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struct InterfaceOperators operators::Any celltooperator::Any ncells::Any numoperators::Any function InterfaceOperators(operators, celltooperator) ncells = length(celltooperator) numcouples, numoperators = size(operators) @assert numcouples == 4 @assert all(celltooperator .>= 0) @assert all(celltooperator .<= numoperators) new(operators, celltooperator, ncells, numoperators) end end function Base.getindex(interfaceoperator::InterfaceOperators, s1, s2, cellid) row = cell_couple_sign_to_row(s1, s2) idx = interfaceoperator.celltooperator[cellid] idx > 0 || error("Cell $cellid does not have an operator") return interfaceoperator.operators[row, idx] end function Base.show(io::IO, interfaceoperator::InterfaceOperators) ncells = number_of_cells(interfaceoperator) nops = number_of_operators(interfaceoperator) str = "InterfaceOperator\n\tNum. Cells: $ncells\n\tNum. Operators: $nops" print(io, str) end function number_of_cells(interfaceoperator::InterfaceOperators) return interfaceoperator.ncells end function number_of_operators(interfaceoperator::InterfaceOperators) return interfaceoperator.numoperators end function interface_mass_operators(basis, interfacequads, cellmap, cellsign, penalty) ncells = length(cellsign) hasinterface = cellsign .== 0 numinterfaces = count(hasinterface) operators = Matrix{Any}(undef, 4, numinterfaces) celltooperator = zeros(Int, ncells) dim = dimension(basis) cellids = findall(hasinterface) for (idx, cellid) in enumerate(cellids) normal = interface_normals(interfacequads, cellid) facescale = scale_area(cellmap, normal) for s1 in [+1, -1] quad1 = interfacequads[s1, cellid] for s2 in [+1, -1] row = cell_couple_sign_to_row(s1, s2) quad2 = interfacequads[s2, cellid] mass = penalty * interface_mass_matrix(basis, quad1, quad2, facescale) operators[row, idx] = mass end end celltooperator[cellid] = idx end return InterfaceOperators(operators, celltooperator) end function interface_mass_operators(basis, interfacequads, cutmesh, penalty) cellmap = cell_map(cutmesh, 1) cellsign = cell_sign(cutmesh) return interface_mass_operators(basis, interfacequads, cellmap, cellsign, penalty) end function interface_incoherent_mass_operators( basis, interfacequads, cellmap, cellsign, penalty, ) ncells = length(cellsign) hasinterface = cellsign .== 0 numinterfaces = count(hasinterface) operators = Matrix{Any}(undef, 4, numinterfaces) celltooperator = zeros(Int, ncells) dim = dimension(basis) cellids = findall(hasinterface) for (idx, cellid) in enumerate(cellids) normal = interface_normals(interfacequads, cellid) components = normal facescale = scale_area(cellmap, normal) for s1 in [+1, -1] quad1 = interfacequads[s1, cellid] for s2 in [+1, -1] row = cell_couple_sign_to_row(s1, s2) quad2 = interfacequads[s2, cellid] mass = penalty * interface_component_mass_matrix( basis, quad1, quad2, components, facescale, ) operators[row, idx] = mass end end celltooperator[cellid] = idx end return InterfaceOperators(operators, celltooperator) end function interface_incoherent_mass_operators(basis, interfacequads, cutmesh, penalty) cellmap = cell_map(cutmesh, 1) cellsign = cell_sign(cutmesh) return interface_incoherent_mass_operators( basis, interfacequads, cellmap, cellsign, penalty, ) end function interface_traction_operators(basis, interfacequads, stiffness, cellmap, cellsign) ncells = length(cellsign) hasinterface = cellsign .== 0 numinterfaces = count(hasinterface) operators = Matrix{Any}(undef, 4, numinterfaces) celltooperator = zeros(Int, ncells) cellids = findall(hasinterface) for (idx, cellid) in enumerate(cellids) normal = interface_normals(interfacequads, cellid) for s1 in [+1, -1] quad1 = interfacequads[s1, cellid] for s2 in [+1, -1] row = cell_couple_sign_to_row(s1, s2) quad2 = interfacequads[s2, cellid] top = coherent_traction_operator( basis, quad1, quad2, normal, stiffness[s2], cellmap, ) operators[row, idx] = top end end celltooperator[cellid] = idx end return InterfaceOperators(operators, celltooperator) end function interface_traction_operators(basis, interfacequads, stiffness, cutmesh) cellmap = cell_map(cutmesh, 1) cellsign = cell_sign(cutmesh) return interface_traction_operators(basis, interfacequads, stiffness, cellmap, cellsign) end function interface_incoherent_traction_operators(basis, interfacequads, stiffness, cutmesh) cellsign = cell_sign(cutmesh) cellmap = cell_map(cutmesh, 1) ncells = length(cellsign) hasinterface = cellsign .== 0 numinterfaces = count(hasinterface) operators = Matrix{Any}(undef, 4, numinterfaces) celltooperator = zeros(Int, ncells) cellids = findall(hasinterface) for (idx, cellid) in enumerate(cellids) normals = interface_normals(interfacequads, cellid) for s1 in [+1, -1] quad1 = interfacequads[s1, cellid] for s2 in [+1, -1] row = cell_couple_sign_to_row(s1, s2) quad2 = interfacequads[s2, cellid] top = incoherent_traction_operator( basis, quad1, quad2, normals, stiffness[s2], cellmap, ) operators[row, idx] = top end end celltooperator[cellid] = idx end return InterfaceOperators(operators, celltooperator) end struct InterfaceCondition tractionoperator::Any massoperator::Any penalty::Any ncells::Any function InterfaceCondition( tractionoperator::InterfaceOperators, massoperator::InterfaceOperators, penalty, ) ncells = number_of_cells(massoperator) @assert number_of_cells(tractionoperator) == ncells new(tractionoperator, massoperator, penalty, ncells) end end function coherent_interface_condition(basis, interfacequads, stiffness, cutmesh, penalty) tractionoperator = interface_traction_operators(basis, interfacequads, stiffness, cutmesh) massoperator = interface_mass_operators(basis, interfacequads, cutmesh, penalty) return InterfaceCondition(tractionoperator, massoperator, penalty) end function incoherent_interface_condition(basis, interfacequads, stiffness, cutmesh, penalty) tractionoperator = interface_incoherent_traction_operators(basis, interfacequads, stiffness, cutmesh) massoperator = interface_incoherent_mass_operators(basis, interfacequads, cutmesh, penalty) return InterfaceCondition(tractionoperator, massoperator, penalty) end function traction_operator(interfacecondition::InterfaceCondition, s1, s2, cellid) return interfacecondition.tractionoperator[s1, s2, cellid] end function mass_operator(interfacecondition::InterfaceCondition, s1, s2, cellid) return interfacecondition.massoperator[s1, s2, cellid] end function Base.show(io::IO, interfacecondition::InterfaceCondition) ncells = interfacecondition.ncells penalty = interfacecondition.penalty str = "InterfaceCondition\n\tNum. Cells: $ncells\n\tDisplacement Penalty: $penalty" print(io, str) end function coherent_traction_operator(basis, quad1, quad2, normals, stiffness, cellmap) numqp = length(quad1) @assert length(quad2) == size(normals)[2] == numqp dim = dimension(basis) nf = number_of_basis_functions(basis) ndofs = dim * nf matrix = zeros(ndofs, ndofs) vectosymmconverter = vector_to_symmetric_matrix_converter() jac = jacobian(cellmap) scalearea = scale_area(cellmap, normals) for qpidx = 1:numqp p1, w1 = quad1[qpidx] p2, w2 = quad2[qpidx] @assert w1 ≈ w2 vals = basis(p1) grad = transform_gradient(gradient(basis, p2), jac) normal = normals[:, qpidx] NK = sum([make_row_matrix(vectosymmconverter[k], grad[:, k]) for k = 1:dim]) N = sum([normal[k] * vectosymmconverter[k]' for k = 1:dim]) NI = interpolation_matrix(vals, dim) matrix .+= NI' * N * stiffness * NK * scalearea[qpidx] * w1 end return matrix end function component_traction_operator( basis, quad1, quad2, components, normals, stiffness, cellmap, ) numqp = length(quad1) @assert length(quad2) == size(normals)[2] == size(components)[2] == numqp dim = dimension(basis) nf = number_of_basis_functions(basis) ndofs = dim * nf matrix = zeros(ndofs, ndofs) vectosymmconverter = vector_to_symmetric_matrix_converter() jac = jacobian(cellmap) scalearea = scale_area(cellmap, normals) for qpidx = 1:numqp p1, w1 = quad1[qpidx] p2, w2 = quad2[qpidx] @assert w1 ≈ w2 vals = basis(p1) grad = transform_gradient(gradient(basis, p2), jac) normal = normals[:, qpidx] component = components[:, qpidx] projector = component * component' NK = sum([make_row_matrix(vectosymmconverter[k], grad[:, k]) for k = 1:dim]) N = sum([normal[k] * vectosymmconverter[k]' for k = 1:dim]) NI = interpolation_matrix(vals, dim) matrix .+= NI' * projector * N * stiffness * NK * scalearea[qpidx] * w1 end return matrix end function incoherent_traction_operator(basis, quad1, quad2, normals, stiffness, cellmap) components = normals return component_traction_operator( basis, quad1, quad2, components, normals, stiffness, cellmap, ) end function interface_mass_matrix(basis, quad1, quad2, scale) numqp = length(quad1) @assert length(quad2) == length(scale) == numqp nf = number_of_basis_functions(basis) dim = dimension(basis) totaldofs = dim * nf matrix = zeros(totaldofs, totaldofs) for qpidx = 1:numqp p1, w1 = quad1[qpidx] p2, w2 = quad2[qpidx] @assert w1 ≈ w2 vals1 = basis(p1) vals2 = basis(p2) NI1 = interpolation_matrix(vals1, dim) NI2 = interpolation_matrix(vals2, dim) matrix .+= NI1' * NI2 * scale[qpidx] * w1 end return matrix end function interface_component_mass_matrix(basis, quad1, quad2, components, scale) numqp = length(quad1) @assert length(quad2) == length(scale) == size(components)[2] == numqp nf = number_of_basis_functions(basis) dim = dimension(basis) totaldofs = dim * nf matrix = zeros(totaldofs, totaldofs) for qpidx = 1:numqp p1, w1 = quad1[qpidx] p2, w2 = quad2[qpidx] @assert w1 ≈ w2 component = components[:, qpidx] projector = component * component' vals1 = basis(p1) vals2 = basis(p2) NI1 = interpolation_matrix(vals1, dim) NI2 = make_row_matrix(projector, vals2) matrix .+= NI1' * NI2 * scale[qpidx] * w1 end return matrix end
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# Script using Distributions, PyPlot, BayesianNonparametricStatistics β=0.5 θ = sumoffunctions(vcat([faberschauderone],[faberschauder(j,k) for j in 0:4 for k in 1:2^j]),vcat([1.0],[(-1)^(j*k)*2^(-β*j) for j in 0:4 for k in 1:2^j])) x = 0.0:0.001:1.0 y = θ.(x) # Uncomment the following lines to plot θ. # clf() # plot(x,y) sde = SDEWithConstantVariance(θ, 1.0, 0.0, 1000.0, 0.01) X = rand(sde) # Uncomment the following lines to plot a sample from sde. # clf() # plot(X) M = SDEModel(1.0, 0.0) Π = FaberSchauderExpansionWithGaussianCoefficients([2^(β*j) for j in 0:4]) postΠ = calculateposterior(Π, X, M ) for k in 1:100 f = rand(postΠ) y = f.(x) plot(x,y) end
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<reponame>JuliaPackageMirrors/Seismic.jl<filename>src/Wavelets/Berlage.jl<gh_stars>0 """ Berlage(; <keyword arguments>) Create a Berlage wavelet. # Arguments **Keyword arguments** * `dt::Real=0.002`: sampling interval in secs. * `f0::Real=20.0`: central frequency in Hz. * `m::Real=2`: exponential parameter of Berlage wavelet. * `alpha::Real=180.0`: alpha parameter of Berlage wavelet in rad/secs. * `phi0::Real`: phase rotation in radians. # Example ```julia julia> w = Berlage(); plot(w); ``` **Reference** * Aldridge, <NAME>., 1990, The berlage wavelet: GEOPHYSICS, 55, 1508--1511. """ function Berlage(; dt::Real=0.002, f0::Real=20.0, m::Real=2, alpha::Real=180.0, phi0::Real=0.0) nw = floor(Int, 2.2/(f0*dt)) t = dt*collect(0:1:nw-1) w = (t.^m).*exp(-alpha*t).*cos(2*pi*f0*t + phi0); w = w/maximum(w) end
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struct Lennnon2000Air <: ThermoState.ThermoModel end const TAU_MAX_EXP_87 = 0.4207493606569795 const lemmon2000_air_R = 8.314510 const lemmon2000_air_T_reducing = 132.6312 const lemmon2000_air_P_reducing = 3.78502E6 const lemmon2000_air_rho_reducing = 10447.7 const lemmon2000_air_rho_reducing_inv = 1.0/lemmon2000_air_rho_reducing const lemmon2000_air_MW = 28.9586 const lemmon2000_air_P_max = 2000E6 const lemmon2000_air_T_max = 2000. molecular_weight(::Lennnon2000Air) = lemmon2000_air_MW function _f0(::Lennnon2000Air, delta,tau) tau_inv = one(tau)/tau A0 = (-0.00019536342*tau*sqrt(tau) + 17.275266575*tau + tau_inv*(tau_inv*(6.057194e-8*tau_inv - 0.0000210274769) - 0.000158860716) + log(delta) + 2.490888032*log(tau) # These two logs both fail for tau < 1e-18, can be truncated but should not be necessary. + 0.791309509*log(1.0 - exp(-25.36365*tau)) + 0.212236768*log(1.0 - exp(-16.90741*tau)) - 13.841928076) if tau < TAU_MAX_EXP_87 A0 -= 0.197938904*log(exp(87.31279*tau) + (2.0/3.0)) else A0 -= 17.282597957782162*tau # 17.282... = 87.31279*0.197938904 return A0 end end function _fr(::Lennnon2000Air,delta,tau) delta2 = delta*delta delta3 = delta*delta2 delta4 = delta2*delta2 delta5 = delta*delta4 delta6 = delta2*delta4 taurt2 = sqrt(tau) taurt4 = sqrt(taurt2) tau2 = tau*tau tau3 = tau*tau2 tau6 = tau3*tau3 tau12 = tau6*tau6 tau_100 = tau^0.01 tau2_100 = tau_100*tau_100 tau4_100 = tau2_100*tau2_100 tau5_100 = tau_100*tau4_100 tau10_100 = tau5_100*tau5_100 tau15_100 = tau5_100*tau10_100 tau8_100 = tau4_100*tau4_100 tau16_100 = tau8_100*tau8_100 tau20_100 = tau4_100*tau16_100 tau32_100 = tau16_100*tau16_100 tau33_100 = tau_100*tau32_100 tau64_100 = tau32_100*tau32_100 tau80_100 = tau16_100*tau64_100 tau40_100 = tau20_100*tau20_100 tau97_100 = tau33_100*tau64_100 tau45_100 = tau5_100*tau40_100 tau90_100 = tau45_100*tau45_100 tau160_100 = tau80_100*tau80_100 tau320_100 = tau160_100*tau160_100 x0 = exp(-delta) x1 = exp(-delta2) x2 = tau3*exp(-delta3) return (-0.101365037911999994*delta*tau160_100*x0 + 0.713116392079000017*delta*tau33_100 - 0.146629609712999986*delta*tau40_100*x1*tau320_100 - 1.61824192067000006*delta*tau4_100*tau97_100 + 0.0148287891978000005*delta*taurt2*x2 + 0.118160747228999996*delta + 0.0714140178971000017*delta2 + 0.134211176704000013*delta3*tau15_100 - 0.031605587982100003*delta3*tau6*x1 - 0.17381369096999999*delta3*tau80_100*x0 - 0.00938782884667000057*delta3*x2*tau12 - 0.0865421396646000041*delta3 - 0.042053322884200002*delta4*tau20_100 + 0.0349008431981999989*delta4*tau2_100*tau33_100 + 0.0112626704218000001*delta4 - 0.0472103183731000034*delta5*tau15_100*x0*tau80_100 + 0.000233594806141999996*delta5*tau3*taurt4*x1*delta6 - 0.0122523554252999996*delta6*tau*taurt4*x0 + 0.000164957183186000006*delta6*tau45_100*tau90_100) end function αR_impl(mt::SingleVT,::Lennnon2000Air, _rho, T) R = lemmon2000_air_R delta = rho*lemmon2000_air_rho_reducing_inv tau = lemmon2000_air_T_reducing/T return _fr(model, delta, tau) end function α0_impl(mt::SingleVT,::Lennnon2000Air, _rho, T) R = lemmon2000_air_R delta = rho*lemmon2000_air_rho_reducing_inv tau = lemmon2000_air_T_reducing/T return _f0(model, delta, tau) end function mol_helmholtzR_impl(mt::SingleVT,::Lennnon2000Air, v, t) rho = 1.0e-3 / v R = lemmon2000_air_R delta = lemmon2000_air_rho_reducing_inv/v tau = lemmon2000_air_T_reducing/T return R*t*_fr(model, delta, tau) end function mol_helmholtz0_impl(mt::SingleVT,::Lennnon2000Air, v, t) rho = 1.0e-3 / v R = lemmon2000_air_R delta = lemmon2000_air_rho_reducing_inv/v tau = lemmon2000_air_T_reducing/T return R*t*_f0(model, delta, tau) end function mol_helmholtz_impl(mt::SingleVT,::Lennnon2000Air, v, t) rho = 1.0e-3 / v R = lemmon2000_air_R delta = lemmon2000_air_rho_reducing_inv/v tau = lemmon2000_air_T_reducing/T return R*t*(_f0(model, delta, tau)+_fr(model, delta, tau)) end
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1.861965
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<gh_stars>1-10 export SingleLayer """ singleLayer σ(K*s+b) where K,b are trainable weights """ struct SingleLayer end mσ(x::AbstractArray{R}) where R<:Real = abs.(x)+log.(R(1) .+ exp.(-R(2)*abs.(x))) mdσ(x::AbstractArray{R}) where R<:Real = tanh.(x) md2σ(x::AbstractArray{R}) where R<:Real = one(eltype(x)) .- tanh.(x).^2 """ evaluate layer for current weights Θ=(K,b) """ function (N::SingleLayer)(S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (K,b) = Θ return mσ(K*S .+ b) end """ compute matvec J_S N(S,Θ)'*Z """ function getJSTmv(N::SingleLayer,Z::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (K,b) = Θ return K'*(mdσ(K*S .+ b) .* Z) end function getJSTmv(N::SingleLayer,Z::AbstractArray{R,3},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = reshape(mdσ(K*S .+ b),size(K,1),1,nex) t2 = K'*reshape(t1 .* Z, size(K,1),:) return reshape(t2,size(K,2),size(Z,2),nex) end """ compute hessian matvec """ function getTraceHessAndGrad(N::SingleLayer, w::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = K*S .+ b Jac = reshape(mdσ(t1),size(K,1),1,nex) .* K return vec(sum(reshape(md2σ(t1) .* w,size(K,1),:,nex).*(K.^2),dims=(1,2))), Jac end function getTraceHessAndGrad(N::SingleLayer, w::AbstractArray{R},Jac::AbstractArray{R,2},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = K*S .+ b; Jac = K * Jac trH = vec(sum(reshape(md2σ(t1) .* w,size(K,1),:,nex).*(Jac).^2,dims=(1,2))) Jac = reshape(mdσ(t1),size(K,1),1,nex) .* Jac return trH,Jac end function getTraceHessAndGrad(N::SingleLayer, w::AbstractArray{R},Jac::AbstractArray{R,3},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = K*S .+ b; Jac = reshape(K* reshape(Jac,size(K,2),:), size(K,1),:,nex) trH = vec(sum(reshape(md2σ(t1) .* w,size(K,1),:,nex).*(Jac).^2,dims=(1,2))) Jac = reshape(mdσ(t1),size(K,1),1,nex) .* Jac return trH,Jac end function getTraceHess(N::SingleLayer, w::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = K*S .+ b return vec(sum(reshape(md2σ(t1) .* w,size(K,1),:,nex).*(K.^2),dims=(1,2))) end function getTraceHess(N::SingleLayer, w::AbstractArray{R},Jac::AbstractArray{R,2},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = K*S .+ b; Jac = K * Jac trH = vec(sum(reshape(md2σ(t1) .* w,size(K,1),:,nex).*(Jac).^2,dims=(1,2))) return trH end function getTraceHess(N::SingleLayer, w::AbstractArray{R},Jac::AbstractArray{R,3},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = K*S .+ b; Jac = reshape(K* reshape(Jac,size(K,2),:), size(K,1),:,nex) trH = vec(sum(reshape(md2σ(t1) .* w,size(K,1),:,nex).*(Jac).^2,dims=(1,2))) return trH end function getDiagHess(N::SingleLayer, w::AbstractArray{R}, Z::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ return sum((md2σ(K*S .+ b) .* w).*(K*Z).^2,dims=1) end function getHessmv(N::SingleLayer, w::AbstractArray{R}, Z::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ return K'*(md2σ(K*S .+ b) .* w .* (K*Z)) end function getHessmv(N::SingleLayer, w::AbstractArray{R}, Z::AbstractArray{R,3},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ t1 = reshape(md2σ(K*S .+ b).*w,size(K,1),1,nex) t2 = t1 .* reshape(K*reshape(Z,size(K,2),:),size(K,1),:,nex) t2 = K'* reshape(t2,size(K,1),:) return reshape(t2,size(K,2),size(Z,2),nex) end """ compute matvec J_S N(S,Θ)*Z """ function getJSmv(N::SingleLayer,Z::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (K,b) = Θ return mdσ(K*S .+ b) .* (K * Z) end """ compute matvec J_S N(S,Θ)*Z """ function getJSmv(N::SingleLayer,Z::AbstractArray{R,3},S::AbstractArray{R,2},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ # K * Z KZ = K*reshape(Z,size(K,2),:) return reshape(mdσ(K*S .+ b),size(K,1),1,nex) .* reshape(KZ,size(K,1),size(Z,2),nex) end """ compute matvec J_S(J_S N(S,Θ)'*Z(S)) here we use product rule J_S N(S,Θ)'*dZ + J_S(N(S,Θ)'*Zfix) """ function getJSJSTmv(N::SingleLayer,dz::AbstractVector{R},d2z::AbstractArray{R},s::AbstractVector{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (K,b) = Θ t1 = K*s + b ndσ = mdσ(t1) return K'* ( Diagonal(md2σ(t1) .* dz) + ndσ .* d2z .* ndσ') *K end function getJSJSTmv(N::SingleLayer,dZ::AbstractArray{R},d2Z::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (K,b) = Θ t1 = K* S .+ b (d,nex) = size(t1) H1 = getJSJSTmv(N,dZ,S,Θ) t2 = mdσ(t1) H2 = reshape(t2,d,1,:) .* d2Z .* reshape(t2,1,d,:) s1 = K' * reshape(H2,size(K,1),:) s1 = permutedims(reshape(s1,size(K,2),size(K,1),nex),(2,1,3)) s2 = K'*reshape(s1,size(K,1),:) return H1 + permutedims(reshape(s2,size(K,2),size(K,2),nex),(2,1,3)) end function getJSJSTmv(N::SingleLayer,dZ::AbstractArray{R},S::AbstractArray{R,2},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R<:Real (K,b) = Θ t1 = K* S .+ b (d,nex) = size(t1) dZ2 = reshape(dZ .* md2σ(t1),size(dZ,1),1,nex) KtdZK = K'*reshape(dZ2.*K,size(K,1),:) return reshape(KtdZK,size(K,2),size(K,2),nex) end function getGradAndHessian(N::SingleLayer,dZ::AbstractArray{R},S::AbstractArray{R,2},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R<:Real # Here, no d2Z is give, so we assume it is zero (K,b) = Θ t1 = K* S .+ b (d,nex) = size(t1) t2 = reshape(dZ .* md2σ(t1),size(dZ,1),1,nex) KtdZK = K'*reshape(t2.*K,size(K,1),:) H = reshape(KtdZK,size(K,2),size(K,2),nex) return K'*(mdσ(t1) .* dZ),H end function getGradAndHessian(N::SingleLayer,dZ::AbstractArray{R},d2Z::AbstractArray{R},S::AbstractArray{R},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (K,b) = Θ t1 = K * S .+ b (d,nex) = size(t1) t2 = reshape(dZ .* md2σ(t1),size(dZ,1),1,nex) KtdZK = K'*reshape(t2.*K,size(K,1),:) H1 = reshape(KtdZK,size(K,2),size(K,2),nex) dσt = mdσ(t1) t3 = reshape(dσt,d,1,:) .* d2Z .* reshape(dσt,1,d,:) s1 = K' * reshape(t3,size(K,1),:) s1 = permutedims(reshape(s1,size(K,2),size(K,1),nex),(2,1,3)) s2 = K'*reshape(s1,size(K,1),:) H2 = permutedims(reshape(s2,size(K,2),size(K,2),nex),(2,1,3)) return K'*(dσt .* dZ), H1+H2 end function getJSJSTmv(N::SingleLayer,d2Z::AbstractArray{R,3},S::AbstractArray{R,2},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}}) where R <: Real (d,nex) = size(S) (K,b) = Θ # K * Z t1 = reshape(mdσ(K*S .+ b),size(K,1),1,nex) t1 = K'*reshape(t1 .* d2Z, size(K,1),:) t1 = reshape(t1,size(K,2),size(d2Z,2),nex) permutedims!(t1,t1,(2,1,3)) t1 = K'*reshape(t1 .* d2Z, size(K,1),:) t1 = reshape(t1,size(K,2),size(d2Z,2),nex) return t1 end function getJSJSTmv(N::SingleLayer,d2Z::AbstractArray{R,3},S::AbstractArray{R,2},Θ::Tuple{AbstractArray{R,2},AbstractArray{R,1}},hk::R) where R <: Real (d,nex) = size(S) (K,b) = Θ # K * Z t1 = reshape(mdσ(K*S .+ b),size(K,1),1,nex) t1 = K'*reshape(t1 .* d2Z, size(K,1),:) t1 = d2Z + hk .* reshape(t1,size(K,2),size(d2Z,2),nex) permutedims!(t1,t1,(2,1,3)) t1 = K'*reshape(t1 .* d2Z, size(K,1),:) t1 = d2Z + hk .* reshape(t1,size(K,2),size(d2Z,2),nex) return t1 end
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""" parseFunctionNode(nodeDict::Dict) Parses a [`FunctionNode`](@ref) from a node set configuration file. """ function parseFunctionNode(nodeDict::Dict) func = get(nodeDict, "function", false) if func == false error("function field is not set in FunctionNode") else aux = 0 try aux = eval(Meta.parse(func)) catch e error("The following function: '$func' in FunctionNode is not defined") end if typeof(aux) <: Function func = aux else error("The following function: '$func' in FunctionNode is not a function") end end arity = get(nodeDict, "arity", false) if arity == false error("arity field is not set in FunctionNode: '$func'") else if !(typeof(arity) <: Integer) error("arity field ($arity) in FunctionNode '$func' must be an integer number") elseif arity <= 0 error("Arity must be an integer greater than 0 in FunctionNode: '$func'") end end returnType = get(nodeDict, "returnType", "") if !(typeof(returnType) <: String) error("returnType field must be a string in FunctionNode: '$func'") end if returnType != "" aux = 0 try aux = eval(Meta.parse(returnType)) catch e error("The following type: '$returnType' in TerminalNode: '$func' is not defined") end if typeof(aux) <: DataType || typeof(aux) <: Union returnType = aux else error("The following type: '$returnType' in TerminalNode: '$func' is not a type") end else error("Function nodes must have a return type in FunctionNode: '$func'") end argTypes = get(nodeDict, "argumentTypes", false) if argTypes == false error("Function nodes must have a list of argument types in FunctionNode: '$func'") end if arity == 1 && !(typeof(argTypes) <: Array) argTypes = [argTypes] elseif arity > 1 && !(typeof(argTypes) <: Array) error("argumentTypes field must be an array of string when arity is greater than 1 in FunctionNode: '$func'") elseif arity > 1 && !(typeof(argTypes) <: Array) error("argumentTypes field must be an array of string when arity is greater than 1 in FunctionNode: '$func'") end if length(argTypes) != arity error("Number of elements in argumentTypes field must match arity field in FunctionNode: '$func'") end for i=1:length(argTypes) if !(typeof(argTypes[i]) <: String) error("The elements of argumentTypes field must be strings in FunctionNode: '$func'") end try aux = eval(Meta.parse(argTypes[i])) catch e error("The following type: '$(argTypes[i])' in TerminalNode: '$func' is not defined") end if typeof(aux) <: DataType || typeof(aux) <: Union argTypes[i] = aux else error("The following type: '$(argTypes[i])' in TerminalNode: '$func' is not a type") end end argTypes = Array{Union{DataType, Union}}(argTypes) return FunctionNode(func, arity, returnType, argTypes) end # function """ parseTerminalNode(nodeDict::Dict) Parses a [`TerminalNode`](@ref) from a node set configuration file. """ function parseTerminalNode(nodeDict::Dict) terminalNode = 0 kind = get(nodeDict, "kind", false) if kind == false error("kind field not specified in TerminalNode") elseif !(typeof(kind) <: String) error("kind field in TerminalNode must be one of these strings: \"variable\", \"constant\", \"ephemeralConstant\" or \"terminalFunction\"") end if kind == "variable" name = get(nodeDict, "name", false) if name == false error("name field is not set in TerminalNode of kind variable") else if !(typeof(name) <: String) error("name field ($name) in TerminalNode of kind variable '$name' must be a string") end end type = get(nodeDict, "type", false) if type == false error("TerminalNode of kind variable '$name' has no type specified") end aux = 0 try aux = eval(Meta.parse(type)) catch e error("The following type: '$type' in TerminalNode: '$name' is not defined") end if typeof(aux) <: DataType type = aux else error("The following type: '$type' in TerminalNode: '$name' is not a type") end terminalNode = VariableNode(name, type) elseif kind == "constant" value = get(nodeDict, "value", nothing) if value == nothing error("TerminalNode of kind constant has no value") end try aux = Meta.parse(value) value = eval(aux) catch e # Empty end terminalNode = ConstantNode(value) elseif kind == "ephemeralConstant" func = get(nodeDict, "function", false) if func == false error("TerminalNode of kind ephemeralConstant has no function") else aux = 0 try aux = eval(Meta.parse(func)) catch e error("The following function: '$func' in TerminalNode of kind ephemeralConstant is not defined") end if typeof(aux) <: Function func = aux else error("The following function: '$func' TerminalNode of kind ephemeralConstant is not a function") end end varArgs = get(nodeDict, "arguments", []) if !(typeof(varArgs) <: Array) aux = Array{Any}(undef, 1) aux[1] = varArgs varArgs = aux end for i=1:length(varArgs) try arg = Meta.parse(varArgs[i]) varArgs[i] = eval(arg) catch e # Empty end end terminalNode = ConstantNode(func, varArgs) elseif kind == "terminalFunction" func = get(nodeDict, "function", false) if func == false error("TerminalNode of kind terminalFunction '$func' has no function") else aux = 0 try aux = eval(Meta.parse(func)) catch e error("The following function: '$func' in TerminalNode of kind terminalFunction is not defined") end if typeof(aux) <: Function func = aux else error("The following function: '$func' TerminalNode of kind terminalFunction is not a function") end end terminalNode = NoArgsFunctionNode(func) else error("kind field of TerminalNode not supported: '$kind'") end return terminalNode end # function """ createNodes(jsonFile::String, verbose::Bool=true) Parses a node set configuration file that contains the information about the nodes of a Genetic Programming problem. """ function createNodes(jsonFile::String) if !isfile(jsonFile) error("File $jsonFile does not exist in working directory") end file=open(jsonFile) dictionary = JSON.parse(file) close(file) if get(dictionary, "FunctionNodes", false) == false error("Nodes configuration file '$jsonFile' must have function nodes") end if get(dictionary, "TerminalNodes", false) == false error("Nodes configuration file '$jsonFile' must have terminal nodes") end nFunctions = length(dictionary["FunctionNodes"]) nTerminals = length(dictionary["TerminalNodes"]) if nFunctions == 0 error("Nodes configuration file '$jsonFile' must have function nodes") end if nTerminals == 0 error("Nodes configuration file '$jsonFile' must have terminal nodes") end functionSet = Array{FunctionNode}(undef, nFunctions) terminalSet = Array{TerminalNode}(undef, nTerminals) for i=1:nFunctions functionSet[i] = parseFunctionNode(dictionary["FunctionNodes"][i]["Node"]) end for i=1:nTerminals terminalSet[i] = parseTerminalNode(dictionary["TerminalNodes"][i]["Node"]) end return functionSet, terminalSet end # function
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2.331475
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export DepthMap import ImageView type DepthMap camera :: M34 depth :: Array{Float32, 2} nxcorr :: Array{Float32, 2} end function DepthMap(view, nbrs, voi, w = 3) cam = view.camera im = view.image (nc, nx, ny) = size(im) mn = LibAminda.mean_and_inverse_deviation(im, w) # determine depth range, resolution bnds = bounds(cam, voi) near = bnds[1][3] far = bnds[2][3] dz = near * camera_resolution(cam) nz = ceil(Int, (far - near) / dz) dz = (far - near) / nz max_nxcorr = LibAminda.fill(nx, ny, -1.0) depth = LibAminda.fill(nx, ny, near) for k in 1:nz z = near + dz*(k-0.5) nxcorr = LibAminda.fill(nx, ny, -1.0) for nbr in nbrs cam2 = nbr.camera hom = Array(homography(cam, cam2, z)) im2 = LibAminda.map_to_plane(nbr.image, hom, nx, ny) mn2 = LibAminda.mean_and_inverse_deviation(im2, w) nxc = LibAminda.normalized_cross_correlation(im, mn, im2, mn2, w) nxcorr = LibAminda.maximum(nxcorr, nxc) end LibAminda.update_depth(nxcorr, max_nxcorr, depth, z) end return DepthMap(cam, depth, max_nxcorr) end
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2.091429
525
# This file is a part of Julia. License is MIT: https://julialang.org/license module REPLCompletions export completions, shell_completions, bslash_completions, completion_text using Base.Meta using Base: propertynames, something abstract type Completion end struct KeywordCompletion <: Completion keyword::String end struct PathCompletion <: Completion path::String end struct ModuleCompletion <: Completion parent::Module mod::String end struct PackageCompletion <: Completion package::String end struct PropertyCompletion <: Completion value property::Symbol end struct FieldCompletion <: Completion typ::DataType field::Symbol end struct MethodCompletion <: Completion func input_types::Type method::Method end struct BslashCompletion <: Completion bslash::String end struct ShellCompletion <: Completion text::String end struct DictCompletion <: Completion dict::AbstractDict key::String end # interface definition function Base.getproperty(c::Completion, name::Symbol) if name === :keyword return getfield(c, :keyword)::String elseif name === :path return getfield(c, :path)::String elseif name === :parent return getfield(c, :parent)::Module elseif name === :mod return getfield(c, :mod)::String elseif name === :package return getfield(c, :package)::String elseif name === :property return getfield(c, :property)::Symbol elseif name === :field return getfield(c, :field)::Symbol elseif name === :method return getfield(c, :method)::Method elseif name === :bslash return getfield(c, :bslash)::String elseif name === :text return getfield(c, :text)::String elseif name === :key return getfield(c, :key)::String end return getfield(c, name) end _completion_text(c::KeywordCompletion) = c.keyword _completion_text(c::PathCompletion) = c.path _completion_text(c::ModuleCompletion) = c.mod _completion_text(c::PackageCompletion) = c.package _completion_text(c::PropertyCompletion) = string(c.property) _completion_text(c::FieldCompletion) = string(c.field) _completion_text(c::MethodCompletion) = sprint(io -> show(io, c.method)) _completion_text(c::BslashCompletion) = c.bslash _completion_text(c::ShellCompletion) = c.text _completion_text(c::DictCompletion) = c.key completion_text(c) = _completion_text(c)::String const Completions = Tuple{Vector{Completion}, UnitRange{Int}, Bool} function completes_global(x, name) return startswith(x, name) && !('#' in x) end function appendmacro!(syms, macros, needle, endchar) for s in macros if endswith(s, needle) from = nextind(s, firstindex(s)) to = prevind(s, sizeof(s)-sizeof(needle)+1) push!(syms, s[from:to]*endchar) end end end function filtered_mod_names(ffunc::Function, mod::Module, name::AbstractString, all::Bool = false, imported::Bool = false) ssyms = names(mod, all = all, imported = imported) filter!(ffunc, ssyms) syms = String[string(s) for s in ssyms] macros = filter(x -> startswith(x, "@" * name), syms) appendmacro!(syms, macros, "_str", "\"") appendmacro!(syms, macros, "_cmd", "`") filter!(x->completes_global(x, name), syms) return [ModuleCompletion(mod, sym) for sym in syms] end # REPL Symbol Completions function complete_symbol(sym::String, ffunc, context_module::Module=Main) mod = context_module name = sym lookup_module = true t = Union{} val = nothing if something(findlast(in(non_identifier_chars), sym), 0) < something(findlast(isequal('.'), sym), 0) # Find module lookup_name, name = rsplit(sym, ".", limit=2) ex = Meta.parse(lookup_name, raise=false, depwarn=false) b, found = get_value(ex, context_module) if found val = b if isa(b, Module) mod = b lookup_module = true elseif Base.isstructtype(typeof(b)) lookup_module = false t = typeof(b) end else # If the value is not found using get_value, the expression contain an advanced expression lookup_module = false t, found = get_type(ex, context_module) end found || return Completion[] end suggestions = Completion[] if lookup_module # We will exclude the results that the user does not want, as well # as excluding Main.Main.Main, etc., because that's most likely not what # the user wants p = let mod=mod, modname=nameof(mod) s->(!Base.isdeprecated(mod, s) && s != modname && ffunc(mod, s)::Bool) end # Looking for a binding in a module if mod == context_module # Also look in modules we got through `using` mods = ccall(:jl_module_usings, Any, (Any,), context_module)::Vector for m in mods append!(suggestions, filtered_mod_names(p, m::Module, name)) end append!(suggestions, filtered_mod_names(p, mod, name, true, true)) else append!(suggestions, filtered_mod_names(p, mod, name, true, false)) end elseif val !== nothing # looking for a property of an instance for property in propertynames(val, false) # TODO: support integer arguments (#36872) if property isa Symbol && startswith(string(property), name) push!(suggestions, PropertyCompletion(val, property)) end end else # Looking for a member of a type if t isa DataType && t != Any # Check for cases like Type{typeof(+)} if t isa DataType && t.name === Base._TYPE_NAME t = typeof(t.parameters[1]) end # Only look for fields if this is a concrete type if isconcretetype(t) fields = fieldnames(t) for field in fields s = string(field) if startswith(s, name) push!(suggestions, FieldCompletion(t, field)) end end end end end suggestions end const sorted_keywords = [ "abstract type", "baremodule", "begin", "break", "catch", "ccall", "const", "continue", "do", "else", "elseif", "end", "export", "false", "finally", "for", "function", "global", "if", "import", "let", "local", "macro", "module", "mutable struct", "primitive type", "quote", "return", "struct", "true", "try", "using", "while"] function complete_keyword(s::Union{String,SubString{String}}) r = searchsorted(sorted_keywords, s) i = first(r) n = length(sorted_keywords) while i <= n && startswith(sorted_keywords[i],s) r = first(r):i i += 1 end Completion[KeywordCompletion(kw) for kw in sorted_keywords[r]] end function complete_path(path::AbstractString, pos::Int; use_envpath=false, shell_escape=false) if Base.Sys.isunix() && occursin(r"^~(?:/|$)", path) # if the path is just "~", don't consider the expanded username as a prefix if path == "~" dir, prefix = homedir(), "" else dir, prefix = splitdir(homedir() * path[2:end]) end else dir, prefix = splitdir(path) end local files try if isempty(dir) files = readdir() elseif isdir(dir) files = readdir(dir) else return Completion[], 0:-1, false end catch return Completion[], 0:-1, false end matches = Set{String}() for file in files if startswith(file, prefix) id = try isdir(joinpath(dir, file)) catch; false end # joinpath is not used because windows needs to complete with double-backslash push!(matches, id ? file * (@static Sys.iswindows() ? "\\\\" : "/") : file) end end if use_envpath && length(dir) == 0 # Look for files in PATH as well local pathdirs = split(ENV["PATH"], @static Sys.iswindows() ? ";" : ":") for pathdir in pathdirs local actualpath try actualpath = realpath(pathdir) catch # Bash doesn't expect every folder in PATH to exist, so neither shall we continue end if actualpath != pathdir && in(actualpath,pathdirs) # Remove paths which (after resolving links) are in the env path twice. # Many distros eg. point /bin to /usr/bin but have both in the env path. continue end local filesinpath try filesinpath = readdir(pathdir) catch e # Bash allows dirs in PATH that can't be read, so we should as well. if isa(e, Base.IOError) || isa(e, Base.ArgumentError) continue else # We only handle IOError and ArgumentError here rethrow() end end for file in filesinpath # In a perfect world, we would filter on whether the file is executable # here, or even on whether the current user can execute the file in question. if startswith(file, prefix) && isfile(joinpath(pathdir, file)) push!(matches, file) end end end end matchList = Completion[PathCompletion(shell_escape ? replace(s, r"\s" => s"\\\0") : s) for s in matches] startpos = pos - lastindex(prefix) + 1 - count(isequal(' '), prefix) # The pos - lastindex(prefix) + 1 is correct due to `lastindex(prefix)-lastindex(prefix)==0`, # hence we need to add one to get the first index. This is also correct when considering # pos, because pos is the `lastindex` a larger string which `endswith(path)==true`. return matchList, startpos:pos, !isempty(matchList) end function complete_expanduser(path::AbstractString, r) expanded = expanduser(path) return Completion[PathCompletion(expanded)], r, path != expanded end # Determines whether method_complete should be tried. It should only be done if # the string endswiths ',' or '(' when disregarding whitespace_chars function should_method_complete(s::AbstractString) method_complete = false for c in reverse(s) if c in [',', '('] method_complete = true break elseif !(c in whitespace_chars) method_complete = false break end end method_complete end # Returns a range that includes the method name in front of the first non # closed start brace from the end of the string. function find_start_brace(s::AbstractString; c_start='(', c_end=')') braces = 0 r = reverse(s) i = firstindex(r) in_single_quotes = false in_double_quotes = false in_back_ticks = false while i <= ncodeunits(r) c, i = iterate(r, i) if !in_single_quotes && !in_double_quotes && !in_back_ticks if c == c_start braces += 1 elseif c == c_end braces -= 1 elseif c == '\'' in_single_quotes = true elseif c == '"' in_double_quotes = true elseif c == '`' in_back_ticks = true end else if !in_back_ticks && !in_double_quotes && c == '\'' && i <= ncodeunits(r) && iterate(r, i)[1] != '\\' in_single_quotes = !in_single_quotes elseif !in_back_ticks && !in_single_quotes && c == '"' && i <= ncodeunits(r) && iterate(r, i)[1] != '\\' in_double_quotes = !in_double_quotes elseif !in_single_quotes && !in_double_quotes && c == '`' && i <= ncodeunits(r) && iterate(r, i)[1] != '\\' in_back_ticks = !in_back_ticks end end braces == 1 && break end braces != 1 && return 0:-1, -1 method_name_end = reverseind(s, i) startind = nextind(s, something(findprev(in(non_identifier_chars), s, method_name_end), 0))::Int return (startind:lastindex(s), method_name_end) end # Returns the value in a expression if sym is defined in current namespace fn. # This method is used to iterate to the value of a expression like: # :(REPL.REPLCompletions.whitespace_chars) a `dump` of this expression # will show it consist of Expr, QuoteNode's and Symbol's which all needs to # be handled differently to iterate down to get the value of whitespace_chars. function get_value(sym::Expr, fn) sym.head !== :. && return (nothing, false) for ex in sym.args fn, found = get_value(ex, fn) !found && return (nothing, false) end return (fn, true) end get_value(sym::Symbol, fn) = isdefined(fn, sym) ? (getfield(fn, sym), true) : (nothing, false) get_value(sym::QuoteNode, fn) = isdefined(fn, sym.value) ? (getfield(fn, sym.value), true) : (nothing, false) get_value(sym, fn) = (sym, true) # Return the value of a getfield call expression function get_value_getfield(ex::Expr, fn) # Example :((top(getfield))(Base,:max)) val, found = get_value_getfield(ex.args[2],fn) #Look up Base in Main and returns the module (found && length(ex.args) >= 3) || return (nothing, false) return get_value_getfield(ex.args[3], val) #Look up max in Base and returns the function if found. end get_value_getfield(sym, fn) = get_value(sym, fn) # Determines the return type with Base.return_types of a function call using the type information of the arguments. function get_type_call(expr::Expr) f_name = expr.args[1] # The if statement should find the f function. How f is found depends on how f is referenced if isa(f_name, GlobalRef) && isconst(f_name.mod,f_name.name) && isdefined(f_name.mod,f_name.name) ft = typeof(eval(f_name)) found = true else ft, found = get_type(f_name, Main) end found || return (Any, false) # If the function f is not found return Any. args = Any[] for ex in expr.args[2:end] # Find the type of the function arguments typ, found = get_type(ex, Main) found ? push!(args, typ) : push!(args, Any) end # use _methods_by_ftype as the function is supplied as a type world = Base.get_world_counter() matches = Base._methods_by_ftype(Tuple{ft, args...}, -1, world) length(matches) == 1 || return (Any, false) match = first(matches) # Typeinference interp = Core.Compiler.NativeInterpreter() return_type = Core.Compiler.typeinf_type(interp, match.method, match.spec_types, match.sparams) return_type === nothing && return (Any, false) return (return_type, true) end # Returns the return type. example: get_type(:(Base.strip("", ' ')), Main) returns (String, true) function try_get_type(sym::Expr, fn::Module) val, found = get_value(sym, fn) found && return Core.Typeof(val), found if sym.head === :call # getfield call is special cased as the evaluation of getfield provides good type information, # is inexpensive and it is also performed in the complete_symbol function. a1 = sym.args[1] if isa(a1,GlobalRef) && isconst(a1.mod,a1.name) && isdefined(a1.mod,a1.name) && eval(a1) === Core.getfield val, found = get_value_getfield(sym, Main) return found ? Core.Typeof(val) : Any, found end return get_type_call(sym) elseif sym.head === :thunk thk = sym.args[1] rt = ccall(:jl_infer_thunk, Any, (Any, Any), thk::Core.CodeInfo, fn) rt !== Any && return (rt, true) elseif sym.head === :ref # some simple cases of `expand` return try_get_type(Expr(:call, GlobalRef(Base, :getindex), sym.args...), fn) elseif sym.head === :. && sym.args[2] isa QuoteNode # second check catches broadcasting return try_get_type(Expr(:call, GlobalRef(Core, :getfield), sym.args...), fn) end return (Any, false) end try_get_type(other, fn::Module) = get_type(other, fn) function get_type(sym::Expr, fn::Module) # try to analyze nests of calls. if this fails, try using the expanded form. val, found = try_get_type(sym, fn) found && return val, found return try_get_type(Meta.lower(fn, sym), fn) end function get_type(sym, fn::Module) val, found = get_value(sym, fn) return found ? Core.Typeof(val) : Any, found end # Method completion on function call expression that look like :(max(1)) function complete_methods(ex_org::Expr, context_module::Module=Main) args_ex = Any[] func, found = get_value(ex_org.args[1], context_module)::Tuple{Any,Bool} !found && return Completion[] funargs = ex_org.args[2:end] # handle broadcasting, but only handle number of arguments instead of # argument types if ex_org.head === :. && ex_org.args[2] isa Expr for _ in (ex_org.args[2]::Expr).args push!(args_ex, Any) end else for ex in funargs val, found = get_type(ex, context_module) push!(args_ex, val) end end out = Completion[] t_in = Tuple{Core.Typeof(func), args_ex...} # Input types na = length(args_ex)+1 ml = methods(func) for method in ml ms = method.sig # Check if the method's type signature intersects the input types if typeintersect(Base.rewrap_unionall(Tuple{(Base.unwrap_unionall(ms)::DataType).parameters[1 : min(na, end)]...}, ms), t_in) !== Union{} push!(out, MethodCompletion(func, t_in, method)) end end return out end include("latex_symbols.jl") include("emoji_symbols.jl") const non_identifier_chars = [" \t\n\r\"\\'`\$><=:;|&{}()[],+-*/?%^~"...] const whitespace_chars = [" \t\n\r"...] # "\"'`"... is added to whitespace_chars as non of the bslash_completions # characters contain any of these characters. It prohibits the # bslash_completions function to try and complete on escaped characters in strings const bslash_separators = [whitespace_chars..., "\"'`"...] # Aux function to detect whether we're right after a # using or import keyword function afterusing(string::String, startpos::Int) (isempty(string) || startpos == 0) && return false str = string[1:prevind(string,startpos)] isempty(str) && return false rstr = reverse(str) r = findfirst(r"\s(gnisu|tropmi)\b", rstr) r === nothing && return false fr = reverseind(str, last(r)) return occursin(r"^\b(using|import)\s*((\w+[.])*\w+\s*,\s*)*$", str[fr:end]) end function bslash_completions(string::String, pos::Int) slashpos = something(findprev(isequal('\\'), string, pos), 0) if (something(findprev(in(bslash_separators), string, pos), 0) < slashpos && !(1 < slashpos && (string[prevind(string, slashpos)]=='\\'))) # latex / emoji symbol substitution s = string[slashpos:pos] latex = get(latex_symbols, s, "") if !isempty(latex) # complete an exact match return (true, (Completion[BslashCompletion(latex)], slashpos:pos, true)) end emoji = get(emoji_symbols, s, "") if !isempty(emoji) return (true, (Completion[BslashCompletion(emoji)], slashpos:pos, true)) end # return possible matches; these cannot be mixed with regular # Julian completions as only latex / emoji symbols contain the leading \ if startswith(s, "\\:") # emoji namelist = Iterators.filter(k -> startswith(k, s), keys(emoji_symbols)) else # latex namelist = Iterators.filter(k -> startswith(k, s), keys(latex_symbols)) end return (true, (Completion[BslashCompletion(name) for name in sort!(collect(namelist))], slashpos:pos, true)) end return (false, (Completion[], 0:-1, false)) end function dict_identifier_key(str::String, tag::Symbol, context_module::Module = Main) if tag === :string str_close = str*"\"" elseif tag === :cmd str_close = str*"`" else str_close = str end frange, end_of_identifier = find_start_brace(str_close, c_start='[', c_end=']') isempty(frange) && return (nothing, nothing, nothing) obj = context_module for name in split(str[frange[1]:end_of_identifier], '.') Base.isidentifier(name) || return (nothing, nothing, nothing) sym = Symbol(name) isdefined(obj, sym) || return (nothing, nothing, nothing) obj = getfield(obj, sym) end (isa(obj, AbstractDict) && length(obj)::Int < 1_000_000) || return (nothing, nothing, nothing) begin_of_key = something(findnext(!isspace, str, nextind(str, end_of_identifier) + 1), # +1 for [ lastindex(str)+1) return (obj::AbstractDict, str[begin_of_key:end], begin_of_key) end # This needs to be a separate non-inlined function, see #19441 @noinline function find_dict_matches(identifier::AbstractDict, partial_key) matches = String[] for key in keys(identifier) rkey = repr(key) startswith(rkey,partial_key) && push!(matches,rkey) end return matches end function project_deps_get_completion_candidates(pkgstarts::String, project_file::String) loading_candidates = String[] d = Base.parsed_toml(project_file) pkg = get(d, "name", nothing)::Union{String, Nothing} if pkg !== nothing && startswith(pkg, pkgstarts) push!(loading_candidates, pkg) end deps = get(d, "deps", nothing)::Union{Dict{String, Any}, Nothing} if deps !== nothing for (pkg, _) in deps startswith(pkg, pkgstarts) && push!(loading_candidates, pkg) end end return Completion[PackageCompletion(name) for name in loading_candidates] end function completions(string::String, pos::Int, context_module::Module=Main) # First parse everything up to the current position partial = string[1:pos] inc_tag = Base.incomplete_tag(Meta.parse(partial, raise=false, depwarn=false)) # if completing a key in a Dict identifier, partial_key, loc = dict_identifier_key(partial, inc_tag, context_module) if identifier !== nothing matches = find_dict_matches(identifier, partial_key) length(matches)==1 && (lastindex(string) <= pos || string[nextind(string,pos)] != ']') && (matches[1]*=']') length(matches)>0 && return Completion[DictCompletion(identifier, match) for match in sort!(matches)], loc::Int:pos, true end # otherwise... if inc_tag in [:cmd, :string] m = match(r"[\t\n\r\"`><=*?|]| (?!\\)", reverse(partial)) startpos = nextind(partial, reverseind(partial, m.offset)) r = startpos:pos expanded = complete_expanduser(replace(string[r], r"\\ " => " "), r) expanded[3] && return expanded # If user expansion available, return it paths, r, success = complete_path(replace(string[r], r"\\ " => " "), pos) if inc_tag === :string && length(paths) == 1 && # Only close if there's a single choice, !isdir(expanduser(replace(string[startpos:prevind(string, first(r))] * paths[1].path, r"\\ " => " "))) && # except if it's a directory (lastindex(string) <= pos || string[nextind(string,pos)] != '"') # or there's already a " at the cursor. paths[1] = PathCompletion(paths[1].path * "\"") end #Latex symbols can be completed for strings (success || inc_tag==:cmd) && return sort!(paths, by=p->p.path), r, success end ok, ret = bslash_completions(string, pos) ok && return ret # Make sure that only bslash_completions is working on strings inc_tag==:string && return Completion[], 0:-1, false if inc_tag === :other && should_method_complete(partial) frange, method_name_end = find_start_brace(partial) # strip preceding ! operator s = replace(partial[frange], r"\!+([^=\(]+)" => s"\1") ex = Meta.parse(s * ")", raise=false, depwarn=false) if isa(ex, Expr) if ex.head === :call return complete_methods(ex, context_module), first(frange):method_name_end, false elseif ex.head === :. && ex.args[2] isa Expr && (ex.args[2]::Expr).head === :tuple return complete_methods(ex, context_module), first(frange):(method_name_end - 1), false end end elseif inc_tag === :comment return Completion[], 0:-1, false end dotpos = something(findprev(isequal('.'), string, pos), 0) startpos = nextind(string, something(findprev(in(non_identifier_chars), string, pos), 0)) # strip preceding ! operator if (m = match(r"^\!+", string[startpos:pos])) !== nothing startpos += length(m.match) end ffunc = (mod,x)->true suggestions = Completion[] comp_keywords = true if afterusing(string, startpos) # We're right after using or import. Let's look only for packages # and modules we can reach from here # If there's no dot, we're in toplevel, so we should # also search for packages s = string[startpos:pos] if dotpos <= startpos for dir in Base.load_path() if basename(dir) in Base.project_names && isfile(dir) append!(suggestions, project_deps_get_completion_candidates(s, dir)) end isdir(dir) || continue for pname in readdir(dir) if pname[1] != '.' && pname != "METADATA" && pname != "REQUIRE" && startswith(pname, s) # Valid file paths are # <Mod>.jl # <Mod>/src/<Mod>.jl # <Mod>.jl/src/<Mod>.jl if isfile(joinpath(dir, pname)) endswith(pname, ".jl") && push!(suggestions, PackageCompletion(pname[1:prevind(pname, end-2)])) else mod_name = if endswith(pname, ".jl") pname[1:prevind(pname, end-2)] else pname end if isfile(joinpath(dir, pname, "src", "$mod_name.jl")) push!(suggestions, PackageCompletion(mod_name)) end end end end end end ffunc = (mod,x)->(Base.isbindingresolved(mod, x) && isdefined(mod, x) && isa(getfield(mod, x), Module)) comp_keywords = false end startpos == 0 && (pos = -1) dotpos < startpos && (dotpos = startpos - 1) s = string[startpos:pos] comp_keywords && append!(suggestions, complete_keyword(s)) # The case where dot and start pos is equal could look like: "(""*"").d","". or CompletionFoo.test_y_array[1].y # This case can be handled by finding the beginning of the expression. This is done below. if dotpos == startpos i = prevind(string, startpos) while 0 < i c = string[i] if c in [')', ']'] if c==')' c_start='('; c_end=')' elseif c==']' c_start='['; c_end=']' end frange, end_of_identifier = find_start_brace(string[1:prevind(string, i)], c_start=c_start, c_end=c_end) startpos = first(frange) i = prevind(string, startpos) elseif c in ('\'', '\"', '\`') s = "$c$c"*string[startpos:pos] break else break end s = string[startpos:pos] end end append!(suggestions, complete_symbol(s, ffunc, context_module)) return sort!(unique(suggestions), by=completion_text), (dotpos+1):pos, true end function shell_completions(string, pos) # First parse everything up to the current position scs = string[1:pos] local args, last_parse try args, last_parse = Base.shell_parse(scs, true)::Tuple{Expr,UnitRange{Int}} catch return Completion[], 0:-1, false end ex = args.args[end]::Expr # Now look at the last thing we parsed isempty(ex.args) && return Completion[], 0:-1, false arg = ex.args[end] if all(s -> isa(s, AbstractString), ex.args) arg = arg::AbstractString # Treat this as a path # As Base.shell_parse throws away trailing spaces (unless they are escaped), # we need to special case here. # If the last char was a space, but shell_parse ignored it search on "". ignore_last_word = arg != " " && scs[end] == ' ' prefix = ignore_last_word ? "" : join(ex.args) # Also try looking into the env path if the user wants to complete the first argument use_envpath = !ignore_last_word && length(args.args) < 2 return complete_path(prefix, pos, use_envpath=use_envpath, shell_escape=true) elseif isexpr(arg, :incomplete) || isexpr(arg, :error) partial = scs[last_parse] ret, range = completions(partial, lastindex(partial)) range = range .+ (first(last_parse) - 1) return ret, range, true end return Completion[], 0:-1, false end end # module
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# This file is a part of JuliaFEM. # License is MIT: see https://github.com/JuliaFEM/JuliaFEM.jl/blob/master/LICENSE.md using JuliaFEM using JuliaFEM.Preprocess using JuliaFEM.Testing function JuliaFEM.get_mesh(::Type{Val{Symbol("two elements 1.0x0.5 with 0.1 gap in y direction")}}) mesh = Mesh() add_node!(mesh, 1, [0.0, 0.0]) add_node!(mesh, 2, [1.0, 0.0]) add_node!(mesh, 3, [1.0, 0.5]) add_node!(mesh, 4, [0.0, 0.5]) add_node!(mesh, 5, [0.0, 0.6]) add_node!(mesh, 6, [1.0, 0.6]) add_node!(mesh, 7, [1.0, 1.1]) add_node!(mesh, 8, [0.0, 1.1]) add_element!(mesh, 1, :Quad4, [1, 2, 3, 4]) add_element!(mesh, 2, :Quad4, [5, 6, 7, 8]) add_element!(mesh, 3, :Seg2, [1, 2]) add_element!(mesh, 4, :Seg2, [7, 8]) add_element!(mesh, 5, :Seg2, [4, 3]) add_element!(mesh, 6, :Seg2, [6, 5]) add_element_to_element_set!(mesh, :LOWER, 1) add_element_to_element_set!(mesh, :UPPER, 2) add_element_to_element_set!(mesh, :LOWER_BOTTOM, 3) add_element_to_element_set!(mesh, :UPPER_TOP, 4) add_element_to_element_set!(mesh, :LOWER_TOP, 5) add_element_to_element_set!(mesh, :UPPER_BOTTOM, 6) return mesh end function JuliaFEM.get_model(::Type{Val{Symbol("two element contact")}}) mesh = get_mesh("two elements 1.0x0.5 with 0.1 gap in y direction") upper = Problem(Elasticity, "UPPER", 2) upper.properties.formulation = :plane_stress upper.elements = create_elements(mesh, "UPPER") update!(upper.elements, "youngs modulus", 288.0) update!(upper.elements, "poissons ratio", 1/3) lower = Problem(Elasticity, "LOWER", 2) lower.properties.formulation = :plane_stress lower.elements = create_elements(mesh, "LOWER") update!(lower.elements, "youngs modulus", 288.0) update!(lower.elements, "poissons ratio", 1/3) bc_upper = Problem(Dirichlet, "UPPER_TOP", 2, "displacement") bc_upper.elements = create_elements(mesh, "UPPER_TOP") #update!(bc_upper.elements, "displacement 1", -17/90) #update!(bc_upper.elements, "displacement 1", -17/90) update!(bc_upper.elements, "displacement 1", -0.2) update!(bc_upper.elements, "displacement 2", -0.2) bc_lower = Problem(Dirichlet, "LOWER_BOTTOM", 2, "displacement") bc_lower.elements = create_elements(mesh, "LOWER_BOTTOM") update!(bc_lower.elements, "displacement 1", 0.0) update!(bc_lower.elements, "displacement 2", 0.0) interface = Problem(Contact, "LOWER_TO_UPPER", 2, "displacement") interface.properties.dimension = 1 interface_slave_elements = create_elements(mesh, "LOWER_TOP") interface_master_elements = create_elements(mesh, "UPPER_BOTTOM") update!(interface_slave_elements, "master elements", interface_master_elements) interface.elements = [interface_master_elements; interface_slave_elements] solver = Solver(Nonlinear) push!(solver, upper, lower, bc_upper, bc_lower, interface) return solver end @testset "test simple two element contact" begin solver = get_model("two element contact") solver() contact = solver["LOWER_TO_UPPER"] master = first(contact.elements) slave = last(contact.elements) u = master("displacement", [0.0], 0.0) la = slave("reaction force", [0.0], 0.0) info("u = $u, la = $la") @test isapprox(u, [-0.2, -0.15]) @test isapprox(la, [0.0, -30.375]) # FIXME end
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using StatsBase # Support some of the weighted statistics function in StatsBase # NOTES: # - Ambiguity errors are still possible for weights with overly specific methods (e.g., UnitWeights) # - Ideally, when the weighted statistics is moved to Statistics.jl we can remove this entire file. # https://github.com/JuliaLang/Statistics.jl/pull/2 function Statistics.mean(A::KeyedArray, wv::AbstractWeights; dims=:, kwargs...) dims === Colon() && return mean(parent(A), wv; kwargs...) numerical_dims = NamedDims.dim(A, dims) data = mean(parent(A), wv; dims=numerical_dims, kwargs...) new_keys = ntuple(d -> d in numerical_dims ? Base.OneTo(1) : axiskeys(A,d), ndims(A)) return KeyedArray(data, map(copy, new_keys))#, copy(A.meta)) end # var and std are separate cause they don't use the dims keyword and we need to set corrected=true for fun in [:var, :std] @eval function Statistics.$fun(A::KeyedArray, wv::AbstractWeights; dims=:, corrected=true, kwargs...) dims === Colon() && return $fun(parent(A), wv; kwargs...) numerical_dims = NamedDims.dim(A, dims) data = $fun(parent(A), wv, numerical_dims; corrected=corrected, kwargs...) new_keys = ntuple(d -> d in numerical_dims ? Base.OneTo(1) : axiskeys(A,d), ndims(A)) return KeyedArray(data, map(copy, new_keys))#, copy(A.meta)) end end for fun in [:cov, :cor] @eval function Statistics.$fun(A::KeyedMatrix, wv::AbstractWeights; dims=1, kwargs...) d = NamedDims.dim(A, dims) data = $fun(keyless_unname(A), wv, d; kwargs...) L1 = dimnames(A, 3 - d) data2 = hasnames(A) ? NamedDimsArray(data, (L1, L1)) : data K1 = axiskeys(A, 3 - d) KeyedArray(data2, (copy(K1), copy(K1))) end end # scattermat is a StatsBase function and takes dims as a kwarg function StatsBase.scattermat(A::KeyedMatrix, wv::AbstractWeights; dims=1, kwargs...) d = NamedDims.dim(A, dims) data = scattermat(keyless_unname(A), wv; dims=d, kwargs...) L1 = dimnames(A, 3 - d) data2 = hasnames(A) ? NamedDimsArray(data, (L1, L1)) : data K1 = axiskeys(A, 3 - d) KeyedArray(data2, (copy(K1), copy(K1))) end for fun in (:std, :var, :cov) full_name = Symbol("mean_and_$fun") @eval StatsBase.$full_name(A::KeyedMatrix, wv::Vararg{<:AbstractWeights}; dims=:, corrected::Bool=true, kwargs...) = ( mean(A, wv...; dims=dims, kwargs...), $fun(A, wv...; dims=dims, corrected=corrected, kwargs...) ) end # Since we get ambiguity errors with specific implementations we need to wrap each supported method # A better approach might be to add `NamedDims` support to CovarianceEstimators.jl in the future. using CovarianceEstimation estimators = [ :SimpleCovariance, :LinearShrinkage, :DiagonalUnitVariance, :DiagonalCommonVariance, :DiagonalUnequalVariance, :CommonCovariance, :PerfectPositiveCorrelation, :ConstantCorrelation, :AnalyticalNonlinearShrinkage, ] for estimator in estimators @eval function Statistics.cov(ce::$estimator, A::KeyedMatrix, wv::Vararg{<:AbstractWeights}; dims=1, kwargs...) d = NamedDims.dim(A, dims) data = cov(ce, keyless_unname(A), wv...; dims=d, kwargs...) L1 = dimnames(A, 3 - d) data2 = hasnames(A) ? NamedDimsArray(data, (L1, L1)) : data K1 = axiskeys(A, 3 - d) KeyedArray(data2, (copy(K1), copy(K1))) end end
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# Compute tracks from entity edge data. using JLD, PyPlot include("findtracks.jl") include("findtrackgraph.jl") # Load edge incidence matrix. # Load the data file file_dir = joinpath(Base.source_dir(),"../1EntityAnalysis/Entity.jld") E = load(file_dir)["E"] #E = loadassoc(file_dir) E = logical(E) # Set prefixes p = StartsWith("PERSON/,") t = StartsWith("TIME/,") x = StartsWith("LOCATION/,") # Build entity tracks with routine. A = findtracks(E,t,p,x) # Track Graph G = findtrackgraph(A) print(G > 5) figure() spy(G) # Track graph pattern o = "ORGANIZATION/international monetary fund," p = StartsWith("PERSON/,") Go = findtrackgraph(A[:,getcol(E[getrow(E[:,o]),p])]); print((Go > 2) & ((Go ./ G) > 0.2))
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<reponame>pourya-b/CIAOAlgorithms.jl struct SVRG_basic_iterable{R<:Real,C<:RealOrComplex{R},Tx<:AbstractArray{C},Tf,Tg} F::Array{Tf} # smooth term g::Tg # nonsmooth term x0::Tx # initial point N::Int # of data points in the finite sum problem L::Maybe{Union{Array{R},R}} # Lipschitz moduli of nabla f_i μ::Maybe{Union{Array{R},R}} # convexity moduli of the gradients γ::Maybe{R} # stepsize m::Maybe{Int} # number of inner loop updates plus::Bool # for SVRG++ variant end mutable struct SVRG_basic_state{R<:Real,Tx} γ::R # stepsize m::Int # number of inner loop updates av::Tx # the running average z::Tx z_full::Tx # the outer loop argument w::Tx # the inner loop variable ind::Array{Int} # running idx set # some extra placeholders ∇f_temp::Tx # placeholder for gradients temp::Tx end function SVRG_basic_state(γ::R, m, av::Tx, z::Tx, z_full::Tx, w::Tx, ind) where {R,Tx} return SVRG_basic_state{R,Tx}(γ, m, av, z, z_full, w, ind, copy(av), copy(av)) end function Base.iterate(iter::SVRG_basic_iterable{R}) where {R} N = iter.N ind = collect(1:N) m = iter.m === nothing ? m = 2 * N : m = iter.m # updating the stepsize if iter.γ === nothing if iter.plus @warn "provide a stepsize γ" return nothing else if iter.L === nothing || iter.μ === nothing @warn "smoothness or convexity parameter absent" return nothing else L_M = maximum(iter.L) μ_M = maximum(iter.μ) γ = 1 / (10 * L_M) # condition Theorem 3.1 rho = (1 + 4 * L_M * γ^2 * μ_M * (N + 1)) / (μ_M * γ * N * (1 - 4L_M * γ)) if rho >= 1 @warn "convergence condition violated...provide a stepsize!" end end end else γ = iter.γ # provided γ end # initializing the vectors av = zero(iter.x0) for i = 1:N ∇f, ~ = gradient(iter.F[i], iter.x0) ∇f ./= N av .+= ∇f end z_full = copy(iter.x0) z = zero(av) w = copy(iter.x0) state = SVRG_basic_state(γ, m, av, z, z_full, w, ind) print() return state, state end function Base.iterate(iter::SVRG_basic_iterable{R}, state::SVRG_basic_state{R}) where {R} # The inner cycle for i in rand(state.ind, state.m) gradient!(state.temp, iter.F[i], state.z_full) gradient!(state.∇f_temp, iter.F[i], state.w) state.temp .-= state.∇f_temp state.temp .-= state.av state.temp .*= state.γ state.temp .+= state.w CIAOAlgorithms.prox!(state.w, iter.g, state.temp, state.γ) state.z .+= state.w # keeping track of the sum of w's end # full update state.z_full .= state.w #state.z ./ state.m # iter.plus || (state.w .= state.z_full) # only for basic SVRG state.z = zero(state.z) # for next iterate state.av .= state.z for i = 1:iter.N gradient!(state.∇f_temp, iter.F[i], state.z_full) state.∇f_temp ./= iter.N state.av .+= state.∇f_temp end # iter.plus && (state.m *= 2) # only for SVRG++ return state, state end solution(state::SVRG_basic_state) = state.z_full
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<reponame>ozmaden/GNSSBenchmarks.jl # CuArray{ComplexF32} function gpu_downconvert!( downconverted_signal::CuVector{ComplexF32}, carrier::CuVector{ComplexF32}, signal::CuVector{ComplexF32}, start_sample::Integer, num_samples_left::Integer ) @. @views downconverted_signal[start_sample:num_samples_left + start_sample - 1] = signal[start_sample:num_samples_left + start_sample - 1] * conj(carrier[start_sample:num_samples_left + start_sample - 1]) end # CuArray{ComplexF32} Matrix function gpu_downconvert!( downconverted_signal::CuMatrix{ComplexF32}, carrier::CuVector{ComplexF32}, signal::CuMatrix{ComplexF32}, start_sample::Integer, num_samples_left::Integer ) @. downconverted_signal = signal * conj(carrier) end # StructArray{CuArray} Vector function gpu_downconvert!( downconverted_signal_re::CuVector{Float32}, downconverted_signal_im::CuVector{Float32}, carrier_re::CuVector{Float32}, carrier_im::CuVector{Float32}, signal_re::CuVector{Float32}, signal_im::CuVector{Float32}, start_sample::Integer, num_samples_left::Integer ) @. downconverted_signal_re = signal_re * carrier_re + signal_im * carrier_im @. downconverted_signal_im = signal_im * carrier_re - signal_re * carrier_im end # StructArray{CuArray} Matrix function gpu_downconvert!( downconverted_signal_re::CuMatrix{Float32}, downconverted_signal_im::CuMatrix{Float32}, carrier_re::CuVector{Float32}, carrier_im::CuVector{Float32}, signal_re::CuMatrix{Float32}, signal_im::CuMatrix{Float32}, start_sample::Integer, num_samples_left::Integer ) @. downconverted_signal_re = signal_re * carrier_re + signal_im * carrier_im @. downconverted_signal_im = signal_im * carrier_re - signal_re * carrier_im end # Float32 implementation of the orig. CPU function function cpu_downconvert!( downconverted_signal_re::Vector{Float32}, downconverted_signal_im::Vector{Float32}, carrier_re::Vector{Float32}, carrier_im::Vector{Float32}, signal_re::Vector{Float32}, signal_im::Vector{Float32}, start_sample::Integer, num_samples_left::Integer ) @avx unroll = 3 for i = start_sample:num_samples_left + start_sample - 1 downconverted_signal_re[i] = signal_re[i] * carrier_re[i] + signal_im[i] * carrier_im[i] downconverted_signal_im[i] = signal_im[i] * carrier_re[i] - signal_re[i] * carrier_im[i] end end # Float32 implementation of the orig. CPU function for matrices function cpu_downconvert!( downconverted_signal_re::Matrix{Float32}, downconverted_signal_im::Matrix{Float32}, carrier_re::Vector{Float32}, carrier_im::Vector{Float32}, signal_re::Matrix{Float32}, signal_im::Matrix{Float32}, start_sample::Integer, num_samples_left::Integer ) @avx unroll = 3 for i = start_sample:num_samples_left + start_sample - 1, j = 1:size(signal_re, 2) # Calculate signal * carrier' downconverted_signal_re[i, j] = signal_re[i, j] * carrier_re[i] + signal_im[i, j] * carrier_im[i] downconverted_signal_im[i, j] = signal_im[i, j] * carrier_re[i] - signal_re[i, j] * carrier_im[i] end end
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module ComradeDynesty using Comrade using AbstractMCMC using TupleVectors using Reexport @reexport using Dynesty Comrade.samplertype(::Type{<:NestedSampler}) = Comrade.IsCube() Comrade.samplertype(::Type{<:DynamicNestedSampler}) = Comrade.IsCube() function AbstractMCMC.sample(post::Comrade.TransformedPosterior, sampler::Union{NestedSampler, DynamicNestedSampler}, args...; kwargs...) ℓ = logdensityof(post) kw = delete!(Dict(kwargs), :init_params) res = sample(ℓ, identity, sampler, args...; kw...) samples, weights = res["samples"].T, exp.(res["logwt"].T .- res["logz"][end]) chain = transform.(Ref(post), eachcol(samples)) |> TupleVector stats = (logl = res["logl"].T, logz = res["logz"][end], logzerr = res["logz"][end], weights = weights, ) return TupleVector(chain), stats end end
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2.137778
