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ytzi
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starcoderdata-gpt2

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<reponame>yihong-zhang/YaoBlocks.jl export PutBlock, put """ PutBlock <: AbstractContainer Type for putting a block at given locations. """ struct PutBlock{N, C, GT <: AbstractBlock} <: AbstractContainer{GT, N} content::GT locs::NTuple{C, Int} function PutBlock{N}(block::GT, locs::NTuple{C, Int}) where {N, M, C, GT <: AbstractBlock{M}} @assert_locs_safe N locs @assert nqubits(block) == C "number of locations doesn't match the size of block" return new{N, C, GT}(block, locs) end end """ put(total::Int, pair) Create a [`PutBlock`](@ref) with total number of active qubits, and a pair of location and block to put on. # Example ```jldoctest julia> put(4, 1=>X) nqubits: 4 put on (1) └─ X gate ``` If you want to put a multi-qubit gate on specific locations, you need to write down all possible locations. ```jldoctest julia> put(4, (1, 3)=>kron(X, Y)) nqubits: 4 put on (1, 3) └─ kron ├─ 1=>X gate └─ 2=>Y gate ``` The outter locations creates a scope which make it seems to be a contiguous two qubits for the block inside `PutBlock`. !!! tips It is better to use [`concentrate`](@ref) instead of `put` for large blocks, since put will use the matrix of its contents directly instead of making use of what's in it. `put` is more efficient for small blocks. """ put(total::Int, pa::Pair{NTuple{M, Int}, <:AbstractBlock}) where M = PutBlock{total}(pa.second, pa.first) put(total::Int, pa::Pair{Int, <:AbstractBlock}) = PutBlock{total}(pa.second, (pa.first, )) put(total::Int, pa::Pair{<:Any, <:AbstractBlock}) = PutBlock{total}(pa.second, Tuple(pa.first)) """ put(pair) -> f(n) Lazy curried version of [`put`](@ref). # Example ```jldoctest julia> put(1=>X) (n -> put(n, 1 => X gate)) ``` """ put(pa::Pair) = @λ(n -> put(n, pa)) occupied_locs(x::PutBlock) = x.locs chsubblocks(x::PutBlock{N}, b::AbstractBlock) where N = PutBlock{N}(b, x.locs) PreserveStyle(::PutBlock) = PreserveAll() cache_key(pb::PutBlock) = cache_key(pb.content) mat(::Type{T}, pb::PutBlock{N, 1}) where {T, N} = u1mat(N, mat(T, pb.content), pb.locs...) mat(::Type{T}, pb::PutBlock{N, C}) where {T, N, C} = unmat(N, mat(T, pb.content), pb.locs) function apply!(r::ArrayReg{B, T}, pb::PutBlock{N}) where {B, T, N} _check_size(r, pb) instruct!(matvec(r.state), mat(T, pb.content), pb.locs) return r end # specialization for G in [:X, :Y, :Z, :T, :S, :Sdag, :Tdag] GT = Expr(:(.), :ConstGate, QuoteNode(Symbol(G, :Gate))) @eval function apply!(r::ArrayReg, pb::PutBlock{N, C, <:$GT}) where {N, C} _check_size(r, pb) instruct!(matvec(r.state), Val($(QuoteNode(G))), pb.locs) return r end end Base.adjoint(x::PutBlock{N}) where N = PutBlock{N}(adjoint(content(x)), x.locs) Base.copy(x::PutBlock{N}) where N = PutBlock{N}(x.content, x.locs) function Base.:(==)(lhs::PutBlock{N, C, GT}, rhs::PutBlock{N, C, GT}) where {N, C, GT} return (lhs.content == rhs.content) && (lhs.locs == rhs.locs) end function YaoBase.iscommute(x::PutBlock{N}, y::PutBlock{N}) where N if x.locs == y.locs return iscommute(x.content, y.content) else return iscommute_fallback(x, y) end end
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<gh_stars>1-10 struct WeightedLeastSquares estimate::Array{Float64,1} rmse::Float64 mae::Float64 wrss::Float64 end struct ErrorMetric rmse::Float64 mae::Float64 wrss::Float64 end struct ErrorMetricWiden rmse::Float64 mae::Float64 wrss::Float64 rmseGBPWLS::Float64 maeGBPWLS::Float64 end ########## Compute the GBP error metrics ########## function errorMetric(gbp::Union{ContinuousModel, ContinuousTreeModel}) observationGBP = gbp.system.jacobian * gbp.inference.mean NactiveRows = activeRows(gbp) rmse = 0.0; mae = 0.0; wrss = 0.0 @inbounds for i = 1:length(gbp.system.observation) rmse += (gbp.system.observation[i] - observationGBP[i])^2 mae += abs(gbp.system.observation[i] - observationGBP[i]) wrss += (gbp.system.observation[i] - observationGBP[i]) / gbp.system.variance[i] end rmse = (rmse / NactiveRows)^(1/2) mae = mae / NactiveRows return ErrorMetric(rmse, mae, wrss) end ########## Compute the GBP error metrics and error according to the WLS ########## @inline function errorMetric(gbp::Union{ContinuousModel, ContinuousTreeModel}, wls::WeightedLeastSquares) observationGBP = gbp.system.jacobian * gbp.inference.mean NactiveRows = activeRows(gbp) rmse = 0.0; mae = 0.0; wrss = 0.0 @inbounds for i = 1:length(gbp.system.observation) rmse += (gbp.system.observation[i] - observationGBP[i])^2 mae += abs(gbp.system.observation[i] - observationGBP[i]) wrss += (gbp.system.observation[i] - observationGBP[i]) / gbp.system.variance[i] end rmse = (rmse / NactiveRows)^(1/2) mae = mae / NactiveRows rmseGBPWLS = 0.0; maeGBPWLS = 0.0 @inbounds for i = 1:gbp.graph.Nvariable rmseGBPWLS += (wls.estimate[i] - gbp.inference.mean[i])^2 maeGBPWLS += abs(wls.estimate[i] - gbp.inference.mean[i]) end rmseGBPWLS = (rmseGBPWLS / gbp.graph.Nvariable)^(1/2) maeGBPWLS = maeGBPWLS / gbp.graph.Nvariable return ErrorMetricWiden(rmse, mae, wrss, rmseGBPWLS, maeGBPWLS) end ########## Compute WLS solution and error metrics ########## function wls(gbp::Union{ContinuousModel, ContinuousTreeModel}) W = spdiagm(0 => @. 1.0 / sqrt(gbp.system.variance)) A = W * gbp.system.jacobian G = A' * A b = A' * W * gbp.system.observation x = G \ b observationWLS = gbp.system.jacobian * x NactiveRows = activeRows(gbp) rmse = 0.0; mae = 0.0; wrss = 0.0 @inbounds for i = 1:length(gbp.system.observation) rmse += (gbp.system.observation[i] - observationWLS[i])^2 mae += abs(gbp.system.observation[i] - observationWLS[i]) wrss += (gbp.system.observation[i] - observationWLS[i]) / gbp.system.variance[i] end rmse = (rmse / NactiveRows)^(1/2) mae = mae / NactiveRows return WeightedLeastSquares(x, rmse, mae, wrss) end ########## Display: GBP and WLS ########## function displayData(args...) gbpIdx = 0; wlsIdx = 0; errorIdx = 0; widenIdx = 0 @inbounds for (k, i) in enumerate(args) T = split.(string(typeof(i)), ".")[end] if T == "ContinuousModel" || T == "ContinuousTreeModel" gbpIdx = k end if T == "WeightedLeastSquares" wlsIdx = k end if T == "ErrorMetric" || T == "ErrorMetricWiden" errorIdx = k end end if gbpIdx == 0 error("FactorGraph model is missing.") end if errorIdx != 0 && wlsIdx == 0 data = ["" "" "" "Error Metrics" "" "" "Root mean square error of the Gaussian belief propagation" "."^10 getfield(args[errorIdx], :rmse); "Mean absolute error of the Gaussian belief propagation" "."^10 getfield(args[errorIdx], :mae); "Weighted residual sum of squares error of the Gaussian belief propagation" "."^10 getfield(args[errorIdx], :wrss)] pretty_table(data, tf = tf_borderless, columns_width = [75, 11, 14], noheader = true, alignment = [:l, :l, :r], formatters = ft_printf("%1.4e", 3), highlighters = (hl_cell( [(2,1)], crayon"bold"), hl_col(2, crayon"dark_gray")), body_hlines = [2], body_hlines_format = Tuple('─' for _ = 1:4)) end if errorIdx != 0 && wlsIdx != 0 data = ["" "" "" "Error Metrics" "" "" "Root mean square error of the Gaussian belief propagation" "."^10 getfield(args[errorIdx], :rmse); "Root mean square error of the weighted least-squares method" "."^10 getfield(args[wlsIdx], :rmse); "Root mean square error ratio" "."^10 getfield(args[errorIdx], :rmse) / getfield(args[wlsIdx], :rmse); "" "" ""; "Mean absolute error of the Gaussian belief propagation" "."^10 getfield(args[errorIdx], :mae); "Mean absolute error of the weighted least-squares method" "."^10 getfield(args[wlsIdx], :mae); "Mean absolute error ratio" "."^10 getfield(args[errorIdx], :mae) / getfield(args[wlsIdx], :mae); "" "" ""; "Weighted residual sum of squares of the belief propagation" "."^10 getfield(args[errorIdx], :wrss); "Weighted residual sum of squares of the weighted least-squares method" "."^10 getfield(args[wlsIdx], :wrss); "Weighted residual sum of squares ratio" "."^10 getfield(args[errorIdx], :wrss) / getfield(args[wlsIdx], :wrss)] high = Highlighter((data,i,j) -> i == 5 && j == 3 && data[5,3] > 1.1, Crayon(bold = true, background = :red)) pretty_table(data, tf = tf_borderless, columns_width = [75, 11, 14], noheader = true, alignment = [:l, :l, :r], formatters = ft_printf("%1.4e", 3), highlighters = (high, hl_cell([(2,1), (8,3), (5,3)], crayon"bold"), hl_col(2, crayon"dark_gray")), body_hlines = [2], body_hlines_format = Tuple('─' for _ = 1:4)) end if wlsIdx == 0 println("\n State Variables Display") pretty_table([collect(1:getfield(args[gbpIdx].graph, :Nvariable)) getfield(args[gbpIdx].inference, :mean)], header = ["State Variable", "Estimate"], alignment=[:r, :r], formatters = ft_printf(["%3.0f", "%3.6f"], [1, 2])) end if wlsIdx != 0 A = [collect(1:getfield(args[gbpIdx].graph, :Nvariable)) getfield(args[gbpIdx].inference, :mean) getfield(args[wlsIdx], :estimate) getfield(args[gbpIdx].inference, :mean) ./ getfield(args[wlsIdx], :estimate)] A = A[reverse(sortperm(A[:, 4])), :] println("\n State Variables Display") pretty_table(A, header = ["State Variable", "Belief Propagation Estimate", "Weighted Least-squares Estimate", "Estimates Ratio"], columns_width = [15, 28, 32, 20], alignment=[:r, :r, :r, :r], formatters = ft_printf(["%3.0f", "%3.6f", "%3.6f", "%3.6e"], [1, 2, 3, 4])) end end ########## Find number of nonzeros rows ########## @inline function activeRows(gbp) NactiveRows = length(gbp.system.observation) @inbounds for (k, i) in enumerate(gbp.system.observation) if i == 0.0 if all(gbp.system.jacobianTranspose[:, k] .== 0.0) NactiveRows -= 1 end end end return NactiveRows end ########## Start row in xlsx-file ########## function startxlsx(xf) start = 1 @inbounds for r in XLSX.eachrow(xf) if !isa(r[1], String) start = XLSX.row_number(r) break end end return start end ########## Check data for import ########## function checkImportFile(args) #### Check the package is installed pathtoFactorGraph = Base.find_package("FactorGraph") if isnothing(pathtoFactorGraph) throw(ErrorException("FactorGraph not found in install packages")) end packagepath = abspath(joinpath(dirname(pathtoFactorGraph), "..")) extension = ".h5"; path = ""; dataname = ""; fullpath = "" @inbounds for i = 1:length(args) try extension = string(match(r"\.[A-Za-z0-9]+$", args[i]).match) catch extension = "" end if extension == ".h5" || extension == ".xlsx" fullpath = args[i] path = dirname(args[i]) dataname = basename(args[i]) break end end if isempty(extension) throw(ErrorException("the input DATA extension is not found")) elseif extension != ".h5" && extension != ".xlsx" throw(DomainError(extension, "the input DATA extension is not supported")) end if path == "" path = joinpath(packagepath, "src/example/") fullpath = joinpath(packagepath, "src/example/", dataname) end if !(dataname in cd(readdir, path)) throw(DomainError(dataname, "the input DATA is not found")) end return fullpath, extension, dataname end ########## Check keyword arguments ########## function checkKeywords(prob, alpha, variance) #### Check convergence parameters if prob <= 0.0 || prob >= 1.0 error("Invalid prob value.") end if alpha <= 0.0 || alpha >= 1.0 error("Invalid alpha value.") end #### Check initial variance if variance < 0.0 error("Invalid variance value.") end end ########## Type of the system data ########## function checkFileOrArguments(args) fromfile = false @inbounds for i in args if typeof(i) == String fromfile = true break end end return fromfile end
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<filename>src/scripts/p10.jl # p10.jl - polynomials and corresponding equipotential curves N = 16; clf(); xx = -1.01:.005:1.01; for i = 1:2 i==1 && ( (s,x) = ("equispaced points", -1 + 2*(0:N)/N) ); i==2 && ( (s,x) = ("Chebyshev points", cos.(pi*(0:N)/N)) ); p = poly(x); # Plot p(x) over [-1,1]: xx = -1:.005:1; pp = p(xx); subplot(2,2,2*i-1); plot(x,0*x,"k.",markersize=6); plot(xx,pp); grid(true); xticks(-1:.5:1); title(s); # Plot equipotential curves: subplot(2,2,2*i) plot(real(x),imag(x),".",markersize=6); axis([-1.4,1.4,-1.12,1.12]); xx = -1.4:.02:1.4; yy = -1.12:.02:1.12; zz = xx' .+ 1im*yy; pp = p(zz); levels = 10.0.^(-4:0); contour(xx,yy,abs.(pp),levels,colors="k"); title(s); end
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<reponame>mortenpi/PSSFSS.jl<gh_stars>1-10 using Test using SafeTestsets @safetestset "Rings Tests" begin include("Rings_test.jl") end @safetestset "RWGSheet Tests" begin include("RWGSheet_test.jl") end @safetestset "Elements Tests" begin include("Elements_test.jl") end @safetestset "RWGData Tests" begin include("RWGData_test.jl") end @safetestset "PGF Tests" begin include("PGF_test.jl") end @safetestset "Zint Tests" begin include("Zint_test.jl") end @safetestset "GSMs Tests" begin include("GSMs_test.jl") end @safetestset "Modes Tests" begin include("Modes_test.jl") end @safetestset "FillZY Tests" begin include("FillZY_test.jl") end @safetestset "SymStripFull Tests" begin include("full_test.jl") end
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<reponame>airicbear/PHYS2502 ### A Pluto.jl notebook ### # v0.18.4 using Markdown using InteractiveUtils # ╔═╡ 1eb34f10-b4b3-11ec-19f5-c7f26108d374 begin using LinearAlgebra using StatsPlots md""" # HW 11 <NAME> """ end # ╔═╡ 6c4b3bf7-26e9-4d6f-9305-65fbc851818f let md""" ### Problem 1 Use the properties of determinants to prove that ``\underline{\underline{A}} \,\underline{\underline{B}} = \underline{\underline{0}}`` implies that either ``|\underline{\underline{A}}| = 0`` or ``|\underline{\underline{B}}| = 0``, or both, where ``\underline{\underline{0}}`` is the matrix of all zeros. Demonstrate this with the matrices $\underline{\underline{A}} = \begin{bmatrix} 1 & 1 \\ 2 & 2 \end{bmatrix} \qquad\text{and}\qquad \underline{\underline{B}} = \begin{bmatrix} a & b \\ -a & -b \end{bmatrix}$ where ``a`` and ``b`` can be any complex numbers. Which matrix has zero determinant? """ let A = [1 1; 2 2] a = rand(Complex{Float64}) b = rand(Complex{Float64}) B = [a b; -a -b] det(A), det(B), eigvals(A), eigvals(B) end md""" ### Problem 1 Given that ``|\underline{\underline{A}} \,\underline{\underline{B}}| = 0`` and using the sixth property of determinants as listed in the course notes, ``|\underline{\underline{A}} \,\underline{\underline{B}}| = |\underline{\underline{A}}| |\underline{\underline{B}}|``, we get $|\underline{\underline{A}} \,\underline{\underline{B}}| = |\underline{\underline{A}}| |\underline{\underline{B}}| = 0$ which implies that at least ``|\underline{\underline{A}}| = 0`` or ``|\underline{\underline{B}}| = 0``. The determinant ``|\underline{\underline{A}}|`` is $|\underline{\underline{A}}| = (1)(2) - (1)(2) = 2 - 2 = 0$ so ``\underline{\underline{A}}`` is the matrix with the zero determinant. """ end # ╔═╡ 2743185f-b0d7-4195-8e8e-a1bfcf132934 let md""" ### Problem 2 Follow the procedure we went through in class to find the symmetry axes of the conic section ``6x^2 + 12xy + y^2 = 16``, and find the angle they make with the ``x,y`` axes. What kind of curve is this? A plot would be helpful. You can do this with MATHEMATICA if you want, but the necessary algebra is rather simple. """ md""" ### Problem 2 $\underline{\underline{A}} = \begin{bmatrix} 6 & 6 \\ 6 & 1 \end{bmatrix} \implies \underline{\underline{R}} = \frac{1}{\sqrt{13}} \begin{bmatrix} -2 & 3 \\ 3 & 2 \end{bmatrix} \implies \underline{\underline{R}}^T \underline{\underline{A}} \,\underline{\underline{R}} = \begin{bmatrix} -3 & 0 \\ 0 & 10 \end{bmatrix}$ """ end # ╔═╡ 982fab25-1414-4789-bc16-343ab29b4514 let A = [6 6; 6 1] v1 = eigvecs(A)[:,1] v2 = eigvecs(A)[:,2] R = [v1 v2] R' * A * R end # ╔═╡ 7e53c83c-ead7-484f-8762-4a0956ba7210 md""" This produces the curve $-3x^2 + 10y^2 = 16$ which is a hyperbola """ # ╔═╡ c8977146-1409-4eb1-bede-13436fd7214b let plot(x -> sqrt(16 + 3x^2) / 10, color=:black, legend=false) plot!(x -> -sqrt(16 + 3x^2) / 10, color=:black, legend=false) end # ╔═╡ 201fb421-39b1-47db-8dec-609706461dca let md""" ### Problem 3 Find the eigenvalues and eigenvectors for the matrix $\underline{\underline{\sigma}}_y = \begin{bmatrix} 0 & -1 \\ i & 0 \end{bmatrix}$ one of the three **Pauli matrices**. Do this by hand, not with MATHEMATICA. Normalize the eigenvectors and show that they are orthogonal. """ md""" ### Problem 3 First, we find the characteristic polynomial. $\det(\underline{\underline{\sigma}}_y - \lambda \,\underline{\underline{I}}) = \begin{vmatrix} -\lambda & -i \\ i & -\lambda \end{vmatrix} = \lambda^2 + i^2 = 0 \implies \lambda = ±1$ The eigenvalues are $\lambda^{(1)} = -1 \qquad \lambda^{(2)} = 1$ For ``\lambda^{(1)} = -1`` we have $\begin{align*} -v_1 - i v_2 &= 0 \\ i v_1 - v_2 &= 0 \end{align*}$ $\implies v_1 = 1, \, v_2 = i$ giving the normalized eigenvector $\underline{v} = \frac{1}{\sqrt{2}} \begin{bmatrix} 1 \\ i \end{bmatrix}$ For ``\lambda^{(2)} = 1`` we have $\begin{align*} u_1 - i u_2 &= 0 \\ i u_1 - u_2 &= 0 \end{align*}$ $\implies u_1 = 1, \, u_2 = i$ giving the normalized eigenvector $\underline{u} = \frac{1}{\sqrt{2}} \begin{bmatrix} 1 \\ -i \end{bmatrix}$ We can show that the eigenvectors are orthogonal: $⟨v \vert u⟩ = v_i u_i^* = \left(\frac{1}{\sqrt{2}}\right)^2 + \left(\frac{i}{\sqrt{2}}\right)^2 = \frac{1}{\sqrt{2}} - \frac{1}{\sqrt{2}} = 0$ """ end # ╔═╡ e12e34c7-90db-4f31-8792-ce2a39acc435 let md""" ### Problem 4 Find the eigenvalues, two of which equal each other, of the real symmetric matrix $\underline{\underline{A}} = \begin{bmatrix} 13 & 4 & -2 \\ 4 & 13 & -2 \\ -2 & -2 & 10 \end{bmatrix}$ Construct the three eigenvectors by hand, not with MATHEMATICA. You will have more freedom than you would have thought. Do you see how to use this freedom to make all three eigenvectors mutually orthogonal? """ md""" ### Problem 4 We first calculate the characteristic equation $\det(\underline{\underline{A}} - \lambda \, \underline{\underline{I}}) = \begin{vmatrix} 13 - \lambda & 4 & -2 \\ 4 & 13 - \lambda & -2 \\ -2 & -2 & 10 - \lambda \end{vmatrix} = -(\lambda - 18)(\lambda - 9)^2$ So the eigenvalues are $\lambda^{(1)} = 9 \qquad \lambda^{(2)} = 18$ For ``\lambda^{(1)} = 9`` we have $\begin{align*} 4v_1 + 4v_2 - 2v_3 &= 0 \\ 4v_1 + 4v_2 - 2v_3 &= 0 \\ -2v_1 - 2v_2 + v_3 &= 0 \end{align*}$ $\implies v_1 = 1, v_2 = 0, v_3 = 2 \qquad \text{or} \qquad v_1 = -1, v_2 = 1, v_3 = 0$ which gives the normalized eigenvectors $\underline{v} = \frac{1}{\sqrt{5}} \begin{bmatrix} 1 \\ 0 \\ 2 \end{bmatrix} \qquad \underline{u} = \frac{1}{\sqrt{2}} \begin{bmatrix} -1 \\ 1 \\ 0 \end{bmatrix}$ For ``\lambda^{(2)} = 18`` we have $\begin{align*} -5w_1 + 4w_2 - 2w_3 &= 0 \\ 4w_1 - 5w_2 - 2w_3 &= 0 \\ -2w_1 - 2w_2 - 8w_3 &= 0 \end{align*}$ $\implies w_1 = -2, w_2 = -2, w_3 = 1$ which gives the normalized eigenvector $\underline{w} = \frac{1}{3} \begin{bmatrix} -2 \\ -2 \\ 1 \end{bmatrix}$ """ end # ╔═╡ 1aaa8364-b72e-4128-b26a-82d4d4601634 let md""" ### Problem 5 Find the eigenfrequencies and eigenmodes for the mechanical system `|~~~k~~~[ m ]~~~k~~~[ m ]~~~k~~~[ m ]~~~k~~~|` Make a plot that shows the motions of each of the three masses, for the three sets of initial conditions where the masses start at rest with position given by each of the three eigenvectors. Briefly describe the motions of the three masses, for each of the eigenmodes. """ md""" ### Problem 5 The coupled differential equations can be written as $\begin{align*} mẍ_1 &= -kx_1 + k(x_2 - x_1) = -2kx_1 + kx_2 \\ mẍ_2 &= -k(x_2 - x_1) + k(x_3 - x_2) = kx_1 - 2kx_2 + kx_3 \\ mẍ_3 &= -k(x_3 - x_2) + k(-x_3) = kx_2 - 2kx_3 \end{align*}$ We can rewrite them using ``\omega_0 = \sqrt{k / m}``, $\begin{align*} ẍ_1 &= -2{\omega_0}^2 x_1 + {\omega_0}^2 x_2 \\ ẍ_2 &= {\omega_0}^2 x_1 - 2{\omega_0}^2 x_2 + {\omega_0}^2 x_3 \\ ẍ_3 &= {\omega_0}^2 x_2 - 2{\omega_0}^2 x_3 \end{align*}$ In terms of vectors and matrices, we can write this as $\underline{ẍ}(t) = -{\omega_0}^2 \underline{\underline{Ω}} \,\underline{x}(t)$ where $\underline{x} = \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} \qquad Ω = \begin{bmatrix} -2 & 1 & 0 \\ 1 & -2 & 1 \\ 0 & 1 & -2 \end{bmatrix}$ with the help of MATHEMATICA, we find the eigenfrequencies to be $\omega_1 = \sqrt{(2 - \sqrt{2}) \omega_0}, \, \omega_2 = \sqrt{2\omega_0}, \, \omega_3 = \sqrt{(2 + \sqrt{2}) \omega_0}$ and the normalized eigenmodes to be $\underline{a}^{(1)} = \frac{1}{2} \begin{bmatrix} 1 \\ -\sqrt{2} \\ 1 \end{bmatrix} \qquad \underline{a}^{(2)} = \frac{1}{\sqrt{2}} \begin{bmatrix} -1 \\ 0 \\ 1 \end{bmatrix} \qquad \underline{a}^{(3)} = \frac{1}{2} \begin{bmatrix} 1 \\ \sqrt{2} \\ 1 \end{bmatrix}$ """ end # ╔═╡ 7387c353-51b7-402b-a500-adec0a34b059 let Ω = [-2 1 0; 1 -2 1; 0 1 -2] eigen(Ω) end # ╔═╡ b40838d0-8391-4f1e-a3fc-a4be028b7bc7 (2 - sqrt(2)) # ╔═╡ ec26fefb-18d1-4ac7-b0c1-0d3c71ae1305 begin ω₁ = sqrt(2 - sqrt(2)) ω₂ = sqrt(2) ω₃ = sqrt(2 + sqrt(2)) a1 = (1/2) .* [1; -sqrt(2); 1] a2 = (1/sqrt(2)) .* [-1; 0; 1] a3 = (1/2) .* [1; sqrt(2); 1] s1(t, A, ϕ) = A .* a1 .* cos(ω₁ * t + ϕ) s2(t, A, ϕ) = A .* a2 .* cos(ω₂ * t + ϕ) s3(t, A, ϕ) = A .* a3 .* cos(ω₃ * t + ϕ) end; # ╔═╡ 355540ec-db9b-4ce5-a5d2-0c10497da934 let x1(t) = s1(t, 1, 0)[1] x2(t) = s1(t, 1, 0)[2] x3(t) = s1(t, 1, 0)[3] plot(x1, label="x₁") plot!(x2, label="x₂") plot!(x3, label="x₃") title!("Eigenmode 1") end # ╔═╡ de44b0c7-eee4-49ed-9264-40a5339e7d6c md""" Eigenmode 1: x1 and x3 oscillate with each other, opposite to x2 """ # ╔═╡ 6f073f29-39b5-496e-b644-45051c81ab54 let x1(t) = s2(t, 1, 0)[1] x2(t) = s2(t, 1, 0)[2] x3(t) = s2(t, 1, 0)[3] plot(x1, label="x₁") plot!(x2, label="x₂") plot!(x3, label="x₃") title!("Eigenmode 2") end # ╔═╡ 5500a012-48fd-4227-a70f-515425c5122a md""" Eigenmode 2: x1 and x3 oscillate against each other while x2 remains in place """ # ╔═╡ e0a8d8ea-e5bd-48ca-b44f-4b6625181a17 let x1(t) = s3(t, 1, 0)[1] x2(t) = s3(t, 1, 0)[2] x3(t) = s3(t, 1, 0)[3] plot(x1, label="x₁") plot!(x2, label="x₂") plot!(x3, label="x₃") title!("Eigenmode 3") end # ╔═╡ cc32c2a5-d377-4a77-8477-b3acc63cca3b md""" Eigenmode 3: x1, x2, and x3 all oscillate with each other, however x2 oscillation is amplified """ # ╔═╡ 00000000-0000-0000-0000-000000000001 PLUTO_PROJECT_TOML_CONTENTS = """ [deps] LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd" 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git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" version = "1.2.12+1" [[deps.Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.2+0" [[deps.libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" version = "5.0.1+0" [[deps.libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" [[deps.libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" [[deps.libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+1" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" version = "1.41.0+1" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" version = "16.2.1+1" [[deps.x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" [[deps.x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" [[deps.xkbcommon_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6" uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" version = "0.9.1+5" """ # ╔═╡ Cell order: # ╟─1eb34f10-b4b3-11ec-19f5-c7f26108d374 # ╟─6c4b3bf7-26e9-4d6f-9305-65fbc851818f # ╟─2743185f-b0d7-4195-8e8e-a1bfcf132934 # ╠═982fab25-1414-4789-bc16-343ab29b4514 # ╟─7e53c83c-ead7-484f-8762-4a0956ba7210 # ╟─c8977146-1409-4eb1-bede-13436fd7214b # ╟─201fb421-39b1-47db-8dec-609706461dca # ╟─e12e34c7-90db-4f31-8792-ce2a39acc435 # ╟─1aaa8364-b72e-4128-b26a-82d4d4601634 # ╠═7387c353-51b7-402b-a500-adec0a34b059 # ╠═b40838d0-8391-4f1e-a3fc-a4be028b7bc7 # ╟─ec26fefb-18d1-4ac7-b0c1-0d3c71ae1305 # ╟─355540ec-db9b-4ce5-a5d2-0c10497da934 # ╟─de44b0c7-eee4-49ed-9264-40a5339e7d6c # ╟─6f073f29-39b5-496e-b644-45051c81ab54 # ╟─5500a012-48fd-4227-a70f-515425c5122a # ╟─e0a8d8ea-e5bd-48ca-b44f-4b6625181a17 # ╟─cc32c2a5-d377-4a77-8477-b3acc63cca3b # ╟─00000000-0000-0000-0000-000000000001 # ╟─00000000-0000-0000-0000-000000000002
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2154, 2414, 5333, 67, 6888, 198, 2, 2343, 243, 253, 7280, 68, 1065, 68, 2682, 66, 22, 12, 3829, 9945, 12, 19, 69, 3132, 12, 23, 48156, 12, 344, 17, 64, 2670, 4134, 40064, 198, 2, 2343, 243, 253, 7280, 16, 46071, 5999, 2414, 12, 65, 4761, 68, 12, 19, 12762, 12, 65, 2075, 64, 12, 6469, 67, 19, 67, 3510, 27037, 2682, 198, 2, 2343, 243, 254, 28670, 22, 32220, 66, 33319, 12, 4349, 65, 22, 12, 32531, 65, 12, 64, 4059, 12, 671, 66, 15, 64, 2682, 65, 46712, 198, 2, 2343, 243, 254, 28670, 65, 26200, 2548, 67, 15, 12, 23, 37710, 12, 19, 69, 16, 68, 12, 64, 18, 16072, 12, 64, 19, 1350, 46957, 65, 22, 15630, 22, 198, 2, 2343, 243, 253, 7280, 721, 2075, 69, 891, 65, 12, 1507, 67, 16, 12, 19, 330, 22, 12, 65, 15, 66, 16, 12, 15, 67, 18, 66, 4869, 3609, 12952, 20, 198, 2, 2343, 243, 253, 7280, 2327, 2816, 1821, 721, 12, 9945, 24, 65, 12, 19, 344, 20, 12, 64, 20, 67, 17, 12, 15, 66, 940, 38073, 6814, 24, 2682, 198, 2, 2343, 243, 253, 7280, 2934, 2598, 65, 15, 66, 22, 12, 1453, 68, 19, 12, 2920, 276, 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1.884621
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<reponame>JuliaPackageMirrors/CUDNN.jl # Julia wrapper for header: /mnt/ai/home/dyuret/src/cudnn/4.0.4/include/cudnn.h # Automatically generated using Clang.jl wrap_c, version 0.0.0 function cudnnGetVersion() ccall((:cudnnGetVersion,libcudnn),Cint,()) end function cudnnGetErrorString(status) ccall((:cudnnGetErrorString,libcudnn),Ptr{UInt8},(cudnnStatus_t,),status) end function cudnnCreate(handle) ccall((:cudnnCreate,libcudnn),cudnnStatus_t,(Ptr{cudnnHandle_t},),handle) end function cudnnDestroy(handle) ccall((:cudnnDestroy,libcudnn),cudnnStatus_t,(cudnnHandle_t,),handle) end function cudnnSetStream(handle,streamId) ccall((:cudnnSetStream,libcudnn),cudnnStatus_t,(cudnnHandle_t,Cint),handle,streamId) end function cudnnGetStream(handle,streamId) ccall((:cudnnGetStream,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Cint}),handle,streamId) end function cudnnCreateTensorDescriptor(tensorDesc) ccall((:cudnnCreateTensorDescriptor,libcudnn),cudnnStatus_t,(Ptr{cudnnTensorDescriptor_t},),tensorDesc) end function cudnnSetTensor4dDescriptor(tensorDesc,format,dataType,n,c,h,w) ccall((:cudnnSetTensor4dDescriptor,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,cudnnTensorFormat_t,cudnnDataType_t,Cint,Cint,Cint,Cint),tensorDesc,format,dataType,n,c,h,w) end function cudnnSetTensor4dDescriptorEx(tensorDesc,dataType,n,c,h,w,nStride,cStride,hStride,wStride) ccall((:cudnnSetTensor4dDescriptorEx,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,cudnnDataType_t,Cint,Cint,Cint,Cint,Cint,Cint,Cint,Cint),tensorDesc,dataType,n,c,h,w,nStride,cStride,hStride,wStride) end function cudnnGetTensor4dDescriptor(tensorDesc,dataType,n,c,h,w,nStride,cStride,hStride,wStride) ccall((:cudnnGetTensor4dDescriptor,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,Ptr{cudnnDataType_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),tensorDesc,dataType,n,c,h,w,nStride,cStride,hStride,wStride) end function cudnnSetTensorNdDescriptor(tensorDesc,dataType,nbDims,dimA,strideA) ccall((:cudnnSetTensorNdDescriptor,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,cudnnDataType_t,Cint,Ptr{Cint},Ptr{Cint}),tensorDesc,dataType,nbDims,dimA,strideA) end function cudnnGetTensorNdDescriptor(tensorDesc,nbDimsRequested,dataType,nbDims,dimA,strideA) ccall((:cudnnGetTensorNdDescriptor,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,Cint,Ptr{cudnnDataType_t},Ptr{Cint},Ptr{Cint},Ptr{Cint}),tensorDesc,nbDimsRequested,dataType,nbDims,dimA,strideA) end function cudnnDestroyTensorDescriptor(tensorDesc) ccall((:cudnnDestroyTensorDescriptor,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,),tensorDesc) end function cudnnTransformTensor(handle,alpha,xDesc,x,beta,yDesc,y) ccall((:cudnnTransformTensor,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,beta,yDesc,y) end function cudnnAddTensor(handle,alpha,bDesc,b,beta,yDesc,y) ccall((:cudnnAddTensor,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,bDesc,b,beta,yDesc,y) end function cudnnAddTensor_v3(handle,alpha,bDesc,b,beta,yDesc,y) ccall((:cudnnAddTensor_v3,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,bDesc,b,beta,yDesc,y) end function cudnnSetTensor(handle,yDesc,y,valuePtr) ccall((:cudnnSetTensor,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void}),handle,yDesc,y,valuePtr) end function cudnnScaleTensor(handle,yDesc,y,alpha) ccall((:cudnnScaleTensor,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void}),handle,yDesc,y,alpha) end function cudnnCreateFilterDescriptor(filterDesc) ccall((:cudnnCreateFilterDescriptor,libcudnn),cudnnStatus_t,(Ptr{cudnnFilterDescriptor_t},),filterDesc) end function cudnnSetFilter4dDescriptor(filterDesc,dataType,k,c,h,w) ccall((:cudnnSetFilter4dDescriptor,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,cudnnDataType_t,Cint,Cint,Cint,Cint),filterDesc,dataType,k,c,h,w) end function cudnnSetFilter4dDescriptor_v4(filterDesc,dataType,format,k,c,h,w) ccall((:cudnnSetFilter4dDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,cudnnDataType_t,cudnnTensorFormat_t,Cint,Cint,Cint,Cint),filterDesc,dataType,format,k,c,h,w) end function cudnnGetFilter4dDescriptor(filterDesc,dataType,k,c,h,w) ccall((:cudnnGetFilter4dDescriptor,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,Ptr{cudnnDataType_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),filterDesc,dataType,k,c,h,w) end function cudnnGetFilter4dDescriptor_v4(filterDesc,dataType,format,k,c,h,w) ccall((:cudnnGetFilter4dDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,Ptr{cudnnDataType_t},Ptr{cudnnTensorFormat_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),filterDesc,dataType,format,k,c,h,w) end function cudnnSetFilterNdDescriptor(filterDesc,dataType,nbDims,filterDimA) ccall((:cudnnSetFilterNdDescriptor,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,cudnnDataType_t,Cint,Ptr{Cint}),filterDesc,dataType,nbDims,filterDimA) end function cudnnSetFilterNdDescriptor_v4(filterDesc,dataType,format,nbDims,filterDimA) ccall((:cudnnSetFilterNdDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,cudnnDataType_t,cudnnTensorFormat_t,Cint,Ptr{Cint}),filterDesc,dataType,format,nbDims,filterDimA) end function cudnnGetFilterNdDescriptor(filterDesc,nbDimsRequested,dataType,nbDims,filterDimA) ccall((:cudnnGetFilterNdDescriptor,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,Cint,Ptr{cudnnDataType_t},Ptr{Cint},Ptr{Cint}),filterDesc,nbDimsRequested,dataType,nbDims,filterDimA) end function cudnnGetFilterNdDescriptor_v4(filterDesc,nbDimsRequested,dataType,format,nbDims,filterDimA) ccall((:cudnnGetFilterNdDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,Cint,Ptr{cudnnDataType_t},Ptr{cudnnTensorFormat_t},Ptr{Cint},Ptr{Cint}),filterDesc,nbDimsRequested,dataType,format,nbDims,filterDimA) end function cudnnDestroyFilterDescriptor(filterDesc) ccall((:cudnnDestroyFilterDescriptor,libcudnn),cudnnStatus_t,(cudnnFilterDescriptor_t,),filterDesc) end function cudnnCreateConvolutionDescriptor(convDesc) ccall((:cudnnCreateConvolutionDescriptor,libcudnn),cudnnStatus_t,(Ptr{cudnnConvolutionDescriptor_t},),convDesc) end function cudnnSetConvolution2dDescriptor(convDesc,pad_h,pad_w,u,v,upscalex,upscaley,mode) ccall((:cudnnSetConvolution2dDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Cint,Cint,Cint,Cint,Cint,cudnnConvolutionMode_t),convDesc,pad_h,pad_w,u,v,upscalex,upscaley,mode) end function cudnnGetConvolution2dDescriptor(convDesc,pad_h,pad_w,u,v,upscalex,upscaley,mode) ccall((:cudnnGetConvolution2dDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{cudnnConvolutionMode_t}),convDesc,pad_h,pad_w,u,v,upscalex,upscaley,mode) end function cudnnGetConvolution2dForwardOutputDim(convDesc,inputTensorDesc,filterDesc,n,c,h,w) ccall((:cudnnGetConvolution2dForwardOutputDim,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,cudnnFilterDescriptor_t,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),convDesc,inputTensorDesc,filterDesc,n,c,h,w) end function cudnnSetConvolutionNdDescriptor(convDesc,arrayLength,padA,filterStrideA,upscaleA,mode,dataType) ccall((:cudnnSetConvolutionNdDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},cudnnConvolutionMode_t,cudnnDataType_t),convDesc,arrayLength,padA,filterStrideA,upscaleA,mode,dataType) end function cudnnGetConvolutionNdDescriptor(convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode,dataType) ccall((:cudnnGetConvolutionNdDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{cudnnConvolutionMode_t},Ptr{cudnnDataType_t}),convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode,dataType) end function cudnnSetConvolutionNdDescriptor_v3(convDesc,arrayLength,padA,filterStrideA,upscaleA,mode,dataType) ccall((:cudnnSetConvolutionNdDescriptor_v3,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},cudnnConvolutionMode_t,cudnnDataType_t),convDesc,arrayLength,padA,filterStrideA,upscaleA,mode,dataType) end function cudnnGetConvolutionNdDescriptor_v3(convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode,dataType) ccall((:cudnnGetConvolutionNdDescriptor_v3,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{cudnnConvolutionMode_t},Ptr{cudnnDataType_t}),convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode,dataType) end function cudnnGetConvolutionNdForwardOutputDim(convDesc,inputTensorDesc,filterDesc,nbDims,tensorOuputDimA) ccall((:cudnnGetConvolutionNdForwardOutputDim,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,cudnnFilterDescriptor_t,Cint,Ptr{Cint}),convDesc,inputTensorDesc,filterDesc,nbDims,tensorOuputDimA) end function cudnnDestroyConvolutionDescriptor(convDesc) ccall((:cudnnDestroyConvolutionDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,),convDesc) end function cudnnFindConvolutionForwardAlgorithm(handle,xDesc,wDesc,convDesc,yDesc,requestedAlgoCount,returnedAlgoCount,perfResults) ccall((:cudnnFindConvolutionForwardAlgorithm,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,cudnnFilterDescriptor_t,cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,Cint,Ptr{Cint},Ptr{cudnnConvolutionFwdAlgoPerf_t}),handle,xDesc,wDesc,convDesc,yDesc,requestedAlgoCount,returnedAlgoCount,perfResults) end function cudnnGetConvolutionForwardAlgorithm(handle,xDesc,wDesc,convDesc,yDesc,preference,memoryLimitInBytes,algo) ccall((:cudnnGetConvolutionForwardAlgorithm,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,cudnnFilterDescriptor_t,cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionFwdPreference_t,Cint,Ptr{cudnnConvolutionFwdAlgo_t}),handle,xDesc,wDesc,convDesc,yDesc,preference,memoryLimitInBytes,algo) end function cudnnGetConvolutionForwardWorkspaceSize(handle,xDesc,wDesc,convDesc,yDesc,algo,sizeInBytes) ccall((:cudnnGetConvolutionForwardWorkspaceSize,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,cudnnFilterDescriptor_t,cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionFwdAlgo_t,Ptr{Cint}),handle,xDesc,wDesc,convDesc,yDesc,algo,sizeInBytes) end function cudnnConvolutionForward(handle,alpha,xDesc,x,wDesc,w,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,yDesc,y) ccall((:cudnnConvolutionForward,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,cudnnConvolutionFwdAlgo_t,Ptr{Void},Cint,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,wDesc,w,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,yDesc,y) end function cudnnConvolutionBackwardBias(handle,alpha,dyDesc,dy,beta,dbDesc,db) ccall((:cudnnConvolutionBackwardBias,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,dyDesc,dy,beta,dbDesc,db) end function cudnnFindConvolutionBackwardFilterAlgorithm(handle,xDesc,dyDesc,convDesc,dwDesc,requestedAlgoCount,returnedAlgoCount,perfResults) ccall((:cudnnFindConvolutionBackwardFilterAlgorithm,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionDescriptor_t,cudnnFilterDescriptor_t,Cint,Ptr{Cint},Ptr{cudnnConvolutionBwdFilterAlgoPerf_t}),handle,xDesc,dyDesc,convDesc,dwDesc,requestedAlgoCount,returnedAlgoCount,perfResults) end function cudnnGetConvolutionBackwardFilterAlgorithm(handle,xDesc,dyDesc,convDesc,dwDesc,preference,memoryLimitInBytes,algo) ccall((:cudnnGetConvolutionBackwardFilterAlgorithm,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionDescriptor_t,cudnnFilterDescriptor_t,cudnnConvolutionBwdFilterPreference_t,Cint,Ptr{cudnnConvolutionBwdFilterAlgo_t}),handle,xDesc,dyDesc,convDesc,dwDesc,preference,memoryLimitInBytes,algo) end function cudnnGetConvolutionBackwardFilterWorkspaceSize(handle,xDesc,dyDesc,convDesc,gradDesc,algo,sizeInBytes) ccall((:cudnnGetConvolutionBackwardFilterWorkspaceSize,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionDescriptor_t,cudnnFilterDescriptor_t,cudnnConvolutionBwdFilterAlgo_t,Ptr{Cint}),handle,xDesc,dyDesc,convDesc,gradDesc,algo,sizeInBytes) end function cudnnConvolutionBackwardFilter(handle,alpha,xDesc,x,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dwDesc,dw) ccall((:cudnnConvolutionBackwardFilter,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,cudnnConvolutionBwdFilterAlgo_t,Ptr{Void},Cint,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dwDesc,dw) end function cudnnConvolutionBackwardFilter_v3(handle,alpha,xDesc,x,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dwDesc,dw) ccall((:cudnnConvolutionBackwardFilter_v3,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,cudnnConvolutionBwdFilterAlgo_t,Ptr{Void},Cint,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dwDesc,dw) end function cudnnFindConvolutionBackwardDataAlgorithm(handle,wDesc,dyDesc,convDesc,dxDesc,requestedAlgoCount,returnedAlgoCount,perfResults) ccall((:cudnnFindConvolutionBackwardDataAlgorithm,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnFilterDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,Cint,Ptr{Cint},Ptr{cudnnConvolutionBwdDataAlgoPerf_t}),handle,wDesc,dyDesc,convDesc,dxDesc,requestedAlgoCount,returnedAlgoCount,perfResults) end function cudnnGetConvolutionBackwardDataAlgorithm(handle,wDesc,dyDesc,convDesc,dxDesc,preference,memoryLimitInBytes,algo) ccall((:cudnnGetConvolutionBackwardDataAlgorithm,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnFilterDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionBwdDataPreference_t,Cint,Ptr{cudnnConvolutionBwdDataAlgo_t}),handle,wDesc,dyDesc,convDesc,dxDesc,preference,memoryLimitInBytes,algo) end function cudnnGetConvolutionBackwardDataWorkspaceSize(handle,wDesc,dyDesc,convDesc,dxDesc,algo,sizeInBytes) ccall((:cudnnGetConvolutionBackwardDataWorkspaceSize,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnFilterDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionDescriptor_t,cudnnTensorDescriptor_t,cudnnConvolutionBwdDataAlgo_t,Ptr{Cint}),handle,wDesc,dyDesc,convDesc,dxDesc,algo,sizeInBytes) end function cudnnConvolutionBackwardData(handle,alpha,wDesc,w,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dxDesc,dx) ccall((:cudnnConvolutionBackwardData,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,cudnnConvolutionBwdDataAlgo_t,Ptr{Void},Cint,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,wDesc,w,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dxDesc,dx) end function cudnnConvolutionBackwardData_v3(handle,alpha,wDesc,w,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dxDesc,dx) ccall((:cudnnConvolutionBackwardData_v3,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,cudnnConvolutionBwdDataAlgo_t,Ptr{Void},Cint,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,wDesc,w,dyDesc,dy,convDesc,algo,workSpace,workSpaceSizeInBytes,beta,dxDesc,dx) end function cudnnIm2Col(handle,xDesc,x,wDesc,convDesc,colBuffer) ccall((:cudnnIm2Col,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnTensorDescriptor_t,Ptr{Void},cudnnFilterDescriptor_t,cudnnConvolutionDescriptor_t,Ptr{Void}),handle,xDesc,x,wDesc,convDesc,colBuffer) end function cudnnSoftmaxForward(handle,algo,mode,alpha,xDesc,x,beta,yDesc,y) ccall((:cudnnSoftmaxForward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnSoftmaxAlgorithm_t,cudnnSoftmaxMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,algo,mode,alpha,xDesc,x,beta,yDesc,y) end function cudnnSoftmaxBackward(handle,algo,mode,alpha,yDesc,y,dyDesc,dy,beta,dxDesc,dx) ccall((:cudnnSoftmaxBackward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnSoftmaxAlgorithm_t,cudnnSoftmaxMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,algo,mode,alpha,yDesc,y,dyDesc,dy,beta,dxDesc,dx) end function cudnnCreatePoolingDescriptor(poolingDesc) ccall((:cudnnCreatePoolingDescriptor,libcudnn),cudnnStatus_t,(Ptr{cudnnPoolingDescriptor_t},),poolingDesc) end function cudnnSetPooling2dDescriptor(poolingDesc,mode,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) ccall((:cudnnSetPooling2dDescriptor,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,cudnnPoolingMode_t,Cint,Cint,Cint,Cint,Cint,Cint),poolingDesc,mode,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) end function cudnnSetPooling2dDescriptor_v4(poolingDesc,mode,maxpoolingNanOpt,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) ccall((:cudnnSetPooling2dDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,cudnnPoolingMode_t,cudnnNanPropagation_t,Cint,Cint,Cint,Cint,Cint,Cint),poolingDesc,mode,maxpoolingNanOpt,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) end function cudnnGetPooling2dDescriptor(poolingDesc,mode,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) ccall((:cudnnGetPooling2dDescriptor,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,Ptr{cudnnPoolingMode_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,mode,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) end function cudnnGetPooling2dDescriptor_v4(poolingDesc,mode,maxpoolingNanOpt,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) ccall((:cudnnGetPooling2dDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,Ptr{cudnnPoolingMode_t},Ptr{cudnnNanPropagation_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,mode,maxpoolingNanOpt,windowHeight,windowWidth,verticalPadding,horizontalPadding,verticalStride,horizontalStride) end function cudnnSetPoolingNdDescriptor(poolingDesc,mode,nbDims,windowDimA,paddingA,strideA) ccall((:cudnnSetPoolingNdDescriptor,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,cudnnPoolingMode_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,mode,nbDims,windowDimA,paddingA,strideA) end function cudnnSetPoolingNdDescriptor_v4(poolingDesc,mode,maxpoolingNanOpt,nbDims,windowDimA,paddingA,strideA) ccall((:cudnnSetPoolingNdDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,cudnnPoolingMode_t,cudnnNanPropagation_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,mode,maxpoolingNanOpt,nbDims,windowDimA,paddingA,strideA) end function cudnnGetPoolingNdDescriptor(poolingDesc,nbDimsRequested,mode,nbDims,windowDimA,paddingA,strideA) ccall((:cudnnGetPoolingNdDescriptor,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,Cint,Ptr{cudnnPoolingMode_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,nbDimsRequested,mode,nbDims,windowDimA,paddingA,strideA) end function cudnnGetPoolingNdDescriptor_v4(poolingDesc,nbDimsRequested,mode,maxpoolingNanOpt,nbDims,windowDimA,paddingA,strideA) ccall((:cudnnGetPoolingNdDescriptor_v4,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,Cint,Ptr{cudnnPoolingMode_t},Ptr{cudnnNanPropagation_t},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,nbDimsRequested,mode,maxpoolingNanOpt,nbDims,windowDimA,paddingA,strideA) end function cudnnGetPoolingNdForwardOutputDim(poolingDesc,inputTensorDesc,nbDims,outputTensorDimA) ccall((:cudnnGetPoolingNdForwardOutputDim,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,cudnnTensorDescriptor_t,Cint,Ptr{Cint}),poolingDesc,inputTensorDesc,nbDims,outputTensorDimA) end function cudnnGetPooling2dForwardOutputDim(poolingDesc,inputTensorDesc,n,c,h,w) ccall((:cudnnGetPooling2dForwardOutputDim,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,cudnnTensorDescriptor_t,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint}),poolingDesc,inputTensorDesc,n,c,h,w) end function cudnnDestroyPoolingDescriptor(poolingDesc) ccall((:cudnnDestroyPoolingDescriptor,libcudnn),cudnnStatus_t,(cudnnPoolingDescriptor_t,),poolingDesc) end function cudnnPoolingForward(handle,poolingDesc,alpha,xDesc,x,beta,yDesc,y) ccall((:cudnnPoolingForward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnPoolingDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,poolingDesc,alpha,xDesc,x,beta,yDesc,y) end function cudnnPoolingBackward(handle,poolingDesc,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) ccall((:cudnnPoolingBackward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnPoolingDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,poolingDesc,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) end function cudnnCreateActivationDescriptor(activationDesc) ccall((:cudnnCreateActivationDescriptor,libcudnn),cudnnStatus_t,(Ptr{cudnnActivationDescriptor_t},),activationDesc) end function cudnnSetActivationDescriptor(activationDesc,mode,reluNanOpt,reluCeiling) ccall((:cudnnSetActivationDescriptor,libcudnn),cudnnStatus_t,(cudnnActivationDescriptor_t,cudnnActivationMode_t,cudnnNanPropagation_t,Cdouble),activationDesc,mode,reluNanOpt,reluCeiling) end function