genkuroki/Animation of Bayesian estimation (Generate Data).ipynb

## Animation of Bayesian estimation (Generate Data).ipynb
{
  "cells": [
    {
      "metadata": {
        "toc": "true"
      },
      "cell_type": "markdown",
      "source": "# Table of Contents\n <p><div class=\"lev1 toc-item\"><a href=\"#ベイズ推定のアニメーション-(データの作成)\" data-toc-modified-id=\"ベイズ推定のアニメーション-(データの作成)-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>ベイズ推定のアニメーション (データの作成)</a></div><div class=\"lev2 toc-item\"><a href=\"#パッケージの読み込みと諸定義\" data-toc-modified-id=\"パッケージの読み込みと諸定義-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>パッケージの読み込みと諸定義</a></div><div class=\"lev3 toc-item\"><a href=\"#MambaパッケージでMCMCを実行するための函数\" data-toc-modified-id=\"MambaパッケージでMCMCを実行するための函数-111\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>MambaパッケージでMCMCを実行するための函数</a></div><div class=\"lev3 toc-item\"><a href=\"#Mambaによるシミュレーション結果のまとめの表示\" data-toc-modified-id=\"Mambaによるシミュレーション結果のまとめの表示-112\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>Mambaによるシミュレーション結果のまとめの表示</a></div><div class=\"lev3 toc-item\"><a href=\"#WAICなどを計算するための函数\" data-toc-modified-id=\"WAICなどを計算するための函数-113\"><span class=\"toc-item-num\">1.1.3&nbsp;&nbsp;</span>WAICなどを計算するための函数</a></div><div class=\"lev3 toc-item\"><a href=\"#情報をまとめて表示するための函数\" data-toc-modified-id=\"情報をまとめて表示するための函数-114\"><span class=\"toc-item-num\">1.1.4&nbsp;&nbsp;</span>情報をまとめて表示するための函数</a></div><div class=\"lev3 toc-item\"><a href=\"#単純な正規分布モデルを最尤法で解くための函数\" data-toc-modified-id=\"単純な正規分布モデルを最尤法で解くための函数-115\"><span class=\"toc-item-num\">1.1.5&nbsp;&nbsp;</span>単純な正規分布モデルを最尤法で解くための函数</a></div><div class=\"lev2 toc-item\"><a href=\"#データの作成-(分散1のガンマ分布で生成)\" data-toc-modified-id=\"データの作成-(分散1のガンマ分布で生成)-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>データの作成 (分散1のガンマ分布で生成)</a></div><div class=\"lev2 toc-item\"><a href=\"#データの作成-(分散1のガンマ分布を2つ合わせた混合分布で生成)\" data-toc-modified-id=\"データの作成-(分散1のガンマ分布を2つ合わせた混合分布で生成)-13\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>データの作成 (分散1のガンマ分布を2つ合わせた混合分布で生成)</a></div><div class=\"lev3 toc-item\"><a href=\"#データの読み込み方\" data-toc-modified-id=\"データの読み込み方-131\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>データの読み込み方</a></div><div class=\"lev3 toc-item\"><a href=\"#プロットのための函数\" data-toc-modified-id=\"プロットのための函数-132\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>プロットのための函数</a></div><div class=\"lev2 toc-item\"><a href=\"#プロットの実行は別ファイルで\" data-toc-modified-id=\"プロットの実行は別ファイルで-14\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>プロットの実行は別ファイルで</a></div>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# ベイズ推定のアニメーション (データの作成)\n\n黒木玄\n\n2017-11-19"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "versioninfo()",
      "execution_count": 1,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Julia Version 0.6.0\nCommit 903644385b* (2017-06-19 13:05 UTC)\nPlatform Info:\n  OS: Windows (x86_64-w64-mingw32)\n  CPU: Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz\n  WORD_SIZE: 64\n  BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)\n  LAPACK: libopenblas64_\n  LIBM: libopenlibm\n  LLVM: libLLVM-3.9.1 (ORCJIT, haswell)\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## パッケージの読み込みと諸定義"
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "@time begin\n    using Mamba\n    \n    using KernelDensity\n    function makefunc_pdfkde(X)\n        local ik = InterpKDE(kde(X))\n        local pdfkde(x) = pdf(ik, x)\n        return pdfkde\n    end\n    function makefunc_pdfkde(X,Y)\n        local ik = InterpKDE(kde((X,Y)))\n        local pdfkde(x, y) = pdf(ik, x, y)\n        return pdfkde\n    end\n\n    using Optim\n    optim_options = Optim.Options(\n        store_trace = true,\n        extended_trace = true\n    )\n    \n    using QuadGK\n\n    import PyPlot\n    plt = PyPlot\n\n    using Distributions\n    @everywhere GTDist(μ, ρ, ν) = LocationScale(Float64(μ), Float64(ρ), TDist(Float64(ν)))\n    @everywhere GTDist(ρ, ν) = GTDist(zero(ρ), ρ, ν)\n\n    using JLD2\n    using FileIO\nend\n\nusing PyCall\n@pyimport matplotlib.animation as anim\nfunction showgif(filename)\n    open(filename) do f\n        base64_video = base64encode(f)\n        display(\"text/html\", \"\"\"<img src=\"data:image/gif;base64,$base64_video\">\"\"\")\n    end\nend\n\nmacro sum(f_k, k, itr)\n    quote\n        begin\n            local s = zero(($(esc(k))->$(esc(f_k)))($(esc(itr))[1]))\n            for $(esc(k)) in $(esc(itr))\n                s += $(esc(f_k))\n            end\n            s\n        end\n    end\nend",
      "execution_count": 2,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": " 63.530875 seconds (15.83 M allocations: 1.103 GiB, 0.74% gc time)\n"
        },
        {
          "data": {
            "text/plain": "@sum (macro with 1 method)"
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### MambaパッケージでMCMCを実行するための函数"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "@everywhere mixnormal(a,b,c) = MixtureModel(Normal[Normal(b, 1.0), Normal(c, 1.0)], [1.0-a, a])\n\nmixnormal(w::AbstractVector) = mixnormal(w[1], w[2], w[3])\nunlink_mixnormal(w) = [logit(w[1]), w[2], w[3]]     # unlink_mixnormal : (0,1)×R^2 -> R^3\nlink_mixnormal(z)   = [invlogit(z[1]), z[2], z[3]]  #   link_mixnormal : R^3 → (0,1)×R^2\n\nfunction sample2model_mixnormal(Y;\n        dist_model = mixnormal,\n        a0 = 0.5,\n        b0 = 0.0,\n        c0 = 0.0,\n        prior_a = Uniform(0.0, 1.0),\n        prior_b = Normal(0.0, 1.0),\n        prior_c = Normal(0.0, 1.0),\n        chains = 2\n    )\n    local data = Dict{Symbol, Any}(\n        :Y => Y,\n        :n => length(Y),\n        :a0 => a0,\n        :b0 => b0,\n        :c0 => c0,\n    )\n    \n    local model = Model(\n        y = Stochastic(1, (a, b, c) -> dist_model(a, b, c), false),\n        a = Stochastic(() -> prior_a, true),\n        b = Stochastic(() -> prior_b, true),\n        c = Stochastic(() -> prior_b, true),\n    )\n    local scheme = [\n        NUTS([:a, :b, :c])\n    ]\n    setsamplers!(model, scheme)\n    \n    local inits = [\n        Dict{Symbol, Any}(\n            :y => data[:Y],\n            :a => data[:a0],\n            :b => data[:b0],\n            :c => data[:c0],\n        )\n        for k in 1:chains\n    ]\n    return model, data, inits\nend",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/plain": "sample2model_mixnormal (generic function with 1 method)"
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "@everywhere normal(mu, sigma) = Normal(mu, sigma)\n\nnormal(w::AbstractVector) = normal(w[1], w[2])\nunlink_normal(w) = [w[1], log(w[2])]  # unlink_normal : R×(0,∞) -> R^2\nlink_normal(z)   = [z[2], exp(z[2])]  #   link_normal : R^2 → R×(0,∞)\n\nfunction sample2model_normal(Y;\n        dist_model = normal,\n        mu0    = 0.0,\n        sigma0 = 1.0,\n        prior_mu = Normal(0.0, 1.0),\n        prior_sigma = Truncated(Normal(1.0, 1.0), 0, Inf),\n        chains = 2\n    )\n    local data = Dict{Symbol, Any}(\n        :Y => Y,\n        :n => length(Y),\n        :mu0 => mu0,\n        :sigma0 => sigma0,\n    )\n    \n    local model = Model(\n        y = Stochastic(1, (mu, sigma) -> dist_model(mu, sigma), false),\n        mu = Stochastic(() -> prior_mu, true),\n        sigma = Stochastic(() -> prior_sigma, true),\n    )\n    local scheme = [\n        NUTS([:mu, :sigma])\n    ]\n    setsamplers!(model, scheme)\n    \n    local inits = [\n        Dict{Symbol, Any}(\n            :y => data[:Y],\n            :mu => data[:mu0],\n            :sigma => data[:sigma0],\n        )\n        for k in 1:chains\n    ]\n    return model, data, inits\nend",
      "execution_count": 4,
      "outputs": [
        {
          "data": {
            "text/plain": "sample2model_normal (generic function with 1 method)"
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "@everywhere normal1(mu::Real) = Normal(mu, 1.0)\n\nnormal1(w::AbstractVector) = normal1(w[1])\nunlink_normal1(w) = w\nlink_normal1(z)   = z\n\nfunction sample2model_normal1(Y;\n        dist_model = normal1,\n        mu0    = 0.0,\n        prior_mu = Normal(0.0, 1.0),\n        chains = 2\n    )\n    local data = Dict{Symbol, Any}(\n        :Y => Y,\n        :n => length(Y),\n        :mu0 => mu0,\n    )\n    \n    local model = Model(\n        y = Stochastic(1, mu -> dist_model(mu), false),\n        mu = Stochastic(() -> prior_mu, true),\n    )\n    local scheme = [\n        NUTS([:mu])\n    ]\n    setsamplers!(model, scheme)\n    \n    local inits = [\n        Dict{Symbol, Any}(\n            :y => data[:Y],\n            :mu => data[:mu0],\n        )\n        for k in 1:chains\n    ]\n    return model, data, inits\nend",
      "execution_count": 5,
      "outputs": [
        {
          "data": {
            "text/plain": "sample2model_normal1 (generic function with 1 method)"
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Mambaによるシミュレーション結果のまとめの表示"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "## Summary\nfunction showsummary(sim;\n        sortkeys=true, figsize_t=(8, 3), figsize_c=(8, 3.5),\n        show_describe=true, show_gelman=true, plot_trace=true, plot_contour=true)\n    ## Summary of MCMC\n    if show_describe\n        println(\"\\n========== Summary:\\n\")\n        display(describe(sim))\n    end\n\n    # Convergence Diagnostics\n    if show_gelman && length(sim.chains) ≥ 2 \n       println(\"========== Gelman Diagnostic:\")\n       show(gelmandiag(sim))\n    end\n\n    ## Plot\n    sleep(0.1)\n    if plot_trace\n        #draw(plot(sim), fmt=:png, width=10inch, height=3.5inch, nrow=1, ncol=2, ask=false)\n        pyplot_trace(sim, sortkeys=sortkeys, figsize=figsize_t)\n    end\n    if plot_contour\n        #draw(plot(sim, :contour), fmt=:png, width=10inch, height=4.5inch, nrow=1, ncol=2, ask=false)\n        pyplot_contour(sim, sortkeys=sortkeys, figsize=figsize_c)\n    end\nend\n\n## plot traces\nfunction pyplot_trace(sim; sortkeys = false, figsize = (8, 3))\n    if sortkeys\n        keys_sim = sort(keys(sim))\n    else\n        keys_sim = keys(sim)\n    end\n    for var in keys_sim\n        plt.figure(figsize=figsize)\n        \n        plt.subplot(1,2,1)\n        for k in sim.chains\n            plt.plot(sim.range, sim[:,var,:].value[:,1,k], label=\"$k\", lw=0.4, alpha=0.8)\n        end\n        plt.xlabel(\"iterations\")\n        plt.grid(ls=\":\")\n        #plt.legend(loc=\"upper right\")\n        plt.title(\"trace of $var\", fontsize=10)\n        \n        plt.subplot(1,2,2)\n        local xmin = quantile(vec(sim[:,var,:].value), 0.005)\n        local xmax = quantile(vec(sim[:,var,:].value), 0.995)\n        for k in sim.chains\n            local chain = sim[:,var,:].value[:,1,k]\n            local pdfkde = makefunc_pdfkde(chain)\n            local x = linspace(xmin, xmax, 201)\n            plt.plot(x, pdfkde.(x), label=\"$k\", lw=0.8, alpha=0.8)\n        end\n        plt.xlabel(\"$var\")\n        plt.grid(ls=\":\")\n        plt.title(\"empirical posterior pdf of $var\", fontsize=10)\n\n        plt.tight_layout()\n    end\nend\n\n# plot contours\nfunction pyplot_contour(sim; sortkeys = true, figsize = (8, 3.5))\n    if sortkeys\n        keys_sim = sort(keys(sim))\n    else\n        keys_sim = keys(sim)\n    end\n    local m = 0\n    local K = length(keys_sim)\n    for i in 1:K\n        for j in i+1:K\n            m += 1\n            mod1(m,2) == 1 && plt.figure(figsize=figsize)\n            plt.subplot(1,2, mod1(m,2))\n\n            local u = keys_sim[i]\n            local v = keys_sim[j]\n            local X = vec(sim[:,u,:].value)\n            local Y = vec(sim[:,v,:].value)\n            local pdfkde = makefunc_pdfkde(X,Y)\n            local xmin = quantile(X, 0.005)\n            local xmax = quantile(X, 0.995)\n            local ymin = quantile(Y, 0.005)\n            local ymax = quantile(Y, 0.995)\n            local x = linspace(xmin, xmax, 200)\n            local y = linspace(ymin, ymax, 200)\n            \n            plt.pcolormesh(x', y, pdfkde.(x',y), cmap=\"CMRmap\")\n            plt.colorbar()\n            plt.grid(ls=\":\")\n            plt.xlabel(u)\n            plt.ylabel(v)\n            plt.title(\"posterior of ($u, $v)\", fontsize=10)\n\n            mod1(m,2) == 2 && plt.tight_layout()\n\n            if 2*m == K*(K-1) && mod1(m,2) == 1\n                plt.subplot(1,2,2)\n                \n                plt.pcolormesh(y', x, pdfkde.(x,y'), cmap=\"CMRmap\")\n                plt.colorbar()\n                plt.grid(ls=\":\")\n                plt.xlabel(v)\n                plt.ylabel(u)\n                plt.title(\"posterior of ($v, $u)\", fontsize=10)\n\n                plt.tight_layout()\n            end\n        end\n    end\nend",
      "execution_count": 6,
      "outputs": [
        {
          "data": {
            "text/plain": "pyplot_contour (generic function with 1 method)"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### WAICなどを計算するための函数"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# loglik[l,i] = lpdf(w_l, Y_i) と chain[l,:] = w_l を取り出す函数を作る函数\n#\nfunction makefunc_loglikchainof(lpdf, symbols, Y)\n    #\n    # loglikchainof(sim) で loglik[l,i] と chain[l,:] が抽出される\n    #\n    local function loglikchainof(sim)\n        local val = sim[:, symbols, :].value\n        local chain = vcat((val[:,:,k] for k in 1:size(val,3))...)\n        local L = size(chain,1)\n        local n = length(Y)\n        local loglik = Array{Float64, 2}(L, n)\n        for i in 1:n\n            for l in 1:L\n                loglik[l,i] = lpdf(chain[l,:], Y[i])\n            end\n        end\n        return loglik, chain\n    end\n    return loglikchainof\nend\n\n# 予測分布函数 p^*(x,y) = mean of { lpdf(w_l, y) }_{l=1}^L を作る函数\n#\nfunction makefunc_pdfpred(lpdf, chain)\n    local L = size(chain,1)\n    local pred_Bayes(y) = @sum(exp(lpdf((@view chain[l,:]), y)), l, 1:L)/L\n    return pred_Bayes\nend\n\n# loglik[l,i] からWAICを計算する函数\n#\nfunction WAICof(loglik)\n    local L, n\n    L, n = size(loglik)\n    local T_n = -mean(log(mean(exp(loglik[l,i]) for l in 1:L)) for i in 1:n)\n    local V_n  = sum(var(loglik[:,i], corrected=false) for i in 1:n)\n    local WAIC = 2*n*T_n + 2*V_n\n    return WAIC, 2*n*T_n, 2*V_n\nend\n\n# loglik[l,i] からLOOCVを素朴に計算する函数\n#\nfunction LOOCVof(loglik)\n    local L, n\n    L, n = size(loglik)\n    local LOOCV = 2*sum(log(mean(exp(-loglik[l,i]) for l in 1:L)) for i in 1:n)\n    return LOOCV\nend\n\n# 自由エネルギー(の2倍)を計算するための函数\n# \n# 自由エネルギーの2の逆温度微分は E^β_w[2n L_n] に等しいので、\n# それを β=0.0 から 1.0 まで数値積分すれば自由エネルギーの2倍を計算できる。\n#\nfunction FreeEnergyof(loglik)\n    local E2nLn = makefunc_E2nLn(loglik)\n    local F = quadgk(E2nLn, 0.0, 1.0)[1]\n    return F, E2nLn\nend\n\nfunction makefunc_E2nLn(loglik)\n    local L = size(loglik)[1]\n    local negloglik = -sum(loglik, 2)\n    local negloglik_n = negloglik .- maximum(negloglik)\n    local function E2nLn(beta)\n        local Edenominator = @sum(             exp((1-beta)*negloglik_n[l]), l, 1:L)/L\n        if Edenominator == zero(Edenominator) || !isfinite(Edenominator)\n            return zero(Edenominator)\n        end\n        local Enumerator   = @sum(negloglik[l]*exp((1-beta)*negloglik_n[l]), l, 1:L)/L\n        return 2*Enumerator/Edenominator\n    end\n    return E2nLn\nend\n\n# loglik[l,i] からWBICを計算する函数\n#\nfunction WBICof(loglik)\n    local E2nLn = makefunc_E2nLn(loglik)\n    local n = size(loglik, 2)\n    local WBIC = E2nLn(1/log(n))\n    return WBIC\nend\n\n# 汎化損失を計算する函数\n#\nfunction GeneralizationLossof(pdftrue, pdfpred; xmin=-10.0, xmax=10.0)\n    local f(x) = -pdftrue(x)*log(pdfpred(x))\n    return quadgk(f, xmin, xmax)\nend\n\n# Shannon情報量を計算する函数\n#\nShannonInformationof(pdftrue; xmin=-10.0, xmax=10.0) = GeneralizationLossof(pdftrue, pdftrue, xmin=xmin, xmax=xmax)[1]\nShannonInformationof(dist::Distribution, n) = entropy(dist)\n\n# 最尤法を実行して AIC と BIC を計算する函数\n#\n# lpdf(w,y) = log p(y|w)\n#\n# w = link_model(z)   ←実ベクトル z 全体をパラメーター w の空間内に移す函数\n# z = unlink_model(w) ←link_model(z)の逆函数\n# これらは optimize() 函数の適用時にパラメーター w が定義域から外れることを防ぐための函数達\n#\n# (X[i], Y[i] はサンプル\n#\n# chain は loglik, chain = loglikchainof(sim) で作った chain\n#\n# optimize函数は1変数函数の場合には初期条件を与えて解を求める函数ではなくなるので、\n# その場合には2変数函数に拡張してから使用している.\n#\nfunction AICandBICof(lpdf, link_model, unlink_model, Y, chain)\n    local n = length(Y)\n    local L = size(chain,1)\n    local nparams = size(chain,2)\n    local negloglik(z) = -@sum(lpdf(link_model(z), Y[i]), i, 1:n)\n    local minnegloglik_chain, l\n    minnegloglik_chain, l = findmin(negloglik(unlink_model(chain[l,:])) for l in 1:L)\n    local o, minnegloglik, param_AIC\n    if size(chain,2) == 1\n        local f(z) = negloglik([z[1]]) + z[2]^2/eps()\n        o = optimize(f, [unlink_model(chain[l,:])[1], 0.0])\n        minnegloglik = o.minimum\n        param_AIC = link_model([o.minimizer[1]])\n    else\n        o = optimize(negloglik, unlink_model(chain[l,:]))\n        minnegloglik = o.minimum\n        param_AIC = link_model(o.minimizer)\n    end\n    local T_AIC = 2.0*minnegloglik\n    local V_AIC = 2.0*nparams\n    local T_BIC = T_AIC\n    local V_BIC = nparams*log(n)\n    local AIC = T_AIC + V_AIC\n    local BIC = T_BIC + V_BIC\n    return AIC, T_AIC, V_AIC, \n        BIC, T_BIC, V_BIC, \n        param_AIC\nend",
      "execution_count": 7,
      "outputs": [
        {
          "data": {
            "text/plain": "AICandBICof (generic function with 1 method)"
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### 情報をまとめて表示するための函数"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "function statsof(sim, Y; \n        dist_true=mixnormal(0.0, 0.0, 0.0), \n        dist_model=mixnormal, link_model=link_mixnormal, unlink_model=unlink_mixnormal)\n    \n    local n = length(Y)\n    \n    local lpdf(w, y) = logpdf(dist_model(w), y)\n    \n    local loglikchainof = makefunc_loglikchainof(lpdf, sort(keys(sim)), Y)\n    local loglik, chain \n    loglik, chain = loglikchainof(sim)\n    \n    local WAIC, T_WAIC, V_WAIC\n    WAIC, T_WAIC, V_WAIC = WAICof(loglik)\n    local LOOCV = LOOCVof(loglik)\n    \n    local WBIC = WBICof(loglik)\n    local FreeEnergy = FreeEnergyof(loglik)[1]\n    \n    local param_Bayes = vec(mean(chain, 1))\n    local pred_Bayes = makefunc_pdfpred(lpdf, chain)\n    \n    local GeneralizationLoss = 2*n*GeneralizationLossof(x->pdf(dist_true,x), pred_Bayes)[1]\n    \n    local AIC, T_AIC, V_AIC, BIC, T_BIC, V_BIC, param_MLE\n    AIC, T_AIC, V_AIC, BIC, T_BIC, V_BIC, param_MLE = AICandBICof(lpdf, link_model, unlink_model, Y, chain)\n    \n    local pred_MLE(y) = exp(lpdf(param_MLE, y))\n    return WAIC, T_WAIC, V_WAIC, LOOCV, GeneralizationLoss,\n        WBIC, FreeEnergy,\n        param_Bayes, pred_Bayes, \n        AIC, T_AIC, V_AIC, \n        BIC, T_BIC, V_BIC,\n        param_MLE, pred_MLE\nend\n\nfunction show_all_results(dist_true, Y, sim; statsfunc=statsof, \n        dist_model=mixnormal, link_model=link_mixnormal, unlink_model=link_mixnormal,\n        figsize=(6,4.2), xmin=-4.0, xmax=6.0)\n    WAIC, T_WAIC, V_WAIC, LOOCV, GeneralizationLoss,\n    WBIC, FreeEnergy,\n    param_Bayes, pred_Bayes, \n    AIC, T_AIC, V_AIC, \n    BIC, T_BIC, V_BIC,\n    param_MLE, pred_MLE = statsfunc(sim, Y, dist_true=dist_true, \n        dist_model=dist_model, link_model=link_model, unlink_model=unlink_model)\n    \n    n = length(Y)\n    println(\"\\n=== Estimates by $dist_model  (n = $n) ===\")\n    @show param_Bayes\n    @show param_MLE\n    \n    println(\"--- Information Criterions\")\n    println(\"* AIC     = $AIC = $T_AIC + $V_AIC\")\n    println(\"* GenLoss = $GeneralizationLoss\")\n    println(\"* WAIC    = $WAIC = $T_WAIC + $V_WAIC\")\n    println(\"* LOOCV   = $LOOCV\")\n    println(\"---\")\n    println(\"* BIC        = $BIC = $T_BIC + $V_BIC\")\n    println(\"* FreeEnergy = $FreeEnergy\")\n    println(\"* WBIC       = $WBIC\")\n\n    println(\"=\"^78 * \"\\n\")\n    \n    sleep(0.1)\n    plt.figure(figsize=figsize)\n    kde_sample = makefunc_pdfkde(Y)\n    x = linspace(xmin, xmax, 201)\n    plt.plot(x, pdf.(dist_true, x), label=\"true distribution\")\n    plt.scatter(Y, kde_sample.(Y), label=\"sample\", s=10, color=\"k\", alpha=0.5)\n    plt.plot(x, kde_sample.(x), label=\"KDE of sample\",   color=\"k\", alpha=0.5)\n    plt.plot(x, pred_Bayes.(x), label=\"Baysian predictive\", ls=\"--\")\n    plt.plot(x, pred_MLE.(x),   label=\"MLE predictive\",     ls=\"-.\")\n    plt.xlabel(\"x\")\n    plt.ylabel(\"probability density\")\n    plt.grid(ls=\":\")\n    plt.legend(fontsize=8)\n    plt.title(\"Estimates by $dist_model: n = $(length(Y))\")\nend\n\nfunction plotsample(dist_true, Y; figsize=(6,4.2), xmin=-4.0, xmax=6.0)\n    sleep(0.1)\n    plt.figure(figsize=figsize)\n    kde_sample = makefunc_pdfkde(Y)\n    x = linspace(xmin, xmax, 201)\n    plt.plot(x, pdf.(dist_true, x), label=\"true dist.\")\n    plt.scatter(Y, kde_sample.(Y), label=\"sample\", s=10, color=\"k\", alpha=0.5)\n    plt.plot(x, kde_sample.(x), label=\"KDE of sample\",   color=\"k\", alpha=0.5)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"probability density\")\n    plt.grid(ls=\":\")\n    plt.legend(fontsize=8)\n    plt.title(\"Sample size n = $(length(Y))\")\nend",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "text/plain": "plotsample (generic function with 1 method)"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### 単純な正規分布モデルを最尤法で解くための函数"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# dist_true 分布に従う乱数で生成したサンプルの配列 Y を与えると\n# 正規分布モデルの最尤法で推定して、AICなどを返す函数\n#\nfunction fit_Normal(dist_true, Y)\n    local n = length(Y)\n    local d = fit(Normal,Y)\n    local mu = d.μ\n    local sigma = d.σ\n    local pred_Normal(y) = pdf(d,y)\n    local T_Normal = -2*sum(logpdf.(d,Y))\n    local V_Normal = 4.0\n    local AIC_Normal = T_Normal + V_Normal\n    local TB_Normal = T_Normal\n    local VB_Normal = 2.0*log(n)\n    local BIC_Normal = TB_Normal + VB_Normal\n    local f(y) = -pdf(dist_true, y)*logpdf(d, y)\n    local GL_Normal = 2*n*quadgk(f, -10, 10)[1]\n    return mu, sigma, pred_Normal, \n        AIC_Normal, T_Normal, V_Normal, \n        BIC_Normal, TB_Normal, VB_Normal, \n        GL_Normal\nend\n\n# グラフをプロットする範囲が xmin から xmax まで\n#\nfunction show_fit_Normal(dist_true, Y; figsize=(6,4.2), xmin=-4.0, xmax=6.0)\n    mu, sigma, pred_Normal, \n    AIC_Normal, T_Normal, V_Normal, \n    BIC_Normal, TB_Normal, VB_Normal, \n    GL_Normal = fit_Normal(dist_true, Y)\n    println(\"--- Normal Fitting\")\n    println(\"* μ = $mu\")\n    println(\"* σ = $sigma\")\n    println(\"* GenLoss = $GL_Normal\")\n    println(\"* AIC     = $AIC_Normal = $T_Normal + $V_Normal\")\n    println(\"* BIC     = $BIC_Normal = $TB_Normal + $VB_Normal\")\n    \n    sleep(0.1)\n    plt.figure(figsize=figsize)\n    kde_sample = makefunc_pdfkde(Y)\n    x = linspace(xmin, xmax, 201)\n    plt.plot(x, pdf.(dist_true, x), label=\"true distribution\")\n    plt.scatter(Y, kde_sample.(Y), label=\"sample\", s=10, color=\"k\", alpha=0.5)\n    plt.plot(x, kde_sample.(x), label=\"KDE of sample\",   color=\"k\", alpha=0.5)\n    plt.plot(x, pred_Normal.(x), label=\"Normal predictive\", ls=\"--\")\n    plt.xlabel(\"x\")\n    plt.ylabel(\"probability density\")\n    plt.grid(ls=\":\")\n    plt.legend(fontsize=8)\n    plt.title(\"Sample size n = $(length(Y))\")\nend",