450
using Test using POMDPs using Random let struct M <: POMDP{Int, Int, Char} end @test_throws MethodError generate_s(M(), 1, 1, MersenneTwister(4)) POMDPs.transition(::M, ::Int, ::Int) = [1] @test generate_s(M(), 1, 1, MersenneTwister(4)) == 1 @test_throws MethodError generate_sor(M(), 1, 1, MersenneTwister(4)) @test_throws MethodError generate_sr(M(), 1, 1, MersenneTwister(4)) POMDPs.reward(::M, ::Int, ::Int, ::Int) = 0.0 @test generate_sr(M(), 1, 1, MersenneTwister(4)) == (1, 0.0) @test_throws MethodError generate_sor(M(), 1, 1, MersenneTwister(4)) POMDPs.generate_o(::M, ::Int, ::Int, ::Int, ::AbstractRNG) = `a` @test generate_sor(M(), 1, 1, MersenneTwister(4)) == (1, `a`, 0.0) end
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2.190476
336
<gh_stars>0 let doc = open("$testdir/example.html") do example example |> readstring |> parsehtml end io = IOBuffer() print(io, doc) seek(io, 0) newdoc = io |> readstring |> parsehtml @test newdoc == doc end
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2.268519
108
#=############################################################################## # DESCRIPTION Utilities. # AUTHORSHIP * Author : <NAME> * Email : <EMAIL> * Created : Sep 2018 * License : MIT License =############################################################################### """ `simplewing(b, ar, tr, twist_root, twist_tip, lambda, gamma; bodytype=RigidWakeBody, span_NDIVS="automatic", rfl_NDIVS="automatic", airfoil_root="naca6412.dat", airfoil_tip="naca6412.dat", airfoil_path=def_rfl_path)` Generates a symmetric single-section wing. **ARGUMENTS** * `b::Real` : Span. * `ar::Real` : Aspect ratio defined as b/c_tip. * `tr::Real` : Taper ratio defined as c_tip/c_root. * `twist_root::Real`: (deg) twist of the root. * `twist_tip::Real` : (deg) twist of the tip. * `lambda::Real` : (deg) sweep. * `gamma::Real` : (deg) dihedral. **OPTIONAL ARGUMENTS** * `bodytype::Type{LBodyTypes}`: Type of lifting body to generate. * `span_NDIVS::ndivstype` : Spanwise divisions. * `rfl_NDIVS::ndivstype` : Chordwise divisions. * `airfoil_root::String` : File to root airfoil contour. * `airfoil_tip::String` : File to tip airfoil contour. * `airfoil_path::String` : Path to airfoil files. NOTE: See gt.multidscretize for a description of arguments of type `ndivstype`. NOTE2: In the current implementation, sweep and dihedral are done about the LE. """ function simplewing(b::RType, ar::RType, tr::RType, twist_root::RType, twist_tip::RType, lambda::RType, gamma::RType; bodytype::Type{LBodyTypes}=RigidWakeBody, span_NDIVS::ndivstype=nothing, rfl_NDIVS::ndivstype=nothing, airfoil_root::String="naca6412.dat", airfoil_tip::String="naca6412.dat", airfoil_path::String=def_rfl_path, spl_s::Real=0.0000001, rflspl_s::Real=0.00000001, verify_spline::Bool=true, verify_rflspline::Bool=true, opt_args... ) # ----------------- GEOMETRY DESCRIPTION ------------------------------------- c_tip = b/ar # Tip chord c_root = c_tip/tr # Root chord semispan = b/2 # (m) semi-span length y_tip = b/2 x_tip = y_tip*tan(lambda*pi/180) z_tip = y_tip*tan(gamma*pi/180) chords = [0.00 c_root/semispan; # (semi-span position, chord c/semib) 1.00 c_tip/semispan] twists = [0.0 twist_root; # (semi-span position, twist (deg)) 1.0 twist_tip] x_pos = [0.00 0; # (semi-span position, LE x-position x/semib) 1.00 x_tip/semispan] z_pos = [0.00 0; # (semi-span position, LE z-position x/semib) 1.00 z_tip/semispan] airfoil_files = [(0.0, airfoil_root), # (semi-span position, airfoil file) (1.0, airfoil_tip)] # ----------------- DISCRETIZATION 0000---------------------------------------- # Defines divisions if span_NDIVS==nothing b_NDIVS = [(1.0, 35, 20.0, true)] # Span cell sections else b_NDIVS = span_NDIVS end if rfl_NDIVS==nothing urfl_NDIVS = [(0.25, 7, 10.0, false), # Cells on upper side of airfoils (0.50, 5, 1.0, true), (0.25, 6, 1/10.0, false)] else urfl_NDIVS = rfl_NDIVS end lrfl_NDIVS = urfl_NDIVS # Cells on lower side of airfoils # ----------------- LOFTING PARAMETERS --------------------------------------- b_low = -1.0 # Lower bound of span lofting b_up = 1.0 # Upper bound of span lofting symmetric = true # Lofting symmetric about b=0 spl_k = 1 # Spline order of distributions along span # spl_s = 0.0000001 # Spline smoothing of distribution along span # rflspl_s = 0.00000001 # Spline smoothing of airfoil cross sections. # verify_spline = false # Plots the splined distributions # verify_rflspline = true # Plots the splined airfoil cross sections return generate_loft_liftbody(bodytype, airfoil_files, airfoil_path, urfl_NDIVS, lrfl_NDIVS, semispan, b_low, b_up, b_NDIVS, chords, twists, x_pos, z_pos; dimsplit=1, symmetric=symmetric, spl_k=spl_k, spl_s=spl_s, verify_spline=verify_spline, verify_rflspline=verify_rflspline, rflspl_s=rflspl_s, opt_args... ) end
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if Base.libllvm_version >= v"7.0" include(joinpath("gcn_intrinsics", "math.jl")) end include(joinpath("gcn_intrinsics", "indexing.jl")) include(joinpath("gcn_intrinsics", "assertion.jl")) include(joinpath("gcn_intrinsics", "synchronization.jl")) include(joinpath("gcn_intrinsics", "extras.jl"))
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using LinearAlgebra export transform, backtransform """ backtransform(Rsets::ReachSolution, options::Options) Undo a coordinate transformation. ### Input - `Rsets` -- flowpipe - `option` -- problem options containing an `:transformation_matrix` entry ### Output A new flowpipe where each reach set has been transformed. ### Notes The transformation is implemented with a lazy `LinearMap`. """ function backtransform(Rsets, options::Options) transformation_matrix = options[:transformation_matrix] if transformation_matrix == nothing return Rsets end return project(Rsets, transformation_matrix) end """ transform(problem::InitialValueProblem, options::Options) Interface function that calls the respective transformation function. ### Input - `problem` -- discrete or continuous initial-value problem - `option` -- problem options ### Output A tuple containing the transformed problem and the transformed options. ### Notes The functions that are called in the background should return a the transformed system components `A`, `X0`, and `U`, and also an inverse transformation matrix `M`. If the system has an invariant, it is transformed as well. """ function transform(problem::InitialValueProblem, options::Options) method = options[:coordinate_transformation] if method == "" nothing # no-op elseif method == "schur" problem, T_inverse = schur_transform(problem) options[:transformation_matrix] = T_inverse else error("the transformation method $method is undefined") end return (problem, options) end """ schur_transform(problem::InitialValueProblem) Applies a Schur transformation to a discrete or continuous initial-value problem. ### Input - `problem` -- discrete or continuous initial-value problem ### Output Transformed problem. ### Algorithm We use Julia's default `schurfact` function to compute a [Schur decomposition](https://en.wikipedia.org/wiki/Schur_decomposition) of the coefficients matrix ``A``. """ function schur_transform(problem::InitialValueProblem{PT, ST} ) where {PT <: Union{ConstrainedLinearControlDiscreteSystem, ConstrainedLinearControlContinuousSystem}, ST<:LazySet} n = size(problem.s.A, 1) # full (dense) matrix is required by schur # result S is a struct such that # - S.Schur is in Schur form and # - A == S.vectors * S.Schur * S.vectors' S = schur(Matrix(problem.s.A)) # recall that for Schur matrices, inv(T) == T' Z_inverse = copy(transpose(S.vectors)) # obtain transformed system matrix A_new = S.Schur # apply transformation to the initial states X0_new = Z_inverse * problem.x0 # apply transformation to the inputs B_new = Z_inverse * problem.s.B U_new = problem.s.U # matrix for reverting the transformation again T_inverse = S.vectors # apply transformation to the invariant if hasmethod(stateset, Tuple{typeof(problem.s)}) invariant_new = T_inverse * problem.s.X else invariant_new = Universe(n) end system_new = _wrap_system(PT, A_new, B_new, invariant_new, U_new) problem_new = InitialValueProblem(system_new, X0_new) return problem_new, T_inverse end function _wrap_system(PT::Type{<:ConstrainedLinearControlDiscreteSystem}, A, B, invariant, U) return ConstrainedLinearControlDiscreteSystem(A, B, invariant, U) end function _wrap_system(PT::Type{<:ConstrainedLinearControlContinuousSystem}, A, B, invariant, U) return ConstrainedLinearControlContinuousSystem(A, B, invariant, U) end
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function renderloop(screen::Screen; framerate = 1/30, prerender = () -> nothing) try while isopen(screen) t = time() GLFW.PollEvents() # GLFW poll prerender() make_context_current(screen) render_frame(screen) GLFW.SwapBuffers(to_native(screen)) diff = framerate - (time() - t) if diff > 0 sleep(diff) else # if we don't sleep, we need to yield explicitely yield() end end catch e destroy!(screen) rethrow(e) end destroy!(screen) return end function setup!(screen) glEnable(GL_SCISSOR_TEST) if isopen(screen) glScissor(0, 0, widths(screen)...) glClearColor(1, 1, 1, 1) glClear(GL_COLOR_BUFFER_BIT) for (id, rect, clear, visible, color) in screen.screens if visible[] a = rect[] rt = (minimum(a)..., widths(a)...) glViewport(rt...) if clear[] c = color[] glScissor(rt...) glClearColor(red(c), green(c), blue(c), alpha(c)) glClear(GL_COLOR_BUFFER_BIT) end end end end glDisable(GL_SCISSOR_TEST) return end const selection_queries = Function[] """ Renders a single frame of a `window` """ function render_frame(screen::Screen) nw = to_native(screen) GLAbstraction.is_context_active(nw) || return fb = screen.framebuffer wh = Int.(framebuffer_size(nw)) resize!(fb, wh) w, h = wh glDisable(GL_STENCIL_TEST) #prepare for geometry in need of anti aliasing glBindFramebuffer(GL_FRAMEBUFFER, fb.id[1]) # color framebuffer glDrawBuffers(2, [GL_COLOR_ATTACHMENT0, GL_COLOR_ATTACHMENT1]) glClearColor(0,0,0,0) glClear(GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT | GL_COLOR_BUFFER_BIT) setup!(screen) glColorMask(GL_TRUE, GL_TRUE, GL_TRUE, GL_TRUE) GLAbstraction.render(screen, true) # transfer color to luma buffer and apply fxaa glBindFramebuffer(GL_FRAMEBUFFER, fb.id[2]) # luma framebuffer glDrawBuffer(GL_COLOR_ATTACHMENT0) glViewport(0, 0, w, h) glClearColor(0,0,0,0) glClear(GL_COLOR_BUFFER_BIT) GLAbstraction.render(fb.postprocess[1]) # add luma and preprocess glBindFramebuffer(GL_FRAMEBUFFER, fb.id[1]) # transfer to non fxaa framebuffer glViewport(0, 0, w, h) glDrawBuffer(GL_COLOR_ATTACHMENT0) GLAbstraction.render(fb.postprocess[2]) # copy with fxaa postprocess #prepare for non anti aliased pass glDrawBuffers(2, [GL_COLOR_ATTACHMENT0, GL_COLOR_ATTACHMENT1]) GLAbstraction.render(screen, false) #Read all the selection queries glReadBuffer(GL_COLOR_ATTACHMENT1) for query_func in selection_queries query_func(fb.objectid, w, h) end glBindFramebuffer(GL_FRAMEBUFFER, 0) # transfer back to window glViewport(0, 0, w, h) glClearColor(0, 0, 0, 0) glClear(GL_COLOR_BUFFER_BIT) GLAbstraction.render(fb.postprocess[3]) # copy postprocess return end function id2rect(screen, id1) # TODO maybe we should use a different data structure for (id2, rect, clear, color) in screen.screens id1 == id2 && return true, rect end false, IRect(0,0,0,0) end function GLAbstraction.render(screen::Screen, fxaa::Bool) for (zindex, screenid, elem) in screen.renderlist found, rect = id2rect(screen, screenid) found || continue a = rect[] glViewport(minimum(a)..., widths(a)...) if fxaa && elem[:fxaa][] render(elem) end if !fxaa && !elem[:fxaa][] render(elem) end end return end
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""" module that holds functions needed to react to scrolling Generally first we need to pass the GLFW callback to the Rocket obeservable code adapted from https://discourse.julialang.org/t/custom-subject-in-rocket-jl-for-mouse-events-from-glfw/65133/3 """ module ReactToScroll using ModernGL, ..DisplayWords,Rocket, GLFW, ..ForDisplayStructs, ..TextureManag,Logging, ..DataStructs, ..StructsManag export reactToScroll export registerMouseScrollFunctions """ configuting Rocket on Subscribe so we get custom handler of input as we see we still need to define actor """ function Rocket.on_subscribe!(handler::ScrollCallbackSubscribable, actor::SyncActor{Any, ActorWithOpenGlObjects}) return subscribe!(handler.subject, actor) end """ we define how handler should act on the subject - observable so it will pass event onto subject If we will scroll fast number will change much and we will skip some slices """ function (handler::ScrollCallbackSubscribable)(_, xoff, yoff) if(!handler.isBusy[]) # if program is ready to repsond handler.numberToSend=0 next!(handler.subject,Int64(handler.numberToSend+yoff))#true if we scroll up else handler.numberToSend+=yoff end end """ uploading data to given texture; of given types associated returns subscription in order to enable unsubscribing in the end window - GLFW window stopListening - atomic boolean able to stop the event listening cycle return scrollback - that holds boolean subject (observable) to which we can react by subscribing appropriate actor """ function registerMouseScrollFunctions(window::GLFW.Window ,stopListening::Base.Threads.Atomic{Bool} ,isBusy::Base.Threads.Atomic{Bool} ) stopListening[]=true # stoping event listening loop to free the GLFW context scrollback = ScrollCallbackSubscribable( isBusy,0 ,Subject(Int64, scheduler = AsyncScheduler())) GLFW.SetScrollCallback(window, (a, xoff, yoff) -> scrollback(a, xoff, yoff)) stopListening[]=false # reactivate event listening loop return scrollback end #registerMouseScrollFunctions """ captures information send from handler that scroll was executed by the """ """ in case of the scroll p true will be send in case of down - false in response to it it sets new screen int variable and changes displayed screen toBeSavedForBack - just marks weather we wat to save the info how to undo latest action - false if we invoke it from undoing """ function reactToScroll(scrollNumb::Int64 ,actor::SyncActor{Any, ActorWithOpenGlObjects} ,toBeSavedForBack::Bool = true) actor.actor.mainForDisplayObjects.stopListening[]=true current = actor.actor.currentDisplayedSlice old = current #when shift is pressed scrolling is 10 times faster if(!actor.actor.mainForDisplayObjects.isFastScroll) current+=scrollNumb else current+=scrollNumb*10 end #isScrollUp ? current+=1 : current-=1 # we do not want to move outside of possible range of slices lastSlice = actor.actor.onScrollData.slicesNumber if(lastSlice>1) actor.actor.isSliceChanged = true actor.actor.isBusy[] = true if(current<1) current=1 end if(lastSlice<1) lastSlice=1 end if(current>=lastSlice) current=lastSlice end #logic to change displayed screen #we select slice that we are intrested in singleSlDat= actor.actor.onScrollData.dataToScroll|> (scrDat)-> map(threeDimDat->threeToTwoDimm(threeDimDat.type,Int64(current),actor.actor.onScrollData.dimensionToScroll,threeDimDat ),scrDat) |> (twoDimList)-> SingleSliceDat(listOfDataAndImageNames=twoDimList ,sliceNumber=current ,textToDisp = getTextForCurrentSlice(actor.actor.onScrollData, Int32(current)) ) updateImagesDisplayed(singleSlDat ,actor.actor.mainForDisplayObjects ,actor.actor.textDispObj ,actor.actor.calcDimsStruct ,actor.actor.valueForMasToSet ) actor.actor.currentlyDispDat=singleSlDat # updating the last mouse position so when we will change plane it will better show actual position currentDim =Int64(actor.actor.onScrollData.dataToScrollDims.dimensionToScroll) lastMouse = actor.actor.lastRecordedMousePosition locArr = [lastMouse[1],lastMouse[2],lastMouse[3]] locArr[currentDim]= current actor.actor.lastRecordedMousePosition=CartesianIndex(locArr[1],locArr[2],locArr[3]) #saving information about current slice for future reference actor.actor.currentDisplayedSlice = current #enable undoing the action if(toBeSavedForBack) func = ()-> reactToScroll(old-=scrollNumb, actor,false ) addToforUndoVector(actor,func ) end end#if actor.actor.isBusy[] = false actor.actor.mainForDisplayObjects.stopListening[]=false end#reactToScroll end #ReactToScroll
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2.610805
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module SMRTypes import Base: show export cSMRWMrkChannel, SMRWMrkChannel, cSMRContChannel, SMRContChannel, cSMREventChannel, SMREventChannel, cSMRMarkerChannel, SMRMarkerChannel, cSMRChannelInfo, cSMRChannelInfoArray, SMRChannelInfo, show, channel_string const MARKER_SIZE = UInt8(4) abstract type SMRCType end abstract type SMRType end # =========================================================================== # struct cSMRWMrkChannel <: SMRCType length::UInt64 npt::UInt64 timestamps::Ptr{Float64} markers::Ptr{UInt8} wavemarks::Ptr{Int16} end mutable struct SMRWMrkChannel <: SMRType timestamps::Vector{Float64} markers::Matrix{UInt8} wavemarks::Matrix{Int16} function SMRWMrkChannel(x::cSMRWMrkChannel) self = new() self.timestamps = Vector{Float64}(undef, x.length) copyto!(self.timestamps, unsafe_wrap(Vector{Float64}, x.timestamps, x.length, own=false)) self.markers = Matrix{UInt8}(undef, MARKER_SIZE, x.length) copyto!(self.markers, unsafe_wrap(Vector{UInt8}, x.markers, x.length * MARKER_SIZE, own=false)) self.wavemarks = Matrix{Int16}(undef, x.npt, x.length) copyto!(self.wavemarks, unsafe_wrap(Vector{Int16}, x.wavemarks, x.length * x.npt, own=false)) return self end end # =========================================================================== # struct cSMRContChannel <: SMRCType length::UInt64 sampling_rate::Float64 data::Ptr{Int16} end mutable struct SMRContChannel <: SMRType data::Vector{Int16} sampling_rate::Float64 function SMRContChannel(x::cSMRContChannel) self = new() self.data = Vector{Int16}(undef, x.length) copyto!(self.data, unsafe_wrap(Vector{Int16}, x.data, x.length, own=false)) self.sampling_rate = x.sampling_rate return self end end # =========================================================================== # struct cSMREventChannel <: SMRCType length::UInt64 data::Ptr{Float64} end mutable struct SMREventChannel <: SMRType data::Vector{Float64} function SMREventChannel(x::cSMREventChannel) self = new() self.data = Vector{Float64}(undef, x.length) copyto!(self.data, unsafe_wrap(Vector{Float64}, x.data, x.length, own=false)) return self end end # =========================================================================== # struct cSMRMarkerChannel <: SMRCType length::UInt64 npt::UInt64 timestamps::Ptr{Float64} markers::Ptr{UInt8} text::Ptr{UInt8} end mutable struct SMRMarkerChannel <: SMRType timestamps::Vector{Float64} markers::Matrix{UInt8} text::Vector{String} function SMRMarkerChannel(mrk::cSMRMarkerChannel) self = new() self.timestamps = Vector{Float64}(undef, mrk.length) copyto!(self.timestamps, unsafe_wrap(Vector{Float64}, mrk.timestamps, mrk.length, own=false)) self.markers = Matrix{UInt8}(undef, MARKER_SIZE, mrk.length) copyto!(self.markers, unsafe_wrap(Vector{UInt8}, mrk.markers, mrk.length * MARKER_SIZE, own=false)) self.text = Vector{String}(undef, mrk.length) ary = unsafe_wrap(Vector{UInt8}, mrk.text, mrk.length * mrk.npt, own=false) for k = 1:mrk.length isrt = ((k-1)*mrk.npt)+1 iend = k*mrk.npt if all(x->x=='\0', ary[isrt:iend]) self.text[k] = "" else self.text[k] = strip(join(map(Char, ary[isrt:iend])), '\0') end end return self end end # =========================================================================== # struct cSMRChannelInfo <: SMRCType title::Cstring index::Int32 kind::UInt8 phy_chan::Int16 end struct cSMRChannelInfoArray <: SMRCType length::UInt32 ifo::Ptr{Ptr{cSMRChannelInfo}} end mutable struct SMRChannelInfo <: SMRType title::String index::Int kind::Int phy_chan::Int function SMRChannelInfo(x::cSMRChannelInfo) self = new() self.title = unsafe_string(x.title) self.index = Int(x.index) self.kind = Int(x.kind) self.phy_chan = Int(x.phy_chan) return self end end # =========================================================================== # function show(io::IO, ifo::SMRChannelInfo) str = "\"" * ifo.title * "\": " * channel_string(ifo.kind) * ", " * string(ifo.index) print(io, str) end # =========================================================================== # function channel_string(x::T) where T<:Integer sx = string(x) if x == 1 str = "continuous" elseif x == 2 str = "event" elseif x == 3 str = "event" elseif x == 4 str = "event" elseif x == 5 str = "marker" elseif x == 6 str = "adc_marker" elseif x == 7 str = "real_marker" elseif x == 8 str = "text_marker" elseif x == 9 str = "real_wave" else @warn(string(x) * " is not a valid channel type") str = "INVALID" end return str end # =========================================================================== # end #END MODULE
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2981, 198, 220, 220, 220, 4129, 3712, 52, 5317, 2414, 198, 220, 220, 220, 299, 457, 3712, 52, 5317, 2414, 198, 220, 220, 220, 4628, 395, 9430, 3712, 46745, 90, 43879, 2414, 92, 198, 220, 220, 220, 19736, 3712, 46745, 90, 52, 5317, 23, 92, 198, 220, 220, 220, 2420, 3712, 46745, 90, 52, 5317, 23, 92, 198, 437, 198, 198, 76, 18187, 2878, 9447, 49, 9704, 263, 29239, 1279, 25, 9447, 49, 6030, 198, 220, 220, 220, 4628, 395, 9430, 3712, 38469, 90, 43879, 2414, 92, 198, 220, 220, 220, 19736, 3712, 46912, 90, 52, 5317, 23, 92, 198, 220, 220, 220, 2420, 3712, 38469, 90, 10100, 92, 628, 220, 220, 220, 2163, 9447, 49, 9704, 263, 29239, 7, 43395, 74, 3712, 66, 12310, 49, 9704, 263, 29239, 8, 198, 220, 220, 220, 220, 220, 220, 220, 2116, 796, 649, 3419, 628, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 16514, 395, 9430, 796, 20650, 90, 43879, 2414, 92, 7, 917, 891, 11, 285, 81, 74, 13, 13664, 8, 198, 220, 220, 220, 220, 220, 220, 220, 4866, 1462, 0, 7, 944, 13, 16514, 395, 9430, 11, 21596, 62, 37150, 7, 38469, 90, 43879, 2414, 5512, 285, 81, 74, 13, 16514, 395, 9430, 11, 285, 81, 74, 13, 13664, 11, 898, 28, 9562, 4008, 628, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 4102, 364, 796, 24936, 90, 52, 5317, 23, 92, 7, 917, 891, 11, 39641, 1137, 62, 33489, 11, 285, 81, 74, 13, 13664, 8, 198, 220, 220, 220, 220, 220, 220, 220, 4866, 1462, 0, 7, 944, 13, 4102, 364, 11, 21596, 62, 37150, 7, 38469, 90, 52, 5317, 23, 5512, 285, 81, 74, 13, 4102, 364, 11, 285, 81, 74, 13, 13664, 1635, 39641, 1137, 62, 33489, 11, 898, 28, 9562, 4008, 628, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 5239, 796, 20650, 90, 10100, 92, 7, 917, 891, 11, 285, 81, 74, 13, 13664, 8, 628, 220, 220, 220, 220, 220, 220, 220, 257, 563, 796, 21596, 62, 37150, 7, 38469, 90, 52, 5317, 23, 5512, 285, 81, 74, 13, 5239, 11, 285, 81, 74, 13, 13664, 1635, 285, 81, 74, 13, 77, 457, 11, 898, 28, 9562, 8, 198, 220, 220, 220, 220, 220, 220, 220, 329, 479, 796, 352, 25, 43395, 74, 13, 13664, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 318, 17034, 796, 14808, 74, 12, 16, 27493, 43395, 74, 13, 77, 457, 47762, 16, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1312, 437, 796, 479, 9, 43395, 74, 13, 77, 457, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 611, 477, 7, 87, 3784, 87, 855, 6, 59, 15, 3256, 257, 563, 58, 271, 17034, 25, 72, 437, 12962, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 5239, 58, 74, 60, 796, 13538, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2073, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 5239, 58, 74, 60, 796, 10283, 7, 22179, 7, 8899, 7, 12441, 11, 257, 563, 58, 271, 17034, 25, 72, 437, 12962, 828, 705, 59, 15, 11537, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 220, 220, 886, 628, 220, 220, 220, 220, 220, 220, 220, 1441, 2116, 198, 220, 220, 220, 886, 198, 437, 198, 2, 38093, 2559, 855, 1303, 198, 7249, 269, 12310, 49, 29239, 12360, 1279, 25, 9447, 49, 4177, 2981, 198, 220, 220, 220, 3670, 3712, 34, 8841, 198, 220, 220, 220, 6376, 3712, 5317, 2624, 198, 220, 220, 220, 1611, 3712, 52, 5317, 23, 198, 220, 220, 220, 872, 88, 62, 3147, 3712, 5317, 1433, 198, 437, 198, 198, 7249, 269, 12310, 49, 29239, 12360, 19182, 1279, 25, 9447, 49, 4177, 2981, 198, 220, 220, 220, 4129, 3712, 52, 5317, 2624, 198, 220, 220, 220, 611, 78, 3712, 46745, 90, 46745, 90, 66, 12310, 49, 29239, 12360, 11709, 198, 437, 198, 198, 76, 18187, 2878, 9447, 49, 29239, 12360, 1279, 25, 9447, 49, 6030, 198, 220, 220, 220, 3670, 3712, 10100, 198, 220, 220, 220, 6376, 3712, 5317, 198, 220, 220, 220, 1611, 3712, 5317, 198, 220, 220, 220, 872, 88, 62, 3147, 3712, 5317, 628, 220, 220, 220, 2163, 9447, 49, 29239, 12360, 7, 87, 3712, 66, 12310, 49, 29239, 12360, 8, 198, 220, 220, 220, 220, 220, 220, 220, 2116, 796, 649, 3419, 198, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 7839, 796, 21596, 62, 8841, 7, 87, 13, 7839, 8, 198, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 9630, 796, 2558, 7, 87, 13, 9630, 8, 198, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 11031, 796, 2558, 7, 87, 13, 11031, 8, 198, 220, 220, 220, 220, 220, 220, 220, 2116, 13, 6883, 62, 3147, 796, 2558, 7, 87, 13, 6883, 62, 3147, 8, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 2116, 198, 220, 220, 220, 886, 198, 437, 198, 2, 38093, 2559, 855, 1303, 198, 8818, 905, 7, 952, 3712, 9399, 11, 611, 78, 3712, 12310, 49, 29239, 12360, 8, 198, 220, 220, 220, 965, 796, 366, 7879, 1, 1635, 611, 78, 13, 7839, 1635, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 366, 30478, 366, 1635, 6518, 62, 8841, 7, 361, 78, 13, 11031, 8, 1635, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 33172, 366, 1635, 4731, 7, 361, 78, 13, 9630, 8, 198, 220, 220, 220, 3601, 7, 952, 11, 965, 8, 198, 437, 198, 2, 38093, 2559, 855, 1303, 198, 8818, 6518, 62, 8841, 7, 87, 3712, 51, 8, 810, 309, 27, 25, 46541, 198, 220, 220, 220, 264, 87, 796, 4731, 7, 87, 8, 198, 220, 220, 220, 611, 2124, 6624, 352, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 18487, 5623, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 362, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 15596, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 513, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 15596, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 604, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 15596, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 642, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 4102, 263, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 718, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 324, 66, 62, 4102, 263, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 767, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 5305, 62, 4102, 263, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 807, 198, 220, 220, 220, 220, 220, 220, 220, 965, 796, 366, 5239, 62, 4102, 263, 1, 198, 220, 220, 220, 2073, 361, 2124, 6624, 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2.379638
2,210
<reponame>Dictino/julia # This file is a part of Julia. License is MIT: https://julialang.org/license using Random using LinearAlgebra function isnan_type(::Type{T}, x) where T isa(x, T) && isnan(x) end @testset "clamp" begin @test clamp(0, 1, 3) == 1 @test clamp(1, 1, 3) == 1 @test clamp(2, 1, 3) == 2 @test clamp(3, 1, 3) == 3 @test clamp(4, 1, 3) == 3 @test clamp(0.0, 1, 3) == 1.0 @test clamp(1.0, 1, 3) == 1.0 @test clamp(2.0, 1, 3) == 2.0 @test clamp(3.0, 1, 3) == 3.0 @test clamp(4.0, 1, 3) == 3.0 @test clamp.([0, 1, 2, 3, 4], 1.0, 3.0) == [1.0, 1.0, 2.0, 3.0, 3.0] @test clamp.([0 1; 2 3], 1.0, 3.0) == [1.0 1.0; 2.0 3.0] @test clamp(-200, Int8) === typemin(Int8) @test clamp(100, Int8) === Int8(100) @test clamp(200, Int8) === typemax(Int8) begin x = [0.0, 1.0, 2.0, 3.0, 4.0] clamp!(x, 1, 3) @test x == [1.0, 1.0, 2.0, 3.0, 3.0] end end @testset "constants" begin @test pi != ℯ @test ℯ != 1//2 @test 1//2 <= ℯ @test ℯ <= 15//3 @test big(1//2) < ℯ @test ℯ < big(20//6) @test ℯ^pi == exp(pi) @test ℯ^2 == exp(2) @test ℯ^2.4 == exp(2.4) @test ℯ^(2//3) == exp(2//3) @test Float16(3.0) < pi @test pi < Float16(4.0) @test widen(pi) === pi @test occursin("3.14159", sprint(show, MIME"text/plain"(), π)) @test repr(Any[pi ℯ; ℯ pi]) == "Any[π ℯ; ℯ π]" @test string(pi) == "π" end @testset "frexp,ldexp,significand,exponent" begin @testset "$T" for T in (Float16,Float32,Float64) for z in (zero(T),-zero(T)) frexp(z) === (z,0) significand(z) === z @test_throws DomainError exponent(z) end for (a,b) in [(T(12.8),T(0.8)), (prevfloat(floatmin(T)), prevfloat(one(T), 2)), (prevfloat(floatmin(T)), prevfloat(one(T), 2)), (prevfloat(floatmin(T)), nextfloat(one(T), -2)), (nextfloat(zero(T), 3), T(0.75)), (prevfloat(zero(T), -3), T(0.75)), (nextfloat(zero(T)), T(0.5))] n = Int(log2(a/b)) @test frexp(a) == (b,n) @test ldexp(b,n) == a @test ldexp(a,-n) == b @test significand(a) == 2b @test exponent(a) == n-1 @test frexp(-a) == (-b,n) @test ldexp(-b,n) == -a @test ldexp(-a,-n) == -b @test significand(-a) == -2b @test exponent(-a) == n-1 end @test_throws DomainError exponent(convert(T,NaN)) @test isnan_type(T, significand(convert(T,NaN))) x,y = frexp(convert(T,NaN)) @test isnan_type(T, x) @test y == 0 @testset "ldexp function" begin @test ldexp(T(0.0), 0) === T(0.0) @test ldexp(T(-0.0), 0) === T(-0.0) @test ldexp(T(Inf), 1) === T(Inf) @test ldexp(T(Inf), 10000) === T(Inf) @test ldexp(T(-Inf), 1) === T(-Inf) @test isnan_type(T, ldexp(T(NaN), 10)) @test ldexp(T(1.0), 0) === T(1.0) @test ldexp(T(0.8), 4) === T(12.8) @test ldexp(T(-0.854375), 5) === T(-27.34) @test ldexp(T(1.0), typemax(Int)) === T(Inf) @test ldexp(T(1.0), typemin(Int)) === T(0.0) @test ldexp(prevfloat(floatmin(T)), typemax(Int)) === T(Inf) @test ldexp(prevfloat(floatmin(T)), typemin(Int)) === T(0.0) @test ldexp(T(0.0), Int128(0)) === T(0.0) @test ldexp(T(-0.0), Int128(0)) === T(-0.0) @test ldexp(T(1.0), Int128(0)) === T(1.0) @test ldexp(T(0.8), Int128(4)) === T(12.8) @test ldexp(T(-0.854375), Int128(5)) === T(-27.34) @test ldexp(T(1.0), typemax(Int128)) === T(Inf) @test ldexp(T(1.0), typemin(Int128)) === T(0.0) @test ldexp(prevfloat(floatmin(T)), typemax(Int128)) === T(Inf) @test ldexp(prevfloat(floatmin(T)), typemin(Int128)) === T(0.0) @test ldexp(T(0.0), BigInt(0)) === T(0.0) @test ldexp(T(-0.0), BigInt(0)) === T(-0.0) @test ldexp(T(1.0), BigInt(0)) === T(1.0) @test ldexp(T(0.8), BigInt(4)) === T(12.8) @test ldexp(T(-0.854375), BigInt(5)) === T(-27.34) @test ldexp(T(1.0), BigInt(typemax(Int128))) === T(Inf) @test ldexp(T(1.0), BigInt(typemin(Int128))) === T(0.0) @test ldexp(prevfloat(floatmin(T)), BigInt(typemax(Int128))) === T(Inf) @test ldexp(prevfloat(floatmin(T)), BigInt(typemin(Int128))) === T(0.0) # Test also against BigFloat reference. Needs to be exactly rounded. @test ldexp(floatmin(T), -1) == T(ldexp(big(floatmin(T)), -1)) @test ldexp(floatmin(T), -2) == T(ldexp(big(floatmin(T)), -2)) @test ldexp(floatmin(T)/2, 0) == T(ldexp(big(floatmin(T)/2), 0)) @test ldexp(floatmin(T)/3, 0) == T(ldexp(big(floatmin(T)/3), 0)) @test ldexp(floatmin(T)/3, -1) == T(ldexp(big(floatmin(T)/3), -1)) @test ldexp(floatmin(T)/3, 11) == T(ldexp(big(floatmin(T)/3), 11)) @test ldexp(floatmin(T)/11, -10) == T(ldexp(big(floatmin(T)/11), -10)) @test ldexp(-floatmin(T)/11, -10) == T(ldexp(big(-floatmin(T)/11), -10)) end end end # We compare to BigFloat instead of hard-coding # values, assuming that BigFloat has an independently tested implementation. @testset "basic math functions" begin @testset "$T" for T in (Float32, Float64) x = T(1//3) y = T(1//2) yi = 4 @testset "Random values" begin @test x^y ≈ big(x)^big(y) @test x^1 === x @test x^yi ≈ big(x)^yi @test acos(x) ≈ acos(big(x)) @test acosh(1+x) ≈ acosh(big(1+x)) @test asin(x) ≈ asin(big(x)) @test asinh(x) ≈ asinh(big(x)) @test atan(x) ≈ atan(big(x)) @test atan(x,y) ≈ atan(big(x),big(y)) @test atanh(x) ≈ atanh(big(x)) @test cbrt(x) ≈ cbrt(big(x)) @test cos(x) ≈ cos(big(x)) @test cosh(x) ≈ cosh(big(x)) @test exp(x) ≈ exp(big(x)) @test exp10(x) ≈ exp10(big(x)) @test exp2(x) ≈ exp2(big(x)) @test expm1(x) ≈ expm1(big(x)) @test hypot(x,y) ≈ hypot(big(x),big(y)) @test hypot(x,x,y) ≈ hypot(hypot(big(x),big(x)),big(y)) @test hypot(x,x,y,y) ≈ hypot(hypot(big(x),big(x)),hypot(big(y),big(y))) @test log(x) ≈ log(big(x)) @test log10(x) ≈ log10(big(x)) @test log1p(x) ≈ log1p(big(x)) @test log2(x) ≈ log2(big(x)) @test sin(x) ≈ sin(big(x)) @test sinh(x) ≈ sinh(big(x)) @test sqrt(x) ≈ sqrt(big(x)) @test tan(x) ≈ tan(big(x)) @test tanh(x) ≈ tanh(big(x)) @test sec(x) ≈ sec(big(x)) @test csc(x) ≈ csc(big(x)) @test secd(x) ≈ secd(big(x)) @test cscd(x) ≈ cscd(big(x)) @test sech(x) ≈ sech(big(x)) @test csch(x) ≈ csch(big(x)) end @testset "Special values" begin @test isequal(T(1//4)^T(1//2), T(1//2)) @test isequal(T(1//4)^2, T(1//16)) @test isequal(acos(T(1)), T(0)) @test isequal(acosh(T(1)), T(0)) @test asin(T(1)) ≈ T(pi)/2 atol=eps(T) @test atan(T(1)) ≈ T(pi)/4 atol=eps(T) @test atan(T(1),T(1)) ≈ T(pi)/4 atol=eps(T) @test isequal(cbrt(T(0)), T(0)) @test isequal(cbrt(T(1)), T(1)) @test isequal(cbrt(T(1000000000)), T(1000)) @test isequal(cos(T(0)), T(1)) @test cos(T(pi)/2) ≈ T(0) atol=eps(T) @test isequal(cos(T(pi)), T(-1)) @test exp(T(1)) ≈ T(ℯ) atol=10*eps(T) @test isequal(exp10(T(1)), T(10)) @test isequal(exp2(T(1)), T(2)) @test isequal(expm1(T(0)), T(0)) @test expm1(T(1)) ≈ T(ℯ)-1 atol=10*eps(T) @test isequal(hypot(T(3),T(4)), T(5)) @test isequal(hypot(floatmax(T),T(1)),floatmax(T)) @test isequal(hypot(floatmin(T)*sqrt(eps(T)),T(0)),floatmin(T)*sqrt(eps(T))) @test isequal(floatmin(T)*hypot(1.368423059742933,1.3510496552495361),hypot(floatmin(T)*1.368423059742933,floatmin(T)*1.3510496552495361)) @test isequal(log(T(1)), T(0)) @test isequal(log(ℯ,T(1)), T(0)) @test log(T(ℯ)) ≈ T(1) atol=eps(T) @test isequal(log10(T(1)), T(0)) @test isequal(log10(T(10)), T(1)) @test isequal(log1p(T(0)), T(0)) @test log1p(T(ℯ)-1) ≈ T(1) atol=eps(T) @test isequal(log2(T(1)), T(0)) @test isequal(log2(T(2)), T(1)) @test isequal(sin(T(0)), T(0)) @test isequal(sin(T(pi)/2), T(1)) @test sin(T(pi)) ≈ T(0) atol=eps(T) @test isequal(sqrt(T(0)), T(0)) @test isequal(sqrt(T(1)), T(1)) @test isequal(sqrt(T(100000000)), T(10000)) @test isequal(tan(T(0)), T(0)) @test tan(T(pi)/4) ≈ T(1) atol=eps(T) @test isequal(sec(T(pi)), -one(T)) @test isequal(csc(T(pi)/2), one(T)) @test isequal(secd(T(180)), -one(T)) @test isequal(cscd(T(90)), one(T)) @test isequal(sech(log(one(T))), one(T)) @test isequal(csch(zero(T)), T(Inf)) end @testset "Inverses" begin @test acos(cos(x)) ≈ x @test acosh(cosh(x)) ≈ x @test asin(sin(x)) ≈ x @test cbrt(x)^3 ≈ x @test cbrt(x^3) ≈ x @test asinh(sinh(x)) ≈ x @test atan(tan(x)) ≈ x @test atan(x,y) ≈ atan(x/y) @test atanh(tanh(x)) ≈ x @test cos(acos(x)) ≈ x @test cosh(acosh(1+x)) ≈ 1+x @test exp(log(x)) ≈ x @test exp10(log10(x)) ≈ x @test exp2(log2(x)) ≈ x @test expm1(log1p(x)) ≈ x @test log(exp(x)) ≈ x @test log10(exp10(x)) ≈ x @test log1p(expm1(x)) ≈ x @test log2(exp2(x)) ≈ x @test sin(asin(x)) ≈ x @test sinh(asinh(x)) ≈ x @test sqrt(x)^2 ≈ x @test sqrt(x^2) ≈ x @test tan(atan(x)) ≈ x @test tanh(atanh(x)) ≈ x end @testset "Relations between functions" begin @test cosh(x) ≈ (exp(x)+exp(-x))/2 @test cosh(x)^2-sinh(x)^2 ≈ 1 @test hypot(x,y) ≈ sqrt(x^2+y^2) @test sin(x)^2+cos(x)^2 ≈ 1 @test sinh(x) ≈ (exp(x)-exp(-x))/2 @test tan(x) ≈ sin(x)/cos(x) @test tanh(x) ≈ sinh(x)/cosh(x) @test sec(x) ≈ inv(cos(x)) @test csc(x) ≈ inv(sin(x)) @test secd(x) ≈ inv(cosd(x)) @test cscd(x) ≈ inv(sind(x)) @test sech(x) ≈ inv(cosh(x)) @test csch(x) ≈ inv(sinh(x)) end @testset "Edge cases" begin @test isinf(log(zero(T))) @test isnan_type(T, log(convert(T,NaN))) @test_throws DomainError log(-one(T)) @test isinf(log1p(-one(T))) @test isnan_type(T, log1p(convert(T,NaN))) @test_throws DomainError log1p(convert(T,-2.0)) @test hypot(T(0), T(0)) === T(0) @test hypot(T(Inf), T(Inf)) === T(Inf) @test hypot(T(Inf), T(x)) === T(Inf) @test hypot(T(Inf), T(NaN)) === T(Inf) @test isnan_type(T, hypot(T(x), T(NaN))) end end end @testset "exp function" for T in (Float64, Float32) @testset "$T accuracy" begin X = map(T, vcat(-10:0.0002:10, -80:0.001:80, 2.0^-27, 2.0^-28, 2.0^-14, 2.0^-13)) for x in X y, yb = exp(x), exp(big(x)) @test abs(y-yb) <= 1.0*eps(T(yb)) end end @testset "$T edge cases" begin @test isnan_type(T, exp(T(NaN))) @test exp(T(-Inf)) === T(0.0) @test exp(T(Inf)) === T(Inf) @test exp(T(0.0)) === T(1.0) # exact @test exp(T(5000.0)) === T(Inf) @test exp(T(-5000.0)) === T(0.0) end end @testset "exp10 function" begin @testset "accuracy" begin X = map(Float64, vcat(-10:0.00021:10, -35:0.0023:100, -300:0.001:300)) for x in X y, yb = exp10(x), exp10(big(x)) @test abs(y-yb) <= 1.2*eps(Float64(yb)) end X = map(Float32, vcat(-10:0.00021:10, -35:0.0023:35, -35:0.001:35)) for x in X y, yb = exp10(x), exp10(big(x)) @test abs(y-yb) <= 1.2*eps(Float32(yb)) end end @testset "$T edge cases" for T in (Float64, Float32) @test isnan_type(T, exp10(T(NaN))) @test exp10(T(-Inf)) === T(0.0) @test exp10(T(Inf)) === T(Inf) @test exp10(T(0.0)) === T(1.0) # exact @test exp10(T(1.0)) === T(10.0) @test exp10(T(3.0)) === T(1000.0) @test exp10(T(5000.0)) === T(Inf) @test exp10(T(-5000.0)) === T(0.0) end end @testset "test abstractarray trig functions" begin TAA = rand(2,2) TAA = (TAA + TAA')/2. STAA = Symmetric(TAA) @test Array(atanh.(STAA)) == atanh.(TAA) @test Array(asinh.(STAA)) == asinh.(TAA) TAA .+= 1 @test Array(acosh.(STAA)) == acosh.(TAA) @test Array(acsch.(STAA)) == acsch.(TAA) @test Array(acoth.(STAA)) == acoth.(TAA) end @testset "check exp2(::Integer) matches exp2(::Float)" begin for ii in -2048:2048 expected = exp2(float(ii)) @test exp2(Int16(ii)) == expected @test exp2(Int32(ii)) == expected @test exp2(Int64(ii)) == expected @test exp2(Int128(ii)) == expected if ii >= 0 @test exp2(UInt16(ii)) == expected @test exp2(UInt32(ii)) == expected @test exp2(UInt64(ii)) == expected @test exp2(UInt128(ii)) == expected end end end @testset "deg2rad/rad2deg" begin @testset "$T" for T in (Int, Float64, BigFloat) @test deg2rad(T(180)) ≈ 1pi @test deg2rad.(T[45, 60]) ≈ [pi/T(4), pi/T(3)] @test rad2deg.([pi/T(4), pi/T(3)]) ≈ [45, 60] @test rad2deg(T(1)*pi) ≈ 180 @test rad2deg(T(1)) ≈ rad2deg(true) @test deg2rad(T(1)) ≈ deg2rad(true) end @test deg2rad(180 + 60im) ≈ pi + (pi/3)*im @test rad2deg(pi + (pi/3)*im) ≈ 180 + 60im end @testset "degree-based trig functions" begin @testset "$T" for T = (Float32,Float64,Rational{Int}) fT = typeof(float(one(T))) fTsc = typeof( (float(one(T)), float(one(T))) ) for x = -400:40:400 @test sind(convert(T,x))::fT ≈ convert(fT,sin(pi/180*x)) atol=eps(deg2rad(convert(fT,x))) @test cosd(convert(T,x))::fT ≈ convert(fT,cos(pi/180*x)) atol=eps(deg2rad(convert(fT,x))) s,c = sincosd(convert(T,x)) @test s::fT ≈ convert(fT,sin(pi/180*x)) atol=eps(deg2rad(convert(fT,x))) @test c::fT ≈ convert(fT,cos(pi/180*x)) atol=eps(deg2rad(convert(fT,x))) end @testset "sind" begin @test sind(convert(T,0.0))::fT === zero(fT) @test sind(convert(T,180.0))::fT === zero(fT) @test sind(convert(T,360.0))::fT === zero(fT) T != Rational{Int} && @test sind(convert(T,-0.0))::fT === -zero(fT) @test sind(convert(T,-180.0))::fT === -zero(fT) @test sind(convert(T,-360.0))::fT === -zero(fT) end @testset "cosd" begin @test cosd(convert(T,90))::fT === zero(fT) @test cosd(convert(T,270))::fT === zero(fT) @test cosd(convert(T,-90))::fT === zero(fT) @test cosd(convert(T,-270))::fT === zero(fT) end @testset "sincosd" begin @test sincosd(convert(T,-360))::fTsc === ( -zero(fT), one(fT) ) @test sincosd(convert(T,-270))::fTsc === ( one(fT), zero(fT) ) @test sincosd(convert(T,-180))::fTsc === ( -zero(fT), -one(fT) ) @test sincosd(convert(T, -90))::fTsc === ( -one(fT), zero(fT) ) @test sincosd(convert(T, 0))::fTsc === ( zero(fT), one(fT) ) @test sincosd(convert(T, 90))::fTsc === ( one(fT), zero(fT) ) @test sincosd(convert(T, 180))::fTsc === ( zero(fT), -one(fT) ) @test sincosd(convert(T, 270))::fTsc === ( -one(fT), zero(fT) ) end @testset "sinpi and cospi" begin for x = -3:0.3:3 @test sinpi(convert(T,x))::fT ≈ convert(fT,sin(pi*x)) atol=eps(pi*convert(fT,x)) @test cospi(convert(T,x))::fT ≈ convert(fT,cos(pi*x)) atol=eps(pi*convert(fT,x)) end @test sinpi(convert(T,0.0))::fT === zero(fT) @test sinpi(convert(T,1.0))::fT === zero(fT) @test sinpi(convert(T,2.0))::fT === zero(fT) T != Rational{Int} && @test sinpi(convert(T,-0.0))::fT === -zero(fT) @test sinpi(convert(T,-1.0))::fT === -zero(fT) @test sinpi(convert(T,-2.0))::fT === -zero(fT) @test_throws DomainError sinpi(convert(T,Inf)) @test cospi(convert(T,0.5))::fT === zero(fT) @test cospi(convert(T,1.5))::fT === zero(fT) @test cospi(convert(T,-0.5))::fT === zero(fT) @test cospi(convert(T,-1.5))::fT === zero(fT) @test_throws DomainError cospi(convert(T,Inf)) end @testset "Check exact values" begin @test sind(convert(T,30)) == 0.5 @test cosd(convert(T,60)) == 0.5 @test sind(convert(T,150)) == 0.5 @test sinpi(one(T)/convert(T,6)) == 0.5 @test_throws DomainError sind(convert(T,Inf)) @test_throws DomainError cosd(convert(T,Inf)) T != Float32 && @test cospi(one(T)/convert(T,3)) == 0.5 T == Rational{Int} && @test sinpi(5//6) == 0.5 end end end @testset "Integer args to sinpi/cospi/sinc/cosc" begin @test sinpi(1) == 0 @test sinpi(-1) == -0 @test cospi(1) == -1 @test cospi(2) == 1 @test sinc(1) == 0 @test sinc(complex(1,0)) == 0 @test sinc(0) == 1 @test sinc(Inf) == 0 @test cosc(1) == -1 @test cosc(0) == 0 @test cosc(complex(1,0)) == -1 @test cosc(Inf) == 0 end @testset "Irrational args to sinpi/cospi/sinc/cosc" begin for x in (pi, ℯ, Base.MathConstants.golden) @test sinpi(x) ≈ Float64(sinpi(big(x))) @test cospi(x) ≈ Float64(cospi(big(x))) @test sinc(x) ≈ Float64(sinc(big(x))) @test cosc(x) ≈ Float64(cosc(big(x))) @test sinpi(complex(x, x)) ≈ Complex{Float64}(sinpi(complex(big(x), big(x)))) @test cospi(complex(x, x)) ≈ Complex{Float64}(cospi(complex(big(x), big(x)))) @test sinc(complex(x, x)) ≈ Complex{Float64}(sinc(complex(big(x), big(x)))) @test cosc(complex(x, x)) ≈ Complex{Float64}(cosc(complex(big(x), big(x)))) end end @testset "trig function type stability" begin @testset "$T $f" for T = (Float32,Float64,BigFloat), f = (sind,cosd,sinpi,cospi) @test Base.return_types(f,Tuple{T}) == [T] end end # useful test functions for relative error, which differ from isapprox (≈) # in that relerrc separately looks at the real and imaginary parts relerr(z, x) = z == x ? 0.0 : abs(z - x) / abs(x) relerrc(z, x) = max(relerr(real(z),real(x)), relerr(imag(z),imag(x))) ≅(a,b) = relerrc(a,b) ≤ 1e-13 @testset "subnormal flags" begin # Ensure subnormal flags functions don't segfault @test any(set_zero_subnormals(true) .== [false,true]) @test any(get_zero_subnormals() .== [false,true]) @test set_zero_subnormals(false) @test !get_zero_subnormals() end @testset "evalpoly" begin @test @evalpoly(2,3,4,5,6) == 3+2*(4+2*(5+2*6)) == @evalpoly(2+0im,3,4,5,6) a0 = 1 a1 = 2 c = 3 @test @evalpoly(c, a0, a1) == 7 @test @evalpoly(1, 2) == 2 end @testset "evalpoly real" begin for x in -1.0:2.0, p1 in -3.0:3.0, p2 in -3.0:3.0, p3 in -3.0:3.0 evpm = @evalpoly(x, p1, p2, p3) @test evalpoly(x, (p1, p2, p3)) == evpm @test evalpoly(x, [p1, p2, p3]) == evpm end end @testset "evalpoly complex" begin for x in -1.0:2.0, y in -1.0:2.0, p1 in -3.0:3.0, p2 in -3.0:3.0, p3 in -3.0:3.0 z = x + im * y evpm = @evalpoly(z, p1, p2, p3) @test evalpoly(z, (p1, p2, p3)) == evpm @test evalpoly(z, [p1, p2, p3]) == evpm end @test evalpoly(1+im, (2,)) == 2 @test evalpoly(1+im, [2,]) == 2 end @testset "cis" begin for z in (1.234, 1.234 + 5.678im) @test cis(z) ≈ exp(im*z) end let z = [1.234, 5.678] @test cis.(z) ≈ exp.(im*z) end end @testset "modf" begin @testset "$elty" for elty in (Float16, Float32, Float64) @test modf( convert(elty,1.2) )[1] ≈ convert(elty,0.2) @test modf( convert(elty,1.2) )[2] ≈ convert(elty,1.0) @test modf( convert(elty,1.0) )[1] ≈ convert(elty,0.0) @test modf( convert(elty,1.0) )[2] ≈ convert(elty,1.0) end end @testset "frexp" begin @testset "$elty" for elty in (Float16, Float32, Float64) @test frexp( convert(elty,0.5) ) == (0.5, 0) @test frexp( convert(elty,4.0) ) == (0.5, 3) @test frexp( convert(elty,10.5) ) == (0.65625, 4) end end @testset "log/log1p" begin # using Tang's algorithm, should be accurate to within 0.56 ulps X = rand(100) for x in X for n = -5:5 xn = ldexp(x,n) for T in (Float32,Float64) xt = T(x) y = log(xt) yb = log(big(xt)) @test abs(y-yb) <= 0.56*eps(T(yb)) y = log1p(xt) yb = log1p(big(xt)) @test abs(y-yb) <= 0.56*eps(T(yb)) if n <= 0 y = log1p(-xt) yb = log1p(big(-xt)) @test abs(y-yb) <= 0.56*eps(T(yb)) end end end end for n = 0:28 @test log(2,2^n) == n end setprecision(10_000) do @test log(2,big(2)^100) == 100 @test log(2,big(2)^200) == 200 @test log(2,big(2)^300) == 300 @test log(2,big(2)^400) == 400 end for T in (Float32,Float64) @test log(zero(T)) == -Inf @test isnan_type(T, log(T(NaN))) @test_throws DomainError log(-one(T)) @test log1p(-one(T)) == -Inf @test isnan_type(T, log1p(T(NaN))) @test_throws DomainError log1p(-2*one(T)) end end @testset "vectorization of 2-arg functions" begin binary_math_functions = [ copysign, flipsign, log, atan, hypot, max, min, ] @testset "$f" for f in binary_math_functions x = y = 2 v = [f(x,y)] @test f.([x],y) == v @test f.(x,[y]) == v @test f.([x],[y]) == v end end @testset "issues #3024, #12822, #24240" begin p2 = -2 p3 = -3 @test_throws DomainError 2 ^ p2 @test 2 ^ -2 == 0.25 == (2^-1)^2 @test_throws DomainError (-2)^(2.2) @test_throws DomainError (-2.0)^(2.2) @test_throws DomainError false ^ p2 @test false ^ -2 == Inf @test 1 ^ -2 === (-1) ^ -2 == 1 ^ p2 === (-1) ^ p2 === 1 @test (-1) ^ -1 === (-1) ^ -3 == (-1) ^ p3 === -1 @test true ^ -2 == true ^ p2 === true end @testset "issue #13748" begin let A = [1 2; 3 4]; B = [5 6; 7 8]; C = [9 10; 11 12] @test muladd(A,B,C) == A*B + C end end @testset "issue #19872" begin f19872a(x) = x ^ 5 f19872b(x) = x ^ (-1024) @test 0 < f19872b(2.0) < 1e-300 @test issubnormal(2.0 ^ (-1024)) @test issubnormal(f19872b(2.0)) @test !issubnormal(f19872b(0.0)) @test f19872a(2.0) === 32.0 @test !issubnormal(f19872a(2.0)) @test !issubnormal(0.0) end # no domain error is thrown for negative values @test invoke(cbrt, Tuple{AbstractFloat}, -1.0) == -1.0 @testset "promote Float16 irrational #15359" begin @test typeof(Float16(.5) * pi) == Float16 end @testset "sincos" begin @test sincos(1.0) === (sin(1.0), cos(1.0)) @test sincos(1f0) === (sin(1f0), cos(1f0)) @test sincos(Float16(1)) === (sin(Float16(1)), cos(Float16(1))) @test sincos(1) === (sin(1), cos(1)) @test sincos(big(1)) == (sin(big(1)), cos(big(1))) @test sincos(big(1.0)) == (sin(big(1.0)), cos(big(1.0))) @test sincos(NaN) === (NaN, NaN) @test sincos(NaN32) === (NaN32, NaN32) end @testset "test fallback definitions" begin @test exp10(5) ≈ exp10(5.0) @test exp10(50//10) ≈ exp10(5.0) @test log10(exp10(ℯ)) ≈ ℯ @test log(ℯ) === 1 @test exp2(Float16(2.0)) ≈ exp2(2.0) @test exp2(Float16(1.0)) === Float16(exp2(1.0)) @test exp10(Float16(1.0)) === Float16(exp10(1.0)) end # #22742: updated isapprox semantics @test !isapprox(1.0, 1.0+1e-12, atol=1e-14) @test isapprox(1.0, 1.0+0.5*sqrt(eps(1.0))) @test !isapprox(1.0, 1.0+1.5*sqrt(eps(1.0)), atol=sqrt(eps(1.0))) # test AbstractFloat fallback pr22716 struct Float22716{T<:AbstractFloat} <: AbstractFloat x::T end Base.:^(x::Number, y::Float22716) = x^(y.x) let x = 2.0 @test exp2(Float22716(x)) === 2^x @test exp10(Float22716(x)) === 10^x end @testset "asin #23088" begin for T in (Float32, Float64) @test asin(zero(T)) === zero(T) @test asin(-zero(T)) === -zero(T) @test asin(nextfloat(zero(T))) === nextfloat(zero(T)) @test asin(prevfloat(zero(T))) === prevfloat(zero(T)) @test asin(one(T)) === T(pi)/2 @test asin(-one(T)) === -T(pi)/2 for x in (0.45, 0.6, 0.98) by = asin(big(T(x))) @test T(abs(asin(T(x)) - by))/eps(T(abs(by))) <= 1 bym = asin(big(T(-x))) @test T(abs(asin(T(-x)) - bym))/eps(T(abs(bym))) <= 1 end @test_throws DomainError asin(-T(Inf)) @test_throws DomainError asin(T(Inf)) @test isnan_type(T, asin(T(NaN))) end end @testset "sin, cos, sincos, tan #23088" begin for T in (Float32, Float64) @test sin(zero(T)) === zero(T) @test sin(-zero(T)) === -zero(T) @test cos(zero(T)) === T(1.0) @test cos(-zero(T)) === T(1.0) @test sin(nextfloat(zero(T))) === nextfloat(zero(T)) @test sin(prevfloat(zero(T))) === prevfloat(zero(T)) @test cos(nextfloat(zero(T))) === T(1.0) @test cos(prevfloat(zero(T))) === T(1.0) for x in (0.1, 0.45, 0.6, 0.75, 0.79, 0.98) for op in (sin, cos, tan) by = T(op(big(x))) @test abs(op(T(x)) - by)/eps(by) <= one(T) bym = T(op(big(-x))) @test abs(op(T(-x)) - bym)/eps(bym) <= one(T) end end @test_throws DomainError sin(-T(Inf)) @test_throws DomainError sin(T(Inf)) @test_throws DomainError cos(-T(Inf)) @test_throws DomainError cos(T(Inf)) @test_throws DomainError tan(-T(Inf)) @test_throws DomainError tan(T(Inf)) @test sin(T(NaN)) === T(NaN) @test cos(T(NaN)) === T(NaN) @test tan(T(NaN)) === T(NaN) end end @testset "rem_pio2 #23088" begin vals = (2.356194490192345f0, 3.9269908169872414f0, 7.0685834705770345f0, 5.497787143782138f0, 4.216574282663131f8, 4.216574282663131f12) for (i, x) in enumerate(vals) for op in (prevfloat, nextfloat) Ty = Float32(Base.Math.rem_pio2_kernel(op(vals[i]))[2].hi) By = Float32(rem(big(op(x)), pi/2)) @test Ty ≈ By || Ty ≈ By-Float32(pi)/2 end end end @testset "atan #23383" begin for T in (Float32, Float64) @test atan(T(NaN)) === T(NaN) @test atan(-T(Inf)) === -T(pi)/2 @test atan(T(Inf)) === T(pi)/2 # no reduction needed |x| < 7/16 @test atan(zero(T)) === zero(T) @test atan(prevfloat(zero(T))) === prevfloat(zero(T)) @test atan(nextfloat(zero(T))) === nextfloat(zero(T)) for x in (T(7/16), (T(7/16)+T(11/16))/2, T(11/16), (T(11/16)+T(19/16))/2, T(19/16), (T(19/16)+T(39/16))/2, T(39/16), (T(39/16)+T(2)^23)/2, T(2)^23) x = T(7/16) by = T(atan(big(x))) @test abs(atan(x) - by)/eps(by) <= one(T) x = prevfloat(T(7/16)) by = T(atan(big(x))) @test abs(atan(x) - by)/eps(by) <= one(T) x = nextfloat(T(7/16)) by = T(atan(big(x))) @test abs(atan(x) - by)/eps(by) <= one(T) end # This case was used to find a bug, but it isn't special in itself @test atan(1.7581305072934137) ≈ 1.053644580517088 end end @testset "atan" begin for T in (Float32, Float64) @test isnan_type(T, atan(T(NaN), T(NaN))) @test isnan_type(T, atan(T(NaN), T(0.1))) @test isnan_type(T, atan(T(0.1), T(NaN))) r = T(randn()) absr = abs(r) # y zero @test atan(T(r), one(T)) === atan(T(r)) @test atan(zero(T), absr) === zero(T) @test atan(-zero(T), absr) === -zero(T) @test atan(zero(T), -absr) === T(pi) @test atan(-zero(T), -absr) === -T(pi) # x zero and y not zero @test atan(one(T), zero(T)) === T(pi)/2 @test atan(-one(T), zero(T)) === -T(pi)/2 # isinf(x) == true && isinf(y) == true @test atan(T(Inf), T(Inf)) === T(pi)/4 # m == 0 (see atan code) @test atan(-T(Inf), T(Inf)) === -T(pi)/4 # m == 1 @test atan(T(Inf), -T(Inf)) === 3*T(pi)/4 # m == 2 @test atan(-T(Inf), -T(Inf)) === -3*T(pi)/4 # m == 3 # isinf(x) == true && isinf(y) == false @test atan(absr, T(Inf)) === zero(T) # m == 0 @test atan(-absr, T(Inf)) === -zero(T) # m == 1 @test atan(absr, -T(Inf)) === T(pi) # m == 2 @test atan(-absr, -T(Inf)) === -T(pi) # m == 3 # isinf(y) == true && isinf(x) == false @test atan(T(Inf), absr) === T(pi)/2 @test atan(-T(Inf), absr) === -T(pi)/2 @test atan(T(Inf), -absr) === T(pi)/2 @test atan(-T(Inf), -absr) === -T(pi)/2 # |y/x| above high threshold atanpi = T(1.5707963267948966) @test atan(T(2.0^61), T(1.0)) === atanpi # m==0 @test atan(-T(2.0^61), T(1.0)) === -atanpi # m==1 @test atan(T(2.0^61), -T(1.0)) === atanpi # m==2 @test atan(-T(2.0^61), -T(1.0)) === -atanpi # m==3 @test atan(-T(Inf), -absr) === -T(pi)/2 # |y|/x between 0 and low threshold @test atan(T(2.0^-61), -T(1.0)) === T(pi) # m==2 @test atan(-T(2.0^-61), -T(1.0)) === -T(pi) # m==3 # y/x is "safe" ("arbitrary values", just need to hit the branch) _ATAN_PI_LO(::Type{Float32}) = -8.7422776573f-08 _ATAN_PI_LO(::Type{Float64}) = 1.2246467991473531772E-16 @test atan(T(5.0), T(2.5)) === atan(abs(T(5.0)/T(2.5))) @test atan(-T(5.0), T(2.5)) === -atan(abs(-T(5.0)/T(2.5))) @test atan(T(5.0), -T(2.5)) === T(pi)-(atan(abs(T(5.0)/-T(2.5)))-_ATAN_PI_LO(T)) @test atan(-T(5.0), -T(2.5)) === -(T(pi)-atan(abs(-T(5.0)/-T(2.5)))-_ATAN_PI_LO(T)) @test atan(T(1235.2341234), T(2.5)) === atan(abs(T(1235.2341234)/T(2.5))) @test atan(-T(1235.2341234), T(2.5)) === -atan(abs(-T(1235.2341234)/T(2.5))) @test atan(T(1235.2341234), -T(2.5)) === T(pi)-(atan(abs(T(1235.2341234)/-T(2.5)))-_ATAN_PI_LO(T)) @test atan(-T(1235.2341234), -T(2.5)) === -(T(pi)-(atan(abs(-T(1235.2341234)/T(2.5)))-_ATAN_PI_LO(T))) end end @testset "atand" begin for T in (Float32, Float64) r = T(randn()) absr = abs(r) # Tests related to the 1-argument version of `atan`. # ================================================== @test atand(T(Inf)) === T(90.0) @test atand(-T(Inf)) === -T(90.0) @test atand(zero(T)) === T(0.0) @test atand(one(T)) === T(45.0) @test atand(-one(T)) === -T(45.0) # Tests related to the 2-argument version of `atan`. # ================================================== # If `x` is one, then `atand(y,x)` must be equal to `atand(y)`. @test atand(T(r), one(T)) === atand(T(r)) # `y` zero. @test atand(zero(T), absr) === zero(T) @test atand(-zero(T), absr) === -zero(T) @test atand(zero(T), -absr) === T(180.0) @test atand(-zero(T), -absr) === -T(180.0) # `x` zero and `y` not zero. @test atand(one(T), zero(T)) === T(90.0) @test atand(-one(T), zero(T)) === -T(90.0) # `x` and `y` equal for each quadrant. @test atand(+absr, +absr) === T(45.0) @test atand(-absr, +absr) === -T(45.0) @test atand(+absr, -absr) === T(135.0) @test atand(-absr, -absr) === -T(135.0) end end @testset "acos #23283" begin for T in (Float32, Float64) @test acos(zero(T)) === T(pi)/2 @test acos(-zero(T)) === T(pi)/2 @test acos(nextfloat(zero(T))) === T(pi)/2 @test acos(prevfloat(zero(T))) === T(pi)/2 @test acos(one(T)) === T(0.0) @test acos(-one(T)) === T(pi) for x in (0.45, 0.6, 0.98) by = acos(big(T(x))) @test T((acos(T(x)) - by))/eps(abs(T(by))) <= 1 bym = acos(big(T(-x))) @test T(abs(acos(T(-x)) - bym))/eps(abs(T(bym))) <= 1 end @test_throws DomainError acos(-T(Inf)) @test_throws DomainError acos(T(Inf)) @test isnan_type(T, acos(T(NaN))) end end #prev, current, next float pcnfloat(x) = prevfloat(x), x, nextfloat(x) import Base.Math: COSH_SMALL_X, H_SMALL_X, H_MEDIUM_X, H_LARGE_X @testset "sinh" begin for T in (Float32, Float64) @test sinh(zero(T)) === zero(T) @test sinh(-zero(T)) === -zero(T) @test sinh(nextfloat(zero(T))) === nextfloat(zero(T)) @test sinh(prevfloat(zero(T))) === prevfloat(zero(T)) @test sinh(T(1000)) === T(Inf) @test sinh(-T(1000)) === -T(Inf) @test isnan_type(T, sinh(T(NaN))) for x in Iterators.flatten(pcnfloat.([H_SMALL_X(T), H_MEDIUM_X(T), H_LARGE_X(T)])) @test sinh(x) ≈ sinh(big(x)) rtol=eps(T) @test sinh(-x) ≈ sinh(big(-x)) rtol=eps(T) end end end @testset "cosh" begin for T in (Float32, Float64) @test cosh(zero(T)) === one(T) @test cosh(-zero(T)) === one(T) @test cosh(nextfloat(zero(T))) === one(T) @test cosh(prevfloat(zero(T))) === one(T) @test cosh(T(1000)) === T(Inf) @test cosh(-T(1000)) === T(Inf) @test isnan_type(T, cosh(T(NaN))) for x in Iterators.flatten(pcnfloat.([COSH_SMALL_X(T), H_MEDIUM_X(T), H_LARGE_X(T)])) @test cosh(x) ≈ cosh(big(x)) rtol=eps(T) @test cosh(-x) ≈ cosh(big(-x)) rtol=eps(T) end end end @testset "tanh" begin for T in (Float32, Float64) @test tanh(zero(T)) === zero(T) @test tanh(-zero(T)) === -zero(T) @test tanh(nextfloat(zero(T))) === nextfloat(zero(T)) @test tanh(prevfloat(zero(T))) === prevfloat(zero(T)) @test tanh(T(1000)) === one(T) @test tanh(-T(1000)) === -one(T) @test isnan_type(T, tanh(T(NaN))) for x in Iterators.flatten(pcnfloat.([H_SMALL_X(T), T(1.0), H_MEDIUM_X(T)])) @test tanh(x) ≈ tanh(big(x)) rtol=eps(T) @test tanh(-x) ≈ tanh(big(-x)) rtol=eps(T) end end end @testset "asinh" begin for T in (Float32, Float64) @test asinh(zero(T)) === zero(T) @test asinh(-zero(T)) === -zero(T) @test asinh(nextfloat(zero(T))) === nextfloat(zero(T)) @test asinh(prevfloat(zero(T))) === prevfloat(zero(T)) @test isnan_type(T, asinh(T(NaN))) for x in Iterators.flatten(pcnfloat.([T(2)^-28,T(2),T(2)^28])) @test asinh(x) ≈ asinh(big(x)) rtol=eps(T) @test asinh(-x) ≈ asinh(big(-x)) rtol=eps(T) end end end @testset "acosh" begin for T in (Float32, Float64) @test_throws DomainError acosh(T(0.1)) @test acosh(one(T)) === zero(T) @test isnan_type(T, acosh(T(NaN))) for x in Iterators.flatten(pcnfloat.([nextfloat(T(1.0)), T(2), T(2)^28])) @test acosh(x) ≈ acosh(big(x)) rtol=eps(T) end end end @testset "atanh" begin for T in (Float32, Float64) @test_throws DomainError atanh(T(1.1)) @test atanh(zero(T)) === zero(T) @test atanh(-zero(T)) === -zero(T) @test atanh(one(T)) === T(Inf) @test atanh(-one(T)) === -T(Inf) @test atanh(nextfloat(zero(T))) === nextfloat(zero(T)) @test atanh(prevfloat(zero(T))) === prevfloat(zero(T)) @test isnan_type(T, atanh(T(NaN))) for x in Iterators.flatten(pcnfloat.([T(2.0)^-28, T(0.5)])) @test atanh(x) ≈ atanh(big(x)) rtol=eps(T) @test atanh(-x) ≈ atanh(big(-x)) rtol=eps(T) end end end # Define simple wrapper of a Float type: struct FloatWrapper <: Real x::Float64 end import Base: +, -, *, /, ^, sin, cos, exp, sinh, cosh, convert, isfinite, float, promote_rule for op in (:+, :-, :*, :/, :^) @eval $op(x::FloatWrapper, y::FloatWrapper) = FloatWrapper($op(x.x, y.x)) end for op in (:sin, :cos, :exp, :sinh, :cosh, :-) @eval $op(x::FloatWrapper) = FloatWrapper($op(x.x)) end for op in (:isfinite,) @eval $op(x::FloatWrapper) = $op(x.x) end convert(::Type{FloatWrapper}, x::Int) = FloatWrapper(float(x)) promote_rule(::Type{FloatWrapper}, ::Type{Int}) = FloatWrapper float(x::FloatWrapper) = x @testset "exp(Complex(a, b)) for a and b of non-standard real type #25292" begin x = FloatWrapper(3.1) y = FloatWrapper(4.1) @test sincos(x) == (sin(x), cos(x)) z = Complex(x, y) @test isa(exp(z), Complex) @test isa(sin(z), Complex) @test isa(cos(z), Complex) end @testset "cbrt" begin for T in (Float32, Float64) @test cbrt(zero(T)) === zero(T) @test cbrt(-zero(T)) === -zero(T) @test cbrt(one(T)) === one(T) @test cbrt(-one(T)) === -one(T) @test cbrt(T(Inf)) === T(Inf) @test cbrt(-T(Inf)) === -T(Inf) @test isnan_type(T, cbrt(T(NaN))) for x in (pcnfloat(nextfloat(nextfloat(zero(T))))..., pcnfloat(prevfloat(prevfloat(zero(T))))..., 0.45, 0.6, 0.98, map(x->x^3, 1.0:1.0:1024.0)..., nextfloat(-T(Inf)), prevfloat(T(Inf))) by = cbrt(big(T(x))) @test cbrt(T(x)) ≈ by rtol=eps(T) bym = cbrt(big(T(-x))) @test cbrt(T(-x)) ≈ bym rtol=eps(T) end end end isdefined(Main, :Furlongs) || @eval Main include("testhelpers/Furlongs.jl") using .Main.Furlongs @test hypot(Furlong(0), Furlong(0)) == Furlong(0.0) @test hypot(Furlong(3), Furlong(4)) == Furlong(5.0) @test hypot(Furlong(NaN), Furlong(Inf)) == Furlong(Inf) @test hypot(Furlong(Inf), Furlong(NaN)) == Furlong(Inf) @test hypot(Furlong(Inf), Furlong(Inf)) == Furlong(Inf)
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8, 198 ]
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@testset "zygote_adjoints" begin rng = MersenneTwister(123456) x = rand(rng, 5) y = rand(rng, 5) r = rand(rng, 5) Q = Matrix(Cholesky(rand(rng, 5, 5), 'U', 0)) @assert isposdef(Q) gzeucl = gradient(:Zygote, [x, y]) do xy evaluate(Euclidean(), xy[1], xy[2]) end gzsqeucl = gradient(:Zygote, [x, y]) do xy evaluate(SqEuclidean(), xy[1], xy[2]) end gzdotprod = gradient(:Zygote, [x, y]) do xy evaluate(KernelFunctions.DotProduct(), xy[1], xy[2]) end gzdelta = gradient(:Zygote, [x, y]) do xy evaluate(KernelFunctions.Delta(), xy[1], xy[2]) end gzsinus = gradient(:Zygote, [x, y]) do xy evaluate(KernelFunctions.Sinus(r), xy[1], xy[2]) end gzsqmaha = gradient(:Zygote, [Q, x, y]) do xy evaluate(SqMahalanobis(xy[1]), xy[2], xy[3]) end gfeucl = gradient(:FiniteDiff, [x, y]) do xy evaluate(Euclidean(), xy[1], xy[2]) end gfsqeucl = gradient(:FiniteDiff, [x, y]) do xy evaluate(SqEuclidean(), xy[1], xy[2]) end gfdotprod = gradient(:FiniteDiff, [x, y]) do xy evaluate(KernelFunctions.DotProduct(), xy[1], xy[2]) end gfdelta = gradient(:FiniteDiff, [x, y]) do xy evaluate(KernelFunctions.Delta(), xy[1], xy[2]) end gfsinus = gradient(:FiniteDiff, [x, y]) do xy evaluate(KernelFunctions.Sinus(r), xy[1], xy[2]) end gfsqmaha = gradient(:FiniteDiff, [Q, x, y]) do xy evaluate(SqMahalanobis(xy[1]), xy[2], xy[3]) end @test all(gzeucl .≈ gfeucl) @test all(gzsqeucl .≈ gfsqeucl) @test all(gzdotprod .≈ gfdotprod) @test all(gzdelta .≈ gfdelta) @test all(gzsinus .≈ gfsinus) @test all(gzsqmaha .≈ gfsqmaha) end
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<reponame>mdneuzerling/advent_of_code module TestDay05 import AOC2021.Day05.part1, AOC2021.Day05.part2 using Test test_input_raw = """ 0,9 -> 5,9 8,0 -> 0,8 9,4 -> 3,4 2,2 -> 2,1 7,0 -> 7,4 6,4 -> 2,0 0,9 -> 2,9 3,4 -> 1,4 0,0 -> 8,8 5,5 -> 8,2""" test_input = [string(x) for x in split(test_input_raw, "\n")] @testset "Day 05" begin @testset "part 1" begin @test part1(test_input) == 5 end @testset "part 2" begin @test part2(test_input) == 12 end end end # module
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<gh_stars>0 @doc raw""" BrownianMotionTorus(n::Int) Returns a hidden state model corresponding to a Brownian motion on an `n`-dimensional torus, with initial condition drawn uniformly at random. """ struct BrownianMotionTorus <: HiddenStateModel{Vector{Float64}, ContinuousTime} n::Int end ##################### ### BASIC METHODS ### ##################### initial_condition(::BrownianMotionTorus) = Uniform(0,2π) state_dim(model::BrownianMotionTorus) = model.n """ noise_dim(model) Returns the dimension of the Brownian motion ``W_t`` in the diffusion model ``dX_t = f(X_t)dt + g(X_t)dW_t``. """ noise_dim(model::BrownianMotionTorus) = model.n function initialize(model::BrownianMotionTorus) x = rand(initial_condition(model), state_dim(model)) return x end function Base.show(io::IO, ::MIME"text/plain", model::BrownianMotionTorus) print(io, "Brownian motion on the ", state_dim(model), "-torus for the hidden state type of hidden state: ", state_dim(model),"-dimensional vector number of independent Brownian motions: ", noise_dim(model)," initial condition: uniform") end function Base.show(io::IO, model::BrownianMotionTorus) print(io, "Brownian motion on the ", state_dim(model), "-torus") end ###################### ### TIME EVOLUTION ### ###################### function (model::BrownianMotionTorus)(x::AbstractVector{T}, dt) where T return mod2pi.(x .+ sqrt(dt) .* randn(T, noise_dim(model))) end function (model::BrownianMotionTorus)(x::AbstractMatrix, dt) N = size(x, 2) out = zeros(eltype(x), state_dim(model), N) sqr = sqrt(dt) @inbounds for b in 1:N for a in 1:state_dim(model) out[a,b] += mod2pi(x[a,b] + sqr * randn(eltype(x))) end end return out end function propagate!(x::AbstractVector, model::BrownianMotionTorus, dt) N = size(x, 2) sqr = sqrt(dt) @inbounds for a in 1:state_dim(model) x[a] += sqr * randn(eltype(x)) x[a] = mod2pi(x[a]) end return x end function propagate!(x::AbstractMatrix, model::BrownianMotionTorus, dt) N = size(x, 2) sqr = sqrt(dt) @inbounds for b in 1:N for a in 1:state_dim(model) x[a,b] += sqr * randn(eltype(x)) x[a,b] = mod2pi(x[a,b]) end end return x end ################################ ### CONVENIENCE CONSTRUCTORS ### ################################ const BrownianMotionCircle() = BrownianMotionTorus(1)
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1.791209
1,911
<gh_stars>1-10 # Helper functions sameSense(pra::Int, ra::Int) = mod(pra,2)==mod(ra,2) downSense(ra::Int) = (ra>0) .& (mod(ra,2)==1) upSense(ra::Int) = (ra>0) .& (mod(ra,2)==0) # Reward function for VerticalCAS MDP function POMDPs.reward(mdp::VerticalCAS_MDP, s::stateType, ra::actType) h = s[1]; vown = s[2]; vint = s[3]; pra = s[4]; resp = s[5]; tau = mdp.currentTau r = 0.0 sep = abs(h) closure = abs(vint-vown) crossing = ((h<0) .& downSense(ra)) .| ((h>0) .& upSense(ra)) deltaVown = 0.0 corrective = false preventitive = false weakening = false strengthening = false reversal = false if ra>COC if pra>COC reversal = mod(pra,2)!=mod(ra,2) end if !reversal weakening = pra>ra strengthening = pra<ra end vLow, vHigh = mdp.velRanges[ra] corrective = (vown>vLow) .& (vown < vHigh) preventitive = !corrective if corrective if downSense(ra) deltaVown = abs(vLow-vown) else deltaVown = abs(vHigh-vown) end end end lolo = (ra==DNC) .| (ra==DND) if (sep<=175) .& (tau==0) r-=1.0 end if mdp.allowedTrans[pra][ra+1]==0 r-=1.0 end if crossing if preventitive r-=1.0 end if sep>500 r-=0.01 end end if corrective r-=1e-5 if (sep>650) .& (closure<2000.0/60.0) r-= 0.1 end if (sep>1000) .& (closure<4000.0/60.0) r-=0.03 end elseif preventitive if (sep>650) .& (closure<2000.0/60.0) r-=0.01 end end if reversal r-= 8e-3 end if strengthening r-=5e-3 end if weakening r-=1e-3 end if lolo r-=1e-4 if closure > 3000.0/60.0 r-=5e-4 end elseif (ra!=COC) .& (closure > 3000.0/60.0) r-=1.5e-3 end if closure < 3000.0/60.0 r-=2.3e-3 end if ra==COC r+=1e-9 else r-=3e-5*deltaVown if closure > 3000.0/60.0 r-=1.5e-3 end end return r end
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1.688066
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name = "LLVM" llvm_full_version = v"11.0.1+3" libllvm_version = v"11.0.1+3" # Include common LLVM stuff include("../common.jl") build_tarballs(ARGS, configure_extraction(ARGS, llvm_full_version, name, libllvm_version; experimental_platforms=true, assert=true)...; skip_audit=true, julia_compat="1.6")
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2.525
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<reponame>tpprescott/mf-abc include("MultiFidelityABC.jl") mkpath("figures") using StatsPlots, Random println("#### Repressilator") println("# Loading data") bm = MakeBenchmarkCloud("repressilator/output") epsilons = (50.0,50.0) sample_size = 10^4 println("# Fig 1") fig1a = view_distances(bm[1:sample_size], epsilons) fig1b = view_distances(bm[1:sample_size], epsilons, 2, L"n") savefig(plot(fig1a, fig1b, layout=2, size=(900,360)), "figures/fig1.pdf") println("# Fig 2 and Table 1") fig2a = compare_efficiencies(bm, sample_size, epsilons, output="theory") fig2b, table1, latex_table1 = compare_efficiencies(bm, sample_size, epsilons, output="plot") savefig(plot(fig2a, fig2b, layout=2, size=(1100,440)), "figures/fig2.pdf") println("# Table 2 and 3") eta_tab, phi_tab = get_eta(bm, epsilons, Repressilator.F) Random.seed!(123) var_tab = variance_table(bm, 10^3, epsilons, eta_tab, Repressilator.F, 30.0) Random.seed!() println("# Supplementary") t,x,p = simulate(Repressilator.tlm) tt,xx = complete(Repressilator.tlm, p) function show_plots(solutions::NTuple{N,Tuple{Times,States,String}}, j::Integer; kwargs...) where N fig = plot(; kwargs...); for (t,x,l) in solutions plot!(t,x[j,:]; label=l) end return fig end titles = ["mRNA1", "mRNA2", "mRNA3", "P1", "P2", "P3"] figs = [show_plots(((t,x,"Tau-leap"),(tt,xx,"Gillespie")),j; title=ttl, legend=:none) for (j, ttl) in enumerate(titles)] figs[1] = plot!(figs[1]; legend=:topleft) repressilator_eg = plot(figs..., layout=6) savefig(repressilator_eg, "./figures/SFig1.pdf") println("#### Viral") println("# Load data") bm = MakeBenchmarkCloud("viral/output") mf_smallBI_location = "./viral/output/mf_smallBI/" mf_largeBI_location = "./viral/output/mf_largeBI/" epsilons = (0.25,0.25) eta_0 = 0.01 smallBI_size = 10^3 largeBI_size = length(bm) function divide_cloud(c::MFABCCloud, s::Integer; stage::String) if stage=="bm" return c[1:s] elseif stage=="inc" return c[s+1:end] end error("What stage? bm or inc") end bm_set = Array{MFABCCloud,1}() mf_set = Array{MFABCCloud,1}() inc_smallBI_set = Array{MFABCCloud,1}() inc_largeBI_set = Array{MFABCCloud,1}() for cloud_location in mf_smallBI_location.*readdir(mf_smallBI_location) c = MakeMFABCCloud(cloud_location) push!(mf_set, c) push!(bm_set, divide_cloud(c, smallBI_size, stage="bm")) push!(inc_smallBI_set, divide_cloud(c, smallBI_size, stage="inc")) end for cloud_location in mf_largeBI_location.*readdir(mf_largeBI_location) c = MakeMFABCCloud(cloud_location) push!(inc_largeBI_set, divide_cloud(c, largeBI_size, stage="inc")) end println("# Fig 3") savefig(view_distances(bm[1:10000], epsilons, epsilons.*2), "figures/fig3.pdf") println("# Fig 4") fig4 = plot_eta_estimates(bm, epsilons, (bm_set,"After burn-in"), (mf_set,"After adaptation"); method="mf", lower_eta=eta_0) plot!(xlim=(0,0.4),ylim=(0,0.2)) savefig(fig4, "figures/fig4.pdf") println("# Fig 5") savefig(plot_apost_efficiencies((inc_largeBI_set,"After large burn-in"), (inc_smallBI_set, "After small burn-in"), (bm_set, "During burn-in")), "figures/fig5.pdf") println("# Supplementary") t,x,p = simulate(Repressilator.tlm) tt,xx = complete(Repressilator.tlm, p) f = [plot(t,x',label=["template" "genome" "struct" "virus"]), plot(tt,xx',legend=:none)] savefig(plot(f..., layout=2, size=(1000,400)), "./figures/SFig2.pdf")