cudnnGetActivationDescriptor(activationDesc,mode,reluNanOpt,reluCeiling) ccall((:cudnnGetActivationDescriptor,libcudnn),cudnnStatus_t,(cudnnActivationDescriptor_t,Ptr{cudnnActivationMode_t},Ptr{cudnnNanPropagation_t},Ptr{Cdouble}),activationDesc,mode,reluNanOpt,reluCeiling) end function cudnnDestroyActivationDescriptor(activationDesc) ccall((:cudnnDestroyActivationDescriptor,libcudnn),cudnnStatus_t,(cudnnActivationDescriptor_t,),activationDesc) end function cudnnActivationForward(handle,mode,alpha,xDesc,x,beta,yDesc,y) ccall((:cudnnActivationForward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnActivationMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,mode,alpha,xDesc,x,beta,yDesc,y) end function cudnnActivationForward_v4(handle,activationDesc,alpha,xDesc,x,beta,yDesc,y) ccall((:cudnnActivationForward_v4,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnActivationDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,activationDesc,alpha,xDesc,x,beta,yDesc,y) end function cudnnActivationBackward(handle,mode,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) ccall((:cudnnActivationBackward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnActivationMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,mode,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) end function cudnnActivationBackward_v4(handle,activationDesc,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) ccall((:cudnnActivationBackward_v4,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnActivationDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,activationDesc,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) end function cudnnCreateLRNDescriptor(normDesc) ccall((:cudnnCreateLRNDescriptor,libcudnn),cudnnStatus_t,(Ptr{cudnnLRNDescriptor_t},),normDesc) end function cudnnSetLRNDescriptor(normDesc,lrnN,lrnAlpha,lrnBeta,lrnK) ccall((:cudnnSetLRNDescriptor,libcudnn),cudnnStatus_t,(cudnnLRNDescriptor_t,UInt32,Cdouble,Cdouble,Cdouble),normDesc,lrnN,lrnAlpha,lrnBeta,lrnK) end function cudnnGetLRNDescriptor(normDesc,lrnN,lrnAlpha,lrnBeta,lrnK) ccall((:cudnnGetLRNDescriptor,libcudnn),cudnnStatus_t,(cudnnLRNDescriptor_t,Ptr{UInt32},Ptr{Cdouble},Ptr{Cdouble},Ptr{Cdouble}),normDesc,lrnN,lrnAlpha,lrnBeta,lrnK) end function cudnnDestroyLRNDescriptor(lrnDesc) ccall((:cudnnDestroyLRNDescriptor,libcudnn),cudnnStatus_t,(cudnnLRNDescriptor_t,),lrnDesc) end function cudnnLRNCrossChannelForward(handle,normDesc,lrnMode,alpha,xDesc,x,beta,yDesc,y) ccall((:cudnnLRNCrossChannelForward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnLRNDescriptor_t,cudnnLRNMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,normDesc,lrnMode,alpha,xDesc,x,beta,yDesc,y) end function cudnnLRNCrossChannelBackward(handle,normDesc,lrnMode,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) ccall((:cudnnLRNCrossChannelBackward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnLRNDescriptor_t,cudnnLRNMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,normDesc,lrnMode,alpha,yDesc,y,dyDesc,dy,xDesc,x,beta,dxDesc,dx) end function cudnnDivisiveNormalizationForward(handle,normDesc,mode,alpha,xDesc,x,means,temp,temp2,beta,yDesc,y) ccall((:cudnnDivisiveNormalizationForward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnLRNDescriptor_t,cudnnDivNormMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,normDesc,mode,alpha,xDesc,x,means,temp,temp2,beta,yDesc,y) end function cudnnDivisiveNormalizationBackward(handle,normDesc,mode,alpha,xDesc,x,means,dy,temp,temp2,beta,dXdMeansDesc,dx,dMeans) ccall((:cudnnDivisiveNormalizationBackward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnLRNDescriptor_t,cudnnDivNormMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void}),handle,normDesc,mode,alpha,xDesc,x,means,dy,temp,temp2,beta,dXdMeansDesc,dx,dMeans) end function cudnnDeriveBNTensorDescriptor(derivedBnDesc,xDesc,mode) ccall((:cudnnDeriveBNTensorDescriptor,libcudnn),cudnnStatus_t,(cudnnTensorDescriptor_t,cudnnTensorDescriptor_t,cudnnBatchNormMode_t),derivedBnDesc,xDesc,mode) end function cudnnBatchNormalizationForwardTraining(handle,mode,alpha,beta,xDesc,x,yDesc,y,bnScaleBiasMeanVarDesc,bnScale,bnBias,exponentialAverageFactor,resultRunningMean,resultRunningInvVariance,epsilon,resultSaveMean,resultSaveInvVariance) ccall((:cudnnBatchNormalizationForwardTraining,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnBatchNormMode_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},Cdouble,Ptr{Void},Ptr{Void},Cdouble,Ptr{Void},Ptr{Void}),handle,mode,alpha,beta,xDesc,x,yDesc,y,bnScaleBiasMeanVarDesc,bnScale,bnBias,exponentialAverageFactor,resultRunningMean,resultRunningInvVariance,epsilon,resultSaveMean,resultSaveInvVariance) end function cudnnBatchNormalizationForwardInference(handle,mode,alpha,beta,xDesc,x,yDesc,y,bnScaleBiasMeanVarDesc,bnScale,bnBias,estimatedMean,estimatedInvVariance,epsilon) ccall((:cudnnBatchNormalizationForwardInference,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnBatchNormMode_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},Ptr{Void},Ptr{Void},Cdouble),handle,mode,alpha,beta,xDesc,x,yDesc,y,bnScaleBiasMeanVarDesc,bnScale,bnBias,estimatedMean,estimatedInvVariance,epsilon) end function cudnnBatchNormalizationBackward(handle,mode,alpha,beta,xDesc,x,dyDesc,dy,dxDesc,dx,dBnScaleBiasDesc,bnScale,dBnScaleResult,dBnBiasResult,epsilon,savedMean,savedInvVariance) ccall((:cudnnBatchNormalizationBackward,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnBatchNormMode_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},Ptr{Void},Cdouble,Ptr{Void},Ptr{Void}),handle,mode,alpha,beta,xDesc,x,dyDesc,dy,dxDesc,dx,dBnScaleBiasDesc,bnScale,dBnScaleResult,dBnBiasResult,epsilon,savedMean,savedInvVariance) end function cudnnSetConvolutionNdDescriptor_v2(convDesc,arrayLength,padA,filterStrideA,upscaleA,mode) ccall((:cudnnSetConvolutionNdDescriptor_v2,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},cudnnConvolutionMode_t),convDesc,arrayLength,padA,filterStrideA,upscaleA,mode) end function cudnnGetConvolutionNdDescriptor_v2(convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode) ccall((:cudnnGetConvolutionNdDescriptor_v2,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{cudnnConvolutionMode_t}),convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode) end function cudnnAddTensor_v2(handle,mode,alpha,bDesc,b,beta,yDesc,y) ccall((:cudnnAddTensor_v2,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnAddMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,mode,alpha,bDesc,b,beta,yDesc,y) end function cudnnConvolutionBackwardFilter_v2(handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) ccall((:cudnnConvolutionBackwardFilter_v2,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) end function cudnnConvolutionBackwardData_v2(handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) ccall((:cudnnConvolutionBackwardData_v2,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) end ### To make things work on v2: function cudnnSetConvolutionNdDescriptor(convDesc,arrayLength,padA,filterStrideA,upscaleA,mode) ccall((:cudnnSetConvolutionNdDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},cudnnConvolutionMode_t),convDesc,arrayLength,padA,filterStrideA,upscaleA,mode) end function cudnnGetConvolutionNdDescriptor(convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode) ccall((:cudnnGetConvolutionNdDescriptor,libcudnn),cudnnStatus_t,(cudnnConvolutionDescriptor_t,Cint,Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{Cint},Ptr{cudnnConvolutionMode_t}),convDesc,arrayLengthRequested,arrayLength,padA,strideA,upscaleA,mode) end function cudnnAddTensor(handle,mode,alpha,bDesc,b,beta,yDesc,y) ccall((:cudnnAddTensor,libcudnn),cudnnStatus_t,(cudnnHandle_t,cudnnAddMode_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,mode,alpha,bDesc,b,beta,yDesc,y) end function cudnnConvolutionBackwardFilter(handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) ccall((:cudnnConvolutionBackwardFilter,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) end function cudnnConvolutionBackwardData(handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) ccall((:cudnnConvolutionBackwardData,libcudnn),cudnnStatus_t,(cudnnHandle_t,Ptr{Void},cudnnFilterDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void},cudnnConvolutionDescriptor_t,Ptr{Void},cudnnTensorDescriptor_t,Ptr{Void}),handle,alpha,xDesc,x,dyDesc,dy,convDesc,beta,dxDesc,dx) end
[ 27, 7856, 261, 480, 29, 16980, 544, 27813, 27453, 5965, 14, 34, 8322, 6144, 13, 20362, 198, 2, 22300, 29908, 329, 13639, 25, 1220, 76, 429, 14, 1872, 14, 11195, 14, 9892, 495, 83, 14, 10677, 14, 66, 463, 20471, 14, 19, 13, 15, 13, 19, 14, 17256, 14, 66, 463, 20471, 13, 71, 198, 2, 17406, 4142, 7560, 1262, 1012, 648, 13, 20362, 14441, 62, 66, 11, 2196, 657, 13, 15, 13, 15, 628, 198, 8818, 269, 463, 20471, 3855, 14815, 3419, 198, 220, 220, 220, 269, 13345, 19510, 25, 66, 463, 20471, 3855, 14815, 11, 8019, 66, 463, 20471, 828, 34, 600, 11, 28955, 198, 437, 198, 198, 8818, 269, 463, 20471, 3855, 12331, 10100, 7, 13376, 8, 198, 220, 220, 220, 269, 13345, 19510, 25, 66, 463, 20471, 3855, 12331, 10100, 11, 8019, 66, 463, 20471, 828, 46745, 90, 52, 5317, 23, 5512, 7, 66, 463, 20471, 19580, 62, 83, 11, 828, 13376, 8, 198, 437, 198, 198, 8818, 269, 463, 20471, 16447, 7, 28144, 8, 198, 220, 220, 220, 269, 13345, 19510, 25, 66, 463, 20471, 16447, 11, 8019, 66, 463, 20471, 828, 66, 463, 20471, 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2.303839
14,195
### Linear Algebra # Import functions from Base import Base:dot, transpose, ctranspose, transpose!, ctranspose!, det, inv, norm, rank, *, A_mul_Bt, At_mul_B, At_mul_Bt, Ac_mul_B, A_mul_Bc, Ac_mul_Bc, chol, lu, lufact!, qr, qrfact!, svd, svdfact!, \, diag # Export Functions export isLAPACKAvailable, chol!, solveLU, upper, lower # Export constants export AF_MAT_NONE, AF_MAT_TRANS, AF_MAT_CTRANS, AF_MAT_CONJ , AF_MAT_UPPER, AF_MAT_LOWER, AF_MAT_DIAG_UNIT, AF_MAT_SYM, AF_MAT_POSDEF, AF_MAT_ORTHOG, AF_MAT_TRI_DIAG, AF_MAT_BLOCK_DIAG, AF_NORM_VECTOR_1, AF_NORM_VECTOR_INF, AF_NORM_VECTOR_2, AF_NORM_VECTOR_P, AF_NORM_MATRIX_1, AF_NORM_MATRIX_INF, AF_NORM_MATRIX_2, AF_NORM_MATRIX_L_PQ, AF_NORM_EUCLID # Constants AF_MAT_NONE = 0 AF_MAT_TRANS = 1 AF_MAT_CTRANS = 2 AF_MAT_CONJ = 4 AF_MAT_UPPER = 32 AF_MAT_LOWER = 64 AF_MAT_DIAG_UNIT = 128 AF_MAT_SYM = 512 AF_MAT_POSDEF = 1024 AF_MAT_ORTHOG = 2048 AF_MAT_TRI_DIAG = 4096 AF_MAT_BLOCK_DIAG = 8192 AF_NORM_VECTOR_1 = 0 AF_NORM_VECTOR_INF = 1 AF_NORM_VECTOR_2 = 2 AF_NORM_VECTOR_P = 3 AF_NORM_MATRIX_1 = 4 AF_NORM_MATRIX_INF = 5 AF_NORM_MATRIX_2 = 6 AF_NORM_MATRIX_L_PQ = 7 AF_NORM_EUCLID = AF_NORM_VECTOR_2 # Dot function dot{T,S}(a::AFVector{T}, b::AFVector{S}, lhsprop = AF_MAT_NONE, rhsprop = AF_MAT_NONE) out = new_ptr() af_dot(out, a, b, lhsprop, rhsprop) AFVector{af_promote(T,S)}(out[]) end # Transpose function transpose{T}(a::AFArray{T}) out = new_ptr() af_transpose(out, a, false) AFArray{T}(out[]) end function ctranspose{T}(a::AFArray{T}) out = new_ptr() af_transpose(out, a, true) AFArray{T}(out[]) end function transpose!{T}(a::AFArray{T}) af_transpose_inplace(a, false) a end function ctranspose!{T}(a::AFArray{T}) af_transpose_inplace(a, true) a end # Matrix Operations function det{T<:Real}(a::AFArray{T}) real = Base.Ref{Cdouble}(0) imag = Base.Ref{Cdouble}(0) af_det(real, imag, a) real[] end function det{T<:Complex}(a::AFArray{T}) real = Base.Ref{Cdouble}(0) imag = Base.Ref{Cdouble}(0) af_det(real, imag, a) complex(real[], imag[]) end function inv(a::AFArray, options::Int = AF_MAT_NONE) out = new_ptr() af_inverse(out, a, options) AFArray{backend_eltype(out[])}(out[]) end function norm{T}(a::AFArray{T}; options = AF_NORM_EUCLID, p::Real = 1, q::Real = 1) out = Base.Ref{Cdouble}(0) af_norm(out, a, options, p, q) if T == Float32 T(out[]) else out[] end end function rank(a::AFArray; tol::Cdouble = 1e-5) out = Base.Ref{Cuint}(0) af_rank(out, a, tol) Int(out[]) end # Matrix Multiply function *{T,S}(a::AFArray{T}, b::AFArray{S}; lhsprop = AF_MAT_NONE, rhsprop = AF_MAT_NONE) out = new_ptr() af_matmul(out, a, b, lhsprop, rhsprop) AFMatrix{af_promote(T,S)}(out[]) end A_mul_Bt{T,S}(a::AFMatrix{T}, b::AFMatrix{S}) = *(a, b, rhsprop = AF_MAT_TRANS) At_mul_B{T,S}(a::AFMatrix{T}, b::AFMatrix{S}) = *(a, b, lhsprop = AF_MAT_TRANS) At_mul_Bt{T,S}(a::AFMatrix{T}, b::AFMatrix{S}) = *(a, b, lhsprop = AF_MAT_TRANS, rhsprop = AF_MAT_TRANS) A_mul_Bc{T,S}(a::AFMatrix{T}, b::AFMatrix{S}) = *(a, b, rhsprop=AF_MAT_CTRANS) Ac_mul_B{T,S}(a::AFMatrix{T}, b::AFMatrix{S}) = *(a, b, lhsprop=AF_MAT_CTRANS) Ac_mul_Bc{T,S}(a::AFMatrix{T}, b::AFMatrix{S}) = *(a, b, lhsprop=AF_MAT_CTRANS, rhsprop=AF_MAT_CTRANS) # LAPACK function isLAPACKAvailable() out = Base.Ref{Bool}(0) af_is_lapack_available(out) out[] end # Matrix Factorizations function _chol{T}(a::AFMatrix{T}, is_upper::Bool) out = new_ptr() info = Base.Ref{Cint}(0) af_cholesky(out, info, a, is_upper) info[] > 0 && throw(PosDefException(info)) AFArray{T}(out[]) end function _chol!{T}(a::AFMatrix{T}, is_upper::Bool) info = Base.Ref{Cint}(0) af_cholesky_inplace(info, a, is_upper) info[] > 0 && throw(PosDefException(info)) a end chol(a::AFMatrix, ::Type{Val{:U}}) = _chol(a, true) chol(a::AFMatrix, ::Type{Val{:L}}) = _chol(a, false) chol(a::AFMatrix) = chol(a,Val{:U}) chol!(a::AFMatrix, ::Type{Val{:U}}) = _chol!(a, true) chol!(a::AFMatrix, ::Type{Val{:L}}) = _chol!(a, false) chol!(a::AFMatrix) = chol!(a,Val{:U}) function lu(a::AFMatrix) l = new_ptr() u = new_ptr() p = new_ptr() af_lu(l, u, p, a) AFArray{backend_eltype(l[])}(l[]), AFArray{backend_eltype(u[])}(u[]), (AFArray{backend_eltype(p[])}(p[]) + 1) end function lufact!(a::AFMatrix) p = new_ptr() af_lu_inplace(p, a, true) a, AFArray{backend_eltype(p[])}(p[]) end function qr(a::AFMatrix) q = new_ptr() r = new_ptr() tau = new_ptr() af_qr(q, r, tau, a) AFArray{backend_eltype(q[])}(q[]), AFArray{backend_eltype(r[])}(r[]), AFArray{backend_eltype(tau[])}(tau[]) end function qrfact!(a::AFMatrix) tau = new_ptr() af_qr_inplace(tau, a) a, AFArray{backend_eltype(tau[])}(tau[]) end function svd(a::AFArray) u = new_ptr() s = new_ptr() vt = new_ptr() af_svd(u, s, vt, a) AFArray{backend_eltype(u[])}(u[]), AFArray{backend_eltype(s[])}(s[]), AFArray{backend_eltype(vt[])}(vt[]) end function svdfact!(a::AFArray) u = new_ptr() s = new_ptr() vt = new_ptr() af_svd_inplace(u, s, vt, a) AFArray{backend_eltype(u[])}(u[]), AFArray{backend_eltype(s[])}(s[]), AFArray{backend_eltype(vt[])}(vt[]) end function \(a::AFArray, b::AFArray; options = AF_MAT_NONE) out = new_ptr() af_solve(out, a, b, options) AFArray{backend_eltype(out[])}(out[]) end function solveLU(a::AFArray, p::AFArray, b::AFArray; options = AF_MAT_NONE) out = new_ptr() af_solve_lu(out, a, p, b, options) AFArray{backend_eltype(out[])}(out[]) end function diag{T}(a::AFMatrix{T}, k::Integer = 1) out = new_ptr() af_diag_extract(out, a, k - 1) AFVector{T}(out[]) end function upper{T}(a::AFMatrix{T}; is_unit_diag = false) out = new_ptr() af_upper(out, a, is_unit_diag) AFMatrix{T}(out[]) end function lower{T}(a::AFMatrix{T}; is_unit_diag = false) out = new_ptr() af_lower(out, a, is_unit_diag) AFMatrix{T}(out[]) end
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<filename>test/dynamic_overlay.jl foo(x) = x^5 bar(x) = x^10 apply(f, x1, x2::Val{T}) where {T} = f(x1, T) function f(x) g = x < 5 ? foo : bar return g(2) end struct DynamicMix <: CompilationContext end allow(ctx::DynamicMix, m::Module) = m == TestMixtape swap(e) = e function swap(e::Expr) new = MacroTools.postwalk(e) do s isexpr(s, :call) || return s s.args[1] == Base.literal_pow || return s return Expr(:call, apply, Base.:(*), s.args[3:end]...) end return new end function transform(::DynamicMix, b) for (v, st) in b replace!(b, v, swap(st)) end return b end # JIT compile an entry and call. @testset "Dynamic overlay" begin fn = Mixtape.jit(DynamicMix(), f, Tuple{Int64}) @test fn(3) == 10 @test fn(6) == 20 # Mixtape cached call. Mixtape.@load_call_interface() @test call(DynamicMix(), f, 3) == 10 @test call(DynamicMix(), f, 6) == 20 # Native. @test f(3) == 2^5 @test f(6) == 2^10 end
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<filename>test/basics.jl<gh_stars>0 @testset "DWDataReader.File basics" begin @test_throws ArgumentError DWDataReader.File("") end
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<filename>src/core/objective.jl ################################################################################ # Copyright 2020, <NAME>, <NAME> # ################################################################################ # PowerModelsDistributionStateEstimation.jl # # An extention package of PowerModels(Distribution).jl for Static Power System # # State Estimation. # ################################################################################ """ objective_mc_se """ function objective_mc_se(pm::_PMD.AbstractUnbalancedPowerModel) return JuMP.@objective(pm.model, Min, sum( sum( sum(_PMD.var(pm, nw, :res, i)[idx] for idx in 1:length(_PMD.var(pm, nw, :res, i)) ) for i in _PMD.ids(pm, nw, :meas)) for (nw, nw_ref) in _PMD.nws(pm) ) ) end
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<reponame>ryanstoner1/COAST.jl using Pkg Pkg.activate(pwd()) using Revise using COAST using JuMP using Test ENV["JULIA_SHELL"]="/bin/bash" @testset "COAST.jl" begin @test loaded_COAST()==true end @testset "app.jl" begin progress_test = [0.0] COAST.App.launchServer(progress_test,8040) end
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using SparseArrays, StatPlots include("util.jl") mutable struct Point position::Tuple{Int64,Int64} velocity::Tuple{Int64,Int64} end function make_sky()::Array{Point} points::Array{Point} = [] for line in getlines("inputs/10_test.txt") m = match(r"position=<\s*(-?\d+),\s*(-?\d+)>\s*velocity=<\s*(-?\d+),\s*(-?\d+)>", line) p = Point((parse(Int64,m[1]),parse(Int64,m[2])),(parse(Int64,m[3]),parse(Int64,m[4]))) push!(points, p) end return points end function make_all_positive(a::Array{Tuple{Int64,Int64}})::Array{Tuple{Int64,Int64}} xmin = Inf ymin = Inf for t in a if t[1] < xmin xmin = t[1] end if t[2] < ymin ymin = t[2] end end newpoints::Array{Tuple{Int64,Int64}} = [] for t in a push!(newpoints, ((t[1]-xmin)+1, (t[2]-ymin)+1)) end return newpoints end function fast_forward_point(p::Point,seconds::Int64)::Tuple{Int64,Int64} x = p.position[1] + seconds*p.velocity[1] y = p.position[2] + seconds*p.velocity[2] return (x,y) end function make_grid(a::Array{Tuple{Int64,Int64}})::Array{Int64,2} xmax = 0 ymax = 0 for t in a if t[1] > xmax xmax = t[1] end if t[2] > ymax ymax = t[2] end end grid = zeros(ymax,xmax) for t in a grid[1+ymax-t[2],t[1]] = 1 end return grid end function get_sky_at(init::Array{Point}, seconds::Int64)::Array{Tuple{Int64,Int64}} newpoints = fast_forward_point.(init, seconds) newview = make_all_positive(newpoints) return newview end function get_size(a::Array{Tuple{Int64,Int64}})::Int64 xmax = 0 ymax = 0 for p in a if p[1] > xmax xmax = p[1] end if p[2] > ymax ymax = p[2] end end return xmax*ymax end function get_message(init::Array{Point})::Array{Int64,2} skies = [] for n in 0:100000 if n % 1000 == 0 println(n) end s = get_sky_at(init, n) area = get_size(s) push!(skies, (area,s)) end smallest_sky = sort(skies, by=x->x[1])[1] return make_grid(smallest_sky[2]) end sky = make_sky() message = get_message(sky) heatmap(message)
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<filename>src/helpers.jl # g + g^2 + ... + g^(T-1) pvfactor(g, T) = (g ^ T - 1.0) / (g - 1.0); # Present value. First entry not discounted. present_value(xV, R) = sum(xV ./ (R .^ (0 : (length(xV)-1)))); # -----------
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""" zipandgutzwiller(mps1, mps2, mode) given two MPS, with the same number of sites and two physical dimensions perform gutzwiller projection of the two by making the matrices using iterative SVD. The final result is an MPS with 2 physical dimension where the final matrices are the tensor product of the given ones according to the `mode` which can be :B14 for bosonic or :F23 for fermionic. """ function zipandgutzwiller!(mps1::MPState{Y}, mps2::MPState{Y}; mode::Symbol=:B14, maxdim::Int64=200) where {Y<:Array} if mode != :B14 error("only Bosinic ↑↑ and ↓↓ is possible for no-symmetry MPSs!") end @assert mps1.d == mps2.d == 2 lx = mps1.lx @assert mps2.lx == lx center_at!(mps1, 1) center_at!(mps2, 1) dims = ones(Int64, lx+1) matrices = Array{T, 3}[] # Gutzwiller projector for mode :B14 gutzp = zeros(T, 2,2,2) gutzp[1,1,1] = 1 gutzp[2,2,2] = 1 E = ones(T, 1,1,1) for site=1:lx-1 mat1 = mps1.matrices[site] mat2 = mps2.matrices[site] diml1, dimr1 = size(mat1, 1), size(mat1, 3) diml2, dimr2 = size(mat2, 1), size(mat2, 3) dimle = size(E, 1) @tensor C[le, dg, r2, r1] := ((E[le, l2, l1] * mat1[l1,d1,r1]) * gutzp[dg,d1,d2]) * mat2[l2,d2, r2] fact = svd(reshape(C, dimle*2, dimr2*dimr1), full=false) S, n, ratio = MPStateTools.truncate(fact.S, maxdim=maxdim) dims[site+1] = n U = fact.U[:, 1:n] push!(matrices, reshape(U, dimle, 2, n)) E = reshape(Diagonal(S) * fact.Vt[1:n, :], n, dimr2, dimr1) end mat1 = mps1.matrices[lx] mat2 = mps2.matrices[lx] @tensor C[le, dg, r2, r1] := ((E[le, l2, l1] * mat1[l1,d1,r1]) * gutzp[dg,d1,d2]) * mat2[l2,d2,r2] push!(matrices, reshape(C, size(E, 1), 2, 1)) return MPState{T}(lx, 2, dims, matrices, lx) end function zipandgutzwiller!(mps1::MPState{Y}, mps2::MPState{Y}; mode::Symbol=:F23, maxdim::Int64=200) where {Y<:SymTensor} if mode==:B14 return _zipandgutzwiller_B14!(mps1, mps2, maxdim=maxdim) elseif mode==:F23 return _zipandgutzwiller_F23!(mps1, mps2, maxdim=maxdim) else error("mode not defined : ", mode) end end #bosonic version function _zipandgutzwiller_B14!(mps1::MPState{Y}, mps2::MPState{Y}; maxdim::Int64=200) where {Y} #@assert mps1.d == mps2.d == 2 lx = length(mps1) @assert length(mps2) == lx center_at!(mps1, 1) center_at!(mps2, 1) mps = MPState{Y}() ## NOTE: in order to make the gutzwiller projector respect the U1 ## symmetry we need to do the follwoing. Assume that ↑↑ ## corresponds to ↑ or 2 and ↓↓ corresponds or ↓ or 0. G = fill(one(T), 0, (STLeg(+1, [0,2], [1,1]), STLeg(-1, [0,1], [1,1]), STLeg(-1, [0,1], [1,1]))) E = fill(one(T), 0, (STLeg(+1, [0], [1]), STLeg(-1, [0], [1]), STLeg(-1, [0], [1]))) for l=1:lx-1 A = mps1.matrices[l] B = mps2.matrices[l] ##NOTE: # contracting (E2, A1) # First tensor has EA = E1 E3 A2 A3 # contracting (A2, G2) # Scond tensor has EAG = E1 E3 G1 A3 G3 # contracting (E3, B1) and (G3, B2) # Final tensor has E1 G1 A3 B3 C = contract(contract(contract(E, (1,-1, 2), A, (-1, 3, 4)), (1, 2, -1, 4), G, (3, -1, 5)), (1,-1, 2, 3,-2), B, (-1,-2, 4)) u,s,v = svdtrunc(fuselegs(fuselegs(C, 1, 2), 2, 2), maxdim=maxdim) normalize!(s) push!(matrices, mapcharges(x->div(x,2), unfuseleg(u, 1, (E.legs[1], G.legs[1])))) E = unfuseleg(s*v, 2, (A.legs[3], B.legs[3])) end A = mps1.As[lx] B = mps2.As[lx] C = contract(contract(contract(E, (1,-1, 2), A, (-1, 3, 4)), (1, 2, -1, 4), G, (3, -1, 5)), (1,-1, 2, 3,-2), B, (-1,-2, 4)) push!(matrices, mapcharges(x->div(x,2), fuselegs(C, 3, 2))) return MPState{Y}(lx, 2, dims, matrices, lx) end function _zipandgutzwiller_F23!(mps1::MPState{Y}, mps2::MPState{Y}; truncation::Bool=true, maxdim::Int64=200) where {Y} #@assert mps1.d == mps2.d == 2 lx = length(mps1) @assert length(mps2) == lx center_at!(mps1, 1) center_at!(mps2, 1) mps = MPState{Y}() ## NOTE: in order to make the gutzwiller projector respect the U1 ## symmetry we need to do the follwoing. Assume the first mps ## corresponds to ↑ or 1 and second mps to ↓ or -1. T = eltype(Y) S = vtype(Y) Vd = VectorSpace{S}(0=>1, 1=>1) G = fill(one(T), (dual(Vd), mapcharges(x->2*x-1, Vd), Vd)) Vdummy = VectorSpace{S}(0=>1) E = fill(one(T), (Vdummy, dual(Vdummy), Vdummy)) u,s,v = _svd_(eye(T, Vdummy)) for l=1:lx-1 A = mps1.As[l] B = dual(mps2.As[l], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) ##NOTE: # contracting (E3, A1) # First tensor has EA = E1 E2 A2 A3 # contracting with swap # second tensor has EAX = E1 XA2 E3 A3 # contracting (A2, G2) # Third tensor has EAG = E1 G1 G3 E3 A3 # contracting (E3, B1) and (G3, B2) # Final tensor has E1 G1 A3 B3 C = contract(contract(contract(contract(E, (1, 2, -1), A, (-1, 3, 4)), (1, -1, -2, 4), fswap, (-1, 2, 3, -2)), (1, -1, 4, 5), G, (-1, 2, 3)), (1,2, -2, -1, 4), B, (-1,-2, 3)) if truncation u,s,v = svdtrunc(SymMatrix(C, [1,2], [3,4]), maxdim=maxdim) else u,s,v = _svd_(SymMatrix(C, [1,2], [3,4])) end normalize!(s) fnl = x->div(x+l-1, 2) fnd = x->div(x+1, 2) fnr = x->div(x+l, 2) push!(mps, mapcharges((fnl,fnd,fnr), splitleg(u, 1, (E.space[1], G.space[2])))) E = splitleg(s*v, 2, (B.space[3], A.space[3])) end A = mps1.As[lx] B = dual(mps2.As[lx], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) C = contract(contract(contract(contract(E, (1, 2, -1), A, (-1, 3, 4)), (1, -1, -2, 4), fswap, (-1, 2, 3, -2)), (1, -1, 4, 5), G, (-1, 2, 3)), (1,2, -2, -1, 4), B, (-1,-2, 3)) if truncation u,s,v = svdtrunc(SymMatrix(C, [1,2], [3,4]), maxdim=maxdim) else u,s,v = _svd_(SymMatrix(C, [1,2], [3,4])) end normalize!(s) C = splitleg(u*s*v, 1, (E.space[1], G.space[2])) fnl = x->div(x+lx-1, 2) fnd = x->div(x+1, 2) fnr = x->div(x+lx, 2) push!(mps, mapcharges((fnl,fnd,fnr), C)) # could the below alone be the issue?! # is this correct (need explanation and stuff) mps.center = lx mps end function gutzwillerexact(mps1::MPState{Y}, mps2::MPState{Y}; mode::Symbol=:F23) where {Y} _zipandgutzwiller_F23(mps1, mps2, truncation=false) end function _tensorproductzip!(mps1::MPState{Y}, mps2::MPState{Y}; truncation::Bool=true, maxdim::Int64=200, verbose::Bool=true) where {Y} #@assert mps1.d == mps2.d == 2 lx = length(mps1) @assert length(mps2) == lx center_at!(mps1, 1) center_at!(mps2, 1) mps = MPState{Y}() ## NOTE: in order to make the gutzwiller projector respect the U1 ## symmetry we need to do the follwoing. Assume the first mps ## corresponds to ↑ or 1 and second mps to ↓ or -1. T = eltype(Y) S = vtype(Y) Vdummy = VectorSpace{S}(0=>1) E = fill(one(T), (Vdummy, dual(Vdummy), Vdummy)) u,s,v = _svd_(eye(T, Vdummy)) for l=1:lx-1 A = mps1.As[l] B = dual(mps2.As[l], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) ##NOTE: # contracting (E3, A1) # First tensor has EA = E1 E2 A2 A3 # contracting with swap # second tensor has EAX = E1 XA2 E2 A3 # contracting (EX2, B1) # Final tensor has E1 XA2 B2 B3 A3 C = contract(contract(contract(E, (1, 2, -1), A, (-1, 3, 4)), (1, -1, -2, 4), fswap, (-1, 2, 3, -2)), (1, 2, -1, 5), B, (-1, 3, 4)) if truncation u,s,v = svdtrunc(SymMatrix(C, [1,2,3], [4,5]), maxdim=maxdim) else u,s,v = _svd_(SymMatrix(C, [1,2,3], [4,5])) end normalize!(s) push!(mps, fuselegs( splitleg(u, 1, (E.space[1], A.space[2], B.space[2])), 2, 2)) E = splitleg(s*v, 2, (B.space[3], A.space[3])) end A = mps1.As[lx] B = dual(mps2.As[lx], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) C = contract(contract(contract(E, (1, 2, -1), A, (-1, 3, 4)), (1, -1, -2, 4), fswap, (-1, 2, 3, -2)), (1, 2, -1, 5), B, (-1, 3, 4)) push!(mps, fuselegs(fuselegs(C, 2, 2), 3, 2)) mps.center = lx mps end function _applygutzwillerzip!(mps::MPState{Y}) where{Y} T = eltype(mps) G = Tensor(ones, zero(U1), (U1Space([-1=>1, 1=>1]), dual(U1Space([-1=>1, 0=>2, 1=>1])) )) mpsgutz = MPState{Y}() center_at!(mps, 1) C = contract(G, (2, -1), mps.As[1], (1, -1, 3)) for l=1:lx-1 u,s,v = svdtrunc(fuselegs(C, 1, 2)) fnl = x->div(x+lx-1, 2) fnd = x->div(x+1, 2) fnr = x->div(x+lx, 2) push!(mpsgutz, mapcharges((fnl,fnd,fnr), splitleg(u, 1, (C.space[1], G.space[1])))) E = s*v C = contract(E, (1, -1), contract(G, (2, -1), mps.As[l+1], (1, -1, 3)), (-1,2,3)) end push!(mpsgutz, C) mpsgutz end function _zipandgutzwiller_F23_analysis!(mps1::MPState{Y}, mps2::MPState{Y}; maxdim::Int64=200) where {Y} lx = length(mps1) @assert length(mps2) == lx center_at!(mps1, 1) center_at!(mps2, 1) mps = MPState{Y}() ## NOTE: in order to make the gutzwiller projector respect the U1 ## symmetry we need to do the follwoing. Assume the first mps ## corresponds to ↑ or 1 and second mps to ↓ or -1. T = eltype(Y) S = vtype(Y) Vd = VectorSpace{S}(0=>1, 1=>1) G = fill(one(T), (dual(Vd), mapcharges(x->2*x-1, Vd), Vd)) Vdummy = VectorSpace{S}(0=>1) ### Making the F tensors Vend1 = rightspace(mps1) Vend2 = rightspace(mps2) Fs = Vector{SymTensor{S,T,3}}(undef, lx) F = fill(one(T), (Vend1, dual(Vend2), fuse(true, dual(Vend1), Vend2))) for l=lx:-1:2 A = mps1.As[l] B = dual(mps2.As[l], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) C = contract(contract(contract(contract(A, (1, 2, -1), F, (-1, 3, 4)), (1, 2, -1, 5), B, (3, 4, -1)), (1, -2, -1, 4, 5), fswap, (2, 3, -1, -2)), (1, 2, -1, -2, 4), G, (-1, 3, -2)) u,s,v = _svd_(SymMatrix(C, [1,2], [3,4])) normalize!(s) F = splitleg(u*s, 1, (C.space[1], C.space[2])) Fs[l] = F end error_ortho = Vector{Float64}() error_ets = Vector{Float64}() E = fill(one(T), (Vdummy, Vdummy, dual(Vdummy))) for l=1:lx-1 A = mps1.As[l] B = dual(mps2.As[l], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) ##NOTE: # contracting (E3, A1) # First tensor has EA = E1 E2 A2 A3 # contracting with swap # second tensor has EAX = E1 XA2 E3 A3 # contracting (A2, G2) # Third tensor has EAG = E1 G1 G3 E3 A3 # contracting (E3, B1) and (G3, B2) # Final tensor has E1 G1 A3 B3 C = contract(contract(contract(contract(E, (1, 2, -1), A, (-1, 3, 4)), (1, -1, -2, 4), fswap, (-1, 2, 3, -2)), (1, -1, 4, 5), G, (-1, 2, 3)), (1,2, -2, -1, 4), B, (-1,-2, 3)) # actual zipgutz process u,s,v = svdtrunc(SymMatrix(C, [1,2], [3,4]), maxdim=maxdim) normalize!(s) println(norm(SymMatrix(C, [1,2], [3,4]))) # The ortho center matrix CFmat = SymMatrix(contract(C, (1,2,-1,-2), Fs[l+1], (-2,-1, 3)), [1, 2], [3]) println(norm(SymMatrix(Fs[l+1], [2,1], [3]))) norm_CFmat = norm(CFmat) println(norm_CFmat) println() # othonormal truncation error _u,_s,_v = svdtrunc(CFmat, maxdim=maxdim) normalize!(_s) push!(error_ortho, dot(_u*_s*_v, CFmat) / norm_CFmat) # zipgutz truncation error CtrFmat = (u*s*v) * SymMatrix(Fs[l+1], [2,1], [3]) norm_CtrFmat = norm(CtrFmat) push!(error_ets, dot(CtrFmat, CFmat) / (norm_CFmat*norm_CtrFmat)) fnl = x->div(x+l-1, 2) fnd = x->div(x+1, 2) fnr = x->div(x+l, 2) push!(mps, mapcharges((fnl,fnd,fnr), splitleg(u, 1, (E.space[1], G.space[2])))) E = splitleg(s*v, 2, (B.space[3], A.space[3])) end A = mps1.As[lx] B = dual(mps2.As[lx], conjugate=false) fswap = fermionswapgate(A.space[2], B.space[1]) C = contract(contract(contract(contract(E, (1, 2, -1), A, (-1, 3, 4)), (1, -1, -2, 4), fswap, (-1, 2, 3, -2)), (1, -1, 4, 5), G, (-1, 2, 3)), (1,2,-2,-1,4), B, (-1,-2, 3)) u,s,v = svdtrunc(SymMatrix(C, [1,2], [3,4]), maxdim=maxdim) normalize!(s) push!(error_ortho, 1.0) push!(error_ets, 1.0) C = splitleg(u*s*v, 1, (E.space[1], G.space[2])) fnl = x->div(x+lx-1, 2) fnd = x->div(x+1, 2) fnr = x->div(x+lx, 2) push!(mps, mapcharges((fnl,fnd,fnr), C)) mps.center = lx mps, error_ortho, error_ets end
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<gh_stars>1-10 using RecordedArrays using Random using EvolutionaryModelingTools using EvolutionaryModelingTools.Scalar # parameters const T = 10.0 # max time const r = 0.5 # growth rate const d = 0.1 # death rate const K = 100 # carrying capacity const n = 10 # initial population size @cfunc growth_c(r, n) = r * n @ufunc growth_u!(ind, n) = n[ind] += 1 @cfunc death_c(d, n) = d * n @ufunc death_u!(ind, n) = n[ind] -= 1 @cfunc comp_c(r, K, n) = r * n * n' / K @ufunc comp_u!(ind::CartesianIndex{0}, n) = n[ind] -= 1 # the ind must be CartesianIndex{0}, this is just for test @ufunc check_extinction(ind::CartesianIndex{0}, n) = n[ind] == 0 ? :extinct : :finnish growth = Reaction(growth_c, growth_u!) death = Reaction(death_c, death_u!) competition = Reaction(comp_c, comp_u!) _maybe_scalar(n::Number) = scalar(n) _maybe_scalar(n) = n function run_gillespie(rng=Random.GLOBAL_RNG, T=T, r=r, d=d, K=K, n=n) ps = (; r, d, K, n=_maybe_scalar(n)) rs = (growth, death, competition) return gillespie(check_extinction, rng, T, ps, rs) end function run_gillespie_record(rng=Random.GLOBAL_RNG, T=T, r=r, d=d, K=K, n=n) clock = ContinuousClock(T) ps = (; r, d, K, n= recorded(DynamicEntry, clock, n)) rs = (growth, death, competition) return gillespie(check_extinction, rng, clock, ps, rs) end function run_manually(rng=Random.GLOBAL_RNG, T=T, r=r, d=d, K=K, n=n) t = 0 while t <= T # calculate rate growth_rate = r * n # intrinsic growth death_rate = d * n # intrinsic death competition_rate = r * n * n / K # resource competition summed = growth_rate + death_rate + competition_rate # sum a_i # break if summed is zero summed == 0 && break # update current time t += -log(rand(rng)) / summed # sample a reaction and adjust population size rn = rand(rng) * summed if rn < growth_rate n += 1 else n -= 1 end end return n end function run_manually_record(rng=Random.GLOBAL_RNG, T=T, r=r, d=d, K=K, n=n) clock = ContinuousClock(T) n = recorded(DynamicEntry, clock, n) for _ in clock # calculate rate growth_rate = r * n # intrinsic growth death_rate = d * n # intrinsic death competition_rate = r * n * n / K # resource competition summed = growth_rate + death_rate + competition_rate # sum a_i # break if summed is zero summed == 0 && break # update current time increase!(clock, -log(rand(rng)) / summed) # sample a reaction and adjust population size rn = rand(rng) * summed if rn < growth_rate n[] += 1 else n[] -= 1 end end return n end
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module PanX using Printf using JSON, GZip using PanGraph struct Gene id :: Int tag :: String low :: Int high :: Int strand :: Bool end function find(database, tag) for (i,cluster) in enumerate(database) tag ∈ cluster && return i end return nothing end askey(name,tag) = "$(name)|$(tag)" function gene(line, name, database) elt = split(line) strand = elt[1] == "+" low, high = parse.(Int, split(elt[2],"..")) tag = elt[3] id = find(database,askey(name,tag)) id !== nothing || return nothing return Gene(id, tag, low, high, strand) end struct Genome name :: String sequence :: Array{UInt8,1} gene :: Array{Gene,1} end Genome(name::String, sequence::Array{UInt8,1}) = Genome(name, sequence, Gene[]) function database(path) data = Set{String}[] meta = String[] re = r"(N[A-Z]_[A-Z0-9]+)_" sub = s"\1|" open(path) do io genes = JSON.parse(io) for (i,gene) in enumerate(genes) loci = map(split(gene["locus"])) do g replace(g, re => sub) end push!(data, Set(loci)) push!(meta, gene["msa"]) end end return ( loci=data, meta=meta, path=dirname(path), ) end function alignment(io::IO) record = [ (name=rec.name, seq=rec.seq) for rec in PanGraph.Utility.read_fasta(io)] return hcat(map(r->r.seq,record)...), map(record) do r entry = split(r.name, "-") return "$(entry[1])|$(entry[2])" end end function pangraph(genomes, genes) vertex = [ let path = "$(genes.path)/geneCluster/$(base)_na_aln.fa.gz" aln, isos = GZip.open(alignment, path) ref = PanGraph.Utility.make_consensus(aln) blk = PanGraph.Graphs.Block(ref) iso = [ PanGraph.Graphs.Node(blk, true) for _ in isos ] blk.uuid = @sprintf("Gene_%05d", i) blk.gaps, blk.mutate, blk.delete, blk.insert, blk.sequence = PanGraph.Utility.alignment_alleles(ref, aln, iso) (block=blk, node=Dict( name => node for (name,node) in zip(isos,iso))) end for (i,(gene,base)) in enumerate(zip(genes.loci,genes.meta)) ] path = [ let nodes = [ let node = pop!(vertex[gene.id].node, askey(genome.name, gene.tag)) node.strand = gene.strand node end for gene in genome.gene ] breaks = [ x for gene in genome.gene for x in [gene.low, gene.high] ] PanGraph.Graphs.Path(genome.name, nodes, 0, true, breaks) end for genome in values(genomes) ] # remove any unpaired nodes we initially added # delete empty blocks index = Int[] for (i,v) in enumerate(vertex) for n in values(v.node) pop!(v.block, n) end if length(v.block.mutate) == 0 push!(index, i) end end deleteat!(vertex, index) block = map(v->v.block, vertex) graph = PanGraph.Graph(Dict(b.uuid => b for b in block), Dict(p.name => p for p in path) ) return graph end function main(database) genomes = Dict{String,Genome}() while !eof(stdin) line = readline(stdin) line[1] == '>' || error("invalid stream detected") name = line[2:end] sbuf = IOBuffer() # grab sequence @label moreseq line = readline(stdin) if line != "ENDSEQ" print(sbuf, line) @goto moreseq end genome = Genome(name, take!(sbuf)) # grab loci @label moregene line = readline(stdin) if line != "ENDGENE" g = gene(line, name, database.loci) g !== nothing && push!(genome.gene, g) @goto moregene end genomes[name] = genome end return pangraph(genomes, database) end # NOTE: just for debugging function echo() while !eof(stdin) println(readline(stdin)) end end if abspath(PROGRAM_FILE) == @__FILE__ database(ARGS[1]) |> main |> (G) -> PanGraph.Graphs.marshal(stdout, G; fmt=:json) end end
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<reponame>hmatuschek/ContinuousWavelet.jl @doc raw""" detrend!(y::AbstractVector{V}, x::AbstractVector{U}=(1:length(y))) In-place detrends the given time-series. That is, fitting a linear function into `y` given `x` and subtracting the fit from the data in `y`. """ function detrend!(y::AbstractVector{V}, x::AbstractVector{U}=(1:length(y))) where {U,V} (length(x) != length(y)) && error("x & y must have the same length!") N = length(x) X = Matrix(zeros(Float64, N, 2)) for i in 1:N X[i,:] = [1, x[i]] end res = transpose(X) * y res = (transpose(X) * X) \ res y[:] -= (res[1] .+ (res[2] .* x)) end @doc raw""" detrend!(y::AbstractArray{V,2}, x::AbstractVector{U}=(1:length(y))) In-place detrends the given time-series. That is, fitting a linear function into every columns of `y` given `x` and subtracting the fit from the data in the corresponding column of `y`. """ function detrend!(y::AbstractArray{V,2}, x::AbstractVector{U}=(1:length(y))) where {U,V} N,M = size(y) for i in 1:M y[:,i] = detrend!(y[:,i]) end y end @doc raw""" detrend(y::AbstractVector{V}, x::AbstractVector{U}=(1:length(y))) Detrends the given time-series. That is, fitting a linear function into `y` given `x` and subtracting the fit from the data in `y`. The function returns the detrended time-series. """ function detrend(y::AbstractVector{V}, x::AbstractVector{U}=(1:length(y))) where {U,V} Y = copy(y) detrend!(Y) Y end @doc raw""" detrend!(y::AbstractArray{V,2}, x::AbstractVector{U}=(1:length(y))) Detrends the given time-series. That is, fitting a linear function into every columns of `y` given `x` and subtracting the fit from the data in the corresponding column of `y`. The fuction returns the detrended time-series. """ function detrend(y::AbstractArray{V,2}, x::AbstractVector{U}=(1:length(y))) where {U,V} Y = copy(y) detrend!(Y) Y end
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struct Transvection <: GSymbol id::Symbol i::UInt16 j::UInt16 inv::Bool function Transvection(id::Symbol, i::Integer, j::Integer, inv = false) @assert id in (:ϱ, :λ) return new(id, i, j, inv) end end ϱ(i, j) = Transvection(:ϱ, i, j) λ(i, j) = Transvection(:λ, i, j) function Base.show(io::IO, t::Transvection) id = if t.id === :ϱ 'ϱ' else # if t.id === :λ 'λ' end print(io, id, subscriptify(t.i), '.', subscriptify(t.j)) t.inv && print(io, "^-1") end Base.inv(t::Transvection) = Transvection(t.id, t.i, t.j, !t.inv) Base.:(==)(t::Transvection, s::Transvection) = t.id === s.id && t.i == s.i && t.j == s.j && t.inv == s.inv Base.hash(t::Transvection, h::UInt) = hash(hash(t.id, hash(t.i)), hash(t.j, hash(t.inv, h))) Base.@propagate_inbounds @inline function evaluate!( v::NTuple{T,N}, t::Transvection, tmp = one(first(v)), ) where {T,N} i, j = t.i, t.j @assert 1 ≤ i ≤ length(v) && 1 ≤ j ≤ length(v) A = alphabet(parent(first(v))) @inbounds begin if t.id === :ϱ if !t.inv append!(word(v[i]), word(v[j])) else # append!(word(v[i]), inv(A, word(v[j]))) for l in Iterators.reverse(word(v[j])) push!(word(v[i]), inv(A, l)) end end else # if t.id === :λ if !t.inv # prepend!(word(v[i]), word(v[j])) for l in Iterators.reverse(word(v[j])) pushfirst!(word(v[i]), l) end else # prepend!(word(v[i]), inv(A, word(v[j]))) for l in word(v[j]) pushfirst!(word(v[i]), inv(A, l)) end end end _setnormalform!(v[i], false) _setvalidhash!(v[i], false) end normalform!(tmp, v[i]) copyto!(v[i], tmp) return v end struct PermRightAut <: GSymbol perm::Vector{UInt8} function PermRightAut(p::AbstractVector{<:Integer}) @assert sort(p) == 1:length(p) return new(p) end end function Base.show(io::IO, p::PermRightAut) print(io, 'σ') join(io, (subscriptify(Int(i)) for i in p.perm)) end Base.inv(p::PermRightAut) = PermRightAut(invperm(p.perm)) Base.:(==)(p::PermRightAut, q::PermRightAut) = p.perm == q.perm Base.hash(p::PermRightAut, h::UInt) = hash(p.perm, hash(PermRightAut, h)) evaluate!(v::NTuple{T,N}, p::PermRightAut, tmp = nothing) where {T,N} = v[p.perm]
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<filename>src/AstrodynamicalModels.jl """ Provides astrodynamical models as `ModelingToolkit.ODESystems`. Check out the `ModelingToolkit` docs to learn how to use these systems for orbit propagation with `DifferentialEquations`, or see `GeneralAstrodynamics` for some convenient orbit propagation wrappers. # Extended help ## Exports $(EXPORTS) ## Imports $(IMPORTS) """ module AstrodynamicalModels # Export every model! export R2BP, CR3BP, NBP # Export every `ODEFunction`! export R2BPFunction, CR3BPFunction, NBPFunction using Symbolics using StaticArrays using LinearAlgebra using ModelingToolkit import Memoize: @memoize using DocStringExtensions @template (FUNCTIONS, METHODS, MACROS) = """ $(SIGNATURES) $(DOCSTRING) """ @template (TYPES, CONSTANTS) = """ $(TYPEDEF) $(DOCSTRING) """ include("R2BP.jl") include("CR3BP.jl") include("NBP.jl") end # module
