01.002. Table1 println("experimental total protein: ", exp_prot_tot) prot_factor = exp_prot_tot/model_prot_tot println("factor exp/model: ", prot_factor) ## ------------------------------------------------------------------ # Rescaling # Carbohydrates for ch in ch_ids biomass[ch] = biomass[ch] * ch_factor end # Aminoacids for aa in aa_ids biomass[aa] = biomass[aa] * prot_factor end # lipids for lip in lip_ids biomass[lip] = biomass[lip] * lip_factor end # DNA for dna in dna_ids biomass[dna] = biomass[dna] * dna_factor end # RNA for rna in rna_ids biomass[rna] = biomass[rna] * rna_factor end ## ------------------------------------------------------------------ # save sdat(HG, biomass, "niklas_biomass", ".jls"; verbose = true )
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""" Axisymmetric Solov'ev equilibra in (R/R₀,Z/R₀,ϕ) coordinates. Based on Cerfon & Freidberg, Physics of Plasmas 17, 032502, 2010, and Freidberg, Ideal Magnetohydrodynamics, 2014. """ module Solovev using RecipesBase using SymEngine: N, symbols, diff, expand, subs import ..ElectromagneticFields import ..ElectromagneticFields: code import ..SolovevAbstract: AbstractSolovevEquilibrium, X, Y, Z, R, r, θ, ϕ, r² export SolovevEquilibrium, SolovevXpointEquilibrium function ψ₀(x::AbstractVector{T}, a) where {T <: Number} x[1]^4 / 8 + a * (x[1]^2 * log(x[1]) / 2 - x[1]^4 / 8 ) end function ψ₁(x::AbstractVector{T}) where {T <: Number} one(T) end function ψ₂(x::AbstractVector{T}) where {T <: Number} x[1]^2 end function ψ₃(x::AbstractVector{T}) where {T <: Number} x[2]^2 - x[1]^2 * log(x[1]) end function ψ₄(x::AbstractVector{T}) where {T <: Number} x[1]^4 - 4 * x[1]^2 * x[2]^2 end function ψ₅(x::AbstractVector{T}) where {T <: Number} 2 * x[2]^4 - 9 * x[2]^2 * x[1]^2 + 3 * x[1]^4 * log(x[1]) - 12 * x[1]^2 * x[2]^2 * log(x[1]) end function ψ₆(x::AbstractVector{T}) where {T <: Number} x[1]^6 - 12 * x[1]^4 * x[2]^2 + 8 * x[1]^2 * x[2]^4 end function ψ₇(x::AbstractVector{T}) where {T <: Number} 8 * x[2]^6 - 140 * x[2]^4 * x[1]^2 + 75 * x[2]^2 * x[1]^4 - 15 * x[1]^6 * log(x[1]) + 180 * x[1]^4 * x[2]^2 * log(x[1]) - 120 * x[1]^2 * x[2]^4 * log(x[1]) end function ψ₈(x::AbstractVector{T}) where {T <: Number} x[2] end function ψ₉(x::AbstractVector{T}) where {T <: Number} x[2] * x[1]^2 end function ψ₁₀(x::AbstractVector{T}) where {T <: Number} x[2]^3 - 3 * x[2] * x[1]^2 * log(x[1]) end function ψ₁₁(x::AbstractVector{T}) where {T <: Number} 3 * x[2] * x[1]^4 - 4 * x[2]^3 * x[1]^2 end function ψ₁₂(x::AbstractVector{T}) where {T <: Number} 8 * x[2]^5 - 45 * x[2] * x[1]^4 - 80 * x[2]^3 * x[1]^2 * log(x[1]) + 60 * x[2] * x[1]^4 * log(x[1]) end @doc raw""" Axisymmetric Solov'ev equilibra in (R/R₀,Z/R₀,ϕ) coordinates. Based on Cerfon & Freidberg, Physics of Plasmas 17, 032502, 2010, and Freidberg, Ideal Magnetohydrodynamics, 2014. The covariant components of the vector potential are given by ```math A (x, y, \phi) = \left( \frac{B_0 R_0}{2} \, \frac{y}{x} , \, - \frac{B_0 R_0}{2} \, \ln x , \, \psi(x,y) \right)^T , ``` with $x = R/R_0$ and $y = Z/R_0$. The normalised poloidal flux $\psi$ is given by ```math \psi (x,y) = \psi_0 + \sum \limits_{i=1}^{7} c_i \psi_i (x,y) , ``` with ```math \begin{aligned} \psi_{0} &= \frac{x^4}{8} + \alpha \left( \frac{1}{2} x^2 \, \ln x - \frac{x^4}{8} \right) , \\ \psi_{1} &= 1 , \\ \psi_{2} &= x^2 , \\ \psi_{3} &= y^2 - x^2 \, \ln x , \\ \psi_{4} &= x^4 - 4 x^2 y^2 , \\ \psi_{5} &= 2 y^4 9 y^2 x^2 + 3 x^4 \, \ln x - 12 x^2 y^2 \, \ln x , \\ \psi_{6} &= x^6 - 12 x^4 y^2 + 8 x^2 y^4 , \\ \psi_{7} &= 8 y^6 - 140 y^4 x^2 + 75 y^2 x^4 - 15 x^6 \, \ln x + 180 x^4 y^2 \, \ln x - 120 x^2 y^4 \, \ln x . \end{aligned} ``` This formula describes exact solutions of the Grad-Shafranov equation with up-down symmetry. The constants $c_i$ are determined from boundary constraints on $\psi$, that are derived from the following analytic model for a smooth, elongated "D" shaped cross section: ```math \begin{aligned} x &= 1 + \epsilon \, \cos (\tau + \delta_0 \, \sin \tau) , \\ y &= \epsilon \kappa \, \sin (\tau) , \end{aligned} ``` where $0 \leq \tau < 2 \pi$, $\epsilon = a / R_0$ is the inverse aspect ratio, $\kappa$ the elongation, and $\sin \delta_0 = \delta$ is the triangularity. Defining three test points, namely - the high point $(1 - \delta \epsilon, \kappa \epsilon)$, - the inner equatorial point $(1 - \epsilon, 0)$, - and the outer equatorial point $(1 + \epsilon, 0)$, the following geometric constraints can be posed on the solution: ```math \begin{aligned} \psi (1 + \epsilon, 0) &= 0 , \\ \psi (1 - \epsilon, 0) &= 0 , \\ \psi (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\ \psi_{x} (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\ \psi_{yy} (1 + \epsilon, 0) &= - N_1 \psi_{x} (1 + \epsilon, 0) , \\ \psi_{yy} (1 - \epsilon, 0) &= - N_2 \psi_{x} (1 - \epsilon, 0) , \\ \psi_{xx} (1 - \delta \epsilon, \kappa \epsilon) &= - N_3 \psi_y (1 - \delta \epsilon, \kappa \epsilon) . \end{aligned} ``` The first three equations define the three test points, the fourth equations enforces the high point to be a maximum, and the last three equations define the curvature at the test points. The coefficients $N_j$ can be found from the analytic model cross section as ```math \begin{aligned} N_1 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = 0} = - \frac{(1 + \delta_0)^2}{\epsilon \kappa^2} , \\ N_2 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi} = \hphantom{-} \frac{(1 - \delta_0)^2}{\epsilon \kappa^2} , \\ N_3 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi/2} = - \frac{\kappa}{\epsilon \, \cos^2 \delta_0} . \end{aligned} ``` For a given value of the constant $a$ above conditions reduce to a set of seven linear inhomogeneous algebraic equations for the unknown $c_i$, which can easily be solved. Parameters: * `R₀`: position of magnetic axis * `B₀`: B-field at magnetic axis * `ϵ`: inverse aspect ratio * `κ`: elongation * `δ`: triangularity * `α`: free constant, determined to match a given beta value """ struct SolovevEquilibrium{T <: Number} <: AbstractSolovevEquilibrium name::String R₀::T B₀::T ϵ::T κ::T δ::T α::T c::Vector{T} function SolovevEquilibrium{T}(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, c::Vector{T}) where T <: Number new("Solovev Equilibrium", R₀, B₀, ϵ, κ, δ, α, c) end end function SolovevEquilibrium(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T) where T <: Number n = 7 A = zeros(n,n+1) x₁, x₂, x₃ = symbols("x₁, x₂, x₃") x = [x₁, x₂, x₃] ψ = [ψ₁(x), ψ₂(x), ψ₃(x), ψ₄(x), ψ₅(x), ψ₆(x), ψ₇(x), -ψ₀(x,α)] for i in axes(A,2) A[1,i] = N(subs(ψ[i], x[1]=>1+ϵ, x[2]=>0)) A[2,i] = N(subs(ψ[i], x[1]=>1-ϵ, x[2]=>0)) A[3,i] = N(subs(ψ[i], x[1]=>1-δ*ϵ, x[2]=>κ*ϵ)) A[4,i] = N(subs(diff(ψ[i], x[1]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ)) A[5,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1+ϵ, x[2]=>0) - (1 + asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1+ϵ, x[2]=>0)) A[6,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1-ϵ, x[2]=>0) + (1 - asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1-ϵ, x[2]=>0)) A[7,i] = N(subs(diff(ψ[i], x[1], 2), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ) - κ / (ϵ * (1 - δ^2)) * subs(diff(ψ[i], x[2]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ)) end c = A[1:n,1:n] \ A[1:n,n+1] SolovevEquilibrium{T}(R₀, B₀, ϵ, κ, δ, α, c) end function init(R₀, B₀, ϵ, κ, δ, α) SolovevEquilibrium(R₀, B₀, ϵ, κ, δ, α) end macro code(R₀, B₀, ϵ, κ, δ, α) code(init(R₀, B₀, ϵ, κ, δ, α); escape=true) end SolovevEquilibriumITER() = SolovevEquilibrium(6.2, 5.3, 0.32, 1.7, 0.33, -0.155) # SolovevEquilibriumTFTR() = SolovevEquilibrium(2.5, 5.6, 0.345, 1.0, 0.0, ) # SolovevEquilibriumJET() = SolovevEquilibrium(3.0, 3.6, 0.333, 1.7, 0.25, ) SolovevEquilibriumNSTX() = SolovevEquilibrium(0.85, 0.30, 0.78, 2.00, 0.35, 1.0) # SolovevEquilibriumMAST() = SolovevEquilibrium(0.85, 0.52, 0.77, 2.45, 0.50, ) SolovevEquilibriumFRC() = SolovevEquilibrium(0.0, 0.0, 0.99, 10., 0.7, 0.0) # SolovevEquilibriumFRC2() = SolovevEquilibrium(0.0, 0.0, 1.00, 10., 1.0, 0.0) function Base.show(io::IO, equ::SolovevEquilibrium) print(io, "SolovevEquilibrium Equilibrium with\n") print(io, " R₀ = ", equ.R₀, "\n") print(io, " B₀ = ", equ.B₀, "\n") print(io, " ϵ = ", equ.ϵ, "\n") print(io, " κ = ", equ.κ, "\n") print(io, " δ = ", equ.δ, "\n") print(io, " α = ", equ.α) end function ElectromagneticFields.A₃(x::AbstractArray{T,1}, equ::SolovevEquilibrium) where {T <: Number} ( ψ₀(x, equ.α) + equ.c[1] * ψ₁(x) + equ.c[2] * ψ₂(x) + equ.c[3] * ψ₃(x) + equ.c[4] * ψ₄(x) + equ.c[5] * ψ₅(x) + equ.c[6] * ψ₆(x) + equ.c[7] * ψ₇(x) ) end @doc raw""" Axisymmetric Solov'ev equilibra with X-point in (R/R₀,Z/R₀,phi) coordinates. Based on Cerfon & Freidberg, Physics of Plasmas 17, 032502, 2010, and Freidberg, Ideal Magnetohydrodynamics, 2014. The covariant components of the vector potential are given by ```math A (x, y, \phi) = \left( \frac{B_0 R_0}{2} \, \frac{y}{x} , \, - \frac{B_0 R_0}{2} \, \ln x , \, \psi(x,y) \right)^T , ``` with $x = R/R_0$ and $y = Z/R_0$. The normalised poloidal flux $\psi$ is given by ```math \psi (x,y) = \psi_0 + \sum \limits_{i=1}^{12} c_i \psi_i (x,y) , ``` with ```math \begin{aligned} \psi_{0} &= \frac{x^4}{8} + \alpha \left( \frac{1}{2} x^2 \, \ln x - \frac{x^4}{8} \right) , \\ \psi_{1} &= 1 , \\ \psi_{2} &= x^2 , \\ \psi_{3} &= y^2 - x^2 \, \ln x , \\ \psi_{4} &= x^4 - 4 x^2 y^2 , \\ \psi_{5} &= 2 y^4 9 y^2 x^2 + 3 x^4 \, \ln x - 12 x^2 y^2 \, \ln x , \\ \psi_{6} &= x^6 - 12 x^4 y^2 + 8 x^2 y^4 , \\ \psi_{7} &= 8 y^6 - 140 y^4 x^2 + 75 y^2 x^4 - 15 x^6 \, \ln x + 180 x^4 y^2 \, \ln x - 120 x^2 y^4 \, \ln x , \\ \psi_{8} &= y , \\ \psi_{9} &= y x^2 , \\ \psi_{10} &= y^3 - 3 y x^2 \, \ln x , \\ \psi_{11} &= 3 y x^4 - 4 y^3 x^2 , \\ \psi_{12} &= 8 y^5 - 45 y x^4 - 80 y^3 x^2 \, \ln x + 60 y x^4 \, \ln x . \end{aligned} ``` This formula describes exact solutions of the Grad-Shafranov equation with up-down asymmetry. The constants $c_i$ are determined from boundary constraints on $\psi$, that are derived from the following analytic model for a smooth, elongated "D" shaped cross section: ```math \begin{aligned} x &= 1 + \epsilon \, \cos (\tau + \arcsin \delta \, \sin \tau) , \\ y &= \epsilon \kappa \, \sin (\tau) , \end{aligned} ``` where $0 \leq \tau < 2 \pi$, $\epsilon = a / R_0$ is the inverse aspect ratio, $\kappa$ the elongation, and $\sin \delta_0 = \delta$ is the triangularity. Defining four test points, namely - the high point $(1 - \delta \epsilon, \kappa \epsilon)$, - the inner equatorial point $(1 - \epsilon, 0)$, - and the outer equatorial point $(1 + \epsilon, 0)$, - the position of the X-point $(x_{\mathrm{sep}}, y_{\mathrm{sep}})$, the following geometric constraints can be posed on the solution: ```math \begin{aligned} \psi (1 + \epsilon, 0) &= 0 , \\ \psi (1 - \epsilon, 0) &= 0 , \\ \psi (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\ \psi (x_{\mathrm{sep}}, y_{\mathrm{sep}}) &= 0 , \\ \psi_{y} (1 + \epsilon, 0) &= 0 , \\ \psi_{y} (1 - \epsilon, 0) &= 0 , \\ \psi_{x} (1 - \delta \epsilon, \kappa \epsilon) &= 0 , \\ \psi_{x} (x_{\mathrm{sep}}, y_{\mathrm{sep}}) &= 0 , \\ \psi_{y} (x_{\mathrm{sep}}, y_{\mathrm{sep}}) &= 0 , \\ \psi_{yy} (1 + \epsilon, 0) &= - N_1 \psi_{x} (1 + \epsilon, 0) , \\ \psi_{yy} (1 - \epsilon, 0) &= - N_2 \psi_{x} (1 - \epsilon, 0) , \\ \psi_{xx} (1 - \delta \epsilon, \kappa \epsilon) &= - N_3 \psi_y (1 - \delta \epsilon, \kappa \epsilon) . \end{aligned} ``` The first four equations define the four test points, the fifth and sixth equations define the up-down symmetry, the seventh equations enforces the high point to be a maximum, the eighth and ninth eqaution set the $x$- and $y$-components of the magnetic field at the X-point to zero, and the last three equations define the curvature at the first three test points. The coefficients $N_j$ can be found from the analytic model cross section as ```math \begin{aligned} N_1 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = 0} = - \frac{(1 + \delta_0)^2}{\epsilon \kappa^2} , \\ N_2 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi} = \hphantom{-} \frac{(1 - \delta_0)^2}{\epsilon \kappa^2} , \\ N_3 &= \left[ \frac{d^2 x}{dy^2} \right]_{\tau = \pi/2} = - \frac{\kappa}{\epsilon \, \cos^2 \delta_0} . \end{aligned} ``` For a given value of the constant $a$ above conditions reduce to a set of seven linear inhomogeneous algebraic equations for the unknown $c_i$, which can easily be solved. Parameters: * `R₀`: position of magnetic axis * `B₀`: B-field at magnetic axis * `ϵ`: inverse aspect ratio * `κ`: elongation * `δ`: triangularity * `α`: free constant, determined to match a given beta value * `xsep`: x position of the X point * `ysep`: y position of the X point """ struct SolovevXpointEquilibrium{T <: Number} <: AbstractSolovevEquilibrium name::String R₀::T B₀::T ϵ::T κ::T δ::T α::T xsep::T ysep::T c::Vector{T} function SolovevXpointEquilibrium{T}(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, xsep::T, ysep::T, c::Vector{T}) where T <: Number new("Solovev Equilibrium with X-point", R₀, B₀, ϵ, κ, δ, α, xsep, ysep, c) end end function SolovevXpointEquilibrium(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, xsep::T, ysep::T) where T <: Number n = 12 A = zeros(n,n+1) x₁, x₂, x₃ = symbols("x₁, x₂, x₃") x = [x₁, x₂, x₃] ψ = [ψ₁(x), ψ₂(x), ψ₃(x), ψ₄(x), ψ₅(x), ψ₆(x), ψ₇(x), ψ₈(x), ψ₉(x), ψ₁₀(x), ψ₁₁(x), ψ₁₂(x), -ψ₀(x,α)] for i in axes(A,2) A[ 1,i] = N(subs(ψ[i], x[1]=>1+ϵ, x[2]=>0)) A[ 2,i] = N(subs(ψ[i], x[1]=>1-ϵ, x[2]=>0)) A[ 3,i] = N(subs(ψ[i], x[1]=>1-δ*ϵ, x[2]=>κ*ϵ)) A[ 4,i] = N(subs(ψ[i], x[1]=>xsep, x[2]=>ysep)) A[ 5,i] = N(subs(diff(ψ[i], x[2]), x[1]=>1+ϵ, x[2]=>0)) A[ 6,i] = N(subs(diff(ψ[i], x[2]), x[1]=>1-ϵ, x[2]=>0)) A[ 7,i] = N(subs(diff(ψ[i], x[1]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ)) A[ 8,i] = N(subs(diff(ψ[i], x[1]), x[1]=>xsep, x[2]=>ysep)) A[ 9,i] = N(subs(diff(ψ[i], x[2]), x[1]=>xsep, x[2]=>ysep)) A[10,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1+ϵ, x[2]=>0) - (1 + asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1+ϵ, x[2]=>0)) A[11,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1-ϵ, x[2]=>0) + (1 - asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1-ϵ, x[2]=>0)) A[12,i] = N(subs(diff(ψ[i], x[1], 2), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ) - κ / (ϵ * (1 - δ^2)) * subs(diff(ψ[i], x[2]), x[1]=>1-δ*ϵ, x[2]=>κ*ϵ)) end c = A[1:n,1:n] \ A[1:n,n+1] SolovevXpointEquilibrium{T}(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, c) end function SolovevDoubleXpointEquilibrium(R₀::T, B₀::T, ϵ::T, κ::T, δ::T, α::T, xsep::T, ysep::T) where T <: Number n = 7 A = zeros(n,n+1) x₁, x₂, x₃ = symbols("x₁, x₂, x₃") x = [x₁, x₂, x₃] ψ = [ψ₁(x), ψ₂(x), ψ₃(x), ψ₄(x), ψ₅(x), ψ₆(x), ψ₇(x), -ψ₀(x,α)] for i in axes(A,2) A[1,i] = N(subs(ψ[i], x[1]=>1+ϵ, x[2]=>0)) A[2,i] = N(subs(ψ[i], x[1]=>1-ϵ, x[2]=>0)) A[3,i] = N(subs(ψ[i], x[1]=>xsep, x[2]=>ysep)) A[4,i] = N(subs(diff(ψ[i], x[1]), x[1]=>xsep, x[2]=>ysep)) A[5,i] = N(subs(diff(ψ[i], x[2]), x[1]=>xsep, x[2]=>ysep)) A[6,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1+ϵ, x[2]=>0) - (1 + asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1+ϵ, x[2]=>0)) A[7,i] = N(subs(diff(ψ[i], x[2], 2), x[1]=>1-ϵ, x[2]=>0) + (1 - asin(δ))^2 / (ϵ * κ^2) * subs(diff(ψ[i], x[1]), x[1]=>1-ϵ, x[2]=>0)) end c = vcat(A[1:n,1:n] \ A[1:n,n+1], zeros(5)) SolovevXpointEquilibrium{T}(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, c) end function init(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, doublex=false) if doublex return SolovevDoubleXpointEquilibrium(R₀, B₀, ϵ, κ, δ, α, xsep, ysep) else return SolovevXpointEquilibrium(R₀, B₀, ϵ, κ, δ, α, xsep, ysep) end end macro code_xpoint(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, doublex=false) code(SolovevXpointEquilibrium(R₀, B₀, ϵ, κ, δ, α, xsep, ysep, doublex); escape=true) end SolovevXpointEquilibriumITER() = SolovevXpointEquilibrium(6.2, 5.3, 0.32, 1.7, 0.33, -0.155, 0.88, -0.60) SolovevXpointEquilibriumNSTX() = SolovevXpointEquilibrium(0.85, 0.3, 0.78, 2.0, 0.35, -0.05, 0.70, -1.71) SolovevDoubleXpointEquilibriumNSTX() = SolovevDoubleXpointEquilibrium(0.85, 0.3, 0.78, 2.0, 0.35, 0.0, 0.70, -1.71) function Base.show(io::IO, equ::SolovevXpointEquilibrium) print(io, "Solovev Xpoint Equilibrium with\n") print(io, " R₀ = ", equ.R₀, "\n") print(io, " B₀ = ", equ.B₀, "\n") print(io, " ϵ = ", equ.ϵ, "\n") print(io, " κ = ", equ.κ, "\n") print(io, " δ = ", equ.δ, "\n") print(io, " α = ", equ.α, "\n") print(io, " xsep = ", equ.xsep, "\n") print(io, " ysep = ", equ.ysep) end function ElectromagneticFields.A₃(x::AbstractArray{T,1}, equ::SolovevXpointEquilibrium) where {T <: Number} ( ψ₀(x, equ.α) + equ.c[1] * ψ₁(x) + equ.c[2] * ψ₂(x) + equ.c[3] * ψ₃(x) + equ.c[4] * ψ₄(x) + equ.c[5] * ψ₅(x) + equ.c[6] * ψ₆(x) + equ.c[7] * ψ₇(x) + equ.c[8] * ψ₈(x) + equ.c[9] * ψ₉(x) + equ.c[10] * ψ₁₀(x) + equ.c[11] * ψ₁₁(x) + equ.c[12] * ψ₁₂(x) ) end function ITER(; xpoint=false) if xpoint return SolovevXpointEquilibriumITER() else return SolovevEquilibriumITER() end end function NSTX(; xpoint=false) if xpoint return SolovevXpointEquilibriumNSTX() else return SolovevEquilibriumNSTX() end end function NSTXdoubleX() SolovevDoubleXpointEquilibriumNSTX() end function FRC() SolovevEquilibriumFRC() end macro code_iter(xpoint=false) code(ITER(xpoint=xpoint); escape=true) end macro code_iter_xpoint() code(ITER(xpoint=true); escape=true) end macro code_nstx(xpoint=false) code(NSTX(xpoint=xpoint); escape=true) end macro code_nstx_xpoint(doublex=false) if doublex equilibrium = NSTXdoubleX() else equilibrium = NSTX(xpoint=true) end code(equilibrium; escape=true) end macro code_nstx_double_xpoint() code(NSTXdoubleX(); escape=true) end macro code_frc() code(FRC(); escape=true) end @recipe function f(equ::SolovevEquilibrium; nx = 100, ny = 120, nτ = 200, levels = 50, size = (300,400), aspect_ratio = :equal, xlims = ( 0.50, 1.50), ylims = (-0.75, +0.75)) xgrid = LinRange(xlims[1], xlims[2], nx) zgrid = LinRange(ylims[1], ylims[2], ny) pot = [ElectromagneticFields.A₃([xgrid[i], zgrid[j], 0.0], equ) / xgrid[i] for i in eachindex(xgrid), j in eachindex(zgrid)] τ = LinRange(0, 2π, nτ) boundary_X = 1 .+ equ.ϵ .* cos.(τ .+ asin(equ.δ) .* sin.(τ) ) boundary_Y = equ.ϵ .* equ.κ .* sin.(τ) aspect_ratio := aspect_ratio size := size xlims := xlims ylims := ylims levels := levels legend := :none @series begin seriestype := :contour (xgrid, zgrid, pot') end @series begin seriestype := :path seriescolor := :red linewidth := 3 (boundary_X, boundary_Y) end end @recipe function f(equ::SolovevXpointEquilibrium; nx = 100, ny = 120, levels = 50, size = (300,400), xlims = ( 0.50, 1.50), ylims = (-0.75, +0.75)) xgrid = LinRange(xlims[1], xlims[2], nx) zgrid = LinRange(ylims[1], ylims[2], ny) pot = [ElectromagneticFields.A₃([xgrid[i], zgrid[j], 0.0], equ) / xgrid[i] for i in eachindex(xgrid), j in eachindex(zgrid)] seriestype := :contour aspect_ratio := :equal size := size xlims := xlims ylims := ylims levels := levels legend := :none (xgrid, zgrid, pot') end end module SolovevFRC import ..Solovev: @code_frc, FRC export @code, init var"@code" = var"@code_frc" init = FRC end module SolovevITER import ..Solovev: @code_iter, ITER export @code, init var"@code" = var"@code_iter" init = ITER end module SolovevITERwXpoint import ..Solovev: @code_iter_xpoint, ITER export @code, init var"@code" = var"@code_iter_xpoint" init() = ITER(xpoint=true) end module SolovevNSTX import ..Solovev: @code_nstx, NSTX export @code, init var"@code" = var"@code_nstx" init = NSTX end module SolovevNSTXwXpoint import ..Solovev: @code_nstx_xpoint, NSTX export @code, init var"@code" = var"@code_nstx_xpoint" init() = NSTX(xpoint=true) end module SolovevNSTXwDoubleXpoint import ..Solovev: @code_nstx_double_xpoint, NSTXdoubleX export @code, init var"@code" = var"@code_nstx_double_xpoint" init = NSTXdoubleX end
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<reponame>emenems/HypoTest<filename>test/runtests.jl # Run the test from HypoTest folder using HypoTest using Test import Distributions import Random # List of test files: tests = ["momentestim_test.jl", "histdata_test.jl", "disttest_test.jl", "othertest_test.jl"] # Run all tests in the list for i in tests include(i) end
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module NaiveBayes # package code goes here include("naive_bayes.jl") end # module
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# Client and server for communicating with the RAFT cluster using Sockets const RAFT_API_VERSION = 1 const RAFT_PROTO_VERSION = 1 const RAFT_JULIA_CLIENT = 0x1 const RAFT_PROTO_CLIENT = 0x2 const COMMS_PORT = 2000 function process_preamble(h::UInt64) size = UInt32(h & 0xffffffff) version = UInt16((h >> 32) & 0xffff) flags = UInt16((h >> 48) & 0xffff) @debug ("size=$size version=$version flags=$flags", " ", h) (size, version, flags) end function check_version(version, flags, lookup_table) if (flags & RAFT_JULIA_CLIENT) == RAFT_JULIA_CLIENT (version != RAFT_API_VERSION) && throw(RavanaException("Unsupported API version $(version)")) elseif (flags & RAFT_PROTO_CLIENT) == RAFT_PROTO_CLIENT (version != RAFT_PROTO_VERSION) && throw(RavanaException("Unsupported proto version $(version)")) else throw(RavanaException("Bad protocol header. Unknown flag $(flags)")) end end # Disassemble protocol header function get_opt(sock, lookup_table) (size::UInt32, version::UInt16, flags::UInt16) = process_preamble(read(sock, UInt64)) check_version(version, flags, lookup_table) (op, argv) = array_to_type(read(sock, size)) !haskey(lookup_table, op) && throw(RavanaException("Invalid op $(op)")) func = lookup_table[op] @debug ("op: ", op, " argv: ", argv) return (op, func, argv) end """ """ function ravana_server(;address=IPv4(0), port=COMMS_PORT) @async begin server = 0 # Init server socket sockErr = true @debug ("In ravana_server") while (sockErr) try server = listen(address, port) sockErr = false @info ("Starting cluster communication server at $(address):$(port)") catch e @info ("Could not listen on port $(port). Trying $(port + 1)") port += 1 # Try next port end end while true sock = accept(server) if isopen(sock) != true throw(RaftException("Error! Socket not open")) end # Disassemble op and arguments @async begin try (op, func, argv) = get_opt(sock, op_table) # Execute on cluster ret = raft_cluster_execute(op, func, argv) # Return result to client b = byte_array(ret) write(sock, length(b), b) catch e @error ("ravana_server(): Exception! ", e) b = byte_array(e) write(sock, length(b), b) end close(sock) end end end end """ ravana_client(address, port, op::Int32, argv...) Low level function that can be called from a Julia prompt/program. ```jldoxctest julia> Ravana.ravana_client(IPv4(0), 2000, Ravana.OP_GET_NODE_PARAMS) ``` """ function ravana_client(address, port, op::Int32, argv...) bytes = byte_array((op, argv)) size = UInt32(length(bytes)) version = UInt16(RAFT_API_VERSION) flags = UInt16(RAFT_JULIA_CLIENT) client = connect(address, port) ret = write(client, size, version, flags, bytes) ret_size = read(client, Int) ret = array_to_type(read(client, ret_size)) close(client) ret end # If leader execute op, otherwise redirect to leader function raft_cluster_execute(op, func, argv) @debug ("In raft_cluster_execute") if op == OP_INIT_CLUSTER ret = raft_execute(op, func, argv) # Bootstrap bypasses append entries elseif current_state == LEADER @debug("Leader before executing raft_run_command") ret = raft_run_command(op, func, argv) # Commits as per RAFT protocol else ret = ravana_client(leaderAddress, leaderPort, op, argv) # Redirect to leader end ret end function init_cluster(;address=IPv4(0), port=2000) ravana_client(address, port, OP_INIT_CLUSTER, nothing) end function get_cluster_config(node::raftNode) ravana_client(node.name, node.port, OP_GET_CLUSTER_CONF, nothing) end get_cluster_config() = get_cluster_config(raftNode(leaderAddress, COMMS_PORT, Int128(0), Int128(0)))
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function build() println("Build this thing!") end
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const CerebellarVemusLobuleI = AnatomicalStructure("cerebellar vermus lobule I") const CerebellarVemusLobuleII = AnatomicalStructure("cerebellar vermus lobule II") const CerebellarVemusLobuleIII = AnatomicalStructure("cerebellar vermus lobule III") const CerebellarVemusLobuleIV = AnatomicalStructure("cerebellar vermus lobule IV") const CerebellarVemusLobuleV = AnatomicalStructure("cerebellar vermus lobule V") const CerebellarVemusLobuleVI = AnatomicalStructure("cerebellar vermus lobule VI") const CerebellarVemusLobuleVII = AnatomicalStructure("cerebellar vermus lobule VII") const CerebellarVemusLobuleVIII = AnatomicalStructure("cerebellar vermus lobule VIII") const CerebellarVemusLobuleIX = AnatomicalStructure("cerebellar vermus lobule IX") const CerebellarVemusLobuleX = AnatomicalStructure("cerebellar vermus lobule X") const FlocculonodularLobe = AnatomicalStructure("flocculonodular lobe") # TODO sub structures need more about cerebellar lob components # children = Flocculonodular lobe const Vestibulocerebellum = BilateralStructure("vestibulocerebellum") const Archicerebellum = Vestibulocerebellum # TODO sub structures for Cerebrocerebellum const Cerebrocerebellum = BilateralStructure("cerebrocerebellum" ) """ Vermis * Catani and <NAME>, 2008 * Dell'Acqua et al., 2013 * Meola et al., 2016a; Meola et al., 2016c; Wakana et al., 2004 """ const Vermis = AnatomicalStructure("vermis") const CerebellumCortex = BilateralStructure("cerebellum cortex") #const CerebellumWhiteMatter = AnatomicalStructure("cerebellum white matter") #= TODO this needs a different separation than white matter vs cortex const Cerebellum = AnatomicalStructure( name = "Cerebellum", children = ( CerebellumCortex, CerebellumWhiteMatter ) ) =# # TODO double check paravermis const Paravermis = BilateralStructure("paravermis") # children Vermis and paravermis const Spinocerebellum = BilateralStructure("spinocerebellum") const Paleocerebellum = Spinocerebellum
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<filename>src/Reconstruction.jl export reconstruction export writePartToImage!, initImage """ This is the most high level reconstruction method using the `MDFDatasetStore` """ function reconstruction(d::MDFDatasetStore, study::Study, exp::Experiment, recoParams) !(haskey(recoParams,:SFPath)) && (recoParams[:SFPath] = sfPath( MPIFile( recoParams[:measPath] ) )) numReco = findReco(d,study,exp,recoParams) haskey(recoParams,:emptyMeasPath) && recoParams[:emptyMeasPath]!=nothing && (recoParams[:emptyMeas] = MPIFile( recoParams[:emptyMeasPath] ) ) #numReco = findReco(d,study,exp,recoParams) if numReco > 0 @info "Reconstruction found in MDF dataset store." reco = getReco(d,study,exp, numReco) c = loadRecoData(reco.path) else c = reconstruction(recoParams) addReco(d,study,exp, c) end return c end # The previous function is somewhat redundant in its arguments. In particular # the measPath and the study/exp are redundant. Should be cleaned up before # it can be used as a user facing API # #function reconstruction(d::MDFDatasetStore, recoParams::Dict) # # study = ??? # # studies = getStudies( activeDatasetStore(m) ) # # for study in studies # push!(m.studyStore, (study.date, study.name, study.subject, study.path, true)) # end # # m.currentStudy = Study(TreeModel(m.studyStoreSorted)[currentIt,4], # TreeModel(m.studyStoreSorted)[currentIt,2], # TreeModel(m.studyStoreSorted)[currentIt,3], # TreeModel(m.studyStoreSorted)[currentIt,1]) # # exp = getExperiment(study, recoParams[:measPath]) # return reconstruction(d, study, exp, recoParams) #end """ This is the most high level reconstruction method that performs in-memory reconstruction """ function reconstruction(recoParams::Dict) @info "Performing in-memory reconstruction." bMeas = MPIFile( recoParams[:measPath] ) !(haskey(recoParams,:SFPath)) && (recoParams[:SFPath] = sfPath( bMeas )) bSF = MPIFile(recoParams[:SFPath]) c = reconstruction(bSF, bMeas; recoParams...) # store reco params with image c.recoParams = recoParams return c end function reconstruction(bMeas::MPIFile; kargs...) bSF = MPIFile(sfPath(bMeas) ) reconstruction(bSF, bMeas; kargs...) end function reconstruction(filenameMeas::AbstractString; kargs...) bMeas = MPIFile(filenameMeas) reconstruction(bMeas; kargs...) end function reconstruction(filenameSF::AbstractString, filenameMeas::AbstractString; kargs...) bSF = MPIFile(filenameSF) bMeas = MPIFile(filenameMeas) reconstruction(bSF,bMeas; kargs...) end function reconstruction(filenameSF::AbstractString, filenameMeas::AbstractString, freq::Array; kargs...) bSF = MPIFile(filenameSF) bMeas = MPIFile(filenameMeas) reconstruction(bSF,bMeas,freq; kargs...) end function reconstruction(bSF::Union{T,Vector{T}}, bMeas::MPIFile; kargs...) where {T<:MPIFile} if haskey(kargs, :periodicMotionCorrection) && kargs[:periodicMotionCorrection] return reconstructionPeriodicMotion(bSF, bMeas; kargs...) elseif acqNumPeriodsPerFrame(bMeas) > 1 && (acqNumPeriodsPerFrame(bSF) == 1 || typeof(bSF) == MultiMPIFile) # This branch is only used of the measurements are multi-patch and if the # system matrix is not fully sampled. This is the case if either we have a # single system matrix that is reused for-multiple patches, or if we have # a MultiMPIFile return reconstructionMultiPatch(bSF, bMeas; kargs...) else return reconstructionSinglePatch(bSF, bMeas; kargs...) end end function reconstructionSinglePatch(bSF::Union{T,Vector{T}}, bMeas::MPIFile; minFreq=0, maxFreq=1.25e6, SNRThresh=-1,maxMixingOrder=-1, numUsedFreqs=-1, sortBySNR=false, recChannels=1:numReceivers(bMeas), bEmpty = nothing, emptyMeas=bEmpty, bgFrames = 1, fgFrames = 1, varMeanThresh = 0, minAmplification=2, numPeriodAverages=1, numPeriodGrouping=1, kargs...) where {T<:MPIFile} freq = filterFrequencies(bSF,minFreq=minFreq, maxFreq=maxFreq,recChannels=recChannels, SNRThresh=SNRThresh, numUsedFreqs=numUsedFreqs, sortBySNR=sortBySNR, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping) if varMeanThresh > 0 bEmptyTmp = (emptyMeas == nothing) ? bMeas : emptyMeas freqVarMean = filterFrequenciesVarMean(bMeas, bEmptyTmp, fgFrames, bgFrames; thresh=varMeanThresh, minAmplification=minAmplification, minFreq=minFreq, maxFreq=maxFreq,recChannels=recChannels) freq = intersect(freq, freqVarMean) end # Ensure that no frequencies are used that are not present in the measurement freq = intersect(freq, filterFrequencies(bMeas, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping)) @debug "selecting $(length(freq)) frequencies" return reconstruction(bSF, bMeas, freq; emptyMeas=emptyMeas, bgFrames=bgFrames, fgFrames=fgFrames, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping, kargs...) end function reconstruction(bSF::Union{T,Vector{T}}, bMeas::MPIFile, freq::Array; bEmpty = nothing, emptyMeas = bEmpty, bgFrames = 1, denoiseWeight = 0, redFactor = 0.0, thresh = 0.0, loadasreal = false, solver = "kaczmarz", sparseTrafo = nothing, saveTrafo=false, gridsize = gridSizeCommon(bSF), fov=calibFov(bSF), center=[0.0,0.0,0.0], useDFFoV=false, deadPixels=Int[], bgCorrectionInternal=false, bgDictSize=nothing, bgFramesDict=nothing, numPeriodAverages=1, numPeriodGrouping=1, kargs...) where {T<:MPIFile} (typeof(bgFrames) <: AbstractRange && emptyMeas==nothing) && (emptyMeas = bMeas) bgCorrection = emptyMeas != nothing ? true : bgCorrectionInternal consistenceCheck(bSF, bMeas) @debug "Loading System matrix" S, grid = getSF(bSF, freq, sparseTrafo, solver; bgCorrection=bgCorrection, loadasreal=loadasreal, thresh=thresh, redFactor=redFactor, saveTrafo=saveTrafo, useDFFoV=useDFFoV, gridsize=gridsize, fov=fov, center=center, deadPixels=deadPixels,numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping) if denoiseWeight > 0 && sparseTrafo == nothing denoiseSF!(S, shape, weight=denoiseWeight) end bgDict = getBackgroundDictionary(bSF, bMeas, freq, bgDictSize, bgFramesDict) return reconstruction(S, bSF, bMeas, freq, grid, emptyMeas=emptyMeas, bgFrames=bgFrames, sparseTrafo=sparseTrafo, loadasreal=loadasreal, bgDict = bgDict, solver=solver, bgCorrectionInternal=bgCorrectionInternal, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping; kargs...) end function reconstruction(S, bSF::Union{T,Vector{T}}, bMeas::MPIFile, freq::Array, grid; frames = nothing, bEmpty = nothing, emptyMeas= bEmpty, bgFrames = 1, nAverages = 1, numAverages=nAverages, bgDict = nothing, bgFramesPost = nothing, sparseTrafo = nothing, loadasreal = false, maxload = 100, maskDFFOV=false, weightType=WeightingType.None, weightingLimit = 0, solver = "kaczmarz", spectralCleaning=true, spectralLeakageCorrection=spectralCleaning, fgFrames=1:10, bgCorrectionInternal=false, noiseFreqThresh=0.0, channelWeights=ones(3), numPeriodAverages=1, numPeriodGrouping=1, kargs...) where {T<:MPIFile} #(typeof(bgFrames) <: AbstractRange && bEmpty==nothing) && (bEmpty = bMeas) bgCorrection = emptyMeas != nothing ? true : false @debug "Loading emptymeas ..." if emptyMeas!=nothing #if acqNumBGFrames(emptyMeas) > 0 # uEmpty = getMeasurementsFD(emptyMeas, false, frequencies=freq, frames=bgFrames, #frames=measBGFrameIdx(bEmpty), # numAverages = =length(bgFrames), bgCorrection=bgCorrectionInternal, # loadasreal=loadasreal, spectralLeakageCorrection=spectralLeakageCorrection) #else uEmpty = getMeasurementsFD(emptyMeas, frequencies=freq, frames=bgFrames, numAverages=length(bgFrames), loadasreal = loadasreal,spectralLeakageCorrection=spectralLeakageCorrection, bgCorrection=bgCorrectionInternal, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping) if bgFramesPost != nothing uEmptyPost = getMeasurementsFD(emptyMeas, false, frequencies=freq, frames=bgFramesPost, numAverages = length(bgFramesPost), bgCorrection=bgCorrectionInternal, loadasreal = loadasreal, spectralLeakageCorrection=spectralLeakageCorrection, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping) end #end end frames == nothing && (frames = 1:acqNumFrames(bMeas)) weights = getWeights(weightType, freq, S, weightingLimit=weightingLimit, emptyMeas = emptyMeas, bMeas = bMeas, bgFrames=bgFrames, bSF=bSF, channelWeights = channelWeights) L = -fld(-length(frames),numAverages) # number of tomograms to be reconstructed p = Progress(L, 1, "Reconstructing data...") # initialize sparseTrafo B = linearOperator(sparseTrafo, shape(grid), eltype(S)) @debug "S: $(eltype(S))" @debug "B: $(eltype(B))" #initialize output image = initImage(bSF,bMeas,L,numAverages,grid,false) currentIndex = 1 iterator = numAverages == 1 ? Iterators.partition(frames,maxload) : Iterators.partition(frames,numAverages*maxload) for partframes in iterator @debug "Loading measurements ..." u = getMeasurementsFD(bMeas, frequencies=freq, frames=partframes, numAverages=numAverages, loadasreal=loadasreal, spectralLeakageCorrection=spectralLeakageCorrection, bgCorrection=bgCorrectionInternal, numPeriodAverages=numPeriodAverages, numPeriodGrouping=numPeriodGrouping) if emptyMeas!=nothing if bgFramesPost == nothing u = u .- uEmpty else for l=1:length(partframes) alpha = (partframes[l] - mean(bgFrames)) / (mean(bgFramesPost) - mean(bgFrames)) u[:,:,l] .-= (1-alpha).*uEmpty[:,:,1] .+ alpha.*uEmptyPost[:,:,1] end end end noiseFreqThresh > 0 && setNoiseFreqToZero(u, freq, noiseFreqThresh, bEmpty = emptyMeas, bMeas = bMeas, bgFrames=bgFrames) # convert measurement data if neccessary if eltype(S)!=eltype(u) @warn "System matrix and measurement have different element data type. Mapping measurment data to system matrix element type." u = map(eltype(S),u) end @debug "Reconstruction ..." c = reconstruction(S, u, bgDict; sparseTrafo=B, progress=p, weights=weights, solver=solver, shape=shape(grid), kargs...) currentIndex = writePartToImage!(image, c, currentIndex, partframes, numAverages) end return image end function writePartToImage!(image, c, currentIndex::Int, partframes, numAverages) # permute c's dimensions into image order colorsize = size(image,1) spatialsize = size(image,2)*size(image,3)*size(image,4) inc = -fld(-length(partframes),numAverages) c = reshape(c,spatialsize,colorsize,inc) c = permutedims(c,[2,1,3]) # write c to image image[Axis{:time}(currentIndex:currentIndex+inc-1)] = c[:] currentIndex += inc return currentIndex end function initImage(bSFs::Union{T,Vector{T}}, bMeas::S, L::Int, numAverages::Int, grid::RegularGridPositions, loadOnlineParams=false) where {T,S<:MPIFile} # the number of channels is determined by the number of system matrices if isa(bSFs,AbstractVector) || isa(bSFs,MultiContrastFile) numcolors = length(bSFs) bSF = bSFs[1] else numcolors = 1 bSF = bSFs end # calculate axis shp = shape(grid) pixspacing = (spacing(grid) ./ acqGradient(bMeas)[1] .* acqGradient(bSF)[1])*1000u"mm" offset = (ffPos(bMeas) .- 0.5 .* calibFov(bSF))*1000u"mm" .+ 0.5 .* pixspacing dtframes = acqNumAverages(bMeas)*dfCycle(bMeas)*numAverages*1u"s" # initialize raw array array = Array{Float32}(undef, numcolors,shp...,L) # create image im = makeAxisArray(array, pixspacing, offset, dtframes) # provide meta data if loadOnlineParams imMeta = ImageMeta(im,generateHeaderDictOnline(bSF,bMeas)) else imMeta = ImageMeta(im,generateHeaderDict(bSF,bMeas)) end return imMeta end """ Low level reconstruction method """ function reconstruction(S, u::Array, bgDict::Nothing=nothing; sparseTrafo = nothing, lambd=0.0, lambda=lambd, λ=lambda, progress=nothing, solver = "kaczmarz", weights=nothing, enforceReal=true, enforcePositive=true, relativeLambda=true, kargs...) N = size(S,2) #prod(shape) M = div(length(S), N) L = size(u)[end] u = reshape(u, M, L) c = zeros(N,L) #c = zeros(real(eltype(u)),N,L) Change by J.Dora if sum(abs.(λ)) > 0 && solver != "fusedlasso" && relativeLambda trace = calculateTraceOfNormalMatrix(S,weights) λ *= trace / N setlambda(S,λ) end solv = createLinearSolver(solver, S; weights=weights, λ=λ, sparseTrafo=sparseTrafo, enforceReal=enforceReal, enforcePositive=enforcePositive, kargs...) progress==nothing ? p = Progress(L, 1, "Reconstructing data...") : p = progress for l=1:L d = solve(solv, u[:,l]) if sparseTrafo != nothing d[:] = sparseTrafo*d #backtrafo from dual space end #if typeof(B)==LinearSolver.DSTOperator # d=onGridReverse(d,shape) #end c[:,l] = real( d ) # this one is allocating next!(p) sleep(0.001) end return c end # old code #if reshapesolution # c = reshape(c, shape..., L) #end #shape(grid)
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### A Pluto.jl notebook ### # v0.12.20 using Markdown using InteractiveUtils # ╔═╡ 63ba08cc-59a8-11eb-0a0f-27efac60d779 using Pkg, DrWatson # ╔═╡ 6db218c6-59a8-11eb-2a8b-7107354cf590 begin #@quickactivate "StatisticalRethinkingStan" using StanSample, StanOptimize using StatisticalRethinking end # ╔═╡ 51fc19b8-59a8-11eb-2214-15aca59b807b md" ## Figure 8.2s" # ╔═╡ 8aaa4bcc-59a8-11eb-2003-f1213b116565 begin df = CSV.read(sr_datadir("rugged.csv"), DataFrame) df_africa = df[df.cont_africa .== 1, [:rgdppc_2000, :rugged]] dropmissing!(df_africa, :rgdppc_2000) dropmissing!(df_africa, :rugged) df_africa.log_gdp = log.(df_africa[:, :rgdppc_2000]) scale!(df_africa, [:log_gdp, :rugged]) PRECIS(df_africa) end # ╔═╡ 25ee6cd8-6a54-11eb-0a72-3fb09ee63b0e begin df_non_africa = df[df.cont_africa .== 0, [:rgdppc_2000, :rugged]] dropmissing!(df_non_africa, :rgdppc_2000) dropmissing!(df_non_africa, :rugged) df_non_africa.log_gdp = log.(df_non_africa[:, :rgdppc_2000]) scale!(df_non_africa, [:log_gdp, :rugged]) end; # ╔═╡ d7d3e626-6a45-11eb-1820-a3ec98e556b1 stan8_0 = " data { int N; vector[N] G; vector[N] R; } parameters { real a; real b; real<lower=0> sigma; } transformed parameters { vector[N] mu; mu = a + b * (R - 0.125); } model { a ~ normal(1, 1); b ~ normal(0, 1); sigma ~ exponential(1); G ~ normal(mu, sigma); } "; # ╔═╡ a3e7c070-6a46-11eb-072b-3943db854020 begin data1 = (N = size(df_africa, 1), G = df_africa.log_gdp_s, R = df_africa.rugged_s) m8_1s = SampleModel("m8.1s", stan8_0) rc8_1_1s = stan_sample(m8_1s; data=data1) if success(rc8_1_1s) post8_1_1s_df = read_samples(m8_1s, :dataframe) PRECIS(post8_1_1s_df[:, [:a, :b, :sigma]]) end end # ╔═╡ cc11715a-6a54-11eb-1955-d38a021a3bb3 begin data2 = (N = size(df_non_africa, 1), G = df_non_africa.log_gdp_s, R = df_non_africa.rugged_s) rc8_1_2s = stan_sample(m8_1s; data=data2) if success(rc8_1_2s) post8_1_2s_df = read_samples(m8_1s, :dataframe) PRECIS(post8_1_2s_df[:, [:a, :b, :sigma]]) end end # ╔═╡ 1968b688-6a49-11eb-2dec-9986830b4a7e begin p1 = plotbounds( df_africa, :rugged, :log_gdp, post8_1_1s_df, [:a, :b, :sigma]; bounds=[:none, :hpdi], colors=[:orange, :lightblue], title="African nations", xlab="ruggedness", ylab="log GDP" ) df_afr = df[df.cont_africa .== 1, [:country, :rgdppc_2000, :rugged]] df_afr = df_afr[df_afr.rugged .> 4, :] for (ind, country) in enumerate(df_afr.country) annotate!([([df_afr.rugged[ind]+0.3], [log(df_afr.rgdppc_2000[ind])+0.15], Plots.text(df_afr.country[ind], 6, :red, :right))]) end end # ╔═╡ 0f36fb46-6a5d-11eb-2d34-91266e770493 begin p2 = plotbounds( df_non_africa, :rugged, :log_gdp, post8_1_2s_df, [:a, :b, :sigma]; bounds=[:none, :hpdi], colors=[:orange, :lightblue], title="Non-African nations", xlab="ruggedness", ylab="log GDP" ) df_na = df[:, [:country, :rgdppc_2000, :rugged]] dropmissing!(df_na, :rgdppc_2000) dropmissing!(df_na, :rugged) df_na = df_na[df_na.rugged .> 4, :] for (ind, country) in enumerate(df_na.country) println(country) if !(country in df_afr.country) annotate!([([df_na.rugged[ind]+0.3], [log(df_na.rgdppc_2000[ind])+0.15], Plots.text(df_na.country[ind], 6, :red, :right))]) end end plot(p1, p2, layout=(1,2)) end # ╔═╡ 45767e2e-6a63-11eb-3e12-354a7e32a374 md" ## End of figure 8.2s" # ╔═╡ Cell order: # ╟─51fc19b8-59a8-11eb-2214-15aca59b807b # ╠═63ba08cc-59a8-11eb-0a0f-27efac60d779 # ╠═6db218c6-59a8-11eb-2a8b-7107354cf590 # ╠═8aaa4bcc-59a8-11eb-2003-f1213b116565 # ╠═25ee6cd8-6a54-11eb-0a72-3fb09ee63b0e # ╠═d7d3e626-6a45-11eb-1820-a3ec98e556b1 # ╠═a3e7c070-6a46-11eb-072b-3943db854020 # ╠═cc11715a-6a54-11eb-1955-d38a021a3bb3 # ╠═1968b688-6a49-11eb-2dec-9986830b4a7e # ╠═0f36fb46-6a5d-11eb-2d34-91266e770493 # ╟─45767e2e-6a63-11eb-3e12-354a7e32a374