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<gh_stars>10-100 struct Lattice{D,T,M,U} site::Vector{Site{D,T}} # sorted by ID coord_order::Vector{Site{D,T}} # sort by coord neighbors::Vector{Vector{Int}} types::Vector{U} end function Lattice(coord, types::Vector; nbhood = VonNeumann(), type_list = unique(types) ) dimension = coord isa Matrix ? size(coord, 1) : length(coord[1]) # unique_types = sort!(type_list) unique_types = type_list number_types = length(unique_types) number_neighbors = capacity(nbhood, dimension) labels = OrderedDict(unique_types[i] => i+1 for i in eachindex(unique_types)) site = build_sites(coord, types, labels) site_by_coord = sort(site, by = coordinates) neighbors = [sizehint!(Int[], number_neighbors) for i in eachindex(site)] mapping = [(label => l) for (label, l) in labels] T = coord isa Matrix ? eltype(coord) : eltype(coord[1]) U = eltype(mapping) return Lattice{dimension,T,typeof(nbhood),U}(site, site_by_coord, neighbors, mapping) end function build_sites(coord::Matrix, types, labels) site = [Site(i, State(labels[types[i]]), tuple(coord[:,i]...)) for i in eachindex(types)] end function build_sites(coord::Vector{NTuple{N,T}}, types, labels) where {N,T} site = [Site(i, State(labels[types[i]]), coord[i]) for i in eachindex(types)] end ##### accessors dimension(::Lattice{D}) where D = D topology(::Lattice{D,T,M}) where {D,T,M} = M() number_types(x::Lattice) = length(x.types) number_sites(x::Lattice) = length(x.site) ##### IO function Base.summary(io::IO, ::Lattice{D,T,M}) where {D,T,M} print(io, "$(D)-D Lattice with $(M) neighborhoods") end function Base.show(io::IO, lattice::Lattice) Base.summary(io, lattice) print(io, "\n species: ") show(io, lattice.types) end function Base.show(io::IO, m::MIME"text/plain", lattice::Lattice) Base.summary(io, lattice) print(io, "\n", "species: ") show(io, m, lattice.types) end ##### other Base overloads Base.copy(lattice::Lattice) = deepcopy(lattice) # this is a dumb hack to make Lattice compatible with SamplePath Base.size(lattice::Lattice) = size(lattice.types) function __simple_copy(lattice) coord = coordinates.(lattice.site) tcode = get_ptype.(lattice.site) idx = findall(!isequal(1), tcode) return coord[idx], tcode[idx] end ##### query API function istracked(lattice::Lattice, coord) idx = searchsorted(lattice.coord_order, coord, by = coordinates, lt = <) return !isempty(idx) end # get the site living at the given coordinates function get_site(lattice::Lattice, coord) idx = searchsorted(lattice.coord_order, coord, by = coordinates, lt = <) lattice.coord_order[idx[1]] end # get the site with the given id function get_site(lattice::Lattice, id::Int) # idx = searchsorted(lattice.site, id, by = label) # # # if idx is empty, site was not found # # # if idx has length > 1, we have a duplicate # # # if idx has length = 1, return the object # lattice.site[idx[1]] lattice.site[id] end function neighborhood(lattice::Lattice, x::Site) lattice.neighbors[label(x)] end ##### update API function spawn_new_site(lattice::Lattice, coord) id = number_sites(lattice) + 1 return Site(id, State(), coord) end # this function assumes new_site is not tracked! function add_site!(lattice::Lattice, new_site::Site) # add neighborhood for site nbmax = capacity(topology(lattice), dimension(lattice)) push!(lattice.neighbors, sizehint!(Int[], nbmax)) # new sites are always added to the end of the list sorted by IDs push!(lattice.site, new_site) # add site to list sorted by coord idx = searchsortedfirst(lattice.coord_order, new_site, by = coordinates) insert!(lattice.coord_order, idx[1], new_site) new_site end function add_neighbor!(lattice::Lattice{D}, x::Site{D}, y::Site{D}) where D nb = neighborhood(lattice, x) nbtype = topology(lattice) neighbor_count = length(nb) nbhood_capacity = capacity(nbtype, D) if label(y) in nb error("$(y) is already a neighbor of $(x)") end if neighbor_count ≥ nbhood_capacity msg = """ Neighborhood of $(x) at capacity ($(nbhood_capacity)). Failed to add $(y) as a neighbor. """ nb_y = neighborhood(lattice, y) @debug """ Neighbor data: x: $(label(x)) / $(get_ptype(x))|$(get_neighbor_class(x)) @ $(x) y: $(label(y)) / $(get_ptype(y))|$(get_neighbor_class(y)) @ $(y) Neighborhood: x: $(nb) y: $(nb_y) Sites: x: $(lattice.site[nb]) y: $(lattice.site[nb_y]) """ throw(ErrorException(msg)) end # store neighbors in sorted fashion i = label(y) idx = searchsortedfirst(nb, i) insert!(nb, idx, i) end function rmv_neighbor!(lattice::Lattice, x::Site, y::Site) i = label(x) j = label(y) nb = neighborhood(lattice, x) idx = findfirst(isequal(j), nb) if idx isa Nothing msg = """ $(y) is not adjacent to $(x) under $(topology(lattice)) structure. Failed to remove neighbor. """ throw(ErrorException(msg)) end # delete item, which preserves ordering deleteat!(nb, idx) end ##### recipes @recipe function f(lattice::Lattice{D,T,VonNeumann}) where {D,T} site = lattice.site types = lattice.types seriestype := :scatter aspect_ratio --> 1 markerstrokewidth --> 0 markershape --> :square # color --> map(get_ptype, site) # label --> map(first, lattice.types) for t in types myseries = filter(x -> get_ptype(x) == last(t), site) @series begin label --> first(t) [tuple(s...) for s in myseries] end end end @recipe function f(lattice::Lattice{2,T,Hexagonal}) where {T} site = lattice.site types = lattice.types seriestype := :scatter aspect_ratio --> 1 markerstrokewidth --> 0 markershape --> :hexagon # color --> map(get_ptype, site) # label --> map(first, lattice.types) A = cos(pi / 3) B = sin(pi / 3) for t in types s = filter(x -> get_ptype(x) == last(t), site) @series begin label --> first(t) new_coords = Vector{NTuple{2,Float64}}(undef, length(s)) for i in eachindex(s) p = s[i] x = p[1] + A * p[2] y = B * p[2] new_coords[i] = (x, y) end new_coords end end end ##### building neighborhoods function build_neighborhoods!(nbtype::VonNeumann, lattice::Lattice{1}) sites = lattice.coord_order sort_by_x_1D!(sites) sweep_neighbors!(nbtype, lattice, sites) return nothing end function build_neighborhoods!(nbtype::VonNeumann, lattice::Lattice{2}) sites = lattice.coord_order sort_by_x_2D!(sites) sweep_neighbors!(nbtype, lattice, sites) sort_by_y_2D!(sites) sweep_neighbors!(nbtype, lattice, sites) return nothing end function build_neighborhoods!(nbtype::VonNeumann, lattice::Lattice{3}) sites = lattice.coord_order sort_by_x_3D!(sites) sweep_neighbors!(nbtype, lattice, sites) sort_by_y_3D!(sites) sweep_neighbors!(nbtype, lattice, sites) sort_by_z_3D!(sites) sweep_neighbors!(nbtype, lattice, sites) return nothing end function sweep_neighbors!(nbtype::VonNeumann, lattice, sites) for i = 2:length(sites) x = sites[i-1] y = sites[i] d = distance(nbtype, x, y) if d == 1 && !(label(y) in neighborhood(lattice, x)) add_neighbor!(lattice, x, y) add_neighbor!(lattice, y, x) end end return sites end function build_neighborhoods!(nbtype::Hexagonal, lattice::Lattice{2}) sites = lattice.coord_order sweep_neighbors!(nbtype, lattice, sites) end function sweep_neighbors!(nbtype::Hexagonal, lattice, sites) n = length(sites) for i in 1:n for j in 1:i-1 # j < i x = sites[i] y = sites[j] d = distance(nbtype, x, y) if d == 1 && !(label(y) in neighborhood(lattice, x)) add_neighbor!(lattice, x, y) add_neighbor!(lattice, y, x) end end end return sites end ### TODO: 3D
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3,217
<gh_stars>1-10 using LinearAlgebra, Jets, JetPack, Test n1,n2 = 33,44 @testset "JopLog, correctness T=$(T)" for T in (Float64,Float32,Complex{Float64},Complex{Float32}) F = JopLog(JetSpace(T,n1,n2)) x1 = rand(domain(F)) .+ T(0.0001) @test F*x1 ≈ log.(x1) end @testset "JopLog, linearity test, T=$(T)" for T in (Float64,Float32,Complex{Float64},Complex{Float32}) F = JopLog(JetSpace(T,n1,n2)) J = jacobian(F, rand(domain(F)) .+ T(0.0001)) lhs,rhs = linearity_test(J) @test lhs ≈ rhs end @testset "JopLog, dot product test, T=$(T)" for T in (Float64,Float32,Complex{Float64},Complex{Float32}) F = JopLog(JetSpace(T,n1,n2)) J = jacobian!(F, rand(domain(F)) .+ T(0.0001)) lhs, rhs = dot_product_test(J, -1 .+ 2 .* rand(domain(J)), -1 .+ 2 .* rand(range(J))) @test lhs ≈ rhs end # note the key here is to increase the size of the nonlinear vector @testset "JopLog, linearization test, T=$(T)" for T in (Float64,Float32,Complex{Float64},Complex{Float32}) F = JopLog(JetSpace(T,n1,n2)) m0 = 1000 .* rand(domain(F)) .+ T(1) μ = sqrt.([1/1,1/2,1/4,1/8,1/16,1/32,1/64,1/128,1/256,1/512,1/1024,1/2048]) δm = rand(domain(F)) .+ T(0.0001) observed, expected = linearization_test(F, m0, μ = μ, δm = δm) δ = minimum(abs, observed - expected) #write(stdout, @sprintf("\nLinearization test -- type(%s)\n", T)) #for i = 1:length(observed) #write(stdout, @sprintf("mu,observed,expected,diff; %12.6f %12.6f %12.6f %12.6f\n", μ[i], observed[i], expected[i], abs(observed[i] - expected[i]))) #end #write(stdout, @sprintf("minimum difference %12.6f\n", minimum(abs,observed .- expected))) @test δ < 0.1 end
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2.106117
801
using FileIO, BedgraphFiles using Bedgraph using IteratorInterfaceExtensions using TableTraits using DataFrames using Query using Test using Logging # old_logger = global_logger(ConsoleLogger(stdout, Logging.Debug)) module Bag using Bedgraph const chroms = ["chr19", "chr19", "chr19", "chr19", "chr19", "chr19", "chr19", "chr19", "chr19"] const firsts = [49302000, 49302300, 49302600, 49302900, 49303200, 49303500, 49303800, 49304100, 49304400] const lasts = [49302300, 49302600, 49302900, 49303200, 49303500, 49303800, 49304100, 49304400, 49304700] const values = [-1.0, -0.75, -0.50, -0.25, 0.0, 0.25, 0.50, 0.75, 1.00] const record = Bedgraph.Record("chr1", 1, 1, 0) const records = Bedgraph.Record.(Bag.chroms, Bag.firsts, Bag.lasts, Bag.values) const file = joinpath(@__DIR__, "data.bedgraph") const file_headerless = joinpath(@__DIR__, "data-headerless.bedgraph") const tmp_output_path = tempname() * ".bedgraph" end # module Bag using .Bag @testset "BedgraphFiles" begin @test isfile(Bag.file) == true @test isfile(Bag.file_headerless) == true # Load tests. loader = load(Bag.file) @test IteratorInterfaceExtensions.isiterable(loader) == true @test TableTraits.isiterabletable(loader) == true loaded = Vector{Bedgraph.Record}(loader) @test Vector{Bedgraph.Record} == typeof(loaded) loader_from_headerless = load(Bag.file_headerless) @test isiterable(loader_from_headerless) == true @test TableTraits.isiterabletable(loader_from_headerless) == true loaded_from_headerless = Vector{Bedgraph.Record}(loader_from_headerless) @test Vector{Bedgraph.Record} == typeof(loaded_from_headerless) @test IteratorInterfaceExtensions.isiterable(Bag.records) == true @test TableTraits.isiterabletable(Bag.records) == true @test Bag.records == loaded @test Bag.records == loaded_from_headerless # Save and load from Vector{Bedgraph.Record}. save(Bag.tmp_output_path, Bag.records) @debug "direct load into Vector{Bedgraph.Record} - commencing" @test Bag.records == Vector{Bedgraph.Record}(load(Bag.tmp_output_path)) @debug "direct load into Vector{Bedgraph.Record} - complete" @test Bag.records == load(Bag.tmp_output_path) |> Vector{Bedgraph.Record} # Save usign query. Bag.records |> save(Bag.tmp_output_path) @test Bag.records == Vector{Bedgraph.Record}(load(Bag.tmp_output_path)) @test Bag.records == load(Bag.tmp_output_path) |> Vector{Bedgraph.Record} # Check return of data from save method. @test Bag.records == Bag.records |> save(Bag.tmp_output_path) # Check piping/continuations through Query.jl. load("data.bedgraph") |> @filter(_.chrom == "chr19" && _.first > 49302900 && _.last < 49303800) |> save(Bag.tmp_output_path) @test [Bedgraph.Record("chr19", 49303200, 49303500, 0.0)] == load(Bag.tmp_output_path) |> Vector{Bedgraph.Record} @testset "Integrations" begin include("integrations/test-DataFrames.jl") end # testset Transformers println() show(load(Bag.file)) println() end
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2.7
1,080
<reponame>cvdlab/ViewerGL.js<filename>test/GLUtils.jl<gh_stars>1-10 using Test using LinearAlgebraicRepresentation Lar = LinearAlgebraicRepresentation using ViewerGL GL = ViewerGL @testset "GLUtils.jl" begin # function lar4mesh(verts,cells) # cells are triangles @testset "lar4mesh" begin @test @test @test @test end # function two2three(points) @testset "two2three" begin @test @test @test @test end # function glGenBuffer() @testset "glGenBuffer" begin @test @test @test @test end # function glGenVertexArray() @testset "glGenVertexArray" begin @test @test @test @test end # function glLineWidth() @testset "glLineWidth" begin @test @test @test @test end # function glPointSize() @testset "glPointSize" begin @test @test @test @test end # function glCheckError(actionName="") @testset "glCheckError" begin @test @test @test @test end # function glErrorMessage() @testset "glErrorMessage" begin @test @test @test @test end # function glDeleteLater(fun::Function) @testset "glDeleteLater" begin @test @test @test @test end # function glDeleteNow() @testset "glDeleteNow" begin @test @test @test @test end # function normalize2(V::Lar.Points; flag=true, fold=-1) @testset "normalize2" begin @test @test @test @test end # function normalize3(V::Lar.Points, flag=true) @testset "normalize3" begin @test @test @test @test end end
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<reponame>sd109/BlackBoxOptim.jl<filename>spikes/subset_tournament_cmsa_es/subset_tournament_cmsa_es.jl # This is Subset Tournament CMSA-ES as proposed by <NAME> in the paper: # <NAME>, "Covariate Subset Tournaments for High-Dimensional Blackbox Optimization with Covariance Matrix Adapting Evolutionary Strategies", 2014 function normalize_utilities(utilities) utilities / sum(utilities) end function linear_utilities(mu, lambda) normalize_utilities(hcat(ones(1, mu), zeros(1, lambda-mu))) end function log_utilities(mu, lambda) normalize_utilities(hcat((log(mu+1) - log(1:mu))', zeros(1, lambda-mu))) end # Optimize the n-dimensional objective func with a (mu,lambda) CMSA-ES using # subset tournaments to optimize subsets of variables in rounds. function st_cmsa_es(p; trace = true, # Stopping criteria related max_seconds = 4*numdims(p), max_evals_per_dim = 1e7, ftol = 1e-7, xtol = 1e-10, stol = 1e-10, max_rounds_without_improvement = 200, # Starting points, will be random unless specified xmean = false, # Algorithm specific params: covarMatrixSampler = SubsetCholeskyCovarSampler, utilitiesFunc = log_utilities, lambda = 4*numdims(p), mu = int(max(ceil(lambda/rand(4:20)), 1.0)), tau = 1 / sqrt(2*numdims(p)), # Equation (1) on page 5 in Beyer2008 tau_c = 1 + numdims(p) * (numdims(p) + 1) / (2 * mu), # Equation (2) on page 5 in Beyer2008 sigma = 0.05*rand(1:8)*minimum(diameters(search_space(p))), decompose_covar_prob = 0.4, # Subsetting specific parameters: subset_size = int(floor(0.20*numdims(p))), subsets_per_tournament = 2, num_rounds_per_tournament = 1, num_rounds_of_optimization_between_tournaments = 30, subset_selection_mechanism = :random ) N = numdims(p) ss = search_space(p) max_evals = max_evals_per_dim * N a = 1 - 1 / tau_c C = covarMatrixSampler(N) utilities = utilitiesFunc(mu, lambda) xbest = xmean = rand_individual(ss) # Current best mean value. fbest = eval1(xbest, p) num_fevals = 1 archive = BlackBoxOptim.TopListArchive(N, 10) add_candidate!(archive, fbest, xbest[:], num_fevals) fevals_last_best = num_fevals next_print_covar = 100 termination_reason = "?" # Init for subset tournaments st_state = :optimization # Can be either :tournament or :optimization and starts with :optimization to ensure we set up a new tournament num_optimization_rounds_for_this_subset = num_rounds_of_optimization_between_tournaments # Also ensures we are end of opt round => new tournament will be set up st_current_subset = 1 # When in :tournament mode this is the index to the subset currently being evaluated, can be in range 1-subsets_per_tournament st_subsets = Array{Int, 1}[] # Subsets that are currently in a tournament num_tournament_rounds_for_this_subset = 0 # Number of eval rounds we have ran with current subset in tournament mode fitness_per_tournament_round = zeros(num_rounds_per_tournament, subsets_per_tournament) # Keep stats per covariate being optimized for how effective the optimization is when they are included. # We save the expected relative change per round of optimization and use a history parameter in [0.0, 1.0] # to decide how much history should be saved when updating it, i.e. newval = oldvalue * h + delta * (1-h). # These stats values are used in multiple tournaments like so: # 1. Repeat until the right number of vars has been added to selected set # 2. Randomly sample two unselected vars # 3. Select the one with the best stats value (lowest value if minimizing) improvement_per_var = 0.01 * ones(N) # Start with some value since it will be adapted as we go... start_time = time() # Now lets optimize! Ensure we run at least one iteration. while(true) if (time() - start_time) > max_seconds termination_reason = "Exceeded time budget" break end if num_fevals > max_evals termination_reason = "Exceeded function eval budget" break end if st_state == :tournament if num_tournament_rounds_for_this_subset < num_rounds_per_tournament num_tournament_rounds_for_this_subset += 1 else st_current_subset += 1 if st_current_subset <= subsets_per_tournament set_subset!(C, st_subsets[st_current_subset]) num_tournament_rounds_for_this_subset = 1 else # No more tournaments needed, select best subset and start optimizing with it winning_subset = select_winning_subset(st_subsets, fitness_per_tournament_round) set_subset!(C, st_subsets[winning_subset]) st_current_subset = winning_subset st_state = :optimization num_optimization_rounds_for_this_subset = 1 end end else # In optimization mode if num_optimization_rounds_for_this_subset < num_rounds_of_optimization_between_tournaments num_optimization_rounds_for_this_subset += 1 else # We have now used the current subset for a full set of optimization rounds so we set up for a new tournament st_subsets = select_new_subsets(st_subsets, N, subsets_per_tournament, st_current_subset, subset_selection_mechanism, subset_size) st_state = :tournament st_current_subset = 1 set_subset!(C, st_subsets[st_current_subset]) num_tournament_rounds_for_this_subset = 1 end end # Decompose only with a given probability => saves time if rand() <= decompose_covar_prob decompose!(C) end # Generate new population sigmas = sigma * exp( tau * randn(1, lambda) ) # 1*lambda s = multivariate_normal_sample(C, N, lambda) z = broadcast(*, sigmas, s) # n*lambda xs = repmat(xmean, 1, lambda) + z # n*lambda # Evaluate fitness fitnesses = eval_fitnesses(p, xs, lambda) num_fevals += lambda # Check if best new fitness is best ever and print some info if tracing. indbest = argmin(fitnesses) fbest_new = fitnesses[indbest] # Save info about the fitnesses if we are in tournament mode if st_state == :tournament fitness_per_tournament_round[num_tournament_rounds_for_this_subset, st_current_subset] = fbest_new end if fbest_new < fbest xbest = xs[:, indbest] fbest = fbest_new add_candidate!(archive, fbest, xbest, num_fevals) fevals_last_best = num_fevals if fitness_is_within_ftol(p, ftol, fbest) termination_reason = "Within ftol" break end if trace println("$(num_fevals): Best fitness = $(fbest)") if num_fevals > next_print_covar next_print_covar = num_fevals + 100 #println("covar summary: ", sumstats(C.C, (x) -> @sprintf("%.2e", x))) println("sigma: ", sigma) end end else if (num_fevals - fevals_last_best) > max_rounds_without_improvement * lambda termination_reason = "Max rounds without improvement reached" break end end # Assign weights to the best individuals according to the utilities vector. weights = assign_weights(lambda, fitnesses, utilities) # Calculate new mean value based on weighted version of steps in population. xmean += (z * weights) # Update the covariance matrix uc = zeros(Float64, N, N) for i in 1:lambda if weights[i] > 0.0 se = s[:,i] uc += weights[i] * (se * se') end end update_covariance_matrix!(C, uc, a) #ws = broadcast(*, weights', s) #update_covariance_matrix!(C, (ws * ws'), covar_learning_rate) # Adapt sigma for next round sigma = sigmas * weights # Terminate if sigma very small #println("sigma = $(sigma[1,1])") if sigma[1,1] < stol termination_reason = "Sigma too small" break end end return xbest, fbest, num_fevals, termination_reason, archive end function select_winning_subset(subsets, fitness_per_tournament_round) if length(size(fitness_per_tournament_round)) > 1 fitness_summary = mean(fitness_per_tournament_round, 1) argmin(fitness_summary) else argmin(fitness_per_tournament_round) end end function select_new_subsets(subsets, n, num_subsets, winning_subset, subset_selection_mechanism, subset_size) if length(subsets) == 0 || subset_selection_mechanism == :random generate_random_subsets(num_subsets, subset_size, n) else # We should keep the winner # Generate new subsets randomly new_subsets = generate_random_subsets(num_subsets-1, subset_size, n) # Add the previous winning subset push!(new_subsets, subsets[winning_subset]) new_subsets end end # Select subsets based on running binary tournaments between stats values function select_new_subsets_based_on_stats(n, num_subsets, subset_size, stats, smaller_stats_is_better) [generate_subset_based_on_stats(subset_size, n, stats, smaller_stats_is_better) for i in 1:num_subsets] end function generate_subset_based_on_stats(subset_size, n, stats, smaller_stats_is_better = false) subset = Int[] candidates = shuffle(collect(1:n)) len = n-1 op = smaller_stats_is_better ? < : > for i in 1:subset_size index = rand(1:len) candidate1 = candidates[index] candidate2 = candidates[index+1] println("Comparing $(candidate1) and $(candidate2)") if op( stats[candidate1], stats[candidate2] ) splice!(candidates, index) push!(subset, candidate1) else splice!(candidates, index+1) push!(subset, candidate2) end len -= 1 end subset end function generate_random_subsets(num_subsets, subset_size, n) new_subsets = Array{Int, 1}[] for i in 1:num_subsets push!(new_subsets, sort(shuffle(collect(1:n))[1:subset_size])) end new_subsets end function eval_fitnesses(problem, xs, lambda = size(xs, 2)) fitnesses = zeros(lambda) for i in 1:lambda fitnesses[i] = eval1(xs[:,i], problem) end fitnesses end function assign_weights(n, fitnesses, utilities; minimize = true) us_ordered = zeros(n, 1) perms = sortperm(fitnesses, rev = !minimize) for i in 1:n us_ordered[perms[i]] = utilities[i] end us_ordered end # We create different types of Covariance matrix samplers based on different # decompositions. abstract type CovarianceMatrixSampler end function update_covariance_matrix!(cms::CovarianceMatrixSampler, delta, a) C = a * cms.C + (1 - a) * delta cms.C = triu(C) + triu(C,1)' # Ensure C is symmetric. Should not be needed, investigate... end mutable struct EigenCovarSampler <: CovarianceMatrixSampler C::Array{Float64,2} B::Array{Float64,2} diagD::Array{Float64,1} EigenCovarSampler(n) = begin new(Matrix{Float64}(I, n,n), Matrix{Float64}(I, n,n), ones(n)) end end function decompose!(cms::EigenCovarSampler) try EV, B = eig(cms.C) cms.B = B cms.diagD = sqrt(EV) catch # We don't update if there is some problem end end function multivariate_normal_sample(cms::CovarianceMatrixSampler, n, m) cms.B * (cms.diagD .* randn(n, m)) end mutable struct CholeskyCovarSampler <: CovarianceMatrixSampler C::Array{Float64,2} sqrtC::Array{Float64,2} CholeskyCovarSampler(n) = begin new(Matrix{Float64}(I, n,n), Matrix{Float64}(I, n,n)) end end function decompose!(cms::CholeskyCovarSampler) try cms.sqrtC = chol(cms.C)' catch error # We don't update if there is some problem end end function multivariate_normal_sample(cms::CholeskyCovarSampler, n, m) cms.sqrtC * randn(n, m) end # The subset CholeskyCovarSampler only uses a subset of the variables in the # (expensive) cholesky decomposition, the other variables are kept constant. # However, the covariance matrix itself is always updated and saved in full # so that the overall learning of the shape of the fitness landscape is not lost. mutable struct SubsetCholeskyCovarSampler <: CovarianceMatrixSampler C::Array{Float64,2} sqrtC::Array{Float64,2} subset::Array{Int, 1} # Indices of the currently active subset of variables SubsetCholeskyCovarSampler(n) = begin new(Matrix{Float64}(I, n,n), Matrix{Float64}(I, n,n), collect(1:n)) end end # Set new subset. function set_subset!(cms::SubsetCholeskyCovarSampler, subset) cms.subset = subset decompose!(cms) end using JSON function decompose!(cms::SubsetCholeskyCovarSampler) try subset_C = cms.C[cms.subset, cms.subset] cms.sqrtC = chol(subset_C)' catch error # We don't update if there is some problem println("ERROR: Could not do cholesky decomposition!") show(error) end end function multivariate_normal_sample(cms::SubsetCholeskyCovarSampler, n, m) subset_size = length(cms.subset) subset_inv_c = 1 # Calc the inverted covar matrix only for the subset try subset_inv_c = cms.sqrtC * randn(subset_size, m) catch error println("Error in subset_inv_c multiplication, size(cms.sqrtC) = $(size(cms.sqrtC)), subset_size = $(subset_size), m = $(m), size(cms.C) = $(size(cms.C))") show(error) end # The rest should be zero. Maybe faster if we do this with a sparse matrix # since most of them will be zero? inv_c = zeros(n, m) for i in 1:subset_size si = cms.subset[i] for j in 1:m inv_c[si, j] = subset_inv_c[i, j] end end inv_c end
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<filename>C/Chafa/build_tarballs.jl using BinaryBuilder name = "Chafa" version = v"1.4.1" sources = [ ArchiveSource("https://hpjansson.org/chafa/releases/chafa-$(version).tar.xz", "46d34034f4c96d120e0639f87a26590427cc29e95fe5489e903a48ec96402ba3"), ] script = raw""" cd ${WORKSPACE}/srcdir/chafa-*/ if [[ "${target}" == *darwin* ]]; then # For some reason building with Clang for macOS doesn't work export CC=gcc fi if [[ "${proc_family}" == intel ]]; then BUILTIN_FUNCS=yes else BUILTIN_FUNCS=no fi ./autogen.sh \ --prefix=${prefix} \ --build=${MACHTYPE} \ --host=${target} \ ax_cv_gcc_check_x86_cpu_init="${BUILTIN_FUNCS}" \ ax_cv_gcc_check_x86_cpu_supports="${BUILTIN_FUNCS}" make -j${nproc} make install """ # Chafa itself does not support Windows platforms = filter!(!Sys.iswindows, supported_platforms()) products = [ LibraryProduct("libchafa", :libchafa), ExecutableProduct("chafa", :chafa), ] dependencies = [ Dependency("FreeType2_jll"), Dependency("Glib_jll", v"2.59.0"; compat="2.59.0"), Dependency("ImageMagick_jll"), ] # Build the tarballs. build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
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""" ULMFiT - Text Classifier This is wrapper around the LanguageMode struct. It has three fields: vocab : contains the same vocabulary from the LanguageModel rnn_layers : contains same DroppedEmebeddings, LSTM (AWD_LSTM) and VarDrop layers of LanguageModel except for last softmax layer linear_layers : contains Chain of two Dense layers [PooledDense and Dense] with softmax layer To train create and instance and give it as first argument to 'train_classifier!' function """ mutable struct TextClassifier vocab::Vector rnn_layers::Flux.Chain linear_layers::Flux.Chain end function TextClassifier(lm::LanguageModel=LanguageModel(), clsfr_out_sz::Integer=1, clsfr_hidden_sz::Integer=50, clsfr_hidden_drop::Float64=0.4) return TextClassifier( lm.vocab, lm.layers[1:8], Chain( gpu(PooledDense(length(lm.layers[7].layer.cell.h), clsfr_hidden_sz)), gpu(BatchNorm(clsfr_hidden_sz, relu)), Dropout(clsfr_hidden_drop), gpu(Dense(clsfr_hidden_sz, clsfr_out_sz)), gpu(BatchNorm(clsfr_out_sz)), softmax ) ) end Flux.@functor TextClassifier """ Cross Validate This function will be used to cross-validate the classifier Arguments: tc : Instance of TextClassfier gen : 'Channel' to get a mini-batch from validation set num_of_batches : specifies the number of batches the validation will be done If num_of_batches is not specified then all the batches which can be given by the gen will be used for validation """ function validate(tc::TextClassifier, gen::Channel, num_of_batches::Union{Colon, Integer}) n_classes = size(tc.linear_layers[end-2].W, 1) classifier = tc Flux.testmode!(classifier) loss = 0 iters = take!(gen) ((num_of_batches != :) & (num_of_batches < iters)) && (iters = num_of_batches) TP, TN = gpu(zeros(Float32, n_classes, 1)), gpu(zeros(Float32, n_classes, 1)) FP, FN = gpu(zeros(Float32, n_classes, 1)), gpu(zeros(Float32, n_classes, 1)) for i=1:num_of_batches X = take!(gen) Y = gpu(take!(gen)) X = map(x -> indices(x, classifier.vocab, "_unk_"), X) H = classifier.rnn_layers.(X) H = classifier.linear_layers(H) l = crossentropy(H, Y) Flux.reset!(classifier.rnn_layers) TP .+= sum(H .* Y, dims=2) FN .+= sum(((-1 .* H) .+ 1) .* Y, dims=2) FP .+= sum(H .* ((-1 .* Y) .+ 1), dims=2) TN .+= sum(((-1 .* H) .+ 1) .* ((-1 .* Y) .+ 1), dims=2) loss += l end precisions = TP ./ (TP .+ FP) recalls = TP ./ (TP .+ FN) F1 = (2 .* (precisions .* recalls)) ./ (precisions .+ recalls) accuracy = (TP[1] + TN[1])/(TP[1] + TN[1] + FP[1] + FN[1]) return (loss, accuracy, precisions, recalls, F1) end """ Forward pass This funciton does the main computation of a mini-batch. It computes the output of the all the layers [RNN and DENSE layers] and returns the predicted output for that pass. It uses Truncated Backprop through time to compute the output. Arguments: tc : Instance of TextClassifier gen : data loader, which will give 'X' of the mini-batch in one call tracked_steps : This is the number of tracked time-steps for Truncated Backprop thorugh time, these will be last time-steps for which gradients will be calculated. """ function forward(tc::TextClassifier, gen::Channel, tracked_steps::Integer=32) # swiching off tracking classifier = tc X = take!(gen) l = length(X) # Truncated Backprop through time Zygote.ignore() do for i=1:ceil(l/tracked_steps)-1 # Tracking is swiched off inside this loop (i == 1 && l%tracked_steps != 0) ? (last_idx = l%tracked_steps) : (last_idx = tracked_steps) H = broadcast(x -> indices(x, classifier.vocab, "_unk_"), X[1:last_idx]) H = classifier.rnn_layers.(H) X = X[last_idx+1:end] end end # set the lated hidden states to original model for (t_layer, unt_layer) in zip(tc.rnn_layers[2:end], classifier.rnn_layers[2:end]) if t_layer isa AWD_LSTM t_layer.layer.state = unt_layer.layer.state continue end if !unt_layer.reset t_layer.mask = unt_layer.mask t_layer.reset = false end end # last part of the sequecnes in X - Tracking is swiched on H = broadcast(x -> tc.rnn_layers[1](indices(x, classifier.vocab, "_unk_")), X) H = tc.rnn_layers[2:end].(H) H = tc.linear_layers(H) return H end """ loss(classifier::TextClassifier, gen::Channel, tracked_steps::Integer=32) LOSS function It takes the output of the forward funciton and returns crossentropy loss. Arguments: classifier : Instance of TextClassifier gen : 'Channel' [data loader], to give a mini-batch tracked_steps : specifies the number of time-steps for which tracking is on """ function loss(classifier::TextClassifier, gen::Channel, tracked_steps::Integer=32) H = forward(classifier, gen, tracked_steps) Y = gpu(take!(gen)) l = crossentropy(H, Y) Flux.reset!(classifier.rnn_layers) return l end function discriminative_step!(layers, classifier::TextClassifier, gen::Channel, tracked_steps::Integer, ηL::Float64, opts::Vector) @assert length(opts) == length(layers) # Gradient calculation grads = Zygote.gradient(() -> loss(classifier, gen, tracked_steps = tracked_steps), get_trainable_params(layers)) # discriminative step ηl = ηL/(2.6^(length(layers)-1)) for (layer, opt) in zip(layers, opts) opt.eta = ηl for ps in get_trainable_params([layer]) Flux.Optimise.update!(opt, ps, grads[ps]) end ηl *= 2.6 end return end """ train_classifier!(classifier::TextClassifier=TextClassifier(), classes::Integer=1, data_loader::Channel=imdb_classifier_data, hidden_layer_size::Integer=50;kw...) It contains main training loops for training a defined classifer for specified classes and data. Usage is discussed in the docs. """ function train_classifier!(classifier::TextClassifier=TextClassifier(), classes::Integer=1, data_loader::Channel=imdb_classifier_data, hidden_layer_size::Integer=50; stlr_cut_frac::Float64=0.1, stlr_ratio::Number=32, stlr_η_max::Float64=0.01, val_loader::Channel=nothing, cross_val_batches::Union{Colon, Integer}=:, epochs::Integer=1, checkpoint_itvl=5000, tracked_steps::Integer=32) trainable = [] append!(trainable, [classifier.rnn_layers[[1, 3, 5, 7]]...]) push!(trainable, [classifier.linear_layers[1:2]...]) push!(trainable, [classifier.linear_layers[4:5]...]) opts = [ADAM(0.001, (0.7, 0.99)) for i=1:length(trainable)] gpu!.(classifier.rnn_layers) for epoch=1:epochs println("Epoch: $epoch") gen = data_loader() num_of_iters = take!(gen) cut = num_of_iters * epochs * stlr_cut_frac for iter=1:num_of_iters # Slanted triangular learning rates t = iter + (epoch-1)*num_of_iters p_frac = (iter < cut) ? iter/cut : (1 - ((iter-cut)/(cut*(1/stlr_cut_frac-1)))) ηL = stlr_η_max*((1+p_frac*(stlr_ratio-1))/stlr_ratio) # Gradual-unfreezing Step with discriminative fine-tuning unfreezed_layers, cur_opts = (epoch < length(trainable)) ? (trainable[end-epoch+1:end], opts[end-epoch+1:end]) : (trainable, opts) discriminative_step!(unfreezed_layers, classifier, gen, tracked_steps,ηL, cur_opts) reset_masks!.(classifier.rnn_layers) # reset all dropout masks end println("Train set accuracy: $trn_accu , Training loss: $trn_loss") if val_loader != nothing val_loss, val_acc, val_precisions, val_reacalls, val_F1_scores = validate(classifer, val_loader) else continue end #!(val_loader isa nothing) ? (val_loss, val_acc, val_precisions, val_reacalls, val_F1_scores = validate(classifer, val_loader)) : continue println("Cross validation loss: $val_loss") println("Cross validation accuracy:\n $val_acc") println("Cross validation class wise Precisions:\n $val_precisions") println("Cross validation class wise Recalls:\n $val_recalls") println("Cross validation class wise F1 scores:\n $val_F1_scores") end end """ predict(tc::TextClassifier, text_sents::Corpus) This function can be used to test the model after training. It returns the predictions done by the model for given `Corpus` of `Documents` All the preprocessing related to the used vocabulary should be done before using this function. Use `prepare!` function to do preprocessing """ function predict(tc::TextClassifier, text_sents::Corpus) classifier = tc Flux.testmode!(classifier) predictions = [] expr(x) = indices(x, classifier.vocab, "_unk_") for text in text_sents tokens_ = tokens(text) h = classifier.rnn_layers.(expr.(tokens_)) probability_dist = classifier.linear_layers(h) class = argmax(probaility_dist) push!(predictions, class) end return predictions end
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## 2D Panels #==========================================================================================# abstract type AbstractPanel2D <: AbstractPanel end struct Panel2D{T <: Real} <: AbstractPanel2D p1 :: SVector{2,T} p2 :: SVector{2,T} end struct WakePanel2D{T <: Real} <: AbstractPanel2D p1 :: SVector{2,T} p2 :: SVector{2,T} end Base.length(:: Panel2D) = 1 Panel2D(p1 :: FieldVector{2,T}, p2 :: FieldVector{2,T}) where T <: Real = Panel2D{T}(p1, p2) WakePanel2D(p1 :: FieldVector{2,T}, p2 :: FieldVector{2,T}) where T <: Real = WakePanel2D{T}(p1, p2) a :: AbstractPanel2D + b :: AbstractPanel2D = Panel2D(p1(a) + p1(b), p2(a) + p2(b)) a :: AbstractPanel2D - b :: AbstractPanel2D = Panel2D(p1(a) - p1(b), p2(a) - p2(b)) collocation_point(panel :: AbstractPanel2D, a = 0.5) = a * (p1(panel) + p2(panel)) panel_vector(panel :: AbstractPanel2D) = p2(panel) - p1(panel) panel_length(panel :: AbstractPanel2D) = (norm ∘ panel_vector)(panel) function transform_panel_points(panel_1 :: AbstractPanel2D, panel_2 :: AbstractPanel2D) x1, y1, x2, y2 = p1(panel_2), p2(panel_2) xs, ys = p1(panel_1) xp1, yp1 = affine_2D(x1, y1, xs, ys, panel_angle(panel_1)) xp2, yp2 = affine_2D(x2, y2, xs, ys, panel_angle(panel_1)) xp1, yp1, xp2, yp2 end function transform_panel(panel :: AbstractPanel2D, point :: SVector{2,<: Real}) xs, ys = p1(panel) affine_2D(first(point), last(point), xs, ys, panel_angle(panel)) end panel_angle(panel :: AbstractPanel2D) = let (x, y) = panel_vector(panel); atan(y, x) end panel_tangent(panel :: AbstractPanel2D) = rotation(1., 0., -panel_angle(panel)) panel_normal(panel :: AbstractPanel2D) = inverse_rotation(0., 1., panel_angle(panel)) panel_location(panel :: AbstractPanel2D) = let angle = panel_angle(panel); ifelse((π/2 <= angle <= π) || (-π <= angle <= -π/2), "lower", "upper") end panel_points(panels) = [ p1.(panels); [(p2 ∘ last)(panels)] ] reverse_panel(panel :: AbstractPanel2D) = Panel2D(panel.p2, panel.p1) trailing_edge_panel(panels) = Panel2D((p2 ∘ last)(panels), (p1 ∘ first)(panels)) function wake_panel(panels, bound, α) firstx, firsty = (p1 ∘ first)(panels) lastx, lasty = (p2 ∘ last)(panels) y_mid = (firsty + lasty) / 2 y_bound, x_bound = bound .* sincos(α) WakePanel2D(SVector(lastx, y_mid), SVector(x_bound * lastx, y_bound * y_mid)) end function wake_panels(panels, chord, length, num) _, firsty = (p1 ∘ first)(panels) _, lasty = (p2 ∘ last)(panels) y_mid = (firsty + lasty) / 2 bounds = cosine_spacing(chord + length / 2, length, num) @. WakePanel2D(SVector(bounds[1:end-1], y_mid), SVector(bounds[2:end], y_mid)) end function panel_scalar(scalar_func, strength, panel :: AbstractPanel2D, x, y) # Transform point to local panel coordinates xp, yp = transform_panel(panel, SVector(x, y)) scalar_func(strength, xp, yp, 0., panel_length(panel)) end function panel_scalar(scalar_func, strength, panel_j :: AbstractPanel2D, panel_i :: AbstractPanel2D, point = 0.5) x, y = collocation_point(panel_i, point) panel_scalar(scalar_func, strength, panel_j, x, y) end function panel_velocity(velocity_func, strength, panel :: AbstractPanel2D, x, y) # Transform point to local panel coordinates xp, yp = transform_panel(panel, SVector(x, y)) # Compute velocity in local panel coordinates u, w = velocity_func(strength, xp, yp, 0., panel_length(panel)) # Transform velocity to original coordinates inverse_rotation(u, w, panel_angle(panel)) end function panel_velocity(velocity_func, strength, panel_j :: AbstractPanel2D, panel_i :: AbstractPanel2D, point = 0.5) x, y = collocation_point(panel_i, point) u, w = panel_velocity(velocity_func, strength, panel_j, x, y) end panel_velocity(f1, f2, str1, str2, panel :: AbstractPanel2D, x, y) = panel_velocity(f1, str1, panel, x, y) .+ panel_velocity(f2, str2, panel, x, y) panel_velocity(f1, f2, str_j1, str_j2, panel_j :: AbstractPanel2D, panel_i :: AbstractPanel2D) = panel_velocity(f1, str_j1, panel_j, panel_i) .+ panel_velocity(f2, str_j2, panel_j, panel_i) get_surface_values(panels, vals, surf = "upper", points = false) = partition(x -> (panel_location ∘ first)(x) == surf, (collect ∘ zip)(panels[2:end], vals), x -> ((first ∘ ifelse(points, p1, collocation_point) ∘ first)(x), last(x)))
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<reponame>bbejanov/ModelBaseEcon.jl<filename>src/transformations.jl ################################################################################## # This file is part of ModelBaseEcon.jl # BSD 3-Clause License # Copyright (c) 2020, Bank of Canada # All rights reserved. ################################################################################## export Transformation, NoTransform, LogTransform, NegLogTransform, transformation, inverse_transformation """ transformation(::Type{<:Transformation}) Return a `Function` that will be substituted into the model equations and will be called to transform the input data before solving. See also [`inverse_transformation`](@ref). It is expected that `transformation(T) ∘ inverse_transformation(T) == identity` and `inverse_transformation(T) ∘ transformation(T) == identity`, but these is not verified. """ function transformation end """ inverse_transformation(::Type{<:Transformation}) Return a `Function` that will be called to transform the simulation data after solving. See also [`transformation`](@ref). It is expected that `transformation(T) ∘ inverse_transformation(T) == identity` and `inverse_transformation(T) ∘ transformation(T) == identity`, but these is not verified. """ function inverse_transformation end """ abstract type Transformation end The base class for all variable transformations. """ abstract type Transformation end transformation(T::Type{<:Transformation}) = error("Transformation of type $T is not defined.") inverse_transformation(T::Type{<:Transformation}) = error("Inverse transformation of type $T is not defined.") """ NoTransform <: Transformation The identity transformation. """ struct NoTransform <: Transformation end transformation(::Type{NoTransform}) = Base.identity inverse_transformation(::Type{NoTransform}) = Base.identity """ LogTransform <: Transformation The `log` transformation. The inverse is of course `exp`. This is the default for variables declared with `@log`. """ struct LogTransform <: Transformation end transformation(::Type{LogTransform}) = Base.log inverse_transformation(::Type{LogTransform}) = Base.exp """ NegLogTransform <: Transformation The `log(-x)`, with the inverse being `-exp(x)`. Use this when the variable is negative with exponential behaviour (toward -∞). """ struct NegLogTransform <: Transformation end "logm(x) = log(-x)" @inline logm(x) = log(-x) "mexp(x) = -exp(x)" @inline mexp(x) = -exp(x) transformation(::Type{NegLogTransform}) = logm inverse_transformation(::Type{NegLogTransform}) = mexp export logm, mexp
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mutable struct Model{T, TV<:AbstractVector{T}, TC<:AbstractVector{<:Function}} dim::Int objective::Function ineq_constraints::TC box_max::TV box_min::TV end GPUUtils.whichdevice(m::Model) = whichdevice(m.box_max) dim(m::Model) = m.dim min(m::Model, i::Integer) = m.box_min[i] max(m::Model, i::Integer) = m.box_max[i] min(m::Model)= m.box_max max(m::Model) = m.box_min objective(m::Model) = m.objective constraints(m::Model) = m.ineq_constraints constraint(m::Model, i::Integer) = m.ineq_constraints[i] eval_objective(m, x::AbstractVector{T}) where {T} = eval_objective(m, x, T[]) eval_objective(m, x, ∇g) = eval_objective(whichdevice(objective(m)), m, x, ∇g) function eval_objective(::CPU, m, x::AbstractVector{T}, ∇g) where {T} return T(m.objective(x, ∇g)) end eval_objective(::GPU, m, x::GPUVector{T}, ∇g) where {T} = T(m.objective(x, ∇g)) function eval_objective(::GPU, m, x::AbstractVector{T}, ∇g) where {T} x_gpu = CuArray(x) ∇g_gpu = CuArray(∇g) obj = T(m.objective(x_gpu, ∇g_gpu)) copyto!(∇g, ∇g_gpu) return obj end function eval_objective(::CPU, m, x::CuVector{T}, ∇g) where {T} error("Optimization on the GPU with the objective evaluation on the CPU is weird!") end eval_constraint(m, i, x::AbstractVector{T}) where {T} = eval_constraint(m, i, x, T[]) eval_constraint(m, i, x, ∇g) = eval_constraint(whichdevice(constraint(m, i)), m, i, x, ∇g) eval_constraint(::CPU, m, i, x::AbstractVector{T}, ∇g) where T = T(constraint(m, i)(x, ∇g)) eval_constraint(::GPU, m, i, x::GPUVector{T}, ∇g) where T = T(constraint(m, i)(x, ∇g)) function eval_constraint(::GPU, m, i, x::AbstractVector{T}, ∇g) where T x_gpu = CuArray(x) ∇g_gpu = CuArray(∇g) constr = T(constraint(m, i)(x_gpu, ∇g_gpu)) copyto!(∇g, ∇g_gpu) return constr end function eval_constraint(::CPU, m, i, x::GPUVector, ∇g) error("Optimization on the GPU with the constraint evaluation on the CPU is weird!") end ftol(m) = m.ftol xtol(m) = m.xtol grtol(m) = m.grtol ftol!(m, v) = m.ftol = v xtol!(m, v) = m.xtol = v grtol!(m, v) = m.grtol = v Model(args...; kwargs...) = Model{CPU}(args...; kwargs...) Model{T}(args...; kwargs...) where T = Model(T(), args...; kwargs...) Model(::CPU, args...; kwargs...) = Model{Float64, Vector{Float64}, Vector{Function}}(args...; kwargs...) Model(::GPU, args...; kwargs...) = Model{Float64, CuVector{Float64}, Vector{Function}}(args...; kwargs...) function Model{T, TV, TC}(dim, objective::Function) where {T, TV, TC} mins = ninfsof(TV, dim) maxs = infsof(TV, dim) Model{T, TV, TC}(dim, objective, Function[], mins, maxs) end # Box constraints function box!(m::Model, i::Integer, minb::T, maxb::T) where {T} if !(1 <= i <= dim(m)) throw(ArgumentError("box constraint need to applied to an existing variable")) end m.box_min[i] = minb m.box_max[i] = maxb end function box!(m::Model, minb::T, maxb::T) where {T} nv = dim(m) m.box_min[1:nv] .= minb m.box_max[1:nv] .= maxb end function box!(m::Model, minbs::AbstractVector{T}, maxbs::AbstractVector{T}) where {T} if (length(minbs) != dim(m)) || (length(minbs) != dim(m)) throw(ArgumentError("box constraint vector must have same size as problem dimension")) end nv = dim(m) map!(identity, m.box_min, minbs) map!(identity, m.box_max, maxbs) end function ineq_constraint!(m::Model, f::Function) push!(m.ineq_constraints, f) end function ineq_constraint!(m::Model, fs::Vector{Function}) for f in fs push!(m.ineq_constraints, f) end end
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#-------- Inverses of DPR1 function inv{T}(A::SymDPR1{T},i::Integer,tols::Vector{Float64}) # COMPUTES: inverse of a shifted SymDPR1 matrix A=diagm(A.D)+A.r*A.u*A.u', # inv(A-A.D[i]*I) which is a SymArrow. # Uses higher precision to compute top of the arrow element accurately, if # needed. # tols=[tolb,tolz] are tolerances, usually [1e3, 10*n] # [0.0,0.0] forces DoubleDouble, [1e50,1e50] would never use it # RETURNS: SymArrow(D,z,b,i), Kb, Kz, Qout # Kb - condition Kb, Kz - condition Kz, Qout = 1 / 0 - double was / was not used n=length(A.D) D=Array(T,n-1) z=Array(T,n-1) wz=one(T)/A.u[i] shift=A.D[i] for k=1:i-1 D[k]=one(T)/(A.D[k]-shift) z[k]=-A.u[k]*D[k]*wz end for k=i+1:n D[k-1]=one(T)/(A.D[k]-shift) z[k-1]=-A.u[k]*D[k-1]*wz end # compute the sum in a plain loop P=zero(T) Q=zero(T) Qout=0 for k=1:i-1 D[k]>0.0 ? P+=A.u[k]^2*D[k] : Q+=A.u[k]^2*D[k] end for k=i:n-1 D[k]>0.0 ? P+=A.u[k+1]^2*D[k] : Q+=A.u[k+1]^2*D[k] end A.r>0 ? P=P+one(T)/A.r : Q=Q+one(T)/A.r Kb=(P-Q)/abs(P+Q) # compute Kz Kz=(sumabs(A.u)-abs(A.u[i]))*abs(wz) # Kz=maxabs(A.z)*abs(wz) if Kb<tols[1] || Kz<tols[2] b=(P+Q)*wz*wz else # recompute in Double Qout=1 shiftd=map(Double,A.D[i]) Dd=[Double{Float64}[Double(A.D[k])-shiftd for k=1:i-1]; Double{Float64}[Double(A.D[k])-shiftd for k=i+1:length(A.D)]] wzd=Double(A.u[i]) Pd,Qd=map(Double,(0.0,0.0)) for k=1:i-1 Dd[k].hi>0.0 ? Pd+=Double(A.u[k])^2/Dd[k] : Qd+=Double(A.u[k])^2/Dd[k] end for k=i+1:n Dd[k-1].hi>0.0 ? Pd+=Double(A.u[k])^2/Dd[k-1] : Qd+=Double(A.u[k])^2/Dd[k-1] # @show P,Q end A.r > 0 ? Pd+=Double(1.0)/Double(A.r) : Qd+=Double(1.0)/Double(A.r) bd=(Pd+Qd)/(wzd*wzd) b=bd.hi+bd.lo end # return this SymArrow(D,z,b,i),Kb,Kz,Qout end # inv function inv{T}(A::SymDPR1{T}, shift::Float64, tolr::Float64) # COMPUTES: inverse of the shifted SymDPR1 A = diagm(A.D)+A.r*A.u*A.u', # inv(A-shift*I) = D + rho*u*u', shift!=A.D[i], which is again a SymDPR1 # uses DoubleDouble to compute A.r accurately, if needed. # tolr is tolerance, usually 1e3, 0.0 forces Double, 1e50 would never use it # RETURNS: SymDPR1(D,u,rho), Krho, Qout # Krho - condition Krho, Qout = 1 / 0 - Double was / was not used n=length(A.D) D=Array(T,n) u=Array(T,n) for k=1:n D[k]=one(T)/(A.D[k]-shift) u[k]=A.u[k]*D[k] end # compute gamma and Kgamma #--- compute the sum in a plain loop P=zero(T) Q=zero(T) Qout=0 for k=1:n D[k]>0.0 ? P=P+A.u[k]^2*D[k] : Q=Q+A.u[k]^2*D[k] end A.r>0 ? P=P+one(T)/A.r : Q=Q+one(T)/A.r # Condition of rho Krho=(P-Q)/abs(P+Q) if Krho < tolr rho=-one(T)/(P+Q) else # recompute in Double Qout=1 Pd,Qd=map(Double,(0.0,0.0)) shiftd=Double(shift) for k=1:n D[k]>0.0 ? Pd=Pd+Double(A.u[k])^2/(Double(A.D[k])-shiftd) : Qd=Qd+Double(A.u[k])^2/(Double(A.D[k])-shiftd) end A.r > 0 ? Pd+=Double(1.0)/Double(A.r) : Qd+=Double(1.0)/Double(A.r) r=Double(1.0)/(Pd+Qd) rho=-(r.hi+r.lo) end # returns the following SymDPR1(D,u,rho), Krho, Qout end # inv function inv{T}(A::SymDPR1{T}, shift::Double) # COMPUTES: inverse of the shifted SymDPR1 A = diagm(A.D)+A.r*A.u*A.u', # inv(A-shift*I) = D + rho*u*u', shift!=A.D[i], which is again a SymDPR1 # here shift is Double so it uses Double to compute everything # RETURNS: SymDPR1(D,u,rho), Qout # Qout = 1 on exit meaning Double was used n=length(A.D) D=Array(Double,n) u=Array(Double,n) for k=1:n D[k]=Double(A.D[k])-shift end u=map(Double,A.u) oned=Double(1.0) zerod=Double(0.0) for k=1:n u[k]=u[k]/D[k] D[k]=oned/D[k] end # compute rho # compute the sum in a plain loop P,Q=zerod,zerod Qout=1 for k=1:n D[k].hi > 0.0 ? P=P+Double(A.u[k])*u[k] : Q=Q+Double(A.u[k])*u[k] end A.r > 0 ? P+=Double(1.0)/Double(A.r) : Q+=Double(1.0)/Double(A.r) r=oned/(P+Q) rho=-(r.hi+r.lo) # returns the following # SymDPR1(T[x.hi+x.lo for x=D],T[x.hi+x.lo for x=u],rho), Qout D1=Array(T,n) u1=Array(T,n) for k=1:n D1[k]=D[k].hi+D[k].lo u1[k]=u[k].hi+u[k].lo end SymDPR1(D1,u1,rho), Qout end # inv function eig( A::SymDPR1,k::Integer,tols::Vector{Float64}) # COMPUTES: k-th eigenpair of an ordered irreducible SymDPR1 # A = diagm(A.D)+A.r*A.u*A.u', A.r > 0 # tols=[tolb,tolz,tolnu,tolrho,tollambda] = [1e3,10.0*n,1e3,1e3,1e3] # RETURNS: lambda, v, Sind, Kb, Kz, Knu, Krho, Qout # lambda - k-th eigenvalue in descending order # v - lambda's normalized eigenvector # Kb, Kz, Knu, Krho - condition numbers # Qout = 1 / 0 - Double was / was not used # Set the dimension n = length(A.D) # Set all conditions initially to zero Kb,Kz,Knu,Krho=0.0,0.0,0.0,0.0 Qout=0 v=zeros(n) # Kz is former kappa_nu # Determine the shift sigma, the shift index i, and whether lambda # is on the left or the right side of the nearest pole # Exterior eigenvalues (k = 1 or k = n): if k == 1 sigma,i,side = A.D[1],1,'R' else # Interior eigenvalues (k in (2,...n-1) ): Dtemp = A.D-A.D[k] middle = Dtemp[k-1]/2.0 Fmiddle = 1.0+A.r*sum(A.u.^2./(Dtemp-middle)) sigma,i,side = Fmiddle > 0.0 ? (A.D[k],k,'R') : (A.D[k-1],k-1,'L') end # Compute the inverse of the shifted matrix, A_i^(-1), Kb and Kz Ainv,Kb,Kz,Qout = inv(A,i,tols[1:2]) # Compute the eigenvalue of the inverse shifted matrix nu = bisect( Ainv,side ) # nu=fastzero([invD1; 0; invD2], [w1;wz;w2], b, side); # implement later # [nu-nu1]/nu, [nu-nueva]/nu, pause # and compare if abs(nu)==Inf # this is nonstandard # Deflation in dpr1eig (nu=Inf) v[i]=1.0 lambda=sigma else # standard case, full computation # nu1 is the F- or 1-norm of the inverse of the shifted matrix # nu10=maximum([sum(abs(Ainv.z))+abs(Ainv.a), maximum(abs(Ainv.D)+abs(Ainv.z))]) nu1=0.0 for k=1:n-1 nu1=max(nu1,abs(Ainv.D[k])+abs(Ainv.z[k])) end nu1=max(sumabs(Ainv.z)+abs(Ainv.a), nu1) Knu=nu1/abs(nu) if Knu<tols[3] # Accuracy is fine, compute the eigenvector mu = 1.0/nu # v=[ A.u./((A.D-sigma)-mu)] for l=1:n v[l]= A.u[l]/((A.D[l]-sigma)-mu) end # for k=1:n-1 # v[k] = A.z[k]/((A.D[k]-sigma)-mu) # end # v[n]=-one lambda, v = mu+sigma, v/norm(v) else # Remedies according to Remark 3 - we shift between original # eigenvalues and compute DPR1 matrix # 1/nu1+sigma, 1/nu+sigma println("Remedy 3 ") nu = side=='R' ? abs(nu) : -abs(nu) nu1=-sign(nu)*nu1 sigma1=(nu1+nu)/(2.0*nu*nu1)+sigma if findfirst(A.D-sigma1,0.0)>0 # we came back with a pole # recompute sigmav more accurately according with dekker sigmav=(Double(nu1)+Double(nu))/(Double(2.0)*Double(nu)*Double(nu1))+Double(sigma) # Compute the inverse of the shifted arrowhead (DPR1) Ainv,Qout1=inv(A,sigmav) # Ainv is Float64 nu1=bisect(Ainv,side) mu1 = 1.0/nu1 D0=map(A.D,Double)-sigmav D1=D0.hi+D0.lo if findfirst(D1-mu1,0.0)>0 ind=find(D1-mu1==0.0); v=zeros(n) v[ind]=1.0; else v=[ A.u./(D1-mu1)] end # Shift the eigenvalue back in Double lam = Double(1.0)/Double(nu1)+sigmav # Return this lambda,v = lam.hi+lam.lo, v/norm(v) else # Compute the inverse of the shifted arrowhead (DPR1) Ainv, Krho,Qout1=inv(A,sigma1,tols[4]) # Ainv is Float64 # Compute the eigenvalue by bisect for DPR1 # Note: instead of bisection could use dlaed4 (need a wrapper) but # it is not faster. There norm(u)==1 nu1= bisect(Ainv,side) mu1=1.0/nu1 # standard v v=A.u./((A.D-sigma1)-mu1) # Return this - shift the eigenvalue back and normalize the vector lambda, v = mu1+sigma1, v/norm(v) end Qout==1 && (Qout=Qout+2) end # Remedy according to Remark 1 - we recompute the the eigenvalue # near zero from the inverse of the original matrix (a DPR1 matrix). if (abs(A.D[i])+abs(1.0/nu))/abs(lambda)>tols[5] if (k==1 && A.D[1]<0.0 || side=='L' && sign(A.D[i])+sign(A.D[i+1])==0 || side=='R' && sign(A.D[i])+sign(A.D[i-1])==0) println("Remedy 1 ") # Compute the inverse of the original arrowhead (DPR1) Ainv,Krho,Qout1 = inv(A,0.0,tols[4]) # Ainv is Float64 Qout==1 && (Qout=Qout+4) if abs(Ainv.r)==Inf lambda=0.0 else # Here we do not need bisection. We compute the Rayleigh # quotient by using already computed vectors which is # componentwise accurate nu1=sum(v.^2.*Ainv.D)+Ainv.r*sum(v.*Ainv.u)^2; lambda=1.0/nu1 end end end end # Return this lambda,v,i,Kb,Kz,Knu,Krho,Qout end # eig (k) function eig(A::SymDPR1, tols::Vector{Float64}) # COMPUTES: all eigenvalues and eigenvectors of a real symmetric SymDPR1 # A = diagm(A.D)+A.r*A.u*A.u' # tols = [tolb,tolz,tolnu,tolrho,tollambda] = [1e3,10.0*n,1e3,1e3,1e3] or similar # RETURNS: U, E, Sind, Kb, Kz, Knu, Krho, Qout # U = eigenvectors, E = eigenvalues in decreasing order # Sind[k] - shift index i for the k-th eigenvalue # Kb, Kz, Knu, Krho [k] - respective conditions for the k-th eigenvalue # Qout[k] = 1 / 0 - Double was / was not used when computing k-th eigenvalue n=length(A.D) n0=n # Checking if A.r > 0 signr = A.r > 0.0 ? 1.0 : -1.0 # Ordering the matrix D=signr*A.D is=sortperm(D,rev=true) D=D[is] z=A.u[is] rho=signr*A.r U=eye(n,n) E=zeros(n) Kb=zeros(n); Kz=zeros(n); Knu=zeros(n); Krho=zeros(n) Qout=zeros(Int,n); Sind=zeros(Int,n) # Quick return for 1x1, this is trivial for SymArrow, not so trivial here :) if n==1 U=1; if (D==0)&&((rho==0)|(z==0)) E=0 else E=A.D[1]+A.r*A.u[1]^2 # Higher accuracy if needed KD=(abs(A.D[1])+abs(A.r)*A.u[1]^2)/abs(E) if KD>tols[1] Ed=Double(A.D[1])+Double(A.r)*Double(A.u[1])^2 E=Ed.hi+Ed.lo end end return U, [E], Sind,Kb,Kz,Knu,Krho,Qout end # test for deflation in z z0=find(z.==0) zx=find(z.!=0) if isempty(zx) # nothing to do E=A.D isE=sortperm(E,rev=true) E=E[isE] U=U[:,isE] return U,E,Sind,Kb,Kz,Knu,Krho,Qout end if !isempty(z0) E[z0]=D[z0] D=D[zx] z=z[zx] if !isempty(z) # return U,E,Sind,Kb,Kz,Knu,Krho,Qout n=length(z) end end # Test for deflation in D g=D[1:n-1]-D[2:n] # Can play with inexact deflation # g0=find(abs(g)<eps) # gx=find(abs(g)>=eps) # Only exact deflation !! g0=find(g.==0.0) gx=find(g.!=0.0) if !isempty(g0) # Deflation Dgx=D[gx]; zgx=z[gx] lg0=length(g0) R=Array(Tuple{Givens{Float64},Float64},lg0) for l=lg0:-1:1 R[l]=givens(z[g0[l]],z[g0[l]+1],zx[g0[l]],zx[g0[l]+1]) z[g0[l]]=R[l][2]; z[g0[l]+1]=0.0 # A_mul_Bc!(U,R) # multiply R'*U later E[zx[g0[l]+1]]=D[g0[l]+1] end # remains gx=[0;gx]+1 nn=length(gx) zxx=zx[gx] for k=1:nn E[zxx[k]],U[zxx,zxx[k]],Sind[zxx[k]],Kb[zxx[k]],Kz[zxx[k]],Knu[zxx[k]],Krho[zxx[k]],Qout[zxx[k]]= eig(SymDPR1(D[gx],z[gx],rho),k,tols) end for l=1:lg0 U=R[l][1]'*U end else # No deflation in D for k=1:n E[zx[k]],U[zx,zx[k]],Sind[zx[k]],Kb[zx[k]],Kz[zx[k]],Knu[zx[k]],Krho[zx[k]],Qout[zx[k]]= eig(SymDPR1(D,z,rho),k,tols) end end # back premutation of vectors isi=sortperm(is) # change the sign if A.r was negative # must sort E once more E=signr*E es=sortperm(E,rev=true) E=E[es] U=U[isi,es] # Return this U,E,Sind[es],Kb[es],Kz[es],Knu[es],Krho[es],Qout[es] end # eig (all)
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# Tests for Mamlmquist DEA Model @testset "MalmquistDEAModel" begin ## Test Mamlmquist DEA Model with 1 input and 1 output X = Array{Float64,3}(undef, 5, 1, 2) X[:, :, 1] = [2; 3; 5; 4; 4]; X[:, :, 2] = [1; 2; 4; 3; 4]; Y = Array{Float64,3}(undef, 5, 1, 2) Y[:, :, 1] = [1; 4; 6; 3; 5]; Y[:, :, 2] = [1; 4; 6; 3; 3]; # Default Malmquist Productivity Index mprod = malmquist(X, Y) @test typeof(mprod) == MalmquistDEAModel @test nobs(mprod) == 5 @test ninputs(mprod) == 1 @test noutputs(mprod) == 1 @test nperiods(mprod) == 2 @test prodchange(mprod) ≈ [2.0000000000; 1.5000000000; 1.2500000000; 1.3333333333; 0.6000000000] @test prodchange(mprod, :Prod) == prodchange(mprod) @test prodchange(mprod, :EC) ≈ [1.3333333333; 1.0000000000; 0.8333333333; 0.8888888889; 0.4000000000]; @test prodchange(mprod, :TC) ≈ [1.5; 1.5; 1.5; 1.5; 1.5]; # Default output oriented mprodoo = malmquist(X, Y, orient = :Output) @test prodchange(mprodoo) == prodchange(mprod) @test prodchange(mprodoo, :Prod) == prodchange(mprod, :Prod) @test prodchange(mprodoo, :EC) == prodchange(mprod, :EC) @test prodchange(mprodoo, :TC) == prodchange(mprod, :TC) # Test geomean is the geometric mean of TC mprodbase = malmquist(X, Y, refperiod = :Base) mprodcomparison = malmquist(X, Y, refperiod = :Comparison) @test prodchange(mprod, :TC) == sqrt.( prodchange(mprodbase, :TC) .* prodchange(mprodcomparison, :TC) ) ## Test Mamlmquist DEA Model with 1 input and 1 output; and 3 years X = Array{Float64,3}(undef, 5, 1, 3) X[:, :, 1] = [2; 3; 5; 4; 4]; X[:, :, 2] = [1; 2; 4; 3; 4]; X[:, :, 3] = [0.5; 1.5; 3; 2; 4] Y = Array{Float64,3}(undef, 5, 1, 3) Y[:, :, 1] = [1; 4; 6; 3; 5]; Y[:, :, 2] = [1; 4; 6; 3; 3]; Y[:, :, 3] = [2; 4; 6; 3; 1]; # Default Malmquist Productivity Index mprod3 = malmquist(X, Y) @test nobs(mprod3) == 5 @test ninputs(mprod3) == 1 @test noutputs(mprod3) == 1 @test nperiods(mprod3) == 3 @test prodchange(mprod3) ≈ [2.0000000000 4.0000000; 1.5000000000 1.3333333; 1.2500000000 1.3333333; 1.3333333333 1.5000000; 0.6000000000 0.3333333] @test prodchange(mprod3, :Prod) == prodchange(mprod3) @test prodchange(mprod3, :EC) ≈ [1.3333333333 2.0000000; 1.0000000000 0.6666667; 0.8333333333 0.6666667; 0.8888888889 0.7500000; 0.4000000000 0.1666667] atol = 1e-7; @test prodchange(mprod3, :TC) ≈ [1.5 2.0; 1.5 2.0; 1.5 2.0; 1.5 2.0; 1.5 2.0]; # Print show(IOBuffer(), mprod) show(IOBuffer(), mprod3) # Test errors @test_throws DimensionMismatch malmquist(X[1:4,:,:], X[1:5,:,:]) # Different number of observations in inputs and outputs @test_throws DimensionMismatch malmquist(X[:,:,1:2], X[:,:,1:3]) # Different number of time periods in inputs and outputs @test_throws ArgumentError malmquist(X, Y, refperiod = :Error) # Invalid reference period @test_throws ArgumentError prodchange(mprod, :Error) end
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1.82359
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# Implements node data types and associated helper functions export SumNode, ProductNode, CategoricalDistribution, IndicatorFunction, GaussianDistribution """ Node Data Structures Implement a labeled sparse matrix. """ abstract type Node end " Sum node data type " struct SumNode <: Node children::Vector{UInt} weights::Vector{Float64} # SumNode() = new(Vector{UInt}(),Vector{Float64}()) # SumNode(children::Vector{<:Integer},weights::Vector{Float64}) = new(children,weights) end " Product node data type " struct ProductNode <: Node children::Vector{UInt} #ProductNode() = new(Vector{UInt}()) #ProductNode(children::Vector{<:Integer}) = new(children) #ProductNode(children) = new(children) end " Abstract leaf node type " abstract type LeafNode <: Node end """ Indicator Function Node. Tolerance sets a maximum discrepancy when evaluating the node at a given value. Its default value is 1e-6. """ struct IndicatorFunction <: LeafNode scope::UInt value::Float64 tolerance::Float64 IndicatorFunction(scope::Integer,value::Float64) = new(scope,value,1e-6) IndicatorFunction(scope::Integer,value::Integer) = new(scope,Float64(value),1e-6) end """ Univariate Categorical Distribution Node """ struct CategoricalDistribution <: LeafNode scope::UInt values::Vector{Float64} end """ Univariate Gaussian Distribution Node """ mutable struct GaussianDistribution <: LeafNode scope::UInt mean::Float64 variance::Float64 end # LeafNode = Union{IndicatorFunction,CategoricalDistribution,GaussianDistribution} """ IndicatorFunction(x::Vector{<:Real})::Float64 Evaluates indicator function at given configuration x. """ function (n::IndicatorFunction)(x::AbstractVector{<:Real})::Float64 return isnan(x[n.scope]) ? 1.0 : n.value ≈ x[n.scope] ? 1.0 : 0.0 end """ Evaluates categorical distribution at given configuration """ function (n::CategoricalDistribution)(x::AbstractVector{<:Real})::Float64 return isnan(x[n.scope]) ? 1.0 : n.values[Int(x[n.scope])] end """ Evaluates Gaussian distribution at given configuration """ function (n::GaussianDistribution)(x::AbstractVector{<:Real})::Float64 return isnan(x[n.scope]) ? 1.0 : exp(-(x[n.scope]-n.mean)^2/(2*n.variance))/sqrt(2*π*n.variance) end "Is this a leaf node?" @inline isleaf(n::Node) = isa(n,LeafNode) "Is this a sum node?" @inline issum(n::Node) = isa(n,SumNode) "Is this a product node?" @inline isprod(n::Node) = isa(n,ProductNode) """ logpdf(node,value) Evaluates leaf `node` at the given `value` in log domain. """ @inline logpdf(n::IndicatorFunction, value::Integer) = isnan(value) ? 0.0 : value == Int(n.value) ? 0.0 : -Inf @inline logpdf(n::IndicatorFunction, value::Float64) = isnan(value) ? 0.0 : abs(value - n.value) < n.tolerance ? 0.0 : -Inf @inline logpdf(n::CategoricalDistribution, value::Integer) = log(n.values[value]) @inline logpdf(n::CategoricalDistribution, value::Float64) = isnan(value) ? 0.0 : logpdf(n,Int(value)) @inline logpdf(n::GaussianDistribution, value::Float64)::Float64 = isnan(value) ? 0.0 : (-(value-n.mean)^2/(2*n.variance)) - log(2*π*n.variance)/2 """ maximum(node) Returns the maximum value of the distribution """ @inline Base.maximum(n::IndicatorFunction) = 1.0 @inline Base.maximum(n::CategoricalDistribution) = Base.maximum(n.values) @inline Base.maximum(n::GaussianDistribution) = 1/sqrt(2*π*n.variance) """ argmax(node) Returns the value at which the distribution is maximum """ @inline Base.argmax(n::IndicatorFunction) = n.value @inline Base.argmax(n::CategoricalDistribution) = Base.argmax(n.values) @inline Base.argmax(n::GaussianDistribution) = n.mean """ scope(node) Returns the scope of a leaf node """ scope(n::LeafNode) = n.scope
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<reponame>KlausC/Multroot.jl<filename>test/testsuit/petk06.m.jl function petk06() # # <NAME> testing polynomials, page 146 # y = [-1.0*[1;1;1;1];3*[1;1;1];-im;-im]; p1 = reverse(poly(y).a) p2 = [1.0;-2;5]; p2 = conv(p2,p2); p = conv(p1,p2); z = [-1.0 4; 3 3; -im 2; 1+2*im 2; 1-2*im 2]; p, PolyZeros(z) end
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<reponame>hasundue/TPT.jl module TPT export TPTSystem, # Basic information ncomp, composition, numberdensity, totalnumberdensity, temperature, # Structural properties structurefactor, paircorrelation, cavityfunction, nndistance, # Interatomic interaction pairpotential, pairpotential_minimum, pairpotential_minimizer, hsdiameter_estimate, # Thermodynamic properties kinetic, entropy, entropy_gas, entropy_conf, internal, helmholtz, # Reference systems AHS, WCA, # Hard-sphere system hsdiameter, packingfraction, totalpackingfraction, contactvalue, contactgradient, # Perturbation LennardJones, NFE, WHTB, NFETB, BOTB, # WCA blipfunction, # NFE Ashcroft, BretonnetSilbert, fermiwavenumber, formfactor, screenedformfactor, dielectric, localfiled, wnechar, # TB bandwidth, # utils spline, # constants kB import Optim import NLopt using Dierckx using Polynomials include("utils.jl") include("types.jl") include("tptsystem.jl") include("constants.jl") include(joinpath("reference", "reference.jl")) include(joinpath("reference", "abstractwca.jl")) include(joinpath("reference", "ahs.jl")) include(joinpath("reference", "wca.jl")) include(joinpath("reference", "lwca.jl")) include(joinpath("perturbation", "perturbation.jl")) include(joinpath("perturbation", "nullpert.jl")) include(joinpath("perturbation", "lennardjones.jl")) include(joinpath("perturbation", "nfetb.jl")) include(joinpath("perturbation", "nfe.jl")) include(joinpath("perturbation", "ashcroft.jl")) include(joinpath("perturbation", "bretonnet_silbert.jl")) include(joinpath("perturbation", "harrison.jl")) include(joinpath("perturbation", "hausleitner.jl")) end # module
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import SMC import Distributions using DataFrames include("hmm_serialization.jl") include("schema.jl") @everywhere begin using SMC using Distributions include("smc_samplers.jl") include("../aide.jl") end function generate_aide_estimates(hmm::HiddenMarkovModel, observations::Vector{Int}, num_particles_list::Vector{Int}, num_metainference_list::Vector{Int}, num_replicates::Int) data = DataFrame() num_particles_column = Int[] num_metainference_column = Int[] proposal_name_column = String[] aide_estimate_column = Float64[] aide_stderr_column = Float64[] gold_standard_name_column = String[] exact_sampler = HMMExactSampler(hmm, observations) gold_standard_sampler = make_optimal_proposal_smc_sampler(hmm, observations, 1000) for num_particles in num_particles_list for num_metainference in num_metainference_list println("generating data for num_partices=$num_particles, num_metainference=$num_metainference...") prior_smc_sampler = make_prior_proposal_smc_sampler(hmm, observations, num_particles) kls = aide(exact_sampler, prior_smc_sampler, 1, num_metainference, num_replicates, num_replicates) push!(num_particles_column, num_particles) push!(num_metainference_column, num_metainference) push!(proposal_name_column, PRIOR_PROPOSAL_NAME) push!(aide_estimate_column, mean(kls)) push!(aide_stderr_column, std(kls)/sqrt(length(kls))) push!(gold_standard_name_column, EXACT_GOLD_STANDARD) prior_smc_sampler = make_prior_proposal_smc_sampler(hmm, observations, num_particles) kls = aide(gold_standard_sampler, prior_smc_sampler, 1, num_metainference, num_replicates, num_replicates) push!(num_particles_column, num_particles) push!(num_metainference_column, num_metainference) push!(proposal_name_column, PRIOR_PROPOSAL_NAME) push!(aide_estimate_column, mean(kls)) push!(aide_stderr_column, std(kls)/sqrt(length(kls))) push!(gold_standard_name_column, APPROXIMATE_GOLD_STANDARD) optimal_smc_sampler = make_optimal_proposal_smc_sampler(hmm, observations, num_particles) kls = aide(exact_sampler, optimal_smc_sampler, 1, num_metainference, num_replicates, num_replicates) push!(num_particles_column, num_particles) push!(num_metainference_column, num_metainference) push!(proposal_name_column, OPTIMAL_PROPOSAL_NAME) push!(aide_estimate_column, mean(kls)) push!(aide_stderr_column, std(kls)/sqrt(length(kls))) push!(gold_standard_name_column, EXACT_GOLD_STANDARD) optimal_smc_sampler = make_optimal_proposal_smc_sampler(hmm, observations, num_particles) kls = aide(gold_standard_sampler, optimal_smc_sampler, 1, num_metainference, num_replicates, num_replicates) push!(num_particles_column, num_particles) push!(num_metainference_column, num_metainference) push!(proposal_name_column, OPTIMAL_PROPOSAL_NAME) push!(aide_estimate_column, mean(kls)) push!(aide_stderr_column, std(kls)/sqrt(length(kls))) push!(gold_standard_name_column, APPROXIMATE_GOLD_STANDARD) end end data[COL_NUM_PARTICLES] = num_particles_column data[COL_NUM_METAINFERENCE] = num_metainference_column data[COL_PROPOSAL_NAME] = proposal_name_column data[COL_AIDE_ESTIMATE] = aide_estimate_column data[COL_AIDE_STDERR] = aide_stderr_column data[COL_GOLD_STANDARD_NAME] = gold_standard_name_column return data end # do experiment data_dir = "data" plot_dir = "plots" # load HMM and observations hmm = load_hmm("$data_dir/hmm.json") num_states = num_states(hmm) num_obs = num_observations(hmm) observations = load_observations("$data_dir/observations.json") num_steps = length(observations) # do AIDE experiment, save data to CSV file num_particles_list = [1, 3, 10, 30, 100] num_metainference_list = [1, 100] num_replicates = 100 aide_estimates = generate_aide_estimates(hmm, observations, num_particles_list, num_metainference_list, num_replicates) writetable("$data_dir/aide_estimates.csv", aide_estimates) println("done!")
