From 8b66f3833bfbc5452c2faf5903ddae22c85be7fc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Tue, 5 Mar 2024 11:00:24 +0100 Subject: [PATCH 01/52] Add the pure dephasing at 0K example --- examples/puredephasing.jl | 101 +++++++++++++++++++++++++++++++ examples/sbm_zero_temperature.jl | 4 +- src/MPSDynamics.jl | 4 +- src/measure.jl | 54 +++++++++++++++++ src/models.jl | 15 +++++ src/run_1TDVP.jl | 10 ++- 6 files changed, 184 insertions(+), 4 deletions(-) create mode 100644 examples/puredephasing.jl diff --git a/examples/puredephasing.jl b/examples/puredephasing.jl new file mode 100644 index 0000000..5b029b3 --- /dev/null +++ b/examples/puredephasing.jl @@ -0,0 +1,101 @@ +#= + Example of a Pure Dephasing Model at zero temperature and with an arbitrary temperature. with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# + + The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. + + H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i +=# + +using MPSDynamics, Plots, LaTeXStrings, QuadGK + +#---------------------------- +# Physical parameters +#---------------------------- + +ΔE = 0.008 # Energy of the electronic states + +d = 5 # number of Fock states of the chain modes + +N = 30 # length of the chain + +α = 0.01 # coupling strength + +s = 1 # ohmicity + +ωc = 0.035 # Cut-off of the spectral density J(ω) + +cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + +#----------------------- +# Simulation parameters +#----------------------- + +dt = 1.0 # time step + +tfinal = 300.0 # simulation time + +method = :TDVP1 # time-evolution method + +D = 6 # MPS bond dimension + +#--------------------------- +# MPO and initial state MPS +#--------------------------- + +H = puredephasingmpo(ΔE, d, N, cpars) + +# Initial electronic system in a superposition of 1 and 2 +ψ = zeros(2) +ψ[1] = 1/sqrt(2) +ψ[2] = 1/sqrt(2) + + +A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> + +#--------------------------- +# Definition of observables +#--------------------------- + +ob1 = OneSiteObservable("sz", sz, 1) + + +#------------- +# Simulation +#------------ + +A, dat = runsim(dt, tfinal, A, H; + name = "pure dephasing model at zero temperature", + method = method, + obs = [ob1], + convobs = [ob1], + params = @LogParams(ΔE, N, d, α, s), + convparams = D, + reduceddensity=true, + verbose = false, + save = true, + plot = true, + ); + + + +#---------- +# Analytical results at zero temperature +# (see The Theory of Open Quantum System, H.-P. Breuer & F. Petruccione 2002, Chapter 4) +#---------- + +Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) +f(ω,t) = (1 - cos(ω*t))/ω^2 +time_analytical = LinRange(0.0,tfinal,Int(tfinal)) + +Γohmic(t) = - quadgk(x -> Johmic(x,s)*f(x,t), 0, ωc)[1] +Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) +ListDecoherence_ohmic = [Decoherence_ohmic(t) for t in time_analytical] + +#------------- +# Plots +#------------ + +ρ12 = sqrt.(real(dat["data/Reduced ρ"][1,2,:]).^2 .+ imag(dat["data/Reduced ρ"][1,2,:]).^2 ) + +(plot(time_analytical, ListDecoherence_ohmic1, label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s \, ,\, T=0K$", linecolor =:black, xlabel="Time (arb. units)",ylabel="Coherence Amplitude", linewidth=4, titlefontsize=16, legend=:best, legendfont=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10))#,ylims=(0.25,0.5)))#,xticks=(0:2.5:10)))) +display(plot!(dat["data/times"],ρ12,lw=4,ls=:dash,label="Numerics")) \ No newline at end of file diff --git a/examples/sbm_zero_temperature.jl b/examples/sbm_zero_temperature.jl index bf8bdc9..4157244 100644 --- a/examples/sbm_zero_temperature.jl +++ b/examples/sbm_zero_temperature.jl @@ -1,10 +1,10 @@ -""" +#= Example of a zero-temperature Spin-Boson Model with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. H = \\frac{ω_0}{2} σ_z + Δ σ_x + c_0 σ_x(b_0^\\dagger + b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N-1} ϵ_i b_i^\\dagger b_i -""" +=# using MPSDynamics, Plots, LaTeXStrings diff --git a/src/MPSDynamics.jl b/src/MPSDynamics.jl index e30c227..4d78756 100644 --- a/src/MPSDynamics.jl +++ b/src/MPSDynamics.jl @@ -153,7 +153,7 @@ end export sz, sx, sy, numb, crea, anih, unitcol, unitrow, unitmat, spinSX, spinSY, spinSZ, SZ, SX, SY -export chaincoeffs_ohmic, spinbosonmpo, methylbluempo, methylbluempo_correlated, methylbluempo_correlated_nocoupling, methylbluempo_nocoupling, ibmmpo, methylblue_S1_mpo, methylbluempo2, twobathspinmpo, xyzmpo +export chaincoeffs_ohmic, spinbosonmpo, methylbluempo, methylbluempo_correlated, methylbluempo_correlated_nocoupling, methylbluempo_nocoupling, ibmmpo, methylblue_S1_mpo, methylbluempo2, twobathspinmpo, xyzmpo, puredephasingmpo export productstatemps, physdims, randmps, bonddims, elementmps @@ -173,5 +173,7 @@ export @LogParams export MPOtoVector, MPStoVector +export rhoreduced_2sites, rhoreduced_1site + end diff --git a/src/measure.jl b/src/measure.jl index 4fe6aaa..7321111 100644 --- a/src/measure.jl +++ b/src/measure.jl @@ -1019,3 +1019,57 @@ function leftcontractmps(A, O::Vector, N::Int=length(A)) end return ρ end + +""" + rhoreduced_1site(A::Vector, site::Int=1) + +Caculate the reduced density matrix of the MPS A at the specified site. + +""" + +function rhoreduced_1site(A::Vector, site::Int=1) + N = length(A) + ρR = Vector{Any}(undef, N-site+1) + ρL = Vector{Any}(undef, site) + ρR[1] = ones(ComplexF64,1,1) + ρL[1] = ones(ComplexF64,1,1) + for i=N:-1:(site+1) # Build the right block, compressing the chain, from right ot left (indir=2) + ρR[N-i+2]= rhoAAstar(ρR[N-i+1], A[i], 2,0) + end + for i=1:(site-1) + ρL[i+1]= rhoAAstar(ρL[i], A[i], 1,0) + end + # Compress final virtual bondimension + @tensoropt ρreduced[a,b,s,s'] := ρR[N-site+1][a0,b0] * conj(A[site][a,a0,s']) * A[site][b,b0,s] + @tensoropt ρreduced2[s,s'] := ρL[site][a0,b0] * ρreduced[a0,b0,s,s'] + return ρreduced2 +end + +""" + rhoreduced_2sites(A::Vector, site::Tuple{Int, Int}) + +Caculate the reduced density matrix of the MPS A of two neigbour sites. The resulting dimensions will be the four physical dimensions in total, +corresponding to the dimensions of the two sites + +""" + +function rhoreduced_2sites(A::Vector, sites::Tuple{Int, Int}) + N = length(A) + site1, site2=sites + ρR = Vector{Any}(undef, N-site2+1) + ρL = Vector{Any}(undef, site1) + ρR[1] = ones(ComplexF64,1,1) + ρL[1] = ones(ComplexF64,1,1) + for i=N:-1:(site2+1) # Build the right block, compressing the chain, from right ot left (indir=2) + ρR[N-i+2]= rhoAAstar(ρR[N-i+1], A[i], 2,0) + end + for i=1:(site1-1) + ρL[i+1]= rhoAAstar(ρL[i], A[i], 1,0) + end + # Compress final virtual bondimension + @tensoropt ρreduced1[a,b,s,s'] := ρR[N-site2+1][a0,b0] * conj(A[site2][a,a0,s']) * A[site2][b,b0,s] + @tensoropt ρreduced2[a,b,s,s'] := ρL[site1][a0,b0] * conj(A[site1][a0,a,s']) * A[site1][b0,b,s] + @tensoropt ρreduced[s,d1,s',d2] := ρreduced2[a0,b0,d1,d2] * ρreduced1[a0,b0,s,s'] + return ρreduced +end + diff --git a/src/models.jl b/src/models.jl index 89a4a6f..3191538 100644 --- a/src/models.jl +++ b/src/models.jl @@ -571,3 +571,18 @@ function nearestneighbourmpo(tree_::Tree, h0, A, Ad = A') Ms[hn] = Ms[hn][D:D, fill(:,nc+2)...] return TreeNetwork(tree, Ms) end + +function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) + u = unitmat(2) + + cparams = only(chainparams[3]) + + Hs = (ΔE/2)*sz + + M=zeros(1,3,2,2) + M[1, :, :, :] = up(Hs, (cparams/2)*sz, u) + + chain = hbathchain(Nchain, dchain, chainparams; tree=false, reverse=false, coupletox=true) + return Any[M, chain...] +end + diff --git a/src/run_1TDVP.jl b/src/run_1TDVP.jl index b43cfe8..64b9c91 100644 --- a/src/run_1TDVP.jl +++ b/src/run_1TDVP.jl @@ -1,4 +1,4 @@ -function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, kwargs...) +function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, reduceddensity=false, kwargs...) A0=deepcopy(A) data = Dict{String,Any}() @@ -11,6 +11,10 @@ function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, kwargs...) for i=1:length(obs) push!(data, obs[i].name => reshape(exp[i], size(exp[i])..., 1)) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + push!(data, "Reduced ρ" => reshape(exprho, size(exprho)..., 1)) + end timed && (ttdvp = Vector{Float64}(undef, numsteps)) @@ -31,6 +35,10 @@ function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, kwargs...) for (i, ob) in enumerate(obs) data[ob.name] = cat(data[ob.name], exp[i]; dims=ndims(exp[i])+1) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) + end end timed && push!(data, "deltat"=>ttdvp) push!(data, "times" => times) From 4ec88bb3b916aa8621ea1ab0fd40cc278688c793 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Tue, 5 Mar 2024 22:25:58 +0100 Subject: [PATCH 02/52] Update puredephasing.jl --- examples/puredephasing.jl | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/examples/puredephasing.jl b/examples/puredephasing.jl index 5b029b3..4b3c58d 100644 --- a/examples/puredephasing.jl +++ b/examples/puredephasing.jl @@ -1,5 +1,5 @@ #= - Example of a Pure Dephasing Model at zero temperature and with an arbitrary temperature. with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# + Example of a Pure Dephasing Model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. @@ -36,7 +36,7 @@ tfinal = 300.0 # simulation time method = :TDVP1 # time-evolution method -D = 6 # MPS bond dimension +D = 2 # MPS bond dimension #--------------------------- # MPO and initial state MPS @@ -84,18 +84,19 @@ A, dat = runsim(dt, tfinal, A, H; #---------- Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) -f(ω,t) = (1 - cos(ω*t))/ω^2 + time_analytical = LinRange(0.0,tfinal,Int(tfinal)) -Γohmic(t) = - quadgk(x -> Johmic(x,s)*f(x,t), 0, ωc)[1] +Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))/x^2, 0, ωc)[1] + Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) -ListDecoherence_ohmic = [Decoherence_ohmic(t) for t in time_analytical] #------------- # Plots #------------ -ρ12 = sqrt.(real(dat["data/Reduced ρ"][1,2,:]).^2 .+ imag(dat["data/Reduced ρ"][1,2,:]).^2 ) +ρ12 = abs.(dat["data/Reduced ρ"][1,2,:]) + +plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $T=0~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) -(plot(time_analytical, ListDecoherence_ohmic1, label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s \, ,\, T=0K$", linecolor =:black, xlabel="Time (arb. units)",ylabel="Coherence Amplitude", linewidth=4, titlefontsize=16, legend=:best, legendfont=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10))#,ylims=(0.25,0.5)))#,xticks=(0:2.5:10)))) -display(plot!(dat["data/times"],ρ12,lw=4,ls=:dash,label="Numerics")) \ No newline at end of file +plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") From 49f3e57514ce3c2330d3891d339956b93a2ac5b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Tue, 19 Mar 2024 09:18:22 +0100 Subject: [PATCH 03/52] Add puredephasing temperature example --- ChainOhmT/chainOhmT.jl | 35 ++++-- ChainOhmT/ohmicT/chaincoeffs.h5 | Bin 0 -> 9848 bytes ...oeffs_ohmic_a0.01wc0.035xc0.035beta100.csv | 31 +++++ ChainOhmT/quadohmT.jl | 12 +- examples/puredephasing_temperature.jl | 110 ++++++++++++++++++ src/MPSDynamics.jl | 2 +- 6 files changed, 176 insertions(+), 14 deletions(-) create mode 100644 ChainOhmT/ohmicT/chaincoeffs.h5 create mode 100644 ChainOhmT/ohmicT/chaincoeffs_ohmic_a0.01wc0.035xc0.035beta100.csv create mode 100644 examples/puredephasing_temperature.jl diff --git a/ChainOhmT/chainOhmT.jl b/ChainOhmT/chainOhmT.jl index 16f8ea3..cfcbc9c 100644 --- a/ChainOhmT/chainOhmT.jl +++ b/ChainOhmT/chainOhmT.jl @@ -1,5 +1,6 @@ module Chaincoeffs using CSV +using HDF5 using Tables include("quadohmT.jl") include("mcdis2.jl") @@ -10,10 +11,12 @@ Code translated in Julia by Thibaut Lacroix in 10/2020 from a previous Matlab co export mc, mp, iq, idelta, irout, AB, a, wc, beta, DM, uv ## Spectral density parameters -a = 0.03 -wc = 1 -beta = 1 -xc = wc +a = 0.01 # Coupling parameter +wc = 0.035 # Cutoff frequency. Often set up as 1 for arbitrary units +s = 1 #Ohmicity parameter +beta = 100 # 1/(kB T) + +xc = wc # Limit of definition segments ## discretisation parameters mc=4 # the number of component intervals @@ -22,7 +25,7 @@ iq=1 # a parameter to be set equal to 1, if the user provides his or her own qua idelta=2 # a parameter whose default value is 1, but is preferably set equal to 2, if iq=1 and the user provides Gauss-type quadrature routines irout=2 # choice between the Stieltjes (irout = 1) and the Lanczos procedure (irout != 1) AB =[[-Inf -xc];[-xc 0];[0 xc];[xc Inf]] # component intervals -N=1000 #Number of bath modes +N=30 #Number of bath modes Mmax=5000 eps0=1e7*eps(Float64) @@ -41,8 +44,26 @@ for i = 1:mc xw = quadfinT(Mcap,i,uv) global eta += sum(xw[:,2]) end -jacerg[N,2] = sqrt(eta/pi) +jacerg[N,2] = sqrt(eta) + +# Write a CSV file +curdir = @__DIR__ + header = Vector([Symbol("α_n"), Symbol("β_n and η")]) -CSV.write("chaincoeffs_ohmic_a$(a)wc$(wc)xc$(xc)beta$(beta).csv", Tables.table(jacerg, header=header)) +CSV.write("$curdir/ohmicT/chaincoeffs_ohmic_a$(a)wc$(wc)xc$(xc)beta$(beta).csv", Tables.table(jacerg, header=header)) + +Nstr=string(N) +astr=string(a) +sstr=string(s) +bstr=string(beta) +# the "path" to the data inside of the h5 file is beta -> alpha -> s -> data (e, t or c) + +# Write onsite energies +h5write("$curdir/ohmicT/chaincoeffs.h5", string("/",Nstr,"/",astr,"/",sstr,"/",bstr,"/e"), jacerg[1:N,1]) +# Write hopping energies +h5write("$curdir/ohmicT/chaincoeffs.h5", string("/",Nstr,"/",astr,"/",sstr,"/",bstr,"/t"), jacerg[1:N-1,2]) +# Write coupling coefficient +h5write("$curdir/ohmicT/chaincoeffs.h5", string("/",Nstr,"/",astr,"/",sstr,"/",bstr,"/c"), jacerg[N,2]) end + diff --git a/ChainOhmT/ohmicT/chaincoeffs.h5 b/ChainOhmT/ohmicT/chaincoeffs.h5 new file mode 100644 index 0000000000000000000000000000000000000000..37df5a09bfe231d8feb39bfc92ce2fda44868803 GIT binary patch literal 9848 zcmeD5aB<`1lHy_j0S*oZ76t(@6Gr@pf(kW=2#gPtPk=HQp>zk7Ucm%mFfxE31A_!q zTo7tLy1I}cS62q0N|^aD8mf)q0V{;X0TURdM^p%SxH<-aJRAY_H7u2$fTlB8U>F-f zSg`cy0h1t;#+80Om>~vcK+`3m^!tJnqJA{}k{J<0TpO ze*P|?Gz?333ea>5Hy!`v}SqlN&kav*>mqOJg%zX_ECA9x|^Vex@#%P4QShXAf} zU;;G#!2Ay`1b`fPK7~n;Nu!qo8qjnLGao%2lWEPc*M_Sch~S2Jy#kuQ36%ps1R?52 z%Yk8^WUzV=S2-{PntouK3=JR+T39~yfJu-^qn85)&~ytkA3YtDY0a?L25M1(!VK20 z6@d0@14JNRhs6`59ALoNuO0RQHR?gUA%I>E!0Lkq(E0(U8J0i6onV+C1vGj&V8IVD z3}#OPl;42n4hpOsmRcDZ*dT2*XuvT-O9Vznh=3f_*D#tHC=ZemRIrEk$s8C=xEW0T zL;GQ@&~k-|i3w}~2h==f#u?C($n|JNO*WZEmp9LxZj+|C6)$`1cEZSl?SVpK*Ue)%9y#BH#8o+*RrOHtFp?hiQ{vR-S#buVL>B zIo`B;`xq+Te)W2KZ68AqFUPyJ7xyttT-ZMC!RdVrpDrZdjytxGp}}C&SIa~D7e}D`O^5ze&iSGt>4l zFf5UI*g0h%p>!k*4KNr@emd%a8u0=a1z>uxr6X9nXpl!u7i`damQjF_v7|#3#Sf#L z(GVC7fzc2c4S~@R7-}JK$B%XOht__32mRh@5j|#p{w7vYAk9#|^xB%&o3+g|Z0cp&34y4BeX)QA_kl(H;fX-0EdQgxQ zfZ49;7*t6ExjmEWAPjdKvUqX1 && i|Vacuum> + +#--------------------------- +# Definition of observables +#--------------------------- + +ob1 = OneSiteObservable("sz", sz, 1) + + +#------------- +# Simulation +#------------ + +A, dat = runsim(dt, tfinal, A, H; + name = "pure dephasing model with temperature", + method = method, + obs = [ob1], + convobs = [ob1], + params = @LogParams(ΔE, N, d, α, s), + convparams = D, + reduceddensity=true, + verbose = false, + save = true, + plot = true, + ); + + + +#---------- +# Analytical results at specified temperature +# (see The Theory of Open Quantum System, H.-P. Breuer & F. Petruccione 2002, Chapter 4) +#---------- + +Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) + +time_analytical = LinRange(0.0,tfinal,Int(tfinal)) + +Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))*coth(β*x/2)/x^2, 0, ωc)[1] + +Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) + +#------------- +# Plots +#------------ + +ρ12 = abs.(dat["data/Reduced ρ"][1,2,:]) + +plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $\beta = %$β ~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) + +plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") + diff --git a/src/MPSDynamics.jl b/src/MPSDynamics.jl index 4d78756..650c2fc 100644 --- a/src/MPSDynamics.jl +++ b/src/MPSDynamics.jl @@ -153,7 +153,7 @@ end export sz, sx, sy, numb, crea, anih, unitcol, unitrow, unitmat, spinSX, spinSY, spinSZ, SZ, SX, SY -export chaincoeffs_ohmic, spinbosonmpo, methylbluempo, methylbluempo_correlated, methylbluempo_correlated_nocoupling, methylbluempo_nocoupling, ibmmpo, methylblue_S1_mpo, methylbluempo2, twobathspinmpo, xyzmpo, puredephasingmpo +export chaincoeffs_ohmic, spinbosonmpo, methylbluempo, methylbluempo_correlated, methylbluempo_correlated_nocoupling, methylbluempo_nocoupling, ibmmpo, methylblue_S1_mpo, methylbluempo2, twobathspinmpo, xyzmpo, puredephasingmpo, tunnelingmpo export productstatemps, physdims, randmps, bonddims, elementmps From 32208721b67657919b8775e40764be17a5349de1 Mon Sep 17 00:00:00 2001 From: angelariva Date: Wed, 27 Mar 2024 17:10:00 +0100 Subject: [PATCH 04/52] Modified README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 55c1fb6..9fd957b 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5106435.svg)](https://doi.org/10.5281/zenodo.5106435) [![license](https://img.shields.io/badge/License-GPL_3.0-orange.svg)](https://github.com/angusdunnett/MPSDynamics/blob/master/LICENSE) [![documentation workflow](https://github.com/angusdunnett/MPSDynamics/actions/workflows/docs.yml/badge.svg)](https://angusdunnett.github.io/MPSDynamics/) + This package is intended to provide an easy to use interface for performing tensor network simulations on Matrix Product States (MPS). MPSDynamics.jl is a versatile package which supports both chain and (loop-free) tree MPS, as well as providing a choice of several time evolution algorithms. The package also provides strong support for the measurement From 9bc7bdba3873d627d83b18b898b1777124b0ee87 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Wed, 3 Apr 2024 11:59:52 +0200 Subject: [PATCH 05/52] Updated doc --- src/MPSDynamics.jl | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/MPSDynamics.jl b/src/MPSDynamics.jl index 650c2fc..21cdb00 100644 --- a/src/MPSDynamics.jl +++ b/src/MPSDynamics.jl @@ -62,8 +62,6 @@ Propagate the MPS `A` with the MPO `H` up to time `tmax` in time steps of `dt`. * `name`: Used to describe the calculation. This name will appear in the log.txt file """ - - function runsim(dt, tmax, A, H; method=:TDVP1, machine=LocalMachine(), From e99990abfe4871e0866520a20cdbbefbc877a30a Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Wed, 3 Apr 2024 16:32:05 +0200 Subject: [PATCH 06/52] Update documentation of models --- src/models.jl | 65 +++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 60 insertions(+), 5 deletions(-) diff --git a/src/models.jl b/src/models.jl index 3191538..1d3fdef 100644 --- a/src/models.jl +++ b/src/models.jl @@ -353,7 +353,7 @@ The spin is on site 1 of the MPS and the bath modes are to the right. This Hamiltonain is unitarily equivalent (before the truncation to `N` sites) to the spin-boson Hamiltonian defined by `` -H = \\frac{ω_0}{2}σ_z + Δσ_x + σ_x\\int_0^∞ dω\\sqrt{\\frac{J(ω)}{π}}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω ωb_ω^\\dagger b_ω +H = \\frac{ω_0}{2}σ_z + Δσ_x + σ_x\\int_0^∞ dω\\sqrt{J(ω)}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω ωb_ω^\\dagger b_ω ``. The chain parameters, supplied by `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. @@ -387,6 +387,27 @@ function spinbosonmpo(ω0, Δ, d, N, chainparams; rwa=false, tree=false) end end +""" + twobathspinmpo(ω0, Δ, Nl, Nr, dl, dr, chainparamsl=[fill(1.0,N),fill(1.0,N-1), 1.0], chainparamsr=chainparamsl; tree=false) + +Generate MPO for a spin-1/2 coupled to a chain of harmonic oscillators on its left and another one on its right, defined by the Hamiltonian + +`` +H = \\frac{ω_0}{2}σ_z + Δσ_x + c_Lσ_x(b_0^\\dagger+b_0) + c_Rσ_x(c_0^\\dagger+c_0) + \\sum_{i=0}^{N-1} t^L_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N} ϵ^L_i c_i^\\dagger c_i + \\sum_{i=0}^{N-1} t^R_i (c_{i+1}^\\dagger c_i + h.c.) + \\sum_{i=0}^{N} ϵ^R_ic_i^\\dagger c_i +``. + +The spin is on site N+2 of the MPS. + +This Hamiltonain is unitarily equivalent (before the truncation to `N` sites) to the two-bath spin-boson Hamiltonian defined by + +`` +H = \\frac{ω_0}{2}σ_z + Δσ_x + σ_x(\\int_0^∞ dω\\sqrt{J_L(ω)}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω\\sqrt{J_R(ω)}(c_ω^\\dagger+c_ω) ) + \\int_0^∞ dω ωb_ω^\\dagger b_ω + \\int_0^∞ dω ωc_ω^\\dagger c_ω +``. + +The chain parameters, supplied by `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. + +The MPO can be considered as a Tree Tensor Network by setting `tree=true`. +""" function twobathspinmpo(ω0, Δ, Nl, Nr, dl, dr, chainparamsl=[fill(1.0,N),fill(1.0,N-1), 1.0], chainparamsr=chainparamsl; tree=false) u = unitmat(2) @@ -410,8 +431,8 @@ end Generate chain coefficients ``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]`` for an Harmonic bath at zero temperature with a power law spectral density given by: -soft cutoff: ``J(ω) = 2παω_c (\\frac{ω}{ω_c})^s \\exp(-ω/ω_c)`` \n -hard cutoff: ``J(ω) = 2παω_c (\\frac{ω}{ω_c})^s θ(ω-ω_c)`` +soft cutoff: ``J(ω) = 2αω_c (\\frac{ω}{ω_c})^s \\exp(-ω/ω_c)`` \n +hard cutoff: ``J(ω) = 2αω_c (\\frac{ω}{ω_c})^s θ(ω-ω_c)`` The coefficients parameterise the chain Hamiltonian @@ -422,8 +443,8 @@ H = H_S + c_0 A_S⊗B_0+\\sum_{i=0}^{N-1}t_i (b_{i+1}^\\dagger b_i +h.c.) + \\su which is unitarily equivalent (before the truncation to `N` sites) to `` -H = H_S + A_S⊗\\int_0^∞dω\\sqrt{\\frac{J(ω)}{π}}B_ω + \\int_0^∞dωωb_ω^\\dagger b_ω -`` +H = H_S + A_S⊗\\int_0^∞dω\\sqrt{J(ω)}B_ω + \\int_0^∞dωωb_ω^\\dagger b_ω +``. """ function chaincoeffs_ohmic(nummodes, α, s; ωc=1, soft=false) @@ -479,6 +500,30 @@ function readchaincoeffs(fdir, params...) return dat end +""" + ibmmpo(ω0, d, N, chainparams; tree=false) + +Generate the MPO for the independent boson model Hamiltonian in a chain mapped representation + +`` +H = \\frac{ω_0}{2}σ_z + c_0σ_z(b_0^\\dagger+b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N} ϵ_ib_i^\\dagger b_i +``. + +The spin is on site 1 of the MPS and the bath modes are to the right. + +This Hamiltonain is unitarily equivalent (before the truncation to `N` sites) to the spin-boson Hamiltonian defined by + +`` +H = \\frac{ω_0}{2}σ_z + σ_z\\int_0^∞ dω\\sqrt{J(ω)}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω ωb_ω^\\dagger b_ω +``. + +This Hamiltonian is equivalent (up to a rotation around the y-axis) to a spin-boson model with without biais. + +The chain parameters, supplied by `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. + +The MPO can be considered as a Tree Tensor Network by setting `tree=true`. + +""" function ibmmpo(ω0, d, N, chainparams; tree=false) u = unitmat(2) @@ -525,6 +570,16 @@ function tunnelingmpo(ϵ, delta, α, s, β, d::Int, nummodes::Int; tree=false, end end +""" + nearestneighbourmpo(N::Int, h0, A, Ad = A') + +Generate the MPO for a N sites system with on-site Hamiltonian `h0` and nearest-neighbour interactions ``A_i A^\\dagger_{i+1}`` + +`` +H = \sum_{i = 1}^{N-1} A_{i}A^\\dagger_{i+1} + Nh_0 +``. + +""" function nearestneighbourmpo(N::Int, h0, A, Ad = A') size(h0) == size(A) || error("physical dimensions don't match") size(h0) == size(Ad) || error("physical dimensions don't match") From 8e1c281965f3a990130fb2010e344e235e3e9fb1 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Wed, 3 Apr 2024 16:46:17 +0200 Subject: [PATCH 07/52] Update models.jl --- src/models.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/models.jl b/src/models.jl index 1d3fdef..a077322 100644 --- a/src/models.jl +++ b/src/models.jl @@ -576,7 +576,7 @@ end Generate the MPO for a N sites system with on-site Hamiltonian `h0` and nearest-neighbour interactions ``A_i A^\\dagger_{i+1}`` `` -H = \sum_{i = 1}^{N-1} A_{i}A^\\dagger_{i+1} + Nh_0 +H = \\sum_{i = 1}^{N-1} A_{i}A^\\dagger_{i+1} + Nh_0 ``. """ From bd8cf56353ab090d9c7bfe855a0927c0c30fcfa8 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Thu, 4 Apr 2024 10:24:59 +0200 Subject: [PATCH 08/52] Update publication list --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 9fd957b..6964efc 100644 --- a/README.md +++ b/README.md @@ -150,6 +150,8 @@ Import["~/MPSDynamics/XXXXX/dat_XXXXX.jld",{"HDF5","Datasets","/data/sz"}] # Publications Publications which make use of MPSDynamics: +* Lacroix et al. From Non-Markovian Dissipation to Spatiotemporal Control of Quantum Nanodevices. *Quantum* 8, 1305, April 2024 + * [https://doi.org/10.22331/q-2024-04-03-1305](https://doi.org/10.22331/q-2024-04-03-1305) * Riva et al. Thermal cycle and polaron formation in structured bosonic environments. *Phys. Rev. B* 108(19):195138, November 2023 * [https://doi.org/10.1103/PhysRevB.108.195138](https://doi.org/10.1103/PhysRevB.108.195138) From bf19a1d529d5a2e73d006e47a829ab775207a2a6 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Thu, 4 Apr 2024 10:35:16 +0200 Subject: [PATCH 09/52] Update references --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 6964efc..e70f771 100644 --- a/README.md +++ b/README.md @@ -9,13 +9,13 @@ States (MPS). MPSDynamics.jl is a versatile package which supports both chain an providing a choice of several time evolution algorithms. The package also provides strong support for the measurement of observables, as well as the storing and logging of data, which makes it a useful tool for the study of many-body physics. The package was originally developed with the aim of studying open system dynamics at finite temperature using -the T-TEDOPA mapping [1], however the methods implemented can equally be applied to other areas of physics. +the T-TEDOPA mapping [^1], however the methods implemented can equally be applied to other areas of physics. The methods currently implemented are -* 1-site TDVP on tree and chain MPS [2] -* 2-site TDVP on chain MPS [2] -* a variant of 1-site TDVP with dynamic bond-dimensions on chain MPS [3] +* 1-site TDVP on tree and chain MPS [^2] +* 2-site TDVP on chain MPS [^2] +* a variant of 1-site TDVP with dynamic bond-dimensions on chain MPS [^3] The elementary tensor operations are implemented in all cases using the [TensorOperations.jl](https://github.com/Jutho/TensorOperations.jl) package. @@ -189,9 +189,9 @@ Contributions are welcome! Don't hesitate to contact us if you # References -* [[1]](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.123.090402) D. Tamascelli, A. Smirne, J. Lim, S. F. Huegla, and M. B. Plenio, Physical Review Letters 123, 090402 (2019) arXiv: 1811.12418 +[^1]: [D. Tamascelli, A. Smirne, J. Lim, S. F. Huegla, and M. B. Plenio, Physical Review Letters 123, 090402 (2019) arXiv: 1811.12418](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.123.090402) -* [[2]](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.165116) J. Haegeman, C. Lubich, I. Oseledets, B. Vandereycken, and F. Verstraete, Physical Review B 94, 165116 (2016), arXiv: 1408.5056 +[^2]: [J. Haegeman, C. Lubich, I. Oseledets, B. Vandereycken, and F. Verstraete, Physical Review B 94, 165116 (2016), arXiv: 1408.5056](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.94.165116) -* [[3]](https://doi.org/10.1103/PhysRevB.104.214302) A. J. Dunnett & A. W. Chin, Physical Review B, 104(21), 214302 (2021) +[^3]: [A. J. Dunnett & A. W. Chin, Physical Review B, 104(21), 214302 (2021)](https://doi.org/10.1103/PhysRevB.104.214302) From e24efc63e20ddc41a9dfe4d84c36f1e12bde830c Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Thu, 4 Apr 2024 11:23:54 +0200 Subject: [PATCH 10/52] Add logo --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index e70f771..c5b5119 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ +MPSDynamics.jl logo + # MPSDynamics.jl *Tensor network simulations for finite temperature, open quantum system dynamics.* From dd8894242e48c96a68ae98d4c0eeb4fcc395e60c Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Wed, 10 Apr 2024 17:19:38 +0200 Subject: [PATCH 11/52] Add DTDVP to the SBM example --- examples/sbm_zero_temperature.jl | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/examples/sbm_zero_temperature.jl b/examples/sbm_zero_temperature.jl index 4157244..6d0772f 100644 --- a/examples/sbm_zero_temperature.jl +++ b/examples/sbm_zero_temperature.jl @@ -3,7 +3,9 @@ The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. - H = \\frac{ω_0}{2} σ_z + Δ σ_x + c_0 σ_x(b_0^\\dagger + b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N-1} ϵ_i b_i^\\dagger b_i + H = \\frac{ω_0}{2} σ_z + Δ σ_x + c_0 σ_x(b_0^\\dagger + b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N-1} ϵ_i b_i^\\dagger b_i + + Two variants of the one-site Time Dependent Variational Principal (TDVP) are presented for the time evolution of the quantum state. =# using MPSDynamics, Plots, LaTeXStrings @@ -34,9 +36,13 @@ dt = 0.5 # time step tfinal = 30.0 # simulation time -method = :TDVP1 # time-evolution method +method = :TDVP1 # Regular one-site TDVP (fixed bond dimension) + +# method = :DTDVP # Adaptive one-site TDVP (dynamically updating bond dimension) + +convparams = [2,4,6] # MPS bond dimension (1TDVP) -D = [2,4,6] # MPS bond dimension +# convparams = [1e-2, 1e-3, 1e-4] # threshold value of the projection error (DTDVP) #--------------------------- # MPO and initial state MPS @@ -56,7 +62,7 @@ ob1 = OneSiteObservable("sz", sz, 1) ob2 = OneSiteObservable("chain mode occupation", numb(d), (2,N+1)) -ob3 = TwoSiteObservable("SXdisp", sx, disp(d), [1], collect(2:N+1)) +ob3 = TwoSiteObservable("SXdisp", sx, MPSDynamics.disp(d), [1], collect(2:N+1)) #------------- # Simulation @@ -68,8 +74,9 @@ A, dat = runsim(dt, tfinal, A, H; obs = [ob2,ob3], convobs = [ob1], params = @LogParams(N, d, α, Δ, ω0, s), - convparams = D, + convparams = convparams, verbose = false, + savebonddims = true, # this keyword argument enables the bond dimension at each time step to be saved when using DTDVP save = true, plot = true, ); @@ -78,6 +85,10 @@ A, dat = runsim(dt, tfinal, A, H; # Plots #---------- -plot(dat["data/times"], dat["convdata/sz"], label=["Dmax = 2" "Dmax = 4" "Dmax = 6"], xlabel=L"t",ylabel=L"\sigma_z") +method == :TDVP1 && plot(dat["data/times"], dat["convdata/sz"], label=["Dmax = 2" "Dmax = 4" "Dmax = 6"], xlabel=L"t",ylabel=L"\sigma_z") + +method == :DTDVP && plot(dat["data/times"], dat["convdata/sz"], label=["p = 1e-2" "p = 1e-3" "p = 1e-4"], xlabel=L"t",ylabel=L"\sigma_z") + +method == :DTDVP && heatmap(dat["data/times"], collect(0:N+1), dat["data/bonddims"], xlabel=L"t",ylabel="bond index") heatmap(dat["data/times"], collect(1:N), abs.(dat["data/SXdisp"][1,:,:]), xlabel=L"t",ylabel="chain mode") From 13f0334c651ac6120e24ab7db8bd3cfc83cd137b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 10 Apr 2024 19:15:56 +0200 Subject: [PATCH 12/52] Add time-dependent example --- examples/sbm_laser_pulse.jl | 98 +++++++++++++++++++++++++++++++++++++ src/run_1TDVP.jl | 12 +++-- 2 files changed, 107 insertions(+), 3 deletions(-) create mode 100644 examples/sbm_laser_pulse.jl diff --git a/examples/sbm_laser_pulse.jl b/examples/sbm_laser_pulse.jl new file mode 100644 index 0000000..29a3267 --- /dev/null +++ b/examples/sbm_laser_pulse.jl @@ -0,0 +1,98 @@ +#= + Example of a time-dependent Hamitloninan on a zero-temperature Spin-Boson Model with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise + + The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. + + H = \\frac{ω_0}{2} σ_z + Δ σ_x + σ_x ϵ sin(ωdrive t) + c_0 σ_x(b_0^\\dagger + b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N-1} ϵ_i b_i^\\dagger b_i +=# + +using MPSDynamics, Plots, LaTeXStrings + +import MPSDynamics: disp + +#---------------------------- +# Physical parameters +#---------------------------- + +d = 4 # number of Fock states of the chain modes + +N = 60 # length of the chain + +α = 0.005 # coupling strength + +Δ = 0.0 # tunneling + +ω0 = 0.8 # TLS gap + +s = 1 # ohmicity + +cpars = chaincoeffs_ohmic(N, α, s) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + +Trabi = 30.0 # Rabi period of the drive + +ϵ = 2*pi / Trabi # Intensity of the drive + +ωdrive = ω0 # Frequency of the drive + +Ndrive = 1 #Number of the site on which the drive is applied +#----------------------- +# Simulation parameters +#----------------------- + +dt = 0.5 # time step + +tfinal = 100.0 # simulation time + +method = :TDVP1 # time-evolution method + +D = [6] # MPS bond dimension + +#--------------------------- +# MPO and initial state MPS +#--------------------------- + +numsteps = length(collect(0:dt:tfinal))-1 +timelist = [(i-1)*dt for i=1:numsteps+1] + +Ht = [ϵ*sx*sin(ωdrive*tstep) for tstep in timelist] # Time dependent Hamiltonian term MPO + +H = spinbosonmpo(ω0, Δ, d, N, cpars) # MPO representation of the Hamiltonian + +ψ = unitcol(2,2) # Initial low-z system state + +A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> + +#--------------------------- +# Definition of observables +#--------------------------- + +ob1 = OneSiteObservable("sz", sz, 1) + +ob2 = OneSiteObservable("chain mode occupation", numb(d), (2,N+1)) + +ob3 = TwoSiteObservable("SXdisp", sx, disp(d), [1], collect(2:N+1)) + +#------------- +# Simulation +#------------ + +A, dat = runsim(dt, tfinal, A, H; + name = "Driving field on ohmic spin boson model", + method = method, + obs = [ob1], + convobs = [ob1], + params = @LogParams(N, d, α, Δ, ω0, s), + convparams = D, + timedep = true, + Ndrive = Ndrive, + Htime = Ht, + verbose = false, + save = true, + plot = true, + ); + +#---------- +# Plots +#---------- + +plot(dat["data/times"], dat["data/sz"], label=["Dmax = $D"], xlabel=L"t",ylabel=L"\sigma_z", title="") \ No newline at end of file diff --git a/src/run_1TDVP.jl b/src/run_1TDVP.jl index 64b9c91..dd8d812 100644 --- a/src/run_1TDVP.jl +++ b/src/run_1TDVP.jl @@ -1,5 +1,6 @@ -function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, reduceddensity=false, kwargs...) +function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, reduceddensity=false, timedep=false, kwargs...) A0=deepcopy(A) + H0=deepcopy(H) data = Dict{String,Any}() numsteps = length(collect(0:dt:tmax))-1 @@ -23,13 +24,18 @@ function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, reduceddensity=fal for tstep=1:numsteps @printf("%i/%i, t = %.3f ", tstep, numsteps, times[tstep]) println() + if timedep + Ndrive = kwargs[:Ndrive] + Htime = kwargs[:Htime] + H0[Ndrive][1,1,:,:] = H[Ndrive][1,1,:,:] + Htime[tstep][:,:] + end if timed - val, t, bytes, gctime, memallocs = @timed tdvp1sweep!(dt, A0, H, F; kwargs...) + val, t, bytes, gctime, memallocs = @timed tdvp1sweep!(dt, A0, H0, F; kwargs...) println("\t","ΔT = ", t) A0, F = val ttdvp[tstep] = t else - A0, F = tdvp1sweep!(dt, A0, H, F; kwargs...) + A0, F = tdvp1sweep!(dt, A0, H0, F; kwargs...) end exp = measure(A0, obs; t=times[tstep]) for (i, ob) in enumerate(obs) From 29c8b3aec275f09d5d492987e13fe59b7b62e929 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 10 Apr 2024 19:17:17 +0200 Subject: [PATCH 13/52] Correct time-dependent example --- examples/{sbm_laser_pulse.jl => sbm_Htimedependent.jl} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/{sbm_laser_pulse.jl => sbm_Htimedependent.jl} (100%) diff --git a/examples/sbm_laser_pulse.jl b/examples/sbm_Htimedependent.jl similarity index 100% rename from examples/sbm_laser_pulse.jl rename to examples/sbm_Htimedependent.jl From 8bc26f1150b47f472bc7846fef83ceb7334c6ac1 Mon Sep 17 00:00:00 2001 From: tl Date: Thu, 11 Apr 2024 11:15:00 +0200 Subject: [PATCH 14/52] Add a couple of comment lines --- examples/sbm_Htimedependent.jl | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/examples/sbm_Htimedependent.jl b/examples/sbm_Htimedependent.jl index 29a3267..8d7896c 100644 --- a/examples/sbm_Htimedependent.jl +++ b/examples/sbm_Htimedependent.jl @@ -35,6 +35,7 @@ Trabi = 30.0 # Rabi period of the drive ωdrive = ω0 # Frequency of the drive Ndrive = 1 #Number of the site on which the drive is applied + #----------------------- # Simulation parameters #----------------------- @@ -51,14 +52,14 @@ D = [6] # MPS bond dimension # MPO and initial state MPS #--------------------------- -numsteps = length(collect(0:dt:tfinal))-1 -timelist = [(i-1)*dt for i=1:numsteps+1] +timelist = collect(0:dt:tfinal) +numsteps = length(timelist)-1 -Ht = [ϵ*sx*sin(ωdrive*tstep) for tstep in timelist] # Time dependent Hamiltonian term MPO +Ht = [ϵ*sx*sin(ωdrive*tstep) for tstep in timelist] # Time-dependent Hamiltonian term H = spinbosonmpo(ω0, Δ, d, N, cpars) # MPO representation of the Hamiltonian -ψ = unitcol(2,2) # Initial low-z system state +ψ = unitcol(2,2) # Initial down-z system state A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> @@ -83,9 +84,9 @@ A, dat = runsim(dt, tfinal, A, H; convobs = [ob1], params = @LogParams(N, d, α, Δ, ω0, s), convparams = D, - timedep = true, - Ndrive = Ndrive, - Htime = Ht, + timedep = true, # the Hamiltonian is time dependent + Ndrive = Ndrive, # the first site of the MPS/MPO (i.e. the system) is concerned + Htime = Ht, # list of time-dependent terms verbose = false, save = true, plot = true, @@ -95,4 +96,4 @@ A, dat = runsim(dt, tfinal, A, H; # Plots #---------- -plot(dat["data/times"], dat["data/sz"], label=["Dmax = $D"], xlabel=L"t",ylabel=L"\sigma_z", title="") \ No newline at end of file +plot(dat["data/times"], dat["data/sz"], label="Dmax = $(D...)", xlabel=L"t",ylabel=L"\sigma_z", title="") From 36423e572968e25057d6fa3c154cd114c59037ce Mon Sep 17 00:00:00 2001 From: BrieucLD <78607376+BrieucLD@users.noreply.github.com> Date: Thu, 11 Apr 2024 16:32:57 +0200 Subject: [PATCH 15/52] Update quadohmT.jl --- ChainOhmT/quadohmT.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ChainOhmT/quadohmT.jl b/ChainOhmT/quadohmT.jl index df9c4a4..2564827 100644 --- a/ChainOhmT/quadohmT.jl +++ b/ChainOhmT/quadohmT.jl @@ -65,9 +65,9 @@ function wf(x,i) if i==1 y = 0 elseif i==2 - y = -( a*abs.(x).^s ./ wc^(s-1)) .* (coth.((beta/2).*x) .+ 1) #.* exp(-abs(x)/wc) + y = -( 2*a*abs.(x).^s ./ wc^(s-1)) .* ((coth.((beta/2).*x) .+ 1)./2) #.* exp(-abs(x)/wc) elseif i==3 - y = ( a*abs.(x).^s ./ wc^(s-1)) .* (coth.((beta/2).*x) .+ 1) #.* exp(-abs(x)/wc) + y = ( 2*a*abs.(x).^s ./ wc^(s-1)) .* ((coth.((beta/2).*x) .+ 1)./2) #.* exp(-abs(x)/wc) elseif i==4 y = 0 end From ce00568fb4c5830c315f04593f9841b3951f8f12 Mon Sep 17 00:00:00 2001 From: tl Date: Mon, 15 Apr 2024 16:50:26 +0200 Subject: [PATCH 16/52] Update ChainOhmT for a new chaincoeffs_finiteT function --- ChainOhmT/mcdis2.jl | 4 +- ChainOhmT/quadohmT.jl | 32 +++++------ ChainOhmT/stieltjes.jl | 34 ++++++------ src/finitetemperature.jl | 114 +++++++++++++++++++++++++++++++++++++++ 4 files changed, 149 insertions(+), 35 deletions(-) create mode 100644 src/finitetemperature.jl diff --git a/ChainOhmT/mcdis2.jl b/ChainOhmT/mcdis2.jl index af80714..4ce8834 100644 --- a/ChainOhmT/mcdis2.jl +++ b/ChainOhmT/mcdis2.jl @@ -69,7 +69,7 @@ MCDIS Multiple-component discretization procedure. in multi-component form. """ -function mcdis(N,eps0,quad::Function,Nmax) +function mcdis(N,eps0,quad::Function,Nmax,idelta,mc,AB,wf,mp,irout) suc = true f = "Ncap exceeds Nmax in mcdis with irout = " @@ -104,7 +104,7 @@ function mcdis(N,eps0,quad::Function,Nmax) xwm = Array{Float64}(undef, mc*Ncap, 2) for i=1:mc im1tn = (i-1)*Ncap - xw = quad(Ncap,i,uv) + xw = quad(Ncap,i,uv,mc,AB,wf) xwm[im1tn+1:im1tn+Ncap,:] = xw end diff --git a/ChainOhmT/quadohmT.jl b/ChainOhmT/quadohmT.jl index 2564827..9636388 100644 --- a/ChainOhmT/quadohmT.jl +++ b/ChainOhmT/quadohmT.jl @@ -1,73 +1,73 @@ -function quadfinT(N,i,uv) +function quadfinT(N,i,uv,mc,AB,wf) if (i>1 && ix!=0, xw[:,2]) #index = min(find(xw(:,2)~=0)) xw = xw[index:length(xw[:,2]),:] - Ibis = sortper(xw[:,1]) #xw = sortrows(xw,1) + Ibis = sortperm(xw[:,1]) #xw = sortrows(xw,1) xw = xw[Ibis,:] Ncap = size(xw,1) @@ -27,7 +27,8 @@ function stieltjes(N,xw) #return ab error("N in sti out of range") end - s0 = ones(1,Ncap)*xw[:,2] + s0 = (ones(1,Ncap)*xw[:,2])[1] + ab = zeros(N, 2) ab[1,1] = transpose(xw[:,1])*xw[:,2]/s0 ab[1,2] = s0 if N==1 @@ -50,23 +51,22 @@ function stieltjes(N,xw) #return ab # % end # % - c = 1e240 - p2 = c*p2 - s0 = c^2*s0 + #c = 1e240 + #p2 = c*p2 + #s0 = c^2*s0 for k=1:N-1 p0 = p1 p1 = p2 - p2 = (xw[:,1] - ab[k,1]).*p1 - ab[k,2]*p0 - s1 = transpose(xw[:,2])*(p2.^2) - s2 = transpose(xw[:,1])*(xw[:,2].*(p2.^2)) - - if (max(abs(p2))>huge)||(abs(s2)>huge) - error("impending overflow in stieltjes for k=$(k)") - end - if abs(s1)huge)||(abs(s2)>huge) + # error("impending overflow in stieltjes for k=$(k)") + # end + # if abs(s1) alpha -> s -> data (e, t or c) + + # Write onsite energies + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/α=",astr,"/s=",sstr,"/β=",bstr,"/e"), jacerg[1:N,1]) + # Write hopping energies + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/α=",astr,"/s=",sstr,"/β=",bstr,"/t"), jacerg[1:N-1,2]) + # Write coupling coefficient + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/α=",astr,"/s=",sstr,"/β=",bstr,"/c"), jacerg[N,2]) + + else + Nstr = string(N) + wstr = string(ωc) + bstr = string(β) + # the "path" to the data inside of the h5 file is N -> ωc -> beta -> data (e, t or c) + + # Write onsite energies + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/ωc=",wstr,"/β=",bstr,"/e"), jacerg[1:N,1]) + # Write hopping energies + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/ωc=",wstr,"/β=",bstr,"/t"), jacerg[1:N-1,2]) + # Write coupling coefficient + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/ωc=",wstr,"/β=",bstr,"/c"), jacerg[N,2]) + end + end + + return [jacerg[:,1], jacerg[1:N-1,2],jacerg[N,2]] +end From 4e6166bf907e130013668e83f340e7d6ced55a9f Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Mon, 15 Apr 2024 17:14:25 +0200 Subject: [PATCH 17/52] Update docs.yml --- .github/workflows/docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 34ffe58..41fe3ba 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -3,7 +3,7 @@ name: Documentation on: push: branches: - - master + - doc-writing tags: '*' pull_request: From e6aca7ba2e6316cfed51406326d6f40e7ca17f43 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Mon, 15 Apr 2024 17:24:57 +0200 Subject: [PATCH 18/52] Update finitetemperature.jl --- src/finitetemperature.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/finitetemperature.jl b/src/finitetemperature.jl index 1c797cb..7701465 100644 --- a/src/finitetemperature.jl +++ b/src/finitetemperature.jl @@ -55,7 +55,7 @@ function chaincoeffs_finiteT(nummodes, β, ohmic=true; α=1, s=1, J=nothing, ωc elseif procedure==:Stieltjes irout = 1 else - throw(ArgumentError("Procedure should be either Lanczos or Stieltjes")) + throw(ArgumentError("Procedure should be either Lanczos or Stieltjes.")) end eps0=1e7*eps(Float64) From bc5f2c5f211b476ba67b19292eb780cb94f9ebf6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Tue, 16 Apr 2024 18:10:14 +0200 Subject: [PATCH 19/52] Update puredephasing_temperature.jl with chaincoeffs_finiteT function --- ChainOhmT/quadohmT.jl | 4 ++-- examples/puredephasing_temperature.jl | 11 ++++++++--- src/MPSDynamics.jl | 3 +++ 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/ChainOhmT/quadohmT.jl b/ChainOhmT/quadohmT.jl index 9636388..4fa693e 100644 --- a/ChainOhmT/quadohmT.jl +++ b/ChainOhmT/quadohmT.jl @@ -65,9 +65,9 @@ function ohmicspectraldensity_finiteT(x,i,α,s,ωc,β) if i==1 y = 0 elseif i==2 - y = -2.*( α*abs.(x).^s ./ ωc^(s-1)) .* (coth.((β/2).*x) .+ 1)./2 #.* exp(-abs(x)/wc) + y = -2 .*( α*abs.(x).^s ./ ωc^(s-1)) .* (coth.((β/2).*x) .+ 1)./2 #.* exp(-abs(x)/wc) elseif i==3 - y = 2.*( α*abs.(x).^s ./ ωc^(s-1)) .* (coth.((β/2).*x) .+ 1)./2 #.* exp(-abs(x)/wc) + y = 2 .*( α*abs.(x).^s ./ ωc^(s-1)) .* (coth.((β/2).*x) .+ 1)./2 #.* exp(-abs(x)/wc) elseif i==4 y = 0 end diff --git a/examples/puredephasing_temperature.jl b/examples/puredephasing_temperature.jl index 459b005..a052a7b 100644 --- a/examples/puredephasing_temperature.jl +++ b/examples/puredephasing_temperature.jl @@ -26,12 +26,17 @@ s = 1 # ohmicity β = 100 -curdir = @__DIR__ +#---------------------------- +# Thermalized ohmic spectral density +#---------------------------- -dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/ohmicT")) +cpars = chaincoeffs_finiteT(N, β; α=α, s=1, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=false) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 -#cpars is built here with the ChainOhmT routine for a thermalized ohmic +#= #If cpars is stored in "../ChainOhmT/ohmicT" +curdir = @__DIR__ +dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/ohmicT")) cpars = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5",N,α,s,β) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 +=# #----------------------- # Simulation parameters diff --git a/src/MPSDynamics.jl b/src/MPSDynamics.jl index 21cdb00..ed282d6 100644 --- a/src/MPSDynamics.jl +++ b/src/MPSDynamics.jl @@ -28,6 +28,7 @@ include("run_DTDVP.jl") include("run_A1TDVP.jl") include("chainA1TDVP.jl") include("switchmpo.jl") +include("finitetemperature.jl") """ runsim(dt, tmax, A, H; @@ -173,5 +174,7 @@ export MPOtoVector, MPStoVector export rhoreduced_2sites, rhoreduced_1site +export chaincoeffs_finiteT + end From 701c23ae0bb88e9f5edc734a7e8633ff5ccd39f3 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Wed, 17 Apr 2024 15:13:43 +0200 Subject: [PATCH 20/52] Add the s=0 case for the Ohmic SD --- src/models.jl | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/src/models.jl b/src/models.jl index a077322..4a2ab93 100644 --- a/src/models.jl +++ b/src/models.jl @@ -453,10 +453,16 @@ function chaincoeffs_ohmic(nummodes, α, s; ωc=1, soft=false) e = [ωc*(2n + 1 + s) for n in 0:(nummodes-1)] t = [ωc*sqrt((n + 1)*(n + s + 1)) for n in 0:(nummodes-2)] return [e, t, c0] - else - c0 = sqrt(2α/(s+1))*ωc - e = [(ωc/2)*(1 + (s^2)/((s+2n)*(2+s+2n))) for n in 0:(nummodes-1)] - t = [ωc*(1+n)*(1+s+n)/((s+2+2n)*(s+3+2n))*sqrt((3+s+2n)/(1+s+2n)) for n in 0:(nummodes-2)] + else + if s==0 + c0 = sqrt(2α)*ωc + e = fill(0,nummodes) + t = [ωc*(n+1)/(2n+1) for n in 0:(nummodes-2)] + else + c0 = sqrt(2α/(s+1))*ωc + e = [(ωc/2)*(1 + (s^2)/((s+2n)*(2+s+2n))) for n in 0:(nummodes-1)] + t = [ωc*(1+n)*(1+s+n)/((s+2+2n)*(s+3+2n))*sqrt((3+s+2n)/(1+s+2n)) for n in 0:(nummodes-2)] + end return [e, t, c0] end end From 5ef5a91bdb4ac1d54da41184cc687480e1541e8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 17 Apr 2024 15:28:59 +0200 Subject: [PATCH 21/52] Update models.jl and fundamentals.jl --- src/fundamentals.jl | 4 +- src/models.jl | 103 +++++++++++++++++++++++++++++++++++++------- 2 files changed, 90 insertions(+), 17 deletions(-) diff --git a/src/fundamentals.jl b/src/fundamentals.jl index c02359c..87f686b 100644 --- a/src/fundamentals.jl +++ b/src/fundamentals.jl @@ -178,9 +178,9 @@ end """ findchainlength(T, cparams...; eps=10^-6) -Estimate length of chain required for a particular set of chain parameters by calulating how long an excitation on the +Estimate length of chain required for a particular set of chain parameters by calculating how long an excitation on the first site takes to reach the end. The chain length is given as the length required for the excitation to have just -reached the last site after time T. +reached the last site after time T. The initial number of sites in cparams has to be larger than the findchainlength result. """ function findchainlength(T, cparams; eps=10^-4, verbose=false) diff --git a/src/models.jl b/src/models.jl index a077322..17da18c 100644 --- a/src/models.jl +++ b/src/models.jl @@ -4,11 +4,13 @@ dn(ops...) = permutedims(cat(reverse(ops)...; dims=3), [3,1,2]) """ xyzmpo(N::Int; Jx=1.0, Jy=Jx, Jz=Jx, hx=0., hz=0.) -Return the MPO representation of the `N`-spin XYZ Hamiltonian with external field ``\\vec{h}=(h_x, 0, h_z)``. +Generate MPO for the `N`-spin XYZ model with external field ``\\vec{h}=(h_x, 0, h_z)``, , defined by the Hamiltonian `` H = \\sum_{n=1}^{N-1} -J_x σ_x^{n} σ_x^{n+1} - J_y σ_y^{n} σ_y^{n+1} - J_z σ_z^{n} σ_z^{n+1} + \\sum_{n=1}^{N}(- h_x σ_x^{n} - h_z σ_z^{n}) -``. +`` + +with ``σ_x^{n}, σ_y^{n}, σ_z^{n}`` the Pauli spin-1/2 matrices of the ``n^\\text{th}`` site. """ function xyzmpo(N::Int; Jx=1.0, Jy=Jx, Jz=Jx, hx=0., hz=0.) @@ -40,13 +42,51 @@ end """ isingmpo(N; J=1.0, h=1.0) -Return the MPO representation of a `N`-spin 1D Ising model with external field ``\\vec{h} = (0,0,h)``. +Generate MPO for the `N`-spin 1D Ising model with external field ``\\vec{h} = (0,0,h)``, defined by the Hamiltonian + +`` +H = \\sum_{n=1}^{N-1} -J_x σ_x^{n} σ_x^{n+1} + \\sum_{n=1}^{N}(- h_z σ_z^{n}) +`` + +with ``σ_x^{n}, σ_y^{n}, σ_z^{n}`` the Pauli spin-1/2 matrices of the ``n^\\text{th}`` site. + """ isingmpo(N::Int; J=1.0, h=1.0) = xyzmpo(N; Jx=J, Jy=0., Jz=0., hz=h, hx=0.) +""" + heisenbergmpo(N::Int, J=1.0) = xyzmpo(N; Jx=J) +Generate MPO for the `N`-spin Heisenberg XXX model, defined by the Hamiltonian + +`` +H = \\sum_{n=1}^{N-1} -J σ_x^{n} σ_x^{n+1} - J σ_y^{n} σ_y^{n+1} - J σ_z^{n} σ_z^{n+1} +`` + +with ``σ_x^{n}, σ_y^{n}, σ_z^{n}`` the Pauli spin-1/2 matrices of the ``n^\\text{th}`` site. + + +""" heisenbergmpo(N::Int, J=1.0) = xyzmpo(N; Jx=J) +""" + xxzmpo(N::Int, Δ = 1.0, J=1.0) = xyzmpo(N; Jx=J, Jy=J, Jz=J*Δ) + +Generate MPO for the `N`-spin XXZ model, defined by the Hamiltonian + +`` +H = \\sum_{n=1}^{N-1} -J σ_x^{n} σ_x^{n+1} - J σ_y^{n} σ_y^{n+1} - \\Delta J σ_z^{n} σ_z^{n+1} +`` + +with ``σ_x^{n}, σ_y^{n}, σ_z^{n}`` the Pauli spin-1/2 matrices of the ``n^\\text{th}`` site. + +""" xxzmpo(N::Int, Δ = 1.0, J=1.0) = xyzmpo(N; Jx=J, Jy=J, Jz=J*Δ) +""" + longrange_xyzmpo(N::Int, α::Float64=0.; Jx=1.0, Jy=Jx, Jz=Jx, hx=0., hz=0.) + +Gennerate MPO for the `N`-spin long-range XYZ model with external field ``\\vec{h}=(h_x, 0, h_z)``, , defined by the Hamiltonian + + +""" function longrange_xyzmpo(N::Int, α::Float64=0.; Jx=1.0, Jy=Jx, Jz=Jx, hx=0., hz=0.) u = unitmat(2) @@ -77,8 +117,18 @@ function longrange_xyzmpo(N::Int, α::Float64=0.; Jx=1.0, Jy=Jx, Jz=Jx, hx=0., h end return Any[M[1:1,:,:,:], fill(M, N-2)..., M[:,D:D,:,:]] end + +""" + longrange_isingmpo(N::Int, α::Float64=0.; J=1.0, h=1.0) = longrange_xyzmpo(N, α; Jx=J, Jy=0., Jz=0., hz=h, hx=0.) + +""" longrange_isingmpo(N::Int, α::Float64=0.; J=1.0, h=1.0) = longrange_xyzmpo(N, α; Jx=J, Jy=0., Jz=0., hz=h, hx=0.) +""" + spinchainmpo(N::Int; J=1.0, hz=1.0, hx=0.0, i=div(N,2)) + + +""" function spinchainmpo(N::Int; J=1.0, hz=1.0, hx=0.0, i=div(N,2)) u = unitmat(2) @@ -103,6 +153,12 @@ function spinchainmpo(N::Int; J=1.0, hz=1.0, hx=0.0, i=div(N,2)) return Any[M0[1:1,:,:,:], fill(M0, i-2)..., Mx, fill(M0, N-i-1)..., M0[:,4:4,:,:]] end +""" + tightbindingmpo(N::Int, d::Int; J=1.0, e=1.0) + + + +""" function tightbindingmpo(N::Int, d::Int; J=1.0, e=1.0) b = anih(d) bd = crea(d) @@ -124,7 +180,7 @@ end """ hbathchain(N::Int, d::Int, chainparams, longrangecc...; tree=false, reverse=false, coupletox=false) -Create an MPO representing a tight-binding chain of `N` oscillators with `d` Fock states each. Chain parameters are supplied in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. The output does not itself represent a complete MPO but will possess an end which is *open* and should be attached to another tensor site, usually representing the *system*. +Generate MPO representing a tight-binding chain of `N` oscillators with `d` Fock states each. Chain parameters are supplied in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. The output does not itself represent a complete MPO but will possess an end which is *open* and should be attached to another tensor site, usually representing the *system*. # Arguments @@ -479,6 +535,13 @@ function getchaincoeffs(nummodes, α, s, beta, ωc=1) end ## +""" + readchaincoeffs(fdir, params...) + + + + +""" function readchaincoeffs(fdir, params...) n = length(params) dat = h5open(fdir, "r") do fid @@ -503,10 +566,10 @@ end """ ibmmpo(ω0, d, N, chainparams; tree=false) -Generate the MPO for the independent boson model Hamiltonian in a chain mapped representation +Generate MPO for a spin-1/2 coupled to a chain of harmonic oscillators with the interacting boson model (IBM), defined by the Hamiltonian `` -H = \\frac{ω_0}{2}σ_z + c_0σ_z(b_0^\\dagger+b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N} ϵ_ib_i^\\dagger b_i +H = \\frac{ω_0}{2}σ_z + c_0σ_z(b_0^\\dagger+b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N} ϵ_ib_i^\\dagger b_i ``. The spin is on site 1 of the MPS and the bath modes are to the right. @@ -514,15 +577,11 @@ The spin is on site 1 of the MPS and the bath modes are to the right. This Hamiltonain is unitarily equivalent (before the truncation to `N` sites) to the spin-boson Hamiltonian defined by `` -H = \\frac{ω_0}{2}σ_z + σ_z\\int_0^∞ dω\\sqrt{J(ω)}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω ωb_ω^\\dagger b_ω +H = \\frac{ω_0}{2}σ_z + σ_z\\int_0^∞ dω\\sqrt{\\frac{J(ω)}{π}}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω ωb_ω^\\dagger b_ω ``. -This Hamiltonian is equivalent (up to a rotation around the y-axis) to a spin-boson model with without biais. - The chain parameters, supplied by `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. -The MPO can be considered as a Tree Tensor Network by setting `tree=true`. - """ function ibmmpo(ω0, d, N, chainparams; tree=false) u = unitmat(2) @@ -546,6 +605,14 @@ function ibmmpo(ω0, d, N, chainparams; tree=false) end end +""" + tunnelingmpo(ϵ, delta, α, s, β, d::Int, nummodes::Int; tree=false, ωc=1) + + + + + +""" function tunnelingmpo(ϵ, delta, α, s, β, d::Int, nummodes::Int; tree=false, ωc=1) cps = chaincoeffs_ohmic(nummodes, α, s, β; ωc=ωc) λ = 2*α*ωc/s + delta @@ -573,11 +640,9 @@ end """ nearestneighbourmpo(N::Int, h0, A, Ad = A') -Generate the MPO for a N sites system with on-site Hamiltonian `h0` and nearest-neighbour interactions ``A_i A^\\dagger_{i+1}`` -`` -H = \\sum_{i = 1}^{N-1} A_{i}A^\\dagger_{i+1} + Nh_0 -``. + + """ function nearestneighbourmpo(N::Int, h0, A, Ad = A') @@ -596,6 +661,14 @@ function nearestneighbourmpo(N::Int, h0, A, Ad = A') return Any[M[D:D,:,:,:], fill(M, N-2)..., M[:,1:1,:,:]] end +""" + nearestneighbourmpo(tree_::Tree, h0, A, Ad = A') + + + + + +""" function nearestneighbourmpo(tree_::Tree, h0, A, Ad = A') size(h0) == size(A) || error("physical dimensions don't match") size(h0) == size(Ad) || error("physical dimensions don't match") From 03faab8140e62b1e388f41a6c9acf31c7012b5bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 17 Apr 2024 15:36:46 +0200 Subject: [PATCH 22/52] Update mpsBasics.jl --- src/mpsBasics.jl | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/mpsBasics.jl b/src/mpsBasics.jl index dde7aac..f04d415 100644 --- a/src/mpsBasics.jl +++ b/src/mpsBasics.jl @@ -650,7 +650,12 @@ function bonddims(M::Vector) return Dims(res) end -#calculates M1*M2 where M1 and M2 are MPOs +""" + multiply(M1::Vector, M2::Vector) + +Calculates M1*M2 where M1 and M2 are MPOs + +""" function multiply(M1::Vector, M2::Vector) N = length(M1) length(M2) == N || throw(ArgumentError("MPOs do not have the same length!")) From 356d083811b0bf24a3970165bab18d037e14fe88 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 17 Apr 2024 15:47:18 +0200 Subject: [PATCH 23/52] Update models.jl --- src/models.jl | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/src/models.jl b/src/models.jl index 3cb8265..340a285 100644 --- a/src/models.jl +++ b/src/models.jl @@ -446,23 +446,22 @@ end """ twobathspinmpo(ω0, Δ, Nl, Nr, dl, dr, chainparamsl=[fill(1.0,N),fill(1.0,N-1), 1.0], chainparamsr=chainparamsl; tree=false) -Generate MPO for a spin-1/2 coupled to a chain of harmonic oscillators on its left and another one on its right, defined by the Hamiltonian +Generate MPO for a spin-1/2 coupled to two chains of harmonic oscillators, defined by the Hamiltonian `` -H = \\frac{ω_0}{2}σ_z + Δσ_x + c_Lσ_x(b_0^\\dagger+b_0) + c_Rσ_x(c_0^\\dagger+c_0) + \\sum_{i=0}^{N-1} t^L_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N} ϵ^L_i c_i^\\dagger c_i + \\sum_{i=0}^{N-1} t^R_i (c_{i+1}^\\dagger c_i + h.c.) + \\sum_{i=0}^{N} ϵ^R_ic_i^\\dagger c_i +H = \\frac{ω_0}{2}σ_z + Δσ_x + c_0^rσ_x(b_0^\\dagger+b_0) + \\sum_{i=0}^{N_r-1} t_i^r (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N_r} ϵ_i^rb_i^\\dagger b_i + c_0^lσ_x(d_0^\\dagger+d_0) + \\sum_{i=0}^{N_l-1} t_i^l (d_{i+1}^\\dagger d_i +h.c.) + \\sum_{i=0}^{N_l} ϵ_i^l d_i^\\dagger d_i ``. -The spin is on site N+2 of the MPS. +The spin is on site ``N_l + 1`` of the MPS, surrounded by the left chain modes and the right chain modes. -This Hamiltonain is unitarily equivalent (before the truncation to `N` sites) to the two-bath spin-boson Hamiltonian defined by +This Hamiltonain is unitarily equivalent (before the truncation to `N` sites) to the spin-boson Hamiltonian defined by `` -H = \\frac{ω_0}{2}σ_z + Δσ_x + σ_x(\\int_0^∞ dω\\sqrt{J_L(ω)}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω\\sqrt{J_R(ω)}(c_ω^\\dagger+c_ω) ) + \\int_0^∞ dω ωb_ω^\\dagger b_ω + \\int_0^∞ dω ωc_ω^\\dagger c_ω +H = \\frac{ω_0}{2}σ_z + Δσ_x + σ_x\\int_0^∞ dω\\sqrt{\\frac{J(ω)}{π}}(b_ω^\\dagger+b_ω) + \\int_0^∞ dω ωb_ω^\\dagger b_ωi + σ_x\\int_0^∞ dω\\sqrt{\\frac{J^l(ω)}{π}}(d_ω^\\dagger+d_ω) + \\int_0^∞ dω ωd_ω^\\dagger d_ω ``. -The chain parameters, supplied by `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. +The chain parameters, supplied by `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. The two chains can have a different spectral density. -The MPO can be considered as a Tree Tensor Network by setting `tree=true`. """ function twobathspinmpo(ω0, Δ, Nl, Nr, dl, dr, chainparamsl=[fill(1.0,N),fill(1.0,N-1), 1.0], chainparamsr=chainparamsl; tree=false) u = unitmat(2) From fd668d9777809414be19cb431a3328127ee15d45 Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Fri, 19 Apr 2024 16:51:14 +0200 Subject: [PATCH 24/52] Add displaced chain --- src/mpsBasics.jl | 48 +++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 45 insertions(+), 3 deletions(-) diff --git a/src/mpsBasics.jl b/src/mpsBasics.jl index f04d415..8706f41 100644 --- a/src/mpsBasics.jl +++ b/src/mpsBasics.jl @@ -524,7 +524,6 @@ end Apply an operator O on the MPS A. O is acting on several sites ::Vector{Int}. The resulting MPS A is the MPS modified by the operator O. """ - function apply1siteoperator!(A, O, sites::Vector{Int}) for i in sites @tensor R[a,b,s] := O[s,s']*A[i][a,b,s'] @@ -538,7 +537,6 @@ end Apply an operator O on the MPS A. O is acting on only one site ::Int. The resulting MPS A is the MPS modified by the operator O. """ - apply1siteoperator!(A, O, site::Int) = apply1siteoperator!(A, O, [site]) """ @@ -547,7 +545,6 @@ apply1siteoperator!(A, O, site::Int) = apply1siteoperator!(A, O, [site]) Apply an MPO H on the MPS A. H must have the same number of site than A. The resulting MPS A is the MPS modified by the mpo H. """ - function applympo!(A, H) N = length(H) N == length(A) || throw(ArgumentError("MPO has $N site while MPS has $(length(A)) sites")) @@ -699,3 +696,48 @@ function mpsembed!(A::Vector, Dmax::Int) return A end +""" + displacedchainmps(A::Vector{Any}, N::Int, Nm::Int, γ::Any) + + Given a mps A, return a mps B where the `Nm`-long chain is displaced by `γ` without displacing the `N`-long system. +""" +function displacedchainmps(A::Vector{Any}, N::Int, Nm::Int, γ::Any) + + if length(A) != N+Nm + throw(ArgumentError("The MPS A should have N+Nm sites.")) + end + + if length(γ)==1 + ι = γ[1] + elseif length(γ) != Nm + throw(ArgumentError("γ should either be a single c-number or a list of length Nm.")) + else + ι = γ + end + + B = Any[] # displaced chain MPS + + # The system part of the MPS should be unaffected by the displacement operator + for i=1:N + push!(B, A[i]) + end + + # Displacement operator for the chain + for n=1:Nm + d1, d2, dhilbert = size(A[N+n]) # left bond dim, right bond dim and system physical dim + χ = d1*d2 + + bd = crea(dhilbert) # chain creation operator + b = anih(dhilbert) # chain anihilation operator + + W = ι[n]*bd - conj(ι[n])*b # Argument of the displacement operator + + Ap = permutedims(reshape(A[N+n], χ, dhilbert), [2,1]) # reshape the MPS tensor as a (d, χ)-matrix + + As = permutedims(exp(W)*Ap, [2,1]) # matrix multiplication B_n = exp(W)*A_n + + push!(B, reshape(As, d1, d2, dhilbert)) + end + + return B +end From f081e660fe503c7b82a7a7f34cc17965671043ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Mon, 22 Apr 2024 17:24:14 +0200 Subject: [PATCH 25/52] Fix measure tensortrace --- src/measure.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/measure.jl b/src/measure.jl index 7321111..464cf10 100644 --- a/src/measure.jl +++ b/src/measure.jl @@ -129,7 +129,7 @@ function measurempo(A::Vector, M::Vector, sites::Tuple{Int, Int}) for k=sites[1]:sites[2] F = updateleftenv(A[k], M[k], F) end - F = tensortrace(F, [1,2,1], [2]) + F = tensortrace([2], F, [1,2,1]) real(only(F)) end From e1ce7026dece189530245a902f877a32706291b0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Tue, 23 Apr 2024 11:39:04 +0200 Subject: [PATCH 26/52] Add options in run_2TDVP and run_DTDVP --- examples/puredephasing.jl | 2 +- examples/puredephasing_temperature.jl | 4 ++-- src/run_2TDVP.jl | 20 +++++++++++++++++--- src/run_DTDVP.jl | 23 +++++++++++++++++++++-- 4 files changed, 41 insertions(+), 8 deletions(-) diff --git a/examples/puredephasing.jl b/examples/puredephasing.jl index 4b3c58d..8fff038 100644 --- a/examples/puredephasing.jl +++ b/examples/puredephasing.jl @@ -1,5 +1,5 @@ #= - Example of a Pure Dephasing Model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# + Example of a Pure Dephasing Model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2α(ω^s)/(ω_c^(s-1)) when ω < ωc and 0 otherwise# The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. diff --git a/examples/puredephasing_temperature.jl b/examples/puredephasing_temperature.jl index a052a7b..c413f7c 100644 --- a/examples/puredephasing_temperature.jl +++ b/examples/puredephasing_temperature.jl @@ -1,5 +1,5 @@ #= - Example of a Pure Dephasing Model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# + Example of a Pure Dephasing Model at finite temperature with an hard cut-off Ohmic spectral density J(ω) = 2α(ω^s)/(ω_c^(s-1)) when ω < ωc and 0 otherwise# The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. @@ -30,7 +30,7 @@ s = 1 # ohmicity # Thermalized ohmic spectral density #---------------------------- -cpars = chaincoeffs_finiteT(N, β; α=α, s=1, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=false) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 +cpars = chaincoeffs_finiteT(N, β; α=α, s=s, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=false) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 #= #If cpars is stored in "../ChainOhmT/ohmicT" curdir = @__DIR__ diff --git a/src/run_2TDVP.jl b/src/run_2TDVP.jl index 2619b99..5166fd4 100644 --- a/src/run_2TDVP.jl +++ b/src/run_2TDVP.jl @@ -1,5 +1,6 @@ -function run_2TDVP(dt, tmax, A, H, truncerr; obs=[], Dlim=50, savebonddims=false, timed=false, kwargs...) +function run_2TDVP(dt, tmax, A, H, truncerr; obs=[], Dlim=50, savebonddims=false, timed=false, reduceddensity=false, timedep=false, kwargs...) A0=deepcopy(A) + H0=deepcopy(H) data = Dict{String,Any}() numsteps = length(collect(0:dt:tmax))-1 @@ -11,6 +12,10 @@ function run_2TDVP(dt, tmax, A, H, truncerr; obs=[], Dlim=50, savebonddims=false for i=1:length(obs) push!(data, obs[i].name => reshape(exp[i], size(exp[i])..., 1)) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + push!(data, "Reduced ρ" => reshape(exprho, size(exprho)..., 1)) + end bonds = bonddims(A0) savebonddims && push!(data, "bonddims" => reshape([bonds...], length(bonds), 1)) @@ -22,19 +27,28 @@ function run_2TDVP(dt, tmax, A, H, truncerr; obs=[], Dlim=50, savebonddims=false maxbond = max(bonds...) @printf("%i/%i, t = %.3f, Dmax = %i ", tstep, numsteps, times[tstep], maxbond) println() + if timedep + Ndrive = kwargs[:Ndrive] + Htime = kwargs[:Htime] + H0[Ndrive][1,1,:,:] = H[Ndrive][1,1,:,:] + Htime[tstep][:,:] + end if timed - val, t, bytes, gctime, memallocs = @timed tdvp2sweep!(dt, A0, H, F; truncerr=truncerr, truncdim=Dlim, kwargs...) + val, t, bytes, gctime, memallocs = @timed tdvp2sweep!(dt, A0, H0, F; truncerr=truncerr, truncdim=Dlim, kwargs...) println("\t","ΔT = ", t) A0, F = val ttdvp[tstep] = t else - A0, F = tdvp2sweep!(dt, A0, H, F; truncerr=truncerr, truncdim=Dlim, kwargs...) + A0, F = tdvp2sweep!(dt, A0, H0, F; truncerr=truncerr, truncdim=Dlim, kwargs...) end bonds = bonddims(A0) exp = measure(A0, obs; t=times[tstep]) for (i, ob) in enumerate(obs) data[ob.name] = cat(data[ob.name], exp[i], dims=ndims(exp[i])+1) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) + end if savebonddims data["bonddims"] = cat(data["bonddims"], [bonds...], dims=2) end diff --git a/src/run_DTDVP.jl b/src/run_DTDVP.jl index c39d34e..1fbc0bc 100644 --- a/src/run_DTDVP.jl +++ b/src/run_DTDVP.jl @@ -1,5 +1,6 @@ -function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, timed=false, savebonddims=false, Dplusmax=nothing, Dlim=50, kwargs...) +function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, timed=false, savebonddims=false, Dplusmax=nothing, Dlim=50, reduceddensity=false, timedep=false, kwargs...) A0=deepcopy(A) + H0=deepcopy(H) data = Dict{String,Any}() numsteps = length(collect(0:dt:tmax))-1 @@ -11,6 +12,10 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim for i=1:length(obs) push!(data, obs[i].name => reshape(exp[i], size(exp[i])..., 1)) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + push!(data, "Reduced ρ" => reshape(exprho, size(exprho)..., 1)) + end bonds = bonddims(A0) savebonddims && push!(data, "bonddims" => reshape([bonds...], length(bonds), 1)) @@ -25,7 +30,12 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim for tstep=1:numsteps maxbond = max(bonds...) @printf("%i/%i, t = %.3f, Dmax = %i \n", tstep, numsteps, times[tstep], maxbond) - A0, Afull, F, info = tdvp1sweep_dynamic!(dt, A0, H, Afull, F; + if timedep + Ndrive = kwargs[:Ndrive] + Htime = kwargs[:Htime] + H0[Ndrive][1,1,:,:] = H[Ndrive][1,1,:,:] + Htime[tstep][:,:] + end + A0, Afull, F, info = tdvp1sweep_dynamic!(dt, A0, H0, Afull, F; obs=obs, prec=prec, Dlim=Dlim, @@ -46,6 +56,10 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim for (i, ob) in enumerate(obs) data[ob.name] = cat(data[ob.name], exp[i]; dims=ndims(exp[i])+1) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) + end end if savebonddims data["bonddims"] = cat(data["bonddims"], bonds, dims=2) @@ -55,6 +69,11 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim for (i, ob) in enumerate(obs) data[ob.name] = cat(data[ob.name], exp[i]; dims=ndims(exp[i])+1) end + if reduceddensity + exprho = rhoreduced_1site(A0,1) + data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) + end + if effects efftarray = efft[1] From 88ce53c069884616ea1fa6f76cadc3dfa7640183 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Thu, 25 Apr 2024 10:45:18 +0200 Subject: [PATCH 27/52] Merge pure dephasing examples --- examples/puredephasing.jl | 48 ++++++++--- examples/puredephasing_temperature.jl | 115 -------------------------- 2 files changed, 35 insertions(+), 128 deletions(-) delete mode 100644 examples/puredephasing_temperature.jl diff --git a/examples/puredephasing.jl b/examples/puredephasing.jl index 8fff038..d8c068f 100644 --- a/examples/puredephasing.jl +++ b/examples/puredephasing.jl @@ -1,30 +1,49 @@ #= - Example of a Pure Dephasing Model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2α(ω^s)/(ω_c^(s-1)) when ω < ωc and 0 otherwise# + Example of a Pure Dephasing Model at finite or zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2α(ω^s)/(ω_c^(s-1)) when ω < ωc and 0 otherwise# The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. - H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i + H = \frac{ω0}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i =# using MPSDynamics, Plots, LaTeXStrings, QuadGK +const ∞ = Inf #---------------------------- # Physical parameters #---------------------------- -ΔE = 0.008 # Energy of the electronic states - -d = 5 # number of Fock states of the chain modes +d = 10 # number of Fock states of the chain modes N = 30 # length of the chain α = 0.01 # coupling strength +ω0 = 0.008 # TLS gap + s = 1 # ohmicity ωc = 0.035 # Cut-off of the spectral density J(ω) -cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 +#β = 100 # Thermalized environment + +β = ∞ # Case zero temperature T=0, β → ∞ + +#---------------------------- +# Ohmic spectral density +#---------------------------- + +if β == ∞ + cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 +else + cpars = chaincoeffs_finiteT(N, β; α=α, s=s, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=false) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + #= #If cpars is stored in "../ChainOhmT/ohmicT" + curdir = @__DIR__ + dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/ohmicT")) + cpars = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5",N,α,s,β) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + =# +end + #----------------------- # Simulation parameters @@ -36,13 +55,15 @@ tfinal = 300.0 # simulation time method = :TDVP1 # time-evolution method +#method = :DTDVP # time-evolution method + D = 2 # MPS bond dimension #--------------------------- # MPO and initial state MPS #--------------------------- -H = puredephasingmpo(ΔE, d, N, cpars) +H = puredephasingmpo(ω0, d, N, cpars) # Initial electronic system in a superposition of 1 and 2 ψ = zeros(2) @@ -63,12 +84,12 @@ ob1 = OneSiteObservable("sz", sz, 1) # Simulation #------------ -A, dat = runsim(dt, tfinal, A, H; - name = "pure dephasing model at zero temperature", +A, dat = runsim(dt, tfinal, A, H, prec=1E-4; + name = "pure dephasing model with temperature", method = method, obs = [ob1], convobs = [ob1], - params = @LogParams(ΔE, N, d, α, s), + params = @LogParams(ω0, N, d, α, s), convparams = D, reduceddensity=true, verbose = false, @@ -79,7 +100,7 @@ A, dat = runsim(dt, tfinal, A, H; #---------- -# Analytical results at zero temperature +# Analytical results at specified temperature # (see The Theory of Open Quantum System, H.-P. Breuer & F. Petruccione 2002, Chapter 4) #---------- @@ -87,7 +108,7 @@ Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) time_analytical = LinRange(0.0,tfinal,Int(tfinal)) -Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))/x^2, 0, ωc)[1] +Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))*coth(β*x/2)/x^2, 0, ωc)[1] Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) @@ -97,6 +118,7 @@ Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) ρ12 = abs.(dat["data/Reduced ρ"][1,2,:]) -plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $T=0~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) +plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $\beta = %$β ~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") + diff --git a/examples/puredephasing_temperature.jl b/examples/puredephasing_temperature.jl deleted file mode 100644 index c413f7c..0000000 --- a/examples/puredephasing_temperature.jl +++ /dev/null @@ -1,115 +0,0 @@ -#= - Example of a Pure Dephasing Model at finite temperature with an hard cut-off Ohmic spectral density J(ω) = 2α(ω^s)/(ω_c^(s-1)) when ω < ωc and 0 otherwise# - - The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. - - H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i -=# - -using MPSDynamics, Plots, LaTeXStrings, QuadGK - -#---------------------------- -# Physical parameters -#---------------------------- - -ΔE = 0.008 # Energy of the electronic states - -d = 10 # number of Fock states of the chain modes - -N = 30 # length of the chain - -α = 0.01 # coupling strength - -s = 1 # ohmicity - -ωc = 0.035 # Cut-off of the spectral density J(ω) - -β = 100 - -#---------------------------- -# Thermalized ohmic spectral density -#---------------------------- - -cpars = chaincoeffs_finiteT(N, β; α=α, s=s, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=false) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 - -#= #If cpars is stored in "../ChainOhmT/ohmicT" -curdir = @__DIR__ -dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/ohmicT")) -cpars = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5",N,α,s,β) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 -=# - -#----------------------- -# Simulation parameters -#----------------------- - -dt = 1.0 # time step - -tfinal = 300.0 # simulation time - -method = :TDVP1 # time-evolution method - -D = 2 # MPS bond dimension - -#--------------------------- -# MPO and initial state MPS -#--------------------------- - -H = puredephasingmpo(ΔE, d, N, cpars) - -# Initial electronic system in a superposition of 1 and 2 -ψ = zeros(2) -ψ[1] = 1/sqrt(2) -ψ[2] = 1/sqrt(2) - - -A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> - -#--------------------------- -# Definition of observables -#--------------------------- - -ob1 = OneSiteObservable("sz", sz, 1) - - -#------------- -# Simulation -#------------ - -A, dat = runsim(dt, tfinal, A, H; - name = "pure dephasing model with temperature", - method = method, - obs = [ob1], - convobs = [ob1], - params = @LogParams(ΔE, N, d, α, s), - convparams = D, - reduceddensity=true, - verbose = false, - save = true, - plot = true, - ); - - - -#---------- -# Analytical results at specified temperature -# (see The Theory of Open Quantum System, H.-P. Breuer & F. Petruccione 2002, Chapter 4) -#---------- - -Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) - -time_analytical = LinRange(0.0,tfinal,Int(tfinal)) - -Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))*coth(β*x/2)/x^2, 0, ωc)[1] - -Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) - -#------------- -# Plots -#------------ - -ρ12 = abs.(dat["data/Reduced ρ"][1,2,:]) - -plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $\beta = %$β ~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) - -plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") - From 43dac2cb5c66a9e72d3d96d96ccc84182b28dff1 Mon Sep 17 00:00:00 2001 From: angelariva Date: Mon, 29 Apr 2024 19:37:19 +0200 Subject: [PATCH 28/52] fixed save procedure in finitetemperature.jl --- ChainOhmT/ohmicT/chaincoeffs.h5 | Bin 9848 -> 18248 bytes ChainOhmT/quadohmT.jl | 23 + examples/anderson_model_double.jl | 63 + examples/anderson_model_interleaved.jl | 81 ++ examples/fermions.ipynb | 1477 +++++++++++++++++++++ examples/plots/chain_occ_bet=2_double.pdf | Bin 0 -> 33746 bytes examples/plots/chain_occ_bet=2_folded.pdf | Bin 0 -> 32537 bytes examples/plots/system_folded_beta2.pdf | Bin 0 -> 11794 bytes examples/puredephasing.jl | 8 +- src/MPSDynamics.jl | 2 +- src/finitetemperature.jl | 109 +- src/models.jl | 254 ++++ 12 files changed, 2004 insertions(+), 13 deletions(-) create mode 100644 examples/anderson_model_double.jl create mode 100644 examples/anderson_model_interleaved.jl create mode 100644 examples/fermions.ipynb create mode 100644 examples/plots/chain_occ_bet=2_double.pdf create mode 100644 examples/plots/chain_occ_bet=2_folded.pdf create mode 100644 examples/plots/system_folded_beta2.pdf diff --git a/ChainOhmT/ohmicT/chaincoeffs.h5 b/ChainOhmT/ohmicT/chaincoeffs.h5 index 37df5a09bfe231d8feb39bfc92ce2fda44868803..0ce402f6488b4f247774baa8920a5164be8ab2ea 100644 GIT binary patch delta 700 zcmez2bE1!Nf(Da^`$VlaMuCkR*_jw6ChugD7xc3=G++P%Mi2o64ih(;Z&qMF#>B`p z`8lhmk^n@Qp+XJ9fT>#mvnGN*#Cj=DO|iT1`>lhwPw zOtgRRx0HLyr^$J8F-#8blh4R0Ktkbz++3yr)5-nvVoVP9V7q_;_hQ?xg|ZHf_5foQ BwhaIP delta 52 zcmV-40L%Z#jsf^gkSGFpCXp(Jvv30t0+YZ37?bcQV6!*_t^$+p2?~?&1t77v7y*+Y K0~oVF2RI)M?h;=B diff --git a/ChainOhmT/quadohmT.jl b/ChainOhmT/quadohmT.jl index 4fa693e..4656047 100644 --- a/ChainOhmT/quadohmT.jl +++ b/ChainOhmT/quadohmT.jl @@ -73,3 +73,26 @@ function ohmicspectraldensity_finiteT(x,i,α,s,ωc,β) end return y end + +""" + fermionicspectraldensity_finiteT(x, i, β, chain; ϵ=x) + + Fermionic spectral density at finite temperature β. A function f(x,i) where x is the frequency + and i ∈ {1,...,mc} labels the intervals on which the SD is defined. + +""" + +function fermionicspectraldensity_finiteT(x, i, β, chain, ϵ) + if i==1 + y = 0 + elseif i==2 || i==3 + if chain==1 + y = (sqrt.(1 .- x.^2) .* sqrt.(1. ./(exp.(-β .* ϵ.(x)) .+ 1))).^2 + elseif chain==2 + y = (sqrt.(1 .- x.^2) .* sqrt.(1. ./(exp.(β .* ϵ.(x)) .+ 1))).