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# This file is a part of Julia. License is MIT: https://julialang.org/license # OffsetArrays (arrays with indexing that doesn't start at 1) # This test file is designed to exercise support for generic indexing, # even though offset arrays aren't implemented in Base. # OffsetArrays v1.3.0 # No compat patch and docstrings module OffsetArrays using Base: tail, @propagate_inbounds using Base: IdentityUnitRange export OffsetArray, OffsetMatrix, OffsetVector struct IdOffsetRange{T<:Integer,I<:AbstractUnitRange{T}} <: AbstractUnitRange{T} parent::I offset::T IdOffsetRange{T,I}(r::I, offset::T) where {T<:Integer,I<:AbstractUnitRange{T}} = new{T,I}(r, offset) end # Construction/coercion from arbitrary AbstractUnitRanges function IdOffsetRange{T,I}(r::AbstractUnitRange, offset::Integer = 0) where {T<:Integer,I<:AbstractUnitRange{T}} rc, o = offset_coerce(I, r) return IdOffsetRange{T,I}(rc, convert(T, o+offset)) end function IdOffsetRange{T}(r::AbstractUnitRange, offset::Integer = 0) where T<:Integer rc = convert(AbstractUnitRange{T}, r)::AbstractUnitRange{T} return IdOffsetRange{T,typeof(rc)}(rc, convert(T, offset)) end IdOffsetRange(r::AbstractUnitRange{T}, offset::Integer = 0) where T<:Integer = IdOffsetRange{T,typeof(r)}(r, convert(T, offset)) # Coercion from other IdOffsetRanges IdOffsetRange{T,I}(r::IdOffsetRange{T,I}) where {T<:Integer,I<:AbstractUnitRange{T}} = r function IdOffsetRange{T,I}(r::IdOffsetRange) where {T<:Integer,I<:AbstractUnitRange{T}} rc, offset = offset_coerce(I, r.parent) return IdOffsetRange{T,I}(rc, r.offset+offset) end function IdOffsetRange{T}(r::IdOffsetRange) where T<:Integer return IdOffsetRange(convert(AbstractUnitRange{T}, r.parent), r.offset) end IdOffsetRange(r::IdOffsetRange) = r AbstractUnitRange{T}(r::IdOffsetRange{T}) where {T} = r AbstractUnitRange{T}(r::IdOffsetRange) where {T} = IdOffsetRange{T}(r) # TODO: uncomment these when Julia is ready # # Conversion preserves both the values and the indexes, throwing an InexactError if this # # is not possible. # Base.convert(::Type{IdOffsetRange{T,I}}, r::IdOffsetRange{T,I}) where {T<:Integer,I<:AbstractUnitRange{T}} = r # Base.convert(::Type{IdOffsetRange{T,I}}, r::IdOffsetRange) where {T<:Integer,I<:AbstractUnitRange{T}} = # IdOffsetRange{T,I}(convert(I, r.parent), r.offset) # Base.convert(::Type{IdOffsetRange{T,I}}, r::AbstractUnitRange) where {T<:Integer,I<:AbstractUnitRange{T}} = # IdOffsetRange{T,I}(convert(I, r), 0) offset_coerce(::Type{Base.OneTo{T}}, r::Base.OneTo) where T<:Integer = convert(Base.OneTo{T}, r), 0 function offset_coerce(::Type{Base.OneTo{T}}, r::AbstractUnitRange) where T<:Integer o = first(r) - 1 return Base.OneTo{T}(last(r) - o), o end # function offset_coerce(::Type{Base.OneTo{T}}, r::IdOffsetRange) where T<:Integer # rc, o = offset_coerce(Base.OneTo{T}, r.parent) # Fallback, specialze this method if `convert(I, r)` doesn't do what you need offset_coerce(::Type{I}, r::AbstractUnitRange) where I<:AbstractUnitRange{T} where T = convert(I, r), 0 @inline Base.parent(r::IdOffsetRange) = r.parent @inline Base.axes(r::IdOffsetRange) = (Base.axes1(r),) @inline Base.axes1(r::IdOffsetRange) = IdOffsetRange(Base.axes1(r.parent), r.offset) @inline Base.length(r::IdOffsetRange) = length(r.parent) Base.reduced_index(i::IdOffsetRange) = typeof(i)(first(i):first(i)) # Workaround for #92 on Julia < 1.4 Base.reduced_index(i::IdentityUnitRange{<:IdOffsetRange}) = typeof(i)(first(i):first(i)) for f in [:firstindex, :lastindex] @eval Base.$f(r::IdOffsetRange) = $f(r.parent) .+ r.offset end @inline function Base.iterate(r::IdOffsetRange) ret = iterate(r.parent) ret === nothing && return nothing return (ret[1] + r.offset, ret[2]) end @inline function Base.iterate(r::IdOffsetRange, i) ret = iterate(r.parent, i) ret === nothing && return nothing return (ret[1] + r.offset, ret[2]) end @inline Base.first(r::IdOffsetRange) = first(r.parent) + r.offset @inline Base.last(r::IdOffsetRange) = last(r.parent) + r.offset @propagate_inbounds Base.getindex(r::IdOffsetRange, i::Integer) = r.parent[i - r.offset] + r.offset @propagate_inbounds function Base.getindex(r::IdOffsetRange, s::AbstractUnitRange{<:Integer}) return r.parent[s .- r.offset] .+ r.offset end @propagate_inbounds function Base.getindex(r::IdOffsetRange, s::IdentityUnitRange) return IdOffsetRange(r.parent[s .- r.offset], r.offset) end @propagate_inbounds function Base.getindex(r::IdOffsetRange, s::IdOffsetRange) return IdOffsetRange(r.parent[s.parent .+ (s.offset - r.offset)] .+ (r.offset - s.offset), s.offset) end # offset-preserve broadcasting Broadcast.broadcasted(::Base.Broadcast.DefaultArrayStyle{1}, ::typeof(-), r::IdOffsetRange{T}, x::Integer) where T = IdOffsetRange{T}(r.parent .- x, r.offset) Broadcast.broadcasted(::Base.Broadcast.DefaultArrayStyle{1}, ::typeof(+), r::IdOffsetRange{T}, x::Integer) where T = IdOffsetRange{T}(r.parent .+ x, r.offset) Broadcast.broadcasted(::Base.Broadcast.DefaultArrayStyle{1}, ::typeof(+), x::Integer, r::IdOffsetRange{T}) where T = IdOffsetRange{T}(x .+ r.parent, r.offset) Base.show(io::IO, r::IdOffsetRange) = print(io, "OffsetArrays.IdOffsetRange(", first(r), ':', last(r), ")") # Optimizations @inline Base.checkindex(::Type{Bool}, inds::IdOffsetRange, i::Real) = Base.checkindex(Bool, inds.parent, i - inds.offset) struct Origin{T <: Union{Tuple,Int}} index::T end Origin(I::NTuple{N,Int}) where N = Origin{typeof(I)}(I) Origin(I::CartesianIndex) = Origin(I.I) Origin(I1::Int, In::Int...) = Origin((I1, In...)) # Origin(0) != Origin((0, )) but they work the same with broadcasting Origin(n::Int) = Origin{Int}(n) (o::Origin)(A::AbstractArray) = o.index .- first.(axes(A)) ### Low-level utilities ### _indexoffset(r::AbstractRange) = first(r) - 1 _indexoffset(i::Integer) = 0 _indexoffset(i::Colon) = 0 _indexlength(r::AbstractRange) = length(r) _indexlength(i::Integer) = i _indexlength(i::Colon) = Colon() _offset(axparent::AbstractUnitRange, ax::AbstractUnitRange) = first(ax) - first(axparent) _offset(axparent::AbstractUnitRange, ax::Integer) = 1 - first(axparent) abstract type AxisConversionStyle end struct SingleRange <: AxisConversionStyle end struct TupleOfRanges <: AxisConversionStyle end AxisConversionStyle(::Type) = SingleRange() AxisConversionStyle(::Type{<:CartesianIndices}) = TupleOfRanges() _convertTupleAbstractUnitRange(x) = _convertTupleAbstractUnitRange(AxisConversionStyle(typeof(x)), x) _convertTupleAbstractUnitRange(::SingleRange, x) = (convert(AbstractUnitRange{Int}, x),) _convertTupleAbstractUnitRange(::TupleOfRanges, x) = convert(Tuple{Vararg{AbstractUnitRange{Int}}}, x) _toAbstractUnitRanges(t::Tuple) = (_convertTupleAbstractUnitRange(first(t))..., _toAbstractUnitRanges(tail(t))...) _toAbstractUnitRanges(::Tuple{}) = () # ensure that the indices are consistent in the constructor _checkindices(A::AbstractArray, indices, label) = _checkindices(ndims(A), indices, label) function _checkindices(N::Integer, indices, label) throw_argumenterror(N, indices, label) = throw(ArgumentError(label * " $indices are not compatible with a $(N)D array")) N == length(indices) || throw_argumenterror(N, indices, label) end # Technically we know the length of CartesianIndices but we need to convert it first, so here we # don't put it in OffsetAxisKnownLength. const OffsetAxisKnownLength = Union{Integer,AbstractUnitRange} const OffsetAxis = Union{OffsetAxisKnownLength,Colon} const ArrayInitializer = Union{UndefInitializer,Missing,Nothing} ## OffsetArray struct OffsetArray{T,N,AA<:AbstractArray} <: AbstractArray{T,N} parent::AA offsets::NTuple{N,Int} function OffsetArray{T,N,AA}(parent::AA, offsets::NTuple{N,Int}) where {T,N,AA <: AbstractArray} @boundscheck overflow_check.(axes(parent), offsets) new{T,N,AA}(parent, offsets) end end const OffsetVector{T,AA <: AbstractArray} = OffsetArray{T,1,AA} const OffsetMatrix{T,AA <: AbstractArray} = OffsetArray{T,2,AA} function overflow_check(r, offset::T) where T # This gives some performance boost https://github.com/JuliaLang/julia/issues/33273 throw_upper_overflow_error() = throw(ArgumentError("Boundary overflow detected: offset $offset should be equal or less than $(typemax(T) - last(r))")) throw_lower_overflow_error() = throw(ArgumentError("Boundary overflow detected: offset $offset should be equal or greater than $(typemin(T) - first(r))")) if offset > 0 && last(r) > typemax(T) - offset throw_upper_overflow_error() elseif offset < 0 && first(r) < typemin(T) - offset throw_lower_overflow_error() end end # Tuples of integers are treated as offsets # Empty Tuples are handled here function OffsetArray(A::AbstractArray, offsets::Tuple{Vararg{Integer}}) _checkindices(A, offsets, "offsets") OffsetArray{eltype(A),ndims(A),typeof(A)}(A, offsets) end # These methods are necessary to disallow incompatible dimensions for # the OffsetVector and the OffsetMatrix constructors for (FT, ND) in ((:OffsetVector, :1), (:OffsetMatrix, :2)) @eval function $FT(A::AbstractArray{<:Any,$ND}, offsets::Tuple{Vararg{Integer}}) _checkindices(A, offsets, "offsets") OffsetArray{eltype(A),$ND,typeof(A)}(A, offsets) end FTstr = string(FT) @eval function $FT(A::AbstractArray, offsets::Tuple{Vararg{Integer}}) throw(ArgumentError($FTstr * " requires a " * string($ND) * "D array")) end end ## OffsetArray constructors for FT in (:OffsetArray, :OffsetVector, :OffsetMatrix) # Nested OffsetArrays may strip off the wrapper and collate the offsets @eval function $FT(A::OffsetArray, offsets::Tuple{Vararg{Integer}}) _checkindices(A, offsets, "offsets") $FT(parent(A), map(+, A.offsets, offsets)) end # In general, indices get converted to AbstractUnitRanges. # CartesianIndices{N} get converted to N ranges @eval function $FT(A::AbstractArray, inds::Tuple{Any,Vararg{Any}}) $FT(A, _toAbstractUnitRanges(to_indices(A, axes(A), inds))) end # convert ranges to offsets @eval function $FT(A::AbstractArray, inds::Tuple{AbstractUnitRange,Vararg{AbstractUnitRange}}) _checkindices(A, inds, "indices") # Performance gain by wrapping the error in a function: see https://github.com/JuliaLang/julia/issues/37558 throw_dimerr(lA, lI) = throw(DimensionMismatch("supplied axes do not agree with the size of the array (got size $lA for the array and $lI for the indices")) lA = size(A) lI = map(length, inds) lA == lI || throw_dimerr(lA, lI) $FT(A, map(_offset, axes(A), inds)) end @eval $FT(A::AbstractArray, inds::Vararg) = $FT(A, inds) @eval $FT(A::AbstractArray, origin::Origin) = $FT(A, origin(A)) end # array initialization function OffsetArray{T,N}(init::ArrayInitializer, inds::Tuple{Vararg{OffsetAxisKnownLength}}) where {T,N} _checkindices(N, inds, "indices") AA = Array{T,N}(init, map(_indexlength, inds)) OffsetArray{T,N,typeof(AA)}(AA, map(_indexoffset, inds)) end function OffsetArray{T,N}(init::ArrayInitializer, inds::Tuple) where {T,N} OffsetArray{T,N}(init, _toAbstractUnitRanges(inds)) end OffsetArray{T,N}(init::ArrayInitializer, inds::Vararg) where {T,N} = OffsetArray{T,N}(init, inds) OffsetArray{T}(init::ArrayInitializer, inds::NTuple{N,OffsetAxisKnownLength}) where {T,N} = OffsetArray{T,N}(init, inds) function OffsetArray{T}(init::ArrayInitializer, inds::Tuple) where {T} OffsetArray{T}(init, _toAbstractUnitRanges(inds)) end OffsetArray{T}(init::ArrayInitializer, inds::Vararg) where {T} = OffsetArray{T}(init, inds) Base.IndexStyle(::Type{OA}) where {OA <: OffsetArray} = IndexStyle(parenttype(OA)) parenttype(::Type{OffsetArray{T,N,AA}}) where {T,N,AA} = AA parenttype(A::OffsetArray) = parenttype(typeof(A)) Base.parent(A::OffsetArray) = A.parent Base.eachindex(::IndexCartesian, A::OffsetArray) = CartesianIndices(axes(A)) Base.eachindex(::IndexLinear, A::OffsetVector) = axes(A, 1) @inline Base.size(A::OffsetArray) = size(parent(A)) @inline Base.size(A::OffsetArray, d) = size(parent(A), d) @inline Base.axes(A::OffsetArray) = map(IdOffsetRange, axes(parent(A)), A.offsets) @inline Base.axes(A::OffsetArray, d) = d <= ndims(A) ? IdOffsetRange(axes(parent(A), d), A.offsets[d]) : IdOffsetRange(axes(parent(A), d)) @inline Base.axes1(A::OffsetArray{T,0}) where {T} = IdOffsetRange(axes(parent(A), 1)) # we only need to specialize this one Base.similar(A::OffsetArray, ::Type{T}, dims::Dims) where T = similar(parent(A), T, dims) function Base.similar(A::AbstractArray, ::Type{T}, inds::Tuple{OffsetAxisKnownLength,Vararg{OffsetAxisKnownLength}}) where T B = similar(A, T, map(_indexlength, inds)) return OffsetArray(B, map(_offset, axes(B), inds)) end # reshape accepts a single colon Base.reshape(A::AbstractArray, inds::OffsetAxis...) = reshape(A, inds) function Base.reshape(A::AbstractArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) AR = reshape(A, map(_indexlength, inds)) return OffsetArray(AR, map(_offset, axes(AR), inds)) end # Reshaping OffsetArrays can "pop" the original OffsetArray wrapper and return # an OffsetArray(reshape(...)) instead of an OffsetArray(reshape(OffsetArray(...))) Base.reshape(A::OffsetArray, inds::Tuple{OffsetAxis,Vararg{OffsetAxis}}) = OffsetArray(reshape(parent(A), map(_indexlength, inds)), map(_indexoffset, inds)) # And for non-offset axes, we can just return a reshape of the parent directly Base.reshape(A::OffsetArray, inds::Tuple{Union{Integer,Base.OneTo},Vararg{Union{Integer,Base.OneTo}}}) = reshape(parent(A), inds) Base.reshape(A::OffsetArray, inds::Dims) = reshape(parent(A), inds) Base.reshape(A::OffsetArray, ::Colon) = reshape(parent(A), Colon()) Base.reshape(A::OffsetVector, ::Colon) = A Base.reshape(A::OffsetVector, ::Tuple{Colon}) = A Base.reshape(A::OffsetArray, inds::Union{Int,Colon}...) = reshape(parent(A), inds) Base.reshape(A::OffsetArray, inds::Tuple{Vararg{Union{Int,Colon}}}) = reshape(parent(A), inds) function Base.similar(::Type{T}, shape::Tuple{OffsetAxis,Vararg{OffsetAxis}}) where {T <: AbstractArray} P = T(undef, map(_indexlength, shape)) OffsetArray(P, map(_offset, axes(P), shape)) end Base.fill(v, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} = fill!(similar(Array{typeof(v)}, inds), v) Base.zeros(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} = fill!(similar(Array{T}, inds), zero(T)) Base.ones(::Type{T}, inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {T, N} = fill!(similar(Array{T}, inds), one(T)) Base.trues(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} = fill!(similar(BitArray, inds), true) Base.falses(inds::NTuple{N, Union{Integer, AbstractUnitRange}}) where {N} = fill!(similar(BitArray, inds), false) ## Indexing # Note this gets the index of the parent *array*, not the index of the parent *range* # Here's how one can think about this: # Δi = i - first(r) # i′ = first(r.parent) + Δi # and one obtains the result below. parentindex(r::IdOffsetRange, i) = i - r.offset @inline function Base.getindex(A::OffsetArray{T,N}, I::Vararg{Int,N}) where {T,N} @boundscheck checkbounds(A, I...) J = map(parentindex, axes(A), I) @inbounds parent(A)[J...] end @inline function Base.getindex(A::OffsetVector, i::Int) @boundscheck checkbounds(A, i) @inbounds parent(A)[parentindex(Base.axes1(A), i)] end @propagate_inbounds Base.getindex(A::OffsetArray, i::Int) = parent(A)[i] @inline function Base.setindex!(A::OffsetArray{T,N}, val, I::Vararg{Int,N}) where {T,N} @boundscheck checkbounds(A, I...) J = @inbounds map(parentindex, axes(A), I) @inbounds parent(A)[J...] = val A end @inline function Base.setindex!(A::OffsetVector, val, i::Int) @boundscheck checkbounds(A, i) @inbounds parent(A)[parentindex(Base.axes1(A), i)] = val A end @propagate_inbounds function Base.setindex!(A::OffsetArray, val, i::Int) parent(A)[i] = val A end # For fast broadcasting: ref https://discourse.julialang.org/t/why-is-there-a-performance-hit-on-broadcasting-with-offsetarrays/32194 Base.dataids(A::OffsetArray) = Base.dataids(parent(A)) Broadcast.broadcast_unalias(dest::OffsetArray, src::OffsetArray) = parent(dest) === parent(src) ? src : Broadcast.unalias(dest, src) ### Special handling for AbstractRange const OffsetRange{T} = OffsetArray{T,1,<:AbstractRange{T}} const IIUR = IdentityUnitRange{S} where S<:AbstractUnitRange{T} where T<:Integer Base.step(a::OffsetRange) = step(parent(a)) @propagate_inbounds Base.getindex(a::OffsetRange, r::OffsetRange) = OffsetArray(a[parent(r)], r.offsets) @propagate_inbounds function Base.getindex(a::OffsetRange, r::IdOffsetRange) OffsetArray(a.parent[r.parent .+ (r.offset - a.offsets[1])], r.offset) end @propagate_inbounds Base.getindex(r::OffsetRange, s::IIUR) = OffsetArray(r[s.indices], s) @propagate_inbounds Base.getindex(a::OffsetRange, r::AbstractRange) = a.parent[r .- a.offsets[1]] @propagate_inbounds Base.getindex(a::AbstractRange, r::OffsetRange) = OffsetArray(a[parent(r)], r.offsets) @propagate_inbounds Base.getindex(r::UnitRange, s::IIUR) = OffsetArray(r[s.indices], s) @propagate_inbounds Base.getindex(r::StepRange, s::IIUR) = OffsetArray(r[s.indices], s) # this method is needed for ambiguity resolution @propagate_inbounds Base.getindex(r::StepRangeLen{T,<:Base.TwicePrecision,<:Base.TwicePrecision}, s::IIUR) where T = OffsetArray(r[s.indices], s) @propagate_inbounds Base.getindex(r::StepRangeLen{T}, s::IIUR) where {T} = OffsetArray(r[s.indices], s) @propagate_inbounds Base.getindex(r::LinRange, s::IIUR) = OffsetArray(r[s.indices], s) function Base.show(io::IO, r::OffsetRange) show(io, r.parent) o = r.offsets[1] print(io, " with indices ", o+1:o+length(r)) end Base.show(io::IO, ::MIME"text/plain", r::OffsetRange) = show(io, r) ### Some mutating functions defined only for OffsetVector ### Base.resize!(A::OffsetVector, nl::Integer) = (resize!(A.parent, nl); A) Base.push!(A::OffsetVector, x...) = (push!(A.parent, x...); A) Base.pop!(A::OffsetVector) = pop!(A.parent) Base.append!(A::OffsetVector, items) = (append!(A.parent, items); A) Base.empty!(A::OffsetVector) = (empty!(A.parent); A) # These functions keep the summary compact function Base.inds2string(inds::Tuple{Vararg{Union{IdOffsetRange,IdentityUnitRange{<:IdOffsetRange}}}}) Base.inds2string(map(UnitRange, inds)) end Base.showindices(io::IO, ind1::IdOffsetRange, inds::IdOffsetRange...) = Base.showindices(io, map(UnitRange, (ind1, inds...))...) function Base.showarg(io::IO, a::OffsetArray, toplevel) print(io, "OffsetArray(") Base.showarg(io, parent(a), false) Base.showindices(io, axes(a)...) print(io, ')') toplevel && print(io, " with eltype ", eltype(a)) end function Base.replace_in_print_matrix(A::OffsetArray{<:Any,2}, i::Integer, j::Integer, s::AbstractString) J = map(parentindex, axes(A), (i,j)) Base.replace_in_print_matrix(parent(A), J..., s) end function Base.replace_in_print_matrix(A::OffsetArray{<:Any,1}, i::Integer, j::Integer, s::AbstractString) ip = parentindex(axes(A,1), i) Base.replace_in_print_matrix(parent(A), ip, j, s) end function no_offset_view(A::AbstractArray) if Base.has_offset_axes(A) OffsetArray(A, map(r->1-first(r), axes(A))) else A end end no_offset_view(A::OffsetArray) = no_offset_view(parent(A)) end # module
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""" set_kit(proj_name, proj_path) Create a folder named `proj_name` in `proj_path`, as well as a Pluto notebook and an internal file structure inside `proj_path/proj_name`. File structure and use of the notebook are detailed [here](https://github.com/FellowsFreiesWissen/computational_notebooks). See also: [`Pluto`](https://juliapackages.com/p/pluto) # Example ```julia-repl julia> include("set_kit.jl") julia> set_kit("example_project", pwd()) ``` """ function set_kit(proj_name::String, proj_path::String) ## Check project path and whether it is empty println(string("Will create a project at and set your working directory to: ", joinpath(proj_path, proj_name))) println("Is that correct? Answer yes or no:") user_ans = readline() if user_ans == "no" println("Check help('set_kit') to set up your project's location.") else if isdir(joinpath(proj_path, proj_name)) println("There already is a project with this name in this path. Move it or change its name.") else ## Create project in path cd(proj_path) ## Create file structure ### first level mkdir(proj_name) ### main folders of a project map(mkdir, map(x -> joinpath(proj_name, x), ["results","text", "submission"])) ### main folders of results map(mkdir, map(x -> joinpath(joinpath(proj_name,"results"), x), ["data", "scripts"])) map(mkdir, map(x -> joinpath(joinpath(proj_name,"results", "data"), x), ["raw", "process", "metadata"])) ### main folders of text map(mkdir, map(x -> joinpath(joinpath(proj_name,"text"), x), ["figures","tables", "supplementary", "references"])) ### main folder touch(joinpath(proj_path, proj_name, "README.txt")) open(joinpath(proj_path, proj_name, "README.txt"), "w") do io write(io, "This folder contains the set up for a reproducible workflow as described by https://github.com/FellowsFreiesWissen/computational_notebooks.git\n") write(io, "\n") write(io, "The file structure is organized as such:\n") write(io, "\n") write(io, "projet_name\n") write(io, "|-- README.txt\n") write(io, "|-- main.Rmd\n") write(io, "|-- results\n") write(io, "| |-- README.txt\n") write(io, "| |-- data\n") write(io, "| | |-- raw\n") write(io, "| | |-- process\n") write(io, "| | |-- metadata\n") write(io, "| |-- scripts\n") write(io, "|-- text\n") write(io, "| |-- README.txt\n") write(io, "| |-- main.doc\n") write(io, "| |-- figures\n") write(io, "| |-- tables\n") write(io, "| |-- supplementary\n") write(io, "| |-- references\n") write(io, "|-- submission\n") write(io, "| |-- README.txt\n") write(io, "| |-- journal1\n") write(io, "| |-- first\n") write(io, "| |-- journal2\n") write(io, "| |-- first\n") write(io, "| |-- revisions\n") end ### results folder touch(joinpath(proj_path, proj_name, "results/README.txt")) open(joinpath(proj_path, proj_name, "results/README.txt"), "w") do io write(io, "This folder contains all files of results or their processing, organized in the following subfolders:") write(io, "\n") write(io, "`data/raw`: your raw data files. These should not be protected against any change after the first storage") write(io, "\n") write(io, "`data/process`: these are files generated by processing the raw data and these are the ones used in the analysis. It can be a copy of the raw data if that is already ready to use. Any processing of the raw data to generate the files here should be documented in the notebook.") write(io, "\n") write(io, "`data/metadata`: files containing information about the data that will be useful for future users, readers, and reviewers of your data (e.g. description of variables names, units, and values)") write(io, "`scripts`: all code used to process the data, and which, for some reason or another is not included in the notebook because they too cumbersome or not of upmost relevancy for comprehension.") end ### text folder touch(joinpath(proj_path, proj_name, "text/README.txt")) open(joinpath(proj_path, proj_name, "text/README.txt"), "w") do io write(io, "This folder contains the main text of the manuscript, folders containing the figures and tables (unformatted) to be included in it, as well as a folder with the supplementary material and one with references.") end ### submission folder touch(joinpath(proj_path, proj_name, "submission/README.txt")) open(joinpath(proj_path, proj_name, "submission/README.txt"), "w") do io write(io, "This folder contains the files specific to journal submissions, e.g. cover letters, submitted versions.") end ## Create the minimal notebook touch(joinpath(proj_path, proj_name, string(proj_name, ".jl"))) open(joinpath(proj_path, proj_name, string(proj_name, ".jl")), "w") do io write(io, "### A Pluto.jl notebook ###") write(io, "\n\n") write(io, "using Markdown") write(io, "\n") write(io, "using InteractiveUtils") write(io, "\n\n") write(io, "# ╔═╡ 307335ba-cf46-11eb-28b0-f199cf048ae6") write(io, "\n") write(io, "md\" ## Your title here\"") write(io, "\n\n") write(io, "# ╔═╡ dfdfa33f-53c1-45e2-8727-86ac01a09398") write(io, "\n") write(io, "md\"\"\"") write(io, "\n\n") write(io, "## Brief intro to Pluto notebooks and Markdown syntax") write(io, "\n\n") write(io, "Feel free to skip this tutorial if you already know how Pluto noteboos work. Also, make sure to delete it once you submit/share your notebook.") write(io, "\n\n") write(io, "This is your notebook. When filling it, you should obey Pluto's own syntax to create cells containing code or text.") write(io, "\n\n") write(io, "To write one single line of narrative text, use simple quotes and include \"md\" before the quotes (as done for the title header above). To write several lines of text, use three pairs of quotes, as in this cell.") write(io, "\n\n") write(io, "Text written with no special markings will appear without any special formatting when you generate a `.html` or `.pdf` version of this file (click the \"Export\" button above to convert it).") write(io, "\n\n") write(io, "It is possible to include code inside Markdown cells (it is not executed, though):") write(io, "\n\n") write(io, "- either as `inline code`") write(io, "\n\n") write(io, "- or as a block of code:") write(io, "\n\n") write(io, "```{julia}") write(io, "\n") write(io, "print(\"This is an example of a code block...\")") write(io, "\n") write(io, "print(\"for several lines of code\")") write(io, "\n") write(io, "```") write(io, "\n\n") write(io, "It is also possible to include hyperlinks: for example, this [link to further details on the Markdown syntax used in Pluto notebooks](https://www.juliapackages.com/p/pluto).") write(io, "\n\n") write(io, "Cells for code do not need any special marking, unless the cell contains several lines of code. In that case, the code should be included inside a `begin ... end` block.") write(io, "\n\n") write(io, "```{julia}") write(io, "\n") write(io, "begin") write(io, "\n") write(io, " # your lines") write(io, "\n") write(io, " # of code") write(io, "\n") write(io, "end") write(io, "\n") write(io, "```") write(io, "\n\n") write(io, "To execute the code in code cell and have its results appear above it, click the \"play\" (\"Run cell\") button below the cells or place your cursor inside the chunk and press `Shift+Enter`.") write(io, "\n\n") write(io, "The outputs of a cell are always show, unless the cell is disabled (available in the `...` - \"Actions\" - button on top of the cell). To hide the contents of the cell itself, click the eye icon on top of the cell.") write(io, "\n") write(io, "\"\"\"") write(io, "\n\n") write(io, "# ╔═╡ 37c7bffc-cf46-11eb-19b8-4175e38e818b") write(io, "\n") write(io, "begin") write(io, "\n") write(io, "# We suggest having a chunk dedicated to variables containing the paths to the folders related to the project.") write(io, "\n") write(io, "# The code in this chunk is not relevant for the reader, and thus is not included in the knitted version (therefore, `include = FALSE`).") write(io, "\n") write(io, "# This is also useful if you are not using the folder structure suggested by this kit, and want to preserve your privacy.") write(io, "\n") write(io, "# Feel free to edit these paths to adapt them to your needs or not use this suggestion at all.") write(io, "\n\n") write(io, "\tdata_dir = joinpath(\"results\", \"data\", \"process\") ## Do NOT play with stuff in data/raw. That is your back-up. Work only on `process`.") write(io, "\n") write(io, "\tscripts_dir = joinpath(\"results\", \"scripts\")") write(io, "\n") write(io, "\tsuppl_dir = joinpath(\"results\", \"supplementary\")") write(io, "\n") write(io, "\tfigs_dir = joinpath(\"text\", \"figures\")") write(io, "\n") write(io, "\ttabs_dir = joinpath(\"text\", \"tables\")") write(io, "\n") write(io, "end") write(io, "\n\n") write(io, "# ╔═╡ f5062ac5-18e0-42fd-8bb7-1602a8298779") write(io, "\n") write(io, "md\"## Load data\"") write(io, "\n\n") write(io, "# ╔═╡ d4465a06-7d32-11ec-3907-59e1f4d60e2f") write(io, "\n") write(io, "# Here, you write the code to read the data from data_dir") write(io, "\n\n") write(io, "# ╔═╡ 6d256dbe-edc9-4132-aac7-62a473601034") write(io, "\n") write(io, "md\"\"\"") write(io, "\n") write(io, "## Data analysis") write(io, "\n\n") write(io, "### Figure 1") write(io, "\n") write(io, "\"\"\"") write(io, "\n\n") write(io, "# ╔═╡ f12401f7-1c08-4800-a64d-88f91f6b59fe") write(io, "\n") write(io, "# Here, you write the code to create a graph (Figure 1) included in the main text.") write(io, "\n") write(io, "# To avoid the repetition of having the same figure here as in the text, include a line that saves the figure in the figures directory.") write(io, "\n\n") write(io, "# ╔═╡ 2af762fe-0173-47eb-98d7-8fa6d3b555d3") write(io, "\n") write(io, "md\"### Figure S1\"") write(io, "\n\n") write(io, "# ╔═╡ 93c7698d-6e5b-4803-b7c6-90d4da7cd9bf") write(io, "\n") write(io, "# your code to plot Figure S1") write(io, "\n\n") write(io, "# ╔═╡ Cell order:") write(io, "\n") write(io, "# ╠═307335ba-cf46-11eb-28b0-f199cf048ae6") write(io, "\n") write(io, "# ╠═dfdfa33f-53c1-45e2-8727-86ac01a09398") write(io, "\n") write(io, "# ╠═37c7bffc-cf46-11eb-19b8-4175e38e818b") write(io, "\n") write(io, "# ╠═f5062ac5-18e0-42fd-8bb7-1602a8298779") write(io, "\n") write(io, "# ╠═d4465a06-7d32-11ec-3907-59e1f4d60e2f") write(io, "\n") write(io, "# ╠═6d256dbe-edc9-4132-aac7-62a473601034") write(io, "\n") write(io, "# ╠═f12401f7-1c08-4800-a64d-88f91f6b59fe") write(io, "\n") write(io, "# ╠═2af762fe-0173-47eb-98d7-8fa6d3b555d3") write(io, "\n") write(io, "# ╠═93c7698d-6e5b-4803-b7c6-90d4da7cd9bf") println("Your project is ready to go") end end end end
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<gh_stars>1-10 immutable DisplacementMesh <: ImageTransformation input_vertices::Matrix{Float64} output_vertices::Matrix{Float64} indices::Matrix{Int} function DisplacementMesh(input_vertices::Matrix{Float64}, output_vertices::Matrix{Float64}, indices::Matrix{Int}) @assert size(input_vertices) == size(output_vertices) new(input_vertices, output_vertices, indices) end end function DisplacementMesh(field::DisplacementField, img_width::Int, img_height::Int) input_vertices, output_vertices = _compute_vertices(field, Float64(img_width), Float64(img_height)) indices = _compute_indices(field) DisplacementMesh(input_vertices, output_vertices, indices) end DisplacementMesh(field::DisplacementField, img_size::Tuple{Int,Int}) = DisplacementMesh(field, img_size[1], img_size[2]) DisplacementMesh(field::DisplacementField, img::AbstractImage) = DisplacementMesh(field, size(img, "x"), size(img, "y")) function Base.show(io::IO, dm::DisplacementMesh) print(io, "DisplacementMesh (vertices: $(size(dm.input_vertices,1)), triangles: $(size(dm.indices,1)))") end @recipe function plot(dm::DisplacementMesh, img::Image) legend --> false yflip := true layout := 2 @series begin title --> "Input" seriestype := :image subplot := 1 img end @series begin title --> "Output" seriestype := :image subplot := 2 transform(dm,img) end dm end @recipe function plot(dm::DisplacementMesh) fillcolor --> :transparent legend --> false yflip := true layout := 2 n_indices = size(dm.indices,1) x_in = zeros(n_indices*4) y_in = zeros(n_indices*4) x_out = zeros(n_indices*4) y_out = zeros(n_indices*4) ti = 1 for i = 1:n_indices for j = 1:3 x_in[ti] = dm.input_vertices[dm.indices[i, j], 2] y_in[ti] = dm.input_vertices[dm.indices[i, j], 1] x_out[ti] = dm.output_vertices[dm.indices[i, j], 2] y_out[ti] = dm.output_vertices[dm.indices[i, j], 1] ti += 1 end x_in[ti] = NaN y_in[ti] = NaN x_out[ti] = NaN y_out[ti] = NaN ti += 1 end @series begin title --> "Input" seriestype := :shape subplot := 1 x_in, y_in end @series begin title --> "Output" seriestype := :shape subplot := 2 x_out, y_out end end function _transform{T}(dm::DisplacementMesh, img::Image{T}) img_sep = separate(img) rawdata = convert(Array{Float64, 3}, data(img_sep)) wrp = PiecewiseAffineTransforms.pa_warp(rawdata, dm.input_vertices, dm.output_vertices, dm.indices) rawdata_new = convert(Array{eltype(T),3}, wrp) img_sep_new = Image(rawdata_new, properties(img_sep)) if isxfirst(img) copyproperties(img, permutedims(convert(Image{T}, img_sep_new), [2, 1])) else copyproperties(img, convert(Image{T}, img_sep_new)) end end function transform{T<:AbstractImage}(dm::DisplacementMesh, img::T) result = _transform(dm, img)::T _log_operation!(result, dm)::T end function _compute_vertices(field::DisplacementField, img_width::Float64, img_height::Float64) height, width = size(field.delta_X) input_vertices = zeros(height*width, 2) output_vertices = zeros(height*width, 2) i = 1 for x = 1:width, y = 1:height input_vertices[i,1] = clamp(clamp(field.y[y], 0., 1.) * img_height, 1., img_height) input_vertices[i,2] = clamp(clamp(field.x[x], 0., 1.) * img_width, 1., img_width) output_vertices[i,1] = clamp(input_vertices[i,1] + field.delta_Y[y,x] * img_height, 1., img_height) output_vertices[i,2] = clamp(input_vertices[i,2] + field.delta_X[y,x] * img_width, 1., img_width) i = i + 1 end input_vertices, output_vertices end function _compute_indices(field::DisplacementField) grid_size = size(field.delta_X) height, width = grid_size w_half = floor(Int, width/2) h_half = floor(Int, height/2) indices = zeros(Int, (height-1)*(width-1)*2, 3) i = 1 for x = 1:(width-1), y = 1:(height-1) if (x <= w_half && y <= h_half) || (x > w_half && y > h_half) # upper left or lower right # *--* # |\ | # | \| # *--* indices[i, 1] = sub2ind(grid_size, y, x ) indices[i, 2] = sub2ind(grid_size, y+1, x+1) indices[i, 3] = sub2ind(grid_size, y+1, x ) i = i + 1 indices[i, 1] = sub2ind(grid_size, y, x ) indices[i, 2] = sub2ind(grid_size, y, x+1) indices[i, 3] = sub2ind(grid_size, y+1, x+1) i = i + 1 else # lower left or upper right # *--* # | /| # |/ | # *--* indices[i, 1] = sub2ind(grid_size, y, x ) indices[i, 2] = sub2ind(grid_size, y, x+1) indices[i, 3] = sub2ind(grid_size, y+1, x ) i = i + 1 indices[i, 1] = sub2ind(grid_size, y+1, x) indices[i, 2] = sub2ind(grid_size, y, x+1) indices[i, 3] = sub2ind(grid_size, y+1, x+1) i = i + 1 end end indices end
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<filename>docs/make.jl using Documenter import Pkg # Fix for https://github.com/trixi-framework/Trixi.jl/issues/668 if (get(ENV, "CI", nothing) != "true") && (get(ENV, "TRIXI_DOC_DEFAULT_ENVIRONMENT", nothing) != "true") push!(LOAD_PATH, dirname(@__DIR__)) end using Trixi using Trixi2Vtk # Get Trixi root directory trixi_root_dir = dirname(@__DIR__) include(joinpath(trixi_root_dir, "docs", "literate", "make.jl")) # Copy list of authors to not need to synchronize it manually authors_text = read(joinpath(trixi_root_dir, "AUTHORS.md"), String) authors_text = replace(authors_text, "in the [LICENSE.md](LICENSE.md) file" => "under [License](@ref)") write(joinpath(@__DIR__, "src", "authors.md"), authors_text) # Define module-wide setups such that the respective modules are available in doctests DocMeta.setdocmeta!(Trixi, :DocTestSetup, :(using Trixi); recursive=true) DocMeta.setdocmeta!(Trixi2Vtk, :DocTestSetup, :(using Trixi2Vtk); recursive=true) # Create tutorials for the following files: # Normal structure: "title" => "filename.jl" # If there are several files for one topic and one folder, the structure is: # "title" => ["subtitle 1" => ("folder 1", "filename 1.jl"), # "subtitle 2" => ("folder 2", "filename 2.jl")] files = [ "Adding a new equation" => ["Scalar conservation law" => ("adding_new_equations", "cubic_conservation_law.jl"), "Nonconservative equation" => ("adding_new_equations", "nonconservative_advection.jl")], "Differentiable programming" => "differentiable_programming.jl", "Unstructured meshes with HOHQMesh.jl" => "hohqmesh_tutorial.jl", ] tutorials = create_tutorials(files) # Make documentation makedocs( # Specify modules for which docstrings should be shown modules = [Trixi, Trixi2Vtk], # Set sitename to Trixi sitename="Trixi.jl", # Provide additional formatting options format = Documenter.HTML( # Disable pretty URLs during manual testing prettyurls = get(ENV, "CI", nothing) == "true", # Explicitly add favicon as asset assets = ["assets/favicon.ico"], # Set canonical URL to GitHub pages URL canonical = "https://trixi-framework.github.io/Trixi.jl/stable" ), # Explicitly specify documentation structure pages = [ "Home" => "index.md", "Getting started" => [ "Overview" => "overview.md", "Visualization" => "visualization.md", ], "Tutorials" => tutorials, "Basic building blocks" => [ "Meshes" => [ "Tree mesh" => joinpath("meshes", "tree_mesh.md"), "Structured mesh" => joinpath("meshes", "structured_mesh.md"), "Unstructured mesh" => joinpath("meshes", "unstructured_quad_mesh.md"), "P4est-based mesh" => joinpath("meshes", "p4est_mesh.md"), "Simplicial mesh" => joinpath("meshes", "mesh_data_meshes.md"), ], "Time integration" => "time_integration.md", "Callbacks" => "callbacks.md", ], "Advanced topics & developers" => [ "Conventions" =>"conventions.md", "Development" => "development.md", "GitHub & Git" => "github-git.md", "Style guide" => "styleguide.md", "Testing" => "testing.md", "Performance" => "performance.md", "Parallelization" => "parallelization.md", ], "Troubleshooting and FAQ" => "troubleshooting.md", "Reference" => [ "Trixi.jl" => "reference-trixi.md", "Trixi2Vtk.jl" => "reference-trixi2vtk.md" ], "Authors" => "authors.md", "Contributing" => "contributing.md", "License" => "license.md" ], strict = true # to make the GitHub action fail when doctests fail, see https://github.com/neuropsychology/Psycho.jl/issues/34 ) deploydocs( repo = "github.com/trixi-framework/Trixi.jl", devbranch = "main", push_preview = true )
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<gh_stars>0 module MNIST using GZip, Colors const Gray = Colors.Gray{Colors.N0f8} const dir = joinpath(@__DIR__, "../../deps/mnist") function load() mkpath(dir) cd(dir) do for file in ["train-images-idx3-ubyte", "train-labels-idx1-ubyte", "t10k-images-idx3-ubyte", "t10k-labels-idx1-ubyte"] isfile(file) && continue info("Downloading MNIST dataset") download("https://cache.julialang.org/http://yann.lecun.com/exdb/mnist/$file.gz", "$file.gz") open(file, "w") do io write(io, GZip.open(read, "$file.gz")) end end end end const IMAGEOFFSET = 16 const LABELOFFSET = 8 const NROWS = 28 const NCOLS = 28 const TRAINIMAGES = joinpath(dir, "train-images-idx3-ubyte") const TRAINLABELS = joinpath(dir, "train-labels-idx1-ubyte") const TESTIMAGES = joinpath(dir, "t10k-images-idx3-ubyte") const TESTLABELS = joinpath(dir, "t10k-labels-idx1-ubyte") function imageheader(io::IO) magic_number = bswap(read(io, UInt32)) total_items = bswap(read(io, UInt32)) nrows = bswap(read(io, UInt32)) ncols = bswap(read(io, UInt32)) return magic_number, Int(total_items), Int(nrows), Int(ncols) end function labelheader(io::IO) magic_number = bswap(read(io, UInt32)) total_items = bswap(read(io, UInt32)) return magic_number, Int(total_items) end function rawimage(io::IO) img = Array{Gray}(NCOLS, NROWS) for i in 1:NCOLS, j in 1:NROWS img[i, j] = reinterpret(Colors.N0f8, read(io, UInt8)) end return img end function rawimage(io::IO, index::Integer) seek(io, IMAGEOFFSET + NROWS * NCOLS * (index - 1)) return rawimage(io) end rawlabel(io::IO) = Int(read(io, UInt8)) function rawlabel(io::IO, index::Integer) seek(io, LABELOFFSET + (index - 1)) return rawlabel(io) end getfeatures(io::IO, index::Integer) = vec(getimage(io, index)) """ images() images(:test) Load the MNIST images. Each image is a 28×28 array of `Gray` colour values (see Colors.jl). Returns the 60,000 training images by default; pass `:test` to retreive the 10,000 test images. """ function images(set = :train) load() io = IOBuffer(read(set == :train ? TRAINIMAGES : TESTIMAGES)) _, N, nrows, ncols = imageheader(io) [rawimage(io) for _ in 1:N] end """ labels() labels(:test) Load the labels corresponding to each of the images returned from `images()`. Each label is a number from 0-9. Returns the 60,000 training labels by default; pass `:test` to retreive the 10,000 test labels. """ function labels(set = :train) load() io = IOBuffer(read(set == :train ? TRAINLABELS : TESTLABELS)) _, N = labelheader(io) [rawlabel(io) for _ = 1:N] end end # module