      "execution_count": 9,
      "outputs": [
        {
          "data": {
            "text/plain": "show_fit_Normal (generic function with 1 method)"
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "function InformationCriterions(dist_true, Y, sim; dist_model=mixnormal)\n    local n = length(Y)\n    local lpdf(w, y) = logpdf(dist_model(w), y)\n\n    local loglikchainof = makefunc_loglikchainof(lpdf, sort(keys(sim)), Y)\n    local loglik, chain \n    loglik, chain = loglikchainof(sim)\n\n    local WAIC, T_WAIC, V_WAIC\n    WAIC, T_WAIC, V_WAIC = WAICof(loglik)\n    local LOOCV = LOOCVof(loglik)\n    \n    local pred_Bayes = makefunc_pdfpred(lpdf, chain)\n    local GL = 2*n*GeneralizationLossof(x->pdf(dist_true,x), pred_Bayes)[1]\n    \n    local WBIC = WBICof(loglik)\n    local FreeEnergy = FreeEnergyof(loglik)[1]\n\n    return chain, WAIC, T_WAIC, V_WAIC, LOOCV, GL, WBIC, FreeEnergy\n end",
      "execution_count": 10,
      "outputs": [
        {
          "data": {
            "text/plain": "InformationCriterions (generic function with 1 method)"
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## データの作成 (分散1のガンマ分布で生成)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# サンプルを生成する真の確率分布の定義\n\ntheta = 0.4\ndist_true = Gamma(1/theta^2, theta)\n@show mu_true = mean(dist_true)\n@show sigma_true = std(dist_true)\ndist_normal_fitting = Normal(mu_true, sigma_true)\nsleep(0.1)\nx = linspace(-1,8,100)\nplt.figure(figsize=(5,3.5))\nplt.plot(x, pdf.(dist_true, x), label=\"true distribution\")\nplt.plot(x, pdf.(dist_normal_fitting, x), label=\"normal fitting\")\nplt.grid(ls=\":\")\nplt.legend()",
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "mu_true = mean(dist_true) = 2.5\nsigma_true = std(dist_true) = 1.0\n"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x00000000574DB5C0>)"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "PyObject <matplotlib.legend.Legend object at 0x00000000570590F0>"
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# シミュレーション\n\nseed = 4649\ndataname = \"Gamma128Sample$seed\"\nN = 2^7\n\n##########\n\niterations_mixnormal = 500\nburnin_mixnormal = 100\nthin_mixnormal = 1\nchains_mixnormal = 4\n\niterations_normal1 = 500\nburnin_normal1 = 100\nthin_normal1 = 1\nchains_normal1 = 4\n\niterations_normal = 500\nburnin_normal = 100\nthin_normal = 1\nchains_normal = 4\n\nSample = rand(dist_true, N)\nShannon = Array{Float64}(N)\nT_true = Array{Float64}(N)\n\nChain_mixnormal = Array{Array{Float64,2}}(N)\nWAIC_mixnormal = Array{Float64}(N)\nT_mixnormal = Array{Float64}(N)\nV_mixnormal = Array{Float64}(N)\nLOOCV_mixnormal = Array{Float64}(N)\nGL_mixnormal = Array{Float64}(N)\nWBIC_mixnormal = Array{Float64}(N)\nFreeEnergy_mixnormal = Array{Float64}(N)\n\nChain_normal1 = Array{Array{Float64,2}}(N)\nWAIC_normal1 = Array{Float64}(N)\nT_normal1 = Array{Float64}(N)\nV_normal1 = Array{Float64}(N)\nLOOCV_normal1 = Array{Float64}(N)\nGL_normal1 = Array{Float64}(N)\nWBIC_normal1 = Array{Float64}(N)\nFreeEnergy_normal1 = Array{Float64}(N)\n\nChain_normal = Array{Array{Float64,2}}(N)\nWAIC_normal = Array{Float64}(N)\nT_normal = Array{Float64}(N)\nV_normal = Array{Float64}(N)\nLOOCV_normal = Array{Float64}(N)\nGL_normal = Array{Float64}(N)\nWBIC_normal = Array{Float64}(N)\nFreeEnergy_normal = Array{Float64}(N)\n\n@time for i in 1:N\n    n = i\n    Y = Sample[1:n]\n    Shannon[i] = 2*n*entropy(dist_true)\n    T_true[i] = -2*@sum(logpdf(dist_true, Y[k]), k, 1:n)\n\n    # mixnormal\n    #\n    model_mixnormal, data_mixnormal, inits_mixnormal = sample2model_mixnormal(Y, chains=chains_mixnormal)\n    sim_mixnormal = mcmc(model_mixnormal, data_mixnormal, inits_mixnormal,\n        iterations_mixnormal, burnin=burnin_mixnormal, thin=thin_mixnormal, chains=chains_mixnormal, verbose=false)\n\n    Chain_mixnormal[i], WAIC_mixnormal[i], T_mixnormal[i], V_mixnormal[i], \n    LOOCV_mixnormal[i], GL_mixnormal[i], WBIC_mixnormal[i], FreeEnergy_mixnormal[i] = \n    InformationCriterions(dist_true, Y, sim_mixnormal, dist_model=mixnormal)\n    \n    # normal1\n    #\n    model_normal1, data_normal1, inits_normal1 = sample2model_normal1(Y, chains=chains_normal1)\n    sim_normal1 = mcmc(model_normal1, data_normal1, inits_normal1,\n        iterations_normal1, burnin=burnin_normal1, thin=thin_normal1, chains=chains_normal1, verbose=false)\n\n    Chain_normal1[i], WAIC_normal1[i], T_normal1[i], V_normal1[i], \n    LOOCV_normal1[i], GL_normal1[i], WBIC_normal1[i], FreeEnergy_normal1[i] = \n    InformationCriterions(dist_true, Y, sim_normal1, dist_model=normal1)\n\n    # normal\n    #\n    model_normal, data_normal, inits_normal = sample2model_normal(Y, chains=chains_normal)\n    sim_normal = mcmc(model_normal, data_normal, inits_normal,\n        iterations_normal, burnin=burnin_normal, thin=thin_normal, chains=chains_normal, verbose=false)\n\n    Chain_normal[i], WAIC_normal[i], T_normal[i], V_normal[i], \n    LOOCV_normal[i], GL_normal[i], WBIC_normal[i], FreeEnergy_normal[i] = \n    InformationCriterions(dist_true, Y, sim_normal, dist_model=normal)\n    \n    print(\"$i \")\n    i == N && println()\nend\n\n#########\n\nvarnames = [\n    \"seed\", \"dist_true\", \"N\", \"Shannon\",\n    \"iterations_mixnormal\", \"burnin_mixnormal\", \"thin_mixnormal\", \"chains_mixnormal\",\n    \"iterations_normal1\",   \"burnin_normal1\",   \"thin_normal1\",   \"chains_normal1\",\n    \"iterations_normal\",    \"burnin_normal\",    \"thin_normal\",    \"chains_normal\",\n    \"Sample\", \"T_true\", \n    \"Chain_mixnormal\", \"WAIC_mixnormal\", \"T_mixnormal\",    \"V_mixnormal\", \n    \"LOOCV_mixnormal\", \"GL_mixnormal\",   \"WBIC_mixnormal\", \"FreeEnergy_mixnormal\", \n    \"Chain_normal1\",   \"WAIC_normal1\",   \"T_normal1\",      \"V_normal1\", \n    \"LOOCV_normal1\",   \"GL_normal1\",     \"WBIC_normal1\",   \"FreeEnergy_normal1\", \n    \"Chain_normal\",    \"WAIC_normal\",    \"T_normal\",       \"V_normal\", \n    \"LOOCV_normal\",    \"GL_normal\",      \"WBIC_normal\",    \"FreeEnergy_normal\", \n]\neval(parse(\"$dataname = Dict()\"))\nfor v in sort(varnames)\n    eval(parse(\"$dataname[\\\"$v\\\"] = $v\"))\nend\n\n@show jld2file = \"$dataname.jld2\"\neval(parse(\"save(jld2file, $dataname)\"))\ndata = load(\"$dataname.jld2\")\n@show keys(data);",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 \n1252.332404 seconds (2.44 G allocations: 55.851 GiB, 1.12% gc time)\njld2file = \"$(dataname).jld2\" = \"Gamma128Sample4649.jld2\"\nkeys(data) = String[\"T_normal\", \"Chain_mixnormal\", \"chains_mixnormal\", \"Shannon\", \"burnin_mixnormal\", \"WBIC_normal\", \"thin_normal\", \"Chain_normal1\", \"FreeEnergy_mixnormal\", \"Sample\", \"V_normal1\", \"V_normal\", \"burnin_normal1\", \"iterations_normal\", \"N\", \"T_mixnormal\", \"WAIC_mixnormal\", \"GL_normal1\", \"iterations_normal1\", \"chains_normal1\", \"WBIC_normal1\", \"WBIC_mixnormal\", \"GL_mixnormal\", \"GL_normal\", \"LOOCV_normal\", \"chains_normal\", \"thin_mixnormal\", \"dist_true\", \"Chain_normal\", \"thin_normal1\", \"WAIC_normal1\", \"burnin_normal\", \"iterations_mixnormal\", \"FreeEnergy_normal\", \"V_mixnormal\", \"WAIC_normal\", \"LOOCV_mixnormal\", \"FreeEnergy_normal1\", \"seed\", \"LOOCV_normal1\", \"T_true\", \"T_normal1\"]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# シミュレーション\n\nseed = 12345\ndataname = \"Gamma128Sample$seed\"\nN = 2^7\n\n##########\n\niterations_mixnormal = 500\nburnin_mixnormal = 100\nthin_mixnormal = 1\nchains_mixnormal = 4\n\niterations_normal1 = 500\nburnin_normal1 = 100\nthin_normal1 = 1\nchains_normal1 = 4\n\niterations_normal = 500\nburnin_normal = 100\nthin_normal = 1\nchains_normal = 4\n\nSample = rand(dist_true, N)\nShannon = Array{Float64}(N)\nT_true = Array{Float64}(N)\n\nChain_mixnormal = Array{Array{Float64,2}}(N)\nWAIC_mixnormal = Array{Float64}(N)\nT_mixnormal = Array{Float64}(N)\nV_mixnormal = Array{Float64}(N)\nLOOCV_mixnormal = Array{Float64}(N)\nGL_mixnormal = Array{Float64}(N)\nWBIC_mixnormal = Array{Float64}(N)\nFreeEnergy_mixnormal = Array{Float64}(N)\n\nChain_normal1 = Array{Array{Float64,2}}(N)\nWAIC_normal1 = Array{Float64}(N)\nT_normal1 = Array{Float64}(N)\nV_normal1 = Array{Float64}(N)\nLOOCV_normal1 = Array{Float64}(N)\nGL_normal1 = Array{Float64}(N)\nWBIC_normal1 = Array{Float64}(N)\nFreeEnergy_normal1 = Array{Float64}(N)\n\nChain_normal = Array{Array{Float64,2}}(N)\nWAIC_normal = Array{Float64}(N)\nT_normal = Array{Float64}(N)\nV_normal = Array{Float64}(N)\nLOOCV_normal = Array{Float64}(N)\nGL_normal = Array{Float64}(N)\nWBIC_normal = Array{Float64}(N)\nFreeEnergy_normal = Array{Float64}(N)\n\n@time for i in 1:N\n    n = i\n    Y = Sample[1:n]\n    Shannon[i] = 2*n*entropy(dist_true)\n    T_true[i] = -2*@sum(logpdf(dist_true, Y[k]), k, 1:n)\n\n    # mixnormal\n    #\n    model_mixnormal, data_mixnormal, inits_mixnormal = sample2model_mixnormal(Y, chains=chains_mixnormal)\n    sim_mixnormal = mcmc(model_mixnormal, data_mixnormal, inits_mixnormal,\n        iterations_mixnormal, burnin=burnin_mixnormal, thin=thin_mixnormal, chains=chains_mixnormal, verbose=false)\n\n    Chain_mixnormal[i], WAIC_mixnormal[i], T_mixnormal[i], V_mixnormal[i], \n    LOOCV_mixnormal[i], GL_mixnormal[i], WBIC_mixnormal[i], FreeEnergy_mixnormal[i] = \n    InformationCriterions(dist_true, Y, sim_mixnormal, dist_model=mixnormal)\n    \n    # normal1\n    #\n    model_normal1, data_normal1, inits_normal1 = sample2model_normal1(Y, chains=chains_normal1)\n    sim_normal1 = mcmc(model_normal1, data_normal1, inits_normal1,\n        iterations_normal1, burnin=burnin_normal1, thin=thin_normal1, chains=chains_normal1, verbose=false)\n\n    Chain_normal1[i], WAIC_normal1[i], T_normal1[i], V_normal1[i], \n    LOOCV_normal1[i], GL_normal1[i], WBIC_normal1[i], FreeEnergy_normal1[i] = \n    InformationCriterions(dist_true, Y, sim_normal1, dist_model=normal1)\n\n    # normal\n    #\n    model_normal, data_normal, inits_normal = sample2model_normal(Y, chains=chains_normal)\n    sim_normal = mcmc(model_normal, data_normal, inits_normal,\n        iterations_normal, burnin=burnin_normal, thin=thin_normal, chains=chains_normal, verbose=false)\n\n    Chain_normal[i], WAIC_normal[i], T_normal[i], V_normal[i], \n    LOOCV_normal[i], GL_normal[i], WBIC_normal[i], FreeEnergy_normal[i] = \n    InformationCriterions(dist_true, Y, sim_normal, dist_model=normal)\n    \n    print(\"$i \")\n    i == N && println()\nend\n\n#########\n\nvarnames = [\n    \"seed\", \"dist_true\", \"N\", \"Shannon\",\n    \"iterations_mixnormal\", \"burnin_mixnormal\", \"thin_mixnormal\", \"chains_mixnormal\",\n    \"iterations_normal1\",   \"burnin_normal1\",   \"thin_normal1\",   \"chains_normal1\",\n    \"iterations_normal\",    \"burnin_normal\",    \"thin_normal\",    \"chains_normal\",\n    \"Sample\", \"T_true\", \n    \"Chain_mixnormal\", \"WAIC_mixnormal\", \"T_mixnormal\",    \"V_mixnormal\", \n    \"LOOCV_mixnormal\", \"GL_mixnormal\",   \"WBIC_mixnormal\", \"FreeEnergy_mixnormal\", \n    \"Chain_normal1\",   \"WAIC_normal1\",   \"T_normal1\",      \"V_normal1\", \n    \"LOOCV_normal1\",   \"GL_normal1\",     \"WBIC_normal1\",   \"FreeEnergy_normal1\", \n    \"Chain_normal\",    \"WAIC_normal\",    \"T_normal\",       \"V_normal\", \n    \"LOOCV_normal\",    \"GL_normal\",      \"WBIC_normal\",    \"FreeEnergy_normal\", \n]\neval(parse(\"$dataname = Dict()\"))\nfor v in sort(varnames)\n    eval(parse(\"$dataname[\\\"$v\\\"] = $v\"))\nend\n\n@show jld2file = \"$dataname.jld2\"\neval(parse(\"save(jld2file, $dataname)\"))\ndata = load(\"$dataname.jld2\")\n@show keys(data);",
      "execution_count": 13,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 \n1576.604257 seconds (2.55 G allocations: 57.980 GiB, 1.07% gc time)\njld2file = \"$(dataname).jld2\" = \"Gamma128Sample12345.jld2\"\nkeys(data) = String[\"T_normal\", \"Chain_mixnormal\", \"chains_mixnormal\", \"Shannon\", \"burnin_mixnormal\", \"WBIC_normal\", \"thin_normal\", \"Chain_normal1\", \"FreeEnergy_mixnormal\", \"Sample\", \"V_normal1\", \"V_normal\", \"burnin_normal1\", \"iterations_normal\", \"N\", \"T_mixnormal\", \"WAIC_mixnormal\", \"GL_normal1\", \"iterations_normal1\", \"chains_normal1\", \"WBIC_normal1\", \"WBIC_mixnormal\", \"GL_mixnormal\", \"GL_normal\", \"LOOCV_normal\", \"chains_normal\", \"thin_mixnormal\", \"dist_true\", \"Chain_normal\", \"thin_normal1\", \"WAIC_normal1\", \"burnin_normal\", \"iterations_mixnormal\", \"FreeEnergy_normal\", \"V_mixnormal\", \"WAIC_normal\", \"LOOCV_mixnormal\", \"FreeEnergy_normal1\", \"seed\", \"LOOCV_normal1\", \"T_true\", \"T_normal1\"]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# シミュレーション\n\nseed = 2017\ndataname = \"Gamma128Sample$seed\"\nN = 2^7\n\n##########\n\niterations_mixnormal = 500\nburnin_mixnormal = 100\nthin_mixnormal = 1\nchains_mixnormal = 4\n\niterations_normal1 = 500\nburnin_normal1 = 100\nthin_normal1 = 1\nchains_normal1 = 4\n\niterations_normal = 500\nburnin_normal = 100\nthin_normal = 1\nchains_normal = 4\n\nSample = rand(dist_true, N)\nShannon = Array{Float64}(N)\nT_true = Array{Float64}(N)\n\nChain_mixnormal = Array{Array{Float64,2}}(N)\nWAIC_mixnormal = Array{Float64}(N)\nT_mixnormal = Array{Float64}(N)\nV_mixnormal = Array{Float64}(N)\nLOOCV_mixnormal = Array{Float64}(N)\nGL_mixnormal = Array{Float64}(N)\nWBIC_mixnormal = Array{Float64}(N)\nFreeEnergy_mixnormal = Array{Float64}(N)\n\nChain_normal1 = Array{Array{Float64,2}}(N)\nWAIC_normal1 = Array{Float64}(N)\nT_normal1 = Array{Float64}(N)\nV_normal1 = Array{Float64}(N)\nLOOCV_normal1 = Array{Float64}(N)\nGL_normal1 = Array{Float64}(N)\nWBIC_normal1 = Array{Float64}(N)\nFreeEnergy_normal1 = Array{Float64}(N)\n\nChain_normal = Array{Array{Float64,2}}(N)\nWAIC_normal = Array{Float64}(N)\nT_normal = Array{Float64}(N)\nV_normal = Array{Float64}(N)\nLOOCV_normal = Array{Float64}(N)\nGL_normal = Array{Float64}(N)\nWBIC_normal = Array{Float64}(N)\nFreeEnergy_normal = Array{Float64}(N)\n\n@time for i in 1:N\n    n = i\n    Y = Sample[1:n]\n    Shannon[i] = 2*n*entropy(dist_true)\n    T_true[i] = -2*@sum(logpdf(dist_true, Y[k]), k, 1:n)\n\n    # mixnormal\n    #\n    model_mixnormal, data_mixnormal, inits_mixnormal = sample2model_mixnormal(Y, chains=chains_mixnormal)\n    sim_mixnormal = mcmc(model_mixnormal, data_mixnormal, inits_mixnormal,\n        iterations_mixnormal, burnin=burnin_mixnormal, thin=thin_mixnormal, chains=chains_mixnormal, verbose=false)\n\n    Chain_mixnormal[i], WAIC_mixnormal[i], T_mixnormal[i], V_mixnormal[i], \n    LOOCV_mixnormal[i], GL_mixnormal[i], WBIC_mixnormal[i], FreeEnergy_mixnormal[i] = \n    InformationCriterions(dist_true, Y, sim_mixnormal, dist_model=mixnormal)\n    \n    # normal1\n    #\n    model_normal1, data_normal1, inits_normal1 = sample2model_normal1(Y, chains=chains_normal1)\n    sim_normal1 = mcmc(model_normal1, data_normal1, inits_normal1,\n        iterations_normal1, burnin=burnin_normal1, thin=thin_normal1, chains=chains_normal1, verbose=false)\n\n    Chain_normal1[i], WAIC_normal1[i], T_normal1[i], V_normal1[i], \n    LOOCV_normal1[i], GL_normal1[i], WBIC_normal1[i], FreeEnergy_normal1[i] = \n    InformationCriterions(dist_true, Y, sim_normal1, dist_model=normal1)\n\n    # normal\n    #\n    model_normal, data_normal, inits_normal = sample2model_normal(Y, chains=chains_normal)\n    sim_normal = mcmc(model_normal, data_normal, inits_normal,\n        iterations_normal, burnin=burnin_normal, thin=thin_normal, chains=chains_normal, verbose=false)\n\n    Chain_normal[i], WAIC_normal[i], T_normal[i], V_normal[i], \n    LOOCV_normal[i], GL_normal[i], WBIC_normal[i], FreeEnergy_normal[i] = \n    InformationCriterions(dist_true, Y, sim_normal, dist_model=normal)\n    \n    print(\"$i \")\n    i == N && println()\nend\n\n#########\n\nvarnames = [\n    \"seed\", \"dist_true\", \"N\", \"Shannon\",\n    \"iterations_mixnormal\", \"burnin_mixnormal\", \"thin_mixnormal\", \"chains_mixnormal\",\n    \"iterations_normal1\",   \"burnin_normal1\",   \"thin_normal1\",   \"chains_normal1\",\n    \"iterations_normal\",    \"burnin_normal\",    \"thin_normal\",    \"chains_normal\",\n    \"Sample\", \"T_true\", \n    \"Chain_mixnormal\", \"WAIC_mixnormal\", \"T_mixnormal\",    \"V_mixnormal\", \n    \"LOOCV_mixnormal\", \"GL_mixnormal\",   \"WBIC_mixnormal\", \"FreeEnergy_mixnormal\", \n    \"Chain_normal1\",   \"WAIC_normal1\",   \"T_normal1\",      \"V_normal1\", \n    \"LOOCV_normal1\",   \"GL_normal1\",     \"WBIC_normal1\",   \"FreeEnergy_normal1\", \n    \"Chain_normal\",    \"WAIC_normal\",    \"T_normal\",       \"V_normal\", \n    \"LOOCV_normal\",    \"GL_normal\",      \"WBIC_normal\",    \"FreeEnergy_normal\", \n]\neval(parse(\"$dataname = Dict()\"))\nfor v in sort(varnames)\n    eval(parse(\"$dataname[\\\"$v\\\"] = $v\"))\nend\n\n@show jld2file = \"$dataname.jld2\"\neval(parse(\"save(jld2file, $dataname)\"))\ndata = load(\"$dataname.jld2\")\n@show keys(data);",