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<gh_stars>0 abstract type Order end """ Trait container for dimension and array ordering in AlignedGrid. The default is `Ordered(Forward()`, `Forward())` All combinations of forward and reverse order for data and indices seem to occurr in real datasets, as strange as that seems. We cover these possibilities by specifying the order of both explicitly. Knowing the order of indices is important for using methods like `searchsortedfirst()` to find indices in sorted lists of values. Knowing the order of the data is then required to map to the actual indices. It's also used to plot the data later - which always happens in smallest to largest order. Base also defines Forward and Reverse, but they seem overly complicated for our purposes. """ struct Ordered{D,A,R} <: Order index::D array::A relation::R end Ordered(; index=Forward(), array=Forward(), relation=Forward()) = Ordered(index, array, relation) indexorder(order::Ordered) = order.index arrayorder(order::Ordered) = order.array relationorder(order::Ordered) = order.relation """ Trait indicating that the array or dimension has no order. """ struct Unordered{R} <: Order relation::R end Unordered() = Unordered(Forward()) indexorder(order::Unordered) = order arrayorder(order::Unordered) = order relationorder(order::Unordered) = order.relation """ Trait indicating that the array or dimension is in the normal forward order. """ struct Forward <: Order end """ Trait indicating that the array or dimension is in the reverse order. Selector lookup or plotting will be reversed. """ struct Reverse <: Order end Base.reverse(::Reverse) = Forward() Base.reverse(::Forward) = Reverse() # Base.reverse(o::Ordered) = # Ordered(indexorder(o), reverse(relationorder(o)), reverse(arrayorder(o))) # Base.reverse(o::Unordered) = # Unordered(reverse(relationorder(o))) reverseindex(o::Unordered) = Unordered(reverse(relationorder(o))) reverseindex(o::Ordered) = Ordered(reverse(indexorder(o)), arrayorder(o), reverse(relationorder(o))) reversearray(o::Unordered) = Unordered(reverse(relationorder(o))) reversearray(o::Ordered) = Ordered(indexorder(o), reverse(arrayorder(o)), reverse(relationorder(o))) isrev(::Forward) = false isrev(::Reverse) = true """ Indicates wether the cell value is specific to the locus point or is related to the whole the span. The span may contain a value if the distance between locii if known. This will often be identical to the distance between any two sequential cell values, but may be distinct due to rounding errors in a vector index, or context-dependent spans such as `Month`. """ abstract type Sampling end """ Each cell value represents a siegle discrete sample taken at the index location. """ struct SingleSample <: Sampling end """ Multiple samples from the span combined using method `M`, where `M` is `typeof(mean)`, `typeof(sum)` etc. """ struct MultiSample{M} <: Sampling end MultiSample() = MultiSample{Nothing}() """ The sampling method is unknown. """ struct UnknownSampling <: Sampling end """ Indicate the position of index values in grid cells. This is frequently `Start` for time series, but may be `Center` for spatial data. """ abstract type Locus end """ Indicates dimensions that are defined by their center coordinates/time/position. """ struct Center <: Locus end """ Indicates dimensions that are defined by their start coordinates/time/position. """ struct Start <: Locus end """ Indicates dimensions that are defined by their end coordinates/time/position. """ struct End <: Locus end struct UnknownLocus <: Locus end """ Traits describing the grid type of a dimension. """ abstract type Grid end dims(g::Grid) = nothing arrayorder(grid::Grid) = arrayorder(order(grid)) indexorder(grid::Grid) = indexorder(order(grid)) relationorder(grid::Grid) = relationorder(order(grid)) Base.reverse(g::Grid) = rebuild(g, reverse(order(g))) reversearray(g::Grid) = rebuild(g, reversearray(order(g))) reverseindex(g::Grid) = rebuild(g, reverseindex(order(g))) slicegrid(grid::Grid, index, I) = grid """ Fallback grid type """ struct UnknownGrid <: Grid end order(::UnknownGrid) = Unordered() # TODO handle unordered in all grids bounds(grid::UnknownGrid, dim) = first(dim), last(dim) """ A grid dimension that is independent of other grid dimensions. """ abstract type IndependentGrid{O} <: Grid end """ A grid dimension aligned exactly with a standard dimension, such as lattitude or longitude. """ abstract type AbstractAlignedGrid{O,L,Sa} <: IndependentGrid{O} end order(g::AbstractAlignedGrid) = g.order locus(g::AbstractAlignedGrid) = g.locus sampling(g::AbstractAlignedGrid) = g.sampling """ An [`AlignedGrid`](@ref) grid without known regular spacing. These grids will generally be paired with a vector of coordinates along the dimension, instead of a range. Bounds are given as the first and last points, which omits the span of one cell, as it is not known. To fix this use either a [`BoundedGrid`](@ref) with specified starting bounds or a [`RegularGrid`](@ref) with a known constand cell span. ## Fields - `order::Order`: `Order` trait indicating array and index order - `locus::Locus`: `Locus` trait indicating the position of the indexed point within the cell span - `sampling::Sampling`: `Sampling` trait indicating wether the grid cells are single samples or means """ struct AlignedGrid{O<:Order,L<:Locus,Sa<:Sampling} <: AbstractAlignedGrid{O,L,Sa} order::O locus::L sampling::Sa end AlignedGrid(; order=Ordered(), locus=Start(), sampling=UnknownSampling()) = AlignedGrid(order, locus, sampling) rebuild(g::AlignedGrid, order=order(g), locus=locus(g), sampling=sampling(g)) = AlignedGrid(order, locus, sampling) bounds(grid::AlignedGrid, dim) = first(dim), last(dim) """ An alligned grid without known regular spacing and tracked bounds. These grids will generally be paired with a vector of coordinates along the dimension, instead of a range. As the size of the cells is not known, the bounds must be actively tracked. ## Fields - `order::Order`: `Order` trait indicating array and index order - `locus::Locus`: `Locus` trait indicating the position of the indexed point within the cell span - `sampling::Sampling`: `Sampling` trait indicating wether the grid cells are single samples or means - `bounds`: the outer edges of the grid (different to the first and last coordinate). """ struct BoundedGrid{O<:Order,L<:Locus,Sa<:Sampling,B} <: AbstractAlignedGrid{O,L,Sa} order::O locus::L sampling::Sa bounds::B end BoundedGrid(; order=Ordered(), locus=Start(), sampling=UnknownSampling(), bounds=nothing) = BoundedGrid(order, locus, sampling, bounds) bounds(g::BoundedGrid) = g.bounds rebuild(g::BoundedGrid, order=order(g), locus=locus(g), sampling=sampling(g), bounds=bounds(g)) = BoundedGrid(order, locus, sampling, bounds) # TODO: deal with unordered AbstractArray slicegrid(g::BoundedGrid, index, I) = rebuild(g, order(g), locus(g), sampling(g), slicebounds(locus(g), bounds(g), index, I)) slicebounds(loci::Start, bounds, index, I) = index[first(I)], last(I) >= lastindex(index) ? bounds[2] : index[last(I) + 1] slicebounds(loci::End, bounds, index, I) = first(I) <= firstindex(index) ? bounds[1] : index[first(I) - 1], index[last(I)] slicebounds(loci::Center, bounds, index, I) = first(I) <= firstindex(index) ? bounds[1] : (index[first(I) - 1] + index[first(I)]) / 2, last(I) >= lastindex(index) ? bounds[2] : (index[last(I) + 1] + index[last(I)]) / 2 abstract type AbstractRegularGrid{O,L,Sa,Sp} <: AbstractAlignedGrid{O,L,Sa} end """ An [`AlignedGrid`](@ref) where all cells are the same size and evenly spaced. ## Fields - `order::Order`: `Order` trait indicating array and index order - `locus::Locus`: `Locus` trait indicating the position of the indexed point within the cell span - `sampling::Sampling`: `Sampling` trait indicating wether the grid cells are single samples or means - `span::Number`: the size of a grid step, such as 1u"km" or `Month(1)` """ struct RegularGrid{O<:Order,L<:Locus,Sa<:Sampling,Sp} <: AbstractRegularGrid{O,L,Sa,Sp} order::O locus::L sampling::Sa span::Sp end RegularGrid(; order=Ordered(), locus=Start(), sampling=UnknownSampling(), span=nothing) = RegularGrid(order, locus, sampling, span) span(grid::RegularGrid) = grid.span rebuild(g::RegularGrid, order=order(g), locus=locus(g), sampling=sampling(g), span=span(g)) = RegularGrid(order, locus, sampling, span) bounds(grid::RegularGrid, dim) = bounds(indexorder(grid), locus(grid), grid, dim) bounds(::Forward, ::Start, grid, dim) = first(dim), last(dim) + span(grid) bounds(::Reverse, ::Start, grid, dim) = last(dim), first(dim) + span(grid) bounds(::Forward, ::Center, grid, dim) = first(dim) - span(grid) / 2, last(dim) + span(grid) / 2 bounds(::Reverse, ::Center, grid, dim) = last(dim) - span(grid) / 2, first(dim) + span(grid) / 2 bounds(::Forward, ::End, grid, dim) = first(dim) - span(grid), last(dim) bounds(::Reverse, ::End, grid, dim) = last(dim) - span(grid), first(dim) abstract type AbstractCategoricalGrid{O} <: IndependentGrid{O} end """ A grid dimension where the values are categories. ## Fields - `order`: `Order` trait indicating array and index order """ struct CategoricalGrid{O<:Order} <: AbstractCategoricalGrid{O} order::O end CategoricalGrid(; order=Ordered()) = CategoricalGrid(order) order(g::CategoricalGrid) = g.order rebuild(g::CategoricalGrid, order=order(g)) = CategoricalGrid(order) bounds(grid::CategoricalGrid, dim) = bounds(indexorder(grid), grid, dim) bounds(::Forward, grid, dim) = first(dim), last(dim) bounds(::Reverse, grid, dim) = last(dim), first(dim) bounds(::Unordered, grid, dim) = error("Cannot call `bounds` on an unordered grid") """ Traits describing a grid dimension that is dependent on other grid dimensions. Indexing into a dependent dimension must provide all other dependent dimensions. """ abstract type DependentGrid <: Grid end locus(g::DependentGrid) = g.locus dims(g::DependentGrid) = g.dims sampling(g::DependentGrid) = g.sampling """ Grid type using an affine transformation to convert dimension from `dim(grid)` to `dims(array)`. ## Fields - `dims`: a tuple containing dimenension types or symbols matching the order needed by the transform function. - `sampling`: a `Sampling` trait indicating wether the grid cells are sampled points or means """ struct TransformedGrid{D,L,Sa<:Sampling} <: DependentGrid dims::D locus::L sampling::Sa end TransformedGrid(dims=(), locus=Start(), sampling=UnknownSampling()) = TransformedGrid(dims, locus, sampling) rebuild(g::TransformedGrid, dims=dims(g), locus=locus(g), sampling=sampling(g)) = TransformedGrid(dims, locus, sampling) """ A grid dimension that uses an array lookup to convert dimension from `dim(grid)` to `dims(array)`. ## Fields - `dims`: a tuple containing dimenension types or symbols matching the order needed to index the lookup matrix. - `sampling`: a `Sampling` trait indicating wether the grid cells are sampled points or means """ struct LookupGrid{D,L,Sa<:Sampling} <: DependentGrid dims::D locus::L sampling::Sa end LookupGrid(dims=(), locus=Start(), sampling=UnknownSampling()) = LookupGrid(dims, locus, sampling) rebuild(g::LookupGrid; dims=dims(g), locus=locus(g), sampling=sampling(g)) = LookupGrid(dims, locus, sampling)
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3.09646
3,701
<reponame>ericphanson/Wandb.jl module Wandb using PyCall, Conda, Random, Requires const wandb = PyNULL() function __init__() copy!(wandb, pyimport("wandb")) @info "Using Wandb version $(wandb.__version__)" msg = Wandb.wandb.sdk.internal.update.check_available(wandb.__version__) if msg !== nothing @info "Trying to update Wandb to the latest stable release" run(`$(PyCall.pyprogramname) -m pip install wandb --upgrade`) end @require FluxTraining = "7bf95e4d-ca32-48da-9824-f0dc5310474f" begin include("fluxtraining.jl") end @require MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195" begin include("mpi.jl") end end using Base.CoreLogging: CoreLogging, AbstractLogger, LogLevel, Info, handle_message, shouldlog, min_enabled_level, catch_exceptions # Base functions like `log`, `Image`, etc. include("main.jl") # Wandb Artifacts API include("artifacts.jl") # AbstractLogger interface include("corelogging.jl") # HyperParameter Tuning: Sweep/Agent API include("sweep.jl") export WandbLogger, WandbArtifact, WandbHyperParameterSweep, update_config!, get_config, save end
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2.519481
462
using MPI testdir = @__DIR__ mpiexec() do cmd run(`$cmd -n 3 $(Base.julia_cmd()) $(joinpath(testdir, "mpi_queue.jl"))`) end for file in readdir() if endswith(file, "log") rm(file) end end mpiexec() do cmd run(`$cmd -n 4 $(Base.julia_cmd()) $(joinpath(testdir, "mpi_updates.jl"))`) end
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2.08
150
# generates array of arrays for a mesh, useful when interpolating the FEM solution # L is the length of a single cell # # index of femCLmap corresponds to cell index # values of femCLmap[jj] correspond to index of elements inside cell jj function generate_femap(mesh::M,totalBounds::Vector{Float64},L::Float64) where M<:eFEM.AbstractMesh Nnodes = length(mesh.xy) numNodesPerElm = (mesh.order == :Linear ? 4 : 9) # generate particle List for FEM nodes particleList = [Particle2D(Point2D(mesh.xy[i].x,mesh.xy[i].y),0.0,0) for i=1:Nnodes] tempCL = StokesParticles.generate_cell_list(particleList,totalBounds,L) femap = Array{Array{Int}}(undef,length(tempCL.cells)) # initialize element array elementArray = Array{Int}(undef,numNodesPerElm) # convert all point indices to corresponding element indices for cellInd = 1:length(tempCL.cells) tempArr = Array{Int}(undef,0) #for each node in that cell for nodeInd in tempCL.cells[cellInd].particleIDList fill!(elementArray,zero(Int)) # map node index to element getElementFromNode!(elementArray,mesh,nodeInd) for ti=1:numNodesPerElm if elementArray[ti] != 0 push!(tempArr,elementArray[ti]) end end end # assign elements to femCL as 'nodes' femap[cellInd] = unique(tempArr) end return femap end """ interpFGT!(v,pList,mesh,h,ε,x,y,q) Takes in particle position and FEM mesh to interpolate values. Also updates particle possitoin arrays in-place to reduce memory consumption POSSIBLE IMPROVEMENTS: 1. add another function that updates the target array only once the first time this code is run. Maybe some sort of interp object that does this for me INPUTS: v: [Vector{Float64}, M] interpolated values at targets pList: [ParticleList] Array of particles, acts as sources mesh: [Mesh] Finite Element mesh, acts as targets h: [Float64] bandwidth, h=sqrt(2)*stddev if standard deviation is known ε: [Float64] maximum absolute error x: [Vector{Float64}, d*N] list of sources -- updated in-place within function y: [Vector{Float64}, d*M] list of targets -- updated in-place within function q: [Vector{Float64}, N] weights on sources -- updated in-place within function W: [Int] number of weight functions (e.g. if W=2, then length(q) = 2N) """ function interpFGT!(v::Array{Float64},pList,mesh,h::Float64,ε::Float64, x::Array{Float64},y::Array{Float64},q::Array{Float64}) N = length(pList) # sources -- needed for fgt function M = length(mesh.xy) # targets -- needed for fgt function d = 2 # dimension of data -- needed for fgt function W = 1 # number of different weights -- needed for fgt function # update source array for ti=1:N x[2*ti-1] = pList[ti].pos.x x[2*ti] = pList[ti].pos.y end # update target array -- really should only have to do this once! room for improvement for ti=1:M y[2*ti-1] = mesh.xy[ti].x # xval of source y[2*ti] = mesh.xy[ti].y end # run interpolation routine StokesParticles.fgt!(v,d,M,N,h,ε,x,y,q,W) # return nothing -- updates in-place nothing end function getElementFromNode!(elementArray::Vector{Int},mesh::M,nodeInd::Int) where M<:eFEM.AbstractMesh nElm = length(mesh.cm) tk = 1 for elmInd=1:nElm if nodeInd in mesh.cm[elmInd].NodeList elementArray[tk] = elmInd tk += 1 end end nothing end
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2.415782
1,508
<gh_stars>0 include("CalcNumCTEC.jl") # f(x,y) = 2x*y + 2x - x^2 - 2y^2 + 72 # # f(x,y) = 1 + x + y # a = 0. # b = 8. # c = 0. # d = 6. # t0 = time() # I_ap = IntegralDupla_Trapezio(f, (a,b), (c,d)) # t1 = time() # I_ex = 2816. # println("Integral aproximada: ", I_ap) # println("Integral exata.....: ", I_ex) # println("Erro absoluto......: ", abs(I_ap - I_ex)) # println("Tempo de execução..: ", round(t1-t0, digits=4), "s") v1 = Vertex2d(1,2) println(v)
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<reponame>JuliaPackageMirrors/SpikingNetworks.jl export @reset_str macro reset_str(reset) reset=replace(reset,r"(.+)(?=\[)", s"net.\1") reset=replace(reset,"i","net.i") str="function reset!(net::Network$timestr)\n" str*="$(reset)\n" str*="end\n" print(str) eval(parse(str)) end
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# Gurobi environment and other supporting facilities mutable struct Env ptr_env::Ptr{Cvoid} end # Note: `Env()` is an outer constructor because sometimes we want to # make an `Env` object from a pointer returned from `GRBgetenv`, and # sometimes we want to create a new `Env`. function Env() a = Ref{Ptr{Cvoid}}() ret = @grb_ccall( loadenv, Cint, (Ptr{Ptr{Cvoid}}, Ptr{UInt8}), a, C_NULL ) if ret == 10009 error("Invalid Gurobi license") elseif ret != 0 error("Failed to create environment (error $ret).") end # TODO(odow): no finalizer is set for Env, because when a model + Env # falls out of scope, Julia's GC will sometimes GC the env first, and # then the model. This causes an error. Thus, `Env`'s are finalized in # the `Model` constructor. # We should probably add a flag to differentiate between envs created # manually, and envs created by `Model()`. CPLEX.jl does something # similar. # finalizer(env, free_env) return Env(a[]) end Base.unsafe_convert(ty::Type{Ptr{Cvoid}}, env::Env) = env.ptr_env::Ptr{Cvoid} function is_valid(env::Env) env.ptr_env != C_NULL end function free_env(env::Env) if env.ptr_env != C_NULL @grb_ccall(freeenv, Nothing, (Ptr{Cvoid},), env.ptr_env) env.ptr_env = C_NULL end end function get_error_msg(env::Env) @assert env.ptr_env != C_NULL sz = @grb_ccall(geterrormsg, Ptr{UInt8}, (Ptr{Cvoid},), env.ptr_env) unsafe_string(sz) end # error mutable struct GurobiError <: Exception code::Int msg::String function GurobiError(env::Env, code::Integer) new(convert(Int, code), get_error_msg(env)) end end
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2.427362
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# recipe.jl using RecipesBase @recipe function g(f::TaylorModel1) ffp = fp_rpa(f) fT = polynomial(ffp) Δ = remainder(ffp) ξ0 = ffp.x0 seriesalpha --> 0.3 seriestype := :shape xs = range(f.dom.lo, stop=f.dom.hi, length=100) evals = fT.(xs .- ξ0) .+ Δ # make polygon: xs = [xs; reverse(xs); xs[1]] ys = [inf.(evals); reverse(sup.(evals)); inf(evals[1])] xs, ys end @recipe function g(f::RTaylorModel1) ffp = fp_rpa(f) fT = polynomial(ffp) Δ = remainder(ffp) ξ0 = ffp.x0 order = get_order(f)+1 seriesalpha --> 0.5 seriestype := :shape xs = range(f.dom.lo, stop=f.dom.hi, length=100) evals = fT.(xs .- ξ0) corrs = (xs .- ξ0) .^ order Δrel = Δ .* corrs evalslo = inf.(evals + Δrel) evalshi = sup.(evals + Δrel) xs = [xs; reverse(xs); xs[1]] ys = [evalslo; reverse(evalshi); evalslo[1]] xs, ys end @recipe function g(sol::TMSol; vars=(0,1), timediv=1) @assert length(vars) == length(unique(vars)) == 2 "`vars` must have 2 distinct integer indices" return _plot_intvbox(sol, vars=vars, timediv=timediv) end function _plot_intvbox(sol::TMSol; vars=(0,1), timediv=1) domT = mince_in_time(sol, var=0, timediv=timediv) if 0 ∈ vars tup0 = findfirst(0 ∈ vars) var1 = vars[3-tup0] v1 = _mince_in_time(sol, domT, var1, timediv) if tup0 == 1 return @. IntervalBox(domT, v1) else return @. IntervalBox(v1, domT) end end var1, var2 = vars v1 = _mince_in_time(sol, domT, var1, timediv) v2 = _mince_in_time(sol, domT, var2, timediv) return @. IntervalBox(v1, v2) end """ mince_in_time(sol::TMSol; var=0, timediv=1) --> ::Vector{Interval} For `var=0`, this function divides the time domain of each entry of `sol` in `timediv` parts (`timediv==1` is the initial domain), and returns the time intervals where the solution holds. This is useful for plotting or finding specific events. For `var ≥ 1`, this function evaluates the flowpipes at the split domain intervals, which is useful to decrease the overapproximations associated to the whole time domain. """ function mince_in_time(sol::TMSol; var::Int=0, timediv::Int=1) @assert timediv > 0 "`timediv must be 1 or larger" @assert 0 ≤ var ≤ get_numvars(sol) domT = _mince_in_time(sol, Val(true), timediv) var == 0 && return domT return _mince_in_time(sol, domT, var, timediv) end # Mince in time var (var == 0) function _mince_in_time(sol::TMSol, ::Val{true}, timediv::Int=1) # Case timediv == 1 if timediv == 1 return expansion_point(sol) .+ domain(sol) end # Case timediv > 1 domT = Vector{typeof(domain(sol,1))}(undef, timediv*length(sol)) i0 = 1 @inbounds for indT in eachindex(sol) i1 = indT*timediv domT[i0:i1] .= expansion_point(sol,indT) .+ mince(domain(sol,indT), timediv) i0 = i1 + 1 end return domT end # Mince other var (var > 0) function _mince_in_time(sol::TMSol, domT::Vector{Interval{T}}, var::Int, timediv::Int=1) where {T} N = get_numvars(sol) @assert 1 ≤ var ≤ N # Case timediv == 1 if timediv == 1 return getindex.(flowpipe(sol), var) end # Case timediv > 1 vv = similar(domT) normalized_box = symmetric_box(N, Float64) δt = mince(domain(sol,1), timediv) i0 = 1 @inbounds for indT in eachindex(sol) i1 = indT*timediv δt .= mince(domain(sol,indT), timediv) @. vv[i0:i1] = evaluate(evaluate(sol[indT,var], δt), (normalized_box,)) i0 = i1 + 1 end return vv end
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2.1331
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#!/usr/bin/env julia using Luxor, Colors function drawbbox(apoly) gsave() setline(0.3) setdash("dotted") box(polybbox(apoly), :stroke) grestore() end function test1(p, x, y) prettypoly(p, :fill, # all these commands are executed for each vertex of the polygon :( scale(0.5, 0.5); randomhue(); opacity = currentdrawing.alpha; setopacity(rand(5:10)/10); ngon(0, 0, 15, rand(3:12), 0, :fill); setopacity(opacity); sethue("black"); # we can pass the origin for the whole polygon into the expression like this # but the current vertex is 0/0 textcentred(string($(x)) * "/" * string($(y))))) end function test2(p) prettypoly(p, :fill, :( ## all these commands are executed for each vertex of the polygon randomhue(); scale(0.5, 0.5); rotate(pi/2); prettypoly($(p), :fill, :( # and all these commands are executed for each vertex of that polygon randomhue(); scale(0.25, 0.25); rotate(pi/2); prettypoly($($(p)), :fill))))) end function get_png_files(folder) cd(folder) imagelist = filter(f -> !startswith(f, ".") && endswith(f, "png"), readdir(folder)) imagelist = filter(f -> !startswith(f, "tiled-images"), imagelist) # don't recurse... :) return map(realpath, imagelist) end function test3(p) imagelist = get_png_files(dirname(@__FILE__)) shuffle!(imagelist) img = readpng(imagelist[1]) w = img.width h = img.height prettypoly(p, :fill, :( ## all these commands are executed for each vertex of the polygon # to make it "through" the expression to the base drawing functions, these have to be made literal using $ placeimage($readpng($imagelist[rand(1:end)]), -$w/2, -$h/2) )) end function draw_lots_of_polys(pagewidth, pageheight) setopacity(0.75) pagetiles = Tiler(width, height, 9, 9, margin=20) for (pos, n) in pagetiles if rand(Bool) p = randompointarray(rand(-140:-10), rand(-140:-10), rand(10:140), rand(10:140), rand(5:12)) else p = ngon(0, 0, rand(50:100), rand(3:12), vertices=true) end gsave() translate(pos) setline(1) randomhue() @eval [test1($p, $pos.x, $pos.y), test2($p), test3($p)][rand(1:end)] # choose a test at random drawbbox(p) grestore() end end width, height = 3000, 3000 fname = "/tmp/pretty-poly-test.pdf" Drawing(width, height, fname) origin() background("ivory") draw_lots_of_polys(width, height) finish() println("finished test: output in $(fname)")
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module Aligator export aligator, extract_loop, closed_forms, invariants using PyCall using SymPy using Singular using AlgebraicDependencies const AppliedUndef = PyCall.PyNULL() include("utils.jl") include("closedform.jl") include("recurrence.jl") include("loop.jl") include("invariants.jl") include("parse_julia.jl") include("ideals.jl") include("singular_imap.jl") function aligator(str::String) _, total = @timed begin loop, time = @timed extract_loop(str) @debug "Recurrence extraction" time cforms, time = @timed closed_forms(loop) @debug "Recurrence solving" time invs, time = @timed invariants(cforms) @debug "Ideal computation" time end @debug "Total time needed" total return InvariantIdeal(invs) end function __init__() copy!(AppliedUndef, PyCall.pyimport_conda("sympy.core.function", "sympy").AppliedUndef) include(joinpath(@__DIR__,"..", "benchmark", "singlepath.jl")) include(joinpath(@__DIR__,"..", "benchmark", "multipath.jl")) singlepath = [:cohencu, :freire1, :freire2, :(petter(1)), :(petter(2)), :(petter(3)), :(petter(4))] multipath = [:divbin, :euclidex, :fermat, :knuth, :lcm, :mannadiv, :wensley] end struct InvariantIdeal ideal::sideal end function Base.show(io::IO, I::InvariantIdeal) println(io, "Invariant ideal with $(ngens(I.ideal))-element basis:") Base.print_array(io, gens(I.ideal)) end end # module
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<reponame>meteorologytoday/IOM include("IOM/src/share/LogSystem.jl") include("IOM/src/share/PolelikeCoordinate.jl") include("IOM/src/models/EMOM/ENGINE_EMOM.jl") include("IOM/src/driver/driver_working.jl") include("IOM/src/share/CyclicData.jl") include("DataLoader.jl") include("func_reinitModel.jl") include("func_data2SOM.jl") using MPI using CFTime, Dates using ArgParse using TOML using DataStructures using NCDatasets using .PolelikeCoordinate using .LogSystem using .CyclicData using .DataLoader function parse_commandline() s = ArgParseSettings() @add_arg_table s begin "--config-file" help = "Configuration file." arg_type = String required = true "--hist-dir" help = "Annual forcing file. It should contain: TAUX, TAUY, SWFLX, NSWFLX, VSFLX" arg_type = String required = true "--year-rng" help = "The year range that the user wants to run. The begin date will be set to 12/31 of the year before the first year." nargs = 2 required = true arg_type = Int64 "--SOM" help = "If set then HMXL will be set to the deepest z_w_bot. Remember to double check Nz_bot file." arg_type = Bool default = false end return parse_args(s) end parsed = parse_commandline() MPI.Init() comm = MPI.COMM_WORLD rank = MPI.Comm_rank(comm) is_master = rank == 0 config = nothing SOM_HMXL = nothing if is_master year_rng = parsed["year-rng"] config = TOML.parsefile(parsed["config-file"]) config["DRIVER"]["sync_thermo_state_before_stepping"] = true # We will strictly use a day Δt_float = 86400.0 Δt = Dates.Second(Δt_float) read_restart = false cfgmc = config["MODEL_CORE"] cfgmm = config["MODEL_MISC"] cfgmc["Qflx"] = "off" cfgmc["weak_restoring"] = "off" cfgmc["transform_vector_field"] = false Nz = length(cfgmc["z_w"])-1 first_run = true if parsed["SOM"] global SOM_HMXL = minimum(cfgmc["z_w"]) cfgmc["Ks_V"] = 0.0 println("`SOM` also set convective_adjustment = off and Ks_V = 0.0") println("HMXL will always be set to $(SOM_HMXL)") end dli = DataLoader.DataLoaderInfo( hist_dir = parsed["hist-dir"], casename = "paper2021_POP2_CTL", state_varnames = ["TEMP", "SALT", "HMXL",], forcing_varnames = ["SFWF", "TAUX", "TAUY", "SHF", "SHF_QSW",], year_rng = year_rng, layers = 1:Nz, ) pred_steps = 5 pred_cnt = 0 t_start = DateTimeNoLeap(year_rng[1], 1, 1, 0, 0, 0) - pred_steps*Δt t_end = DateTimeNoLeap(year_rng[2], 1, 1, 0, 0, 0) end global data = nothing global QFLXT = nothing global QFLXS = nothing global Nz_bot = nothing coupler_funcs = ( master_before_model_init = function() return read_restart, t_start end, master_after_model_init! = function(OMMODULE, OMDATA) # setup forcing println("# Model beg time: $(string(t_start))") println("# Model end time: $(string(t_end))") if parsed["SOM"] println("SOM specific: Load Nz_bot for later usage.") global Nz_bot = OMDATA.mb.ev.topo.Nz_bot_sT end end, master_before_model_run! = function(OMMODULE, OMDATA) global first_run, data, SOM_HMXL if first_run first_run = false # only put the TEMP and SALT of # the day before start date into the model data_init = DataLoader.loadInitDataAndForcing(dli, OMDATA.clock.time) if parsed["SOM"] println("SOM would propagate SST and SSS to the whole column") data2SOM!(data_init["TEMP"], Nz_bot) data2SOM!(data_init["SALT"], Nz_bot) end reinitModel!( OMDATA, data_init; forcing = true, thermal = true, SOM_HMXL = SOM_HMXL, ) global QFLXT = copy(data_init["TEMP"]) global QFLXS = copy(QFLXT) QFLXT .= 0.0 QFLXS .= 0.0 global pred_reinit = false global pred_cnt = 0 end write_restart = OMDATA.clock.time == t_end end_phase = OMDATA.clock.time > t_end if ! end_phase return_values = ( :RUN, Δt, write_restart ) else return_values = ( :END, 0.0, write_restart ) end return return_values end, master_after_model_run! = function(OMMODULE, OMDATA) global pred_reinit, pred_cnt, data fi = OMDATA.mb.fi data_next = DataLoader.loadInitDataAndForcing(dli, OMDATA.clock.time + Δt) if parsed["SOM"] println("SOM would propagate SST and SSS to the whole column") data2SOM!(data_next["TEMP"], Nz_bot) data2SOM!(data_next["SALT"], Nz_bot) end #_, m, d = DataLoader.getYMD(OMDATA.clock.time) pred_cnt += 1 #if any(d .== compute_qflx_dates[m]) if pred_cnt == pred_steps writeLog("# [Warning] Compare to OGCM, compute QFLX and reinitialize") # compute QFLX _Δt = Δt_float * pred_steps # @. fi.sv[:QFLXT] = (data_next["TEMP"] - fi.sv[:TEMP]) / _Δt # @. fi.sv[:QFLXS] = (data_next["SALT"] - fi.sv[:SALT]) / _Δt @. QFLXT = (data_next["TEMP"] - fi.sv[:TEMP]) / _Δt @. QFLXS = (data_next["SALT"] - fi.sv[:SALT]) / _Δt reinitModel!(OMDATA, data_next; forcing=true, thermal=true, SOM_HMXL=SOM_HMXL) pred_cnt = 0 else reinitModel!(OMDATA, data_next; forcing=true, thermal=false, SOM_HMXL=SOM_HMXL) #fi._QFLXX_ .= 0.0 end fi.sv[:QFLXT] .= QFLXT fi.sv[:QFLXS] .= QFLXS end, master_finalize! = function(OMMODULE, OMDATA) writeLog("[Coupler] Finalize") end, ) runModel( ENGINE_EMOM, coupler_funcs, config, )
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using SparseArrays using Distributions using StatsBase using GVFHordes import GVFHordes: AbstractHorde using MinimalRLCore mutable struct Agent{#IR<:IntrinsicReward, # H, # BH, # DL<:Learner, # BL<:Learner, O, Φ, SC} <: AbstractAgent behaviour_learner::Learner behaviour_demons::Union{AbstractHorde,Nothing} # Round Robin learners have behaviour demons set to nothing behaviour_gamma::Float64 demons::AbstractHorde demon_learner::Learner last_obs::O last_state::Φ last_action::Int num_actions::Int intrinsic_reward::IntrinsicReward state_constructor::SC use_external_reward::Bool exploration::ExplorationStrategy random_first_action::Bool end function Agent(horde, behaviour_feature_size::Int, behaviour_learner, behaviour_horde, behaviour_gamma, demon_learner, observation_size::Int, num_actions::Int, intrinsic_reward_type, state_constructor, use_external_reward, exploration_strategy, random_first_action) behaviour_weight_dims = (num_actions, behaviour_feature_size) intrinsic_reward = if intrinsic_reward_type == "weight_change" #TODO: The intrinsic reward is defined by how the components of the agent are put together. For example, an intrinsic reward # could be the model error, which would then require different components that are assembled in the agent # Not sure how the construction of intrinisic reward could be abstracted out of this constructor WeightChange(demon_learner) elseif intrinsic_reward_type == "no_reward" NoReward() else throw(ArgumentError("Not a valid intrinsic reward")) end Agent(behaviour_learner, behaviour_horde, behaviour_gamma, horde, demon_learner, zeros(observation_size), spzeros(behaviour_feature_size), 0, # last_action num_actions, intrinsic_reward, state_constructor, use_external_reward, exploration_strategy, random_first_action) end function proc_input(agent, obs) return agent.state_constructor(obs) end function get_action(agent, state, obs) action_probs = if agent.behaviour_learner isa OneDTMazeUtils.RoundRobinPolicy || agent.behaviour_learner isa TwoDGridWorldUtils.RoundRobinPolicy get_action_probs(agent.behaviour_learner, state, obs) elseif agent.behaviour_learner.update isa TabularRoundRobin get_action_probs(agent.behaviour_learner.update, state, obs) elseif agent.behaviour_learner isa BaselineUtils.FollowDemon agent.behaviour_learner(obs) elseif agent.behaviour_learner isa BaselineUtils.RandomDemons qs = agent.behaviour_learner(obs) agent.exploration(qs) else qs = agent.behaviour_learner(state) agent.exploration(qs) end action = sample(1:agent.num_actions, Weights(action_probs)) return action, action_probs end function assign_horde!(agent, horde) agent.demons = horde end MinimalRLCore.end!(agent, obs, reward, is_terminal) = MinimalRLCore.step!(agent, obs, reward, is_terminal) function MinimalRLCore.start!(agent::Agent, obs, args...) next_state = proc_input(agent, obs) #Always exploring starts step!(agent.exploration) next_action = if agent.random_first_action sample(1:agent.num_actions, Weights(ones(agent.num_actions))) else a, probs = get_action(agent,next_state, obs) a end # NOTE: IS THIS RIGHT, seems wierd!? _, discounts, _ = get(agent.demons; state_t = obs, action_t = next_action, state_tp1 = obs, action_tp1 = next_action) agent.last_state = next_state agent.last_action = next_action agent.last_obs = obs zero_eligibility_traces!(agent.demon_learner) return next_action end function MinimalRLCore.step!(agent::Agent, obs, r, is_terminal, args...) next_state = proc_input(agent, obs) step!(agent.exploration) next_action, next_action_probs = get_action(agent, next_state, obs) update_demons!(agent, agent.last_obs, obs, agent.last_state, agent.last_action, next_state, next_action, is_terminal, r) #get intrinssic reward r_int = update_reward!(agent.intrinsic_reward, agent) total_reward = agent.use_external_reward ? r_int + r : r_int update_behaviour!(agent, agent.last_obs, obs, agent.last_state, agent.last_action, next_state, next_action, is_terminal, total_reward) agent.last_state = next_state agent.last_action = next_action agent.last_obs = obs return next_action end get_behaviour_pis(agent::Agent, state, obs) = get_action(agent, state, obs)[2] function μ_π(agent::Agent, obs) state = proc_input(agent, obs) get_behaviour_pis(agent,state,obs) end function update_demons!(agent,obs, next_obs, state, action, next_state, next_action, is_terminal, env_reward) update!(agent.demon_learner, agent.demons, obs, next_obs, state, action, next_state, next_action, is_terminal, (state, obs) -> get_behaviour_pis(agent, state, obs), env_reward) end function update_behaviour!(agent, obs, next_obs, state, action, next_state, next_action, is_terminal, reward) # behaviour_pis = get_behaviour_pis(agent, state, obs) # next_behaviour_pis = get_behaviour_pis(agent, next_state, next_obs) #NOTE: Different call than demon updates as the reward and environment pseudotermination function update!(agent.behaviour_learner, agent.behaviour_demons, obs, next_obs, state, action, next_state, next_action, is_terminal, (state, obs) -> get_behaviour_pis(agent, state, obs), reward) end function get_demon_prediction(agent::Agent, obs, action) ϕ = agent.state_constructor(obs) agent.demon_learner(ϕ, action) end
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2.163465
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<filename>src/types/reference_frames.jl # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Description # ============================================================================== # # Structures related to reference frames. # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # export T_ECEFs, T_ECIs, T_ECIs_of_date, T_ECEFs_IAU_2006, T_ECIs_IAU_2006, T_ECIs_IAU_2006_Equinox_of_date, T_ROT """ T_ECEFs Union of all Earth-Centered Earth-Fixed (ECEF) frames supported by the IAU-76/FK5 theory. """ T_ECEFs = Union{Val{:ITRF}, Val{:PEF}} """ T_ECIs Union of all Earth-Centered Inertial (ECI) frames supported by the IAU-76/FK5 theory. """ T_ECIs = Union{Val{:GCRF}, Val{:J2000}, Val{:TOD}, Val{:MOD}, Val{:TEME}} """ T_ECIs_of_date Union of all *of date* Earth-Centered Inertial (ECI) frames supported by the IAU-76/FK5 theory. """ T_ECIs_of_date = Union{Val{:TOD}, Val{:MOD}, Val{:TEME}} """ T_ECEFs_IAU_2006 Union of all Earth-Centered Earth-Fixed (ECEF) frames supported by IAU-2006/2010 theory. """ T_ECEFs_IAU_2006 = Union{Val{:ITRF}, Val{:TIRS}} """ T_ECIs_IAU_2006_CIO Union of all Earth-Centered Inertial (ECI) frames supported by CIO-based IAU-2006/2010 theory. """ T_ECIs_IAU_2006_CIO = Union{Val{:GCRF}, Val{:CIRS}} """ T_ECIs_IAU_2006_Equinox Union of all Earth-Centered Inertial (ECI) frames supported by Equinox-based IAU-2006/2010 theory. """ T_ECIs_IAU_2006_Equinox = Union{Val{:GCRF}, Val{:MJ2000}, Val{:MOD06}, Val{:ERS}} """ T_ECIs_IAU_2006 Union of all Earth-Centered Inertial (ECI) frames supported by IAU-2006/2010 theory. """ T_ECIs_IAU_2006 = Union{T_ECIs_IAU_2006_CIO, T_ECIs_IAU_2006_Equinox} """ T_ECIs_IAU_2006_Equinox_of_date Union of all *of date* Earth-Centered Inertial (ECI) frames supported by the equinox-based IAU-2006/2010 theory. """ T_ECIs_IAU_2006_Equinox_of_date = Union{Val{:MOD06}, Val{:ERS}} """ T_ROT Union of all supported rotation descriptions. """ T_ROT = Union{Type{DCM}, Type{Quaternion}}
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<gh_stars>10-100 is_constraint(::typeof(voltage_magnitude_constraints)) = true is_linear(polar::PolarForm, ::typeof(voltage_magnitude_constraints)) = true # We add constraint only on vmag_pq function voltage_magnitude_constraints(polar::PolarForm, cons, vm, va, pinj, qinj) index_pq = polar.indexing.index_pq cons .= @view vm[index_pq] return end function voltage_magnitude_constraints(polar::PolarForm, cons, buffer) index_pq = polar.indexing.index_pq cons .= @view buffer.vmag[index_pq] return end function size_constraint(polar::PolarForm, ::typeof(voltage_magnitude_constraints)) return PS.get(polar.network, PS.NumberOfPQBuses()) end function bounds(polar::PolarForm, ::typeof(voltage_magnitude_constraints)) npv = PS.get(polar.network, PS.NumberOfPVBuses()) npq = PS.get(polar.network, PS.NumberOfPQBuses()) fr_ = npq + npv + 1 to_ = 2*npq + npv return polar.x_min[fr_:to_], polar.x_max[fr_:to_] end function adjoint!( polar::PolarForm, pbm::AutoDiff.TapeMemory{F, S, I}, cons, ∂cons, vm, ∂vm, va, ∂va, pinj, ∂pinj, ) where {F<:typeof(voltage_magnitude_constraints), S, I} index_pq = polar.indexing.index_pq ∂vm[index_pq] .= ∂cons end function matpower_jacobian( polar::PolarForm, X::Union{State,Control}, cons::typeof(voltage_magnitude_constraints), V, ) m = size_constraint(polar, cons) nᵤ = get(polar, NumberOfControl()) nₓ = get(polar, NumberOfState()) npv = PS.get(polar.network, PS.NumberOfPVBuses()) npq = PS.get(polar.network, PS.NumberOfPQBuses()) shift = npq + npv I = 1:m J = (shift+1):(shift+npq) V = ones(m) if isa(X, State) return sparse(I, J, V, m, nₓ) elseif isa(X, Control) return spzeros(m, nᵤ) end end function matpower_hessian(polar::PolarForm, ::typeof(voltage_magnitude_constraints), buffer, λ) nu = get(polar, NumberOfControl()) nx = get(polar, NumberOfState()) return FullSpaceHessian( spzeros(nx, nx), spzeros(nu, nx), spzeros(nu, nu), ) end
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<gh_stars>1-10 using WAVI function MISMIP_PLUS_Ice1r(initial_thickness) #Grid and boundary conditions nx = 80 ny = 10 nσ = 4 x0 = 0.0 y0 = -40000.0 dx = 8000.0 dy = 8000.0 h_mask=trues(nx,ny) u_iszero = falses(nx+1,ny); u_iszero[1,:].=true v_iszero=falses(nx,ny+1); v_iszero[:,1].=true; v_iszero[:,end].=true grid = Grid(nx = nx, ny = ny, nσ = nσ, x0 = x0, y0 = y0, dx = dx, dy = dy, h_mask = h_mask, u_iszero = u_iszero, v_iszero = v_iszero) #Bed bed = WAVI.mismip_plus_bed #function definition #solver parameters maxiter_picard = 30 solver_params = SolverParams(maxiter_picard = maxiter_picard) #Physical parameters accumulation_rate = 0.3 params = Params(default_thickness = default_thickness, accumulation_rate = accumulation_rate) #initial conditions initial_conditions = InitialConditions(initial_thickness = initial_thickness) #make the model model = Model(grid = grid, bed_elevation = bed, params = params, solver_params = solver_params, initial_conditions = initial_conditions) #embed the model with melt rate model function m1(h, b) draft = -(918.0 / 1028.0) * h cavity_thickness = draft .- b cavity_thickness = max.(cavity_thickness, 0) m = 0.2*tanh.(cavity_thickness./75).*max.((-100 .- draft), 0) return m end arguments = (h = model.fields.gh.h, b = model.fields.gh.b) melt_rate_model = AnalyticMeltRate(melt_partial_cell = true, melt_rate_function = m1, function_arguments = arguments) add_melt_rate_model!(model,melt_rate_model) #timestepping parameters dt = 0.1 end_time = 100. timestepping_params = TimesteppingParams(dt = dt, end_time = end_time) simulation = Simulation(model = model, timestepping_params = timestepping_params) #perform the simulation run_simulation!(simulation) return simulation end #@time simulation = MISMIP_PLUS_test();
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# Automatically generated using Clang.jl wrap_c, version 0.0.0 using Compat const VULKAN_H_ = 1 const VK_VERSION_1_0 = 1 # Skipping MacroDefinition: VK_MAKE_VERSION ( major , minor , patch ) ( ( ( major ) << 22 ) | ( ( minor ) << 12 ) | ( patch ) ) # Skipping MacroDefinition: VK_API_VERSION_1_0 VK_MAKE_VERSION ( 1 , 0 , 0 ) # Skipping MacroDefinition: VK_VERSION_MAJOR ( version ) ( ( uint32_t ) ( version ) >> 22 ) # Skipping MacroDefinition: VK_VERSION_MINOR ( version ) ( ( ( uint32_t ) ( version ) >> 12 ) & 0x3ff ) # Skipping MacroDefinition: VK_VERSION_PATCH ( version ) ( ( uint32_t ) ( version ) & 0xfff ) const VK_HEADER_VERSION = 8 const VK_NULL_HANDLE = 0 # Skipping MacroDefinition: VK_DEFINE_HANDLE ( object ) typedef struct object ## _T * object ; # Skipping MacroDefinition: VK_DEFINE_NON_DISPATCHABLE_HANDLE ( object ) typedef struct object ## _T * object ; const VK_LOD_CLAMP_NONE = Float32(1000.0) const VK_REMAINING_MIP_LEVELS = ~(UInt32(0)) const VK_REMAINING_ARRAY_LAYERS = ~(UInt32(0)) const VK_WHOLE_SIZE = ~(UInt64(0)) const VK_ATTACHMENT_UNUSED = ~(UInt32(0)) const VK_TRUE = 1 const VK_FALSE = 0 const VK_QUEUE_FAMILY_IGNORED = ~(UInt32(0)) const VK_SUBPASS_EXTERNAL = ~(UInt32(0)) const VK_MAX_PHYSICAL_DEVICE_NAME_SIZE = 256 const VK_UUID_SIZE = 16 const VK_MAX_MEMORY_TYPES = 32 const VK_MAX_MEMORY_HEAPS = 16 const VK_MAX_EXTENSION_NAME_SIZE = 256 const VK_MAX_DESCRIPTION_SIZE = 256 const VK_KHR_surface = 1 const VK_KHR_SURFACE_SPEC_VERSION = 25 const VK_KHR_SURFACE_EXTENSION_NAME = "VK_KHR_surface" const VK_KHR_swapchain = 1 const VK_KHR_SWAPCHAIN_SPEC_VERSION = 67 const VK_KHR_SWAPCHAIN_EXTENSION_NAME = "VK_KHR_swapchain" const VK_KHR_display = 1 const VK_KHR_DISPLAY_SPEC_VERSION = 21 const VK_KHR_DISPLAY_EXTENSION_NAME = "VK_KHR_display" const VK_KHR_display_swapchain = 1 const VK_KHR_DISPLAY_SWAPCHAIN_SPEC_VERSION = 9 const VK_KHR_DISPLAY_SWAPCHAIN_EXTENSION_NAME = "VK_KHR_display_swapchain" const VK_KHR_xcb_surface = 1 const VK_KHR_XCB_SURFACE_SPEC_VERSION = 6 const VK_KHR_XCB_SURFACE_EXTENSION_NAME = "VK_KHR_xcb_surface" const VK_KHR_wayland_surface = 1 const VK_KHR_WAYLAND_SURFACE_SPEC_VERSION = 5 const VK_KHR_WAYLAND_SURFACE_EXTENSION_NAME = "VK_KHR_wayland_surface" const VK_KHR_mir_surface = 1 const VK_KHR_MIR_SURFACE_SPEC_VERSION = 4 const VK_KHR_MIR_SURFACE_EXTENSION_NAME = "VK_KHR_mir_surface" const VK_KHR_win32_surface = 1 const VK_KHR_WIN32_SURFACE_SPEC_VERSION = 5 const VK_KHR_WIN32_SURFACE_EXTENSION_NAME = "VK_KHR_win32_surface" const VK_KHR_sampler_mirror_clamp_to_edge = 1 const VK_KHR_SAMPLER_MIRROR_CLAMP_TO_EDGE_SPEC_VERSION = 1 const VK_KHR_SAMPLER_MIRROR_CLAMP_TO_EDGE_EXTENSION_NAME = "VK_KHR_sampler_mirror_clamp_to_edge" const VK_EXT_debug_report = 1 const VK_EXT_DEBUG_REPORT_SPEC_VERSION = 2 const VK_EXT_DEBUG_REPORT_EXTENSION_NAME = "VK_EXT_debug_report" @cenum(VkStructureType, VK_STRUCTURE_TYPE_APPLICATION_INFO = 0, VK_STRUCTURE_TYPE_INSTANCE_CREATE_INFO = 1, VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO = 2, VK_STRUCTURE_TYPE_DEVICE_CREATE_INFO = 3, VK_STRUCTURE_TYPE_SUBMIT_INFO = 4, VK_STRUCTURE_TYPE_MEMORY_ALLOCATE_INFO = 5, VK_STRUCTURE_TYPE_MAPPED_MEMORY_RANGE = 6, VK_STRUCTURE_TYPE_BIND_SPARSE_INFO = 7, VK_STRUCTURE_TYPE_FENCE_CREATE_INFO = 8, VK_STRUCTURE_TYPE_SEMAPHORE_CREATE_INFO = 9, VK_STRUCTURE_TYPE_EVENT_CREATE_INFO = 10, VK_STRUCTURE_TYPE_QUERY_POOL_CREATE_INFO = 11, VK_STRUCTURE_TYPE_BUFFER_CREATE_INFO = 12, VK_STRUCTURE_TYPE_BUFFER_VIEW_CREATE_INFO = 13, VK_STRUCTURE_TYPE_IMAGE_CREATE_INFO = 14, VK_STRUCTURE_TYPE_IMAGE_VIEW_CREATE_INFO = 15, VK_STRUCTURE_TYPE_SHADER_MODULE_CREATE_INFO = 16, VK_STRUCTURE_TYPE_PIPELINE_CACHE_CREATE_INFO = 17, VK_STRUCTURE_TYPE_PIPELINE_SHADER_STAGE_CREATE_INFO = 18, VK_STRUCTURE_TYPE_PIPELINE_VERTEX_INPUT_STATE_CREATE_INFO = 19, VK_STRUCTURE_TYPE_PIPELINE_INPUT_ASSEMBLY_STATE_CREATE_INFO = 20, VK_STRUCTURE_TYPE_PIPELINE_TESSELLATION_STATE_CREATE_INFO = 21, VK_STRUCTURE_TYPE_PIPELINE_VIEWPORT_STATE_CREATE_INFO = 22, VK_STRUCTURE_TYPE_PIPELINE_RASTERIZATION_STATE_CREATE_INFO = 23, VK_STRUCTURE_TYPE_PIPELINE_MULTISAMPLE_STATE_CREATE_INFO = 24, VK_STRUCTURE_TYPE_PIPELINE_DEPTH_STENCIL_STATE_CREATE_INFO = 25, VK_STRUCTURE_TYPE_PIPELINE_COLOR_BLEND_STATE_CREATE_INFO = 26, VK_STRUCTURE_TYPE_PIPELINE_DYNAMIC_STATE_CREATE_INFO = 27, VK_STRUCTURE_TYPE_GRAPHICS_PIPELINE_CREATE_INFO = 28, VK_STRUCTURE_TYPE_COMPUTE_PIPELINE_CREATE_INFO = 29, VK_STRUCTURE_TYPE_PIPELINE_LAYOUT_CREATE_INFO = 30, VK_STRUCTURE_TYPE_SAMPLER_CREATE_INFO = 31, VK_STRUCTURE_TYPE_DESCRIPTOR_SET_LAYOUT_CREATE_INFO = 32, VK_STRUCTURE_TYPE_DESCRIPTOR_POOL_CREATE_INFO = 33, VK_STRUCTURE_TYPE_DESCRIPTOR_SET_ALLOCATE_INFO = 34, VK_STRUCTURE_TYPE_WRITE_DESCRIPTOR_SET = 35, VK_STRUCTURE_TYPE_COPY_DESCRIPTOR_SET = 36, VK_STRUCTURE_TYPE_FRAMEBUFFER_CREATE_INFO = 37, VK_STRUCTURE_TYPE_RENDER_PASS_CREATE_INFO = 38, VK_STRUCTURE_TYPE_COMMAND_POOL_CREATE_INFO = 39, VK_STRUCTURE_TYPE_COMMAND_BUFFER_ALLOCATE_INFO = 40, VK_STRUCTURE_TYPE_COMMAND_BUFFER_INHERITANCE_INFO = 41, VK_STRUCTURE_TYPE_COMMAND_BUFFER_BEGIN_INFO = 42, VK_STRUCTURE_TYPE_RENDER_PASS_BEGIN_INFO = 43, VK_STRUCTURE_TYPE_BUFFER_MEMORY_BARRIER = 44, VK_STRUCTURE_TYPE_IMAGE_MEMORY_BARRIER = 45, VK_STRUCTURE_TYPE_MEMORY_BARRIER = 46, VK_STRUCTURE_TYPE_LOADER_INSTANCE_CREATE_INFO = 47, VK_STRUCTURE_TYPE_LOADER_DEVICE_CREATE_INFO = 48, VK_STRUCTURE_TYPE_SWAPCHAIN_CREATE_INFO_KHR = 1000001000, VK_STRUCTURE_TYPE_PRESENT_INFO_KHR = 1000001001, VK_STRUCTURE_TYPE_DISPLAY_MODE_CREATE_INFO_KHR = 1000002000, VK_STRUCTURE_TYPE_DISPLAY_SURFACE_CREATE_INFO_KHR = 1000002001, VK_STRUCTURE_TYPE_DISPLAY_PRESENT_INFO_KHR = 1000003000, VK_STRUCTURE_TYPE_XLIB_SURFACE_CREATE_INFO_KHR = 1000004000, VK_STRUCTURE_TYPE_XCB_SURFACE_CREATE_INFO_KHR = 1000005000, VK_STRUCTURE_TYPE_WAYLAND_SURFACE_CREATE_INFO_KHR = 1000006000, VK_STRUCTURE_TYPE_MIR_SURFACE_CREATE_INFO_KHR = 1000007000, VK_STRUCTURE_TYPE_ANDROID_SURFACE_CREATE_INFO_KHR = 1000008000, VK_STRUCTURE_TYPE_WIN32_SURFACE_CREATE_INFO_KHR = 1000009000, VK_STRUCTURE_TYPE_DEBUG_REPORT_CALLBACK_CREATE_INFO_EXT = 1000011000, VK_STRUCTURE_TYPE_BEGIN_RANGE = 0, VK_STRUCTURE_TYPE_END_RANGE = 48, VK_STRUCTURE_TYPE_RANGE_SIZE = 49, VK_STRUCTURE_TYPE_MAX_ENUM = 2147483647, ) const VK_STRUCTURE_TYPE_DEBUG_REPORT_CREATE_INFO_EXT = VK_STRUCTURE_TYPE_DEBUG_REPORT_CALLBACK_CREATE_INFO_EXT const VK_NV_glsl_shader = 1 const VK_NV_GLSL_SHADER_SPEC_VERSION = 1 const VK_NV_GLSL_SHADER_EXTENSION_NAME = "VK_NV_glsl_shader" const VK_IMG_filter_cubic = 1 const VK_IMG_FILTER_CUBIC_SPEC_VERSION = 1 const VK_IMG_FILTER_CUBIC_EXTENSION_NAME = "VK_IMG_filter_cubic" typealias VkFlags UInt32 typealias VkBool32 UInt32 typealias VkDeviceSize UInt64 typealias VkSampleMask UInt32 typealias VkInstance_T Void typealias VkInstance Ptr{VkInstance_T} typealias VkPhysicalDevice_T Void typealias VkPhysicalDevice Ptr{VkPhysicalDevice_T} typealias VkDevice_T Void typealias VkDevice Ptr{VkDevice_T} typealias VkQueue_T Void typealias VkQueue Ptr{VkQueue_T} typealias VkSemaphore_T Void typealias VkSemaphore Ptr{VkSemaphore_T} typealias VkCommandBuffer_T Void typealias VkCommandBuffer Ptr{VkCommandBuffer_T} typealias VkFence_T Void typealias VkFence Ptr{VkFence_T} typealias VkDeviceMemory_T Void typealias VkDeviceMemory Ptr{VkDeviceMemory_T} typealias VkBuffer_T Void typealias VkBuffer Ptr{VkBuffer_T} typealias VkImage_T Void typealias VkImage Ptr{VkImage_T} typealias VkEvent_T Void typealias VkEvent Ptr{VkEvent_T} typealias VkQueryPool_T Void typealias VkQueryPool Ptr{VkQueryPool_T} typealias VkBufferView_T Void typealias VkBufferView Ptr{VkBufferView_T} typealias VkImageView_T Void typealias VkImageView Ptr{VkImageView_T} typealias VkShaderModule_T Void typealias VkShaderModule Ptr{VkShaderModule_T} typealias VkPipelineCache_T Void typealias VkPipelineCache Ptr{VkPipelineCache_T} typealias VkPipelineLayout_T Void typealias VkPipelineLayout Ptr{VkPipelineLayout_T} typealias VkRenderPass_T Void typealias VkRenderPass Ptr{VkRenderPass_T} typealias VkPipeline_T Void typealias VkPipeline Ptr{VkPipeline_T} typealias VkDescriptorSetLayout_T Void typealias VkDescriptorSetLayout Ptr{VkDescriptorSetLayout_T} typealias VkSampler_T Void typealias VkSampler Ptr{VkSampler_T} typealias VkDescriptorPool_T Void typealias VkDescriptorPool Ptr{VkDescriptorPool_T} typealias VkDescriptorSet_T Void typealias VkDescriptorSet Ptr{VkDescriptorSet_T} typealias VkFramebuffer_T Void typealias VkFramebuffer Ptr{VkFramebuffer_T} typealias VkCommandPool_T Void typealias VkCommandPool Ptr{VkCommandPool_T} @cenum(VkPipelineCacheHeaderVersion, VK_PIPELINE_CACHE_HEADER_VERSION_ONE = 1, VK_PIPELINE_CACHE_HEADER_VERSION_BEGIN_RANGE = 1, VK_PIPELINE_CACHE_HEADER_VERSION_END_RANGE = 1, VK_PIPELINE_CACHE_HEADER_VERSION_RANGE_SIZE = 1, VK_PIPELINE_CACHE_HEADER_VERSION_MAX_ENUM = 2147483647, ) @cenum(VkResult, VK_SUCCESS = 0, VK_NOT_READY = 1, VK_TIMEOUT = 2, VK_EVENT_SET = 3, VK_EVENT_RESET = 4, VK_INCOMPLETE = 5, VK_ERROR_OUT_OF_HOST_MEMORY = -1, VK_ERROR_OUT_OF_DEVICE_MEMORY = -2, VK_ERROR_INITIALIZATION_FAILED = -3, VK_ERROR_DEVICE_LOST = -4, VK_ERROR_MEMORY_MAP_FAILED = -5, VK_ERROR_LAYER_NOT_PRESENT = -6, VK_ERROR_EXTENSION_NOT_PRESENT = -7, VK_ERROR_FEATURE_NOT_PRESENT = -8, VK_ERROR_INCOMPATIBLE_DRIVER = -9, VK_ERROR_TOO_MANY_OBJECTS = -10, VK_ERROR_FORMAT_NOT_SUPPORTED = -11, VK_ERROR_SURFACE_LOST_KHR = -1000000000, VK_ERROR_NATIVE_WINDOW_IN_USE_KHR = -1000000001, VK_SUBOPTIMAL_KHR = 1000001003, VK_ERROR_OUT_OF_DATE_KHR = -1000001004, VK_ERROR_INCOMPATIBLE_DISPLAY_KHR = -1000003001, VK_ERROR_VALIDATION_FAILED_EXT = -1000011001, VK_ERROR_INVALID_SHADER_NV = -1000012000, VK_RESULT_BEGIN_RANGE = -11, VK_RESULT_END_RANGE = 5, VK_RESULT_RANGE_SIZE = 17, VK_RESULT_MAX_ENUM = 2147483647, ) @cenum(VkSystemAllocationScope, VK_SYSTEM_ALLOCATION_SCOPE_COMMAND = 0, VK_SYSTEM_ALLOCATION_SCOPE_OBJECT = 1, VK_SYSTEM_ALLOCATION_SCOPE_CACHE = 2, VK_SYSTEM_ALLOCATION_SCOPE_DEVICE = 3, VK_SYSTEM_ALLOCATION_SCOPE_INSTANCE = 4, VK_SYSTEM_ALLOCATION_SCOPE_BEGIN_RANGE = 0, VK_SYSTEM_ALLOCATION_SCOPE_END_RANGE = 4, VK_SYSTEM_ALLOCATION_SCOPE_RANGE_SIZE = 5, VK_SYSTEM_ALLOCATION_SCOPE_MAX_ENUM = 2147483647, ) @cenum(VkInternalAllocationType, VK_INTERNAL_ALLOCATION_TYPE_EXECUTABLE = 0, VK_INTERNAL_ALLOCATION_TYPE_BEGIN_RANGE = 0, VK_INTERNAL_ALLOCATION_TYPE_END_RANGE = 0, VK_INTERNAL_ALLOCATION_TYPE_RANGE_SIZE = 1, VK_INTERNAL_ALLOCATION_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkFormat, VK_FORMAT_UNDEFINED = 0, VK_FORMAT_R4G4_UNORM_PACK8 = 1, VK_FORMAT_R4G4B4A4_UNORM_PACK16 = 2, VK_FORMAT_B4G4R4A4_UNORM_PACK16 = 3, VK_FORMAT_R5G6B5_UNORM_PACK16 = 4, VK_FORMAT_B5G6R5_UNORM_PACK16 = 5, VK_FORMAT_R5G5B5A1_UNORM_PACK16 = 6, VK_FORMAT_B5G5R5A1_UNORM_PACK16 = 7, VK_FORMAT_A1R5G5B5_UNORM_PACK16 = 8, VK_FORMAT_R8_UNORM = 9, VK_FORMAT_R8_SNORM = 10, VK_FORMAT_R8_USCALED = 11, VK_FORMAT_R8_SSCALED = 12, VK_FORMAT_R8_UINT = 13, VK_FORMAT_R8_SINT = 14, VK_FORMAT_R8_SRGB = 15, VK_FORMAT_R8G8_UNORM = 16, VK_FORMAT_R8G8_SNORM = 17, VK_FORMAT_R8G8_USCALED = 18, VK_FORMAT_R8G8_SSCALED = 19, VK_FORMAT_R8G8_UINT = 20, VK_FORMAT_R8G8_SINT = 21, VK_FORMAT_R8G8_SRGB = 22, VK_FORMAT_R8G8B8_UNORM = 23, VK_FORMAT_R8G8B8_SNORM = 24, VK_FORMAT_R8G8B8_USCALED = 25, VK_FORMAT_R8G8B8_SSCALED = 26, VK_FORMAT_R8G8B8_UINT = 27, VK_FORMAT_R8G8B8_SINT = 28, VK_FORMAT_R8G8B8_SRGB = 29, VK_FORMAT_B8G8R8_UNORM = 30, VK_FORMAT_B8G8R8_SNORM = 31, VK_FORMAT_B8G8R8_USCALED = 32, VK_FORMAT_B8G8R8_SSCALED = 33, VK_FORMAT_B8G8R8_UINT = 34, VK_FORMAT_B8G8R8_SINT = 35, VK_FORMAT_B8G8R8_SRGB = 36, VK_FORMAT_R8G8B8A8_UNORM = 37, VK_FORMAT_R8G8B8A8_SNORM = 38, VK_FORMAT_R8G8B8A8_USCALED = 39, VK_FORMAT_R8G8B8A8_SSCALED = 40, VK_FORMAT_R8G8B8A8_UINT = 41, VK_FORMAT_R8G8B8A8_SINT = 42, VK_FORMAT_R8G8B8A8_SRGB = 43, VK_FORMAT_B8G8R8A8_UNORM = 44, VK_FORMAT_B8G8R8A8_SNORM = 45, VK_FORMAT_B8G8R8A8_USCALED = 46, VK_FORMAT_B8G8R8A8_SSCALED = 47, VK_FORMAT_B8G8R8A8_UINT = 48, VK_FORMAT_B8G8R8A8_SINT = 49, VK_FORMAT_B8G8R8A8_SRGB = 50, VK_FORMAT_A8B8G8R8_UNORM_PACK32 = 51, VK_FORMAT_A8B8G8R8_SNORM_PACK32 = 52, VK_FORMAT_A8B8G8R8_USCALED_PACK32 = 53, VK_FORMAT_A8B8G8R8_SSCALED_PACK32 = 54, VK_FORMAT_A8B8G8R8_UINT_PACK32 = 55, VK_FORMAT_A8B8G8R8_SINT_PACK32 = 56, VK_FORMAT_A8B8G8R8_SRGB_PACK32 = 57, VK_FORMAT_A2R10G10B10_UNORM_PACK32 = 58, VK_FORMAT_A2R10G10B10_SNORM_PACK32 = 59, VK_FORMAT_A2R10G10B10_USCALED_PACK32 = 60, VK_FORMAT_A2R10G10B10_SSCALED_PACK32 = 61, VK_FORMAT_A2R10G10B10_UINT_PACK32 = 62, VK_FORMAT_A2R10G10B10_SINT_PACK32 = 63, VK_FORMAT_A2B10G10R10_UNORM_PACK32 = 64, VK_FORMAT_A2B10G10R10_SNORM_PACK32 = 65, VK_FORMAT_A2B10G10R10_USCALED_PACK32 = 66, VK_FORMAT_A2B10G10R10_SSCALED_PACK32 = 67, VK_FORMAT_A2B10G10R10_UINT_PACK32 = 68, VK_FORMAT_A2B10G10R10_SINT_PACK32 = 69, VK_FORMAT_R16_UNORM = 70, VK_FORMAT_R16_SNORM = 71, VK_FORMAT_R16_USCALED = 72, VK_FORMAT_R16_SSCALED = 73, VK_FORMAT_R16_UINT = 74, VK_FORMAT_R16_SINT = 75, VK_FORMAT_R16_SFLOAT = 76, VK_FORMAT_R16G16_UNORM = 77, VK_FORMAT_R16G16_SNORM = 78, VK_FORMAT_R16G16_USCALED = 79, VK_FORMAT_R16G16_SSCALED = 80, VK_FORMAT_R16G16_UINT = 81, VK_FORMAT_R16G16_SINT = 82, VK_FORMAT_R16G16_SFLOAT = 83, VK_FORMAT_R16G16B16_UNORM = 84, VK_FORMAT_R16G16B16_SNORM = 85, VK_FORMAT_R16G16B16_USCALED = 86, VK_FORMAT_R16G16B16_SSCALED = 87, VK_FORMAT_R16G16B16_UINT = 88, VK_FORMAT_R16G16B16_SINT = 89, VK_FORMAT_R16G16B16_SFLOAT = 90, VK_FORMAT_R16G16B16A16_UNORM = 91, VK_FORMAT_R16G16B16A16_SNORM = 92, VK_FORMAT_R16G16B16A16_USCALED = 93, VK_FORMAT_R16G16B16A16_SSCALED = 94, VK_FORMAT_R16G16B16A16_UINT = 95, VK_FORMAT_R16G16B16A16_SINT = 96, VK_FORMAT_R16G16B16A16_SFLOAT = 97, VK_FORMAT_R32_UINT = 98, VK_FORMAT_R32_SINT = 99, VK_FORMAT_R32_SFLOAT = 100, VK_FORMAT_R32G32_UINT = 101, VK_FORMAT_R32G32_SINT = 102, VK_FORMAT_R32G32_SFLOAT = 103, VK_FORMAT_R32G32B32_UINT = 104, VK_FORMAT_R32G32B32_SINT = 105, VK_FORMAT_R32G32B32_SFLOAT = 106, VK_FORMAT_R32G32B32A32_UINT = 107, VK_FORMAT_R32G32B32A32_SINT = 108, VK_FORMAT_R32G32B32A32_SFLOAT = 109, VK_FORMAT_R64_UINT = 110, VK_FORMAT_R64_SINT = 111, VK_FORMAT_R64_SFLOAT = 112, VK_FORMAT_R64G64_UINT = 113, VK_FORMAT_R64G64_SINT = 114, VK_FORMAT_R64G64_SFLOAT = 115, VK_FORMAT_R64G64B64_UINT = 116, VK_FORMAT_R64G64B64_SINT = 117, VK_FORMAT_R64G64B64_SFLOAT = 118, VK_FORMAT_R64G64B64A64_UINT = 119, VK_FORMAT_R64G64B64A64_SINT = 120, VK_FORMAT_R64G64B64A64_SFLOAT = 121, VK_FORMAT_B10G11R11_UFLOAT_PACK32 = 122, VK_FORMAT_E5B9G9R9_UFLOAT_PACK32 = 123, VK_FORMAT_D16_UNORM = 124, VK_FORMAT_X8_D24_UNORM_PACK32 = 125, VK_FORMAT_D32_SFLOAT = 126, VK_FORMAT_S8_UINT = 127, VK_FORMAT_D16_UNORM_S8_UINT = 128, VK_FORMAT_D24_UNORM_S8_UINT = 129, VK_FORMAT_D32_SFLOAT_S8_UINT = 130, VK_FORMAT_BC1_RGB_UNORM_BLOCK = 131, VK_FORMAT_BC1_RGB_SRGB_BLOCK = 132, VK_FORMAT_BC1_RGBA_UNORM_BLOCK = 133, VK_FORMAT_BC1_RGBA_SRGB_BLOCK = 134, VK_FORMAT_BC2_UNORM_BLOCK = 135, VK_FORMAT_BC2_SRGB_BLOCK = 136, VK_FORMAT_BC3_UNORM_BLOCK = 137, VK_FORMAT_BC3_SRGB_BLOCK = 138, VK_FORMAT_BC4_UNORM_BLOCK = 139, VK_FORMAT_BC4_SNORM_BLOCK = 140, VK_FORMAT_BC5_UNORM_BLOCK = 141, VK_FORMAT_BC5_SNORM_BLOCK = 142, VK_FORMAT_BC6H_UFLOAT_BLOCK = 143, VK_FORMAT_BC6H_SFLOAT_BLOCK = 144, VK_FORMAT_BC7_UNORM_BLOCK = 145, VK_FORMAT_BC7_SRGB_BLOCK = 146, VK_FORMAT_ETC2_R8G8B8_UNORM_BLOCK = 147, VK_FORMAT_ETC2_R8G8B8_SRGB_BLOCK = 148, VK_FORMAT_ETC2_R8G8B8A1_UNORM_BLOCK = 149, VK_FORMAT_ETC2_R8G8B8A1_SRGB_BLOCK = 150, VK_FORMAT_ETC2_R8G8B8A8_UNORM_BLOCK = 151, VK_FORMAT_ETC2_R8G8B8A8_SRGB_BLOCK = 152, VK_FORMAT_EAC_R11_UNORM_BLOCK = 153, VK_FORMAT_EAC_R11_SNORM_BLOCK = 154, VK_FORMAT_EAC_R11G11_UNORM_BLOCK = 155, VK_FORMAT_EAC_R11G11_SNORM_BLOCK = 156, VK_FORMAT_ASTC_4x4_UNORM_BLOCK = 157, VK_FORMAT_ASTC_4x4_SRGB_BLOCK = 158, VK_FORMAT_ASTC_5x4_UNORM_BLOCK = 159, VK_FORMAT_ASTC_5x4_SRGB_BLOCK = 160, VK_FORMAT_ASTC_5x5_UNORM_BLOCK = 161, VK_FORMAT_ASTC_5x5_SRGB_BLOCK = 162, VK_FORMAT_ASTC_6x5_UNORM_BLOCK = 163, VK_FORMAT_ASTC_6x5_SRGB_BLOCK = 164, VK_FORMAT_ASTC_6x6_UNORM_BLOCK = 165, VK_FORMAT_ASTC_6x6_SRGB_BLOCK = 166, VK_FORMAT_ASTC_8x5_UNORM_BLOCK = 167, VK_FORMAT_ASTC_8x5_SRGB_BLOCK = 168, VK_FORMAT_ASTC_8x6_UNORM_BLOCK = 169, VK_FORMAT_ASTC_8x6_SRGB_BLOCK = 170, VK_FORMAT_ASTC_8x8_UNORM_BLOCK = 171, VK_FORMAT_ASTC_8x8_SRGB_BLOCK = 172, VK_FORMAT_ASTC_10x5_UNORM_BLOCK = 173, VK_FORMAT_ASTC_10x5_SRGB_BLOCK = 174, VK_FORMAT_ASTC_10x6_UNORM_BLOCK = 175, VK_FORMAT_ASTC_10x6_SRGB_BLOCK = 176, VK_FORMAT_ASTC_10x8_UNORM_BLOCK = 177, VK_FORMAT_ASTC_10x8_SRGB_BLOCK = 178, VK_FORMAT_ASTC_10x10_UNORM_BLOCK = 179, VK_FORMAT_ASTC_10x10_SRGB_BLOCK = 180, VK_FORMAT_ASTC_12x10_UNORM_BLOCK = 181, VK_FORMAT_ASTC_12x10_SRGB_BLOCK = 182, VK_FORMAT_ASTC_12x12_UNORM_BLOCK = 183, VK_FORMAT_ASTC_12x12_SRGB_BLOCK = 184, VK_FORMAT_BEGIN_RANGE = 0, VK_FORMAT_END_RANGE = 184, VK_FORMAT_RANGE_SIZE = 185, VK_FORMAT_MAX_ENUM = 2147483647, ) @cenum(VkImageType, VK_IMAGE_TYPE_1D = 0, VK_IMAGE_TYPE_2D = 1, VK_IMAGE_TYPE_3D = 2, VK_IMAGE_TYPE_BEGIN_RANGE = 0, VK_IMAGE_TYPE_END_RANGE = 2, VK_IMAGE_TYPE_RANGE_SIZE = 3, VK_IMAGE_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkImageTiling, VK_IMAGE_TILING_OPTIMAL = 0, VK_IMAGE_TILING_LINEAR = 1, VK_IMAGE_TILING_BEGIN_RANGE = 0, VK_IMAGE_TILING_END_RANGE = 1, VK_IMAGE_TILING_RANGE_SIZE = 2, VK_IMAGE_TILING_MAX_ENUM = 2147483647, ) @cenum(VkPhysicalDeviceType, VK_PHYSICAL_DEVICE_TYPE_OTHER = 0, VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU = 1, VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU = 2, VK_PHYSICAL_DEVICE_TYPE_VIRTUAL_GPU = 3, VK_PHYSICAL_DEVICE_TYPE_CPU = 4, VK_PHYSICAL_DEVICE_TYPE_BEGIN_RANGE = 0, VK_PHYSICAL_DEVICE_TYPE_END_RANGE = 4, VK_PHYSICAL_DEVICE_TYPE_RANGE_SIZE = 5, VK_PHYSICAL_DEVICE_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkQueryType, VK_QUERY_TYPE_OCCLUSION = 0, VK_QUERY_TYPE_PIPELINE_STATISTICS = 1, VK_QUERY_TYPE_TIMESTAMP = 2, VK_QUERY_TYPE_BEGIN_RANGE = 0, VK_QUERY_TYPE_END_RANGE = 2, VK_QUERY_TYPE_RANGE_SIZE = 3, VK_QUERY_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkSharingMode, VK_SHARING_MODE_EXCLUSIVE = 0, VK_SHARING_MODE_CONCURRENT = 1, VK_SHARING_MODE_BEGIN_RANGE = 0, VK_SHARING_MODE_END_RANGE = 1, VK_SHARING_MODE_RANGE_SIZE = 2, VK_SHARING_MODE_MAX_ENUM = 2147483647, ) @cenum(VkImageLayout, VK_IMAGE_LAYOUT_UNDEFINED = 0, VK_IMAGE_LAYOUT_GENERAL = 1, VK_IMAGE_LAYOUT_COLOR_ATTACHMENT_OPTIMAL = 2, VK_IMAGE_LAYOUT_DEPTH_STENCIL_ATTACHMENT_OPTIMAL = 3, VK_IMAGE_LAYOUT_DEPTH_STENCIL_READ_ONLY_OPTIMAL = 4, VK_IMAGE_LAYOUT_SHADER_READ_ONLY_OPTIMAL = 5, VK_IMAGE_LAYOUT_TRANSFER_SRC_OPTIMAL = 6, VK_IMAGE_LAYOUT_TRANSFER_DST_OPTIMAL = 7, VK_IMAGE_LAYOUT_PREINITIALIZED = 8, VK_IMAGE_LAYOUT_PRESENT_SRC_KHR = 1000001002, VK_IMAGE_LAYOUT_BEGIN_RANGE = 0, VK_IMAGE_LAYOUT_END_RANGE = 8, VK_IMAGE_LAYOUT_RANGE_SIZE = 9, VK_IMAGE_LAYOUT_MAX_ENUM = 2147483647, ) @cenum(VkImageViewType, VK_IMAGE_VIEW_TYPE_1D = 0, VK_IMAGE_VIEW_TYPE_2D = 1, VK_IMAGE_VIEW_TYPE_3D = 2, VK_IMAGE_VIEW_TYPE_CUBE = 3, VK_IMAGE_VIEW_TYPE_1D_ARRAY = 4, VK_IMAGE_VIEW_TYPE_2D_ARRAY = 5, VK_IMAGE_VIEW_TYPE_CUBE_ARRAY = 6, VK_IMAGE_VIEW_TYPE_BEGIN_RANGE = 0, VK_IMAGE_VIEW_TYPE_END_RANGE = 6, VK_IMAGE_VIEW_TYPE_RANGE_SIZE = 7, VK_IMAGE_VIEW_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkComponentSwizzle, VK_COMPONENT_SWIZZLE_IDENTITY = 0, VK_COMPONENT_SWIZZLE_ZERO = 1, VK_COMPONENT_SWIZZLE_ONE = 2, VK_COMPONENT_SWIZZLE_R = 3, VK_COMPONENT_SWIZZLE_G = 4, VK_COMPONENT_SWIZZLE_B = 5, VK_COMPONENT_SWIZZLE_A = 6, VK_COMPONENT_SWIZZLE_BEGIN_RANGE = 0, VK_COMPONENT_SWIZZLE_END_RANGE = 6, VK_COMPONENT_SWIZZLE_RANGE_SIZE = 7, VK_COMPONENT_SWIZZLE_MAX_ENUM = 2147483647, ) @cenum(VkVertexInputRate, VK_VERTEX_INPUT_RATE_VERTEX = 0, VK_VERTEX_INPUT_RATE_INSTANCE = 1, VK_VERTEX_INPUT_RATE_BEGIN_RANGE = 0, VK_VERTEX_INPUT_RATE_END_RANGE = 1, VK_VERTEX_INPUT_RATE_RANGE_SIZE = 2, VK_VERTEX_INPUT_RATE_MAX_ENUM = 2147483647, ) @cenum(VkPrimitiveTopology, VK_PRIMITIVE_TOPOLOGY_POINT_LIST = 0, VK_PRIMITIVE_TOPOLOGY_LINE_LIST = 1, VK_PRIMITIVE_TOPOLOGY_LINE_STRIP = 2, VK_PRIMITIVE_TOPOLOGY_TRIANGLE_LIST = 3, VK_PRIMITIVE_TOPOLOGY_TRIANGLE_STRIP = 4, VK_PRIMITIVE_TOPOLOGY_TRIANGLE_FAN = 5, VK_PRIMITIVE_TOPOLOGY_LINE_LIST_WITH_ADJACENCY = 6, VK_PRIMITIVE_TOPOLOGY_LINE_STRIP_WITH_ADJACENCY = 7, VK_PRIMITIVE_TOPOLOGY_TRIANGLE_LIST_WITH_ADJACENCY = 8, VK_PRIMITIVE_TOPOLOGY_TRIANGLE_STRIP_WITH_ADJACENCY = 9, VK_PRIMITIVE_TOPOLOGY_PATCH_LIST = 10, VK_PRIMITIVE_TOPOLOGY_BEGIN_RANGE = 0, VK_PRIMITIVE_TOPOLOGY_END_RANGE = 10, VK_PRIMITIVE_TOPOLOGY_RANGE_SIZE = 11, VK_PRIMITIVE_TOPOLOGY_MAX_ENUM = 2147483647, ) @cenum(VkPolygonMode, VK_POLYGON_MODE_FILL = 0, VK_POLYGON_MODE_LINE = 1, VK_POLYGON_MODE_POINT = 2, VK_POLYGON_MODE_BEGIN_RANGE = 0, VK_POLYGON_MODE_END_RANGE = 2, VK_POLYGON_MODE_RANGE_SIZE = 3, VK_POLYGON_MODE_MAX_ENUM = 2147483647, ) @cenum(VkFrontFace, VK_FRONT_FACE_COUNTER_CLOCKWISE = 0, VK_FRONT_FACE_CLOCKWISE = 1, VK_FRONT_FACE_BEGIN_RANGE = 0, VK_FRONT_FACE_END_RANGE = 1, VK_FRONT_FACE_RANGE_SIZE = 2, VK_FRONT_FACE_MAX_ENUM = 2147483647, ) @cenum(VkCompareOp, VK_COMPARE_OP_NEVER = 0, VK_COMPARE_OP_LESS = 1, VK_COMPARE_OP_EQUAL = 2, VK_COMPARE_OP_LESS_OR_EQUAL = 3, VK_COMPARE_OP_GREATER = 4, VK_COMPARE_OP_NOT_EQUAL = 5, VK_COMPARE_OP_GREATER_OR_EQUAL = 6, VK_COMPARE_OP_ALWAYS = 7, VK_COMPARE_OP_BEGIN_RANGE = 0, VK_COMPARE_OP_END_RANGE = 7, VK_COMPARE_OP_RANGE_SIZE = 8, VK_COMPARE_OP_MAX_ENUM = 2147483647, ) @cenum(VkStencilOp, VK_STENCIL_OP_KEEP = 0, VK_STENCIL_OP_ZERO = 1, VK_STENCIL_OP_REPLACE = 2, VK_STENCIL_OP_INCREMENT_AND_CLAMP = 3, VK_STENCIL_OP_DECREMENT_AND_CLAMP = 4, VK_STENCIL_OP_INVERT = 5, VK_STENCIL_OP_INCREMENT_AND_WRAP = 6, VK_STENCIL_OP_DECREMENT_AND_WRAP = 7, VK_STENCIL_OP_BEGIN_RANGE = 0, VK_STENCIL_OP_END_RANGE = 7, VK_STENCIL_OP_RANGE_SIZE = 8, VK_STENCIL_OP_MAX_ENUM = 2147483647, ) @cenum(VkLogicOp, VK_LOGIC_OP_CLEAR = 0, VK_LOGIC_OP_AND = 1, VK_LOGIC_OP_AND_REVERSE = 2, VK_LOGIC_OP_COPY = 3, VK_LOGIC_OP_AND_INVERTED = 4, VK_LOGIC_OP_NO_OP = 5, VK_LOGIC_OP_XOR = 6, VK_LOGIC_OP_OR = 7, VK_LOGIC_OP_NOR = 8, VK_LOGIC_OP_EQUIVALENT = 9, VK_LOGIC_OP_INVERT = 10, VK_LOGIC_OP_OR_REVERSE = 11, VK_LOGIC_OP_COPY_INVERTED = 12, VK_LOGIC_OP_OR_INVERTED = 13, VK_LOGIC_OP_NAND = 14, VK_LOGIC_OP_SET = 15, VK_LOGIC_OP_BEGIN_RANGE = 0, VK_LOGIC_OP_END_RANGE = 15, VK_LOGIC_OP_RANGE_SIZE = 16, VK_LOGIC_OP_MAX_ENUM = 2147483647, ) @cenum(VkBlendFactor, VK_BLEND_FACTOR_ZERO = 0, VK_BLEND_FACTOR_ONE = 1, VK_BLEND_FACTOR_SRC_COLOR = 2, VK_BLEND_FACTOR_ONE_MINUS_SRC_COLOR = 3, VK_BLEND_FACTOR_DST_COLOR = 4, VK_BLEND_FACTOR_ONE_MINUS_DST_COLOR = 5, VK_BLEND_FACTOR_SRC_ALPHA = 6, VK_BLEND_FACTOR_ONE_MINUS_SRC_ALPHA = 7, VK_BLEND_FACTOR_DST_ALPHA = 8, VK_BLEND_FACTOR_ONE_MINUS_DST_ALPHA = 9, VK_BLEND_FACTOR_CONSTANT_COLOR = 10, VK_BLEND_FACTOR_ONE_MINUS_CONSTANT_COLOR = 11, VK_BLEND_FACTOR_CONSTANT_ALPHA = 12, VK_BLEND_FACTOR_ONE_MINUS_CONSTANT_ALPHA = 13, VK_BLEND_FACTOR_SRC_ALPHA_SATURATE = 14, VK_BLEND_FACTOR_SRC1_COLOR = 15, VK_BLEND_FACTOR_ONE_MINUS_SRC1_COLOR = 16, VK_BLEND_FACTOR_SRC1_ALPHA = 17, VK_BLEND_FACTOR_ONE_MINUS_SRC1_ALPHA = 18, VK_BLEND_FACTOR_BEGIN_RANGE = 0, VK_BLEND_FACTOR_END_RANGE = 18, VK_BLEND_FACTOR_RANGE_SIZE = 19, VK_BLEND_FACTOR_MAX_ENUM = 2147483647, ) @cenum(VkBlendOp, VK_BLEND_OP_ADD = 0, VK_BLEND_OP_SUBTRACT = 1, VK_BLEND_OP_REVERSE_SUBTRACT = 2, VK_BLEND_OP_MIN = 3, VK_BLEND_OP_MAX = 4, VK_BLEND_OP_BEGIN_RANGE = 0, VK_BLEND_OP_END_RANGE = 4, VK_BLEND_OP_RANGE_SIZE = 5, VK_BLEND_OP_MAX_ENUM = 2147483647, ) @cenum(VkDynamicState, VK_DYNAMIC_STATE_VIEWPORT = 0, VK_DYNAMIC_STATE_SCISSOR = 1, VK_DYNAMIC_STATE_LINE_WIDTH = 2, VK_DYNAMIC_STATE_DEPTH_BIAS = 3, VK_DYNAMIC_STATE_BLEND_CONSTANTS = 4, VK_DYNAMIC_STATE_DEPTH_BOUNDS = 5, VK_DYNAMIC_STATE_STENCIL_COMPARE_MASK = 6, VK_DYNAMIC_STATE_STENCIL_WRITE_MASK = 7, VK_DYNAMIC_STATE_STENCIL_REFERENCE = 8, VK_DYNAMIC_STATE_BEGIN_RANGE = 0, VK_DYNAMIC_STATE_END_RANGE = 8, VK_DYNAMIC_STATE_RANGE_SIZE = 9, VK_DYNAMIC_STATE_MAX_ENUM = 2147483647, ) @cenum(VkFilter, VK_FILTER_NEAREST = 0, VK_FILTER_LINEAR = 1, VK_FILTER_CUBIC_IMG = 1000015000, VK_FILTER_BEGIN_RANGE = 0, VK_FILTER_END_RANGE = 1, VK_FILTER_RANGE_SIZE = 2, VK_FILTER_MAX_ENUM = 2147483647, ) @cenum(VkSamplerMipmapMode, VK_SAMPLER_MIPMAP_MODE_NEAREST = 0, VK_SAMPLER_MIPMAP_MODE_LINEAR = 1, VK_SAMPLER_MIPMAP_MODE_BEGIN_RANGE = 0, VK_SAMPLER_MIPMAP_MODE_END_RANGE = 1, VK_SAMPLER_MIPMAP_MODE_RANGE_SIZE = 2, VK_SAMPLER_MIPMAP_MODE_MAX_ENUM = 2147483647, ) @cenum(VkSamplerAddressMode, VK_SAMPLER_ADDRESS_MODE_REPEAT = 0, VK_SAMPLER_ADDRESS_MODE_MIRRORED_REPEAT = 1, VK_SAMPLER_ADDRESS_MODE_CLAMP_TO_EDGE = 2, VK_SAMPLER_ADDRESS_MODE_CLAMP_TO_BORDER = 3, VK_SAMPLER_ADDRESS_MODE_MIRROR_CLAMP_TO_EDGE = 4, VK_SAMPLER_ADDRESS_MODE_BEGIN_RANGE = 0, VK_SAMPLER_ADDRESS_MODE_END_RANGE = 3, VK_SAMPLER_ADDRESS_MODE_RANGE_SIZE = 4, VK_SAMPLER_ADDRESS_MODE_MAX_ENUM = 2147483647, ) @cenum(VkBorderColor, VK_BORDER_COLOR_FLOAT_TRANSPARENT_BLACK = 0, VK_BORDER_COLOR_INT_TRANSPARENT_BLACK = 1, VK_BORDER_COLOR_FLOAT_OPAQUE_BLACK = 2, VK_BORDER_COLOR_INT_OPAQUE_BLACK = 3, VK_BORDER_COLOR_FLOAT_OPAQUE_WHITE = 4, VK_BORDER_COLOR_INT_OPAQUE_WHITE = 5, VK_BORDER_COLOR_BEGIN_RANGE = 0, VK_BORDER_COLOR_END_RANGE = 5, VK_BORDER_COLOR_RANGE_SIZE = 6, VK_BORDER_COLOR_MAX_ENUM = 2147483647, ) @cenum(VkDescriptorType, VK_DESCRIPTOR_TYPE_SAMPLER = 0, VK_DESCRIPTOR_TYPE_COMBINED_IMAGE_SAMPLER = 1, VK_DESCRIPTOR_TYPE_SAMPLED_IMAGE = 2, VK_DESCRIPTOR_TYPE_STORAGE_IMAGE = 3, VK_DESCRIPTOR_TYPE_UNIFORM_TEXEL_BUFFER = 4, VK_DESCRIPTOR_TYPE_STORAGE_TEXEL_BUFFER = 5, VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER = 6, VK_DESCRIPTOR_TYPE_STORAGE_BUFFER = 7, VK_DESCRIPTOR_TYPE_UNIFORM_BUFFER_DYNAMIC = 8, VK_DESCRIPTOR_TYPE_STORAGE_BUFFER_DYNAMIC = 9, VK_DESCRIPTOR_TYPE_INPUT_ATTACHMENT = 10, VK_DESCRIPTOR_TYPE_BEGIN_RANGE = 0, VK_DESCRIPTOR_TYPE_END_RANGE = 10, VK_DESCRIPTOR_TYPE_RANGE_SIZE = 11, VK_DESCRIPTOR_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkAttachmentLoadOp, VK_ATTACHMENT_LOAD_OP_LOAD = 0, VK_ATTACHMENT_LOAD_OP_CLEAR = 1, VK_ATTACHMENT_LOAD_OP_DONT_CARE = 2, VK_ATTACHMENT_LOAD_OP_BEGIN_RANGE = 0, VK_ATTACHMENT_LOAD_OP_END_RANGE = 2, VK_ATTACHMENT_LOAD_OP_RANGE_SIZE = 3, VK_ATTACHMENT_LOAD_OP_MAX_ENUM = 2147483647, ) @cenum(VkAttachmentStoreOp, VK_ATTACHMENT_STORE_OP_STORE = 0, VK_ATTACHMENT_STORE_OP_DONT_CARE = 1, VK_ATTACHMENT_STORE_OP_BEGIN_RANGE = 0, VK_ATTACHMENT_STORE_OP_END_RANGE = 1, VK_ATTACHMENT_STORE_OP_RANGE_SIZE = 2, VK_ATTACHMENT_STORE_OP_MAX_ENUM = 2147483647, ) @cenum(VkPipelineBindPoint, VK_PIPELINE_BIND_POINT_GRAPHICS = 0, VK_PIPELINE_BIND_POINT_COMPUTE = 1, VK_PIPELINE_BIND_POINT_BEGIN_RANGE = 0, VK_PIPELINE_BIND_POINT_END_RANGE = 1, VK_PIPELINE_BIND_POINT_RANGE_SIZE = 2, VK_PIPELINE_BIND_POINT_MAX_ENUM = 2147483647, ) @cenum(VkCommandBufferLevel, VK_COMMAND_BUFFER_LEVEL_PRIMARY = 0, VK_COMMAND_BUFFER_LEVEL_SECONDARY = 1, VK_COMMAND_BUFFER_LEVEL_BEGIN_RANGE = 0, VK_COMMAND_BUFFER_LEVEL_END_RANGE = 1, VK_COMMAND_BUFFER_LEVEL_RANGE_SIZE = 2, VK_COMMAND_BUFFER_LEVEL_MAX_ENUM = 2147483647, ) @cenum(VkIndexType, VK_INDEX_TYPE_UINT16 = 0, VK_INDEX_TYPE_UINT32 = 1, VK_INDEX_TYPE_BEGIN_RANGE = 0, VK_INDEX_TYPE_END_RANGE = 1, VK_INDEX_TYPE_RANGE_SIZE = 2, VK_INDEX_TYPE_MAX_ENUM = 2147483647, ) @cenum(VkSubpassContents, VK_SUBPASS_CONTENTS_INLINE = 0, VK_SUBPASS_CONTENTS_SECONDARY_COMMAND_BUFFERS = 1, VK_SUBPASS_CONTENTS_BEGIN_RANGE = 0, VK_SUBPASS_CONTENTS_END_RANGE = 1, VK_SUBPASS_CONTENTS_RANGE_SIZE = 2, VK_SUBPASS_CONTENTS_MAX_ENUM = 2147483647, ) typealias VkInstanceCreateFlags VkFlags @cenum(VkFormatFeatureFlagBits, VK_FORMAT_FEATURE_SAMPLED_IMAGE_BIT = 1, VK_FORMAT_FEATURE_STORAGE_IMAGE_BIT = 2, VK_FORMAT_FEATURE_STORAGE_IMAGE_ATOMIC_BIT = 4, VK_FORMAT_FEATURE_UNIFORM_TEXEL_BUFFER_BIT = 8, VK_FORMAT_FEATURE_STORAGE_TEXEL_BUFFER_BIT = 16, VK_FORMAT_FEATURE_STORAGE_TEXEL_BUFFER_ATOMIC_BIT = 32, VK_FORMAT_FEATURE_VERTEX_BUFFER_BIT = 64, VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BIT = 128, VK_FORMAT_FEATURE_COLOR_ATTACHMENT_BLEND_BIT = 256, VK_FORMAT_FEATURE_DEPTH_STENCIL_ATTACHMENT_BIT = 512, VK_FORMAT_FEATURE_BLIT_SRC_BIT = 1024, VK_FORMAT_FEATURE_BLIT_DST_BIT = 2048, VK_FORMAT_FEATURE_SAMPLED_IMAGE_FILTER_LINEAR_BIT = 4096, VK_FORMAT_FEATURE_SAMPLED_IMAGE_FILTER_CUBIC_BIT_IMG = 8192, VK_FORMAT_FEATURE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkFormatFeatureFlags VkFlags @cenum(VkImageUsageFlagBits, VK_IMAGE_USAGE_TRANSFER_SRC_BIT = 1, VK_IMAGE_USAGE_TRANSFER_DST_BIT = 2, VK_IMAGE_USAGE_SAMPLED_BIT = 4, VK_IMAGE_USAGE_STORAGE_BIT = 8, VK_IMAGE_USAGE_COLOR_ATTACHMENT_BIT = 16, VK_IMAGE_USAGE_DEPTH_STENCIL_ATTACHMENT_BIT = 32, VK_IMAGE_USAGE_TRANSIENT_ATTACHMENT_BIT = 64, VK_IMAGE_USAGE_INPUT_ATTACHMENT_BIT = 128, VK_IMAGE_USAGE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkImageUsageFlags VkFlags @cenum(VkImageCreateFlagBits, VK_IMAGE_CREATE_SPARSE_BINDING_BIT = 1, VK_IMAGE_CREATE_SPARSE_RESIDENCY_BIT = 2, VK_IMAGE_CREATE_SPARSE_ALIASED_BIT = 4, VK_IMAGE_CREATE_MUTABLE_FORMAT_BIT = 8, VK_IMAGE_CREATE_CUBE_COMPATIBLE_BIT = 16, VK_IMAGE_CREATE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkImageCreateFlags VkFlags @cenum(VkSampleCountFlagBits, VK_SAMPLE_COUNT_1_BIT = 1, VK_SAMPLE_COUNT_2_BIT = 2, VK_SAMPLE_COUNT_4_BIT = 4, VK_SAMPLE_COUNT_8_BIT = 8, VK_SAMPLE_COUNT_16_BIT = 16, VK_SAMPLE_COUNT_32_BIT = 32, VK_SAMPLE_COUNT_64_BIT = 64, VK_SAMPLE_COUNT_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkSampleCountFlags VkFlags @cenum(VkQueueFlagBits, VK_QUEUE_GRAPHICS_BIT = 1, VK_QUEUE_COMPUTE_BIT = 2, VK_QUEUE_TRANSFER_BIT = 4, VK_QUEUE_SPARSE_BINDING_BIT = 8, VK_QUEUE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkQueueFlags VkFlags @cenum(VkMemoryPropertyFlagBits, VK_MEMORY_PROPERTY_DEVICE_LOCAL_BIT = 1, VK_MEMORY_PROPERTY_HOST_VISIBLE_BIT = 2, VK_MEMORY_PROPERTY_HOST_COHERENT_BIT = 4, VK_MEMORY_PROPERTY_HOST_CACHED_BIT = 8, VK_MEMORY_PROPERTY_LAZILY_ALLOCATED_BIT = 16, VK_MEMORY_PROPERTY_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkMemoryPropertyFlags VkFlags @cenum(VkMemoryHeapFlagBits, VK_MEMORY_HEAP_DEVICE_LOCAL_BIT = 1, VK_MEMORY_HEAP_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkMemoryHeapFlags VkFlags typealias VkDeviceCreateFlags VkFlags typealias VkDeviceQueueCreateFlags VkFlags @cenum(VkPipelineStageFlagBits, VK_PIPELINE_STAGE_TOP_OF_PIPE_BIT = 1, VK_PIPELINE_STAGE_DRAW_INDIRECT_BIT = 2, VK_PIPELINE_STAGE_VERTEX_INPUT_BIT = 4, VK_PIPELINE_STAGE_VERTEX_SHADER_BIT = 8, VK_PIPELINE_STAGE_TESSELLATION_CONTROL_SHADER_BIT = 16, VK_PIPELINE_STAGE_TESSELLATION_EVALUATION_SHADER_BIT = 32, VK_PIPELINE_STAGE_GEOMETRY_SHADER_BIT = 64, VK_PIPELINE_STAGE_FRAGMENT_SHADER_BIT = 128, VK_PIPELINE_STAGE_EARLY_FRAGMENT_TESTS_BIT = 256, VK_PIPELINE_STAGE_LATE_FRAGMENT_TESTS_BIT = 512, VK_PIPELINE_STAGE_COLOR_ATTACHMENT_OUTPUT_BIT = 1024, VK_PIPELINE_STAGE_COMPUTE_SHADER_BIT = 2048, VK_PIPELINE_STAGE_TRANSFER_BIT = 4096, VK_PIPELINE_STAGE_BOTTOM_OF_PIPE_BIT = 8192, VK_PIPELINE_STAGE_HOST_BIT = 16384, VK_PIPELINE_STAGE_ALL_GRAPHICS_BIT = 32768, VK_PIPELINE_STAGE_ALL_COMMANDS_BIT = 65536, VK_PIPELINE_STAGE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkPipelineStageFlags VkFlags typealias VkMemoryMapFlags VkFlags @cenum(VkImageAspectFlagBits, VK_IMAGE_ASPECT_COLOR_BIT = 1, VK_IMAGE_ASPECT_DEPTH_BIT = 2, VK_IMAGE_ASPECT_STENCIL_BIT = 4, VK_IMAGE_ASPECT_METADATA_BIT = 8, VK_IMAGE_ASPECT_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkImageAspectFlags VkFlags @cenum(VkSparseImageFormatFlagBits, VK_SPARSE_IMAGE_FORMAT_SINGLE_MIPTAIL_BIT = 1, VK_SPARSE_IMAGE_FORMAT_ALIGNED_MIP_SIZE_BIT = 2, VK_SPARSE_IMAGE_FORMAT_NONSTANDARD_BLOCK_SIZE_BIT = 4, VK_SPARSE_IMAGE_FORMAT_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkSparseImageFormatFlags VkFlags @cenum(VkSparseMemoryBindFlagBits, VK_SPARSE_MEMORY_BIND_METADATA_BIT = 1, VK_SPARSE_MEMORY_BIND_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkSparseMemoryBindFlags VkFlags @cenum(VkFenceCreateFlagBits, VK_FENCE_CREATE_SIGNALED_BIT = 1, VK_FENCE_CREATE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkFenceCreateFlags VkFlags typealias VkSemaphoreCreateFlags VkFlags typealias VkEventCreateFlags VkFlags typealias VkQueryPoolCreateFlags VkFlags @cenum(VkQueryPipelineStatisticFlagBits, VK_QUERY_PIPELINE_STATISTIC_INPUT_ASSEMBLY_VERTICES_BIT = 1, VK_QUERY_PIPELINE_STATISTIC_INPUT_ASSEMBLY_PRIMITIVES_BIT = 2, VK_QUERY_PIPELINE_STATISTIC_VERTEX_SHADER_INVOCATIONS_BIT = 4, VK_QUERY_PIPELINE_STATISTIC_GEOMETRY_SHADER_INVOCATIONS_BIT = 8, VK_QUERY_PIPELINE_STATISTIC_GEOMETRY_SHADER_PRIMITIVES_BIT = 16, VK_QUERY_PIPELINE_STATISTIC_CLIPPING_INVOCATIONS_BIT = 32, VK_QUERY_PIPELINE_STATISTIC_CLIPPING_PRIMITIVES_BIT = 64, VK_QUERY_PIPELINE_STATISTIC_FRAGMENT_SHADER_INVOCATIONS_BIT = 128, VK_QUERY_PIPELINE_STATISTIC_TESSELLATION_CONTROL_SHADER_PATCHES_BIT = 256, VK_QUERY_PIPELINE_STATISTIC_TESSELLATION_EVALUATION_SHADER_INVOCATIONS_BIT = 512, VK_QUERY_PIPELINE_STATISTIC_COMPUTE_SHADER_INVOCATIONS_BIT = 1024, VK_QUERY_PIPELINE_STATISTIC_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkQueryPipelineStatisticFlags VkFlags @cenum(VkQueryResultFlagBits, VK_QUERY_RESULT_64_BIT = 1, VK_QUERY_RESULT_WAIT_BIT = 2, VK_QUERY_RESULT_WITH_AVAILABILITY_BIT = 4, VK_QUERY_RESULT_PARTIAL_BIT = 8, VK_QUERY_RESULT_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkQueryResultFlags VkFlags @cenum(VkBufferCreateFlagBits, VK_BUFFER_CREATE_SPARSE_BINDING_BIT = 1, VK_BUFFER_CREATE_SPARSE_RESIDENCY_BIT = 2, VK_BUFFER_CREATE_SPARSE_ALIASED_BIT = 4, VK_BUFFER_CREATE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkBufferCreateFlags VkFlags @cenum(VkBufferUsageFlagBits, VK_BUFFER_USAGE_TRANSFER_SRC_BIT = 1, VK_BUFFER_USAGE_TRANSFER_DST_BIT = 2, VK_BUFFER_USAGE_UNIFORM_TEXEL_BUFFER_BIT = 4, VK_BUFFER_USAGE_STORAGE_TEXEL_BUFFER_BIT = 8, VK_BUFFER_USAGE_UNIFORM_BUFFER_BIT = 16, VK_BUFFER_USAGE_STORAGE_BUFFER_BIT = 32, VK_BUFFER_USAGE_INDEX_BUFFER_BIT = 64, VK_BUFFER_USAGE_VERTEX_BUFFER_BIT = 128, VK_BUFFER_USAGE_INDIRECT_BUFFER_BIT = 256, VK_BUFFER_USAGE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkBufferUsageFlags VkFlags typealias VkBufferViewCreateFlags VkFlags typealias VkImageViewCreateFlags VkFlags typealias VkShaderModuleCreateFlags VkFlags typealias VkPipelineCacheCreateFlags VkFlags @cenum(VkPipelineCreateFlagBits, VK_PIPELINE_CREATE_DISABLE_OPTIMIZATION_BIT = 1, VK_PIPELINE_CREATE_ALLOW_DERIVATIVES_BIT = 2, VK_PIPELINE_CREATE_DERIVATIVE_BIT = 4, VK_PIPELINE_CREATE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkPipelineCreateFlags VkFlags typealias VkPipelineShaderStageCreateFlags VkFlags @cenum(VkShaderStageFlagBits, VK_SHADER_STAGE_VERTEX_BIT = 1, VK_SHADER_STAGE_TESSELLATION_CONTROL_BIT = 2, VK_SHADER_STAGE_TESSELLATION_EVALUATION_BIT = 4, VK_SHADER_STAGE_GEOMETRY_BIT = 8, VK_SHADER_STAGE_FRAGMENT_BIT = 16, VK_SHADER_STAGE_COMPUTE_BIT = 32, VK_SHADER_STAGE_ALL_GRAPHICS = 31, VK_SHADER_STAGE_ALL = 2147483647, VK_SHADER_STAGE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkPipelineVertexInputStateCreateFlags VkFlags typealias VkPipelineInputAssemblyStateCreateFlags VkFlags typealias VkPipelineTessellationStateCreateFlags VkFlags typealias VkPipelineViewportStateCreateFlags VkFlags typealias VkPipelineRasterizationStateCreateFlags VkFlags @cenum(VkCullModeFlagBits, VK_CULL_MODE_NONE = 0, VK_CULL_MODE_FRONT_BIT = 1, VK_CULL_MODE_BACK_BIT = 2, VK_CULL_MODE_FRONT_AND_BACK = 3, VK_CULL_MODE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkCullModeFlags VkFlags typealias VkPipelineMultisampleStateCreateFlags VkFlags typealias VkPipelineDepthStencilStateCreateFlags VkFlags typealias VkPipelineColorBlendStateCreateFlags VkFlags @cenum(VkColorComponentFlagBits, VK_COLOR_COMPONENT_R_BIT = 1, VK_COLOR_COMPONENT_G_BIT = 2, VK_COLOR_COMPONENT_B_BIT = 4, VK_COLOR_COMPONENT_A_BIT = 8, VK_COLOR_COMPONENT_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkColorComponentFlags VkFlags typealias VkPipelineDynamicStateCreateFlags VkFlags typealias VkPipelineLayoutCreateFlags VkFlags typealias VkShaderStageFlags VkFlags typealias VkSamplerCreateFlags VkFlags typealias VkDescriptorSetLayoutCreateFlags VkFlags @cenum(VkDescriptorPoolCreateFlagBits, VK_DESCRIPTOR_POOL_CREATE_FREE_DESCRIPTOR_SET_BIT = 1, VK_DESCRIPTOR_POOL_CREATE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkDescriptorPoolCreateFlags VkFlags typealias VkDescriptorPoolResetFlags VkFlags typealias VkFramebufferCreateFlags VkFlags typealias VkRenderPassCreateFlags VkFlags @cenum(VkAttachmentDescriptionFlagBits, VK_ATTACHMENT_DESCRIPTION_MAY_ALIAS_BIT = 1, VK_ATTACHMENT_DESCRIPTION_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkAttachmentDescriptionFlags VkFlags typealias VkSubpassDescriptionFlags VkFlags @cenum(VkAccessFlagBits, VK_ACCESS_INDIRECT_COMMAND_READ_BIT = 1, VK_ACCESS_INDEX_READ_BIT = 2, VK_ACCESS_VERTEX_ATTRIBUTE_READ_BIT = 4, VK_ACCESS_UNIFORM_READ_BIT = 8, VK_ACCESS_INPUT_ATTACHMENT_READ_BIT = 16, VK_ACCESS_SHADER_READ_BIT = 32, VK_ACCESS_SHADER_WRITE_BIT = 64, VK_ACCESS_COLOR_ATTACHMENT_READ_BIT = 128, VK_ACCESS_COLOR_ATTACHMENT_WRITE_BIT = 256, VK_ACCESS_DEPTH_STENCIL_ATTACHMENT_READ_BIT = 512, VK_ACCESS_DEPTH_STENCIL_ATTACHMENT_WRITE_BIT = 1024, VK_ACCESS_TRANSFER_READ_BIT = 2048, VK_ACCESS_TRANSFER_WRITE_BIT = 4096, VK_ACCESS_HOST_READ_BIT = 8192, VK_ACCESS_HOST_WRITE_BIT = 16384, VK_ACCESS_MEMORY_READ_BIT = 32768, VK_ACCESS_MEMORY_WRITE_BIT = 65536, VK_ACCESS_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkAccessFlags VkFlags @cenum(VkDependencyFlagBits, VK_DEPENDENCY_BY_REGION_BIT = 1, VK_DEPENDENCY_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkDependencyFlags VkFlags @cenum(VkCommandPoolCreateFlagBits, VK_COMMAND_POOL_CREATE_TRANSIENT_BIT = 1, VK_COMMAND_POOL_CREATE_RESET_COMMAND_BUFFER_BIT = 2, VK_COMMAND_POOL_CREATE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkCommandPoolCreateFlags VkFlags @cenum(VkCommandPoolResetFlagBits, VK_COMMAND_POOL_RESET_RELEASE_RESOURCES_BIT = 1, VK_COMMAND_POOL_RESET_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkCommandPoolResetFlags VkFlags @cenum(VkCommandBufferUsageFlagBits, VK_COMMAND_BUFFER_USAGE_ONE_TIME_SUBMIT_BIT = 1, VK_COMMAND_BUFFER_USAGE_RENDER_PASS_CONTINUE_BIT = 2, VK_COMMAND_BUFFER_USAGE_SIMULTANEOUS_USE_BIT = 4, VK_COMMAND_BUFFER_USAGE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkCommandBufferUsageFlags VkFlags @cenum(VkQueryControlFlagBits, VK_QUERY_CONTROL_PRECISE_BIT = 1, VK_QUERY_CONTROL_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkQueryControlFlags VkFlags @cenum(VkCommandBufferResetFlagBits, VK_COMMAND_BUFFER_RESET_RELEASE_RESOURCES_BIT = 1, VK_COMMAND_BUFFER_RESET_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkCommandBufferResetFlags VkFlags @cenum(VkStencilFaceFlagBits, VK_STENCIL_FACE_FRONT_BIT = 1, VK_STENCIL_FACE_BACK_BIT = 2, VK_STENCIL_FRONT_AND_BACK = 3, VK_STENCIL_FACE_FLAG_BITS_MAX_ENUM = 2147483647, ) typealias VkStencilFaceFlags VkFlags typealias PFN_vkAllocationFunction Ptr{Void} typealias PFN_vkReallocationFunction Ptr{Void} typealias PFN_vkFreeFunction Ptr{Void} typealias PFN_vkInternalAllocationNotification Ptr{Void} typealias PFN_vkInternalFreeNotification Ptr{Void} typealias PFN_vkVoidFunction Ptr{Void} immutable VkApplicationInfo sType::VkStructureType pNext::Ptr{Void} pApplicationName::Cstring applicationVersion::UInt32 pEngineName::Cstring engineVersion::UInt32 apiVersion::UInt32 end immutable VkInstanceCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkInstanceCreateFlags pApplicationInfo::Ptr{VkApplicationInfo} enabledLayerCount::UInt32 ppEnabledLayerNames::Ptr{Cstring} enabledExtensionCount::UInt32 ppEnabledExtensionNames::Ptr{Cstring} end immutable VkAllocationCallbacks pUserData::Ptr{Void} pfnAllocation::PFN_vkAllocationFunction pfnReallocation::PFN_vkReallocationFunction pfnFree::PFN_vkFreeFunction pfnInternalAllocation::PFN_vkInternalAllocationNotification pfnInternalFree::PFN_vkInternalFreeNotification end immutable VkPhysicalDeviceFeatures robustBufferAccess::VkBool32 fullDrawIndexUint32::VkBool32 imageCubeArray::VkBool32 independentBlend::VkBool32 geometryShader::VkBool32 tessellationShader::VkBool32 sampleRateShading::VkBool32 dualSrcBlend::VkBool32 logicOp::VkBool32 multiDrawIndirect::VkBool32 drawIndirectFirstInstance::VkBool32 depthClamp::VkBool32 depthBiasClamp::VkBool32 fillModeNonSolid::VkBool32 depthBounds::VkBool32 wideLines::VkBool32 largePoints::VkBool32 alphaToOne::VkBool32 multiViewport::VkBool32 samplerAnisotropy::VkBool32 textureCompressionETC2::VkBool32 textureCompressionASTC_LDR::VkBool32 textureCompressionBC::VkBool32 occlusionQueryPrecise::VkBool32 pipelineStatisticsQuery::VkBool32 vertexPipelineStoresAndAtomics::VkBool32 fragmentStoresAndAtomics::VkBool32 shaderTessellationAndGeometryPointSize::VkBool32 shaderImageGatherExtended::VkBool32 shaderStorageImageExtendedFormats::VkBool32 shaderStorageImageMultisample::VkBool32 shaderStorageImageReadWithoutFormat::VkBool32 shaderStorageImageWriteWithoutFormat::VkBool32 shaderUniformBufferArrayDynamicIndexing::VkBool32 shaderSampledImageArrayDynamicIndexing::VkBool32 shaderStorageBufferArrayDynamicIndexing::VkBool32 shaderStorageImageArrayDynamicIndexing::VkBool32 shaderClipDistance::VkBool32 shaderCullDistance::VkBool32 shaderFloat64::VkBool32 shaderInt64::VkBool32 shaderInt16::VkBool32 shaderResourceResidency::VkBool32 shaderResourceMinLod::VkBool32 sparseBinding::VkBool32 sparseResidencyBuffer::VkBool32 sparseResidencyImage2D::VkBool32 sparseResidencyImage3D::VkBool32 sparseResidency2Samples::VkBool32 sparseResidency4Samples::VkBool32 sparseResidency8Samples::VkBool32 sparseResidency16Samples::VkBool32 sparseResidencyAliased::VkBool32 variableMultisampleRate::VkBool32 inheritedQueries::VkBool32 end immutable VkFormatProperties linearTilingFeatures::VkFormatFeatureFlags optimalTilingFeatures::VkFormatFeatureFlags bufferFeatures::VkFormatFeatureFlags end immutable VkExtent3D width::UInt32 height::UInt32 depth::UInt32 end immutable VkImageFormatProperties maxExtent::VkExtent3D maxMipLevels::UInt32 maxArrayLayers::UInt32 sampleCounts::VkSampleCountFlags maxResourceSize::VkDeviceSize end immutable VkPhysicalDeviceLimits maxImageDimension1D::UInt32 maxImageDimension2D::UInt32 maxImageDimension3D::UInt32 maxImageDimensionCube::UInt32 maxImageArrayLayers::UInt32 maxTexelBufferElements::UInt32 maxUniformBufferRange::UInt32 maxStorageBufferRange::UInt32 maxPushConstantsSize::UInt32 maxMemoryAllocationCount::UInt32 maxSamplerAllocationCount::UInt32 bufferImageGranularity::VkDeviceSize sparseAddressSpaceSize::VkDeviceSize maxBoundDescriptorSets::UInt32 maxPerStageDescriptorSamplers::UInt32 maxPerStageDescriptorUniformBuffers::UInt32 maxPerStageDescriptorStorageBuffers::UInt32 maxPerStageDescriptorSampledImages::UInt32 maxPerStageDescriptorStorageImages::UInt32 maxPerStageDescriptorInputAttachments::UInt32 maxPerStageResources::UInt32 maxDescriptorSetSamplers::UInt32 maxDescriptorSetUniformBuffers::UInt32 maxDescriptorSetUniformBuffersDynamic::UInt32 maxDescriptorSetStorageBuffers::UInt32 maxDescriptorSetStorageBuffersDynamic::UInt32 maxDescriptorSetSampledImages::UInt32 maxDescriptorSetStorageImages::UInt32 maxDescriptorSetInputAttachments::UInt32 maxVertexInputAttributes::UInt32 maxVertexInputBindings::UInt32 maxVertexInputAttributeOffset::UInt32 maxVertexInputBindingStride::UInt32 maxVertexOutputComponents::UInt32 maxTessellationGenerationLevel::UInt32 maxTessellationPatchSize::UInt32 maxTessellationControlPerVertexInputComponents::UInt32 maxTessellationControlPerVertexOutputComponents::UInt32 maxTessellationControlPerPatchOutputComponents::UInt32 maxTessellationControlTotalOutputComponents::UInt32 maxTessellationEvaluationInputComponents::UInt32 maxTessellationEvaluationOutputComponents::UInt32 maxGeometryShaderInvocations::UInt32 maxGeometryInputComponents::UInt32 maxGeometryOutputComponents::UInt32 maxGeometryOutputVertices::UInt32 maxGeometryTotalOutputComponents::UInt32 maxFragmentInputComponents::UInt32 maxFragmentOutputAttachments::UInt32 maxFragmentDualSrcAttachments::UInt32 maxFragmentCombinedOutputResources::UInt32 maxComputeSharedMemorySize::UInt32 maxComputeWorkGroupCount::NTuple{3,UInt32} maxComputeWorkGroupInvocations::UInt32 maxComputeWorkGroupSize::NTuple{3,UInt32} subPixelPrecisionBits::UInt32 subTexelPrecisionBits::UInt32 mipmapPrecisionBits::UInt32 maxDrawIndexedIndexValue::UInt32 maxDrawIndirectCount::UInt32 maxSamplerLodBias::Cfloat maxSamplerAnisotropy::Cfloat maxViewports::UInt32 maxViewportDimensions::NTuple{2,UInt32} viewportBoundsRange::NTuple{2,Cfloat} viewportSubPixelBits::UInt32 minMemoryMapAlignment::Csize_t minTexelBufferOffsetAlignment::VkDeviceSize minUniformBufferOffsetAlignment::VkDeviceSize minStorageBufferOffsetAlignment::VkDeviceSize minTexelOffset::Int32 maxTexelOffset::UInt32 minTexelGatherOffset::Int32 maxTexelGatherOffset::UInt32 minInterpolationOffset::Cfloat maxInterpolationOffset::Cfloat subPixelInterpolationOffsetBits::UInt32 maxFramebufferWidth::UInt32 maxFramebufferHeight::UInt32 maxFramebufferLayers::UInt32 framebufferColorSampleCounts::VkSampleCountFlags framebufferDepthSampleCounts::VkSampleCountFlags framebufferStencilSampleCounts::VkSampleCountFlags framebufferNoAttachmentsSampleCounts::VkSampleCountFlags maxColorAttachments::UInt32 sampledImageColorSampleCounts::VkSampleCountFlags sampledImageIntegerSampleCounts::VkSampleCountFlags sampledImageDepthSampleCounts::VkSampleCountFlags sampledImageStencilSampleCounts::VkSampleCountFlags storageImageSampleCounts::VkSampleCountFlags maxSampleMaskWords::UInt32 timestampComputeAndGraphics::VkBool32 timestampPeriod::Cfloat maxClipDistances::UInt32 maxCullDistances::UInt32 maxCombinedClipAndCullDistances::UInt32 discreteQueuePriorities::UInt32 pointSizeRange::NTuple{2,Cfloat} lineWidthRange::NTuple{2,Cfloat} pointSizeGranularity::Cfloat lineWidthGranularity::Cfloat strictLines::VkBool32 standardSampleLocations::VkBool32 optimalBufferCopyOffsetAlignment::VkDeviceSize optimalBufferCopyRowPitchAlignment::VkDeviceSize nonCoherentAtomSize::VkDeviceSize end immutable VkPhysicalDeviceSparseProperties residencyStandard2DBlockShape::VkBool32 residencyStandard2DMultisampleBlockShape::VkBool32 residencyStandard3DBlockShape::VkBool32 residencyAlignedMipSize::VkBool32 residencyNonResidentStrict::VkBool32 end immutable VkPhysicalDeviceProperties apiVersion::UInt32 driverVersion::UInt32 vendorID::UInt32 deviceID::UInt32 deviceType::VkPhysicalDeviceType deviceName::NTuple{256,UInt8} pipelineCacheUUID::NTuple{16,UInt8} limits::VkPhysicalDeviceLimits sparseProperties::VkPhysicalDeviceSparseProperties end immutable VkQueueFamilyProperties queueFlags::VkQueueFlags queueCount::UInt32 timestampValidBits::UInt32 minImageTransferGranularity::VkExtent3D end immutable VkMemoryType propertyFlags::VkMemoryPropertyFlags heapIndex::UInt32 end immutable VkMemoryHeap size::VkDeviceSize flags::VkMemoryHeapFlags end immutable VkPhysicalDeviceMemoryProperties memoryTypeCount::UInt32 memoryTypes::NTuple{32,VkMemoryType} memoryHeapCount::UInt32 memoryHeaps::NTuple{16,VkMemoryHeap} end immutable VkDeviceQueueCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkDeviceQueueCreateFlags queueFamilyIndex::UInt32 queueCount::UInt32 pQueuePriorities::Ptr{Cfloat} end immutable VkDeviceCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkDeviceCreateFlags queueCreateInfoCount::UInt32 pQueueCreateInfos::Ptr{VkDeviceQueueCreateInfo} enabledLayerCount::UInt32 ppEnabledLayerNames::Ptr{Cstring} enabledExtensionCount::UInt32 ppEnabledExtensionNames::Ptr{Cstring} pEnabledFeatures::Ptr{VkPhysicalDeviceFeatures} end immutable VkExtensionProperties extensionName::NTuple{256,UInt8} specVersion::UInt32 end immutable VkLayerProperties layerName::NTuple{256,UInt8} specVersion::UInt32 implementationVersion::UInt32 description::NTuple{256,UInt8} end immutable VkSubmitInfo sType::VkStructureType pNext::Ptr{Void} waitSemaphoreCount::UInt32 pWaitSemaphores::Ptr{VkSemaphore} pWaitDstStageMask::Ptr{VkPipelineStageFlags} commandBufferCount::UInt32 pCommandBuffers::Ptr{VkCommandBuffer} signalSemaphoreCount::UInt32 pSignalSemaphores::Ptr{VkSemaphore} end immutable VkMemoryAllocateInfo sType::VkStructureType pNext::Ptr{Void} allocationSize::VkDeviceSize memoryTypeIndex::UInt32 end immutable VkMappedMemoryRange sType::VkStructureType pNext::Ptr{Void} memory::VkDeviceMemory offset::VkDeviceSize size::VkDeviceSize end immutable VkMemoryRequirements size::VkDeviceSize alignment::VkDeviceSize memoryTypeBits::UInt32 end immutable VkSparseImageFormatProperties aspectMask::VkImageAspectFlags imageGranularity::VkExtent3D flags::VkSparseImageFormatFlags end immutable VkSparseImageMemoryRequirements formatProperties::VkSparseImageFormatProperties imageMipTailFirstLod::UInt32 imageMipTailSize::VkDeviceSize imageMipTailOffset::VkDeviceSize imageMipTailStride::VkDeviceSize end immutable VkSparseMemoryBind resourceOffset::VkDeviceSize size::VkDeviceSize memory::VkDeviceMemory memoryOffset::VkDeviceSize flags::VkSparseMemoryBindFlags end immutable VkSparseBufferMemoryBindInfo buffer::VkBuffer bindCount::UInt32 pBinds::Ptr{VkSparseMemoryBind} end immutable VkSparseImageOpaqueMemoryBindInfo image::VkImage bindCount::UInt32 pBinds::Ptr{VkSparseMemoryBind} end immutable VkImageSubresource aspectMask::VkImageAspectFlags mipLevel::UInt32 arrayLayer::UInt32 end immutable VkOffset3D x::Int32 y::Int32 z::Int32 end immutable VkSparseImageMemoryBind subresource::VkImageSubresource offset::VkOffset3D extent::VkExtent3D memory::VkDeviceMemory memoryOffset::VkDeviceSize flags::VkSparseMemoryBindFlags end immutable VkSparseImageMemoryBindInfo image::VkImage bindCount::UInt32 pBinds::Ptr{VkSparseImageMemoryBind} end immutable VkBindSparseInfo sType::VkStructureType pNext::Ptr{Void} waitSemaphoreCount::UInt32 pWaitSemaphores::Ptr{VkSemaphore} bufferBindCount::UInt32 pBufferBinds::Ptr{VkSparseBufferMemoryBindInfo} imageOpaqueBindCount::UInt32 pImageOpaqueBinds::Ptr{VkSparseImageOpaqueMemoryBindInfo} imageBindCount::UInt32 pImageBinds::Ptr{VkSparseImageMemoryBindInfo} signalSemaphoreCount::UInt32 pSignalSemaphores::Ptr{VkSemaphore} end immutable VkFenceCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkFenceCreateFlags end immutable VkSemaphoreCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkSemaphoreCreateFlags end immutable VkEventCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkEventCreateFlags end immutable VkQueryPoolCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkQueryPoolCreateFlags queryType::VkQueryType queryCount::UInt32 pipelineStatistics::VkQueryPipelineStatisticFlags end immutable VkBufferCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkBufferCreateFlags size::VkDeviceSize usage::VkBufferUsageFlags sharingMode::VkSharingMode queueFamilyIndexCount::UInt32 pQueueFamilyIndices::Ptr{UInt32} end immutable VkBufferViewCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkBufferViewCreateFlags buffer::VkBuffer format::VkFormat offset::VkDeviceSize range::VkDeviceSize end immutable VkImageCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkImageCreateFlags imageType::VkImageType format::VkFormat extent::VkExtent3D mipLevels::UInt32 arrayLayers::UInt32 samples::VkSampleCountFlagBits tiling::VkImageTiling usage::VkImageUsageFlags sharingMode::VkSharingMode queueFamilyIndexCount::UInt32 pQueueFamilyIndices::Ptr{UInt32} initialLayout::VkImageLayout end immutable VkSubresourceLayout offset::VkDeviceSize size::VkDeviceSize rowPitch::VkDeviceSize arrayPitch::VkDeviceSize depthPitch::VkDeviceSize end immutable VkComponentMapping r::VkComponentSwizzle g::VkComponentSwizzle b::VkComponentSwizzle a::VkComponentSwizzle end immutable VkImageSubresourceRange aspectMask::VkImageAspectFlags baseMipLevel::UInt32 levelCount::UInt32 baseArrayLayer::UInt32 layerCount::UInt32 end immutable VkImageViewCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkImageViewCreateFlags image::VkImage viewType::VkImageViewType format::VkFormat components::VkComponentMapping subresourceRange::VkImageSubresourceRange end immutable VkShaderModuleCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkShaderModuleCreateFlags codeSize::Csize_t pCode::Ptr{UInt32} end immutable VkPipelineCacheCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineCacheCreateFlags initialDataSize::Csize_t pInitialData::Ptr{Void} end immutable VkSpecializationMapEntry constantID::UInt32 offset::UInt32 size::Csize_t end immutable VkSpecializationInfo mapEntryCount::UInt32 pMapEntries::Ptr{VkSpecializationMapEntry} dataSize::Csize_t pData::Ptr{Void} end immutable VkPipelineShaderStageCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineShaderStageCreateFlags stage::VkShaderStageFlagBits _module::VkShaderModule