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<filename>src/general_variables.jl<gh_stars>0 ################################################################################ # BASE EXTENSIONS ################################################################################ # Extend Base.copy for new variable types Base.copy(v::GeneralVariableRef) = v Base.copy(v::DispatchVariableRef) = v # Extend Base.:(==) for GeneralVariableRef function Base.:(==)(v::T, w::T)::Bool where {T <: GeneralVariableRef} return v.model === w.model && v.raw_index == w.raw_index && v.index_type == w.index_type && v.param_index == w.param_index end # Extend Base.:(==) for DispatchVariableRef function Base.:(==)(v::T, w::U)::Bool where {T <: DispatchVariableRef, U <: DispatchVariableRef} return v.model === w.model && v.index == w.index end # Extend Base.broadcastable Base.broadcastable(v::GeneralVariableRef) = Ref(v) Base.broadcastable(v::DispatchVariableRef) = Ref(v) # Extend Base.length Base.length(v::GeneralVariableRef)::Int = 1 Base.length(v::DispatchVariableRef)::Int = 1 # Extend JuMP functions JuMP.isequal_canonical(v::GeneralVariableRef, w::GeneralVariableRef)::Bool = v == w JuMP.isequal_canonical(v::DispatchVariableRef, w::DispatchVariableRef)::Bool = v == w JuMP.variable_type(model::InfiniteModel)::DataType = GeneralVariableRef # Extract the root name of a variable reference (removes the bracketed container indices) function _remove_name_index(vref::GeneralVariableRef)::String name = JuMP.name(vref) first_bracket = findfirst(isequal('['), name) if first_bracket === nothing return name else # Hacky fix to handle invalid Unicode try return name[1:first_bracket-1] catch return name[1:first_bracket-2] end end end # Define basic attribute getters _index_type(vref::GeneralVariableRef)::DataType = vref.index_type _raw_index(vref::GeneralVariableRef)::Int = vref.raw_index _param_index(vref::GeneralVariableRef)::Int = vref.param_index ################################################################################ # BASIC REFERENCE ACCESSERS ################################################################################ """ JuMP.index(vref::GeneralVariableRef)::AbstractInfOptIndex Extend [`JuMP.index`](@ref JuMP.index(::JuMP.VariableRef)) to return the appropriate index of `vref`. **Example** ```jldoctest; setup = :(using InfiniteOpt, JuMP; m = InfiniteModel(); @hold_variable(m, vref)) julia> index(vref) HoldVariableIndex(1) ``` """ function JuMP.index(vref::GeneralVariableRef)::AbstractInfOptIndex index_type = _index_type(vref) if index_type == DependentParameterIndex return index_type(DependentParametersIndex(_raw_index(vref)), _param_index(vref)) else return index_type(_raw_index(vref)) end end """ JuMP.index(vref::DispatchVariableRef)::AbstractInfOptIndex Extend [`JuMP.index`](@ref JuMP.index(::JuMP.VariableRef)) to return the appropriate index of `vref`. """ function JuMP.index(vref::DispatchVariableRef)::AbstractInfOptIndex return vref.index end """ JuMP.owner_model(vref::GeneralVariableRef)::InfiniteModel Extend [`JuMP.owner_model`](@ref JuMP.owner_model(::JuMP.AbstractVariableRef)) to return the model where `vref` is stored. **Example** ```jldoctest; setup = :(using InfiniteOpt, JuMP; m = InfiniteModel(); @hold_variable(m, 0 <= vref <= 1)) julia> owner_model(vref) An InfiniteOpt Model Feasibility problem with: Finite Parameters: 0 Infinite Parameters: 0 Variable: 1 Derivatives: 0 Measures: 0 `HoldVariableRef`-in-`MathOptInterface.GreaterThan{Float64}`: 1 constraint `HoldVariableRef`-in-`MathOptInterface.LessThan{Float64}`: 1 constraint Names registered in the model: vref Optimizer model backend information: Model mode: AUTOMATIC CachingOptimizer state: NO_OPTIMIZER Solver name: No optimizer attached. ``` """ function JuMP.owner_model(vref::GeneralVariableRef)::InfiniteModel return vref.model end """ JuMP.owner_model(vref::DispatchVariableRef)::InfiniteModel Extend [`JuMP.owner_model`](@ref JuMP.owner_model(::JuMP.AbstractVariableRef)) to return the model where `vref` is stored. """ function JuMP.owner_model(vref::DispatchVariableRef)::InfiniteModel return vref.model end ################################################################################ # DISPATCH VARIABLE MAKERS ################################################################################ """ dispatch_variable_ref(model::InfiniteModel, index::AbstractInfOptIndex) Return the variable reference associated the type of `index`. This needs to be defined for each variable reference type. """ function dispatch_variable_ref end """ dispatch_variable_ref(vef::GeneralVariableRef)::DispatchVariableRef Return the concrete [`DispatchVariableRef`](@ref) this associated with `vref`. This relies on `dispatch_variable_ref` being extended for the index type, otherwise an `MethodError` is thrown. """ function dispatch_variable_ref(vref::GeneralVariableRef)::DispatchVariableRef model = JuMP.owner_model(vref) idx = JuMP.index(vref) return dispatch_variable_ref(model, idx) end ################################################################################ # CORE DATA METHODS ################################################################################ """ _add_data_object(model::InfiniteModel, object::AbstractDataObject)::ObjectIndex Add `object` to the appropriate `CleverDict` in `model` and return the its index. This needs to be defined for the type of `object`. These definitions need to use `MOIUC.add_item` to add the object to the `CleverDict`. """ function _add_data_object end """ _data_dictionary(vref::DispatchVariableRef)::MOIUC.CleverDict Return the `CleverDict` that stores data objects for the type of `vref`. This needs to be defined for the type of `vref`. """ function _data_dictionary end """ _data_dictionary(vref::GeneralVariableRef)::MOIUC.CleverDict Return the `CleverDict` that stores data objects for the type of `vref`. It relies on `_data_dictionary` being defined for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown. """ function _data_dictionary(vref::GeneralVariableRef)::MOIUC.CleverDict return _data_dictionary(dispatch_variable_ref(vref)) end """ _data_object(vref::DispatchVariableRef)::AbstractDataObject Return the data object associated with `vref`, in other words the object its index points to in the `InfiniteModel`. This needs to be defined for the type of `vref`. This should use `_data_dictionary` to access the `CleverDict` that the object is stored in. """ function _data_object end """ _data_object(vref::GeneralVariableRef)::AbstractDataObject Return the data object associated with `vref`, in other words the object its index points to in the `InfiniteModel`. It relies on `_data_object` being defined for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown. """ function _data_object(vref::GeneralVariableRef)::AbstractDataObject return _data_object(dispatch_variable_ref(vref)) end """ _delete_data_object(vref::DispatchVariableRef)::Nothing Delete the concrete `AbstractDataObject` associated with `vref`. """ function _delete_data_object(vref::DispatchVariableRef)::Nothing delete!(_data_dictionary(vref), JuMP.index(vref)) return end ################################################################################ # NAME METHODS ################################################################################ # Dispatch fallback function JuMP.name(vref::DispatchVariableRef) throw(ArgumentError("`JuMP.name` not defined for variable reference type " * "`$(typeof(vref))`.")) end """ JuMP.name(vref::GeneralVariableRef)::String Extend [`JuMP.name`](@ref JuMP.name(::JuMP.VariableRef)) to return the name of `vref`. It relies on `JuMP.name` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. """ function JuMP.name(vref::GeneralVariableRef)::String return JuMP.name(dispatch_variable_ref(vref)) end # Dispatch fallback function JuMP.set_name(vref::DispatchVariableRef, name::String) throw(ArgumentError("`JuMP.set_name` not defined for variable reference type " * "`$(typeof(vref))`.")) end """ JuMP.set_name(vref::GeneralVariableRef, name::String)::Nothing Extend [`JuMP.set_name`](@ref JuMP.name(::JuMP.VariableRef, ::String)) to set the name of `vref`. It relies on `JuMP.set_name` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. """ function JuMP.set_name(vref::GeneralVariableRef, name::String)::Nothing return JuMP.set_name(dispatch_variable_ref(vref), name) end ################################################################################ # VALIDITY METHODS ################################################################################ """ JuMP.is_valid(model::InfiniteModel, vref::DispatchVariableRef)::Bool Extend [`JuMP.is_valid`](@ref JuMP.is_valid(::JuMP.Model, ::JuMP.VariableRef)) to return `Bool` if `vref` is a valid reference. """ function JuMP.is_valid(model::InfiniteModel, vref::DispatchVariableRef)::Bool return model === JuMP.owner_model(vref) && haskey(_data_dictionary(vref), JuMP.index(vref)) end # DependentParameterRef function JuMP.is_valid(model::InfiniteModel, vref::DependentParameterRef)::Bool return model === JuMP.owner_model(vref) && haskey(_data_dictionary(vref), JuMP.index(vref).object_index) end """ JuMP.is_valid(model::InfiniteModel, vref::GeneralVariableRef)::Bool Extend [`JuMP.is_valid`](@ref JuMP.is_valid(::JuMP.Model, ::JuMP.VariableRef)) to return `Bool` if `vref` is a valid reference. **Example** ```jldoctest; setup = :(using InfiniteOpt, JuMP; model = InfiniteModel(); @hold_variable(model, vref)) julia> is_valid(model, vref) true ``` """ function JuMP.is_valid(model::InfiniteModel, vref::GeneralVariableRef)::Bool return JuMP.is_valid(model, dispatch_variable_ref(vref)) end ################################################################################ # CORE OBJECT METHODS ################################################################################ """ _core_variable_object(vref::DispatchVariableRef)::Union{InfOptParameter, InfOptVariable, Measure} Return the core object that `vref` points to. This needs to be extended for type of `vref`. This should use `_data_object` to access the data object where the variable object is stored. """ function _core_variable_object end """ _core_variable_object(vref::GeneralVariableRef)::Union{InfOptParameter, InfOptVariable, Measure} Return the core object that `vref` points to. This is enabled with appropriate definitions of `_core_variable_object` for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown. """ function _core_variable_object(vref::GeneralVariableRef) return _core_variable_object(dispatch_variable_ref(vref)) end """ _set_core_variable_object(vref::DispatchVariableRef, object)::Nothing Sets the core object that `vref` points to `object`. This needs to be extended for types of `vref` and `object`. This should use `_data_object` to access the data object where the variable object is stored. """ function _set_core_variable_object end ################################################################################ # DEPENDENCY METHODS ################################################################################ # Define wrappers for internal usage methods and their templates for op = (:_infinite_variable_dependencies, :_reduced_variable_dependencies, :_point_variable_dependencies, :_measure_dependencies, :_constraint_dependencies, :_derivative_dependencies, :_derivative_constraint_dependencies, :_parameter_function_dependencies, :_generative_measures) @eval begin # define the api template func = $op """ $func(vref::DispatchVariableRef)::Vector{AbstractInfOptIndex} Return the indices of these entities that depend on `vref`. This needs to be extended for type of `vref`. This should use `_data_object` to access the data object where the name is stored if appropriate. """ function $(op) end # define the dispatch version """ $func(vref::GeneralVariableRef)::Vector{AbstractInfOptIndex} Return the indices of these entities that depend on `vref`. This is enabled with appropriate definitions of `$func` for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown. """ function $op(vref::GeneralVariableRef) return $op(dispatch_variable_ref(vref)) end end end ################################################################################ # USED BY METHODS ################################################################################ # Define the usage method wrappers and their fallbacks for op = (:used_by_infinite_variable, :used_by_reduced_variable, :used_by_point_variable, :used_by_measure, :used_by_constraint, :used_by_objective, :used_by_derivative, :is_used, :has_derivative_constraints, :used_by_parameter_function) @eval begin # define the fallback method func = $op function $op(vref::DispatchVariableRef) str = string("`", func, "` not defined for variable reference type " * "`$(typeof(vref))`.") throw(ArgumentError(str)) end # define the dispatch version """ $func(vref::GeneralVariableRef)::Bool Define `$func` for general variable references. It relies on `$func` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. See the underlying docstrings for more information. """ function $op(vref::GeneralVariableRef)::Bool return $op(dispatch_variable_ref(vref)) end end end ################################################################################ # DELETE METHODS ################################################################################ # Dispatch fallback function JuMP.delete(model::InfiniteModel, vref) throw(ArgumentError("`JuMP.delete` not defined for variable reference type " * "`$(typeof(vref))`.")) end """ JuMP.delete(model::InfiniteModel, vref::GeneralVariableRef)::Nothing Extend [`JuMP.delete`](@ref JuMP.delete(::JuMP.Model, ::JuMP.VariableRef)) to delete `vref` and its dependencies. It relies on `JuMP.delete` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. """ function JuMP.delete(model::InfiniteModel, vref::GeneralVariableRef)::Nothing return JuMP.delete(model, dispatch_variable_ref(vref)) end """ JuMP.delete(model::InfiniteModel, prefs::AbstractArray{<:GeneralVariableRef})::Nothing Extend `JuMP.delete` to delete a group of dependent infinite parameters and their dependencies. An `ArugmentError` is thrown if `prefs` are not dependent infinite parameters. """ function JuMP.delete(model::InfiniteModel, prefs::AbstractArray{<:GeneralVariableRef})::Nothing return JuMP.delete(model, dispatch_variable_ref.(prefs)) end ################################################################################ # PARAMETER METHODS ################################################################################ """ _parameter_number(pref::DispatchVariableRef)::Int Return the parameter creation number for `pref` assuming it is an infinite parameter. This needs to be defined for the type of `pref`. This should use the `_data_object` to get the number. """ function _parameter_number end """ _parameter_number(pref::GeneralVariableRef)::Int Return the parameter creation number for `pref` assuming it is an infinite parameter. It relies on `_parameter_number` being properly defined for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown. """ function _parameter_number(pref::GeneralVariableRef)::Int return _parameter_number(dispatch_variable_ref(pref)) end """ _object_number(pref::DispatchVariableRef)::Int Return the object number for `pref` assuming it is an infinite parameter. This needs to be defined for the type of `pref`. This should use the `_data_object` to get the number. """ function _object_number end """ _object_number(pref::GeneralVariableRef)::Int Return the object number for `pref` assuming it is an infinite parameter. It relies on `_object_number` being properly defined for the underlying `DispatchVariableRef`, otherwise an `MethodError` is thrown. """ function _object_number(pref::GeneralVariableRef)::Int return _object_number(dispatch_variable_ref(pref)) end # Define 1 argument user method wrappers and their fallbacks for op = (:infinite_set, :num_supports, :significant_digits, :has_supports, :supports, :delete_supports, :fill_in_supports!, :parameter_value, :derivative_method, :has_generative_supports, :has_internal_supports, :add_generative_supports, :raw_function, :generative_support_info) @eval begin # define the fallback method func = $op function $op(pref; kwargs...) str = string("`", func, "` not defined for variable reference type " * "`$(typeof(pref))`.") throw(ArgumentError(str)) end # define the dispatch version """ $func(prefs; [kwargs...]) Define `$func` for general variable references. It relies on `$func` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. See the underlying docstrings for more information. Note that this is a auto generated wrapper and the underlying method may or may not use `kwargs`. """ function $op(prefs::Union{GeneralVariableRef, AbstractArray{<:GeneralVariableRef}}; kwargs...) return $op(dispatch_variable_ref.(prefs); kwargs...) end end end # Dispatch fallback function set_infinite_set(pref, set::AbstractInfiniteSet) throw(ArgumentError("`set_infinite_set` not defined for variable reference type(s) " * "`$(typeof(pref))`.")) end """ set_infinite_set(pref::GeneralVariableRef, set::InfiniteScalarSet)::Nothing Specify the scalar infinite set of the infinite parameter `pref` to `set`. Note this will reset/delete all the supports contained in the underlying parameter object. Also, errors if `pref` is used by a measure. An `ArgumentError` is thrown if `pref` is not an infinite parameter. """ function set_infinite_set(pref::GeneralVariableRef, set::InfiniteScalarSet)::Nothing return set_infinite_set(dispatch_variable_ref(pref), set) end """ set_infinite_set(prefs::AbstractArray{<:GeneralVariableRef}, set::InfiniteArraySet)::Nothing Specify the multi-dimensional infinite set of the dependent infinite parameters `prefs` to `set`. Note this will reset/delete all the supports contained in the underlying [`DependentParameters`](@ref) object. This will error if the not all of the dependent infinite parameters are included or if any of them are used by measures. An `ArgumentError` is thrown if `prefs` are not dependent infinite parameters. """ function set_infinite_set(prefs::AbstractArray{<:GeneralVariableRef}, set::InfiniteArraySet)::Nothing return set_infinite_set(dispatch_variable_ref.(prefs), set) end # Better fallbacks for supports function supports(pref::DispatchVariableRef; kwargs...) throw(ArgumentError("`supports` not defined for variable reference type(s) " * "`$(typeof(pref))`.")) end function supports(prefs::AbstractArray; kwargs...) throw(ArgumentError("`supports` not defined for variable reference type(s) " * "`$(typeof(prefs))`.")) end # Dispatch fallback function set_supports(pref, supports; kwargs...) throw(ArgumentError("`set_supports` not defined for variable reference type(s) " * "`$(typeof(pref))`.")) end """ set_supports(pref::GeneralVariableRef, supports::Union{Real, Vector{<:Real}}; [force::Bool = false])::Nothing Set the support points associated with a single infinite parameter `pref`. An `ArgumentError` is thrown if `pref` is not an independent infinite parameter. """ function set_supports(pref::GeneralVariableRef, supports::Union{Real, Vector{<:Real}}; force::Bool = false, label::Type{<:AbstractSupportLabel} = UserDefined )::Nothing return set_supports(dispatch_variable_ref(pref), supports, force = force, label = label) end """ set_supports( prefs::Union{Vector{GeneralVariableRef}, AbstractArray{<:GeneralVariableRef}}, supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}}; [force::Bool = false] )::Nothing Set the support points associated with dependent infinite parameters `prefs`. An `ArgumentError` is thrown if `prefs` is are not dependent infinite parameters. """ function set_supports(prefs::AbstractArray{<:GeneralVariableRef}, supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}}; label::Type{<:AbstractSupportLabel} = UserDefined, force::Bool = false )::Nothing return set_supports(dispatch_variable_ref.(prefs), supports, label = label, force = force) end # Dispatch fallback function add_supports(pref, supports; kwargs...) throw(ArgumentError("`add_supports` not defined for variable reference type(s) " * "`$(typeof(pref))`.")) end """ add_supports(pref::GeneralVariableRef, supports::Union{Real, Vector{<:Real}})::Nothing Add the support points `supports` to a single infinite parameter `pref`. An `ArgumentError` is thrown if `pref` is not an independent infinite parameter. """ function add_supports(pref::GeneralVariableRef, supports::Union{Real, Vector{<:Real}}; check::Bool = true, label::Type{<:AbstractSupportLabel} = UserDefined )::Nothing return add_supports(dispatch_variable_ref(pref), supports, check = check, label = label) end """ add_supports( prefs::Union{Vector{GeneralVariableRef}, AbstractArray{<:GeneralVariableRef}}, supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}} )::Nothing Add the support points `supports` to the dependent infinite parameters `prefs`. An `ArgumentError` is thrown if `prefs` is are not dependent infinite parameters. """ function add_supports(prefs::AbstractArray{<:GeneralVariableRef}, supports::Union{Array{<:Real, 2}, AbstractArray{<:Vector{<:Real}}}; label::Type{<:AbstractSupportLabel} = UserDefined, check::Bool = true )::Nothing return add_supports(dispatch_variable_ref.(prefs), supports, label = label, check = check) end # Fallback function JuMP.set_value(vref::DispatchVariableRef, value::Real) throw(ArgumentError("`JuMP.set_value` not defined for variable reference type " * "`$(typeof(vref))`.")) end """ JuMP.set_value(vref::DispatchVariableRef, value::Real)::Nothing Extend `JuMP.set_value` to set the value of `vref`. It relies on `JuMP.set_value` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. """ function JuMP.set_value(vref::GeneralVariableRef, value::Real)::Nothing return JuMP.set_value(dispatch_variable_ref(vref), value) end # Dispatch fallback function set_derivative_method(pref::DispatchVariableRef, method) throw(ArgumentError("`set_derivative_method` not defined for variable reference type(s) " * "`$(typeof(pref))`.")) end """ set_derivative_method(pref::GeneralVariableRef, method::AbstractDerivativeMethod )::Nothing Specify the numerical derivative evaluation technique associated with `pref`. An `ArgumentError` is thrown if `pref` is not an infinite parameter. """ function set_derivative_method(pref::GeneralVariableRef, method::AbstractDerivativeMethod )::Nothing return set_derivative_method(dispatch_variable_ref(pref), method) end # Define parameter status setters for op = (:_set_has_generative_supports, :_set_has_internal_supports, :_set_has_derivative_constraints) @eval begin # define the fallback method func = $op function $op(vref::DispatchVariableRef, status) str = string("`", func, "` not defined for variable reference type " * "`$(typeof(vref))`.") throw(ArgumentError(str)) end # define the dispatch version function $op(vref::GeneralVariableRef, status::Bool)::Nothing return $op(dispatch_variable_ref(vref), status) end end end ################################################################################ # VARIABLE METHODS ################################################################################ # Define single argument variable method wrappers and their fallbacks for op = (:raw_parameter_refs, :parameter_refs, :parameter_list, :start_value_function, :reset_start_value_function, :infinite_variable_ref, :eval_supports, :raw_parameter_values, :parameter_values, :parameter_bounds, :delete_parameter_bounds) @eval begin # define the fallback method func = $op function $op(vref::DispatchVariableRef) str = string("`", func, "` not defined for variable reference type " * "`$(typeof(vref))`.") throw(ArgumentError(str)) end # define the dispatch version """ $func(vref::GeneralVariableRef) Define `$func` for general variable references. It relies on `$func` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. See the underlying docstrings for more information. """ function $op(vref::GeneralVariableRef) return $op(dispatch_variable_ref(vref)) end end end # Dispatch fallback function set_start_value_function(vref::DispatchVariableRef, start) throw(ArgumentError("`set_start_value_function` not defined for variable reference type " * "`$(typeof(vref))`.")) end """ set_start_value_function(vref::GeneralVariableRef, start::Union{Real, Function})::Nothing Set the start value function of `vref`. It relies on `set_start_value_function` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. """ function set_start_value_function(vref::GeneralVariableRef, start)::Nothing return set_start_value_function(dispatch_variable_ref(vref), start) end """ has_parameter_bounds(vref::GeneralVariableRef)::Bool Return a `Bool` indicating if `vref` is limited to a sub-domain as defined by parameter bound. """ function has_parameter_bounds(vref::GeneralVariableRef)::Bool return has_parameter_bounds(dispatch_variable_ref(vref)) end # Dispatch fallback function set_parameter_bounds(vref::DispatchVariableRef, bounds; kwargs...) throw(ArgumentError("`set_parameter_bounds` not defined for variable reference type(s) " * "`$(typeof(vref))`.")) end """ set_parameter_bounds(vref::GeneralVariableRef, bounds::ParameterBounds{GeneralVariableRef}; [force::Bool = false])::Nothing Specify a new set of parameter bounds for a hold variable `vref`. An `ArgumentError` is thrown if `vref` is not a hold variable. """ function set_parameter_bounds(vref::GeneralVariableRef, bounds::ParameterBounds{GeneralVariableRef}; force::Bool = false, _error::Function = error )::Nothing return set_parameter_bounds(dispatch_variable_ref(vref), bounds, force = force, _error = _error) end # Dispatch fallback function add_parameter_bounds(vref::DispatchVariableRef, bounds; kwargs...) throw(ArgumentError("`add_parameter_bounds` not defined for variable reference type(s) " * "`$(typeof(vref))`.")) end """ add_parameter_bounds(vref::GeneralVariableRef, bounds::ParameterBounds{GeneralVariableRef} )::Nothing Specify more parameter bounds for a hold variable `vref`. An `ArgumentError` is thrown if `vref` is not a hold variable. """ function add_parameter_bounds(vref::GeneralVariableRef, bounds::ParameterBounds{GeneralVariableRef}; _error::Function = error )::Nothing return add_parameter_bounds(dispatch_variable_ref(vref), bounds, _error = _error) end ################################################################################ # MEASURE METHODS ################################################################################ # Define measure queries and their fallbacks for op = (:measure_function, :measure_data, :is_analytic, :expand) @eval begin # define the fallback method func = $op function $op(mref::DispatchVariableRef) str = string("`", func, "` not defined for variable reference type " * "`$(typeof(mref))`.") throw(ArgumentError(str)) end # define the dispatch version """ $func(mref::GeneralVariableRef) Define `$func` for general variable references. Errors if `mref` does not correspond to a `MeasureRef`. See the underlying docstrings for more information. """ function $op(mref::GeneralVariableRef) return $op(dispatch_variable_ref(mref)) end end end ################################################################################ # DERIVATIVE METHODS ################################################################################ # Define measure queries and their fallbacks for op = (:derivative_argument, :operator_parameter, :evaluate, :derivative_constraints, :delete_derivative_constraints) @eval begin # define the fallback method func = $op function $op(dref::DispatchVariableRef) str = string("`", func, "` not defined for variable reference type " * "`$(typeof(dref))`.") throw(ArgumentError(str)) end # define the dispatch version """ $func(dref::GeneralVariableRef) Define `$func` for general variable references. Errors if `dref` does not correspond to a `DerivativeRef`. See the underlying docstrings for more information. """ function $op(dref::GeneralVariableRef) return $op(dispatch_variable_ref(dref)) end end end ################################################################################ # VARIABLE INFO METHODS ################################################################################ # Define the 1 argument JuMP variable info methods for op = (:has_lower_bound, :has_upper_bound, :is_fixed, :is_binary, :is_integer, :lower_bound, :upper_bound, :fix_value, :start_value, :set_binary, :set_integer, :LowerBoundRef, :UpperBoundRef, :FixRef, :BinaryRef, :IntegerRef, :delete_lower_bound, :delete_upper_bound, :unfix, :unset_binary, :unset_integer) @eval begin # define the fallback method func = JuMP.$op function JuMP.$op(vref::DispatchVariableRef) str = string("`JuMP.", func, "` not defined for variable reference type " * "`$(typeof(vref))`.") throw(ArgumentError(str)) end # define the dispatch version """ JuMP.$func(vref::GeneralVariableRef) Define `JuMP.$func` for general variable references. It relies on `JuMP.$func` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. See the underlying docstrings for more information. """ function JuMP.$op(vref::GeneralVariableRef) return JuMP.$op(dispatch_variable_ref(vref)) end end end # Define the 2 argument setting methods (except for fix) for op = (:set_lower_bound, :set_upper_bound, :set_start_value) @eval begin # define the fallback method func = JuMP.$op function JuMP.$op(vref::DispatchVariableRef, value) str = string("`JuMP.", func, "` not defined for variable reference type " * "`$(typeof(vref))`.") throw(ArgumentError(str)) end # define the dispatch version """ JuMP.$func(vref::GeneralVariableRef, value::Real)::Nothing Define `JuMP.$func` for general variable references. It relies on `JuMP.$func` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. See the underlying docstrings for more information. """ function JuMP.$op(vref::GeneralVariableRef, value::Real)::Nothing return JuMP.$op(dispatch_variable_ref(vref), value) end end end # Dispatch fallback for JuMP.fix function JuMP.fix(vref::DispatchVariableRef, value::Real; force::Bool = false) throw(ArgumentError("`JuMP.fix` not defined for variable reference type " * "`$(typeof(vref))`.")) end """ JuMP.fix(vref::GeneralVariableRef, value::Real; force::Bool = false)::Nothing Define `JuMP.fix` for general variable references. It relies on `JuMP.fix` being defined for the underlying `DispatchVariableRef`, otherwise an `ArugmentError` is thrown. See the underlying docstrings for more information. """ function JuMP.fix(vref::GeneralVariableRef, value::Real; force::Bool = false)::Nothing return JuMP.fix(dispatch_variable_ref(vref), value, force = force) end
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using PyPlot, Random, TransD_GP, GP, Statistics, HDF5 function geomprogdepth(n, dy, c) dy*(1.0-c^n)/(1-c) end function getn(z, dy, c) log(1 - z/dy*(1-c))/log(c) end function nicenup(g::PyPlot.Figure;fsize=14) for ax in gcf().axes ax.tick_params("both",labelsize=fsize) ax.xaxis.label.set_fontsize(fsize) ax.yaxis.label.set_fontsize(fsize) ax.title.set_fontsize(fsize) if typeof(ax.get_legend_handles_labels()[1]) != Array{Any,1} ax.legend(loc="best", fontsize=fsize) end end g.tight_layout() end n, dz, extendfrac = 18, .5, 1.2 znrange = 1.0:n zboundaries = geomprogdepth.(znrange, dz, extendfrac) thickness = [zboundaries[1]; diff(zboundaries)[1:end-1]] zall = [zboundaries[1]/2; 0.5*(zboundaries[1:end-1] + zboundaries[2:end])] znall = getn.(zall, dz, extendfrac) figure() plot(znall, zall) xlabel("depth index") ylabel("depth associated km") grid() nicenup(gcf()) f, ax = plt.subplots(1, 2, figsize=(10,5)) ax[1].stem(zboundaries[1:end-1], zboundaries[1:end-1], markerfmt="") ax[1].stem(zall, zall, "k--", markerfmt=" ") ax[1].set_xlabel("depth km") ax[1].set_ylabel("depth km") ax[2].stem(znrange[1:end-1], znrange[1:end-1], markerfmt="") ax[2].stem(znall, znall, "k--", markerfmt=" ") ax[2].set_ylabel("depth index") ax[2].set_xlabel("depth index") nicenup(gcf()) f, ax = plt.subplots(1, 2, figsize=(10,5), sharey=true) ax[1].stem(zall[1:end-1],thickness, "k--", markerfmt=" ") ax[1].set_xlabel("depth km") ax[1].set_ylabel("thickness km") ax[1].yaxis.grid(which="major") ax[2].stem(znall[1:end-1],thickness, "k--", markerfmt=" ") ax[2].set_xlabel("depth index") ax[2].yaxis.grid(which="major") nicenup(f) nmin, nmax = 2, 200 λ, δ = [8, 5, 2], 0.1 fbounds = [-2.8 0.25] demean = true sdev_prop = 0.1 sdev_pos = [0.5;0.5;0.2] pnorm = 2. λx,λy = 100.0, 100.0 dx, dy = 0.05λx, 0.05λy x = 0:(0.05λx):λx-dx y = 0:(0.05λy):λy-dy xall = zeros(3,length(x)*length(y)*length(znall)) for i in 1:size(xall,2) xid, yid, zid = Tuple(CartesianIndices((length(x),length(y),length(znall)))[i]) xall[:,i] = [x[xid]; y[yid]; znall[zid]] end xbounds = zeros(Float64,size(xall,1),2) for dim in 1:size(xall, 1) xbounds[dim,:] = [minimum(xall[dim,:]), maximum(xall[dim,:])] end ## Initialize a model using these options Random.seed!(2) opt = TransD_GP.Options(nmin = nmin, nmax = nmax, xbounds = xbounds, fbounds = fbounds, xall = xall, λ = λ, δ = δ, demean = demean, sdev_prop = sdev_prop, sdev_pos = sdev_pos, pnorm = pnorm, quasimultid = false ) @time m = TransD_GP.init(opt) TransD_GP.birth!(m, opt) @time for i = 1:48# 17 also works well TransD_GP.birth!(m, opt) end v = reshape(m.fstar,length(x), length(y), length(znall)) meshgrid(xs, ys) = [xs[i] for i in 1:length(xs), j in 1:length(ys)], [ys[j] for i in 1:length(xs), j in 1:length(ys)] xx,yy = meshgrid(x,y) f = figure(figsize=(10,10)) l = [7,12, 15, 18] for i in l c=(v[:,:,i].-minimum(v))./(maximum(v)-minimum(v)) plot_surface(xx, yy, zall[i]*ones(size(yy)), facecolors=plt.cm.jet(c), shade=false, alpha=0.9) end timebirth = true if timebirth ts = time(); ndo = 100 for i = 1:ndo TransD_GP.birth!(m, opt) TransD_GP.death!(m, opt) end dt = time() - ts @info "avg time per move is $(dt/ndo/2)" end yy,zz = meshgrid(y,zall) l = [1, 18] for i in l c=(v[i,:,:].-minimum(v))./(maximum(v)-minimum(v)) plot_surface(x[i]*ones(size(yy)), yy, zz, facecolors=plt.cm.jet(c), shade=false, alpha=0.9) end scatter3D(m.xtrain[1,1:m.n], m.xtrain[2,1:m.n], geomprogdepth.(m.xtrain[3,1:m.n], dz, extendfrac), c=m.ftrain[1:m.n], vmin=minimum(v), vmax=maximum(v), s=50, cmap="jet", alpha=0.8) xlabel("x km") ylabel("y km") zlabel("depth km") cbar = f.colorbar(plt.cm.ScalarMappable(cmap="jet",norm=matplotlib.colors.Normalize(vmin=minimum(v), vmax=maximum(v))),ax=gca()) cbar.set_label(L"\log_{10} \sigma") gca().zaxis.label.set_fontsize(16); nicenup(gcf()) gca().invert_zaxis()
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<reponame>ScottJordan/EvaluationOfRLAlgs function sample_normal!(z, μ, σ, rng) @. z = μ + σ * randn((rng,), eltype(μ)) end function logpdf_normal(z, μ, σ) logp = -log(sqrt(2.0 * π)) - (z - μ)^2 / (2.0 * σ^2) - log(σ) return logp end # something might be wrong with the gradient of the log probability calculation. mutable struct LinearNormalPolicy{TP, TB, TS} <: AbstractPolicy where {TP<:Real,TB<:AbstractFuncApprox,TS<:Bool} θ::Array{TP} ϕ::TB σ::Array{TP, 1} μ::Array{TP,1} action::Array{TP, 1} feats::Array{TP, 1} function LinearNormalPolicy(::Type{T}, state_dim::Int, num_actions::Int, sigma, train_sigma::Bool=true) where {T} θmu = vec(zeros(T,state_dim, num_actions)) σ = ones(T, num_actions) .* sigma ϕ = IdentityBasis(state_dim) if train_sigma θ = cat(θmu, σ, dims=1) else θ = θmu end a = zeros(tp, num_actions) μ = zeros(tp, num_actions) feats = zeros(T, get_num_outputs(ϕ)) new{T,typeof(ϕ),train_sigma}(θ, ϕ, σ, μ, a, feats) end function LinearNormalPolicy(ϕ::AbstractFuncApprox, num_actions::Int, sigma, train_sigma::Bool=true) tp = eltype(get_params(ϕ)) θmu = vec(zeros(tp,get_num_outputs(ϕ), num_actions)) σ = ones(tp, num_actions) .* sigma if train_sigma θ = cat(θmu, σ, dims=1) else θ = θmu end a = zeros(tp, num_actions) μ = zeros(tp, num_actions) feats = zeros(tp, get_num_outputs(ϕ)) new{tp,typeof(ϕ),train_sigma}(θ, clone(ϕ), σ, μ, a, feats) end end function get_num_params(π::LinearNormalPolicy)::Int return length(π.θ) end function get_thetamu(π::LinearNormalPolicy{T,TB,false}) where {T,TB} N = get_num_outputs(π.ϕ) return reshape(π.θ, N, :) end function get_thetamu(π::LinearNormalPolicy{T,TB,true}) where {T,TB} N = get_num_outputs(π.ϕ) A = length(π.σ) return reshape(view(π.θ, 1:N*A), N, A) end function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Int)::Array{TP,1} where {TP<:Real} θ = get_thetamu(fun) return θ[x, :] end function call!(out::Array{TP, 1}, fun::LinearNormalPolicy{TP,IdentityBasis}, x::Int) where {TP<:Real} θ = get_thetamu(fun) out .= θ[x, :] end function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Int, y::Int)::TP where{TP<:Real} θ = get_thetamu(fun) return θ[x, y] end function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Array{TP,1})::Array{TP,1} where {TP<:Real} θ = get_thetamu(fun) return θ'*x end function call!(out::Array{TP, 1}, fun::LinearNormalPolicy{TP,IdentityBasis}, x::Array{TP,1}) where {TP<:Real} θ = get_thetamu(fun) out .= θ'*x end function call(fun::LinearNormalPolicy{TP}, x::Array{TP,1})::Array{TP,1} where {TP<:Real} call!(fun.feats, fun.ϕ, x) θ = get_thetamu(fun) return θ'*fun.feats end function call!(out::Array{TP, 1}, fun::LinearNormalPolicy{TP}, x::Array{TP,1}) where {TP<:Real} call!(fun.feats, fun.ϕ, x) θ = get_thetamu(fun) out .= θ'*fun.feats end function call(fun::LinearNormalPolicy{TP,IdentityBasis}, x::Array{TP,1}, y::Int)::TP where {TP<:Real} θ = get_thetamu(fun) return dot(θ[:, y],x) end function call(fun::LinearNormalPolicy{TP}, x::Array{TP,1}, y::Int)::TP where {TP<:Real} call!(fun.feats, fun.ϕ, x) θ = get_thetamu(fun) return dot(θ[:, y], fun.feats) end function get_mean!(π::LinearNormalPolicy, x) call!(π.μ,π,x) end function get_mean(π::LinearNormalPolicy{T}, x)::Array{T,1} where {T<:Real} get_mean!(π, x) return π.μ end function get_action!(π::LinearNormalPolicy{T}, x, rng::AbstractRNG)::T where {T<:Real} get_mean!(π, x) sample_normal!(π.action, π.μ, π.σ, rng) logp = sum(logpdf_normal.(π.action, π.μ, π.σ)) return logp end function grad_mu!(grad, gmu, x) grad .= x*gmu' end function grad_mu!(grad, gmu, x::Int) grad[x, :] .= gmu end function grad_std!(grad, amu, std) @. grad = (-1 + (amu / std)^2) / std end function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,true}, x, action)::T where {T<:Real} fill!(grad, 0.) θmu = get_thetamu(π) num_theta = length(θmu) gtheta = reshape(view(grad, 1:num_theta), size(θmu)) get_mean!(π, x) std = π.σ amu = @. (action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, x) grad_std!(view(grad, num_theta+1:length(grad)), amu, std) logp = sum(logpdf_normal.(π.action, π.μ, std)) # TODO make this not redundant computation return logp end function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,false}, x, action)::T where {T<:Real} fill!(grad, 0.) num_theta = get_num_params(π) gtheta = reshape(view(grad, 1:num_theta), size(get_thetamu(π))) get_mean!(π, x) std = π.σ amu = @. (action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, x) logp = sum(logpdf_normal.(π.action, π.μ, std)) return logp end function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T,TB,true}, x, action::Int)::T where {T<:Real,TB} fill!(grad, 0.) θmu = get_thetamu(π) num_theta = length(θmu) gtheta = reshape(view(grad, 1:num_theta), size(θmu)) get_mean!(π, x) std = π.σ amu = @. (action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, π.feats) grad_std!(view(grad, num_theta+1:length(grad)), amu, std) logp = sum(logpdf_normal.(π.action, π.μ, std)) return logp end function gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T,TB,false}, x, action::Int)::T where {T<:Real,TB} fill!(grad, 0.) num_theta = get_num_params(π) gtheta = reshape(view(grad, 1:num_theta), size(get_thetamu(π))) get_mean!(π, x) std = π.σ amu = @. (action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, π.feats) logp = sum(logpdf_normal.(π.action, π.μ, std)) return logp end function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,true}, x, rng::AbstractRNG)::T where {T<:Real} fill!(grad, 0.) θmu = get_thetamu(π) num_theta = length(θmu) gtheta = reshape(view(grad, 1:num_theta), size(θmu)) get_mean!(π, x) sample_normal!(π.action, π.μ, π.σ, rng) logp = sum(logpdf_normal.(π.action, π.μ, std)) std = π.σ amu = @. (action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, x) grad_std!(view(grad, num_theta+1:length(grad)), amu, std) return logp end function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, IdentityBasis,false}, x, rng::AbstractRNG)::T where {T<:Real} fill!(grad, 0.) θmu = get_thetamu(π) num_theta = length(θmu) gtheta = reshape(view(grad, 1:num_theta), size(θmu)) get_mean!(π, x) sample_normal!(π.action, π.μ, π.σ, rng) logp = sum(logpdf_normal.(π.action, π.μ, std)) std = π.σ amu = @. (action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, x) return logp end function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, TB,true}, x, rng::AbstractRNG)::T where {T<:Real,TB} fill!(grad, 0.) θmu = get_thetamu(π) num_theta = length(θmu) gtheta = reshape(view(grad, 1:num_theta), size(θmu)) get_mean!(π, x) sample_normal!(π.action, π.μ, π.σ, rng) std = π.σ logp = sum(logpdf_normal.(π.action, π.μ, std)) amu = @. (π.action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, π.feats) grad_std!(view(grad, num_theta+1:length(grad)), amu, std) return logp end function get_action_gradient_logp!(grad::Array{T}, π::LinearNormalPolicy{T, TB,false}, x, rng::AbstractRNG)::T where {T<:Real,TB} fill!(grad, 0.) θmu = get_thetamu(π) num_theta = length(θmu) gtheta = reshape(view(grad, 1:num_theta), size(θmu)) get_mean!(π, x) sample_normal!(π.action, π.μ, π.σ, rng) std = π.σ logp = sum(logpdf_normal.(π.action, π.μ, std)) amu = @. (π.action - π.μ) gmu = @. amu / std^2 grad_mu!(gtheta, gmu, π.feats) return logp end function set_params!(π::LinearNormalPolicy{T,TB,true}, θ::Array{T}) where {T,TB} π.θ .= θ clamp!(view(π.θ, length(π.θ)-length(π.σ)+1:length(π.θ)), 0.001, 100) π.σ .= π.θ[end-length(π.σ)+1:end] end function set_params!(π::LinearNormalPolicy{T,TB,false}, θ::Array{T}) where {T,TB} π.θ .= θ end function get_params(π::LinearNormalPolicy) π.θ end function copy_params!(params::Array{T}, π::LinearNormalPolicy{T}) where {T} vec(params) .= vec(π.θ) end function copy_params(π::LinearSoftmaxPolicy{T})::Array{T} where {T} return deepcopy(π.θ) end function add_to_params!(π::LinearNormalPolicy, Δθ) @. π.θ += Δθ end function add_to_params!(π::LinearNormalPolicy{T,TB,true}, Δθ) where {T,TB} @. π.θ += Δθ clamp!(view(π.θ,length(π.θ)-length(π.σ)+1:length(π.θ)), 0.001, 100) π.σ .= π.θ[end-length(π.σ)+1:end] end function clone(π::LinearNormalPolicy{T, TB, TS})::LinearNormalPolicy{T,TB,TS} where {T,TB,TS} A = length(π.σ) π₂ = LinearNormalPolicy(π.ϕ, A, π.σ, TS) π₂.action = deepcopy(π.action) π₂.μ = deepcopy(π.μ) return π₂ end
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1.983409
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struct GUI id::UUID widgets::Vector{Widget} expression::Expr end macro gui(expr) if expr.head != :for error( "@gui syntax is @gui for ", " [<variable>=<domain>,]... <expression> end" ) end is_gui = true quote_widgets, cur_block, cur_bindings, cur_symbols = _manipulate_outer(expr, is_gui) return quote cur_widgets = make_gui_list($(quote_widgets...)) cur_listener = $(esc(make_gui_block(cur_block, cur_symbols))) cur_id = _manipulate_inner(cur_widgets, cur_listener, $(is_gui)) nothing end end macro manipulate(expr) if expr.head != :for error( "@manipulate syntax is @manipulate for ", " [<variable>=<domain>,]... <expression> end" ) end is_gui = false quote_widgets, cur_block, cur_bindings, cur_symbols = _manipulate_outer(expr, is_gui) return quote cur_widgets = make_gui_list($(quote_widgets...)) cur_listener = $(esc(make_gui_block(cur_block, cur_symbols))) cur_id = _manipulate_inner(cur_widgets, cur_listener, $(is_gui)) nothing end end export @gui, @manipulate
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<reponame>JeffBezanson/MemPool.jl<filename>src/lock.jl # Copied from CUDA.jl/src/pool.jl """ NonReentrantLock Simple non-reentrant lock that errors when trying to reenter on the same task. """ struct NonReentrantLock # <: Base.AbstractLock rl::ReentrantLock NonReentrantLock() = new(ReentrantLock()) end function Base.lock(nrl::NonReentrantLock) @assert !islocked(nrl.rl) || nrl.rl.locked_by !== current_task() lock(nrl.rl) end function Base.trylock(nrl::NonReentrantLock) @assert !islocked(nrl.rl) || nrl.rl.locked_by !== current_task() trylock(nrl.rl) end Base.unlock(nrl::NonReentrantLock) = unlock(nrl.rl) # NonReentrantLock may be taken around code that might call the GC, which might # reenter through finalizers. Avoid that by temporarily disabling finalizers # running concurrently on this thread. enable_finalizers(on::Bool) = ccall(:jl_gc_enable_finalizers, Cvoid, (Ptr{Cvoid}, Int32,), Core.getptls(), on) macro safe_lock(l, ex) quote temp = $(esc(l)) lock(temp) enable_finalizers(false) try $(esc(ex)) finally unlock(temp) enable_finalizers(true) end end end # If we actually want to acquire a lock from a finalizer, we can't cause a task # switch. As a NonReentrantLock can only be taken by another thread that should # be running, and not a concurrent task we'd need to switch to, we can safely # spin. macro safe_lock_spin(l, ex) quote temp = $(esc(l)) while !trylock(temp) # we can't yield here end enable_finalizers(false) # retains compatibility with non-finalizer callers try $(esc(ex)) finally unlock(temp) enable_finalizers(true) end end end
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<reponame>Kitty-Cats/MaxHelpingHand module MaxHelpingHandMovingFlagTouchSwitch using ..Ahorn, Maple @mapdef Entity "MaxHelpingHand/MovingFlagTouchSwitch" MovingFlagTouchSwitch(x::Integer, y::Integer, flag::String="moving_flag_touch_switch", icon::String="vanilla", persistent::Bool=false, inactiveColor::String="5FCDE4", movingColor::String="FF8080", activeColor::String="FFFFFF", finishColor::String="F141DF") const placements = Ahorn.PlacementDict( "Flag Touch Switch (Moving)\n(max480's Helping Hand + Outback Helper)" => Ahorn.EntityPlacement( MovingFlagTouchSwitch ) ) const bundledIcons = String["vanilla", "tall", "triangle", "circle", "diamond", "double", "heart", "square", "wide", "winged"] Ahorn.editingOptions(entity::MovingFlagTouchSwitch) = Dict{String,Any}( "icon" => bundledIcons ) Ahorn.editingOrder(entity::MovingFlagTouchSwitch) = String["x", "y", "width", "height", "inactiveColor", "movingColor", "activeColor", "finishColor"] function Ahorn.renderSelectedAbs(ctx::Ahorn.Cairo.CairoContext, entity::MovingFlagTouchSwitch) px, py = Ahorn.position(entity) sprite = "collectables/outback/movingtouchswitch/container.png" for node in get(entity.data, "nodes", ()) nx, ny = Int.(node) theta = atan(py - ny, px - nx) Ahorn.drawArrow(ctx, px, py, nx + cos(theta) * 8, ny + sin(theta) * 8, Ahorn.colors.selection_selected_fc, headLength=6) Ahorn.drawSprite(ctx, sprite, nx, ny) px, py = nx, ny end end function Ahorn.selection(entity::MovingFlagTouchSwitch) nodes = get(entity.data, "nodes", ()) x, y = Ahorn.position(entity) sprite = "collectables/outback/movingtouchswitch/container.png" res = Ahorn.Rectangle[Ahorn.getSpriteRectangle(sprite, x, y)] for node in nodes nx, ny = Int.(node) push!(res, Ahorn.getSpriteRectangle(sprite, nx, ny)) end return res end Ahorn.nodeLimits(entity::MovingFlagTouchSwitch) = 0, -1 function Ahorn.render(ctx::Ahorn.Cairo.CairoContext, entity::MovingFlagTouchSwitch) icon = get(entity.data, "icon", "vanilla") iconPath = "objects/touchswitch/icon00.png" if icon != "vanilla" iconPath = "objects/MaxHelpingHand/flagTouchSwitch/$(icon)/icon00.png" end Ahorn.drawSprite(ctx, "collectables/outback/movingtouchswitch/container.png", 0, 0) Ahorn.drawSprite(ctx, iconPath, 0, 0) end end
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<reponame>vtjnash/Coverage.jl ####################################################################### # Coverage.jl # Take Julia test coverage results and bundle them up in JSONs # https://github.com/IainNZ/Coverage.jl ####################################################################### module Coverage using Requests using JSON # process_cov # Given a .cov file, return the counts for each line, where the # lines that can't be counted are denoted with a -1 export process_cov function process_cov(filename) fp = open(filename, "r") lines = readlines(fp) num_lines = length(lines) coverage = Array(Union(Nothing,Int), num_lines) for i = 1:num_lines cov_segment = lines[i][1:9] coverage[i] = cov_segment[9] == '-' ? nothing : int(cov_segment) end close(fp) return coverage end # process_src_coveralls # Given a .jl file, return the Coveralls.io dictionary for this # file by reading in the file and its matching .cov. Don't convert # to JSON yet, just return dictionary. # https://coveralls.io/docs/api # { # "name" : "$filename" # "source": "...\n....\n...." # "coverage": [null, 1, null] # } export process_src_coveralls function process_src_coveralls(filename) return ["name" => filename, "source" => readall(filename), "coverage" => process_cov(filename*".cov")] end # create_coveralls_post # Create the request to submit to Coveralls.io (as a dictionary, # not a JSON string) # https://coveralls.io/docs/api # { # "service_job_id": "1234567890", # "service_name": "travis-ci", # "source_files": [ # { # "name": "example.rb", # "source": "def four\n 4\nend", # "coverage": [null, 1, null] # }, # { # "name": "lib/two.rb", # "source": "def seven\n eight\n nine\nend", # "coverage": [null, 1, 0, null] # } # ] # } export create_coveralls_travis_post function create_coveralls_travis_post(source_files) return ["service_job_id" => ENV["TRAVIS_JOB_ID"], "service_name" => "travis-ci", "source_files" => source_files] end # submit_coveralls # Submit coverage to Coveralls.io export submit_coveralls function submit_coveralls(data) println(JSON.json(data)) post("https://coveralls.io/api/v1/jobs"; data = {"json_file" => JSON.json(data)}) end end