^2 + end + elseif i==4 + y = 0 + end + return y +end diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl new file mode 100644 index 0000000..7d71879 --- /dev/null +++ b/examples/anderson_model_double.jl @@ -0,0 +1,63 @@ +using MPSDynamics, Plots, LaTeXStrings, QuadGK + +import MPSDynamics: disp, measuremodes, eigenchain, mpsembed! +import MPSDynamics: setleftbond, setrightbond, mpsrightnorm! +import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_mpo + +#---------------------------- +# Physical parameters +#---------------------------- + +N = 20 # number of chain sites +β = 2.0 # inverse temperature +μ = 0. # chemical potential +Ed = 0.6 +ϵd = Ed - μ # energy of the impurity + +dt = 0.5 +T = 30.0 + +dir = "/Users/ariva/Documents/fermions/" + +#chainparams2 = readchaincoeffs(dir*"fermionic.h5", β, 2.0) #filled +#chainparams1 = readchaincoeffs(dir*"fermionic.h5", β, 1.0) #empty + +chainparams2 = readchaincoeffs(dir*"fermionic.h5", β, 2.0) # filled +chainparams1 = readchaincoeffs(dir*"fermionic.h5", β, 1.0) # empty + + +c1 = chainparams1[3] +c2 = chainparams2[3] + +H = tightbinding_mpo(N, ϵd, chainparams1, chainparams2) + +ψ = unitcol(2,2) # (0,1) filled impurity state +A = productstatemps(physdims(H), state=[fill(unitcol(2,2), N)..., ψ, fill(unitcol(1,2), N)...]) # MPS +mpsembed!(A, 4) + +dt = 0.5 +T = 30.0 + + +# For the double chain +ob1 = OneSiteObservable("chain1_filled_occup", numb(2), (1,N)) +ob2 = OneSiteObservable("chain2_empty_occup", numb(2), (N+2, 2N+1)) +ob3 = OneSiteObservable("system_occup", numb(2), N+1) + +A, dat = runsim(dt, T, A, H; + name = "Anderson impurity problem (folded chain)", + #method = :TDVP2, + #method = :TDVP1, + method = :DTDVP, + obs = [ob1, ob2, ob3], # for the double chain + convobs = [ob1], + params = @LogParams(N, ϵd, β, c1, c2), + #convparams = [4], + convparams = [0.1], # for the adaptive TDVP we set the precision that we want to obtain + Dlim = 10, # max bond dimension + savebonddims = true, # we want to save the bond dimension + verbose = false, + save = true, + savedir = dir, + plot = true, + ); \ No newline at end of file diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl new file mode 100644 index 0000000..268796e --- /dev/null +++ b/examples/anderson_model_interleaved.jl @@ -0,0 +1,81 @@ +using MPSDynamics, Plots, LaTeXStrings, QuadGK, Revise + +import MPSDynamics: disp, measuremodes, eigenchain, mpsembed! +import MPSDynamics: setleftbond, setrightbond, mpsrightnorm! +import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_mpo + +#---------------------------- +# Physical parameters +#---------------------------- + +N = 20 # number of chain sites +β = 2.0 # inverse temperature +μ = 0. # chemical potential +Ed = 0.6 +ϵd = Ed - μ # energy of the impurity + +dt = 0.5 +T = 30.0 + +dir = "/Users/ariva/Documents/fermions/" + + +# define the dispersion relation + +function ϵ(x) + return x +end + + +chainparams1 = chaincoeffs_fermionic(20, 2.0, 1.0; ϵ, save=true) +chainparams2 = chaincoeffs_fermionic(20, 2.0, 2.0; ϵ, save=true) + +curdir = @__DIR__ +dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/fermionicT/")) +chainparams2 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 2.0) #filled +chainparams1 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 1.0) #empty + +#chainparams2 = readchaincoeffs(dir*"fermionic.h5", β, 2.0) +#chainparams1 = readchaincoeffs(dir*"fermionic.h5", β, 1.0) + +c1 = chainparams1[3] +c2 = chainparams2[3] + +H = interleaved_tightbinding_mpo(N, ϵd, chainparams1, chainparams2) + +ψ = unitcol(2,2) # (0,1) filled impurity state +Tot = Any[] +push!(Tot, ψ) +for i in 1:(N) + push!(Tot, unitcol(2,2)) + push!(Tot, unitcol(1,2)) +end + +A = productstatemps(physdims(H), state=Tot) # MPS +mpsembed!(A, 2) + +dt = 0.5 +T = 30.0 + +# Observables for the interleaved chain +ob1 = OneSiteObservable("system_occup", numb(2), 1) +ob2 = OneSiteObservable("folded_chain_occup", numb(2), (2,2N)) + +A, dat = runsim(dt, T, A, H; + name = "Anderson impurity problem (folded chain)", + #method = :TDVP2, + #method = :TDVP1, + method = :DTDVP, + obs = [ob1, ob2], # for the interleaved chain + #obs = [ob1, ob2, ob3], # for the double chain + convobs = [ob1], + params = @LogParams(N, ϵd, β, c1, c2), + #convparams = [4], + convparams = [0.1], # for the adaptive TDVP we set the precision that we want to obtain + Dlim = 10, # max bond dimension + savebonddims = true, # we want to save the bond dimension + verbose = false, + save = true, + savedir = dir, + plot = true, + ); \ No newline at end of file diff --git a/examples/fermions.ipynb b/examples/fermions.ipynb new file mode 100644 index 0000000..8a327ad --- /dev/null +++ b/examples/fermions.ipynb @@ -0,0 +1,1477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Single Impurity Anderson Model\n", + "______________________________\n", + "\n", + "\n", + "We use the fermionic chain mapping proposed in [1] to perform tensor network simulations of the Single Impurity Anderson Model (SIAM). The SIAM Hamiltonian is defined as:\n", + "\\begin{equation}\n", + " \\hat H^\\text{SIAM} = \\hat H_\\text{loc} + \\hat H_\\text{hyb} + \\hat H_\\text{cond} = \\overbrace{\\epsilon_d \\hat d^\\dagger \\hat d}^{\\hat H_\\text{loc}} + \\underbrace{\\sum_{k} V_k \\Big( \\hat d^\\dagger \\hat c_k + \\hat c_k^\\dagger \\hat d \\Big)}_{H_\\text{hyb}} + \\underbrace{\\sum_k \\epsilon_k \\hat c_k^\\dagger \\hat c_k}_{H_I^\\text{chain}}.\n", + "\\end{equation}\n", + "All of the operators obey to the usual fermionic anti-commutation relations: $\\{\\hat c_i, \\hat c_j^\\dagger \\} = \\delta_{ij}$, $\\{\\hat c_i, \\hat c_j \\} =\\{\\hat c_i^\\dagger, \\hat c_j^\\dagger \\} =0$ $\\forall i,j$. The chain mapping is based on a thermofield-like transformation [2], performed with fermions: ancillary fermionic operators $\\hat c_{2k}$ are defined, one for each of the original fermionic modes $\\hat c_{1k}$. A Bogoliubov transformation is then applied, so that two new fermionic modes $\\hat f_{1k}$ and $\\hat f_{2k}$ are defined as a linear combination of $\\hat c_{1k}$ and $\\hat c_{2k}$. Two chains are defined: the chain labelled $1$ for the empty modes, the chain labelled $2$ for the filled modes.\n", + "The following relations are used to define the functions equivalent to the spectral density of the bosonic case, one for each chain:\n", + "\\begin{equation}\n", + "\\begin{split}\n", + " &V_{1k} = V_{k} \\sin \\theta_k = \\sqrt{\\frac{1}{e^{\\beta \\epsilon_k}+1}} \\\\\n", + " &V_{2k} = V_{k} \\cos \\theta_k = \\sqrt{\\frac{1}{e^{-\\beta \\epsilon_k}+1}}, \n", + "\\end{split}\n", + "\\end{equation} \n", + "where we choose the spectral function that characterizes the fermionic bath to be: $V_k= \\sqrt{1-k^2}$, and we define the dispersion relation as: $e_k = k$, that is, a linear dispersion relation with propagation speed equal to $1$. This latter choice corresponds to a model of metals (gapless energy spectrum). We select a filled state as the initial state of the defect.\n", + "Using the mapping proposed in [1], the chain Hamiltonian becomes:\n", + "\\begin{equation}\\label{chain_ham_siam}\n", + " \\begin{split}\n", + " \\hat H^\\text{chain} = \\hat H_\\text{loc} &+ \\sum_{i = \\{1,2\\}}\\bigg[ J_{i,0} \\Big(\\hat d^\\dagger \\hat a_{i,0} + \\hat d \\hat a_{i,0}^\\dagger \\Big) + \\\\ &+ \\sum_{n=1}^\\infty \\Big( J_{i,n} \\hat a_{i,n}^\\dagger \\hat a_{i,n-1} + J_{i,n} \\hat a_{i,n-1}^\\dagger \\hat a_{i,n} \\Big) + \\sum_{n=0}^\\infty E_{i,n} \\hat a_{i,n}^\\dagger \\hat a_{i,n} \\bigg],\n", + " \\end{split}\n", + "\\end{equation}\n", + "where the $J_{i,n}$ coefficients are the couplings between the chain sites and the $E_{i,n}$ coefficients are the energies associated to each chain site. Clearly, the interactions are between nearest neighbors. This, combined with the fact that the fermions in our model are spinless, enables a straightforward mapping into fermionic operators of the bosonic creation and annihilation operators, that on their part obey to the bosonic commutation relations: $[\\hat b_i, \\hat b_j^\\dagger] = \\delta_{ij}$, $[\\hat b_i, \\hat b_j] =[\\hat b_i^\\dagger, \\hat b_j^\\dagger] =0$ $\\forall i,j$. The mapping derived from Jordan-Wigner transformations for spinless fermions is:\n", + "\\begin{equation}\n", + " \\hat a_{i}^\\dagger \\hat a_{i+1} + \\hat a_{i+1}^\\dagger \\hat a_{i} = \\hat b_{i}^\\dagger \\hat b_{i+1} + \\hat b_{i+1}^\\dagger \\hat b_{i}. \n", + "\\end{equation}\n", + "\n", + "\n", + "## Double chain mapping\n", + "\n", + "The corresponding MPO representation is:\n", + "\\begin{equation}\n", + "\\begin{split}\n", + "&\n", + "\\begin{bmatrix}\n", + " \\hat{\\mathbb I} & J_{2,N} \\hat b_{2,N}^\\dagger & J_{2,N} \\hat b_{2,N} & E_{2,N} \\hat b_{2,N}^\\dagger \\hat b_{2,N} \n", + "\\end{bmatrix}\\cdot ... \\cdot\n", + "\\begin{bmatrix}\n", + " \\hat{ \\mathbb I} & J_{2,0} \\hat b_{2,0}^\\dagger & J_{2,0} \\hat b_{2,0} & E_{2,0} \\hat b_{2,0}^\\dagger \\hat b_{2,0}\\\\\n", + "0 &0 & 0 & \\hat b_{2,0} \\\\\n", + "0 &0 & 0 & \\hat b_{2,0}^\\dagger \\\\\n", + "0 &0 & 0 & \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\cdot \\\\ \\cdot &\n", + "\\begin{bmatrix}\n", + " \\hat{ \\mathbb I} & \\hat d^\\dagger & \\hat d & \\epsilon_d \\hat d^\\dagger \\hat d\\\\\n", + "0 &0 & 0 & \\hat d \\\\\n", + "0 &0 & 0 & \\hat d^\\dagger \\\\\n", + "0 &0 & 0 & \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\cdot \n", + "\\begin{bmatrix}\n", + " \\hat{ \\mathbb I} & \\hat b_{1,0}^\\dagger & \\hat b_{1,0} & E_{1,0} \\hat b_{1,0}^\\dagger \\hat b_{1,0}\\\\\n", + "0 &0 & 0 & \\hat J_{1,0}b_{1,0} \\\\\n", + "0 &0 & 0 & \\hat J_{1,0}b_{1,0}^\\dagger \\\\\n", + "0 &0 & 0 & \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\cdot ... \\cdot\n", + "\\begin{bmatrix}\n", + " E_{2,N} \\hat b_{2,N}^\\dagger \\hat b_{2,N} \\\\ J_{2,N} \\hat b_{2,N} \\\\ J_{2,N} \\hat b_{2,N}^\\dagger \\\\ \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\end{split}\n", + "\\end{equation}\n", + "\n", + "The system starts from a filled state, the chain starts as in the Fermi sea.\n", + "\n", + "## Interleaved chain mapping\n", + "\n", + "The drawback of such a representation though, is that the particle-hole pairs are spatially separated in the MPS, creating correlations and therefore leading to a dramatic increase in the bond dimensions. This is why Kohn and Santoro propose an interleaved geometry, the advantages of which are thoroughly explained in \\cite{Kohn_Santoro_2021b}. Exploiting the interleaved representation, the interaction comes to be between next-nearest neighbors: a string operator appears in the Jordan-Wigner transformation from bosons to fermions:\n", + "\\begin{equation}\n", + " \\hat a_{i}^\\dagger \\hat a_{i+2} + \\hat a_{i+2}^\\dagger \\hat a_{i} = \\hat b_{i}^\\dagger \\hat F_{i+1} \\hat b_{i+2} + \\hat b_{i} \\hat F_{i+1} \\hat b_{i+2}^\\dagger,\n", + "\\end{equation}\n", + "where the string operator $\\hat F_i$ is defined as: $\\hat F_i = (-1)^{\\hat n_i} = \\hat{\\mathbb I} -2 \\hat n_i = \\hat{\\mathbb I}-2 \\hat b_i^\\dagger \\hat b_i$. It is possible to find the analytical form also for MPOs with long range interaction \\cite{mpo}. In the case of next-nearest neighbors interactions between spinless fermions, the MPO representation will require a bond dimension $\\chi=6$. We explicitly write it as:\n", + "\\begin{equation}\n", + "\\begin{split}\n", + "&\n", + "\\begin{bmatrix}\n", + " \\hat{\\mathbb I} & \\hat d & \\hat d^\\dagger & 0 & 0 & E_{d} \\hat d^\\dagger \\hat d \n", + "\\end{bmatrix}\\cdot\n", + "\\begin{bmatrix}\n", + " \\hat{ \\mathbb I} & \\hat b_{2,0} & \\hat b_{2,0}^\\dagger & 0 & 0 & E_{2,0} \\hat b_{2,0}^\\dagger \\hat b_{2,0}\\\\\n", + "0 &0 & 0 & \\hat{F}_{2,0} & 0 & J_{2,0} \\hat b_{2,0}^\\dagger \\\\\n", + "0 &0 & 0 & 0 & \\hat{F}_{2,0} & J_{2,0} \\hat b_{2,0} \\\\\n", + "0 &0 & 0 & 0 & 0 & 0\\\\\n", + "0 &0 & 0 & 0 & 0 & 0 \\\\\n", + "0 &0 & 0 & 0 & 0 & \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\cdot \\\\ \\cdot &\n", + "\\begin{bmatrix}\n", + " \\hat{ \\mathbb I} & \\hat b_{1,0} & \\hat b_{1,0}^\\dagger & 0 & 0 & E_{1,0} \\hat b_{1,0}^\\dagger \\hat b_{1,0}\\\\\n", + "0 &0 & 0 & \\hat{ F}_{1,0} & 0 & 0 \\\\\n", + "0 &0 & 0 & 0 & \\hat{F}_{1,0} & 0 \\\\\n", + "0 &0 & 0 & 0 & 0 & J_{1,0} \\hat b_{1,0}^\\dagger \\\\\n", + "0 &0 & 0 & 0 & 0 & J_{1,0} \\hat b_{1,0} \\\\\n", + "0 &0 & 0 & 0 & 0 & \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\cdot ... \\cdot \n", + "\\begin{bmatrix}\n", + " \\hat{ \\mathbb I} & \\hat b_{2,N} & \\hat b_{2,N}^\\dagger & 0 & 0 & E_{2,N} \\hat b_{2,N}^\\dagger \\hat b_{2,N}\\\\\n", + "0 &0 & 0 & \\hat{F}_{2,N} & 0 & 0 \\\\\n", + "0 &0 & 0 & 0 & \\hat{F}_{2,N} & 0 \\\\\n", + "0 &0 & 0 & 0 & 0 & J_{2,N} \\hat b_{2,N}^\\dagger \\\\\n", + "0 &0 & 0 & 0 & 0 & J_{2,N} \\hat b_{2,N} \\\\\n", + "0 &0 & 0 & 0 & 0 & \\hat{\\mathbb I}\n", + "\\end{bmatrix}\n", + "\\cdot \\\\ \\cdot &\n", + "\\begin{bmatrix}\n", + " E_{1,N} \\hat b_{1,N}^\\dagger \\hat b_{1,N} \\\\ 0 \\\\0 \\\\ J_{1,N} \\hat b_{1,N}^\\dagger \\\\ J_{1,N} \\hat b_{1,N} \\\\ \\hat{\\mathbb I}\n", + "\\end{bmatrix} \n", + "\\end{split}\n", + "\\end{equation}\n", + "________________\n", + "### References\n", + "\n", + "[1] Lucas Kohn and Giuseppe E. Santoro. Efficient mapping for anderson impurity problems with matrix product states. Physical Review B, 104(1):014303, Jul 2021. arXiv: [2012.01424](https://arxiv.org/abs/2012.01424).\n", + "\n", + "\n", + "[2] Ines de Vega and Mari-Carmen Banuls. Thermofield-based chain mapping approach for open quantum systems. Physical Review A, 92(5):052116, Nov 2015. arXiv:[1504.07228](https://arxiv.org/abs/1504.07228).\n", + "\n", + "[3] L. Kohn and G. E. Santoro. Quenching the anderson impurity model at finite temperature: Entanglement and bath dynamics using matrix product states. arXiv:2107.02807 [cond-mat, physics:quant-ph], Jul 2021. arXiv: [2107.02807](https://arxiv.org/abs/2107.02807)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import numpy as np\n", + "#import cmath for complex number operations\n", + "import cmath\n", + "import matplotlib\n", + "import matplotlib.pyplot as pl\n", + "import matplotlib.style as style \n", + "import matplotlib.pyplot as pl\n", + "import scipy.misc\n", + "from scipy import ndimage\n", + "\n", + "from scipy.optimize import curve_fit\n", + "style.use('tableau-colorblind10')\n", + "matplotlib.rcParams.update({'font.size': 12})" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Collecting scipy\n", + " Downloading scipy-1.13.0-cp39-cp39-macosx_12_0_arm64.whl.metadata (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy<2.3,>=1.22.4 in /Users/ariva/Library/Python/3.9/lib/python/site-packages (from scipy) (1.26.2)\n", + "Downloading scipy-1.13.0-cp39-cp39-macosx_12_0_arm64.whl (30.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.3/30.3 MB\u001b[0m \u001b[31m201.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:05\u001b[0m\n", + "\u001b[?25hInstalling collected packages: scipy\n", + "Successfully installed scipy-1.13.0\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def system(tit, res, times, pr, title):\n", + " f = pl.figure(figsize=(6, 4))\n", + " pl.plot(times, res, '-o', markersize=\"4\")\n", + " pl.xlabel(r\"$t$\")\n", + " pl.ylabel(r'$\\langle \\hat n_{d} \\rangle$', fontsize=14)\n", + " pl.ylim((0,1))\n", + " if pr!= True: pl.title(tit)\n", + " pl.grid()\n", + " if pr==True: pl.savefig(\"plots/\"+title+\".pdf\", bbox_inches='tight')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def occupation(res, times, chain):\n", + " f, ax = pl.subplots(figsize=(7,5))\n", + " img = ax.imshow(res.T, cmap='Blues', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", + "\n", + " y_label_list = times\n", + " x_label_list = chain\n", + "\n", + " ax.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + " ax.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + "\n", + " ax.set_yticklabels(y_label_list)\n", + " ax.set_xticklabels(x_label_list)\n", + "\n", + " ax.set_ylabel(\"$t$\")\n", + " ax.set_xlabel(\"$N_{i,j}$ chain sites\")\n", + " \n", + " pl.title(\"Chain occupation\")\n", + " f.colorbar(img)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def bonddim(res, times, chain):\n", + " f, ax = pl.subplots(figsize=(7,5))\n", + " img = ax.imshow(res, cmap='hot', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", + "\n", + " y_label_list = times\n", + " x_label_list = chain\n", + "\n", + " ax.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + " ax.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + "\n", + " ax.set_yticklabels(y_label_list)\n", + " ax.set_xticklabels(x_label_list)\n", + "\n", + " ax.set_ylabel(\"$t$\")\n", + " ax.set_xlabel(\"$N_{i,j}$ chain sites\")\n", + " \n", + " pl.title(\"Bond dimensions\")\n", + " f.colorbar(img)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def alltogether(resocc, resbond, times, timespl, chain, double, pr, title):\n", + " f = pl.figure(figsize=(13, 9))\n", + " y_label_list = times\n", + " x_label_list = chain\n", + " \n", + " ax1 = pl.subplot(2,2,1) \n", + " if double==True: pl.imshow(resocc.T, cmap='Blues', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", + " else: pl.imshow(resocc, cmap='Blues', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", + " pl.colorbar()\n", + "\n", + " ax1.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + " ax1.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + " ax1.set_yticklabels(y_label_list)\n", + " ax1.set_xticklabels(x_label_list)\n", + " ax1.set_ylabel(\"$t$\")\n", + " ax1.set_xlabel(\"$N_{i,j}$ chain sites\")\n", + " \n", + " pl.title(\"Chain occupation\")\n", + " \n", + " ax2 = pl.subplot(2,2,2)\n", + " pl.imshow(resbond, cmap='hot', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", + " pl.colorbar()\n", + " pl.clim(0,35)\n", + "\n", + " ax2.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + " ax2.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", + " ax2.set_yticklabels(y_label_list)\n", + " ax2.set_xticklabels(x_label_list)\n", + " ax2.set_ylabel(\"$t$\")\n", + " ax2.set_xlabel(\"$N_{i,j}$ chain sites\")\n", + " \n", + " pl.title(\"Bond dimensions\")\n", + " \n", + " pl.subplot(2,2,3) \n", + " if double==True:\n", + " sitespos = [i for i in range(1,N+1)]\n", + " sitesneg = [-N+i for i in range(0,N+1)]\n", + " sites = sitesneg + sitespos\n", + " else:\n", + " sites = [i for i in range(2*N)]\n", + " for t in range(1, len(timespl)+1):\n", + " i = int(timespl[t-1])\n", + " x = int(timespl[t-1])*0.5\n", + " if double==True: pl.plot(sites, resocc.T[i], '-o', markersize=\"4\", label=r\"$t =$\"+str(x))\n", + " else: pl.plot(sites, resocc[i], '-o', markersize=\"4\", label=r\"$t =$\"+str(x))\n", + " pl.legend(loc=\"best\",prop={'size': 10})\n", + " pl.xlabel(r\"$N_{i,j}$ chain sites\")\n", + " pl.ylabel(r'$\\langle \\hat n_{i,j} \\rangle$', fontsize=14)\n", + " #pl.title(\"Occupation evolution\")\n", + " \n", + " pl.subplot(2,2,4)\n", + " if double==True:\n", + " sitespos = [i for i in range(1,N+1)]\n", + " sitesneg = [-N+i for i in range(0,N+2)]\n", + " sites = sitesneg + sitespos\n", + " else:\n", + " sites = [i for i in range(0,2*N+2)]\n", + " for t in range(1, len(timespl)+1):\n", + " i = int(timespl[t-1])\n", + " x = int(timespl[t-1])*0.5\n", + " pl.plot(sites, resbond[i], '-o', markersize=\"4\", label=r\"$t =$\"+str(x))\n", + " pl.legend(loc=\"best\",prop={'size': 10})\n", + " pl.xlabel(\"$N_{i,j}$ chain sites\")\n", + " pl.ylabel('$\\chi_{i,j}$', fontsize=14)\n", + " pl.ylim(-1,35)\n", + " #pl.title(\"Occupation evolution\")\n", + " if pr==True: pl.savefig(\"plots/\"+title+\".pdf\", bbox_inches='tight')\n", + " \n", + " pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#path = \"/home/berkane/Documents/stage/simulations/\"\n", + "#path2 = \"/home/berkane/Documents/stage/Julia/\"\n", + "\n", + "path = \"/home/angela/Documents/stage/simulations/\"\n", + "path2 = \"/home/angela/Documents/insp/\"\n", + "\n", + "\n", + "chain_coeff = h5py.File(path+\"fermions/fermionic.h5\", \"r\")\n", + "toymod = h5py.File(path2+\"toy_model/results/HxBBT/dat_HxBBT.jld\", \"r\")\n", + "\n", + "toymod[\"data\"].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sites = [i for i in range(10)]\n", + "pl.plot(sites, toymod[\"data/occup\"][60])\n", + "pl.plot(sites, toymod[\"data/occup\"][30])\n", + "pl.plot(sites, toymod[\"data/occup\"][10])\n", + "pl.plot(sites, toymod[\"data/occup\"][20])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.46065886596178074\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.24367008165136503,\n", + " 0.2465439160674058,\n", + " 0.24795478868354134,\n", + " 0.24868721474295313,\n", + " 0.24909378199697457,\n", + " 0.2493399585116368,\n", + " 0.24950013242550662,\n", + " 0.2496105102818235,\n", + " 0.24969025876653497,\n", + " 0.2497502822281343,\n", + " 0.249797179435281,\n", + " 0.2498351720344085,\n", + " 0.24986711058460603,\n", + " 0.24989503162469592,\n", + " 0.2499204832940156,\n", + " 0.24994472571721438,\n", + " 0.24996886071974184,\n", + " 0.24999392000269427,\n", + " 0.2500209275744485]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(chain_coeff[\"2000.0/1.0\"].keys())\n", + "print(chain_coeff[\"2000.0/2.0/c\"][()])\n", + "list(chain_coeff[\"2000.0/2.0/t\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Double chain results $\\beta = 2.0$" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t name : Anderson impurity problem (folded chain)\n", + "\t machine : local\n", + "\t method : DTDVP\n", + "\t dt = 0.5\n", + "\t tmax = 30.0\n", + "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.46065886596178063, c2 = 0.4606588659617807, \n", + "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", + "\t convparams : 0.1\n", + "\t options : Dlim = 10, savebonddims = true, verbose = false, \n", + "\n" + ] + } + ], + "source": [ + "\n", + "# path to work from personal computer\n", + "\n", + "path = \"/Users/ariva/Documents/fermions/plkIX/\"\n", + "\n", + "# Results for alpha_lit=0.01\n", + "res = h5py.File(path+\"/dat_plkIX.jld\", \"r\")[\"data\"]\n", + "info = open(path+\"/info.txt\", \"r\")\n", + "data = info.read()\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['bonddims',\n", + " 'chain1_filled_occup',\n", + " 'chain2_empty_occup',\n", + " 'system_occup',\n", + " 'times']" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 20\n", + "epsd = -0.4\n", + "beta = 2.0\n", + "\n", + "times = [\"0\", \"7.5\", \"15\", \"22.5\", \"30\"]\n", + "tim = [\"0\", \"20\", \"40\", \"60\"]\n", + "chain_fol = [\"0\", \"10\", \"20\", \"30\", \"40\"]\n", + "chain = [\"-20\", \"-10\", \"0\", \"10\", \"20\"]\n", + "\n", + "occ = np.column_stack((res[\"chain1_filled_occup\"][()], res[\"system_occup\"][()]))\n", + "occ = np.concatenate((occ.T, res[\"chain2_empty_occup\"][()].T))\n", + "\n", + "\n", + "list(res.keys())" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "occupation(occ, times, chain)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bonddim(res[\"bonddims\"][()], times, chain)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(occ, res[\"bonddims\"][()], times, tim, chain, True, True, \"chain_occ_bet=2_double\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Folded chain results $\\beta = 2.0$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t name : Anderson impurity problem (folded chain)\n", + "\t machine : local\n", + "\t method : DTDVP\n", + "\t dt = 0.5\n", + "\t tmax = 30.0\n", + "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.46065886596178063, c2 = 0.4606588659617807, \n", + "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", + "\t convparams : 0.1\n", + "\t options : Dlim = 10, savebonddims = true, verbose = false, \n", + "\n" + ] + } + ], + "source": [ + "\n", + "# path to work from personal computer\n", + "\n", + "path = \"/Users/ariva/Documents/fermions/obiSh/\"\n", + "\n", + "# Results for alpha_lit=0.01\n", + "res_fol = h5py.File(path+\"/dat_obiSh.jld\", \"r\")[\"data\"]\n", + "info = open(path+\"/info.txt\", \"r\")\n", + "data = info.read()\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"Unable to open object (object 'folded_chain_occup' doesn't exist)\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m occ_fol \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mcolumn_stack((res_fol[\u001b[39m\"\u001b[39m\u001b[39msystem_occup\u001b[39m\u001b[39m\"\u001b[39m][()], res_fol[\u001b[39m\"\u001b[39;49m\u001b[39mfolded_chain_occup\u001b[39;49m\u001b[39m\"\u001b[39;49m][()]))\n\u001b[1;32m 3\u001b[0m \u001b[39mlist\u001b[39m(res_fol\u001b[39m.\u001b[39mkeys())\n", + "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/h5py/_hl/group.py:357\u001b[0m, in \u001b[0;36mGroup.__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInvalid HDF5 object reference\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 356\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(name, (\u001b[39mbytes\u001b[39m, \u001b[39mstr\u001b[39m)):\n\u001b[0;32m--> 357\u001b[0m oid \u001b[39m=\u001b[39m h5o\u001b[39m.\u001b[39;49mopen(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mid, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_e(name), lapl\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_lapl)\n\u001b[1;32m 358\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 359\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mAccessing a group is done with bytes or str, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 360\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnot \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mtype\u001b[39m(name)))\n", + "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mh5py/h5o.pyx:241\u001b[0m, in \u001b[0;36mh5py.h5o.open\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"Unable to open object (object 'folded_chain_occup' doesn't exist)\"" + ] + } + ], + "source": [ + "occ_fol = np.column_stack((res_fol[\"system_occup\"][()], res_fol[\"folded_chain_occup\"][()]))\n", + "\n", + "list(res_fol.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The occupation measures the number of electrons or holes on each site:\n", + "\n", + "The following cell can be executed to switch from filled_by_electrons-unfilled_by_electrons representation to the filled_by_electrons-filled_by_holes representation." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "#list(occ_fol)\n", + "for i in range(len(occ_fol)):\n", + " for j in range(len(occ_fol[i])):\n", + " #print(j, \")\", occ_fol[i,j])\n", + " if j%2!=0: occ_fol[i,j] = 1-occ_fol[i,j]\n", + " #print(occ_fol[i,j])\n", + " #print(\"#########\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.0,\n", + " 0.9870176494540611,\n", + " 0.8919698620297826,\n", + " 0.7464379947477169,\n", + " 0.6161634018854681,\n", + " 0.5522477965356296,\n", + " 0.5666502795226769,\n", + " 0.6319453340780932,\n", + " 0.7024830060119678,\n", + " 0.7359934219639208,\n", + " 0.7124381207373258,\n", + " 0.6451042074839143,\n", + " 0.5673426216695034,\n", + " 0.5150195889022545,\n", + " 0.5095481614975812,\n", + " 0.5491013412596275,\n", + " 0.6114661181500118,\n", + " 0.6674654651247499,\n", + " 0.6950890994103728,\n", + " 0.6865314704334547,\n", + " 0.6482378623922909,\n", + " 0.5966290273779772,\n", + " 0.5512030497704176,\n", + " 0.526640953803126,\n", + " 0.5268942235928347,\n", + " 0.5443781568562909,\n", + " 0.565422871548877,\n", + " 0.5787781027272988,\n", + " 0.5818291881187583,\n", + " 0.5807038614072185,\n", + " 0.5853035895407249,\n", + " 0.6020620370781531,\n", + " 0.630270827167768,\n", + " 0.6624160353318125,\n", + " 0.68742027274086,\n", + " 0.6949376166445541,\n", + " 0.6796932962998955,\n", + " 0.6452235302947861,\n", + " 0.6038016695930509,\n", + " 0.5700765455894988,\n", + " 0.5536473344128495,\n", + " 0.5552398597383836,\n", + " 0.5676928952064143,\n", + " 0.580971003884192,\n", + " 0.58816789214906,\n", + " 0.5888815165361093,\n", + " 0.5887977273019762,\n", + " 0.595517048051502,\n", + " 0.6131930032441936,\n", + " 0.6386617799784853,\n", + " 0.6621951663634985,\n", + " 0.6718039292907736,\n", + " 0.6596157241929744,\n", + " 0.6268162711631243,\n", + " 0.5839235849677901,\n", + " 0.5455891197894346,\n", + " 0.5226057361360607,\n", + " 0.5161508113080157,\n", + " 0.5193841981281505,\n", + " 0.5243301594820952,\n", + " 0.528374719494914]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(res[\"system_occup\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#system(\"Double chain system occupation\", res[\"system_occup\"][()], res[\"times\"][()], True, \"system_double_beta2\")\n", + "system(\"Folded chain system occupation\", res_fol[\"system_occup\"][()], res_fol[\"times\"][()], True, \"system_folded_beta2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABEkAAAMZCAYAAAD8zGo0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXxU1fnH8e9kkkwSIIFAgATDoojI7gIItagtCrKpyGZFFm3RKi4gLmgtoBa0oNX+qFu1gIoLBKxCVQoqKoIgKmoRmrqgAcJOEkL2yf39QTN1yMw9mSXJJPm8ed2X5t577j33znbmmXOe47AsyxIAAAAAAEADF1XbFQAAAAAAAIgEBEkAAAAAAABEkAQAAAAAAEASQRIAAAAAAABJBEkAAAAAAAAkESQBAAAAAACQRJAEAAAAAABAEkESAAAAAAAASQRJAAAAAAAAJBEkAardrl275HA4NGnSpGo9j8Ph0IUXXlit50DwFi9eLIfDocWLF9d2VQAAqDXhbhf5av/Mnj1bDodD69evD8s56oL27durffv2tV0NoF4gSAIEYefOnbr55pvVrVs3JSUlKTY2VmlpaRo6dKiee+45FRcX13YVUcPWr18vh8Oh2bNn13ZVAAANhMPhqLS4XC61b99eEydO1I4dO2q7igBQ50TXdgWAuub+++/XnDlzVF5ern79+mnixIlq3Lix9u/fr/Xr1+vXv/61nnzySW3durVG67Vjxw4lJCTU6DlRdVdccYXOO+88paam1nZVAAD1zKxZszz/n5ubqy1btuj555/XihUrtGHDBvXq1av2KlcLpk6dqnHjxqlt27a1XZUa884779R2FYB6gyAJEIC5c+dq1qxZSk9P1/Lly9W3b99K+6xevVqPPPJIjdetc+fONX5OVF1SUpKSkpJquxoAgHrIVy/Gm2++WQsXLtRjjz3W4IZ6tmjRQi1atKjtatSo0047rbarANQbDLcBqmjXrl2aPXu2YmJi9Oabb/oMkEjSsGHD9Pbbb/s9xrhx49SiRQvFxcXp3HPP1erVqyvtl5ubq/nz5+sXv/iFTjnlFMXGxiolJUUjRozQpk2bfB7bNCY3IyNDffr0UUJCgpKTkzVu3Djt2bMnoHtQXFyshx56SN27d1dCQoISExP185//XMuWLfNbZsuWLRo7dqzatGkjl8ul1NRUXXLJJT7LVGVf07AWX2Nyf5oP5B//+If69++vRo0aqVmzZho1apT+85//VDpOZmam7r77bp177rlKSUmRy+VSu3btNGXKFO3evdtr30mTJumiiy6SJM2ZM8er23PFeGi7nCSffvqprrzySrVs2dJznhtvvFHZ2dmV9p00aZIcDod27dqlp59+Wt27d1dcXJxatWqlKVOmKDc31+d9AQA0LJdccokk6eDBg5W2BfJ5/tMcIlVtx0jSsWPHNH36dJ1yyimKi4tT586d9eijj6q8vDzgaykpKdEDDzyg0047TS6XSx06dNDvfvc7v8Ob/eUkqWgr7d+/X9dee61atWqlRo0aqX///vrwww8lScePH9cdd9yhdu3ayeVyqWvXrlq+fLnfur388su66KKL1LRpU8XFxenMM8/Ugw8+6LNuFec/dOiQpkyZotTUVM85Fi1aVGl/y7K0ZMkS9e/fXykpKYqLi1N6eroGDRqkV1991WtffzlJqvuxLikp0Z///GedffbZatasmRISEtS+fXtddtllWrdund/7BkQyepIAVbRo0SKVlpZq3Lhx6tatm+2+Lper0roffvhBffr00amnnqprrrlGR44c0auvvur5EKn4ki2dGDpz7733asCAARo6dKiaNWumH3/8UW+88YbeeustrVq1SoMHD65y3Z944gm98cYbGjFihC644AJt3rxZr776qr744gtt27bNZ31PVlJSokGDBun9999X586dddNNN6mgoEAZGRkaO3astm3bprlz53qV+etf/6rf/va3cjqdGjFihE4//XQdOHBAW7du1RNPPKExY8YEtW+wVq5cqbfeektXXHGFLrzwQm3btk0rVqzQe++9p40bN+qMM87w2vepp57SRRddpP79+ys2Nlbbt2/Xs88+q1WrVmnr1q1q06aNJOnyyy+XJC1ZskQXXHCBV7DKlERt9erVuvLKK2VZlkaNGqV27drp008/1ZNPPqnXX39dGzZsUIcOHSqVu/POO7VmzRoNHz5cl1xyid577z399a9/1TfffKN333035HsFAKjbKr6gnnvuuV7rg/k8lwJrxxQXF+uXv/ylPvnkE/Xs2VNXX321cnJy9MADD+j9998P6Dosy9KYMWP0+uuv67TTTtPUqVNVUlKiv/3tb/rqq68Cvi85OTn62c9+piZNmuiqq67SkSNH9Morr2jQoEHatGmTrr/+eh05ckTDhg1TaWmpXn75ZY0dO1bp6ek677zzvI517bXXatGiRTrllFN05ZVXqmnTpvr4449133336Z133tHatWsVHR3t8/yxsbEaNWqUiouLtXz5cl177bWKiorSxIkTPfvee++9mjdvnjp06KAxY8YoKSlJ2dnZ+uSTT7R8+XKNHTvW9lpr4rGeNGmSXn75ZXXr1k0TJkxQfHy89u7dqw0bNujtt9/WwIEDA36MgFpnAaiSX/ziF5Yk669//WtA5b7//ntLkiXJmj17tte2t99+25JkXXrppV7rc3JyrIMHD1Y6VlZWlpWammp17ty50jZJ1gUXXOC1btasWZYkq0mTJtaXX37pte2qq66yJFmvvvpqla5j7ty5nrqWlpZ61u/fv99q166dJcn66KOPPOu3b99uRUdHW82aNbP+9a9/+byWYPZ97733LEnWrFmzfNazXbt2Vrt27bzWLVq0yPMYrFq1ymvbY489ZkmyfvGLX3it3717t1VUVFTp+GvWrLGioqKsG264wWu9qV4VdVi0aJFn3bFjx6zk5GQrKirK+uCDD7z2f+ihhyxJ1sUXX+y1fuLEiZYkKz093frhhx8860tLS62f//znliRr8+bNPusAAKhfKj7bZs2a5VmmTZtmnX/++ZbD4bCGDRtm5eXleZUJ9PM8mHbMH/7wB0uSNXLkSMvtdnvWf/fdd1azZs0sSdbEiROrdI1Lly61JFnnnXeeVVhY6Fl/+PBh69RTT7Vt/7z33ns+79f111/vVa/nn3/ekmQ1a9bMGjZsmNd5PvjgA0uSdfnll3sdq+Jz/YorrrAKCgp8nv+xxx7zef7rrrvOKisr86zfvn275XQ6rTPPPNNr/+TkZKtNmzbW8ePHK92Xk9uJvto/1f1Y5+TkWA6HwzrnnHO8rqfCoUOHKq0D6gKCJEAVnXnmmZYk66233gqoXMUHTrt27Xx+gLRt29Zq3rx5lY938803W5K8viBbln2Q5N577610nHfffdeSZN1+++1VOm/Hjh0th8Nh7dixo9K2Z5991pJkTZ482bNu6tSpliTr0UcfNR47kH1DCZKcHAixLMsqKyuzTjvtNEuStWvXLuP5LcuyunfvbnXo0CGgevkKkrz44ouWJOuqq66qtH9paanVvn37So91RZDEV7Dub3/7myXJ+r//+78qXQcAoG6r+ELra+nSpYu1dOnSSmUC/TwPph3TsWNHKyoqyvrmm28q7V/RNqlqkGTgwIGWJOvdd9+ttK3iszWQIElCQkKlwFFZWZkVHR1tSbK+/fbbSudp37691b59e691vXr1sqKjo62jR49W2r+srMxq3ry51bt3b5/nz83NrVRmwIABliTr2LFjnnXJyclW+/btff5oczJf7Z/qfqxzc3MtSVb//v2t8vJyYx2BuoKcJEAN6dWrl5xOZ6X16enpOnr0aKX1H330kcaMGaP09HS5XC5Pjov/+7//k6SA8omc3NW24rySfJ77ZMeOHdM333yjtLQ0nwlif/GLX0iSPv/8c8+6jz/+WJJ06aWXGo8fyL6huOCCCyqtczqdOv/88yV519+yLL344osaOHCgUlJSFB0d7XkMvvrqq4Dzufjy2WefSfrf/fup6OhoDRgwoFK9KoT6mAIA6g/rxA+fsixL+fn52rx5s1q1aqWrr75a9957r2e/YD7PK1S1HVNxjjZt2vhMJnpy/jSTzz77TFFRUZ7P6lCOJUmdOnVSkyZNvNY5nU61atVKTZs21amnnlqpTJs2bbzykRUUFOiLL75Qs2bN9Nhjj2n27NleywMPPCCXy+VzCubTTz9diYmJldb7+gy/+uqrtWvXLnXp0kUzZ87U22+/XeXcYzXxWCcmJmr48OHauHGjevXqpfvvv1/vvfeeCgoKqlRHIFKRkwSootTUVO3YsSPoL8dNmzb1uT46OrpSErPXXntNo0aNUlxcnC6++GKddtppatSokaKiorR+/Xq9//77fpOVVfXcFWNk3W63sXzFB7K/6Wsr1ufk5HjWVfx/Rd4OO4HsG4pWrVr5XN+6dWtJ8mp4TJ8+XY899phSU1M1aNAgtWnTRvHx8ZJOJGH94YcfQq5PMPe1QqiPKQCgfmrUqJH69OmjlStX6pRTTtEf//hH3XDDDUpPTw/7545UuR1TcQ7TZ25V5ebmKjk5WTExMSEfS5Lfmeaio6Ntt5WVlXn+Pnr0qCzL0sGDBzVnzpyAzm93HyXvz/A//elPOvXUU7Vo0SI99NBDeuihhxQdHa0hQ4bokUceUceOHf2epyYea0l69dVX9fDDD+ull17yTEUdFxenUaNGacGCBX6fB0AkI0gCVNH555+vd999V++8846uu+66aj3Xfffdp9jYWG3dulVnnnmm17brr78+4KRnoapoNOzbt8/n9opZWH7auKj4gN2zZ49xeuJA9o2KOtEB7qeNlZ/Kycnx++G+f/9+n+srrqui/gcOHNCf//xndevWTRs3bqz0i9PLL79sW8eqCua+AgBQFU2bNtUZZ5yhzz77TJ999pnS09Nr5HOnoqzpMzeQ4x05ckSlpaWVAiWBHitcKq7xrLPO8vQKrQ5Op1O33XabbrvtNh04cEAbNmzQK6+8ouXLl2v79u3avn273+T7NdXGiI+P9/SgycrK0gcffKDFixfrxRdf1K5duzyzBgF1CcNtgCqaPHmyYmJitGLFCn399de2+wbSy8OXb775Rl26dKkUICkvL9eGDRtCOnYwmjRpotNOO0179uzxOV3ue++9J0k6++yzPesqMsC/9dZbxuMHsm+zZs0kSVlZWZW2ffPNN7bdUH0Fl9xut+eennXWWZKk7777TuXl5brkkksqBUh2796t7777rtJxKrqlBtKLo+J8J09RKJ0IAlU0LH56XwEAqKqKoREVv/4H83keqCZNmqhjx47as2ePvv3220rbfX3m2Tn77LP9tn8CPVa4NG7cWF27dtX27dt15MiRGjlny5YtNXLkSC1btky/+MUv9O233+pf//qX3/1r4rE+WXp6uq6++mqtWbNGHTt21IYNG3T48OGwHR+oKQRJgCpq3769Zs+erZKSEg0dOlRbt271ud/bb78dcm6N9u3b6z//+Y/27t3rWWdZlmbPnm0M0FSXa6+9VpZl6Y477vAKBBw6dEgPPPCAZ58Kv/3tbxUdHa0HHnjAZ51/OrY3kH07d+6sxMREvf766zpw4IBnfWFhoW655Rbba3j33Xe1evVqr3ULFy7Ut99+q4suukjt2rWT9L9pezds2OB1rfn5+frNb37jsxdL8+bNJUk//vijbR1+6vLLL1dycrJefvllT16WCo899pi+//57DRw4UG3btq3yMQEAkKS///3v+v777xUTE6P+/ft71gf6eR6MyZMnq7y8XHfddZfX8Izvv/9ef/7znwM+lnRiOtyioiLP+iNHjujBBx8MqZ6hmD59ukpKSnTttdf6HLJy9OjRkHqZFBcX66OPPqq0vrS01BOYSUhIsD1GdT/WBw8e9DkN8/Hjx5Wfn6/o6GjFxsYGfXygtjDcBgjAPffco7KyMs2ZM0e9e/dW//79de6556px48bav3+/PvjgA/3nP//xmVQzENOmTdMNN9ygs846S1deeaViYmL00Ucf6euvv9bw4cO1atWqMF1R1c2YMUNvvfWWXn/9dfXs2VNDhgxRQUGBli9frgMHDujOO+/0SqrWpUsXPfHEE57ruOyyy3T66afr8OHD+uSTT5SYmOj5FSOQfWNiYnTrrbfqgQce0FlnnaUrrrhCZWVlWrt2rdLS0pSWlub3GoYPH64rrrhCV1xxhTp27Kht27bprbfeUnJysp544gnPfq1bt9a4ceP0yiuvqFevXrrkkkuUm5urtWvXKi4uTr169dK2bdu8jn3GGWeoTZs2euWVVxQTE6N27drJ4XDommuu8QRfTta4cWP97W9/0+jRo3XBBRdo9OjRatu2rT799FP985//VOvWrfX0008H+5ABABqI2bNne/7/+PHj+vrrrz29M+fOneuVFyLQz/Ng3H777fr73/+uFStW6Oyzz9agQYOUk5OjZcuWacCAAXrjjTeqfKyrrrpKr776qt544w1169ZNl112mUpLS5WRkaHevXv77K1SE6699lp9+umneuKJJ3Taaadp0KBBatu2rY4cOaLvv/9eH3zwgSZPnqynnnoqqOMXFhbq/PPPV8eOHXXOOeeoXbt2Kioq0tq1a7Vjxw6NGDGiUo/jk1X3Y71nzx6dddZZ6t69u3r06KH09HTl5eVp9erV2rdvn2655ZZKPXKBOqHW5tUB6rCvv/7amjp1qtW1a1erSZMmVkxMjNW6dWtr8ODB1rPPPus1VVvFdGr+prq74IILLF8vxUWLFlk9e/a0EhISrObNm1uXX3659eWXX9pOa1fVKfCqUi9fCgsLrT/84Q9W165drbi4OKtx48bWz372M+ull17yW2bjxo3WyJEjrZSUFCsmJsZKTU21Bg0aZC1fvjzofcvLy6158+ZZp556qhUTE2Olp6dbd9xxh3X8+HHbKYAXLVpkrVq1yjrvvPOshIQEKykpyRo5cqT173//u1Jdjh8/bt1zzz3WaaedZrlcLuuUU06xbrzxRuvQoUN+H7MtW7ZYv/jFL6zExETL4XB43XtfUwD/tNzll19utWjRwnM9N9xwg7Vnz55K+1ZMAfz9999X2maahhgAUL/Ix9S/TqfTat26tTVixAjrn//8p89ygXyeB9uOyc3NtaZNm2alpaVZLpfLOuOMM6wFCxZY3377bcDtj+LiYmvOnDlWhw4drNjYWKtdu3bWPffcYxUVFQU8BfDJ+1bw1X4wXaNlWdaqVausoUOHetourVq1snr37m3de++9labetTv/yZ/vJSUl1sMPP2wNHjzYSk9Pt1wul9WiRQurb9++1pNPPmkVFxdXqf7V+VgfPXrUmjNnjnXRRRdZaWlpVmxsrNW6dWvrggsusF566SWmBUad5bAsy6qZcAwA1I7Fixdr8uTJWrRokSZNmlTb1QEAAAAQochJAgAAAAAAIIIkEWv79u0aPXq0Tj31VCUkJKhFixYaMGCAz1wUO3bs0ODBg9W4cWMlJyfrmmuu0cGDB2uh1gCAmpSfn69Zs2Zp8ODBSk5OlsPh0OLFi6tcPicnR1OmTFFKSooaNWqkiy66qFqns0TdRJsEAGBS1c+KSZMmyeFwVFo6d+5cSzWvjMStEeqHH37QsWPHNHHiRKWlpamgoEArVqzQiBEj9PTTT2vKlCmSTsz6MWDAACUlJWnu3LnKz8/XggUL9NVXX2nLli1klAaAeuzQoUO6//771bZtW/Xs2TOg6TDLy8s1dOhQffHFF7rjjjvUokULPfHEE7rwwgv16aef6vTTT6++iqNOoU0CADCp6meFJLlcLj377LNe5ZOSkmq6yn6Rk6QOcbvdOuecc1RUVKSdO3dKkm688UYtXrxYO3fu9EwTum7dOl188cWVnowAgPqluLhYR48eVevWrbV161b17t27yrl3li1bprFjx2r58uUaNWqUpBPTOXbq1EmXXnqpXnrppWquPeoy2iQAABNfnxWTJk1SRkaG8vPza7l2/jHcpg5xOp1KT0/3mot9xYoVGjZsmKcxIkkDBw5Up06dtGzZslqoJQCgprhcLrVu3TqoshkZGWrVqpVGjhzpWZeSkqIxY8bo9ddfV3FxcbiqiXqINgkAwMTXZ0UFt9utvLy8mq9UFRAkiXDHjx/XoUOH9O233+pPf/qT3nrrLf3yl7+UdGJu8gMHDujcc8+tVK5Pnz76/PPPa7q6AIA64vPPP9fZZ5+tqCjvpkCfPn1UUFCgzMzMWqoZIhVtEgCAid1nRYWCggIlJiYqKSlJycnJuummmyKqZwk5SSLc7bffrqefflqSFBUVpZEjR2rhwoWSpOzsbElSampqpXKpqak6cuSIiouL5XK5Km0vLi72+pWwvLxcR44cUfPmzeVwOKrjUgCg1lmWpWPHjiktLa1ScCAcioqKVFJSEnR5y7IqvQe7XC6f7+Ohys7O1oABAyqtr/hM2bt3r7p37x7286Luqq42iUS7BEDDVB/bJXafFdKJz4Q777xTZ599tsrLy/X222/riSee0BdffKH169crOrr2QxS1XwPYuu222zRq1Cjt3btXy5Ytk9vt9jzRCwsLJcnnkzQuLs6zj6/t8+bN05w5c6qx5gAQubKysnTKKaeE9ZhFRUWKb9JcKisI+hiNGzeu9EvKrFmzNHv27BBrV5m/z4effn4AP1VdbRKJdgmAhq262iUdOnTQvn37gj5GMO0Su88K6cT7/U+NGzdOnTp10r333quMjAyNGzcu6PqGC0GSCNe5c2fPdEgTJkzQJZdcouHDh2vz5s2Kj4+XJJ/jxouKiiTJs8/JZs6cqenTp3v+zs3NVdu2bTVm4VrFxDfyWebOi06zrevmHw/bbu/Ryn/G4ijDr0TlhvzC8S77p3JslP3xs3OKbLc3T7T/FbewuMx2e3rzBL/bvj9w3Lbstv1HbbeP7JFuu/1ovn30uGmjGNvtoT420U7/UfGiUrdt2bgYp+12E3e5fd2chudFuaH8oXz7nA3JjYKfycHuvknmxzXBZX/vikvLbbeb7k1Bif1zvmmC/2s3/SicV1Bquz0m2v7eNIn3/5w+lpenjh3S1aRJE/tKBKGkpEQqK5Cr62TJGcRj7y5R/vZFysrKUmJiomd1dfQikU58PgTz+YGGq7raJJL/dklr+R4bnmyoazPDdjstbLYditCywR431GOHwr51AwQnlNe+nVCer0dstpVL2idVW7tk3759ysr63qtdUVV5eXlKT+8QcLvE7rPCX8/AadOm6b777tO6desIkiBwo0aN0vXXX6/MzExPl9aKLq4/lZ2dreTkZL9PYn/dpGLiGyk2obHPMk2a2L+44hvbf2lrbFM+1C/iCaYgidP++Mfc9l9omjSxfzNwxtp/YUxM9B8kaVxo/2U2/rjp2PaPS1mU/eOSWItBktg6HiQpdtgHSRIbV1+QxPS4NjIESYpCDJI4DYHBRJsAkSlIYkWHFiRJtAmS/K8O1dh9PzpWDmfggQ3rv1VKTEwMqjETqNTUVL+fH5KUlpZW7XVA3RauNonkv10SJd9BEtOnQygNXLt3ENNxa6tssMcN9dihCO0THvCtup7PoTxfqzKIpjrbJYmJCbbfRfwr+2/50NolP/2sOOOMM3zuEx8fr+bNm+vIEbuQUs0hcWsdU9GdNTc3V23atFFKSoq2bt1aab8tW7aoV69eNVw7AIAcUcEvNahXr1767LPPVF7uHTDbvHmzEhIS1KlTpxqtD+oe2iQAUBeUhbCE7qefFf4cO3ZMhw4dUkpKSljOGSqCJBHqwIEDldaVlpbq+eefV3x8vLp06SJJuvLKK7V69WplZWV59nvnnXeUmZmp0aNH11h9AQD/5XAEv1ST7Oxs7dy5U6Wl/+ulM2rUKO3fv18rV670rDt06JCWL1+u4cOHV9swH9Q9tEkAoC6rmSBJVT4rioqKdOzYsUr7PfDAA7IsS4MHDw7onNWF4TYR6vrrr1deXp4GDBigNm3aaN++fVq6dKl27typRx55RI0bnxgSc88992j58uW66KKLdOuttyo/P1/z589X9+7dNXny5IDPa9dOjzZ0vzdsVqnb/9CF+Fj7eF18tH0nt1K3/dABd7l95Vwx9uePNXTv33vUfnhAG5thG4kJ9h1hD+Tbv0GZvlYlxof2MjcOjbC/9bachoNbhqE8tT3jgen8dkOVTMOUTGIMQ8hMRzed32l4Ztm9niUpyuYNwfS4mp50puJ2w6RMQ6jqooULFyonJ0d79+6VJK1atUq7d++WJN18881KSkrSzJkztWTJEn3//fdq3769pBNBkvPOO0+TJ0/W119/rRYtWuiJJ56Q2+0mgSa81FabBADCyTSQw5TrCPaq8lmxa9cunXXWWbrqqqs8eUvWrFmjN998U4MHD9Zll11Wy1dxAkGSCDV27Fg999xzevLJJ3X48GE1adJE55xzjh5++GGNGDHCs196erref/99TZ8+XXfffbdiY2M1dOhQPfLII/wKCAC1IdihM0EOt1mwYIF++OEHz98rV6709A4ZP368kpJ8J812Op168803dccdd+jPf/6zCgsL1bt3by1evNjvmGE0TLRJAKAucyu4oTP2uQNPVpXPiqZNm2rYsGFau3atlixZIrfbrY4dO2ru3LmaMWNGtUyDHAyHZfxJDw1BXl6ekpKSNP5vG/0mbp19sf349A93HbTd3qWF/9lt4mPte4q4DD05TD1JTL/4FxiSUDY3JG7dddB+hpqup/hPdnT0uH0vlFe/3GO7/ZbzT7Xdbro3xgSdhu1lhh4Fdkk2S8vs6xZt6C1helyrf3Yb++SpLWwSt4aS8FaSjhWGlty0sMT+gy/GcP5cwww0rZvG+d1m+tjJLbR/PZp6tTWJ8x//z8vLU2pKU+Xm5oY9OWrF+6jrnJuDS9zqLlbxp/9XLXUD6pqK11OafI8Nb24oH8ovwnYj4u1bOrVXNtjjhnrsUERGekbUN3av/VB6koTyfLWb/7Nc0l6pWtslubmZSkwMfPacvLxjSkrq1CDbJfQkAQAgrIJNwhoZv54AAID6JNgkrOFJ3FoXESQBACCcgk3CWss5dgAAQH1EkCRQBEngxRnl8DsEwTQuq6DUvvt+mc2wj+go+6eiaTjO8WP2L2LTsAvT8eMMQxcOFxXbbi8o9n9vGrvsz93IkNTW9LiYks6ahsuYhrQ4HMGP2DMNdzEm6AxxtGCoSTxNwz5sb53h1KbnrGk4TqgDKU1f1031C+HSZRhlZTy33fMi1OcMAOB/7IYgkAQTQF1FkAQAgHCq4cStAAAA/rkVaBLW/5VrmAiSAAAQTgy3AQAAEaNmZrepTwiSAAAQTvQkAQAAEYOcJIEiSAIAQDjRkwQAAEQMgiSB4mcrAAAAAAAA0ZMEAbCbnUaSDh23jzbaTUgRY5jOwmWYoaWo1L5ulmFGi6SEGNvtphliSg2zbRw9XuJ3W3rzBNuybRPjbbfXNtPsN3b3PsowO4xp9hnT42qqm2mmE9PsO6bnhR1j3Uyzxxg6HRjL2xc3PzbGe29T1v7larzvZWX2ByizuXa7bWHDcBsAsJ35BqguPO98oSdJoAiSAAAQTg5HkEEShtsAAIBwI3FroAiSAAAQTlGOE0sw5QAAAMKKniSBIkgCAEA4MdwGAABEDIIkgSJIAgBAODG7DQAAiBgESQJFkAReyi3/CVZLDMkS9+eX2m532yRMNCWJNDEllTUlgjTkoDSWj3c6bbdn5xX53dauhSFxa5L9dmPy0ijTr9OhJbI0PXR21TN9JzTVzO45JZkTAptyeJqSq0Ybjh8Kt+lxNZQ31d30mgt15Idd9UNNmGt63OwOb3qtAwAAoGEjSAIAQDgx3AYAAEQMepIEiiAJAADhxHAbAAAQMZjdJlAESQAACCd6kgAAgIhBT5JAESQBACCc6EkCAAAiBkGSQBEkgZfiUresEt9dq0yJW3ML7BO3ltgkVy03ZGK0K1sVMU77X2iLSkPrTpYcF2u7/atDuX639XM0ty3bookrqDpVVajfy0wJQu0Sy5qSzprUdhJO0/PKlFjWjuk1YUpuanpcTeVD3W6XnNX0uIWayLk6n3MAAACo3wiSAAAQTgy3AQAAEYOeJIEiSAIAQDgx3AYAAEQMgiSBIkgCAEBYBdmTRPQkAQAA4cbsNoEiSAIAQDjRkwQAAEQMt4ILeBAkASRJ+UWlinH4TsBa6rZPeFhmSK5a7Pb/QissDS0xa2y0/S+wcbFO2+3HCu2TztoloZSkxIQY2+37jtkf306S4dihigoh8apUhcStNttMyUmNdbPdav7OGep3UlPy0pIy/zUMMTep8dqdposzdFowPa6mpLV2D63p9RRtOLfTdGk25U3PKQAAADRsBEkAAAgnhyPIxK0EcAAAQLiRkyRQBEkAAAgnZrcBAAARgyBJoAiSAAAQTuQkAQAAEYPErYEiSAIAQDjRkwQAAEQMepIEiiAJvBwrLFW0VeJzmymBZ7QhkWNhmf9o5PEi+xehZdknXo2Lsd8eb0jcujen0HZ7SZl9YtnEePuXkuHW2DIlpTUlzDWJMmQQNSVXNacQtTm2oagpuanpd3dT8lHT8d2GCpoSt9rduyhD9lFT3U233fR922m8e/ZiDPW3Tc5qqrvh2k3vNXbPadPzPSzoSQIgghw0bD9isy05nBUBIpzda6FuI0gSKH62AgAAAAAAED1JAAAIL4bbAACAiEFPkkDRIgMAIJwqhtsEswAAAIRVWQhL1W3fvl2jR4/WqaeeqoSEBLVo0UIDBgzQqlWrKu27Y8cODR48WI0bN1ZycrKuueYaHTxoGhxYc+hJAgBAGDkcDnNOGd8Fw18ZAADQwNXM7DY//PCDjh07pokTJyotLU0FBQVasWKFRowYoaefflpTpkyRJO3evVsDBgxQUlKS5s6dq/z8fC1YsEBfffWVtmzZotjY2CDqGl4ESeClqKhM0X5eRKYklabkqCXl/hOM5haVmitnIykhxnZ7XIx9p6mCMvs3jvxi+zeJRi77a2+T6P/FbkqIa0pPavoyZjq+sbztVslUfbuj2yb3VBUSr4aYhNNUvtSQsNd0ervrsyzT42p/bNMDE2U4QLnhAKbnTYwhoXAIeVuNKWWjDUlj7R6XmsnbSpAEQP1QfxNZAg1JmST77yr+y1XdkCFDNGTIEK91U6dO1TnnnKNHH33UEySZO3eujh8/rk8//VRt27aVJPXp00cXX3yxFi9e7NmvNjHcBgAAAAAAhJXT6VR6erpycnI861asWKFhw4Z5AiSSNHDgQHXq1EnLli2rhVpWRk8SAADCySFzdxh/5QAAAMKqZnqSVDh+/LgKCwuVm5urN954Q2+99ZbGjh0rSdqzZ48OHDigc889t1K5Pn366M033wzqnOFGkAQAgDBiuA0AAIgcoQVJ8vLyvNa6XC65XC6/pW6//XY9/fTTkqSoqCiNHDlSCxculCRlZ2dLklJTUyuVS01N1ZEjR1RcXGx7/JrAcBsAAMKoIkgSzAIAABBeFYlbA11O5GRMT09XUlKSZ5k3b57t2W677TatXbtWS5Ys0aWXXiq3262SkhJJUmFhoST5DILExcV57VOb6EkCL+6ycslPssq4GPsIZNN4+6dTqU3i1sNFxbZlY6Ls43lNG9knbo112pd3Ouy35xwvsd3eLKGR7fZ2if63h/rFyJSI0phYNcTvZe5y+xNEh5Ap03RvDA+ryg11MyVuNV2bKZmxXf1NyUudhmt3G49gOL6h7mVuQ+JWw823S/xqTiZsu9n4nLK77zURiKAnCQAAiBxlCq5vxImeJFlZWUpMTPSsNfXy6Ny5szp37ixJmjBhgi655BINHz5cmzdvVnx8vCSpuLjyd7+ioiJJ8uxTm+hJAgAAAAAAKklMTPRaAh0KM2rUKH3yySfKzMz0DLOpGHbzU9nZ2UpOTq71oTYSPUkAAAgrepIAAIDIEVpPklBVDJ/Jzc3VGWecoZSUFG3durXSflu2bFGvXr3Ccs5Q0ZMEAIBwcoSwAAAAhFUw+Ugqlqo7cOBApXWlpaV6/vnnFR8fry5dukiSrrzySq1evVpZWVme/d555x1lZmZq9OjRgV5ctaAnCQAAYURPEgAAEDncqkjCGni5qrv++uuVl5enAQMGqE2bNtq3b5+WLl2qnTt36pFHHlHjxo0lSffcc4+WL1+uiy66SLfeeqvy8/M1f/58de/eXZMnTw6inuFHkAQAgDByOIJMEEuMBAAAhF3F7DbBlKu6sWPH6rnnntOTTz6pw4cPq0mTJjrnnHP08MMPa8SIEZ790tPT9f7772v69Om6++67FRsbq6FDh+qRRx6JiHwkEkESnCTK6ZDT6bulnuAyzW5jv91uppCc0lLbsomx9rPXmGbiMH1fSYy1fykcNsxuc1qrxrbbWzWJs69ACExfxsoNM4mYZoAxfW8zHd92phHDsU1ME+eYZqdxGCaIMd67EGdpsWOaecfyP1nUie2m4xufN/YnMM0w42eSrP8eO/jnjGSe1ciuaiFMtlRlDgU7nS9REgAAEG5lCq6NEVhgZdy4cRo3blyV9u3atavWrFkTRJ1qBjlJAAAAAAAARE8SAADCipwkAAAgctRMT5L6hCAJAADhFOxMNcRIAABA2BEkCRRBEgAAwinIniQWPUkAAEDYESQJFEESeImNjVa0nySm8bH2iVmbGBK72skvts+eXJRgv72wxH67KbFr4zj7l8K+I/m2202SG8f63WZKJGmFmOTSxJRE05Tg05Qh1K5+TlNy0hCv3V1un3zUeG0GhurJGcLxTSVNzxtTQl7jvTed31A+yubmGBO3Gs5tetxtkwXXQCAi2OE2wZQpLi7W73//e73wwgs6evSoevTooQcffFAXX3yxsey6dev0hz/8QV999ZXKysrUqVMn3XzzzbrmmmsCrgcAAIhUbgUXJAlm2uD6gcStAADUUZMmTdKjjz6qq6++Wo8//ricTqeGDBmiDRs22JZ74403dMkll6ikpESzZ8/WH/7wB8XHx2vChAn605/+VEO1BwAAiDz0JAEAIIxqqifJli1b9Morr2j+/PmaMWOGJGnChAnq1q2b7rzzTm3cuNFv2YULFyo1NVXvvvuuXC6XJOn6669X586dtXjxYk2bNi3g+gMAgEgU7LCZhjvchp4kAACEkyOEJQAZGRlyOp2aMmWKZ11cXJyuu+46bdq0SVlZWX7L5uXlqVmzZp4AiSRFR0erRYsWio+PD6wiAAAggpWFsDRMBEkAAAijip4kwSyB+Pzzz9WpUyclJiZ6re/Tp48kadu2bX7LXnjhhdq+fbvuu+8+ffPNN/r222/1wAMPaOvWrbrzzjsDvmYAABCpCJIEiuE28BIX41S0nwStMU77mFqs076BH22T6LGozD6RY6khCWV+sf2LuLDUPvFQk/gY2+2mBJymrzaNbJLamr4YmRJwmlNs2nObEnxGG64uhDyYpsSpprpFG55zplsXatJaY3LTEO6N6dihJq0NlTmxrP89TI+rKVZgfEnUslCH2+Tl5Xmtd7lcXj0+KmRnZys1NbXS+op1e/fu9Xuu++67T99//73+8Ic/6MEHH5QkJSQkaMWKFbrssssCrjsAAIhUwSZgJXErAACIAOnp6UpKSvIs8+bN87lfYWGhz+BJXFycZ7s/LpdLnTp10qhRo/Tyyy/rxRdf1Lnnnqvx48fr448/Ds+FAAAA1EH0JAEAIIxC7UmSlZXlNYTGVyBEkuLj41VcXFxpfVFRkWe7P1OnTtXHH3+szz77TFFRJ34vGTNmjLp27apbb71VmzdvDrj+AAAgEpUpuJ7n9CQBAABhEGpOksTERK/FX5AkNTVV2dnZldZXrEtLS/NZrqSkRM8995yGDh3qCZBIUkxMjC699FJt3bpVJSUlod4GAAAQEchJEiiCJAAAhFMNzW7Tq1cvZWZmVsphUtELpFevXj7LHT58WGVlZXK7K/9CVFpaqvLycp/bAABAXUSQJFAMt4GX6GiHYqJ9x84sQ5JLU3LTmCj/MblSt/2xTQk280pKbbfnF9m/yJs1irXdnhLv+5fcqnKGkMHTmLbVsIPpzKEmCDU97naiDPelzJDQ15TU1vSctSz785vqZ2KXvNT0nDa+3gx1Mz0vTMcP9Xljd+9Mj5sxmbHbPiltKK+3cAh1uE1VjRo1SgsWLNAzzzyjGTNmSJKKi4u1aNEi9e3bV+np6ZKkH3/8UQUFBercubMkqWXLlmratKlee+013X///YqNPfH+l5+fr1WrVqlz585MAwzUMUdstiXXWC0ARCaG2wSKIAkAAGFUU0GSvn37avTo0Zo5c6YOHDigjh07asmSJdq1a5eee+45z34TJkzQ+++/7wmMOZ1OzZgxQ7/73e903nnnacKECXK73Xruuee0e/duvfjiiwHXHQAARCq3gguSVO9MiZGMIAkAAHXU888/r/vuu08vvPCCjh49qh49emj16tUaMGCAbbl7771XHTp00OOPP645c+aouLhYPXr0UEZGhq688soaqj0AAEDkIUgCAEAY1VRPEunEdL/z58/X/Pnz/e6zfv16n+t/9atf6Ve/+lXA5wQAAHUJPUkCRZAEAIBwCiIJq6ccAABAWJUpuPlaCJIAkk4kPPSX9NCUXNXE5XT63WZKYuk2bC8ss08slFtgn9i1ddM42+3Jje0Tu5p+ALarfgh5TyWZk2BGO6v3m5cpuald/UIpK0nlhksz/TJvTD4awuNqOr5dUldJKjUkJ412hjY5manupvqF8rwL8a3E+H4QE9rhQ1aTPUkAwMQuqSuAhoAgSaCYAriafPLJJ5o6daq6du2qRo0aqW3bthozZowyMzM9+5SXl2vx4sUaMWKE0tPT1ahRI3Xr1k0PPvigioqKqnSeCy+80NMg/+kyePDg6ro0AIANX+/JVV2A6kK7BAAaKqYADhQ9SarJww8/rI8++kijR49Wjx49tG/fPi1cuFBnn322Pv74Y3Xr1k0FBQWaPHmyzjvvPN1www1q2bKlNm3apFmzZumdd97Ru+++W6VG8ymnnKJ58+Z5rUtLS6uuSwMAAHUM7RIAAKqGIEk1mT59ul566SXFxv5vmMbYsWPVvXt3PfTQQ3rxxRcVGxurjz76SP379/fs85vf/Ebt27f3NEgGDhxoPFdSUpLGjx9fLdcBAAiMQ0EOtyEpCaoR7RIAaKjcCm7oTIjjo+swhttUk/79+3s1RCTp9NNPV9euXbVjxw5JUmxsrFdDpMIVV1whSZ79qqKsrEz5+fkh1BgAEA4Mt0Ekol0CAA0Vw20CRU+SGmRZlvbv36+uXbva7rdv3z5JUosWLap03MzMTDVq1EglJSVq1aqVfvOb3+j3v/+9YmL8py8sLi5WcXGx5++8vDzjeYpL7ZOjlhoSOcZH+0/cahJqYtcjRSW222MMSTCbJtingjR9ubGrvykBpulrk+neOBz212ZK0GniL9FvhdIy/5Fr06lN12ZZ9gcwJ161P35UlP29KzdE2O2Ob0paa0qsahTivTXVz+5xlaSYaP/3znTfTUyvmVrH7DaoI+p6uwQAUBVlCq6REeHtrWpEkKQGLV26VHv27NH9999vu98f//hHJSYm6tJLLzUe87TTTtNFF12k7t276/jx48rIyNCDDz6ozMxMvfrqq37LzZs3T3PmzAn4GgAA9pjdBnUF7RIAaAgIkgSKIEkN2blzp2666Sb169dPEydO9Lvf3LlztW7dOj3xxBNq2rSp8bjPPfec19/XXHONpkyZor/+9a+aNm2azjvvPJ/lZs6cqenTp3v+zsvLU3p6etUuBgAA1Gm0SwAA8I2cJDVg3759Gjp0qJKSkpSRkSGn0/ewk1dffVW/+93vdN111+m3v/1t0Oe7/fbbJUnr1q3zu4/L5VJiYqLXAgAIHTlJEOlolwBAA2KVS5Y7iCWYZK/1Az1Jqllubq4uvfRS5eTk6MMPP/Q7Bd7atWs1YcIEDR06VE899VRI56z45eXIkSMhHQcAEDiHw5xvx185oLrRLgGABqZcwU1u03BjJARJqlNRUZGGDx+uzMxMrVu3Tl26dPG53+bNm3XFFVfo3HPP1bJlyxQdHdrD8t1330mSUlJSQjoOACBwJ4IkweQkqYbKAD9BuwQAGiD3f5dgyjVQBEmqidvt1tixY7Vp0ya9/vrr6tevn8/9duzYoaFDh6p9+/ZavXq14uPj/R5z586dSkhIUNu2bSWdGK/rcrnkcrk8+1iWpQcffFCSNGjQoDBekVTitk/eUxbC7DamGVZMx3Y57csXlNm/yk2zoCS4qu+lYpxlxHBvQk2pFOoMMKYvg3YzD0UZZqcxMV27aeadUCdJMV17mdt/CD7aMKOSqWrGmXlCfE3FGl5TpTbXJklx8v96D3XmnpBn/qluQfYkYXYbVKf62C6pbw7WdgUA1E8ESQJGkKSa3H777XrjjTc0fPhwHTlyRC+++KLX9vHjx+vYsWMaNGiQjh49qjvuuEP/+Mc/vPY57bTTvBoxZ555pi644AKtX79ekvTZZ5/pqquu0lVXXaWOHTuqsLBQr732mj766CNNmTJFZ599drVfJwDAG7PbIBLRLgGABqqGhtt88sknWrJkid577z3t2rVLzZs313nnnacHH3xQnTp18uw3adIkLVmypFL5M844Qzt37gyiouFHkKSabNu2TZK0atUqrVq1qtL28ePH6/Dhw8rKypIk3X333ZX2mThxot9feiSpXbt2+vnPf67XXntN+/btU1RUlM4880w99dRTmjJlSnguBAAA1Hm0SwAA1enhhx/WRx99pNGjR6tHjx7at2+fFi5cqLPPPlsff/yxunXr5tnX5XLp2Wef9SqflJRU01X2iyBJNan4VcVO+/btjV3mf+rkfTt06KBly5YFWjUAQDUicSsiEe0SAGigami4zfTp0/XSSy8pNjbWs27s2LHq3r27HnroIa8ejNHR0Ro/fnwQlaoZBEkAAAijqCiHokzJfnywgigDAABgq4aG2/Tv37/SutNPP11du3bVjh07Km1zu906fvx4RE75TpAEXqIcDr8JH92GRI/FZaZEkP4TVcYYkkSWGJJENnPZJ8F0hzjPtykBaCC/vFUua7/dYX9pIed6NF2b6XGPNjx25TblrShD8tEQ77sjyv7mRRnSo5qS6pqfF7abbZkeV9PjYvq+XWp4vUbFhJb41U597zFBTxIAwbCbIDm5xmoBoN4pV3A9ScIwBbBlWdq/f7+6du3qtb6goECJiYkqKChQs2bNdNVVV+nhhx9W48aNQz9pGBAkAQAgjEjcCgAAIkaIw23y8vK8Vp88i5mdpUuXas+ePbr//vs961JTU3XnnXfq7LPPVnl5ud5++2098cQT+uKLL7R+/fqQp50Ph9qvAQAAAAAAiDjp6elef8+aNUuzZ882ltu5c6duuukm9evXTxMnTvSsnzdvntd+48aNU6dOnXTvvfcqIyND48aNC0u9Q2HoyA8AAAJRMdwmmAUAACCsykNYJGVlZSk3N9ezzJw503jKffv2aejQoUpKSlJGRoacTqft/tOmTVNUVJTWrVsX3DWGGT1JAAAII4bbAACAiBHicJvExMSAkqvm5ubq0ksvVU5Ojj788EOlpaUZy8THx6t58+Y6csQuO1PNIUgCL9HOKEXbJFi1U1Rmn93HLsFno1j7c5qSRMaYEnSGkECzKkw5LO2++piSgzoMmVtDTSpr+mLmLrd/XKMcwd9c031zGupmvHemChiv3ZC01lDerrT5cbHdbKxbjOF1HEri1aqc347pOWsU4bEEgiQAACBi1NAUwJJUVFSk4cOHKzMzU+vWrVOXLl2qVO7YsWM6dOiQUlJSAj9pNSBIAgBAGDG7DQAAiBg1NAWw2+3W2LFjtWnTJr3++uvq169fpX2KiopUWlqqJk2aeK1/4IEHZFmWBg8eHERFw48gCQAAYeRQkD1JIr2LDAAAqHtqqCfJ7bffrjfeeEPDhw/XkSNH9OKLL3ptHz9+vPbt26ezzjpLV111lTp37ixJWrNmjd58800NHjxYl112WRAVDT+CJAAAAAAAIGjbtm2TJK1atUqrVq2qtH38+PFq2rSphg0bprVr12rJkiVyu93q2LGj5s6dqxkzZijKkEKhphAkAQAgjBhuAwAAIoal4IbbBJh+bv369cZ9mjZtqhdeeCGIytQsgiTwEu10+E2wasq1WFRq/+qzS9aYFGf/VCxxm45tu1nl7ur99mFKwhkVaqJKG6Zu/ebkqPbbTeVNyVOjbOoX6n2LMgxPML23mx6W0hCTm9oxPi6GypUYEiXHGt7dywyvKRPDQ2cr1MStds+pSEDiVgAAEDFqMHFrfUGQBACAMKInCQAAiBgESQJGkAQAgDCiJwkAAIgYNTS7TX0SGZlRAAAAAAAAahk9SQAACCOG2wAAgIjBcJuAESSBF7tu4qZki6EkuWzqirHdXlBm/yp1GBJ4lln25U1JKE3fXUyXbteNPtQklMbHxZDg05RD05RctdwyJFe1OYExcaspKa0hNWu54YFxRtt3pjMlpTXV3676blPdTI+r2758guHeGYpXq9ATt4apItWE4TYAACBiECQJGEESAADCKcieJMZoLAAAQKDISRIwgiQAAIQRPUkAAEDEKFdwvUIacJCExK0AAAAAAACiJwkAAGFF4lYAABAxGG4TMIIk8FJaVi75SfQZaoJRu0SVyS6XbdlGMfZ9xEwJNkvc9q9yUw5LY3JTQ3k7pttqSj5qlxhVMicINZ3fNATAlLzULklnuSEJg/lxsS9vetzt0wWbmRL2Om3qV2qqm9Npu70shETJkszZig1Mzzs70SEnbo3saALDbQAAQMQgcWvACJIAABBG9CQBAAARgyBJwAiSAAAQRvQkAQAAEYPhNgEjSAIAQBgRJAEAABGDniQBY3YbAAAAAAAA0ZMEAICwIicJAACIGPQkCRhBEng5XlSmaEdpUGWjTTON+Jk1R5Iauexn8mjssH+q7jtWZLu9yDS7jWmmD8O1hfLdxm72F6kKs9MYqm6a+ceyDNcW4sw+9jORmOpmmDnHad8ZznzthpmDDBdvemzsHtvSEvuyCSGe2yjEb+ShzFATbXjcTCI9mMBwGwDhdqS2KwCg7rIUXH6REJuadRlBEgAAwoieJAAAIGLQkyRgBEkAAAgjepIAAICIwew2ASNxKwAAAAAAgOhJAgBAWDkU5HCbsNcEAAA0eAy3CRhBEnjJLyxVtOU7caspd0+0076Jn19a5ndbeqME27Ix0fadnvbmFdpuLy0Prb+YqRu8w5A91S5BqOnYbkPdQ+2ib0puakrQGUoCUVNi1BJDwt1o+3y/xuesqepRIV57jM1roizExKvGZMMGIeRdlWROOFyd5zY9b2pblMMRVB0j/boAAEAdRJAkYAy3AQAgjCoStwazBKq4uFh33XWX0tLSFB8fr759+2rt2rVVLv/qq6+qX79+atSokZo2bar+/fvr3XffDbwiAAAgMpWHsDRQBEkAAAijisStwSyBmjRpkh599FFdffXVevzxx+V0OjVkyBBt2LDBWHb27Nm66qqrlJ6erkcffVQPPvigevTooT179gRz2QAAIBK5Q1gaKIbbAABQB23ZskWvvPKK5s+frxkzZkiSJkyYoG7duunOO+/Uxo0b/Zb9+OOPdf/99+uRRx7RtGnTaqrKAAAAEY+eJAAAhFGUI/glEBkZGXI6nZoyZYpnXVxcnK677jpt2rRJWVlZfss+9thjat26tW699VZZlqX8/PxgLxcAAESycgXXi6QBD7ehJwm8FBaUylnuJ3FriAk+j9skbo2Ltc/AGRdjH8+zDCk6qzsaaEq4aJej05Dv1phc1PS4mOpmSv9pGgJg+mJnlxjWlPyz3HDxoV676fimaysyJJaNt3leh5LwVgp9JpRQEq+GWj7Uc0d8flNHkAmVAyzy+eefq1OnTkpMTPRa36dPH0nStm3blJ6e7rPsO++8o/79++vPf/6zHnzwQR0+fFitW7fWvffeq6lTpwZedwAAEJmCzS9CkAQAAIRDsElYK8rk5eV5rXe5XHK5XJX2z87OVmpqaqX1Fev27t3r8zxHjx7VoUOH9NFHH+ndd9/VrFmz1LZtWy1atEg333yzYmJidP311wd+AQAAIPIwu03AGG4DAEAYOUL4J0np6elKSkryLPPmzfN5nsLCQp/Bk7i4OM92XyqG1hw+fFjPPvusZsyYoTFjxugf//iHunTpogcffDActwEAAEQCZrcJGD1JAAAIo2Dyi1SUk6SsrCyvITS+AiGSFB8fr+Li4krri4qKPNv9lZOkmJgYjRo16n/nj4rS2LFjNWvWLP34449q27Zt4BcBAAAiCz1JAkaQBACACJKYmFgpz4gvqampPqfrzc7OliSlpaX5LJecnKy4uDg1bdpUTqd33pyWLVtKOjEkhyAJAABoiBhuAy/u8nK53X6Wcst2MSkrt/wusU6H7eJ02C+mf41iom2XUEVFOWyX8nLL72JZoS2mx8VUNxOHYTEd365uDofDdrEk+8WyX0wzidg9J8vKLUU5HLZLqduyXezYPSdMCWWr8pwziY5y2C7G84cwS4vpcTcJtXx1M9UvXHXv1auXMjMzK+Uw2bx5s2e7L1FRUerVq5cOHjyokpISr20VeUxSUlICqgsAAIhQwcxsE0Tvk08++URTp05V165d1ahRI7Vt21ZjxoxRZmZmpX137NihwYMHq3HjxkpOTtY111yjgwcPBn2J4UaQBACAMKpI3BrMEohRo0bJ7XbrmWee8awrLi7WokWL1LdvX8/MNj/++KN27tzpVXbs2LFyu91asmSJZ11RUZGWLl2qLl26+O2FAgAA6pgaykny8MMPa8WKFfrlL3+pxx9/XFOmTNEHH3ygs88+W//61788++3evVsDBgzQN998o7lz52rGjBn6xz/+oYsvvrjSjze1heE2AACEUUVPo2DKBaJv374aPXq0Zs6cqQMHDqhjx45asmSJdu3apeeee86z34QJE/T+++97TZl9/fXX69lnn9VNN92kzMxMtW3bVi+88IJ++OEHrVq1KuC6AwCACFWu4PKLBBgkmT59ul566SXFxsZ61o0dO1bdu3fXQw89pBdffFGSNHfuXB0/flyffvqpZ2hvnz59dPHFF2vx4sWaMmVKEJUNL3qSAAAQRjXVk0SSnn/+ed1222164YUXdMstt6i0tFSrV6/WgAEDbMvFx8fr3Xff1a9+9Sv97W9/0x133KGoqCj94x//0KWXXhrklQMAgIhTQz1J+vfv7xUgkaTTTz9dXbt21Y4dOzzrVqxYoWHDhnnlPhs4cKA6deqkZcuWBXhx1YOeJAAA1FFxcXGaP3++5s+f73ef9evX+1zfsmVLLV68uHoqBgAAGjzLsrR//3517dpVkrRnzx4dOHBA5557bqV9+/TpozfffLOmq+gTQRJ4iXL4T/poyiVp+hU07qRZFAJRXGYfyox12neKckXH2m43JUw0JdI0Jcp0W/7LOwxRWlPdbA4tSTLcGp1Iv2pzfENpU5LOqiT19cf0w3q54eKdhsoVl9j3PYyLsb95bnfwE8iHml/UdG2RXD7EU0e8YBPIRkLSWQAAUM+EOAXwyQniXS6XXC5XlQ6xdOlS7dmzR/fff7+k/83Al5qaWmnf1NRUHTlyRMXFxVU+fnVhuA0AAGFUk8NtAAAAbIU4u016erqSkpI8y7x586p02p07d+qmm25Sv379NHHiRElSYWGhJPkMgsTFxXntU5voSQIAQBjVVOJWAAAAoyDyi3jKScrKylJiYqJndVV6eezbt09Dhw5VUlKSMjIy5PzviIL4+HhJJ2bjO1lRUZHXPrWJIAkAAGHkkHmomL9yAAAAYRXicJvExESvIIlJbm6uLr30UuXk5OjDDz9UWlqaZ1vFMJuKYTc/lZ2dreTk5FofaiMRJAEAAAAAACEqKirS8OHDlZmZqXXr1qlLly5e29u0aaOUlBRt3bq1UtktW7aoV69eNVRTewRJ4CXW5VS0y3eCVWPyUsPPoImxMX63lbjtj11cah/+bBxj/1R2GRJwhpogNMqU/NSmuCmvqem+mhK3mrvw2x/AlHjVGW1/b22vPcSEuGWG502soW6lhvLGhL7B56QNeWhFbSduDSXJaH1PUEriVgAAEDFC7ElS5d3dbo0dO1abNm3S66+/rn79+vnc78orr9SSJUuUlZWl9PR0SdI777yjzMxMTZs2LYiKhh9BEgAAwijKEdwMPvV91h8AAFALLAWXkyTAHwNvv/12vfHGGxo+fLiOHDmiF1980Wv7+PHjJUn33HOPli9frosuuki33nqr8vPzNX/+fHXv3l2TJ08OoqLhR5AEAIAwoicJAACIGDXUk2Tbtm2SpFWrVmnVqlWVtlcESdLT0/X+++9r+vTpuvvuuxUbG6uhQ4fqkUceiYh8JBJBEgAAwo54BwAAiAghzm5TVevXr6/yvl27dtWaNWsCO0ENIkgCAEAY0ZMEAABEjBrqSVKf2Gc1BAAAAAAAaCDoSQIvcXExio7zPQuNaYYXp+FX0EZ+Zs2RpILiMtuyx4vtQ5kJNsc+sd3+qW6cRaXM/tpNs7TYljXc12hT3UKZYkXmWVZK3cH0z6uaUJ9TRWX2zwvTrEbuarw2k1BnlzE9L6r7/CQZ9Y/ErQAAIGLQkyRgBEkAAAgjhtsAAICIUUM5SeoTgiQAAISR479LMOUAAADCip4kASNIAgBAGEU5HMZhbP7KAQAAhFW5ggt4NOCeJCRuBQAAAAAAED1JcJK4GKeiY30nQTXk2FRCrH3MLd7PcSUpv8g+cWtucant9g7NG9lub2RI3GpiSsxabvgB2O4HYtN9Nf267DSc25gY1mn/uJmu3TIc3y4prinpbIyhbiVu+/KmHA+G4kahJNqMNj1wBqEmXg01/wX5M/xzOOxf83blAAAAwoqcJAEjSAIAQBiRuBUAAEQMcpIEjOE2ESo/P1+zZs3S4MGDlZycLIfDocWLF1fab9KkSZ4G+U+Xzp0713ylAQCeniTBLECkol0CAHVUeQhLA0VPkgh16NAh3X///Wrbtq169uyp9evX+93X5XLp2Wef9VqXlJRUzTUEAPhC4lbUR7RLAKCOoidJwAiSRKjU1FRlZ2erdevW2rp1q3r37u133+joaI0fP74GawcAABoS2iUAgIaiwQdJkpOT9eKLL2rIkCG1XRUvLpdLrVu3rvL+brdbx48fV2JiYkjnjY2JUkyM7wSrpgSgCX7KebbbJG7NyimwLZtXYp/YtUmc/VPZlADUlHzUlN/TbUhA6rT5hdhtytxqYJcYVZJKy+z7ykXbP2yhX7tN/UoMdTPl23W7a7cfoNPwvLItG2Li1VDLh1gcNkjcilDQLgEAhBU9SQLW4HOS5OTkKCcnx+/2zz77TH/5y19qrkJBKCgoUGJiopKSkpScnKybbrpJ+fn5tmWKi4uVl5fntQAAQucrH0NVF4B2Ce0SAAgrcpIErEEGST766CNlZGTou+++k2Q/o8COHTt0yy231FTVApaamqo777xTixYt0ssvv6wRI0boiSee0ODBg1VW5r/3xbx585SUlORZ0tPTa7DWAFB/RYWwoGGiXUK7BACqTbn+15skkKUBB0ka5HCbd999V7NmzfL8cjdnzhytW7dOPXr0UI8ePdSzZ08lJydLkvbu3avGjRvXco39mzdvntff48aNU6dOnXTvvfcqIyND48aN81lu5syZmj59uufvvLw8GiQAEAZMAYxA0S6hXQIA1cat4H6JYbhNw3Lfffdp586deuGFF2RZlmJjY/XOO+9o2rRp+uUvf6mUlBSdcsopOu+88/T73/9eP/vZz2q7ygGZNm2aoqKitG7dOr/7uFwuJSYmei0AgNA5HCdyvgS6ECNpuGiX0C4BgGrDcJuANcieJJLUqVMnderUSY8//rjuvvtuXXHFFcrLy9OXX37pWX788Udde+21+t3vflfb1Q1IfHy8mjdvriNHjgRc1vHfxRdTftEEQwbQ2Gj/MbljhsSspqSx8TZJYSVzclHDZr/3pKrlo53+j2C6NtN2UwJP07WbktaavrhVZ+LWxoaEvGWmG28QavLS6BAOEEpZKfReB/RaACIL7ZL64WBtVwAAELIGGySpsHnzZs//JyYm6vzzz9f5559fizUK3bFjx3To0CGlpKTUdlUAoMGp6BkSTDmAdgkAIKwYbhOwBh8kqcuKiopUWlqqJk2aeK1/4IEHZFmWBg8eXEs1A4CGi5wkaKholwBABAp26AzDbRCJFi5cqJycHO3du1eStGrVKu3evVuSdPPNN+vo0aM666yzdNVVV6lz586SpDVr1ujNN9/U4MGDddlll9Va3QGgoaInCeor2iUAUAfRkyRgBEki2IIFC/TDDz94/l65cqVWrlwpSRo/fryaNm2qYcOGae3atVqyZIncbrc6duyouXPnasaMGYqKapB5eQGgVjmCTMJKRxJEOtolAFAHESQJGEGSCLZr1y7jPi+88EL1V+S/TCkyXU775Kl2XclNx441NKyinfbby9z2r/JyQwJQ05cXU3m7X4hDTbxqunZzYljbzcb6lbrtDxBjU79Sd+324zNdm4ldQt7qPje9DgDUtEhrl0Si+p+aFgDqP4IkAACEUZTDoagguoUEUwYAAMCWpeDyi4Q2iWSdRpAEAIAwilJwvVoZiAAAAMLOLSmY32EYbgMAAMKBnCQAACBiECQJGEESAADCKEpBDrcJqgUDAABggymAA0aQBFVmGRKAmhJJ2iUgjTMkHzUlhTXVzcScHNX+4kynt9tu+jJVXG7/DhVrOLldwlypCtdueGALiu3DzI3j/L/NmM5tEmoOB7ukslURSvJV0+NS3eUBAAAAVEaQBACAMGK4DQAAiBgMtwkYQRIAAMIoyhHcFM1M6wwAAMKO4TYBI0gCAEAYORzBDQWjJwkAAAg7epIEjBkHAQAIo4rhNsEsAAAAYVWuEwGPQJcAe5Lk5+dr1qxZGjx4sJKTk+VwOLR48eJK+02aNEkOh6PS0rlz52CvMOzoSQIAQBgx3AYAAESMcgXXkyTAIMmhQ4d0//33q23bturZs6fWr1/vd1+Xy6Vnn33Wa11SUlIQlaweBEngJSY6SjHRvjsYmbqPlxpmYSkp87+9SWyMbVmXYRYS4yQphjeGcsMBohz2548y9Mmym8XF3/2uSlnJfO2mGVhK3faPW1yM/cxCpvJ2QpyUKKTZZSTzzD3VWZ4vxAAAAKgvUlNTlZ2drdatW2vr1q3q3bu3332jo6M1fvz4GqxdYAiSAAAQRo7//gumHAAAQFgFm1skwHIul0utW7eu+uHdbh0/flyJiYkBVqz6kZMEAIAwqhhuE8wCAAAQVsHkI6lYqklBQYESExOVlJSk5ORk3XTTTcrPz6++EwaIniQAAIQROUkAAEDECDEnSV5entdql8sll8sVdHVSU1N155136uyzz1Z5ebnefvttPfHEE/riiy+0fv16RUfXfoii9msAAEA9UpGlPZhyAAAAYRXicJv09HSv1bNmzdLs2bODrs68efO8/h43bpw6deqke++9VxkZGRo3blzQxw4XgiTwEhvtVGy070Sd0U77BnyRIYFnUan/V2gTl/1T0ZSg05Tc1Gn48uGWfXnTdxfTL8ClbpvErfZFjUxJZ033rqjY/p0zIdY+cWuZzbWZhPqdMMbwnDRxhlg+lC+1fCEGAABApMvKyvLKGxJKLxJ/pk2bpvvuu0/r1q0jSAIAQH3DcBsAABAxQhxuk5iYWO3JVePj49W8eXMdOXKkWs9TVSRuBQAgjByO4JdAFRcX66677lJaWpri4+PVt29frV27NuDjXHzxxXI4HJo6dWrglQAAAJGrXMElbbUfJBBWx44d06FDh5SSklJzJ7VBkAQAgDCKcjiCXgI1adIkPfroo7r66qv1+OOPy+l0asiQIdqwYUOVj7Fy5Upt2rQp4HMDAIA6IIJmtykqKtKxY8cqrX/ggQdkWZYGDx4c/pMGgeE2AACEUU0Nt9myZYteeeUVzZ8/XzNmzJAkTZgwQd26ddOdd96pjRs3Go9RVFSk22+/XXfddZd+//vfB15pAAAQ2YLtERJEuYULFyonJ0d79+6VJK1atUq7d++WJN188806evSozjrrLF111VXq3LmzJGnNmjV68803NXjwYF122WVBVja8CJLAS2x0lGJjfHcwcsXYJ/AsNSRuPW6TILSRy/7YMU77Tk9lhnOb6h5VHmJiWMO3m8IS/9deXm5/baaks6b7Hhttf/ySMvvypgSjwadtlaINj6uJ6XlhPH+ISSDIIYHalJGRIafTqSlTpnjWxcXF6brrrtM999yjrKysShnpT/bHP/5R5eXlmjFjBkESAAAQkgULFuiHH37w/L1y5UqtXLlSkjR+/Hg1bdpUw4YN09q1a7VkyRK53W517NhRc+fO1YwZMxQVFRkDXQiSAAAQTkHmFwk0qdrnn3+uTp06VUqm1qdPH0nStm3bbIMkP/74ox566CH97W9/U3x8fMDVBQAAdYBbwf2qGURPkl27dhn3eeGFFwI/cA0jSAIAQBhFyaGoINLIV5TJy8vzWu9yuXxOt5edna3U1NRK6yvWVXR19ef222/XWWedFRFT7QEAgGpSg0GS+iIy+rMAAFBPhDq7TXp6upKSkjzLvHnzfJ6nsLDQZ/AkLi7Os92f9957TytWrNBjjz0W8vUCAIAIVh7C0kDRkwQAgDAKNXFrVlaW1xAaX4EQSYqPj1dxcXGl9UVFRZ7tvpSVlemWW27RNddco969ewdeUQAAUHeUK7ieJKEkHqzjCJKgylyGBKAm+cVlfrc1bxxrf24/yWQrGPKqGr+wmBKvlhlOYEpAale83DIkhXXa163IJimsJMXH2ietLXOH9g4YSu7SGMO1mZjujYkpKW11l0f9FOx0vhVlEhMTK+UZ8SU1NVV79uyptD47O1uSlJaW5rPc888/r3//+996+umnK40dPnbsmHbt2qWWLVsqISEhwCsAAAARp1zBNdgbcJCE4TYAANRBvXr1UmZmZqUcJps3b/Zs9+XHH39UaWmpfvazn6lDhw6eRToRQOnQoYP++c9/VmvdAQAAIhU9SQAACKOf5hcJtFwgRo0apQULFuiZZ57RjBkzJEnFxcVatGiR+vbt65nZ5scff1RBQYE6d+4sSRo3bpzPAMoVV1yhIUOG6De/+Y369u0b+AUAAIDI4xY9SQJEkAQAgDCKUpDDbQJswfTt21ejR4/WzJkzdeDAAXXs2FFLlizRrl279Nxzz3n2mzBhgt5//31Z/x3a17lzZ0/A5GQdOnTQ5ZdfHnDdAQBAhCJIEjCCJAAAhFFN9SSRTgyPue+++/TCCy/o6NGj6tGjh1avXq0BAwYEfjAAAFD/kJMkYARJ4KWo1C23n0Sg0YYkmbGG5KXHS/0nbjUl8IyPMSQfNWVuNTAlbi0psZ8Dy4q2P7/dl59SQ+JU070pKbOvmym5qClxrInpeWEnxvCcMXGGmDg1mBlIAJMoBZfwK5gycXFxmj9/vubPn+93n/Xr11fpWFaI7wUAACAC0ZMkYCRuBQAAAAAAED1JAAAIK4fDEdT00EwpDQAAwo6eJAEjSAIAQBg5FFxbhBAJAAAIO0sNOuARDIIkAACEUZQjyNlt6EkCAADCzP3fJZhyDRVBEnjJKyhRjFXic5vbkBy1cUzwTydTN/OYaPv0OeWl9i9jU91NiVtN5U3bo22Ob0q86oq2v68lhsSv1S06hOSrdvelKqJCLM/wBlQXnllA3XWwtisAAGFEkCRwJG4FAAAAAAAQPUkAAAgrh8N+2m+7cgAAAOFU/t8lmHINFUESAADCiNltAABApGC4TeAIkgAAEEZRCm4sK+NfAQBAuNGTJHAESQAACCN6kgAAgEhBT5LAESSBl7zjpYp2+57dpqDY/qUSF+203R4T5f93UtP8LKbZZ0zbTTPIxMfa192y7GtYZphhxm4GmPwi3/e7QmK8/cvUNLOOSSiz00hSrDP4L3amx80kxOJAtXAouNlteDoDke9IbVcAAAJUruACHg25Jwm9ewEAAAAAAERPEgAAworhNgAAIFKQkyRwBEkAAAgjErcCAIBIQU6SwBEkAQAgjOhJAgAAIgVBksARJIGXguMlcrpjfG7LKyy1LWuXmFWSmrj8P91MXw3KDclJTclHi0rLbLe7DLlPjYlh3fYd0hpF+7/2YkNS2er+4hQTQuJVKbTEr1EhZl7lSyUiEYlbgbqN5KwA6hOG2wSO3r0AAAAAAACiJwkAAGHlcJxYgikHAAAQTgy3CRxBEgAAwihKDkUFMXgmmDIAAAB2GG4TOIIkAACEET1JAABApChXcL1CCJIA/1VuWXL4SZJ6pLDEtqwpcWtivO+EsJI5MWqZIXGrKfmo21C+zJB41ZSc9FiRfWLYJnH+t5nqZhIdYvLTmBASr4Z6/hCrDkQkx3//BVMOAAAgnBhuEzgStwIAAAAAAIggCQAAYVUx3CaYBQAAIJzKQ1gCkZ+fr1mzZmnw4MFKTk6Ww+HQ4sWLfe67Y8cODR48WI0bN1ZycrKuueYaHTx4MIirqx4MtwEAIIwcQSZuZbgNAAAIt5oabnPo0CHdf//9atu2rXr27Kn169f73G/37t0aMGCAkpKSNHfuXOXn52vBggX66quvtGXLFsXGxgZR2/AiSAIAQBiRuBUAAESKmgqSpKamKjs7W61bt9bWrVvVu3dvn/vNnTtXx48f16effqq2bdtKkvr06aOLL75Yixcv1pQpU4KobXgRJIEXlyta0XG+nxYFZfbJSV1Op+32xn6OK5kTrxaX2r9MnVH2T2XTl4+SMvsOZXEx9tdmqp/DpgJWaHlbFRMd2qi52BDLR4WQfdXuvgB1FUESAAAQKWpqCmCXy6XWrVsb91uxYoWGDRvmCZBI0sCBA9WpUyctW7YsIoIk5CQBACCMHCH8AwAACCd3CEu47dmzRwcOHNC5555baVufPn30+eefV8NZA0dPEgAAAAAAUEleXp7X3y6XSy6XK6hjZWdnSzoxNOdkqampOnLkiIqLi4M+frjQkwQAgDCKcgS/AAAAhJOl4Ga2qcgIkJ6erqSkJM8yb968oOtSWFgoST6DIHFxcV771CZ6kgAAEEbBDp1huA0AAAi3UBO3ZmVlKTEx0bM+lF4e8fHxkqTi4uJK24qKirz2qU0ESeAlqbFL0fG+n/gxUfYdj8rK7TOQumwShDoNP6EWlhqSxhrOHW04flGpfWqiRi77l4op8asd07Wb2N3XqjDdGxN+/Qa8kbgVAABEilCDJImJiV5BklBUDLOpGHbzU9nZ2UpOTq71oTYSQRIAAMLKoeB6hRAjAQAA4VZTs9tURZs2bZSSkqKtW7dW2rZlyxb16tWrGs4aOHKSAAAAAACAanfllVdq9erVysrK8qx75513lJmZqdGjR9dizf6HniQAAIRRsElYGboGAADCLdThNoFYuHChcnJytHfvXknSqlWrtHv3bknSzTffrKSkJN1zzz1avny5LrroIt16663Kz8/X/Pnz1b17d02ePDmIs4YfPUkiwKRJk+RwOPwue/bs8Vt29uzZPstUZAcGANQsRwj/gNpGmwQA6hd3CEugFixYoPvuu09PPvmkJGnlypW67777dN999+no0aOSTsyW8/777+u0007T3XffrT/+8Y8aMmSI1q5dGxH5SCR6kkSE66+/XgMHDvRaZ1mWbrjhBrVv315t2rQxHuPJJ59U48aNPX87nc6g6tI8yaWYeN+NmeQ4+yftkaLKWYp/yi61qilhYbkhMWupIXFqjCG5aU5Bqe326kyoGBti4tUYZ4iJV0P8+dpBtknAC4lbUZdFUpsEABC6msxJsmvXrirt17VrV61ZsyaIM9QMgiQRoF+/furXr5/Xug0bNqigoEBXX311lY4xatQotWjRojqqBwAIgEPBJWElRoJIQJsEAOqXcgXXK6Q6ErfWFQy3iVAvvfSSHA6HfvWrX1Vpf8uylJeXJ8uy73EBAAAQCNokAICGhCBJBCotLdWyZcvUv39/tW/fvkplTj31VCUlJalJkyYaP3689u/fb7t/cXGx8vLyvBYAQOii5FCUI4iFviSIQDXRJpFolwBAdSkPYWmoGG4TgdasWaPDhw9XqVtrs2bNNHXqVPXr108ul0sffvih/vKXv2jLli3aunWrEhMTfZabN2+e5syZE+6qA0CDx3Ab1Cc10SaRaJcAQHWpydlt6guCJBHopZdeUkxMjMaMGWPc99Zbb/X6+8orr1SfPn109dVX64knntDdd9/ts9zMmTM1ffp0z995eXlKT08PreIAAKIkqFdqok0i0S4BgOpCkCRwBEkiTH5+vl5//XUNGjRIzZs3D+oYv/rVr3T77bdr3bp1fhskLpfL5xRLjV0xio2L8VkmMd7+6VLstn8pFZX63+6Msj+2aQaWQptjS1LTGN/XVKG41L5DmWkGl6gQpqVwxYQ26i3aGVr5ECe3AXCSYKfzZQpgRJqaapNI/tsl1eFIjZwFACJDTc5uU18QJIkwf//73wPKIO9Penq6jhyhGQAANS7IKYCJkSDS0CYBgLqPniSBI3FrhFm6dKkaN26sESNGBH0My7K0a9cupaSkhLFmAACgIaFNAgBoiAiSRJCDBw9q3bp1uuKKK5SQkFBp+48//qidO3dWKnOyJ598UgcPHtTgwYOrra4AAN8cISxApKBNAgD1gzuEpaFiuE0EefXVV1VWVua3W+uECRP0/vvvy7Isz7p27dpp7Nix6t69u+Li4rRhwwa98sor6tWrl66//vqaqjoAoAKJW1EP0CYBgPrBUnD5RSzzLvUWQZIIsnTpUrVs2VIDBw6scpmrr75aGzdu1IoVK1RUVKR27drpzjvv1L333uvzlx8Tyzqx+BJjSBDaONb+6VRY4j8eGWs4dqzT/tvD0eNlttuTG8XabneXh/Y2EBsdfKesUMpKkjPEzKumpLQAAkPiVtQHkdAmAQCEjpwkgSNIEkE2bdpku339+vWV1v31r3+tptoAAILhCDJxK/FKRBLaJABQPzC7TeDISQIAAAAAACCCJAAAhFVNJm4tLi7WXXfdpbS0NMXHx6tv375au3atsdzKlSs1duxYnXrqqUpISNAZZ5yh22+/XTk5OUHUAgAARCoStwaOIAkAAOFUg1GSSZMm6dFHH9XVV1+txx9/XE6nU0OGDNGGDRtsy02ZMkU7duzQ+PHj9ec//1mDBw/WwoUL1a9fPxUWFgZeEQAAEJEIkgSOnCTwcvR4sWLKY3xuKy6zH5kWF+O03W6XHLWw1P5l2Dgu+KSwkhRlSG4aai6AuJjg442mhLgmIeZtBRBmNZW4dcuWLXrllVc0f/58zZgxQ9KJGUe6deumO++8Uxs3bvRbNiMjQxdeeKHXunPOOUcTJ07U0qVL9etf/zrg+gMAgMhDTpLA0ZMEAIAwqkjcGswSiIyMDDmdTk2ZMsWzLi4uTtddd502bdqkrKwsv2VPDpBI0hVXXCFJ2rFjR2AVAQAAEYueJIEjSAIAQB30+eefq1OnTkpMTPRa36dPH0nStm3bAjrevn37JEktWrQIS/0AAADqIobbAAAQRsEmYa0ok5eX57Xe5XLJ5XJV2j87O1upqamV1les27t3b0Dnf/jhh+V0OjVq1KiAygEAgMhVruB6hTDcBgAAhEeIiVvT09OVlJTkWebNm+fzNIWFhT6DJ3FxcZ7tVfXSSy/pueee0+23367TTz+9yuUAAEBkKw9haajoSQIv+48WKrrQd+zsQH6Rbdk2SfG22502GUbzi8psyzZN8J1MtkKpO7SXcWx0aPFClyFprR27+1IVjlCzzgIIq1ATt2ZlZXkNofEVCJGk+Ph4FRcXV1pfVFTk2V4VH374oa677joNGjRIf/jDHwKtNgAAiGDB5hdpyDlJCJIAABBGwSRhrSgnSYmJiZXyjPiSmpqqPXv2VFqfnZ0tSUpLSzMe44svvtCIESPUrVs3ZWRkKDqaZgEAAPUJs9sEjuE2AACEUYijbaqsV69eyszMrJTDZPPmzZ7tdr799lsNHjxYLVu21JtvvqnGjRsHWAMAABDpmN0mcARJAACog0aNGiW3261nnnnGs664uFiLFi1S3759lZ6eLkn68ccftXPnTq+y+/bt0yWXXKKoqCitWbNGKSkpNVp3AACASEW/WgAAwinU6W2qqG/fvho9erRmzpypAwcOqGPHjlqyZIl27dql5557zrPfhAkT9P7778uyLM+6wYMH67vvvtOdd96pDRs2aMOGDZ5trVq10sUXXxzEBQAAgEhDTpLAESSBl6NHC+R0+W6pf5OTb1u2dZM42+12yU2P5JfYlg01ualJQmzwiVclKcYZfP2q+dIA1LBQE7cG4vnnn9d9992nF154QUePHlWPHj20evVqDRgwwLbcF198IUn64x//WGnbBRdcQJAEAIB6gpwkgSNIAgBAGIWauDUQcXFxmj9/vubPn+93n/Xr11da99NeJQAAoP4qV3C9QgiSAACAsKih0TYAAABGDLcJHIlbAQAAAAAARE8SAADCi64kAAAgQpCTJHAESeCltKRcbofvzlU/HrVPrlrQ0r5TVuum/jsuHSsusy3rMAzWj40OrVNUXIiJW6OdwZ/fdG0A6paaTNwKAABgh+E2gWO4DQAAYVSRuDWYBQAAIJzKQ1gCsX79ejkcDp/Lxx9/HKarqRn0JAEAIIwYbQMAACJFTfckueWWW9S7d2+vdR07dgzyaLWDIAkAAAAAAAjZz3/+c40aNaq2qxESgiQAAIQTXUkAAECEqI2cJMeOHVN8fLyio+tmuIGcJAAAhJEjhH8AAADhZCm4fCRWkOebPHmyEhMTFRcXp4suukhbt24N8QpqXt0M7aDaxMVHy+mKCars3vxC2+2d05r43VZmhTbJVIIrtKdyqLPjRPHdBkCFYJOw8j4ChM2R2q4AAESIUHuS5OXlea13uVxyuVyV9o+NjdWVV16pIUOGqEWLFvr666+1YMEC/fznP9fGjRt11llnBVGL2kGQBACAMGK0DQAAiBShBknS09O91s+aNUuzZ8+utH///v3Vv39/z98jRozQqFGj1KNHD82cOVNvv/12ELWoHQRJAAAIJ6IkAAAgQgQznW9FOUnKyspSYmKiZ72vXiT+dOzYUZdddplWrlwpt9stp9MZRE1qHkESAAAAAABQSWJioleQJFDp6ekqKSnR8ePHQzpOTSJIAgBAGAWbhJXErQAAINxqY3abn/ruu+8UFxenxo0bh+mI1Y8gCbwkJycoOi7B57Y2SbG2Zb/PKbDdHmWT3TTeGdpTsZErtK5bMc7QErc6gsrSCKA+cgSZuJW3EQAAEG6hDrepqoMHDyolJcVr3RdffKE33nhDl156qaKi6s7EugRJAAAII1KSAACASFFTPUnGjh2r+Ph49e/fXy1bttTXX3+tZ555RgkJCXrooYeCqEHtIUgCAEA4ESUBAAARolzBBUkC7Uly+eWXa+nSpXr00UeVl5enlJQUjRw5UrNmzVLHjh2DqEHtIUgCAAAAAACCdsstt+iWW26p7WqEBUESAADCiMStAAAgUtRUTpL6hCAJvDRv4lJMfJzPbZ2a2WckfvubQ7bbbfK2qklcaE/F+JjQErfa1Q0AAuFQkIlbw14TAADQ0LklBZMyNVyz29RFBEkAAAgjUpIAAIBIQZAkcARJAAAII6YABgAAkYLhNoGrO5MVAwAAAAAAVCN6kgAAEFYMuAEAAJGB4TaBI0gCLzHRUYqJ9v0ySkuKD+nYDpu+5EkJMSEdO9oZ2pcLu7oBQCAYbgMAACIFw20CR5AEAIAwoh8JAACIFOUKrlcIQRIAABAW9CQBAACRwq3gfohhuA0AAAgLx3//BVMOAAAgnBhuEzhmtwEAAAAAABA9SXCSw3nFii71/bRo3jjWtmz7ZFfQ520SF9pT0RnFL7AAIgRJSQAAQIRguE3gCJIAABBGxEgAAECkIEgSOIIkAACEEYlbAQBApCAnSeAIkgAAEEYkbgUAAJGCniSBI3ErAAAAAACA6EmCk3z3/VFFuYp9bmvkctqW7ZXSNOjzumJCi9c56KcOIFKQlAQAAEQIS8ENnbHCXZE6hCAJAABhRIwEAABEimCHzTTk4TYESQAACCMStwIAgEhBkCRwBEkAAAir4BK30pcEAACEW7mCa2E05NltSNwKAAAAAAAgepIAABBWDLcBAACRguE2gSNIAi/7vtklR0y8z23RTvuOR21bJAR9XtOxAQAAAACBIUgSOIIkAACEET1JAABApCAnSeAIkgAAEEaOIBO3BpfsFQAAwL9ggx0ESQAAQFjQkwQAAEQKgiSBIxEEAAAAAACA6EmCkx34VnK6giravHFsmCsDAHWPQ8GN/aUjCQAACDe3JCuIcg25JwlBEgAAwokoCQAAiBAESQJHkAQAgDAicSsAAIgU5CQJHEESAADCiMStAAAgUtCTJHAkbq0HiouLdddddyktLU3x8fHq27ev1q5dW9vVAgBUs1De//fs2aMxY8aoadOmSkxM1GWXXabvvvuummuMhoB2CQA0PPXpvZ8gST0wadIkPfroo7r66qv1+OOPy+l0asiQIdqwYUPgB3O7JXeZ78XAGeWwXQCgIXCEsAQq2Pf//Px8XXTRRXr//fd1zz33aM6cOfr88891wQUX6PDhw0HUBPifsLZLAAAhKdeJ3iSBLoH2JKlP7/0Oy7KC6X2DCLFlyxb17dtX8+fP14wZMyRJRUVF6tatm1q2bKmNGzdW6Th5eXlKSkqSq/tv5HD6nqXm6CcLw1ZvAKgNeXl5atU8Sbm5uUpMTAz7sZOSkpR9KCeoY+fl5Sm1RdMq1y2U9/8//vGPuuuuu7Rlyxb17t1bkrRz505169ZNd955p+bOnRtw/QEp/O2SNPn+Ra95+KoMANXK7qeHckl7pWptlyQpuB9iLEm5VaxbuN77IwU9Seq4jIwMOZ1OTZkyxbMuLi5O1113nTZt2qSsrKxarB0ANDyOEP4FIpT3/4yMDPXu3dsTIJGkzp0765e//KWWLVsW+EUD/0W7BAAiSzC9SCqWqqpv7/0ESeq4zz//XJ06daoU3evTp48kadu2bbVQKwBouCoStwazBCLY9//y8nJ9+eWXOvfccytt69Onj7799lsdO3YssMoA/0W7BAAiS3kIS1XVt/d+Zrep47Kzs5WamlppfcW6vXv3+ixXXFys4uJiz9+5ubmSJMtd4vdceXl5oVQVAGrdsf++j1XnSNNg3ysryp1c3uVyyeVyVdo/2Pf/I0eOqLi42Fj2jDPOCOwCAIW/XeKvkR7IL5wAUJvsgg0V26qzXRLskSvKVaVdEux7f6QiSFLHFRYW+mw8x8XFebb7Mm/ePM2ZM6fS+pKvl/g9V6vmfw2ylgAQWQ4fPqykpKSwHjM2NlatW7fW6R3Sgz5G48aNlZ7uXX7WrFmaPXt2pX2Dff+vWB9MWcAk3O2SfX7OU7ea2wBgrzrbJfv2+XsnNatquyTY9/5IRZCkjouPj/f65aVCUVGRZ7svM2fO1PTp0z1/5+TkqF27dvrxxx/D/gKt7/Ly8pSenq6srKywJ1yq77h3wePeBSc3N1dt27ZVcnJy2I8dFxen77//XiUl/nvkmViWJcdJ4258NTqk4N//K9YHUxYwoV1S+/h8CA73LXjcu+DVl3ZJsO/9kYogSR2XmpqqPXv2VFqfnZ0tSUpLS/NZzl/37aSkJN7cgpSYmMi9CxL3Lnjcu+BERVVPSq64uDjPrybVLdj3/+TkZLlcLs9+gZQFTGiXRA4+H4LDfQse9y54db1dEux7f6QicWsd16tXL2VmZlYaK7Z582bPdgBA/RPs+39UVJS6d++urVu3Vtq2efNmnXrqqWrSpEnY64uGgXYJADQ89e29nyBJHTdq1Ci53W4988wznnXFxcVatGiR+vbtW2kMGQCgfqjq+/+PP/6onTt3Vir7ySefeAVK/v3vf+vdd9/V6NGja+YCUC/RLgGAhqe+vfcz3KaO69u3r0aPHq2ZM2fqwIED6tixo5YsWaJdu3bpueeeq/JxXC6XZs2a5XfsO/zj3gWPexc87l1w6tN9q+r7/4QJE/T+++97Zc6/8cYb9de//lVDhw7VjBkzFBMTo0cffVStWrXS7bffXhuXg3qCdknt494Fh/sWPO5d8OrLvQvXe3+kcFjVOd8QakRRUZHuu+8+vfjiizp69Kh69OihBx54QIMGDartqgEAqlFV3v8vvPDCSkESSdq9e7emTZumf/7znyovL9eFF16oP/3pT+rYsWNNXwbqGdolANDw1Kf3foIkAAAAAAAAIicJAAAAAACAJIIkAAAAAAAAkgiSAAAAAAAASCJI0uAVFxfrrrvuUlpamuLj49W3b1+tXbu2tqsVcfLz8zVr1iwNHjxYycnJcjgcWrx4sc99d+zYocGDB6tx48ZKTk7WNddco4MHD9ZshSPEJ598oqlTp6pr165q1KiR2rZtqzFjxigzM7PSvty3/9m+fbtGjx6tU089VQkJCWrRooUGDBigVatWVdqX+2b2hz/8QQ6HQ926dau0bePGjTr//POVkJCg1q1b65ZbblF+fn4t1BKARLukKmiTBIc2SfBol4QPbZK6gcStDdxVV12ljIwM3XbbbTr99NO1ePFiffLJJ3rvvfd0/vnn13b1IsauXbvUoUMHtW3bVqeeeqrWr1+vRYsWadKkSV777d69W2eddZaSkpI8b2wLFixQ27ZttWXLFsXGxtbOBdSSUaNG6aOPPtLo0aPVo0cP7du3TwsXLlR+fr4+/vhjzwcE983bm2++qT//+c/q16+f0tLSVFBQoBUrVujDDz/U008/rSlTpkjivlXF7t27dcYZZ8jhcKh9+/b617/+5dm2bds29evXT2eeeaamTJmi3bt3a8GCBbrooov01ltv1WKtgYaLdokZbZLg0CYJHu2S8KBNUodYaLA2b95sSbLmz5/vWVdYWGiddtppVr9+/WqxZpGnqKjIys7OtizLsj755BNLkrVo0aJK+/32t7+14uPjrR9++MGzbu3atZYk6+mnn66p6kaMjz76yCouLvZal5mZablcLuvqq6/2rOO+mZWVlVk9e/a0zjjjDM867pvZ2LFjrV/84hfWBRdcYHXt2tVr26WXXmqlpqZaubm5nnV//etfLUnWmjVrarqqQINHu6RqaJMEhzZJeNEuCRxtkrqD4TYNWEZGhpxOpyf6K0lxcXG67rrrtGnTJmVlZdVi7SKLy+VS69atjfutWLFCw4YNU9u2bT3rBg4cqE6dOmnZsmXVWcWI1L9//0q/Gpx++unq2rWrduzY4VnHfTNzOp1KT09XTk6OZx33zd4HH3ygjIwMPfbYY5W25eXlae3atRo/frwSExM96ydMmKDGjRtz/4BaQLukamiTBIc2SXjRLgkMbZK6hSBJA/b555+rU6dOXi9GSerTp4+kE92+UHV79uzRgQMHdO6551ba1qdPH33++ee1UKvIY1mW9u/frxYtWkjivtk5fvy4Dh06pG+//VZ/+tOf9NZbb+mXv/ylJO6bidvt1s0336xf//rX6t69e6XtX331lcrKyirdv9jYWPXq1avB3z+gNtAuCR8+I6qGNklgaJcEhzZJ3RNd2xVA7cnOzlZqamql9RXr9u7dW9NVqtOys7Mlye89PXLkiIqLi+VyuWq6ahFl6dKl2rNnj+6//35J3Dc7t99+u55++mlJUlRUlEaOHKmFCxdK4r6ZPPXUU/rhhx+0bt06n9tN9+/DDz+s1voBqIx2SfjwGVE1tEkCQ7skOLRJ6h6CJA1YYWGhzzequLg4z3ZUXcX9Mt3ThvjhUGHnzp266aab1K9fP02cOFES983ObbfdplGjRmnv3r1atmyZ3G63SkpKJHHf7Bw+fFi///3vdd999yklJcXnPqb7x/sfUPNol4QPnxFmtEkCR7skcLRJ6iaG2zRg8fHxKi4urrS+qKjIsx1VV3G/uKe+7du3T0OHDlVSUpJn3LnEfbPTuXNnDRw4UBMmTNDq1auVn5+v4cOHy7Is7puN3/3ud0pOTtbNN9/sdx/T/Wuo9w6oTbRLwofPCHu0SYJDuyRwtEnqJnqSNGCpqanas2dPpfUVXb7S0tJqukp1WkUXuYr791PZ2dlKTk5ucNHzCrm5ubr00kuVk5OjDz/80Ou5xX2rulGjRun6669XZmYm982P//znP3rmmWf02GOPeXXNLyoqUmlpqXbt2qXExETj/eP9D6h5tEvCh88I/2iThA/tEnu0SeouepI0YL169VJmZqby8vK81m/evNmzHVXXpk0bpaSkaOvWrZW2bdmypcHez6KiIg0fPlyZmZlavXq1unTp4rWd+1Z1Fd0tc3NzuW9+7NmzR+Xl5brlllvUoUMHz7J582ZlZmaqQ4cOuv/++9WtWzdFR0dXun8lJSXatm1bg71/QG2iXRI+fEb4RpskvGiX2KNNUncRJGnARo0aJbfbrWeeecazrri4WIsWLVLfvn2Vnp5ei7Wrm6688kqtXr3aa5rCd955R5mZmRo9enQt1qx2uN1ujR07Vps2bdLy5cvVr18/n/tx37wdOHCg0rrS0lI9//zzio+P9zTquG+VdevWTa+99lqlpWvXrmrbtq1ee+01XXfddUpKStLAgQP14osv6tixY57yL7zwgvLz8xvs/QNqE+2S8OIzwhttkuDRLgkObZK6y2FZllXblUDtGTNmjF577TVNmzZNHTt21JIlS7Rlyxa98847GjBgQG1XL6IsXLhQOTk52rt3r5588kmNHDlSZ511liTp5ptvVlJSkrKysnTWWWepadOmuvXWW5Wfn6/58+frlFNO0SeffNLguhnedtttevzxxzV8+HCNGTOm0vbx48dLEvftJFdccYXy8vI0YMAAtWnTRvv27dPSpUu1c+dOPfLII5o+fbok7lsgLrzwQh06dEj/+te/POs+++wz9e/fX126dNGUKVO0e/duPfLIIxowYIDWrFlTi7UFGi7aJVVDmyRwtEmCR7skvGiT1AEWGrTCwkJrxowZVuvWrS2Xy2X17t3bevvtt2u7WhGpXbt2liSfy/fff+/Z71//+pd1ySWXWAkJCVbTpk2tq6++2tq3b1/tVbwWXXDBBX7v2clvP9y3/3n55ZetgQMHWq1atbKio6OtZs2aWQMHDrRef/31Svty36rmggsusLp27Vpp/Ycffmj179/fiouLs1JSUqybbrrJysvLq4UaArAs2iVVRZskcLRJgke7JLxok0Q+epIAAAAAAACInCQAAAAAAACSCJIAAAAAAABIIkgCAAAAAAAgiSAJAAAAAACAJIIkAAAAAAAAkgiSAAAAAAAASCJIAgAAAAAAIIkgCQAAAAAAgCSCJAAAAAAAAJIIkgAAAAAAAEgiSAIgCLNnz5bD4dChQ4fCdszFixfL4XBo165dYTtmJJ4TAACED20SAOFGkASIYGvWrJHD4ZDD4dDXX39dafvw4cN1yimn1ELN6q+NGzdq9uzZysnJqe2qAAAQMWiT1DzaJEDtIEgCRLAvvvhCkhQVFaXVq1f73N6jR4+arla1uOaaa1RYWKh27drV6jk3btyoOXPm0CABAOAnaJPU/DlpkwC1gyAJEMG+/PJLJSYmatCgQVq1apXXtqNHjyorK0s9e/aspdqFl9PpVFxcnBwOR70+JwAAdRFtkvp3TgC+ESQBItgXX3yh7t27a9iwYdq0aZMOHz7stU1Stfxqs2fPHl133XVKS0uTy+VShw4d9Nvf/lYlJSVe++Xk5GjSpElq2rSpkpKSNHnyZBUUFHjt88MPP+jGG2/UGWecofj4eDVv3lyjR4+uNObW11jcinHG33zzjfE8Jzt27Jhuu+02tW/fXi6XSy1bttTFF1+szz77zO85Z8+erTvuuEOS1KFDB0+34orte/bs0bXXXqtWrVrJ5XKpa9eu+tvf/hbweQEAqGtok9AmARqK6NquAADfSkpK9O9//1u/+c1vNGzYMN1000168803dc0110g68YuOpLD/arN371716dNHOTk5mjJlijp37qw9e/YoIyNDBQUFio2N9ew7ZswYdejQQfPmzdNnn32mZ599Vi1bttTDDz/s2eeTTz7Rxo0bNW7cOJ1yyinatWuXnnzySV144YX6+uuvlZCQYKxTVc5zshtuuEEZGRmaOnWqunTposOHD2vDhg3asWOHzj77bJ9lRo4cqczMTL388sv605/+pBYtWkiSUlJStH//fp133nlyOByaOnWqUlJS9NZbb+m6665TXl6ebrvttqDPCwBAJKNN8j+0SYAGwAIQkT7//HNLkvXUU09ZlmVZ3bt3t0aPHu3Zfu2111oul8sqKysL63knTJhgRUVFWZ988kmlbeXl5ZZlWdasWbMsSda1117rtf2KK66wmjdv7rWuoKCg0nE2bdpkSbKef/55z7pFixZZkqzvv//esy6Q85wsKSnJuummm2z38XXO+fPnV1pnWZZ13XXXWampqdahQ4e81o8bN85KSkryXGdVzgsAQF1Cm4Q2CdCQMNwGiFAVv8pUdF0dNmyY1qxZo9LSUkknurZ27dpVTqdTklRcXKxWrVopLy/P7zHLy8vVpk0b7d+/3+/2v//97xo+fLjOPffcSttPHid7ww03eP3985//XIcPH/aqQ3x8vOf/S0tLdfjwYXXs2FFNmzatcnfPqpznZE2bNtXmzZu1d+/eKp3DjmVZWrFihYYPHy7LsnTo0CHPMmjQIOXm5nquJZznBQAgEtAmCew8J6NNAtQtBEmACPXFF1/I4XCoe/fukk40SPLy8vTBBx/I7XZr+/btXmN/XS6X9u/fr8TERL/HjIqK0p49e9SqVSuf2w8ePKi8vDx169atSnVs27at19/NmjWTdCKBW4XCwkL9/ve/V3p6ulwul1q0aKGUlBTl5OQoNzc3bOc52R//+Ef961//Unp6uvr06aPZs2fru+++q9L5Tnbw4EHl5OTomWeeUUpKitcyefJkSdKBAwfCfl4AACIBbZLAznMy2iRA3UJOEiBCffnllzr11FPVuHFjSdJ5552nFi1aaNWqVUpLS1NRUVGtZ5Gv+MXoZJZlef7/5ptv1qJFi3TbbbepX79+SkpKksPh0Lhx41ReXh6285xszJgx+vnPf67XXntN//znPzV//nw9/PDDWrlypS699NIqnbdCRT3Hjx+viRMn+tynonEYzvMCABAJaJMEdp6T0SYB6haCJECE+vLLL/Wzn/3M83dUVJQuvfRSrVq1Suedd54k7yzyjz/+uL788ks999xzfo/5zDPPaP369XrppZd8bk9JSVFiYqL+9a9/hekqpIyMDE2cOFGPPPKIZ11RUZFycnLCdg5/UlNTdeONN+rGG2/UgQMHdPbZZ+sPf/iDbcPA19R7KSkpatKkidxutwYOHFgt5wUAIFLRJgkdbRKg7mC4DRCB9u3bpwMHDlT6VWbYsGH67rvv9PLLL0vyziL/5ZdfGqfe2759u2231aioKF1++eVatWqVtm7dWmm73a8k/jidzkrl/u///k9utzvgY1WV2+2u1G22ZcuWSktLU3FxsW3ZRo0aSZJXg8npdOrKK6/UihUrfDbWDh48GPJ5AQCIRLRJQkObBKh76EkCRKAvvvhCkio1MAYNGqSYmBhP99bmzZt7tn355ZcaP3687XG3b9+uX/ziF7b7zJ07V//85z91wQUXaMqUKTrzzDOVnZ2t5cuXa8OGDWratGlA1zJs2DC98MILSkpKUpcuXbRp0yatW7fOq+7hduzYMZ1yyikaNWqUevbsqcaNG2vdunX65JNPvH498uWcc86RJN17770aN26cYmJiNHz4cD300EN677331LdvX/3mN79Rly5ddOTIEX322Wdat26djhw5EtJ5AQCIRLRJQkObBKh7CJIAEejkLPIVkpKSdP755+u9997z2lZeXq6vv/66Sr/adO3a1XafNm3aaPPmzbrvvvu0dOlS5eXlqU2bNrr00kuVkJAQ8LU8/vjjcjqdWrp0qYqKivSzn/1M69at06BBgwI+VlUlJCToxhtv1D//+U+tXLlS5eXl6tixo5544gn99re/tS3bu3dvPfDAA3rqqaf09ttvq7y8XN9//73at2+vLVu26P7779fKlSv1xBNPqHnz5uratasefvjhkM8LAEAkok0SGtokQN3jsILpqwYgomRmZurCCy+0neLtyJEjOuWUU5Sfn6+oKEbaAQCA8KNNAqCu410JqAdOHvs7adIkTZo0yWuf7du368wzz6QxAgAAqg1tEgB1He9MQD3w1VdfeTVIdu/e7ZWFXqpat1YAAIBQ0CYBUNeRkwSoB+bMmeP5/7KyMu3du7fSrzZffvllpcz0AAAA4USbBEBdR04SoAHIz89X9+7dtWzZMvXu3bu2qwMAABoo2iQAIh3DbYB6bvPmzerUqZNGjhxJYwQAANQa2iQA6gJ6kgAAAAAAAIieJAAAAAAAAJIIkgAAAITd9u3bNXr0aJ166qlKSEhQixYtNGDAAK1atcprv0mTJsnhcFRaOnfuXEs1BwCgYWN2GwAAgDD74YcfdOzYMU2cOFFpaWkqKCjQihUrNGLECD399NOaMmWKZ1+Xy6Vnn33Wq3xSUlJNVxkAAIicJAAAADXC7XbrnHPOUVFRkXbu3CnpRE+SjIwM5efn13LtAACAxHAbAACAGuF0OpWenq6cnJxK29xut/Ly8mq+UgAAwAtBEgAAgGpy/PhxHTp0SN9++63+9Kc/6a233tIvf/lLr30KCgqUmJiopKQkJScn66abbqJnCQAAtYThNiEoLy/X3r171aRJEzkcjtquDgAAEcWyLB07dkxpaWmKimqYv8vccMMNevrppyVJUVFRGjlypJ555hk1a9ZMkjRz5kxZlqWzzz5b5eXlevvtt7VkyRL97Gc/0/r16xUd7T99XHFxsYqLiz1/l5eX68iRI2revDntEgAATlLVdglBkhDs3r1b6enptV0NAAAiWlZWlk455ZTarkat2Llzp3bv3q29e/dq2bJlio2N1ZNPPqlWrVr5LTN37lzde++9evnllzVu3Di/+82ePVtz5sypjmoDAFBvmdolBElCkJubq6ZNmyorK0uJiYm1XR0AACJKXl6eJwcHs7WccMkllygnJ0ebN2/229ujsLBQjRs31uTJkyvNevNTJ/ckyc3NVdu2bWmXAADgQ1XbJUwBHIKKxk1iYiKNEQAA/GDox/+MGjVK119/vTIzM3XGGWf43Cc+Pl7NmzfXkSNHbI/lcrnkcrkqraddAgCAf6Z2ScMcIAwAAFALCgsLJZ3o9eHPsWPHdOjQIaWkpNRUtQAAwH8RJAEAAAizAwcOVFpXWlqq559/XvHx8erSpYuKiop07NixSvs98MADsixLgwcPromqAgCAn2C4DQCgznC73SotLa3tauC/YmJi5HQ6a7saEen6669XXl6eBgwYoDZt2mjfvn1aunSpdu7cqUceeUSNGzfWrl27dNZZZ+mqq65S586dJUlr1qzRm2++qcGDB+uyyy6r5asAAPhDmyTyhKtdQpAEABDxLMvSvn37lJOTU9tVwUmaNm2q1q1bk3fkJGPHjtVzzz2nJ598UocPH1aTJk10zjnn6OGHH9aIESMknbh3w4YN09q1a7VkyRK53W517NhRc+fO1YwZMxrstMkAEMlok0S2cLRLmN0mBHl5eUpKSlJubi4J0gCgGmVnZysnJ0ctW7ZUQkICX8gjgGVZKigo0IEDB9S0aVOlpqZW2ofPyZrF/QaA6kebJDKFs11CTxIAQERzu92exkjz5s1ruzr4ifj4eEkn8m+0bNmSoTcAgHqNNklkC1e7pM7248zPz9esWbM0ePBgJScny+FwaPHixVUun5OToylTpiglJUWNGjXSRRddpM8++6z6KgwACErFeN+EhIRargl8qXhcGJcNAKjvaJNEvnC0S+pskOTQoUO6//77tWPHDvXs2TOgsuXl5Ro6dKheeuklTZ06VX/84x914MABXXjhhfrPf/5TTTUGAISC7qyRiccFANDQ8NkXucLx2NTZ4TapqanKzs5W69attXXrVvXu3bvKZTMyMrRx40YtX75co0aNkiSNGTNGnTp10qxZs/TSSy9VV7Vtrdz2o+a8+ZUyD+SpU8tEzRrSXSN7ta2VugAAAAAA0NDU2Z4kLpdLrVu3DqpsRkaGWrVqpZEjR3rWpaSkaMyYMXr99ddVXFwcrmpW2cptP+rKZz/UV3tzVFRWrq+yc3Tlsx9q5bYfa7wuAAAAAAA0RHU2SBKKzz//XGeffXalqfX69OmjgoICZWZm+ixXXFysvLw8ryVc5rz5lRySKqYasizJ4ZDuf+ursJ0DAAAAAAD41yCDJNnZ2T6nBKpYt3fvXp/l5s2bp6SkJM+Snp4etjplHsjTyXMxW5b07/3hC8QAAGretGnTvHouhstf/vIXtW/fXnFxcerbt6+2bNlSLWUAAED9QbvErEEGSQoLC+VyuSqtj4uL82z3ZebMmcrNzfUsWVlZYatTp5aJOjnFjMMhndHK//zNAICqW7ntR/Wc+w/F3/ayes79R40NZ9yyZYvOPffcsB7z1Vdf1fTp0zVr1ix99tln6tmzpwYNGqQDBw6EtQwAAKgetEsit13SIIMk8fHxPvOOFBUVebb74nK5lJiY6LWEy6wh3b16kjgcJ3qSzBrSI2znAID6wLIsHS8uC2h56ZNd3nmf9p7I+/TSJ7sCOo5lndznz7+SkhLFxMRo48aNuvfee+VwOHTeeeeF5R48+uij+s1vfqPJkyerS5cueuqpp5SQkKC//e1vYS0DAADs0S6pf+2SOju7TSgqZsY5WcW6tLS0mq6SRvZqq+XXna/Rz22QJHVpnaQHhvXUFT3DN6QHAOqDghK3Gt/+alBlrZP+e/WSjwIqn//IWDVyVe2jMzo6Wh999JH69u2rbdu2qVWrVp4eixXmzp2ruXPn2h7n66+/Vtu2/5vprKSkRJ9++qlmzpzpWRcVFaWBAwdq06ZNPo8RTBkAAGBGu6T+tUsaZJCkV69e+vDDD1VeXu6VvHXz5s1KSEhQp06daqVeo85qp9jojSopK9dbN16k9GaNaqUeAIDQRUVFae/evWrevLl69uzpc58bbrhBY8aMsT3OyYH7Q4cOye12q1WrVl7rW7VqpZ07d/o8RjBlAABA/UG7pOrqfZAkOztbubm5Ou200xQTEyNJGjVqlDIyMrRy5UqNGjVK0okHavny5Ro+fLjPfCU1JSEmWiVlJSoocddaHQAgkiXEOpX/yNiAypy34G1tz86tNKyxW2pTbbp9UEDnDsTnn3/utyEiScnJyUpOTg7omAAAIHLQLql/6nSQZOHChcrJyfHMRrNq1Srt3r1bknTzzTcrKSlJM2fO1JIlS/T999+rffv2kk4ESc477zxNnjxZX3/9tVq0aKEnnnhCbrdbc+bMqa3LkSTFxziVUygVlJTVaj0AIFI5HI4qdy2tMGdoD1357IeefE8V/50ztEfAxwrEtm3bbBsjwXRrbdGihZxOp/bv3++13/79+9W6dWufxwimDAAAMKNdUv/aJXU6SLJgwQL98MMPnr9XrlyplStXSpLGjx+vpKQkn+WcTqfefPNN3XHHHfrzn/+swsJC9e7dW4sXL9YZZ5xRI3X3pyIaWFhKTxIACJeRvdpqxa9/rvvf+kr/3p+nM1olataQHtWe9+mrr77SlVde6Xd7MN1aY2Njdc455+idd97R5ZdfLkkqLy/XO++8o6lTp/o8RjBlAABA9aBdEtntkjodJNm1a5dxn8WLF2vx4sWV1jdr1kzPPvusnn322fBXLATxMSeCJPQkAYDwGtmrrUb2amveMYzKy8v173