pName::Cstring pSpecializationInfo::Ptr{VkSpecializationInfo} end immutable VkVertexInputBindingDescription binding::UInt32 stride::UInt32 inputRate::VkVertexInputRate end immutable VkVertexInputAttributeDescription location::UInt32 binding::UInt32 format::VkFormat offset::UInt32 end immutable VkPipelineVertexInputStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineVertexInputStateCreateFlags vertexBindingDescriptionCount::UInt32 pVertexBindingDescriptions::Ptr{VkVertexInputBindingDescription} vertexAttributeDescriptionCount::UInt32 pVertexAttributeDescriptions::Ptr{VkVertexInputAttributeDescription} end immutable VkPipelineInputAssemblyStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineInputAssemblyStateCreateFlags topology::VkPrimitiveTopology primitiveRestartEnable::VkBool32 end immutable VkPipelineTessellationStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineTessellationStateCreateFlags patchControlPoints::UInt32 end immutable VkViewport x::Cfloat y::Cfloat width::Cfloat height::Cfloat minDepth::Cfloat maxDepth::Cfloat end immutable VkOffset2D x::Int32 y::Int32 end immutable VkExtent2D width::UInt32 height::UInt32 end immutable VkRect2D offset::VkOffset2D extent::VkExtent2D end immutable VkPipelineViewportStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineViewportStateCreateFlags viewportCount::UInt32 pViewports::Ptr{VkViewport} scissorCount::UInt32 pScissors::Ptr{VkRect2D} end immutable VkPipelineRasterizationStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineRasterizationStateCreateFlags depthClampEnable::VkBool32 rasterizerDiscardEnable::VkBool32 polygonMode::VkPolygonMode cullMode::VkCullModeFlags frontFace::VkFrontFace depthBiasEnable::VkBool32 depthBiasConstantFactor::Cfloat depthBiasClamp::Cfloat depthBiasSlopeFactor::Cfloat lineWidth::Cfloat end immutable VkPipelineMultisampleStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineMultisampleStateCreateFlags rasterizationSamples::VkSampleCountFlagBits sampleShadingEnable::VkBool32 minSampleShading::Cfloat pSampleMask::Ptr{VkSampleMask} alphaToCoverageEnable::VkBool32 alphaToOneEnable::VkBool32 end immutable VkStencilOpState failOp::VkStencilOp passOp::VkStencilOp depthFailOp::VkStencilOp compareOp::VkCompareOp compareMask::UInt32 writeMask::UInt32 reference::UInt32 end immutable VkPipelineDepthStencilStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineDepthStencilStateCreateFlags depthTestEnable::VkBool32 depthWriteEnable::VkBool32 depthCompareOp::VkCompareOp depthBoundsTestEnable::VkBool32 stencilTestEnable::VkBool32 front::VkStencilOpState back::VkStencilOpState minDepthBounds::Cfloat maxDepthBounds::Cfloat end immutable VkPipelineColorBlendAttachmentState blendEnable::VkBool32 srcColorBlendFactor::VkBlendFactor dstColorBlendFactor::VkBlendFactor colorBlendOp::VkBlendOp srcAlphaBlendFactor::VkBlendFactor dstAlphaBlendFactor::VkBlendFactor alphaBlendOp::VkBlendOp colorWriteMask::VkColorComponentFlags end immutable VkPipelineColorBlendStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineColorBlendStateCreateFlags logicOpEnable::VkBool32 logicOp::VkLogicOp attachmentCount::UInt32 pAttachments::Ptr{VkPipelineColorBlendAttachmentState} blendConstants::NTuple{4,Cfloat} end immutable VkPipelineDynamicStateCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineDynamicStateCreateFlags dynamicStateCount::UInt32 pDynamicStates::Ptr{VkDynamicState} end immutable VkGraphicsPipelineCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineCreateFlags stageCount::UInt32 pStages::Ptr{VkPipelineShaderStageCreateInfo} pVertexInputState::Ptr{VkPipelineVertexInputStateCreateInfo} pInputAssemblyState::Ptr{VkPipelineInputAssemblyStateCreateInfo} pTessellationState::Ptr{VkPipelineTessellationStateCreateInfo} pViewportState::Ptr{VkPipelineViewportStateCreateInfo} pRasterizationState::Ptr{VkPipelineRasterizationStateCreateInfo} pMultisampleState::Ptr{VkPipelineMultisampleStateCreateInfo} pDepthStencilState::Ptr{VkPipelineDepthStencilStateCreateInfo} pColorBlendState::Ptr{VkPipelineColorBlendStateCreateInfo} pDynamicState::Ptr{VkPipelineDynamicStateCreateInfo} layout::VkPipelineLayout renderPass::VkRenderPass subpass::UInt32 basePipelineHandle::VkPipeline basePipelineIndex::Int32 end immutable VkComputePipelineCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineCreateFlags stage::VkPipelineShaderStageCreateInfo layout::VkPipelineLayout basePipelineHandle::VkPipeline basePipelineIndex::Int32 end immutable VkPushConstantRange stageFlags::VkShaderStageFlags offset::UInt32 size::UInt32 end immutable VkPipelineLayoutCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkPipelineLayoutCreateFlags setLayoutCount::UInt32 pSetLayouts::Ptr{VkDescriptorSetLayout} pushConstantRangeCount::UInt32 pPushConstantRanges::Ptr{VkPushConstantRange} end immutable VkSamplerCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkSamplerCreateFlags magFilter::VkFilter minFilter::VkFilter mipmapMode::VkSamplerMipmapMode addressModeU::VkSamplerAddressMode addressModeV::VkSamplerAddressMode addressModeW::VkSamplerAddressMode mipLodBias::Cfloat anisotropyEnable::VkBool32 maxAnisotropy::Cfloat compareEnable::VkBool32 compareOp::VkCompareOp minLod::Cfloat maxLod::Cfloat borderColor::VkBorderColor unnormalizedCoordinates::VkBool32 end immutable VkDescriptorSetLayoutBinding binding::UInt32 descriptorType::VkDescriptorType descriptorCount::UInt32 stageFlags::VkShaderStageFlags pImmutableSamplers::Ptr{VkSampler} end immutable VkDescriptorSetLayoutCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkDescriptorSetLayoutCreateFlags bindingCount::UInt32 pBindings::Ptr{VkDescriptorSetLayoutBinding} end immutable VkDescriptorPoolSize _type::VkDescriptorType descriptorCount::UInt32 end immutable VkDescriptorPoolCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkDescriptorPoolCreateFlags maxSets::UInt32 poolSizeCount::UInt32 pPoolSizes::Ptr{VkDescriptorPoolSize} end immutable VkDescriptorSetAllocateInfo sType::VkStructureType pNext::Ptr{Void} descriptorPool::VkDescriptorPool descriptorSetCount::UInt32 pSetLayouts::Ptr{VkDescriptorSetLayout} end immutable VkDescriptorImageInfo sampler::VkSampler imageView::VkImageView imageLayout::VkImageLayout end immutable VkDescriptorBufferInfo buffer::VkBuffer offset::VkDeviceSize range::VkDeviceSize end immutable VkWriteDescriptorSet sType::VkStructureType pNext::Ptr{Void} dstSet::VkDescriptorSet dstBinding::UInt32 dstArrayElement::UInt32 descriptorCount::UInt32 descriptorType::VkDescriptorType pImageInfo::Ptr{VkDescriptorImageInfo} pBufferInfo::Ptr{VkDescriptorBufferInfo} pTexelBufferView::Ptr{VkBufferView} end immutable VkCopyDescriptorSet sType::VkStructureType pNext::Ptr{Void} srcSet::VkDescriptorSet srcBinding::UInt32 srcArrayElement::UInt32 dstSet::VkDescriptorSet dstBinding::UInt32 dstArrayElement::UInt32 descriptorCount::UInt32 end immutable VkFramebufferCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkFramebufferCreateFlags renderPass::VkRenderPass attachmentCount::UInt32 pAttachments::Ptr{VkImageView} width::UInt32 height::UInt32 layers::UInt32 end immutable VkAttachmentDescription flags::VkAttachmentDescriptionFlags format::VkFormat samples::VkSampleCountFlagBits loadOp::VkAttachmentLoadOp storeOp::VkAttachmentStoreOp stencilLoadOp::VkAttachmentLoadOp stencilStoreOp::VkAttachmentStoreOp initialLayout::VkImageLayout finalLayout::VkImageLayout end immutable VkAttachmentReference attachment::UInt32 layout::VkImageLayout end immutable VkSubpassDescription flags::VkSubpassDescriptionFlags pipelineBindPoint::VkPipelineBindPoint inputAttachmentCount::UInt32 pInputAttachments::Ptr{VkAttachmentReference} colorAttachmentCount::UInt32 pColorAttachments::Ptr{VkAttachmentReference} pResolveAttachments::Ptr{VkAttachmentReference} pDepthStencilAttachment::Ptr{VkAttachmentReference} preserveAttachmentCount::UInt32 pPreserveAttachments::Ptr{UInt32} end immutable VkSubpassDependency srcSubpass::UInt32 dstSubpass::UInt32 srcStageMask::VkPipelineStageFlags dstStageMask::VkPipelineStageFlags srcAccessMask::VkAccessFlags dstAccessMask::VkAccessFlags dependencyFlags::VkDependencyFlags end immutable VkRenderPassCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkRenderPassCreateFlags attachmentCount::UInt32 pAttachments::Ptr{VkAttachmentDescription} subpassCount::UInt32 pSubpasses::Ptr{VkSubpassDescription} dependencyCount::UInt32 pDependencies::Ptr{VkSubpassDependency} end immutable VkCommandPoolCreateInfo sType::VkStructureType pNext::Ptr{Void} flags::VkCommandPoolCreateFlags queueFamilyIndex::UInt32 end immutable VkCommandBufferAllocateInfo sType::VkStructureType pNext::Ptr{Void} commandPool::VkCommandPool level::VkCommandBufferLevel commandBufferCount::UInt32 end immutable VkCommandBufferInheritanceInfo sType::VkStructureType pNext::Ptr{Void} renderPass::VkRenderPass subpass::UInt32 framebuffer::VkFramebuffer occlusionQueryEnable::VkBool32 queryFlags::VkQueryControlFlags pipelineStatistics::VkQueryPipelineStatisticFlags end immutable VkCommandBufferBeginInfo sType::VkStructureType pNext::Ptr{Void} flags::VkCommandBufferUsageFlags pInheritanceInfo::Ptr{VkCommandBufferInheritanceInfo} end immutable VkBufferCopy srcOffset::VkDeviceSize dstOffset::VkDeviceSize size::VkDeviceSize end immutable VkImageSubresourceLayers aspectMask::VkImageAspectFlags mipLevel::UInt32 baseArrayLayer::UInt32 layerCount::UInt32 end immutable VkImageCopy srcSubresource::VkImageSubresourceLayers srcOffset::VkOffset3D dstSubresource::VkImageSubresourceLayers dstOffset::VkOffset3D extent::VkExtent3D end immutable VkImageBlit srcSubresource::VkImageSubresourceLayers srcOffsets::NTuple{2,VkOffset3D} dstSubresource::VkImageSubresourceLayers dstOffsets::NTuple{2,VkOffset3D} end immutable VkBufferImageCopy bufferOffset::VkDeviceSize bufferRowLength::UInt32 bufferImageHeight::UInt32 imageSubresource::VkImageSubresourceLayers imageOffset::VkOffset3D imageExtent::VkExtent3D end immutable VkClearColorValue _VkClearColorValue::NTuple{4,Cfloat} end immutable VkClearDepthStencilValue depth::Cfloat stencil::UInt32 end immutable VkClearValue _VkClearValue::VkClearColorValue end immutable VkClearAttachment aspectMask::VkImageAspectFlags colorAttachment::UInt32 clearValue::VkClearValue end immutable VkClearRect rect::VkRect2D baseArrayLayer::UInt32 layerCount::UInt32 end immutable VkImageResolve srcSubresource::VkImageSubresourceLayers srcOffset::VkOffset3D dstSubresource::VkImageSubresourceLayers dstOffset::VkOffset3D extent::VkExtent3D end immutable VkMemoryBarrier sType::VkStructureType pNext::Ptr{Void} srcAccessMask::VkAccessFlags dstAccessMask::VkAccessFlags end immutable VkBufferMemoryBarrier sType::VkStructureType pNext::Ptr{Void} srcAccessMask::VkAccessFlags dstAccessMask::VkAccessFlags srcQueueFamilyIndex::UInt32 dstQueueFamilyIndex::UInt32 buffer::VkBuffer offset::VkDeviceSize size::VkDeviceSize end immutable VkImageMemoryBarrier sType::VkStructureType pNext::Ptr{Void} srcAccessMask::VkAccessFlags dstAccessMask::VkAccessFlags oldLayout::VkImageLayout newLayout::VkImageLayout srcQueueFamilyIndex::UInt32 dstQueueFamilyIndex::UInt32 image::VkImage subresourceRange::VkImageSubresourceRange end immutable VkRenderPassBeginInfo sType::VkStructureType pNext::Ptr{Void} renderPass::VkRenderPass framebuffer::VkFramebuffer renderArea::VkRect2D clearValueCount::UInt32 pClearValues::Ptr{VkClearValue} end immutable VkDispatchIndirectCommand x::UInt32 y::UInt32 z::UInt32 end immutable VkDrawIndexedIndirectCommand indexCount::UInt32 instanceCount::UInt32 firstIndex::UInt32 vertexOffset::Int32 firstInstance::UInt32 end immutable VkDrawIndirectCommand vertexCount::UInt32 instanceCount::UInt32 firstVertex::UInt32 firstInstance::UInt32 end typealias PFN_vkCreateInstance Ptr{Void} typealias PFN_vkDestroyInstance Ptr{Void} typealias PFN_vkEnumeratePhysicalDevices Ptr{Void} typealias PFN_vkGetPhysicalDeviceFeatures Ptr{Void} typealias PFN_vkGetPhysicalDeviceFormatProperties Ptr{Void} typealias PFN_vkGetPhysicalDeviceImageFormatProperties Ptr{Void} typealias PFN_vkGetPhysicalDeviceProperties Ptr{Void} typealias PFN_vkGetPhysicalDeviceQueueFamilyProperties Ptr{Void} typealias PFN_vkGetPhysicalDeviceMemoryProperties Ptr{Void} typealias PFN_vkGetInstanceProcAddr Ptr{Void} typealias PFN_vkGetDeviceProcAddr Ptr{Void} typealias PFN_vkCreateDevice Ptr{Void} typealias PFN_vkDestroyDevice Ptr{Void} typealias PFN_vkEnumerateInstanceExtensionProperties Ptr{Void} typealias PFN_vkEnumerateDeviceExtensionProperties Ptr{Void} typealias PFN_vkEnumerateInstanceLayerProperties Ptr{Void} typealias PFN_vkEnumerateDeviceLayerProperties Ptr{Void} typealias PFN_vkGetDeviceQueue Ptr{Void} typealias PFN_vkQueueSubmit Ptr{Void} typealias PFN_vkQueueWaitIdle Ptr{Void} typealias PFN_vkDeviceWaitIdle Ptr{Void} typealias PFN_vkAllocateMemory Ptr{Void} typealias PFN_vkFreeMemory Ptr{Void} typealias PFN_vkMapMemory Ptr{Void} typealias PFN_vkUnmapMemory Ptr{Void} typealias PFN_vkFlushMappedMemoryRanges Ptr{Void} typealias PFN_vkInvalidateMappedMemoryRanges Ptr{Void} typealias PFN_vkGetDeviceMemoryCommitment Ptr{Void} typealias PFN_vkBindBufferMemory Ptr{Void} typealias PFN_vkBindImageMemory Ptr{Void} typealias PFN_vkGetBufferMemoryRequirements Ptr{Void} typealias PFN_vkGetImageMemoryRequirements Ptr{Void} typealias PFN_vkGetImageSparseMemoryRequirements Ptr{Void} typealias PFN_vkGetPhysicalDeviceSparseImageFormatProperties Ptr{Void} typealias PFN_vkQueueBindSparse Ptr{Void} typealias PFN_vkCreateFence Ptr{Void} typealias PFN_vkDestroyFence Ptr{Void} typealias PFN_vkResetFences Ptr{Void} typealias PFN_vkGetFenceStatus Ptr{Void} typealias PFN_vkWaitForFences Ptr{Void} typealias PFN_vkCreateSemaphore Ptr{Void} typealias PFN_vkDestroySemaphore Ptr{Void} typealias PFN_vkCreateEvent Ptr{Void} typealias PFN_vkDestroyEvent Ptr{Void} typealias PFN_vkGetEventStatus Ptr{Void} typealias PFN_vkSetEvent Ptr{Void} typealias PFN_vkResetEvent Ptr{Void} typealias PFN_vkCreateQueryPool Ptr{Void} typealias PFN_vkDestroyQueryPool Ptr{Void} typealias PFN_vkGetQueryPoolResults Ptr{Void} typealias PFN_vkCreateBuffer Ptr{Void} typealias PFN_vkDestroyBuffer Ptr{Void} typealias PFN_vkCreateBufferView Ptr{Void} typealias PFN_vkDestroyBufferView Ptr{Void} typealias PFN_vkCreateImage Ptr{Void} typealias PFN_vkDestroyImage Ptr{Void} typealias PFN_vkGetImageSubresourceLayout Ptr{Void} typealias PFN_vkCreateImageView Ptr{Void} typealias PFN_vkDestroyImageView Ptr{Void} typealias PFN_vkCreateShaderModule Ptr{Void} typealias PFN_vkDestroyShaderModule Ptr{Void} typealias PFN_vkCreatePipelineCache Ptr{Void} typealias PFN_vkDestroyPipelineCache Ptr{Void} typealias PFN_vkGetPipelineCacheData Ptr{Void} typealias PFN_vkMergePipelineCaches Ptr{Void} typealias PFN_vkCreateGraphicsPipelines Ptr{Void} typealias PFN_vkCreateComputePipelines Ptr{Void} typealias PFN_vkDestroyPipeline Ptr{Void} typealias PFN_vkCreatePipelineLayout Ptr{Void} typealias PFN_vkDestroyPipelineLayout Ptr{Void} typealias PFN_vkCreateSampler Ptr{Void} typealias PFN_vkDestroySampler Ptr{Void} typealias PFN_vkCreateDescriptorSetLayout Ptr{Void} typealias PFN_vkDestroyDescriptorSetLayout Ptr{Void} typealias PFN_vkCreateDescriptorPool Ptr{Void} typealias PFN_vkDestroyDescriptorPool Ptr{Void} typealias PFN_vkResetDescriptorPool Ptr{Void} typealias PFN_vkAllocateDescriptorSets Ptr{Void} typealias PFN_vkFreeDescriptorSets Ptr{Void} typealias PFN_vkUpdateDescriptorSets Ptr{Void} typealias PFN_vkCreateFramebuffer Ptr{Void} typealias PFN_vkDestroyFramebuffer Ptr{Void} typealias PFN_vkCreateRenderPass Ptr{Void} typealias PFN_vkDestroyRenderPass Ptr{Void} typealias PFN_vkGetRenderAreaGranularity Ptr{Void} typealias PFN_vkCreateCommandPool Ptr{Void} typealias PFN_vkDestroyCommandPool Ptr{Void} typealias PFN_vkResetCommandPool Ptr{Void} typealias PFN_vkAllocateCommandBuffers Ptr{Void} typealias PFN_vkFreeCommandBuffers Ptr{Void} typealias PFN_vkBeginCommandBuffer Ptr{Void} typealias PFN_vkEndCommandBuffer Ptr{Void} typealias PFN_vkResetCommandBuffer Ptr{Void} typealias PFN_vkCmdBindPipeline Ptr{Void} typealias PFN_vkCmdSetViewport Ptr{Void} typealias PFN_vkCmdSetScissor Ptr{Void} typealias PFN_vkCmdSetLineWidth Ptr{Void} typealias PFN_vkCmdSetDepthBias Ptr{Void} typealias PFN_vkCmdSetBlendConstants Ptr{Void} typealias PFN_vkCmdSetDepthBounds Ptr{Void} typealias PFN_vkCmdSetStencilCompareMask Ptr{Void} typealias PFN_vkCmdSetStencilWriteMask Ptr{Void} typealias PFN_vkCmdSetStencilReference Ptr{Void} typealias PFN_vkCmdBindDescriptorSets Ptr{Void} typealias PFN_vkCmdBindIndexBuffer Ptr{Void} typealias PFN_vkCmdBindVertexBuffers Ptr{Void} typealias PFN_vkCmdDraw Ptr{Void} typealias PFN_vkCmdDrawIndexed Ptr{Void} typealias PFN_vkCmdDrawIndirect Ptr{Void} typealias PFN_vkCmdDrawIndexedIndirect Ptr{Void} typealias PFN_vkCmdDispatch Ptr{Void} typealias PFN_vkCmdDispatchIndirect Ptr{Void} typealias PFN_vkCmdCopyBuffer Ptr{Void} typealias PFN_vkCmdCopyImage Ptr{Void} typealias PFN_vkCmdBlitImage Ptr{Void} typealias PFN_vkCmdCopyBufferToImage Ptr{Void} typealias PFN_vkCmdCopyImageToBuffer Ptr{Void} typealias PFN_vkCmdUpdateBuffer Ptr{Void} typealias PFN_vkCmdFillBuffer Ptr{Void} typealias PFN_vkCmdClearColorImage Ptr{Void} typealias PFN_vkCmdClearDepthStencilImage Ptr{Void} typealias PFN_vkCmdClearAttachments Ptr{Void} typealias PFN_vkCmdResolveImage Ptr{Void} typealias PFN_vkCmdSetEvent Ptr{Void} typealias PFN_vkCmdResetEvent Ptr{Void} typealias PFN_vkCmdWaitEvents Ptr{Void} typealias PFN_vkCmdPipelineBarrier Ptr{Void} typealias PFN_vkCmdBeginQuery Ptr{Void} typealias PFN_vkCmdEndQuery Ptr{Void} typealias PFN_vkCmdResetQueryPool Ptr{Void} typealias PFN_vkCmdWriteTimestamp Ptr{Void} typealias PFN_vkCmdCopyQueryPoolResults Ptr{Void} typealias PFN_vkCmdPushConstants Ptr{Void} typealias PFN_vkCmdBeginRenderPass Ptr{Void} typealias PFN_vkCmdNextSubpass Ptr{Void} typealias PFN_vkCmdEndRenderPass Ptr{Void} typealias PFN_vkCmdExecuteCommands Ptr{Void} typealias VkSurfaceKHR_T Void typealias VkSurfaceKHR Ptr{VkSurfaceKHR_T} @cenum(VkColorSpaceKHR, VK_COLORSPACE_SRGB_NONLINEAR_KHR = 0, VK_COLOR_SPACE_BEGIN_RANGE_KHR = 0, VK_COLOR_SPACE_END_RANGE_KHR = 0, VK_COLOR_SPACE_RANGE_SIZE_KHR = 1, VK_COLOR_SPACE_MAX_ENUM_KHR = 2147483647, ) @cenum(VkPresentModeKHR, VK_PRESENT_MODE_IMMEDIATE_KHR = 0, VK_PRESENT_MODE_MAILBOX_KHR = 1, VK_PRESENT_MODE_FIFO_KHR = 2, VK_PRESENT_MODE_FIFO_RELAXED_KHR = 3, VK_PRESENT_MODE_BEGIN_RANGE_KHR = 0, VK_PRESENT_MODE_END_RANGE_KHR = 3, VK_PRESENT_MODE_RANGE_SIZE_KHR = 4, VK_PRESENT_MODE_MAX_ENUM_KHR = 2147483647, ) @cenum(VkSurfaceTransformFlagBitsKHR, VK_SURFACE_TRANSFORM_IDENTITY_BIT_KHR = 1, VK_SURFACE_TRANSFORM_ROTATE_90_BIT_KHR = 2, VK_SURFACE_TRANSFORM_ROTATE_180_BIT_KHR = 4, VK_SURFACE_TRANSFORM_ROTATE_270_BIT_KHR = 8, VK_SURFACE_TRANSFORM_HORIZONTAL_MIRROR_BIT_KHR = 16, VK_SURFACE_TRANSFORM_HORIZONTAL_MIRROR_ROTATE_90_BIT_KHR = 32, VK_SURFACE_TRANSFORM_HORIZONTAL_MIRROR_ROTATE_180_BIT_KHR = 64, VK_SURFACE_TRANSFORM_HORIZONTAL_MIRROR_ROTATE_270_BIT_KHR = 128, VK_SURFACE_TRANSFORM_INHERIT_BIT_KHR = 256, VK_SURFACE_TRANSFORM_FLAG_BITS_MAX_ENUM_KHR = 2147483647, ) typealias VkSurfaceTransformFlagsKHR VkFlags @cenum(VkCompositeAlphaFlagBitsKHR, VK_COMPOSITE_ALPHA_OPAQUE_BIT_KHR = 1, VK_COMPOSITE_ALPHA_PRE_MULTIPLIED_BIT_KHR = 2, VK_COMPOSITE_ALPHA_POST_MULTIPLIED_BIT_KHR = 4, VK_COMPOSITE_ALPHA_INHERIT_BIT_KHR = 8, VK_COMPOSITE_ALPHA_FLAG_BITS_MAX_ENUM_KHR = 2147483647, ) typealias VkCompositeAlphaFlagsKHR VkFlags immutable VkSurfaceCapabilitiesKHR minImageCount::UInt32 maxImageCount::UInt32 currentExtent::VkExtent2D minImageExtent::VkExtent2D maxImageExtent::VkExtent2D maxImageArrayLayers::UInt32 supportedTransforms::VkSurfaceTransformFlagsKHR currentTransform::VkSurfaceTransformFlagBitsKHR supportedCompositeAlpha::VkCompositeAlphaFlagsKHR supportedUsageFlags::VkImageUsageFlags end immutable VkSurfaceFormatKHR format::VkFormat colorSpace::VkColorSpaceKHR end typealias PFN_vkDestroySurfaceKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceSurfaceSupportKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceSurfaceCapabilitiesKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceSurfaceFormatsKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceSurfacePresentModesKHR Ptr{Void} typealias VkSwapchainKHR_T Void typealias VkSwapchainKHR Ptr{VkSwapchainKHR_T} typealias VkSwapchainCreateFlagsKHR VkFlags immutable VkSwapchainCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkSwapchainCreateFlagsKHR surface::VkSurfaceKHR minImageCount::UInt32 imageFormat::VkFormat imageColorSpace::VkColorSpaceKHR imageExtent::VkExtent2D imageArrayLayers::UInt32 imageUsage::VkImageUsageFlags imageSharingMode::VkSharingMode queueFamilyIndexCount::UInt32 pQueueFamilyIndices::Ptr{UInt32} preTransform::VkSurfaceTransformFlagBitsKHR compositeAlpha::VkCompositeAlphaFlagBitsKHR presentMode::VkPresentModeKHR clipped::VkBool32 oldSwapchain::VkSwapchainKHR end immutable VkPresentInfoKHR sType::VkStructureType pNext::Ptr{Void} waitSemaphoreCount::UInt32 pWaitSemaphores::Ptr{VkSemaphore} swapchainCount::UInt32 pSwapchains::Ptr{VkSwapchainKHR} pImageIndices::Ptr{UInt32} pResults::Ptr{VkResult} end typealias PFN_vkCreateSwapchainKHR Ptr{Void} typealias PFN_vkDestroySwapchainKHR Ptr{Void} typealias PFN_vkGetSwapchainImagesKHR Ptr{Void} typealias PFN_vkAcquireNextImageKHR Ptr{Void} typealias PFN_vkQueuePresentKHR Ptr{Void} typealias VkDisplayKHR_T Void typealias VkDisplayKHR Ptr{VkDisplayKHR_T} typealias VkDisplayModeKHR_T Void typealias VkDisplayModeKHR Ptr{VkDisplayModeKHR_T} @cenum(VkDisplayPlaneAlphaFlagBitsKHR, VK_DISPLAY_PLANE_ALPHA_OPAQUE_BIT_KHR = 1, VK_DISPLAY_PLANE_ALPHA_GLOBAL_BIT_KHR = 2, VK_DISPLAY_PLANE_ALPHA_PER_PIXEL_BIT_KHR = 4, VK_DISPLAY_PLANE_ALPHA_PER_PIXEL_PREMULTIPLIED_BIT_KHR = 8, VK_DISPLAY_PLANE_ALPHA_FLAG_BITS_MAX_ENUM_KHR = 2147483647, ) typealias VkDisplayPlaneAlphaFlagsKHR VkFlags typealias VkDisplayModeCreateFlagsKHR VkFlags typealias VkDisplaySurfaceCreateFlagsKHR VkFlags immutable VkDisplayPropertiesKHR display::VkDisplayKHR displayName::Cstring physicalDimensions::VkExtent2D physicalResolution::VkExtent2D supportedTransforms::VkSurfaceTransformFlagsKHR planeReorderPossible::VkBool32 persistentContent::VkBool32 end immutable VkDisplayModeParametersKHR visibleRegion::VkExtent2D refreshRate::UInt32 end immutable VkDisplayModePropertiesKHR displayMode::VkDisplayModeKHR parameters::VkDisplayModeParametersKHR end immutable VkDisplayModeCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkDisplayModeCreateFlagsKHR parameters::VkDisplayModeParametersKHR end immutable VkDisplayPlaneCapabilitiesKHR supportedAlpha::VkDisplayPlaneAlphaFlagsKHR minSrcPosition::VkOffset2D maxSrcPosition::VkOffset2D minSrcExtent::VkExtent2D maxSrcExtent::VkExtent2D minDstPosition::VkOffset2D maxDstPosition::VkOffset2D minDstExtent::VkExtent2D maxDstExtent::VkExtent2D end immutable VkDisplayPlanePropertiesKHR currentDisplay::VkDisplayKHR currentStackIndex::UInt32 end immutable VkDisplaySurfaceCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkDisplaySurfaceCreateFlagsKHR displayMode::VkDisplayModeKHR planeIndex::UInt32 planeStackIndex::UInt32 transform::VkSurfaceTransformFlagBitsKHR globalAlpha::Cfloat alphaMode::VkDisplayPlaneAlphaFlagBitsKHR imageExtent::VkExtent2D end typealias PFN_vkGetPhysicalDeviceDisplayPropertiesKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceDisplayPlanePropertiesKHR Ptr{Void} typealias PFN_vkGetDisplayPlaneSupportedDisplaysKHR Ptr{Void} typealias PFN_vkGetDisplayModePropertiesKHR Ptr{Void} typealias PFN_vkCreateDisplayModeKHR Ptr{Void} typealias PFN_vkGetDisplayPlaneCapabilitiesKHR Ptr{Void} typealias PFN_vkCreateDisplayPlaneSurfaceKHR Ptr{Void} immutable VkDisplayPresentInfoKHR sType::VkStructureType pNext::Ptr{Void} srcRect::VkRect2D dstRect::VkRect2D persistent::VkBool32 end typealias PFN_vkCreateSharedSwapchainsKHR Ptr{Void} typealias VkXcbSurfaceCreateFlagsKHR VkFlags immutable VkXcbSurfaceCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkXcbSurfaceCreateFlagsKHR connection::Ptr{xcb_connection_t} window::xcb_window_t end typealias PFN_vkCreateXcbSurfaceKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceXcbPresentationSupportKHR Ptr{Void} typealias VkWaylandSurfaceCreateFlagsKHR VkFlags immutable VkWaylandSurfaceCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkWaylandSurfaceCreateFlagsKHR display::Ptr{wl_display} surface::Ptr{wl_surface} end typealias PFN_vkCreateWaylandSurfaceKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceWaylandPresentationSupportKHR Ptr{Void} typealias VkMirSurfaceCreateFlagsKHR VkFlags immutable VkMirSurfaceCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkMirSurfaceCreateFlagsKHR connection::Ptr{Cint} mirSurface::Ptr{Cint} end typealias PFN_vkCreateMirSurfaceKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceMirPresentationSupportKHR Ptr{Void} typealias VkWin32SurfaceCreateFlagsKHR VkFlags immutable VkWin32SurfaceCreateInfoKHR sType::VkStructureType pNext::Ptr{Void} flags::VkWin32SurfaceCreateFlagsKHR hinstance::Cint hwnd::Cint end typealias PFN_vkCreateWin32SurfaceKHR Ptr{Void} typealias PFN_vkGetPhysicalDeviceWin32PresentationSupportKHR Ptr{Void} typealias VkDebugReportCallbackEXT_T Void typealias VkDebugReportCallbackEXT Ptr{VkDebugReportCallbackEXT_T} @cenum(VkDebugReportObjectTypeEXT, VK_DEBUG_REPORT_OBJECT_TYPE_UNKNOWN_EXT = 0, VK_DEBUG_REPORT_OBJECT_TYPE_INSTANCE_EXT = 1, VK_DEBUG_REPORT_OBJECT_TYPE_PHYSICAL_DEVICE_EXT = 2, VK_DEBUG_REPORT_OBJECT_TYPE_DEVICE_EXT = 3, VK_DEBUG_REPORT_OBJECT_TYPE_QUEUE_EXT = 4, VK_DEBUG_REPORT_OBJECT_TYPE_SEMAPHORE_EXT = 5, VK_DEBUG_REPORT_OBJECT_TYPE_COMMAND_BUFFER_EXT = 6, VK_DEBUG_REPORT_OBJECT_TYPE_FENCE_EXT = 7, VK_DEBUG_REPORT_OBJECT_TYPE_DEVICE_MEMORY_EXT = 8, VK_DEBUG_REPORT_OBJECT_TYPE_BUFFER_EXT = 9, VK_DEBUG_REPORT_OBJECT_TYPE_IMAGE_EXT = 10, VK_DEBUG_REPORT_OBJECT_TYPE_EVENT_EXT = 11, VK_DEBUG_REPORT_OBJECT_TYPE_QUERY_POOL_EXT = 12, VK_DEBUG_REPORT_OBJECT_TYPE_BUFFER_VIEW_EXT = 13, VK_DEBUG_REPORT_OBJECT_TYPE_IMAGE_VIEW_EXT = 14, VK_DEBUG_REPORT_OBJECT_TYPE_SHADER_MODULE_EXT = 15, VK_DEBUG_REPORT_OBJECT_TYPE_PIPELINE_CACHE_EXT = 16, VK_DEBUG_REPORT_OBJECT_TYPE_PIPELINE_LAYOUT_EXT = 17, VK_DEBUG_REPORT_OBJECT_TYPE_RENDER_PASS_EXT = 18, VK_DEBUG_REPORT_OBJECT_TYPE_PIPELINE_EXT = 19, VK_DEBUG_REPORT_OBJECT_TYPE_DESCRIPTOR_SET_LAYOUT_EXT = 20, VK_DEBUG_REPORT_OBJECT_TYPE_SAMPLER_EXT = 21, VK_DEBUG_REPORT_OBJECT_TYPE_DESCRIPTOR_POOL_EXT = 22, VK_DEBUG_REPORT_OBJECT_TYPE_DESCRIPTOR_SET_EXT = 23, VK_DEBUG_REPORT_OBJECT_TYPE_FRAMEBUFFER_EXT = 24, VK_DEBUG_REPORT_OBJECT_TYPE_COMMAND_POOL_EXT = 25, VK_DEBUG_REPORT_OBJECT_TYPE_SURFACE_KHR_EXT = 26, VK_DEBUG_REPORT_OBJECT_TYPE_SWAPCHAIN_KHR_EXT = 27, VK_DEBUG_REPORT_OBJECT_TYPE_DEBUG_REPORT_EXT = 28, VK_DEBUG_REPORT_OBJECT_TYPE_BEGIN_RANGE_EXT = 0, VK_DEBUG_REPORT_OBJECT_TYPE_END_RANGE_EXT = 28, VK_DEBUG_REPORT_OBJECT_TYPE_RANGE_SIZE_EXT = 29, VK_DEBUG_REPORT_OBJECT_TYPE_MAX_ENUM_EXT = 2147483647, ) @cenum(VkDebugReportErrorEXT, VK_DEBUG_REPORT_ERROR_NONE_EXT = 0, VK_DEBUG_REPORT_ERROR_CALLBACK_REF_EXT = 1, VK_DEBUG_REPORT_ERROR_BEGIN_RANGE_EXT = 0, VK_DEBUG_REPORT_ERROR_END_RANGE_EXT = 1, VK_DEBUG_REPORT_ERROR_RANGE_SIZE_EXT = 2, VK_DEBUG_REPORT_ERROR_MAX_ENUM_EXT = 2147483647, ) @cenum(VkDebugReportFlagBitsEXT, VK_DEBUG_REPORT_INFORMATION_BIT_EXT = 1, VK_DEBUG_REPORT_WARNING_BIT_EXT = 2, VK_DEBUG_REPORT_PERFORMANCE_WARNING_BIT_EXT = 4, VK_DEBUG_REPORT_ERROR_BIT_EXT = 8, VK_DEBUG_REPORT_DEBUG_BIT_EXT = 16, VK_DEBUG_REPORT_FLAG_BITS_MAX_ENUM_EXT = 2147483647, ) typealias VkDebugReportFlagsEXT VkFlags typealias PFN_vkDebugReportCallbackEXT Ptr{Void} immutable VkDebugReportCallbackCreateInfoEXT sType::VkStructureType pNext::Ptr{Void} flags::VkDebugReportFlagsEXT pfnCallback::PFN_vkDebugReportCallbackEXT pUserData::Ptr{Void} end typealias PFN_vkCreateDebugReportCallbackEXT Ptr{Void} typealias PFN_vkDestroyDebugReportCallbackEXT Ptr{Void} typealias PFN_vkDebugReportMessageEXT Ptr{Void}
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2.201432
39,656
<reponame>DavidJArnold/churrosJulia.jl using churrosJulia churro("{o}====} {======={o} {{==o}") res = churro(str,diagnostics=false) print("done: $res")
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2.152778
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<reponame>singularitti/Thermo.jl using Thermo using Test @testset "Thermo.jl" begin # Write your tests here. end
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# SD_AOU Functions for Keysight M32XXA/M33XXA AWGs export SD_AOU_initialize, SD_AOU_clockGetFrequency, SD_AOU_clockGetSyncFrequency, SD_AOU_clockSetFrequency, SD_AOU_clockResetPhase, SD_AOU_channelWaveShape, SD_AOU_channelFrequency, SD_AOU_channelPhase, SD_AOU_channelPhaseReset, SD_AOU_channelPhaseResetMultiple, SD_AOU_channelAmplitude, SD_AOU_channelOffset, SD_AOU_modulationAngleConfig, SD_AOU_modulationAmplitudeConfig, SD_AOU_modulationIQconfig, SD_AOU_clockIOconfig, SD_AOU_triggerIOconfigV5, SD_AOU_triggerIOconfig, SD_AOU_triggerIOwrite, SD_AOU_triggerIOread, SD_AOU_waveformGetAddress, SD_AOU_waveformGetMemorySize, SD_AOU_waveformMemoryGetWriteAddress, SD_AOU_waveformMemorySetWriteAddress, SD_AOU_waveformReLoadArrayInt16, SD_AOU_waveformReLoad, SD_AOU_waveformLoadArrayInt16, SD_AOU_waveformLoad, SD_AOU_waveformFlush, SD_AOU_AWGqueueWaveform, SD_AOU_AWGstreamingConfig, SD_AOU_AWGstreamingRelease, SD_AOU_AWGidleValue, SD_AOU_AWGidleValueRead, SD_AOU_waveformLoadP2Pstart, SD_AOU_waveformLoadP2Pclose, SD_AOU_waveformLoadP2PgetPhysicalAddress, SD_AOU_waveformLoadP2PwriteDataTest, SD_AOU_AWGstart, SD_AOU_AWGstop, SD_AOU_AWGresume, SD_AOU_AWGpause, SD_AOU_AWGtrigger, SD_AOU_AWGjumpNextWaveform, SD_AOU_AWGjumpNextWaveformMultiple, SD_AOU_AWGisRunning, SD_AOU_AWGnWFplaying, SD_AOU_AWGstartMultiple, SD_AOU_AWGstopMultiple, SD_AOU_AWGresumeMultiple, SD_AOU_AWGpauseMultiple, SD_AOU_AWGtriggerMultiple, SD_AOU_AWGflush, SD_AOU_AWGfromFile, SD_AOU_AWGfromArray, SD_AOU_AWGtriggerExternalConfig, SD_AOU_AWGqueueConfig, SD_AOU_AWGqueueConfigRead, SD_AOU_AWGqueueMarkerConfig, SD_AOU_AWGqueueSyncMode ## int SD_AOU_initialize(int moduleID); SD_AOU_initialize(moduleID::Integer) = ccall((:SD_AOU_initialize, klib), Cint, (Cint,), moduleID) ## double SD_AOU_clockGetFrequency(int moduleID); """ This function returns the real hardware clock frequency. It may differ from the frequency set with the function clockSetFrequency, due to the hardware frequency resolution. """ SD_AOU_clockGetFrequency(moduleID::Integer) = ccall((:SD_AOU_clockGetFrequency, klib), Cdouble, (Cint,), moduleID) ## double SD_AOU_clockGetSyncFrequency(int moduleID); """ This function returns the frequency of Clock System """ SD_AOU_clockGetSyncFrequency(moduleID::Integer) = ccall((:SD_AOU_clockGetSyncFrequency, klib), Cint, (Cint,), moduleID) ## double SD_AOU_clockSetFrequency(int moduleID, double frequency, int mode); """ This function sets the module clock frequency. ### CLK Set Frequency Mode - Low Jitter Mode : The clock system is set to achieve the lowest jitter, sacrificing tuning speed --> CLK_LOW_JITTER = 0 - Fast Tuning Mode : The clock system is set to achieve the lowest tuning time, sacrificing jitter performance --> CLK_FAST_TUNE = 1 """ SD_AOU_clockSetFrequency(moduleID::Integer, frequency::Real, mode::Integer) = ccall((:SD_AOU_clockSetFrequency, klib), Cdouble, (Cint, Cdouble, Cint), moduleID, frequency, mode) ## int SD_AOU_clockResetPhase(int moduleID, int triggerBehavior, int PXItrigger, double skew); SD_AOU_clockResetPhase(moduleID::Integer, triggerBehavior::Integer, PXItrigger::Integer, skew::Real) = ccall((:SD_AOU_clockResetPhase, klib), Cint, (Cint, Cint, Cint, Cdouble), moduleID, triggerBehavior, PXItrigger, skew) ## int SD_AOU_channelWaveShape(int moduleID, int nChannel, int waveShape); """ This function sets the channel output waveform type. ### Output Signal Selection - HIZ : The output signal is set to HIZ (no output signal is provided, only available for M3202A) AOU_HIZ = -1 - No Signal : The output signal is set to 0. All other channel settings are maintained --> AOU_OFF = 0 (default) - Sinusoidal : Generated by the Function Generator --> AOU_SINUSOIDAL = 1 - Triangular : Generated by the Function Generator --> AOU_TRIANGULAR = 2 - Square : Generated by the Function Generator --> AOU_SQUARE = 4 - DC Voltage : The output DC voltage is set by the channel amplitude setting --> AOU_DC = 5 - Arbitrary Waveform : Generated by the Arbitrary Waveform Generator --> AOU_AWG = 6 - Partner Channel : Only for odd channels. It is the output of the previous channel (to create differential signals, etc.) --> AOU_PARTNER = 8 """ SD_AOU_channelWaveShape(moduleID::Integer, nChannel::Integer, waveShape::Integer) = ccall((:SD_AOU_channelWaveShape, klib), Cint, (Cint, Cint, Cint), moduleID, nChannel, waveShape) ## int SD_AOU_channelFrequency(int moduleID, int nChannel, double frequency); """ This function sets the frequency for the periodic signals generated by the Function Generators. """ SD_AOU_channelFrequency(moduleID::Integer, nChannel::Integer, frequency::Real) = ccall((:SD_AOU_channelFrequency, klib), Cint, (Cint, Cint, Cdouble), moduleID, nChannel, frequency) ## int SD_AOU_channelPhase(int moduleID, int nChannel, double phase); """ This function sets the phase for the periodic signals generated by the Function Generators. """ SD_AOU_channelPhase(moduleID::Integer, nChannel::Integer, phase::Real) = ccall((:SD_AOU_channelPhase, klib), Cint, (Cint, Cint, Cdouble), moduleID, nChannel, phase) ## int SD_AOU_channelPhaseReset(int moduleID, int channel); """ This function resets the accumulated phase of the selected signal. This accumulated phase is the result of the phase continuous operation of the product. """ SD_AOU_channelPhaseReset(moduleID::Integer, nChannel::Integer) = ccall((:SD_AOU_channelPhaseReset, klib), Cint, (Cint, Cint), moduleID, nChannel) ## int SD_AOU_channelPhaseResetMultiple(int moduleID, int channelMask); """ This function resets the accumulated phase of the selected channels simultaneously. This accumulated phase is the result of the phase continuous operation of the product. """ SD_AOU_channelPhaseResetMultiple(moduleID::Integer, channelMask::Integer) = ccall((:SD_AOU_channelPhaseResetMultiple, klib), Cint, (Cint, Cint), moduleID, channelMask) ## int SD_AOU_channelAmplitude(int moduleID, int nChannel, double amplitude); """ This function sets the amplitude of a channel. """ SD_AOU_channelAmplitude(moduleID::Integer, nChannel::Integer, amplitude::Real) = ccall((:SD_AOU_channelAmplitude, klib), Cint, (Cint, Cint, Cdouble), moduleID, nChannel, amplitude) ## int SD_AOU_channelOffset(int moduleID, int nChannel, double offset); """ This function sets the DC offset of a channel. """ SD_AOU_channelOffset(moduleID::Integer, nChannel::Integer, offset::Real) = ccall((:SD_AOU_channelOffset, klib), Cint, (Cint, Cint, Cdouble), moduleID, nChannel, offset) ## int SD_AOU_modulationAngleConfig(int moduleID, int nChannel, int modulationType, double deviationGain); """ This function configures the modulation in frequency/phase for the selected channel. ### Angle Modulation Types - No Modulation : Modulation is disabled --> AOU_MOD_OFF = 0 (default) - Frequency Modulation : The AWG is used to modulate the channel frequency --> AOU_MOD_FM = 1 - Frequency Modulation (32 bits): The AWG is used to modulate the channel frequency. For models with -DM1 dual modulation option --> AOU_MOD_FM_32b = 1 - Phase Modulation : The AWG is used to modulate the channel phase --> AOU_MOD_PM = 2 """ SD_AOU_modulationAngleConfig(moduleID::Integer, nChannel::Integer, modulationType::Integer, deviationGain::Real) = ccall((:SD_AOU_modulationAngleConfig, klib), Cint, (Cint, Cint, Cint, Cdouble), moduleID, nChannel, modulationType, deviationGain) ## int SD_AOU_modulationAmplitudeConfig(int moduleID, int nChannel, int modulationType, double deviationGain); """ This function configures the modulation in amplitude/offset for the selected channel. """ SD_AOU_modulationAmplitudeConfig(moduleID::Integer, nChannel::Integer, modulationType::Integer, deviationGain::Real) = ccall((:SD_AOU_modulationAmplitudeConfig, klib), Cint, (Cint, Cint, Cint, Cdouble), moduleID, nChannel, modulationType, deviationGain) ## int SD_AOU_modulationIQconfig(int moduleID, int nChannel, int enable); """ This function sets the IQ modulation for the selected channel. ### Amplitude Modulation Options - No Modulation : Modulation is disabled. The channel amplitude and offset are only set by the main registers set by the main registers --> AOU_MOD_OFF = 0 (default) - Amplitude Modulation : The modulating signal is used to modulate the channel amplitude --> AOU_MOD_AM = 1 - Offset Modulation : The modulating signal is used to modulate the channel offset --> AOU_MOD_OFFSET = 2 """ SD_AOU_modulationIQconfig(moduleID::Integer, nChannel::Integer, enable::Integer) = ccall((:SD_AOU_modulationIQconfig, klib), Cint, (Cint, Cint, Cint), moduleID, nChannel, enable) # Clock ## int SD_AOU_clockIOconfig(int moduleID, int clockConfig); """ This function configures the operation of the clock output connector. ### CLK Output Configurations - Disable : The CLK connector is disabled --> CLK_CONN_DISABLED = 0 (default) - CLKref Output : A copy of the reference clock is available at the CLK connector --> CLK_REF_OUTPUT = 1 """ SD_AOU_clockIOconfig(moduleID::Integer, clockConfig::Integer) = ccall((:SD_AOU_clockIOconfig, klib), Cint, (Cint, Cint), moduleID, clockIOconfig) # Trigger ## int SD_AOU_triggerIOconfigV5(int moduleID, int direction, int syncMode); SD_AOU_triggerIOconfigV5(moduleID::Integer, direction::Integer, syncMode::Integer) = ccall((:SD_AOU_triggerIOconfigV5, klib), Cint, (Cint, Cint, Cint), moduleID, direction, syncMode) ## int SD_AOU_triggerIOconfig(int moduleID, int direction); """ This function configures the trigger connector/line direction and synchronization/sampling method ### Trigger I/O Options - Trigger Output (readable) : TRG operates as a general purpose digital output signal, which can be written by the user software --> AOU_TRG_OUT = 0 - Trigger Input : TRG operates as a trigger input, or as general purpose digital input signal, which can be read by the user software --> AOU_TRG_IN = 1 ### Trigger Synchronization/Sampling Options - Non-synchronized mode : The trigger is sampled with an internal 100 MHz clock --> SYNC_NONE = 0 - Synchronized mode : (PXI form factor only) The trigger is sampled using CLK10 --> SYNC_CLK_0 = 1 """ SD_AOU_triggerIOconfig(moduleID::Integer, direction::Integer) = ccall((:SD_AOU_triggerIOconfig, klib), Cint, (Cint, Cint), moduleID, direction) ## int SD_AOU_triggerIOwrite(int moduleID, int value, int syncMode); """ This function sets the trigger output. The trigger must be configured as output using function triggerIOconfig. """ SD_AOU_triggerIOwrite(moduleID::Integer, value::Integer, syncMode::Integer) = ccall((:SD_AOU_triggerIOwrite, klib), Cint, (Cint, Cint, Cint), moduleID, value, syncMode) ## int SD_AOU_triggerIOread(int moduleID); """ This function reads the trigger input. """ SD_AOU_triggerIOread(moduleID::Integer) = ccall((:SD_AOU_triggerIOread, klib), Cint, (Cint,), moduleID) ## int SD_AOU_waveformGetAddress(int moduleID, int waveformNumber); SD_AOU_waveformGetAddress(moduleID::Integer, waveformNumber::Integer) = ccall((:SD_AOU_waveformGetAddress, klib), Cint, (Cint, Cint), moduleID, waveformNumber) ## int SD_AOU_waveformGetMemorySize(int moduleID, int waveformNumber); SD_AOU_waveformGetMemorySize(moduleID::Integer, waveformNumber::Integer) = ccall((:SD_AOU_waveformGetMemorySize, klib), Cint, (Cint, Cint), moduleID, waveformNumber) ## int SD_AOU_waveformMemoryGetWriteAddress(int moduleID); SD_AOU_waveformMemoryGetWriteAddress(moduleID::Integer) = ccall((:SD_AOU_waveformMemoryGetWriteAddress, klib), Cint, (Cint,), moduleID) ## int SD_AOU_waveformMemorySetWriteAddress(int moduleID, int writeAddress); SD_AOU_waveformMemorySetWriteAddress(moduleID::Integer, writeAddress::Integer) = ccall((:SD_AOU_waveformMemorySetWriteAddress, klib), Cint, (Cint, Cint), moduleID, writeAddress) ## int SD_AOU_waveformReLoadArrayInt16(int moduleID, int waveformType, int waveformPoints, short *waveformDataRaw, int waveformNumber, int paddingMode SD_DEFAULT_NULL); function SD_AOU_waveformReLoadArrayInt16(moduleID::Integer, waveformType::Integer, waveformDataRaw::Vector, waveformNumber::Integer, paddingMode::Integer=0) c_waveformDataRaw = Vector{Cshort}(waveformDataRaw) waveformPoints = length(c_waveformDataRaw) val = ccall((:SD_AOU_waveformReLoadArrayInt16, klib), Cint, (Cint, Cint, Cint, Ref{Cshort}, Cint, Cint), moduleID, waveformType, waveformPoints, c_waveformDataRaw, waveformNumber, paddingMode) return val end ## int SD_AOU_waveformReLoad(int moduleID, int waveformID, int waveformNumber, int paddingMode SD_DEFAULT_NULL); """ This function replaces a waveform located in the module onboard RAM. The size of the new waveform must be smaller than or equal to the existing waveform. """ SD_AOU_waveformReLoad(moduleID::Integer, waveformID::Integer, waveformNumber::Integer, paddingMode::Integer=0) = ccall((:SD_AOU_waveformReLoad, klib), Cint, (Cint, Cint, Cint, Cint), moduleID, waveformID, waveformNumber, paddingMode) ## int SD_AOU_waveformLoadArrayInt16(int moduleID, int waveformType, int waveformPoints, short *waveformDataRaw, int waveformNumber, int paddingMode SD_DEFAULT_NULL); function SD_AOU_waveformLoadArrayInt16(moduleID::Integer, waveformType::Integer, waveformDataRaw::Vector, waveformNumber::Integer, paddingMode::Integer=0) c_waveformDataRaw = Vector{Cshort}(waveformDataRaw) waveformPoints = length(c_waveformDataRaw) # Not sure.. val = ccall((:SD_AOU_waveformLoadArrayInt16, klib), Cint, (Cint, Cint, Cint, Ref{Cshort}, Cint, Cint), moduleID, waveformType, waveformPoints, c_waveformDataRaw, waveformNumber, paddingMode) return val end ## int SD_AOU_waveformLoad(int moduleID, int waveformID, int waveformNumber, int paddingMode SD_DEFAULT_NULL); """ This function loads the specified waveform into the module onboard RAM. Waveforms must be created first with the SD-Wave class. """ SD_AOU_waveformLoad(moduleID::Integer, waveformID::Integer, waveformNumber::Integer, paddingMode::Integer=0) = ccall((:SD_AOU_waveformLoad, klib), Cint, (Cint, Cint, Cint, Cint), moduleID, waveformID, waveformNumber, paddingMode) ## int SD_AOU_waveformFlush(int moduleID); """ This function deletes all the waveforms from the module onboard RAM and flushes all the AWG queues. """ SD_AOU_waveformFlush(moduleID::Integer) = ccall((:SD_AOU_waveformFlush, klib), Cint, (Cint,), moduleID) ## int SD_AOU_AWGqueueWaveform(int moduleID, int nAWG, int waveformNumber, int triggerMode, int startDelay, int cycles, int prescaler); """ This function queues the specified waveform in one of the AWGs of the module. The waveform must be already loaded in the module onboard RAM. """ SD_AOU_AWGqueueWaveform(moduleID::Integer, nAWG::Integer, waveformNumber::Integer, triggerMode::Integer, startDelay::Integer, cycles::Integer, prescaler::Integer) = ccall((:SD_AOU_AWGqueueWaveform, klib), Cint, (Cint, Cint, Cint, Cint, Cint, Cint, Cint), moduleID, nAWG, waveformNumber, triggerMode, startDelay, cycles, prescaler) ## int SD_AOU_AWGstreamingConfig(int moduleID, int nAWG, int triggerMode, int startDelay, int prescaler, int waveformPoints, callbackWGPtr callbackFunction, void *callbackUserObj); SD_AOU_AWGstreamingConfig(moduleID::Integer, nAWG::Integer, triggerMode::Integer, startDelay::Integer, prescaler::Integer, waveformPoints::Integer) = ccall((:SD_AOU_AWGstreamingConfig, klib), Cint, (Cint, Cint, Cint, Cint, Cint, Cint, Ptr{Void}, Ptr{Void}), moduleID, nAWG, triggerMode, startDelay, prescaler, waveformPoints, C_NULL, C_NULL) ## int SD_AOU_AWGstreamingRelease(int moduleID, int nAWG); SD_AOU_AWGstreamingRelease(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGstreamingRelease, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGidleValue(int moduleID, int nAWG, double value); SD_AOU_AWGidleValue(moduleID::Integer, nAWG::Integer, value::Real) = ccall((:SD_AOU_AWGidleValue, klib), Cint, (Cint, Cint, Cdouble), moduleID, nAWG, value) ## double SD_AOU_AWGidleValueRead(int moduleID, int nAWG); SD_AOU_AWGidleValueRead(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGidleValueRead, klib), Cdouble, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_waveformLoadP2Pstart(int moduleID, int wfType, int points, int waveformNumber); SD_AOU_waveformLoadP2Pstart(moduleID::Integer, wfType::Integer, points::Integer, waveformNumber::Integer) = ccall((:SD_AOU_waveformLoadP2Pstart, klib), Cint, (Cint, Cint, Cint, Cint), moduleID, wfType, points, waveformNumber) ## int SD_AOU_waveformLoadP2Pclose(int moduleID, int nDWwritten); SD_AOU_waveformLoadP2Pclose(moduleID::Integer, nDWwritten::Integer) = ccall((:SD_AOU_waveformLoadP2Pclose, klib), Cint, (Cint, Cint), moduleID, nDWwritten) ## unsigned long long SD_AOU_waveformLoadP2PgetPhysicalAddress(int moduleID); SD_AOU_waveformLoadP2PgetPhysicalAddress(moduleID::Integer) = ccall((:SD_AOU_waveformLoadP2PgetPhysicalAddress, klib), Culonglong, (Cint,), moduleID) ## int SD_AOU_waveformLoadP2PwriteDataTest(int moduleID, int wfType, int points, short *dataRaw, int paddingMode