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# This file is a part of Julia. License is MIT: https://julialang.org/license """ Some{T} A wrapper type used in `Union{Some{T}, Nothing}` to distinguish between the absence of a value ([`nothing`](@ref)) and the presence of a `nothing` value (i.e. `Some(nothing)`). Use [`something`](@ref) to access the value wrapped by a `Some` object. """ struct Some{T} value::T end promote_rule(::Type{Some{T}}, ::Type{Some{S}}) where {T, S<:T} = Some{T} promote_rule(::Type{Some{T}}, ::Type{Nothing}) where {T} = Union{Some{T}, Nothing} convert(::Type{Some{T}}, x::Some) where {T} = Some{T}(convert(T, x.value)) convert(::Type{Union{Some{T}, Nothing}}, x::Some) where {T} = convert(Some{T}, x) convert(::Type{Union{T, Nothing}}, x::Any) where {T} = convert(T, x) convert(::Type{Nothing}, x::Any) = throw(MethodError(convert, (Nothing, x))) convert(::Type{Nothing}, x::Nothing) = nothing function show(io::IO, x::Some) if get(io, :typeinfo, Any) == typeof(x) show(io, x.value) else print(io, "Some(") show(io, x.value) print(io, ')') end end """ notnothing(x) Throw an error if `x === nothing`, and return `x` if not. """ notnothing(x::Any) = x notnothing(::Nothing) = throw(ArgumentError("nothing passed to notnothing")) """ something(x, y...) Return the first value in the arguments which is not equal to [`nothing`](@ref), if any. Otherwise throw an error. Arguments of type [`Some`](@ref) are unwrapped. # Examples ```jldoctest julia> something(nothing, 1) 1 julia> something(Some(1), nothing) 1 julia> something(missing, nothing) missing julia> something(nothing, nothing) ERROR: ArgumentError: No value arguments present ``` """ function something end something() = throw(ArgumentError("No value arguments present")) something(x::Nothing, y...) = something(y...) something(x::Some, y...) = x.value something(x::Any, y...) = x
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<filename>src/FEMMDeforLinearModule.jl """ FEMMDeforLinearModule Module for operations on interiors of domains to construct system matrices and system vectors for linear deformation models. """ module FEMMDeforLinearModule using FinEtools.FTypesModule: FInt, FFlt, FCplxFlt, FFltVec, FIntVec, FFltMat, FIntMat, FMat, FVec, FDataDict import FinEtools.FENodeSetModule: FENodeSet import FinEtools.FESetModule: FESet, manifdim import FinEtools.IntegDataModule: IntegData import FinEtools.FEMMDeforLinearBaseModule: FEMMDeforLinearAbstract import FinEtools.DeforModelRedModule: DeforModelRed, DeforModelRed2DAxisymm import FinEtools.MatDeforModule: MatDefor import FinEtools.CSysModule: CSys """ FEMMDeforLinear{S<:FESet, F<:Function, P<:PropertyDeformationLinear} Class for linear deformation finite element modeling machine. """ mutable struct FEMMDeforLinear{MR<:DeforModelRed, S<:FESet, F<:Function, M<:MatDefor} <: FEMMDeforLinearAbstract mr::Type{MR} integdata::IntegData{S, F} # geometry data mcsys::CSys # updater of the material orientation matrix material::M # material object end function FEMMDeforLinear(mr::Type{MR}, integdata::IntegData{S, F}, material::M) where {MR<:DeforModelRed, S<:FESet, F<:Function, M<:MatDefor} # @show mr # @show material.mr @assert mr === material.mr "Model reduction is mismatched" @assert (integdata.axisymmetric) || (mr != DeforModelRed2DAxisymm) "Axially symmetric requires axisymmetric to be true" return FEMMDeforLinear(mr, integdata, CSys(manifdim(integdata.fes)), material) end function FEMMDeforLinear(mr::Type{MR}, integdata::IntegData{S, F}, material::M, print) where {MR<:DeforModelRed, S<:FESet, F<:Function, M<:MatDefor} @show mr @show material.mr @assert mr === material.mr "Model reduction is mismatched" @assert (integdata.axisymmetric) || (mr != DeforModelRed2DAxisymm) "Axially symmetric requires axisymmetric to be true" return FEMMDeforLinear(mr, integdata, CSys(manifdim(integdata.fes)), material) end end
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<gh_stars>1-10 # # Single-thread `sum_gather` include("plots.jl"); # ## Throughput, L1, and last-level (LL) cache misses # # (Note: the LL cache miss data may not be available in some machines.) plt_throughput_cache_miss # ## Tuned `evals` plt_evals
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module CompatHelper import Base64 import Dates import GitHub import HTTP import Pkg import Printf import TOML import TimeZones import UUIDs include("types.jl") include("main.jl") include("assert.jl") include("ci_service.jl") include("envdict.jl") include("get_latest_version_from_registries.jl") include("get_project_deps.jl") include("git.jl") include("new_versions.jl") include("pull_requests.jl") include("ssh_keys.jl") include("stdlib.jl") include("timestamps.jl") include("utils.jl") include("version_numbers.jl") end # module
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<gh_stars>1-10 using LightGraphs import Base.== input = joinpath(@__DIR__, "input") raw = readlines(input) struct Coord x::Int y::Int end ==(left::Coord, right::Coord) = left.x == right.x && left.y == right.y function adjacent(coord::Coord) x, y = coord.x, coord.y return [ Coord(x, y + 1), Coord(x, y - 1), Coord(x + 1, y), Coord(x - 1, y) ] end function map_maze!(cs::Array{String,1}, c2t::Dict{Coord,Char}) for j = 1:length(cs) line = cs[j] for i = 1:length(line) t = line[i] c2t[Coord(i, j)] = t end end end function isalpha(char::Union{Char,Nothing}) return char !== nothing && char >= 'A' && char <= 'Z' end function portals(c2t::Dict{Coord, Char}) potentials = [] for ct in c2t c, t = ct if t >= 'A' && t <= 'Z' push!(potentials, c) end end portals = Array{Pair{String, Coord}, 1}(undef, (0)) for p in potentials x, y = p.x, p.y i = get(c2t, Coord(x - 1, y), nothing) j = c2t[p] k = get(c2t, Coord(x + 1, y), nothing) if i == '.' && isalpha(j) push!(portals, Pair(string(j, k), Coord(x - 1, y))) elseif isalpha(i) && k == '.' push!(portals, Pair(string(i, j), Coord(x + 1, y))) end end for p in potentials x, y = p.x, p.y i = get(c2t, Coord(x, y - 1), nothing) j = c2t[p] k = get(c2t, Coord(x, y + 1), nothing) if i == '.' && isalpha(j) push!(portals, Pair(string(j, k), Coord(x, y - 1))) elseif isalpha(i) && k == '.' push!(portals, Pair(string(i, j), Coord(x, y + 1))) end end return portals end function warp(coord, portals) label, _ = first(filter(kv -> kv.second.x == coord.x && kv.second.y == coord.y, portals)) for w in filter(kv -> kv.first == label, portals) if w.second.x != coord.x && w.second.y != coord.y return w.second end end end function seen!(coord::Coord, level::Int, cache::Set{Pair{Coord, Int}}) if Pair(coord, level) in cache return true else push!(cache, Pair(coord, level)) return false end end function explore(start::Coord, finish::Coord, level::Int, c2t::Dict{Coord, Char}, portals::Array{Pair{String, Coord}, 1}) nodes = [(start, 0, level)] seen = Set{Pair{Coord, Int}}() warps = [w.second for w in portals] xs = map(kv -> kv.second.x, portals) ys = map(kv -> kv.second.y, portals) minx = minimum(xs) maxx = maximum(xs) miny = minimum(ys) maxy = maximum(ys) while !isempty(nodes) node = popfirst!(nodes) coord, steps, level = node # skip exploring if we've already started from this node if seen!(coord, level, seen) continue else ## found exit, bail if coord == finish && level in [0, 1] return steps ## found exit but not at right level elseif coord == finish continue ## not a corridor elseif c2t[coord] != '.' continue elseif coord in warps && coord != start warp_to = warp(coord, portals) if (coord.x in [minx, maxx] || coord.y in [miny, maxy]) && level < 0 for neighbor in adjacent(warp_to) push!(nodes, (neighbor, steps + 2, level + 1)) end elseif (coord.x in [minx, maxx] || coord.y in [miny, maxy]) && level == 0 continue elseif level <= 0 for neighbor in adjacent(warp_to) push!(nodes, (neighbor, steps + 2, level - 1)) end else for neighbor in adjacent(warp_to) push!(nodes, (neighbor, steps + 2, level)) end end else for neighbor in adjacent(coord) push!(nodes, (neighbor, steps + 1, level)) end end end end end c2t = Dict{Coord,Char}() map_maze!(raw, c2t) ps = portals(c2t) _, start = first(filter(n -> n.first == "AA", ps)) _, finish = first(filter(n -> n.first == "ZZ", ps)) p1 = explore(start, finish, 1, c2t, ps) @assert p1 == 580 p2 = explore(start, finish, 0, c2t, ps) @assert p2 == 6362 print("-----------------------------------------------------------------------\n") print("donut maze -- part one\n : $p1\n") print("donut maze -- part two\n : $p2\n") print("-----------------------------------------------------------------------\n")
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<reponame>UnofficialJuliaMirrorSnapshots/SimpleANOVA.jl-fff527a3-8410-504e-9ca3-60d5e79bb1e4<gh_stars>0 @testset "Display Tests" begin @testset "AnovaData" begin data = [AnovaValue( "Total", 1827.6975, 19), AnovaResult( "A", 70.3125, 1, 70.3125, 3.0706495, 0.098856175), AnovaResult( "B", 1386.1125, 1, 1386.1125, 60.533556, 7.9430782e-7), AnovaResult("A × B", 4.9005, 1, 4.9005, 0.21401199, 0.64987001), AnovaFactor( "C", 366.372, 16, 22.89825)] result = AnovaData(data, AnovaFactor[], AnovaFactor[], Float64[], 1) expectedlines = ["", "Analysis of Variance Results", "", "Effect SS DF MS F p", "-------------------------------------------------------", " Total 1827.7 19 ", " A 70.3125 1 70.3125 3.07065 0.0988562 ", " B 1386.11 1 1386.11 60.5336 7.94308e-7", " A × B 4.9005 1 4.9005 0.214012 0.64987 ", " C 366.372 16 22.8983 ", ""] @test sprint(show, result) == join(expectedlines, "\n") end end
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1.59292
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<filename>test/runtest.jl using Logging io = open("test_log.txt", "w+") logger = SimpleLogger(io) with_logger(logger) do testdirectory = dirname(@__FILE__) cd(testdirectory) directories = readdir() for directory in directories if isdir(directory) include(joinpath(directory, "runtest.jl")) end cd(testdirectory) end end
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2.35
160
using Comonicon using Comonicon.Types using Comonicon.BuildTools: precompile_script, install using Test module Dummy using Comonicon using Test """ foo foo # Arguments - `x`: an argument # Options - `--foo <foo>`: foo foo """ @cast function foo(x::Int, y; foo::Int = 1, hulu::Float64 = 2.0, flag::Bool = false) where {T} @test x == 1 @test y == "2.0" @test foo == 2 @test hulu == 3.0 @test flag == true end """ goo goog gooasd dasdas goo asdas dasd assadas # Arguments - `ala`: ala ahjsd asd wvxzj - `gaga`: djknawd sddasd kw # Options - `-g,--giao <name>`: huhuhuhuhuuhu # Flags - `-f,--flag`: dadsa fasf gas """ @cast function goo(ala::Int, gaga; giao = "Bob", flag::Bool = false) @test ala == 1 @test gaga == "2.0" @test giao == "Sam" @test flag == true end """ tick tick. # Arguments - `xx`: xxxxxxxxxxxx - `yy`: yyyyyyyyyyyy """ @cast function tick(xx::Int, yy::Float64 = 1.0) @test xx == 1 @test yy in [1.0, 2.0] end @main name = "dummy" doc = """ dummy command. dasdas dsadasdnaskdas dsadasdnaskdas sdasdasdasdasdasd adsdasdas dsadasdas dasdasd dasda """ end @test Dummy.command_main(String["foo", "1.0", "2.0", "--foo", "2", "--hulu=3.0", "--flag"]) == 0 @test Dummy.command_main(String["foo", "1.0", "2.0", "--foo", "2", "--hulu=3.0", "-f"]) == 0 @test Dummy.command_main(String["goo", "1.0", "2.0", "-gSam", "-f"]) == 0 @test_throws ErrorException LeafCommand( identity; name = "foo", options = [Option("huhu"; short = true)], ) @test Dummy.command_main(String["tick", "1.0", "2.0"]) == 0 @test Dummy.command_main(String["tick", "1.0"]) == 0 @test precompile_script(Dummy) == """ using Main.Dummy; Main.Dummy.command_main(["-h"]); Main.Dummy.command_main(["goo", "-h"]); Main.Dummy.command_main(["tick", "-h"]); Main.Dummy.command_main(["foo", "-h"]); """ empty!(ARGS) append!(ARGS, ["2", "--opt1", "3"]) """ ArgParse example implemented in Comonicon. # Arguments - `x`: an argument # Options - `--opt1 <arg>`: an option - `-o, --opt2 <arg>`: another option # Flags - `-f, --flag`: a flag """ @main function main(x; opt1 = 1, opt2::Int = 2, flag = false) @test flag == false @test x == "2" @test opt1 == "3" @test opt2 == 2 end Comonicon.install( Dummy; bin = Comonicon.PATH.project("test", "bin"), completion = false, quiet = false, ) @test isfile(Comonicon.PATH.project("test", "bin", "dummy")) @test isfile(Comonicon.PATH.project("test", "bin", "dummy.jl")) @testset "default_name" begin @test Comonicon.Parse.default_name("Foo.jl") == "foo" @test Comonicon.Parse.default_name(sin) == "sin" end cmd = @cast(f_issue_47(xs::Int...) = xs) @testset "issue/#47" begin @test cmd.args[1].type == Int @test cmd.args[1].vararg == true end
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2.239775
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module DVeTest using Base.Test using Quantum xb = basis(:X,[1:5]) d = DiracVector([1:5], xb) @test d==d'' @test d[:]==d.coeffs @test d'*d == 55 @test d*d' == DiracMatrix(kron(d.coeffs, d.coeffs'), d.basis, d.basis') @test isdual(d,d') @test filterstates(s->label(s)%2==0, d) == DiracVector([2,4], basis(xb[2], xb[4])) @test filtercoeffs(c->c%2==0, d) == DiracVector([2,4], basis(xb[2], xb[4])) @test map(x->x*2, d)==d+d @test xb[1]'*d == 1 @test d'*xb[1] == 1 @test norm(normalize(d)) < 1+1e-8 && norm(normalize(d)) > 1-1e-8 @test reduce(+,[i*xb[i]' for i=1:length(xb)]) == d' @test (1/sqrt(2)*xb[1]) + (1/sqrt(2)*xb[2]) == dvec([1/sqrt(2), 1/sqrt(2)], basis(xb[1], xb[2])) @test ket(:X,1)+ket(:X,1)+ket(:X,1) = dvec([3], basis(xb[1])) @test ket(:X,1)+(bra(:S,"1")*ket(:X, 1)) * ket(:X,1)==dvec([1+1*bra(:S,"1")*ket(:X,1)], basis(ket(:X,1))) end
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using LightGraphs """ solve """ export solve! function solve!(::LPModel, ::JuMP.Model, ::AbstractSolveAttribute) <: Bool end # Will construct the constraint graph and find a feasible solution or deem the problem infeasible # NB: Assumes the model to have difference constraints only! function solve!(lpmodel::LPModel, model::JuMP.Model, ::ShortestPath) A, b = get_constraint_matrices(lpmodel) edges = fill((0,0), size(A)[1]) # Number of constraints num_vars = size(A)[2] distmx = zeros(num_vars, num_vars) # Get list of edges and distance matrix index = 1 for row in eachrow(A) i = findall(x->x==-1, row)[1] j = findall(x->x==1, row)[1] edge = (i,j) edges[index] = edge distmx[i,j] = b[index] index += 1 end # Directed graph digraph = SimpleDiGraph(Edge.(edges)) # Use all vertices as sources, as we omit the starting source sources = collect(1:num_vars) try bf = bellman_ford_shortest_paths(digraph, sources, distmx) distances = bf.dists print("\nFeasible solution found:\n") show(distances) print("\n") # Update to feasible unbounded solution return true catch error if isa(error, LightGraphs.NegativeCycleError) # Update no feasible solution print("\nInfeasible solution.\n") return true else throw(error) return false end end end """ function solve!(model::Model, ::ConstantObjective) # Set optimal objective value (if legal) MOI.set(model, MOI.ObjectiveValue(), objective_function(model, objective_function_type(model)).constant) # Check if there is a legal solution for the variables, and set result status accordingly for (F, S) in list_of_constraint_types(model) for cref in all_constraints(model, F, S) setVariable(model, cref, F, S) end end end function setVariable(model::Model, ::Any, ::VariableRef, ::Union{}) MOI.set(model, MOI.PrimalStatus(), MOI.INFEASIBLE_POINT) MOI.set(model, MOI.PrimalStatus(), MOI.FEASIBLE_POINT) end """
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# %% # <NAME> # Code based off Emmanuel's Code # cd("/Volumes/SSD Hans/Github/MacroFall2020/AdvMacro/Assignment8") # mkpath("Figures") # Pkg.add("Plots") # Pkg.add.(["Optim" ,"Roots","Parameters","Distributions","QuadGK"]) # Pkg.add.(["Latexify","PrettyTables","StatsPlots"]) using Plots # using LateXStrings # Pkg.add("LaTeXStrings") # https://github.com/stevengj/LaTeXStrings.jl # Pkg.add("Dierckx") using Dierckx # Pkg.add("Dierckx") # https://github.com/kbarbary/Dierckx.jl # Pkg.add("Interpolations") using Interpolations # Pkg.add("Interpolations") # https://github.com/JuliaMath/Interpolations.jl # Pkg.add("ForwardDiff") using ForwardDiff # Pkg.add("ForwardDiff") # https://github.com/JuliaDiff/ForwardDiff.jl using Optim # Pkg.add("Optim") # https://julianlsolvers.github.io/Optim.jl/stable/ using Optim: converged, maximum, maximizer, minimizer, iterations using Roots # Pkg.add("Roots") # https://github.com/JuliaMath/Roots.jl using Parameters # Pkg.add("Parameters") # https://github.com/mauro3/Parameters.jl using Distributions #Pkg.add("Distributions") using QuadGK # Pkg.add("QuadGK") # https://juliamath.github.io/QuadGK.jl/latest/ using LinearAlgebra using Random using Statistics using Latexify # Pkg.add("StatsPlots") using StatsPlots # Call Scaled Interpolation Functions include("../Scaled_Interpolation_Functions.jl") # Pkg.add("SparseArrays") using SparseArrays # mkpath("Figures") # # using Random, Distributions # using Statistics # using LinearAlgebra # using Plots # using Interpolations # using Dierckx # using ForwardDiff # using Optim # using Optim: converged, maximum, maximizer, minimizer, iterations # using Roots # using Parameters # include("Scaled_Interpolation_Functions.jl") # # using .VFI_Toolbox # println(" ") # println("------------------------") # println("Hugget (1993) in Julia") # println("PWD: ",pwd()) # println("Solve Hugget's model with EGM and several implementations of histogram method") # println("------------------------") # println(" ") #----------------------------------------------------------- #----------------------------------------------------------- # Paramters and Model Structure # Generate structure for parameters using Parameters module # We can set default values for our parameters @with_kw struct Par # Model Parameters α::Float64 = 1/3 ; # Production function β::Float64 = 0.96 ; # Discount factor γ::Float64 = 2.0 ; # Relative risk aversion parameter δ::Float64 = 0.05 ; # Depreciation rate ρ::Float64 = 0.90 ; # Persistence of labor efficiency process σ::Float64 = 0.08 ; # Standard devaiation of labor efficiency innovation z_bar::Float64 = 1; # Reference level for productivity # VFI Paramters max_iter::Int64 = 100000; # Maximum number of iterations dist_tol::Float64 = 1E-6 ; # Tolerance for distance # Histogram iteration parameters Hist_max_iter = 10000 ; Hist_tol = 1E-5 ; # Histogram iteration parameters N_eq = 1000 ; tol_eq = 1E-6 ; # Minimum consumption for numerical optimization c_min::Float64 = 1E-16 end # Allocate paramters to object p for future calling p = Par() # Function to distretize AR(1) markov process with Rouwenhorst (1995) function Rouwenhorst95(N,p::Par) @unpack ρ,σ=p # INPUTS: # ρ: persistence of unerlying AR(1) process where log(z') = ρlog(z)+η # σ_z: Std dev of inovation η in AR(1) process where η∼N(0,σ^2) # N: Size of grid for discrete process # OUTPUT: # z: All possible values of discretized AR(1) process, equally spaced grid of size N # Π: Matrix of transition probabilities # PDF_z: Stationary PDF of z #--------------------------------------------------------------------------- Π = zeros(N,N) Π_Nm = zeros(N-1,N-1) P = (1+ρ)/2 ϕ = σ*(sqrt((N-1)/(1-ρ^2))) z = range(-ϕ,ϕ;length=N) if N==2 Π = [P 1-P;1-P P] else Π_Nm = Rouwenhorst95(N-1,p)[2] o = zeros(N-1) Π = P*[Π_Nm o; o' 0] + (1-P)*[o Π_Nm; 0 o'] + (1-P)*[o' 0; Π_Nm o] + P*[0 o';o Π_Nm] Π = Π./repeat(sum(Π,dims=2),1,N) end PDF_z = pdf.(Binomial(N-1,0.5),(0:N-1)) return (z,Π,P) end # Function to get grid on assets function Make_A_Grid(n_a,θ_a,a_min,a_max,p::Par) # Get k_grid if θ_a≠1 a_grid = PolyRange(a_min,a_max;θ=θ_a,N=n_a) else a_grid = range(a_min,a_max,length=n_a) end # Return return a_grid end # %% # Generate structure of model objects @with_kw struct Model # Parameters p::Par = Par() # Model paramters in their own structure # Parameters for Asset Grid a_max::Float64 = 50 # Default max node of a_grid θ_a::Float64 = 2.5 # Default Curvature of a_grid n_a::Int64 = 200 # Default Size of a_grid n_a_fine::Int64 = 1000 # Default Size of fine grid for interpolation and distribution # Productivity process n_ϵ::Int64 = 11 # Default size of discretized grid for productivity as a markov process log_ϵ = Rouwenhorst95(n_ϵ,p)[1] Π = Rouwenhorst95(n_ϵ,p)[2] ϵ_grid = exp.(log_ϵ) # Prices and aggregates r::Float64 = 0.05 # Initial guess for rate of return on assets # Lower bound on Asset Grid (borrowing limit) a_min = -ϵ_grid[1]/r # Capital grid a_grid = Make_A_Grid(n_a,θ_a,a_min,a_max,p) # Grid on assets for model solution a_grid_fine = Make_A_Grid(n_a_fine,1,a_min,a_max,p) # Fine grid on assets for interpolation # State matrices a_mat = repeat(a_grid',n_ϵ,1) a_mat_fine= repeat(a_grid_fine',n_ϵ,1) ϵ_mat = repeat(ϵ_grid,1,n_a) # Value and policy functions V = Array{Float64}(undef,n_ϵ,n_a) # Value Function G_ap = Array{Float64}(undef,n_ϵ,n_a) # Policy Function G_c = Array{Float64}(undef,n_ϵ,n_a) # Policy Function V_fine = Array{Float64}(undef,n_ϵ,n_a_fine) # Value Function on fine grid G_ap_fine = Array{Float64}(undef,n_ϵ,n_a_fine) # Policy Function on fine grid G_c_fine = Array{Float64}(undef,n_ϵ,n_a_fine) # Policy Function on fine grid # Distribution Γ = 1/(n_ϵ*n_a_fine)*ones(n_ϵ,n_a_fine) # Distribution (initiliazed to uniform) # Error in Euler equation Euler = Array{Float64}(undef,n_ϵ,n_a_fine) # Errors in Euler equation end # Allocate model to object M for future calling M = Model() # Utility function function utility(c,p::Par) if p.γ>1 return (c).^(1-p.γ)/(1-p.γ) else return log.(c) end end function d_utility(c,p::Par) return (c).^(-p.γ) end function d_utility_inv(x,p::Par) return x.^(-1/p.γ) end # Get index for histogram method function get_ind(a_grid,g_a) n = length(a_grid) if g_a > maximum(a_grid) return n elseif g_a < minimum(a_grid) return 1 else return floor(((g_a-minimum(a_grid))/(maximum(a_grid)-minimum(a_grid)))*(n-1)+1) end end # %% # PFI Fixed Point - Iterate over policy functions for a given guess of the interest rate r function PFI_Fixed_Point(T::Function,M::Model,G_ap_old=nothing) # Unpack model structure @unpack p, n_ϵ, n_a, n_a_fine, θ_a, a_grid, a_grid_fine, r = M # PFI paramters @unpack max_iter, dist_tol = p # Initialize variables for loop if G_ap_old==nothing G_ap_old = (1+r)*M.a_mat end G_dist = 1 ; # Initialize distance println(" ") println("------------------------") println("PFI - n_ϵ=$n_ϵ, n_a=$n_a - θ_a=$θ_a - r=$r") for iter=1:max_iter # Update value function G_ap_new, G_c = T(Model(M,G_ap=copy(G_ap_old))) # Update distance and iterations G_dist = sqrt(norm(G_ap_new-G_ap_old,2)) # Update old function G_ap_old = G_ap_new # Report progress every 250 iterations if mod(iter,250)==0 println(" PFI Loop: iter=$iter, dist=",G_dist) end # Check convergence and return results if G_dist<=dist_tol println("PFI - n_ϵ=$n_ϵ, n_a=$n_a - θ_a=$θ_a - r=$r") println("Iterations = $iter and Distance = ",G_dist) println("------------------------") println(" ") # Interpolate to fine grid G_ap_fine = zeros(n_ϵ,n_a_fine) G_c_fine = zeros(n_ϵ,n_a_fine) for i_ϵ=1:n_ϵ G_ap_ip = ScaledInterpolations(a_grid,G_ap_new[i_ϵ,:] , BSpline(Cubic(Line(OnGrid())))) G_ap_fine[i_ϵ,:].= G_ap_ip.(collect(a_grid_fine)) G_c_ip = ScaledInterpolations(a_grid,G_c[i_ϵ,:] , BSpline(Cubic(Line(OnGrid())))) G_c_fine[i_ϵ,:] .= G_c_ip.(collect(a_grid_fine)) end # Update model M = Model(M; G_ap=G_ap_new,G_c=G_c,G_ap_fine=G_ap_fine,G_c_fine=G_c_fine) # Return results return M end end # If loop ends there was no convergence -> Error! error("Error in PFI - Solution not found") end # Bellman operator - EGM - Iterate on Policy Functions function T_EGM_G(M::Model) @unpack p, n_ϵ, n_a, G_ap, r, a_min, Π = M @unpack β = p # Define RHS of Euler equation for each (ϵ,a') # Rows are present ϵ and columns are tomorrow's a in fixed grid Euler_RHS = β*(1+r)*Π*d_utility( (1+r)*M.a_mat + M.ϵ_mat - G_ap , p ) # Check Monotonicity if any( Euler_RHS.<0 ) error("RHS must be monotone for EGM to work") end # Define consumption from Euler equation C_endo = max.(d_utility_inv(Euler_RHS,p),p.c_min) # Define endogenous grid on assets A_endo = (C_endo .+ M.a_mat - M.ϵ_mat)/(1+r) # Interpolate functions on exogenous grid G_c = Array{Float64}(undef,n_ϵ,n_a) for i_ϵ=1:n_ϵ # Sort A_endo for interpolation sort_ind = sortperm(A_endo[i_ϵ,:]) A_aux = A_endo[i_ϵ,:][sort_ind] C_aux = C_endo[i_ϵ,:][sort_ind] # Check boundary condition # Ap(ϵ,a)=a_min for all a<min(A_aux) # Note that in that case C is linear between a_min and min(A_aux) if minimum(A_aux)>a_min a_vec = M.a_grid[M.a_grid.<minimum(A_aux)] # All assets today between a_min and min(A_aux) A_aux = [a_vec ; A_aux] # Append to existing grid of today's assets C_aux = [((1+r)*a_vec.+M.ϵ_grid[i_ϵ].-a_min) ; C_aux] # I can know consumption directly because all those assets imply a'=a_min end C_ip = Spline1D(A_aux,C_aux;k=1) G_c[i_ϵ,:] .= C_ip.(M.a_grid) # interpolate consumption to exogenous grid of assets today #Ap_aux = (1+r)*collect(M.a_grid) .+ M.ϵ_grid[i_ϵ] .- G_c[i_ϵ,:] # Find corresponding assets tomorrow end # Update policy function G_ap .= (1+r)*M.a_mat .+ M.ϵ_mat .- G_c # Adjust for numerical error for ind = findall(<=(1e-10),abs.(G_ap.-a_min)) G_ap[ind] = a_min G_c[ind] = (1+r)*M.a_mat[ind] + M.ϵ_mat[ind] - a_min end # Check for borrowing constraint if any( G_ap.<a_min ) error("Borrowing Constraint Violated") end # Return Results return G_ap, G_c end # Value function (given policy function) function Value_Function(M::Model) # Unpack model structure @unpack p, n_ϵ, n_a, n_a_fine, a_grid, a_grid_fine, r, G_ap, G_c, Π = M @unpack β, dist_tol = p # Compute value function with policy function iteration V = zeros(n_ϵ,n_a) V_new = zeros(n_ϵ,n_a) V_dist = 1 U_mat = utility(G_c,p) while V_dist>dist_tol for i_ϵ=1:n_ϵ Pr = Π[i_ϵ,:]' for i_a=1:n_a ap = G_ap[i_ϵ,i_a] Vp = zeros(n_ϵ) for i_ϵp=1:n_ϵ Vp_ip = ScaledInterpolations(a_grid,V[i_ϵp,:], BSpline(Cubic(Line(OnGrid())))) Vp[i_ϵp] = Vp_ip(ap) end V_new[i_ϵ,i_a] = U_mat[i_ϵ,i_a] + β*(Pr*Vp) end end V_dist = maximum(abs.(V_new./V.-1)) V = V_new end # Interpolate to fine grid V_fine = zeros(n_ϵ,n_a_fine) for i_ϵ=1:n_ϵ V_ip = ScaledInterpolations(a_grid,V[i_ϵ,:], BSpline(Cubic(Line(OnGrid())))) V_fine[i_ϵ,:] .= V_ip.(collect(a_grid_fine)) end # Update model M = Model(M; V=V,V_fine=V_fine) return M end # %% # Compute stationary distribution with Histogram method # Histogram method function Histogram_Method_Loop(M::Model,N_H=nothing,Γ_0=nothing) @unpack a_min, p, n_ϵ, Π, n_a_fine, a_grid_fine, G_ap_fine = M @unpack Hist_max_iter, Hist_tol = p println("\n--------------------------------\nBegining Histogram Method with Loops") # Change max iter if N_H==nothing N_H = Hist_max_iter end # Initial distribution (uniorm in income shock and assets) if Γ_0==nothing Γ_0 = M.Γ end # Discretize distribution H_ind = Array{Int64}(undef,n_ϵ,n_a_fine) H_weight = Array{Float64}(undef,n_ϵ,n_a_fine) a_max = maximum(a_grid_fine) for i_ϵ=1:n_ϵ for i_a=1:n_a_fine H_ind[i_ϵ,i_a] = get_ind(a_grid_fine,G_ap_fine[i_ϵ,i_a]) # weight on node corresponding to H_ind if H_ind[i_ϵ,i_a] == n_a_fine || H_ind[i_ϵ,i_a] == 1 # Mass points above max or below min in the fine grid H_weight[i_ϵ,i_a] = 1 else H_weight[i_ϵ,i_a] = 1-(G_ap_fine[i_ϵ,i_a]-a_grid_fine[H_ind[i_ϵ,i_a]])/(a_grid_fine[H_ind[i_ϵ,i_a]+1]-a_grid_fine[H_ind[i_ϵ,i_a]]) end end end #Γ = zeros(n_ϵ,n_a_fine) ## Loop for updating histogram #H_dist = 1 #for l = 1:N_H # for i = 1:n_ϵ # for j = 1:n_a_fine # # all combinations of (ϵ,a) today that may lead to a_j tomorrow # index = findall(x->x==j,H_ind) # index_ = findall(x->x==j,H_ind.+1) # # from the above, take only ϵ for the income probability transition # transindex = map(i->i[1], index) # transindex_ = map(i->i[1], index_) # # Update the distribution # Γ[i,j] = Π[i,transindex]'*(H_weight[index].*Γ_0[index])+Π[i,transindex_]'*((-H_weight[index_].+1).*Γ_0[index_]) # end # end # # Update distance # H_dist = maximum(abs.(Γ-Γ_0)) # # Update initial distribution # Γ_0 .= Γ # # Report progress # println(" Histogram Loop: iter=$l, dist=$H_dist") # #if mod(l,250)==0 # # println(" Histogram Loop: iter=$l, dist=$H_dist") # #end # # Check convergence # if H_dist<Hist_tol # println("Histogram iteartion converged in iteration $i_H. H_dist=$H_dist\n--------------------------------\n") # M = Model(M; Γ=Γ) # return M # end #end Γ = zeros(n_ϵ,n_a_fine) # Loop for updating histogram H_dist = 1 for i_H=1:N_H # Update histogram Γ = zeros(n_ϵ,n_a_fine) for i_ϵ=1:n_ϵ # Current ϵ for i_a=1:n_a_fine # Current a i_ap = H_ind[i_ϵ,i_a] ω_ap = H_weight[i_ϵ,i_a] for i_ϵp=1:n_ϵ # Future ϵ Γ[i_ϵp,i_ap] = Γ[i_ϵp,i_ap] + ω_ap *Π[i_ϵ,i_ϵp]*Γ_0[i_ϵ,i_a] if i_ap < n_a_fine Γ[i_ϵp,i_ap+1] = Γ[i_ϵp,i_ap+1] + (1-ω_ap)*Π[i_ϵ,i_ϵp]*Γ_0[i_ϵ,i_a] end end end end # Update distance H_dist = maximum(abs.(Γ-Γ_0)) # Update initial distribution Γ_0 .= Γ # Report progress if mod(i_H,50)==0 println(" Histogram Loop: iter=$i_H, dist=$H_dist") end # Check convergence if H_dist<Hist_tol println("Histogram iteartion converged in iteration $i_H. H_dist=$H_dist\n--------------------------------\n") M = Model(M; Γ=Γ) return M end end # Return Results println("Histogram updated for $N_H iteartions. Current H_dist=$H_dist \n--------------------------------\n") M = Model(M; Γ=Γ_0) return M end # %% # Do optimization then check market clearing function market_clearing(r,M::Model) # Iterate over policy function given r M = PFI_Fixed_Point(T_EGM_G,Model(r=r)) # Get value function M = Value_Function(M) # Find stationary distribution M = Histogram_Method_Loop(M) # Check for market clearing distance = sum(sum(M.G_ap_fine.*M.Γ,dims=2)).^2 println("Squared distance from market clearing=$distance at intertest rate = $r") return distance end # Outer loop to find interest rate that clears market function S_RCE(M::Model) @unpack p = M @unpack β = p # Maximum interest rate r_max = 1/β-1 # Minimum interest rate r_min = 0 # Find equilibrium interest rate min_result = optimize(x->market_clearing(x,M).^2,r_min,r_max) # Check result converged(min_result) || error("Failed to clear asset market in $(iterations(min_result)) iterations") # Upddate policy function r = min_result.minimizer println("Equilibrium found in $(iterations(min_result)) iterations: r=$r") # Load equilibrium interest rate into model M = Model(M; r=r) # Compute policy function, value function and stationary distribution M = PFI_Fixed_Point(T_EGM_G,M) M = Value_Function(M) M = Histogram_Method_Loop(M) return M end # %% # Function that makes 3-d plots of the value function, policy function and asset distributon function plot_hugget(M::Model) # Interpolate the value function, policy function and asset distribution along the income shock dimension ϵ_grid_fine = range(M.ϵ_grid[1],M.ϵ_grid[end],length=M.n_a_fine) V_fine3d = zeros(M.n_a_fine,M.n_a_fine) G_ap_fine3d = zeros(M.n_a_fine,M.n_a_fine) Γ_fine3d = zeros(M.n_a_fine,M.n_a_fine) for i in 1:M.n_a_fine Γ_fine3d[:,i] = Spline1D(M.ϵ_grid,M.Γ[:,i];k=1).(collect(ϵ_grid_fine)) V_fine3d[:,i] = Spline1D(M.ϵ_grid,M.V_fine[:,i];k=1).(collect(ϵ_grid_fine)) G_ap_fine3d[:,i] = Spline1D(M.ϵ_grid,M.G_ap_fine[:,i];k=1).(collect(ϵ_grid_fine)) end # Normalize distribution such that it sums to one (need because I interpolated) Γ_fine3d = Γ_fine3d./(sum(sum(Γ_fine3d))) # Plots of the value function, policy function and asset distributon gr() plot(ϵ_grid_fine,M.a_grid_fine,V_fine3d, st=:contour,xlabel="Income",ylabel="Assets",title="Value Function - Hugget") # Surface plot savefig("./Figures/surface_vf_hugget") plot(ϵ_grid_fine,M.a_grid_fine,G_ap_fine3d, st=:contour,xlabel="Income",ylabel="assets",title="Asset policy - Hugget") # Surface plot savefig("./Figures/surface_policy_hugget") plot(ϵ_grid_fine,M.a_grid_fine,Γ_fine3d, st=:surface,xlabel="Income",ylabel="Assets",title="Distribution - Hugget") # Surface plot savefig("./Figures/surface_dist_hugget") end # %% # Call PFI M_hugget = S_RCE(Model(n_a=100,n_ϵ=11)) # Get plots plot_hugget(M_hugget)
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2.035016
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struct HessianNull <: Hessian end struct HessianD21{H1,H2} <: Hessian h1::H1 h2::H2 ref::MyRef{Float64} ref1::MyRef{Float64} ref2::MyRef{Float64} end struct Hessian11{F,H1,H11} <: Hessian h1::H1 h11::H11 fref::MyRef{Float64} ref::MyRef{Float64} function Hessian11(e::Expression1{F,E},d, indexer = nothing) where {F,E} h1 = Hessian(e.e1,d.d1,indexer) h11= Hessian(d.d1,d.d1,indexer) return new{F,typeof(h1),typeof(h11)}(h1,h11,ref(e.e1),ref(d)) end end struct Hessian11F1{F,H1,H11,R} <: Hessian a::R h1::H1 h11::H11 fref::MyRef{Float64} ref::MyRef{Float64} function Hessian11F1(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1 <: Real,E2} h1 = Hessian(e.e2,d.d1,indexer) h11= Hessian(d.d1,d.d1,indexer) return new{F,typeof(h1),typeof(h11),typeof(e.e1)}(e.e1,h1,h11,ref(e.e2),ref(d)) end end struct Hessian11F2{F,H1,H11,R} <: Hessian a::R h1::H1 h11::H11 fref::MyRef{Float64} ref::MyRef{Float64} function Hessian11F2(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1,E2 <: Real} h1 = Hessian(e.e1,d.d1,indexer) h11= Hessian(d.d1,d.d1,indexer) return new{F,typeof(h1),typeof(h11),typeof(e.e2)}(e.e2,h1,h11,ref(e.e1),ref(d)) end end struct Hessian02{H11,H12,H21,H22} <: Hessian h11::H11 h12::H12 h21::H21 h22::H22 ref11::MyRef{Float64} ref12::MyRef{Float64} ref21::MyRef{Float64} ref22::MyRef{Float64} end struct Hessian22{F,H1,H2,H11,H12,H21,H22} <: Hessian h1::H1 h2::H2 h11::H11 h12::H12 h21::H21 h22::H22 fref1::MyRef{Float64} fref2::MyRef{Float64} ref1::MyRef{Float64} ref2::MyRef{Float64} function Hessian22(e::Expression2{F,E1,E2},d,indexer = nothing) where {F,E1,E2} h1 = Hessian(e.e1,d.d1,indexer) h2 = Hessian(e.e2,d.d2,indexer) h11= Hessian(d.d1,d.d1,indexer) h12= Hessian(d.d1,d.d2,indexer) h21= Hessian(d.d2,d.d1,indexer) h22= Hessian(d.d2,d.d2,indexer) new{F,typeof(h1),typeof(h2),typeof(h11),typeof(h12),typeof(h21),typeof(h22)}(h1,h2,h11,h12,h21,h22,ref(e.e1),ref(e.e2),ref1(d),ref2(d)) end end # struct Hessian20{H1,H2} <: Hessian # h1::H1 # h2::H2 # ref1::MyRef{Float64} # ref2::MyRef{Float64} # end struct HessianD00 <: Hessian index1::Int index2::Int islower::Bool end struct HessianD00S <: Hessian index::Int islower::Bool end struct HessianD20{H1,H2} <: Hessian h1::H1 h2::H2 ref1::MyRef{Float64} ref2::MyRef{Float64} end struct HessianD10{H} <: Hessian h::H ref::MyRef{Float64} end struct HessianD11{H} <: Hessian h::H ref1::MyRef{Float64} ref2::MyRef{Float64} end struct Hessian22a{H1,H2} <: Hessian h1::H1 h2::H2 ref1::MyRef{Float64} ref2::MyRef{Float64} end struct Hessian22m{H1,H2,H12,H21} <: Hessian h1::H1 h2::H2 h12::H12 h21::H21 ref1::MyRef{Float64} ref2::MyRef{Float64} end struct Hessian11a{H} <: Hessian h1::H ref::MyRef{Float64} end struct HessianSum{I,H} <: Hessian inner::I hs::Vector{H} end const HESSIAN_NULL = HessianNull() @inline function (h::HessianSum{I,H})(z,x,p=nothing,h0=1) where {I,H} inner(h)(z,x,p,h0) @simd for i in eachindex(h.hs) @inbounds h.hs[i](z,x,p,h0) end end @inline function (h::HessianSum{Nothing,H})(z,x,p=nothing,h0=1) where H @simd for i in eachindex(h.hs) @inbounds h.hs[i](z,x,p,h0) end end @inline (::HessianNull)(z,x,p=nothing,h0=1) = nothing @inline function (h::HessianD00)(z,x,p=nothing,h0 = 1) islower(h) && @inbounds z[index1(h)::Int,index2(h)::Int] += h0 return end @inline function (h::HessianD00S)(z,x,p=nothing,h0 = 1) islower(h) && @inbounds z[index(h)::Int] += h0 return end @inline function (h::HessianD10{H})(z,x,p=nothing,h0 = 1) where H h.h(z,x,p,h0*refval(h)) return end @inline function (h::HessianD11{H})(z,x,p=nothing,h0 = 1) where H h.h(z,x,p,h0*refval1(h)*refval2(h)) return end @inline function (h::HessianD21{H1,H2})(z,x,p=nothing,h0=1) where {H1,H2} h.h1(z,x,p,h0*refval(h)*refval1(h)) h.h2(z,x,p,h0*refval(h)*refval2(h)) return end @inline function (h::HessianD20{H1,H2})(z,x,p=nothing,h0=1) where {H1,H2} h.h1(z,x,p,h0*refval1(h)) h.h2(z,x,p,h0*refval2(h)) end @inline function (h::Hessian02{H11,H12,H21,H22})(z,x,p=nothing,h0=1) where {H11,H12,H21,H22} h.h11(z,x,p,h0*refval11(h)*refval21(h)) h.h12(z,x,p,h0*refval11(h)*refval22(h)) h.h21(z,x,p,h0*refval12(h)*refval21(h)) h.h22(z,x,p,h0*refval12(h)*refval22(h)) return end @inline function (h::Hessian11a{H})(z,x,p=nothing,h0=1) where {H} h.h1(z,x,p,h0*refval(h)) return end @inline function (h::Hessian22a{H1,H2})(z,x,p=nothing,h0=1) where {H1,H2} h.h1(z,x,p,h0*refval1(h)) h.h2(z,x,p,h0*refval2(h)) return end @inline function (h::Hessian22m{H1,H2,H12})(z,x,p=nothing,h0=1) where {H1,H2,H12} h.h1(z,x,p,h0*refval1(h)) h.h2(z,x,p,h0*refval2(h)) h.h12(z,x,p,h0) h.h21(z,x,p,h0) return end Hessian(e::ExpressionSum{E,I1},d::GradientSum{D,I2},indexer = nothing) where {E,D,I1,I2} = HessianSum(Hessian(inner(e),inner(d),indexer),[Hessian(e,d,indexer) for (e,d) in zip(e.es,d.ds)]) Hessian(e::ExpressionSum{E,Nothing},d::GradientSum{D,Nothing},indexer = nothing) where {E,D} = HessianSum(nothing,[Hessian(e,d,indexer) for (e,d) in zip(e.es,d.ds)]) Hessian(e::Variable,::G,indexer) where G <: Gradient = HESSIAN_NULL Hessian(e::Parameter,::G,indexer) where G <: Gradient = HESSIAN_NULL Hessian(e::Constant,::G,indexer) where G <: Gradient = HESSIAN_NULL Hessian(d1::G,d2::GradientNull,indexer) where G <: Gradient = HESSIAN_NULL Hessian(d1::GradientNull,d2::G,indexer) where G <: Gradient = HESSIAN_NULL Hessian(d1::GradientNull,d2::GradientNull,indexer) = HESSIAN_NULL Hessian(d1::Gradient0,d2::Gradient0,indexer)= HessianD00S(index(d1)>=index(d2) ? set_indexer!(indexer,index(d1),index(d2)) : 0,index(d1) >= index(d2)) Hessian(d1::Gradient0,d2::Gradient0,::Nothing) = HessianD00(index(d1),index(d2),index(d1) >= index(d2)) Hessian(d1::Gradient0,d2::G1, indexer = nothing) where G1 <: Union{Gradient1,Gradient2F1,Gradient2F2} = HessianD10(Hessian(d1,d2.d1,indexer),ref(d2)) Hessian(d1::G1,d2::Gradient0, indexer = nothing) where G1 <: Union{Gradient1,Gradient2F1,Gradient2F2} = HessianD10(Hessian(d1.d1,d2,indexer),ref(d1)) Hessian(d1::G1,d2::G2, indexer = nothing) where {G1 <: Union{Gradient1,Gradient2F1,Gradient2F2}, G2 <: Union{Gradient1,Gradient2F1,Gradient2F2}}= HessianD11(Hessian(d1.d1,d2.d1,indexer),ref(d1),ref(d2)) Hessian(d1::G1,d2::G2, indexer = nothing) where {G1 <: Union{Gradient1,Gradient2F1,Gradient2F2}, G2 <: Gradient2} = HessianD21(Hessian(d1.d1,d2.d1,indexer),Hessian(d1.d1,d2.d2,indexer),ref(d1),ref1(d2),ref2(d2)) Hessian(d1::G1,d2::G2, indexer = nothing) where {G1 <: Gradient2, G2 <: Union{Gradient1,Gradient2F1,Gradient2F2}} = HessianD21(Hessian(d1.d1,d2.d1,indexer),Hessian(d1.d2,d2.d1,indexer),ref(d2),ref1(d1),ref2(d1)) Hessian(d1::Gradient0,d2::Gradient2{F,F1,F2}, indexer = nothing) where {F,F1,F2} = HessianD20(Hessian(d1,d2.d1,indexer),Hessian(d1,d2.d2,indexer),ref1(d2),ref2(d2)) Hessian(d1::Gradient2{F,F1,F2},d2::Gradient0, indexer = nothing) where {F,F1,F2} = HessianD20(Hessian(d1.d1,d2,indexer),Hessian(d1.d2,d2,indexer),ref1(d1),ref2(d1)) Hessian(d1::Gradient2{F,F1,F2} where {F,F1,F2},d2::Gradient2{F,F1,F2} where {F,F1,F2}, indexer = nothing) = Hessian02(Hessian(d1.d1,d2.d1,indexer),Hessian(d1.d1,d2.d2,indexer),Hessian(d1.d2,d2.d1,indexer),Hessian(d1.d2,d2.d2,indexer),ref1(d1),ref2(d1),ref1(d2),ref2(d2)) Hessian(e::Expression1{F,E},d, indexer = nothing) where {F,E} = Hessian11(e,d,indexer) Hessian(e::Expression2{F,E1,E2},d,indexer = nothing) where {F,E1,E2} = Hessian22(e,d,indexer) Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1 <: Real,E2} = Hessian11F1(e,d,indexer) Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F,E1,E2 <: Real} = Hessian11F2(e,d,indexer) Hessian(e::Expression2{typeof(*),E1,E2},d,indexer = nothing) where {E1,E2} = Hessian22m(Hessian(e.e1,d.d1,indexer),Hessian(e.e2,d.d2,indexer),Hessian(d.d1,d.d2,indexer),Hessian(d.d2,d.d1,indexer),ref1(d),ref2(d)) Hessian(e::Expression2{F,E1,E2},d,indexer = nothing) where {F<:Union{typeof(+),typeof(-)},E1,E2} = Hessian22a(Hessian(e.e1,d.d1,indexer),Hessian(e.e2,d.d2,indexer),ref1(d),ref2(d)) Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F<:Union{typeof(+),typeof(-),typeof(*)},E1 <: Real,E2 <: Expression} = Hessian11a(Hessian(e.e2,d.d1,indexer),ref(d)) Hessian(e::Expression2{F,E1,E2}, d,indexer = nothing) where {F<:Union{typeof(+),typeof(-),typeof(*),typeof(/)},E1 <: Expression,E2 <: Real} = Hessian11a(Hessian(e.e1,d.d1,indexer),ref(d)) # performance killer --------------- function Hessian(d1::GradientSum{D1,I1},d2::GradientSum{D2,I2},indexer = nothing) where {D1,D2,I1,I2} @warn "This operation is expensive" hinner = Hessian(inner(d1),d2,indexer) hs = [Hessian(d,d2,indexer) for d in d1.ds] @inline function (z,x,p=nothing,h0=1) hinner(z,x,p,h0) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::GradientSum{D1,Nothing},d2::GradientSum{D2,I2},indexer = nothing) where {D1,D2,I2} @warn "This operation is expensive" hs = [Hessian(d,d2,indexer) for d in d1.ds] @inline function (z,x,p=nothing,h0=1) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::GradientSum{D1,I1},d2::GradientSum{D2,Nothing},indexer = nothing) where {D1,D2,I1} @warn "This operation is expensive" hinner = Hessian(inner(d1),d2,indexer) hs = [Hessian(d,d2,indexer) for d in d1.ds] @inline function (z,x,p=nothing,h0=1) hinner(z,x,p,h0) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::GradientSum{D1,Nothing},d2::GradientSum{D2,Nothing},indexer = nothing) where {D1,D2} @warn "This operation is expensive" hs = [Hessian(d,d2,indexer) for d in d1.ds] @inline function (z,x,p=nothing,h0=1) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::GradientSum{D1,I1},d2::G,indexer = nothing) where {D1,D2,I1,I2,G <: Gradient} @warn "This operation is expensive" hinner = Hessian(inner(d1),d2,indexer) hs = [Hessian(d,d2,indexer) for d in d1.ds] @inline function (z,x,p=nothing,h0=1) hinner(z,x,p,h0) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::GradientSum{D1,Nothing},d2::G,indexer = nothing) where {D1,D2,I2,G <: Gradient} @warn "This operation is expensive" hs = [Hessian(d,d2,indexer) for d in d1.ds] @inline function (z,x,p=nothing,h0=1) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::G,d2::GradientSum{D2,I2},indexer = nothing) where {G <: Gradient,D2,I2} @warn "This operation is expensive" hinner = Hessian(d1,inner(d2),indexer) hs = [Hessian(d1,d,indexer) for d in d2.ds] @inline function (z,x,p=nothing,h0=1) hinner(z,x,p,h0) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end function Hessian(d1::G,d2::GradientSum{D2,Nothing},indexer = nothing) where {G <: Gradient, D2} @warn "This operation is expensive" hs = [Hessian(d1,d,indexer) for d in d2.ds] @inline function (z,x,p=nothing,h0=1) @simd for i in eachindex(hs) @inbounds hs[i](z,x,p,h0) end end end islower(h) = h.islower