//W3v37lWjRo0qBe+D7dY6ffp0TZw4Ueeee6769Omjxx57TMePH9fkyZM9+yxcuFCvvfaa3nnnnSqXAQAANYN2SeS2S+p0kKQ+Sog98ZDQkwQA6r4HH3xQd911l+bOnasZM2Zo/vz5YTnu2LFjdfDgQf3+97/Xvn371KtXL7399tteCdAOHTqkb7/9NqAyAACg/qJdUjUOK5DJleElLy9PSUlJys3NVWJiYliOedHj67T+P/v1yuSfaew57cNyTACoy4qKivT999+rQ4cOlaaqQ+2ze3yq43MS/nG/AaB60SaJfOFol0T53YJaUTHchp4kAAAAAADULIIkEaYicStTAAMAAAAAULMIkkQYepIAAAAAAFA7CJJEmIrErcxuAwAAAABAzSJIEmHoSQIAAAAAQO0gSBJh6EkCAAAAAEDtIEgSYSp6kpC4FQAAAACAmkWQJMJU9CQpLKUnCQAAAAAANYkgSYShJwkAAAAAALWDIEmESYglcSsAAAAAALWBIEmEIXErANQv06ZN08iRI8N6zA8++EDDhw9XWlqaHA6H/v73v/vc7y9/+Yvat2+vuLg49e3bV1u2bDEeO5gyAACgbqBdYkaQJMIwBTAAVJMdb0hP9ZcebHnivzveqJHTbtmyReeee25Yj3n8+HH17NlTf/nLX/zu8+qrr2r69OmaNWuWPvvsM/Xs2VODBg3SgQMHwloGAAAEgXZJxLZLHJZlWdV6hnosLy9PSUlJys3NVWJiYliO+c6/92ng/72jbqlJ+ureYWE5JgDUZUVFRfr+++/VoUMHxcXFSZYllRYEdpB/vymt/LUkhyTrf/8d+ax0xpCqHycmQXI4qrRrSUmJGjVqpLKy//UM7Nu3rz7++ONAam7kcDj02muv6fLLL/da37dvX/Xu3VsLFy6UJJWXlys9PV0333yz7r77bp/HCqZMpcfnJ6rjcxL+cb8BoHr5/MyjXeKlPrRLoqt6sagZ9CQBAIPSAmleWpCFLe//rvx1YMVn7pViG1Vp1+joaH300Ufq27evtm3bplatWlX6sJ47d67mzp1re5yvv/5abdu2DaiaJSUl+vTTTzVz5kzPuqioKA0cOFCbNm0KWxn4t337ds2ePVuffvqp9u3bp4SEBHXp0kV33HGHhg8f7rXvjh07NG3aNG3YsEGxsbEaOnSoHn30UaWkpNRS7QEAVUa7xKiutUsIkkSYisStzG4DAHVbVFSU9u7dq+bNm6tnz54+97nhhhs0ZswY2+OkpQXe8Dp06JDcbrdatWrltb5Vq1bauXNn2MrAvx9++EHHjh3TxIkTlZaWpoKCAq1YsUIjRozQ008/rSlTpkiSdu/erQEDBigpKUlz585Vfn6+FixYoK+++kpbtmxRbGxsLV8JAKA+oF1SdQRJIkx8zImHhJ4kAOBHTMKJX04C8dwvpQM79b9fbCTJIbU8U7puXWDnDsDnn3/utyEiScnJyUpOTg7omKgbhgwZoiFDvLtMT506Veecc44effRRT5Bk7ty5On78uD799FPPL3N9+vTRxRdfrMWLF3v2AwBEKNol9Q6JWyOMpydJKbPbAIBPDseJrqWBLBfeo/+N+ZU8Y38vvCew41Rx3G+F/2fvzuOauPP/gb8mgYRwHwoCgvd9V4WirT3W29pDbe2pvdbtrj3U9udqD3u5atce2rXf3e32Uttut7XYWttqveuB4oXVouKFnIIKBALkIJnfH+MMBBJMIBzR1/PxoIHJZz4zCaZM3nl/3u+0tLR6L0YWLVqEwMDAer+ysrLcforatGkDtVqNgoICu+0FBQVo166dx/Yh96jVasTFxaGkpETZ9u233+KOO+6wS10eOXIkunfvjq+//roFzpKIiNzC65Kr8rbrEgZJWhm5Jom5ygarzdbCZ0NEdI3odSdw32ogqg/go5Vu7/sc6DXx6vs2wtGjRzFw4ECn9z/11FNIS0ur96shaa0ajQaDBw/Gli1blG02mw1btmxBUlKSx/ahqysvL8elS5dw5swZvPfee/j555/xhz/8AQCQm5uLwsJCh10GEhIScPjw4XrnNplMKC0ttfsiIiIvwOuSVn1dwuU2rYy/pvpXUmmxIlDLOBYRkUf0ulP6akY2mw0nT55EXl4eAgICEBISYnd/Q9NaDQYDTp8+rfx87tw5pKWlITw8XMlImDNnDqZPn44hQ4YgISEBy5YtQ3l5OR577DFlvxUrVmDt2rXKBYgr+5B7nn/+efz73/8GIK0HnzRpklKlPz8/HwAQHR1dZ7/o6GgUFRXBZDJBq9U6nHvx4sV4/fXXm+jMiYioSfG6pNVel/AdeCvj56NWvmfxViIi77Zw4UJ89tlniI2NxcKFCz0274EDBzBo0CAMGjQIgHQRMWjQICxYsEAZM3XqVLz99ttYsGABBg4ciLS0NGzYsMGuAJqc4eDOPuSeWbNmYdOmTVi5ciXGjRsHq9UKs9kMAKisrAQAh0EQueOAPMaR+fPnQ6/XK1/Z2dlN8AiIiOhawesS1wiiKIpXH0aOuNpn2V262V/BaLEi84270CE80GPzEhF5o/r63VPLq+/301R/J73Z6NGjUVJSgn379uHgwYMYOnQoVq1ahUceecRu3Ny5c7F06VIYjUanmSS18fkmImpavCZp/TxxXcJMklbI35dtgImIiK5FU6ZMwf79+5GRkaEss5GX3dSUn5+P8PBwlwMkRERE5BkMkrRCuisdbtgGmIiI6NoiL5/R6/WIjY1F27ZtceDAgTrjUlNT6y2uR0RERE2DQZJWyN9XKt5aYWYbYCIiIm9UWFhYZ5vFYsGqVaug0+nQu3dvAMDkyZOxfv16u3oiW7ZsQUZGBu69995mO18iIiKSsLtNK6RkknC5DRERkVf605/+hNLSUowYMQKxsbG4cOECvvjiC5w4cQLvvPMOAgOlmmMvvvgivvnmG9x222147rnnYDAYsHTpUvTr149dhYiIiFoAgyStkJJJYmEmCRERkTeaOnUqPv74Y/zzn//E5cuXERQUhMGDB+Ott97CnXdWt3yMi4vDjh07MGfOHMybNw8ajQYTJkzAO++8w3okRERELYBBklaImSRERETe7f7778f999/v0tg+ffpg48aNTXxGRERE5ArWJGmFlO42LNxKRERERERE1GwYJGmFdBopwaeShVuJiIiIiIiImg2DJK0QM0mIiIiIiIiImh+DJK2QTg6SMJOEiIiIiIiIqNkwSNIK+cvLbZhJQkTk9WbPno1JkyZ5dM7Fixdj6NChCAoKQmRkJO6++26cPHmyzrgPPvgAHTt2hJ+fHxITE5GamnrVuRuyDxEREXkHXpdcHYMkrZC/Rs4kYZCEiMhTcnNzsWnTJiQnJ2PTpk3Izc1tluOmpqZiyJAhHp1zx44dmDlzJvbu3YtNmzbBYrFg9OjRKC8vV8b873//w5w5c/Dqq6/i0KFDGDBgAMaMGYPCwkKn8zZkHyIiInIfr0ta73WJIIqi2KRHuIaVlpYiJCQEer0ewcHBHpv3bxuO4eX1R/DksC74z4M3emxeIiJvZDQace7cOXTq1Al+fn4QRRFWq3tB5Ly8PIefPCQkJCAmJsbledRqNQRBcGms2WxGQEAAqqqql04mJiZi7969Lh/PVRcvXkRkZCR27NiBESNGKMcaOnQoVqxYAQCw2WyIi4vDM888g3nz5jmcpyH71P791NRUfyfJMT7fRERNy9HfPF6X1OXt1yU+bj9ianLMJCEics5qteK7777zyFzupmzefffd8PFx7U+nj48Pdu/ejcTERKSlpSEqKqrOH+tFixZh0aJF9c6Tnp6O+Pj4esfo9XoAQHh4OADpQujgwYOYP3++MkalUmHkyJFISUlxOEdD9iEiIrre8bqkLm+/LmGQpBWSC7eyJgkRkfdSqVTIy8tDREQEBgwY4HDMU089hfvuu6/eea72iZLNZsOsWbMwfPhw9O3bFwBw6dIlWK1WREVF2Y2NiorCiRMnHM7TkH2IiIjIO/C6xHUMkrRCcuFWdrchIqpLrVbj7rvvdmufrVu3orS0tM724OBg3H777W4d2x2HDx92eiECSJ+wyJ+yNNTMmTNx7Ngx7Nq1q1HzEBERkft4XWLvWrguYeHWVoiZJEREzgmCAB8fH7e++vTp43CuPn36uDWPq+t+ZWlpafVejCxatAiBgYH1fmVlZTnd/+mnn8b69euxbds2tG/fXtnepk0bqNVqFBQU2I0vKChAu3btHM7VkH2IiIiud7wuqXatXJcwSNIKMZOEiMizYmNjkZSUhJCQEKhUKoSEhCApKQmxsbFNetyjR49i4MCBTu9/6qmnkJaWVu+Xo7RWURTx9NNPY+3atdi6dSs6depkd79Go8HgwYOxZcsWZZvNZsOWLVuQlJTk8Fwasg8RERG5j9clrfu6hMttWiFmkhAReV5sbGyTX3zUZrPZcPLkSeTl5SEgIAAhISF29zc0rXXmzJn48ssv8f333yMoKAgXLlwAAISEhECn0wEA5syZg+nTp2PIkCFISEjAsmXLUF5ejscee0yZZ8WKFVi7dq1yAeLKPkRERNR4vC5pvdclzCRphaozSRgkISLyZgsXLsRnn32G2NhYLFy40GPz/vOf/4Rer8ett96K6Oho5et///ufMmbq1Kl4++23sWDBAgwcOBBpaWnYsGGDXQG0S5cu4cyZM27tQ0RERN6J1yWuEURRFJv0CE3EZDJhwYIFWL16NYqLi9G/f38sXLgQo0aNuuq+mzdvxt/+9jccPXoUVVVV6N69O5555hk88sgjbp2Dq32W3XUkpxgDl/yEqCA/XFg82WPzEhF5o/r63VPLq+/301R/J8kxPt9ERE2L1yStnyeuS7w2k+TRRx/Fu+++i4ceegjLly+HWq3G+PHjr1pFd926dRg9ejTMZjNee+01/O1vf4NOp8O0adPw3nvvNdPZ189fw+U2RERERERERM3NK2uSpKam4quvvsLSpUvxwgsvAACmTZuGvn37Yu7cudizZ4/TfVesWIHo6Ghs3boVWq0WAPCnP/0JPXv2xGeffYbZs2c3y2Ooj86XhVuJiIiIiIiImptXZpKsWbMGarUaM2bMULb5+fnhiSeeQEpKCrKzs53uW1pairCwMCVAAgA+Pj5o06aNUlSmpcmZJFU2ERarrYXPhoiIiIiIiOj64JVBksOHD6N79+511hElJCQAkPo/O3Prrbfi999/xyuvvILTp0/jzJkzePPNN3HgwAHMnTu33uOaTCaUlpbafTUFuXArAFSyeCsRERERERFRs/DK5Tb5+fmIjo6us13elpeX53TfV155BefOncPf/vY3paKvv78/vv32W9x11131Hnfx4sV4/fXXG3HmrtH6qCAIgCgClZYqBOt8m/yYRERERERERNc7r8wkqaystFsuI5Or11ZWVjrdV6vVonv37pgyZQr++9//4vPPP8eQIUPw8MMPY+/evfUed/78+dDr9cpXfct6GkMQBOh8pSU3bANMRERERERE1Dy8MpNEp9PBZDLV2W40GpX7nXn66aexd+9eHDp0CCqVFCO677770KdPHzz33HPYt2+f0321Wq3D4ExT0Pn6oMJsZYcbIiIiIiIiombilZkk0dHRyM/Pr7Nd3hYTE+NwP7PZjI8//hgTJkxQAiQA4Ovri3HjxuHAgQMwm81Nc9Jukou3ssMNERERERERUfPwyiDJwIEDkZGRUadwqpwFMnDgQIf7Xb58GVVVVbBa62ZnWCwW2Gw2h/e1BHm5DTNJiIiIiIiIiJqHVwZJpkyZAqvVig8//FDZZjKZ8OmnnyIxMRFxcXEAgKysLJw4cUIZExkZidDQUKxdu9YuY8RgMOCHH35Az549W1EbYGklFDNJiIiIiIiIiJqHVwZJEhMTce+992L+/PmYO3cuPvzwQ9x+++3IzMzE3//+d2XctGnT0KtXL+VntVqNF154ARkZGbjxxhuxbNkyvPPOO0hISEBOTg5efvnllng4DjGThIjo2jB79mxMmjTJo3P+85//RP/+/REcHIzg4GAkJSXh559/rjPugw8+QMeOHeHn54fExESkpqZede6G7ENERETegdclV+eVQRIAWLVqFWbNmoXVq1fj2WefhcViwfr16zFixIh693vppZfwxRdfwNfXF6+//jpeeeUVBAcHY82aNXjooYea6eyvjpkkRESelZqainnz5mH69OmYN29es735T01NxZAhQzw6Z/v27bFkyRIcPHgQBw4cwO2334677roLv//+uzLmf//7H+bMmYNXX30Vhw4dwoABAzBmzBgUFhY6nbch+xAREZH7eF3Seq9LBFEUxSY9wjWstLQUISEh0Ov1CA4O9ujcE/+1HeuP5eI/DybiyWFdPTo3EZE3MRqNOHfuHDp16gQ/Pz+Iouiww1l9Dh48iA8++ACCIEAUReV25syZGDx4sMvzaLVaCILg0liz2YyAgABUVVUHuxMTE6/abr6hwsPDsXTpUjzxxBPKsYYOHYoVK1YAAGw2G+Li4vDMM89g3rx5DudoyD61fz81NeXfSaqLzzcRUdNy9DeP1yWOefN1iVe2AL4eyN1tKs1cbkNEVJPJZMLjjz/eoH3lzwXk2w8++MCt/T/55JM6f3Cd8fHxwe7du5GYmIi0tDRERUXV2XfRokVYtGhRvfOkp6cjPj7e6f1WqxXffPMNysvLkZSUBEC6EDp48CDmz5+vjFOpVBg5ciRSUlIcztOQfYiIiK53vC6xdy1clzBI0kr5+15ZbmPhchsiIm+kUqmQl5eHiIgIDBgwwOGYp556Cvfdd1+98zhra3/06FEkJSXBaDQiMDAQa9euRe/evQEAly5dgtVqRVRUlN0+UVFRdgXNa2rIPkREROQdeF3iOgZJWikdM0mIiBzSarX45JNP3NpnwYIFyM3NRc0VpoIgoH379nj99dfdOrY7Dh8+7PRCBJBSUcPDw92aU9ajRw+kpaVBr9djzZo1mD59Onbs2KFckFDL2r9/P1auXIlt27YhMzMTERERuPHGG7Fw4UJ0795dGffoo49i5cqVdfbv0aMHg1NERF6A1yWSa+m6hEGSVoqZJEREjgmC4HJqqWzKlClYtmxZnbW/U6ZMcXsud6SlpdV7MdKYtFaNRoOuXaWaVYMHD8b+/fuxfPly/Pvf/0abNm2gVqtRUFBgt09BQQHatWvn8DgN2Yece+utt7B7927ce++96N+/Py5cuIAVK1bghhtuwN69e9G3b19lrFarxUcffWS3f0hISHOfMhERNQCvSyTX0nUJgyStFDNJiIg8JyEhAbNmzUJycjLy8/MRHR2NyZMnY+jQoU163KNHj2Ly5MlO729MWmttNptNKRyn0WgwePBgbNmyBXfffbdy/5YtW/D000873L8h+5Bzc+bMwZdffgmNRqNsmzp1Kvr164clS5bg888/V7b7+Pjg4YcfbonTJCKiFsDrktZ9XcIgSSvl7ysFSSosDJIQEXlCQkICEhISmvWYNpsNJ0+eRF5eHgICAupkBzQ0rXX+/PkYN24c4uPjUVZWhi+//BLbt2/Hxo0blTFz5szB9OnTMWTIECQkJGDZsmUoLy/HY489poxZsWIF1q5diy1btri8D7lm2LBhdbZ169YNffr0wfHjx+vcZ7VaUV5ezq40RETXCV6XtN7rEgZJWimdRvrVVJq53IaIyFstXLgQf/3rX7Fo0SK88MILWLp0qUfmLSwsxLRp05Cfn4+QkBD0798fGzduxKhRo5QxU6dOxcWLF7FgwQJcuHABAwcOxIYNG+wKoF26dAlnzpxxax9qOFEUUVBQgD59+thtr6ioQHBwMCoqKhAWFoYHHngAb731FgIDA+udz2Qy2bWdLC0tbZLzJiKiawOvS1wjiDWrxZBbXO2z3BAf7jqFP32Virv6t8d3M27x6NxERN6kvn731PLq+/005d9Jb/T555/jkUcewccff6y0i5w/fz5EUcQNN9wAm82GDRs2YOXKlRg+fDi2b98OHx/nn2e99tprDgv88fkmImoavCZp/TxxXcJMklaKNUmIiIiuHSdOnMDMmTORlJSE6dOnK9sXL15sN+7+++9H9+7d8dJLL2HNmjW4//77nc45f/58zJkzR/m5tLQUcXFxnj95IiKi64iqpU+AHGN3GyIiomvDhQsXMGHCBISEhGDNmjVQq9X1jp89ezZUKhU2b95c7zitVovg4GC7LyIiImocZpK0UnImSQUzSYiIiLyWXq/HuHHjUFJSgp07d7rUFUCn0yEiIgJFRUXNcIZERERUE4MkrZScSVLJTBIiIiKvZDQaMXHiRGRkZGDz5s3o3bu3S/uVlZXh0qVLaNu2bROfIREREdXGIEkr5c9MEiIiOzabraVPgRzg78Uxq9WKqVOnIiUlBd9//z2SkpLqjDEajbBYLAgKCrLb/uabb0IURYwdO7a5TpeIiNzAv32tlyd+NwyStFI63yuFWy0MkhDR9U2j0UClUiEvLw9t27aFRqOBIAgtfVrXPVEUYTabcfHiRahUKmg0mpY+pVbl+eefx7p16zBx4kQUFRXh888/t7v/4YcfxoULFzBo0CA88MAD6NmzJwBg48aN+OmnnzB27FjcddddLXHqRETkBK9JWi9PXpcwSNJK+WuuFG41c7kNEV3fVCoVOnXqhPz8fOTl5bX06VAt/v7+iI+Ph0rFWvA1paWlAQB++OEH/PDDD3Xuf/jhhxEaGoo77rgDmzZtwsqVK2G1WtG1a1csWrQIL7zwAp9TIqJWhtckrZ8nrksYJGmlamaSiKLICCURXdc0Gg3i4+NRVVUFq5UZdq2FWq2Gj48P/0Y5sH379quOCQ0NxerVq5v+ZIiIyGN4TdJ6eeq6hEGSVkrOJLHaRFisNmh86m8XSER0rRMEAb6+vvD19W3pUyEiIqLrGK9Jrm3M42yl5EwSgHVJiIiIiIiIiJoDgyStlMZHBdWVNCF2uCEiIiIiIiJqegyStFKCINSoS8LirURERERERERNjUGSVsxfIwVJmElCRERERERE1PQYJGnF5EwStgEmIiIiIiIianoMkrRicocbFm4lIiIiIiIianoMkrRicpCEmSRERERERERETY9BklasunArM0mIiIiIiIiImhqDJK0YC7cSERERERERNR8GSVoxnS9rkhARERERERE1FwZJWrHqTBLWJCEiIiIiIiJqagyStGKsSUJERERERETUfBgkacXY3YaIiIiIiIio+TBI0ooxk4SIiIiIiIio+TBI0ooxk4SIiIiIiIio+fi09AlQDcfXATuWAJdOA226YkDIQwBCmUlCRERERERE1AwYJGktjq8Dvn6k+ueCdEwqeBH3aP6ICnOHljsvIiIiIiIiousEl9u0FjuWABBqbBBhg4AF/j9xuQ0RERERERFRM2CQpLW4dBqAaLdJBRE9fAq43IaIiIiIiIioGTBI0lq06Qr7TBJAhICTVVGosDCThIiIiIiIiKipMUjSWtwyD/aZJAIEiHi9YgIqzcwkISIiIiIiImpqDJK0Fr3uBO5bDag10s9hHfH7iBX4zjwQFVxuQ0RERERERNTkGCRpTXrdCbTrL30/eiEqOo8HAFSycCsRERERERFRk2OQpLXxD5duK4rgr1FL3zKThIiIiIiIiKjJMUjS2vhHSLeVRdD5SkES1iQhIiIiIiIianoMkrQ2OjmT5DL8NT7St5YqiKJYz05ERERERERE1FheGyQxmUz461//ipiYGOh0OiQmJmLTpk0u7/+///0PSUlJCAgIQGhoKIYNG4atW7c24Rm7SBcm3VZUZ5KIImCusrXgSRERERERERFd+7w2SPLoo4/i3XffxUMPPYTly5dDrVZj/Pjx2LVr11X3fe211/DAAw8gLi4O7777LhYuXIj+/fsjNze3Gc78KuSaJJXFSiYJIGWTEBEREREREVHT8bn6kNYnNTUVX331FZYuXYoXXngBADBt2jT07dsXc+fOxZ49e5zuu3fvXrzxxht45513MHv27OY6ZdfVqEniq1bBRyWgyiaiwmxFmH/LnhoRERERERHRtcwrM0nWrFkDtVqNGTNmKNv8/PzwxBNPICUlBdnZ2U73XbZsGdq1a4fnnnsOoijCYDA0xym7rkZNEgDQXelwU8kON0RERF5j//79ePrpp9GnTx8EBAQgPj4e9913HzIyMuqMPX78OMaOHYvAwECEh4fjkUcewcWLF1vgrImIiMgrgySHDx9G9+7dERwcbLc9ISEBAJCWluZ03y1btmDo0KF4//330bZtWwQFBSE6OhorVqy46nFNJhNKS0vtvjyuRgtgAPD3vVK81czlNkRERN7irbfewrfffos//OEPWL58OWbMmIFff/0VN9xwA44dO6aMy8nJwYgRI3D69GksWrQIL7zwAn788UeMGjUKZrO5BR8BERHR9ckrl9vk5+cjOjq6znZ5W15ensP9iouLcenSJezevRtbt27Fq6++ivj4eHz66ad45pln4Ovriz/96U9Oj7t48WK8/vrrnnkQzuiqa5JAFJlJQkRE5IXmzJmDL7/8EhqNRtk2depU9OvXD0uWLMHnn38OAFi0aBHKy8tx8OBBxMfHA5A+9Bk1ahQ+++wzu6xZIiIianpemUlSWVkJrVZbZ7ufn59yvyPy0prLly/jo48+wgsvvID77rsPP/74I3r37o2FCxfWe9z58+dDr9crX/Ut62kwOZNEtAImPTNJiIiIvNCwYcPsAiQA0K1bN/Tp0wfHjx9Xtn377be44447lAAJAIwcORLdu3fH119/3WznS0RERBKvDJLodDqYTKY6241Go3K/s/0AwNfXF1OmTFG2q1QqTJ06FTk5OcjKynJ6XK1Wi+DgYLsvj/PxA3wDpO9rtAFmJgkREZF3E0URBQUFaNOmDQAgNzcXhYWFGDJkSJ2xCQkJOHz4cL3zNcsyYCIiouuMVwZJoqOjkZ+fX2e7vC0mJsbhfuHh4fDz80NERATUarXdfZGRkQCkJTktrkZdEv8ry20qzAySEBERebMvvvgCubm5mDp1KoDq6xZnS4iLioocfigkW7x4MUJCQpSvuLi4pjlxIiKi64hXBkkGDhyIjIyMOp+Y7Nu3T7nfEZVKhYEDB+LixYt1iqHJdUzatm3r+RN2ly5Muq0sgu7KcptKC5fbEBEReasTJ05g5syZSEpKwvTp0wFULw9uyBJioJmWARMREV1nvDJIMmXKFFitVnz44YfKNpPJhE8//RSJiYnKJylZWVk4ceKE3b5Tp06F1WrFypUrlW1GoxFffPEFevfu7TQLpVkxk4SIiOiaceHCBUyYMAEhISFYs2aNks0qLwNuyBJioJmWARMREV1nvLK7TWJiIu69917Mnz8fhYWF6Nq1K1auXInMzEx8/PHHyrhp06Zhx44dEEVR2fanP/0JH330EWbOnImMjAzEx8dj9erVOH/+PH744YeWeDh1KR1uiqDzbS99y5okREREXkev12PcuHEoKSnBzp077T6MkZfZOFtCHB4e7jDLhIiIiJqOVwZJAGDVqlV45ZVXsHr1ahQXF6N///5Yv349RowYUe9+Op0OW7duxdy5c/HJJ5+gvLwcAwcOxI8//ogxY8Y009lfhX+EdFtRBH8Nu9sQERF5I6PRiIkTJyIjIwObN29G79697e6PjY1F27ZtceDAgTr7pqamOl0+TERERE3Ha4Mkfn5+WLp0KZYuXep0zPbt2x1uj4yMxGeffdY0J+YJciZJxWWluw2DJERERN7DarVi6tSpSElJwffff4+kpCSH4yZPnoyVK1ciOztbWS68ZcsWZGRkYPbs2c15ykRERAQvDpJc0/yrl9vImSRcbkNEROQ9nn/+eaxbtw4TJ05EUVERPv/8c7v7H374YQDAiy++iG+++Qa33XYbnnvuORgMBixduhT9+vXDY4891hKnTkREdF3zaJBkz549WLt2Lfbs2YNbb70Vd999N4YOHerJQ1wfatQk8Q9g4VYiIiJvk5aWBgD44YcfHNY8k4MkcXFx2LFjB+bMmYN58+ZBo9FgwoQJeOedd1iPhIiIqAU0KkhisViwZcsWrF27FuvWrUNhYSHUajV69OiBt956C0uWLEFMTAzuuece3HPPPbjlllugUnllQ51mkZubi/T0dJSVqhDU5TX0Nqcpy22YSUJEROQ9nC35daRPnz7YuHFj050MERERuaxBQZKvv/4a3333HX766SeUlZVBq9Vi9OjRmDRpEiZOnIiwsDAUFhbiu+++Q3JyMv7973/jgw8+QFhYGCZOnIgpU6ZgwoQJnn4sXi03NxcpKSnKz3ptLFL84hAmlgNgTRIiIiIiIiKiptagIMn999+PkJAQ3HHHHZg0aRLGjh0Lf39/uzGRkZGYMWMGZsyYgdLSUqxfvx5r167FmjVr8OOPP6KwsNAjD+BakZ6ebr9BUAGiDcEV0vPETBIiIiIiIiKiptWgIMmGDRtw++23w8fHtd2Dg4Px4IMP4sEHH4TJZMLOnTsbcthrWllZWd2NggqqKhMAZpIQERERERERNbUGFQgZPXq0ywGS2rRaLUaOHNmgfa9lQUFBdTeKNqh92N2GiIiIiIiIqDmwimor0bt3b/sNog0QVAgNkirbs7sNERERERERUdPyeJDkscceQ2VlJQAgLy/P09Nfs2JjY5GUlKR0//G3liIpawXaaqTnstLC5TZERESewGsVIiIicqZRLYAd0el0MJvN0Ol0aN++PSIiItC/f3/la8CAAejTpw+0Wq2nD+31cnNzsXXrVpSUlCAyQECboHy0sZYC8GEmCRERkYfwWoWIiIic8XiQ5P/+7/+U73NycnDkyBHla+PGjTh16hQEQUDXrl0xdOhQjB07FlOnTlUyKK5XqampWLZsmfLzhTJgWdlgTD99DkA31iQhIiLyEF6rEBERkTMeD5LUFBMTg5iYGIwbN07ZZjKZcOzYMRw5cgT79+/HrFmzsHv3bqxYsaIpT6XVS05OrrNNgIjNh88DQjd2tyEiImoCvFYhIiKimgRRFMWWPIGDBw9i1KhRKCoqasnTaJDS0lKEhIRAr9cjODi4UXNNnz4dFoulznYfFbA6dAIAwPaPByEIQqOOQ0RE1Fw8+XeyJXnLtcq18nwTERE1BVf/TrZ43mjfvn3xyiuvtPRptLjo6Og6ARABIqIDq7cZueSGiIio2fFahYiI6PrR4kESrVaL2bNnt/RptLhJkyahdlKPCAGTupqUn1m8lYiIqPnxWoWIiOj60eJBEpIkJCRg1qxZ8PGRysQEBARgdswBJIZchq9a+jWxeCsRERERERFR02mxIIlKpcLtt9+OgwcPttQptDoJCQno1q0bAKBXr14YElQIVBbBX6MGABZvJSIiaka8ViEiIrr+tFiQ5JNPPsEtt9yCZ599tqVOoVUKDw8HABiNRpjV/kBFEXS+UpCEmSRERETNh9cqRERE1x+PB0meeeYZGI1GAIBer3c67tFHH8Wrr76K3bt3e/oUvFpoaCgAqf2gyScYMJUiWCPdx0wSIiKi5sNrFSIiouuPx4Mker1eCZKEhYWhY8eOuOuuu/DKK69gzZo1yMjIqFOglKqFhIQAkDJJjGrp+3Y+0vPJTBIiIqLGc/UDHSIiIrr++Hh6wlWrVinfnzhxAkeOHMFvv/2GI0eOYPXq1cjKyoK/vz/69OmDffv2efrwXk8OkphMJph07YCKE2jnWwlAx0wSIiIiD5A/0PHz80NYWBji4+MxYMAA9O/fX7nt1q0bBEFo6VMlIiKiZubxIElN3bt3R/fu3XHvvfcq2/R6vRI4obrk5TZGoxEmXRQAIFJdDkDHTBIiIiIP4Ac6RERE5EyTBkkcCQkJwYgRIzBixIjmPrRXqJlJYtS2AyAHSdqgwswgCRERkSfxAx0iIiKqqcW625BjNYMklVdqkkQI5QCACguX2xARETXEH/7wB1y4cMGlsfIHOk8//XQTnxURERG1Nm4HSWw2m93PBQUFuOeeexAUFASNRoMePXpg6tSpWLJkCTZs2ICCggKPnez1ICgoSFkDXWzVAQDCVVKQpJKZJERERA2ybds2DBgwAD/++GNLnwoRERG1Ym4FSX7//XckJibabXv66afx/fffIyIiAsOHD4fRaMQ333yDF198ERMmTEBMTAxiYmIwYcIEj574tUqlUiEgIAAAoDdLv54wGAAwk4SIiKihvvvuO1RVVeHOO+/ErFmzYLFYrrrP9u3b7eqXEBER0bXPpSCJzWbDm2++iSFDhmDUqFF2923duhVDhgzBqVOnsG3bNpw/fx6XLl3CL7/8giVLluC+++5DYGAgNm7c2CQP4FokL7nRm6SsnRCxDAAzSYiIiBrqzjvvxG+//YabbroJ77//PhITE5GRkVHvPl999RX++Mc/NtMZEhERUWvgUuHWt956C++88w6Sk5Mxbtw4u/tEUcTtt98OX19fZVt4eDhGjhyJkSNHKtsMBoOHTvnaFxISgtzcXJRVSp9yBdukIEkFu9sQERE1WGxsLLZt24Y333wTCxcuxODBg7F8+XI8/vjjyhi9Xo+0tDT8+uuvWLlyJTp06NCCZ0xERETNzaUgSUxMDAwGA3bt2oUxY8ZApapOQBk2bBjOnz9/1TkCAwMbfpbXmbCwMABARaUJVYIGgbZSAEClmcttiIiIGkOlUuHVV1/F7bffjkmTJuGPf/wjkpOTodVqcfjwYeWaRhRFqNVqvPjiiy18xkRERNScXAqSTJ8+HR06dMCTTz6JXbt2YceOHcp9c+fOxYQJE3D+/Hl+2uIhoaGhAKQONya/YARYpSBJQzJJcnNzkZ6ejrKyMgQFBaF3796IjY315OkSERG1ejabDWvXrsXu3buRlpaGI0eOoLi4GADw008/QRAEhISE4J577sHAgQMxcOBAJCYmom3bti185kRERNScXAqSAMCtt96KI0eOYP78+XbbT5w4gfHjx2Ps2LH4+uuv0a9fP4+f5PVGDpIYjUYYA4Ohq5Iu4irdDJLk5uYiJSVF+Vmv1yMlJQVJSUkMlBAR0XXlhRdewPLlyyGKIjQaDfr27YtJkyZh0KBBqKiowIIFC2AwGDBo0CC89NJLLX26RERE1EJcDpIAQEBAAN5//327bU899RQEQYAoisqnLiNHjsQNN9yAwYMHIy4uzqMnfD2QC7eaTCaYfIIQVpkJATZUuLncJj093el2BkmIiOh68t///hcxMTH46quvkJiYCB8f+0ugMWPG4P7778eCBQuwbds2fP7552jXrl0LnS0RERG1FLdaADvy5ZdfYu7cuRgzZgwiIyOxd+9eLFy4EJMnT0bHjh0RGRmJMWPGcE2vG+wySXxCoIINIYLR7UySsrIyt7YTERFdqwoLC/Hggw9i+PDhdQIkANCvXz8cPHgQTz75JLZu3YoBAwbgp59+atQxDQYDXn31VYwdOxbh4eEQBAGfffZZnXGPPvooBEGo89WzZ89GHZ+IiIjc51YmiSP3338/7r//fuXngoICpKWl2X1t2bIFmzdvxqJFixp7uOuCXSaJJgIAECEY3M4kCQoKgl6vd7idiIjoepKVlYWqqvr/jvr5+eHf//43Ro0ahT/+8Y+YOHEinnvuObz77rsNOualS5fwxhtvID4+HgMGDMD27dudjtVqtfjoo4/stsnXA0RERNR8Gh0kqS0qKgpjxozBmDFjlG2VlZX47bffPH2oa5Z8UWQ2m1HhGwMAiFCVu51J0rt3b7uaJDW3ExERXU/cWWY6ZcoUJCQk4IEHHsDy5csbHCSJjo5Gfn4+2rVrhwMHDmDo0KFOx/r4+ODhhx9u0HGIiIjIcxq93MYVOp0OiYmJzXGoa0JgYKDSZrkYUtZHhKocFWb3giSxsbHo37+/8rNarWbRViIiIhfEx8dj586djSriqtVq3aprYrVaUVpa2uDjERERUeM1KEjSsWNHzJkzB7/++itEUXRpH6vVis2bN+Mvf/kLBgwY0JDDXjdUKhUCAgIAACVVWgBAmwYstwGA4OBg5XuNRsMACRERkYtUKhXeeOONZjlWRUUFgoODERISgvDwcMycORMGg6FZjk1ERETVGrTcpmvXrlixYgWWL1+ONm3a4K677sI999yDkSNHwtfXVxlnNBrx888/Y+3atfjxxx9RUlICX19f3HnnnR57ANeq4OBglJWVodQixbEastwGkC66ZJWVlbBarVCr1R47TyIiImqc6OhozJ07FzfccANsNhs2bNiA//u//8ORI0ewfft2h4VmgSu1y0wm5WdmoRARETVeg4IkmzdvRklJCdatW4e1a9fiiy++wMcff4ygoCCMHz8eN954I7Zv345ffvkFFRUVCAoKwrhx43DPPfdg/PjxLBzqgpCQEOTm5qLUKGXqRAjlDcokqaystPvZYDCwEBwREVErsnjxYruf77//fnTv3h0vvfQS1qxZY1cgv/Z+r7/+enOcIhER0XWjwTVJQkNDMW3aNKxduxaXLl3Ct99+i7vuugu//PILZs2ahd27d+OBBx7A+vXrcfHiRXz11VeYOnUqAyQuCgsLAwCUm6pggwoRqnKYqmyw2Vxb3iSrmUkCAOXl5R47RyIiImoas2fPhkqlwubNm52OmT9/PvR6vfKVnZ3djGdIRER0bfJIdxudToe7774bd999N6xWK06cOIFevXopxUfJfeHh4QCkJUsmvyC0UUnrkistVgRoXf+1yUESQRAgiiLXNxMREXkBnU6HiIgIFBUVOR2j1Wqh1Wqb8ayIiIiufR6PYqjVavTp04cBkkYKDQ0FcGW9sToIEYKUAVJpcW/JjbzcRg66MEhCRETU+pWVleHSpUto27ZtS58KERHRdYWRjFZKrhtiNBph9AlBG5UUJHGnDbAoikomiXyRxSAJERFR62E0GlFWVlZn+5tvvglRFDF27NgWOCsiIqLrl0eW27QEk8mEBQsWYPXq1SguLkb//v2xcOFCjBo1yq15Ro0ahc2bN2PmzJlYsWJFE52t++QgiclkgsknCBGqPABwq8ON2WyGzWYDIAVJTpw4wZokREREzWjFihUoKSlBXp70d/yHH35ATk4OAOCZZ55BcXExBg0ahAceeAA9e/YEAGzcuBE//fQTxo4di7vuuqvFzp2IiOh65LVBkkcffRRr1qzBrFmz0K1bN3z22WcYP348tm3bhptuusmlOZKTk5GSktLEZ9owNTNJTD7BiBQMAES3OtzIWSRarRbBwcEApMKtNpuNy6GIiIiawdtvv43z588rPycnJyM5ORkA8PDDDyM0NBR33HEHNm3ahJUrV8JqtaJr165YtGgRXnjhBf69JiIiamZeGSRJTU3FV199haVLl+KFF14AAEybNg19+/bF3LlzsWfPnqvOYTQa8fzzz+Ovf/0rFixY0NSn7Da5JonFYoFBFQSdYIE/zG5lksj1SPz9/eHn5we1Wg2r1Yry8nJ2GSIiImoGmZmZVx2zevXqpj8RIiIicolXfjyxZs0aqNVqzJgxQ9nm5+eHJ554AikpKS61wPv73/8Om82mBFlam4CAAOXToxKblAUSoSpvUCaJTqeDIAgIDAwEwLokRERERERERI54ZZDk8OHD6N69u7KERJaQkAAASEtLq3f/rKwsLFmyBG+99RZ0Ol1TnWaj1AxqFIvSrRQkcT2TRA6S+Pv7A5ACLwBYl4SIiIiIiIjIAa9cbpOfn4/o6Og62+VtcnE0Z55//nkMGjQI999/v1vHNZlMMJlMys+lpaVu7e+u4OBglJaWQm/VAAAihHK3WgDLy23kQBAzSYiIiIiIiIic88pMksrKSmi12jrb/fz8lPud2bZtG7799lssW7bM7eMuXrwYISEhyldcXJzbc7hDLt5aZlYDACJUhkZlkjBIQkREREREROScVwZJdDqdXUaHzGg0Kvc7UlVVhWeffRaPPPIIhg4d6vZx58+fD71er3y5UvukMeTirQazFSKANkJ5gwq3ys8Hl9sQEREREREROeeVy22io6ORm5tbZ3t+fj4AICYmxuF+q1atwsmTJ/Hvf/+7TrX5srIyZGZmIjIyUsm8qE2r1TrMYGkq4eHhAIBKoxkWrc6twq2iKNp1twHsM0lEUYQgCE1w1kRERERERETeySuDJAMHDsS2bdtQWlpqV7x13759yv2OZGVlwWKxYPjw4XXuW7VqFVatWoW1a9fi7rvvborTdltYWBiAK7VQAoIRIRhQ6mImidFoVAIh8jIkf39/qFQq2Gw2VFRUKJkl5H1SU1ORnJys1OeZNGmSUriYiIiIiIiIGsYrl9tMmTIFVqsVH374obLNZDLh008/RWJiolIrJCsrCydOnFDG3H///Vi7dm2dLwAYP3481q5di8TExOZ9MPWQa5KYTCYYfYLdyiSR65H4+fkprYQFQVACI6xL4r1SU1OxbNkyZGdnw2KxIDs7G8uWLUNqampLnxoREREREZFX88pMksTERNx7772YP38+CgsL0bVrV6xcuRKZmZn4+OOPlXHTpk3Djh07IIoiAKBnz57o2bOnwzk7derUajJIZHJNEqPRCJNPMNqoXK9JUnupjSwgIABlZWWsS+LFkpOTIQiC8u9azhhKTk5mNgkREREREVEjeGWQBJCWx7zyyitYvXo1iouL0b9/f6xfvx4jRoxo6VPzGHkpkclkgkkdjAjhosvdbeRMktpFbNnhxvvl5+crARKZKIpKTR4iIiIiIiJqGK8Nkvj5+WHp0qVYunSp0zHbt293aa7abzhbCzmTxGKxwKAKQoSqHJUW95bb1M4kYZDE+0VHRyMrK8tumyAIiI6ObqEzIiIiIiIiujZ4bZDkeuDv7w+1Wg2r1YoiWwA6CuUuZ5LIy23OnTuHL7/8UinwefvttwNgG2BvNmnSJCxbtkz5WV56M3ny5JY7KSIiIiIiomsAgyStmCAICAwMhF6vR7HVHyEqI0xmo0v7VlRUIDc3F/v27VPeRGdnZ+Ozzz5DYmIi1Go12wB7qf79+9v93L59e0yZMgVDhw5toTMiIiIiIiK6Nnhld5vriVyXpLRKCwDQmEpc2q+yshLHjx93WODzxIkTsFqtMBpdC7hQ63Lu3Dm7n+fMmcMACRERERERkQcwSNLKyUESfZUvAEBj0V91H5vNBqPRCIPB4LDAZ1lZGQDWJfFWZ8+etfu5uLi4hc6EiIiIiIjo2sIgSSsXFhYGADBYpGUxfpaSq+4j1yMJCgqqs5xGEARlTtYl8U5nzpyx+5lBEiIiIiIiIs9gTZJWTg5oVJqtsGp8oKsqveo+cmebgQMHYseOHXb3iaKIm266CUDzZJLk5uYiPT0dZWVlCAoKQu/evREbG9vkx72WyZkkwcHBKC0tZZCEiIiIiIjIQ5hJ0sqFh4cDAIxGI0w+wQhwIUgiZ5L07NkTd911l7JdEAQ899xzGDRoEICmD5Lk5uYiJSUFer0eNpsNer0eKSkpyM3NbdLjXsvKyspQWFgIAMrvkUESIiIiIiIiz2CQpJULCQkBAJhMJhjVwQi0uZ5JotPpEBAQoGwXRRHx8fEIDAwE0PTLbdLT093aTlcnZ5G0a9cO7du3B8AgCRERERERkacwSNLKhYaGAqjOJAkWy666j5xJ4u/vj7y8PLv7zp49qwRJHBV29RSbzYbSUscBHblwLLlPrkfSuXNnZSkWgyRERERERESewSBJK2eXSeITjFDRAKvNVu8+ciZJzSCJPM/Zs2eV7BKLxQKz2ezxc66oqMCvv/7qNAATFBTk8WNeL+RMki5dujBIQkRERERE5GEMkrRycnCjqqoKBiEIEapyVFqs9e4jZ5LodDolSDJs2DAA0ptstVoNnU4HwPN1SfLz87F582ZcunQJKpXjf17yMhFyjyiKTjNJmiojiIiIiIiI6HrCIEkrp9Pp4OMjNSG6bAtEG5UBFeb6gyRyJonNZlOCIMOHDwcAZGZmwmazebwuic1mw2+//Ybdu3fDbDYjNDQUo0aNQlJSEkJCQqBSqaDRaAAA2dnZsF0lG4bqKioqgl6vh0qlQseOHZWlWCaTSQmMERERERERUcOxBXArJwgCAgICoNfrUWL1R0+hHJWWKqfjq6qqlCU0RUVFAIA2bdqgY8eO8PPzg9FoRF5eHgIDA3Hx4kWPZJKUl5dj3759yvG6du2Kfv36Qa1WIygoSGn5azabsXHjRpSWliIjIwM9e/Zs9LGvJ3IWSVxcHLRaLQBpSVVFRQWKi4vh7+/fkqdHRERERETk9Rgk8QLBwcHQ6/XQW7WIUJXjUj2ZJHJGgY+Pj9IqNjY2Vsk+OHHiBM6cOYOoqCgADV9uk5ubi/T0dJSWlipLPXx9fTFkyBAlKFKbRqNB//79sX//fqSnp6N9+/ZKRktDj19WVoagoCD07t3b6XGvFXI9ks6dOyvbwsLClCDJtf74iYiIiIiImhqX23iB4OBgAECpxQfhQjkqTM6LrdZs/5ufnw8AiImJAVD95rp2hxt35ebmIiUlBXq93q4WRt++fa/6Rj0+Ph6RkZGw2Ww4fPhwg2pp1Dy+zWaDXq9HSkoKcnNz3Z7Lm9SsRyJj8VYiIiIiIiLPYZDEC8hvhMuqVFALIszlJU7HOmr/WztIcu7cuUbVJElPT3e4Xc50qI8gCBg0aBBUKhUKCgqQk5PjseM7234tsNlsOHfuHACps42MQRIiIiIiIiLPYZDEC8hvhMvNIkQIsBouOR3rqP1v7SDJ+fPnlZoWJpMJFovFrfMpKytza3ttQUFBSj2StLQ0t9sQl5aWNur43ujChQuoqKiAr6+vXXcgBkmIiIiIiIg8h0ESLxAeHg4AMJnMMKkDYau47HSsnEni6+uLixcvAqgOkkRFRcHf3x8WiwUFBQVKoMTdJTfO6ogEBQW5PEePHj0QFBQEk8mEY8eOubSPKIrIyMhwukTHneN7GzlLp2PHjkq3I4BBEiIiIiIiIk9ikMQLyG+EjUYjTD7BEMuLnI6VM0nKy8shiiICAgKUmiaCIKBTp04AGleXpF27dg639+7d2+U51Go1brjhBuVcLl92HvgBpK49qamp+O2335yOcef43sZRPRKAQRIiIiIiIiJPYpDEC4SEhACQlsYYfYIhGJ2/IZaDJHq9HoCURSIIgnK/XM/izJkzDa5LYjKZAEjdalQqFUJCQpCUlOR2d5W2bduiY8eOAICDBw/CZrM5HGcwGLB161ZkZ2dDEAQMHDgQQ4cOVe4PDg5u0PG9iZxJUrMeCcAgCRFRa2YwGPDqq69i7NixCA8PhyAI+OyzzxyOPX78OMaOHYvAwECEh4fjkUceUTJCiYiIqPmwBbAXkIMkRqMRJnUQhErHmSSiKCrLbeQ3zfJSG1nN4q3Dhw8H4F4miSiKStecG2+8EZGRkW48krr69euHvLw8lJaWIiMjQ6lVIsvPz0dqaiosFgu0Wi1uvPFGtG3bFoBUz8RisSAxMVF5jq5FVVVVyMzMBFB/kEQURbuAGBERtaxLly7hjTfeQHx8PAYMGIDt27c7HJeTk4MRI0YgJCQEixYtgsFgwNtvv42jR48iNTUVGo2meU+ciIjoOsYgiRcIDQ0FAFitVpQKwfAxOc4asFgsqKqqAoA69Uhk8nKb7OzsBtUkKSoqgtlshq+vL9q0aePW43BEq9Wif//+OHDgAI4fP464uDgEBARAFEUcP35c6VgTHh6OpKQk6HQ6Zd+AgACUlJSgoqLimg6S5OTkwGKxwN/fH1FRUXb31fy3UVZWpiytIiKilhcdHY38/Hy0a9cOBw4csMuCrGnRokUoLy/HwYMHER8fDwBISEjAqFGj8Nlnn2HGjBnNedpERETXNQZJvICfnx98fX1hsVhw2RaEAFOJw3FyFolGo8GFCxcA1A2StGnTBsHBwSgtLVWW5LgTJJGzSKKioqBSeWa1VocOHXD+/HlcvHgRmzZtgs1mg0qlUgI+Xbp0wYABA+ocz9/fHyUlJQ1qY+xN5HoknTp1qvMc+Pj4KL/PkpISBkmIiFoRrVbrtI5XTd9++y3uuOMOJUACACNHjkT37t3x9ddfM0hCRETUjFiTxEsEBAQAAEqs/tCYSxyOkeuR6HQ6JZhRO0hSs3irHEgxGo1KQOJq5Hmjo6PdewD1EARBOc+qqirYbDa7AMmgQYMcBmT8/f0BVD/ua5WzeiQy1iUhIvJeubm5KCwsxJAhQ+rcl5CQgMOHDzvd12QyobS01O6LiIiIGodBEi8ht7fVW7Xws+gdjpGDBVVVVTCZTFCr1Ygs2gf8axiwMFK6Pb5OqUuSlZUFX19fAK4Vb62oqFCyT1z5ZMwdcs2N2i5duuR0HzlwdL0ESWp3tpHJQZKiIuddj4iIqHWq78OH6OhoFBUVKQXTa1u8eDFCQkKUr7i4uCY9VyIiousBgyReQq65oa/yhX+V4yCJvNxGvm0XFgD1mulAQTpgNUm3Xz+Czr5S4OHcuXNutQGWL+QiIiKUeiaeUlZW5tZ2oDqT5FpebmMymZCdnQ2AmSRERNci+W+2o7+rfn5+dmNqmz9/PvR6vfIl/70gIiKihmNNEi8hF+g0WFTwh+N0WjmjQg4sxCAfgABAvDJCuu18+mMA3ZGTk+NW8damWGojCwoKUrJUam935npYbpOZmQmbzYbQ0FCEh4c7HMMgCRGR95ILkjvKFjEajXZjatNqtR7/0IKIiOh6x0wSLyG/ETZYgABrKSCKdcbInzSVlJQAAGJQiOoASY25yk8hzNcMURRRduEcgCvZGMfX1VmaI6uqqkJhYSGApgmS9O7d263tQPVyG5PJBKvV6vFzag1qLrVx1t5X/rch/96JiMh7yH9T5Q8iasrPz0d4eDgDIURERM2IQRIvERERAQAwmixQqdSApW72hJxRcfnyZQBAbEQgpEySWgQVOmul+hVFB74FABjO7AW+fqTO0hw5UHLx4kXYbDb4+/s3SQeV2NhYJCUlISQkBCqVCiEhIUhKSkJsbKzTfXx9feHjIyVDXavZJHJnG2f1SIDqLCNmkhAReZ/Y2Fi0bdsWBw4cqHNfamoqBg4c2PwnRUREdB1jkMRLyNkCRqMRJp9goNK+SKcoikomiZzxEXPTQ7DPJLkSMJn8CToPHQsAuGCQ7jdY5GBKzaU5ArDjLQD2S22cZTQ0VmxsLEaNGoVJkyZh1KhR9QZIAKkrzrVel0QOkjirRwKwcCsRkbebPHky1q9fb1dTZMuWLcjIyMC9997bgmdGRER0/WFNEi8hF241mUwwqoMQWHEZCKmuYm8ymWCz2WA2m5UWgNGJk4C984CqSkDtC7TpAdwyD+g1EZ2rjgDb0pDj2wkdAFT4RsAq+EAt1mwFLAKXT0EURSVI4umuNo3l7++P0tLSazKTxGAwoKCgAED9mSRyrRK9Xg+r1Qq1Wt0s50dERFe3YsUKlJSUIC8vDwDwww8/ICcnBwDwzDPPICQkBC+++CK++eYb3HbbbXjuuedgMBiwdOlS9OvXD4899lhLnj4REdF1h0ESLyEHSYxGI4zqYKDCPmtADhKYzWYA0htn3cU0KUDi3wZ44RQgVCcOderUCQBQUFAIm80GlUqFCt82CDJfqDGrAER0g16vR2VlpdRSODKy6R5kA1zLbYDPnZPqxURGRtZbwDY4OBgqlQo2mw2lpaVKZgkREbW8t99+G+fPn1d+Tk5ORnJyMgDg4YcfVlr37tixA3PmzMG8efOg0WgwYcIEvPPOO6xHQkRE1MwYJPEScpDEZrOhVKi73KZ2+9/o6Gjg1Ebpzm6j7QIkgPTGum3btrh48SIqKysREBAAg6ZtjSDJla44t8xTskgiIyNbXZbCtdzhxpV6JACgUqkQGhqKoqIiFBcXM0hCRNSKZGZmujSuT58+2LhxY9OeDBEREV0Va5J4CT8/P/j4+gIALotBQIV9kU45SCDX5oiJiQEyrlxsdR/jcE45m0RuGWxIfB4IuJIp4hcM3Pc50Gtik7b+baxruSaJ3NmmvnokMhZvJSIiIiIiajwGSbyIv7+0tKSkyt9pJolcjyQ2VAtcPgWofIAutzucT85QkAt+lgd2AiZ9KN3pFwr0mgiTyaTc3xqDJNfychtXirbK5OwRBkmIiIiIiIgajkESLxIYFAgAKLbqgIrLdvfJQQL5TXJMVaZ0R4dhgNZxy145SCIXBzUYDEDMDQAEoOQ8UH4JFy5Iy29CQ0Oh0+k8+XA8Qs4kqayshM1ma+Gz8Zzi4mIUFxdDEAR07NjxquPl4q3scENERERERNRwDJJ4keBgKdhRWuUDW7l9kEQOEihBkpJU6Y5uY53OJy+3KS4uhslkkoIkfiFAm+7SgNwDrbarjUyr1UKlkv4ZX0vZJHIWSfv27eHn53fV8XImSUlJSVOeFhERERER0TWNQRIvEhYqvREuq1LD5qC7jcFggM1mg5+fFmH5u6Q7ujsPkgQEBCjBj5KSEpSXl0vZGO2HAABs2QeUTJLWuNQGAARBuCaX3LhTjwRgTRIiIiIiIiJPYJDEi0SES0GScjMg1lhuY7PZUFlZqRRgjQnzhyBagIiuQET9b7LlJTclJSUQRVGqbRI7GABw6UI2qqqqoNVqleUcrVF9HW5SU1Mxb948TJ8+HfPmzUNqampzn16DuNrZRsaaJERERERERI3HIIkXiYiIAABUmK2wVeiV7UajEcCVmiIAYjTSLbo57mpTk/wmXC74ajAYgFgpkyS/Quqm065dOwiC4IFH0DScdbhJTU3FsmXLkJ2dDYvFguzsbCxbtqzVB0pEUVQySRgkISIiIiIiaj4MkngROZvDZDLBUmVRtssZFHKHmxiL9AbbWevfmmp3uDEYDEBUH8BHh3z/ngBa71IbmbNMkuTkZAiCAFEUAUjBB0EQkJyc3Ozn6I6CggKUl5fDx8cH8fHxLu0jB0nKyspgsViuMpqIiIiIiIgcYZDEi8hvhI1GIyyiGrCaAVQHR5TlNiiUOtrEJ111zo4dO0IQBJSXl6OyslIKkqh8UBY7AgZtOwgQERUV1USPyDOc1STJz89XAiQyURSVYrStlZxF0qFDB/j4+Li0T2BgIHx9pcwfFm8lIiIiIiJqGAZJvEhISAgAKZPEqA4GKqWlFRUVFRBFEXq9tAQnRmMAutwGqDVXndPPzw+xsbEAqou3AkB+xDAAQFuVXnnz3Vo5yyRxlAEjCEKrz4yR65G4WrQVkB4Xi7cSERERERE1DoMkXkQOkthsNpQgBLjS4aayshJGoxFmsxkqiGinKa+39W9t8pKb4uJipa7JBXV7AEB02RFPPoQmUTNIUjNzZNKkSXXGiqKIyZMnN9u5NYS7RVtlrEtCRERERETUOF4bJDGZTPjrX/+KmJgY6HQ6JCYmYtOmTVfdLzk5GVOnTkXnzp3h7++PHj164Pnnn/eKJQoajQYqHymro8gWCFzpcFNRUaEstYnyLYePAKDbKJfn7dSpEwDpzXV5eTnMZjMuVkqFWqPzfgEsrbu1rk6nU2qPyEuPACAhIQEdO3a0GztgwAAMHTq0mc/QdVarFZmZmQDcyyQBGCQhIiIiIiJqLK8Nkjz66KN499138dBDD2H58uVQq9UYP348du3aVe9+M2bMwPHjx/Hwww/j/fffx9ixY7FixQokJSXZvcFurdQaPwBXgiSV1ZkkSj0SrQFoPwQIaOvynPKb8ZKSElRVVeH8+fMQRRFBlosINF8A8lt3NokgCA6X3JjNZuTl5QEAHnzwQQBSlkZVVVXzn6SLcnNzYTabodPp3F4WxCAJERERERFR47hWFbKVSU1NxVdffYWlS5fihRdeAABMmzYNffv2xdy5c7Fnzx6n+65Zswa33nqr3bbBgwdj+vTp+OKLL/Dkk0825ak3mkqrAyrKUGLVKsttamaSxGjKgW5T3JozPj4earUaJpMJlZWVOH36NACgnbpEGpBzwKUisC3J398f5eXldkGS48ePw2w2Izw8HOPGjcNPP/2EkpISHDlyBIMHD27Bs3VOXmrTqVMnqFTuxTAZJCEiIiIiImocr8wkWbNmDdRqNWbMmKFs8/PzwxNPPIGUlBRkZ2c73bd2gAQA7rnnHgDSm+rWTqWVMiZKqzRAZRGsVitMJpNSSyRaY3Cp9W9NGo0G7dtLNUjkJTcAEB0RJA3IPeChs286ciaJfO4AkJaWBkBaYqNWqzFsmFSMdufOnc1+fq5ITU3Fl19+CQDIzs5GamqqW/szSEJERERERNQ4XhkkOXz4MLp3747g4GC77QkJCQCq3xy76sKFCwCANm3a1DvOZDKhtLTU7qu5yUGSMosaqLhct/1vqB8Q1c/teWsWbwUAHx8ftOnUV7oz92BjT7vJOWoDfOSItExowIABAICbb74ZAHDo0CElqNRapKamYtmyZUqQp6ysDMuWLXMrUMIgCRERERERUeN4ZZAkPz/fYb0GeZtch8JVb731FtRqNaZMqX+ZyuLFixESEqJ8xcXFuXUcT1DrAgEABosAVBajoqICFotFCZbE9B0OCILb88pBkpoFbPNVsQAEQJ8NGAoafe5NqXZNkgsXLuDChQtQq9Xo21cK9nTo0AFxcXGoqqrCvn37WuxcHUlOToZQ6/cmCAKSk5NdnoNBEiIiIiIiosbxyiBJZWUltFptne1+fn7K/a768ssv8fHHH+P5559Ht27d6h07f/586PV65au+ZT1Nxddfyp6pMFshVhShsrJSyYoIURsR2Gdcg+YNDJSCL8XFxRBFEVVVVUg5cAS57SdKA3Ja95Kb2stt5CySHj16KPcBwE033QSg9S25yc/Pt2tfDEjtivPz812eQw6SyC2hiYiIiIiIyD1eGSTR6XQwmUx1tstvDHU6nUvz7Ny5E0888QTGjBmDv/3tb1cdr9VqERwcbPfV3DSB0jGNJjPMxnL7oq3aSqDzLQ2at7i4GCqVChaLxa6uR3rI7dI3rbwuSc3lNqIo2tUjqemmm26CIAjIyMhAQUHryY5xlBklCIJbHW50Op0SKGQ2CRERERERkfu8MkgSHR3t8BN2eVtMTMxV5zhy5AjuvPNO9O3bF2vWrIGPj3c0+vEPkoIkJpMJJqPZvv1vm2DA17++3Z2qqKhASEgIAPs32GWClGHS2oMkcmDMZrOhrKwM6enpAICBAwfajQsLC1OW3+zevbvBx0tNTcW8efMwffp0zJs3z+0iq7VNmjTJ7mdBECCKIiZPnuzWPFxyQ0RERERE1HBeGSQZOHAgMjIy6hROletM1H5jXNuZM2cwduxYREZG4qefflKWmniDoGApkGE0GlFprrLPJOnUs+HzBgVBo9EAAA4cOIDNmzcjNzcXQf5SZgJyDwM2a+NOvgmpVColUHLkyBFYLBaEh4crXXtqqrnkpvYSF1fIRVazsrJgsViQnZ3tdpHV2hISEpTMJLVajbi4OMyePRtDhw51ax4GSYiIiIiIiBrOK4MkU6ZMgdVqxYcffqhsM5lM+PTTT5GYmKgUVM3KysKJEyfs9r1w4QJGjx4NlUqFjRs3om3bts167o0lv5EWRRElFjUqy8uUmiQx/W9u8LxWq1VZfiKKIkpLS7Fv3z5Y4QP4BgDmMuBShtvzejrjoj5y7ZGaXW1qF0MFgKFDh0Kr1aKgoACnTp1y+zi1i6mKouh2kdXaanZL+te//oUlS5a4HSABGCQhIiIiIiJqDO9YY1JLYmIi7r33XsyfPx+FhYXo2rUrVq5ciczMTHz88cfKuGnTpmHHjh122QJjx47F2bNnMXfuXOzatQu7du1S7ouKisKoUaOa9bG4K1DnB0HtC9FqwWVrIAyl+uogSdcBV9nbue3btzvcvv7HH5HY7waEXdgpLbmJ7OXynHLGhbx0RM64mDVrltKu2ZMCAgJw+fJlJTDmLKPIz88PQ4cOVX7/3bt3d+s4jronuVtktbYzZ84AkJaKyfVVGoJBEiIiIiIioobzyiAJAKxatQqvvPIKVq9ejeLiYvTv3x/r16/HiBEj6t1PzjL4+9//Xue+W265pdUHSfw1asBXC1gtuCiGQV9uhM1mg0YtICIiosHzOnuDX1RUhJk7gtFRexMG/bQDg4KS0LlzZxw4cADJyclKO+ZJkyZh6NChKC4uRnZ2NnJycvDdd98BgBKkqplx0RRBEn9/fxgMBhQVFUGtVqNPnz5Ox958883YtWsXUlJS8Mgjj8DX19elY9hsNqjValRVVdltd7fIam2nT58GAHTt2rXBcwAMkhARERERETWG1wZJ/Pz8sHTpUixdutTpGEfZEQ2pQdGa6HzVsPpooYIBhYiA8Uo9kujICKhUDV89FR0djezs7DrPj0ajgcVsRqYpBJm/V2DtggXQ6XR2bZazsrKwbNkyaDQamM3meo/T2IyL+vj7+ytLhmq3/q2tT58+CAsLQ3FxMdLS0lxe2vLLL7847KzUkCKrNclBki5dujR4DqA6SFJSUtKoeYiIiIiIiK5HXlmT5Hrmr/GBRaUFABTZgqqLtnZoXAbCpEmTlEwPAMrtzJkz8X9/fxV/ancECUH5dQIkNZnNZqhUKsTExNgVIq2tMRkX9fH398eFCxcA1G39W5tKpcLw4cMBwG7JVX3y8/Px1VdfAQBuu+02BAUFAQACAwMbVGRVZrPZlOU2ngqSFBUVNWoeIiIiIiKi6xGDJF5G56tG5ZUgSaXZqgRJYh10cXFHQkICZs2ahbi4OPj6+tp1Vwlp3wO3tLdhVswh/Pul6VCr1Q7n8PHxwaeffoq3334bs2bNwuOPPw4AdYqnNlWxXF9fX1y8eBHA1YMkQHWXm0OHDinPozM2mw3//Oc/YTab0bdvXzz55JN48sknAQAhISENDpAAQEFBASoqKuDr64v4+PgGzwPYL7cRRRE4vg741zBgYaR0e3xdo+YnIiIiIiK6ljFI4mX8NT4oh9Sq12g0VmeS2OoWE3VXQkIClixZgpUrV9btrtJ+MADAJ/8wYmNj6wQ+BEFATEyMXW2P2oGXNm3aAAAOHjzYJF1uzp8/D5vNBp1Oh8jIyKuOj4+PR4cOHWC1WrF37956x65fvx6nT5+GTqfDjBkzIAgCevToAQDIzc2t047aHfJSm06dOsHHp3Er4EJDQwEAFosF5Ye/Bb5+BChIB6wm6fbrRxgoISIiIiIicoJBEi+j81UjTn0ZgNT2WAmSHFzStG9+Y4dIt7n7HS7NcVaTo2bg5f3338eECRMAAP/+978ddolpjKNHjwKQuhQ5WxJUm5xNUt+Sm+zsbKxZswYA8MgjjyjBnuDgYMTGxgIATp482eDz9lQ9EkCqIRMYGAgAKNn2DwACALnOjCj9vOOtRh+HiIiIiIjoWsQgiZfx1/jgLu1hAEBpaSksFgsEiGinqWjaN79ykCTnYL1Lc67m/vvvR8+ePVFZWYlly5bBaDR67BTT0tIAAO3atUNFRYVL+wwbNgyCIODUqVNKPZOaqqqq8M9//hNVVVUYNGgQbrnlFrv75WySjIyMBp+3p+qRyJQlNxcLUB0gkYnA5VMeOQ4REREREdG1hkESL6PzVaOPTw4AKNkS4ZoqaFTWpn3zGzMQEFRAWR5Qmlf/0px6qNVqPPvsswgNDUVOTg4++ugjj3Qcys/PR0FBAVQqFdq2bYvy8nKX9gsLC0P//v0BOM4m+f7775GZmYmAgAA8+eSTdZYZ9ezZEwBw4sSJBp232WzG+fPnATS+/a9MXnJT5BcPKZOkJgGI6OaR4xARkWds374dgiA4/LraclAiIiLyLAZJvIy/Ro3Lgn3XmGhtOZr8za8mEIjsLX2fe6BRU4WGhuLZZ5+FSqXCnj17sGnTpkaf3pEjRwBAyW5xNZMEsF9yUzNgc+7cOXz33XcAgMcee0zJ0KhJziTJzMxsUFbM+fPnYbVaERwc7LGCtuHh4QCA4tjb4DCT5JZ5HjkOERF51rPPPovVq1fbfXkqgE5ERESuYZDEy/iqVVhuGmW3Lc6nBM3y5jdWKt6KnMYFSQApA+OBBx4AAKxevRqnTjUuC0YOksiZHe4ESYYMGQI/Pz8UFhYqy2YsFgv++c9/wmq1IiEhAUlJSQ73bdu2LSIiImC1WpXaIu6oWY+kdpZKQynLbXzaAZ3slwdhxF+BXhM9chwiIvKsm2++GQ8//LDdl1wHi4iIiJoHgyReRhAEbBSGwqdGFxl9/Ejkjl3Z9G9+leKtBz0y3fjx45GQkACr1Yrly5c3uEOMyWRCeno6AChLZ9wJkmi1WiQkJAAAdu7cCQD49ttvkZOTg+DgYDz++OP1BjDkbJKGLLnxdD0SoDpIUlJSAuizpY2B7aRb0eax4xARkeeVlZWhqqqqpU+DiIjousUgiRdKilRD5+en/OwTEoOU7Crk5uY27YHbXwmS5B0GbNZGTycIAmbMmIHo6GgUFRVhxYoVsNncfxOfnp4Oi8WCNm3aoFOnTgDgck0S2c033wwA2Lt3L44fP44ffvgBAPDEE08gODi4vl2VIElDOtzIQRJPplPLNUmKLxUARWcBCEDS09KdWXs8c5Dj64B/DQMWRkq3bCtMRNRojz32GIKDg+Hn54fbbrsNBw7Un7lpMplQWlpq90VERESNwyCJF7ojxv7nw4cPIzc3V8mmaDJteki1SSzlwMXjHpnS398fs2fPhlarxbFjx/CXv/wF06dPx7x585CamurSHPJSmwEDBijtby0WCywWi8vn0atXLwQGBqKiogJvvvkmRFFEz549XSpIKy/xOX36tFuf/pWWlqKgoAAA0LlzZ5f3uxpluc3li9KG6P5Aj/HS9zkHgKpGdhQ6vg74+hGgIB2wmqTbrx9hoISIqIE0Gg0mT56M5cuX4/vvv8fChQtx9OhR3HzzzTh8+LDT/RYvXoyQkBDlKy4urhnPmoiI6NrEIIkXqrqci7KyMuXnsrIy7Nu3r8EdVlymUgMxg6TvPVCXRNa+fXvcdtttAKrbGmdnZ2PZsmVXDZSIoqi0/h0wYAB8fHyg0WgAuLfk5sCBAzAYDHbbTpw44VKgJjY2FgEBATCZTMjMzHT5mHIWSXR0tBLc8QS5cGtJWSVsIoAONwPhnYHAKCmokXuocQfYsQRS1xy5KKwo/dyULaiJiK5hw4YNw5o1a/D444/jzjvvxLx587B3714IgoD58+c73W/+/PnQ6/XKV3Z2djOeNRER0bWJQRIvlH7ccTCkIcs93Nb+SmZFIzvc1FY7C0YURQiCgOTk5Hr3u3DhAgoLC6FWq9GnTx8AUnYK4N6SG0fHceX4AKBSqRpUl6Qp6pEAQEhICARBgFUEyqwaoONwQBCA+CvFZ8/vbtwBLp2Gw645TdmCmojoOtO1a1fcdddd2LZtG6xWx0tctVotgoOD7b6IiIiocXxa+gTIfRWGMofbm2UtsoeLt8ry8/PrbBNFEXl5efXuJ2eR9OzZEzqdDoAUJCkpKXErk8TZ8R1td6RHjx44dOgQTp48iTvuuMOlfZqiHgkAqNVqhAQFoqS0DMVVOoTIwZEOw4H07xpfl6RNV6Dg91obm7gFNTVIamoqkpOTkZ+fj+joaEyaNEkpUkwNx+eVmktcXBzMZjPKy8sZACEiImomzCTxQlW6kDqf4wuCgJiYGIfjPUou3lp4HDA5DtY0RHR0tMMOMqIo1ttaV65HMnDgQGVbQEAAAPeW2zg6viAIiI6Odml/uS7JyZMnXSo+K4pi3SCJB4uhhvqrAQDFQd0BnVSjBB2GSbfZqYCtEZ0Ths+uteHK0pumbkFNbklNTcWyZcuQnZ3t1hK2a0lubi42bdqE5ORkbNq0ySPFrfm8UnM6e/Ys/Pz8PLokk4iIiOrHIIkXsnYaAunt/JX/CgJEUcTkyZOb/uCBUUBIHABR6nLjIZMmTVKW2ABQbq1WK15//XWsW7euTvDBZDLh+HGpgOyAAQOU7fJyG3eCJI6O785z2qlTJ2g0GhgMhqtmvwDSMiGDwQBfX1/Ex8d7vBhqmEp67MWBPas3RvYG/EIAswG48FudfVJTUzFv3ryrF87VBNj/rA0E7vu86VtQk1uSk5OVf8eA60vYrhW5ublISUmBXq+HzWaDXq9HSkpKowMl1/vzSk3j4sWLdbYdOXIE69atw+jRo6FS8XKNiIiouXC5jRfSxHTDzvOXMUaTC0tZEaKjozF58mSXOrF4ROxgQJ8t1SXpNMIjUyYkJGDWrFl2Kex33HEHDh06hL179+Krr77C77//jr/85S8ICQkBYN/6NzY2VpmrITVJHB3fnefUx8cHXbt2RXp6Ok6ePIn27dvXO17OIunQoQN8fHzqL4ba606XH4cszHoRQAiKtdXPCwQVED8MyPgZOL8HiLlBuUv+dFx+8yd/Oj5r1qy6ywhO/izddhgu1TexVgFdR7p9jtS08vPzlTfyMneWkHk7Z92+0tPT7f5/4a7r/XmlpjF16lTodDoMGzYMkZGRSE9Px4cffgh/f38sWbKkpU+v1dqwYQN++OEH6PV6hISEYOLEiRg7dmyrmQ9oueV5XBZIRNRwDJJ4IX+ND3I00egycSzmj+nb/CcQO0SqbeHhuiQJCQl1/oAPHz4c/fr1w8qVK3H06FHMmzcPf/nLX9CvXz+7rjY1l8o0ZLmNs+O7o0ePHkhPT8eJEyfwhz/8od6xdZbaeLIYqqEQ4VUXAISgSAyyv69DjSBJ0tPK5vo+Hbd7TkQbcOoX6fub5gDF54HSHCBzJ9BttPvnSk0mOjoaWVlZDrdfD2p2AHNlu6t0Op3D9uJt2rRp1Lx0fbv77rvxxRdf4N1330VpaSnatm2LSZMm4dVXX/V43aprxYYNG7Bq1Srl5+LiYuXnhgQ2PD0fUP0BhKzeDyA8yK0PPoiIqA7mb3ohna9Ub6LS4rjafZOT65LkHADE2m/sPUsQBNx2221YuHAh2rdvD71ej8WLF+Pdd9/F1q1bAUgpyTWXhsiZJCaTyWlHgKZQsy7J1ch1VpSL3/CODkY1sBjq+V0I8zEBkNoA2+kwXLrN2iMFPK5w+dPx/DTAcAHQBAIdbwa6j5G2y9kl1GpMmjTJ4fZmWZbXCgQFBbm13RVnzpxxGmTp1atXg+d1l8tL48hrPPvss9i3bx8uX74Mi8WCvLw8rF69mgGSevzwww8Ot69fv75VzAcA3377rd3PzbU8T56fywKJiBqGQRIv5K+RgiQV5hYKkkQPAKCS3iz/rfFFRl3Rvn17LFy4UMnQOHDggBIAuXz5sl3hRF9fX2kJC9xbctNY3bp1g0qlwqVLl3Dp0iWn4ywWC86fPw+gRvvfNj0djGxgMdTM3Qj1MQKQPgmz064/4BsAVBYDF6vbFTvKLnBYuDZjo3Tb5TbARwt0v/Lp2qmNTR4wI/d061YdYFOr1cr318ubrt69e7u1/WrKy8vx/vvvQxRFhIWF2XUa6dixI8LDwxs0r7vkT4izsrJYOJauayUlJW5tvxq9Xu/R+SorK5GTk1Nne3Msz3NUe4nLAomIXMcgiRfS+UoBgEpLIzqUNMbpzQCuZCFYzY0uMuoqjUaDJ554ok5ae+1PSARBaPCSm8bw8/NDx44dAQAnTpxwOu78+fOoqqpCUFAQIiMjpS5B53ZId4bEA/LSoX73NawYao1MkjpBErUvEHcl1fb8bmXzXXfdVWcah4VrM65kjHQfJ912vBnw0QGluUDBUffPlZrMnj1Sq+cePXpg9erV6N69OwBg586dLXlaTnk6OyI2NhZhYWF22yIiIhpUj0QURXz44Ye4ePEiAgMDMXz4cIwcORL9+/cHIAVqm6v7SO1PgvkJMV1vrFYrli9fXif7UaZWq2E0Gt2ac+vWrfV2pjt37pxb812+fBmvv/6603MMDQ11az5XiaKIdevWOc2i9fHxcfu5ISK6HjFI4oVaPJNkR+0icjWKjDYDR5/21P6EpCEdbjyhR48eAOpfciPXI+nSpYtUS+XQSsBYIi2tee4IcMf70sCC390/gfKLwMUTCLuSSVJaWoqqqlrBNHnJzfk9yib5Qq5mxsFNN91kX7i2NA/IPwJAqK4/4quTskqA6iwTahV27doFQPo9AsAtt9wCANixY4fTC/eW0hRtdc1ms/L/iiFDpCWCJSUldV8PLti4cSP2798PtVqNBx54ABqNBkB14eWysjIle62pOfokWBRFl7pqEXm7yspKvPbaa9i3b5/TMRaLBS+99BKKiopcmvO///0vPvroI6f322w2vPHGGzhw4IBL82VmZmLBggXIyspSrkVq1k0DpG5GP/74o0f/X1xVVYWPPvoIX331lbKt9nErKyvxxhtv1P0AhYiI7DBI4oX8lUySFgqSXDrtYGMDi4w2QHR0dJ0//LWXhjSkw40nuFKXxK4eSZUJSFkh3TH8OakDTa+JgFoDFP7ufqDkSnZIUHRXqNVqiKJYN6gUn3Rl7B5liYxc3+Wuu+7CjBkzAEjZMHafrJ26EgRpPwQIaFu9vduVuiQZrEvSWmRnZ+P8+fNQq9VITEwEACQmJkKr1SI/Px+nTjXPa9VVTdFWNycnBzabDSEhIejQoQMCAgJgtVrdDiacPXsWX3zxBQDgoYceUrJTfHx84Ovriw4dOgAADh5sXCHr3NxcbNq0CcnJydi0aZPTVsUREREOt1utVqSkpDTqHIhas8LCQsybNw9nzpyBSqXCfffdh2nTpiE8PBwqlQrh4eG46aab4Ovri/z8fLz44os4e/as0/msVivee+89pRZJ79698eCDD9rNd8899yAiIgImkwnLli3DTz/9VO85Hjp0CK+//jqKi4vRvn17LF68GLNmzUJcXBx8fX0RHx+PAQMGAAC++OILfPrppx6pnVZRUYGlS5di27ZtEAQB06dPr3Pc+++/H8HBwcjMzMQrr7zisLA3ERFJ2N3GC+mUTJIWWm7Tpqu0xMauG0sDi4w2wKRJk+yqtsu3NZeGNCiT5Pg6KUvm0mnpMd4yz+32u3ImSU5ODsrKyhwWiZSDJF26dAGOfg2U5QNB0dLyGgDQhUmZGifWA8fWAFF9XD+BTCl7QNXxJoSdMOHSpUsoLi62f2MVO1gKwhguAMVnkVvph/T0dKVIblBQEL788ktcunQJBw8erM4mydgg3XavVeVfLt6aexAwFAKBka6fLzWJ3bulYNnAgQOVZSD+/v5ITEzEr7/+ih07dijLb1qDpmirm52dDQCIi4uDIAiIi4vDiRMnkJ2djfj4eJfmKC8vx/Lly2G1WjF06FCMGjVKeZOUmJiIvLw8GAwGnDlzBocPH1ZabborNzfXLsCh1+uRkpKCpKSkOsuDQkNDceHCBeVn+f9/oijiH//4B44dO4Zp06ZBq9W6fR5ErdXJkyfxzjvvwGAwQKPR4E9/+hOSkqSAf+3OM7feeivee+89lJaW4s0338Sf//znOh1dysvLsXjxYiWIcuutt+LJJ5+ESqXCHXfcYTd23LhxWLRoETIzM/H555/jwoULePTRR6FS2X/OuHHjRqxatQqiKKJv376YNWsW/P390bZtW7vji6KIn3/+GV988QU2b96Mixcv4tlnn4VOp2vQc3Px4kUsXboUOTk50Gq1eOaZZ3DDDTcAQJ3HnZiYiKVLlyIvLw+vv/46nn32WSVoQ0RE1ZhJ4oX8W7q7zS3z4LBdbUOKjDZAQkKC3SckcXFxmD17tt3SELdrkhxfJ9VVKUgHrKYG11kJDg5GTEwMACAjI6PO/QaDAQUFBQCALp07AbuXS3fcOFMqhCrrO0W6PbrGvYKocp2Rjjcpa57rpNX66qRACQCcT1GySG644QZERERAo9HgttukJTS//HKl3a+lAji7XfperkciC4oGogdK35/ikpuWZrPZlCCJvNRGJi+5SUlJaVXr0p0FFhrarriiogIXL15Ebm4uPvnkE0yfPh0rV65Ebm4uLly4ALPZfNU5RFHEf/7zH1y8eBFt27bFjBkzUFBQAJPJBK1Wi6ioKMTHxyMwMFA5T+X14qb09HSXtl+8eFH5/0q7du2U//8999xzuPvuuyEIArZt24aXX35ZCRIRebvdu3dj0aJFMBgMCAwMxEsvvaQESBzp3bs33njjDbRp0wYmkwnvv/++XYeagoICJctEpVLh/vvvx4wZM+oEPWSBgYF48803lcDD5s2bsXTpUqUVuM1mw+rVq7Fy5UqIoohbb70Vc+fOVT6sqU0QBIwfPx6zZs2CRqPBkSNH8Prrr+Py5ctuPzdnz57Fq6++ipycHISGhmLBggXKeToSFRWF1157Db1790ZlZSWWLl2KLVu2uH1cIqJrHTNJvJBOI/3aKlqqcGuvO4H7VgPb/lbdIWXUmw0rMtpACQkJdT4hqcntTJIdSwAIqA7+1Kiz0oBskry8PJw4cQKDBw+2u0+uR9KuXTsE5uyQlij5hQCDH7WfpPtYqc2uPgvISQXiEq9+4IrLQOGVN1UdhiMsTPqEzOHa4/hhQFYKzGd349dfpX9HcucgABg5ciTWr1+P33//HTk5OWhfcQyoMgIhcUCkg+4g3cdJ7YEzNgCDHrn6uVKTOXnyJC5fvgydTodBgwbZ3dezZ09ERUWhoKAAqampGDFiRAudpb1hw4Y5TP0eNmxYg+bLzs5Gbm4u9u3bp2Ra5OXlKUtYcnJy0Llz53rn+OWXX5Camgq1Wo1nnnkGAQEBOHpUKk4cFxcHlUqFNm3aICAgAJ07d0Z+fj5+/fVX3HvvvU7fHDnjrK1w7e0///wzbDYb+vbtixdffNHuvsTERPTp0wcffPABcnNz8fLLL2P69OkIDAxEcnKykuUyadKkev/fSdSUNnyxAj/sTIPeYERIoB8m3jwQYx962um4krJKJcssKioKL774Itq2rbHc00kGaHR0NBYvXoxFixbh3Llz+PLLL5GcnAyz2axkXmk0Gvz5z39WliTWR61W44UXXsDq1avx888/48iRI5g9ezasVqvdktapU6fizjvvrLMk2JGhQ4filVdewdtvv42srCzMnTsXvr6+MBgMCAkJwcSJE+tkyQBSDafk5GTk5uYqS3Xi4uIwd+5cp8vxagoMDMS8efPwn//8Bzt37sTHH3+Mr7/+Gkajsd7/R8jHdeX/Ja6OdWdOIro6T7/2rufXKIMkXkjOJGmxwq2AFDjodSfw7RPSkpCLzru5tAT5TUplZSVsNpvTT4gUl07DYXZMA+qs9OjRA9u2bXNYl0SpR9KlC7D7PWnj0BmAttayHF+dFHQ68l/g6DeuBUnkLJK2vYCANkrtBIdBkg7DgF3vYG/a7ygvb482bdoonToAoG3bthgyZAj279+PjRs34onI36Q7uo+t7r5TU/cxwI7FwJltUp0Vnwak+ntguRNVF2xNTExUCozKBEHAiBEj8M0332DHjh2tJkiSmZkJAHavU5vNhtTUVNx5p/v/BrKzs3H8+HEAsKtzAki1dgYOHFhvkKRmHZIHH3wQXbt2hdlsVuqZyHVIBEFAfHw8DAYDwsLCUFxcjF9//dXhG5v6BAUFOSxIXXO5XllZGbZt2wYAmDjRcUC6T58+WLx4Mf71r3/hyJEjSjFKOVAkF8SdNWvWdXORQ63Hhi9WYNWP1QXDi0srsOrHPdh96Diiwqu7QxUUGXAmv+7frdvaV6Htb/+s3nDpFHCiRrannAF632qg150ICAjAG2+8gZdffhnnz5+vkz03atQolwIkNT3yyCNo164dVq5c6bAwrFardSlAIuvSpQveeOMNLFiwAHq9HpWVlQCkv9urVq3CoUOHpKW5V+Tl5WH//v115rnjjjtcCpDIfHx88NRTT6GqqgopKSlKQDYrKwvLli3D0KFDlaxYR8d1Ns6dsbXH8f9PRI0jF8GXNfb1fL2/Rhkk8UJyTZIWW25TU8KfpCDJ0TXAyDeAgDZX36cZaLVaqNVqWK1WVFRUXL09Z0h7oOhM3e1BMXW3XYVcvPXcuXMwGo3w8/NT7lPqkYQBOHUQ8PEDEv/keKK+U6Qgye9rgTGLpfa99blSj0TuXlNvkCQuERBU2JorBZNuv/32OoGk0aNHY//+/di1axfu75WKAKBuPRJZ9EBp2U1ZPpC5E+g6sv5zrU1e7iRn89S62CXXmM1mpetD7aU2shEjRmDNmjU4fvw4CgoKEBUV1ZynWIcoivj9d6lAcb9+/dClSxcUFRVh+/btOHv2LPbv32/fZekqSktLUVJSAoPB4PB+vV6PzZs3w9/fHwMHDoSvr/S6kj8tycvLgyiKsFqtGDx4sBLwkAvBBgcH27XvjI+Px/Hjx9GhQwcUFxdj48aNGD169NUDszW0b9/eYZCkd+/qrK3NmzfDZDKhQ4cO6Nu3r9O5QkJC8P/+3//DTz/9hC+//BIAHBbEvR4ucKh1WfdrmsPtZ/KLHQZFavslowJ3Gt6pZ0TdDFC1Wo3S0lKHo1NSUvDQQw9d9bi1jRo1CmvWrHGYAbZ+/Xq3g6Rt27Z1Glg5duwYjh07Vu/+giDgxx9/xM033+zWcQVBcFog2lEgpjHjXBnL/z8RNc63337rcLunXs/X22uUQRIvJHe3abHCrTW1Hyq9Qc5Pk1rZ3vx8S58RAOmPv7+/P8rKylwLkviFON5eli8VJI0d7Ph+B9q2bYvw8HAUFRXhzJkz6NNHKrwqimJ1+9+iK2uABz1i3ymmpk63AP5tgIpLwLkdVw881KhHAlwlSKINQlbQYGQYw6FWCbj11lvrDOnduzfi4uKQnZ2NHbm+GB8VoMxdh3ClLfChlVIrYHeDJB5c7nQ9S0tLQ0VFBcLDw5VgXW0RERHo168ffvvtN+zYsQP33XdfM5+lvWPHjiktdOWCquHh4ejevTsyMjLw8ccfo0ePHggODnZpPnnZTkhIiNM2l2fOnMF7770HrVaLvn37IjQ0FFu2bLHrsANI6fDymxd53vj4eLs3NEFBQQgPD0dVVRVOnDiBgoICHDlypM5Sp/rI56nRaFBVVQWbzQZBEBAeHg5ACn5t3CjV+5k4ceJVP6mWi09+/fXXdVoeN7YgLlFDnD2yGyVljpe/CoKAG7q2U34+dPqCw9a4JeUmIPHP1RtSPwTE2h8WiVKGSQ2OApCA1BK8oZx1zmvInJWVlU4DOYIgYMyYMcrPv/zyi33XOTTuNe1sP5VKhdGjR9d7XEfj3Bnr6cdCdD0zGo3IyclxeF9DX8/OXqPudgn0VgySeKFWlUkiCFI2yfd/Bg58IrWxVbWOf1ZykOSqbYDzDklfEICIrlIdkPCuAESpxscXk4FHfwYie7l0XEEQ0KNHD6SkpODkyZNKkKSwsBAGgwE+ajU6XNwGqNVAUt212Aq1L9DnHmD/f6RsnfoCDxVFQMGVT5tcySQBsKW0C4AyDI7V2H0yXvNxjB49Gh9//DE2lXTA2KTeUPn41Rmn6D7uSpBkAzDu746X5TjjweVO1zO5YOvw4cPrzWS45ZZb8Ntvv+HXX3/FlClT3Mp68LQNG6SuSR06dFCyOgCgV69e0Ov1KCgowMqVK/HMM89cdS55SYlcULEmOQBy0003ITs7GxcvXkRFRYVd696ab8wEQcBPP/2EESNGwGAw4NKlSwDgsDNOhw4dUFRUhK5du+LYsWPYsGGDy0GSsrIy5YLj1ltvRXBwMLZt24bLly/j9OnT6NevH3799VeUlpaiTZs2bi0PiImJQXZ2dp3H1dCCuEQNkbrxa/zzvz86vT8syB/Pv16dITLzqSdRXFo3oBIaqAPGLqnekPmrg057V5ScB0KlZXHOAqaO/u65ytmcrgZzZXq9Hrt27UJgYKDDQElwcDAefPBB+PhI11Xp6ekefU1HR0c7nK99+/aYNm2ass3ZcWuPc2eso3FAdbBYU+AzZAAAUK1JREFUfsxEVL/i4mIsXbrUYXC5Ma9nZ69Rq9WKY8eO1ZvVei1gdxsv1KoySQCg7yTAPwIozQFOOL8Qam4ud7jZvli6HXA/8PQB4KVC4M97gMd/AWKHAJXFwOf3AMWZLh9b/hT/xInqWi3yUpuOwTb4qmxA38lAWMf6J+p3r3R7/AfAUul8XNaVdd5teigteOsLkhiNRuw6J803MuSc02mHDx8Ofx8bCiwBOOJzlWyazrdIy4f0WcDF4/WPrS2iq4ONzddW+lpgMBhw+PBhANLvrT6DBw9GQEAAioqKrprK3ZQKCgqQlpYGAOjcubPden4/Pz/8+c9/hkqlQkpKirKMqD5FRUUoLS3F3r17UVxcDJ1Oh5iYGLsuWI8//jgGDx6MMWPG4JVXXsF9993nMDOj5ieachZJZGSkw6Ks7du3hyAIiI2NhSAIOHr0qNNPdGo7dUoKBEZHRytvsOT2zGfPnoXZbMaPP0r/X50wYQLUarVL8wJSu3RHrZVrtksnakrrVy3H+6vXwWS2INDfcZD9jpvtW9BOvHmgS+OqO+3Jr98rt1YT8NFIKQsUzmv41G716w5nc/bo0QNpaWkO36zUduHCBWzbtg2VlZVO2/D27NkTO3bsUOqpyK9p+f9ZcvC3oa9pV+dz57gNnVNWXl6OJUuWOF0ySUTVsrKysGDBAmRmZiptxD31enY0DpCuI9566y1s3769KR9ai2OQxAvVzCRx5Q9xk/PxA254VPo+9cPmOebxdcC/hgELI6VbB616Xepwk7MfOPULIKiBEf/P/j5tEPDQN1I3l7J8YPVdQNkFl06vR48eAKQ3QHL1eaUeCa4EJYbPuvpE7ROA0HjAbJAyNJypVY8EqA6SlJeX12l5mpKSgkpzFaJ8y9HbfBgov+RwWj9LMW4NygQAbDx5lYwcX3+g05VCoCd/BgDk5uZi06ZNSE5OxqZNm5yuf0a8oy4mzddW2ts4el737duHqqoqxMfHO8x2qEmj0SidY1ryj9wvv/wCURQRFRWF6OhojB8/HnfffTf8/f1hsVhgs9mUwq2ffvqp05R0WWZmJvbv34+LFy9Cp9Ph5Zdfxttvv42VK1diyZIlGDp0KPz8/BAZGan8sb/77rsRFxdX50Jd/nRWFEUlSCIXbK1Nq9UiOjoaAQEB6NZNCuzJy2PqYzKZlKK1cmAEkDJAAgMDYbFY8NNPP6GgoACBgYFKC2dX1WyXLhs2bJhbNV6IGsJmteKjpS/jyw37YLPZ0KldGN5bugTTJgxDeHAAVCoVwoMDMG3CsDrdbcY+9LRL45ROe1F9pGLhUX2Aie8DUf2A8kLgswnA8R8wduxYTJs2DeHh4dJ84eGYNm2a27VD7M7RwZx33HEHYmNjcfr0aezZs6fOUreazp49i927d6Oqqgpt27bFE088UWe+e++9F506dUJxcTG2bt0KvV5v95quGfxt6Gva1fncOW5D54yPj8c999wDnU6H9PR0vPbaaygoKGjQ4yK6HtRsHx4dHY1FixZ59PXsaNwzzzyDYcOGwWq14sMPP8TXX3/dOt6LNgHmsnkhOZNEFAFTlQ1+vq5/sthkhj4B7F4GnN8lLfuIasIULBeLfMpBknqX22xfJN0OeAAI71L3fl048PBa4NMxUibJ5/cAj/4oba9H+/btERAQgPLycmRmZqJLly7V9Uj8iqX6HVF9rv5YBUEq4LrrXWnJTZ97HI87fyVIUqNmiL+/PzQaDcxmM4qLi+0KdG7ZItVE+UOsESoBQFaK4xbOGRsxKjQTPxd3wm+/n0BeXl6dSvZ2uo+Vgk6nNiK38/1ISUlR7tLr9UhJSUFSUhJiY2Or9zEbqjsUBLaTarDYqqTgmytdfa4zubm5Dp9XV7NIZLfeeis2bdqEgwcPwmAwXL1uj4cZjUYlQNOlSxcMGjRIyZLo06cP9u/fj5MnT2LChAk4ePAgsrOz8emnn+K5555zOJ/VasV3332HvLw8qNVqzJkzB506dXI4Nj4+HoWFhcjOzkbPnj0xadIkLFu2TPk0peanKkVFRTAYDFCr1fb/bmvp0KED8vLyEBcXh4yMDOzatQtTp06t93k9c+YMbDYbwsLC0KZNddFrQRDQvXt3HDx4UHmtjh492q4ItKvkdul79uzBihUrcPDgQRQXFytBVCJPM1eW452FL+HouUIAwODu0Zj10mKofTUY+9DTGOtCrVRXxymd9mrqMwlY8xhwepN0bTB6IcaOmdmooIjDcxw7ts6cOTk5SE1NRX5+PrZv347hw4crn+4C0iewR48eRUZGBgDp/xuDBw+GSqVyOJ/BYMCuXbtgMBiwbds2JCUlKa9pT3F1PneO25g5ExMT8fe//x15eXl49dVX8fzzzyvBZyKSbNmyBZ9++ilsNht69eqF2bNnIzAwEFFRUR59PTt7jUZGRuK7777Dd999h8LCQsyYMaNOR0VvxyCJF5IzSQCg0lLVOoIkwbFArzuA9O+lbJKJ7zfdsba7VuTzqpkkWSnAma1SDZXaWSQ1BbUDHvke+HTslRol9wLTvgc0zt/8qFQqdO/eHYcPH8aJEyfQoUMHnL/yiXFXvxJg+GxXH6205GbXu1LwobIY0NV6c1NZDFy4smSiRpBEEASEhYWhoKDALkhy9uxZnD17Fj4+PhgxuBdwbK/zIMmpjYjSVGJghxAcPl+KTZs2Yfr06c7PtduVAnPZqUg/9pvDIenp6fZvNnctAwwFQFgn4C/7pFosH/1BqhOzbWHT/lvyQunp6XW2VVRU4Ny5cxAEQckQuZqOHTsiPj4eWVlZ2LNnT53ie01t586dqKysRGBgIIYMGWIXxIuPj8fJkydRWlqKs2fP4qmnnsKCBQuwb98+7N27FzfeeGOd+VatWqUEImfOnKnUAnIkJiYGKpUKpaWldp/OJicnIz8/H9HR0Zg8eTKGDh2KQ4cOAQBiY2PrXSPfrl07+Pr6Ijg4GDExMcjLy8P27dudpvRbrVYlu6x79+51Mlk6dOiALVu24PLly/Dx8Wn07ycpKQkbNmzA6dOn8c0332DGjBmNmo+opk8++DsO/JaB0nIjVIKAqisZlBOSeuKhZxYo45LTsvD6T0eRUViK7pHBeHV8P0waWDfzrdHjHvgK+HkucOBj4JeXcOHwTyi9mIV4MR/nhRgUD52NG8c/Xme+vT99grD976GDmFfvuPrG6nQ67N69GyUlJdi6dSs0VUUwlelh1kQAohU2tRTs7N27N3r16qW89p3Nd9tttyElJQWXLl3Crl274K82wmoohlkTAY35MgKCg3HbndPqnN+2datQXlra7OM8Meebb76JpUuXIjMzEwsXLkTPzrHIzitEabkRwQF+GNK/Ox6fObfOfDX/HTbnuJY8dmsf5w3n6G2P2ddHBdOVkgs33XQTZsyYoVyfNNfr/r777kNkZCQ+/vhj7NmzB78flZYZGipMHntuWhqX23ghX7UKPirpj2qFuRUUb5UlXGll+9vXQGWR5+c3lQH7/iUFKlwo8inXJKmsrHRYxVnJIhn48NVrg4R1lDJKdGFA7gHg41HAP5PqXe4jL7k5efIkzp8/D0tVFQJVZkR17gfEJ1398coiewORfQCrWapNUltWCgBRqt8RaN/O1VFdEvmT6YSEBAT3uBJUkTvj1GSpBM5sAwCMGTsOAPDrr7+isrKe2igh7YF2/QCIKHOynri0tLQ6NU+fDaT8Q/p+1BtSyrSgAsZc+d0cXl1dkLYBUlNTMW/ePEyfPh3z5s1Dampqg+dqLRy1nczOzgYgFTuNiIhwaR5BEJTlGzt27PDcCbpAFEWsX78eANC1a9c66/EFQVAKgp0+fRrt2rXDXXfdBQD45JNP6nSr2LhxIzZt2gRAas/pKIhSk0ajQbt2UjcN+blLSEjAkiVL7JbmWK1W5X5nS21karVaWbYjt+795ZdflOV2tZ0/fx5msxn+/v4OM1TUarWyzKdbt24ICgqq9/hXIwgCHn74YQDS7/v8+fONmo9I9skHf8fm3WkoKauAzWZTAiQDu0bXCZBM/mgnjuaXwFhlw9H8Ekz+aCeS07Ls5vPIOJUPMP4dYPTfIAJod3E3uonZ8BOq0E3Mxo37Z2PvT5/Yzbf3p09w4/7ZVx13tbERERG4/fbbERQUhMrKSugtOhi1UbCpfJUASYCPCb1797YLkDibT6vV4uabb0Z8fDxEUUR5lVaZz6iNxGWzP7atW2V3ftvWrcJlsz+M2shmHeepOcPCwrBgwQIMGjQIFosFR09mKv++SsoqsHl3Gj754O9289X+d9hc41ry2K19nDecozc+ZjlA0imuHf785z/bBUia83V/66234q9//St81CroyypQaqj02HPTGgjitbqQqBmUlpYiJCQEer3e7YrmjRXywtcoNVpw6tU70bVt4y6ePUYUgX/fJL2pHfUmMOxZz8xbfA7Y9yGQ9jlgclaTQJCWrzxV/WZfFEUkJydDFEWMHz/evuBi5k5g5R2AWgM8cwgIiXMwpwO5B4HPxgNVRvtjQ6yz3CcjIwOvvfYagoKCMGniOKz88msMCCjEX194HugxzuWHD0DKJNnyulTzY1qtQMnGF4G9H0h1YSYut7vrH//4B1JSUvDwww9j/PjxqKiowMyZM2EymbBgwQL0jAkG3uslBSb+eh7Q1vh3fOoX4Mt7geD2sD37G/7f3LnIz8/H9OnT7VoS1rF1IbBzKX7q/T4qhACHQ8LCwtC/f3+03TkfOPoN0OEmYPp6+44430wH0r+TWiE/8r173XIgBUiWLVtWZ/usWbO8ur/7unXr7GrMiKKIzZs3o6ysDDNmzHDYztmZ0tJSzJw5E1arFbNnz0ZJSQnKysoQFBSE3r1717u8pDEOHjyId955Bz4+Ppg7d67DCumiKGLbtm0oKipCly5d0K9fP7zyyis4f/48hg4dilmzZkEQBOzduxf/+Mc/IIoievXqhb/85S8uBYqys7Oxb98++Pv7Y9y4cQ6Lt8pLm/z8/DBhwoSrtt69fPkytm2TAou//PILDAaDw3W+oihi48aNMBgMGDBggMNU8qysLMybJ9XkGT16NCZMmIC2bZ20C69Hbm4u0tPTld/roUOHcOTIEfTp0wcvvvjiVR9TY7Xk38nrUUs833/505MO2/uGBvnjSI97lZ/TcoodfrDjr1FjYPuwJhv3ZclTiFcV2f0JEUWgAhqcUVcvyetiPQd/mK86ztWxVpUfMrv+CVa11v4ERRuCjLmIzvyvW/OJAM53fQpm31q/V1GEymaGxnRR2WTWtoVNpUHtCZt6nKfntIkikremweygY5larUZYUPVSpuKyClitdT8Qa+pxLXns1j7OG87Rmx9zSHAQJgyr7rzZUq/7dTt/h8FBaQN3npvQIH/8378/qrO9Kbj6d5LLbbyUzleNUqOl9XS4Aa60A54B/PCs1Lb2xpmAyo2lQMfXATuWSO1gI7oCve8C8o8AJ3+CkjkS0Q3oMExqNVt7yc0w+xahgiDA398f5eXlKC8vrw6SiCKw7Uqmwg3TXQ+QAEDsYGn5jV2nmyvLfTa/KtXkUEtr8jp37gxfHzXKysqwM/k/AELQJcQGdK8nwOBM38lSkOTcTqmIbFCNdn9KPZKb6+xWO5Nk9+7dMJlMiI2NlTJdBEFa5lJ8DshOtW8zfKX4KrqPgUqtxujRo7Fy5Ur88ssvGDVqlPO2sT3GoejAGhhFTXXTgRpUKhWKi4uxY8cOxJRGoZ+mHYLG/K1uEGTk69Lv/twOqWhtjcBS7Td+jt7Q/+9//3N4ev/73/+8NkiSlZVVpwivXq9HWVkZfHx83H5cwcHBGDx4MFJTU7Fu3Tolo8Np/RgPWbt2LQBpmYmzZTFyNsmvv/6Ks2fPolu3bnjqqafw8ssvY//+/Xj22WdRUlKiZGp07twZgwcPRnh4/fWCZNHR0fDx8UFFRQWKioocBlbkbIv4+HiXggnh4eEIDAyEwWDAkCFDsH37dmzYsKFOkCQ/Px8GgwG+vr5O66bImTY9evRAYGAgMjIy3A6SOKpf065dO/z+++/4/fffcfjwYdxwww1uzUlUm97gOLuwtNyIPWcdFwWvqcJsbdJxkW1K6/x5EQQgAGb0t52ssbHuXA7HuTrWBpwTHPydFFQo10Y16Nhn1DqHA21qLYz+7eve1xrGNXLOKutBh0OtVisulVy9A05LjfOGc+Rz03zjmmLOMkN5q3jdV1QecDjUneemtNx49UHNjEESL+WvkdsAt6LlNoBUP2PTAqAkCzi1Eegx3rX9ahdjLfxd+pJ1+QNw41+ALrdLWQ9dR0o1SC5lAKJNKvR5+HOpYJu6unCQHCSxq0tybrvUMletBW6a4/5jLM13sFEEis4Ci9sDsTcAcYnwgYCuvoU4XhWBs8YQAEBXIQs4sb5uobmrCe0AxN0IZO8FjiUDSTOl7cYSIP9K7Y+OdQt21gySyBkHAPCHP/yh+k1fh+FSkOT87uogiShKvz9ACvwAGDFiBL7++mvk5+fj2LFj6N+/v8NTLQvqhl0dZsGm8kWITg1oAu2CGREREUj//XecO3sGecGDkB88AJ0viAg1nsPp06ftAx83/kUqCLzpZenc1L5OC5cOHjwYERERMJlMMJlMTqviFxYWuvikty5FRUU4cED6QxQTE4Py8nKUlZUprWZjY2Pdag8ru+WWW5Camors7Gz069fPLvh16NAhWK1WBAUFISgoSEnpdCVI5czx48dx9uxZAMDkyZPrDT5ERkYiKioKBQUFSE9PVwqIpaSk4PLly3Zj27Rp43IwAwB8fHwQExODrKwsZGdn1wmSmEwmpQXw1ZbayARBQHx8PNLT09G+fXuoVCocP34c58+ft5vj5EnpTU+XLl0c1jm5dOmS8m980qRJOHnyJPLz81FaWupWdoCj+jUBAQHo1asXjh49ii+++AL9+/evt9YKkTPWqiosX/KK084GwQF+WPvHEcrPs749iKyicrvFsgKADuEBeG/y4CYbl7k2Gj3EHKiE6pE2EchFG+QlvqJsi9n3BmJxGaoa/wtxNM6dsdpLF1Hp1066bpGJNviZLmFfwnKPzaexlCCoxie2ZWWVMPuG1foEuOnHNcWcQQE66MvqfkodoNNi2JDqIPvuA7+jotLU7ONa8titfZw3nKM3P+bgQB0itNVB6pZ63bv6Gq33sQS4X5i+qfHKyEvpfOU2wK0okwSQ2sDeMA3Y8z6Q+m/XgyQ7ahdjvUIXDjy2AWjbw357zYr2+UekJTCZO4F1zwB3/0t5QQcEBODixYvVQZKaWSRDHgeC6+nU4kybrsit8EN65ESUadohyHwBvQvXIdZwBLCapBohWdIbnB7+3XG8svrNV2c/fZ0Cs7KrvvHsN+VKkOSb6iBJ1l7pOQvvYp9dcoUcJCkpKcGpU6eQnZ0NX19f3HRTdYFXdBgGpH2O3Nw8pG/aJB3fzwe9xSjE+hYrbX11Oh1GjBiBjRs3YuPGjQ6DJEajEbt274ZZHYiwykzcElAEn1GL6oy7QZuJrqcX4Gj0VOQH9lMKbsrkwEfPbnciJPIozBYbzFu+hDlqoNIytbaDB+0/bfL19YXJVPd/xFqtts62ptKYYEJNRqMRKSkpsNlsaNeuHZKSkiAIAmw2m1KLIzo6Gnv27MGIESPcCpb0798ffn5+MBqNyM/Ptzs/k8lkV8dFp9PB19fXrhWvO1knVqsV3377LQCpcGyvXr3qHQ8Affv2RUFBAbKystC9e3clKFTbyZMn8fjjjgssOhMXF6cESfr3728XIMrJyYEoiggNDUVISIjLc8pBkoqKCgwePBj79+/HwoULYTabER0djVGjRqG4uBgqlQpdujjoqAXg559/htVqRZ8+fdCvXz+UlZUhLy8PGRkZGDJkiMvn4qh+DSBl3Zw/fx75+fnYsmVL/cvniByorCjHW6/PR0a288yOIQN64O4B1ZmaNlHE5I92QhCkP8Xy7buTBzfpuL25c6DaPxtWUYBaEJXb3ISXceO4R6vHiTbEuTDOnbHb1q1CpVklfZgjVN/qQsOQOO4Rj80XFKTDbRMfsRt32Sw0+7immPNcVj42705DbUmDe+OxP7+g/CxeqXXQ3ONa8titfZw3nKM3P+Yh/bu3ite9q6/R+h6LKAJZmWcR37FznftaCgu3ein/Kx1uWl0mCQAMfVJ6MZ3dDlw8edXhEMUr4xx8GmUprxsgqS16AHDvSkBQA799VV2QFQ463JzZAuSkAj464CY3OszUkDtoLlLi/wK9NhY2lS/02likxM9E7phPgKcPAnd+AAyaBkCAUOMx+cCKE5XhdQrMAtVp8Xq9HjabTXnjmZubWz2o9z3SY8w7DFyWumIgc6d0W6OrTU01M0m2bt0KQOpyYdeWtMMw5AbdgBT/UdXHrzBJj6nLQ1Ir3ivkDhtpaWl1MjUsFgt27dqF8vJyBGiA4eeXw+fUj9Lvt6YqI7D5NQSb8zG8kz9uueUWqFQq5ObmYvPmzfjuu++wefNm5Obm4sSpc9jX9kEcjnkYv5cG4NSpU7A4WJss02g0CAwMRG5ursMACSD9W5CL1zYll36nLrBardizZw8qKysRFBSExMREJVvi999/h16vR0BAANq3b4/Lly9j//79bvWsP3/+POLj45Xva/L19UVERIQSWKqsrERpaanD35WjrIXajh07hlOnpH//99xzj0vnFxYWhvbt2yuP98KFCw7HGQwGt2swREVFQaPRwGQy4eJF+3X1NZfauCMwMFDJSpGLrZaXl8NisSA7Oxsff/wxcnNzER8fb9catObjkF+rcmec7t27A5CWWxmNrqWkXr582XHBakjLgu69V6oV8e2338LgpMgykSMX8nPwyvznlQDJDT3jcfuwAQgN8odKpUJokD9G3jQIj//FvmvcpIHx+PbJm9E/JhR+Pir0jwlF8h9H4J4aAY2mGHfj+Mexd+h7OK2Kg1H0wWlVHPYmLMON4x5r0Dh3xt525zREaCrgZyqEymaBn6kQEdrKOoEFT8/XUuOaYs7HZ87FyOEDr/rvq6XGecM58rm5Ph5zS73uG/PcBOj8oFKpoDdUYPGiRdi/99c6+7QUFm5thJYsSDfivV+w88xFfPPEzZgyyL2L+Gbxv4ekZSVDngQmvON8nLEE+H6mNLaOusVY63VopVQPBQAm/gO4YRoyMzNx4MABREZGYsTNNwMf3S61lk16Ghj9N3cfFcrLy7Fly5Y6dSEAICQkBKNGjVJ+Tl04EsvSI6HULLlyO6t3IRJe3my378aNGx1+6lt7TnwxBTi9CbhlPnDrPOA/t0pBk3v+A/S/r87+Fy5cwJw5c+Dr6wtACmS88cYb6Nq1a/UgUcTGr/6JMp+2dVLqQrQiRt051W7OJUuW4LfffsP48eOVbhk2mw27du1CYWEhtFotbrspEYEfXOnI85dU+0CXXIQ2OBZ4+gDg668UmK0tMTERAwcOgCZnDzTl+fCN6o7zqo4OAyDyc7Vx40asXLkSgLQExVBWhjKDAYGBgdBoNLh06RIEQcBdd92FSZMmNWipgbMMEVEUUV5ejkuXLuHIkSMOAzp1fqf1EEURBw8eRGZmJnx9fZWOCbJ//etf+PXXX/GHP/wBd955J3799VeIooju3bs7XQ4ls1qtSEtLw7lz51BWVqZkpIwbN055814zO8RsNqOsrAyrVq3C/v3768yXmJiIhx56CG3atHF4PIPBgH/96184dOgQIiIisHz5cud1bWopKyvDL7/8AlEUsXfvXuTn59cJBEVFReG9995zab6aDh06hLNnz6Jjx45KlkZZWRk2bpSWm91xxx3w83MvDfTs2bM4dOgQtm3bZtdZShYUFISlS5c6/Lvx3Xff4euvv0Z8fDwWL16sBMS2bt2KoqIi9OzZ02Gh25ry8/Oxd+9ep511unbtin79+mH+/PnIycmxey17Ggu3Nq+mfr6Ppu3Hv/71IYpLy6FSqf5/e3ceF1W9P378NTPAsIMgKKtLiiIKYgpuaZnlkraYer1fTU3LX2madu9tvdpyy+paafd2rbxtV7PuTbNbmqnZ1TYVMBVcUBB39kUYEIZtzu+PkdFhBh2Ubcb38/HgUZxz5rPMZxw+530+C7cN6sPseU81eT5CCCFuDFs3reeLDd9Soa/EVevC/XffyV33/V+z5ScLtzq4S2uStLHpNnXi5hgDH8mfw+1LwNXKcPXsZFg33bgIqtrJuK7IZcEEUGD407bn2W8GFJ+Fn5fBpoXgHYyHj3EhyvLycuMaG1n7jFOChiy0OVlFUcjJySEjI6PBp9hgObR9Q0F3VBSjmFZkM44s2VAYQd3ymgaDgfT09AaHxddtl2taZ6HPJGOQ5NA6GPio8T0Eq+uRAPj6+gKYbtY7depkNsTfYDCQkZFBqXMAFivHqdSUVEFWVhZBQUGmMtx5552kpKTw3XffsW3bNoKCgoiOjkar1aLRaBgyZAie7fyMC8lm/GBcdLUuSFKWBz+/Zfz/258HZ3dKSko4cOCA1fKnp6fz+OOPQ4YBPr0Pcp3wn7iF3YdPW1zbq1cvfvjhB1OA5N72JxjcMZvU6PGUajviVZlD14LvWesUwtGccr7++muKioqYMGECgYGBVvO3pqE1Ufz9/SkvL7/yFskY27Smpsam4ExGRoZpelF8fLxZgOTyqTBDhw4lICCAAQMGkJiYSFpaGh4eHg1O56ioqGD37t0UFRm36h40aBAHDhwgPz+fLVu24Ovry7hx49BoNCQlJZmmpJw9e9a0Tkd9R48eZefOnQQEBNCzZ08CAwNNnxlFUdi3bx/HjxtHQI0ZM8bmAAkYgwqdO3fm5MmT9OrVi6ysLFQqlVmgZOLEiTand7mwsDBOnDhBZmYmsbGxaDQa0yiSjh07NjpAAhAaGsqBAwcstiquU1paypIlS4iKijL9HDt2jPXr15umE0VGRpqtr9KjRw92795NRkYGPXv2bPDzc+bMGdNoog4dOtCpUyeOHTtGaWkpGo2G6upqjh8/joeHB1OnTuX1119n69atjBw50rQtshDWbPv2S/7z5SYq9JVoXZy5b9zt3D1xemsXSwghhB0bNW4iHYNCeX/VBxSXlvPvDZvJzc60OhKlJUmQxE5dWpOkDU63Aeg8DAIiIT8VDnxmvKGvoyjw28ew5WnjGh6+4cbpMiXnjOt1FKYbd7EZ/jREjm9cvrc9ByVnIOU/sG4G7r//BjAGSZQdy41hgLg54GG5S0T90QHdu3dHr9dz4sQJs4VfNRqN1Se0BoOBo0ePEhERgVqtJvt8+WUBkotVR0V2kXFxo+LiYvbu3UtxcXGD1VEUhZ07d9K/f3/jDXKPscapQoXHIeF94/zAdl2MozKsSElJMbuZvOmmm0w3XkVFRezbt+9i/pdN6r6UOahU7Nq1i3bt2hEVFUWHDh1MARdFUaipqTHdPA8cOJDJkydf2l0kYsylIMmQx43HdrwMVaUYgmI5oorkf3/7G0lJSQ0+8S4uLqayshLtTSOg+yhI30pI8lsMuuUNi5Ec6enpfPjhhwCMi/Znkv5bVGUQWrbPLM0/+6hZ7vMwvx07x88//0xlZSVDhw7F39/fFLCytn5I3SiR5ORkq2WtW0hUpVLh5+dHaWkpJ0+eJDU1lbKLI1kiIyMJCQlh69atxMbGEhzc8Jo4eXl5prz69OljdgObmJjImjVr0Ov1aDQa0814eHg4Fy5cMO1c4ubmZpFHQUEBe/bsQa/X4+zsTHx8PGfOnDFNN1EUhfPnz7NmzRrWrFnTYPnqqwv05efnk5+fj5+fHwEBAeTk5KDT6cjNzUWn06HVahu1TXGdyMhITp8+jbe3NzNmzGDHjh1kZmbi6elJfHw8Q4ZYDxReTfv27XFzc6OiooLc3FyCgoI4c+YM0PipNnVcXFwICgrC09PTbP2Wy+Xl5ZGXl2faMri+rVu3EhkZadqxKDg42LRzzqlTp8xHg12Unp5u+syEhYUxYMAA1Gq1qR6KopCcnMzx48dJTk6mZ8+eREdHk5KSwueff86iRdc2BVE4po/+8Vf2pqShu6DHTetCRWUVBoMBbw83Zj34AHGDb23tIgohhHAAMTcPZPGSYN54fSnZBTq2/7KfjIx5FJVVUnpBj7eHK/2jI5g178kWK5PdTreprKxkyZIlrFmzhvPnzxMdHc3LL79s0zD2zMxMFi1axLZt2zAYDNx2220sX76crl0bt1hMaw4jnvrJr3y29xRvTejHohFXX/ywVez9EL59Avy6GtfqUKmhqgw2LYKDXxiviRgD974Lbu2aLt/aKvh0Apz6mbOBI0kI+D0AXvpMehdtIeShT8HdfCeL+qMD6nN2dqZz58507doVnU53xWu9vb3p168fb7zxBmfPnjV72q1SqQgLC2Pq1KmkpaWhKArOzs6Eh4dbLF4Kxu1yDQYDarWaXr16GQMwG2bD4Q3GtUJq9BD7ANz9jsVrExMTWbFihcXx+fPn4+bmZnqq7+zsTGh7D05mF1sszhQcHExubq4piOHn58fmzZvJysqySFer1XLPPfcQERFB165dcdXnkvjynWwo7E52rR9BgX6MNmyntMaZ/ykDyS289JTdxcXF6hQmMN7ETps2jQFdfFC9N9g44mj6N9BluOmaX375hXfffRdFURgdXMIDnr9cFu+pNzoJ464BKy+MZVemCpVKRVxcnNVFR7t06YJaraa4uJiSkhJqahoeuaVSqbjlllvw8/PDycmJLVu2sHr1aovrhgwZQocOHQDjjW/fvn0vbU99Ud26FFVVVYSHhzNgwABTcKuhdl24cCFxcXGmURsnT55Eo9EwfPhw/Pz8UBSFEydOcODAARRFwdvbm8GDB+Pp6cnTTz9t8Vmt07lzZ8LDwwkLCyM8PJzVq1c3uK5KZGQkQ4YMobi42GI9jN27d5Odnc2gQYOYP3++1ddfTUpKCmlpafj4+DBy5Ei+//57dDodN998c4Nb6doiOTmZ9PR0QkNDuemmm/jxxx9xcnJi3Lhx17zzS1ZWFuvWrSMhIcFi1MvcuXPx8PAwbcVbfy0YuPRd8dprr5mOZWRksH//fjw8PBg1apRpNI6iKBw6dMi0a063bt2IiYmxutOPoigcPXqUw4eNu4d5eXmxevVqFEVh8eLFNi2m2xgy3aZlNdX7/VEDi+t5e7rxzDNP0alLxHWUUgghhLBUUX6B1154mvRzhVbPjxzS97oDJQ4/3WbmzJmsX7+ehQsX0r17dz755BPGjh3Ljh07zHfuqKesrIzbbruNkpISnn32WZydnVm+fDnDhw/nwIEDFttAtlV1C7e22ZEkANG/g61/Nm6N+3IgtOsEVeVQmmVcgPT2JTB4gflWdk1B4wK/+5TMtY+R4HOvaUREqTaY3cGzaZ94EK1WS21tLTU1NdTW1jY4LF6j0RAbG0toaKjpZsnLy4tBgwaZjWSIjIyktraW5ORkdDodO3fupG/fvqYn0nUURaFr166mm5mQkBBiY2NxdXUlMDDQYnREu3bt+O2338jNzeXQoUOcO3eO/t3ux/fwBmOABCDjf8YtlOvtmLNhwwardVqzZg0jRowAjE/Ko6OjcdW60PEf93DE5zbT1JResfGE9B6MXq/n2LFjZGRkUFRU1OCUo8rKSr74whj8UqvV+Pv7k59/M8bARA1nsnJZRd06GSW4ubkxZMgQRowYQV5eHitWrDDdTNb918vLi4KCAlasWEGfPn2Y3n06Icc+gq3PwpyfQK1hz549pgDJSN/TPOB5CJVHe+NaOCq1+eikYX+CopOod77KXI/NqH3780txBxITE60GSk6ePGn2u1ptDCAZDPVH3Rjw1mpM03aKiorYuHGj1ffpyJEjxMTEkJubS1ZWFnl5eURFReHm5kZqaqppRIbBYKBdu3bcfPPNppvd6upqPvvsM4s0VSoVGzZsIC4uDpVKRWxsLOXl5eTm5rJjxw5UKpVpqgUYp4P079/f9Jm2tsYHGANoS5ea7040adIkq22l0WhITU0lPT2d0aNH4+rqagqUXLhwwTRN53qmdPTo0YMTJ05QUlLCoUOH0Ol0qNXqa9ox6HJhYWGkp6eTnZ1teq8v/zd/LTp27GgK3Jw5c4bc3Fy8vLy4++67TX+jYmNjAZg+fbpFAE5RFIupTZ06deLw4cNcuHCBrKwsQkNDMRgM7Nu3zzQtq3fv3vTo0aPBrZBVKhWRkZFotVr27dtHaWkpUVFRHDp0yBSQCQoKYsKECaZRLOLGszclzepxlUolARIhhBDNws3dgyVLl/Pwww+hr7R8eLo3JY3G7WN47ewySJKYmMi///1vli1bxh//aNxaaPr06fTu3Zsnn3ySXbt2NfjalStXkp6eTmJiIgMGDACM8+N79+7Nm2++aXFD0FZlFhvXPVjybQr/+e00z4/tw4S+lkPDNxw4w4ubD5KWpyMi0LvB65rl2oz/Qc3FaSqGapTC48bJJ66+MOUz6DSk8Wnaeq2rL0faj4cqw6UgzMWbhoKChrcsrE9RFDp37mxxPCG/lheTqkjLqyQiUMvzAQYm9O1Ex44dOXjwIKdOncLJyYn4+HhSU49SVlaKp6cXkZE98fPzw9XVldjYWLObO8s0a5kQ4s7QoUM5ffo0ycnJFBcX80MxBIc+Qqm2I2UuHfGsyiHqu+WEgFmgxNpoDzBGUD08POjXr59pRAOAW20Rd5x4EYBqNPzmt4yQ3kNwdXUlJiaGiIgIjh071uAUAjc3N2JiYkhLS6OoqOiy3UJUZv91cXZmxsyZDBo0yLTeQ+fOnRl07zT+t2UTLnodVVpvRowex5x7bmfjxo1s3LiRgwcP8vQRNWP8YgjX5fPVrAnkVblRiwpQcZvPGWYGHkLVZyKM+Su4+xs/J0XPkpavI0LlzfNVfZgw9F7oeRfqr+fyCElgiOEXXSgJCQm4ublRWVlpNjUmIiLCuA2shyte+nNkb3yF9ZXDLKbRdCWBr/+rIWnvXk6cONHgZ+r8+fOsXLmSkJAQAgIC8PX1pbq6mqysLIs0o6OjSU9P58iRIxw5cqTB3X3q31DXTbHIzc1FURQURTEFLOqPTAHMpphcLijIclvpuLg4xg6L5reEBIqqXPBzqaJ/fDwj73uQTz75hOTkZDZt2oSHhwchISHk5OSYAj/e3t4Nbk+8Z/NHtEtaTicli9OqYM4PWMTAseZ/CrVaLT169ODw4cOmQCPV5fy65d/cdrfl2gi2pAnGHXTU1FJbC2fPngWg8NxRaGC7XVvSVavVhIWFUVVVRUhwR2Ng2FCLtjrfIr3g4GCb3n8nJyduuukmUlNTSdj1M4kASi0GjfHfkbURNQ2VtWvXrmi1WhISEnBxcTG+lRc/W2fOnGHFihWm0UnixqO7YH0XpdIGjgshhBBNQePkRFW19ZHbDf1tag52Od3mySef5K233qKoqMhsmMyrr77Ks88+y5kzZwgLC7P62roOX92Ch3VGjRpFRkaGaQqCLVprGPGGA2e4/4OfTb+rLi4n8eVDt5gFCuquqzvf0HXNdW3x8gF4l6ShrrfMRan3TXg/se+a0mzMtRvW/RuDyjIOqFJq6duvPxqNxvSzK/E3DNV61JfdOBoUBbXWnYl339Xo/PPz8/nxx5+wuq2xWsPd4+4y3ZjYmqZer2f//v2XpjpcfqFKha/+DJ7dBptuiNd8+ik6KyNkfHx8eOedd8xuVvds/oiBSZfWI6hLes+A5RY3f2+//TcSEvZYpBs/cCCPLzDuLlRYWMjjCxZgsPL1olGrWfPpp416T3Nzc1mzZg379l3+uambQmM0N/QIQ2e9Aj3vsu09NdRCwnsYfvgLf8i+m9zzltug9uvVjamhp3EpSsO5OAMXVQ3JZQG8nX2zxbWXUwFOaqg2mJcRFJw0GmoNitnIDScnJ6tTedQqlcV7aKyHeboqFPzbefG3f6wyHfvvl/+hxqAyH6mlGHBWK9xzv/mORZ++9xqbf0pBhYJycYFhBRV3DYtm6iPmiyfXfVYMigq1SjH9d8+A5cSPeZCkpCRWr15tWhS2viEDBzBvwSKb06z/+fvhm9Wcr7psetLFxvV3KTcLlDQmzR3frKbQhjQbm+62/65BV+N21XQb8/5v/2YNxVWWWwd7OlUy+t6pjS5rXl4eS5YssRr49PP14p2V71scbwyZbtOymur9nvv/HqK4tNziuK+XOyvf/+B6iiiEEEJcUXP+DbL176RdBknuuOMO0yKbl/vhhx8YOXIk33zzDePHWy74aTAYcHd3Z9asWaxcudLs3OLFi3n55ZfR6XRmO0hcrrKy0mzrUZ1OR1hYWIt3/mKWfktKVrHFca2Tmq7tPU2/nygoo7LGcNXrmuvafTUzcVVZ3vjpFSf6OX3S7Pkv73ScUtcQi5tEb30mC0+bL3rop1xgYaSTMTBy8cZUrVKxIrWGIpXHNeX/5wg9LpdHiC6qMii8nGa+Y0Zj6v/Xrlnonf3Mp3tYkZmZSUJCgsXxm+Pi+bDMfP2df1c+RW9NpllAq1ZRcag2hN9rXzO79qFQPeriTNOOGV5eXvTo2ZNanyA+PHfp5u2O4x9RUOVqtnitCoUAFz3bupnfTNpaf+/STLqd3YkB8ylaxnQr2NZtdqPTDFdyiDz1PbkVjRlYVz/4YTwW45FPf88cbvbMI62iHSuybra48X086Dd6uhdx8EIA+y4EknIhgHKDc4M5eWsq6eVeSC/3Qrq66citdOXv2ZbpPha0jzDvGtQoqFDYE/kKBrVlumpDNQNTn0NBhQEVCio6KIUklwawoag72VUeBLlcYIJfGtFehWSqAlFdFuwLVvLRUmOxxm8lTmSpjAsi6w0a3jrZm/Iay1EjHd2qmRd+yOxYiJLXYJqZKvOdh453nUOpa6jFdCcvfSbdTlwKEjU+TcvvivppNmdZU0rbN/j+X0uajSnr8+n9jFPI6lGrVXz66VqL440hQZKW1dxrkowcGtvqOw4IIYRwbM35N8ih1yTJzs62Ogy87lhD0wyKioqorKy86mt79Ohh9fWvvvoqL7744rUWu8mk5VnfLaGyxkBqjvVz13Ld9V57rF0H+miyUKsudb5rFRVHazqQWtD8+Xu6/kJp599bLEbqnvMLqTmWW75W1ypM6qwh2A2yKhTWnaohscAAXFv+WaHOhHtgMTolq1y5rve/MsLbeoBEqeWj4wo1ikKNASZ1DiYuLs4imFHjE0TqcfM0u7fPpX48R6NSiNDkWOT/nxo1f+odSnBIiFlAadmharN2XeV/lJXZsRY385P8j/L2NdffiwiVQv37OQUVRdXaa/r8p+JOoL6hgJNxzQ2DUj8kYmVBTFT822MkR9SZJFed43HPHSwM/s24cO3FG9/7/dMY4JULwFCfTIb6ZKI3aHjo+Biro27UKhXv3rTdrLm7akGjajjdOl5VOZRordz4V2YTTL0FsVQQ551DnLflejM3kWVxbX0qFbhSQ1cuTvlRQ3Vtb8sLgUK9ulFp1r82WdvR8vOvUnNB28H82kanWW9tJGtpNmNZbX3/bU6zEWX18hxGic5yG3IvT+sPDITjMy6Md2l3G28PV/rH9JAAiRBCiGbXFv4G2WWQpKKiAq1Wa3G8bn2DioqKBl8HXNNrAZ555hmeeOIJ0+91I0laWkSgNwezi7n8nkoFdG3vyYdTB5qOzVq7h5MFZWYTPqxd11zXvrc2lXdVf6dWUaFRKab/vu90PzsfH9ns+X+6NpU/n/kHhwPGU6btiGdlDlH53/DyhVus5p9UUHYxKHIpzZuuI/83N+5iumetxeiUnwud2Pn48GtKE+DEnm14uWosbn5L9VU8NfFO8/zDLYMZa05rLPI/tTaIHso5i4DWSYKtvldvHCpnYr2AUiHu7Hz8Uln9177K4/zGV5c9Hb/PLx0vLxd2PnLt7f/VK+vJtzJCxd+lyqysTZFmoFbPvc++BxhHotVWV/POG0txq9LVm0QD5VpfVsx70HQsbe3t9Pc8R5zXpRtfgwLHlWDyJqzD4OSGQaNFUTvj99pCCios72j9XGv56fepqGv1aGr1qGsqCPz6/+jvmWOR7imlA0Wj/m6spUqF/66VlIRPtQgS+uf9xL5R641DClBQKQrtts0nnDyzQJlBgTMEUnzHcrMytft+EWE2XOt/8gNyq9ws3tP2LpUcuN18ulW77U9YTfMsgZwf+ZbZtdqTp6hw7WDx+ddWFpil2xxpOmpZo3f/xs/7LYMk0d0sHyiI5nM9u/Y1h1nznmyxBfKEEEKIy7X23yC7DJLULa5Yn16vN51v6HXANb0WjMEVawGWlvb82D5W11pYdl8/hne/tBDnsntjbbquua4tvHcOE1brWeKxmR6aXI7VduDFC3cxfcacFst//mo9S85/wC0X83+sJfMfcTOv//dXi9EpT98Xf135F+T2Qck5bnHz69M1+prz3xP3BOqkRRYBLV3cEw2WNbHQYFbWDQ+bl3VP3BMMTFpEf69cszT3xK24rvqfHRhvdf2G/gMHXvPn39Y0AVLH3s3u/35qtqmwCrhj7HiL+lt7TwvinmRwtPkoi7MDYtj8Uwr1xcfFMrxHsHlbZT9FVyvp5sQ9w8BBoy5dV3SWQUctg4Rne81g4EDzm649xc/Q2WqazzJwyDjza0vy6GTDtYcOHrL6nvYbOIi+Q82nQ+7R5VtNMzvuWQbWu/Z80WoqqtQWn39333Zm6TZHmo5a1vNF56lyacfR1FRKy8rw8vSkZ2QkPbv6IVrOte7aJ4QQQoimJWuSXGTLmiT1teZc6w0HzvDSdwc5lqujRwdvnh8bzX0xlqNabL2uua6V/Jsn/y9/OUD26Qz8nA0UVasJ7tyNCUNirivNPZs/ot3e5XQyZHFaHcz5AU8wcMyDVq+1Nd3mSBOMC12a7a4ycCBT/99TLZImwN+/2MyOLZtw1uuodvVmxJjxPDZpzHXVvzH525puY/Jvjmubo05gXGj1gk5HlYs/LlWFePj4cNv4B1okTUcta2PSbAxZk8Q2iYmJxMfHm+3ap9fr6d27N4GBgVfcte9y8n4LIYQQDXPohVv/9Kc/sXz5covdbZYuXcpzzz13xd1t6ra+rL+7zZ133klGRgYZGRk2l0M6I0IIIUTD5O+kba5n177LyfsthBBCNMzWv5PqBs+0YRMnTqS2tpZVqy6t5F9ZWcnHH39MfHy8qSNx5swZjh49avHapKQk9u7dazp27Ngx/ve//zFp0qSWqYAQQgghxEX79+8nIiLCosMWFxcHwIEDB6y+rrKyEp1OZ/YjhBBCiOtjl2uSxMfHM2nSJJ555hny8vLo1q0b//rXvzh16hQffvih6brp06fz448/cvlgmblz5/LPf/6Tu+66iz/+8Y84Ozvz1ltv0aFDB/7whz+0RnWEEEIIcQO71l372sque0IIIYQjscuRJACrV69m4cKFrFmzhgULFlBdXc2mTZsYNmzYFV/n5eXFzp07GTZsGC+//DKLFy8mJiaGH3/8kYCAgBYqvRBCCCGE0bXu2vfMM89QUlJi+jl79myzllMIIYS4EdjlSBIwdhyWLVvGsmXLGrxm586dVo+Hhoaybt26ZiqZEEIIIYTtrnXXvray654QQgjhSOx2JIkQQgghhCMICgoiOzvb4njdseDgYItzQgghhGgeEiQRQgghhGhFffv2JS0tzWLh1YSEBNN5IYQQQrQMCZIIIYQQQrQiW3ftE0IIIUTzs9s1SYQQQgghHIGtu/YJIYQQovlJkEQIIYQQopWtXr2axYsXs2bNGs6fP090dLRNu/YJIYQQommpFEVRWrsQ9qqkpARfX1/Onj2Lt7d3axdHCCGEaFN0Oh1hYWEUFxfj4+PT2sVxeNIvEUIIIRpma79ERpJch9LSUgCZKyyEEEJcQWlpqQRJWoD0S4QQQoiru1q/REaSXAeDwUBWVhZeXl6oVKomSbMuuuVIT4EcsU7gmPWSOtkPR6yXI9YJHLNettZJURRKS0sJDg5GrZa14ptbU/dLHPGz6wikXdomaZe2R9qkbWrNdrG1XyIjSa6DWq0mNDS0WdL29vZ2uH/MjlgncMx6SZ3shyPWyxHrBI5ZL1vqJCNIWk5z9Usc8bPrCKRd2iZpl7ZH2qRtaq12saVfIo91hBBCCCGEEEIIIZAgiRBCCCGEEEIIIQQgQZI2R6vV8vzzz6PValu7KE3GEesEjlkvqZP9cMR6OWKdwDHr5Yh1EpakndsmaZe2Sdql7ZE2aZvsoV1k4VYhhBBCCCGEEEIIZCSJEEIIIYQQQgghBCBBEiGEEEIIIYQQQghAgiRCCCGEEEIIIYQQgARJhBBCCCGEEEIIIQAJkrQZlZWVPPXUUwQHB+Pm5kZ8fDzff/99axfrmu3cuROVSmX1Z8+ePa1dPJuUlZXx/PPPM3r0aPz8/FCpVHzyySdWr01NTWX06NF4enri5+fHAw88QH5+fssW2Aa21mnmzJlW265nz54tX+irSEpK4rHHHiMqKgoPDw/Cw8OZPHkyaWlpFtfaSzvZWid7aieAw4cPM2nSJLp27Yq7uzvt27dn2LBhbNy40eJae2krW+tkb21V3yuvvIJKpaJ3794W53bt2sXQoUNxd3enY8eOLFiwgLKyslYopWgqjtYnsTeO2P+wd47Y13AEjtivcET22Idwau0CCKOZM2eyfv16Fi5cSPfu3fnkk08YO3YsO3bsYOjQoa1dvGu2YMECBgwYYHasW7durVSaxikoKOCll14iPDycmJgYdu7cafW6c+fOMWzYMHx8fFi6dCllZWW88cYbHDx4kMTERFxcXFq24Fdga53AuD3XBx98YHbMx8enmUvYeK+//jq//vorkyZNIjo6mpycHN555x369evHnj17TF/I9tROttYJ7KedAE6fPk1paSkzZswgODiY8vJyvvzyS+6++27ef/995syZA9hXW9laJ7CvtrrcuXPnWLp0KR4eHhbnDhw4wO23305kZCRvvfUW586d44033iA9PZ3vvvuuFUormoKj9knshSP2P+ydI/Y1HIEj9iscjd32IRTR6hISEhRAWbZsmelYRUWFctNNNymDBg1qxZJdux07diiAsm7dutYuyjXT6/VKdna2oiiKkpSUpADKxx9/bHHdo48+qri5uSmnT582Hfv+++8VQHn//fdbqrg2sbVOM2bMUDw8PFq4dNfm119/VSorK82OpaWlKVqtVpk6darpmD21k611sqd2akhNTY0SExOj9OjRw3TMntrKGmt1sue2+t3vfqeMGDFCGT58uBIVFWV2bsyYMUpQUJBSUlJiOvbPf/5TAZStW7e2dFFFE3DEPom9ccT+h71zxL6Go3LEfoU9s9c+hEy3aQPWr1+PRqMxe+Lo6urK7Nmz2b17N2fPnm3F0l2/0tJSampqWrsYjabVaunYseNVr/vyyy8ZN24c4eHhpmMjR44kIiKCL774ojmL2Gi21qlObW0tOp2uGUt0/QYPHmzxBKB79+5ERUWRmppqOmZP7WRrnerYQzs1RKPREBYWRnFxsemYPbWVNdbqVMfe2uqnn35i/fr1rFixwuKcTqfj+++/Z9q0aXh7e5uOT58+HU9PT7toK2HJ0fsk9sAR+x/2zhH7Go7KEfsV9sqe+xASJGkD9u/fT0REhNkHBCAuLg4wDkWyVw8++CDe3t64urpy2223sXfv3tYuUpPKzMwkLy+P/v37W5yLi4tj//79rVCqplFeXo63tzc+Pj74+fkxb968NjFH0BaKopCbm0v79u0Bx2in+nWqY4/tdOHCBQoKCsjIyGD58uV899133H777YD9ttWV6lTH3tqqtraW+fPn89BDD9GnTx+L8wcPHqSmpsairVxcXOjbt2+bbStxZY7cJ3Ek9vpd6Ugcsa9hrxyxX2Hv7L0PIWuStAHZ2dkEBQVZHK87lpWV1dJFum4uLi7cf//9jB07lvbt23PkyBHeeOMNbrnlFnbt2kVsbGxrF7FJZGdnAzTYfkVFRVRWVqLValu6aNclKCiIJ598kn79+mEwGNiyZQsrV64kOTmZnTt34uTUtr861q5dS2ZmJi+99BLgGO1Uv05gv+30hz/8gffffx8AtVrNhAkTeOeddwD7basr1Qnss63ee+89Tp8+zfbt262ev1pb/fzzz81aPtE8HLFP4ojs9bvSkThiX8NeOWK/wt7Zex+i7fXKbkAVFRVW/2G6urqaztubwYMHM3jwYNPvd999NxMnTiQ6OppnnnmGLVu2tGLpmk5d21yt/ezti/fVV181+33KlClERETw3HPPsX79eqZMmdJKJbu6o0ePMm/ePAYNGsSMGTMA+28na3UC+22nhQsXMnHiRLKysvjiiy+ora2lqqoKsN+2ulKdwP7aqrCwkCVLlrB48WICAgKsXnO1trLHv13CMfskjshevysdhSP2NeyZI/Yr7Jkj9CFkuk0b4ObmRmVlpcVxvV5vOu8IunXrxj333MOOHTuora1t7eI0ibq2uRHab9GiRajV6gYjwm1BTk4Od911Fz4+PqZ59WDf7dRQnRpiD+3Us2dPRo4cyfTp09m0aRNlZWWMHz8eRVHstq2uVKeGtOW2+vOf/4yfnx/z589v8JqrtVVbbCdxdTdKn8Te2et3pSNwxL6GvXPEfoU9c4Q+hARJ2oCgoCDTkKPL1R0LDg5u6SI1m7CwMKqqqrhw4UJrF6VJ1A0Ra6j9/Pz8HCYy7ebmhr+/P0VFRa1dFKtKSkoYM2YMxcXFbNmyxezfjb2205Xq1JC23k7WTJw4kaSkJNLS0uy2req7vE4NaattlZ6ezqpVq1iwYAFZWVmcOnWKU6dOodfrqa6u5tSpUxQVFV21rRzpb9eN5Ebqk9gzR/mutDeO2NdwRI7Yr7AXjtKHkCBJG9C3b1/S0tIsdjxISEgwnXcUJ06cwNXVFU9Pz9YuSpMICQkhICDA6oK0iYmJDtV2paWlFBQUNDhsrjXp9XrGjx9PWloamzZtolevXmbn7bGdrlanhrTldmpI3ZDKkpISu2wray6vU0PaaltlZmZiMBhYsGABXbp0Mf0kJCSQlpZGly5deOmll+jduzdOTk4WbVVVVcWBAwfspq2EuRupT2LPHOW70p44Yl/DUTliv8JeOEofQoIkbcDEiROpra1l1apVpmOVlZV8/PHHxMfHExYW1oqluzb5+fkWx5KTk/nmm2+48847Uasd56N3//33s2nTJrNtEX/44QfS0tKYNGlSK5bs2uj1ekpLSy2O/+Uvf0FRFEaPHt0KpWpYbW0tv/vd79i9ezfr1q1j0KBBVq+zp3aypU721k4AeXl5Fseqq6tZvXo1bm5upg6nPbWVLXWyt7bq3bs3X331lcVPVFQU4eHhfPXVV8yePRsfHx9GjhzJp59+ala/NWvWUFZW1ubaStjGEfskjsqevivtnSP2NRyBI/Yr7J2j9CFUypUmTIsWM3nyZL766isWLVpEt27d+Ne//kViYiI//PADw4YNa+3iNdqIESNwc3Nj8ODBBAYGcuTIEVatWoWzszO7d+8mMjKytYtok3feeYfi4mKysrJ49913mTBhgmlnnvnz5+Pj48PZs2eJjY3F19eXxx9/nLKyMpYtW0ZoaChJSUltbgjf1ep0/vx5YmNj+f3vf0/Pnj0B2Lp1K5s3b2b06NF8++23bSrItXDhQt5++23Gjx/P5MmTLc5PmzYNwK7ayZY6nTp1yq7aCeC+++5Dp9MxbNgwQkJCyMnJYe3atRw9epQ333yTJ554ArCvtrKlTvbYVtbceuutFBQUcOjQIdOxffv2MXjwYHr16sWcOXM4d+4cb775JsOGDWPr1q2tWFpxPRytT2KPHLH/Yc8csa/hCByxX+Go7K4PoYg2oaKiQvnjH/+odOzYUdFqtcqAAQOULVu2tHaxrtnbb7+txMXFKX5+foqTk5MSFBSkTJs2TUlPT2/tojVKp06dFMDqz8mTJ03XHTp0SLnzzjsVd3d3xdfXV5k6daqSk5PTegW/gqvV6fz588q0adOUbt26Ke7u7opWq1WioqKUpUuXKlVVVa1dfAvDhw9vsD71v+LspZ1sqZO9tZOiKMrnn3+ujBw5UunQoYPi5OSktGvXThk5cqTy9ddfW1xrL21lS53ssa2sGT58uBIVFWVx/Oeff1YGDx6suLq6KgEBAcq8efMUnU7XCiUUTcXR+iT2yBH7H/bMEfsajsAR+xWOyt76EDKSRAghhBBCCCGEEAJZk0QIIYQQQgghhBACkCCJEEIIIYQQQgghBCBBEiGEEEIIIYQQQghAgiRCCCGEEEIIIYQQgARJhBBCCCGEEEIIIQAJkgghhBBCCCGEEEIAEiQRQgghhBBCCCGEACRIIoQQQgghhBBCCAFIkEQIIYQQQgghhBACkCCJEEIIIYQQQgghBCBBEiFEG/DCCy+gUqkoKChosjQ/+eQTVCoVp06darI022KeQgghhGg60icRQkiQRIgbyNatW1GpVKhUKo4cOWJxfvz48YSGhrZCyRzXrl27eOGFFyguLm7togghhBBthvRJWp70SYSwjQRJhLiBJCcnA6BWq9m0aZPV89HR0S1drGbxwAMPUFFRQadOnVo1z127dvHiiy9Kh0QIIYS4jPRJWj5P6ZMIYRsJkghxA0lJScHb25tRo0axceNGs3Pnz5/n7NmzxMTEtFLpmpZGo8HV1RWVSuXQeQohhBD2SPokjpenEI5CgiRC3ECSk5Pp06cP48aNY/fu3RQWFpqdA5rlqU1mZiazZ88mODgYrVZLly5dePTRR6mqqjK7rri4mJkzZ+Lr64uPjw8PPvgg5eXlZtecPn2auXPn0qNHD9zc3PD392fSpEkWc26tzcWtm2d8/Pjxq+ZTX2lpKQsXLqRz585otVoCAwO544472LdvX4N5vvDCC/zpT38CoEuXLqZhxXXnMzMzmTVrFh06dECr1RIVFcVHH33U6HyFEEIIeyN9EumTCNFWObV2AYQQLaOqqopjx47x8MMPM27cOObNm8fmzZt54IEHAOMTHaDJn9pkZWURFxdHcXExc+bMoWfPnmRmZrJ+/XrKy8txcXExXTt58mS6dOnCq6++yr59+/jggw8IDAzk9ddfN12TlJTErl27mDJlCqGhoZw6dYp3332XW2+9lSNHjuDu7n7VMtmST32PPPII69ev57HHHqNXr14UFhbyyy+/kJqaSr9+/ay+ZsKECaSlpfH555+zfPly2rdvD0BAQAC5ubkMHDgQlUrFY489RkBAAN999x2zZ89Gp9OxcOHCa85XCCGEaMukT3KJ9EmEaIMUIcQNYf/+/QqgvPfee4qiKEqfPn2USZMmmc7PmjVL0Wq1Sk1NTZPmO336dEWtVitJSUkW5wwGg6IoivL8888rgDJr1iyz8/fdd5/i7+9vdqy8vNwind27dyuAsnr1atOxjz/+WAGUkydPmo41Jp/6fHx8lHnz5l3xGmt5Llu2zOKYoijK7NmzlaCgIKWgoMDs+JQpUxQfHx9TPW3JVwghhLAn0ieRPokQbZlMtxHiBlH3VKZu6Oq4cePYunUr1dXVgHFoa1RUFBqNBoDKyko6dOiATqdrME2DwUBISAi5ubkNnv/vf//L+PHj6d+/v8X5+vNkH3nkEbPfb7nlFgoLC83K4ObmZvr/6upqCgsL6datG76+vjYP97Qln/p8fX1JSEggKyvLpjyuRFEUvvzyS8aPH4+iKBQUFJh+Ro0aRUlJiakuTZmvEEII0RZIn6Rx+dQnfRIhmpcESYS4QSQnJ6NSqejTpw9g7JDodDp++uknamtrOXz4sNncX61WS25uLt7e3g2mqVaryczMpEOHDlbP5+fno9Pp6N27t01lDA8PN/u9Xbt2gHEBtzoVFRUsWbKEsLAwtFot7du3JyAggOLiYkpKSposn/r++te/cujQIcLCwoiLi+OFF17gxIkTNuVXX35+PsXFxaxatYqAgACznwcffBCAvLy8Js9XCCGEaAukT9K4fOqTPokQzUvWJBHiBpGSkkLXrl3x9PQEYODAgbRv356NGzcSHByMXq9v9VXk654Y1acoiun/58+fz8cff8zChQsZNGgQPj4+qFQqpkyZgsFgaLJ86ps8eTK33HILX331Fdu2bWPZsmW8/vrrbNiwgTFjxtiUb526ck6bNo0ZM2ZYvaauc9iU+QohhBBtgfRJGpdPfdInEaJ5SZBEiBtESkoKQ4YMMf2uVqsZM2YMGzduZODAgYD5KvJvv/02KSkpfPjhhw2muWrVKnbu3Mlnn31m9XxAQADe3t4cOnSoiWoB69evZ8aMGbz55pumY3q9nuLi4ibLoyFBQUHMnTuXuXPnkpeXR79+/XjllVeu2DGwtvVeQEAAXl5e1NbWMnLkyGbJVwghhGirpE9y/aRPIkTzkek2QtwAcnJyyMvLs3gqM27cOE6cOMHnn38OmK8in5KSctWt9w4fPnzFYatqtZp7772XjRs3snfvXovzV3pK0hCNRmPxur///e/U1tY2Oi1b1dbWWgybDQwMJDg4mMrKyiu+1sPDA8Csw6TRaLj//vv58ssvrXbW8vPzrztfIYQQoi2SPsn1kT6JEM1PRpIIcQNITk4GsOhgjBo1CmdnZ9PwVn9/f9O5lJQUpk2bdsV0Dx8+zIgRI654zdKlS9m2bRvDhw9nzpw5REZGkp2dzbp16/jll1/w9fVtVF3GjRvHmjVr8PHxoVevXuzevZvt27eblb2plZaWEhoaysSJE4mJicHT05Pt27eTlJRk9vTImptvvhmA5557jilTpuDs7Mz48eN57bXX2LFjB/Hx8Tz88MP06tWLoqIi9u3bx/bt2ykqKrqufIUQQoi2SPok10f6JEI0PwmSCHEDqL+KfB0fHx+GDh3Kjh07zM4ZDAaOHDli01ObqKioK14TEhJCQkICixcvZu3ateh0OkJCQhgzZgzu7u6Nrsvbb7+NRqNh7dq16PV6hgwZwvbt2xk1alSj07KVu7s7c+fOZdu2bWzYsAGDwUC3bt1YuXIljz766BVfO2DAAP7yl7/w3nvvsWXLFgwGAydPnqRz584kJiby0ksvsWHDBlauXIm/vz9RUVG8/vrr152vEEII0RZJn+T6SJ9EiOanUq5lbJkQwqGlpaVx6623XnGLt6KiIkJDQykrK0Otlpl7QgghhGh60icRQrQ0+RYRQlioP/d35syZzJw50+yaw4cPExkZKZ0RIYQQQjQb6ZMIIVqafJMIISwcPHjQrENy7tw5s1XowbZhrUIIIYQQ10P6JEKIliZrkgghLLz44oum/6+pqSErK8viqU1KSorFyvRCCCGEEE1J+iRCiJYma5IIIRqtrKyMPn368MUXXzBgwIDWLo4QQgghblDSJxFCNDWZbiOEaJSEhAQiIiKYMGGCdEaEEEII0WqkTyKEaA4ykkQIIYQQQgghhBACGUkihBBCCCGEEEIIAUiQRAghhBBCCCGEEAKQIIkQQgghhBBCCCEEIEESIYQQQgghhBBCCECCJEIIIYQQQgghhBCABEmEEEIIIYQQQgghAAmSCCGEEEIIIYQQQgASJBFCCCGEEEIIIYQAJEgihBBCCCGEEEIIAUiQRAghhBBCCCGEEAKQIIkQQgghhBBCCCEEAP8fwNI4HHfO+uQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(occ_fol, res_fol[\"bonddims\"][()], times, tim, chain_fol, False, True, \"chain_occ_bet=2_folded\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results at low temperature, $\\beta = 2000.0$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t name : Anderson impurity problem\n", + "\t machine : local\n", + "\t method : DTDVP\n", + "\t dt = 0.5\n", + "\t tmax = 30.0\n", + "\t parameters : N = 20.0, ϵd = 0.6, β = 2000.0, c1 = 0.4606588659617806, c2 = 0.46065886596178074, \n", + "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", + "\t convparams : 0.001\n", + "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", + "\n" + ] + } + ], + "source": [ + "res_cold = h5py.File(path+\"fermions/results/qc37d/dat_qc37d.jld\", \"r\")[\"data\"]\n", + "info_cold = open(path+\"fermions/results/qc37d/info.txt\", \"r\")\n", + "data_cold = info_cold.read()\n", + "print(data_cold)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "occ_cold = np.column_stack((res_cold[\"chain1_filled_occup\"][()], res_cold[\"system_occup\"][()]))\n", + "occ_cold = np.concatenate((occ_cold.T, res_cold[\"chain2_empty_occup\"][()].T))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(occ_cold, res_cold[\"bonddims\"][()], times, tim, chain, True, True, \"chain_occ_bet=2000_double\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t name : Anderson impurity problem (folded chain)\n", + "\t machine : local\n", + "\t method : DTDVP\n", + "\t dt = 0.5\n", + "\t tmax = 30.0\n", + "\t parameters : N = 20.0, ϵd = 0.6, β = 2000.0, c1 = 0.4606588659617806, c2 = 0.46065886596178074, \n", + "\t observables : system_occup, folded_chain_occup, \n", + "\t convparams : 0.001\n", + "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", + "\n" + ] + } + ], + "source": [ + "res_fol_cold = h5py.File(path+\"fermionsnn/results/nqs0Q/dat_nqs0Q.jld\", \"r\")[\"data\"]\n", + "info_fol_cold = open(path+\"fermionsnn/results/nqs0Q/info.txt\", \"r\")\n", + "data_fol_cold = info_fol_cold.read()\n", + "print(data_fol_cold)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "occ_fol_cold = np.column_stack((res_fol_cold[\"system_occup\"][()], res_fol_cold[\"folded_chain_occup\"][()]))\n", + "\n", + "alltogether(occ_fol_cold, res_fol_cold[\"bonddims\"][()], times, tim, chain_fol, False, True, \"chain_occ_bet=2000_folded\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "system(\"Double chain system occupation\", res_cold[\"system_occup\"][()], res_cold[\"times\"][()], True, \"system_double_beta2000\")\n", + "system(\"Folded chain system occupation\", res_fol_cold[\"system_occup\"][()], res_fol_cold[\"times\"][()], True, \"system_folded_beta2000\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 4, 6, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 6, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 7, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 8, 6, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 9, 6, 7, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 9, 7, 7, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 9, 9, 7, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([1, 2, 4, 8, 9, 9, 8, 5, 7, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 8, 8, 7, 4, 2, 4, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 7, 4, 5, 4, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 7, 5, 6, 4, 3, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 7, 11, 6, 4, 3, 4, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 11, 11, 6, 4, 4, 4, 3, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 11, 11, 7, 4, 5, 4, 3, 4, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 13, 11, 7, 4, 6, 4, 3, 4, 3,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 8, 13, 11, 8, 4, 6, 4, 3, 4, 3,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 10, 9, 13, 11, 8, 4, 7, 4, 5, 4, 3,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 4, 8, 4, 5, 4, 3,\n", + " 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 4, 8, 5, 6, 4, 3,\n", + " 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 5, 8, 5, 7, 4, 5,\n", + " 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 5, 8, 5, 7, 4, 5,\n", + " 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 10, 5, 8, 5, 7, 4, 6,\n", + " 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 10, 6, 8, 5, 7, 5, 7,\n", + " 4, 5, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 8, 5, 7, 5, 7,\n", + " 4, 5, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", + " 4, 6, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", + " 5, 7, 4, 5, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", + " 5, 8, 4, 5, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", + " 5, 8, 4, 6, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 8, 9, 6, 7, 5, 7,\n", + " 5, 8, 5, 7, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 10, 9, 6, 10, 5, 7,\n", + " 5, 8, 5, 7, 5, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", + " 5, 8, 5, 7, 6, 6, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", + " 5, 8, 5, 7, 6, 6, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", + " 6, 8, 5, 7, 8, 7, 4, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", + " 6, 8, 6, 7, 8, 7, 4, 5, 4, 2, 2, 2, 2, 3, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", + " 6, 8, 6, 7, 8, 7, 6, 7, 4, 3, 2, 2, 2, 3, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 10, 6, 10, 5, 8,\n", + " 6, 8, 6, 7, 8, 7, 6, 7, 6, 3, 2, 2, 2, 3, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 10, 6, 10, 5, 10,\n", + " 7, 8, 6, 7, 8, 7, 6, 7, 6, 3, 2, 2, 2, 3, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 10, 6, 10, 6, 10,\n", + " 7, 8, 6, 7, 8, 7, 6, 7, 6, 3, 4, 3, 2, 3, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 7, 8, 6, 11, 8, 7, 9, 7, 6, 5, 5, 3, 2, 3, 2, 2, 2,\n", + " 3, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 7, 10, 7, 11, 8, 13, 9, 7, 6, 5, 5, 3, 3, 3, 2, 2, 2,\n", + " 3, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 7, 11, 8, 11, 8, 13, 9, 7, 6, 5, 6, 4, 3, 3, 2, 3, 2,\n", + " 3, 2, 2, 2, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 7, 11, 10, 11, 8, 13, 10, 8, 7, 7, 6, 4, 5, 3, 2, 3, 2,\n", + " 3, 3, 2, 3, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 7, 11, 10, 11, 8, 13, 11, 10, 9, 8, 6, 5, 5, 3, 2, 3, 2,\n", + " 3, 3, 2, 3, 2, 2, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 7, 11, 10, 11, 9, 13, 11, 10, 9, 8, 6, 6, 5, 3, 3, 3, 2,\n", + " 3, 3, 3, 3, 3, 3, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 8, 11, 10, 11, 9, 13, 11, 12, 9, 8, 6, 6, 6, 4, 5, 3, 2,\n", + " 4, 3, 3, 5, 3, 3, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 8, 11, 10, 11, 9, 13, 11, 12, 9, 9, 8, 6, 7, 6, 5, 3, 2,\n", + " 4, 3, 3, 5, 3, 3, 2, 1]),\n", + " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", + " 8, 11, 10, 11, 9, 13, 11, 12, 9, 10, 8, 6, 7, 6, 5, 3, 4,\n", + " 4, 3, 4, 5, 3, 3, 2, 1])]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(res_fol[\"bonddims\"][()])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "unfolded_occ = []\n", + "unfolded_bonds = []\n", + "for i in range(1, 2*N, 2):\n", + " unfolded_occ.append(occ_fol.T[2*N-i])\n", + "\n", + "for i in range(1, (2*N+2), 2):\n", + " unfolded_bonds.append(res_fol[\"bonddims\"][()].T[2*N+2-i])\n", + " \n", + "unfolded_occ.append(occ_fol.T[0])\n", + "unfolded_bonds.append(res_fol[\"bonddims\"][()].T[0])\n", + "\n", + "for i in range(2, (2*N), 2):\n", + " unfolded_occ.append(occ_fol.T[i])\n", + "\n", + "for i in range(2, (2*N+2), 2):\n", + " unfolded_bonds.append(res_fol[\"bonddims\"][()].T[i])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "42" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(unfolded_bonds)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(np.array(unfolded_occ).T, np.array(unfolded_bonds).T, times, tim, chain, False, True, \"chain_occ_bet=2_unf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "unfolded_occ_cold = []\n", + "unfolded_bonds_cold = []\n", + "for i in range(1, 2*N, 2):\n", + " unfolded_occ_cold.append(occ_fol_cold.T[2*N-i])\n", + "\n", + "for i in range(1, (2*N+2), 2):\n", + " unfolded_bonds_cold.append(res_fol_cold[\"bonddims\"][()].T[2*N+2-i])\n", + " \n", + "unfolded_occ_cold.append(occ_fol.T[0])\n", + "unfolded_bonds_cold.append(res_fol_cold[\"bonddims\"][()].T[0])\n", + "\n", + "for i in range(2, (2*N), 2):\n", + " unfolded_occ_cold.append(occ_fol_cold.T[i])\n", + "\n", + "for i in range(2, (2*N+2), 2):\n", + " unfolded_bonds_cold.append(res_fol_cold[\"bonddims\"][()].T[i])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(np.array(unfolded_occ_cold).T, np.array(unfolded_bonds_cold).T, times, tim, chain, False, True, \"chain_occ_bet=2000_unf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# weak coupling results" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t name : Anderson impurity problem (folded chain)\n", + "\t machine : local\n", + "\t method : DTDVP\n", + "\t dt = 0.5\n", + "\t tmax = 30.0\n", + "\t parameters : N = 20.0, ϵd = 0.3, β = 2000.0, c1 = 0.04606588659617806, c2 = 0.04606588659617808, \n", + "\t observables : system_occup, folded_chain_occup, \n", + "\t convparams : 0.001\n", + "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", + "\n" + ] + } + ], + "source": [ + "res_fol_weak = h5py.File(path+\"fermionsnn/results/6OcGt/dat_6OcGt.jld\", \"r\")[\"data\"]\n", + "info_fol_weak = open(path+\"fermionsnn/results/6OcGt/info.txt\", \"r\")\n", + "data_fol_weak = info_fol_weak.read()\n", + "print(data_fol_weak)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "occ_fol_weak = np.column_stack((res_fol_weak[\"system_occup\"][()], res_fol_weak[\"folded_chain_occup\"][()]))\n", + "\n", + "alltogether(occ_fol_weak, res_fol_weak[\"bonddims\"][()], times, tim, chain_fol, False, True, \"chain_occ_weak_folded\")" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "unfolded_occ_weak = []\n", + "unfolded_bonds_weak = []\n", + "for i in range(1, 2*N, 2):\n", + " unfolded_occ_weak.append(occ_fol_weak.T[2*N-i])\n", + "\n", + "for i in range(1, (2*N+2), 2):\n", + " unfolded_bonds_weak.append(res_fol_weak[\"bonddims\"][()].T[2*N+2-i])\n", + " \n", + "unfolded_occ_weak.append(occ_fol_weak.T[0])\n", + "unfolded_bonds_weak.append(res_fol_weak[\"bonddims\"][()].T[0])\n", + "\n", + "for i in range(2, (2*N), 2):\n", + " unfolded_occ_weak.append(occ_fol_weak.T[i])\n", + "\n", + "for i in range(2, (2*N+2), 2):\n", + " unfolded_bonds_weak.append(res_fol_weak[\"bonddims\"][()].T[i])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(np.array(unfolded_occ_weak).T, np.array(unfolded_bonds_weak).T, times, tim, chain, False, True, \"chain_occ_weak_unf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t name : Anderson impurity problem\n", + "\t machine : local\n", + "\t method : DTDVP\n", + "\t dt = 0.5\n", + "\t tmax = 30.0\n", + "\t parameters : N = 20.0, ϵd = 0.3, β = 2000.0, c1 = 0.4606588659617806, c2 = 0.46065886596178074, \n", + "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", + "\t convparams : 0.001\n", + "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", + "\n" + ] + } + ], + "source": [ + "res_weak = h5py.File(path+\"fermions/results/jIRi5/dat_jIRi5.jld\", \"r\")[\"data\"]\n", + "info_weak = open(path+\"fermions/results/jIRi5/info.txt\", \"r\")\n", + "data_weak = info_weak.read()\n", + "print(data_weak)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "occ_weak = np.column_stack((res_weak[\"chain1_filled_occup\"][()], res_weak[\"system_occup\"][()]))\n", + "occ_weak = np.concatenate((occ_weak.T, res_cold[\"chain2_empty_occup\"][()].T))" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "alltogether(occ_weak, res_weak[\"bonddims\"][()], times, tim, chain, True, True, \"chain_occ_weak_double\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "system(\"Double chain system occupation\", res_weak[\"system_occup\"][()], res_weak[\"times\"][()], True, \"system_double_weak\")\n", + "system(\"Folded chain system occupation\", res_fol_weak[\"system_occup\"][()], res_fol_weak[\"times\"][()], True, \"system_folded_weak\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/plots/chain_occ_bet=2_double.pdf b/examples/plots/chain_occ_bet=2_double.pdf new file mode 100644 index 0000000000000000000000000000000000000000..a3816d1092480336068d914bb861515cbdd17aee GIT binary patch literal 33746 zcmeFZbzD{3*Dox+Nohf9g96g9H*C5a1Su)$2I&$|kd{JXl zQP1)B9PZW>YdWH+2F-fC?