SD_DEFAULT_NULL); function SD_AOU_waveformLoadP2PwriteDataTest(moduleID::Integer, wfType::Integer, dataRaw::Vector, paddingMode::Integer) c_dataRaw = Vector{Cshort}(dataRaw) points = length(c_dataRaw) val = ccall((:SD_AOU_waveformLoadP2PwriteDataTest, klib), Cint, (Cint, Cint, Cint, Ref{Cshort}, Cint), moduleID, wfType, points, c_dataRaw, paddingMode) return val end ## int SD_AOU_AWGstart(int moduleID, int nAWG); """ This function starts he selected AWG from the beginning of its queue. The generation will start immediately or when a trigger is received, depending on the trigger selection of the first waveform in the queue and provided that at least one waveform is queued in the AWG. """ SD_AOU_AWGstart(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGstart, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGstop(int moduleID, int nAWG); """ This function stops the selected AWG, setting the output to zero and resetting the AWG queue to its initial position. All following incoming triggers are ignored. """ SD_AOU_AWGstop(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGstop, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGresume(int moduleID, int nAWG); """ The function resumes the operation of the selected AWG from the current position of the queue. """ SD_AOU_AWGresume(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGresume, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGpause(int moduleID, int nAWG); """ This function pauses the selected AWG, leaving the last waveform point at the output, and ignoring all incoming triggers. The waveform generation can be resumed calling AWGresume. """ SD_AOU_AWGpause(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGpause, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGtrigger(int moduleID, int nAWG); """ This function triggers the selected AWG. The waveform waiting in the current position of the queue is launched provided it is configured with VI/HVI Trigger. """ SD_AOU_AWGtrigger(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGtrigger, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGjumpNextWaveform(int moduleID, int nAWG); """ This function forces a jump to the next waveform in the AWG queue. The jump is executed once the current waveform has finished a complete cycle. """ SD_AOU_AWGjumpNextWaveform(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGjumpNextWaveform, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGjumpNextWaveformMultiple(int moduleID, int AWGmask); """ This function forces a jump to the next waveform in the queue of several AWGs. The jumps are executed once the current waveforms have finished a complete cycle. """ SD_AOU_AWGjumpNextWaveformMultiple(moduleID::Integer, AWGmask::Integer) = ccall((:SD_AOU_AWGjumpNextWaveformMultiple, klib), Cint, (Cint, Cint), moduleID, AWGmask) ## int SD_AOU_AWGisRunning(int moduleID, int nAWG); """ This function returns if the AWG is running or stopped. """ SD_AOU_AWGisRunning(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGisRunning, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGnWFplaying(int moduleID, int nAWG); """ This function returns the waveformNumber of the waveform which is currently being generated. """ SD_AOU_AWGnWFplaying(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGnWFplaying, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGstartMultiple(int moduleID, int AWGmask); """ This function starts the selected AWGs from the beginning of their queues. The generation will start immediately or when a trigger is received, depending on the trigger selection of the first waveform in their queues and provided that at least one waveform is queued in these AWGs (functions AWGqueueWaveform, or AWG). """ SD_AOU_AWGstartMultiple(moduleID::Integer, AWGmask::Integer) = ccall((:SD_AOU_AWGstartMultiple, klib), Cint, (Cint, Cint), moduleID, AWGmask) ## int SD_AOU_AWGstopMultiple(int moduleID, int AWGmask); """ This function stops the selected AWGs, setting their outputs to zero and resetting their respective queues to the initial positions. All following incoming triggers are ignored. """ SD_AOU_AWGstopMultiple(moduleID::Integer, AWGmask::Integer) = ccall((:SD_AOU_AWGstopMultiple, klib), Cint, (Cint, Cint), moduleID, AWGmask) ## int SD_AOU_AWGresumeMultiple(int moduleID, int AWGmask); """ This function resumes the operation of the selected AWGs from the current position of their respective queues. """ SD_AOU_AWGresumeMultiple(moduleID::Integer, AWGmask::Integer) = ccall((:SD_AOU_AWGresumeMultiple, klib), Cint, (Cint, Cint), moduleID, AWGmask) ## int SD_AOU_AWGpauseMultiple(int moduleID, int AWGmask); """ This function pauses the selected AWGs, leaving the last waveform point at the output, and ignoring all incoming triggers. The waveform generation can be resumed calling AWGresume. """ SD_AOU_AWGpauseMultiple(moduleID::Integer, AWGmask::Integer) = ccall((:SD_AOU_AWGpauseMultiple, klib), Cint, (Cint, Cint), moduleID, AWGmask) ## int SD_AOU_AWGtriggerMultiple(int moduleID, int AWGmask); """ This function triggers the selected AWGs. The waveform waiting in the current position of the queue is launched provided it is configured with VI/HVI Trigger. """ SD_AOU_AWGtriggerMultiple(moduleID::Integer, AWGmask::Integer) = ccall((:SD_AOU_AWGtriggerMultiple, klib), Cint, (Cint, Cint), moduleID, AWGmask) ## int SD_AOU_AWGflush(int moduleID, int nAWG); """ This function empties the queue of the selected AWGs. Waveforms are not removed from the module onboard RAM. """ SD_AOU_AWGflush(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGflush, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGfromFile(int moduleID, int nAWG, const char *waveformFile, int triggerMode, int startDelay, int cycles, int prescaler, int paddingMode SD_DEFAULT_NULL); SD_AOU_AWGfromFile(moduleID::Integer, nAWG::Integer, waveformFile::String, triggerMode::Integer, startDelay::Integer, cycles::Integer, prescaler::Integer, paddingMode::Integer) = ccall((:SD_AOU_AWGfromFile, klib), Cint, (Cint, Cint, Cstring, Cint, Cint, Cint, Cint, Cint), moduleID, nAWG, waveformFile, triggerMode, startDelay, cycles, prescaler, paddingMode) # not sure if waveformFile is a pointer to the file or just a string of filename ## int SD_AOU_AWGfromArray(int moduleID, int nAWG, int triggerMode, int startDelay, int cycles, int prescaler, int waveformType, int waveformPoints, double* waveformDataA, double* waveformDataB SD_DEFAULT_NULL, int paddingMode SD_DEFAULT_NULL); """ This function provides a one-step method to load, queue and start a single waveform in one of the module AWGs. The waveform can be loaded from an array of points in memory or from a file. ### AWG Trigger Mode - Auto : The waveform is launched automatically after function AWGstart, or when the previous waveform in the queue finishes --> AUTOTRIG = 0 - Software / HVI : Software trigger. The AWG is triggered by the function AWGtrigger, provided that the AWG is running. AWGtrigger can be executed from the user application (VI) or from an HVI --> SWHVITRIG = 1 - Software / HVI (per cycle) : Software trigger. Identical to the previous option, but the trigger is required per each waveform cycle --> SWHVITRIG_CYCLE = 5 - External Trigger : Hardware trigger. The AWG waits for an external trigger --> EXTTRIG = 2 - External Trigger (per cycle) : Hardware trigger. Identical to the previous option, but the trigger is required per each waveform cycle --> EXTTRIG_CYCLE = 6 """ function SD_AOU_AWGfromArray(moduleID::Integer, nAWG::Integer, triggerMode::Integer, startDelay::Integer, cycles::Integer, prescaler::Integer, waveformType::Integer, waveformDataA::Vector, waveformDataB::Vector=0, paddingMode::Integer=0) waveformPoints = length(waveformDataA) c_waveformDataA = Vector{Cdouble}(waveformDataA) c_waveformDataB = (waveformDataB==0)? 0: Vector{Cdouble}(waveformDataB) val = ccall((:SD_AOU_AWGfromArray, klib), Cint, (Cint, Cint, Cint, Cint, Cint, Cint, Cint, Cint, Ref{Cdouble}, Ref{Cdouble}), moduleID, nAWG, triggerMode, startDelay, cycles, prescaler, waveformType, waveformPoints, c_waveformDataA, c_waveformDataB, paddingMode) return val end ## int SD_AOU_AWGtriggerExternalConfig(int moduleID, int nAWG, int externalSource, int triggerBehavior, int sync); """ This function configures the external triggers for the selected AWG. The external trigger is used in case the waveform is queued with the external trigger mode option (function AWGqueueWaveform). ### AWG External Trigger Source - External I/O Trigger : The AWG trigger is a TRG connector/line of the module I/O Triggers. PXI form factor only: this trigger can be synchronized to CLK10 --> TRIG_EXTERNAL = 0 - PXI Trigger [0..n] : PXI form factor only. The AWG external trigger is a PXI trigger line and it is synchronized to CLK10 --> TRIG_PXI_AWG(=4000) + Trigger No. ### AWG External Trigger Behavior - Active High : Trigger is active when it is at level high --> TRIG_HIGH = 1 - Active Low : Trigger is active when it is at level low --> TRIG_LOW = 2 - Rising Edge : Trigger is active on the rising edge --> TRIG_RISE = 3 - Falling Edge : Trigger is active on the falling edge --> TRIG_FALL = 4 """ SD_AOU_AWGtriggerExternalConfig(moduleID::Integer, nAWG::Integer, externalSource::Integer, triggerBehavior::Integer, sync::Integer) = ccall((:SD_AOU_AWGtriggerExternalConfig, klib), Cint, (Cint, Cint, Cint, Cint, Cint), moduleID, nAWG, externalSource, triggerBehavior, sync) ## int SD_AOU_AWGqueueConfig(int moduleID, int nAWG, int mode); """ This function configures the cyclic mode of the queue. All waveforms must be already queued in one of the AWGs of the module. """ SD_AOU_AWGqueueConfig(moduleID::Integer, nAWG::Integer, mode::Integer) = ccall((:SD_AOU_AWGqueueConfig, klib), Cint, (Cint, Cint, Cint), moduleID, nAWG, mode) ## int SD_AOU_AWGqueueConfigRead(int moduleID, int nAWG); """ This function reads the value of a cyclic mode of the queue. All waveforms must be already queued in one of the AWGs of the module. """ SD_AOU_AWGqueueConfigRead(moduleID::Integer, nAWG::Integer) = ccall((:SD_AOU_AWGqueueConfigRead, klib), Cint, (Cint, Cint), moduleID, nAWG) ## int SD_AOU_AWGqueueMarkerConfig(int moduleID, int nAWG, int markerMode, int trgPXImask, int trgIOmask, int value, int syncMode, int length, int delay); """ This function configures the Marker generation for each AWG. All waveforms must be already queued in one of the AWGs of the module. """ SD_AOU_AWGqueueMarkerConfig(moduleID::Integer, nAWG::Integer, markerMode::Integer, trgPXImask::Integer, trgIOmask::Integer, value::Integer, syncMode::Integer, length::Integer, delay::Integer) = ccall((:SD_AOU_AWGqueueMarkerConfig, klib), Cint, (Cint, Cint, Cint, Cint, Cint, Cint, Cint, Cint, Cint), moduleID, nAWG, markerMode, trgPXImask, trgIOmask, value, syncMode, length, delay) ## int SD_AOU_AWGqueueSyncMode(int moduleID, int nAWG, int syncMode); """ This function configures the sync mode of the queue. All waveforms must be already queued in one of the AWGs of the module. """ SD_AOU_AWGqueueSyncMode(moduleID::Integer, nAWG::Integer, syncMode::Integer) = ccall((:SD_AOU_AWGqueueSyncMode, klib), Cint, (Cint, Cint, Cint), moduleID, nAWG, syncMode) # Functions that exist in the manual, but not in the library `SD_AIN.h` file # ## AWGreset # """ # This function resets the selected AWG to its initial position. # """ # SD_AOU_AWGreset(moduleID::Integer, nAWG::Integer) = # ccall((:SD_AOU_AWGreset, klib), Cint, (Cint, Cint), moduleID, nAWG)
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include("Graph.jl") include("Centrality.jl") function doExp(ags1) G0 = readDiGraph("data/"*ags1*".txt") G = LSCC(G0) lg = open("log.txt", "a") println(lg, ags1, " ", G0.n, " ", G0.m, " ", G.n, " ", G.m) println(lg, "PageRank : ", getPc(PageRank(G))) println(lg, "Eigenvector : ", getPc(EigenvectorCentrality(G))) println(lg) end doExp(ARGS[1])
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<filename>src/loglikelihood.jl<gh_stars>1-10 abstract type LogLikelihood{T} end struct GaussianLogLikelihood{T, V<:AbstractVector{T}, S<:AbstractMatrix{T}} <: LogLikelihood{T} μ::V Σ::S end Base.copy(logf::GaussianLogLikelihood) = GaussianLogLikelihood(copy(logf.μ), copy(logf.Σ)) function (logf::GaussianLogLikelihood{T})(x::V) where {T, V<:AbstractVector{T}} T(-0.5)*dot(x-logf.μ, logf.Σ\(x-logf.μ)) end (logf::GaussianLogLikelihood)(x, ::Val{:v}) = logf(x) (logf::GaussianLogLikelihood)(x, ::Val{:g}) = -logf.Σ\(x - logf.μ) function (logf::GaussianLogLikelihood{T})(x::V, ::Val{:vg}) where {T, V<:AbstractVector{T}} invΣx = logf.Σ\(x - logf.μ) return T(-0.5)*dot(x - logf.μ, invΣx), -invΣx end struct RLogLikelihood{T, V <: LogLikelihood{T}, W <: AbstractMatrix{T}} <: LogLikelihood{T} orglogf::V R::W end Base.copy(logf::RLogLikelihood) = RLogLikelihood(copy(logf.orglogf), copy(logf.R)) function (logf::RLogLikelihood{T})(x::V) where {T, V<:AbstractVector{T}} rx = logf.R*x return logf.orglogf(rx) end (logf::RLogLikelihood)(x, ::Val{:v}) = logf(x) function (logf::RLogLikelihood{T})(x::V, ::Val{:g}) where {T, V<:AbstractVector{T}} rx = logf.R*x return R'*logf.orglogf(rx, Val(:g)) end function (logf::RLogLikelihood{T})(x::V, ::Val{:vg}) where {T, V<:AbstractVector{T}} rx = logf.R*x v, g = logf.orglogf(rx, Val(:vg)) return v, logf.R'*g end
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<reponame>JuliaAstro/BoxLeastSquares.jl using LoopVectorization """ BLSPeriodogram A convenient wrapper for outputs from [`BLS`](@ref). # Methods * [`BoxLeastSquares.params`](@ref) * [`BoxLeastSquares.power`](@ref) * [`BoxLeastSquares.periods`](@ref) # Attributes * `t` - input time grid * `y` - input data * `yerr` - input data uncertainty * `periods` - the input periods * `duration_in` - the input durations * `objective` - the objective that was maximized * `power` - the power calculated at each period * `duration` - the best duration at each period * `t0` - the best transit time at each period * `depth` - the best transit depth at each period * `snr` - the signal-to-noise ratio at each period * `loglike` - the log-likeilhood at each period # Plotting Plotting recipes are provided for `BLSPeriodogram` which automatically plots the period and the power """ struct BLSPeriodogram{TT,FT,FET,PT,DT,VTT<:AbstractVector{TT},VFT<:AbstractVector{FT},VFET<:AbstractVector{FET},T1,T2,T3,T4,T5,T6} t::VTT y::VFT yerr::VFET periods::PT duration_in::DT objective::Symbol power::T1 duration::T2 t0::T3 depth::T4 snr::T5 loglike::T6 end """ BoxLeastSquares.periods(::BLSPeriodogram) Return the period grid for the periodogram """ periods(bls::BLSPeriodogram) = bls.periods """ BoxLeastSquares.power(::BLSPeriodogram) Return the power calculated for each period for the periodogram """ power(bls::BLSPeriodogram) = bls.power """ BoxLeastSquares.params(::BLSPeriodogram) Return the transit parameters for the best fitting period. Returns period, duration, t0, and power as well as the index of the max-power period. """ function params(bls::BLSPeriodogram) ind = argmax(power(bls)) pow = power(bls)[ind] per = periods(bls)[ind] dur = bls.duration[ind] t0 = bls.t0[ind] depth = bls.depth[ind] snr = bls.snr[ind] depth_err = depth / snr loglike = bls.loglike[ind] return (index=ind, power=pow, period=per, duration=dur, t0=t0, depth=depth, depth_err=depth_err, snr=snr, loglike=loglike) end """ autoperiod(t, duration; minimum_n_transit=3, frequency_factor=1.0, [minimum_period, maximum_period]) Automatically determine a period grid from the given times and duration(s). Periods are selected such that at least `minimum_n_trasnit` transits occur. The default minimum period is twice the maximum duration. The default maximum period is `(maximum(t) - minimum(t)) / (minimum_n_transit - 1)`. The frequency factor changes the granularity in frequency space- a smaller frequency factor will create a finer period grid. """ function autoperiod(t::AbstractVector{T}, duration; minimum_n_transit=3, minimum_period=default_minimum_period(duration), maximum_period=default_maximum_period(t, minimum_n_transit), frequency_factor=1.0) where T baseline = maximum(t) - minimum(t) df = frequency_factor * minimum(duration) / baseline^2 if maximum_period < minimum_period minimum_period, maximum_period = maximum_period, minimum_period end minimum_period ≥ zero(T) || error("minimum period must be positive") minimum_frequency = inv(maximum_period) maximum_frequency = inv(minimum_period) nf = 1 + (maximum_frequency - minimum_frequency) ÷ df return @. convert(T, inv(maximum_frequency - df * (0:nf))) end default_minimum_period(duration) = 2 * maximum(duration) function default_maximum_period(t, minimum_n_transit) minimum_n_transit > 1 || error("minimum number of transits must be greater than 1") baseline = maximum(t) - minimum(t) return baseline / (minimum_n_transit - 1) end @inline wrap(x, period) = x - period * trunc(x / period) @inline function wrap_index(t::Real, min_t, P, bin_duration) unsafe_trunc(Int, wrap(t - min_t, P) / bin_duration) + 1 end # this method will catch e.g. AbstractQuantity <: Number # which is important because unsafe_trunc not defined @inline function wrap_index(t, min_t, P, bin_duration) trunc(Int, wrap(t - min_t, P) / bin_duration) + 1 end """ BLS(t, y, [yerr]; duration, periods=autoperiod(t, duration, kwargs...), objective=:likelihood, oversample=10, kwargs...) Compute the box-least-squares periodogram. # Parameters * `t` - the time for each observation. Units are irrelevant, except that they must be consistent for all temporal parameters (e.g., `duration`). `Unitful.jl` units work seamlessly without needing to convert. * `y` - the flux value for each observation * `yerr`, optional - the uncertainty for each observation, if not provided, will default to ones * `duration` - The duration or durations to consider. Same units as `t` * `periods`, optional - The period grid to computer the BLS power over. If not provided, [`autoperiod`](@ref) will be called along with any extra keyword arguments (like `minimum_period`) * `objective`, optional - Choose between maximizing the likeilhood (`:likeilhood`, default) or the signal-to-noise ratio (`:snr`). * `oversample`, optional - The number of bins per duration that should be used. Larger values of `oversample` will lead to a finer grid. The returned values are wrapped into a convenience type [`BoxLeastSquares.BLSPeriodogram`](@ref) """ function BLS(t, y, yerr=fill!(similar(y), one(eltype(y))); duration, periods=nothing, objective=:likelihood, oversample=10, kwargs...) if isnothing(periods) periods = autoperiod(t, duration; kwargs...) end # set up arrays powers = similar(periods, Float64) durations = similar(powers, eltype(duration)) t0s = similar(powers, eltype(t)) depths = similar(powers, eltype(y)) snrs = similar(powers) loglikes = similar(powers) # find parameter ranges max_P = maximum(periods) min_τ = minimum(duration) bin_duration = min_τ / oversample max_bins = ceil(Int, max_P / bin_duration) + oversample # work arrays mean_y = similar(y, typeof(inv(first(y))), max_bins + 1) mean_ivar = similar(yerr, typeof(inv(first(yerr)^2)), max_bins + 1) _begin = firstindex(mean_y) # need this because @turbo can't use implicit begin # pre-accumulate some factors ymed = median(y) min_t = minimum(t) sum_y = zero(eltype(mean_y)) sum_ivar = zero(eltype(mean_ivar)) @inbounds for i in eachindex(y, yerr) iv = inv(yerr[i]^2) sum_y += (y[i] - ymed) * iv sum_ivar += iv end # loop over periods in grid search @inbounds for idx in eachindex(periods, powers, durations, t0s, depths, snrs, loglikes) P = periods[idx] n_bins = ceil(Int, P / bin_duration) + oversample @turbo for n in 0:n_bins mean_y[_begin + n] = zero(eltype(mean_y)) mean_ivar[_begin + n] = zero(eltype(mean_ivar)) end @turbo for idx in eachindex(t, y, yerr) ind = wrap_index(t[idx], min_t, P, bin_duration) iv = inv(yerr[idx]^2) mean_y[_begin + ind] += (y[idx] - ymed) * iv mean_ivar[_begin + ind] += iv end # simplify calcualations by wrapping binned values ind = n_bins - oversample for n in 1:oversample mean_y[begin + ind] = mean_y[begin + n] mean_ivar[begin + ind] = mean_ivar[begin + n] ind += 1 end for n in 1:n_bins mean_y[begin + n] += mean_y[begin + n - 1] mean_ivar[begin + n] += mean_ivar[begin + n - 1] end # now, loop over phases powers[idx] = -Inf for dur in duration τ = round(Int, dur / bin_duration) for n in 0:n_bins - τ # estimate in- and out-of-transit flux y_in = mean_y[begin + n + τ] - mean_y[begin + n] ivar_in = mean_ivar[begin + n + τ] - mean_ivar[begin + n] y_out = sum_y - y_in ivar_out = sum_ivar - ivar_in # check if no points in transit (iszero(ivar_in) || iszero(ivar_out)) && continue # normalize y_in /= ivar_in y_out /= ivar_out # check if negative SNR y_out < y_in && continue # compute best-fit depth depth = y_out - y_in depth_err = sqrt(inv(ivar_in) + inv(ivar_out)) # calculate objectives snr = depth / depth_err loglike = 0.5 * ivar_in * (y_out - y_in)^2 if objective === :snr power = snr elseif objective === :likelihood power = loglike else error("invalid objective $objective. Should be one of `:snr` or `:likelihood`") end if power > powers[idx] powers[idx] = power durations[idx] = τ * bin_duration t0s[idx] = mod(n * bin_duration + 0.5 * durations[idx] + min_t, P) depths[idx] = depth snrs[idx] = snr loglikes[idx] = loglike end end end end return BLSPeriodogram(t, y, yerr, periods, duration, objective, powers, durations, t0s, depths, snrs, loglikes) end
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# returns the variable (or matrix), lagged from 1 to p times, # with the first p rows filled with ones (to avoid divide errors) # remember to drop those rows before doing analysis function lags(x,p) n = size(x,1) k = size(x,2) lagged_x = zeros(eltype(x),n,p*k) for i = 1:p lagged_x[:,i*k-k+1:i*k] = lag(x,i) end return lagged_x end
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<reponame>grahamas/AxisIndices.jl @testset "BroadcastStyle" begin A = typeof(AxisArray(ones(2,2))) B = typeof(ones(2,2)) @test Base.BroadcastStyle(A) isa AxisIndices.AxisArrayStyle SA = Base.BroadcastStyle(A) SB = Base.BroadcastStyle(B) @test Base.BroadcastStyle(SA, SA) isa AxisIndices.AxisArrayStyle @test Base.BroadcastStyle(SA, SB) isa AxisIndices.AxisArrayStyle @test Base.BroadcastStyle(SB, SA) isa AxisIndices.AxisArrayStyle end @testset "combine" begin #= @testset "broadcast_axis" begin @test @inferred(broadcast_axis(Axis(1:2), Axis(1:2))) isa Axis{Int64,Int64,UnitRange{Int64},Base.OneTo{Int64}} @test @inferred(broadcast_axis(Axis(1:2), SimpleAxis(1:2))) isa Axis{Int64,Int64,UnitRange{Int64},UnitRange{Int64}} @test @inferred(broadcast_axis(Axis(1:2), 1:2)) isa Axis{Int64,Int64,UnitRange{Int64},UnitRange{Int64}} @test @inferred(broadcast_axis(SimpleAxis(1:2), SimpleAxis(1:2))) isa SimpleAxis{Int,UnitRange{Int}} @test @inferred(broadcast_axis(SimpleAxis(1:2), 1:2)) isa SimpleAxis{Int,UnitRange{Int}} @test @inferred(broadcast_axis(SimpleAxis(1:2), Axis(1:2))) isa Axis{Int64,Int64,UnitRange{Int64},UnitRange{Int64}} @test @inferred(broadcast_axis(1:2, SimpleAxis(1:2))) isa SimpleAxis{Int,UnitRange{Int}} @test @inferred(broadcast_axis(1:2, 1:2)) isa UnitRange{Int} @test @inferred(broadcast_axis(1:2, Axis(1:2))) isa Axis{Int64,Int64,UnitRange{Int64},UnitRange{Int64}} @test @inferred(broadcast_axis(1:2, Symbol.(1:2))) isa Vector{Symbol} @test @inferred(broadcast_axis(Symbol.(1:2), 1:2)) isa Vector{Symbol} @test @inferred(broadcast_axis(1:2, string.(1:2))) isa Vector{String} @test @inferred(broadcast_axis(string.(1:2), 1:2)) isa Vector{String} #@test_throws ErrorException("No method available for combining keys of type Int64 and String.") combine_keys(12, "x") @test @inferred(broadcast_axis(1:2, SimpleAxis(1:2))) isa SimpleAxis{Int,UnitRange{Int}} end @test @inferred(broadcast_axis(SimpleAxis(1:2), SimpleAxis(1:2))) == SimpleAxis(1:2) =# @testset "Multiple broadcasts" begin @test (AxisArray(ones(2)) .- AxisArray(ones(2))) .^ 2 == zeros(2) end @test @inferred(Base.Broadcast.broadcast_shape((1:10,), (1:10, 1:10), (1:10,))) == (1:10, 1:10) b1 = Broadcast.broadcast_shape( axes(CartesianIndices((1,))), axes(CartesianIndices((3, 2, 2))), axes(CartesianIndices((3, 2, 2))) ) b2 = Broadcast.broadcast_shape( axes(CartesianAxes((1,))), axes(CartesianIndices((3, 2, 2))), axes(CartesianAxes((3, 2, 2))) ) @test b1 == b2 b1 = Broadcast.broadcast_shape( axes(LinearIndices((1,))), axes(LinearIndices((3, 2, 2))), axes(LinearIndices((3, 2, 2))) ) b2 = Broadcast.broadcast_shape( axes(LinearAxes((1,))), axes(LinearIndices((3, 2, 2))), axes(LinearAxes((3, 2, 2))) ) @test b1 == b2 @test_throws DimensionMismatch Broadcast.broadcast_shape(axes(LinearAxes((3, 2, 2))), axes(LinearAxes((3, 2, 5)))) @testset "combine_axes" begin @test Broadcast.combine_axes(CartesianIndices((1,)), CartesianIndices((3, 2, 2)), CartesianIndices((3, 2, 2))) == @inferred(Broadcast.combine_axes(CartesianAxes((1,)), CartesianIndices((3, 2, 2)), CartesianAxes((3, 2, 2)))) @test Broadcast.combine_axes(LinearIndices((1,)), LinearIndices((3, 2, 2)), LinearIndices((3, 2, 2))) == @inferred(Broadcast.combine_axes(LinearAxes((1,)), CartesianAxes((3, 2, 2)), CartesianAxes((3, 2, 2)))) cartinds = CartesianIndices((2, 2)); cartaxes = CartesianAxes((2:3, 3:4)); @test keys.(Broadcast.combine_axes(cartaxes, cartaxes, cartaxes)) == (2:3, 3:4) @test keys.(Broadcast.combine_axes(cartaxes, cartaxes, cartinds)) == (2:3, 3:4) @test keys.(Broadcast.combine_axes(cartaxes, cartinds, cartaxes)) == (2:3, 3:4) @test keys.(Broadcast.combine_axes(cartinds, cartaxes, cartaxes)) == (2:3, 3:4) end end @testset "Broadcasting" begin x = Axis(1:10, 1:10) y = SimpleAxis(1:10) z = 1:10 @test @inferred(broadcasted(bstyle, -, 2, x)) == @inferred(broadcasted(bstyle, -, 2, y)) == broadcasted(bstyle, -, 2, z) @test @inferred(broadcasted(bstyle, -, x, 2)) == @inferred(broadcasted(bstyle, -, y, 2)) == broadcasted(bstyle, -, z, 2) @test @inferred(broadcasted(bstyle, +, 2, x)) == @inferred(broadcasted(bstyle, +, 2, y)) == broadcasted(bstyle, +, 2, z) @test isa(broadcasted(bstyle, +, 2, x), Axis) @test isa(broadcasted(bstyle, +, 2, y), SimpleAxis) @test @inferred(broadcasted(bstyle, +, x, 2)) == @inferred(broadcasted(bstyle, +, y, 2)) == broadcasted(bstyle, +, z, 2) @test isa(broadcasted(bstyle, +, x, 2), Axis) @test isa(broadcasted(bstyle, +, y, 2), SimpleAxis) @test @inferred(broadcasted(bstyle, *, 2, x)) == @inferred(broadcasted(bstyle, *, 2, y)) == broadcasted(bstyle, *, 2, z) @test @inferred(broadcasted(bstyle, *, x, 2)) == @inferred(broadcasted(bstyle, *, y, 2)) == broadcasted(bstyle, *, z, 2) @test broadcasted(bstyle, -, x, 1//2) == 1//2:19//2 @test broadcasted(bstyle, -, y, 1//2) == 1//2:19//2 end @testset "Binary broadcasting operations (.+)" begin #= TODO moved to docs @testset "standard case" begin a = AxisArray(ones(3), (2:4,)) @test a .+ a == 2ones(3) @test keys(axes(a .+ a, 1)) == 2:4 @test a .+ a.+ a == 3ones(3) @test keys(axes(a .+ a .+ a, 1)) == 2:4 @test (a .= 0 .* a .+ 7) == [7, 7, 7] end =# #= TODO Need to explain how order matters @testset "Order matters" begin x = AxisArray(ones(3, 5), (:, nothing)) y = AxisArray(ones(3, 5), (nothing, :y)) lhs = x .+ y rhs = y .+ x @test dimnames(lhs) == (:x, :y) == dimnames(rhs) @test lhs == 2ones(3, 5) == rhs end =# @testset "broadcasting" begin v = AxisArray(zeros(3,), (2:4,)) m = AxisArray(ones(3, 3), (2:4, 3:5)) s = 0 @test @inferred(v .+ m) == ones(3, 3) == @inferred(m .+ v) @test @inferred(s .+ m) == ones(3, 3) == @inferred(m .+ s) @test @inferred(s .+ v .+ m) == ones(3, 3) == @inferred(m .+ s .+ v) @test keys.(axes(v .+ m)) == (2:4, 3:5) == keys.(axes(m .+ v)) @test keys.(axes(s .+ m)) == (2:4, 3:5) == keys.(axes(m .+ s)) @test keys.(axes(s .+ v .+ m)) == (2:4, 3:5) == keys.(axes(m .+ s .+ v)) end end
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2.061683
3,291
struct ZeroPolynomialSet{DT <: AbstractSemialgebraicSet, BT <: AbstractPolynomialBasis, MT <: AbstractMonomial, MVT <: AbstractVector{MT}} <: MOI.AbstractVectorSet domain::DT basis::Type{BT} monomials::MVT end # `x`-in-`PlusMinusSet(set)` iff `x`-in-`set` and `-x`-in-`set`. struct PlusMinusSet{ST <: MOI.AbstractVectorSet} <: MOI.AbstractVectorSet set::ST end
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<filename>src/wstp.jl struct MathLinkError <: Exception err::Error msg::String end function MathLinkError(link::Link) err = ccall((:MLError, mlib), Error, (CLink,), link) msg = unsafe_string(ccall((:MLErrorMessage, mlib), Cstring, (CLink,), link)) clearerror(link) newpacket(link) MathLinkError(err, msg) end clearerror(link::Link) = ccall((:MLClearError, mlib), Cint, (CLink,), link) endpacket(link::Link) = ccall((:MLEndPacket, mlib), Cint, (CLink,), link) newpacket(link::Link) = ccall((:MLNewPacket, mlib), Cint, (CLink,), link) nextpacket(link::Link) = ccall((:MLNextPacket, mlib), Packet, (CLink,), link) macro wschk(expr) link = expr.args[5] # first argument quote if $(esc(expr)) == 0 throw(MathLinkError($(esc(link)))) end end end function getargcount(link::Link) r = Ref{Cint}() # int WSGetArgCount(WSLINK link,int *n) @wschk ccall((:MLGetArgCount, mlib), Cint, (CLink, Ptr{Cint}), link, r) r[] end function putargcount(link::Link, n::Integer) # int WSPutArgCount(WSLINK link,int n) @wschk ccall((:MLPutArgCount, mlib), Cint, (CLink, Cint), link, n) nothing end function putfunction(link::Link, w::WSymbol, nargs::Integer) # int MLPutUTF8Function(( MLLINK l , const unsigned char * s , int v , int n ) @wschk ccall((:MLPutUTF8Function, mlib), Cint, (CLink, Ptr{UInt8}, Cint, Cint), link, w.name, sizeof(w.name), nargs) nothing end function getfunction(link::Link) r_str = Ref{Ptr{UInt8}}() r_bytes = Ref{Cint}() r_nargs = Ref{Cint}() # int MLGetUTF8Function( MLLINK l , const unsigned char ** s , int * v , int * n ) @wschk ccall((:MLGetUTF8Function, mlib), Cint, (CLink, Ptr{Ptr{UInt8}}, Ptr{Cint}, Ptr{Cint}), link, r_str, r_bytes, r_nargs) str = unsafe_string(r_str[], r_bytes[]) # void MLReleaseUTF8Symbol(MLLINK link,const unsigned char *s,int len) ccall((:MLReleaseUTF8Symbol, mlib), Cvoid, (CLink, Ptr{UInt8}, Cint), link, r_str[], r_bytes[]) return WSymbol(str), r_nargs[] end # WSymbol function put(link::Link, w::WSymbol) # int MLPutUTF8Symbol(MLLINK link,const unsigned char *s,int len) @wschk ccall((:MLPutUTF8Symbol, mlib), Cint, (CLink, Ptr{UInt8}, Cint), link, w.name, sizeof(w.name)) nothing end function get(link::Link, ::Type{WSymbol}) r_str = Ref{Ptr{UInt8}}() r_bytes = Ref{Cint}() r_chars = Ref{Cint}() # int MLGetUTF8Symbol(MLLINK link,const unsigned char **s,int *b,int *c) @wschk ccall((:MLGetUTF8Symbol, mlib), Cint, (CLink, Ptr{Ptr{UInt8}}, Ptr{Cint}, Ptr{Cint}), link, r_str, r_bytes, r_chars) str = unsafe_string(r_str[], r_bytes[]) # void MLReleaseUTF8Symbol(MLLINK link,const unsigned char *s,int len) ccall((:MLReleaseUTF8Symbol, mlib), Cvoid, (CLink, Ptr{UInt8}, Cint), link, r_str[], r_bytes[]) return WSymbol(str) end # WReal/WInteger function put(link::Link, w::WReal) # int MLPutRealNumberAsUTF8String(MLLINK l, const unsigned char *s, int n) @wschk ccall((:MLPutRealNumberAsUTF8String, mlib), Cint, (CLink, Ptr{UInt8}, Cint), link, w.value, sizeof(w.value)) nothing end function get(link::Link, ::Type{W}) where W <: Union{WReal,WInteger} r_str = Ref{Ptr{UInt8}}() r_bytes = Ref{Cint}() r_chars = Ref{Cint}() # int MLGetNumberAsUTF8String(MLLINK l, const unsigned char **s, int *v, int *c) @wschk ccall((:MLGetNumberAsUTF8String, mlib), Cint, (CLink, Ptr{Ptr{UInt8}}, Ptr{Cint}, Ptr{Cint}), link, r_str, r_bytes, r_chars) str = unsafe_string(r_str[], r_bytes[]) # void MLReleaseUTF8String(MLLINK link,const unsigned char *s,int len) ccall((:MLReleaseUTF8String, mlib), Cvoid, (CLink, Ptr{UInt8}, Cint), link, r_str[], r_bytes[]) return W(str) end # String function put(link::Link, str::Union{String,SubString}) # int MLPutUTF8String(MLLINK link,const unsigned char *s,int len) @wschk ccall((:MLPutUTF8String, mlib), Cint, (CLink, Ptr{UInt8}, Cint), link, str, sizeof(str)) nothing end function get(link::Link, ::Type{String}) r_str = Ref{Ptr{UInt8}}() r_bytes = Ref{Cint}() r_chars = Ref{Cint}() # int MLGetUTF8String(MLLINK link,const unsigned char **s,int *b,int *c) @wschk ccall((:MLGetUTF8String, mlib), Cint, (CLink, Ptr{Ptr{UInt8}}, Ptr{Cint}, Ptr{Cint}), link, r_str, r_bytes, r_chars) str = unsafe_string(r_str[], r_bytes[]) # void MLReleaseUTF8String(MLLINK link,const unsigned char *s,int len) ccall((:MLReleaseUTF8String, mlib), Cvoid, (CLink, Ptr{UInt8}, Cint), link, r_str[], r_bytes[]) return str end for (f, T) in [ (:Integer8, Int8) (:Integer16, Int16) (:Integer32, Int32) (:Integer64, Int64) (:Real32, Float32) (:Real64, Float64) ] @eval begin function put(link::Link, x::$T) # note slightly bizarre handling of Float32 @wschk ccall(($(QuoteNode(Symbol(:MLPut, f))), mlib), Cint, (CLink, $(f == :Real32 ? Float64 : T)), link, x) nothing end function get(link::Link, ::Type{$T}) r = Ref{$T}() @wschk ccall(($(QuoteNode(Symbol(:MLGet, f))), mlib), Cint, (CLink, Ptr{$T}), link, r) r[] end end end # Get fns gettype(link::Link) = ccall((:MLGetType, mlib), Token, (CLink,), link) getrawtype(link::Link) = ccall((:MLGetRawType, mlib), Token, (CLink,), link) puttype(link::Link, t::Token) = @wschk ccall((:MLPutType, mlib), Cint, (CLink, Token), link, t) getnext(link::Link) = ccall((:MLGetNext, mlib), Token, (CLink,), link) getnextraw(link::Link) = ccall((:MLGetNextRaw, mlib), Token, (CLink,), link)
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""" Abstract population. The top node of the Population type hierarchy. """ abstract type AbstractPopulation end """ Abstract network population. Interactors are nodes of some network. """ abstract type AbstractNetworkPopulation <: AbstractPopulation end """ Abstract spatial population. Represents any type of population in which interactors have a spatial representation (e.g. a point on a plane). """ abstract type AbstractSpatialPopulation <: AbstractPopulation end """ Abstract lattice population: interactors on some lattice. """ abstract type AbstractLatticePopulation <: AbstractSpatialPopulation end
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<reponame>apatlpo/UnderwaterAcoustics.jl using Documenter using Plots push!(LOAD_PATH,"../src/") using UnderwaterAcoustics makedocs( sitename = "UnderwaterAcoustics.jl", format = Documenter.HTML(prettyurls = false), linkcheck = !("skiplinks" in ARGS), pages = Any[ "Home" => "index.md", "Manual" => Any[ "uw_basic.md", "pm_basic.md", "pm_envref.md", "pm_api.md" ], "Propagation models" => Any[ "pm_pekeris.md", "pm_rays.md", "pm_bellhop.md" ], "Tutorials" => Any[ "tut_turing.md", "tut_autodiff.md" ] ] ) deploydocs( repo = "github.com/org-arl/UnderwaterAcoustics.jl.git", branch = "gh-pages", devbranch = "master", devurl = "dev", versions = ["stable" => "v^", "v#.#", "dev" => "dev"] )
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<reponame>JuliaTagBot/DESPOT.jl import DESPOT: lower_bound mutable struct DESPOTDefaultLowerBound #placeholder for now end function init_bound{S,A,O}(ub::DESPOTDefaultLowerBound, pomdp::POMDP{S,A,O}, config::DESPOTConfig) error("Function init_bound for $(typeof(ub)) has not been implemented yet") end function lower_bound{S,A,O}(lb::DESPOTDefaultLowerBound, pomdp::POMDP{S,A,O}, particles::Vector{DESPOTParticle{S}}, config::DESPOTConfig) error("Function lower_bound for $(typeof(lb)) has not been implemented yet") end
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@testset "Select" begin @testset "Dense" begin #dense first X = GBMatrix(rand(-10.0:0.05:10.0, 10, 10)) test_frule(map, UnaryOps.SIN, X) test_rrule(map, UnaryOps.SIN, X) test_frule(map, UnaryOps.COS, X) test_rrule(map, UnaryOps.COS, X) test_frule(map, UnaryOps.COSH, X) test_rrule(map, UnaryOps.COSH, X) test_frule(map, UnaryOps.SINH, X) test_rrule(map, UnaryOps.SINH, X) test_frule(map, UnaryOps.TANH, X) test_rrule(map, UnaryOps.TANH, X) test_frule(map, UnaryOps.MINV, X) test_rrule(map, UnaryOps.MINV, X) test_frule(map, UnaryOps.ABS, X) test_rrule(map, UnaryOps.ABS, X) test_frule(map, UnaryOps.IDENTITY, X) test_rrule(map, UnaryOps.IDENTITY, X) test_frule(map, UnaryOps.EXP, X) test_rrule(map, UnaryOps.EXP, X) end @testset "Sparse" begin X = GBMatrix(sprand(50, 50, 0.15)) end end
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<filename>src/grammar.jl<gh_stars>0 import ParserCombinator: PInt64, Eos, set_fix, Drop, Star, Space, @with_pre, Parse, @p_str, Pattern, Alt, Delayed, parse_one, @E_str import Dates space = Drop(Star(Space())) PNonZeroInt64() = Parse(p"-?[1-9][0-9]*", Int64) PPosInt64() = Parse(p"[1-9][0-9]*", Int64) PPosZeroInt64() = Parse(p"[0-9][0-9]*", Int64) PCalendarNames() = p"[a-zA-Z0-9-\/\s|]+" @with_pre space begin sum = Delayed() weekday_short = Alt(map(x -> Pattern(uppercase(x)), Dates.ENGLISH.days_of_week_abbr)...) month_short = Alt(map(x -> Pattern(uppercase(x)), Dates.ENGLISH.months_abbr)...) brackets = E"(" + space + sum + space + E")" repeat = E"Repeat(" + space + sum + space + E")" > rd -> Repeat(rd) rdate_term = Alt() next = E"Next(" + (space + sum) + (E"," + space + sum)[0:end] + space + E")" |> parts -> Next(parts) next_inc = E"Next!(" + (space + sum) + (E"," + space + sum)[0:end] + space + E")" |> parts -> Next(parts, true) previous = E"Previous(" + (space + sum) + (E"," + space + sum)[0:end] + space + E")" |> parts -> Previous(parts) previous_inc = E"Previous!(" + (space + sum) + (E"," + space + sum)[0:end] + space + E")" |> parts -> Previous(parts, true) rdate_expr = rdate_term | brackets | repeat | next | next_inc | previous | previous_inc neg = Delayed() neg.matcher = rdate_expr | (E"-" + neg > -) cal_adj = neg + (E"@" + PCalendarNames() + E"[" + Alt(map(Pattern, collect(keys(HOLIDAY_ROUNDING_MAPPINGS)))...) + E"]")[0:1] |> xs -> length(xs) == 1 ? xs[1] : CalendarAdj(map(String, split(xs[2], "|")), xs[1], HOLIDAY_ROUNDING_MAPPINGS[xs[3]]) mult = Delayed() mult = cal_adj | ((PPosInt64() + space + E"*" + space + cal_adj) > (c,rd) -> multiply(rd, c)) | ((cal_adj + space + E"*" + space + PPosInt64()) > (rd,c) -> multiply(rd, c)) add = E"+" + mult sub = E"-" + mult > - sum.matcher = (sub | mult) + (add | mult)[0:end] |> Base.sum entry = sum + Eos() end function register_grammar!(term) # Handle the spacing correctly push!(rdate_term.matchers[2].matchers, term) end macro rd_str(arg::String) val = parse_one(arg, entry)[1] isa(val, RDate) || error("Unable to parse $(arg) as RDate") return val end function rdate(arg::String) val = parse_one(arg, entry)[1] isa(val, RDate) || error("Unable to parse $(arg) as RDate") return val end
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<gh_stars>1-10 using Test using LinearAlgebra using LinearAlgebra: norm using Ferrite using StaticArrays using TopOpt using TrussTopOpt.TrussTopOptProblems using TrussTopOpt.TrussTopOptProblems: buckling using Crayons.Box using GLMakie problem_json = ["mgz_geom_stiff_ex9.1.json", "buckling_2d_debug.json"] ins_dir = joinpath(@__DIR__, "instances", "fea_examples"); # @testset "Buckling problem solve - $(problem_json[i])" for i in 1:length(problem_json) i = 1 file_name = problem_json[i] problem_file = joinpath(ins_dir, file_name) node_points, elements, mats, crosssecs, fixities, _ = parse_truss_json(problem_file); P_val = 400 # kN P = Dict(2 => SVector{2}(P_val * [0.05, -1.0])) nnode = length(node_points) # Newton iteration setup max_newton_itr = 50 NEWTON_TOL = 1e-8 function dict2vec(dict, veclen) v = zeros(veclen) for (nid, val) in dict v[2*nid-1:2*nid] = val end return v end # Δu = Dict(nid => SVector{2,Float64}(0,0) for nid in keys(node_points)) updated_nodes = copy(node_points) problem = TrussProblem(Val{:Linear}, node_points, elements, P, fixities, mats, crosssecs); solver = FEASolver(Displacement, Direct, problem) solver() K, Kσ = buckling(problem, solver.globalinfo, solver.elementinfo; u=zeros(2*nnode)) Kt = K + Kσ P_vec = solver.globalinfo.f # * initialize displacements as zero ut = zeros(nnode*2) # tracking iteration history for plotting ub_t = [] p_t = [] newton_itr = -1 # Newton-Raphson Iterations # https://people.duke.edu/~hpgavin/cee421/truss-finite-def.pdf # https://kristofferc.github.io/JuAFEM.jl/dev/examples/hyperelasticity/ while true global newton_itr, Kt, P_vec, ut, updated_nodes newton_itr += 1 reaction = Kt * ut @show res_norm = norm(reaction - P_vec) push!(ub_t, ut[2*2-1]) push!(p_t,-reaction[2*2]) if res_norm < NEWTON_TOL break elseif newton_itr > max_newton_itr break # error("Reached maximum Newton iterations, aborting") end # construct new stiffness matrix ut = Kt \ P_vec updated_nodes = Dict(nid => pt + ut[2*nid-1:2*nid] for (nid, pt) in updated_nodes) problem = TrussProblem(Val{:Linear}, updated_nodes, elements, P, fixities, mats, crosssecs); solver = FEASolver(Displacement, Direct, problem) solver() K, Kσ = buckling(problem, solver.globalinfo, solver.elementinfo; u=ut) Kt = K + Kσ end scatter(ub_t, p_t) lines!(ub_t, p_t, color = :blue, linewidth = 3) current_figure() # scene, layout = draw_truss_problem(problem; u=v, default_exagg=1.0, exagg_range=10.0, # default_load_scale=0.2, default_support_scale=0.2, default_arrow_size=0.03) # end # end test set # =#
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<filename>src/data/competitions.jl<gh_stars>0 # This file contains functions to get access to the list of all available competitions and seasons depending on the loader. """ PublicWyscoutCompetitions """ Base.@kwdef mutable struct PublicWyscoutCompetitions data::DataFrame end """ function competitions() Return a dataframe with all available competitions and seasons in PublicWyscoutLoader """ function competitions(events_data::PublicWyscoutLoader)::PublicWyscoutCompetitions competitions_data = PublicWyscoutCompetitions(data = get_df(joinpath(events_data.path, "competitions.json"))) rename!(competitions_data.data, :wyId => :competition_id, :name => :competition_name) country_names = DataFrame(competitions_data.data[:, :area])[:,:name] for i in eachindex(country_names) if country_names[i] == "" country_names[i] = "International" end end insertcols!(competitions_data.data, :country_name => country_names, :competition_gender => "male") competitions_data.data = competitions_data.data[:,[:competition_id, :competition_name, :country_name, :competition_gender]] competitions_data.data = leftjoin(competitions_data.data, unique(events_data.match_index[:,[:competition_id, :season_id, :season_name]]), on = [:competition_id]) return competitions_data end
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# Note that this script can accept some limited command-line arguments, run # `julia build_tarballs.jl --help` to see a usage message. using BinaryBuilder, Pkg name = "rubberband" version = v"1.9.0" # Collection of sources required to complete build sources = [ GitSource("https://github.com/breakfastquay/rubberband.git", "6659d61f010e19080d62aa2fd2eae88f16e5d9c9") ] # Bash recipe for building across all platforms script = raw""" cd $WORKSPACE/srcdir/rubberband/ ./configure --prefix=${prefix} --build=${MACHTYPE} --host=${target} CXXFLAGS="${CXXFLAGS} -DHAVE_FFTW3 -DHAVE_LIBSAMPLERATE" enable_vamp=no enable_ladspa=no make DYNAMIC_LDFLAGS="${LDFLAGS} -shared" DYNAMIC_EXTENSION=".$dlext" DYNAMIC_EXTENSION=".$dlext" PROGRAM_TARGET="bin/rubberband$exeext" make install DYNAMIC_EXTENSION=".$dlext" PROGRAM_TARGET="bin/rubberband$exeext" """ # 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()) # The products that we will ensure are always built products = [ LibraryProduct("librubberband", :librubberband) ExecutableProduct("rubberband", :rubberband) ] # Dependencies that must be installed before this package can be built dependencies = Dependency[ Dependency("FFTW_jll"), Dependency("libsamplerate_jll"), Dependency("libsndfile_jll"), ] # Build the tarballs, and possibly a `build.jl` as well. build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
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<filename>test/examples/example_18_new_sliders.jl using Makie using MakieLayout using Printf begin scene = Scene(camera=campixel!) screen = display(scene) outergrid = GridLayout(scene; alignmode=Outside(30)) innergrid = outergrid[1, 1] = GridLayout() lagrid = innergrid[1, 1] = GridLayout() la = lagrid[1, 1] = LAxis(scene) # la.xoppositespinevisible = false # la.yoppositespinevisible = false la.xgridvisible = false la.ygridvisible = false la.xautolimitmargin = (0, 0) la.yautolimitmargin = (0, 0) slidergrid = innergrid[2, 1] = GridLayout() ls1 = slidergrid[1, 1] = LSlider(scene, range=50:0.01:100, height=50) slidergrid[1, 2] = LText(scene, text=lift(x->@sprintf("%.2f", x), ls1.value), alignment=(:right, :center), width=140, height=40, padding=(10, 10, 5, 5)) slidergrid[1, 2] = LRect(scene, color=:white, strokewidth=2f0, strokecolor=RGBf0(0.9, 0.9, 0.9)) ls2 = slidergrid[2, 1] = LSlider(scene, range=LinRange(0.1, 3, 1000), startvalue=1, height=50) t2 = slidergrid[2, 2] = LText(scene, text=lift(x->@sprintf("%.2f", x), ls2.value), alignment=(:right, :center), width=140, height=40, padding=(10, 10, 5, 5)) slidergrid[2, 2] = LRect(scene, color=:white, strokewidth=2f0, strokecolor=RGBf0(0.9, 0.9, 0.9)) ls3 = innergrid[1, end+1] = LSlider(scene, range=0.1:0.01:1, width=50, horizontal=false) buttongrid = slidergrid[3, :] = GridLayout() buttongrid[1, 1] = LButton(scene, height=40) b2 = buttongrid[1, 2] = LButton(scene, autoshrink=(false, false), label="Change colormap") data = lift(ls2.value, ls3.value) do v1, v2 [sin(x * y / 1000 * v1) * v2 for x in 1:100, y in 1:100] end hmap = heatmap!(la, data) lc = lagrid[1, 2] = LColorbar(scene, hmap, width=40) tight_ticklabel_spacing!(lc) on(b2.clicks) do c hmap.colormap = rand(setdiff([:viridis, :heat, :rainbow, :blues], [hmap.colormap[]])) end end