      "execution_count": 14,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 \n1636.631460 seconds (2.53 G allocations: 57.627 GiB, 1.16% gc time)\njld2file = \"$(dataname).jld2\" = \"Gamma128Sample2017.jld2\"\nkeys(data) = String[\"T_normal\", \"Chain_mixnormal\", \"chains_mixnormal\", \"Shannon\", \"burnin_mixnormal\", \"WBIC_normal\", \"thin_normal\", \"Chain_normal1\", \"FreeEnergy_mixnormal\", \"Sample\", \"V_normal1\", \"V_normal\", \"burnin_normal1\", \"iterations_normal\", \"N\", \"T_mixnormal\", \"WAIC_mixnormal\", \"GL_normal1\", \"iterations_normal1\", \"chains_normal1\", \"WBIC_normal1\", \"WBIC_mixnormal\", \"GL_mixnormal\", \"GL_normal\", \"LOOCV_normal\", \"chains_normal\", \"thin_mixnormal\", \"dist_true\", \"Chain_normal\", \"thin_normal1\", \"WAIC_normal1\", \"burnin_normal\", \"iterations_mixnormal\", \"FreeEnergy_normal\", \"V_mixnormal\", \"WAIC_normal\", \"LOOCV_mixnormal\", \"FreeEnergy_normal1\", \"seed\", \"LOOCV_normal1\", \"T_true\", \"T_normal1\"]\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## データの作成 (分散1のガンマ分布を2つ合わせた混合分布で生成)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# サンプルを生成する真の確率分布の定義\n\ntheta1 = 0.4\ntheta2 = 0.18\nr = 0.7\ndist_true = MixtureModel(Gamma[Gamma(1/theta1^2, theta1), Gamma(1/theta2^2, theta2)], [1.0-r, r])\n\n@show mu_true = mean(dist_true)\n@show sigma_true = std(dist_true)\ndist_normal_fitting = Normal(mu_true, sigma_true)\ndist_normal1_fitting = Normal(mu_true, 1.0)\n\nmu1 = mean(Gamma(1/theta1^2, theta1))\nmu2 = mean(Gamma(1/theta2^2, theta2))\ndist_mixnormal_fitting = MixtureModel(Normal[Normal(mu1,1.0), Normal(mu2,1.0)], [1.0-r, r])\n\nsleep(0.1)\nx = linspace(-1,11,100)\nplt.figure(figsize=(5,3.5))\nplt.plot(x, pdf.(dist_true, x), label=\"true dist\", color=\"black\", lw=1)\nplt.plot(x, pdf.(dist_normal_fitting, x), label=\"normal\", ls=\"--\")\nplt.plot(x, pdf.(dist_normal1_fitting, x), label=\"normal1\", ls=\"-.\")\nplt.plot(x, pdf.(dist_mixnormal_fitting, x), label=\"mixnormal\", ls=(0,(5,2,2,2,2,2)))\nplt.grid(ls=\":\")\nplt.legend()",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "mu_true = mean(dist_true) = 4.638888888888888\nsigma_true = std(dist_true) = 1.7206534073276198\n"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000005751A6D8>)"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "PyObject <matplotlib.legend.Legend object at 0x0000000057059208>"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# シミュレーション\n\nseed = 4649\ndataname = \"MixGamma128Sample$seed\"\nN = 2^7\n\n##########\n\niterations_mixnormal = 500\nburnin_mixnormal = 100\nthin_mixnormal = 1\nchains_mixnormal = 4\n\niterations_normal1 = 500\nburnin_normal1 = 100\nthin_normal1 = 1\nchains_normal1 = 4\n\niterations_normal = 500\nburnin_normal = 100\nthin_normal = 1\nchains_normal = 4\n\nSample = rand(dist_true, N)\nShannon = Array{Float64}(N)\nT_true = Array{Float64}(N)\n\nChain_mixnormal = Array{Array{Float64,2}}(N)\nWAIC_mixnormal = Array{Float64}(N)\nT_mixnormal = Array{Float64}(N)\nV_mixnormal = Array{Float64}(N)\nLOOCV_mixnormal = Array{Float64}(N)\nGL_mixnormal = Array{Float64}(N)\nWBIC_mixnormal = Array{Float64}(N)\nFreeEnergy_mixnormal = Array{Float64}(N)\n\nChain_normal1 = Array{Array{Float64,2}}(N)\nWAIC_normal1 = Array{Float64}(N)\nT_normal1 = Array{Float64}(N)\nV_normal1 = Array{Float64}(N)\nLOOCV_normal1 = Array{Float64}(N)\nGL_normal1 = Array{Float64}(N)\nWBIC_normal1 = Array{Float64}(N)\nFreeEnergy_normal1 = Array{Float64}(N)\n\nChain_normal = Array{Array{Float64,2}}(N)\nWAIC_normal = Array{Float64}(N)\nT_normal = Array{Float64}(N)\nV_normal = Array{Float64}(N)\nLOOCV_normal = Array{Float64}(N)\nGL_normal = Array{Float64}(N)\nWBIC_normal = Array{Float64}(N)\nFreeEnergy_normal = Array{Float64}(N)\n\n@time for i in 1:N\n    n = i\n    Y = Sample[1:n]\n    Shannon[i] = 2*n*ShannonInformationof(x->pdf(dist_true,x), xmin=0.0, xmax=20.0)\n    T_true[i] = -2*@sum(logpdf(dist_true, Y[k]), k, 1:n)\n\n    # mixnormal\n    #\n    model_mixnormal, data_mixnormal, inits_mixnormal = sample2model_mixnormal(Y, chains=chains_mixnormal)\n    sim_mixnormal = mcmc(model_mixnormal, data_mixnormal, inits_mixnormal,\n        iterations_mixnormal, burnin=burnin_mixnormal, thin=thin_mixnormal, chains=chains_mixnormal, verbose=false)\n\n    Chain_mixnormal[i], WAIC_mixnormal[i], T_mixnormal[i], V_mixnormal[i], \n    LOOCV_mixnormal[i], GL_mixnormal[i], WBIC_mixnormal[i], FreeEnergy_mixnormal[i] = \n    InformationCriterions(dist_true, Y, sim_mixnormal, dist_model=mixnormal)\n    \n    # normal1\n    #\n    model_normal1, data_normal1, inits_normal1 = sample2model_normal1(Y, chains=chains_normal1)\n    sim_normal1 = mcmc(model_normal1, data_normal1, inits_normal1,\n        iterations_normal1, burnin=burnin_normal1, thin=thin_normal1, chains=chains_normal1, verbose=false)\n\n    Chain_normal1[i], WAIC_normal1[i], T_normal1[i], V_normal1[i], \n    LOOCV_normal1[i], GL_normal1[i], WBIC_normal1[i], FreeEnergy_normal1[i] = \n    InformationCriterions(dist_true, Y, sim_normal1, dist_model=normal1)\n\n    # normal\n    #\n    model_normal, data_normal, inits_normal = sample2model_normal(Y, chains=chains_normal)\n    sim_normal = mcmc(model_normal, data_normal, inits_normal,\n        iterations_normal, burnin=burnin_normal, thin=thin_normal, chains=chains_normal, verbose=false)\n\n    Chain_normal[i], WAIC_normal[i], T_normal[i], V_normal[i], \n    LOOCV_normal[i], GL_normal[i], WBIC_normal[i], FreeEnergy_normal[i] = \n    InformationCriterions(dist_true, Y, sim_normal, dist_model=normal)\n    \n    print(\"$i \")\n    i == N && println()\nend\n\n#########\n\nvarnames = [\n    \"seed\", \"dist_true\", \"N\", \"Shannon\",\n    \"iterations_mixnormal\", \"burnin_mixnormal\", \"thin_mixnormal\", \"chains_mixnormal\",\n    \"iterations_normal1\",   \"burnin_normal1\",   \"thin_normal1\",   \"chains_normal1\",\n    \"iterations_normal\",    \"burnin_normal\",    \"thin_normal\",    \"chains_normal\",\n    \"Sample\", \"T_true\", \n    \"Chain_mixnormal\", \"WAIC_mixnormal\", \"T_mixnormal\",    \"V_mixnormal\", \n    \"LOOCV_mixnormal\", \"GL_mixnormal\",   \"WBIC_mixnormal\", \"FreeEnergy_mixnormal\", \n    \"Chain_normal1\",   \"WAIC_normal1\",   \"T_normal1\",      \"V_normal1\", \n    \"LOOCV_normal1\",   \"GL_normal1\",     \"WBIC_normal1\",   \"FreeEnergy_normal1\", \n    \"Chain_normal\",    \"WAIC_normal\",    \"T_normal\",       \"V_normal\", \n    \"LOOCV_normal\",    \"GL_normal\",      \"WBIC_normal\",    \"FreeEnergy_normal\", \n]\neval(parse(\"$dataname = Dict()\"))\nfor v in sort(varnames)\n    eval(parse(\"$dataname[\\\"$v\\\"] = $v\"))\nend\n\n@show jld2file = \"$dataname.jld2\"\neval(parse(\"save(jld2file, $dataname)\"))\ndata = load(\"$dataname.jld2\")\n@show keys(data);",
      "execution_count": 25,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 \n848.269957 seconds (2.86 G allocations: 64.576 GiB, 4.25% gc time)\njld2file = \"$(dataname).jld2\" = \"MixGamma128Sample4649.jld2\"\nkeys(data) = String[\"T_normal\", \"Chain_mixnormal\", \"chains_mixnormal\", \"Shannon\", \"burnin_mixnormal\", \"WBIC_normal\", \"thin_normal\", \"Chain_normal1\", \"FreeEnergy_mixnormal\", \"Sample\", \"V_normal1\", \"V_normal\", \"burnin_normal1\", \"iterations_normal\", \"N\", \"T_mixnormal\", \"WAIC_mixnormal\", \"GL_normal1\", \"iterations_normal1\", \"chains_normal1\", \"WBIC_normal1\", \"WBIC_mixnormal\", \"GL_mixnormal\", \"GL_normal\", \"LOOCV_normal\", \"chains_normal\", \"thin_mixnormal\", \"dist_true\", \"Chain_normal\", \"thin_normal1\", \"WAIC_normal1\", \"burnin_normal\", \"iterations_mixnormal\", \"FreeEnergy_normal\", \"V_mixnormal\", \"WAIC_normal\", \"LOOCV_mixnormal\", \"FreeEnergy_normal1\", \"seed\", \"LOOCV_normal1\", \"T_true\", \"T_normal1\"]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# シミュレーション\n\nseed = 12345\ndataname = \"MixGamma128Sample$seed\"\nN = 2^7\n\n##########\n\niterations_mixnormal = 500\nburnin_mixnormal = 100\nthin_mixnormal = 1\nchains_mixnormal = 4\n\niterations_normal1 = 500\nburnin_normal1 = 100\nthin_normal1 = 1\nchains_normal1 = 4\n\niterations_normal = 500\nburnin_normal = 100\nthin_normal = 1\nchains_normal = 4\n\nSample = rand(dist_true, N)\nShannon = Array{Float64}(N)\nT_true = Array{Float64}(N)\n\nChain_mixnormal = Array{Array{Float64,2}}(N)\nWAIC_mixnormal = Array{Float64}(N)\nT_mixnormal = Array{Float64}(N)\nV_mixnormal = Array{Float64}(N)\nLOOCV_mixnormal = Array{Float64}(N)\nGL_mixnormal = Array{Float64}(N)\nWBIC_mixnormal = Array{Float64}(N)\nFreeEnergy_mixnormal = Array{Float64}(N)\n\nChain_normal1 = Array{Array{Float64,2}}(N)\nWAIC_normal1 = Array{Float64}(N)\nT_normal1 = Array{Float64}(N)\nV_normal1 = Array{Float64}(N)\nLOOCV_normal1 = Array{Float64}(N)\nGL_normal1 = Array{Float64}(N)\nWBIC_normal1 = Array{Float64}(N)\nFreeEnergy_normal1 = Array{Float64}(N)\n\nChain_normal = Array{Array{Float64,2}}(N)\nWAIC_normal = Array{Float64}(N)\nT_normal = Array{Float64}(N)\nV_normal = Array{Float64}(N)\nLOOCV_normal = Array{Float64}(N)\nGL_normal = Array{Float64}(N)\nWBIC_normal = Array{Float64}(N)\nFreeEnergy_normal = Array{Float64}(N)\n\n@time for i in 1:N\n    n = i\n    Y = Sample[1:n]\n    Shannon[i] = 2*n*ShannonInformationof(x->pdf(dist_true,x), xmin=0.0, xmax=20.0)\n    T_true[i] = -2*@sum(logpdf(dist_true, Y[k]), k, 1:n)\n\n    # mixnormal\n    #\n    model_mixnormal, data_mixnormal, inits_mixnormal = sample2model_mixnormal(Y, chains=chains_mixnormal)\n    sim_mixnormal = mcmc(model_mixnormal, data_mixnormal, inits_mixnormal,\n        iterations_mixnormal, burnin=burnin_mixnormal, thin=thin_mixnormal, chains=chains_mixnormal, verbose=false)\n\n    Chain_mixnormal[i], WAIC_mixnormal[i], T_mixnormal[i], V_mixnormal[i], \n    LOOCV_mixnormal[i], GL_mixnormal[i], WBIC_mixnormal[i], FreeEnergy_mixnormal[i] = \n    InformationCriterions(dist_true, Y, sim_mixnormal, dist_model=mixnormal)\n    \n    # normal1\n    #\n    model_normal1, data_normal1, inits_normal1 = sample2model_normal1(Y, chains=chains_normal1)\n    sim_normal1 = mcmc(model_normal1, data_normal1, inits_normal1,\n        iterations_normal1, burnin=burnin_normal1, thin=thin_normal1, chains=chains_normal1, verbose=false)\n\n    Chain_normal1[i], WAIC_normal1[i], T_normal1[i], V_normal1[i], \n    LOOCV_normal1[i], GL_normal1[i], WBIC_normal1[i], FreeEnergy_normal1[i] = \n    InformationCriterions(dist_true, Y, sim_normal1, dist_model=normal1)\n\n    # normal\n    #\n    model_normal, data_normal, inits_normal = sample2model_normal(Y, chains=chains_normal)\n    sim_normal = mcmc(model_normal, data_normal, inits_normal,\n        iterations_normal, burnin=burnin_normal, thin=thin_normal, chains=chains_normal, verbose=false)\n\n    Chain_normal[i], WAIC_normal[i], T_normal[i], V_normal[i], \n    LOOCV_normal[i], GL_normal[i], WBIC_normal[i], FreeEnergy_normal[i] = \n    InformationCriterions(dist_true, Y, sim_normal, dist_model=normal)\n    \n    print(\"$i \")\n    i == N && println()\nend\n\n#########\n\nvarnames = [\n    \"seed\", \"dist_true\", \"N\", \"Shannon\",\n    \"iterations_mixnormal\", \"burnin_mixnormal\", \"thin_mixnormal\", \"chains_mixnormal\",\n    \"iterations_normal1\",   \"burnin_normal1\",   \"thin_normal1\",   \"chains_normal1\",\n    \"iterations_normal\",    \"burnin_normal\",    \"thin_normal\",    \"chains_normal\",\n    \"Sample\", \"T_true\", \n    \"Chain_mixnormal\", \"WAIC_mixnormal\", \"T_mixnormal\",    \"V_mixnormal\", \n    \"LOOCV_mixnormal\", \"GL_mixnormal\",   \"WBIC_mixnormal\", \"FreeEnergy_mixnormal\", \n    \"Chain_normal1\",   \"WAIC_normal1\",   \"T_normal1\",      \"V_normal1\", \n    \"LOOCV_normal1\",   \"GL_normal1\",     \"WBIC_normal1\",   \"FreeEnergy_normal1\", \n    \"Chain_normal\",    \"WAIC_normal\",    \"T_normal\",       \"V_normal\", \n    \"LOOCV_normal\",    \"GL_normal\",      \"WBIC_normal\",    \"FreeEnergy_normal\", \n]\neval(parse(\"$dataname = Dict()\"))\nfor v in sort(varnames)\n    eval(parse(\"$dataname[\\\"$v\\\"] = $v\"))\nend\n\n@show jld2file = \"$dataname.jld2\"\neval(parse(\"save(jld2file, $dataname)\"))\ndata = load(\"$dataname.jld2\")\n@show keys(data);",
      "execution_count": 23,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 \n773.950815 seconds (2.85 G allocations: 64.527 GiB, 3.89% gc time)\njld2file = \"$(dataname).jld2\" = \"MixGamma128Sample12345.jld2\"\nkeys(data) = String[\"T_normal\", \"Chain_mixnormal\", \"chains_mixnormal\", \"Shannon\", \"burnin_mixnormal\", \"WBIC_normal\", \"thin_normal\", \"Chain_normal1\", \"FreeEnergy_mixnormal\", \"Sample\", \"V_normal1\", \"V_normal\", \"burnin_normal1\", \"iterations_normal\", \"N\", \"T_mixnormal\", \"WAIC_mixnormal\", \"GL_normal1\", \"iterations_normal1\", \"chains_normal1\", \"WBIC_normal1\", \"WBIC_mixnormal\", \"GL_mixnormal\", \"GL_normal\", \"LOOCV_normal\", \"chains_normal\", \"thin_mixnormal\", \"dist_true\", \"Chain_normal\", \"thin_normal1\", \"WAIC_normal1\", \"burnin_normal\", \"iterations_mixnormal\", \"FreeEnergy_normal\", \"V_mixnormal\", \"WAIC_normal\", \"LOOCV_mixnormal\", \"FreeEnergy_normal1\", \"seed\", \"LOOCV_normal1\", \"T_true\", \"T_normal1\"]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# シミュレーション\n\nseed = 2017\ndataname = \"MixGamma128Sample$seed\"\nN = 2^7\n\n##########\n\niterations_mixnormal = 500\nburnin_mixnormal = 100\nthin_mixnormal = 1\nchains_mixnormal = 4\n\niterations_normal1 = 500\nburnin_normal1 = 100\nthin_normal1 = 1\nchains_normal1 = 4\n\niterations_normal = 500\nburnin_normal = 100\nthin_normal = 1\nchains_normal = 4\n\nSample = rand(dist_true, N)\nShannon = Array{Float64}(N)\nT_true = Array{Float64}(N)\n\nChain_mixnormal = Array{Array{Float64,2}}(N)\nWAIC_mixnormal = Array{Float64}(N)\nT_mixnormal = Array{Float64}(N)\nV_mixnormal = Array{Float64}(N)\nLOOCV_mixnormal = Array{Float64}(N)\nGL_mixnormal = Array{Float64}(N)\nWBIC_mixnormal = Array{Float64}(N)\nFreeEnergy_mixnormal = Array{Float64}(N)\n\nChain_normal1 = Array{Array{Float64,2}}(N)\nWAIC_normal1 = Array{Float64}(N)\nT_normal1 = Array{Float64}(N)\nV_normal1 = Array{Float64}(N)\nLOOCV_normal1 = Array{Float64}(N)\nGL_normal1 = Array{Float64}(N)\nWBIC_normal1 = Array{Float64}(N)\nFreeEnergy_normal1 = Array{Float64}(N)\n\nChain_normal = Array{Array{Float64,2}}(N)\nWAIC_normal = Array{Float64}(N)\nT_normal = Array{Float64}(N)\nV_normal = Array{Float64}(N)\nLOOCV_normal = Array{Float64}(N)\nGL_normal = Array{Float64}(N)\nWBIC_normal = Array{Float64}(N)\nFreeEnergy_normal = Array{Float64}(N)\n\n@time for i in 1:N\n    n = i\n    Y = Sample[1:n]\n    Shannon[i] = 2*n*ShannonInformationof(x->pdf(dist_true,x), xmin=0.0, xmax=20.0)\n    T_true[i] = -2*@sum(logpdf(dist_true, Y[k]), k, 1:n)\n\n    # mixnormal\n    #\n    model_mixnormal, data_mixnormal, inits_mixnormal = sample2model_mixnormal(Y, chains=chains_mixnormal)\n    sim_mixnormal = mcmc(model_mixnormal, data_mixnormal, inits_mixnormal,\n        iterations_mixnormal, burnin=burnin_mixnormal, thin=thin_mixnormal, chains=chains_mixnormal, verbose=false)\n\n    Chain_mixnormal[i], WAIC_mixnormal[i], T_mixnormal[i], V_mixnormal[i], \n    LOOCV_mixnormal[i], GL_mixnormal[i], WBIC_mixnormal[i], FreeEnergy_mixnormal[i] = \n    InformationCriterions(dist_true, Y, sim_mixnormal, dist_model=mixnormal)\n    \n    # normal1\n    #\n    model_normal1, data_normal1, inits_normal1 = sample2model_normal1(Y, chains=chains_normal1)\n    sim_normal1 = mcmc(model_normal1, data_normal1, inits_normal1,\n        iterations_normal1, burnin=burnin_normal1, thin=thin_normal1, chains=chains_normal1, verbose=false)\n\n    Chain_normal1[i], WAIC_normal1[i], T_normal1[i], V_normal1[i], \n    LOOCV_normal1[i], GL_normal1[i], WBIC_normal1[i], FreeEnergy_normal1[i] = \n    InformationCriterions(dist_true, Y, sim_normal1, dist_model=normal1)\n\n    # normal\n    #\n    model_normal, data_normal, inits_normal = sample2model_normal(Y, chains=chains_normal)\n    sim_normal = mcmc(model_normal, data_normal, inits_normal,\n        iterations_normal, burnin=burnin_normal, thin=thin_normal, chains=chains_normal, verbose=false)\n\n    Chain_normal[i], WAIC_normal[i], T_normal[i], V_normal[i], \n    LOOCV_normal[i], GL_normal[i], WBIC_normal[i], FreeEnergy_normal[i] = \n    InformationCriterions(dist_true, Y, sim_normal, dist_model=normal)\n    \n    print(\"$i \")\n    i == N && println()\nend\n\n#########\n\nvarnames = [\n    \"seed\", \"dist_true\", \"N\", \"Shannon\",\n    \"iterations_mixnormal\", \"burnin_mixnormal\", \"thin_mixnormal\", \"chains_mixnormal\",\n    \"iterations_normal1\",   \"burnin_normal1\",   \"thin_normal1\",   \"chains_normal1\",\n    \"iterations_normal\",    \"burnin_normal\",    \"thin_normal\",    \"chains_normal\",\n    \"Sample\", \"T_true\", \n    \"Chain_mixnormal\", \"WAIC_mixnormal\", \"T_mixnormal\",    \"V_mixnormal\", \n    \"LOOCV_mixnormal\", \"GL_mixnormal\",   \"WBIC_mixnormal\", \"FreeEnergy_mixnormal\", \n    \"Chain_normal1\",   \"WAIC_normal1\",   \"T_normal1\",      \"V_normal1\", \n    \"LOOCV_normal1\",   \"GL_normal1\",     \"WBIC_normal1\",   \"FreeEnergy_normal1\", \n    \"Chain_normal\",    \"WAIC_normal\",    \"T_normal\",       \"V_normal\", \n    \"LOOCV_normal\",    \"GL_normal\",      \"WBIC_normal\",    \"FreeEnergy_normal\", \n]\neval(parse(\"$dataname = Dict()\"))\nfor v in sort(varnames)\n    eval(parse(\"$dataname[\\\"$v\\\"] = $v\"))\nend\n\n@show jld2file = \"$dataname.jld2\"\neval(parse(\"save(jld2file, $dataname)\"))\ndata = load(\"$dataname.jld2\")\n@show keys(data);",
      "execution_count": 24,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 \n679.179733 seconds (2.81 G allocations: 63.562 GiB, 4.30% gc time)\njld2file = \"$(dataname).jld2\" = \"MixGamma128Sample2017.jld2\"\nkeys(data) = String[\"T_normal\", \"Chain_mixnormal\", \"chains_mixnormal\", \"Shannon\", \"burnin_mixnormal\", \"WBIC_normal\", \"thin_normal\", \"Chain_normal1\", \"FreeEnergy_mixnormal\", \"Sample\", \"V_normal1\", \"V_normal\", \"burnin_normal1\", \"iterations_normal\", \"N\", \"T_mixnormal\", \"WAIC_mixnormal\", \"GL_normal1\", \"iterations_normal1\", \"chains_normal1\", \"WBIC_normal1\", \"WBIC_mixnormal\", \"GL_mixnormal\", \"GL_normal\", \"LOOCV_normal\", \"chains_normal\", \"thin_mixnormal\", \"dist_true\", \"Chain_normal\", \"thin_normal1\", \"WAIC_normal1\", \"burnin_normal\", \"iterations_mixnormal\", \"FreeEnergy_normal\", \"V_mixnormal\", \"WAIC_normal\", \"LOOCV_mixnormal\", \"FreeEnergy_normal1\", \"seed\", \"LOOCV_normal1\", \"T_true\", \"T_normal1\"]\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### データの読み込み方"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "seed = 4649\n#seed = 12345\n#seed = 2017\n\ndataname = \"Gamma128Sample$seed\"\n#dataname = \"MixGamma128Sample$seed\"\n\ndata = load(\"$dataname.jld2\")\nfor v in sort(collect(keys(data)))\n    ex = parse(\"$v = data[\\\"$v\\\"]\")\n    # println(ex)\n    eval(ex)\nend\n\n@show mu_true = mean(dist_true)\n@show sigma_true = std(dist_true)\n@show dist_normal_fitting = Normal(mu_true, sigma_true)\n\nshannon_true = Shannon[1]/2\ngl_normal_fitting = GeneralizationLossof(x->pdf(dist_true,x), x->pdf(dist_normal_fitting,x))[1]\nkl_normal_fitting = gl_normal_fitting - shannon_true\n\nKL_normal    = GL_normal    - Shannon\nKL_normal1   = GL_normal1   - Shannon\nKL_mixnormal = GL_mixnormal - Shannon\n\nWT_normal    = WAIC_normal    - T_true\nWT_normal1   = WAIC_normal1   - T_true\nWT_mixnormal = WAIC_mixnormal - T_true\n\nkl_normal    = [KL_normal[n]/(2n) for n in 1:N]\nkl_normal1   = [KL_normal1[n]/(2n) for n in 1:N]\nkl_mixnormal = [KL_mixnormal[n]/(2n) for n in 1:N]\n\nwt_normal    = [WT_normal[n]/(2n) for n in 1:N]\nwt_normal1   = [WT_normal1[n]/(2n) for n in 1:N]\nwt_mixnormal = [WT_mixnormal[n]/(2n) for n in 1:N]\n\n@show shannon_true\n@show gl_normal_fitting\n@show kl_normal_fitting\n@show exp(-kl_normal_fitting)\nprintln()\n@show kl_normal\n@show kl_normal1\n@show kl_mixnormal\n;",