&LAt4M?TN8Lip5Gcs+1WaSp_did zH18Q%nHoETxqnDT%Wv5xn*ad%LOwKQY1Lf%EiFuyF3F-=n{l^~Xsd_#Eg?Mb+Ms*fLO zPZpc09PgXMihRn{!ByKQ;nFkauWL?b;`;T^*GgU#K0Vpr&pZEez9p%zKUZj1xc(L6 z>9?(Sg|C4NHVk3Ur)e^(cVh*-JXpN;sqZ$rol}|Izc`s%+K=llnaPMOKUpv7rcLOK zIHuOiKT-{$#D4IK?mmIOeEK7^^t;UZH?+6s+c%D0bAGqpA|ZHFasI84t>D7J)5-Jb zar2Nt-pPg?^T!VsbBHYG6}tyH;c1;oui9Qeed>9{_VsJ$^-(rfeVwN|57Y<1rfNczrP#*(tbi`L=%0O;um@#Gz9~r!-X6s=6@f?Aya6x^E;1&eGR1&Iz>< z80YJ*Nv$z>*e#=zU=C~u#dl6Svjw9~J?4&d>D2OkcQ(DtPd7dG&Feyaqf;>b!sGjr zgi%dZd~vYr`-kEoy+nOsbSa)zu`uDULZiE%utBLxEm>MK zs3>N&+QaJ%&JZ zeYZ)X?>=ieEV^E=bMEil-^`Bu-K#;yHHuMnqv!WReP!Gwi+Afa zTf^we@-YcEP~u7Gt&leIeeeR+Hkp!=IKDgJ(0S0`HJCUP+cENmOct4(|p+i=-gSbX)#QoSzp=0>mKaU1!I zr@EwB*GxZYAFJ8tQWjS-Vx1=8lKRG_^ z<{qU=5xzKmTK9cz>AKzGZDx?X5%&j!^3V@n`=Fdp9n@9qwDX|;h_oWjfEdfv42yPmIOJ8`mMq3zZK4j7m-*5c*YB|}traE9-j=&z z;`yfJJ(Sc8GMCsD*!KY~DZl#ua|LqO7zWq5NEIUhgpQlfMS3Xet?pLq#6e&0U@(>Qzd z8_st}ee3YEPzH=oH(#p1PbIRK)BKNxi4VBjlY^ zJm5_okCrujbWY?T%wmt6xoEA_f?s2-xB4ncj7dat@mt))=*8+&ZniHe?2$>dGY3sG zYI+p}#@=ibBom<=xY_Yz)mecm-YncPh~&4QvF?6)@!)%nlj|LcJ8NmUb9aQD8=H5{ zuz4;D(9>fKx^c6R(HLmHz6vYr>?zsp<25p`%roI>Vhtz$p28V9NEJLMV41?Bt(<$? zhR~B~`9L$vjP>30L-zQ6TDsOg5|0 z^GXD*zon^*ZGjN(o$_afSWP&Zo3z=uKqMbUFt{R{uFYxWe|hy`Bx~tGfbb5t%r7R)?;*0~t{iJZ_;n@W&bmC0D+^Fvu^=9|-V zm$T9c6OnE6ECZBuw;fg8)6?aZa{1@ADfTpEG=5DyQIXxEUm?v=YvJ9&W1!pGxISQ^Rf7R zS*;Ccd|gbS!4ozxsazlFmbB*%RXLIvitqz0Yt)FLN$&ezw~`nqszOF|9%M%?d#Va5 zi*I_|PdqtC;1TP`l@>k{vD%=}v*x8get#@Bxh0tP%6%esoYI~6!(oBXS^~g=OG@ zBT!;4E*YYBG=kUa~8HayZp3I zWm+ug^-K-tmv$b%pb0yU0J+Kv2@Lh`Ui@8*C8N)Mnh_0{UteF6Wy+ab$bxYf#p^)2 zsU`%MA-wHwC_aLIUh#yVnj3J(jMTHepKA8x$d3wtnBb(jp2K{dQrf?0I-U}OPhd2E z_#leFjMG5MSm6^!_+w^T7I&mQE(&zQe)l5msZ@7NQ&^Kl?g?`=;s<9Vq-8R4E>FKX zu5q&I+L25$J}-90+g7dU7L;$MtFq{X*A@jnG_$Z*y}4z!P`^e7*(2&ABy%{i$_nHx z#zO<+Gu16oL?r4N*o#Y_>1~kY-tX1q+|iNaA_}%Girf{KJhC7&U>XmsX`BfmA1GC3 z?@a8;M*jR}1cXmS<@-UR4Ld$k>N%nZEolm``!${W-G;o_qKvs>;&geF)bFa~v95FI z2UJNTxG=q6z(R{22Q`h-)XYUFMb0f~(x=-ZBgjcAVO8N}icDez=1ugRO@AM?i|ZY~ z6>Cb7&`%Yo$)0_d^pSa^^KiVLQQ}>bP^HN&v(}{2+t!Z)`J10L?|SdD6sCV)psj+Q z*PFW0IA{B8*Jx*)a@pl&ojVEmZ5!DOI^XaaH48@q=H{id6$c{}V zXWdR_sU-F;P1n0g+(1)BoWDu!t+gcA`{vD@ycajeNiq2DDf(>+2=i7+x(fFF6FitL8S-~veHzS>^0+m2QmO)v^qe`6J7a1*jPoqft zw{fy1pg*trgn_=B}hgi^bj5lnrR00>qNPIuh z$744_o5Dw?m2o(jjETbbDKgD zdyv{S&>sXXz0J?Na_5ajku8qfX`MWT5>q*q`oM1ivlw@bX7@Q)c*JvDqhi)fgSHe? zI%aGHW$w1L1S4H_kPPv##WNOT-Vl%~&RAnI$t`iRdst|FW2NPw{p{objVAB-4Y!XZ z>wWt^sM>F47441v)%{eP3GR_lo`E@ydU+W45MDIh#8VK+dhXvq5|aTn)VOu?nrVwY zBQq&Z=3q*gq5XqyR(sajfW-Lr;P}|ILL2OMl zwdCzkZb?Scn?Kw(LBk!s=87PH|XC-?j__`69{q{UE*vvSzmsGDw z!a1}T;T;z-!<3OzoySczsrUFvNZkjFT*gPfy{F$(miz)ylZ=}vhHdAM zlzk8i?8VLCh+&TVV*>k+yDX4CjwRn;zm_W&-gls8ELz0HC;g(Y!Sbdv&MR#L)7Lkh z`yRFV>?!#58M_u+pKcW7I&YfBeW0Ppuyb{}8>%0B%Za4hKyqNZDuQa?s$B%d%8@=^ z*PnxKvEP{CS^_Rv5O?@i1Zg+RU8Kr>k42Z39?UyWDE9ioTZ7mcvFih}hJ$0#wb7bX zI>X|%xT%RGXxNTY+EIX8(<7?T;tQnYy2#1lWKL2WL#;p3Bjb|u0 zjXLPsSDu!50_T2}E;lhD5c|i9QLyNI+;INfIzTMBx<4 zqmmYT-X2r#F^_I;B;49X>A97CFR*m${V@u%lYi;Ug)JG8k(-uK5>&5U>><=xKLpYB zo0j|{hDYo(V4FIXkRqZ7nY|MruW_myGD&l6>LUX*HvK!N`Lv@VHl;hoI_pPKNUM0b z5PdH~C9+-~k`H!iupOAuZFj%Qrq78p%KkOx48^cz|7GuS0~-k!6Z#Tji_5pmk0zWvdF%tT_3Gh-dGtW&|hH$E}kerN!<65}B>JVO?#Y!7c_ z-f;^By$qdT#ai+0?J?N-l(vbD_=JxtwGj8)L0AXtZ4EygO;I_<+d!9c$y}#TAT*7x zvm<>y-nHIaB$3hXU1m%TFlRZ2@d58G!c#)pWq&bmn=yGBEa|$;=(<{88`;lj6G`Pv z_1nl3Q@Be9WH+aKv>e)ncKjq826W7xZJD-&TeL+sM84;om#mPoEi{<3WDaN~Z062y z!{9Sn#%iVU^YaUTuxuZGcj!HBSUiQWrPytQJi&QW`x}N%yG!hZh}cM5(Q*#0@n=2B z5_EaeK_tfa8!%4ysiogjvo&EfU?~YGU1)+Vm=bKs|b_0 z3XAmQl)XB=b!MVTbShwxW6>NzC7UF>9Tsg75c`UTtL#>9XjoGd+0fX&`-e7i)cj|| zuZ}^8^hOTM0Zr*XFJG6xZj)R7NX;OG^upNe8A==YW*htSXV3jO!GSaEU*>irUNoHr z>F0$>x`Nm=r>1npKOe%X8ONtAfIo49ktrgq>y6VX@ZGSyo}@@uAHSGm?-@lViSEwDmWs4Cj*3lV36ZbYc<3Ld1L?Oufd!<|J z{=Bx3P-&=lw;t-y&gsh@TTepT^$a(IN;P4>L8f+La~!X)FC@l0+wHB~1)gO#GoF67 z)z<4rFvTm9KWjaj^s2OWvu|D_jjHTobAx9_ zPIh$`Sa?)V*>g5dGGWQM;$xMItU}ZeSSi&ysV5a=SaejelvJ|#tiHYz7#Bd$?%rX;o)OHw zEmOlHU8Va_^d6y`xW@CIccvy{XiWJ>gH2l2x@B4d8Bro9%6J+T$t0QzQxd5uO(rEt zwpmz?l$kB2pIXMsPPa6h76hWXLGx#a@(0u3_Gj;kf2EzVPtu5aNfUTDL7_JfW43F=F<& zZdlPAf*OZ&r6bBvV`0iFp&e_jbS=4K(DaSZ z!PDq>)+>(n)>enAm+=d1j+u%lgp-AtYcrna&s&_-7SYhoQoJEU2aZ3`Pi(tHe0^|E zLi)pzG55ldGo|@f@5jD8Q5?+2Q3sAUPT0*-{q#(=7!c}vD=rU28mc03JYJgela$A% zCLdwbf($eLs@~@@8$CyU17R?cdmyj5Igh`~liFWwsW#CS8}!}(Lw;V#Lk5OhK11~v z#~X3N-_PggP9L!qrYEZnZj-;AKJ;2LR@^fwXne+Y5azgKEF}`imN%F@y-S3bE#AY% zwJ8>woLo(U%;Mhn907)@u!s_X+LPd%BWt0U>+M`U5Vo>k#~N%~`@vm2r0~$D)OQYr zCGLD#`eDE_g3#M}42y7P;dBFK`YZ?3Mt`C$P()J!wr${r%8O&f< zUk?a;Dh}HW-;A9>!kg*FE5*LRz@OReum50?fk3gyl&ZdqP^lPL2n(?#&EKE8Cm&Td zHz`(e&K@4? z3-O`UoRhe1mBe(E_GfHkJqkSTOBK@W+{UU`4}1ol+e*WUzE{2AqFKFyQ@Sm(fm51d z@N5Gwz!}F|jRfhfXDq(uk^9K2xxU?L(h)SK@mF)BUAy*lHG(6C`@udV%9gSG5nsVR zgcoHS#GEdc8!Rt@YiXpn2a+l{-h|;kBSP}QoM!^-efH*VSk2WpU*d;;{vK!UCWOo~ zNh(ARkU%$1@@efoS#3Mblm!vu`TEm1XADUBz+B-;qU?y=;z{Vs?T0^E~Lqrao48^dr1DLIjB} zsb=x%g3_lxtL6_BG8vJkteOrt=M%rq`;6)^>NJr)hPw8EF(Zlca81spL$5ZX6{X|~H79W%BjJ!;g}VGNqUYBlo|gOOw{X?^BoyG> zy{DT#n661J%7rPZ!(nPb zbozihu`CJP^buRcFWv^+*`}3*)ea`q(cQiGFkLInfLel!Lehxik&*qo?BM8DPXC4P zM;jKP8?2_B{zG!LSaWqdDiY#lq;k@&9vbi14^+ZLzxp#zfdwsc55Q0I)%+t`^4$P= ztlw3$DWGLwM3DG~!LX&iDTecqC6u~Nf8AUri6tz_p_@3Aeq`!KJ>L7o>72WE=WntZ z2F(WF;-)5g)8xnJL-HW`+y_rTV?W)Hg!&V7C6bDnzUK_IZ+Vm(Pd)MReYMet09LG; z5$%X+FcVZ}l);7EKkjAJ^{6}R;xhZ{chjw0nFN&4sk_u1Gr{7Fw^J7ZfRS7bx zk8+2)jN9&oY6s0rsF)0sy;^(N^re(8kqCoWOl?^CHn9^uYWCwq9xXa?p4Gw%v9=p= z7f6XQs-o~H=FiC6&v@o#m<}Gg<7S6Ie+4-&<9qO%55T&=?m@1I@$o=`xZWS)dt71F z*bO2e@yBlrkY`W)J=5=?jMrn+R3e^r65|L)D5cZ-pW)v_JtBW%5Q#-Z!eTN+cjK(& z+mMaPJ;I!?6*+15n%~#mZaZ>r#2&@myGx?>PadG~aOAJ>=!(1h^-tg`)zU2^gKnK8kEO6+EKER=y1)pHc& z-8Wn>yukq_PszHTtFDQj2;7F4VJSC0Amzmxmke%A`^_WMfi;v9u%or>zS7gV9sU%n!meuZ&gA*0^|#Xs5P=KdF4 zhE>9+583Hms{cYZxZBZwTlkRLpTM>BI(aZ=Yu_9$mcARF60DAW85Jg`AuRD-Rq^XJ zh78LQsYJ!TLpQ(DyIJeUXc1mFLarbR9wYuWaevAF-%#~J9v_JsBypUa&L;8_9Fk(L z@>VPU%nnI$J|x@X=?EHzrbMGQ`8v)H3LABCpxN{5mYx=oIesgFDfLk;4SLk38=PUp zC5k$W?!zcB=ho=CE7%Ny-(<(|Bb)4j=zHZy79~DQYxxN(}dygXs#h&$>OP) zIUHO{S?~h8Ki)Ic4yi?B)z*8E@Lp%5vDd5%cU_qx0#&irGhRTB{qDy?r3=4f9w9|S zX<1^w_2`Yq{!GwjozI=7b^iPA*RPjNJZYGdA|H;N>7Z4e=1myC30D1Lg?{ASW19JC zSZoDi&)H?$8a-ZpNK&O+L820BeZ)nr8a{pZ6*Q;L#OZA|*pxFP;9;4OnB41wnFTJd z8GbCqk}CuR$4LAa0deqf{0jj^{8WRK^cP^^!Gqf`yNnz!Y;MwOJ!{pSQC5CLM*5;Hmm7tQG1WLVzD49nAsW9<$oWc7QZtRWs9>85D#R zO0ecAA+9U$TbO0cTD|eD^K4O1I3&ISl0>9qHfQ?&8fkcgm;#G9ev7-iMBoTx*qOQK zaiIHk$y;8iOWL*urulxa11S=Z%Pj6(EYd$FcKh(?u_uDb6LErnau0z+Mt*VsWA(ZO zmavH#I+^|`NsDV}N~kkQm|7XCyQmu4In-Z zz-)@ZG7Ye031+hba{wla%@)Xj*U0$?B8E-F(!|*U==~C6^BYFylJS2E_&3p9it5jD zur@RYkT5We%j*BFM8IsKPR4Nj4KH9e+2A9=FIXT@fLw7ydl^$pa|>tqjA{NV0_Yw9 zcVuZSYHMz73W$zP)!EcW9bQl{bi3pX%mL+M|D7#3pK!JwfMM+H{}T!0f43hf2M?GN zm~&3Rl=47%z%VEbgBKu{n};0?0YF7iUiQnx1<(n94zCY|as0@k-0=GUUBdYF917Hh zCwL#f5*HM(;p}h$@NfbN=!lmG%*_p4Lm^-;U~q1D;(~x7yj&PS0>ijqU@k786BrcC z%?Z@y0SI6R%J3Zi910`=SH%sqhxf_L!wcj;?!og*W}w^{@ElGAoGEyMaX>HIasiD0 z=mV$=jC)yzasq%o7J3g_uknE-`?xB9CWI8p5E7?;m}cEAhc1zyM>39bk~bO5dg|Cugj4d?}2Cw@zp z`T*C7U+Gd;e(D5VAAV18x-PGQKH$3XE5SL3vGV|P2+#`Gfh!U(Fvg``aKI(?clxCh zzj8nq0QxvFE^~PKw{$tzKc2(u{FUH(0iRWP{MB>2eF2{&%&{q+xt8_3peofgR$j`wc zk9SS<*jAaBOxT9D`^EW~sk!W8>~X5iepUK9>f0o+Q>Q~Gkx}e^1^#Vf7E`)U=I{LW z9k8v4>UIgB1jE#_E%=TqFKF^!6#9)Gl27V$ubtl{rE<1b@{o_pO;u~Ib1cQ@k6qXG z+w^;rG_(#KF4*+W+6&AZ$y9twaaezgxhZp6MiPyhxPG{s;yrUF`N%}27t);Wmapc$ ze1T0|%V~8Fd7_@>9^|{EHvDn{UeMxa!@rvD7DVA>>Ah#G&G^K|7^cW@hD! zv^`2|LPj|7M#IC2tETq4=>-04r|8*YT8t%sW-_ z(@!VpY1cx?$B8AaVyo_IsZvI8M*BZq+qEx#wa;(eZ)P^DogNpw6tglwWVD_BWV}a8 zSBt)N-QSF_d{vqE1{zG^-RoTDtSMGEoKd=z2S<3qH3O-XBV4TL+|C*qrVO0M@6MhM zJw6YE%v#;mwN}Y}OeLTvaes9cO*)vyIPS*Ef++f$T$6=%(91`XNTS>OrCpEFwRB#lfI9rG+h?<*cF5e`>2*J9xwT1$#EI7NP3*Kh8_d>Cs@ z>?ZdWg)(D`P98^nd?H72CtqNE;FwB#)S2oE*bah&i5O`b$?Wzcz=HrHN^D+(=>k!7t0t})$pzm*cH1y;BeuO@`A!jmDrWoxitp3I63dLLm*1*oDgLhjztJ3CmPhL(!iJ3 zAT}5w!Vq-3@up*ifwofvANajeKCcd`j^#CnbbRwGXt`oz1pzF;ABo|*#~X!Ajn(ZQ zAjE#3WJ07qJG%@vcKWm`DnT(~m%4M9fFi*y;nuApv-SyX6R_E=>fP4*qtfdH?~Z3F zX<|Y#4Feja9iWpWE|r)`)nL2QI zkqi|!*4iOh6UU4}mR9nJG~=yQe?#>cPSh?%!KU=Qe{ z1Q)?`Dk{O9S+8Tdgl)QFF!;;{k(u!;XH5tcc)F`)Rl-PPAnTNGTxmS>1UfSKR!tQ8 z@@^%J58s1@0{&{utn81>eO{JPb`<0gq z{>v$n6%zb7^QWXUCKsn@-=AsITp>aDCiB-`>!$>vfb;Wb39883wu*ozRtR-chjx?t z-Bs>S)tuQq6jyo6SQE_A#_rh_QMn|qi|-K7S*};`K;X?2)(6@N?m^X|;j}_!lX2VB zWxCF+#XJ zDw8JSi{crsL=xp>D!tZfno-`~IEJ8D*@b0c8k2uPFGUfvk!e9(7r7>}8KagT@!b42 zK|Rrwxe>X+%HVs10#Y@J*vFggL2vtV!fGU1gzhPPy%T$vMZL{dl~n&cCT!%1GYaEd z&i$DEf%_!`n3}=v_(#0wGAApatf>R}Y(MP>FHu2TA^3d;^1KFVaSPJ36Iw2K1r(JP zea=2qFF)DeE#2_yIK21Wc|yC_rN$AIF0!N`x?7D?AXVJb0ug!@&ft;GTy!(BMheQ2>xVZ!A<~PS0T+Zx=G^Ls}Ngs?P!b7Z`1Z$B$-Q8*B{nSj*WWDq1Q`&ndl#$x^ zb5)?#hKOrP-GP1CYhmbWy3G}H`uhRJ&p8G5E`OZT6819qipI&V3cxB<*tuaU>>OP1 z6%7&HC%#gLy^)-~nsc~2FTWkZkvqqIWwRfJkEx+YsZL44(6vMiejW=oboxpIrM?9b+S?tQgTk244ult0vT z(+?1ef1BXC9VO;5r@&9sLy3_I;b&p`@VI5&*5|8mx61Cf&f^n`!t1@~I>}c^@Cv`4 z_g~DZWOyT90~mySp(cusf${ksT{K|S!*L-`3|4Tq{ZTN>+@+{cUef6iX`;y)Pb$3- zshx9V>6}ElZcqR8^MG1%?C>MYNZI=glbQJL7dYm05jpOI#;=Ysjn8fg{F6#xC;gAr zgyY}rJ|rXB9Z&$rZ|5B%ExR|yVP5;e^^Na^fnSD2JxIi`jP5lR>1a3@Iyb*4XMtir zl_uS8`wB6x`KSq|Uq!--49*?Q{Oa0D`tHl(N27v_bR9N68vLH9gfMo|yK6k?A3&)n^TVT|Bu7A=Ye&;#4h? z_FV1DT*1#f!j!Ij`&Te~g_q0qFQxB1g6Xm<3uzf2p|+^O$7zL>_#Ez zMko<^=U9>2upjG?3zZ%lmv$AX?6pqw)fV-Dd)Gjm!DwFL&z=KJyBpJG*D+ey>T{ z+9O_deliq7$vg3#_g9M%bNN+U5*`LyZ5W#_Rt)o_wvzSf^1d~3$Ov;$b78c<+yC-V zdFs?vE5=hA#}s2M&xzc6w#%lOgkCBse;emG7<`XjRl0QxJid!YAMW`wm&yK>_0ga_ zI}Y+UF@pHTP>~HRpSE7rFN^q_3k$9*J_dH~crxR-9g)-|6uz#nbS~yUoz`AB1oHQP zAT1!@nST`j$+cbID2m7lA#{=aP;UxV3l;V&Y)c&leFSj4D!R<>Hc zbl$UoIWb}86Hu7pc*JCorsDDuO>dbMdtK~W{_A?g^?>Eih<1~hE_{;n(s+$3yyo>lC24h zmQj)|x`_680AtTHc$x5c>SJt)IA@#%uNNki@f{h(uskf$o{^v`b+Q@y72C!cN&OjR z@dNNcrom_#*s=XO307kQK`0HDoC4}af#ZwUdZA=O&3od6kx@9yTo;<(_MNZ`RbS9Y z+S=94>m|R%LBb=CO02o1t69v7k7vmxs?gt{&Ej2MuJ@g+^i~y?$Wd^ML!!#_wEBnk zS^aEF^r~Z5i0BH}a+o7@2na)6Kz-}uckx`6?p&csdyK9RGfwUeA<4@gV-+9<6FFTomETX&lF!; z?%NLZ78ZZcz(RDrP*TIqr!AK2(HXKW=9Fa{Z{`V)d-&n`%G<~OS{ZJ;3uD6!K5K%I zMzfMyUiMtUKAZ@}inhY_Vg8r5k%Frt4#cuBHYi7E(_b)6#H+kgWLYAKd%gPj$@6Q1 zt<&L?gYDfn!lXX1-E=DI1uDx&UZz6DM>-{;qwX6}R?enowEu6IQsn(GSB0|>+aQEWf=$DHhF zQBr{-v5~=IA=EcyuC??m`Jyj-YoUtVOy@x>pnJyT$Ao=tt`s}|pHl`zC;s71K!GFA zKaS0!BJbD=#+q=#&`pJ&pe+ilIaj&WUX(>eOG@DweBoFM43>MM1uoC{)KVVaBN8xI znA;Q3FEi1>7t|dQS`oz(@K}O*k)uVR#djxnkCO52 zmsvz^ea}gcPG8%+l8>? zj#S94h}Umv1X43Qd+0FcUnEmHG9nqSCl)w6lHGU6PU+!F3N44qnr+m{6el#f>++O* z$5J)>;7;G~m_G6rv{P&@KQO>skWw3~W#ODeP?PE3-==s^u}%ZJLCs5q{?3Rj1ZJlvPFGCJ6^;)OocQCcK%p>I!1Z89#DDW95Mx;? z_R{qb`sMcYF!%T{-@=TDC1(}KG-kIkvd%OxGeR}d0&{??;EqsyA_3AB)Lh{RK_UNw z94=r7i^2h#=x3-9$=bcwcku3AXdSSFC8#C3%Wkm1z@{}MUpGQKk=4BTfa;jGSZw;m zR$`Az>oIW&VLwZubU$7>3kI?>fuLpr8xhS&|Cd$19DBLfJ{q8MLb4h^*=v4QCuo#e zP_WX9NL#fLL?erDPiL9K-DnP~Z}0SX1hRfN{AR@qct z+0)d7csYrvxDC%@u`huQQ368J9G%{quwa{X6uK44EZUwHLcXGQ#2T~2kM~y%nkb(! zglS-wY`-1b`v^2> z@tusgYu1=3%3C_S7$4h`ry-~zMUSUd?|^_8ry_?c!2-LI$kam-pz#K1)SF?mXTDh==pbt=%-J^0eCon)Hul(@;Jyp zU?NXd92*U0+$oKx(DX_?wX|rncqY6EN)e?_a8BJueE)V1IE`YE49`pZr?e~dT8C#Oau<}E4aDBWr6}>hCgz{rNV0q7$N{(;tBS)o!A)%%S4{n zW4|3IL~?LeiQ(5)bM1Z>p!Q+Y`Ly)qXMFC(N2UQS=$*OvTPlGvIW_9kA9oTUh2>o4 z$Q>$KO^ca}4xPTj%~{!)?WbkMg-ab2FDNyMZly^yKw4W?Z^rg*7nwcqinVNb;pU&r z^ogZi6Geq=if1e2IL)2qJ!(*GLTm^Ig?cctm*`%qbhzgciJB&OaEn~lJ}g)%(%aSN zvkVsRV`f)nsxQzx90aSM$s{L5eQ2ZNd0VTTzs#Hr6sLY43*)ZmV_bWntz|qUM79;v z#td2W(EQ?j@TuJolXEBieB~n+KBsATR2y3UXWqc{1Z8!%a`9;FUXFI^1Dup*Qgpem z0)+K1XbC7{I%aUN)1K;6W`rffrV*FZg>8t?aOO*z^(uXc`0^9+>3cLwE|7t^SX=AwuiSk_pHE6sJ!;|t}OLp*v<^rs>$v%Kr)VGNV2 zt#cft=H;WNBepM zkf9EEmz3FuEQ%jiAQcXCBs|Mvd*K{-KRkzH(}~4Kzh8mp;lQS5-u_|?rLF@j{SCG} z@-K%lq#MeXdHW(V4c~09A3D8{o^g|3=_4Lqc--5$-=t>Hnlx%F0{b{WQxmjs7FM{j za(a%WZ}1@K3K?GE1akbF11N^V~s`9ebp*m!=B=c2PhsD(pxDxF0$&b58K2j8g)!hu*O)j2MZ-f+EL z2ZV1DM^)DVrqO+Is(zpq{iVO0f9ge5shPfDdPDFgsI>?|!~0uRyN`U+sowBn93!nU z!Lxmzbs7~Ie;V!wwN1x&Ath5uO!h=y&IiS}w@^OZ5}3Jy?cW{D|A}oIH|M|DwheK; zXTM*>&aTw4G{gnrM1?w4IJ1Nk^cA!x0?}aS{-D+| z2ZXP^-(&TLH@wH?&=ofp2NPuCcKKWN&2_;~BQVQxu7L0X?#TZE;vq2hU(|C#)q%JW z3?TnKyLWLI7E8mB0f&>SJQyhxr^$D%_XqJv$2dIyR3rc^@i+0mPD=q0!XKr_rOb;1 z?5Q`8fspg34d+*Y}1Es#7-V1eV57C+w?4=TI-H3V9^3CRa_4_E>SQ#Fnx+ zc~_Lxu;oWJ1e*os5DD2v~q92m(`y;C%aEEH01dwt6IgHK!;^-3_< z=ng5fXzyD|0%Dtf`b@5RV0Sqkw>8zFn$;Fd;)9By&O<)BI=cH(@E+ZuGZADYm#dou zoj{UC;Pm;Mj|3aqoI?=&kv6e|-uKWwHBE{4`%7yyU^zHlWD<^{s}hG1S`|7;9qGXWk0 zpdesi3pi$lgPj+H4FGQg?ST>O0n{I$yiRc7ADasRc>&NWzp#G*kOD61-|#EH+uy(E z_G;|kKc)hh||u!c8X7&*cISn1LS`z?u&R&=Qvy764WgUc&fgG=NRdESJ^bCI|L&m>*UQ zh#_2dlVDZn&QUC(> zNAYj_^ZQAID97(W_rE?6$tEre^v7mw2%Jm7O_?a{x7SsvjT=0s0h!6 zgnlz0zpMvf{{JTNHw*dqO*kLFSd;|-x<4mM76Rt@DLz>sHuO_=vOt9Br|1*_WbsdN zC;-iV`W*^Dv!8mQ00jewX_sPF05H)%=Su;2Eq+dy0t^iKIhq1D81hpcr64T-{PhEi zu>}BV{n?uo$L}?4ENxw!z(Dx)a?=0vnTxF@JCq$*7Jus0Kb}IkB>|}LCBcB*|Brrw zw!l+f@a2Z!cb&MD&@YMoueLXrAM^zOa8>{$F5t!dg#SZ1fX$4!owc2#>ZQ*GdRIr=>3+m%a=y91JZS49xBK z&+Yfl?Dfp-bxrMdPVKf$>@<&WH;!+C!~$zZH%fho7p(#4Q}Lq*1#1|5B00#@Tp~cS_4u^W_nyj?eGD_?Lukup8R3a zP0IpO^ZXm8xx~ir2n}-x^t17Fvz}Ib#?ea0(n!U;{~BHO6-dwv#9_rI>~e_h!@KAGD*p4~hK@w!c|AAz8DpH~k@ zm%ofG9t_U!_s#BgPw#e2?zDW~ZW!AF@p*t0GXcouILj;BG@Qr z$l%SOR(ij3au4WUf=q0ONJJ}0$QvXV#TU}Z>0i(8UCZiK!~CS0(W4S1AER@}t%A$*alpFq80djtc}y< z0;A*zd@9x${sSWTPi`)C6!-w_Wy!_H*6D{DU(q{!Df_wE1aJiK7aq9t_g7zEt;Olw zCz2Kip;LQ9{I5xgw2(>@s!4=ngh)vsKf5#Z#xH_B9#`anJ@PZq%~n!!B|P>VJU^L> zM+Hk$)mq1SjADB4o_$y^uXnlcQhVs?c~a}t?|z)`fwfF_;oINb8on_Hi+`x2Gdq>v zkiAUli%AyONIY9TgLJRynC!73(x!DyW>Kz#(_BkicoDJU>M4FQA{m+awP%y3`)w3O zjN9|9+w->ClY%(O+oEnHJcaoIp4k5VPfvvR1i38WFcff z%O!Amq?#c&L@j6DaPfI$45^C0+JU=8cd}J?QX#xL*JpTSO5W!{B}z-in^E@BlddNp zzsy`SZ`lf{dhl^zivHT8q`BJ_St};9Q|mZ$8-@yL6$i%13=Y+UY_xkj0TrRuKmMDm zs)_9%VY_)H#}UiTb!Bx4z1%O@u92EU8`GcE8r{#X??tj>1*L|u-#o&~+PK{JNCg$nQZgi1V)F7+c=>F{aj@GstV@-@) zc*Iz@JMDZ#f9_fM!7P%}q}D^?M`T~U@5{NgcegjXHjHn>^AOsMSEF- zk9G*#ntX}-$Hejx_wTHHw`~R6^k;qAP-cTui}KtN>{m}u7zVB_@F?y)~7S{Uqa_kd*?y0x_uJ3q#9|$vW!S!X7i1t{>($J)?DFD z7yayE_)bHplZI9CVUN@!$~aW3s=5 zHOlb%AxZG`zFb|^Z4dM?+a#@|Q(CUT|EIj`4r?N7_eWhdf~ZR~f*=+Ir6!cnQIw#F zAQq4=T||1vu7Dy5R*F&-0R=&-6cJDZ1Vsq4^lrqJYCusy;bZQZfUG2Y@9w?N^ZjwZ zjXcM9e)FC)bNbBhyyu<%8;uHYUI(7$YOw1LdPlp(v6PWg@i^Pg&jNj$a zyK3>Fh&X9cKEERQ@YIfKV`JoKQNT;NbB8q2%6~M5@!(e$-Y4KCc71eq%~F2dC{)}lfCN;ceGAv*miU-LMxE@chP5j zOgKuS#E6cks2KT~6O~;19!%!=(wzC?A~j4*7WufAIp4ga*n!Y^tbMZ^vI*fCw$AI* za`%t5iH$C`F74c5TKkT%_7bxj#u)iE<;6>1JG#WQIWoI=Lu$HD5&fZR#sID0m8TBk zJd_Z)dR=$N@@I6dcofwrq>Gy|8`0<)LWBp zCY6tjwRz7&9ysy8qqXE!9BOmOue+&D`<_Gn#u#8kkq5$sjZ!P9T!wE`l0J=bXMISk zJhHOj0O!fj6Uc1!r>SNumYokx-4vVP9;2K6Rx&s*daqE^u=z)cNrMyZsU`7N3f_)I z#I)RVdOoJ3h0}uFDF0a&-wAvhJ8{>@zQ=?-}`eghu`_8 z36}j@FfBcH<8=L-I1{B0OXBNBf%C-8QSc#+PiSx4J3Y0eQ^_CI=PGMQ=d!4+@vdub zwHv*)7wxAlj10ThMzpnPbP=A)=~f)lRc_JGeVAdx?-#$0;cb+$SD!K1ym(*#gO2>e>BIUxTMx?@hjrY7MLJTq?LierRa5`dYn@%o08Qkj+z%sk^N^Z_(ZlVf{p{To@A%IDHS9-pPDwBKYN5)krzkaW3M`rYowT zkhA^F^Wxv_-8OwK749&(p?t{wEddhk(IDKlwRdJWx_KwTvRE(5W zB=;mFeOf=}ULTj^-n;cfm++uTC(gz&zEI9RTWZBJ3>gSWRsvIncjh6SZdl+CuE?ON=(!{k}nh?JhC zp8SvEay{Q*wsQ^LS8to&QbFSjJpoaKli_C-Q(in&9a*s8K3-MFU`|(>VHBkL$uzix zFV=1R-uoL|W5g1Uh^3khS?fO>`_M_QdD|w|EFRudGqzfc$A^D*f(zT$AET^B->1lE z5&XL410#OIqU?~KeOz;k`4REpYU4K?tJhBOOr?9QGY3{Kg|59k z3;ZSf_P=+Yt?G(SU0-9j%;faE_{L6EK{wEO>Jj~V%2V-?FZIdpKeD^$VnK5b&jl~P zrrhCUeYB#;*stqdH7H)oMiqWEv=yU~ab3la##WAuhA=F-8f>}><#LxV5S-20o)s=~ zI+D|vXGhgQk!?Zm^~^m2Su@>sQRq|pssau5hRMK}tp(=?{f_tN-z?)nPd5MRR|e0P zcN*HZS4&}|Es8P4-$KP0l0A95(G2|SPNATF2Ulgbn()`LmnQ^Qo>yf^o+;Js;1Xkm z@G#@1i}+FW-MozDOSu?T`5n~wxijUR?nZZ2M=N2c;~9*L5O80>A1Lv zhq1~c;M>+82#l9|zVn5rpn$#~09g#<@s`!M7v}WldNIT> z$akdH{+?XZ)5G`%S&zGr_>gn|_+<(bAKgRaw<_O8RCHe}>msA{M<=8|&OdEuavu=^ zd98kVt=_pU9@)({9C?6>o7WGv&1)t6KWyt(lIc0@J`MC zxN3u#*L!bO?T&gyjCi^8M&vjKVq4!>E6SmK zZ9-|3FL$cwb?-*spk&JD8kA4@RE1J1Bb7{6rL2tsS(Vb3p|!`dF#6F&O@mLE+)5sk zTRB(M4!M;yCbx2*$*m++_dt3Dqk1x~ihld$+bC-H&GPQ+3L`U`_a(S zj^N^spyGBlQW+#%P8GHV=C}GhZt=`%am#AP?7Vb5qv=>i)6w)s%_|l54;yS#>LKBU zQ9lFuk~gOJYK`yGjPBA7#MeM#M*sHfJ<(OV(XSvqqkXe-oolYvbt;pmIj0d`j?ulQ za+#uZ>4jp*b4b|W&zJt-Y>FUfv)Q+BlXt-e&wP}rF}TH~X)Z&KW{tz68JdRoND8EB zel<5whBVEJ!}n%cnq~XrAaMq1uYWI5gLccm;Av)f*PnSB@=IeZ<`?X?y0m@y_?+OK z4)(SCFKv>ybF+^u(Kf`#Xc0MFHl7$3+IoUG)^*}kNjH)_>R%{UEQ9-u5e-@$WGqVD zLAOY_Gm<(|QXVauzG|9x`Wp5{BBh2CvgJz@mNR&VmLgp{w9YJgL+ond zNNjS@Q!Oic_!}LY4*s@f1woeFJ@?-nC|v*LDz-V8K~YGEaX)0^&6k0R3x3U=c*A{n zpST{s$}SQolDC#Aor^9PdX2bC{3bsU zb7dE)f#RN!a#hzRi8o}~j$mHyjZb8EVkI~6UEf7A2)a($@mceoEq&wq?5n$Mmh&#b ztmIgUS*J@{8T8BNb<+abhyP^aK^q=A$0#T>?N^xd&QFUKv!CW-ck0GMsQ*0i{2fAF zUPSx^08RiH0U!gwS^&17+g#Xs$)6ie{a?7!KkI|?C^I_8mCh`${M-jWHX3p*!~y8g zOq~1qg1ob5_I}}BTm1Bwmud@QzO{d9dz2SH-ZdfLTU?P^xmhUZ+(q~sA1>y(Kced| zTFh;^EYq9M4f&{M&5;>oOp%~R%;A4}nDJI1P_woArcx604D&w zsH|n~3BYqG$phpbKpp~aC{M!^8VCj;22fBzAi@U_XK)t)Q7HEVF&H9>$W9=I3LeNo z7!D!`9!UX+0MLY>C`5V@L>dVPcJOc^KnftzgG2x%4hmb57=U;{As9ir5hEyUM%Dr3 z0u(xsO8{{I$Z05VKoIMlFnSw6WB^hGkrw0`Kq{fI4rzd41A+)5-_S0IAa9Ub7_LT; zY6Rgy5Fg|&Kz@VBOJoR!E0NtWEJqMd!~`JLP=1~Zz!Z#_2j!*T02qhzR`3OY&uD7) z2vR%@g`B-p-cf`sMZvZ@?90rb~$Sq_QaDajTR)J^$TmkqEMReg%j2HsM2`B)d zh9Z;y<_99U{>nDgqbufr48}7H+&>S-_Lar}^9%2`I*F}Mb8-%ibtPEuyjDVJH6vcW zA7{ubP$jJ0dg4m@P>Hdxc34PC-(+C#@N;#B>BKN~LC?mG4}yCMmj#l^j^rwS%gz*+ z1~~yUf3L)dx<>y-N;fVcXi`&z!*4zU;v;{;6oJra{VUWq%$X|(@d#n;;gAMSO_(;JMQpL%J)R)#%w)86kIwS|`B5Du+bXxW+ zCFd{sBk|NS>ILd3|8!zqoqyR&$Jm0A2jnM2VcV<0j`9vzXL166Y56Y4wF(@9Yb1-w zbzOZ&ymLlqL_&{!;>BPw0=K{^QX0{U6M5zT+P{6GoiZu7i2Q=Oit|h})>_SugxAO# zSxL?&I``Oeu5&x-peM_1Het6h7grPQI%Q8MkYq?Qh|2vZlgG_86;^@iJZ?|k1OU2w3FPVPF~EeZ?PZB9yvwS?zwucnKFzWUsFj! zNH_RTSNT&WXfGY;CZyL?zMiF=&m5+XPdOwhPG^nWB;qI6trJ^K`I9;4$72t4=QTIy z{A`W!-`N(-_isxOk%HdK;J-h1?c(Hc(3;^d45nSKs;@XJiwXAAf3etd(P6|$CzEcY zSBz{W=Q|x(twsOZbMCV?MK$2;r@h?N?f3NhledQ)Qg4)%N!y!ie2-Wkt!w4GuYbcC zSt1=9=B)NXYw?N3#f`FF!#o5vFKzV&acj@)^G-DUMj7PI3;6PsKE9;SEjW^7_kB}a z--2yxwXy6a!ZafJD+1y_H}|L+%0D*uOjDtMZthX*sDEzmQLB-EZtj1)68~YU&;I)R zA3N$X&HnzmbTnJ>j^)BcT#kTxpYU=N4 z@4x}w0uwl(L)qCM^vA0Jw{19x%wec4Df$y}oyFvE%Cc}8oQL6|%YIl4EEu|HgcZZ1 zrpqjPc+|j`6;p!tpz|@Tjxv`ROSuLAU<%F*#ZI?8k=5LC3$WdWxvg_`LMwWZ`$=Suq?A zH2|NlheQ4HvSP4qSuj~;dDIkmUR+saJX^iVDk-5BD~N z^l-2c*vbG0Q)Jb{DWKNM^RJ6jf?H%+FuW|fnm1n$`h#T6FAi>zWV=5(csHzZ<>1O4 z8>WB`LG!O6M?kk5uwcs24>k)Xk2-i~#qj8sg86af6`*5rRt)v=&59|r^=X^}p8dKC zaJ*uTs{lJS8>Ylw&Wg~z5v!h}JbRfcDxt&n{Cp@Xv-KGq0ea?U)q`1M%P&ERy}c;O zDzjZf2@m~Vvs_n69{RRs#T41{0sU>UjSQh%CE)%gE&F9hL#vY zLLd&+r*FOk{;$+}t0^<5{L(#y=$;811#0|T|;7~C)Ft)HU1M&QP>R@Z6V&VkS z0fxOR1(0Im>ICArZw-(j{xg^OncoNL{YDP>pA5k0hEeZe0#NsZd=6z3M_XqHBY+sTn%I~*nS;PThnBLi0vHEzNLc~WxNBl$Yit6e-_gmz#K0QE_4SsX zOc;lTypDX3803whj6z2#j06r`dXh$N}S(s};%d!3t!v5T5X@8%7^ zFAv$X6BM1KjE(Ouk({lQq}G4m;Vpi6dDh)u^zz%ve#W=s^|+wAy4Km9;`MXlmoMwr zH{wpeUP#VY&~SiTX-z1EUp!-caX^dJ==zPuSncw>d3k@VJ4j)$gX45KE>4EWJ8;I| z>BDDS{-<14&eW=M&lv+}5@ZzNPc^+cNx`|`hm)*DSYMS_pPI&*`l3d4iWBQ z6!+HI*}Rbtn)3)O--Hgz#@bpMlakvWUAVgMDV(35DU20!JbLuiT)wfbs+HgBjU{Vun>(Gh=ljKrH$Hyb z-rpHh7^W$@w0A!nSytcxx|E_~|C~)*(ZsS)=d^XGU)bHZuCj3C_dN644ec|cJ<%8P z-flk6%joaKH_mj#wYZ(M_V$G;e94U~K5tw*lx zr0xSrblvk!nEbZ%=)0Z!*|9hWx)I-*$EYnP};Qu#*_zhpT-o5=rXAyUHY0`9CcD_$-fxv1~n0!`A1Otr`1yEBz4U8 z8E+Jhd0q5Q3AjZ?m9boY-7-CnJ~L5ob4{jqFFt!@hA6qLChL27lwk_Vv(~E|S9cRz z2(Z@v=v)%OPIO5=-Aj5|iA0wrjit}xo@0H#-8g+{f7Mcg{IJw%X_4-%&p7^VZ2bVw z@+xh=``D6`gg|U@SnC_kT=j3VllVJBbIGGmA$2n3WxeUHMxkHpdxDfbAsWZ$JJ#eq zr3mGn99B!7y-%UH2Z`jCdeHaEVYUzvPBO?i`__$42g4sG5G3|BovEltlu}K1M_il;eTgSB;PyF{p?>rDNPg zCnO4mSZ6i`s^&j_BJ?2qnSBwe(aB;h$+zXj6x285Nwx>Vh* z?QUG7p42#?hK;M+;`gungC}Bl#J}69t+`Vw?=3!4J5^Hi6(mDhd)He+LB=4SykMxn zf|ZT%*yJ`5-)(Fv4>j^>2j#iI59x~Og#O~bnd<|{xa~=-bKg|DCGr}zX&zwXVBGDX z3ND|J&D>qWus3>F;KV1+9zygzh$dSE(?$ zCHd=T9@)QM=~sP~dU5!@X5)$Ni{}^LNrnUKU$YTr(vLSQnoUUNt{X`L%S$%P!lJ@$ z!jbAJdEK^hp7py=6d7%9KiI<4C_JWc%?7mSkYuF5#*Vs&G& zkcPblO0NHX>olFe+*4Y2nr}f|DBV+|ZZ#mO@|fq&Fgc|xcOeR64*AP!sTq0u*qEIc zuQpab9xWytSJGbG4cWZVZ_^tEGX+b!@9)YM2J4yc-0Ho{E&Hr^LmKlw!_rnFVS#A0 zXuV__sL|PlSW}$TV~h|%IjcTDEAr;X-Mi?Y zJvXg9Tgt>H`dhn0H+!x1D}(Oy+dVUmVKe{wj?F>Zj>(Jwas6A$X5kBI1lue`+Ujte zTXg42Po<*KM_W^tE_%&GF;0V)D<>L~;T<0tpWY)T8-0q>m{+KxVzvA`|<3<0X)JTud;!NKeGuA!@flxiuK1gryIQH!#Dd zqL?s{vPuU-n2|A@AHl!J6aMIQH~GyIl>c}s|DNI9n7#Ytkl&6~xT+nAz^9Y9ZcSci**|h9l zrqhVLyf~h$|90G4vGelj>DC1W!DVy~`M2Y;!~yt&{b(lhZx=4nYNo|h-X6$J@pooM z82feD7ENV>V%Si46VJk?rw+k%D=#p8g~d2^yS`9{BjZ8CsdqVo@SM-iS6wE==N7sS z(0aw+cd6y!=CZrWzT@VGl9^?$bL9~|R_MWEd+OUvl;Fxr_ryO@mNO2{nj8|2#CaE` zq_Fd;=q|j}*b;&_%ez$p^LxxA#`=84^C{S5exLXyM;(dC@|b!hBSFQd6&d)?&jt<$ z;VMl~Ww=$@sneGIWCaa!hOw(WW?sp*AivMhacJ{FP_XPMAy}u*w3cJg^=%ml#~dx} zcpuf~G#F{XXrkJUEm-Blm-o^0HW9k;Mj|W7MsDSdF4alGFHhftblsq$4Tr2bhXNvp zj95N}JfUv9&BY;YibPfq=MG;wY)t*h>2@^*L@Dn>(fq?Pm6PUE+u8p7aB0>9B(U}S zUNM{YY8ULorx}%Xr)Iu{s^e8j5n9n&-nvC^hpeewoXsksQ)Js%G?>lMbO^TU%1C5E zAUeA0=`(@5CZZAE&<50WaT6bb!P|rkM642D5N)_#GwbFg!^hiAtfIslEVTGUh$Hq0 z^~Wp+>UxmnV9S=dS>A<%%9e!&32<{FR(J_J+IVO!y{PTZfj5P3Ak**RYQR$I*Ze-# zz`D=lKN!`45pS@LT?%iSz{1k${~WQ0a}rer9=e}MTJDh$);8L}8rrV0U%BRl*rO;l z(!e_2s3RIorEf~EkKzHZ#FL6_I=B(NucdFwxlVAxq`z`=YF>!*K-WYNx5r5~48ipK zOQLu@^DW7Jte#LU7taau4UzFZ5TQf32%M?C#~5PD(_jn#1PZM=A@QjE#`jlX6i;?HupI%O?vv-Lu=GhDP#1w1)dq)@V-Z zZX){Aju?#&G|?AU>W&ymiM|c5#0{^mB_{~yIVGZ9_^PchApYt&;QlNx3Ixuv{q(ZV z_LebRvZ~Ldi?#F$9F1hP?78Yt0{WEW!!LFe-lDwsbnlTewuMjGVYLt=ylYN=il2zN zH+r}g@y2KkfEZSZ^%V@Wglj9-fEBnirqqc3udWbAMTN^B%G+eb$7)& zXv&Y~FSPIeCR9nDD**R8#~3XZ*WRgN3gebeu?>PFzIGl3Wg++tyx!PGQm1%gv++!W zc>}b?KZHA29_xLN&G1Ij97B#AcQC`;C!P>el_FbH>eN1!Nn|PM+z>KW?u{fdZ`#j_ zndT|aRfE&WB~km-o-&XSV$X$=kuwx3If1+xHwzV=7N+o=LoynZI9qEJGuQZBml(&= z%()C+H|tKNtu@H_iY1sn9YQQ6+fEw0{WIJI3|!eM00I*i_I`+zldZbhA&As!T!&B7G{nmFxUy{Rzzc}!3g)l zvfjG-FdltSM2w_%U_}vpt#3L_K@=f5OLz~C`SvK>MT8WF^DE5f?9L-|b-W;4%57N; z?$<2vFK*%@I3@MfI$>c2M({`2R(W{ZiHmJiA9J2!mpQE%O5b_cnVg0=5VvuxL_uvd zpqmlM7}?em!fKQ+B0`~Be!#3M+pfsptbk2b?bY=e=ea@iFeCio>ANs{x;0ise#gg= z7jM%cwi3%0r79p3IBJqIGLmD0(@YQe7=Iw_9Xu=CC@5 z=VajV>$b#b|fDC>E-%hD)t|#Upwz)Y=E+wBQT;-IgNskrl3UhAHsu!pC zGDYsD&?n8ZIjrXjFR`$dP1VE$wCR=uN(ZP@ZAOz8D@nOiR*CPCIir|?&B$|u4SX^P z_&5~#TN=d?XA5`_aH#FVEN~Ez;9hE4c+DfTWLjmkPmnVcbef?^W5>`LuUfg6O;NU~ zgj=fWh?*4chZ`i>V@epB%-vK4S83VEbD|3|ax0SEIpf<_Ma$|rX;O@d9{?@eKWbK( zGW^Y)_1fR&cL)#F?{mwfUEWVo-Fk7RIBtRFWS6=`} z{T|c*m|3%7$iz>`Qj@+d>ZH^bviVtIubLBl#TFEQHRrYArnp4w zkS0#}{bGhzE%RP4HQx9Ku<2-z795{JUUz744N1CKsS#5@J_f<^V&TWX{k3>@lFTSX|b z4>}sdnd8!!1noZSt4T?c0c?4}?4#%S@{3#?nbb zUsCc{E*alkQMRv<>8)r~l+dCt4#Pfh=A=>?X0Axp+)?R&vx|eXLfKXXZLY)|L$OUC z8)W&??_!f1TVNSNOvB+shWi{14h~_fV-0>JT^qL=M?ajPpZJ_vohq!2o;`wlSWB0x zDgK)k!sj7~4?mr0G)mp5*9QM6L8&TONO(a~_u)~0;YHM_ojCUVYGh4|k5$!O#cY2x z-t-S>a)V;`V^(s%1nn7jw+<-d@-r)MK>XhMTMkjbUE7P-T=5$CR)tH_=2> zS~oL4iMA*yY52Te)0ERw3(!G{>Mbgd5$LcFCy!GiaMx*n(o8^2lHli0d&f#Q5tlj+ zmEs13ottF9z0$MG_hEZZR_a5(LY>hMEv-SAdTzBV*c0}GqHXP!hG|uxN0PT`M1vcB z-FT3R95MQLG=@LWIfw5XzNP37?e?P@cG8u|eLtrZ)#JVB9Bp|=e^a^0Ohqy7`~AnR z>3+MWoq9r#%5@jAK1m?O3}_>zt)*o>aJ=URPO+*Y)n2ESmbBDsQ#@9*{VYKuOifUh zh)UvqS~4*HB0N{v&7}9K&7j?^PHl1Xkc|V=QZgv!2zzg^U1+Vj{p-6YQ`VaF4?ce% zq6vLO>S^x@9ayEl%)zB8R=J&-e1py6Ylf0ch+yO{98q;~sFeu2%xXvgCaumVoQDwQ z(00^)*U&Yl{z;!D)F`;fY;!Te8@)S*c59WmjV`I96VIOoeYKx(Y%V#COWW!WwJTmr z?oRPMT=IYTd?|)D`vcXZwW>RZ`*^~KHz7A(B#zdfroG8GPs(dS5uj$E&N*R8o!_Xf ztK0Lkh)6lCy}_E*|DNCzp7FZo`RLa?NdX1NDW?~-HeIL=IAu=Fg@?l%-_@m}$V)Kl z2lW|J1{?BH(0OJ{iO5?@47`DZ0PoyqJmU`v9dK0lMo}>Ld{A^tw#W51E;#exbKX%< zumU@M&9SmZ^;feE@E=f2q_gtH(1a<5Z38>wo>$#r*26G&ux2uNmqb8Ka2}oHhWmLu zaxisIjACPR|3z@bPW!iwmvzq$4s45Q9$xIOFwcZDjV41zRB(fZP2Wf?l; z!{jQQ-293FNfxPbvD89rah^#emEL^f;58AAj-JKCJuVF%p+i;kr@E45;(n+DW2D6( zW7p3^0r7a z(WSqISL|1oTAA1C%{2BRMtu>LLI6QKNyEU6A=HL{<;C-m5nZYSO?S|v!j+Z|^tL_j zEE}sxX1%4@9PhnPs>6F_lrVhz1C1^;Ap}1+i7x#B)aILV12=cR(6g8j4r`f{y$Bc9 zmHGrx;4o%0-laeZ4xPXtd*1TdkxjiLAoqjP+m!-(?E5#0@_n&HqF=od=VwBkC!(86 zjGpJlJ>Px%F0Bx)eBcIDh;nTKA4;=y5y#CHV}{2SM1C@$_i@tZ!wQ~`$tNP}StF$< zeuB-(^L@LQN%QIQG5xO^sXrK95MmKu)F$2plg5ek>zDZMgf?!IZql{rzr2hHQ6cR) z0gsDd3b7u#Qp>-0^jl3x{HvEn(nQw7+I&NQj?gK*aVpPgyzzrVFX4r}a6})X^KjGD zY*2xFia){D>-ncSro+I%cECRJIj0{3rZBWgz%6$nv}7A?BeWz1&mj4w!2+B?t4P*_ z857#$)YbXBf@ZEMd1Qa>JcH9a#ox^Gj|(K%Kiy23rI}6nd})6$EK-mAeJbHHv5azS z(&Ff}&^xzhKYf1~KBplz;EZkGRZsQ))I6iLfi4YPc*{{TqL^+ffB5^O3wS2e7rCOj zWg-rs!-unc0S|CvAGGW%W%Jxc%g~!tDg3mO z;o}8|n1{+^HJ56c-B9=#lm$8XyzO&1v60eRmx6H+CibZw1hQe6l;A(^49La;j3Po zPb~`vo%u&i4#Ps-#<-TF@)vTiIgT}~*=^xeF#=E#v&uI%5Z>u@3nFD)E;k~4Df6{= zwwW*t^wEAENRggDu^uzAUO(|J+PrLB^|m6m=%9MA`WJPw(WJdQRDLvHS&(m{Ez==4 z8Sk?p-~}i=%MdNk&#qkf;LA=~t7{WC2r6#s9$+F_m5h!NJdV80z65ap4B-Cbmq$#r zGsxlO#bIcw$p_hvt@sa_mA+{}ii_AM+3b~Al)Ub}wJLWrD`zt(FJMR{LLq*j+N{Jy zY{V$=rh?UeUk?E*6I2F9U2-Tdsx`+XQ=6C0PiX;1&dYj@Nz?bMfI)%TCkfs zAiZDrAlC%7_@F=_>kk1f?%*2i22nJLXF2*vbEiJ;8Ml!q>alNDA$;v5#t{xz%%J!A zim!ljO!-_t0*i>0&G-`o$=9-rPu9i?gt_OHx#Y)?Z0K|q$ds~mum*96{|NH2fjPlEAT}sB7zF0w;RGUhJbVDF9KV8f z+t!1EGMIP zKGItsYGf@^DoiA;(kMyc@?osS^wZ>szQelT$*Zv6wMxJJu8OxbSI)mvUVZM8CTQmH z9KqzAhxZy8{T>s3O0R6j&IX^7Tq=DN zDDofO%uTBjG9#M|&8;(b z%zFbe;mY)VTy7}-AoXB&k^9B0AeMaDH3EWx0{%llU_S7_5K#C}HAu+_1Kk-sxE0f7 z=x}LG&S;u`frLR}#U$W&;J|IHA(Tzz$0*YK)yr(b((*z&5+lJ~#Xq9=~!;@$3 z@W#(22>!`E1cs6L#r==f>k3K1A#UJk@}ndzp`m$Ko#n2HrGdJ$ih+$In}VU0g}pNX zY>={WaCDL|H*f%P0?U`d&oYFY8+Zhu74Dii8aY_lIoUdZAV0wsf31kHv7i_n@&?vG zKmXkjKtF$2H(^7`88|suTp=jfIXPjg4e;-;5)3>65UB_NI{*;LPt=8^4FG?D!9MvW zuP`OQfkLhr|EGX|6U~*V{wxP812X{n!Gm#C{lAq6h(pZL2nIOe2h1i1Y$VtP8w3iF zD`8-F&&0ya+zB>gn!kzwoCN^dSQv@fm|2+sqT^6;GO<>N732(DuQ&sNq1>FmvjyW5 z#+D9O*9x9w>;H3#iKn5WoqPVL9wM6i5J&h!yw|4AIN{)gXLGu zKzT7>IgAJxQ?SGXhF-Ph1{nX*2T&In_o@u#0tCT#mAHBNuX27~fL`D}4;TV4_+wly zU|enpAQ*mFJ1!76kn;h;g5_5UP;QtoU_yZ9u<@@Z4JJT<20*YFuraxTHdjLVMI0=@ z(jVAJT(ApHn22Cx!&rmmK)RY2*l<4*1~|5Afr z)AcI{sKxk2$Q4ywykMZgk9maY!G9qY#?zHD0SX0c^;a)2qBuD*uAcqufS-pScp-lz zm?Hep0hk{A*K{RoKrdiA@mspm2bfO$N>{q_Qzu~h@Oy&Mb#)E&0n?3N3C1}OCm%3} z0Ie_`xF+!fV_fM47$&K|(=VO)l>@o}(8q;wmBY%vrK`FA@f=p?uLRQz*sQ|xzrFR} zfqC)(f52Ly3|l(^AzhU$K>!O^B^yASev|<8-Vb=*-}pZ0HC{i2hwERMX632~L@-u& z1x9ScBW2;bDtrBtRO$v{K`>Yb4^HgFMv0%|jx~LE*tc;Lv-?Z2Cu!FE)fww3c}XD0 zPWw(G!`S^w{9D9qCJduyZ@u^Ju`P+}b_t*aL$vo>@Ew%HZWe?UdygGaPCerN@{OE~ z#>q<2O(rrwO|`ks;XS@!?7E)!rgu)#r*-I1(WXcCo?pRmmi$Yqqk1~lrmUHJlBl%A z^+VlM?^v@ahbOCEAkOP;d1)#X3T@u7m{E6=A?jJ_L6Rl27A)}ipk>sgioQ$SOFOI` z&_g0n7;v^UHQ30d_LTf)v$C)EmT)Gw?ixA1O!L|OHl&sM-SdJD>z9{scIIZ)|Frg8 zzwcFky1Woz_WtM$ax2}pfdiw}tP*_}%`RcwNqD=SesTH1Pl*E&aeVyNPNUQhhn8rBp}2=_q0q!5{Lzf73Es&U6#1K z#R4pJrMtgy4hgNHSBa3?8@M@`Nw3@^09f)1g5L@;}qjDyfD4x+La=|lgU+k zEH-rX@tK&k4TCXrg`yO5N�}aQbeb-Ox#=H( zo0`sPXT$|AN3RYL8E$7hpXia&(_(C0_c0ZySX1IBLFJKqo0`v>J zh5qo~xF6JkaofCYWw>J@{QFD@l1x~UIqkt*vCSyEeyxgtq@l6ALxDw*n*50(;ZRlc zN30u1R+17-ju9j4kDB{1AIDk|yFNHarp}yZkik)(n9P;mDHNI*IHA!VbE3IM_SbCD zAi&b`uk^Kr6RYMc(YFS;(d}Bs)-?a)%Q59HLNrnO@=AI`BRT{Q@F{=5)M+GlCc_Ie z2aRv63ck`!g>&}_J>%vdx_EU{w3*z&fzs5*srO~IMPYSF@(2szLB{EC2yoV*IFFlt z4y>b{FQZfLm5Lo2!$17QjMSx4KKsQB-#Y)?@oNF5_1Dh?@-ENT?=KSsti~2Jm@E=S zwQ8aWD^M@&iC6~Qjk|3&7ueW%djMy%YRhavjn-Um`i-?wuGE9} zp?YjNxk$9LyXa=+T-(IoU-knp##dZ(--qr9Lz?-e-}Ie9e(xVgC0 zI3W;4PA-VjJ@68QiwhO%Sf%g9uOAx-A8vqltC8HHQeWG#K>+klv5;SvOxFU=J_FzE z8d|Q|SU~^_@JC{}75F2OXtBDV`igL>kxq)%zj;&6Gk*56Ix;~%e3!O!h=3}=HGz(< z#I$`<+Zbdzry|>0fBYVo;O)sA_08xYOas3NyU~J0o0L$JOP82_P1GHp(^py#Z;Us| zj%pB_u}>l!S-fuH@3aZlBPb1VE_|b+Db1OV<>OS|91U_*%{uH1g{-XcMKGoW?Ho_C zy70fi*}&upp(R>9Wp?x8@y^(Mx`ROOAa?DOcOixc&S z+xU*+iDK*ow7p-Lb8aLm7L;YCDJ$9KiM&}WNPW5dNSEyzdHr&!VH=WPi!&GC!T;%y zOIT4b^e!#d2EQ-I?yY?~J8G$XHq)KiBE&={McEhS&|3p|p3+$0=;B5C!n7jK`XKQ= zn}rSUCQ!bFGN{iDH^6B#hbMwSfa*2$jsdSxXoDme<@Fb*9AQ)j{;y3KK8_S7o9g!% z1K-{ZU5AD;r7%BBbtPuq>fq5~j1*o1%_}Phc4ntWcipk>ipCHy9YkWqubML^kmKvF zxvv~d77baa&T+ZvULe$wDX?ZN*H=K7FfpVsnQ%&JSMlhxv3yLs6Lrt&>-T-${6y6! zi-A0-av`7BHctpFm^qYdsI-$`IOE4mldh8D$CD^XL;dcree)U#!Zw+|_F6wB z2nC##iZE}@RH|^7IQvXwBwdvZgwvQ#0^O$P`!EK!GUEx*BGJ5#-zMT~h zi*$r?p0n#{Cp-9t7%qgd*cLxY-t;ujcG)A zi2fc~-1=S%!n!Ej-OXs#!thYDTLkq)(`JU0`m2NQ;ETvq@5VmcY!Arm%MGr*+ajVM zcYZromQB6QMuqIrx9H&E=T6AXd0hL^`vYoa1DKkDPw|iWzuh}s9krtM6R;WG4_u~! zwnFgx^kw+<)8iJU=O(qB@rtObD*K!~X<|n0WZ!SR=r~gN?lh_0>s;%AmLa+ zX&GlZY(?YZRN>^}Rp#X7QRW15!&Wo|bkF!IUCu^I&Ti6NR#es?eKBKbgB6{MN-p&c z%#8LMMfiRQcGjc|m(nC}hg! ztCXn;gAaC?vG^3JM*s+jDUh;8^Y9QrwE7UYusgA}^~drkl!CliqDRr4GSpk%_RN*7 zjJohjuHvtHm%ozOq21a{K@SWUqHX7?c(-foW!IDR{C5$HKRFpdh^4{$fW7yBI! ziMf%AV;L$mmFQ~N8#pzGRj@&^UrLj0x1B?bYDY9djBAKk5rO%GS?4aTWN#0bMhuHG zGjurwZsPYuF6N8UJr{V_9#viry>NO+vq`%{aLLTPYGVM;{&rjROqphHIBY^`Juy9u zndLFHNLpZ`I6Bv<0ff5Q9}q{3~SlR%IHql$Fv7HDD@ zl`+IUCZEMF+3I*uh7$+rLYyFeDM)k!%d@RlUnN`?ypaSkkrXY%QEr*w!S{iYBmo)cT6 zaVzD9Ax)5X4T0D2XRp9DFaL7-Y2=+kBr~ILUwCY|@t2UCJ=C)n&7+mwt6D8$oc7Fl z99b|638+kQ+@dqd(ryS4&1_{XIpl2jok`doJvyp4oaRpo^z7`SLhD^unL6VU%&4yJ z{GQc5RWSB~WmC<-LQAF_y&}2BR1|$mh5vKD@EsZqf#Yjk%#s{Y zCG;l)7<=x4D}*Q0Be7)?Tyf_7FEF9ZZz-sTWOx$omi*QFI-r3p z{jqY8Lpv@hR$~G|&`qodawwNY4q>SegD6Cr_aq1-B5_u@FEuar9kGj5!WbiLZ0i;t zzRtrz#G{N%tfkY_EM>>Xv)~q!>u=Cz^QftK_?_ZCT{V{IabSymqH<_@{bRfAevV~E zmGNstbd8(&Z$hb(uuv)|+Fe*E^##MRh*EUZOg;7udYekdX<=O|^aLgwXtM-L3KpxM zp1tQ`EGMR}v|?!TW~RYN-8xdpQI7J#4sLYHX2F8r20Vf|x#=~eUE{w&fJ2}^&YqlN zr!_EpUu%GqzJ@i*8fw%ap?4QACA!cfpsUsGhayD;}4;$dCz>bGF^8U$A_3azX(GbP0K#= zbLI>8;e^XqwiT}r3C7$)46F`65YNWgpdO~r2xFOyS5B5^TPBT5U3>OC6i&ExCS+=` zy_+Oh>H}Nupk)jnE+>J?V2pi!gl9AXiiDZ81{Y>_N0*y8cT)&|dcwzMJAK6NGM}vM zLgiXq6Zka*z0U%RTo1fL;hJV|2OpiIy#4m@n(!)cp8O9>1q7G>D7K%wJ}yqwNGU(j z*oZ*!SF|Me;97c?z0g-Yv`|FJGx$)882nkhS+L>e-($yLGiBE}Lr~x#?~h}%Da$yt zg0LnXG4#@)r>ILpU!1C4KVFtcMoCKH=pTZY{Q@66*8){!jPt*q%!bcsyE4Ee@SKizK!g-{_SO|Mf?ZAv^u%1}t=KwpoYsa`S^B1iD8^bKFsK?*`3WvQkpS_Z)Bu5*f9NL^ z@-N8Y2KHshV6@48rb^N5U4_1bw{L^$fPERkMo#ASs2Y{k<3col3INJ<34nh6|4H;4NV*93Cy9;A9|pj8l3)Oz2C^R_%i zrOt+e6jw#tY78ODh+enG9WCt90v#9vUuM;&%rX{cr5_4wC3Me5$z%^u$;q$5w|2GO zPt&`9mX;9z;BI8xhI^^_p1%^^4$NLiMYng`#09{YN7CGCX^`Rf}>hNRKBL?Yy34ZaJ2tNjgt(M!9n@~ zrU-3lr&+a!eDQU>-111aAutm5RrRd$N*88-jF?PBLrYe^SCJPRDyb!+>ZYuY+d1c7 z(AG^mmBza3b)VUfRxv1^Jc7-{rov;A2m9T-7?teMlAuh^u;ceuMAVNmbZ$CF-tlf> z=9pTnrs~KZt(nOPs(YCQ4@SR+n`>MqC~$cGM{c;4`E3A01b`;oK^``fI}@OL5#Q>u z^Ck!p?VXgP1+`UOy8V4sKWsXky^r~f&%5-*#J2^#Gar9T+3#L%tvc<IfD-mq|!u8*1Tae!q+aC3V*di74z|a69b*PD(Qw`h#;J z!ul|J0;=eaSsd*2myf73gJ1K^AgpBEu_i*rStx6MSmjCdfaG!H34EUHn&+3x;=K=b zV`DT_TOMuKOwM&a-oG(-Y)bbX5XI1H`dJQrOyA5J#j*G=U5=r3p`u_1i8^U7qy?vf zST$;wBl69gXQ5!PS!DkgEMwN1OQNcv`$me~=`MS2ht%Wt@#sBKqa|3T1-RdWnWolS z=fPxV6=NpDH#YT(g%iX&3v^rP2}oo|4Cu^o?j%6i-#ZHPf>xz8;|#)W)XAavp6dRsGhaF&j~yk%igXfW@!D#jC4l z-w+?^>jYdQ!)u&C@V_}$kqf_%BLVCQPF^>WdPP|!pc14* z9WBa@Q=~myJUsq^iKOvOM54W2^IaafJlx)-SSDUALvR~M#8>#p{|cYv`Q7jPpJ0a@ z1~2&&JMT(VIeEC$JC-33hz2L`2i1;wASmnc4y!k$;oS|eo`jJEh#>1mmyc!Nd>3qo zk6D3p4KNJ|OZ@E={C`F~gopDN^;}SOAlktLkpGU;qcj+ct>KeC*il6WgqVfX48feKc*t zzH(G9?Xrr9TLSy*>Z!3FORm+}_iT+`rUBlK7V(wvi9+xaV}rR$5+u|E z!`UjgOQw)SURBuOP8)sjj4P*I4Fnn9CSw)r&66Y`w(e)l;;sjFmosr&(;X_=ZLy@D zDDdihlw)gSyD`EF41-QYkl}ppZc=mt$(urFp*bT28`@l-Ao#;=cmowE+NH0&&=xI9S*kg8<|QhntCmEr`R`2EfEQ zJpoEi<_;!6*%bI-5b(bR@WlZRM*!x=;b`JwVgus%f#d=(EF32G&IVQ>9-uei8$}Wz zejq+=0OAL>&PE^(W8g7>askFPhk-D_oWN}WjckL#0gPY=z}Nu2b%Y_=IGh0t2LQkL z1!e<~0Wc~52FUp5`uQId^Bb7$AF}{V@D+#+3i(&_@&5{ljSKQSJHM>p{{gXGfuQ~m zi0zLcHb8&=XF+TLWb1z)#0KE=fOY8q0f-Iq7k~{0Wc&XY#K!&KgV^{0U?B|L_#1!? zR{jNE`<4HXf!MAV_+PVDbJ5XEOo;YH?+celF@iA#K2#eg$d6 z0M=iwA8YB)B^y}wuj>C;wz*)|Qu)UM4>JQlE?{du5dOcqFbBYguo4Eaegj*bt9#Y} z^zlat_>$ev5p6--S0nzgApj2hs-_+ACAlB1f0~-B14CdyM-UKFxvKKRpm6^j4rXJn z4sBp{t}GT{W3K8sfq1S4#rSEO0KfRheZX1)v9YVNGk|jaQMxi`fMUUJ|7Jyi>tBME z0-&)!xt9VkoIi?xSN(mfCkFldil%LU<(sKXjj@TV3Ep zGEtTm2c!qaxQ)j7_~h&5(b@OIv+w&S7keicJ4fHPkG^dleBC(sy0&+|ymPj+bGEQ` zI=^)~yKyqJaWeJgc=F5f#Ol%b>e1-(;mGpg(Bi?T#e;$Q{r>s=-r2pL*}bmm-OlOV z_Q{>L$(`nj?Z%0%y0OjLv5l&c_4h+-#e*w3y^9&$3uqDn$(?hF?Q`+%voWo+kT4CSLyrj+eD;?lrnhPpjyiE9e}{sjc5pJ}x1*C?Yc} zBsO|W@aPSmUN(+a2BumnnnD8Fy|Cl+?+2$ByT{*lj=yake%(C$y1sw@W&eD2_iSbN zY-#&+ar<%F?Uv8m4dYw&Sw$J7k#qNk3;$FKa+6qhBMfPw91!d}5bOeCNH`4yovN z$>?_R$TrdNR*}#aGyylhfCh+H9qaQNX16Nc+pd)iE)_Q&K7j1rQ`@|wvMMDvFCx%? zga0t+hOTawu7hSemUj)7@UuE`3nxcK69)-fYdgTP27U=~eK-*C)~`N@34E;6 z+VN^_P;>x3%xMJs^$P6w9#^X82rM$NlC!mq;}7k;rgzvX^y_@!$J+(m`THB2$rcq< zlJJr{n1N!dgp4dqx3pnGR8Pt-aLgX_3_?(-7`A;>^`ujo^?C(asAoGS>Q%wpI( z4^1{96R1k8ZiUW_>+uX#eH1U8lH1{%(jp+V7e52TA1S$;*14#^a`DH~KJpcPd@3Ya zv&hL;P*a^>Q>_zR^R=06p4VX3M@M=iLjMWno$b?jbuF+RUm}?Xp)J~GsLY5{Xagy)FIH%I}2!A56R!Y%?bDtM_SP zFQj$c4Be8iMoJ?(aK?Rt!=&kG9W~vlb{Sde$bA++<+K&uX<*s9+=j#yn*YWhCW8E$ z)~aVpu7;AKs7Nf1^K5XHE}^&Nu6(|6p&>YgDq)@|`Y!8In2;Zyyr;#;-DS z$EuEdSZ{hnZ>R<4j(rPugw<8hu)B z3DbGlN6~q$AIkLVNek!Jk`SIOS&5Vj=G-2=8XjnGJvn`+=}A*+^ApCY2Ng4`w}#`w z?3+6x_ZbmC?q>yJapi?*AEN=^`QkF&e%-R&hj|t+@-BhqwZo6W4X5l=HxuS(X}5(O zkhBAbF2utK_%-a?w|nx^o;*e9{-8L@;WW(g#-1gV4=Z^!F7vs3$2MhhuMmm)ui2~c zcWi#yL`2}SsuZsGNw@cn5d&pmGMTqEc9R25aK_6j{wE-_xxO;N$une?rB@rT*MADW zlFsaavcI*bK1f-2B3WmH!XS~ZQI^-m49gE`7EPt7FI%86L!%}XJK1ElLq5v-+k$PcPL}g_$H&(w;QWdy_6$7C*4XR4l%_ORFfCnRi;NY zb$vFlx8~|^(xtujn8b(1SLlw+W8gqGw-1-wM3PdV{WNB#M;I<&d%VX$_qY=c@`<1P zzS)^CkJ&gMe(X{DW@^UvdeQc}4S4$no-1GU(;Y3T;cfE|bloJp%Y8vb_C%i|Z}%^~ z9hy$o*l~4w)jJGX>>}ZPD%-QZ0W>SxCSKic^?ce@kEwKjBQLhOOc@8g-3iH!EXK)? zb}~`dxkNxCZ~E+kEn$I++4A>l1|?43q+X#`onMkJ#+*f~$6i$!V46ieFy3fKbxQz~ zK4OfGm_KbRH7@K{v&`frcd~a{UC${$;IH2e7M$h1(s9_EeuA{3u1jU}I?b6f)-pLi z`ir$8DT&joedneB~wr3{d~PE7ljd~7N5Eggt%`<3P@c%0B z+T)>2+yA|7Z4@juID~p_vyL5_chlul4+bUJxL(W$luTvaY=1V-En;GI zo59uAuXA+e(3f(h4#9#Xv78XEONB2s=Q!T_=H&$oU$IPT@(z`p12I|OxA>OyE%Hl9 z8gNeDJvLqNcwnvh1OH0A-R=cB-6=n~)k>a?yw~dT)O)?3|H(bKi0fD-eFdorLCL;; zz1iN)_Ui9m6XMTUPW9Ic|Lg@{Mz*oKh}QnsQ`k$og)~|v>j!ZxRB%tQ!l`SDu2GbC$P|7`aL=IH{{D({^ir{fn!2 za|X-pQY~+Hzpba4L9iyuoN6e@9bKTnjCfW5;A2yy6;y2Kjvq`D71ibe38ikjS2zh&z4nmUT=l?j;Gn*ch$t=xCv0nw*uH za<94J!nKF(q>{da_HQMX$Ai`Dnv1HeKtKb!rbVffTAb5T+Qy}IAcK}aWyLc8Ck z5cTSM3o%}Ew;9=gVnw9F$CZw`Gpppv;x`uWVo(_()e`nV%Qmt&T=uFrnZQ`ercEtGJ&t$)zh2HU+DbE+Nny2#)43TuV z`e87U!m@Zz$M;;VWU-yjq+HM6WSCjDX3xI7lxG$>Q#)JsmX;HfUIDv4v1>{#ine#w zvs;!`93|FOD!e0z|m_Yz<7?oPU~fA^zK%BEMvg}EOZZ&wIjcVH#Ehej7FmejeD zuYSDKkhneKXr|MyT#ZN78~fanN+o_DWL_!rywUaT=U;~VhAS48dF37{C^``r`MA@) z>)oZDB3p_cyf<>MKOErQjIy3CC3{l%rkj6At+*S@>@2;&%h$P5=*x;olV?Htm$CMF{2x(KuAEdP@WZx>4kePIdq$~-wRtWP;?+i}?2eiy6DtmM#iJd|W#<5H`| z?tJGB+v42cjXRvl?HQZb;W*FUFw;11Fx!GOT?0;KSS>1bE7y3-=GJ##_Z|!yJh8>M zt4DHG<&k{jb2hs?9g}0?r<;h1Xs*<1VV7(eJ2KX@L08qH#N|}Nu3*hestTo-&*!Vx z$K*i6*%ev+z5CMQQ?~`ZbB~d_dNVfY1uTBG&^tD3i<+sC4HLlB4%4M=5zwq)mwRE_`r`>M%XhL^q1J{(Wio;}D?BuuTw zbE6LTTP^AzIxiz9YqD#W;QKDJBae?eJf6H>HFqnqiCLqw>Ve2CtA!f9cN$!$CKyMu zg(qSyTUpDDmakwC^{k1-vbgG2mV$~g9G_ZeNB^fz>IB1sA9 z;@eKl=;BD#o>Iq=E{@xih;;G3(ea?-cBGN_%vH*9nxX$vDaY-MK}xyR<)*wpdS>); z21hT)5#1^{s`*V!HGf_Bus4#?b*-rIz zGao*~_ZSW^If6j}Ul0_KbdSw}0>(K|z-I&nARORX|0kTv;m*E~xb3Ifdp|tJ(17#UBk5x7zww9LmG(jxTL_Q^ElX zia0>Q@zN(b4_;+)u!7617YM&dukS{P0d7lBaveLN_89^VVr!nBt?3FWX~*eB(W*Kh zz8DS1(1VW9@+WGv+c>r0U=EDnd%ex8pv^t66{r2HOK!`d+?JoSo3}>ZvA_DrHuE9E zX>jTx2m*Lu&cPJ+U1AxhG;olG`#a<7^f}-HLKarL6;6Q*IE~{NzMvni8o{9aTCIGt zN-4Aw0S)+L6`PNi%LSDpxM73e&GkMAZMcEZ1_Y@fv;n6RfdB_Fha3^RtmQUWgbrpR zjYez>Br0Y;*LZDQU}=pAXl%_D(HfkU{+owPp2CWb?5LY|&Yxtxp8 zRX&OJ%p<80dHVwNY_fmY$e$IaucZ^7udllyTS!=!W);TAAX;Ck39vL>t0pZcP`8tI zLQBQ^7T(Y%cKItl`%o1D+bH~gQ7Ivj2tC>b#*o$z)}=M^8`L(btuu%dty~lqrls{g zTO{0QTdl%`+{Ajq$fB)$TXBYZv~tG!)YHT1QTuIzqz!~pmU-#X+`|@W?PmLi6D&og z?L=zAL^r5T2vWo#eW`rMYiZcToa zuFYbh?{G``mf}|H)0T$LA6Y#qo^SdW8xJ<{usw#P!m**?>^rkS^sis};dPhA1p?3B zc>WFutSBk74**R76aZKOfGlRP!!t#&Lug)8m;VF>{HqDbIcAR|XQF z41~}R096290046UgjBc#KqAOMw=nz+0Q>;-V6>Kn7XVd=QUdTd0Ivc8L{Fjz8W0Dd zAtF>E;w%Ty8C(KD3em5DGzy_I)CTex;R*Mm@C}4Sme%W_SkJq1$-? z79&nKi~}$k5gTD5fS!m5gYYTbhlmYuHGsb$q67X4paXzM5xo{dYi*Rg9Y6&DOA)6H zRswhz5v$=N6h4Ab0*(PEtQrqsBML@zY}fh(a_2oW`OZhw#!# zM63ib1N;KOaYV;}qbNL#s^SI?W08`fCGY{j6Va!E3&yzsmjMSvxB)bJtP$}7z^%Z- z0(%x`27n0wdokM52mnI>3@|!cAB8B&4(!Gp!cq7ZXdyZfKm!0505YPFp-20OoV5g2 zAes)3gB9F`y-43FdnQ0KoT+Yz)BR#fu{h71JDkDD7uBap(a=kfE57eu-lKz zP*?}z0dNE04~(N9h(aR(od5{{HH!;c%l@tjz|9xh z*0gD3SwX>1AG%VlwWBM@kM~m}E+-oaiPx>tc^nv--Ctqir*rybX7Biso}nuBLGw>T z%x}8at@}N!ha4fEL3gCr30rn%x;|1A-yh(e7T44q(9C#BI4RLh3uhYe!*)}LY{CuG zPj20`=o%S&ww2gL33u@&w0_~q@V6;VcLG?9Vu^9OAaemfymQmO!#y4UhdKJ#5MkYU zqbkNuQA4riw5!x5{MVcxkrTw_<;s(r8g*?K=M5(?otb;PFA?rA5=G7EgG{pUR$N;jbm(Y2`?`5Rs96;~0&%>z8s z(mn6VII#KYr>UB@aYii+D{>0qjKoX%iM;%_wu6fcho$N7sT;bj(vrdqiI&835`psN z8Ai-n>j_(Sr1%b+1M^#ExA0_E{ymEGyBotP%-hUgn4`jzX-!Q5w`(1fiidxvmrz&P zMuj;lIfy#bQ-yEU=sK<-@kxlumD8KLdJp;(46~@@Zu_)TVbWv)@nBjO)rue93%DQf zgYUVQjkXBJ^H9HznBX4*qJ)i2_ZOgyfJ;=cTFR=fU-MT_TL%iFBJ)2BZ zpefMPg*BdtI@J?i?f*2-Hrn%v>7n&`!^@fa-Iw#*8O~yFXj)7vf2Es?@(uAPGUwcWICoBYd%can-DfWEUdUhRFyS)ckghzL zHylePj;~%VEyDPVJqH_dQ?CmgV^_}hGy5d{|2s3D>s^y9DUW<7p}#-PbP0-tzufJy z0Y7sv=NhH{{V_Z)%zts!^ht45@2fn zX7D;g^W;zJ&3J{ZU4x@eIngfc-$==D`O&j~H zu#C1U5l>V?F0)h!WFnqK-h?Mg;SP#((Nk`d78uQP3Q24$fFIC6Bmz!nGlJS^L>zs0l z6f#e_M2ZS#usS!M3g!Tto2SHEKNT`&KgpF=g-3qKP!e-zIVYYXUI}@Foy)_k5HRE3 zIq~r5OV8YG!&6Aeb?w}E1SC@)Ig^RlbeNkTSrN0@=Lkv#2pWLN#>pl1R{k;E?BWVWhg2l7tvhhDq`+txp}v6E9vO4j%*`WVFGF1M$e2r6F5X{^4GQuz%oPu`@?f}Qn-}Uye zHKI`A=76lKI$(w3PlY2i-2e;9^R>!+tqHeS#13lt1|Ys6?!761yE*m|h7=zUUvFEm zABcZIUzcL%WUcJs4=5tRABKdJL6a~992`x+%8&?X9H4?w0s8}s_`peXTq?P{dw?qx zG-I($pxX;V)SZf`2`pE zHcIS^L+pDz_3>@-?2oChFFErp8;%5EUb>}sA5N+Go|eAPe*1&|{!yWKyT5-&2Pi7& z!8#U7dWwOR2gB&WJTWsRJ>1sh(WE@hjY{WA7)zZY(z^+;*jr=HomSC;41 zW$3=LVw4YEGAVVfe9cAIvFp`#!poO#8$MgzynID5jhw&HBC@Ld30-vK;}*wfR-0bQ zZx|_Z4C}MdCk5Lc@e|*8HkSdek(j0)cHOhHB49^f^{I7l>JGu`4){rntu_)AJDcqQ zN2!Uumlyk1bV?v^*R{>3hMTU3*`cHeP*7D)fDCuG>dh^4AEw zJcnOeu*ww`RC?>dkIlylj_$W-w$_Xh=h7P*l@cDdQ95L=s#FsflRr_YHFb6BiNh|R zZvGzLewo)lMyk}QexsLWjHexo{9mNXes}Yj`S38htN6^P?%B!P+q=I{_O^UH$@!rA zi`%(i;mwxMsWU(XnZ6am0GTsTMxDSq~+qOA2p2pPT7R z>OAA0RZhye`R(ZLQK#U*yHVS9#7WoUZp9w(Dj}Rv4_H@E*1nvaWhN#bi8D}JwOND> zE1k8+rNAarXZ_AyTM{#6`r$TG&z>qdmsfSv2*u=?hllaGZFLsh(Cs56A7I)lKcwOE zEMsi6$U#e9RdpAR)2kw!Qp_4Tq=+aLy7n-5OQwft5=NA}dxQLu(?UkWHI3Q4tI~tb zM`|9|1xc}-KU|)4c_2@>@jXll#$l>8)P3AEOgOau?3QoiC3j0g$nF@g8uxV5`Y3vt zN<$Y;6UnbkVdSW*GRK|HpIQ@07p!=?;ri429VUB=(@M04J;d1$&m1VnABk)0SI%K; zT@f9&a`$`Q})7H|xaNteFvwx7MKI7`uK`!4R=ha7^q`Sm7e zuI&iBmsv*Qa*R8?vqO3dqstm}tM=BY{cdW(cVmrjh3e@Qvgh3@+h?8oF^id>k1O+& zJ+h+-L%AKoW#Meq63WzZC&=ns#vnC6zOdC`wdnZuxM!g+{S2oQ>B)fuU$vQIDfgJ# zwGun;YtRoyDVIHZ?)<=_@X_?)&ogSQ35{PK1i>SDIe#?Hq{^5oZ3)`rGkhQ}*hY?UX`MM*AI`ZW=x|ahXZV zzga-f%WJrf6`y@4-HV#R*j&XjnSDzHhT&98oz@nY-J=Ar!v+6}Px3cB( zM##raGA&wd4PoSm$fZJ#D&ZtL%w3V0QGR`bX>+xdsMTOGH4U_MiT1$?pWyF`dN9P4|RAPwm0ruFkz?&?SoYJ4-#@B0x zm&Bi-3zpQjhT+F;yZoPqjD)x2PsBFAe!tycF{_v2e@tpBY)FL=B#Y5X__}_dy7*Js z&-RkO(MNsd`%Itf**Ttl?822hv7!2whTPLmHvK%WXl%5Y+xhJ`yu-+j69bgJ zqEytdl6H&Zg|593+?&G8*FUl))5GYKV-YzDj42xsCe;e_nJUs0Ez`ZjpPgSv0K3ZQ$!@hWcFpo4dVG}f#-STIZZT~v_1<*( z$7h(uo8Mg=KDst@|F)-coe6idbPQ6e=cPj%zAL_dG&@2UFf(~(T~lD4Uxx=l z=10Bzlt}i>>_x~IoA)5*y)v4+0l6S(6cV%Gs?q!usM8#7@w0ES|2FTdV9+Gsp!~s6 z!NooDVnDH}e7a1}Zm0fT>#_qTSt#{K9$jCbYRcZ=R~O`9JROra$Tj7Ik$Qqof?Xq+ z*J93_?!97N%Yl)9*i^n+TX-UST2Ardfu4pEePS@xr0d3sYSR}^eo?z5(pK*}M#>kM z99o|$;9yi9d&uopthq|e31^wfzQ6iyr>>;2(x>=QYH{FOBj+}B=kvU#zwysUi@++b1 z>bhc^4Oyqmg}ocHWlsBzPTz@TcHEIXB#gKu=Wyk(cZS~atd?or-X9+fF@89U@7-5@ z`IF7C7-3&Y4%w@E`;DWYOApi8ckidHaC9qpk7yMB&nKdwkBVbJu?)??(yYifA0~zy zb)$-BZ^w4%z|7g`MRUS*wFhlaci$8d>7yjK{8;T5H6@rNB8Bquy(zsm;!&%fwOQ2t z)iP$5=E((nhsqo6>$wIEgikT=ydRh(uZ7fU&C{O^oggaiT&JPQ7djX}bTDii=HcGX zI!bBSmjF)AqOL<_z3M_Ou|2ioM%|?3jty|5G3Pa3gBmDVZ7s_EXisdtdsTc#@wmV} zi#q~_$6deTM2%v)bgJ^exx(B4DkuG@g;pAJSvp`3TvTCH5`lx;8gqTF!Nh8 zcPq-Z(DVr5l!+q8Ewg=_5Ayln+J7*R&hC&3&+^>^P58C@xd@FGb|I9leJFD+4VB%d zswUf1DbCg=zDCyWKGJ$Nu1;RQAPQ1<^7f&sI9hwdkuZdg^?VzR!+~2MxKX9}*m^s8 zQa!xkC|Y0!M;Q6qP-$@>L<<8$?6h_R>--%Fu#WOvBqfbP0(PY_I20U($0I?YgeL;i zLnEbYP4#x7gRgzJ*zIITbp(sfg`vOVSlWIUwCa~!7fFQ1_Mf-! zYV805MEu+?|5gxigp!Xf6qJ%6g9}lDE~L>Iz^;n5=MIXKgCi9>DAR=|2zfy;?qsXv z?%+xR5igF`NkhoODW*?c?@%7381{@pb&C?XsSjv5Ozfruf( z@fbXe1ehfdks!*(ftVYLyOD4l;1jwJjmO|ob2WwljsLp>Teyz_W1#}Au~2Xrpn^z9 z07NV(U?CC_P9T753>uCD#0gNrq2Xu}4h9MwkHf=pIIsvF11Dg?SR!Bm3EEH%-N%3e zzy<=C53NZel0ZGz2i3IAU}2BVhcG4x@shwtE$srK^A4-t$jCpKS^--1V|#+QUV~ zFn4Q7B&r%2Sbys7TWt$&ukgCtmeYr6T?@Z+W9$ywIY!8;|(IHy!{6Ion8gB(hae9lFHTY2J2I}mDXDK z$MWW6>8J0h=rQ;>G=XMv_K41AwG|p$bCXHgZAT^jAbFMQaDwr*Qzsn6cpmV+bg&V! z>TfQfyT0C7HSyqZ&9U6aIngDmm5O@0Q(F>sq)n>bjn?n`o)G=)5S3|bF7`{pmnXXl zpRk%n25^5Recv(J-{vYBA?x1uCGxEZrV7pd*h-sZm2u{c#;dM9zH6@w-@Wsg8Z2_U z%~R**(Bax~y&u#rvqs+%ZH-_0zewi~1d3Lh-3UQcaRorp0dHC9_Sw7~4 z`?R_}MIwy-q@D5$fuLjXA^HLVsRTTp2fgPp!w2>{b~CH?TtS25A=?t#)=)D|Dla#@ zDty=GM^KUe@+v2AWXjRxj8%BE_PDA4zHr5)++@FxamojJb>%iSY=mW@<)pXWI#@aA z9z3=Ek>Tf=x`|2QJkG}NdsA1C;NmA~UV#cJ-7 ze38W+Fw5Z}aVjUE+4kH7tL^k(@+)w;La;}~`~xndi`mPNEUE`DS}`7$_^N1-P|^K> zolD&P&gPd2dxh5|OS)qosxYUqNkv$A1&<{nS@&w_pEx&s!Me3{kTGWbqT%Q#&V;n# ztFI%5=;)O<*ss9a3Q-Sm8~NiX>`BW>1B<-eq5-@2N7^`G(U?#yT}_gA$l0Iu9*A|~MZm$z83DsN`>O8r6V>Sk^-%qYCwm};*gor)&aq?n`^p=P` zl2ww{v-w(9#+bsM6XbP(Z4ZUyrFWP?2Q`w68n_aT?1AX8;?px?`AE`+35Pkoac0w@U=JVJCNOv zkVZBcy*HSiy|bZ&e0=)`msfG@gF(E-L(h+ijr&=e9e)V&)xWakjSJGal+VrXL@Ya2 zmS#z;vK-|hag-giJ(nWIX6q<%>A;D|vL|VU+1AxH8_mW?IgQ^RI=aF^EY965yjrmT z=pab$j3_n``3Ay++`B$?z<0!cf54vGv5vuuYM3BrX6*OqXt?pMVd}S=r#ra`efufl zm22v*aDOn2*pX9WBHH>X8J%|<=fGHNm|fAA)#p_gvi)K9CDxj6MfrJeYlTm4H0Av( zL$wTDRXMOB@$ttS_U4BZoyt!7hox?7lddskG8BAC{17#f5g=W_eC&R5ViZi+B$Ds2 z(#Uy@n82?B#-{M*4?>!r(UJPGL4LuVJJ?7ErTh#;#xPq@JOiDAYA=+6#p9AlRrjc4 zJ(ExJ&;NKHO?V)?^}V^-9^0pif*%s9rO@vWn2u3L+iF5tv7a)(_qVcfV<|Cl)vK>{ zk|Hvb4NUxRtHiT6qH07(Ino}kU!yf8zwW_FaUS7>+8z$}j3fItUWiV`chkSi-0sG^ zn&WlhL(99tyjuJ&aT9d8Is?J)XY)pGmA1Ew2!9BwUg2<{1lEGDu<(xfCr_xffu=Wu z5=(K@wy`k#OFgz{wjJ%8ZD*-p&CGtBOf`FbJ~|yPwY%hP>=nm{Js4C-57W`Z+uGes z`^d&{Ogh(`DI{uYT!E*l6J;vzZ@pRcLG+&%kfsboC2)N7;-!=eSQn ze2>3&|6{(EHwPQ*zEl`nRh?~jC*xaR_mmuaGaa4R-~a79!#*qXV=KrI%8f3_a4yDN zNR$4Gv(-Jk-Qb8{fg|vvrLiCiK@q?U1%<|7piJca0T8et%=`mT_vR_C0J-Z15~*MN z_9+gHWybj2p-RW_Hn264rBpB~0xh+bhUG5=8nAf$(a02mqzYbgV|{OZP!1j!D!@hw zsccy_G|q=!VLJ=4<*$JJzqcJ66gB^8I|Vn4k$9|0?OQY&y&FlmWn9~f#$$sD*cxNX z3b;@fDz++co-D5VFz3g;^{=!TR*9EX4t1_?q}6&uR1f7$ju;ku3E+8^@!dsT`QrmuhJubtdM#4)DOCJ4dcY+@jp;r<>sBLXZF!c!MvW>9+pFI%YpW4Ia3_%7JGIpau!#SG^SCk;=5E+AWqp8o<4 zva|pSg{y;IxyJ1_7py(n+x>+XcaAkhHH{ug)A1T~=K^Y21+)RMX2E(xIPw7I`1(?~6x z-aqsF2ykft^Mb!$t~UyU`m?j@c1nRG2+0z@e`h(9C4=d#iAG+pjZVqz)|etEBYx%H z@i)O=)GFixf<|{4h&BChBb?#@(iNz&Tqr~iJ_5u|#rz;PG(cgjo$ zTbWvsKA(69@;+(`bmxSQ=uqKZf+0jyGbDBr{+R+!JqiA|C|5X9VC;XbM0=`&;5`|lU)8gssL3MSqf%{=( zh%#6yxFeP7=_89kxc$5(fRdiN+$aXcCHmM&j_>km$`wcz(o+7AKu?#^AmAWN2K|1WIf1bJd alpha -> s -> data (e, t or c) # Write onsite energies - h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/α=",astr,"/s=",sstr,"/β=",bstr,"/e"), jacerg[1:N,1]) + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/", Nstr,"/",astr,"/",sstr,"/",bstr,"/e"), jacerg[1:N,1]) # Write hopping energies - h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/α=",astr,"/s=",sstr,"/β=",bstr,"/t"), jacerg[1:N-1,2]) + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/", Nstr,"/",astr,"/",sstr,"/",bstr,"/t"), jacerg[1:N-1,2]) # Write coupling coefficient - h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/α=",astr,"/s=",sstr,"/β=",bstr,"/c"), jacerg[N,2]) + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/", Nstr,"/",astr,"/",sstr,"/",bstr,"/c"), jacerg[N,2]) + else Nstr = string(N) wstr = string(ωc) bstr = string(β) # the "path" to the data inside of the h5 file is N -> ωc -> beta -> data (e, t or c) - + # Write onsite energies - h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/ωc=",wstr,"/β=",bstr,"/e"), jacerg[1:N,1]) + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/", Nstr, "/", wstr, "/", bstr, "/e"), jacerg[1:N,1]) # Write hopping energies - h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/ωc=",wstr,"/β=",bstr,"/t"), jacerg[1:N-1,2]) + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/", Nstr, "/", wstr, "/", bstr,"/t"), jacerg[1:N-1,2]) # Write coupling coefficient - h5write("$curdir/ohmicT/chaincoeffs.h5", string("/N=",Nstr,"/ωc=",wstr,"/β=",bstr,"/c"), jacerg[N,2]) + h5write("$curdir/ohmicT/chaincoeffs.h5", string("/", Nstr, "/", wstr, "/", bstr,"/c"), jacerg[N,2]) + end end return [jacerg[:,1], jacerg[1:N-1,2],jacerg[N,2]] end + +""" + chaincoeffs_fermionic(nummodes, β, chain; ϵ=x, ωc=1, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=true) + +Generate chain coefficients ``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]`` for a fermionic bath at the inverse temperature β. + +# Arguments +* nummodes: Number of bath modes +* β: inverse temperature +* chain: 1 if the chain modes are empty, 2 if the chain modes are filled +* ϵ: user-provided dispersion relation. Should be a function f(x) where x is the frequency +* ωc: the maximum frequency of the dispersion relation +* mc: the number of component intervals +* mp: the number of points in the discrete part of the measure (mp=0 if there is none) +* iq: a parameter to be set equal to 1, if the user provides his or her own quadrature routine, and different from 1 otherwise +* idelta: a parameter whose default value is 1, but is preferably set equal to 2, if iq=1 and the user provides Gauss-type quadrature routines +* procedure: choice between the Stieltjes and the Lanczos procedure +* AB: component intervals +* Mmax: maximum number of integration points +* save: if true the coefficients are saved +""" + + +function chaincoeffs_fermionic(nummodes, β, chain; ϵ=nothing, ωc=1, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=true) + + N = nummodes # Number of bath modes + + if AB==nothing + if mc==4 + AB = [[-Inf -ωc];[-ωc 0];[0 ωc];[ωc Inf]] + else + throw(ArgumentError("An interval AB with mc = $mc components should have been provided.")) + end + elseif length(AB) != mc + throw(ArgumentError("AB has a different number of intervals than mc = $mc.")) + end + + if ϵ==nothing + throw(ArgumentError("A dispersion relation should have been provided.")) + else + wf(x,i) = fermionicspectraldensity_finiteT(x, i , β, chain, ϵ) + end + + if procedure==:Lanczos # choice between the Stieltjes (irout = 1) and the Lanczos procedure (irout != 1) + irout = 2 + elseif procedure==:Stieltjes + irout = 1 + else + throw(ArgumentError("Procedure should be either Lanczos or Stieltjes.")) + end + + eps0=1e7*eps(Float64) + + jacerg = zeros(N,2) + + ab = 0. + ab, Mcap, kount, suc, uv = mcdis(N,eps0,quadfinT,Mmax,idelta,mc,AB,wf,mp,irout) + for m = 1:N-1 + jacerg[m,1] = ab[m,1] #site energy e + jacerg[m,2] = sqrt(ab[m+1,2]) #hopping parameter t + end + jacerg[N,1] = ab[N,1] + + eta = 0. + for i = 1:mc + xw = quadfinT(Mcap,i,uv,mc,AB,wf) + eta += sum(xw[:,2]) + end + jacerg[N,2] = sqrt(eta) # system-chain coupling c + + if save==true + # Write a HDF5 file + curdir = @__DIR__ + + Nstr=string(N) + cstr=string(chain) + bstr=string(β) + # the "path" to the data inside of the h5 file is beta -> alpha -> s -> data (e, t or c) + + # Write onsite energies + h5write("$curdir/fermionicT/chaincoeffs.h5", string("/", Nstr, "/", bstr, "/", cstr, "/e"), jacerg[1:N,1]) + # Write hopping energies + h5write("$curdir/fermionicT/chaincoeffs.h5", string("/", Nstr, "/", bstr, "/", cstr, "/t"), jacerg[1:N-1,2]) + # Write coupling coefficient + h5write("$curdir/fermionicT/chaincoeffs.h5", string("/", Nstr, "/", bstr, "/", cstr, "/c"), jacerg[N,2]) + end + + return [jacerg[:,1], jacerg[1:N-1,2],jacerg[N,2]] +end \ No newline at end of file diff --git a/src/models.jl b/src/models.jl index 340a285..0fd3d52 100644 --- a/src/models.jl +++ b/src/models.jl @@ -719,3 +719,257 @@ function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) return Any[M, chain...] end +""" + tightbinding_mpo(N, ϵd, chainparams1, chainparams2) + + Generate MPO for a tight-binding chain of N fermionic sites with a single impurity site (fermionic as well) + of energy ϵd at the center. The impurity is coupled to two leads, each described by a set of chain parameters. + The interactions are nearest-neighbour, with the first N/2-1 sites corresponding to the first lead, + the Nth site corresponding to the impurity, and the rest of the sites corresponding to the second + lead. + + # Arguments + + * `N::Int`: number of sites in the chain + * `ϵd::Real`: energy of the impurity site at the center, as Ed - μ, where μ is the chemical potential + * chainparams1::Array{Real,1}: chain parameters for the first lead + * chainparams2::Array{Real,1}: chain parameters for the second lead + + The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. + + +""" +function tightbinding_mpo(N, ϵd, chainparams1, chainparams2) + + e1 = chainparams1[1] + t1 = chainparams1[2] + c1 = chainparams1[3] + + e2 = chainparams2[1] + t2 = chainparams2[2] + c2 = chainparams2[3] + + d = 2 # physical dimension for fermionic Hilbert space + D = 4 # bond dimensions of the MPO + u = unitmat(d) + n = numb(d) + b = anih(d) + bd = crea(d) + + W = Any[] + + # SECOND CHAIN: filled modes + + # the initial matrix for the first filled site: + Min = zeros(ComplexF64, 1, D, d, d) + Min[1,1,:,:] = u + Min[1,D,:,:] = e2[N]*n + Min[1,2,:,:] = t2[N-1]*bd + Min[1,3,:,:] = t2[N-1]*b + push!(W, Min) + + if N!=1 + + M2 = zeros(ComplexF64, D, D, d, d) + for i in 1:(N-2) + M2[1,1,:,:] = u + M2[D,D,:,:] = u + M2[1,D,:,:] = e2[N-i]*n + M2[1,2,:,:] = t2[N-i-1]*bd + M2[3,D,:,:] = bd + M2[1,3,:,:] = t2[N-i-1]*b + M2[2,D,:,:] = b + push!(W, M2) + end + end + + # MPO for the filled site of the second chain coupled to the system: + Mcoup2 = zeros(ComplexF64, D, D, d, d) + Mcoup2[1,1,:,:] = u + Mcoup2[D,D,:,:] = u + Mcoup2[1,D,:,:] = e2[1]*n + Mcoup2[1,2,:,:] = c2*bd + Mcoup2[3,D,:,:] = bd + Mcoup2[1,3,:,:] = c2*b + Mcoup2[2,D,:,:] = b + push!(W, Mcoup2) + + + #system MPO + Md = zeros(ComplexF64, D, D, d, d) + Md[1,1,:,:] = u + Md[D,D,:,:] = u + Md[1,D,:,:] = ϵd*n + Md[1,2,:,:] = bd + Md[3,D,:,:] = bd + Md[1,3,:,:] = b + Md[2,D,:,:] = b + push!(W, Md) + + # FIRST CHAIN: empty modes + + # MPO for the empty site of the first chain coupled to the system: + Mcoup1 = zeros(ComplexF64, D, D, d, d) + Mcoup1[1,1,:,:] = u + Mcoup1[D,D,:,:] = u + Mcoup1[1,D,:,:] = e1[1]*n + Mcoup1[1,2,:,:] = bd + Mcoup1[3,D,:,:] = c1*bd + Mcoup1[1,3,:,:] = b + Mcoup1[2,D,:,:] = c1*b + push!(W, Mcoup1) + + + if N!=1 + M1 = zeros(ComplexF64, D, D, d, d) + for i in 2:(N-1) + M1[1,1,:,:] = u + M1[D,D,:,:] = u + M1[1,D,:,:] = e1[i]*n + M1[1,2,:,:] = bd + M1[3,D,:,:] = t1[i-1]*bd + M1[1,3,:,:] = b + M1[2,D,:,:] = t1[i-1]*b + push!(W, M1) + end + end + # the final matrix for the last empty site: + Mfin = zeros(ComplexF64, D, 1, d, d) + Mfin[1,1,:,:] = e1[N]*n + Mfin[D,1,:,:] = u + Mfin[3,1,:,:] = t1[N-1]*bd + Mfin[2,1,:,:] = t1[N-1]*b + push!(W, Mfin) + return W +end + +""" + interleaved_tightbinding_mpo(N, ϵd, chainparams1, chainparams2) + + Generate MPO for a tight-binding chain of N fermionic sites with a single impurity site (fermionic as well) + of energy ϵd. The impurity is coupled to two leads, each described by a set of chain parameters. + The interactions are next-nearest-neighbour, with the first site corresponding to the impurity, and the + two chains organised in an interleaved fashion. + + # Arguments + + * `N::Int`: number of sites in the chain + * `ϵd::Real`: energy of the impurity site at the first site, as Ed - μ, where μ is the chemical potential + * chainparams1::Array{Real,1}: chain parameters for the first lead + * chainparams2::Array{Real,1}: chain parameters for the second lead + + The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. +""" + +function interleaved_tightbinding_mpo(N, ϵd, chainparams1, chainparams2) + + e1 = chainparams1[1] + t1 = chainparams1[2] + c1 = chainparams1[3] +# c1 = 0. + + e2 = chainparams2[1] + t2 = chainparams2[2] + c2 = chainparams2[3] +# c2 = 0. + + d = 2 # physical dimension for fermionic Hilbert space + D = 6 # bond dimensions of the MPO + u = unitmat(d) + n = numb(d) + b = anih(d) + bd = crea(d) + F = u .- 2.0.*n + + W = Any[] + + + # the initial matrix for the system: + Min = zeros(ComplexF64, 1, D, d, d) + Min[1,1,:,:] = u + Min[1,D,:,:] = ϵd*n + Min[1,2,:,:] = b + Min[1,3,:,:] = bd + + push!(W, Min) + + if N!=1 + # SECOND CHAIN: filled modes + M2in = zeros(ComplexF64, D, D, d, d) + # FIRST CHAIN: empty modes + M1in = zeros(ComplexF64, D, D, d, d) + # first site, COUPLED TO THE SYSTEM + M2in[1,1,:,:] = u + M2in[1,2,:,:] = b + M2in[1,3,:,:] = bd + M2in[1,D,:,:] = e2[1]*n + M2in[2,4,:,:] = F + M2in[3,5,:,:] = F + M2in[2,D,:,:] = c2*bd + M2in[3,D,:,:] = c2*b + M2in[D,D,:,:] = u + push!(W, M2in) + + M1in[1,1,:,:] = u + M1in[1,2,:,:] = b + M1in[1,3,:,:] = bd + M1in[1,D,:,:] = e1[1]*n + M1in[2,4,:,:] = F + M1in[3,5,:,:] = F + M1in[4,D,:,:] = c1*bd + M1in[5,D,:,:] = c1*b + M1in[D,D,:,:] = u + push!(W, M1in) + + for i in 2:(N-1) + # SECOND CHAIN: filled modes + M2 = zeros(ComplexF64, D, D, d, d) + # FIRST CHAIN: empty modes + M1 = zeros(ComplexF64, D, D, d, d) + + M2[1,1,:,:] = u + M2[1,2,:,:] = b + M2[1,3,:,:] = bd + M2[1,D,:,:] = e2[i]*n + M2[2,4,:,:] = F + M2[3,5,:,:] = F + M2[4,D,:,:] = t2[i-1]*bd + M2[5,D,:,:] = t2[i-1]*b + M2[D,D,:,:] = u + push!(W, M2) + + M1[1,1,:,:] = u + M1[1,2,:,:] = b + M1[1,3,:,:] = bd + M1[1,D,:,:] = e1[i]*n + M1[2,4,:,:] = F + M1[3,5,:,:] = F + M1[4,D,:,:] = t1[i-1]*bd + M1[5,D,:,:] = t1[i-1]*b + M1[D,D,:,:] = u + push!(W, M1) + + end + end + # The final matrix for the SECOND chain: + M2fin = zeros(ComplexF64, D, D, d, d) + M2fin[1,1,:,:] = u + M2fin[1,2,:,:] = b + M2fin[1,3,:,:] = bd + M2fin[1,D,:,:] = e2[N]*n + M2fin[2,4,:,:] = F + M2fin[3,5,:,:] = F + M2fin[4,D,:,:] = t2[N-1]*bd + M2fin[5,D,:,:] = t2[N-1]*b + M2fin[D,D,:,:] = u + push!(W, M2fin) + + # The final matrix for the FIRST chain: + M1fin = zeros(ComplexF64, D, 1, d, d) + M1fin[1,1,:,:] = e1[N]*n + M1fin[4,1,:,:] = t1[N-1]*bd + M1fin[5,1,:,:] = t1[N-1]*b + M1fin[D,1,:,:] = u + push!(W, M1fin) + return W +end \ No newline at end of file From 1e951b9e48d004b2e08a448b98c70bd1c7598566 Mon Sep 17 00:00:00 2001 From: angelariva Date: Tue, 30 Apr 2024 15:05:15 +0200 Subject: [PATCH 29/52] added examples --- examples/anderson_model_double.jl | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index 7d71879..082ebfc 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -19,13 +19,23 @@ T = 30.0 dir = "/Users/ariva/Documents/fermions/" -#chainparams2 = readchaincoeffs(dir*"fermionic.h5", β, 2.0) #filled -#chainparams1 = readchaincoeffs(dir*"fermionic.h5", β, 1.0) #empty +# define the dispersion relation -chainparams2 = readchaincoeffs(dir*"fermionic.h5", β, 2.0) # filled -chainparams1 = readchaincoeffs(dir*"fermionic.h5", β, 1.0) # empty +function ϵ(x) + return x +end +chainparams1 = chaincoeffs_fermionic(20, 2.0, 1.0; ϵ, save=false) # empty +chainparams2 = chaincoeffs_fermionic(20, 2.0, 2.0; ϵ, save=false) # filled + +#= +curdir = @__DIR__ +dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/fermionicT/")) +chainparams2 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", N, β, 2.0) #filled +chainparams1 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", N, β, 1.0) #empty +=# + c1 = chainparams1[3] c2 = chainparams2[3] @@ -60,4 +70,5 @@ A, dat = runsim(dt, T, A, H; save = true, savedir = dir, plot = true, - ); \ No newline at end of file + ); + From fdf1ed19d0ed0006aa4417098a6b1c01aac6264c Mon Sep 17 00:00:00 2001 From: angelariva Date: Tue, 30 Apr 2024 15:06:03 +0200 Subject: [PATCH 30/52] worked on fermionic examples --- ChainOhmT/fermionicT/chaincoeffs.h5 | Bin 0 -> 15120 bytes examples/anderson_model_interleaved.jl | 101 +++++++---- examples/fermions.ipynb | 202 ++++++++++------------ examples/plots/chain_occ_bet=2_double.pdf | Bin 33746 -> 34169 bytes examples/plots/chain_occ_bet=2_folded.pdf | Bin 32537 -> 32537 bytes examples/plots/system_folded_beta2.pdf | Bin 11794 -> 11794 bytes src/finitetemperature.jl | 4 +- 7 files changed, 157 insertions(+), 150 deletions(-) create mode 100644 ChainOhmT/fermionicT/chaincoeffs.h5 diff --git a/ChainOhmT/fermionicT/chaincoeffs.h5 b/ChainOhmT/fermionicT/chaincoeffs.h5 new file mode 100644 index 0000000000000000000000000000000000000000..b93f9642abe2a721b29664dc71829e66250a8ba0 GIT binary patch literal 15120 zcmeHOZ)jUp6u(K=EloF@G@E3p*lx3^otC$(%B+qP?OKCwYv=}TVGe1rirSfV5j#-` z8^+Erb~>;QaZ#Ba`yk>luvWRn!HBRAK?fG=d=;$PLfN#g>kzHE@17HztRXC$V`*** zIrrbYIp=rIJMX;vqX)d38kSjIRwh0c3tPl&Mw7l~@nd^Xj!>WGpTvd_@X4xbmVs_4O>ilJ+Nwm$A`8*|N}pl6i%IxBg+DFt`P+rYMbKoDmpqO|~c= zy)==fOc%f20=eNc7?+vi_qQUsUd3;gj>z%L(#3BN#t)h2u9g9Oz2bM<*2YaDhAHkU zFm4n3r^GSop=2@w>GFVYiCpK$`#Vz}xUfvFr~H-tE2_895lGDgR2TJP{E+?YFb|~W z0}m~@N|~Am?!vgGJdhg4SGj%OEEkznU`BqkVLj``8=C43nFm1zRr0zS>^twcMul?pauZu@{io1x# zpP$q^80$`&ztJ6YA|uk9J4vaD740LvC~ta7<%q0$8GGr|&gwnyoQCPHOFuk%xgVNa zci%pB;teR@{_@Dgp%>xeu5+-C4H|*qCEuBvJ*zQbpskk#jgGWn0E(2D$`tKvd zw*p)Kk-gLD0QOT|^IPRMV8P>!U+gUg7S)Fe4ljXBes`dOB(wLswo-1@N01rvX7fAw zao8o*BtI_3=c37GG6ghyk`1b(5Kssx1QY@afm}ntTF8gmHh&ME!255Wcl-#hUV9|q z&|%B-4J*7|I<#9uw#l##O}Femdt^`t?eWXQ9cOg-u;{O+`@hp+kR5M)Ti0Ro-lb=r z7}Md;KlaB4#&x)D&AW$JPR6fm+QGg$uft>e9x40z_xQT$SIR$_(qXjCamaR2hfv$j zP~Ef+ul5HHHeAxd{@jeVW=5Z9my)5Kssx1ab?3jrT0|e_1gRuLI^kpAJ__d%S3tI-kxh|K4bA z)16OmNICDN`qFou6wGwKM(3#{v#)DocT1JSNt$0-*XHKAYH+PIi_YVcj8q+kfI?tF zBG4`Q6JD~%;7@q<4ue18&5}Ri-7N-x!oN9S@F(2oHuw|%-meCK!e_b+{)B7)>=OJ5 z*EEAa;X{k;fK(;RSPm!l~D=-g@8gpA&@@^5P#xD z{=|v=NiFgxUC5toM*idh=1.22.4 in /Users/ariva/Library/Python/3.9/lib/python/site-packages (from scipy) (1.26.2)\n", - "Downloading scipy-1.13.0-cp39-cp39-macosx_12_0_arm64.whl (30.3 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.3/30.3 MB\u001b[0m \u001b[31m201.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:05\u001b[0m\n", - "\u001b[?25hInstalling collected packages: scipy\n", - "Successfully installed scipy-1.13.0\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install scipy" - ] - }, { "cell_type": "code", "execution_count": 25, @@ -446,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -458,7 +430,7 @@ "\t method : DTDVP\n", "\t dt = 0.5\n", "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.46065886596178063, c2 = 0.4606588659617807, \n", + "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.8164965809277259, c2 = 0.8164965809277259, \n", "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", "\t convparams : 0.1\n", "\t options : Dlim = 10, savebonddims = true, verbose = false, \n", @@ -470,10 +442,10 @@ "\n", "# path to work from personal computer\n", "\n", - "path = \"/Users/ariva/Documents/fermions/plkIX/\"\n", + "path = \"/Users/ariva/Documents/fermions/38d74/\"\n", "\n", "# Results for alpha_lit=0.01\n", - "res = h5py.File(path+\"/dat_plkIX.jld\", \"r\")[\"data\"]\n", + "res = h5py.File(path+\"/dat_38d74.jld\", \"r\")[\"data\"]\n", "info = open(path+\"/info.txt\", \"r\")\n", "data = info.read()\n", "print(data)" @@ -481,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -494,7 +466,7 @@ " 'times']" ] }, - "execution_count": 64, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -518,12 +490,12 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 88, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -538,12 +510,12 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAHlCAYAAAC6fvUNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA+DUlEQVR4nO3dd3xUVf7/8fckyCQQJhgSgUBIKCLSi1QRxHUFpChIc0HqChZAxbKyqIAFZNVVdlmxo66gNBuujbCiNClfVBThl10FjIHQQjIEUkhyfn/wzXwdZpJMyGSGO3k9H4/7eDDnnnvumTsx+fj5nHvHZowxAgAACICwYE8AAABUHQQeAAAgYAg8AABAwBB4AACAgCHwAAAAAUPgAQAAAobAAwAABAyBBwAACBgCDwAAEDAEHoAF7N+/XzabTePHj/fLeDabTVdffbVb25w5c2Sz2bR+/Xq/nMMKkpKSlJSUFOxpAFUKgQeqHJvN5rHZ7XYlJSVp3Lhx2rNnT7CnCAAhq1qwJwAEy+zZs13/zsrK0rZt2/Tmm29q9erV2rhxo9q3bx+8yQXB1KlTNWrUKDVq1CjYUwmYdevWBXsKQJVD4IEqa86cOR5t06ZN06JFi/Tcc8/p9ddfD/icgik2NlaxsbHBnkZANW3aNNhTAKocSi3Ab1x33XWSpKNHj3rsy8vL05NPPqk2bdqoRo0acjgcuuqqq7RixQqPvr9dk7F//36NGjVKsbGxioiI0BVXXKGPPvrI6/lPnjypGTNmqGHDhoqIiFCLFi3017/+VUVFReV+L/n5+XrsscfUtGlT2e12NW7cWA899JDy8vK89i9pjUfxepDDhw9r4sSJqlu3rmrWrKkePXpow4YNkqRTp07p/vvvV2Jioux2u1q1aqWVK1eWOLe3335bffr0Ue3atRUREaHLL79cjz/+uNe5FZ//2LFjmjx5surXr+86x5IlSzz6G2P0xhtvqEePHoqLi1NERIQSEhLUt29fLV++3K1vSWs8Kvuzzs/P19/+9jd17NhRF198sWrUqKGkpCTdcMMNSk5OLvG6AaGAjAfwG8W/9K+44gq39vz8fPXt21dffvmlWrRooTvvvFOnT5/WqlWrNHLkSH377beaN2+ex3gHDhxQly5d1KRJE91yyy3KyMjQ8uXLXX9g+vTp4+qbl5en3/3ud9q+fbvatWun0aNHKzMzU4899pi+/PLLcr0PY4xGjBihDz74QE2bNtXUqVOVn5+v1157Td9//325r0tmZqauvPJK1apVSzfffLMyMjL0zjvvqG/fvtqyZYumTJmijIwMDRw4UGfOnNHbb7+tkSNHKiEhQd26dXMba+LEiVqyZIkaNmyom266SbVr19bXX3+thx9+WOvWrdPatWtVrVo1r+evXr26hg0bpry8PK1cuVITJ05UWFiYxo0b5+o7a9YszZ8/X40bN9aIESMUHR2tQ4cOafv27Vq5cqVGjhxZ6nsNxGc9fvx4vf3222rdurXGjh2ryMhIHTx4UBs3btSnn36qa6+9ttyfEWAZBqhiJBlJZvbs2a7tnnvuMT179jQ2m80MHDjQOJ1Ot2PmzZtnJJn+/fubM2fOuNoPHz5sEhMTjSSzadMmV/u+fftc55kzZ47bWJ9++qlrrN964oknjCQzdOhQU1hY6Gr/+eefzcUXX2wkmXHjxvn0HpcuXWokmW7dupmcnBxX+/Hjx02TJk2MJNO7d2+3Y2bPnm0kmS+++MLr9ZoyZYrbvN58800jyVx88cVm4MCBbuf56quvjCRz4403uo21ZMkSI8kMGTLEnD592uv5n3vuOa/nnzRpkikoKHC1796924SHh5vLL7/crX9MTIxp0KCBOXXqlMd1OXr0qNvrxMREk5iY6NZW2Z91ZmamsdlsplOnTm7vp9ixY8c82oBQQuCBKqf4j4S3rWXLlmbp0qUexzRr1szYbDazZ88ej32vvPKKkWQmTJjgaiv+Y5SYmOj1j0ujRo1MnTp1PM4RFhZm/vvf/3r0L/6j7Gvgce211xpJ5t///rfHvuI//uUJPGrUqOERjBUUFJhq1aoZSeann37yOE9SUpJJSkpya2vfvr2pVq2aOXHihEf/goICU6dOHdO5c2ev58/KyvI4plevXkaSOXnypKstJibGJCUlmdzcXI/+5/IWeFT2Z52VlWUkmR49epiioqIy5wiEGkotqLKMMa5/nzp1Srt379aDDz6o0aNHa/fu3XriiScknV138d///lcNGjRQixYtPMa55pprJEnffPONx7727dsrPDzcoz0hIUFbtmxxvS4+R0JCgtcFj1dffbXmzp3r83vbuXOnwsLC1LNnT69jlVfz5s1Vq1Ytt7bw8HDVrVtXp06dUpMmTTyOadCggbZu3ep6ffr0aX333XeKjY3Vc8895/U8drvd6+3Ml156qRwOh0d7QkKCJOnEiROKioqSJI0ePVp///vf1bJlS40YMUK9e/dW9+7dFR0dXeb7DMRn7XA4NGjQIK1Zs0bt27fXTTfdpKuuukpdu3ZVjRo1ypwjYHUEHoCkmjVrqkuXLnr33XfVsGFD/eUvf9Ftt92mhIQEZWVlSZLq16/v9dji9szMTI99tWvX9npMtWrV3BaMFp+jbt26XvvXq1fP17fiGi8mJkYXXXRRhceSVOIf7WrVqpW6r6CgwPX6xIkTMsbo6NGj5QqipNKvoyQVFha62p599lk1adJES5Ys0ZNPPqknn3xS1apV0/XXX69nnnlGzZo1K/E8gfisJWn58uVasGCBli1b5rqtOyIiQsOGDdPTTz9d4s8BEAq4qwX4jdq1a+uyyy5TQUGBdu7cKen//uimp6d7PebQoUNu/c5H8bGHDx/2ur+kc5c2XkZGhs6cOVPhsfyl+D126NBB5myZt8StIsLDw3X33Xfru+++0+HDh7V69WoNGTJEH374ofr161fiXT2/nWNlftaSFBkZqTlz5iglJUW//PKL3nrrLfXs2VNvvfWWhg0bVqGxgQsdgQdwjhMnTkiS6/9Sa9WqpaZNmyotLU3/+c9/PPp/8cUXkqSOHTue9zlr1aqlZs2aKS0tTT/99JPH/vI+xrxjx44qKirSxo0bKzyWv0RFRalVq1bavXu3MjIyAnLOSy65REOHDtWKFSt0zTXX6KefftIPP/xQYv9AfNbnSkhI0OjRo/XZZ5+pWbNm2rhxo44fP+638YELDYEH8Bvvv/++9u3bp4suukg9evRwtU+cOFHGGN1///1uaf1jx47psccec/WpiAkTJqioqEh/+tOf3FLz+/bt09/+9rdyjyWdvbU0NzfX1Z6RkaHHH3+8QvOsiBkzZig/P18TJ070Wq44ceKEK9N0PvLy8rRp0yaP9jNnzriCnbLWUVT2Z3306FGvtzSfOnVK2dnZqlatmqpXr37e4wMXOtZ4oMr67ZNLT506pR9//FGffPKJJGnevHludfb77rtPn3zyiT744AO1a9dO119/vU6fPq2VK1fqyJEjeuCBB7wu5CyPe++9V++//75Wr16tjh07qm/fvsrMzNSKFSvUq1cvffjhhz6PdfPNN2v58uX68MMP1bp1a91www06c+aMVq1apc6dO3vNqgTCxIkT9T//8z96/vnn1bRpU/Xt21eNGjVSRkaG9u3bp6+++koTJkzQCy+8cF7j5+TkqGfPnmrWrJk6deqkxMRE5ebmau3atdqzZ48GDx6syy+/vNQxKvuzTktLU4cOHdSmTRu1bdtWCQkJcjqd+uijj5Senq7p06d7LOQFQkpwbqYBgkdebqMNDw839erVM4MHDzaff/651+NycnLME088YVq1amUiIiJMVFSUufLKK82yZcs8+hbfYlnS7a+9e/c23v7zy8rKMvfcc4+Jj483drvdXHbZZebpp582P/30U7lupzXGmLy8PDN37lzTuHFjU716dZOYmGj+/Oc/m9zc3HLfTntu32Lebkct6z0aY8yaNWvMgAEDTFxcnLnoootM3bp1TefOnc2sWbM8bmMt7fzjxo0zksy+ffuMMcbk5+ebBQsWmH79+pmEhARjt9tNbGys6dq1q1m8eLHJy8vzaf6V+VmfOHHCzJ071/Tp08fEx8eb6tWrm3r16pnevXubZcuWcYstQp7NmAqu5AIAAPARazwAAEDAhFTgsXv3bg0fPlxNmjRRjRo1FBsbq169emnNmjUefffs2aN+/fopKipKMTExuuWWW7x+MRgAADj7YMLBgwcrJiZGNWrUUOvWrcu98F0KscWlBw4c0MmTJzVu3DjFx8fr9OnTWr16tQYPHqwXX3xRkydPliT9+uuv6tWrl6KjozVv3jxlZ2fr6aef1vfff69t27axohwAgN/4/PPPNWjQIHXo0EEPP/ywoqKi9NNPP+nXX38t91ghv8ajsLBQnTp1Um5urvbu3StJuuOOO/T6669r7969atSokaSz30r6+9//3i1AAQCgqnM6nWrevLl69OihVatWKSysYsWSkCq1eBMeHq6EhAS3ZwasXr1aAwcOdAUdknTttdeqefPmWrFiRRBmCQDAhWnZsmU6fPiwnnjiCYWFhenUqVMeXwNQHiEZeJw6dUrHjh3TTz/9pGeffVaffPKJfve730k6ew/9kSNHdMUVV3gc16VLF69f/gQAQFWVnJwsh8OhtLQ0XXbZZYqKipLD4dDtt9/u9oBCX4XUGo9i9957r1588UVJUlhYmIYOHapFixZJ+r/vWvD2JVD169dXRkaG8vLyZLfbPfbn5eW5fc9DUVGRMjIyVKdOHdlstsp4KwCAC5QxRidPnlR8fHyFyw++yM3NVX5+vt/GM8Z4/O2y2+0ef//+85//qKCgQDfccIMmTZqk+fPna/369fr73/+uzMxMvf322+U+ccjZs2ePWbt2rXnjjTfMgAEDzJAhQ0x6eroxxpivvvrKSDLLly/3OO7hhx82ksyJEye8jlv8gCU2NjY2NrbiLTU1tTL/pBljzj7Url69en6dd1RUlEfb7NmzPc7dpEkTI8ncdtttbu1TpkwxkkxKSkq53ktIZjxatGihFi1aSJLGjh2r6667ToMGDdLWrVsVGRkpSV6/obI4ZVTc51wzZ87UjBkzXK+zsrLUqFEjRUgqLd8x24c5377Ih05AIN2S5cfBrim7yz//x4/nA0q3eGrZfeaWsd9IypUC8oj7/Px8paenKzV1nxwOR4XHczqdSkhorNTUVLfxvGX7i/8m3nzzzW7tf/jDH/Tiiy9qy5YtuvTSS30+d0gGHucaNmyYpkyZopSUFFeJpbjk8luHDh1STEyM1wsveU9BSWeDjtICjwgf5ujwHusAweOHX27/J7zsLvw3gADy5feyrwX0QJbaHQ6HXwKP8owXHx+v3bt3u31/lXT225+l//tGb1+F5OLSc+Xk5Eg6m6Fo0KCB4uLitGPHDo9+27ZtU/v27QM8OwAAfFXgx803nTp1knT25ozfOnjwoCQpLi6uXO8gpAKPI0eOeLSdOXNGb775piIjI9WyZUtJ0k033aSPPvpIqamprn7r1q1TSkqKhg8fHrD5AgBQPoEPPEaMGCFJevXVV93aX3nlFVWrVk1XX311ud5BSJVapkyZIqfTqV69eqlBgwZKT0/X0qVLtXfvXj3zzDOKioqSJP35z3/WypUr1adPH911113Kzs7WU089pTZt2mjChAlBfhcAAFw4OnTooIkTJ+q1115TQUGBevfurfXr12vlypWaOXOm4uPjyzVeSAUeI0eO1KuvvqrFixfr+PHjqlWrljp16qQFCxZo8ODBrn4JCQn68ssvNWPGDD344IOqXr26BgwYoGeeeabE9R0AAARf+bIVpY/juxdeeEGNGjXSkiVL9N577ykxMVHPPvus7r777nKfOeQfmV6ZnE6noqOjFanSFyHN82Gsu14tuw8QUBP9+auha9ldXtvmx/MBpVs4qew+fy5jv5GUo7PrB/254NOb4r83WVk/y+Go+F00TudJRUc3CcjczxVSazwAAMCFLaRKLQAAhLbglFr8icADAADLsH7gQakFAAAEDBkPAAAsw/oZDwIPAJWPO1YAPyn8380f4wQHpRYAABAwZDwAALCMQvmnTBK8jAeBBwAAlmH9NR6UWgAAQMCQ8QAAwDKsn/Eg8AAAwDKsH3hQagEAAAFDxgMAAMvgrhYAABAwlFoAAAB8RsYDAADLsH7Gg8ADQAV0DfYEgCrG+oEHpRYAABAwZDwAALAM62c8CDwAALAM699OS6kFAAAEDBkPAAAsg1ILAAAIGOsHHpRaAABAwJDxAADAMqyf8SDwAADAMqwfeFBqAQAAAUPGAwAAy7D+czwIPAAAsIxC+Sdo4AFiAACgCiDjAQCAZVh/cSmBBwAAlmH9wINSCwAACBgyHgAAWAZ3tQAAgICh1AIAAOAzMh4AAFiG9TMeBB4AAFiG9QMPSi0AACBgyHgAAGAZ1s94EHgAAGAZ1r+dllILAAAIGDIeAABYRoGkcD+NExwEHgAAWIb1Aw9KLQAAIGDIeAAAYBnWz3gQeAAAYBnc1QIAAOAzMh4AAFhGgfyTM6DUAgAAymT9wINSCwAACBgyHgAAWIb1Mx4EHgAAWEah/HNHCne1AACAKoCMBwAAlmH953gQeAAAYBkFkmx+Gic4KLUAAICAIeMBAIBlWD/jQeABAIBlWD/woNQCAAAChowHAACWYf2MB4EHAACWUSj/BB48QAwAAFyg1q9fL5vN5nX7+uuvyzUWGQ8AACzDXyWS8xtn+vTp6ty5s1tbs2bNyjUGgQcAAJYR3MDjqquu0rBhwyp0ZkotAADAZydPnlRBwfkHQAQeAABYRoEft/KbMGGCHA6HIiIi1KdPH+3YsaPcY1BqAeDdaz6snJ/YpfLnAeA3/HU3ytlxnE6nW6vdbpfdbvfoXb16dd100026/vrrFRsbqx9//FFPP/20rrrqKm3evFkdOnTw+cwEHgAAVFEJCQlur2fPnq05c+Z49OvRo4d69Ojhej148GANGzZMbdu21cyZM/Xpp5/6fE4CDwAALKNAkvHDOGczHqmpqXI4HK5Wb9mOkjRr1kw33HCD3n33XRUWFio8PNyn4wg8AACwDP8GHg6Hwy3wKK+EhATl5+fr1KlTPo/D4lIAAHBefv75Z0VERCgqKsrnYwg8AACwjODc1XL06FGPtu+++04ffvihrrvuOoWF+R5OUGoBAMAy/Ftq8dXIkSMVGRmpHj166JJLLtGPP/6ol156STVq1NCTTz5ZrrEIPAAAQKluvPFGLV26VH/961/ldDoVFxenoUOHavbs2TwyHQCA0FUo/2Q8isrVe/r06Zo+fbofzkvgAQCAhQQn8PAnFpcCAICAIeMBAIBlFMg/OYPgZTwIPAAAsAzrBx6UWgAAQMCQ8QAAwDLIePjV9u3bNXXqVLVq1Uo1a9ZUo0aNNGLECKWkpLj6FBUV6fXXX9fgwYOVkJCgmjVrqnXr1nr88ceVm5vr03muvvpq2Ww2j61fv36V9dYAAPCDQvnnqaXle4CYP11QGY8FCxZo06ZNGj58uNq2bav09HQtWrRIHTt21Ndff63WrVvr9OnTmjBhgrp166bbbrtNl1xyibZs2aLZs2dr3bp1+ve//y2bzVbmuRo2bKj58+e7tcXHx1fWWwMAALrAAo8ZM2Zo2bJlql69uqtt5MiRatOmjZ588km99dZbql69ujZt2qQePXq4+tx6661KSkpyBR/XXnttmeeKjo7WmDFjKuV9AABQOQoklf0/12Xzx7NAzs8FVWrp0aOHW9AhSZdeeqlatWqlPXv2SJKqV6/uFnQUGzJkiCS5+vmioKBA2dnZFZgxAACBFJwvifOnCyrj4Y0xRocPH1arVq1K7Zeeni5Jio2N9WnclJQU1axZU/n5+apbt65uvfVWPfLII7roootKPCYvL095eXmu106n06dzASHrtW3BngEAi7ngA4+lS5cqLS1Njz76aKn9/vKXv8jhcKh///5ljtm0aVP16dNHbdq00alTp7Rq1So9/vjjSklJ0fLly0s8bv78+Zo7d2653wMAAP5h/VKLzRgTvLOXYe/everatatatWqlDRs2KDw83Gu/efPmadasWXr++ed1++23n9e5Jk+erJdffllbtmxRt27dvPbxlvFISEhQpEr/MZjnw/nverVc0wUAVMDCSWX3+XMZ+42kHElZWVlyOBx+mFXJnE6noqOjlZUp+eNUTqcUXTswcz/XBbXG47fS09M1YMAARUdHa9WqVSUGHcuXL9dDDz2kSZMmnXfQIUn33nuvJCk5ObnEPna7XQ6Hw20DAAC+uyBLLVlZWerfv78yMzO1YcOGEm9zXbt2rcaOHasBAwbohRdeqNA5ExISJEkZGRkVGgcAgEpTJP88+yt4zw+78AKP3NxcDRo0SCkpKUpOTlbLli299tu6dauGDBmiK664QitWrFC1ahV7Kz///LMkKS4urkLjAABQaQrln2d/Be/5YRdWqaWwsFAjR47Uli1btHLlSnXv3t1rvz179mjAgAFKSkrSRx99pMjIyBLH3Lt3r3755RfXa6fT6bZOQzp758zjjz8uSerbt68f3gkAAPDmgsp43Hvvvfrwww81aNAgZWRk6K233nLbP2bMGJ08eVJ9+/bViRMndP/99+tf//qXW5+mTZu6BSyXX365evfurfXr10uSdu7cqZtvvlk333yzmjVrppycHL333nvatGmTJk+erI4dO1b6+wQA4LyEQMbjggo8vv32W0nSmjVrtGbNGo/9Y8aM0fHjx5WamipJevDBBz36jBs3rsRMiSQlJibqqquu0nvvvaf09HSFhYXp8ssv1wsvvKDJkyf7540AAFAZWOPhX8VZidIkJSWpPHcAn9u3cePGWrFiRXmnBgAA/OCCCjwAAEApKLUAAICACYFSywV1VwsAAAhtZDwAALCKIvmnTMLiUgAAUKYQWONBqQUAAAQMGQ8AAKwiBBaXEngAAGAVlFoAAAB8R8YDAACrCIGMB4EHAABWEQJrPCi1AACAgCHjAQCAVVBqAQAAAWPknzKJ71/y7neUWgAAQMCQ8QAAwCootQAAgIAJgcCDUgsAAAgYMh4AAFhFCDzHg8ADAACroNQCAADgOzIeAABYRQhkPAg8AACwihBY40GpBQAABAwZDwAArKJI/imTcFcLAAAoE6UWAAAA35HxAADAKrirBQAABEwIBB6UWgAAQMCQ8QAAwCpCYHEpgQcAAFZBqQUAAMB3ZDwAALCKEMh4EHgAAGAVRv5Zn2H8MMZ5otQCAAAChowHAABWQakFAAAETAjcTkupBQAABAwZDwAArIJSCwAACJgQCDwotQAAgIAh4wEAgFWwuBQAAARMoR+3CnjiiSdks9nUunXrch9L4AEAAHz266+/at68eapZs+Z5HU+pBQAAqyiSfxaGVqDUct9996lbt24qLCzUsWPHyn08GQ8AAKyiyI/befjqq6+0atUqPffcc+f9Fgg8AABAmQoLCzVt2jT98Y9/VJs2bc57HEotAABYhZ+f4+F0Ot2a7Xa77Ha710NeeOEFHThwQMnJyRU6NRkPAACsws+lloSEBEVHR7u2+fPnez3t8ePH9cgjj+jhhx9WXFxchd4CGQ8AAKqo1NRUORwO1+uSsh0PPfSQYmJiNG3atAqfk8ADAACr8HOpxeFwuAUe3vznP//RSy+9pOeee04HDx50tefm5urMmTPav3+/HA6HYmJifDo1gQcAAFYRhO9qSUtLU1FRkaZPn67p06d77G/cuLHuuusun+90IfAAAAAlat26td577z2P9oceekgnT57UwoUL1bRpU5/HI/AAAMAqgvBdLbGxsbrxxhs92oszHN72lYbAAwAAq7gAnlxaUQQeAACg3NavX39exxF4AABgFUEotfgbgQcAAFYRhLta/I0nlwIAgIAh4wEAgFWEQMaDwAMAAKsIgTUelFoAAEDAkPEAAMAqKLUAAICACYHAg1ILAAAIGDIeAABYhZF/FoYaP4xxngg8AACwCkotAAAAviPjAQCAVYTAczwIPAAAsApKLQAAAL4j4wEAgFWEQMaDwAMAAKsIgTUelFoAAEDAkPEAAMAqKLUAAICAKZJ/ggZKLQAAoCog4wEAgFWwuBQAAMB3ZDwAALAKFpcCAICAodRyYcnOztbs2bPVr18/xcTEyGaz6fXXX/foN378eNlsNo+tRYsWgZ80AABVSEhlPI4dO6ZHH31UjRo1Urt27bR+/foS+9rtdr3yyitubdHR0ZU8QwAAKoBSy4Wlfv36OnTokOrVq6cdO3aoc+fOJfatVq2axowZE8DZAQBQQSEQeJxXqSUmJkYff/yxv+dSYXa7XfXq1fO5f2FhoZxOZyXOCAAA/NZ5BR6ZmZnKzMwscf/OnTv1j3/843znFBCnT5+Ww+FQdHS0YmJidOeddyo7O7vUY/Ly8uR0Ot02AAACpsiPW5D4HHhs2rRJq1at0s8//yxJstlsJfbds2ePpk+fXvHZVZL69evrgQce0JIlS/T2229r8ODBev7559WvXz8VFBSUeNz8+fMVHR3t2hISEgI4awBAlVf8yPSKbkEMPHxe4/Hvf/9bs2fPdt0BMnfuXCUnJ6tt27Zq27at2rVrp5iYGEnSwYMHFRUVVWmTrqj58+e7vR41apSaN2+uWbNmadWqVRo1apTX42bOnKkZM2a4XjudToIPAADKweeMx8MPP6y9e/fqn//8p4wxql69utatW6d77rlHv/vd7xQXF6eGDRuqW7dueuSRR3TllVdW5rz97p577lFYWJiSk5NL7GO32+VwONw2AAACxh/ZDn8tUD1P5bqrpXnz5mrevLkWLlyoBx98UEOGDJHT6dSuXbtc2y+//KKJEyfqoYceqqw5V4rIyEjVqVNHGRkZwZ4KAADehcADxM7rdtqtW7e6/u1wONSzZ0/17NnTb5MKhpMnT+rYsWOKi4sL9lQAAAhZIfUcD1/k5ubqzJkzqlWrllv7Y489JmOM+vXrF6SZAQBQhkL555njVim1WMGiRYuUmZmpgwcPSpLWrFmjX3/9VZI0bdo0nThxQh06dNDNN9/sekT6Z599po8//lj9+vXTDTfcELS5AwBQqqpaarmQPf300zpw4IDr9bvvvqt3331XkjRmzBjVrl1bAwcO1Nq1a/XGG2+osLBQzZo107x583TfffcpLCykvr4GAIALSsgFHvv37y+zzz//+c/KnwgAAP5GqQUAAARMCAQe1BUAAEDAkPEAAMAqjPyzMNT4YYzzROABAIBVFEoq+avSyjdOkFBqAQAAAUPGAwAAqwiBjAeBBwAAVhECDxCj1AIAAAKGjAcAAFZBqQUAAAQMpRYAAADfkfEAAMAqKLUAAICAKZJ/ggZKLQAAoCog4wEAgFUUyT+lliBmPAg8AACwCn+tzeC7WgAAQFVAxgMAAKsIgYwHgQcAAFYRAms8KLUAAICAIeMBAIBVUGoBAAABQ6kFAADAd2Q8AACwCn9lKsh4AACAMhX6cSuH3bt3a/jw4WrSpIlq1Kih2NhY9erVS2vWrCn3WyDjAQAASnXgwAGdPHlS48aNU3x8vE6fPq3Vq1dr8ODBevHFFzV58mSfx7IZY0wlzjWkOZ1ORUdHK1Klr/WZ58NYd73qp0kBAMq0cFLZff5cxn4jKUdSVlaWHA6HH2ZVsuK/N1kNJIcfahXOIik6rWJzLywsVKdOnZSbm6u9e/f6fBylFgAArCJIpRZvwsPDlZCQoMzMzHIdR6kFAAD45NSpU8rJyVFWVpY+/PBDffLJJxo5cmS5xiDwAADAKgp1tsZTUf97V4vT6XRrttvtstvtJR5277336sUXX5QkhYWFaejQoVq0aFG5Tk2pBQAAqyjy4yYpISFB0dHRrm3+/Pmlnv7uu+/W2rVr9cYbb6h///4qLCxUfn5+ud4CGQ8AAKqo1NRUt8WlpWU7JKlFixZq0aKFJGns2LG67rrrNGjQIG3dulU2m2+PVCXjAQCAVRTJPwtL/zfj4XA43LayAo9zDRs2TNu3b1dKSorPx5DxAADAKvz1XS1+epBGTk6OpLO35fqKjAcAACjVkSNHPNrOnDmjN998U5GRkWrZsqXPY5HxAADAKgoVlIzHlClT5HQ61atXLzVo0EDp6elaunSp9u7dq2eeeUZRUVE+j0XgAQCAVQQp8Bg5cqReffVVLV68WMePH1etWrXUqVMnLViwQIMHDy7XWAQeAACgVKNGjdKoUaP8MhaBBwAAVnGBLS49HwQeAABYRZBKLf7EXS0AACBgyHgAAGAVIZDxIPAAAMAqjIIaNPgDpRYAABAwZDwAALCI4q9a8cc4wULgAQCARYRC4EGpBQAABAwZDwAALKJIrm+0r/A4wULgAQCARVBqAQAAKAcyHgAAWASlFgAAEDCUWgAAAMqBjAcAABZRJP9kKyi1AACAMoXCGg9KLQAAIGDIeAAAYBGhsLiUwAMAAIsIhcCDUgsAAAgYMh4AAFhEKCwuJfAAAMAiKLUAAACUAxkPAAAsglILAAAImFB4cimlFgAAEDBkPAAAsIhQWFxK4AEAgEWEwhoPSi0AACBgyHgAAGARlFoAAEDAhELgQakFAAAEDBkPAAAsIhQWlxJ4AABgEZRaAAAAyoGMBwAAFmHknzKJ8cMY54vAAwAAi6DUAgAAUA5kPAAAsIhQyHgQeAAAYBGhcDstpRYAABAwZDwAALAISi0AACBgQiHwsGypZfz48bLZbCVuaWlpJR47Z84cr8dEREQE8B0AAFD1WDbjMWXKFF177bVubcYY3XbbbUpKSlKDBg3KHGPx4sWKiopyvQ4PD/f7PAEA8JdQWFxq2cCje/fu6t69u1vbxo0bdfr0aY0ePdqnMYYNG6bY2NjKmB4AAH5XJP+USbirxU+WLVsmm82mP/zhDz71N8bI6XTKmGA+PBYAgKojZAKPM2fOaMWKFerRo4eSkpJ8OqZJkyaKjo5WrVq1NGbMGB0+fLjU/nl5eXI6nW4bAACBUuTHLVgsW2o512effabjx4/7VGa5+OKLNXXqVHXv3l12u10bNmzQP/7xD23btk07duyQw+Hwetz8+fM1d+5cf08dAACfhMJdLSETeCxbtkwXXXSRRowYUWbfu+66y+31TTfdpC5dumj06NF6/vnn9eCDD3o9bubMmZoxY4brtdPpVEJCQsUmDgBAFRISpZbs7Gx98MEH6tu3r+rUqXNeY/zhD39QvXr1lJycXGIfu90uh8PhtgEAECiFftyCJSQyHu+//3657mYpSUJCgjIyMvw0KwAA/CsUbqcNiYzH0qVLFRUVpcGDB5/3GMYY7d+/X3FxcX6cGQAA+C3LBx5Hjx5VcnKyhgwZoho1anjs/+WXX7R3716PY861ePFiHT16VP369au0uQIAUBGUWi4Ay5cvV0FBQYlllrFjx+rLL790e1ZHYmKiRo4cqTZt2igiIkIbN27UO++8o/bt22vKlCmBmjoAAOXCXS0XgKVLl+qSSy7xeHx6aUaPHq3Nmzdr9erVys3NVWJioh544AHNmjXLa9YEAAD4h+UDjy1btpS6f/369R5tL7/8ciXNBgCAymPkn4WhwXxet+UDDwAAqopQKLVYfnEpAACwDjIeAABYRCg8x4PAAwAAi6DUAgAAUA4EHgAAWESwHiC2fft2TZ06Va1atVLNmjXVqFEjjRgxQikpKeV+D5RaAACwiGCt8ViwYIE2bdqk4cOHq23btkpPT9eiRYvUsWNHff3112rdurXPYxF4AACAUs2YMUPLli1T9erVXW3FTwB/8skn9dZbb/k8FoEHAAAWEazFpT169PBou/TSS9WqVSvt2bOnXGOxxgMAAIsokn/Wd/jl6afG6PDhw4qNjS3XcQQeAABUUU6n023Ly8vz+dilS5cqLS1NI0eOLNc5CTwAALCIIj9ukpSQkKDo6GjXNn/+fJ/msXfvXt15553q3r27xo0bV673wBoPAAAswt9rPFJTU+VwOFztdru9zGPT09M1YMAARUdHa9WqVQoPDy/XuQk8AACoohwOh1vgUZasrCz1799fmZmZ2rBhg+Lj48t9TgIPAAAsIpjf1ZKbm6tBgwYpJSVFycnJatmy5Xmdm8ADAACLCNbttIWFhRo5cqS2bNmiDz74QN27dz/vcxN4AACAUt1777368MMPNWjQIGVkZHg8MGzMmDE+j0XgAQCARQQr4/Htt99KktasWaM1a9Z47CfwAAAgBAVrjcf69ev9cNazeI4HAAAIGDIeAABYRPEj0/0xTrAQeAAAYBHBWuPhT5RaAABAwJDxAADAIoL5ADF/IfAAAMAiKLUAAACUAxkPAAAsglILAAAIGEotAAAA5UDGAwAAiwiFjAeBBwAAFmHkn/UZxg9jnC9KLQAAIGDIeAAAYBGUWgAAQMCEQuBBqQUAAAQMGQ8AACyCB4gBAICAodQCAABQDmQ8AACwCEotAAAgYCi1AAAAlAMZDwAALKJI/slWUGoBAABlCoU1HpRaAABAwJDxAADAIgrln4wB39UCAADKFAqBB6UWAAAQMGQ8AACwiFBYXErgAQCARVBqAQAAKAcyHgAAWASlFgAAEDCh8ORSSi0AACBgyHgAAGARhZJsfhonWAg8AACwiFBY40GpBQAABAwZDwAALIJSCwAACJhQCDwotQAAgIAh4wEAgEWEwuJSAg8AACyCUgsAAEA5kPEAAMAijPxTJjF+GON8EXgAAGAR/iqRUGoBAABVAhkPAAAsIhQyHgQeAABYRJH8c1cL39UCAACqBDIeAABYBKUWAAAQMKEQeFBqAQAAAUPGAwAAiwiFxaUEHgAAWIS/AgbuagEAAFUCGQ8AACwiFDIeBB4AAFhEofzzBW+UWgAAQJVAxgMAAIsIhYwHgQcAABYRCms8KLUAAICAIeMBAIBFhEKphYwHAAAImCobeOTl5elPf/qT4uPjFRkZqa5du2rt2rXBnhYAACUq0tmsR0W38mY8srOzNXv2bPXr108xMTGy2Wx6/fXXz+s9VNnAY/z48frrX/+q0aNHa+HChQoPD9f111+vjRs3BntqAAB4VeTHrTyOHTumRx99VHv27FG7du0q9B6q5BqPbdu26Z133tFTTz2l++67T5I0duxYtW7dWg888IA2b94c5BkCAHDhqF+/vg4dOqR69eppx44d6ty583mPVSUzHqtWrVJ4eLgmT57saouIiNCkSZO0ZcsWpaamBnF2AAB4548yS/FWHna7XfXq1fPDO6iigcc333yj5s2by+FwuLV36dJFkvTtt98GYVYAAJQuWKUWf6qSpZZDhw6pfv36Hu3FbQcPHvR6XF5envLy8lyvs7KyJJV9a1OuD3Ny5vjQCQDgF778Xi7rd3vxfmP8cYOrb/x1puJxnE6nW7vdbpfdbvfTWbyrkoFHTk6O1wsbERHh2u/N/PnzNXfuXI/2sn6AH/RhTg9O9aETAOCCc/z4cUVHR1fqOapXr6569eopPT3db2NGRUUpISHBrW327NmaM2eO387hTZUMPCIjI90yF8Vyc3Nd+72ZOXOmZsyY4XqdmZmpxMRE/fLLL5X+QwfvnE6nEhISlJqa6lE6Q+Xj+gcX1z+4srKy1KhRI8XExFT6uSIiIrRv3z7l5+f7bUxjjGw2m1tbZWc7pCoaeNSvX19paWke7YcOHZIkxcfHez2upBRUdHQ0/9EHmcPh4DMIIq5/cHH9gyssLDDLJSMiIlyZeSurkotL27dvr5SUFI/a1tatW137AQCA/1XJwGPYsGEqLCzUSy+95GrLy8vTkiVL1LVrV4+aFwAA8I8qWWrp2rWrhg8frpkzZ+rIkSNq1qyZ3njjDe3fv1+vvvqqz+PY7XbNnj07IDUxeMdnEFxc/+Di+gdXVbv+ixYtUmZmpuvOzzVr1ujXX3+VJE2bNs3ntY42E8j7gC4gubm5evjhh/XWW2/pxIkTatu2rR577DH17ds32FMDAOCCk5SUpAMHDnjdt2/fPiUlJfk0TpUNPAAAQOBVyTUeAAAgOAg8AABAwBB4AACAgCHw8NG6des0ceJENW/eXDVq1FCTJk30xz/+0fXQsXNt3rxZPXv2VI0aNVSvXj1Nnz5d2dnZAZ516Dh06JAefPBB9enTR7Vq1ZLNZtP69etL7M/1r1x5eXn605/+pPj4eEVGRqpr165au3ZtsKcVcrKzszV79mz169dPMTExstlsev3117323bNnj/r166eoqCjFxMTolltu0dGjRwM74RCzfft2TZ06Va1atVLNmjXVqFEjjRgxQikpKR59uf7lYOCTTp06mcaNG5sHHnjAvPzyy2bmzJmmVq1apm7duubQoUNufb/55hsTERFhOnToYBYvXmxmzZpl7Ha76devX5Bmb31ffPGFkWQuvfRS0717dyPJfPHFF177cv0r36hRo0y1atXMfffdZ1588UXTvXt3U61aNbNhw4ZgTy2k7Nu3z0gyjRo1MldffbWRZJYsWeLRLzU11cTGxpqmTZuahQsXmieeeMJcfPHFpl27diYvLy/wEw8RN910k6lXr56ZNm2aefnll81jjz1m6tata2rWrGm+//57Vz+uf/kQePjoyy+/NIWFhR5tksysWbPc2vv372/q169vsrKyXG0vv/yykWQ+++yzgMw31DidTnP8+HFjjDErV64sNfDg+leurVu3GknmqaeecrXl5OSYpk2bmu7duwdxZqEnNzfX9T8227dvLzHwuP32201kZKQ5cOCAq23t2rVGknnxxRcDNd2Qs2nTJo/AISUlxdjtdjN69GhXG9e/fCi1+KhXr14ez+Pv1auXYmJitGfPHleb0+nU2rVrNWbMGLfvThg7dqyioqK0YsWKgM05lNSqVcunL2Li+le+VatWKTw8XJMnT3a1RUREaNKkSdqyZYtSU1ODOLvQYrfbVa9evTL7rV69WgMHDlSjRo1cbddee62aN2/Oz3wF9OjRQ9WrV3dru/TSS9WqVSu33/tc//Ih8KiA7OxsZWdnKzY21tX2/fffq6CgQFdccYVb3+rVq6t9+/b65ptvAj3NKoXrX/m++eYbNW/e3ONLybp06SJJ+vbbb4Mwq6orLS1NR44c8fiZl85+JvzM+5cxRocPH3b93uf6lx+BRwU899xzys/P18iRI11txYtN69ev79G/fv36rkfNonJw/SvfoUOHSry+krjGAVbWz3xGRoby8vICPa2QtXTpUqWlpbl+73P9y4/A4zx99dVXmjt3rkaMGKFrrrnG1Z6TkyNJXp/dHxER4dqPysH1r3w5OTklXt/i/Qicsn7mf9sHFbN3717deeed6t69u8aNGyeJ638+quSXxJUmPz9fGRkZbm1xcXEKDw93vd67d6+GDBmi1q1b65VXXnHrGxkZKUleI9zc3FzXfnjny/UvDde/8kVGRpZ4fYv3I3DK+pn/bR+cv/T0dA0YMEDR0dGudU4S1/98EHicY/PmzerTp49b22+//CY1NVXXXXedoqOj9fHHH6tWrVpufYvTbd6e73Ho0CHFx8dXzsRDRFnXvyxc/8pXv359paWlebQXX3OucWCV9TMfExNTZb49tbJkZWWpf//+yszM1IYNG9x+xrn+5UfgcY527dp5PAipeFX58ePHdd111ykvL0/r1q3zWtNr3bq1qlWrph07dmjEiBGu9vz8fH377bdubfBU2vX3Bde/8rVv315ffPGFnE6n2wLTrVu3uvYjcBo0aKC4uDjt2LHDY9+2bdv4PCooNzdXgwYNUkpKipKTk9WyZUu3/Vz/8xDs+3mtIjs723Tp0sXUqlXL7Nixo9S+/fr1M/Xr1zdOp9PV9sorrxhJ5pNPPqnsqYa8sp7jwfWvXF9//bXHczxyc3NNs2bNTNeuXYM4s9BW2nM8brvtNhMZGWl++eUXV1tycrKRZBYvXhzAWYaWgoICM3jwYFOtWjXzr3/9q8R+XP/ysRljTBDjHsu48cYb9cEHH2jixIkepYCoqCjdeOONrtc7d+5Ujx491LJlS02ePFm//vqrnnnmGfXq1UufffZZgGceOh5//HFJ0u7du/XOO+9o4sSJaty4sSTpoYcecvXj+le+ESNG6L333tM999yjZs2a6Y033tC2bdu0bt069erVK9jTCymLFi1SZmamDh48qMWLF2vo0KHq0KGDJGnatGmKjo5WamqqOnTooNq1a+uuu+5Sdna2nnrqKTVs2FDbt28n1X+e7r77bi1cuFCDBg3ymi0dM2aMJHH9yyvYkY9VJCYmGklet8TERI/+GzZsMD169DAREREmLi7O3HnnnW7/B47yK+n6e/sx5vpXrpycHHPfffeZevXqGbvdbjp37mw+/fTTYE8rJJX2u2ffvn2ufj/88IO57rrrTI0aNUzt2rXN6NGjTXp6evAmHgJ69+7t8+8drr/vyHgAAICA4TkeAAAgYAg8AABAwBB4AACAgCHwAAAAAUPgAQAAAobAAwAABAyBBwAACBgCDwAAEDAEHgAAIGAIPAAAQMAQeABVzJw5c2Sz2XTs2DG/jfn666/LZrNp//79fhvzQjwngIoj8AAqyWeffSabzSabzaYff/zRY/+gQYPUsGHDIMwsdG3evFlz5sxRZmZmsKcCoAQEHkAl+e677yRJYWFh+uijj7zub9u2baCnVSluueUW5eTkKDExMajn3Lx5s+bOnUvgAVzACDyASrJr1y45HA717dtXa9ascdt34sQJpaamql27dkGanX+Fh4crIiJCNpstpM8JoOIIPIBK8t1336lNmzYaOHCgtmzZouPHj7vtk1QpGY+0tDRNmjRJ8fHxstvtaty4sW6//Xbl5+e79cvMzNT48eNVu3ZtRUdHa8KECTp9+rRbnwMHDuiOO+7QZZddpsjISNWpU0fDhw/3WFfhbb1F8VqS//73v2We51wnT57U3XffraSkJNntdl1yySX6/e9/r507d5Z4zjlz5uj++++XJDVu3NhV5iren5aWpokTJ6pu3bqy2+1q1aqVXnvttXKfF0DFVAv2BIBQlJ+fr//3//6fbr31Vg0cOFB33nmnPv74Y91yyy2SzmZDJPk943Hw4EF16dJFmZmZmjx5slq0aKG0tDStWrVKp0+fVvXq1V19R4wYocaNG2v+/PnauXOnXnnlFV1yySVasGCBq8/27du1efNmjRo1Sg0bNtT+/fu1ePFiXX311frxxx9Vo0aNMufky3nOddttt2nVqlWaOnWqWrZsqePHj2vjxo3as2ePOnbs6PWYoUOHKiUlRW+//baeffZZxcbGSpLi4uJ0+PBhdevWTTabTVOnTlVcXJw++eQTTZo0SU6nU3ffffd5nxdAORkAfvfNN98YSeaFF14wxhjTpk0bM3z4cNf+iRMnGrvdbgoKCvx63rFjx5qwsDCzfft2j31FRUXGGGNmz55tJJmJEye67R8yZIipU6eOW9vp06c9xtmyZYuRZN58801X25IlS4wks2/fPldbec5zrujoaHPnnXeW2sfbOZ966imPNmOMmTRpkqlfv745duyYW/uoUaNMdHS06336cl4AFUOpBagExRmN4lLKwIED9dlnn+nMmTOSzpZaWrVqpfDwcElSXl6e6tatK6fTWeKYRUVFatCggQ4fPlzi/vfff1+DBg3SFVdc4bH/3LUQt912m9vrq666SsePH3ebQ2RkpOvfZ86c0fHjx9WsWTPVrl3b5/KDL+c5V+3atbV161YdPHjQp3OUxhij1atXa9CgQTLG6NixY66tb9++ysrKcr0Xf54XgHcEHkAl+O6772Sz2dSmTRtJZwMPp9Opr776SoWFhdq9e7fb+g673a7Dhw/L4XCUOGZYWJjS0tJUt25dr/uPHj0qp9Op1q1b+zTHRo0aub2++OKLJZ1d+FosJydHjzzyiBISEmS32xUbG6u4uDhlZmYqKyvLb+c511/+8hf98MMPSkhIUJcuXTRnzhz9/PPPPp3vXEePHlVmZqZeeuklxcXFuW0TJkyQJB05csTv5wXgHWs8gEqwa9cuNWnSRFFRUZKkbt26KTY2VmvWrFF8fLxyc3ODfkdLcbblXMYY17+nTZumJUuW6O6771b37t0VHR0tm82mUaNGqaioyG/nOdeIESN01VVX6b333tPnn3+up556SgsWLNC7776r/v37+3TeYsXzHDNmjMaNG+e1T3EQ6M/zAvCOwAOoBLt27dKVV17peh0WFqb+/ftrzZo16tatmyT3O1oWLlyoXbt26dVXXy1xzJdeeknr16/XsmXLvO6Pi4uTw+HQDz/84Kd3Ia1atUrjxo3TM88842rLzc0NyHMy6tevrzvuuEN33HGHjhw5oo4dO+qJJ54oNQDwdmttXFycatWqpcLCQl177bWVcl4AvqPUAvhZenq6jhw54pHRGDhwoH7++We9/fbbktzvaNm1a1eZt9bu3r271DJKWFiYbrzxRq1Zs0Y7duzw2F9ahqEk4eHhHsf9/e9/V2FhYbnH8lVhYaFHGeeSSy5RfHy88vLySj22Zs2akuQWGIWHh+umm27S6tWrvQZlR48erfB5AfiOjAfgZyU9o6Nv37666KKLXOWWOnXquPbt2rVLY8aMKXXc3bt365prrim1z7x58/T555+rd+/emjx5si6//HIdOnRIK1eu1MaNG1W7du1yvZeBAwfqn//8p6Kjo9WyZUtt2bJFycnJbnP3t5MnT6phw4YaNmyY2rVrp6ioKCUnJ2v79u1umRdvOnXqJEmaNWuWRo0apYsuukiDBg3Sk08+qS+++EJdu3bVrbfeqpYtWyojI0M7d+5UcnKyMjIyKnReAL4j8AD87Nw7WopFR0erZ8+e+uKLL9z2FRUV6ccff/Qp49GqVatS+zRo0EBbt27Vww8/rKVLl8rpdKpBgwbq37+/T8/cONfChQsVHh6upUuXKjc3V1deeaWSk5PVt2/fco/lqxo1auiOO+7Q559/rnfffVdFRUVq1qyZnn/+ed1+++2lHtu5c2c99thjeuGFF/Tpp5+qqKhI+/btU1JSkrZt26ZHH31U7777rp5//nnVqVNHrVq1cj1PpCLnBeA7mzmf/CsAv0lJSdHVV19d6i2cGRkZatiwobKzsxUWRoUUgHXxGwwIsnPXd4wfP17jx49367N7925dfvnlBB0ALI/fYkCQff/9926Bx6+//up2R4zkW5kFAKyANR5AkM2dO9f174KCAh08eNAj47Fr166gP/cDAPyBNR7ABS47O1tt2rTRihUr1Llz52BPBwAqhFILcAHbunWrmjdvrqFDhxJ0AAgJZDwAAEDAkPEAAAABQ+ABAAAChsADAAAEDIEHAAAIGAIPAAAQMAQeAAAgYAg8AABAwBB4AACAgCHwAAAAAUPgAQAAAobAAwAABMz/B1g4UmqcnW6gAAAAAElFTkSuQmCC", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh4AAAHlCAYAAAC6fvUNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA+q0lEQVR4nO3deXgUVdr38V8nkE4gdDAkQAIhASIi+yKrLOIwArIoCAQFWWdAZRFxGRl1ABGRcRlxGHEd1BGQzQUcFQkjCogsDyqK8MZR0BgIW0iaQBaS1PuHT/qxSSfpTjrdVOf7ua66LvrUqVOnq2Nye9+nqi2GYRgCAADwgSB/TwAAAFQfBB4AAMBnCDwAAIDPEHgAAACfIfAAAAA+Q+ABAAB8hsADAAD4DIEHAADwGQIPAADgMwQegAkcPXpUFotFEydO9Mp4FotF1113nVPb/PnzZbFYtG3bNq+cwwwSEhKUkJDg72kA1QqBB6odi8VSYrNarUpISNCECRN06NAhf08RAAJWDX9PAPCXefPmOf6dlZWlPXv26I033tCGDRu0Y8cOdejQwX+T84MZM2ZozJgxatKkib+n4jNbt2719xSAaofAA9XW/PnzS7TNnDlTy5Yt07PPPqvXXnvN53Pyp6ioKEVFRfl7Gj7VvHlzf08BqHYotQC/ccMNN0iSTp06VWJfXl6ennjiCbVt21a1atWSzWZT7969tXbt2hJ9f7sm4+jRoxozZoyioqIUGhqqa665Ru+//77L8587d05z5sxR48aNFRoaqpYtW+qZZ55RUVGRx+8lPz9fCxcuVPPmzWW1WtW0aVM9/PDDysvLc9m/tDUexetBTpw4ocmTJ6tBgwaqXbu2evbsqe3bt0uSzp8/r/vvv1/x8fGyWq1q3bq11q1bV+rcVq9erX79+qlu3boKDQ3V1Vdfrccee8zl3IrPf/r0aU2dOlUxMTGOc6xYsaJEf8Mw9Prrr6tnz56Kjo5WaGio4uLiNGDAAK1Zs8apb2lrPKr6s87Pz9dzzz2nTp066YorrlCtWrWUkJCgm266ScnJyaVeNyAQkPEAfqP4l/4111zj1J6fn68BAwbo008/VcuWLTV9+nRduHBB69evV1JSkr766is9/vjjJcb76aef1LVrVzVr1ky33367MjIytGbNGscfmH79+jn65uXl6Xe/+5327t2r9u3ba+zYscrMzNTChQv16aefevQ+DMPQ6NGj9d5776l58+aaMWOG8vPz9c9//lPffPONx9clMzNT1157rerUqaNbb71VGRkZeuuttzRgwADt2rVL06ZNU0ZGhoYMGaKLFy9q9erVSkpKUlxcnLp37+401uTJk7VixQo1btxYt9xyi+rWrasvvvhCjzzyiLZu3aotW7aoRo0aLs8fEhKikSNHKi8vT+vWrdPkyZMVFBSkCRMmOPo+9NBDWrx4sZo2barRo0crIiJCx48f1969e7Vu3TolJSWV+V598VlPnDhRq1evVps2bTR+/HiFhYXp2LFj2rFjhz766CP179/f488IMA0DqGYkGZKMefPmObZ77rnH6NWrl2GxWIwhQ4YYdrvd6ZjHH3/ckGQMGjTIuHjxoqP9xIkTRnx8vCHJ2Llzp6P9yJEjjvPMnz/faayPPvrIMdZvLVq0yJBkjBgxwigsLHS0//jjj8YVV1xhSDImTJjg1ntcuXKlIcno3r27kZOT42g/c+aM0axZM0OS0bdvX6dj5s2bZ0gyPvnkE5fXa9q0aU7zeuONNwxJxhVXXGEMGTLE6TyfffaZIcm4+eabncZasWKFIckYPny4ceHCBZfnf/bZZ12ef8qUKUZBQYGj/eDBg0ZwcLBx9dVXO/WPjIw0GjVqZJw/f77EdTl16pTT6/j4eCM+Pt6prao/68zMTMNisRidO3d2ej/FTp8+XaINCCQEHqh2iv9IuNpatWplrFy5ssQxiYmJhsViMQ4dOlRi3yuvvGJIMiZNmuRoK/5jFB8f7/KPS5MmTYx69eqVOEdQUJDx3//+t0T/4j/K7gYe/fv3NyQZ//nPf0rsK/7j70ngUatWrRLBWEFBgVGjRg1DkvHDDz+UOE9CQoKRkJDg1NahQwejRo0axtmzZ0v0LygoMOrVq2d06dLF5fmzsrJKHNOnTx9DknHu3DlHW2RkpJGQkGDk5uaW6H8pV4FHVX/WWVlZhiSjZ8+eRlFRUblzBAINpRZUW4ZhOP59/vx5HTx4UA8++KDGjh2rgwcPatGiRZJ+XXfx3//+V40aNVLLli1LjHP99ddLkr788ssS+zp06KDg4OAS7XFxcdq1a5fjdfE54uLiXC54vO6667RgwQK339v+/fsVFBSkXr16uRzLUy1atFCdOnWc2oKDg9WgQQOdP39ezZo1K3FMo0aNtHv3bsfrCxcu6Ouvv1ZUVJSeffZZl+exWq0ub2e+8sorZbPZSrTHxcVJks6ePavw8HBJ0tixY/X3v/9drVq10ujRo9W3b1/16NFDERER5b5PX3zWNptNQ4cO1aZNm9ShQwfdcsst6t27t7p166ZatWqVO0fA7Ag8AEm1a9dW165d9fbbb6tx48b661//qjvuuENxcXHKysqSJMXExLg8trg9MzOzxL66deu6PKZGjRpOC0aLz9GgQQOX/Rs2bOjuW3GMFxkZqZo1a1Z6LEml/tGuUaNGmfsKCgocr8+ePSvDMHTq1CmPgiip7OsoSYWFhY62v/3tb2rWrJlWrFihJ554Qk888YRq1KihG2+8UU8//bQSExNLPY8vPmtJWrNmjZYsWaJVq1Y5busODQ3VyJEj9dRTT5X6cwAEAu5qAX6jbt26uuqqq1RQUKD9+/dL+r8/uunp6S6POX78uFO/iig+9sSJEy73l3bussbLyMjQxYsXKz2WtxS/x44dO8r4tcxb6lYZwcHBmj17tr7++mudOHFCGzZs0PDhw7Vx40YNHDiw1Lt6fjvHqvysJSksLEzz589XSkqKfv75Z7355pvq1auX3nzzTY0cObJSYwOXOwIP4BJnz56VJMf/pdapU0fNmzdXWlqavv/++xL9P/nkE0lSp06dKnzOOnXqKDExUWlpafrhhx9K7Pf0MeadOnVSUVGRduzYUemxvCU8PFytW7fWwYMHlZGR4ZNz1q9fXyNGjNDatWt1/fXX64cfftC3335ban9ffNaXiouL09ixY7V582YlJiZqx44dOnPmjNfGBy43BB7Ab7z77rs6cuSIatasqZ49ezraJ0+eLMMwdP/99zul9U+fPq2FCxc6+lTGpEmTVFRUpD/96U9OqfkjR47oueee83gs6ddbS3Nzcx3tGRkZeuyxxyo1z8qYM2eO8vPzNXnyZJflirNnzzoyTRWRl5ennTt3lmi/ePGiI9gpbx1FVX/Wp06dcnlL8/nz55Wdna0aNWooJCSkwuMDlzvWeKDa+u2TS8+fP6/vvvtOH374oSTp8ccfd6qz33ffffrwww/13nvvqX379rrxxht14cIFrVu3TidPntQDDzzgciGnJ+699169++672rBhgzp16qQBAwYoMzNTa9euVZ8+fbRx40a3x7r11lu1Zs0abdy4UW3atNFNN92kixcvav369erSpYvLrIovTJ48Wf/zP/+j559/Xs2bN9eAAQPUpEkTZWRk6MiRI/rss880adIkvfDCCxUaPycnR7169VJiYqI6d+6s+Ph45ebmasuWLTp06JCGDRumq6++uswxqvqzTktLU8eOHdW2bVu1a9dOcXFxstvtev/995Wenq5Zs2aVWMgLBBT/3EwD+I9c3EYbHBxsNGzY0Bg2bJjx8ccfuzwuJyfHWLRokdG6dWsjNDTUCA8PN6699lpj1apVJfoW32JZ2u2vffv2NVz955eVlWXcc889RmxsrGG1Wo2rrrrKeOqpp4wffvjBo9tpDcMw8vLyjAULFhhNmzY1QkJCjPj4eOPPf/6zkZub6/HttJf2LebqdtTy3qNhGMamTZuMwYMHG9HR0UbNmjWNBg0aGF26dDEeeuihErexlnX+CRMmGJKMI0eOGIZhGPn5+caSJUuMgQMHGnFxcYbVajWioqKMbt26GcuXLzfy8vLcmn9VftZnz541FixYYPTr18+IjY01QkJCjIYNGxp9+/Y1Vq1axS22CHgWw6jkSi4AAAA3scYDAAD4TEAFHgcPHtSoUaPUrFkz1apVS1FRUerTp482bdpUou+hQ4c0cOBAhYeHKzIyUrfffrvLLwYDAKC6mzhxoiwWS6lbWlqa22MFVKnlgw8+0HPPPacePXooNjZWFy5c0IYNG7R9+3a9+OKLmjp1qiTpl19+UceOHRUREaFZs2YpOztbTz31lJo0aaI9e/awohwAgN/YtWtXiUXphmHojjvuUEJCgg4ePOj2WAEVeLhSWFiozp07Kzc3V4cPH5Yk3XXXXXrttdd0+PBhNWnSRNKv30r6+9//3ilAAQAAru3YsUO9e/fWokWL9Oc//9nt4wKq1OJKcHCw4uLinJ4ZsGHDBg0ZMsQRdEhS//791aJFC61du9YPswQAwFxWrVoli8Wi2267zaPjAvI5HufPn1dOTo6ysrK0ceNGffjhh0pKSpL06z30J0+e1DXXXFPiuK5du+qDDz7w9XQBADCVixcvau3aterZs6cSEhI8OjYgA497771XL774oiQpKChII0aM0LJlyyT933ctuPoSqJiYGGVkZCgvL09Wq7XE/ry8PKfveSgqKlJGRobq1asni8VSFW8FAHCZMgxD586dU2xsrIKCqr6AkJubq/z8fK+NZxhGib9dVqvV5d+/S23evFlnzpzR2LFjPT5vQAYes2fP1siRI3Xs2DGtXbtWhYWFjg8rJydHklxe2NDQUEcfV/sXL17s8bdqAgACW2pqqho3blyl58jNzVXTpk29+iWP4eHhys7OdmqbN2+e01OdS7Nq1SrVrFlTo0eP9vi8ARl4tGzZUi1btpQkjR8/XjfccIOGDh2q3bt3KywsTJJcfkNl8XdaFPe51Ny5czVnzhzH66ysLDVp0kShksrKd9zhxpzne/7ZARX3sjvfgPpq+V32ufktrS+61w3wlfluLOcr78H9hqRcySePuM/Pz1d6erpSU4/IZrNVejy73a64uKZKTU11Gs+dbEd2drbee+89DRgwQPXq1fP43AEZeFxq5MiRmjZtmlJSUhwlluKSy28dP35ckZGRpV740lJQFpUdeJT/MUq2mm50ArzFrR84N365hbt5Pn6+cZlx5/eyuwV0X5babTabVwKPyoz37rvv6sKFCxUqs0jVJPAoLq9kZWXpqquuUnR0tPbt21ei3549e9ShQwcfzw4AAHcV/O/mjXEqZuXKlQoPD9ewYcMqdHxA3U578uTJEm0XL17UG2+8obCwMLVq1UqSdMstt+j9999Xamqqo9/WrVuVkpKiUaNG+Wy+AAB4psCLm+dOnTql5ORkDR8+XLVq1arQGAGV8Zg2bZrsdrv69OmjRo0aKT09XStXrtThw4f19NNPKzz817zwn//8Z61bt079+vXT3XffrezsbD355JNq27atJk2a5Od3AQDA5WnNmjUqKCiocJlFCrDAIykpSa+++qqWL1+uM2fOqE6dOurcubOWLFnilBKKi4vTp59+qjlz5ujBBx9USEiIBg8erKefftqthTUAAPiHf0stK1euVP369dW/f/8KnzmgAo8xY8ZozJgxbvVt3bq1Nm/eXMUzAgDAmwrlncCjsEJH7dq1q9JnDqg1HgAA4PIWUBkPAAACm//vaqksAg8AAEzD/IEHpRYAAOAzZDwAADAN82c8CDwAADCNQlX0jpSS4/gHpRYAAOAzZDwAADAN/z7HwxsIPAAAMA3zr/Gg1AIAAHyGjAcAAKZh/owHgQcAAKZh/sCDUgsAAPAZMh4AAJgGd7UAAACfodQCAADgNjIeAACYhvkzHgQeAACYhvkDD0otAADAZ8h4AABgGubPeBB4AABgGua/nZZSCwAA8BkyHgAAmAalFgAA4DPmDzwotQAAAJ8h4wEAgGmYP+NB4AEAgGmYP/Cg1AIAAHyGjAcAAKZh/ud4EHgAAGAahfJO0MADxAAAQDVAxgMAANMw/+JSAg8AAEzD/IEHpRYAAOAzZDwAADAN7moBAAA+Q6kFAADAbWQ8AAAwDfNnPAg8AAAwDfMHHpRaAACAz5DxAADANMyf8SDwAADANMx/Oy2lFgAA4DNkPAAAMI0CScFeGsc/CDwAADAN8wcelFoAAIDPkPEAAMA0zJ/xIPAAAMA0uKsFAADAbWQ8AAAwjQJ5J2fA4lIAAFCuAi9untm/f7+GDRumyMhI1apVS23atNFzzz3n8ThkPAAAQJk+/vhjDR06VB07dtQjjzyi8PBw/fDDD/rll188HovAAwAA0/B9qcVut2v8+PEaPHiw1q9fr6Cgyp2fwAMAANMolHfuSHF/jFWrVunEiRNatGiRgoKCdP78eYWFhVU4AGGNBwAAKFVycrJsNpvS0tJ01VVXKTw8XDabTXfeeadyc3M9Ho+MBwAApuHd53jY7XanVqvVKqvV6tT2/fffq6CgQDfddJOmTJmixYsXa9u2bfr73/+uzMxMrV692qMzk/EAAMA0vHtXS1xcnCIiIhzb4sWLS5wxOztbFy5c0Pjx4/Xcc89pxIgReu655zRt2jS99dZb+v777z16B2Q8AACoplJTU2Wz2RyvL812SFJYWJgk6dZbb3Vqv+222/Tiiy9q165duvLKK90+J4EHAACmUSDJ4qVxJJvN5hR4uBIbG6uDBw+qQYMGTu3169eXJJ09e9ajM1NqAQDANHz/ALHOnTtLktLS0pzajx07JkmKjo726B0QeAAAgFKNHj1akvTqq686tb/yyiuqUaOGrrvuOo/Go9QCAIBpeLfU4o6OHTtq8uTJ+uc//6mCggL17dtX27Zt07p16zR37lzFxsZ6dGYCDwAATKNQ3gk8PHsI2QsvvKAmTZpoxYoVeueddxQfH6+//e1vmj17tsdnJvAAAABlqlmzpubNm6d58+ZVeiwCDwAATMNbX2fvrXE8R+ABAIBpmD/w4K4WAADgM2Q8AAAwDfNnPAg8AAAwDc/uRqn6cTxHqQUAAPgMGQ8AAEyjQJLhhXH8l/Eg8AAAwDTMH3hQagEAAD5DxgMAANMwf8aDwAMAANMwf+BBqQUAAPgMGQ8AAEyjUN7JeBR5YYyKIfAAAMA0zB94UGoBAAA+Q8YDAADTKJB3cgaUWgAAQLnMH3hQagEAAD5DxgMAANMg4+FVe/fu1YwZM9S6dWvVrl1bTZo00ejRo5WSkuLoU1RUpNdee03Dhg1TXFycateurTZt2uixxx5Tbm6uW+e57rrrZLFYSmwDBw6sqrcGAIAXFOrX4KOyG08ulSQtWbJEO3fu1KhRo9SuXTulp6dr2bJl6tSpk7744gu1adNGFy5c0KRJk9S9e3fdcccdql+/vnbt2qV58+Zp69at+s9//iOLxVLuuRo3bqzFixc7tcXGxlbVWwMAALrMAo85c+Zo1apVCgkJcbQlJSWpbdu2euKJJ/Tmm28qJCREO3fuVM+ePR19/vjHPyohIcERfPTv37/cc0VERGjcuHFV8j4AAKgaBZLK/5/r8nnjWSAVc1mVWnr27OkUdEjSlVdeqdatW+vQoUOSpJCQEKego9jw4cMlydHPHQUFBcrOzq7EjAEA8CVvlFmKN/+4rAIPVwzD0IkTJxQVFVVmv/T0dEkqt1+xlJQU1a5dW3Xq1FHDhg31yCOP6OLFi2Uek5eXJ7vd7rQBAAD3XValFldWrlyptLQ0Pfroo2X2++tf/yqbzaZBgwaVO2bz5s3Vr18/tW3bVufPn9f69ev12GOPKSUlRWvWrCn1uMWLF2vBggUevwcAALzD/KWWyzrwOHz4sKZPn64ePXpowoQJpfZ7/PHHlZycrOeff15169Ytd9xXX33V6fXtt9+uqVOn6uWXX9Y999yj7t27uzxu7ty5mjNnjuO13W5XXFyce28GAIDKMoq8EzP4L+64fEst6enpGjx4sCIiIrR+/XoFBwe77LdmzRo9/PDDmjJliu68884Kn+/ee++VJCUnJ5fax2q1ymazOW0AAMB9l2XGIysrS4MGDVJmZqa2b99e6m2uW7Zs0fjx4zV48GC98MILlTpnceYiIyOjUuMAAFBliuSdZ3/57/lhl1/gkZubq6FDhyolJUXJyclq1aqVy367d+/W8OHDdc0112jt2rWqUaNyb+XHH3+UJEVHR1dqHAAAqkyhvPPsL/89P+zyKrUUFhYqKSlJu3bt0rp169SjRw+X/Q4dOqTBgwcrISFB77//vsLCwkod8/Dhw/r5558dr+12u/Ly8pz6GIahxx57TJI0YMAAL7wTAADgymWV8bj33nu1ceNGDR06VBkZGXrzzTed9o8bN07nzp3TgAEDdPbsWd1///3697//7dSnefPmTgHL1Vdfrb59+2rbtm2SpP379+vWW2/VrbfeqsTEROXk5Oidd97Rzp07NXXqVHXq1KnK3ycAABUSABmPyyrw+OqrryRJmzZt0qZNm0rsHzdunM6cOaPU1FRJ0oMPPliiz4QJE0rNlEhSfHy8evfurXfeeUfp6ekKCgrS1VdfrRdeeEFTp071zhsBAKAqsMbDu4qzEmVJSEiQYbh/H9ClfZs2baq1a9d6OjUAAOAFl1XgAQAAykCpBQAA+EwAlFouq7taAABAYCPjAQCAWRTJO2USFpcCAIByBcAaD0otAADAZ8h4AABgFgGwuJTAAwAAs6DUAgAA4D4yHgAAmEUAZDwIPAAAMIsAWONBqQUAAPgMGQ8AAMyCUgsAAPAZQ94pk7j/Je9eR6kFAAD4DBkPAADMglILAADwmQAIPCi1AAAAnyHwAADALIq8uHlg27ZtslgsLrcvvvjCo7EotQAAYBZ+LrXMmjVLXbp0cWpLTEz0aAwCDwAA4JbevXtr5MiRlRqDwAMAALO4DBaXnjt3TmFhYapRo2IhBGs8AAAwCy+v8bDb7U5bXl5emaefNGmSbDabQkND1a9fP+3bt8/jt0DgAQBANRUXF6eIiAjHtnjxYpf9QkJCdMstt2jp0qV677339Nhjj+mbb75R79699eWXX3p0TkotAACYRZG8U2r534xHamqqbDabo9lqtbrs3rNnT/Xs2dPxetiwYRo5cqTatWunuXPn6qOPPnL71AQeAACYRQVuhS11HEk2m80p8PBEYmKibrrpJr399tsqLCxUcHCwW8dRagEAABUSFxen/Px8nT9/3u1jyHgAAGAWl8FdLb/1448/KjQ0VOHh4W4fQ8YDAACzKPTi5oFTp06VaPv666+1ceNG3XDDDQoKcj+cIOMBAADKlJSUpLCwMPXs2VP169fXd999p5deekm1atXSE0884dFYBB4AAJiFlxeXuuvmm2/WypUr9cwzz8hutys6OlojRozQvHnzeGQ6AAABy09rPGbNmqVZs2Z54cSs8QAAAD5ExgMAALO4zO5qqQgCDwAAzMKQd9Z4GF4Yo4IotQAAAJ8h4wEAgFlQagEAAD7jp9tpvYlSCwAA8BkyHgAAmAWlFgAA4DMBEHhQagEAAD5DxgMAALMIgMWlBB4AAJgFpRYAAAD3kfEAAMAsiuSdbAWlFgA+tWd1+X26rqr6eQDwTACs8aDUAgAAfIaMBwAAZhEAi0sJPAAAMAtKLQAAAO4j4wEAgFlQagEAAD4TAIEHpRYAAOAzZDwAADCLAFhcSuABAIBZBMCTSym1AAAAnyHjAQCAWVBqAQAAPsNdLQAAAO4j4wEAgFkEQMaDwAMAALMIgDUelFoAAIDPkPEAAMAsKLUAAACfCYDAg1ILAADwGTIeAACYhSHvLAw1vDBGBRF4AABgFpRaAAAA3EfGAwAAswiA53gQeAAAYBaUWgAAANxHxgMAALMIgIwHgQcAAGYRAGs8KLUAAACfIeMBAIBZUGoBAAA+UyTvBA2UWgAAQHVAxgMAALNgcSkAAID7yHgAAGAWLC4FAAA+Q6nl8pKdna158+Zp4MCBioyMlMVi0WuvvVai38SJE2WxWEpsLVu29P2kAQAwmUWLFslisahNmzYeHxtQGY/Tp0/r0UcfVZMmTdS+fXtt27at1L5Wq1WvvPKKU1tEREQVzxAAgEq4DEotv/zyix5//HHVrl27QscHVOARExOj48ePq2HDhtq3b5+6dOlSat8aNWpo3LhxPpwdAACVdBkEHvfdd5+6d++uwsJCnT592uPjK1RqiYyM1AcffFCRQ6uU1WpVw4YN3e5fWFgou91ehTMCACBwfPbZZ1q/fr2effbZCo9RocAjMzNTmZmZpe7fv3+//vGPf1R0Tj5x4cIF2Ww2RUREKDIyUtOnT1d2dnaZx+Tl5clutzttAAD4TJEXN6nE37S8vLxST11YWKiZM2fqD3/4g9q2bVvht+B24LFz506tX79eP/74oyTJYrGU2vfQoUOaNWtWhSdV1WJiYvTAAw9oxYoVWr16tYYNG6bnn39eAwcOVEFBQanHLV68WBEREY4tLi7Oh7MGAFR7xY9Mr+z2v4FHXFyc09+1xYsXl3rqF154QT/99JMWLlxYqbfg9hqP//znP5o3b57jDpAFCxYoOTlZ7dq1U7t27dS+fXtFRkZKko4dO6bw8PBKTawqXXphx4wZoxYtWuihhx7S+vXrNWbMGJfHzZ07V3PmzHG8ttvtBB8AANNKTU2VzWZzvLZarS77nTlzRn/5y1/0yCOPKDo6ulLndDvj8cgjj+jw4cP617/+JcMwFBISoq1bt+qee+7R7373O0VHR6tx48bq3r27/vKXv+jaa6+t1MR87Z577lFQUJCSk5NL7WO1WmWz2Zw2AAB8xhvZjt8sUL30b1ppgcfDDz+syMhIzZw5s9JvwaO7Wlq0aKEWLVpo6dKlevDBBzV8+HDZ7XYdOHDAsf3888+aPHmyHn744UpPzpfCwsJUr149ZWRk+HsqAAC45ocHiH3//fd66aWX9Oyzz+rYsWOO9tzcXF28eFFHjx6VzWZzVD3KU6HbaXfv3u34t81mU69evdSrV6+KDHXZOHfunE6fPl3pFBIAAIEkLS1NRUVFmjVrlsv1m02bNtXdd9/t9p0uAfUcD3cUR2h16tRxal+4cKEMw9DAgQP9NDMAAMpRKO88c9yD53i0adNG77zzTon2hx9+WOfOndPSpUvVvHlzt8cLuMBj2bJlyszMdKSDNm3apF9++UWSNHPmTJ09e1YdO3bUrbfe6nhE+ubNm/XBBx9o4MCBuummm/w2dwAAyuSHUktUVJRuvvnmEu3FGQ5X+8oScIHHU089pZ9++snx+u2339bbb78tSRo3bpzq1q2rIUOGaMuWLXr99ddVWFioxMREPf7447rvvvsUFBRQX18DAMBlJeACj6NHj5bb51//+lfVTwQAAG/zQ6mlNGV9H1pZAi7wAAAgYF1GgUdFUVcAAAA+Q8YDAACzMOSdxaWGF8aoIAIPAADMolBS6V+V5tk4fkKpBQAA+AwZDwAAzCIAMh4EHgAAmIUfHiDmbQQeQHX0nBt93qzyWQCohgg8AAAwC0otAADAZwKg1MJdLQAAwGfIeAAAYBaUWgAAgM8UyTtBA6UWAABQHZDxAADALIrknVILz/EAAADl8tbaDL6rBQAAVAdkPAAAMIsAyHgQeAAAYBYBsMaDUgsAAPAZMh4ASnGbvycA4FKUWgAAgM9QagEAAHAfGQ8AAMzCW5kKHiAGAADKVSjJ8MI4lFoAAEB1QMYDgGt7Vpff57mqnwaA36DUAgAAfIZSCwAAgPvIeAAAYBYBkPEg8AAAwCwCYI0HpRYAAOAzZDwAADCLInmn1OKNMSqIwAMAALPw1ne1+DHwoNQCAAB8howHAABmUSjTZzwIPAAAMIsACDwotQAAAJ8h4wEAgFkEwOJSAg8AAMyCUgsAAID7yHgAAGAWAZDxIPAAAMAsDPk1aPAGSi0AAMBnyHgAAGAShf+7eWMcfyHwAADAJAIh8KDUAgAAfIaMBwAAJlH0v5s3xvEXAg8AAEyCUgsAAIAHCDwAADCJIi9unjh48KBGjRqlZs2aqVatWoqKilKfPn20adMmj98DpRYAAEzCX6WWn376SefOndOECRMUGxurCxcuaMOGDRo2bJhefPFFTZ061e2xCDwAAECZbrzxRt14441ObTNmzFDnzp31zDPPeBR4UGoBAMAkivR/WY/KbN64qyU4OFhxcXHKzMz06DgyHgAAmIS3b6e12+1O7VarVVartdTjzp8/r5ycHGVlZWnjxo368MMPlZSU5NG5yXgAAFBNxcXFKSIiwrEtXry4zP733nuvoqOjlZiYqPvuu0/Dhw/XsmXLPDonGQ8AAEzC24tLU1NTZbPZHO1lZTskafbs2Ro5cqSOHTumtWvXqrCwUPn5+R6dm8ADAACT8HbgYbPZnAKP8rRs2VItW7aUJI0fP1433HCDhg4dqt27d8tisbg1BqUWAABQISNHjtTevXuVkpLi9jFkPAAAMInL7btacnJyJElZWVluH0PGAwAAk/DGrbQVKdecPHmyRNvFixf1xhtvKCwsTK1atXJ7LDIeAACgTNOmTZPdblefPn3UqFEjpaena+XKlTp8+LCefvpphYeHuz0WgQcAACbhr1JLUlKSXn31VS1fvlxnzpxRnTp11LlzZy1ZskTDhg3zaCwCDwAATKL4yaXeGMcTY8aM0ZgxY7xwZtZ4AAAAHyLjAQCASfjr22m9icADAACTuNxup60ISi0AAMBnyHgAAGASlFoAAIDPBELgQakFAAD4DBkPAABMIhAWlxJ4AABgEpRaAAAAPEDGAwAAkzDknTKJ4YUxKorAAwAAk6DUAgAA4AEyHgAAmEQgZDwIPAAAMIlAuJ2WUgsAAPAZMh4AAJgEpRYAAOAzgRB4mLbUMnHiRFksllK3tLS0Uo+dP3++y2NCQ0N9+A4AAKh+TJvxmDZtmvr37+/UZhiG7rjjDiUkJKhRo0bljrF8+XKFh4c7XgcHB3t9ngAAeEsgLC41beDRo0cP9ejRw6ltx44dunDhgsaOHevWGCNHjlRUVFRVTA8AAK8rknfKJNzV4iWrVq2SxWLRbbfd5lZ/wzBkt9tlGP58eCwAANVHwAQeFy9e1Nq1a9WzZ08lJCS4dUyzZs0UERGhOnXqaNy4cTpx4kSZ/fPy8mS32502AAB8pciLm7+YttRyqc2bN+vMmTNulVmuuOIKzZgxQz169JDVatX27dv1j3/8Q3v27NG+fftks9lcHrd48WItWLDA21MHAMAtgXBXS8AEHqtWrVLNmjU1evTocvvefffdTq9vueUWde3aVWPHjtXzzz+vBx980OVxc+fO1Zw5cxyv7Xa74uLiKjdxAACqkYAotWRnZ+u9997TgAEDVK9evQqNcdttt6lhw4ZKTk4utY/VapXNZnPaAADwlUIvbv4SEBmPd99916O7WUoTFxenjIwML80KAADvCoTbaQMi47Fy5UqFh4dr2LBhFR7DMAwdPXpU0dHRXpwZAAD4LdMHHqdOnVJycrKGDx+uWrVqldj/888/6/DhwyWOudTy5ct16tQpDRw4sMrmCgBAZVBquQysWbNGBQUFpZZZxo8fr08//dTpWR3x8fFKSkpS27ZtFRoaqh07duitt95Shw4dNG3aNF9NHQAAj3BXy2Vg5cqVql+/fonHp5dl7Nix+vzzz7Vhwwbl5uYqPj5eDzzwgB566CGXWRMAAOAdpg88du3aVeb+bdu2lWh7+eWXq2g2AABUHUPeWRjqz+d1mz7wAACgugiEUovpF5cCAADzIOMBAIBJBMJzPAg8AAAwCUotAAAAHiDjAQCASQRCxoPAA4Brz/l7AgAuFQhrPCi1AAAAnyHjAQCASVBqAQAAPlMk7wQNlFoAAEC1QMYDAACTCITFpQQeAACYRCCs8aDUAgAAfIaMBwAAJkGpBQAA+AylFgAAAA+Q8QAAwCTIeAAAAJ8p8uLmib1792rGjBlq3bq1ateurSZNmmj06NFKSUnx+D2Q8QAAAGVasmSJdu7cqVGjRqldu3ZKT0/XsmXL1KlTJ33xxRdq06aN22MReAAAYBL+emT6nDlztGrVKoWEhDjakpKS1LZtWz3xxBN688033R6LwAMAAJPw1xqPnj17lmi78sor1bp1ax06dMijsQg8AACopux2u9Nrq9Uqq9Xq1rGGYejEiRNq3bq1R+dkcSkAACbh7cWlcXFxioiIcGyLFy92ey4rV65UWlqakpKSPHoPZDwAADAJb5daUlNTZbPZHO3uZjsOHz6s6dOnq0ePHpowYYJH5ybwAACgmrLZbE6BhzvS09M1ePBgRUREaP369QoODvboeAIPAABMwt/f1ZKVlaVBgwYpMzNT27dvV2xsrMdjEHgAAGAS/nxyaW5uroYOHaqUlBQlJyerVatWFTo3gQcAAChTYWGhkpKStGvXLr333nvq0aNHhcci8AAAwCT8lfG49957tXHjRg0dOlQZGRklHhg2btw4t8ci8AAAwCQMeWeNh+Fh/6+++kqStGnTJm3atKnEfgIPAADgNdu2bfPaWAQeAACYhD8Xl3oLgQcAACYRCIEHj0wHAAA+Q8YDAACT8PcDxLyBwAMAAJOg1AIAAOABMh4AAJgEpRYAAOAzlFoAAAA8QMYDAACTKJJ3shWUWgAAQLkCYY0HpRYAAOAzZDwAADCJQnknY8B3tQAAgHIFQuBBqQUAAPgMGQ8AAEwiEBaXEngAAGASlFoAAAA8QMYDAACToNQCAAB8JhCeXEqpBQAA+AwZDwAATKJQksVL4/gLgQcAACYRCGs8KLUAAACfIeMBAIBJUGoBAAA+EwiBB6UWAADgM2Q8AAAwiUBYXErgAQCASVBqAQAA8AAZDwAATMKQd8okhhfGqCgCDwAATMJbJRJKLQAAoFog4wEAgEkEQsaDwAMAAJMoknfuauG7WgAAQLVAxgMAAJOg1AIAAHwmEAIPSi0AAMBnyHgAAGASgbC4lMADAACT8FbAwF0tAACgWiDjAQCASQRCxoPAAwAAkyiUd77gjVILAACoFsh4AABgEoGQ8SDwAADAJAJhjQelFgAA4DNkPAAAMIlAKLWQ8QAAAD5TbQOPvLw8/elPf1JsbKzCwsLUrVs3bdmyxd/TAgCgVEX6NetR2c3TjEd2drbmzZungQMHKjIyUhaLRa+99lqF3kO1DTwmTpyoZ555RmPHjtXSpUsVHBysG2+8UTt27PD31AAAcKnIi5snTp8+rUcffVSHDh1S+/btK/UequUajz179uitt97Sk08+qfvuu0+SNH78eLVp00YPPPCAPv/8cz/PEACAy0dMTIyOHz+uhg0bat++ferSpUuFx6qWGY/169crODhYU6dOdbSFhoZqypQp2rVrl1JTU/04OwAAXPNGmaV484TValXDhg298A6qaeDx5ZdfqkWLFrLZbE7tXbt2lSR99dVXfpgVAABl81epxZuqZanl+PHjiomJKdFe3Hbs2DGXx+Xl5SkvL8/xOisrS1L5tzbllbNfkuwX3egEAPAKd34vl/e7vXi/YXjjBlf3eOtMxePY7XandqvVKqvV6qWzuFYtA4+cnByXFzY0NNSx35XFixdrwYIFJdpzyznf39yY09/WutEJAHDZOXPmjCIiIqr0HCEhIWrYsKHS09O9NmZ4eLji4uKc2ubNm6f58+d77RyuVMvAIywszClzUSw3N9ex35W5c+dqzpw5jteZmZmKj4/Xzz//XOU/dHDNbrcrLi5OqampJUpnqHpcf//i+vtXVlaWmjRposjIyCo/V2hoqI4cOaL8/HyvjWkYhiwWi1NbVWc7pGoaeMTExCgtLa1E+/HjxyVJsbGxLo8rLQUVERHBf/R+ZrPZ+Az8iOvvX1x//woK8s1yydDQUEdm3syq5eLSDh06KCUlpURta/fu3Y79AADA+6pl4DFy5EgVFhbqpZdecrTl5eVpxYoV6tatW4maFwAA8I5qWWrp1q2bRo0apblz5+rkyZNKTEzU66+/rqNHj+rVV191exyr1ap58+b5pCYG1/gM/Ivr719cf/+qbtd/2bJlyszMdNz5uWnTJv3yyy+SpJkzZ7q91tFi+PI+oMtIbm6uHnnkEb355ps6e/as2rVrp4ULF2rAgAH+nhoAAJedhIQE/fTTTy73HTlyRAkJCW6NU20DDwAA4HvVco0HAADwDwIPAADgMwQeAADAZwg83LR161ZNnjxZLVq0UK1atdSsWTP94Q9/cDx07FKff/65evXqpVq1aqlhw4aaNWuWsrOzfTzrwHH8+HE9+OCD6tevn+rUqSOLxaJt27aV2p/rX7Xy8vL0pz/9SbGxsQoLC1O3bt20ZcsWf08r4GRnZ2vevHkaOHCgIiMjZbFY9Nprr7nse+jQIQ0cOFDh4eGKjIzU7bffrlOnTvl2wgFm7969mjFjhlq3bq3atWurSZMmGj16tFJSUkr05fp7wIBbOnfubDRt2tR44IEHjJdfftmYO3euUadOHaNBgwbG8ePHnfp++eWXRmhoqNGxY0dj+fLlxkMPPWRYrVZj4MCBfpq9+X3yySeGJOPKK680evToYUgyPvnkE5d9uf5Vb8yYMUaNGjWM++67z3jxxReNHj16GDVq1DC2b9/u76kFlCNHjhiSjCZNmhjXXXedIclYsWJFiX6pqalGVFSU0bx5c2Pp0qXGokWLjCuuuMJo3769kZeX5/uJB4hbbrnFaNiwoTFz5kzj5ZdfNhYuXGg0aNDAqF27tvHNN984+nH9PUPg4aZPP/3UKCwsLNEmyXjooYec2gcNGmTExMQYWVlZjraXX37ZkGRs3rzZJ/MNNHa73Thz5oxhGIaxbt26MgMPrn/V2r17tyHJePLJJx1tOTk5RvPmzY0ePXr4cWaBJzc31/E/Nnv37i018LjzzjuNsLAw46effnK0bdmyxZBkvPjii76absDZuXNnicAhJSXFsFqtxtixYx1tXH/PUGpxU58+fUo8j79Pnz6KjIzUoUOHHG12u11btmzRuHHjnL47Yfz48QoPD9fatXwNbUXUqVPHrS9i4vpXvfXr1ys4OFhTp051tIWGhmrKlCnatWuXUlNT/Ti7wGK1WtWwYcNy+23YsEFDhgxRkyZNHG39+/dXixYt+JmvhJ49eyokJMSp7corr1Tr1q2dfu9z/T1D4FEJ2dnZys7OVlRUlKPtm2++UUFBga655hqnviEhIerQoYO+/PJLX0+zWuH6V70vv/xSLVq0KPGlZF27dpUkffXVV36YVfWVlpamkydPlviZl379TPiZ9y7DMHTixAnH732uv+cIPCrh2WefVX5+vpKSkhxtxYtNY2JiSvSPiYlxPGoWVYPrX/WOHz9e6vWVxDX2sfJ+5jMyMpSXl+fraQWslStXKi0tzfF7n+vvOQKPCvrss8+0YMECjR49Wtdff72jPScnR5JcPrs/NDTUsR9Vg+tf9XJyckq9vsX74Tvl/cz/tg8q5/Dhw5o+fbp69OihCRMmSOL6V0S1/JK4suTn5ysjI8OpLTo6WsHBwY7Xhw8f1vDhw9WmTRu98sorTn3DwsIkyWWEm5ub69gP19y5/mXh+le9sLCwUq9v8X74Tnk/87/tg4pLT0/X4MGDFRER4VjnJHH9K4LA4xKff/65+vXr59T22y+/SU1N1Q033KCIiAh98MEHqlOnjlPf4nSbq+d7HD9+XLGxsVUz8QBR3vUvD9e/6sXExCgtLa1Ee/E15xr7Vnk/85GRkdXm21OrSlZWlgYNGqTMzExt377d6Wec6+85Ao9LtG/fvsSDkIpXlZ85c0Y33HCD8vLytHXrVpc1vTZt2qhGjRrat2+fRo8e7WjPz8/XV1995dSGksq6/u7g+le9Dh066JNPPpHdbndaYLp7927HfvhOo0aNFB0drX379pXYt2fPHj6PSsrNzdXQoUOVkpKi5ORktWrVymk/178C/H0/r1lkZ2cbXbt2NerUqWPs27evzL4DBw40YmJiDLvd7mh75ZVXDEnGhx9+WNVTDXjlPceD61+1vvjiixLP8cjNzTUSExONbt26+XFmga2s53jccccdRlhYmPHzzz872pKTkw1JxvLly304y8BSUFBgDBs2zKhRo4bx73//u9R+XH/PWAzDMPwY95jGzTffrPfee0+TJ08uUQoIDw/XzTff7Hi9f/9+9ezZU61atdLUqVP1yy+/6Omnn1afPn20efNmH888cDz22GOSpIMHD+qtt97S5MmT1bRpU0nSww8/7OjH9a96o0eP1jvvvKN77rlHiYmJev3117Vnzx5t3bpVffr08ff0AsqyZcuUmZmpY8eOafny5RoxYoQ6duwoSZo5c6YiIiKUmpqqjh07qm7durr77ruVnZ2tJ598Uo0bN9bevXtJ9VfQ7NmztXTpUg0dOtRltnTcuHGSxPX3lL8jH7OIj483JLnc4uPjS/Tfvn270bNnTyM0NNSIjo42pk+f7vR/4PBcadff1Y8x179q5eTkGPfdd5/RsGFDw2q1Gl26dDE++ugjf08rIJX1u+fIkSOOft9++61xww03GLVq1TLq1q1rjB071khPT/ffxANA37593f69w/V3HxkPAADgMzzHAwAA+AyBBwAA8BkCDwAA4DMEHgAAwGcIPAAAgM8QeAAAAJ8h8AAAAD5D4AEAAHyGwAMAAPgMgQcAAPAZAg+gmpk/f74sFotOnz7ttTFfe+01WSwWHT161GtjXo7nBFB5BB5AFdm8ebMsFossFou+++67EvuHDh2qxo0b+2Fmgevzzz/X/PnzlZmZ6e+pACgFgQdQRb7++mtJUlBQkN5//32X+9u1a+fraVWJ22+/XTk5OYqPj/frOT///HMtWLCAwAO4jBF4AFXkwIEDstlsGjBggDZt2uS07+zZs0pNTVX79u39NDvvCg4OVmhoqCwWS0CfE0DlEXgAVeTrr79W27ZtNWTIEO3atUtnzpxx2iepSjIeaWlpmjJlimJjY2W1WtW0aVPdeeedys/Pd+qXmZmpiRMnqm7duoqIiNCkSZN04cIFpz4//fST7rrrLl111VUKCwtTvXr1NGrUqBLrKlyttyheS/Lf//633PNc6ty5c5o9e7YSEhJktVpVv359/f73v9f+/ftLPef8+fN1//33S5KaNm3qKHMV709LS9PkyZPVoEEDWa1WtW7dWv/85z89Pi+Ayqnh7wkAgSg/P1//7//9P/3xj3/UkCFDNH36dH3wwQe6/fbbJf2aDZHk9YzHsWPH1LVrV2VmZmrq1Klq2bKl0tLStH79el24cEEhISGOvqNHj1bTpk21ePFi7d+/X6+88orq16+vJUuWOPrs3btXn3/+ucaMGaPGjRvr6NGjWr58ua677jp99913qlWrVrlzcuc8l7rjjju0fv16zZgxQ61atdKZM2e0Y8cOHTp0SJ06dXJ5zIgRI5SSkqLVq1frb3/7m6KioiRJ0dHROnHihLp37y6LxaIZM2YoOjpaH374oaZMmSK73a7Zs2dX+LwAPGQA8Lovv/zSkGS88MILhmEYRtu2bY1Ro0Y59k+ePNmwWq1GQUGBV887fvx4IygoyNi7d2+JfUVFRYZhGMa8efMMScbkyZOd9g8fPtyoV6+eU9uFCxdKjLNr1y5DkvHGG2842lasWGFIMo4cOeJo8+Q8l4qIiDCmT59eZh9X53zyySdLtBmGYUyZMsWIiYkxTp8+7dQ+ZswYIyIiwvE+3TkvgMqh1AJUgeKMRnEpZciQIdq8ebMuXrwo6ddSS+vWrRUcHCxJysvLU4MGDWS320sds6ioSI0aNdKJEydK3f/uu+9q6NChuuaaa0rsv3QtxB133OH0unfv3jpz5ozTHMLCwhz/vnjxos6cOaPExETVrVvX7fKDO+e5VN26dbV7924dO3bMrXOUxTAMbdiwQUOHDpVhGDp9+rRjGzBggLKyshzvxZvnBeAagQdQBb7++mtZLBa1bdtW0q+Bh91u12effabCwkIdPHjQaX2H1WrViRMnZLPZSh0zKChIaWlpatCggcv9p06dkt1uV5s2bdyaY5MmTZxeX3HFFZJ+XfhaLCcnR3/5y18UFxcnq9WqqKgoRUdHKzMzU1lZWV47z6X++te/6ttvv1VcXJy6du2q+fPn68cff3TrfJc6deqUMjMz9dJLLyk6OtppmzRpkiTp5MmTXj8vANdY4wFUgQMHDqhZs2YKDw+XJHXv3l1RUVHatGmTYmNjlZub6/c7WoqzLZcyDMPx75kzZ2rFihWaPXu2evTooYiICFksFo0ZM0ZFRUVeO8+lRo8erd69e+udd97Rxx9/rCeffFJLlizR22+/rUGDBrl13mLF8xw3bpwmTJjgsk9xEOjN8wJwjcADqAIHDhzQtdde63gdFBSkQYMGadOmTerevbsk5ztali5dqgMHDujVV18tdcyXXnpJ27Zt06pVq1zuj46Ols1m07fffuuldyGtX79eEyZM0NNPP+1oy83N9clzMmJiYnTXXXfprrvu0smTJ9WpUyctWrSozADA1a210dHRqlOnjgoLC9W/f/8qOS8A91FqAbwsPT1dJ0+eLJHRGDJkiH788UetXr1akvMdLQcOHCj31tqDBw+WWUYJCgrSzTffrE2bNmnfvn0l9peVYShNcHBwieP+/ve/q7Cw0OOx3FVYWFiijFO/fn3FxsYqLy+vzGNr164tSU6BUXBwsG655RZt2LDBZVB26tSpSp8XgPvIeABeVtozOgYMGKCaNWs6yi316tVz7Dtw4IDGjRtX5rgHDx7U9ddfX2afxx9/XB9//LH69u2rqVOn6uqrr9bx48e1bt067dixQ3Xr1vXovQwZMkT/+te/FBERoVatWmnXrl1KTk52mru3nTt3To0bN9bIkSPVvn17hYeHKzk5WXv37nXKvLjSuXNnSdJDDz2kMWPGqGbNmho6dKieeOIJffLJJ+rWrZv++Mc/qlWrVsrIyND+/fuVnJysjIyMSp0XgPsIPAAvu/SOlmIRERHq1auXPvnkE6d9RUVF+u6779zKeLRu3brMPo0aNdLu3bv1yCOPaOXKlbLb7WrUqJEGDRrk1jM3LrV06VIFBwdr5cqVys3N1bXXXqvk5GQNGDDA47HcVatWLd111136+OOP9fbbb6uoqEiJiYl6/vnndeedd5Z5bJcuXbRw4UK98MIL+uijj1RUVKQjR44oISFBe/bs0aOPPqq3335bzz//vOrVq6fWrVs7nidSmfMCcJ/FqEj+FYDXpKSk6LrrrivzFs6MjAw1btxY2dnZCgqiQgrAvPgNBvjZpes7Jk6cqIkTJzr1OXjwoK6++mqCDgCmx28xwM+++eYbp8Djl19+cbojRnKvzAIAZsAaD8DPFixY4Ph3QUGBjh07ViLjceDAAb8/9wMAvIE1HsBlLjs7W23bttXatWvVpUsXf08HACqFUgtwGdu9e7datGihESNGEHQACAhkPAAAgM+Q8QAAAD5D4AEAAHyGwAMAAPgMgQcAAPAZAg8AAOAzBB4AAMBnCDwAAIDPEHgAAACfIfAAAAA+Q+ABAAB8hsADAAD4zP8HyFUS3GYZf5QAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -558,12 +530,12 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -585,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -597,7 +569,7 @@ "\t method : DTDVP\n", "\t dt = 0.5\n", "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.46065886596178063, c2 = 0.4606588659617807, \n", + "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.8164965809277259, c2 = 0.8164965809277259, \n", "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", "\t convparams : 0.1\n", "\t options : Dlim = 10, savebonddims = true, verbose = false, \n", @@ -609,10 +581,10 @@ "\n", "# path to work from personal computer\n", "\n", - "path = \"/Users/ariva/Documents/fermions/obiSh/\"\n", + "path = \"/Users/ariva/Documents/fermions/sUuWp/\"\n", "\n", "# Results for alpha_lit=0.01\n", - "res_fol = h5py.File(path+\"/dat_obiSh.jld\", \"r\")[\"data\"]\n", + "res_fol = h5py.File(path+\"/dat_sUuWp.jld\", \"r\")[\"data\"]\n", "info = open(path+\"/info.txt\", \"r\")\n", "data = info.read()\n", "print(data)" @@ -620,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -630,7 +602,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[62], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m occ_fol \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mcolumn_stack((res_fol[\u001b[39m\"\u001b[39m\u001b[39msystem_occup\u001b[39m\u001b[39m\"\u001b[39m][()], res_fol[\u001b[39m\"\u001b[39;49m\u001b[39mfolded_chain_occup\u001b[39;49m\u001b[39m\"\u001b[39;49m][()]))\n\u001b[1;32m 3\u001b[0m \u001b[39mlist\u001b[39m(res_fol\u001b[39m.\u001b[39mkeys())\n", + "Cell \u001b[0;32mIn[92], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m occ_fol \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mcolumn_stack((res_fol[\u001b[39m\"\u001b[39m\u001b[39msystem_occup\u001b[39m\u001b[39m\"\u001b[39m][()], res_fol[\u001b[39m\"\u001b[39;49m\u001b[39mfolded_chain_occup\u001b[39;49m\u001b[39m\"\u001b[39;49m][()]))\n\u001b[1;32m 3\u001b[0m \u001b[39mlist\u001b[39m(res_fol\u001b[39m.\u001b[39mkeys())\n", "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/h5py/_hl/group.py:357\u001b[0m, in \u001b[0;36mGroup.__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInvalid HDF5 object reference\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 356\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(name, (\u001b[39mbytes\u001b[39m, \u001b[39mstr\u001b[39m)):\n\u001b[0;32m--> 357\u001b[0m oid \u001b[39m=\u001b[39m h5o\u001b[39m.\u001b[39;49mopen(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mid, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_e(name), lapl\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_lapl)\n\u001b[1;32m 358\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 359\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mAccessing a group is done with bytes or str, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 360\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnot \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mtype\u001b[39m(name)))\n", @@ -658,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -673,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 83, "metadata": { "scrolled": true }, @@ -682,69 +654,69 @@ "data": { "text/plain": [ "[1.0,\n", - " 0.9870176494540611,\n", - " 0.8919698620297826,\n", - " 0.7464379947477169,\n", - " 0.6161634018854681,\n", - " 0.5522477965356296,\n", - " 0.5666502795226769,\n", - " 0.6319453340780932,\n", - " 0.7024830060119678,\n", - " 0.7359934219639208,\n", - " 0.7124381207373258,\n", - " 0.6451042074839143,\n", - " 0.5673426216695034,\n", - " 0.5150195889022545,\n", - " 0.5095481614975812,\n", - " 0.5491013412596275,\n", - " 0.6114661181500118,\n", - " 0.6674654651247499,\n", - " 0.6950890994103728,\n", - " 0.6865314704334547,\n", - " 0.6482378623922909,\n", - " 0.5966290273779772,\n", - " 0.5512030497704176,\n", - " 0.526640953803126,\n", - " 0.5268942235928347,\n", - " 0.5443781568562909,\n", - " 0.565422871548877,\n", - " 0.5787781027272988,\n", - " 0.5818291881187583,\n", - " 0.5807038614072185,\n", - " 0.5853035895407249,\n", - " 0.6020620370781531,\n", - " 0.630270827167768,\n", - " 0.6624160353318125,\n", - " 0.68742027274086,\n", - " 0.6949376166445541,\n", - " 0.6796932962998955,\n", - " 0.6452235302947861,\n", - " 0.6038016695930509,\n", - " 0.5700765455894988,\n", - " 0.5536473344128495,\n", - " 0.5552398597383836,\n", - " 0.5676928952064143,\n", - " 0.580971003884192,\n", - " 0.58816789214906,\n", - " 0.5888815165361093,\n", - " 0.5887977273019762,\n", - " 0.595517048051502,\n", - " 0.6131930032441936,\n", - " 0.6386617799784853,\n", - " 0.6621951663634985,\n", - " 0.6718039292907736,\n", - " 0.6596157241929744,\n", - " 0.6268162711631243,\n", - " 0.5839235849677901,\n", - " 0.5455891197894346,\n", - " 0.5226057361360607,\n", - " 0.5161508113080157,\n", - " 0.5193841981281505,\n", - " 0.5243301594820952,\n", - " 0.528374719494914]" + " 1.0,\n", + " 1.0,\n", + " 0.9492303375801012,\n", + " 0.8221085853332134,\n", + " 0.6776828500834261,\n", + " 0.5720971694543087,\n", + " 0.5273477604814314,\n", + " 0.5261365902560947,\n", + " 0.5331311087158226,\n", + " 0.5240071064983182,\n", + " 0.500500369997204,\n", + " 0.4831515300615675,\n", + " 0.49133011998777504,\n", + " 0.5279373298700886,\n", + " 0.5791050367950586,\n", + " 0.625543266082979,\n", + " 0.6547108142994459,\n", + " 0.666001361425089,\n", + " 0.6681321792284813,\n", + " 0.6722056628395839,\n", + " 0.6849149978637048,\n", + " 0.7054750817005258,\n", + " 0.7274258471230926,\n", + " 0.743479474587771,\n", + " 0.7500925072701339,\n", + " 0.7491501723867717,\n", + " 0.7461678932365814,\n", + " 0.7465253372351754,\n", + " 0.7522534218517924,\n", + " 0.7612329507560163,\n", + " 0.7689431907676597,\n", + " 0.7713276667603857,\n", + " 0.7668640266921565,\n", + " 0.7566683468480119,\n", + " 0.7428839503819253,\n", + " 0.7267807275057369,\n", + " 0.7080566225327188,\n", + " 0.6857203388884013,\n", + " 0.6595988435402462,\n", + " 0.6311147021603905,\n", + " 0.6027238309646079,\n", + " 0.5765187693263762,\n", + " 0.5530535487834857,\n", + " 0.5311178350965653,\n", + " 0.5084556908686778,\n", + " 0.4770857131620225,\n", + " 0.4324425961703501,\n", + " 0.37923973111800374,\n", + " 0.33209156818307095,\n", + " 0.3087635822297634,\n", + " 0.3119192944956135,\n", + " 0.33370407426998305,\n", + " 0.36026524261149695,\n", + " 0.3782498515625513,\n", + " 0.38045564280831085,\n", + " 0.36846194185463504,\n", + " 0.3474060428171668,\n", + " 0.32955133728903474,\n", + " 0.3318857976652411,\n", + " 0.3574666398262629]" ] }, - "execution_count": 32, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -755,12 +727,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 84, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -776,12 +748,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 85, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] diff --git a/examples/plots/chain_occ_bet=2_double.pdf b/examples/plots/chain_occ_bet=2_double.pdf index a3816d1092480336068d914bb861515cbdd17aee..768b0f2cc0adb2fffb1322471db383f656632dcf 100644 GIT binary patch delta 13114 zcmb_>bySpH*FFuBN_T@aGYk`e0wN^>(%mT{-2x8XDP2+m(j|>BG}7Heiu6cH2;#@{ zJihPue7e@}uiw4yHEU+iIeYKx+Hux7XCf5iG6JJ22_0U^0Cir@Hg-y8WNzp=H;)en zGtvR$uowvvWa5HZZ7uG+p^x6b_`1b=7AV8$p0M?OAUP2;`ayHSiyED>KJAz8JLB4; zKhHO7x8Og%99@pCWHWANzx^oj>4f+CwG-u*_Qj7q-M8%>U#afo56b${jU&GF|` zB=-}iH=K&qZ`+trqk?XkpTQXYA!rJ7pQbU$yM56U zkjTnz6^&17{5Ompag^TuUSw>c+&CMc7G_?{(}U#q6J(+#8ipaj#I+0f8hl$&DEf#YZ)g_ zKJ=#H!1OH0s~s=DEGn}NdOaE$&|1|QI`q?r0`y)bc0{p^!bB?7he1b>XvqA@eA}BQ zZp%s+0Hmc4XQo#0!_2-sl~7sx$U^WF#?hECAK`Y@dnG(u#x}#38{{cxYI;^18L&fI-ga&h`7{E4zSVb97e<0%wCVUmyN89jInWykfzfYJ*#i~+EQI_!r04(?J#q++{jwKi5*BI z7@_h~cs;;9vVu>jnk0B)n4YY5;pg?nWQqfPRN|rc+2GFyKQ1?)Ib3q`;YFH5t4$gb zX54G=-s>H4&_ZQY?;5+984-B5b{d3A?q>QWvfCQ@kv}RCrdHZ{&Rj%ESYSoib2u51 zW%PkP_(Dsukg`2qr2K6*2S;gcKcwyrl&s^^lMKC}U>IvaL6V2GN+>I{pjrf%j7~Tv zoRu1#+(eW$)%;%nq#=K&6#cyp=8(u3Jd{q*_I*8R?!P4HBlHGxL5^G)1Q@zO`SSYV zfV(d&ZDQomokvGE-<`H)pJz%)EQjMo&#R|ih-<@F1dhUcI0IRQ(sS}Gg}l4PjY)42 zyiI&))WgZscf3M--CV)`U^@OKHx^u1sS)IZ|NK=I*UO4_W>gAxFD5t5LD2m>C|xQ& zb53mnmAmKIRkSKQwCH-l@nqy;yy*Dr{%G-#&OR>^lqQuZLeL5L@ILC<*Eh4?TfsEx zZFScn<=Vtm675v4-)IlD>r?6NRmW#=tn7A8C?`b*Y!j(9OTyN%`ce)RH1xbo zw%|E2Pfth=`@`#!+w5I|S&I>tSfJ=Xr`<9ZhJD%C!2hbi&v$ zJZJfpV>C_Py|v))P@s=PgaMlNF!@suIh;*#CM+@Xe!Ft67?|!Q$*10w(rGJ;M7vQg99$Na@J#13r3KiODzqH8s6|1Xv zA5*iYE zqt&ox4T4B~I^7$yQ4>#Wd3c4vHUs-TXMx(2Y_4NC|JSxxH@iD4uP<+Q7SBB#jM{!| z{!lZobbzT6dGUTw*L|V9NF^ra!1yh;*66)ywb%Y7zZn}ncR0V9n)O86m|E9eB7+_* zyX(d&l6U)(aSwCeo11|a{FlO>qXUfe=r$VbnPle`!k_3>s&YD*xh%lj=I~)y;4)_M zsa~b(SYycq7p@|<6EkJ8)P(?J*uBIcg0n(<3rrat;%%-}373VADamTY_Hw?bHc}J1 z;+>Et%C7b)fGYoBv|QC%2HVzoE(PukcwoDwj18r3W&VJT=@pE9D|7CwOCDcU%7Dill* zSA2!ROjLX+MRsI($;gvma(V_$+Ehf7MN&xSK5uXqd)9Q!uR#e%utafyc(D4Xe9-|R z4HgA=EJ@k}&2UBfGsoB?*xPem#M0=MO50V23>=WBCW*JO^T#Db?Cd&6Xo<+qXo z-jnY-zti{nA}=>aMN@$9#yeU%?Q6Yn0@*kD@-w@jHRx`8t8ekOaVH_GKz0u$zG){Z zxBTVR1|I*^G7KM1OyF1jU^(f2MNWv3;B=jT@0V9~*Av6D20YW$mCRKfVuHT9-1Qar$Fl8Gm*@PX1d4y*Ede#*e*@&y2Edt zjl|w)GOJmheUfIV;??RYFlhlUQHwK%hy^*kruY_{vfT?m{F%JwKbD2nTx%BnWy`gS za&puZ3yICEzdcfv=(X6 zvhE~|cfh#}{lzF$21;`@@1RulNRt7qiL1)8JmXaY+5uwRo8(Ka3xp`Nip=xW1}qN& z69@W%B+@0Bpl4Gis!6NLQat;DyFW;gPws682WL20g?IqX)HbX>X?s!;vQvzAlP*6Z z)hWy_vN#l&(3iEd^|aoBO)B`Jf6ckSwa?K?FbB_m>S;~>V~2EE*Ia{OnR`;oLny5q zyE#Fqek{vX$rVaclOrZ&>%H8T(_)^?*i5ifH8kX;fU;H-vSZ<^9}IqERi3Ht>}p}| zrywVHW>gX8n(g%^dLTZU=xD;W6~=|tnb1A@ct{O9^k>YWLL+rf1jGEj@N#K(VYb$B zHWT=`8`Fv$G#pGPg3B@STauzzBfb2Sox(T(|5d|qJh-vf{wmT zv+O)j6^p!H#VuPf3%^&m#l3dk_~|STbe9yT9$N?Z34S5;F0JJN8xFc?6qcc%9Co6` zkV=rORy4V1pA6Fv54!l}%L z<2jff=HLZ+9EwOXnSI9=*PheV&uYUU8Bg&WNWP$7!!6^{D5?x{G<8?izMw~webgE` zsg027wlTN$iJ8VAE>Y=lk;d9Ebq9;%zQ7=^uNU>HBdz5DKJ7Xbf%z@;1ayJKwb_XR zO3DvKSZh09>lm)ND0zRev*nOJ(Au%n z@gHS*9R#rq9?g22r+bgTYb%0KP*(qK+bcf&wlqjGx+}@Bx8Gx94A-rkedYE8gf7C;PNxOvHU!jJ7gp%e6gyfm9>3 z@*PS3x&fz!C(0x{ei<|AklunU@jRWO&3Vg+Sf5~m$+y*Y>=AV~$*$bFD~bEk&4CTp9R;j?2!B}^)F?ztz|>C&b< z-aTp^+KAJKxeQZ}R)r_h6hmp&qUZ@(^w0PMKllq%pj)*NysN}qop3cCg1h2|kmU;z z&OKtU5@?vXQ^uCXtPrJ>RbVEf;*!zE9=4%})47LPVYV%WqXrx}M7LL{4&d=bu1L?d zYQ<@3_lbFxgf*jPC$b+l3^dg{dZ+>a1KDFWIC*DC0J?#gJmCrjTVWxT{M_6_PK^hn49|)k#>T--&dTbJ zIl<6DNI`U5wbw!cAT$Y-jv(^2l92HOeRkTp8ghk%Nyv+e9`MAV<*_!(-{m|Jr#_n* zRHa_uMn6jqlIOC2PwJh3#>t7+p92lvdT)r)NBHWoJnnld-tBTsxYK)l&r*QylZQDx zt4dEhL-aM#9S8;R_%slU)Kd1&vJ-@!8?pWY)x7=YHJ27Wb{UQv8v zB-;E=>&}g-^jPA)Nvq=lg_p~O!12gPMMGllxjL0@DK_DR`$JKrbz6S&okK~U%o)%6 zQKB*_yH{26%}-|N(oxEj8N&VdT88G_WwJ@-cg#J)`-PNM8zD7fLKEWdX)m}_Wm3`> zp1~?pmf8EH;5kduI(|yq;OPU9ZH>_*YnMHdOL2$PyXz+Btf!VaPx~DSu*hZ^wAFMK zioVadQurhN&~31wiz{Ny0eB8_s~VVeL}fvSGipr&wYjZP+r{^Un=#qMa+DE9jR#z3 z@>b91S#6jDP7Nb48oJh15{OYEGK5_Wej6joa zNs@CekGpz$&%>sw57u3Jp$DTh9b#udc54Hs6{eH%lyta{NLOGLSOt^RIGa>s`sz|W z&+!olJx1$uqUlXKirt~ivG-`6=N!``nWM^?@ZD2Z(?`-)4d~3PLq&OC2sMrYX_OPS zQrE%qcw=fm{F#%ODC@xlMZ0$L-Yi#pZ_;N!SuF@pjBSIy1HK&<`^x=TRoLy}=j`6L zb3`0mn&%X#8Y^*4yb6n!I^hVWAXyjW{&De1N&ZYH}}W*?Gbvlrjw8 z5)La!A{!4Rx52_~&^*08NAM9r;B=&>i$0UZ1USoE_X0S@`F53K?f7IQW5AFs+Dv?Z z)0irp#NeKV^=3##XsTr#dY<_U{%< zGq|*iG7z~DaZ-^$BVP}Jv)oS>+*NV5>5%BiNPf19mcDJK^rQQX=*AiIXti@Z_iK6( zDA|Np|8&Azb+rkBs>vgZQ0D-;55%a7Zn#-~F2oeeey(2B$bsTxk?3K*wR4i2Oe zYw7Ler-2qlnLysMEa)fsd;%^a^;K`0!&mk3H@fVbH-!#;S4p=F&21OY8sMgPX1zF* zB-XmV7kpoR?RBUecvUbpd93?-CoRqBJ7*CIQ%y44pmXnZ1Sm_t^<8Du4p#diN%J!v zb_NI)qry4aM^MF3VGe82*zjaPGygkRweO9yupBcX`|KzqWxtN>T2h64&(bObIba)BY>n(AW2JUINRqDjouE*91xX&{TQB`xFMOHzQd&^vL-_2O&kujLEYe_b_{@ZZz=& z_bGmeO$L9_8Y57lhc?OScB{EZ+r&W3r|kYw3Jv zg`>oMa~{wXpPi0C`vhedvlrQN7qVRz| zUv*cGeXOu3P^)DgrGbs9Elz01adV^={Oiqg6NsOA9 zg_Pv({zR}oj{^phHo+A*y~vS}A;~nTPez|F^K=@Buqv!>W}fsD$#>haL{W(Fexa^&DBjIJwsEf5qIt5rkSRW#rTC0#}h z04{S7dnLTo(A95F7?tJ8?QrzUXPBNp&IaWkBL>g>XT_h!C|*5S?{ze93^7HZPzw*% z^W%Q@9*O{$qPrUh7@5xI_Nod-iR^x?J&(L8?xnSpNl>;YuyBh)*gxNkefXt0uVDl| z-T6mZ{pcIEVe#0;+Z}hshcxubmPH!5s;M(4-u2$YJHY+4MdJ6)OlWWohHo#L{7dNG zH~W`_qG@i}sS`^keYLLlO{TXw^XiUX>vb#`CQMyMj=<6n zH^|%f!LJSPo*d8dWiSfVU6?Sy9w4Ha783uYFRTkWi99?X8cL| z6wULEhvVT?m!jv3Bh6E9=4KXyJzW<()-OGLcI*o43kUw{4QJ#%@EqIwky02lX91Bd z-}<>IDI0<<8KgP=)86H`jbif-4XXXTFjMJl8=pZ+Jn$tC6Xo2NGMLs&eX%Q9H`RkP z9AQaH-V)bisozU!MJGzV*MyF=f96wo07=b1wx?6nZY{=fnI@UE4IJfdgv}R%veMwF z350L(x$A07l9(g_&NMX4s<1vY?w`;AA@8ZuAB>Q~n^_Hb+UnNDMQMG!Uc*U9#i|dn zUe5H3&3ITy&e}pnrCX;}w2-dCUtVpR7Lazg$K4`)e!03QJed=R?j@k_$F;XDfNG>1 zoI2r5LiDBW+@`Ff&($mMP$S7ohbq_*68gm9aB~aQTUiB5Kvg z^vGIT^!L)WH^l4+FSEwF??A*EVcK9t9(=dV4LfD7!dD=oXTG(u?9O0t|Jh&~RDEm1 zs+}viU%6XGt9-h+o>Swg?Mt0P6He)B23@mykC#q01>qetG((Rh)+%ULvTjIh5c=K; zxo%_xLd*bW2eTLWPyno!fguMGPNFr^^{#}o4& zS~(g(9)3a|_AP$xUHsZLkLs8|z>^ATo851n-D{fOt()BWFu7edzEwH?1y44rbab<1 zbhB`HqhNR=Z*V<#a2@Y)Sk|XiJk`wfzSZHCqtw2Yq@I<~k4u3qi+BKAzorE|>2N&7 zWbcM~&$>Cc+BwJSS-Z*^ya&EiaLZEDq6s{(Gal4UFMm`gcSJLLSUqb73u zVYI8Ex2>YJsi3hgZ=c(zG%q1HeNSdwbkDGWL_eQcH;+&|hd?v?j(QdzflMSG@Fkvn zEFR1wVMUW2^XU9X0;U!_Vb21pdjW-*JHS(TlK{}7gBOi_=J##M8Cb)6n)r5LEu()m zp?f*5a~bbRd?W%H*0vPfx){*B_#mna-nih~IRC1C-s8iZOUdZpjn3l~79<(g|Pd?5#euOkKG^e}L(}(-|u0!?{1{$>sycY@2Q* zJ`1pI-oK+QJTS;x@#4aCg%N%E2JnE$9(4@VnQz2)4{vFVY-yyU^k@$di4I$%;SaH)kVa^2xAi)Dh@A@+S0*vfhbvpIQ;HgVqCBzSpDRhqMhWw zzR~NkuHl&x*ZlNg?iVYhqrPp9 zH5_fks?uf&uenP$yb?#}yP5V*jDsPPM&#Vihcp@UC%JERl2LR^w&YTe;ZhHBtWk`q zzeB@@RF+Fv1{B?yw)r55wJU{(zbOr+IPK^`(wY}J%oEC0|2A?**_zY*X>)3ZZoF8I zsZU)@=vr5Y1RnRovwt|Vf4=mPupjPqcYDF0y(Cwx9nv({g3utEjA&?uGDB~#e`mYS z{e+g4R6@932!uqLc`i)Jmam97Hnc`MJhP$4Y5Vy8h&Jq>%%Jt?m3FRycV(EyhOFGM zOGY@IJ(TPgrp?HpQHktS>$}3fzMDz_BPNXQ$-1lUR`hOtq?G7Ah_($btYEx=n9lp* z{{+SF1o>|?^-+A16Za)mYuhr$9$V1R7 z*OBJMH&U4VK&x&!QPBa`k17(hrN@XdILu{w{2$tC1)3U7eWn=#-QU%fwx)+YAz$8L znVSN@N4^)onbq=;UP~WcUiK6qbhI{nGn33Q-iL|WcRj!*zP)(*S>qu(yTS!HbdJfHR>9Btz#m!gVn-K>20o94e`@F2e*8p4{69#G#kR?5fA z!O_^@6HGGeW~a({q&i86O1J;u?z$LH0M7$j6&6=60dU9ss1ubsm59!|dna;OydZAQH>yf7`)#b) z8`yoDE{01)(JisAN0HQz;@2N%$LJ-_#5!z@l!8B&45E3o`|BDq!U??How_?G=$>eQ z>Pa@y(Qdxtit9+LnV(|OJbAIfUs%r7`?scBWplPs~g#r#jf zK41FSP5|HYQa&PxpGo$8a5fxOYos)DrCc>{wL<7o*u=4XLySUOFP+byxJd8SzP(YO zCgr|2shr|J#YAR3Y_7SO)tGvNvHIBl)!((&NJr}-h`pqWVQ7nG`F&xb$Qq!sBy9Ey zT*z=y@B2j=8kO;tD6BSyud3@Hc=ZtLo|*>^Tw{rd_5&tw0UHvnRBh4Pz>afk(Zars z;jbp;1a(CrL>V_cBh&KT3#B{~zbV!aea6cX1ug3>KhAZbgv;D;9YZ0yC4h~x2 zx| zP+YoD^A_B>juMv4B}h+_@tN+#%_Uq&Q)cO^wDkceu(i>KM2baUg|2=zm-@SVIzjhQ zQ4NaD`u>ynkLTN5tM9LAP4?<~OMJGtnd6EUdnSBAbIA2RE6K}HLBPtT?~xdRyR;3j1r*q$r`AzRSX5K!%ReP&7 z1etYI|6%xgVSnT3!j36`hiz#@qZMC1WGjW1%jRGav2cIU;VqmfMD}=2^m*t!DR+v; z*1oJ_M5)=Kx@AGea08N#>R0_%wUBj|+kpm?p-TfI`DjqMZk%- z#d~)8u~B8++@GSwIH*oiR4aLpzL~)@5Agq4b#6cNKUaJhhDlGt z$Lpn|qu+7oU-^9G`qhZ*aX0wwK+ye*F|uetc4kefRjW+#8{rr+q55Ey-s<>!b3}qC zUIW-!NX5G3%k+oAsM6+1*rR4~At^phj6RTgI8yKKcqk4k#6_g%bbJ+OMo=57~O`4-EhwXR9M#J9i%iqQS#gB?|nzP^O` z>8I~#NRfd9Gqhw9o#iXKoC&z|TU^{S+wF>%OTbc|okR-Q`SI!yY_Eu($)vEddsYg`R|OJKy9!ALWcx3&05G$>ITJHvQ= z9I1WjKVA_$j@ds8LhmB`4;He-`gya63}SQrrBg855VvlD1IZmzZ zV>Ga`xjvHTM>WYtO_eF#|D8(@@t_4FOAnTxh1{1PPEHH5d!E`+ZN4U8Rfjyc5tRuo zEib(dP=XRx`E@(A^#FLhH7 zMg!*&@cs3BCJK7TI&|hs8T1&-rDIelJkMUvRj-BrGD|0;usC$&ak6l{R2IP(p^YyjE#b1>UEheFm587N_6+>^eVu_BqRm$tU7-(< zYmfq9$$vw)Zm-sOV${<{Q%b#5ZDJmEzpyo57gIw%%f-Wkm_tR5zcrX8woXcP;-d)7 zw0FXP$ip;P>58gnqufGZ5>a*NURm6XfbU2Tam%OQ2{8?+c?i!MW_!@b+V*RaM^c-t zL~VH*H2l!^wCzfuqpyS2uBl}Ah#@Gwm z|JEfp(%%jgnVBdlGH8e7GkHE*lo^8?vjU0Tbglo`ceKp@E4;moPjgY5rehGp4RQby^Iw{KNkVIj-QJ>mbRwhG*I=t6j7&v!!lM5k(09x=m4)rH_Bmv; zwGxdxodgwS-SN*RL;%G}s3(yLbg8()K-pZA4t*kiPHlYuKW)f(y+XoimPwY@FzXc= z;7nQ1y8#ZOAmm2j+#-J%-g47z2w%4^(j^(u@TqlT@@&Sq@2Q*v`t`?ObF-g(9y!Qy z@E41724h(iyMVZ?!b; zJzM&AAN3t{2a2TNTo^O?+p+(O$^NDEU!(sKUo&9@=g}q*Skhxcg+aAEmY;E`z|356 zXlUMOXf|kQT4-p3XlV3kXxz*RRZipyRS?PqW^LgFzn5V6h!{nuBpO9>Sl4E?U=ry}Fujjs@1w+fizl&a(5*hlKwBd(JDteHLr~kH_#$LnrTA z(xHMUN9#oD8Us;fkhW-eeZnI<81_y;D_K{9s@+4}AD+fGi52(>boTTZK%rU@dma)z z5CA9)1c8AGw!TlTg+QR&2K$y_)G=YQw(0|*wwSW7F2t!4F^AI2+^asZPm&Chcl!9%|YNTL4V)|3JL%2IuHmE`cozX`aKB1TgwUP{$%j~ z6NCqCK)^q=f*_#Z(*g*B0e+7JP#6IE$4mIXAPNH^zb80Q7zFqO zjxa>{56*;Pz(0(D0e=(%5DfZd_?O+kk`D+L7Wo4S81_dk1HmGHati_di5MdMC#w(` z^pE&KL?8+A0k>)PNBTf-&Hq*nK&Z&CwD=cgFd+C3tuPSyk66LL!hhI+L4SE}{J#o? yAfSJm!oPZg5Df4~$qB(ALcbdU0d7bB<}^qMBqEd$5Gad(%P~F&hs-ls{Qm)?7aGw3 delta 12673 zcmZX21ymL5_crYzr9`@qhjb%wXpoW;knZl5?rsj<-A6)75I*#J@Av!5 ztT}6)+4JuG?C05g&pT^wLSZI@VJbdDgQ|(@?N`$E?c*I(A?V)_a5OdIb3=<_;utSa^XecGlnAo%NjF4J2OK-kjO&HU^+pqpq z4!S-&E4llAcPyc=zg})ze((bc^z*o=JpJ*48A-?slqsz&7tinQ!RUQPCfDHxx+5`G zeYjrUK1&#?Ud@iKzdooQqWm%tbw#FEdZ`>rjH;bVrHZ95o8@YnCC8xuQVX)tcX*l3 zasU1p7b~yf?q@l3*@L~8qu1q!u1SNE>qA?H>00x37{yr7$ zkAdg2%uM<^ARTSBanky-@!lmt5Y4i$Y|`iWBq4XUiYBqe0FCnqEb> zD<^zZD|0`+AJh&91hO7H?zhDan;R1=Ke|*|iG_~57!9XN^RkTR68cfUs@vw_{Ei-R zK9l5ooS#8h?2sojJ4uZNV^2<^>IlEb=1u=xupdaTW#@*-i)<^? z8fEaP`<{)(VfOA9VyZSI{#a1kka3dV8B;x1Y@1&HjCMKv?NF|BIF*DM_dWMd)5`$& zCV)}1d`#=?-PaH_geWUNE@L|kf1*uzp^j3AXG`s8HRdwVo04{AAs z#aq$Ci^y%!zKgNxAmIMy3P$v&AFfm)s?HDerHi1XNj8^}6r0e#pNF7Lh*6@-;!n{- z=Wm-^sWTR8tzr0wBk!(y2|s~!@p7R|Y6&A&7lB`lpxkF36MLKN0um*#! zEc#EM6Ag2nxcj=ZE`>ZnR~JLvvm|Lk4>zFJ`~B_bwp$bo09ixsT7&wqTJJMJ!Aw6{ zBMYS}hQ6#JUf6YWhfc8YL6UErsg<4&wph^;t}9+crU4F$i75)@$y8`+>+x%()0QM1 zdT+Ds?=JW8pWn+%)D_mdA!GQZl~_q=gqVq+@Sphtb`;=B!kWcFVDge~EOH<`_8J0t zgAvBulo)-dDE})zF%)%JjLu5HM=D}*#$Ohg6rLboz^MWp23#mWD!LA1UA$nrmWLR= zUqEO!w({0n(Bb~4k!d{5c4lZZwy+Su;vyt9jYO*N3u*A~K zc5UI@KESwaxZlRyJU{{CIh-^W%^FXAqSv7&?W6X%+8gOq$2x#M66DT0|!W|~0- zxliMWo*?-HW6wG&rfxqn%-;@d#m*37 zJ7|#zFeuX-sV`W)X3xpHxpTg)i82<3nB^M4XTkkoBtpWJGcyb8VTY6Nmn}W(uc!nK zt`6g&l*0@}ApZosj!T<5IdC*a|{-}59yQc)%Z|2OQ$B$zVQZA; zw_e&?ngi`gFs5d6&SrCorLQ#)oiKHgA00fIN%3Sx@s6duzLC_!ioy$Nqr3+pL`^Pn zt9rjmrdwmd8j5`-$4UK$IzvOE`==*iS*v{P+wJDi7p)rWTv_= z#?KSGsidulZ$9vK?WSslthXZa9hEwmk-uOQ*$!xBGIfSI9i1usmnH7~mVt zAQ~$jV=lJq?Le8*?vjdP;DN?pZ6+oWs&WZ_=073b8z3?$E!8Wmg62#)S<3I2W3(6c zA{Wt_eyn)f%j{^4>u7BUG^(^B8j`-+%<;Vs=pV9Z%Ni*2wLu(7?cN*2H%M1K8|m8x zW5AI9eET(h!TM${7k5RX4%-mPqQDLtPoEpSuYkXIBKAyIJNlfVTD}iRW4J(eR;YH7 zgZy~`!*gP(fQpqwVm3@H!;K5=7%Wo`14*MdGe{917$_OtVNW@U5V6PHD^Qm++>uSV zI?ao(8De2-oeW`j2nabrUjFNx^8_m`;2Z*8Zx%WV%N|5?;=Gl{Tq>dcE&kdrMwZ6B zSEie7`vh#KF9xv*?5{0zgE%TNo{?hGw{8Og;yhf2_AZa)sZXpj0z@uE(_ug0%Tdy;D? z-n$0zbp>~w_?$47gR<^7DI3}E+DzQYo$`Gl&05(voVF#ocaHr25JNiwrmk-nHkZ8-O2<_+*HRPy5^ zfzBWgFmETaor+GH(nEw?cSEHI&H3;_73cVVevb+XM?gb%0YTJ!__pG<;_5RWBajqm zV&U7FMW>)swe-3qne`P3s~GXYs7P<&znAg|O=6$NUzE@wQV7k&U(AIACWOdE9LFTb z_+7X5x{zv5#+j)a@X^4qppwYE?<25K5G~5o_30Ed&`=WN>r^RxFTq*WbyQID34b0B ziTAC%{}I0sh^JA)WxV!~tO?xZT=9jtY>NGBkF(ZS!Ln*EotH>uMk#f>yU1mHrg-a( zfNHkT9YF5YtOCc-Lw2_wCuTCn2?_Rg%kS;m)SjIK+oT!**s zD0~Rx0);&bDUUf-DQI&J*KdY85w(NOlZ_1J%Y`v2sK+Pwb%fdCn>cjA7?*wVA#?oo z-fe+v)_VVRso~2)G6cO09SS0O)Cn?|pa1}S4Fw<1E=`_!g$;=q^43A6WKew~MWw*(h7eLi`rt%!n!iq1s26|;z#z5j#zqTX}h2#i3%1I@mj5_LT z>wo1D)CHdcy8_b&)yTC+nDOcwPJAW9(I6R4=g821)Ot{F*3WV1!_P?gf)1WN9|Axc zCvZ1^_v3Rz0z1eC9I2^M*a)6pr?^wt2y_uA4(+m`W2*M%fKWZ-)Ypf*Z1^||EQ5m<@=k%vSOzrlZ0AwqHJ3iXSp!_ z_*ag&Lk1G#D~(YkXO?}!@RknLiMj!-R9j<4v`}Br2|~Cdj-&8~80BEUj(Kc3cMpS* zX*`KeMr@&DPt+U=1?szWox)4HGqt+}!|b{py*JpgufD_7 zw3XgBFcuaHCs2<3VPwjn9LWrv{Fq4=0`&vfors0{3MA#SsSAANFnCxJdJsNB04I3e zEYwkQgF``1*(r?M1lSP3QKXPJ?wkV!V2Yy0^IR5H5<)c@`?Sd<#rigXz$)Cb`fwh5fsNs3Wb!yJffSxG=}y?~Fku2E#$m zoVwUzdZ|P_1ChQ!cjG4Wzq^3v5H2B+5{>MOtM}MIG&2w@RIZ%NSVD9tJv_zBjf4!3Yqy);5#t8S`Rg8J9b7Z|@#{NrX5wZvPhqD=|JK`f}T=4guzmymKxjI%LXg zA#YLV5LzW4sJb{1>&RNJat%-`u^6!j>lva(*^_}WARgt>;ss( zfj4#|iAfQ5ljG=Lk*wGwvR}j2onnAD9FE};>kzSa-^&+L%yHc1$sQX)Mh{(`UJjRqs;k@x=Bkyoo1CI{-wD=t3M#s7jn$ov(Imyt@kNX^B-(XYj z1c>@r&&iUbNVVp~wzh!$tY0rYTTHH}Z-c-sE~9Us6X355Yufh-e)E@TAJ;JrJ~ru& zFz=0SkFMg_kZ6#!DK}WRUr<3@pd>xoT@}B1lLHl9qHzbOsWb*#@vb2j-tS?@Sp+M zdl?x{=xT10LIa&t2n7g?Dy>2qx#w+-rLY5lVxnQm+6o)Q0v9ft^{tpVRBpdu4W}$!G;(KN*<@ z!}pTn_p(F=NBVP+2CcGuUq6ZZ)Oj1CUlK0i0$|oy>9o6a+IqDfGVV<7X1EO>a?+SG zLkRC%@HtF+=R(6^E5dWBojV{nZ{QO_$XK=!vtX$;)PRZh>0+*Wc*lXLHaq;n+!Z+Kcpc&@1`^H%04RB5uP%C%yT zZII$c`v;=P6AELq&jsFGzX^GN|JAMMY(q;>uqMoBNDpE1+s)@;8!v3igKRg0uPQ?R z6ZCyTW@sSqAD_hM2m0(R-T8xay6A2eY_#;opiM9;WN&*em%P8eceCr-8+VOXlxjoJ zUe2Cfq2GV&0$l{roW?BW(%YLPX^@Abjd01iI_X6)<7sp;h{D4$*T10)GL9Ar1S=&# zEMN9+1P44fCDlz-7tgtvnN>PL>2V;-7a=tcepF4UKCY&Lqq{I1qcmL%cZ@pKyB9bhSI|G4>_B zf9Sa_t#mn;@bGDGzPjb{M4-oy{u&$3{#G?By~0U|NHwB0IE=ACydu-IlCwKFr%q4p z+>{Jd2v$hI07oLxk?E>UYYly4MqL#srjTxClxozq5_ya5CZ--aTx4P_`i#Exa-vi7 zy>6W*e|C)UwIYUkLkh0On`QBgv`*vdWSd+R2jZM=lbP2tCPO`M>39N^5eoxhL!o7gRDyF}ZV$wVW#tk&sO5l7(6#~2>LiVz z{!`ra?dq`YM;&V~TilKnF$8y~<3#N!WMCA#H>m4H^^le3SJBf2^H#8CUAA6j(}dPY zFL>u-?|u#U6PetZ-|=3Ld8=pd%rTUZI#?E)LDZiL7T4>72Vz|Z$_lzClzSIbP-#q} zA-%_A3PgD3a~yk{8z%TJ^xd%*{nr5ywfQrLXWin0pE>M4en)=kg%pwzZ9D#zye{WB zj0k->P6or1>!?86deWs>wo5>1QW8uNTjO7*yHRSSfGgp=@3wbO}@Lo0|ne0+i(8mYpfvf}L0m-J7z$X1OywGl@ecZmx=T zXcbncuMY5&9p!0z*tSe9QZ0lIAuBHzKCU3r95h_%?e9%C?O>K!U(r`C3Z)1!v}A)y zH_WeFD#$6uITpk;(v5dB?#?c1m z2{2=<(n4i%Fh1lyptiK6+KQI;mG5NR!_{Ge(EZ)U`i(1dc~*+b1cVS&xN_mWZ6tqc zT-FiHd>-zwZ6qli#9T6wvU2hQBVTNone#|AEG4B07mm?=G!mK%`OOx5Aj0q$r-Ik! z^2I(*P2=IaXRS;hZCYyG#X`$3tZV$%;TaR|cBHHVcc29eH;~LD7=*GIIW|7<1S}~{ za@FXXPn_FIOUu+HFz73Ss3<*9k&Z>`wPLOXlzp6$v_yIXO~(n_nl53JwwJNU-^2t7 z*7PDVnrlF1pQ*WME7)hAT9Ktm(aIRCBE5ee7z7gII*K@oUxme39m1$VeL%uoJsE4O zHP417+M>@;JAwWxA5_j2YJ*pLw)|E$rfz*nv^q_eN&1lN{@At`6dk+Vc`j{4Wm12` znzp{mSjXe*Qyo^_oUkFE{I)1%o>_N0j100?)kIWzGO~p6sgZpNZ-vkQhXG|1`<)gZ z@n}GOFt3~-?TH8YML6F{BWyX}i3iNu#g)DwFMRVIuG?{STz}12HUz~r=xQ`WP-6gs z(xw&^%)Wk{i5B}7)U@fW(X@+J0}(z%t4T8mKEw!gLi16NwY%YQIxXXb{|)r9{UGi>2L!OTq%j&TW3knk}Fs(BFZ%zhO?OCR~NI%&u)_b2Jr zZ-V5|y93lToMvAm>ZcCu_feHhe-@~$kP0^ig_uNKOA;2+$jGo*n=BnDN^hvSh@saP z5yaGe?uuGwaApqxYLB3jU8yyKgnezg>}2ALeHrV1y_TpRc8$rejTjV#Q1l!4**3$1HcTiEFdcV?c&KBP4H7O3k5RaHUbpBXa9bS{2^r=xz-(aKlui@w~^r(=`WkG>$tG5T!zyz`mtyO5*? zRokeo(k=1nBek?FOi}FWC`{YW%takv*&4DCsRL=^8NYB-M;+sQDh#B7P)cyMOnhmj zl6z|f%F@g2XZRbWt6mZ(BeOlXphy7MA57m!^nE| z56q;K8N-w9hj7BE!OJh(FshPP3gm3>^73gx6Q&b|=ov{q61>BLJZqt+}WMyZBE0qLN>&ejHd`v_pLg=*H9&q zy+FbdRhd$xz;UET$p4T8)T9yv?v*!)_P$JbfK7^17K!B}B7$bC)+EZ{e#r{tM4|x* z`(NEYT-@A$zqvoV`gwZwGZtM{6g0AWI=p&1xO_6OeA2u4t!p0AF@FpY4Qie}teM)Y znAk0!*eM>{E*jg;8rsMl+5m`!qzECE=oe7-IKIP3v( z@eY-aarckVte7Kr(#w@QUYS2X}BgXJ8w9>@zhqelq)7 z0Jk%-brqpi1-?ZYo>}QjlVTjBBJ6hsSo--Gy1Af+1vJep6!i>b)pSJVRDggtfb~59 z=n9bj6sxU95$mT$0qS0Ww9g#_cs&5}2~9JHjWY-J;Db8we%0h&<>YSZ_)f|APQmC_ z{^%B?S5x-z=5y`rjKOsP--oop_2mAwsLmC@>*$cyB>=ydW7Vu}#SB3FqhZ0MLEeOB z)|g_-FyQSM>G*!(s2+fz4?re{H?)HzppC_+g~_{_!Lx~u&f_aUHcp4et%1s=p2De) z#I~B;p_bIH1|Z}|Y*R(_z7il2MqpWvZ(bG~rrrcf&{2IMB?dqw^I;2sk`(U7lfqUJ zk3b5Q6o(6@nauN#Vo5|7*0zHS2H^ML5h`G?6kzyEKe(1{FE?pkmC%Xm6$RHG)VDdS zI=5W7cwM(Rj=5izdZ6qOJot@u^+X)5b0u2o=&UW5w&(8<`ymq~bl|Ktt-`+TydwDU z4)zH2zB#9&*xqryyEmc&M}F@HGX;i#zziyQ>E^7LsDci%!35c`(OMEfOM!^E;R4G` z1HDiK#z63)?;_eT6|P;c$&YNWVeepD7aOpg)N`TpgNw18U6r$CCdp*X+8-9cbFhtk zP4?W~x=TH}OK&2YihZZR%d)=bN->(!K6IehuAYOQ)8AL2%({;Q8?~p$m#Lv#lh-L4 za(9i_mJiU@58u7XY&bW9qqT3EV5U6%7T6Hh^v~JV*o0%}it6T_l7J(#KA1Zw`1!01 z(k!|DY)*ekbM`F1Z4A1h&HkkutAmx8E4Z>r{$PD!i)!o0=L<~P-IVOhgSlnU+H>Eg zSFwNKn%A@)=a*k zvYb_Itsh|GACKSN=C7J%@EcqX%HkL~Q zvqs{L3Vm#8kYVl7#+s2h9tN zyA}g*|x?G?>fA+3*Sxjqe z140<`dm59~TxV+2n4--Qv}WTgo5>4HoEz7R_{t7O>jxa99-OWqLARC(Uyxo+OcCq$ z9gOyju-!A^^guwnb{{@@u`Mwk6%+l_V7ZWzkwIX9?}59u6)=96jw%wnT*<2D{>n`w zm~u#n7qPMt@ZG-arl@2@wa8#A<~-y{L+ywSt_}OU#O$;y#1t^~%VeA3r?&X@O$C zO0F)ykA)N|nRdxCm1_)vwTW+tX1?hh)8D@RuvplVrqAhQ))-ONQ}@X}cs`~V&1fug zbaracI~`Knp5VUsPsuD?)z9kEm=yOIfoV*xjz9LR02&)E#ROd#_4iDdpjhvz@l;LV z4no?*Ih56P+&R=v@e86isT*?qIbONP0{<0EY#A|uF3kq-%c9zv3FZDMOng}Mf~1k| zv^DWv)hgGK49}c=by1lij%ykT#7V8}jXdOV7G7aLf2l5C=MSeIT_dN(VQqcg+dD$^ zw!G7eK+yes*6mQjeAv|TGXgY@qobDsZ{%}7pDSTf7NpbNcnt1&E-xpHa^ISG*88vi zNoH1Y!BopGPt-M~cFMyf-Y_kyZr^l2h|h6`WUC96Tj5-ME@xzm0b2Xnafo)41(g|X zCw~y6p}bEoMZdAp9`+xBdvv8Nl0oy_j%tg}{MbcccwYKOHre%Kp`5DTpWi-l5t`G$ zx*9Ff4w!E#tEsxQ%KU03&2su1UPrOFzn`0zndUtiPoN{T>D^2Wq`yyNP!u z2m2eNXKDl{iJcE;T7k%EvPk^IW0)$gIstP4M>m+)e1nv7UnBOv!*KI`#`}@Fy%50b z;`}F<7TjU&1yd#)US289L$Rg^{msM_XY-XF`QaI!t3CqB97@_4C$sjAVUIev5|PGA zpH8;LdQj$8G5q^K5r`0TtxZ4oXdaF>Pb1~Yg5D$3=iKj$9w?($kLA~q^^s1$`zkT` zHnw~k@9pS{zY>TNRc+>>gQUtTn=u12beRrqy2!Ri@rz90 zYqdU5EaZlmdn>f|{(fKSS)LGH91i4heOtlB2e#8JU%Je;xHUF%sQa|2NwWSigT0*cJ|O950rvx;M&fmdT$_Ydg3tv=hNw zvR+*|sn=D33|x&ze>wd=Z;Yryuq*f|}l_J2_9#4>gxr4HeTG z*zGFW|KLysTeGLPtlclfflMD?qC)h!_g!+oPXt|wTko;ze+#Jg@JxP#opO{W{htnS zr`>LJv)JV;ocQc3{I3_$J{=AqW8d~p2@-K~THZJF{ zB?VO;4qtvFU~1vf7V%kuHxtur>pEeS)05+P6b$q`BO5=kwm9Cpi=43QGXi!0P4@(D z&qyDxf{r4-6KLo>Ha@AhzZU<=UM+y{R4cIMMT+fJ)>bU)rDO}z=VwCT3bGx`o#lv; zwFT-~n#j$O%a_zx&NzRzyD;JXWFG!?1un=O!3FIOB<)(aOI6M!6*N1iV?eW5bk!he z@CU#qw+U;(G}3Mnm!4;cp(p#BZ9~!x5~*3~z_4c8*8Z_zSk)ZfzhR%-TP+SRgbRLw z2*P}`NM~G~$G(n;N zsn+wj=+zScRjk=C;WgG>i9Lx9l)iO?%`O(S8^fN`merqf_bkh}sN0t3mei(AFY^)D zLM?lhuE!KK(VTpr%C-BKuxxPsY!#Njqw7rdH_N@z#`u&AG8@58N5pf^f13^MJU$h) z(p-A^{Ny|cqK67LTxnphMu8^yLrxaWnq7+F9?)ja57lT?zZ+97i(m22_`-f{T1T%J z6ae3P^v~D(9lE~XuZ!!#`gL;_!?o-2ZuhdH(c&;8ZlZ28p!wz_HH2Za_Vq6YKhqls zKv`8kioj8-n9SjbT8Yf zW$>NHFIo}}>Y)x$>02{cCyjtM*&&a3H{c`#<3ygZ%6D zE0zBZ{*V0ux;woAIrg127780F3#qN4B@#Oa7i;x{=@tSl2Pw@5C@2dkC^aZ3W+*5^ zC@5M|T~cOI3nxbf69+L{Ydc#T6B{Q|j@V4M7qMxUY_V!K+;Y>3s4%Qh;vHHvnC;ND zKA<<>F$tTS;QINYlW!JJ7fNf>u2!x&cAI+&`>2Sje1jj4t8dL%c_%V%l+BoWh~-W% zY#)yK-yxL;sK<)fl4F&v5v_mz?{0v4=dE9?uPtw^oh@RloGs)3{ICE&Fjfd?x+3g; zF+%3}m(e4kHCwvp05%{i4+lF3Pb?-#7R1WK!b8gP>maouMf&}Rjg^Iq^@)v@)1HBp4zy$pW1-Hzif|zxHuneu}dIw7!Ix&A1V+B$5T*F9`?T+**JMvo`7<5ay{1V z|0V}yWBUd0c>I52JV5ThlVfLLW&e!}`k#gDEbPF)o!MEq*#B0Fodx(LL3UPFjwc|j z94vp6V`t^!{9lYm{KtcbgY|C^79O@I(z5`8oPP&=ED-nKDzI>K0H4~p*#AzDg_{S& z_5>Kn!u_Ofz(>x1$;txc;EzSwz0GR&6t&q>nRiV$8`P% zW@qDk5|5pe`|k#3W#{Gq{aq(kb{_60S#vz<@HYqt8^_aioWLgva&U7#!QkNecQir& zUusUar@)+#PmI5TIk}(AKPxBjsnlF-PZ@J@{H5xn{;XU;)+hDk;(0PUtlX?DPa5u# z+!My!z^6R}WZ`*&0c7I=g8pU)hde%llj+qI&Z0aX Date: Tue, 30 Apr 2024 21:55:25 +0200 Subject: [PATCH 31/52] finished fermionic examples --- examples/anderson_model_double.jl | 125 +++++++++++++++++++------ examples/anderson_model_interleaved.jl | 99 ++++++++++++++++---- 2 files changed, 177 insertions(+), 47 deletions(-) diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index 082ebfc..eddb80f 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -8,67 +8,138 @@ import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_ # Physical parameters #---------------------------- -N = 20 # number of chain sites -β = 2.0 # inverse temperature -μ = 0. # chemical potential -Ed = 0.6 -ϵd = Ed - μ # energy of the impurity +N = 40 # number of chain sites +β = 2.0 # inverse temperature +μ = 0. # chemical potential +Ed = 0.6 # energy of the impurity +ϵd = Ed - μ # energy of the impurity minus the chemical potential -dt = 0.5 -T = 30.0 - -dir = "/Users/ariva/Documents/fermions/" - -# define the dispersion relation +#----------------------------------------- +# Dispersion relation and chain parameters +#----------------------------------------- function ϵ(x) return x end - -chainparams1 = chaincoeffs_fermionic(20, 2.0, 1.0; ϵ, save=false) # empty -chainparams2 = chaincoeffs_fermionic(20, 2.0, 2.0; ϵ, save=false) # filled +chainparams1 = chaincoeffs_fermionic(N, β, 1.0; ϵ, save=false) # empty +chainparams2 = chaincoeffs_fermionic(N, β, 2.0; ϵ, save=false) # filled #= curdir = @__DIR__ dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/fermionicT/")) -chainparams2 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", N, β, 2.0) #filled -chainparams1 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", N, β, 1.0) #empty +chainparams2 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 2.0) #filled +chainparams1 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 1.0) #empty =# c1 = chainparams1[3] c2 = chainparams2[3] +#----------------------- +# Simulation parameters +#----------------------- + +dt = 0.5 # time step +T = 10.0 # simulation time +method = :DTDVP # time-evolution method +Dmax = 100 # MPS max bond dimension +prec = 0.0001 # precision for the adaptive TDVP + +dir = "/Users/ariva/Documents/fermions/" + +#--------------------------- +# MPO and initial state MPS +#--------------------------- + H = tightbinding_mpo(N, ϵd, chainparams1, chainparams2) ψ = unitcol(2,2) # (0,1) filled impurity state A = productstatemps(physdims(H), state=[fill(unitcol(2,2), N)..., ψ, fill(unitcol(1,2), N)...]) # MPS mpsembed!(A, 4) -dt = 0.5 -T = 30.0 - +#---------------------------------------------------- +# Definition of observables for the double chain +#---------------------------------------------------- # For the double chain ob1 = OneSiteObservable("chain1_filled_occup", numb(2), (1,N)) ob2 = OneSiteObservable("chain2_empty_occup", numb(2), (N+2, 2N+1)) ob3 = OneSiteObservable("system_occup", numb(2), N+1) +#------------- +# Simulation +#------------ + A, dat = runsim(dt, T, A, H; name = "Anderson impurity problem (folded chain)", - #method = :TDVP2, - #method = :TDVP1, - method = :DTDVP, - obs = [ob1, ob2, ob3], # for the double chain + method = method, + obs = [ob1, ob2, ob3], convobs = [ob1], params = @LogParams(N, ϵd, β, c1, c2), - #convparams = [4], - convparams = [0.1], # for the adaptive TDVP we set the precision that we want to obtain - Dlim = 10, # max bond dimension - savebonddims = true, # we want to save the bond dimension + convparams = [prec], + Dlim = Dmax, + savebonddims = true, # we want to save the bond dimension verbose = false, save = true, savedir = dir, plot = true, ); +#---------------- +# Post-processing +#---------------- + +# Reshaping the vector to a column matrix and horizontal concatenation +system_occup_col = reshape(dat["data/system_occup"], :, 1) +occ = hcat(dat["data/chain1_filled_occup"]', system_occup_col) +occ = vcat(occ', dat["data/chain2_empty_occup"]) + +#------------- +# Plots +#------------- + +# Plot the occupation of the chain sites +heatmap( + collect(1:2*N+1), + dat["data/times"], + transpose(occ), # Use the matrix form + cmap = :coolwarm, + aspect_ratio = :auto, + xlabel = L"$N_{i,j}$ chain sites", + ylabel = L"$t$", + title = "Chain Occupation", + colorbar = true, + size = (700, 500) +) + +# Plot the bond dimensions +heatmap( + collect(1:2*N+2), + dat["data/times"], + transpose(dat["data/bonddims"]), + cmap = :magma, + aspect_ratio = :auto, + xlabel = L"$N_{i,j}$ chain sites", + ylabel = L"$t$", + title = "Bond Dimensions", + colorbar = true, + size = (700, 500) +) + +# Define indices for columns to be plotted +columns_to_plot = [1, 5, 10, 15, 20] + +# Plot vertical slices for occupancy +p1 = plot(title = "Chain occupation") +for col in columns_to_plot + plot!(p1, unfolded_occ_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = "chain occupation") +end + +# Plot vertical slices for bond dimensions +p2 = plot(title = "Bond Dimensions") +for col in columns_to_plot + plot!(p2, unfolded_bonds_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = L"$\chi$") +end + +# Display the plots +plot(p1, p2, layout = (2, 1), size = (600, 800)) diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl index ee8b5f9..d780a9c 100644 --- a/examples/anderson_model_interleaved.jl +++ b/examples/anderson_model_interleaved.jl @@ -7,7 +7,7 @@ import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_ # Physical parameters #---------------------------- -N = 20 # number of chain sites +N = 40 # number of chain sites β = 2.0 # inverse temperature μ = 0. # chemical potential Ed = 0.6 # energy of the impurity @@ -21,8 +21,8 @@ function ϵ(x) return x end -chainparams1 = chaincoeffs_fermionic(20, 2.0, 1.0; ϵ, save=false) # empty -chainparams2 = chaincoeffs_fermionic(20, 2.0, 2.0; ϵ, save=false) # filled +chainparams1 = chaincoeffs_fermionic(N, β, 1.0; ϵ, save=false) # empty +chainparams2 = chaincoeffs_fermionic(N, β, 2.0; ϵ, save=false) # filled #= curdir = @__DIR__ @@ -39,10 +39,10 @@ c2 = chainparams2[3] #----------------------- dt = 0.5 # time step -T = 30.0 # simulation time +T = 10.0 # simulation time method = :DTDVP # time-evolution method -Dmax = 2 # MPS max bond dimension -prec = 0.1 # precision for the adaptive TDVP +Dmax = 40 # MPS max bond dimension +prec = 0.0001 # precision for the adaptive TDVP dir = "/Users/ariva/Documents/fermions/" @@ -91,24 +91,83 @@ A, dat = runsim(dt, T, A, H; ); -#---------------------------- -# Post-processing of the data -#---------------------------- +#------------------------------------------------------------- +# Post-processing of the data: unfolding the chain for clarity +#------------------------------------------------------------- +unfolded_occ = Vector{Vector{Float64}}() # Assuming the elements are of type Float64 +unfolded_bonds = Vector{Vector{Float64}}() # Adjust the type based on actual data + +# Populate unfolded_occ by iterating in the specific order mentioned +for i in 1:N # Adjusted for 1-based indexing + push!(unfolded_occ, dat[ "data/folded_chain_occup"][2N + 1 - 2i, :]) +end -occ_unfold = similar(dat["data/folded_chain_occup"]) - -# Loop through each element, modifying as necessary -for i in 1:size(dat["data/folded_chain_occup"], 1) - for j in 1:size(dat["data/folded_chain_occup"], 2) - if j % 2 != 0 # Check if the column index is odd - occ_unfold[i, j] = 1 - dat["data/folded_chain_occup"][i, j] - else - occ_unfold[i, j] = dat["data/folded_chain_occup"][i, j] - end - end +push!(unfolded_occ, dat["data/folded_chain_occup"][1,:]) + +for i in 2:N + push!(unfolded_occ, dat["data/folded_chain_occup"][2i,:]) +end + +# Populate unfolded_bonds similarly +for i in 1:(N+1) # Adjusted for 1-based indexing + push!(unfolded_bonds, dat["data/bonddims"][2N + 3 - 2i,:]) # Assuming bonddims is directly accessible end +push!(unfolded_bonds, dat["data/bonddims"][1,:]) + +for i in 2:(N+1) + push!(unfolded_bonds, dat["data/bonddims"][2i,:]) +end + +unfolded_bonds_matrix = hcat(unfolded_bonds...)' +unfolded_occ_matrix = hcat(unfolded_occ...)' + #------------- # Plots #------------- +# Plot the occupation of the chain sites +heatmap( + collect(1:2*N), + dat["data/times"], + transpose(unfolded_occ_matrix), # Use the matrix form + cmap = :coolwarm, + aspect_ratio = :auto, + xlabel = L"$N_{i,j}$ chain sites", + ylabel = L"$t$", + title = "Chain Occupation", + colorbar = true, + size = (700, 500) +) + +# Plot the bond dimensions +heatmap( + collect(1:2*N+2), + dat["data/times"], + transpose(unfolded_bonds_matrix), + cmap = :magma, + aspect_ratio = :auto, + xlabel = L"$N_{i,j}$ chain sites", + ylabel = L"$t$", + title = "Bond Dimensions", + colorbar = true, + size = (700, 500) +) + +# Define indices for columns to be plotted +columns_to_plot = [1, 5, 10, 15, 20] + +# Plot vertical slices for occupancy +p1 = plot(title = "Chain occupation") +for col in columns_to_plot + plot!(p1, unfolded_occ_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = "chain occupation") +end + +# Plot vertical slices for bond dimensions +p2 = plot(title = "Bond Dimensions") +for col in columns_to_plot + plot!(p2, unfolded_bonds_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = L"$\chi$") +end + +# Display the plots +plot(p1, p2, layout = (2, 1), size = (600, 800)) From 984c0084fcb299f168680198f4f4227425955cf9 Mon Sep 17 00:00:00 2001 From: angelariva Date: Tue, 30 Apr 2024 21:56:57 +0200 Subject: [PATCH 32/52] deleted file --- examples/fermions.ipynb | 1449 --------------------------------------- 1 file changed, 1449 deletions(-) delete mode 100644 examples/fermions.ipynb diff --git a/examples/fermions.ipynb b/examples/fermions.ipynb deleted file mode 100644 index 865d382..0000000 --- a/examples/fermions.ipynb +++ /dev/null @@ -1,1449 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Single Impurity Anderson Model\n", - "______________________________\n", - "\n", - "\n", - "We use the fermionic chain mapping proposed in [1] to perform tensor network simulations of the Single Impurity Anderson Model (SIAM). The SIAM Hamiltonian is defined as:\n", - "\\begin{equation}\n", - " \\hat H^\\text{SIAM} = \\hat H_\\text{loc} + \\hat H_\\text{hyb} + \\hat H_\\text{cond} = \\overbrace{\\epsilon_d \\hat d^\\dagger \\hat d}^{\\hat H_\\text{loc}} + \\underbrace{\\sum_{k} V_k \\Big( \\hat d^\\dagger \\hat c_k + \\hat c_k^\\dagger \\hat d \\Big)}_{H_\\text{hyb}} + \\underbrace{\\sum_k \\epsilon_k \\hat c_k^\\dagger \\hat c_k}_{H_I^\\text{chain}}.\n", - "\\end{equation}\n", - "All of the operators obey to the usual fermionic anti-commutation relations: $\\{\\hat c_i, \\hat c_j^\\dagger \\} = \\delta_{ij}$, $\\{\\hat c_i, \\hat c_j \\} =\\{\\hat c_i^\\dagger, \\hat c_j^\\dagger \\} =0$ $\\forall i,j$. The chain mapping is based on a thermofield-like transformation [2], performed with fermions: ancillary fermionic operators $\\hat c_{2k}$ are defined, one for each of the original fermionic modes $\\hat c_{1k}$. A Bogoliubov transformation is then applied, so that two new fermionic modes $\\hat f_{1k}$ and $\\hat f_{2k}$ are defined as a linear combination of $\\hat c_{1k}$ and $\\hat c_{2k}$. Two chains are defined: the chain labelled $1$ for the empty modes, the chain labelled $2$ for the filled modes.\n", - "The following relations are used to define the functions equivalent to the spectral density of the bosonic case, one for each chain:\n", - "\\begin{equation}\n", - "\\begin{split}\n", - " &V_{1k} = V_{k} \\sin \\theta_k = \\sqrt{\\frac{1}{e^{\\beta \\epsilon_k}+1}} \\\\\n", - " &V_{2k} = V_{k} \\cos \\theta_k = \\sqrt{\\frac{1}{e^{-\\beta \\epsilon_k}+1}}, \n", - "\\end{split}\n", - "\\end{equation} \n", - "where we choose the spectral function that characterizes the fermionic bath to be: $V_k= \\sqrt{1-k^2}$, and we define the dispersion relation as: $e_k = k$, that is, a linear dispersion relation with propagation speed equal to $1$. This latter choice corresponds to a model of metals (gapless energy spectrum). We select a filled state as the initial state of the defect.\n", - "Using the mapping proposed in [1], the chain Hamiltonian becomes:\n", - "\\begin{equation}\n", - " \\begin{split}\n", - " \\hat H^\\text{chain} = \\hat H_\\text{loc} &+ \\sum_{i = \\{1,2\\}}\\bigg[ J_{i,0} \\Big(\\hat d^\\dagger \\hat a_{i,0} + \\hat d \\hat a_{i,0}^\\dagger \\Big) + \\\\ &+ \\sum_{n=1}^\\infty \\Big( J_{i,n} \\hat a_{i,n}^\\dagger \\hat a_{i,n-1} + J_{i,n} \\hat a_{i,n-1}^\\dagger \\hat a_{i,n} \\Big) + \\sum_{n=0}^\\infty E_{i,n} \\hat a_{i,n}^\\dagger \\hat a_{i,n} \\bigg],\n", - " \\end{split}\n", - "\\end{equation}\n", - "where the $J_{i,n}$ coefficients are the couplings between the chain sites and the $E_{i,n}$ coefficients are the energies associated to each chain site. Clearly, the interactions are between nearest neighbors. This, combined with the fact that the fermions in our model are spinless, enables a straightforward mapping into fermionic operators of the bosonic creation and annihilation operators, that on their part obey to the bosonic commutation relations: $[\\hat b_i, \\hat b_j^\\dagger] = \\delta_{ij}$, $[\\hat b_i, \\hat b_j] =[\\hat b_i^\\dagger, \\hat b_j^\\dagger] =0$ $\\forall i,j$. The mapping derived from Jordan-Wigner transformations for spinless fermions is:\n", - "\\begin{equation}\n", - " \\hat a_{i}^\\dagger \\hat a_{i+1} + \\hat a_{i+1}^\\dagger \\hat a_{i} = \\hat b_{i}^\\dagger \\hat b_{i+1} + \\hat b_{i+1}^\\dagger \\hat b_{i}. \n", - "\\end{equation}\n", - "\n", - "\n", - "## Double chain mapping\n", - "\n", - "The corresponding MPO representation is:\n", - "\\begin{equation}\n", - "\\begin{split}\n", - "&\n", - "\\begin{bmatrix}\n", - " \\hat{\\mathbb I} & J_{2,N} \\hat b_{2,N}^\\dagger & J_{2,N} \\hat b_{2,N} & E_{2,N} \\hat b_{2,N}^\\dagger \\hat b_{2,N} \n", - "\\end{bmatrix}\\cdot ... \\cdot\n", - "\\begin{bmatrix}\n", - " \\hat{ \\mathbb I} & J_{2,0} \\hat b_{2,0}^\\dagger & J_{2,0} \\hat b_{2,0} & E_{2,0} \\hat b_{2,0}^\\dagger \\hat b_{2,0}\\\\\n", - "0 &0 & 0 & \\hat b_{2,0} \\\\\n", - "0 &0 & 0 & \\hat b_{2,0}^\\dagger \\\\\n", - "0 &0 & 0 & \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\cdot \\\\ \\cdot &\n", - "\\begin{bmatrix}\n", - " \\hat{ \\mathbb I} & \\hat d^\\dagger & \\hat d & \\epsilon_d \\hat d^\\dagger \\hat d\\\\\n", - "0 &0 & 0 & \\hat d \\\\\n", - "0 &0 & 0 & \\hat d^\\dagger \\\\\n", - "0 &0 & 0 & \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\cdot \n", - "\\begin{bmatrix}\n", - " \\hat{ \\mathbb I} & \\hat b_{1,0}^\\dagger & \\hat b_{1,0} & E_{1,0} \\hat b_{1,0}^\\dagger \\hat b_{1,0}\\\\\n", - "0 &0 & 0 & \\hat J_{1,0}b_{1,0} \\\\\n", - "0 &0 & 0 & \\hat J_{1,0}b_{1,0}^\\dagger \\\\\n", - "0 &0 & 0 & \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\cdot ... \\cdot\n", - "\\begin{bmatrix}\n", - " E_{2,N} \\hat b_{2,N}^\\dagger \\hat b_{2,N} \\\\ J_{2,N} \\hat b_{2,N} \\\\ J_{2,N} \\hat b_{2,N}^\\dagger \\\\ \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\end{split}\n", - "\\end{equation}\n", - "\n", - "The system starts from a filled state, the chain starts as in the Fermi sea.\n", - "\n", - "## Interleaved chain mapping\n", - "\n", - "The drawback of such a representation though, is that the particle-hole pairs are spatially separated in the MPS, creating correlations and therefore leading to a dramatic increase in the bond dimensions. This is why Kohn and Santoro propose an interleaved geometry, the advantages of which are thoroughly explained in \\cite{Kohn_Santoro_2021b}. Exploiting the interleaved representation, the interaction comes to be between next-nearest neighbors: a string operator appears in the Jordan-Wigner transformation from bosons to fermions:\n", - "\\begin{equation}\n", - " \\hat a_{i}^\\dagger \\hat a_{i+2} + \\hat a_{i+2}^\\dagger \\hat a_{i} = \\hat b_{i}^\\dagger \\hat F_{i+1} \\hat b_{i+2} + \\hat b_{i} \\hat F_{i+1} \\hat b_{i+2}^\\dagger,\n", - "\\end{equation}\n", - "where the string operator $\\hat F_i$ is defined as: $\\hat F_i = (-1)^{\\hat n_i} = \\hat{\\mathbb I} -2 \\hat n_i = \\hat{\\mathbb I}-2 \\hat b_i^\\dagger \\hat b_i$. It is possible to find the analytical form also for MPOs with long range interaction \\cite{mpo}. In the case of next-nearest neighbors interactions between spinless fermions, the MPO representation will require a bond dimension $\\chi=6$. We explicitly write it as:\n", - "\\begin{equation}\n", - "\\begin{split}\n", - "&\n", - "\\begin{bmatrix}\n", - " \\hat{\\mathbb I} & \\hat d & \\hat d^\\dagger & 0 & 0 & E_{d} \\hat d^\\dagger \\hat d \n", - "\\end{bmatrix}\\cdot\n", - "\\begin{bmatrix}\n", - " \\hat{ \\mathbb I} & \\hat b_{2,0} & \\hat b_{2,0}^\\dagger & 0 & 0 & E_{2,0} \\hat b_{2,0}^\\dagger \\hat b_{2,0}\\\\\n", - "0 &0 & 0 & \\hat{F}_{2,0} & 0 & J_{2,0} \\hat b_{2,0}^\\dagger \\\\\n", - "0 &0 & 0 & 0 & \\hat{F}_{2,0} & J_{2,0} \\hat b_{2,0} \\\\\n", - "0 &0 & 0 & 0 & 0 & 0\\\\\n", - "0 &0 & 0 & 0 & 0 & 0 \\\\\n", - "0 &0 & 0 & 0 & 0 & \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\cdot \\\\ \\cdot &\n", - "\\begin{bmatrix}\n", - " \\hat{ \\mathbb I} & \\hat b_{1,0} & \\hat b_{1,0}^\\dagger & 0 & 0 & E_{1,0} \\hat b_{1,0}^\\dagger \\hat b_{1,0}\\\\\n", - "0 &0 & 0 & \\hat{ F}_{1,0} & 0 & 0 \\\\\n", - "0 &0 & 0 & 0 & \\hat{F}_{1,0} & 0 \\\\\n", - "0 &0 & 0 & 0 & 0 & J_{1,0} \\hat b_{1,0}^\\dagger \\\\\n", - "0 &0 & 0 & 0 & 0 & J_{1,0} \\hat b_{1,0} \\\\\n", - "0 &0 & 0 & 0 & 0 & \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\cdot ... \\cdot \n", - "\\begin{bmatrix}\n", - " \\hat{ \\mathbb I} & \\hat b_{2,N} & \\hat b_{2,N}^\\dagger & 0 & 0 & E_{2,N} \\hat b_{2,N}^\\dagger \\hat b_{2,N}\\\\\n", - "0 &0 & 0 & \\hat{F}_{2,N} & 0 & 0 \\\\\n", - "0 &0 & 0 & 0 & \\hat{F}_{2,N} & 0 \\\\\n", - "0 &0 & 0 & 0 & 0 & J_{2,N} \\hat b_{2,N}^\\dagger \\\\\n", - "0 &0 & 0 & 0 & 0 & J_{2,N} \\hat b_{2,N} \\\\\n", - "0 &0 & 0 & 0 & 0 & \\hat{\\mathbb I}\n", - "\\end{bmatrix}\n", - "\\cdot \\\\ \\cdot &\n", - "\\begin{bmatrix}\n", - " E_{1,N} \\hat b_{1,N}^\\dagger \\hat b_{1,N} \\\\ 0 \\\\0 \\\\ J_{1,N} \\hat b_{1,N}^\\dagger \\\\ J_{1,N} \\hat b_{1,N} \\\\ \\hat{\\mathbb I}\n", - "\\end{bmatrix} \n", - "\\end{split}\n", - "\\end{equation}\n", - "________________\n", - "### References\n", - "\n", - "[1] Lucas Kohn and Giuseppe E. Santoro. Efficient mapping for anderson impurity problems with matrix product states. Physical Review B, 104(1):014303, Jul 2021. arXiv: [2012.01424](https://arxiv.org/abs/2012.01424).\n", - "\n", - "\n", - "[2] Ines de Vega and Mari-Carmen Banuls. Thermofield-based chain mapping approach for open quantum systems. Physical Review A, 92(5):052116, Nov 2015. arXiv:[1504.07228](https://arxiv.org/abs/1504.07228).\n", - "\n", - "[3] L. Kohn and G. E. Santoro. Quenching the anderson impurity model at finite temperature: Entanglement and bath dynamics using matrix product states. arXiv:2107.02807 [cond-mat, physics:quant-ph], Jul 2021. arXiv: [2107.02807](https://arxiv.org/abs/2107.02807)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "import h5py\n", - "import numpy as np\n", - "#import cmath for complex number operations\n", - "import cmath\n", - "import matplotlib\n", - "import matplotlib.pyplot as pl\n", - "import matplotlib.style as style \n", - "import matplotlib.pyplot as pl\n", - "import scipy.misc\n", - "from scipy import ndimage\n", - "\n", - "from scipy.optimize import curve_fit\n", - "style.use('tableau-colorblind10')\n", - "matplotlib.rcParams.update({'font.size': 12})" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "def system(tit, res, times, pr, title):\n", - " f = pl.figure(figsize=(6, 4))\n", - " pl.plot(times, res, '-o', markersize=\"4\")\n", - " pl.xlabel(r\"$t$\")\n", - " pl.ylabel(r'$\\langle \\hat n_{d} \\rangle$', fontsize=14)\n", - " pl.ylim((0,1))\n", - " if pr!= True: pl.title(tit)\n", - " pl.grid()\n", - " if pr==True: pl.savefig(\"plots/\"+title+\".pdf\", bbox_inches='tight')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "def occupation(res, times, chain):\n", - " f, ax = pl.subplots(figsize=(7,5))\n", - " img = ax.imshow(res.T, cmap='Blues', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", - "\n", - " y_label_list = times\n", - " x_label_list = chain\n", - "\n", - " ax.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - " ax.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - "\n", - " ax.set_yticklabels(y_label_list)\n", - " ax.set_xticklabels(x_label_list)\n", - "\n", - " ax.set_ylabel(\"$t$\")\n", - " ax.set_xlabel(\"$N_{i,j}$ chain sites\")\n", - " \n", - " pl.title(\"Chain occupation\")\n", - " f.colorbar(img)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "def bonddim(res, times, chain):\n", - " f, ax = pl.subplots(figsize=(7,5))\n", - " img = ax.imshow(res, cmap='hot', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", - "\n", - " y_label_list = times\n", - " x_label_list = chain\n", - "\n", - " ax.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - " ax.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - "\n", - " ax.set_yticklabels(y_label_list)\n", - " ax.set_xticklabels(x_label_list)\n", - "\n", - " ax.set_ylabel(\"$t$\")\n", - " ax.set_xlabel(\"$N_{i,j}$ chain sites\")\n", - " \n", - " pl.title(\"Bond dimensions\")\n", - " f.colorbar(img)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "def alltogether(resocc, resbond, times, timespl, chain, double, pr, title):\n", - " f = pl.figure(figsize=(13, 9))\n", - " y_label_list = times\n", - " x_label_list = chain\n", - " \n", - " ax1 = pl.subplot(2,2,1) \n", - " if double==True: pl.imshow(resocc.T, cmap='Blues', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", - " else: pl.imshow(resocc, cmap='Blues', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", - " pl.colorbar()\n", - "\n", - " ax1.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - " ax1.