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""" Find all types that may be considered ansestors of this type. For number types it is the same as Julia type hierarchy. For array types it includes unparametrized versions of types, i.e.: type_ansestors(Vector{Float64}) # ==> [Array{Float64,1}, Array{T,1}, DenseArray{T,1}, AbstractArray{T,1}, AbstractArray{T,N}, Any] """ function type_ansestors(t::Type{T}) where T<:Number types = Type[] while t != Any push!(types, t) t = supertype(t) end push!(types, Any) return types end type_ansestors(t::Type{Vector{T}}) where {T} = [t, Vector, DenseVector, AbstractVector, AbstractArray, Any] type_ansestors(t::Type{Matrix{T}}) where {T} = [t, Matrix, DenseMatrix, AbstractMatrix, AbstractArray, Any] function bcast_to_call(pex::Expr) @assert pex.head == :(.) return Expr(:call, pex.args[1], pex.args[2].args...) end deriv_name(z::Symbol, x::Symbol) = Symbol("d$(z)!d$(x)") split_deriv_name(vname) = Symbol.(split(String(vname), "!")) function replace_node(g::AbstractExGraph, vname::Symbol, nds::Vector{ExNode}) i = indexof(g, vname) delete!(g, vname) insert!(g, nds) end function find_related(g::AbstractExGraph, dydx_v::Symbol) subderivs = Symbol[] i = 1 name = Symbol("$(dydx_v)__$(i)") while haskey(g, name) push!(subderivs, name) i += 1 name = Symbol("$(dydx_v)__$(i)") end return subderivs end # (symbolic) derivative size propagation const DERIV_NAME_PATTERN = r"(d.+)!(d.+)" function propagate_deriv_size!(g::AbstractExGraph, dd_name::Symbol) sizes = @get_or_create(g.ctx, :sizes, Dict()) rg = match(DERIV_NAME_PATTERN, String(dd_name)) @assert length(rg.captures) == 2 str_dnames = rg.captures zname = Symbol(str_dnames[1][2:end]) xname = Symbol(split(str_dnames[2][2:end], "__")[1]) # cut down `__$(i)` part if any zsize, xsize = (sizes[zname], sizes[xname]) if zsize == :(()) # output var is constant sizes[dd_name] = xsize else sizes[dd_name] = :(($zsize..., $xsize...)) |> simplify end end function propagate_deriv_size!(g::AbstractExGraph) for nd in g.tape vname = varname(nd) if match(DERIV_NAME_PATTERN, String(vname)) != nothing propagate_deriv_size!(g, vname) end end end # (numeric) derivative size propagation function infer_deriv_size!(g::AbstractExGraph, dd_name::Symbol) rg = match(DERIV_NAME_PATTERN, String(dd_name)) @assert length(rg.captures) == 2 str_dnames = rg.captures zname = Symbol(str_dnames[1][2:end]) xname = Symbol(split(str_dnames[2][2:end], "__")[1]) # cut down `__$(i)` part if any # in case z or x haven't been evaluated and their size isn't known yet evaluate!(g, zname) evaluate!(g, xname) sizes = @get_or_create(g.ctx, :rsizes, Dict()) zsize, xsize = (sizes[zname], sizes[xname]) sizes[dd_name] = (zsize..., xsize...) end function infer_deriv_size!(g::AbstractExGraph) for nd in g.tape vname = varname(nd) if match(DERIV_NAME_PATTERN, String(vname)) != nothing infer_deriv_size!(g, vname) end end end # top type "The top type describing given data" top_type(x::AbstractArray{T,N}) where {T,N} = AbstractArray{T,N} top_type(x::Number) = Number top_type(::Type{AT}) where {AT <: AbstractArray{T,N}} where {T,N} = AbstractArray{T,N} top_type(::Type{T}) where {T <: Number} = Number
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module Menus using Genie, Stipple, StippleUI, StippleUI.API import Genie.Renderer.Html: HTMLString, normal_element, register_normal_element export menu register_normal_element("q__menu", context = @__MODULE__) function menu( fieldname::Union{Symbol,Nothing} = nothing, args...; content::Union{String,Vector} = "", wrap::Function = StippleUI.DEFAULT_WRAPPER, kwargs...) wrap() do q__menu(args...; attributes([:fieldname => fieldname, kwargs...], StippleUI.API.ATTRIBUTES_MAPPINGS)...) do join(content) end end end function menu(content::Function, fieldname::Union{Symbol,Nothing} = nothing, args...; wrap::Function = StippleUI.DEFAULT_WRAPPER, kwargs...) menu(label, fieldname, args...; wrap = wrap, content = content(), kwargs...) end end
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<filename>test/test_core_radius.jl tol = 1e-10 # # Sanity check. # @test size(Planets.mr_table) == (91,42) @test haskey(Planets.mr_table,:Mearth) @test haskey(Planets.mr_table,:rocky) @test haskey(Planets.mr_table,Symbol("5%fe")) @test haskey(Planets.mr_table,Symbol("5%h2o")) @test isa(Planets.mr_table[:Mearth][1], Float64) @test Planets.mr_table[:Mearth][1] == 0.0625 # # Test grid points, including edge cases. # @test abs(core_radius(0.07179, fe=0.0 ) - 0.4888) < tol @test abs(core_radius(0.07179, h2o=0.0) - 0.4888) < tol @test abs(core_radius(0.07179, fe=1.0 ) - 0.3733) < tol @test abs(core_radius(0.07179, fe=0.5 ) - 0.4394) < tol @test abs(core_radius(0.07179, fe=0.05) - 0.4841) < tol @test abs(core_radius(0.07179, h2o=.05) - 0.5041) < tol @test abs(core_radius(0.07179, h2o=1.0) - 0.6588) < tol @test abs(core_radius(1.0, fe=1.0) - 0.8228) < tol @test abs(core_radius(1.0, fe=0.0) - 1.0667) < tol @test abs(core_radius(1.0, fe=.05) - 1.0568) < tol @test abs(core_radius(1.0, fe=0.1) - 1.0466) < tol @test abs(core_radius(1.0, fe=0.2) - 1.0260) < tol @test abs(core_radius(1.0, fe=0.3) - 1.0050) < tol @test abs(core_radius(1.0, fe=0.4) - 0.9834) < tol @test abs(core_radius(1.0, fe=.45) - 0.9723) < tol @test abs(core_radius(1.0, fe=.35) - 0.9943) < tol @test abs(core_radius(1.0, fe=1.0) - 0.8228) < tol # # Test interpolation across columns. # @test abs(core_radius(1.0, fe=0.975) - 0.83350) < tol @test abs(core_radius(1.0, fe=0.025) - 1.06175) < tol # # Test interpolation across rows. # @test abs(core_radius(1.0359, fe=1.0) - 0.8307) < tol # # Test interpolation across columns AND rows. # @test abs(core_radius(1.035900, fe =0.975) - 0.841525) < tol @test abs(core_radius(0.064745, h2o=0.025) - 0.480450) < tol # # Test extrapolation of mass. # M_small = 0.0625 R_small = core_radius(M_small, fe=0.3) M_large = 32.0 R_large = core_radius(M_large, fe=0.3) @test abs(core_radius(0.01, fe=0.3) - R_small * (0.01/M_small)^(1/3.7)) < tol @test abs(core_radius(35.0, fe=0.3) - R_large * (35.0/M_large)^(1/3.7)) < tol
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# Various correlation # # spearman correlation functions # # spearman correlation between two vectors function cor_spearman(x::AbstractVector, y::AbstractVector) if any(isnan(x)) || any(isnan(y)) return NaN end return cor(tiedrank(x), tiedrank(y)) end # spearman correlation over all pairs of columns of two matrices function cor_spearman(X::AbstractMatrix, Y::AbstractMatrix) return cor(mapslices(tiedrank, X, 1), mapslices(tiedrank, Y, 1)) end function cor_spearman(X::AbstractMatrix, y::AbstractVector) return cor(mapslices(tiedrank, X, 1), tiedrank(y)) end function cor_spearman(x::AbstractVector, Y::AbstractMatrix) return cor(tiedrank(x), mapslices(tiedrank, Y, 1)) end # spearman correlation over all pairs of columns of a matrix function cor_spearman(X::AbstractMatrix) csp = cor(mapslices(tiedrank, X, 1)) nanindex = vec(mapslices(any, isnan(X), 1)) csp[nanindex, :] = NaN csp[:, nanindex] = NaN return csp end # # Kendall's rank correlation # # Knigh JASA (1966) function cor_kendall!{T<:Real,S<:Real}(x::AbstractVector{T}, y::AbstractVector{S}) if any(isnan(x)) || any(isnan(y)) return NaN end n = length(x) if n != length(y) error("Vectors must have same length") end # Initial sorting pm = sortperm(y) x[:] = x[pm] y[:] = y[pm] pm[:] = sortperm(x) x[:] = x[pm] # Counting ties in x and y iT = 1 nT = 0 iU = 1 nU = 0 for i = 2:n if x[i] == x[i-1] iT += 1 else nT += iT*(iT - 1) iT = 1 end if y[i] == y[i-1] iU += 1 else nU += iU*(iU - 1) iU = 1 end end if iT > 1 nT += iT*(iT - 1) end nT = div(nT,2) if iU > 1 nU += iU*(iU - 1) end nU = div(nU,2) # Sort y after x y[:] = y[pm] # Calculate double ties iV = 1 nV = 0 jV = 1 for i = 2:n if x[i] == x[i-1] && y[i] == y[i-1] iV += 1 else nV += iV*(iV - 1) iV = 1 end end if iV > 1 nV += iV*(iV - 1) end nV = div(nV,2) nD = div(n*(n - 1),2) return (nD - nT - nU + nV - 2swaps!(y))/sqrt((nD - nT)*(nD - nU)) end cor_kendall(x::AbstractVector, y::AbstractVector) = cor_kendall!(copy(x), copy(y)) cor_kendall(x::AbstractVector, Y::AbstractMatrix) = [cor_kendall(x, Y[:,i]) for i in 1:size(Y, 2)] cor_kendall(X::AbstractMatrix, y::AbstractVector) = [cor_kendall(X[:,i], y) for i in 1:size(X, 2)] cor_kendall(X::AbstractMatrix, Y::AbstractMatrix) = [cor_kendall(X[:,i], Y[:,j]) for i in 1:size(X, 2), j in 1:size(Y, 2)] function cor_kendall(X::AbstractMatrix) n = size(X, 2) C = eye(n) for j = 2:n for i = 1:j-1 C[i,j] = cor_kendall!(X[:,i],X[:,j]) C[j,i] = C[i,j] end end return C end # Auxilliary functions for Kendall's rank correlation function swaps!(x::AbstractVector) n = length(x) if n == 1 return 0 end n2 = div(n, 2) xl = sub(x, 1:n2) xr = sub(x, n2+1:n) nsl = swaps!(xl) nsr = swaps!(xr) sort!(xl) sort!(xr) return nsl + nsr + mswaps(xl,xr) end function mswaps(x::AbstractVector, y::AbstractVector) i = 1 j = 1 nSwaps = 0 n = length(x) while i <= n && j <= length(y) if y[j] < x[i] nSwaps += n - i + 1 j += 1 else i += 1 end end return nSwaps end # autocorrelation for range function autocor(x::AbstractVector, lags::Ranges) lx = length(x) if max(lags) > lx error("Autocorrelation distance must be less than sample size") end mx = mean(x) sxinv = 1/stdm(x, mx) xs = Array(typeof(sxinv), lx) for i = 1:lx xs[i] = (x[i] - mx)*sxinv end acf = Array(typeof(sxinv), length(lags)) for i in 1:length(lags) acf[i] = dot(xs[1:end - lags[i]], xs[lags[i] + 1:end])/(lx - 1) end return acf end # autocorrelation at a specific lag autocor(x::AbstractVector, lags::Real) = autocor(x, lags:lags)[1] # autocorrelation at a default of zero to 10log10(length(v)) lags autocor(v::AbstractVector) = autocor(v, 0:min(length(v) - 1, 10log10(length(v))))
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using ClimateMachine const clima_dir = dirname(dirname(pathof(ClimateMachine))); if parse(Bool, get(ENV, "CLIMATEMACHINE_PLOT_EDMF_COMPARISON", "false")) plot_dir = joinpath(clima_dir, "output", "bomex_edmf", "pycles_comparison") else plot_dir = nothing end include(joinpath(@__DIR__, "compute_mse.jl")) #! format: off best_mse = Dict() best_mse[:Bomex] = Dict() best_mse[:Bomex]["ρ"] = 3.4943021267397123e-02 best_mse[:Bomex]["ρu[1]"] = 3.0714039084256679e+03 best_mse[:Bomex]["ρu[2]"] = 1.3375796498101822e-03 best_mse[:Bomex]["moisture.ρq_tot"] = 4.8463531712319707e-02 best_mse[:Bomex]["turbconv.environment.ρatke"] = 6.6626422991098286e+02 best_mse[:Bomex]["turbconv.environment.ρaθ_liq_cv"] = 8.5667099987715503e+01 best_mse[:Bomex]["turbconv.environment.ρaq_tot_cv"] = 1.6439116552012851e+02 best_mse[:Bomex]["turbconv.updraft[1].ρa"] = 7.9577348413012515e+01 best_mse[:Bomex]["turbconv.updraft[1].ρaw"] = 8.4352188057391225e-02 best_mse[:Bomex]["turbconv.updraft[1].ρaθ_liq"] = 9.0101464252959325e+00 best_mse[:Bomex]["turbconv.updraft[1].ρaq_tot"] = 1.0768120864370509e+01 #! format: on sufficient_mse(computed_mse, best_mse) = computed_mse <= best_mse + eps() function test_mse(computed_mse, best_mse, key) mse_not_regressed = sufficient_mse(computed_mse[key], best_mse[key]) @test mse_not_regressed mse_not_regressed || @show key end computed_mse = Dict( k => compute_mse( solver_config.dg.grid, solver_config.dg.balance_law, time_data, dons_arr, data_files[k], k, best_mse[k], plot_dir, ) for k in keys(data_files) ) @testset "BOMEX EDMF Solution Quality Assurance (QA) tests" begin #! format: off test_mse(computed_mse[:Bomex], best_mse[:Bomex], "ρ") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "ρu[1]") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "moisture.ρq_tot") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρa") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρaw") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρaθ_liq") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.updraft[1].ρaq_tot") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.environment.ρatke") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.environment.ρaθ_liq_cv") test_mse(computed_mse[:Bomex], best_mse[:Bomex], "turbconv.environment.ρaq_tot_cv") #! format: on end
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1.940541
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#Controlled HF noise plot (test data reduction) using InspectDR using Colors #==Input constants ===============================================================================# dfltline = line(color=RGB24(0, 0, 1), width=1, style=:solid) #==Input data ===============================================================================# function gennoise(xv, yv; step=.01, npts=20, lo=0, hi=1) xmax = xv[end] xnew = xmax+collect(step:step:(npts*step)) ynew = similar(xnew) v = Float64[lo, hi] for i in 1:length(ynew) ynew[i] = v[(i&0x1)+1] end append!(xv, xnew) append!(yv, ynew) end function addpts(ynew, xv, yv; step=1.0) xmax = xv[end] npts = length(ynew) xnew = xmax+collect(step:step:(npts*step)) append!(xv, xnew) append!(yv, ynew) end #Generate dataset: x = Float64[0] y = Float64[0] addpts(Float64[1.1,-.1,], x,y) #Force maximum y-extents addpts(Float64[1,1,1,0,0], x,y) gennoise(x,y) addpts(Float64[0,1,1,1,0,0], x,y) gennoise(x,y) addpts(Float64[1,1,1,0,0], x,y) gennoise(x,y) addpts(Float64[1,1,1,0,0], x,y) #==Generate plot ===============================================================================# plot = InspectDR.Plot2D() wfrm = add(plot, x, y+1) wfrm.line = dfltline plot.xres=1000 #Force resolution a = plot.annotation a.title = "Sample Plot (λ)" a.xlabel = "Time (s)" a.ylabels = ["Signal Voltage (V)"] gplot = display(InspectDR.GtkDisplay(), plot) :Test_Complete
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""" Only packs `A`. Primitively does column-major packing: it packs blocks of `A` into a column-major temporary. """ function matmul_st_only_pack_A!( C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, ::StaticFloat64{W₁}, ::StaticFloat64{W₂}, ::StaticFloat64{R₁}, ::StaticFloat64{R₂} ) where {T, W₁, W₂, R₁, R₂} mᵣ, nᵣ = matmul_params() ((Mblock, Mblock_Mrem, Mremfinal, Mrem, Miter), (Kblock, Kblock_Krem, Krem, Kiter)) = solve_McKc(T, M, K, N, StaticFloat64{W₁}(), StaticFloat64{W₂}(), StaticFloat64{R₁}(), StaticFloat64{R₂}(), mᵣ) for ko ∈ CloseOpen(Kiter) ksize = ifelse(ko < Krem, Kblock_Krem, Kblock) let A = A, C = C for mo in CloseOpen(Miter) msize = ifelse((mo+1) == Miter, Mremfinal, ifelse(mo < Mrem, Mblock_Mrem, Mblock)) # if ko == 0 # loopmul!(C, A, B, α, β, msize, ksize, N) # else # loopmul!(C, A, B, α, One(), msize, ksize, N) # end if ko == 0 packaloopmul!(C, A, B, α, β, msize, ksize, N) else packaloopmul!(C, A, B, α, One(), msize, ksize, N) end A = gesp(A, (msize, Zero())) C = gesp(C, (msize, Zero())) end end A = gesp(A, (Zero(), ksize)) B = gesp(B, (ksize, Zero())) end nothing end function matmul_st_pack_A_and_B!( C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, W₁, W₂, R₁, R₂, tid ) where {T} mᵣ, nᵣ = matmul_params() # TODO: if this is nested in other threaded code, use only a piece of BCACHE and make R₂ (and thus L₂ₑ) smaller (Mblock, Mblock_Mrem, Mremfinal, Mrem, Miter), (Kblock, Kblock_Krem, Krem, Kiter), (Nblock, Nblock_Nrem, Nrem, Niter) = solve_block_sizes(T, M, K, N, W₁, W₂, R₁, R₂, mᵣ) bcache = _use_bcache(tid) L3ptr = Base.unsafe_convert(Ptr{T}, pointer(bcache)) for n ∈ CloseOpen(Niter) nsize = ifelse(n < Nrem, Nblock_Nrem, Nblock) let A = A, B = B for k ∈ CloseOpen(Kiter) ksize = ifelse(k < Krem, Kblock_Krem, Kblock) _B = default_zerobased_stridedpointer(L3ptr, (One(),ksize)) unsafe_copyto_avx!(_B, B, ksize, nsize) let A = A, C = C, B = _B for m in CloseOpen(Miter) msize = ifelse((m+1) == Miter, Mremfinal, ifelse(m < Mrem, Mblock_Mrem, Mblock)) if k == 0 packaloopmul!(C, A, B, α, β, msize, ksize, nsize) else packaloopmul!(C, A, B, α, One(), msize, ksize, nsize) end A = gesp(A, (msize, Zero())) C = gesp(C, (msize, Zero())) end end A = gesp(A, (Zero(), ksize)) B = gesp(B, (ksize, Zero())) end end B = gesp(B, (Zero(), nsize)) C = gesp(C, (Zero(), nsize)) end _free_bcache!(bcache) nothing end @inline contiguousstride1(A) = ArrayInterface.contiguous_axis(A) === One() @inline contiguousstride1(A::AbstractStridedPointer{T,N,1}) where {T,N} = true # @inline bytestride(A::AbstractArray, i) = VectorizationBase.bytestrides(A)[i] @inline bytestride(A::AbstractStridedPointer, i) = strides(A)[i] @inline firstbytestride(A::AbstractStridedPointer) = bytestride(A, One()) @inline function vectormultiple(bytex, ::Type{Tc}, ::Type{Ta}) where {Tc,Ta} Wc = pick_vector_width(Tc) * static_sizeof(Ta) - One() iszero(bytex & (VectorizationBase.register_size() - One())) end @inline function dontpack(pA::AbstractStridedPointer{Ta}, M, K, ::StaticInt{mc}, ::StaticInt{kc}, ::Type{Tc}) where {mc, kc, Tc, Ta} (contiguousstride1(pA) && ((((MᵣW_mul_factor() + StaticInt(5)) * pick_vector_width(Tc)) ≥ M) || (vectormultiple(bytestride(pA, StaticInt{2}()), Tc, Ta) && ((M * K) ≤ (mc * kc)) && iszero(reinterpret(Int, pointer(pA)) & (VectorizationBase.register_size() - One()))))) end @inline function alloc_matmul_product(A::AbstractArray{TA}, B::AbstractArray{TB}) where {TA,TB} # TODO: if `M` and `N` are statically sized, shouldn't return a `Matrix`. M, KA = size(A) KB, N = size(B) @assert KA == KB "Size mismatch." Matrix{promote_type(TA,TB)}(undef, M, N), (M, KA, N) end @inline function matmul_serial(A::AbstractMatrix, B::AbstractMatrix) C, (M,K,N) = alloc_matmul_product(A, B) _matmul_serial!(C, A, B, One(), Zero(), (M,K,N)) return C end # These methods must be compile time constant maybeinline(::Any, ::Any, ::Any, ::Any) = false function maybeinline(::StaticInt{M}, ::StaticInt{N}, ::Type{T}, ::Val{true}) where {M,N,T} mᵣ, nᵣ = matmul_params() static_sizeof(T) * StaticInt{M}() * StaticInt{N}() < StaticInt{176}() * mᵣ * nᵣ end function maybeinline(::StaticInt{M}, ::StaticInt{N}, ::Type{T}, ::Val{false}) where {M,N,T} StaticInt{M}() * static_sizeof(T) ≤ StaticInt{2}() * VectorizationBase.register_size() end @inline function matmul_serial!(C::AbstractMatrix{T}, A::AbstractMatrix, B::AbstractMatrix) where {T} matmul_serial!(C, A, B, One(), Zero(), nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α) matmul_serial!(C, A, B, α, Zero(), nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β) matmul_serial!(C, A, B, α, β, nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, MKN, ::StaticInt{2}) _matmul_serial!(C', B', A', α, β, nothing) return C end @inline function matmul_serial!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, MKN, ::StaticInt) _matmul_serial!(C, A, B, α, β, nothing) return C end """ matmul_serial!(C, A, B[, α = 1, β = 0]) Calculates `C = α * (A * B) + β * C` in place. A single threaded matrix-matrix-multiply implementation. Supports dynamically and statically sized arrays. Organizationally, `matmul_serial!` checks the arrays properties to try and dispatch to an appropriate implementation. If the arrays are small and statically sized, it will dispatch to an inlined multiply. Otherwise, based on the array's size, whether they are transposed, and whether the columns are already aligned, it decides to not pack at all, to pack only `A`, or to pack both arrays `A` and `B`. """ @inline function _matmul_serial!( C::AbstractMatrix{T}, A::AbstractMatrix, B::AbstractMatrix, α, β, MKN ) where {T} M, K, N = MKN === nothing ? matmul_sizes(C, A, B) : MKN if M * N == 0 return elseif K == 0 matmul_only_β!(C, β) return end pA = zstridedpointer(A); pB = zstridedpointer(B); pC = zstridedpointer(C); Cb = preserve_buffer(C); Ab = preserve_buffer(A); Bb = preserve_buffer(B); Mc, Kc, Nc = block_sizes(T); mᵣ, nᵣ = matmul_params(); GC.@preserve Cb Ab Bb begin if maybeinline(M, N, T, ArrayInterface.is_column_major(A)) # check MUST be compile-time resolvable inlineloopmul!(pC, pA, pB, One(), Zero(), M, K, N) return elseif (nᵣ ≥ N) || dontpack(pA, M, K, Mc, Kc, T) loopmul!(pC, pA, pB, α, β, M, K, N) return else matmul_st_pack_dispatcher!(pC, pA, pB, α, β, M, K, N) return end end end # function function matmul_only_β!(C::AbstractMatrix{T}, β::StaticInt{0}) where T @avx for i=1:length(C) C[i] = zero(T) end end function matmul_only_β!(C::AbstractMatrix{T}, β) where T @avx for i=1:length(C) C[i] = β * C[i] end end function matmul_st_pack_dispatcher!(pC::AbstractStridedPointer{T}, pA, pB, α, β, M, K, N) where {T} Mc, Kc, Nc = block_sizes(T) if (contiguousstride1(pB) ? (Kc * Nc ≥ K * N) : (firstbytestride(pB) ≤ 1600)) matmul_st_only_pack_A!(pC, pA, pB, α, β, M, K, N, W₁Default(), W₂Default(), R₁Default(), R₂Default()) # elseif notnested !== nothing && notnested # matmul_st_pack_A_and_B!(pC, pA, pB, α, β, M, K, N, W₁Default(), W₂Default(), R₁Default(), R₂Default(), nothing) else matmul_st_pack_A_and_B!(pC, pA, pB, α, β, M, K, N, W₁Default(), W₂Default(), R₁Default(), R₂Default()/Threads.nthreads(), Threads.threadid() - 1) end nothing end """ matmul(A, B) Multiply matrices `A` and `B`. """ @inline function matmul(A::AbstractMatrix, B::AbstractMatrix) C, (M,K,N) = alloc_matmul_product(A, B) _matmul!(C, A, B, One(), Zero(), nothing, (M,K,N)) return C end """ matmul!(C, A, B[, α, β, max_threads]) Calculates `C = α * A * B + β * C` in place, overwriting the contents of `A`. It may use up to `max_threads` threads. It will not use threads when nested in other threaded code. """ @inline function matmul!(C::AbstractMatrix{T}, A::AbstractMatrix, B::AbstractMatrix) where {T} matmul!(C, A, B, One(), Zero(), nothing, nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α) matmul!(C, A, B, α, Zero(), nothing, nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β) matmul!(C, A, B, α, β, nothing, nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, nthread) matmul!(C, A, B, α, β, nthread, nothing, ArrayInterface.contiguous_axis(C)) end @inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, nthread, MKN, ::StaticInt{2}) _matmul!(C', B', A', α, β, nthread, MKN) return C end @inline function matmul!(C::AbstractMatrix, A::AbstractMatrix, B::AbstractMatrix, α, β, nthread, MKN, ::StaticInt) _matmul!(C, A, B, α, β, nthread, MKN) return C end @inline function dontpack(pA::AbstractStridedPointer{Ta}, M, K, ::StaticInt{mc}, ::StaticInt{kc}, ::Type{Tc}, nspawn) where {mc, kc, Tc, Ta} # TODO: perhaps consider K vs kc by themselves? (contiguousstride1(pA) && ((M * K) ≤ (mc * kc) * nspawn >>> 1)) end # passing MKN directly would let osmeone skip the size check. @inline function _matmul!(C::AbstractMatrix{T}, A, B, α, β, nthread, MKN) where {T}#::Union{Nothing,Tuple{Vararg{Integer,3}}}) where {T} M, K, N = MKN === nothing ? matmul_sizes(C, A, B) : MKN if M * N == 0 return elseif K == 0 matmul_only_β!(C, β) return end W = pick_vector_width(T) pA = zstridedpointer(A); pB = zstridedpointer(B); pC = zstridedpointer(C); Cb = preserve_buffer(C); Ab = preserve_buffer(A); Bb = preserve_buffer(B); mᵣ, nᵣ = matmul_params() GC.@preserve Cb Ab Bb begin if maybeinline(M, N, T, ArrayInterface.is_column_major(A)) # check MUST be compile-time resolvable inlineloopmul!(pC, pA, pB, One(), Zero(), M, K, N) return else (nᵣ ≥ N) && @goto LOOPMUL if (Sys.ARCH === :x86_64) || (Sys.ARCH === :i686) (M*K*N < (StaticInt{4_096}() * W)) && @goto LOOPMUL else (M*K*N < (StaticInt{32_000}() * W)) && @goto LOOPMUL end __matmul!(pC, pA, pB, α, β, M, K, N, nthread) return @label LOOPMUL loopmul!(pC, pA, pB, α, β, M, K, N) return end end end # This funciton is sort of a `pun`. It splits aggressively (it does a lot of "splitin'"), which often means it will split-N. function matmulsplitn!(C::AbstractStridedPointer{T}, A, B, α, β, ::StaticInt{Mc}, M, K, N, nspawn, ::Val{PACK}) where {T, Mc, PACK} Mᵣ, Nᵣ = matmul_params() W = pick_vector_width(T) MᵣW = Mᵣ*W _Mblocks, Nblocks = divide_blocks(M, cld_fast(N, Nᵣ), nspawn, W) Mbsize, Mrem, Mremfinal, Mblocks = split_m(M, _Mblocks, W) # Nblocks = min(N, _Nblocks) Nbsize, Nrem = divrem_fast(N, Nblocks) _nspawn = Mblocks * Nblocks Mbsize_Mrem, Mbsize_ = promote(Mbsize + W, Mbsize) Nbsize_Nrem, Nbsize_ = promote(Nbsize + One(), Nbsize) let _A = A, _B = B, _C = C, n = 0, tnum = 0, Nrc = Nblocks - Nrem, Mrc = Mblocks - Mrem, __Mblocks = Mblocks - One() while true nsize = ifelse(Nblocks > Nrc, Nbsize_Nrem, Nbsize_); Nblocks -= 1 let _A = _A, _C = _C, __Mblocks = __Mblocks while __Mblocks != 0 msize = ifelse(__Mblocks ≥ Mrc, Mbsize_Mrem, Mbsize_); __Mblocks -= 1 launch_thread_mul!(_C, _A, _B, α, β, msize, K, nsize, (tnum += 1), Val{PACK}()) _A = gesp(_A, (msize, Zero())) _C = gesp(_C, (msize, Zero())) end if Nblocks != 0 launch_thread_mul!(_C, _A, _B, α, β, Mremfinal, K, nsize, (tnum += 1), Val{PACK}()) else call_loopmul!(_C, _A, _B, α, β, Mremfinal, K, nsize, Val{PACK}()) waitonmultasks(CloseOpen(One(), _nspawn)) return end end _B = gesp(_B, (Zero(), nsize)) _C = gesp(_C, (Zero(), nsize)) end end end function __matmul!( C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, nthread ) where {T} Mᵣ, Nᵣ = matmul_params() W = pick_vector_width(T) Mc, Kc, Nc = block_sizes(T) MᵣW = Mᵣ*W # Not taking the fast path # But maybe we don't want to thread anyway # Maybe this is nested, or we have ≤ 1 threads nt = _nthreads() _nthread = nthread === nothing ? nt : min(nt, nthread) if _nthread < 2 matmul_st_pack_dispatcher!(C, A, B, α, β, M, K, N) return end # We are threading, but how many threads? nspawn = if (Sys.ARCH === :x86_64) || (Sys.ARCH === :i686) clamp(div_fast(M * N, StaticInt{128}() * W), 1, _nthread) else clamp(div_fast(M * N, StaticInt{256}() * W), 1, _nthread) end # nkern = cld_fast(M * N, MᵣW * Nᵣ) # Approach: # Check if we don't want to pack A, # if not, aggressively subdivide # if so, check if we don't want to pack B # if not, check if we want to thread `N` loop anyway # if so, divide `M` first, then use ratio of desired divisions / divisions along `M` to calc divisions along `N` # if not, only thread along `M`. These don't need syncing, as we're not packing `B` # if so, `matmul_pack_A_and_B!` # # MᵣW * (MᵣW_mul_factor - One()) # gives a smaller Mc, then # if 2M/nspawn is less than it, we don't don't `A` # First check is: do we just want to split aggressively? mᵣ, nᵣ = matmul_params() if dontpack(A, M, K, Mc, Kc, T, nspawn) || (W ≥ M) || (nᵣ*((num_cores() ≥ StaticInt(8)) ? max(nspawn,8) : 8) ≥ N) # `nᵣ*nspawn ≥ N` is needed at the moment to avoid accidentally splitting `N` to be `< nᵣ` while packing # Should probably handle that with a smarter splitting function... matmulsplitn!(C, A, B, α, β, Mc, M, K, N, nspawn, Val{false}()) elseif ((nspawn*(W+W) > M) || (contiguousstride1(B) ? (roundtostaticint(Kc * Nc * R₂Default()) ≥ K * N) : (firstbytestride(B) ≤ 1600))) matmulsplitn!(C, A, B, α, β, Mc, M, K, N, nspawn, Val{true}()) else # TODO: Allow splitting along `N` for `matmul_pack_A_and_B!` matmul_pack_A_and_B!(C, A, B, α, β, M, K, N, nspawn, W₁Default(), W₂Default(), R₁Default(), R₂Default()) end nothing end # If tasks is [0,1,2,3] (e.g., `CloseOpen(0,4)`), it will wait on `MULTASKS[i]` for `i = [1,2,3]`. function waitonmultasks(tasks) for tid ∈ tasks wait(tid) end end @inline allocref(::StaticInt{N}) where {N} = Ref{NTuple{N,UInt8}}() function matmul_pack_A_and_B!( C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, tospawn::Int, ::StaticFloat64{W₁}, ::StaticFloat64{W₂}, ::StaticFloat64{R₁}, ::StaticFloat64{R₂}#, ::Val{1} ) where {T,W₁,W₂,R₁,R₂} W = pick_vector_width(T) mᵣ, nᵣ = matmul_params() mᵣW = mᵣ * W # atomicsync = Ref{NTuple{16,UInt}}() Mbsize, Mrem, Mremfinal, _to_spawn = split_m(M, tospawn, W) # M is guaranteed to be > W because of `W ≥ M` condition for `jmultsplitn!`... atomicsync = allocref(StaticInt{2}()*num_cores()*cache_linesize()) p = reinterpret(Ptr{UInt}, Base.unsafe_convert(Ptr{UInt8}, atomicsync)) GC.@preserve atomicsync begin for i ∈ CloseOpen(2_to_spawn) _atomic_store!(p + i*cache_linesize(), zero(UInt)) end Mblock_Mrem, Mblock_ = promote(Mbsize + W, Mbsize) u_to_spawn = _to_spawn % UInt tid = 0 bc = _use_bcache() bc_ptr = Base.unsafe_convert(typeof(pointer(C)), pointer(bc)) last_id = _to_spawn - One() for m ∈ CloseOpen(last_id) # ...thus the fact that `CloseOpen()` iterates at least once is okay. Mblock = ifelse(m < Mrem, Mblock_Mrem, Mblock_) launch_thread_mul!(C, A, B, α, β, Mblock, K, N, p, bc_ptr, m % UInt, u_to_spawn, StaticFloat64{W₁}(),StaticFloat64{W₂}(),StaticFloat64{R₁}(),StaticFloat64{R₂}()) A = gesp(A, (Mblock, Zero())) C = gesp(C, (Mblock, Zero())) end sync_mul!(C, A, B, α, β, Mremfinal, K, N, p, bc_ptr, last_id % UInt, u_to_spawn, StaticFloat64{W₁}(), StaticFloat64{W₂}(), StaticFloat64{R₁}(), StaticFloat64{R₂}()) waitonmultasks(CloseOpen(One(), _to_spawn)) end _free_bcache!(bc) return end function sync_mul!( C::AbstractStridedPointer{T}, A::AbstractStridedPointer, B::AbstractStridedPointer, α, β, M, K, N, atomicp::Ptr{UInt}, bc::Ptr, id::UInt, total_ids::UInt, ::StaticFloat64{W₁}, ::StaticFloat64{W₂}, ::StaticFloat64{R₁}, ::StaticFloat64{R₂} ) where {T, W₁, W₂, R₁, R₂} (Mblock, Mblock_Mrem, Mremfinal, Mrem, Miter), (Kblock, Kblock_Krem, Krem, Kiter), (Nblock, Nblock_Nrem, Nrem, Niter) = solve_block_sizes(T, M, K, N, StaticFloat64{W₁}(), StaticFloat64{W₂}(), StaticFloat64{R₁}(), StaticFloat64{R₂}(), One()) # atomics = atomicp + 8sizeof(UInt) sync_iters = zero(UInt) myp = atomicp + id *cache_linesize() atomicp -= cache_linesize() atomics = atomicp + total_ids*cache_linesize() mys = myp + total_ids*(cache_linesize() % UInt) Npackb_r_div, Npackb_r_rem = divrem_fast(Nblock_Nrem, total_ids) Npackb_r_block_rem, Npackb_r_block_ = promote(Npackb_r_div + One(), Npackb_r_div) Npackb___div, Npackb___rem = divrem_fast(Nblock, total_ids) Npackb___block_rem, Npackb___block_ = promote(Npackb___div + One(), Npackb___div) pack_r_offset = Npackb_r_div * id + min(id, Npackb_r_rem) pack___offset = Npackb___div * id + min(id, Npackb___rem) pack_r_len = ifelse(id < Npackb_r_rem, Npackb_r_block_rem, Npackb_r_block_) pack___len = ifelse(id < Npackb___rem, Npackb___block_rem, Npackb___block_) for n in CloseOpen(Niter) # Krem # pack kc x nc block of B nfull = n < Nrem nsize = ifelse(nfull, Nblock_Nrem, Nblock) pack_offset = ifelse(nfull, pack_r_offset, pack___offset) pack_len = ifelse(nfull, pack_r_len, pack___len) let A = A, B = B for k ∈ CloseOpen(Kiter) ksize = ifelse(k < Krem, Kblock_Krem, Kblock) _B = default_zerobased_stridedpointer(bc, (One(), ksize)) unsafe_copyto_avx!(gesp(_B, (Zero(), pack_offset)), gesp(B, (Zero(), pack_offset)), ksize, pack_len) # synchronize before starting the multiplication, to ensure `B` is packed _mv = _atomic_add!(myp, one(UInt)) sync_iters += one(UInt) let atomp = atomicp for _ ∈ CloseOpen(total_ids) atomp += cache_linesize() atomp == myp && continue while _atomic_load(atomp) != sync_iters pause() end end end # multiply let A = A, B = _B, C = C for m in CloseOpen(Miter) msize = ifelse((m+1) == Miter, Mremfinal, ifelse(m < Mrem, Mblock_Mrem, Mblock)) if k == 0 packaloopmul!(C, A, B, α, β, msize, ksize, nsize) else packaloopmul!(C, A, B, α, One(), msize, ksize, nsize) end A = gesp(A, (msize, Zero())) C = gesp(C, (msize, Zero())) end end A = gesp(A, (Zero(), ksize)) B = gesp(B, (ksize, Zero())) # synchronize on completion so we wait until every thread is done with `Bpacked` before beginning to overwrite it _mv = _atomic_add!(mys, one(UInt)) let atoms = atomics for _ ∈ CloseOpen(total_ids) atoms += cache_linesize() atoms == mys && continue while _atomic_load(atoms) != sync_iters pause() end end end end end B = gesp(B, (Zero(), nsize)) C = gesp(C, (Zero(), nsize)) end nothing end
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220, 220, 220, 2073, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2353, 7335, 404, 76, 377, 0, 7, 34, 11, 317, 11, 347, 11, 26367, 11, 1881, 22784, 285, 7857, 11, 479, 7857, 11, 299, 7857, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 317, 796, 308, 9774, 7, 32, 11, 357, 907, 1096, 11, 12169, 3419, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 327, 796, 308, 9774, 7, 34, 11, 357, 907, 1096, 11, 12169, 3419, 4008, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 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<gh_stars>0 import Base: linspace # Multidimensional Linspace function linspace{D, T}(start ::NTuple{D, T}, stop ::NTuple{D, T}, n=50) zip([linspace(start[d], stop[d], n+1)[1:n] for d in 1:D]...) end function flatten{T}(a :: Array{T}) return reshape(a, prod(size(a))) end import Iterators function linpath{D, T <: Number}(points ::Vector{NTuple{D, T}}, n=50) segments = [] for ip in 2:length(points) push!(segments, linspace(points[ip-1], points[ip], n)) end Iterators.chain(segments...) end
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function cpu_reduce_partial_sum( partial_sum::StructArray ) return sum(replace_storage(Array, partial_sum), dims=1) end function cuda_reduce_partial_sum( partial_sum::StructArray ) return CUDA.sum(partial_sum.re, dims=1), CUDA.sum(partial_sum.im, dims=1) end function gen_code_replica_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, latest_shift, code_length ) # thread_idx goes from 1:2502 # sample_idx converted to -1:2500 -> [-1 0 +1] thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) if thread_idx <= num_samples @inbounds code_replica[thread_idx] = codes[1+mod(floor(Int32, code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase), code_length), prn] end return nothing end function gen_code_replica_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, latest_shift, code_length ) # thread_idx goes from 1:2502 # sample_idx converted to -1:2500 -> [-1 0 +1] stride = blockDim().x * gridDim().x thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) @inbounds for i = thread_idx:stride:num_samples code_replica[i] = codes[1+mod(floor(Int32, code_frequency/sampling_frequency * (i + latest_shift) + start_code_phase), code_length), prn] end return nothing end function gen_code_replica_texture_mem_strided_kernel!( code_replica, codes, # texture memory codes code_frequency, sampling_frequency, start_code_phase, prn::Int, num_samples, latest_shift, code_length ) # thread_idx goes from 1:2502 # sample_idx converted to -1:2500 -> [-1 0 +1] thread_idx = (blockIdx().x - 1) * blockDim().x + threadIdx().x if thread_idx <= num_samples code_replica[thread_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn] end return nothing end function gen_code_replica_texture_mem_strided_nsat_kernel!( code_replica, codes, # texture memory codes code_frequency, sampling_frequency, start_code_phase, prn, num_samples, latest_shift, code_length ) # thread_idx goes from 1:2502 # sample_idx converted to -1:2500 -> [-1 0 +1] sat_idx = blockIdx().y thread_idx = (blockIdx().x - 1) * blockDim().x + threadIdx().x if thread_idx <= num_samples code_replica[thread_idx, sat_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn[sat_idx]] end return nothing end function gen_code_replica_texture_mem_strided_kernel!( code_replica, codes, # texture memory codes code_frequency, sampling_frequency, start_code_phase, prn, num_samples, latest_shift, code_length ) # thread_idx goes from 1:2502 # sample_idx converted to -1:2500 -> [-1 0 +1] thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) if thread_idx <= num_samples code_replica[thread_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn] end return nothing end function gen_code_replica_texture_mem_kernel!( code_replica, codes, # texture memory codes code_frequency, sampling_frequency, start_code_phase, prn, num_samples, latest_shift, code_length ) # thread_idx goes from 1:2502 # sample_idx converted to -1:2500 -> [-1 0 +1] thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) if thread_idx <= num_samples code_replica[thread_idx] = codes[(code_frequency/sampling_frequency * (thread_idx + latest_shift) + start_code_phase) / code_length, prn] end return nothing end function downconvert_and_correlate_kernel_1330!( res_re, res_im, signal_re, signal_im, codes, code_frequency, correlator_sample_shifts, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, code_length, prn, num_samples, num_ants, num_corrs ) cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs)) sample_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1)) corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1)) iq_offset = blockDim().x cache_index = threadIdx().x - 1 code_phase = accum_re = accum_im = dw_re = dw_im = carrier_re = carrier_im = 0.0f0 mod_floor_code_phase = Int(0) if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs # generate carrier carrier_im, carrier_re = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconvert with the conjugate of the carrier dw_re = signal_re[sample_idx, antenna_idx] * carrier_re + signal_im[sample_idx, antenna_idx] * carrier_im dw_im = signal_im[sample_idx, antenna_idx] * carrier_re - signal_re[sample_idx, antenna_idx] * carrier_im # calculate the code phase code_phase = code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase # wrap the code phase around the code length e.g. phase = 1024 -> modfloorphase = 1 mod_floor_code_phase = 1 + mod(floor(Int32, code_phase), code_length) # multiply elementwise with the code accum_re += codes[mod_floor_code_phase, prn] * dw_re accum_im += codes[mod_floor_code_phase, prn] * dw_im end cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im ## Reduction # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] end sync_threads() i ÷= 2 end if (threadIdx().x - 1) == 0 res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx] res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx] end return nothing end function downconvert_and_correlate_kernel_1331!( res_re, res_im, signal_re, signal_im, codes, code_frequency, correlator_sample_shifts, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, code_length, prn, num_samples, num_ants, num_corrs ) cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs)) sample_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1)) corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1)) iq_offset = blockDim().x cache_index = threadIdx().x - 1 accum_re = accum_im = dw_re = dw_im = carrier_re = carrier_im = 0.0f0 if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs # generate carrier carrier_im, carrier_re = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconvert with the conjugate of the carrier dw_re = signal_re[sample_idx, antenna_idx] * carrier_re + signal_im[sample_idx, antenna_idx] * carrier_im dw_im = signal_im[sample_idx, antenna_idx] * carrier_re - signal_re[sample_idx, antenna_idx] * carrier_im # multiply elementwise with the code accum_re += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re accum_im += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im end cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im ## Reduction # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] end sync_threads() i ÷= 2 end if (threadIdx().x - 1) == 0 res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx] res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx] end return nothing end function downconvert_and_correlate_kernel_1431!( res_re, res_im, signal_re, signal_im, codes, code_frequency, correlator_sample_shifts, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, code_length, prn, num_samples, num_ants, num_corrs ) block_dim_x = 2 * blockDim().x # launched with half the grid sample_idx = 1 + ((blockIdx().x - 1) * block_dim_x + (threadIdx().x - 1)) antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1)) corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1)) iq_offset = blockDim().x cache_index = threadIdx().x - 1 # allocate shared memory cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs)) # wipe values cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = 0.0f0 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = 0.0f0 # define local variables accum_re_1 = accum_im_1 = dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0 accum_re_2 = accum_im_2 = dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0 if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs # generate carrier carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconvert with the conjugate of the carrier dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1 dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1 # multiply elementwise with the code accum_re_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_1 accum_im_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_1 # write results to shared memory cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re_1 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im_1 if sample_idx + blockDim().x <= num_samples # generate carrier for the remaining samples carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconvert with the conjugate of the carrier for the remaining samples dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 # multiply elementwise with the code for the remaining samples accum_re_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_2 accum_im_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_2 # append results to shared memory cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += accum_re_2 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += accum_im_2 end end ## Reduction in shared memory # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] end sync_threads() i ÷= 2 end if (threadIdx().x - 1) == 0 res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx] res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx] end return nothing end # function downconvert_and_correlate_kernel_3431!( # res_re, # res_im, # signal_re, # signal_im, # codes, # code_frequency, # correlator_sample_shifts, # carrier_frequency, # sampling_frequency, # start_code_phase, # carrier_phase, # code_length, # prn, # num_samples, # num_ants, # num_corrs # ) # block_dim_x = 2 * blockDim().x # launched with half the grid # sample_idx = 1 + ((blockIdx().x - 1) * block_dim_x + (threadIdx().x - 1)) # antenna_idx = 1 + ((blockIdx().y - 1) * blockDim().y + (threadIdx().y - 1)) # corr_idx = 1 + ((blockIdx().z - 1) * blockDim().z + (threadIdx().z - 1)) # iq_offset = blockDim().x # cache_index = threadIdx().x - 1 # # allocate shared memory # cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, num_ants, num_corrs)) # # wipe values # cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = 0.0f0 # cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = 0.0f0 # # define local variables # accum_re_1 = accum_im_1 = dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0 # accum_re_2 = accum_im_2 = dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0 # if sample_idx <= num_samples && antenna_idx <= num_ants && corr_idx <= num_corrs # # generate carrier # carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # # downconvert with the conjugate of the carrier # dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1 # dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1 # # multiply elementwise with the code # accum_re_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_1 # accum_im_1 += codes[(code_frequency / sampling_frequency * ((sample_idx - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_1 # # write results to shared memory # cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = accum_re_1 # cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = accum_im_1 # if sample_idx + blockDim().x <= num_samples # # generate carrier for the remaining samples # carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # # downconvert with the conjugate of the carrier for the remaining samples # dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 # dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 # # multiply elementwise with the code for the remaining samples # accum_re_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_re_2 # accum_im_2 += codes[(code_frequency / sampling_frequency * ((sample_idx + blockDim().x - 1) + correlator_sample_shifts[corr_idx]) + start_code_phase) / code_length, prn] * dw_im_2 # # append results to shared memory # cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += accum_re_2 # cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += accum_im_2 # end # end # ## Reduction in shared memory # # wait until all the accumulators have done writing the results to the cache # sync_threads() # i::Int = blockDim().x ÷ 2 # @inbounds while i != 0 # if cache_index < i # cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] # cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] # end # sync_threads() # i ÷= 2 # end # if (threadIdx().x - 1) == 0 # res_re[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx] # res_im[blockIdx().x, antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx] # end # return nothing # end function downconvert_and_correlate_kernel_3431!