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<gh_stars>10-100 @testset "AutoFlux" begin @testset "ImageClassifier smoketest" begin using NaiveGAflux.AutoFlux import NaiveGAflux.AutoFlux.ImageClassification: TrainSplitAccuracy, TrainIterConfig, BatchedIterConfig, ShuffleIterConfig, AccuracyVsSize, TrainAccuracyVsSize, EliteAndTournamentSelection, EliteAndSusSelection, GlobalOptimizerMutation, modelname using Random rng = MersenneTwister(123) x = randn(rng, Float32, 32,32,2,4) y = Float32.(Flux.onehotbatch(rand(rng, 0:5,4), 0:5)) dummydir = joinpath(NaiveGAflux.modeldir, "ImageClassifier_smoketest") c = ImageClassifier(popsize = 5, seed=1, mdir=dummydir, insize=size(x), outsize=size(y,1)) f = TrainSplitAccuracy(;split=0.25, accuracyconfig=BatchedIterConfig(batchsize=1), accuracyfitness=data -> AccuracyVsSize(data, 3;accwrap=EwmaFitness), trainconfig=TrainIterConfig(nbatches_per_gen=1, baseconfig=ShuffleIterConfig(batchsize=1))) # Logs are mainly to prevent CI timeouts @info "\tSmoke test with TrainSplitAccuracy and EliteAndSusSelection" pop = @test_logs (:info, "Begin generation 1") (:info, "Begin generation 2") (:info, "Begin generation 3") (:info, r"Mutate model") match_mode=:any fit(c, x, y, fitnesstrategy=f, evolutionstrategy = GlobalOptimizerMutation(EliteAndSusSelection(popsize=c.popsize, nelites=1)), stopcriterion = pop -> generation(pop) == 3) @test length(pop) == c.popsize @test modelname.(pop) == ["model$i" for i in 1:length(pop)] globallearningrate(c::AbstractCandidate) = globallearningrate(c.c) globallearningrate(c::CandidateOptModel) = globallearningrate(c.opt) globallearningrate(o::Flux.Optimiser) = prod(globallearningrate.(o.os)) globallearningrate(o) = 1 globallearningrate(o::ShieldedOpt{Descent}) = o.opt.eta @test unique(globallearningrate.(pop)) != [1] @test length(unique(globallearningrate.(pop))) == 1 # Now try TrainAccuracyVsSize and EliteAndTournamentSelection f = TrainAccuracyVsSize(;trainconfig= TrainIterConfig(nbatches_per_gen=1, baseconfig=ShuffleIterConfig(batchsize=1))) @info "\tSmoke test with TrainAccuracyVsSize and EliteAndTournamentSelection" pop = @test_logs (:info, "Begin generation 1") (:info, "Begin generation 2") (:info, "Begin generation 3") (:info, r"Mutate model") match_mode=:any fit(c, x, y, fitnesstrategy=f, evolutionstrategy = GlobalOptimizerMutation(EliteAndTournamentSelection(popsize=c.popsize, nelites=1, k=2)), stopcriterion = pop -> generation(pop) == 3) @test length(pop) == c.popsize @test modelname.(pop) == ["model$i" for i in 1:length(pop)] @test unique(globallearningrate.(pop)) != [1] @test length(unique(globallearningrate.(pop))) == 1 end @testset "sizevs" begin import NaiveGAflux.AutoFlux.ImageClassification: sizevs struct SizeVsTestFitness <: AbstractFitness fitness end NaiveGAflux._fitness(s::SizeVsTestFitness, c) = s.fitness struct SizeVsTestFakeModel <: AbstractCandidate nparams::Int end NaiveGAflux.nparams(s::SizeVsTestFakeModel) = s.nparams basefitness = SizeVsTestFitness(0.12345678) testfitness = sizevs(basefitness, 2) @test fitness(testfitness, SizeVsTestFakeModel(1)) == 0.121 @test fitness(testfitness, SizeVsTestFakeModel(100_000)) ≈ 0.12001 testfitness = sizevs(basefitness, 3) @test fitness(testfitness, SizeVsTestFakeModel(1)) == 0.1231 @test fitness(testfitness, SizeVsTestFakeModel(100_000)) ≈ 0.12301 testfitness = sizevs(basefitness, 4) @test fitness(testfitness, SizeVsTestFakeModel(1)) == 0.12351 @test fitness(testfitness, SizeVsTestFakeModel(100_000)) ≈ 0.12351 end @testset "TrainIterConfig" begin import NaiveGAflux.AutoFlux.ImageClassification: TrainIterConfig, ShuffleIterConfig, dataiter nexamples = 10 x = randn(Float32, 4,4,3, nexamples) y = rand(0:7, nexamples) @testset "Test $ne epochs and $nbpg batches per generation" for ne in (1, 2, 10), nbpg in (2, 10) bs = 3 s = TrainIterConfig(nbatches_per_gen=nbpg, baseconfig=ShuffleIterConfig(batchsize=bs, iterwrap=identity)) itr = Iterators.take(dataiter(s, x, y), cld(ne * nexamples, nbpg)) totnrofexamples = ne * nexamples @test mapreduce(length ∘ collect, +, itr) == totnrofexamples @test length(itr |> first) == min(nbpg, totnrofexamples) @test size(itr |> first |> first |> first, 4) == bs # All models shall see the same examples for iitr in itr @test collect(iitr) == collect(iitr) end end end end
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<gh_stars>10-100 module ScenTrees include("TreeStructure.jl") include("TreeApprox.jl") include("StochPaths.jl") include("LatticeApprox.jl") include("KernelDensityEstimation.jl") include("bushinessNesDistance.jl") export tree_approximation!,lattice_approximation,Tree,Lattice,stage,height,leaves,nodes,root, part_tree,build_probabilities!,tree_plot,plot_hd,plot_lattice,gaussian_path1D,gaussian_path2D, running_maximum1D,running_maximum2D,path,bushiness_nesdistance,kernel_scenarios end
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module TestTraits using MLJBase using Test using MLJIteration using ..DummyModel model = DummyIterativeModel() imodel = IteratedModel(model=model, measure=mae) @test is_wrapper(imodel) @test !MLJBase.caches_data_by_default(imodel) @test !supports_weights(imodel) @test !supports_class_weights(imodel) @test load_path(imodel) == "MLJIteration.DeterministicIteratedModel" @test package_name(imodel) == "MLJIteration" @test package_uuid(imodel) == "614be32b-d00c-4edb-bd02-1eb411ab5e55" @test package_url(imodel) == "https://github.com/JuliaAI/MLJIteration.jl" @test package_license(imodel) == "MIT" @test is_pure_julia(imodel) == is_pure_julia(model) @test input_scitype(imodel) == input_scitype(model) @test output_scitype(imodel) == output_scitype(model) @test target_scitype(imodel) == target_scitype(model) end true
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export Editor, editor, setbars, barson, barsoff, setcursor type Editor file w::Window end filetitle(e) = e.file == nothing ? "Julia" : basename(e.file) Blink.msg(e::Editor, args...) = Blink.msg(e.w, args...) Blink.handlers(e::Editor) = Blink.handlers(e.w) # I know, I know, this code is a mess. function loadeditor(p::Page; value = "", ver = "5.0.0") for f in ["codemirror.min.js" "codemirror.min.css" "addon/display/rulers.min.js" "addon/selection/active-line.min.js" "addon/search/searchcursor.min.js" "keymap/sublime.min.js"] load!(p, "https://cdnjs.cloudflare.com/ajax/libs/codemirror/$ver/$f") end for f in ["julia.js", "editor.css", "june.css", "bars.js", "bars.css"] load!(p, Pkg.dir("DevTools", "res", f)) end @js_ p CodeMirror.keyMap.blink = d("fallthrough"=>"sublime") body!(p, "", fade = false) @js_ p cm = CodeMirror(document.body, $(d(:value => value, :mode=>"julia2", :theme=>"june", :keyMap=>"blink", :lineNumbers=>true, :styleActiveLine=>true, :rulers=>[80], :showCursorWhenSelecting=>true))) @js_ p Bars.hook(cm) handle_eval(p) end function Editor(value = ""; file = nothing) w = Window(d(:title=>file == nothing ? "Julia" : basename(file))) loadeditor(w.content, value = value) ed = Editor(file, w) handle_dirty(ed) handle_save(ed) return ed end editor(f) = Editor(read(f, String), file = f) setbars(e, ls) = @js_ e Bars.set(cm, $ls) barson(e) = @js_ e Bars.on(cm) barsoff(e) = @js_ e Bars.off(cm) function centrecursor(ed) @js_ ed begin @var l = cm.getCursor().line @var y = cm.charCoords(d("line"=>l, "ch"=>0), "local").top @var height = cm.getScrollerElement().offsetHeight cm.scrollTo(0, y - height/2 + 55) end end function setcursor(ed, line, ch = 0) @js_ ed cm.setCursor($(line-1), $ch) centrecursor(ed) end norm(key::AbstractString) = replace(key, r"\bc\b"i, Sys.is_apple() ? "Cmd" : "Ctrl") function keymap(ed, key, res) @js_ ed begin @var map = CodeMirror.keyMap.blink map[$(norm(key))] = $res # CodeMirror.normalizeKeyMap(map) end end function handle_dirty(e::Editor) @js_ e cm.on("changes", () -> Blink.msg("change", d("clean"=>cm.isClean()))) handle(e, "change") do data t = filetitle(e) data["clean"] || (t *= "*") title(e.w, t) end end function handle_save(ed::Editor) keymap(ed, "C-S", :(cm -> Blink.msg("save", d("code"=>cm.getValue())))) handle(ed, "save") do data if ed.file != nothing && isfile(ed.file) @js_ ed cm.markClean() title(ed.w, filetitle(ed)) open(io -> write(io, data["code"]), ed.file, "w") end end end function handle_eval(ed) keymap(ed, "Shift-Enter", :(cm -> Blink.msg("eval", d("code"=>cm.getSelection())))) handle(ed, "eval") do data try result = eval(Main, parse(data["code"])) @js_ ed console.log($(sprint(show, result))) catch e @js_ ed console.log($(sprint(showerror, e, catch_backtrace()))) end end end
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<reponame>erwanlecarpentier/IICGP.jl<filename>scripts/atari_monocgp.jl<gh_stars>1-10 using ArcadeLearningEnvironment using ArgParse using Cambrian using CartesianGeneticProgramming using Dates using IICGP using Random # function extension import Cambrian.mutate import Cambrian.evaluate s = ArgParseSettings() @add_arg_table! s begin "--cfg" help = "configuration script" default = "cfg/test_mono.yaml" "--game" help = "game rom name" default = "assault" "--seed" help = "random seed for evolution" arg_type = Int default = 0 "--ind" help = "individual for evaluation" arg_type = String default = "" end args = parse_args(ARGS, s) const game = args["game"] const seed = args["seed"] Random.seed!(seed) main_cfg, cont_cfg, reducer, bootstrap = IICGP.monocgp_config(args["cfg"], game) const max_frames = main_cfg["max_frames"] const stickiness = main_cfg["stickiness"] const grayscale = main_cfg["grayscale"] const downscale = main_cfg["downscale"] const logid = cont_cfg.id const state_ref = get_state_ref(game, seed) function play_atari( reducer::Reducer, controller::CGPInd, lck::ReentrantLock; rom=game, seed=seed, rom_state_ref=state_ref, max_frames=max_frames, grayscale=grayscale, downscale=downscale, stickiness=stickiness ) # Random.seed!(seed) mt = MersenneTwister(seed) game = Game(rom, seed, lck=lck, state_ref=rom_state_ref) reward = 0.0 frames = 0 prev_action = Int32(0) while ~game_over(game.ale) if rand(mt) > stickiness || frames == 0 s = get_state(game, grayscale, downscale) output = IICGP.process(reducer, controller, s) action = game.actions[argmax(output)] else action = prev_action end reward += act(game.ale, action) frames += 1 prev_action = action if frames > max_frames break end end close!(game) [reward] end if length(args["ind"]) > 0 ind = CGPInd(cfg, read(args["ind"], String)) ftn = fitness(ind, inps, outs) println("Fitness: ", ftn) else mutate(ind::CGPInd) = goldman_mutate(cont_cfg, ind) lck = ReentrantLock() fit(controller::CGPInd) = play_atari(reducer, controller, lck) evaluate(e::CGPEvolution) = IICGP.fitness_evaluate(e, e.fitness) e = CartesianGeneticProgramming.CGPEvolution(cont_cfg, fit) init_backup(logid, args["cfg"]) run!(e) end
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<reponame>Leticia-maria/IndividualDisplacements.jl<filename>examples/basics/solid_body_rotation.jl<gh_stars>1-10 ### A Pluto.jl notebook ### # v0.17.0 using Markdown using InteractiveUtils # ╔═╡ 0fea38f6-2c9e-43c6-bfc1-267ff220a0cc begin using IndividualDisplacements, DataFrames, MeshArrays, PlutoUI import CairoMakie as Mkie "done with loading packages" end # ╔═╡ 5c7e4e72-3f69-11ec-258b-6bcfdc7b1d65 md"""# Simple Three Dimensional Flow Field Simulate the trajectory of an individual point, first in a perfectly circular flow (a.k.a. solid body rotation). Then add a convergent term to obtain a spiraling trajectory, and a constant vertical velocity for the third dimension. These simple flow configurations can be thought of as idealized models e.g. ocean meso-scale eddies. For additional documentation e.g. see : [1](https://JuliaClimate.github.io/IndividualDisplacements.jl/dev/), [2](https://JuliaClimate.github.io/MeshArrays.jl/dev/), [3](https://docs.juliadiffeq.org/latest/solvers/ode_solve.html), [4](https://en.wikipedia.org/wiki/Displacement_(vector)) ### Exercise examples: - make the sinking velocity decrease with time (hint: it increases in the original notebook) - change the number of times the particle goes around the origin - flow field parameters (e.g. intensity and sign of the convergent term) """ # ╔═╡ aadd082a-3ed9-41b7-ba5e-7c5f77d59508 TableOfContents() # ╔═╡ 011c89a7-f3d1-4cea-b71a-3d6f96a1801c begin function solid_body_rotation(np,nz) Γ=simple_periodic_domain(np) Γ = UnitGrid(Γ.XC.grid;option="full") γ=Γ.XC.grid; #Solid-body rotation around central location ... i=Int(np/2+1) u=-(Γ.YG.-Γ.YG[1][i,i]) v=(Γ.XG.-Γ.XG[1][i,i]) #... plus a convergent term to / from central location d=-0.01 u=u+d*(Γ.XG.-Γ.XG[1][i,i]) v=v+d*(Γ.YG.-Γ.YG[1][i,i]) #Replicate u,v in vertical dimension uu=MeshArray(γ,γ.ioPrec,nz) [uu[k]=u[1] for k=1:nz] vv=MeshArray(γ,γ.ioPrec,nz) [vv[k]=v[1] for k=1:nz] #Vertical velocity component w w=fill(-0.01,MeshArray(γ,γ.ioPrec,nz+1)) return write(uu),write(vv),write(w) end md"""## 1. Problem Configuration ### 1.1 Gridded Flow Fields **solid\_body\_rotation(np,nz)** Set up an idealized flow field which consists of [rigid body rotation](https://en.wikipedia.org/wiki/Rigid_body), plus a convergent term, plus a sinking term. """ end # ╔═╡ 2200503c-605c-4dfd-878f-ffea2431f7ba begin np,nz=16,4 #gridded domain size (horizontal and vertical) u,v,w=solid_body_rotation(np,nz) #staggered velocity arrays 𝐹=FlowFields(u,u,v,v,0*w,1*w,[0,19.95*2*pi]); #FlowFields data structure "Done with defining the flow fields." end # ╔═╡ bde49eaf-3747-4edf-b712-3ee6f08ffe20 md"""### 1.2 Initialize Individuals Here just one individual is initialized at [np*1/3,np*1/3,nz*1/3] in the three-dimensional space of the flow fields.""" # ╔═╡ c60a48e0-6f51-44b0-bd6e-332e04d2d332 begin (x,y,z)=(np*1/3,np*1/3,nz*1/3) 𝐼=Individuals(𝐹,x,y,z) end # ╔═╡ 45762eb1-7404-4524-8d1d-788a70f8f5ef md"""### 1.3 A Closer Look The above `Individuals` constructor wraps up 𝐹, the initial position, and other needed components within 𝐼. **At this point, you can either jump to section 2 or read through the next code cell** to learn more about how the details as needed e.g. if you wanted to overide default options that were selected for you by the section 1.2 constructor. """ # ╔═╡ b70d76d1-09f3-462a-b550-8fa51cb90c2c begin # Initial position is 📌=[x,y,z] # and the data structure ([DataFrame](http://juliadata.github.io/DataFrames.jl/stable/)) # to record properties along the individual's path accordingly. 🔴 = DataFrame(ID=Int[], x=Float64[], y=Float64[], z=Float64[], t=Float64[]) # It is the postprocessing function's responsibility to provide the record. It is thus # important that this intermediary (`postproc`) be consistent with the solver setup (`sol`) # and the expected record format (`🔴`). function postproc(sol,𝐹::FlowFields;id=missing,𝑇=missing) df=postprocess_xy(sol,𝐹,id=id,𝑇=𝑇) #add third coordinate z=sol[3,:] df.z=z[:] return df end # The velocity function `🚄` relies only on flow fields obtained from # `𝐹` (which is defined above) to interpolate velocity at the specified # space-time position (e.g. those of individuals). 🚄 = dxdt! # Now that every thing needed to carry out the computation is in place, # we wrap up the problem configuration in a struct (`Individuals`) which # links to the initial positions, flow fields, etc. all that will be # necessary to compute trajectories over time (`∫!(𝐼,𝑇)`). #assemble as a NamedTuple: I=(position=📌,record=🔴,velocity=🚄, postprocessing=postproc,parameters=𝐹) #construct Individuals from NamedTuple: 𝐽=Individuals(I) end # ╔═╡ 8cc54cc2-5284-40b1-9a30-0ad6f725053f md"""## 2 Trajectory Simulations The `∫!` function call below returns the final positions & updates `𝐼.📌` accordingly. It also records properties observed along the trajectory in `𝐼.🔴`. Simple methods to visualize the individual trajectory (plot or movie) are provided at the end. ### 2.1 Compute Trajectory """ # ╔═╡ 503c8702-df0b-4495-8245-4ba6608431e3 begin 𝑇=(0.0,𝐼.𝑃.𝑇[2]) ∫!(𝐼,𝑇) end # ╔═╡ 32a8b0ee-e61d-495b-ba53-56a6c6126950 md"""### 2.2 Visualize Trajectory""" # ╔═╡ dd62433c-2274-4c6f-9e27-34bc2c620df1 begin nt=length(𝐼.🔴.x) time = Mkie.Node(nt) xx=Mkie.@lift( [𝐼.🔴.x[1:$time];fill(NaN,nt-$time)] ) yy=Mkie.@lift( [𝐼.🔴.y[1:$time];fill(NaN,nt-$time)] ) zz=Mkie.@lift( [𝐼.🔴.z[1:$time];fill(NaN,nt-$time)] ) Mkie.set_theme!(Mkie.theme_light()) f=Mkie.Figure(resolution = (900, 600)) a = Mkie.Axis3(f[1, 1],xlabel="x",ylabel="y",zlabel="z", title="Solid body rotation / Spiral example") Mkie.lines!(a,xx,yy,zz,linewidth=1.0,color=:black) Mkie.scatter!(a,[𝐼.🔴.x[1]],[𝐼.🔴.y[1]],[𝐼.🔴.z[1]],color=:red) Mkie.scatter!(a,[𝐼.🔴.x[nt]],[𝐼.🔴.y[nt]],[𝐼.🔴.z[nt]],color=:green) f end # ╔═╡ 250d011e-57db-4d80-b16e-dbc28fec9fba begin #plt=myplot(nt) #scatter!(plt,[📌[1]],[📌[2]],[📌[3]]) #scatter!(plt,[𝐼.🔴.x[end]],[𝐼.🔴.y[end]],[𝐼.🔴.z[end]]) #scatter!(plt,[𝐼.🔴.x[1]],[𝐼.🔴.y[1]],[𝐼.🔴.z[1]]) end # ╔═╡ a0bf6d10-5d42-481b-8720-dadd3f727e8b md"""### 2.3 Animation example""" # ╔═╡ 2b8ee43d-1225-4475-91c9-161cf612bfb2 begin if false p=Int(ceil(nt/50)) tt=1:p:nt tt=tt.+(nt-maximum(tt)) fil=joinpath(tempdir(),"solid_body_rotation.mp4") Mkie.record(f,fil, tt; framerate = 20) do t time[] = t end LocalResource(fil) else """try code in this cell to generate animation""" end end # ╔═╡ 00000000-0000-0000-0000-000000000001 PLUTO_PROJECT_TOML_CONTENTS = """ [deps] CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" IndividualDisplacements = "b92f0c32-5b7e-11e9-1d7b-238b2da8b0e6" MeshArrays = "cb8c808f-1acf-59a3-9d2b-6e38d009f683" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" [compat] CairoMakie = "~0.6.6" DataFrames = "~1.2.2" IndividualDisplacements = "~0.3.3" MeshArrays = "~0.2.26" PlutoUI = "~0.7.18" """ # ╔═╡ 00000000-0000-0000-0000-000000000002 PLUTO_MANIFEST_TOML_CONTENTS = """ # This file is machine-generated - editing it directly is not advised [[AbstractFFTs]] deps = ["LinearAlgebra"] git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" version = "1.0.1" [[AbstractPlutoDingetjes]] deps = ["Pkg"] git-tree-sha1 = "0ec322186e078db08ea3e7da5b8b2885c099b393" uuid = "6e696c72-6542-2067-7265-42206c756150" version = "1.1.0" [[AbstractTrees]] git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" version = "0.3.4" [[Adapt]] deps = ["LinearAlgebra"] git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.3.1" [[Animations]] deps = ["Colors"] git-tree-sha1 = "e81c509d2c8e49592413bfb0bb3b08150056c79d" uuid = "27a7e980-b3e6-11e9-2bcd-0b925532e340" version = "0.4.1" [[ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" [[ArnoldiMethod]] deps = ["LinearAlgebra", "Random", "StaticArrays"] git-tree-sha1 = "f87e559f87a45bece9c9ed97458d3afe98b1ebb9" uuid = "ec485272-7323-5ecc-a04f-4719b315124d" version = "0.1.0" [[ArrayInterface]] deps = ["Compat", "IfElse", "LinearAlgebra", "Requires", "SparseArrays", "Static"] git-tree-sha1 = "e527b258413e0c6d4f66ade574744c94edef81f8" uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9" version = "3.1.40" [[Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" [[Automa]] deps = ["Printf", "ScanByte", "TranscodingStreams"] git-tree-sha1 = "d50976f217489ce799e366d9561d56a98a30d7fe" uuid = "67c07d97-cdcb-5c2c-af73-a7f9c32a568b" version = "0.8.2" [[AxisAlgorithms]] deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"] git-tree-sha1 = "66771c8d21c8ff5e3a93379480a2307ac36863f7" uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950" version = "1.0.1" [[Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[BitTwiddlingConvenienceFunctions]] deps = ["Static"] git-tree-sha1 = "bc1317f71de8dce26ea67fcdf7eccc0d0693b75b" uuid = "62783981-4cbd-42fc-bca8-16325de8dc4b" version = "0.1.1" [[Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" version = "1.0.8+0" [[CEnum]] git-tree-sha1 = "215a9aa4a1f23fbd05b92769fdd62559488d70e9" uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" version = "0.4.1" [[CFTime]] deps = ["Dates", "Printf"] git-tree-sha1 = "bca6cb6ee746e6485ca4535f6cc29cf3579a0f20" uuid = "179af706-886a-5703-950a-314cd64e0468" version = "0.1.1" [[CPUSummary]] deps = ["Hwloc", "IfElse", "Static"] git-tree-sha1 = "87b0c9c6ee0124d6c1f4ce8cb035dcaf9f90b803" uuid = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9" version = "0.1.6" [[CSV]] deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings"] git-tree-sha1 = "74147e877531d7c172f70b492995bc2b5ca3a843" uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" version = "0.9.10" [[Cairo]] deps = ["Cairo_jll", "Colors", "Glib_jll", "Graphics", "Libdl", "Pango_jll"] git-tree-sha1 = "d0b3f8b4ad16cb0a2988c6788646a5e6a17b6b1b" uuid = "159f3aea-2a34-519c-b102-8c37f9878175" version = "1.0.5" [[CairoMakie]] deps = ["Base64", "Cairo", "Colors", "FFTW", "FileIO", "FreeType", "GeometryBasics", "LinearAlgebra", "Makie", "SHA", "StaticArrays"] git-tree-sha1 = "774ff1cce3ae930af3948c120c15eeb96c886c33" uuid = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" version = "0.6.6" [[Cairo_jll]] deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] git-tree-sha1 = "f2202b55d816427cd385a9a4f3ffb226bee80f99" uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" version = "1.16.1+0" [[CatViews]] deps = ["Random", "Test"] git-tree-sha1 = 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"DataStructures", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "OffsetArrays", "PkgVersion", "ProgressMeter", "UUIDs"] git-tree-sha1 = "c342ae2abf4902d65a0b0bf59b28506a6e17078a" uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69" version = "0.5.2" [[TranscodingStreams]] deps = ["Random", "Test"] git-tree-sha1 = "216b95ea110b5972db65aa90f88d8d89dcb8851c" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" version = "0.9.6" [[TreeViews]] deps = ["Test"] git-tree-sha1 = "8d0d7a3fe2f30d6a7f833a5f19f7c7a5b396eae6" uuid = "a2a6695c-b41b-5b7d-aed9-dbfdeacea5d7" version = "0.3.0" [[TriangularSolve]] deps = ["CloseOpenIntervals", "IfElse", "LayoutPointers", "LinearAlgebra", "LoopVectorization", "Polyester", "Static", "VectorizationBase"] git-tree-sha1 = "ec9a310324dd2c546c07f33a599ded9c1d00a420" uuid = "d5829a12-d9aa-46ab-831f-fb7c9ab06edf" version = "0.1.8" [[UUIDs]] deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" [[UnPack]] git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b" uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed" version = "1.0.2" [[Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" [[UnicodeFun]] deps = ["REPL"] git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf" uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1" version = "0.4.1" [[Unitful]] deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"] git-tree-sha1 = "880f77d2cd4c6948e6bd55425b7b52f34dcd7f4b" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" version = "1.9.1" [[VectorizationBase]] deps = ["ArrayInterface", "CPUSummary", "HostCPUFeatures", "Hwloc", "IfElse", "LayoutPointers", "Libdl", "LinearAlgebra", "SIMDTypes", "Static"] git-tree-sha1 = "5239606cf3552aff43d79ecc75b1af1ce4625109" uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f" version = "0.21.21" [[VertexSafeGraphs]] deps = ["LightGraphs"] git-tree-sha1 = "b9b450c99a3ca1cc1c6836f560d8d887bcbe356e" uuid = "19fa3120-7c27-5ec5-8db8-b0b0aa330d6f" version = "0.1.2" [[WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.1" [[WoodburyMatrices]] deps = ["LinearAlgebra", "SparseArrays"] git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3" uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" version = "0.5.5" [[XML2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"] git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a" uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a" version = "2.9.12+0" [[XSLT_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"] git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a" uuid = "aed1982a-8fda-507f-9586-7b0439959a61" version = "1.1.34+0" [[Xorg_libX11_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"] git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527" uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc" version = "1.6.9+4" [[Xorg_libXau_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e" uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec" version = "1.0.9+4" [[Xorg_libXdmcp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4" uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05" version = "1.1.3+4" [[Xorg_libXext_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3" uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3" version = "1.3.4+4" [[Xorg_libXrender_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96" uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa" version = "0.9.10+4" [[Xorg_libpthread_stubs_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb" uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74" version = "0.1.0+3" [[Xorg_libxcb_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6" uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b" version = "1.13.0+3" [[Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" [[Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" [[ZygoteRules]] deps = ["MacroTools"] git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.2" [[isoband_jll]] deps = ["Libdl", "Pkg"] git-tree-sha1 = "a1ac99674715995a536bbce674b068ec1b7d893d" uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4" version = "0.2.2+0" [[libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.15.1+0" [[libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "2.0.2+0" [[libpng_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c" uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" version = "1.6.38+0" [[libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+0" [[nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" [[p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" [[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2021.5.5+0" [[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.5.0+0" """ # ╔═╡ Cell order: # ╟─5c7e4e72-3f69-11ec-258b-6bcfdc7b1d65 # ╟─0fea38f6-2c9e-43c6-bfc1-267ff220a0cc # ╟─aadd082a-3ed9-41b7-ba5e-7c5f77d59508 # ╟─011c89a7-f3d1-4cea-b71a-3d6f96a1801c # ╠═2200503c-605c-4dfd-878f-ffea2431f7ba # ╟─bde49eaf-3747-4edf-b712-3ee6f08ffe20 # ╠═c60a48e0-6f51-44b0-bd6e-332e04d2d332 # ╟─45762eb1-7404-4524-8d1d-788a70f8f5ef # ╟─b70d76d1-09f3-462a-b550-8fa51cb90c2c # ╟─8cc54cc2-5284-40b1-9a30-0ad6f725053f # ╠═503c8702-df0b-4495-8245-4ba6608431e3 # ╟─32a8b0ee-e61d-495b-ba53-56a6c6126950 # ╟─dd62433c-2274-4c6f-9e27-34bc2c620df1 # ╟─250d011e-57db-4d80-b16e-dbc28fec9fba # ╟─a0bf6d10-5d42-481b-8720-dadd3f727e8b # ╟─2b8ee43d-1225-4475-91c9-161cf612bfb2 # ╟─00000000-0000-0000-0000-000000000001 # ╟─00000000-0000-0000-0000-000000000002
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1.927845
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using Documenter, SlimPlotting makedocs(sitename="Slim Plotting toolbox", doctest=false, clean=true, authors="<NAME>", pages = Any[ "Home" => "index.md", "About" => "README.md", "API reference" => "API.md", ]) deploydocs(repo="github.com/slimgroup/SlimPlotting.jl")
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1.988372
172
using NCDatasets using Distributed using SharedArrays using Formatting s_map_file = "b.e11.B1850C5CN.f09_g16.005.pop.h.SST.100001-109912.nc" d_map_file = "domain.ocn.gx3v7.120323.nc" wgt_file = "wgt_gx1v6_to_gx3v7.nc" const NNN = 9 missing_value = 1e20 Dataset(s_map_file, "r") do ds global s_mask, s_Nx, s_Ny, s_N, s_lat, s_lon s_mask = reshape(ds["REGION_MASK"][:], :) s_Nx = ds.dim["nlon"] s_Ny = ds.dim["nlat"] s_N = s_Nx * s_Ny s_lon = reshape(ds["TLONG"][:] .|> deg2rad, :) s_lat = reshape(ds["TLAT"][:] .|> deg2rad, :) end Dataset(d_map_file, "r") do ds global d_mask, d_Nx, d_Ny, d_N, d_lat, d_lon d_mask = reshape(ds["mask"][:], :) d_Nx = ds.dim["ni"] d_Ny = ds.dim["nj"] d_N = d_Nx * d_Ny d_lon = reshape(ds["xc"][:] .|> deg2rad, :) d_lat = reshape(ds["yc"][:] .|> deg2rad, :) end #trans = spzeros(Float64, d_N, s_N) trans = SharedArray{Float64}(NNN, d_N) # s_coord and d_coord are the coordinates of grid points # in 3-dimensional cartesian coordinate s_coord = zeros(Float64, s_N, 3) d_coord = zeros(Float64, d_N, 3) s_coord[:, 1] .= cos.(s_lat) .* cos.(s_lon) s_coord[:, 2] .= cos.(s_lat) .* sin.(s_lon) s_coord[:, 3] .= sin.(s_lat) d_coord[:, 1] .= cos.(d_lat) .* cos.(d_lon) d_coord[:, 2] .= cos.(d_lat) .* sin.(d_lon) d_coord[:, 3] .= sin.(d_lat) s_NaN_idx = (s_mask .== 0) println("Start making transform matrix... ") #idx_arr = zeros(Integer, s_N) #dist2 = zeros(Float64, s_N) @time @sync @distributed for i = 1:d_N # For every point find its nearest-neighbors print("\r", i, "/", d_N) if d_mask[i] == 0 trans[:, i] .= missing_value continue end dist2 = ( (s_coord[:, 1] .- d_coord[i, 1]).^2 + (s_coord[:, 2] .- d_coord[i, 2]).^2 + (s_coord[:, 3] .- d_coord[i, 3]).^2 ) # Decided not to apply this condition because in # extreme cases there might be a small area of water # that is surrounded by lands. dist2[s_NaN_idx] .= NaN idx_arr = 1:s_N |> collect sort!(idx_arr; by=(k)->dist2[k]) trans[:, i] = idx_arr[1:NNN] end println("done.") s_lat = reshape(s_lat, s_Nx, :) s_lon = reshape(s_lon, s_Nx, :) d_lat = reshape(d_lat, d_Nx, :) d_lon = reshape(d_lon, d_Nx, :) Dataset(wgt_file, "c") do ds defDim(ds, "s_Nx", s_Nx) defDim(ds, "s_Ny", s_Ny) defDim(ds, "s_N", s_N) defDim(ds, "d_Nx", d_Nx) defDim(ds, "d_Ny", d_Ny) defDim(ds, "d_N", d_N) defDim(ds, "NNN", NNN) for (varname, vardata, dims) in ( ("NN_idx", trans, ("NNN", "d_N")), ("s_lat", s_lat, ("s_Nx", "s_Ny")), ("s_lon", s_lon, ("s_Nx", "s_Ny")), ("d_lat", d_lat, ("d_Nx", "d_Ny")), ("d_lon", d_lon, ("d_Nx", "d_Ny")), ) print(format("Output data: {} ...", varname)) v = defVar(ds, varname, Float64, dims) v.attrib["_FillValue"] = missing_value v[:] = vardata println("done.") end end
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<reponame>rmsrosa/adventofcode2020 list_test_str = split( "forward 5 down 5 forward 8 up 3 down 8 forward 2", '\n' ) list_str = readlines("day2_input.txt") # Part 1 function part1(list) depth = 0 pos = 0 for cmd in list m, k = split(cmd) k = parse(Int, k) pos += m == "forward" ? k : 0 depth += m == "up" ? -k : m == "down" ? k : 0 end return pos * depth end @info "Part 1" @info "test:" @show part1(list_test_str) == 150 @info "challenge:" @show part1(list_str) == 1714680 # Part 2 function part2(list) depth = 0 pos = 0 aim = 0 for cmd in list m, k = split(cmd) k = parse(Int, k) pos += m == "forward" ? k : 0 depth += m == "forward" ? aim * k : 0 aim += m == "up" ? -k : m == "down" ? k : 0 end return pos * depth end @info "Part 2" @info "test:" @show part2(list_test_str) == 900 @info "challenge:" @show part2(list_str) == 1963088820 # Alternative # Part 1 forward(k) = [0, k] # depth and pos up(k) = [-k, 0] down(k) = [k, 0] function part1_alt(list) commands = [split(cmd) |> u -> (Symbol(first(u)), parse(Int, last(u))) for cmd in list] return prod(mapreduce(u -> eval(first(u))(last(u)), +, commands)) end @info "Part 1 alt" @info "test:" @show part1_alt(list_test_str) == 150 @info "challenge:" @show part1_alt(list_str) == 1714680 # Part 2 function forward!(u, k) u[1] += u[3] * k u[2] += k end function up!(u, k) u[3] -= k end function down!(u, k) u[3] += k end function part2_alt2(list) u = [0, 0, 0] for l in list cmd = l[1] k = parse(Int, l[end]) if cmd == 'f' forward!(u, k) elseif cmd == 'u' up!(u, k) else down!(u, k) end end return u[1] * u[2] end @info "Part 2 alt" @info "test:" @show part2_alt2(list_test_str) == 900 @info "challenge:" @show part2_alt2(list_str) == 1963088820 nothing
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<gh_stars>10-100 # Fire a state transition function fire(fsm::StateMachine, name::AbstractString, args...) # Reserved event names: is, can, finished # is: check whether the current state is the same as the requested state # returns true if they are the same, false otherwise if name == "is" event = args[1] fsm.current == event # can: ensure a specified event can be triggered from the current state # returns true if it can be triggered, false otherwise elseif name == "can" event = args[1] haskey(fsm.map[event], fsm.current) # finished: check if the terminal state has been reached # returns true if current = terminal state, false otherwise elseif name == "finished" fsm.current == fsm.terminal # User-defined events and callbacks elseif haskey(fsm.actions, name) fsm.actions[name]() # Throw an error if there weren't any matching names else error(name * " not found") end end
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# Julia wrapper for header: /usr/include/scip/pub_history.h # Automatically generated using Clang.jl wrap_c function SCIPhistoryGetVSIDS(history, dir) ccall((:SCIPhistoryGetVSIDS, libscip), Cdouble, (Ptr{SCIP_HISTORY}, SCIP_BRANCHDIR), history, dir) end function SCIPhistoryGetCutoffSum(history, dir) ccall((:SCIPhistoryGetCutoffSum, libscip), Cdouble, (Ptr{SCIP_HISTORY}, SCIP_BRANCHDIR), history, dir) end function SCIPvaluehistoryGetNValues(valuehistory) ccall((:SCIPvaluehistoryGetNValues, libscip), Cint, (Ptr{SCIP_VALUEHISTORY},), valuehistory) end function SCIPvaluehistoryGetHistories(valuehistory) ccall((:SCIPvaluehistoryGetHistories, libscip), Ptr{Ptr{SCIP_HISTORY}}, (Ptr{SCIP_VALUEHISTORY},), valuehistory) end function SCIPvaluehistoryGetValues(valuehistory) ccall((:SCIPvaluehistoryGetValues, libscip), Ptr{Cdouble}, (Ptr{SCIP_VALUEHISTORY},), valuehistory) end
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@testset "rng.jl" begin @testset "sample distribution" begin rng = AdvancedPS.TracedRNG() vns = rand(rng, Distributions.Normal()) AdvancedPS.save_state!(rng) rand(rng, Distributions.Normal()) AdvancedPS.load_state!(rng) new_vns = rand(rng, Distributions.Normal()) @test new_vns ≈ vns end @testset "split" begin rng = AdvancedPS.TracedRNG() key = rng.rng.key new_key, = AdvancedPS.split(key, 1) @test key ≠ new_key Random.seed!(rng, new_key) @test rng.rng.key === new_key end end
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# complex dense, projected w/ transient # Compare to figures in S&P, chap. 22. # ∃ small discrepancies for large ϵ, # but we are consistent w/ EigTool. using Pseudospectra, Test @testset "Projection/Transients" begin # for a sensible projection, must provide axes opts = Dict{Symbol,Any}(:ax => [-1,0.3,-1.2,0.2],:npts=>100,:proj_lev=>1.5) A = Pseudospectra.orrsommerfeld(200,10000,1.02) ps_data = new_matrix(A,opts) driver!(ps_data,opts,gs) @test iscomputed(ps_data) mtxexpsplot(gs,ps_data,1.0,100,lbmethod=:old) # additional plots to compare w/ book Fig. 22.6: # modeplot(ps_data,gs,0,0.0-0.2im) # modeplot(ps_data,gs,1,-0.1-0.8im) end
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function iscyclic(a::Vector) for i=1:length(a)-1 if a == circshift(a, i) return true end end # else return false end export iscyclic function isunique(a)::Bool return length(a) == length(unique(a)) end export isunique function mapArray(arr::AbstractArray, low, high) """ map array values to region [0,1] where: * arr<=low -> 0 * arr>=high -> 1 * arr∈[low,high] -> values are linearily mapped from within [low,high] to [0,1] * quick test: * mapArray(rand(3,3,3),0.25,0.75) """ _arr, _low, _high = promote(arr, low, high) _arr = clamp.(_arr, _low, _high) return (_arr-_low)/(_high-_low) end export mapArray
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<reponame>matrixfunctions/GraphMatFunData using LinearAlgebra struct ValueOne; end ValueOne() # Compute X <- a X + b I. function matfun_axpby!(X,a,b,Y::UniformScaling) m,n=size(X) if ~(a isa ValueOne) rmul!(X,a) end @inbounds for i=1:n X[i,i]+=(b isa ValueOne) ? 1 : b end end @inline function exp_ps_m9(A) T=promote_type(eltype(A),Float64) A_copy=similar(A,T); A_copy .= A; return exp_ps_m9!(A_copy) end @inline function exp_ps_m9!(A) T=promote_type(eltype(A),Float64) # Make it work for many 'bigger' types (matrices and scalars) # max_memslots=8 n=size(A,1) # The first slots are precomputed nodes [:A] memslots2 = similar(A,T) memslots3 = similar(A,T) memslots4 = similar(A,T) memslots5 = similar(A,T) memslots6 = similar(A,T) memslots7 = similar(A,T) memslots8 = similar(A,T) # Assign precomputed nodes memslots memslots1=A # overwrite A # Uniform scaling is exploited. # No matrix I explicitly allocated. value_one=ValueOne() # Computation order: A2 A3 A4 A5 A6 P4 C3 P3 C2 P2 C1 P1 C0 P0 # Computing A2 with operation: mult mul!(memslots2,memslots1,memslots1) # Computing A3 with operation: mult mul!(memslots3,memslots1,memslots2) # Computing A4 with operation: mult mul!(memslots4,memslots1,memslots3) # Computing A5 with operation: mult mul!(memslots5,memslots1,memslots4) # Computing A6 with operation: mult mul!(memslots6,memslots1,memslots5) # Computing P4 = x*I+x*A+x*A2+x*A3+x*A4+x*A5+x*A6 coeff1=1.6117375710961184e-24 coeff2=6.446950284384474e-26 coeff3=2.4795962632247976e-27 coeff4=9.183689863795546e-29 coeff5=3.279889237069838e-30 coeff6=1.1309962886447716e-31 coeff7=3.7699876288159054e-33 memslots7 .= coeff2.*memslots1 .+ coeff3.*memslots2 .+ coeff4.*memslots3 .+ coeff5.*memslots4 .+ coeff6.*memslots5 .+ coeff7.*memslots6 mul!(memslots7,true,I*coeff1,true,true) # Computing C3 with operation: mult mul!(memslots8,memslots7,memslots6) # Deallocating P4 in slot 7 # Computing P3 = x*I+x*A+x*A2+x*A3+x*A4+x*A5+x*C3 coeff1=1.5619206968586225e-16 coeff2=8.22063524662433e-18 coeff3=4.110317623312165e-19 coeff4=1.9572941063391263e-20 coeff5=8.896791392450574e-22 coeff6=3.868170170630684e-23 coeff7=1.0 # Smart lincomb recycle C3 memslots8 .= coeff2.*memslots1 .+ coeff3.*memslots2 .+ coeff4.*memslots3 .+ coeff5.*memslots4 .+ coeff6.*memslots5 .+ coeff7.*memslots8 mul!(memslots8,true,I*coeff1,true,true) # Computing C2 with operation: mult mul!(memslots7,memslots8,memslots6) # Deallocating P3 in slot 8 # Computing P2 = x*I+x*A+x*A2+x*A3+x*A4+x*A5+x*C2 coeff1=2.08767569878681e-9 coeff2=1.6059043836821613e-10 coeff3=1.1470745597729725e-11 coeff4=7.647163731819816e-13 coeff5=4.779477332387385e-14 coeff6=2.8114572543455206e-15 coeff7=1.0 # Smart lincomb recycle C2 memslots7 .= coeff2.*memslots1 .+ coeff3.*memslots2 .+ coeff4.*memslots3 .+ coeff5.*memslots4 .+ coeff6.*memslots5 .+ coeff7.*memslots7 mul!(memslots7,true,I*coeff1,true,true) # Computing C1 with operation: mult mul!(memslots8,memslots7,memslots6) # Deallocating P2 in slot 7 # Computing P1 = x*I+x*A+x*A2+x*A3+x*A4+x*A5+x*C1 coeff1=0.001388888888888889 coeff2=0.0001984126984126984 coeff3=2.48015873015873e-5 coeff4=2.7557319223985893e-6 coeff5=2.755731922398589e-7 coeff6=2.505210838544172e-8 coeff7=1.0 # Smart lincomb recycle C1 memslots8 .= coeff2.*memslots1 .+ coeff3.*memslots2 .+ coeff4.*memslots3 .+ coeff5.*memslots4 .+ coeff6.*memslots5 .+ coeff7.*memslots8 mul!(memslots8,true,I*coeff1,true,true) # Computing C0 with operation: mult mul!(memslots7,memslots8,memslots6) # Deallocating P1 in slot 8 # Deallocating A6 in slot 6 # Computing P0 = x*I+x*A+x*A2+x*A3+x*A4+x*A5+x*C0 coeff1=1.0 coeff2=1.0 coeff3=0.5 coeff4=0.16666666666666666 coeff5=0.041666666666666664 coeff6=0.008333333333333333 coeff7=1.0 # Smart lincomb recycle A memslots1 .= coeff2.*memslots1 .+ coeff3.*memslots2 .+ coeff4.*memslots3 .+ coeff5.*memslots4 .+ coeff6.*memslots5 .+ coeff7.*memslots7 mul!(memslots1,true,I*coeff1,true,true) # Deallocating A2 in slot 2 # Deallocating A3 in slot 3 # Deallocating A4 in slot 4 # Deallocating A5 in slot 5 # Deallocating C0 in slot 7 return memslots1 # Returning P0 end
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<filename>__site/__generated/A-composing-models/tutorial-raw.jl using MLJ using PrettyPrinting KNNRegressor = @load KNNRegressor X = (age = [23, 45, 34, 25, 67], gender = categorical(['m', 'm', 'f', 'm', 'f'])) height = [178, 194, 165, 173, 168]; scitype(X.age) pipe = Pipeline( coercer = X -> coerce(X, :age=>Continuous), one_hot_encoder = OneHotEncoder(), transformed_target_model = TransformedTargetModel( model = KNNRegressor(K=3); target=UnivariateStandardizer() ) ) pipe.transformed_target_model.model.K = 2 pipe.one_hot_encoder.drop_last = true; evaluate( pipe, X, height, resampling=Holdout(), measure=rms ) |> pprint # This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl
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function horner(coeff, x0) degree = length(coeff) - 1 y = coeff[1] z = 0 for j ∈ 2:(degree + 1) println(coeff[j]) z = y + x0 * z y = x0 * y + coeff[j] end return (y, z) end println(horner([5, 2, 1], 5)[1])
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<gh_stars>1-10 using GapFindFill, Test # using JuMP, Gurobi, CPLEX, GLPK println("Starting tests....................") @time begin include("testGapFind.jl") end @time begin include("testGapFill.jl") end
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2.956522
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using Rankings, Test @testset "Литвак (1982), p.78" begin @test ⦸(ranking"a₂,a₄,a₁,a₃", ranking"a₁,a₃∼a₄,a₂", ranking"a₂∼a₃,a₄,a₁", ranking"a₃,a₂,a₁∼a₄") == [ 0 2 0 1; -2 0 1 -2; 0 -1 0 -1; -1 2 1 0 ] end @testset "<NAME> (2008), p.154" begin @test ranking"O₂,O₃,O₄,O₁,O₅" ⦸ ranking"(O₂,O₃),O₄,(O₁,O₅)" ⦸ ranking"O₂,(O₁,O₃,O₄),O₅" ⦸ ranking"O₂,(O₁,O₃,O₄,O₅)" == [ 0 4 2 2 -2; -4 0 -3 -4 -4; -2 3 0 -2 -3; -2 4 2 0 -3; 2 4 3 3 0 ] end @testset "Bredereck (2009), p.21" begin @test ranking"a≻b≻c≻d" ⦸ ranking"a≻b≻d≻c" ⦸ ranking"a≻c≻b≻d" ⦸ ranking"b≻a≻c≻d" ⦸ ranking"b≻a≻d≻c" == [ 0 -1 -5 -5; 1 0 -3 -5; 5 3 0 -1; 5 5 1 0 ] end @testset "Muravyov, Tarakanov (2011)" begin @test ranking"A K D N J H E∼L P I B∼O F R M T Q S G C" ⦸ ranking"T I C A Q F H N M G B O D R P L E K J S" ⦸ ranking"R N T P∼I C G∼D K O H J Q S∼M B E A F L" ⦸ ranking"H B M A Q N D O T L I G R S E∼C J F K P" == [ 0 0 0 -2 -2 -4 -2 0 0 -2 -2 -4 0 -2 -2 -2 -2 -2 -2 0; 0 0 0 0 -2 -2 0 4 2 0 0 -2 0 2 -1 0 0 -2 -2 0; 0 0 0 0 -1 -2 0 0 4 -2 -2 0 0 2 0 0 0 2 0 4; 2 0 0 0 -4 -2 -1 0 0 -4 -2 -4 0 2 -2 -2 0 -2 -4 0; 2 2 1 4 0 -2 2 4 2 0 0 1 2 4 2 0 2 2 0 2; 4 2 2 2 2 0 0 2 4 2 0 0 0 2 2 0 2 0 0 2; 2 0 0 1 -2 0 0 2 4 -2 -2 0 2 4 0 0 2 0 -2 4; 0 -4 0 0 -4 -2 -2 0 0 -2 0 -4 -4 0 -2 -2 -2 -2 -4 0; 0 -2 -4 0 -2 -4 -4 0 0 -2 -2 0 -2 2 -2 -1 -2 -2 -4 2; 2 0 2 4 0 -2 2 2 2 0 2 0 0 4 2 0 0 2 -2 2; 2 0 2 2 0 0 2 0 2 -2 0 0 0 2 0 0 0 2 -2 2; 4 2 0 4 -1 0 0 4 0 0 0 0 2 4 2 0 2 0 -2 2; 0 0 0 0 -2 0 -2 4 2 0 0 -2 0 2 0 0 0 0 -3 0; 2 -2 -2 -2 -4 -2 -4 0 -2 -4 -2 -4 -2 0 -4 -4 0 -2 -4 -2; 2 1 0 2 -2 -2 0 2 2 -2 0 -2 0 4 0 0 0 -2 -4 0; 2 0 0 2 0 0 0 2 1 0 0 0 0 4 0 0 0 2 -2 2; 2 0 0 0 -2 -2 -2 2 2 0 0 -2 0 0 0 0 0 0 -4 2; 2 2 -2 2 -2 0 0 2 2 -2 -2 0 0 2 2 -2 0 0 -4 0; 2 2 0 4 0 0 2 4 4 2 2 2 3 4 4 2 4 4 0 4; 0 0 -4 0 -2 -2 -4 0 -2 -2 -2 -2 0 2 0 -2 -2 0 -4 0 ] @test ranking"AIFDHCBEJG" ⦸ ranking"BHDAFCIEGJ" ⦸ ranking"DIEBCJAHGF" ⦸ ranking"CJHADIFGBE" ⦸ ranking"ICAJHDGBFE" == [ 0 -1 1 -1 -3 -5 -5 -1 -1 -1; 1 0 1 3 -3 -1 -1 1 3 -1; -1 -1 0 1 -3 -1 -5 -1 1 -5; 1 -3 -1 0 -5 -3 -5 1 -1 -1; 3 3 3 5 0 3 -1 3 5 -1; 5 1 1 3 -3 0 -1 3 3 1; 5 1 5 5 1 1 0 5 5 3; 1 -1 1 -1 -3 -3 -5 0 1 1; 1 -3 -1 1 -5 -3 -5 -1 0 -3; 1 1 5 1 1 -1 -3 -1 3 0 ] @test ranking"J∼K A∼B∼C∼D∼E∼F∼G∼H∼I∼L∼M" ⦸ ranking"J A∼B∼C∼D∼E∼F∼G∼H∼I∼K∼L∼M" ⦸ ranking"J∼K A∼B∼C∼D∼E∼F∼G∼H∼I∼L∼M" ⦸ ranking"I∼J∼K A∼B∼C∼D∼E∼F∼G∼H∼L∼M" ⦸ ranking"H∼I∼J∼K L∼A∼B∼C∼D∼E∼F∼G∼M" ⦸ ranking"H∼I∼J A∼B∼C∼D∼E∼F∼G∼K∼L∼M" ⦸ ranking"K∼L A∼B∼C∼D∼E∼F∼G∼H∼I∼J∼M" ⦸ ranking"I∼J∼K A∼B∼C∼D∼E∼F∼G∼H∼L∼M" ⦸ ranking"B∼C A∼D∼E∼F∼G∼H∼I∼J∼K∼L∼M" ⦸ ranking"H∼I A∼B∼C∼D∼E∼F∼G∼J∼K∼L∼M" == [ 0 1 1 0 0 0 0 3 5 7 6 1 0; -1 0 0 -1 -1 -1 -1 2 4 6 5 0 -1; -1 0 0 -1 -1 -1 -1 2 4 6 5 0 -1; 0 1 1 0 0 0 0 3 5 7 6 1 0; 0 1 1 0 0 0 0 3 5 7 6 1 0; 0 1 1 0 0 0 0 3 5 7 6 1 0; 0 1 1 0 0 0 0 3 5 7 6 1 0; -3 -2 -2 -3 -3 -3 -3 0 2 4 3 -2 -3; -5 -4 -4 -5 -5 -5 -5 -2 0 2 1 -4 -5; -7 -6 -6 -7 -7 -7 -7 -4 -2 0 -1 -6 -7; -6 -5 -5 -6 -6 -6 -6 -3 -1 1 0 -5 -6; -1 0 0 -1 -1 -1 -1 2 4 6 5 0 -1; 0 1 1 0 0 0 0 3 5 7 6 1 0 ] end @testset "<NAME> (2014), p.6427" begin @test ranking"1423" ⦸ ranking"1432" ⦸ ranking"2314" ⦸ ranking"4231" ⦸ ranking"3241" == [ 0 1 1 -1; -1 0 -1 1; -1 1 0 1; 1 -1 -1 0 ] @test ranking"54132" ⦸ ranking"15423" ⦸ ranking"43512" ⦸ ranking"13452" ⦸ ranking"42531" ⦸ ranking"12534" ⦸ ranking"24351" == [ 0 -3 -1 1 1; 3 0 -1 3 1; 1 1 0 3 1; -1 -3 -3 0 -1; -1 -1 -1 1 0 ] @test ranking"123" ⦸ ranking"123" ⦸ ranking"321" ⦸ ranking"213" == [ 0 0 -2; 0 0 -2; 2 2 0 ] end @testset "<NAME> (2014), p.55" begin @test ranking"a₃,a₅,a₂,a₄,a₁" ⦸ ranking"a₃,a₅,a₄,a₂,a₁" ⦸ ranking"a₃,a₅,a₂∼a₄,a₁" == [ 0 3 3 3 3; -3 0 3 0 3; -3 -3 0 -3 -3; -3 0 3 0 3; -3 -3 3 -3 0 ] end @testset "<NAME> (2018), p.55" begin @test ranking"c≻e≻d≻b≻a" ⦸ ranking"c≻e≻d≻b≻a" ⦸ ranking"c≻e≻b≻d≻a" ⦸ ranking"c≻e≻d≻b≻a" ⦸ ranking"e≻c≻d≻a≻b" ⦸ ranking"e≻d≻c≻a≻b" ⦸ ranking"e≻c≻d≻a≻b" ⦸ ranking"b≻e≻d≻c≻a" ⦸ ranking"d≻b≻e≻c≻a" == [ 0 3 9 9 9; -3 0 5 5 5; -9 -5 0 -3 1; -9 -5 3 0 7; -9 -5 -1 -7 0 ] end @testset "Muravyov, Baranov, Emelyanova (2019)" begin @test ranking"A∼C,B" ⦸ ranking"C,A∼B" ⦸ ranking"B∼C,A" == [ 0 0 2; 0 0 2; -2 -2 0 ] @test ranking"A B F D (C E)" ⦸ ranking"D E A B C F" ⦸ ranking"B (E A)(C D) F" ⦸ ranking"(F C) D B (A E)" ⦸ ranking"C D (B F) E A" == [ 0 1 -1 1 1 -1; -1 0 -1 1 -3 -2; 1 1 0 0 0 -2; -1 -1 0 0 -3 -1; -1 3 0 3 0 1; 1 2 2 1 -1 0 ] end