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - " ax1.set_yticklabels(y_label_list)\n", - " ax1.set_xticklabels(x_label_list)\n", - " ax1.set_ylabel(\"$t$\")\n", - " ax1.set_xlabel(\"$N_{i,j}$ chain sites\")\n", - " \n", - " pl.title(\"Chain occupation\")\n", - " \n", - " ax2 = pl.subplot(2,2,2)\n", - " pl.imshow(resbond, cmap='hot', interpolation='nearest', extent=[-1,1,-1,1], origin='lower')\n", - " pl.colorbar()\n", - " pl.clim(0,35)\n", - "\n", - " ax2.set_yticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - " ax2.set_xticks([-1.0, -0.5, 0, 0.5, 1.0])\n", - " ax2.set_yticklabels(y_label_list)\n", - " ax2.set_xticklabels(x_label_list)\n", - " ax2.set_ylabel(\"$t$\")\n", - " ax2.set_xlabel(\"$N_{i,j}$ chain sites\")\n", - " \n", - " pl.title(\"Bond dimensions\")\n", - " \n", - " pl.subplot(2,2,3) \n", - " if double==True:\n", - " sitespos = [i for i in range(1,N+1)]\n", - " sitesneg = [-N+i for i in range(0,N+1)]\n", - " sites = sitesneg + sitespos\n", - " else:\n", - " sites = [i for i in range(2*N)]\n", - " for t in range(1, len(timespl)+1):\n", - " i = int(timespl[t-1])\n", - " x = int(timespl[t-1])*0.5\n", - " if double==True: pl.plot(sites, resocc.T[i], '-o', markersize=\"4\", label=r\"$t =$\"+str(x))\n", - " else: pl.plot(sites, resocc[i], '-o', markersize=\"4\", label=r\"$t =$\"+str(x))\n", - " pl.legend(loc=\"best\",prop={'size': 10})\n", - " pl.xlabel(r\"$N_{i,j}$ chain sites\")\n", - " pl.ylabel(r'$\\langle \\hat n_{i,j} \\rangle$', fontsize=14)\n", - " #pl.title(\"Occupation evolution\")\n", - " \n", - " pl.subplot(2,2,4)\n", - " if double==True:\n", - " sitespos = [i for i in range(1,N+1)]\n", - " sitesneg = [-N+i for i in range(0,N+2)]\n", - " sites = sitesneg + sitespos\n", - " else:\n", - " sites = [i for i in range(0,2*N+2)]\n", - " for t in range(1, len(timespl)+1):\n", - " i = int(timespl[t-1])\n", - " x = int(timespl[t-1])*0.5\n", - " pl.plot(sites, resbond[i], '-o', markersize=\"4\", label=r\"$t =$\"+str(x))\n", - " pl.legend(loc=\"best\",prop={'size': 10})\n", - " pl.xlabel(\"$N_{i,j}$ chain sites\")\n", - " pl.ylabel('$\\chi_{i,j}$', fontsize=14)\n", - " pl.ylim(-1,35)\n", - " #pl.title(\"Occupation evolution\")\n", - " if pr==True: pl.savefig(\"plots/\"+title+\".pdf\", bbox_inches='tight')\n", - " \n", - " pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#path = \"/home/berkane/Documents/stage/simulations/\"\n", - "#path2 = \"/home/berkane/Documents/stage/Julia/\"\n", - "\n", - "path = \"/home/angela/Documents/stage/simulations/\"\n", - "path2 = \"/home/angela/Documents/insp/\"\n", - "\n", - "\n", - "chain_coeff = h5py.File(path+\"fermions/fermionic.h5\", \"r\")\n", - "toymod = h5py.File(path2+\"toy_model/results/HxBBT/dat_HxBBT.jld\", \"r\")\n", - "\n", - "toymod[\"data\"].keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "sites = [i for i in range(10)]\n", - "pl.plot(sites, toymod[\"data/occup\"][60])\n", - "pl.plot(sites, toymod[\"data/occup\"][30])\n", - "pl.plot(sites, toymod[\"data/occup\"][10])\n", - "pl.plot(sites, toymod[\"data/occup\"][20])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.46065886596178074\n" - ] - }, - { - "data": { - "text/plain": [ - "[0.24367008165136503,\n", - " 0.2465439160674058,\n", - " 0.24795478868354134,\n", - " 0.24868721474295313,\n", - " 0.24909378199697457,\n", - " 0.2493399585116368,\n", - " 0.24950013242550662,\n", - " 0.2496105102818235,\n", - " 0.24969025876653497,\n", - " 0.2497502822281343,\n", - " 0.249797179435281,\n", - " 0.2498351720344085,\n", - " 0.24986711058460603,\n", - " 0.24989503162469592,\n", - " 0.2499204832940156,\n", - " 0.24994472571721438,\n", - " 0.24996886071974184,\n", - " 0.24999392000269427,\n", - " 0.2500209275744485]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(chain_coeff[\"2000.0/1.0\"].keys())\n", - "print(chain_coeff[\"2000.0/2.0/c\"][()])\n", - "list(chain_coeff[\"2000.0/2.0/t\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Double chain results $\\beta = 2.0$" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t name : Anderson impurity problem (folded chain)\n", - "\t machine : local\n", - "\t method : DTDVP\n", - "\t dt = 0.5\n", - "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.8164965809277259, c2 = 0.8164965809277259, \n", - "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", - "\t convparams : 0.1\n", - "\t options : Dlim = 10, savebonddims = true, verbose = false, \n", - "\n" - ] - } - ], - "source": [ - "\n", - "# path to work from personal computer\n", - "\n", - "path = \"/Users/ariva/Documents/fermions/38d74/\"\n", - "\n", - "# Results for alpha_lit=0.01\n", - "res = h5py.File(path+\"/dat_38d74.jld\", \"r\")[\"data\"]\n", - "info = open(path+\"/info.txt\", \"r\")\n", - "data = info.read()\n", - "print(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['bonddims',\n", - " 'chain1_filled_occup',\n", - " 'chain2_empty_occup',\n", - " 'system_occup',\n", - " 'times']" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "N = 20\n", - "epsd = -0.4\n", - "beta = 2.0\n", - "\n", - "times = [\"0\", \"7.5\", \"15\", \"22.5\", \"30\"]\n", - "tim = [\"0\", \"20\", \"40\", \"60\"]\n", - "chain_fol = [\"0\", \"10\", \"20\", \"30\", \"40\"]\n", - "chain = [\"-20\", \"-10\", \"0\", \"10\", \"20\"]\n", - "\n", - "occ = np.column_stack((res[\"chain1_filled_occup\"][()], res[\"system_occup\"][()]))\n", - "occ = np.concatenate((occ.T, res[\"chain2_empty_occup\"][()].T))\n", - "\n", - "\n", - "list(res.keys())" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "occupation(occ, times, chain)" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bonddim(res[\"bonddims\"][()], times, chain)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(occ, res[\"bonddims\"][()], times, tim, chain, True, True, \"chain_occ_bet=2_double\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Folded chain results $\\beta = 2.0$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t name : Anderson impurity problem (folded chain)\n", - "\t machine : local\n", - "\t method : DTDVP\n", - "\t dt = 0.5\n", - "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.6, β = 2.0, c1 = 0.8164965809277259, c2 = 0.8164965809277259, \n", - "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", - "\t convparams : 0.1\n", - "\t options : Dlim = 10, savebonddims = true, verbose = false, \n", - "\n" - ] - } - ], - "source": [ - "\n", - "# path to work from personal computer\n", - "\n", - "path = \"/Users/ariva/Documents/fermions/sUuWp/\"\n", - "\n", - "# Results for alpha_lit=0.01\n", - "res_fol = h5py.File(path+\"/dat_sUuWp.jld\", \"r\")[\"data\"]\n", - "info = open(path+\"/info.txt\", \"r\")\n", - "data = info.read()\n", - "print(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"Unable to open object (object 'folded_chain_occup' doesn't exist)\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[92], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m occ_fol \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mcolumn_stack((res_fol[\u001b[39m\"\u001b[39m\u001b[39msystem_occup\u001b[39m\u001b[39m\"\u001b[39m][()], res_fol[\u001b[39m\"\u001b[39;49m\u001b[39mfolded_chain_occup\u001b[39;49m\u001b[39m\"\u001b[39;49m][()]))\n\u001b[1;32m 3\u001b[0m \u001b[39mlist\u001b[39m(res_fol\u001b[39m.\u001b[39mkeys())\n", - "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/h5py/_hl/group.py:357\u001b[0m, in \u001b[0;36mGroup.__getitem__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mInvalid HDF5 object reference\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 356\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(name, (\u001b[39mbytes\u001b[39m, \u001b[39mstr\u001b[39m)):\n\u001b[0;32m--> 357\u001b[0m oid \u001b[39m=\u001b[39m h5o\u001b[39m.\u001b[39;49mopen(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mid, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_e(name), lapl\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_lapl)\n\u001b[1;32m 358\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 359\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mAccessing a group is done with bytes or str, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 360\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnot \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mtype\u001b[39m(name)))\n", - "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mh5py/h5o.pyx:241\u001b[0m, in \u001b[0;36mh5py.h5o.open\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: \"Unable to open object (object 'folded_chain_occup' doesn't exist)\"" - ] - } - ], - "source": [ - "occ_fol = np.column_stack((res_fol[\"system_occup\"][()], res_fol[\"folded_chain_occup\"][()]))\n", - "\n", - "list(res_fol.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The occupation measures the number of electrons or holes on each site:\n", - "\n", - "The following cell can be executed to switch from filled_by_electrons-unfilled_by_electrons representation to the filled_by_electrons-filled_by_holes representation." - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "#list(occ_fol)\n", - "for i in range(len(occ_fol)):\n", - " for j in range(len(occ_fol[i])):\n", - " #print(j, \")\", occ_fol[i,j])\n", - " if j%2!=0: occ_fol[i,j] = 1-occ_fol[i,j]\n", - " #print(occ_fol[i,j])\n", - " #print(\"#########\")" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.0,\n", - " 1.0,\n", - " 1.0,\n", - " 0.9492303375801012,\n", - " 0.8221085853332134,\n", - " 0.6776828500834261,\n", - " 0.5720971694543087,\n", - " 0.5273477604814314,\n", - " 0.5261365902560947,\n", - " 0.5331311087158226,\n", - " 0.5240071064983182,\n", - " 0.500500369997204,\n", - " 0.4831515300615675,\n", - " 0.49133011998777504,\n", - " 0.5279373298700886,\n", - " 0.5791050367950586,\n", - " 0.625543266082979,\n", - " 0.6547108142994459,\n", - " 0.666001361425089,\n", - " 0.6681321792284813,\n", - " 0.6722056628395839,\n", - " 0.6849149978637048,\n", - " 0.7054750817005258,\n", - " 0.7274258471230926,\n", - " 0.743479474587771,\n", - " 0.7500925072701339,\n", - " 0.7491501723867717,\n", - " 0.7461678932365814,\n", - " 0.7465253372351754,\n", - " 0.7522534218517924,\n", - " 0.7612329507560163,\n", - " 0.7689431907676597,\n", - " 0.7713276667603857,\n", - " 0.7668640266921565,\n", - " 0.7566683468480119,\n", - " 0.7428839503819253,\n", - " 0.7267807275057369,\n", - " 0.7080566225327188,\n", - " 0.6857203388884013,\n", - " 0.6595988435402462,\n", - " 0.6311147021603905,\n", - " 0.6027238309646079,\n", - " 0.5765187693263762,\n", - " 0.5530535487834857,\n", - " 0.5311178350965653,\n", - " 0.5084556908686778,\n", - " 0.4770857131620225,\n", - " 0.4324425961703501,\n", - " 0.37923973111800374,\n", - " 0.33209156818307095,\n", - " 0.3087635822297634,\n", - " 0.3119192944956135,\n", - " 0.33370407426998305,\n", - " 0.36026524261149695,\n", - " 0.3782498515625513,\n", - " 0.38045564280831085,\n", - " 0.36846194185463504,\n", - " 0.3474060428171668,\n", - " 0.32955133728903474,\n", - " 0.3318857976652411,\n", - " 0.3574666398262629]" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(res[\"system_occup\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#system(\"Double chain system occupation\", res[\"system_occup\"][()], res[\"times\"][()], True, \"system_double_beta2\")\n", - "system(\"Folded chain system occupation\", res_fol[\"system_occup\"][()], res_fol[\"times\"][()], True, \"system_folded_beta2\")" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(occ_fol, res_fol[\"bonddims\"][()], times, tim, chain_fol, False, True, \"chain_occ_bet=2_folded\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Results at low temperature, $\\beta = 2000.0$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t name : Anderson impurity problem\n", - "\t machine : local\n", - "\t method : DTDVP\n", - "\t dt = 0.5\n", - "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.6, β = 2000.0, c1 = 0.4606588659617806, c2 = 0.46065886596178074, \n", - "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", - "\t convparams : 0.001\n", - "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", - "\n" - ] - } - ], - "source": [ - "res_cold = h5py.File(path+\"fermions/results/qc37d/dat_qc37d.jld\", \"r\")[\"data\"]\n", - "info_cold = open(path+\"fermions/results/qc37d/info.txt\", \"r\")\n", - "data_cold = info_cold.read()\n", - "print(data_cold)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "occ_cold = np.column_stack((res_cold[\"chain1_filled_occup\"][()], res_cold[\"system_occup\"][()]))\n", - "occ_cold = np.concatenate((occ_cold.T, res_cold[\"chain2_empty_occup\"][()].T))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxcAAAIyCAYAAAC5NjcNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACc60lEQVR4nOzdeZwcdZ3/8ddnrszRM7kTcgBJCCQhUQIEOQKCgkYjntl1QVBYdVEQV0VQ1h8oKh6rUVcXRFnwlkMRvDgigigEAQMkYAIhQBLInck5M0nm/Pz+qJ6kM1010z1TPd3T837yqEemv/Wtqm/1MP3tT30vc3dERERERET6qiTfBRARERERkeKg4EJERERERGKh4EJERERERGKh4EJERERERGKh4EJERERERGKh4EJERERERGKh4ELyysweMrObCuU8g4GZrTGzq/JdDhGRQtGbz0Uzm2Rmbmanhr0udGZ2jZm9mO9ySPFRcCE5Y2YjzewbZrbSzPaZ2RYz+5uZfcDMymK+3HuAy2I+54BmZjeZ2UMhu04AvtPPxRERCWVmP0l+Ke/cdpnZ381sfr7LlqVXgXHA4/kuSIYWAifluxBSfOL+gicCgJkdCjwCtAGfB54GWoFTgMuBZ4ClcV3P3bfHda5i5+5b810GEZEuHgbem/x5OHAp8Fszm+HuL+WvWJlz93ZgU77LkSl3bwQa810OKT5quZBc+T4wBDjO3X/p7ivcfZW7/xQ4HliVmtnMrjazTWa23cx+ZmaJlH3Hmdm9yZaPRjP7h5m9pcvxB3WL6nzd3XnDmNk4M7vNzHaa2d7keeZ0yXOEmd2RPOceM3vGzM5O2X+8md1nZruT5X3CzE5M7ktrhjazU5NP6yYlX19oZm1mdpaZLU+2+jxuZrNTjhluZr8ws1eS5VxpZp82M+u8DvAh4PSUp4EXJvcd1PxvZrVm9kMz22pmzWa2xMzenLK/s6n/vWb2x+Q9v9x5PhGRGLS4+6bk9hxwJVAOvLYzQ0+fz2Z2RvKz6k3JVvI9ZrbCzN6aeiEzO8bMHk1+3q0ys/eSgeRn4IvJz+RHU8uW3B/VTep9ZrYoWZ7nzex0M5tgZveYWVOyjKd1OddUM/tN8l53mNmfzOw1Kfs764m5ZvZU8txPmtkJKXnKzezbZrYuea8bzey2lP1h9dEFyfK0JI+71lJ6GlgGdauZzUze787k/T1nZu/P5D2W4qDgQmJnZiOA+cB17r6r6353b3X3ppSkfwFGAGcA5wBnA59N2V8H3A68ATgOWAT83syO6qEoPZ23a7kN+C0wPZn3dcBm4H4zG5XMcwjwKDAMeAfwGuBqoCO5fybwN2AH8EbgWIIuSNn+rZUA3wAuSZZjK3C3mVUl9w8B/gm8Czga+DLwReDC5P6FwC3A3wma6ccRvIdhfgTMA84HZgOLgT+a2fQu+b4O/IygQr0NuCmD34GISFbMrAL4D6AZeCqZ1uPnc4qFwFeBYwi6KN1uZsOT56kC7gF2Js/xAeAKYEwPZToWuBX4dfK8C4HvZnhLXwZuIPh8fY7g8/OnwP8R1BErgFvMrDx5rbEELf9bgNMIui6tBB4ys9Ep5y0BvgZ8gqBu3AL8KiUY+DhBa9D5wJEEddZj3dzj2wjqg58Ds4BPAx8DvtAla091663ANoKeCq8h6LK8o5v3R4qNu2vTFutG8IHtwHsyyPsQsKxL2g3A33s4bhnw/7qc56a+nBc4M1nuo1PShgAbgc8nX3+ZoNm7JuIcP0+WrSRi/zXAi13STk1ed1Ly9YXJ12em5BlO0Hz9oW7K/13g/pTXNwEPheRbA1yV/Hlq8lrzu+R5CvhR8udJyTyXpewvBRqAj+T7/zdt2rQN7A34CUEX2s5uOh3Jf9+TkieTz+czutY9wNhk2rzk6w8nzz08Jc+sZJ6ruinjL4DFXdIuTR53avL1pIjXn0w55oRk2qdT0o5Nps1Kvr4GeKzLtQx4qfNcKfXEcSl5TkymTUu+/i7wIGAR93RQfUTQNe1XXfJ8AtgLVCRfP0QPdSuwC7gw3/9facvfppYLyQXLMv+yLq83EFQIwcnMRpvZ95PNyTvNrBGYCRzel/OGmAlsc/cVnQnu3kzw5GtmMul44FE/uOUl1fHAA+7e0UPZMvH3lHLsIHjiNRPAzErM7EozW2pm9cn35KP0/J50dXTy3791Sf8bB+6509KU8rQTPCXr7v0UEcnU4wRP9mcDc4DrgZ+ldHvK5PO509KUPJuBdg58Vh0NPJf8TO3M80+CL8TdOZqg1TrVIz0c0ym1Luock/FMSFpn68kJwPEWdKttTH6+NxAEK0emHOddzr0h+W/nvf6YoOXgRTP7gZktSLYKRelseU/1V6ASOCLifjqvm1oXLCRo2X4o2fXquG6uKUVIwYXkwiqCJ09H95QxqaXLa+fg/zd/QtA0/Jnkv7MJKo/uPiQzOW8+dJAefJX34jyfBv4L+B7wJoL35CZ6fk/6ohDfTxEpDnvd/cXk9pS7fxZYB3yyF+fq+lkF+f2sak352btJK0n59wEOBFud2zSC1oZOHckHPaHncfelwGSCSVRaCFoylppZXe9uY79u6wJ3/zJwFPArglahx8zs2j5eUwYQfTGQ2Hkwc9O9wKVmNrTr/uQgs5osTvl64Pvu/nt3f5agGXxKPKU9yHJgpJntD4rMbAhBU/M/k0lPAqd0U/4ngTPNLOpvawswxsxKU9KinursnyLQzIYBMwj65kLwntzn7j9y96fd/UUOfqIFQQVQSveWp5wv1es5cM8iIvnQDnSOM8vk8zkTK4AZyc/UzvPMBNLqqpDjTumSNjeL62ZjCUErwrqUgKtzy2q2P3dvdPe73P0/CVqEZgCnR2RfTnpdcDpBt6isZuxy95fd/fvu/i8EM0ZenM3xMrApuJBcuYTgycyTyZkyjk7OfnE+wQdn1y/C3VkJnGdmr7FgxqRb6flLc288CDxBMLBurpnNIhjAXEnQpxSCWbBKgN8l80w2s7PtwGwk3yC4t1+a2RwLZpb6VzM7Obn/L0A18KXOfQQD5rpy4Btm9vrkDCE/I2gWvyW5fyVwhpm9wcyOSj4VOrHLOVYD05Mzd4xKVsQHXySY4vHXwPfNbJ6ZTTez7xI8bfpmFu+diEhfVJjZIcntSDO7mqD1+67k/kw+nzNxC8Fn6S8smDXqJIJBzHt7OO47wMlm9pXkZ+67CVqQc+E6gjrud2Z2mgWzTp2avHbXACeSmV1hZucl64DJwAcJArYXIg75GrAg2eX2KAtm0boG+Ja7h7UGhV0zYWbXm9kbk/XjscBbOPBgTAYBBReSE+7+CsET+d8SfDg9RdBf9T8IvrRm86Tp3wn+X30ieb77gH/EVtgkd3eC2ZeeB+5OXuMQ4E3uXp/Ms5FgAHYDwYwjy4GvkOzqlGxZOQMYTdBXdSlBBdSe3L+S4D04l+A9+CDwuZDidCTTf0gQjB0CvM3d9yT3fzl5/t8RjM0YTtBFKtXNyXt4lGC2qXMjbv3DBDNw/YKgL+1c4Gx3fz4iv4hI3E4jaJXeSFBfLAD+w91/AZl9Pmci+Rk6HxhJUKf8kiBw2NLDcU8C7yOYHelZgqlyP5Xx3WUhOU7kZKAeuJPgYdIvCcbUbcziVLsJZmr6O0GZ3w0sSNZDYde9h6BOuoCgfvoOwQO1L2ZxzTaC+uhmgnGCiwhm9XpfFueQAc6Cv1cRKRQWrB9xk7trkUsREREZUNRyISIiIiIisVBw0UcWrJK80YLVmF8wsw+n7DszOX3qHjP7i5llO02oiAxCZnapBSulN5vZT3rI+ykLVsrdbWY/ChtbI4VB9YWI5ELUZ4sdWCW+MWW7OuflUbeovknOMvGiuzdbsKLxQ8DbgLUEsyt8GPgDQR/509z9pKhziYgAmNl7CMbdzAOq3P3CiHzzCAa1vpFgrvm7CBbfurKfiipZUH0hIrnQzWfLNoLJXcrdva2/yqOWiz5y9+XJhXwgmOHHCRabeQ+w3N1/7e77CAY1H5P8pYuIRHL3O939twQVQ3cuAG5Ofg7tIPhSemGOiye9pPpCRHKhm8+WvFBwEQMLVo/eQzCLxUaCWYRmkrKKZXJF55dIX0lURKS3DvqcSf481sxG5qk80gPVFyKSCxGfLZ3Wmtk6M/uxmY3KdVk0G00M3P0SM/s4wdRxZwDNQIJg+s9Uu4Darseb2UXARcGLsuOtcngui9sz67qANKQvKt1NcmhPuyy634VeP7tTRJ4jLV9EfB1yfEl5+OLXQ+sq09JqK8P/tGrK09MryjKP8cNuK6pnY6ZvQdQ5sjm+I+QE2ZTLIi6Wxf+JffbUU0/Wu/vo3h5fWne4e9u+jPL63i3LgdTMN7r7jb24bILgc6VT58+19NzqIXkQZ31hcHzXT5SoSj39U0pEUkV9enftS9QGdLj3qSp6y1vmeX19ZjMoP/nkUxnVFxGfLfXACQTT4o8ErieY1nheH4rfIwUXMXH3duCR5CJxFwONQF2XbHUE6yN0PfZG4EaAkuoxPmTae3Nc2qSob49hX7hLI/5XKQlZy66jPT2tPaKrX9jxUdfKZnxQWXggkKY8osoNOX7IIRNCs86bNyst7U3TR4TmnTMuPf3QkVUhOQkNpsICkdb2jtDDy0vT87Z3hL+HbSHpQyKCnrAz7GtJ/51HlasspFxRAVZpSfr/o2Fpcagqt7V9Od7b9zFkxjkZ5d331Pf2ufucvlwvqevnTOfPaZ8zUjjiqi8qzHxsl/1RTVbT+lRikeIXuvgH6U9pNsdwrfr6epYsWZxRXrOqjOuLrp8t7v49grWyADab2aXARjOrdfec1RPqFhW/MoJ+bsuBYzoTzawmJV1EipGVZLbF56DPmeTPm91drRYDg+oLkUHJCdpAMtl6pfOzJezCkOPv/wou+sDMxpjZOcnl7kuTM7ecCzxAMGvLLDNbYGaVwOeBZwpq1WP38K2veUO/UFn45h3pW5Sw47OR6fW9I6trlZVY2tbc1hG6lRhp2/6hV103S9/CsmVzux3uoVvIpaJ/Ze5pW3vIZmYRW8h5w283dCtoUW9alv/fmllZ8nOjFCg1s0ozC2vS+xnwITM72syGAVcBP4nxjiQmA76+EJEYxRdcdPfZYmYnmtk0MytJjsX7HvCQu+/q/qx9o+Cib5ygSXsdsANYCHzS3X/v7luBBcBXkvtOBDLrMyEiA5DF2XJxFbAXuBI4P/nzVWZ2WHKe8sMA3P0+4BvAX4BXCKY0/UIu7k76TPWFiCTF2nIR+dkCTAHuI+hi+U+CcRjnxngjoTTmog+SFcLp3ez/M6CpBEUGi2xb0yK4+zUE05GGSXTJ+23g27FcWHJG9YWIHNAZXMRwpm4+W9z9VuDWWC6UBQUX0jd9XYQx7Pg4pj8KPT6LhrqQvCUl4ceXhwxGbmgOGdRO+GDksJmWAEpD7jds0cuomZbCxDGDU3tHete1bP43KAk5b+TcAgXfDyqFEfd4ChERKUrxBReFSMGFiEgsLHz2MxERkYM4QQ+l4qTgQkQkLgOqqUVERPJDLRciItIjU7coERHJgIILGWzCpoONXIwypBtIaAf+iC9dHj42IVTYOaKOD53Stm9dViLHXIQsCrdrXzZjLsKvVx7yPobljfrNhI2DiBrfUR5yb2HjO6LKEJa3JGKxu6J9tt85f6+IiEiPFFyIiEhP1HIhIiI9UsuFiIj0SN2iREQkEwouRESkJwaUarYoERHpSQewL9+FyBkFF9I3YWMbLMNxGNmcE+jrmImsyhC2FkPEGIKwcRS7I8ZclIWMz2iPGHQRVtyOkLxRYxuixldkeq2ow8PKEJY16t3OZk2NbNbwKAgDrbwiIpInarkQEZFuqVuUiIhkQt2iREQkE2q5EBGRHim4kMEmrD9MFl1sQkU90c2mP06mx2d7jgxFTUUb1nWnpS28a1dpH797ht1VScR7ENXdKkzYOaK6VbVn+N5GlSssvWi+kqvlQkREeqTgQkREemKmlgsREcmAggsREcmEWi5ERCQjCi5ERKRbBiWailZERHqiqWhFomU6tiGWqWjDzhvxpDibc2R6qYh78JD3IGq8Qdj7EHZ8pJCsUZcKGxsRVa7waW8jihBWhgzPGZVeNL2JiuZGREQkdxwIn7K+GCi4EBGJg6FuUSIikgGNuRARkR5pnQsREcmUggsREemJukWJiEiP1HIh0s0YhpABrKGd8qM64Ic86Y26Vlh61ADasGEMYeWKWLsim3Uy2trT8yaGhJerI2TtCSvL4ml3NkNXwm43chxE+o72iEEXYetfhI3liBx3UszUciEiIj1ScCEiIj0xzRYlIiKZ0GxRIiKSicHYWiMiIr2glguRcKFdmEKe3kZ1Fwn7MhbVIymbKVsz7Z4S9WUw5L46IroJtYWkD6saEpE3rEtReBFCsob2iop6W8K6L5VFdAMLmw43rAtX1PXC3u7oqWjTd0RN8zvQFMt9iIhILsXbLcrMfgGcCdQAm4BvuPtNyX1nAtcDhwGPAxe6+9rYLh5CHYRFRGJgBMFFJpuIiAxmncFFJltGvgZMcvc64B3AtWZ2vJmNAu4ErgZGAEuA2+O7j3BquRARiYOR1YB7EREZrOJtuXD35V1O7sARwPHAcnf/NYCZXQPUm9l0d38+tgJ0oZYLEZFYZNZqoZYLEZHBLquWi1FmtiRluyjsjGb2fTPbAzwPbATuAWYCy/Zf1b0JeCmZnjNquZC+CeuAn+k4DIgYG9Eeca2Q80aNrfCQc0ROp5uZqDEXrW3p6cOrw++3rT09b0nEoIuwMQ+lIXnDxlZEibxWyCnaszlvFuMoivmrtQIHERHJTMYtF/XuPqenTO5+iZl9HDgZOANoBhLA1i5ZdwG1mZczewouRERiUhK1boqIiMh+uZmK1t3bgUfM7HzgYqARqOuSrQ5oiP3iKVQTiojEwbLYRERkEIt9QHdXZQRjLpYDx3QmmllNSnrOKLgQEYmBacyFiIhkrD3DrXtmNsbMzjGzhJmVmtk84FzgAeAuYJaZLTCzSuDzwDO5HMwN6hYlmYrqf5/p96So8Q7ZfNHKap2LsPUzsjg+JK9HrPsQNjahtiJ8zEV72DiKiPcgdJ2KkLwRxQoV9W6HlStqnYuw4oalRa3fUczfrRU4iIhIz2KdLcoJukD9gKDRYC3wSXf/PYCZLQCuA35BsM7FOXFdOIqCCxGRmCi4EBGRnsUXXLj7VuD0bvb/GZgey8UypOBCRCQmCi5ERKRn8a5zUWgUXEjfhHV3yqr7Usiwn6jpZTO9frbnyFDUVLRhykvDrx/a0yji+2jY2xj25bUjZHpbiJoeNvxaod2iIn6NYd2dwq416GiwtoiIZETBhYiI9MAwTUUrIiIZcHIxFW2hUE0oIhKTOGeLMrMRZnaXmTWZ2Voze19EviFm9gMz22xm283sD2Y2IdYbExGRGOV8Ktq8UnAhIhKXeNe5uB5oAcYC5wE3mNnMkHyfIFiR9bXAeGAH8L+9vQUREcm14g4u1C1K4hc1DiJTUU92O0Lme44a3xE2MKCvU9lGKAvpCpPN9LDZCCtV2JS1AKVRc8GGCBtzEXULYU/ew6enDb9+0Q56tvjuLbnQ0QJglrs3Eqy4+nvg/cCVXbJPBha5++bksbcD346lICIikgPFPeZCLRciIjHJolvUKDNbkrJd1OVURwFt7v5CStoyIKzl4mZgrpmNN7NqglaOe3NxfyIiEge1XIiISAayaLmod/c53exPALu7pO0CakPyrgJeBdYTLOf6LHBppgUREZF8GJiBQyYUXIiIxMAwLIuuaD1oBOq6pNUBDSF5rweGACOBJuAzBC0XJ8ZVGBERiVMHxTxblIIL6R9R4x3CvotFrlERNuaij+M7ooSUIeqpdFlpevre1pCyEj4OIpu3Jixr9LCTzK/VHrYjInPYeUPX1Ai/VPGKccwF8AJQZmZHuvuqZNoxwPKQvLOB/+fu2wHM7H+BL5nZKHevj6tAIiISF425EBGRDMQ1Fa27NwF3EgQJNWY2F3gn8POQ7P8APmBmQ82sHLgE2KDAQkSkUBX3mAsFFyIiMYlznQuCIKEK2ALcClzs7svN7DQza0zJdzlB+/oqYCswH3h3fHclIiLxa89wG3jULUr6JtPuNJHdl0rTk6K+fIV1l4rsUxQWN4eUIfL4kK4/EasvDylLv4ed+8KfNpSFdIvqiJi3NrwLVeZTxpaEXSvifsOmoo36PWTaBapYZ5ztVoz3nOzm9K6Q9IcJBnx3vt5GMEOUiIgMCMXdLUrBhYhITIp2DQ8REYlRcQcX6hbVhZkNMbObzWytmTWY2VIze2ty30lmdr+ZbTezrWb2azMb1825HjKzfWbWmNxW9t+diEh/yrRLlAKQ4qC6QkR6T2MuBpsygjnjTweGAlcBvzKzScBw4EZgEnA4wbSQP+7hfJe6eyK5TctVoUUk/0pKSjLapCiorhCRXnKCoXKZbAOPukV1kZyl5ZqUpD+a2WrgeHf/TWpeM7sO+Gs/Fm9gCBtfETW2IUzkVLQZXqu79EzzhZShpDS8XBVl6U+it++JGHMRMm1tW8SYi7C8YWMjop6Dhy250Nwefq3QIRcR5w178K6H8Ul6HwYN1RUi0nvqFjWomdlY4CjC55d/fUR6qq+ZWb2ZLTazMyKucZGZLTGzJd62t0/lFZH8Ubeowas/6orkdfbXFzla5UdEcq64u0Wp5aIbyTnjfwn81N2f77LvtcDnCeaej/JZYAXQApwD/MHMZrv7S6mZ3P1GgiZ0SqrHZPGIX0QKRryL6MkA0l91BRxcX1SYqb4QGah8YE4zmwkFFxHMrIRgwaoW4NIu+6YC9wKfSE4LGcrdH095+VMzO5dgDvr/jb/EA1RYt6SSkOlpIbzvTa66W2UxFW1pSN6mlvBnimUhXauipocNm4q2NaRbUzbfZ0OnnCV8ituw60PEVLQZphUzQ93DBiPVFSLSK0Xc9KjgIoQF34puBsYC8929NWXf4cCfgS+7e9hqud1x1CtbpEipy9Ngo7pCRHrFGajr42VEYy7C3QDMAN7u7vsHQZjZBOBB4Dp3/0F3JzCzYWY2z8wqzazMzM4j6Hd7Xy4LLiL5U1JiGW1SNFRXiEj2HGjNcBuAFFx0kXza9BFgNrApZd7x84APA1OAa1LSG1OO/ZyZ3Zt8WQ5cC2wF6oGPA+9y9xf68XZEpL9Y0C0qk00GPtUVItJrnS0XmWw96GHNnUlm5qmfQ2Z2dW5u6gB1i+rC3dfSfXP0F7s59qspP28FToixaANbplPDdidsLEZ7xEwKYeMY+vjE2LI4PmpsQ9gpIrKG6gjJXBoyZS2EvwVR5QoT1cUndCrajM9avAzUKjGIqK4QkT6Jb8xF6po7rxCM1/qVmb0mJc8wd++3qacUXIiIxEStEiIi0qMYx1x0t+YO8GQ8V8mOggsRkZhoQLeIiGQk8+BilJktSXl9Y3JK6lARa+6stWDq6vuBK9y9PsvSZkXBhYhIHDSeQkREMuFk0y2q3t3nZJKx65o7ZpYg6Ha5FBgJXJ/cPy/LEmdFwYXEL6yzf9R6FLlapyJsjEc2x2ehNWQcQ3kW4yCy+UIatiZGRcR9heXNZsxFdutcZHzaohWsc6E3QkREMhDzVLRha+64eyPQ2eqx2cwuBTaaWa27N8RbggMUXIiIxELTzIqISAY6p6KNSXdr7oRcGXI8W6yCCxGRmKjlQkREehT/Inqda+6c1WXNnROBncAqYDjwPeAhd98V69W7UHAh+ZXNFLWhc6BGfJnr6ONfbRbdtdra0/OOSoT/abWFdEuqyqILVVipop6Wh3WBCpvKFsLn04x6a/X9OYLGXIiISKZimoo2Zc2dZoI1dzp3fSR5la8CY4DdBAO6z43nytEUXIiIxEBjLkREJCPxTkXb05o7t8ZzpcwpuBARiYliCxER6VH83aIKioILEZGYqOVCREQyEt8K3QVHwYX0j6ixFdlMW1tSmp4WNb1spmMmIo9PL69HjFdobkt//DCyuio0b1t7+nlLhoTcF9Aecg9hX12jJihqDrlWyPAQAMpCTlIa8UU57Au0vlQH9DaIiEiP1HIhIiI9MYseXC8iIrJfzFPRFhoFFyIisTC14IiISGbUciEiIj1RbCEiIj1yNOZCJGfCxmJErn0RNuYi4ttc6FiOkPOGjeOA0HUyOjrCyxW2zsXwqvA/rdaQvKVZrFNRksW317A1NaLGopSWpI89iVznIuMSDD5quRARkYyo5UJERLqlRfRERCQTGtAtIiI90SJ6IiKSMXWLEumjqKlhM50yNkrUVLIdIdMwhF0ri6lso7pFeUje6rKI6WVDuipFTfnaEjKVbEVpenkjZsgNvVaUsCJEdcHS9+domi1KRER65EBLvguROwouRERiopYLERHpkQZ0i4hIjzTmQkREMqUxFyIi0h3TOhciIpIJtVyI9LNsxmFkMZVs6FS02XwZjChW2FSyUeMVwsZnRM3t2hE2FW15euawfBA+RW7Ul9+wcR/6npw9vWciIpIRtVyIiEhPslmHREREBqkin4o2YqocERHJlllmW2bnshFmdpeZNZnZWjN7Xzd5jzOzv5lZo5ltNrNPxHVPIiKSAx0ZbgOQWi5ERGJgFr3aei9dTzBZ4VhgNnC3mS1z9+UHX9dGAfcBnwLuACqAiXEWREREYtSBpqIVyZmwcRBhaVHpUWMuwh4Ph43DiEF5WXoDYEvEmhihYx4ixnKEDaUI++7aGjK2Ijg+Pb0s4stv2PoMUeMzNGg5WlzvjZnVAAuAWe7eCDxiZr8H3g9c2SX7ZcAid/9l8nUz8FwsBRERkdwYoK0SmVC3KBGRmMTYLeoooM3dX0hJWwbMDMl7ErDdzB41sy1m9gczO6zvdyMiIjnROeYik20AUnAhIhIDIzkdbQb/AaPMbEnKdlGX0yWA3V3SdgG1IZeeCFwAfAI4DFgN3BrrzYmISLw05kKkH2UzFW0UC4mb+zrFbcQT54qy9LwNzW2hecO6JbVHlCvsCXfYbERt7eGPNkJnvY3s6hSSFppTupPFkIt6d5/Tzf5GoK5LWh3QEJJ3L3CXu/8DwMy+CNSb2VB335VxiUREpH8U+WxRCi5EROJgsS6i9wJQZmZHuvuqZNoxwPKQvM9w8MidGKJzERHJqSIOLtQtSkQkJnGNuXD3JuBO4EtmVmNmc4F3Aj8Pyf5j4N1mNtvMyoGrgUfUaiEiUqA6V+iOoVuUmQ0xs5uTU5Y3mNlSM3tryv4zzex5M9tjZn8xs8Pjv6GDKbgQEYmBEUxFm8mWoUuAKmALwRiKi919uZmdZmaNnZnc/UHgc8DdybxTgcg1MUREJM8caM1w61kZ8CpwOjAUuAr4lZlNSk5VfifBQ6cRwBLg9hjvJLJAIvkTNjAgcirasEEEEfFx6JSvfRtzUVISfq3KsvRrbd8bPuYibNratoipZDNd7TlqKtqw1KgvtmHX0oyz2Ytzml533w68KyT9YYIB36lpNwA3xHZxERHJrZi6RSVbuq9JSfqjma0GjgdGAsvd/dcAZnYNwZi86e7+fDwlSKeWCxGRGGTaJUpBm4jIIJfdVLQ9zS54EDMbSzCd+XKC6cuX7b9sEIi8RPi05rFRy4WISEwybW0SEZFBLvNpZnuaXXC/5Li7XwI/dffnzSwBbO2SLWpa89gouBARiYlCCxER6VEOpqI1sxKCST9agEuTydlMax4bBRdSeKLGRkSNxQgTtk5Fe8g4iI6Iv+4sxlwMCRlHsWNP+JiLitKwdSrC76uyPL0MHSHvTVtHxDoZIWnRYy5CkyVLcY65EBGRIhVzcGFB5XMzMBaY7+6dQ8GXEyyy2pmvBjiC8GnNY6MxFyIiMTDLbKaoLGaLEhGRYhTvbFEQTOgxA3i7u+9NSb8LmGVmC8ysEvg88EwuB3ODggsRkdhoQLeIiGQkvnUuDgc+AswGNplZY3I7z923AguArwA7gBOBc+K+la7ULUoKT1T3p7AuTFF5o6aoTTs+ogtWSLcoi3jiHPZlcU9reLnCnlpHdWsqDetCFZK3Per4kGtFPTQP686jLj7Z03smIiI9irFblLuvpZshf+7+Z2B6PFfLjIILEZEYGBq7IiIiGYp5QHchUXAhIhITtVyIiEiPnGymoh1wFFyIiMREoYWIiGRELRciBSBsfETkmImQr3lhT5U7wqeMDRuzETUVbdiYh2y6x0QMmaAs5CT7QsZydEScIGza25KocSPdlE8yY6ZF9EREJAM5WOeikCi4EBGJSVTwJiIisl/nVLRFSlPR9oGZXWpmS8ys2cx+kpI+ycw8ZTqwRjO7Oo9FFZF+oKloJYrqCxE5SExT0RYitVz0zQbgWmAeUBWyf5i7R/S7EZFiYpi6RUl3VF+ISEDdogYWMytxj1r8IF7ufmfymnOAif1xzUEhahxF2K81m3UuQtauyOpaEd8b97Wln6OmIuRaQHvI5aK+jobNPNTSll6uiDsI7aIT9eVX34ljoFaJAaU/6wpQfSEiXRRxcFGM3aI257sAKdaa2Toz+7GZjcp3YUQkt8wso00KQiHVFaD6QmTw6JyKtki7RRVNcGFmx5hZOVAZsf+VfixOPXACcDhwPFAL/DIqs5ldlOyLu8Tb9vZTEUUkbiUZbpI/BVZXQB/qiwH6vUNEIGi5yGQbgIqpW9QfgTFAiZndCiwFliX/LQGG9ldB3L0RWJJ8udnMLgU2mlmtuzeE5L8RuBGgpHpMVC8XyWYq2jBhXaWiekVk0VuiOWR62EOHVoTmbQ3p1lReFv5100Pura09/fio5+Clod2iIjJLnxnh77kUnIKpK6Bv9UWFmeoLkYGoyGeLKprgwt0PTTYlvwI8DBwDvAeYRfCE6gf5LF7yXz20FCliii0KX4HXFaD6QqT4aUD3wOHu9Wb2Gnd/qTPNgg7OVe6+J+7rmVkZwXtYCpSaWSXQRtC0vRNYBQwHvgc85O674i6DiBSGYJpZRRcDQX/XFcnzq74QkUDnmIsiVXRPRlIri+Rrz1VlAVwF7AWuBM5P/nwVMAW4D2gA/gk0A+fmqAwiUiBKLLNN8q+f6wpQfSEiqTTmQsK4+zXANRG7b+2/kgxiHRF/eWFjJsKmog0ZwxB53ojezc1t6XlH15SH5g2bSrZ6SMS0tR3pF2wNmcs26mF5aciOqCfreuIeD72NEkX1hYjsp25RIiLSEyN6HREREZGDFHG3KAUXIiIxKbp+piIiEj+1XIiISE/MTFPRiohIzzQVrUiBCBtHEblORcgAiZIsniu3t6UldXSEX6sjZGxEbUX4n1ZryBiP8tLwcrWFjK8IG4dRWR5+fNgXXX31zS31ihIRkYyo5UJERHqihgsREelRkU9Fq+BCRCQGGtAtIiIZU8uFSIEK6/4E4d2lwr74hU1PC6FT0XpIl6TgtOnnHVIaft6wrk5lEY+797aml6Ej5H6j+vmH3a6+++aW3l8REelRjAO6zexS4ELgNcCt7n5hMn0SsBpoSsn+3+7+5XiuHE3BhYhIHLRAnoiIZCq+blEbgGuBeUBVyP5h7p4+kDSHFFyIiMTENGReRER6EmPLhbvfCWBmc4CJ8Zy1bxRciIjEwIAyLXQhIiI96d+paNeamQP3A1e4e32uL6jgQgaOsPEVUVPRhoyZwEK++ZVE/AmEHR+hrDT9aXXY2AgIPk/SihDRl6a5NbM206gxF1pzof+Fjb8RERE5SHYtF6PMbEnK6xvd/cYMjqsHTgCWAiOB64FfEnSfyikFFyIiMQhmi8p3KUREZEDIfMxFvbvPyfb07t4IdAYlm5MDvzeaWa27N2R7vmwouBARiYNptigREclAjGMusrwqQM478KqHsIhITErMMtoyYWYjzOwuM2sys7Vm9r4e8leY2XNmti6WmxERkdxpz3DrgZmVmVklUAqUmlllMu1EM5tmZiVmNhL4HvCQu+/Kyf2kUMuFDGyR61xEpHcVtc5F1FiOEBVl6efY0xb+iRC2poVHlLWlLb0MYeMoykrDnxGE9f/XmIDcyUG3qOuBFmAsMBu428yWufvyiPxXAFuB2lhLISIi8Yp3he6rgC+kvD4f+CKwEvgqMAbYTTCg+9zYrtoNBRciIrEwSmMK3sysBlgAzEr2m33EzH4PvB+4MiT/ZIIK5TLg/2IphIiI5IYTPDqK41Tu1wDXROy+NZ6rZEfBhYhIDIysxlz0NPvHUUCbu7+QkrYMOD3ifP8LfA7Ym3EJREQkf+JruSg4Ci5kYMtmKtqwvKXlEcenJ1lEn5fq8vRuSdv3hT+SKA9ZCKGtI7xbVGt7eiEqy9O7YEV1xVEHqH6W3QrdPc3+kSBoxk61i5AuT2b2bqDU3e8yszMyLoGIiORHfgZ09xsFFyIiMcl0sHYGGoG6Lml1wEHTBya7T30DmB/XhUVEpB+o5UJERLqTZbeonrwAlJnZke6+Kpl2DNB1MPeRwCTg4eRg/QpgqJltAk5y9zWxlUhEROKhlgsREclEXC0X7t5kZncCXzKzDxPMFvVO4JQuWf8JHJry+hTgOuA4gpmjRESkECm4EBlgwsZXhI3DKK/M+JRR07iGjbnY0tAWmrcyJG9za3jbaHvIWIyy0vQyhE1PC1rQLR9ifs8vAX4EbAG2ARe7+3IzOw24190T7t4GbDpwfdsOdLj7ptAziohI/sU7FW3BUXAhIhIDM2KbihbA3bcD7wpJf5hgwHfYMQ8BE2MrhIiIxC/GqWgLkYILEZGYqLFIREQyopYLERHpTrBCt8ILERHpWREPuVBwIQOch68RETq+Iiwt6stgafqfRtSYi7BxEDv2hI+5GBKyzsW+1sw/YspK04+P+kIbVV7JHb3jIiLSkyKfLErBhYhIXBTPiYhIJoq4V5SCCxGReJhai0REpEdquRAZiMK6S4V1i4pSWp5x1rCZYJvbw7trhXVr2rmnNeK86ScuC7lYScRUtNK/jHhnixIRkeLkQHjNXxwUXIiIxEShhYiI9EQtFyIi0jPTIHoREcmMxlyIiEi3DEjv9CYiInIwtVyIDEQe8kwg0+lpAcorM75US1v6+IrKsvAn2B4yFqS5Nfz5RVVFaVpa2LS3elZeONRyISIimVBwISIiPVJoISIiPXHULUpERDKghgsREcmEWi5ERKRbmopWREQyoaloRYpF2PiKsPUwAErTxzuEjZcA2BMyZmJ0InydjJaQ9S9a28MbR2tL0/88S0PWtND32UJhmDpGiYhIDzSgW0REMqJAT0REMlHMYy40c6KISAyCqWgto01ERAavzpaLTLaemNmlZrbEzJrN7Cdd9p1pZs+b2R4z+4uZHR7nfURRy4UUp7AuTGHdotoz7/XY0RH+nKGpJf28rzmkOjTvnua29PNG9MwaUpYe+4f16df0pwXC1HIhIiI9i7lb1AbgWmAeUNWZaGajgDuBDwN/AL4M3A6cFN+lwym4EBGJiYILERHJRFzdotz9TgAzmwNMTNn1HmC5u/86uf8aoN7Mprv78zFdPpSCCxGRmGhAt4iI9CTLlotRZrYk5fWN7n5jBsfNBJbtv6Z7k5m9lExXcCEiUug0Fa2IiGQiy6lo6919Ti8ukwC2dknbBdT24lxZUXAhg4eHNEKGjcOIPD48eV9r+jlGV1WG5m3clz7mImR2WQDKStPHXJREZZaCoNhCREQy0Q9T0TYCdV3S6oCGXF9Ys0WJiMTEMvxPREQGLycYc5HJ1gfLgWM6X5hZDXBEMj2nFFyIiMTACFqhMtlERGRwi3Eq2jIzqwRKgVIzqzSzMuAuYJaZLUju/zzwTK4Hc4OCi6yYWWOXrd3M/jci74XJ/an5z+jfEotI/8m03ULRxWCg+kJEosS5zgVwFbAXuBI4P/nzVe6+FVgAfAXYAZwInBPfXUTTmIssuHui82czSwCbgF93c8jf3f3UnBdMMhO29kUM61x0hCxUUVsR/qe1c1/69YZVlofmrQhZ50JfSwuY1rmQFKovRKQ7MU5Few1wTcS+PwPTY7pUxhRc9N4CYAvwcL4LIiL5p9mipBuqL0Rkvyxnixpw1C2q9y4AfuYe9jh8v2PNrN7MXjCzq5N94NKY2UXJpduXeNve3JRWRHLOMtxk0MlJfRHXk08R6V8xd4sqOGq56AUzOxw4HfhQN9n+BswC1hIsWHI70AZ8rWvG5GIoNwKUVI/prvKRvgibirY9fWpYAEK6OlnEU+mw9KgvkDubW9LSxtaGT1tbFjLyVw/GC5x+P9JFLuuLCjPVFyID1EANHDKhloveeT/wiLuvjsrg7i+7+2p373D3Z4EvAf/SbyUUkX6nAd0SQvWFiBykn6aizRsFF73zAeCnWR7j6LmmSFEzy2yTQUX1hYikKeZuUQousmRmpwAT6H7WD8zsrWY2NvnzdOBq4He5L6GI5IvGXEgq1RciEkZjLqSrC4A73f2g5dPN7DBgBXC0u78CnAn8JDkF4WbgF8BX+7uwkiJsLGVHxJ9uyPiMsojpZcOeRO9uCR/Lsbct/XqV5eExfmnomAt9NS1o+vXIwVRfiEiogdrlKRMKLrLk7h+JSH8FSKS8vhy4vL/KJSL5ZQYlCv4kheoLEQnTQXFPRavgQkQkJgotREQkEwO1y1MmFFzI4BbVLSpk5e7qmvApY8O6L23cE75eSVnIk+0h5aWheUtCzisFLsZfmZmNAG4G3gzUA//l7reE5LuCoPvN4cl833f3b8ZXEhERiVPnmItipeBCRCQWsU8zez3QAowFZgN3m9kyd1+eduFgRqJngCOAP5nZq+5+W5yFERGR+BTzmAvNFiUiEpO4pqI1sxpgAXC1uze6+yPA7wnWTDiIu3/D3Z9y9zZ3X0kwy9DceO9MRETiUuyzRSm4EBGJQabT0CZji1FmtiRlu6jL6Y4C2tz9hZS0ZQSrN0eXIZhO7DSga+uGiIgUkGIOLtQtSga3qDEXbS1pSYnEkNCsYTMELd+0JzTvSYfWpaUNKQuP8TXkYgDK/HdW7+5zutmfAHZ3SdsF1PZw3msIHhr9OOOSiIhIv+pcobtYKbgQEYlJjFPRNgJdI9E6oCEkLwBmdinB2IvT3L05roKIiEi8HE1FKyIiGYixsekFoMzMjnT3Vcm0Y4jo7mRmHwSuBF7v7uviK4aIiOTCQO3ylAmNuRARiUOWgy664+5NwJ3Al8ysxszmAu8Efp52WbPzCFZzfpO7vxzHrYiISO4U+4ButVzI4OYRvR5b03uVVFWF/7lUhIyZWLUpvPfKW48cldHxAKbVngecmKeivQT4EbAF2AZc7O7Lzew04F5371zh+VpgJPCPlP9nfuHuH42zMCIiEh+NuRARkW4ZmU0zmyl33w68KyT9YYIB352vJ8d3VRERyTUtoiciIhlRW5OIiPREwYVIMXMPT2/dl5bU0RGet6YivVvTxq1NoXmHV1akpZVpztmioa5sIiLSE80WJSIiGVFsISIimSjmMReaLUpEJCYxTRYlIiJFLO7ZoszsITPbZ2aNyW1lDoqdMbVciIjERZGDiIhkIAdjLi5195viP232FFyIhAkZc7FrV3oaQGXIVLLNzeEfG7Uh09mWasxFUQhaJfS7FBGR7jnqFiUiIj2xYMxFJpuIiAxuWXSLGmVmS1K2iyJO+TUzqzezxWZ2Rm5L3z21XIiIxERxg4iI9CTLqWjr3X1OD3k+C6wAWoBzgD+Y2Wx3f6m3ZewLtVyIiMTCMMtsExGRwatzKtpMtozO5/64uze4e7O7/xRYDMyPveAZUsuFSJj2trSkbVt2hWZtDVn/IpEoD82bqEz/k9N3zeKh36WIiGQix2MunDw2pqvlQkQkBplOQ6v4Q0RkcItzKlozG2Zm88ys0szKzOw84PXAfTkpfAbUciEiEhdFDiIikoEYp6ItB64FpidP+zzwLnd/Ib5LZEfBhUiYjvQ/+4bNW0OzbtjZnJY2dcKw0LxV5aVpaeqDXzw0Fa2IiPQkzqlo3X0rcEJMp4uFggsRkZgoThQRkUzkYBG9gqHgQkQkDgZaD1FERHrSQeYzQQ1ECi5ERGKj6EJERHqmlgsRgZ0bQ5OfW7s9Le2jb5wcmre8TBO0FStD3aJERKRncY65KEQKLkREYqLYQkREMqGWCxER6ZFaLkREpCed61wUKwUXIiIx0VS0IiKSCXWLEhHY2xCavOq59LEYx77vuNC8pZpOqLjp1ysiIj1Qy4WIiPTINBWtiIhkwNFUtCIikgF1ixIRkUyo5UJEoCP8o6DppRVpaRNG/EuuSyOFSLGFiIj0QFPRiohIRhRbiIhIJtRyISIiPdJUtCIi0hMN6BYRkQyYxlyIiEhG1C1KRKI1bk9LqhmiP63BxlDLhYiI9EwtFyIikhEFFyIi0hNNRSsiIhlRtygREemJWi5ERKRnppYLERHJjMZciIhItwxNRSsiIj1Ty4WIiGRG0YWIiGRAwYWIiPRIYy5ERKQnWqFbREQyUqLYQkREeuBAS74LkUMl+S5AMTOzEWZ2l5k1mdlaM3tfvsskIjlkGW6ZnCrDzw8L/LeZbUtu/22moeUDjeoLkcGlI8MtE4X2+aGWi9y6niA4HQvMBu42s2XuvjyvpRKRnIi5W1Smnx8XAe8CjiF4IHY/sBr4QZyFkZxTfSEySORgQHdBfX6o5SJHzKwGWABc7e6N7v4I8Hvg/fktmYjkQucK3ZlsPZ4ru8+PC4Bvufs6d18PfAu4MK77ktxTfSEy+MTVclGInx9qucido4A2d38hJW0ZcHrXjGZ2EcHTR4DmfUuv/2c/lC9fRgH1+S5Ejuy/t6ry6/NclJwo5t8dwLS+HPzUU08uqiq3URlmrzSzJSmvb3T3G1NeZ/z5AcxM7kvNNzPDckhh6HV9sQ4Oqi/WRVxgWUR6ASv2zxvd38DVp7oCoAMWNQXvUSbirC/6hYKL3EkAu7uk7QJqu2ZM/k9yI4CZLXH3ObkvXn4U8/0V873B4Li/vhzv7m+Jqyxk8fmRzLurS76EmZm7e4xlktxRfdFFMd8b6P4Gsr7WFZDX+qJfqFtU7jQCdV3S6oCGPJRFRAaWbD4/uuatAxoVWAwoqi9EpLcK7vNDwUXuvACUmdmRKWnHABqcJyI9yebzY3lyX0/5pHCpvhCR3iq4zw8FFzni7k3AncCXzKzGzOYC7wR+3sOhN/awf6Ar5vsr5nsD3V+/yfLz42fAZWY2wczGA58GftJvhZU+U30RqpjvDXR/A1lB3VsfPj9yxtRynjtmNgL4EfAmYBtwpbvfkt9SichAEPX5YWanAfe6eyKZz4D/Bj6cPPQm4LPqFjWwqL4Qkd4qtM8PBRciIiIiIhILdYsSEREREZFYKLgQEREREZFYKLjIIzO7wMyeNLPdZrbOzL5hZmUp+0eY2V1m1mRma83sffksb7bMbJaZLTKzejNL63830O+vU7HcRyczu9TMlphZs5n9pMu+M83seTPbY2Z/MbPD81TMXjGzIWZ2c/L31GBmS83srSn7B/T9SfEq5vpisNQVUHT3UrR1Bai+6AsFF/lVDXySYJXGE4EzgctT9l8PtABjgfOAG8xsIK282wr8CvhQxP6Bfn+diuU+Om0AriUYHLafmY0imJHiamAEsAS4vd9L1zdlwKsEK5cOBa4CfmVmk4rk/qR4FXN9MVjqCiiueynmugJUX/SaBnQXEDO7DHiDu7/dzGqAHcCsziXdzeznwHp3vzKf5cyWmU0FVrm7paQVxf0Vy32EMbNrgYnufmHy9UXAhe5+SvJ1DVAPHOvuz+etoH1kZs8AXwRGUoT3J8WpGOuLYq4roLjuJdVgqStA9UWm1HJRWF7PgUVPjgLaOj+AkpYBA/UJR1fFcn/Fch+ZmElwb8D+ubVfYgDfq5mNJfgdLqcI70+K2mCpL4rp3orpXrpTlJ+lqi8yV9ZzFukPZvZBYA4H5qpPALu7ZNsF1PZnuXKoWO6vWO4jEwlga5e0AXuvZlYO/BL4qbs/b2ZFdX9SvAZZfVFM91ZM99KdovssVX2RHbVc9CMzO8/MGpPbvSnp7wK+BrzV3euTyY1AXZdT1AEN/VLYXoi6vwgD7v4iFMt9ZKJo7tXMSghWL20BLk0mF839ycBXzPXFIK0roLjupTtFdZ+qL7Kn4KIfufsv3T2R3N4KYGZvAf4PeLu7P5uS/QWgzMyOTEk7hgPN4AUn7P66MeDuL0Kx3EcmlhPcG7C/j+kRDLB7NTMDbiYYULnA3VuTu4ri/qQ4FHN9MUjrCiiue+lO0XyWqr7oHQUXeWRmbyRoZlvg7k+k7kv237sT+JKZ1ZjZXOCdBNHzgGCBSqAi+brSzIZAcdwfFM99pDKzsuTvrRQoTf7eyoC7gFlmtiC5//PAMwNw8NoNwAyCL2h7U9KL5f6kCBVzfTEY6goornuBQVFXgOqL3nF3bXnagL8AbQTNa53bvSn7RwC/BZqAV4D35bvMWd7fJMC7bGuK5f6K7T5S7ueakN/bNcl9ZwHPA3uBh4BJ+S5vlvd2ePJ+9nX5uzuvGO5PW/FuxVxfDJa6ogjvpWjriuQ9qL7o5aapaEVEREREJBbqFiUiIiIiIrFQcCEiIiIiIrFQcCEiIiIiIrFQcCEiIiIiIrFQcCEiIiIiIrFQcCEiIiIiIrFQcCEiIiIiIrFQcCHShZmtMbOzennscjM7I+byxH5OERHpO9UXIukUXEjBM7PhZuZm9vcu6T8ws+/kq1xh3H2muz+Uy3P2pTITESlmqi9UX0j+KbiQgWA2sAk42swOSUk/FliajwKJiEhBmo3qC5G8UnAhA8FsYAlwP/BOADMrBV4DPN3bk5rZoWZ2p5ltNbNtZnZd6jXN7Bkz22Vmt5tZZcpxV5rZS2bWYGYrzOzdKfsOekqUfH151Lm6lOezZrY+ed6VZnZm13Oa2c+Bw4A/mFmjmX3GzMab2W+S97HazP4zk/OKiBSh2ai+UH0heaXgQgaCzidOvwXelUybTvD/73O9OWGysvkjsBaYBEwAbkvJ8l7gLcBk4LXAhSn7XgJOA4YCXwR+YWbjurlcd+fqLM804FLgBHevBeYBa7rmc/f3A68Ab3f3BLAQ+AOwLHkPZwKfNLN52ZxXRKRIqL5IUn0h+aLgQgaC2QSVxd3AaWZWm0xb7u6tZnapmR0ZdqCZfdjMZobseh0wHrjC3ZvcfZ+7P5Ky/3vuvsHdtxN8GM/u3OHuv07u63D324FVyfNFiTxXinZgCEFTfrm7r3H3l7o5Z6cTgNHu/iV3b3H3l4H/A87p43lFRAai2ai+iKL6QvqFggspaGY2BJgBLHX3HcATwFtJ6T/r7te5+6qw4939JndfHrLrUGCtu7dFXHpTys97gERKmT5gZkvNbKeZ7QRmAaO6uY3Ic6WU80Xgk8A1wBYzu83Mxndzzk6HA+M7y5Isz+eAsX08r4jIgKL6okeqL6RfKLiQQjeL4AP25eTr3xI0dR9Lsv+smT0UdXA3+14FDjOzsmwKY2aHEzzpuRQY6e7DgH8Cls15wrj7Le5+KkEF4MB/R2VN+flVYLW7D0vZat19fi/OKyIykKm+CMma8rPqC+kXCi6k0B0LPOPunR+QvwfmJ9OXmtkoYEvYgcnm8IaI8z4BbAS+bmY1ZlZpZnMzKE8NwQfu1uQ1/p2gQusTM5tmZm9MPnnbB+wFOiKybwamJH9+AmhIDsKrMrNSM5tlZif04rwiIgOZ6ot0qi+k3ym4kEI3m5TpA919DcEAs2EEg9JeCzwbcewsgqdEady9HXg7MJVgwNs64N96Koy7rwC+Bfyd4EP7NcDiHu+iZ0OArwP1BM3iY4D/isj7NeCqZJP2p4CzCd6n1cnjbyIYPJjteUVEBrLZqL7oSvWF9Ds7EOCLDDxm9klgjbv/Nvl6oruvS/58EdDo7rfkr4QiIlIIVF+I9A+1XMhA9xrgGYBkf9hbu+yLekolIiKDi+oLkX6Q1eAkkULj7h9KeXkc8LOU168Bnu/fEomISCFSfSHSP9QtSoqSmf2aYF7za/JdFhERKVyqL0TipeBCRERERERioTEXIiIyoJnZL8xso5ntNrMXzOzDyfRJZuZm1piyXZ3v8oqIFDO1XIiIyIBmZjOBF9292cymAw8BbwO2EUy5Wd7N6soiIhIjtVyIiMiA5u7L3b2582VyOyKPRRIRGbQG1WxRo0aN8kmTJuW7GCIiBenJJ5+sd/fR+S5Hb5jZ94ELgSrgaeAeYFRy91ozc+B+4Ap3rw85/iLgIoCamprjp0+f3h/FFhEZkLqrLwZVt6g5c+b4kiVL8l0MEZGCZGZPuvucfJejt8ysFDgZOAP4b4IVh6cTrNo8ErgeqHX3ed2dR3WFiEj3uqsv1C1KRESKgru3u/sjwETgYndvdPcl7t7m7puBS4E3m1ltfksqIlK8FFyIiEixKSN8zEVnU73qPhGRHNEHrIiIDFhmNsbMzjGzhJmVmtk84FzgATM70cymmVmJmY0Evgc85O678ltqEZHiNagGdItIcWttbWXdunXs27cv30UpaJWVlUycOJHy8vJ8FyUODlwM/IDggdla4JPu/nszOxf4KjAG2E0woPvcfBVURAqD6orM9aa+UHAhIkVj3bp11NbWMmnSJMws38UpSO7Otm3bWLduHZMnT853cfrM3bcCp0fsuxW4tX9LJCKFTnVFZnpbXxRMtygzu9TMlphZs5n9pIe8nzKzTcnVWH9kZkP6qZgiUsD27dvHyJEjVVl0w8wYOXKkntiJyKCluiIzva0vCia4ADYA1wI/6i5Tsj/tlcCZwOHAFOCLOS+diAwIqix6pvdIRAY7fQ5mpjfvU8F0i3L3OwHMbA7BNIJRLgBudvflyfxfBn5JEHDE7u9/u58f/fRWmvbuo6aqktcdexSjx4wDYOuWjTzx9AsH7RszdhwQ/CLqN2/ksadXBvurKznluOmMGTeBEjO2bN7A4n+soGnPPmpqqnjjKa9l4mGTKC0ppaTE2LBuLYv++hQNTXuprali3unHMeHQSQCsf3VN5D6Ahh1baGpqoaV8GBWtO6mpqaB2+JiQfTuoqRlC3YhgnwG7t2+mqbGZlorhDGneTqJuCCPGjMfM2LF5Pbt376V5yCiGNNdTV1fF8LETDlx366tMXPI1JrCN9Yxk3Zz/onb0oT3uC9v/6gn/j9oxkwCjYctaJv7jWiZQz6uM4eXTvk1i4iw6SiuhpJSd61/giAc+zBQ28TKH8NKZNzFswlHBe7VzD1f89mle2bGHw4ZX8813HcuEYdX7r9vd/lwf+9nfPc3aHXuYNqaOP3z0dKaM0uyYIiIiMrAV3CJ6ZnYtMNHdL4zYvwz4qrvfnnw9CtgKjHL3bd2duzcLI330Pz7E7qa9WR2TC2VlZRx66KEMGTKE1atX09zcvH9fbW0tb3rTm3J6fetow60EMDADd8rb9zBt231Ut26juqUew1ky/kIahxxConkTc1/5HonWtIVwY7XXyymnjVIcM+hwWN0xkmnbr6Gd0pxeOy4lBtPHDmX5VWfnuygD3nPPPceMGTPyXYwBIey9GuiL6MVFi+iJFDfVFdnJtr4omJaLLCSA1GkEO3+uBdKCCzO7CLgI4LDDDsv6Yg170vuZve3MkwG4+4G/p+2bn9yHO/c8+Fja/nmnvw7H+dNf/5G27+TjZ9Le3kF7eztPPvvCQfva2trYuHE9+/a1pJexoYGnl/ydkcOHMmp4HU1t5Sxd9gyNjY0kEglOOfkkJh9SA8CWBsBSesN5B6NTHphvDdk/rBrcS9m114PAAsCM1tJq/jl2wf6sTU1NLP7bYpqaniCRSLBrzsVMOSJ4z4/920WU2oFAtt2Np19/4/7XYfuXnXo94BzzyKVp+1485tOUtu2jtH0Phz//o/3FKjE4onQb+8Z9lp0jj+Wnq4fwniFLObRkJyvbx/LO3Rdz/Uf+df+55t/wFzpS4usSg3sufkOP++I8tsNh5ZbdiIiIiAx0AzG4aATqUl53/twQltndbwRuhOBpVLYXq62pZHfjgZaLukQV533o4wA8/PjStH3nJ/cBPPLEsrT9F3zkkwA89uSH0vZ9/NP/b//rj16Uvv8HN95MR0cHF3/kwzQ0HQh6ykpL2dcKTz6zkq4tUQ0NDTzy8N94///dTElJCX/4xfdpHjISrBS8nSHN2zj9Xy/Znz9s/1nJ/WH73vJv/8GLL77Ik08+yf33309HR8f+6/7hb8uYvqWZQw89lOXNZ/Lo1joa97ZQVzOE88a/xKlvfO/+67748DVM9g2UmtPuxmobz3FnnRfsW/zVtH3T3n31gWO/uOig/VttOIccu4BRax/l09XL9+ebXrqJ+0b8kKlHf+JA2tihPL95Fx1+oAVh3tHje9zX12OPHF3Lyi3B/7IlBtPGpP4vLQPdunXrWLx4Mf/2b//Wq+Pvu+8+PvGJT9De3s6HP/xhrrwyvddnJnlERKRwFWtdUUgDujO1HDgm5fUxwOaeukT11oUXnEddogozoy5RxYUXnJfRvlwdW1JSwr9feP5B+y756L/zrW99i5tvvpkvfelLdB1707S3mUsuuYTvfe97bN4Df1q0iLvuuos/3/8nRk+eflDeacedxJDmbVgyeJh23En7942ePJ0/3/8n7rrrLu679z6Wv7qdyy+/nC9/+cvcc889+wOLTu3t7axcuZL77ruPRWuH0LCnGXdnV+M+frr+qIPyVpz/K1bbeNq8hNU2norzf5XRvrD9Lef/AeYvhIsfxe1A16hSc45g40HH/uGjpzN97FBKS4zpY4fyh4+entG+vh57138Ery0ZeHTdLwPbAw88wFNPPdWrY9vb2/nYxz7Gvffey4oVK7j11ltZsWJF1nlERKSwFWtdUTAtF2ZWRlCeUqDUzCqBNndv65L1Z8BPzOyXBDNMXQX8JFflOmnuGzlp7huz3pePYysrK5k6dSrjx09gw4YNuDtmxrBhw5g5cybLly9nx44d+/PvbtzLj356G81tpUycOJEJEyYwdOQE/rZsNRs3Psq4ceM46oQzefbZZ9mwYQN33HEHTcnxJ3v2NfPMsys49thjecc73sGxxx7LV77yFTZuPPDlfdy4cSxcuJDNmzdz2WWXHVTWpqYmPvOZz3DmmWcyd+5cDjtiFnwh+B9+apf7GpIYzfca5rNp0ybGjh3LJ8rraGlpoaSkhNLSUtqsmm9tOp3du3dTV1fHZR1D9t9705g5LE6cTeOQsQfGgdx/NZx5DZSUMmVUbeRYh+729bS/p2OnH1KHGfy/ebP48tnHROaT3Hq5voG3/+CvrNyyO7aB9Y888giXXXYZw4YNY9GiRdx5551MmTIl4+OfeOIJpk6duv+Yc845h9/97nccffTRWeUREZF4qK7ITsEEFwRBwhdSXp8PfNHMfgSsAI5291fc/T4z+wbwF6AK+E2X4wa9yy+/nIULF7Jx40bGjRvH5ZdfztixY3F33v/+9x/UwtDY2MgPf/jD/a9LS0tpb28HYP369Xzxi9Gz/JaUlHDFFVfsf/2Zz3wm7bpmxiGHHMLQoUPZtevAUJkhQ4bQ0NDAT3/6U375y18yY8YMXn75ZZqamqiqquKkk06itbWVLVu28OKLL+4v88aNG7tt0tu1axcLFy7kTW96E6WlpXSMuijoKmZGw5BxLD7qauY9+gnY8hwsuAkqh2X9/sbBzKgqL2VvS3terj8YfPKOJSxdt6PbPP9Yu409rcHvYMWmXbzmK3dzwuEjI/PPnjic//mX7sc7n3rqqZxwwgksXLiQWbNm7U8/7bTTaGhI7725cOFCzjrrrP2v169fz6GHHphJbeLEiTz++OMHHZNJHhER6ZnqivgVTHDh7tcA10TsTnTJ+23g2zku0oA1duxYvvnNb6almxnjxo07qFVj/PjxXHbZZaxbt45XX32VO+64I+2Yz33uc0yYMIGvfOUrBx07bty4jK4LQeDxjW98Y3/rwqc//WnMjMcee4znnnuOZ599dn/evXv38pe//IVRo0YxevTotO5WZsYpp5zCzp07aW9v5/nnnz9of0NDA+Xl5Rx++OG8+OKLKYPQS2ggAW/7Dtx7Bdx0FpxzK4w6MuP3Nk7VFWXsae3aMCf9qbOyiHrdWytXrmT69IO7HD788MOxnFtERPqX6orsFExwIf0jqlVj3LhxnHDCCfz9739PCz5mzpwZeWymJk+ezA033JCWPmXKFDZt2sTll19+0GB0M+Od73wniUSC9evXs3v3gdmUEokEEyZM4OSTT2by5Ml87nOfO6hVxMy4/fbbGT9+PGPHjuXFF188MHPWKaewYfxbGP+BafCr97P5+reycO1r2bivgnFVbVx+5X8x9sjje/PWZk0tF7nV01MjgJnX/jFt4P1Dn+zbtM719fUMHTqUsrKDP14zfRo1YcIEXn311f2v161bx4QJEw46JpM8IiLSM9UV8VNwMch017oA3QcQPR3bG50tIHV1dQcFCLW1tVRUVLB9+3ZOPvlkHn300YMChLPPPnv/H2RYq8imTZu4++67efrpp/efs6GhgQcffJDnnnuOuro6EkMuYumqp9jXUQoYG/aWsfDrX+ObN9/RtZg5oZaL/PvDR09P60fbV2vWrGH8+PFp6Zk+jTrhhBNYtWoVq1evZsKECdx2223ccsstWecREZF4qK7IjoILOUguAohMdA0QPvOZzzB58mQAFi1adNAigbW1tQdF+mGtIlOnTmXu3Lmcf/75B7WItLa2smPHDrZt20ZFRcX+wALAKWHD3vL9QUyuqeUi/3oaeN8b06dPp76+nlmzZnHjjTdyyimnZHV8WVkZ1113HfPmzaO9vZ0PfvCD+1sP58+fz0033cT48eMj84iISLxUV2Sn4FboziWtujowNTY2snjx4v1f+ufOnZvxl/8rrrgirZvXN77xDV544QWeffZZHvjTfexq3HPQMUOGDOGMM85gzpw5/OQnP0nrQhaXud9eRFV5GX/++JmxnXOw06qrmdMK3dFUV4gUN9UV2RkMK3TLIJNIJJg3b16vjg3r5mVmTJs2jaFDh9LU1HRQl6tjZx5JU4vz5z//mUWLFu0/z/r16/nGN77Bt771rbhui6ryMvaqW5SIiIgUEQUXUtS66+Z1yCGHkEgkDupyBTAKGDP2EO69996D0jdt2hRr2arLS9nW1BzrOUVERETyaSCu0C0Sm67dqxIlLZz10jWcNbGZ2tqDF8hxd7797W/zyiuvxHLtqopS9sY0nZ2IiIhIIVDLhQxqc+fOPXg8xymnkGi9m2EPX8Gb3vBd7v9LsK+mpoaxY8fyzDPP8OSTT3LiiSdyxhln8POf/7zXYzKqy8vY06JuUSIiIlI8FFzIoBY6nuPdP4Sb38xb1n+H2nd+mca9zSQSCSZNmsTSpUt54YUXeOqpp3jsscf2H7JhwwYWLlyY1UxbarkQERGRYqPgQqSrigSccwuJH76eeU/9O3S0B6t4v+42JrztbYwZM4YpU6Zw99137z/E3dm4cWNWl1HLhYiIiBQbjbkQCTN8ElTWQXsLeDtsXQm3vJdEIsHpp5/OcccdlzYmo+vrnnS2XAym6aBFRESkuCm4EImye0PKC4f6F+DXH6Dk+d8zY+okTjnlFKqrqwGorKzkpJNOyur01eWluENzW0eMhRYZfMzsF2a20cx2m9kLZvbhlH1nmtnzZrbHzP5iZofns6wiIsVOwYVIlFFHgnX+iRhUDoO1j8KvL4BvHsEh1W3Me/ObGTp0KEMqKhg7ekRWp6+qCHolaq2L4rNu3Tpuv/32Xh//wQ9+kDFjxjBr1qyD0u+77z6mTZvG1KlT+frXvx55fKb5isjXgEnuXge8A7jWzI43s1HAncDVwAhgCdD7X4yISIyKta5QcCES5dzbYNRRYKUwehpc9BBc9jy8/3fwmn9h7otfp6p9F1OmTGHX7t2MeeaGrE5fXV4KwJ4WDeouNg888ABPPfVUr4+/8MILue+++w5Ka29v52Mf+xj33nsvK1as4NZbb2XFihVpx2aar5i4+3J371w0xpPbEcB7gOXu/mt33wdcAxxjZtPzU1IRkQOKta5QcCESZfhkuORx+Pz24N/hk6GkDKacAW//HonWbbzppS9w2MTxVJbB31bvy+r0VRVBcKEZo/Jox2r4/onwpRHBvztW9/mUjzzyCJdddhl33HEHs2fP5uWXX876HK9//esZMeLglrAnnniCqVOnMmXKFCoqKjjnnHP43e9+l3ZspvmKjZl938z2AM8DG4F7gJnAss487t4EvJRM73r8RWa2xMyWbN26tZ9KLSIDguqKrGi2KJHeGnUkQ7auZFzzixxx2HiWvOxs27aNkSNHZnR4dXnw56cZo3Lkvith07Pd59nwJLTuDX7e+jzccDKMPz46/yGvgbd033R86qmncsIJJ7Bw4cKDmqpPO+00Ghoa0vIvXLiQs846q/tyAuvXr+fQQw/d/3rixIk8/vjjvc5XbNz9EjP7OHAycAbQDCSArpHCLiBt9gV3vxG4EWDOnDmaZUFksFBd0W2+3lBwIdJb594GP3sHE3Y/yaFH/QsrVm/k/vvv55xzzsnocLVcFIDOyiLqdS+tXLmS6dMP7nnz8MMPx3Juiebu7cAjZnY+cDHQCNR1yVYHpNfcIiJRVFdkRcGFSG8NnwwfX8qE786hurqKo446igcffJD3vOc9VFRU9Hi4Wi5yrIenRkDQvF3/AnhHMHh/1FFw4d09H9eN+vp6hg4dSlnZwR+vfX0aNWHCBF599dX9r9etW8eECRN6na/IlRGMuVgOXNCZaGY1KekiIqoresjXGwouRPqipJTKqacxcufLTJ50OCtXruTRRx/ljDPO6PFQtVwUgHNvg1vPgfpVwexg597W51OuWbOG8ePHp6X39WnUCSecwKpVq1i9ejUTJkzgtttu45Zbbul1vmJhZmOANwJ/BPYCZwHnJre/A980swXA3cDngWfc/fk8FVdEBiLVFVnRgG6Rvpr+dibufIKq6hrGjx/Pn/70p4wWxtNsUQUgbNB+H02fPp36+npmzZrFo48+2qtznHvuuZx88smsXLmSiRMncvPNN1NWVsZ1113HvHnzmDFjBu9973uZOTMYlzx//nw2bAjWZekuX5Fygi5Q64AdwELgk+7+e3ffCiwAvpLcdyKQWb9FEZFOqiuyYoNpdeA5c+b4kiVL8l0MKTZtzTR9Zw73Tv4Czc3N3H333XzhC19g2rRp3R724tYGjvzi7/nZB07m/a+b0k+FLW7PPfccM2bMyHcxBoSw98rMnnT3OXkqUsFQXSFS3FRXZCfb+kItFyJ9VTaEmslzGN78KiNHjqS6uppFixb1eJhaLkRERKTYKLgQicP0s5mw83EaGxs59dRTeeKJJ9i+fXu3h2jMhYiIiBQbBRcicTjyTUxoDObJnjFjBu7On//8524P0WxRIiIiUmwUXIjEYUgdtRNnUNe6hb1793Lcccfx4IMP0traGnlIRVkJZmq5EBERkeKh4EIkLtPPZsKOv1NfX8/s2bPZvXs3F154IVdccQWbN29Oy25mVJeXsVdjLkRERKRIKLgQicu0+Uzc/RQAv//97wFwdzZs2MDChQtDD6mqKGVPq7pFiYiISHFQcCESl8QY6sZMJNG+g/r6+v3J7s7GjRtDD6kuL1XLhYiIiBQNBRciMbLpb2PC9r+TSCQOpJkxbty40PxVFWVquRAREZGioeBCJE7Tz2bC7ic55ZRTGDp0KACjRo3i8ssvD82ulovitG7dOm6//fZeHfvqq6/yhje8gaOPPpqZM2fy3e9+d/++++67j2nTpjF16lS+/vWvR54j03wiIpI/xVpXKLgQidOIKQyvq2FUlfO2t70NgH/9139l7NixodmDMRcKLorNAw88wFNPPdWrY8vKyvjWt77FihUreOyxx7j++utZsWIF7e3tfOxjH+Pee+9lxYoV3HrrraxYsSLt+EzziYhIfhVrXVFQwYWZjTCzu8ysyczWmtn7IvINMbMfmNlmM9tuZn8wswn9XV6RMDb9bUzY8Rh79+4BYMuWLZF5q8vL2KtuUXnT2NjIokWL+M1vfsOiRYtobGzs8zkfeeQRLrvsMu644w5mz57Nyy+/nNXx48aN47jjjgOgtraWGTNmsH79ep544gmmTp3KlClTqKio4JxzzuF3v/td2vGZ5hMRkcyorshOWSxnic/1QAswFpgN3G1my9x9eZd8nwBOBl4L7AJuBP4XeE//FVUkwvSzGbH0MmzkWVRWVrJ06VLe/OY3HzQOo1NVeSn1Tc15KGTxW7p0KTt37uw2z44dO2hvD1qOGhoauP/++xk+fHhk/mHDhjF79uxuz3nqqadywgknsHDhQmbNmrU//bTTTqOhoSEt/8KFCznrrLNCz7VmzRqefvppTjzxRP70pz9x6KGH7t83ceJEHn/88bRj1q9fn1E+ERFRXdFTvt4omODCzGqABcAsd28EHjGz3wPvB67skn0ysMjdNyePvR34dn+WVyTSIa/luUMWgDs1NTXs3LmTxYsXM2/evLSs1RVlWkQvjzori6jXvbVy5UqmT59+UNrDDz+c1TkaGxtZsGAB//M//0NdXV0s5RIRkeyprshOwQQXwFFAm7u/kJK2DDg9JO/NwHfNbDywEzgPuDfspGZ2EXARwGGHHRZneUXCmdFYPhowampq2Lp1a2QTalV5KXta1C0qF3p6agSwaNGig54Q1dbWcsYZZ/TpuvX19QwdOpSysoM/XrN5GtXa2sqCBQs477zzeM97ggbZCRMm8Oqrr+7Ps27dOiZMSO8Nmmk+ERFRXdFTvt4opOAiAezukrYLqA3Juwp4FVgPtAPPApeGndTdbyToNsWcOXM8rsKKdCdRAQ3NHVRXV7N3716qhoT/qVVXlKrlIo/mzp3L4sWLaWxsJJFIMHfu3D6fc82aNYwfPz4tPdOnUe7Ohz70IWbMmMFll122P/2EE05g1apVrF69mgkTJnDbbbdxyy23pB2faT4REcmM6orsFNKA7kaga3tOHZAevgVjM4YAI4Ea4E4iWi5E8mHuhhupbNtJTU0NANNW3RCar6q8TC0XeZRIJJg3bx4LFixg3rx5oeNisjV9+nTq6+uZNWsWjz76aNbHL168mJ///Oc8+OCDzJ49m9mzZ3PPPfdQVlbGddddx7x585gxYwbvfe97mTlzJgDz589nw4YNAN3mExGR7KmuyE4htVy8AJSZ2ZHuviqZdgzQdTA3BIO9/5+7bwcws/8FvmRmo9y9PiS/SL9KbHmSubs2s27oJQA0bXopNF9VsuXC3TGz/iyi5EgikeCJJ57o9fGnnnoq7uGNrPPnz2f+/Plp6ffcc09G+UREpDAUc11RMC0X7t5E0ALxJTOrMbO5wDuBn4dk/wfwATMbamblwCXABgUWUjBGHUlNSz3V1dUAbKmYFJqturwUd2hu6+jHwomIiIjkRsEEF0mXAFXAFuBW4GJ3X25mp5lZ6ojYy4F9BGMvtgLzgXf3d2FFIp17GxXVCerKOyg12DolfJbkqoqg8VBrXYiIiEgxKKRuUSS7Ob0rJP1hggHfna+3EcwQJVKYhk+Gt3+P2sf/SW1dHZt3ha9lUV1eCsCelnaGV/dnAUVERETiV2gtFyLFo3YcNS1bqK6uilylu6oiCC40Y5SIiIgUAwUXIrlSN56alq1UVlWzdevW0CzV5UHjoWaMEukdMxtiZjeb2VozazCzpWb21uS+SWbmZtaYsl2d7zKLiBSzguoWJVJUqkeSaNtBdfWRNDU10dTUtH9q2k5quRDpszKCdY9OB14hGIP3KzN7TUqeYe6uCF5EpB+o5UIkV6yEmrL2/QFFWOuFWi5E+sbdm9z9Gndf4+4d7v5HYDVwfL7LJiIyGCm4EMmhmsqK/cFF2LgLtVyIxMvMxgJHcfAaSWvNbJ2Z/djMRkUcd5GZLTGzJVHdGEVEpGcKLkRyqLq2jkR1JQCbN29O358yW5QUj3Xr1nH77bf36th9+/bxute9jmOOOYaZM2fyhS98Yf++++67j2nTpjF16lS+/vWvR54j03zFJrnu0S+Bn7r780A9cAJwOEFLRm1yfxp3v9Hd57j7nNGjR/dXkUVkECvWukLBhUgOWe14hrGbysrK0G5RWueiOD3wwAM89dRTvTp2yJAhPPjggyxbtoylS5dy33338dhjj9He3s7HPvYx7r33XlasWMGtt97KihUr0o7PNF+xMbMSgkVXW4BLAdy90d2XuHubu29Opr/ZzGrzWFQREaB46woFFyK5VDeOmubNJGpqQrtFqeUivzZv3swVV1zB+eefzxVXXBHaupStRx55hMsuu4w77riD2bNn8/LLL2d1vJmRSATL+rS2ttLa2oqZ8cQTTzB16lSmTJlCRUUF55xzDr/73e/Sjs80XzExMwNuBsYCC9y9NSKrJ/9V3SciGVNdkR3NFiWSS3XjSbSuoKoqEdFyoTEXufKzn/2MtWvXdpvnpZdeoqWlBYD169fz2c9+liOOOCIy/+GHH84HPvCBbs956qmncsIJJ7Bw4UJmzZq1P/20006joaEhLf/ChQs566yzDkprb2/n+OOP58UXX+RjH/sYJ554InfccQeHHnro/jwTJ07k8ccfTzvf+vXrM8pXZG4AZgBnufvezkQzOxHYCawChgPfAx5y9135KKSIFB7VFd3n6w0FFyK5VBusdVFVPYaXX36Zjo4OSkoOPDTVbFH51VlZRL3urZUrVzJ9+vSD0h5++OGMjy8tLWXp0qXs3LmTd7/73fzzn/+MpVzFyMwOBz4CNAObgkYMSKZ1AF8FxgC7gfuBc/NQTBEZwFRXZEfBhUgu1Y2jpmUr1dXVtLW1sWPHDkaOHLl/d0VZCWZquciFnp4aAVxxxRVs2LABd8fMGD9+PFdf3bc11urr6xk6dChlZQd/vGbzNKrTsGHDeMMb3sB9993H3LlzefXVV/fvW7duHRMmTEg7ZsKECRnlKxbuvhawbrLc2l9lEZGBR3VF9/l6Q8GFSC7VjiPRsoWa2gPT0aYGF2ZGdXmZWi7y5PLLL2fhwoVs3LiRcePGcfnll/f5nGvWrGH8+PFp6Zk+jdq6dSvl5eUMGzaMvXv3cv/99/PZz36WE044gVWrVrF69WomTJjAbbfdxi233JJ2fKb5REQkM6orsqPgQiSXSiuoKeegtS5mzJhxUJaqilK1XOTJ2LFj+eY3vxnrOadPn059fT2zZs3ixhtv5JRTTsnq+I0bN3LBBRfQ3t5OR0cH733vezn77LMBuO6665g3bx7t7e188IMfZObMmQDMnz+fm266ifHjx1NWVhaZT0REsqe6IjsKLkRyrCwxkmFVhplFrNJdqtmiikgikeCJJ57o9fGvfe1refrpp0P3zZ8/n/nz56el33PPPRnlExGRwlDMdYWm4xPJtboJ1HXsoiZiOtqqijKtcyEiIiJFQcGFSK7VjiOxb2NkcKGWCxERESkWCi5Ecq1uPDV711NZWRnRcqExF3Fy954zDXJ6j0RksNPnYGZ68z4puBDJtbrxwYxRNTXs3LkzbX5szRYVn8rKSrZt26ZKoxvuzrZt26isrMx3UURE8kJ1RWZ6W19oQLdIrtWOo6Y1WOsCgunjUueSriovpb6pOV+lKyoTJ05k3bp1oQPn5YDKykomTpyY72KIiOSF6orM9aa+UHAhkmt1wSrdNaMOTEebGlxUV6jlIi7l5eVMnjw538UQEZECproit9QtSiTX6sZT2baLupoqgLRxF1XlGnMhIiIixUHBhUiuDanDKhKMqIKysrK04KK6QrNFiYiISHFQcCHSH2rHkejYRSKRSOvjWVWudS5ERESkOCi4EOkPyXEXVVVVkS0XmrVCREREBjoFFyL9oW48iT2vUl1dzebNmw8KJKrKSwFobuvIV+lEREREYqHgQqQ/1I6npuElampqaG5upqGhYf+u6opg0jbNGCUiIiIDnYILkf5QN45E86b9a12kdo3qbLnQjFEiIiIy0Cm4EOkPteOpbt1GTU2w1kXqoG61XIiIiEixUHAh0h/qxlPqbYwcemAhvU5quRAREZFioeBCpD/UjQdg2BBPmzGquiIILrTWhYiIiAx0BRVcmNkIM7vLzJrMbK2Zva+bvMeZ2d/MrNHMNpvZJ/qzrCJZqRkNJWUkvJHq6uouLRdBtyitdSEiIiIDXVm+C9DF9UALMBaYDdxtZsvcfXlqJjMbBdwHfAq4A6gAJvZvUUWyYCVQO46a1m1quRAREZGiVTAtF2ZWAywArnb3Rnd/BPg98P6Q7JcBi9z9l+7e7O4N7v5cf5ZXJGu140jsW09NTQ3btm2jvT0IJjTmQqT3zGyImd2cbO1uMLOlZvbWlP1nmtnzZrbHzP5iZofns7wiIsWuYIIL4Cigzd1fSElbBswMyXsSsN3MHjWzLWb2BzM7LOykZnaRmS0xsyWpM/SI9Lu6cdQ0rqampoaOjg62bdsGaLYokT4qA14FTgeGAlcBvzKzSclW7juBq4ERwBLg9nwVVERkMCik4CIB7O6StguoDck7EbgA+ARwGLAauDXspO5+o7vPcfc5o0ePjrG4IlmqnUDNjufTpqNVy4VI77l7k7tf4+5r3L3D3f9IUCccD7wHWO7uv3b3fcA1wDFmNj2PRRYRKWqFFFw0AnVd0uqAhpC8e4G73P0fyQrji8ApZjY0x2UU6b26cVQ0b6WuLvjfvHPchVouROJjZmMJWsKXE7R8L+vc5+5NwEuEtIirlVtEJB6FFFy8AJSZ2ZEpaccQVBBdPQN4ymsPySNSWGrHY8Do4bWUlJSwefNmQC0XInExs3Lgl8BP3f15ghbxXV2yhbaIq5VbRCQeBRNcJJ8o3Ql8ycxqzGwu8E7g5yHZfwy828xmJyuTq4FH3L1rJSJSOJJrXdRWONXV1fu7RVWUlVBippYLkT4wsxKC+qIFuDSZnE2LuIiIxKBggoukS4AqYAvBGIqL3X25mZ1mZo2dmdz9QeBzwN3JvFOByDUxRApC3TgAath70FoXZkZVealaLkR6ycwMuJlgGvMF7t6a3LWcoAW8M18NcAThLeIiIhKDglrnwt23A+8KSX+YoHk7Ne0G4Ib+KZlIDGqD4CLRtiNtIb3qilKtcyHSezcAM4Cz3H1vSvpdwDfNbAHBw6jPA88ku0yJiEgO9LnlwswmmllFHIURKWpllVA9kprmTdTU1NDQ0MDevcH3ILVciPROct2KjxAsvLrJzBqT23nuvpVg/aSvADuAE4Fz8lZYEZFBoFctF2Z2LEELw7uAWUCTmS0Cfgf80d13xlQ+keJSO57EnlcoLZ0GwIc//GHGjx/PsMrjNOZCpBfcfS1g3ez/M6CpZ0VE+knGLRdmNsPMvmdma4EHgCOBrwLDgVMJpvv7BLDZzB4ws4/nosAiA1rdOKp2v8iLL74IgLuzYcMGpq77m1ouREREZMDLplvU6wieDn0IGOPu73P32919t7s/4+7XuvsJwBTgN8DbclBekYGtdjwluzfs7w4FQYBR0bxbwYWIiIgMeBl3i3L3nwI/zSDfeuD7yU1EUtWNhz31DBs2lB07dgLBbFFtlUPVLUpEREQGvD4N6Daz/0r+e5yZDYmnSCJFLDlj1IK3v5Xy8nIAxo8fT8tr3qqWCxERERnw+jpb1EPJf68EnjKzZ83sNjP7nJmdbWZj+3h+keKSXEhvwvAhHH300QBceeWVVNYNV8uFDBp6MCUiUrz6tM6Fu/89+e97AcysimD2qNcCbwK+aGb3uPvVfS2oSFFIBhdlLbsZPnw4AL/5zW8YWTNDLRcymDyU/PdKYKaZdRAsbPdMcvuHu2/OU9lERKQPYl1EL7l40T+SGwBm9iSg4EIE9neLeqG+jaFDh2JmbNiwgdfNHMPtWkRPBgk9mBIRKV79sUL3Sf1wDZGBoXIYlFezp72U0lJj6NCh7Nixg5nWxt5WdYuSwUkPpkREikefV+gOY2bjOvvRuntrLq4hMiCZQe04Et4AwPDhw9mxYwf7KGNPSzvunucCihQMPZgSERmAchJcAD8HnjezhTk6v8jAVTeeuY13U1VVxfDhw2lra2Ntey0AzW0deS6cSP7owZSIyMCXk+DC3c9y98nATbk4v8iAVjeexK4XmDdvHqNGjQJg365tAMGMUTtWw/dPhC+NCP7dsTqfpRXpT3owJSIywPV1nYtPJf+daWalXfe7+/N9Ob9IUaodBw0bKCstYerUqZSVldG8bSNAMGPUredA/Qvg7cG/t56T5wKL9A89mBIRGfj62nKxNPnvV4EVZrbUzH5pZlea2dl9PLdIcaqbAB1t0FTPoYceytChQ2nasg5ItlzUrwJPdo/yjuC1SBHRgykRkeLV13Uu/pL8950AZpYAZgKvAc4C/tjXAooUneR0tDRsYNy4oxkxYgQvvfwyVtcRtFxUDYc99UEeK4FRR+avrCK5sTT571eB6Wa2l2Cdi2eBf7q76g4RkQEq1jEX7t7o7o+7+03u/sk4zy1SNJIL6bF7AxUVFRx++OF0tLczrL2B9m0vQ/NuwII8I46Ac2/LW1FF4mRm74WDH0y5+zTgVOB7QD3BgykRERmgMgouzOxfurw+3cyeNLNXzOxBM/u2mX3AzF5rZv2xdobIwJUSXAC85jWvAWBq6U4Oe+wLUFoBb7wqyPO+X8HwyfkopUgu/NjMfmxmNamJejAlIlI8ug0uzGyUmd0GvLPLrpuBV4HPAX8DjgCuJWjqboy/mCJFpGYMWCk0BIO4Z86cSUVFBceVbmLkxofhDVfBIccEeZu25rGgIrGbA8wGlpnZ68IymNmhZvZAv5ZKRERi01Mrw8eAand/R5f0Q4A3u/vLqYlmNgI4NsbyiRSfklJIjN3fclFdXc3IUaPY17CDHUNnMPx1/wGb/xnkVXAhRcTdnzOzE4H/Bh42sy8DX3F3N7NK4LXAJ4Gj81hMERHpg566RV0PtJjZz7qkPwxM6prZ3be7u544ifSkbjw0bNj/clJdOzsb9/HQ1M9ASVnQugHQuCVPBRTJDXdvAb4N/B/wRYJWjBVAA/AowQOqK/JXQhER6Ytugwt3r3f3fwHu7bLrBuBqMxuds5KJFLO6cbA76BbFhqc4ds/DuDtrd3iQVhMsrqeWCykmZvafZrYNWAO8j6Bb7XZgOkG9UuvuM9z9F/krpYiI9EVGg6/d/dYuSb9N/vuCmd0N/B14Gljq7nviK55IkaodDy89BB3t8MdPMmPoXgAaNqwJ9pdWQOUwBRdSbD4P/BL4lruv7Uw0s48B3wCGm9nF7q6xeyIiA1Rvp6I9FDgb+CZQDvwnwROoXcnmbRHpTt14aGmAR74NG5dRN/+LeHklDds2s2/fviBPYgw0qVuUFJU/E4yxWJua6O7XAycQrJG0zMxOyuakZnapmS0xs2Yz+0lK+iQzczNrTNmujuE+REQkQq+mjXX39cB64J7ONDOrBo4hGJAnIt0pKQ/+/cu1UF5D2cTj2F72KIkdO9iwYQNTpkyBmtHQqJYLKR7ufk43+1YkZ5D6b+CvwJAsTr2BYMbCeUBVyP5h7t6WTVlFRKR3Mm65MLPDzKwuar+773H3v7v7D5P5FWSIRHn8hgM/t+3Fbj+XzaXDaWpq4qWXXgrSa0arW5QMKu7e4u6fImgZz+a4O939t8C2nBRMREQylk23qLcBW83sT2b2MTM7NHWnmZWY2RvM7H/MbDXBkycRCbP7wExReAfUr2Jf9UgAVq1aRUtLSzBjlLpFySDk7vfHfMq1ZrYuuYDfqLAMZnZRsmvVkq1bFdSLiPRWxsGFu98AHAn8HngX8GJyle4vm9nPgXrgZ0AF8FFgTPzFFSkSo44ES/75WQmMOpKWZHCxfft2Nm3aFLRc7NsFbc15LKjIgFZPMJbjcOB4oJZgQHkad7/R3ee4+5zRozURoohIb2U1oNvdX3H369z9TQTBw3eAKcArwDx3P9TdL3H3Re7emoPyihSHc2+DUUcFK3WPOgrOvY0hVdV41VB2797N+vXrIZH8grOnPr9lFRmg3L3R3Ze4e5u7bwYuBd5sZrX5LpuISLHq1YBuAHffBfwiuYlINoZPhksePyipqvw52mpHs3PnJjZt2kTbtFHBH2jjFqibkJdiihSZ5EIyvZ4pUUREelBQH7BmNsLM7jKzJjNba2bv6yF/hZk9Z2br+quMIrlSXVHG3qqRNDY20tjYyG9XNLPoiC/RuF3jLkS6Y2ZlZlYJlAKlZlaZTDvRzKYlxwSOBL4HPJR8OCYiIjlQUMEFcD3QAowFzgNuMLOZ3eS/AtDIOykKVeWlNFWOAGDHjh0ANAw5hMUv6XuQSA+uAvYCVwLnJ3++iqDb7n1AA/BPoBk4N09lFBEZFAomuDCzGmABcHWyn+wjBIPH3x+RfzJBJfK1/iulSO5UV5Sxs2woZrY/uMBKaWy1/BZMpMC5+zXubl22a9z9Vnef7O417j7O3T/g7pvyXV4RkWJWMMEFcBTQ5u4vpKQtA6JaLv4X+BzBEyqRAa+qvJQ97TBs2LADwYV3kCjZl9+CiYiIiGSokIKLBLC7S9ougqkDD2Jm7wZK3f2unk6quctloKiuKGVPSzuzZs1i586duDs17TuZ6//Id9FEREREMlJIwUUj0HUF8DqCvrL7JbtPfQP4z0xOqrnLZaCoKi9lb2s706ZNo6WlhaamJl7b/DiJPWvzXTQRERGRjBRScPECUGZmR6akHQMs75LvSGAS8LCZbQLuBMaZ2SYzm9QfBRXJheqKMva0tDFlyhQgGNTdWDkBmrTOhYiIiAwMvV7nIm7u3mRmdwJfMrMPA7OBdwKndMn6T+DQlNenANcBx6GZo2QA62y5KC8vB+Af//gHL9dWcdVhuxib57KJiIiIZKKQWi4ALgGqgC3ArcDF7r7czE4zs0aA5Eqrmzo3YDvQkXzdnr+ii/RNdUUQ63/nO9/Zn7a9YS8LV02GDv2vLSIiIoWvYFouANx9O/CukPSHCQZ8hx3zEDAxpwUT6QdV5aUAbNp0YKZMBza21MDeHVAzKk8lExEREclMobVciAxanS0Xo8aMxezA2hbjKvZAk1bpFhERkcKn4EKkQHS2XJz74UvonNmssrKSj05eA00aTiQiIiKFT8GFSIGorgiCi8q6EXzrW9+ivLyciRMnUpUYBo0KLkRERKTwKbgQKRCdLRd7W9spLS3l0EMPZefOnTQOGatuUSIiIjIgKLgQKRCdYy72tLQBMHnyZHbt2kVDxRh1ixIREZEBQcGFSIFIbbmAILhobW1lQ8cYaFTLhYiIiBQ+BRciBSKs5QJg/Z5ytVyIiIjIgKDgQqRAdG25mDhxIiUlJWza3UxH07Z8Fk1EREQkIwouRApE15aL8vJyDjnkEHbu3EVTc1s+iyYiIiKSEQUXIgWia8sFwOGHH87OnTtpaCkF93wVTURERCQjCi5ECkTnOhd7Wg4EF1OnTqWlpYX1HcOhpSFfRRMRERHJiIILkQJRXlpCiRl7Ww90gTriiCMAWN08XAvpiYiISMFTcCFSIMyM6orSg1ouDj/8cMzg1T2VmjFKRERECp6CC5ECUlVeetCYiyFDhjB82DA2NblW6RaJYGaXmtkSM2s2s5902XemmT1vZnvM7C9mdnieiikiMigouBApINUVZftni+o0ftwhbNu9h44GBRciETYA1wI/Sk00s1HAncDVwAhgCXB7v5dORGQQUXAhUkC6tlwAHD5pMvv2NbNhc32eSiVS2Nz9Tnf/LdB1QZj3AMvd/dfuvg+4BjjGzKb3cxFFRAYNBRciBSSs5eKIqUcC8ML67fkokshANhNY1vnC3ZuAl5LpIiKSAwouRApIWMvFtGnTAHh56558FElkIEsAu7qk7QJqu2Y0s4uS4zaWbN2qyRNERHpLwYVIAek6WxTAsGHDSCRqeHVHS55KJTJgNQJ1XdLqgLRFY9z9Rnef4+5zRo8e3S+FExEpRgouRApIVXlZWsuFmTF6WIJNu5rzVCqRAWs5cEznCzOrAY5IpouISA4ouBApIEHLRVta+vgRNTTsbaWhQat0i3RlZmVmVgmUAqVmVmlmZcBdwCwzW5Dc/3ngGXd/Pp/lFREpZgouRApI2JgLgMNGBz07Xl6l70QiIa4C9gJXAucnf77K3bcCC4CvADuAE4Fz8lVIEZHBQMGFSAGprkjvFgUwZULQB3zVc8/2d5FECp67X+Pu1mW7Jrnvz+4+3d2r3P0Md1+T39KKiBQ3BRciBaSqPLxb1JjRo6murubll1fnoVQiIiIimVFwIVJAOlsu3P2g9MTwUQwbNoxXNmzOU8lEREREeqbgQqSAVJWXArCvS9eoyuGHMGJogu27GtmzR+tdiIiISGFScCFSQKorguAibTraxBjG1QStGWvXru33comIiIhkQsGFSAGpKi8DSFtIj/JqDq0JFtFbvVrjLkRERKQwKbgQKSAHWi5CBnUPaaayslLBhYiIiBQsBRciBaRzzEVaywWQKG1j2LBhCi5ERESkYCm4ECkg1RVBt6iwtS4SFcawYcPYuHEj+/bt6++iiYiIiPSooIILMxthZneZWZOZrTWz90Xku8LM/mlmDWa22syu6O+yiuTCgZaL9G5RiepKhg0bhrvzyiuv9HfRRERERHpUUMEFcD3QAowFzgNuMLOZIfkM+AAwHHgLcKmZndNvpRTJke5aLqpqhjJiaAKANWvW9GexRERERDJSMMGFmdUAC4Cr3b3R3R8Bfg+8v2ted/+Guz/l7m3uvhL4HTC3f0ssEr/uWi4sMZrRJbupqqxUcCEiIiIFqWCCC+AooM3dX0hJWwaEtVzsZ2YGnAYsj9h/kZktMbMlW7duja2wIrkQtc4FAInRJFq3Mny4BnWLiIhIYSqk4CIB7O6Stguo7eG4awju48dhO939Rnef4+5zRo8e3edCiuTSgXUu0lsuqBlNomULlZWVrF27lvPPP58rrriCzZs393MpRURERMIVUnDRCNR1SasDGqIOMLNLCcZevM3dm3NYNpF+0dktKrTlomYMiZYtbNi4CYCOjg42bNjAwoULM7/AjtXw/RPhSyOCf3eoBURERETiU0jBxQtAmZkdmZJ2DNHdnT4IXAmc6e7r+qF8IjnX2S0qbJ0LEkHLRXPzgTja3dm4cWP3J21vhVceg79+A244BbY+D94O9S/ArZoHQUREROJTlu8CdHL3JjO7E/iSmX0YmA28Ezila14zOw/4KvAGd3+5XwsqkkPlpSWUmIWu0M2QoSTat5NIvJaGhqBBz8wYN27cgTw7VsMt/wbbVkH1SBh1FGxYCq1NBJOs+YG83gH1q3J5OyIiIjLIFFLLBcAlQBWwBbgVuNjdl5vZaWbWmJLvWmAk8A8za0xuP8hDeUViZWZUV5SGt1yYUV1RximnnMSQIUMAGD9+PJdffvmBPLeeA/Urg8ChaSus+wfMfh+89+dwxcswenrK+Upg1JGIiIiIxKVgWi4A3H078K6Q9IcJBnx3vp7cj8US6VdV5aXhYy4AqxnFIdXOaaedxp///Gc+/vGPM3bs2AMZurZEdLTD/JQxGefeBj85G3avg9rxwWsRERGRmBRay4XIoFddURY+WxRAzWhqWrcxfPhwAFasWNFl/6gDP4e1TAyfDP/5NFQOhUmnBq9FREREYqLgQqTAdNdyQWIMieZgtqjRo0enBxdVw6F0CFhpMN4irGWitAKmvx1W3gNt+2IuvYiIiAxmCi5ECkxPLRfl++ppb2+npqaGZ555ht27k8vDbHommAnqTV+Gz2+HSx6PbpmY+W5o3g0vPZibmxAREZFBScGFSIHptuWiZjRrhp4MwKhRo2hpaeHuu+8O9j3186DV4rX/2vNFJp8OlcNgxW9jKbNIITOzh8xsX8oEICvzXSYRkWKl4EKkwETOFgVQM4Z95cF4i1GjgvEVa9euDbo3PfsrmHE2VI3o+SKl5TD9bHheXaNk0LjU3RPJbVq+CyMiUqwUXIgUmKrysm7GXIzeP+aiurqampoatm/fDs/9EfbthGPfn/mFZr4bWhrgxQf6XmgRERERFFyIFJyg5SJ6zMXcV75HdXnw8pBDDmHbtm10PP1zGHZY0N0pU5NPDwaAq2uUDA5fM7N6M1tsZmfkuzAiIsVKwYVIgel+zMUYEq31zB+/gzFjxjB27Fj27t3LmueegtnnB9PPZqqza9TKe6B1bzyFFylMnwWmABOAG4E/mNkRqRnM7CIzW2JmS7Zu3ZqPMoqIFAUFFyIFptvZoqpHAgaNW5k+fTpDhw4FYMWeUcFK3Nma+W5oaYSX1DVKipe7P+7uDe7e7O4/BRYD87vkudHd57j7nNGjR+enoCIiRUDBhUiB6bbloqQ0CDCatjB69GgmjB9PXaKK5RwFQw/N/mKTXh8MAFfXKBlcHLB8F0JEpBiV5bsAInKw6opgQLe7Yxby/ScxBpq2YmZMH9bCyNGH8Py6V2lvb6e0tDS7i5WWw4y3wz9/E3SNKq+K5ya6aGxsZPHixTQ2NpJIJJg7dy6JRCIn1xJJZWbDgBOBvwJtwL8Brwc+kcdiiYgULQUXIgWmqjwIEPa1tlNVEfInWjMamoI+4eNevo3Dhk9i9eo2Vq9ezdSpU7O/4NHvgqd+Ci/+OQg0cmDx4sU0NDQA0NDQwOLFi5k3b15OriXSRTlwLTAdaAeeB97l7i/ktVQpHlv8ID/56S9paNpHbU0lF15wHifNfWNG+3Xs4D72Vz/7Ib//09/o6HBKSox3vPn1vPcDHynoMuvY/jk2n8zd812GfjNnzhxfsmRJvosh0q3vPfQ8n7jjSeq//i+MTAxJz/CbD8H6J+HDD8C3prF82qf4ym+XM3/+fM4///zsL9jRBguPhCPeCAtu7vsNhLjjjjsOem1mLFiwICfXkt4zsyfdfU6+y5Fv/V1XfPSiD7G78cCkCiUlRqK6cv/rxj376Ojw0P3d7dOxxX9s6v83neoSVQVdZh2bm2PrElX84Mbc1OFhuqsv1HIhUmCqyoM/y+5mjKKpHp65HTpamfH6dzL0L+tYtmwZ5513XnhXqu6UlMGMd8Czv85J16jGxsa0NHfnySefZPr06dTU1MR6PZGBpqHp4IUsOzqcYTUHHix0/QKZur+7fTp28B0LDLgy69h4ju36OZJPCi5ECkx1RdAtKnLGqMToYPG7JTfD+OMoOWQW06dPZ8mSJWzatIlx48Zlf9GZ74KnfgIv3h8EGjHpDCJKS0upqqqiqamJ6upqRowYwdq1a1mzZg3jx49n586d7NmzR+MxZFCqKC+juaV1/+u6RBVf/87397/u2rKRur+7fTq2+I89//zz0p5sF3qZdWxujk1Uh/R0yBPNFiVSYDrHXES3XCSnydz24v4VuU888UTa29t5+OGHe3fRSacFs1DFPGvUmjVr2Lp1K8cccwxvectbWLBgAW9961s58cQTeetb38oRRxzB+vXraWpqwt33j8cQGSyWLX2ctrZgMgYzoy5RxYUXnHdQngsvOI+6RFXo/u726djiP/Ydb349JSVBa3XnmItCL7OOjffYTkNrCie40JgLkQJz34oNvPX7f+HRy97MyVNC5tt/6mfwh48HP488Es77NbtLR/LRj36Uo48+mosvvpiRI0dmf+E/fhKe+RVc8SKUV/fpHgD27t3Ln/70J4YOHcrpp58e2V3rN7/5DamfQ/01HmPz5s0sXLiQjRs3Mm7cOC6//HLGjh2b8+sWMo25CPRnXXHlZZewbstuPvbRf+fkU8/sl2uKSHG59v99ihWrN/Ov73gT7z7n3/vlmt3VF2q5ECkwPbZcPLzwwM/bX4Jbz6Guro5DDz2Ubdu2sXLlyt5d+LCTobUJvjYBvn8i7Fjdu/MkPf3007S3t3P88cd3Ow6kaxeoysrKiJzxcXeuvfZa1q9fT0dHBxs2bGDhwoU9HygSo1t/8n1e2bSTWVMOUWAhIr120cc/Q3XlEO7/y2L27mnKd3EUXIgUmurk9LORYy52rTvws3dA/SoAjj76aLZt28arr77KHXfcwaJFi0IHU0dpfPQmFh3xJX4z4wcsqjuPxts/0ut7WLduHRs2bGDmzJnU1tZ2m3fu3LnU1tZiZpSUlNDa2rp/2tpcWLt2LV/72tfYtm3b/jR3Z/369Tz66KN0dHTk7NoinXbt2M5Di58kUV3JRz/1X/kujogMYGMOGccpc45mZ8Mevv/ta/NdHAUXIoWmx5aLUUeCJf90rSR4TRBctLW1sWPHDoCsxy8sTrydhiHjcCulYchYFifO7lX5W1paePrppxk2bBhHHnlkj/kTiQTz5s1jwYIFzJs3j5KSEh599FFaW1t7PDYb27dv54c//CGf+9znWLNmDUOHDt3fomJmlJWVcd111/Ff//VfPPHEEwymLqPS/3743a/R0LSX1588m2HDe9GNUUQkxQUXfYpxo+pY9sI6nl36j7yWRbNFiRSYA7NFRQQX594Gt54TtFiMOjJ4DcyYMQOArVu3MmrUKCB8GtgoDUMOSQlaSmkcckivyv/MM8/Q0tLCaaedRklJds8vampqOPnkk/nb3/7G448/zty5c7OfWjfF5s2b+eY3v8nGjRsBKCkpYf78+bzzne+kqanpoDEXn/70p3n55Zf5zW9+w//8z/8wYcIE9u3bx44dOzQmQ2L1j8f+xrMvbeTQMcM4/0P/me/iiEgRKC0r41/e+69c/4Mfc8vPfszXZp+Qt7IouBApMAfWuYjoFjV8MlzyeFpyIpFgxIgR1NfXH5ReX1+/P9gI097ezrJly4LAwh3MwDtIlKTPn96TzZs3s2bNGqZNm8awYcOyPh5g9OjRHHvssTz11FM8++yzvPa1r+3VeQC+9rWvsWXLlv2vR40axXnnBbNtJBIJvvnNbx6U/5BDDuHEE09k8eLF/N///R/t7UGA1zkmo2t+kd741a23AsY5vVn0UkQkwsmnnslf/3Qvz7y4gdt+egPnXHBxXsqhblEiBabHlotuHHfccWzbto2Ojg5qamqoqqrir3/9K6tWrQrt5rNr1y4eeOABXn75ZSZNmkRtXV2ww0qYtu522PBURtdtbGxk0aJFPPzww5gZhx9+eMZl3rx5M1dccQXnn38+V1xxBZv/f3v3Hh5Vde9//P2d3JOZgKANSYoiXkAFQQVFAeEoVrEqiBzrKbTiY0W0HsUjXurRals9taj4/OyhKuqvRaXetRUvqD8rWEHEUEXFGyLIJSSCQG6Q+/r9MUOYmWRynWQ24fN6njxm9tp77W+We+8va9ZeexcX079/fw477DC++uorvv3221bXFR1TeMcCgqM6LUlKSuLUU0+NaC/nXMPoh0hHzH9oDpu3lnDsEXkcN+yURIcjIt3MjGt/hT8znbffLaBkx/aExKDOhYjHtDjnohlDhgyhvr6ewYMHM378eMaNG0dubi6rVq1ixYoV1NYGR0Occ6xdu5a33nqLqqoqRo0axbBhwzjzzDM5//zzCfgz+SznfGr/fi3UVbe436VLlzZMwnbO8d5777U65nvuuYfCwsJGT20aMmQIBx10EAUFBbz66qs8//zzrZ6kXl9fz9y5cwEi5lW05QWDubm5EbdkOefQo6ylvZYv/QczLruU15cU4PMZJ55yassbiYi0Uc8DenPqiCGUVexm5rXXMmXKFGZMv5TlS//RZTGocyHiMSlJPpJ8FvtpUc3Y836LO+64g+uvv54dO3Zw8sknM2jQIDZs2MBzzz3HjBkzmDp1KrNnzyYpKYkzzjiDPn32zq9ISkrihGEnsiv5AFbbAPjnvS3uN/rpTq2d61FZWUlhYWHDKEH4CIHP52PEiBEA7Nq1q00v2Xv66adZtWoVF154IXl5efh8PvLy8pg1a1ar4gKYNWtWw7Z9+vQhPz+fOXPm8NRTTzXcLiXSWn+Zv4DSiuCthvX1jiefeSHBEYlIdzX1F9eQnJxEVXUNzjlKy3fzl/kLumz/mnMh4jFmRkZKUrtGLvZ8W7/n0arXXXcdGRkZVFdXN3r6UllZGYsWLWLChAmN6jnwwAPp378/aziDvh/8nl5HnUsxB3H33XdTVFQUMcF5/fr1jbaPfndFNOcc77//Pk888USj27XC52qkpaU1Km+p47Js2TIWLlzIuHHjmDhxIhMnTmx2/VhycnIi5lhUV1czf/58XnrpJT7//HPKy8spLi7WZG9p0e5dFZSWR85hKquoTFA0IrI/qKuLfKx6V15zNHIh4kGZqcntGrmInhdQX1/PyJEjOeuss5g0aVKj9UtLS2PWNXjwYNLT0liZfwm7X/hPbr311ka3LxUWFrJy5Up69+7d8K6KQCDAyJEjY9a7adMm7rzzTu6//34CgQBXX301+fn5+Hw+UlNT2b59O4sXL25YP/o9GcnJyTHfRbF+/XrmzZvHgAED+PnPfx4zhvZITU3lsssuY/r06axZs4YtW7boBXzSog3rv+GWG69rtDyQ1fkvixSR/Vf0NSYtNaXL9q2RCxEPau/IRW5ubsNtRmZGXl4e06ZNayh/6623KCkpafjsnOPDDz/kuOOOa1RXSkoKx58wjBdf3MiLBX0oryqP2K6wsJDly5fTs2dPRo0aRUpK7AtXcXExs2fPpqioCOccGRkZXHLJJZx++ukRtz9VVlZy3333MW/ePHbv3s348eMZOXIkS5cupby8nOTkZGpqaliyZAknnXQSmZmZDfsoLS1lzpw5+P1+Zs6cSXJy51zexo4dy8MPPxxxK1dhYWGn7Ev2bUuXvMFjTzxN+a5Kjj0ij/VbdlBWUUkgK51pF09JdHgi0o1Nu3gKf5m/gLKKSnw+o7Kqmt/dPJMbb7uL1LTO/XLD9qcXRQ0bNsxpQqbsC46+YyHH5Pbk2UtHt2m74uLiiHc3RN+us27dOmbPnk1paSl+v5+srCyKioqYMGECkydPJikpqWHdkpISHn/8cZYtW0bA7ye16nu216ZH3KZ0/PHH88tf/pKMjIyYMe3YsYObbropYl5Gbm4u997b9FyOmpoa5s6dy4oVK5g0aRIXXHBBxMTqjRs3snLlSnw+HyeddBI5OTnU1tZy1113sWbNGm677Tb69+/fpnZrq+uvvz5irgjA8OHDmTx5Mn379u3UfXcmM1vpnBuW6DgSLR654sk/z2XR4vcxM845YxSTp1wWp+hERNqmoryMu3/333y1cRu5B2Yz8/ob6dv30A7V2Vy+UOdCxINO+MNr5Gan8/IV/9ap+9kzj+Dtt9/m8MMPp6KiguLiYrKzs6mqqqKmpoazzz6b1JRkkret5p/LV7KlKousrCz82dls2VLE0UcfzRVXXNEwmRyC3+Z/9tlnvPnmmxQUFDS6jcnn8/HEE0/EjKuuro5HHnmEJUuWMHr0aL755puIDlNGRgbLly9ny5YtvPfeew23d02ePLnJ27/iLbwTl5OTw7HHHss777xDZWUlI0aMYOzYsTz22GMxO3lepc5FUHtyxfKl/2j4ljAtNYXKqmp6BjKZdvFUTjxlbOcEKiLSBn+c/WuWr1pLVnoqmFGxu6phJHXEyNPaVNc+07kws17Ao8CPgG3Ar5xzf21iPQPuAn4RWvQIcJNr4Y9R50L2FaPve4OUJB//uHpcl+zvnXfe4cEHH4xYlpqayp133kl+fj7r1q1j5cqVpNWWUJWUDThO3vYUXx91NfMfewyAzMxMdu7cSSAQID09neLiYvx+P2PGjKGgoIDvvvsu4natll5IV19fz4IFC3jttdcalpkZPXr04Mc//jGVlZUsXLiQ6uq9j8rt0aMHf/rTnzr0Vu/2Kisr45VXXuH111+nqqoqIubW/L1eoM5FUHtyxYzpl0ZM2k5OSuIPd91Jbv7B8Q5PRKTdnlvwMC+88nbEsmx/Bg/Oe7RN9TSXL7w252IuUA3kAEOBV8xslXNuddR604GJwBDAAW8C64AHEekGMlKSKKts+4Tu9jr11FN56KGHIm7zqa2tJT8/H4B+/frx0QfLqEruESx0jk8DYzhz9Q0MnDSRG575nO3bgy/rKSkpYdeuCi6//HJOPvlkUlNTGTduXKPbtVri8/mYOnUqixYtipjfsHPnThYsaPqReqWlpSxcuJCcnBz69OmD3++noKCA8vJy/H4/I0eObPFJVu0VCAS46KKLGD9+PFdeeWVEzJs3b2bu3LkceeSR9O7dmyeffDLmqEZLt7aJN0U/iaWuvl4dCxHxnMlTLuPFV98m/Ov4eD9JyjOdCzPLAi4ABjnnyoF3zewl4GfATVGrXwzc65zbFNr2XuAy1LmQbuCbbWUsX7eNsqpajvrdQp66ZCT9eu/9B/H678u56M9LWbO1jCMOCkSUN1fWUnmvg3LY9l0RRrDHfsCBP6Bk995RgToLm7BtPsrTcqlN70nOyj9QVzue8IfP1dXUMKzn99SsXUx1aoDyrd9xpf8l+h5ezEbLoaRwPBlJtWA+tmz4ipSXLudgV8wGy6F6wiPkHjKgoa7evXry/ffbcRiG44ADenLLLTeTmprKzf99W8QTrwIBP717H0BxcREbN24MLnQOzCgrLeXNN17n0P6HkpKSTMm2LWzdvIGalB6kVu8g99DDye93BD4zzIxN67+icO2XVKf2IrV6O/mHD6TvoQMB2LjuCzav+YLqtF6kVm3nh0cMpG//gQ1x9DqgB99v39nwOTUlhU8++aTROzo2b97MjTfewMknjyAzMxO/389rr75GecWuhvLbb/s1M664nOTkJNZ8/imvLnqLit1V+DPSOO+cH3HUsSdgBp9//C/+/vIblO+qJJCZzrRpbR/mlvYLZKVHjFzoaVAi4lWBrIxOvV555rYoMzsOWOqcywxbNgsY45w7N2rdEuBHzrn3Q5+HAW875yKfWRlFt0XJvuCYO17ms6KSlleMs6y6XYwp+4BAfQVlviyWBIZTkbT3aUx/HO6jT2YSmA9cPUW76vjPD+rp59vG5NL/R2F1Fg4fRj15qRXcfeg7cYmruDqDezYPZ0t1FrmpFczK/4Cc1OBFcZ3LZ3bhCEorqsjOSuOGvOUcaptxGDvT+/JW/1uD8e4R6mh0toqKCpYtW9YwYnLKKaeQmZnJ7t27WbRoUaP1U1JSGr2HpKPiPcy9P+nonIv23sMsItIV4nG92ldui/ID0Q/dLwGa6jD4Q2Xh6/nNzKLnXZjZdIK3UXHwwRqiFu/78rvI08BncM/5xzd8nvXiv6h3TZc3V9aabV/tOSaibE7Ytne+9i9uGJREXqajcBfM/rSeOZOC5cPfeJS/Fx3S0AG4oM83PH7c46TWVZBat4vzPr2OJNu74zpnLDn8OsAx5us5jcr+edg1DZ9Hr/0/ER2VOmcs7n9NqOx+Hsh/vskygEDVFsrS+oAlgasjUFVEqttOvSWzI21AVMejHn/NJiDY+ShPyW9UnlUTfI9IRUpuE2VFez9n9uGMM85otK0/OXj7VPiTswKBABPHHEN9fT1VNbW8tuwLKkIjFwAZGekMP7ov9fWOdz/8mmgnDuoHwIpP10cs10va9mrtfL6OGDHyNHUmRGSf0NnXKy91LsqB7Khl2UBZK9bNBsqbmtDtnJsHzIPgt1HxCVWk8wz4QTZfFJdQ74L/wB+Y04NrTzuqofyRZWtjljdXFo9tZxU0XX7m0pnM6TubAUnFfFmXw3+l3sDr553XsO3Xq+/lUFdIkjnqnLHO8jht6q3Bst881ahs7M9+s3fb3zzbRPlvQ2XPxSwDWP0/I9l08IWUp/XBX1XEDzc8wzE3B29NWvjEn6hK693Q8Uir+p6zpv5Xw7ZNlY+fek0zZVe3sG2wfGf5HBav/LJhVGPsCQMY/9O921bVNC7/j+nBuD6OmjSc7c9g5s3/AzSeUKzbciK0dj6fiIh0kJfe0P0VkGxmR4QtGwI0dfFfHSpraT2Rfc7CGWMYmNODJJ8xMKcHC2eMaXV5orZ94Ip/58K0e0jfPpcL0+7hgSv+PWLb1KnPsM7yqHU+1lkeqVOfaVVZR7cN/OQhDlv7EBNWz+CwtQ8R+MlDDWUDjh9BWtX3WOgf/wOOHxGxbXPlHdn2hFFjOWf0ECZNPI9zRg/hhFFjI7ZtrnzaxVPI9mdgZmT7MyJexNZc2f4sbD7frc65cufcu8Ce+XwiIhJnnplzAWBmTxGcS/oLgt8uvQqcEv3tkpnNAK4BxrH3aVF/dM41O6Fbcy5ERGLrjnMu2jKfbw/lChGR5jWXL7w0cgFwJZABfAc8CVzhnFttZqPNrDxsvYeAhcAnwKfAK6FlIiIi4Vo1n8/MpptZgZkVbN26tcuCExHpbrw05wLn3HaC76+IXv5Pggliz2cH3BD6ERERiaVV8/k0P09EJD68NnIhIiIST22ZzyciIh2kzoWIiHRbzrkK4AXgt2aWZWYjgQnA44mNTESke1LnQkREursm5/MlNiQRke7JU3MuRERE4i3WfD4REYk/Tz2KtrOZ2Vbg23ZufiDBN7t6iRdjAm/G5cWYwJtxeTEm8GZc3S2mQ5xzB8UzmH1RN8wV4M24vBgTeDMuL8YE3ozLizGBN+PqlHyxX3UuOsLMCrz2/HcvxgTejMuLMYE34/JiTODNuBSTRPNq+3sxLi/GBN6My4sxgTfj8mJM4M24OismzbkQEREREZG4UOdCRERERETiQp2L1puX6ACa4MWYwJtxeTEm8GZcXowJvBmXYpJoXm1/L8blxZjAm3F5MSbwZlxejAm8GVenxKQ5FyIiIiIiEhcauRARERERkbhQ50JEREREROJCnYsYzCzNzB41s2/NrMzMPjKz8VHrnG5mX5jZLjN728wO6aLYrjKzAjOrMrO/RJX1MzNnZuVhP7cmMqZQeULaqok4FptZZVjbfJmgOHqZ2YtmVhE6xn6aiDiiYkp427RwbCfsGIoVV6LOt9C+m71GeeWc2x8oX8QvplB5wo9dL1wPQ3F4LleAN9rHi/lCuSJInYvYkoGNwBigB3AL8IyZ9QMwswOBF4BbgV5AAfB0F8VWCNwB/N9m1unpnPOHfn6XyJgS3FZNuSqsbQYkKIa5QDWQA0wBHjCzYxIUS7hEt02Tx5EHjqGWzrmuPt+gmWuUB9prf6N8EaeYPHbsJvp6CN7NFZD49vFivlCuCO1QmuCcqwBuD1v0spmtA04A1gOTgNXOuWcBzOx2YJuZDXTOfdHJsb0Q2ucw4Iedua/WaiGmhLWVF5lZFnABMMg5Vw68a2YvAT8DbkpocAnWzHGU0GPIo+dcc9eo3uic6zLKF22jfNE6yhXN82K+8Oj51uW5QiMXrWRmOcCRwOrQomOAVXvKQ//z1oaWe8G3ZrbJzP4c6pkmktfa6vdmts3MlprZ2ATs/0ig1jn3VdiyVXjj2El028TitWMoWsLPt6hrlNfbq1tTvugQL7VVoq+HXs4VkPj2icVLx1C0hJ9rXZEr1LloBTNLARYA88N6cn6gJGrVEiDQlbE1YRswHDiEYK80QDD2RPJSW90I9AfyCT7feaGZHdbFMfiB0qhlXjh2vNA2sXjpGArnifOtiWuUV9ur21O+6DCvtJUXrodezRXgjfaJxSvHUDhPnGtdlSv2285FaDKSi/Hzbth6PuBxgvc8XhVWRTmQHVVtNlDWFXHF4pwrd84VOOdqnXPFoZh/ZGbtPlA6GhOd1FbtidM5975zrsw5V+Wcmw8sBc6OZxyt0CXt0VYeaZtYvNpmcT/f2irGNcqT7bWvUr7oupjogmNXuaLjPNI+sXiu3fa3XLHfzrlwzo1taR0zM+BRghOpznbO1YQVrwYuDls3CziMvcPgnRZXW6sM/bfdHck4xNQpbRWtnXE6wOIZRyt8BSSb2RHOuTWhZUOIc3vEQSLaJpYuOYbioMPnW1s0c43aV9prn6B80YYK9oF8oVzRKZQv2qZb54r9duSilR4AjgLOdc7tjip7ERhkZheYWTrwa+DjrphwZmbJoX0mAUlmlm5myaGyk8xsgJn5zKw3cD+w2DkXPezVZTGRwLaKirGnmZ25JzYzmwKcCizqyjhC9zS+APzWzLLMbCQwgeA3CgnhlbZp5jhK6DEUK65EnW9hYl2jPHHO7WeUL+IQEx44dr1yPfRirgDvtI8X84VyRYhzTj9N/BC8L84BlQSHjfb8TAlbZxzwBbAbWAz066LYbg/FFv5ze6jsP4B1QAWwBXgM6JPImBLZVlExHgR8QHC4byewHDgjQcdXL+Bvof9PG4CfJiIOr7VNC8d2wo6hWHEl6nwLxdTsNcoL59z+8qN8Eb+YEtlWYfv3xPUwFIuncoWX2seL+UK5IvhjoYpFREREREQ6RLdFiYiIiIhIXKhzISIiIiIicaHOhYiIiIiIxIU6FyIiIiIiEhfqXIiIiIiISFyocyEiIiIiInGhzoWIiIiIiMSFOhcicWBm681sXDu3XW1mY+McT9zrFBGRjlGukP2BOhfSLZnZAWbmzOy9qOUPmtl9iYqrKc65Y5xzizuzzo4kNBGR7kq5QrlC4k+dC+muhgJFwNFm1ids+XHAR4kISEREPGcoyhUicaXOhXRXQ4EC4E1gAoCZJQGDgQ/bW6mZ9TWzF8xsq5l9b2b/G75PM/vYzErM7GkzSw/b7iYzW2tmZWb2mZmdH1YW8U1R6POsWHVFxXOjmW0O1fulmZ0eXaeZPQ4cDCw0s3Izu8HM8szs+dDfsc7Mrm5NvSIi3cxQlCuUKySu1LmQ7mrPt05/AyaGlg0keMx/3p4KQwnnZeBboB+QDzwVtsqFwFnAocCxwLSwsrXAaKAH8BvgCTPLbWZ3zdW1J54BwFXAcOdcADgTWB+9nnPuZ8AG4FznnB+4B1gIrAr9DacDM83szLbUKyLSDShXhChXSLyocyHd1VCCCeMVYLSZBULLVjvnaszsKjM7oqkNzewXZnZME0UnAnnA9c65CudcpXPu3bDy+51zhc657QQvyEP3FDjnng2V1TvnngbWhOqLJWZdYeqANILD+SnOufXOubXN1LnHcOAg59xvnXPVzrlvgIeBizpYr4jIvmYoyhWxKFdIu6hzId2OmaUBRwEfOed2ACuA8YTdQ+uc+1/n3JqmtnfOPeKcW91EUV/gW+dcbYxdF4X9vgvwh8X0czP7yMx2mtlOYBBwYDN/Rsy6wuL8GpgJ3A58Z2ZPmVleM3XucQiQtyeWUDw3AzkdrFdEZJ+hXNEi5QppF3UupDsaRPAi+03o898IDncfR+geWjNbHGvjZso2AgebWXJbgjGzQwh+23MV0Ns51xP4FLC21NMU59xfnXOjCCYBB/wh1qphv28E1jnneob9BJxzZ7ejXhGRfZVyRROrhv2uXCHtos6FdEfHAR875/ZcJF8Czg4t/8jMDgS+a2rD0JB4WYx6VwBbgLvMLMvM0s1sZCviySJ40d0a2sclBJNah5jZADM7LfTtWyWwG6iPsXox0D/0+wqgLDQRL8PMksxskJkNb0e9IiL7KuWKxpQrpMPUuZDuaChhjxB0zq0nOMmsJ8GJaccCn8TYdhDBb4oacc7VAecChxOc9LYJ+ElLwTjnPgPuBd4jeOEeDCxt8a9oWRpwF7CN4ND4D4BfxVj398AtoWHta4FzCLbTutD2jxCcQNjWekVE9lVDUa6IplwhHWZ7O+wi+wczmwmsd879LfT5h865TaHfpwPlzrm/Ji5CERFJNOUKkfbRyIXsjwYDHwOE7ol9Mqos1jdVIiKy/1CuEGmHNk02EukOnHOXhn08Hngs7PNg4IuujUhERLxGuUKkfXRblEiImT1L8Nnmtyc6FhER8SblCpHmqXMhIiIiIiJxoTkXIiIiIiISF+pciIiIiIhIXKhzISIiIiIicaHOhYiIiIiIxIU6FyIiIiIiEhfqXIiIiIiISFyocyEiIiIiInGhzoWIiIiIiMSFOhciIiIiIhIX/x/j26tvTy5OywAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(occ_cold, res_cold[\"bonddims\"][()], times, tim, chain, True, True, \"chain_occ_bet=2000_double\")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t name : Anderson impurity problem (folded chain)\n", - "\t machine : local\n", - "\t method : DTDVP\n", - "\t dt = 0.5\n", - "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.6, β = 2000.0, c1 = 0.4606588659617806, c2 = 0.46065886596178074, \n", - "\t observables : system_occup, folded_chain_occup, \n", - "\t convparams : 0.001\n", - "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", - "\n" - ] - } - ], - "source": [ - "res_fol_cold = h5py.File(path+\"fermionsnn/results/nqs0Q/dat_nqs0Q.jld\", \"r\")[\"data\"]\n", - "info_fol_cold = open(path+\"fermionsnn/results/nqs0Q/info.txt\", \"r\")\n", - "data_fol_cold = info_fol_cold.read()\n", - "print(data_fol_cold)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "occ_fol_cold = np.column_stack((res_fol_cold[\"system_occup\"][()], res_fol_cold[\"folded_chain_occup\"][()]))\n", - "\n", - "alltogether(occ_fol_cold, res_fol_cold[\"bonddims\"][()], times, tim, chain_fol, False, True, \"chain_occ_bet=2000_folded\")" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "system(\"Double chain system occupation\", res_cold[\"system_occup\"][()], res_cold[\"times\"][()], True, \"system_double_beta2000\")\n", - "system(\"Folded chain system occupation\", res_fol_cold[\"system_occup\"][()], res_fol_cold[\"times\"][()], True, \"system_folded_beta2000\")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 4, 6, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 6, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 7, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 8, 6, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 9, 6, 7, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 9, 7, 7, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 9, 9, 7, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([1, 2, 4, 8, 9, 9, 8, 5, 7, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 8, 8, 7, 4, 2, 4, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 7, 4, 5, 4, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 7, 5, 6, 4, 3, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 7, 11, 6, 4, 3, 4, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 11, 11, 6, 4, 4, 4, 3, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 11, 11, 7, 4, 5, 4, 3, 4, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 13, 11, 7, 4, 6, 4, 3, 4, 3,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 8, 13, 11, 8, 4, 6, 4, 3, 4, 3,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 10, 9, 13, 11, 8, 4, 7, 4, 5, 4, 3,\n", - " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 4, 8, 4, 5, 4, 3,\n", - " 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 4, 8, 5, 6, 4, 3,\n", - " 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 5, 8, 5, 7, 4, 5,\n", - " 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 11, 8, 5, 8, 5, 7, 4, 5,\n", - " 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 10, 5, 8, 5, 7, 4, 6,\n", - " 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 10, 6, 8, 5, 7, 5, 7,\n", - " 4, 5, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 8, 5, 7, 5, 7,\n", - " 4, 5, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", - " 4, 6, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", - " 5, 7, 4, 5, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", - " 5, 8, 4, 5, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 6, 9, 5, 7, 5, 7,\n", - " 5, 8, 4, 6, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 8, 9, 6, 7, 5, 7,\n", - " 5, 8, 5, 7, 4, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 11, 10, 9, 6, 10, 5, 7,\n", - " 5, 8, 5, 7, 5, 3, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", - " 5, 8, 5, 7, 6, 6, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", - " 5, 8, 5, 7, 6, 6, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", - " 6, 8, 5, 7, 8, 7, 4, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", - " 6, 8, 6, 7, 8, 7, 4, 5, 4, 2, 2, 2, 2, 3, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 9, 6, 10, 5, 8,\n", - " 6, 8, 6, 7, 8, 7, 6, 7, 4, 3, 2, 2, 2, 3, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 10, 6, 10, 5, 8,\n", - " 6, 8, 6, 7, 8, 7, 6, 7, 6, 3, 2, 2, 2, 3, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 10, 6, 10, 5, 10,\n", - " 7, 8, 6, 7, 8, 7, 6, 7, 6, 3, 2, 2, 2, 3, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 13, 10, 10, 6, 10, 6, 10,\n", - " 7, 8, 6, 7, 8, 7, 6, 7, 6, 3, 4, 3, 2, 3, 2, 2, 2,\n", - " 2, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 7, 8, 6, 11, 8, 7, 9, 7, 6, 5, 5, 3, 2, 3, 2, 2, 2,\n", - " 3, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 7, 10, 7, 11, 8, 13, 9, 7, 6, 5, 5, 3, 3, 3, 2, 2, 2,\n", - " 3, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 7, 11, 8, 11, 8, 13, 9, 7, 6, 5, 6, 4, 3, 3, 2, 3, 2,\n", - " 3, 2, 2, 2, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 7, 11, 10, 11, 8, 13, 10, 8, 7, 7, 6, 4, 5, 3, 2, 3, 2,\n", - " 3, 3, 2, 3, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 7, 11, 10, 11, 8, 13, 11, 10, 9, 8, 6, 5, 5, 3, 2, 3, 2,\n", - " 3, 3, 2, 3, 2, 2, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 7, 11, 10, 11, 9, 13, 11, 10, 9, 8, 6, 6, 5, 3, 3, 3, 2,\n", - " 3, 3, 3, 3, 3, 3, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 8, 11, 10, 11, 9, 13, 11, 12, 9, 8, 6, 6, 6, 4, 5, 3, 2,\n", - " 4, 3, 3, 5, 3, 3, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 8, 11, 10, 11, 9, 13, 11, 12, 9, 9, 8, 6, 7, 6, 5, 3, 2,\n", - " 4, 3, 3, 5, 3, 3, 2, 1]),\n", - " array([ 1, 2, 4, 8, 11, 9, 11, 9, 13, 12, 14, 10, 10, 6, 10, 6, 10,\n", - " 8, 11, 10, 11, 9, 13, 11, 12, 9, 10, 8, 6, 7, 6, 5, 3, 4,\n", - " 4, 3, 4, 5, 3, 3, 2, 1])]" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(res_fol[\"bonddims\"][()])" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "unfolded_occ = []\n", - "unfolded_bonds = []\n", - "for i in range(1, 2*N, 2):\n", - " unfolded_occ.append(occ_fol.T[2*N-i])\n", - "\n", - "for i in range(1, (2*N+2), 2):\n", - " unfolded_bonds.append(res_fol[\"bonddims\"][()].T[2*N+2-i])\n", - " \n", - "unfolded_occ.append(occ_fol.T[0])\n", - "unfolded_bonds.append(res_fol[\"bonddims\"][()].T[0])\n", - "\n", - "for i in range(2, (2*N), 2):\n", - " unfolded_occ.append(occ_fol.T[i])\n", - "\n", - "for i in range(2, (2*N+2), 2):\n", - " unfolded_bonds.append(res_fol[\"bonddims\"][()].T[i])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "42" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(unfolded_bonds)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(np.array(unfolded_occ).T, np.array(unfolded_bonds).T, times, tim, chain, False, True, \"chain_occ_bet=2_unf\")" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "unfolded_occ_cold = []\n", - "unfolded_bonds_cold = []\n", - "for i in range(1, 2*N, 2):\n", - " unfolded_occ_cold.append(occ_fol_cold.T[2*N-i])\n", - "\n", - "for i in range(1, (2*N+2), 2):\n", - " unfolded_bonds_cold.append(res_fol_cold[\"bonddims\"][()].T[2*N+2-i])\n", - " \n", - "unfolded_occ_cold.append(occ_fol.T[0])\n", - "unfolded_bonds_cold.append(res_fol_cold[\"bonddims\"][()].T[0])\n", - "\n", - "for i in range(2, (2*N), 2):\n", - " unfolded_occ_cold.append(occ_fol_cold.T[i])\n", - "\n", - "for i in range(2, (2*N+2), 2):\n", - " unfolded_bonds_cold.append(res_fol_cold[\"bonddims\"][()].T[i])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(np.array(unfolded_occ_cold).T, np.array(unfolded_bonds_cold).T, times, tim, chain, False, True, \"chain_occ_bet=2000_unf\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# weak coupling results" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t name : Anderson impurity problem (folded chain)\n", - "\t machine : local\n", - "\t method : DTDVP\n", - "\t dt = 0.5\n", - "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.3, β = 2000.0, c1 = 0.04606588659617806, c2 = 0.04606588659617808, \n", - "\t observables : system_occup, folded_chain_occup, \n", - "\t convparams : 0.001\n", - "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", - "\n" - ] - } - ], - "source": [ - "res_fol_weak = h5py.File(path+\"fermionsnn/results/6OcGt/dat_6OcGt.jld\", \"r\")[\"data\"]\n", - "info_fol_weak = open(path+\"fermionsnn/results/6OcGt/info.txt\", \"r\")\n", - "data_fol_weak = info_fol_weak.read()\n", - "print(data_fol_weak)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "occ_fol_weak = np.column_stack((res_fol_weak[\"system_occup\"][()], res_fol_weak[\"folded_chain_occup\"][()]))\n", - "\n", - "alltogether(occ_fol_weak, res_fol_weak[\"bonddims\"][()], times, tim, chain_fol, False, True, \"chain_occ_weak_folded\")" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "unfolded_occ_weak = []\n", - "unfolded_bonds_weak = []\n", - "for i in range(1, 2*N, 2):\n", - " unfolded_occ_weak.append(occ_fol_weak.T[2*N-i])\n", - "\n", - "for i in range(1, (2*N+2), 2):\n", - " unfolded_bonds_weak.append(res_fol_weak[\"bonddims\"][()].T[2*N+2-i])\n", - " \n", - "unfolded_occ_weak.append(occ_fol_weak.T[0])\n", - "unfolded_bonds_weak.append(res_fol_weak[\"bonddims\"][()].T[0])\n", - "\n", - "for i in range(2, (2*N), 2):\n", - " unfolded_occ_weak.append(occ_fol_weak.T[i])\n", - "\n", - "for i in range(2, (2*N+2), 2):\n", - " unfolded_bonds_weak.append(res_fol_weak[\"bonddims\"][()].T[i])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(np.array(unfolded_occ_weak).T, np.array(unfolded_bonds_weak).T, times, tim, chain, False, True, \"chain_occ_weak_unf\")" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\t name : Anderson impurity problem\n", - "\t machine : local\n", - "\t method : DTDVP\n", - "\t dt = 0.5\n", - "\t tmax = 30.0\n", - "\t parameters : N = 20.0, ϵd = 0.3, β = 2000.0, c1 = 0.4606588659617806, c2 = 0.46065886596178074, \n", - "\t observables : chain1_filled_occup, chain2_empty_occup, system_occup, \n", - "\t convparams : 0.001\n", - "\t options : Dlim = 100, savebonddims = true, verbose = false, \n", - "\n" - ] - } - ], - "source": [ - "res_weak = h5py.File(path+\"fermions/results/jIRi5/dat_jIRi5.jld\", \"r\")[\"data\"]\n", - "info_weak = open(path+\"fermions/results/jIRi5/info.txt\", \"r\")\n", - "data_weak = info_weak.read()\n", - "print(data_weak)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "occ_weak = np.column_stack((res_weak[\"chain1_filled_occup\"][()], res_weak[\"system_occup\"][()]))\n", - "occ_weak = np.concatenate((occ_weak.T, res_cold[\"chain2_empty_occup\"][()].T))" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alltogether(occ_weak, res_weak[\"bonddims\"][()], times, tim, chain, True, True, \"chain_occ_weak_double\")" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "system(\"Double chain system occupation\", res_weak[\"system_occup\"][()], res_weak[\"times\"][()], True, \"system_double_weak\")\n", - "system(\"Folded chain system occupation\", res_fol_weak[\"system_occup\"][()], res_fol_weak[\"times\"][()], True, \"system_folded_weak\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 10883d079bbdab764125f2df63cb9259260a1f8f Mon Sep 17 00:00:00 2001 From: angelariva Date: Thu, 2 May 2024 17:03:07 +0200 Subject: [PATCH 33/52] added plots to the SIAM example --- examples/anderson_model_double.jl | 15 +++++++++++---- examples/anderson_model_interleaved.jl | 11 +++++++++++ 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index eddb80f..a34e090 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -1,8 +1,5 @@ using MPSDynamics, Plots, LaTeXStrings, QuadGK - -import MPSDynamics: disp, measuremodes, eigenchain, mpsembed! -import MPSDynamics: setleftbond, setrightbond, mpsrightnorm! -import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_mpo +import MPSDynamics: productstatemps, tightbinding_mpo #---------------------------- # Physical parameters @@ -98,6 +95,16 @@ occ = vcat(occ', dat["data/chain2_empty_occup"]) # Plots #------------- +# Plot the system occupation +plot( + dat["data/times"], + system_occup_col, + xlabel = L"$t$", + ylabel = L"$n_d$", + title = "System Occupation", + size = (700, 500) +) + # Plot the occupation of the chain sites heatmap( collect(1:2*N+1), diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl index d780a9c..1ded9e2 100644 --- a/examples/anderson_model_interleaved.jl +++ b/examples/anderson_model_interleaved.jl @@ -126,6 +126,16 @@ unfolded_occ_matrix = hcat(unfolded_occ...)' # Plots #------------- +# Plot the system occupation +plot( + dat["data/times"], + system_occup_col, + xlabel = L"$t$", + ylabel = L"$n_d$", + title = "System Occupation", + size = (700, 500) +) + # Plot the occupation of the chain sites heatmap( collect(1:2*N), @@ -157,6 +167,7 @@ heatmap( # Define indices for columns to be plotted columns_to_plot = [1, 5, 10, 15, 20] + # Plot vertical slices for occupancy p1 = plot(title = "Chain occupation") for col in columns_to_plot From b4d043cc1e1170353b9abc31da2c3e3bd5cd6675 Mon Sep 17 00:00:00 2001 From: angelariva Date: Thu, 2 May 2024 18:17:17 +0200 Subject: [PATCH 34/52] fixed some plots in the Anderson model examples --- examples/anderson_model_double.jl | 30 ++++++++++++------------ examples/anderson_model_interleaved.jl | 32 ++++++++++++-------------- 2 files changed, 30 insertions(+), 32 deletions(-) diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index a34e090..fb26a6b 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -1,5 +1,5 @@ -using MPSDynamics, Plots, LaTeXStrings, QuadGK -import MPSDynamics: productstatemps, tightbinding_mpo +using MPSDynamics, Plots, LaTeXStrings +import MPSDynamics: productstatemps, tightbinding_mpo, mpsembed! #---------------------------- # Physical parameters @@ -8,7 +8,7 @@ import MPSDynamics: productstatemps, tightbinding_mpo N = 40 # number of chain sites β = 2.0 # inverse temperature μ = 0. # chemical potential -Ed = 0.6 # energy of the impurity +Ed = 0.3 # energy of the impurity ϵd = Ed - μ # energy of the impurity minus the chemical potential #----------------------------------------- @@ -36,10 +36,10 @@ c2 = chainparams2[3] # Simulation parameters #----------------------- -dt = 0.5 # time step -T = 10.0 # simulation time +dt = 0.25 # time step +T = 15.0 # simulation time method = :DTDVP # time-evolution method -Dmax = 100 # MPS max bond dimension +Dmax = 150 # MPS max bond dimension prec = 0.0001 # precision for the adaptive TDVP dir = "/Users/ariva/Documents/fermions/" @@ -96,9 +96,9 @@ occ = vcat(occ', dat["data/chain2_empty_occup"]) #------------- # Plot the system occupation -plot( +p1 = plot( dat["data/times"], - system_occup_col, + dat["data/system_occup"], xlabel = L"$t$", ylabel = L"$n_d$", title = "System Occupation", @@ -106,7 +106,7 @@ plot( ) # Plot the occupation of the chain sites -heatmap( +p2 = heatmap( collect(1:2*N+1), dat["data/times"], transpose(occ), # Use the matrix form @@ -120,7 +120,7 @@ heatmap( ) # Plot the bond dimensions -heatmap( +p3 = heatmap( collect(1:2*N+2), dat["data/times"], transpose(dat["data/bonddims"]), @@ -137,16 +137,16 @@ heatmap( columns_to_plot = [1, 5, 10, 15, 20] # Plot vertical slices for occupancy -p1 = plot(title = "Chain occupation") +p4 = plot(title = "Chain occupation") for col in columns_to_plot - plot!