( partial_sum_re, partial_sum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, code_replica, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, sampling_frequency, carrier_phase, num_samples::Int, num_ants::NumAnts{NANT} ) where {NCOR, NANT} cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, NANT, NCOR)) sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) # double the grid iq_offset = blockDim().x # indexing offset for complex values I/Q samples cache_index = threadIdx().x - 1 dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0 dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0 @inbounds if sample_idx <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1 dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1 for corr_idx = 1:NCOR sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_re_1 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_im_1 end end if sample_idx + blockDim().x <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 for corr_idx = 1:NCOR sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_re_2 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_im_2 end end end end ## Partial Reduction # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i for antenna_idx = 1:NANT for corr_idx = 1:NCOR cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] end end end sync_threads() i ÷= 2 end @inbounds if (threadIdx().x - 1) == 0 for antenna_idx = 1:NANT for corr_idx = 1:NCOR partial_sum_re[blockIdx().x, antenna_idx, corr_idx] = cache[1 + 0 * iq_offset, antenna_idx, corr_idx] partial_sum_im[blockIdx().x, antenna_idx, corr_idx] = cache[1 + 1 * iq_offset, antenna_idx, corr_idx] end end end return nothing end function downconvert_and_correlate_kernel_4431!( accum_re, accum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, code_replica, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, sampling_frequency, carrier_phase, num_samples::Int, num_ants::NumAnts{NANT} ) where {NCOR, NANT} cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, NANT, NCOR)) sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) # double the grid iq_offset = blockDim().x # indexing offset for complex values I/Q samples cache_index = threadIdx().x - 1 dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0 dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0 @inbounds if sample_idx <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1 dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1 for corr_idx = 1:NCOR sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_re_1 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_im_1 end end if sample_idx + blockDim().x <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 for corr_idx = 1:NCOR sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_re_2 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_im_2 end end end end ## Partial Reduction # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i for antenna_idx = 1:NANT for corr_idx = 1:NCOR cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] end end end sync_threads() i ÷= 2 end @inbounds if threadIdx().x == 1 for antenna_idx = 1:NANT for corr_idx = 1:NCOR CUDA.@atomic accum_re[antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx] CUDA.@atomic accum_im[antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx] end end end return nothing end function downconvert_and_correlate_kernel_3d_4431!( accum_re, accum_im, signal_re, signal_im, code_replica, correlator_sample_shifts, carrier_frequency, sampling_frequency, carrier_phase, num_samples::Int, num_ants::NumAnts{NANT} ) where {NANT} cache = CuDynamicSharedArray(Float32, (2 * blockDim().x, NANT)) sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) # double the grid iq_offset = blockDim().x # indexing offset for complex values I/Q samples cache_index = threadIdx().x - 1 antenna_idx = threadIdx().y corr_idx = blockIdx().y sat_idx = blockIdx().z dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0 dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0 sample_shift = correlator_sample_shifts[corr_idx] @inbounds if sample_idx <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_1 = signal_re[sample_idx, antenna_idx, sat_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx, sat_idx] * carrier_im_1 dw_im_1 = signal_im[sample_idx, antenna_idx, sat_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx, sat_idx] * carrier_im_1 # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx] = code_replica[sample_idx + sample_shift, sat_idx] * dw_re_1 cache[1 + cache_index + 1 * iq_offset, antenna_idx] = code_replica[sample_idx + sample_shift, sat_idx] * dw_im_1 end if sample_idx + blockDim().x <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_im_2 dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx, sat_idx] * carrier_im_2 # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx] += code_replica[sample_idx + blockDim().x + sample_shift, sat_idx] * dw_re_2 cache[1 + cache_index + 1 * iq_offset, antenna_idx] += code_replica[sample_idx + blockDim().x + sample_shift, sat_idx] * dw_im_2 end end end ## Partial Reduction # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i for antenna_idx = 1:NANT cache[1 + cache_index + 0 * iq_offset, antenna_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx] end end sync_threads() i ÷= 2 end @inbounds if threadIdx().x == 1 for antenna_idx = 1:NANT CUDA.@atomic accum_re[antenna_idx, corr_idx, sat_idx] += cache[1 + 0 * iq_offset, antenna_idx] CUDA.@atomic accum_im[antenna_idx, corr_idx, sat_idx] += cache[1 + 1 * iq_offset, antenna_idx] end end return nothing end function downconvert_and_correlate_kernel_5431!( accum_re, accum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, codes, code_length, code_replica, prn, correlator_sample_shifts::SVector{NCOR, Int64}, num_of_shifts, code_frequency, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, num_samples::Int, num_ants::NumAnts{NANT}, ) where {NCOR, NANT} cache = @cuDynamicSharedMem(Float32, (2 * blockDim().x, NANT, NCOR)) sample_idx = 1 + ((blockIdx().x - 1) * (2 * blockDim().x) + (threadIdx().x - 1)) iq_offset = blockDim().x # indexing offset for complex values I/Q samples cache_index = threadIdx().x - 1 # local dw_re_1 = dw_im_1 = carrier_re_1 = carrier_im_1 = 0.0f0 dw_re_2 = dw_im_2 = carrier_re_2 = carrier_im_2 = 0.0f0 # Code replica generation if sample_idx <= num_samples + num_of_shifts @inbounds code_replica[sample_idx] = codes[(code_frequency/sampling_frequency * (sample_idx - num_of_shifts) + start_code_phase)/code_length, prn] if sample_idx + blockDim().x <= num_samples + num_of_shifts @inbounds code_replica[sample_idx + blockDim().x] = codes[(code_frequency/sampling_frequency * (sample_idx + blockDim().x - num_of_shifts) + start_code_phase)/code_length, prn] end end sync_threads() @inbounds if sample_idx <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_1, carrier_re_1 = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_1 = signal_re[sample_idx, antenna_idx] * carrier_re_1 + signal_im[sample_idx, antenna_idx] * carrier_im_1 dw_im_1 = signal_im[sample_idx, antenna_idx] * carrier_re_1 - signal_re[sample_idx, antenna_idx] * carrier_im_1 for corr_idx = 1:NCOR sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_re_1 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] = code_replica[sample_idx + sample_shift] * dw_im_1 end end if sample_idx + blockDim().x <= num_samples # carrier replica generation, sin->im , cos->re carrier_im_2, carrier_re_2 = CUDA.sincos(2π * ((sample_idx + blockDim().x - 1) * carrier_frequency / sampling_frequency + carrier_phase)) # downconversion / carrier wipe off for antenna_idx = 1:NANT dw_re_2 = signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 + signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 dw_im_2 = signal_im[sample_idx + blockDim().x, antenna_idx] * carrier_re_2 - signal_re[sample_idx + blockDim().x, antenna_idx] * carrier_im_2 for corr_idx = 1:NCOR sample_shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] # write to shared memory cache cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_re_2 cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += code_replica[sample_idx + blockDim().x + sample_shift] * dw_im_2 end end end end ## Partial Reduction # wait until all the accumulators have done writing the results to the cache sync_threads() i::Int = blockDim().x ÷ 2 @inbounds while i != 0 if cache_index < i for antenna_idx = 1:NANT for corr_idx = 1:NCOR cache[1 + cache_index + 0 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 0 * iq_offset + i, antenna_idx, corr_idx] cache[1 + cache_index + 1 * iq_offset, antenna_idx, corr_idx] += cache[1 + cache_index + 1 * iq_offset + i, antenna_idx, corr_idx] end end end sync_threads() i ÷= 2 end # Last block reduction via atomic add @inbounds if (threadIdx().x - 1) == 0 for antenna_idx = 1:NANT for corr_idx = 1:NCOR CUDA.@atomic accum_re[antenna_idx, corr_idx] += cache[1 + 0 * iq_offset, antenna_idx, corr_idx] CUDA.@atomic accum_im[antenna_idx, corr_idx] += cache[1 + 1 * iq_offset, antenna_idx, corr_idx] end end end return nothing end function downconvert_and_accumulate_strided_kernel!( accum_re, accum_im, code_replica, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, carrier_frequency, sampling_frequency, carrier_phase, num_samples::Int, num_ants::NumAnts{NANT}, correlator_sample_shifts::SVector{NCOR, Int64} ) where {NANT, NCOR} stride = blockDim().x * gridDim().x thread_idx = 1 + ((blockIdx().x - 1) * blockDim().x + (threadIdx().x - 1)) @inbounds for sample_idx = thread_idx:stride:num_samples # gen carrier replica carrier_replica_im[sample_idx], carrier_replica_re[sample_idx] = CUDA.sincos(2π * ((sample_idx - 1) * carrier_frequency / sampling_frequency + carrier_phase)) for antenna_idx = 1:NANT # downconvert downconverted_signal_re[sample_idx, antenna_idx] = signal_re[sample_idx, antenna_idx] * carrier_replica_re[sample_idx] + signal_im[sample_idx, antenna_idx] * carrier_replica_im[sample_idx] downconverted_signal_im[sample_idx, antenna_idx] = signal_im[sample_idx, antenna_idx] * carrier_replica_re[sample_idx] - signal_re[sample_idx, antenna_idx] * carrier_replica_im[sample_idx] for corr_idx = 1:NCOR # accumulate shift = correlator_sample_shifts[corr_idx] - correlator_sample_shifts[1] accum_re[sample_idx, antenna_idx, corr_idx] = downconverted_signal_re[sample_idx, antenna_idx] * code_replica[sample_idx + shift] accum_im[sample_idx, antenna_idx, corr_idx] = downconverted_signal_im[sample_idx, antenna_idx] * code_replica[sample_idx + shift] end end end return nothing end # KERNEL 1_3_cplx_multi function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, partial_sum, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{1330}; ) where {NANT, NCOR} @cuda threads=threads_per_block[1] blocks=blocks_per_grid shmem=shmem_size[1] downconvert_and_correlate_kernel_1330!( partial_sum.re, partial_sum.im, signal_re, signal_im, codes, code_frequency, correlator_sample_shifts, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, code_length, prn, num_samples, NANT, NCOR ) @cuda threads=512 blocks=1 shmem=shmem_size[2] reduce_cplx_multi_3( partial_sum.re, partial_sum.im, partial_sum.re, partial_sum.im, blocks_per_grid, num_ants, correlator_sample_shifts ) end # KERNEL 1_3_cplx_multi_textmem function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, partial_sum, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{1331}; ) where {NANT, NCOR} NVTX.@range "downconvert_and_correlate_kernel_1331!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid shmem=shmem_size[1] downconvert_and_correlate_kernel_1331!( partial_sum.re, partial_sum.im, signal_re, signal_im, codes, code_frequency, correlator_sample_shifts, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, code_length, prn, num_samples, NANT, NCOR ) end NVTX.@range "reduce_cplx_multi_3" begin @cuda threads=threads_per_block[2] blocks=1 shmem=shmem_size[2] reduce_cplx_multi_3( partial_sum.re, partial_sum.im, partial_sum.re, partial_sum.im, blocks_per_grid, num_ants, correlator_sample_shifts ) end end # KERNEL 1_4_cplx_multi_textmem # 1431 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, partial_sum, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{1431}; ) where {NANT, NCOR} NVTX.@range "downconvert_and_correlate_kernel_1431!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid shmem=shmem_size[1] downconvert_and_correlate_kernel_1431!( partial_sum.re, partial_sum.im, signal_re, signal_im, codes, code_frequency, correlator_sample_shifts, carrier_frequency, sampling_frequency, start_code_phase, carrier_phase, code_length, prn, num_samples, NANT, NCOR ) end NVTX.@range "reduce_cplx_multi_4" begin @cuda threads=threads_per_block[2] blocks=1 shmem=shmem_size[2] reduce_cplx_multi_4( partial_sum.re, partial_sum.im, partial_sum.re, partial_sum.im, blocks_per_grid, num_ants, correlator_sample_shifts ) end end # KERNEL 2_3_cplx_multi # 2330 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, accum_re, accum_im, phi_re, phi_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{2330} ) where {NANT, NCOR} NVTX.@range "gen_code_replica_kernel!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length ) end NVTX.@range "downconvert_and_accumulate!" begin @cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!( accum_re, accum_im, code_replica, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, carrier_frequency, sampling_frequency, carrier_phase, num_samples, num_ants, correlator_sample_shifts ) end NVTX.@range "reduce_cplx_multi_3" begin @cuda threads=threads_per_block[3] blocks=blocks_per_grid[3] shmem=shmem_size reduce_cplx_multi_3( phi_re, phi_im, accum_re, accum_im, num_samples, num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) NVTX.@range "reduce_cplx_multi_3" begin @cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_3( phi_re, phi_im, phi_re, phi_im, blocks_per_grid[3], num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) end # KERNEL 2_3_cplx_multi_textmem # 2331 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, accum_re, accum_im, phi_re, phi_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{2331} ) where {NANT, NCOR} NVTX.@range "gen_code_replica_kernel!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length ) end NVTX.@range "downconvert_and_accumulate!" begin @cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!( accum_re, accum_im, code_replica, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, carrier_frequency, sampling_frequency, carrier_phase, num_samples, num_ants, correlator_sample_shifts ) end NVTX.@range "reduce_cplx_multi_3" begin @cuda threads=threads_per_block[3] blocks=blocks_per_grid[3] shmem=shmem_size reduce_cplx_multi_3( phi_re, phi_im, accum_re, accum_im, num_samples, num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) NVTX.@range "reduce_cplx_multi_3" begin @cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_3( phi_re, phi_im, phi_re, phi_im, blocks_per_grid[3], num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) end # KERNEL 2_4_cplx_multi # 2430 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, accum_re, accum_im, phi_re, phi_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{2430} ) where {NANT, NCOR} NVTX.@range "gen_code_replica_kernel!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length ) end NVTX.@range "downconvert_and_accumulate!" begin @cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!( accum_re, accum_im, code_replica, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, carrier_frequency, sampling_frequency, carrier_phase, num_samples, num_ants, correlator_sample_shifts ) end NVTX.@range "reduce_cplx_multi_4" begin @cuda threads=threads_per_block[3] blocks=cld(blocks_per_grid[3], 2) shmem=shmem_size reduce_cplx_multi_4( phi_re, phi_im, accum_re, accum_im, num_samples, num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) NVTX.@range "reduce_cplx_multi_4" begin @cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_4( phi_re, phi_im, phi_re, phi_im, blocks_per_grid[3], num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) end # KERNEL 2_4_cplx_multi_textmem # 2431 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, accum_re, accum_im, phi_re, phi_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{2431} ) where {NANT, NCOR} NVTX.@range "gen_code_replica_kernel!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length ) end NVTX.@range "downconvert_and_accumulate!" begin @cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] downconvert_and_accumulate_strided_kernel!( accum_re, accum_im, code_replica, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, carrier_frequency, sampling_frequency, carrier_phase, num_samples, num_ants, correlator_sample_shifts ) end NVTX.@range "reduce_cplx_multi_4" begin @cuda threads=threads_per_block[3] blocks=cld(blocks_per_grid[3], 2) shmem=shmem_size reduce_cplx_multi_4( phi_re, phi_im, accum_re, accum_im, num_samples, num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) NVTX.@range "reduce_cplx_multi_4" begin @cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size reduce_cplx_multi_4( phi_re, phi_im, phi_re, phi_im, blocks_per_grid[3], num_ants, correlator_sample_shifts ) end # print(Array(phi_re)) end # KERNEL 3_4_cplx_multi_textmem # 3431 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, accum_re, accum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{3431} ) where {NANT, NCOR} NVTX.@range "gen_code_replica_texture_mem_strided_kernel!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length ) end NVTX.@range "downconvert_and_correlate_kernel_3431!" begin @cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] shmem=shmem_size[1] downconvert_and_correlate_kernel_3431!( accum_re, accum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, code_replica, correlator_sample_shifts, carrier_frequency, sampling_frequency, carrier_phase, num_samples, num_ants ) end NVTX.@range "reduce_cplx_multi_4" begin @cuda threads=threads_per_block[3] blocks=1 shmem=shmem_size[2] reduce_cplx_multi_4( accum_re, accum_im, accum_re, accum_im, blocks_per_grid[2], num_ants, correlator_sample_shifts ) end end # KERNEL 4_4_cplx_multi_textmem # 4431 function kernel_algorithm( threads_per_block, blocks_per_grid, shmem_size, code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length, accum_re, accum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, correlator_sample_shifts::SVector{NCOR, Int64}, carrier_frequency, carrier_phase, num_ants::NumAnts{NANT}, num_corrs, algorithm::KernelAlgorithm{4431} ) where {NANT, NCOR} NVTX.@range "gen_code_replica_texture_mem_strided_kernel!" begin @cuda threads=threads_per_block[1] blocks=blocks_per_grid[1] gen_code_replica_texture_mem_strided_kernel!( code_replica, codes, code_frequency, sampling_frequency, start_code_phase, prn, num_samples, num_of_shifts, code_length ) end NVTX.@range "downconvert_and_correlate_kernel_3431!" begin @cuda threads=threads_per_block[2] blocks=blocks_per_grid[2] shmem=shmem_size[1] downconvert_and_correlate_kernel_4431!( accum_re, accum_im, carrier_replica_re, carrier_replica_im, downconverted_signal_re, downconverted_signal_im, signal_re, signal_im, code_replica, correlator_sample_shifts, carrier_frequency, sampling_frequency, carrier_phase, num_samples, num_ants ) end end
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using DataFrames using CSV cd("/Users/kiristern/Documents/GitHub/SDM/") predators = CSV.read("data/preds/originals/Nasua_nasua2.csv") predators = select(predators, [:species, :decimalLatitude, :decimalLongitude, :year]) # select only ursus arctos from imported all predators csv file #df = predators[predators.species .== "Ursus arctos", :] p1 = CSV.read("data/prey_class/Diplopoda2.csv") p1 = select(p1,[:class, :decimalLatitude, :decimalLongitude, :year]) rename!(p1, :class => :species) #create one dataframe with predators/prey dataframes pred_prey = vcat(predators, p1) pred_prey = dropmissing!(pred_prey, [:decimalLatitude, :decimalLongitude, :year]) CSV.write("data/pred_prey/Nasua_nasua.csv", pred_prey) show(by(pred_prey, :species, :species => length), allcols=true)
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function zad35(d, el1, el2) A = zeros(d,d) A[:, 1:2:end] .= el1 A[:, 2:2:end] .= el2 return A end
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<gh_stars>0 @testset "Database IO" begin @testset "SQLite" begin fname = joinpath(testpath, "test.db") db = SQLite.DB(fname) write_table!(db, "test1", df) @test filesize(fname) > 0 df_recovered = DataFrame(read_table(fname, "test1"); copycols=false) @test df == df_recovered df_sql = DataFrame(read_sql(db, "select * from test1 where a < 5"); copycols=false) @test df[df.a .< 5, :] == df_sql write_table!(fname, "test2", nt) nt_recovered = read_table(db, "test2") @test DataFrame(nt) == DataFrame(nt_recovered) end @testset "PostgreSQL" begin # the following tests require a running PostgreSQL database. # `docker run --rm --detach --name test-libpqjl -e POSTGRES_HOST_AUTH_METHOD=trust -p 5432:5432 postgres` conn = LibPQ.Connection("dbname=postgres user=postgres") execute(conn, """CREATE TEMPORARY TABLE test1 ( a integer PRIMARY KEY, b numeric, c character varying, d boolean, e date, f character varying );""") write_table!(conn, "test1", df) df_recovered = DataFrame(read_table(conn, "test1"); copycols=false) @test df == df_recovered df_sql = DataFrame(read_sql(conn, "select * from test1 where a < 5"); copycols=false) @test df[df.a .< 5, :] == df_sql execute(conn, """CREATE TEMPORARY TABLE test2 ( a integer PRIMARY KEY, b numeric, c character varying );""") write_table!(conn, "test2", nt) nt_recovered = read_table(conn, "test2") @test DataFrame(nt) == DataFrame(nt_recovered) close(conn) end end
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# abstract type AbstractRule{T} end # abstract type ConditionalRule{T} <: AbstractRule{T} end # struct SimpleRule{T} <: AbstractReplacementRule{T} # r::T # priority::Int # end # struct UpwindRule{T, T2} # r::T # priority::Int # condition::T2 # end # struct RuleSet{T, T2} # derivrules::T # valrules::T2 # end # function RuleSet(rules::Vector{T}, conditional_rules::Vector{C}) where {T<:SimpleRule, C<:ConditionalRule} # priorities = vcat(map(r -> r.priority, rules), map(r -> r.priority, conditional_rules)) # for (i,r) in enumerate(vcat(rules, conditional_rules)) # end # ModelingToolkit.substitute(expr, rule::AbstractRule{T}) where T = substitute(expr, rule.r) """ `interpolate_discrete_param` Interpolate gridpoints by taking the average of the values of the discrete points, or if the offset is outside the grid, extrapolate the value with dx. """ @inline function interpolate_discrete_param(i, s, itap, x, bpc) return s.grid[x][i+itap]+s.dxs[x]*.5 end """ `cartesian_nonlinear_laplacian` Differential(x)(expr(x)*Differential(x)(u(x))) Given an internal multiplying expression `expr`, return the correct finite difference equation for the nonlinear laplacian at the location in the grid given by `II`. The inner derivative is discretized with the half offset centered scheme, giving the derivative at interpolated grid points offset by dx/2 from the regular grid. The outer derivative is discretized with the centered scheme, giving the nonlinear laplacian at the grid point `II`. For first order returns something like this: `d/dx( a du/dx ) ~ (a(x+1/2) * (u[i+1] - u[i]) - a(x-1/2) * (u[i] - u[i-1]) / dx^2` For 4th order, returns something like this: ``` first_finite_diffs = [a(x-3/2)*finitediff(u, i-3/2), a(x-1/2)*finitediff(u, i-1/2), a(x+1/2)*finitediff(u, i+1/2), a(x+3/2)*finitediff(u, i+3/2)] dot(central_finite_diff_weights, first_finite_diffs) ``` where `finitediff(u, i)` is the finite difference at the interpolated point `i` in the grid. And so on. """ function cartesian_nonlinear_laplacian(expr, II, derivweights, s::DiscreteSpace{N}, b, depvars, x, u) where N # Based on the paper https://web.mit.edu/braatzgroup/analysis_of_finite_difference_discretization_schemes_for_diffusion_in_spheres_with_variable_diffusivity.pdf # See scheme 1, namely the term without the 1/r dependence. See also #354 and #371 in DiffEqOperators, the previous home of this package. ndims(u,s) == 0 && return Num(0) jx = j, x = (x2i(s, u, x), x) @assert II[j] != 1 "The nonlinear laplacian is only defined on the interior of the grid, it is unsupported in boundary conditions." @assert II[j] != length(s, x) "The nonlinear laplacian is only defined on the interior of the grid, it is unsupported in boundary conditions." D_inner = derivweights.halfoffsetmap[Differential(x)] inner_interpolater = derivweights.interpmap[x] # Get the outer weights and stencil. clip() essentially removes a point from either end of the grid, for this reason this function is only defined on the interior, not in bcs# cliplen = length(s, x) - 1 outerweights, outerstencil = get_half_offset_weights_and_stencil(D_inner, II-unitindex(N,j), s, b, u, jx, cliplen) # Get the correct weights and stencils for this II inner_deriv_weights_and_stencil = [get_half_offset_weights_and_stencil(D_inner, I, s, b, u, jx) for I in outerstencil] interp_weights_and_stencil = [get_half_offset_weights_and_stencil(inner_interpolater, I, s, b, u, jx) for I in outerstencil] # map variables to symbolically inerpolated/extrapolated expressions map_vars_to_interpolated(stencil, weights) = [v => dot(weights, s.discvars[v][stencil]) for v in depvars] # Map parameters to interpolated values. Using simplistic extrapolation/interpolation for now as grids are uniform #TODO: make this more efficient map_params_to_interpolated(stencil, weights) = vcat([x => dot(weights, getindex.((s.grid[x],), getindex.(stencil, (j,))))], [s.x̄[k] => s.grid[s.x̄[k]][II[k]] for k in setdiff(1:N, [j])]) # Take the inner finite difference inner_difference = [dot(inner_weights, s.discvars[u][inner_stencil]) for (inner_weights, inner_stencil) in inner_deriv_weights_and_stencil] # Symbolically interpolate the multiplying expression interpolated_expr = map(interp_weights_and_stencil) do (weights, stencil) Num(substitute(substitute(expr, map_vars_to_interpolated(stencil, weights)), map_params_to_interpolated(stencil, weights))) end # multiply the inner finite difference by the interpolated expression, and finally take the outer finite difference return dot(outerweights, inner_difference .* interpolated_expr) end """ `spherical_diffusion` Based on https://web.mit.edu/braatzgroup/analysis_of_finite_difference_discretization_schemes_for_diffusion_in_spheres_with_variable_diffusivity.pdf See scheme 1 in appendix A. The r = 0 case is treated in a later appendix """ function spherical_diffusion(innerexpr, II, derivweights, s, b, depvars, r, u) # Based on the paper https://web.mit.edu/braatzgroup/analysis_of_finite_difference_discretization_schemes_for_diffusion_in_spheres_with_variable_diffusivity.pdf D_1 = derivweights.map[Differential(r)] D_2 = derivweights.map[Differential(r)^2] #TODO!: Update this to use indvars of the pde # What to replace parameter x with given I _rsubs(x, I) = x => s.grid[x][I[s.x2i[x]]] # Full rules for substituting parameters in the inner expression rsubs(I) = vcat([v => s.discvars[v][I] for v in depvars], [_rsubs(x, I) for x in s.x̄]) # Discretization func for u ufunc_u(v, I, x) = s.discvars[v][I] # 2nd order finite difference in u exprhere = Num(substitute(innerexpr, rsubs(II))) # Catch the r ≈ 0 case if Symbolics.unwrap(substitute(r, _rsubs(r, II))) ≈ 0 D_2_u = central_difference(D_2, II, s, b, (s.x2i[r], r), u, ufunc_u) return 3exprhere*D_2_u # See appendix B of the paper end D_1_u = central_difference(D_1, II, s, b, (s.x2i[r], r), u, ufunc_u) # See scheme 1 in appendix A of the paper return exprhere*(D_1_u/Num(substitute(r, _rsubs(r, II))) + cartesian_nonlinear_laplacian(innerexpr, II, derivweights, s, b, depvars, r, u)) end @inline function generate_cartesian_rules(II, s, depvars, derivweights, pmap, indexmap, terms) central_ufunc(u, I, x) = s.discvars[u][I] return reduce(vcat, [reduce(vcat, [[(Differential(x)^d)(u) => central_difference(derivweights.map[Differential(x)^d], Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], (x2i(s,u,x),x), u, central_ufunc) for d in (let orders = derivweights.orders[x]; orders[iseven.(orders)] end)] for x in params(u, s)]) for u in depvars]) end @inline function upwind_difference(expr, d::Int, II::CartesianIndex{N}, s::DiscreteSpace{N}, b, depvars, derivweights, (j,x), u, central_ufunc, indexmap) where N # TODO: Allow derivatives in expr expr = substitute(expr, valmaps(s, u, depvars, Idx(II, s, depvar(u, s), indexmap), indexmap)) IfElse.ifelse(expr > 0, expr*upwind_difference(d, II, s, b, derivweights, (j,x), u, central_ufunc, true), expr*upwind_difference(d, II, s, b, derivweights, (j,x), u, central_ufunc, false)) end @inline function generate_winding_rules(II, s, depvars, derivweights, pmap, indexmap, terms) wind_ufunc(v, I, x) = s.discvars[v][I] # for all independent variables and dependant variables rules = vcat(#Catch multiplication reduce(vcat, [reduce(vcat, [[@rule *(~~a, $(Differential(x)^d)(u), ~~b) => upwind_difference(*(~a..., ~b...), d, Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], depvars, derivweights, (x2i(s,u,x),x), u, wind_ufunc, indexmap) for d in (let orders = derivweights.orders[x]; orders[isodd.(orders)] end)] for x in params(u, s)]) for u in depvars]), #Catch division and multiplication, see issue #1 reduce(vcat, [reduce(vcat, [[@rule /(*(~~a, $(Differential(x)^d)(u), ~~b), ~c) => upwind_difference(*(~a..., ~b...)/~c, d, Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], depvars, derivweights, (x2i(s,u,x), x), u, wind_ufunc, indexmap) for d in (let orders = derivweights.orders[x]; orders[isodd.(orders)] end)] for x in params(u, s)]) for u in depvars]) ) wind_rules = [] # wind_exprs = [] for t in terms for r in rules if r(t) !== nothing push!(wind_rules, t => r(t)) end end end return vcat(wind_rules, vec(mapreduce(vcat, depvars) do u mapreduce(vcat, params(u, s)) do x j = x2i(s,u,x) let orders = derivweights.orders[x] oddorders = orders[isodd.(orders)] # for all odd orders if length(oddorders) > 0 map(oddorders) do d (Differential(x)^d)(u) => upwind_difference(d, Idx(II, s, u, indexmap), s, pmap.map[operation(u)][x], derivweights, (j,x), u, wind_ufunc, true) end else [] end end end end)) end @inline function generate_nonlinlap_rules(II, s, depvars, derivweights, pmap, indexmap, terms) rules = reduce(vcat, [vec([@rule *(~~c, $(Differential(x))(*(~~a, $(Differential(x))(u), ~~b)), ~~d) => *(~c...,cartesian_nonlinear_laplacian(*(a..., b...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][x], depvars, x, u), ~d...) for x in params(u, s)]) for u in depvars]) rules = vcat(rules, reduce(vcat, [vec([@rule $(Differential(x))(*(~~a, $(Differential(x))(u), ~~b)) => cartesian_nonlinear_laplacian(*(a..., b...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][x], depvars, x, u) for x in params(u, s)]) for u in depvars])) rules = vcat(rules, reduce(vcat, [vec([@rule ($(Differential(x))($(Differential(x))(u)/~a)) => cartesian_nonlinear_laplacian(1/~a, Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][x], depvars, x, u) for x in params(u, s)]) for u in depvars])) nonlinlap_rules = [] for t in terms for r in rules if r(t) !== nothing push!(nonlinlap_rules, t => r(t)) end end end return nonlinlap_rules end @inline function generate_spherical_diffusion_rules(II, s, depvars, derivweights, pmap, indexmap, terms) rules = reduce(vcat, [vec([@rule *(~~a, 1/(r^2), ($(Differential(r))(*(~~c, (r^2), ~~d, $(Differential(r))(u), ~~e))), ~~b) => *(~a..., spherical_diffusion(*(~c..., ~d..., ~e...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][r], depvars, r, u), ~b...) for r in params(u,s)]) for u in depvars]) rules = vcat(rules, reduce(vcat, [vec([@rule /(*(~~a, $(Differential(r))(*(~~c, (r^2), ~~d, $(Differential(r))(u), ~~e)), ~~b), (r^2)) => *(~a..., ~b..., spherical_diffusion(*(~c..., ~d..., ~e...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][r], depvars, r, u)) for r in params(u,s)]) for u in depvars])) rules = vcat(rules, reduce(vcat, [vec([@rule /(($(Differential(r))(*(~~c, (r^2), ~~d, $(Differential(r))(u), ~~e))), (r^2)) => spherical_diffusion(*(~c..., ~d..., ~e...), Idx(II, s, u, indexmap), derivweights, s, pmap.map[operation(u)][r], depvars, r, u) for r in params(u, s)]) for u in depvars])) spherical_diffusion_rules = [] for t in terms for r in rules if r(t) !== nothing push!(spherical_diffusion_rules, t => r(t)) end end end return spherical_diffusion_rules end """ `generate_finite_difference_rules` Generate a vector of finite difference rules to dictate what to replace variables in the `pde` with at the gridpoint `II`. Care is taken to make sure that the rules only use points that are actually in the discretized grid by progressively up/downwinding the stencils when the gridpoint `II` is close to the boundary. There is a genral catch all ruleset that uses the cartesian centered difference scheme for derivatives, and simply the discretized variable at the given gridpoint for particular variables. There are of course more specific schemes that are used to improve stability/speed/accuracy when particular forms are encountered in the PDE. These rules are applied first to override the general ruleset. ##Currently implemented special cases are as follows: - Spherical derivatives - Nonlinear laplacian uses a half offset centered scheme for the inner derivative to improve stability - Spherical nonlinear laplacian. - Up/Downwind schemes to be used for odd ordered derivatives multiplied by a coefficient, downwinding when the coefficient is positive, and upwinding when the coefficient is negative. Please submit an issue if you know of any special cases which impact stability or accuracy that are not implemented, with links to papers and/or code that demonstrates the special case. """ function generate_finite_difference_rules(II, s, depvars, pde, derivweights, pmap, indexmap) terms = split_terms(pde, s.x̄) # Standard cartesian centered difference scheme central_deriv_rules_cartesian = generate_cartesian_rules(II, s, depvars, derivweights, pmap, indexmap, terms) # Nonlinear laplacian scheme nonlinlap_rules = generate_nonlinlap_rules(II, s, depvars, derivweights, pmap, indexmap, terms) # Because winding needs to know about multiplying terms, we can't split the terms into additive and multiplicative terms. winding_rules = generate_winding_rules(II, s, depvars, derivweights, pmap, indexmap, terms) # Spherical diffusion scheme spherical_diffusion_rules = generate_spherical_diffusion_rules(II, s, depvars, derivweights, pmap, indexmap, split_additive_terms(pde)) return vcat(vec(spherical_diffusion_rules), vec(nonlinlap_rules), vec(winding_rules), vec(central_deriv_rules_cartesian)) end
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2.510446
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<filename>src/requires/cairomakie.jl function savefig_monitoring_results(r::MonitoringResults, symbols=keys(r.results); ext=:pdf) for s in symbols savefig_monitoring_results(r, s; ext) end return nothing end function savefig_monitoring_results(r::MonitoringResults, s::Symbol; ext=:pdf) times = r.times values = r.results[s] title, ylabel = _symbol2title_and_label(s) ylims = _defaultylims(values) device_labels = [str for (str, uuid) in r.devices] f = CairoMakie.Figure(; resolution=(1000, 500)) ax = f[1, 1] = CairoMakie.Axis( f; xlabel="Time [s]", ylabel=ylabel, title=title, ylims=ylims ) CairoMakie.scatterlines!(times, getindex.(values, 1); label=device_labels[1]) for i in 2:length(first(values)) CairoMakie.scatterlines!(times, getindex.(values, i); label=device_labels[i]) end f[1, 2] = CairoMakie.Legend(f, ax, "Devices"; framevisible=false) filename = replace(replace(replace(lowercase(title), " " => "_"), "(" => ""), ")" => "") * "_plot.$(string(ext))" CairoMakie.save(filename, f) return nothing end
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2.354772
482
using .Instantiation using .ModelElaboration using Unitful using .StructuralTransform using .Synchronous: sample, Clock, previous, hold, positive, positiveChange, positiveEdge """ Shortcut for `Variable` """ Var(; args...) = Variable(; args...) """ Create a floating-point `Variable` """ Float(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits, min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing, variability=continuous, flow::Bool=false, state::Bool=true) = Variable(variability, Float64, size, value, unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general) """ Create a boolean `Variable` """ Boolean(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits, min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing, variability=continuous, flow::Bool=false, state::Bool=true) = Variable(variability, Bool, size, value, unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general) """ Create an integer `Variable` """ Integ(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits, min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing, variability=continuous, flow::Bool=false, state::Bool=true) = Variable(variability, Int, size, value, unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general) """ Create a string `Variable` """ Str(value=nothing; info="", size=nothing, unit=NoUnits, displayUnit=NoUnits, min=nothing, max=nothing, start=nothing, fixed::Bool=false, nominal=nothing, variability=continuous, flow::Bool=false, state::Bool=true) = Variable(variability, String, size, value, unit, displayUnit, min, max, start, fixed, nominal, info, flow, state, general) #= Float(; args...) = Var(T=Float64; args...) Float0(; args...) = Float(size=(); args...) Boolean(; args...) = Var(T=Bool; args...) Boolean0(; args...) = Boolean(size=(); args...) Integ(; args...) = Var(T=Int; args...) Integ0(; args...) = Integ(size=(); args...) Str(; args...) = Var(T=String; args...) Str0(; args...) = Str(size=(); args...) =# """ Create a `Variable` with `parameter` variability, meaning it is an input variable that is constant with time """ Parameter(; args...) = Variable(variability=parameter; args...) Parameter(value; args...) = Variable(variability=parameter, value=value; args...) """ Shortcut for `Parameter` """ Par(; args...) = Variable(variability=parameter; args...) Par(value; args...) = Variable(variability=parameter, value=value; args...) """ A value meant to be filled in later """ const undefined = nothing
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3.03491
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@testset "Joint Life" begin @testset "Joint Last Survivor unknown status" begin @testset "ALMCR §9.4" begin ℓ₁ = [43302,42854,42081,41351,40050] ℓ₂ = [47260,47040,46755,46500,46227] ps₁ = ℓ₁ ./ ℓ₁[1] qs₁ = [1 - ps₁[t] / ps₁[t - 1] for t in 2:5 ] ps₂ = ℓ₂ ./ ℓ₂[1] qs₂ = [1 - ps₂[t] / ps₂[t - 1] for t in 2:5 ] m1 = UltimateMortality(qs₁, start_age=65) m2 = UltimateMortality(qs₂, start_age=60) @test decrement(m1, 65, 66) == 1 - ℓ₁[2] / ℓ₁[1] @test decrement(m2, 60, 61) == 1 - ℓ₂[2] / ℓ₂[1] l1 = SingleLife(mort = m1, issue_age = 65) l2 = SingleLife(mort = m2, issue_age = 60) jl = JointLife(lives=(l1, l2), contingency = LastSurvivor(), joint_assumption=Frasier()) @test isapprox( survival(jl, 2) , 0.9997, atol = 1e-4) ins = LifeContingency(jl,Yields.Constant(0.05)) ins_l1 = LifeContingency(jl.lives[1],Yields.Constant(0.05)) ins_l2 = LifeContingency(jl.lives[2],Yields.Constant(0.05)) # problem 9.1.f @test isapprox( present_value(AnnuityDue(ins,n=5)), 4.5437, atol = 1e-4) @test isapprox( present_value(AnnuityDue(ins)), 4.5437, atol = 1e-4) end @testset "CIA tables" begin m1 = MortalityTables.table("1986-92 CIA – Male Smoker, ANB") m2 = MortalityTables.table("1986-92 CIA – Female Nonsmoker, ANB") l1 = SingleLife(mort = m1.ultimate, issue_age = 40) l2 = SingleLife(mort = m2.ultimate, issue_age = 37) jl = JointLife(lives=(l1, l2), contingency=LastSurvivor(), joint_assumption=Frasier()) @testset "independent lives" begin for time in 1:40 tpx = survival(l1,time) tpy = survival(l2,time) @test survival(jl, time) == tpx + tpy - tpx * tpy end end q_annual = [0.00000141120,0.00000478349,0.00000921100,0.00001508953,0.00002255325,0.00003179413,0.00004327035,0.00005782855,0.00007524178,0.00009695039,0.00012370408,0.00015606419,0.00019612090,0.00024529620,0.00030502659,0.00037768543,0.00046587280,0.00057325907,0.00070238876,0.00085952824,0.00104786439,0.00127420204,0.00154640464,0.00187374100,0.00226186101,0.00272457482,0.00327193743,0.00391984214,0.00468084563,0.00557778027,0.00662135901,0.00783684326,0.00924475551,0.01086696400,0.01272977389,0.01485150445,0.01727277853,0.02000566587,0.02308982834,0.02654184882,0.03039819991,0.03469299691,0.03944737905,0.04469931521,0.05048294677,0.05683522818,0.06379854263,0.07142629169,0.07976664920,0.08888579420,0.09884411368,0.10971505011,0.12159455247,0.13456410066,0.14873755448,0.16421406941,0.18111392905,0.19954137714,0.21962070363,0.24144744481,0.26515968560,0.29099967654,0.31901974549,0.35111309929,0.39733960046,0.47004540325,0.58255000000,0.74852030000,1.00000000000] q_cumulative = [0.00000141120,0.00000619469,0.00001540563,0.00003049492,0.00005304748,0.00008483992,0.00012810660,0.00018592774,0.00026115553,0.00035808060,0.00048174038,0.00063772939,0.00083372522,0.00107881691,0.00138351443,0.00176067732,0.00222572986,0.00279771301,0.00349813669,0.00435465818,0.00539795948,0.00666528343,0.00820138084,0.01005975458,0.01229886182,0.01498992747,0.01821281880,0.02206126957,0.02663884979,0.03206804441,0.03847706939,0.04601237389,0.05483175625,0.06510286552,0.07700389465,0.09071177542,0.10641770955,0.12429441828,0.14451430984,0.16722048170,0.19253547997,0.22054884408,0.25129614928,0.28476269870,0.32086998531,0.35946849466,0.40033347120,0.44316542761,0.48758225561,0.53312891378,0.57927637250,0.62543608637,0.67098101782,0.71525516126,0.75760741222,0.79741168545,0.83410325108,0.86720651682,0.89637071504,0.92139174110,0.94223548231,0.95904493828,0.97211041164,0.98190281145,0.98909354112,0.99422007198,0.99758716905,0.99939322200,1.00000000000] @testset "precalced vectors" begin for time in 1:40 @test isapprox(decrement(jl,time), q_cumulative[time], atol = 1e-6) end for time in 1:40 q′ = 1 - survival(jl, time) / survival(jl, time-1) @test isapprox(q′, q_annual[time], atol = 1e-6) end for time in 1:40 @test isapprox(survival(jl, time), 1 - q_cumulative[time], atol = 1e-6) end end end end end