      "execution_count": 11,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "mu_true = mean(dist_true) = 2.5\nsigma_true = std(dist_true) = 1.0\ndist_normal_fitting = Normal(mu_true, sigma_true) = Distributions.Normal{Float64}(μ=2.5, σ=1.0)\nshannon_true = 1.3634322389764533\ngl_normal_fitting = 1.4188708216864312\nkl_normal_fitting = 0.055438582709977924\nexp(-kl_normal_fitting) = 0.9460701269495115\n\nkl_normal = [1.31064, 0.943061, 0.750522, 0.656015, 0.467435, 0.325275, 0.280714, 0.183553, 0.14322, 0.109568, 0.133723, 0.114118, 0.134027, 0.13493, 0.165084, 0.129665, 0.0916437, 0.109228, 0.122627, 0.128549, 0.144589, 0.118252, 0.12343, 0.104349, 0.0963167, 0.0944973, 0.10444, 0.0742909, 0.0781926, 0.0830617, 0.0784852, 0.0842484, 0.0801226, 0.0893429, 0.0927317, 0.0942377, 0.100268, 0.108295, 0.081999, 0.079874, 0.0838082, 0.0843932, 0.0866203, 0.0807927, 0.0828459, 0.0811817, 0.0795774, 0.0762512, 0.0768373, 0.0720845, 0.072021, 0.0710833, 0.069372, 0.0677738, 0.0695583, 0.0721299, 0.0692544, 0.0710969, 0.0747382, 0.0721481, 0.07313, 0.0730386, 0.0720626, 0.0750822, 0.0726765, 0.0700707, 0.071154, 0.0701254, 0.0693048, 0.0688835, 0.0698035, 0.0727116, 0.0711797, 0.0741647, 0.0717155, 0.0710162, 0.0686571, 0.0685826, 0.0663693, 0.0664087, 0.0675754, 0.0677752, 0.0660047, 0.0678938, 0.0659807, 0.0710998, 0.0713109, 0.0702581, 0.0713986, 0.070322, 0.0716038, 0.0703248, 0.0687681, 0.0724746, 0.070599, 0.0704589, 0.0687648, 0.0675405, 0.0690222, 0.0677945, 0.069507, 0.0676838, 0.0727986, 0.0720131, 0.0738819, 0.0729581, 0.0730847, 0.0709425, 0.0707688, 0.0697003, 0.0694465, 0.0677609, 0.0663643, 0.0741785, 0.0723012, 0.0716901, 0.0712664, 0.0707643, 0.0698307, 0.0691062, 0.0689839, 0.0694474, 0.0698461, 0.0693909, 0.0711229, 0.0698827, 0.0690576, 0.0686411]\nkl_normal1 = [1.47104, 1.04922, 0.856708, 0.646608, 0.505585, 0.350638, 0.301355, 0.159513, 0.151451, 0.125463, 0.151449, 0.134613, 0.15219, 0.152594, 0.174633, 0.141745, 0.109988, 0.127805, 0.134692, 0.141619, 0.160252, 0.134758, 0.134227, 0.111601, 0.102443, 0.101496, 0.108661, 0.0845866, 0.0882789, 0.0921233, 0.0870597, 0.0909659, 0.089052, 0.092995, 0.0945692, 0.0912503, 0.0920543, 0.0970527, 0.0715839, 0.0695211, 0.0740948, 0.0805521, 0.0693679, 0.0694012, 0.0701721, 0.0724424, 0.0742791, 0.0728815, 0.0720971, 0.0718206, 0.0705532, 0.0632158, 0.0634734, 0.0624744, 0.0668962, 0.0671967, 0.0662609, 0.0693644, 0.0721963, 0.0699896, 0.0741157, 0.072758, 0.0711214, 0.072918, 0.068315, 0.0677923, 0.064398, 0.0656126, 0.0673253, 0.0626848, 0.0641893, 0.0644017, 0.0622744, 0.0645114, 0.0631903, 0.0618102, 0.0608189, 0.059143, 0.0598792, 0.0592656, 0.0609539, 0.0608831, 0.0623464, 0.0630496, 0.0619223, 0.0587734, 0.0587298, 0.0595798, 0.0601699, 0.0613004, 0.0624615, 0.0627157, 0.0628518, 0.0597006, 0.0599679, 0.0591537, 0.0592126, 0.0590257, 0.0599146, 0.060057, 0.0609821, 0.0601873, 0.0576759, 0.0579047, 0.0568314, 0.0575244, 0.0569019, 0.0564451, 0.0561743, 0.0562749, 0.0561539, 0.0562237, 0.0561309, 0.055473, 0.0554871, 0.0554596, 0.0554911, 0.0554578, 0.055464, 0.055468, 0.0554557, 0.0554974, 0.0554629, 0.0555053, 0.0554721, 0.0554648, 0.0554687, 0.0555168]\nkl_mixnormal = [1.51714, 1.23032, 0.956264, 0.764635, 0.611714, 0.461523, 0.386516, 0.242406, 0.227114, 0.192356, 0.210525, 0.189439, 0.204289, 0.198312, 0.229041, 0.184636, 0.146015, 0.165102, 0.170953, 0.17621, 0.189366, 0.167973, 0.164655, 0.140756, 0.132038, 0.125436, 0.132522, 0.110978, 0.112889, 0.114524, 0.106492, 0.112621, 0.106069, 0.11177, 0.113272, 0.11073, 0.111814, 0.114561, 0.0845257, 0.0841041, 0.0865583, 0.094262, 0.0841411, 0.0810539, 0.083699, 0.0863985, 0.0887248, 0.0836628, 0.0846884, 0.0831397, 0.081651, 0.0718591, 0.0712221, 0.0727205, 0.0739206, 0.0764266, 0.074292, 0.0750328, 0.0811272, 0.0781057, 0.0813954, 0.0800016, 0.080839, 0.0804213, 0.0752033, 0.0760047, 0.0724008, 0.07307, 0.0724738, 0.0677019, 0.0692192, 0.0692594, 0.0665389, 0.0674687, 0.0682824, 0.0668629, 0.0660012, 0.0649428, 0.0652945, 0.0644028, 0.0655677, 0.0657117, 0.0666502, 0.0680592, 0.0677797, 0.0608641, 0.0609913, 0.0623759, 0.0632366, 0.0641064, 0.0632388, 0.0634444, 0.0637755, 0.0597194, 0.0590873, 0.0577823, 0.05877, 0.0578065, 0.0596795, 0.0596639, 0.0607269, 0.0602364, 0.0547192, 0.0559958, 0.0561995, 0.0557398, 0.0549923, 0.0552078, 0.0553165, 0.0546666, 0.0547095, 0.055189, 0.0535805, 0.0541584, 0.053423, 0.0520137, 0.0519102, 0.0518535, 0.05307, 0.0520342, 0.0513495, 0.0521865, 0.0526227, 0.052231, 0.0531176, 0.0521187, 0.0523198, 0.052091]\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### プロットのための函数"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "function plotsim(t; modelname=\"normal\", xmin=-3, xmax=8, y1max=0.45, y2max=0.40)\n    plt.clf()\n    \n    local n = t\n    local Y = Sample[1:n]\n    local Chain = eval(Symbol(:Chain_, modelname))\n    local chain = Chain[n]\n    local dist_model = eval(Symbol(modelname))\n    local lpdf(w,x) = logpdf(dist_model(w),x)\n    local pred = makefunc_pdfpred(lpdf, chain)\n    local x = linspace(xmin,xmax,201)\n    local kl = eval(Symbol(:kl_, modelname))\n    local wt = eval(Symbol(:wt_, modelname))\n    \n    plt.subplot(121)\n    plt.plot(x, pdf.(dist_true,x), color=\"black\", ls=\":\", label=\"true dist\")\n    plt.scatter(Y, pdf.(dist_true,Y), color=\"red\", s=10, alpha=0.5, label=\"sample\") \n    plt.plot(x, pred.(x), label=\"predictive\")\n    plt.ylim(-y1max/40, y1max)\n    plt.grid(ls=\":\")\n    plt.legend()\n    plt.title(\"$modelname: n = $n\")\n    \n    plt.subplot(122)\n    plt.plot(1:n, kl[1:n], label=\"KL information\")\n    plt.plot(1:n, wt[1:n], label=\"(WAIC \\$-\\$ T\\$_\\\\mathrm{true}\\$)/2n\")\n    plt.xlabel(\"sample size n\")\n    plt.ylabel(\"\\$-\\$ log(probability)\")\n    plt.xlim(-1, N+1)\n    plt.ylim(min(minimum(kl), minimum(wt[5:end]))-y2max/40, y2max)\n    plt.grid(ls=\":\")\n    plt.legend()\n    plt.title(\"$modelname: n = $n\")\n    \n    plt.tight_layout()\n    plt.plot()\nend",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/plain": "plotsim (generic function with 1 method)"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(10,3.5))\nplotsim(N, modelname=\"normal\")",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x00000000574AB630>)"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "0-element Array{Any,1}"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(10,3.5))\nplotsim(N, modelname=\"normal1\")",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000005661BB70>)"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "0-element Array{Any,1}"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(10,3.5))\nplotsim(N, modelname=\"mixnormal\")",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "image/png": 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WZRmTyaS6ZVlGp9MV6jaZTCiKorotaS/MbcsmWZbZvn079913H25uboXaZDAY0Ol0SJKkup3VpuI+p/w2ZWdns3v3bu6//36ACmFTSZ9TXl4eu3fvpn379mg0mgphU2meE1BoveCqNpX0OSmKUmi94Ko2leY5WeqFBx54QL1nediUnp5OWFhYidp8l/gFZple5uvrW6E63bIsExkZSdWqVV1iOmJ5IrSxjdClcIQ2thG62MZeurjKdOgaNWqg1Wq5cuWKlf+VK1cIDg4uED4jI4N9+/Zx8OBBRowYAZg1tPwI2bBhAx07diQ4OLjYcVpwd3fH3d29gH+1atXw9vamXbt2VK9evUyfU43q1dDcMGHSeVC9evUyi9ceyLJMu3bt8Pf3r9Rl2JIXqlWrVml1sNRblT0vWMpEWdcLroSoF8xY6oXy/o2j1WqBkrX5lfcpOQEajYbAwMBKXVgKQ2hjG6FL4QhtbCN0sU1l18XNzY1WrVqxadMm1U+WZTZt2kT79u0LhPf19eXIkSMcOnRI/bzwwguEh4dz6NAhIiMjAWjfvr1VnAAbN260GWdxsNdz8vXQA5CebSzTeO1BZc+rFoQOQgMLQgehgQVX0sH5U1iBMRgM/PrrrxgMBkcnxekQ2thG6FI4QhvbCF1sI3SBMWPG8MUXX7B48WISEhJ48cUXyczMZPDgwYB52ndMTAxg/mHTpEkTq09gYCAeHh40adKEKlWqADBq1CjWr1/P9OnTOX78OJMmTWLfvn3q6HhJsddz8vEwT/RLz3b+5y/yqhmhg9DAgtBBaGDBlXRwienlFRWtVkubNm3UKQqC/yG0sY3QpXCENrYRuthG6GI+3uuff/7h7bffJjk5mRYtWrB+/Xp1I7TLly9z/vz5EsV533338fXXX/Pmm2/yxhtv0KBBA1auXEmTJk1KlUZ7PSdfz39HunOc/4eayKtmhA5CAwtCB6GBBVfSwSXO6U5PT8fPz4+0tLQKtaZbIBBUXEwmk0u8eRUUD71eX2SjLtqpsqM8tJzz+yk+3nCSvq1D+eDJZna5h0AgKBrRTgqcFXu0+WKk24EYDAY2bNhA165d0ev1jk6OUyG0sY3QpXCcRRtFUUhOTiY1NdVhaciPoijk5OTg4eHhMpt8lQel0aVq1aoEBwcLHcsRe5Vry0h3Rq7z/+B3lrrN0QgdKo4Gd9pOinZNaGDBnjqUdZsvOt0ORKfT8eCDD4pjfGwgtLGN0KVwnEUbyw+JwMBAvLy8HN4YKoqCLMtoNBqHp8WZKIkuiqKQlZXF1atXAahZs2Z5JFGA/cq1K22k5ix1m6MROlQcDe60nRTtmtDAgj10sFeb79ql1sWRJElMQywEoY1tVF327oXERPDyAjc385eNGkFIiGMT6ECcIc+YTCb1h4SzH0MkKBmenp4AXL16lcDAQJdYP1YRsFe5tmykluYCG6k5Q93mDAgdKoYGop0UuAL2aPPF7uUOxGAwsGrVKrGexQZCG9uouvTrB889B336QN++MGgQdO0K48dDUpKjk+kQnCHPWO7t5eXlsDTciizLpKamIsuyo5PiVJRGF8tzFfVS+WGvcu3lZu50ZxtMZRqvPXCGus0ZEDpUDA3Kop0U7ZrQwII9dSjrNl90uh2ITqeja9euLj9NyB4IbWyjmzaNri+9hC4xETIyeDYnh26pqWy6cgUSEuCjj6BLF9i40dFJLXecKc8401Qvy8iIM6XJGSiNLkLD8sde5dpdb/75k2t0/k63M9VtjkToULE0uJP6VLRrQgML9tShrOMUnW4HUxEqTnshtLmFOXNg1ix0KSmq1wlgAyBbDiGQZTh5El591TwFvZIh8oxAUPGwR7l21/3b6Ta4xiiRqNvMCB2EBgKBqyI63Q7EaDSydu1ajEbn38ilvBHa3MLs2ZyePJlHMjL4Zv58jO7uALwDzASiLeEkiSuyTM7x4/DWW5VqxFvkGdsoikJ6ejoucDqkTcLCwpg1a5b6vyRJrFy58o7jdXVdKgv2KtceevP6vBwXmF4u6jYzQgehgYWKVH/HxsbSu3fvEl2TnJxMly5d8PHxoVq1anZKWdlz9uxZJEni0KFDZRanK+UF0el2IDqdjh49eoi3ljYQ2uTjnXdQJk1iwPXrrDMaWf3ss+iyswHoCIwGLAeHnFAU2gEDZBlTVhZ89lmlGfEWecY2xZ16FR0dzejRo8spVaXn8uXLPPzww8UKW1QHXUzNcw3sVa7VkW6j8490i7rNjNBBaGDBUfW3rQ7yihUr8PDwYPr06YWGKYpPPvmERYsWlSgdM2fOJDk5mQMHDnDixIkSXVte2NIhNDSUy5cv06RJkzK7jyu15aLT7WAq+9vKohDaABMmwAcfIKWksAToJEl8XK0aaDTmXcsbN4aHHjK7JYlk4BJwGPgnKQl27qxUI94iz9gXRVEcrnFwcDDu/870EFQO7JHn3HXmke5co+wSIySOLnfOgtBBaOBMLFiwgAEDBjBv3jxeffXVUsXh5+dH1apVS3TNmTNnaNWqFQ0aNCAwMLBU983LyyvVdXeCVqslODi40r40Ep1uB2I0GtmwYYOoQG0gtME8Qv3VV2A0grs74RoN67y8+HPBAowhIfDKK7B+PWzZAh9/DC1bElWjBqtr1GCHvz/B165BdjYcOQLvvVfhdzUXecY2xZl6FRsby5YtW/jkk0+QJAlJkjh79izx8fFIksS6deto1aoV7u7ubN++3eYb7NGjRxMdHa3+L8sy06ZNo169enh6etK8eXNWrFhRZFqvXr3Ko48+iqenJ/Xq1WPZsmUFwuQfvc7Ly2PEiBHUrFkTDw8P6taty7Rp0wDztHSAxx57DEmS1P9LoovA8dirXFs2UgPIMzn3aLeo28wIHYQGFpyh/v7www8ZOXIk3377LYMHDy51PLe2p9HR0bz88suMGzcOf39/goODmTRpkvp9WFgYP/zwA0uWLEGr1RIbGwvA+fPn6dWrF97e3vj6+vLUU09x5coV9bpJkybRokULFixYQL169fDw8FDvN3LkSEaPHk21atUICgriiy++IDMzk8GDB+Pj48Pdd9/NunXr1LhMJhNDhgxR2/fw8HA++eQTq3stXryYVatWqb8p4uPjbU4v37JlC23btsXd3Z2aNWsyfvx4q/x9Oz2cIS8Ul8r5qsFJ0Ov19OrVy9HJcEqENsCqVZz95x9kWaY+gFaLPiuLXkOGwMSJMHLk/8KOHAmPPQZr1tDt22/NHXaNBhQFUlLgjz/MHfgJExxljd1x9jyTmZkJmI+gsEyDysvLw2AwoNPprEZvLWE9PT3RaMydA4PBQF5eHlqtVm0siwqr15sXHWg0mtu+Rf/kk084efIkTZo0YcqUKQAEBARw9uxZAMaPH8/HH39M/fr1i71+bNq0aSxdupT58+fToEEDtm7dyjPPPENAQABRUVE2r4mNjeXSpUts3rwZvV7Pyy+/zNWrVwu9x6effsrq1av57rvvqFOnDhcuXODChQsA7N27l8DAQOLi4ujevXuBMzaLo4vA8dirXFuml4N5tNsy8u2MOHvdVl4IHSquBoqilPj4Pjcvb3KMMnBnL8089doST01+/fXX+eyzz1izZg2dOnW6o/vbYvHixYwZM4bdu3eza9cuYmNjuf/+++nSpQt79+4lJiYGX19fPvnkEzw9PZFlWe1wb9myBaPRyPDhw+nbty/x8fFqvKdPn+aHH37gxx9/tGoTFy9ezLhx49izZw/Lly/nxRdf5KeffuKxxx7jjTfeYObMmQwcOJDz58/j5eWFLMuEhITw/fffU716dXbu3MmwYcOoWbMmTz31FGPHjiUhIYH09HTi4uIA8Pf359KlS1Z2Xrx4kR49ehAbG8uSJUs4fvw4Q4cOxcPDw6pjXZQertSWi063A1EUhYyMDHx8fFxiLUJ5Uum1SUqCdet43WBgjaKwVJJ4zGRC8fQkY9w4fEaMoIAqISHwwgvg42M+Piw1lV8MBlYrCvMB6csvYeBAc7gKiLPnGW9vb8A8mhsQEADARx99xJtvvslzzz3HF198oYYNDAwkKyuLxMREdYR27ty5vPLKK/Tv399qBDgsLIxr165x9OhRGjduDMCiRYsYOnQoYNZFlmU0Gk2huvj5+eHm5oaXlxfBwcEFvp8yZQpdunQptq25ubm89957/Pbbb7Rv3x6A+vXrs337dj7//HObne6TJ0+ybt069uzZQ5s2bQBYuHAhDRs2LPQ+58+fp0GDBjzwwANIkkTdunXV7ywaV61a1aZNxdFF4HjsVa7dtBokyfxeMsdgwtdDf/uLHISz123lhdCh4mqQbTDR6O1fHXLvY1O64eVW/O7QunXrWLVqFZs2baJjx452SVOzZs2YOHEiAA0aNGDOnDls2rSJLl26EBAQgLu7O56engQEBKDRaPjtt984cuQIiYmJhIaGArBkyRIaN27M3r171TY1Ly+PJUuWqO2jhebNm/Pmm28CMGHCBN5//31q1Kih/o54++23mTdvHocPH6Zdu3bo9XomT56sXl+vXj127drFd999x1NPPYW3tzeenp7k5ubabH8tfPbZZ4SGhjJnzhwkSSIiIoJLly7x+uuv8/bbb6sDCUXp4UptuZhe7kCMRiPbtm2r9NOEbFHptfnqK3JPnOACkAXcrSig0WDs3ZttjRsXrUtUFAQFcTEvj8cVhf8D1ms0cOmSeSp6BaXS55lCsPxIu5OpV61bty5R+NOnT5OVlUWXLl3w9vZWP0uWLOHMmTM2r0lISECn09GqVSvVLyIiosg32LGxsRw6dIjw8HBefvllNmzYUOw0loUuAvtjr3ItSZLLHBsm6jYzQgehgTPQrFkzwsLCmDhxIjdv3rTbPfJTs2bNArO+8rdhCQkJhIaGqh1ugEaNGlG1alUSEhJUv7p16xbocN96P61WS/Xq1WnatKnqFxQUBGCVhrlz59KqVSsCAgLw9vbm//7v/zh//nyJ7ExISKB9+/ZWneX777+fmzdvkpRvSWRRerhSWy5Guh2IXq/nkUcecXQynJJKrU1SEmzZgjuwo0YNDuXk0FSWoUoV9IMG8UjXrkVfHxIC0dHU/usvXlMUjFot93l5mdeGp6eXiwmOwNnzjKVx9vLyUv1ee+01Ro8eXWBTEUtj4unpqfoNHz6coUOHFpgmbZkCnj+sZY0XlM006ipVqlj9r9FoCjRwBoNBdVts/eWXX6hdu7ZVuLLcBO3ee+8lMTGRdevW8dtvv/HUU0/RuXPn264dBzG93FWwZ7l212nJMchOv4O5s9dt5YXQoeJq4KnXcmxKN4fduyTUrl2bFStW0KFDB7p37866devw8fEp0zRZlodZkCQJWZYL+JW0Dbu1LS/qfvn9LJ1iSxq+/fZbxo4dy/Tp02nfvj0+Pj589NFH7N69u0TpKS5F6eFKbbnodDsQWZZJTU2latWq6hQKgZlKrc3162AwQNWqSDdv0tLNDXJy4K67kBs2JPXGjdvrMnAgfP89U9PTzTubm0zg62v+JCVVyCnmzp5nbDV2bm5uuLm5FSusXq8v0PAUFdaCoiiYTCa02qLXrbm5uWEyFW9NXUBAAEePHrXyO3TokHrfRo0a4e7uzvnz5wtdv30rERERGI1G9u/fr06FO3HiBKmpqUVe5+vrS9++fenbty9PPvkk3bt358aNG/j7+6PX6wu1qbi6CByLPcv1/44Nc+6zup29bisvhA4VVwNJkko0xdvR9XfdunXZsmWL2vFev359mXe8i4PRaESr1dKwYUN1TxPLaPexY8dITU2lUaNGZX7fHTt2cN999/HSSy+pfrfOYivOb4qGDRvyww8/oCiK+hx37NiBj48PIcX8nerovFASKk6JdUFMJhN79+4t9g/dykSl1uavvzh64oT5nG1ZNn8CA2HECEzBwcXTpU0bGDUKgoLMne4qVSAgABYtgtdeq5BHiFXqPFMEiqKQmZl526lXYWFh7N69m7Nnz3Lt2rUCb9Xz07FjR/bt28eSJUs4deoUEydOtOqE+/j4MHbsWF555RUkTqGKAAAgAElEQVQWL17MmTNnOHDgALNnz2bx4sU24wwPD6d79+48//zz7N69m/379/Pcc89ZjeDfyowZM/jmm284fvw4J0+e5Pvvvyc4OFh96x0WFsamTZtITk4mJSWlVLoIHIs9y7VlB3NnH+kWdZsZoYPQwIIz1N+hoaHEx8dz9epVunXrRnq+mYRpaWkcOnTI6mPZ5LOsyK9B586dadq0KQMGDODAgQPs2bOHmJgYoqKiSrw8rDg0aNCAffv28euvv3Ly5Eneeust9u7daxUmLCyMw4cPc+LECa5du2Y1G87CSy+9xIULFxg5ciTHjx9n1apVTJw4kTFjxhT7pZIz5IXiUqpO99y5cwkLC8PDw4PIyEj27NlTrOt27NiBTqejRYsWpblthUOv19OtWzebo1eVnUqrTVISmV9+yX2XLhGWmso5gwF0OhgzBvr3L5ku48fD8uUwaRIngoKIOXuWeX/+CTt2wLvvVrgjxCptnrkNGo0GPz+/2zZgY8eORavV0qhRIwICAopcm9WtWzfeeustxo0bR5s2bcjIyCAmJsYqzDvvvMNbb73FtGnTaNiwId27d+eXX36hXr16hcYbFxdHrVq1iIqK4vHHH2fYsGFFnkHq4+PDhx9+SOvWrWnTpg1nz55l7dq1qq3Tp09n48aNhIaG0rJly1LpInAs9izXHv/uWJ5Twl2TyxtRt5kROggNLDhL/R0SEkJ8fDzXrl2z6njHx8fTsmVLq0/+jcfKAkmSVA0kSWLVqlVUq1aNhx56iM6dO1O/fn2WL19epve08Pzzz/P444/Tt29fIiMjuX79utWoN8DQoUMJDw+ndevWBAQEsGPHjgLx1K5dm7Vr17Jnzx6aN2/OCy+8wJAhQ9RN3YqDs+SF4iApJXw1sHz5cmJiYpg/fz6RkZHMmjWL77//nhMnThT54yg1NZVWrVpx9913c+XKFasz2m5Heno6fn5+pKWl4evrW5LkOjWyLHPt2jVq1KjhEpmlPKm02mzYwJ7+/emRkkI1nY4Tdeuiyc6GhQuha9fS6bJhA/P69eOlGzcI1Wj428PDvK7kzTcr1BFizpBncnJySExMtDoD09EoioLRaESn0zn91KvypDS6FPV8K2o75Qjya+nt7W23cv2f2ds4ejGduMFt6BBe+O8XR+MMdZszIHSoGBqURTsp2jWhgQV76lDWbX6JS+yMGTMYOnQogwcPplGjRsyfPx8vLy++/PLLIq974YUX6N+/v3p8jMBceR49erTIaZyVlUqrzbff0jYtjYvAz4AmPd080v0vpdVlsKcnsTodP0gSurw88xrxH36oUKPdlTbPFIPs7GxHJ8EpEbo4P/Ys15azuZ1993JRt5kROggN8iPqb6GBBVfRoUSd7ry8PPbv30/nzp3/F4FGQ+fOndm1a1eh18XFxfH333+rZ6wJzOh0Ojp27Fhg52JBJdVm716Ijwe9Hne9nghZNm+qFhwM/26EUSpdGjXCIyiIOKORNiaTeRdzWYaTJ+HYMfvY4gAqZZ4pBpIk4evrW6nfhNtC6OIa2LNcu8pGaqJuMyN0EBpYEPW30MCCK+lQok73tWvXMJlM6nltFoKCgkhOTrZ5zalTpxg/fjxLly4tdiWRm5tLenq61QdQN44wmUw23Uaj0cpteRNYmNtgMFi5LTPtLW5FUQq4ASu3LMtWbsvZiYW5TSaT6jYYDFy4cAFZliuMTYXZUVKb8vLySEpKUtNSEWy67XNKTCQdUOrXR/H1xVC9OopOh9KpE4Z/y5zJZOL8+fPIslx8m4KDITISk06H0c0NNBpMbm6YjEY4frzC5D1nK0+W6251W+IoL7eiKOTm5qr+siwX6bakN7/b2Wy61V0amxRFIS8vr9Q2FVZHCMoWWZa5ePGinUa6XWMjNXtq4EoIHYQGFiz1tytsnmUvhAZmXEkHuy4IMZlM9O/fn8mTJ3PPPfcU+7pp06bh5+enfizb31t2x01ISFAPez98+DCnTp0C4ODBgyQmJgKwZ88edafAnTt3cvnyZQC2bt3KtWvXAPj999/V42g2bNhARkYGAGvXriUnJwej0cjatWsxGo3k5OSwdu1aADIyMtiwYQNgXqv++++/A+aXElu3bgXg8uXL7Ny5E4ALFy6om80lJiZy8OBBwPxCwjJNqCLZdPjw4Tt+Ttu2bePEiRPIslxhbLrdc1LCwojIyeH+s2c5HhLC2s8/xxgWRs5//qPalJ6ezsGDB9VjQ4ptU9OmJD76KFteeIFpisLTISEcHjIETp+uUHnP0eXJkl5Zlq1eFlquMxqNqttgMKjnWefl5ZGZmQmYXzpmZWUB5vVElmlTOTk55OTkAOapVBZ3VlYWubm5AGRmZpKXlweYz8q2vATIzs5W3RkZGWoHMT09Xf3xlp6ernYqb3U7o00ZGRlqp7e0NuXk5JTIJoPBoKbFVt4TlD2yLHPmzBm7dDI89Jbp5c79wsSeGrgSQgehQX4sbURlRmhgxlV0KNFGanl5eXh5ebFixQp69+6t+g8aNIjU1FRWrVplFT41NZVq1aqh1f7v4HnLCINWq2XDhg107NixwH1yc3OtBExPTyc0NJQbN25QrVo19ceVVqu1chuNRiRJUt0ajQaNRlOo22AwoNVqVbdlEb7FDaiL8y1uvV6vLtrX6/XqqJrFLcsyOp2uULfJZEJRFNVtyw5hU+W06eTWrURER+MpSVwNDsa9Rg10/fvD66/fuU0HD2J6/HFO/fMPjXJzUYAET08iWrfGtHQp1K4tnlMZ2HTz5k2SkpIICwvD3d0djUajdvQsbjBPh3KUW5ZlJEkq1A3mN8f53bbsqIw2ZWdnk5iYSP369dW8a8l76enp+Pv7i43UyoDy2pRu9LcHWXnoEm8+0pDnHqxvt/sIBIL/4YwbjgoEtijrjdRKtCjEzc2NVq1asWnTJrXTLcsymzZtYsSIEQXC+/r6cuTIESu/zz77jN9//50VK1YUenSMu7s77u7uBfwtnff8nfj87vzT14vjzn/kQknckiSpbsuP8+K686dXkiT1IPuKYlNx3MWxQ6PRqNpUFJuKfE4bNxL+5ZckPfAAh27exPuRR6BXL/N52/nCK4rCpUuXCA0NLZlNbdqgffxxIhYs4FVJoqmiEJaXB/v3o50+HT75pOxtKqa7opYnSxotHUCLO396y8NtmXrl5uYGYLXjbWFuW/HcaocjbbrVXRqbbtWluDZZ/r81j+X/X1B2yLKsluuy3q1Z3UjNBaaX20sDV0LoIDSwkL/+doW1vPZAaGDGlXQocYkdM2YMX3zxBYsXLyYhIYEXX3yRzMxMBg8eDMCECRPU81o1Gg1NmjSx+gQGBuLh4UGTJk2oUqVK2VrjYoi1OYVTqbRJSjKfm71zJ7WTk3kkMxP27YOaNQsEvSNdXnsNWrXiI52OGJ0OD0ky72K+dKl5EzcXp1LlmRJimZItsEboAnPnziUsLAwPDw8iIyPVpRu22L59O/fffz/Vq1fH09OTiIgIZs6caRVm0aJF6ssJy+dORrPsuqZb/++abheYXi7qNqEDCA3yI+pvoYEFV9GhxNsf9u3bl3/++Ye3336b5ORkWrRowfr169XN1S5fvsz58+fLPKEVEZ1Ox3333efoZDgllUqbr74yd3olCTw9wccHTpww7yweEmIV9I50CQmBFi1g505QFPMO5gA3bpg7/StX3qEhjqVS5ZkSIEkS3t7ejk6G0yF0geXLlzNmzBjmz59PZGQks2bNolu3bpw4cYLAwILnVlepUoURI0bQrFkzqlSpwvbt23n++eepUqUKw4YNU8P5+vpy4sQJ9f87GX2wZ7l2lY3URN1mRuggNLAg6m+hgQVX0qFUc1NGjBjBuXPnyM3NZffu3URGRqrfLVq0iPj4+EKvnTRpEocOHSrNbSscJpOJ06dPi11vbVBptElKgh9+4LPsbJ7LzmZXejpcvWo+1ssGd6xLZCTodOTKMt8Dr1v8t2xx+dHuSpNnSoiiKOTk5LjEzp7lidAFZsyYwdChQxk8eDCNGjVi/vz5eHl58eWXX9oM37JlS/r160fjxo0JCwvjmWeeoVu3bmzbts0qnCRJBAcHq59bTzwpCfYs15aN1HKcfKRb1G1mhA5CAwui/hYaWHAlHSrvghAnQFEUUlJSXCKjlDeVRptjx+DKFZZKEgtlmSMmk3nKd1CQejZ3fu5Yl6goCAzkKtAX+BA4A5CVZZ7S7sJUmjxTCpztB1psbKzVZpzR0dGMHj36juIsTRzOpkt5kpeXx/79++ncubPqp9Fo6Ny5M7t27SpWHAcPHmTnzp1ERUVZ+d+8eZO6desSGhpKr169+Ouvv0qdTnuWa1cZ6RZ1mxmhg9AgP5W5/rYgNDDjKjqITrcD0el0tGnTptjnl1cmKpU2Oh2TAwMZ5eFBD3d3cHeHJ54oMLXcHPQOdQkJgf79CdVoiAXeAKoA5OVBETNUXIFKlWdKgCRJVKlSxak3GPnxxx955513ihU2Pj4eSZLUo9xKEwe4hi725Nq1a5hMpgKj0EFBQSQnJxd5bUhICO7u7rRu3Zrhw4fz3HPPqd+Fh4fz5ZdfsmrVKpYuXYosy9x3330kJSUVGl9ubi7p6elWHzD/kNLpdNx7773qc8p/JrrRaLRyW9a5FuY2GAxWbrd/O93ZeUZ193qDwWDlBqzcsixbuS3H1hXmLuw898LctmzS6XS0bNlS3TirKJssnbFb7XA2m0rynCw2KYpC69at0Wq1Fcamkj4nRVFo06aNetKGq9qU/5la4rjVvyi3JEl4eXlhoSTXFuW2aFyY25Lea9euERgYyN9//23TjtLYZC8NimtTfrejbLrV/fTTT/Pxxx8XaZOiKHh5eaknltjLpsLKU0kQnW4HYjKZOH78uMu8oSlPKo02jRpBRARdfHyYVbs2ITVqQNu2MHCgzeBlosuIEVC/Pl8C7wLBYF5PvnmzS08xrzR5poQoikJ2dnaZj4xYzqsuC/z9/fHx8SnXOOylS2Vg27Zt7Nu3j/nz5zNr1iy++eYb9bv27dsTExNDixYtiIqK4scffyQgIIDPP/+80PimTZuGn5+f+gkNDQXg6NGjmEwmduzYoY6WHz58mFOnTgHmkfbExEQA9uzZw4ULFwDYuXMnly9fBmDr1q1cu3YNgN9//119WbNhwwYwmjsB5y5cJCcnB6PRyNq1azEajeTk5LB27VrAfA78hg0bAPNRqL///jtgfnGxdetWwLyfzc6dOwG4cOGCuiHdree5Hz58GICEhAQSEhKKZZPJZGLTpk1cvHjxtjZZzpdfu3atU9tUkueU36ajR4+Sm5tboWwq6XM6fvw4V65ccVmbMjMzycnJAczttuU6o9Goug0GAzdv3gTMbU1mZiZgfkGXlZWFoijcvHmTrKwswHy0kyXO7Oxs1Z2VlaUeQZyZmam2Wzdv3lRfAmRkZKidqYyMDPU3RHp6utopS09PVztg6enpvPvuu/Ts2RN/f3/mz5+Pr68vN27cUG26fPkyer2e6OhoK5vWrVuHJEkkJCRY2bR582a0Wi0PP/ww2dnZVjbFxsbSs2dPK5vOnTvHiBEj1KOsQkND6dGjB7/++mupbcrvBnPH1uLWaDRotVr1ZBaNRmPlnjRpks3nZLHjVpuK+5xeeeUV3nvvPdLS0mzaNHjwYMaNG8eUKVNo06YNfn5+BAUF0bt3b/bv31+kTcXJewaDQU3LreXp6NGjlBjFBUhLS1MAJS0tzdFJKVOMRqNy4MABxWg0OjopTkel0mbDBkV5+mlF6dzZ/HfDhkKDlpkuI0cqikZj/dFqzf4uijPkmezsbOXYsWNKdna2w9JwK7IsK5mZmYosy0WGi4qKUoYPH64MHz5c8fX1VapXr668+eab6nV169ZVpkyZogwcOFDx8fFRBg0apCiKopw/f17p06eP4ufnp1SrVk3p2bOnkpiYqMZrNBqVV155RfHz81P8/f2V1157TYmJiVF69eplde9Ro0ap/+fk5Cjjxo1TQkJCFDc3N+Wuu+5SFixYoCQmJiqA1ceSjvxxTJgwQWnbtm0BG5s1a6ZMnjxZ1WXu3LlKRESE4u7uroSHhytz584tUqOinq+rtVO5ubmKVqtVfvrpJyv/mJgYpWfPnsWO55133lHuueeeIsM8+eSTytNPP13o9zk5OUpaWpr6uXDhggIoN27cUIxGo7J//34lNzdXURRzfrKUcYPBYOU2mUxFuvPy8qzc3+w+p9R9fY0S++UfiizLiizLSl5enpVbURQrt8lksnIbDIYi3Uaj0cptSW9hbls2GY1GZd++fep9i7LJUl5vtcPZbCrJc7LYlJ2drezfv18xGAwVxqaSPqecnBzlwIEDSl5ensvalJWVpfz1119Kdna2IsuyGoclXHHcsiwrN2/eLNW1RblNJlORblmWlYyMDMXX11fZuXOnYjKZlOPHjyuAsmPHDjW+NWvWKCEhIYqHh4eSlZWl+r/11ltKnTp1Ctz/2WefVUaNGqV4e3srSUlJVukaNGiQ0qtXLzX833//rdSqVUtp1KiRsnTpUiUhIUE5evSo8vHHHyvh4eGlsulWtyUOi/vSpUvKxYsXlcuXLyszZ85UfH19lcuXLyuXLl1SLl26pGRkZJTZM7jV3bp1a2XOnDkFbDIYDEqNGjWUXbt2KZ07d1a+/PJL5fDhw8rBgweVHj16KHXq1FHTZcum4uS9/Hn11vJ048aNErf5Yh6mA9FqtbRs2dLRyXBKKos2iqIw79QpOr3wAvf4+SHVqGFzWrmFMtMlMhK++ALFYOCQVstJk4m+YB7pTkoqMg3OirPmGUVRyHbQRk2eem2BKWhFsXjxYoYMGcKePXvYt28fw4YNo06dOgwdOhSAjz/+mLfffpuJEycC5rfA3bp1o3379mzbtg2dTsfUqVPp3r07hw8fxs3NjenTp7No0SK+/PJLGjZsyPTp0/npp5/o2LFjoemIiYlh165dfPrppzRv3pzz589z5coVQkND+eGHH3jiiSc4ceIEvr6+eHp6Frh+wIABTJs2jTNnznDXXXcB8Ndff3H48GF++OEHAL7++mumTp3KnDlzaNmyJQcPHmTo0KFUqVKFQYMGlUhnV8TNzY1WrVqxadMmdX29LMts2rSJESNGFDseWZbVEQpbmEwmjhw5Qo8ePQoN4+7ujru7ewF/y/nn9957r5WfhfxLSYrj1uv1Vm5PN3NcBtP/dli/NQyYv7O4LSM7xXXfep777dyFpb1Vq1bFsqm4bmewqbjPyYKHh4dVXqgINpX0Obm7uxdo51zRJkt5u9VtoTju/McOl/Tawtz5zz4vzL1+/Xrc3d1p3749YF5SU7NmTbZu3cp9992HJEls3bqVXr168fvvv7N7926io6ORJIktW7bQoUMHq3tmZmby3XffsW/fPpKTk1m8eDFvvPFGgaVPlv+HDx+OJEns2bPHSoPGjRszZMiQUtlkS4/8z6ZmviNsq1atqm6WWRhl9TwkSeLRRx/l22+/Zfjw4VZp37FjB3q9nsjISDZu3Gh1/0WLFhEYGMiBAwd46KGHkCSJ6OhomjVrhoeHBwsWLMDNzY0XXniBSZMmFXl/y/+3lpv8/xcX0el2ICaTiYSEBBo2bFiqh1eRqSza/PnnnwwfPpwqVapw/fp1mz8881NmukRFQb167DlxgnZ5efgAPfV6PJOSYM0aeOGF0sftIJw1z2QbTDR6+1eH3PvYlG546rXk5OTg4eFx2/XLoaGhzJw5E0mSCA8P58iRI8ycOVPtdHfs2JFXX31VDW9Zs7tgwQI17ri4OKpWrUp8fDxdu3Zl1qxZTJgwgccffxyA+fPnq1PgbHHy5Em+++47Nm7cqG7yVb9+ffV7f39/AAIDA6latarNOBo3bkzz5s35+uuveeuttwBYtmwZkZGR3H333QBMnDiRadOm8dhjjyFJEvXq1ePYsWN8/vnnlaLTDTBmzBgGDRpE69atadu2LbNmzSIzM5PBgwcDMGHCBC5evMiSJUsA85nederUISIiAjBPNf344495+eWX1TinTJlCu3btuPvuu0lNTeWjjz7i3LlzVuu+S4I9y7VlIzVX2L3cGeu28kboIDSwoChKsdu1smbbtm1WL8EAOnTowObNmxk/fjwAmzdvZty4cZhMJjZv3kx0dDTZ2dns3r2bZ5991ura7777joiICMLDw3nmmWcYPXo0EyZMsGnXjRs3WL9+Pe+++y5eXl5kZ2dbaVBYm+jKtG3blnfffZfc3Fyr38irV6/m0UcfBSigQ1paGvC/3wsWFi9ezJgxY9i9eze7du0iNjaW+++/ny5dupSLLWJNt0DgQHIvXKBzZCQ9OnS4bYe7TAkJgTffpE1gIOGSRHedjpTgYPM54Vu2mEe7BZWOdu3aWTX07du359SpU+p6sNatW1uF//PPPzl9+jQ+Pj54e3vj7e2Nv78/OTk5nDlzhrS0NC5fvmx1rKROpysQT34OHTqEVqstsCN2SRkwYABff/01YP6B9s033zBgwADAPLJw5swZXnzxRau0T506lTNnztzRfV2Jvn37qrMXWrRowaFDh1i/fr26udrly5c5f/68Gl6WZSZMmECLFi1o3bo1c+fO5YMPPmDKlClqmJSUFIYOHUrDhg3p0aMH6enp7Ny5k0Y2TmNwNO46c6fF2XcvFwgqPIoCeZmO+ZRwX49z585Rq1YtK78OHTqwY8cOdW3wwYMHiYqK4qGHHlKPUd61axe5ubl06NDB6tqFCxfyzDPPANC9e3fS0tLYsmWLzXufPn0aRVHUF5/ORGpqKv