(p1, unfolded_occ_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = "chain occupation") + plot!(p4, occ[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = "chain occupation") end # Plot vertical slices for bond dimensions -p2 = plot(title = "Bond Dimensions") +p5 = plot(title = "Bond Dimensions") for col in columns_to_plot - plot!(p2, unfolded_bonds_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = L"$\chi$") + plot!(p5, dat["data/bonddims"][:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = L"$\chi$") end # Display the plots -plot(p1, p2, layout = (2, 1), size = (600, 800)) +plot(p2, p3, p4, p5, p1, layout = (3, 2), size = (1400, 1200)) diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl index 1ded9e2..e3b48b6 100644 --- a/examples/anderson_model_interleaved.jl +++ b/examples/anderson_model_interleaved.jl @@ -1,7 +1,5 @@ -using MPSDynamics, Plots, LaTeXStrings, QuadGK, Revise -import MPSDynamics: disp, measuremodes, eigenchain, mpsembed! -import MPSDynamics: setleftbond, setrightbond, mpsrightnorm! -import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_mpo +using MPSDynamics, Plots, LaTeXStrings +import MPSDynamics: mpsembed!, interleaved_tightbinding_mpo, productstatemps #---------------------------- # Physical parameters @@ -10,7 +8,7 @@ import MPSDynamics: interleaved_tightbinding_mpo, productstatemps, tightbinding_ N = 40 # number of chain sites β = 2.0 # inverse temperature μ = 0. # chemical potential -Ed = 0.6 # energy of the impurity +Ed = 0.3 # energy of the impurity ϵd = Ed - μ # energy of the impurity minus the chemical potential #----------------------------------------- @@ -38,10 +36,10 @@ c2 = chainparams2[3] # Simulation parameters #----------------------- -dt = 0.5 # time step -T = 10.0 # simulation time +dt = 0.25 # time step +T = 15.0 # simulation time method = :DTDVP # time-evolution method -Dmax = 40 # MPS max bond dimension +Dmax = 150 # MPS max bond dimension prec = 0.0001 # precision for the adaptive TDVP dir = "/Users/ariva/Documents/fermions/" @@ -127,9 +125,9 @@ unfolded_occ_matrix = hcat(unfolded_occ...)' #------------- # Plot the system occupation -plot( +p1 = plot( dat["data/times"], - system_occup_col, + dat["data/system_occup"], xlabel = L"$t$", ylabel = L"$n_d$", title = "System Occupation", @@ -137,7 +135,7 @@ plot( ) # Plot the occupation of the chain sites -heatmap( +p2 = heatmap( collect(1:2*N), dat["data/times"], transpose(unfolded_occ_matrix), # Use the matrix form @@ -151,7 +149,7 @@ heatmap( ) # Plot the bond dimensions -heatmap( +p3 = heatmap( collect(1:2*N+2), dat["data/times"], transpose(unfolded_bonds_matrix), @@ -169,16 +167,16 @@ columns_to_plot = [1, 5, 10, 15, 20] # Plot vertical slices for occupancy -p1 = plot(title = "Chain occupation") +p4 = plot(title = "Chain occupation") for col in columns_to_plot - plot!(p1, unfolded_occ_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = "chain occupation") + plot!(p4, unfolded_occ_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = "chain occupation") end # Plot vertical slices for bond dimensions -p2 = plot(title = "Bond Dimensions") +p5 = plot(title = "Bond Dimensions") for col in columns_to_plot - plot!(p2, unfolded_bonds_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = L"$\chi$") + plot!(p5, unfolded_bonds_matrix[:, col], label = L"$t =$"*"$col", xlabel = L"$N_{i,j}$ chain sites", ylabel = L"$\chi$") end # Display the plots -plot(p1, p2, layout = (2, 1), size = (600, 800)) +plot(p2, p3, p4, p5, p1, layout = (3, 2), size = (1400, 1200)) \ No newline at end of file From 0b5d3db457eb71cef81502456f1ec49b512c7f3e Mon Sep 17 00:00:00 2001 From: angelariva Date: Fri, 3 May 2024 10:19:19 +0200 Subject: [PATCH 35/52] corrected examples --- examples/anderson_model_double.jl | 6 +++--- examples/anderson_model_interleaved.jl | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index fb26a6b..e98a300 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -6,7 +6,7 @@ import MPSDynamics: productstatemps, tightbinding_mpo, mpsembed! #---------------------------- N = 40 # number of chain sites -β = 2.0 # inverse temperature +β = 10.0 # inverse temperature μ = 0. # chemical potential Ed = 0.3 # energy of the impurity ϵd = Ed - μ # energy of the impurity minus the chemical potential @@ -40,7 +40,7 @@ dt = 0.25 # time step T = 15.0 # simulation time method = :DTDVP # time-evolution method Dmax = 150 # MPS max bond dimension -prec = 0.0001 # precision for the adaptive TDVP +prec = 0.00001 # precision for the adaptive TDVP dir = "/Users/ariva/Documents/fermions/" @@ -52,7 +52,7 @@ H = tightbinding_mpo(N, ϵd, chainparams1, chainparams2) ψ = unitcol(2,2) # (0,1) filled impurity state A = productstatemps(physdims(H), state=[fill(unitcol(2,2), N)..., ψ, fill(unitcol(1,2), N)...]) # MPS -mpsembed!(A, 4) +mpsembed!(A, 2) #---------------------------------------------------- # Definition of observables for the double chain diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl index e3b48b6..3d6bdd1 100644 --- a/examples/anderson_model_interleaved.jl +++ b/examples/anderson_model_interleaved.jl @@ -6,7 +6,7 @@ import MPSDynamics: mpsembed!, interleaved_tightbinding_mpo, productstatemps #---------------------------- N = 40 # number of chain sites -β = 2.0 # inverse temperature +β = 10.0 # inverse temperature μ = 0. # chemical potential Ed = 0.3 # energy of the impurity ϵd = Ed - μ # energy of the impurity minus the chemical potential From c4bfc9c21f7b2ca4337c15e06a21d90a8e2bb468 Mon Sep 17 00:00:00 2001 From: angelariva Date: Fri, 3 May 2024 17:16:39 +0200 Subject: [PATCH 36/52] added SDF J as input of chain coefficients function --- ChainOhmT/quadohmT.jl | 6 +++--- examples/anderson_model_double.jl | 8 ++++++-- examples/anderson_model_interleaved.jl | 8 ++++++-- src/finitetemperature.jl | 11 +++++++---- 4 files changed, 22 insertions(+), 11 deletions(-) diff --git a/ChainOhmT/quadohmT.jl b/ChainOhmT/quadohmT.jl index 4656047..6afed43 100644 --- a/ChainOhmT/quadohmT.jl +++ b/ChainOhmT/quadohmT.jl @@ -82,14 +82,14 @@ end """ -function fermionicspectraldensity_finiteT(x, i, β, chain, ϵ) +function fermionicspectraldensity_finiteT(x, i, β, chain, ϵ, J) if i==1 y = 0 elseif i==2 || i==3 if chain==1 - y = (sqrt.(1 .- x.^2) .* sqrt.(1. ./(exp.(-β .* ϵ.(x)) .+ 1))).^2 + y = (J.(x) .* sqrt.(1. ./(exp.(-β .* ϵ.(x)) .+ 1))).^2 elseif chain==2 - y = (sqrt.(1 .- x.^2) .* sqrt.(1. ./(exp.(β .* ϵ.(x)) .+ 1))).^2 + y = (J.(x) .* sqrt.(1. ./(exp.(β .* ϵ.(x)) .+ 1))).^2 end elseif i==4 y = 0 diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index e98a300..108ff99 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -19,8 +19,12 @@ function ϵ(x) return x end -chainparams1 = chaincoeffs_fermionic(N, β, 1.0; ϵ, save=false) # empty -chainparams2 = chaincoeffs_fermionic(N, β, 2.0; ϵ, save=false) # filled +function J(x) + return sqrt(1 - x^2) # semi-circular density of states +end + +chainparams1 = chaincoeffs_fermionic(N, β, 1.0; ϵ, J, save=false) # empty +chainparams2 = chaincoeffs_fermionic(N, β, 2.0; ϵ, J, save=false) # filled #= curdir = @__DIR__ diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl index 3d6bdd1..ea177a8 100644 --- a/examples/anderson_model_interleaved.jl +++ b/examples/anderson_model_interleaved.jl @@ -19,8 +19,12 @@ function ϵ(x) return x end -chainparams1 = chaincoeffs_fermionic(N, β, 1.0; ϵ, save=false) # empty -chainparams2 = chaincoeffs_fermionic(N, β, 2.0; ϵ, save=false) # filled +function J(x) + return sqrt(1 - x^2) # semi-circular density of states +end + +chainparams1 = chaincoeffs_fermionic(N, β, 1.0; ϵ, J, save=false) # empty +chainparams2 = chaincoeffs_fermionic(N, β, 2.0; ϵ, J, save=false) # filled #= curdir = @__DIR__ diff --git a/src/finitetemperature.jl b/src/finitetemperature.jl index 54e852d..f4c4c5f 100644 --- a/src/finitetemperature.jl +++ b/src/finitetemperature.jl @@ -126,8 +126,9 @@ Generate chain coefficients ``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]`` for a fermio * nummodes: Number of bath modes * β: inverse temperature * chain: 1 if the chain modes are empty, 2 if the chain modes are filled -* ϵ: user-provided dispersion relation. Should be a function f(x) where x is the frequency -* ωc: the maximum frequency of the dispersion relation +* ϵ: user-provided dispersion relation. Should be a function f(x) where x is the wavenumber +* J: user-provided spectral density. Should be a function f(x) where x is the wavenumber +* ωc: the maximum frequency allowwed in the spectral density * mc: the number of component intervals * mp: the number of points in the discrete part of the measure (mp=0 if there is none) * iq: a parameter to be set equal to 1, if the user provides his or her own quadrature routine, and different from 1 otherwise @@ -139,7 +140,7 @@ Generate chain coefficients ``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]`` for a fermio """ -function chaincoeffs_fermionic(nummodes, β, chain; ϵ=nothing, ωc=1, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=true) +function chaincoeffs_fermionic(nummodes, β, chain; ϵ=nothing, J=nothing, ωc=1, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=true) N = nummodes # Number of bath modes @@ -155,8 +156,10 @@ function chaincoeffs_fermionic(nummodes, β, chain; ϵ=nothing, ωc=1, mc=4, mp= if ϵ==nothing throw(ArgumentError("A dispersion relation should have been provided.")) + elseif J==nothing + throw(ArgumentError("The spectral density should be provided.")) else - wf(x,i) = fermionicspectraldensity_finiteT(x, i , β, chain, ϵ) + wf(x,i) = fermionicspectraldensity_finiteT(x, i , β, chain, ϵ, J) end if procedure==:Lanczos # choice between the Stieltjes (irout = 1) and the Lanczos procedure (irout != 1) From dbf57af351041f2292d28754a270652e2af43821 Mon Sep 17 00:00:00 2001 From: angelariva Date: Fri, 3 May 2024 17:43:31 +0200 Subject: [PATCH 37/52] cleaned up fermionic examples --- examples/anderson_model_interleaved.jl | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/examples/anderson_model_interleaved.jl b/examples/anderson_model_interleaved.jl index ea177a8..e845cd5 100644 --- a/examples/anderson_model_interleaved.jl +++ b/examples/anderson_model_interleaved.jl @@ -33,21 +33,16 @@ chainparams2 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 2.0) #fill chainparams1 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 1.0) #empty =# -c1 = chainparams1[3] -c2 = chainparams2[3] - #----------------------- # Simulation parameters #----------------------- -dt = 0.25 # time step +dt = 0.25 # time step T = 15.0 # simulation time method = :DTDVP # time-evolution method -Dmax = 150 # MPS max bond dimension +Dmax = 150 # MPS max bond dimension prec = 0.0001 # precision for the adaptive TDVP -dir = "/Users/ariva/Documents/fermions/" - #--------------------------- # MPO and initial state MPS #--------------------------- @@ -87,8 +82,7 @@ A, dat = runsim(dt, T, A, H; Dlim = Dmax, savebonddims = true, # we want to save the bond dimension verbose = false, - save = true, - savedir = dir, + save = false, plot = true, ); From e2a4d30760930293c22f9023a0a39d51998634d5 Mon Sep 17 00:00:00 2001 From: angelariva Date: Fri, 3 May 2024 17:44:06 +0200 Subject: [PATCH 38/52] cleaned up fermionic examples --- examples/anderson_model_double.jl | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/examples/anderson_model_double.jl b/examples/anderson_model_double.jl index 108ff99..91028ac 100644 --- a/examples/anderson_model_double.jl +++ b/examples/anderson_model_double.jl @@ -33,9 +33,6 @@ chainparams2 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 2.0) #fill chainparams1 = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", β, 1.0) #empty =# -c1 = chainparams1[3] -c2 = chainparams2[3] - #----------------------- # Simulation parameters #----------------------- @@ -46,8 +43,6 @@ method = :DTDVP # time-evolution method Dmax = 150 # MPS max bond dimension prec = 0.00001 # precision for the adaptive TDVP -dir = "/Users/ariva/Documents/fermions/" - #--------------------------- # MPO and initial state MPS #--------------------------- @@ -81,8 +76,7 @@ A, dat = runsim(dt, T, A, H; Dlim = Dmax, savebonddims = true, # we want to save the bond dimension verbose = false, - save = true, - savedir = dir, + save = false, plot = true, ); From 160c163ddc89a6921b228e2aefcf5c952903779a Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Fri, 3 May 2024 18:06:47 +0200 Subject: [PATCH 39/52] Add correlated environment mpo --- src/models.jl | 347 +++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 346 insertions(+), 1 deletion(-) diff --git a/src/models.jl b/src/models.jl index 0fd3d52..fed0568 100644 --- a/src/models.jl +++ b/src/models.jl @@ -972,4 +972,349 @@ function interleaved_tightbinding_mpo(N, ϵd, chainparams1, chainparams2) M1fin[D,1,:,:] = u push!(W, M1fin) return W -end \ No newline at end of file +end + +""" + correlatedenvironmentmpo(R::Vector, Nm::Int, d::Int; chainparams, fnamecc::String, s=1, α=1, ωc=1, c_phonon=1, β="inf", issoft=false) + +Generate a MPO for a one-dimensional bosonic bath spatially correlated to a multi-component system + +`` +H_B + H_int = \\int_{-∞}^{+∞} dk ω_k b_k^\\dagger b_k + ∑_j \\int_{-∞}^{+∞}dk \\sqrt{J(k)}(A_j b_k e^{i k R_j} + h.c.) +``. + +The interactions between the system and the chain-mapped bath are long range, i.e. each site interacts with all the chain modes. The spectral density is assumed to be Ohmic ``J(ω) = 2αωc(ω/ωc)^s``. + +# Arguments + +* `R`: List of system's components positions +* `Nm`: Number of chain modes. The actual number of mode will be doubled to account for the left and right moving excitations. +* `d`: Local Hilbert space dimension of the bath modes +* `chainparams`: chain parameters, of the form `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. +* `fnamecc`: Path to a file containing pre-computed long-range coupling coefficient. If not provided, the coupling coefficients will be computed and stored. +* `s`: Ohmicity +* `α`: Kondo parameter +* `ωc`: Bath cut-off frequency +* `c_phonon`: Speed of sound in the bath +* `β`: Inverse temperature +* `issoft`: Is the cut-off of the Ohmic SD soft or hard? +""" +function correlatedenvironmentmpo(R::Vector, Nm::Int, d::Int; chainparams, fnamecc::String, s=1, α=1, ωc=1, c_phonon=1, β="inf", issoft=false) + + function polybeta(t::Float64, n::Int, a::Array, b::Array, temp::Array) + """ + polybeta recursively constructs the polynomials used to compute the coupling coefficients given the coefficients a and b + This function is useful when working at finite temperature (β != inf) + """ + if n==-1 + return 0 + elseif n==0 + if length(temp)>=2 + temp[2] = 1 + else + push!(temp,1) + end + + return 1 + elseif n==1 + pn = (t - a[n]) + if length(temp) == 2 + push!(temp,pn) + elseif length(temp) == 1 + push!(temp, 1) + push!(temp,pn) + end + + return pn + else + if length(temp) n+1 && temp[1] == t + pn = temp[n+2] + + return pn + else + temp = [t] + pn = (t - a[n])*polybeta(t,n-1,a,b,temp) - b[n-1]*polybeta(t,n-2,a,b,temp) #P_{n}(t) = (t-a_{n-1})P_{n-1} - b_{n-1}P_{n-2} + push!(temp, pn) + + return pn + end + end + end + + function SDOhmic(t) + """ + Bath Ohmic Spectral Density for zero temperature chain mapping of the bath + """ + if t==0 + return 0 + elseif t>-1 && t<1 + return 2*α*abs(t)*ωc + elseif abs(t)==1 + return 2*α*ωc + else + return 0 + end + end + + + function SDTOhmic(t) + """ + Bath Ohmic Spectral Density after the finite temperature chain mapping of the bath + """ + if t==0 + return 2*α/β + elseif t>-1 && t<1 + return α*t*ωc*(1+coth(β*t*ωc*0.5)) + elseif abs(t)==1 + return α*t*ωc*(1+coth(β*t*ωc*0.5)) + else + return 0 + end + end + + a_chain = chainparams[1] + b_chain = chainparams[2].^2 + + Norm = zeros(Nm) + + function γ(x::Int, n::Int, issoft::Bool; β="inf", temp=[1.]) + """ + Definition of the coupling coefficient between the site x and the mode n for a Ohmic spectral density with a hard cut-off (Jacobi Polynomials) or a soft cut-off Laguerre Polynomials + """ + if β=="inf" + if issoft==true + polynomial0(t) = sf_laguerre_n(n,s,t)*exp(-im*t*R[x]*ωc/c_phonon)*t^s*exp(-s) + return sqrt(2*α*gamma(n+s + 1)/gamma(n+1))*ωc*quadgk(polynomial0, 0, 1)[1] + else + polynomial(t) = jacobi(2*t-1,n-1, 0, s)*exp(-im*t*R[x]*ωc/c_phonon)*t^s + return sqrt(2*α*(2*(n-1) + s + 1))*ωc*quadgk(polynomial, 0, 1)[1] + end + elseif β!="inf" + polynomial1(t) = polybeta(t,n-1,a_chain,b_chain,[t]) + integrand(t) = polynomial1(t)*exp(im*t*R[x]*ωc/c_phonon)*SDTOhmic(t) + N2(t) = polynomial1(t)^2*SDTOhmic(t) + if Norm[n]==0 + Norm[n] = sqrt(quadgk(N2,-1,1)[1]) + end + return (ωc/Norm[n])*quadgk(integrand, -1, 1)[1] + + end + end + + # Construction of the MPO + W = Any[] # list of the MPO's tensors + + ### Construction of the bosonic bath MPO ### + e = chainparams[1] # list of the energy of each modes + t = chainparams[2] # list of the hopping energy between modes + u = unitmat(d) + # modes creation, anihilation and number operators + bd = crea(d) + b = anih(d) + n = numb(d) + + N = length(R) # Number of system's sites + + coupling_stored = zeros(ComplexF64,N,Nm) # just need NxNm because the two chains have the same coupling coeff up to complex conjugation + arestored = 1 # are the coupling coefficient stored + Nstored, Nmstored = N, Nm # number of stored coeff. + couplinglist = [] + try + couplinglist = readdlm(fnamecc,',',ComplexF64,'\n') + catch stored_error + if isa(stored_error, ArgumentError) + print("Coupling coefficient not found. They will be computed and stored.\n") + arestored = 0 + end + end + if couplinglist != [] + Nstored, Nmstored = size(couplinglist) + if Nmstored>=Nm && Nstored>=N + coupling_stored = couplinglist[1:N,1:Nm] + else + coupling_stored[1:min(N,Nstored),1:min(Nm,Nmstored)] = couplinglist[1:min(N,Nstored),1:min(Nm,Nmstored)] + print("Less coupling coefficient stored than needed. Available ones will be used and missing one will be computed and stored.\n") + end + end + + ## First chain MPO + D = 2*(N + 2) #Bond dimension + M = zeros(ComplexF64,D-2, D, d, d) + M[1,1,:,:] = M[D-2,D,:,:] = u + M[1,D,:,:] = e[1]*n + i = 2 + M[1,i,:,:] = t[1]*bd + M[1,i+1,:,:] = t[1]*b + + a = 0 #site counter + while i Date: Mon, 6 May 2024 14:25:22 +0200 Subject: [PATCH 40/52] new file added --- examples/bath-observables.jl | 115 +++++++++++++++++++++++++++++++++++ 1 file changed, 115 insertions(+) create mode 100644 examples/bath-observables.jl diff --git a/examples/bath-observables.jl b/examples/bath-observables.jl new file mode 100644 index 0000000..c92defb --- /dev/null +++ b/examples/bath-observables.jl @@ -0,0 +1,115 @@ +using MPSDynamics, Plots, LaTeXStrings, QuadGK + +const ∞ = Inf +#---------------------------- +# Physical parameters +#---------------------------- + +d = 10 # number of Fock states of the chain modes +N = 30 # length of the chain +α = 0.01 # coupling strength +ω0 = 0.008 # TLS gap +s = 1 # ohmicity +ωc = 0.035 # Cut-off of the spectral density J(ω) +#β = 100 # Thermalized environment +β = ∞ # Case zero temperature T=0, β → ∞ + +#---------------------------- +# Ohmic spectral density +#---------------------------- + +if β == ∞ + cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 +else + cpars = chaincoeffs_finiteT(N, β; α=α, s=s, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=true) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + #=#If cpars is stored in "../ChainOhmT/ohmicT" + curdir = @__DIR__ + dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/ohmicT")) + cpars = readchaincoeffs("$dir_chaincoeff/chaincoeffs.h5", N, α, s, β) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + =# +end + + +#----------------------- +# Simulation parameters +#----------------------- + +dt = 1.0 # time step +tfinal = 10.0 # simulation time +method = :DTDVP # time-evolution method +prec = 0.0001 # precision for the adaptive TDVP + +#--------------------------- +# MPO and initial state MPS +#--------------------------- + +H = puredephasingmpo(ω0, d, N, cpars) + +# Initial electronic system in a superposition of 1 and 2 +ψ = zeros(2) +ψ[1] = 1/sqrt(2) +ψ[2] = 1/sqrt(2) + + +A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> + +#--------------------------- +# Definition of observables +#--------------------------- + +ob1 = OneSiteObservable("sz", sz, 1) + + +#------------- +# Simulation +#------------ + +ob1 = OneSiteObservable("sz", sz, 1) +ob2 = OneSiteObservable("chain_mode_occupation", numb(d), (2,N+1)) +ob3 = TwoSiteObservable("SXdisp", sx, disp(d), [1], collect(2:N+1)) +ob4 = TwoSiteObservable("cdagc", crea(d), anih(d), collect(2:N+1), collect(2:N+1)) +ob5 = OneSiteObservable("sx", sx, 1) + +A, dat = runsim(dt, tfinal, A, H, prec=1E-4; + name = "pure dephasing model with temperature", + method = method, + obs = [ob1], + convobs = [ob1], + params = @LogParams(ω0, N, d, α, s), + convparams = prec, + verbose = false, + save = false, + plot = true, + ); + +#--------------------------- +# Post-processing +#--------------------------- + +occup = mapslices(X -> measuremodes(X, cpars[1], cpars[2]), dat["data/cdagc"], dims = [1,2]) + + +#---------- +# Analytical results at specified temperature +# (see The Theory of Open Quantum System, H.-P. Breuer & F. Petruccione 2002, Chapter 4) +#---------- + +Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) + +time_analytical = LinRange(0.0,tfinal,Int(tfinal)) + +Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))*coth(β*x/2)/x^2, 0, ωc)[1] + +Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) + +#------------- +# Plots +#------------ + +ρ12 = abs.(dat["data/Reduced ρ"][1,2,:]) + +plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $\beta = %$β ~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) + +plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") + + From 4d916ef745b5581968000439e4c1e3d6e0126992 Mon Sep 17 00:00:00 2001 From: angelariva Date: Mon, 6 May 2024 14:37:21 +0200 Subject: [PATCH 41/52] documented the puredephasing function --- examples/bath-observables.jl | 2 +- src/models.jl | 19 ++++++++++++++++++- 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/examples/bath-observables.jl b/examples/bath-observables.jl index c92defb..8f234b9 100644 --- a/examples/bath-observables.jl +++ b/examples/bath-observables.jl @@ -71,7 +71,7 @@ ob4 = TwoSiteObservable("cdagc", crea(d), anih(d), collect(2:N+1), collect(2:N+1 ob5 = OneSiteObservable("sx", sx, 1) A, dat = runsim(dt, tfinal, A, H, prec=1E-4; - name = "pure dephasing model with temperature", + name = "Bath observables in the pure dephasing model", method = method, obs = [ob1], convobs = [ob1], diff --git a/src/models.jl b/src/models.jl index fed0568..a81a4dc 100644 --- a/src/models.jl +++ b/src/models.jl @@ -705,6 +705,23 @@ function nearestneighbourmpo(tree_::Tree, h0, A, Ad = A') return TreeNetwork(tree, Ms) end +""" + puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) + + Generate MPO for a pure dephasing model, defined by the Hamiltonian + ``H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i `` + + The spin is on site 1 of the MPS and the bath modes are to the right. + + ### Arguments + * `ΔE::Real`: energy splitting of the spin + * `dchain::Int`: physical dimension of the chain sites truncated Hilbert spaces + * `Nchain::Int`: number of sites in the chain + * `chainparams::Array{Real,1}`: chain parameters for the bath chain. The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. + * `tree::Bool`: if true, return a `TreeNetwork` object, otherwise return a vector of MPO tensors + """ + + function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) u = unitmat(2) @@ -728,7 +745,7 @@ end the Nth site corresponding to the impurity, and the rest of the sites corresponding to the second lead. - # Arguments + ### Arguments * `N::Int`: number of sites in the chain * `ϵd::Real`: energy of the impurity site at the center, as Ed - μ, where μ is the chemical potential From 465d2394de2f687e744347a6b377e5e247745df8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Mon, 6 May 2024 18:35:53 +0200 Subject: [PATCH 42/52] Add proton transfer example (prblm puredephas comment) --- examples/protontransfer.jl | 266 +++++++++++++++++++++++++++++++++++++ src/MPSDynamics.jl | 2 +- src/fundamentals.jl | 1 + src/models.jl | 35 ++++- src/run_1TDVP.jl | 5 +- src/run_2TDVP.jl | 5 +- src/run_DTDVP.jl | 7 +- 7 files changed, 310 insertions(+), 11 deletions(-) create mode 100644 examples/protontransfer.jl diff --git a/examples/protontransfer.jl b/examples/protontransfer.jl new file mode 100644 index 0000000..e914b85 --- /dev/null +++ b/examples/protontransfer.jl @@ -0,0 +1,266 @@ +#= + Example of a Proton Transfer model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# + + The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. + + H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i +=# + +using MPSDynamics, Plots, LaTeXStrings, QuadGK, ColorSchemes, PolyChaos, LinearAlgebra + +#---------------------------- +# Physical parameters +#---------------------------- + +ω0e= 0.8 +ω0k= 0.8 + +x0e = -0.25 +x0k = 0.25 + +Δ = 0.05 + +m=1.83E3 +ħ=1 + +dFockRC = 25 + +ωRC = 0.0347 + +γ = sqrt(m*ωRC/2)*x0e # γ = displacement RC ; γ = \sqrt{m\omega/2} x_disp + +cparsRC = [ωRC,ωRC*sqrt(m*ωRC/2)] # Energy and g coupling parameter + +isolated = true +# If isolated system, set up d=1, α =0 and N = 2 +if isolated + d=1; N=2; α = 0.0 +else + d=4; N=40; α = 0.008 +end + +s = 1 # ohmicity + +ωc = 2*ωRC # Cut-off of the spectral density J(ω) + +λreorg = (2*α*ωc)/s + +cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + +#----------------------- +# Simulation parameters +#----------------------- + +dt = 10.0 # time step + +tfinal = 2000.0 #1000.0 # simulation time + +numsteps = length(collect(0:dt:tfinal))-1 + +method = :TDVP1 # time-evolution method + +D = 5 # MPS bond dimension + +#----------------------- +# Plot parameters +#----------------------- + +xlist_rho =collect(-1.2:0.05:1.2) # x definition for the reduced density matrix expressed in space +# other values of xlist_rho can be chosen to gain numerical time + +palette = ColorSchemes.okabe_ito + +#--------------------------- +# MPO and initial state MPS +#--------------------------- + +# Initial electronic system in the left well, at mean displacement . It results to a superposition of 1 and 2 +# This state is obtained with the diagonalization of the diabatic Hamiltonian at x=γ +ψ = zeros(2) +X2 = zeros(2) + +Ae = ω0e - 0.5*m*ωRC^2*(x0e)^2 +Ak = ω0k - 0.5*m*ωRC^2*(x0k)^2 +xini = γ/(sqrt(m*ωRC/2)) + +Adumb = (ω0e-ω0k+0.5*m*ωRC^2*((xini-x0e)^2-(xini-x0k)^2)) +Bdumb = sqrt((Adumb)^2 + 4*Δ^2) +X2[1] = (Adumb-Bdumb)/(2*Δ) +X2[2] = 1 + +mod_X2 = sqrt(sum(X2[i]^2 for i=1:2)) + +ψ[1] = X2[1] / mod_X2 +ψ[2] = X2[2] / mod_X2 + +#### Coherent state . Initialise the RC oscillator at displacement γ thanks to the Taylor expression of the displacement operator + +coherent_RC = [fill(unitcol(1,dFockRC), 1)...] +chainpop_RCini =zeros(dFockRC,2) + +chainpop_RCini[1,2]=1 +chainpop_RCini[1,1]=exp(-(γ^2)/2) + +for j=2:dFockRC + chainpop_RCini[j,1]=chainpop_RCini[j-1,1]*(γ/sqrt(j-1)) +end + +coherent_RC[1] = hcat(chainpop_RCini[:,1]...) #Manipulation to get good format for MPS, does not change value + +H = protontransfermpo(ω0e, ω0k, x0e, x0k, Δ, length(ψ), dFockRC, d, N, cpars, cparsRC, λreorg) + +A = productstatemps(physdims(H), state=[ψ, coherent_RC..., fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> + +Eini = measurempo(A,H) +print("\n Energy =",Eini) + +#--------------------------- +# Definition of observables +#--------------------------- + +ob1 = OneSiteObservable("sz", sz, 1) +#------------- +ob2 = OneSiteObservable("RC displacement", MPSDynamics.disp(dFockRC,ωRC,m), 2) + +#------------- +# Simulation +#------------ + +A, dat = runsim(dt, tfinal, A, H; + name = "proton transfer and tunneling at zero temperature", + method = method, + obs = [ob1, ob2], + convobs = [ob1, ob2], + params = @LogParams(N, d, α, s), + convparams = D, + Nrho = 2, # Need to precise that the reduced density matrix is + # calculated for the 2nd tensor. The value by default is 1. + reduceddensity= true, + verbose = false, + save = true, + plot = false, + ); + + +#------------- +# Plots +#------------ + +#### Plot the doublewell in the adiabatic basis ##### + +function fp(ɸ) + H11 = Ae + 0.5*m*ωRC^2*(ɸ-x0e)^2 + H22 = Ak + 0.5*m*ωRC^2*(ɸ-x0k)^2 + H12 = Δ + λp = ((H11+H22)/2)+0.5*sqrt((H11-H22)^2+4*H12^2) + return λp +end + +function fm(ɸ) + H11 = Ae + 0.5*m*ωRC^2*(ɸ-x0e)^2 + H22 = Ak + 0.5*m*ωRC^2*(ɸ-x0k)^2 + H12 = Δ + λm = ((H11+H22)/2)-0.5*sqrt((H11-H22)^2+4*H12^2) + return λm +end + +function gaussian(x) + return Eini .+((1/0.06*sqrt(2*pi)).*exp.(-(x.+(0.25)).^2 ./(2*0.06^2))) .*0.001 +end + +ɸ = [i for i in -0.75:0.001:0.75] +ɸwp = [i for i in -0.45:0.001:-0.05] +xmin = -0.75 +xmax = 0.75 +ymin = 0.7 +ymax =0.9 + +plt=plot(ɸ,fm, label="LOW",xlabel="Reaction Coordinate (arb. units)",ylabel=L"$\omega$ (arb. units)",linewidth=4,thickness_scaling = 1, bg_legend=:transparent, legendfont=16, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=(12), xlims=(xmin,xmax),ylims=(ymin,ymax),xticks=(xmin:0.25:xmax),yticks=(ymin:0.05:ymax),legend=(0.6,0.6), grid=false,linecolor =palette[8]) + +plt=plot!(ɸ,fp,label="UP",lc =palette[3], linewidth=3,thickness_scaling = 1) + +plt = hline!([Eini], lw=2, lc=:black, ls=:dash, label=L"\omega_{system}") + +plt=plot!(ɸwp,gaussian,linecolor=:black,linewidth=3, label="", fillrange = (Eini , gaussian(ɸwp)),c = :black) + +display(plt) + +##### Results ##### + +display(plot(dat["data/times"], dat["data/RC displacement"],label=" (arb. units)", linecolor =:black, xlabel="Time (arb. units)",ylabel="", title="", linewidth=4, legend=:none, tickfontsize=10,xlims=(0,2000))) + +#### Reduced Density Matrix in space##### + +# Loads the Hermite coefficients to calculate the eigenstates of the harmonic oscillator. The coefficient 2 comes from the definition of the physics field. +αherm, βherm = 2.0 .*rm_hermite(dFockRC) + +#Fct building the nth harmonic eigenstate at coordinate x +# sqrt(m*ωRC/ħ)*sqrt(2)*x in the Hermite function to recover the form of the physical Hermite polynomial in the atomic units +function Ψ(n,x) +return Acoef(n)*(m*ωRC/(ħ*π))^(1/4)*exp(-m*ωRC*(x)^2/(2*ħ))*PolyChaos.evaluate(n,(sqrt(m*ωRC/ħ)*sqrt(2)*x),αherm,βherm) +end + + +#Function to compute the pre factor of Ψ(n,x) + function Acoef(n) + if n == 0 + return 1.0 + else + return (1/sqrt(n*2))*2^(1/2)*Acoef(n-1) + end +end + +#### Calculation of functions to express the reduced densty matrix in space. These functions build the matrix of oscillator eigenstates +# Ψ(i,x) Ψ(j,x) ∀i,j ∈ dFockRC^2 and ∀ x ∈ xlist_rho +Pmatrix = zeros(length(xlist_rho),dFockRC) +Pmatrixnormed = zeros(length(xlist_rho),dFockRC) +normalization = zeros(dFockRC) + +for n=1:dFockRC + for x=1:length(xlist_rho) + Pmatrix[x,n] = Ψ(n-1,xlist_rho[x]) + end +end + +for n=1:dFockRC + normalization[n] = sqrt(sum(Pmatrix[k,n]^2 for k=1:length(xlist_rho))) +end + + +for n=1:dFockRC + Pmatrixnormed[:,n] = Pmatrix[:,n]/normalization[n] +end + +### Converts the reduced density matrix expressed in Fock states in a reduced density matrix expressed in space. +ρx=zeros(ComplexF64,length(xlist_rho),length(xlist_rho),length(dat["data/times"])) +for t=1:length(dat["data/times"]) + print(" \n t = ", dat["data/times"][t]) + for x=1:length(xlist_rho) + for y=1:length(xlist_rho) + for n=1:dFockRC + for m=1:dFockRC + ρx[x,y,t] += Pmatrixnormed[x,n]*Pmatrixnormed[y,m]*dat["data/Reduced ρ"][n,m,t] + end + end + end + end +end + +#Creates a GIF of the diagonal elements of the reduced density over time, ie the population dynamics. Here, the reduced density matrix is expressed in space. + +anim = @animate for t=1:length(dat["data/times"]) + k=(dat["data/times"][t]) + print("\n k = ", k) + plot(xlist_rho, abs.(diag(ρx[:,:,t])), title="Time = $k (arb. units)", xlabel=L"Diag( $||\rho_{RC}(x,x)||$ )", ylabel="Amplitude", xlims=(-0.5, 0.5), ylims=(0,0.25),legend=:none) +end +display(gif(anim, "gif_population.gif", fps = 2.5)) + +#Creates a GIF of the reduced density expressed over time. Here, the reduced density matrix is expressed in space. Diagonal elements are populations +#whereas anti-diagonal elements represent coherences + +anim = @animate for t=1:length(dat["data/times"]) + k=(dat["data/times"][t]) + print("\n k = ", k) + (plot(heatmap(xlist_rho, xlist_rho, abs.(ρx[:,:,t]), c=:Blues ), title="Time = $k (arb. units)", xlabel=L"$x$ (arb. units)",ylabel=L"$x$ (arb. units)", tickfontsize=(12),colorbar_title = L"||\rho_{RC}(x,x)||", legend=:none, xlims=(-0.5, 0.5), ylims=(-0.5,0.5), clims=(0,0.18),aspect_ratio=:equal)) +end +display(gif(anim, "gif_reducedrho.gif", fps = 2.5)) \ No newline at end of file diff --git a/src/MPSDynamics.jl b/src/MPSDynamics.jl index be6e349..40df16c 100644 --- a/src/MPSDynamics.jl +++ b/src/MPSDynamics.jl @@ -172,7 +172,7 @@ export @LogParams export MPOtoVector, MPStoVector -export rhoreduced_2sites, rhoreduced_1site +export rhoreduced_2sites, rhoreduced_1site, protontransfermpo export chaincoeffs_finiteT, chaincoeffs_fermionic, fermionicspectraldensity_finiteT diff --git a/src/fundamentals.jl b/src/fundamentals.jl index 87f686b..ecf8d2d 100644 --- a/src/fundamentals.jl +++ b/src/fundamentals.jl @@ -4,6 +4,7 @@ crea(d) = diagm(-1 => [sqrt(i) for i=1:d-1]) anih(d) = Matrix(crea(d)') numb(d) = crea(d)*anih(d) disp(d) = (1/sqrt(2))*(crea(d)+anih(d)) +disp(d,ωvib,m) = (1/(2*sqrt(m*ωvib/2)))*(crea(d)+anih(d)) mome(d) = (1/sqrt(2))*(im*(crea(d)-anih(d))) sx = [0. 1.; 1. 0.] sz = [1. 0.; 0. -1.] diff --git a/src/models.jl b/src/models.jl index a81a4dc..e4c85b8 100644 --- a/src/models.jl +++ b/src/models.jl @@ -704,7 +704,7 @@ function nearestneighbourmpo(tree_::Tree, h0, A, Ad = A') Ms[hn] = Ms[hn][D:D, fill(:,nc+2)...] return TreeNetwork(tree, Ms) end - +#= """ puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) @@ -719,9 +719,8 @@ end * `Nchain::Int`: number of sites in the chain * `chainparams::Array{Real,1}`: chain parameters for the bath chain. The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. * `tree::Bool`: if true, return a `TreeNetwork` object, otherwise return a vector of MPO tensors - """ - +"""=# function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) u = unitmat(2) @@ -1335,3 +1334,33 @@ function correlatedenvironmentmpo(R::Vector, Nm::Int, d::Int; chainparams, fname return W end + +function protontransfermpo(ω0e,ω0k,x0e,x0k, Δ, dsystem, dRC, d, N, chainparams, RCparams, λreorg) + u = unitmat(dsystem) + + b = anih(dRC) + bd = crea(dRC) + n = numb(dRC) + e = RCparams[1] + uRC = unitmat(dRC) + + spos = [-x0e 0.; 0. -x0k] + + c0 = only(chainparams[3]) + cRC = only(RCparams[2]) + + Hs = (ω0e)*[1. 0.; 0. 0.] + (ω0k)*[0. 0.; 0. 1.] + Δ*sx + + M=zeros(1,3,dsystem,dsystem) + M[1,:,:,:] = up(Hs, cRC*spos, u) + + MRC = zeros(3,3,dRC,dRC) + + MRC[:, 1, :, :] = dn(e[1]*(n.+diagm([1/2 for i=1:dRC]))+(λreorg)*(b+bd)^2,(b+bd), uRC) + MRC[3, :, :, :] = up(e[1]*(n.+diagm([1/2 for i=1:dRC]))+(λreorg)*(b+bd)^2, -c0*(b+bd), uRC) + + chain = hbathchain(N, d, chainparams; coupletox=true) + + return Any[M,MRC,chain...] +end + diff --git a/src/run_1TDVP.jl b/src/run_1TDVP.jl index dd8d812..627df14 100644 --- a/src/run_1TDVP.jl +++ b/src/run_1TDVP.jl @@ -13,7 +13,8 @@ function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, reduceddensity=fal push!(data, obs[i].name => reshape(exp[i], size(exp[i])..., 1)) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + :Nrho in keys(kwargs) ? Nrho = kwargs[:Nrho] : Nrho = 1 + exprho = rhoreduced_1site(A0,Nrho) push!(data, "Reduced ρ" => reshape(exprho, size(exprho)..., 1)) end @@ -42,7 +43,7 @@ function run_1TDVP(dt, tmax, A, H, Dmax; obs=[], timed=false, reduceddensity=fal data[ob.name] = cat(data[ob.name], exp[i]; dims=ndims(exp[i])+1) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + exprho = rhoreduced_1site(A0,Nrho) data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) end end diff --git a/src/run_2TDVP.jl b/src/run_2TDVP.jl index 5166fd4..805ff87 100644 --- a/src/run_2TDVP.jl +++ b/src/run_2TDVP.jl @@ -13,7 +13,8 @@ function run_2TDVP(dt, tmax, A, H, truncerr; obs=[], Dlim=50, savebonddims=false push!(data, obs[i].name => reshape(exp[i], size(exp[i])..., 1)) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + :Nrho in keys(kwargs) ? Nrho = kwargs[:Nrho] : Nrho = 1 + exprho = rhoreduced_1site(A0,Nrho) push!(data, "Reduced ρ" => reshape(exprho, size(exprho)..., 1)) end @@ -46,7 +47,7 @@ function run_2TDVP(dt, tmax, A, H, truncerr; obs=[], Dlim=50, savebonddims=false data[ob.name] = cat(data[ob.name], exp[i], dims=ndims(exp[i])+1) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + exprho = rhoreduced_1site(A0,Nrho) data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) end if savebonddims diff --git a/src/run_DTDVP.jl b/src/run_DTDVP.jl index 1fbc0bc..928abe4 100644 --- a/src/run_DTDVP.jl +++ b/src/run_DTDVP.jl @@ -13,7 +13,8 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim push!(data, obs[i].name => reshape(exp[i], size(exp[i])..., 1)) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + :Nrho in keys(kwargs) ? Nrho = kwargs[:Nrho] : Nrho = 1 + exprho = rhoreduced_1site(A0,Nrho) push!(data, "Reduced ρ" => reshape(exprho, size(exprho)..., 1)) end @@ -57,7 +58,7 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim data[ob.name] = cat(data[ob.name], exp[i]; dims=ndims(exp[i])+1) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + exprho = rhoreduced_1site(A0,Nrho) data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) end end @@ -70,7 +71,7 @@ function run_DTDVP(dt, tmax, A, H, prec; obs=[], effects=false, error=false, tim data[ob.name] = cat(data[ob.name], exp[i]; dims=ndims(exp[i])+1) end if reduceddensity - exprho = rhoreduced_1site(A0,1) + exprho = rhoreduced_1site(A0,Nrho) data["Reduced ρ"] = cat(data["Reduced ρ"], exprho; dims=ndims(exprho)+1) end From 42253065e1bcf1a3d6d58c029b46a2a0c0d9eeba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Tue, 7 May 2024 16:00:19 +0200 Subject: [PATCH 43/52] Add details for proton transfer --- examples/protontransfer.jl | 59 +++++++++++++++++++++----------------- src/models.jl | 42 +++++++++++++++++++++++---- 2 files changed, 69 insertions(+), 32 deletions(-) diff --git a/examples/protontransfer.jl b/examples/protontransfer.jl index e914b85..2d7de1b 100644 --- a/examples/protontransfer.jl +++ b/examples/protontransfer.jl @@ -1,9 +1,18 @@ #= - Example of a Proton Transfer model at zero temperature with an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# + Example of a Proton Transfer model at zero temperature for an isolated system or with an environment characterised by an hard cut-off Ohmic spectral density J(ω) = 2αω when ω < ωc and 0 otherwise# The dynamics is simulated using the T-TEDOPA method that maps the normal modes environment into a non-uniform tight-binding chain. - H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i + The RC tensor is initially displaced at the value γ corresponding to a space coordinate. + + The intiial electronic system is initialized in the adiabatic LOW surface at the RC displacement. + + H_S + H_RC + H_int^{S-RC} = ω^0_{e} |e⟩ ⟨e| + ω^0_{k} |k⟩ ⟨k| + \\Delta (|e⟩ ⟨k| + |k⟩ ⟨ e|) + ω_{RC} (d^{\dagger}d + \\frac{1}{2}) + g_{e} |e⟩ ⟨e|( d + d^{\dagger})+ g_{k} |k⟩ ⟨k|( d + d^{\dagger}) +`` + H_B + H_int^{RC-B} = ∫_{-∞}^{+∞} dk ω_k b_k^\dagger b_k - (d + d^{\dagger})∫_0^∞ dω\\sqrt{J(ω)}(b_ω^\\dagger+b_ω) + λ_{reorg}(d + d^{\\dagger})^2 +``. + λ_{reorg} = ∫ \frac{J(ω)}{ω}dω + =# using MPSDynamics, Plots, LaTeXStrings, QuadGK, ColorSchemes, PolyChaos, LinearAlgebra @@ -11,39 +20,40 @@ using MPSDynamics, Plots, LaTeXStrings, QuadGK, ColorSchemes, PolyChaos, LinearA #---------------------------- # Physical parameters #---------------------------- +# Enol / Keto -ω0e= 0.8 -ω0k= 0.8 +ω0e= 0.8 # Enol energy at x=0 +ω0k= 0.8 # Keto energy at x=0 -x0e = -0.25 -x0k = 0.25 +x0e = -0.25 # Enol Equilibrium displacement +x0k = 0.25 # Keto Equilibrium displacement -Δ = 0.05 +Δ = 0.05 # Direct coupling between enol and keto -m=1.83E3 -ħ=1 +m=1.83E3 # Mass of the reaction coordinate particle. 1.83E3 is the proton mass in atomic units. +ħ=1 # Atomic Units convention -dFockRC = 25 +dFockRC = 25 # Fock space of the RC tensor -ωRC = 0.0347 +ωRC = 0.0347 # Frequency of the RC tensor γ = sqrt(m*ωRC/2)*x0e # γ = displacement RC ; γ = \sqrt{m\omega/2} x_disp -cparsRC = [ωRC,ωRC*sqrt(m*ωRC/2)] # Energy and g coupling parameter +cparsRC = [ωRC,ωRC*sqrt(m*ωRC/2)] # Energy and g RC coupling parameter -isolated = true -# If isolated system, set up d=1, α =0 and N = 2 +isolated = true # isolated = true : no environment +# Creates the chain depending on the isolated condition. The parameters for isolated = false can be modified as desired. if isolated - d=1; N=2; α = 0.0 + d=1; N=2; α = 0.0 # number of Fock states of the chain modes ; length of the chain ; coupling strength else - d=4; N=40; α = 0.008 + d=4; N=40; α = 0.008 # number of Fock states of the chain modes ; length of the chain ; coupling strength end s = 1 # ohmicity ωc = 2*ωRC # Cut-off of the spectral density J(ω) -λreorg = (2*α*ωc)/s +λreorg = (2*α*ωc)/s # Reorganisation Energy taken into account in the Hamiltonian cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 @@ -53,7 +63,7 @@ cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site en dt = 10.0 # time step -tfinal = 2000.0 #1000.0 # simulation time +tfinal = 2000.0 # simulation time numsteps = length(collect(0:dt:tfinal))-1 @@ -68,7 +78,7 @@ D = 5 # MPS bond dimension xlist_rho =collect(-1.2:0.05:1.2) # x definition for the reduced density matrix expressed in space # other values of xlist_rho can be chosen to gain numerical time -palette = ColorSchemes.okabe_ito +palette = ColorSchemes.okabe_ito # color palette for plots #--------------------------- # MPO and initial state MPS @@ -107,9 +117,9 @@ end coherent_RC[1] = hcat(chainpop_RCini[:,1]...) #Manipulation to get good format for MPS, does not change value -H = protontransfermpo(ω0e, ω0k, x0e, x0k, Δ, length(ψ), dFockRC, d, N, cpars, cparsRC, λreorg) +H = protontransfermpo(ω0e, ω0k, x0e, x0k, Δ, dFockRC, d, N, cpars, cparsRC, λreorg) -A = productstatemps(physdims(H), state=[ψ, coherent_RC..., fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> +A = productstatemps(physdims(H), state=[ψ, coherent_RC..., fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Displaced RC>|Vacuum> Eini = measurempo(A,H) print("\n Energy =",Eini) @@ -146,7 +156,7 @@ A, dat = runsim(dt, tfinal, A, H; # Plots #------------ -#### Plot the doublewell in the adiabatic basis ##### +#### Plot the doublewell in the adiabatic basis. It is obtained with the diagonalization of the diabatic space Hamiltonian ##### function fp(ɸ) H11 = Ae + 0.5*m*ωRC^2*(ɸ-x0e)^2 @@ -170,10 +180,7 @@ end ɸ = [i for i in -0.75:0.001:0.75] ɸwp = [i for i in -0.45:0.001:-0.05] -xmin = -0.75 -xmax = 0.75 -ymin = 0.7 -ymax =0.9 +xmin = -0.75 ; xmax = 0.75 ; ymin = 0.7 ; ymax = 0.9 plt=plot(ɸ,fm, label="LOW",xlabel="Reaction Coordinate (arb. units)",ylabel=L"$\omega$ (arb. units)",linewidth=4,thickness_scaling = 1, bg_legend=:transparent, legendfont=16, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=(12), xlims=(xmin,xmax),ylims=(ymin,ymax),xticks=(xmin:0.25:xmax),yticks=(ymin:0.05:ymax),legend=(0.6,0.6), grid=false,linecolor =palette[8]) diff --git a/src/models.jl b/src/models.jl index e4c85b8..b07585d 100644 --- a/src/models.jl +++ b/src/models.jl @@ -704,12 +704,12 @@ function nearestneighbourmpo(tree_::Tree, h0, A, Ad = A') Ms[hn] = Ms[hn][D:D, fill(:,nc+2)...] return TreeNetwork(tree, Ms) end -#= + """ puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) Generate MPO for a pure dephasing model, defined by the Hamiltonian - ``H = \frac{ΔE}{2} σ_z + \frac{σ_z}{2} c_0 (b_0^\dagger + b_0) + \sum_{i=0}^{N-1} t_i (b_{i+1}^\dagger b_i +h.c.) + \sum_{i=0}^{N-1} ϵ_i b_i^\dagger b_i `` + ``H = \\frac{ΔE}{2} σ_z + \\frac{σ_z}{2} c_0 (b_0^\\dagger + b_0) + \\sum_{i=0}^{N-1} t_i (b_{i+1}^\\dagger b_i +h.c.) + \\sum_{i=0}^{N-1} ϵ_i b_i^\\dagger b_i `` The spin is on site 1 of the MPS and the bath modes are to the right. @@ -720,7 +720,7 @@ end * `chainparams::Array{Real,1}`: chain parameters for the bath chain. The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. * `tree::Bool`: if true, return a `TreeNetwork` object, otherwise return a vector of MPO tensors -"""=# +""" function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) u = unitmat(2) @@ -1335,8 +1335,38 @@ function correlatedenvironmentmpo(R::Vector, Nm::Int, d::Int; chainparams, fname return W end -function protontransfermpo(ω0e,ω0k,x0e,x0k, Δ, dsystem, dRC, d, N, chainparams, RCparams, λreorg) - u = unitmat(dsystem) +""" + protontransfermpo(ω0e,ω0k,x0e,x0k, Δ, dRC, d, N, chainparams, RCparams, λreorg) + +Generate a MPO for a system described in space with a reaction coordinate (RC) tensor. The RC tensor is coupled to a bosonic bath, taking into account the induced reorganization energy. + +`` +H_S + H_RC + H_int^{S-RC} = \\omega^0_{e} |e\\rangle \\langle e| + \\omega^0_{k} |k\\rangle \\langle k| + \\Delta (|e\\rangle \\langle k| + |k\\rangle \\langle e|) + \\omega_{RC} (d^{\\dagger}d + \\frac{1}{2}) + g_{e} |e\\rangle \\langle e|( d + d^{\\dagger})+ g_{k} |k \\rangle \\langle k|( d + d^{\dagger}) +`` +`` +H_B + H_int^{RC-B} = \\int_{-∞}^{+∞} dk ω_k b_k^\\dagger b_k - (d + d^{\\dagger})\\int_0^∞ dω\\sqrt{J(ω)}(b_ω^\\dagger+b_ω) + \\lambda_{reorg}(d + d^{\\dagger})^2 +``. +`` +\\lambda_{reorg} = \\int \\frac{J(\\omega)}{\\omega}d\\omega +``. + + +# Arguments + +* `ω0e`: enol energy at x=0 +* `ω0k`: keto energy at x=0 +* `x0e`: enol equilibrium displacement +* `x0k`: keto equilibrium displacement +* `Δ`: direct coupling between enol and keto +* `dRC`: fock space of the RC tensor +* `d`: number of Fock states of the chain modes +* `N`: length of the chain +* `chainparams`: chain parameters, of the form `chainparams`=``[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``, can be chosen to represent any arbitrary spectral density ``J(ω)`` at any temperature. +* `RCparams`: RC tensor parameter, of the form `RCparams`=``[ωRC,-g/x]`` +* `λreorg`: reorganization energy +""" +function protontransfermpo(ω0e,ω0k,x0e,x0k, Δ, dRC, d, N, chainparams, RCparams, λreorg) + u = unitmat(2) b = anih(dRC) bd = crea(dRC) @@ -1351,7 +1381,7 @@ function protontransfermpo(ω0e,ω0k,x0e,x0k, Δ, dsystem, dRC, d, N, chainparam Hs = (ω0e)*[1. 0.; 0. 0.] + (ω0k)*[0. 0.; 0. 1.] + Δ*sx - M=zeros(1,3,dsystem,dsystem) + M=zeros(1,3,2,2) M[1,:,:,:] = up(Hs, cRC*spos, u) MRC = zeros(3,3,dRC,dRC) From 23947f513eae03313e4b57449723504d47aafb55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Fri, 10 May 2024 17:07:01 +0200 Subject: [PATCH 44/52] Fix typo --- examples/protontransfer.jl | 4 ++-- src/models.jl | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/protontransfer.jl b/examples/protontransfer.jl index 2d7de1b..19a00d5 100644 --- a/examples/protontransfer.jl +++ b/examples/protontransfer.jl @@ -15,7 +15,7 @@ =# -using MPSDynamics, Plots, LaTeXStrings, QuadGK, ColorSchemes, PolyChaos, LinearAlgebra +using MPSDynamics, Plots, LaTeXStrings, ColorSchemes, PolyChaos, LinearAlgebra #---------------------------- # Physical parameters @@ -41,7 +41,7 @@ dFockRC = 25 # Fock space of the RC tensor cparsRC = [ωRC,ωRC*sqrt(m*ωRC/2)] # Energy and g RC coupling parameter -isolated = true # isolated = true : no environment +isolated = true # isolated = true : no environment ; isolated = false : system coupled to a bosonic environment # Creates the chain depending on the isolated condition. The parameters for isolated = false can be modified as desired. if isolated d=1; N=2; α = 0.0 # number of Fock states of the chain modes ; length of the chain ; coupling strength diff --git a/src/models.jl b/src/models.jl index b07585d..288d6c1 100644 --- a/src/models.jl +++ b/src/models.jl @@ -1341,7 +1341,7 @@ end Generate a MPO for a system described in space with a reaction coordinate (RC) tensor. The RC tensor is coupled to a bosonic bath, taking into account the induced reorganization energy. `` -H_S + H_RC + H_int^{S-RC} = \\omega^0_{e} |e\\rangle \\langle e| + \\omega^0_{k} |k\\rangle \\langle k| + \\Delta (|e\\rangle \\langle k| + |k\\rangle \\langle e|) + \\omega_{RC} (d^{\\dagger}d + \\frac{1}{2}) + g_{e} |e\\rangle \\langle e|( d + d^{\\dagger})+ g_{k} |k \\rangle \\langle k|( d + d^{\dagger}) +H_S + H_RC + H_int^{S-RC} = \\omega^0_{e} |e\\rangle \\langle e| + \\omega^0_{k} |k\\rangle \\langle k| + \\Delta (|e\\rangle \\langle k| + |k\\rangle \\langle e|) + \\omega_{RC} (d^{\\dagger}d + \\frac{1}{2}) + g_{e} |e\\rangle \\langle e|( d + d^{\\dagger})+ g_{k} |k \\rangle \\langle k|( d + d^{\\dagger}) `` `` H_B + H_int^{RC-B} = \\int_{-∞}^{+∞} dk ω_k b_k^\\dagger b_k - (d + d^{\\dagger})\\int_0^∞ dω\\sqrt{J(ω)}(b_ω^\\dagger+b_ω) + \\lambda_{reorg}(d + d^{\\dagger})^2 From 2435457aeb1a5f2afd9ddd2636103c197294d563 Mon Sep 17 00:00:00 2001 From: angelariva Date: Mon, 13 May 2024 20:36:31 +0200 Subject: [PATCH 45/52] added examples on bath observables --- examples/bath-observables.jl | 103 +++++++++++++++++++++++++++-------- examples/puredephasing.jl | 2 + src/fundamentals.jl | 32 +++++++++++ src/models.jl | 1 + 4 files changed, 114 insertions(+), 24 deletions(-) diff --git a/examples/bath-observables.jl b/examples/bath-observables.jl index 8f234b9..ebbae6e 100644 --- a/examples/bath-observables.jl +++ b/examples/bath-observables.jl @@ -1,4 +1,6 @@ -using MPSDynamics, Plots, LaTeXStrings, QuadGK +using MPSDynamics, Plots, LaTeXStrings, QuadGK, LinearAlgebra + +import MPSDynamics: measuremodes, measurecorrs, mpsembed!, eigenchain const ∞ = Inf #---------------------------- @@ -34,10 +36,16 @@ end # Simulation parameters #----------------------- -dt = 1.0 # time step -tfinal = 10.0 # simulation time -method = :DTDVP # time-evolution method -prec = 0.0001 # precision for the adaptive TDVP +#method = :TDVP1 # time-evolution method +#conv = 2 # MPS bond dimension +#dt = 1.0 # time step +#tfinal = 10.0 # simulation time + +method = :TDVP1 # time-evolution method +conv = 3 # bond dimension for the TDVP1 +dt = 0.5 # time step +tfinal = 100.0 # simulation time +Tsteps = Int(tfinal / dt) #--------------------------- # MPO and initial state MPS @@ -52,31 +60,34 @@ H = puredephasingmpo(ω0, d, N, cpars) A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> - +mpsembed!(A, 2) #--------------------------- # Definition of observables #--------------------------- ob1 = OneSiteObservable("sz", sz, 1) +ob2 = OneSiteObservable("sx", sx, 1) +ob3 = OneSiteObservable("chain_mode_occupation", numb(d), (2,N+1)) +ob4 = OneSiteObservable("c", crea(d), collect(2:N+1)) +ob5 = OneSiteObservable("cdag", crea(d), collect(2:N+1)) +ob6 = TwoSiteObservable("cdagc", crea(d), anih(d), collect(2:N+1), collect(2:N+1)) +ob7 = TwoSiteObservable("cdagcdag", crea(d), crea(d), collect(2:N+1), collect(2:N+1)) +ob8 = TwoSiteObservable("cc", anih(d), anih(d), collect(2:N+1), collect(2:N+1)) #------------- # Simulation #------------ -ob1 = OneSiteObservable("sz", sz, 1) -ob2 = OneSiteObservable("chain_mode_occupation", numb(d), (2,N+1)) -ob3 = TwoSiteObservable("SXdisp", sx, disp(d), [1], collect(2:N+1)) -ob4 = TwoSiteObservable("cdagc", crea(d), anih(d), collect(2:N+1), collect(2:N+1)) -ob5 = OneSiteObservable("sx", sx, 1) - A, dat = runsim(dt, tfinal, A, H, prec=1E-4; name = "Bath observables in the pure dephasing model", method = method, - obs = [ob1], + obs = [ob1, ob2, ob3, ob4, ob5, ob6, ob7, ob8], convobs = [ob1], - params = @LogParams(ω0, N, d, α, s), - convparams = prec, + params = @LogParams(ω0, N, d, α, s, ψ), + convparams = conv, + Dlim = 100, + reduceddensity = true, verbose = false, save = false, plot = true, @@ -86,21 +97,55 @@ A, dat = runsim(dt, tfinal, A, H, prec=1E-4; # Post-processing #--------------------------- -occup = mapslices(X -> measuremodes(X, cpars[1], cpars[2]), dat["data/cdagc"], dims = [1,2]) +T = length(dat["data/times"]) +constr = Array{ComplexF64}(undef, N, N, T) +destr = Array{ComplexF64}(undef, N, N, T) +for t in 1:T + constr[:,:,t] = diagm(0 => dat["data/cdag"][:,t]) + destr[:,:,t] = diagm(0 => dat["data/c"][:,t]) +end -#---------- -# Analytical results at specified temperature -# (see The Theory of Open Quantum System, H.-P. Breuer & F. Petruccione 2002, Chapter 4) -#---------- +omeg = eigenchain(cpars, nummodes=N).values +bath_occup = mapslices(X -> measuremodes(X, cpars[1], cpars[2]), dat["data/cdagc"], dims = [1,2]) +cdag_average = mapslices(X -> measuremodes(X, cpars[1], cpars[2]), constr, dims = [1,2]) +c_average = mapslices(X -> measuremodes(X, cpars[1], cpars[2]), destr, dims = [1,2]) +cc_average = mapslices(X -> measurecorrs(X, cpars[1], cpars[2]), dat["data/cc"], dims = [1,2]) +cdagcdag_average = mapslices(X -> measurecorrs(X, cpars[1], cpars[2]), dat["data/cdagcdag"], dims = [1,2]) + +correlations_c = [ + cc_average[i, j, t] - c_average[i, 1, t] .* c_average[j, 1, t] + for i in 1:size(cc_average, 1), j in 1:size(cc_average, 2), t in 1:size(cc_average, 3) +] +correlations_cdag = [ + cdagcdag_average[i, j, t] - cdag_average[i, 1, t] .* cdag_average[j, 1, t] + for i in 1:size(cdagcdag_average, 1), j in 1:size(cdagcdag_average, 2), t in 1:size(cdagcdag_average,3) +] +#-------------------- +# Analytical results +#-------------------- Johmic(ω,s) = (2*α*ω^s)/(ωc^(s-1)) -time_analytical = LinRange(0.0,tfinal,Int(tfinal)) +time_analytical = LinRange(0.0, tfinal, Int(tfinal)) Γohmic(t) = - quadgk(x -> Johmic(x,s)*(1 - cos(x*t))*coth(β*x/2)/x^2, 0, ωc)[1] -Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) +Decoherence_ohmic(t) = 0.5 * exp(Γohmic(t)) + + +α_theo = 0.25 * α +function Jtherm(x) + if 1 >= x >= 0 + return +α_theo * abs(x)^s * (1 + coth(β*0.5*x)) + elseif -1 <= x <= 0 + return -α_theo * abs(x)^s * (1 + coth(β*0.5*x)) + else + return 0 + end +end + +bath_occup_analytical(ω, t) = abs(Jtherm(ω))/(ω^2)*2*(1-cos(ω*t)) #------------- # Plots @@ -108,8 +153,18 @@ Decoherence_ohmic(t) = 0.5*exp(Γohmic(t)) ρ12 = abs.(dat["data/Reduced ρ"][1,2,:]) -plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $\beta = %$β ~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) +p1 = plot(time_analytical, t->Decoherence_ohmic(t), label="Analytics", title=L"Pure Dephasing, Ohmic $s=%$s$, $\beta = %$β ~\mathrm{K}$", linecolor=:black, xlabel="Time (arb. units)", ylabel=L"Coherence $|\rho_{12}(t)|$", linewidth=4, titlefontsize=16, legend=:best, legendfontsize=16, xguidefontsize=16, yguidefontsize=16, tickfontsize=10) +p1 = plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") + +cumul = [bath_occup_analytical(omeg[i], tfinal)*(omeg[i+1]-omeg[i]) for i in 1:(length(omeg)-1)] -plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") +p2 = plot(omeg[1:length(omeg)-1], cumul, + xlabel=L"\omega", ylabel=L"\langle n^b_\omega \rangle", label="Analytics", + title="Mode occupation") +p2 = plot!(omeg, bath_occup[:, :, Tsteps], label="Numerics") +p3 = heatmap(omeg, omeg, abs.(real.(correlations_cdag[:,:,Tsteps]) .+ im*imag.(correlations_cdag[:,:,Tsteps])), + xlabel=L"\omega", + ylabel=L"\omega", title="Environmental correlations") +plot(p1, p2, p3, layout = (2, 2), size = (1400, 1200)) diff --git a/examples/puredephasing.jl b/examples/puredephasing.jl index 364c8a7..ce8c0db 100644 --- a/examples/puredephasing.jl +++ b/examples/puredephasing.jl @@ -8,6 +8,8 @@ using MPSDynamics, Plots, LaTeXStrings, QuadGK +import MPSDynamics: chaincoeffs_ohmic, puredephasingmpo + const ∞ = Inf #---------------------------- # Physical parameters diff --git a/src/fundamentals.jl b/src/fundamentals.jl index ecf8d2d..85ba888 100644 --- a/src/fundamentals.jl +++ b/src/fundamentals.jl @@ -176,6 +176,38 @@ function measuremodes(adaga, e::Vector, t::Vector) return real.(diag(U' * adaga * U)) end +""" + measurecorrs(oper, , e::Vector, t::Vector) + + ### Parameters + + `oper``: Square matrix (Matrix{Float64}) representing the operator to be transformed. + `e``: Vector (Vector{Float64}) of diagonal (on-site energy) chain coefficients. + `t`: Vector (Vector{Float64}) of off-diagonal (hopping terms) chain coefficients. + + ### Returns + + Matrix{Float64}: This matrix is the operator `oper` transformed back from the chain + representation to the representation corresponding to the extended bath. The resulting + operator represents quantities like mode occupations or other properties in the basis + of environmental modes associated with specific frequencies $\omega_i$. + + ### Description + + This function performs a basis transformation of the operator `oper`. Specifically, + this transformation reverses the unitary transformation that maps the extended bath + Hamiltonian into the chain representation. + +""" + +function measurecorrs(oper, e::Vector, t::Vector) + N = size(oper)[1] + hmat = diagm(0=>e[1:N], 1=>t[1:N-1], -1=>t[1:N-1]) + F = eigen(hmat) + U = F.vectors + return (U' * oper * U) +end + """ findchainlength(T, cparams...; eps=10^-6) diff --git a/src/models.jl b/src/models.jl index 288d6c1..d30f5f2 100644 --- a/src/models.jl +++ b/src/models.jl @@ -719,6 +719,7 @@ end * `Nchain::Int`: number of sites in the chain * `chainparams::Array{Real,1}`: chain parameters for the bath chain. The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. * `tree::Bool`: if true, return a `TreeNetwork` object, otherwise return a vector of MPO tensors +""" """ function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) From a65db9d33f65fff38e0526aee7eeee0efc3412d8 Mon Sep 17 00:00:00 2001 From: angelariva Date: Tue, 14 May 2024 16:31:20 +0200 Subject: [PATCH 46/52] added function to undo the thermofield transformation --- examples/bath-observables.jl | 34 +++++++++----- src/fundamentals.jl | 89 +++++++++++++++++++++++++++++++++++- 2 files changed, 111 insertions(+), 12 deletions(-) diff --git a/examples/bath-observables.jl b/examples/bath-observables.jl index ebbae6e..f7284cd 100644 --- a/examples/bath-observables.jl +++ b/examples/bath-observables.jl @@ -1,6 +1,7 @@ -using MPSDynamics, Plots, LaTeXStrings, QuadGK, LinearAlgebra +using MPSDynamics, Plots, LaTeXStrings, QuadGK, LinearAlgebra, Interpolations + +import MPSDynamics: measuremodes, measurecorrs, mpsembed!, eigenchain, physical_occup -import MPSDynamics: measuremodes, measurecorrs, mpsembed!, eigenchain const ∞ = Inf #---------------------------- @@ -8,13 +9,13 @@ const ∞ = Inf #---------------------------- d = 10 # number of Fock states of the chain modes -N = 30 # length of the chain +N = 60 # length of the chain α = 0.01 # coupling strength -ω0 = 0.008 # TLS gap +ω0 = 0.2 # TLS gap s = 1 # ohmicity -ωc = 0.035 # Cut-off of the spectral density J(ω) -#β = 100 # Thermalized environment -β = ∞ # Case zero temperature T=0, β → ∞ +ωc = 1. # Cut-off of the spectral density J(ω) +β = 20 # Thermalized environment +#β = ∞ # Case zero temperature T=0, β → ∞ #---------------------------- # Ohmic spectral density @@ -23,7 +24,7 @@ s = 1 # ohmicity if β == ∞ cpars = chaincoeffs_ohmic(N, α, s; ωc=ωc) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 else - cpars = chaincoeffs_finiteT(N, β; α=α, s=s, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=true) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 + cpars = chaincoeffs_finiteT(N, β; α=α, s=s, J=nothing, ωc=ωc, mc=4, mp=0, AB=nothing, iq=1, idelta=2, procedure=:Lanczos, Mmax=5000, save=false) # chain parameters, i.e. on-site energies ϵ_i, hopping energies t_i, and system-chain coupling c_0 #=#If cpars is stored in "../ChainOhmT/ohmicT" curdir = @__DIR__ dir_chaincoeff = abspath(joinpath(curdir, "../ChainOhmT/ohmicT")) @@ -44,7 +45,7 @@ end method = :TDVP1 # time-evolution method conv = 3 # bond dimension for the TDVP1 dt = 0.5 # time step -tfinal = 100.0 # simulation time +tfinal = 60.0 # simulation time Tsteps = Int(tfinal / dt) #--------------------------- @@ -121,6 +122,9 @@ correlations_cdag = [ cdagcdag_average[i, j, t] - cdag_average[i, 1, t] .* cdag_average[j, 1, t] for i in 1:size(cdagcdag_average, 1), j in 1:size(cdagcdag_average, 2), t in 1:size(cdagcdag_average,3) ] + +bath_occup_phys = physical_occup(correlations_cdag[:,:,Tsteps], correlations_c[:,:,Tsteps], omeg, bath_occup[:,:,Tsteps], β, N) + #-------------------- # Analytical results #-------------------- @@ -160,11 +164,19 @@ cumul = [bath_occup_analytical(omeg[i], tfinal)*(omeg[i+1]-omeg[i]) for i in 1:( p2 = plot(omeg[1:length(omeg)-1], cumul, xlabel=L"\omega", ylabel=L"\langle n^b_\omega \rangle", label="Analytics", - title="Mode occupation") + title="Mode occupation in the extended bath") p2 = plot!(omeg, bath_occup[:, :, Tsteps], label="Numerics") p3 = heatmap(omeg, omeg, abs.(real.(correlations_cdag[:,:,Tsteps]) .+ im*imag.(correlations_cdag[:,:,Tsteps])), xlabel=L"\omega", ylabel=L"\omega", title="Environmental correlations") -plot(p1, p2, p3, layout = (2, 2), size = (1400, 1200)) + +Mhalf = Int(length(omeg)*0.5)+1 +M = length(omeg) + +p4 = plot(omeg[Mhalf:M], bath_occup_phys, + xlabel=L"\omega", ylabel=L"\langle n^b_\omega \rangle", label="Analytics", + title="Mode occupation in the physical bath") + +plot(p1, p2, p3, p4, layout = (2, 2), size = (1400, 1200)) diff --git a/src/fundamentals.jl b/src/fundamentals.jl index 85ba888..d30c748 100644 --- a/src/fundamentals.jl +++ b/src/fundamentals.jl @@ -190,7 +190,7 @@ end Matrix{Float64}: This matrix is the operator `oper` transformed back from the chain representation to the representation corresponding to the extended bath. The resulting operator represents quantities like mode occupations or other properties in the basis - of environmental modes associated with specific frequencies $\omega_i$. + of environmental modes associated with specific frequencies omega_i . ### Description @@ -208,6 +208,93 @@ function measurecorrs(oper, e::Vector, t::Vector) return (U' * oper * U) end + +""" + cosineh(omega, bet) + + Calculates the hyperbolic cosine function function based on the input parameters, + for the Bogoliubov transformation necessary for the thermofield transformation. + + # Arguments + - `omega::Float64`: The frequency parameter. + - `bet::Float64`: The beta parameter. + + # Returns + - `Float64`: The result of the modified cosine function. +""" + +function cosineh(omega, bet) + return 1/sqrt(1 - exp(-omega * (bet))) +end + +" + sineh(omega, bet) + + Calculates the hyperbolic sine function function based on the input parameters, + for the Bogoliubov transformation necessary for the thermofield transformation. + + # Arguments + - `omega::Float64`: The frequency parameter. + - `bet::Float64`: The beta parameter. + + # Returns + - `Float64`: The result of the modified cosine function. +""" + +function sineh(omega, bet) + return 1/sqrt(-1 + exp(omega * float(bet))) +end + +""" + physical_occup(corr_constr, corr_destr, omega, occup, b, M) + + Calculates the physical occupation based on correlation matrices, omega values, + and other parameters. The physical occupation in the original frequency environment + is computed by reverting the thermofield transformation. + + # Arguments + - `corr_constr::Matrix{ComplexF64}`: The correlation construction matrix. + - `corr_destr::Matrix{ComplexF64}`: The correlation destruction matrix. + - `omega::Vector{Float64}`: The omega values. + - `occup::Matrix{Float64}`: The occupation matrix. + - `b::Float64`: The beta parameter. + - `M::Int`: The number of points for interpolation. + + # Returns + - `Vector{Float64}`: The physical occupation values. +""" + +function physical_occup(corr_constr, corr_destr, omega, occup, b, M) + x = range(-1, stop=1, length=M) + + # Ensure occup is a vector + occup_vector = vec(occup) + + occup_interp = LinearInterpolation(omega, occup_vector, extrapolation_bc=Line()) + corr_constr_interp = interpolate((omega, omega), abs.(corr_constr), Gridded(Linear())) + corr_destr_interp = interpolate((omega, omega), abs.(corr_destr), Gridded(Linear())) + + Mhalf = div(M, 2) + phys_occ = [] + + omega_min = minimum(omega) + omega_max = maximum(omega) + x_rescaled = (x .+ 1) .* (omega_max - omega_min) / 2 .+ omega_min + + for el in 1:Mhalf + ipos = Mhalf + el + ineg = Mhalf - el + 1 + occ = (cosineh(x_rescaled[ipos], b) * sineh(x_rescaled[ipos], b) * + (corr_destr_interp(x_rescaled[ineg], x_rescaled[ipos]) + corr_constr_interp(x_rescaled[ipos], x_rescaled[ineg])) + + cosineh(x_rescaled[ipos], b)^2 * occup_interp(x_rescaled[ipos]) + + sineh(x_rescaled[ipos], b)^2 * (1 + occup_interp(x_rescaled[ineg]))) + push!(phys_occ, occ) + end + + return phys_occ +end + + """ findchainlength(T, cparams...; eps=10^-6) From 840cb9231bf8c1da22b8e83ac0b7f5d0d55eccad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 15 May 2024 15:52:09 +0200 Subject: [PATCH 47/52] Fix typos --- src/fundamentals.jl | 2 +- src/models.jl | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/src/fundamentals.jl b/src/fundamentals.jl index 85ba888..a531b1e 100644 --- a/src/fundamentals.jl +++ b/src/fundamentals.jl @@ -190,7 +190,7 @@ end Matrix{Float64}: This matrix is the operator `oper` transformed back from the chain representation to the representation corresponding to the extended bath. The resulting operator represents quantities like mode occupations or other properties in the basis - of environmental modes associated with specific frequencies $\omega_i$. + of environmental modes associated with specific frequencies ``\\omega_i``. ### Description diff --git a/src/models.jl b/src/models.jl index d30f5f2..0256428 100644 --- a/src/models.jl +++ b/src/models.jl @@ -720,8 +720,6 @@ end * `chainparams::Array{Real,1}`: chain parameters for the bath chain. The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. * `tree::Bool`: if true, return a `TreeNetwork` object, otherwise return a vector of MPO tensors """ - -""" function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) u = unitmat(2) From 31a4040b42c21d3bc6a03e4feb71926275d6b952 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brieuc=20Le=20D=C3=A9?= Date: Wed, 15 May 2024 16:10:19 +0200 Subject: [PATCH 48/52] Update tensorcontract TensorOperations --- src/treeTDVP.jl | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/src/treeTDVP.jl b/src/treeTDVP.jl index 696ade5..0b65ce8 100644 --- a/src/treeTDVP.jl +++ b/src/treeTDVP.jl @@ -50,8 +50,9 @@ function mpsrightnorm!(net::TreeNetwork, id::Int) IC=collect(1:nc+2) IA=collect(1:nc+2) IC[i+1]=-1 + #net[id] = tensorcontract(net[id], IA, C, [i+1,-1], IC) #Old tensoroperation version + net[id] = tensorcontract(IC,net[id], IA, C, [i+1,-1]) - net[id] = tensorcontract(net[id], IA, C, [i+1,-1], IC) end end @@ -405,7 +406,8 @@ function tdvp1sweep_lc!(dt, A::TreeNetwork, M::TreeNetwork, lc::TreeLightCone, F IA = collect(1:ngc+2) IB = collect(1:ngc+2) IA[1] = -1 - A[child] = tensorcontract(A[child], IA, C, [1,-1], IB) + #A[child] = tensorcontract(A[child], IA, C, [1,-1], IB) #old tensoroperations + A[child] = tensorcontract(IB, A[child], IA, C, [1,-1]) #(OC is now on child) #evolve child forward one full time step @@ -427,7 +429,8 @@ function tdvp1sweep_lc!(dt, A::TreeNetwork, M::TreeNetwork, lc::TreeLightCone, F IA = collect(1:nc+2) IB = collect(1:nc+2) IA[i+1] = -1 - AC = tensorcontract(AL, IA, C, [i+1,-1], IB) + #AC = tensorcontract(AL, IA, C, [i+1,-1], IB) #old tensoroperations + AC = tensorcontract(IB, AL, IA, C, [i+1,-1]) #(OC is now on headnode) end @@ -496,7 +499,8 @@ function tdvp1sweep_lc!(dt, A::TreeNetwork, M::TreeNetwork, lc::TreeLightCone, F IA = collect(1:ngc+2) IB = collect(1:ngc+2) IA[1] = -1 - A[child] = tensorcontract(A[child], IA, C, [1,-1], IB) + #A[child] = tensorcontract(A[child], IA, C, [1,-1], IB) + A[child] = tensorcontract(IB, A[child], IA, C, [1,-1]) #(OC is now on child) #evolve child forward one full time step @@ -518,7 +522,8 @@ function tdvp1sweep_lc!(dt, A::TreeNetwork, M::TreeNetwork, lc::TreeLightCone, F IA = collect(1:nc+2) IB = collect(1:nc+2) IA[i+1] = -1 - AC = tensorcontract(AL, IA, C, [i+1,-1], IB) + #AC = tensorcontract(AL, IA, C, [i+1,-1], IB) + AC = tensorcontract(IB, AL, IA, C, [i+1,-1]) #(OC is now on node) end From a72ac785ff70d0ce0c09daaf456d465896b6401b Mon Sep 17 00:00:00 2001 From: angelariva Date: Wed, 15 May 2024 18:19:15 +0200 Subject: [PATCH 49/52] corrected variable name --- examples/bath-observables.jl | 42 ++++++++++++++---------------------- 1 file changed, 16 insertions(+), 26 deletions(-) diff --git a/examples/bath-observables.jl b/examples/bath-observables.jl index f7284cd..8a344d7 100644 --- a/examples/bath-observables.jl +++ b/examples/bath-observables.jl @@ -1,4 +1,4 @@ -using MPSDynamics, Plots, LaTeXStrings, QuadGK, LinearAlgebra, Interpolations +using MPSDynamics, Plots, LaTeXStrings, QuadGK, LinearAlgebra, Interpolations, Revise import MPSDynamics: measuremodes, measurecorrs, mpsembed!, eigenchain, physical_occup @@ -17,6 +17,16 @@ s = 1 # ohmicity β = 20 # Thermalized environment #β = ∞ # Case zero temperature T=0, β → ∞ + +#----------------------- +# Simulation parameters +#----------------------- + +method = :TDVP1 # time-evolution method +conv = 3 # bond dimension for the TDVP1 +dt = 0.5 # time step +tfinal = 60.0 # simulation time + #---------------------------- # Ohmic spectral density #---------------------------- @@ -32,22 +42,6 @@ else =# end - -#----------------------- -# Simulation parameters -#----------------------- - -#method = :TDVP1 # time-evolution method -#conv = 2 # MPS bond dimension -#dt = 1.0 # time step -#tfinal = 10.0 # simulation time - -method = :TDVP1 # time-evolution method -conv = 3 # bond dimension for the TDVP1 -dt = 0.5 # time step -tfinal = 60.0 # simulation time -Tsteps = Int(tfinal / dt) - #--------------------------- # MPO and initial state MPS #--------------------------- @@ -61,7 +55,6 @@ H = puredephasingmpo(ω0, d, N, cpars) A = productstatemps(physdims(H), state=[ψ, fill(unitcol(1,d), N)...]) # MPS representation of |ψ>|Vacuum> -mpsembed!(A, 2) #--------------------------- # Definition of observables #--------------------------- @@ -71,7 +64,6 @@ ob2 = OneSiteObservable("sx", sx, 1) ob3 = OneSiteObservable("chain_mode_occupation", numb(d), (2,N+1)) ob4 = OneSiteObservable("c", crea(d), collect(2:N+1)) ob5 = OneSiteObservable("cdag", crea(d), collect(2:N+1)) - ob6 = TwoSiteObservable("cdagc", crea(d), anih(d), collect(2:N+1), collect(2:N+1)) ob7 = TwoSiteObservable("cdagcdag", crea(d), crea(d), collect(2:N+1), collect(2:N+1)) ob8 = TwoSiteObservable("cc", anih(d), anih(d), collect(2:N+1), collect(2:N+1)) @@ -87,7 +79,6 @@ A, dat = runsim(dt, tfinal, A, H, prec=1E-4; convobs = [ob1], params = @LogParams(ω0, N, d, α, s, ψ), convparams = conv, - Dlim = 100, reduceddensity = true, verbose = false, save = false, @@ -123,7 +114,6 @@ correlations_cdag = [ for i in 1:size(cdagcdag_average, 1), j in 1:size(cdagcdag_average, 2), t in 1:size(cdagcdag_average,3) ] -bath_occup_phys = physical_occup(correlations_cdag[:,:,Tsteps], correlations_c[:,:,Tsteps], omeg, bath_occup[:,:,Tsteps], β, N) #-------------------- # Analytical results @@ -162,12 +152,12 @@ p1 = plot!(dat["data/times"], ρ12, lw=4, ls=:dash, label="Numerics") cumul = [bath_occup_analytical(omeg[i], tfinal)*(omeg[i+1]-omeg[i]) for i in 1:(length(omeg)-1)] -p2 = plot(omeg[1:length(omeg)-1], cumul, +p2 = plot(omeg[1:length(omeg)-1], cumul, lw = 4, linecolor=:black, xlabel=L"\omega", ylabel=L"\langle n^b_\omega \rangle", label="Analytics", title="Mode occupation in the extended bath") -p2 = plot!(omeg, bath_occup[:, :, Tsteps], label="Numerics") +p2 = plot!(omeg, bath_occup[:, :, T], lw=4, ls=:dash, label="Numerics") -p3 = heatmap(omeg, omeg, abs.(real.(correlations_cdag[:,:,Tsteps]) .+ im*imag.(correlations_cdag[:,:,Tsteps])), +p3 = heatmap(omeg, omeg, abs.(real.(correlations_cdag[:,:,T]) .+ im*imag.(correlations_cdag[:,:,T])), xlabel=L"\omega", ylabel=L"\omega", title="Environmental correlations") @@ -175,8 +165,8 @@ p3 = heatmap(omeg, omeg, abs.(real.(correlations_cdag[:,:,Tsteps]) .+ im*imag.