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<reponame>andrew-saydjari/disCovErr.jl<gh_stars>1-10 ## utility functions import OffsetArrays import ImageFiltering import ShiftedArrays export cov_avg! export boxsmooth! export outest_bounds # """ outest_bounds(cx,sx) -> px0 Helper function to find maximum padding in pixels required to accomodate all query points `cx` outside of the image size 1:`sx`. # Arguments: - `cx`: list of integer star centers (in either x or y) - `sx`: image dimension along the axis indexed by `cx` # Outputs: - `px0`: maximum padding in pixels required to accomodate all query points """ function outest_bounds(cx,sx) px0 = 0 sortcx = sort(cx) if sortcx[1] < 1 px0 = abs(sortcx[1]-1) end if sortcx[end] > sx if px0 < (sortcx[end]-sx) px0 = (sortcx[end]-sx) end end return px0 end """ boxsmooth!(out::AbstractArray, arr::AbstractArray, tot::Array{T,1}, widx::Int, widy::Int) Boxcar smooths an input image (or paddedview) `arr` with window size `widx` by `widy`. We pass the original image size `sx` and `sy` to help handle image views. # Arguments: - `out::AbstractArray`: preallocated output array for the boxcar smoothed image - `arr::AbstractArray`: input array for which boxcar smoothing is computed (generally paddedview) - `tot::Array{T,1}`: preallocated array to hold moving sums along 1 dimension - `widx::Int`: size of boxcar smoothing window in x - `widy::Int`: size of boxcar smoothing window in y """ function boxsmooth!(out::AbstractArray, arr::AbstractArray, tot::Array{T,1}, widx::Int, widy::Int) where T (sx, sy) = size(arr) Δx = (widx-1)÷2 Δy = (widy-1)÷2 for j=1:(sy-widy+1) if (j==1) for n = 1:widy @simd for m = 1:sx @inbounds tot[m] += arr[m,n] end end else @simd for m = 1:sx @inbounds tot[m] += arr[m,j+widy-1]-arr[m,j-1] end end tt=zero(eltype(out)) for i=1:(sx-widx+1) if (i==1) @simd for n=1:widx @inbounds tt += tot[n] end else @inbounds tt += tot[i+widx-1]-tot[i-1] end @inbounds out[i,j] = tt end end end """ cov_avg!(bimage, ism, bism, in_image; Np::Int=33, widx::Int=129, widy::Int=129, ftype::Int=32) Key function for constructing the (shifted and multiplied) versions of the input image used to quickly estimate the local covariance matrix at a large number of locations. The main output is in the preallocated `bism` which is used as an input to `build_cov!`. # Arguments: - `bimage`: preallocated output array for the boxcar smoothed unshifted image - `ism`: preallocated intermediate array for the input image times itself shifted - `bism`: preallocated output array to store boxcar-smoothed image products for all shifts - `in_image`: input image the local covariance of which we want to estimate # Keywords: - `Np::Int`: size of local covariance matrix in pixels (default 33) - `widx::Int`: width of boxcar window in x which determines size of region used for samples for the local covariance estimate (default 129) - `widy::Int`: width of boxcar window in y which determines size of region used for samples for the local covariance estimate (default 129) - `ftype::Int`: determine the Float precision, 32 is Float32, otherwise Float64 """ function cov_avg!(bimage, ism, bism, in_image; Np::Int=33, widx::Int=129, widy::Int=129, ftype::Int=32) if ftype == 32 T = Float32 else T = Float64 end Δx = (widx-1)÷2 Δy = (widy-1)÷2 halfNp = (Np-1) ÷ 2 (sx1, sy1) = size(in_image) tot = zeros(T,sx1); boxsmooth!(bimage,in_image,tot,widx,widy) # loop over shifts for dc=0:Np-1 # column shift loop for dr=1-Np:Np-1 # row loop, incl negatives if (dr < 0) & (dc == 0) continue end # ism = image, shifted and multipled @inbounds ism .= in_image .* ShiftedArrays.circshift(in_image,(-dr, -dc)) fill!(tot,0) boxsmooth!(view(bism,:,:,dr+Np,dc+1),ism,tot,widx,widy) # bism = boxcar(ism) end end return end
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<reponame>justinjhlo/TextGrids.jl # insert functions """ insert_boundary!(tier, time; split_at = 0) insert_boundary!(tg, num, time; split_at = 0) Insert a boundary at `time` in an interval `tier`, which can also be specified by its number in a `TextGrid`. This action splits an existing interval and increases the size of the tier by 1. The keyword argument `split_at` indicates the starting position in the label of the interval being split that belongs to the right interval. The remaining part of the label belongs to the left interval. A value outside any valid index for the label (i.e. the default value 0, any negative integer, or any positive integer greater than its length) means that the whole original label belongs to the left interval. """ function insert_boundary!(tier::Tier, time::Real; split_at::Int = 0) tier.class == "interval" || error("Cannot insert boundaries in point tiers.") index = findfirst(x -> x.xmin ≤ time ≤ x.xmax, tier.contents) isnothing(index) && error("Time out of bounds of tier.") if tier.contents[index].xmin == time || tier.contents[index].xmax == time error("Boundary already exists in Tier $tier at $(time)s.") end label_length = length(tier.contents[index].label) split_at ∈ 1:label_length || (split_at = label_length + 1) insert!(tier.contents, index + 1, Interval(index + 1, time, tier.contents[index].xmax, tier.contents[index].label[split_at:end])) tier.contents[index].xmax = time tier.contents[index].label = tier.contents[index].label[1:(split_at - 1)] tier.size += 1 reindex_intervals!(tier, from = index + 1) end insert_boundary!(tg::TextGrid, num::Int, time::Real; split_at::Int = 0) = insert_boundary!(tg[num], time, split_at = split_at) """ insert_interval!(tier, start, stop, label; split_at = 0) insert_interval!(tg, num, start, stop, label; split_at = 0) Insert an interval from `start` to `stop` in an interval `tier`. Inserted intervals must not straddle an existing boundary, but if either `start` or `stop` coincides with an existing edge of an interval, then the other boundary is added to split the original interval. The keyword argument `split_at` indicates the starting position in the label of the interval being split that belongs to the interval immediately to the right of the inserted interval. Note that if either `start` or `stop` coincides with an existing edge, `split_at` is automatically overridden and the whole original label goes to the split interval. See also `insert_boundary!`. """ function insert_interval!(tier::Tier, start::Real, stop::Real, label::AbstractString; split_at::Int = 0) tier.class == "interval" || error("Cannot insert intervals in point tiers.") start_index, stop_index = is_interval_insertable(tier, start, stop) if start == tier.contents[start_index].xmin # If only left edge already exists, insert new right edge and force original label to the right insert_boundary!(tier, stop, split_at = 1) relabel!(tier, start_index, label) elseif stop == tier.contents[stop_index].xmax # If only right edge already exists, insert new left edge and force original label to the left insert_boundary!(tier, start) relabel!(tier, start_index + 1, label) else insert_boundary!(tier, stop, split_at = split_at) insert_boundary!(tier, start) relabel!(tier, start_index + 1, label) end return tier end insert_interval!(tg::TextGrid, num::Int, start::Real, stop::Real, label::AbstractString; split_at::Int = 0) = insert_interval!(tg[num], start, stop, label, split_at = split_at) """ copy_interval!(tier, source_tier, index; split_at = 0) copy_interval!(tg, num, source_num, index; split_at = 0) Copy the `index`-th interval from `source_tier` to `tier`. See also `insert_interval!`. """ copy_interval!(tier::Tier, source_tier::Tier, index::Int; split_at::Int = 0) = insert_interval!(tier, source_tier.contents[index].xmin, source_tier.contents[index].xmax, source_tier.contents[index].label, split_at = split_at) copy_interval!(tg::TextGrid, num::Int, source_num::Int, index::Int; split_at::Int = 0) = copy_interval!(tg[num], tg[source_num], index, split_at = split_at) """ Insert multiple intervals to an interval `tier`, with boundaries and labels defined by `starts`, `stops` and `labels`. The keyword argument `split_at` can be either a single integer if constant across inserted intervals or a vector of integers if custom per interval. Intervals are inserted in the specified order, and intervals that cannot be inserted are simply discarded rather than throw an error. See also `insert_interval!` """ function insert_intervals!(tier::Tier, starts::Vector{<:Real}, stops::Vector{<:Real}, labels::Vector{<:AbstractString}; split_at::Union{Int, Vector{Int}} = 0) num_added = minimum(length, [starts, stops, labels]) if split_at isa Vector length(split_at) < num_added && error("`split_at must not be shorter than `starts`, `stops` and `labels`.") for (start, stop, label, split_at_i) in zip(starts, stops, labels, split_at) try insert_interval!(tier, start, stop, label, split_at = split_at_i) catch continue end end else for (start, stop, label) in zip(starts, stops, labels) try insert_interval!(tier, start, stop, label, split_at = split_at) catch continue end end end return tier end insert_intervals!(tg::TextGrid, num::Int, starts::Vector{<:Real}, stops::Vector{<:Real}, labels::Vector{<:AbstractString}; split_at::Union{Int, Vector{Int}} = 0) = insert_intervals!(tg[num], starts, stops, labels, split_at = split_at) function is_interval_insertable(tier::Tier, start::Real, stop::Real) start ≥ stop && error("Start time must come before stop time.") start_index = find_interval(tier, start) stop_index = find_low_interval(tier, stop) start_index * stop_index == 0 && error("Time out of bounds of tier.") start_index != stop_index && error("Cannot insert interval that straddles a boundary.") start == tier.contents[start_index].xmin && stop == tier.contents[stop_index].xmax && error("Interval already exists with the same boundaries.") return start_index, stop_index end # remove functions """ remove_left_boundary!(tier, index; delim = "") remove_left_boundary!(tg, num, index; delim = "") Remove the left boundary of the `index`-th interval in a `tier`. The keyword argument `delim` defines the string used to join the labels from the two intervals being combined. """ function remove_left_boundary!(tier::Tier, index::Int; delim::AbstractString = "") tier.class == "interval" || error("Cannot remove boundaries from point tiers.") index == 1 && error("Cannot remove left edge of tier.") tier.contents[index - 1].xmax = tier.contents[index].xmax relabel!(tier, index - 1, tier.contents[index - 1].label * delim * tier.contents[index].label) deleteat!(tier.contents, index) tier.size -= 1 reindex_intervals!(tier, from = index) end remove_left_boundary!(tg::TextGrid, num::Int, index::Int; delim::AbstractString = "") = remove_left_boundary!(tg[num], index, delim = delim) """ remove_right_boundary!(tier, index; delim = "") remove_right_boundary!(tg, num, index; delim = "") Remove the right boundary of the `index`-th interval in an interval `tier`. The keyword argument `delim` defines the string used to join the labels from the two intervals being combined. """ function remove_right_boundary!(tier::Tier, index::Int; delim::AbstractString = "") tier.class == "interval" || error("Cannot remove boundaries from point tiers.") index == tier.size && error("Cannot remove right edge of tier.") remove_left_boundary!(tier, index + 1, delim = delim) end remove_right_boundary!(tg::TextGrid, num::Int, index::Int; delim::AbstractString = "") = remove_right_boundary!(tg[num], index, delim = delim) """ remove_boundary!(tier, time; delim = "", tolerance = 0.0001) remove_boundary!(tier, num, time; delim = "", tolerance = 0.0001) Remove the boundary at `time` (± `tolerance`) from an interval `tier`. The keyword argument `delim` defines the string used to join the labels from the two intervals being combined. """ function remove_boundary!(tier::Tier, time::AbstractFloat; delim::AbstractString = "", tolerance::Real = 0.0001) index = findlast(x -> abs(x.xmin - time) ≤ tolerance, tier.contents) if !isnothing(index) remove_left_boundary!(tier, index, delim) end end remove_boundary!(tg::TextGrid, num::Int, time::AbstractFloat; delim::AbstractString = "", tolerance::Real = 0.0001) = remove_boundary!(tg[num], time, delim = delim, tolerance = tolerance) """ remove_boundary!(tier, index, edge; delim = "") remove_boundary!(tier, num, index, edge; delim = "") Remove left and/or right boundaries of the `index`-th interval in `tier`. Depending on value of `edge`, equivalent to: - `"left"`: `remove_left_boundary!` - `"right"`: `remove_right_boundary!` - `"both"`: a combination of the two The keyword argument `delim` defines the string used to join the labels from the two intervals being combined. """ function remove_boundary!(tier::Tier, index::Int, edge::AbstractString; delim::AbstractString = "") edge ∈ ("left", "right", "both") || error("`edge` must be `left`, `right` or `both`.") edge != "left" && remove_right_boundary!(tier, index, delim = delim) edge != "right" && remove_left_boundary!(tier, index, delim = delim) return tier end remove_boundary!(tg::TextGrid, num::Int, index::Int, edge::AbstractString; delim::AbstractString = "") = remove_boundary!(tg[num], index, edge, delim = delim) function remove_interval!(tier::Tier, index::Int) tier.xmin == tier.contents[index].xmin && tier.xmax == tier.contents[index].xmax && error("Cannot remove only interval in tier $tier.") relabel!(tier, index, "") edge = if tier.contents[index].xmin == tier.xmin "right" elseif tier.contents[index].xmax == tier.xmax "left" else "both" end remove_boundary!(tier, index, edge) end remove_interval!(tg::TextGrid, num::Int, index::Int) = remove_interval!(tg[num], index) # move functions """ move_left_boundary!(tier, index; by = 0, to = -1) move_left_boundary!(tg, num, index; by = 0, to = -1) Move the left boundary of the `index`-th interval in an interval `tier`, either `by` a specified shift or `to` a specified destination. Boundary movement is not allowed to cross or overlap with other boundaries. See also `move_right_boundary!`, `move_boundary!` and `move_interval!`. """ function move_left_boundary!(tier::Tier, index::Int; by::Real = 0, to::Real = -1) index == 1 && error("Cannot move left edge of tier.") by == 0 && to == -1 && error("Boundary movement not specified.") by != 0 && to != -1 && error("Both `by` and `to` specified.") by != 0 && (to = tier.contents[index].xmin + by) (to < tier.xmin || to > tier.xmax) && error("Cannot move boundary out of bounds.") (to ≤ tier.contents[index - 1].xmin || to ≥ tier.contents[index].xmax) && error("Cannot move boundary beyond interval duration.") tier.contents[index].xmin = to tier.contents[index - 1].xmax = to return tier end move_left_boundary!(tg::TextGrid, num::Int, index::Int; by::Real = 0, to::Real = -1) = move_left_boundary!(tg[num], index, by = by, to = to) """ move_right_boundary!(tier, index; by = 0, to = -1) move_right_boundary!(tg, num, index; by = 0, to = -1) Move the right boundary of the `index`-th interval in an interval `tier`, either `by` a specified shift or `to` a specified destination. Boundary movement is not allowed to cross or overlap with other boundaries. See also `move_left_boundary!`, `move_boundary!` and `move_interval!`. """ function move_right_boundary!(tier::Tier, index::Int; by::Real = 0, to::Real = -1) index == tier.size && error("Cannot move right edge of tier.") move_left_boundary!(tier, index + 1, by = by, to = to) end move_right_boundary!(tg::TextGrid, num::Int, index::Int; by::Real = 0, to::Real = -1) = move_right_boundary!(tg[num], index, by = by, to = to) """ move_interval!(tier, index; by = 0, to = -1) move_interval!(tg, num, index; by = 0, to = -1) Move both left and right boundaries of the `index`-th interval in an interval `tier`, either `by` a specified shift or `to` a specified destination. `to` specifies the destination of the left boundary. Boundary movement is not allowed to cross or overlap with other boundaries. See also `move_left_boundary!`, `move_right_boundary!` and `move_boundary!`. """ function move_interval!(tier::Tier, index::Int; by::Real = 0, to::Real = -1) index == 1 && error("Cannot move left edge of tier.") index == tier.size && error("Cannot move right edge of tier.") by == 0 && to == -1 && error("Boundary movement not specified.") by != 0 && to != -1 && error("Both `by` and `to` specified.") if by != 0 left_to = tier.contents[index].xmin + by right_to = tier.contents[index].xmax + by else left_to = to right_to = to + tier.contents[index].xmax - tier.contents[index].xmin end (left_to < tier.xmin || right_to > tier.xmax) && error("Cannot move boundary out of bounds.") (left_to ≤ tier.contents[index - 1].xmin || right_to ≥ tier.contents[index + 1].xmax) && error("Cannot move boundary beyond interval duration.") tier.contents[index].xmin = left_to tier.contents[index - 1].xmax = left_to tier.contents[index].xmax = right_to tier.contents[index + 1].xmin = right_to return tier end move_interval!(tg::TextGrid, num::Int, index::Int; by::Real = 0, to::Real = -1) = move_interval!(tg[num], index, by = by, to = to) """ move_boundary!(tier, index, edge; by = 0, to = -1) move_boundary!(tg, num, index, edge; by = 0, to = -1) Move one or both boundaries of the `index`-th interval in an interval `tier`, either `by` a specified shift or `to` a specified destination. Equivalent to the following depending on value of `edge`: - "left": `move_left_boundary!` - "right": `move_right_boundary!` - "both": `move_interval!` """ function move_boundary!(tier::Tier, index::Int, edge::AbstractString; by::Real = 0, to::Real = -1) if edge == "left" return move_left_boundary!(tier, index, by = by, to = to) elseif edge == "right" return move_right_boundary!(tier, index, by = by, to = to) elseif edge == "both" return move_interval!(tier, index, by = by, to = to) else error("`edge` must be `left`, `right` or `both`.") end end move_boundary!(tg::TextGrid, num::Int, index::Int, edge::AbstractString; by::Real = 0, to::Real = -1) = move_boundary!(tg[num], index, edge, by = by, to = to) """ move_boundary!(tier, time; by = 0, to = -1, tolerance = 0.0001) move_boundary!(tg, num, time; by = 0, to = -1, tolerance = 0.0001) Move an interval boundary located at `time` (± `tolerance`), either `by` a specified shift or `to` a specified destination. Boundary movement is not allowed to cross or overlap with other boundaries. Nothing happens if no boundary is found within the tolerance limit. """ function move_boundary!(tier::Tier, time::AbstractFloat; by::Real = 0, to::Real = -1, tolerance::Real = 0.0001) index = find_interval(tier, time) if time - tier.contents[index].xmin ≤ tolerance return move_left_boundary!(tier, index, by = by, to = to) elseif tier.contents[index].xmax - time ≤ tolerance return move_right_boundary!(tier, index, by = by, to = to) end end move_boundary!(tg::TextGrid, num::Int, time::AbstractFloat; by::Real = 0, to::Real = -1, tolerance::Real = 0.0001) = move_boundary!(tg[num], time, by = by, to = to, tolerance = tolerance)
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###################### # 1: The Julia type for ToricVarieties ###################### abstract type AbstractNormalToricVariety end struct NormalToricVariety <: AbstractNormalToricVariety polymakeNTV::Polymake.BigObject end export NormalToricVariety struct AffineNormalToricVariety <: AbstractNormalToricVariety polymakeNTV::Polymake.BigObject end export AffineNormalToricVariety function pm_ntv(v::AbstractNormalToricVariety) return v.polymakeNTV end ###################### # 2: Generic constructors ###################### @doc Markdown.doc""" AffineNormalToricVariety(C::Cone) Construct the affine normal toric variety $U_{C}$ corresponding to a polyhedral cone `C`. # Examples Set `C` to be the positive orthant in two dimensions. ```jldoctest julia> C = Oscar.positive_hull([1 0; 0 1]) A polyhedral cone in ambient dimension 2 julia> antv = AffineNormalToricVariety(C) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function AffineNormalToricVariety(C::Cone) pmc = Oscar.pm_cone(C) fan = Polymake.fan.check_fan_objects(pmc) pmntv = Polymake.fulton.NormalToricVariety(fan) return AffineNormalToricVariety(pmntv) end @doc Markdown.doc""" NormalToricVariety(C::Cone) Construct the (affine) normal toric variety $X_{\Sigma}$ corresponding to a polyhedral fan $\Sigma = C$ consisting only of the cone `C`. # Examples Set `C` to be the positive orthant in two dimensions. ```jldoctest julia> C = Oscar.positive_hull([1 0; 0 1]) A polyhedral cone in ambient dimension 2 julia> ntv = NormalToricVariety(C) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function NormalToricVariety(C::Cone) return AffineNormalToricVariety(C) end @doc Markdown.doc""" NormalToricVariety(PF::PolyhedralFan) Construct the normal toric variety $X_{PF}$ corresponding to a polyhedral fan `PF`. # Examples Take `PF` to be the normal fan of the square. ```jldoctest julia> square = Oscar.cube(2) A polyhedron in ambient dimension 2 julia> nf = Oscar.normal_fan(square) A polyhedral fan in ambient dimension 2 julia> ntv = NormalToricVariety(nf) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function NormalToricVariety(PF::PolyhedralFan) fan = Oscar.pm_fan(PF) pmntv = Polymake.fulton.NormalToricVariety(fan) if fan.N_MAXIMAL_CONES == 1 return AffineNormalToricVariety( pmntv ) end return NormalToricVariety(pmntv) end @doc Markdown.doc""" NormalToricVariety(P::Polyhedron) Construct the normal toric variety $X_{\Sigma_P}$ corresponding to the normal fan $\Sigma_P$ of the given polyhedron `P`. Note that this only coincides with the projective variety associated to `P`, if `P` is normal. # Examples Set `P` to be a square. ```jldoctest julia> square = Oscar.cube(2) A polyhedron in ambient dimension 2 julia> ntv = NormalToricVariety(square) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function NormalToricVariety(P::Polyhedron) fan = normal_fan(P) return NormalToricVariety(fan) end @doc Markdown.doc""" NormalToricVariety( r::Matrix{Int}, c::Vector{Vector{Int}} ) Construct the normal toric variety whose fan has ray generators `r` and maximal cones `c`. # Examples ```jldoctest julia> NormalToricVariety( [-1 5; 0 1; 1 0; 0 -1], [[1,2],[2,3],[3,4],[4,1]] ) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function NormalToricVariety( rays::Matrix{Int}, cones::Vector{Vector{Int}} ) Incidence = Oscar.IncidenceMatrix(cones) arr = Polymake.@convert_to Array{Set{Int}} Polymake.common.rows(Incidence.pm_incidencematrix) pmntv = Polymake.fulton.NormalToricVariety( RAYS = Oscar.matrix_for_polymake(rays), MAXIMAL_CONES = arr, ) if length( cones ) == 1 return AffineNormalToricVariety( pmntv ) end return NormalToricVariety( pmntv ) end export NormalToricVariety ###################### # 3: Special constructors ###################### @doc Markdown.doc""" projective_space( d::Int ) Construct the projective space of dimension `d`. # Examples ```jldoctest julia> projective_space( 2 ) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function projective_space( d::Int ) f = normal_fan(Oscar.simplex(d)) pm_ntv = Polymake.fulton.NormalToricVariety(Oscar.pm_fan(f)) return NormalToricVariety(pm_ntv) end export projective_space @doc Markdown.doc""" hirzebruch_surface( r::Int ) Constructs the r-th Hirzebruch surface. # Examples ```jldoctest julia> hirzebruch_surface( 5 ) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function hirzebruch_surface( r::Int ) Rays = [ 1 0; 0 1; -1 r; 0 -1] Cones = [[1,2],[2,3],[3,4],[4,1]] return NormalToricVariety( Rays, Cones ) end export hirzebruch_surface @doc Markdown.doc""" delPezzo( b::Int ) Constructs the delPezzo surface with b blowups for b at most 3. # Examples ```jldoctest julia> del_pezzo( 3 ) A normal toric variety corresponding to a polyhedral fan in ambient dimension 2 ``` """ function del_pezzo( b::Int ) if b < 0 throw(ArgumentError("Number of blowups for construction of delPezzo surfaces must be non-negative.")) return 0 end if b == 0 return projective_space( 2 ) end if b == 1 Rays = [ 1 0; 0 1; -1 0; -1 -1 ] Cones = [ [1,2],[2,3],[3,4],[4,1] ] return NormalToricVariety( Rays, Cones ) end if b == 2 Rays = [ 1 0; 0 1; -1 0; -1 -1; 0 -1 ] Cones = [ [1,2],[2,3],[3,4],[4,5],[5,1] ] return NormalToricVariety( Rays, Cones ) end if b == 3 Rays = [ 1 0; 1 1; 0 1; -1 0; -1 -1; 0 -1 ] Cones = [ [1,2],[2,3],[3,4],[4,5],[5,6],[6,1] ] return NormalToricVariety( Rays, Cones ) end if b > 3 throw(ArgumentError("delPezzo surfaces with more than 3 blowups are realized as subvarieties of toric ambient spaces. This is currently not supported.")) return 0 end end export del_pezzo ############################################################################### ############################################################################### ### Display ############################################################################### ############################################################################### function Base.show(io::IO, ntv::AbstractNormalToricVariety) # fan = get_polyhedral_fan(ntv) pmntv = pm_ntv(ntv) ambdim = pmntv.FAN_AMBIENT_DIM print(io, "A normal toric variety corresponding to a polyhedral fan in ambient dimension $(ambdim)") end
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<reponame>joshday/OfflinePluto.jl # Download links in `assets.csv` dir = joinpath(@__DIR__, "Pluto", "frontend", "offline_assets") rm(dir, force=true, recursive=true) mkpath(dir) for url in readlines(joinpath(@__DIR__, "assets.csv") ) @info "Downloading: $url" file = touch(joinpath(dir, basename(url))) download(url, file) end
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import sbp err = sbp.MMS using Test using LinearAlgebra using DataStructures @testset "extract_vars" begin n = [4, 8, 12] data = Array(1:sum(n)) vars = OrderedDict("u" => n[1], "v" => n[2], "w" => n[3]) ans_u = Array(1:n[1]) ans_v = Array(n[1]+1:n[1] + n[2]) ans_w = Array(n[1]+n[2]+1:sum(n)) expected = Dict("u" => ans_u, "v" => ans_v, "w" => ans_w) extracted_vars = err.extract_vars(data, vars) @test extracted_vars == expected end @testset "l2_errors" begin u = ones(4) v = ones(4) H = Matrix(I, 4, 4) solution = Dict("u" => u, "v" => v) norms = Dict("u" => H, "v" => H) mms = Dict("u" => u, "v" => v) errors = err.l2_errors(solution, mms, norms) expected = Dict("u" => 0.0, "v" => 0.0) @test errors == expected end @testset "functional_errors" begin u = ones(4) v = ones(4) H = Matrix(I, 4, 4) solution = Dict("u" => u, "v" => v) norms = Dict("u" => H, "v" => H) mms = Dict("u" => 4.0, "v" => 4.0) errors = err.functional_errors(solution, mms, norms) expected = Dict("u" => 0.0, "v" => 0.0) @test errors == expected end @testset "log2_convergence_rates" begin u = [16, 8, 4, 2] q = err.log2_convergence_rates(u) ans = [Inf, 1, 1, 1] @test q == ans end
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<filename>src/pruning.jl #================================ All kinds of functions related to pruning =================================# """ get_next_prune_constraint(com::CS.CoM, constraint_idxs_vec) Check which function will be called for pruning next. This is based on `constraint_idxs_vec`. The constraint with the lowest value is chosen and if two have the same lowest value the first one is chosen. Return the best value and the constraint index. Return a constraint index of 0 if there is no constraint with a less than maximal value """ function get_next_prune_constraint(com::CS.CoM, constraint_idxs_vec) best_ci = 0 best_open = typemax(Int) for ci in 1:length(constraint_idxs_vec) if constraint_idxs_vec[ci] < best_open best_ci = ci best_open = constraint_idxs_vec[ci] end end return best_open, best_ci end """ open_possibilities(search_space, indices) Return the sum of possible values for the given list of indices. Does not count 1 if the value is fixed """ function open_possibilities(search_space, indices) open = 0 for vi in indices if !isfixed(search_space[vi]) open += nvalues(search_space[vi]) end end return open end """ prune!(com::CS.CoM; pre_backtrack=false, all=false, only_once=false, initial_check=false) Prune based on changes by initial solve or backtracking. The information for it is stored in each variable. There are several parameters: `pre_backtrack` when set to true `com.info.in_backtrack_calls` is incremented `all` instead of only looking at changes each constraint is check at least once (if there are open values) `only_once` Just run on the changed constraints or `all` once instead of repeatedly until nothing can be pruned `initial_check` Checks on `all` constraints and also checks if variables are set for the whole constraint whether the constraint is fulfilled or the problem is infeasible. Return whether it's still feasible """ function prune!( com::CS.CoM; pre_backtrack = false, all = false, only_once = false, initial_check = false, ) feasible = true N = typemax(Int) search_space = com.search_space prev_var_length = zeros(Int, length(search_space)) constraint_idxs_vec = fill(N, length(com.constraints)) # get all constraints which need to be called (only once) for var in search_space new_var_length = num_changes(var, com.c_step_nr) if new_var_length > 0 || all || initial_check prev_var_length[var.idx] = new_var_length for ci in com.subscription[var.idx] com.constraints[ci].is_deactivated && continue inner_constraint = com.constraints[ci] constraint_idxs_vec[inner_constraint.idx] = open_possibilities(search_space, inner_constraint.indices) end end end # while we haven't called every constraint while true open_pos, ci = get_next_prune_constraint(com, constraint_idxs_vec) # no open values => don't need to call again if open_pos == 0 && !initial_check constraint_idxs_vec[ci] = N continue end # checked all if open_pos == N break end constraint_idxs_vec[ci] = N constraint = com.constraints[ci] changed!(com, constraint, constraint.fct, constraint.set) feasible = prune_constraint!(com, constraint, constraint.fct, constraint.set; logs = false) if !pre_backtrack com.info.in_backtrack_calls += 1 else com.info.pre_backtrack_calls += 1 end if !feasible break end # if we changed another variable increase the level of the constraints to call them later for vidx in constraint.indices var = search_space[vidx] new_var_length = num_changes(var, com.c_step_nr) if new_var_length > prev_var_length[var.idx] prev_var_length[var.idx] = new_var_length for ci in com.subscription[var.idx] com.constraints[ci].is_deactivated && continue # don't call the same constraint again. # Each constraint should prune as much as possible if ci != constraint.idx inner_constraint = com.constraints[ci] # if initial check or don't add constraints => update only those which already have open possibilities if (only_once || initial_check) && constraint_idxs_vec[inner_constraint.idx] == N continue end constraint_idxs_vec[inner_constraint.idx] = open_possibilities(search_space, inner_constraint.indices) end end end end end return feasible end """ restore_prune!(com::CS.CoM, prune_steps) Prune the search space based on a list of backtracking indices `prune_steps`. """ function restore_prune!(com::CS.CoM, prune_steps) search_space = com.search_space for backtrack_idx in prune_steps step_nr = com.backtrack_vec[backtrack_idx].step_nr for var in search_space for change in view_changes(var, step_nr) fct_symbol = change[1] val = change[2] if fct_symbol == :fix fix!(com, var, val; changes = false, check_feasibility = false) elseif fct_symbol == :rm rm!(com, var, val; changes = false, check_feasibility = false) elseif fct_symbol == :remove_above remove_above!(com, var, val; changes = false, check_feasibility = false) elseif fct_symbol == :remove_below remove_below!(com, var, val; changes = false, check_feasibility = false) else throw(ErrorException("There is no pruning function for $fct_symbol")) end end end com.c_backtrack_idx = backtrack_idx end call_restore_pruning!(com, prune_steps) end """ single_reverse_pruning!(search_space, vidx::Int, prune_int::Int, prune_fix::Int) Reverse a single variable using `prune_int` (number of value removals) and `prune_fix` (new last_ptr if not 0). """ function single_reverse_pruning!(search_space, vidx::Int, prune_int::Int, prune_fix::Int) if prune_int > 0 var = search_space[vidx] l_ptr = max(1, var.last_ptr) new_l_ptr = var.last_ptr + prune_int min_val, max_val = extrema(var.values[l_ptr:new_l_ptr]) if min_val < var.min var.min = min_val end if max_val > var.max var.max = max_val end var.last_ptr = new_l_ptr end if prune_fix > 0 var = search_space[vidx] var.last_ptr = prune_fix min_val, max_val = extrema(var.values[1:prune_fix]) if min_val < var.min var.min = min_val end if max_val > var.max var.max = max_val end var.first_ptr = 1 end end """ reverse_pruning!(com, backtrack_idx) Reverse the changes made by a specific backtrack object """ function reverse_pruning!(com::CS.CoM, backtrack_idx::Int) com.c_backtrack_idx = backtrack_idx step_nr = com.backtrack_vec[backtrack_idx].step_nr search_space = com.search_space for var in search_space v_idx = var.idx changes = var.changes.changes ch_indices = var.changes.indices for change_id in ch_indices[step_nr+1]-1:-1:ch_indices[step_nr] single_reverse_pruning!(search_space, v_idx, changes[change_id][4], changes[change_id][3]) end end subscriptions = com.subscription constraints = com.constraints for var in search_space num_changes(var, step_nr) == 0 && continue var.idx > length(subscriptions) && continue @inbounds for ci in subscriptions[var.idx] constraint = constraints[ci] single_reverse_pruning_constraint!( com, constraint, constraint.fct, constraint.set, var, backtrack_idx, ) end end for constraint in constraints reverse_pruning_constraint!( com, constraint, constraint.fct, constraint.set, backtrack_idx, ) end com.c_backtrack_idx = com.backtrack_vec[backtrack_idx].parent_idx end
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2.186191
4,012
using SuiteSparseMatrixCollection using MatrixMarket using SuiteSparseGraphBLAS SuiteSparseGraphBLAS.gbset(SuiteSparseGraphBLAS.FORMAT, SuiteSparseGraphBLAS.BYROW) using BenchmarkTools using SparseArrays using LinearAlgebra include("tc.jl") include("pr.jl") graphs = [ #"karate", #"com-Youtube", #"as-Skitter", #"com-LiveJournal", #"com-Orkut", "com-Friendster", ] ssmc = ssmc_db() matrices = filter(row -> row.name ∈ graphs, ssmc) BenchmarkTools.DEFAULT_PARAMETERS.gcsample = true for name ∈ graphs path = fetch_ssmc(matrices[matrices.name .== name, :])[1] G = GBMatrix(convert(SparseMatrixCSC{Float64}, MatrixMarket.mmread(joinpath(path, "$name.mtx")))) SuiteSparseGraphBLAS.gbset(G, SuiteSparseGraphBLAS.FORMAT, SuiteSparseGraphBLAS.BYROW) GC.gc() G[:,:, mask=G, desc=SuiteSparseGraphBLAS.S] = 1 diag(G) println("$name | $(size(G)) | $(nnz(G)) edges") d = reduce(+, G; dims=2) # for centrality in [PR, TC1, TC3] for centrality in [TC3] println("Benchmarking $(string(centrality)) on $(name)") i = 0.0 centrality(G, d) #warmup for run ∈ 1:3 i += @elapsed centrality(G, d) end println("$(string(centrality)) on $(name) over 3 runs took an average of: $(i / 3)s") end end
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2.270506
573
### A Pluto.jl notebook ### # v0.17.4 using Markdown using InteractiveUtils # ╔═╡ 3668f786-9597-11eb-01a1-87d34b49eef9 begin #packages for I/O, interpolation, etc using MITgcmTools, MeshArrays, Plots, PlutoUI PICKUP_hs94_download() 🏁 = "🏁" "Downloads and packages : complete." end # ╔═╡ 19095067-33f5-495f-bc4d-ee6dacbf6ca8 begin imgB="https://user-images.githubusercontent.com/20276764/113531401-b1780d00-9596-11eb-8e96-990cf9533ada.png" md"""# Simple Atmosphere ### In this notebook we: 1. run the Held and Suarez Benchmark 1. read MITgcm output for temperature 1. interpolate it via **`MeshArrays.jl`** 1. plot and animate This should generate something like this: $(Resource(imgB, :width => 240)) $(TableOfContents()) !!! note If you use a live version of this notebook, selecting a different configuration from the list below will make the other notebook cells react (e.g. displayed contents). If you visualize an html version of this notebook, then cells wont react. """ end # ╔═╡ 207e4c15-7818-4dc3-a048-1dd36ba5a73e begin myexp=verification_experiments("hs94.cs-32x32x5") pth_run=joinpath(myexp.folder,string(myexp.ID),"run") md""" ## Model Configuration This is a data structure of type `MITgcm_config` (a concrete type of `AbstractModelConfig`). """ end # ╔═╡ 2fd1ddf0-c3ee-4076-9f7f-b066da2baf50 myexp # ╔═╡ bd0803d8-c70d-47b8-a76e-5765f4ba01c6 md""" ## Worflow Steps The animation just above results from the following workflow steps: - setup & compile - run model - process output """ # ╔═╡ 9238b863-dd69-42ce-8f36-995b4757cc1a md""" #### Contents of model run folder The first cell below list all files found in files the run directory. The second displays the end of the standard output file (e.g. `output.txt`) generated during the model run. The third is the result of the `scan_rundir` function. """ # ╔═╡ 88a5819f-6f35-40e8-9a82-8fd6f97001b1 md""" #### Contents of the pickup folder""" # ╔═╡ 37294d8a-a70e-419a-a60b-11d09930c6b0 readdir(PICKUP_hs94_path) # ╔═╡ f0185b52-2297-4e8c-b44b-8c29b634607a md""" ## Appendices""" # ╔═╡ fa968801-6892-4475-9b27-56472ca611b4 begin #function used to modify model parameters (e.g. duration) function modify_params_HS94(myexp) par_path=joinpath(myexp.folder,string(myexp.ID),"log","tracked_parameters") fil=joinpath(par_path,"data") nml=read(fil,MITgcm_namelist()) nml.params[1][:useSingleCpuIO]=true nml.params[3][:nIter0]=43200 nml.params[3][:nTimeSteps]=720 nml.params[3][:monitorFreq]= 21600.0 write(fil,nml) #ClimateModels.git_log_fil(myexp,fil,"update parameter file : "*split(fil,"/")[end]) fil=joinpath(par_path,"data.pkg") nml=read(fil,MITgcm_namelist()) nml.params[1][:useDiagnostics]=false nml.params[1][:useMNC]=false write(fil,nml) #ClimateModels.git_log_fil(myexp,fil,"update parameter file : "*split(fil,"/")[end]) end "helper function" end # ╔═╡ aad7e042-ba39-4518-8f3e-da59b77c13cb begin #set up run directory setup(myexp) #compile model if not already done build(myexp,"--allow-skip") #modify parameters to start from time step 43200, etc modify_params_HS94(myexp) #provide initial condition for time step 43200 fil1=joinpath(PICKUP_hs94_path,"pickup.0000043200.data") fil2=joinpath(pth_run,"pickup.0000043200.data") !isfile(fil2) ? cp(fil1,fil2) : nothing fil1=joinpath(PICKUP_hs94_path,"pickup.0000043200.meta") fil2=joinpath(pth_run,"pickup.0000043200.meta") !isfile(fil2) ? cp(fil1,fil2) : nothing #readdir(joinpath(myexp.folder,string(myexp.ID),"log")) step1=🏁 end # ╔═╡ 0aa37844-b4b9-4f58-adf7-15ae9a490993 begin step1==🏁 MITgcmTools.launch(myexp) step2=🏁 end # ╔═╡ b77f7ff2-da7e-41b3-b3f6-3819b09cd33c begin step2==🏁 # isfile(joinpath(MITgcm_path[1],"verification",myexp.configuration,"build","mitgcmuv")) isfile(joinpath(pth_run,"output.txt")) ? sc=scan_rundir(pth_run) : sc=(completed=false,) #copy files to known location for subsequent notebooks (Makie, particles, etc) function cp_run_dir() p2=joinpath(PICKUP_hs94_path,"run") tst = sc.completed&(!isdir(p2)) tst ? run(`cp -pr $pth_run $p2`) : nothing isdir(p2) end cp_run_dir() ## read grid variables (for interpolation) Γ = GridLoad_mdsio(myexp) ## setup interpolation (for plotting) lon=[i for i=-179.5:1.0:179.5, j=-89.5:1.0:89.5] lat=[j for i=-179.5:1.0:179.5, j=-89.5:1.0:89.5] (f,i,j,w,_,_,_)=InterpolationFactors(Γ,vec(lon),vec(lat)) IntFac=(f,i,j,w) #list of output files (1 per time record) ff=readdir(pth_run); fil="T.0000" ff=ff[findall(occursin.(fil,ff).*occursin.(".data",ff))] nt=length(ff) γ=Γ.XC.grid step3=🏁 end # ╔═╡ ee0e6f28-aa26-48de-8ddd-8bb2d1102ee9 begin #function used to plot one time record function myplot(fil,pth) T=read(joinpath(pth,fil),MeshArray(γ,Float64)) TT=Interpolate(T,IntFac...) contourf(vec(lon[:,1]),vec(lat[1,:]),TT,clims=(260.,320.)) end #loop over model output files dt=6 anim = @animate for i ∈ 1:dt:nt myplot(ff[i],pth_run) end pp=tempdir()*"/" gif(anim,pp*"hs94.cs.gif", fps = 8) end # ╔═╡ 0ca84f4e-f5bf-40d0-bf46-7a0e70b7aded begin step3==🏁 ls_run_dir=readdir(pth_run) ls_run_dir end # ╔═╡ ca299148-6aa8-4379-88e3-c4500ddc779f begin step3==🏁 stdout=readlines(joinpath(pth_run,"output.txt")) Dump(stdout) end # ╔═╡ b1ca8b16-7b63-470b-90d0-6ea41eeb5211 sc # ╔═╡ 00000000-0000-0000-0000-000000000001 PLUTO_PROJECT_TOML_CONTENTS = """ [deps] MITgcmTools = "62725fbc-3a66-4df3-9000-e33e85b3a198" MeshArrays = "cb8c808f-1acf-59a3-9d2b-6e38d009f683" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8" [compat] MITgcmTools = "~0.1.32" MeshArrays = "~0.2.31" Plots = "~1.25.2" PlutoUI = "~0.7.25" """ # ╔═╡ 00000000-0000-0000-0000-000000000002 PLUTO_MANIFEST_TOML_CONTENTS = """ # This file is machine-generated - editing it directly is not advised julia_version = "1.7.0" manifest_format = "2.0" [[deps.AWS]] deps = ["Base64", "Compat", "Dates", "Downloads", "GitHub", "HTTP", "IniFile", "JSON", "MbedTLS", "Mocking", "OrderedCollections", "Retry", "Sockets", "URIs", "UUIDs", "XMLDict"] git-tree-sha1 = "07d944e4d9946c2061f97c1564d1b7ae8ea8f189" uuid = "fbe9abb3-538b-5e4e-ba9e-bc94f4f92ebc" version = "1.61.0" [[deps.AbstractPlutoDingetjes]] deps = ["Pkg"] git-tree-sha1 = "abb72771fd8895a7ebd83d5632dc4b989b022b5b" uuid = "6e696c72-6542-2067-7265-42206c756150" version = "1.1.2" [[deps.Adapt]] deps = ["LinearAlgebra"] git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.3.1" [[deps.ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" [[deps.Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" [[deps.Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.Blosc]] deps = ["Blosc_jll"] git-tree-sha1 = "575bdd70552dd9a7eaeba08ef2533226cdc50779" uuid = "a74b3585-a348-5f62-a45c-50e91977d574" version = "0.7.2" [[deps.Blosc_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Lz4_jll", "Pkg", "Zlib_jll", 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version = "1.23.0+0" [[deps.WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] git-tree-sha1 = "c69f9da3ff2f4f02e811c3323c22e5dfcb584cfa" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.1" [[deps.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" [[deps.XMLDict]] deps = ["EzXML", "IterTools", "OrderedCollections"] git-tree-sha1 = "d9a3faf078210e477b291c79117676fca54da9dd" uuid = "228000da-037f-5747-90a9-8195ccbf91a5" version = "0.4.1" [[deps.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" [[deps.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" [[deps.Xorg_libXau_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e" uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec" version = "1.0.9+4" [[deps.Xorg_libXcursor_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"] git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd" uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724" version = "1.2.0+4" [[deps.Xorg_libXdmcp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4" uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05" version = "1.1.3+4" [[deps.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" [[deps.Xorg_libXfixes_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4" uuid = "d091e8ba-531a-589c-9de9-94069b037ed8" version = "5.0.3+4" [[deps.Xorg_libXi_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"] git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246" uuid = "a51aa0fd-4e3c-5386-b890-e753decda492" version = "1.7.10+4" [[deps.Xorg_libXinerama_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"] git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123" uuid = "d1454406-59df-5ea1-beac-c340f2130bc3" version = "1.1.4+4" [[deps.Xorg_libXrandr_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"] git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631" uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484" version = "1.5.2+4" [[deps.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" [[deps.Xorg_libpthread_stubs_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb" uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74" version = "0.1.0+3" [[deps.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" [[deps.Xorg_libxkbfile_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"] git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2" uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a" version = "1.1.0+4" [[deps.Xorg_xcb_util_image_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97" uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b" version = "0.4.0+1" [[deps.Xorg_xcb_util_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"] git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1" uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5" version = "0.4.0+1" [[deps.Xorg_xcb_util_keysyms_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00" uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7" version = "0.4.0+1" [[deps.Xorg_xcb_util_renderutil_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e" uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e" version = "0.3.9+1" [[deps.Xorg_xcb_util_wm_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"] git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67" uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361" version = "0.4.1+1" [[deps.Xorg_xkbcomp_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"] git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b" uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" version = "1.4.2+4" [[deps.Xorg_xkeyboard_config_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"] git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d" uuid = "33bec58e-1273-512f-9401-5d533626f822" version = "2.27.0+4" [[deps.Xorg_xtrans_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845" uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" version = "1.4.0+3" [[deps.Zarr]] deps = ["AWS", "Blosc", "CodecZlib", "DataStructures", "Dates", "DiskArrays", "HTTP", "JSON", "LRUCache", "OffsetArrays", "Pkg", "URIs"] git-tree-sha1 = "7238cf588d2def313a65b63ea9bba07aa762f26b" uuid = "0a941bbe-ad1d-11e8-39d9-ab76183a1d99" version = "0.7.0" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" [[deps.Zstd_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" version = "1.5.0+0" [[deps.libass_jll]] deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" version = "0.14.0+4" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" [[deps.libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" version = "0.1.6+4" [[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.libsodium_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "848ab3d00fe39d6fbc2a8641048f8f272af1c51e" uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8" version = "1.0.20+0" [[deps.libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" version = "1.3.7+0" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" [[deps.x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" version = "2020.7.14+2" [[deps.x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" version = "3.0.0+3" [[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: # ╟─19095067-33f5-495f-bc4d-ee6dacbf6ca8 # ╟─207e4c15-7818-4dc3-a048-1dd36ba5a73e # ╟─2fd1ddf0-c3ee-4076-9f7f-b066da2baf50 # ╟─ee0e6f28-aa26-48de-8ddd-8bb2d1102ee9 # ╟─bd0803d8-c70d-47b8-a76e-5765f4ba01c6 # ╠═aad7e042-ba39-4518-8f3e-da59b77c13cb # ╠═0aa37844-b4b9-4f58-adf7-15ae9a490993 # ╟─b77f7ff2-da7e-41b3-b3f6-3819b09cd33c # ╟─9238b863-dd69-42ce-8f36-995b4757cc1a # ╟─0ca84f4e-f5bf-40d0-bf46-7a0e70b7aded # ╟─ca299148-6aa8-4379-88e3-c4500ddc779f # ╟─b1ca8b16-7b63-470b-90d0-6ea41eeb5211 # ╟─88a5819f-6f35-40e8-9a82-8fd6f97001b1 # ╟─37294d8a-a70e-419a-a60b-11d09930c6b0 # ╟─f0185b52-2297-4e8c-b44b-8c29b634607a # ╟─3668f786-9597-11eb-01a1-87d34b49eef9 # ╟─fa968801-6892-4475-9b27-56472ca611b4 # ╟─00000000-0000-0000-0000-000000000001 # ╟─00000000-0000-0000-0000-000000000002
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<filename>src/Geometry/Primitives/Chebyshev.jl # MIT License # Copyright (c) Microsoft Corporation. # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE """ Module to enclose [Chebyshev polynomial](https://en.wikipedia.org/wiki/Chebyshev_polynomials) specific functionality. """ module Chebyshev using OpticSim: NaNsafeacos """ T(n::Int, q::R, fast::Bool = true) -> R Evaluate Chebyshev polynomial of the first kind ``T_n(q)``. `fast` will use trigonometric definition, rather than the recursive definition which is much faster but slightly less precise. """ @inline function T(n::Int, q::R, fast::Bool = true)::R where {R<:Real} @assert n >= 0 if n === 0 return one(R) elseif n === 1 return q elseif fast && n > 3 if abs(q) < one(R) return cos(n * NaNsafeacos(q)) elseif q >= one(R) return cosh(n * acosh(q)) else return (-1)^n * cosh(n * acosh(-q)) end else return 2q * T(n - 1, q, fast) - T(n - 2, q, fast) end end """ U(n::Int, q::R, fast::Bool = true) -> R Evaluate Chebyshev polynomial of the second kind ``U_n(q)``. `fast` will use trigonometric definition, rather than the recursive definition which is much faster but slightly less precise. """ @inline function U(n::Int, q::R, fast::Bool = true)::R where {R<:Real} @assert n >= 0 if n === 0 return one(R) elseif n === 1 return 2q elseif abs(q) < one(R) && fast && q > 3 # much faster but not stable at |q| = 1 aq = NaNsafeacos(q) return sin((n + 1) * aq) / sin(aq) else return 2q * U(n - 1, q, fast) - U(n - 2, q, fast) end end """ dTdq(n::Int, q::R, fast::Bool = true) -> R Evaluate derivative of Chebyshev polynomial of the first kind ``\\frac{dT_n}{dq}(q)``. `fast` will use trigonometric definition, rather than the recursive definition which is much faster but slightly less precise. """ @inline function dTdq(n::Int, q::R, fast::Bool = true)::R where {R<:Real} @assert n >= 0 if n === 0 return zero(R) elseif n === 1 return one(R) elseif fast && n > 4 if abs(q) == one(R) return q^(n + 1) * n^2 elseif abs(q) < one(R) return n * sin(n * acos(q)) / sqrt(1 - q^2) elseif q > one(R) return n * sinh(n * acosh(q)) / sqrt(q^2 - 1) else return -n * (-1)^n * sinh(n * acosh(-q)) / sqrt(q^2 - 1) end else return n * U(n - 1, q, fast) end end # dUdq(n::Int, q::R)::R where {R<:Real} = ((n + 1) * T(n + 1, q) - q * U(n, q)) / (q^2 - one(T)) # d2Tq2(n::Int, q::R)::R where {R<:Real} = n * ((n + 1) * T(n, q) - U(n, q)) / (q^2 - 1) end # module Chebyshev """ ChebyshevSurface{T,N,P,Q} <: ParametricSurface{T,N} Rectangular surface incorporating Chebyshev polynomials as well as radius and conic terms. `T` is the datatype, `N` is the dimensionality, `P` is the number of Chebyshev terms in u and `Q` is the number of Chebyshev terms in v. The surface is centered at the origin and treated as being the cap of an infinite rectangular prism, thus creating a true half-space. **Note that the surface is vertically offset so that the center (i.e., `(u,v) == (0,0)`) lies at 0 on the z-axis.** ```julia ChebyshevSurface(halfsizeu, halfsizev, chebycoeff; radius = Inf, conic = 0) ``` `chebycoeff` is a vector containing tuples of the form `(i, j, v)` where `v` is the value of the coefficient ``c_{ij}``. The sag is defined by the equation ```math z(u,v) = \\frac{c(u^2 + v^2)^2}{1 + \\sqrt{1 - (1+k)c^2(u^2 + v^2)}} + \\sum_{i}^{P}\\sum_{j}^{Q}c_{ij}T_i(u)T_j(v) ``` where ``c = \\frac{1}{\\texttt{radius}}``, ``k = \\texttt{conic}`` and ``T_n`` is the nᵗʰ Chebyshev polynomial of the first kind. """ struct ChebyshevSurface{T,N,P} <: ParametricSurface{T,N} halfsizeu::T halfsizev::T curvature::T conic::T boundingprism::BoundingBox{T} chebycoeff::SVector{P,Tuple{Int,Int,T}} offset::T function ChebyshevSurface(halfsizeu::T, halfsizev::T, chebycoeff::Union{Nothing,Vector{Tuple{Int,Int,T}}}; radius::T = typemax(T), conic::T = zero(T)) where {T<:Real} @assert !isnan(halfsizeu) && !isnan(halfsizev) && !isnan(radius) && !isnan(conic) @assert halfsizeu > zero(T) && halfsizev > zero(T) @assert one(T) - (1 / radius)^2 * (conic + one(T)) * (halfsizeu^2 + halfsizev^2) > 0 "Invalid surface (conic/radius combination: $radius, $conic)" offset = zero(T) if chebycoeff === nothing P = 0 else for (i, j, c) in chebycoeff @assert i >= 0 && j >= 0 "i and j must be non-negative" if i % 2 == 0 && j % 2 == 0 offset += c * (-1)^(i ÷ 2) * (-1)^(j ÷ 2) end end chebycoeff = filter(k -> abs(k[3]) > zero(T), chebycoeff) P = length(chebycoeff) end bounding_prism = BoundingBox(-halfsizeu, halfsizeu, -halfsizev, halfsizev, typemin(T), typemax(T)) return new{T,3,P}(halfsizeu, halfsizev, 1 / radius, conic, bounding_prism, SVector{P,Tuple{Int,Int,T}}(P === 0 ? [] : chebycoeff), offset) end end export ChebyshevSurface uvrange(::Type{ChebyshevSurface{T,N,P}}) where {T<:Real,N,P} = ((-one(T), one(T)), (-one(T), one(T))) boundingobj(z::ChebyshevSurface{T}) where {T<:Real} = z.boundingprism halfsizeu(z::ChebyshevSurface{T}) where {T<:Real} = z.halfsizeu halfsizev(z::ChebyshevSurface{T}) where {T<:Real} = z.halfsizev function point(s::ChebyshevSurface{T,3,P}, u::T, v::T)::SVector{3,T} where {T<:Real,P} x = u * s.halfsizeu y = v * s.halfsizev r2 = (x^2 + y^2) q = (one(T) + s.conic) * s.curvature^2 * r2 if q > one(T) return SVector{3,T}(NaN, NaN, NaN) end z = s.curvature * r2 / (one(T) + sqrt(one(T) - q)) @inbounds @simd for ci in 1:P i, j, c = s.chebycoeff[ci] z += c * Chebyshev.T(i, u) * Chebyshev.T(j, v) end return SVector{3,T}(x, y, z - s.offset) end function partials(s::ChebyshevSurface{T,3,P}, u::T, v::T)::Tuple{SVector{3,T},SVector{3,T}} where {T<:Real,P} x = u * s.halfsizeu y = v * s.halfsizev r2 = x^2 + y^2 t = one(T) - s.curvature^2 * (1 + s.conic) * r2 if t < zero(T) return SVector{3,T}(NaN, NaN, NaN), SVector{3,T}(NaN, NaN, NaN) end q = s.curvature * sqrt(t) / t dhdu = x * q * s.halfsizeu dhdv = y * q * s.halfsizev @inbounds @simd for k in s.chebycoeff i, j, c = k dhdu += c * Chebyshev.dTdq(i, u) * Chebyshev.T(j, v) dhdv += c * Chebyshev.T(i, u) * Chebyshev.dTdq(j, v) end return SVector{3,T}(s.halfsizeu, 0.0, dhdu), SVector{3,T}(0.0, s.halfsizev, dhdv) end function normal(s::ChebyshevSurface{T,3,P}, u::T, v::T)::SVector{3,T} where {T<:Real,P} du, dv = partials(s, u, v) return normalize(cross(du, dv)) end function uv(s::ChebyshevSurface{T,3,P}, p::SVector{3,T}) where {T<:Real,P} return SVector{2,T}(p[1] / s.halfsizeu, p[2] / s.halfsizev) end function onsurface(surf::ChebyshevSurface{T,3,P}, p::SVector{3,T}) where {T<:Real,P} u, v = uv(surf, p) if abs(u) > one(T) || abs(v) > one(T) return false else surfpoint = point(surf, u, v) return samepoint(p[3], surfpoint[3]) end end function inside(surf::ChebyshevSurface{T,3,P}, p::SVector{3,T}) where {T<:Real,P} u, v = uv(surf, p) if abs(u) > one(T) || abs(v) > one(T) return false else surfpoint = point(surf, u, v) return p[3] < surfpoint[3] end end ######################################################################################################### # Assumes the ray has been transformed into the canonical coordinate frame which has the vertical axis passing through (0,0,0) and aligned with the z axis. function surfaceintersection(surf::AcceleratedParametricSurface{T,3,ChebyshevSurface{T,3,P}}, r::AbstractRay{T,3}) where {T<:Real,P} bboxint = surfaceintersection(surf.surface.boundingprism, r) if bboxint isa EmptyInterval{T} return EmptyInterval(T) else if doesintersect(surf.triangles_bbox, r) || inside(surf.triangles_bbox, origin(r)) surfint = triangulatedintersection(surf, r) if !(surfint isa EmptyInterval{T}) return intervalintersection(bboxint, surfint) end end # hasn't hit the surface if lower(bboxint) isa RayOrigin{T} && upper(bboxint) isa Infinity{T} if inside(surf.surface, origin(r)) return Interval(RayOrigin(T), Infinity(T)) else return EmptyInterval(T) end # otherwise check that the intersection is underneath the surface else p = point(closestintersection(bboxint, false)) ρ, ϕ = uv(surf, p) surfpoint = point(surf.surface, ρ, ϕ) if p[3] < surfpoint[3] return bboxint # TODO!! UV (and interface) issues? else return EmptyInterval(T) end end end end function BoundingBox(surf::ChebyshevSurface{T,3,P}) where {T<:Real,P} xmin = -surf.halfsizeu xmax = surf.halfsizeu ymin = -surf.halfsizev ymax = surf.halfsizev # polynomials range between -1 and 1 so we have to sum the absolute value of every coefficient to get the theoretical max zmax = P > 0 ? sum(abs(c) for (_, _, c) in surf.chebycoeff) : zero(T) zmin = -zmax q = one(T) - (one(T) + surf.conic) * surf.curvature^2 * (surf.halfsizeu^2 + surf.halfsizev^2) if q < zero(T) throw(ErrorException("The surface is invalid, no bounding box can be constructed")) end hmax = surf.curvature * (surf.halfsizeu^2 + surf.halfsizev^2) / (one(T) + sqrt(q)) if hmax > zero(T) zmax += hmax else zmin += hmax end return BoundingBox(xmin, xmax, ymin, ymax, zmin, zmax) end
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<filename>distributed/dfract.jl using Distributed using Images using FileIO using Mmap # calculate a distributed fractal width = 40000 height = 20000 rmin = -2.5 rmax = 1.5 imin = -1.25 imax = 1.25 iter = 500 epsilon = 0.25 mapfile = "zset-$(width)-$(height)" s = open(mapfile) n = read(s, Int) println("size of zset is $n") if n != (width * height) @warn("zset doesn't match dims") exit() end zset = Mmap.mmap(s, Array{ComplexF64,1}, n) close(s) num_procs = 0 hosts = ["<EMAIL>"] for i in 1:num_procs println("started proc $(i)") sshflags=`-i /Users/robert/src/julia/juliaSnippets/distributed/ms-keys` addprocs(hosts, tunnel=false, sshflags=sshflags, max_parallel=10, dir="/Users/robert/tmp", topology=:all_to_all) end # define mandel everywhere include("mandel.jl") println("starting pmap on $(workers())") # #generate image from results # time measurements print("starting...\n") tStart=time() #zset = Array{ComplexF64,1}(undef,0) m_set = mandelbrot_set(zset, rmin, rmax, imin, imax, width, height, iter, epsilon) tStop = time() # write the image-file img = colorview(RGB, m_set) save("mandel.jpg", img) print("done. took ", tStop-tStart, " seconds\n");
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<gh_stars>100-1000 const POWER_SYSTEM_DESCRIPTOR_FILE = joinpath(dirname(pathof(PowerSystems)), "descriptors", "power_system_inputs.json") const INPUT_CATEGORY_NAMES = [ ("branch", InputCategory.BRANCH), ("bus", InputCategory.BUS), ("dc_branch", InputCategory.DC_BRANCH), ("gen", InputCategory.GENERATOR), ("load", InputCategory.LOAD), ("reserves", InputCategory.RESERVE), ("storage", InputCategory.STORAGE), ] struct PowerSystemTableData base_power::Float64 category_to_df::Dict{InputCategory, DataFrames.DataFrame} timeseries_metadata_file::Union{String, Nothing} directory::String user_descriptors::Dict descriptors::Dict generator_mapping::Dict{NamedTuple, DataType} end function PowerSystemTableData( data::Dict{String, Any}, directory::String, user_descriptors::Union{String, Dict}, descriptors::Union{String, Dict}, generator_mapping::Union{String, Dict}; timeseries_metadata_file = joinpath(directory, "timeseries_pointers"), ) category_to_df = Dict{InputCategory, DataFrames.DataFrame}() if !haskey(data, "bus") throw(DataFormatError("key 'bus' not found in input data")) end if !haskey(data, "base_power") @warn "key 'base_power' not found in input data; using default=$(DEFAULT_BASE_MVA)" end base_power = get(data, "base_power", DEFAULT_BASE_MVA) for (name, category) in INPUT_CATEGORY_NAMES val = get(data, name, nothing) if isnothing(val) @debug "key '$name' not found in input data, set to nothing" _group = IS.LOG_GROUP_PARSING else category_to_df[category] = val end end if !isfile(timeseries_metadata_file) if isfile(string(timeseries_metadata_file, ".json")) timeseries_metadata_file = string(timeseries_metadata_file, ".json") elseif isfile(string(timeseries_metadata_file, ".csv")) timeseries_metadata_file = string(timeseries_metadata_file, ".csv") else timeseries_metadata_file = nothing end end if user_descriptors isa AbstractString user_descriptors = _read_config_file(user_descriptors) end if descriptors isa AbstractString descriptors = _read_config_file(descriptors) end if generator_mapping isa AbstractString generator_mapping = get_generator_mapping(generator_mapping) end return PowerSystemTableData( base_power, category_to_df, timeseries_metadata_file, directory, user_descriptors, descriptors, generator_mapping, ) end """ Reads in all the data stored in csv files The general format for data is folder: gen.csv branch.csv bus.csv .. load.csv # Arguments - `directory::AbstractString`: directory containing CSV files - `base_power::Float64`: base power for System - `user_descriptor_file::AbstractString`: customized input descriptor file - `descriptor_file=POWER_SYSTEM_DESCRIPTOR_FILE`: PowerSystems descriptor file - `generator_mapping_file=GENERATOR_MAPPING_FILE`: generator mapping configuration file """ function PowerSystemTableData( directory::AbstractString, base_power::Float64, user_descriptor_file::AbstractString; descriptor_file = POWER_SYSTEM_DESCRIPTOR_FILE, generator_mapping_file = GENERATOR_MAPPING_FILE, timeseries_metadata_file = joinpath(directory, "timeseries_pointers"), ) files = readdir(directory) REGEX_DEVICE_TYPE = r"(.*?)\.csv" REGEX_IS_FOLDER = r"^[A-Za-z]+$" data = Dict{String, Any}() if length(files) == 0 error("No files in the folder") else data["base_power"] = base_power end encountered_files = 0 for d_file in files try if match(REGEX_IS_FOLDER, d_file) !== nothing @info "Parsing csv files in $d_file ..." d_file_data = Dict{String, Any}() for file in readdir(joinpath(directory, d_file)) if match(REGEX_DEVICE_TYPE, file) !== nothing @info "Parsing csv data in $file ..." encountered_files += 1 fpath = joinpath(directory, d_file, file) raw_data = DataFrames.DataFrame(CSV.File(fpath)) d_file_data[split(file, r"[.]")[1]] = raw_data end end if length(d_file_data) > 0 data[d_file] = d_file_data @info "Successfully parsed $d_file" end elseif match(REGEX_DEVICE_TYPE, d_file) !== nothing @info "Parsing csv data in $d_file ..." encountered_files += 1 fpath = joinpath(directory, d_file) raw_data = DataFrames.DataFrame(CSV.File(fpath)) data[split(d_file, r"[.]")[1]] = raw_data @info "Successfully parsed $d_file" end catch ex @error "Error occurred while parsing $d_file" exception = ex throw(ex) end end if encountered_files == 0 error("No csv files or folders in $directory") end return PowerSystemTableData( data, directory, user_descriptor_file, descriptor_file, generator_mapping_file, timeseries_metadata_file = timeseries_metadata_file, ) end """ Return the custom name stored in the user descriptor file. Throws DataFormatError if a required value is not found in the file. """ function get_user_field( data::PowerSystemTableData, category::InputCategory, field::AbstractString, ) if !haskey(data.user_descriptors, category) throw(DataFormatError("Invalid category=$category")) end try for item in data.user_descriptors[category] if item["name"] == field return item["custom_name"] end end catch (err) if err == KeyError msg = "Failed to find category=$category field=$field in input descriptors $err" throw(DataFormatError(msg)) else throw(err) end end end """Return a vector of user-defined fields for the category.""" function get_user_fields(data::PowerSystemTableData, category::InputCategory) if !haskey(data.user_descriptors, category) throw(DataFormatError("Invalid category=$category")) end return [x["name"] for x in data.user_descriptors[category]] end """Return the dataframe for the category.""" function get_dataframe(data::PowerSystemTableData, category::InputCategory) df = get(data.category_to_df, category, DataFrames.DataFrame()) isempty(df) && @warn("Missing $category data.") return df end """ Return a NamedTuple of parameters from the descriptor file for each row of a dataframe, making type conversions as necessary. Refer to the PowerSystems descriptor file for field names that will be created. """ function iterate_rows(data::PowerSystemTableData, category; na_to_nothing = true) df = get_dataframe(data, category) field_infos = _get_field_infos(data, category, names(df)) Channel() do channel for row in eachrow(df) obj = _read_data_row(data, row, field_infos; na_to_nothing = na_to_nothing) put!(channel, obj) end end end """ Construct a System from PowerSystemTableData data. # Arguments - `time_series_resolution::Union{DateTime, Nothing}=nothing`: only store time_series that match this resolution. - `time_series_in_memory::Bool=false`: Store time series data in memory instead of HDF5 file - `time_series_directory=nothing`: Store time series data in directory instead of tmpfs - `runchecks::Bool=true`: Validate struct fields. Throws DataFormatError if time_series with multiple resolutions are detected. - A time_series has a different resolution than others. - A time_series has a different horizon than others. """ function System( data::PowerSystemTableData; time_series_resolution = nothing, time_series_in_memory = false, time_series_directory = nothing, runchecks = true, kwargs..., ) sys = System( data.base_power; time_series_in_memory = time_series_in_memory, time_series_directory = time_series_directory, runchecks = runchecks, kwargs..., ) set_units_base_system!(sys, IS.UnitSystem.DEVICE_BASE) loadzone_csv_parser!(sys, data) bus_csv_parser!(sys, data) # Services and time_series must be last. parsers = ( (get_dataframe(data, InputCategory.BRANCH), branch_csv_parser!), (get_dataframe(data, InputCategory.DC_BRANCH), dc_branch_csv_parser!), (get_dataframe(data, InputCategory.GENERATOR), gen_csv_parser!), (get_dataframe(data, InputCategory.LOAD), load_csv_parser!), (get_dataframe(data, InputCategory.RESERVE), services_csv_parser!), ) for (val, parser) in parsers if !isnothing(val) parser(sys, data) end end timeseries_metadata_file = get(kwargs, :timeseries_metadata_file, getfield(data, :timeseries_metadata_file)) if !isnothing(timeseries_metadata_file) add_time_series!(sys, timeseries_metadata_file; resolution = time_series_resolution) end check(sys) return sys end """ Add buses and areas to the System from the raw data. """ function bus_csv_parser!(sys::System, data::PowerSystemTableData) for bus in iterate_rows(data, InputCategory.BUS) name = bus.name bus_type = isnothing(bus.bus_type) ? nothing : get_enum_value(BusTypes, bus.bus_type) voltage_limits = make_minmaxlimits(bus.voltage_limits_min, bus.voltage_limits_max) area_name = string(get(bus, :area, "area")) area = get_component(Area, sys, area_name) if isnothing(area) area = Area(area_name) add_component!(sys, area) end zone = get(bus, :zone, nothing) ps_bus = Bus(; number = bus.bus_id, name = name, bustype = bus_type, angle = bus.angle, magnitude = bus.voltage, voltage_limits = voltage_limits, base_voltage = bus.base_voltage, area = area, load_zone = get_component(LoadZone, sys, string(zone)), ) add_component!(sys, ps_bus) # add load if the following info is nonzero if (bus.max_active_power != 0.0) || (bus.max_reactive_power != 0.0) load = PowerLoad( name = name, available = true, bus = ps_bus, model = LoadModels.ConstantPower, active_power = bus.active_power, reactive_power = bus.reactive_power, base_power = bus.base_power, max_active_power = bus.max_active_power, max_reactive_power = bus.max_reactive_power, ) add_component!(sys, load) end end end """ Add branches to the System from the raw data. """ function branch_csv_parser!(sys::System, data::PowerSystemTableData) available = true for branch in iterate_rows(data, InputCategory.BRANCH) bus_from = get_bus(sys, branch.connection_points_from) bus_to = get_bus(sys, branch.connection_points_to) name = get(branch, :name, get_name(bus_from) * "_" * get_name(bus_to)) connection_points = Arc(bus_from, bus_to) pf = branch.active_power_flow qf = branch.reactive_power_flow #TODO: noop math...Phase-Shifting Transformer angle alpha = (branch.primary_shunt / 2) - (branch.primary_shunt / 2) branch_type = get_branch_type(branch.tap, alpha, get(branch, :is_transformer, nothing)) if branch_type == Line b = branch.primary_shunt / 2 value = Line( name = name, available = available, active_power_flow = pf, reactive_power_flow = qf, arc = connection_points, r = branch.r, x = branch.x, b = (from = b, to = b), rate = branch.rate, angle_limits = ( min = branch.min_angle_limits, max = branch.max_angle_limits, ), ) elseif branch_type == Transformer2W value = Transformer2W( name = name, available = available, active_power_flow = pf, reactive_power_flow = qf, arc = connection_points, r = branch.r, x = branch.x, primary_shunt = branch.primary_shunt, rate = branch.rate, ) elseif branch_type == TapTransformer value = TapTransformer( name = name, available = available, active_power_flow = pf, reactive_power_flow = qf, arc = connection_points, r = branch.r, x = branch.x, primary_shunt = branch.primary_shunt, tap = branch.tap, rate = branch.rate, ) elseif branch_type == PhaseShiftingTransformer # TODO create PhaseShiftingTransformer error("Unsupported branch type $branch_type") else error("Unsupported branch type $branch_type") end add_component!(sys, value) end end """ Add DC branches to the System from raw data. """ function dc_branch_csv_parser!(sys::System, data::PowerSystemTableData) function make_dc_limits(dc_branch, min, max) min_lim = dc_branch[min] if isnothing(dc_branch[min]) && isnothing(dc_branch[max]) throw(DataFormatError("valid limits required for $min , $max")) elseif isnothing(dc_branch[min]) min_lim = dc_branch[max] * -1.0 end return (min = min_lim, max = dc_branch[max]) end for dc_branch in iterate_rows(data, InputCategory.DC_BRANCH) available = true bus_from = get_bus(sys, dc_branch.connection_points_from) bus_to = get_bus(sys, dc_branch.connection_points_to) connection_points = Arc(bus_from, bus_to) if dc_branch.control_mode == "Power" mw_load = dc_branch.mw_load activepowerlimits_from = make_dc_limits( dc_branch, :min_active_power_limit_from, :max_active_power_limit_from, ) activepowerlimits_to = make_dc_limits( dc_branch, :min_active_power_limit_to, :max_active_power_limit_to, ) reactivepowerlimits_from = make_dc_limits( dc_branch, :min_reactive_power_limit_from, :max_reactive_power_limit_from, ) reactivepowerlimits_to = make_dc_limits( dc_branch, :min_reactive_power_limit_to, :max_reactive_power_limit_to, ) loss = (l0 = 0.0, l1 = dc_branch.loss) #TODO: Can we infer this from the other data?, value = HVDCLine( name = dc_branch.name, available = available, active_power_flow = dc_branch.active_power_flow, arc = connection_points, active_power_limits_from = activepowerlimits_from, active_power_limits_to = activepowerlimits_to, reactive_power_limits_from = reactivepowerlimits_from, reactive_power_limits_to = reactivepowerlimits_to, loss = loss, ) else rectifier_taplimits = ( min = dc_branch.rectifier_tap_limits_min, max = dc_branch.rectifier_tap_limits_max, ) rectifier_xrc = dc_branch.rectifier_xrc #TODO: What is this?, rectifier_firingangle = dc_branch.rectifier_firingangle inverter_taplimits = ( min = dc_branch.inverter_tap_limits_min, max = dc_branch.inverter_tap_limits_max, ) inverter_xrc = dc_branch.inverter_xrc #TODO: What is this? inverter_firingangle = ( min = dc_branch.inverter_firing_angle_min, max = dc_branch.inverter_firing_angle_max, ) value = VSCDCLine( name = dc_branch.name, available = true, active_power_flow = pf, arc = connection_points, rectifier_taplimits = rectifier_taplimits, rectifier_xrc = rectifier_xrc, rectifier_firingangle = rectifier_firingangle, inverter_taplimits = inverter_taplimits, inverter_xrc = inverter_xrc, inverter_firingangle = inverter_firingangle, ) end add_component!(sys, value) end end """ Add generators to the System from the raw data. """ struct _HeatRateColumns columns::Base.Iterators.Zip{Tuple{Array{Symbol, 1}, Array{Symbol, 1}}} end struct _CostPointColumns columns::Base.Iterators.Zip{Tuple{Array{Symbol, 1}, Array{Symbol, 1}}} end function gen_csv_parser!(sys::System, data::PowerSystemTableData) output_point_fields = Vector{Symbol}() heat_rate_fields = Vector{Symbol}() cost_point_fields = Vector{Symbol}() fields = get_user_fields(data, InputCategory.GENERATOR) for field in fields if occursin("output_point", field) push!(output_point_fields, Symbol(field)) elseif occursin("heat_rate_", field) push!(heat_rate_fields, Symbol(field)) elseif occursin("cost_point_", field) push!(cost_point_fields, Symbol(field)) end end @assert length(output_point_fields) > 0 if length(heat_rate_fields) > 0 && length(cost_point_fields) > 0 throw(IS.ConflictingInputsError("Heat rate and cost points are both defined")) elseif length(heat_rate_fields) > 0 cost_colnames = _HeatRateColumns(zip(heat_rate_fields, output_point_fields)) elseif length(cost_point_fields) > 0 cost_colnames = _CostPointColumns(zip(cost_point_fields, output_point_fields)) end for gen in iterate_rows(data, InputCategory.GENERATOR) @debug "making generator:" _group = IS.LOG_GROUP_PARSING gen.name bus = get_bus(sys, gen.bus_id) if isnothing(bus) throw(DataFormatError("could not find $(gen.bus_id)")) end generator = make_generator(data, gen, cost_colnames, bus) @debug "adding gen:" _group = IS.LOG_GROUP_PARSING generator if !isnothing(generator) add_component!(sys, generator) end end end """ load_csv_parser!(sys::System, data::PowerSystemTableData) Add loads to the System from the raw load data. """ function load_csv_parser!(sys::System, data::PowerSystemTableData) for rawload in iterate_rows(data, InputCategory.LOAD) bus = get_bus(sys, rawload.bus_id) if isnothing(bus) throw( DataFormatError( "could not find bus_number=$(rawload.bus_id) for load=$(rawload.name)", ), ) end load = PowerLoad( name = rawload.name, available = rawload.available, bus = bus, model = LoadModels.ConstantPower, active_power = rawload.active_power, reactive_power = rawload.reactive_power, max_active_power = rawload.max_active_power, max_reactive_power = rawload.max_reactive_power, base_power = rawload.base_power, ) add_component!(sys, load) end end """ loadzone_csv_parser!(sys::System, data::PowerSystemTableData) Add branches to the System from the raw data. """ function loadzone_csv_parser!(sys::System, data::PowerSystemTableData) buses = get_dataframe(data, InputCategory.BUS) zone_column = get_user_field(data, InputCategory.BUS, "zone") if !in(zone_column, names(buses)) @warn "Missing Data : no 'zone' information for buses, cannot create loads based on zones" return end zones = unique(buses[!, zone_column]) for zone in zones bus_numbers = Set{Int}() active_powers = Vector{Float64}() reactive_powers = Vector{Float64}() for bus in iterate_rows(data, InputCategory.BUS) if bus.zone == zone bus_number = bus.bus_id push!(bus_numbers, bus_number) active_power = bus.max_active_power push!(active_powers, active_power) reactive_power = bus.max_reactive_power push!(reactive_powers, reactive_power) end end name = string(zone) load_zone = LoadZone(name, sum(active_powers), sum(reactive_powers)) add_component!(sys, load_zone) end end """ Add services to the System from the raw data. """ function services_csv_parser!(sys::System, data::PowerSystemTableData) bus_id_column = get_user_field(data, InputCategory.BUS, "bus_id") bus_area_column = get_user_field(data, InputCategory.BUS, "area") # Shortcut for data that looks like "(val1,val2,val3)" make_array(x) = isnothing(x) ? x : split(strip(x, ['(', ')']), ",") function _add_device!(contributing_devices, device_categories, name) component = [] for dev_category in device_categories component_type = _get_component_type_from_category(dev_category) components = get_components_by_name(component_type, sys, name) if length(components) == 0 # There multiple categories, so we might not find a match in some. continue elseif length(components) == 1 push!