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<filename>src/Hangul.jl<gh_stars>0 module Hangul # Hangul Compatibility Jamo export Jamo export Letter, Consonant, Vowel export Syllable export ComposeError, DecomposeError export to_char, to_syllable include("jamo.jl") # Hangul Jamo export YetJamo export Initial, Medial, Final export YetSyllable export to_string include("yetjamo.jl") end # module
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################## # Society Type # ################## abstract type Society{N, K, T} end ########################### # Society Redefinitions # ########################### length(soc::Society) = length(soc.agents) size(soc::Society) = (length(soc.agents), length(soc.agents[1])) size(soc::Society, i::Int) = size(soc)[i] getindex(soc::Society, i::Int) = soc.agents[i] getindex(soc::Society, c::Colon) = soc.agents[:] getindex(soc::Society, c1::Colon, c2::Colon) = soc.links[:, :] endof(soc::Society) = soc.agents[end] getindex(soc::Society, i::Int, j::Int) = soc.links[i, j] getindex(soc::Society, i::Int, r::UnitRange) = soc.links[i, r] getindex(soc::Society, r::UnitRange, j::Int) = soc.links[r, j] getindex(soc::Society, r::UnitRange, s::UnitRange) = soc.links[r, s] getindex(soc::Society, i::Int, c::Colon) = soc.links[i, c] getindex(soc::Society, c::Colon, j::Int) = soc.links[c, j] getindex(soc::Society, c::Colon, r::UnitRange) = soc.links[c, r] getindex(soc::Society, r::UnitRange, c::Colon) = soc.links[r, c] setindex!(soc::Society, x::MoralVector, i::Int64) = soc.agents[i] = x start(soc::Society) = 1 done(soc::Society, s::Int) = s > length(soc) next(soc::Society, s::Int) = (soc[s], s+1) ######################### # Society Definitions # ######################### """ agents(soc::Society) Returns the agents vector of a Society soc """ agents(soc::Society) = soc[:] """ agents(soc::Society, i::Int) Returns the ith agent of a Society soc """ agents(soc::Society, i::Int) = soc[i] """ interactions(soc::Society) Returns the links of a Society soc """ interactions(soc::Society) = soc.links """ insertagent!{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}, i::Int) Insert new agent x at position i in Society soc """ insertagent!{N, K, T}(soc::Society{N,K,T}, x::MoralVector{K,T}, i::Int) = soc.agents[i] = x """ hamiltonian{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) Evaluates the hamiltonian, that is, the total social cost of a Society given some MoralVector x """ function hamiltonian{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) hamil = 0 for j in 1:N for i in 1:N Rij = soc[i, j] if Rij != 0 hamil += Rij*cogcost(soc,i,j,x) end end end return hamil end """ magnetization{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) Evaluates the magnetization of a Society, that is, the total sum of the MoralVectors opinions """ function magnetization{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) mag = 0. for agi in agents(soc) mag += agi ⋅ x end return mag / N end """ believeness{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) Evaluates the believeness of a Society, that is, the sum of the absolute values of MoralVectors opinions """ function believeness{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) bel = 0. for agi in agents(soc) bel += abs(agi ⋅ x) end return bel / N end """ quadrupole{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) Evaluates the quadrupole of a Society, that is, something very nasty and obscure: * \frac{1}{N^2 K} \sum_{\langle ij \rangle} R_{ij} j_i h_j """ function quadrupole{N,K,T}(soc::Society{N,K,T}, x::MoralVector{K,T}) qd = 0. for i in 1:N for j in (i+1):N qd += soc[i, j] * (soc[i] ⋅ x) * (soc[j] ⋅ x) end end return qd / N^2 end """ consensus{N,K,T}(soc::Society{N,K,T}, i::Int, j::Int) Evaluates the consensus of MoralVectors i and j within a Society, that is, the between the agents inner representations """ function consensus{N,K,T}(soc::Society{N,K,T}, i::Int, j::Int) if 0 < i <= N && 0 < j <= N return soc[i] ⋅ soc[j] else error("The indices given are out of range") end end """ consensus{N,K,T}(soc::Society{N,K,T}) Evaluates the consensus of a Society, that is, the matrix of the correlations between the MoralVectors inner representations """ function consensus{N,K,T}(soc::Society{N,K,T}; format = :none) ψ = zeros(N, N) for i in 1:N for j in (i+1):N ψ[i, j] = consensus(soc, i, j) end end if format == :ordered return ψ[ψ .!= 0.] end return ψ end
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<filename>src/Poisson/core_obsolete.jl<gh_stars>1-10 """ This module represents an explicit, direct sparse 2D finite-difference Poisson solver for heterogeneous media, i.e. media having spatially varying (space-dependent) medium parameters. The following functionality is currently available in this module: * 'Forward' problem: given the source, and the conductivity distribution, solve for the electrical potential ψ: Example problem: solve Aψ=∇⋅j; A=∇⋅(σ(x,z)∇), for ψ. * 'Inverse' problem: given wave propagation related pore pressure, and mechanical medium properties, calculate the source term: Example problem: solve Aψ=-∇⋅j; A=∇⋅([Q*k/η](x,z)∇B*P), for ∇⋅j; * The Boundary Value Problem that is currently implemented assumes Neumann boundary conditions at all boundaries. Developed by:\\\n <NAME>, Massachusetts Institute of Technology, USA.\\\n In collaboration with: <NAME> & <NAME>, Massachusetts Institute of Technology, USA.\\\n Date: October, 2017 \\\n Contact: <EMAIL> """ module Poisson using SparseArrays using StatsBase """ Poisson.solve(field,mpars,solflag) solves the forward or inverse Poisson problem in a heterogeneous medium. # Function input arguments: * `field` : either source term used in 'forward' mode to get field, or the field (e.g. pore pressure) used in 'inverse' mode to calculate source-term * `mpars` : medium parameters for forward or inverse mode * `solflag` : Solution flag determining whether to use forward (solflag=1) or inverse mode (solflag=-1) of Poisson solver. """ function solve(field,mpars,solflag::Int64) # fields: either field or source, mpars=medium pars if(solflag==-1) pp=field; # elseif(solflag==1) src=field; else error("In Poisson.solve: change solflag to solflag=1 (forward problem) or solflag=-1 (inverse problem)") end nz=size(mpars,1); nx=size(mpars,2); dz= inv(nz - 1); dx= inv(nx - 1); z=(0 : nz - 1)*dz; x=(0:nx-1)*dx; #-------------------- Build explicit operator matrix A=∇⋅(mpars(x,z)∇) for direct Poisson solver -------------------- # Some notes: # We build the operator matrix explicitly like this to properly deal with the Neumann boundary conditions at all boundaries. # The standard Kronecker product approach for building sparse operator matrices seemed to generate some errors at the boundaries. # Medium properties calculated on staggered grid. # Directions: z-direction row-wise, x-direction column-wise. # Allocate non-zero operator entries k = 5*(nz - 2)*(nx - 2)+ # pentadiagonal operator entry allocation: 5, since using 5-point finite-difference stencil (star-shaped) 2*(2*(nz - 2) + 2*(nx - 2)+ # bidiagonal non-corner 4-boundary allocation # for boundaries (2 points only) 4); # bidiagonal corner boundary allocation # The sparse() function is often a handy way to construct sparse matrices. # It takes as its input a vector I of row indices, a vector J of column indices, and a vector V of nonzero values. # Sparse(I,J,V) constructs a sparse matrix such that S[I[k], J[k]] = V[k]. iz=Array{Float64,1}; ix=Array{Float64,1}; iz=collect(1:nz*nx); # same row: row index=1 --> vectors must have same length [k] ix=collect(1:nz*nx); # column indices val=zeros(nz*nx); # actual values global A=sparse(ix,iz,val,nz*nx,nz*nx); # pre-allocate sparse operator matrix A dz = dz.^2; # for simplicity below dx = dx.^2; println("fffffff", dz*dx) #-------------- Create operator A for the interior points (without boundary)--------------------------------------- for j = 2 : nx - 1 # interior x (column) loop (for amount of internal nodes) k = 2 + nz*(j - 1); # initial interior z for this x. To jump from j (new column), need nz*... to proceed to next column vector k for i = 2 : nz - 1 # interior z (row) loop: A[k,k - nz] = .5*(mpars[i,j] + mpars[i,j - 1])/dx; # moving left (and placing actual staggered 2D grid medium value there) A[k,k - 1] = .5*(mpars[i,j] + mpars[i - 1,j])/dz; # moving up the grid A[k,k] = -(mpars[i,j] + .5*(mpars[i,j - 1] + mpars[i,j + 1]))/dx - (mpars[i,j] + .5*(mpars[i - 1,j] + mpars[i + 1,j]))/dz; # diagonal elements: ∇⋅\medpars(x,z)∇ calculated using 5 mpars points A[k,k + 1] = .5*(mpars[i,j] + mpars[i + 1,j])/dz; # moving down the grid A[k,k + nz] = .5*(mpars[i,j]+ mpars[i,j + 1])/dx; # moving right on grid (need nz values to go to next column) k = k + 1; # to next z end # The foregoing A terms are operations by hand ... end #------------- Create operator A for the top and bottom boundaries including Neumann conditions (without the corners)------------- if(solflag==-1) # for 'inverse' problem: source plays no role here, so just allocate zeros with proper size. src=zeros(nz,nx); elseif(solflag==1) # for 'forward' problem src = src[:]; # vectorize source (which was function input argument) end for j = 2 : nx - 1 # interior x (column) loop: k = 1 + nz*(j - 1);# initial boundary z for this x (k = i + m*(j - 1)) for i = [1 nz] # % north & south boundary z (row) loop: o = k - Int(sign(i - .5*nz)); #% sign function determines correct direction to: inward from north or south boundary # to define direction(from boundary down or up) A[k,k] = -A[o,k]; #% build in symmetry ... --> trick to get symmetry, the next line is leading. A[k,o] = -A[k,k]; #% cancelation implies equation # Neumann: value of psi = same. The operator does not know value. Just knows has to subtract two values --> minus sign src[k] = 0; # 0 to equate boundary values in source term ... (?) k = k + nz - 1; #% from north to south boundary end end # Create A for the east and west boundaries without corner points for j = [1 nx] # west & east boundary x (column) loop: k = 2 + nz*(j - 1); #% initial interior z for this x (k = i + m*(j - 1)) for i = 2 : nz - 1 # interior z (row) loop: o = k - Int(sign(j - .5*nx))*nz; # inward from west or east boundary A[k,k] = -A[o,k]; #% build in symmetry ... A[k,o] = -A[k,k]; #% cancelation implies equation src[k]= 0; #% 0 to equate boundary values k = k + 1; #% to next z end end # Create A for the corner points for j = [1 nx] #% west & east boundary x (column) loop: k = 1 + nz*(j - 1); #% start on north boundary for i = [1 nz] #% north & south boundary y (row) loop: o = k - Int(sign(i - .5*nz))- #% inward from north or south boundary Int(sign(j - .5*nx))*nz; # inward from west or east boundary # two terms to move diagonal A[k,k] = A[o,o]; #% nonzero, alas asymmetric ... A[k,o] = -A[k,k]; #% ... cancelation implies equation src[k] = 0; #% 0 to equate boundary values k = k + nz - 1; #% from north to south boundary end end if(solflag == -1) #for inverse problem: fields + medium --> source term pp_vec=pp[:]; # vectorize 2D pore pressure field (according to organization operator A). divj=-reshape(A*pp_vec,nz,nx); return divj # function command --> return these variables, in this case electrical source term ∇⋅j (used in inverse problem) elseif(solflag==1) # forward problem: source+medium --> fields # To 'fix' the nullspace, and make the source consistent with the nullspace --> not always fast/memory allowable since # julia backslash operator does not like non-square matrices... (?) #ix=Array{Float64,1}; #ix=ones(nz*nx); # same row: row index=1 #iz=collect(1:nz*nx); # column indices #v=ones(nz*nx); # actual values #onemat=sparse(ix,iz,v); # A = [onemat;A]; # fix null space # psi = reshape(A\[0; src], nz, nx); # make source consistent with null space psi_vec=A\src[:]; # direct inverse solve using backslash operator psi = reshape(psi_vec, nz, nx); # reshape electrical potential vector psi into 2D electrical potential map (z,x) psi = psi .- StatsBase.mean(StatsBase.mean(psi)); # alternative to 'fix' nullspace: make zero-mean... # src=reshape(src,nz,nx); # reshape source vector into 2D source map for plotting purposes only. return psi,A # function command --> return these variables, in this case electrical potential ψ end # end solflag if statement end # function command end # end module Poisson
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<gh_stars>1-10 #function testNHHill_L() using CGDycore # Physical parameters Param=CGDycore.PhysParameters(); nx=60; ny=2; lx=6000000; ly=200000; x0=-3000000; y0=0; Boundary = (;WE="Period", BT="Period") Param.Grid=CGDycore.CartGrid(nx,ny,lx,ly,x0,y0,CGDycore.OrientFaceCart,Boundary,Param); Param.TopoS="AgnesiCartX"; Param.H=15600; nz=40; zP=zeros(nz,1); z=zeros(nz+1,1); dz=Param.H/nz; zP[1]=dz/2; for i=2:nz zP[i]=zP[i-1]+dz; end for i=2:nz+1 z[i]=z[i-1]+dz; end Param.Grid.nz=nz; Param.Grid.z=z; Param.Grid.zP=zP; Param.Grid.dz=dz; # Model Param.ModelType="Curl"; Param.Coriolis=false; Param.Thermo=""; Param.Source=false; Param.StrideDamp=6000; Param.Relax=1.e-1; Param.Damping=true; Param.Flat=false; Param.Coriolis=false; Param.Buoyancy=true; Param.hC=400; Param.x0C=0; Param.aC=100000; Param.Th0=300; Param.uMax=10; Param.vMax=0; Param.NBr=1.e-2; Param.Equation="Compressible"; Param.RefProfile=false # Discretization OrdPoly=4; OrdPolyZ=1; (CG,Param)=CGDycore.Discretization(OrdPoly,OrdPolyZ,CGDycore.JacobiDG3,Param); Param.HyperVisc=true; Param.HyperDCurl=1.e4; Param.HyperDGrad=1.e4; Param.HyperDDiv=1.e4; Param.Upwind=false # Initial conditions Param.NumV=5; U=zeros(CG.NumG,nz,Param.NumV); Param.ProfRho="GravityHill"; Param.ProfTheta="GravityHill"; Param.ProfVel="Const"; Param.RhoPos=1; Param.uPos=2; Param.vPos=3; Param.wPos=4; Param.ThPos=5; U[:,:,Param.RhoPos]=CGDycore.Project(CGDycore.fRho,CG,Param); (U[:,:,Param.uPos],U[:,:,Param.vPos])=CGDycore.ProjectVec(CGDycore.fVel,CG,Param); U[:,:,Param.ThPos]=CGDycore.Project(CGDycore.fTheta,CG,Param).*U[:,:,Param.RhoPos]; # Output Param.vtkFileName="Hill_L"; Param.vtk=0; vtkGrid=CGDycore.vtkCGGrid(CG,CGDycore.TransCart,CGDycore.Topo,Param); Param.cNames = [ "Rho", "u", "v", "w", "Th" ] IntMethod="Rosenbrock"; #IntMethod="RungeKutta"; if strcmp(IntMethod,"Rosenbrock") dtau=30; else dtau=.4; end nIter=6000; Param.RK=CGDycore.RungeKuttaMethod("RK4"); Param.ROS=CGDycore.RosenbrockMethod("SSP-Knoth"); CFL=0.125; time=0; Param.EndTime=216000; nIter=Param.EndTime/dtau; PrintTime=10000; PrintInt=floor(PrintTime/dtau); Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param); nIter=20 # Print initial conditions Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param); # str = IntMethod if str == "Rosenbrock" @time begin for i=1:nIter @info "Iteration: $i" @show dtau U .= CGDycore.RosenbrockSchur(U,dtau,CGDycore.FcnNHCurlVec,CGDycore.JacSchur,CG,Param); if mod(i,PrintInt)==0 Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param); end end end elseif str == "RungeKutta" for i=1:nIter @info "Iteration: $i" U .= CGDycore.RungeKuttaExplicit(U,dtau,CGDycore.FcnNHCurlVec,CG,Param); time[1] += dtau; if mod(i,PrintInt)==0 Param.vtk=CGDycore.vtkOutput(U,vtkGrid,CG,Param); end end else error("Bad str") end
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1.974255
1,476
const FIELDS = Dict( :name => (String, 0, 100), :mode => (Int, 100, 8), :uid => (Int, 108, 8), :gid => (Int, 116, 8), :size => (Int, 124, 12), :mtime => (Int, 136, 12), :chksum => (Int, 148, 8), :typeflag => (Char, 156, 1), :linkname => (String, 157, 100), :magic => (String, 257, 6), :version => (String, 263, 2), :uname => (String, 265, 32), :gname => (String, 297, 32), :devmajor => (Int, 329, 8), :devminor => (Int, 337, 8), :prefix => (String, 345, 155), ) function field_data(buf::Vector{UInt8}, field::Symbol) type, offset, len = FIELDS[field] return String(buf[offset .+ (1:len)]) end function parse_header(buf::Vector{UInt8}) Dict(f => field_data(buf, f) for f in keys(FIELDS)) end
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1.909871
466
""" x = admm(A, B; ...) Solves non-negative least-squares problem by the Alternating Direction Method of Multipliers (ADMM). Optional arguments: ρ: penalty parameter (set heuristically by default) ε: tolerance for stopping (small number times sqrt[m*n] by default) References: <NAME>, <NAME>, <NAME>, <NAME>, and <NAME> (2011). Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends in Machine Learning. http://stanford.edu/~eryu/nnlsqr.html """ function admm(A::Matrix{Float64}, B::Matrix{Float64}; ρ=max(0.1,vecnorm(A)^2/size(A,2)), ε=sqrt(size(A,2)*size(B,2))*1e-15, kwargs...) # Dimensions m,k = size(A) n = size(B,2) # Cache matrices AtB = A'*B AtAρ = A'*A + eye(k)*ρ # Cache cholesky factorization L = cholfact(AtAρ) # Matrix storing the solutions X = zeros(k,n) # Initialize variables Z,U = zeros(k,n),zeros(k,n) X = L \ (AtB+ρ*(Z-U)) # Solve while vecnorm(X-Z) > ε Z = max(0,X+U) U = U+X-Z X = L \ (AtB+ρ*(Z-U)) end # Z ≈ X, return Z because nonnegativity is strictly enforced return Z end
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2.294347
513
@testset "lmm" begin rng = MersenneTwister(123) FDM = FiniteDifferences.central_fdm(5, 1) N = 10 in_dim = 3 out_dim = 6 x1 = MOInput([rand(rng, in_dim) for _ in 1:N], out_dim) x2 = MOInput([rand(rng, in_dim) for _ in 1:N], out_dim) H = rand(4, 6) k = LinearMixingModelKernel( [Matern32Kernel(), SqExponentialKernel(), FBMKernel(), Matern32Kernel()], H ) @test k isa LinearMixingModelKernel @test k isa MOKernel @test k isa Kernel @test k(x1[1], x2[1]) isa Real @test string(k) == "Linear Mixing Model Multi-Output Kernel" @test repr("text/plain", k) == ( "Linear Mixing Model Multi-Output Kernel. Kernels:\n" * "\tMatern 3/2 Kernel (metric = Euclidean(0.0))\n" * "\tSquared Exponential Kernel (metric = Euclidean(0.0))\n" * "\tFractional Brownian Motion Kernel (h = 0.5)\n" * "\tMatern 3/2 Kernel (metric = Euclidean(0.0))" ) k = LinearMixingModelKernel(SEKernel(), H) @test k isa LinearMixingModelKernel @test k isa MOKernel @test k isa Kernel @test length(k.K) == 4 for kernel in k.K @test isa(kernel, SEKernel) end @test string(k) == "Linear Mixing Model Multi-Output Kernel" @test repr("text/plain", k) == ( "Linear Mixing Model Multi-Output Kernel. Kernels:\n" * "\tSquared Exponential Kernel (metric = Euclidean(0.0))\n" * "\tSquared Exponential Kernel (metric = Euclidean(0.0))\n" * "\tSquared Exponential Kernel (metric = Euclidean(0.0))\n" * "\tSquared Exponential Kernel (metric = Euclidean(0.0))" ) test_ADs(k) end
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2.198391
746
using Gadfly, DataFrames plot(readtable(joinpath(dirname(@__FILE__), "issue120.csv")), x=:x1, y=:x2)
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2.318182
44
""" center_predictors(X::AbstractMatrix) Centers the columns of a matrix `X` of predictors to mean 0. Returns a tuple with: 1. `μ_X`: 1xK `Matrix` of `Float64`s of the means of the K columns in the original `X` matrix. 2. `X_centered`: A `Matrix` of `Float64`s with the same dimensions as the original matrix `X` with the columns centered on mean μ=0. # Arguments - `X::AbstractMatrix`: a matrix of predictors where rows are observations and columns are variables. """ function center_predictors(X::AbstractMatrix) μ_X = mapslices(mean, X; dims=1) X_centered = similar(X, Float64) for (idx, val) in enumerate(eachcol(X)) X_centered[:, idx] = val .- μ_X[1, idx] end return vec(μ_X), X_centered end """ standardize_predictors(X::AbstractMatrix) Standardizes the columns of a matrix `X` of predictors to mean 0 and standard deviation 1. Returns a tuple with: 1. `μ_X`: 1xK `Matrix` of `Float64`s of the means of the K columns in the original `X` matrix. 2. `σ_X`: 1xK `Matrix` of `Float64`s of the standard deviations of the K columns in the original `X` matrix. 3. `X_std`: A `Matrix` of `Float64`s with the same dimensions as the original matrix `X` with the columns centered on mean μ=0 and standard deviation σ=1. # Arguments - `X::AbstractMatrix`: a matrix of predictors where rows are observations and columns are variables. """ function standardize_predictors(X::AbstractMatrix) μ_X = mapslices(mean, X; dims=1) σ_X = mapslices(std, X; dims=1) X_std = similar(X, Float64) for (idx, val) in enumerate(eachcol(X)) X_std[:, idx] = (val .- μ_X[1, idx]) ./ σ_X[1, idx] end return vec(μ_X), vec(σ_X), X_std end """ standardize_predictors(x::AbstractVector) Standardizes the vector `x` to mean 0 and standard deviation 1. Returns a tuple with: 1. `μ_X`: `Float64`s of the mean of the original vector `x`. 2. `σ_X`: `Float64`s of the standard deviations of the original vector `x`. 3. `x_std`: A `Vector` of `Float64`s with the same length as the original vector `x` with the values centered on mean μ=0 and standard deviation σ=1. # Arguments - `x::AbstractVector`: a vector. """ function standardize_predictors(x::AbstractVector) μ_x = mean(x) σ_x = std(x) x_std = (x .- μ_x) ./ σ_x return μ_x, σ_x, x_std end """ tuple_length(::NTuple{N, Any}) where {N} = Int(N) This is a hack to get the length of any tuple. """ tuple_length(::NTuple{N,Any}) where {N} = Int(N) """ convert_str_to_indices(v::AbstractVector) Converts a vector `v` to a vector of indices, i.e. a vector where all the entries are integers. Returns a tuple with the first element as the converted vector and the second element a `Dict` specifying which string is which integer. This function is especially useful for random-effects varying-intercept hierarchical models. Normally `v` would be a vector of group membership with values such as `"group_1"`, `"group_2"` etc. For random-effect models with varying-intercepts, Turing needs the group membership values to be passed as `Int`s. """ function convert_str_to_indices(v::AbstractVector) d = Dict{eltype(v),Int}() v_int = Int[] for i in v n = get!(d, i, length(d) + 1) push!(v_int, n) end return v_int, d end
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2.699093
1,213
using LightXML """ Verision Comment v0.1 hf, firstversion v0.2 hf, use exiftool instead of tiffcommit ref: http://web.mit.edu/jhawk/mnt/cgs/Image-ExifTool-6.99/html/exiftool_pod.html https://exiftool.org/TagNames/EXIF.html https://docs.openmicroscopy.org/ome-model/5.6.3/ome-tiff/specification.html https://micro-manager.org/wiki/Micro-Manager_File_Formats """ """ Create and return XML string with xyzt """ function generatexml(SizeX::Integer, SizeY::Integer, SizeZ::Integer, SizeT::Integer) tiffXML = XMLDocument(); OME = create_root(tiffXML, "OME"); xmlns="http://www.openmicroscopy.org/Schemas/OME/2016-06" xmlns_xsi="http://www.w3.org/2001/XMLSchema-instance" Creator="OME Bio-Formats 5.2.2" UUID="urn:uuid:a5ae8c1b-ac04-4544-97c1-bbdd0bdf8629" # use fixed value now xsi_schemaLocation="http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd" OME_attributes = Dict("xmlns"=>xmlns, "xmlns:xsi"=>xmlns_xsi, "Creator"=>Creator, "UUID"=>UUID, "xsi:schemaLocation"=>xsi_schemaLocation) set_attributes(OME, OME_attributes) name = "test.tiff" Image= new_child(OME, "Image") set_attributes(Image, Dict("ID"=>"Image:0", "Name"=>name)) Pixels = new_child(Image, "Pixels"); SizeC=1; #SizeT=10; #SizeX=1300; #SizeY=1900; #SizeZ=2; PhysicalSizeX=PhysicalSizeY=0.108; PhysicalSizeZ=0.5; TimeIncrement=600; Pixels_attributes = Dict("ID"=>"Pixels:0","Type"=>"uint16", "BigEndian"=>"false", "DimensionOrder"=>"XYZCT", "SizeC"=>1,"SizeX"=>SizeX, "SizeY"=>SizeY, "SizeZ"=>SizeZ, "SizeT"=>SizeT, "PhysicalSizeX"=>PhysicalSizeX, "PhysicalSizeY"=>PhysicalSizeY, "PhysicalSizeZ"=>PhysicalSizeZ, "PhysicalSizeXUnit"=>"µm", "PhysicalSizeYUnit"=>"µm", "PhysicalSizeZUnit"=>"µm", "TimeIncrement"=>TimeIncrement, "TimeIncrementUnit"=>"s"); set_attributes(Pixels, Pixels_attributes) Channel_ = new_child(Pixels, "Channel"); set_attributes(Channel_, Dict("ID"=>"Channel:0:0", "SamplesPerPixel"=>"1")); LightPath =new_child(Channel_, "LightPath"); for i in 1:SizeC*SizeZ*SizeT new_child(Pixels, "TiffData") end """ for c in 0:SizeC-1 for t in 0:SizeT-1 for z in 0:SizeZ-1 TiffData = new_child(Pixels, "TiffData") TiffData_attributes = Dict("FirstC"=>c, "FirstT"=>t, "FirstZ"=>z, "IFD"=>t*SizeZ+z,"PlaneCount"=>1) set_attributes(TiffData, TiffData_attributes) end end end """ tiffXML; end """ Embed OME-XML to tiff with XYZT informance """ function embedxml(SizeX::Integer, SizeY::Integer, SizeZ::Integer, SizeT::Integer, img_name::String) tiffxml = generatexml(SizeX, SizeY, SizeZ, SizeT); print("Embedding OME-XML") #run(`/home/hf/Bin/bftools/tiffcomment -set $tiffxml $img_name`); if Sys.which("exiftool") == Nothing print("Please install exiftool") else run(`exiftool -ImageDescription=$tiffxml $img_name -overwrite_original`, wait=false) #run(pipeline(`echo $tiffxml`, `exiftool -ImageDescription\<= $img_name -overwrite_original`), wait=false) #run(`ulimit -s 65536 ; exiftool -ImageDescription\<= $img_name -overwrite_original`, wait=true) # run in backgroud to continue next computing work # I assume that run computing takes more time to embed xml #run(`rm $img_name"_original"`) end println("") nothing; end
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44872, 7203, 4943, 198, 220, 220, 220, 2147, 26, 198, 437, 198 ]
2.244516
1,550
rootbase = pwd() function webserverpath(folder::AbstractString) global rootbase = joinpath(pwd(),folder) end """ File(file::String) Return a file content located in "rootbase" """ function File(file::String) return File("", file) end """ File(folder::String, file::String) Return a file content located in "joinpath(rootbase, folder)" """ function File(folder::String, file::String) path = joinpath(rootbase, folder, file) f = open(path) return read(f, String) end """ files(roop::String, file::String) Create a GET route to file """ function files(folder::String, file::String) extension="text/plain" ext= split(file,".") if(length(ext)>1) my_extension = ext[end] if (haskey(mimetypes, my_extension)) extension = mimetypes[my_extension] end end data = File(folder, file) routefile = replace(joinpath(folder,file),"\\" => "/") Get( string("/",routefile) , (request,HTTP)->(begin HTTP.Response(200 , HTTP.mkheaders(["Content-Type" => extension]) , body=data) end) ) end """ Create routes to files inside "rootbase" """ function WebServer(folder::String, exten::String) path = joinpath(rootbase, folder) ls= readdir(path) for i = 1:length(ls) if isfile( joinpath(path, ls[i]) ) ext = split(ls[i], ".") if length(ext) > 1 && ext[1] != "" && occursin(exten, ext[end] ) files(folder,ls[i]) end elseif isdir( joinpath(path, ls[i]) ) WebServer(joinpath(folder, ls[i]) ,exten) end end end function webserverfiles(load::AbstractString) if load == "*" WebServer("", "") else WebServer("", load) end end
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2.415385
715
<reponame>vikashdwd/JuML struct FileCovariate{T<:AbstractFloat} <: AbstractCovariate{T} name::String length::Int64 datapath::String end Base.length(covariate::FileCovariate{T}) where {T<:AbstractFloat} = covariate.length function slice(covariate::FileCovariate{T}, fromobs::Integer, toobs::Integer, slicelength::Integer) where {T<:AbstractFloat} #len = toobs - fromobs + 1 slicelength = verifyslicelength(fromobs, toobs, slicelength) sizeT = sizeof(T) iostream = open(covariate.datapath) seek(iostream, sizeT * (fromobs - 1)) buffer = Vector{T}(slicelength) map(Seq(Vector{T}, (buffer, iostream, fromobs, toobs, slicelength), nextdatachunk), SubArray{T,1,Array{T,1},Tuple{UnitRange{Int64}},true}) do slice view(slice, 1:length(slice)) end #data = Vector{UInt8}(sizeof(T) * len) # open(covariate.datapath) do f # seek(f, sizeof(T) * (fromobs - 1)) # readbytes!(f, data) # end # covdata = reinterpret(T, data) # slice(covdata, 1, len, slicelength) end function Base.map(covariate::FileCovariate, dataframe::AbstractDataFrame) dataframe[getname(covariate)] end
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<gh_stars>1-10 using ApproxFun using SpecialExponentialFamilies using Test @testset "SpecialExponentialFamilies.jl" begin include("test_twintower.jl") end
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<reponame>brenhinkeller/NaNStatistics.jl function _nancov(x::AbstractVector, y::AbstractVector, corrected::Bool, μᵪ::Number, μᵧ::Number) # Calculate covariance σᵪᵧ = ∅ = zero(promote_type(typeof(μᵪ), typeof(μᵧ), Int)) n = 0 @turbo for i ∈ indices((x,y)) δᵪ = x[i] - μᵪ δᵧ = y[i] - μᵧ δ² = δᵪ * δᵧ notnan = δ²==δ² n += notnan σᵪᵧ += ifelse(notnan, δ², ∅) end σᵪᵧ = σᵪᵧ / (n-corrected) return σᵪᵧ end """ ```julia nancov(x::AbstractVector, y::AbstractVector; corrected::Bool=true) ``` Compute the covariance between the vectors `x` and `y`. As `Statistics.cov`, but ignoring `NaN`s. If `corrected` is `true` as is the default, _Bessel's correction_ will be applied, such that the sum is scaled by `n-1` rather than `n`, where `n = length(x)`. """ function nancov(x::AbstractVector, y::AbstractVector; corrected::Bool=true) # Check lengths nᵪ = length(x) nᵧ = length(y) @assert nᵪ == nᵧ μᵪ = _nanmean(x,:) μᵧ = _nanmean(y,:) σᵪᵧ = _nancov(x, y, corrected, μᵪ, μᵧ) return σᵪᵧ end """ ```julia nancov(X::AbstractMatrix; dims::Int=1, corrected::Bool=true) ``` Compute the covariance matrix of the matrix `X`, along dimension `dims`. As `Statistics.cov`, but ignoring `NaN`s. If `corrected` is `true` as is the default, _Bessel's correction_ will be applied, such that the sum is scaled by `n-1` rather than `n`, where `n = length(x)`. """ function nancov(X::AbstractMatrix; dims::Int=1, corrected::Bool=true) Tₒ = Base.promote_op(/, eltype(X), Int) n = size(X, dims) m = size(X, mod(dims,2)+1) Σ = similar(X, Tₒ, (m, m)) # Only two dimensions are possible, so handle each manually if dims == 1 # Precalculate means for each column μ = ntuple(m) do d nanmean(view(X,:,d)) end # Fill covariance matrix symmetrically @inbounds for i = 1:m for j = 1:i σᵢⱼ = _nancov(view(X,:,i), view(X,:,j), corrected, μ[i], μ[j]) Σ[i,j] = Σ[j,i] = σᵢⱼ end end elseif dims == 2 # Precalculate means for each row μ = ntuple(m) do d nanmean(view(X,d,:)) end # Fill covariance matrix symmetrically @inbounds for i = 1:m for j = 1:i σᵢⱼ = _nancov(view(X,i,:), view(X,j,:), corrected, μ[i], μ[j]) Σ[i,j] = Σ[j,i] = σᵢⱼ end end else throw("Dimension not in range") end return Σ end export nancov """ ```julia nancor(x::AbstractVector, y::AbstractVector) ``` Compute the (Pearson's product-moment) correlation between the vectors `x` and `y`. As `Statistics.cor`, but ignoring `NaN`s. Equivalent to `nancov(x,y) / (nanstd(x) * nanstd(y))`. """ function nancor(x::AbstractVector, y::AbstractVector; corrected::Bool=true) # Check lengths nᵪ = length(x) nᵧ = length(y) @assert nᵪ == nᵧ μᵪ = nanmean(x) μᵧ = nanmean(y) σᵪ = nanstd(x, mean=μᵪ, corrected=corrected) σᵧ = nanstd(y, mean=μᵧ, corrected=corrected) σᵪᵧ = _nancov(x, y, corrected, μᵪ, μᵧ) ρᵪᵧ = σᵪᵧ / (σᵪ * σᵧ) return ρᵪᵧ end """ ```julia nancor(X::AbstractMatrix; dims::Int=1) ``` Compute the (Pearson's product-moment) correlation matrix of the matrix `X`, along dimension `dims`. As `Statistics.cor`, but ignoring `NaN`s. """ function nancor(X::AbstractMatrix; dims::Int=1, corrected::Bool=true) Tₒ = Base.promote_op(/, eltype(X), Int) n = size(X, dims) m = size(X, mod(dims,2)+1) Ρ = similar(X, Tₒ, (m, m)) # Diagonal must be unity @inbounds for i = 1:m Ρ[i,i] = one(Tₒ) end # Only two dimensions are possible, so handle each manually if dims == 1 # Precalculate means and standard deviations μ = ntuple(m) do d nanmean(view(X,:,d)) end σ = ntuple(m) do d nanstd(view(X,:,d), mean=μ[d], corrected=corrected) end # Fill off-diagonals symmetrically @inbounds for i = 1:m for j = 1:i σᵢⱼ = _nancov(view(X,:,i), view(X,:,j), corrected, μ[i], μ[j]) Ρ[i,j] = Ρ[j,i] = σᵢⱼ / (σ[i] * σ[j]) end end elseif dims == 2 # Precalculate means and standard deviations μ = ntuple(m) do d nanmean(view(X,d,:)) end σ = ntuple(m) do d nanstd(view(X,d,:), mean=μ[d], corrected=corrected) end @inbounds for i = 1:m for j = 1:i-1 σᵢⱼ = _nancov(view(X,i,:), view(X,j,:), corrected, μ[i], μ[j]) Ρ[i,j] = Ρ[j,i] = σᵢⱼ / (σ[i] * σ[j]) end end else throw("Dimension not in range") end return Ρ end export nancor
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# To update the reference data, run # julia --project=. update_reference_data.jl using ParticleDA, HDF5, StableRNGs include(joinpath(@__DIR__, "model", "model.jl")) using .Model reference_file = joinpath(@__DIR__, "reference_data.h5") rm(reference_file; force=true) x_true, _, _ = ParticleDA.run_particle_filter(Model.init, joinpath(@__DIR__, "integration_test_1.yaml"), BootstrapFilter()) h5write(reference_file, "integration_test_1", x_true) x_true, _, _ = ParticleDA.run_particle_filter(Model.init, joinpath(@__DIR__, "integration_test_2.yaml"), BootstrapFilter()) h5write(reference_file, "integration_test_2", x_true) rng = StableRNG(123) _, x_avg, _ = ParticleDA.run_particle_filter(Model.init, joinpath(@__DIR__, "integration_test_4.yaml"), BootstrapFilter(); rng) h5write(reference_file, "integration_test_4", x_avg) rng = StableRNG(123) _, x_avg, _ = ParticleDA.run_particle_filter(Model.init, joinpath(@__DIR__, "optimal_filter_test_1.yaml"), OptimalFilter(); rng) h5write(reference_file, "integration_test_6", x_avg)
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""" Get global settings. """ function get_options() opt = Dict() opt["base_folder"] = "/data02/Ican/vic_sim/past_1km" opt["target_folder"] = "/data02/Ican/vic_sim/jan_past_1031" opt["eval_folder"] = "/data02/Ican/vic_sim/jan_eval_1031" opt["stat_sel"] = ["191.2", "122.11", "2.32", "2.279", "224.1", "12.70", "62.5", "22.4"] opt["resolutions"] = ["1km", "5km", "10km", "25km", "50km"] opt["startyear"] = 1982 opt["endyear"] = 2012 opt["timestep"] = 3 opt["output_force"] = "FALSE" opt["full_energy"] = "FALSE" opt["output_binary"] = "TRUE" return opt end """ Get wsh_info file. """ function get_wsh_info() @load Pkg.dir("IcanProj", "data", "wsh_info.jld2") wsh_info return wsh_info end
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<filename>src/renderers/json.jl # module Bukdu import JSON function render(T::Type{Val{:JSON}}, obj::Any)::Conn filtering(render,T,obj) do Conn(200, Dict("Content-Type"=>"application/json"), JSON.json(obj)) end end
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<reponame>Asaber5492/MCPhylo.jl #################### Empirical Move #################### #################### Types and Constructors #################### mutable struct EmpiricalTune <: SamplerTune samplerfun::Union{Function, Missing} width::Int64 replacement::Bool EmpiricalTune() = new() EmpiricalTune(samplerfun::Union{Function, Missing}, width::Int64, replacement::Bool=false)= new(samplerfun, width, false) end EmpiricalTune(x::Vector, width::Int64, replacement::Bool=false)= EmpiricalTune(missing, width, replacement) const EmpiricalVariate = SamplerVariate{EmpiricalTune} #################### Sampler Constructor #################### """ function Empirical(params::ElementOrVector{Symbol}, width::Int64; args...) This Sampler generates Samples from a distribution by selecting a random value from this distribution. The move will always be accepted. """ function Empirical(params::ElementOrVector{Symbol}, width::Int64; args...) samplerfx = function(model::Model, block::Integer) block = SamplingBlock(model, block) v = SamplerVariate(block, width, args...) sample!(v, x -> rand!(block, x)) relist(block, v) end Sampler(params, samplerfx, EmpiricalTune()) end sample!(v::EmpiricalVariate) = sample(v, v.tune.samplerfun) function sample!(v::EmpiricalVariate, sampler_func::Function) width = v.tune.width sam = sampler_func(width) if width > 1 && v.tune.replacement while length(Set(sam)) != width sam = sampler_func(width) end end v[:] = sam v end
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<filename>test/runtests.jl using MaximumLikelihoodPower const MLE = MaximumLikelihoodPower import MaximumLikelihoodPower.Example using Test include("testmle.jl")
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export PostponeScheduler import DataStructures: Queue, enqueue!, dequeue! import Base: show, similar, wait abstract type AbstractPostponedAction end ## struct PostponeScheduler <: AbstractScheduler postponed_actions :: Queue{AbstractPostponedAction} PostponeScheduler() = new(Queue{AbstractPostponedAction}()) end Base.show(io::IO, ::PostponeScheduler) = print(io, "PostponeScheduler()") Base.similar(::PostponeScheduler) = PostponeScheduler() ## mutable struct PostponedSubscriptionProps is_subscribed :: Bool is_unsubscribed :: Bool subscription :: Teardown PostponedSubscriptionProps() = new(false, false, voidTeardown) end ## struct PostponedSubscriptionAction <: AbstractPostponedAction scheduler :: PostponeScheduler source actor props :: PostponedSubscriptionProps end function release!(action::PostponedSubscriptionAction) if !action.props.is_unsubscribed && !action.props.is_subscribed action.props.subscription = on_subscribe!(action.source, action.actor, action.scheduler) action.props.is_subscribed = true end end ## struct PostponedUnsubscriptionAction <: AbstractPostponedAction props :: PostponedSubscriptionProps end function release!(action::PostponedUnsubscriptionAction) if !action.props.is_unsubscribed && action.props.is_subscribed unsubscribe!(action.props.subscription) action.props.is_unsubscribed = true end end ## struct PostponedNextAction <: AbstractPostponedAction actor data end release!(action::PostponedNextAction) = next!(action.actor, action.data) ## struct PostponedErrorAction <: AbstractPostponedAction actor error end release!(action::PostponedErrorAction) = error!(action.actor, action.error) ## struct PostponedCompleteAction <: AbstractPostponedAction actor end release!(action::PostponedCompleteAction) = complete!(action.actor) ## function release!(scheduler::PostponeScheduler) index = 1 n = length(getactions(scheduler)) while index <= n action = dequeue!(getactions(scheduler)) release!(action) index += 1 end end function Base.wait(scheduler::PostponeScheduler) while length(getactions(scheduler)) !== 0 release!(scheduler) end end makeinstance(::Type, scheduler::PostponeScheduler) = scheduler instancetype(::Type, ::Type{ <: PostponeScheduler }) = PostponeScheduler getactions(scheduler::PostponeScheduler) = scheduler.postponed_actions scheduled_next!(actor, data, scheduler::PostponeScheduler) = enqueue!(getactions(scheduler), PostponedNextAction(actor, data)) scheduled_error!(actor, error, scheduler::PostponeScheduler) = enqueue!(getactions(scheduler), PostponedErrorAction(actor, error)) scheduled_complete!(actor, scheduler::PostponeScheduler) = enqueue!(getactions(scheduler), PostponedCompleteAction(actor)) function scheduled_subscription!(source, actor, scheduler::PostponeScheduler) postponed_subscription_props = PostponedSubscriptionProps() enqueue!(getactions(scheduler), PostponedSubscriptionAction(scheduler, source, actor, postponed_subscription_props)) return PostponedSubscription(scheduler, postponed_subscription_props) end struct PostponedSubscription <: Teardown scheduler :: PostponeScheduler props :: PostponedSubscriptionProps end as_teardown(::Type{ <: PostponedSubscription }) = UnsubscribableTeardownLogic() function on_unsubscribe!(subscription::PostponedSubscription) enqueue!(getactions(subscription.scheduler), PostponedUnsubscriptionAction(subscription.props)) return nothing end
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<filename>src/environments/3rd_party/structs.jl struct GymEnv{T,Ta,To,P} <: AbstractEnv pyenv::P observation_space::To action_space::Ta state::P end export GymEnv mutable struct AtariEnv{IsGrayScale,TerminalOnLifeLoss,N,S<:AbstractRNG} <: AbstractEnv ale::Ptr{Nothing} name::String screens::Tuple{Array{UInt8,N},Array{UInt8,N}} # for max-pooling actions::Vector{Int} action_space::Base.OneTo{Int} observation_space::Space{Array{ClosedInterval{UInt8},N}} noopmax::Int frame_skip::Int reward::Float32 lives::Int rng::S end export AtariEnv mutable struct OpenSpielEnv{S,G} <: AbstractEnv state::S game::G end export OpenSpielEnv # ??? can we safely ignore the `game` field here Base.hash(e::OpenSpielEnv, h::UInt) = hash(e.state, h) Base.:(==)(e::OpenSpielEnv, ee::OpenSpielEnv) = e.state == ee.state mutable struct SnakeGameEnv{A,N,G} <: AbstractEnv game::G latest_snakes_length::Vector{Int} latest_actions::Vector{CartesianIndex{2}} is_terminated::Bool end export SnakeGameEnv
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<gh_stars>0 # indicator of the free cone """ IndFree() Returns the indicator function of the whole space, or "free cone", i.e., a function which is identically zero. """ immutable IndFree <: IndicatorConvexCone end """ Zero() Returns the identically zero function. """ typealias Zero IndFree function (f::IndFree)(x::AbstractArray) return 0.0 end function prox!(f::IndFree, x::AbstractArray, y::AbstractArray, gamma::Real=1.0) y[:] = x return 0.0 end fun_name(f::IndFree) = "indicator of the free cone" fun_dom(f::IndFree) = "AbstractArray{Real}, AbstractArray{Complex}" fun_expr(f::IndFree) = "x ↦ 0" fun_params(f::IndFree) = "none" function prox_naive(f::IndFree, x::AbstractArray, gamma::Real=1.0) return x, 0.0 end
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<reponame>glwagner/OceanLES.jl """ VelocityFields(arch, grid) Return a NamedTuple with fields `u`, `v`, `w` initialized on the architecture `arch` and `grid`. """ function VelocityFields(arch, grid) u = FaceFieldX(arch, grid) v = FaceFieldY(arch, grid) w = FaceFieldZ(arch, grid) return (u=u, v=v, w=w) end """ TracerFields(arch, grid) Return a NamedTuple with tracer fields initialized as `CellField`s on the architecture `arch` and `grid`. """ function TracerFields(arch, grid, tracernames) tracerfields = Tuple(CellField(arch, grid) for c in tracernames) return NamedTuple{tracernames}(tracerfields) end TracerFields(arch, grid, ::Union{Tuple{}, Nothing}) = NamedTuple{()}(()) TracerFields(arch, grid, tracer::Symbol) = TracerFields(arch, grid, tuple(tracer)) TracerFields(arch, grid, tracers::NamedTuple) = tracers tracernames(::Nothing) = () tracernames(name::Symbol) = tuple(name) tracernames(names::NTuple{N, Symbol}) where N = :u ∈ names ? names[4:end] : names tracernames(::NamedTuple{names}) where names = tracernames(names) """ PressureFields(arch, grid) Return a NamedTuple with pressure fields `pHY′` and `pNHS` initialized as `CellField`s on the architecture `arch` and `grid`. """ function PressureFields(arch, grid) pHY′ = CellField(arch, grid) pNHS = CellField(arch, grid) return (pHY′=pHY′, pNHS=pNHS) end """ Tendencies(arch, grid, tracernames) Return a NamedTuple with tendencies for all solution fields (velocity fields and tracer fields), initialized on the architecture `arch` and `grid`. """ function Tendencies(arch, grid, tracernames) velocities = (u = FaceFieldX(arch, grid), v = FaceFieldY(arch, grid), w = FaceFieldZ(arch, grid)) tracers = TracerFields(arch, grid, tracernames) return merge(velocities, tracers) end
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<gh_stars>10-100 struct CG{T <: AbstractFloat} u::Vector{T} r::Vector{T} c::Vector{T} function CG{T}(dim_b::Integer, dim_x::Integer) where {T <: AbstractFloat} new{T}(zeros(T, dim_b), zeros(T, dim_b), zeros(T, dim_x)) end end CG{T}(dims_A::Dims{2}) where {T} = CG{T}(dims_A...) function (cg::CG{T})(x, A, b; tol::Real = sqrt(eps(real(eltype(b)))), maxiter::Int = size(A, 2), initiallyzero::Bool = false) where {T} if !(T == eltype(x) == eltype(A) == eltype(b)) @warn "eltype mismatch: expected x, A, and b to have eltype $T. CG may run slower as a result" maxlog=1 error() end d1, d2 = size(A) u = cg.u r = cg.r c = cg.c if !(length(r) == d1 && length(u) == length(c) == d2) resize!(u, d2) resize!(r, d1) resize!(c, d2) end fill!(u, zero(T)) copyto!(r, b) # do not need to zero out c as e overwrite it below if initiallyzero # ignore x and treat x0 = 0 --> r0 = b residual = norm(b) reltol = residual * tol else # x0 = x --> r0 = b - A*x0 mul!(c, A, x) # A*x0 r .-= c # b - A*x0 residual = norm(r) reltol = norm(b) * tol # TODO norm(b) or norm(r)? end prev_residual = one(residual) for _ = 1:maxiter residual <= reltol && break β = residual^2 / prev_residual^2 u .= r .+ β .* u mul!(c, A, u) α = residual^2 / dot(u, c) # Improve solution and residual x .+= α .* u r .-= α .* c prev_residual = residual residual = norm(r) end x end
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<gh_stars>0 #!/usr/bin/env julia const prgm = basename(Base.source_path()) if length(ARGS) < 2 println("usage: ", prgm, " <file> [line]...") exit(1) end file = ARGS[1] const numbers = map(x -> begin try parse(Uint, x) catch println(prgm, ": ", x, ": not a number") exit(1) end end, ARGS[2:end]) f = open(file) lines = readlines(f) close(f) if maximum(numbers) > length(lines) println(prgm, ": detected extraneous line number") exit(1) end deleteat!(lines, sort(unique(numbers))) f = open(file, "w") write(f, join(lines)) close(f)
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