diff --git a/Huggett1993.ipynb b/Huggett1993.ipynb new file mode 100644 index 0000000..d1172ab --- /dev/null +++ b/Huggett1993.ipynb @@ -0,0 +1,698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# This notebook solves the Huggett Model (1993) using Dolo." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a heterogeneous-agent model where each period agents are hit with idiosyncratic income shocks $y_t$ that follow an $AR1$ process. There are incomplete markets and agents only have access to a risk-free asset $s_t$ that pays $(1+r)s_t$ next period, where $r$ is the interest rate.\n", + "\n", + "The value function for an agent with current assets $s$ and current income $y$ is: $v(y,s)=\\max_{c,s'} u(c)+\\beta \\mathbf{E}v(y',s')$ where the expectation is taken over the value of the income shock.\n", + "\n", + "The agent's budget constraint is: $c+s'=(1+r)s+y$ where s' is his asset choice next period. The agent will also be subject to a borrowing constraint: $s'\\geq \\bar{s}$.\n", + "\n", + "Here, we define the control in the model as $a=s'-s$, i.e. $a$ is the change in assets.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[31mWARNING: Using Calculus.jl for symbolic differentiation. This will be slower than SymEngine.jl\n", + ". To use SymEngine call Pkg.add(\"SymEngine\")\u001b[0m\n" + ] + } + ], + "source": [ + "# First import the packages\n", + "Pkg.dir(\"Dolo\")\n", + "import Dolo\n", + "using AxisArrays\n", + "using PyPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"huggett_1993.yaml\"" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the model file\n", + "filename=(\"huggett_1993.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model \n" + ] + }, + { + "ename": "MethodError", + "evalue": "MethodError: no method matching sanitize(::Expr, ::Dolo.Model{Symbol(\"##336\")})\u001b[0m\nClosest candidates are:\n sanitize(::Expr, \u001b[1m\u001b[31m::Array{Symbol,1}\u001b[0m) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:14\n sanitize(::Any, \u001b[1m\u001b[31m::Array{Symbol,1}\u001b[0m) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:3\n sanitize(::Any, \u001b[1m\u001b[31m::Dolo.SModel{ID}\u001b[0m) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:32\u001b[0m", + "output_type": "error", + "traceback": [ + "MethodError: no method matching sanitize(::Expr, ::Dolo.Model{Symbol(\"##336\")})\u001b[0m\nClosest candidates are:\n sanitize(::Expr, \u001b[1m\u001b[31m::Array{Symbol,1}\u001b[0m) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:14\n sanitize(::Any, \u001b[1m\u001b[31m::Array{Symbol,1}\u001b[0m) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:3\n sanitize(::Any, \u001b[1m\u001b[31m::Dolo.SModel{ID}\u001b[0m) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:32\u001b[0m", + "", + " in show(::IOContext{Base.AbstractIOBuffer{Array{UInt8,1}}}, ::MIME{Symbol(\"text/html\")}, ::Dolo.Model{Symbol(\"##336\")}) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\printing.jl:75", + " in limitstringmime(::MIME{Symbol(\"text/html\")}, ::Dolo.Model{Symbol(\"##336\")}) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\IJulia\\src\\inline.jl:25", + " in display_dict(::Dolo.Model{Symbol(\"##336\")}) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\IJulia\\src\\execute_request.jl:40", + " in execute_request(::ZMQ.Socket, ::IJulia.Msg) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\IJulia\\src\\execute_request.jl:188", + " in eventloop(::ZMQ.Socket) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\IJulia\\src\\eventloop.jl:8", + " in (::IJulia.##13#19)() at .\\task.jl:360" + ] + } + ], + "source": [ + "# Convert the file into Dolo model\n", + "model=Dolo.yaml_import(filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Now let's look at solving the model. We will use Dolo's time iteration function (which iterates on the residuals of the arbitrage equation)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It SA gain nit \n", + "-----------------------------------\n", + "0 1.11e+00 NaN 0 \n", + "1 1.42e+00 1.28e+00 5 \n", + "2 8.12e-01 5.71e-01 4 \n", + "3 4.94e-01 6.08e-01 4 \n", + "4 3.19e-01 6.46e-01 4 \n", + "5 2.16e-01 6.79e-01 3 \n", + "6 1.53e-01 7.05e-01 3 \n", + "7 1.11e-01 7.27e-01 3 \n", + "8 8.27e-02 7.45e-01 3 \n", + "9 6.29e-02 7.61e-01 3 \n", + "10 4.87e-02 7.74e-01 3 \n", + "11 3.82e-02 7.85e-01 3 \n", + "12 3.04e-02 7.95e-01 3 \n", + "13 2.54e-02 8.36e-01 3 \n", + "14 2.38e-02 9.35e-01 3 \n", + "15 2.25e-02 9.49e-01 2 \n", + "16 2.11e-02 9.35e-01 2 \n", + "17 1.98e-02 9.40e-01 2 \n", + "18 1.86e-02 9.41e-01 2 \n", + "19 1.73e-02 9.30e-01 2 \n", + "20 1.63e-02 9.40e-01 2 \n", + "21 1.51e-02 9.29e-01 2 \n", + "22 1.41e-02 9.33e-01 2 \n", + "23 1.31e-02 9.29e-01 2 \n", + "24 1.22e-02 9.29e-01 2 \n", + "25 1.13e-02 9.30e-01 2 \n", + "26 1.05e-02 9.30e-01 2 \n", + "27 9.79e-03 9.30e-01 2 \n", + "28 9.12e-03 9.31e-01 2 \n", + "29 8.50e-03 9.32e-01 2 \n", + "30 7.94e-03 9.34e-01 2 \n", + "31 7.43e-03 9.37e-01 2 \n", + "32 6.99e-03 9.40e-01 2 \n", + "33 6.60e-03 9.44e-01 2 \n", + "34 6.26e-03 9.49e-01 2 \n", + "35 5.97e-03 9.54e-01 2 \n", + "36 5.73e-03 9.60e-01 2 \n", + "37 5.54e-03 9.66e-01 2 \n", + "38 5.39e-03 9.73e-01 2 \n", + "39 5.60e-03 1.04e+00 2 \n", + "40 6.05e-03 1.08e+00 2 \n", + "41 6.49e-03 1.07e+00 2 \n", + "42 6.94e-03 1.07e+00 2 \n", + "43 7.39e-03 1.06e+00 2 \n", + "44 7.84e-03 1.06e+00 2 \n", + "45 8.29e-03 1.06e+00 2 \n", + "46 8.75e-03 1.05e+00 2 \n", + "47 9.20e-03 1.05e+00 2 \n", + "48 9.65e-03 1.05e+00 2 \n", + "49 1.01e-02 1.05e+00 2 \n", + "50 1.05e-02 1.04e+00 2 \n", + "51 1.10e-02 1.04e+00 2 \n", + "52 1.14e-02 1.04e+00 2 \n", + "53 1.19e-02 1.04e+00 2 \n", + "54 1.23e-02 1.04e+00 2 \n", + "55 1.27e-02 1.03e+00 2 \n", + "56 1.31e-02 1.03e+00 2 \n", + "57 1.35e-02 1.03e+00 2 \n", + "58 1.39e-02 1.03e+00 2 \n", + "59 1.43e-02 1.03e+00 2 \n", + "60 1.47e-02 1.03e+00 2 \n", + "61 1.51e-02 1.03e+00 2 \n", + "62 1.55e-02 1.03e+00 2 \n", + "63 1.59e-02 1.02e+00 2 \n", + "64 1.62e-02 1.02e+00 2 \n", + "65 1.66e-02 1.02e+00 2 \n", + "66 1.70e-02 1.02e+00 2 \n", + "67 1.73e-02 1.02e+00 2 \n", + "68 1.77e-02 1.02e+00 2 \n", + "69 1.81e-02 1.02e+00 2 \n", + "70 1.84e-02 1.02e+00 2 \n", + "71 1.88e-02 1.02e+00 2 \n", + "72 1.91e-02 1.02e+00 2 \n", + "73 1.95e-02 1.02e+00 2 \n", + "74 1.99e-02 1.02e+00 2 \n", + "75 2.02e-02 1.02e+00 2 \n", + "76 2.06e-02 1.02e+00 2 \n", + "77 2.10e-02 1.02e+00 2 \n", + "78 2.13e-02 1.02e+00 2 \n", + "79 2.17e-02 1.02e+00 2 \n", + "80 2.21e-02 1.02e+00 2 \n", + "81 2.24e-02 1.02e+00 2 \n", + "82 2.28e-02 1.02e+00 2 \n", + "83 2.32e-02 1.02e+00 2 \n", + "84 2.36e-02 1.02e+00 2 \n", + "85 2.39e-02 1.02e+00 2 \n", + "86 2.43e-02 1.02e+00 2 \n", + "87 2.47e-02 1.02e+00 2 \n", + "88 2.51e-02 1.01e+00 2 \n", + "89 2.54e-02 1.01e+00 2 \n", + "90 2.57e-02 1.01e+00 2 \n", + "91 2.61e-02 1.01e+00 2 \n", + "92 2.64e-02 1.01e+00 2 \n", + "93 2.66e-02 1.01e+00 2 \n", + "94 2.69e-02 1.01e+00 2 \n", + "95 2.71e-02 1.01e+00 2 \n", + "96 2.72e-02 1.01e+00 2 \n", + "97 2.73e-02 1.00e+00 2 \n", + "98 2.74e-02 1.00e+00 2 \n", + "99 2.74e-02 1.00e+00 2 \n", + "100 2.73e-02 9.97e-01 2 \n", + "101 2.72e-02 9.95e-01 2 \n", + "102 2.70e-02 9.92e-01 2 \n", + "103 2.67e-02 9.90e-01 2 \n", + "104 2.63e-02 9.87e-01 2 \n", + "105 2.59e-02 9.84e-01 2 \n", + "106 2.54e-02 9.81e-01 2 \n", + "107 2.48e-02 9.78e-01 2 \n", + "108 2.42e-02 9.75e-01 2 \n", + "109 2.35e-02 9.72e-01 2 \n", + "110 2.28e-02 9.69e-01 2 \n", + "111 2.20e-02 9.66e-01 2 \n", + "112 2.12e-02 9.63e-01 2 \n", + "113 2.04e-02 9.60e-01 2 \n", + "114 1.95e-02 9.58e-01 2 \n", + "115 1.86e-02 9.55e-01 2 \n", + "116 1.77e-02 9.52e-01 2 \n", + "117 1.69e-02 9.50e-01 2 \n", + "118 1.60e-02 9.48e-01 2 \n", + "119 1.51e-02 9.45e-01 2 \n", + "120 1.42e-02 9.43e-01 2 \n", + "121 1.34e-02 9.41e-01 2 \n", + "122 1.26e-02 9.39e-01 2 \n", + "123 1.18e-02 9.38e-01 2 \n", + "124 1.11e-02 9.36e-01 2 \n", + "125 1.03e-02 9.35e-01 2 \n", + "126 9.64e-03 9.33e-01 2 \n", + "127 8.99e-03 9.32e-01 2 \n", + "128 8.37e-03 9.31e-01 2 \n", + "129 7.78e-03 9.30e-01 2 \n", + "130 7.22e-03 9.29e-01 2 \n", + "131 6.70e-03 9.28e-01 2 \n", + "132 6.21e-03 9.27e-01 2 \n", + "133 5.75e-03 9.26e-01 2 \n", + "134 5.33e-03 9.26e-01 2 \n", + "135 4.93e-03 9.25e-01 2 \n", + "136 4.55e-03 9.24e-01 2 \n", + "137 4.21e-03 9.24e-01 2 \n", + "138 3.88e-03 9.23e-01 2 \n", + "139 3.58e-03 9.23e-01 2 \n", + "140 3.31e-03 9.23e-01 2 \n", + "141 3.05e-03 9.22e-01 2 \n", + "142 2.81e-03 9.22e-01 2 \n", + "143 2.59e-03 9.22e-01 2 \n", + "144 2.39e-03 9.21e-01 2 \n", + "145 2.20e-03 9.21e-01 2 \n", + "146 2.03e-03 9.21e-01 1 \n", + "147 1.87e-03 9.21e-01 1 \n", + "148 1.72e-03 9.21e-01 1 \n", + "149 1.58e-03 9.20e-01 1 \n", + "150 1.46e-03 9.20e-01 1 \n", + "151 1.34e-03 9.20e-01 1 \n", + "152 1.23e-03 9.20e-01 1 \n", + "153 1.13e-03 9.20e-01 1 \n", + "154 1.04e-03 9.20e-01 1 \n", + "155 9.60e-04 9.20e-01 1 \n", + "156 8.83e-04 9.20e-01 1 \n", + "157 8.12e-04 9.20e-01 1 \n", + "158 7.47e-04 9.20e-01 1 \n", + "159 6.87e-04 9.20e-01 1 \n", + "160 6.32e-04 9.20e-01 1 \n", + "161 5.82e-04 9.20e-01 1 \n", + "162 5.35e-04 9.20e-01 1 \n", + "163 4.92e-04 9.20e-01 1 \n", + "164 4.52e-04 9.20e-01 1 \n", + "165 4.16e-04 9.20e-01 1 \n", + "166 3.83e-04 9.20e-01 1 \n", + "167 3.52e-04 9.20e-01 1 \n", + "168 3.24e-04 9.20e-01 1 \n", + "169 2.98e-04 9.20e-01 1 \n", + "170 2.74e-04 9.20e-01 1 \n", + "171 2.52e-04 9.20e-01 1 \n", + "172 2.32e-04 9.20e-01 1 \n", + "173 2.13e-04 9.20e-01 1 \n", + "174 1.96e-04 9.20e-01 1 \n", + "175 1.80e-04 9.20e-01 1 \n", + "176 1.66e-04 9.20e-01 1 \n", + "177 1.52e-04 9.20e-01 1 \n", + "178 1.40e-04 9.20e-01 1 \n", + "179 1.29e-04 9.20e-01 1 \n", + "180 1.18e-04 9.20e-01 1 \n", + "181 1.09e-04 9.20e-01 1 \n", + "182 1.00e-04 9.20e-01 1 \n", + "183 9.21e-05 9.20e-01 1 \n", + "184 8.47e-05 9.20e-01 1 \n", + "185 7.79e-05 9.20e-01 1 \n", + "186 7.16e-05 9.20e-01 1 \n", + "187 6.59e-05 9.20e-01 1 \n", + "188 6.06e-05 9.20e-01 1 \n", + "189 5.57e-05 9.20e-01 1 \n", + "190 5.12e-05 9.20e-01 1 \n", + "191 4.71e-05 9.20e-01 1 \n", + "192 4.33e-05 9.20e-01 1 \n", + "193 3.98e-05 9.20e-01 1 \n", + "194 3.66e-05 9.20e-01 1 \n", + "195 3.37e-05 9.20e-01 1 \n", + "196 3.10e-05 9.20e-01 1 \n", + "197 2.85e-05 9.20e-01 1 \n", + "198 2.62e-05 9.20e-01 1 \n", + "199 2.41e-05 9.20e-01 1 \n", + "200 2.21e-05 9.20e-01 1 \n", + "201 2.04e-05 9.20e-01 1 \n", + "202 1.87e-05 9.20e-01 1 \n", + "203 1.72e-05 9.20e-01 1 \n", + "204 1.58e-05 9.20e-01 1 \n", + "205 1.46e-05 9.20e-01 1 \n", + "206 1.34e-05 9.20e-01 1 \n", + "207 1.23e-05 9.20e-01 1 \n", + "208 1.13e-05 9.20e-01 1 \n", + "209 1.04e-05 9.20e-01 1 \n", + "210 9.58e-06 9.20e-01 1 \n", + "211 8.81e-06 9.20e-01 1 \n", + "212 8.10e-06 9.20e-01 1 \n", + "213 7.45e-06 9.20e-01 1 \n", + "214 6.85e-06 9.20e-01 1 \n", + "215 6.30e-06 9.20e-01 1 \n", + "216 5.79e-06 9.20e-01 1 \n", + "217 5.33e-06 9.20e-01 1 \n", + "218 4.90e-06 9.20e-01 1 \n", + "219 4.50e-06 9.20e-01 1 \n", + "220 4.14e-06 9.20e-01 1 \n", + "221 3.81e-06 9.20e-01 1 \n", + "222 3.50e-06 9.20e-01 1 \n", + "223 3.22e-06 9.20e-01 1 \n", + "224 2.96e-06 9.20e-01 1 \n", + "225 2.72e-06 9.20e-01 1 \n", + "226 2.50e-06 9.20e-01 1 \n", + "227 2.30e-06 9.20e-01 1 \n", + "228 2.12e-06 9.20e-01 1 \n", + "229 1.95e-06 9.20e-01 1 \n", + "230 1.79e-06 9.20e-01 1 \n", + "231 1.65e-06 9.20e-01 1 \n", + "232 1.51e-06 9.20e-01 1 \n", + "233 1.39e-06 9.20e-01 1 \n", + "234 1.28e-06 9.20e-01 1 \n", + "235 1.18e-06 9.20e-01 1 \n", + "236 0.00e+00 0.00e+00 0 \n", + " 15.609431 seconds (25.56 M allocations: 1.637 GB, 2.39% gc time)\n", + "It SA gain nit \n", + "-----------------------------------\n", + "0 1.32e-01 NaN 0 \n", + "1 0.00e+00 0.00e+00 0 \n", + " 1.111583 seconds (686.69 k allocations: 26.390 MB, 1.53% gc time)\n" + ] + }, + { + "data": { + "text/plain": [ + "Results of Time Iteration Algorithm\n", + " * Complementarities: true\n", + " * Decision Rule type: Dolo.TimeIterationResult\n", + " * Number of iterations: 1\n", + " * Convergence: true\n", + " * |x - x'| < 1.0e-08: true\n" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@time sol=Dolo.time_iteration(model,verbose=true, maxit=1000, details=true)\n", + "dr=sol.dr\n", + "@time res = Dolo.time_iteration(model, dr; maxit=200, details=true)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Dolo tabulate gives us the decision rules." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2-dimensional AxisArray{Float64,2,...} with axes:\n", + " :V, Symbol[:lny,:s,:a]\n", + " :s, [-2.0,-1.77778,-1.55556,-1.33333,-1.11111,-0.888889,-0.666667,-0.444444,-0.222222,0.0 … 18.0,18.2222,18.4444,18.6667,18.8889,19.1111,19.3333,19.5556,19.7778,20.0]\n", + "And data, a 3×100 Array{Float64,2}:\n", + " 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 \n", + " -2.0 -1.77778 -1.55556 -1.33333 19.5556 19.7778 20.0 \n", + " 0.271821 0.241945 0.212451 0.18372 -0.897489 -0.905228 -0.91296" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drtab = Dolo.tabulate(model, dr, :s) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we plot the consumption policy function. We see that it is concave because of the precautionary savings motive noting as well that there is more curvature closer to the borrowing constraint." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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XrtZNN91k+fv7W9WqVUs15fla9VtW2mne6bna+0xPRupJ73WvnDKd4vPPP7fuvvtuK0+ePFZQUJBVu3Zt66OPPkrzfFu2bLEAq1mzZtetMUXK9PLTp09f87zTp09b7dq1s/LmzWsVKlTI6t27t7Vz5850p5fny5fvqq/zX4mJidbYsWOt22+/3fL397eKFi1qtWjRwtq2bZvjnD179lgNGjSw8uTJYwGOqeZX+6ymTp1q3X777Vbu3Lmt4sWLW08//bR19uzZVOc0bNjQqlKlSpoar/ydF8kMH8vSCC8RbxcaGkrVqlX54osv7C7FI+3YsYMaNWowb968VDPBRCTraYyOiEgW+7//+z/y589P27Zt7S5FxOtojI6ISBZZsWIFu3btYsaMGTz77LOOwdwikn0UdEREskjfvn2JioqiZcuW6c5iEpGspzE6IiIi4rE0RkdEREQ8loKOiIiIeCyvG6OTnJzM8ePHKVCggFM78YqIiEj2syyLf//9l5CQkDSb816L1wWd48ePp9nNWURERNzD0aNHufnmmzN8vtcFnZRl3o8ePUpQUJDN1YiIiEhGxMbGUrp06UxvH+N1QSeluyooKEhBR0RExM1kdtiJBiOLiIiIx1LQEREREY+loCMiIiIeS0FHREREPJaCjoiIiHgsBR0RERHxWAo6IiIi4rEUdERERMRjKeiIiIiIx1LQEREREY+loCMiIiIeS0FHREREPJaCjoiIiLjMiRMQGWl3FZcp6IiIiMgNO3wY+vSBsmWhWzewLLsrMnLZXYCIiIi4rz//hLfegg8/hMRE05YnD/zzD9x0k721ga7oiIiIiBN++w0efRQqVYLZs03IadoU1q2DjRtzRsgBXdERERGRTNiyBd54Az7//HJb69bwyitQp459dV2Ngo6IiIhc14YN8PrrsGaNOfbxgYcfhpdfhrAwe2u7FgUdERERSZdlwerVJuBs3Gja/PzgiSdgyBC47TZ768sIBR0RERFJJTkZVqwwAWfrVtPm729mU73wgplZ5S4UdERERASApCRYssSMwfntN9OWJw889RQMHAilStlbnzMUdERERLzcpUuwcCG8+aaZLg5QoIBZF6d/fyhWzN76boSCjoiIiJeKj4e5c806OAcPmrZCheD556FvX3Pf3SnoiIiIeJkLF+CDD+Dtt+Gvv0xbsWIwYAA884y5muMpFHRERES8xPnz8N57MG4cnDxp2kJCzADjnj0hb15768sKCjoiIiIeLjYWpk2DCRPg779NW5ky8NJL0LUrBATYW19WUtARERHxUNHRMGUKTJoEZ8+atvLlzSJ/Tz4JuXPbW192UNARERHxMP/8Y8LNlCnmag6Yxf2GDoWOHSGXF337e9FbFRER8WynT8P48aab6tw501aligk4Dz9sVjX2Ngo6IiIibi4qygwwnj4d4uJMW1gYDBsGbdqAr6+99dlJQUdERMRNnThhpoi//76ZMg5w553w6qtmR3EfH3vrywkUdERERNzMsWMm4MyYARcvmrY6dcwVnBYtFHD+S0FHRETETfz1l1nF+IMPzKrGAHXrwvDh0KyZAk56FHRERERyuKNHYfRomDkTEhJM2z33mIDTpIkCzrUo6IiIiORQR45cDjiXLpm2hg1NwGnUSAEnIxR0REREcpjDh03AmTXrcsBp1OhywJGMU9ARERHJIdILOBERJuA0bGhvbe5KQUdERMRm6QWcxo1NwGnQwN7a3J2CjoiIiE3SCzhNmpiAU7++vbV5CgUdERGRbHbkCLz5pgJOdlDQERERySZHj5qA899ZVAo4WUtBR0REJIv99Zfpovrgg8vr4EREwIgRGoOT1Wzd5mvDhg20bt2akJAQfHx8WL58+XUfs2DBAsLCwsibNy8lS5akW7du/PPPP9lQrYiISOYcPw59+0L58mbDzYQEMz38u+9g7VqFnOxga9A5f/48YWFhTJs2LUPnb9q0iU6dOtG9e3d+//13Fi9ezJYtW+jZs2cWVyoiIpJxJ07Ac89BuXIwdaoJOA0awLp15qap4tnH1q6rFi1a0KJFiwyfv3nzZkJDQ+nXrx8AZcuWpXfv3owZMyarShQREcmwqCgYMwbefffyZpv33AMjR5quKq1knP1svaKTWXXr1uXo0aN8+eWXWJZFVFQUS5YsoWXLlld9THx8PLGxsaluIiIirnT6NLzwApQtCxMnmpBz992wZg1s2GDWxFHIsYdbBZ169eqxYMECOnTogL+/PyVKlCA4OPiaXV+jR48mODjYcStdunQ2ViwiIp7sn3/g5ZdNwBk7Fi5cgDp1YNUq2LgRmjZVwLGbWwWdXbt28dxzzzFs2DC2bdvGqlWrOHToEE899dRVHzNkyBBiYmIct6NHj2ZjxSIi4omio2HYMBNwRo+G8+ehZk1YuRI2b4bmzRVwcgq3ml4+evRo6tWrx+DBgwGoXr06+fLlo379+rz++uuULFkyzWMCAgIICAjI7lJFRMQDxcbC5MkwfjzExJi2sDAzBueBBxRuciK3CjpxcXHkypW6ZD8/PwAsy7KjJBER8QLnzpnZU2PHwpkzpq1KFRNwHnoIfN2qf8S72Bp0zp07x759+xzHBw8eJDIyksKFC3PLLbcwZMgQjh07xrx58wBo3bo1PXv25N1336V58+acOHGC559/ntq1axMSEmLX2xAREQ914YKZQfXWW2bAMcBtt5mF/h55RAHHHdgadLZu3UpERITjeMCAAQB07tyZOXPmcOLECY4cOeL4eZcuXfj333+ZOnUqAwcOpGDBgjRu3FjTy0VExKXi480qxm+8YdbEAbPo3/Dh8Nhj8L/OBHEDPpaX9fnExsYSHBxMTEwMQUFBdpcjIiI5yKVLMHcujBplNt4EuOUWM/C4UyfIndve+ryZs9/fbjVGR0REJCskJcFHH5kuqf37TVtICLzyCnTvDprT4r4UdERExGslJ8PSpeaKze7dpq1oURgyBJ56CvLksbc+uXEKOiIi4nUsy6x58+qrEBlp2goVgsGDzSac+fPbW5+4joKOiIh4lW+/haFD4ccfzXGBAjBgAPTvD8HB9tYmrqegIyIiXmHzZjPmZt06c5wnj7l688ILUKSIvbVJ1lHQERERjxYZabqovvjCHPv7Q+/eZo+qEiXsrU2ynoKOiIh4pD/+MIOMFy0yx35+0KWLabvlFltLk2ykoCMiIh7l8GGzNcPcuWZWlY8PdOxopo5XrGh3dZLdFHRERMQjnDwJb74J771nFv4Ds9HmqFFQvbq9tYl9FHRERMStnT1rNtucPBni4kxbkyZm+4Y6deytTeynoCMiIm7p/HmYMgXefhuio01bnTrmqk7jxvbWJjmHgo6IiLiV+HiYMcNcsYmKMm1Vq5rj1q3NmByRFAo6IiLiFpKSYP58s4P44cOmrVw5eO01M9hYO4pLehR0REQkR7MsWL7crGa8a5dpK1nSTBPv3l07isu1KeiIiEiOtXat2WBzyxZzXKgQvPQSPPss5M1rb23iHhR0REQkx9m2zQScNWvMcd68Zi+qQYOgYEF7axP3oqAjIiI5xh9/mO0aFi82x7lzQ69epttK2zWIMxR0RETEdseOmdWMZ80yg459fODxx01buXJ2VyfuTEFHRERsc/YsvPWWWQ/n4kXTdv/9Zqq4VjMWV1DQERGRbHfhggk3b711ebG/evXM8T332FubeBYFHRERyTaJiTBnjlkL5/hx01alCoweba7kaLE/cTUFHRERyXIpa+G8/DLs2WPabrnFLPb3xBNa7E+yjoKOiIhkqQ0b4MUX4ccfzXGRIvDKK/D00xAYaG9t4vkUdEREJEvs3GnWwvniC3OcNy8MGGDWwgkOtrc28R4KOiIi4lJHj5rtGebONV1Wfn5mLZxXXzVbN4hkJwUdERFxibNnzaDiKVPMDuMA7dubqeIVK9pbm3gvBR0REbkhFy/C1Knw5psm7AA0bAhjxkCdOvbWJqKgIyIiTklOhgULzPYMR46YtqpVzVo4LVtqqrjkDAo6IiKSaatXwwsvwI4d5vjmm81U8U6dNFVcchYFHRERybDISBNwUnYVDwoya+P06wd58thbm0h6FHREROS6jhwxXVTz55uZVLlzQ58+Zj2cm26yuzqRq1PQERGRq4qONjOpJk++PJOqY0czk0q7ios7UNAREZE0EhJg+nQYNQrOnDFtjRrB2LFw5522liaSKQo6IiLiYFmweLFZ0fjAAdNWuTK8/bZmUol7UtAREREANm0y2zOk7ElVooSZSdW1K+TSt4W4Kf3qioh4ub174aWXYOlSc5wvHwweDAMHQv789tYmcqMUdEREvNTff5sxONOnQ2Ii+PpC9+4wcqT2pBLPoaAjIuJlLl6Ed94xM6diYkxbixZmHE7VqvbWJuJqCjoiIl7CsmDRItNNdeiQaateHcaNg3vvtbU0kSyjoCMi4gU2b4YBAy4PNC5Z0lzR0ZYN4ukUdEREPNjBg+YKzqJF5jhvXnjxRTPQOF8+e2sTyQ4KOiIiHig6Gt5806xonJBg1r/p1s0MPtZAY/EmCjoiIh4kMRFmzIDhw82sKoAmTWD8eAgLs7c2ETso6IiIeIivvjJdUrt3m+PbbzcDjbWisXgzX7sLEBGRG/P773DffSbQ7N4NRYrA1Knw66/QqpVCjng3XdEREXFTp0+bLqr334fkZMidG/r1g6FDoWBBu6sTyRlsvaKzYcMGWrduTUhICD4+Pixfvvy6j4mPj+eVV16hTJkyBAQEEBoayqxZs7KhWhGRnCE+3nRJ3XorvPuuCTlt25qrOePGKeSI/JetV3TOnz9PWFgY3bp1o23bthl6zCOPPEJUVBQzZ86kQoUKnDhxguTk5CyuVETEfpYFn31mNt7cv9+0hYfDxInQsKG9tYnkVLYGnRYtWtCiRYsMn79q1SrWr1/PgQMHKFy4MAChoaFZVJ2ISM7x66/w/POwbp05LlHCTB/Xgn8i1+ZWg5E///xz7rzzTt5++21KlSpFxYoVGTRoEBcuXLjqY+Lj44mNjU11ExFxF6dOQe/e5srNunUQEAAvv2x2HO/aVSFH5HrcajDygQMH2LhxI4GBgSxbtoy///6bZ555hn/++YfZs2en+5jRo0czcuTIbK5UROTGJCTAlClmgb+Uf5898ojZeLNMGXtrE3EnbnVFJzk5GR8fHxYsWEDt2rVp2bIlEyZMYO7cuVe9qjNkyBBiYmIct6NHj2Zz1SIiGWdZsGIFVKkCgwebkFOzJnz/PXzyiUKOSGa51RWdkiVLUqpUKYKDgx1tlSpVwrIs/vrrL2699dY0jwkICCAgICA7yxQRccrvv0P//rBmjTlOGYfTuTP4utU/S0VyDrf6o1OvXj2OHz/OuXPnHG1//vknvr6+3HzzzTZWJiLivDNnzPo3YWEm5Pj7m404//zTjMNRyBFxnq1/fM6dO0dkZCSRkZEAHDx4kMjISI4cOQKYbqdOnTo5zn/ssccoUqQIXbt2ZdeuXWzYsIHBgwfTrVs38uTJY8t7EBFxVmIiTJ9u1sN55x1ISoI2bcx6OKNHQ4ECdlco4v5sDTpbt24lPDyc8PBwAAYMGEB4eDjDhg0D4MSJE47QA5A/f37WrFlDdHQ0d955J48//jitW7dmypQpttQvIuKsdevgjjugTx9zRadqVfjmG1i2DMqVs7s6Ec/hY1mWZXcR2Sk2Npbg4GBiYmIICgqyuxwR8TKHDpkF/z791BwXLmxmVvXqBbncatSkSPZy9vtbf6xERLJBXBy89RaMHQsXL5pxN08/Da+9ZsKOiGQNBR0RkSxkWbB4sbmKk7K6RUQETJ4M1arZW5uIN1DQERHJIr/9ZmZTffedOS5TBsaPNxtw+vjYWpqI19CkRRERFzt7Fvr2hRo1TMgJDISRI81sqnbtFHJEspOu6IiIuEhSEsyaZfai+vtv09a+PYwbpxWNReyioCMi4gI//gjPPgvbtpnjKlXMXlWNG9tbl4i3U9eViMgNiIoyqxfXrWtCTlAQTJoEv/yikCOSE+iKjoiIExLCp1ieAAAgAElEQVQTYdo0GDbs8u7iXbuaFY2LF7e3NhG5TEFHRCST1q833VQ7d5rjmjVh6lS46y576xKRtNR1JSKSQcePw2OPQaNGJuQULgzvvw8//aSQI5JTKeiIiFzHpUtm/ZvbboOPPjLTw596yuwu3qsX+PnZXaGIXI26rkREruG778zGm7t2meM6dczYnJo1bS1LRDJIV3RERNJx4oTppoqIMCHnpptg5kz44QeFHBF3oqAjIvIfiYkwceLlbipfX3jmGdNN1a2bORYR96GuKxGR/9m40YSa334zx3XqwPTpcMcd9tYlIs7Tv01ExOudOgVdukD9+ibkFCkC//d/pptKIUfEvemKjoh4raQkmDHD7E0VHW1mU/XoYRb9K1LE7upExBUUdETEK23dCk8/bf4LEB4O775ruqtExHOo60pEvEp0tFnVuHZtE3KCg+Gdd+DnnxVyRDyRruiIiFewLFi4EAYONBtxAjz+OIwbByVK2FubiGQdBR0R8Xh//GFmU61da45vu83MptLu4iKeT11XIuKxLl6E4cOhenUTcgID4fXXYccOhRwRb6ErOiLikdasMVdx9u0zxy1amB3Gy5Wzty4RyV66oiMiHuXkSbN1Q7NmJuSEhMCSJbBypUKOiDdS0BERj5CcDO+9B7fffnnrhn79YPduaNfOrJEjIt5HXVci4vZ+/RV694YffzTHNWvC++9r800R0RUdEXFjcXHw0ksm0Pz4IxQoAFOmwE8/KeSIiKErOiLillatMisbHzpkjtu2NSGnVClbyxKRHEZXdETErURFwaOPmllUhw5B6dLw+efw6acKOSKSllNXdJKSkpgzZw7ffvstp06dIjk5OdXP16asyiUi4iKWBbNmwaBBZhsHX1947jl47TXIn9/u6kQkp3Iq6Dz33HPMmTOHVq1aUbVqVXw0nUFEstAff5jBxuvXm+M77jC7jmscjohcj1NB5+OPP2bRokW0bNnS1fWIiDgkJMCYMWY144QEyJvX3O/bF3JphKGIZIBTf1X4+/tToUIFV9ciIuLwww/Qsyfs2mWOW7Qw+1OFhtpaloi4GacGIw8cOJDJkydjWZar6xERLxcbC336wD33mJBTrJhZAHDlSoUcEck8p67obNy4kXXr1vHVV19RpUoVcufOnernS5cudUlxIuJdVqwwU8aPHTPHXbvCuHFQuLC9dYmI+3Iq6BQsWJCHHnrI1bWIiJeKijLbNSxaZI7LlzcrGzdpYm9dIuL+nAo6s2fPdnUdIuKFLAvmzYP+/eHsWfDzg4EDYfhwM/BYRORG3dC8hdOnT/PHH38AcNttt1G0aFGXFCUinu/QIejVC9asMcc1asDMmWbquIiIqzg1GPn8+fN069aNkiVL0qBBAxo0aEBISAjdu3cnLi7O1TWKiAdJSjJbNVStakJOYCC89RZs2aKQIyKu51TQGTBgAOvXr2fFihVER0cTHR3NZ599xvr16xk4cKCraxQRD7F7N9Svb1Y0Pn8eGjSAHTvgxRfhijkNIiIu4WM5MUf8pptuYsmSJTRq1ChV+7p163jkkUc4ffq0q+pzudjYWIKDg4mJiSEoKMjuckS8wqVL8PbbZruGhASzy/jbb5uuK1/tuCciGeDs97dTY3Ti4uIoXrx4mvZixYqp60pEUvnlF+jWDSIjzXHLlvDee2YzThGRrObUv6Xq1q3L8OHDuXjxoqPtwoULjBw5krp167qsOBFxX/HxMHQo1KplQk7hwvDhh/DFFwo5IpJ9nLqiM3nyZJo3b87NN99MWFgYADt27CAwMJCvv/7apQWKiPv56Sez2N/u3ea4fXuYOhXSuRAsIpKlnBqjA6b7asGCBezZsweASpUq8fjjj5MnTx6XFuhqGqMjknUuXIBhw2DCBEhONsFm2jRo187uykTE3WXrGB2AvHnz0rNnT2cfDsCGDRsYO3Ys27Zt48SJEyxbtow2bdpk6LGbNm2iYcOGVK1alciUzn8Rsc0PP5irOH/+aY6feAImTYIiReytS0S8W4aDzueff06LFi3InTs3n3/++TXPfeCBBzL0nOfPnycsLIxu3brRtm3bjJZCdHQ0nTp1okmTJkRFRWX4cSLienFxZizOpElmpeOQELN9w/33212ZiEgmuq58fX05efIkxYoVw/ca80F9fHxISkrKfCE+Phm+otOxY0duvfVW/Pz8WL58eaau6KjrSsR1Nm0yV3H27jXHXbqYbqtChWwtS0Q8kLPf3xmedZWcnEyxYsUc9692cybkZMbs2bM5cOAAw4cPz9LXEZGri4uDAQPM4n9790KpUrByJcyerZAjIjmLU9PL582bR3x8fJr2hIQE5s2bd8NFXc3evXt56aWXmD9/PrlyZazXLT4+ntjY2FQ3EXHepk1mX6qJE01XVdeusHOnWR9HRCSncSrodO3alZiYmDTt//77L127dr3hotKTlJTEY489xsiRI6lYsWKGHzd69GiCg4Mdt9JawEPEKRcvwuDBaa/izJoFBQvaXZ2ISPqcml7u6+tLVFRUmt3Kd+zYQUREBGfOnMl8IdcZoxMdHU2hQoXw8/NztCUnJ2NZFn5+fqxevZrGjRuneVx8fHyqq0+xsbGULl1aY3REMmHLFujcGf63mgRdupgrOgo4IpJdsmV6eXh4OD4+Pvj4+NCkSZNU3UdJSUkcPHiQ++67LzNPmWFBQUH89ttvqdqmT5/O2rVrWbJkCWXLlk33cQEBAQQEBGRJTSKeLj7e7E/11ltmXZwSJWDGDGjd2u7KREQyJlNBJ+VqS2RkJM2bNyd//vyOn/n7+xMaGkq7TKwMdu7cOfbt2+c4PnjwIJGRkRQuXJhbbrmFIUOGcOzYMebNm4evry9Vq1ZN9fhixYoRGBiYpl1EbtyOHdCpE/z6qzl+7DF45x2zlYOIiLvIVNBJmekUGhpKhw4dCAwMvKEX37p1KxEREY7jAQMGANC5c2fmzJnDiRMnOHLkyA29hohkTmIijBkDI0eaXcdvuslswqnVjUXEHTm9BQSYoLL7f5vZVK5cmZo1a7qssKyidXREru6PP8xVnC1bzHGbNmbxv/+tLCEiYpts3QLi2LFjdOzYkU2bNlHwf6MRo6Ojufvuu/n444+5+eabnXlaEbFJcrLZk+qFF8zsquBg0031xBPg42N3dSIiznNqenn37t25dOkSu3fv5syZM5w5c4bdu3eTnJxMjx49XF2jiGSho0ehWTPo18+EnKZN4bff4MknFXJExP051XWVJ08efvjhB8LDw1O1b9u2jfr16xMXF+eyAl1NXVcihmXB/PnQty/ExECePDB2LDz9NFxjlxcREVtka9dV6dKluXTpUpr2pKQkQkJCnHlKEclG//wDTz0FS5aY4zp1YN48yMRanCIibsGpf7eNHTuWvn37snXrVkfb1q1bee655xg3bpzLihMR11u1CqpVMyEnVy4YNQo2blTIERHP5FTXVaFChYiLiyMxMdGxaGDK/Xz58qU615lVkrOSuq7EW8XFmS0cpk83x7ffbrqu3GCypIhI9nZdTZo0yZmHiYhNtm6Fxx+HP/80x/36mdWO8+Sxty4RkazmVNDp3Lmzq+sQkSyQlGQCzYgRZiHAUqVg9my49167KxMRyR5OBZ0Up06d4tSpUyQnJ6dqr169+g0VJSI37uBBM0V80yZz/PDDZoVjbeEgIt7EqaCzbds2OnfuzO7du7lyiI+Pjw9JSUkuKU5EMs+y4MMP4dln4d9/oUABsxigFv8TEW/kVNDp1q0bFStWZObMmRQvXhwf/e0pkiOcPWvWwfnkE3N8zz0m9ISG2lqWiIhtnAo6Bw4c4NNPP6VChQqurkdEnLR+vemqOnoU/PzMppwvvWTui4h4K6fW0WnSpAk7duxwdS0i4oRLl+CVVyAiwoSc8uXhhx9Mm0KOiHg7p67ofPDBB3Tu3JmdO3dStWpVcufOnernDzzwgEuKE5Fr278fHnvs8m7j3brB5MmQP7+9dYmI5BROBZ3NmzezadMmvvrqqzQ/02BkkayXsk/VM8/AuXNQsCDMmGFmVomIyGVOdV317duXJ554ghMnTpCcnJzqppAjkrViY80Mqk6dTMipXx927FDIERFJj1NB559//qF///4UL17c1fWIyDX89BPUqAELF5rxN6NGwbp1cMstdlcmIpIzORV02rZty7p161xdi4hcRXIyjBljposfPAhlysD338PQoRpwLCJyLU6N0alYsSJDhgxh48aNVKtWLc1g5H79+rmkOBGBEydMN9U335jjRx6B998343JEROTanNq9vGzZsld/Qh8fDhw4cENFZSXtXi7uZNUqE3JOn4a8eWHKFDOzSmt0ioi3ydbdyw8ePOjMw0Qkgy5dMt1Sb79tjqtXh48/hkqV7K1LRMTd3NCmniLieocOQceOZuAxQJ8+MG4cBAbaWpaIiFtyeq+ra5k1a5ZTxYh4u6VLTddUTIwZgzNzJrRta3dVIiLuy6mgc/bs2VTHly5dYufOnURHR9O4cWOXFCbiTS5ehMGDYepUc1y3Lnz0kZldJSIiznMq6CxbtixNW3JyMk8//TTly5e/4aJEvMm+fWYm1S+/mOMXXoDXX4crJjOKiIgTnFpHJ90n8vVlwIABTJw40VVPKeLxPvkE7rjDhJwiRWDlSrNejkKOiIhruCzoAOzfv5/ExERXPqWIR7p40exT1bEj/Puv2cYhMhJatrS7MhERz+JU19WAAQNSHVuWxYkTJ1i5ciWdO3d2SWEinmr/ftNVtX27OX75ZRg5EnJpDqSIiMs59VfrLymDCf7H19eXokWLMn78+OvOyBLxZkuXQteuZmPOIkXgww+hRQu7qxIR8VxOBR3tcyWSOZcuwYsvQsoQtrvvNgsAli5tb10iIp7OqTE6Fy5cIC4uznF8+PBhJk2axOrVq11WmIin+OsvaNTocsgZOBC++04hR0QkOzgVdB588EHmzZsHQHR0NLVr12b8+PE8+OCDvPvuuy4tUMSdffMNhIfDDz9AcDAsW2ZWOdasKhGR7OFU0Nm+fTv169cHYMmSJZQoUYLDhw8zb948pkyZ4tICRdxRcjKMGgXNmsHff0ONGrBtG7RpY3dlIiLexakxOnFxcRQoUACA1atX07ZtW3x9fbnrrrs4fPiwSwsUcTdnzsATT8BXX5njnj1h8mTIk8feukREvJFTV3QqVKjA8uXLOXr0KF9//TXNmjUD4NSpU5naOl3E02zfDjVrmpATGAizZ8OMGQo5IiJ2cSroDBs2jEGDBhEaGkqdOnWoW7cuYK7uhIeHu7RAEXcxc6aZTXXoEJQrB5s3Q5cudlclIuLdfCzLspx54MmTJzlx4gRhYWH4+pq8tGXLFoKCgrj99ttdWqQrxcbGEhwcTExMjK4+iUtcvAh9+8IHH5jj1q1h3jyz+7iIiLiGs9/fTq/FWqJECUqUKJGqrXbt2s4+nYhbOnoU2rWDn38GHx8zAHnIEPB16eYqIiLiLKeCzvnz53nrrbf49ttvOXXqFMnJyal+fuDAAZcUJ5KTrVsHHTrA6dNQuDB89JGZZSUiIjmHU0GnR48erF+/nieffJKSJUvi4+Pj6rpEcizLggkT4IUXzDTy8HCztUNoqN2ViYjIlZwKOl999RUrV66kXr16rq5HJEeLi4MePczVG4DOneHddzWrSkQkp3Iq6BQqVIjChQu7uhaRHO3gQXjoIdixw+w0PmkSPPOMGZsjIiI5k1NDJkeNGsWwYcNS7Xcl4sm++QbuvNOEnGLFYO1a6NNHIUdEJKdz6orO+PHj2b9/P8WLFyc0NJTcV2zcs337dpcUJ2K3K8fj1KplxuPcfLPdlYmISEY4FXTaaMMe8QIXLkCvXjB/vjnu2hWmTzcrHouIiHtwesFAd6UFAyUj/vrLbMC5bRv4+ZnxOOqqEhGxj7Pf3ze0rNm2bduYP38+8+fP55dffsn04zds2EDr1q0JCQnBx8eH5cuXX/P8pUuXcu+991K0aFGCgoKoW7cuX3/9tbPli6Rr0yYzHmfbNihSBNasgWefVcgREXFHTgWdU6dO0bhxY2rVqkW/fv3o168fNWvWpEmTJpw+fTrDz3P+/HnCwsKYNm1ahs7fsGED9957L19++SXbtm0jIiKC1q1bOxWyRNIzaxZEREBUFFSvblY8joiwuyoREXGWU11XHTp04MCBA8ybN49KlSoBsGvXLjp37kyFChX4KGWRkcwU4uPDsmXLMj3+p0qVKnTo0IFhw4Zl6Hx1XUl6EhPNgOOJE81xu3YwZw7kz29rWSIi8j/ZutfVqlWr+OabbxwhB6By5cpMmzaNZtm4Bn5ycjL//vvvNdf0iY+PJz4+3nEcGxubHaWJG4mOho4dIaUXdMQIePVV7VclIuIJnPqrPDk5Oc2UcoDcuXOn2fcqK40bN45z587xyCOPXPWc0aNHExwc7LiVLl062+qTnG/vXrjrLhNy8uSBxYth+HCFHBERT+HUX+eNGzfmueee4/jx4462Y8eO0b9/f5o0aeKy4q5l4cKFjBw5kkWLFlGsWLGrnjdkyBBiYmIct6NHj2ZLfZLzrV0LderAH3+YdXE2boT27e2uSkREXMmprqupU6fywAMPEBoa6rhCcvToUapWrcr8lEVHstDHH39Mjx49WLx4MU2bNr3muQEBAQQEBGR5TeJeZsww08UTE03YWb4cSpSwuyoREXE1p4JO6dKl2b59O9988w179uwBoFKlStcNHa7w0Ucf0a1bNz7++GNatWqV5a8nniUpCQYNMuviADz6qJlppUUARUQ8U6a6rtauXUvlypWJjY3Fx8eHe++9l759+9K3b19q1apFlSpV+P777zP8fOfOnSMyMpLIyEgADh48SGRkJEeOHAFMt1OnTp0c5y9cuJBOnToxfvx46tSpw8mTJzl58iQxMTGZeRvipWJj4YEHLoec116DBQsUckREPFmmgs6kSZPo2bNnutO6goOD6d27NxMmTMjw823dupXw8HDCw8MBGDBgAOHh4Y6p4idOnHCEHoAZM2aQmJhInz59KFmypOP23HPPZeZtiBc6cgTuuQe+/NIEm08+MTOrtAigiIhny9Q6OmXKlGHVqlWpppX/1549e2jWrFmqcJLTaB0d77N1K7RuDSdPmnE4n39uNucUERH3kS1bQERFRaU7rTxFrly5MrUyskhWW7YMGjQwIadaNfjpJ4UcERFvkqmgU6pUKXbu3HnVn//666+ULFnyhosSuVGWBePHmxWOL1yA++4z08dvucXuykREJDtlKui0bNmSV199lYsXL6b52YULFxg+fDj333+/y4oTcUZiopk6PmiQCTxPPw0rVoB6KkVEvE+mxuhERUVxxx134Ofnx7PPPsttt90GmLE506ZNIykpie3bt1O8ePEsK/hGaYyOZzt3zmznsHKlGWg8bhz0769BxyIi7i5b9roqXrw4P/zwA08//TRDhgwhJSP5+PjQvHlzpk2blqNDjni2Eyfg/vth+3Yzs2r+fNN1JSIi3ivTCwaWKVOGL7/8krNnz7Jv3z4sy+LWW2+lUKFCWVGfSIbs2gUtWphp5DfdZLqq7rrL7qpERMRuTq2MDFCoUCFqafqK5ADff28WAoyOhltvha++gvLl7a5KRERyAu3RLG5tyRK4914Tcu6+GzZvVsgREZHLFHTEbU2eDI88AvHx0KYNfPMNFClid1UiIpKTKOiI20lOhsGD4fnnzfTxPn3MlZ08eeyuTEREchqnx+iI2CEhAbp1M5txArz1FrzwgqaPi4hI+hR0xG2cO2emi69eDblywaxZ8OSTdlclIiI5mYKOuIXTp6FVK/j5Z8iXz3RV3Xef3VWJiEhOp6AjOd6hQ9CsGezda9bIWbkSate2uyoREXEHCjqSo+3cCc2bw/HjUKaM6baqWNHuqkRExF1o1pXkWD/+CA0amJBTtSr88INCjoiIZI6CjuRIq1dDkyZw9izUrQvr10NIiN1ViYiIu1HQkRxn8WKzOWdcnBmbs2YNFC5sd1UiIuKOFHQkR5k5Ezp0gEuXzKrHK1aYWVYiIiLOUNCRHGPSJOjRw6x23KsXLFwI/v52VyUiIu5MQUdsZ1kwahT072+OBw2C994DPz976xIREfen6eViK8syWziMG2eOX3sNhg7Vlg4iIuIaCjpim+RkePZZePddczxxotmoU0RExFUUdMQWSUlmHM6sWebqzYwZZnyOiIiIKynoSLZLTIQuXcwO5L6+MG8ePP643VWJiIgnUtCRbHXpkgk1ixebHcgXLoSHH7a7KhER8VQKOpJt4uPNGjmffQa5c5uw8+CDdlclIiKeTEFHskV8vLlys2IFBATAsmXQooXdVYmIiKdT0JEsFx8P7dvDF19AYCB8/jnce6/dVYmIiDdQ0JEsdWXIWbECmja1uyoREfEWWhlZsoxCjoiI2E1BR7JEQoJCjoiI2E9dV+Jyly5Bx46XQ84XX0CTJnZXJSIi3khXdMSlEhPhiSfMrCp/fzOVXCFHRETsoqAjLpOUBF27wqJFZp2cpUuhWTO7qxIREW+moCMukZwMPXvC/PlmxeNFi6BVK7urEhERb6egIzfMsqBvX5g92+xdtXAhtGljd1UiIiIKOnKDLAuGDIHp080u5HPnau8qERHJORR05Ia8+SaMGWPuv/eeGYgsIiKSUyjoiNMmT4ahQ8398eOhVy976xEREbmSgo44ZdYseP55c3/ECBgwwNZyRERE0qWgI5m2dKmZYQUwcCAMG2ZvPSIiIlejoCOZ8s038OijZjp5jx4wdqwZhCwiIpITKehIhm3ZYqaNJyRAu3Zm8LFCjoiI5GQKOpIhu3dDixZw/rzZnHPBAvDzs7sqERGRa1PQkes6fBjuvRfOnIHatc0+VgEBdlclIiJyfbYGnQ0bNtC6dWtCQkLw8fFh+fLl133Md999xx133EFAQAAVKlRgzpw5WV+oF/vnH2jeHI4dg0qV4MsvIX9+u6sSERHJGFuDzvnz5wkLC2PatGkZOv/gwYO0atWKiIgIIiMjef755+nRowdff/11FlfqneLi4P774Y8/4Oab4euvoUgRu6sSERHJuFx2vniLFi1o0aJFhs9/7733KFu2LOPHjwegUqVKbNy4kYkTJ9K8efOsKtMrJSZChw7w449QqJAJOaVL212ViIhI5rjVGJ3NmzfTtGnTVG3Nmzdn8+bNV31MfHw8sbGxqW5ybZYFvXvDF19AYCCsWAGVK9tdlYiISOa5VdA5efIkxYsXT9VWvHhxYmNjuXDhQrqPGT16NMHBwY5baV2WuK5XXzUrH/v6wiefQL16dlckIiLiHLcKOs4YMmQIMTExjtvRo0ftLilHmzED3njD3H//fXjgAXvrERERuRG2jtHJrBIlShAVFZWqLSoqiqCgIPLkyZPuYwICAgjQXOgMWbkSnn7a3B8+3Kx8LCIi4s7c6opO3bp1+fbbb1O1rVmzhrp169pUkefYuhUeecRs7dC1qwk6IiIi7s7WoHPu3DkiIyOJjIwEzPTxyMhIjhw5Aphup06dOjnOf+qppzhw4AAvvPACe/bsYfr06SxatIj+/fvbUr+nOHjQTCOPi4NmzUyXlbZ2EBERT2Br0Nm6dSvh4eGEh4cDMGDAAMLDwxn2v+2wT5w44Qg9AGXLlmXlypWsWbOGsLAwxo8fzwcffKCp5TfgzBlo2RKioiAsDBYvhty57a5KRETENXwsy7LsLiI7xcbGEhwcTExMDEFBQXaXY6uEBHMFZ/16syDgjz9CqVJ2VyUiIpKWs9/fbjVGR1wnZa2c9euhQAGztYNCjoiIeBoFHS81ZgzMmWPWylm0CKpVs7siERER11PQ8UJLlsCQIeb+lClw33321iMiIpJVFHS8zJYt8OST5n7fvtCnj731iIiIZCUFHS/y11/w4INw8aKZaTVhgt0ViYiIZC0FHS9x4QI89BCcPAlVq8LHH0Mut1oXW0REJPMUdLyAZUGvXmb148KF4fPPzUwrERERT6eg4wUmToT588HPzywIWLas3RWJiIhkDwUdD7d6NQwebO5PmACNG9tbj4iISHZS0PFg+/ZBhw6XN+rs29fuikRERLKXgo6HiouDtm0hOhruugvefVcbdYqIiPdR0PFAlgXPPAO//QbFi8Onn0JAgN1ViYiIZD8FHQ80axbMnWu2d/joIwgJsbsiEREReyjoeJjIyMurHb/+OkRE2FuPiIiInRR0PEh0NLRvD/Hx0KoVvPii3RWJiIjYS0HHQ1iWmVm1fz+UKQPz5pmuKxEREW+mr0IPMW0aLF8O/v5md/LChe2uSERExH4KOh5g504YNMjcHzsW7rzT3npERERyCgUdN3fhAjz6qBmX07KlFgUUERH5LwUdN/fii+aKTrFiMHu2FgUUERH5LwUdN/bll/DOO+b+nDkm7IiIiMhlCjpuKirKzLICeO45aNHC3npERERyIgUdN2RZ0L07nDoF1avDW2/ZXZGIiEjOpKDjhubNg5UrzVTyBQsgMNDuikRERHImBR03c+yY6aoCeO01qFrV3npERERyMgUdN2JZ0Ls3xMRArVowcKDdFYmIiORsCjpuZP78y11Ws2dDrlx2VyQiIpKzKei4iRMnoF8/c3/ECKhSxdZyRERE3IKCjhtI6bKKjoaaNWHwYLsrEhERcQ8KOm5g0SJYsQJy5zYLA6rLSkREJGMUdHK4mBh4/nlz/5VXNMtKREQkMxR0crihQ+HkSahYEV56ye5qRERE3IuCTg62dStMm2buv/suBATYW4+IiIi7UdDJoZKSzABky4InnoDGje2uSERExP0o6ORQ06fD9u1QsCCMG2d3NSIiIu5JQScHOn7cDDwGs2Fn8eL21iMiIuKuFHRyoAED4N9/4a67oGdPu6sRERFxXwo6Ocx338Enn4CvrxmA7Kv/QyIiIk7T12gOkph4eWfyp56CGjXsrWktHJIAABFGSURBVEdERMTdKejkIDNmwK+/QqFC8NprdlcjIiLi/hR0cogzZ+DVV839UaOgSBF76xEREfEECjo5xLBhJuxUq2bWzxEREZEbp6CTA/z2mxl4DDB5sjbtFBERcRUFHZtZlhmAnJwM7dtDRITdFYmIiHgOBR2bLVsG69ZBYKBWQBYREXG1HBF0pk2bRmhoKIGBgdSpU4ctW7Zc8/xJkyZx2223kSdPHkqXLk3//v25ePFiNlXrOvHx8MIL5v7gwVCmjL31iIiIeBrbg84nn3zCgAEDGD58ONu3bycsLIzmzZtz6tSpdM9fuHAhL730EsOHD2f37t3MnDmTTz75hJdffjmbK79x06bB/v1QosTlwCMiIiKuY3vQmTBhAj179qRr165UrlyZ9957j7x58zJr1qx0z//hhx+oV68ejz32GKGhoTRr1oxHH330uleBcpp//jHTyAFefx3y57e3HhEREU9ka9BJSEhg27ZtNG3a1NHm6+tL06ZN2bx5c7qPufvuu9m2bZsj2Bw4cIAvv/ySli1bZkvNrjJqFERHQ/Xq0KWL3dWIiIh4JlsnMv/9998kJSVR/IrtuYsXL86ePXvSfcxjjz3G33//zT333INlWSQmJvLUU09dtesqPj6e+Ph4x3FsbKzr3oCT/vzTdFsBjB8Pfn721iMiIuKpbO+6yqzvvvuON998k+nTp7N9+3aWLl3KypUrGZXSD3SF0aNHExwc7LiVLl06mytO68UXzb5WLVvCfy5miYiIiIv5WJZl2fXiCQkJ5M2blyVLltCmTRtHe+fOnYmOjuazzz5L85j69etz1113MXbsWEfb/Pnz6dWrF+fOncP3iu2+07uiU7p0aWJiYggKCsqCd3Vt69dDo0bmKs6vv0LlytlegoiIiNuJjY0lODg409/ftl7R8ff3p2bNmnz77beOtuTkZL799lvq1q2b7mPi4uLShBm///X9pJfZAgICCAoKSnWzS3IyDBxo7vfqpZAjIiKS1WzfbGDAgAF07tyZO++8k9q1azNp0iTOnz9P165dAejUqROlSpVi9OjRALRu3ZoJEyYQHh5OnTp12LdvH6+++iqtW7d2BJ6c6pNPYNs2KFAARoywuxoRERHPZ3vQ6dChA6dPn2bYsGGcPHmSGjVqsGrVKscA5SNHjqS6gjN06FB8fHwYOnQox44do2jRorRu3Zo33njDrreQIfHxkDJe+sUXoVgxe+sRERHxBraO0bGDs318N2rSJOjfH0JCYO9eyJs3215aRETE7bnlGB1vER19eXHAkSMVckRERLKLgk42GDMGzpwxg4+1OKCIiEj2UdD5//buPybquoED+Ps4jp93J/4IDvRAqAecIVgaV7CsBgo2URKLHCuZRv44a9VcixTxD5MHmrYkJrU1pabM0OEsp5uAR6WgeWk+at3QMG38qmvI7w65z/MHj/d06tNjE+57fb/v18Z2P7587+199pH3vt/v3WeMXbs2ctoKAP75T8BX8quiiIiIlINFZ4wVFQGDg8CcOcCCBVKnISIiUhYWnTH0r38Bu3aN3C4tBVQqSeMQEREpDovOGCooAIQAliwBTCap0xARESkPi84YaWgADh0aWephyxap0xARESkTi84YEGLkSwGBkaUe/vEPafMQEREpFYvOGDhwADh5cuT7cjZulDoNERGRcrHojLIbN0auzQFGFvA0GKTNQ0REpGQsOqNs507AZgMmTQLWrZM6DRERkbKx6Iyi/v6R780BgA0bAA8upUVERER3wKIzirZvB9ragKlTgVWrpE5DRERELDqjxG4fWeIBADZvBvz9pc1DREREAFdeGiXXrgFhYSNHc5YulToNERERASw6o2bmTOD8+ZFTVz48TkZEROQV+Cd5FGk0QGSk1CmIiIjoJhYdIiIiki0WHSIiIpItFh0iIiKSLRYdIiIiki0WHSIiIpItFh0iIiKSLRYdIiIiki0WHSIiIpItFh0iIiKSLRYdIiIiki0WHSIiIpItFh0iIiKSLRYdIiIiki1fqQN4mhACANDd3S1xEiIiIrpbN/9u3/w7frcUV3R6enoAAEajUeIkRERE9Ff19PRg3Lhxd729SvzVavQ353Q60draCp1OB5VKJXUcj+vu7obRaMS1a9eg1+uljkP/wXHxXhwb78Rx8V5jNTZCCPT09CAiIgI+Pnd/5Y3ijuj4+PhgypQpUseQnF6v538OXojj4r04Nt6J4+K9xmJs/sqRnJt4MTIRERHJFosOERERyZZ606ZNm6QOQZ6lVqvx5JNPwtdXcWcuvRrHxXtxbLwTx8V7edPYKO5iZCIiIlIOnroiIiIi2WLRISIiItli0SEiIiLZYtEhIiIi2WLRUZDy8nJMnToVAQEBMJlMOHXqlNSRFG/Tpk1QqVRuP9OmTZM6liJ9+eWXyMzMREREBFQqFQ4cOOD2vBACGzduRHh4OAIDA5GWlobm5maJ0irH/xuXvLy82+ZQRkaGRGmVo7i4GI888gh0Oh1CQ0ORlZUFm83mts3g4CDMZjMmTpwIrVaL7OxsdHR0eDwri45C7N27F2+88QaKiorw7bffIjExEenp6ejs7JQ6muI9+OCDaGtrc/18/fXXUkdSpL6+PiQmJqK8vPyOz5eWlmL79u2oqKjAyZMnERwcjPT0dAwODno4qbL8v3EBgIyMDLc5VFVV5cGEytTQ0ACz2YympiYcPXoUQ0NDmDdvHvr6+lzbvP766/j8889RXV2NhoYGtLa2YvHixZ4PK0gRkpKShNlsdt0fHh4WERERori4WMJUVFRUJBITE6WOQbcAIGpqalz3nU6nMBgM4t1333U91tXVJfz9/UVVVZUUERXp1nERQohly5aJRYsWSZSIburs7BQARENDgxBiZH5oNBpRXV3t2ub7778XAERjY6NHs/GIjgI4HA5YrVakpaW5HvPx8UFaWhoaGxslTEYA0NzcjIiICMTExCA3NxdXr16VOhLdoqWlBe3t7W5zaNy4cTCZTJxDXsBisSA0NBRxcXFYvXo17Ha71JEU5/r16wCACRMmAACsViuGhobc5sy0adMQGRnp8TnDoqMAv/76K4aHhxEWFub2eFhYGNrb2yVKRQBgMpmwa9cuHDlyBDt27EBLSwsef/xx9PT0SB2N/uDmPOEc8j4ZGRn45JNPUFdXh5KSEjQ0NGD+/PkYHh6WOppiOJ1OvPbaa0hJSUF8fDyAkTnj5+eHkJAQt22lmDPSfzczkYLNnz/fdTshIQEmkwlRUVH47LPPsGLFCgmTEf09PP/8867bM2bMQEJCAu6//35YLBakpqZKmEw5zGYzzp8/77XXF/KIjgJMmjQJarX6tqvdOzo6YDAYJEpFdxISEoLY2FhcunRJ6ij0BzfnCeeQ94uJicGkSZM4hzxk7dq1+OKLL3Ds2DFMmTLF9bjBYIDD4UBXV5fb9lLMGRYdBfDz88OsWbNQV1fneszpdKKurg6PPfaYhMnoVr29vbh8+TLCw8OljkJ/EB0dDYPB4DaHuru7cfLkSc4hL/Pzzz/DbrdzDo0xIQTWrl2Lmpoa1NfXIzo62u35WbNmQaPRuM0Zm82Gq1evenzOcPVyhdDr9SgsLITRaIS/vz8KCwtx9uxZfPzxx9BqtVLHU6x169bB398fAHDx4kWsWrUKnZ2dqKioQHBwsMTplKW3txcXL15Ee3s7PvzwQ5hMJgQGBsLhcCAkJATDw8PYsmULpk+fDofDgVdffRX9/f0oKyvzihWa5erPxkWtVmP9+vXQ6/W4ceMGrFYrVqxYAa1Wi61bt3JcxpDZbMbu3buxb98+REREoLe3F729vVCr1dBoNAgICEBrays++OADzJw5E7/99htWrlwJo9GIoqIiz4b16Ge8SFJlZWUiMjJS+Pn5iaSkJNHU1CR1JMXLyckR4eHhws/PT0yePFnk5OSIS5cuSR1LkY4dOyYA3PazbNkyIcTIR8wLCwtFWFiY8Pf3F6mpqcJms0kbWgH+bFz6+/vFvHnzxH333Sc0Go2IiooS+fn5or29XerYsnenMQEgdu7c6dpmYGBArFmzRowfP14EBQWJZ555RrS1tXk8q+o/gYmIiIhkh9foEBERkWyx6BAREZFssegQERGRbLHoEBERkWyx6BAREZFssegQERGRbLHoEBERkWyx6BCRbFy5cgUqlQpnz56VOgoReQkWHSLymF9++QWrV69GZGQk/P39YTAYkJ6ejuPHj4/K/o1GI9ra2hAfHz8q+yOivz8uBEJEHpOdnQ2Hw4HKykrExMSgo6MDdXV1sNvto7J/tVrN1cSJyA2P6BCRR3R1deGrr75CSUkJnnrqKURFRSEpKQkFBQVYuHAhAGDbtm2YMWMGgoODYTQasWbNGvT29gIYWS08MDAQhw8fdttvTU0NdDod+vv7bzt1ZbFYoFKpUFdXh9mzZyMoKAjJycmw2Wxu+9i8eTNCQ0Oh0+nw0ksv4a233sLMmTNdz1ssFiQlJSE4OBghISFISUnBTz/9NJZvFxGNEhYdIvIIrVYLrVaLAwcO4Pfff7/jNj4+Pti+fTsuXLiAyspK1NfX48033wQA6PV6LFiwAHv27HH7nd27dyMrKwtBQUH/87XXr1+PrVu34vTp0/D19cXy5cvdfv+dd95BSUkJrFYrIiMjsWPHDtfzN27cQFZWFp544gmcO3cOjY2NePnll6FSqe7l7SAiT/H4MqJEpFj79u0T48ePFwEBASI5OVkUFBSI77777n9uX11dLSZOnOi6X1NTI7Rarejr6xNCCHH9+nUREBAgDh8+LIQQoqWlRQAQZ86cEUL8d+Xr2tpa1z4OHTokAIiBgQEhhBAmk0mYzWa3101JSRGJiYlCCCHsdrsAICwWyyi8A0TkaTyiQ0Qek52djdbWVhw8eBAZGRmwWCx4+OGHsWvXLgBAbW0tUlNTMXnyZOh0Orzwwguw2+3o7+8HADz99NPQaDQ4ePAgAGD//v3Q6/VIS0v709dNSEhw3Q4PDwcAdHZ2AgBsNhuSkpLctv/j/QkTJiAvLw/p6enIzMzE+++/j7a2tnt7I4jIY1h0iMijAgICMHfuXBQWFuLEiRPIy8tDUVERrly5ggULFiAhIQH79++H1WpFeXk5AMDhcAAA/Pz8sGTJEtfpqz179iAnJwe+vn/+uQqNRuO6ffOUk9PpvOvMO3fuRGNjI5KTk7F3717ExsaiqanpL/27iUgaLDpEJKnp06ejr68PVqsVTqcTW7duxaOPPorY2Fi0trbetn1ubi6OHDmCCxcuoL6+Hrm5uff0+nFxcfjmm2/cHrv1PgA89NBDKCgowIkTJxAfH3/btUJE5J348XIi8gi73Y5nn30Wy5cvR0JCAnQ6HU6fPo3S0lIsWrQIDzzwAIaGhlBWVobMzEwcP34cFRUVt+1nzpw5MBgMyM3NRXR0NEwm0z3leuWVV5Cfn4/Zs2e7jticO3cOMTExAICWlhZ89NFHWLhwISIiImCz2dDc3IwXX3zxnl6XiDyDRYeIPEKr1cJkMuG9997D5cuXMTQ0BKPRiPz8fLz99tsIDAzEtm3bUFJSgoKCAsyZMwfFxcW3FQqVSoWlS5eitLQUGzduvOdcubm5+PHHH7Fu3ToMDg7iueeeQ15eHk6dOgUACAoKwg8//IDKykrY7XaEh4fDbDZj5cqV9/zaRDT2VEIIIXUIIiJvMnfuXBgMBnz66adSRyGie8QjOkSkaP39/aioqEB6ejrUajWqqqpQW1uLo0ePSh2NiEYBj+gQkaINDAwgMzMTZ86cweDgIOLi4rBhwwYsXrxY6mhENApYdIiIiEi2+PFyIiIiki0WHSIiIpItFh0iIiKSLRYdIiIiki0WHSIiIpItFh0iIiKSLRYdIiIiki0WHSIiIpItFh0iIiKSrX8DsMGhA9W/t70AAAAASUVORK5CYII=", + "text/plain": [ + "PyPlot.Figure(PyObject )" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Plot the consumption policy function\n", + "import PyPlot\n", + "plt=PyPlot\n", + "r=model.calibration.flat[:r]\n", + "c=exp(drtab[Axis{:V}(:lny)])+drtab[:s]*r-drtab[Axis{:V}(:a)]\n", + "\n", + "plt.plot(drtab[Axis{:V}(:s)],c, color=\"blue\")\n", + "plt.xlabel(\"Savings\")\n", + "plt.ylabel(\"Consumption\")\n", + "plt.title(\"Consumption Policy Function\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Simulations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Here we run simulations for N agents and look at the asset distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "200" + ], + "text/plain": [ + "200" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Simulations\n", + "import PyPlot\n", + "plt=PyPlot\n", + "\n", + "\n", + "mc_ar=model.exogenous\n", + "\n", + "sim_armc = Dolo.simulate(model,dr;N=1000,T=200)\n", + "\n", + "\n", + "T=200" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: Method definition plot_simulations(Int64" + ] + }, + { + "data": { + "text/plain": [ + "plot_simulations (generic function with 1 method)" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N=1000\n", + "n=200 # number of periods to plot\n", + "hor=linspace(1,n,n)\n", + "function plot_simulations(N::Int64,T::Int64,n::Int64,sim_armc)\n", + " assets_end=zeros(N)\n", + " for ii=1:N # number of simulations\n", + " c=exp(sim_armc[Axis{:N}(ii), Axis{:V}(:lny)])[T-n+1:T]+sim_armc[Axis{:N}(ii), Axis{:V}(:s)][T-n+1:T]*r-sim_armc[Axis{:N}(ii), Axis{:V}(:a)][T-n+1:T]\n", + " \n", + " assets_end[ii]=exp(sim_armc[Axis{:N}(ii), Axis{:V}(:lny)])[T]-c[end]\n", + " \n", + " end\n", + "\n", + " return assets_end\n", + "end\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ", Int64, Int64, Any) in module Main at In[30]:5 overwritten" + ] + } + ], + "source": [ + "assets_end=plot_simulations(N,T,n,sim_armc);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We see that the average asset holdings is very close to zero so we have market clearing." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using Plots\n", + "histogram(assets_end,nbins=80,xlims=(-2.0,4.0), ylims=(0.0,120.0), label=\"\", xlabel=\"Assets\", ylabel=\"Frequency\", title=\"Assets Distribution\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "-0.00468303900072091" + ], + "text/plain": [ + "-0.00468303900072091" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean(assets_end)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 0.5.1", + "language": "julia", + "name": "julia-0.5" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "0.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Huggett1996.yaml b/Huggett1996.yaml new file mode 100644 index 0000000..42856c6 --- /dev/null +++ b/Huggett1996.yaml @@ -0,0 +1,69 @@ +name: Huggett 1996 + +symbols: + exogenous: [lny,t] # exogenous states: y is the income and t is an indicator for whether the agent is alive or not( if t=0 the agent is dead) + states: [s] # endogenous states + controls: [a] # here the control is the difference in savings between tomorrow's assets and today's assets: a=s(+1)-s + expectations: [m] + values: [V] + parameters: [beta, sigma, r, rho, ybar,sig_y, abar, tbar, sig_t] # abar is the borrowing constraint + rewards: [u] + +definitions: # for now let's assume that the age plays the role of increasing life-cycle profile of earnings + y: exp(lny) + c: y+r*s-a + +equations: + + arbitrage: + - (1-beta*(c/c(1))^(sigma)*(1+r))*t+(1-t)*(10000-c)| abar-s<= a <= inf + + transition: + - s=a(-1)+s(-1) + + value: + - V=c^(1-sigma)/(1-sigma) +beta*V(1) + + felicity: + - u=c^(1-sigma)/(1-sigma) + + expectation: + - m=beta/(c(1)^sigma)*(1+r) + + +calibration: + + + beta: 0.98 + sigma: 3 + r: 0.002 + rho: 0.85 + ybar: 0.0 + sig_y: 0.017 + abar: -2.0 + tbar: 1.0 + sig_t: 0.4 + m: 0 + V0: (c^(1-sigma)/(1-sigma))/(1-beta) + + # endogenous variables - initial conditions + a: 0.0 + s: 0.0 + lny: ybar + y: exp(lny) + t: tbar + c: y+r*s-a # budget constraint + V: (c^(1-sigma)/(1-sigma))/(1-beta) + u: c^(1-sigma)/(1-sigma) + + +exogenous: !MarkovChain + values: [[-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 1.0], [-0.0968141, 0.0], [-0.0580885, 1.0], [-0.0580885, 1.0], [-0.0580885, 1.0], [-0.0580885, 1.0], [-0.0580885, 1.0], 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"metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This is a heterogeneous-agent overlapping generations model where each period agents are hit with idiosyncratic income shocks $y_t$ that follow an $AR1$ process. Also, each period, agents die with probability $\\tau$, and at age $T$, agents die with certainty. There are incomplete markets and agents only have access to a risk-free asset $s_t$ that pays $(1+r)s_t$ next period, where $r$ is the interest rate.\n", + "\n", + "The value function for an agent with current assets $s$ and current income $y$ is: $v(y,s)=\\max_{c,s'} u(c)+\\beta \\mathbf{E}v(y',s')$ where the expectation is taken over the value of the income shock and the probability of dying.\n", + "\n", + "The agent's budget constraint is: $c+s'=(1+r)s+y$ where s' is his asset choice next period. The agent will also be subject to a borrowing constraint: $s'\\geq \\bar{s}$.\n", + "\n", + "Here, we define the control in the model as $a=s'-s$, i.e. $a$ is the change in assets.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "#### Some notes on the solution method:\n", + "\n", + "The solution method is almost identical to Huggett (1993) except that we have an additional exogenous process, $t$, for keeping track of whether an agent is alive or not (if $t=1$, the agent is alive). $t$ follows a Markov Process. To illustrate, let us suppose an agent can live for three periods at most, and each period he has a probability $\\tau$ of dying. Then, the set of values for $t$ is $[1, 1,1,0]$ and the transition matrix for $t$ is:\n", + "\n", + "\\begin{bmatrix}\n", + " 0 & 1-\\tau & 0 & \\tau \\\\\n", + " 0 & 0 & 1-\\tau & \\tau \\\\\n", + " 0 & 0 & 0 & 1\\\\\n", + " 0 & 0 & 0 & 1\\\\\n", + "\\end{bmatrix}\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1m\u001b[31mWARNING: Using Calculus.jl for symbolic differentiation. This will be slower than SymEngine.jl\n", + ". To use SymEngine run the following code: `Pkg.add(\"SymEngine\")`\u001b[0m\n" + ] + } + ], + "source": [ + "# importing packages\n", + "# First import the packages\n", + "Pkg.dir(\"Dolo\")\n", + "import Dolo\n", + "using AxisArrays\n", + "using PyPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Huggett1996.yaml\"" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get the model file\n", + "filename=(\"Huggett1996.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Model
nameHuggett 1996
filenameHuggett1996.yaml
\n", + "\n", + "
TypeEquation
value\\[V_{t} = \\frac{\\left(c_{t}\\right)^{1-\\sigma}}{1-\\sigma}+\\beta V_{t+1}\\]
expectation\\[m_{t} = \\frac{\\beta}{\\left(c_{t+1}\\right)^{\\sigma}} 1+r\\]
felicity\\[u_{t} = \\frac{\\left(c_{t}\\right)^{1-\\sigma}}{1-\\sigma}\\]
transition\\[s_{t} = a_{t-1}+s_{t-1}\\]
arbitrage\\[1-\\beta \\left(\\frac{c_{t}}{c_{t+1}}\\right)^{\\sigma} 1+r t_{t}+1-t_{t} 10000-c_{t}\\]
" + ], + "text/plain": [] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model\n" + ] + } + ], + "source": [ + "model=Dolo.yaml_import(filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "MethodError: no method matching time_iteration(::Dolo.Model{Symbol(\"##340\")}, ::Dolo.DiscreteMarkovProcess, ::Dolo.CartesianGrid, ::Dolo.ConstantDecisionRule; verbose=true, maxit=1000, details=true)\u001b[0m\nClosest candidates are:\n time_iteration(::Dolo.Model{ID}, ::Dolo.AbstractDiscretizedProcess, ::Any, ::Any; verbose, maxit, trace, tol_η, solver) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos/time_iteration.jl:172\u001b[1m\u001b[31m got unsupported keyword argument \"details\"\u001b[0m\n time_iteration(::Any, ::Any, ::Any; grid, kwargs...) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos/time_iteration.jl:261\n time_iteration(::Any, ::Dolo.AbstractDiscretizedProcess; grid, kwargs...) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos/time_iteration.jl:268\n ...\u001b[0m", + "output_type": "error", + "traceback": [ + "MethodError: no method matching time_iteration(::Dolo.Model{Symbol(\"##340\")}, ::Dolo.DiscreteMarkovProcess, ::Dolo.CartesianGrid, ::Dolo.ConstantDecisionRule; verbose=true, maxit=1000, details=true)\u001b[0m\nClosest candidates are:\n time_iteration(::Dolo.Model{ID}, ::Dolo.AbstractDiscretizedProcess, ::Any, ::Any; verbose, maxit, trace, tol_η, solver) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos/time_iteration.jl:172\u001b[1m\u001b[31m got unsupported keyword argument \"details\"\u001b[0m\n time_iteration(::Any, ::Any, ::Any; grid, kwargs...) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos/time_iteration.jl:261\n time_iteration(::Any, ::Dolo.AbstractDiscretizedProcess; grid, kwargs...) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos/time_iteration.jl:268\n ...\u001b[0m", + "", + " in (::Dolo.#kw##time_iteration)(::Array{Any,1}, ::Dolo.#time_iteration, ::Dolo.Model{Symbol(\"##340\")}, ::Dolo.DiscreteMarkovProcess, ::Dolo.CartesianGrid, ::Dolo.ConstantDecisionRule) at .\\:0", + " in #time_iteration#101(::Dict{Any,Any}, ::Array{Any,1}, ::Function, ::Dolo.Model{Symbol(\"##340\")}, ::Dolo.DiscreteMarkovProcess, ::Dolo.ConstantDecisionRule) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos\\time_iteration.jl:262", + " in (::Dolo.#kw##time_iteration)(::Array{Any,1}, ::Dolo.#time_iteration, ::Dolo.Model{Symbol(\"##340\")}, ::Dolo.DiscreteMarkovProcess, ::Dolo.ConstantDecisionRule) at .\\:0", + " in #time_iteration#104(::Dict{Any,Any}, ::Array{Any,1}, ::Function, ::Dolo.Model{Symbol(\"##340\")}) at C:\\Users\\Angela\\AppData\\Local\\JuliaPro-0.5.1.1\\pkgs-0.5.1.1\\v0.5\\Dolo\\src\\algos\\time_iteration.jl:282", + " in (::Dolo.