/3f/9X5vHWqlWLvLy8Aht8rlq1ip49exYIL8syo0eP5v7776dJkyZW3zVr1oyJEyfSoEEDYmJiaN26NZs2bSrzNBeGGOl2IFqttkCGEJipFNps3EjkqlVsDA1F8fWFjRvhNm/bylSX/v3RnDvHX7Nno/XyAl9faNjQvJP59esuN8XcWfOMp17LsSndHHZvSZJsTsEuDfmnsoF5M5pWrVqxbNmyAmEDAgJKdY+ySmu/fv14/fXXOXDgANnZ2Vy4cIG+ffsCqJukfPHFF1YvBIBKN3o0YsSIQqeTL1q0yOr/kSNHMnLkyCLjmzlzJjNnziyr5Nm1XP/vyDDnHul21rqtvBE6VGANDFnwXq3bh/sXCSiblgJ44xK4Vbl9uH+xjKrmJzo6mszMTPbu3UtKSgr33HMPAQEBREVFMXjwYHJycoiPj6d+/frUqVNHve7EiRPs2bOHn376CTC/lO7bty8LFy4kOjq6wL2VfC8IStq2jx8/ng8++KDIMAkJCaXu0Fs63cOGDSvwnSzLVtPZS4LFRsvGbJZ0Xrp0iU6dOhXQYfjw4Rw9epTt27cXiKtZs2ZW/9esWZOrV6+WKl2lQXS6HYjJZOLw4cM0a9as0v3Qux0VXpukJFi+HGQZIiKQLl40/9+wYZGd3TLXZeBAtIcPQ3Y21K0LGRnm0e7q1e887nLGWfOMJEl4uTmuqlX+3aXb09PzttPwdu/ebfX/H3/8QYMGDQrV895772X58uUEBgbi6+trM0zNmjXZvXs3Dz30EGDeJXT//v0212YCNG3aFFmW2bJli9UZ0hbc3NyA25/LGRISQlRUFMuWLSM7O5suXboQGBgImI/FqlWrFidOnKB///6V9tgwV8Ce5dpd7xrndDtr3VbeCB2EBs5AjRo1SElJsfK7++67CQkJYfPmzaSkpKgztWrVqkVoaCg7d+5k8+bNBfYyWbhwIUaj0WrkXFEU3N3dmTNnDn5+flbhGzRogCRJHD9+vERtO8Crr75KbGxskWHyL+UqKf/97385duwYLVq04PHHHycmJoZHH32Uxo0bc+jQIX766ScGDhzIvn37ABg7dixNmjQhNjaWr776ik8//ZS8vDw6derEjBkz1Hgtu8Lnf5G/evVqunTpgoeHh5UOI0eOZM2aNWzdupUQG7+l8+8xAKjHjJUXotPtYMpqVKciUqG1uX6dzOvX0TdsiJtWC7Vrw/HjxRphLlNdQkLg2Wdh+XJSDhwgxWikfv/+LjfKbaFC55k7oLidyvPnzzNmzBief/55Dhw4wOzZs5k+fXqh4QcMGMBHH31Er169mDJlCiEhIZw7d44ff/yRcePGERISwqhRo3j//fdp0KABERERzJgxo8AZ2/kJCwtj0KBBPPvss+pGaufOnePq1as89dRT1K1bF0mSWLNmDT169MDT0xNvb+9C0zdx4kTy8vIKjL5OmjSJUaNGUb16dR5++GFyc3PZt28fKSkpjBkzplh6CcoHe5VrdXq5wbk73SDqNgtChwqqgd7LPOJcTMp0Tbe+eBuNWmjZsiVLly4t4N+hQwfi4+NJSUnhtddeU/0feugh1q1bx549e3jxxRdVf6PRyJIlS5g+fTpdu3a1iqt379588803vHDL/jr+/v5069aNuXPnMnLkyAIvXlJTUwtd1x0QEFDqGWjF4d133+XEiRNqp/rs2bMkJCSwbNkymjVrxtmzZ21el5CQwKpVq9i1axc6nY6YmBh++eUXHnnkEcB8dGRISAg1atRQr1m1alWBEfURI0awcuVK4uPjqVevnn2MvEPEmm4HotVqiYiIEG8rbVDhtalenflnz1Ltww95e906uHgR/PxuO8JcWl2u38xl/7kUfjt2hf3nbpCdl2+UsEsXliQnE7R9O68ePAhz5sD775fGKodS4fNMKbFMvSrOD5OYmBiys7Np27Ytw4cPZ9SoUTanilnw8vJi69at1KlTh8cff5yGDRsyZMgQcnJy1JHvV199lYEDBzJo0CDat2+Pj48Pjz32WJHpmDdvHk8++SQvvfQSERERDB06VD3/s3bt2kyePJnx48cTFBRU5E7bTz75JNevXycrK0vdodvC0KFDWbBgAYsWLaJp06ZERUWxaNEip22sKyv2LNce/45057jA9HJRtwkdoMnW33EAACAASURBVAJrIEnmKd7F/Eju3nj61UBy9y7RdTY/Jey0d+vWjb/++qvAaHeHDh3Yvn07hw4dstqTJCoqis8//5y8vDyr9dxr1qwhJSWFIUOG0KRJE6vPE088wcKFC23ef+7cuZhMJiIjI1m7di2nT58mISGBTz/9VN1R3Vm45557CkzpvpVNmzbxxx9/0Lp1a1q0aMEff/zB6dOn1e+3bdtm9VLi6tWr7Nu3j//85z+A+TfO2LFjWbZsGV9//TU+Pj4kJyeTnJysng/uLIiRbgdiNBo5ePAgLVu2tDqGQVAJtElIYN/Fi2SZTPidOAH+/jBmzG1HmEuii6IorD+azNd7zrPj9DXkfHuFaCS4/+4avBB1F/fd+JsWR49iAJL0emSTCU1cHHTqBG3alIGx5UOFzzOlRFEUsrKy8PLyum3HW6/XM2vWLObNm1fgu8LeUgcHB7N48eJC49TpdMyaNYtZs2YVGsay0YwFDw8PZsyYYTXFLD9vvfWWuit5YXGAeSfXnJwcm3EoikKvXr3o16+fmF7uxNizXLvKSLeo28wIHYQGFkrSrpU1TZs25d577+W7777j+eefV/07dOhAdnY2ERER6maUYO50Z2RkqEeLWVi4cCGdO3cuMIUc4IknnuDDDz/k8OHDBb6rX78+Bw4cYOrUqYwZM4bk5GQCAgJo1qxZkTPTHEH+40p1Op3VVG7LUZOyLDN06FD1KNL85OTksHLlStavX6/6/fzzz7Rt21Yd+VYURf3Ncus6+Li4uNtOqS9PKm+JdQIkSaJatWriB58NKrQ2SUkwYwbL3Nz4r78/NTQayMkxr+e+DcXV5cKNLN746QjbTl1T/WpX9cS/ihtX0nO4mpHLtlPX2HbqGg/5GvlY68HJe++lgY8PGI1w9iwkJrpUp7tC55k7pMKNipQRQhfnx57lOv9GaoqiOG3dIeo2M0IHoUF+HFl/v/3227z22msMHTpU3SAsLCzMaqMzC3Xr1rXp//PPPxcaf9u2bdVrbt3QEsx7pcyZM4fp06fj7u5ervkhNjbWZkfWx8eHjIyMQq8LDAzk0qVLZGRkIEkSGzdupFWrVnTq1Ik+ffowYsQIqlevztWrVzGZTNSsWZO4uDjatm1Lu3bt1Hhs7VqenZ19Wx1svZhfuXLl7Q0uQ0Sn24FotVr1zFiBNRVam2PHYNcuNNnZNFEU89SmgwfN/rcZ6S6OLnsSbzBk8V4ycoy46TQMfbAeT7UOpW71/+3Oef56Fl/uSOSbPefZmq7jkUcn8tkfcWDMgitXoEoVcLFpthU6z9wBkiQV2GlVIHRxFexZri0j3bICRllBr3XOjoyo28wIHYQGFhxdfz/yyCOcOnWKixcvEhoa6pA0OFqDW6levTr33nsvTZs2pU+fPsTExFh97+bmxrhx42jZsiV16tShadOmADRu3Jj//ve/dOrUCVmWcXd3Z9GiRdSsWRO9Xs/s2bOt4nnggQfo16+f+r+z6VAUkmLr9YuTkZ6ejp+fH2lpaYXukOuKGI1G9uzZQ9u2bSv1NCFbVGhtPv3UPJVcksDd3XxElyzDjBnw8stFXno7XTb8lcyIbw6SZ5RpWacqM55qQb0ahR+FcfJKBsOXHeDU1Zt4GPOI2zybdhlJmAYNQvff/96xqeWJM+SZnJwcEhMTqVevntM0AoqikJmZSZUqVcToSD5Ko0tRz7eitlOOIL+WXl5edivXOQYTEW+Zpy0endwNb3fnbGucoW5zBoQOFUODsmgnRbsmNLBgTx3Kus0XG6k5EI1GQ+3atUt9dl1FpkJrc/o0U2WZ141Gjmdng6KATge3HGVgi6J02ZN4gxFfmzvcnRsG8c3QdkV2uAHuCfJh9YgHiLongBydGwM7jaKuRxA/uOCb9AqdZ+6QW4/JEJgRujg/9izXlunlYO6AOyuibjMjdBAa5EfU30IDC66igyi1DkSj0VC3bl1RedqgwmqTlATnzrEQ+BBIAnOnu1o1aN36tpcXpkvitUyGfbWPPJNM98bBzH/mXjz0xVvv5Omm5fOBrYi6JwCjRocS/SLL1/xWYtMcTYXNM3eIJEnlvubLFRC6uAb2LNeSJOGmc/6zukXdZkboIDSwIOpvoYEFV9KhcpdaB2M0Gtm6dStGo9HRSXE6Kqw216+j6PVMDA9nqJsbbd3dzVPMn3qqWJuW2dIlx2Bi2JJ9pGYZaB5alZl9W6DTlqxoe+jNHe8G1d3RevmR06wvOfsPmV8SuAgVNs/cIYqikJGRYXMjl8qM0MU1sHe5VjdTc+KRblG3mRE6CA0siPpbaGDBlXQQnW4HotFouOuuuyr9G0tbVFhtqldH0uuJDQ/n/3r3xjcyEjp3htdeK9bltnR5f91xTl29SYCPO1/EtMLTrXQ7enrotcQ9dx/+ejh2PZd3FmyCSZNg48ZSxVfeVNg8Uwa4u7s7OglOidDF+bF3uVaPDXPykW5RtwkdQGiQH1F/Cw0suIoOotQ6ELE2p3AqrDYJCZCaCn/+CXv2mEe5R4y47a7lFm7VZevJf1i08ywAHz3ZjECfO9u8KyTzBp9e2w7AMr8ItmtrwPLlLjHiXWHzzB0iSRJubm4uMfWqPBG6uAb2LtceeteYXi7qNqEDCA0siPpbaGDBlXSo3KXWwRj/n71zj4uqzB//e27cVEAB0UTRlPJ+v0Q3yzTLbtZWrptllta3zXJju6y7rrVtP+1iWttWpmW33cps21wLDZMMdzHxghcUERUVEERA7gzMmXN+fwxnBB0MBmbmnPF5v17z4uFwOOd53s8zZ+Y5z3M+jySRnJx80U8TcoVfusnLg9WrSVYUTk6YAMOGOZ7lbsH63CqNvdTW25n/9T4AHoiP5brLu7Y9jyUljDmVxcjivQA823kMlRU1UFLS9mN7GL9sM+2AoihUVFToYuqVNxFe9IGn39fq9HItB1IT1zYHwoN/OWjLtVdcv4UDFU96aO9jik63DzEajQwePPiiv2PpCr90U1JCXWkpN//wAz1WreJIjx4gSa3q0Db28vaPh8kvq6VHeDDzb255x/2CRERg6dyZ1DWvYSsr5CRBvN59HEREtM/xPYhftpl2Ijg42NdZcIsHH3yQqVOneuz4evVyMeHp97VeppeLa5vwAP7hQI00XVNT06bjiOu3cKDiKQ9qG22v6Oj6XOTPTzAajXTt2g6jk36IX7qJiKDAYqF/aCin6uu5tKYGwsJa1aFVveQUV7Mi5SgAf751gNvPcZ9HTAzGX/+apzMyOH5gLeuufJRPg/tynyWMuPY5g8fwyzbTDhgMBt0sp+FNhBd94On3daBF+4HUxLXNgfDgHw5MJhPh4eEUFRUBEBIS4vbUYLtdu+9bbyEcOGhPD4qiUFNTQ1FREeHh4ZhM7fMdW3S6fYjNZiM5OZkJEyaIL3/n4JduYmLoPXs2ezp1wnbmDAaTCaZNa/Hz3HDWy5cFnam3y1wTF8nkQd3aN5+TJvH06dOwZg115UdJCruUFz/ewifP3KzpZ2b8ss20A7IsU1lZSadOnXQ9OtLeCC/6wNPv60AdLBkmrm0OhAf/cdCtm+N7i9rxbi2KomC1WgkKCtL09xJPIhw48KSH8PBwZ1ttD0Sn24eYTCbGjBnTbndQ/Am/dTNpEgwYgKWkxDHC3YoONzi8BPboz6YfMzAa4PnbBrb/xTYvD5KToXNn/hRRzmabnS2lJpL/m8kN1wxs33O1I37bZtqIwWCgQ4cOv9hOvvrqK/7yl79w+PBhQkJCGDFiBGvXruXAgQP88Y9/JD09HZvNxvDhw1m2bBkjR45sco7ly5ezbt06kpOTiY2NZdWqVURFRTF79my2b9/OsGHD+PTTT+nbty8AL7zwAt988w2PPfYYL730EiUlJdx6662sXLmSsLAwl3mUZZlXXnmFFStWUFhYyGWXXcaf//xn7r77bo95EfgWT7+vgyzan14urm0OhAf/cWAwGOjevTtdu3bFZrO1+v9lWaaiooLQ0NCL9qapcODAUx4sFku7v89Ep9uHGI1GunTp4utsaBK/dhMT0+rOtorBYOC91AIA7h4VQ7+undozZw5KSqC8HOmyy8jJ2sN1nWSSuo7itf/lcf1VAzAatdlJ8cs2k5fnqA83btCoGAwGzOYLX+oLCgqYPn06r776KnfeeSeVlZVs2bLFuf7lzJkzeeutt1AUhddff50pU6aQnZ1Np05n299f//pXli5dytKlS3nuuef4zW9+w6WXXsr8+fPp1asXDz30EHPnzmX9+vXO/zl8+DBffvkl69ato6Kigocffpjf/va3/POf/3SZz8WLF/OPf/yD5cuXExcXR0pKCjNmzCAqKorx48e3uxeB7/H0+1oPgdT88trmBsKD/zkwmUxud2xCQkLaOTf6QzhwoBcPF++tEQ1gs9n47rvv3LrL5+/4o5uqqir69OnDXXfdhdVqdesYP2WdYuvREiwmA0/e4KGnrCMiICyMf65dy/VffUXaV2/SyVbLwTIbiRkFnjlnO+B3bWbjRsc66S+91Kb10mVZpqysDFlufiSvoKAASZK466676N27N0OGDOG3v/0tHTt2ZMKECcyYMYP+/fszYMAAVqxYQU1NDT/99FOTY8yaNYt7772Xyy67jOeee45jx45x3333MXnyZAYMGMC8efPYvHlzk/+xWq188sknDB8+nGuvvZa33nqLL774gsLCwvPyWFdXx6JFi1i1ahWTJ0/m0ksv5cEHH2TGjBm89957HvEi8D2efl/rIZCa313b3ER4EA5UhAfhQEVPHkSn24eYzWauueYaMdriAn90k56ezrFjx0hLSyMoyL31tP++2RE87b5xscR09tCdvZgYmDCBKbW1dDUaGWFWmHV6NwDL1h/ALmtzeQq/ajMNy8shy9C/v+Onm+ulGwwGOnXqdMFp1MOGDeOGG25gyJAh3HPPPaxcuZIzZ84AcOrUKebMmUNcXBxhYWGEhoZSVVXFiRMnmhxj6NChznR0dDQAQ4YMabLNarVSUVHh3NarVy969Ojh/D0+Ph5ZlsnKyjovj4cPH6ampoZJkybRsWNH5+uTTz7hyJEjrbTSMi8C3+Pp9/XZZ7q1O9LtV9e2NiA8CAcqwoNwoKInD9rPoR9jMBgIDQ31dTY0iT+6GTVqFD/99BOlpaVu/f/2Y6XsOHaGAJORx67r2865O4dBg4jq35+CCRMwVldTUXuYT6ThHDkD6/acZOqIHr98DC/jV22mYYo//fuDyQQ9esDBg47trZxmbjAYfnH6nslkYuPGjaSmppKUlMRbb73Fn/70J7Zt28Zjjz1GSUkJb775JrGxsQQGBhIfH099fX2TYzQO6qN2ZF1tc3dkuaqqCoDvvvuuSUcdIDAwsNXHa4kXge/x9Pv6bPRy7Y50+9W1rQ0ID8KBivAgHKjoyYMY6fYhNpuNtWvX6mJKhLfxRzchISFce+21bq87/O5mx2jeqAiJLsEe7ixERED37hjT02HDBkK/+w9zdv7HmQ9F0d5ot1+1mYYp/uTng93u+NnK5eVUWjqN2mAwcNVVV/GXv/yF9PR0AgIC+Pe//83//vc/nnzySaZMmcKgQYMIDAykuLjY3ZI14cSJE5w8edL5+88//4zRaOTyyy8/b9+BAwcSGBjIiRMn6NevX5NXz549W31uMb1cH3j6fR2kk+nlfnNtawPCg3CgIjwIByp68iA63T7EbDZz44036mJKhLcRbpqSWVBB8sEiR8TyaVd63ktMDAwfDtnZUF9PQVAQv8raTEdbLVmnKvkxy71lPjyJX7WZmBjHcnJGo2OE22hs9fJyKupd4AtNo962bRuLFi1ix44dnDhxgq+//prTp08zYMAA4uLi+PTTT8nMzGTbtm3cd999BAcHt6V0ToKCgpg5cyZ79uxhy5YtPPnkk9x7770ul+jo1KkTTz/9NE899RQff/wxR44cYdeuXbz11lt8/PHHrT53S7wIfI+n39fqSLeWA6n51bWtDQgPwoGK8CAcqOjJg/Zz6OfooZH4Cn9yU5GZyYqVKxkTH8/4e+5p9f+v3OJ4lvvmwd3p29VL02i6doWQEO6rquKzM2f4oqON3+zbyIqRt7N881Em9I/2Tj5agT+1GXV5ubZGL28JoaGhpKSk8MYbb1BRUUFsbCyvv/46N998M926deORRx5h5MiR9OzZk0WLFvH000+3y3n79evHXXfdxZQpUygtLeXWW2/lnXfeaXb/v/71r0RFRbF48WKOHj1KeHg4I0eO5I9//GO75EegTTz5vtZDIDXws2tbGxAehAMV4UE4UNGLBzHS7UMkSSIxMRFJknydFc3hV242biT96ad5ZtkyZj70UKujUJ+urOPbPY6o4bOu7OU9L336QHAwPerqANhns/Hw3g0EyHbSjpWy8/gZz+ehFfhVm1GJiYFhw9rU4VYUhYqKigs+EjBgwAA2bNhAUVERVquVrKws5s6dC8CIESPYvn07tbW1HDp0iLvvvptjx47xu9/9rsk5Gj820bt3bxRFYfjw4c5t1113HYqiEB4e3uTcjz32GPn5+dTW1rJmzRo6d+7s/NtHH33EN9984/zdYDAwb948Dh48SH19PUVFRWzYsIFrr73WI14EvsfT72s9BFLzy2ubGwgPwoGK8CAcqOjJg+h0+xCz2cyUKVN0c4fGm/iNm4Yo1EF1ddwZG8st0dGtjkL9edoJ6u0yw3uGM6p3hPe8jBkDt93GUxYLJ0NCeCk4mOieXZlafgiAD/+X4/k8tAK/aTPtjJhG7RrhxcHbb79N7969CQoKYty4caSlpTW773//+1+uuuoqIiIiCA4Opn///ixbtuy8/dasWUP//v0JCgpiyJAhJCYmup0/T7+vz3a6tTvSLa5tDoQH4UBFeBAOVPTkQXS6fYwe7sz4Cr9wU1IChw4xrqSEr0NCeLtTJzh0yLG9BdjsMv/4+TgAs67qDXjZyzPP0H3iRLr36wedO0N+Pg+mrQVgfUYhBeW13stLC/CLNiMQuMBms5Gbm0tWVpbbKyCcy+rVq0lISOD5559n165dDBs2jMmTJ1NU5DpmQ4cOHZg7dy4pKSlkZmayYMECFixYwIoVK5z7pKamMn36dB5++GHS09OZOnUqU6dOJSMjw+18evJ9HWRpmF6u4ejlIK5tKsKDcKAiPAgHKnrxIDrdPkSSJJKSknTTWLyJ37ipr4dTp6CqCrp0cfw8dcqxvQWszyikqLKOqE6B3Dy4u/e9qAG9SkqgqAhqahhYdJRxVfnYZYV//nzil4/hJfymzbQzWp1G/cILL7B7926fnV+rXhpTWVnJu+++y/jx4wkNDaV3794MGDCAqKgoYmNjmTNnDtu3b3f7+EuXLmXOnDnMmjWLgQMHsnz5ckJCQli1apXL/UeMGMH06dMZNGgQvXv3ZsaMGUyePJktW7Y493nzzTe56aabeOaZZxgwYAB//etfGTlyJH//+9/dyqOn39fOJcM0Pr1cXNuEBxAOVIQH4UBFTx5Ep9uHWCwW7rjjjiZr2Qoc+I2bgADsUVHUBgdDaSl06OAIUBYQ0KJ//2fDKPdvxvYiwGz0jZfISI7b7TxoMnGTJIHFwqztjtHuz9JOaCbqr9+0mXbGaDQSHh6O0Sgu943RupelS5fSu3dvPvzwQyZOnMg333zD7t27OXToEFu3buX5559HkiRuvPFGbrrpJrKzs1t1/Pr6enbu3MnEiROd24xGIxMnTmTr1q0tOkZ6ejqpqamMHz/euW3r1q1NjgkwefLkCx6zrq6OioqKJi8Au92OxWLh1ltvddaT3W7HbndccyRJapJWl39rLm2z2ZqkFUVxBlKz2uwoiuLcrqaBJmlZlpuk1S96zaXtdnuTtJrf5tKuymSxWLjllluc68r/Upkap7VaptbWk8rtt9+O2Wz2mzK1tp4A7rjjDkwmk9+UyZ16utB1Qa9lam09Xei6oNcyuVNP4LguWCwWn5SpNWjz28ZFgh5GWnyF37iJiGB3hw50yshgfHExXH6549WC9ZaPnq5iW04pRgP8eqxjHWJfeQm2WPikooLvq6s5mZfHxF0b6WGvprS6nsR9BV7NS3P4TZtpZxRFwW63Cy/noHUv27dvJyUlhbS0NP785z8zefJkhgwZQr9+/Rg7diwPPfQQH374IYWFhUydOrXJaHNLKC4uxm63Ex3ddBWC6OhoCgsLL/i/MTExBAYGMnr0aB5//HFmz57t/FthYWGrj7l48WLCwsKcL3Xd9YyMDBRFYffu3Rw4cACAvXv3Om8wpKenk5PjiC2RlpZGbm4u4JjiXlDguC6lpKQ415VPTk6mrKwMgKSkJCorK53PdFvrpSYBeaxWq/NZ9MrKSpKSkgAoKysjOTnZ6TAlJQWAgoICUlNTAcjNzXU+G5+Tk0N6ejoA2dnZ7N27F4DMzEwyMzNbVCZFUdiyZUuLywSQmJiI1WrVbJlaW09qmU6fPo3NZvOrMrW2nioqKjh9+rRflam19aQoChkZGX5VptbWk6IopKamcuLECb8pk7v1VFhYiKIoXi2TW49MKTqgvLxcAZTy8nJfZ6Vdqa+vV7799lulvr7e11nRHH7jJilJ+fDyyxVAuSEwUFEmT1aUpKQW/ev/++6AEvvct8pDH6Y5t/nES26uolxxhbLUaFQ2GY1KfUCAogQHK3+b8n9K7HPfKvcuT/VeXi6AFtpMbW2tcuDAAaW6utpneTgXu92unDlzRrHb7b7OiqZwx0t1dbVy4MABpba29ry/6e1zKj8/XwGU1NSm799nnnlGGTt27AX/9+jRo8revXuVFStWKF26dFE+++wz598sFkuT3xVFUd5++22la9euzR7ParUq5eXlzldubq4CKKWlpUp9fb2ybt06p3NJkhRJkhRFURSbzdYkrdZlc+n6+vomaVmWlS2HTiuxz32r3LjsJ0WWZed2Na0oSpO03W5vkrbZbBdMS5LUJK3mt7m0qzKpDqxWa4vK1Dit1TK1tp4UxfH+W7dunVJXV+c3ZWptPdXU1CjffvutYrVa/aZM7tTTha4Lei1Ta+vpQtcFvZbJnXpSrwv19fVeLVNpaWmrP/MNiqLR2/yNqKioICwsjPLyckJDvbRGsUDQVvLyYM4clMOHOSlJVFitDLj8cvjHP35xCag6yU784mRKq+tZ+cBoJg308ZrYCxbA6687psUHBkLXrhTUw1V3vYyMgeTfj+fSqI6+zaMGkGWZ7OxsTCYTUVFRBAQEXPTRsf0BRVGor6/n9OnT2O124uLizpuW7snPqZkzZ/Lwww+7tTRac9TX1xMSEsJXX33VZLm3mTNnUlZWxtq1a1t0nJdeeolPP/2UrKwsAHr16kVCQkKTJeWef/55vvnmG/bs2dOiY3rzM3/HsVLuXr6VPpEd+PHp6zx6LoFAIBD4B+58Tmk/vrofI8syZWVlmn6u0Ff4hZsDB+DgQQzBwfQIDaVHbS0cP+7Y/gud7o0HTlFaXU90aCDXXx7l3O4zL3fcAZ99BpIE0dFQVkZ3o5Hx3QL5sbCeL3fk8Yeb+3svPy7QQpsxGo306dOHgoICTp486ZM8nIvS8EyTwWAQNwAa4Y6XkJAQevXq5fX2VV5ezsSJE4mNjWXWrFnMnDmTHj16tOmYAQEBjBo1ik2bNjk73bIss2nTJuca7S1BlmXq6uqcv8fHx7Np06Ymne6NGzcSHx/vVj49/b5u/Ey3VtHCtU0LCA/CgYrwIByo6MmD6HT7ELvdzvbt25kwYYLmG4q38Rs3NTVw5szZ3wMDW/RvX+10rON996gYzKaz5feZlzFj4JFHyHrvPf5z6BDXh4cz+rHHmDZxCD/+Yydf7czj9zdehsXku7rSSpsJCAigV69eTQKD+BKbzca2bdsYN26cCDLXiNZ6MZlMmM1mn9y4+Oabbzh9+jSffvopH3/8Mc8//zwTJ07k4YcfblPwwISEBGbOnMno0aMZO3Ysb7zxBtXV1cyaNQuA+fPnk5+fzyeffAI41vTu1asX/fs7brClpKSwZMkSnnzySecx582bx/jx43n99de55ZZb+OKLL9ixY0eTZcVag6ff12ejl2t3yTCtXNt8jfAgHKgID8KBip48iOnlAoGn2L6dEzfeyCvl5YwxGnnQYICwMFi/3tGJbYaiCitXLN6ErKC5adsP3347q9at43e//jXLPv8cm10mfvEmiqvqee/+UUwe1M3XWRQIfII3P6d27drFhx9+yPvvv0/Hjh2ZMWMGv/3tb4mLi2v1sf7+97/z2muvUVhYyPDhw/nb3/7GuHHjAHjwwQc5duwYmzdvBuCtt97ivffeIycnB7PZTN++fZkzZw6PPvpoky87a9asYcGCBRw7doy4uDheffVVpkyZ0uI8edPl8ZJqxr+2mQ4BJva/eJNHzyUQCAQC/8Cdzylt3xLwc2RZpqioqEnYe4EDv3Bz5gw7FIV3FIW/g2O5MLO56ci3C77ZnY+swMhe4ed1uH3tZeqcOdx0002Mvf12ACwmI78a5Zgqv3p7rk/ypOJrN1pFeHGNXr0UFBSwceNGNm7ciMlkYsqUKezbt4+BAweybNmyVh9v7ty5HD9+nLq6OufIv8pHH33k7HADPPHEE2RkZFBdXU15eTm7du3iscceO2904Z577iErK4u6ujoyMjJa1eE+F0/Xkzq9XMsj3Xptq+2N8CAcqAgPwoGKnjyITrcPkWWZjIwMXTQUb+Mvbvp26MCzERHc160bXHIJBAdfcH9FUfjXznwAZ2e2Mb72ctttt7F+/XqmT5/u3DZttGN5n81ZRRSWW32SL/C9G60ivLhGT15sNhv/+te/uPXWW4mNjWXNmjX87ne/4+TJk3z88cf88MMPfPnll7z44ou+zmq74+l6CmqYXi7JCpJdm21BT23VkwgPwoGK8CAcqOjJg1vTy99++23ndLRhw4bx1ltvMXbsWJf7/ve//+W5557j4MGD1NTUEBsbzjtlWQAAIABJREFUy6OPPspTTz3V4vOJ6eUCXdIQvZwjR8BkArsd+vaFlSubDaSWkV/OrW/9lwCzke1/mkhYsD6ewb33va2k5ZTy9I2XMXdC66e4CgR6x5OfU5GRkciyzPTp05kzZw7Dhw8/b5+ysjJGjBjhXOtUz3jzM7+23s6AhRsA2P+XyXQIFKFuBAKBQHBhvDK9fPXq1SQkJPD888+za9cuhg0bxuTJkykqKnK5f4cOHZg7dy4pKSlkZmayYMECFixY4HZQFX9ClmXy8/N1cXfG2/iFm5gYSEiAUaMc6VGjHL9fIHL52t2OUe5JA6Nddri14qW2tpaff/7Z+fuvxzhGu1fvyEWWfRMmQitutIbw4ho9eZk3bx55eXm8/fbbTTrciqJw4sQJAMLDw/2iw30unq6nAPPZr0FanWKup7bqSYQH4UBFeBAOVPTkodWd7qVLlzJnzhxmzZrFwIEDWb58OSEhIaxatcrl/iNGjGD69OkMGjSI3r17M2PGDCZPnsyWLVvanHm9I8syR44c0UVD8Tb+4Kauro6sXr2wv/wyLFkCr70GkyY1u78sK3y7twCAO4Zd0sw+vvdy8uRJunTpwrXXXktVVRUANw/uTqcgM7mltWzLKfVJvrTgRosIL67Rk5cXXnjB+V5rTGlpKX369PFBjryHp+vJZDRgMTki0tdJvl9twBV6aqueRHgQDlSEB+FARU8eWtXprq+vZ+fOnUycOPHsAYxGJk6cyNatW1t0jPT0dFJTUxk/fnyz+9TV1VFRUdHkBTiX37Hb7S7TjZfokSTJWQHNpW02W5O0OtNeTSuKcl4aaJKWZblJWpKkC6btdrszbTAYuOqqqzCbzX5TpubK0doyAVx99dWYzWbdlmnPnj3079+fftddB8OGIXXrdsF62nH8DAXlVjoFmbn2skiXZTKZTMTHx2M2m31WT1FRUfTo0YNu3bqRlZWFLMsEB5i4aVA04BitF+8n7byfzGYzV155pTPYlT+UqT3qyWw2c8011zj3ba8yeYLmngKrqqoiKCjIY+fVAmazmWuvvRaz2XPTvp3B1Gza/NLmDQd6QHgQDlSEB+FARU8eWtXpLi4uxm63Ex0d3WR7dHQ0hYWFF/zfmJgYAgMDGT16NI8//jizZ89udt/FixcTFhbmfPXs6Zi6mpGRAUBmZiaZmZkA7N27l+zsbMDRoVen16WlpZGb64imnJqaSkGBYwQxJSWF4uJiAJKTkykrKwMgKSmJyspKABITE7FarUiSRGJiIpIkYbVaSUxMBKCyspKkpCTA8RxdcnKy009KSgrgiDCbmpoKQG5uLmlpaQDk5OSQnp4OwKFDh0hNTUWWZb8pU3Z2Nnv37m1zPf3000/s378fWZZ1W6b8/HyCgoLo1atXi+pp3Z6TAEzsH4kJxWWZKioq2LBhA7Is+6yetm7dyldffcXx//2P8lOnKG743552R90l7ivguw3i/aSV95Msy2zatMn5CJA/lKk96kmWZbKystqtTOrnU3uSkJBAQkICBoOBhQsXOn9PSEhg3rx5TJs2zeXz3f6ELMscP37co6MYQRpfq9sbDvSA8CAcqAgPwoGKnjy0KpDayZMn6dGjB6mpqcTHxzu3P/vss/z0009s27at2f/NycmhqqqKn3/+mT/84Q/8/e9/bxIBuTF1dXXU1dU5f6+oqKBnz56UlpbSuXNn54iCyWRqkpYkCYPB4EwbjUaMRmOzaZvNhslkcqbNZjMGg8GZBsdoSuO0xWJBURRnWpZl7Ha7M62OLDWXttvtKIqC2Wymrq6OHTt2MG7cOAwGg1+Uqbm6aW2ZrFYrO3fuZNy4cSiKotsy1dXVUVlZSWRk5AXrCYORKxYnU1Jdz0ezxjD+siiXZbLZbGzbto0rrrgCo9Hou3pKTsb45ZdI1dUYO3bEeO+9WK8dz/jXUzhVUcc704dz89BLxPtJA+8nWZbZtm0bY8aMISAgwC/K1B71ZDAY2LZtGyNHjiQ4OLjNZaqoqKBLly7tGvzr+uuvBxw3IePj4wkICHD+LSAggN69e/P000+7tT63lmkcoCYkJIS0tDTGjh3rsZGMq15OJr+slrWPX8WwnuEeOUdbkCTJ4w70gPAgHKgID8KBiq88uBNIrVWd7vr6ekJCQvjqq6+YOnWqc/vMmTMpKytj7dq1LTrOSy+9xKeffkpWVlaL9hfRywX+zpbs09z/QRpdOgSw7Y83YDFpeDW/vDx44QWQZejRA3tuLiazGV54gf+3p4KVW3KYMqQb79w3ytc5FQi8hic/p2bNmsWbb7550Xz+efszf8KSzRwtrubLR+MZ26eLx88nEAgEAn3j8ejlAQEBjBo1ik2bNjm3qVMXG498/xKyLDcZyb5YsdvtHD582KPPAuqVi82NOrX85sHdLtjh1oSXkhIoL+eTkhIGvfceLx8/DuXlUFLCHcN7APBDZhEVVptXs6UJNxpEeHGNnrx8+OGHF02H+1y8UU9qBHOtBlLTU1v1JMKDcKAiPAgHKnry0Opx+ISEBGbOnMno0aMZO3Ysb7zxBtXV1cyaNQuA+fPnk5+fzyeffAI41vTu1asX/fv3BxzP4S1ZsoQnn3yyHYuhTxRF4cyZM/Tu3dvXWdEcendTVFTEzJkzGTJkCK+88opzurMr6iQ76zMcMRFubyZquYomvEREQFgY1sxMDpw+zfoDB/jTbbdBRASDLgmlX9eOHC6qYkNGIfeO7um1bGnCjQYRXlyjdS8JCQn89a9/pUOHDiQkJFxw36VLl3opV97HG/UUaNF2IDWtt1VvITwIByrCg3CgoicPre50T5s2jdOnT7Nw4UIKCwsZPnw4GzZscAZXKygocK4bCo5R7fnz55OTk4PZbKZv37688sorPProo+1XCp1iNpsZM2aMr7OhSfTuZv/+/WzYsIHs7GxeffXVC+6bcqiYSqtEdGggY3pfeGqjJrzExMC0adzx0UeE2u1MjouDadMgJgYDMHX4JSxJOsR/dp/0aqdbE240iPDiGq17SU9Pd0ZTVwO2ueJCN/T8AW/UU6BZ24HUtN5WvYXwIByoCA/CgYqePLTqmW5f4a/PdNvtdrKzs4mLi8NkMvk6O5pC725OnjzJt99+i8FgYM6cORfc98nP0/nPnpM8fHUf/nzrwAvuqykveXmOqeYREY6OeAO5pTVc8+qPGA3w8/wb6BrqnSWNNOVGQwgvrmlvL/76OeULGrvs0KGDx9vvA6vSSDl0mtfvGcavRsX88j94GfEediA8CAcqwoNwoOIrDx5/plvQ/tTW1vo6C5pFz24uueQSHnnkkV/scNfUS2w8cAqA235harmKZrzExMCwYY70nj2OTjjQs0sIo2I7Iyvwn4Zn1b2FZtxoDOHFNcKLPvB0PWl9pBtEW1URHoQDFeFBOFDRi4eLN8a8BjCZTIwYMcLX2dAkF4ubzVmnqbXZ6dklmGExYb+4v9a81Ccm8vkrr5B8/DgfTJiAefp0mDSJqcMvYefxM6zdfZLZ11zqlbxozY1WEF5co3Uvv/Qcd2P8+Zlub9RToMYDqWm9rXoL4UE4UBEehAMVPXkQI90+xG63k5GRoYuIe95G725++Pxzjq9fj5Kbe8H9NjQEUJsyuHuLns3UlJe8PExffUXCtm18cvw4P2dnw6pVkJfHLUMvwWQ0sC+/nGPF1V7JjqbcaAjhxTVa95Kent6i1+7du32dVY/ijXoKNDcEUtPoSLfW26q3EB6EAxXhQThQ0ZMHMdItELQzZStXMumRRwAov/9+Qu+/HyZNOm+/OslO8sEiACYP7ubVPLYLJSWYKiuZGxeHUlxMzMmTjinm335Ll//7P67sG8GW7GK+21fA49f383VuBQJd8eOPP/o6CxcNgZaGkW6NRi8XCAQCgf4RI90+xGQyMXjw4Is6AEJz6NbNZ59xetEiBlksXBoQQOipU7B6tfN558akHi6hqs4RtXx4THiLDq8pLxERYDbzl7o6XoyOpnfHjmCxwE8/QV4etw7tDsB3ewu8kh1NudEQwotrhBd94I16CmoY6bZqeHq5aKvCAwgHKsKDcKCiJw+i0+1D7HY76enpupgS4W106SYvD9asIU5RyBg7luwBA+DUKSgocET5Pgd1avmNA7thNLZs2R9NeYmJgfHjwWaDujowmWDMGJAkKCnhxoHdMBkNHCioIMcLU8w15UZDCC+u0bqXhIQEqqurnekLvfwZb9RToMXIbcZUBp382mPnaAtab6veQngQDlSEB+FARU8exPRyHxMcHOzrLGgW3bkpKXF0QDt3hupqjB07Ojriffo4RoUbIdllNmY6opbf1Mqp5Zrycuut8NNPyDU17FIUyMtj9CWXQEQEnTsEOKeYJ3ppirmm3GgI4cU1WvYi1uk+i6frKdAESyzvEXjCBhWzILS7R8/nDlpuq95EeBAOVIQH4UBFLx5Ep9uHmEwm+vfv7+tsaBJduomIgG7doKrKMbp9+jR06AD33NNkHWuA7cfOUFpdT3iIhbF9urT4FJrzEhMDDz3EG088we+zsrglMJBv4+MhMxNiYrh1aHe2ZBfz7V7Pd7o150YjCC+u0bqXxs90X8zPd3ujnjoZrAQaHDc4KD2quU631tuqtxAehAMV4UE4UNGTBzG93IdIksT27duRJMnXWdEcunQTEwP9+nFDWhp3HD/OMUWBu+6C3/zmvF2/3++YWj5xQDQWU8vfhpr0MmAAEzt2pKPBQLjFgpKXB6+/Dnl5zinmmQUVHD1d5dFsaNKNBhBeXKNXL4qioCiKr7PhNbxRTx2pOfvLmRyPncdd9NpW2xvhQThQER6EAxU9eRCdbh9iMBjo3LnzRTE9sLXo0k1eHtWZmSTX1vKfujo6jB7tGO0+J4iaoijOTvdNg1o3tVyTXg4cYEhxMcWDBvGPESMwhIRAVhYcOEDnDgFc1S8SgMR9ng2opkk3GkB4cY3evHzwwQcMHjyYoKAggoKCGDx4MO+//76vs+VxvFFPnZRGMSfOHPPYedxFb23VUwgPwoGK8CAcqOjJg5he7kNMJhP9+omllFyhSzclJVhOn2bTlClk19URNXQoHDzoeNa70fTyvXnlFJRbCQkwcXVcZKtOoVUvBoOBQDVy5DkXvluGdCPl0Gm+21fI3AlxHsuDVt34GuHFNXrysnDhQpYuXcoTTzxBfHw8AFu3buWpp57ixIkTvPjiiz7OoefwRj2FKI1Huo959FzuoKe26kmEB+FARXgQDlT05EGMdPsQSZJITU3VxZQIb6NLN/v3E3DkCBP27+fRkhJIS4OwsPOCqG1oGOW+/vKuBFlat8SBJr0MHAj9+0NNDZw+TUVlJcrllzu244jObvbCFHNNutEAwotr9OTl3XffZeXKlSxevJjbb7+d22+/ncWLF7NixQreeecdX2fPo3ijnkKURtelUm1OL9dLW/UkwoNwoCI8CAcqevIgOt0+xGg00qNHD4xGUQ3nojs3eXmQnOyIXA5QWAjHjsGECU1GuRVFcS4VNrmVUctBo15iYiAhAWXkSKacPk1ETg6Zd9/tLHfnDgFc6YUp5pp0owGEF9foyYvNZmP06NHnbR81alSbv2i8/fbb9O7dm6CgIMaNG0daWlqz+3799ddMmjSJqKgoQkNDiY+P5/vvv2+yz0cffYTBYGjyCgoKcjt/3qinYFnbI916aqueRHgQDlSEB+FARU8etJ9DP8ZoNBIbG6uLhuJtdOempAR+/pnE/fvZdOoUFQ3r6zJoUJPdDhdVkVNcTYDJyPWXR7X6NJr1MmkShiVLUPr3R1IUtpyzXuKtQxzRgL/d69lOtybd+BjhxTV68nL//ffz7rvvnrd9xYoV3HfffW4fd/Xq1SQkJPD888+za9cuhg0bxuTJkykqKnK5f0pKCpMmTSIxMZGdO3dy/fXXc9ttt523pFloaCgFBQXO1/Hjx93OozfqKcheefaXmmKoq2x+Zx+gp7bqSYQH4UBFeBAOVPTkQfs59GMkSSIlJUUXUyK8je7c5OZCbi6/r6lhotXKNklyjHbn5jbZTV2bO75vBJ2CLK0+jaa9xMTwyjvvcPjwYR599NEmf7pxUDRmo4GDhZXkFFc3c4C2oWk3PkR4cY3WvSQkJDhfBoOB999/n8GDBzN79mxmz57NkCFDWLlyZZu+aCxdupQ5c+Ywa9YsBg4cyPLlywkJCWHVqlUu93/jjTd49tlnGTNmDHFxcSxatIi4uDjWrVvXZD+DwUC3bt2cr+joaLfz6I16CrKfc03S2Gi31tuqtxAehAMV4UE4UNGTBxFIzYcYjUb69u2ri7sz3kZ3bmpqUMxmBlks2G02BoSEOLc3ZlOmYwRp4kD3voRq3cvQoUNdbg8PCSC+bwRbsotZn1HAb69r/6AXWnfjK4QX12jdy7mjx6NGjQLgyJEjAERGRhIZGcn+/fvdOn59fT07d+5k/vz5zm1Go5GJEyeydevWFh1DlmUqKyvp0qVLk+1VVVXExsYiyzIjR45k0aJFDDpn1k9L8UY9BUrnxJo4cwy6DfHY+VqL1tuqtxAehAMV4UE4UNGTB9Hp9iHqcwiC89Gdmz59MERG8lVoKISHQ1kZBARAnz7OXYqr6th14gwAEwd0des0uvPSiJsHd3d0uvcVeqzTrVc3nkR4cY3Wvfz4448ePX5xcTF2u/28Uejo6GgOHjzYomMsWbKEqqoq7r33Xue2yy+/nFWrVjF06FDKy8tZsmQJV155Jfv37yemUXyLxtTV1VFXV+f8vaKiAgC73Y7RaKRbt27OtcntDY+umEwmJEnCYDA400ajEaPR2GzaZrNhMpmcabPZjMFgwGyraJIfpTQHFAVJkrBYLCiN0rIsY7fbnWlZljGbzc2m7XY7iqI402rem0s3V6bG9dSSMqlpdf/Gaa2UqbX1ZLfbueSSSwDOK59ey+ROPfXo0QNZlp37+0OZ3Kmn5q4Lei5Ta+upueuCnsvU2npSrwveLpOa/9ag/dsCfowkSSQnJ+tiSoS30Z2bMWNg1ixHR/v0acfPWbMc2xtIPliEosCgS0LpHhbs1mn04CUzM5O5c+fyhz/8ocn2GwdFYzTAvvxycktrmvlv99GDG18gvLhGj14OHDjAhg0b+M9//uN8nTu121t89tln/OUvf+HLL7+ka9ezNxHj4+N54IEHGD58OOPHj+frr78mKiqK9957r9ljLV68mLCwMOerZ8+eAGRkZCBJEt9//71zRH/v3r1kZ2cDjtkAOTmOaONpaWnkNjzOk5qaSkGBI35ESkoKxcXFACQnJ1NWVgZAUlISlZWOZ7fP5DtmD1QrjoBvcslRrFYriYmJAFRWVpKUlARAWVkZycnJgOPGRUpKCgAFBQWkpqYCkJub6wxIl5OT45y1kJ2dzd69ewHHdTIzM7NFZZIkifXr15Ofn9/iMiUmJmK1WpEkicTERCRJ0lSZ3KmnxMRENm3a5CyHv5SptfWUnJzMqVOn/KpMra0nSZJISkpiz549flOm1taTJEls2LCBY8eO+U2Z3K2nH374wVkOb5UpIyOD1mJQ1NtEGqaiooKwsDDKy8sJDQ31dXbaDVmWKS4uJjIyUhfTIryJbt1s3w45OY4R7kYdboBHP93B9/tPMe+GOJ6adJlbh9eDl82bN3P99dcTGRlJYWEhJtPZZdF+vWIrPx8t5U9TBjDn2kvb9bx6cOMLhBfXtLcXT35OHT16lDvvvJN9+/ZhMBicozsGgwHArTvu9fX1hISE8NVXXzF16lTn9pkzZ1JWVsbatWub/d8vvviChx56iDVr1nDLLbf84rnuuecezGYzn3/+ucu/uxrp7tmzJ6WlpYSFhVFUVERERAQWi8Ujo1i1q+4g+MRmtsv9GWM8iNL3BpjxL82M+ACcOnWKqKgozGbzRTWK1bhMdXV1lJWVERkZ6cyL3svU2nqSJImysjLnIx3+UCZ36sloNDZ7XdBrmVpbTwaDodnrgl7L5E49qdeFqKgo7Ha718pUUVFBly5dWvWZL76B+RCj0UjXrl3FF2EX6NHNg7feyqC77uKb48fP63BbbXZSDjnu0E0c4H5QIT14ueqqq5g7dy4ffPAB597Tm9IQxTwxo/2jmOvBjS8QXlyjJy/z5s2jT58+FBUVERISwv79+0lJSWH06NFs3rzZrWMGBAQwatQoNm3a5NwmyzKbNm0iPj6+2f/7/PPPmTVrFp9//nmLOtx2u519+/bRvXv3ZvcJDAwkNDS0yQtwfqnq1q0bFovFuU29kWc2m5uk1bpsLm2xWJqk1ZsWZskRSC1DjgXAcOYYBoPBec7GaaPR2CStfqlrLm0ymZqk1fw2l3ZVJqPRSPfu3Z3HaUmZ1LSa98ZpLZTJnXoKDAwkOjoak8nkN2VqbT0FBATQtWtXzGaz35TJnXq60HVBr2VqbT1d6Lqg1zK5U0/qdUHNo7fL1Bq0/23Dj7HZbHz//ffYbDZfZ0Vz6M7Nyy+zOymJA3l5mF59FV5+ucmftx4todZmJzo0kME93B8F04MXi8XCW2+9xe233+68QKlMHuRYmzz9RBkF5bXtel49uPEFwotr9ORl69atvPjii85ReaPRyNVXX83ixYt58skn3T5uQkICK1eu5OOPPyYzM5PHHnuM6upqZs2aBcD8+fN54IEHnPt/9tlnPPDAA7z++uuMGzeOwsJCCgsLKS8vd+7z4osvkpSUxNGjR9m1axczZszg+PHjzJ492608eqOeTPWOZ7oz5IYYHGUnQG797AFPoae26kmEB+FARXgQDlT05EF0un2IyWRizJgxbt0t8Xd05Wb7dliyhLWyTKLJxJVnzsCSJY7tDfxwwLFU2MQB0c67jO6gKy8uiA4NYnRsZwA2ZBS267H17sZTCC+u0ZMXu91Op06dAEfU8pMnTwIQGxtLVlaW28edNm0aS5YsYeHChQwfPpzdu3ezYcMGZ3CegoICTpw44dx/xYoVSJLE448/Tvfu3Z2vefPmOfc5c+YMc+bMYcCAAUyZMoWKigpSU1MZOHCgW3n0Rj0ZGtblPqTEoBgtINugIt9j52stemqrnkR4EA5UhAfhQEVPHkT0ch9iNBrPW2pF4EBXbjZuhLIyYs1mYgMCoL7eEb1840YYMwZFUc4uFdaGqeWgLy+5ubmsW7eOqVOnOiPOAtw8pDs7jp9hfUYhs67qc4EjtA49ufEmwotr9ORl8ODB7Nmzhz59+jBu3DheffVVAgICWLFiBZde2rbYCHPnzmXu3Lku//bRRx81+b0lU9mXLVvGsmXL2pSnxnijngxWx0h9OR2wdowhuCLHsWxYeC+Pnrel6KmtehLhQThQER6EAxU9eRAj3T7EZrPx3Xff6WJKhLfRpRtFAYPB8bMR+09WUFhhJdhiIr5vRJtOoScvv/71r3n88cfPC8h002DHFPPtx0opqrS22/n05MabCC+u0ZOXBQsWIMsy4Ji+nZOTwzXXXENiYiJ/+9vffJw7z+LxerJLYHM8012pBJNPw43R0hzPnM8N9NRWPYnwIByoCA/CgYqePIiRbh9iNpu55pprznvuVaAzN5MmkbxoEYdrarhGkhhgNDrW6p40CYCNDVPLr4mLJMjStukvevJy5513YrTZ6FpTA3l50LBGb4/wYIb1DGdPbhlJ+08x44rYdjmfntx4E+HFNXryMnnyZGe6X79+HDx4kNLSUjp37tymx1X0gMfrqe7sGt2VhLCvpjP9wDHSrRH01FY9ifAgHKgID8KBip48iJFuH2IwGAgNDfX7L03uoCs3ZWV8GhTEo4rCVwaDo8P99NPOCOabDjY8zz2wbVPLQV9efj90KFuGDuVXP/8ML7zgmG7fwM0No93r2zGKuZ7ceBPhxTV69aIoCoqi0KVLF93l3R08Xk8NnW7FEoLRFEBGbcM0RQ11uvXaVtsb4UE4UBEehAMVPXkQnW4fYrPZWLt2rS6mRHgb3bjJy4PVqxnZrx839ejBmIED4corYcYMAArKa8nIr8BggAn9u7b5dHryYvjyS5Bl6N/f8XP1aocvzna6fz5aSml1fbucUjduvIzw4hq9efnggw8YPHgwQUFBBAUFMXjwYN5//31fZ8vjeLyeGp7nNgSGEt83glyl4Tp9RlvTy/XUVj2F8CAcqAgPwoGKnjyITrcPMZvN3HjjjbqYEuFtdOOmpATKy3li0iTWz57NTXfcAQEBju3ADw0B1Eb0DCeyY2CbT6c3L/TogQ3IDAx0/N7gJTaiA4MuCcUuK2w80D5RzHXjxssIL67Rk5eFCxcyb948brvtNtasWcOaNWu47bbbeOqpp1i4cKGvs+dRPF5P1obp5UGhTBzQlRNKw4wkDY1066mtehLhQThQER6EAxU9edB+Dv0cPTQSX6ELNxEREBYG+fnQo4fjZ1iYYzuwKbP9ppar6MnLoQMHuCIxEWSZU/fdhyXibCC5mwd3Y//JChL3FTJtTPtECdaFGx8gvLhGL17effddVq5cyfTp053bbr/9doYOHcoTTzzBiy++6MPceR6P1pP6THdgKBMGRLN4bcNId+0ZqC2D4HDPnbsV6KWtehrhQThQER6EAxW9eBAj3T5EkiQSExORJMnXWdEcunETE0PdoEHIOTmQmgpGI0ybBjExVNdJpB52jOxOauNSYSp68sK0afQNC8Moy5iMRo5cdZUzmBo4lg4DSD1STHlN26cF6caNlxFeXKMnLzabjdGjR5+3fdSoUbrIf1vweD05R7rD6BEeTO/uXTmthDq2aWS0W09t1ZMID8KBivAgHKjoyYNBUc5Z30iDVFRUEBYWRnl5OaGhob7OTruhKAqSJGE2m3URAMCb6MbNyy/z5tKlzD99msc6deL1xx+HxYsB2JBRyP/9Yye9uoTw0zPXtUs5dONFJS+Pw+np9Bk6FFPs+VHKb1z2E4dOVfH6PcP41agYFwdoObpz4yWEF9e0txdPfk498cQTWCwWli5d2mTk/0DHAAAgAElEQVT7008/TW1tLW+//Xa7ns/XNHbZqVMnz7bfn5fDhudg0J1wz0csTcriuv/+hpHGw/CrD2DI3W0+RVpOKSYjjIp1by1Z8R52IDwIByrCg3Cg4isP7nzm62M83o9RG4rgfDTvZvt2+PBDsmpqqAWCAL7+Gu66C8aM4Qd1avmA6Ha9EGjeS2NiYugX03xn+ubB3Tl0Kpv1GYVt7nSDztx4EeHFNVr2kpCQ4EwbDAbef/99kpKSuOKKKwDYtm0bJ06c4IEHHvBVFr2GR+up0fRygBsGRLMrpS8jjYexH07G1MZOd1WdxP0fbMNkNJC+cBKBZveWjdRyW/UmwoNwoCI8CAcqevEgppf7EEmSSEpK0sWUCG+jCzc5OVBQwN8kiUMWC4/V10NBAeTkYJcVfjzoCKI2cUDbo5ar6MLLBTh3Ys3NQxxRzFOyT1NpbdsUc7278RTCi2u07iU9Pd352rdvH6NGjSIqKoojR45w5MgRIiMjGTlyJPv37/d1Vj2Kx+upIXo5QY5O95AeYewIdNzYsB9cD7K9TYc/UlRFnSRTU2+nqKLOrWNova16C+FBOFARHoQDFT150P5tAT/GYrFwxx13+DobmkQXbmpqoLYWMxAXFARWK9TWQk0Nu3PPUFJdT6cgM2P6uDel0BW68OKCNWvW8NprrzF16lT++Mc/OrdfHt2JSyM7cLS4muSDRdwxvIfb59CrG08jvLhG615+/PFHX2dBE3i8nurOPtMNYDQaMF56NeWHQgirK4W87dDrCrcPn1Nc7UyfqrDSs0tIq4+h9bbqLYQH4UBFeBAOVPTkQYx0+xBFUaioqDhv9E+gEzchIY6XyQT19Y6fDds2HnCMcl9/eVcspvZ7m+nCiwvKysrYvn07a9eubbLdYDA4R7s3ZLRt6TC9uvE0wotr9OalrKyM119/ndmzZzN79myWLVtGeXm5r7PlcTxeT+pId2CYc1PPiFB+lIc7fjn4XZsOf/R0lTNdWGF16xh6a6ueQngQDlSEB+FARU8eRKfbh0iSxJYtW3QxJcLb6MJNnz5kdO7McxYL/46MhKgo6NYN+vRxLhV2QztOLQedeHHB7bffzooVK87rdIPjuW6AH7OKqKl3v1x6deNphBfX6MnLjh076Nu3L8uWLaO0tJTS0lKWLl1K37592bVrl6+z51E8Xk+N1ulW6dUlhI32hmjxWevbdPijjUa6C8vd63Trqa16EuFBOFARHoQDFT15ENHLBYI28N6dd/J/33zDzYGBJMbGwqxZHJ/zBONf24zZaGDngkmEhVh8nU3tkJcHJSWOdbwbAqwpisK1r/1Ibmkt79430rmUmECgJzz5OXXNNdfQr18/Vq5c6QwWI0kSs2fP5ujRo6SkpLTr+XyNVz/zV06A/J3w68+h/xQAUg8X88j7P5Ie9H9YkGDuDoiMc+vwU97cwoECR8f+kWsv5Y9TBrRb1gUCgUDgG9z5nBIj3T5ElmVKS0uRZdnXWdEcenEz6Pe/Z+4993DH/ffDP/4Bf/gDP2Q6ppaP7dOl3TvcevHiko0b4Zln4OmnHT83bgQappg3jHYntmGKua7deBDhxTV68rJjxw6ee+65JtFZzWYzzz77LDt27PBhzjyPx+vJ2vSZboCeXUKoIoSf5YGODW5OMVcUpckz3e6OdOuprXoS4UE4UBEehAMVPXkQnW4fYrfb2b59O3Z726Kj+iN6cXP11Vfz1pdf8ujKlTBmDAA/HFCnlke3+/n04uU88vKQlyzh3z/9xENbt1KVnAwLFzpGvoGbBzue607OPIXV5l7ZdOvGwwgvrtGTl9DQUE6cOHHe9tzcXDp16uSDHHkPj9dT3fnTyy8JD8ZsNPC9fZRjQ1aiW4curLBS2+h65u4z3Xpqq55EeBAOVIQH4UBFTx5Ep9uHWCwWJk+ejMUiph+fi17dlNfYSDtWCrTvUmEqevXCgQMYsrJ4uqiID6urSSoudqxzvnAhAMN7hnNJWBDV9Xa2ZBe7dQrduvEwwotr9ORl2rRpPPzww6xevZrc3Fxyc3P54osvmD17NtOnT/d19jyKx+vJGUjtbKfbZDQQ0zmYH+wjHRty06CqqNWHzjld3eT3Ijc73Xpqq+2KtRy+/xOcOgBcxB4aIRw4EB6EAxU9eRCdbh8iyzJFRUW6mBLhbfTgpr6+nvz8/CYREzcfKsIuK8R17UhsRId2P6cevDSHoa6OR4GnTCYuVy+OSUmwfTsGg4HJDaPd6zMK3Dq+nt14EuHFNXrysmTJEu666y4eeOABevfuTe/evXnwwQe5++67eeWVV3ydPY/i0XqS6kFq6AgHNX0mr2eXEAqJoDRsIKDAoQ2tPrwaRK1f146AY6TbnTA6emqr7UrGv2Dr3+HLB8AuXbweGiEcOBAehAMVPXkQnW4fIssyGRkZumgo3kYPbnbv3k1MTAz9+/d3blOf5544sP2nloM+vLhk4ECIjuZZYKnZzCCA4GDHUms5OQBMaQigtvHAKeql1pdPt248jPDiGj15CQgI4M033+TMmTPs3r2b3bt3U1payrJlywgMDPR19jyKR+tJnVoOTUa6wRHBHCAz9GrHhsM/tPrwRxtGuuMvjQDAapOpsLY+wq6e2mq7UnvG8bMkG/Z8dvF6aIRw4EB4EA5U9ORBdLp9iNlsZsKECU2C4wgc6MFNbm4uJpOJmIYo3Da7zOashk63B57nBn14cUlMDDz8sKOjrSiOlyRBXR2ccjwDP6pXZ6I6BVJplfjfkdZPMdetGw8jvLhGL15sNhs33HAD2dnZhISEMGTIEIYMGUJISIivs+YVPFpP6tTygE5gNDX5k9rp3mloCKaWu73Vh88pdqzRPaB7KGHBjtk9p9yYYq6Xttru2GrPpje/jFmxXZweGnHRtoVzEB6EAxU9eRCdbh8iyzL5+fm6uDvjbfTg5le/+hXV1dV89tlnAGzPKaXSKhHRIYDhPcM9ck49eGmWJ56AmTPBYuGgopCoKNC9O+zZA3l5GI0GbhrkmGK+YV/ro5jr2o0HEV5coxcvFouFvXv3+jobPsOj9aR2uoPOX+5F7XSn1vYCgwkqT0J5HseKqzldWdeiw6vTyy+N6kB0qGNGgjsRzPXSVtud+pqz6Yp85LSVF6eHRly0beEchAfhQEVPHtzqdL/99tv07t2boKAgxo0bR1paWrP7fv3110yaNImoqChCQ0OJj4/n+++/dzvD/oQsyxw5ckQXDcXb6MVNYGAg0dGOUe2NmY4R2wn9u2IyGjxyPr14aZY5c0jp1IkBNhuz6uqwnzoFP//sWLsbuHmIo9OddKAQyd66MurejYcQXlyjJy8zZszggw8+8HU2fIJH60mdXh54fqe7Z0On+/AZGboNBqA067/c9GYK973/8y8+m10vyeSWOjqNl0Z2IDo0CHBvpFtPbbVdsTV0urtcCoDhv8s4lrX34vPQiIu2LZyD8CAcqOjJQ6vH4levXk1CQgLLly9n3LhxvPHGG0yePJmsrCy6dj0/WnNKSgqTJk1i0aJFhIeH8+GHH3Lbbbexbds2RowY0S6F0Ctms5lrr73W19nQJHpzoygKP2R6bqkwFb15OY/cXOKrq4k2GBgZGEiJzUbX3FzIzYVhwxjbuwtdOgRQWl3PtpxSruoX2eJD696NhxBeXKMnL5IksWrVKn744QdGjRpFhw5NgzQuXbrURznzPB6tJ+v5y4Wp9IpwdLqLq+qxDR2NpWAPRQe2YLVN4dCpKgrKrVwSHtzsoU+UViMr0DHQTFSnQLq1odOtp7barqid7hH3w57PMRQf4io5Dcw3+zZfPuSibQvnIDwIByp68tDqke6lS5cyZ84cZs2axcCBA1m+fDkhISGsWrXK5f5vvPEGzz77LGPGjCEuLo5FixYRFxfHunXr2px5vSPLMsePH9fF3Rlvo3U3VquVX/3qVyxYsACbzUbWqUpyS2sJNBu59rKWdxRbi9a9/CI1NVgCAjgRGUlix4507dABLBaocXy5MpuMTB7kuGmRuK91Ucx178ZDCC+u0ZOXjIwMRo4cSadOnTh06BDp6enO1+7du32dPY/i0XpyrtEddt6fQoMshIc4nsM+HT7MsVvhTuff00+UXfDQahC1PpEdMBgMjUa6WzY1vTF6aqvtitrpDuwEE/7sSP93Kcq/5kB1ie/y5UMu2rZwDsKDcKCiJw+t6nTX19ezc+dOJk6cePYARiMTJ05k69atLTqGLMtUVlbSpUuXZvepq6ujoqKiyQtwLnxut9tdpiVJapJWK6C5tM1ma5JWp4upaUVRzksDTdKyLDdJS5J0wbTdbnembTYbeXl5yLLsN2VqrhytLVN9fb3TjRbLdOjQIb7++mveeecdzGYzSQ3LXF3dL5IAIx6rJ7vdTm5uLrIsa6KeWl2m2FiUiAgsHTti69sXQkJQIiOxxcY68zupfxQA3+8vpK7+wuUT76dfLpMsy+Tl5TXxpPcytUc9qV7q6uratUye4Mcff2z2lZyc7LHzagGPPq/nYo3uxqjPdR8JdART62HNJpB6AHadOHPBQzd+nhsgOszR6S50c3q5Xp5ZbFfUQGoBHWDAbcjxT6BgwLDvS3h7DOxZDReZk4u2LZyD8CAcqOjJQ6s63cXFxdjtduczrCrR0dEUFrYs8NGSJUuoqqri3nvvbXafxYsXExYW5nz17NkTcNztB8jMzCQzMxOAvXv3kp2dDUB6ejo5DcsPpaWlkZubC0BqaioFBY5OUUpKCsXFjsjIycnJlJU57lYnJSVRWVkJQGJiIlarFUmSSExMRJIkrFYriYmJAFRWVpKUlARAWVmZ80tPcXExKSkpABQUFJCamgo4olyrz73n5OSQnp7uTIeEhGA2m/2mTNnZ2c6gP20pU2pqKnFxcZjNZk2WKcpm4/ePPcas6dMxGAys3XkMcCwV5sl6qq2tpby8HLPZrIl6anWZCguRHn4Ya2QkifPnU202c+w3vyGp4RhlZWVYj+8hLNhCcVU9q/6zucVlEu8n12Uym83U1dU5y+EPZWqPejKbzQwZMsRZjraWSf188jTqjYP2whMxWtasWUP//v0JCgpiyJAhzjbkDmazmSuvvNJD0cubn14OZ5/rzqrrghQcgQWJQYZjAKSf0+lOPniKW9/aQlpOKQA5jUa6gTZPL/eYAy2jBlKzBIPBgHHySxhm/wBdB0JNCfz7EVh+Nez/90XT+b5o28I5CA/CgYquPCitID8/XwGU1NTUJtufeeYZZezYsb/4///85z+VkJAQZePGjRfcz2q1KuXl5c5Xbm6uAiilpaWKoiiKJEmKJEnnpW02W5O03W6/YLq+vr5JWpblJmlZls9LK4rSJG2325ukbTbbBdOSJDnTdXV1SlZWlrMM/lCm5srR2jJZrVbl0KFDiiRJ2ivT998rysMPK9K0aYo0Z45SuO57Jfa5b5Xef/hWOVVR69F6stlsysGDBxVJkjRRT26Xads2ZemDDyrBgYHKU089dV49/f7L3Ursc98qf/733haXSbyfXJdDkiTl4MGDzjz4Q5nao54kSVIOHTqk1NbWtkuZSktLFUApLy9XPMH777+vDBo0SAkICFACAgKUQYMGKStXrmzTMb/44gslICBAWbVqlbJ//35lzpw5Snh4uHLq1CmX+8+bN0955ZVXlLS0NOXQoUPK/PnzFYvFouzatcu5z//+9z/FZDIpr776qnLgwAFlwYIFisViUfbt29fifJWXlztdSpKkZGdnO9tDu7L+D4ryfKiiJC10+eeX12cqsc99qyz8Zp/y/9k77/AoqvWPf7akkx4gkoTQRYqE0ERFUEAEpKhIEQWx/LwoFvCK5SroVa9XLIiCBQQRkSJeitIEaaGXkBAChE4aSUgPpO/u/P442U2WbEI22c3uJvN5nn1yMjsze97vOTO7Z8573jdhwUhJmu0lffPRK1Lomxul9v/aLBWVltfp0W/3S6FvbpTC/71NuppTII35Tvy/ITpZkiRJOpGYLYW+uVHq/XH1v39MYVUN7Jnv7xPtc/YvSZIq6FBcIEl7PpOk/wSL92d7SdL8PpJ0YaeNK2x9Gm1fuAlZB1kDPbbSoeL3VE0xa6Y7ICAAlUpFWlleXT1paWkEBgZWe+yqVat47rnn+O2334zc003h4uKCl5eX0QtApVIZ/poqq9Vqo7JSqay27OTkZFRWKBRGZYVCUakMGJWVSqVRWf+kpaqySqUy2p6bm4skSQ3GpqrsMNcmlUpFTk4OkiTZl00pKah++w10OlTt26PSaNi+VcwMhYX40MzT1artBJCXl4ckSXbRTrW2qXdvWo0cSWFxMVFRUZXaaWgXfRTza+h0knw91aGdJEkiLy/PqO6ObpMl2kmSJHJycozqawmbrMGsWbN49dVXGTFiBGvWrGHNmjWMGDGC6dOnM2vWrFqf1xoxWubNm8dDDz3EG2+8wR133MGHH35IeHg48+fPr1UdJUkiOzvborP7BoqqXtMN5e7lCVkFRGrbAzDEKxFfdydKNDrOpAiPivTrxQZ388z8El769TgXy2a629w0051+vdjszAxW1cCeKa0w000FHZROcN8/4bUY6P8WuHhD+hn4ZTSsfQHyM2xYaevSaPvCTcg6yBrocSQdzBp0Ozs706NHD3bs2GHYptPp2LFjB3379q3yuJUrVzJlyhRWrlzJ8OHDa1/bBoZaraZXr16O4RJRz9itNpmZkJvLESC9qAiCgtiuFGuQB3eyXtRyPXarSy146KGHOH78ODuXLTPk6tZzb/sAmrioSc0rIiqx+oBFehqSNpZE1sU0jqTLd999x6JFi/jkk08YOXIkI0eO5JNPPmHhwoV8++23tTqntWK0HDx4sNKD9SFDhlR7zuriuKjVasLDww0PUiy69r9I3Fu0Th4m1/638HIGID6rgI3ZQQCEFp6iW7AYpB+Pz0Kj0bDjTBqSBK383fF0VXM8IYesfLH2u6WvKxqNBv8mLqiUCnSSGJibE89ArVbTvXt3w4OlhhZ3okqbytZ0S07uhjr37NkTlUolbHL1QRrwFqXTjkPvF5BQQMwqmN8LXdQKSktK7M+mOraTJEn06tULpVLZYGyqTd+r7r7gqDaZ207V3Rcc1abatJP+vqBWq21ikzmYHb18xowZLFq0iJ9//pkzZ84wdepU8vPzmTJlCgBvv/02kyZNMuy/YsUKJk2axBdffEGfPn1ITU0lNTWV3Nxcsyvb0NBqtcTFxVk1AI+jYrfa+Puj8fTk3p9/ptnnnxN37iIH3W4D4MF6GHTbrS61wM3Nje4ZGShmzoR//hPeeAO2bwfARa1i4B0iBeHW2JpFMW9I2lgSWRfTOJIupaWl9OzZs9L2Hj16GH4EmIu1YrSkpqaafc7q4rhotVr279/PqVOnAMuu/dfmi9npqDOXTK79v3jiMCAike8vaIlGUuJckErLonMAHDqfRkREBNtOC++/nv4avhwbVm63lwvXriYSFRWFSqnA11X85ErNLTIrnoFWq2XHjh0kJyff0qYGFaOhRHgL5JfoDDbFxsZSXFxsbNPO/TBsDgUTNpDnFgKFWSg3TCVvwQOQedG+bLJAO8XFxZGWltagbALz+p5Wq+XgwYOcOHGiwdhkbjtptVp27dpFfHx8g7Gptu0UExODVqutV5tqFcelxo7oFfjmm2+kli1bSs7OzlLv3r2lQ4cOGd6bPHmy1L9/f8P//fv3l4BKr8mTJ9f482rjN+8IaDQa6fjx441+PYYp7FmbpBUrpNaenpKnWi39+Y93pdA3N0oDPttlWEdqTexZF7NJTJSkIUMkqX17SXf77ZKuXTvxf2KiJEmStOVkihT65kbp7k921EjbBqWNBZF1MY2ldbHm99S0adOk6dOnV9r++uuvSy+++GKtzmmtGC1OTk7SihUrjLYtWLBAatasWZXnqi6Oi0ajkSIjI6Xi4mJJkiy89r9szXDpqY0m1/4XFBZJbd7eJIW+uVEKfXOjdOmjcEma7SXF/rVYCn1zo3Tvf3dIOTcKpfb/2iyFvrlROpWULUmSJP1382kp9M2N0jM/HTFa+z/ym71S6Jsbpa2xKWbFM9BoNNKxY8eMYns0pLgTVdr0YXNJmu0l6bIuS5IkSYWFhVJkZKRUWlpatU2F+ZIU8YWk+7CZWOv9YTNJG/GlVFpSbB821bGdioqKpOPHj0slJSX20051tKk2fa+6+4Kj2mRuO1V3X3BUm2rTTvr7gqYsBlR92VSbOC618qubNm0a06ZNM/ne0qVLjf7fvXt3bT6iUaBSqejevbutq2GX2LM2QRMmcKlfP4pTUngrthTOZjO4U3ODm5M1sWddzOb0aTh7lv8WFbE4J4flwcH0OXtWbA8Opn+Hprg5qUjOKSQ2OY+uwabXXeppUNpYEFkX0ziaLosXL2bbtm3cddddABw+fJiEhAQmTZrEjBkzDPt9+eWXNTqfJWK0rFmzppIreWBgoNnndHFxwcXFpdJ2/Vr58PBwo216Ki4NqEnZKT8FDnwDfafh5BtqyNOt9vCDCnEA9Li5uhDk40ZCllhbfKNpOFy9QLuSOBSK5iRmF/JHTColGh2h/u7c0ULco94Y0pHwUD+6Bnkb1Vfk6s7lWl6R0faa2NSjRw/TNlWorznlm2M06F1Ua1Kuqu7m2lRlOzk5iWjkGuFernAS6+JdXV2N+oJJm1zdod8MFJ1Hw8bpcGk3yh3vo4zfB48sROnhL+zIjkd57i+UShW4+aJy84WWdwFq69lUi/LN7eTi4lLpvmXTdrKATbXte1XdFxzZJnPbqar7giPbZG47VbwvVDyntW2qTRwXs93LZSyHVqs1uM/JGGP32gQHowzvwc6EGwAMusP6ruXgALrUgpNFRVwoKmJ1jvHabTdnFQ90FC7mm2vgYt4QtbEEsi6mcSRdYmNjCQ8Pp2nTply8eJGLFy8SEBBAeHg4sbGxREVFERUVRXR0dI3Paa0YLX379jU6J8D27durPWd1WLSdDi6AIwth/VSQpFsGUoPyYGoAnu3vBsAlJZL2zZoA8PXOC4BYXqR/8KpUKhjcqTmBZbm59QTWMle3I/VVi1E24AYMgdTM0sGvDTy1HkZ8DWpXuPA3/HAfHP8FVk6Aed1gyxuwaQb8PkUEYfvuHijIspJBlqFR9gUTyDrIGuhxJB3sP4KMjIydcvRKFrmFpfh5ONMj1NfW1XE8OnWCjh155dQphru5MdLdHdq3F9vLGNo1kE0nU9hyMoWZQ26vF28CGRl7Y9euXVY574wZM5g8eTI9e/akd+/efPXVV5VitCQnJ7Ns2TJAxGiZPHky8+bNM8RoARGfwdtbDFxfffVV+vfvzxdffMHw4cNZtWoVx44dY+HChVaxwSzSz4q/8fvFIKyoLLZMFXm6oTxXdxMXNcFd+8MeIOUEvTq6cy7tBunXiwEY3Kl67wDQz3RDam5x7W1oLJRWHHS7V71fdSgU0GMyBIXDb5Mg6xL8UcFLM/RecPOBwmy4dhqyLsLvz8DE30El/zyWkZGxLPJdxYaoVCq6dOli62rYJfaszRNPPIEkSTQf+hIAD3RshkpZP4NBe9bFbIKDYcYM+ixZQp+MDAgIgGeeEdvLuP/2ZriolVzJLCAu9Tp33Fb1j+MGpY0FkXUxjb3rkpCQQMuWLWu8f3JyMkFBQWZ9xrhx40hPT2fWrFmkpqYSFhbG1q1bDYHQUlJSSEhIMOy/cOFCNBoNL730Ei+99JJh++TJkw1Ly+6++25WrFjBu+++yzvvvEP79u1Zv359rbW2aDtlXSwvb3sXdCJyLS5V31f0Kb96t/ZD7d8a3AOgIIMB3in8inAvrOmDV/2g+9p182a67b2vWoWyIGqoXaGCe2etdAjsCv+3GzbOgEu7oMsY6PUcNO1Qvk9qLCweLN7/ezYM+bjOJliDRtkXTCDrIGugx5F0kN3LbYhWqzVEYZQxxl610el0rF+/nlWrVnEwQfwoqI9UYXrsVZdaM3gwfPYZfP65+Dt4sNHbHi5q+ncQKdm2nKzexbzBaWMhZF1MY++69OrVixdeeIGjR49WuU9ubi6LFi2iS5cu/O9//6vV50ybNo34+HiKi4s5fPgwffr0Mby3dOlSo7gsu3fvNqRgqfi6OZbL448/ztmzZykuLiY2NpZhw4bVqm5gwXbSFENuWVpCtRukx5W9oQDnJlUeNr53CC/c14Z3h98hZk5DegPQTXHesM+gO2r24DXQMNNtvnt5RQ1OXc2lsMQ++63F0M90V5jlrlNfcPWGMYth5iUYNsd4wA0Q2AVGl6XfOzgfYn6rZcWti73ft+oLWQdZAz2OpIM86LYxbm5utq6C3WKP2uh0OlavXs0bH39F2g0NLmol/doH1Gsd7FGXOhEcTGmnTizfvZuxY8dSUpZbVc/QrsJtc0vsrdMYNThtLISsi2nsWZfTp0/j4eHB4MGDCQwMZPjw4Tz//PO8/PLLPPnkk4SHh9OsWTOWLFnCnDlzeOWVV2xdZathkXbKvgKSDpw9od/r5dtdvQwzqabwdHXi7WF30KZp2cA8uBcATbNP4OkinAVr4loOIoUYQJqZa7qhXIONMVcZ/vU+Ptp02uxzOBSlZTPdN7mWW/Wa7fxIed9Y9wKsfhLiD4r1/3aEPd+36hNZB1kDPY6igzzotiEqlYqOHTvWKgJeQ8detVGr1YwYMYKQPmLmpl/7ANyd62+Vhr3qUleUSiVvvPEGa9asYXtZrm49A+9ojpNKwflrN7hw7XqV52io2tQVWRfT2Lsu/v7+fPnll6SkpDB//nzat29PRkaGIa/pxIkTiYyM5ODBg3WaSbZ3LNZOmSLgGf5toO+L4CGCNOJSfVaESpTNdCuSjvKfR7vyj/5tDQEfb0XzskBqeUUas2aqK2qw6ojIX7v7bLp59XY09DPdzuWD7nq5Zu//F3SbIB7QnPkTfnoIfhwo1oPbAfZ+36ovZB1kDcBTViUAACAASURBVPQ4kg7yoNuGaDQajh49ikajsXVV7A5712b7GZESpz5dy8H+daktKpWK6dOnM2vWrEprc7xcnejXXu9iXvVsd0PVpq7IupjGUXRxc3NjzJgxfPXVV6xbt46tW7eyfPlyXn/9dYdZx1YXLNZOmWXruf3agrMH9J8p/ves2Sy1gRbdQaGC61cZ0UrHW0M71jimh6eLGndn8cPQnAjmeg2uZudz4GIGAMk5hWavDXcoSkSaNn3kcqina1apgke+hxcPQfhksaY8ORJWPVleJxviKPctayPrIGugx5F0kAfdNkShUODr6ytHZDaBvWqzb+1aNi9ZTkxSLgoFPNCxfgfd9qqLJZg5cyYffPABoaGhld57qIv4Yby5GhfzhqxNXZB1MY2si2NgsXbSB1Hzbyv+9nwWRn4DD9csr7kBZw+x/hcg8YhZhyoUigoRzGs+YNZrsDk2FV0FT+fohJyqD3J0SvWDbg/Dpnq9ZpvdASO/hpcjhVfEtVOw6XWbu5rL9y2BrIOsgR5H0kGOXm5DVCoV7dq1s3U17BK71Gb7dmZOncrJFj3wH/IS3b0UNPV0qdcq2KUu9cCDnZrzjlLBmZQ8rmTk0yrAo9I+jVWbWyHrYhpH0mXGjBkmtysUClxdXWnXrh2jRo3Cz8+vnmtmfSzWThVnukGs4w6fVLtzBfeGlBOQdBS6PGrWoYFerlzOyCc1r/DWO5eh1+D1v/YDIn3ZjWIN0Yk5PNjZzJl6R8EQSK18ptsm16x3MIxZAstGwokV0LIP9HjaeB9Jgs1vQORS4ZauUIgZ8k6j4Z5XoOntFquOI923rImsg6yBHkfSQZ7ptiEajYYDBw44hEtEfWN32iQlwerVBLm749upHwCDr54U2+sRu9PFwkiSxNGjR/n++++Ntvu4O9O3rT9QdUC1hq5NbZF1MY0j6RIVFcWSJUtYuHAhe/bsYc+ePSxatIjFixezY8cOZsyYQbt27Th9uuEF17JYO+kH3f4W+HFWtq7b3JlugCBfMYhMzq75oFuj0bB2215OJOagVMAL97UBIDqxIc906wOpGbuX2+Sabd0PHnhPlDfPhOTjxu8f+haOLhIp6CQt6DRQcgOil8OC3rByAqRZ5tp0pPuWNZF1kDXQ40g6yINuG6JUKgkKCkJZTeTUxordaZOZCbm5LJo4GecQ4Vr4UOZZsb0esTtdLMyFCxfo3bs306ZNIy0tzei9oV1uA2BLrOnUYQ1dm9oi62IaR9Ll0UcfZeDAgVy9epXIyEgiIyNJSkpi8ODBTJgwgeTkZO677z6mT59u66paHIu0U0kBXL8qynr38rpQFsGc1BiRiswMWviUDbpzau5erlQqOXVDuKXf0y6AQWWxRE4k5qDV2VdkbYthCKRW7tVk02v2ntegw1DQFsOy0XBxp9iecAi2zxLlBz+GGXHiNWUrdHwYUMDZzbBkCCRF1rkajnTfsiayDrIGehxJB/uvYQNGqVQSGhrqEB2lvrE7bfz9wdub7VkKNCjpqMmltbtSbK9H7E4XC9O+fXv69+/P2LFjKSgwDlrzYOfmKBUQk5RLYlblgDYNXZvaIutiGkfSZc6cOXz44Yd4eXkZtnl7e/P+++8zZ84c3N3dmTVrFpGRdf9Rb29YpJ30kaddfcDdAi74vq3APQC0JcLN3AyCDYPums90KxQKdl8WM7+jwoLo0NwTd2cV+SVaLly7YdbnOwwmAqnZ9JpVKkWAtZZ3Q3EuLB8D++bCmiliZrvLY9D3JfC6TbxC+8L4X2Ha0bJj8uCXR0RQNoDSIuGOvvVtKMgyoxqOc9+yJrIOsgZ6HEkH+69hA0aj0RAREeEQLhH1jd1pExwM48ax1TUIgIeKr8K4cWJ7PWJ3uliBXbt2sWLFClq3bm20PaCJC31ai4ccm05Wnu1uDNrUBlkX0ziSLtnZ2Vy7dq3S9vT0dPLy8gDw8fGplOO+IWCRdro5iFpdUShq7WKudy+/asag+2RSNhfT83FWKxnSuTkqpYI7g0Wqs+jEbLM+32EwEUjN5tesmw9MWl+WUkwLf78vPCgCOsCIeaJf3ExAe5i4pnywvuwRcdxXXeHPV4Vr+oaXahygzeYa2AmyDrIGehxJB3nQbUOUSiVt27Z1iKcz9Y09ajN9+052qEXqqqHPjYLBg+u9Dvaoi6WpLgLlqLAWAKyPSq70XmPQpjbIupjGkXQZNWoUzzzzDOvWrSMpKYmkpCTWrVvHs88+y+jRowE4cuQIHTp0sHFNLY9F2smQo9uCwXb0LuZJ5g26De7l2YVINRxorTwiYocM7NgMT1cnAMJCfIEGvK671PRMt82vWbULjP6ufI23kzuMXQYunlUf49KkbODdVwy8982F/GvgFQQqZ+F+fmxJjT7eLjSwA2QdZA30OJIO9l/DBowjrUOob+xRm70Xs0Gpxt9ZS4c7bRMp0R51sRbp6elEREQYbRva9TacVUriUq9zJiXP6L3GpI05yLqYxpF0+eGHHxg4cCDjx48nNDSU0NBQxo8fz8CBAw1BBzt27MiPP/5o45paHou0U2aZe7mfhWa6ocJM91GzDrvNW6zNLizVkl1Qesv9fz0cz8qjiQBM6N3SsD0sxAeAqIaaNkw/6HZ2N2yym2tWoYD7/gkv7IWp+0V6sVuhH3h3GAotwuGRH+DVEzDoffH+X+/AtbhbnsZuNLAxsg6yBnocSQf7r2EDRqPRsHPnTodwiahv7FGbOwZPAGBY19tslg/QHnWxBkePHqVFixaMGTOG0tLyH6bebk480LEZAOujjWe7G4s25iLrYhpH0qVJkyYsWrSIzMxMoqKiiIqKIjMzk4ULF+LhIdxvw8LCCAsLs3FNLY9F2snS7uUALbqDQiXci3MSa3yYq5PKkGryVi7mO86k8d76WABGt1Vzdxtfw3vdW4pB97m06+QX238fNhtDyrDyQbfdXbO33Ql+bWq+v4snPLEK/m8XdBsPKifoMxXaPgCaIvjfc7cMzGd3GtgIWQdZAz2OpIM86LYhSqWSLl26OMTTmfrG3rQpLNFyLFn8CBjb13YunPami7UICwvDz8+P0NBQrl69avTe6O5iXf2GqKvoKkTubSzamIusi2kcUZcmTZpw5513cuedd9KkSRNbV6desEg76d3LzRkg3QpnDzHwBri4w6xD9S7mSdWkDTuRmMO0FVHoJBjbM5i3R4YZadDcy5UW3q7oJBFcssFhCKRmPNPtaNfsLVEqYfT3IjBf2klYeD8cXQzF16vYvQFqUAtkHWQN9DiSDvZfwwaMUqmkWbNmDtFR6ht702bPuWsUlmoJ9nWjS5DXrQ+wEvami7VwcnLi1KlTHD16lFCVCk6cMOREv79jU7xc1aTmFXHocnnKtsaijbnIupjG0XTJycnhiy++4LnnnuO5557jyy+/JDe3AQ62bqLO7VSUB/npomzJmW6ADg+Jv2e3mnXYrSKYp+YW8ezPxygs1XJfh6Z8/EhXmjdvXkmDsLLZ7qiGGEyt1PSg25Gu2Rrj2Rwe/UHYeu0UbJoBX3SEnR+DTme0a4PVwExkHWQN9DiSDvZfwwZMaWkpf/31l5H7rIzA3rRZtku4+N0V5Goz13KwP12sSUBAAGzfDu+/Dx99JP5u346LWsXwO0XO7ooB1RqTNuYg62IaR9Ll2LFjtG3blrlz55KVlUVWVhZz586lbdu2HD9+3NbVsyp1bie9a7lHU3D1tlzFAG4vG3Rf2l3uDl0DWviIdd2m3MuLNVqm/hpJxo1iOgZ68u3EcNBpTWqgX9cd3RDXdZsIpOZI16zZtBsE00/BkE/Avz2U3ICIOfC/Z41czhu0BmYg6yBroMeRdJAH3TZEpVLRq1cvVCqVratid9iTNsUaLUeSRI7UvNMRt9jbutiTLlYnKQlWr0aTksLlvDxIS4PVqyEpidFhwsV8y8lUikq1QCPTxgxkXUzjSLpMnz6dkSNHcuXKFdauXcvatWu5fPkyDz/8MK+99pqtq2dV6txOmWWDbksGUdPTvAt4BYOmEC7X/LshqEIE85v5cONpohJy8HJV88NTPWjioq5SA30E84MXMzlwMaMOhtgh+ocYFQKpOdI1Wyvc/aDviyK39+jvQOkEp9bCr2OExwaNQIMaIusga6DHkXSQB902RKlU4ufn5xAuEfWNPWmz/0IGGoUTqpLrDOl5u03rYk+6WJ3MTA7v2kXoli2M+PtvpP374fBhyMykVys/gnzcuF6sYWecyF/cqLQxA1kX0ziSLseOHePNN99ErVYbtqnVambOnMmxY8dsWDPrU+d2yrRCEDU9CkX5bPfZLTU+LMhXDCRvdi//7Vgiyw8loFDAvPHdCfUXQfKq0iAsxIdOt3lxvVjDxB8PM2drHKVaY3dkh6VEPOi+2b3cUa7ZOqFQQNgTMPE3cG4iHuj8NBRSYhqPBrdA1kHWQI8j6WD/NWzAlJaWsmnTJodwiahv7EmbjTEpAEzs34XHHn3EpnWxJ12sTmIit6enky1JXAOSSkogIQESE1EqFYwsy9m9rszFvFFpYwayLqZxJF28vLxISEiotD0xMRFPz2ryAzcA6txOevdySwZRq0iHoeLvub+ghnm3TbmXX80p5N2ySOXTB3Xg/rIsDVC1Bs5qJb9P7cv4XiFIEny7+yLjFx6iWKOti0X2gYno5Y50zVqEtg/A05vE0oi0WFjYH+2mN/hrw5rGo0EVNLq+YAJZA4Ej6SAPum2IWq2mX79+RrMXMgJ70aaoVMu2U2kAjOjWwqZ1AfvRpV4oKMDH2Zk9vr4k+foS4uEBTk5QINb6PVIWxXz32Wtk55c0Lm3MQNbFNI6ky7hx43j22WdZvXo1iYmJJCYmsmrVKp577jkmTJhg6+pZlTq3U1ZZjm5rzHQDtLoXnDxE6rDUmBodEuwjBpKZ+SUUlogB8p5z6ZRodNwZ7M20+9sZ7V+dBu7Oav772J18OzGcJi5qIuOziTjXAFzNTQRSc6Rr1mK0CBP5wDs/CpIO1dGFDD73DuqkQ7aumU1plH3hJmQNBI6kgzzotiEKhQIvLy+bBuayV+xFm91nr3GjWEMLb1d6tPS99QFWxl50qRdatwZ/f3p5e+PcsiW4u4O/v9gOdGjuSafbvCjVSvxx4mrj0sYMZF1M40i6fP755zz66KNMmjSJVq1aERoaytNPP82YMWP49NNPbV09q1LndrqeKv56h1iuUhVxcoW294tyDaOYe7mp8XAW6w+v5ooZ3SOXswAY0KEpSqWxrTXRYFjX2xjSORCAU1cbQFR7/aC7wppuR7pmLYrXbfD4T/DUevBvhzL/Gorlj0HcZlvXzGY02r5QAVkDgSPpIA+6bUhpaSkbNmxwCJeI+sZetPnzhHAtvxzxP957712b1gXsR5d6oVcvmDIFnJ3h2jVwdiZvwgSxvYyxPYMBsRayUWljBrIupnEkXZydnZk3bx7Z2dlER0dz4sQJQwRzFxcXW1fPqtS5nQrK0gq6+1muUjejTx12rmbruhUKBUG+xsHU9IPu3q39K+1fUw06txDpLE9dzatRPewWbSnoNKJ8U/RyR7lmrULb+yl9dhcp3uGgLYbVT0L0SlvXqlJas/qg0fcFZA30OJIO9j8X34BRq9U8+OCDDuESUd/YgzY3ijXsiBOu5VlR21D2tL0bpz3oUq+89RYMHMj5AweY8tNPZK5ezenZsw1PNEeFBfGfzXGcuprH2Wv5jUubGtLo+kwNsXddZsyYUeN9v/zySyvWxLbUqZ1KCspnTN0DLFuxinQYIv5ejYK8FDEzeQuCfNw4l3aD5JxCkrILSM4pRK1UEB7qU2nfmmpgGHQnO/hMtz6IGlRyL7fna7Y+ULt54vN/fyBtn4kiegWs/wfkJkG/16E+A0mln4PT6+HUOsg4B13HwpCPrftwqwJyX5A10ONIOth/DRs4jtBJbIWttfn7dBpFpTpC/dxYv/V3fH1t714Ottel3unVi8COHTnx7rsUFhZy6tQpunTpAoCvhzMPdm7OxpgUfjuWxHvDbBtd3l5pdH2mhtizLlFRUTXazxFc6upKrdtJP8utdAIXKwaca9IMgnpAciSc2wo9p9zykBZlacOu5hQaZrm7BHnj7mza1ppo0Kls0H01t4is/BL8PJxraoF9oQ+iplCBytgGe75m6wu1syuMnA9ufnBwPuz6CK5EwCMLb/3Ap/g6bHsXJB0E3gmBXcXL2aNmH67TwdrnIfZ34+0nVsD5bTD0U+jymIjAXpGSfLHUw4KxFeS+IGugx1F0kN3LbYhGo2Hz5s1oNBpbV8XusAdt/jxxFYBR3YMJCwsjNDTUZnXRYw+62AJPT09WrVpFQkKCYcCtZ2xPsVZzQ3QyGzY2Pm1uRWPtM7fC3nXZtWtXjV47d+60dVWtSp3aqaAsoJhHQOVBgKXp+LD4e2ptjXav6F6uH3T3aW16lrCmGni6OtHKX8wMO/S67opB1Cq0m71fs/WBQQOtDh78CEYtEDpdjoDv7oboFVCQVcXBJcIlPXIpHF8Gm/8JS4bA5x1g69uQU5YhIf0sbHkL5naF3f81jsq/+z9iwK1QQfsHRT7xSRug6R3ievvfs/D9veK41JOQcAg2vCQ+45tw2Di9/KGKJXSQ+0Kj1gAcSweFJNUwx4UNycvLw9vbm9zcXLy8vGxdHYshSRIajQa1Wt0oZivMwdba5BSU0OvjvynVSmyffh/tm9tHWh5b62IXJCVBZqYIqhYcjFYn0e/TnVzNLeKrsXcyqntw49XGBHKfMY2ldXHU76kFCxbw2WefkZqaSrdu3fjmm2/o3bu3yX1TUlJ4/fXXOXbsGBcuXOCVV17hq6++Mtpn6dKlTJliPNPr4uJCUVFRjetUUUtPT8/at9OFv2H5Y9C8C0zdb96x5pIdD/PuBBTwehx4Bla7+4boZF5dFU3v1n5k3CjmUno+iyf3ZOAdzSvta05ffWnFcTbFpPDW0I78o7+VIrZbm9STYuDWpDn885xhs3wvq0KDjPPw+zMVoucrxOx1u4HQ8xnwaWk8Q+3kAb2eFYPrlBNwoyzYoEIFzTsJ/SvS42kY/iXEbYTfJoltjyyEbuPK99GUwP55EPGZWG9eHc06i8BwTWvvmSb3BTM00OmgNN+63j42xFZ9oTbf+fJMt41xhCcztsKW2myNTaVUK9HKA36d/QbbV6ywWV1uplH3me3b4f33Kf33v+H992H7dlRKBWN6iIBqayKTbFs/O6VR95lqaOy6rF69mhkzZjB79myOHz9Ot27dGDJkCNeuXTO5f3FxMU2bNuXdd9+lW7duVZ7Xy8uLlJQUwys+Pr5O9ax1O+Xrg6hVDk5mcXxDIaQPIEHsrWe7g8rcy8+k5HEpPR+FAnqGVr0etqYa6Nd1xzryuu4S/Uy3W6W3Gvs1CyY0CGgPz/0NA94WM85IYgC+by7MC4M1U+DPl8WAW6mGcb/Agx/CxN/EA6In/wet+4OkFQNuhRJuHwb3zRTlyKWwYhysmyo+r+804wE3gNoZ+r8hzjfqW7h9OKhdxSx82ESYskV8jkdTuHYKFg6A0xssq4OjUZIP2rrZUCMNNv8TPm0N5/6qfr+zW0Wf0V9/DoSj9AV50G1DNBoN27Ztc5jOUp/YWps/ylzLb4vZwQc//MDCf/1LDPhsjK11sSlJSWQtW8azERF02LGD4tJSWL0akpIY00O4mB+4mEl8xnUbV9S+aNR9phpkXUQAtueff54pU6bQqVMnvv/+e9zd3VmyZInJ/Vu1asW8efOYNGkS3t7eVZ5XoVAQGBhoeDVvXnn2tqbUqZ0K6nHQDdD1cfH35Jpb7qp3L79eJOzqGOiFt7uTyX3N0aBLC9Eupx05grnBvdx4nbF8zVajgdoFBrwFLx2C18/Co4ugzQAxkD61FqKWi/1GfydmwPUoFNBuEEz+A/5vD4z8Bl6NgQkr4YF/weNLxbr6C9vFbGnr/jDog6or6O4H3SfChBXwVgK8GQ+jv4XQu8Xn/GO/qFdpAax5GmJufa2YpUN9odPBxZ1w8nc48yec2yYC2t3qmBOrxMOLBXfBJ8Ewp40IRlcLaqRBbjIc/xl0pbD+RbiRbnq/+AOw6gn4+31Y9ABcO1OrOtkCm/cFM5AH3TbEycmJUaNG4eRk+ou2MWNLba7lFXHwovix9nBRIlO6deOhoCDDAM+WNOo+k5lJk4ICtqamciU3l7+KiiA3FzIzaenvzt1t/ZFQsOFEmq1ralc06j5TDY1dl5KSEiIjIxk0aJBhm1KpZNCgQRw8eLBO575x4wahoaGEhIQwatQoTp06Vetz1amdKq7prg86jRYuulePQ+bFandt5umKukI+7t6tqg7UaY4G+pnuSxn53Ci2/x+hJtGv+b1ppruxX7NQQw08A+HOsWKt9Qt74c5x4OoDQz8T26uiRRiETwKfCjntO42CiWvAxRsCOpQNwmsYtErtImbAjerWHJ5cC2FPimBu6/6vVmnPbNoXinLF2vhfHhFr2Fc/CSseh6/D4eiPxmvgDcfkif3WvSCCzqWfEfYX54qHD1vfEanyzKBGGhxdVJ5+ryAD/nylcv3yM8TyBEkrPBvSz8DC+8W6f/tfgexQ9wV50G1DJEkiLy8PB1hWX+/YUps/Y1KQgLCidCaGdWTJ6NE8e//9hgGeLWnUfcbfH2dfXxb06sX+yZMZ4e4O3t5ibTfwuMHFPBGdrhHqUwWNus9UQ2PXJSMjA61WW2kWunnz5qSmptb6vLfffjtLlixhw4YNLF++HJ1Ox913301SNQ8si4uLycvLM3oBaLVaJEkiOzvbMIuh1WrRarWAmOGoWNaV5Qs2lMtmunVuwm27tLTUsE9paamh7fVlSZIqlQGjsk6nMyrr66XT6dC4+opZPEBbNoOn1WpN1h1JR6C3q0GDnqG+Vdqk18CUrTfb5OfhzG1l5z19NbfuNpkoV2VTVeVbttPN5aKyWXpnd6N2KikpITc311BfR7LJkn0vLy/PqL7V2tS0Ezy6EO0bl9D2fLZ2NrW8F92M0/CP/Wicvepuk0JJ6bAvxVpxSYe0fipsfgPd0cVoz26H/IxbtpMkSeTk5NyyzSzeTqmxSD/0h7ObkFTO6ELvgZA+SP7txVr2Ta8jrXma0uuZ5XW/dhZ+HFR2jAvcOx3d+JVoXomBe14DgEML4Keh6PZ/jTZuC2RdvqVN1d0XNBoNuqLrcOwnUY8B/xIeC2c3Ix1fVm6TTou09v/gegpSQAekl4+ja3M/aArhj5fRrZsKpUV2fT3p7wum+lt93CPMQR502xCNRsPevXsdwiWivrGlNv8rWxf8mCYZkpNBqxV/KwzwbEWj7jPBwTBuHKNbt+bu/HwUKhWMGye2A4M6BuCmhqTsQiLOV+FC1Qhp1H2mGhxVl3Pnztl1nfv27cukSZMICwujf//+rF27lqZNm/LDDz9Uecwnn3yCt7e34RUSImbaYmNjDe2kny2PiYnh/PnzgEirdvnyZQCOHDlCYmIiAAcOHCAlJUXM4AD5khiE7ty5k5ycHAC2bdvG9etiKcrmzZspKioyioJbVFTE5s2bAbh+/Trbtm0DICcnxxAxPiMjg4iICEAEmTtw4IDBxbz46C8gSVy+fNmQ/u38+fPExIhgV2fOnMHHSWfQwF+XXaVNGo2GiIgIkpOTAYiIiCAjI6NKm/Sz3SfiMy1jE5CYmMiRI0cAqrXpzJkz5rfTTTbFxZSly3NyN2qnLVu2sHfvXoMdjmSTJfve3r17SUtLq1+bMnJB7Ww5m7b/DcPnUtT1SRRIcGQhyk0zUK0cA191JTNyQ7U26a+JEydO1L2d9uwm79hqOLaES7+8SvGWd+Gvf5G0aCKl61+B9S+RPn8Iup+GweLBKLIvI3kFUzzxD/70ewGe3cb1STs4HTIRlGoUp9ejmxcG3/ZFN/8u+KE/ZJxF696M6O7/gUHvk+jWmSNxyTD4A1L6f4FG7QFJR1Fufw/VqvHwdRj5i0dz9mRklTbp7436mBk39738Az9CUQ4FLs3J6TIFHngPALa+BUcXE/3rbDRb3kZxcQcahTOaRxZT5BbIn16TYeBsJIUSZcxK+Okh8hJP2+31tH37dsN3uUXv5bewKTY2FnORo5fLyFTg9NU8hn29F2eVkj09tbisXY1fUZEYcI8bB4MH27qKMhWil+tatECpLH92+O8/T7Nk/2UGdmzG4qd72bCSMo2N+vqeUqlUnDlzhg4dOtTpPCUlJbi7u/P7778zevRow/bJkyeTk5PDhg3VBzkaMGAAYWFhlaKXm+Lxxx9HrVazcqVpN9Li4mKKi8sjHufl5RESEkJWVha+vr6GGQWVSmVU1mg0KBQKQ1mpVKJUKsvLS4dDwgF0jy1B2fUxSktLUalUKJVKSktLDdFu9WXAEAVXX3ZycjJEx3VyckKn06HVag1lnU6HWq0uL2sKkD5vj0JTBC9EoG3WBUmSUKvVlex4fU0M66Ov0jrAnb+n31czm24qm7Jp7t/n+XrHecb0COKzMd3qbtNNZf0smymbatVON5W1Bxag2vYOdH6E0tGLrNNO9WxTvfQ9R7RJq0UXsxp1ShRSdjykx6HIiUdyboL2yfWoW/ayuk3aPZ+h2vXRrW5jBnStB6AYsxjc/SvblBqN9PsUFLmJxgcF90b3+M/oPJqZbqesK6hjV6O7Foci8zyKjHOg0yAF3oniid/QejQDSYcq+Rjaq9EodBqUkhZtSSHKgnQUN9KQbqRDm/4o+s1AgwrV931RZF5A++B/UNw1FSUSuqUPo0w4UMkmzfB5qHpONm6ni7vh9ykoCrOQ3APQPrYEddv+Dafv1fF6ysvLw8/Pz6zvfMfIJt5A0el05OTk4OPjYzRwkLGdNv87Lma5B3VqxvG8WB5evpz7u3Vj58aNhhlVWyL3GSA4GCkoiM8//5wFCxbw999/065dO3Q6HSPu8GbJfth59hqJWQWE+LnburY2R3yH/gAAIABJREFUR+4zpnFUXSz1nNzZ2ZkePXqwY8cOw6Bbp9OxY8cOpk2bZpHPAOGGd/LkSYYNG1blPi4uLri4uFTarlKp0Ol05Obm4uPjY9imR/+Dqspy2ZpuZZOmAEZr/swpKxQKQ1n/o6/KstoLRYchIjLzyTWoHiyP8l6x7iqVig6BIoXPPe0CDO+ZskOn05GXl2fQoOI+purbpWym+9TV60Z1r7VNN5VvtuNW5Vu2001llT7llJOHUd1VKhXZ2dn4+Pg4nE2W6nsAWVlZ+Pj4GM7v0DapVCi7PwHdn0ABUFoEKx5HcTkC9cqxMGULShdPlKfXw8WdqG4fBj2fqfa+YJZN19NQ7S97cNi6P3i1EKm1nNxEpHelWqxNd/UW6+I9A1G27AtKlWmbQnqheOkwXI0Sa6klSURxD+6JUuVkcC+u1E5N28L975S7HycegZXjUaTGwKIHUHUaBWf+gLxkyq3DqKwASDoMp9ai7vo4ZF4AFy9UPSaBvn+M/Rn2zIHcRCi+Ll7tB6PuOVkE1atoU9sB8H+7YfVEFKknUf/6GIyaj7LbeLu6nm51XzCyyYL3iIr/1xR50G1DtFotR48e5YEHHnCoH331gS20KdXqWB8l3PfGKDOIK3OHbJaXB2fO2MWgW+4zAoVCwa5du4iPj2fhwoXMmTMHrVbLtYux3NPWn/0XM/n1cAJvDe1o66raHLnPmEbWBWbMmMHkyZPp2bMnvXv35quvviI/P9+QZ/vtt98mOTmZZcuWGY6Jjo4GRLC09PR0oqOjcXZ2plOnTgD8+9//5q677qJdu3bk5OTw2WefER8fz3PPPVerOtapneo7ermeLmPEoPv0Bhj8oeHH7M08fXcrPF3UjAwLqvZ05mrQOUhEMD+fdp1ijRYXtfk/Dm1KqemUYfI12wg0cHKF8Stg2ShIjhSRtEvzy9+/8DdcjkA79EuOHj1Wdx12fQwlN6BFODy13jA4rRPOHtDq3rqdI6Q3PLcDVoyFjHNw+Dux3cULWvUDZ3d0ChXJqem0aB+GyqcFoBAD6swLsPsTsX/4JOP83E2awfDPa14P31B4Zhusnwqn14tAcFmXRaT8ivc1TbH43LyrcFs38Tn1hCNdE7J7uYxMGdtPp/H8smMEuKs5lPA7ap2WvIAArsfHE+TpKfJC28HAW0Zw8OBB4uLimDBhAq6u5QGJtp1K5f9+icTX3YmDbw/E1cnBfnDKOCT19T2lVCqJi4urs3u5nvnz5/PZZ5+RmppKWFgYX3/9NX369AHg6aef5sqVK+zevduwv8LEADI0NJQrV64AMH36dNauXUtqaiq+vr706NGDjz76iO7du9e4ThbRUqeFDwNEhODXz4qIzvVFSb5IBaQpEimSArvU32cjvCHCP9xOdkEpf067l67BVad3s0v++hccnA/3vAqD/23r2sjYgoIs+GmYiKSNAlr2hcCucGyxmEH2bQ2j5ovtZbPO5CaLnOKXdoFfW2h5l3jfxROK88SsbpPmYiAJkHYKvr9X3COmbIXQvraytmoKs2HLm+J+1vkRkXbNybWa/XNg+yyRJkzlAtOOlttbF3Q62PlvkccboFln8VBM0olo7tmXRRkABQT1gNsfEpHzfVoanyvpGJzbCv7tIaSXaMsqHkzaM7X5npJnum2ITqcjIyODgIAAu386U9/YQht9ALVHWrqiPpkDHTvipVLh1aEDxMWJdcQ2HnTLfaacvn370rdv+ZekXpsBHQJo4e3K1dwiNp9M4dHwxv2gRO4zppF1EUybNq1Kd/KlS5dW2nar5/Rz585l7ty5lqgaUId2Kswp/xFY3zPdzh7Q9gE4uxniNtV50G2uBgqFgs4tvNl3IYOTybmON+guKZvZdDJeHiRfs41IA3c/mLJZ5MIOvVu4fYNIebbmaTHIWzocycUTReg9IlXfua0i7RVA0lGIWWXixAroNh4GvC0e7kg6kRbNHgfcAG6+8OhCk2+Z7AtuPjDya+j9f+J/Swy4QXgADHpfDJA3TodrJtJAunhDk6Zixjv5mHhFfAED34M+/xD7RHwOe/5bYYAOeDSD/jOh9/O1qpojXRP2XbsGjk6nIzY21hD2Xqac+tYmO7+EHXEiv/NjYS1E4DQ7i1wOcp+pCn0aiNjYWJQKmHiX+KJZdjDexjWzPXKfMY2si2NQ63bSu5a7eIPKBvlbOw4Xf+M21vlUtdGgR1kKspVHEnC4FIqGPN2VB92N/ZptVBq4+0HXMeUDboDgnvBCBLo7x1OqckdRfF0Mts9uEgPuVv1gxNdw30xRVpetzXbzK5txleDESvgmXMyIq5zFYNIBqbYvBHaxjodNj8nw0hEYt1wsA5iwGib/CTPi4K14eDkSZpyBEfMg5C6Reuyvd2DJEPh5BOz+jxhwtx0Iwb1A6QT512DzP2HzTDGjbyaOdE3I7uUyMsDPB64w+49TdAnyYuPL/Tj944989Mkn3OvtzYvh4XLkcjtm7969vPPOO4wYMYKZM2cCkHGjmL6f7KBUKzmme6WMw+Go7uX2iEW0jD8APw0FvzbwSpRlK1gT8jPg8/biB+ZrseATUq8fn3mjmPvm7CK/RMuCJ8IZfudt9fr5dWL1UyJo1LDPaz37JdMI0GkhNQYu7xXu413GQLOb4rjodMJ1We++nBwJO/4Nl3aL//tOgyEf12u1Gw2SJNz9t70HJSKdHM5NYPiX0G2c+L+0CA5/D3/PFv93eAgeWwwuTUyfrzo3dEmCs1vELPxdL4GzdQPp1uZ7Sp7ptiE6nY7k5GSHeDpT39S3Nr+XuZaPKXNFPqJWs/LSJX5Xq8VabjsZcMt9pjIXL15k3759LFiwgISEBHQ6HQFNXBjWVfzI/OXQFdtW0MbIfcY0si6OQa3byVZB1PR4BIiZHhBu5nWgNhr4N3Hh+fvaAPDFtrNotA7Uz/WB1Jw9jDbL16ysgR6dTkdySiq6wG5wzyvwwLuVB9wg3KIrDtSCesCkDTDpD3jwI3Gcg2L3fUGhgJ5T4KXDYj1624HwQkT5gBvE+vR7X4Oxy0SU93Nb4Yf7IOa38lnvtFPw2yT4JAQOLqj0MTqdjrSYHUhLH4ZVE2DnR/D3+zWvp7YUrsWJQbuVkQfdNkSn03Hx4kX7vWBsSH1qE5eax8nkXJxUCkMU2V69evHxxx/z9LRpNl/HXRG5z1Rm4sSJzJ49m71793LlyhWDNpP6ChfzDdFXybhRXN0pGjRynzGNo+ry5ptv4m8HS13qi1q3U75IF4Z7gOUrVVMMLuabyrflJIqo5pqSGp+mtho8168Nfh7OXMrINzxYdggM7uXG0csd9Zq1JLIGgjrr0KY/3P1ypT7mSDhMX/AOgseXwlNrwb+t6X06jYLJG8X67qyLsPZ5WNAbVk6A7+4W98yS68JV/a9/CQ8GgJwE+OMVmq0dgyJ+nwgeB3BkISQcvnXdUmLgh/7wbR9YOADObrXq4Ft2L5dp9Ly3PpZfDsUztEsg3z3Zw9bVkbEQkiQx+tsDnEjM4ZWB7ZkxuOG648rYHvl7ynJYRMuIz2HnhxD2JIyuPDtSL2Rdgq+7iyBPMy+KH4jLRkNhFvi2goGzxQyQFSP3Lt53mQ83nibQy5XdbwxwjGwOP/SHlGh44jfoMMTWtZGRkakvivLEgPngfBG5HQAFdB4tlgrt/UJs6vKYSJ8WtRx0pWJb50dEtoPd/4XoXyHgdvjHXpFn/Wa0Gtg/F3Z/Wn68nhbhwgui1T3VVrXe3MsXLFhAq1atcHV1pU+fPhw5cqTKfVNSUnjiiSfo0KEDSqWS1157rTYf2SDR6XTEx8fb/1MqG1Bf2two1rCuLDf3U3dZKMqjFZH7TNXotSkuFrPaCoWCF8rcK5cdvEJBicaGtbMdcp8xjayLY1DrdtK7l3vY0CvArw006yQCPO35TAQSKswCFJB9BX6fAj8OgsyL1Z6mLn11Yp+WtPB2JTWviJ8PXKn0/ld/n+Oe/+4kIbPA7HNbjWoCqTX2a1bWQCDr0EA1cPWC+/4Jr50UA9++0+DFg2KmfOAseOQHERgv9n8Q+RPoSpFa9yd12DJ0jy0RwfIe/EjMmGecFQ9fAXKTIOpXkX7t5xHwxe3CDV1XCh0fhhcPixSFTu5w9TgsHQZ/virSoVkQswfdq1evZsaMGcyePZvjx4/TrVs3hgwZwrVr10zuX1xcTNOmTXn33Xfp1q1bnSvckLD79Rg2pL60WR+VzI1iDW2aetC3rfhxlpeXx+HDh8nPz7fqZ9cGuc9UTXp6Oq+++irdunWjpES4bg7pHEiovzs5BaWsOeZA7pUWRO4zppF1cQwcdk23Hr2L+aEF4gdcSB8R3XfA2+DkIdLqrJks1hVWQV36qquTitfKvHzm7TjPlYzy77XDlzL56u/zJOcUsjk2xexzWw39mm4Tg+7Gfs3KGghkHRq4Bi6ewv1/yMfQ7I7y7d3GwxOroUkgtO4PU7agnbiWS5qm5Tq4+8Gwz0R535cwvzfM7QwbXhRB2y5HQEEGuHqLQfy45SIewOB/w6sx0ONpcWzkUljQB44tgbTTtYqsfjNmu5f36dOHXr16MX/+fEA0ekhICC+//DJvvfVWtccOGDCAsLAwvvrqK7MqKbvtyVgDSZIYOm8vcanXee/hTjx7b2sANm/ezPDhw+ncuTOxsbE2rqVMTSkoKKBNmzakpaWxfv16Ro0aBcAvB6/w3oZThPi5sev1AahVcigLGcsjf09ZDoto+cujcHEHjPoWuk+0bAXNIfk4LLpflEPvFT8Y9ZF5c5Pg+3uFG+UD74kZHiug00k88eMhDl3KIizEh9//0ReNTnz/XS4bhD/UOZDvn7KT5VVz2oiHJi8eMv7BLSMjI1MTJAlWP1meslGhFEH0QvqIe0qzO6DpHVVHOL+yD/54Rawv1+PkDoF3irzjre61vnt5SUkJkZGRDBo0qPwESiWDBg3i4MGD5pyqWoqLi8nLyzN6AWi1WsNfU2WNRmNU1j/1qKpcWlpqVNY/f9CX9bl/K5YBo7JOpzMqazSaastardZQLikp4dy5cwYbGoJNVdlhrk3FxcWcP38erVZrNZuOJ2QTl3odVyclY8KDDdvz8vJo3rw5Xbt2tahNlmgnjUbD2bNn0Wq1dtFO9tT31Go1s2bNYufOnTz88MOGuj8S1gI/dycSswrZFHPVoWyyRDtptVrOnj1rqENDsMkS7aTVajl//jxFRUUWtUnGsmi1Wi5cuGC+vvYy092iO4RPgm4TYOIa41Q43sHw0KeivOdTSD9n8hS11qAMpVLBF2PD8HRVE52Yw4JdF5m7/RyXM/JxVoufgdGJObU6t1WoIpBaXXVoCMgaCGQdZA30mNRBoYBRC4RL+thlMPMSPPe3mDnv/qQYgFeXUqzVvTB1v3gYGnqvSHVWWgCJh0SMjlpi1qA7IyMDrVZL8+bNjbY3b96c1NTUWlfiZj755BO8vb0Nr5AQkd9SP+t45swZzpw5A0BMTAznz58HICoqisuXLwNw5MgREhMTAThw4AApKcJ1KiIigowMEdV0586d5OSIL5pt27Zx/brII7d582aKiorQaDRs3rwZjUZDUVERmzeLtB/Xr19n27ZtAOTk5LBz506DPhEREYBYy37gwAEAEhMTDeveL1++TFSUyBl64cIF4uPjkSSpwdh0/vx5YmJi6txO+/btIzU1FUmSrGbT8kMJAPRqrsTb3clg0/jx49m3bx8vvfSSRW2yVDudPXsWSZLsop3sqe9duHCB9u3b069fPyObzsedYmh78UP3622nuHTpksPYZIl2kiSJCxcuNCibLNFOkiSRnp7Ojh07LGKT7BVjHSRJIjs72/AgpcYY1nTbMHo5iB9/I7+BR743/SPvzrHQ/kHQlsAf00y6MNZagwoE+bjx4aguAHy98zyL9or74OePd0OlVJCaV0RqblGtz28xdLoK7uXGKcMsoYOjI2sgkHWQNdBTpQ5uPtDvdREZ3c3X/BM7uQnvoymb4K0EeOmIcEe/rfZLpc1yL7969SpBQUEcOHCAvn37GrbPnDmTPXv2cPhw9eHZa+peXlxcbAiGBMLVLCQkhKysLHx9fQ1PM1QqlVFZo9GgUCgMZaVSiVKprLJcWlqKSqUylNVqNQqFwlAGMZtSsezk5IQkSYayfiZJX9bpdKjV6irLWq0WSZIMZVN2yDZZ36b060X0m7OHEq2OdVPvonuov8Pb1BDbqS42FVy4gC49nSYtW5Lp05R+c3ZTrNHx67O9uKd9M4e0qSG2U0OxKS8vDz8/P9m93AJYxL3849vE4O2VKBHQzJ7JTYIFd4mUOPf/C/r9U+QXtjCSJPHyyig2xogHVyO7teDrCd0ZOm8vZ1Ly+P7JcB7qcpvFP9csSgrgP2V1eDvZ2DNARkZGxk6wunt5QEAAKpWKtLQ0o+1paWkEBgaac6pqcXFxwcvLy+gF4seY/q+pslqtNiory760qio7OTkZlRVlaTv0ZYVCUakMGJWVSqVRWf+jrqqySqUylAGDC3VDsakqO8y1SaFQGFzvrWHTuugUSrQ67gz2pnuof73YZIl20ulEXkatVmsX7WRPfQ/Kr6dNH3/M7eHhzHrqKVSvvkqzjet4vKfIt75w7xWHsckS7aR3vdI/X20INlminfRu9/rjLGWTjGXRarXExcWZ50JZUlA+W2rLPN01xTsYBn8gyrs+hkUD4NJuw9u10sAECoWCj0d3pZW/O0E+bswe0QmAsBAfAKLswcW8tEIUdRPu5ZbQwZGRNRDIOsga6HEkHcwadDs7O9OjRw+DOx6IQcCOHTuMZr5lak5hYaGtq2C3WEsbnU5ixWHhWv5kH+M0Ybt376Z379588MEHVvlsSyD3maopLCyE5GQ8Dh0ipaCArSkplBw/Dh98wPPZp1ApFew5l87xhOxbn6wBIfcZ08i6OAZmt5PetVzpJKLgOgI9n4FBH4CzJ6ScgGWj4OeRELcZdFqL9VVvdyf+mn4fO//ZH/8mIn9t97JBd3SCHQ261a6grPwgS75mZQ30yDrIGuhxFB3M9l+aMWMGixYt4ueff+bMmTNMnTqV/Px8pkyZAsDbb7/NpEmTjI6Jjo4mOjqaGzdukJ6eTnR0NKdPn7aMBQ6MSqWie/fu8gyJCaypzfYzaSRkFeDlqmZEtxZG7x05coSjR4/abf+U+0zVGLTJzmagQsHvoaHE3H47ziEhUFxM6B+reex2sa5n7nbTAYsaInKfMY2si2NQq3YqEGv98QgQa6odAYUC7n0NXo2G3i+IXLSX98CqCai+6U73G7tQYZm1my5qFS7qcj3DWopB98nkXLQ6G68PLdGv53ar9JZ8zcoa6JF1kDXQ40g6mD3oHjduHJ9//jmzZs0iLCyM6Ohotm7dagiulpKSQkJCgtEx3bt3p3v37kRGRrJixQq6d+/OsGHDLGOBA6PVaomNjXUIl4j6xpraLIwQAWSevCsUN2fji3TixIn89ttvTJ061eKfawnkPlM1Bm18fcHZmccAF09PyM4GV1fIy+Plds6olQr2ns/gyOUsW1e5XpD7jGlkXRyDWrWTvUQurw0eATBsDrwcCXe/Am5+kJcEOz5At2eOVT6ybdMmNHFRU1Ci5Vzadat8Ro2pIogayNcsyBrokXWQNdDjSDrUKlLHtGnTiI+Pp7i4mMOHD9OnTx/De0uXLmX37t1G++vTtFR8XblypS71lpGpFceuZBEZn42zSsnT97Sq9H5QUBCPP/44AwYMqPe6yViIoCB4/HFwcYHz55FSUtiamoomMZGQ+LOM7SWyITSm2W4ZmUZFvgMPuvX4toIHP4QZZ9A9MBsAxaEFUGD5h4UqpYKuQd6AHaQOqyJdmIyMjIyjY/nwmDI1RqVS0aVLF4dwiahvrKXN93vELPej4UE083S16LnrA7nPVI2RNk88AdOmgasrTxYVMTQ3l+9UKti5k2kdPXBWKTl4KZMDFzNsXW2rI/cZ08i6OAa1aidHnum+GSdXlPe+BoFdUZTcgH1zrfIxehfzKtd167QinZe10c90m0ivJl+zsgZ6ZB1kDfQ4kg7yoNuGaLVaoqKiHMIlor6xhjYXrt3g7zNpKBTw/H2VU8icP3+e5cuXG3IF2yNyn6maStrcdx907Uq/8HCcVSoK/PwgPp4WiReY0Lt8truh57iU+4xpZF0cg1q1k73k6LYQWkniYugT4p8ji+B6qsU/Qx/B/ERS2aC7tBAu7oRd/4Flo5H+2xLtF7dDTkI1Z6k7JUU3RMGp8qBbvmZlDfTIOsga6HEkHeRBt41xc5NdqKrC0tosKlvLPeiO5rRtWjn357p163jqqad4++23Lfq5lkbuM1VjpI2/PwQG8ny7dpwZPpw309PhwgX46SdeVF3FWa3k6JVs9l1o+LPdcp8xjayLY2B2O+kDqTWEme4ySlvdjxTcCzSFEPG5xc/fPcQHF0q4K/13ND8/Ap+2gl8egT2fwqVdKEpuoMq/Rv6aqVDxQeWNa7Dpdbiyr9rzZ+WXcOhSJgUlGpPvS5LElpMpfPrHcbF/idrkfvI1K2ugR9ZB1kCPo+ggD7ptiEqlomPHjg7hElHfWFqba3lFrItKBuAf/SvPcgMEBgZyzz330L9/f4t8pjWQ+0zVVNImOBjGjUNVXEybmBix7e67Qaej+eLveLKVSJfz6dY4dLaO2GtF5D5jGlkXx6BW7dSQ3Msp0+COO1AMnCU2RC6F7HiLfkYzL1emeezkfaefUV/eCZoiUiU/1mrv5d3SKTxdMpNCyRmP5H1Ix5aIgwr/n73zDo+qSv/4Z2Yyk15IQhoJoUPooRelKAiKuq6uiguiCFgABVEEWRULK5bVVQFFXduu6CLWVamCUn6AlNACSSgphFTSJpMy7d77+2PImMAEkpBkZpLzeZ48XO6cufc93/Pec+bc855zijF9civs/xelX13UGQdKjRb+syedv364l8F//4XJH+xlzOu/8d99Z+2rpFslmUNni7n3o308siYBY4VtpLvQfGl5i2dWaFCF0EFoUIU76SA63U7EarWyf/9+rFbHb35bM42tzSe70zFLMgNj2zAwNthhmmnTprFr1y4effTRRrlnUyB8pnYcajN+PEyfDl26wI03klFRweO//opp3z7mbPwQfw0kZpXydcI55xnexAifcYzQxT1oUDm1hIXUqmHXIGYEdBwNsgU+uxn2vgemxlttfKL2EABfWscy3vQaw0wreDvgSYLHzGbG9Ad5Q54MgLTpGchPQvr8TjwLkwAIqDjLqf2b7NcyGC385b3dPPvDcXafKUSSFfw9Pcg3mFj87TFufHsH9370O/1e2Myf393NrtMF6DzUdAmy/Swttlw60i2eWaFBFUIHoUEV7qSD6HQ7EZVKRZs2bVC5yz6izUhjaqOvtPD5XtuowEMO5nK7E8JnaqdWbXr2hNhYLNnZjFm/nrfOn2eZ1UqIRuax0mMAvL4phTKT61fYDUH4jGOELu5Bg8qphc3prqHBxOXgE2qbW71xMbzZE7a+BObyq7tJZQmdTScAeF++jW69B/PlrOH89uQYFozvxrVd2+Ix/BF+l3vgYa1AeX8Umqz96BUfdkh9AEjb9C7lJiuyrPD42iOczCujrb8nT9/Yg+0Lx3Dg2XE8MymOQG8tJ/PK2HmqgHKzhL+XBzf3jWTL46MY1dG2VViB6dJRK/HMCg2qEDoIDapwJx1UihusIlRaWkpgYCB6vZ6AgABnmyNwM/655SRvbz1F1zA/Ns0fhVp96YNZWVmJl5eXWzy0ggawZQu8+y7f7NjBG0Yjn44bR7e+fTEnn+SGfg+QXmZl9pjOPDWxh7MtFbgpop1qPK5ay9c62Trej+yG8F6Nb6CzMVfAkS9tI92FFxb+DIyBia9Aj0nQkHbs+Pew7j4qAjphmLmH8IBLd/coM1mZ+tqXfGF9Ah+ViQrFk3stT7Pwxl4M23onRkXLq71/wD+oLe9sPYXOQ81XDw23L9JWhb7CwjcJ59BqVAzuGEy3MH97u6z/3xICE1bxqXwj057/0mF7LRAIBM6mIe2UGOl2Ilarld27d7tFSERz01ja6CssfLwrDYD547rV2oAvXbqUkJAQ3nnnnau6X1MjfKZ2LqvN+PGwZAl3jBrFrkmT6Na3L2RloQv0Z8moaAD+tSuNzKKKZra66RE+4xihi3tQ73KSJagsth37tIyR7ks00PnA4BkwZx/c/TkEtQd9JqydAh+Nhy/vgf9Oge9nQ97xut3k9BYAfHpOdNjhBvDz9OCvE8ew0PIQh+VOzLQ8wdhxNzPsmvGUB3XDS2XBcngd72y1vQhY/uc+l3S4AQJ9tDxwTUfuHd6BHhEBNdplf40FAIPsSba+8vI6tEKEBjaEDkKDKtxJB9HpdiJqtZp27dqhVotiuJjG0uajXakYTFZ6RPhzY++IWtMdPHiQ4uJi/P39r+p+TY3wmdq5ojaDB8Ps2agDAiA5GdRqSiZNYvy1PRnZJQSzVWb5hqTmNboZED7jGKGLe1DvcjLqQbmwn7SP4/U73I1aNVCrIe4WmP07XPskqLVwbj+krIfkn+DwGvjXODjxw+VvoChweqvtuMu4yyb9y8BoMiJv4DbzMjy6jGX2mC6gUuE77AEA7tb8CsADIztyx0DbC02sZsg+BEk/2crncnm12jralYqOM+drhsyLZ1ZoUIXQQWhQhTvp4PoWtmDUajWxsbFu4SjNTWNoU1hm4uP/Swdg3vVdLxumtnHjRg4cOMAtt9zS4Ps1B8JnaqdO2owfD88/j/K3v7EiNpaYadPYs2cPz0zqiVoF64/lsje1sPmMbgaEzzhG6GJj1apVdOjQAS8vL4YOHcq+fftqTZuTk8Nf//pXunXrhlqtZv78+Q7TrVu3jh49euDl5UWfPn1Yv359g+2rdzlVjXLr/ECjbfB9XYkraqDzgeufhbn74E/vwi3vwKQ3bYuuWSrgq2mw7e8gy44cdnaqAAAgAElEQVS/n5cIhhzb3tixI69gi4r3pgxk4YTurLgn/o92te/dKBodfdTpPNXPxJKx4bDzTVunf3k0fDDGNhL/7nA4/Yvji0sWe/lV4knq+bL66dAKEBrYEDoIDapwJx1c38IWjNVqZceOHW4REtHcNIY2K389TZnJSu92AUzoVfsoN4BWq2XgwIGEhrp2OKLwmdqpszbR0aj692f/qVOUlZXx+eefExcZwOQh7QH423fHMFqkZrC4eRA+4xihC6xdu5YFCxawdOlSEhIS6NevHxMmTCA/P99hepPJRNu2bXnmmWfo16+fwzS7d+/mnnvuYcaMGRw6dIjbbruN2267jcTExAbZWO9yMpXa/vVsOfPq66xBcCeInwID77OFn0/9FobNsX224zXYsNDx907ZQsvpcC1oHYeWVycm2Ic5Y7sQ6F3tpYZPMKoeNwMwu/AVPN7qBVtfsI28SybwCgK/CCjNgs/vgB/mQuK38Oty+Oo+WDUM/h5pG6EHKvAi9aKRbvHMCg2qEDoIDapwJx1Ep9uJqNVqOnfu7BZvZ5qbq9Ums6jCvmL54olxLWYxFuEztVNfbd555x1WrVrFypUrAXhqQndC/Tw5c76cldtON6WpzYrwGccIXeDNN99k1qxZTJ8+nZ49e7J69Wp8fHz4+OOPHabv0KEDb7/9NtOmTSMwMNBhmrfffpuJEyeycOFC4uLieOmllxgwYID9Oasv9S6nqi20PF17qlB9aLCvajxg4su20W+AAx9D4ZlL01WFlncdf3WGDrjX9m9BClgrIaKPbdT90QRYlA6PHYKhjwAqOPQf+Ho6bH8FTnwP55NsW6FpfckPGcyvUn/OOBjpbu3PrNDAhtBBaFCFO+ng+ha2YNxpHkJzc7Xa/GNzChZJ4dquoVzT9fKj1y+++CLPP/88aWlpDbpXcyJ8pnbqq01QUBCzZ8+2pw/y0bHsNttKx6u3n+F49uXnHroLwmcc09p1MZvNHDx4kHHj/pjDq1arGTduHHv27Gnwdffs2VPjmgATJky47DVNJhOlpaU1/gAkSUKtVhMREUHVRiuSJCFJtkgUq9Va41iWZTDavqtcGOm2nwcsFkuN46prVh0rinLJMVDjWJblGsdVoyu1HUuSVOO4yt7ajh3lSa1WEx4ebter3nnqfRdK1wmgyMjb/1EzT8ZSlMy9tgt3GXd1eYq9FnngA9DrdqRpPyLN/A0G3oc1MBZJlkHng3X8MuT7foT2w1HaDULpPxVuWIZ0z1fI847C0+dIn/Ql+bQh9Xx5jTxJkkRUVBQqlcoly+lyx43le5Ik0a5dO/v1W0KeGlJOl6sX3DVP9S2ny9UL7pqnhpRTVb2gVqudkqf60Dp/bbgIVquVbdu2uUVIRHNzNdoknC3mh8PZACy6whZQiqKwatUqXnjhBbKyshpka3MifKZ2rkYbSZJ44YUXCChN48beEVhlhUXfHMUq1TIH0o0QPuOY1q5LQUEBkiTV+NEGEB4eTm5uboOvm5ubW+9rLl++nMDAQPtfTEwMAImJiVitVjZt2sTx47ZVuI8ePcqpU7bVsQ8dOmR/Wbpv3z4yMzPtI90mlScAO3bsoKCgAIBt27ZRUlICwObNmzEYbGnXr1+P0WjEarWyfv16rFYrRqPRPhfdYDCwefNmAEpKSti2bRtg03DHjh2Abb777t27AcjMzLTPjU9LS+PQoUMAnDp1iqNHjwKQlJREUlJSnfJktVrZsGGDvY1qSJ6kkY/bxD66Fooz/shT6m+oZCvlXpEQ3PHq8pScwomOM+DOTziq9+fU6dOO86RuDw9sZGf3pWQPXgIjHmV7lpYCizeo1Zw7cRCA3FIj/1tfM09bt261l42rlRPYplfk5OQ0qe9t27aNvLy8FpWn+paT1Wpl8+bNHDlypMXkqb7lZLVa2bhxI+np6S0mTw0tp19++cWej+bKU4OmTClugF6vVwBFr9c725RGRZIkJS8vT5EkydmmuBwN1UaSZOXWlbuU2EU/KU9+dfiK6c1ms7J69Wpl6tSpitFobKi5zYbwmdq5Gm2WL1+uAEp0dLSSlpCo9H32ZyV20U/Ke7+dbgJLmxfhM45pbF3crZ3KyspSAGX37t01zi9cuFAZMmTIFb8/evRoZd68eZec12q1yhdffFHj3KpVq5SwsLBar2U0GhW9Xm//y8zMVAClqKhIkSRJycnJUcxms6IoimK1WhWr1aooiqJYLJYax5IkKcre1YqyNECR106reV6x1ffVj2VZrnEsy/Ilx4qi1DiWJKnGscViueyx1WqtcVxlb23HjvIkSZKSnZ1tv06D8/TZrYqyNEBRfpxvO19Zrihr7lKUpQGK9acnmzVPlzs2m83KgBc3K7GLflIS0grseTIajUpubq5itVpdspyulKfG8D2TyaTk5eUpFoulxeSpIeV0uXrBXfNU33K6XL3grnlqSDlV1QtVNjZXnoqKiurd5nvUv5suaCzUajVhYWHONsMlaag23x3K4khmCb46DQsndr9ieq1Wy0MPPcRDDz3UEDObHeEztXM12syZM4d169bx+LhxdHhtGc/KbXmy4wT+uSmZ8T3D6dzWr5GtbT6EzzimtesSGhqKRqMhLy+vxvm8vDwiIi6/8OTliIiIqPc1PT098fT0vOS8RqOxh5FWP1eFh4fHpccXwstVXgGXpNFqtQ06VqlU9mO1Wm2fklCX4+r21uXYYZ6AyMhIh+frladRT0Hqb3Doc1SDZqDduBjSd9ps6Htns+eptmOtVkvntn4UlhdxtsRIfAfbmiyenp72KIrq13elcrpcnhpyfHGedDrdJfWWu+epoeVUW73gznmqbznVVi+4c57qW07V64Xq08WaOk/V/19XRHi5E7FYLGzatMk+p0DwBw3RRl9pYfmGZAAevb4rYf5XXoXV3RA+UztXo42/vz/7v/uOqUeOwMGD3HF0C6PykjDJMPez3916NXPhM45p7brodDoGDhzI1q1b7edkWWbr1q0MHz68wdcdPnx4jWsCbNmypcHXrHc5tcDVyxvNVzuMhPYjQDLD+6NsHW6dH9y9BmKGNI6xjUSntr4AnMn/YzG12nTYeeo8d7y3m8SslrEOx+Vo7fVWFUIHoUEV7qSD6HQ7EY1Gw+DBgxv0tqSl0xBtXtuYTEGZic5tfZk+skOdvrNhwwaKiooaaGXzI3ymdq5WG3VyMqSkgLc35cHBXLvzY0KMpSQVGFn284lGtrb5ED7jGKELLFiwgA8//JDPPvuMpKQkHnnkEcrLy5k+fToATz/9NNOmTavxncOHD3P48GHKyso4f/48hw8f5sSJP56PefPmsXHjRt544w2Sk5N5/vnnOXDgAHPnzm2QjfUupxbY6W5UXx31pO1fRYI2HWDGFoi7+eqv28jYO90Ff2wb5kgHg9HCgq+OcDCjmJfXJzW7nc2NqLdsCB2EBlW4kw4ivNyJqNVqgoODnW2GS1JfbQ5mFPPFvrMA/P3PffD0uPLDl56ezk033YRWq0Wv1+Pt7d1ge5sL4TO101jaVCgKoxMSSDAaWbr+LT69/Tk+33uW4Z1CmdQ38soXcDGEzzhG6AJ3330358+f57nnniM3N5f+/fuzceNGe6heTk4OZ8+erfGd+Ph4+/HBgwf54osviI2NtS/mM2LECL744gueeeYZlixZQteuXfn+++/p3bt3g2ysdzldCC/Hq+V0uhvVVztfByMeg8piGP8i+LjmM1A1paf6Xt2OdHjrl1OcN5gA2H2mkMQsPb3bOd7OriUg6i0bQgehQRXupIMY6XYiFouFn3/+2S1CIpqb+mhjsko8/e1RFAX+MjCaYZ1C6nSP3Nxc4uLiGDx4sFt0uEH4zOW4am169oQePfAxGBgry7RVqZhQfo5HSo4BsPjrw2QUll/hIq6H8BnHCF1szJ07l4yMDEwmE7///jtDhw61f/bpp5/y22+/1UivXNiCpfpfVYe7ijvvvJOUlBRMJhOJiYncdNNNDbav3uXUAvfpblRfVanghpfgTytdtsMN0OlCpzutoAxZ/mOLo+o6pOQa+HR3OgDdwm3pP9iR2vzGNiOi3rIhdBAaVOFOOohOtxPx8PDg2muvrbE4gMBGfbR565dTnMwrI9RPx5Kb4up8j2HDhnHixAm2bNlyNaY2K8JnaueqtYmOhgULoHt3XgsM5HCXLgy/7jqeCC1nUGUeBrPM3C8OYbK61/xu4TOOEbq4B/UupxYYXt4afTWmjTdajQqjRSZbXwnU1EFRFJ77IRFJVpjQK5w37+oPwM/HcjhXXFHn+1gkma/2Z3Lj2zt5+ttjSBc6+K5Ka/QFRwgdhAZVuJMOotPtRFQqFQEBAahUKmeb4nLUVZtDZ4t5f/sZwBZWHuyrq/e9fHx8GmSjMxA+