(component, components[1]) else msg = "Found duplicate names type=$component_type name=$name" throw(DataFormatError(msg)) end end if length(component) > 1 msg = "Found duplicate components with name=$name" throw(DataFormatError(msg)) elseif length(component) == 1 push!(contributing_devices, component[1]) end end for reserve in iterate_rows(data, InputCategory.RESERVE) device_categories = make_array(reserve.eligible_device_categories) device_subcategories = make_array(get(reserve, :eligible_device_subcategories, nothing)) devices = make_array(get(reserve, :contributing_devices, nothing)) regions = make_array(reserve.eligible_regions) #TODO: rename to "area" requirement = get(reserve, :requirement, nothing) contributing_devices = Vector{Device}() if isnothing(device_subcategories) @info("Adding contributing components for $(reserve.name) by component name") for device in devices _add_device!(contributing_devices, device_categories, device) end else @info("Adding contributing generators for $(reserve.name) by category") for gen in iterate_rows(data, InputCategory.GENERATOR) buses = get_dataframe(data, InputCategory.BUS) bus_ids = buses[!, bus_id_column] gen_type = get_generator_type(gen.fuel, gen.unit_type, data.generator_mapping) sys_gen = get_component( get_generator_type(gen.fuel, gen.unit_type, data.generator_mapping), sys, gen.name, ) area = string( buses[bus_ids .== get_number(get_bus(sys_gen)), bus_area_column][1], ) if gen.category in device_subcategories && area in regions _add_device!(contributing_devices, device_categories, gen.name) end end unused_categories = setdiff( device_subcategories, get_dataframe(data, InputCategory.GENERATOR)[ !, get_user_field(data, InputCategory.GENERATOR, "category"), ], ) for cat in unused_categories @warn( "Device category: $cat not found in generators data; adding contributing devices by category only supported for generator data" ) end end if length(contributing_devices) == 0 throw( DataFormatError( "did not find contributing devices for service $(reserve.name)", ), ) end direction = get_reserve_direction(reserve.direction) if isnothing(requirement) service = StaticReserve{direction}(reserve.name, true, reserve.timeframe, 0.0) else service = VariableReserve{direction}( reserve.name, true, reserve.timeframe, requirement, ) end add_service!(sys, service, contributing_devices) end end function get_reserve_direction(direction::AbstractString) if direction == "Up" return ReserveUp elseif direction == "Down" return ReserveDown else throw(DataFormatError("invalid reserve direction $direction")) end end """Creates a generator of any type.""" function make_generator(data::PowerSystemTableData, gen, cost_colnames, bus) generator = nothing gen_type = get_generator_type(gen.fuel, get(gen, :unit_type, nothing), data.generator_mapping) if isnothing(gen_type) @error "Cannot recognize generator type" gen.name elseif gen_type == ThermalStandard generator = make_thermal_generator(data, gen, cost_colnames, bus) elseif gen_type == ThermalMultiStart generator = make_thermal_generator_multistart(data, gen, cost_colnames, bus) elseif gen_type <: HydroGen generator = make_hydro_generator(gen_type, data, gen, cost_colnames, bus) elseif gen_type <: RenewableGen generator = make_renewable_generator(gen_type, data, gen, cost_colnames, bus) elseif gen_type == GenericBattery storage = get_storage_by_generator(data, gen.name).head generator = make_storage(data, gen, storage, bus) else @error "Skipping unsupported generator" gen.name gen_type end return generator end function calculate_variable_cost( data::PowerSystemTableData, gen, cost_colnames::_HeatRateColumns, base_power, ) fuel_cost = gen.fuel_price / 1000.0 vom = isnothing(gen.variable_cost) ? 0.0 : gen.variable_cost if fuel_cost > 0.0 var_cost = [(getfield(gen, hr), getfield(gen, mw)) for (hr, mw) in cost_colnames.columns] var_cost = unique([ (tryparse(Float64, string(c[1])), tryparse(Float64, string(c[2]))) for c in var_cost if !in(nothing, c) ]) else var_cost = [(0.0, 0.0)] end if length(var_cost) > 1 var_cost[2:end] = [ ( ( var_cost[i][1] * fuel_cost * (var_cost[i][2] - var_cost[i - 1][2]) + var_cost[i][2] * vom ), var_cost[i][2], ) .* gen.active_power_limits_max .* base_power for i in 2:length(var_cost) ] var_cost[1] = ((var_cost[1][1] * fuel_cost + vom) * var_cost[1][2], var_cost[1][2]) .* gen.active_power_limits_max .* base_power fixed = max( 0.0, var_cost[1][1] - (var_cost[2][1] / (var_cost[2][2] - var_cost[1][2]) * var_cost[1][2]), ) var_cost[1] = (var_cost[1][1] - fixed, var_cost[1][2]) for i in 2:length(var_cost) var_cost[i] = (var_cost[i - 1][1] + var_cost[i][1], var_cost[i][2]) end elseif length(var_cost) == 1 # if there is only one point, use it to determine the constant $/MW cost var_cost = var_cost[1][1] * fuel_cost + vom fixed = 0.0 end return var_cost, fixed, fuel_cost end function calculate_variable_cost( data::PowerSystemTableData, gen, cost_colnames::_CostPointColumns, base_power, ) vom = isnothing(gen.variable_cost) ? 0.0 : gen.variable_cost var_cost = [(getfield(gen, c), getfield(gen, mw)) for (c, mw) in cost_colnames.columns] var_cost = unique([ (tryparse(Float64, string(c[1])), tryparse(Float64, string(c[2]))) for c in var_cost if !in(nothing, c) ]) var_cost = [ ((var_cost[i][1] + vom) * var_cost[i][2], var_cost[i][2]) .* gen.active_power_limits_max .* base_power for i in 1:length(var_cost) ] if length(var_cost) > 1 fixed = max( 0.0, var_cost[1][1] - (var_cost[2][1] / (var_cost[2][2] - var_cost[1][2]) * var_cost[1][2]), ) var_cost = [(var_cost[i][1] - fixed, var_cost[i][2]) for i in 1:length(var_cost)] elseif length(var_cost) == 1 var_cost = var_cost[1][1] + vom fixed = 0.0 end return var_cost, fixed, 0.0 end function calculate_uc_cost(data, gen, fuel_cost) startup_cost = gen.startup_cost if isnothing(startup_cost) if hasfield(typeof(gen), :startup_heat_cold_cost) startup_cost = gen.startup_heat_cold_cost * fuel_cost * 1000 else startup_cost = 0.0 @warn "No startup_cost defined for $(gen.name), setting to $startup_cost" maxlog = 5 end end shutdown_cost = get(gen, :shutdown_cost, nothing) if isnothing(shutdown_cost) @warn "No shutdown_cost defined for $(gen.name), setting to 0.0" maxlog = 1 shutdown_cost = 0.0 end return startup_cost, shutdown_cost end function make_minmaxlimits(min::Union{Nothing, Float64}, max::Union{Nothing, Float64}) if isnothing(min) && isnothing(max) minmax = nothing else minmax = (min = min, max = max) end return minmax end function make_ramplimits( gen; ramplimcol = :ramp_limits, rampupcol = :ramp_up, rampdncol = :ramp_down, ) ramp = get(gen, ramplimcol, nothing) if !isnothing(ramp) up = ramp down = ramp else up = get(gen, rampupcol, ramp) up = typeof(up) == String ? tryparse(Float64, up) : up down = get(gen, rampdncol, ramp) down = typeof(down) == String ? tryparse(Float64, down) : down end ramplimits = isnothing(up) && isnothing(down) ? nothing : (up = up, down = down) return ramplimits end function make_timelimits(gen, up_column::Symbol, down_column::Symbol) up_time = get(gen, up_column, nothing) up_time = typeof(up_time) == String ? tryparse(Float64, up_time) : up_time down_time = get(gen, down_column, nothing) down_time = typeof(down_time) == String ? tryparse(Float64, down_time) : down_time timelimits = isnothing(up_time) && isnothing(down_time) ? nothing : (up = up_time, down = down_time) return timelimits end function make_reactive_params( gen; powerfield = :reactive_power, minfield = :reactive_power_limits_min, maxfield = :reactive_power_limits_max, ) reactive_power = get(gen, powerfield, 0.0) reactive_power_limits_min = get(gen, minfield, nothing) reactive_power_limits_max = get(gen, maxfield, nothing) if isnothing(reactive_power_limits_min) && isnothing(reactive_power_limits_max) reactive_power_limits = nothing elseif isnothing(reactive_power_limits_min) reactive_power_limits = (min = 0.0, max = reactive_power_limits_max) else reactive_power_limits = (min = reactive_power_limits_min, max = reactive_power_limits_max) end return reactive_power, reactive_power_limits end function make_thermal_generator(data::PowerSystemTableData, gen, cost_colnames, bus) @debug "Making ThermaStandard" _group = IS.LOG_GROUP_PARSING gen.name active_power_limits = (min = gen.active_power_limits_min, max = gen.active_power_limits_max) (reactive_power, reactive_power_limits) = make_reactive_params(gen) rating = calculate_rating(active_power_limits, reactive_power_limits) ramplimits = make_ramplimits(gen) timelimits = make_timelimits(gen, :min_up_time, :min_down_time) primemover = parse_enum_mapping(PrimeMovers, gen.unit_type) fuel = parse_enum_mapping(ThermalFuels, gen.fuel) base_power = gen.base_mva var_cost, fixed, fuel_cost = calculate_variable_cost(data, gen, cost_colnames, base_power) startup_cost, shutdown_cost = calculate_uc_cost(data, gen, fuel_cost) op_cost = ThreePartCost(var_cost, fixed, startup_cost, shutdown_cost) return ThermalStandard( name = gen.name, available = gen.available, status = gen.status_at_start, bus = bus, active_power = gen.active_power, reactive_power = reactive_power, rating = rating, prime_mover = primemover, fuel = fuel, active_power_limits = active_power_limits, reactive_power_limits = reactive_power_limits, ramp_limits = ramplimits, time_limits = timelimits, operation_cost = op_cost, base_power = base_power, ) end function make_thermal_generator_multistart( data::PowerSystemTableData, gen, cost_colnames, bus, ) thermal_gen = make_thermal_generator(data, gen, cost_colnames, bus) @debug "Making ThermalMultiStart" _group = IS.LOG_GROUP_PARSING gen.name base_power = get_base_power(thermal_gen) var_cost, fixed, fuel_cost = calculate_variable_cost(data, gen, cost_colnames, base_power) if var_cost isa Float64 no_load_cost = 0.0 var_cost = VariableCost(var_cost) else no_load_cost = var_cost[1][1] var_cost = VariableCost([(c - no_load_cost, pp - var_cost[1][2]) for (c, pp) in var_cost]) end lag_hot = isnothing(gen.hot_start_time) ? get_time_limits(thermal_gen).down : gen.hot_start_time lag_warm = isnothing(gen.warm_start_time) ? 0.0 : gen.warm_start_time lag_cold = isnothing(gen.cold_start_time) ? 0.0 : gen.cold_start_time startup_timelimits = (hot = lag_hot, warm = lag_warm, cold = lag_cold) start_types = sum(values(startup_timelimits) .> 0.0) startup_ramp = isnothing(gen.startup_ramp) ? 0.0 : gen.startup_ramp shutdown_ramp = isnothing(gen.shutdown_ramp) ? 0.0 : gen.shutdown_ramp power_trajectory = (startup = startup_ramp, shutdown = shutdown_ramp) hot_start_cost = isnothing(gen.hot_start_cost) ? gen.startup_cost : gen.hot_start_cost if isnothing(hot_start_cost) if hasfield(typeof(gen), :startup_heat_cold_cost) hot_start_cost = gen.startup_heat_cold_cost * fuel_cost * 1000 else hot_start_cost = 0.0 @warn "No hot_start_cost or startup_cost defined for $(gen.name), setting to $startup_cost" maxlog = 5 end end warm_start_cost = isnothing(gen.warm_start_cost) ? START_COST : gen.hot_start_cost #TODO cold_start_cost = isnothing(gen.cold_start_cost) ? START_COST : gen.cold_start_cost startup_cost = (hot = hot_start_cost, warm = warm_start_cost, cold = cold_start_cost) shutdown_cost = gen.shutdown_cost if isnothing(shutdown_cost) @warn "No shutdown_cost defined for $(gen.name), setting to 0.0" maxlog = 1 shutdown_cost = 0.0 end op_cost = MultiStartCost(var_cost, no_load_cost, fixed, startup_cost, shutdown_cost) return ThermalMultiStart(; name = get_name(thermal_gen), available = get_available(thermal_gen), status = get_status(thermal_gen), bus = get_bus(thermal_gen), active_power = get_active_power(thermal_gen), reactive_power = get_reactive_power(thermal_gen), rating = get_rating(thermal_gen), prime_mover = get_prime_mover(thermal_gen), fuel = get_fuel(thermal_gen), active_power_limits = get_active_power_limits(thermal_gen), reactive_power_limits = get_reactive_power_limits(thermal_gen), ramp_limits = get_ramp_limits(thermal_gen), power_trajectory = power_trajectory, time_limits = get_time_limits(thermal_gen), start_time_limits = startup_timelimits, start_types = start_types, operation_cost = op_cost, base_power = get_base_power(thermal_gen), time_at_status = get_time_at_status(thermal_gen), must_run = gen.must_run, ) end function make_hydro_generator(gen_type, data::PowerSystemTableData, gen, cost_colnames, bus) @debug "Making HydroGen" _group = IS.LOG_GROUP_PARSING gen.name active_power_limits = (min = gen.active_power_limits_min, max = gen.active_power_limits_max) (reactive_power, reactive_power_limits) = make_reactive_params(gen) rating = calculate_rating(active_power_limits, reactive_power_limits) ramp_limits = make_ramplimits(gen) min_up_time = gen.min_up_time min_down_time = gen.min_down_time time_limits = make_timelimits(gen, :min_up_time, :min_down_time) base_power = gen.base_mva if gen_type == HydroEnergyReservoir || gen_type == HydroPumpedStorage if !haskey(data.category_to_df, InputCategory.STORAGE) throw(DataFormatError("Storage information must defined in storage.csv")) end storage = get_storage_by_generator(data, gen.name) var_cost, fixed, fuel_cost = calculate_variable_cost(data, gen, cost_colnames, base_power) operation_cost = TwoPartCost(var_cost, fixed) if gen_type == HydroEnergyReservoir @debug "Creating $(gen.name) as HydroEnergyReservoir" _group = IS.LOG_GROUP_PARSING hydro_gen = HydroEnergyReservoir( name = gen.name, available = gen.available, bus = bus, active_power = gen.active_power, reactive_power = reactive_power, prime_mover = parse_enum_mapping(PrimeMovers, gen.unit_type), rating = rating, active_power_limits = active_power_limits, reactive_power_limits = reactive_power_limits, ramp_limits = ramp_limits, time_limits = time_limits, operation_cost = operation_cost, base_power = base_power, storage_capacity = storage.head.storage_capacity, inflow = storage.head.input_active_power_limit_max, initial_storage = storage.head.energy_level, ) elseif gen_type == HydroPumpedStorage @debug "Creating $(gen.name) as HydroPumpedStorage" _group = IS.LOG_GROUP_PARSING pump_active_power_limits = ( min = gen.pump_active_power_limits_min, max = gen.pump_active_power_limits_max, ) (pump_reactive_power, pump_reactive_power_limits) = make_reactive_params( gen, powerfield = :pump_reactive_power, minfield = :pump_reactive_power_limits_min, maxfield = :pump_reactive_power_limits_max, ) pump_rating = calculate_rating(pump_active_power_limits, pump_reactive_power_limits) pump_ramp_limits = make_ramplimits( gen; ramplimcol = :pump_ramp_limits, rampupcol = :pump_ramp_up, rampdncol = :pump_ramp_down, ) pump_time_limits = make_timelimits(gen, :pump_min_up_time, :pump_min_down_time) hydro_gen = HydroPumpedStorage( name = gen.name, available = gen.available, bus = bus, active_power = gen.active_power, reactive_power = reactive_power, rating = rating, base_power = base_power, prime_mover = parse_enum_mapping(PrimeMovers, gen.unit_type), active_power_limits = active_power_limits, reactive_power_limits = reactive_power_limits, ramp_limits = ramp_limits, time_limits = time_limits, rating_pump = pump_rating, active_power_limits_pump = pump_active_power_limits, reactive_power_limits_pump = pump_reactive_power_limits, ramp_limits_pump = pump_ramp_limits, time_limits_pump = pump_time_limits, storage_capacity = ( up = storage.head.storage_capacity, down = storage.head.storage_capacity, ), inflow = storage.head.input_active_power_limit_max, outflow = storage.tail.input_active_power_limit_max, initial_storage = ( up = storage.head.energy_level, down = storage.tail.energy_level, ), storage_target = ( up = storage.head.storage_target, down = storage.tail.storage_target, ), operation_cost = operation_cost, pump_efficiency = storage.tail.efficiency, ) end elseif gen_type == HydroDispatch @debug "Creating $(gen.name) as HydroDispatch" _group = IS.LOG_GROUP_PARSING hydro_gen = HydroDispatch( name = gen.name, available = gen.available, bus = bus, active_power = gen.active_power, reactive_power = reactive_power, rating = rating, prime_mover = parse_enum_mapping(PrimeMovers, gen.unit_type), active_power_limits = active_power_limits, reactive_power_limits = reactive_power_limits, ramp_limits = ramp_limits, time_limits = time_limits, base_power = base_power, ) else error("Tabular data parser does not currently support $gen_type creation") end return hydro_gen end function get_storage_by_generator(data::PowerSystemTableData, gen_name::AbstractString) head = [] tail = [] for s in iterate_rows(data, InputCategory.STORAGE) if s.generator_name == gen_name position = get(s, :position, "head") if position == "tail" push!(tail, s) else push!(head, s) end end end if length(head) != 1 @warn "storage generator should have exactly 1 head storage defined: this will throw an error in v1.3.x" maxlog = 1 # this currently selects the first head storage with no control on how to make that selection, in the future throw an error. #throw(DataFormatError("storage generator must have exactly 1 head storage defined")) #TODO: uncomment this in next version elseif length(tail) > 1 throw( DataFormatError( "storage generator cannot have more than 1 tail storage defined", ), ) end tail = length(tail) > 0 ? tail[1] : nothing return (head = head[1], tail = tail) end function make_renewable_generator( gen_type, data::PowerSystemTableData, gen, cost_colnames, bus, ) @debug "Making RenewableGen" _group = IS.LOG_GROUP_PARSING gen.name generator = nothing active_power_limits = (min = gen.active_power_limits_min, max = gen.active_power_limits_max) (reactive_power, reactive_power_limits) = make_reactive_params(gen) rating = calculate_rating(active_power_limits, reactive_power_limits) base_power = gen.base_mva var_cost, fixed, fuel_cost = calculate_variable_cost(data, gen, cost_colnames, base_power) operation_cost = TwoPartCost(var_cost, fixed) if gen_type == RenewableDispatch @debug "Creating $(gen.name) as RenewableDispatch" _group = IS.LOG_GROUP_PARSING generator = RenewableDispatch( name = gen.name, available = gen.available, bus = bus, active_power = gen.active_power, reactive_power = reactive_power, rating = rating, prime_mover = parse_enum_mapping(PrimeMovers, gen.unit_type), reactive_power_limits = reactive_power_limits, power_factor = gen.power_factor, operation_cost = operation_cost, base_power = base_power, ) elseif gen_type == RenewableFix @debug "Creating $(gen.name) as RenewableFix" _group = IS.LOG_GROUP_PARSING generator = RenewableFix( name = gen.name, available = gen.available, bus = bus, active_power = gen.active_power, reactive_power = reactive_power, rating = rating, prime_mover = parse_enum_mapping(PrimeMovers, gen.unit_type), power_factor = gen.power_factor, base_power = base_power, ) else error("Unsupported type $gen_type") end return generator end function make_storage(data::PowerSystemTableData, gen, storage, bus) @debug "Making Storge" _group = IS.LOG_GROUP_PARSING storage.name state_of_charge_limits = (min = storage.min_storage_capacity, max = storage.storage_capacity) input_active_power_limits = ( min = storage.input_active_power_limit_min, max = storage.input_active_power_limit_max, ) output_active_power_limits = ( min = storage.output_active_power_limit_min, max = isnothing(storage.output_active_power_limit_max) ? gen.active_power_limits_max : storage.output_active_power_limit_max, ) efficiency = (in = storage.input_efficiency, out = storage.output_efficiency) (reactive_power, reactive_power_limits) = make_reactive_params(storage) battery = GenericBattery(; name = gen.name, available = storage.available, bus = bus, prime_mover = parse_enum_mapping(PrimeMovers, gen.unit_type), initial_energy = storage.energy_level, state_of_charge_limits = state_of_charge_limits, rating = storage.rating, active_power = storage.active_power, input_active_power_limits = input_active_power_limits, output_active_power_limits = output_active_power_limits, efficiency = efficiency, reactive_power = reactive_power, reactive_power_limits = reactive_power_limits, base_power = storage.base_power, ) return battery end const CATEGORY_STR_TO_COMPONENT = Dict{String, DataType}( "Bus" => Bus, "Generator" => Generator, "Reserve" => Service, "LoadZone" => LoadZone, "ElectricLoad" => ElectricLoad, ) function _get_component_type_from_category(category::AbstractString) component_type = get(CATEGORY_STR_TO_COMPONENT, category, nothing) if isnothing(component_type) throw(DataFormatError("unsupported category=$category")) end return component_type end function _read_config_file(file_path::String) return open(file_path) do io data = YAML.load(io) # Replace keys with enums. config_data = Dict{InputCategory, Vector}() for (key, val) in data # TODO: need to change user_descriptors.yaml to use reserve instead. if key == "reserves" key = "reserve" end config_data[get_enum_value(InputCategory, key)] = val end return config_data end end """Stores user-customized information for required dataframe columns.""" struct _FieldInfo name::String custom_name::String per_unit_conversion::NamedTuple{ (:From, :To, :Reference), Tuple{UnitSystem, UnitSystem, String}, } unit_conversion::Union{NamedTuple{(:From, :To), Tuple{String, String}}, Nothing} default_value::Any # TODO unit, value ranges and options end function _get_field_infos(data::PowerSystemTableData, category::InputCategory, df_names) if !haskey(data.user_descriptors, category) throw(DataFormatError("Invalid category=$category")) end if !haskey(data.descriptors, category) throw(DataFormatError("Invalid category=$category")) end # Cache whether PowerSystems uses a column's values as system-per-unit. # The user's descriptors indicate that the raw data is already system-per-unit or not. per_unit = Dict{String, IS.UnitSystem}() unit = Dict{String, Union{String, Nothing}}() custom_names = Dict{String, String}() for descriptor in data.user_descriptors[category] custom_name = descriptor["custom_name"] if descriptor["custom_name"] in df_names per_unit[descriptor["name"]] = get_enum_value( IS.UnitSystem, get(descriptor, "unit_system", "NATURAL_UNITS"), ) unit[descriptor["name"]] = get(descriptor, "unit", nothing) custom_names[descriptor["name"]] = custom_name else @warn "User-defined column name $custom_name is not in dataframe." end end fields = Vector{_FieldInfo}() for item in data.descriptors[category] name = item["name"] item_unit_system = get_enum_value(IS.UnitSystem, get(item, "unit_system", "NATURAL_UNITS")) per_unit_reference = get(item, "base_reference", "base_power") default_value = get(item, "default_value", "required") if default_value == "system_base_power" default_value = data.base_power end if name in keys(custom_names) custom_name = custom_names[name] if item_unit_system == IS.UnitSystem.NATURAL_UNITS && per_unit[name] != IS.UnitSystem.NATURAL_UNITS throw(DataFormatError("$name cannot be defined as $(per_unit[name])")) end pu_conversion = ( From = per_unit[name], To = item_unit_system, Reference = per_unit_reference, ) expected_unit = get(item, "unit", nothing) if !isnothing(expected_unit) && !isnothing(unit[name]) && expected_unit != unit[name] unit_conversion = (From = unit[name], To = expected_unit) else unit_conversion = nothing end else custom_name = name pu_conversion = ( From = item_unit_system, To = item_unit_system, Reference = per_unit_reference, ) unit_conversion = nothing end push!( fields, _FieldInfo(name, custom_name, pu_conversion, unit_conversion, default_value), ) end return fields end """Reads values from dataframe row and performs necessary conversions.""" function _read_data_row(data::PowerSystemTableData, row, field_infos; na_to_nothing = true) fields = Vector{String}() vals = Vector() for field_info in field_infos if field_info.custom_name in names(row) value = row[field_info.custom_name] else value = field_info.default_value value == "required" && throw(DataFormatError("$(field_info.name) is required")) @debug "Column $(field_info.custom_name) doesn't exist in df, enabling use of default value of $(field_info.default_value)" _group = IS.LOG_GROUP_PARSING maxlog = 1 end if ismissing(value) throw(DataFormatError("$(field_info.custom_name) value missing")) end if na_to_nothing && value == "NA" value = nothing end if !isnothing(value) if field_info.per_unit_conversion.From == IS.UnitSystem.NATURAL_UNITS && field_info.per_unit_conversion.To == IS.UnitSystem.SYSTEM_BASE @debug "convert to $(field_info.per_unit_conversion.To)" _group = IS.LOG_GROUP_PARSING field_info.custom_name value = value isa String ? tryparse(Float64, value) : value value = data.base_power == 0.0 ? 0.0 : value / data.base_power elseif field_info.per_unit_conversion.From == IS.UnitSystem.NATURAL_UNITS && field_info.per_unit_conversion.To == IS.UnitSystem.DEVICE_BASE reference_idx = findfirst( x -> x.name == field_info.per_unit_conversion.Reference, field_infos, ) isnothing(reference_idx) && throw( DataFormatError( "$(field_info.per_unit_conversion.Reference) not found in table with $(field_info.custom_name)", ), ) reference_info = field_infos[reference_idx] @debug "convert to $(field_info.per_unit_conversion.To) using $(reference_info.custom_name)" _group = IS.LOG_GROUP_PARSING field_info.custom_name maxlog = 1 reference_value = get(row, reference_info.custom_name, reference_info.default_value) reference_value == "required" && throw( DataFormatError( "$(reference_info.name) is required for p.u. conversion", ), ) value = value isa String ? tryparse(Float64, value) : value value = reference_value == 0.0 ? 0.0 : value / reference_value elseif field_info.per_unit_conversion.From != field_info.per_unit_conversion.To throw( DataFormatError( "conversion not supported from $(field_info.per_unit_conversion.From) to $(field_info.per_unit_conversion.To) for $(field_info.custom_name)", ), ) end else @debug "$(field_info.custom_name) is nothing" _group = IS.LOG_GROUP_PARSING maxlog = 1 end # TODO: need special handling for units if !isnothing(field_info.unit_conversion) @debug "convert units" _group = IS.LOG_GROUP_PARSING field_info.custom_name maxlog = 1 value = convert_units!(value, field_info.unit_conversion) end # TODO: validate ranges and option lists push!(fields, field_info.name) push!(vals, value) end return NamedTuple{Tuple(Symbol.(fields))}(vals) end
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220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 20984, 796, 583, 62, 20850, 62, 35790, 11, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1267, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 4326, 62, 1102, 9641, 796, 2147, 198, 220, 220, 220, 220, 220, 220, 220, 886, 628, 220, 220, 220, 220, 220, 220, 220, 4574, 0, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 7032, 11, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 4808, 15878, 12360, 7, 3672, 11, 2183, 62, 3672, 11, 47574, 62, 1102, 9641, 11, 4326, 62, 1102, 9641, 11, 4277, 62, 8367, 828, 198, 220, 220, 220, 220, 220, 220, 220, 1267, 198, 220, 220, 220, 886, 628, 220, 220, 220, 1441, 7032, 198, 437, 198, 198, 37811, 5569, 82, 3815, 422, 1366, 14535, 5752, 290, 17706, 3306, 32626, 526, 15931, 198, 8818, 4808, 961, 62, 7890, 62, 808, 7, 7890, 3712, 13434, 11964, 10962, 6601, 11, 5752, 11, 2214, 62, 10745, 418, 26, 12385, 62, 1462, 62, 22366, 796, 2081, 8, 198, 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318, 22366, 7, 35790, 62, 312, 87, 8, 11405, 3714, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 6060, 26227, 12331, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 17971, 7, 3245, 62, 10951, 13, 525, 62, 20850, 62, 1102, 9641, 13, 26687, 8, 407, 1043, 287, 3084, 351, 29568, 3245, 62, 10951, 13, 23144, 62, 3672, 42501, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 10612, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1267, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 4941, 62, 10951, 796, 2214, 62, 10745, 418, 58, 35790, 62, 312, 87, 60, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2488, 24442, 366, 1102, 1851, 284, 29568, 3245, 62, 10951, 13, 525, 62, 20850, 62, 1102, 9641, 13, 2514, 8, 1262, 29568, 35790, 62, 10951, 13, 23144, 62, 3672, 16725, 4808, 8094, 796, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 3180, 13, 25294, 62, 46846, 62, 27082, 50, 2751, 2214, 62, 10951, 13, 23144, 62, 3672, 3509, 6404, 796, 352, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 4941, 62, 8367, 796, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 651, 7, 808, 11, 4941, 62, 10951, 13, 23144, 62, 3672, 11, 4941, 62, 10951, 13, 12286, 62, 8367, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 4941, 62, 8367, 6624, 366, 35827, 1, 11405, 3714, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 6060, 26227, 12331, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 17971, 7, 35790, 62, 10951, 13, 3672, 8, 318, 2672, 329, 279, 13, 84, 13, 11315, 1600, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 10612, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1267, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1988, 796, 1988, 318, 64, 10903, 5633, 1949, 29572, 7, 43879, 2414, 11, 1988, 8, 1058, 1988, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1988, 796, 4941, 62, 8367, 6624, 657, 13, 15, 5633, 657, 13, 15, 1058, 1988, 1220, 4941, 62, 8367, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2073, 361, 2214, 62, 10951, 13, 525, 62, 20850, 62, 1102, 9641, 13, 4863, 14512, 2214, 62, 10951, 13, 525, 62, 20850, 62, 1102, 9641, 13, 2514, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 3714, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 6060, 26227, 12331, 7, 198, 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220, 886, 628, 220, 220, 220, 220, 220, 220, 220, 1303, 16926, 46, 25, 761, 2041, 9041, 329, 4991, 198, 220, 220, 220, 220, 220, 220, 220, 611, 5145, 271, 22366, 7, 3245, 62, 10951, 13, 20850, 62, 1102, 9641, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2488, 24442, 366, 1102, 1851, 4991, 1, 4808, 8094, 796, 3180, 13, 25294, 62, 46846, 62, 27082, 50, 2751, 2214, 62, 10951, 13, 23144, 62, 3672, 3509, 6404, 796, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 352, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1988, 796, 10385, 62, 41667, 0, 7, 8367, 11, 2214, 62, 10951, 13, 20850, 62, 1102, 9641, 8, 198, 220, 220, 220, 220, 220, 220, 220, 886, 198, 220, 220, 220, 220, 220, 220, 220, 1303, 16926, 46, 25, 26571, 16069, 290, 3038, 8341, 198, 220, 220, 220, 220, 220, 220, 220, 4574, 0, 7, 25747, 11, 2214, 62, 10951, 13, 3672, 8, 198, 220, 220, 220, 220, 220, 220, 220, 4574, 0, 7, 12786, 11, 1988, 8, 198, 220, 220, 220, 886, 198, 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2.18274
25,342
<filename>test/runtests.jl using Inequality using StatsBase using Test using DataFrames df_1 = DataFrame(v = [8,5,1,3,5,6,7,6,3], w = collect(0.1:0.1:0.9)) df_2 = DataFrame(v = repeat([8,5,1,3,5,6,7,6,3],2), w = repeat(collect(0.1:0.1:0.9),2), group = [1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2]) @testset "atkinson checks" begin @test_throws ArgumentError atkinson([8, 5, 1, 3, 5], -1) @test_throws ArgumentError atkinson([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4, 0.5], -1) @test_throws ArgumentError atkinson([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4], 1) @test_throws ArgumentError atkinson([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4], 1) @test_throws MethodError atkinson([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4], 1) @test_throws ArgumentError atkinson([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4], 1) # different length `v` and `w` end @testset "atkinson" begin @test atkinson([8,5,1,3,5], 1) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 1) atol=0.00000001 @test atkinson([8,5,1,3,5], 0.8) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 0.8) atol=0.00000001 @test atkinson([8,5,1,3,5], 1.2) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 1.2) atol=0.00000001 @test atkinson([8,5,1,3,5], 1) ≈ 0.183083677559 atol=0.00000001 @test atkinson([8,5,1,3,5], 0.8) ≈ 0.14249378376024 atol=0.00000001 @test atkinson([8,5,1,3,5], 1.2) ≈ 0.2248733447899 atol=0.00000001 @test atkinson([8,5,1,3,5], [1, 2, 1, 3, 1], 1) ≈ atkinson([8,5,5,1,3,3,3,5], 1) atol=0.00000001 # same result for probability and frequency weights @test atkinson([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 1) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 1) atol=0.00000001 @test atkinson([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 1) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 1) atol=0.00000001 @test atkinson([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 0.8) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 0.8) atol=0.00000001 @test atkinson([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 0.8) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 0.8) atol=0.00000001 @test atkinson([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 1.2) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 1.2) atol=0.00000001 @test atkinson([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 1.2) ≈ atkinson([8,5,1,3,5], [1,1,1,1,1], 1.2) atol=0.00000001 @test watkinson([8,5,1,3,5], [1, 2, 1, 3, 1], 1) ≈ atkinson([8,5,1,3,5], [1, 2, 1, 3, 1], 1) @test watkinson([8,5,1,3,5], weights([1, 2, 1, 3, 1]), 1) ≈ atkinson([8,5,1,3,5], [1, 2, 1, 3, 1], 1) end @testset "atkinson with DataFrames" begin @test combine(df_1, :v => x -> atkinson(x,2))[!,:v_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3],2) @test combine(df_1, :v => x -> atkinson(x,1))[!,:v_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3],1) @test combine(df_1, :v => x -> atkinson(x,0.8))[!,:v_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3],0.8) @test combine(df_1, [:v, :w] => (x, y) -> atkinson(x,weights(y),2))[!,:v_w_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2) @test combine(df_1, [:v, :w] => (x, y) -> atkinson(x,y,2))[!,:v_w_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2) @test combine(groupby(df_2, :group), :v => x -> atkinson(x,2))[!,:v_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3],2) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> atkinson(x,y,2))[!,:v_w_function][1] ≈ atkinson([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2) end @testset "gini checks" begin @test_throws ArgumentError gini([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError gini([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4]) @test_throws MethodError gini([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError gini([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4]) # different length `v` and `w` end @testset "gini" begin @test gini([8,5,1,3,5]) ≈ gini([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test gini([8,5,1,3,5,6,7,6,3]) ≈ 0.237373737373737 atol=0.00000001 @test gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) ≈ 0.2065239551478077 atol=0.00000001 @test gini([8,5,1,3,5], [1, 2, 1, 3, 1]) ≈ gini([8,5,5,1,3,3,3,5]) atol=0.00000001 # same result for probability and frequency weights @test gini([8,5,1,3,5], StatsBase.weights([1,1,1,1,1])) ≈ gini([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test gini([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1])) ≈ gini([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test wgini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) ≈ gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test wgini([8,5,1,3,5,6,7,6,3], weights(collect(0.1:0.1:0.9))) ≈ gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) end @testset "gini with DataFrames" begin @test combine(df_1, :v => gini)[!,:v_gini][1] ≈ gini([8,5,1,3,5,6,7,6,3]) @test combine(df_1, [:v, :w] => (x, y) -> gini(x, weights(y)))[!,:v_w_function][1] ≈ gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test combine(df_1, [:v, :w] => (x, y) -> gini(x, y))[!,:v_w_function][1] ≈ gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test combine(groupby(df_2, :group), :v => gini)[!,:v_gini][1] ≈ gini([8,5,1,3,5,6,7,6,3]) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> gini(x, y))[!,:v_w_function][1] ≈ gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) # @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> gini(x, weights(y)))[!,:v_w_function][1] ≈ gini([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) broken=true end @testset "lorenz_curve checks" begin @test_throws ArgumentError lorenz_curve([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError lorenz_curve([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4]) @test_throws MethodError lorenz_curve([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError lorenz_curve([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4]) # different length `v` and `w` end @testset "lorenz_curve" begin @test all(lorenz_curve([8,5,1,3,5])[2] .≈ lorenz_curve([8,5,1,3,5], [1,1,1,1,1])[2]) @test all(lorenz_curve([8,5,1,3,5,6,7,6,3])[1] .≈ Vector([0.0, 0.1111111111111111, 0.2222222222222222, 0.3333333333333333, 0.4444444444444444, 0.5555555555555556, 0.6666666666666666, 0.7777777777777778, 0.8888888888888888, 1.0])) @test all(lorenz_curve([8,5,1,3,5,6,7,6,3])[2] .≈ [0.0, 0.022727272727272728, 0.09090909090909091, 0.1590909090909091, 0.2727272727272727, 0.38636363636363635, 0.5227272727272727, 0.6590909090909091, 0.8181818181818182, 1.0]) @test all(lorenz_curve([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9))[2] .≈ [0.0, 0.013761467889908256, 0.05045871559633028, 0.0963302752293578, 0.1513761467889908, 0.2660550458715596, 0.38990825688073394, 0.555045871559633, 0.7752293577981653, 1.0]) @test all(lorenz_curve([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]))[2] ≈ lorenz_curve([8,5,1,3,5], [1,1,1,1,1])[2]) @test all(lorenz_curve([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]))[2] ≈ lorenz_curve([8,5,1,3,5], [1,1,1,1,1])[2]) @test all(wlorenz_curve([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9))[2] .≈ [0.0, 0.013761467889908256, 0.05045871559633028, 0.0963302752293578, 0.1513761467889908, 0.2660550458715596, 0.38990825688073394, 0.555045871559633, 0.7752293577981653, 1.0]) @test all(wlorenz_curve([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]))[2] ≈ lorenz_curve([8,5,1,3,5], [1,1,1,1,1])[2]) end @testset "lorenz_curve with DataFrames" begin @test all(combine(df_1, :v => lorenz_curve)[!,:v_lorenz_curve][1][2] .≈ lorenz_curve([8,5,1,3,5,6,7,6,3])[2]) @test all(combine(df_1, [:v, :w] => (x, y) -> lorenz_curve(x, weights(y)))[!,:v_w_function][1][2] .≈ lorenz_curve([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9))[2]) @test all(combine(df_1, [:v, :w] => (x, y) -> lorenz_curve(x, y))[!,:v_w_function][1][2] .≈ lorenz_curve([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9))[2]) @test all(combine(groupby(df_2, :group), :v => lorenz_curve)[!,:v_lorenz_curve][1][2] .≈ lorenz_curve([8,5,1,3,5,6,7,6,3])[2]) @test all(combine(groupby(df_2, :group), [:v, :w] => (x, y) -> lorenz_curve(x, y))[!,:v_w_function][1][2] ≈ lorenz_curve([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9))[2]) end @testset "mld checks" begin @test_throws ArgumentError mld([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError mld([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4]) @test_throws MethodError mld([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError mld([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4]) # different length `v` and `w` end @testset "mld" begin @test mld([8,5,1,3,5]) ≈ mld([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test mld([8,5,1,3,5,6,7,6,3]) ≈ 0.1397460530936332 atol=0.00000001 @test mld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) ≈ 0.10375545537468207 atol=0.00000001 @test mld([8,5,1,3,5], [1, 2, 1, 3, 1]) ≈ mld([8,5,5,1,3,3,3,5]) atol=0.00000001 # same result for probability and frequency weights @test mld([8,5,1,3,5], StatsBase.weights([1,1,1,1,1])) ≈ mld([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test mld([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1])) ≈ mld([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test wmld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) ≈ mld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test wmld([8,5,1,3,5,6,7,6,3], StatsBase.weights(collect(0.1:0.1:0.9))) ≈ mld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) end @testset "mld with DataFrames" begin @test combine(df_1, :v => mld)[!,:v_mld][1] ≈ mld([8,5,1,3,5,6,7,6,3]) @test combine(df_1, [:v, :w] => (x, y) -> mld(x, weights(y)))[!,:v_w_function][1] ≈ mld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test combine(df_1, [:v, :w] => (x, y) -> mld(x, y))[!,:v_w_function][1] ≈ mld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test combine(groupby(df_2, :group), :v => mld)[!,:v_mld][1] ≈ mld([8,5,1,3,5,6,7,6,3]) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> mld(x, y))[!,:v_w_function][1] ≈ mld([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) end @testset "watts checks" begin @test_throws ArgumentError watts([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4], 4) @test_throws ArgumentError watts([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4], 4) @test_throws MethodError watts([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4], 4) @test_throws ArgumentError watts([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4], 4) # different length `v` and `w` end @testset "watts" begin @test watts([8,5,1,3,5], 4) ≈ watts([8,5,1,3,5], [1,1,1,1,1], 4) atol=0.00000001 @test watts([8,5,1,3,5,6,7,6,3], 4) ≈ 0.217962056224828 atol=0.00000001 @test watts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) ≈ 0.17552777833850716 atol=0.00000001 @test watts([8,5,1,3,5], [1, 2, 1, 3, 1], 4) ≈ watts([8,5,5,1,3,3,3,5], 4) atol=0.00000001 # same result for probability and frequency weights @test watts([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 4) ≈ watts([8,5,1,3,5], [1,1,1,1,1], 4) atol=0.00000001 @test watts([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 4) ≈ watts([8,5,1,3,5], [1,1,1,1,1], 4) atol=0.00000001 @test wwatts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) ≈ watts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) @test wwatts([8,5,1,3,5,6,7,6,3], weights(collect(0.1:0.1:0.9)), 4) ≈ watts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) end @testset "watts with DataFrames" begin @test combine(df_1, :v => x -> watts(x,4))[!,:v_function][1] ≈ watts([8,5,1,3,5,6,7,6,3],4) @test combine(df_1, [:v, :w] => (x, y) -> watts(x, weights(y),4))[!,:v_w_function][1] ≈ watts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) @test combine(df_1, [:v, :w] => (x, y) -> watts(x,y,4))[!,:v_w_function][1] ≈ watts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) @test combine(groupby(df_2, :group), :v => x-> watts(x,4))[!,:v_function][1] ≈ watts([8,5,1,3,5,6,7,6,3],4) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> watts(x,y,4))[!,:v_w_function][1] ≈ watts([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) end @testset "theil checks" begin @test_throws ArgumentError theil([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError theil([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4]) @test_throws MethodError theil([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4]) @test_throws ArgumentError theil([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4]) # different length `v` and `w` end @testset "theil" begin @test theil([8,5,1,3,5]) ≈ theil([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test theil([8,5,1,3,5,6,7,6,3]) ≈ 0.10494562214323544 atol=0.00000001 @test theil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) ≈ 0.08120013911680612 atol=0.00000001 @test theil([8,5,1,3,5], [1, 2, 1, 3, 1]) ≈ theil([8,5,5,1,3,3,3,5]) atol=0.00000001 # same result for probability and frequency weights @test theil([8,5,1,3,5], StatsBase.weights([1,1,1,1,1])) ≈ theil([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test theil([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1])) ≈ theil([8,5,1,3,5], [1,1,1,1,1]) atol=0.00000001 @test wtheil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) ≈ theil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test wtheil([8,5,1,3,5,6,7,6,3], weights(collect(0.1:0.1:0.9))) ≈ theil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) end @testset "theil with DataFrames" begin @test combine(df_1, :v => theil)[!,:v_theil][1] ≈ theil([8,5,1,3,5,6,7,6,3]) @test combine(df_1, [:v, :w] => (x, y) -> theil(x, weights(y)))[!,:v_w_function][1] ≈ theil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test combine(df_1, [:v, :w] => (x, y) -> theil(x, y))[!,:v_w_function][1] ≈ theil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) @test combine(groupby(df_2, :group), :v => theil)[!,:v_theil][1] ≈ theil([8,5,1,3,5,6,7,6,3]) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> theil(x, y))[!,:v_w_function][1] ≈ theil([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9)) end @testset "gen_entropy checks" begin @test_throws ArgumentError gen_entropy([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4],2) @test_throws ArgumentError gen_entropy([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4],2) @test_throws MethodError gen_entropy([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4],2) @test_throws ArgumentError gen_entropy([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4],2) # different length `v` and `w` end @testset "gen_entropy" begin @test gen_entropy([8,5,1,3,5], 0) ≈ mld([8,5,1,3,5]) @test gen_entropy([8,5,1,3,5], 1) ≈ theil([8,5,1,3,5]) @test gen_entropy([8,5,1,3,5], 2) ≈ gen_entropy([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test gen_entropy([8,5,1,3,5,6,7,6,3], 2) ≈ 0.09039256198347094 atol=0.00000001 @test gen_entropy([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 2) ≈ 0.0709746654322026 atol=0.00000001 @test gen_entropy([8,5,1,3,5], [1, 2, 1, 3, 1], 2) ≈ gen_entropy([8,5,5,1,3,3,3,5], 2) atol=0.00000001 # same result for probability and frequency weights @test gen_entropy([8,5,1,3,5], collect(0.1:0.1:0.5), 0) ≈ mld([8,5,1,3,5],collect(0.1:0.1:0.5)) @test gen_entropy([8,5,1,3,5], collect(0.1:0.1:0.5), 1) ≈ theil([8,5,1,3,5],collect(0.1:0.1:0.5)) @test gen_entropy([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 2) ≈ gen_entropy([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test gen_entropy([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 2) ≈ gen_entropy([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test gen_entropy([8,5,1,3,5], StatsBase.weights(collect(0.1:0.1:0.5)), 0) ≈ mld([8,5,1,3,5],collect(0.1:0.1:0.5)) @test gen_entropy([8,5,1,3,5], StatsBase.weights(collect(0.1:0.1:0.5)), 1) ≈ theil([8,5,1,3,5],collect(0.1:0.1:0.5)) @test wgen_entropy([8,5,1,3,5], [1,1,1,1,1], 2) ≈ gen_entropy([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test wgen_entropy([8,5,1,3,5], weights(collect(0.1:0.1:0.5)), 2) ≈ gen_entropy([8,5,1,3,5], collect(0.1:0.1:0.5), 2) end @testset "gen_entropy with DataFrames" begin @test combine(df_1, :v => x -> gen_entropy(x,0))[!,:v_function][1] ≈ mld([8,5,1,3,5,6,7,6,3]) @test combine(df_1, :v => x -> gen_entropy(x,1))[!,:v_function][1] ≈ theil([8,5,1,3,5,6,7,6,3]) @test combine(df_1, :v => x -> gen_entropy(x,2))[!,:v_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3],2) @test combine(df_1, :v => x -> gen_entropy(x,1))[!,:v_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3],1) @test combine(df_1, :v => x -> gen_entropy(x,0.8))[!,:v_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3],0.8) @test combine(df_1, [:v, :w] => (x, y) -> gen_entropy(x,weights(y),2))[!,:v_w_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2) @test combine(df_1, [:v, :w] => (x, y) -> gen_entropy(x,y,2))[!,:v_w_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2) @test combine(groupby(df_2, :group), :v => x -> gen_entropy(x,2))[!,:v_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3],2) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> gen_entropy(x,y,2))[!,:v_w_function][1] ≈ gen_entropy([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2) end @testset "headcount checks" begin @test_throws ArgumentError headcount([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4],2) @test_throws ArgumentError headcount([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4],2) @test_throws MethodError headcount([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4],2) @test_throws ArgumentError headcount([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4],2) # different length `v` and `w` end @testset "headcount" begin @test headcount([8,5,1,3,5,6,7,6,3], 4) ≈ 0.3333333333333333 atol=0.00000001 @test headcount([8,5,1,3,5,6,7,6,3], fill(1,9), 4) ≈ 0.3333333333333333 atol=0.00000001 @test headcount([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) ≈ 0.35555555555555557 atol=0.00000001 @test headcount([8,5,1,3,5], [1, 2, 1, 3, 1], 2) ≈ headcount([8,5,5,1,3,3,3,5], 2) atol=0.00000001 # same result for probability and frequency weights @test headcount([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 2) ≈ headcount([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test headcount([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 2) ≈ headcount([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test wheadcount([8,5,1,3,5], [1, 2, 1, 3, 1], 2) ≈ headcount([8,5,5,1,3,3,3,5], 2) atol=0.00000001 @test wheadcount([8,5,1,3,5], StatsBase.pweights([1, 2, 1, 3, 1]), 2) ≈ headcount([8,5,5,1,3,3,3,5], 2) atol=0.00000001 end @testset "headcount with DataFrames" begin @test combine(df_1, :v => x -> headcount(x,4))[!,:v_function][1] ≈ headcount([8,5,1,3,5,6,7,6,3],4) @test combine(df_1, [:v, :w] => (x, y) -> headcount(x, weights(y),4))[!,:v_w_function][1] ≈ headcount([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) @test combine(df_1, [:v, :w] => (x, y) -> headcount(x,y,4))[!,:v_w_function][1] ≈ headcount([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) @test combine(groupby(df_2, :group), :v => x-> headcount(x,4))[!,:v_function][1] ≈ headcount([8,5,1,3,5,6,7,6,3],4) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> headcount(x,y,4))[!,:v_w_function][1] ≈ headcount([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) end @testset "poverty_gap checks" begin @test_throws ArgumentError poverty_gap([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4],2) @test_throws ArgumentError poverty_gap([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4],2) @test_throws MethodError poverty_gap([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4],2) @test_throws ArgumentError poverty_gap([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4],2) # different length `v` and `w` end @testset "poverty_gap" begin @test poverty_gap([8,5,1,3,5,6,7,6,3], 4) ≈ 0.1388888888888889 atol=0.00000001 @test poverty_gap([8,5,1,3,5,6,7,6,3], fill(1,9), 4) ≈ 0.1388888888888889 atol=0.00000001 @test poverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) ≈ 0.1222222222222222 atol=0.00000001 @test poverty_gap([8,5,1,3,5], [1, 2, 1, 3, 1], 2) ≈ poverty_gap([8,5,5,1,3,3,3,5], 2) atol=0.00000001 # same result for probability and frequency weights @test poverty_gap([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 2) ≈ poverty_gap([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test poverty_gap([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 2) ≈ poverty_gap([8,5,1,3,5], [1,1,1,1,1], 2) atol=0.00000001 @test wpoverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) ≈ poverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) @test wpoverty_gap([8,5,1,3,5,6,7,6,3], weights(collect(0.1:0.1:0.9)), 4) ≈ poverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 4) end @testset "poverty_gap with DataFrames" begin @test combine(df_1, :v => x -> poverty_gap(x,4))[!,:v_function][1] ≈ poverty_gap([8,5,1,3,5,6,7,6,3],4) @test combine(df_1, [:v, :w] => (x, y) -> poverty_gap(x, weights(y),4))[!,:v_w_function][1] ≈ poverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) @test combine(df_1, [:v, :w] => (x, y) -> poverty_gap(x,y,4))[!,:v_w_function][1] ≈ poverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) @test combine(groupby(df_2, :group), :v => x-> poverty_gap(x,4))[!,:v_function][1] ≈ poverty_gap([8,5,1,3,5,6,7,6,3],4) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> poverty_gap(x,y,4))[!,:v_w_function][1] ≈ poverty_gap([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),4) end @testset "fgt checks" begin @test_throws ArgumentError fgt([8, 5, 1, 3, 5], [-1, 0.1, 0.2, 0.3, 0.4], 2, 4) @test_throws ArgumentError fgt([8, 5, 1, 3, 5], [NaN, 0.1, 0.2, 0.3, 0.4],2, 4) @test_throws MethodError fgt([8, 5, 1, 3, 5], [missing, 0.1, 0.2, 0.3, 0.4],2, 4) @test_throws ArgumentError fgt([8, 5, 1, 3, 5], [0.1, 0.2, 0.3, 0.4], 2, 4) # different length `v` and `w` end @testset "fgt" begin @test fgt([8,5,1,3,5], 0, 4) ≈ headcount([8,5,1,3,5], 4) @test fgt([8,5,1,3,5], 1, 4) ≈ poverty_gap([8,5,1,3,5], 4) @test fgt([8,5,1,3,5], 0, 4) ≈ headcount([8,5,1,3,5], 4) @test fgt([8,5,1,3,5], 1, 4) ≈ poverty_gap([8,5,1,3,5], 4) @test fgt([8,5,1,3,5], 2, 4) ≈ fgt([8,5,1,3,5], [1,1,1,1,1], 2, 4) atol=0.00000001 @test fgt([8,5,1,3,5,6,7,6,3], 2, 4) ≈ 0.0763888888888889 atol=0.00000001 @test fgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 2, 4) ≈ 0.05555555555555555 atol=0.00000001 @test fgt([8,5,1,3,5], [1, 2, 1, 3, 1], 2, 4) ≈ fgt([8,5,5,1,3,3,3,5], 2, 4) atol=0.00000001 # same result for probability and frequency weights @test fgt([8,5,1,3,5], StatsBase.weights([1,1,1,1,1]), 2, 4) ≈ fgt([8,5,1,3,5], [1,1,1,1,1], 2, 4) atol=0.00000001 @test fgt([8,5,1,3,5], StatsBase.pweights([1,1,1,1,1]), 2, 4) ≈ fgt([8,5,1,3,5], [1,1,1,1,1], 2, 4) atol=0.00000001 @test wfgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 2, 4) ≈ fgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 2, 4) @test wfgt([8,5,1,3,5,6,7,6,3], weights(collect(0.1:0.1:0.9)), 2, 4) ≈ fgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9), 2, 4) end @testset "fgt with DataFrames" begin @test combine(df_1, :v => x -> fgt(x,2,4))[!,:v_function][1] ≈ fgt([8,5,1,3,5,6,7,6,3],2,4) @test combine(df_1, :v => x -> fgt(x,1,4))[!,:v_function][1] ≈ poverty_gap([8,5,1,3,5,6,7,6,3],4) @test combine(df_1, :v => x -> fgt(x,0,4))[!,:v_function][1] ≈ headcount([8,5,1,3,5,6,7,6,3],4) @test combine(df_1, [:v, :w] => (x, y) -> fgt(x, weights(y),2,4))[!,:v_w_function][1] ≈ fgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2,4) @test combine(df_1, [:v, :w] => (x, y) -> fgt(x,y,2,4))[!,:v_w_function][1] ≈ fgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2,4) @test combine(groupby(df_2, :group), :v => x-> fgt(x,2,4))[!,:v_function][1] ≈ fgt([8,5,1,3,5,6,7,6,3],2,4) @test combine(groupby(df_2, :group), [:v, :w] => (x, y) -> fgt(x,y,2,4))[!,:v_w_function][1] ≈ fgt([8,5,1,3,5,6,7,6,3], collect(0.1:0.1:0.9),2,4) end
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<filename>src/StructuredQueries.jl module StructuredQueries using Compat export Cursor, Grouped, @with, source, graph include("utils.jl") # grouped include("grouped/grouped.jl") include("grouped/show.jl") #verbs include("verbs/verbs.jl") include("verbs/primitives.jl") include("verbs/expr/assignment_expr_ops.jl") include("verbs/expr/scalar.jl") include("verbs/expr/sym_analysis.jl") include("verbs/process/generic.jl") include("verbs/process/select.jl") include("verbs/process/filter.jl") include("verbs/process/orderby.jl") include("verbs/process/groupby.jl") include("verbs/process/join.jl") include("verbs/process/summarize.jl") #nodes include("nodes/nodes.jl") include("nodes/primitives.jl") include("nodes/show.jl") # cursors include("cursor/cursor.jl") include("cursor/primitives.jl") include("cursor/show.jl") include("cursor/macros.jl") include("cursor/graph.jl") # collect include("collect/lift.jl") end
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<gh_stars>0 # This file is a part of Julia. License is MIT: http://julialang.org/license struct Rational{T<:Integer} <: Real num::T den::T function Rational{T}(num::Integer, den::Integer) where T<:Integer num == den == zero(T) && throw(ArgumentError("invalid rational: zero($T)//zero($T)")) g = den < 0 ? -gcd(den, num) : gcd(den, num) new(div(num, g), div(den, g)) end end Rational(n::T, d::T) where T<:Integer = Rational{T}(n,d) Rational(n::Integer, d::Integer) = Rational(promote(n,d)...) Rational(n::Integer) = Rational(n,one(n)) function divgcd(x::Integer,y::Integer) g = gcd(x,y) div(x,g), div(y,g) end """ //(num, den) Divide two integers or rational numbers, giving a `Rational` result. ```jldoctest julia> 3 // 5 3//5 julia> (3 // 5) // (2 // 1) 3//10 ``` """ //(n::Integer, d::Integer ) = Rational(n,d) function //(x::Rational, y::Integer ) xn,yn = divgcd(x.num,y) xn//checked_mul(x.den,yn) end function //(x::Integer, y::Rational) xn,yn = divgcd(x,y.num) checked_mul(xn,y.den)//yn end function //(x::Rational, y::Rational) xn,yn = divgcd(x.num,y.num) xd,yd = divgcd(x.den,y.den) checked_mul(xn,yd)//checked_mul(xd,yn) end //(x::Complex, y::Real) = complex(real(x)//y,imag(x)//y) //(x::Number, y::Complex) = x*y'//abs2(y) //(X::AbstractArray, y::Number) = X .// y function show(io::IO, x::Rational) show(io, numerator(x)) print(io, "//") show(io, denominator(x)) end function read{T<:Integer}(s::IO, ::Type{Rational{T}}) r = read(s,T) i = read(s,T) r//i end function write(s::IO, z::Rational) write(s,numerator(z),denominator(z)) end convert{T<:Integer}(::Type{Rational{T}}, x::Rational) = Rational{T}(convert(T,x.num),convert(T,x.den)) convert{T<:Integer}(::Type{Rational{T}}, x::Integer) = Rational{T}(convert(T,x), convert(T,1)) convert(::Type{Rational}, x::Rational) = x convert(::Type{Rational}, x::Integer) = convert(Rational{typeof(x)},x) convert(::Type{Bool}, x::Rational) = x==0 ? false : x==1 ? true : throw(InexactError()) # to resolve ambiguity convert(::Type{Integer}, x::Rational) = (isinteger(x) ? convert(Integer, x.num) : throw(InexactError())) convert{T<:Integer}(::Type{T}, x::Rational) = (isinteger(x) ? convert(T, x.num) : throw(InexactError())) convert(::Type{AbstractFloat}, x::Rational) = float(x.num)/float(x.den) function convert{T<:AbstractFloat,S}(::Type{T}, x::Rational{S}) P = promote_type(T,S) convert(T, convert(P,x.num)/convert(P,x.den)) end function convert{T<:Integer}(::Type{Rational{T}}, x::AbstractFloat) r = rationalize(T, x, tol=0) x == convert(typeof(x), r) || throw(InexactError()) r end convert(::Type{Rational}, x::Float64) = convert(Rational{Int64}, x) convert(::Type{Rational}, x::Float32) = convert(Rational{Int}, x) big(z::Complex{<:Rational{<:Integer}}) = Complex{Rational{BigInt}}(z) promote_rule{T<:Integer,S<:Integer}(::Type{Rational{T}}, ::Type{S}) = Rational{promote_type(T,S)} promote_rule{T<:Integer,S<:Integer}(::Type{Rational{T}}, ::Type{Rational{S}}) = Rational{promote_type(T,S)} promote_rule{T<:Integer,S<:AbstractFloat}(::Type{Rational{T}}, ::Type{S}) = promote_type(T,S) widen{T}(::Type{Rational{T}}) = Rational{widen(T)} """ rationalize([T<:Integer=Int,] x; tol::Real=eps(x)) Approximate floating point number `x` as a `Rational` number with components of the given integer type. The result will differ from `x` by no more than `tol`. If `T` is not provided, it defaults to `Int`. ```jldoctest julia> rationalize(5.6) 28//5 julia> a = rationalize(BigInt, 10.3) 103//10 julia> typeof(numerator(a)) BigInt ``` """ function rationalize{T<:Integer}(::Type{T}, x::AbstractFloat, tol::Real) if tol < 0 throw(ArgumentError("negative tolerance $tol")) end isnan(x) && return zero(T)//zero(T) isinf(x) && return (x < 0 ? -one(T) : one(T))//zero(T) p, q = (x < 0 ? -one(T) : one(T)), zero(T) pp, qq = zero(T), one(T) x = abs(x) a = trunc(x) r = x-a y = one(x) tolx = oftype(x, tol) nt, t, tt = tolx, zero(tolx), tolx ia = np = nq = zero(T) # compute the successive convergents of the continued fraction # np // nq = (p*a + pp) // (q*a + qq) while r > nt try ia = convert(T,a) np = checked_add(checked_mul(ia,p),pp) nq = checked_add(checked_mul(ia,q),qq) p, pp = np, p q, qq = nq, q catch e isa(e,InexactError) || isa(e,OverflowError) || rethrow(e) return p // q end # naive approach of using # x = 1/r; a = trunc(x); r = x - a # is inexact, so we store x as x/y x, y = y, r a, r = divrem(x,y) # maintain # x0 = (p + (-1)^i * r) / q t, tt = nt, t nt = a*t+tt end # find optimal semiconvergent # smallest a such that x-a*y < a*t+tt a = cld(x-tt,y+t) try ia = convert(T,a) np = checked_add(checked_mul(ia,p),pp) nq = checked_add(checked_mul(ia,q),qq) return np // nq catch e isa(e,InexactError) || isa(e,OverflowError) || rethrow(e) return p // q end end rationalize{T<:Integer}(::Type{T}, x::AbstractFloat; tol::Real=eps(x)) = rationalize(T, x, tol)::Rational{T} rationalize(x::AbstractFloat; kvs...) = rationalize(Int, x; kvs...) """ numerator(x) Numerator of the rational representation of `x`. ```jldoctest julia> numerator(2//3) 2 julia> numerator(4) 4 ``` """ numerator(x::Integer) = x numerator(x::Rational) = x.num """ denominator(x) Denominator of the rational representation of `x`. ```jldoctest julia> denominator(2//3) 3 julia> denominator(4) 1 ``` """ denominator(x::Integer) = one(x) denominator(x::Rational) = x.den sign(x::Rational) = oftype(x, sign(x.num)) signbit(x::Rational) = signbit(x.num) copysign(x::Rational, y::Real) = copysign(x.num,y) // x.den copysign(x::Rational, y::Rational) = copysign(x.num,y.num) // x.den typemin{T<:Integer}(::Type{Rational{T}}) = -one(T)//zero(T) typemax{T<:Integer}(::Type{Rational{T}}) = one(T)//zero(T) isinteger(x::Rational) = x.den == 1 -(x::Rational) = (-x.num) // x.den function -{T<:Signed}(x::Rational{T}) x.num == typemin(T) && throw(OverflowError()) (-x.num) // x.den end function -{T<:Unsigned}(x::Rational{T}) x.num != zero(T) && throw(OverflowError()) x end for (op,chop) in ((:+,:checked_add), (:-,:checked_sub), (:rem,:rem), (:mod,:mod)) @eval begin function ($op)(x::Rational, y::Rational) xd, yd = divgcd(x.den, y.den) Rational(($chop)(checked_mul(x.num,yd), checked_mul(y.num,xd)), checked_mul(x.den,yd)) end end end function *(x::Rational, y::Rational) xn,yd = divgcd(x.num,y.den) xd,yn = divgcd(x.den,y.num) checked_mul(xn,yn) // checked_mul(xd,yd) end /(x::Rational, y::Rational) = x//y /(x::Rational, y::Complex{<:Union{Integer,Rational}}) = x//y fma(x::Rational, y::Rational, z::Rational) = x*y+z ==(x::Rational, y::Rational) = (x.den == y.den) & (x.num == y.num) <( x::Rational, y::Rational) = x.den == y.den ? x.num < y.num : widemul(x.num,y.den) < widemul(x.den,y.num) <=(x::Rational, y::Rational) = x.den == y.den ? x.num <= y.num : widemul(x.num,y.den) <= widemul(x.den,y.num) ==(x::Rational, y::Integer ) = (x.den == 1) & (x.num == y) ==(x::Integer , y::Rational) = y == x <( x::Rational, y::Integer ) = x.num < widemul(x.den,y) <( x::Integer , y::Rational) = widemul(x,y.den) < y.num <=(x::Rational, y::Integer ) = x.num <= widemul(x.den,y) <=(x::Integer , y::Rational) = widemul(x,y.den) <= y.num function ==(x::AbstractFloat, q::Rational) if isfinite(x) (count_ones(q.den) == 1) & (x*q.den == q.num) else x == q.num/q.den end end ==(q::Rational, x::AbstractFloat) = x == q for rel in (:<,:<=,:cmp) for (Tx,Ty) in ((Rational,AbstractFloat), (AbstractFloat,Rational)) @eval function ($rel)(x::$Tx, y::$Ty) if isnan(x) || isnan(y) $(rel == :cmp ? :(throw(DomainError())) : :(return false)) end xn, xp, xd = decompose(x) yn, yp, yd = decompose(y) if xd < 0 xn = -xn xd = -xd end if yd < 0 yn = -yn yd = -yd end xc, yc = widemul(xn,yd), widemul(yn,xd) xs, ys = sign(xc), sign(yc) if xs != ys return ($rel)(xs,ys) elseif xs == 0 # both are zero or ±Inf return ($rel)(xn,yn) end xb, yb = ndigits0z(xc,2) + xp, ndigits0z(yc,2) + yp if xb == yb xc, yc = promote(xc,yc) if xp > yp xc = (xc<<(xp-yp)) else yc = (yc<<(yp-xp)) end return ($rel)(xc,yc) else return xc > 0 ? ($rel)(xb,yb) : ($rel)(yb,xb) end end end end # needed to avoid ambiguity between ==(x::Real, z::Complex) and ==(x::Rational, y::Number) ==(z::Complex , x::Rational) = isreal(z) & (real(z) == x) ==(x::Rational, z::Complex ) = isreal(z) & (real(z) == x) for op in (:div, :fld, :cld) @eval begin function ($op)(x::Rational, y::Integer ) xn,yn = divgcd(x.num,y) ($op)(xn, checked_mul(x.den,yn)) end function ($op)(x::Integer, y::Rational) xn,yn = divgcd(x,y.num) ($op)(checked_mul(xn,y.den), yn) end function ($op)(x::Rational, y::Rational) xn,yn = divgcd(x.num,y.num) xd,yd = divgcd(x.den,y.den) ($op)(checked_mul(xn,yd), checked_mul(xd,yn)) end end end trunc{T}(::Type{T}, x::Rational) = convert(T,div(x.num,x.den)) floor{T}(::Type{T}, x::Rational) = convert(T,fld(x.num,x.den)) ceil{ T}(::Type{T}, x::Rational) = convert(T,cld(x.num,x.den)) function round{T, Tr}(::Type{T}, x::Rational{Tr}, ::RoundingMode{:Nearest}) if denominator(x) == zero(Tr) && T <: Integer throw(DivideError()) elseif denominator(x) == zero(Tr) return convert(T, copysign(one(Tr)//zero(Tr), numerator(x))) end q,r = divrem(numerator(x), denominator(x)) s = q if abs(r) >= abs((denominator(x)-copysign(Tr(4), numerator(x))+one(Tr)+iseven(q))>>1 + copysign(Tr(2), numerator(x))) s += copysign(one(Tr),numerator(x)) end convert(T, s) end round{T}(::Type{T}, x::Rational) = round(T, x, RoundNearest) function round{T, Tr}(::Type{T}, x::Rational{Tr}, ::RoundingMode{:NearestTiesAway}) if denominator(x) == zero(Tr) && T <: Integer throw(DivideError()) elseif denominator(x) == zero(Tr) return convert(T, copysign(one(Tr)//zero(Tr), numerator(x))) end q,r = divrem(numerator(x), denominator(x)) s = q if abs(r) >= abs((denominator(x)-copysign(Tr(4), numerator(x))+one(Tr))>>1 + copysign(Tr(2), numerator(x))) s += copysign(one(Tr),numerator(x)) end convert(T, s) end function round{T, Tr}(::Type{T}, x::Rational{Tr}, ::RoundingMode{:NearestTiesUp}) if denominator(x) == zero(Tr) && T <: Integer throw(DivideError()) elseif denominator(x) == zero(Tr) return convert(T, copysign(one(Tr)//zero(Tr), numerator(x))) end q,r = divrem(numerator(x), denominator(x)) s = q if abs(r) >= abs((denominator(x)-copysign(Tr(4), numerator(x))+one(Tr)+(numerator(x)<0))>>1 + copysign(Tr(2), numerator(x))) s += copysign(one(Tr),numerator(x)) end convert(T, s) end function round{T}(::Type{T}, x::Rational{Bool}) if denominator(x) == false && issubtype(T, Union{Integer, Bool}) throw(DivideError()) end convert(T, x) end round{T}(::Type{T}, x::Rational{Bool}, ::RoundingMode{:Nearest}) = round(T, x) round{T}(::Type{T}, x::Rational{Bool}, ::RoundingMode{:NearestTiesAway}) = round(T, x) round{T}(::Type{T}, x::Rational{Bool}, ::RoundingMode{:NearestTiesUp}) = round(T, x) round{T}(::Type{T}, x::Rational{Bool}, ::RoundingMode) = round(T, x) trunc{T}(x::Rational{T}) = Rational(trunc(T,x)) floor{T}(x::Rational{T}) = Rational(floor(T,x)) ceil{ T}(x::Rational{T}) = Rational(ceil(T,x)) round{T}(x::Rational{T}) = Rational(round(T,x)) function ^(x::Rational, n::Integer) n >= 0 ? power_by_squaring(x,n) : power_by_squaring(inv(x),-n) end ^(x::Number, y::Rational) = x^(y.num/y.den) ^{T<:AbstractFloat}(x::T, y::Rational) = x^convert(T,y) ^{T<:AbstractFloat}(x::Complex{T}, y::Rational) = x^convert(T,y) ^(z::Complex{<:Rational}, n::Bool) = n ? z : one(z) # to resolve ambiguity function ^(z::Complex{<:Rational}, n::Integer) n >= 0 ? power_by_squaring(z,n) : power_by_squaring(inv(z),-n) end iszero(x::Rational) = iszero(numerator(x)) function lerpi(j::Integer, d::Integer, a::Rational, b::Rational) ((d-j)*a)/d + (j*b)/d end
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# Use baremodule to shave off a few KB from the serialized `.ji` file baremodule CGAL2_jll using Base using Base: UUID import JLLWrappers JLLWrappers.@generate_main_file_header("CGAL2") JLLWrappers.@generate_main_file("CGAL2", UUID("a133c068-ba04-5466-9207-ec1c2ac43820")) end # module CGAL2_jll
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function warning_callback(message::String) global TEST_CALLBACK = true error("checking that error are supported") end @testset "Errors" begin err = ChemfilesError("oops") iobuf = IOBuffer() show(iobuf, err) @test String(iobuf.data[1:(19 + length(err.message))]) == "\"Chemfiles error: oops\"" Chemfiles.clear_errors() @test Chemfiles.last_error() == "" remove_chemfiles_warning() do @test_throws ChemfilesError Residue(Topology(), 3) end @test_throws UndefVarError TEST_CALLBACK == false @test Chemfiles.last_error() == "residue index out of bounds in topology: we have 0 residues, but the index is 3" Chemfiles.clear_errors() @test Chemfiles.last_error() == "" Chemfiles.set_warning_callback(warning_callback) @test_throws ChemfilesError Residue(Topology(), 3) @test TEST_CALLBACK == true Chemfiles.set_warning_callback(Chemfiles.__default_warning_callback) end @testset "Configuration" begin config = joinpath(@__DIR__, "data", "config.toml") Chemfiles.add_configuration(config) trajectory = joinpath(@__DIR__, "data", "water.xyz") frame = read(Trajectory(trajectory)) @test name(Atom(frame, 9)) == "Oz" @test type(Atom(frame, 9)) == "F" end @testset "Format list" begin for metadata in Chemfiles.format_list() if metadata.name == "XYZ" @test metadata.description == "XYZ text format" @test metadata.extension == ".xyz" @test metadata.reference == "https://openbabel.org/wiki/XYZ" @test metadata.read == true @test metadata.write == true @test metadata.memory == true @test metadata.positions == true @test metadata.velocities == false @test metadata.unit_cell == true @test metadata.atoms == true @test metadata.bonds == false @test metadata.residues == false end if metadata.name == "LAMMPD Data" @test metadata.extension === nothing end end end @testset "Guess format" begin @test Chemfiles.guess_format("file.xyz.gz") == "XYZ / GZ" @test Chemfiles.guess_format("file.nc") == "Amber NetCDF" end
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<filename>test/runtests.jl<gh_stars>1-10 using PySerial using Base.Test @test typeof(list_ports()) == Array{Any,1}
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using KiteConnect using Test @test_throws ArgumentError KiteConnect.ltp("INFY")
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# ---------------------- # -- read -- # ---------------------- function read_nodenum(skipnum) """ xmax : 仮想セルも含めたnodeのxの数 ymax : 仮想セルも含めたnodeのyの数 """ fff=[] open("grid/nodesnum", "r") do f fff=read(f,String) end fff=split(fff,"\n",keepempty=false) num_nodes=length(fff)-skipnum for i in 1+skipnum:length(fff) fff[i]=replace(fff[i]," \r" => "") end temp = split(fff[2]," ") xmax = parse(Int64,temp[1]) ymax = parse(Int64,temp[2]) return xmax, ymax end function read_nodes(skipnum,xmax,ymax) """ nodes[i][j][k] i : x点の番号 j : y点の番号 k=1 : 点のx座標 k=2 : 点のy座標 """ fff=[] open("grid/nodes", "r") do f fff=read(f,String) end fff=split(fff,"\n",keepempty=false) num_nodes=length(fff)-skipnum for i in 1+skipnum:length(fff) fff[i]=replace(fff[i]," \r" => "") end nodes = zeros(xmax,ymax,2) for i in 1:num_nodes temp=split(fff[i+skipnum]," ") xnum = parse(Int64,temp[1]) ynum = parse(Int64,temp[2]) nodes[xnum,ynum,1]=parse(Float64,temp[3]) nodes[xnum,ynum,2]=parse(Float64,temp[4]) end return nodes end function read_nodes_vtk(skipnum) fff=[] open("grid/nodes_forvtk", "r") do f fff=read(f,String) end fff=split(fff,"\n",keepempty=false) num_nodes=length(fff)-skipnum for i in 1+skipnum:length(fff) fff[i]=replace(fff[i]," \r" => "") end nodes=zeros(num_nodes,3) for i in 1:num_nodes temp=split(fff[i+skipnum]," ") # x = parse(Float64,temp[1]) # y = parse(Float64,temp[2]) # z = parse(Float64,temp[3]) x = parse(Float64,temp[2]) y = parse(Float64,temp[3]) z = 0.0 nodes[i,1] = x nodes[i,2] = y nodes[i,3] = z end return nodes end function read_elements_vtk(skipnum) fff=[] open("grid/element_forvtk", "r") do f fff=read(f,String) end fff=split(fff,"\n",keepempty=false) num_elements=length(fff)-skipnum for i in 1+skipnum:length(fff) fff[i]=replace(fff[i]," \r" => "") end elements = zeros(Int64,num_elements,4) for i in 1:num_elements temp=split(fff[i+skipnum]," ") elements[i,1] = parse(Int64,temp[2]) elements[i,2] = parse(Int64,temp[3]) elements[i,3] = parse(Int64,temp[4]) elements[i,4] = parse(Int64,temp[5]) end return elements end function read_result(skipnum) fff=[] open("test333.dat", "r") do f fff=read(f,String) end fff=split(fff,"\n",keepempty=false) num_point=length(fff)-skipnum for i in 1+skipnum:length(fff) fff[i]=replace(fff[i]," \r" => "") end readQ = zeros(num_point,5) for i in 1:num_point temp=split(fff[i+skipnum]," ") k = 1 for j in 1:length(temp) if temp[j] != "" readQ[i,k] = parse(Float64,temp[j]) k += 1 end end end return readQ end function read_allgrid() skip=1 xmax,ymax = read_nodenum(skip) nodes = read_nodes(skip,xmax,ymax) nodes_vtk = read_nodes_vtk(skip) elements = read_elements_vtk(skip) readQ = read_result(skip) println("fin read grid") return xmax,ymax,nodes,nodes_vtk,elements,readQ end
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# This file describes how we decide which logger (e.g. LogText vs LogValue vs LogHistograms) # to use for what data, and any preprocessing """ preprocess(name, val, data) This method takes a tag `name` and the value `val::T` pair. If type `T` can be serialized to TensorBoard then the pair is pushed to `data`, otherwise it should call `preprocess` recursively with some simpler types, until a serializable type is finally hit. For a struct, it calls preprocess on every field. """ function preprocess(name, val::T, data) where T if isstructtype(T) fn = logable_propertynames(val) for f=fn prop = getfield(val, f) preprocess(name*"/$f", prop, data) end else # If we do not know how to serialize a type, then # it will be simply logged as text push!(data, name=>val) end data end """ logable_propertynames(val::Any) Returns a tuple with the name of the fields of the structure `val` that should be logged to TensorBoard. This function should be overridden when you want TensorBoard to ignore some fields in a structure when logging it. The default behaviour is to return the same result as `propertynames`. See also: [`Base.propertynames`](@ref) """ logable_propertynames(val::Any) = propertynames(val) ## Default unpacking of key-value dictionaries function preprocess(name, dict::AbstractDict, data) for (key, val) in dict # convert any key into a string, via interpolating it preprocess("$name/$key", val, data) end return data end ## Default behaviours ########## For things going to LogImage ############################## function preprocess(name, img::AbstractArray{<:Colorant}, data) # If it has three dimensions (and we don't have 3D monitors) we log several # 2D slices under the same tag so that TB shows a slider along the z direction. dimensions = ndims(img) if dimensions == 3 #3rd is channel dim as observed in testimages channels = size(img, 3) for c in 1:channels preprocess(name, convert(PngImage, img[:, :, c]), data) end else preprocess(name, convert(PngImage, img), data) end return data end preprocess(name, val::PngImage, data) = push!(data, name=>val) summary_impl(name, value::PngImage) = image_summary(name, value) ########## For things going to LogText ############################## preprocess(name, val::AbstractString, data) where T<:String = push!(data, name=>val) summary_impl(name, value::Any) = text_summary(name, value) ########## For things going to LogHistograms ######################## # Only consider 1D histograms for histogram plotting preprocess(name, hist::StatsBase.Histogram{T,1}, data) where T = push!(data, name=>hist) summary_impl(name, hist::StatsBase.Histogram) = histogram_summary(name, hist) # TODO: maybe deprecate? tuple means histogram (only if bins/weights match) function preprocess(name, (bins,weights)::Tuple{AbstractVector,AbstractVector}, data) # if ... this is an histogram if length(bins) == length(weights)+1 return preprocess(name, Histogram(bins,weights), data) end preprocess(name*"/1", bins, data) preprocess(name*"/2", weights, data) end preprocess(name, val::AbstractArray{<:Real}, data) = push!(data, name=>val) summary_impl(name, val::AbstractArray{<:Real}) = histogram_arr_summary(name, val) # Split complex numbers into real/complex pairs preprocess(name, val::AbstractArray{<:Complex}, data) = push!(data, name*"/re"=>real.(val), name*"/im"=>imag.(val)) ########## For things going to LogValue ############################# preprocess(name, val::Real, data) = push!(data, name=>val) summary_impl(name, value::Real) = scalar_summary(name, value) # Split complex numbers into real/complex pairs preprocess(name, val::Complex, data) = push!(data, name*"/re"=>real(val), name*"/im"=>imag(val))