(c Mhalf = Int(length(omeg)*0.5)+1 M = length(omeg) -p4 = plot(omeg[Mhalf:M], bath_occup_phys, - xlabel=L"\omega", ylabel=L"\langle n^b_\omega \rangle", label="Analytics", +p4 = plot(omeg[Mhalf:M], bath_occup_phys, lw=4, + xlabel=L"\omega", ylabel=L"\langle n^b_\omega \rangle", title="Mode occupation in the physical bath") plot(p1, p2, p3, p4, layout = (2, 2), size = (1400, 1200)) From 3e82e6cd908a8619f9de382f71a83f7cf0752c94 Mon Sep 17 00:00:00 2001 From: angelariva Date: Thu, 16 May 2024 17:01:24 +0200 Subject: [PATCH 50/52] minor correction --- src/models.jl | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/src/models.jl b/src/models.jl index d30f5f2..2dbe98e 100644 --- a/src/models.jl +++ b/src/models.jl @@ -721,7 +721,6 @@ end * `tree::Bool`: if true, return a `TreeNetwork` object, otherwise return a vector of MPO tensors """ -""" function puredephasingmpo(ΔE, dchain, Nchain, chainparams; tree=false) u = unitmat(2) @@ -745,17 +744,14 @@ end the Nth site corresponding to the impurity, and the rest of the sites corresponding to the second lead. - ### Arguments - * `N::Int`: number of sites in the chain * `ϵd::Real`: energy of the impurity site at the center, as Ed - μ, where μ is the chemical potential - * chainparams1::Array{Real,1}: chain parameters for the first lead - * chainparams2::Array{Real,1}: chain parameters for the second lead + * `chainparams1::Array{Real,1}`: chain parameters for the first lead + * `chainparams2::Array{Real,1}`: chain parameters for the second lead The chain parameters are given in the standard form: `chainparams` ``=[[ϵ_0,ϵ_1,...],[t_0,t_1,...],c_0]``. - - """ + function tightbinding_mpo(N, ϵd, chainparams1, chainparams2) e1 = chainparams1[1] From c94b984618f741fd91e3ab4afd3973944f2a70ca Mon Sep 17 00:00:00 2001 From: angelariva Date: Fri, 17 May 2024 19:04:25 +0200 Subject: [PATCH 51/52] corrected some typos, updated dependencies --- Manifest.toml | 1633 +++++++++++++++++++++++++++------- Project.toml | 8 +- examples/bath-observables.jl | 3 +- src/MPSDynamics.jl | 2 +- src/fundamentals.jl | 2 +- 5 files changed, 1343 insertions(+), 305 deletions(-) diff --git a/Manifest.toml b/Manifest.toml index 5415b49..8b7d8de 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -1,130 +1,262 @@ # This file is machine-generated - editing it directly is not advised [[AbstractFFTs]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0" +deps = ["ChainRulesCore", "LinearAlgebra", "Test"] +git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.0.1" +version = "1.5.0" [[AbstractTrees]] -git-tree-sha1 = "03e0550477d86222521d254b741d470ba17ea0b5" +git-tree-sha1 = "2d9c9a55f9c93e8887ad391fbae72f8ef55e1177" uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" -version = "0.3.4" +version = "0.4.5" + +[[Accessors]] +deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "MacroTools", "Markdown", "Requires", "Test"] +git-tree-sha1 = "c0d491ef0b135fd7d63cbc6404286bc633329425" +uuid = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697" +version = "0.1.36" [[Adapt]] -deps = ["LinearAlgebra"] -git-tree-sha1 = "345a14764e43fe927d6f5c250fe4c8e4664e6ee8" +deps = ["LinearAlgebra", "Requires"] +git-tree-sha1 = "cde29ddf7e5726c9fb511f340244ea3481267608" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "2.4.0" +version = "3.7.2" + +[[ArgCheck]] +git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" +uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" +version = "2.3.0" + +[[ArgTools]] +uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" +version = "1.1.1" [[ArnoldiMethod]] deps = ["LinearAlgebra", "Random", "StaticArrays"] -git-tree-sha1 = "f87e559f87a45bece9c9ed97458d3afe98b1ebb9" +git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6" uuid = "ec485272-7323-5ecc-a04f-4719b315124d" -version = "0.1.0" +version = "0.4.0" + +[[ArrayInterface]] +deps = ["Adapt", "LinearAlgebra", "Requires", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "16267cf279190ca7c1b30d020758ced95db89cd0" +uuid = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9" +version = "7.5.1" + +[[ArrayLayouts]] +deps = ["FillArrays", "LinearAlgebra", "SparseArrays"] +git-tree-sha1 = "33207a8be6267bc389d0701e97a9bce6a4de68eb" +uuid = "4c555306-a7a7-4459-81d9-ec55ddd5c99a" +version = "1.9.2" [[Artifacts]] -deps = ["Pkg"] -git-tree-sha1 = "c30985d8821e0cd73870b17b0ed0ce6dc44cb744" uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" -version = "1.3.0" + +[[Atomix]] +deps = ["UnsafeAtomics"] +git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be" +uuid = "a9b6321e-bd34-4604-b9c9-b65b8de01458" +version = "0.1.0" [[AxisAlgorithms]] deps = ["LinearAlgebra", "Random", "SparseArrays", "WoodburyMatrices"] -git-tree-sha1 = "a4d07a1c313392a77042855df46c5f534076fab9" +git-tree-sha1 = "01b8ccb13d68535d73d2b0c23e39bd23155fb712" uuid = "13072b0f-2c55-5437-9ae7-d433b7a33950" -version = "1.0.0" +version = "1.1.0" + +[[BFloat16s]] +deps = ["LinearAlgebra", "Printf", "Random", "Test"] +git-tree-sha1 = "dbf84058d0a8cbbadee18d25cf606934b22d7c66" +uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b" +version = "0.4.2" + +[[BangBang]] +deps = ["Accessors", "Compat", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires"] +git-tree-sha1 = "08e5fc6620a8d83534bf6149795054f1b1e8370a" +uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66" +version = "0.4.2" [[Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" -[[BinaryProvider]] -deps = ["Libdl", "Logging", "SHA"] -git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058" -uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232" -version = "0.5.10" +[[Baselet]] +git-tree-sha1 = "aebf55e6d7795e02ca500a689d326ac979aaf89e" +uuid = "9718e550-a3fa-408a-8086-8db961cd8217" +version = "0.1.1" + +[[BitFlags]] +git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" +uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" +version = "0.1.8" + +[[BitIntegers]] +deps = ["Random"] +git-tree-sha1 = "a55462dfddabc34bc97d3a7403a2ca2802179ae6" +uuid = "c3b6d118-76ef-56ca-8cc7-ebb389d030a1" +version = "0.3.1" + +[[BlockArrays]] +deps = ["ArrayLayouts", "FillArrays", "LinearAlgebra"] +git-tree-sha1 = "9a9610fbe5779636f75229e423e367124034af41" +uuid = "8e7c35d0-a365-5155-bbbb-fb81a777f24e" +version = "0.16.43" [[Blosc]] deps = ["Blosc_jll"] -git-tree-sha1 = "84cf7d0f8fd46ca6f1b3e0305b4b4a37afe50fd6" +git-tree-sha1 = "310b77648d38c223d947ff3f50f511d08690b8d5" uuid = "a74b3585-a348-5f62-a45c-50e91977d574" -version = "0.7.0" +version = "0.7.3" [[Blosc_jll]] -deps = ["Libdl", "Lz4_jll", "Pkg", "Zlib_jll", "Zstd_jll"] -git-tree-sha1 = "aa9ef39b54a168c3df1b2911e7797e4feee50fbe" +deps = ["Artifacts", "JLLWrappers", "Libdl", "Lz4_jll", "Zlib_jll", "Zstd_jll"] +git-tree-sha1 = "19b98ee7e3db3b4eff74c5c9c72bf32144e24f10" uuid = "0b7ba130-8d10-5ba8-a3d6-c5182647fed9" -version = "1.14.3+1" +version = "1.21.5+0" [[Bzip2_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "c3598e525718abcc440f69cc6d5f60dda0a1b61e" +git-tree-sha1 = "9e2a6b69137e6969bab0152632dcb3bc108c8bdd" uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0" -version = "1.0.6+5" +version = "1.0.8+1" [[CEnum]] -git-tree-sha1 = "215a9aa4a1f23fbd05b92769fdd62559488d70e9" +git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90" uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" -version = "0.4.1" +version = "0.4.2" [[CUDA]] -deps = ["AbstractFFTs", "Adapt", "BinaryProvider", "CEnum", "DataStructures", "ExprTools", "GPUArrays", "GPUCompiler", "LLVM", "Libdl", "LinearAlgebra", "Logging", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SparseArrays", "Statistics", "TimerOutputs"] -git-tree-sha1 = "83bfd180e2f842f6d4ee315a6db8665e9aa0c19b" +deps = ["AbstractFFTs", "Adapt", "BFloat16s", "CEnum", "CUDA_Driver_jll", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "Crayons", "DataFrames", "ExprTools", "GPUArrays", "GPUCompiler", "KernelAbstractions", "LLVM", "LLVMLoopInfo", "LazyArtifacts", "Libdl", "LinearAlgebra", "Logging", "NVTX", "Preferences", "PrettyTables", "Printf", "Random", "Random123", "RandomNumbers", "Reexport", "Requires", "SparseArrays", "Statistics", "UnsafeAtomicsLLVM"] +git-tree-sha1 = "95ac638373ac40e29c1b6d086a3698f5026ff6a6" uuid = "052768ef-5323-5732-b1bb-66c8b64840ba" -version = "1.3.3" +version = "5.1.2" + +[[CUDA_Driver_jll]] +deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "Pkg"] +git-tree-sha1 = "d01bfc999768f0a31ed36f5d22a76161fc63079c" +uuid = "4ee394cb-3365-5eb0-8335-949819d2adfc" +version = "0.7.0+1" + +[[CUDA_Runtime_Discovery]] +deps = ["Libdl"] +git-tree-sha1 = "38f830504358e9972d2a0c3e5d51cb865e0733df" +uuid = "1af6417a-86b4-443c-805f-a4643ffb695f" +version = "0.2.4" + +[[CUDA_Runtime_jll]] +deps = ["Artifacts", "CUDA_Driver_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] +git-tree-sha1 = "9704e50c9158cf8896c2776b8dbc5edd136caf80" +uuid = "76a88914-d11a-5bdc-97e0-2f5a05c973a2" +version = "0.10.1+0" + +[[CUTENSOR_jll]] +deps = ["Artifacts", "CUDA_Runtime_jll", "CompilerSupportLibraries_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] +git-tree-sha1 = "e231d9b8894558e22bb35910a2c5e7458655744f" +uuid = "35b6c64b-1ee1-5834-92a3-3f624899209a" +version = "1.7.0+1" + +[[Cairo_jll]] +deps = ["Artifacts", "Bzip2_jll", "CompilerSupportLibraries_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"] +git-tree-sha1 = "a2f1c8c668c8e3cb4cca4e57a8efdb09067bb3fd" +uuid = "83423d85-b0ee-5818-9007-b63ccbeb887a" +version = "1.18.0+2" [[ChainRulesCore]] deps = ["Compat", "LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "644c24cd6344348f1c645efab24b707088be526a" +git-tree-sha1 = "575cd02e080939a33b6df6c5853d14924c08e35b" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" -version = "0.9.34" +version = "1.23.0" + +[[ChangesOfVariables]] +deps = ["InverseFunctions", "LinearAlgebra", "Test"] +git-tree-sha1 = "2fba81a302a7be671aefe194f0525ef231104e7f" +uuid = "9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0" +version = "0.1.8" + +[[CodecZlib]] +deps = ["TranscodingStreams", "Zlib_jll"] +git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73" +uuid = "944b1d66-785c-5afd-91f1-9de20f533193" +version = "0.7.4" [[ColorSchemes]] -deps = ["ColorTypes", "Colors", "FixedPointNumbers", "Random", "StaticArrays"] -git-tree-sha1 = "3141757b5832ee7a0386db87997ee5a23ff20f4d" +deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] +git-tree-sha1 = "4b270d6465eb21ae89b732182c20dc165f8bf9f2" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" -version = "3.10.2" +version = "3.25.0" [[ColorTypes]] deps = ["FixedPointNumbers", "Random"] -git-tree-sha1 = "32a2b8af383f11cbb65803883837a149d10dfe8a" +git-tree-sha1 = "b10d0b65641d57b8b4d5e234446582de5047050d" uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" -version = "0.10.12" +version = "0.11.5" + +[[ColorVectorSpace]] +deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "Requires", "Statistics", "TensorCore"] +git-tree-sha1 = "a1f44953f2382ebb937d60dafbe2deea4bd23249" +uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4" +version = "0.10.0" [[Colors]] -deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Reexport"] -git-tree-sha1 = "ac5f2213e56ed8a34a3dd2f681f4df1166b34929" +deps = ["ColorTypes", "FixedPointNumbers", "Reexport"] +git-tree-sha1 = "362a287c3aa50601b0bc359053d5c2468f0e7ce0" uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" -version = "0.12.6" +version = "0.12.11" [[Compat]] -deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"] -git-tree-sha1 = "919c7f3151e79ff196add81d7f4e45d91bbf420b" +deps = ["Dates", "LinearAlgebra", "TOML", "UUIDs"] +git-tree-sha1 = "b1c55339b7c6c350ee89f2c1604299660525b248" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "3.25.0" +version = "4.15.0" [[CompilerSupportLibraries_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "8e695f735fca77e9708e795eda62afdb869cbb70" +deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" -version = "0.3.4+0" +version = "0.5.2+0" + +[[CompositionsBase]] +git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" +uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" +version = "0.1.2" + +[[ConcurrentUtilities]] +deps = ["Serialization", "Sockets"] +git-tree-sha1 = "6cbbd4d241d7e6579ab354737f4dd95ca43946e1" +uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" +version = "2.4.1" + +[[ConstructionBase]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "260fd2400ed2dab602a7c15cf10c1933c59930a2" +uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" +version = "1.5.5" [[Contour]] -deps = ["StaticArrays"] -git-tree-sha1 = "9f02045d934dc030edad45944ea80dbd1f0ebea7" +git-tree-sha1 = "439e35b0b36e2e5881738abc8857bd92ad6ff9a8" uuid = "d38c429a-6771-53c6-b99e-75d170b6e991" -version = "0.5.7" +version = "0.6.3" + +[[Crayons]] +git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" +uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" +version = "4.1.1" [[DataAPI]] -git-tree-sha1 = "dfb3b7e89e395be1e25c2ad6d7690dc29cc53b1d" +git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" -version = "1.6.0" +version = "1.16.0" + +[[DataFrames]] +deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"] +git-tree-sha1 = "04c738083f29f86e62c8afc341f0967d8717bdb8" +uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +version = "1.6.1" [[DataStructures]] deps = ["Compat", "InteractiveUtils", "OrderedCollections"] -git-tree-sha1 = "4437b64df1e0adccc3e5d1adbc3ac741095e4677" +git-tree-sha1 = "1d0a14036acb104d9e89698bd408f63ab58cdc82" uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.18.9" +version = "0.18.20" [[DataValueInterfaces]] git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6" @@ -135,137 +267,299 @@ version = "1.0.0" deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" +[[DefineSingletons]] +git-tree-sha1 = "0fba8b706d0178b4dc7fd44a96a92382c9065c2c" +uuid = "244e2a9f-e319-4986-a169-4d1fe445cd52" +version = "0.1.2" + [[DelimitedFiles]] deps = ["Mmap"] uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" [[Dictionaries]] -deps = ["Indexing", "Random"] -git-tree-sha1 = "12e8b690de74848e9f41853817a94900a9e13bff" +deps = ["Indexing", "Random", "Serialization"] +git-tree-sha1 = "35b66b6744b2d92c778afd3a88d2571875664a2a" uuid = "85a47980-9c8c-11e8-2b9f-f7ca1fa99fb4" -version = "0.3.8" +version = "0.4.2" [[Distributed]] deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" +[[DocStringExtensions]] +deps = ["LibGit2"] +git-tree-sha1 = "2fb1e02f2b635d0845df5d7c167fec4dd739b00d" +uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" +version = "0.9.3" + +[[Downloads]] +deps = ["ArgTools", "FileWatching", "LibCURL", "NetworkOptions"] +uuid = "f43a241f-c20a-4ad4-852c-f6b1247861c6" +version = "1.6.0" + [[EarCut_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "92d8f9f208637e8d2d28c664051a00569c01493d" +git-tree-sha1 = "e3290f2d49e661fbd94046d7e3726ffcb2d41053" uuid = "5ae413db-bbd1-5e63-b57d-d24a61df00f5" -version = "2.1.5+1" +version = "2.2.4+0" + +[[EllipsisNotation]] +deps = ["StaticArrayInterface"] +git-tree-sha1 = "3507300d4343e8e4ad080ad24e335274c2e297a9" +uuid = "da5c29d0-fa7d-589e-88eb-ea29b0a81949" +version = "1.8.0" + +[[EnzymeCore]] +deps = ["Adapt"] +git-tree-sha1 = "18394bc78ac2814ff38fe5e0c9dc2cd171e2810c" +uuid = "f151be2c-9106-41f4-ab19-57ee4f262869" +version = "0.7.2" + +[[EpollShim_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "8e9441ee83492030ace98f9789a654a6d0b1f643" +uuid = "2702e6a9-849d-5ed8-8c21-79e8b8f9ee43" +version = "0.0.20230411+0" + +[[ExceptionUnwrapping]] +deps = ["Test"] +git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a" +uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" +version = "0.1.10" + +[[Expat_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1c6317308b9dc757616f0b5cb379db10494443a7" +uuid = "2e619515-83b5-522b-bb60-26c02a35a201" +version = "2.6.2+0" [[ExprTools]] -git-tree-sha1 = "10407a39b87f29d47ebaca8edbc75d7c302ff93e" +git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec" uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04" -version = "0.1.3" +version = "0.1.10" + +[[Extents]] +git-tree-sha1 = "2140cd04483da90b2da7f99b2add0750504fc39c" +uuid = "411431e0-e8b7-467b-b5e0-f676ba4f2910" +version = "0.1.2" + +[[ExternalDocstrings]] +git-tree-sha1 = "1224740fc4d07c989949e1c1b508ebd49a65a5f6" +uuid = "e189563c-0753-4f5e-ad5c-be4293c83fb4" +version = "0.1.1" [[FFMPEG]] -deps = ["FFMPEG_jll", "x264_jll"] -git-tree-sha1 = "9a73ffdc375be61b0e4516d83d880b265366fe1f" +deps = ["FFMPEG_jll"] +git-tree-sha1 = "b57e3acbe22f8484b4b5ff66a7499717fe1a9cc8" uuid = "c87230d0-a227-11e9-1b43-d7ebe4e7570a" -version = "0.4.0" +version = "0.4.1" [[FFMPEG_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "LibVPX_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "Pkg", "Zlib_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] -git-tree-sha1 = "3cc57ad0a213808473eafef4845a74766242e05f" +deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "LAME_jll", "Libdl", "Ogg_jll", "OpenSSL_jll", "Opus_jll", "PCRE2_jll", "Zlib_jll", "libaom_jll", "libass_jll", "libfdk_aac_jll", "libvorbis_jll", "x264_jll", "x265_jll"] +git-tree-sha1 = "466d45dc38e15794ec7d5d63ec03d776a9aff36e" uuid = "b22a6f82-2f65-5046-a5b2-351ab43fb4e5" -version = "4.3.1+4" +version = "4.4.4+1" [[FileIO]] deps = ["Pkg", "Requires", "UUIDs"] -git-tree-sha1 = "b4fdad5fe06e0226348301b5163925ac1ae8b64b" +git-tree-sha1 = "82d8afa92ecf4b52d78d869f038ebfb881267322" uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549" -version = "1.6.5" +version = "1.16.3" + +[[FileWatching]] +uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" + +[[FillArrays]] +deps = ["LinearAlgebra", "PDMats", "SparseArrays", "Statistics"] +git-tree-sha1 = "0653c0a2396a6da5bc4766c43041ef5fd3efbe57" +uuid = "1a297f60-69ca-5386-bcde-b61e274b549b" +version = "1.11.0" [[FixedPointNumbers]] deps = ["Statistics"] -git-tree-sha1 = "335bfdceacc84c5cdf16aadc768aa5ddfc5383cc" +git-tree-sha1 = "05882d6995ae5c12bb5f36dd2ed3f61c98cbb172" uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" -version = "0.8.4" +version = "0.8.5" -[[Formatting]] -deps = ["Printf"] -git-tree-sha1 = "8339d61043228fdd3eb658d86c926cb282ae72a8" -uuid = "59287772-0a20-5a39-b81b-1366585eb4c0" -version = "0.4.2" +[[Folds]] +deps = ["Accessors", "BangBang", "Baselet", "DefineSingletons", "Distributed", "ExternalDocstrings", "InitialValues", "MicroCollections", "Referenceables", "Requires", "Test", "ThreadedScans", "Transducers"] +git-tree-sha1 = "7eb4bc88d8295e387a667fd43d67c157ddee76cf" +uuid = "41a02a25-b8f0-4f67-bc48-60067656b558" +version = "0.2.10" + +[[Fontconfig_jll]] +deps = ["Artifacts", "Bzip2_jll", "Expat_jll", "FreeType2_jll", "JLLWrappers", "Libdl", "Libuuid_jll", "Zlib_jll"] +git-tree-sha1 = "db16beca600632c95fc8aca29890d83788dd8b23" +uuid = "a3f928ae-7b40-5064-980b-68af3947d34b" +version = "2.13.96+0" + +[[Format]] +git-tree-sha1 = "9c68794ef81b08086aeb32eeaf33531668d5f5fc" +uuid = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" +version = "1.3.7" [[FreeType2_jll]] -deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "cbd58c9deb1d304f5a245a0b7eb841a2560cfec6" +deps = ["Artifacts", "Bzip2_jll", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "d8db6a5a2fe1381c1ea4ef2cab7c69c2de7f9ea0" uuid = "d7e528f0-a631-5988-bf34-fe36492bcfd7" -version = "2.10.1+5" +version = "2.13.1+0" [[FriBidi_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "0d20aed5b14dd4c9a2453c1b601d08e1149679cc" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1ed150b39aebcc805c26b93a8d0122c940f64ce2" uuid = "559328eb-81f9-559d-9380-de523a88c83c" -version = "1.0.5+6" +version = "1.0.14+0" + +[[Functors]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "d3e63d9fa13f8eaa2f06f64949e2afc593ff52c2" +uuid = "d9f16b24-f501-4c13-a1f2-28368ffc5196" +version = "0.4.10" + +[[Future]] +deps = ["Random"] +uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820" + +[[GLFW_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libglvnd_jll", "Xorg_libXcursor_jll", "Xorg_libXi_jll", "Xorg_libXinerama_jll", "Xorg_libXrandr_jll"] +git-tree-sha1 = "ff38ba61beff76b8f4acad8ab0c97ef73bb670cb" +uuid = "0656b61e-2033-5cc2-a64a-77c0f6c09b89" +version = "3.3.9+0" [[GPUArrays]] -deps = ["AbstractFFTs", "Adapt", "LinearAlgebra", "Printf", "Random", "Serialization"] -git-tree-sha1 = "da6398282abd2a8c0dc3e55b49d984fcc2c582e5" +deps = ["Adapt", "GPUArraysCore", "LLVM", "LinearAlgebra", "Printf", "Random", "Reexport", "Serialization", "Statistics"] +git-tree-sha1 = "85d7fb51afb3def5dcb85ad31c3707795c8bccc1" uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7" -version = "5.2.1" +version = "9.1.0" + +[[GPUArraysCore]] +deps = ["Adapt"] +git-tree-sha1 = "2d6ca471a6c7b536127afccfa7564b5b39227fe0" +uuid = "46192b85-c4d5-4398-a991-12ede77f4527" +version = "0.1.5" [[GPUCompiler]] -deps = ["DataStructures", "InteractiveUtils", "LLVM", "Libdl", "TimerOutputs", "UUIDs"] -git-tree-sha1 = "05097d81898c527e3bf218bb083ad0ead4378e5f" +deps = ["ExprTools", "InteractiveUtils", "LLVM", "Libdl", "Logging", "Scratch", "TimerOutputs", "UUIDs"] +git-tree-sha1 = "a846f297ce9d09ccba02ead0cae70690e072a119" uuid = "61eb1bfa-7361-4325-ad38-22787b887f55" -version = "0.6.1" +version = "0.25.0" [[GR]] -deps = ["Base64", "DelimitedFiles", "HTTP", "JSON", "LinearAlgebra", "Printf", "Random", "Serialization", "Sockets", "Test", "UUIDs"] -git-tree-sha1 = "cd0f34bd097d4d5eb6bbe01778cf8a7ed35f29d9" +deps = ["Artifacts", "Base64", "DelimitedFiles", "Downloads", "GR_jll", "HTTP", "JSON", "Libdl", "LinearAlgebra", "Preferences", "Printf", "Random", "Serialization", "Sockets", "TOML", "Tar", "Test", "p7zip_jll"] +git-tree-sha1 = "ddda044ca260ee324c5fc07edb6d7cf3f0b9c350" uuid = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71" -version = "0.52.0" +version = "0.73.5" + +[[GR_jll]] +deps = ["Artifacts", "Bzip2_jll", "Cairo_jll", "FFMPEG_jll", "Fontconfig_jll", "FreeType2_jll", "GLFW_jll", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Libtiff_jll", "Pixman_jll", "Qt6Base_jll", "Zlib_jll", "libpng_jll"] +git-tree-sha1 = "278e5e0f820178e8a26df3184fcb2280717c79b1" +uuid = "d2c73de3-f751-5644-a686-071e5b155ba9" +version = "0.73.5+0" + +[[GeoInterface]] +deps = ["Extents"] +git-tree-sha1 = "801aef8228f7f04972e596b09d4dba481807c913" +uuid = "cf35fbd7-0cd7-5166-be24-54bfbe79505f" +version = "1.3.4" [[GeometryBasics]] -deps = ["EarCut_jll", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"] -git-tree-sha1 = "4136b8a5668341e58398bb472754bff4ba0456ff" +deps = ["EarCut_jll", "Extents", "GeoInterface", "IterTools", "LinearAlgebra", "StaticArrays", "StructArrays", "Tables"] +git-tree-sha1 = "b62f2b2d76cee0d61a2ef2b3118cd2a3215d3134" uuid = "5c1252a2-5f33-56bf-86c9-59e7332b4326" -version = "0.3.12" +version = "0.4.11" [[GeometryTypes]] deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "StaticArrays"] -git-tree-sha1 = "07194161fe4e181c6bf51ef2e329ec4e7d050fc4" +git-tree-sha1 = "d796f7be0383b5416cd403420ce0af083b0f9b28" uuid = "4d00f742-c7ba-57c2-abde-4428a4b178cb" -version = "0.8.4" +version = "0.8.5" + +[[Gettext_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "XML2_jll"] +git-tree-sha1 = "9b02998aba7bf074d14de89f9d37ca24a1a0b046" +uuid = "78b55507-aeef-58d4-861c-77aaff3498b1" +version = "0.21.0+0" + +[[Glib_jll]] +deps = ["Artifacts", "Gettext_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Libiconv_jll", "Libmount_jll", "PCRE2_jll", "Zlib_jll"] +git-tree-sha1 = "7c82e6a6cd34e9d935e9aa4051b66c6ff3af59ba" +uuid = "7746bdde-850d-59dc-9ae8-88ece973131d" +version = "2.80.2+0" [[GraphRecipes]] -deps = ["AbstractTrees", "GeometryTypes", "InteractiveUtils", "Interpolations", "LightGraphs", "LinearAlgebra", "NaNMath", "NetworkLayout", "PlotUtils", "RecipesBase", "SparseArrays", "Statistics"] -git-tree-sha1 = "82a923cd1497adc7dfd07c1dad5e8eb79e5c4499" +deps = ["AbstractTrees", "GeometryTypes", "Graphs", "InteractiveUtils", "Interpolations", "LinearAlgebra", "NaNMath", "NetworkLayout", "PlotUtils", "RecipesBase", "SparseArrays", "Statistics"] +git-tree-sha1 = "10920601dc51d2231bb3d2111122045efed8def0" uuid = "bd48cda9-67a9-57be-86fa-5b3c104eda73" -version = "0.5.5" +version = "0.5.13" + +[[Graphite2_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "344bf40dcab1073aca04aa0df4fb092f920e4011" +uuid = "3b182d85-2403-5c21-9c21-1e1f0cc25472" +version = "1.3.14+0" + +[[Graphs]] +deps = ["ArnoldiMethod", "Compat", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"] +git-tree-sha1 = "4f2b57488ac7ee16124396de4f2bbdd51b2602ad" +uuid = "86223c79-3864-5bf0-83f7-82e725a168b6" +version = "1.11.0" [[Grisu]] -git-tree-sha1 = "03d381f65183cb2d0af8b3425fde97263ce9a995" +git-tree-sha1 = "53bb909d1151e57e2484c3d1b53e19552b887fb2" uuid = "42e2da0e-8278-4e71-bc24-59509adca0fe" -version = "1.0.0" +version = "1.0.2" + +[[H5Zblosc]] +deps = ["Blosc", "HDF5"] +git-tree-sha1 = "d778420e524bcf56066e8c63c7aa315ae7269da2" +uuid = "c8ec2601-a99c-407f-b158-e79c03c2f5f7" +version = "0.1.2" [[HDF5]] -deps = ["Blosc", "Compat", "HDF5_jll", "Libdl", "Mmap", "Random", "Requires"] -git-tree-sha1 = "8a21f34a34491833bcda29a3ec2188b4ec6e558f" +deps = ["Compat", "HDF5_jll", "Libdl", "MPIPreferences", "Mmap", "Preferences", "Printf", "Random", "Requires", "UUIDs"] +git-tree-sha1 = "e856eef26cf5bf2b0f95f8f4fc37553c72c8641c" uuid = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" -version = "0.15.4" +version = "0.17.2" [[HDF5_jll]] -deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "OpenSSL_jll", "Pkg", "Zlib_jll"] -git-tree-sha1 = "fd83fa0bde42e01952757f01149dd968c06c4dba" +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LazyArtifacts", "LibCURL_jll", "Libdl", "MPICH_jll", "MPIPreferences", "MPItrampoline_jll", "MicrosoftMPI_jll", "OpenMPI_jll", "OpenSSL_jll", "TOML", "Zlib_jll", "libaec_jll"] +git-tree-sha1 = "82a471768b513dc39e471540fdadc84ff80ff997" uuid = "0234f1f7-429e-5d53-9886-15a909be8d59" -version = "1.12.0+1" +version = "1.14.3+3" [[HTTP]] -deps = ["Base64", "Dates", "IniFile", "MbedTLS", "Sockets"] -git-tree-sha1 = "c7ec02c4c6a039a98a15f955462cd7aea5df4508" +deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] +git-tree-sha1 = "d1d712be3164d61d1fb98e7ce9bcbc6cc06b45ed" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "0.8.19" +version = "1.10.8" + +[[HalfIntegers]] +git-tree-sha1 = "9c3149243abb5bc0bad0431d6c4fcac0f4443c7c" +uuid = "f0d1745a-41c9-11e9-1dd9-e5d34d218721" +version = "1.6.0" + +[[HarfBuzz_jll]] +deps = ["Artifacts", "Cairo_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "Graphite2_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg"] +git-tree-sha1 = "129acf094d168394e80ee1dc4bc06ec835e510a3" +uuid = "2e76f6c2-a576-52d4-95c1-20adfe4de566" +version = "2.8.1+1" + +[[Hwloc_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "ca0f6bf568b4bfc807e7537f081c81e35ceca114" +uuid = "e33a78d0-f292-5ffc-b300-72abe9b543c8" +version = "2.10.0+0" [[ITensors]] -deps = ["Compat", "HDF5", "KrylovKit", "LinearAlgebra", "NDTensors", "PackageCompiler", "Pkg", "Printf", "Random", "StaticArrays", "TimerOutputs"] -git-tree-sha1 = "7ff6203cf39ed6ed8ed62a62618c8faa28167eb3" +deps = ["Adapt", "BitIntegers", "ChainRulesCore", "Compat", "Dictionaries", "DocStringExtensions", "Functors", "IsApprox", "KrylovKit", "LinearAlgebra", "LinearMaps", "NDTensors", "PackageExtensionCompat", "Pkg", "Printf", "Random", "Requires", "SerializedElementArrays", "SimpleTraits", "StaticArrays", "Strided", "TimerOutputs", "TupleTools", "Zeros"] +git-tree-sha1 = "4d5bb90170b17f60a531c64d18c3a35e206e1ef0" uuid = "9136182c-28ba-11e9-034c-db9fb085ebd5" -version = "0.1.41" +version = "0.6.9" + +[[IfElse]] +git-tree-sha1 = "debdd00ffef04665ccbb3e150747a77560e8fad1" +uuid = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173" +version = "0.1.1" [[Indexing]] git-tree-sha1 = "ce1566720fd6b19ff3411404d4b977acd4814f9f" @@ -273,30 +567,57 @@ uuid = "313cdc1a-70c2-5d6a-ae34-0150d3930a38" version = "1.1.1" [[Inflate]] -git-tree-sha1 = "f5fc07d4e706b84f72d54eedcc1c13d92fb0871c" +git-tree-sha1 = "ea8031dea4aff6bd41f1df8f2fdfb25b33626381" uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9" -version = "0.1.2" +version = "0.1.4" -[[IniFile]] -deps = ["Test"] -git-tree-sha1 = "098e4d2c533924c921f9f9847274f2ad89e018b8" -uuid = "83e8ac13-25f8-5344-8a64-a9f2b223428f" -version = "0.5.0" +[[InitialValues]] +git-tree-sha1 = "4da0f88e9a39111c2fa3add390ab15f3a44f3ca3" +uuid = "22cec73e-a1b8-11e9-2c92-598750a2cf9c" +version = "0.3.1" + +[[InlineStrings]] +deps = ["Parsers"] +git-tree-sha1 = "9cc2baf75c6d09f9da536ddf58eb2f29dedaf461" +uuid = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48" +version = "1.4.0" [[InteractiveUtils]] deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" [[Interpolations]] -deps = ["AxisAlgorithms", "LinearAlgebra", "OffsetArrays", "Random", "Ratios", "SharedArrays", "SparseArrays", "StaticArrays", "WoodburyMatrices"] -git-tree-sha1 = "2b7d4e9be8b74f03115e64cf36ed2f48ae83d946" +deps = ["Adapt", "AxisAlgorithms", "ChainRulesCore", "LinearAlgebra", "OffsetArrays", "Random", "Ratios", "Requires", "SharedArrays", "SparseArrays", "StaticArrays", "WoodburyMatrices"] +git-tree-sha1 = "88a101217d7cb38a7b481ccd50d21876e1d1b0e0" uuid = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" -version = "0.12.10" +version = "0.15.1" + +[[InverseFunctions]] +deps = ["Dates", "Test"] +git-tree-sha1 = "e7cbed5032c4c397a6ac23d1493f3289e01231c4" +uuid = "3587e190-3f89-42d0-90ee-14403ec27112" +version = "0.1.14" + +[[InvertedIndices]] +git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038" +uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f" +version = "1.3.0" + +[[IrrationalConstants]] +git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" +uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" +version = "0.2.2" + +[[IsApprox]] +deps = ["Dictionaries", "LinearAlgebra"] +git-tree-sha1 = "ea55bdf2fcdbf558d7926db52966c2361e969edd" +uuid = "28f27b66-4bd8-47e7-9110-e2746eb8bed7" +version = "0.1.8" [[IterTools]] -git-tree-sha1 = "05110a2ab1fc5f932622ffea2a003221f4782c18" +git-tree-sha1 = "42d5f897009e7ff2cf88db414a389e5ed1bdd023" uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e" -version = "1.3.0" +version = "1.10.0" [[IteratorInterfaceExtensions]] git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" @@ -304,369 +625,783 @@ uuid = "82899510-4779-5014-852e-03e436cf321d" version = "1.0.0" [[JLD]] -deps = ["FileIO", "HDF5", "Printf"] -git-tree-sha1 = "c19ab1257ef0042a45209f56a1a4794edc3c8f2e" +deps = ["Compat", "FileIO", "H5Zblosc", "HDF5", "Printf"] +git-tree-sha1 = "e42f32690d41f758e126a48ee43459ef91179d1f" uuid = "4138dd39-2aa7-5051-a626-17a0bb65d9c8" -version = "0.12.2" +version = "0.13.5" + +[[JLFzf]] +deps = ["Pipe", "REPL", "Random", "fzf_jll"] +git-tree-sha1 = "a53ebe394b71470c7f97c2e7e170d51df21b17af" +uuid = "1019f520-868f-41f5-a6de-eb00f4b6a39c" +version = "0.1.7" [[JLLWrappers]] -git-tree-sha1 = "a431f5f2ca3f4feef3bd7a5e94b8b8d4f2f647a0" +deps = ["Artifacts", "Preferences"] +git-tree-sha1 = "7e5d6779a1e09a36db2a7b6cff50942a0a7d0fca" uuid = "692b3bcd-3c85-4b1f-b108-f13ce0eb3210" -version = "1.2.0" +version = "1.5.0" [[JSON]] deps = ["Dates", "Mmap", "Parsers", "Unicode"] -git-tree-sha1 = "81690084b6198a2e1da36fcfda16eeca9f9f24e4" +git-tree-sha1 = "31e996f0a15c7b280ba9f76636b3ff9e2ae58c9a" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -version = "0.21.1" +version = "0.21.4" + +[[Jacobi]] +deps = ["Polynomials", "SpecialFunctions"] +git-tree-sha1 = "1e950c2a726191435ddadce2726749476a779e93" +uuid = "83f21c0b-4282-5fbc-9e3f-f6da3d2e584c" +version = "0.7.0" + +[[JpegTurbo_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "c84a835e1a09b289ffcd2271bf2a337bbdda6637" +uuid = "aacddb02-875f-59d6-b918-886e6ef4fbf8" +version = "3.0.3+0" + +[[JuliaNVTXCallbacks_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "af433a10f3942e882d3c671aacb203e006a5808f" +uuid = "9c1d0b0a-7046-5b2e-a33f-ea22f176ac7e" +version = "0.2.1+0" + +[[KernelAbstractions]] +deps = ["Adapt", "Atomix", "EnzymeCore", "InteractiveUtils", "LinearAlgebra", "MacroTools", "PrecompileTools", "Requires", "SparseArrays", "StaticArrays", "UUIDs", "UnsafeAtomics", "UnsafeAtomicsLLVM"] +git-tree-sha1 = "db02395e4c374030c53dc28f3c1d33dec35f7272" +uuid = "63c18a36-062a-441e-b654-da1e3ab1ce7c" +version = "0.9.19" [[KrylovKit]] -deps = ["LinearAlgebra", "Printf"] -git-tree-sha1 = "f66c3e1a5fb1433622dcefe4ffa09e11776a095c" +deps = ["ChainRulesCore", "GPUArraysCore", "LinearAlgebra", "Printf", "VectorInterface"] +git-tree-sha1 = "3f3a92bbe8f568b689a7f7bc193f7c717d793751" uuid = "0b1a1467-8014-51b9-945f-bf0ae24f4b77" -version = "0.5.2" +version = "0.7.1" [[LAME_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "df381151e871f41ee86cee4f5f6fd598b8a68826" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "170b660facf5df5de098d866564877e119141cbd" uuid = "c1c5ebd0-6772-5130-a774-d5fcae4a789d" -version = "3.100.0+3" +version = "3.100.2+0" + +[[LERC_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "bf36f528eec6634efc60d7ec062008f171071434" +uuid = "88015f11-f218-50d7-93a8-a6af411a945d" +version = "3.0.0+1" [[LLVM]] -deps = ["CEnum", "Libdl", "Printf", "Unicode"] -git-tree-sha1 = "a662366a5d485dee882077e8da3e1a95a86d097f" +deps = ["CEnum", "LLVMExtra_jll", "Libdl", "Preferences", "Printf", "Requires", "Unicode"] +git-tree-sha1 = "839c82932db86740ae729779e610f07a1640be9a" uuid = "929cbde3-209d-540e-8aea-75f648917ca0" -version = "2.0.0" +version = "6.6.3" + +[[LLVMExtra_jll]] +deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] +git-tree-sha1 = "88b916503aac4fb7f701bb625cd84ca5dd1677bc" +uuid = "dad2f222-ce93-54a1-a47d-0025e8a3acab" +version = "0.0.29+0" + +[[LLVMLoopInfo]] +git-tree-sha1 = "2e5c102cfc41f48ae4740c7eca7743cc7e7b75ea" +uuid = "8b046642-f1f6-4319-8d3c-209ddc03c586" +version = "1.0.0" + +[[LLVMOpenMP_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "d986ce2d884d49126836ea94ed5bfb0f12679713" +uuid = "1d63c593-3942-5779-bab2-d838dc0a180e" +version = "15.0.7+0" [[LRUCache]] -git-tree-sha1 = "5a9338dce0811619e42c9e9aa9ae044c3c82a58f" +deps = ["Serialization"] +git-tree-sha1 = "b3cc6698599b10e652832c2f23db3cab99d51b59" uuid = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637" -version = "1.2.0" +version = "1.6.1" + +[[LZO_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "70c5da094887fd2cae843b8db33920bac4b6f07d" +uuid = "dd4b983a-f0e5-5f8d-a1b7-129d4a5fb1ac" +version = "2.10.2+0" [[LaTeXStrings]] -git-tree-sha1 = "c7f1c695e06c01b95a67f0cd1d34994f3e7db104" +git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec" uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" -version = "1.2.1" +version = "1.3.1" [[Latexify]] -deps = ["Formatting", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "Printf", "Requires"] -git-tree-sha1 = "7c72983c6daf61393ee8a0b29a419c709a06cede" +deps = ["Format", "InteractiveUtils", "LaTeXStrings", "MacroTools", "Markdown", "OrderedCollections", "Requires"] +git-tree-sha1 = "e0b5cd21dc1b44ec6e64f351976f961e6f31d6c4" uuid = "23fbe1c1-3f47-55db-b15f-69d7ec21a316" -version = "0.14.12" +version = "0.16.3" + +[[LazyArtifacts]] +deps = ["Artifacts", "Pkg"] +uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3" + +[[LibCURL]] +deps = ["LibCURL_jll", "MozillaCACerts_jll"] +uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" +version = "0.6.3" [[LibCURL_jll]] -deps = ["LibSSH2_jll", "Libdl", "MbedTLS_jll", "Pkg", "Zlib_jll", "nghttp2_jll"] -git-tree-sha1 = "897d962c20031e6012bba7b3dcb7a667170dad17" +deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" -version = "7.70.0+2" +version = "7.84.0+0" [[LibGit2]] -deps = ["Printf"] +deps = ["Base64", "NetworkOptions", "Printf", "SHA"] uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" [[LibSSH2_jll]] -deps = ["Libdl", "MbedTLS_jll", "Pkg"] -git-tree-sha1 = "717705533148132e5466f2924b9a3657b16158e8" +deps = ["Artifacts", "Libdl", "MbedTLS_jll"] uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8" -version = "1.9.0+3" - -[[LibVPX_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "85fcc80c3052be96619affa2fe2e6d2da3908e11" -uuid = "dd192d2f-8180-539f-9fb4-cc70b1dcf69a" -version = "1.9.0+1" +version = "1.10.2+0" [[Libdl]] uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" -[[LightGraphs]] -deps = ["ArnoldiMethod", "DataStructures", "Distributed", "Inflate", "LinearAlgebra", "Random", "SharedArrays", "SimpleTraits", "SparseArrays", "Statistics"] -git-tree-sha1 = "432428df5f360964040ed60418dd5601ecd240b6" -uuid = "093fc24a-ae57-5d10-9952-331d41423f4d" -version = "1.3.5" +[[Libffi_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "0b4a5d71f3e5200a7dff793393e09dfc2d874290" +uuid = "e9f186c6-92d2-5b65-8a66-fee21dc1b490" +version = "3.2.2+1" + +[[Libgcrypt_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgpg_error_jll"] +git-tree-sha1 = "9fd170c4bbfd8b935fdc5f8b7aa33532c991a673" +uuid = "d4300ac3-e22c-5743-9152-c294e39db1e4" +version = "1.8.11+0" + +[[Libglvnd_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll", "Xorg_libXext_jll"] +git-tree-sha1 = "6f73d1dd803986947b2c750138528a999a6c7733" +uuid = "7e76a0d4-f3c7-5321-8279-8d96eeed0f29" +version = "1.6.0+0" + +[[Libgpg_error_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "fbb1f2bef882392312feb1ede3615ddc1e9b99ed" +uuid = "7add5ba3-2f88-524e-9cd5-f83b8a55f7b8" +version = "1.49.0+0" + +[[Libiconv_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "f9557a255370125b405568f9767d6d195822a175" +uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531" +version = "1.17.0+0" + +[[Libmount_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "0c4f9c4f1a50d8f35048fa0532dabbadf702f81e" +uuid = "4b2f31a3-9ecc-558c-b454-b3730dcb73e9" +version = "2.40.1+0" + +[[Libtiff_jll]] +deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "LERC_jll", "Libdl", "XZ_jll", "Zlib_jll", "Zstd_jll"] +git-tree-sha1 = "2da088d113af58221c52828a80378e16be7d037a" +uuid = "89763e89-9b03-5906-acba-b20f662cd828" +version = "4.5.1+1" + +[[Libuuid_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "5ee6203157c120d79034c748a2acba45b82b8807" +uuid = "38a345b3-de98-5d2b-a5d3-14cd9215e700" +version = "2.40.1+0" [[LinearAlgebra]] -deps = ["Libdl"] +deps = ["Libdl", "libblastrampoline_jll"] uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +[[LinearMaps]] +deps = ["ChainRulesCore", "LinearAlgebra", "SparseArrays", "Statistics"] +git-tree-sha1 = "9948d6f8208acfebc3e8cf4681362b2124339e7e" +uuid = "7a12625a-238d-50fd-b39a-03d52299707e" +version = "3.11.2" + +[[LogExpFunctions]] +deps = ["ChainRulesCore", "ChangesOfVariables", "DocStringExtensions", "InverseFunctions", "IrrationalConstants", "LinearAlgebra"] +git-tree-sha1 = "18144f3e9cbe9b15b070288eef858f71b291ce37" +uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" +version = "0.3.27" + [[Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" +[[LoggingExtras]] +deps = ["Dates", "Logging"] +git-tree-sha1 = "c1dd6d7978c12545b4179fb6153b9250c96b0075" +uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" +version = "1.0.3" + [[Lz4_jll]] -deps = ["Libdl", "Pkg"] -git-tree-sha1 = "51b1db0732bbdcfabb60e36095cc3ed9c0016932" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "6c26c5e8a4203d43b5497be3ec5d4e0c3cde240a" uuid = "5ced341a-0733-55b8-9ab6-a4889d929147" -version = "1.9.2+2" +version = "1.9.4+0" + +[[MPICH_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "Hwloc_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "MPIPreferences", "TOML"] +git-tree-sha1 = "4099bb6809ac109bfc17d521dad33763bcf026b7" +uuid = "7cb0a576-ebde-5e09-9194-50597f1243b4" +version = "4.2.1+1" + +[[MPIPreferences]] +deps = ["Libdl", "Preferences"] +git-tree-sha1 = "c105fe467859e7f6e9a852cb15cb4301126fac07" +uuid = "3da0fdf6-3ccc-4f1b-acd9-58baa6c99267" +version = "0.1.11" + +[[MPItrampoline_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "MPIPreferences", "TOML"] +git-tree-sha1 = "ce0ca3dd147c43de175c5aff161315a424f4b8ac" +uuid = "f1f71cc9-e9ae-5b93-9b94-4fe0e1ad3748" +version = "5.3.3+1" [[MacroTools]] deps = ["Markdown", "Random"] -git-tree-sha1 = "6a8a2a625ab0dea913aba95c11370589e0239ff0" +git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df" uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" -version = "0.5.6" +version = "0.5.13" + +[[MakieCore]] +deps = ["Observables", "REPL"] +git-tree-sha1 = "248b7a4be0f92b497f7a331aed02c1e9a878f46b" +uuid = "20f20a25-4f0e-4fdf-b5d1-57303727442b" +version = "0.7.3" + +[[MappedArrays]] +git-tree-sha1 = "2dab0221fe2b0f2cb6754eaa743cc266339f527e" +uuid = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900" +version = "0.4.2" [[Markdown]] deps = ["Base64"] uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" [[MbedTLS]] -deps = ["Dates", "MbedTLS_jll", "Random", "Sockets"] -git-tree-sha1 = "1c38e51c3d08ef2278062ebceade0e46cefc96fe" +deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"] +git-tree-sha1 = "c067a280ddc25f196b5e7df3877c6b226d390aaf" uuid = "739be429-bea8-5141-9913-cc70e7f3736d" -version = "1.0.3" +version = "1.1.9" [[MbedTLS_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "0eef589dd1c26a3ac9d753fe1a8bcad63f956fa6" +deps = ["Artifacts", "Libdl"] uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" -version = "2.16.8+1" +version = "2.28.0+0" [[Measures]] -git-tree-sha1 = "e498ddeee6f9fdb4551ce855a46f54dbd900245f" +git-tree-sha1 = "c13304c81eec1ed3af7fc20e75fb6b26092a1102" uuid = "442fdcdd-2543-5da2-b0f3-8c86c306513e" -version = "0.3.1" +version = "0.3.2" + +[[MicroCollections]] +deps = ["Accessors", "BangBang", "InitialValues"] +git-tree-sha1 = "44d32db644e84c75dab479f1bc15ee76a1a3618f" +uuid = "128add7d-3638-4c79-886c-908ea0c25c34" +version = "0.2.0" + +[[MicrosoftMPI_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "f12a29c4400ba812841c6ace3f4efbb6dbb3ba01" +uuid = "9237b28f-5490-5468-be7b-bb81f5f5e6cf" +version = "10.1.4+2" [[Missings]] deps = ["DataAPI"] -git-tree-sha1 = "f8c673ccc215eb50fcadb285f522420e29e69e1c" +git-tree-sha1 = "ec4f7fbeab05d7747bdf98eb74d130a2a2ed298d" uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28" -version = "0.4.5" +version = "1.2.0" [[Mmap]] uuid = "a63ad114-7e13-5084-954f-fe012c677804" +[[MozillaCACerts_jll]] +uuid = "14a3606d-f60d-562e-9121-12d972cd8159" +version = "2022.2.1" + [[NDTensors]] -deps = ["Compat", "Dictionaries", "HDF5", "LinearAlgebra", "Random", "Requires", "StaticArrays", "Strided", "TimerOutputs", "TupleTools"] -git-tree-sha1 = "e7892580c1d2e4cdc3751f40124076f74b2c5775" +deps = ["Accessors", "Adapt", "ArrayLayouts", "BlockArrays", "Compat", "Dictionaries", "EllipsisNotation", "FillArrays", "Folds", "Functors", "GPUArraysCore", "HalfIntegers", "InlineStrings", "LinearAlgebra", "MacroTools", "MappedArrays", "PackageExtensionCompat", "Random", "SimpleTraits", "SparseArrays", "SplitApplyCombine", "StaticArrays", "Strided", "StridedViews", "TimerOutputs", "TupleTools", "VectorInterface"] +git-tree-sha1 = "41a3fd1fdf468ad37a88e1a2e3f2983d780d4f08" uuid = "23ae76d9-e61a-49c4-8f12-3f1a16adf9cf" -version = "0.1.28" +version = "0.3.7" + +[[NVTX]] +deps = ["Colors", "JuliaNVTXCallbacks_jll", "Libdl", "NVTX_jll"] +git-tree-sha1 = "53046f0483375e3ed78e49190f1154fa0a4083a1" +uuid = "5da4648a-3479-48b8-97b9-01cb529c0a1f" +version = "0.3.4" -[[NNlib]] -deps = ["ChainRulesCore", "Compat", "LinearAlgebra", "Pkg", "Requires", "Statistics"] -git-tree-sha1 = "ab1d43fead2ecb9aa5ae460d3d547c2cf8d89461" -uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.7.17" +[[NVTX_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "ce3269ed42816bf18d500c9f63418d4b0d9f5a3b" +uuid = "e98f9f5b-d649-5603-91fd-7774390e6439" +version = "3.1.0+2" [[NaNMath]] -git-tree-sha1 = "bfe47e760d60b82b66b61d2d44128b62e3a369fb" +deps = ["OpenLibm_jll"] +git-tree-sha1 = "0877504529a3e5c3343c6f8b4c0381e57e4387e4" uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3" -version = "0.3.5" +version = "1.0.2" [[NetworkLayout]] -deps = ["DelimitedFiles", "GeometryTypes", "LinearAlgebra", "SparseArrays", "Test"] -git-tree-sha1 = "adde9ad01842a4f62c3724b8707bed90293f0f3f" +deps = ["GeometryBasics", "LinearAlgebra", "Random", "Requires", "StaticArrays"] +git-tree-sha1 = "91bb2fedff8e43793650e7a677ccda6e6e6e166b" uuid = "46757867-2c16-5918-afeb-47bfcb05e46a" -version = "0.2.0" +version = "0.4.6" + +[[NetworkOptions]] +uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" +version = "1.2.0" + +[[Observables]] +git-tree-sha1 = "7438a59546cf62428fc9d1bc94729146d37a7225" +uuid = "510215fc-4207-5dde-b226-833fc4488ee2" +version = "0.5.5" [[OffsetArrays]] deps = ["Adapt"] -git-tree-sha1 = "b3dfef5f2be7d7eb0e782ba9146a5271ee426e90" +git-tree-sha1 = "e64b4f5ea6b7389f6f046d13d4896a8f9c1ba71e" uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" -version = "1.6.2" +version = "1.14.0" [[Ogg_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "a42c0f138b9ebe8b58eba2271c5053773bde52d0" +git-tree-sha1 = "887579a3eb005446d514ab7aeac5d1d027658b8f" uuid = "e7412a2a-1a6e-54c0-be00-318e2571c051" -version = "1.3.4+2" +version = "1.3.5+1" + +[[OpenBLAS_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] +uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" +version = "0.3.20+0" + +[[OpenLibm_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "05823500-19ac-5b8b-9628-191a04bc5112" +version = "0.8.1+0" + +[[OpenMPI_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "MPIPreferences", "TOML"] +git-tree-sha1 = "e25c1778a98e34219a00455d6e4384e017ea9762" +uuid = "fe0851c0-eecd-5654-98d4-656369965a5c" +version = "4.1.6+0" + +[[OpenSSL]] +deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"] +git-tree-sha1 = "38cb508d080d21dc1128f7fb04f20387ed4c0af4" +uuid = "4d8831e6-92b7-49fb-bdf8-b643e874388c" +version = "1.4.3" [[OpenSSL_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "71bbbc616a1d710879f5a1021bcba65ffba6ce58" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "3da7367955dcc5c54c1ba4d402ccdc09a1a3e046" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" -version = "1.1.1+6" +version = "3.0.13+1" [[OpenSpecFun_jll]] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "9db77584158d0ab52307f8c04f8e7c08ca76b5b3" +git-tree-sha1 = "13652491f6856acfd2db29360e1bbcd4565d04f1" uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e" -version = "0.5.3+4" +version = "0.5.5+0" [[Opus_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "f9d57f4126c39565e05a2b0264df99f497fc6f37" +git-tree-sha1 = "51a08fb14ec28da2ec7a927c4337e4332c2a4720" uuid = "91d4177d-7536-5919-b921-800302f37372" -version = "1.3.1+3" +version = "1.3.2+0" [[OrderedCollections]] -git-tree-sha1 = "4fa2ba51070ec13fcc7517db714445b4ab986bdf" +git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -version = "1.4.0" +version = "1.6.3" + +[[PCRE2_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "efcefdf7-47ab-520b-bdef-62a2eaa19f15" +version = "10.40.0+0" -[[PackageCompiler]] -deps = ["Libdl", "Pkg", "UUIDs"] -git-tree-sha1 = "d448727c4b86be81b225b738c88d30334fda6779" -uuid = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d" -version = "1.2.5" +[[PDMats]] +deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] +git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65" +uuid = "90014a1f-27ba-587c-ab20-58faa44d9150" +version = "0.11.31" + +[[PackageExtensionCompat]] +deps = ["Requires", "TOML"] +git-tree-sha1 = "fb28e33b8a95c4cee25ce296c817d89cc2e53518" +uuid = "65ce6f38-6b18-4e1d-a461-8949797d7930" +version = "1.0.2" [[Parsers]] -deps = ["Dates"] -git-tree-sha1 = "c8abc88faa3f7a3950832ac5d6e690881590d6dc" +deps = ["Dates", "PrecompileTools", "UUIDs"] +git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "1.1.0" +version = "2.8.1" + +[[Pipe]] +git-tree-sha1 = "6842804e7867b115ca9de748a0cf6b364523c16d" +uuid = "b98c9c47-44ae-5843-9183-064241ee97a0" +version = "1.3.0" + +[[Pixman_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "LLVMOpenMP_jll", "Libdl"] +git-tree-sha1 = "35621f10a7531bc8fa58f74610b1bfb70a3cfc6b" +uuid = "30392449-352a-5448-841d-b1acce4e97dc" +version = "0.43.4+0" [[Pkg]] -deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"] +deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" +version = "1.8.0" [[PlotThemes]] -deps = ["PlotUtils", "Requires", "Statistics"] -git-tree-sha1 = "a3a964ce9dc7898193536002a6dd892b1b5a6f1d" +deps = ["PlotUtils", "Statistics"] +git-tree-sha1 = "1f03a2d339f42dca4a4da149c7e15e9b896ad899" uuid = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a" -version = "2.0.1" +version = "3.1.0" [[PlotUtils]] -deps = ["ColorSchemes", "Colors", "Dates", "Printf", "Random", "Reexport", "Statistics"] -git-tree-sha1 = "ae9a295ac761f64d8c2ec7f9f24d21eb4ffba34d" +deps = ["ColorSchemes", "Colors", "Dates", "PrecompileTools", "Printf", "Random", "Reexport", "Statistics"] +git-tree-sha1 = "7b1a9df27f072ac4c9c7cbe5efb198489258d1f5" uuid = "995b91a9-d308-5afd-9ec6-746e21dbc043" -version = "1.0.10" +version = "1.4.1" [[Plots]] -deps = ["Base64", "Contour", "Dates", "FFMPEG", "FixedPointNumbers", "GR", "GeometryBasics", "GeometryTypes", "JSON", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "Requires", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs"] -git-tree-sha1 = "f4425bbd5f313b074d6ce3b86d80c0ad16bf7326" +deps = ["Base64", "Contour", "Dates", "Downloads", "FFMPEG", "FixedPointNumbers", "GR", "JLFzf", "JSON", "LaTeXStrings", "Latexify", "LinearAlgebra", "Measures", "NaNMath", "Pkg", "PlotThemes", "PlotUtils", "PrecompileTools", "Printf", "REPL", "Random", "RecipesBase", "RecipesPipeline", "Reexport", "RelocatableFolders", "Requires", "Scratch", "Showoff", "SparseArrays", "Statistics", "StatsBase", "UUIDs", "UnicodeFun", "UnitfulLatexify", "Unzip"] +git-tree-sha1 = "442e1e7ac27dd5ff8825c3fa62fbd1e86397974b" uuid = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" -version = "1.6.12" +version = "1.40.4" + +[[Polynomials]] +deps = ["LinearAlgebra", "MakieCore", "RecipesBase", "Setfield", "SparseArrays"] +git-tree-sha1 = "89620a0b5458dca4bf9ea96174fa6422b3adf6f9" +uuid = "f27b6e38-b328-58d1-80ce-0feddd5e7a45" +version = "4.0.8" + +[[PooledArrays]] +deps = ["DataAPI", "Future"] +git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3" +uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720" +version = "1.4.3" + +[[PrecompileTools]] +deps = ["Preferences"] +git-tree-sha1 = "5aa36f7049a63a1528fe8f7c3f2113413ffd4e1f" +uuid = "aea7be01-6a6a-4083-8856-8a6e6704d82a" +version = "1.2.1" + +[[Preferences]] +deps = ["TOML"] +git-tree-sha1 = "9306f6085165d270f7e3db02af26a400d580f5c6" +uuid = "21216c6a-2e73-6563-6e65-726566657250" +version = "1.4.3" + +[[PrettyTables]] +deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "Reexport", "StringManipulation", "Tables"] +git-tree-sha1 = "88b895d13d53b5577fd53379d913b9ab9ac82660" +uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d" +version = "2.3.1" [[Printf]] deps = ["Unicode"] uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" +[[Qt6Base_jll]] +deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Vulkan_Loader_jll", "Xorg_libSM_jll", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_cursor_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "libinput_jll", "xkbcommon_jll"] +git-tree-sha1 = "37b7bb7aabf9a085e0044307e1717436117f2b3b" +uuid = "c0090381-4147-56d7-9ebc-da0b1113ec56" +version = "6.5.3+1" + +[[QuadGK]] +deps = ["DataStructures", "LinearAlgebra"] +git-tree-sha1 = "9b23c31e76e333e6fb4c1595ae6afa74966a729e" +uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" +version = "2.9.4" + [[REPL]] -deps = ["InteractiveUtils", "Markdown", "Sockets"] +deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" [[Random]] -deps = ["Serialization"] +deps = ["SHA", "Serialization"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +[[Random123]] +deps = ["Random", "RandomNumbers"] +git-tree-sha1 = "4743b43e5a9c4a2ede372de7061eed81795b12e7" +uuid = "74087812-796a-5b5d-8853-05524746bad3" +version = "1.7.0" + +[[RandomNumbers]] +deps = ["Random", "Requires"] +git-tree-sha1 = "043da614cc7e95c703498a491e2c21f58a2b8111" +uuid = "e6cf234a-135c-5ec9-84dd-332b85af5143" +version = "1.5.3" + [[Ratios]] -git-tree-sha1 = "37d210f612d70f3f7d57d488cb3b6eff56ad4e41" +deps = ["Requires"] +git-tree-sha1 = "1342a47bf3260ee108163042310d26f2be5ec90b" uuid = "c84ed2f1-dad5-54f0-aa8e-dbefe2724439" -version = "0.4.0" +version = "0.4.5" [[RecipesBase]] -git-tree-sha1 = "b3fb709f3c97bfc6e948be68beeecb55a0b340ae" +deps = ["PrecompileTools"] +git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff" uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01" -version = "1.1.1" +version = "1.3.4" [[RecipesPipeline]] -deps = ["Dates", "NaNMath", "PlotUtils", "RecipesBase"] -git-tree-sha1 = "4a325c9bcc2d8e62a8f975b9666d0251d53b63b9" +deps = ["Dates", "NaNMath", "PlotUtils", "PrecompileTools", "RecipesBase"] +git-tree-sha1 = "45cf9fd0ca5839d06ef333c8201714e888486342" uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c" -version = "0.1.13" +version = "0.6.12" [[Reexport]] -deps = ["Pkg"] -git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0" +git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" uuid = "189a3867-3050-52da-a836-e630ba90ab69" -version = "0.2.0" +version = "1.2.2" + +[[Referenceables]] +deps = ["Adapt"] +git-tree-sha1 = "02d31ad62838181c1a3a5fd23a1ce5914a643601" +uuid = "42d2dcc6-99eb-4e98-b66c-637b7d73030e" +version = "0.1.3" + +[[RelocatableFolders]] +deps = ["SHA", "Scratch"] +git-tree-sha1 = "ffdaf70d81cf6ff22c2b6e733c900c3321cab864" +uuid = "05181044-ff0b-4ac5-8273-598c1e38db00" +version = "1.0.1" [[Requires]] deps = ["UUIDs"] -git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621" +git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7" uuid = "ae029012-a4dd-5104-9daa-d747884805df" -version = "1.1.3" +version = "1.3.0" [[SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" +version = "0.7.0" + +[[Scratch]] +deps = ["Dates"] +git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386" +uuid = "6c6a2e73-6563-6170-7368-637461726353" +version = "1.2.1" + +[[SentinelArrays]] +deps = ["Dates", "Random"] +git-tree-sha1 = "363c4e82b66be7b9f7c7c7da7478fdae07de44b9" +uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c" +version = "1.4.2" [[Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" +[[SerializedElementArrays]] +deps = ["Serialization"] +git-tree-sha1 = "8e73e49eaebf73486446a3c1eede403bff259826" +uuid = "d3ce8812-9567-47e9-a7b5-65a6d70a3065" +version = "0.1.0" + +[[Setfield]] +deps = ["ConstructionBase", "Future", "MacroTools", "StaticArraysCore"] +git-tree-sha1 = "e2cc6d8c88613c05e1defb55170bf5ff211fbeac" +uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46" +version = "1.1.1" + [[SharedArrays]] deps = ["Distributed", "Mmap", "Random", "Serialization"] uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383" [[Showoff]] deps = ["Dates", "Grisu"] -git-tree-sha1 = "ee010d8f103468309b8afac4abb9be2e18ff1182" +git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de" uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f" -version = "0.3.2" +version = "1.0.3" + +[[SimpleBufferStream]] +git-tree-sha1 = "874e8867b33a00e784c8a7e4b60afe9e037b74e1" +uuid = "777ac1f9-54b0-4bf8-805c-2214025038e7" +version = "1.1.0" [[SimpleTraits]] deps = ["InteractiveUtils", "MacroTools"] -git-tree-sha1 = "daf7aec3fe3acb2131388f93a4c409b8c7f62226" +git-tree-sha1 = "5d7e3f4e11935503d3ecaf7186eac40602e7d231" uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d" -version = "0.9.3" +version = "0.9.4" [[Sockets]] uuid = "6462fe0b-24de-5631-8697-dd941f90decc" [[SortingAlgorithms]] -deps = ["DataStructures", "Random", "Test"] -git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd" +deps = ["DataStructures"] +git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085" uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "0.3.1" +version = "1.2.1" [[SparseArrays]] deps = ["LinearAlgebra", "Random"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" [[SpecialFunctions]] -deps = ["ChainRulesCore", "OpenSpecFun_jll"] -git-tree-sha1 = "5919936c0e92cff40e57d0ddf0ceb667d42e5902" +deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] +git-tree-sha1 = "2f5d4697f21388cbe1ff299430dd169ef97d7e14" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "1.3.0" +version = "2.4.0" + +[[SplitApplyCombine]] +deps = ["Dictionaries", "Indexing"] +git-tree-sha1 = "c06d695d51cfb2187e6848e98d6252df9101c588" +uuid = "03a91e81-4c3e-53e1-a0a4-9c0c8f19dd66" +version = "1.2.3" + +[[SplittablesBase]] +deps = ["Setfield", "Test"] +git-tree-sha1 = "e08a62abc517eb79667d0a29dc08a3b589516bb5" +uuid = "171d559e-b47b-412a-8079-5efa626c420e" +version = "0.1.15" + +[[Static]] +deps = ["IfElse"] +git-tree-sha1 = "b366eb1eb68075745777d80861c6706c33f588ae" +uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3" +version = "0.8.9" + +[[StaticArrayInterface]] +deps = ["ArrayInterface", "Compat", "IfElse", "LinearAlgebra", "PrecompileTools", "Requires", "SparseArrays", "Static", "SuiteSparse"] +git-tree-sha1 = "5d66818a39bb04bf328e92bc933ec5b4ee88e436" +uuid = "0d7ed370-da01-4f52-bd93-41d350b8b718" +version = "1.5.0" [[StaticArrays]] -deps = ["LinearAlgebra", "Random", "Statistics"] -git-tree-sha1 = "da4cf579416c81994afd6322365d00916c79b8ae" +deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore", "Statistics"] +git-tree-sha1 = "9ae599cd7529cfce7fea36cf00a62cfc56f0f37c" uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "0.12.5" +version = "1.9.4" + +[[StaticArraysCore]] +git-tree-sha1 = "36b3d696ce6366023a0ea192b4cd442268995a0d" +uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c" +version = "1.4.2" [[Statistics]] deps = ["LinearAlgebra", "SparseArrays"] uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +[[StatsAPI]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "1ff449ad350c9c4cbc756624d6f8a8c3ef56d3ed" +uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0" +version = "1.7.0" + [[StatsBase]] -deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"] -git-tree-sha1 = "a83fa3021ac4c5a918582ec4721bc0cf70b495a9" +deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"] +git-tree-sha1 = "5cf7606d6cef84b543b483848d4ae08ad9832b21" uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -version = "0.33.4" +version = "0.34.3" [[Strided]] -deps = ["LinearAlgebra", "TupleTools"] -git-tree-sha1 = "82111d3fe7982756d98e8b6606394efc6bb8df40" +deps = ["LinearAlgebra", "StridedViews", "TupleTools"] +git-tree-sha1 = "40c69be0e1b72ee2f42923b7d1ff13e0b04e675c" uuid = "5e0ebb24-38b0-5f93-81fe-25c709ecae67" -version = "1.1.1" +version = "2.0.4" + +[[StridedViews]] +deps = ["CUDA", "LinearAlgebra", "PackageExtensionCompat"] +git-tree-sha1 = "5b765c4e401693ab08981989f74a36a010aa1d8e" +uuid = "4db3bf67-4bd7-4b4e-b153-31dc3fb37143" +version = "0.2.2" + +[[StringManipulation]] +deps = ["PrecompileTools"] +git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5" +uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e" +version = "0.3.4" [[StructArrays]] -deps = ["Adapt", "DataAPI", "Tables"] -git-tree-sha1 = "26ea43b4be7e919a2390c3c0f824e7eb4fc19a0a" +deps = ["Adapt", "ConstructionBase", "DataAPI", "GPUArraysCore", "SparseArrays", "StaticArrays", "Tables"] +git-tree-sha1 = "f4dc295e983502292c4c3f951dbb4e985e35b3be" uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" -version = "0.5.0" +version = "0.6.18" + +[[SuiteSparse]] +deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] +uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" + +[[TOML]] +deps = ["Dates"] +uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" +version = "1.0.0" [[TableTraits]] deps = ["IteratorInterfaceExtensions"] -git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e" +git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39" uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c" -version = "1.0.0" +version = "1.0.1" [[Tables]] -deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"] -git-tree-sha1 = "a9ff3dfec713c6677af435d6a6d65f9744feef67" +deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"] +git-tree-sha1 = "cb76cf677714c095e535e3501ac7954732aeea2d" uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c" -version = "1.4.1" +version = "1.11.1" + +[[Tar]] +deps = ["ArgTools", "SHA"] +uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e" +version = "1.10.1" + +[[TensorCore]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6" +uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50" +version = "0.1.1" [[TensorOperations]] -deps = ["CUDA", "LRUCache", "LinearAlgebra", "Requires", "Strided", "TupleTools"] -git-tree-sha1 = "5da1079f1cdfa239a16f7f05b8bb325945dffb7c" +deps = ["CUDA", "ChainRulesCore", "LRUCache", "LinearAlgebra", "PackageExtensionCompat", "Strided", "StridedViews", "TupleTools", "VectorInterface", "cuTENSOR"] +git-tree-sha1 = "e450689f53b2a9f8261d899e60977e8561df75a6" uuid = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2" -version = "3.1.0" +version = "4.1.1" [[Test]] -deps = ["Distributed", "InteractiveUtils", "Logging", "Random"] +deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" +[[ThreadedScans]] +deps = ["ArgCheck"] +git-tree-sha1 = "ca1ba3000289eacba571aaa4efcefb642e7a1de6" +uuid = "24d252fe-5d94-4a69-83ea-56a14333d47a" +version = "0.1.0" + [[TimerOutputs]] -deps = ["Printf"] -git-tree-sha1 = "32cdbe6cd2d214c25a0b88f985c9e0092877c236" +deps = ["ExprTools", "Printf"] +git-tree-sha1 = "f548a9e9c490030e545f72074a41edfd0e5bcdd7" uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f" -version = "0.5.8" +version = "0.5.23" + +[[TranscodingStreams]] +deps = ["Random", "Test"] +git-tree-sha1 = "5d54d076465da49d6746c647022f3b3674e64156" +uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" +version = "0.10.8" + +[[Transducers]] +deps = ["Accessors", "Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "ConstructionBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "SplittablesBase", "Tables"] +git-tree-sha1 = "5215a069867476fc8e3469602006b9670e68da23" +uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999" +version = "0.4.82" [[TupleTools]] -git-tree-sha1 = "62a7a6cd5a608ff71cecfdb612e67a0897836069" +git-tree-sha1 = "41d61b1c545b06279871ef1a4b5fcb2cac2191cd" uuid = "9d95972d-f1c8-5527-a6e0-b4b365fa01f6" -version = "1.2.0" +version = "1.5.0" + +[[URIs]] +git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b" +uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" +version = "1.5.1" [[UUIDs]] deps = ["Random", "SHA"] @@ -675,56 +1410,356 @@ uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" [[Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" +[[UnicodeFun]] +deps = ["REPL"] +git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf" +uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1" +version = "0.4.1" + +[[Unitful]] +deps = ["ConstructionBase", "Dates", "InverseFunctions", "LinearAlgebra", "Random"] +git-tree-sha1 = "dd260903fdabea27d9b6021689b3cd5401a57748" +uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" +version = "1.20.0" + +[[UnitfulLatexify]] +deps = ["LaTeXStrings", "Latexify", "Unitful"] +git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd" +uuid = "45397f5d-5981-4c77-b2b3-fc36d6e9b728" +version = "1.6.3" + +[[UnsafeAtomics]] +git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278" +uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f" +version = "0.2.1" + +[[UnsafeAtomicsLLVM]] +deps = ["LLVM", "UnsafeAtomics"] +git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e" +uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" +version = "0.1.3" + +[[Unzip]] +git-tree-sha1 = "ca0969166a028236229f63514992fc073799bb78" +uuid = "41fe7b60-77ed-43a1-b4f0-825fd5a5650d" +version = "0.2.0" + +[[VectorInterface]] +deps = ["LinearAlgebra"] +git-tree-sha1 = "833b06acd39f0abc97bc1170bd28e4f713619b37" +uuid = "409d34a3-91d5-4945-b6ec-7529ddf182d8" +version = "0.4.5" + +[[Vulkan_Loader_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Wayland_jll", "Xorg_libX11_jll", "Xorg_libXrandr_jll", "xkbcommon_jll"] +git-tree-sha1 = "2f0486047a07670caad3a81a075d2e518acc5c59" +uuid = "a44049a8-05dd-5a78-86c9-5fde0876e88c" +version = "1.3.243+0" + +[[Wayland_jll]] +deps = ["Artifacts", "EpollShim_jll", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"] +git-tree-sha1 = "7558e29847e99bc3f04d6569e82d0f5c54460703" +uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89" +version = "1.21.0+1" + +[[Wayland_protocols_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "93f43ab61b16ddfb2fd3bb13b3ce241cafb0e6c9" +uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91" +version = "1.31.0+0" + [[WoodburyMatrices]] deps = ["LinearAlgebra", "SparseArrays"] -git-tree-sha1 = "59e2ad8fd1591ea019a5259bd012d7aee15f995c" +git-tree-sha1 = "c1a7aa6219628fcd757dede0ca95e245c5cd9511" uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6" -version = "0.5.3" +version = "1.0.0" + +[[XML2_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"] +git-tree-sha1 = "532e22cf7be8462035d092ff21fada7527e2c488" +uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a" +version = "2.12.6+0" + +[[XSLT_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"] +git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a" +uuid = "aed1982a-8fda-507f-9586-7b0439959a61" +version = "1.1.34+0" + +[[XZ_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "ac88fb95ae6447c8dda6a5503f3bafd496ae8632" +uuid = "ffd25f8a-64ca-5728-b0f7-c24cf3aae800" +version = "5.4.6+0" + +[[Xorg_libICE_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "326b4fea307b0b39892b3e85fa451692eda8d46c" +uuid = "f67eecfb-183a-506d-b269-f58e52b52d7c" +version = "1.1.1+0" + +[[Xorg_libSM_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libICE_jll"] +git-tree-sha1 = "3796722887072218eabafb494a13c963209754ce" +uuid = "c834827a-8449-5923-a945-d239c165b7dd" +version = "1.2.4+0" + +[[Xorg_libX11_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"] +git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495" +uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc" +version = "1.8.6+0" + +[[Xorg_libXau_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "6035850dcc70518ca32f012e46015b9beeda49d8" +uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec" +version = "1.0.11+0" + +[[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" + +[[Xorg_libXdmcp_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "34d526d318358a859d7de23da945578e8e8727b7" +uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05" +version = "1.1.4+0" + +[[Xorg_libXext_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] +git-tree-sha1 = "d2d1a5c49fae4ba39983f63de6afcbea47194e85" +uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3" +version = "1.3.6+0" + +[[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" + +[[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" + +[[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" + +[[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" + +[[Xorg_libXrender_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] +git-tree-sha1 = "47e45cd78224c53109495b3e324df0c37bb61fbe" +uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa" +version = "0.9.11+0" + +[[Xorg_libpthread_stubs_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "8fdda4c692503d44d04a0603d9ac0982054635f9" +uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74" +version = "0.1.1+0" + +[[Xorg_libxcb_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"] +git-tree-sha1 = "b4bfde5d5b652e22b9c790ad00af08b6d042b97d" +uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b" +version = "1.15.0+0" + +[[Xorg_libxkbfile_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"] +git-tree-sha1 = "730eeca102434283c50ccf7d1ecdadf521a765a4" +uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a" +version = "1.1.2+0" + +[[Xorg_xcb_util_cursor_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_jll", "Xorg_xcb_util_renderutil_jll"] +git-tree-sha1 = "04341cb870f29dcd5e39055f895c39d016e18ccd" +uuid = "e920d4aa-a673-5f3a-b3d7-f755a4d47c43" +version = "0.1.4+0" + +[[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" + +[[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" + +[[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" + +[[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" + +[[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" + +[[Xorg_xkbcomp_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxkbfile_jll"] +git-tree-sha1 = "330f955bc41bb8f5270a369c473fc4a5a4e4d3cb" +uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4" +version = "1.4.6+0" + +[[Xorg_xkeyboard_config_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"] +git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3" +uuid = "33bec58e-1273-512f-9401-5d533626f822" +version = "2.39.0+0" + +[[Xorg_xtrans_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77" +uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10" +version = "1.5.0+0" + +[[Zeros]] +deps = ["Test"] +git-tree-sha1 = "7eb4fd47c304c078425bf57da99a56606150d7d4" +uuid = "bd1ec220-6eb4-527a-9b49-e79c3db6233b" +version = "0.3.0" [[Zlib_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6" +deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" -version = "1.2.11+18" +version = "1.2.12+3" [[Zstd_jll]] -deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "2c1332c54931e83f8f94d310fa447fd743e8d600" +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "e678132f07ddb5bfa46857f0d7620fb9be675d3b" uuid = "3161d3a3-bdf6-5164-811a-617609db77b4" -version = "1.4.8+0" +version = "1.5.6+0" + +[[cuTENSOR]] +deps = ["CEnum", "CUDA", "CUDA_Runtime_Discovery", "CUTENSOR_jll", "LinearAlgebra"] +git-tree-sha1 = "248d804aee9226e010199e7b196c133809c49a8c" +uuid = "011b41b2-24ef-40a8-b3eb-fa098493e9e1" +version = "1.2.1" + +[[eudev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"] +git-tree-sha1 = "431b678a28ebb559d224c0b6b6d01afce87c51ba" +uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06" +version = "3.2.9+0" + +[[fzf_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "a68c9655fbe6dfcab3d972808f1aafec151ce3f8" +uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09" +version = "0.43.0+0" + +[[gperf_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "3516a5630f741c9eecb3720b1ec9d8edc3ecc033" +uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70" +version = "3.1.1+0" + +[[libaec_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "46bf7be2917b59b761247be3f317ddf75e50e997" +uuid = "477f73a3-ac25-53e9-8cc3-50b2fa2566f0" +version = "1.1.2+0" + +[[libaom_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl"] +git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d" +uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b" +version = "3.9.0+0" [[libass_jll]] -deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] -git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c" +deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"] +git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47" uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0" -version = "0.14.0+4" +version = "0.15.1+0" + +[[libblastrampoline_jll]] +deps = ["Artifacts", "Libdl", "OpenBLAS_jll"] +uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" +version = "5.1.1+0" + +[[libevdev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22" +uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc" +version = "1.11.0+0" [[libfdk_aac_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab" +git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55" uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280" -version = "0.1.6+4" +version = "2.0.2+0" + +[[libinput_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"] +git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce" +uuid = "36db933b-70db-51c0-b978-0f229ee0e533" +version = "1.18.0+0" + +[[libpng_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"] +git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4" +uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f" +version = "1.6.43+1" [[libvorbis_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"] -git-tree-sha1 = "fa14ac25af7a4b8a7f61b287a124df7aab601bcd" +git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c" uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a" -version = "1.3.6+6" +version = "1.3.7+1" + +[[mtdev_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] +git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11" +uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e" +version = "1.1.6+0" [[nghttp2_jll]] -deps = ["Libdl", "Pkg"] -git-tree-sha1 = "8e2c44ab4d49ad9518f359ed8b62f83ba8beede4" +deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" -version = "1.40.0+2" +version = "1.48.0+0" + +[[p7zip_jll]] +deps = ["Artifacts", "Libdl"] +uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" +version = "17.4.0+0" [[x264_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f" +git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2" uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a" -version = "2020.7.14+2" +version = "2021.5.5+0" [[x265_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] -git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab" +git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9" uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76" -version = "3.0.0+3" +version = "3.5.0+0" + +[[xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+1" diff --git a/Project.toml b/Project.toml index 05f1023..49cecd7 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "MPSDynamics" uuid = "8fc8f346-e1eb-498f-94db-02ffe92d8134" authors = ["angus-dunnett ", "Thibaut Lacroix "] -version = "1.0" +version = "1.0.0" [deps] Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" @@ -10,17 +10,19 @@ Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b" GraphRecipes = "bd48cda9-67a9-57be-86fa-5b3c104eda73" HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f" ITensors = "9136182c-28ba-11e9-034c-db9fb085ebd5" +Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8" +Jacobi = "83f21c0b-4282-5fbc-9e3f-f6da3d2e584c" KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" Logging = "56ddb016-857b-54e1-b83d-db4d58db5568" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Preferences = "21216c6a-2e73-6563-6e65-726566657250" Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" +QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b" TensorOperations = "6aa20fa7-93e2-5fca-9bc0-fbd0db3c71a2" -QuadGK = "1fd47b50-473d-5c70-9696-f719f8f3bcdc" -Jacobi = "83f21c0b-4282-5fbc-9e3f-f6da3d2e584c" [compat] TensorOperations = "4.0.7" diff --git a/examples/bath-observables.jl b/examples/bath-observables.jl index 8a344d7..caf0f39 100644 --- a/examples/bath-observables.jl +++ b/examples/bath-observables.jl @@ -25,7 +25,7 @@ s = 1 # ohmicity method = :TDVP1 # time-evolution method conv = 3 # bond dimension for the TDVP1 dt = 0.5 # time step -tfinal = 60.0 # simulation time +tfinal = 50.0 # simulation time #---------------------------- # Ohmic spectral density @@ -114,6 +114,7 @@ correlations_cdag = [ for i in 1:size(cdagcdag_average, 1), j in 1:size(cdagcdag_average, 2), t in 1:size(cdagcdag_average,3) ] +bath_occup_phys = physical_occup(correlations_cdag[:,:,T], correlations_c[:,:,T], omeg, bath_occup[:,:,T], β, N) #-------------------- # Analytical results diff --git a/src/MPSDynamics.jl b/src/MPSDynamics.jl index 40df16c..8fd1049 100644 --- a/src/MPSDynamics.jl +++ b/src/MPSDynamics.jl @@ -1,6 +1,6 @@ module MPSDynamics -using JLD, HDF5, Random, Dates, Plots, Printf, Distributed, LinearAlgebra, DelimitedFiles, KrylovKit, TensorOperations, GraphRecipes, SpecialFunctions, ITensors +using JLD, HDF5, Random, Dates, Plots, Printf, Distributed, LinearAlgebra, DelimitedFiles, KrylovKit, TensorOperations, GraphRecipes, SpecialFunctions, ITensors, Interpolations include("fundamentals.jl") include("reshape.jl") diff --git a/src/fundamentals.jl b/src/fundamentals.jl index 6dcac84..fcffbae 100644 --- a/src/fundamentals.jl +++ b/src/fundamentals.jl @@ -227,7 +227,7 @@ function cosineh(omega, bet) return 1/sqrt(1 - exp(-omega * (bet))) end -" +""" sineh(omega, bet) Calculates the hyperbolic sine function function based on the input parameters, From 38bc32900dcc42adb551a629eea30ec993d007ee Mon Sep 17 00:00:00 2001 From: Thibaut Lacroix <57836508+tfmlaX@users.noreply.github.com> Date: Fri, 24 May 2024 10:59:29 +0200 Subject: [PATCH 52/52] Fix typo --- examples/protontransfer.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/protontransfer.jl b/examples/protontransfer.jl index 19a00d5..1a85c2b 100644 --- a/examples/protontransfer.jl +++ b/examples/protontransfer.jl @@ -5,7 +5,7 @@ The RC tensor is initially displaced at the value γ corresponding to a space coordinate. - The intiial electronic system is initialized in the adiabatic LOW surface at the RC displacement. + The initial electronic system is initialized in the adiabatic LOW surface at the RC displacement. H_S + H_RC + H_int^{S-RC} = ω^0_{e} |e⟩ ⟨e| + ω^0_{k} |k⟩ ⟨k| + \\Delta (|e⟩ ⟨k| + |k⟩ ⟨ e|) + ω_{RC} (d^{\dagger}d + \\frac{1}{2}) + g_{e} |e⟩ ⟨e|( d + d^{\dagger})+ g_{k} |k⟩ ⟨k|( d + d^{\dagger}) `` @@ -270,4 +270,4 @@ anim = @animate for t=1:length(dat["data/times"]) print("\n k = ", k) (plot(heatmap(xlist_rho, xlist_rho, abs.(ρx[:,:,t]), c=:Blues ), title="Time = $k (arb. units)", xlabel=L"$x$ (arb. units)",ylabel=L"$x$ (arb. units)", tickfontsize=(12),colorbar_title = L"||\rho_{RC}(x,x)||", legend=:none, xlims=(-0.5, 0.5), ylims=(-0.5,0.5), clims=(0,0.18),aspect_ratio=:equal)) end -display(gif(anim, "gif_reducedrho.gif", fps = 2.5)) \ No newline at end of file +display(gif(anim, "gif_reducedrho.gif", fps = 2.5))