UzuNos348fDSS6hHjiQqPh46dMAjO4sF+TvwU8scy9Kz6N97URTX/mFWHeEzjhG6uAf1LqcWGF7eGn3VQ6MmNuTCvO4LIebVdfjuUBa/pxXhpVXz7M096d0ukJFdQpBkhY92pV3x+pKssO5AJte/sZ2nvjlKUk4pX+47y3M/JLp0/d4afcERQgehQRXupIPodDsRi8XCDz/84BYhEc1NXbQpM1l5fO1hZAVu6x/FhF513+bGbDa7dMNaG8JnaqfRtBk8GGbPBn9/SE4mtayMO9d/R+66l1ArMt+fLOGtDzY2jtHNgPAZxwhd3IN6l1MLDS9vjb7aKdTW6U49b1vB3GKx8NHaH5j3ZQJPrDsCwNyxXYhuY3tx/uCozgCs3Z+JvqJ2rXafKWDSOztZ+PVRzhZVEOKrY+qw9qhUsOb3s7z1y6ka6a2S3Oh5ayit1RcuRuggNKjCnXRQKW7Q8ygtLSUwMBC9Xk9AQMt5e60oCkajES8vL7d4Q9Oc1EWbx9ce5rtDWUQFerFh3igCfbQO0zniueee49///jfPPPMMM2fObCyzmxzhM7XT6NqcOweFhZTr9dx4110UmUw89sASXtb2BOCNcTHcMa7v1d+niRE+45jG1qWltlPOoLqW/v7+9Sun5TG2EPO5ByG0S9Mb2wy01mf4lQ3JrN5+hiAfLR1CfAnw8mDPmUIsF0LA/xzfjlfu+GPhVEVRuPHtnSTnGnh4dGcWTexeQ6/MogpeXp/EhsRcAAK8PJg9tgvThsfio/PgP3szePb7RAD+OrQ9xeVmDp0tIc9gZEiHYG7qE8nE3hGEBzhvO9LW6gsXI3QQGlThLB0a0ua7fgB8C8cd5iA4i8tp8/XBc3x3KAu1Ct6+J75eHW6Abdu2kZGR4RZbDFyM8JnaaVRtoqMhOhrfI0f4edgwjJ060da/nGJzFu/J7Vi8LZOoju0Y3rluC/c5E+EzjhG6uAd1LidZbpEj3dA6ffWaLqF8sOMMJRUWDleU2M9f2zWUpyb0oE90zVXKVSoVD4/uzPy1h1m9/Qyn8gy8fHsf/Dw9eO+3M3ywMxWzVUatgilDY3l8fLcaU9LuHRZLYZmJt345xRe/11y1//e0In5PK+L5H48zKLYNN/aO5MY+EUQGNv8irK3RFxwhdBAaVOEuOojwciditVpZv349VqvV2aa4HJfTJjFLz9++s60oPX9cNwZ3qP8KrJs2beLnn3/m5ptdb3/SyyF8pnaaTJuQEPxDQ2lbUgKSxMLcvcQXn8Qiw0P/OUBilr5x79fICJ9xjNDFPahXOZnLgAvBey1oTndr9dVruoay9+nr+eaREayeOpAXb43jsV5WPp424JIOdxV/6h/FUxO7o9Wo2Jqcz/g3t3PdG7+x8tfTmK0yIzqHsH7etbx0W2+Ha8DMu74rCyd0Z2KvCJ6a2J3/PjiMbU+M5plJcQxoH4SiwP70Yl786QTDl29j4bojTT5V7UB6EV/uO4vJKtXwhTPny3j622O8siGZA+lF9kXgKsxWDmeWsOVEHgczijlbWEGl2b0WAL0SrfWZqI7QwIY76SDCy52IoihYrVY8PDxadWiII2rTprjczM0rdpFVUsnY7m356L7BqNWtRzvhM7XTpNps2QJr14Jez0mg//pNBPz5WbyiexLorWXNzKEuuzes8BnHNLYuLbWdcgYXh5fXuZz05+CfvUCthWfP27bHagGIZ9hGfXRIyTXw5LojHLvwUjQm2Ju/3dSTCb3Cr0rDHH0lGxNz2XAsl/0ZRSgKfDFzKCO6hDb4mpfjeLaeP7+7G7NVpmuYH6/e0Zfekb58fSiHl35KotLyR2c62FdHkLeWtMJyLv5lr9WoWDSxBzOv7dQkdjY34pkQGlThLB1EeLkbUuUogku5WBuTVeKh/xwkq6SS2BAf3ro7vlV1uKsQPlM7TabN+PEQFweFhXQNDmZel3dJOLYL7+HDOZypZ8q/fnfpjrfwGccIXdyDOpdT9dDyFvYjVPiqjbrq0D3Cn29nj+CL388iKwr3DGmPl/bqp5NFBnozfWRHpo/syNIfEvlsTwbv/namSTrdpUYLc9YkYLbKqFRwKr+Mv6zeTa9If45l23x9eKcQwgI8+TU5n6JyM0XlZgBC/TyJCvKiqNxMQZkJo0Vm2c9JBPvquH1AdKPb6gzEMyE0qMJddBDh5U7EarWyefNmtwiJaG4u1kZRFJ76+ij70ovw9/Tgg3sH1XseN0BxcTETJkzg/fffR5ZdZ0XSuiJ8pnaaXJvoaOjXD1VMDMuXL+fn77/hPzOGMqB9EPpKC5M/2MOxc64Xai58xjFCF/egXuXUArcLA+GrVdRXB61GzX0jOjB9ZMdG6XBfzKxRnfBQq9h1uoCj50qu/IWLsEgyhWUmh58pisLib46SXlhBuyBvfn1iDH+Ob4eswLFsA1qNir/dFMeamUN5e3I8B58dz1cPDec/M4aw/2/jOPDMOP439xp2LbqOpBcn8tAo2wj3U18fZeep81eVb1dAPBNCgyrcSQcRXi5weRRFYfmGZD7YkYqHWsWn04dwTdeGvVX+7LPPuP/+++nTpw9Hjx5tZEsFrRGD0cL4Zd+Ta/XBSy3z4agwrg1Rg04HISG2zrqgxSPaqcajwVqe2gJr/gIRfeDhXU1noEBwgQVfHebbhCwm9opg9b0D6/QdWVb44UgW/9h0kqySSgZ3aMNDozpzXY8w1GoVZqvMp7vTeHl9MlqNinUPj6B/TBAAvybnsyExh2nDO9QrskqWFeatPcyPR7Lx8/Rg5rUdSSsoJyXXQFiAFy/e2osOF7ZoEwgEV0aEl7sZiqJgMBjw9/dv1fMxHFFdmxXbTvPBjlQAXr69T4M73ABjx47l1VdfpW3bto1larMifKZ2nKWNr06Dz76PMYaOhvZ9uP/XfF44/A1TK85A9+5w99228HQnIXzGMUIX96Be5WS6MNLt6ZrTPBqK8FUbrqjDI6M7821CFptO5HI6v4wuYX61pi2pMPN/pwtZ9etpTuSU2s/vTy9mf/oBOoT4oFapyCiqsC+KtuSmOHuHG2BM97YMjPLC379+q/Or1Sr+cWdfzhuM7E0tqrEXeXKugVtW7uLNu/ozvmd4va7rLFzRF5oboYENd9JBhJc7EavVys6dO90iJKK5qdJm1bZTvLnlJADP3tyTuwbFXNV127dvz1NPPcX06dMbw8xmR/hM7ThLG7VazeYP3mNZ1iZuM5xBUql5Jv5OlkZdg2Qosy3Adu5cs9pUHeEzjhG6uAf1KqcWHF4ufNU1dega7s/4nuEoCry//UyNz2RZIeFsMa9sSObmFTuJf2kLc75I4EROKf6eHjw1sTu/PTmGh0Z3wt/Tg/TCClILypFkxTYafU1H7h/RocY1r0YDTw8N7987iNvj2/Gn/lEsnNCd1VMHMjC2DQajlVn/PsDSHxJ5bWMys9cc5E8rdzHt43387btj9i3YXAVX9IXmRmhgw510EOHlApdEURTe3HKSFdtOA/DE+G48en1XJ1slENTCkSOwbBlKUBAr8714I24iACG5SXxVup/OS5dAv35ONlLQlIh2qvFosJb/9zZseQ763g23f9B0BgoE1Th0tpg/v7sbtQp6twukfbAPfp4e/JZyntxSY420XcL8GBcXzkOjOtGm2pZlpUYL21PO08ZHR5cwP8IDPJtt1M5slXl5fRKf7k6/bDpPDzWf3D+4yVZqFwjcCRFe7mbIskxJSQlBQUGo1SLooAqrJPPs94l8uT8TgKdv7MFDoztf1TUVRWHx4sXccsstjBw50uVDUGpD+EztOFWbkBAIDERVWsqjZw9Qkp/BhyPupzAijrvaduDNMi2jm9ciO8JnHCN0cQ/qVU721ctb1ksP4as2XFWH+PZtuL5HGFuT8zl6Ts/Ragtq+uo0XBcXznU92jKicyjhAV4OrxHgpeWWflFXvFdTaKDzUPP8rb0Y3CGY/x3Joq2/Jx1CfIkJ9kFfYSGzuIL/O11AwtkSZnx2gH/PGMLgDsH275dUmDFbZaQLY3jh/l5NvrOMq/pCcyI0sOFOOohOtxORJIn9+/dz3XXXubyjNBelRguPfXmI31LOowKW3tyD+6+5ug43wJ49e3jttddYuXIlOTk5bjsSJXymdpyqTXS0be722rXg78+zeSl03vse7wyaSq53KPf9mMaMYpgQYWTwwPhmfekjfMYxQhf3oF7l1ELDy4Wv2nBlHT6YNojT+WVkFJaTUVhBQbmJwbHBXNM1tFFXTm9KDSb1jWRS30iHn829rgsP/vsg20+eZ/on+3n1jr6czDOwITGHk3llNdJ2D/dn6a09GdH50hHxUqOFpOxSMgor6NUugJ6RAQ1qDxuiQ0ZhObl6IwHeWvy9PAj182ySVe2bC1d+HpoTd9JBhJcLXIaTeQYe/s9BUgvK8dKqeWdyPDf0imiUa6ekpPD666/j7e3NihUrGuWaAsElnDsHhYVgNoNOhzGwDcuPlPLZngwALIWZtM/fw85vPnKLPSUFdUe0U41Hg7X87hE48gWMex6uebypzBMIWiVGi8T0T/azJ7Xwks/UKtCoVUiywoU14JjUJ5Jb+kVxMs/AiexSjufoySyqrPG9dkHejIsLY3jnUHpFBRDdxrvRXkpXmK0cO6fn15Tz/JKUx+n8mi8HArw8WD11oAiXFzSIhrRTotPtRGRZpqCggNDQUJd/O9OUKIrCVwcyeeHHE1SYJaICvXh3ygCivCytXpuLET5TO66szdakPB77/HfKJdtb9Vv6RfG3m+KICLwQaljVWW+CLcZcWRdn0ti6tNR2yhlU19LPz6/u5fTfKZD8E0x6EwbPaB5jmwHxDNsQOjhfgwqzlRmfHuBgRjGjuoVyY+9IxsWFE+ijBWyh5m9sPsma3zPsne+LaRfkTbs23hw9V4LRItf4zN/Lg15RAQyMbcPA2Db0igqkwixRXGFGX2mhXZA3nUJ9UaugoKAAWefH7+nFnMkvw2SVMVll9JUWTmSXcirfUMMGD7WK6DbelJkkSistmCWZqEAvNj4+igAvbVNJ1mQ42xdcBWfpIOZ0uxmyLJOYmMioUaNa7QOTbzDy7PeJbDqeB8DILiG8MzmeQC8NO3bsaNXaOEL4TO24sjbXx4Wz+5kbefG7BL47VsCPR7LZlpTHlIHhrFt8L4+EBDEzIgJ1UFCjbzHmyro4E6GLe1CvcjJemEvbAud0C18VOoDzNfDRebBm5lAkRUGrufT+QT46XrqtN/cMac/rm5LJ0RvpGRlAzyhbKHlcZIB9ATmjRWLXqYILc+FLOJlnwGC0sje1iL2pRbXa4KVV0z3cn7yiUnIrLj9uGObvybBOIYzvGc7o7m3tnesKs5Ub395JRmEFL/+cxCt39K2zBvoKCydybJ36EF9PekYFEBvsY5/HLssK5WYrZSYrBqMVWVHoHt7421k52xdcBXfSQYx0C5yCJCus3Z/JKxuSKDVa0WpUPHFDd2Zd2wlNIy7AkZ2dzT//+U+efPJJwsPdY/9JQcsmMUvPcz8kknC2BACp0kBQ0hZ2D4nBP/ssqNXw/PONPuItaFpEO9V4NFjL90dDzmH461fQbULTGSgQCBods1XmdH4ZR8+VcCCjmIMZxaQVlOOr0xDko8Pfy4OzRRVUmCX7d1Qq6BUVQN/oIHx1Gjw9NHjrNHQL96dvdGCtC9cB/J5ayN0f7AXgsweGMLpb20vSnDeYOJhRzImcUk5kl5KUU0pWSeUl6Xx0Gtr46Cg1WigzWbm4ZzWqW1venTIAP08x1tlSEOHlboYsy+Tk5BAZGenyb2caC0VR2HOmkGU/J3Eix7boTZ92gbxyRx96RQXa0zWWNo899hgrVqxg3LhxbNmy5artdzat0WfqijtpI8sKPx/L4Z8/HiG1zBZeF4iVe9R53Hl6FzNST/CnKVOYM2cOOp3ukrni9QlDdyddmpPG1qWltlPO4OLw8jqX0zvxUJQK0zdC7PDmMbYZEM+wDaFD69NAkpUaAzGSrJBeWM7xLD2VZXpuiO9MG1/PBl//+f8d59Pd6UQGerF66kBkRaHSInEwvZhfkvM5klni8Hsxwd50C/OnoNxMck4pJqt8SRqtRoW/l5YyoxWzJNMzMoBPpg++7IuA+nCxL5isEh5qdaMOXLkDznomRHi5myHLMmfOnCE8PLzFV56KovBbynlW/nqagxnFgG3uzvxx3bhveCweF4UpNZY2U6dOZevWrSxevPiq7HcVWpPP1Bd30katVnFLvyhuaiPx0ysf8Y5Pd854BLBabsf7He+iXNpH2trNPPrYPNiyBdaupfj4cYIKC1GFh0P37nUOQ3cnXZoToYt7UK9yqlq93NO/6Q1rRoSv2hA6tD4NLu5AatQqOrf1I7aNF7t3n8Hfs9tVXf+pid35NSWfjMIK/rTq/xymiYsMoE87W2h8z8gAekQGEOj9xxxwqySTXlhOuUnC38sDPy8PAry0eHqoUalUHD1XwgOf7udETil/XvV/vHRbb7qG+RMV5HXJb98rkVlUwfHsUkBBlmWOHE+mwruIhLMlJOWUolKpaOvnSRhukCsAACAASURBVESgF9d2DWX+uG4tvhPuTs+EGOkWNClGi8TGxFw+3Jl6oaKw7Qk5eXAM867vSohfw99Q1hWLxYKHh4fb7s0taOFs2YK0di2/WAL5T0hvdunC7B+F+ngwqTCFW4pPct+6jym3WviuY0fi4+PB31+EobsQop1qPBqs5UthIJlg/jEIat90BgoEghZDwtli5v33ECaLjM5Djc5DTadQX66PC+e6HmGNMjJ9trCC+z/ZR2pBuf2cx4UXCNd2DWV097b0aRdISq6Bw5klpOQaiAj0om90EH2iAzmepWfN72fZcer8JaHrl+OhUZ14+qa4q7ZfcCkivNzNkGWZzMxMYmJiXP7tTH1QFIXDmSWsO3iOH49kYzBaAduclylD2zPr2k6EXaESu1pt8vPzCQsLu3JCN6Ol+kxj4NbaVFu9/IxnEJ/vzeC7Q1mUVFjsSaTS81SmHuR9Uw7ju4URUHSeNfHxbDl5ksmTJzNx4kSHl3ZrXZqQxtalpbZTzuDi8PI6lZPVBMsu1PmL0sG7TbPY2hyIZ9iG0EFoUIU76lBcbmbZz0kcziwms7gSs4OQ9LrQp10gnh5qFEXBajET36EtgzoGM6B9GzRqFbl6I/vSivj7+iQAXvtLX+4aFNOYWXEpnOULIrzczZBlmaysLNq1a+c2lUZtGC0Se84Usi05n23J+TUWmmgX5M2dg6K5b3gH+6qVV+JqtPn888955JFH+Prrr5kwoWUtptOSfKaxcWttoqPtI9adgaW39GLJTXHs2pvMj+u2s1kXQVlAW/z6T+QJQKPI9PcrJjs1kyP/l0RM58P2TndZWRmLFy+mT58+zJo1y711aUKELu5BncupKrQcWuTq5cJXhQ4gNKjCHXVo46vjjbv6AbZ1XXJLjSScLWZ7ynm2nzxPvsFERIAX/WOC6BkVQHZJJYczbau6B/nouGtQDPcMiSE2xBcAq9XKvn37GDKkBx4ef3TnwgO86BcThMFk5Z2tp/jbd8eIDfYh0EfLzpMF7EsvQl9podIsUWG24qPzIMzfk7AAT6ICvekS5keXMD/ah/ggy7bf90arhNEi244tEn6eHsQE++Cl1TjMq0WSKSo308ZHh86jacvHnXyhQSPdq1at4vXXXyc3N5d+/fqxYsUKhgwZUmv63377jQULFnD8+HFiYmJ45plnuP/+++t8PzGC4HoUl5s5lFlMQkYJBzOKOZRZXGO/RS+tmht7R3LnwGiGdQqxb6XQ1CiKwq233spPP/3Ek08+yeuvv94s9xUImowtWzCuXcfefBPbdRFsj+pFqk/IJcnaB/vQI8Iff6mU1a8+RyDlZJ5IsM/neuWVV0hLS+OBBx5g6NChgO15AcTUi0bAXdupxm7PP/30U6ZPn17jO56enhiNxjrb1CAtC8/AigGg84MlWXW+l0AgEDgbRVEoNVprzBWvwmiR0Grqv0CaLCs8+t9D/Hw0p7HMrIFKBZEBXoQHeqEotjyYJYXzBhOF5SYUBQK8PLixdyR/6h9Fr6hAskoqOVdcgb7SQmyIL13C/Aiu42Ccq9EsI91r165lwYIFrF69mqFDh/LWW28xYcIEUlJSHIbzpqWlMWnSJB5++GHWrFnD1q1bmTlzJpGRkS1uFLK+SJJEWloaHTt2RKNx/LbImZitMrl6I+dKKjhXXMnp/DJScg2czDOQo7/0B1RUoBdje4RxXY8wRnQOxVvX8Dw1VBuVSsU333zDJ598wqxZsxp8f1fF1X3GmbRYbcaPxysujjGFhYy5sHp5ptaPvRU6DmYUcyCjmNP5ZZwtquBsUQUAbW97GoCez22kfbAPIZ4KRxMrOZeSR+TgbPxi9IT5e5Fy9AATbriBoUOHsm3bNvstf/jhB8rKyhg7dixRUVGA7W2ySqW6fAe9Wpi8q881b7H+Ug+aqj0PCAggJSXF/v+realT53IytcxF1ED4ahVCB6FBFS1NB5VK5bDDDdQ6mnwlDdRqFf/4Sz/OFVVw5JweL62aoR1DuLZrKBGBXvjoNHhpNVSYJPINJvINRs4WVXAmv4zT+WWUV9uazUOtwkurwUurxtNDQ2mlBYPJSrbeSLaD/kAVpUYraw9ksvZAZq1pgn11DO8UwrieYYztHkaQT+2d8PMGE7vPFJCQUUywrycDYoPoExVAQU6mQx2skkxhuZnzBhPnDSa0GjXBvjpC/XRUWiSOZ5dyPFtPjt5Ijwh/+se0oU+7QLy0aiotEhVmCY1KhY+nbTu6q6XeI91Dhw5l8ODBrFy5ErD9EIuJieHRRx91uEL0okWL+Pnnn0lMTLSfmzx5MiUlJWzcuLFO93TXEYQrYbVaOXToEPHx8TVCQxobWVYwWiXKTZItnMRipdwkYTBaKK4wU1x+4d8Lxzn6SrJKKsk3mC67YEOntr4MbN+GgbG2vy5hfo02YlZXbYxGI++88w779u3j66+/bpR7uzLN5TPuSGvWpqTCzInsUpJzDSTnlpKSayAlz1Aj+sQRKhSsZSV4YmZ4fC9CfHUEemv55r9ryExN4eEZ9zNq2GD8vTw4nXSMuQ/NpG+vOP73/Te2BthDw6vLXyY9PY1HBgxg0KFDoNeTp9OxPjKSsLFjmTRpkv1+p0+fxmg0EhMTQ2CgbYtASZIwm814eno2a2hYY/uLO7ZTTdGef/rpp8yfP5+SEsdb7dSF6lr6+PjUrZzSdsBnt0Bod5i7r8H3dkVac91WHaGD0KAKoUPdNagwW0nONdAzMqDWDvzFKIpCSYUFrYcaLw/1JausK4pCUbmZ9MIKCspMqFUq1Crb6vKhF1ZPD/LWsi+9iB+PZLP+WC76SgvBvjqi23gT4KUlraD8kj3PNWoVEQFeaDUqNGqVfQs0rUZFuVnidH7ZJbaqVBDpqyEuOpjOYf4EeHmQkldGck4pqQXlSHL9ArqrujEX93+0GhU+Og9W/XUA13QNbfqF1MxmMz4+Pnz99dfcdttt9vP33XcfJSUl/PDDD5d8Z9SoUQwYMIC33nrLfu6TTz5h/vz56PX6Ot23MX/M/GtnKpt37MVgMNCrd2+Cg4MBKCoq4lhiIr6+fsTHx9tCJVA4duwYen0p3bt3JzQ0FEVR0JcaSEw8hpeXN/3j+wO2wklKSqakpIROnTvRtm1bAMrKyjmWmIhOq6N/fP8L17X9+CwqKiI2Npa2YWGgQKXRSGJiImqNhv79/7hueno6BUXFhEdG0iY4FIskYzJbOJOajqJS0y6mPVZZwSrJFOtLqag04untg9pDR4VZotIiOdSiLnh6qGkX5I3OWkZlXjoDOkcy9ZaxdAnzJ9Bby+zZs5FlmVdeeYWgoCAANm3axLfffsvw4cNrhB3OmzePiooKli1bRnh4OAC//vora9asYdCgQTz88MP2tE888QQlJSUsXbqU9u1tq9Du2rWLjz/+mBEjRjBz5kwATCYTgYGBmEwmdu/ezfDhLWdvVoHgapBkhcyiCjKKKsgsskWrZBZXcO7CcVGFuV6roNaKIiNbzPirFfxVCl4aFWZjBemlpXhqPejbtw8ealsDeuzIYYoKC+jXtw/to6PxUKvQlxTzy+ZN+Ph4c8cdt6NChUoFu3buJDs7iyFDhtClc2dUKtDr9WzevBkfb2/+dOutqFSgAvbt28e5c+fo378fXbt0RaWCiopyftnyCzqdlpsnTbIlBI4cPkLmuUziesTRuUtnxnQLY3jnS8P164u7dbqbqj3/9NNPmTlzJu3atUOWZQYMGMDLL79Mr1696mxbg7RM+gnWToHowTDzlzrfSyAQCARNi1WSMUsyPrqaLwcqzRJJuaX8mpzPlhN5JOcarnitnpEBDO0UTGGZmYSzxZwrrrxserUKQv08CfXzxCrb5poXlZvxUKvpHuFPr6gAIgO9OZGj59DZEvINpste778PDmNYp5CmDy8vKChAkiR7h6mK8PBwkpOTHX4nNzfXYfrS0lIqKyvx9va+5DsmkwmT6Y9Ml5bawsYkSarxr0ajqXFstVpRqVT2Y7VajVqtrnH85b6znDEEAoEkJZYA1d/GR0M5JOxKq3bOH1T+nDpZBiervWFRtQMTJO49Wy2tD6h9SE0zQlq1UAp1FEhw8sC5amm9QBPF2XMWOFdt/pkmEoCMw9nV0upAE052vgz5+dXS2lZnLcqq/vJCDWofKkyAyXyxtPjoNHhrNfjoNBiK8snJOE232HaMGT6YQC8NQT5a5j14P1Z9PkkHdhEbEYKiKCxbtowX3nqBng8/zMC5dyDLMlarlQ8//BCr1cqSJUsICgpCkiQOHTrEBx98gNFo5N5777WX02effYZer2fBggWEhoai0Wg4duwYH330EaWlpcycOfOPcvryS3Jycpg9ezaRkZFoNBpOnz7NJ598Qk5ODjNnzsRisaDT6Zg1axb9+/e/8LJEwWq1otVqaxzLsowkSfZjWZbx8PCo9ViSJBRFsR878rf6+l71Y4vFgkajsR9XbWlWdQy2t5jVj7VaLVarlZSUFHr06IFKpWoReWqscjKbzZw+fZru3bv/8Yi4eZ6uppwUWaJ9sDcxbbxIsZ7n7oFd0Wq19jzJCuTpKyiplCgoN5NbUk5xpS0CprTCTJlZptRoobTSQplJorTSjKHSglmSMUnKHx12lRq1zotywL4Zipc3Oq8QFOBIZrU6NqA93gHtOWmAk0l59tO+vcYA8G1CtbrQpyPqLh05UAQHiqrVp7HDKAX+szejWs0WDpHh7MyDnXnV6u+QvgC8vyO1Wlp/8O9JVhb8kpWKn1bF8M4hV11OVWXiLjRVe969e3c+/vhj+vbti16v5x//+AcjRozg+PHjRNcy5eBybb4kSaSkpNClSxd0Ol2tvi9VFKMB8PRvcc+zoiikpKTQtWtXezvg7nlqSL1rNBpJS0uja9eu9vu7e57qW04Wi4W0tDQ6d+6MSqVqEXlqSDmpVKpa6wV3zVN9ywmotV5wtTypAB/dpfnTaWBA+zb0jfJn/vVdyDOYyS2pQFJAVsBksSIrICmgyDL9YoJoG+BdI09nz+vZdfQ0Fq9gUgvKMBgluob70bWtD3FRQYQHeKHIUo08qTVVdik18qTRaMgvNaIoMv7ennhqVFglCZOswlBppsxooX2oX4PbfJdc5m358uUEBgba/2JibEvdV4W0JSUlkZRkWwr/6NGjnDp1CoBDhw6Rlmb7wbVv3z4yM20/1Hbv3k1Ojm0hgfggE7088uhizWBooIF7B0fy0KhODA000NmcygDvAsZFycwaGcvDozrSqfIk3aU0/tzdmxvayTx6XRfu7h1AZOEhBnrl8+CIaG7uoGb+uK4M8yskuvgQd/f0ZdawCP7STceDwyPpWplE18oTLJzQnRmDQ5nS25cJEZV0KU/k2oBCFk/szgMDgrgnzpN4UuleeZwHB4fw7M09ub+vH2MDCxikSuWGNoU8NaYdb97VjwfiVAw2HWGMx0nmx+t45/ZufDFrKDfpkhlrPchzwzxZMgDWPzKIXU9ew6D0L5jMLhKWjOG5vhVsW3AtL470YSyJPHt9OxZe1544KZXpIzvy6G3XcNe4oUSFBFBYWMiOHTsYM2YMTzzxBB07dgQgMzOTffv2sXTpUubNm0dqqu1H7alTp4iIiOCll15i0KBBNcrpwQcfZNmyZWRlZdnLyc/Pj4ULFzJ58uQa5XT77bfz8MMPExkZybZt2ygpKWHgwIHcf//99lGZ9evXYzQaefPNN+2deKPRyPr16wEwGAxs3rwZgJKSEvuc1YKCAnbs2AFATk4Ou3fvrpEnsM1dPHTokD1PR48evWrf27FjBwUFBQD2PAFs3rwZg8FQI09Wq5X169djtVpr5KmsrIwzZ860qDw1VjmdPn2a7OzsFpWnxiqn9PT0S/LkoVFzeM92YvxVjO7WFq+cI9w3JIr513VmkDqN127vxcq7ejM18jwb5l3LhtmDWT5EInnZTRx4chgrR2s4svQG1j/Yj1eGadhc/AsfaY7yt04GvijewT88T/LUAE/+NW0Qz4+LYsHQAN6e3J8nrw1j7uAgXrm9D7OHBPPgwCCeGt+JyT08mTEomMU39mBKb1/u6OrBlD7+3NoBZg4J44nx3bilg4rxkWb+3FXHrZ08mDE0ikev68IQ3yKuCS5jcr9gboiGB4ZHc//QdnQynWFIQCkzRsYyNlJmxshYrotSaF9xikmdvZjUxQtLXuM8T9VDrlszw4cPZ9q0afTv35/Ro0fz7bff0rZtW95///1av3OlNj83N9f+IqA23884dUF/z4AW+TxnZGSQm5vbovJU33LatGkTZWVlLSpP9S2n3377jcrKyhaVp4aWU15eXqP0C1wpT/Utp8zMTM6dO9di8hQV5I0xO4UwdRlDO4VAXgpd/a1cHxeOOi8JxWi4JE/H9u2is5+VqcPaM4BU/n5rd2aOjKX89H7C/LRYzKZL8qRRqygzlF6SJ5VKhbWsiJNHD+Lr6UFW1jkSDh4g0FuLsSgHQ9YpfHQeDW7zXTK83NFb75iYGIqKimjTpk2rfKMl8iTyJPIk8uTSefr1V+SvvkI2GPDw80O+6y7k665z7zzVo5xKS0sJDg5u9eHljrjzzjvx8PDgyy+/dPh5Y7T50q+voNm+HOLvxTrpLZf1E0f5cHffF3kSeRJ5EnlqbXlqSJvfoIXUhgwZwooVKwDbwivt27dn7ty5tS68sn79eo4dO2Y/99e//pWioqJWv5CaJEkkJSURFxeHRuP+qy82JkIbxwhdakdo45hm1cXNVi9vTF3csZ1qjvZckiR69erFTTfdxJtvvlknu6pr6evrW7dy2vwM7F4Bw+fChL/X6T7ugqjbbAgdhAZVCB2EBlU4S4eGtPn1Di9fsGABH374IZ999hlJSUk88sgjlJeX2/flfPrpp5k2bZo9/cMPP0xqaipPPfUUycnJvPvuu3z11Vc8/vjj9b21QCAQCFyZ6Gjo18/lO9wCG03Rnr/44ots3ryZ1NRUEhISmDp1KhkZGfbFL5sM04UFeDzd44WHQCAQCFoX9V5n/+677+b8+fM899xz5Obm0r9/fzZu3GhfXCUnJ4ezZ/9YXKxjx478/PPPPP7447z99ttER0fzr3/9q9Xv0Q22kIrevXs72wyXRGjjGKFL7QhtHCN0cYzQpWna8+LiYmbNmkVubi5t2rRh4MCB7N69m549ezbIxjqXk7Hl7tMtfNWG0EFoUIXQQWhQhTvpUO/wcmeg1+sJCgoiMzPTbcL26oIkSSQmJtK7d+9WHRriCKGNY4QutSO0cYzQxTGNrUvVPOSSkhL7HuSChlG9zff19a1bOa29F1J/hZvegH53N5+xzYB4hm0IHYQGVQgdhAZVOEuHhrT5btHpPnfunH01U4FAIBAIXJXMzMxat8YS1A3R5gsEAoHAHahPm+8WnW5ZlsnOzsbf39++N11LoOotSUsbwW8MhDaOEbrUjtDGMUIXxzS2LoqiYDAYiIqKQq12yd043Ybqbb7BYGj1/iueYRtCB6FBFUIHoUEVztKhIW1+ved0OwO1Wt2iRw4CAgJa9QNzOYQ2jhG61I7QxjFCF8c0pi4irLxxqN7mV71oF/4rNKhC6CA0qELoIDSowhk61LfNF6/jBQKBQCAQCAQCgUAgaCJEp1sgEAgEAoFAIBAIBIImQvP8888/72wjWjMajYYxY8bg4eEWkf7NitDGMUKX2hHaOEbo4hihi3sgykloUIXQQWhQhdBBaFCFu+jgFgupCQQCgUAgEAgEAoFA4I6I8HKBQCAQCAQCgUAgEAiaCNHpFggEAoFAIBAIBAKBoIkQnW6BQCAQCAQCgUAgEAiaCNHpdhHS09OZMWMGHTt2xNvbm86dO7N06VLMZrOzTWt2Vq1aRYcOHfDy8mLo0KHs27fP2SY5neXLlzN48GD8/f0JCwvjtttuIyUlxdlmuRyvvPIKKpWK+fPnO9sUlyArK4upU6cSEhKCt7c3ffr04cCBA842y6lIksSzzz5bo6596aWXEMubuB6tqS2oSx2vKArPPfcckZGReHt7M27cOE6dOuUki5sHR3V6a9HhSvV3S9ehLnV1S9Rgx44d3HLLLURFRaFSqfj+++9rfF6XPBuNRubMmUNISAh+fn7ccccd5OXlNWc2rorLaWCxWFi0aBF9+vTB19eXqKgopk2bRnZ2do1ruKIGotPtIiQnJyPLMu+//z7Hjx/nn//8J6tXr2bJkiXONq1ZWbt2LQsWLGDp0qUkJCTQr18/JkyYQH5+vrNNcyrbt29nzpw57N27ly1btmCxWLjhhhsoLy93tmkuw/79+3n//ffp27evs01xCYqLixk5ciRarZYNGzZw4sQJ3njjDdq0aeNs05zKq6++ynvvvcfKlStJSkri1Vdf5bXXXmPFihXONk1QjdbWFtSljn/ttdd45513WL16Nb///ju+vr5MmDABo9HoRMubjtrq9NagQ13q75auQ13q6paoQXl5Of369WPVqlUOP69Lnh9//HF+/PFH1q1bx/bt28nOzub2229vrixcNZfToKKigoSEBJ599lkSEhL49ttvSUlJ4dZbb62RziU1UAQuy2uvvaZ07NjR2WY0K0OGDFHmzJlj/78kSUpUVJSyfPlyJ1rleuTn5yuAsn37dmeb4hIYDAala9euypYtW5TRo0cr8+bNc7ZJTmfRokXKNddc42wzXI5JkyYpDzzwQI1zt99+uzJlyhQnWSRwRGtvCy6u42VZViIiIpTXX3/dnqakpETx9PRUvvzyS2eZ2WTUVqe3Fh2uVH+3Bh2uVFe3Bg0A5bvvvrP/vy55LikpUbRarbJu3Tp7mqSkJAVQ9uzZ03zGNxIXa+CIffv2KYCSkZGhKIrraiBGul0YvV5PcHCws81oNsxmMwcPHmTcuHH2c2q1mnHjxrFnzx4nWuZ66PV6gFblH5djzpw5TJo0qYbvtHb+97//MWjQIO68807CwsKIj4/nww8/dLZZTmfEiBFs3bqVkydPAnDkyBF27drFjTfe6GTLBFWItuDSOj4tLY3c3NwamgQGBjJ06NAWqUltdXpr0eFK9Xdr0OFKdXVr0OBi6pLngwcPYrFYaqTp0aMH7du3b7G66PV6VCoVQUFBgOtq4Nq7iLdiTp8+zYoVK/jHP/7hbFOajYKCAiRJIjw8vMb58PBwkpOTnWSV6yHLMvPnz2fkyJH07t3b2eY4nf/+978kJCSwf/9+Z5viUqSmpvLee++xYMEClixZwv79+3nsscfQ6XTcd999zjbPaSxevJjS0lJ69OiBRqNBkiT+/ve/M2XKFGebJrhAa28LHNXxubm5AA41qfqspXC5Or216HCl+rs16HCluro1aHAxdclzbm4uOp3O3gF1lKYlYTQaWbRoEffccw8BAQGA62ogOt1NzOLFi3n11VcvmyYpKYkePXrY/5+VlcXEiRO58847mTVrVlObKHAz5syZQ2JiIrt27XK2KU4nMzOTefPmsWXLFry8vJxtjkshyzKDBg3i5ZdfBiA+Pp7ExERWr17dqjvdX331FWvWrOGLL76gV69eHD58mPnz5xMVFdWqdRG4Dq25jhd1ug1Rf4u6WnBlLBYLd911F4qi8N577znbnCsiOt1NzBNPPMH9999/2TSdOnWyH2dnZzN27FhGjBjBBx980MTWuRahoaFoNJpLVhfMy8sjIiLCSVa5FnPnzuWnn35ix44dREdHO9scp3Pw4EHy8/MZMGCA/ZwkSezYsYOVK1diMpnQaDROtNB5REZG0rNnzxrn4uLi+Oabb5xkkWuwcOFCFi1axOTJkwHo06cPGRkZLF++XPyQcxFac1tQWx1fle+8vDwiIyPt5/Py8ujfv3+z29lUXKlOr1rRvaXrcKX6uzX4w5Xq6tagwcXUJc8RERGYzWZKSkpqjPS2tPqzqsOdkZHBtm3b7KPc4LoaiDndTUzbtm3p0aPHZf90Oh1gG+EeM2YMAwcO5JNPPkGtbl3Fo9PpGDhwIFu3brWfk2WZrVu3Mnz4cCda5nwURWHu3Ll89913bNu2jY4dOzrbJJfg+uuv59ixYxw+fNj+N2jQIKZMmcLhw4dbbYcbYOTIkZdsOXTy5EliY2OdZJFrUFFRgYdHzffNGo0GWZadZJHgYlpjW3ClOr5jx47/3979x1RV/3Ecf13A7uU2+RFMJIs77qAkQ4t+LLSJix8Sc5lENlt5XW2l1CBbuTURMTP6w2zOxaTNSVmzOaMcWI4MqARBSbAfNiZmQxZWtubFkFDu5/tH325dBaS+3u8FeT62s3E/5/P5nPd57+5z9t4596DJkyf75MTtdqu5ufmKysml1nSn0zku8nCp9Xs8fB8utVaPhxxcaCTnfNttt2nChAk+fdrb29XZ2XnF5OXPgvvo0aPau3evoqKifPaP2hwE7BVu8NHV1WUSEhJMenq66erqMt3d3d5tPHn33XeN1Wo1FRUV5siRI+aJJ54wERER5uTJk4EOLaCWLVtmwsPDTX19vc93o7e3N9ChjTq8vfwPBw4cMCEhIWbdunXm6NGj5p133jF2u928/fbbgQ4toFwul5kyZYqprq42x48fN5WVlSY6OtqsWLEi0KHhb8bbtWAka/wrr7xiIiIizK5du8yXX35p5s+fb+Lj483Zs2cDGLn/Xbimj4c8jGT9vtLzMJK1+krMQU9Pj2ltbTWtra1GktmwYYNpbW31vpl7JOe8dOlSExcXZ2pra01LS4tJTU01qampgTqlf2y4HPT395v77rvPXHfddaatrc1nvfz999+9c4zGHFB0jxJbt241kgbdxptNmzaZuLg4c9VVV5k777zTNDU1BTqkgBvqu7F169ZAhzbqUHT/paqqytx8883GarWaqVOnmjfeeCPQIQWc2+02hYWFJi4uzthsNuN0Os3KlSt9LtYYHcbTtWAka7zH4zGrVq0yMTExxmq1mvT0dNPe3h64oP9PLlzTx0seLrV+X+l5moRTcQAAB5xJREFUGMlafSXmoK6ubtC1wOVyGWNGds5nz541+fn5JjIy0tjtdrNgwYIxdRNvuBwcP358yPWyrq7OO8dozIHFGGP8ey8dAAAAAIDxaXz9aBgAAAAAgP8jim4AAAAAAPyEohsAAAAAAD+h6AYAAAAAwE8ougEAAAAA8BOKbgAAAAAA/ISiGwAAAAAAP6HoBgAAAADATyi6AQAAgDFqyZIluv/++/02/5w5c/TMM8/4bX5gPAgJdAAAAAAARqfKykpNmDAh0GEAYxpFNwAAAIBBXXPNNYEOARjzeLwcAAAAuISdO3cqOTlZoaGhioqKUkZGhn777TdJ0sGDB5WZmano6GiFh4crLS1Nhw4d8hlvsVhUXl6uefPmyW63KykpSfv371dHR4fmzJmjq6++WjNnztSxY8e8Y0pKSnTLLbeovLxc119/vex2uxYuXKjTp08PGafH41Fpaani4+MVGhqqGTNmaOfOncOeW1lZmRITE2Wz2RQTE6O8vDzvvr8/Xl5fXy+LxXLRtmTJEm//Xbt2KSUlRTabTU6nU2vWrNH58+eHPPafj8evX79esbGxioqK0lNPPaVz584NGzMwllB0AwAAAMPo7u7WokWL9Nhjj+nbb79VfX29cnNzZYyRJPX09Mjlcmnfvn1qampSYmKicnJy1NPT4zPP2rVrtXjxYrW1tWnq1Kl6+OGH9eSTT+qFF15QS0uLjDF6+umnfcZ0dHRox44dqqqq0p49e9Ta2qr8/PwhYy0tLdVbb72lzZs365tvvtHy5cv1yCOP6NNPPx20f0tLiwoKCvTiiy+qvb1de/bs0ezZswftO3PmTHV3d3u32tpa2Ww2b//PP/9cixcvVmFhoY4cOaLy8nJVVFRo3bp1w+a3rq5Ox44dU11dnd58801VVFSooqJi2DHAmGIAAAAADOmLL74wksz3338/ov4DAwNm4sSJpqqqytsmyRQVFXk/79+/30gyW7Zs8bZt377d2Gw27+fVq1eb4OBg09XV5W376KOPTFBQkOnu7jbGGONyucz8+fONMcb09fUZu91uGhsbfeJ5/PHHzaJFiwaN9b333jNhYWHG7XYPuj8tLc0UFhZe1H7q1CnjdDpNfn6+ty09Pd28/PLLPv22bdtmYmNjB537z/gdDoc5f/68t+3BBx80Dz300JBjgLGG33QDAAAAw5gxY4bS09OVnJysuXPnKisrS3l5eYqMjJQk/fjjjyoqKlJ9fb1++uknDQwMqLe3V52dnT7zTJ8+3ft3TEyMJCk5Odmnra+vT263W2FhYZKkuLg4TZkyxdsnNTVVHo9H7e3tmjx5ss/8HR0d6u3tVWZmpk97f3+/br311kHPLTMzUw6HQ06nU9nZ2crOztaCBQtkt9uHzMe5c+f0wAMPyOFwaOPGjd72w4cPq6GhwefO9sDAgPr6+tTb2zvknNOmTVNwcLD3c2xsrL766qshjw+MNRTdAAAAwDCCg4P18ccfq7GxUTU1Ndq0aZNWrlyp5uZmxcfHy+Vy6ZdfftHGjRvlcDhktVqVmpqq/v5+n3n+/hZwi8UyZJvH4/lXcZ45c0aStHv3bp9CXZKsVuugYyZOnKhDhw6pvr5eNTU1Ki4uVklJiQ4ePKiIiIhBxyxbtkwnTpzQgQMHFBLyVzlx5swZrVmzRrm5uReNsdlsQ8Z94dvRLRbLv84BMBpRdAMAAACXYLFYNGvWLM2aNUvFxcVyOBx6//339eyzz6qhoUFlZWXKycmRJJ04cUKnTp26LMft7OzUDz/8oGuvvVaS1NTUpKCgIN14440X9b3ppptktVrV2dmptLS0ER8jJCREGRkZysjI0OrVqxUREaHa2tpBi+cNGzZox44damxsVFRUlM++lJQUtbe3KyEh4R+eJXBlo+gGAAAAhtHc3KxPPvlEWVlZmjRpkpqbm/Xzzz8rKSlJkpSYmKht27bp9ttvl9vt1vPPP6/Q0NDLcmybzSaXy6X169fL7XaroKBACxcuvOjRcumPu9bPPfecli9fLo/Ho7vvvlunT59WQ0ODwsLC5HK5LhpTXV2t7777TrNnz1ZkZKQ+/PBDeTyeQYv6vXv3asWKFXr99dcVHR2tkydPSpJCQ0MVHh6u4uJizZs3T3FxccrLy1NQUJAOHz6sr7/+Wi+99NJlyQcwFvH2cgAAAGAYYWFh+uyzz5STk6MbbrhBRUVFevXVV3XvvfdKkrZs2aJff/1VKSkpevTRR1VQUKBJkyZdlmMnJCQoNzdXOTk5ysrK0vTp01VWVjZk/7Vr12rVqlUqLS1VUlKSsrOztXv3bsXHxw/aPyIiQpWVlbrnnnuUlJSkzZs3a/v27Zo2bdpFffft26eBgQEtXbpUsbGx3q2wsFCSNHfuXFVXV6umpkZ33HGH7rrrLr322mtyOByXJRfAWGUx5r//6wAAAADAqFFSUqIPPvhAbW1tgQ4FwP+AO90AAAAAAPgJRTcAAAAAAH7C4+UAAAAAAPgJd7oBAAAAAPATim4AAAAAAPyEohsAAAAAAD+h6AYAAAAAwE8ougEAAAAA8BOKbgAAAAAA/ISiGwAAAAAAP6HoBgAAAADATyi6AQAAAADwk/8AW83O0VsDMwwAAAAASUVORK5CYII=",
            "text/plain": "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x000000005B741A58>)"
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": "0-element Array{Any,1}"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
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