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2.941485
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<filename>abc171-180/abc171/a.jl<gh_stars>0 function solve() a = readline() a == uppercase(a) ? "A" : "a" end println(solve())
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2.15873
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<gh_stars>0 using BinaryBuilder, Pkg name = "GLPK" version = v"5.0" # Collection of sources required to build GLPK sources = [ ArchiveSource("http://ftpmirror.gnu.org/gnu/glpk/glpk-$(version.major).$(version.minor).tar.gz", "4a1013eebb50f728fc601bdd833b0b2870333c3b3e5a816eeba921d95bec6f15"), ] # Bash recipe for building across all platforms script = raw""" cd $WORKSPACE/srcdir/glpk* if [[ ${target} == *mingw* ]]; then export CPPFLAGS="-I${prefix}/include -D__WOE__=1" else export CPPFLAGS="-I${prefix}/include" fi autoreconf -vi ./configure --prefix=${prefix} --host=${target} --build=${MACHTYPE} --with-gmp make -j${nproc} make install """ # Build for all platforms platforms = supported_platforms() # The products that we will ensure are always built products = [ LibraryProduct("libglpk", :libglpk) ] # Dependencies that must be installed before this package can be built dependencies = [ Dependency("GMP_jll", v"6.1.2"), ] # Build the tarballs, and possibly a `build.jl` as well. # Use the same preferred_gcc_version as GMP. build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies; preferred_gcc_version=v"6")
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2.624176
455
<gh_stars>10-100 module UI import GLFW using ModernGL include("gltools.jl") GLFW.Init() # OS X-specific GLFW hints to initialize the correct version of OpenGL @osx_only begin GLFW.WindowHint(GLFW.CONTEXT_VERSION_MAJOR, 3) GLFW.WindowHint(GLFW.CONTEXT_VERSION_MINOR, 2) GLFW.WindowHint(GLFW.OPENGL_PROFILE, GLFW.OPENGL_CORE_PROFILE) GLFW.WindowHint(GLFW.OPENGL_FORWARD_COMPAT, GL_TRUE) end # Create a windowed mode window and its OpenGL context window = GLFW.CreateWindow(600, 600, "OpenGL Example") # Make the window's context current GLFW.MakeContextCurrent(window) # The data for our triangle data = GLfloat[ 0.0, 0.5, 0.5, -0.5, -0.5,-0.5 ] # Generate a vertex array and array buffer for our data vao = glGenVertexArray() glBindVertexArray(vao) vbo = glGenBuffer() glBindBuffer(GL_ARRAY_BUFFER, vbo) glBufferData(GL_ARRAY_BUFFER, sizeof(data), data, GL_STATIC_DRAW) # Create and initialize shaders const vsh = """ #version 330 in vec2 position; void main() { gl_Position = vec4(position, 0.0, 1.0); } """ const fsh = """ #version 330 out vec4 outColor; void main() { outColor = vec4(1.0, 1.0, 1.0, 1.0); } """ vertexShader = createShader(vsh, GL_VERTEX_SHADER) fragmentShader = createShader(fsh, GL_FRAGMENT_SHADER) program = createShaderProgram(vertexShader, fragmentShader) glUseProgram(program) positionAttribute = glGetAttribLocation(program, "position"); glEnableVertexAttribArray(positionAttribute) glVertexAttribPointer(positionAttribute, 2, GL_FLOAT, false, 0, 0) t = 0 # Loop until the user closes the window while !GLFW.WindowShouldClose(window) # Pulse the background blue t += 1 glClearColor(0.0, 0.0, 0.5 * (1 + sin(t * 0.02)), 1.0) glClear(GL_COLOR_BUFFER_BIT) # Draw our triangle glDrawArrays(GL_TRIANGLES, 0, 3) # Swap front and back buffers GLFW.SwapBuffers(window) # Poll for and process events GLFW.PollEvents() end GLFW.Terminate() end
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abstract type AbstractBC{T} <: AbstractDiffEqAffineOperator{T} end abstract type AtomicBC{T} <: AbstractBC{T} end """ Robin, General, and in general Neumann, Dirichlet and Bridge BCs are not necessarily linear operators. Instead, they are affine operators, with a constant term Q*x = Qa*x + Qb. """ abstract type AffineBC{T} <: AtomicBC{T} end struct NeumannBC{N} end struct Neumann0BC{N} end struct DirichletBC{N} end struct Dirichlet0BC{N} end """ q = PeriodicBC{T}() Qx, Qy, ... = PeriodicBC{T}(size(u)) #When all dimensions are to be extended with a periodic boundary condition. ------------------------------------------------------------------------------------- Creates a periodic boundary condition, where the lower index end of some u is extended with the upper index end and vice versa. It is not reccomended to concretize this BC type in to a BandedMatrix, since the vast majority of bands will be all 0s. SpatseMatrix concretization is reccomended. """ struct PeriodicBC{T} <: AtomicBC{T} PeriodicBC(T::Type) = new{T}() end """ q = RobinBC(left_coefficients, right_coefficients, dx::T, approximation_order) where T # When this BC extends a dimension with a uniform step size q = RobinBC(left_coefficients, right_coefficients, dx::Vector{T}, approximation_order) where T # When this BC extends a dimension with a non uniform step size. dx should be the vector of step sizes for the whole dimension ------------------------------------------------------------------------------------- The variables in l are [αl, βl, γl], and correspond to a BC of the form αl*u(0) + βl*u'(0) = γl imposed on the lower index boundary. The variables in r are [αl, βl, γl], and correspond to an analagous boundary on the higher index end. Implements a robin boundary condition operator Q that acts on a vector to give an extended vector as a result Referring to (https://github.com/JuliaDiffEq/DiffEqOperators.jl/files/3267835/ghost_node.pdf) Write vector b̄₁ as a vertical concatanation with b0 and the rest of the elements of b̄ ₁, denoted b̄`₁, the same with ū into u0 and ū`. b̄`₁ = b̄`_2 = fill(β/Δx, length(stencil)-1) Pull out the product of u0 and b0 from the dot product. The stencil used to approximate u` is denoted s. b0 = α+(β/Δx)*s[1] Rearrange terms to find a general formula for u0:= -b̄`₁̇⋅ū`/b0 + γ/b0, which is dependent on ū` the robin coefficients and Δx. The non identity part of Qa is qa:= -b`₁/b0 = -β.*s[2:end]/(α+β*s[1]/Δx). The constant part is Qb = γ/(α+β*s[1]/Δx) do the same at the other boundary (amounts to a flip of s[2:end], with the other set of boundary coeffs) """ struct RobinBC{T, V<:AbstractVector{T}} <: AffineBC{T} a_l::V b_l::T a_r::V b_r::T function RobinBC(l::NTuple{3,T}, r::NTuple{3,T}, dx::T, order = 1) where {T} αl, βl, γl = l αr, βr, γr = r s = calculate_weights(1, one(T), Array(one(T):convert(T,order+1))) #generate derivative coefficients about the boundary of required approximation order a_l = -s[2:end]./(αl*dx/βl + s[1]) a_r = s[end:-1:2]./(αr*dx/βr - s[1]) # for other boundary stencil is flippedlr with *opposite sign* b_l = γl/(αl+βl*s[1]/dx) b_r = γr/(αr-βr*s[1]/dx) return new{T, typeof(a_l)}(a_l, b_l, a_r, b_r) end function RobinBC(l::Union{NTuple{3,T},AbstractVector{T}}, r::Union{NTuple{3,T},AbstractVector{T}}, dx::AbstractVector{T}, order = 1) where {T} αl, βl, γl = l αr, βr, γr = r s_index = Array(one(T):convert(T,order+1)) dx_l, dx_r = dx[1:length(s_index)], dx[(end-length(s_index)+1):end] s = calculate_weights(1, one(T), s_index) #generate derivative coefficients about the boundary of required approximation order denom_l = αl+βl*s[1]/dx_l[1] denom_r = αr-βr*s[1]/dx_r[end] a_l = -βl.*s[2:end]./(denom_l*dx_l[2:end]) a_r = βr.*s[end:-1:2]./(denom_r*dx_r[1:(end-1)]) # for other boundary stencil is flippedlr with *opposite sign* b_l = γl/denom_l b_r = γr/denom_r return new{T, typeof(a_l)}(a_l, b_l, a_r, b_r) end end stencil(q::AffineBC{T}, N::Int) where T = ([transpose(q.a_l) transpose(zeros(T, N-length(q.a_l)))], [transpose(zeros(T, N-length(q.a_r))) transpose(q.a_r)]) affine(q::AffineBC) = (q.b_l, q.b_r) stencil(q::PeriodicBC{T}, N::Int) where T= ([transpose(zeros(T, N-1)) one(T)], [one(T) transpose(zeros(T, N-1))]) affine(q::PeriodicBC{T}) where T = (zero(T), zero(T)) """ q = GeneralBC(α_leftboundary, α_rightboundary, dx::T, approximation_order) ------------------------------------------------------------------------------------- Implements a generalization of the Robin boundary condition, where α is a vector of coefficients. Represents a condition of the form α[1] + α[2]u[0] + α[3]u'[0] + α[4]u''[0]+... = 0 Implemented in a similar way to the RobinBC (see above). This time there are multiple stencils for multiple derivative orders - these can be written as a matrix S. All components that multiply u(0) are factored out, turns out to only involve the first column of S, s̄0. The rest of S is denoted S`. the coeff of u(0) is s̄0⋅ᾱ[3:end] + α[2]. the remaining components turn out to be ᾱ[3:end]⋅(S`ū`) or equivalently (transpose(ᾱ[3:end])*S`)⋅ū`. Rearranging, a stencil q_a to be dotted with ū` upon extension can readily be found, along with a constant component q_b """ struct GeneralBC{T, L<:AbstractVector{T}, R<:AbstractVector{T}} <:AffineBC{T} a_l::L b_l::T a_r::R b_r::T function GeneralBC(αl::AbstractVector{T}, αr::AbstractVector{T}, dx::T, order = 1) where {T} nl = length(αl) nr = length(αr) S_l = zeros(T, (nl-2, order+nl-2)) S_r = zeros(T, (nr-2, order+nr-2)) for i in 1:(nl-2) S_l[i,:] = [transpose(calculate_weights(i, one(T), Array(one(T):convert(T, order+i)))) transpose(zeros(T, Int(nl-2-i)))]./(dx^i) #am unsure if the length of the dummy_x is correct here end for i in 1:(nr-2) S_r[i,:] = [transpose(calculate_weights(i, convert(T, order+i), Array(one(T):convert(T, order+i)))) transpose(zeros(T, Int(nr-2-i)))]./(dx^i) end s0_l = S_l[:,1] ; Sl = S_l[:,2:end] s0_r = S_r[:,end] ; Sr = S_r[:,(end-1):-1:1] denoml = αl[2] .+ αl[3:end] ⋅ s0_l denomr = αr[2] .+ αr[3:end] ⋅ s0_r a_l = -transpose(transpose(αl[3:end]) * Sl) ./denoml a_r = reverse(-transpose(transpose(αr[3:end]) * Sr) ./denomr) b_l = -αl[1]/denoml b_r = -αr[1]/denomr new{T, typeof(a_l), typeof(a_r)}(a_l,b_l,a_r,b_r) end function GeneralBC(αl::AbstractVector{T}, αr::AbstractVector{T}, dx::AbstractVector{T}, order = 1) where {T} nl = length(αl) nr = length(αr) dx_l, dx_r = (dx[1:(order+nl-2)], reverse(dx[(end-order-nr+3):end])) S_l = zeros(T, (nl-2, order+nl-2)) S_r = zeros(T, (nr-2, order+nr-2)) for i in 1:(nl-2) S_l[i,:] = [transpose(calculate_weights(i, one(T), Array(one(T):convert(T, order+i)))) transpose(zeros(T, Int(nl-2-i)))]./(dx_l.^i) end for i in 1:(nr-2) S_r[i,:] = [transpose(calculate_weights(i, convert(T, order+i), Array(one(T):convert(T, order+i)))) transpose(zeros(T, Int(nr-2-i)))]./(dx_r.^i) end s0_l = S_l[:,1] ; Sl = S_l[:,2:end] s0_r = S_r[:,end] ; Sr = S_r[:,(end-1):-1:1] denoml = αl[2] .+ αl[3:end] ⋅ s0_l denomr = αr[2] .+ αr[3:end] ⋅ s0_r a_l = -transpose(transpose(αl[3:end]) * Sl) ./denoml a_r = reverse(-transpose(transpose(αr[3:end]) * Sr) ./denomr) b_l = -αl[1]/denoml b_r = -αr[1]/denomr new{T, typeof(a_l), typeof(a_r)}(a_l,b_l,a_r,b_r) end end #implement Neumann and Dirichlet as special cases of RobinBC NeumannBC(α::NTuple{2,T}, dx::Union{AbstractVector{T}, T}, order = 1) where T = RobinBC((zero(T), one(T), α[1]), (zero(T), one(T), α[2]), dx, order) DirichletBC(αl::T, αr::T) where T = RobinBC((one(T), zero(T), αl), (one(T), zero(T), αr), one(T), 2one(T) ) #specialized constructors for Neumann0 and Dirichlet0 Dirichlet0BC(T::Type) = DirichletBC(zero(T), zero(T)) Neumann0BC(dx::Union{AbstractVector{T}, T}, order = 1) where T = NeumannBC((zero(T), zero(T)), dx, order) # other acceptable argument signatures #RobinBC(al::T, bl::T, cl::T, dx_l::T, ar::T, br::T, cr::T, dx_r::T, order = 1) where T = RobinBC([al,bl, cl], [ar, br, cr], dx_l, order) Base.:*(Q::AffineBC, u::AbstractVector) = BoundaryPaddedVector(Q.a_l ⋅ u[1:length(Q.a_l)] + Q.b_l, Q.a_r ⋅ u[(end-length(Q.a_r)+1):end] + Q.b_r, u) Base.:*(Q::PeriodicBC, u::AbstractVector) = BoundaryPaddedVector(u[end], u[1], u) Base.size(Q::AtomicBC) = (Inf, Inf) #Is this nessecary? gettype(Q::AbstractBC{T}) where T = T
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const HubbardConf = Array{Int8, 2} # conf === hsfield === discrete Hubbard Stratonovich field (Hirsch field) const HubbardDistribution = Int8[-1,1] """ Famous attractive (negative U) Hubbard model on a cubic lattice. Discrete Hubbard Stratonovich transformation (Hirsch transformation) in the density/charge channel, such that HS-field is real. HubbardModelAttractive(; dims, L[, kwargs...]) Create an attractive Hubbard model on `dims`-dimensional cubic lattice with linear system size `L`. Additional allowed `kwargs` are: * `mu::Float64=0.0`: chemical potential * `U::Float64=1.0`: onsite interaction strength, "Hubbard U" * `t::Float64=1.0`: hopping energy """ @with_kw_noshow struct HubbardModelAttractive{C<:AbstractCubicLattice} <: Model # user mandatory dims::Int L::Int # user optional mu::Float64 = 0.0 U::Float64 = 1.0 @assert U >= 0. "U must be positive." t::Float64 = 1.0 # non-user fields l::C = choose_lattice(HubbardModelAttractive, dims, L) neighs::Matrix{Int} = neighbors_lookup_table(l) flv::Int = 1 end function choose_lattice(::Type{HubbardModelAttractive}, dims::Int, L::Int) if dims == 1 return Chain(L) elseif dims == 2 return SquareLattice(L) else return CubicLattice(dims, L) end end """ HubbardModelAttractive(params::Dict) HubbardModelAttractive(params::NamedTuple) Create an attractive Hubbard model with (keyword) parameters as specified in the dictionary/named tuple `params`. """ HubbardModelAttractive(params::Dict{Symbol, T}) where T = HubbardModelAttractive(; params...) HubbardModelAttractive(params::NamedTuple) = HubbardModelAttractive(; params...) # cosmetics import Base.summary import Base.show Base.summary(model::HubbardModelAttractive) = "$(model.dims)D attractive Hubbard model" Base.show(io::IO, model::HubbardModelAttractive) = print(io, "$(model.dims)D attractive Hubbard model, L=$(model.L) ($(model.l.sites) sites)") Base.show(io::IO, m::MIME"text/plain", model::HubbardModelAttractive) = print(io, model) # implement `Model` interface @inline nsites(m::HubbardModelAttractive) = nsites(m.l) # implement `DQMC` interface: mandatory @inline Base.rand(::Type{DQMC}, m::HubbardModelAttractive, nslices::Int) = rand(HubbardDistribution, nsites(m), nslices) """ Calculates the hopping matrix \$T_{i, j}\$ where \$i, j\$ are site indices. Note that since we have a time reversal symmetry relating spin-up to spin-down we only consider one spin sector (one flavor) for the attractive Hubbard model in the DQMC simulation. This isn't a performance critical method as it is only used once before the actual simulation. """ function hopping_matrix(mc::DQMC, m::HubbardModelAttractive) N = nsites(m) neighs = m.neighs # row = up, right, down, left; col = siteidx T = diagm(0 => fill(-m.mu, N)) # Nearest neighbor hoppings @inbounds @views begin for src in 1:N for nb in 1:size(neighs,1) trg = neighs[nb,src] T[trg,src] += -m.t end end end return T end """ Calculate the interaction matrix exponential `expV = exp(- power * delta_tau * V(slice))` and store it in `result::Matrix`. This is a performance critical method. """ @inline function interaction_matrix_exp!(mc::DQMC, m::HubbardModelAttractive, result::Matrix, conf::HubbardConf, slice::Int, power::Float64=1.) dtau = mc.p.delta_tau lambda = acosh(exp(m.U * dtau/2)) result .= spdiagm(0 => exp.(sign(power) * lambda * conf[:,slice])) nothing end @inline function propose_local(mc::DQMC, m::HubbardModelAttractive, i::Int, slice::Int, conf::HubbardConf) # see for example dos Santos (2002) greens = mc.s.greens dtau = mc.p.delta_tau lambda = acosh(exp(m.U * dtau/2)) ΔE_boson = -2. * lambda * conf[i, slice] γ = exp(ΔE_boson) - 1 detratio = (1 + γ * (1 - greens[i,i]))^2 # squared because of two spin sectors. return detratio, ΔE_boson, γ end @inline function accept_local!(mc::DQMC, m::HubbardModelAttractive, i::Int, slice::Int, conf::HubbardConf, delta, detratio, ΔE_boson::Float64) greens = mc.s.greens γ = delta u = -greens[:, i] u[i] += 1. # TODO: OPT: speed check, maybe @views/@inbounds greens .-= kron(u * 1. /(1 + γ * u[i]), transpose(γ * greens[i, :])) conf[i, slice] *= -1 nothing end # implement DQMC interface: optional """ Green's function is real for the attractive Hubbard model. """ @inline greenseltype(::Type{DQMC}, m::HubbardModelAttractive) = Float64 """ Calculate energy contribution of the boson, i.e. Hubbard-Stratonovich/Hirsch field. """ @inline function energy_boson(m::HubbardModelAttractive, hsfield::HubbardConf) dtau = mc.p.delta_tau lambda = acosh(exp(m.U * dtau/2)) return lambda * sum(hsfield) end include("observables.jl")
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<gh_stars>1-10 export expandModelNearest, getSimilarLinearModel, addAbsorbingLayer export addAbsorbingLayer, smoothModel, smooth3 export velocityToSlowSquared,slowSquaredToVelocity,velocityToSlow,slowToSlowSquared,slowSquaredToSlow export slowToLeveledSlowSquared,getModelInvNewton using Statistics using jInv.Mesh function slowToLeveledSlowSquared(s,mid::Float64 = 0.32,a::Float64 = 0.0,b::Float64 = 0.05) d = (b-a)./2.0; dinv = 200; tt = dinv.*(mid-s); t = -d.*(tanh(tt).+1) + a; dt = (dinv*d)*(sech(tt)).^2 .+ 1; # up until here it's just the slowness dt = spdiagm(2.0.*(t+s).*dt); t = (t + s).^2; return t,dt end function getModelInvNewton(m,modFun::Function,m0 = copy(m)) # m0: initial guess for the model inverse # modFun: the model function to invert. err_prev = Inf; s_prev = copy(m0); s = m0; for k=1:50 k (fs,dfs) = modFun(s); err = vecnorm(fs - m,Inf); println(err) if err < 1e-5 break; end err_prev = err; s_prev[:] = s; s = s - 0.4*(dfs\(fs - m)); end return s; end function velocityToSlowSquared(v::Array) s = (1.0./(v.+1e-16)).^2 ds = sparse(Diagonal((-2.0)./(v[:].^3))); return s,ds end function slowSquaredToVelocity(s::Array) m = 1.0./sqrt.(s.+1e-16); dm = sparse(Diagonal((-0.5*(1.0/(s[:].^(3.0/2.0)))))); return m,dm end function velocityToSlow(v::Array) s = (1.0./(v.+1e-16)) ds = sparse(Diagonal(((-1.0)./(v[:].^2)))); return s,ds end function slowToSlowSquared(v::Array) s = v.^2; ds = sparse(Diagonal((2.0.*v[:]))); return s,ds end function slowSquaredToSlow(v::Array) s = sqrt.(v); ds = sparse(Diagonal((0.5./s[:]))); return s,ds end function expandModelNearest(m,n,ntarget) if length(size(m))==2 mnew = zeros(Float64,ntarget[1],ntarget[2]); for j=1:ntarget[2] for i=1:ntarget[1] jorig = convert(Int64,ceil((j/ntarget[2])*n[2])); iorig = convert(Int64,ceil((i/ntarget[1])*n[1])); mnew[i,j] = m[iorig,jorig]; end end elseif length(size(m))==3 mnew = zeros(Float64,ntarget[1],ntarget[2],ntarget[3]); for k=1:ntarget[3] for j=1:ntarget[2] for i=1:ntarget[1] korig = convert(Int64,floor((k/ntarget[3])*n[3])); jorig = convert(Int64,floor((j/ntarget[2])*n[2])); iorig = convert(Int64,floor((i/ntarget[1])*n[1])); mnew[i,j,k] = m[iorig,jorig,korig]; end end end end return mnew end function getSimilarLinearModel(m::Array{Float64},mtop::Float64=0.0,mbottom::Float64=0.0) # m here is assumed to be a velocity model. if length(size(m))==2 (nx,nz) = size(m); m_vel = copy(m) ; if mtop==0.0 mtop = m_vel[1:10,5:6]; mtop = Statistics.mean(mtop[:]); println("Mref top = ",mtop); end if mbottom==0.0 mbottom = m_vel[1:10,end-10:end]; mbottom = Statistics.mean(mbottom[:]); println("Mref bottom = ",mbottom); end m_vel = ones(nx)*range(mtop,stop=mbottom,length=nz)'; mref = m_vel; elseif length(size(m))==3 (nx,ny,nz) = size(m); m_vel = copy(m); if mtop==0.0 mtop = m_vel[1:10,:,5:15]; mtop = Statistics.mean(mtop[:]); end if mbottom==0.0 mbottom = m_vel[1:10,:,end-10:end]; mbottom = Statistics.mean(mbottom[:]); end lin = range(mtop,stop=mbottom,length=nz); m_vel = copy(m); Oplane = ones(nx,ny); for k=1:nz m_vel[:,:,k] = lin[k]*Oplane; end mref = m_vel; else error("Unhandled Dimensions"); end return mref; end function addAbsorbingLayer2D(m::Array{Float64},pad::Int64) if pad<=0 return m; end mnew = zeros(size(m,1)+2*pad,size(m,2)+pad); mnew[pad+1:end-pad,1:end-pad] = m; mnew[1:pad,1:end-pad] = repeat(m[[1],:],pad,1); mnew[end-pad+1:end,1:end-pad] = repeat(m[[end],:],pad,1); mnew[:,end-pad+1:end] = repeat(mnew[:,end-pad],1,pad); return mnew; end function addAbsorbingLayer(m::Array{Float64},Msh::RegularMesh,pad::Int64) if pad<=0 return m,Msh; end Omega = Msh.domain; if length(size(m))==2 mnew = addAbsorbingLayer2D(m,pad); MshNew = getRegularMesh([Omega[1],Omega[2] + 2*pad*Msh.h[1],Omega[3],Omega[4]+pad*Msh.h[2]],Msh.n.+[2*pad,pad]); elseif length(size(m))==3 mnew = zeros(size(m,1)+2*pad,size(m,2)+2*pad,size(m,3)+pad); mnew[pad+1:end-pad,pad+1:end-pad,1:end-pad] = m; extendedPlane1 = addAbsorbingLayer2D(reshape(m[1,:,:],size(m,2),size(m,3)),pad); extendedPlaneEnd = addAbsorbingLayer2D(reshape(m[end,:,:],size(m,2),size(m,3)),pad); for k=1:pad mnew[k,:,:] = extendedPlane1; mnew[end-k+1,:,:] = extendedPlaneEnd; mnew[pad+1:end-pad,end-k+1,1:end-pad] = m[:,end,:]; mnew[pad+1:end-pad,k,1:end-pad] = m[:,1,:]; end t = mnew[:,:,end-pad]; for k=1:pad mnew[:,:,end-pad+k] = t; end MshNew = getRegularMesh([Omega[1],Omega[2] + 2*pad*Msh.h[1],Omega[3],Omega[4] + 2*pad*Msh.h[2],Omega[5],Omega[6]+pad*Msh.h[2]],Msh.n.+[2*pad,2*pad,pad]); end return mnew,MshNew; end function smoothModel(m,Mesh,times = 0) ms = addAbsorbingLayer2D(m,times); for k=1:times for j = 2:size(ms,2)-1 for i = 2:size(ms,1)-1 @inbounds ms[i,j] = (2*ms[i,j] + (ms[i-1,j-1]+ms[i-1,j]+ms[i-1,j+1]+ms[i,j-1]+ms[i,j+1]+ms[i+1,j-1]+ms[i+1,j]+ms[i,j+1]))/10.0; end end end return ms[(times+1):(end-times),1:end-times]; end function smooth3(m,Mesh,times = 0) pad = 50 println("Smoothing ", times," times"); ms, = addAbsorbingLayer(m, Mesh, pad) for k=1:times for l = 2:size(ms,3)-1 for j = 2:size(ms,2)-1 for i = 2:size(ms,1)-1 @inbounds ms[i,j,l] = (2*ms[i,j,l] + (ms[i,j,l+1] + ms[i,j,l-1] + ms[i,j-1,l] + ms[i,j-1,l-1] + ms[i,j-1,l+1] + ms[i,j+1,l] + ms[i,j+1,l-1] + ms[i,j+1,l+1] + ms[i-1,j,l] + ms[i-1,j,l+1] + ms[i-1,j,l-1] + ms[i-1,j-1,l] + ms[i-1,j-1,l-1] + ms[i-1,j-1,l+1] + ms[i-1,j+1,l] + ms[i-1,j+1,l-1] + ms[i-1,j+1,l+1] + ms[i+1,j,l] + ms[i+1,j,l+1] + ms[i+1,j,l-1] + ms[i+1,j-1,l] + ms[i+1,j-1,l-1] + ms[i+1,j-1,l+1] + ms[i+1,j+1,l] + ms[i+1,j+1,l-1] + ms[i+1,j+1,l+1]))/28.0; end end end end return ms[(pad+1):(end-pad),(pad+1):(end-pad),1:end-pad]; end
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1.895064
3,059
using Test using MarketData using TimeSeries using MarketTechnicals @testset "Levels" begin @testset "floor pivots" begin # values verified by various website calculators @test isapprox(values(floorpivots(ohlc)[:r3])[1] , 123.310, atol=.01) @test isapprox(values(floorpivots(ohlc)[:r2])[1] , 119.52, atol=.01) @test isapprox(values(floorpivots(ohlc)[:r1])[1] , 115.73, atol=.01) @test isapprox(values(floorpivots(ohlc)[:pivot])[1], 108.71, atol=.01) @test isapprox(values(floorpivots(ohlc)[:s1])[1] , 104.92, atol=.01) @test isapprox(values(floorpivots(ohlc)[:s2])[1] , 97.900, atol=.01) @test isapprox(values(floorpivots(ohlc)[:s3])[1] , 94.110, atol=.01) @test timestamp(floorpivots(ohlc))[end] == Date(2001,12,31) end @testset "woodiespivots" begin # @test_approx_eq 97.37500000000001 value(wr4)[2] # values NEED to be verified with online calculators # @test_approx_eq 88.62500000000001 value(ws4)[2] @test isapprox(values(woodiespivots(ohlc)[:r3])[1] , 124.465, atol=.01) @test isapprox(values(woodiespivots(ohlc)[:r2])[1] , 118.480, atol=.01) @test isapprox(values(woodiespivots(ohlc)[:r1])[1] , 113.655, atol=.01) @test isapprox(values(woodiespivots(ohlc)[:pivot])[1], 107.670, atol=.01) @test isapprox(values(woodiespivots(ohlc)[:s1])[1] , 102.845, atol=.01) @test isapprox(values(woodiespivots(ohlc)[:s2])[1] , 96.8625, atol=.01) @test isapprox(values(woodiespivots(ohlc)[:s3])[1] , 92.035, atol=.01) @test timestamp(woodiespivots(ohlc))[end] == Date(2001,12,31) end end # @testset "Levels"
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2.117955
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""" # Description Rewrite an expression to remove all use of backticks. # Arguments 1. `e::Any`: An expression. # Return Values 1. `e::Any`: An expression in which backticks have been removed. # Examples ``` julia> remove_backticks(:(`mean(a)`)) :(mean(a)) ``` """ function remove_backticks(@nospecialize(e::Any)) if isa(e, Expr) && e.head == :macrocall && isa(e.args[1], GlobalRef) && e.args[1].name == Symbol("@cmd") Meta.parse(e.args[3]) elseif isa(e, Expr) Expr( e.head, [remove_backticks(a) for a in e.args]..., ) else e end end
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<reponame>f-ttok/Gadfly.jl<gh_stars>0 using Measures const CategoricalAesthetic = Union{Nothing, IndirectArray} const NumericalAesthetic = Union{Nothing, AbstractMatrix, AbstractVector} const NumericalOrCategoricalAesthetic = Union{CategoricalAesthetic, NumericalAesthetic} @varset Aesthetics begin x, Union{NumericalOrCategoricalAesthetic, Distribution} y, Union{NumericalOrCategoricalAesthetic, Distribution} z, Union{Nothing, Function, NumericalAesthetic} xend, NumericalAesthetic yend, NumericalAesthetic size, Union{CategoricalAesthetic,Vector,Nothing} shape, Union{CategoricalAesthetic,Vector,Nothing} color, Union{CategoricalAesthetic,Vector,Nothing} alpha, NumericalOrCategoricalAesthetic linestyle, Union{CategoricalAesthetic,Vector,Nothing} label, CategoricalAesthetic group, CategoricalAesthetic xmin, NumericalAesthetic xmax, NumericalAesthetic ymin, NumericalAesthetic ymax, NumericalAesthetic # hexagon sizes used for hexbin xsize, NumericalAesthetic ysize, NumericalAesthetic # fixed lines xintercept, NumericalAesthetic yintercept, NumericalAesthetic intercept, NumericalAesthetic slope, NumericalAesthetic # boxplots middle, NumericalAesthetic lower_hinge, NumericalAesthetic upper_hinge, NumericalAesthetic lower_fence, NumericalAesthetic upper_fence, NumericalAesthetic outliers, NumericalAesthetic width, NumericalAesthetic # subplots xgroup, CategoricalAesthetic ygroup, CategoricalAesthetic # guides xtick, NumericalAesthetic ytick, NumericalAesthetic xgrid, NumericalAesthetic ygrid, NumericalAesthetic color_key_colors, Maybe(AbstractDict) color_key_title, Maybe(AbstractString) color_key_continuous, Maybe(Bool) color_function, Maybe(Function) titles, Maybe(Dict{Symbol, AbstractString}) shape_key_title, Maybe(AbstractString) size_key_title, Maybe(AbstractString) size_key_vals, Maybe(AbstractDict) # mark some ticks as initially invisible xtickvisible, Maybe(Vector{Bool}) ytickvisible, Maybe(Vector{Bool}) # scale at which ticks should become visible xtickscale, Maybe(Vector{Float64}) ytickscale, Maybe(Vector{Float64}) # plot viewport extents xviewmin, Any xviewmax, Any yviewmin, Any yviewmax, Any # labeling functions x_label, Function, showoff y_label, Function, showoff xtick_label, Function, showoff ytick_label, Function, showoff color_label, Function, showoff xgroup_label, Function, showoff ygroup_label, Function, showoff shape_label, Function, showoff size_label, Function, showoff # pseudo-aesthetics pad_categorical_x, Union{Missing,Bool}, missing pad_categorical_y, Union{Missing,Bool}, missing end # Calculating fieldnames at runtime is expensive const valid_aesthetics = fieldnames(Aesthetics) function show(io::IO, data::Aesthetics) maxlen = 0 print(io, "Aesthetics(") for name in valid_aesthetics val = getfield(data, name) if !ismissing(val) && val != nothing print(io, "\n ", string(name), "=") show(io, getfield(data, name)) end end print(io, "\n)\n") end # Alternate aesthetic names const aesthetic_aliases = Dict{Symbol, Symbol}(:colour => :color, :x_min => :xmin, :x_max => :xmax, :y_min => :ymin, :y_max => :ymax, :x_group => :xgroup, :y_group => :ygroup, :x_viewmin => :xviewmin, :x_viewmax => :xviewmax, :y_viewmin => :yviewmin, :y_viewmax => :yviewmax, :x_group_label => :xgroup_label, :y_group_label => :ygroup_label, :x_tick => :xtick, :y_tick => :ytick, :x_grid => :xgrid, :y_grid => :ygrid) # Index as if this were a data frame getindex(aes::Aesthetics, i::Integer, j::AbstractString) = getfield(aes, Symbol(j))[i] # Return the set of variables that are non-nothing. function defined_aesthetics(aes::Aesthetics) vars = Set{Symbol}() for name in valid_aesthetics getfield(aes, name) === nothing || push!(vars, name) end vars end # Checking aesthetics and giving reasonable error messages. # Raise an error if any of the given aesthetics are not defined. # # Args: # who: A string naming the caller which is printed in the error message. # aes: An Aesthetics object. # vars: Symbol that must be defined in the aesthetics. # undefined_aesthetics(aes::Aesthetics, vars::Symbol...) = setdiff(Set(vars), defined_aesthetics(aes)) function assert_aesthetics_defined(who::AbstractString, aes::Aesthetics, vars::Symbol...) undefined_vars = undefined_aesthetics(aes, vars...) isempty(undefined_vars) || error("The following aesthetics are required by ",who, " but are not defined: ", join(undefined_vars,", "),"\n") end function assert_aesthetics_undefined(who::AbstractString, aes::Aesthetics, vars::Symbol...) defined_vars = intersect(Set(vars), defined_aesthetics(aes)) isempty(defined_vars) || error("The following aesthetics are defined but incompatible with ", who,": ",join(defined_vars,", "),"\n") end function assert_aesthetics_equal_length(who::AbstractString, aes::Aesthetics, vars::Symbol...) defined_vars = filter(var -> !(getfield(aes, var) === nothing), [vars...]) if !isempty(defined_vars) n = length(getfield(aes, first(defined_vars))) for var in defined_vars length(getfield(aes, var)) != n && error( "The following aesthetics are required by ",who, " to be of equal length: ",join(defined_vars,", "),"\n") end end nothing end # Replace values in a with non-nothing values in b. # # Args: # a: Destination. # b: Source. # # Returns: nothing # # Modifies: a # function update!(a::Aesthetics, b::Aesthetics) for name in valid_aesthetics issomething(getfield(b, name)) && setfield(a, name, getfield(b, name)) end nothing end # Serialize aesthetics to JSON. # Args: # a: aesthetics to serialize. # # Returns: # JSON data as a string. # json(a::Aesthetics) = join([string(a, ":", json(getfield(a, var))) for var in aes_vars], ",\n") # Concatenate aesthetics. # # A new Aesthetics instance is produced with data vectors in each of the given # Aesthetics concatenated, nothing being treated as an empty vector. # # Args: # aess: One or more aesthetics. # # Returns: # A new Aesthetics instance with vectors concatenated. # function concat(aess::Aesthetics...) cataes = Aesthetics() for aes in aess for var in valid_aesthetics if var in [:xviewmin, :yviewmin] mu, mv = getfield(cataes, var), getfield(aes, var) setfield!(cataes, var, mu === nothing ? mv : mv == nothing ? mu : min(mu, mv)) elseif var in [:xviewmax, :yviewmax] mu, mv = getfield(cataes, var), getfield(aes, var) setfield!(cataes, var, mu === nothing ? mv : mv == nothing ? mu : max(mu, mv)) else setfield!(cataes, var, cat_aes_var!(getfield(cataes, var), getfield(aes, var))) end end end cataes end cat_aes_var!(a::(Nothing), b::(Nothing)) = a cat_aes_var!(a::(Nothing), b::Union{Function,AbstractString}) = b cat_aes_var!(a::(Nothing), b) = copy(b) cat_aes_var!(a, b::(Nothing)) = a cat_aes_var!(a::Function, b::Function) = a === string || a == showoff ? b : a function cat_aes_var!(a::Dict, b::Dict) merge!(a, b) a end cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{T}) where {T <: Base.Callable} = append!(a, b) cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T <: Base.Callable, U <: Base.Callable} = a = [a...,b...] # Let arrays of numbers clobber arrays of functions. This is slightly odd # behavior, comes up with with function statistics applied on a layer-wise # basis. cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T <: Base.Callable, U} = b cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T, U <: Base.Callable} = a cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{T}) where {T} = append!(a, b) cat_aes_var!(a, b) = a function cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T, U} V = promote_type(T, U) ab = Array{V}(undef, length(a) + length(b)) i = 1 for x in a ab[i] = x i += 1 end for x in b ab[i] = x i += 1 end return ab end function cat_aes_var!(xs::IndirectArray{T,1}, ys::IndirectArray{S,1}) where {T, S} TS = promote_type(T, S) return append!(IndirectArray(xs.index, convert(Array{TS},xs.values)), IndirectArray(ys.index, convert(Array{TS},ys.values))) end cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T<:Measure, U<:Measure} = [a..., b...] cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T<:Measure, U} = isabsolute(T) ? [a..., b...] : b cat_aes_var!(a::AbstractVector{T}, b::AbstractVector{U}) where {T, U<:Measure} = isabsolute(U) ? a : [a..., b...] # Summarizing aesthetics # Produce a matrix of Aesthetic or Data objects partitioning the original # Aesthetics or Data object by the cartesian product of xgroup and ygroup. # # This is useful primarily for drawing facets and subplots. # # Args: # aes: Aesthetics or Data objects to partition. # # Returns: # A Array{Aesthetics} of size max(1, length(xgroup)) by # max(1, length(ygroup)) # function by_xy_group(aes::T, xgroup, ygroup, num_xgroups, num_ygroups) where T <: Union{Data, Aesthetics} @assert xgroup === nothing || ygroup === nothing || length(xgroup) == length(ygroup) n = num_ygroups m = num_xgroups xrefs = xgroup === nothing ? [1] : xgroup yrefs = ygroup === nothing ? [1] : ygroup aes_grid = Array{T}(undef, n, m) staging = Array{AbstractArray}(undef, n, m) for i in 1:n, j in 1:m aes_grid[i, j] = T() end xgroup === nothing && ygroup === nothing && return aes_grid function make_pooled_array(::Type{IndirectArray{T,N,A,V}}, arr::AbstractArray) where {T,N,A,V} uarr = unique(arr) return IndirectArray(A(indexin(arr, uarr)), V(uarr)) end make_pooled_array(::Type{IndirectArray{T,R,N,RA}}, arr::IndirectArray{T,R,N,RA}) where {T,R,N,RA} = arr for var in fieldnames(T) # Skipped aesthetics. Don't try to partition aesthetics for which it # makes no sense to pass on to subplots. if var == :xgroup || var == :ygroup|| var == :xtick || var == :ytick || var == :xgrid || var == :ygrid || var == :x_viewmin || var == :y_viewmin || var == :x_viewmax || var == :y_viewmax || var == :color_key_colors continue end vals = getfield(aes, var) if isa(vals, AbstractArray) && length(vals)>1 if xgroup !== nothing && length(vals) !== length(xgroup) || ygroup !== nothing && length(vals) !== length(ygroup) continue end for i in 1:n, j in 1:m staging[i, j] = similar(vals, 0) end for (i, j, v) in zip(cycle(yrefs), cycle(xrefs), vals) push!(staging[i, j], v) end for i in 1:n, j in 1:m if typeof(vals) <: IndirectArray setfield!(aes_grid[i, j], var, make_pooled_array(typeof(vals), staging[i, j])) else if !applicable(convert, typeof(vals), staging[i, j]) T2 = eltype(vals) if T2 <: Color T2 = Color end da = Array{Union{Missing,T2}}(undef, length(staging[i, j])) copy!(da, staging[i, j]) setfield!(aes_grid[i, j], var, da) else setfield!(aes_grid[i, j], var, convert(typeof(vals), copy(staging[i, j]))) end end end else for i in 1:n, j in 1:m setfield!(aes_grid[i, j], var, vals) end end end aes_grid end function inherit(a::Aesthetics, b::Aesthetics; clobber=[]) acopy = copy(a) inherit!(acopy, b, clobber=clobber) return acopy end function inherit!(a::Aesthetics, b::Aesthetics; clobber=[]) clobber_set = Set{Symbol}(clobber) for field in valid_aesthetics aval = getfield(a, field) bval = getfield(b, field) if field in clobber_set setfield!(a, field, bval) elseif aval === missing || aval === nothing || aval === string || aval == showoff setfield!(a, field, bval) elseif field == :xviewmin || field == :yviewmin bval != nothing && (aval == nothing || aval > bval) && setfield!(a, field, bval) elseif field == :xviewmax || field == :yviewmax bval != nothing && (aval == nothing || aval < bval) && setfield!(a, field, bval) elseif typeof(aval) <: Dict && typeof(bval) <: Dict merge!(aval, getfield(b, field)) end end nothing end
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<reponame>Fypsilonn/MPIMeasurements.jl export RobotBasedMagneticFieldStaticProtocolParams, RobotBasedMagneticFieldStaticProtocol, measurement, filename Base.@kwdef struct RobotBasedMagneticFieldStaticProtocolParams <: RobotBasedProtocolParams positions::Union{Positions, Missing} = missing postMoveWaitTime::typeof(1.0u"s") = 0.5u"s" numCooldowns::Integer = 0 robotVelocity::typeof(1.0u"m/s") = 0.01u"m/s" switchBrakes::Bool = false end RobotBasedMagneticFieldStaticProtocolParams(dict::Dict) = createRobotBasedProtocolParams(RobotBasedMagneticFieldStaticProtocolParams, dict) Base.@kwdef mutable struct RobotBasedMagneticFieldStaticProtocol <: RobotBasedProtocol name::AbstractString description::AbstractString scanner::MPIScanner params::RobotBasedMagneticFieldStaticProtocolParams biChannel::BidirectionalChannel{ProtocolEvent} done::Bool = false cancelled::Bool = false finishAcknowledged::Bool = false executeTask::Union{Task, Nothing} = nothing measurement::Union{MagneticFieldMeasurement, Missing} = missing filename::Union{AbstractString, Missing} = missing safetyTask::Union{Task, Nothing} = nothing safetyChannel::Union{Channel{ProtocolEvent}, Nothing} = nothing end measurement(protocol::RobotBasedMagneticFieldStaticProtocol) = protocol.measurement measurement(protocol::RobotBasedMagneticFieldStaticProtocol, measurement::Union{MagneticFieldMeasurement, Missing}) = protocol.measurement = measurement filename(protocol::RobotBasedMagneticFieldStaticProtocol) = protocol.filename filename(protocol::RobotBasedMagneticFieldStaticProtocol, filename::String) = protocol.filename = filename function init(protocol::RobotBasedMagneticFieldStaticProtocol) measurement_ = MagneticFieldMeasurement() MPIFiles.description(measurement_, "Generated by protocol $(name(protocol)) with the following description: $(description(protocol))") MPIFiles.positions(measurement_, positions(protocol)) measurement(protocol, measurement_) # For inner protocol communication protocol.safetyChannel = Channel{ProtocolEvent}(32) # I'd prefer to only start task during execution and also close it in cleanup protocol.safetyTask = @tspawnat protocol.scanner.generalParams.protocolThreadID watchTemperature(protocol.scanner, protocol.safetyChannel) # For user I/O return BidirectionalChannel{ProtocolEvent}(protocol.biChannel) end function preMoveAction(protocol::RobotBasedMagneticFieldStaticProtocol, pos::Vector{<:Unitful.Length}) if isready(protocol.safetyChannel) # Shut down system appropiately and then end execution close(protocol.safetyChannel) throw(IllegalStateException(take!(protocol.safetyChannel).message)) end @info "moving to position" pos end function postMoveAction(protocol::RobotBasedMagneticFieldStaticProtocol, pos::Vector{<:Unitful.Length}) gaussmeter = getGaussMeter(scanner(protocol)) field_ = getXYZValues(gaussmeter) fieldError_ = calculateFieldError(gaussmeter, field_) fieldFrequency_ = getFrequency(gaussmeter) timestamp_ = now() temperature_ = getTemperature(gaussmeter) addMeasuredPosition(measurement(protocol), pos, field=field_, fieldError=fieldError_, fieldFrequency=fieldFrequency_, timestamp=timestamp_, temperature=temperature_) end function cleanup(protocol::RobotBasedMagneticFieldStaticProtocol) saveMagneticFieldAsHDF5(measurement(protocol), filename(protocol)) close(protocol.scanner) end # Here I'd like to also dispatch on protocol and not only scanner function watchTemperature(scanner::MPIScanner, channel::Channel) while isopen(channel) temp = getTemperature(scanner) if temp > 1.0 put!(channel, IllegaleStateEvent("Temperature is too high. Aborting protocol")) end sleep(0.05) end end function handleEvent(protocol::RobotBasedMagneticFieldStaticProtocol, event::FinishedAckEvent) # End SafetyTask close(protocol.safetyChannel) end
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<reponame>UnofficialJuliaMirror/Merlin.jl-80f3d04f-b880-5e6d-8e06-6a7e799169ac doc""" tanh(x::Var) Hyperbolic tangent function. """ Base.tanh(x::Var) = Var(tanh(x.data), ∇tanh!, (x,)) Base.tanh(x::Array) = tanh.(x) Base.tanh(x::CuArray) = CUDNN.tanh(x) Base.tanh(x::Node) = Node(tanh, (x,)) function ∇tanh!(y::Var, x::Var) isnothing(x.grad) && return ∇tanh!(y.data, y.grad, x.data, x.grad) end function ∇tanh!(y::Array{T}, gy::Array{T}, x::Array{T}, gx::Array{T}) where T @inbounds for i = 1:length(gx) gx[i] += gy[i] * (T(1) - y[i] * y[i]) end end ∇tanh!(y::CuArray, gy::CuArray, x::CuArray, gx::CuArray) = CUDNN.∇tanh!(y, gy, x, gx)
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""" static_analysis(assembly; kwargs...) Perform a static analysis of the system of nonlinear beams contained in `assembly`. Return the resulting system and a flag indicating whether the iteration procedure converged. # Keyword Arguments - `prescribed_conditions = Dict{Int,PrescribedConditions{Float64}}()`: A dictionary with keys corresponding to the points at which prescribed conditions are applied and elements of type [`PrescribedConditions`](@ref) which describe the prescribed conditions at those points. If time varying, this input may be provided as a function of time. - `distributed_loads = Dict{Int,DistributedLoads{Float64}}()`: A dictionary with keys corresponding to the elements to which distributed loads are applied and elements of type [`DistributedLoads`](@ref) which describe the distributed loads at those points. If time varying, this input may be provided as a function of time. - `linear = false`: Set to `true` for a linear analysis - `linearization_state`: Linearization state variables. Defaults to zeros. - `update_linearization_state`: Flag indicating whether to update the linearization state variables for a linear analysis with the instantaneous state variables. - `method = :newton`: Method (as defined in NLsolve) to solve nonlinear system of equations - `linesearch = LineSearches.BackTracking(maxstep=1e6)`: Line search used to solve nonlinear system of equations - `ftol = 1e-9`: tolerance for solving nonlinear system of equations - `iterations = 1000`: maximum iterations for solving the nonlinear system of equations - `tvec = 0`: Time vector/value. May be used in conjunction with time varying prescribed conditions and distributed loads to gradually increase displacements/loads. - `reset_state = true`: Flag indicating whether the state variables should be reset prior to performing the analysis. This keyword argument is only valid for the pre-allocated version of this function. """ function static_analysis(assembly; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, tvec=0.0, ) static = true pc = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(tvec[1]) system = System(assembly, static; prescribed_points=keys(pc)) return static_analysis!(system, assembly; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, update_linearization_state=update_linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, tvec=tvec, reset_state=false) end """ static_analysis!(system, assembly; kwargs...) Pre-allocated version of `static_analysis`. """ function static_analysis!(system, assembly; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, tvec=0.0, reset_state=true) # check to make sure system is static @assert system.static == true # reset state, if specified if reset_state reset_state!(system) end # unpack pre-allocated storage and pointers x = system.x F = system.r J = system.K force_scaling = system.force_scaling mass_scaling = system.mass_scaling irow_point = system.irow_point irow_elem = system.irow_elem irow_elem1 = system.irow_elem1 irow_elem2 = system.irow_elem2 icol_point = system.icol_point icol_elem = system.icol_elem converged = true for t in tvec # update stored time system.t = t # current parameters pcond = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(t) dload = typeof(distributed_loads) <: AbstractDict ? distributed_loads : distributed_loads(t) # solve the system of equations f! = (F, x) -> system_residual!(F, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem) j! = (J, x) -> system_jacobian!(J, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem) if linear # linear analysis if !update_linearization_state if isnothing(linearization_state) x .= 0 else x .= linearization_state end end f!(F, x) j!(J, x) x .-= safe_lu(J) \ F else # nonlinear analysis df = NLsolve.OnceDifferentiable(f!, j!, x, F, J) result = NLsolve.nlsolve(df, x, linsolve=(x, A, b) -> ldiv!(x, safe_lu(A), b), method=method, linesearch=linesearch, ftol=ftol, iterations=iterations) # update the solution x .= result.zero J .= df.DF # update convergence flag converged = result.f_converged end end return system, converged end """ steady_state_analysis(assembly; kwargs...) Perform a steady-state analysis for the system of nonlinear beams contained in `assembly`. Return the resulting system and a flag indicating whether the iteration procedure converged. # Keyword Arguments - `prescribed_conditions = Dict{Int,PrescribedConditions{Float64}}()`: A dictionary with keys corresponding to the points at which prescribed conditions are applied and elements of type [`PrescribedConditions`](@ref) which describe the prescribed conditions at those points. If time varying, this input may be provided as a function of time. - `distributed_loads = Dict{Int,DistributedLoads{Float64}}()`: A dictionary with keys corresponding to the elements to which distributed loads are applied and elements of type [`DistributedLoads`](@ref) which describe the distributed loads at those points. If time varying, this input may be provided as a function of time. - `linear = false`: Set to `true` for a linear analysis - `linearization_state`: Linearization state variables. Defaults to zeros. - `update_linearization_state`: Flag indicating whether to update the linearization state variables for a linear analysis with the current state variables. - `method = :newton`: Method (as defined in NLsolve) to solve nonlinear system of equations - `linesearch = LineSearches.LineSearches.BackTracking(maxstep=1e6)`: Line search used to solve nonlinear system of equations - `ftol = 1e-9`: tolerance for solving nonlinear system of equations - `iterations = 1000`: maximum iterations for solving the nonlinear system of equations - `origin = zeros(3)`: Global frame origin vector. If time varying, this input may be provided as a function of time. - `linear_velocity = zeros(3)`: Global frame linear velocity vector. If time varying, this vector may be provided as a function of time. - `angular_velocity = zeros(3)`: Global frame angular velocity vector. If time varying, this vector may be provided as a function of time. - `tvec = 0.0`: Time vector/value. May be used in conjunction with time varying prescribed conditions, distributed loads, and global motion to gradually increase displacements/loads. - `reset_state = true`: Flag indicating whether the state variables should be reset prior to performing the analysis. This keyword argument is only valid for the pre-allocated version of this function. """ function steady_state_analysis(assembly; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), tvec=0.0, ) static = false pc = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(tvec[1]) system = System(assembly, static; prescribed_points=keys(pc)) return steady_state_analysis!(system, assembly; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, update_linearization_state=update_linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, origin=origin, linear_velocity=linear_velocity, angular_velocity=angular_velocity, tvec=tvec, reset_state=false, ) end """ steady_state_analysis!(system, assembly; kwargs...) Pre-allocated version of `steady_state_analysis`. """ function steady_state_analysis!(system, assembly; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), tvec=0.0, reset_state=true, ) # check to make sure the simulation is dynamic @assert system.static == false # reset state, if specified if reset_state reset_state!(system) end # unpack pointers to pre-allocated storage x = system.x F = system.r J = system.K force_scaling = system.force_scaling mass_scaling = system.mass_scaling irow_point = system.irow_point irow_elem = system.irow_elem irow_elem1 = system.irow_elem1 irow_elem2 = system.irow_elem2 icol_point = system.icol_point icol_elem = system.icol_elem converged = true for t in tvec # update stored time system.t = t # current parameters pcond = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(t) dload = typeof(distributed_loads) <: AbstractDict ? distributed_loads : distributed_loads(t) x0 = typeof(origin) <: AbstractVector ? SVector{3}(origin) : SVector{3}(origin(t)) v0 = typeof(linear_velocity) <: AbstractVector ? SVector{3}(linear_velocity) : SVector{3}(linear_velocity(t)) ω0 = typeof(angular_velocity) <: AbstractVector ? SVector{3}(angular_velocity) : SVector{3}(angular_velocity(t)) f! = (F, x) -> system_residual!(F, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0) j! = (J, x) -> system_jacobian!(J, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0) # solve the system of equations if linear # linear analysis if !update_linearization_state if isnothing(linearization_state) x .= 0 else x .= linearization_state end end f!(F, x) j!(J, x) x .-= safe_lu(J) \ F else # nonlinear analysis df = NLsolve.OnceDifferentiable(f!, j!, x, F, J) result = NLsolve.nlsolve(df, x, linsolve=(x, A, b) -> ldiv!(x, safe_lu(A), b), method=method, linesearch=linesearch, ftol=ftol, iterations=iterations) # update the solution x .= result.zero J .= df.DF # update the convergence flag convergence = result.f_converged end end return system, converged end """ eigenvalue_analysis(assembly; kwargs...) Compute the eigenvalues and eigenvectors of the system of nonlinear beams contained in `assembly`. Return the modified system, eigenvalues, eigenvectors, and a convergence flag indicating whether the corresponding steady-state analysis converged. # Keyword Arguments - `prescribed_conditions = Dict{Int,PrescribedConditions{Float64}}()`: A dictionary with keys corresponding to the points at which prescribed conditions are applied and elements of type [`PrescribedConditions`](@ref) which describe the prescribed conditions at those points. If time varying, this input may be provided as a function of time. - `distributed_loads = Dict{Int,DistributedLoads{Float64}}()`: A dictionary with keys corresponding to the elements to which distributed loads are applied and elements of type [`DistributedLoads`](@ref) which describe the distributed loads at those points. If time varying, this input may be provided as a function of time. - `linear = false`: Set to `true` for a linear analysis - `linearization_state`: Linearization state variables. Defaults to zeros. - `update_linearization_state`: Flag indicating whether to update the linearization state variables for a linear analysis with the current state variables. - `method = :newton`: Method (as defined in NLsolve) to solve nonlinear system of equations - `linesearch = LineSearches.LineSearches.BackTracking(maxstep=1e6)`: Line search used to solve nonlinear system of equations - `ftol = 1e-9`: tolerance for solving nonlinear system of equations - `iterations = 1000`: maximum iterations for solving the nonlinear system of equations - `reset_state = true`: Flag indicating whether the state variables should be reset prior to performing the steady-state analysis. This keyword argument is only valid for the pre-allocated version of this function. - `find_steady_state = reset_state && !linear`: Flag indicating whether the steady state solution should be found prior to performing the eigenvalue analysis. - `origin = zeros(3)`: Global frame origin. If time varying, this vector may be provided as a function of time. - `linear_velocity = zeros(3)`: Global frame linear velocity vector. If time varying, this vector may be provided as a function of time. May be provided either as a constant or as a function of time. - `angular_velocity = zeros(3)`: Global frame angular velocity vector. If time varying, this vector may be provided as a function of time. - `tvec`: Time vector. May be used in conjunction with time varying prescribed conditions, distributed loads, and global motion to gradually increase displacements/loads during the steady-state analysis. - `nev = 6`: Number of eigenvalues to compute """ function eigenvalue_analysis(assembly; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), method=:newton, linear=false, linearization_state=nothing, update_linearization_state=false, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, find_steady_state=!linear, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), tvec=0.0, nev=6 ) static = false pc = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(tvec[1]) system = System(assembly, static; prescribed_points=keys(pc)) return eigenvalue_analysis!(system, assembly; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, update_linearization_state=update_linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, reset_state=false, find_steady_state=find_steady_state, origin=origin, linear_velocity=linear_velocity, angular_velocity=angular_velocity, tvec=tvec, nev=nev, ) end """ eigenvalue_analysis!(system, assembly; kwargs...) Pre-allocated version of `eigenvalue_analysis`. Uses the state variables stored in `system` as an initial guess for iterating to find the steady state solution. """ function eigenvalue_analysis!(system, assembly; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, reset_state=true, find_steady_state=!linear && reset_state, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), tvec=0.0, nev=6, ) if reset_state reset_state!(system) end # perform steady state analysis (if nonlinear) if find_steady_state system, converged = steady_state_analysis!(system, assembly; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, update_linearization_state=update_linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, origin=origin, linear_velocity=linear_velocity, angular_velocity=angular_velocity, tvec=tvec, ) else # set linearization state variables if linear && !update_linearization_state if isnothing(linearization_state) system.x .= 0 else system.x .= linearization_state end end # converged by default converged = true end # unpack state vector, stiffness, and mass matrices x = system.x # populated during steady state solution K = system.K # needs to be updated M = system.M # still needs to be populated # unpack scaling parameter force_scaling = system.force_scaling mass_scaling = system.mass_scaling # also unpack system indices irow_point = system.irow_point irow_elem = system.irow_elem irow_elem1 = system.irow_elem1 irow_elem2 = system.irow_elem2 icol_point = system.icol_point icol_elem = system.icol_elem # current time t = system.t # current parameters pcond = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(t) dload = typeof(distributed_loads) <: AbstractDict ? distributed_loads : distributed_loads(t) x0 = typeof(origin) <: AbstractVector ? SVector{3}(origin) : SVector{3}(origin(t)) v0 = typeof(linear_velocity) <: AbstractVector ? SVector{3}(linear_velocity) : SVector{3}(linear_velocity(t)) ω0 = typeof(angular_velocity) <: AbstractVector ? SVector{3}(angular_velocity) : SVector{3}(angular_velocity(t)) # solve for the system stiffness matrix K = system_jacobian!(K, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0) # solve for the system mass matrix M = system_mass_matrix!(M, x, assembly, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem) # construct linear map T = eltype(system) nx = length(x) Kfact = safe_lu(K) f! = (b, x) -> ldiv!(b, Kfact, M * x) fc! = (b, x) -> mul!(b, M', Kfact' \ x) A = LinearMap{T}(f!, fc!, nx, nx; ismutating=true) # compute eigenvalues and eigenvectors λ, V = partialeigen(partialschur(A; nev=min(nx, nev), which=LM())[1]) # sort eigenvalues by magnitude perm = sortperm(λ, by=(λ) -> (abs(λ), imag(λ)), rev=true) λ .= λ[perm] V .= V[:,perm] # eigenvalues are actually -1/λ, no modification necessary for eigenvectors λ .= -1 ./ λ return system, λ, V, converged end """ initial_condition_analysis(assembly, t0; kwargs...) Perform an analysis to obtain a consistent set of initial conditions. Return the final system with the new initial conditions. # Keyword Arguments - `prescribed_conditions: A dictionary with keys corresponding to the points at which prescribed conditions are applied and elements of type [`PrescribedConditions`](@ref) which describe the prescribed conditions at those points. If time varying, this input may be provided as a function of time. - `distributed_loads: A dictionary with keys corresponding to the elements to which distributed loads are applied and elements of type [`DistributedLoads`](@ref) which describe the distributed loads at those points. If time varying, this input may be provided as a function of time. - `linear = false`: Set to `true` for a linear analysis - `linearization_state`: Linearization state variables. Defaults to zeros. - `method = :newton`: Method (as defined in NLsolve) to solve nonlinear system of equations - `linesearch = LineSearches.LineSearches.BackTracking(maxstep=1e6)`: Line search used to solve nonlinear system of equations - `ftol = 1e-9`: tolerance for solving nonlinear system of equations - `iterations = 1000`: maximum iterations for solving the nonlinear system of equations - `reset_state = true`: Flag indicating whether the state variables should be reset prior to performing the analysis. This keyword argument is only valid for the pre-allocated version of this function. - `origin = zeros(3)`: Global frame origin. If time varying, this vector may be provided as a function of time. - `linear_velocity = zeros(3)`: Global frame linear velocity vector. If time varying, this vector may be provided as a function of time. May be provided either as a constant or as a function of time. - `angular_velocity = zeros(3)`: Global frame angular velocity vector. If time varying, this vector may be provided as a function of time. - `u0=fill(zeros(3), length(assembly.elements))`: Initial displacment of each beam element, - `theta0=fill(zeros(3), length(assembly.elements))`: Initial angular displacement of each beam element, - `udot0=fill(zeros(3), length(assembly.elements))`: Initial time derivative with respect to `u` - `thetadot0=fill(zeros(3), length(assembly.elements))`: Initial time derivative with respect to `theta` - `save=1:length(tvec)`: Steps at which to save the time history """ function initial_condition_analysis(assembly, t0; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), u0=fill((@SVector zeros(3)), length(assembly.elements)), theta0=fill((@SVector zeros(3)), length(assembly.elements)), udot0=fill((@SVector zeros(3)), length(assembly.elements)), thetadot0=fill((@SVector zeros(3)), length(assembly.elements)), ) static = false pc = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(t0) system = System(assembly, static; prescribed_points=keys(pc)) return initial_condition_analysis!(system, assembly, t0; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, reset_state=false, origin=origin, linear_velocity=linear_velocity, angular_velocity=angular_velocity, u0=u0, theta0=theta0, udot0=udot0, thetadot0=thetadot0, ) end """ initial_condition_analysis!(system, assembly, t0; kwargs...) Pre-allocated version of `initial_condition_analysis`. """ function initial_condition_analysis!(system, assembly, t0; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, reset_state=true, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), u0=fill((@SVector zeros(3)), length(assembly.elements)), theta0=fill((@SVector zeros(3)), length(assembly.elements)), udot0=fill((@SVector zeros(3)), length(assembly.elements)), thetadot0=fill((@SVector zeros(3)), length(assembly.elements)), ) # check to make sure the simulation is dynamic @assert system.static == false if reset_state reset_state!(system) end # unpack pre-allocated storage and pointers for system x = system.x F = system.r J = system.K force_scaling = system.force_scaling mass_scaling = system.mass_scaling irow_point = system.irow_point irow_elem = system.irow_elem irow_elem1 = system.irow_elem1 irow_elem2 = system.irow_elem2 icol_point = system.icol_point icol_elem = system.icol_elem udot = system.udot θdot = system.θdot Pdot = system.Pdot Hdot = system.Hdot nelem = length(assembly.elements) # set current time step system.t = t0 # set current parameters pcond = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(t0) dload = typeof(distributed_loads) <: AbstractDict ? distributed_loads : distributed_loads(t0) x0 = typeof(origin) <: AbstractVector ? SVector{3}(origin) : SVector{3}(origin(t0)) v0 = typeof(linear_velocity) <: AbstractVector ? SVector{3}(linear_velocity) : SVector{3}(linear_velocity(t0)) ω0 = typeof(angular_velocity) <: AbstractVector ? SVector{3}(angular_velocity) : SVector{3}(angular_velocity(t0)) # construct residual and jacobian functions f! = (F, x) -> system_residual!(F, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0, u0, theta0, udot0, thetadot0) j! = (J, x) -> system_jacobian!(J, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0, u0, theta0, udot0, thetadot0) # solve system of equations if linear # linear analysis if isnothing(linearization_state) x .= 0 else x .= linearization_state end f!(F, x) j!(J, x) x .-= safe_lu(J) \ F else # nonlinear analysis df = OnceDifferentiable(f!, j!, x, F, J) result = NLsolve.nlsolve(df, x, linsolve=(x, A, b) -> ldiv!(x, safe_lu(A), b), method=method, linesearch=linesearch, ftol=ftol, iterations=iterations) x .= result.zero J .= df.DF end # get convergence flag converged = result.f_converged # save states and state rates for ielem = 1:nelem icol = icol_elem[ielem] # extract rotation parameters for this beam element C = get_C(theta0[ielem]) Cab = assembly.elements[ielem].Cab CtCab = C' * Cab # save states and state rates udot[ielem] = udot0[ielem] θdot[ielem] = thetadot0[ielem] Pdot[ielem] = CtCab' * SVector(x[icol], x[icol + 1], x[icol + 2]) .* mass_scaling Hdot[ielem] = CtCab' * SVector(x[icol + 3], x[icol + 4], x[icol + 5]) .* mass_scaling # restore original state vector x[icol:icol + 2] .= u0[ielem] x[icol + 3:icol + 5] .= theta0[ielem] end return system, converged end """ time_domain_analysis(assembly, tvec; kwargs...) Perform a time-domain analysis for the system of nonlinear beams contained in `assembly` using the time vector `tvec`. Return the final system, a post-processed solution history, and a convergence flag indicating whether the iterations converged for each time step. # Keyword Arguments - `prescribed_conditions: A dictionary with keys corresponding to the points at which prescribed conditions are applied and elements of type [`PrescribedConditions`](@ref) which describe the prescribed conditions at those points. If time varying, this input may be provided as a function of time. - `distributed_loads: A dictionary with keys corresponding to the elements to which distributed loads are applied and elements of type [`DistributedLoads`](@ref) which describe the distributed loads at those points. If time varying, this input may be provided as a function of time. - `linear = false`: Set to `true` for a linear analysis - `linearization_state`: Linearization state variables. Defaults to zeros. - `update_linearization_state`: Flag indicating whether to update the linearization state variables for a linear analysis with the current state variables. - `method = :newton`: Method (as defined in NLsolve) to solve nonlinear system of equations - `linesearch = LineSearches.LineSearches.BackTracking(maxstep=1e6)`: Line search used to solve nonlinear system of equations - `ftol = 1e-9`: tolerance for solving nonlinear system of equations - `iterations = 1000`: maximum iterations for solving the nonlinear system of equations - `reset_state = true`: Flag indicating whether the state variables should be reset prior to performing the analysis. This keyword argument is only valid for the pre-allocated version of this function. - `initialize = true`: Flag indicating whether a consistent set of initial conditions should be found using [`initial_condition_analysis`](@ref). If `false`, the keyword arguments `u0`, `theta0`, `udot0` and `thetadot0` will be ignored and the system state vector will be used as the initial state variables. - `origin`: Global frame origin vector. If time varying, this input may be provided as a function of time. - `linear_velocity`: Global frame linear velocity vector. If time varying, this vector may be provided as a function of time. - `angular_velocity`: Global frame angular velocity vector. If time varying, this vector may be provided as a function of time. - `u0=fill(zeros(3), length(assembly.elements))`: Initial displacment of each beam element, - `theta0=fill(zeros(3), length(assembly.elements))`: Initial angular displacement of each beam element, - `udot0=fill(zeros(3), length(assembly.elements))`: Initial time derivative with respect to `u` - `thetadot0=fill(zeros(3), length(assembly.elements))`: Initial time derivative with respect to `theta` - `save=1:length(tvec)`: Steps at which to save the time history """ function time_domain_analysis(assembly, tvec; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, initialize=true, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), u0=fill((@SVector zeros(3)), length(assembly.elements)), theta0=fill((@SVector zeros(3)), length(assembly.elements)), udot0=fill((@SVector zeros(3)), length(assembly.elements)), thetadot0=fill((@SVector zeros(3)), length(assembly.elements)), save=1:length(tvec) ) static = false pc = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(tvec[1]) system = System(assembly, static; prescribed_points=keys(pc)) return time_domain_analysis!(system, assembly, tvec; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, update_linearization_state=update_linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, reset_state=false, initialize=initialize, origin=origin, linear_velocity=linear_velocity, angular_velocity=angular_velocity, u0=u0, theta0=theta0, udot0=udot0, thetadot0=thetadot0, save=save, ) end """ time_domain_analysis!(system, assembly, tvec; kwargs...) Pre-allocated version of `time_domain_analysis`. """ function time_domain_analysis!(system, assembly, tvec; prescribed_conditions=Dict{Int,PrescribedConditions{Float64}}(), distributed_loads=Dict{Int,DistributedLoads{Float64}}(), linear=false, linearization_state=nothing, update_linearization_state=false, method=:newton, linesearch=LineSearches.BackTracking(maxstep=1e6), ftol=1e-9, iterations=1000, reset_state=true, initialize=true, origin=(@SVector zeros(3)), linear_velocity=(@SVector zeros(3)), angular_velocity=(@SVector zeros(3)), u0=fill((@SVector zeros(3)), length(assembly.elements)), theta0=fill((@SVector zeros(3)), length(assembly.elements)), udot0=fill((@SVector zeros(3)), length(assembly.elements)), thetadot0=fill((@SVector zeros(3)), length(assembly.elements)), save=1:length(tvec) ) # check to make sure the simulation is dynamic @assert system.static == false if reset_state reset_state!(system) end # perform initial condition analysis if initialize system, converged = initial_condition_analysis!(system, assembly, tvec[1]; prescribed_conditions=prescribed_conditions, distributed_loads=distributed_loads, linear=linear, linearization_state=linearization_state, method=method, linesearch=linesearch, ftol=ftol, iterations=iterations, reset_state=false, origin=origin, linear_velocity=linear_velocity, angular_velocity=angular_velocity, u0=u0, theta0=theta0, udot0=udot0, thetadot0=thetadot0, ) else # converged by default converged = true end # unpack pre-allocated storage and pointers for system x = system.x F = system.r J = system.K force_scaling = system.force_scaling mass_scaling = system.mass_scaling irow_point = system.irow_point irow_elem = system.irow_elem irow_elem1 = system.irow_elem1 irow_elem2 = system.irow_elem2 icol_point = system.icol_point icol_elem = system.icol_elem udot = system.udot θdot = system.θdot Pdot = system.Pdot Hdot = system.Hdot # number of beam elements nelem = length(assembly.elements) # initialize storage for each time step isave = 1 history = Vector{AssemblyState{eltype(system)}}(undef, length(save)) # add initial state to the solution history if isave in save pcond = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(tvec[1]) history[isave] = AssemblyState(system, assembly, prescribed_conditions=pcond) isave += 1 end # --- Begin Time Domain Simulation --- # for it = 2:length(tvec) # update current time system.t = tvec[it] # current time step size dt = tvec[it] - tvec[it - 1] # current parameters pcond = typeof(prescribed_conditions) <: AbstractDict ? prescribed_conditions : prescribed_conditions(tvec[it]) dload = typeof(distributed_loads) <: AbstractDict ? distributed_loads : distributed_loads(tvec[it]) x0 = typeof(origin) <: AbstractVector ? SVector{3}(origin) : SVector{3}(origin(tvec[it])) v0 = typeof(linear_velocity) <: AbstractVector ? SVector{3}(linear_velocity) : SVector{3}(linear_velocity(tvec[it])) ω0 = typeof(angular_velocity) <: AbstractVector ? SVector{3}(angular_velocity) : SVector{3}(angular_velocity(tvec[it])) # current initialization parameters for ielem = 1:nelem icol = icol_elem[ielem] # get beam element states u = SVector(x[icol], x[icol + 1], x[icol + 2]) θ = SVector(x[icol + 3], x[icol + 4], x[icol + 5]) P = SVector(x[icol + 12], x[icol + 13], x[icol + 14]) .* mass_scaling H = SVector(x[icol + 15], x[icol + 16], x[icol + 17]) .* mass_scaling # extract rotation parameters C = get_C(θ) Cab = assembly.elements[ielem].Cab CtCab = C' * Cab # store `udot_init` in `udot` udot[ielem] = 2 / dt * u + udot[ielem] # store `θdot_init` in `θdot` θdot[ielem] = 2 / dt * θ + θdot[ielem] # store `CtCabPdot_init` in `Pdot` Pdot[ielem] = 2 / dt * CtCab * P + CtCab * Pdot[ielem] # store `CtCabHdot_init` in `Hdot` Hdot[ielem] = 2 / dt * CtCab * H + CtCab * Hdot[ielem] end # solve for the state variables at the next time step f! = (F, x) -> system_residual!(F, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0, udot, θdot, Pdot, Hdot, dt) j! = (J, x) -> system_jacobian!(J, x, assembly, pcond, dload, force_scaling, mass_scaling, irow_point, irow_elem, irow_elem1, irow_elem2, icol_point, icol_elem, x0, v0, ω0, udot, θdot, Pdot, Hdot, dt) # solve system of equations if linear # linear analysis if !update_linearization_state if isnothing(linearization_state) x .= 0 else x .= linearization_state end end f!(F, x) j!(J, x) x .-= safe_lu(J) \ F else df = OnceDifferentiable(f!, j!, x, F, J) result = NLsolve.nlsolve(df, x, linsolve=(x, A, b) -> ldiv!(x, safe_lu(A), b), method=method, linesearch=linesearch, ftol=ftol, iterations=iterations) x .= result.zero J .= df.DF end # set new state rates for ielem = 1:nelem icol = icol_elem[ielem] # get beam element states u = SVector(x[icol], x[icol + 1], x[icol + 2]) θ = SVector(x[icol + 3], x[icol + 4], x[icol + 5]) P = SVector(x[icol + 12], x[icol + 13], x[icol + 14]) .* mass_scaling H = SVector(x[icol + 15], x[icol + 16], x[icol + 17]) .* mass_scaling # extract rotation parameters C = get_C(θ) Cab = assembly.elements[ielem].Cab CtCab = C' * Cab # save state rates udot[ielem] = 2 / dt * u - udot[ielem] θdot[ielem] = 2 / dt * θ - θdot[ielem] Pdot[ielem] = 2 / dt * P - CtCab' * Pdot[ielem] Hdot[ielem] = 2 / dt * H - CtCab' * Hdot[ielem] end # add state to history if it in save history[isave] = AssemblyState(system, assembly, prescribed_conditions=prescribed_conditions) isave += 1 end # stop early if unconverged if !linear && !result.f_converged converged = false break end end return system, history, converged end
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2.498696
16,489
# Multidimensional arrays z = zeros(Float64, 2, 3) println(typeof(z)) # Declare array of dimension n x m n, m = 2, 4 arr = Array{Int}(undef, 2, 4) println(arr) println(size(arr)) arr2 = Array{Int}(undef, 3, 2, 2) println(arr2) println(size(arr2)) s = ones(String, 1, 3) println(s) # Note that s is considered a "row matrix / vector". # The typical array is considered a "column matrix / vector". println(s == ["", "", ""]) a = [1 2 3; 4 5 6] println(a) # 2 rows, 3 columns println(a[1, 2]) a[2, 3] = 10 println(a) u = a[:,2:end] println(ℯ.^(im*u))
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2.369099
233
function randuint() :: UInt32 Base.llvmcall(( """ define i32 @randuint() #0 { %1 = tail call { i32, i32 } @llvm.x86.rdrand.32() #1 %2 = extractvalue { i32, i32 } %1, 0 ret i32 %2 } ; Function Attrs: nounwind declare { i32, i32 } @llvm.x86.rdrand.32() #1 attributes #0 = { nounwind ssp uwtable "less-precise-fpmad"="false" "no-frame-pointer-elim"="true" "no-frame-pointer-elim-non-leaf" "no-infs-fp-math"="true" "no-nans-fp-math"="true" "stack-protector-buffer-size"="8" "target-cpu"="core2" "target-features"="+ssse3,+cx16,+rdrnd,+sse,+sse2,+sse3" "unsafe-fp-math"="true" "use-soft-float"="false" } attributes #1 = { nounwind } """ , """ %1 = call i32 @randuint() ret i32 %1 """), UInt32, Tuple{} ) end
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2.127907
344
using LazyTaylorSeries using Test @testset "Basic usage" begin t = Taylor1((t, i) -> (i == 1) ? 1.0 : 0.0, Float64[]) # define variable t2 = Taylor1((t, i) -> (i == 1) ? 1.0 : 0.0, Dict{Int,Float64}()) # define variable @test t[0] == 0 == t2[0] @test t[1] == 1 == t2[1] @test t[2] == 0 == t2[2] @test t[100] == 0 == t2[100] s = 1 + t @test (s[0], s[1], s[2], s[3]) == (1, 1, 0, 0) s = t + t @test (s[0], s[1], s[2], s[3]) == (0, 2, 0, 0) s = t * t @test (s[0], s[1], s[2], s[3]) == (0, 0, 1, 0) s = t^2 @test (s[0], s[1], s[2], s[3]) == (0, 0, 1, 0) s = (1 + t)^2 @test (s[0], s[1], s[2], s[3]) == (1, 2, 1, 0) end
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using Documenter, MyPkg1 makedocs(; modules=[MyPkg1], format=Documenter.HTML(), pages=[ "Home" => "index.md", ], repo="https://github.com/XiaodongMa-MRI/MyPkg1.jl/blob/{commit}{path}#L{line}", sitename="MyPkg1.jl", authors="<NAME>", assets=String[], ) deploydocs(; repo="github.com/XiaodongMa-MRI/MyPkg1.jl", )