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<gh_stars>0 using Test, Printf t1 = @elapsed using Tullio @info @sprintf("Loading Tullio took %.1f seconds", t1) @info "Testing with $(Threads.nthreads()) threads" if Threads.nthreads() > 1 # use threading even on small arrays Tullio.BLOCK[] = 32 Tullio.TILE[] = 32 end #===== stuff =====# t2 = time() @testset "parsing all the things" begin include("parsing.jl") end @testset "tests from Einsum.jl" begin include("einsum.jl") end @info @sprintf("Basic tests took %.1f seconds", time()-t2) @testset "internal pieces" begin include("utils.jl") end @testset "matrix multiplication" begin # size 200 is big enough to test block_halves even with MINIBLOCK = 64^3 @testset "size $N, elements $T" for N in [2, 20, 200], T in [1:99, Float32, Float64, ComplexF64] for f in [identity, adjoint] A = f(rand(T, N,N)); B = f(rand(T, N,N)); @test A * B ≈ @tullio C[i,k] := A[i,j] * B[j,k] end if N < 200 X = rand(T, N,N+1); Y = rand(T, N+1,N+2); Z = rand(T, N+2,N+1); @test X * Y * Z ≈ @tullio C[a,d] := X[a,b] * Y[b,c] * Z[c,d] end end @testset "@allocated" begin m!(C,A,B) = @tullio C[i,k] = A[i,j] * B[j,k] threads=false C1, A1, B1 = rand(4,4), rand(4,4), rand(4,4) @allocated m!(C1, A1, B1) @test 0 == @allocated m!(C1, A1, B1) end end #===== Tracker =====# t3 = time() using Tracker GRAD = :Tracker _gradient(x...) = Tracker.gradient(x...) @tullio grad=Base @testset "gradients: Tracker + DiffRules" begin include("gradients.jl") end @tullio grad=Dual @testset "gradients: Tracker + ForwardDiff" begin include("gradients.jl") end @info @sprintf("Tracker tests took %.1f seconds", time()-t3) #===== KernelAbstractions =====# t4 = time() using KernelAbstractions @testset "KernelAbstractions + gradients" begin A = (rand(3,4)); B = (rand(4,5)); @tullio C[i,k] := A[i,j] * B[j,k] threads=false # verbose=2 @test C ≈ A * B @tullio threads=false # else KernelAbstractions CPU kernels not used include("gradients.jl") @tullio threads=true for sy in Tullio.SYMBOLS @test !isdefined(@__MODULE__, sy) end end using CUDA if CUDA.has_cuda_gpu() @info "===== found a GPU, starting CUDA tests =====" @testset "===== CUDA tests on GPU =====" begin include("cuda.jl") end end @info @sprintf("KernelAbstractions tests took %.1f seconds", time()-t4) @tullio cuda=false #===== Zygote =====# t5 = time() using Zygote GRAD = :Zygote _gradient(x...) = Zygote.gradient(x...) @tullio grad=Base @testset "gradients: Zygote + DiffRules" begin include("gradients.jl") end @tullio grad=Dual @testset "gradients: Zygote + ForwardDiff" begin include("gradients.jl") end @tullio grad=Base if VERSION >= v"1.4" # mysterious failures on 1.3 @testset "complex gradients with Zygote" begin x0 = [1,2,3] .+ [5im, 0, -11im] # y0 = rand(Int8,3) .+ im .* rand(Int8,3) .+ 0.0 @testset "analytic" begin g1 = _gradient(x -> real(sum(x)), x0)[1] g1i = _gradient(x -> imag(sum(x)), x0)[1] @test g1 ≈ _gradient(x -> real(@tullio y := x[i]), x0)[1] @test g1i ≈ _gradient(x -> imag(@tullio y := x[i]), x0)[1] g2 = _gradient(x -> real(sum(exp, x)), x0)[1] g2i = _gradient(x -> imag(sum(exp, x)), x0)[1] @test g2 ≈ _gradient(x -> real(@tullio y := exp(x[i])), x0)[1] @test g2i ≈ _gradient(x -> imag(@tullio y := exp(x[i])), x0)[1] g3 = _gradient(x -> real(sum(1 ./ (x.+im).^2)), x0)[1] g3i = _gradient(x -> imag(sum(1 ./ (x.+im).^2)), x0)[1] @test g3 ≈ _gradient(x -> real(@tullio y := 1/(x[i] + im)^2), x0)[1] @test g3 ≈ _gradient(x -> real(@tullio y := inv(x[i] + im)^2), x0)[1] @test g3i ≈ _gradient(x -> imag(@tullio y := 1/(x[i] + im)^2), x0)[1] @test g3i ≈ _gradient(x -> imag(@tullio y := inv(x[i] + im)^2), x0)[1] # with finaliser g7 = _gradient(x -> real(sum(sqrt.(sum(exp.(x), dims=2)))), x0 .+ x0')[1] g7i = _gradient(x -> imag(sum(sqrt.(sum(exp.(x), dims=2)))), x0 .+ x0')[1] @test g7 ≈ _gradient(x -> real(sum(@tullio y[i] := sqrt <| exp(x[i,j]) )), x0 .+ x0')[1] @test g7i ≈ _gradient(x -> imag(sum(@tullio y[i] := sqrt <| exp(x[i,j]) )), x0 .+ x0')[1] end @testset "non-analytic" begin g4 = _gradient(x -> real(sum(x * x')), x0)[1] g4i = _gradient(x -> imag(sum(x * x')), x0)[1] # zero! @test_broken g4 ≈ _gradient(x -> real(@tullio y := x[i] * conj(x[j])), x0)[1] @test_broken g4i ≈ _gradient(x -> imag(@tullio y := x[i] * conj(x[j])), x0)[1] @test_broken g4 ≈ _gradient(x -> real(@tullio y := x[i] * adjoint(x[j])), x0)[1] @test_broken g4i ≈ _gradient(x -> imag(@tullio y := x[i] * adjoint(x[j])), x0)[1] g5 = _gradient(x -> real(sum(abs2.(x .+ 2 .+ im))), x0)[1] g5i = _gradient(x -> imag(sum(abs2.(x .+ 2 .+ im))), x0)[1] # zero! @test_broken g5 ≈ _gradient(x -> real(@tullio y := abs2(x[i] + 2 + im)), x0)[1] @test_broken g5i ≈ _gradient(x -> real(@tullio y := abs2(x[i] + 2 + im)), x0)[1] g6 = _gradient(x -> real(sum(abs.(x.^3))), x0)[1] g6i = _gradient(x -> imag(sum(abs.(x.^3))), x0)[1] # zero! @test_broken g6 ≈ _gradient(x -> real(@tullio y := abs(x[i]^3)), x0)[1] @test_broken g6i ≈ _gradient(x -> real(@tullio y := abs(x[i]^3)), x0)[1] end end end # VERSION @info @sprintf("Zygote tests took %.1f seconds", time()-t5) #===== ReverseDiff =====# #= t6 = time() using ReverseDiff GRAD = :ReverseDiff _gradient(x...) = ReverseDiff.gradient(x...) # ?? @tullio grad=Base @testset "gradients: ReverseDiff + DiffRules" begin include("gradients.jl") end @tullio grad=Dual @testset "gradients: ReverseDiff + ForwardDiff" begin include("gradients.jl") end @info @sprintf("ReverseDiff tests took %.1f seconds", time()-t6) =# #===== Yota =====# #= t7 = time() using Yota GRAD = :Yota _gradient(x...) = Yota.grad(x...)[2] @tullio grad=Base @testset "gradients: Yota + DiffRules" begin include("gradients.jl") end @tullio grad=Dual @testset "gradients: Yota + ForwardDiff" begin include("gradients.jl") end @info @sprintf("Yota tests took %.1f seconds", time()-t7) =# #===== LoopVectorization =====# #= t8 = time() using LoopVectorization using LoopVectorization.VectorizationBase: SVec, Mask, prevpow2 sv = SVec{4,Int}(1,2,3,4) # SVec{4,Int64}<1, 2, 3, 4> ms = Mask(0x03) # Mask{8,Bool}<1, 1, 0, 0, 0, 0, 0, 0> @test Tullio.onlyone(ms, 0) == Mask(0x02) @test Tullio.onlyone(ms, sv) == Mask(0x00) @test Tullio.onlyone(ms, zero(sv)) == Mask(0x02) GRAD = :Tracker _gradient(x...) = Tracker.gradient(x...) @tullio grad=Base @testset "gradients: Tracker + DiffRules + LoopVectorization" begin include("gradients.jl") end @tullio grad=Dual @testset "gradients: Tracker + ForwardDiff + LoopVectorization" begin include("gradients.jl") end GRAD = :Zygote _gradient(x...) = Zygote.gradient(x...) @tullio grad=Base @testset "gradients: Zygote + LoopVectorization" begin include("gradients.jl") end @testset "parsing + LoopVectorization" begin include("parsing.jl") end @info @sprintf("LoopVectorization tests took %.1f seconds", time()-t8) =# #===== TensorOperations =====# t9 = time() using TensorOperations using Tracker GRAD = :Tracker _gradient(x...) = Tracker.gradient(x...) @tullio grad=Base @testset "gradients: Tracker + TensorOperations" begin include("gradients.jl") end using Zygote GRAD = :Zygote _gradient(x...) = Zygote.gradient(x...) @tullio grad=Base @testset "gradients: Zygote + TensorOperations" begin include("gradients.jl") end @testset "complex gradients with TensorOperations" begin x0 = [1 2; 3 4] .+ [5im 0; 7im -8im] @testset "analytic" begin g1 = _gradient(x -> real(sum(x * x)), x0)[1] g1i = _gradient(x -> imag(sum(x * x)), x0)[1] @test g1 ≈ _gradient(x -> real(sum(@tullio y[i,j] := x[i,k] * x[k,j])), x0)[1] @test g1i ≈ _gradient(x -> imag(sum(@tullio y[i,j] := x[i,k] * x[k,j])), x0)[1] end @testset "non-analytic" begin g2 = _gradient(x -> real(sum(x * x')), x0)[1] g2i = _gradient(x -> imag(sum(x * x')), x0)[1] # zero @test_broken g2 ≈ _gradient(x -> real(sum(@tullio y[i,j] := x[i,k] * conj(x[j,k]))), x0)[1] @test_broken g2i ≈ _gradient(x -> imag(sum(@tullio y[i,j] := x[i,k] * conj(x[j,k]))), x0)[1] end end @testset "parsing + TensorOperations" begin include("parsing.jl") end # testing correct fallback @info @sprintf("TensorOperations tests took %.1f seconds", time()-t9) #===== done! =====#
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using LispSyntax using Test #------------------------------------------------------------------------------- # Setup macro incr(x) esc(quote $x = $x + 1 $x end) end #------------------------------------------------------------------------------- @testset "Reader" begin @test LispSyntax.read("1.1f") == 1.1f0 @test LispSyntax.read("1.2f") == 1.2f0 @test LispSyntax.read("2f") == 2f0 @test LispSyntax.read("3.0d") == 3.0 @test LispSyntax.read("4") == 4 @test LispSyntax.read("\\u2312") == '\u2312' @test LispSyntax.read("\\040") == ' ' @test LispSyntax.read("\\c") == 'c' @test LispSyntax.read("\"test\"") == "test" @test LispSyntax.read("true") == true @test LispSyntax.read("false") == false @test LispSyntax.read("test") == :test @test LispSyntax.read("()") == sx() @test LispSyntax.read("(1.1f)") == sx(1.1f0) @test LispSyntax.read("(1.1f 2.2f)") == sx(1.1f0, 2.2f0) @test LispSyntax.read("(+ 1.1f 2)") == sx(:+, 1.1f0, 2) @test LispSyntax.read("(this (+ 1.1f 2))") == sx(:this, sx(:+, 1.1f0, 2)) @test LispSyntax.read("(this (+ 1.1f 2) )") == sx(:this, sx(:+, 1.1f0, 2)) @test LispSyntax.read("#{1 2 3 4}") == Set([1, 2, 3, 4]) @test LispSyntax.read("""#{ 1 2 3 4 }""") == Set([1, 2, 3, 4]) @test LispSyntax.read("{a 2 b 3}") == Dict(:a => 2, :b => 3) @test LispSyntax.read("""{ a 2 b 3 }""") == Dict(:a => 2, :b => 3) @test LispSyntax.read("[1 2 3 4]") == sx(1, 2, 3, 4) @test LispSyntax.read("""[ 1 2 3 4 ]""") == sx(1, 2, 3, 4) @test LispSyntax.read("[]") == sx() @test LispSyntax.read("[1]") == sx(1) @test LispSyntax.read("'test") == sx(:quote, :test) @test LispSyntax.read("`test") == sx(:quasi, :test) @test LispSyntax.read("~test") == sx(:splice, :test) @test LispSyntax.read("~@(1 2 3)") == sx(:splice_seq, sx(1, 2, 3)) @test LispSyntax.read("`~test") == sx(:quasi, sx(:splice, :test)) @test desx(sx(:splice_seq, sx(1, 2, 3))) == Any[ :splice_seq, [1, 2, 3] ] @test desx(sx(:splice_seq, sx(1, 2, sx(3)))) == Any[ :splice_seq, Any[ 1, 2, [3] ] ] @test LispSyntax.read("""(defn multiline [x] (+ x 1))""") == sx(:defn, :multiline, sx(:x), sx(:+, :x, 1)) @test LispSyntax.read(""" (defn f1 [n] (if (< n 2) 1 (+ (f1 (- n 1)) (f1 (- n 2))))) """) == sx(:defn, :f1, sx(:n), sx(:if, sx(:<, :n, 2), 1, sx(:+, sx(:f1, sx(:-, :n, 1)), sx(:f1, sx(:-, :n, 2))))) assign_reader_dispatch(:sx, x -> sx(x.vector...)) assign_reader_dispatch(:hash, x -> Dict(x.vector[i] => x.vector[i+1] for i = 1:2:length(x.vector))) @test LispSyntax.read("#sx[a b c]") == sx(:a, :b, :c) @test LispSyntax.read("#sx [ 1 2 3 ]") == sx(1, 2, 3) end #------------------------------------------------------------------------------- @testset "Code generation" begin @test codegen(desx(LispSyntax.read("(if true a)"))) == :(true && a) @test codegen(desx(LispSyntax.read("(if true a b)"))) == :(true ? a : b) @test codegen(desx(LispSyntax.read("(call)"))) == :(call()) @test codegen(desx(LispSyntax.read("(call a)"))) == :(call(a)) @test codegen(desx(LispSyntax.read("(call a b)"))) == :(call(a,b)) @test codegen(desx(LispSyntax.read("(call a b c)"))) == :(call(a,b,c)) @test codegen(desx(LispSyntax.read("(lambda (x) (call x))"))) == Base.remove_linenums!(:(function (x) call(x) end)) @test codegen(desx(LispSyntax.read("(def x 3)"))) == :(global x = 3) @test codegen(desx(LispSyntax.read("(def x (+ 3 1))"))) == :(global x = 3 + 1) construct_sexpr = LispSyntax.construct_sexpr @test codegen(desx(LispSyntax.read("test"))) == :test @test codegen(desx(LispSyntax.read("'test"))) == QuoteNode(:test) @test codegen(desx(LispSyntax.read("'(1 2)"))) == :($construct_sexpr(1, 2)) @test codegen(desx(LispSyntax.read("'(1 x)"))) == :($construct_sexpr(1, :x)) @test codegen(desx(LispSyntax.read("'(1 (1 2))"))) == :($construct_sexpr(1, $construct_sexpr(1, 2))) @test codegen(desx(LispSyntax.read("'(1 (test x))"))) == :($construct_sexpr(1, $construct_sexpr(:test, :x))) @test codegen(desx(LispSyntax.read("(call 1 '2)"))) == :(call(1,2)) end #------------------------------------------------------------------------------- @testset "Scope and variables" begin x = 10 @test lisp"x" == 10 let # In clojure, def affects global bindings lisp"(def w (+ 3 1))" end let @test w == 4 end end #------------------------------------------------------------------------------- @testset "Quoting and splicing" begin x = 10 @test lisp"`~x" == 10 @test lisp"'test" == :test @test lisp"'(1 2)" == Any[1, 2] @test lisp"'(1 x)" == Any[1, :x] @test lisp"'(1 (1 2))" == Any[1, Any[1, 2]] @test lisp"'(1 (test x))" == Any[1, Any[:test, :x]] @test lisp"`(test ~x)" == Any[ :test, 10 ] @test lisp"`(~x ~x)" == Any[ 10, 10 ] global y = Any[ 1, 2 ] @test lisp"`(~x ~@y)" == Any[ 10, 1, 2 ] @test lisp"`(~x ~y)" == Any[ 10, Any[1, 2] ] @test lisp"`(10 ~(+ 10 x))" == Any[10, 20] @test lisp"(quote (+ 1 2))" == Any[:+, 1, 2] end #------------------------------------------------------------------------------- @testset "Functions" begin lisp"(defn xxx [a b] (+ a b))" @test lisp"(xxx 1 2)" == 3 global z = 10 lisp"(defn yyy [a] (+ a z))" @test lisp"(yyy 1)" == 11 @test lisp"(yyy z)" == 20 # recursion lisp"(defn fib [a] (if (< a 2) a (+ (fib (- a 1)) (fib (- a 2)))))" @test lisp"(fib 2)" == 1 @test lisp"(fib 4)" == 3 @test lisp"(fib 30)" == 832040 @test lisp"(fib 40)" == 102334155 # Note this version is slow due to the non-const global binding fib2 lisp"(def fib2 (lambda [a] (if (< a 2) a (+ (fib2 (- a 1)) (fib2 (- a 2))))))" @test lisp"(fib2 2)" == 1 @test lisp"(fib2 4)" == 3 @test lisp"(fib2 30)" == 832040 lisp"(defn dostuff [a] (@incr a) (@incr a) (@incr a))" @test lisp"(dostuff 3)" == 6 @test lisp"(dostuff 6)" == 9 lisp"(def dostuff2 (lambda [a] (@incr a) (@incr a) (@incr a)))" @test lisp"(dostuff2 3)" == 6 @test lisp"(dostuff2 6)" == 9 lisp"(def dostuff3 (fn [a] (@incr a) (@incr a) (@incr a)))" @test lisp"(dostuff3 3)" == 6 @test lisp"(dostuff3 6)" == 9 @test lisp"((lambda [x] (+ x 1)) 5)" == 6 @test lisp"#{1 2 z}" == Set([1, 2, 10]) @test lisp"{1 2 2 z}" == Dict(1 => 2, 2 => 10) @test lisp"#sx[+ 1 2]" == 3 @test lisp"#hash['+ 1 '- z]" == Dict(:+ => 1, :- => 10) end #------------------------------------------------------------------------------- # Macros. Note that LispSyntax currently uses the Julia macro system, which # includes the hygenic-by-default hygiene rules. This is very different from # Clojure style explicit hygiene. lisp"(defmacro fapply [f a] (esc `(~f ~a)))" fcount = 0 lisp"(defmacro fapply_trace [f a] (esc `(do (global fcount) (@incr fcount) (~f ~a))))" @testset "Macros" begin lisp"(defn fact [a] (if (< a 1) 1 (* a (fact (- a 1)))))" @test @fapply(fib2, 2) == 1 @test @fapply(fact, 3 + 1) == 24 @test lisp"(@fapply fib2 2)" == 1 @test lisp"(@fapply fact (+ 3 1))" == 24 @test @fapply_trace(fib2, 2) == 1 @test fcount == 1 @test @fapply_trace(fact, 3 + 1) == 24 @test fcount == 2 end #------------------------------------------------------------------------------- @testset "Loops" begin number = 0 output = 0 lisp"(while (< number 2) (@incr number) (@incr output))" @test number == 2 @test output == 2 r = output lisp"(for [i (: 1 10)] (@incr r))" @test r == 12 r = 0 lisp"(for [i (: 1 10) j (: 1 10)] (@incr r))" @test r == 100 end #------------------------------------------------------------------------------- @testset "Let and do" begin number = 2 r = 100 output = 2 @test lisp"(let [x 10] x)" == 10 @test lisp"(let [x 10 y 20] (+ x y))" == 30 @test lisp"(let [x 10 y 20 z 20] (+ x y z))" == 50 @test lisp"(let [x 10 y 20 z 20] (+ x y z number))" == 52 @test lisp"(let [x 10 y 20 z 20 number 10] (+ x y z number))" == 60 @test lisp"(let [x 10 y 20 z 20] (- (+ x y z number) output))" == 50 lisp"(do (@incr r) (@incr number))" @test number == 3 @test r == 101 end #------------------------------------------------------------------------------- lisp"(import ParserCombinator)" @testset "Module import" begin @test lisp"(@E_str \"S\")" == E"S" end #------------------------------------------------------------------------------- @testset "Include from file" begin # Return value is value of last expression @test include_lisp(@__MODULE__, "lisp.clj") == 100 # Test objects defined in lisp.clj @test func_in_clj_file(1, 2) == "x = 1; y = 2" @test func_in_clj_file(10, 20) == "x = 10; y = 20" @test some_global === 1.23f0 @test !isdefined(@__MODULE__, :not_a_global) end #------------------------------------------------------------------------------- # Bug reports @testset "Bug reports" begin @test lisp"""(def game_map (Dict (=> 'living_room '((you are in the living room of a wizards house - there is a wizard snoring loudly on the couch -) (west door garden) (upstairs stairway attic)))))""" == Dict(:living_room => Any[ Any[ :you, :are, :in, :the, :living, :room, :of, :a, :wizards, :house, :-, :there, :is, :a, :wizard, :snoring, :loudly, :on, :the, :couch, :- ], Any[ :west, :door, :garden ], Any[ :upstairs, :stairway, :attic ] ]) end
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2.148413
4,757
abstract type Topology <: Component end
[ 397, 8709, 2099, 5849, 1435, 1279, 25, 35100, 886, 628 ]
4.1
10
<gh_stars>1-10 ################################################################################ # # DESCRIPTION # Wrapper for Python Paraview functions. # # AUTHORSHIP # * Author : <NAME> # * Email : <EMAIL> # * Created : Nov 2017 # * License : MIT License ################################################################################ # Wrap VTKtools_paraview.py @pyimport imp (file, filename, data) = imp.find_module("VTKtools_paraview", [module_path]) parapy = imp.load_module("VTKtools_paraview", file, filename, data)
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@testset "Orders in absolute number fields" begin println("NfOrd.jl") @time include("NfOrd/NfOrd.jl") println("Elem.jl") @time include("NfOrd/Elem.jl") println("Ideal.jl") @time include("NfOrd/Ideal.jl") println("FracIdl.jl") @time include("NfOrd/FracIdl.jl") println("ResidueRing.jl") @time include("NfOrd/ResidueRing.jl") println("Clgp.jl") @time include("NfOrd/Clgp.jl") println("RayClassGroup.jl") @time include("NfOrd/RayClassGroup.jl") println("ResidueRingMultGrp.jl") @time include("NfOrd/ResidueRingMultGrp.jl") println("Overorders.jl") @time include("NfOrd/Overorders.jl") println("LinearAlgebra.jl") @time include("NfOrd/LinearAlgebra.jl") #println("PicardGroup.jl") #@time include("NfOrd/PicardGroup.jl") end
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<reponame>lemauee/RoME.jl<filename>src/variables/deprecated/Point3D.jl """ $(TYPEDEF) XYZ Euclidean manifold variable node softtype. Example ------- ```julia p3 = Point3() ``` """ struct Point3 <: IncrementalInference.InferenceVariable end getDimension(::Point3) = 3 getManifolds(::Point3) = (:Euclid,:Euclid,:Euclid)
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<gh_stars>10-100 @testset "537.complex-number-multiplication.jl" begin @test complex_number_multiply("1+1i", "1+1i") == "0+2i" @test complex_number_multiply("1+-1i", "1+-1i") == "0+-2i" end
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<filename>src/NSFVector.jl mutable struct NSFVecCoefs A::Matrix{Float64} B::Matrix{Float64} C::Matrix{Float64} T::Union{Matrix{Float64},Nothing} NSFVecCoefs() = new() NSFVecCoefs(A,B,C,T) = new(A,B,C,T) end mutable struct NSFVector size::Int moments::Int scales::Int coefs::Vector{NSFVecCoefs} end function NSFVector_decomp(size::Int, moments::Int, scales::Int, X::Matrix{Float64} )::NSFVector if size%(1<<scales) != 0 throw("size not divisible by 2^scales") end H,G = get_qmf(moments) Y = NSFVector(size,moments,scales,Vector{NSFVecCoefs}(undef,scales)) T = X for j in 1:scales A,B,C,T = dwtmat(size,T,moments,H,G) Y.coefs[j] = NSFVecCoefs(A,B,C,nothing) size ÷= 2 end Y.coefs[scales].T = T return Y end function NSFVector_reconst(X::NSFVector)::Matrix{Float64} n = X.size÷(2^X.scales) H,G = get_qmf(X.moments) T = X.coefs[X.scales].T for j in X.scales:-1:1 T = idwtmat(n,X.moments,H,G,X.coefs[j].A,X.coefs[j].B,X.coefs[j].C,T) n *= 2 end return T end
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<filename>src/Surrogates.jl<gh_stars>0 module Surrogates using LinearAlgebra using Distributions using Requires abstract type AbstractSurrogate <: Function end include("utils.jl") include("Radials.jl") include("Kriging.jl") include("Sampling.jl") include("Optimization.jl") include("Lobachevsky.jl") include("LinearSurrogate.jl") include("InverseDistanceSurrogate.jl") include("SecondOrderPolynomialSurrogate.jl") function __init__() @require Stheno="8188c328-b5d6-583d-959b-9690869a5511" begin include("SthenoKriging.jl") end end include("RandomForestSurrogate.jl") include("NeuralSurrogate.jl") include("Wendland.jl") include("MOE.jl") #rewrite gaussian mixture with own algorithm to fix deps issue include("VariableFidelity.jl") include("PolynomialChaos.jl") include("Earth.jl") include("GEK.jl") current_surrogates = ["Kriging","LinearSurrogate","LobachevskySurrogate","NeuralSurrogate", "RadialBasis","RandomForestSurrogate","SecondOrderPolynomialSurrogate", "Wendland","GEK","PolynomialChaosSurrogate"] #Radial structure: function RadialBasisStructure(;radial_function,scale_factor,sparse) return (name = "RadialBasis", radial_function = radial_function, scale_factor = scale_factor, sparse = sparse) end #Kriging structure: function KrigingStructure(;p,theta) return (name = "Kriging", p = p, theta = theta) end function GEKStructure(;p,theta) return (name = "GEK", p = p, theta = theta) end #Linear structure function LinearStructure() return (name = "LinearSurrogate") end #InverseDistance structure function InverseDistanceStructure(;p) return (name = "InverseDistanceSurrogate", p = p) end #Lobachevsky structure function LobachevskyStructure(;alpha,n,sparse) return (name = "LobachevskySurrogate", alpha = alpha, n = n, sparse = sparse) end #Neural structure function NeuralStructure(;model,loss,opt,n_echos) return (name ="NeuralSurrogate", model = model ,loss = loss,opt = opt,n_echos = n_echos) end #Random forest structure function RandomForestStructure(;num_round) return (name = "RandomForestSurrogate", num_round = num_round) end #Second order poly structure function SecondOrderPolynomialStructure() return (name = "SecondOrderPolynomialSurrogate") end #Wendland structure function WendlandStructure(; eps, maxiters, tol) return (name = "Wendland", eps = eps, maxiters = maxiters, tol = tol) end #Polychaos structure function PolyChaosStructure(; op) return (name = "PolynomialChaosSurrogate", op = op) end export current_surrogates export RadialBasisStructure, KrigingStructure, LinearStructure, InverseDistanceStructure export LobachevskyStructure, NeuralStructure, RandomForestStructure, SecondOrderPolynomialStructure export WendlandStructure export AbstractSurrogate, SamplingAlgorithm export Kriging, RadialBasis, add_point!, current_estimate, std_error_at_point export linearRadial,cubicRadial,multiquadricRadial,thinplateRadial export sample, GridSample, UniformSample, SobolSample, LatinHypercubeSample, LowDiscrepancySample export RandomSample, KroneckerSample, GoldenSample export SRBF,LCBS,EI,DYCORS,SOP,EGO,RTEA,SMB,surrogate_optimize export LobachevskySurrogate, lobachevsky_integral, lobachevsky_integrate_dimension export LinearSurrogate export RandomForestSurrogate export SVMSurrogate export NeuralSurrogate export InverseDistanceSurrogate export SecondOrderPolynomialSurrogate export SthenoKriging export Wendland export RadialBasisStructure, KrigingStructure, LinearStructure, InverseDistanceStructure export LobachevskyStructure, NeuralStructure, RandomForestStructure, SecondOrderPolynomialStructure export WendlandStructure #export MOE export VariableFidelitySurrogate export PolynomialChaosSurrogate export EarthSurrogate export GEK end
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<filename>contents/bogo_sort/code/julia/bogo.jl using Random function is_sorted(a::Vector{Float64}) for i = 1:length(a)-1 if (a[i] > a[i + 1]) return false end end return true end function bogo_sort(a::Vector{Float64}) while(!is_sorted(a)) shuffle!(a) end end function main() a = [1, 3, 2, 4] bogo_sort(a) println(a) end main()
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""" UniversalFallback The `UniversalFallback` can be applied on a [`MathOptInterface.ModelLike`](@ref) `model` to create the model `UniversalFallback(model)` supporting *any* constraint and attribute. This allows to have a specialized implementation in `model` for performance critical constraints and attributes while still supporting other attributes with a small performance penalty. Note that `model` is unaware of constraints and attributes stored by `UniversalFallback` so this is not appropriate if `model` is an optimizer (for this reason, [`MathOptInterface.optimize!`](@ref) has not been implemented). In that case, optimizer bridges should be used instead. """ mutable struct UniversalFallback{MT} <: MOI.ModelLike model::MT objective::Union{MOI.AbstractScalarFunction, Nothing} constraints::OrderedDict{Tuple{DataType, DataType}, OrderedDict} # See https://github.com/jump-dev/JuMP.jl/issues/1152 and https://github.com/jump-dev/JuMP.jl/issues/2238 nextconstraintid::Int64 con_to_name::Dict{CI, String} name_to_con::Union{Dict{String, MOI.ConstraintIndex}, Nothing} optattr::Dict{MOI.AbstractOptimizerAttribute, Any} modattr::Dict{MOI.AbstractModelAttribute, Any} varattr::Dict{MOI.AbstractVariableAttribute, Dict{VI, Any}} conattr::Dict{MOI.AbstractConstraintAttribute, Dict{CI, Any}} function UniversalFallback{MT}(model::MOI.ModelLike) where {MT} new{typeof(model)}(model, nothing, OrderedDict{Tuple{DataType, DataType}, OrderedDict}(), 0, Dict{CI, String}(), nothing, Dict{MOI.AbstractOptimizerAttribute, Any}(), Dict{MOI.AbstractModelAttribute, Any}(), Dict{MOI.AbstractVariableAttribute, Dict{VI, Any}}(), Dict{MOI.AbstractConstraintAttribute, Dict{CI, Any}}()) end end UniversalFallback(model::MOI.ModelLike) = UniversalFallback{typeof(model)}(model) function Base.show(io::IO, U::UniversalFallback) s(n) = n == 1 ? "" : "s" indent = " "^get(io, :indent, 0) MOIU.print_with_acronym(io, summary(U)) !(U.objective === nothing) && print(io, "\n$(indent)with objective") for (attr, name) in ( (U.constraints, "constraint"), (U.optattr, "optimizer attribute"), (U.modattr, "model attribute"), (U.varattr, "variable attribute"), (U.conattr, "constraint attribute") ) n = length(attr) if n > 0 print(io, "\n$(indent)with $n $name$(s(n))") end end print(io, "\n$(indent)fallback for ") show(IOContext(io, :indent => get(io, :indent, 0)+2), U.model) end function MOI.is_empty(uf::UniversalFallback) return MOI.is_empty(uf.model) && uf.objective === nothing && isempty(uf.constraints) && isempty(uf.modattr) && isempty(uf.varattr) && isempty(uf.conattr) end function MOI.empty!(uf::UniversalFallback) MOI.empty!(uf.model) uf.objective = nothing empty!(uf.constraints) uf.nextconstraintid = 0 empty!(uf.con_to_name) uf.name_to_con = nothing empty!(uf.modattr) empty!(uf.varattr) empty!(uf.conattr) end function MOI.copy_to(uf::UniversalFallback, src::MOI.ModelLike; kws...) MOIU.automatic_copy_to(uf, src; kws...) end function supports_default_copy_to(uf::UniversalFallback, copy_names::Bool) return supports_default_copy_to(uf.model, copy_names) end # References MOI.is_valid(uf::UniversalFallback, idx::VI) = MOI.is_valid(uf.model, idx) function MOI.is_valid(uf::UniversalFallback, idx::CI{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.is_valid(uf.model, idx) else haskey(uf.constraints, (F, S)) && haskey(uf.constraints[(F, S)], idx) end end function MOI.delete(uf::UniversalFallback, ci::CI{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.delete(uf.model, ci) else if !MOI.is_valid(uf, ci) throw(MOI.InvalidIndex(ci)) end delete!(uf.constraints[(F, S)], ci) delete!(uf.con_to_name, ci) uf.name_to_con = nothing end for d in values(uf.conattr) delete!(d, ci) end end function _remove_variable(uf::UniversalFallback, constraints::OrderedDict{<:CI{MOI.SingleVariable}}, vi::VI) to_delete = keytype(constraints)[] for (ci, constraint) in constraints f::MOI.SingleVariable = constraint[1] if f.variable == vi push!(to_delete, ci) end end MOI.delete(uf, to_delete) end function _remove_variable(uf::UniversalFallback, constraints::OrderedDict{CI{MOI.VectorOfVariables, S}}, vi::VI) where S to_delete = keytype(constraints)[] for (ci, constraint) in constraints f::MOI.VectorOfVariables, s = constraint if vi in f.variables if length(f.variables) > 1 if MOI.supports_dimension_update(S) constraints[ci] = remove_variable(f, s, vi) else throw_delete_variable_in_vov(vi) end else push!(to_delete, ci) end end end MOI.delete(uf, to_delete) end function _remove_variable(::UniversalFallback, constraints::OrderedDict{<:CI}, vi::VI) for (ci, constraint) in constraints f, s = constraint constraints[ci] = remove_variable(f, s, vi) end end function _remove_vector_of_variables( uf::UniversalFallback, constraints::OrderedDict{<:CI{MOI.VectorOfVariables}}, vis::Vector{VI} ) to_delete = keytype(constraints)[] for (ci, constraint) in constraints f::MOI.VectorOfVariables = constraint[1] if vis == f.variables push!(to_delete, ci) end end MOI.delete(uf, to_delete) end function _remove_vector_of_variables( ::UniversalFallback, ::OrderedDict{<:CI}, ::Vector{VI}) end function MOI.delete(uf::UniversalFallback, vi::VI) MOI.delete(uf.model, vi) for d in values(uf.varattr) delete!(d, vi) end if uf.objective !== nothing uf.objective = remove_variable(uf.objective, vi) end for (_, constraints) in uf.constraints _remove_variable(uf, constraints, vi) end end function MOI.delete(uf::UniversalFallback, vis::Vector{VI}) MOI.delete(uf.model, vis) for d in values(uf.varattr) for vi in vis delete!(d, vi) end end if uf.objective !== nothing uf.objective = remove_variable(uf.objective, vis) end for (_, constraints) in uf.constraints _remove_vector_of_variables(uf, constraints, vis) for vi in vis _remove_variable(uf, constraints, vi) end end end # Attributes _get(uf, attr::MOI.AbstractOptimizerAttribute) = uf.optattr[attr] _get(uf, attr::MOI.AbstractModelAttribute) = uf.modattr[attr] function _get(uf, attr::MOI.AbstractVariableAttribute, vi::VI) attribute_dict = get(uf.varattr, attr, nothing) if attribute_dict === nothing # It means the attribute is not set to any variable so in particular, it # is not set for `vi` return nothing end return get(attribute_dict, vi, nothing) end function _get(uf, attr::MOI.AbstractConstraintAttribute, ci::CI) attribute_dict = get(uf.conattr, attr, nothing) if attribute_dict === nothing # It means the attribute is not set to any constraint so in particular, # it is not set for `ci` return nothing end return get(attribute_dict, ci, nothing) end function _get(uf, attr::MOI.CanonicalConstraintFunction, ci::MOI.ConstraintIndex) return MOI.get_fallback(uf, attr, ci) func = MOI.get(uf, MOI.ConstraintFunction(), ci) if is_canonical(func) return func else return canonical(func) end end function MOI.get(uf::UniversalFallback, attr::Union{MOI.AbstractOptimizerAttribute, MOI.AbstractModelAttribute}) if !MOI.is_copyable(attr) || MOI.supports(uf.model, attr) MOI.get(uf.model, attr) else _get(uf, attr) end end function MOI.get(uf::UniversalFallback, attr::MOI.AbstractConstraintAttribute, idx::MOI.ConstraintIndex{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) && (!MOI.is_copyable(attr) || MOI.supports(uf.model, attr, typeof(idx))) MOI.get(uf.model, attr, idx) else _get(uf, attr, idx) end end function MOI.get(uf::UniversalFallback, attr::MOI.AbstractVariableAttribute, idx::MOI.VariableIndex) if !MOI.is_copyable(attr) || MOI.supports(uf.model, attr, typeof(idx)) MOI.get(uf.model, attr, idx) else _get(uf, attr, idx) end end function MOI.get(uf::UniversalFallback, attr::MOI.NumberOfConstraints{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) return MOI.get(uf.model, attr) else return length(get(uf.constraints, (F, S), OrderedDict{CI{F, S}, Tuple{F, S}}())) end end function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfConstraintIndices{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.get(uf.model, listattr) else collect(keys(get(uf.constraints, (F, S), OrderedDict{CI{F, S}, Tuple{F, S}}()))) end end function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfConstraints) list = MOI.get(uf.model, listattr) for (FS, constraints) in uf.constraints if !isempty(constraints) push!(list, FS) end end list end function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfOptimizerAttributesSet) list = MOI.get(uf.model, listattr) for attr in keys(uf.optattr) push!(list, attr) end list end function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfModelAttributesSet) list = MOI.get(uf.model, listattr) if uf.objective !== nothing push!(list, MOI.ObjectiveFunction{typeof(uf.objective)}()) end for attr in keys(uf.modattr) push!(list, attr) end list end function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfVariableAttributesSet) list = MOI.get(uf.model, listattr) for attr in keys(uf.varattr) push!(list, attr) end list end function MOI.get(uf::UniversalFallback, listattr::MOI.ListOfConstraintAttributesSet{F, S}) where {F, S} list = MOI.get(uf.model, listattr) for attr in keys(uf.conattr) push!(list, attr) end return list end # Objective function MOI.set(uf::UniversalFallback, attr::MOI.ObjectiveSense, sense::MOI.OptimizationSense) where T if sense == MOI.FEASIBILITY_SENSE uf.objective = nothing end MOI.set(uf.model, attr, sense) end function MOI.get(uf::UniversalFallback, attr::MOI.ObjectiveFunctionType) if uf.objective === nothing return MOI.get(uf.model, attr) else return typeof(uf.objective) end end function MOI.get(uf::UniversalFallback, attr::MOI.ObjectiveFunction{F})::F where F if uf.objective === nothing return MOI.get(uf.model, attr) else return uf.objective end end function MOI.set(uf::UniversalFallback, attr::MOI.ObjectiveFunction, func::MOI.AbstractScalarFunction) if MOI.supports(uf.model, attr) MOI.set(uf.model, attr, func) # Clear any fallback objective uf.objective = nothing else uf.objective = copy(func) # Clear any `model` objective sense = MOI.get(uf.model, MOI.ObjectiveSense()) MOI.set(uf.model, MOI.ObjectiveSense(), MOI.FEASIBILITY_SENSE) MOI.set(uf.model, MOI.ObjectiveSense(), sense) end end function MOI.modify(uf::UniversalFallback, obj::MOI.ObjectiveFunction, change::MOI.AbstractFunctionModification) where F if uf.objective === nothing MOI.modify(uf.model, obj, change) else uf.objective = modify_function(uf.objective, change) end end # Name # The names of constraints not supported by `uf.model` need to be handled function MOI.set(uf::UniversalFallback, attr::MOI.ConstraintName, ci::CI{F, S}, name::String) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.set(uf.model, attr, ci, name) else uf.con_to_name[ci] = name uf.name_to_con = nothing # Invalidate the name map. end return end function MOI.get(uf::UniversalFallback, attr::MOI.ConstraintName, ci::CI{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) return MOI.get(uf.model, attr, ci) else return get(uf.con_to_name, ci, EMPTYSTRING) end end MOI.get(uf::UniversalFallback, ::Type{VI}, name::String) = MOI.get(uf.model, VI, name) check_type_and_multiple_names(::Type, ::Nothing, ::Nothing, name) = nothing check_type_and_multiple_names(::Type{T}, value::T, ::Nothing, name) where T = value check_type_and_multiple_names(::Type, ::Any, ::Nothing, name) where T = nothing check_type_and_multiple_names(::Type{T}, ::Nothing, value::T, name) where T = value check_type_and_multiple_names(::Type, ::Nothing, ::Any, name) where T = nothing function check_type_and_multiple_names(T::Type, ::Any, ::Any, name) throw_multiple_name_error(T, name) end function MOI.get(uf::UniversalFallback, ::Type{CI{F, S}}, name::String) where {F, S} if uf.name_to_con === nothing uf.name_to_con = build_name_to_con_map(uf.con_to_name) end if MOI.supports_constraint(uf.model, F, S) ci = MOI.get(uf.model, CI{F, S}, name) else # There is no `F`-in-`S` constraint in `b.model`, `ci` is only queried # to check for duplicate names. ci = MOI.get(uf.model, CI, name) end ci_uf = get(uf.name_to_con, name, nothing) throw_if_multiple_with_name(ci_uf, name) return check_type_and_multiple_names(CI{F, S}, ci_uf, ci, name) end function MOI.get(uf::UniversalFallback, ::Type{CI}, name::String) if uf.name_to_con === nothing uf.name_to_con = build_name_to_con_map(uf.con_to_name) end ci_uf = get(uf.name_to_con, name, nothing) throw_if_multiple_with_name(ci_uf, name) return check_type_and_multiple_names( CI, ci_uf, MOI.get(uf.model, CI, name), name) end _set(uf, attr::MOI.AbstractOptimizerAttribute, value) = uf.optattr[attr] = value _set(uf, attr::MOI.AbstractModelAttribute, value) = uf.modattr[attr] = value function _set(uf, attr::MOI.AbstractVariableAttribute, vi::VI, value) if !haskey(uf.varattr, attr) uf.varattr[attr] = Dict{VI, Any}() end uf.varattr[attr][vi] = value end function _set(uf, attr::MOI.AbstractConstraintAttribute, ci::CI, value) if !haskey(uf.conattr, attr) uf.conattr[attr] = Dict{CI, Any}() end uf.conattr[attr][ci] = value end MOI.supports(::UniversalFallback, ::Union{MOI.AbstractModelAttribute, MOI.AbstractOptimizerAttribute}) = true function MOI.set(uf::UniversalFallback, attr::Union{MOI.AbstractOptimizerAttribute, MOI.AbstractModelAttribute}, value) if MOI.supports(uf.model, attr) return MOI.set(uf.model, attr, value) else return _set(uf, attr, value) end end MOI.supports(::UniversalFallback, ::Union{MOI.AbstractVariableAttribute, MOI.AbstractConstraintAttribute}, ::Type{<:MOI.Index}) = true function MOI.set(uf::UniversalFallback, attr::MOI.AbstractVariableAttribute, idx::VI, value) if MOI.supports(uf.model, attr, typeof(idx)) return MOI.set(uf.model, attr, idx, value) else return _set(uf, attr, idx, value) end end function MOI.set(uf::UniversalFallback, attr::MOI.AbstractConstraintAttribute, idx::CI{F, S}, value) where {F, S} if MOI.supports_constraint(uf.model, F, S) && MOI.supports(uf.model, attr, CI{F, S}) return MOI.set(uf.model, attr, idx, value) else return _set(uf, attr, idx, value) end end # Constraints MOI.supports_constraint(uf::UniversalFallback, ::Type{F}, ::Type{S}) where {F<:MOI.AbstractFunction, S<:MOI.AbstractSet} = true function _new_constraint_index(uf, f::MOI.SingleVariable, s::MOI.AbstractScalarSet) return CI{MOI.SingleVariable, typeof(s)}(f.variable.value) end function _new_constraint_index(uf, f::MOI.AbstractFunction, s::MOI.AbstractSet) uf.nextconstraintid += 1 return CI{typeof(f), typeof(s)}(uf.nextconstraintid) end function MOI.add_constraint(uf::UniversalFallback, f::MOI.AbstractFunction, s::MOI.AbstractSet) F = typeof(f) S = typeof(s) if MOI.supports_constraint(uf.model, F, S) return MOI.add_constraint(uf.model, f, s) else constraints = get!(uf.constraints, (F, S)) do OrderedDict{CI{F, S}, Tuple{F, S}}() end::OrderedDict{CI{F, S}, Tuple{F, S}} ci = _new_constraint_index(uf, canonical(f), copy(s)) constraints[ci] = (f, s) return ci end end function MOI.modify(uf::UniversalFallback, ci::CI{F, S}, change::MOI.AbstractFunctionModification) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.modify(uf.model, ci, change) else (f, s) = uf.constraints[(F, S)][ci] uf.constraints[(F, S)][ci] = (modify_function(f, change), s) end end function MOI.get(uf::UniversalFallback, attr::MOI.ConstraintFunction, ci::CI{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.get(uf.model, attr, ci) else MOI.throw_if_not_valid(uf, ci) uf.constraints[(F, S)][ci][1] end end function MOI.get(uf::UniversalFallback, attr::MOI.ConstraintSet, ci::CI{F, S}) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.get(uf.model, attr, ci) else MOI.throw_if_not_valid(uf, ci) uf.constraints[(F, S)][ci][2] end end function MOI.set(uf::UniversalFallback, ::MOI.ConstraintFunction, ci::CI{F,S}, func::F) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.set(uf.model, MOI.ConstraintFunction(), ci, func) else MOI.throw_if_not_valid(uf, ci) if F == MOI.SingleVariable throw(MOI.SettingSingleVariableFunctionNotAllowed()) end (_, s) = uf.constraints[(F, S)][ci] uf.constraints[(F, S)][ci] = (func, s) end end function MOI.set(uf::UniversalFallback, ::MOI.ConstraintSet, ci::CI{F,S}, set::S) where {F, S} if MOI.supports_constraint(uf.model, F, S) MOI.set(uf.model, MOI.ConstraintSet(), ci, set) else MOI.throw_if_not_valid(uf, ci) (f, _) = uf.constraints[(F, S)][ci] uf.constraints[(F, S)][ci] = (f, set) end end # Variables MOI.add_variable(uf::UniversalFallback) = MOI.add_variable(uf.model) MOI.add_variables(uf::UniversalFallback, n) = MOI.add_variables(uf.model, n)
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2.187314
8,734
<filename>test/dynamics_constraints.jl model = Dynamics.Cartpole() prob = Problems.Cartpole()[1] bnd = prob.constraints.constraints[1] n,m = size(prob) N = prob.N rollout!(prob) Z = prob.Z vals = [@SVector zeros(model.n) for k = 1:N-1] ∇c = [SizedMatrix{n,2n+m}(zeros(n,2n+m)) for k = 1:N-1] dyn_con = DynamicsConstraint{RK3}(model, N) @test TO.width(dyn_con) == 2n+m evaluate!(vals, dyn_con, Z) jacobian!(∇c, dyn_con, Z) @test (@allocated evaluate!(vals, dyn_con, Z)) == 0 @test (@allocated jacobian!(∇c, dyn_con, Z)) == 0 con_rk3 = ConstraintVals(dyn_con, 1:N-1) evaluate!(con_rk3, Z) jacobian!(con_rk3, Z) TO.max_violation!(con_rk3) maximum(con_rk3.c_max) @test (@allocated evaluate!(con_rk3, Z)) == 0 @test (@allocated jacobian!(con_rk3, Z)) == 0 ∇c = [zeros(SizedMatrix{n,2n+2m}) for k = 1:N-1] dyn_con = DynamicsConstraint{HermiteSimpson}(model, N) @test TO.width(dyn_con) == 2(n+m) evaluate!(vals, dyn_con, Z) jacobian!(∇c, dyn_con, Z) @test (@allocated evaluate!(vals, dyn_con, Z)) == 0 @test (@allocated jacobian!(∇c, dyn_con, Z)) == 0 con_hs = ConstraintVals(dyn_con, 1:N-1) evaluate!(con_hs, Z) jacobian!(con_hs, Z) @test (@allocated evaluate!(con_hs, Z)) == 0 @test (@allocated jacobian!(con_hs, Z)) == 0 # Test default dyn_con = DynamicsConstraint(model, N) @test integration(dyn_con) == RK3 == TO.DEFAULT_Q
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2.08805
636
module MixedModels using BlockArrays using BlockDiagonals using CategoricalArrays using Distributions using GLM using LinearAlgebra using NamedArrays using NLopt using Random using ProgressMeter using Showoff using SparseArrays using StaticArrays using Statistics using StatsBase using StatsModels using Tables using TypedTables using LinearAlgebra: BlasFloat, BlasReal, HermOrSym, PosDefException, copytri! using Printf: @printf, @sprintf using GLM: Link, canonicallink using StatsFuns: log2π import Base: * import GLM: dispersion, dispersion_parameter import NLopt: Opt import StatsBase: fit, fit! export @formula, Bernoulli, Binomial, Block, BlockedSparse, DummyCoding, EffectsCoding, Gamma, GeneralizedLinearMixedModel, HelmertCoding, InverseGaussian, InverseLink, LinearMixedModel, LogitLink, LogLink, MixedModel, Normal, OptSummary, Poisson, RaggedArray, RandomEffectsTerm, ReMat, UniformBlockDiagonal, VarCorr, aic, aicc, bic, coef, coefnames, coeftable, cond, describeblocks, condVar, deviance, dispersion, dispersion_parameter, dof, dof_residual, fit, fit!, fitted, fixef, fulldummy, fnames, GHnorm, loglikelihood, lowerbd, nblocks, nobs, objective, parametricbootstrap, pirls!, predict, pwrss, ranef, refit!, residuals, response, shortestCovInt, sdest, setθ!, simulate!, sparse, statscholesky, std, stderror, updateL!, varest, vcov, zerocorr, zerocorr! import Base: ==, * abstract type MixedModel{T} <: StatsModels.RegressionModel end # model with fixed and random effects include("utilities.jl") include("arraytypes.jl") include("varcorr.jl") include("femat.jl") include("remat.jl") include("optsummary.jl") include("randomeffectsterm.jl") include("linearmixedmodel.jl") include("gausshermite.jl") include("generalizedlinearmixedmodel.jl") include("mixed.jl") include("linalg/statschol.jl") include("linalg/cholUnblocked.jl") include("linalg/rankUpdate.jl") include("linalg/logdet.jl") include("linalg.jl") include("simulate.jl") end # module
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2.147986
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<filename>examples/imagelike/contourf.jl using GLVisualize, GeometryTypes if !isdefined(:runtests) window = glscreen() timesignal = loop(linspace(0f0,1f0,360)) end description = """ Simple animated contour plot. You need to press Ctrl to move the camera. Most 2D camera's use the Ctrl modifier to make editing easier. """ # use the performance tips to speed this up # (http://docs.julialang.org/en/release-0.4/manual/performance-tips/) # the array is 512x512 after all const N = 256 const range = linspace(-5f0, 5f0, N) const data = zeros(Intensity{Float32}, N, N) function contour_inner(i, x, y) Intensity{Float32}(sin(1.3*x*i)*cos(0.9*y)+cos(.8*x)*sin(1.9*y)+cos(y*.2*x)) end function contourdata(t) for i=1:size(data, 1) for j=1:size(data, 2) @inbounds data[i,j] = contour_inner(t, range[i], range[j]) end end data end renderable = visualize(map(contourdata, timesignal), color_norm=Vec2f0(-3, 3)) _view(renderable, window, camera=:orthographic_pixel) if !isdefined(:runtests) renderloop(window) end
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2.417234