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<filename>src/grammar.jl import Base include("production.jl") """ A grammar, represented as a tuple ``G=(N,T,P,S)`` """ struct Grammar "The nonterminal symbols Set" N::Set "The terminal symbols Set" T::Set "The productions Array" P::Array "The starting symbol" S::AbstractString iscontextfree::Bool end ### Constructors ### """ Grammar(N::Set, T::Set, P::Array, S::AbstractString)::Grammar Builds a [`Grammar`](@ref) obtained from the given string of productions. """ function Grammar(N, T, P, S) cf = all(x -> isa(x.left, AbstractString), P) if (N ∩ T) ≠ Set() throw(ArgumentError("The set of terminals and nonterminals are not disjoint, but have " * string(collect(N ∩ T)) * " in common")) end if S ∉ N throw(ArgumentError("The start symbol is not a nonterminal.")) end if cf badprods = [p for p ∈ P if p.left ∉ N] if !isempty(badprods) throw(ArgumentError("The following productions have a left-hand side that is not a nonterminal: " * string(badprods))) end end badprods = [p for p ∈ P if (Set(astype0(p).left) ∪ Set(p.right)) ⊈ (N ∪ T ∪ Set(["ε"]))] if ~isempty(badprods) throw(ArgumentError("The following productions contain symbols that are neither terminals or nonterminals: " * string(badprods))) end Grammar(N, T, P, S, cf) end """ Grammar(prods::AbstractString, iscontextfree = true)::Grammar Builds a [`Grammar`](@ref) obtained from the given string of productions. """ function Grammar(prods::AbstractString, iscontextfree = true)::Grammar P = parseproduction(prods, iscontextfree) S = nothing N = nothing T = nothing if iscontextfree S = P[1].left N = Set(map(x -> x.left, P)) T = Set(vcat(map(x -> x.right, P)...)) - N - "ε" else S = P[1].left[1] symbols = Set(vcat(map(x -> x.left, P)...)) ∪ Set(vcat(map(x -> x.right, P)...)) N = Set(filter(x -> isuppercase(x[1]), symbols)) T = symbols - N - "ε" end G = Grammar(N, T, P, S) if iscontextfree if ~G.iscontextfree throw(ArgumentError("The resulting grammar is not context-free, even if so requested.")) end end return G end ### Functions ### """ alternatives(g::Grammar, N::Array)::Array Returns all the right-hand sides alternatives matching the given nonterminal. """ alternatives(g::Grammar, N::Union{AbstractString, Iterable})::Array = [P.right for P ∈ g.P if P.left == (isa(N,Iterable) ? collect(N) : N)] """ restrict(g::Grammar, symbols::Set) Returns a [`Grammar`](@ref) using only the given symbols. """ restrict(g::Grammar, symbols::Set) = if g.S ∉ symbols throw(ArgumentError("The start symbol must be present among the symbols to keep.")) else Grammar(g.N ∩ symbols, g.T ∩ symbols, [P for P ∈ g.P if (Set([P.left]) ∪ Set(P.right)) ≤ symbols], g.S) end ### Operators ### Base.:(==)(x::Grammar, y::Grammar) = (x.N, x.T, sort(x.P), x.S) == (y.N, y.T, sort(y.P), y.S) Base.hash(g::Grammar) = Base.hash((g.N, g.T, sort(g.P), g.S)) Base.show(io::IO, g::Grammar) = Base.show(io, string("Grammar(N=", g.N, ", T=", g.T, ", P=", g.P, "S=", g.S, ")"))
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<gh_stars>10-100 const ALWB_URI = URI(scheme="http", host="www.bom.gov.au", path="/jsp/awra/thredds/fileServer/AWRACMS") abstract type DataMode end """ Values <: DataMode Get the dataset as regular measured values. """ struct Values <: DataMode end """ Deciles <: DataMode Get the dataset in relative deciles. """ struct Deciles <: DataMode end # Docs below struct ALWB{M<:DataMode,D<:Union{Day,Month,Year}} <: RasterDataSource end layers(::Type{<:ALWB}) = ( :rain_day, :s0_pct, :ss_pct, :sd_pct, :sm_pct, :qtot, :etot, :e0, :ma_wet, :pen_pet, :fao_pet, :asce_pet, :msl_wet, :dd ) # Days are in 1 year nc files date_step(::Type{<:ALWB{<:Any,Day}}) = Year(1) # Months and years are in single files date_step(::Type{<:ALWB{<:Any,Month}}) = Year(100) date_step(::Type{<:ALWB{<:Any,Year}}) = Year(100) has_constant_dims(::Type{<:ALWB}) = false @doc """ ALWB{Union{Deciles,Values},Union{Day,Month,Year}} <: RasterDataSource Data from the Australian Landscape Water Balance (ALWB) data source. See: [www.bom.gov.au/water/landscape](http://www.bom.gov.au/water/landscape) The dataset contains NetCDF files. They have a time dimension so that multiple dates are stored in each file. The available layers are: `$(layers(ALWB))`, available in daily, monthly and annual resolutions, and as `Values` or relative `Deciles`. `getraster` for `ALWB` must use a `date` keyword to specify the date to download. See the [`getraster`](@ref) docs for implementation details. """ ALWB # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/rain_day_2017.nc # Precipiation = "rain_day" # SoilMoisture_Upper = "s0_pct" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/ss_pct_2017.nc # SoilMoisture_Lower = "ss_pct" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/sd_pct_2017.nc # SoilMoisture_Deep = "sd_pct" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/sm_pct_2017.nc # SoilMoisture_RootZone = "sm_pct" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/qtot_2017.nc # Runoff = "qtot" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/etot_2017.nc # Evapotrans_Actual = "etot" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/e0_2017.nc # Evapotrans_Potential_Landscape = "e0" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/ma_wet_2017.nc # Evapotrans_Potential_Areal = "ma_wet" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/pen_pet_2017.nc # Evapotrans_Potential_SyntheticPan = "pen_pet" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/fao_pet_2017.nc # Evapotrans_RefCrop_Short = "fao_pet" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/asce_pet_2017.nc # Evapotrans_RefCrop_Tall = "asce_pet" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/etot_2017.nc # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/msl_wet_2017.nc # Evaporation_OpenWater = "msl_wet" # http://www.bom.gov.au/jsp/awra/thredds/fileServer/AWRACMS/values/day/dd_2017.nc # DeepDrainage = "dd" """ getraster(source::Type{<:ALWB{Union{Deciles,Values},Union{Day,Month,Year}}}, [layer]; date) Download [`ALWB`](@ref) weather data from [www.bom.gov.au/water/landscape](http://www.bom.gov.au/water/landscape) as values or deciles with timesteps of `Day`, `Month` or `Year`. # Arguments - `layer`: `Symbol` or `Tuple` of `Symbol` from `$(layers(ALWB))`. Without a `layer` argument, all layers will be downloaded, and a `NamedTuple` of paths returned. # Keywords - `date`: a `DateTime`, `AbstractVector` of `DateTime` or a `Tuple` of start and end dates. For multiple dates, a `Vector` of multiple filenames will be returned. ALWB is available with a daily, monthly, and yearly, timestep. # Example This will return the file containing annual averages, including your date: ```julia julia> getraster(ALWB{Values,Year}, :ss_pct; date=Date(2001, 2)) "/your/RASTERDATASOURCES_PATH/ALWB/values/month/ss_pct.nc" ``` Returns the filepath/s of the downloaded or pre-existing files. """ function getraster(T::Type{<:ALWB}, layers::Union{Tuple,Symbol}; date) _getraster(T, layers, date) end function _getraster(T::Type{<:ALWB{M,P}}, layers, dates::Tuple) where {M,P} _getraster(T, layers, date_sequence(T, dates)) end function _getraster(T::Type{<:ALWB}, layers, dates::AbstractArray) _getraster.(T, Ref(layers), dates) end function _getraster(T::Type{<:ALWB}, layers::Tuple, date::Dates.TimeType) _map_layers(T, layers, date) end function _getraster(T::Type{<:ALWB}, layer::Symbol, date::Dates.TimeType) _check_layer(T, layer) mkpath(rasterpath(T)) url = rasterurl(T, layer; date=date) path = rasterpath(T, layer; date=date) _maybe_download(url, path) path end rastername(T::Type{<:ALWB{M,P}}, layer; date) where {M,P} = string(layer, _pathsegment(P, date), ".nc") rasterpath(::Type{ALWB}) = joinpath(rasterpath(), "ALWB") rasterpath(::Type{ALWB{M,P}}) where {M,P} = joinpath(joinpath(rasterpath(), "ALWB"), map(_pathsegment, (M, P))...) rasterpath(T::Type{<:ALWB}, layer; date=nothing) = joinpath(rasterpath(T), rastername(T, layer; date)) rasterurl(T::Type{<:ALWB{M,P}}, layer; date) where {M,P} = joinpath(ALWB_URI, _pathsegments(T)..., rastername(T, layer; date)) # Utility methods _pathsegments(::Type{ALWB{M,P}}) where {M,P} = _pathsegment(M), _pathsegment(P) _pathsegment(::Type{Values}) = "values" _pathsegment(::Type{Deciles}) = "deciles" _pathsegment(::Type{Day}) = "day" _pathsegment(::Type{Month}) = "month" _pathsegment(::Type{Year}) = "year" # Days are in whole-year files _pathsegment(::Type{Day}, date) = "_" * string(year(date)) # Months and years are all in one file _pathsegment(::Type{<:Union{Year,Month}}, date) = ""
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<gh_stars>1-10 """ scattering_field(args) Returns a function which gives the average scattering coefficients for any vector `x` inside the material. This field is defined by Equation (3.13) in [<NAME> and <NAME>, "Effective waves for random three-dimensional particulate materials", (2021)](https://arxiv.org/pdf/2010.00934.pdf) """ scattering_field "Calculates the effective wavenumbers and return Vector{EffectivePlaneWaveMode}." function WaveModes(ω::T, source::AbstractSource, material::Material{Dim,S,Sps}; kws...) where {T,Dim,S<:Shape{Dim},Sps<:Species{T,Dim}} # without the parametric types we get a "Unreachable reached" error # The wavenumbers are calculated without knowledge of the materail symmetry. This is because the plane-wave symmetry leads to all possible wavenumbers and is simple to calculate. k_effs = wavenumbers(ω, source.medium, material.species; numberofparticles = material.numberofparticles, kws... ) # The wavemodes need to know the material symmetry as the eigenvectors do depend on material shape and symetry. wave_effs = [ WaveMode(ω, k_eff, source, material; kws...) for k_eff in k_effs] return wave_effs end """ WaveMode(ω::T, wavenumber::Complex{T}, eigenvectors::Array{Complex{T}}, ::SetupSymmetry; kws...) Returns a concrete subtype of AbstractWaveMode depending on the SetupSymmetry. The returned type should have all the necessary fields to calculate scattered waves (currently not true for EffectivePlanarWaves). """ function WaveMode(ω::T, wavenumber::Complex{T}, source::AbstractSource, material::Material{Dim}; kws...) where {T,Dim} eigvectors = eigenvectors(ω, wavenumber, source, material; kws...) α = solve_boundary_condition(ω, wavenumber, eigvectors, source, material; kws...) # After this normalisation, sum(eigvectors, dims = 3) will satisfy the boundary conditions eigvectors = [eigvectors[i] * α[i[3]] for i in CartesianIndices(eigvectors)] return EffectiveRegularWaveMode(ω, wavenumber, source, material, eigvectors; kws...) end function WaveMode(ω::T, wavenumber::Complex{T}, psource::PlaneSource{T,Dim,1}, material::Material{Dim,Halfspace{T,Dim}}; tol::T = 1e-6, kws...) where {T,Dim} direction = transmission_direction(wavenumber, (ω / psource.medium.c) * psource.direction, material.shape.normal) eigvectors = eigenvectors(ω, wavenumber, psource, material; direction_eff = direction, kws...) α = solve_boundary_condition(ω, wavenumber, eigvectors, psource, material; kws...) # After this normalisation, sum(eigvectors, dims = 3) will satisfy the boundary conditions eigvectors = [eigvectors[i] * α[i[3]] for i in CartesianIndices(eigvectors)] return EffectivePlaneWaveMode(ω, wavenumber, direction, eigvectors) end function WaveMode(ω::T, wavenumber::Complex{T}, psource::PlaneSource{T,Dim,1}, material::Material{Dim,Plate{T,Dim}}; kws...) where {T,Dim} # First we calculate the outward pointing normal n = material.shape.normal; n = - n .* sign(real(dot(n,psource.direction))); k = ω / psource.medium.c direction1 = transmission_direction(wavenumber, k * psource.direction, n) eigvectors1 = eigenvectors(ω, wavenumber, psource, material; direction_eff = direction1, kws...) # we choose direction2 so that k2 .* direction2 = - k1 .* direction1, where k1 = wavenumber, and k2 = - wavenumber direction2 = direction1 eigvectors2 = eigenvectors(ω, - wavenumber, psource, material; direction_eff = direction2, kws...) α = solve_boundary_condition(ω, wavenumber, eigvectors1, eigvectors2, psource, material; kws...) # apply normalisation eigvectors1 = eigvectors1 .* α[1] eigvectors2 = eigvectors2 .* α[2] mode1 = EffectivePlaneWaveMode(ω, wavenumber, direction1, eigvectors1) mode2 = EffectivePlaneWaveMode(ω, - wavenumber, direction2, eigvectors2) return [mode1,mode2] end # eigensystem(ω::T, source::AbstractSource, material::Material; kws...) where T<:AbstractFloat = eigensystem(ω, source.medium, material.species, Symmetry(source,material); numberofparticles = material.numberofparticles, kws...) function solve_boundary_condition(ω::T, wavenumber::Complex{T}, eigvectors::Array, source::AbstractSource, material::Material; kws...) where T return solve_boundary_condition(ω, wavenumber, eigvectors, source, material, Symmetry(source,material); kws...) end function solve_boundary_condition(ω::T, wavenumber::Complex{T}, eigvectors1::Array, eigvectors2::Array, source::AbstractSource, material::Material; kws...) where T return solve_boundary_condition(ω, wavenumber, eigvectors1, eigvectors2, source, material, Symmetry(source,material); kws...) end """ convert_eigenvector_basis(medium::PhysicalMedium,sym::AbstractSymmetry,eigvecs::Array) The eigenvectors from high symmetric scenarios are smaller then the more general scenarios. This function pads with zeros the more symmetric cases to match more general cases, so that we can use the functions for both. """ convert_eigenvector_basis(medium::PhysicalMedium,sym::AbstractSymmetry,eigvecs::Array) = eigvecs eigenvectors(ω::T, k_eff::Complex{T}, source::AbstractSource, material::Material; kws...) where T<:AbstractFloat = eigenvectors(ω, k_eff::Complex{T}, source.medium, material.species, Symmetry(source,material); numberofparticles = material.numberofparticles, kws...) # For plane waves, it is simpler to write all cases in the format for the most general case. For example, for PlanarAzimuthalSymmetry the eignvectors are much smaller. So we will turn these into the more general eigvector case by padding it with zeros. # function eigenvectors(ω::T, k_eff::Complex{T}, source::PlaneSource{T}, material::Material{Dim,S}; kws...) where {T<:AbstractFloat,Dim,S<:Union{Plate,Halfspace}} # # eigvecs = eigenvectors(ω, k_eff, source.medium, material.species, Symmetry(source,material); kws...) # # if Symmetry(source,material) == PlanarAzimuthalSymmetry{Dim}() # eigvecs = azimuthal_to_planar_eigenvector(typeof(source.medium),eigvecs) # end # # return eigvecs # # end function eigenvectors(ω::T, k_eff::Complex{T}, medium::PhysicalMedium, species::Vector{Sp}, symmetry::AbstractSymmetry; tol::T = 1e-4, kws... ) where {T<:AbstractFloat, Sp<:Specie{T}} MM = eigensystem(ω, medium, species, symmetry; kws...) # calculate eigenvectors MM_svd = svd(MM(k_eff)) inds = findall(MM_svd.S .< tol) if isempty(inds) @warn("No eigenvectors found with the tolerance tol = $tol. Will use only one eigenvector with the eigenvalue $(MM_svd.S[end]), which should be less than tol.") inds = [length(MM_svd.S)] end #NOTE: MM(k_eff) ≈ MM_svd.U * diagm(0 => MM_svd.S) * MM_svd.Vt eigvectors = MM_svd.V[:,inds] # Reshape to separate different species and eigenvectors S = length(species) # pads with zeros if necessary to match the more general case with less symmetry eigvectors = convert_eigenvector_basis(medium,symmetry,reshape(eigvectors,(:,S,size(eigvectors,2)))) return eigvectors end
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############## # Owner Type # ############## @ghdef mutable struct Owner typ::Union{String, Nothing} email::Union{String, Nothing} name::Union{String, Nothing} login::Union{String, Nothing} bio::Union{String, Nothing} company::Union{String, Nothing} location::Union{String, Nothing} avatar_url::Union{HTTP.URI, Nothing} gravatar_id::Union{String, Nothing} id::Union{Int, Nothing} public_repos::Union{Int, Nothing} owned_private_repos::Union{Int, Nothing} total_private_repos::Union{Int, Nothing} public_gists::Union{Int, Nothing} private_gists::Union{Int, Nothing} followers::Union{Int, Nothing} following::Union{Int, Nothing} collaborators::Union{Int, Nothing} blog::Union{HTTP.URI, Nothing} url::Union{HTTP.URI, Nothing} html_url::Union{HTTP.URI, Nothing} updated_at::Union{Dates.DateTime, Nothing} created_at::Union{Dates.DateTime, Nothing} date::Union{Dates.DateTime, Nothing} hireable::Union{Bool, Nothing} site_admin::Union{Bool, Nothing} end Owner(login::AbstractString, isorg = false) = Owner(Dict("login" => login, "type" => isorg ? "Organization" : "User")) namefield(owner::Owner) = owner.login typprefix(isorg) = isorg ? "orgs" : "users" ############# # Owner API # ############# isorg(owner::Owner) = something(owner.typ, "") == "Organization" @api_default function whoami(api::GitHubAPI; options...) result = gh_get_json(api, "/user"; options...) return Owner(result) end @api_default owner(api::GitHubAPI, owner_obj::Owner; options...) = owner(api, name(owner_obj), isorg(owner_obj); options...) @api_default function owner(api::GitHubAPI, owner_obj, isorg = false; options...) result = gh_get_json(api, "/$(typprefix(isorg))/$(name(owner_obj))"; options...) return Owner(result) end @api_default function users(api::GitHubAPI; options...) results, page_data = gh_get_paged_json(api, "/users"; options...) return map(Owner, results), page_data end @api_default function check_membership(api::GitHubAPI, org, user; public_only = false, options...) scope = public_only ? "public_members" : "members" resp = gh_get(api, "/orgs/$(name(org))/$scope/$(name(user))"; handle_error = false, allowredirects = false, options...) if resp.status == 204 return true elseif resp.status == 404 return false elseif resp.status == 302 # For convenience, still check public membership. Otherwise, we don't know, so error @assert !public_only is_public_member = check_membership(org, user; public_only = true, options...) is_public_member && return true error("Enquiring about an Organization to which you do not have access.\n"* "Set `public_only=true` or provide authentication.") else handle_response_error(resp) end end @api_default function orgs(api::GitHubAPI, owner; options...) results, page_data = gh_get_paged_json(api, "/users/$(name(owner))/orgs"; options...) return map(Owner, results), page_data end @api_default function followers(api::GitHubAPI, owner; options...) results, page_data = gh_get_paged_json(api, "/users/$(name(owner))/followers"; options...) return map(Owner, results), page_data end @api_default function following(api::GitHubAPI, owner; options...) results, page_data = gh_get_paged_json(api, "/users/$(name(owner))/following"; options...) return map(Owner, results), page_data end @api_default function pubkeys(api::GitHubAPI, owner; options...) Base.depwarn("`pubkeys` is deprecated in favor of `sshkeys`, " * "which return a vector of keys, instead of a Dict from key-id to key.", :pubkeys) results, page_data = sshkeys(api, owner; options...) output = Dict{Int,String}([(key["id"], key["key"]) for key in results]) return output, page_data end @api_default function sshkeys(api::GitHubAPI, owner; options...) results, page_data = gh_get_paged_json(api, "/users/$(name(owner))/keys"; options...) output = convert(Vector{Dict{String,Any}}, results) return output, page_data end @api_default function gpgkeys(api::GitHubAPI, owner; options...) results, page_data = gh_get_paged_json(api, "/users/$(name(owner))/gpg_keys"; options...) output = convert(Vector{Dict{String,Any}}, results) return output, page_data end repos(api::GitHubAPI, owner::Owner; options...) = repos(api, name(owner), isorg(owner); options...) @api_default function repos(api::GitHubAPI, owner, isorg = false; options...) results, page_data = gh_get_paged_json(api, "/$(typprefix(isorg))/$(name(owner))/repos"; options...) return map(Repo, results), page_data end @api_default function teams(api::GitHubAPI, owner; options...) results, page_data = gh_get_paged_json(api, "/orgs/$(name(owner))/teams"; options...) return map(Team, results), page_data end
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################################ ## Generic DataFile interface ## ################################ # This provides common methods that could be applicable to any # interface for reading variables out of a file, e.g. HDF5, # JLD, or MAT files. This is the super class of HDF5File, HDF5Group, # JldFile, JldGroup, Matlabv5File, and MatlabHDF5File. # # Types inheriting from DataFile should have names, read, and write # methods abstract DataFile import Base: read, write # Convenience macros macro read(fid, sym) if !isa(sym, Symbol) error("Second input to @read must be a symbol (i.e., a variable)") end esc(:($sym = read($fid, $(string(sym))))) end macro write(fid, sym) if !isa(sym, Symbol) error("Second input to @write must be a symbol (i.e., a variable)") end esc(:(write($fid, $(string(sym)), $sym))) end # Read a list of variables, read(parent, "A", "B", "x", ...) read(parent::DataFile, name::ASCIIString...) = tuple([read(parent, x) for x in name]...) # Read one or more variables and pass them to a function. This is # convenient for avoiding type inference pitfalls with the usual # read syntax. read(f::Base.Callable, parent::DataFile, name::ASCIIString...) = f(read(parent, name...)...) # Read every variable in the file if VERSION < v"0.4.0-dev+980" function read(f::DataFile) vars = names(f) vals = Array(Any, length(vars)) for i = 1:length(vars) vals[i] = read(f, vars[i]) end Dict(vars, vals) end else function read(f::DataFile) vars = names(f) vals = Array(Any, length(vars)) for i = 1:length(vars) vals[i] = read(f, vars[i]) end Dict(zip(vars, vals)) end end
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688
<filename>src/umfpack_lu.jl mutable struct LUFactorization{Tv,Ti} <: AbstractLUFactorization{Tv,Ti} A::Union{Nothing,ExtendableSparseMatrix{Tv,Ti}} fact::Union{Nothing,SuiteSparse.UMFPACK.UmfpackLU{Tv,Ti}} phash::UInt64 end """ ``` LUFactorization(;valuetype=Float64, indextype=Int64) LUFactorization(matrix) ``` Default Julia LU Factorization based on umfpack. """ LUFactorization(;valuetype::Type=Float64,indextype::Type=Int64)=LUFactorization{valuetype,indextype}(nothing,nothing,0) function update!(lufact::LUFactorization) flush!(lufact.A) if lufact.A.phash!=lufact.phash lufact.fact=lu(lufact.A.cscmatrix) lufact.phash=lufact.A.phash else lufact.fact=lu!(lufact.fact,lufact.A.cscmatrix) end lufact end
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2.203966
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<gh_stars>0 # CEP """ setup_cep_opt_sets(ts_data::ClustData,opt_data::CEPData,opt_config::Dict{String,Any}) fetching sets from the time series (ts_data) and capacity expansion model data (opt_data) and returning Dictionary with Sets as Symbols """ function setup_opt_cep_set(ts_data::ClustData, opt_data::OptDataCEP, opt_config::Dict{String,Any}) #`costs::OptVariable`: costs[tech,node,year,account,impact] - annulized costs [USD in USD/MW_el, CO2 in kg-CO₂-eq./MW_el]` costs = opt_data.costs #`techs::OptVariable`: techs[tech] - OptDataCEPTech techs = opt_data.techs #`nodes::OptVariable`: nodes[tech,node] - OptDataCEPNode nodes = opt_data.nodes #`lines::OptVarible`: lines[tech,line] - OptDataCEPLine lines = opt_data.lines set=Dict{String,Array}() set["nodes"]=axes(nodes,"node") #Seperate sets for fossil and renewable technology set["tech"]=Array{String,1}() for categ in unique(getfield.(techs[:],:categ)) if opt_config[categ] set["tech_"*categ]=axes(techs,"tech")[getfield.(techs[:], :categ).==categ] set["tech"]=[set["tech"];set["tech_"*categ]] end end #Compose a set of technologies without transmission set["tech_cap"]=deepcopy(set["tech"]) set["tech_trans"]=Array{String,1}() set["tech_power"]=deepcopy(set["tech"]) set["tech_energy"]=Array{String,1}() for (k,v) in set if occursin("tech",k) && occursin("_transmission",k) setdiff!(set["tech_cap"],v) set["tech_trans"]=[set["tech_trans"];v] end if occursin("tech",k) && String(k[end-1:end])=="_e" setdiff!(set["tech_power"],v) set["tech_energy"]=[set["tech_energy"];v] end end set["impact"]=axes(costs,"impact") set["impact_mon"]=[set["impact"][1]] set["impact_env"]=set["impact"][2:end] set["year"]=axes(costs,"year") set["account"]=axes(costs,"account") if opt_config["transmission"] set["lines"]=axes(opt_data.lines,"line") set["dir_transmission"]=["uniform","opposite"] end if opt_config["existing_infrastructure"] set["infrastruct"]=["new","ex"] else set["infrastruct"]=["new"] end set["sector"]=unique(getfield.(techs[:],:sector)) set["time_K"]=1:ts_data.K set["time_T"]=1:ts_data.T set["time_T_e"]=0:ts_data.T if opt_config["seasonalstorage"] set["time_I_e"]=0:length(ts_data.k_ids) set["time_I"]=1:length(ts_data.k_ids) end return set end """ setup_cep_opt_basic(ts_data::ClustData,opt_data::CEPData,opt_config::Dict{String,Any},optimizer::DataType,optimizer_config::Dict{Symbol,Any}) setting up the basic core elements for a CEP-model - a JuMP Model is setup and the optimizer is configured. The optimizer itself is passed on as a `optimizer`. It's configuration with `optimizer_config` - Each Symbol and the corresponding value in the Dictionary is passed on to the `with_optimizer` function in addition to the optimizer. For Gurobi an example Dictionary could look like `Dict{Symbol,Any}(:Method => 2, :OutputFlag => 0, :Threads => 2)` - the sets are setup """ function setup_opt_cep_basic(ts_data::ClustData, opt_data::OptDataCEP, opt_config::Dict{String,Any}, optimizer::DataType, optimizer_config::Dict{Symbol,Any}) ## MODEL CEP ## # Initialize model model = JuMP.Model(with_optimizer(optimizer;optimizer_config...)) # Initialize info info=[opt_config["descriptor"]] # Setup set set=setup_opt_cep_set(ts_data, opt_data, opt_config) # Setup Model CEP return OptModelCEP(model,info,set) end """ setup_opt_cep_basic_variables!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) Adding basic variables COST, CAP and GEN based on set """ function setup_opt_cep_basic_variables!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set ## VARIABLES ## # Cost push!(cep.info,"Variable COST[account, impact, tech] in $(set["impact"].*" "...)") @variable(cep.model, COST[account=set["account"],impact=set["impact"],tech=set["tech"]]) # Capacity push!(cep.info,"Variable CAP[tech_cap, infrastruct, nodes] ≥ 0 in MW]") @variable(cep.model, CAP[tech=set["tech_cap"],infrastruct=set["infrastruct"] ,node=set["nodes"]]>=0) # Generation # push!(cep.info,"Variable GEN[sector, tech_power, t, k, node] in MW") @variable(cep.model, GEN[sector=set["sector"], tech=set["tech_power"], t=set["time_T"], k=set["time_K"], node=set["nodes"]]) #end return cep end """ setup_opt_cep_lost_load!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) set::Dict) Adding variable SLACK, LL (LostLoad - if demand cannot be met with installed capacity -> Lost Load can be "purchased" to meet demand) """ function setup_opt_cep_lost_load!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #ts_weights: k - weight of each period: ts_weights=ts_data.weights #ts_deltas: t x k - Δt of each segment x period ts_deltas=ts_data.delta_t ## LOST LOAD ## # Slack variable # push!(cep.info,"Variable SLACK[sector, t, k, node] ≥ 0 in MW") @variable(cep.model, SLACK[sector=set["sector"], t=set["time_T"], k=set["time_K"], node=set["nodes"]] >=0) # Lost Load variable # push!(cep.info,"Variable LL[sector, node] ≥ 0 in MWh") @variable(cep.model, LL[sector=set["sector"], node=set["nodes"]] >=0) # Calculation of Lost Load push!(cep.info,"LL[sector, node] = Σ SLACK[sector, t, k, node] ⋅ ts_weights[k] ⋅ Δt[t,k] ∀ sector, node") @constraint(cep.model, [sector=set["sector"], node=set["nodes"]], cep.model[:LL][sector, node]==sum(cep.model[:SLACK][sector, t, k, node]*ts_weights[k]*ts_deltas[t,k] for t=set["time_T"], k=set["time_K"])) return cep end """ setup_opt_cep_lost_emission!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) Adding variable LE (LostEmission - if demand cannot be met without breaking Emission-constraint -> Lost Emission can be "purchased" to meet demand with "dirty" production) """ function setup_opt_cep_lost_emission!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set ## LOST EMISSION ## # Lost Emission variable # push!(cep.info,"Variable LE[impact_{environment}] ≥ 0 in kg") @variable(cep.model, LE[impact=set["impact"][2:end]] >=0) return cep end """ setup_opt_cep_fix_design_variables!(cep::OptModelCEP,ts_data::ClustData, opt_data::OptDataCEP,fixed_design_variables::Dict{String,Any}) Fixing variables CAP based on first stage vars """ function setup_opt_cep_fix_design_variables!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP, fixed_design_variables::Dict{String,Any}) ## DATA ## set=cep.set cap=fixed_design_variables["CAP"] ## VARIABLES ## # Transmission if "tech_transmission" in keys(set) trans=fixed_design_variables["TRANS"] push!(cep.info,"TRANS[tech, 'new', line] = existing infrastructure ∀ tech_trans, line") @constraint(cep.model, [line=set["lines"], tech=set["tech_trans"]], cep.model[:TRANS][tech,"new",line]==trans[tech, "new", line]) end # Capacity push!(cep.info,"CAP[tech, 'new', node] = existing infrastructure ∀ tech_cap, node") @constraint(cep.model, [node=set["nodes"], tech=set["tech_cap"]], cep.model[:CAP][tech,"new",node]==cap[tech, "new", node]) return cep end """ setup_opt_cep_generation_el!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) add variable and fixed Costs and limit generation to installed capacity (and limiting time_series, if dependency in techs defined) for fossil and renewable power plants """ function setup_opt_cep_generation_el!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #`costs::OptVariable`: costs[tech,node,year,account,impact] - annulized costs [USD in USD/MW_el, CO2 in kg-CO₂-eq./MW_el]` costs = opt_data.costs #`techs::OptVariable`: techs[tech] - OptDataCEPTech techs = opt_data.techs #`nodes::OptVariable`: nodes[tech,node] - OptDataCEPNode nodes = opt_data.nodes #ts Dict( tech-node ): t x k ts=ts_data.data #ts_weights: k - weight of each period: ts_weights=ts_data.weights #ts_deltas: t x k - Δt of each segment x period ts_deltas=ts_data.delta_t ## GENERATION ELECTRICITY ## # Calculate Variable Costs push!(cep.info,"COST['var',impact,tech] = Σ_{t,k,node}GEN['el',t,k,node]⋅ ts_weights[k] ⋅ ts_deltas[t,k]⋅ var_costs[tech,impact] ∀ impact, tech_generation") @constraint(cep.model, [impact=set["impact"], tech=set["tech_generation"]], cep.model[:COST]["var",impact,tech]==sum(cep.model[:GEN]["el",tech,t,k,node]*ts_weights[k]*ts_deltas[t,k]*costs[tech,node,set["year"][1],"var",impact] for node=set["nodes"], t=set["time_T"], k=set["time_K"])) # Calculate Fixed Costs push!(cep.info,"COST['cap_fix',impact,tech] = Σ_{t,k}(ts_weights ⋅ ts_deltas[t,k])/8760h ⋅ Σ_{node}CAP[tech,'new',node] ⋅ cap_costs[tech,impact] ∀ impact, tech_generation") @constraint(cep.model, [impact=set["impact"], tech=set["tech_generation"]], cep.model[:COST]["cap_fix",impact,tech]==sum(ts_weights[k]*ts_deltas[t,k] for t=set["time_T"], k=set["time_K"])/8760* sum(cep.model[:CAP][tech,"new",node] *costs[tech,node,set["year"][1],"cap_fix",impact] for node=set["nodes"])) # Limit the generation of dispathables to the infrastructing capacity of dispachable power plants push!(cep.info,"0 ≤ GEN['el',tech, t, k, node] ≤ Σ_{infrastruct} CAP[tech,infrastruct,node] ∀ node, tech_generation{dispatchable}, t, k") # Limit the generation of dispathables to the infrastructing capacity of non-dispachable power plants push!(cep.info,"0 ≤ GEN['el',tech, t, k, node] ≤ Σ_{infrastruct}CAP[tech,infrastruct,node]*ts[tech-node,t,k] ∀ node, tech_generation{non_dispatchable}, t, k") for tech in set["tech_generation"] # Limit the generation of dispathables to the infrastructing capacity of dispachable power plants if techs[tech].time_series=="none" @constraint(cep.model, [node=set["nodes"], t=set["time_T"], k=set["time_K"]], 0 <=cep.model[:GEN]["el",tech, t, k, node]) @constraint(cep.model, [node=set["nodes"], t=set["time_T"], k=set["time_K"]], cep.model[:GEN]["el",tech, t, k, node] <=sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"])) else # Limit the generation of dispathables to the infrastructing capacity of non-dispachable power plants @constraint(cep.model, [node=set["nodes"], t=set["time_T"], k=set["time_K"]], 0 <=cep.model[:GEN]["el",tech, t, k, node]) @constraint(cep.model, [node=set["nodes"], t=set["time_T"], k=set["time_K"]], cep.model[:GEN]["el",tech,t,k,node] <= sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"])*ts[techs[tech].time_series*"-"*node][t,k]) end end return cep end """ setup_opt_cep_storage!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) add variables INTRASTORGEN and INTRASTOR, variable and fixed Costs, limit generation to installed power-capacity, connect simple-storage levels (within period) with generation basis for either simplestorage or seasonalstorage """ function setup_opt_cep_storage!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #`costs::OptVariable`: costs[tech,node,year,account,impact] - annulized costs [USD in USD/MW_el, CO2 in kg-CO₂-eq./MW_el]` costs = opt_data.costs #`techs::OptVariable`: techs[tech] - OptDataCEPTech techs = opt_data.techs #ts_weights: k - weight of each period: ts_weights=ts_data.weights #ts_deltas: t x k - Δt of each segment x period ts_deltas=ts_data.delta_t ## VARIABLE ##existing_infrastructure # Storage has additional element 0 for storage at hour 0 of day push!(cep.info,"Variable INTRASTOR[sector, tech_storage_e, t, k, node] ≥ 0 in MWh") @variable(cep.model, INTRASTOR[sector=set["sector"], tech=set["tech_storage_e"], t=set["time_T_e"], k=set["time_K"], node=set["nodes"]] >=0) # Storage generation is necessary for the efficiency #push!(cep.info,"Variable INTRASTORGEN[sector, dir, tech, t, k, node] ≥ 0 in MW") #@variable(cep.model, INTRASTORGEN[sector=set["sector"], dir=set["dir_storage"], tech=set["tech_storage_p"], t=set["time_T"], k=set["time_K"], node=set["nodes"]] >=0) ## STORAGE ## # Calculate Variable Costs push!(cep.info,"COST['var',impact,tech] = 0 ∀ impact, tech_storage") @constraint(cep.model, [impact=set["impact"], tech=[set["tech_storage_in"];set["tech_storage_out"];set["tech_storage_e"]]], cep.model[:COST]["var",impact,tech]==0) # Fix Costs storage push!(cep.info,"COST['fix',impact,tech] = Σ_{t,k}(ts_weights ⋅ ts_deltas[t,k])/8760h ⋅ Σ_{node}CAP[tech,'new',node] ⋅ costs[tech,node,year,'cap_fix',impact] ∀ impact, tech_storage") @constraint(cep.model, [tech=[set["tech_storage_in"];set["tech_storage_out"];set["tech_storage_e"]], impact=set["impact"]], cep.model[:COST]["cap_fix",impact,tech]==sum(ts_weights[k]*ts_deltas[t,k] for t=set["time_T"], k=set["time_K"])/8760* sum(cep.model[:CAP][tech,"new",node]*costs[tech,node,set["year"][1],"cap_fix",impact] for node=set["nodes"])) # Limit the Generation of the theoretical power part of the battery to its installed power push!(cep.info,"0 ≤ GEN['el',tech, t, k, node] ≤ Σ_{infrastruct} CAP[tech,infrastruct,node] ∀ node, tech_storage_out, t, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_out"], t=set["time_T"], k=set["time_K"]], 0 <= cep.model[:GEN]["el",tech,t,k,node]) @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_out"], t=set["time_T"], k=set["time_K"]], cep.model[:GEN]["el",tech,t,k,node]<=sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"])) push!(cep.info,"0 ≥ GEN['el',tech, t, k, node] ≥ (-1) ⋅ Σ_{infrastruct} CAP[tech,infrastruct,node] ∀ node, tech_storage_in, t, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_in"], t=set["time_T"], k=set["time_K"]], 0 >= cep.model[:GEN]["el",tech,t,k,node]) @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_in"], t=set["time_T"], k=set["time_K"]], cep.model[:GEN]["el",tech,t,k,node]>=(-1)*sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"])) # Connect the previous storage level and the integral of the flows with the new storage level push!(cep.info,"INTRASTOR['el',tech, t, k, node] = INTRASTOR['el',tech, t-1, k, node] η[tech]^(ts_deltas[t,k]/732h) + ts_deltas[t,k] ⋅ (-1) ⋅ (GEN['el',tech_{in}, t, k, node] ⋅ η[tech_{in}] + GEN['el',tech_{out}, t, k, node] / η[tech_{out}]) ∀ node, tech_storage_e, t, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"], t in set["time_T"], k=set["time_K"]], cep.model[:INTRASTOR]["el",tech,t,k,node]==cep.model[:INTRASTOR]["el",tech,t-1,k,node]*(techs[tech].eff)^(ts_deltas[t,k]/732) - ts_deltas[t,k] * (cep.model[:GEN]["el",split(tech,"_")[1]*"_in",t,k,node] * techs[split(tech,"_")[1]*"_in"].eff + cep.model[:GEN]["el",split(tech,"_")[1]*"_out",t,k,node] / techs[split(tech,"_")[1]*"_out"].eff)) push!(cep.info,"CAP[tech_{out}, 'new', node] = CAP[tech_{in}, 'new', node] ∀ node, tech_{EUR-Cap-Cost out/in==0}") for tech in set["tech_storage_out"] for node in set["nodes"] if costs[tech,node,set["year"][1],"cap_fix",set["impact"][1]]==0 || costs[split(tech,"_")[1]*"_in",node,set["year"][1],"cap_fix",set["impact"][1]]==0 @constraint(cep.model, cep.model[:CAP][tech,"new",node]==cep.model[:CAP][split(tech,"_")[1]*"_in","new",node]) end end end return cep end """ setup_opt_cep_simplestorage!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) Adding only intra-day storage: Looping constraint for each period (same start and end level for all periods) and limit storage to installed energy-capacity """ function setup_opt_cep_simplestorage!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set ## INTRASTORAGE ## # Limit the storage of the theoretical energy part of the battery to its installed power push!(cep.info,"INTRASTOR['el',tech, t, k, node] ≤ Σ_{infrastruct} CAP[tech,infrastruct,node] ∀ node, tech_storage, t, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"], t=set["time_T"], k=set["time_K"]], cep.model[:INTRASTOR]["el",tech,t,k,node]<=sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"])) # Set storage level at beginning and end of day equal push!(cep.info,"INTRASTOR['el',tech, '0', k, node] = INTRASTOR['el',tech, 't[end]', k, node] ∀ node, tech_storage_e, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"], k=set["time_K"]], cep.model[:INTRASTOR]["el",tech,0,k,node]== cep.model[:INTRASTOR]["el",tech,set["time_T_e"][end],k,node]) # Set the storage level at the beginning of each representative day to the same push!(cep.info,"INTRASTOR['el',tech, '0', k, node] = INTRASTOR['el',tech, '0', k, node] ∀ node, tech_storage_e, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"], k=set["time_K"]], cep.model[:INTRASTOR]["el",tech,0,k,node]== cep.model[:INTRASTOR]["el",tech,0,1,node]) return cep end """ setup_opt_cep_seasonalstorage!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) Adding inter-day storage: add variable INTERSTOR, calculate seasonal-storage-level and limit total storage to installed energy-capacity """ function setup_opt_cep_seasonalstorage!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #K identification numbers k_ids=ts_data.k_ids ## VARIABLE ## # Storage push!(cep.info,"Variable INTERSTOR[sector, tech, i, node] ≥ 0 in MWh") @variable(cep.model, INTERSTOR[sector=set["sector"], tech=set["tech_storage_e"], i=set["time_I_e"], node=set["nodes"]]>=0) ## INTERSTORAGE ## # Set storage level at the beginning of the year equal to the end of the year push!(cep.info,"INTERSTOR['el',tech, '0', node] = INTERSTOR['el',tech, 'end', node] ∀ node, tech_storage, t, k") @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"]], cep.model[:INTERSTOR]["el",tech,0,node]== cep.model[:INTERSTOR]["el",tech,set["time_I_e"][end],node]) # Connect the previous seasonal-storage level and the daily difference of the corresponding simple-storage with the new seasonal-storage level push!(cep.info,"INTERSTOR['el',tech, i+1, node] = INTERSTOR['el',tech, i, node] + INTRASTOR['el',tech, 'k[i]', 't[end]', node] - INTRASTOR['el',tech, 'k[i]', '0', node] ∀ node, tech_storage_e, i") # Limit the total storage (seasonal and simple) to be greater than zero and less than total storage cap push!(cep.info,"0 ≤ INTERSTOR['el',tech, i, node] + INTRASTOR['el',tech, t, k[i], node] ≤ Σ_{infrastruct} CAP[tech,infrastruct,node] ∀ node, tech_storage_e, i, t") push!(cep.info,"0 ≤ INTERSTOR['el',tech, i, node] + INTRASTOR['el',tech, t, k[i], node] ≤ Σ_{infrastruct} CAP[tech,infrastruct,node] ∀ node, tech_storage_e, i, t") for i in set["time_I"] @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"]], cep.model[:INTERSTOR]["el",tech,i,node] == cep.model[:INTERSTOR]["el",tech,i-1,node] + cep.model[:INTRASTOR]["el",tech,set["time_T"][end],k_ids[i],node] - cep.model[:INTRASTOR]["el",tech,0,k_ids[i],node]) @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"], t=set["time_T_e"]], 0 <= cep.model[:INTERSTOR]["el",tech,i,node]+cep.model[:INTRASTOR]["el",tech,t,k_ids[i],node]) @constraint(cep.model, [node=set["nodes"], tech=set["tech_storage_e"], t=set["time_T_e"]], cep.model[:INTERSTOR]["el",tech,i,node]+cep.model[:INTRASTOR]["el",tech,t,k_ids[i],node] <= sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"])) end return cep end """ setup_opt_cep_transmission!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) Setup variable FLOW and TRANS, calculate fixed and variable COSTs, set CAP-trans to zero, limit FLOW with TRANS, calculate GEN-trans for each node """ function setup_opt_cep_transmission!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #`costs::OptVariable`: costs[tech,node,year,account,impact] - annulized costs [USD in USD/MW_el, CO2 in kg-CO₂-eq./MW_el]` costs = opt_data.costs #`lines::OptVarible`: lines[tech,line] - OptDataCEPLine lines = opt_data.lines #ts_weights: k - weight of each period: ts_weights=ts_data.weights #ts_deltas: t x k - Δt of each segment x period ts_deltas=ts_data.delta_t ## VARIABLE ## # Add varibale FLOW push!(cep.info,"Variable FLOW[sector, dir, tech_transmission, t, k, line] ≥ 0 in MW") @variable(cep.model, FLOW[sector=set["sector"], dir=set["dir_transmission"], tech=set["tech_transmission"], t=set["time_T"], k=set["time_K"], line=set["lines"]] >= 0) # Add variable TRANS push!(cep.info,"Variable TRANS[tech_transmission, infrastruct, lines] ≥ 0 in MW") @variable(cep.model, TRANS[tech=set["tech_transmission"], infrastruct=set["infrastruct"], line=set["lines"]] >= 0) ## TRANSMISSION ## # Calculate Variable Costs push!(cep.info,"COST['var',impact,tech] = 0 ∀ impact, tech_transmission") @constraint(cep.model, [impact=set["impact"], tech=set["tech_transmission"]], cep.model[:COST]["var",impact,tech] == 0) # Calculate Fixed Costs push!(cep.info,"COST['cap-fix',impact,tech] = Σ_{t,k}(ts_weights ⋅ ts_deltas[t,k])/8760h ⋅ Σ_{node}(TRANS[tech,'new',line] ⋅ length[line]) ⋅ (cap_costs[tech,impact]+fix_costs[tech,impact]) ∀ impact, tech_transmission") @constraint(cep.model, [impact=set["impact"], tech=set["tech_transmission"]], cep.model[:COST]["cap_fix",impact,tech] == sum(ts_weights[k]*ts_deltas[t,k] for t=set["time_T"], k=set["time_K"])/8760* sum(cep.model[:TRANS][tech,"new",line]*lines[tech,line].length *(costs[tech,lines[tech,line].node_start,set["year"][1],"cap_fix",impact]) for line=set["lines"])) # Limit the flow per line to the existing infrastructure push!(cep.info,"| FLOW['el', dir, tech, t, k, line] | ≤ Σ_{infrastruct}TRANS[tech,infrastruct,line] ∀ line, tech_transmission, t, k") @constraint(cep.model, [line=set["lines"], dir=set["dir_transmission"], tech=set["tech_transmission"], t=set["time_T"], k=set["time_K"]], cep.model[:FLOW]["el",dir, tech, t, k, line] <= sum(cep.model[:TRANS][tech,infrastruct,line] for infrastruct=set["infrastruct"])) # Calculate the sum of the flows for each node push!(cep.info,"GEN['el',tech, t, k, node] = Σ_{line-end(node)} FLOW['el','uniform',tech, t, k, line] - Σ_{line_pos} FLOW['el','opposite',tech, t, k, line] / (η[tech]⋅length[line]) + Σ_{line-start(node)} Σ_{line_pos} FLOW['el','opposite',tech, t, k, line] - FLOW['el','uniform',tech, t, k, line] / (η[tech]⋅length[line])∀ tech_transmission, t, k") for node in set["nodes"] @constraint(cep.model, [tech=set["tech_transmission"], t=set["time_T"], k=set["time_K"]], cep.model[:GEN]["el",tech, t, k, node] == sum(cep.model[:FLOW]["el","uniform",tech, t, k, line_end] - cep.model[:FLOW]["el","opposite",tech, t, k, line_end]/lines[tech,line_end].eff for line_end=set["lines"][getfield.(lines[tech,:], :node_end).==node]) + sum(cep.model[:FLOW]["el","opposite",tech, t, k, line_start] - cep.model[:FLOW]["el","uniform",tech, t, k, line_start]/lines[tech,line_start].eff for line_start=set["lines"][getfield.(lines[tech,:], :node_start).==node])) end return cep end """ setup_opt_cep_demand!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP,lost_load_cost::Dict{String,Number}=Dict{String,Number}("el"=>Inf)) Add demand which shall be matched by the generation (GEN) """ function setup_opt_cep_demand!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP; lost_load_cost::Dict{String,Number}=Dict{String,Number}("el"=>Inf)) ## DATA ## set=cep.set #ts Dict( tech-node ): t x k ts=ts_data.data ## DEMAND ## if "tech_transmission" in keys(set) && lost_load_cost["el"]!=Inf # Force the demand and slack to match the generation either with transmission push!(cep.info,"Σ_{tech}GEN['el',tech,t,k,node] = ts[el_demand-node,t,k]-SLACK['el',t,k,node] ∀ node,t,k") @constraint(cep.model, [node=set["nodes"], t=set["time_T"], k=set["time_K"]], sum(cep.model[:GEN]["el",tech,t,k,node] for tech=set["tech_power"]) == ts["el_demand-"*node][t,k]-cep.model[:SLACK]["el",t,k,node]) elseif !("tech_transmission" in keys(set)) && lost_load_cost["el"]!=Inf # or on copperplate push!(cep.info,"Σ_{tech,node}GEN['el',tech,t,k,node]= Σ_{node}ts[el_demand-node,t,k]-SLACK['el',t,k,node] ∀ t,k") @constraint(cep.model, [t=set["time_T"], k=set["time_K"]], sum(cep.model[:GEN]["el",tech,t,k,node] for node=set["nodes"], tech=set["tech_power"]) == sum(ts["el_demand-"*node][t,k]-cep.model[:SLACK]["el",t,k,node] for node=set["nodes"])) elseif "tech_transmission" in keys(set) && lost_load_cost["el"]==Inf # Force the demand without slack to match the generation either with transmission push!(cep.info,"Σ_{tech}GEN['el',tech,t,k,node] = ts[el_demand-node,t,k] ∀ node,t,k") @constraint(cep.model, [node=set["nodes"], t=set["time_T"], k=set["time_K"]], sum(cep.model[:GEN]["el",tech,t,k,node] for tech=set["tech_power"]) == ts["el_demand-"*node][t,k]) else # or on copperplate push!(cep.info,"Σ_{tech,node}GEN['el',tech,t,k,node]= Σ_{node}ts[el_demand-node,t,k]∀ t,k") @constraint(cep.model, [t=set["time_T"], k=set["time_K"]], sum(cep.model[:GEN]["el",tech,t,k,node] for node=set["nodes"], tech=set["tech_power"]) == sum(ts["el_demand-"*node][t,k] for node=set["nodes"])) end return cep end """ setup_opt_cep_co2_limit!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP;co2_limit::Number=Inf,lost_emission_cost::Dict{String,Number}=Dict{String,Number}("CO2"=>Inf)) Add co2 emission constraint """ function setup_opt_cep_co2_limit!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP; co2_limit::Number=Inf, lost_emission_cost::Dict{String,Number}=Dict{String,Number}("CO2"=>Inf)) ## DATA ## set=cep.set #ts Dict( tech-node ): t x k ts=ts_data.data #ts_weights: k - weight of each period: ts_weights=ts_data.weights #ts_deltas: t x k - Δt of each segment x period ts_deltas=ts_data.delta_t ## EMISSIONS ## if lost_emission_cost["CO2"]!=Inf # Limit the Emissions with co2_limit if it exists push!(cep.info,"ΣCOST_{account,tech}[account,'$(set["impact"][1])',tech] ≤ LE['CO2'] + co2_limit Σ_{node,t,k} ts[el_demand-node,t,k] ⋅ ts_weights[k] ⋅ ts_deltas[t,k]") @constraint(cep.model, sum(cep.model[:COST][account,"CO2",tech] for account=set["account"], tech=set["tech"])<= cep.model[:LE]["CO2"] + co2_limit*sum(ts["el_demand-"*node][t,k]*ts_deltas[t,k]*ts_weights[k] for t=set["time_T"], k=set["time_K"], node=set["nodes"])) else # Limit the Emissions with co2_limit if it exists # Total demand can also be determined with the function get_total_demand() edit both in case of changes of e.g. ts_deltas push!(cep.info,"ΣCOST_{account,tech}[account,'$(set["impact"][1])',tech] ≤ co2_limit ⋅ Σ_{node,t,k} ts[el_demand-node,t,k] ⋅ ts_weights[k] ⋅ ts_deltas[t,k]") @constraint(cep.model, sum(cep.model[:COST][account,"CO2",tech] for account=set["account"], tech=set["tech"])<= co2_limit*sum(ts["el_demand-$node"][t,k]*ts_weights[k]*ts_deltas[t,k] for node=set["nodes"], t=set["time_T"], k=set["time_K"])) end return cep end """ setup_opt_cep_existing_infrastructure!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) fixing existing infrastructure to CAP[tech, 'ex', node] """ function setup_opt_cep_existing_infrastructure!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #`nodes::OptVariable`: nodes[tech,node] - OptDataCEPNode nodes = opt_data.nodes #`lines::OptVarible`: lines[tech,line] - OptDataCEPLine lines = opt_data.lines ## ASSIGN VALUES ## # Assign the existing capacity from the nodes table push!(cep.info,"CAP[tech, 'ex', node] = existing infrastructure ∀ node, tech") @constraint(cep.model, [node=set["nodes"], tech=set["tech_cap"]], cep.model[:CAP][tech,"ex",node]==nodes[tech,node].power_ex) if "transmission" in keys(set) push!(cep.info,"TRANS[tech, 'ex', line] = existing infrastructure ∀ tech, line") @constraint(cep.model, [line=set["lines"], tech=set["tech_trans"]], cep.model[:TRANS][tech,"ex",line]==lines[tech,line].power_ex) end return cep end """ setup_opt_cep_limit_infrastructure!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) limit infrastructure setup of CAP[tech, sum(infrastuct), node] NOTE just for CAP not for TRANS implemented """ function setup_opt_cep_limit_infrastructure!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP) ## DATA ## set=cep.set #`nodes::OptVariable`: nodes[tech,node] - OptDataCEPNode nodes = opt_data.nodes #`lines::OptVarible`: lines[tech,line] - OptDataCEPLine lines = opt_data.lines ## ASSIGN VALUES ## # Limit the capacity for each tech at each node with the limit provided in nodes table in column infrastruct push!(cep.info,"∑_{infrastuct} CAP[tech, infrastruct, node] <= limit infrastructure ∀ tech_cap, node") @constraint(cep.model, [node=set["nodes"], tech=set["tech_cap"]], sum(cep.model[:CAP][tech,infrastruct,node] for infrastruct=set["infrastruct"]) <= nodes[tech,node].power_lim) if "transmission" in keys(set) push!(cep.info,"∑_{infrastuct} TRANS[tech, infrastruct, line] <= limit infrastructure ∀ tech_trans, line") @constraint(cep.model, [line=set["lines"], tech=set["tech_trans"]], sum(cep.model[:TRANS][tech,infrastruct,line] for infrastruct=set["infrastruct"]) <= lines[tech,line].power_lim) end return cep end """ setup_opt_cep_objective!(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP) Calculate total system costs and set as objective """ function setup_opt_cep_objective!(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP; lost_load_cost::Dict{String,Number}=Dict{String,Number}("el"=>Inf), lost_emission_cost::Dict{String,Number}=Dict{String,Number}("CO2"=>Inf)) ## DATA ## set=cep.set ## OBJECTIVE ## # Minimize the total €-Costs s.t. the Constraints introduced above if lost_load_cost["el"]==Inf && lost_emission_cost["CO2"]==Inf push!(cep.info,"min Σ_{account,tech}COST[account,'$(set["impact"][1])',tech] st. above") @objective(cep.model, Min, sum(cep.model[:COST][account,set["impact"][1],tech] for account=set["account"], tech=set["tech"])) elseif lost_load_cost["el"]!=Inf && lost_emission_cost["CO2"]==Inf push!(cep.info,"min Σ_{account,tech}COST[account,'$(set["impact"][1])',tech] + Σ_{node} LL['el'] ⋅ $(lost_load_cost["el"]) st. above") @objective(cep.model, Min, sum(cep.model[:COST][account,set["impact"][1],tech] for account=set["account"], tech=set["tech"]) + sum(cep.model[:LL]["el",node] for node=set["nodes"])*lost_load_cost["el"]) elseif lost_load_cost["el"]==Inf && lost_emission_cost["CO2"]!=Inf push!(cep.info,"min Σ_{account,tech}COST[account,'$(set["impact"][1])',tech] + LE['CO2'] ⋅ $(lost_emission_cost["CO2"]) st. above") @objective(cep.model, Min, sum(cep.model[:COST][account,set["impact"][1],tech] for account=set["account"], tech=set["tech"]) + cep.model[:LE]["CO2"]*lost_emission_cost["CO2"]) else push!(cep.info,"min Σ_{account,tech}COST[account,'$(set["impact"][1])',tech] + Σ_{node} LL['el'] ⋅ $(lost_load_cost["el"]) + LE['CO2'] ⋅ $(lost_emission_cost["CO2"]) st. above") @objective(cep.model, Min, sum(cep.model[:COST][account,set["impact"][1],tech] for account=set["account"], tech=set["tech"]) + sum(cep.model[:LL]["el",node] for node=set["nodes"])*lost_load_cost["el"] + cep.model[:LE]["CO2"]*lost_emission_cost["CO2"]) end return cep end """ solve_opt_cep(cep::OptModelCEP,ts_data::ClustData,opt_data::OptDataCEP,opt_config::Dict{String,Any}) solving the cep model and writing it's results and `co2_limit` into an OptResult-Struct """ function solve_opt_cep(cep::OptModelCEP, ts_data::ClustData, opt_data::OptDataCEP, opt_config::Dict{String,Any}) optimize!(cep.model) status=Symbol(termination_status(cep.model)) objective=objective_value(cep.model) total_demand=get_total_demand(cep,ts_data) variables=Dict{String,Any}() # cv - Cost variable, dv - design variable, which is used to fix variables in a dispatch model, ov - operational variable variables["COST"]=OptVariable(cep,:COST,"cv") variables["CAP"]=OptVariable(cep,:CAP,"dv") variables["GEN"]=OptVariable(cep,:GEN,"ov") lost_load=0 lost_emission=0 if opt_config["lost_load_cost"]["el"]!=Inf variables["SLACK"]=OptVariable(cep,:SLACK,"sv") variables["LL"]=OptVariable(cep,:LL,"sv") lost_load=sum(variables["LL"].data) end if opt_config["lost_emission_cost"]["CO2"]!=Inf variables["LE"]=OptVariable(cep,:LE,"sv") lost_emission=sum(variables["LE"].data) end if opt_config["storage_in"] && opt_config["storage_out"] && opt_config["storage_e"] variables["INTRASTOR"]=OptVariable(cep,:INTRASTOR,"ov") if opt_config["seasonalstorage"] variables["INTERSTOR"]=OptVariable(cep,:INTERSTOR,"ov") end end if opt_config["transmission"] variables["TRANS"]=OptVariable(cep,:TRANS,"dv") variables["FLOW"]=OptVariable(cep,:FLOW,"ov") end get_met_cap_limit(cep, opt_data, variables) currency=variables["COST"].axes[2][1] if lost_load==0 && lost_emission==0 opt_config["print_flag"] && @info("Solved Scenario $(opt_config["descriptor"]): "*String(status)*" min COST: $(round(objective,sigdigits=4)) [$currency] ⇨ $(round(objective/total_demand,sigdigits=4)) [$currency per MWh] s.t. Emissions ≤ $(opt_config["co2_limit"]) [kg-CO₂-eq. per MWh]") else cost=variables["COST"] opt_config["print_flag"] && @info("Solved Scenario $(opt_config["descriptor"]): "*String(status)*" min COST: $(round(sum(cost[:,axes(cost,"impact")[1],:]),sigdigits=4)) [$currency] ⇨ $(round(sum(cost[:,axes(cost,"impact")[1],:])/total_demand,sigdigits=4)) [$currency per MWh] with LL: $lost_load [MWh] s.t. Emissions ≤ $(opt_config["co2_limit"]) + $(round(lost_emission/total_demand,sigdigits=4)) (violation) [kg-CO₂-eq. per MWh]") end opt_info=Dict{String,Any}("total_demand"=>total_demand,"model"=>cep.info,) return OptResult(status,objective,variables,cep.set,opt_config,opt_info) end
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<reponame>SV-97/ares # requires LinearAlgebra, Images, ImageView include("GrahamScan.jl") using ImageView, Images using SparseArrays using Random using LinearAlgebra using Statistics function FindRoughEdge(A) # Find the rough coordinates of the non-horizontal edges of shapes in A B = Float64.(A); abs.(B[:, 2:end] - B[:, 1:end - 1]) |> rough_outline -> findall(px -> abs.(px - 1.0) < 0.8, rough_outline) end function FitLinear(x, y, deg) # Use linear regression to approximate f(x) = y with a polynomial of degree deg # returns coefficients of polynomial vs = hcat(map(p -> x.^p, (deg:-1:0))...); Q, R = qr(vs); R_pad = [R; zeros(size(Q, 1) - size(R, 1), size(R, 2))]; params = R_pad \ (Q' * y); params end function FitLinearModel(x, y, deg) # Use linear regression to approximate f(x) = y with a polynomial of degree deg # returns a ready to use function params = FitLinear(x, y, deg); t -> (t.^(deg:-1:0))' * params end function CleanUpEdges(sensitivity, contour) # Try to remove unnecessary points from contour # contour is a matrix where the rows are x,y coordinate pairs reduced_contour = []; current_xs = Array{eltype(contour),1}(); current_ys = Array{eltype(contour),1}(); for point in eachrow(contour) # the first element is always equal to GrahamScan.EdgePoint(contour) x, y = point[1], point[2] if isempty(current_xs) push!(current_xs, x); push!(current_ys, y); else next_xs = [x; current_xs]; next_ys = [y; current_ys]; # approximate all points in current segment by a line approx = FitLinearModel(next_xs, next_ys, 1); # calculate deviations from approximation and actual values # and find the maximum deviation md = vcat(approx.(next_xs)...) - next_ys |> ds -> abs.(ds) |> maximum; if md <= sensitivity # if the maximum deviation doesn't exceed `sensitivity` pixels # try the next line current_xs = next_xs; current_ys = next_ys; continue else # otherwise save ends of the last line to the reduced contour push!(reduced_contour, [current_xs[1], current_ys[1]]); push!(reduced_contour, [current_xs[end], current_ys[end]]); # and begin a new segment starting at the current point current_xs = [x]; current_ys = [y]; end end end reduced_contour |> unique |> x -> hcat(x...)' end function Center(C) # find "center" of a point cloud mean.(eachcol(C))' end function Triangulate(center, points) start = points[1, :]; current = start; Channel() do channel for point in eachrow(points[2:end, :]) put!(channel, (current', center, point')); current = point; end put!(channel, (current', center, start')); end end function CalculateArea(C) # calculate area of star domain with center Center(C) triangleArea((a, b, c)) = abs(det([a 1; b 1; c 1])) / 2; map(triangleArea, Triangulate(Center(C), C)) |> sum end function CartesianToCoords(idxs) # convert CartesianIndex array to (no of pixels) x (2) Matrix hcat(map(idx -> [idx[1], idx[2]], idxs)...)' end function CoordsToImage(A) S = sparse(A[:,1], A[:,2], ones(size(A)[1])); I, J, V = findnz(S); I = I .- (minimum(I) - 1); J = J .- (minimum(J) - 1); sparse(I, J, V) |> Matrix end function Show(A) imshow(CoordsToImage(A)) A end if isempty(ARGS) norm_path = "norm.bmp"; img_path = "in.bmp"; else norm_path = ARGS[1]; img_path = ARGS[2]; end # calculate area of a square we know to have an area of 1mm² # (this could of course be replaced with a arbitrary shape of known area) area_square = load(norm_path) |> img -> Gray.(img) |> FindRoughEdge |> CartesianToCoords |> GrahamScan.FindConvexHull |> Show |> CalculateArea pixelsToMillimeters(x) = x / sqrt(area_square) area(path) = load(path) |> img -> Gray.(img) |> FindRoughEdge |> CartesianToCoords |> GrahamScan.FindConvexHull |> Show |> hull -> CleanUpEdges(10, hull) |> Show |> pixelsToMillimeters |> CalculateArea |> area -> println("The area is $(area) mm². ~$(Int64(round(sqrt(area_square)))) pixels are 1 mm.") area(img_path) println("Press any button to continue...") readline()
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764, 12, 357, 39504, 7, 40, 8, 532, 352, 1776, 198, 220, 220, 220, 449, 796, 449, 764, 12, 357, 39504, 7, 41, 8, 532, 352, 1776, 198, 220, 220, 220, 29877, 7, 40, 11, 449, 11, 569, 8, 930, 29, 24936, 198, 437, 198, 198, 8818, 5438, 7, 32, 8, 198, 220, 220, 220, 545, 12860, 7, 7222, 3669, 2514, 5159, 7, 32, 4008, 198, 220, 220, 220, 317, 198, 437, 198, 198, 361, 318, 28920, 7, 1503, 14313, 8, 198, 220, 220, 220, 2593, 62, 6978, 796, 366, 27237, 13, 65, 3149, 8172, 198, 220, 220, 220, 33705, 62, 6978, 796, 366, 259, 13, 65, 3149, 8172, 198, 17772, 198, 220, 220, 220, 2593, 62, 6978, 796, 5923, 14313, 58, 16, 11208, 198, 220, 220, 220, 33705, 62, 6978, 796, 5923, 14313, 58, 17, 11208, 198, 437, 198, 198, 2, 15284, 1989, 286, 257, 6616, 356, 760, 284, 423, 281, 1989, 286, 352, 3020, 31185, 198, 2, 357, 5661, 714, 286, 1781, 307, 6928, 351, 257, 14977, 5485, 286, 1900, 1989, 8, 198, 20337, 62, 23415, 796, 3440, 7, 27237, 62, 6978, 8, 930, 29, 198, 220, 220, 220, 33705, 4613, 12723, 12195, 9600, 8, 930, 29, 198, 220, 220, 220, 9938, 49, 619, 37021, 930, 29, 198, 220, 220, 220, 13690, 35610, 2514, 7222, 3669, 930, 29, 198, 220, 220, 220, 11520, 33351, 13, 16742, 3103, 303, 87, 39, 724, 930, 29, 5438, 930, 29, 198, 220, 220, 220, 27131, 378, 30547, 198, 198, 79, 14810, 2514, 22603, 31551, 7, 87, 8, 796, 2124, 1220, 19862, 17034, 7, 20337, 62, 23415, 8, 198, 198, 20337, 7, 6978, 8, 796, 3440, 7, 6978, 8, 930, 29, 198, 220, 220, 220, 33705, 4613, 12723, 12195, 9600, 8, 930, 29, 198, 220, 220, 220, 9938, 49, 619, 37021, 930, 29, 198, 220, 220, 220, 13690, 35610, 2514, 7222, 3669, 930, 29, 198, 220, 220, 220, 11520, 33351, 13, 16742, 3103, 303, 87, 39, 724, 930, 29, 5438, 930, 29, 198, 220, 220, 220, 23644, 4613, 5985, 4933, 7407, 3212, 7, 940, 11, 23644, 8, 930, 29, 5438, 930, 29, 198, 220, 220, 220, 17848, 2514, 22603, 31551, 930, 29, 198, 220, 220, 220, 27131, 378, 30547, 930, 29, 198, 220, 220, 220, 1989, 4613, 198, 220, 220, 220, 220, 220, 220, 220, 44872, 7203, 464, 1989, 318, 29568, 20337, 8, 8085, 31185, 13, 5299, 3, 7, 5317, 2414, 7, 744, 7, 31166, 17034, 7, 20337, 62, 23415, 35514, 17848, 389, 352, 8085, 19570, 198, 198, 20337, 7, 9600, 62, 6978, 8, 198, 198, 35235, 7203, 13800, 597, 4936, 284, 2555, 9313, 8, 198, 961, 1370, 3419, 198 ]
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module NamedIndicesMeta using NamedDims, ImageCore, ImageMetadata, ImageAxes, FieldProperties, AxisIndices, LightGraphs, SimpleWeightedGraphs, Reexport, MappedArrays using Base: tail import ImageAxes: timeaxis, timedim, colordim, checknames, isstreamedaxis export NamedDimsArray, AxisIndicesArray, NIMArray, NIArray, IMArray, MetaArray, properties, dimnames, getdim, named_axes, @defdim include("types.jl") include("properties.jl") include("data_format.jl") include("defdim.jl") include("time.jl") using .TimeData include("color.jl") using .ColorData include("observation.jl") using .ObservationData include("spatial.jl") using .SpatialData include("graphs.jl") using .AxisIndicesGraphs include("deprecations.jl") @reexport using NamedIndicesMeta.TimeData @reexport using NamedIndicesMeta.ColorData @reexport using NamedIndicesMeta.ObservationData @reexport using NamedIndicesMeta.SpatialData @reexport using NamedIndicesMeta.AxisIndicesGraphs end # module
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<gh_stars>10-100 ### A Pluto.jl notebook ### # v0.14.4 using Markdown using InteractiveUtils # ╔═╡ d01a9b4f-8b55-4607-abb6-717d227fcd48 begin using PlutoUI, LinearAlgebra PlutoUI.TableOfContents(aside=true) end # ╔═╡ 479a40d9-b81e-442b-9962-f972b110a4dd # Pkg.checkout("SpecialMatrices") using SpecialMatrices # ╔═╡ 574e86bb-159c-4141-8d8a-21bdcc9b5304 # Plot the eigenvalues (singular values) and left singular vectors using Plots # ╔═╡ 5eb73af5-f78a-4811-83a5-ac39063a4516 # pkg> add Arrowhead#master using Arrowhead # ╔═╡ 5d95dc2c-bf94-4b13-b9d5-b7b261e86cf6 md""" # Algorithms for Structured Matrices For matrices with some special structure, it is possible to derive versions of algorithms which are faster and/or more accurate than the standard algorithms. __Prerequisites__ The reader should be familiar with concepts of eigenvalues and eigen vectors, singular values and singular vectors, related perturbation theory, and algorithms. __Competences__ The reader should be able to recognise matrices which have rank-revealing decomposition and apply adequate algorithms, and to apply forward stable algorithms to arrowhead and diagonal-plus-rank-one matrices. """ # ╔═╡ e3e43840-d0a3-4cde-9b1e-5785759912b2 md""" # Rank revealing decompositions For more details, see [<NAME>, Computing Eigenvalues and Singular Values to High Relative Accuracy](https://www.routledge.com/Handbook-of-Linear-Algebra/Hogben/p/book/9781138199897) and [<NAME> et al, Computing the singular value decomposition with high relative accuracy](http://www.sciencedirect.com/science/article/pii/S0024379599001342), and the references therein. Let $A\in\mathbb{R}^{m\times n}$ with $\mathop{\mathrm{rank}}(A)=n$ (therefore, $m\geq n$) and $A=U\Sigma V^T$ its thin SVD. ## Definitions Let $A\in\mathbb{R}^{m\times n}$. The singular values of $A$ are (__perfectly__) __well determined to high relative accuracy__ if changing any entry $A_{kl}$ to $\theta A_{kl}$, $\theta \neq 0$, causes perturbations in singular values bounded by $$ \min\{|\theta|,1/|\theta|\}\sigma_j \leq\tilde \sigma_j \leq \max\{|\theta|,1/|\theta|\}\sigma_j,\quad \forall j.$$ The __sparsity pattern__ of $A$, $Struct(A)$, is the set of indices for which $A_{kl}$ is permitted to be non-zero. The __bipartite graph__ of the sparsity pattern $S$, $\mathcal{G}(S)$, is the graph with vertices partitioned into row vertices $r_1,\ldots,r_m$ and column vertices $c_1,\ldots,c_n$, where $r_k$ and $c_l$ are connected if and only if $(k,l)\in S$. If $\mathcal{G}(S)$ is acyclic, matrices with sparsity pattern $S$ are __biacyclic__. A decomposition $A=XDY^T$ with diagonal matrix $D$ is called a __rank revealing decomposition__ (RRD) if $X$ and $Y$ are full-column rank well-conditioned matrices. __Hilbert matrix__ is a square matrix $H$ with elements $H_{ij}=\displaystyle\frac{1}{i+j-1}$. __Hankel matrix__ is a square matrix with constant elements along skew-diagonals. __Cauchy matrix__ is an $m\times n$ matrix $C$ with elements $C_{ij}=\displaystyle\frac{1}{x_i+y_j}$ with $x_i+y_j\neq 0$ for all $i,j$. """ # ╔═╡ 03f797ac-8764-4688-9e7e-e144cafb3b4c md""" ## Facts 1. The singular values of $A$ are perfectly well determined to high relative accuracy if and only if the bipartite graph $\mathcal{G}(S)$ is acyclic (forest of trees). Examples are bidiagonal and arrowhead matrices. Sparsity pattern $S$ of acyclic bipartite graph allows at most $m+n-1$ nonzero entries. A bisection algorithm computes all singular values of biacyclic matrices to high relative accuracy. 2. An RRD of $A$ can be given or computed to high accuracy by some method. Typical methods are Gaussian elimination with complete pivoting or QR factorization with complete pivoting. 3. Let $\hat X \hat D \hat Y^T$ be the computed RRD of $A$ satisfying $|D_{jj}-\hat D_{jj}| \leq O(\varepsilon)|D_{jj}|$, $\| X-\hat X\|\leq O(\varepsilon) \|X\|$, and $\| Y-\hat Y\|\leq O(\varepsilon) \|Y\|$. The following algorithm computes the EVD of $A$ with high relative accuracy: 1. Perform QR factorization with pivoting to get $\hat X\hat D=QRP$, where $P$ is a permutation matrix. Thus $A=QRP\hat Y^T$. 2. Multiply $W=RP\hat Y^T$ (_NOT_ Strassen's multiplication). Thus $A=QW$ and $W$ is well-scaled from the left. 3. Compute the SVD of $W^T=V\Sigma^T \bar U^T$ using one-sided Jacobi method. Thus $A=Q\bar U \Sigma V^T$. 4. Multiply $U=Q\bar U$. Thus $A=U\Sigma V^T$ is the computed SVD of $A$. 4. Let $R=D'R'$, where $D'$ is such that the _rows_ of $R'$ have unit norms. Then the following error bounds hold: $$ \frac{|\sigma_j-\tilde\sigma_j|}{\sigma_j}\leq O(\varepsilon \kappa(R')\cdot \max\{\kappa(X),\kappa(Y)\})\leq O(\varepsilon n^{3/2}\kappa(X)\cdot \max\{\kappa(X),\kappa(Y)\}).$$ 5. Hilbert matrix is Hankel matrix and Cauchy matrix, it is symmetric positive definite and _very_ ill-conditioned. 6. Every sumbatrix of a Cauchy matrix is itself a Cauchy matrix. 7. Determinant of a square Cauchy matrix is $$ \det(C)=\frac{\prod_{1\leq i<j\leq n}(x_j-x_i)(y_j-y_i)} {\prod_{1\leq i,j\leq n} (x_i+y_j)}.$$ It is computed with elementwise high relative accuracy. 8. Let $A$ be square and nonsingular and let $A=LDR$ be its decomposition with diagonal $D$, lower unit-triangular $L$, and upper unit-triangular $R$. The closed formulas using quotients of minors are (see [A. S. Householder, The Theory of Matrices in Numerical Analysis](https://books.google.hr/books?id=hCre109IpRcC&printsec=frontcover&hl=hr&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false)): $$ \begin{aligned} D_{11}&=A_{11}, \\ D_{jj}&=\frac{\det(A_{1:j,1:j})}{\det(A_{1:j-1,1:j-1})}, \quad j=2,\ldots,n, \\ L_{jj}&=1, \\ L_{ij}&=\frac{\det(A_{[1,2,\ldots,j-1,i],[1:j]}\, )} {\det(A_{1:j,1:j})}, \quad j < i, \\ R_{jj}&=1, \\ R_{ji}&=\frac{\det(A_{[1,2,\ldots,j],[1,2, \ldots,j-1,i]}\, )} {\det(A_{1:j,1:j})}, \quad i > j, \end{aligned}$$ """ # ╔═╡ f280b119-76a7-4ee8-b6fd-608d977af0c6 md""" ## Examples ### Positive definite matrix Let $A=DA_S D$ be strongly scaled symmetric positive definite matrix. Then Cholesky factorization with complete (diagonal) pivoting is an RRD. Consider the following three step algorithm: 1. Compute $P^T A P=LL^T$ (_Cholesky factorization with complete pivoting_). 2. Compute the $L=\bar U\Sigma V^T$ (_one-sided Jacobi, V is not needed_). 3. Set $\Lambda=\Sigma^2$ and $U=P\bar U$. Thus $A=U\Lambda U^T$ is an EVD of $A$. The Cholesky factorization with pivoting can be implemented very fast with block algorithm (see [C. Lucas, LAPack-Style Codes for Level 2 and 3 Pivoted Cholesky Factorizations](http://www.netlib.org/lapack/lawnspdf/lawn161.pdf)). The eigenvalues $\tilde \lambda_j$ computed using the above algorithm satisfy relative error bounds: $$ \frac{|\lambda_j-\tilde\lambda_j|}{\lambda_j} \leq O(n\varepsilon \|A_S\|_2^{-1}).$$ """ # ╔═╡ 28c1a9e7-6c65-4184-b41b-b5cfd17645b5 function JacobiR(A₁::AbstractMatrix) A=deepcopy(A₁) m,n=size(A) T=typeof(A[1,1]) V=Matrix{T}(I,n,n) # Tolerance for rotation tol=√n*eps(T) # Counters p=n*(n-1)/2 sweep=0 pcurrent=0 # First criterion is for standard accuracy, second one is for relative accuracy # while sweep<30 && vecnorm(A-diagm(diag(A)))>tol while sweep<30 && pcurrent<p sweep+=1 # Row-cyclic strategy for i = 1 : n-1 for j = i+1 : n # Compute the 2 x 2 sumbatrix of A'*A # F=A[:,[i,j]]'*A[:,[i,j]] F=view(A,:,[i,j])'*view(A,:,[i,j]) # Check the tolerance - the first criterion is standard, # the second one is for relative accuracy # if A[i,j]!=zero(T) # if abs(F[1,2])>tol*√(F[1,1]*F[2,2]) # Compute c and s τ=(F[2,2]-F[1,1])/(2*F[1,2]) t=sign(τ)/(abs(τ)+√(1+τ^2)) c=1/√(1+t^2) s=c*t G=LinearAlgebra.Givens(i,j,c,s) # A*=G' rmul!(A,G) # V*=G' rmul!(V,G) pcurrent=0 else pcurrent+=1 end end end end σ=[norm(A[:,k]) for k=1:n] for k=1:n A[:,k]./=σ[k] end # A, σ, V SVD(A,σ,adjoint(V)) end # ╔═╡ bc8b94b7-7e20-4cd3-be68-7e9152fe6d7b begin n=20 import Random Random.seed!(421) B=randn(n,n) # Scaled matrix As=Matrix(Symmetric(B'*B)) # Scaling D₀=exp.(50*(rand(n).-0.5)) # Parentheses are necessary! A=[As[i,j]*(D₀[i]*D₀[j]) for i=1:n, j=1:n] issymmetric(A), cond(As), cond(A) end # ╔═╡ 66dedda0-24a0-48a0-9286-4ce688e5da72 # ?cholesky; # ╔═╡ cc924252-137a-4241-b178-2eabf653ff71 md""" We will not use the Cholesky factorization with complete pivoting. Instead, we will just sort the diagonal of $A$ in advance, which is sufficient for this example. _Write the function for Cholesky factorization with complete pivoting as an excercise._ """ # ╔═╡ c41e4534-6b29-4cbe-9b17-2c69c89e5570 # ?sortperm; # ╔═╡ bcee7b90-2dd4-47b0-a781-67d35962d5f2 begin p=sortperm(diag(A), rev=true) L=cholesky(A[p,p]) end # ╔═╡ f471d60f-1c70-4bd5-bb67-2bb17c18f3f8 U,σ,V=JacobiR(Matrix(L.L)); # ╔═╡ c474702a-8af8-4640-9f45-bca748ab3952 begin U₁=U[invperm(p),:] λ=σ.^2 end # ╔═╡ 25143fe5-3965-468a-8cb1-7c3e8e8027ea U'*A[p,p]*U # ╔═╡ 86dc734d-f45d-491f-9f80-7d958e642fbd # Due to large condition number, this is not # as accurate as expected Ξ=U₁'*A*U₁ # ╔═╡ 001f801a-06d2-48be-a5ad-4c07f7a92890 # Orthogonality norm(U₁'*U₁-I) # ╔═╡ 5196b508-5aad-4811-bfc0-1c1fa00eec37 begin DΞ=sqrt.(diag(Ξ)) Ξs=[Ξ[i,j]/(DΞ[i]*DΞ[j]) for i=1:n, j=1:n] end # ╔═╡ bcfdae20-040f-492e-bc67-60cf5f420cc0 begin K=U₁*Diagonal(σ) K'*K end # ╔═╡ 3ad5b60e-3fb9-443e-90eb-40b2fd78d9a3 # Explain why is the residual so large. norm(A*U₁-U₁*Diagonal(λ)) # ╔═╡ 3e863b09-dc68-4560-adeb-e56ab22d9afd # Relative residual is percfect norm(A*U₁-U₁*Diagonal(λ))/norm(A) # ╔═╡ a5555731-c229-43de-9d88-cc3b91d7db67 [λ sort(eigvals(A),rev=true)] # ╔═╡ c3fcbaf1-b98f-401f-9244-aad632805ecb md""" ### Hilbert matrix We need the newest version of the package [SpecialMatrices.jl](https://github.com/JuliaMatrices/SpecialMatrices.jl). """ # ╔═╡ 8d7e4960-9b04-4f5e-b2e6-acce80a6812f varinfo(SpecialMatrices) # ╔═╡ 80d64fc6-33e7-4069-a6f4-8e85369dad9f C=Cauchy([1,2,3,4,5],[0,1,2,3,4]) # ╔═╡ 804c4972-bf3d-4284-8426-b50b3ea5bb1b H=Hilbert(5) # ╔═╡ e9bf2b02-a60d-495b-bbdb-d87e31d8d9e0 Hf=Matrix(H) # ╔═╡ c113b7fe-87b9-454c-aeb9-166af79bbe61 begin # Exact formula for the determinant of a Cauchy matrix from Fact 7. import LinearAlgebra.det function det(C::Cauchy{T}) where T n=length(C.x) F=triu([(C.x[j]-C.x[i])*(C.y[j]-C.y[i]) for i=1:n, j=1:n],1) num=prod(F[findall(!iszero,F)]) den=prod([(C.x[i]+C.y[j]) for i=1:n, j=1:n]) if all(isinteger,C.x)&all(isinteger,C.y) return num//den else return num/den end end end # ╔═╡ c6c0e7b2-400c-478e-90af-496659fed9c4 # This is exact det(Hf) # ╔═╡ d057146d-4a5c-415f-9332-9b3bb7e82fc4 det(C) # ╔═╡ 66115e0a-a5c8-4a66-958b-27d9a7865855 md""" Compute componentwise highly accurate $A=LDL ^T$ factorization of a Hilbert (Cauchy) matrix. Using `Rational` numbers gives high accuracy. """ # ╔═╡ 43656da7-93db-4de3-b420-098f64dfba85 # Exact LDLT factorization from Fact 8, no pivoting. function LDLT(C::Cauchy) n=length(C.x) T=typeof(C.x[1]) D=Array{Rational{T}}(undef,n) L=Matrix{Rational{T}}(I,n,n) δ=[det(Cauchy(C.x[1:j],C.y[1:j])) for j=1:n] D[1]=map(Rational{T},C[1,1]) D[2:n]=δ[2:n]./δ[1:n-1] for i=2:n for j=1:i-1 L[i,j]=det(Cauchy( C.x[[1:j-1;i]], C.y[1:j])) / δ[j] end end L,D end # ╔═╡ d0b35d9b-8b87-4831-846c-b63fe8912ec7 L₁,D₁=LDLT(C) # ╔═╡ d25dcaa1-2448-49ec-b863-b224edc4ec2c L₁*Diagonal(D₁)*L₁' # -Matrix(H) # ╔═╡ d668a5a2-8060-48ef-adf0-6be0b1c3859e # L*D*L' is an RRD cond(L₁) # ╔═╡ a4a40d52-94fc-4ccc-8e35-09c75c5de755 cond(C) # ╔═╡ dbfbbb00-ab00-4c85-a4ce-32468f18088a md""" We now compute the accurate EVD of the Hilbert matrix of order $n=100$. We cannot use the function `LDLT()` since the _computation of determinant causes overflow_ and _there is no pivoting_. Instead, we use Algorithm 3 from [<NAME>, Computing the singular value decomposition with high relative accuracy, SIAM J. Matrix Anal. Appl, 21 (1999) 562-580](http://www.netlib.org/lapack/lawnspdf/lawn130.pdf). """ # ╔═╡ 0176739c-2846-4b07-a8d3-68454c1251a6 function GECP(C::Cauchy) n=length(C.x) G=Matrix(C) x=copy(C.x) y=copy(C.y) pr=collect(1:n) pc=collect(1:n) # Find the maximal element for k=1:n-1 i,j=Tuple(argmax(abs.(G[k:n,k:n]))) i+=k-1 j+=k-1 if i!=k || j!=k G[[i,k],:]=G[[k,i],:] G[:, [j,k]]=G[:, [k,j]] x[[k,i]]=x[[i,k]] y[[k,j]]=y[[j,k]] pr[[i,k]]=pr[[k,i]] pc[[j,k]]=pc[[k,j]] end for r=k+1:n for s=k+1:n G[r,s]=G[r,s]*(x[r]-x[k])*(y[s]-y[k])/ ((x[k]+y[s])*(x[r]+y[k])) end end G=Matrix(Symmetric(G)) end D=diag(G) X=tril(G,-1)*Diagonal(1.0./D)+I Y=Diagonal(1.0./D)*triu(G,1)+I X,D,Y', pr,pc end # ╔═╡ 8b844cf7-c574-45e9-b682-fa9cc6e9cb73 X,D,Y,pr,pc=GECP(C) # ╔═╡ b3877ea1-d479-4d08-af8c-26e17de77106 # Check norm(X*Diagonal(D)*Y'-Matrix(C)[pr,pc]), norm(X[invperm(pr),:]*Diagonal(D)*Y[invperm(pc),:]'-C) # ╔═╡ 9bff72d8-68b6-41b6-a954-ee39f90ec7b0 begin # Now the big test. n₂=100 H₂=Hilbert(n₂) C₂=Cauchy(collect(1:n₂), collect(0:n₂-1)) end # ╔═╡ 72390bf0-c5d6-46de-b8d0-0bee2cbb0af7 md""" We need a function to compute RRD from `GECP()` """ # ╔═╡ 46656cee-2202-4eb9-9725-1e3e3af4df42 function RRD(C::Cauchy) X,D,Y,pr,pc=GECP(C) X[invperm(pr),:], D, Y[invperm(pc),:] end # ╔═╡ 6b544eaf-858a-40e5-b59b-75cfa2237a6f X₂,D₂,Y₂=RRD(C₂); # ╔═╡ 8eb2d15e-53c0-40ab-98ce-7b0bbf4d6cd1 # Check norm((X₂*Diagonal(D₂)*Y₂')-C₂) # ╔═╡ c7fd604c-9e63-4ac0-8839-82371f968ba7 cond(C₂) # ╔═╡ 23df1242-1b43-4d52-a8ed-1b12b0d5d4b9 # Is this RRD? here X=Y cond(X₂), cond(Y₂) # ╔═╡ 14374962-2eea-4c8f-9390-73ef886c25d9 # Algorithm from Fact 3 function RRDSVD(X,D,Y) Q,R,p=qr(X*Diagonal(D),Val(true)) W=R[:,p]*Y' V,σ,U₁=JacobiR(W') U=Q*U₁ U,σ,V end # ╔═╡ d4a86d03-0afe-4b1d-8828-52eae055aa9f U₂,σ₂,V₂=RRDSVD(X₂,D₂,Y₂); # ╔═╡ 1dfe86bf-7553-444b-a94e-340acccf5375 # Residual and orthogonality norm(Matrix(C₂)*V₂-U₂*Diagonal(σ₂)), norm(U₂'*U₂-I), norm(V₂'*V₂-I) # ╔═╡ cc123376-b657-4d8f-88e0-0f7577058c2b # Observe the differences!! [sort(σ₂) sort(svdvals(C₂)) sort(eigvals(Matrix(C₂)))] # ╔═╡ 337ade70-accb-417b-b655-9640ed61b375 plot(σ₂,yscale = :log10,legend=false, title="Singular values of Hilbert matrix") # ╔═╡ f7556414-693d-4d46-889b-ed6b091a235e begin # Better spy # some options :bluesreds,clim=(-1.0,1.0) import Plots.spy spy(A)=heatmap(A, yflip=true, color=:bluesreds, aspectratio=1) end # ╔═╡ 9ee70a5d-59f7-460c-92af-8872966cec40 spy(U₂) # ╔═╡ abb64f41-6817-49e9-9d44-81ef0ce32934 md""" # Symmetric arrowhead and DPR1 matrices For more details, see [<NAME>, <NAME> and <NAME>, Accurate eigenvalue decomposition of real symmetric arrowhead matrices and applications](https://arxiv.org/abs/1302.7203) and [<NAME>, <NAME> and <NAME>, Forward stable eigenvalue decomposition of rank-one modifications of diagonal matrices](https://arxiv.org/abs/1405.7537). """ # ╔═╡ b24b1c96-c73e-4c8e-83af-f35e2b9df304 md""" ## Definitions An __arrowhead matrix__ is a real symmetric matrix of order $n$ of the form $A=\begin{bmatrix} D & z \\ z^{T} & \alpha \end{bmatrix}$, where $D=\mathop{\mathrm{diag}}(d_{1},d_{2},\ldots ,d_{n-1})$, $z=\begin{bmatrix} \zeta _{1} & \zeta _{2} & \cdots & \zeta _{n-1} \end{bmatrix}^T$ is a vector, and $\alpha$ is a scalar. An arrowhead matrix is __irreducible__ if $\zeta _{i}\neq 0$ for all $i$ and $d_{i}\neq d_{j}$ for all $i\neq j$. A __diagonal-plus-rank-one matrix__ (DPR1 matrix) is a real symmetric matrix of order $n$ of the form $A= D +\rho z z^T$, where $D=\mathop{\mathrm{diag}}(d_{1},d_{2},\ldots ,d_{n})$, $z=\begin{bmatrix} \zeta _{1} & \zeta _{2} & \cdots & \zeta _{n} \end{bmatrix}^T$ is a vector, and $\rho \neq 0$ is a scalar. A DPR1 matrix is __irreducible__ if $\zeta _{i}\neq 0$ for all $i$ and $d_{i}\neq d_{j}$ for all $i\neq j$. """ # ╔═╡ 5e068291-cb05-4fd5-acaa-8e5d766e375a md""" ## Facts on arrowhead matrices Let $A$ be an arrowhead matrix of order $n$ and let $A=U\Lambda U^T$ be its EVD. 1. If $d_i$ and $\lambda_i$ are nonincreasingy ordered, the Cauchy Interlace Theorem implies $$\lambda _{1}\geq d_{1}\geq \lambda _{2}\geq d_{2}\geq \cdots \geq d_{n-2}\geq\lambda _{n-1}\geq d_{n-1}\geq \lambda _{n}.$$ 2. If $\zeta _{i}=0$ for some $i$, then $d_{i}$ is an eigenvalue whose corresponding eigenvector is the $i$-th unit vector, and we can reduce the size of the problem by deleting the $i$-th row and column of the matrix. If $d_{i}=d_{j}$, then $d_{i}$ is an eigenvalue of $A$ (this follows from the interlacing property) and we can reduce the size of the problem by annihilating $\zeta_j$ with a Givens rotation in the $(i,j)$-plane. 3. If $A$ is irreducible, the interlacing property holds with strict inequalities. 4. The eigenvalues of $A$ are the zeros of the __Pick function__ (also, _secular equation_) $$ f(\lambda )=\alpha -\lambda -\sum_{i=1}^{n-1}\frac{\zeta _{i}^{2}}{% d_{i}-\lambda }=\alpha -\lambda -z^{T}(D-\lambda I)^{-1}z,$$ and the corresponding eigenvectors are $$ U_{:,i}=\frac{x_{i}}{\left\Vert x_{i}\right\Vert _{2}},\quad x_{i}=\begin{bmatrix} \left( D-\lambda _{i}I\right) ^{-1}z \\ -1% \end{bmatrix}, \quad i=1,\ldots ,n.$$ 5. Let $A$ be irreducible and nonsingular. If $d_i\neq 0$ for all $i$, then $A^{-1}$ is a DPR1 matrix $$ A^{-1}=\begin{bmatrix} D^{-1} & \\ & 0 \end{bmatrix} + \rho uu^{T},$$ where $u=\begin{bmatrix} D^{-1}z \\ -1 \end{bmatrix}$, and $\rho =\displaystyle\frac{1}{\alpha-z^{T}D^{-1}z}$. If $d_i=0$, then $A^{-1}$ is a permuted arrowhead matrix, $$ A^{-1}\equiv \begin{bmatrix} D_{1} & 0 & 0 & z_{1} \\ 0 & 0 & 0 & \zeta _{i} \\ 0 & 0 & D_{2} & z_{2} \\ z_{1}^{T} & \zeta _{i} & z_{2}^{T} & \alpha \end{bmatrix}^{-1} = \begin{bmatrix} D_{1}^{-1} & w_{1} & 0 & 0 \\ w_{1}^{T} & b & w_{2}^{T} & 1/\zeta _{i} \\ 0 & w_{2} & D_{2}^{-1} & 0 \\ 0 & 1/\zeta _{i} & 0 & 0 \end{bmatrix},$$ where $$ \begin{aligned} w_{1}&=-D_{1}^{-1}z_{1}\displaystyle\frac{1}{\zeta _{i}},\\ w_{2}&=-D_{2}^{-1}z_{2}\displaystyle\frac{1}{\zeta _{i}},\\ b&= \displaystyle\frac{1}{\zeta _{i}^{2}}\left(-\alpha +z_{1}^{T}D_{1}^{-1}z_{1}+z_{2}^{T}D_{2}^{-1}z_{2}\right). \end{aligned}$$ 6. The algorithm based on the following approach computes all eigenvalues and _all components_ of the corresponding eigenvectors in a forward stable manner to almost full accuracy in $O(n)$ operations per eigenpair: 1. Shift the irreducible $A$ to $d_i$ which is closer to $\lambda_i$ (one step of bisection on $f(\lambda)$). 2. Invert the shifted matrix. 3. Compute the absolutely largest eigenvalue of the inverted shifted matrix and the corresponding eigenvector. 7. The algorithm is implemented in the package [Arrowhead.jl](https://github.com/ivanslapnicar/Arrowhead.jl). In certain cases, $b$ or $\rho$ need to be computed with extended precision. For this, we use the functions from file [DoubleDouble.jl](https://github.com/ivanslapnicar/Arrowhead.jl/blob/master/src/DoubleDouble.jl), originally from the the package [DoubleDouble.jl](https://github.com/simonbyrne/DoubleDouble.jl). """ # ╔═╡ 6d7f330f-3c88-44bd-aff9-65daa7f1ea1c md" ## Examples ### Extended precision arithmetic " # ╔═╡ eea30449-2410-448d-885f-e27b7aa657c0 begin # Extended precision arithmetic a=2.0 b=3.0 √a end # ╔═╡ 21f8e11a-09fe-49a9-91cd-b118876910a8 √BigFloat(a) # ╔═╡ 6d484719-e545-4742-9e46-515525af8244 # Double numbers according to Dekker, 1971 ad=Arrowhead.Double(a) # ╔═╡ 310ace86-6193-43ee-bdac-fe1a79a2cb26 bd=Arrowhead.Double(b) # ╔═╡ be91a6f1-2594-4084-8b23-312c51f35553 roota=√ad # ╔═╡ 5762ccae-2a28-4041-8a02-4bec918e192a rootb=√bd # ╔═╡ 4c1a73a1-bdfe-4939-8282-54b7fa193e5a # 30 digits should match BigFloat(roota.hi)+BigFloat(roota.lo) # ╔═╡ f5b83025-64e7-44d1-a841-f9a7fc8614fb √BigFloat(a)*√BigFloat(b) # ╔═╡ 89e578d9-7608-42f2-a2f0-0ec2c89bd510 rootab=roota*rootb # ╔═╡ 24f1cb57-ed20-4399-996f-80294c7b1bb2 BigFloat(rootab.hi)+BigFloat(rootab.lo) # ╔═╡ 8339adb3-b0fa-45e4-b107-62954547345b md""" ### Random arrowhead matrix """ # ╔═╡ 321070d6-c0c9-482d-9a5f-0e5a4f7b522f varinfo(Arrowhead) # ╔═╡ 9c207955-174d-474f-be95-061c71761023 methods(GenSymArrow) # ╔═╡ bd8ea662-f4c2-4f2d-943b-b1a5f59cbe74 begin n₃=8 A₃=GenSymArrow(n₃,n₃) end # ╔═╡ 9fe07467-4033-4374-b4f2-b0ceecf91a03 # Elements of the type SymArrow A₃.D, A₃.z, A₃.a, A₃.i # ╔═╡ a3ba9c15-1d55-4e2b-b32e-4202fbeca671 E₃,info₃=eigen(A₃) # ╔═╡ 34d1bc3f-b703-4b31-887e-36d036e5c3f9 @which eigen(A₃) # ╔═╡ 8677d8b8-f68b-4013-864c-89902ffff8fd # Residual and orthogonality norm(A₃*E₃.vectors-E₃.vectors*Diagonal(E₃.values)), norm(E₃.vectors'*E₃.vectors-I) # ╔═╡ 360829ce-2790-4d70-8699-687650fc51b4 begin # Timings - notice the O(n^2) @time eigen(GenSymArrow(1000,1000)) @time eigen(GenSymArrow(2000,2000)) 1 end # ╔═╡ 9f2a5e5d-a705-4d65-8ebf-6b6d9d548999 md""" ### Numerically demanding matrix """ # ╔═╡ 19d95a91-8c03-4b12-a8d3-46a6861991a1 A₄=SymArrow( [ 1e10+1.0/3.0, 4.0, 3.0, 2.0, 1.0 ], [ 1e10 - 1.0/3.0, 1.0, 1.0, 1.0, 1.0 ], 1e10, 6 ) # ╔═╡ 4a86007c-1137-4964-be3c-da51ab7fca3c begin E₄,info₄=eigen(A₄) [sort(E₄.values) sort(eigvals(Matrix(A₄))) sort(E₄.values)-sort(eigvals(Matrix(A₄)))] end # ╔═╡ 33e70165-1fab-4996-99e6-a5d27bf976c5 # Residual and orthogonality norm(A₄*E₄.vectors-E₄.vectors*Diagonal(E₄.values)), norm(E₄.vectors'*E₄.vectors-I) # ╔═╡ 65e00890-d4d7-4d36-a359-4606378401b7 md""" ## Facts on DPR1 matrices The properties of DPR1 matrices are very similar to those of arrowhead matrices. Let $A$ be a DPR1 matrix of order $n$ and let $A=U\Lambda U^T$ be its EVD. 1. If $d_i$ and $\lambda_i$ are nonincreasingy ordered and $\rho>0$, then $$\lambda _{1}\geq d_{1}\geq \lambda _{2}\geq d_{2}\geq \cdots \geq d_{n-2}\geq\lambda _{n-1}\geq d_{n-1}\geq \lambda _{n}\geq d_n.$$ If $A$ is irreducible, the inequalities are strict. 2. Fact 2 on arrowhead matrices holds. 3. The eigenvalues of $A$ are the zeros of the __secular equation__ $$ f(\lambda )=1+\rho\sum_{i=1}^{n}\frac{\zeta _{i}^{2}}{d_{i}-\lambda } =1 +\rho z^{T}(D-\lambda I)^{-1}z=0,$$ and the corresponding eigenvectors are $$ U_{:,i}=\frac{x_{i}}{\left\Vert x_{i}\right\Vert _{2}},\quad x_{i}=( D-\lambda _{i}I) ^{-1}z.$$ 4. Let $A$ be irreducible and nonsingular. If $d_i\neq 0$ for all $i$, then $$ A^{-1}=D^{-1} +\gamma uu^{T},\quad u=D^{-1}z, \quad \gamma =-\frac{\rho}{1+\rho z^{T}D^{-1}z},$$ is also a DPR1 matrix. If $d_i=0$, then $A^{-1}$ is a permuted arrowhead matrix, $$ A^{-1}\equiv \left(\begin{bmatrix} D_{1} & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & D_{2} \end{bmatrix} +\rho \begin{bmatrix} z_{1} \\ \zeta _{i} \\ z_{2} \end{bmatrix} \begin{bmatrix} z_{1}^{T} & \zeta _{i} & z_{2}^{T} \end{bmatrix}\right)^{-1}= \begin{bmatrix} D_{1}^{-1} & w_{1} & 0 \\ w_{1}^{T} & b & w_{2}^{T} \\ 0 & w_{2} & D_{2}^{-1} \end{bmatrix},$$ where $$ \begin{aligned} w_{1}&=-D_{1}^{-1}z_{1}\displaystyle\frac{1}{\zeta _{i}},\\ w_{2}&=-D_{2}^{-1}z_{2}\displaystyle\frac{1}{\zeta _{i}},\\ b &=\displaystyle\frac{1}{\zeta _{i}^{2}}\left( \frac{1}{\rho}+z_{1}^{T}D_{1}^{-1}z_{1}+z_{2}^{T}D_{2}^{-1}z_{2}\right). \end{aligned}$$ 5. The algorithm based on the same approach as above, computes all eigenvalues and all components of the corresponding eigenvectors in a forward stable manner to almost full accuracy in $O(n)$ operations per eigenpair. The algorithm is implemented in the package `Arrowhead.jl`. In certain cases, $b$ or $\gamma$ need to be computed with extended precision. """ # ╔═╡ 6b3c7336-f1fc-428b-8b1e-94546b6622e8 md""" ## Examples ### Random DPR1 matrix """ # ╔═╡ 71d0022d-3cd8-488c-80b8-4c3c958ed8fa begin n₅=8 A₅=GenSymDPR1(n₅) end # ╔═╡ 4655eb27-0ae8-46db-9deb-575c89d08e7e # Elements of the type SymDPR1 A₅.D, A₅.u, A₅.r # ╔═╡ 6642fa72-8b1b-40b2-87f2-91199a96d7f9 begin E₅,info₅=eigen(A₅) norm(A₅*E₅.vectors-E₅.vectors*Diagonal(E₅.values)), norm(E₅.vectors'*E₅.vectors-I) end # ╔═╡ 827bb865-c361-4a98-a6d6-90dca425eddc md""" ### Numerically demanding DPR1 matrix """ # ╔═╡ 56c0c2ec-220f-4e04-a0cf-a144d89dd225 # Choose one A₆=SymDPR1( [ 1e10, 5.0, 4e-3, 0.0, -4e-3,-5.0 ], [ 1e10, 1.0, 1.0, 1e-7, 1.0,1.0 ], 1.0 ) # ╔═╡ 31114bf7-450b-458d-9a1d-c22123f5f291 begin E₆,info₆=eigen(A₆) [sort(E₆.values) sort(eigvals(Matrix(A₆)))] end # ╔═╡ ad305a98-6cdc-424e-8f06-386b801b53e5 # Residual and orthogonality norm(A₆*E₆.vectors-E₆.vectors*Diagonal(E₆.values)), norm(E₆.vectors'*E₆.vectors-I) # ╔═╡ Cell order: # ╟─d01a9b4f-8b55-4607-abb6-717d227fcd48 # ╟─5d95dc2c-bf94-4b13-b9d5-b7b261e86cf6 # ╟─e3e43840-d0a3-4cde-9b1e-5785759912b2 # ╟─03f797ac-8764-4688-9e7e-e144cafb3b4c # ╟─f280b119-76a7-4ee8-b6fd-608d977af0c6 # ╠═28c1a9e7-6c65-4184-b41b-b5cfd17645b5 # ╠═bc8b94b7-7e20-4cd3-be68-7e9152fe6d7b # ╠═66dedda0-24a0-48a0-9286-4ce688e5da72 # ╟─cc924252-137a-4241-b178-2eabf653ff71 # ╠═c41e4534-6b29-4cbe-9b17-2c69c89e5570 # ╠═bcee7b90-2dd4-47b0-a781-67d35962d5f2 # ╠═f471d60f-1c70-4bd5-bb67-2bb17c18f3f8 # ╠═c474702a-8af8-4640-9f45-bca748ab3952 # ╠═25143fe5-3965-468a-8cb1-7c3e8e8027ea # ╠═86dc734d-f45d-491f-9f80-7d958e642fbd # ╠═001f801a-06d2-48be-a5ad-4c07f7a92890 # ╠═5196b508-5aad-4811-bfc0-1c1fa00eec37 # ╠═bcfdae20-040f-492e-bc67-60cf5f420cc0 # ╠═3ad5b60e-3fb9-443e-90eb-40b2fd78d9a3 # ╠═3e863b09-dc68-4560-adeb-e56ab22d9afd # ╠═a5555731-c229-43de-9d88-cc3b91d7db67 # ╟─c3fcbaf1-b98f-401f-9244-aad632805ecb # ╠═479a40d9-b81e-442b-9962-f972b110a4dd # ╠═8d7e4960-9b04-4f5e-b2e6-acce80a6812f # ╠═80d64fc6-33e7-4069-a6f4-8e85369dad9f # ╠═804c4972-bf3d-4284-8426-b50b3ea5bb1b # ╠═e9bf2b02-a60d-495b-bbdb-d87e31d8d9e0 # ╠═c6c0e7b2-400c-478e-90af-496659fed9c4 # ╠═c113b7fe-87b9-454c-aeb9-166af79bbe61 # ╠═d057146d-4a5c-415f-9332-9b3bb7e82fc4 # ╟─66115e0a-a5c8-4a66-958b-27d9a7865855 # ╠═43656da7-93db-4de3-b420-098f64dfba85 # ╠═d0b35d9b-8b87-4831-846c-b63fe8912ec7 # ╠═d25dcaa1-2448-49ec-b863-b224edc4ec2c # ╠═d668a5a2-8060-48ef-adf0-6be0b1c3859e # ╠═a4a40d52-94fc-4ccc-8e35-09c75c5de755 # ╟─dbfbbb00-ab00-4c85-a4ce-32468f18088a # ╠═0176739c-2846-4b07-a8d3-68454c1251a6 # ╠═8b844cf7-c574-45e9-b682-fa9cc6e9cb73 # ╠═b3877ea1-d479-4d08-af8c-26e17de77106 # ╠═9bff72d8-68b6-41b6-a954-ee39f90ec7b0 # ╟─72390bf0-c5d6-46de-b8d0-0bee2cbb0af7 # ╠═46656cee-2202-4eb9-9725-1e3e3af4df42 # ╠═6b544eaf-858a-40e5-b59b-75cfa2237a6f # ╠═8eb2d15e-53c0-40ab-98ce-7b0bbf4d6cd1 # ╠═c7fd604c-9e63-4ac0-8839-82371f968ba7 # ╠═23df1242-1b43-4d52-a8ed-1b12b0d5d4b9 # ╠═14374962-2eea-4c8f-9390-73ef886c25d9 # ╠═d4a86d03-0afe-4b1d-8828-52eae055aa9f # ╠═1dfe86bf-7553-444b-a94e-340acccf5375 # ╠═cc123376-b657-4d8f-88e0-0f7577058c2b # ╠═574e86bb-159c-4141-8d8a-21bdcc9b5304 # ╠═337ade70-accb-417b-b655-9640ed61b375 # ╠═f7556414-693d-4d46-889b-ed6b091a235e # ╠═9ee70a5d-59f7-460c-92af-8872966cec40 # ╟─abb64f41-6817-49e9-9d44-81ef0ce32934 # ╟─b24b1c96-c73e-4c8e-83af-f35e2b9df304 # ╟─5e068291-cb05-4fd5-acaa-8e5d766e375a # ╟─6d7f330f-3c88-44bd-aff9-65daa7f1ea1c # ╠═5eb73af5-f78a-4811-83a5-ac39063a4516 # ╠═eea30449-2410-448d-885f-e27b7aa657c0 # ╠═21f8e11a-09fe-49a9-91cd-b118876910a8 # ╠═6d484719-e545-4742-9e46-515525af8244 # ╠═310ace86-6193-43ee-bdac-fe1a79a2cb26 # ╠═be91a6f1-2594-4084-8b23-312c51f35553 # ╠═5762ccae-2a28-4041-8a02-4bec918e192a # ╠═4c1a73a1-bdfe-4939-8282-54b7fa193e5a # ╠═f5b83025-64e7-44d1-a841-f9a7fc8614fb # ╠═89e578d9-7608-42f2-a2f0-0ec2c89bd510 # ╠═24f1cb57-ed20-4399-996f-80294c7b1bb2 # ╟─8339adb3-b0fa-45e4-b107-62954547345b # ╠═321070d6-c0c9-482d-9a5f-0e5a4f7b522f # ╠═9c207955-174d-474f-be95-061c71761023 # ╠═bd8ea662-f4c2-4f2d-943b-b1a5f59cbe74 # ╠═9fe07467-4033-4374-b4f2-b0ceecf91a03 # ╠═a3ba9c15-1d55-4e2b-b32e-4202fbeca671 # ╠═34d1bc3f-b703-4b31-887e-36d036e5c3f9 # ╠═8677d8b8-f68b-4013-864c-89902ffff8fd # ╠═360829ce-2790-4d70-8699-687650fc51b4 # ╟─9f2a5e5d-a705-4d65-8ebf-6b6d9d548999 # ╠═19d95a91-8c03-4b12-a8d3-46a6861991a1 # ╠═4a86007c-1137-4964-be3c-da51ab7fca3c # ╠═33e70165-1fab-4996-99e6-a5d27bf976c5 # ╟─65e00890-d4d7-4d36-a359-4606378401b7 # ╟─6b3c7336-f1fc-428b-8b1e-94546b6622e8 # ╠═71d0022d-3cd8-488c-80b8-4c3c958ed8fa # ╠═4655eb27-0ae8-46db-9deb-575c89d08e7e # ╠═6642fa72-8b1b-40b2-87f2-91199a96d7f9 # ╟─827bb865-c361-4a98-a6d6-90dca425eddc # ╠═56c0c2ec-220f-4e04-a0cf-a144d89dd225 # ╠═31114bf7-450b-458d-9a1d-c22123f5f291 # ╠═ad305a98-6cdc-424e-8f06-386b801b53e5
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1.829519
15,773
# This file is a part of Julia. License is MIT: https://julialang.org/license using Test # code_native / code_llvm (issue #8239) # It's hard to really test these, but just running them should be # sufficient to catch segfault bugs. module ReflectionTest using Test, Random function test_ir_reflection(freflect, f, types) @test !isempty(freflect(f, types)) nothing end function test_bin_reflection(freflect, f, types) iob = IOBuffer() freflect(iob, f, types) str = String(take!(iob)) @test !isempty(str) nothing end function test_code_reflection(freflect, f, types, tester) tester(freflect, f, types) tester(freflect, f, (types.parameters...,)) nothing end function test_code_reflections(tester, freflect) test_code_reflection(freflect, occursin, Tuple{Regex, AbstractString}, tester) # abstract type test_code_reflection(freflect, +, Tuple{Int, Int}, tester) # leaftype signature test_code_reflection(freflect, +, Tuple{Array{Float32}, Array{Float32}}, tester) # incomplete types test_code_reflection(freflect, Module, Tuple{}, tester) # Module() constructor (transforms to call) test_code_reflection(freflect, Array{Int64}, Tuple{Array{Int32}}, tester) # with incomplete types test_code_reflection(freflect, muladd, Tuple{Float64, Float64, Float64}, tester) end test_code_reflections(test_ir_reflection, code_lowered) test_code_reflections(test_ir_reflection, code_typed) io = IOBuffer() Base.print_statement_costs(io, map, (typeof(sqrt), Tuple{Int})) str = String(take!(io)) @test occursin("map(f, t::Tuple{Any})", str) @test occursin("sitofp", str) @test occursin(r"20 .*sqrt_llvm.*::Float64", str) end # module ReflectionTest # isbits, isbitstype @test !isbitstype(Array{Int}) @test isbitstype(Float32) @test isbitstype(Int) @test !isbitstype(AbstractString) @test isbitstype(Tuple{Int, Vararg{Int, 2}}) @test !isbitstype(Tuple{Int, Vararg{Int}}) @test !isbitstype(Tuple{Integer, Vararg{Int, 2}}) @test isbitstype(Tuple{Int, Vararg{Any, 0}}) @test isbitstype(Tuple{Vararg{Any, 0}}) @test isbits(1) @test isbits((1,2)) @test !isbits([1]) @test isbits(nothing) # issue #16670 @test isconcretetype(Int) @test isconcretetype(Vector{Int}) @test isconcretetype(Tuple{Int, Vararg{Int, 2}}) @test !isconcretetype(Tuple{Any}) @test !isconcretetype(Tuple{Integer, Vararg{Int, 2}}) @test !isconcretetype(Tuple{Int, Vararg{Int}}) @test !isconcretetype(Type{Tuple{Integer, Vararg{Int}}}) @test !isconcretetype(Type{Vector}) @test !isconcretetype(Type{Int}) @test !isconcretetype(Tuple{Type{Int}}) @test isconcretetype(DataType) @test isconcretetype(Union) @test !isconcretetype(Union{}) @test isconcretetype(Tuple{Union{}}) @test !isconcretetype(Complex) @test !isconcretetype(Complex.body) @test !isconcretetype(AbstractArray{Int,1}) struct AlwaysHasLayout{T} x end @test !isconcretetype(AlwaysHasLayout) && !isconcretetype(AlwaysHasLayout.body) @test isconcretetype(AlwaysHasLayout{Any}) @test isconcretetype(Ptr{Cvoid}) @test !isconcretetype(Ptr) && !isconcretetype(Ptr.body) # issue #10165 i10165(::Type) = 0 i10165(::Type{AbstractArray{T,n}}) where {T,n} = 1 @test i10165(AbstractArray{Int,n} where n) == 0 @test which(i10165, Tuple{Type{AbstractArray{Int,n} where n},}).sig == Tuple{typeof(i10165),Type} # fullname @test fullname(Base) == (:Base,) @test fullname(Base.Iterators) == (:Base, :Iterators) const a_const = 1 not_const = 1 @test isconst(@__MODULE__, :a_const) == true @test isconst(Base, :pi) == true @test isconst(@__MODULE__, :pi) == true @test isconst(@__MODULE__, :not_const) == false @test isconst(@__MODULE__, :is_not_defined) == false @test ismutable(1) == false @test ismutable([]) == true @test ismutabletype(Int) == false @test ismutabletype(Vector{Any}) == true @test ismutabletype(Union{Int, Vector{Any}}) == false ## find bindings tests @test ccall(:jl_get_module_of_binding, Any, (Any, Any), Base, :sin)==Base # For curmod_* include("testenv.jl") module TestMod7648 using Test import Base.convert import ..curmod_name, ..curmod export a9475, foo9475, c7648, foo7648, foo7648_nomethods, Foo7648 const c7648 = 8 d7648 = 9 const f7648 = 10 foo7648(x) = x function foo7648_nomethods end mutable struct Foo7648 end module TestModSub9475 using Test using ..TestMod7648 import ..curmod_name export a9475, foo9475 a9475 = 5 b9475 = 7 foo9475(x) = x let @test Base.binding_module(@__MODULE__, :a9475) == @__MODULE__ @test Base.binding_module(@__MODULE__, :c7648) == TestMod7648 @test Base.nameof(@__MODULE__) == :TestModSub9475 @test Base.fullname(@__MODULE__) == (curmod_name..., :TestMod7648, :TestModSub9475) @test Base.parentmodule(@__MODULE__) == TestMod7648 end end # module TestModSub9475 using .TestModSub9475 let @test Base.binding_module(@__MODULE__, :d7648) == @__MODULE__ @test Base.binding_module(@__MODULE__, :a9475) == TestModSub9475 @test Base.nameof(@__MODULE__) == :TestMod7648 @test Base.parentmodule(@__MODULE__) == curmod end end # module TestMod7648 let @test Base.binding_module(TestMod7648, :d7648) == TestMod7648 @test Base.binding_module(TestMod7648, :a9475) == TestMod7648.TestModSub9475 @test Base.binding_module(TestMod7648.TestModSub9475, :b9475) == TestMod7648.TestModSub9475 @test Set(names(TestMod7648))==Set([:TestMod7648, :a9475, :foo9475, :c7648, :foo7648, :foo7648_nomethods, :Foo7648]) @test Set(names(TestMod7648, all = true)) == Set([:TestMod7648, :TestModSub9475, :a9475, :foo9475, :c7648, :d7648, :f7648, :foo7648, Symbol("#foo7648"), :foo7648_nomethods, Symbol("#foo7648_nomethods"), :Foo7648, :eval, Symbol("#eval"), :include, Symbol("#include")]) @test Set(names(TestMod7648, all = true, imported = true)) == Set([:TestMod7648, :TestModSub9475, :a9475, :foo9475, :c7648, :d7648, :f7648, :foo7648, Symbol("#foo7648"), :foo7648_nomethods, Symbol("#foo7648_nomethods"), :Foo7648, :eval, Symbol("#eval"), :include, Symbol("#include"), :convert, :curmod_name, :curmod]) @test isconst(TestMod7648, :c7648) @test !isconst(TestMod7648, :d7648) end let using .TestMod7648 @test Base.binding_module(@__MODULE__, :a9475) == TestMod7648.TestModSub9475 @test Base.binding_module(@__MODULE__, :c7648) == TestMod7648 @test nameof(foo7648) == :foo7648 @test parentmodule(foo7648, (Any,)) == TestMod7648 @test parentmodule(foo7648) == TestMod7648 @test parentmodule(foo7648_nomethods) == TestMod7648 @test parentmodule(foo9475, (Any,)) == TestMod7648.TestModSub9475 @test parentmodule(foo9475) == TestMod7648.TestModSub9475 @test parentmodule(Foo7648) == TestMod7648 @test nameof(Foo7648) == :Foo7648 @test basename(functionloc(foo7648, (Any,))[1]) == "reflection.jl" @test first(methods(TestMod7648.TestModSub9475.foo7648)) == which(foo7648, (Int,)) @test TestMod7648 == which(@__MODULE__, :foo7648) @test TestMod7648.TestModSub9475 == which(@__MODULE__, :a9475) end @test which(===, Tuple{Int, Int}) isa Method @test length(code_typed(===, Tuple{Int, Int})) === 1 @test only(Base.return_types(===, Tuple{Int, Int})) === Any module TestingExported using Test include("testenv.jl") # for curmod_str import Base.isexported global this_is_not_defined export this_is_not_defined @test_throws ErrorException("\"this_is_not_defined\" is not defined in module Main") which(Main, :this_is_not_defined) @test_throws ErrorException("\"this_is_not_exported\" is not defined in module Main") which(Main, :this_is_not_exported) @test isexported(@__MODULE__, :this_is_not_defined) @test !isexported(@__MODULE__, :this_is_not_exported) const a_value = 1 @test which(@__MODULE__, :a_value) === @__MODULE__ @test_throws ErrorException("\"a_value\" is not defined in module Main") which(Main, :a_value) @test which(Main, :Core) === Main @test !isexported(@__MODULE__, :a_value) end # PR 13825 let ex = :(a + b) @test string(ex) == "a + b" end foo13825(::Array{T, N}, ::Array, ::Vector) where {T, N} = nothing @test startswith(string(first(methods(foo13825))), "foo13825(::Array{T, N}, ::Array, ::Vector) where {T, N} in") mutable struct TLayout x::Int8 y::Int16 z::Int32 end tlayout = TLayout(5,7,11) @test fieldnames(TLayout) == (:x, :y, :z) == Base.propertynames(tlayout) @test hasfield(TLayout, :y) @test !hasfield(TLayout, :a) @test hasfield(Complex, :re) @test !hasfield(Complex, :qxq) @test hasproperty(tlayout, :x) @test !hasproperty(tlayout, :p) @test [(fieldoffset(TLayout,i), fieldname(TLayout,i), fieldtype(TLayout,i)) for i = 1:fieldcount(TLayout)] == [(0, :x, Int8), (2, :y, Int16), (4, :z, Int32)] @test fieldnames(Complex) === (:re, :im) @test_throws BoundsError fieldtype(TLayout, 0) @test_throws ArgumentError fieldname(TLayout, 0) @test_throws BoundsError fieldoffset(TLayout, 0) @test_throws BoundsError fieldtype(TLayout, 4) @test_throws ArgumentError fieldname(TLayout, 4) @test_throws BoundsError fieldoffset(TLayout, 4) @test fieldtype(Tuple{Vararg{Int8}}, 1) === Int8 @test fieldtype(Tuple{Vararg{Int8}}, 10) === Int8 @test_throws BoundsError fieldtype(Tuple{Vararg{Int8}}, 0) # issue #30505 @test fieldtype(Union{Tuple{Char},Tuple{Char,Char}},2) === Char @test_throws BoundsError fieldtype(Union{Tuple{Char},Tuple{Char,Char}},3) @test fieldnames(NTuple{3, Int}) == ntuple(i -> fieldname(NTuple{3, Int}, i), 3) == (1, 2, 3) @test_throws ArgumentError fieldnames(Union{}) @test_throws BoundsError fieldname(NTuple{3, Int}, 0) @test_throws BoundsError fieldname(NTuple{3, Int}, 4) @test fieldnames(NamedTuple{(:z,:a)}) === (:z,:a) @test fieldname(NamedTuple{(:z,:a)}, 1) === :z @test fieldname(NamedTuple{(:z,:a)}, 2) === :a @test_throws ArgumentError fieldname(NamedTuple{(:z,:a)}, 3) @test_throws ArgumentError fieldnames(NamedTuple) @test_throws ArgumentError fieldnames(NamedTuple{T,Tuple{Int,Int}} where T) @test_throws ArgumentError fieldnames(Real) @test_throws ArgumentError fieldnames(AbstractArray) @test fieldtype((NamedTuple{T,Tuple{Int,String}} where T), 1) === Int @test fieldtype((NamedTuple{T,Tuple{Int,String}} where T), 2) === String @test_throws BoundsError fieldtype((NamedTuple{T,Tuple{Int,String}} where T), 3) @test fieldtype(NamedTuple, 42) === Any @test_throws BoundsError fieldtype(NamedTuple, 0) @test_throws BoundsError fieldtype(NamedTuple, -1) @test fieldtype(NamedTuple{(:a,:b)}, 1) === Any @test fieldtype(NamedTuple{(:a,:b)}, 2) === Any @test fieldtype((NamedTuple{(:a,:b),T} where T<:Tuple{Vararg{Integer}}), 2) === Integer @test_throws BoundsError fieldtype(NamedTuple{(:a,:b)}, 3) # issue #32697 @test fieldtype(NamedTuple{(:x,:y), T} where T <: Tuple{Int, Union{Float64, Missing}}, :x) == Int @test fieldtype(NamedTuple{(:x,:y), T} where T <: Tuple{Int, Union{Float64, Missing}}, :y) == Union{Float64, Missing} @test fieldtypes(NamedTuple{(:a,:b)}) == (Any, Any) @test fieldtypes((NamedTuple{T,Tuple{Int,String}} where T)) === (Int, String) @test fieldtypes(TLayout) === (Int8, Int16, Int32) import Base: datatype_alignment, return_types @test datatype_alignment(UInt16) == 2 @test datatype_alignment(TLayout) == 4 let rts = return_types(TLayout) @test length(rts) == 2 # general constructor and specific constructor @test all(rts .== TLayout) end # issue #15447 f15447_line = @__LINE__() + 1 @noinline function f15447(s, a) if s return a else nb = 0 return nb end end @test functionloc(f15447)[2] == f15447_line # issue #14346 @noinline function f14346(id, mask, limit) if id <= limit && mask[id] return true end end @test functionloc(f14346)[2] == @__LINE__() - 5 # issue #15714 # show variable names for slots and suppress spurious type warnings function f15714(array_var15714) for index_var15714 in eachindex(array_var15714) array_var15714[index_var15714] += 0 end end function g15714(array_var15714) for index_var15714 in eachindex(array_var15714) array_var15714[index_var15714] += 0 end let index_var15714 for outer index_var15714 in eachindex(array_var15714) array_var15714[index_var15714] += 0 end index_var15714 end let index_var15714 for outer index_var15714 in eachindex(array_var15714) array_var15714[index_var15714] += 0 end index_var15714 end end import InteractiveUtils.code_warntype used_dup_var_tested15714 = false used_unique_var_tested15714 = false function test_typed_ir_printing(Base.@nospecialize(f), Base.@nospecialize(types), must_used_vars) src, rettype = code_typed(f, types, optimize=false)[1] dupnames = Set() slotnames = Set() for name in src.slotnames if name in slotnames || name === Symbol("") push!(dupnames, name) else push!(slotnames, name) end end # Make sure must_used_vars are in slotnames for name in must_used_vars @test name in slotnames end must_used_checked = Dict{Symbol,Bool}() for sym in must_used_vars must_used_checked[sym] = false end for str in (sprint(io -> code_warntype(io, f, types, optimize=false)), repr("text/plain", src)) for var in must_used_vars @test occursin(string(var), str) end # Check that we are not printing the bare slot numbers for i in 1:length(src.slotnames) name = src.slotnames[i] if name in dupnames if name in must_used_vars && occursin(Regex("_$i\\b"), str) must_used_checked[name] = true global used_dup_var_tested15714 = true end else @test !occursin(Regex("_$i\\b"), str) if name in must_used_vars global used_unique_var_tested15714 = true end end end end for sym in must_used_vars if sym in dupnames @test must_used_checked[sym] end must_used_checked[sym] = false end # Make sure printing an AST outside CodeInfo still works. str = sprint(show, src.code) # Check that we are printing the slot numbers when we don't have the context # Use the variable names that we know should be present in the optimized AST for i in 2:length(src.slotnames) name = src.slotnames[i] if name in must_used_vars && occursin(Regex("_$i\\b"), str) must_used_checked[name] = true end end for sym in must_used_vars @test must_used_checked[sym] end end test_typed_ir_printing(f15714, Tuple{Vector{Float32}}, [:array_var15714, :index_var15714]) test_typed_ir_printing(g15714, Tuple{Vector{Float32}}, [:array_var15714, :index_var15714]) #This test doesn't work with the new optimizer because we drop slotnames #We may want to test it against debug info eventually #@test used_dup_var_tested15715 @test used_unique_var_tested15714 let li = typeof(fieldtype).name.mt.cache.func::Core.MethodInstance, lrepr = string(li), mrepr = string(li.def), lmime = repr("text/plain", li), mmime = repr("text/plain", li.def) @test lrepr == lmime == "MethodInstance for fieldtype(...)" @test mrepr == mmime == "fieldtype(...) in Core" end # Linfo Tracing test function tracefoo end # Method Tracing test methtracer(x::Ptr{Cvoid}) = (@test isa(unsafe_pointer_to_objref(x), Method); global didtrace = true; nothing) let cmethtracer = @cfunction(methtracer, Cvoid, (Ptr{Cvoid},)) ccall(:jl_register_newmeth_tracer, Cvoid, (Ptr{Cvoid},), cmethtracer) end didtrace = false tracefoo2(x, y) = x*y @test didtrace didtrace = false tracefoo(x::Int64, y::Int64) = x*y @test didtrace didtrace = false ccall(:jl_register_newmeth_tracer, Cvoid, (Ptr{Cvoid},), C_NULL) # test for reflection over large method tables for i = 1:100; @eval fLargeTable(::Val{$i}, ::Any) = 1; end for i = 1:100; @eval fLargeTable(::Any, ::Val{$i}) = 2; end fLargeTable(::Any...) = 3 @test length(methods(fLargeTable, Tuple{})) == 1 fLargeTable(::Complex, ::Complex) = 4 fLargeTable(::Union{ComplexF32, ComplexF64}...) = 5 @test length(methods(fLargeTable, Tuple{})) == 1 fLargeTable() = 4 @test length(methods(fLargeTable)) == 204 @test length(methods(fLargeTable, Tuple{})) == 1 @test fLargeTable(1im, 2im) == 4 @test fLargeTable(1.0im, 2.0im) == 5 @test_throws MethodError fLargeTable(Val(1), Val(1)) @test fLargeTable(Val(1), 1) == 1 @test fLargeTable(1, Val(1)) == 2 fLargeTable(::Union, ::Union) = "a" @test fLargeTable(Union{Int, Missing}, Union{Int, Missing}) == "a" fLargeTable(::Union, ::Union) = "b" @test length(methods(fLargeTable)) == 205 @test fLargeTable(Union{Int, Missing}, Union{Int, Missing}) == "b" # issue #15280 function f15280(x) end @test functionloc(f15280)[2] > 0 # bug found in #16850, Base.url with backslashes on Windows function module_depth(from::Module, to::Module) if from === to || parentmodule(to) === to return 0 else return 1 + module_depth(from, parentmodule(to)) end end function has_backslashes(mod::Module) for n in names(mod, all = true, imported = true) isdefined(mod, n) || continue Base.isdeprecated(mod, n) && continue f = getfield(mod, n) if isa(f, Module) && module_depth(Main, f) <= module_depth(Main, mod) continue end h = has_backslashes(f) h === nothing || return h end return nothing end function has_backslashes(f::Function) for m in methods(f) h = has_backslashes(m) h === nothing || return h end return nothing end function has_backslashes(meth::Method) if '\\' in string(meth.file) return meth else return nothing end end has_backslashes(x) = nothing h16850 = has_backslashes(Base) if Sys.iswindows() if h16850 === nothing @warn """No methods found in Base with backslashes in file name, skipping test for `Base.url`""" else @test !('\\' in Base.url(h16850)) end else @test h16850 === nothing end # PR #18888: code_typed shouldn't cache, return_types should f18888() = nothing let world = Core.Compiler.get_world_counter() m = first(methods(f18888, Tuple{})) ft = typeof(f18888) code_typed(f18888, Tuple{}; optimize=false) @test !isempty(m.specializations) # uncached, but creates the specializations entry mi = Core.Compiler.specialize_method(m, Tuple{ft}, Core.svec()) interp = Core.Compiler.NativeInterpreter(world) @test !Core.Compiler.haskey(Core.Compiler.code_cache(interp), mi) @test !isdefined(mi, :cache) code_typed(f18888, Tuple{}; optimize=true) @test !isdefined(mi, :cache) Base.return_types(f18888, Tuple{}) @test Core.Compiler.getindex(Core.Compiler.code_cache(interp), mi) === mi.cache @test mi.cache isa Core.CodeInstance @test !isdefined(mi.cache, :next) end # code_typed_by_type @test Base.code_typed_by_type(Tuple{Type{<:Val}})[1][2] == Val @test Base.code_typed_by_type(Tuple{typeof(sin), Float64})[1][2] === Float64 # New reflection methods in 0.6 struct ReflectionExample{T<:AbstractFloat, N} x::Tuple{T, N} end @test !isabstracttype(Union{}) @test !isabstracttype(Union{Int,Float64}) @test isabstracttype(AbstractArray) @test isabstracttype(AbstractSet{Int}) @test !isabstracttype(ReflectionExample) @test !isabstracttype(Int) @test !isabstracttype(TLayout) @test !isprimitivetype(Union{}) @test !isprimitivetype(Union{Int,Float64}) @test !isprimitivetype(AbstractArray) @test !isprimitivetype(AbstractSet{Int}) @test !isprimitivetype(ReflectionExample) @test isprimitivetype(Int) @test !isprimitivetype(TLayout) @test !isstructtype(Union{}) @test !isstructtype(Union{Int,Float64}) @test !isstructtype(AbstractArray) @test !isstructtype(AbstractSet{Int}) @test isstructtype(ReflectionExample) @test !isstructtype(Int) @test isstructtype(TLayout) let wrapperT(T) = Base.typename(T).wrapper @test @inferred wrapperT(ReflectionExample{Float64, Int64}) == ReflectionExample @test @inferred wrapperT(ReflectionExample{Float64, N} where N) == ReflectionExample @test @inferred wrapperT(ReflectionExample{T, Int64} where T) == ReflectionExample @test @inferred wrapperT(ReflectionExample) == ReflectionExample @test @inferred wrapperT(Union{ReflectionExample{Union{},1},ReflectionExample{Float64,1}}) == ReflectionExample @test_throws(ErrorException("typename does not apply to unions whose components have different typenames"), Base.typename(Union{Int, Float64})) end # sizeof and nfields @test sizeof(Int16) == 2 @test sizeof(ComplexF64) == 16 primitive type ParameterizedByte__{A,B} 8 end @test sizeof(ParameterizedByte__) == 1 @test sizeof(nothing) == 0 @test sizeof(()) == 0 struct TypeWithIrrelevantParameter{T} x::Int32 end @test sizeof(TypeWithIrrelevantParameter) == sizeof(Int32) @test sizeof(TypeWithIrrelevantParameter{Int8}) == sizeof(Int32) @test sizeof(:abc) == 3 @test sizeof(Symbol("")) == 0 @test_throws(ErrorException("Abstract type Real does not have a definite size."), sizeof(Real)) @test sizeof(Union{ComplexF32,ComplexF64}) == 16 @test sizeof(Union{Int8,UInt8}) == 1 @test_throws ErrorException sizeof(AbstractArray) @test_throws ErrorException sizeof(Tuple) @test_throws ErrorException sizeof(Tuple{Any,Any}) @test_throws ErrorException sizeof(String) @test_throws ErrorException sizeof(Vector{Int}) @test_throws ErrorException sizeof(Symbol) @test_throws ErrorException sizeof(Core.SimpleVector) @test_throws ErrorException sizeof(Union{}) @test nfields((1,2)) == 2 @test nfields(()) == 0 @test nfields(nothing) == fieldcount(Nothing) == 0 @test nfields(1) == 0 @test_throws ArgumentError fieldcount(Union{}) @test fieldcount(Tuple{Any,Any,T} where T) == 3 @test fieldcount(Complex) == fieldcount(ComplexF32) == 2 @test fieldcount(Union{ComplexF32,ComplexF64}) == 2 @test fieldcount(Int) == 0 @test_throws(ArgumentError("type does not have a definite number of fields"), fieldcount(Union{Complex,Pair})) @test_throws ArgumentError fieldcount(Real) @test_throws ArgumentError fieldcount(AbstractArray) @test_throws ArgumentError fieldcount(Tuple{Any,Vararg{Any}}) # PR #22979 function test_similar_codeinfo(a, b) @test a.code == b.code @test a.slotnames == b.slotnames @test a.slotflags == b.slotflags end @generated f22979(x...) = (y = 1; :(x[1] + x[2])) let x22979 = (1, 2.0, 3.0 + im) T22979 = Tuple{typeof(f22979), typeof.(x22979)...} world = Core.Compiler.get_world_counter() match = Base._methods_by_ftype(T22979, -1, world)[1] instance = Core.Compiler.specialize_method(match) cinfo_generated = Core.Compiler.get_staged(instance) @test_throws ErrorException Base.uncompressed_ir(match.method) test_similar_codeinfo(code_lowered(f22979, typeof(x22979))[1], cinfo_generated) cinfos = code_lowered(f22979, typeof.(x22979), generated=true) @test length(cinfos) == 1 cinfo = cinfos[1] test_similar_codeinfo(cinfo, cinfo_generated) @test_throws ErrorException code_lowered(f22979, typeof.(x22979), generated=false) end module MethodDeletion using Test, Random # Deletion after compiling top-level call bar1(x) = 1 bar1(x::Int) = 2 foo1(x) = bar1(x) faz1(x) = foo1(x) @test faz1(1) == 2 @test faz1(1.0) == 1 m = first(methods(bar1, Tuple{Int})) Base.delete_method(m) @test bar1(1) == 1 @test bar1(1.0) == 1 @test foo1(1) == 1 @test foo1(1.0) == 1 @test faz1(1) == 1 @test faz1(1.0) == 1 # Deletion after compiling middle-level call bar2(x) = 1 bar2(x::Int) = 2 foo2(x) = bar2(x) faz2(x) = foo2(x) @test foo2(1) == 2 @test foo2(1.0) == 1 m = first(methods(bar2, Tuple{Int})) Base.delete_method(m) @test bar2(1.0) == 1 @test bar2(1) == 1 @test foo2(1) == 1 @test foo2(1.0) == 1 @test faz2(1) == 1 @test faz2(1.0) == 1 # Deletion after compiling low-level call bar3(x) = 1 bar3(x::Int) = 2 foo3(x) = bar3(x) faz3(x) = foo3(x) @test bar3(1) == 2 @test bar3(1.0) == 1 m = first(methods(bar3, Tuple{Int})) Base.delete_method(m) @test bar3(1) == 1 @test bar3(1.0) == 1 @test foo3(1) == 1 @test foo3(1.0) == 1 @test faz3(1) == 1 @test faz3(1.0) == 1 # Deletion before any compilation bar4(x) = 1 bar4(x::Int) = 2 foo4(x) = bar4(x) faz4(x) = foo4(x) m = first(methods(bar4, Tuple{Int})) Base.delete_method(m) @test bar4(1) == 1 @test bar4(1.0) == 1 @test foo4(1) == 1 @test foo4(1.0) == 1 @test faz4(1) == 1 @test faz4(1.0) == 1 # Methods with keyword arguments fookw(x; direction=:up) = direction fookw(y::Int) = 2 @test fookw("string") == :up @test fookw(1) == 2 m = collect(methods(fookw))[2] Base.delete_method(m) @test fookw(1) == 2 @test_throws MethodError fookw("string") # functions with many methods types = (Float64, Int32, String) for T1 in types, T2 in types, T3 in types @eval foomany(x::$T1, y::$T2, z::$T3) = y end @test foomany(Int32(5), "hello", 3.2) == "hello" m = first(methods(foomany, Tuple{Int32, String, Float64})) Base.delete_method(m) @test_throws MethodError foomany(Int32(5), "hello", 3.2) struct EmptyType end Base.convert(::Type{EmptyType}, x::Integer) = EmptyType() m = first(methods(convert, Tuple{Type{EmptyType}, Integer})) Base.delete_method(m) @test_throws MethodError convert(EmptyType, 1) # parametric methods parametric(A::Array{T,N}, i::Vararg{Int,N}) where {T,N} = N @test parametric(rand(2,2), 1, 1) == 2 m = first(methods(parametric)) Base.delete_method(m) @test_throws MethodError parametric(rand(2,2), 1, 1) # Deletion and ambiguity detection foo(::Int, ::Int) = 1 foo(::Real, ::Int) = 2 foo(::Int, ::Real) = 3 Base.delete_method(first(methods(foo))) @test_throws MethodError foo(1, 1) foo(::Int, ::Int) = 1 foo(1, 1) Base.delete_method(first(methods(foo))) @test_throws MethodError foo(1, 1) # multiple deletions and ambiguities typeparam(::Type{T}, a::Array{T}) where T<:AbstractFloat = 1 typeparam(::Type{T}, a::Array{T}) where T = 2 for mth in collect(methods(typeparam)) Base.delete_method(mth) end typeparam(::Type{T}, a::AbstractArray{T}) where T<:AbstractFloat = 1 typeparam(::Type{T}, a::AbstractArray{T}) where T = 2 @test typeparam(Float64, rand(2)) == 1 @test typeparam(Int, rand(Int, 2)) == 2 # prior ambiguities (issue #28899) uambig(::Union{Int,Nothing}) = 1 uambig(::Union{Float64,Nothing}) = 2 @test uambig(1) == 1 @test uambig(1.0) == 2 @test_throws MethodError uambig(nothing) m = which(uambig, Tuple{Int}) Base.delete_method(m) @test_throws MethodError uambig(1) @test uambig(1.0) == 2 @test uambig(nothing) == 2 end module HasmethodKwargs using Test f(x::Int; y=3) = x + y @test hasmethod(f, Tuple{Int}) @test hasmethod(f, Tuple{Int}, ()) @test hasmethod(f, Tuple{Int}, (:y,)) @test !hasmethod(f, Tuple{Int}, (:jeff,)) @test !hasmethod(f, Tuple{Int}, (:y,), world=typemin(UInt)) g(; b, c, a) = a + b + c h(; kwargs...) = 4 for gh = (g, h) @test hasmethod(gh, Tuple{}) @test hasmethod(gh, Tuple{}, ()) @test hasmethod(gh, Tuple{}, (:a,)) @test hasmethod(gh, Tuple{}, (:a, :b)) @test hasmethod(gh, Tuple{}, (:a, :b, :c)) end @test !hasmethod(g, Tuple{}, (:a, :b, :c, :d)) @test hasmethod(h, Tuple{}, (:a, :b, :c, :d)) end # issue #31353 function f31353(f, x::Array{<:Dict}) end @test hasmethod(f31353, Tuple{Any, Array{D}} where D<:Dict) @test !hasmethod(f31353, Tuple{Any, Array{D}} where D<:AbstractDict) # issue #26267 module M26267 import Test foo(x) = x end @test !(:Test in names(M26267, all=true, imported=false)) @test :Test in names(M26267, all=true, imported=true) @test :Test in names(M26267, all=false, imported=true) # issue #20872 f20872(::Val{N}, ::Val{N}) where {N} = true f20872(::Val, ::Val) = false @test which(f20872, Tuple{Val{N},Val{N}} where N).sig == Tuple{typeof(f20872), Val{N}, Val{N}} where N @test which(f20872, Tuple{Val,Val}).sig == Tuple{typeof(f20872), Val, Val} @test which(f20872, Tuple{Val,Val{N}} where N).sig == Tuple{typeof(f20872), Val, Val} @test_throws ErrorException which(f20872, Tuple{Any,Val{N}} where N) @test which(Tuple{typeof(f20872), Val{1}, Val{2}}).sig == Tuple{typeof(f20872), Val, Val} module M29962 end # make sure checking if a binding is deprecated does not resolve it @test !Base.isdeprecated(M29962, :sin) && !Base.isbindingresolved(M29962, :sin) # @locals using Base: @locals let local x, y global z @test isempty(keys(@locals)) x = 1 @test @locals() == Dict{Symbol,Any}(:x=>1) y = "" @test @locals() == Dict{Symbol,Any}(:x=>1,:y=>"") for i = 8:8 @test @locals() == Dict{Symbol,Any}(:x=>1,:y=>"",:i=>8) end for i = 42:42 local x @test @locals() == Dict{Symbol,Any}(:y=>"",:i=>42) end @test @locals() == Dict{Symbol,Any}(:x=>1,:y=>"") x = (y,) @test @locals() == Dict{Symbol,Any}(:x=>("",),:y=>"") end function _test_at_locals1(::Any, ::Any) x = 1 @test @locals() == Dict{Symbol,Any}(:x=>1) end _test_at_locals1(1,1) function _test_at_locals2(a::Any, ::Any, c::T) where T x = 2 @test @locals() == Dict{Symbol,Any}(:x=>2,:a=>a,:c=>c,:T=>typeof(c)) end _test_at_locals2(1,1,"") _test_at_locals2(1,1,0.5f0) @testset "issue #31687" begin import InteractiveUtils._dump_function @noinline f31687_child(i) = f31687_nonexistent(i) f31687_parent() = f31687_child(0) params = Base.CodegenParams() _dump_function(f31687_parent, Tuple{}, #=native=#false, #=wrapper=#false, #=strip=#false, #=dump_module=#true, #=syntax=#:att, #=optimize=#false, :none, #=binary=#false, params) end @test nameof(Any) === :Any @test nameof(:) === :Colon @test nameof(Core.Intrinsics.mul_int) === :mul_int @test nameof(Core.Intrinsics.arraylen) === :arraylen module TestMod33403 f(x) = 1 f(x::Int) = 2 g() = 3 module Sub import ..TestMod33403: f f(x::Char) = 3 end end @testset "methods with module" begin using .TestMod33403: f, g @test length(methods(f)) == 3 @test length(methods(f, (Int,))) == 1 @test length(methods(f, TestMod33403)) == 2 @test length(methods(f, [TestMod33403])) == 2 @test length(methods(f, (Int,), TestMod33403)) == 1 @test length(methods(f, (Int,), [TestMod33403])) == 1 @test length(methods(f, TestMod33403.Sub)) == 1 @test length(methods(f, [TestMod33403.Sub])) == 1 @test length(methods(f, (Char,), TestMod33403.Sub)) == 1 @test length(methods(f, (Int,), TestMod33403.Sub)) == 0 @test length(methods(g, ())) == 1 end module BodyFunctionLookup f1(x, y; a=1) = error("oops") f2(f::Function, args...; kwargs...) = f1(args...; kwargs...) end @testset "bodyfunction" begin m = first(methods(BodyFunctionLookup.f1)) f = Base.bodyfunction(m) @test occursin("f1#", String(nameof(f))) m = first(methods(BodyFunctionLookup.f2)) f = Base.bodyfunction(m) @test f !== Core._apply_iterate @test f !== Core._apply @test occursin("f2#", String(nameof(f))) end @testset "code_typed(; world)" begin mod = @eval module $(gensym()) end @eval mod foo() = 1 world1 = Base.get_world_counter() @test only(code_typed(mod.foo, ())).second == Int @test only(code_typed(mod.foo, (); world=world1)).second == Int @eval mod foo() = 2. world2 = Base.get_world_counter() @test only(code_typed(mod.foo, ())).second == Float64 @test only(code_typed(mod.foo, (); world=world1)).second == Int @test only(code_typed(mod.foo, (); world=world2)).second == Float64 end @testset "default_tt" begin m = Module() @eval m f1() = return @test Base.default_tt(m.f1) == Tuple{} @eval m f2(a) = return @test Base.default_tt(m.f2) == Tuple{Any} @eval m f3(a::Integer) = return @test Base.default_tt(m.f3) == Tuple{Integer} @eval m f4() = return @eval m f4(a) = return @test Base.default_tt(m.f4) == Tuple end
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2.341793
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<reponame>nveldt/SparseCardDSFM using Plots include("src/pwl_approx.jl") ## Define a submodular cardinality based function via Combined gadget k = 2 w = random_scb_function(k::Int64) w = 10*w/maximum(w) pl = scatter(0:k,w,legend = false, xticks = 0:k) epsi = 10.0 z0, zk, a, b, cgf = SubCardFun_to_CGF_weights(w,epsi,false) Jtrue = length(a) + 1 J = Jtrue xs = 0:0.01:k ys = cgf(xs) plot!(pl,xs,ys,color= :blue) # Refine to get better approximation z0l, zkl, al, bl, cgfl, best_eps = Refined_SCB_to_CGF(w,epsi) xs = 0:0.01:k ys = cgfl(xs) plot!(pl,xs,ys,color= :red)
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@enum Shape CIRCLE RECTANGLE ROUNDED_RECTANGLE DISTANCEFIELD TRIANGLE @enum CubeSides TOP BOTTOM FRONT BACK RIGHT LEFT struct Grid{N, T <: AbstractRange} dims::NTuple{N, T} end Base.ndims(::Grid{N,T}) where {N,T} = N Grid(ranges::AbstractRange...) = Grid(ranges) function Grid(a::Array{T, N}) where {N, T} s = Vec{N, Float32}(size(a)) smax = maximum(s) s = s./smax Grid(ntuple(Val{N}) do i range(0, stop=s[i], length=size(a, i)) end) end Grid(a::AbstractArray, ranges...) = Grid(a, ranges) """ This constructor constructs a grid from ranges given as a tuple. Due to the approach, the tuple `ranges` can consist of NTuple(2, T) and all kind of range types. The constructor will make sure that all ranges match the size of the dimension of the array `a`. """ function Grid(a::AbstractArray{T, N}, ranges::Tuple) where {T, N} length(ranges) =! N && throw(ArgumentError( "You need to supply a range for every dimension of the array. Given: $ranges given Array: $(typeof(a))" )) Grid(ntuple(Val(N)) do i range(first(ranges[i]), stop=last(ranges[i]), length=size(a, i)) end) end Base.length(p::Grid) = prod(size(p)) Base.size(p::Grid) = map(length, p.dims) function Base.getindex(p::Grid{N,T}, i) where {N,T} inds = ind2sub(size(p), i) return Point{N, eltype(T)}(ntuple(Val(N)) do i p.dims[i][inds[i]] end) end Base.iterate(g::Grid, i = 1) = i <= length(g) ? (g[i], i + 1) : nothing GLAbstraction.isa_gl_struct(x::Grid) = true GLAbstraction.toglsltype_string(t::Grid{N,T}) where {N,T} = "uniform Grid$(N)D" function GLAbstraction.gl_convert_struct(g::Grid{N, T}, uniform_name::Symbol) where {N,T} return Dict{Symbol, Any}( Symbol("$uniform_name.start") => Vec{N, Float32}(minimum.(g.dims)), Symbol("$uniform_name.stop") => Vec{N, Float32}(maximum.(g.dims)), Symbol("$uniform_name.lendiv") => Vec{N, Cint}(length.(g.dims) .- 1), Symbol("$uniform_name.dims") => Vec{N, Cint}(map(length, g.dims)) ) end function GLAbstraction.gl_convert_struct(g::Grid{1, T}, uniform_name::Symbol) where T x = g.dims[1] return Dict{Symbol, Any}( Symbol("$uniform_name.start") => Float32(minimum(x)), Symbol("$uniform_name.stop") => Float32(maximum(x)), Symbol("$uniform_name.lendiv") => Cint(length(x) - 1), Symbol("$uniform_name.dims") => Cint(length(x)) ) end import Base: getindex, length, iterate, ndims, setindex!, eltype struct GridZRepeat{G, T, N} <: AbstractArray{Point{3, T}, N} grid::G z::Array{T, N} end Base.size(g::GridZRepeat) = size(g.z) Base.size(g::GridZRepeat, i) = size(g.z, i) Base.IndexStyle(::Type{<:GridZRepeat}) = Base.IndexLinear() function Base.getindex(g::GridZRepeat{G, T}, i) where {G,T} pxy = g.grid[i] Point{3, T}(pxy[1], pxy[2], g.z[i]) end struct GLVisualizeShader <: AbstractLazyShader paths::Tuple kw_args::Dict{Symbol, Any} function GLVisualizeShader(paths::String...; view = Dict{String, String}(), kw_args...) # TODO properly check what extensions are available @static if !Sys.isapple() view["GLSL_EXTENSIONS"] = "#extension GL_ARB_conservative_depth: enable" view["SUPPORTED_EXTENSIONS"] = "#define DETPH_LAYOUT" end args = Dict{Symbol, Any}(kw_args) args[:view] = view args[:fragdatalocation] = [(0, "fragment_color"), (1, "fragment_groupid")] new(map(x-> assetpath("shader", x), paths), args) end end
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<filename>src/nodes/variable.jl # Variable reference. mutable struct VariableNode <: AbstractSQLNode name::Symbol VariableNode(; name::Union{Symbol, AbstractString}) = new(Symbol(name)) end VariableNode(name) = VariableNode(name = name) """ Var(; name) Var(name) Var.name Var."name" Var[name] Var["name"] A reference to a query parameter. # Examples ```jldoctest julia> person = SQLTable(:person, columns = [:person_id, :year_of_birth]); julia> q = From(person) |> Where(Get.year_of_birth .> Var.year); julia> print(render(q)) SELECT "person_1"."person_id", "person_1"."year_of_birth" FROM "person" AS "person_1" WHERE ("person_1"."year_of_birth" > :year) ``` """ Var(args...; kws...) = VariableNode(args...; kws...) |> SQLNode dissect(scr::Symbol, ::typeof(Var), pats::Vector{Any}) = dissect(scr, VariableNode, pats) Base.getproperty(::typeof(Var), name::Symbol) = Var(name) Base.getproperty(::typeof(Var), name::AbstractString) = Var(name) Base.getindex(::typeof(Var), name::Union{Symbol, AbstractString}) = Var(name) PrettyPrinting.quoteof(n::VariableNode, ctx::QuoteContext) = Expr(:., nameof(Var), quoteof(n.name)) label(n::VariableNode) = n.name
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<reponame>Pooksoft/PooksoftOptionsKit.jl # packages - using Dates using Optim using JSON using DataFrames using Statistics using LsqFit using Reexport @reexport using PooksoftBase # include my code - include("./base/Types.jl") include("./base/Checks.jl") include("./base/Intrinsic.jl") include("./base/Binary.jl") include("./base/Ternary.jl") include("./base/Greeks.jl") include("./base/Compute.jl") include("./base/Factory.jl") include("./base/Volatility.jl") include("./base/Utility.jl") include("./base/Longstaff.jl") include("./base/Breakeven.jl")
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<reponame>Jagirhussan/ModiaMath.jl<gh_stars>0 module test_Variables import ModiaMath # Desired: # using Test # # In order that Test needs not to be defined in the user environment, it is included via ModiaMath: @static if VERSION < v"0.7.0-DEV.2005" using Base.Test else using ModiaMath.Test end mutable struct Revolute <: ModiaMath.AbstractComponentWithVariables _internal::ModiaMath.ComponentInternal phi::ModiaMath.RealScalar w::ModiaMath.RealScalar a::ModiaMath.RealScalar tau::ModiaMath.RealScalar drive::Bool function Revolute(;phi0::Float64=0.0, w0::Float64=0.0, drive::Bool=false) this = new(ModiaMath.ComponentInternal(:Revolute)) phi = ModiaMath.RealScalar(:phi, this, start=phi0, unit="rad", fixed=true, info="Relative rotation angle", numericType=drive ? ModiaMath.WR : ModiaMath.XD_EXP) w = ModiaMath.RealScalar("w", this, start=w0, unit="rad/s", fixed=true, info="Relative angular velocity", integral=phi, numericType=drive ? ModiaMath.WR : ModiaMath.XD_EXP, analysis=ModiaMath.OnlyDynamicAnalysis) a = ModiaMath.RealScalar("a", this, start=0.0, unit="rad/s^2", info="Relative angular acceleration", integral=w, numericType=drive ? ModiaMath.WR : ModiaMath.DER_XD_EXP, analysis=ModiaMath.OnlyDynamicAnalysis) tau = ModiaMath.RealScalar(:tau, this, start=0.0, unit="N*m", info="Driving torque", numericType=ModiaMath.WR, analysis=ModiaMath.QuasiStaticAndDynamicAnalysis) return this end end function Base.show(io::IO, rev::Revolute) print("Revolute(", "\n phi = ", rev.phi, "\n w = ", rev.w, "\n a = ", rev.a, "\n tau = ", rev.tau, "\n )") end mutable struct Frame <: ModiaMath.AbstractComponentWithVariables _internal::ModiaMath.ComponentInternal r::ModiaMath.RealSVector3 q::ModiaMath.RealSVector{4} derq::ModiaMath.RealSVector{4} v::ModiaMath.RealSVector3 w::ModiaMath.RealSVector3 a::ModiaMath.RealSVector3 z::ModiaMath.RealSVector3 f::ModiaMath.RealSVector3 t::ModiaMath.RealSVector3 residue_w::ModiaMath.RealSVector3 residue_f::ModiaMath.RealSVector3 residue_t::ModiaMath.RealSVector3 residue_q::ModiaMath.RealScalar drive::Bool function Frame(;r0=zeros(3), q0=[0,0,0,1], v0=zeros(3), w0=zeros(3), drive::Bool=false) this = new(ModiaMath.ComponentInternal(:Frame)) r = ModiaMath.RealSVector3(:r, this, start=r0, unit="m", fixed=true, info="Relative position", numericType=drive ? ModiaMath.WR : ModiaMath.XD_EXP) q = ModiaMath.RealSVector{4}(:q, this, start=q0, fixed=true, info="Relative quaternion", numericType=drive ? ModiaMath.WR : ModiaMath.XD_IMP) derq = ModiaMath.RealSVector{4}(:derq, this, unit="1/s", info="der(q)", integral=q, numericType=drive ? ModiaMath.WR : ModiaMath.DER_XD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) v = ModiaMath.RealSVector3(:v, this, start=v0, unit="m/s", fixed=true, info="Relative velocity", integral=r, numericType=drive ? ModiaMath.WR : ModiaMath.XD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) w = ModiaMath.RealSVector3(:w, this, start=w0, unit="rad/s", fixed=true, info="Relative angular velocity", numericType=drive ? ModiaMath.WR : ModiaMath.XD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) a = ModiaMath.RealSVector3(:a, this, unit="m/s^2", info="Relative acceleration", integral=v, numericType=drive ? ModiaMath.WR : ModiaMath.DER_XD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) z = ModiaMath.RealSVector3(:z, this, unit="rad/s^2", info="Relative angular acceleration", integral=w, numericType=drive ? ModiaMath.WR : ModiaMath.DER_XD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) f = ModiaMath.RealSVector3(:f, this, unit="N", info="Driving force", numericType=ModiaMath.WR, analysis=ModiaMath.QuasiStaticAndDynamicAnalysis) t = ModiaMath.RealSVector3(:t, this, unit="N*m", info="Driving torque", numericType=ModiaMath.WR, analysis=ModiaMath.QuasiStaticAndDynamicAnalysis) residue_w = ModiaMath.RealSVector3(:residue_w, this, info="Angular velocity residue", numericType=ModiaMath.FD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) residue_f = ModiaMath.RealSVector3(:residue_f, this, info="Momentum equation residue", numericType=ModiaMath.FD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) residue_t = ModiaMath.RealSVector3(:residue_t, this, info="Angular momentum equation residue", numericType=ModiaMath.FD_IMP, analysis=ModiaMath.OnlyDynamicAnalysis) residue_q = ModiaMath.RealScalar(:residue_q, this, info="Quaternion constraint residue", numericType=ModiaMath.FC, analysis=ModiaMath.OnlyDynamicAnalysis) this.drive = drive return this end end function Base.show(io::IO, frame::Frame) print("Revolute(", "\n r = ", frame.r, "\n q = ", frame.q, "\n v = ", frame.v, "\n w = ", frame.w, "\n a = ", frame.a, "\n z = ", frame.z, "\n f = ", frame.f, "\n t = ", frame.t, "\n )") end ModiaMath.@component Robot(;phi10=0.0, phi20=0.0, var10=0.0, r0=zeros(3), q0=zeros(4)) begin rev1 = Revolute(phi0=phi10) rev2 = Revolute(phi0=phi20) var1 = ModiaMath.RealScalar(start=var10, numericType=ModiaMath.XA) res1 = ModiaMath.RealScalar(numericType=ModiaMath.FD_IMP) frame = Frame(r0=r0, q0=q0) end ModiaMath.@component Robot2(;phi10=0.0, phi20=0.0, r0=zeros(3), q0=[0.0, 0.0, 0.0, 1.0]) begin rev1 = Revolute(phi0=phi10, drive=true) rev2 = Revolute(phi0=phi20, drive=true) frame = Frame(r0=r0, q0=q0, drive=true) end ModiaMath.@component Robot3(;phi10=0.0, phi20=0.0, phi30=-2.0) begin rev1 = Revolute(phi0=phi10, drive=true) rev2 = Revolute(phi0=phi20, drive=true) rev3 = Revolute(phi0=phi30) res1 = ModiaMath.RealScalar(numericType=ModiaMath.FC) end @testset "ModiaMath: test_Variables.jl" begin @testset "Dynamic analysis" begin # ------------------------------ Dynamic analysis r0 = [1.0,2.0,3.0] q0 = [0.5,0.5,0.0,sqrt(0.5^2 + 0.5^2)] robot = Robot(phi10=1.0, phi20=2.0, var10=3.0, r0=r0, q0=q0) robot.rev1.a.value = 10 * 2.22 robot.rev2.a.value = 10 * 4.44 println("\n... robot = ", robot) println("\n... Print variables of robot") m = ModiaMath.ModelVariables(robot) ModiaMath.print_ModelVariables(m) println("\n... Copy start values to x") x = zeros(5 + 7 + 6) x_fixed = fill(false, 5 + 7 + 6) ModiaMath.copy_start_to_x!(m, x, x_fixed) x0 = [1.0, 0.0, 2.0, 0.0, 1.0, 2.0, 3.0, 0.5, 0.5, 0.0, sqrt(0.5^2 + 0.5^2), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0 ] x0_fixed = fill(true, 17) append!(x0_fixed, false) @test isapprox(x, x0) @test x_fixed == x0_fixed println("\n... Copy x and der_x to variables") x = [1.11, 2.22 , 3.33, 4.44, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6 , 5.55] derx = [2.22, 22.2, 4.44, 44.4, 1.1, 2.2, 3.3, 4.44, 5.55, 6.66, 7.77, 1.11, 2.22, 3.33, 4.44, 5.55, 6.66, 0.0] ModiaMath.copy_x_and_derx_to_variables!(0.5, x, derx, m) @test isapprox(x, [robot.rev1.phi.value, robot.rev1.w.value, robot.rev2.phi.value, robot.rev2.w.value, robot.frame.r.value[1], robot.frame.r.value[2], robot.frame.r.value[3], robot.frame.q.value[1], robot.frame.q.value[2], robot.frame.q.value[3], robot.frame.q.value[4], robot.frame.v.value[1], robot.frame.v.value[2], robot.frame.v.value[3], robot.frame.w.value[1], robot.frame.w.value[2], robot.frame.w.value[3], robot.var1.value], rtol=1e-15) @test isapprox(derx[2], robot.rev1.a.value, rtol=1e-15) @test isapprox(derx[4], robot.rev2.a.value, rtol=1e-15) println("\n... Copy variables to residues") residues = zeros(5 + 7 + 6) ModiaMath.copy_variables_to_residue!(m, x, derx, residues) println("residue = ", residues) @test isapprox(residues, zeros(5 + 7 + 6), atol=1e-12) end @testset "Kinematic analysis 1" begin # ---------------------------- Kinematic analysis 1 robot2 = Robot2(phi10=1.0, phi20=2.0) println("\n... robot2 = ", robot2) println("\n... Print variables of robot2") m = ModiaMath.ModelVariables(robot2, analysis=ModiaMath.KinematicAnalysis) ModiaMath.print_ModelVariables(m) println("\n... Copy start values to x") x = [1.11] x_fixed = [false] ModiaMath.copy_start_to_x!(m, x, x_fixed) @test isapprox(x, [0.0]) @test x_fixed == [true] println("\n... Copy x and der_x to variables") x = fill(2.1, 1) derx = fill(3.2, 1) ModiaMath.copy_x_and_derx_to_variables!(0.5, x, derx, m) @test isapprox(x, [m.var[2].value], rtol=1e-15) # var[2] = _dummy_x println("\n... Copy variables to residues") residues = zeros(m.nx) ModiaMath.copy_variables_to_residue!(m, x, derx, residues) @test isapprox(residues, [3.2 - (-2.1)], atol=1e-12) end @testset "Kinematic analysis 2" begin # ---------------------------- Kinematic analysis 2 robot3 = Robot3(phi10=1.0, phi20=2.0, phi30=-2.0) println("\n... robot3 = ", robot3) println("\n... Print variables of robot3") m = ModiaMath.ModelVariables(robot3, analysis=ModiaMath.KinematicAnalysis) ModiaMath.print_ModelVariables(m) println("\n... Copy start values to x") x = zeros(m.nx) x_fixed = fill(false, m.nx) ModiaMath.copy_start_to_x!(m, x, x_fixed) @test isapprox(x, [-2.0]) @test x_fixed == [true] println("\n... Copy x and der_x to variables") x = [1.11] derx = [2.22] ModiaMath.copy_x_and_derx_to_variables!(0.5, x, derx, m) @test isapprox(x, [robot3.rev3.phi.value], rtol=1e-15) println("\n... Copy variables to residues") residues = zeros(m.nx) ModiaMath.copy_variables_to_residue!(m, x, derx, residues) @test isapprox(residues, zeros(1), atol=1e-12) end end end
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1.942311
5,807
<gh_stars>0 # Note that this script can accept some limited command-line arguments, run # `julia build_tarballs.jl --help` to see a usage message. using BinaryBuilder name = "iso_codes" version = v"4.3" # Collection of sources required to build iso-codes sources = [ "https://salsa.debian.org/iso-codes-team/iso-codes/-/archive/iso-codes-$(version.major).$(version.minor)/iso-codes-iso-codes-$(version.major).$(version.minor).tar.bz2" => "6b539f915d02c957c45fce8133670811f1c36a1f1535d5af3dd95dc519d3c386" ] # Bash recipe for building across all platforms script = raw""" cd $WORKSPACE/srcdir/iso-codes-*/ apk add gettext ./configure --prefix=${prefix} --host=${target} make -j${nproc} make install """ # These are the platforms we will build for by default, unless further # platforms are passed in on the command line platforms = supported_platforms() # The products that we will ensure are always built products = Product[ FileProduct("share/iso-codes", :iso_codes_dir), ] # Dependencies that must be installed before this package can be built dependencies = [ ] # Build the tarballs, and possibly a `build.jl` as well. build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
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3.042079
404
<reponame>typedb-osi/TypeDBClient.jl<gh_stars>1-10 # This file is a part of TypeDBClient. License is MIT: https://github.com/Humans-of-Julia/TypeDBClient.jl/blob/main/LICENSE # --------------------------------------------------------------------------------- module DatabaseManagerRequestBuilder using ..TypeDBClient: Proto create_req(name::String) = Proto.CoreDatabaseManager_Create_Req(; name) contains_req(name::String) = Proto.CoreDatabaseManager_Contains_Req(; name) all_req() = Proto.CoreDatabaseManager_All_Req() end # --------------------------------------------------------------------------------- module DatabaseRequestBuilder using ..TypeDBClient: Proto schema_req(name::String) = Proto.CoreDatabase_Schema_Req(; name) delete_req(name::String) = Proto.CoreDatabase_Delete_Req(; name) end # --------------------------------------------------------------------------------- module SessionRequestBuilder using ..TypeDBClient: Proto, EnumType, Bytes function open_req(database::String, _type::EnumType, options::Proto.Options) return Proto.Session_Open_Req(; database, _type, options) end close_req(session_id::Bytes) = Proto.Session_Close_Req(; session_id) pulse_req(session_id::Bytes) = Proto.Session_Pulse_Req(; session_id) end # --------------------------------------------------------------------------------- module TransactionRequestBuilder using ..TypeDBClient: Proto, EnumType, Bytes using UUIDs: UUID function client_msg(reqs::AbstractVector{Proto.Transaction_Req}) return Proto.Transaction_Client(; reqs) end function stream_req(req_id::Bytes) stream_req = Proto.Transaction_Stream_Req() return Proto.Transaction_Req(; req_id, stream_req) end function open_req( session_id::Bytes, _type::EnumType, options::Proto.Options, network_latency_millis::Int ) open_req = Proto.Transaction_Open_Req(; session_id, _type, options, network_latency_millis ) return Proto.Transaction_Req(; open_req) end function commit_req() # metadata = tracing_data() commit_req = Proto.Transaction_Commit_Req() return Proto.Transaction_Req(; commit_req) end end # --------------------------------------------------------------------------------- module QueryManagerRequestBuilder using ..TypeDBClient: Proto for (f, t) in ( (:define_req, :Define_Req), (:undefine_req, :Undefine_Req), (:match_req, :Match_Req), (:match_aggregate_req, :MatchAggregate_Req), (:match_group_req, :MatchGroup_Req), (:match_group_aggregate_req, :MatchGroupAggregate_Req), (:insert_req, :Insert_Req), (:delete_req, :Delete_Req), (:update_req, :Update_Req), ) func = Symbol("$f") type = Symbol("QueryManager_$t") @eval begin function $func(query::String, options::Proto.Options = Proto.Options()) $f = Proto.$type(; query) query_manager_req = Proto.QueryManager_Req(; $f, options) return Proto.Transaction_Req(; query_manager_req) end end end end # --------------------------------------------------------------------------------- module ConceptManagerRequestBuilder using ..TypeDBClient: Proto, EnumType, bytes function _treq(; kwargs...) return Proto.Transaction_Req( concept_manager_req = Proto.ConceptManager_Req(; kwargs...) ) end function put_entity_type_req(label::String) return _treq( put_entity_type_req = Proto.ConceptManager_PutEntityType_Req(; label) ) end function put_relation_type_req(label::String) return _treq( put_relation_type_req = Proto.ConceptManager_PutRelationType_Req(; label) ) end function put_attribute_type_req(label::String, value_type::EnumType) return _treq( put_attribute_type_req = Proto.ConceptManager_PutAttributeType_Req(; label, value_type) ) end function get_thing_type_req(label::String) return _treq( get_thing_type_req = Proto.ConceptManager_GetThingType_Req(; label) ) end function get_thing_req(iid::String) return _treq( get_thing_req = Proto.ConceptManager_GetThing_Req(; iid = bytes(iid)) ) end end # --------------------------------------------------------------------------------- module LogicManagerRequestBuilder using ..TypeDBClient: Proto function _treq(; kwargs...) return Proto.Transaction_Req( logic_manager_req = Proto.LogicManager_Req( ; kwargs... ) ) end function put_rule_req(label::String, when::String, then::String) return _treq( put_rule_req = Proto.LogicManager_PutRule_Req(; label, when, then) ) end function get_rule_req(label::String) return _treq( get_rule_req = Proto.LogicManager_GetRule_Req(; label) ) end function get_rules_req() return _treq( get_rules_req = Proto.LogicManager_GetRules_Req() ) end end # --------------------------------------------------------------------------------- module TypeRequestBuilder using ..TypeDBClient: Proto, Label # Ignore linter error here function _treq(label, scope; kwargs...) return Proto.Transaction_Req( type_req = Proto.Type_Req(; label, scope, kwargs...) ) end function is_abstract_req(label::Label) return _treq(label.name, label.scope; type_is_abstract_req = Proto.Type_IsAbstract_Req() ) end function set_label_req(label::Label, new_label::String) return _treq(label.name, label.scope; type_set_label_req = Proto.Type_SetLabel_Req( label = new_label ) ) end function get_supertypes_req(label::Label) return _treq(label.name, label.scope; type_get_supertypes_req = Proto.Type_GetSupertypes_Req() ) end function get_subtypes_req(label::Label) return _treq(label.name, label.scope; type_get_subtypes_req = Proto.Type_GetSubtypes_Req() ) end function get_supertype_req(label::Label) return _treq(label.name, label.scope; type_get_supertype_req = Proto.Type_GetSupertype_Req() ) end function delete_req(label::Label) return _treq(label.name, label.scope; type_delete_req = Proto.Type_Delete_Req() ) end end # --------------------------------------------------------------------------------- module RoleTypeRequestBuilder using ..TypeDBClient: Proto, EnumType, Label using ..TypeRequestBuilder: _treq # TODO to be deprecated, see porting note at RoleType.jl function proto_role_type(label::Label, encoding::EnumType) @assert label.scope !== nothing return Proto._Type( scope = label.scope, label = label.name, encoding = encoding, ) end function get_relation_types_req(label::Label) return _treq(label.name, label.scope; role_type_get_relation_types_req = Proto.RoleType_GetRelationTypes_Req() ) end function get_players_req(label::Label) return _treq(label.name, label.scope; role_type_get_players_req = Proto.RoleType_GetPlayers_Req() ) end end # --------------------------------------------------------------------------------- module ThingTypeRequestBuilder using ..TypeDBClient: Proto, EnumType, Label, Optional using ..TypeRequestBuilder: _treq # TODO to be deprecated, see porting note at RoleType.jl function proto_thing_type(label::Label, encoding::EnumType) return Proto._Type( label = label.name, encoding = encoding ) end function set_abstract_req(label::Label) return _treq(label.name, label.scope; thing_type_set_abstract_req = Proto.ThingType_SetAbstract_Req() ) end function unset_abstract_req(label::Label) return _treq(label.name, label.scope; thing_type_unset_abstract_req = Proto.ThingType_UnsetAbstract_Req() ) end function set_supertype_req(label::Label) return _treq(label.name, label.scope; type_set_supertype_req = Proto.Type_SetSupertype_Req() ) end function get_plays_req(label::Label) return _treq(label.name, label.scope; thing_type_get_plays_req = Proto.ThingType_GetPlays_Req() ) end function set_plays_req( label::Label, role_type::Proto.RoleType, overridden_role_type::Optional{Proto.RoleType} = nothing ) return _treq(label.name, label.scope; thing_type_set_plays_req = Proto.ThingType_SetPlays_Req( role = role_type, overridden_role = overridden_role_type ) ) end function unset_plays_req( label::Label, role_type::Proto.RoleType ) return _treq(label.name, label.scope; thing_type_unset_plays_req = Proto.ThingType_UnsetPlays_Req( role = role_type, ) ) end # Porting note: keys_only is defaulted to false function get_owns_req( label::Label, value_type::Optional{EnumType} = nothing, keys_only::Bool = false ) # TODO this code can be simplified later (ProtoBuf PR#77) thing_type_get_owns_req = value_type === nothing ? Proto.ThingType_GetOwns_Req(; keys_only) : Proto.ThingType_GetOwns_Req(; keys_only, value_type) return _treq(label.name, label.scope; thing_type_get_owns_req) end # Porting note: the order of `is_key` is moved upfront function set_owns_req( label::Label, is_key::Bool, attribute_type::Proto.AttributeType, overridden_type::Optional{Proto.AttributeType} = nothing ) # TODO this code can be simplified later (ProtoBuf PR#77) thing_type_set_owns_req = overridden_type === nothing ? Proto.ThingType_SetOwns_Req(; is_key, attribute_type) : Proto.ThingType_SetOwns_Req(; is_key, attribute_type, overridden_type) return _treq(label.name, label.scope; thing_type_set_owns_req) # return _treq(label.name, label.scope; # thing_type_set_owns_req = Proto.ThingType_SetOwns_Req(; # is_key, attribute_type, overridden_type # ) # ) end function unset_owns_req(label::Label, attribute_type::Proto.AttributeType) return _treq(label.name, label.scope; thing_type_unset_owns_req = Proto.ThingType_UnsetOwns_Req(; attribute_type) ) end function get_instances_req(label::Label) return _treq(label.name, label.scope; thing_type_get_instances_req = Proto.ThingType_GetInstances_Req() ) end end # --------------------------------------------------------------------------------- module EntityTypeRequestBuilder using ..TypeDBClient: Proto, Label using ..TypeRequestBuilder: _treq function create_req(label::Label) return _treq(label.name, label.scope; entity_type_create_req = Proto.EntityType_Create_Req() ) end end # --------------------------------------------------------------------------------- module RelationTypeRequestBuilder using ..TypeDBClient: Proto, Label, Optional using ..TypeRequestBuilder: _treq function create_req(label::Label) return _treq(label.name, label.scope; relation_type_create_req = Proto.RelationType_Create_Req() ) end function get_relates_req(label::Label, role_label::Optional{String}) return _treq(label.name, label.scope; relation_type_get_relates_req = Proto.RelationType_GetRelates_Req(; label = role_label ) ) end function set_relates_req( label::Label, role_label::String, overridden_label::Optional{String} ) return _treq(label.name, label.scope; relation_type_set_relates_req = Proto.RelationType_SetRelates_Req(; label = role_label, overridden_label ) ) end function unset_relates_req(label::Label, role_label::Optional{String}) return _treq(label.name, label.scope; relation_type_unset_relates_req = Proto.RelationType_UnsetRelates_Req(; label = role_label ) ) end end # --------------------------------------------------------------------------------- module AttributeTypeRequestBuilder using ..TypeDBClient: Proto, Label using ..TypeRequestBuilder: _treq function get_owners_req(label::Label, only_key::Bool) return _treq(label.name, label.scope; attribute_type_get_owners_req = Proto.AttributeType_GetOwners_Req(; only_key) ) end function put_req(label::Label, value::Proto.Attribute_Value) return _treq(label.name, label.scope; attribute_type_put_req = Proto.AttributeType_Put_Req(; value) ) end function get_req(label::Label, value::Proto.Attribute_Value) return _treq(label.name, label.scope; attribute_type_get_req = Proto.AttributeType_Get_Req(; value) ) end function get_regex_req(label::Label) return _treq(label.name, label.scope; attribute_type_get_regex_req = Proto.AttributeType_GetRegex_Req() ) end function set_regex_req(label::Label, regex::AbstractString) return _treq(label.name, label.scope; attribute_type_set_regex_req = Proto.AttributeType_SetRegex_Req(; regex) ) end end # --------------------------------------------------------------------------------- module ThingRequestBuilder using ..TypeDBClient: Proto, Label, Bytes, bytes proto_thing(iid::Bytes) = Proto.Thing(; iid) proto_thing(iid::String) = proto_thing(bytes(iid)) function _thing_req(iid::String; kwargs...) return Proto.Transaction_Req( thing_req = Proto.Thing_Req( ; iid = bytes(iid), kwargs... ) ) end function is_inferred_req(iid::String) return _thing_req(iid; thing_is_inferred_req = Proto.Thing_IsInferred_Req() ) end function get_has_req(iid::String, attribute_types::AbstractVector{Proto.Type}) return _thing_req(iid; thing_get_has_req = Proto.Thing_GetHas_Req(; attribute_types) ) end function get_has_req(iid::String, only_key::Bool) return _thing_req(iid; thing_get_has_req = Proto.Thing_GetHas_Req(; only_key) ) end function set_has_req(iid::String, attribute::Proto.Thing) return _thing_req(iid; thing_set_has_req = Proto.Thing_SetHas_Req(; attribute) ) end function unset_has_req(iid::String, attribute::Proto.Thing) return _thing_req(iid; thing_unset_has_req = Proto.Thing_UnsetHas_Req(; attribute) ) end function get_playing_req(iid::String) return _thing_req(iid; thing_get_playing_req = Proto.Thing_GetPlaying_Req() ) end function get_relations_req(iid::String, role_types::AbstractVector{Proto._Type}) return _thing_req(iid; thing_get_relations_req = Proto.Thing_GetRelations_Req(; role_types) ) end function delete_req(iid::String) return _thing_req(iid; thing_delete_req = Proto.Thing_Delete_Req() ) end end # --------------------------------------------------------------------------------- module RelationRequestBuilder using ..TypeDBClient: Proto using ..ThingRequestBuilder: _thing_req function add_player_req(iid::String, role_type::Proto._Type, player::Proto.Thing) return _thing_req(iid; relation_add_player_req = Proto.Relation_AddPlayer_Req(; role_type, player ) ) end function remove_player_req(iid::String, role_type::Proto._Type, player::Proto.Thing) return _thing_req(iid; relation_remove_player_req = Proto.Relation_RemovePlayer_Req(; role_type, player ) ) end function get_players_req(iid::String, role_types::AbstractVector{Proto._Type}) return _thing_req(iid; relation_get_players_req = Proto.Relation_GetPlayers_Req(; role_types) ) end function get_players_by_role_type_req(iid::String) return _thing_req(iid; relation_get_players_by_role_type_req = Proto.Relation_GetPlayersByRoleType_Req() ) end function get_relating_req(iid::String) return _thing_req(iid; relation_get_players_req = Proto.Relation_GetRelating_Req() ) end end # --------------------------------------------------------------------------------- module AttributeRequestBuilder using ..TypeDBClient: Proto, Optional using ..ThingRequestBuilder: _thing_req using TimeZones: ZonedDateTime function get_owners_req(iid::String, owner_type::Optional{Proto._Type}) return _thing_req(iid; relation_get_owners_req = Proto.Relation_GetOwners_Req(), thing_type = owner_type ) end proto_boolean_attribute_value(value::Bool) = Proto.Attribute_Value(; boolean = value) proto_long_attribute_value(value::Int64) = Proto.Attribute_Value(; long = value) proto_double_attribute_value(value::Float64) = Proto.Attribute_Value(; double = value) proto_string_attribute_value(value::String) = Proto.Attribute_Value(; string = value) function proto_date_time_attribute_value(value::ZonedDateTime) epoch_millis = value.utc_datetime.instant Proto.Attribute_Value(; date_time = epoch_millis) end end # --------------------------------------------------------------------------------- module RuleRequestBuilder using ..TypeDBClient: Proto function set_label_req(current_label::String, new_label::String) return Proto.Transaction_Req( rule_req = Proto.Rule_Req( label = current_label, rule_set_label_req = Proto.Rule_SetLabel_Req( label = new_label ) ) ) end function delete_req(label::String) return Proto.Transaction_Req( rule_req = Proto.Rule_Req( rule_delete_req = Proto.Rule_Delete_Req() ) ) end end
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