441
using Test using Statistics using LinearAlgebra using Random using EnhancedGJK using EnhancedGJK: projection_weights, projection_weights_reference using CoordinateTransformations: IdentityTransformation, Translation using StaticArrays: SVector import GeometryTypes const gt = GeometryTypes using FileIO const mesh_dir = joinpath(dirname(@__FILE__), "meshes") @testset "reference distance" begin mesh = load(joinpath(mesh_dir, "base_link.obj")) for x in range(0.05, stop=1, length=10) for y in range(-1, stop=1, length=10) for z in range(-1, stop=1, length=10) point = SVector(x, y, z) gjk_dist = gjk(mesh, point) simple_dist = ReferenceDistance.signed_distance(mesh, point) @test isapprox(gjk_dist.signed_distance, simple_dist, atol=2e-4) end end end end @testset "reference interior distance" begin verts = [gt.Point(1., -1, 1), gt.Point(1., 1, 1), gt.Point(-1., 1, 1), gt.Point(-1., -1, 1), gt.Point(1., -1, -1), gt.Point(1., 1, -1), gt.Point(-1., 1, -1), gt.Point(-1., -1, -1)] ft = gt.Face{3, Int} faces = [ft(1, 2, 5), ft(6, 5, 2), ft(2, 3, 6), ft(7, 6, 3), ft(3, 4, 7), ft(8, 7, 4), ft(4, 1, 8), ft(5, 8, 1), ft(2, 1, 3), ft(4, 3, 1), ft(5, 6, 7), ft(7, 8, 5)] mesh = gt.HomogenousMesh(verts, faces) ReferenceDistance.signed_distance(mesh, SVector(0, 0, 0)) for x in range(-1, stop=1, length=10) for y in range(-1, stop=1, length=10) for z in range(-1, stop=1, length=10) point = SVector(x, y, z) expected = -min(1 - abs(x), 1 - abs(y), 1 - abs(z)) actual = ReferenceDistance.signed_distance(mesh, point) @test isapprox(expected, actual, atol=1e-15) end end end end @testset "table" begin width = 0.5 thickness = 0.05 surface_points = Vector{SVector{3, Float64}}() for z in [-thickness, thickness] for x in [-width, width] for y in [-width, width] push!(surface_points, SVector(x, y, z)) end end end geometry = SVector{length(surface_points)}(surface_points) point = zeros(SVector{3, Float64}) for x in range(-width, stop=width, length=21) for y in range(-width, stop=width, length=21) z = 0.1 @test isapprox(gjk(geometry, point, IdentityTransformation(), Translation(SVector(x, y, z))).signed_distance, 0.05) z = 0.06 @test isapprox(gjk(geometry, point, IdentityTransformation(), Translation(SVector(x, y, z))).signed_distance, 0.01, atol=1e-12) z = 0.05 @test isapprox(gjk(geometry, point, IdentityTransformation(), Translation(SVector(x, y, z))).signed_distance, 0.0, atol=1e-12) end end end @testset "johnson distance subalgorithm" begin include("johnson_distance.jl") end @testset "simplex distance" begin simplex = SVector{3}(SVector{2, Float64}[[1., 0], [2., 0], [1., 1]]) pt = SVector(0., 0) cache = CollisionCache(simplex, pt); result = gjk!(cache, IdentityTransformation(), IdentityTransformation()) @test isapprox(result.signed_distance, 1.0) @test isapprox(result.closest_point_in_body.a, [1.0, 0.0]) @test isapprox(result.closest_point_in_body.b, [0.0, 0.0]) end @testset "mesh to mesh" begin mesh = load(joinpath(mesh_dir, "r_foot_chull.obj")) dx = 1.0 foot_length = 0.172786 + 0.090933 cache = CollisionCache(mesh, mesh) result = gjk!(cache, IdentityTransformation(), Translation(SVector(dx, 0, 0))) @test isapprox(result.signed_distance, dx - foot_length, atol=1e-3) cache = CollisionCache(mesh, mesh) result = gjk!(cache, Translation(SVector(dx, 0, 0)), IdentityTransformation()) @test isapprox(result.signed_distance, dx - foot_length, atol=1e-3) cache = CollisionCache(mesh, mesh) expected_penetration = 0.01 result = gjk!(cache, IdentityTransformation(), Translation(foot_length - expected_penetration, 0, 0)) # TODO: penetration distance is inconsistent and inaccurate @test result.signed_distance < 0 end @testset "neighbor mesh to mesh" begin mesh = NeighborMesh(load(joinpath(mesh_dir, "r_foot_chull.obj"))) dx = 1.0 foot_length = 0.172786 + 0.090933 cache = CollisionCache(mesh, mesh) result = gjk!(cache, IdentityTransformation(), Translation(SVector(dx, 0, 0))) @test isapprox(result.signed_distance, dx - foot_length, atol=1e-3) cache = CollisionCache(mesh, mesh) result = gjk!(cache, Translation(SVector(dx, 0, 0)), IdentityTransformation()) @test isapprox(result.signed_distance, dx - foot_length, atol=1e-3) cache = CollisionCache(mesh, mesh) expected_penetration = 0.01 result = gjk!(cache, IdentityTransformation(), Translation(foot_length - expected_penetration, 0, 0)) # TODO: penetration distance is inconsistent and inaccurate @test result.signed_distance < 0 end @testset "geometry types" begin # Adapted from # https://github.com/JuliaGeometry/GeometryTypes.jl/blob/master/test/gjk.jl @testset "gjk examples" begin c1 = gt.Simplex(gt.Vec(-1.)) c2 = gt.Simplex(gt.Vec(4.)) cache = CollisionCache(c1, c2) result = gjk!(cache, IdentityTransformation(), IdentityTransformation()) @test isapprox(result.signed_distance, 5.0) c1 = gt.Simplex(gt.Vec(-1.,0,0)) c2 = gt.Simplex(gt.Vec(4.,0,0)) cache = CollisionCache(c1, c2) result = gjk!(cache, IdentityTransformation(), IdentityTransformation()) @test isapprox(result.signed_distance, 5.0) c1 = gt.FlexibleConvexHull([gt.Vec(0.,0), gt.Vec(0.,1), gt.Vec(1.,0),gt.Vec(1.,1)]) c2 = gt.Simplex(gt.Vec(4.,0.5)) cache = CollisionCache(c1, c2) result = gjk!(cache, IdentityTransformation(), IdentityTransformation()) @test isapprox(result.signed_distance, 3.0) pt1 = gt.Vec(1,2,3.) pt2 = gt.Vec(3,4,5.) cache = CollisionCache(pt1, pt2) result = gjk!(cache, IdentityTransformation(), IdentityTransformation()) @test isapprox(result.signed_distance, norm(pt1 - pt2)) end @testset "gjk intersecting lines" begin c1 = gt.Simplex(gt.Vec(1,1.), gt.Vec(1, 2.)) @test gjk(c1, c1).signed_distance == 0. c2 = gt.Simplex(gt.Vec(1,1.), gt.Vec(10, 2.)) @test gjk(c1, c2).signed_distance == 0. end end @testset "benchmarks" begin include("../perf/runbenchmarks.jl") end
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<gh_stars>1-10 using MathOptInterface const MOI = MathOptInterface # Some tests are excluded because UniversalFallback accepts absolutely # everything. MOI.Test.runtests( MOI.Utilities.MockOptimizer( MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()), ), MOI.Test.Config(), exclude = [ "test_model_ScalarFunctionConstantNotZero", "test_model_copy_to_UnsupportedAttribute", "test_model_copy_to_UnsupportedConstraint", "test_model_supports_constraint_ScalarAffineFunction_EqualTo", "test_model_supports_constraint_VariableIndex_EqualTo", "test_model_supports_constraint_VectorOfVariables_Nonnegatives", ], warn_unsupported = true, ) # Run the previously excluded tests, this time without UniversalFallback. MOI.Test.runtests( MOI.Utilities.MockOptimizer( MOI.Utilities.Model{Float64}(), scalar_function_constant_non_zero = true, ), MOI.Test.Config(), include = [ "test_model_ScalarFunctionConstantNotZero", "test_model_copy_to_UnsupportedAttribute", "test_model_copy_to_UnsupportedConstraint", "test_model_supports_constraint_ScalarAffineFunction_EqualTo", "test_model_supports_constraint_VariableIndex_EqualTo", "test_model_supports_constraint_VectorOfVariables_Nonnegatives", ], ) # Test for Issue #1757 MOI.Test.test_model_ScalarFunctionConstantNotZero( MOI.Utilities.MockOptimizer( MOI.Utilities.Model{Float64}(), scalar_function_constant_non_zero = false, ), MOI.Test.Config(exclude = Any[MOI.ConstraintFunction]), ) # Test exclude_tests_after. This should work despite no methods being added for IncompleteOptimizer # because every test should get skipped. struct IncompleteOptimizer <: MOI.AbstractOptimizer end MOI.Test.runtests( IncompleteOptimizer(), MOI.Test.Config(); exclude_tests_after = v"0.0.1", ) # Non-Float64 tests MOI.Test.runtests( MOI.Utilities.MockOptimizer( MOI.Utilities.UniversalFallback(MOI.Utilities.Model{BigFloat}()), BigFloat, ), MOI.Test.Config(BigFloat), exclude = [ # ========================= Expected failures ========================== # UniversalFallback supports these tests. "test_model_copy_to_UnsupportedAttribute", "test_model_copy_to_UnsupportedConstraint", "test_model_supports_constraint_ScalarAffineFunction_EqualTo", "test_model_supports_constraint_VariableIndex_EqualTo", "test_model_supports_constraint_VectorOfVariables_Nonnegatives", ], )
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<filename>src/functions/utils/generate_file_template.jl function generate_file_template(parent_folder, file_type, file_name="", file_params=Dict()) file_suffix = "jl" if contains(file_type, ".") file_type, file_suffix = map(s -> String(s), split(file_type, "."; limit=2)) end if file_name == "" ; file_name = file_type ; end template_path = "$(dirname(@__FILE__))/../../../templates/$parent_folder/$file_type.$file_suffix" template = readstring(template_path) template_dictionary = Dict() template_dictionary["app"] = replace(rsplit(pwd(), "/"; limit=2)[2], ".jl", "") template_dictionary["user"] = LibGit2.getconfig("github.user", "") template_dictionary["name"] = file_name template_dictionary["title"] = title(file_name) template_dictionary["class"] = get_class_name(file_name) template_dictionary["params"] = join(file_params, ", ") template_dictionary["fields"] = join(file_params, "\n ") render(template, template_dictionary) end
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339
using Plots, Test, Colors const PLOTS_DEFAULTS = Dict(:theme => :wong2, :fontfamily => :palantino) Plots.__init__() @testset "Loading theme" begin pl = plot(1:5) @test pl[1][1][:seriescolor] == RGBA(colorant"black") @test Plots.guidefont(pl[1][:xaxis]).family == "palantino" end empty!(PLOTS_DEFAULTS) Plots.__init__() @testset "Legend defaults" begin p = plot() @test p[1][:legend_font_family] == "sans-serif" @test p[1][:legend_font_pointsize] == 8 @test p[1][:legend_font_halign] == :hcenter @test p[1][:legend_font_valign] == :vcenter @test p[1][:legend_font_rotation] == 0.0 @test p[1][:legend_font_color] == RGB{Colors.N0f8}(0.0, 0.0, 0.0) @test p[1][:legend_position] == :best @test p[1][:legend_title] == nothing @test p[1][:legend_title_font_family] == "sans-serif" @test p[1][:legend_title_font_pointsize] == 11 @test p[1][:legend_title_font_halign] == :hcenter @test p[1][:legend_title_font_valign] == :vcenter @test p[1][:legend_title_font_rotation] == 0.0 @test p[1][:legend_title_font_color] == RGB{Colors.N0f8}(0.0, 0.0, 0.0) @test p[1][:legend_background_color] == RGBA{Float64}(1.0, 1.0, 1.0, 1.0) @test p[1][:legend_foreground_color] == RGB{Colors.N0f8}(0.0, 0.0, 0.0) end # testset @testset "Legend API" begin p = plot(; legendfontfamily = "serif", legendfontsize = 12, legendfonthalign = :left, legendfontvalign = :top, legendfontrotation = 1, legendfontcolor = :red, legend = :outertopleft, legendtitle = "The legend", legendtitlefontfamily = "helvetica", legendtitlefontsize = 3, legendtitlefonthalign = :right, legendtitlefontvalign = :bottom, legendtitlefontrotation = -5.2, legendtitlefontcolor = :blue, background_color_legend = :cyan, foreground_color_legend = :green, ) @test p[1][:legend_font_family] == "serif" @test p[1][:legend_font_pointsize] == 12 @test p[1][:legend_font_halign] == :left @test p[1][:legend_font_valign] == :top @test p[1][:legend_font_rotation] == 1.0 @test p[1][:legend_font_color] == :red @test p[1][:legend_position] == :outertopleft @test p[1][:legend_title] == "The legend" @test p[1][:legend_title_font_family] == "helvetica" @test p[1][:legend_title_font_pointsize] == 3 @test p[1][:legend_title_font_halign] == :right @test p[1][:legend_title_font_valign] == :bottom @test p[1][:legend_title_font_rotation] == -5.2 @test p[1][:legend_title_font_color] == :blue @test p[1][:legend_background_color] == RGBA{Float64}(0.0, 1.0, 1.0, 1.0) @test p[1][:legend_foreground_color] == RGBA{Float64}(0.0, 0.5019607843137255, 0.0, 1.0) #setting whole font sp = plot( 1:5, legendfont = font(12), legend_font_halign = :left, foreground_color_subplot = :red, )[1] @test Plots.legendfont(sp).pointsize == 12 @test Plots.legendfont(sp).halign == :left # match mechanism @test sp[:legend_font_color] == sp[:foreground_color_subplot] @test Plots.legendfont(sp).color == sp[:foreground_color_subplot] end # testset
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2.123923
1,509
<filename>test/test_float.jl using Push using Test cfg_path = joinpath(dirname(@__FILE__), "configuration/float.cfg") cfg = Push.load_configuration(cfg_path) # FLOAT.* s = Push.run("(1.0 FLOAT.*)", cfg) @test s.float == [1.0] s = Push.run("(2.5 3.0 50.0 0.1 FLOAT.*)", cfg) @test s.float == [2.5, 3.0, 5.0] s = Push.run("(2.5 3.0 -50.0 10.0 FLOAT.*)", cfg) @test s.float == [2.5, 3.0, -500.0] # FLOAT.+ s = Push.run("(1.0 FLOAT.+)", cfg) @test s.float == [1.0] s = Push.run("(7.0 3.0 2.5 10.25 FLOAT.+)", cfg) @test s.float == [7.0, 3.0, 12.75] s = Push.run("(7.0 3.0 2.75 -10.25 FLOAT.+)", cfg) @test s.float == [7.0, 3.0, -7.5] # FLOAT.- s = Push.run("(1.0 FLOAT.-)", cfg) @test s.float == [1.0] s = Push.run("(7.0 3.0 10.75 2.25 FLOAT.-)", cfg) @test s.float == [7.0, 3.0, 8.5] s = Push.run("(7.0 3.0 10.0 25.0 FLOAT.-)", cfg) @test s.float == [7.0, 3.0, -15.0] # FLOAT./ s = Push.run("(1.0 FLOAT./)", cfg) @test s.float == [1.0] s = Push.run("(7.0 3.0 10.0 2.0 FLOAT./)", cfg) @test s.float == [7.0, 3.0, 5.0] s = Push.run("(7.0 3.0 -10.0 4.0 FLOAT./)", cfg) @test s.float == [7.0, 3.0, -2.5] s = Push.run("(7.0 3.0 -10.0 0.0 FLOAT./)", cfg) @test s.float == [7.0, 3.0, -10.0, 0.0] # FLOAT.< s = Push.run("(1.0 FLOAT.<)", cfg) @test s.float == [1.0] && isempty(s.boolean) s = Push.run("(100.0 900.0 2.0 3.0 FLOAT.<)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [true] s = Push.run("(100.0 900.0 5.0 -10.0 FLOAT.<)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [false] s = Push.run("(100.0 900.0 5.0 5.0 FLOAT.<)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [false] # FLOAT.= s = Push.run("(1.0 FLOAT.=)", cfg) @test s.float == [1.0] && isempty(s.boolean) s = Push.run("(100.0 900.0 -10.0 90.0 FLOAT.=)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [false] s = Push.run("(100.0 900.0 -10.0 -10.0 FLOAT.=)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [true] # FLOAT.> s = Push.run("(1.0 FLOAT.>)", cfg) @test s.float == [1.0] && isempty(s.boolean) s = Push.run("(100.0 900.0 2.0 3.0 FLOAT.>)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [false] s = Push.run("(100.0 900.0 5.0 -10.0 FLOAT.>)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [true] s = Push.run("(100.0 900.0 5.0 5.0 FLOAT.>)", cfg) @test s.float == [100.0, 900.0] && s.boolean == [false] # FLOAT.DUP s = Push.run("(10.0 20.0 30.0 FLOAT.DUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 30.0] # FLOAT.FLUSH s = Push.run("(10.0 20.0 30.0 FLOAT.FLUSH)", cfg) @test s.float == [] # FLOAT.FROMBOOLEAN s = Push.run("(10.0 20.0 TRUE FALSE FLOAT.FROMBOOLEAN)", cfg) @test s.float == [10.0, 20.0, 0.0] && s.boolean == [true] s = Push.run("(10.0 20.0 FALSE TRUE FLOAT.FROMBOOLEAN)", cfg) @test s.float == [10.0, 20.0, 1.0] && s.boolean == [false] # FLOAT.FROMINTEGER s = Push.run("(89 FLOAT.FROMINTEGER)", cfg) @test s.integer == [] && s.float == [89.0] # FLOAT.MAX s = Push.run("(10.0 10.0 FLOAT.MAX)", cfg) @test s.float == [10.0] s = Push.run("(20.0 10.0 FLOAT.MAX)", cfg) @test s.float == [20.0] s = Push.run("(10.0 20.0 FLOAT.MAX)", cfg) @test s.float == [20.0] # FLOAT.MIN s = Push.run("(10.0 10.0 FLOAT.MIN)", cfg) @test s.float == [10.0] s = Push.run("(20.0 10.0 FLOAT.MIN)", cfg) @test s.float == [10.0] s = Push.run("(10.0 20.0 FLOAT.MIN)", cfg) @test s.float == [10.0] # FLOAT.POP s = Push.run("(10.0 20.0 30.0 FLOAT.POP)", cfg) @test s.float == [10.0, 20.0] # FLOAT.ROT s = Push.run("(1.0 FLOAT.ROT)", cfg) @test s.float == [1.0] s = Push.run("(0.0 10.0 20.0 30.0 FLOAT.ROT)", cfg) @test s.float == [0.0, 30.0, 10.0, 20.0] # FLOAT.SHOVE s = Push.run("(1.0 FLOAT.SHOVE)", cfg) @test s.float == [1.0] s = Push.run("(2.0 0 FLOAT.SHOVE)", cfg) @test s.float == [2.0] s = Push.run("(2.0 9000 FLOAT.SHOVE)", cfg) @test s.float == [2.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 -20 FLOAT.SHOVE)", cfg) @test s.float == [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,900.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 0 FLOAT.SHOVE)", cfg) @test s.float == [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 900.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 1 FLOAT.SHOVE)", cfg) @test s.float == [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 900.0, 8.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 2 FLOAT.SHOVE)", cfg) @test s.float == [1.0,2.0,3.0,4.0,5.0,6.0,900.0,7.0,8.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 9 FLOAT.SHOVE)", cfg) @test s.float == [900.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 900.0 1000 FLOAT.SHOVE)", cfg) @test s.float == [900.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0] # FLOAT.STACKDEPTH s = Push.run("(FLOAT.STACKDEPTH)", cfg) @test s.integer == [0] s = Push.run("(1 20.0 30.0 40.0 FLOAT.STACKDEPTH)", cfg) @test s.integer == [1, 3] && s.float == [20.0, 30.0, 40.0] # FLOAT.SWAP s = Push.run("(1.0 FLOAT.SWAP)", cfg) @test s.float == [1.0] s = Push.run("(1.0 2.0 3.0 4.0 5.0 6.0 FLOAT.SWAP)", cfg) @test s.float == [1.0,2.0,3.0,4.0,6.0,5.0] # FLOAT.YANK s = Push.run("(1.0 FLOAT.YANK)", cfg) @test s.float == [1.0] s = Push.run("(10.0 0 FLOAT.YANK)", cfg) @test s.float == [10.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 -90 FLOAT.YANK)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 0 FLOAT.YANK)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 1 FLOAT.YANK)", cfg) @test s.float == [10.0, 20.0, 30.0, 50.0, 40.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 2 FLOAT.YANK)", cfg) @test s.float == [10.0, 20.0, 40.0, 50.0, 30.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 5 FLOAT.YANK)", cfg) @test s.float == [20.0, 30.0, 40.0, 50.0, 10.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 987 FLOAT.YANK)", cfg) @test s.float == [20.0, 30.0, 40.0, 50.0, 10.0] # FLOAT.YANKDUP s = Push.run("(1.0 FLOAT.YANKDUP)", cfg) @test s.float == [1.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 0 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 50.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 1 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 40.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 2 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 30.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 3 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 20.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 4 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 10.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 987 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 10.0] s = Push.run("(10.0 20.0 30.0 40.0 50.0 -987 FLOAT.YANKDUP)", cfg) @test s.float == [10.0, 20.0, 30.0, 40.0, 50.0, 50.0] # FLOAT.TAN s = Push.run("(90.0 FLOAT.TAN)", cfg) @test s.float == Float32[tan(90.0)] # FLOAT.COS s = Push.run("(90.0 FLOAT.COS)", cfg) @test s.float == Float32[cos(90.0)] # FLOAT.SIN s = Push.run("(90.0 FLOAT.SIN)", cfg) @test s.float == Float32[sin(90.0)] # FLOAT.DEFINE s = Push.run("(X FLOAT.DEFINE X)", cfg) @test isempty(s.float) && s.name == [:X, :X] s = Push.run("(3.0 X FLOAT.DEFINE X)", cfg) @test isempty(s.name) && s.float == [3.0] # FLOAT.RAND s = Push.run("(FLOAT.RAND)", cfg) @test length(s.float) == 1 # FLOAT.%
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7203, 7, 3064, 13, 15, 15897, 13, 15, 642, 13, 15, 642, 13, 15, 9977, 46, 1404, 29847, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 3064, 13, 15, 11, 15897, 13, 15, 60, 11405, 264, 13, 2127, 21052, 6624, 685, 9562, 60, 198, 198, 2, 9977, 46, 1404, 13, 28, 198, 82, 796, 23691, 13, 5143, 7203, 7, 16, 13, 15, 9977, 46, 1404, 13, 28, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 16, 13, 15, 60, 11405, 318, 28920, 7, 82, 13, 2127, 21052, 8, 198, 82, 796, 23691, 13, 5143, 7203, 7, 3064, 13, 15, 15897, 13, 15, 532, 940, 13, 15, 4101, 13, 15, 9977, 46, 1404, 13, 28, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 3064, 13, 15, 11, 15897, 13, 15, 60, 11405, 264, 13, 2127, 21052, 6624, 685, 9562, 60, 198, 82, 796, 23691, 13, 5143, 7203, 7, 3064, 13, 15, 15897, 13, 15, 532, 940, 13, 15, 532, 940, 13, 15, 9977, 46, 1404, 13, 28, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 3064, 13, 15, 11, 15897, 13, 15, 60, 11405, 264, 13, 2127, 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7, 16, 13, 15, 9977, 46, 1404, 13, 49, 2394, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 16, 13, 15, 60, 198, 82, 796, 23691, 13, 5143, 7203, 7, 15, 13, 15, 838, 13, 15, 1160, 13, 15, 1542, 13, 15, 9977, 46, 1404, 13, 49, 2394, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 15, 13, 15, 11, 1542, 13, 15, 11, 838, 13, 15, 11, 1160, 13, 15, 60, 198, 198, 2, 9977, 46, 1404, 13, 9693, 46, 6089, 198, 82, 796, 23691, 13, 5143, 7203, 7, 16, 13, 15, 9977, 46, 1404, 13, 9693, 46, 6089, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 16, 13, 15, 60, 198, 82, 796, 23691, 13, 5143, 7203, 7, 17, 13, 15, 657, 9977, 46, 1404, 13, 9693, 46, 6089, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 17, 13, 15, 60, 198, 82, 796, 23691, 13, 5143, 7203, 7, 17, 13, 15, 50138, 9977, 46, 1404, 13, 9693, 46, 6089, 42501, 30218, 70, 8, 198, 31, 9288, 264, 13, 22468, 6624, 685, 17, 13, 15, 60, 198, 82, 796, 23691, 13, 5143, 7203, 7, 16, 13, 15, 362, 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<reponame>JJMinton/TensorBoardLogger.jl import .Plots: Plots function Base.convert(t::Type{PngImage}, plot::Plots.Plot) pb = PipeBuffer() show(pb, MIME("image/png"), plot) return PngImage(pb) end preprocess(name, plot::Plots.Plot, data) = preprocess(name, convert(PngImage, plot), data) preprocess(name, plots::AbstractArray{<:Plots.Plot}, data) = begin for (i, plot)=enumerate(plots) preprocess(name*"/$i", plot, data) end return data end
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# This file contains utilities to facilities the use of # DataFrames.jl for generating benchmark settings # The idea is to benchmark different "features" and obtain # "measurements". Dependent features are supported. using FileIO, DataFrames; @doc """ generate_all_settings( features_dict, dependent_features = nothing ) Return a DataFrame, the columns of which correspond to every possible combination of feature values. ## Arguments * `features_dict` is a Dict or NamedTuple. The keys must be Strings or Symbols and provide a feature name. This is also the column name in the returned DataFrame. The values must be Vectors of possible feature values. E.g. if feature `"x"` can take values `[1,2,3]` provide an entry `"x" => [1,2,3]`. * `dependent_features` (optional). A Vector of Dicts or NamedTuples, one for each **dependent** feature. A dependent feature depends on some features from `features_dict`; we call these **arguments**. Each `dependent_feature` is defined by an entry in `dependent_features` with keys `"name"`, `"depends_on"` and `"values"`. The value of `"values"` must either be a function that takes as keyword arguments the arguments and returns a vector of possible values.\n It can also be a Dict/NamedTuple with keys being Tuples corresponding to all possible value combinations of features in `depends_on` (in the order given there). ## Example ```jldoctest features = Dict( :X => [1,], :Y => [1,2,] ); dep_features = [ Dict( "name" => :Z, "depends_on" => [:X, :Y], "values" => function(;X,Y) fill( X+Y, X+Y ) end ), ]; s = generate_all_settings(features, dep_features) # output 5×3 DataFrame Row │ Y X Z │ Int64 Int64 Int64 ─────┼───────────────────── 1 │ 1 1 2 2 │ 1 1 2 3 │ 2 1 3 4 │ 2 1 3 5 │ 2 1 3 ``` """ function generate_all_settings( features_dict :: Union{Dict, NamedTuple}, dependent_features :: Union{Nothing,Vector{D}} where D<:Union{Dict, NamedTuple} = nothing ) # turn entries of `features_dict` into Dict with key=feature name and # value = single column DataFrame of values simple_dfs = Dict{Union{String},DataFrame}() for (feature, feature_values) ∈ pairs(features_dict) simple_dfs[string(feature)] = DataFrame( feature => feature_values ) end # combine to first settings DataFrame # rows = all possible combinations of feature values from `features_dict` settings_df = crossjoin( (simple_dfs[string(k)] for k in keys(features_dict))... ) if !isnothing( dependent_features ) for dependent_feature ∈ dependent_features # for each possible value combination of feature values of # features in `dependent_feature["depends_on"]` put a dataframe # into `dependent_dfs`; these are later stacked vertically dependent_dfs = DataFrame[]; if length( dependent_feature["depends_on"] ) > 1 input_args = crossjoin( (simple_dfs[ string(dependency) ] for dependency ∈ dependent_feature["depends_on"] )... ); else input_args = simple_dfs[ string(dependent_feature["depends_on"][1]) ] end for arg_row = eachrow( input_args ) # a 1-col DF with colname=dependent_feature["name"] and values=f(;arg_row...) row_df = DataFrame( dependent_feature["name"] => let dp_vals = dependent_feature["values"]; if isa(dp_vals, Function) dp_vals(; arg_row... ) elseif isa( dp_vals, Union{Dict,NamedTuple} ) dp_vals[ Tuple(arg_row) ]; else error( "Field `values` of dependent feature $(dependent_features["name"]) must be a function with kwargs or a Dict/NamedTuple with keys corresponding to all possible value combinations of $(dependent_feature["depends_on"])." ); end#if end#let ); # add redundant columns containing input_args information # and colnames for joining with settings_df push!( dependent_dfs, crossjoin( DataFrame(arg_row), row_df ) ) end settings_df = innerjoin( settings_df, vcat( dependent_dfs... ); on = dependent_feature["depends_on"] ); end end return settings_df; end #= features = Dict( :X => [1,], :Y => [1,2,] ); dep_features = [ Dict( "name" => :Z, "depends_on" => [:X, :Y], "values" => function(;X,Y) fill( X+Y, X+Y ) end ), ]; s = generate_all_settings(features, dep_features) =# function feature_names( features_dict :: Union{Dict, NamedTuple}, dependent_features :: Union{Nothing,Vector{D}} where D<:Union{Dict, NamedTuple} = nothing ) return [ string.(collect( keys( features_dict ) )); [ string(dep_feature["name"]) for dep_feature ∈ dependent_features ] ] end #%% function unpack_feature( feature_name :: String, some_dict ) feature_symbol = Symbol( feature_name ); feature_symbol_symbol = :(Symbol( $feature_name )); @eval $feature_symbol = getindex($some_dict, $feature_symbol_symbol); end #%% @doc """ add_observation_columns!(df, observations ::Union{Dict, NamedTuple} ) Add one or several new empty column(s) to DataFrame `df` or overwrite if exists. Columns are specified by `observations` where each key gives a column name and each value the corresponding data type. Each column will have then have this data type in union with `Nothing`. """ function add_observation_columns!(df :: DataFrame, observations ::Union{Dict, NamedTuple} ) n_rows = size(df, 1); for (obs_name, obs_type) ∈ pairs(observations) df[!, string(obs_name)] = Vector{Union{obs_type,Missings.Missing}}(Missings.missing, n_rows) end nothing end #= observations = (; :ω => Float64, ) add_observation_columns!( s, observations ); =# #%% function load_previous_results( filename; result_key = "results" ) file_data = load( filename ); if !haskey( file_data, result_key ) error("Cannot retrieve previous data."); end return file_data[result_key]; end function scan_feature_values( df :: DataFrame, feature_name :: Union{Symbol, String}) if feature_name in names(df) return unique( df[ !, feature_name ] ); else @warn "No column $(feature_name) found in DataFrame." return [] end end #%% function scan_dependent_feature_values( df :: DataFrame, feature_name :: Union{Symbol, String}, depends_on :: Union{S, Vector{S}} where S<:Union{Symbol, String} ) feature_name = string(feature_name); if feature_name in names(df) dep_args = isa( depends_on, Union{Symbol, String} ) ? [ string( depends_on ), ] : string.(depends_on); new_dep_dict = Dict() for sub_df in groupby( df, dep_args ) new_dep_dict[ Tuple( sub_df[1, dep_args]) ] = unique( sub_df[:, feature_name ] ); end return new_dep_dict else @warn "No column $(feature_name) found in DataFrame." return [] end end function scan_dependent_feature_values(df :: DataFrame, feature :: Union{Dict, NamedTuple}) return scan_dependent_feature_values( feature["name"], feature["depends_on"] ) end #= z_vals = scan_dependent_feature_values( s, "Z", ["X","Y"]) =# #%% function find_observation_rows( df :: DataFrame, observations :: Vector{<:Symbol} = Symbol[] ) return findall( .!(vec( prod( hcat( ( ismissing.( df[:,obs] ) for obs in observations)... ), dims = 2 ) )) ); end find_observation_rows(df :: DataFrame, obs :: Union{Dict, NamedTuple}) = find_observation_rows( df, collect(keys(obs)) ) function fill_from_partial_results!(target :: DataFrame, source :: DataFrame, feature_names :: Vector{ S } where S<:Union{Symbol, String}, observations :: Union{Nothing, Union{Dict, NamedTuple} } = nothing, required_observations :: Union{Nothing, Union{Dict, NamedTuple} } = nothing; ) if isnothing( observations ) observations = intersect( setdiff( names(target), string.(feature_names) ), names( source ) ) else observations = collect(keys(observations)); end if !isnothing( observations ) if isnothing( required_observations ) required_observations = observations; end investigated_row_indices = Int[]; non_missing_indices = find_observation_rows( source, required_observations ); for non_missing_index in non_missing_indices src_row = source[ non_missing_index, : ]; for target_row ∈ eachrow( target ) if Tuple(target_row[feature_names]) == Tuple(src_row[feature_names]) target_row[observations] = src_row[observations]; push!( investigated_row_indices, rownumber(target_row) ) # break here if we expect/assume unique features end end end to_do_row_indices = setdiff( 1 : size(target,1), investigated_row_indices ) println("Filled in data from $(length(investigated_row_indices)) rows.") return investigated_row_indices, to_do_row_indices end return nothing end function fill_from_partial_results!( target :: DataFrame, source :: DataFrame, features_dict :: Union{Dict, NamedTuple}, dependent_features :: Union{Nothing,Vector{D}} where D<:Union{Dict, NamedTuple} = nothing, observations :: Union{Nothing, Union{Dict, NamedTuple} } = nothing ) feature_names = collect(keys( features_dict )); if !isnothing( dependent_features ) push!( feature_names, [dep_feat["name"] for dep_feat ∈ dependent_features]... ) end return fill_from_partial_results!( target, source, feature_names, observations) end #= new_df = DataFrame( X=[1,1,2,2], Y=[1,2,1,2] ) add_observation_columns!( new_df, (; :Z => Int,)) src = DataFrame( X = [1,2], Y= [2,2], Z = [10,11]) fill_from_partial_results!( new_df, src, ["X", "Y"] ) =#
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<filename>src/RepeatingStructures/Multilens/HexTilings.jl # MIT license # Copyright (c) Microsoft Corporation. All rights reserved. # See LICENSE in the project root for full license information. # All of the objects can be displayed with Vis.draw. Example: # Vis.draw(hex4RGB()) function clustercolors(lattice) elements = Repeat.clusterelements(lattice) colors = Vector{String}(undef, length(elements)) for (i, element) in pairs(elements) colors[i] = pointcolor(element, lattice) end return colors end export clustercolors function hex3RGB() lattice = hex3() colors = clustercolors(lattice) names = ["R","G","B"] properties = DataFrames.DataFrame(Color=colors, Name=names) return Repeat.ClusterWithProperties(hex3(), properties) end export hex3RGB function hex4RGB() lattice = hex4() colors = clustercolors(lattice) names = ["R","G","B","W"] properties = DataFrames.DataFrame(Color=colors, Name=names) return Repeat.ClusterWithProperties(hex4(), properties) end export hex4RGB function hex7RGB() lattice = hex7() colors = clustercolors(lattice) names = ["R1","G1","B1","W","R2","G2","B2"] properties = DataFrames.DataFrame(Color=colors, Name=names) return Repeat.ClusterWithProperties(hex7(), properties) end export hex7RGB function hex9RGB() lattice = hex9() colors = clustercolors(lattice) names = ["R-1","G-1","B-1","R0","G0","B0","R1","G1","B1"] properties = DataFrames.DataFrame(Color=colors, Name=names) return Repeat.ClusterWithProperties(hex9(), properties) end export hex9RGB function hex12RGB() lattice = hex12() colors = clustercolors(lattice) names = [ "G3","B0", "B2","R1","G2", "R0","G1","B1","R3", "B3","R2","G0" ] properties = DataFrames.DataFrame(Color=colors, Name=names) return Repeat.ClusterWithProperties(hex12(), properties) end export hex12RGB function hex19RGB() lattice = hex19() colors = clustercolors(lattice) names = [ "W", "B0","G1","R2","B3","G4","R5", "G0","B1","R1","G2","B2","R3","G3","B4","R4","G5","B5","R0" ] properties = DataFrames.DataFrame(Color=colors, Name=names) # properties = DataFrames.DataFrame(Color = colors) return Repeat.ClusterWithProperties(lattice, properties) end export hex19RGB function hex19fov1() lattice = hex19() offset = 1 colornames = ["R","G","B"] colors = ["red","green","blue"] fov1 = [8,19,18,17,7,2] fov2 = [16,15,14,13,5,6] fov3 = [12,11,10,9,3,4] allfov = vcat([0],fov1,fov2,fov3) names = [x -> string("fov",x) for x in 1:6] allnames = Vector{String}(undef,19) allcolors = Vector{String}(undef,19) for (index,fov) in pairs((fov1,fov2,fov3)) for i in eachindex(fov) println("$index $i") latticeindex = (index-1)*length(fov1) + i + offset allcolors[allfov[latticeindex]] = colors[index] allnames[allfov[latticeindex]] = string("fov",string(index)) end end allcolors[offset] = "white" allnames[offset] = "W" properties = DataFrames.DataFrame(Color=allcolors, Name=allnames) # properties = DataFrames.DataFrame(Color = colors) return Repeat.ClusterWithProperties(lattice, properties) end export hex19fov1 function hex19fov2() lattice = hex19() colors = clustercolors(lattice) names = [string("fov",x÷3) for x in 1:18] names = vcat("W",names) properties = DataFrames.DataFrame(Color=colors, Name=names) # properties = DataFrames.DataFrame(Color = colors) return Repeat.ClusterWithProperties(lattice, properties) end export hex19fov2
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module includes import Random import REPL import TOML using Dates """ note: works on mac & windows safe_path credits to: the Julia Programming Team (from [Pkg.jl/src/manifest.jl](https://github.com/JuliaLang/Pkg.jl/blob/master/src/manifest.jl)): (ordered as-is by github.com) [10 members]: <NAME> @KristofferC <NAME> @IanButterworth #PRO <NAME> @00vareladavid <NAME> @timholy #PRO [neuroscientist, professor, julia tutor] <NAME> @GunnarFarneback <NAME> @fredrikekre #PRO [postdoc] <NAME> @DilumAluthge <NAME> @tpapp <NAME> @devmotion #PRO <NAME> @aviatesk """ # turn /-paths into \-paths on Windows """safe_path source: https://github.com/JuliaLang/Pkg.jl/blob/b4da4946735fe4c7c6bb3e2bc16af95b4e76e487/src/manifest.jl#L57 helps in resolving uris' slashes for windows users """ function safe_path(path::String) if Sys.iswindows() && !isabspath(path) path = joinpath(split(path, "/")...) # what if: if we can do all urls as "\": thus we'll only Modify for windows users while other OSs can communicate too worry-free -newly standardized end return path end paths = ["functions", "functions/Generators", "Distributions", "NeuralNet/Networks", "src/DataStructures/Arrays/Array.jl", "tests"] #Idea: all string paths in here # used in a for loop - in a custom iterable function #parallizable #TODO: Iterate all locations in paths #include(safe_path(".")) #why refeing to thyself? #ERROR:PermissionDenied #TODO: find a solution on windows pc to solve previlages in julia include(safe_path("functions")) include(safe_path("functions/Generators")) include(safe_path("Distributions")) include(safe_path("NeuralNet/Networks")) include(safe_path("tests")) include(safe_path("src/constants.jl")) include(safe_path("src/DataStructures/Arrays/Array.jl")) """ to be called upon path error """ #= #systemError: Permission Denied (on Windows)#TODO: check common # solution? #nice try !, needs further checking to see why it defaults to last catch function pathRevise(path::String) # TRY TO FIX (possible) url typos strings = split(path, "/") try strings[1] = Upper(strings[1])#Gatcha#1: try Uppercasing the first letter #(input is a miniscule, while actual directory is Majiscule) path = joinpath(strings...) catch end #=for i in enumerate length(strings) #TODO: do minor changes to path - i.e. uppercase other words #TODO: how to spot others , first? [Hint: names are CamelToed!] #possible-fix #available end=# end paths = ["functions", "functions/Generators", "Distributions", "NeuralNet/Networks", "src/DataStructures/Arrays/Array.jl", "tests"] #TODO: Iterate all locations in paths # try #include(safe_path(".")) #why refeing to thyself? include(safe_path("functions")) include(safe_path("functions/Generators")) include(safe_path("Distributions")) include(safe_path("NeuralNet/Networks")) include(safe_path("tests")) include(safe_path("src/constants.jl")) include(safe_path("src/DataStructures/Arrays/Array.jl")) catch try strings = split(path, "/") n = length(strings) for i in enumerate(n) strings[1] = Upper(strings[1])#try Uppercasing the first letter path = joinpath(strings...) end catch println("path does not exist") end end end =# """ DEPOT_PATH A stack of "depot" locations where the package manager, as well as Julia's code loading mechanisms, look for package registries, installed packages, named environments, repo clones, cached compiled package images, and configuration files. By default it includes: 1. ~/.julia where ~ is the user home as appropriate on the system; 2. an architecture-specific shared system directory, e.g. /usr/local/share/julia; 3. an architecture-independent shared system directory, e.g. /usr/share/julia. So DEPOT_PATH might be: [joinpath(homedir(), ".julia"), "/usr/local/share/julia", "/usr/share/julia"] The first entry is the "user depot" and should be writable by and owned by the current user. The user depot is where: registries are cloned, new package versions are installed, named environments are created and updated, package repos are cloned, newly compiled package image files are saved, log files are written, development packages are checked out by default, and global configuration data is saved. Later entries in the depot path are treated as read-only and are appropriate for registries, packages, etc. installed and managed by system administrators. DEPOT_PATH is populated based on the JULIA_DEPOT_PATH environment variable if set. DEPOT_PATH contents ===================== Each entry in DEPOT_PATH is a path to a directory which contains subdirectories used by Julia for various purposes. Here is an overview of some of the subdirectories that may exist in a depot: • clones: Contains full clones of package repos. Maintained by Pkg.jl and used as a cache. • compiled: Contains precompiled *.ji files for packages. Maintained by Julia. • dev: Default directory for Pkg.develop. Maintained by Pkg.jl and the user. <--- ok • environments: Default package environments. For instance the global environment for a specific julia version. Maintained by Pkg.jl. • logs: Contains logs of Pkg and REPL operations. Maintained by Pkg.jl and Julia. • packages: Contains packages, some of which were explicitly installed and some which are implicit dependencies. Maintained by Pkg.jl. • registries: Contains package registries. By default only General. Maintained by Pkg.jl. See also: JULIA_DEPOT_PATH, and Code Loading. """ depots() = Base.DEPOT_PATH logdir(depot = depots1()) = joinpath(depot, "logs") devdir(depot = depots1()) = get(ENV, "JULIA_PKG_DEVDIR", joinpath(depot, "dev")) envdir(depot = depots1()) = joinpath(depot, "environments") const UPDATED_REGISTRY_THIS_SESSION = Ref(false) const OFFLINE_MODE = Ref(false) function depots1() d = depots() isempty(d) && Pkg.Types.pkgerror("no depots found in DEPOT_PATH") return d[1] end function pkg_server() server = get(ENV, "JULIA_PKG_SERVER", "https://pkg.julialang.org") isempty(server) && return nothing startswith(server, r"\w+://") || (server = "https://$server") return rstrip(server, '/') end #--- For globally overriding in e.g. tests const DEFAULT_IO = Ref{Union{IO,Nothing}}(nothing) stderr_f() = something(DEFAULT_IO[], stderr) stdout_f() = something(DEFAULT_IO[], stdout) const PREV_ENV_PATH = Ref{String}("") #a goo rule of thumb can_fancyprint(io::IO) = (io isa Base.TTY) && (get(ENV, "CI", nothing) != "true") # fancyprint: <interesting> end
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function plot(tb::TBModel; plothopping::Bool=false) figure() if plothopping x_cncts = Vector{Float64}[] y_cncts = similar(x_cncts) for site in eachindex(tb.neighbors) for neighbor in tb.neighbors[site] push!(x_cncts, tb.pos[[site,neighbor],1]) push!(y_cncts, tb.pos[[site,neighbor],2]) end end # faster to collect all the connections in one array than to issue multiple calls to # PyPlot for each connection plot(hcat(x_cncts...), hcat(y_cncts...), "-", color="gray") # TODO: Avoid plotting same connection twice... end plot(tb.pos[:,1], tb.pos[:,2], ".k") axis("equal") end
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module TestConversions using Compat, Compat.Test, DataFrames, Compat.InteractiveUtils using DataStructures: OrderedDict, SortedDict const ≅ = isequal df = DataFrame() df[:A] = 1:5 df[:B] = [:A, :B, :C, :D, :E] @test isa(convert(Array, df), Matrix{Any}) @test isa(convert(Array{Any}, df), Matrix{Any}) df = DataFrame() df[:A] = 1:5 df[:B] = 1.0:5.0 @test isa(convert(Array, df), Matrix{Float64}) @test isa(convert(Array{Any}, df), Matrix{Any}) @test isa(convert(Array{Float64}, df), Matrix{Float64}) df = DataFrame() df[:A] = Vector{Union{Float64, Missing}}(1.0:5.0) df[:B] = Vector{Union{Float64, Missing}}(1.0:5.0) a = convert(Array, df) aa = convert(Array{Any}, df) ai = convert(Array{Int}, df) @test isa(a, Matrix{Union{Float64, Missing}}) @test a == convert(Array, convert(Array{Union{Float64, Missing}}, df)) @test a == convert(Matrix, df) @test isa(aa, Matrix{Any}) @test aa == convert(Matrix{Any}, df) @test isa(ai, Matrix{Int}) @test ai == convert(Matrix{Int}, df) df[1,1] = missing @test_throws ErrorException convert(Array{Float64}, df) na = convert(Array{Union{Float64, Missing}}, df) naa = convert(Array{Union{Any, Missing}}, df) nai = convert(Array{Union{Int, Missing}}, df) @test isa(na, Matrix{Union{Float64, Missing}}) @test na ≅ convert(Matrix, df) @test isa(naa, Matrix{Union{Any, Missing}}) @test naa ≅ convert(Matrix{Union{Any, Missing}}, df) @test isa(nai, Matrix{Union{Int, Missing}}) @test nai ≅ convert(Matrix{Union{Int, Missing}}, df) a = Union{Float64, Missing}[1.0,2.0] b = Union{Float64, Missing}[-0.1,3] c = Union{Float64, Missing}[-3.1,7] di = Dict("a"=>a, "b"=>b, "c"=>c) df = convert(DataFrame, di) @test isa(df, DataFrame) @test names(df) == Symbol[x for x in sort(collect(keys(di)))] @test df[:a] == a @test df[:b] == b @test df[:c] == c od = OrderedDict("c"=>c, "a"=>a, "b"=>b) df = convert(DataFrame,od) @test isa(df, DataFrame) @test names(df) == Symbol[x for x in keys(od)] @test df[:a] == a @test df[:b] == b @test df[:c] == c sd = SortedDict("c"=>c, "a"=>a, "b"=>b) df = convert(DataFrame,sd) @test isa(df, DataFrame) @test names(df) == Symbol[x for x in keys(sd)] @test df[:a] == a @test df[:b] == b @test df[:c] == c a = [1.0] di = Dict("a"=>a, "b"=>b, "c"=>c) @test_throws DimensionMismatch convert(DataFrame,di) end
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# Autogenerated wrapper script for SOLAR_jll for armv7l-linux-gnueabihf-cxx03 export solar JLLWrappers.@generate_wrapper_header("SOLAR") JLLWrappers.@declare_executable_product(solar) function __init__() JLLWrappers.@generate_init_header() JLLWrappers.@init_executable_product( solar, "bin/solar", ) JLLWrappers.@generate_init_footer() end # __init__()
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