#kw##time_iteration)(::Array{Any,1}, ::Dolo.#time_iteration, ::Dolo.Model{Symbol(\"##340\")}) at .\\:0" + ] + } + ], + "source": [ + "@time sol=Dolo.time_iteration(model,verbose=true, maxit=1000, details=true)\n", + "dr=sol.dr\n", + "@time res = Dolo.time_iteration(model, dr; maxit=200, details=true)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Let's look at some consumption policy functions." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "drtab = Dolo.tabulate(model, dr, :s) \n", + "\n", + "# First we have to get the policy functions \n", + "s0 = model.calibration[:states]\n", + "num_ages= 9 # number of ages + 1 for death state\n", + "y_states=[num_ages*n-(num_ages-1) for n in 1:5]\n", + "num_yplots=length(y_states)\n", + "dr_ylist=[Dolo.tabulate(model, dr, :s, s0, y) for y in y_states]\n", + "\n", + "r=model.calibration.flat[:r]\n", + "\n", + "ygrid=[Dolo.node(model.exogenous,num_ages*i-(num_ages-1))[1] for i in 1:6]\n", + "c_ylist=[exp(ygrid[y])+dr_ylist[y][:s]*r-dr_ylist[y][Axis{:V}(:a)] for y in 1:num_yplots];" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "PyPlot.Figure(PyObject )" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Plot the consumption policy function across different income levels for age 1\n", + "import PyPlot\n", + "plt=PyPlot\n", + "end_y=20\n", + "\n", + "for i=1:num_yplots\n", + " plt.plot(dr_ylist[i][Axis{:V}(:s)][1:end_y],c_ylist[i][1:end_y], label=i)\n", + "end\n", + "\n", + "plt.legend()\n", + "plt.xlabel(\"Savings\")\n", + "plt.ylabel(\"Consumption\")\n", + "plt.title(\"Consumption Policy Function across different income levels\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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6dYvXXnvtsfP8mYLCpV69en86X9++fdHpdMbiJDk5mU2bNvG3v/2txEVD69at6dChg/GnZcuWJc55v4L2Nve6f9+8dOkSnp6euLi4PPb73KtgvT7zzDOFpltaWuLr61tkvdeoUaPIurk/44McPHiQDh06YGtri5OTE+7u7sb2LgWFTkm3HUD16tWLNNgt6fLUqlWLd999l2+++QY3Nzc6duzI7NmzC7XP6du3Ly1btmTYsGF4eHjQr18/fvzxx4cWPQXHJD8/v0LT789UGiIjI0lOTqZKlSq4u7sX+klLS+Pu3buF5q9Vq1axr6GUIiAgoMhrnDt3rshr/JV94EH0ej0rVqygXbt2REdHExUVRVRUFM2aNSMmJoZdu3YZ57169SrVqlUr0vD8/nNAVFQUSin+7//+r8hyFXxQKFi2J3HML4uk11UZ4eDggKenJ6dPn36k55X0xPSgHidKKePvrVu35tKlS6xfv57t27fzzTff8PXXXzNv3jyGDRtmfL97n1Pg/oajD3vfkuQpC/5qzvr169OhQ4fSjPSXOTs706VLF8LDw/nPf/7D6tWryc7OLvRp9a940D75qPtIWfK4+8GlS5d48cUXCQoK4quvvsLLywtLS0s2b97M119//UhXTAr81d5CU6dOZfDgwcb/87Fjx/LZZ59x+PBhY+eAffv2sWfPHiIiIti6dSsrV66kffv2bN++vUxsL4PBQJUqVR54JfH+wrm4dWYwGNBoNGzZsqXYZbp/+IQncczavXs3t2/fZsWKFaxYsaLI38PDwwkJCXmk1yzYp957770iV7EKFBRHJTnmVwRS6JQhXbp0YcGCBfzyyy80b978T+f19vbGYDAQGRlZqOFtTEwMSUlJeHt7P1YGFxcXhgwZwpAhQ0hLS6N169Z88MEHxp3e2dm52Eu1j3Ll4VEUfDq59+R58eJFAGMvr0e5deHt7c0ff/yBwWAodBXl/Pnzxr8/TaWZp+CT9OnTpx86BsfAgQN5+eWXOXr0KOHh4Tz33HPUrVv3MZagqIKrXvc35vwr+4ifnx/btm0jISHhT6/qlHRfKFivFy5cwNfX1zg9JyeH6OjoUitON27cSHZ2Nhs2bCh0dfD+2yKPsu2K86jLU79+ferXr8+///1vDh06RMuWLZk3bx4ff/wxkH9L5MUXX+TFF1/kq6++4tNPP+Vf//oXe/bseeC6KTgmXbp0qdBVnAsXLjzy8jyMn58fO3fupGXLlo9d+Pn5+aGUolatWsarxH/Vo95GDQ8Pp0qVKkUapkP+rbWffvqJefPmYW1tjbe3N3v27CkynEBUVFSh5xVsfwsLixLtxw875lcEcuuqDJk4cSK2trYMGzaMmJiYIn+/dOmSsSdO586dAZg2bVqheb766iuAEvV0uF98fHyhx3Z2dvj7+xfqru7n58f58+cLdU8+efJkiXpkPI5bt24V6iWQkpLC0qVLadiwIVWrVgXA1tYWKHpiLU7nzp25c+dOoV5HeXl5zJw5Ezs7uxK3USktpZknJCQEe3t7PvvssyJdxO//1BkaGoqbmxuff/45e/fuLbWrOZB/wtNqtezbt6/Q9Dlz5jz2a/bs2ROlFJMnTy7yt3uXzdbWtkT7QYcOHbC0tGTGjBmFnv/tt9+SnJz8WP8/xSm4CnDveyQnJ7N48eJC8z3KtitOSZcnJSWFvLy8Qs+tX78+ZmZmxv/z+4d5AIy9lO4fuuJeoaGhAMyYMaPQ9PuPUaWhT58+6PV6PvrooyJ/y8vLK9E+0KNHD7RaLZMnTy6yjpVSRY6HJfEox6LMzEzWrl1Lly5d6NWrV5Gft956i9TUVGOX8ILeZAsXLjS+hsFgKFIkValShbZt2zJ//nxu375d5H3vPXaX5JhfEcgVnTLEz8+P5cuX07dvX2rXrl1oZORDhw6xatUq47ggDRo0YNCgQSxYsICkpCTatGnDkSNH+O677+jevbuxYdujqFOnDm3btqVRo0a4uLhw7NgxVq9ezVtvvWWcZ+jQoXz11Vd07NiR119/nbt37zJv3jzq1q1rbFBdmgIDA3n99dc5evQoHh4eLFq0iJiYmEInioYNG6LVavn8889JTk5Gp9MZxy253/Dhw5k/fz6DBw/m+PHj+Pj4sHr1ag4ePMi0adNK3Bi8tJRmHgcHB77++muGDRtGkyZNGDBgAM7Ozpw8eZKMjIxCY/NYWFjQr18/Zs2ahVarLdQY+q9ydHSkd+/ezJw5E41Gg5+fH5s2bSrS5uFRtGvXjtdee40ZM2YQGRlJp06dMBgM7N+/n3bt2hn30UaNGrFz506++uorPD09qVWrVrEjNru7uzNp0iQmT55Mp06d6NatGxcuXGDOnDk0adKk1Aq/kJAQLC0t6dq1KyNGjCAtLY2FCxdSpUqVQiehR9l2xSnp8uzevZu33nqL3r17ExgYSF5eHsuWLUOr1dKzZ08gv93Gvn37CAsLw9vbm7t37zJnzhxq1KhBq1atHpihYcOG9O/fnzlz5pCcnEyLFi3YtWtXkSsOpaFNmzaMGDGCzz77jBMnThASEoKFhQWRkZGsWrWK6dOnGwfqfBA/Pz8+/vhjJk2axJUrV+jevTv29vZER0fz008/MXz48GKHvHjYazo5OTFv3jzs7e2xtbWlWbNmxbYR2rBhA6mpqXTr1q3Y13rhhRdwd3cnPDycvn370r17d5o2bcr48eOJiooiKCiIDRs2GAvTe68mzZ49m1atWlG/fn3eeOMNfH19iYmJ4ZdffuHGjRucPHkSKNkxv0J4yr28RAlcvHhRvfHGG8rHx0dZWloqe3t71bJlSzVz5sxC3QVzc3PV5MmTVa1atZSFhYXy8vJSkyZNKjSPUvndK4vrQnh/1+uPP/5YNW3aVDk5OSlra2sVFBSkPvnkE5WTk1Poed9//73y9fVVlpaWqmHDhmrbtm0P7F4+ZcqUQs8t6Hp8f/fWgu6mBd2u7829bds29eyzzyqdTqeCgoKK7Rq7cOFC5evrq7RabaGuzcV1h4+JiVFDhgxRbm5uytLSUtWvX79Id9AH5VeqaDfv4jxoOYtTkjzFvW9xXXSVUmrDhg2qRYsWytraWjk4OKimTZuqH374ocjrHTlyRAEqJCTkoRkLFHQvf1jX49jYWNWzZ09lY2OjnJ2d1YgRI9Tp06eL7V5eXJfigve5V15enpoyZYoKCgpSlpaWyt3dXYWGhqrjx48b5zl//rxq3bq1sra2VoCxq/mD1tWsWbNUUFCQsrCwUB4eHmrkyJEqMTGx0DzFdW8uyH7vPv8gGzZsUM8++6yysrJSPj4+6vPPP1eLFi16rG33oCwlXZ7Lly+roUOHKj8/P2VlZaVcXFxUu3bt1M6dO43z7Nq1S7388svK09NTWVpaKk9PT9W/f3918eLFhy5rZmamGjt2rHJ1dVW2traqa9eu6vr166XevbzAggULVKNGjZS1tbWyt7dX9evXVxMnTlS3bt0yzvOg41+BNWvWqFatWilbW1tla2urgoKC1OjRo9WFCxcemqG4fWD9+vWqTp06ytzc/E+7mnft2lVZWVmp9PT0B2YbPHiwsrCwUHFxcUqp/P+rAQMGKHt7e+Xo6KgGDx6sDh48qAC1YsWKQs+9dOmSGjhwoKpataqysLBQ1atXV126dFGrV682zlPSY355p1GqjLX+FOJ/fHx8qFevHps2bTJ1lArp5MmTNGzYkKVLlxbqCSaEKD/WrVvHK6+8woEDB/5S78eKTNroCFFJLVy4EDs7O3r06GHqKEKIEsjMzCz0WK/XM3PmTBwcHHj++edNlKrskzY6QlQyGzdu5OzZsyxYsIC33nrL2IBSCFG2jRkzhszMTJo3b052djZr167l0KFDfPrpp2XqS0rLGil0hKhkxowZQ0xMDJ07dy62F5MQomxq3749U6dOZdOmTWRlZeHv78/MmTMrXuPhUiZtdIQQQghRYUkbHSGEEEJUWFLoCCGEEKLCqnRtdAwGA7du3cLe3v6xvvVYCCGEEE+fUorU1FQ8PT2LfBHyn6l0hc6tW7eKfHO2EEIIIcqH69evU6NGjRLPX+kKnYIh9a9fv46Dg4OJ0wghhBCiJFJSUvDy8nrkr+qpdIVOwe0qBwcHKXSEEEKIcuZRm51IY2QhhBBCVFhS6AghhBCiwpJCRwghhBAVlhQ6QgghhKiwpNARQgghRIUlhY4QQgghKiwpdIQQQghRYUmhI4QQQogKy6SFzr59++jatSuenp5oNBrWrVv30OeEh4fToEEDbGxsqFatGkOHDiU+Pv4ppBVCCCFEeWPSQic9PZ0GDRowe/bsEs1/8OBBBg4cyOuvv86ZM2dYtWoVR44c4Y033njCSYUQQghRHpn0KyBCQ0MJDQ0t8fy//PILPj4+jB07FoBatWoxYsQIPv/88ycVUQghhBDlWLlqo9O8eXOuX7/O5s2bUUoRExPD6tWr6dy58wOfk52dTUpKSqEfIYQQQlQO5arQadmyJeHh4fTt2xdLS0uqVq2Ko6Pjn976+uyzz3B0dDT+eHl5PcXEQgghROWScOsGCbdumjqGUbkqdM6ePcu4ceP4z3/+w/Hjx9m6dStXrlzhzTfffOBzJk2aRHJysvHn+vXrTzGxEEIIUTkopTi1ZzvL/jGOTdP+S15OjqkjASZuo/OoPvvsM1q2bMmECRMAePbZZ7G1tSU4OJiPP/6YatWqFXmOTqdDp9M97ahCCCFEpZGVnsbOhbO58Mt+AKztHcjNzsLc0tLEycpZoZORkYG5eeHIWq0WyK8khRBCCPF03bxwjs0zp5ASexczrZaWfV+jSdceaMzKxk0jkxY6aWlpREVFGR9HR0dz4sQJXFxcqFmzJpMmTeLmzZssXboUgK5du/LGG28wd+5cOnbsyO3bt3n77bdp2rQpnp6eploMIYQQotIxGPQcWbeaQ6vCUQYDjlU8CBs7kWoBz5g6WiEmLXSOHTtGu3btjI/fffddAAYNGsSSJUu4ffs2165dM/598ODBpKamMmvWLMaPH4+TkxPt27eX7uVCCCHEU5QaH8eWWVO5fvYUAEEt29Bh2Gh0NjYmTlaURlWyez4pKSk4OjqSnJyMg4ODqeMIIYQQ5UrU0cNsmzedrLRULHRWvPj6SOq0bo9Go3mi7/u45+9y1UZHCCGEEKaRm5PNvu8XcWJbBAAevv6EjZ2Ac7XqJk7256TQEUIIIcSfir9xjU3TvyDu2hUAGnftQat+r6E1tzBtsBKQQkcIIYQQxVJK8cfOrfz83ULycnOwcXQidNQ7+DRsZOpoJSaFjhBCCCGKyExLZcf8mUQeOQSAT4Pn6TTqHWydnE2c7NFIoSOEEEKIQm6cPU3ErC9Ji4/DTGtO8IBBNOr8cpkZG+dRSKEjhBBCCAAMej2H167g8JqVKGXAqWo1uoz7Ox6+/qaO9tik0BFCCCEEKbF3iZj5JbcunAWgbpsXaT9kBJbWZW9snEchhY4QQghRyV08fIDtC2aSnZ6OpbU1HYaNpnartqaOVSqk0BFCCCEqqdzsLPZ8t5BTu7YBUNU/kLCxE3HyqGriZKVHCh0hhBCiErp75TIRM6aQcPM6aDQ07daTFn1eRWtesUqDirU0QgghhPhTSil+37qJfeGL0OfmYuvsQujod/Gu39DU0Z4IKXSEEEKISiIjJZlt86Zz+fgRAHyfb0LHkW9j4+Bo4mRPjhQ6QgghRCVw7fRJNs+aSnpiAlpzc1q/OpTnOnV94l/GaWpS6AghhBAVmD4vj19WL+fXdatAKVw8axA2biJVfHxNHe2pkEJHCCGEqKCS794hYvoUbkddAKB++xDaDRqOhZWViZM9PVLoCCGEEBXQuYN72blwNjmZGehsbHlp+Biead7K1LGeOil0hBBCiAokJyuT3Yvmc2bvTgA8A2sTNnYCDu5VTJzMNKTQEUIIISqImMtRRMz4gsTbt9BozGjWoy/Ne/bDTKs1dTSTkUJHCCGEKOeUwcBvWzawL3wJBn0edq5udH5rPF516ps6mslJoSOEEEKUYxnJSWyd8zV7pJrJAAAgAElEQVTRJ44D4N+kOSFvjsXazt7EycoGKXSEEEKIcurKyd/YMvsrMpKTMLewpO2gYTzbIbTCj43zKKTQEUIIIcoZfV4uB1Ys49jGtQC4eXkTNnYCbjV9TBusDJJCRwghhChHEm/fJGLGl8RcjgSgQUgYbV4bioWlzsTJyiYpdIQQQohy4uy+3ez8di65WZlY2doRMnIcAU2amzpWmSaFjhBCCFHGZWdksGvRXM7t3wNAjdr1CH1rPA5u7iZOVvZJoSOEEEKUYXeiLhIxYwpJMbfRmJnRotcAmr7SGzOzyjs2zqOQQkcIIYQog5TBwNGNazm4chkGvR57N3fCxkygelAdU0crV6TQEUIIIcqYtMQEtsz+imunTgAQ+EIrXhr+Fla2diZOVv5IoSOEEEKUIZd/P8rWOdPITEnGXKej/eAR1Gv3koyN85ik0BFCCCHKgLzcXPYvX8Jvm9cD4O5di7BxE3Gt7mXiZOWbFDpCCCGEiSXcusGm6V8Qe+UyAM+HdiN4wGDMLS1NnKz8k0JHCCGEMBGlFKd/3sHuxfPJy87G2t6BTqPewff5JqaOVmFIoSOEEEKYQFZ6GjsXzubCL/sBqFmvAaFvjcfO2cXEySoWKXSEEEKIp+zWxXNEzPiSlNgYzLRaWvZ9jSZde6AxMzN1tApHCh0hhBDiKTEY9BxZt5pDq8JRBgOOVTwIGzuRagHPmDpahSWFjhBCCPEUpCbEsWXmVK6fPQVAUMs2dBg2Gp2NjYmTVWxS6AghhBBPWNSxX9k2dxpZaalY6Kx48fWR1GndXsbGeQqk0BFCCCGekNycbPZ9v4gT2yIAqFLLj7CxE3HxrG7iZJWHFDpCCCHEExB/4xqbpn9B3LUrADTq8grB/QeiNbcwbbBKRgodIYQQohQppTi1axt7vltIXk42No5OhI56B5+GjUwdrVKSQkcIIYQoJZlpqeyYP5PII4cA8GnwPJ1GvYOtk7OJk1VeUugIIYQQpeDGudNsnjmV1PhYzLTmBPcfSKOw7jI2jolJoSOEEEL8BQa9nsNrV3B4zUqUMuBUtRpdxv0dD19/U0cTSKEjhBBCPLaUuLtsnvklN8+fBaBumxdpP2QEltYyNk5ZIYWOEEII8Rgu/nqQ7fNnkJ2ejqW1NR1eH0Xt4HamjiXuI4WOEEII8Qhys7P4eek3/LFzKwBV/QMJGzMBp6rVTJxMFEcKHSGEEKKEYq9GEzFjCvE3roFGQ9NuPWnR51W05nI6LatkywghhBAPoZTixPYI9i77Fn1uLrZOzoSOHo/3sw1NHU08hBQ6QgghxJ/ISElm+/wZXDr2KwC1nmtMp1HvYOPgaOJkoiSk0BFCCCEe4NrpP9gy60vSEhPQmpvT+m9DeC60m3wZZzkihY4QQghxH31eHr+s/oFf1/0ISuHsWYMu4yZSxcfX1NHEI5JCRwghhLhH8t0YImZO4fbF8wDUaxdC+8HDsbCyMnEy8Tik0BFCCCH+5/yhfexYMIuczAx0Nra8NPwtnmkebOpY4i+QQkcIIUSll5uVxe4l8zm9ZwcA1QKDCBszAccqHiZOVj7p9Qa02rLxHV9S6AghhKjUYqIvETFjCom3boBGwwuv9KF5rwGYabWmjlbuJMVkcGBVJLbOOtr9LcjUcQApdIQQQlRSSil+27yB/csXo8/Lw87Flc5vjcer7rOmjlbu5GTlcWzzFU7uuo5Br9Cam9G0Sy1sHXWmjiaFjhBCiMonIzmJrXOnEf37MQD8Gr9AxzfHYm3vYOJk5YsyKC4cucMvay+RkZIDQM26LrTqHVAmihyQQkcIIUQlc+WP39k6+yvSkxLRWljQ9rVhNAjpLGPjPKKYKynsX3mRmOgUABzcrQnuHYB3fdcytS6l0BFCCFEp6PPyOLhyGUc3rAHAtUZNwsZNxL2mj2mDlTMZKTkcXneJc7/cBgXmOi2NQ71p+GJNtBZlowHyvaTQEUIIUeEl3blNxIwvuHMpEoAGL4XS5rXXsdDJ2DglpdcbOLXnBkc3RZOTpQcgsJkHLV7xx9apbNymKo4UOkIIISq0c/v3sPPbOeRkZmJla0fIiLEENGth6ljlyrWz8Rz4MZLEOxkAuNe0J7hvINX8yv73fUmhI4QQokLKycxg17dzObt/DwDVg+rSecx7OLi5mzhZ+ZEcm8nB1ZFEn4wDwMrOgubd/QhqUQ0zs7LTDufPSKEjhBCiwrlzKZKIGV+QdOc2Go0ZzXv1p1mPPpiZydg4JZGbref41iuc2HEdfZ4BjZmGZ9vWoEkXH3Q2FqaO90ik0BFCCFFhKIOBYxHrOPDDdxj0euxd3ek89j1qBNU1dbRyQSlF5LEYDq25RHpSNgA1gpwJ7hOIi6etidM9Hil0hBBCVAjpSYlsmf0VV//4HYCAZi0IGT4WKzs7EycrH2KvpbL/x4vcjkoGwN7Vila9AqjV0K1MdRd/VFLoCCGEKPeifz/G1rnTyEhOwtxSR7vBb1C/fcdyfYJ+WjLTcvh1/WXOHLiV313cwoxGod407FATc8vyf6tPCh0hhBDlVl5uLgd++I7jEesAcKvpQ5dxE3GtUdPEyco+g97A6X23OLLxMtkZeQD4N65Cix7+2LtUnG73Jh3ZZ9++fXTt2hVPT080Gg3r1q176HOys7P517/+hbe3NzqdDh8fHxYtWvQU0gohhChLEm7d5If/e89Y5DTs2IW/ffKVFDklcON8Ais/Ocr+lRfJzsjDtbod3d99jo7D6lWoIgdMfEUnPT2dBg0aMHToUHr06FGi5/Tp04eYmBi+/fZb/P39uX37NgaD4QknFUIIUVYopTizdxe7F80jNzsLK3sHOr45Dv/GzUwdrcxLic/k0JooLv0WC4DO1pwXuvlSp5UnZtqyN6pxaTBpoRMaGkpoaGiJ59+6dSt79+7l8uXLuLi4AODj4/OE0gkhhChrsjMy2PnNbM4f3AuAV91nCX3rXexd3EycrGzLzdHz+7ar/Lb9GvpcAxoN1GtdnabdfLGyLV/dxR9VuWqjs2HDBho3bswXX3zBsmXLsLW1pVu3bnz00UdYW1sX+5zs7Gyys7ONj1NSUp5WXCGEEKXoduQFImZ8QfLdGDRmZrTs8ypNXu4pY+P8CaUUl36L5eCaSNIS8s+FngFOBPcNwK2GvYnTPR3lqtC5fPkyBw4cwMrKip9++om4uDhGjRpFfHw8ixcvLvY5n332GZMnT37KSYUQQpQWZTBwZMMaDv34PQa9Hgd3D8LGvodnYG1TRyvT4m+msX/lRW5eTALAzllHi57++DeqUql6o2mUUsrUIQA0Gg0//fQT3bt3f+A8ISEh7N+/nzt37uDomP/9GmvXrqVXr16kp6cXe1WnuCs6Xl5eJCcn4+DgUPoLIoQQotSkJcSzZfZXXDt9EoBnmgfz0vC30NmUz8Hrnoas9FyObIzm9N4bKAVaCzOeC6nJ8x29sSjH3cVTUlJwdHR85PN3ubqiU61aNapXr24scgBq166NUoobN24QEBBQ5Dk6nQ6drux+q6oQQojiXTp+hG1zp5GZmoK5TseLQ96kbtsOlepqxKMwGBRnD9zi1/WXyUrPBcDvOXda9PTHwa345h2VQbkqdFq2bMmqVatIS0vD7n8jXV68eBEzMzNq1Khh4nRCCCFKQ15uLvvCF/H7lo0AuPv40mXcRFw85Tj/ILcik9j/40XirqcB4OJpS6s+AXgFuZg4memZtNBJS0sjKirK+Dg6OpoTJ07g4uJCzZo1mTRpEjdv3mTp0qUADBgwgI8++oghQ4YwefJk4uLimDBhAkOHDn1gY2QhhBDlR/zN60RM/4LYq9EAPN/5ZYIHDMbcomL3DHpcaYlZHFp7icijMQDobMxp0qUW9dpUR1tBu4s/KpMWOseOHaNdu3bGx++++y4AgwYNYsmSJdy+fZtr164Z/25nZ8eOHTsYM2YMjRs3xtXVlT59+vDxxx8/9exCCCFKj1KK03t2sHvJfPKys7F2cKTTqLfxfa6JqaOVSXm5ek7svM7xLVfIyzGABuq08uSFbr5Y21uaOl6ZUmYaIz8tj9uYSQghxJORlZ7GjoWzufjLfgBq1m9I6Oh3sXOW2y73U0oRfTKOg6sjSYnLAqCanyPBfQNxr1mxu4tXisbIQgghKpabF86xeeYUUmLvYqbV0rLvazTp2gONmdx2uV/C7XQOrIrk+tkEAGwdLWnR05+AJh7SQPtPSKEjhBDiqTMY9Bz5aRWHVi9HGQw4eVSj89j3qOb/jKmjlTnZmXkc3RTNqT03MBgUZuYaGnaoSaNO3lhayWn8YWQNCSGEeKpS4+PYMmsq18+eAqB2cDteHDoSnY2NiZOVLcqgOPfLbQ6vu0Rman53cZ9n3WjV2x9Hd1lXJSWFjhBCiKcm6uhhts2bTlZaKhZW1nR4fSR1Wrc3dawy587lZPavvMjdq6kAOHnYENwngJp1XU2crPyRQkcIIcQTl5uTzd5lizi5PQIAD19/wsZOwLladRMnK1vSk7P55adLXDh8BwBLKy1NutSiftsaaM2l3dLjkEJHCCHEExV3/SoR078g7vpVABp37UGrfq+hNZexcQro8wyc3H2dYxFXyM3WA1C7RTVe6O6HjYN0F/8rpNARQgjxRCil+GPnFn7+7hvycnOwcXQidPS7+DR43tTRypQrp+I4uDqKpJgMADxqORDcJxCPWjIESmmQQkcIIUSpy0xLZcf8mUQeOQSAT8NGdBr5NrZOziZOVnYkxWRwYHUkV0/FA2DtYEmLHn4807QqGjPpLl5apNARQghRqm6cPU3ErC9Ji4/DTGtO8IBBNOr8soyN8z85WXkc33KFEzuvY9ArzLQaGrT3onFnHyyt5bRc2mSNCiGEKBUGvZ7Da1dweM1KlDLgXM2TsLET8fD1N3W0MkEpxcUjMRxaG0VGcg4ANeu60Kp3AM5VbU2cruKSQkcIIcRflhJ7l4iZX3LrwlkA6rbpQPuhI7C0ki9cBrh7NYX9Ky9y53IKAI7u1rTqHYB3fVcZ1fgJk0JHCCHEX3Lx8AG2L5hJdno6ltbWdBg2mtqt2po6VpmQkZLDr+svcfbQbVBgrtPSpLMPDdp7obWQW3lPgxQ6QgghHktudhY/f/cNf+zaCkBV/0DCxk7EyaOqiZOZnl5v4PTPNzmyKZqczDwAApt50OIVf2yddCZOV7lIoSOEEOKRxV6NZtP0L0i4eR00Gpp260mLPq+iNZfTyvVzCexfeZHEO/ndxd1r2hPcN5Bqfo4mTlY5yR4phBCixJRSnNi2ib3fL0Kfm4utswuho9/Fu35DU0czuZS4TA6ujuLyiVgArOwsaN7dj6AW1TCT7uImI4WOEEKIEslISWbbvOlcPn4EAN/nm9Bx5NvYOFTuKxW52Xp+23aV37dfQ59nQGOmoX7b6jTtUgudTeUa/dmQk0Py2rUAOPfrZ+I0+aTQEUII8VDXTp9ky6yppCUmoDU3p/WrQ3muU9dK3WNIKUXU8bscWhNFWmI2ADWCnGnVJwBXTzsTp3u6DFlZJK1aTfw335AXE4OZoyMOXbqitTN9t3kpdIQQQjyQPi+PX1Yv59d1q0ApnD1r0GXcRKr4+Jo6mknF3Uhl/8pIbkUmAWDvakXLXv74NnSvVMWfITOTxJUrif/2W/SxcQCYe3jg+sYbaCzKRolRNlIIIYQoc5Lv3iFixhRuR14AoF67ENoPHo6FlZWJk5lOVlouv264zJn9N1EKzC3MeL6TN8+9VBNzS62p4z01hvR0En/4gfjFS9DH53+FhblnNdyGj8CxxyuYWZadLyKVQkcIIUQR5w/tY8eCWeRkZqCzseWl4W/xTPNgU8cyGYPewJn9t/h1w2WyM/K7i/s3qkKLnv7Yu1Sewk+flkbi9+EkLFmCPin/apaFlxduI4bj2K0bmjJU4BSQQkcIIYRRTlYmuxfP58zPOwGoFhhE2JgJOFbxMHEy07l5MZH9KyOJv5kGgGt1W4L7BlI9sPJ8Qak+OZmEZd+TsHQphpT80Z0tvb1xHfkmjl26oCnDwwqU3WRCCCGeqpjLUUTMmELi7Zug0fDCK31o3msAZtrKc0vmXqkJWRxaE0XU8bsA6GzMadbNl7rBnphpK8eoxnmJiSQsXUrisu8xpOUXepZ+fri9+SYOnUPRlIN9QwodIYSo5JRS/LZ5PfvCl2DQ52Hn4krnt8bjVfdZU0czibwcPb/vuMZvW6+Sl2tAo4G6ravTrKsvVnaVo7t4XkICCYsXkxi+HENG/sCHuoAA3EaNxD4kpFwUOAWk0BFCiEosIzmJrXO+JvrEcQD8Gr9AxzfHYm3vYOJkT59SiugTcRxYHUlqfBYAngFOBPcNwK2GvYnTPR15sbHEL1pM4ooVqMxMAHS1a+M28k3sO3RAY1b+rmRJoSOEEJXUlT9+Z8usqWQkJ6G1sKDta8NoENK5UnWPLhB/K40DP0Zy43wiAHbOOlr09Me/UZVKsT5yY2KI/+Zbkn78EZWdPyaQVb16uI0ahV27tuV6HUihI4QQlYw+L5cDK5ZxbGP+CLauNWoSNm4i7jV9TBvMBLIzcjmyKZpTP99EGRRaczOeC6nJ8x29sdCVn9szjyv31i3iv/mGpFWrUbm5AFg3aIDb6FHYBgeX6wKngBQ6QghRiSTeuUXE9CnEXI4EoMFLobR57XUsdJWnizSAwaA4d/AWh9dfJist/wRfq4EbLXsF4OhubeJ0T17OjRvEz19A0rp1UFDgNGqE++hR2DRvXiEKnAJS6AghRCVxdv8edn4zh9ysTKxs7Qh5cywBTVuYOtZTd/tSMvtXXiT2WioAzlVtCO4TiFcdFxMne/Jyrl4lbv4CktevB70eAJtmzXAbNQqbpk0qVIFTQAodIYSo4HIyM9j17VzO7t8DQPWgunQe8x4Obu4mTvZ0pSdlc+inKC7+GgOApZWWpl19qde2OtoK3l08+3I08fPnkbxxExgMANi2bInbqJHYNGpk4nRPlhQ6QghRgd2JukjEjCkkxdxGozGjea/+NOvRBzOzit/+pIA+18DJ3dc5uvkKedl60EDtFtV44WU/bBzK3ki+pSk7MpK4ufNI2bIFlALAtk1r3EeOxLphQxOnezqk0BFCiApIGQwc2/QTB1YsxaDXY+/mTucx71EjqK6poz01SimunornwKpIkmPzu0p71HIguG8gHj4Vu/t81vnzxM2dR+q2bcZpdi++iNubb2Jdv54Jkz19UugIIUQFk5aYwNY5X3P1j98BCGzWkpeGj8HKzs7EyZ6exDvpHFgVxbUz+V84aeNgSYsefgQ2rYrGrOK1QymQefoMcXPnkrZrl3GafUgIbiPfxKp2bRMmMx0pdIQQogK5/PtRts6ZRmZKMuaWOtoNHk799iEVspFpcXIy8zi6+Qp/7LqOwaAw02po8KIXjTv7YGlVcU95mSdPEjdnLml79+ZP0GhwCO2E65tvYhUYaNpwJlZxt7oQQlQiebm5HPhhCccj1gPgXtOHsHF/x7WGl4mTPR3KoLjw6x0O/XSJzJQcAGrWdSW4TwBOHjYmTvfkZPz2G3Fz5pJ+4ED+BDMzHLqE4TZiBDo/P9OGKyOk0BFCiHIu4dYNIqZP4e6VSwA8F9qV1gOGYG5ZsRvaFoi5ksL+lReJic7/Vm1Hd2ta9QnAp76biZM9OelHjhA3Zy4Zhw/nT9BqcezWDbcRw7H08TFptrJGCh0hhCinlFKc+XknuxbPIy87Gyt7BzqNHIdfo2amjvZUZKTkcHjdJc4dug2AhU5L484+NGjvhdai4nUXV0qRcfgwcbPnkHHsWP5ECwucunfHdfgbWHpVjqt3j0oKHSGEKIeyM9LZsXA2Fw7tA8Cr7rOEvvUu9i4V9ypGAb3ewKk9Nzi6KZqcrPxB755pVpXmr/hh66QzcbrSp5Qi/cBB4ubMIfP3/AbmGgsLnHr3wnXYMCw8PU2csGyTQkcIIcqZWxfPETHjS1JiY9CYmdGyz6s0eblnpRgb59rZeA78GEninQwA3GvaE9w3kGp+jiZOVvqUUqT9/DNxc+aSdeoUABqdDqc+fXAd9joWHh4mTlg+SKEjhBDlhMGg58i61RxaFY4yGHCs4kHnMRPwDAwydbQnLjk2k4OrI4k+GQeAtb0FL7zsR1CLaphVsO7iymAgbfduYufMIfvsOQA0VlY49+uHy9AhWFSpYuKE5YsUOkIIUQ6kJsSxZeZUrp/N/2Qf1LINHYaNQmdja+JkT1Zutp7jW69wYsd19HkGNGYanm1bgyZdfNDZWJg6XqlSBgOp23cQN3cu2RcuAKCxscHlbwNwGTwYc1dXEycsn6TQEUKIMi7q2K9smzuNrLRULHRWvPj6SOq0bl+hx8ZRShF17C6H1kaRlpgNQI0gZ4L7BOLiWbGKO6XXk7JlK3Hz5pITld9zzszWFufXXsVl0CDMnZ1NnLB8k0JHCCHKqLycHPZ+v4gT2zYBUKWWH2FjJ+LiWd3EyZ6s2Oup7F95kdtRyQDYu1rRqncAtRq4VajiTuXlkRIRQdy8+eRERwNg5uCAy8CBuLz2KlrHitfuyBQeq9DR6/UsWbKEXbt2cffuXQz/+ybUArt37y6VcEIIUVnF37jGpulfEHftCgCNurxCq34DMbeoWLdr7pWVlsuvGy5zZv9NlAJzCzMahXrTsENNzC0rTkNrlZtL8oaNxM2fT+61awBoHR1xGTIY57/9Da29vYkT/jVXU64SlxlHI4+y8a3oj1XojBs3jiVLlhAWFka9evUqVIUthBCmpJTi1K5t7PluIXk52dg4OtFp1DvUalg2ThpPgkFv4Mz+W/y64TLZGXkA+DeuQose/ti7WJk4XelROTkkrVtH/PwF5N68CYDW2RmXoUNw7j8ArV35vSVnUAYO3DzA8vPLOXjzILUca7H+5fVloj54rEJnxYoV/Pjjj3Tu3Lm08wghRKWVmZbKjvkziTxyCADvZ58jdPS72DpV3DYaNy8ksv/Hi8TfTAfAtbodwX0DqB5YcZbZkJ1N0po1xC/8hrzb+YMbat3ccB06FOd+fTGzKb9fUZGSk8L6qPWsOL+Ca6n5V6c0aPCy9yIlJwVHnelvvz1WoWNpaYm/v39pZxFCiErrxrnTbJ45ldT4WMy05rTqP5DGYd3RmFW8EX4BUhOyOLQmiqjjdwHQ2ZrzQjdf6rTyxExbMZbZkJVF0o+riP/mG/Lu5i+neZUquA4bhlOf3phZld+rVVGJUfxw/gc2Xt5IZl4mAPYW9nQP6E6/Z/pR06GmiRP+f49V6IwfP57p06cza9asMnFZSgghyiuDXs/htSs4vGYlShlwqlqNsLETqeoXYOpoT0Rejp7fd1zjt61Xycs1oNFA3dbVadbVFyu7itH+yJCRQeLKH4n/9lv0cfnj/phXrYrr8Ddw6tkTM135HL05z5DH3ut7+eH8D/x651fjdH8nf/oH9aeLbxdsLMre1anHKnQOHDjAnj172LJlC3Xr1sXivsZxa9euLZVwQghRkaXE3WXzzC+5ef4sAHVat+fFoW9iaV32ThZ/lVKK6BNxHFgdSWp8FgCeAU4E9w3ArUb5bnxbQJ+WTuIPy0lYvAR9QgIAFp6euI4YgeMr3TErp1+ympiVyNrItay8sJLb6fm33sw0ZrT3as+A2gNo7NG4TF/0eKxCx8nJiVdeeaW0swghRKVx8deDbJ8/g+z0dCytrenw+ihqB7czdawnIuFWOvt/vMiN84kA2DnraNHTH/9GVcr0CbKk9KmpJIaH5xc4yfld4i1q1sRtxAgcu3VFU057yp2LP8fy88vZEr2FbH3+WEbOOmd6BvakT2AfqtlVM3HCknmsQmfx4sWlnUMIISqF3Owsfv7uG/7YtRWAqv6BhI2ZgFPV8nHSeBTZGbkc3XSFP36+gTIotOZmPBdSk+c7emOhK//dxfXJySQs+56EpUsxpKQAYOnjg9vIN3EIC0NjXv6Gqss15LLz6k6Wn1vOidgTxul1XOswIGgAnWp1QqctX7fe/tJWiI2N5cL/hql+5plncHd3L5VQQghREcVejSZixhTib1wDjYam3XrSos+raMvhCfHPGAyK84duc3j9JTJTcwHwbehOy17+OLhZmzjdX5eXmEjC0qUkLvseQ1oaAJZ+friNHIlDaCc02vJXxMVlxrHqwipWXVxFbGYsAOZm5oR4h9A/qD8N3BuU26tvj/XflZ6ezpgxY1i6dKlxsECtVsvAgQOZOXMmNuW4q5wQQpQ2pRQntkewd9m36HNzsXVyJnT0eLyfbWjqaKXu9qVk9q+8SOy1VACcq9oQ3CcQrzouJk721+UlJJCweAmJ4eEYMvK/PV0XGIjbqJHYh4SUux5ySin+iPuD5eeWs/3qdvIM+WMYuVm70SewD70Ce+FuU/4vYDxWofPuu++yd+9eNm7cSMuWLYH8Bspjx45l/PjxzJ07t1RDCiFEeZWRksz2+TO4dCy/l4rv803oOPJtbBxMP75IaUpPyuaXny5x4dc7AFhaaWna1Zd6baujLefdxfNiY4lftJjEFStQmfldqXW1a+M28k3sO3QodwVOtj6brdFbWX5+OWfjzxqnN3RvyIDaA+hQswMW2vLZrqg4GqWUetQnubm5sXr1atq2bVto+p49e+jTpw+xsbGlla/UpaSk4OjoSHJyMg4ODqaOI4SowK6d/oMts74kLTEBrbk5rV8dynOdupbbWwDF0ecaOLn7Osc2XyE3Ww8aqN2iGi+87IeNQ/nsZVQgN+Yu8d9+Q9LKH1HZ+Y1xrerVw230KOzati132/FO+h1WXljJmotrSMzObxhuaWZJZ9/O9A/qTx3XOiZO+Oce9/z9WFd0MjIy8PDwKDK9SpUqZPzvcp4QQlRW+rw8flm9nF/XrQKlcPasQZdxE6ni42vqaKXqyqk4DqyKJPlu/lUOj1oOBPcNxMOnfH+IzL19m/iF35C0ejUqJ9/T5soAACAASURBVAcA6wYNcHtrNLatWpWrAkcpxbGYY/xw/gd2X9uNXukBqGpblb7P9KVnQE+crSrOKNTFeaxCp3nz5rz//vssXboUq/+N7JiZmcnkyZNp3rx5qQYUQojyJPnuHSJmTOF2ZH5HjXrtQmg/eDgW5XgU3PslxWRwYHUkV0/FA2DjYEnzHn4807QqGrPyUwTcL+fGTeIXLiRp7VrIzW9Ebd2oEe6jR2HTvHm5KnAycjOIiI7gh/M/EJkYaZzetGpTBgQNoI1XG8zNKlYj+Ad5rKWcPn06HTt2pEaNGjRo0ACAkydPYmVlxbZt20o1oBBClBfnD+5lx8LZ5GRmoLP5f+y9Z1iU957//5oZht57EUU6iqBJ7C2W2MCeKKCJJ4k51iSbs3tKrut/sr9z7V67ezbJ2ZOTqElOugpq7IIaY4y9xQYWEBCUNvTep9z/BzeCJjEqDjOD+b4eyfdm5v4oyP3m094OPPPb1USMHGvusIxGe6uOc3tvkv5dIQa9hFKlIHZiIE/NCMLarvc+NNsLCqj8+GPqdu4CndyQaz98OJ4rV2I/bGivEjiFDYVsztrM9tztNLTLDeF2VnbMDJ5JYmQioW6/PvumbvXogFy+2rhxI1lZWQBERUWxaNEi7Owse3RQ9OgIBAJj097awqHPP+Lq4YMA+IVHEvfq73Hx/mmJvzciGSSyz5ZycvsNmuvlUk7fge6MeS4MN9/e67jdlp9P1YcfUZeaCnq5pOMwahSeK1dg/9RTZo7uwTFIBk6XnCY5K5mjRUeRkB/rgU6BJEQkMCdsDs7Wvf95Z9IeHQB7e3teeeWV7r5cIBAIHgvK8nJJ+8fb1GiKQaFgxLyFjJyfiLIX7lL5Ocpv1XNsczalefJCPBcvO8Y8F0a/QR69KtNxJ225uVR++BH1e/dCx4oUh3Fj8VyxAvshQ8wc3YPT2N7Irhuyc/jN+pud56MDRpMUmcSYgDEoFb1rIqwneGChs3v3bqZPn45arWb37t2/+LmzZs165MAEAoHAkpEMBi7s283RjV9g0OtwdPdgxup/JXBgjLlDMwrN9e2c2XWDayc1IIGVjYqhM4KInRiISt07H56t17Op/HAdDfu/gY5ihuOECXiuXIHdoEFmju7Bya/LJyUrhV25u2jWyQNADmoH5oTKzuFBLkHmDdDCeODSlVKppLS0FG9vb5S/sDNAoVCg70gBWiKidCUQCB6V5rpa9q/9P/IvnQcgdOgIpix7DTun3v8zRa83cOVwMWdT82lvkftVwof7MGpuKA6uvWv1/21aMzOpXLuOhm+/7TxzemYynitWYDvAskeqb6M36DlWfIzkzGROaU51nge7BJMYmcjMkJk4qHtvGfFB6PHS1e0NyD/+s0AgEPyauJl+gX1r/kZzXS1WamueXrKUmMnTe20Z504KM6s5tiWHGk0TAJ6BjoxbGI5fqKuZI+seLZevULluHY2HDskHCgVOU6fiuWI5thER5g3uAalrq2Nn7k5SslIobiwGQIGCpwOfJjEykRF+Ix6L772epFs9Ol999RULFy7ExuZudd/e3s6mTZt44YUXjBKcQCAQWAp6nZbjm9Zzbs92ADwD+xH32u/x7Btk3sCMQH1lCye25ZJ3UV72auuoZsTsYKJG+6PshePiLZcuUbF2LU1Hj8kHSiXO06fjuXwZNmFh5g3uAcmuySY5M5m0vDRa9a0AOFs7Mz9sPgsjFxLgGGDmCO9BezNkpYK2BZ5cYu5ogG5OXalUKjQaDd7e3nedV1VV4e3tLUpXAoHgsaJGU0zaP96hLE/eRxI7JY7xz7+E2rp3lnJuo23Xc+GbW1w8UIBea0ChVDBofABD4/tj69D7LACaz5+ncs1amk6elA+USlxmzsRj2TJsgvubN7gHQGfQ8X3h9yRnJnOu7FzneYRbBElRSUzvPx07KwucbJYkKDwDlzbClR3Q3gAO3vC7a2BEKwmTTl1JkvSzqbKioiJcXB4v/xaBQPDr5trRQxz8dB3a1hZsHZ2Ysvw1wob27sWokiRx40IFJ7bm0FgjWxsERLgxdkEYHgGOZo7u4Wk6c5bKtWtpPiP7iaFS4TJ7Np7Lfot1v37mDe4BqG6tZlv2NjZf30xZcxkAKoWKSX0nkRSVxBPeT1hmeaq2EDI2waVkqM7rOnftC7FJoGszqtDpLg8ldIYMGYJCoUChUDBp0iSsrLpertfryc/PZ9q0aUYPUiAQCExNW3Mz3326lszjhwHoMyCaGav/DScPT/MG9ohUFTdybHM2xdm1ADi62zDm2TCCh3hZ5sP0HkiSRPOpU1SsXUvLObkpHLUa1zlz8Fj2W6z79DFvgA/A1aqrJGcmsz9/P+0GeT+Ru607z4Y/y4LwBfg4WOAeptulqUsbIe8IdOzsQe0AA+fA4CToOwosyOj0oYTOnDlzALh06RJTp07F0bFL+VtbWxMUFMT8+fONG6FAIBCYGE3uddL+8TZ1ZaUolEpGPZvEsLnPoVT23t04rU1azu7O48rRYiQJVGolT0ztx5ApfVFb956/lyRJNB0/TuWatbRcugSAQq3G9bln8Vi6FLW/v5kj/GW0ei0Hbh0gOSuZjIqMzvNoj2iSopKYGjQVa5WFmaH+XGnqNkFjZXETNQtsLDMb+FBC59///d8BCAoKYuHChZ0+VwKBQPA4IBkM/LBnOyc2r8eg1+Ps5c2MV39PQESUuUPrNgaDxLXjJZzZlUdrk+zfFPKEF6Pmh+LsYYH9HvdAkiQaDx+mcu06Wi9fBkBhY4PrggV4LH0Z9c8YTVsSFc0VfJ39NV9nf01lSyUAVkorpgZNJSkyiRgvC9y/dM/SVD9Z3MQmgFuQ2cJ7ULrVo7NkidxJfe7cOTIzMwEYMGAATz755EO9z9GjR3n77bc5f/48Go2GHTt2dGaN7seJEycYP3480dHRXOpQ9QKBQNBdGqur2LfmbxRcSQcgfORYnnllFbYOlvlb6oNQklPLsS3ZVBY2AuDu78DYBWH0iXQ3c2QPjmQw0HjoEBVr19J2TX7eKGxtcUtIwP2lF1H/aCjGkpAkifSKdJKzkvn25rfoJHkvkZedFwsiFvBs+LN42llYKbS9GTL3yNmb/KPcXZqa21GaGmlRpan70S2hU1xcTEJCAidOnMDVVd6vUFtby6hRo9i0aRN9HrA22tTURGxsLC+99BLz5s174PvX1tbywgsvMGnSJMrKyrrzVxAIBIJObpw/yzfr/k5LQz1WNjZMfHEZ0U8/06t6Vu6ksaaVk9tvkPOD/PPRxt6KYTODiR7nj1LVOx5QksFAw4FvqVy3jrbrshO8wt4e96RE3F98ESsPDzNHeG/a9G3sy99HcmYymdWZnedDvIeQFJnEpH6TUCvN36TbiSRBwWlZ3Fzd+TOlqUUQNdNiS1P3o1tC5+WXX0ar1ZKZmUlEx9Kl69ev8+KLL7J06VL279//QO8zffp0pk+f/tD3X758OUlJSahUKnbu3PnQrxcIBAIAXXs7R5M/5+K+PQB4BQUT99rv8QgINHNk3UOn1XPpYCHn991E124ABQwc48/wWcHYOVlY38c9kPR66vfvp+rDD2nLyQVA6eCA2+LFuP9mCVZubmaO8N6UNpWy+fpmtmVvo6atBgAblQ0z+s8gMTKRKA8LK4HWFkL6Jkj/udLUoo7SlOVPrd2PbgmdI0eOcPLkyU6RAxAREcH777/P2LFjjRbcz/H555+Tl5fHhg0b+M///M/7fn5bWxttbW2dH9fX1/dkeAKBoJdQVVxI2nv/S8WtfACemDGbsUm/wUptQb9pPyCSJHEzo5LjX+dQXykvl/MLcWHswnC8+jqZOboHQ9LpqN+7l8p1H9KeL39NlE5OuD//PO4vPI/K1TK3M0uSxLmyc6RkpXCo4BB6Sd4j5+fgx8KIhcwPm4+rrQXF/hiWpu5Ht4ROYGAgWq32J+d6vR7/Hux4z8nJ4U9/+hPHjh27a7T9l/jv//5v/vKXv/RYTAKBoHchSRJXvv+WQ198hK6tDTsnZ6atfIPgJ4aaO7RuUVPaxPEtORRcqwbAwcWaUfNDCRvq0ytKb5JWS92eVCo/+hDtrQIAlC4uuC95AffFi1FZ6GLXZm0zaflppGSlkFOT03k+zHcYSZFJjA8cj5WyW49Y4/OYl6buR7e+Cm+//Tavvvoqa9as4amnngLkxuTXX3+dd955x6gB3kav15OUlMRf/vIXwsPDH/h1b775Jr/73e86P66vrycwsHempQUCwaPR2tTIt/9cQ/Yp2Rqgb3Qs01f/K45uvac59zbtLTp+SMsn41ARBoOE0krB4Ml9eXJaP6xtLeQB+wtI7e3U7d5N5Ucfoy0sBEDl6or7iy/itigJlaNlPnQLGwrZnLWZ7bnbaegQDHZWdswMnkliZCKhbqFmjvAOfrE0lQSxiY9Faep+dMsCws3NjebmZnQ6XWdm5fafHRzudk+trq5+sEAUil+cuqqtrcXNzQ2Vqmvfg8FgQJIkVCoVBw4cYOLEife9j7CAEAh+nRRfz2Tv+29TX1GOUqVi9MLnGTpzHopelqKXDBJZp0s5tfMGLfXykrmgGE9GPxuKq7e9maO7P4b2duq2b6fy44/RlWgAULm74/HyS7glJKB0sDwHbkmSOKU5RUpmCkeKjiB1lHsCnQJJiEhgTtgcnK0t5HnyGJemTGoB8fe//707L3sknJ2dudyxO+E2a9eu5dChQ2zdupX+/S3fx0QgEJgeg0HP2R1fc3JrMpLBgIuPL3Gv/R6/0N7hXn0nZfn1HN2cTflNudfQ1ceeMc+F0S/acieQbmNoa6N261aq/vkJutJSAFRenni8/DJuCxagtLc8kdakbWL3jd0kZyZzs/5m5/lo/9EkRSUxJmAMSoUFCIbbC/0ubvhVlqbuxyPt0XlUGhsbyc3N7fw4Pz+fS5cu4e7uTt++fXnzzTcpLi7mq6++QqlUEh0dfdfrvb29sbW1/cm5QCAQADRUVbL3g3counYFgMjR45m8dBU2FvhQ/SWa69s5tfMGWSflDIjaRsXQuP7ETOyDysoCHrS/gKG1ldotW2SBUyG7o1t5e+Pxyiu4PvcsSgtcPHuz7iYpWSnsurGLJm0TAA5qB2aHzCYhMoH+Lhbyi/UvLvR7fKamHpVHKuSWl5dTXl6OwWC46zwm5sE2PJ47d44JEyZ0fny7l2bJkiV88cUXaDQaCgoKHiVEgUDwKyX3h9N88+F7tDY2oLaxZdLLKxgwbmKvaNC9jV5v4PL3RfyQmk97qzzNEznClxFzQ3BwsWzndENzMzWbNlP12WfoKzs2Afv54fnbV3CZNw+ljWXFb5AMHC8+TnJmMidKTnSeBzkHkRiZyOzQ2TioLaCs9oteU+YtTUmSREZRHWmXNSiAN2dYxjh9t3p0zp8/z5IlS8jMzOTHL1coFOj1eqMFaGxEj45A8HijbW/jyPrPSD+QBoB3/xDiX/8Dbn4BZo7s4Si4VsXxLTnUlDYD4N3PibELw/ENdjFzZL+MoamJ6uRkqj/7HH2NvEtG7e+Px7JluM6dg8Lasvb51LfXszNnJ5uub6KwQW6KVqBgXJ9xJEUmMcJ/hPnLU/f1mjJfaUqSJK4U15N6uYS0DA1FNS0AONpYce7/m4yt2ng+aibt0XnppZcIDw/n008/xcend4wwCgSCx5/Kwlukvfe/VBbeAuDJ+LmMTXwBlVXv2Y1TV9HCia055KfLWRA7JzUj5oQQNdIPhdJyf9bqGxup2bCR6i++QF8rO6OrAwPxXL4Ml1mzUFjYfqLcmlxSslLYk7eHFp38cHaydmJu6FwSIhMIdLKA6VwLLU1JksQ1TT1pGRrSLmu4VdXcec1OrWJSlDfxMX4oLUQbdEvo5OXlsW3bNkJDLWiMTiAQ/GqRJImMg/s5/OU/0WnbsXdxZfrKNwga/HD+e+ZE26bn/P6bXPq2EL3OgFKpYNCEPgyNC8LG3rJEwp3o6+upXr+e6i+/wtCxkNW6Xz88VizHJT4exQPuPDMFeoOew0WHSclM4Uzpmc7zUNdQkqKSiOsfh73azP1bFlqakiSJrNKGTnGTX9nUec1WrWRSpA9xMX5MiPDGztp4WRxj0K3vwEmTJpGeni6EjkAgMDstjQ18+9H75Jw9CUBQ7BNMW/kGDq6WaxVwJ5IkkXuunJPbc2mskbe494l0Y+yCcNz9LaAn5B7oa2up/mo91evXY2iQSynWwcF4rliB84zpKFSW87Crba1lW842Nl/fjKZJbuhWKpRMDJxIUlQST/k8Zd7KhIWWpiRJIruskbSMElIva8ir6BI3NlZKJkZ6Exfjx8RIb+ytLUfQ/phuRfbJJ5+wZMkSrly5QnR0NOofpSRnzZpllOAEAoHglyjKvMLe99+loaoCpcqKsYkv8GTcnF6zG6eyqIGjm7LR5NYB4ORhy5jnwugf62mxLQG6mhqqv/iSmg0bMDTJDz6bsFA8V6zAaepUixI4WdVZJGcmszd/L216WUS62rgyP2w+CyMW4ufoZ94A64ogPQUupUD1ja5zMy/0yylrILUjc5Nb3th5bm2lZEKEF3Ex/kyK9MbBxnLFzZ10K8pTp05x4sQJ9u3b95Nrlt6MLBAIej8GvZ7T2zdxettmJMmAq68fca/9Ad+QMHOH9kC0Nmo5szuPq8eKkSSwUit5cno/Bk/ui5WFpf1vo6uqovrzz6lOTkFqlnsybCIi8Fy5EqdnJluMuNQatHxX8B0pmSlcKL/QeR7lHkViZCLT+0/H1sqMI+3tzZCV1lGaOszdpak5HaWpUSYvTeWWN3aUpUrILrtD3KiUjAv3Ij7Gj0lR3jjZWm4Z9V50S+i8+uqrLF68mD//+c/4+PgYOyaBQCC4J/WV5ex9/12Ks64CMGDcRCa9tBxrO8vfjWPQG7h6rIQzu/Noa9YBEPqUN6PmheLkbnn7ZAB0FRVUffoZNZs2IbXKhqG2AwbguWoljhMmWIzAqWqpYmv2VrZkb6G8uRwAK4UVk/tNJikqicFeg82XJZMkKDzb4TW1A9ruMJcOGiuLm6hZJi9N5VU0dvbcZJV2lcvUKgXjwryIi/Fj8gAfnHuhuLmTbgmdqqoq3njjDSFyBAKBSck+c4IDH/2DtqYm1LZ2PLN0JVFjJ9z/hRZAcXYNxzbnUFUs/7bsEeDA2IXhBIRbZi+Rtqycqk8/oXbzFqQ2uexjO2iQLHDGj7eY0tqVyiskZyaz/+Z+tAbZbNrd1p3nwp/jufDn8HEw43Oqrkj2mrqU/KPSVF+ITYLBieAWZNKQblY2kXZZQ1qGhmuaLsFlpVQwNsyTuBh/nhngg4td7xY3d9ItoTNv3jy+//57QkJCjB2PQCAQ/ARtWyuHv/qEjIP7AfANCSPutT/g6mvmHosHoKG6lZPbc8k9J2cZbOytGD4rmIFj/VGqLCMbcidajYaqf35C7datSO2yl5ZdbCyeq1fhMGaMRQgcrV7LgVsHSM5KJqMio/N8kOcgEiMTmRo0FWuVmfb1aFsgM/VnSlP2MKCjNNVvtElLUwVVzbK4uVzCleIucaNSKhgd6kl8jB9TBvjgam9ZO46MRbeETnh4OG+++SbHjx9n0KBBP2lGfu2114wSnEAgEFQU3CTtvf+lqkjekj509rOMXrDI4nfj6LR6Ln1bwPl9t9BpDSgUMHBsAMNnBWPraHmxa4uLqfz4n9Ru3w5aOTNi9+STeK1aif3IkRYhcCqaK/g6+2u+zv6aypaObctKK6YFTSMpMolBXoPME5gkQdEPHVNT2+8uTfUbI4ubAbPAxslkIRXVNLO3I3OTXlTXea5SKhgV4sGMQX5MG+iLm8PjKW7upFubkX/JQFOhUJCXl3fP6+ZGbEYWCHoHkiSRfmAvh9d/gl6rxcHVjWmrfkdQzBBzh/aLSJJEfnolJ7bmUF8p97T4hbowdmE4XoGme9A9KO2FhVR9/DG1O3aCTu4bsh82DM9Vq7AfNtTsAkeSJNIr0knOSubbm9+ik+QYvey8WBCxgGfDn8XTztM8wdUVdy30q+rybcSlr1yWik0A92CThVNS28LeyxpSMzRcKqztPFcqYESwB/Ex/kwd6IOHo2XZbzwoJt2MnJ+f352XCQQCwQPR0tjAgQ/fI/eH0wD0H/wk01a+gb2Lq5kj+2WqNU0c35JNYaZsfeDgasOoeSGEDbW8DfLtt25R+dHH1O3aBR2Tsg6jRuK5YgX2Q4eaOTpo07exP38/yVnJXKu61nk+xHsISZFJTOo3CbXSDJkxbUvH1FQy5H0PUofXo9oeBszuKE2NMVlpqrSuVc7cXNZw/lZN57lCAcP7uxMX48+0gb54OfVOcWMMescQvEAg+NVQlHmFtPffobGqEqXKinGLfsMT02dZzHTPz9HWouOH1Hwuf1+EwSChtFIw5Jm+PDG1H9a2lvVjti0vn6qPPqRuTyp0GDI7jBmD58qV2D9h/mxZaVMpW65vYWv2Vmra5Ae3tdKaGcEzSIxMZIDHANMHJUlQdO6O0lRXKYi+o2RxM3COyUpT5fWt7LtSSmpGCT/cvFvcDO3nTlyMH9OjffF2tsxJPlPTba+rX+Kzzz7rVjACgeDXi8Gg5/S2zZzetqlzN07863/EJ9hyN7BLBoms0xpO7bhBS4Pc1xIU48mY50Jx8bKscfe23FwqP/yI+r17OwWO4/jxeK5cgV1srFljkySJ82XnSc5K5lDBIfSSnGHysfchITKB+WHzcbM1w3RavaarNFWZ3XXuEigv84tNAA/TDOVUNLSx/4pcljp7s5o7m06e7OdGfIwf06P98HUR4ubHdEvo1NTU3PWxVqvlypUr1NbWMnHiRKMEJhAIfj3UV1aw74N3Kcq8AvSO3Til+XUc25RN+S15/4irjz1jFoTRb6CHmSO7m9br2VR+uI6G/d9w++noOHEinitXYhc90Kyxteha2Ju3l5SsFK7XXO88f8rnKZKikpgQOAErpYkzYtpWuL5Xzt7cONRVmrKykxuKBydB0DiTlKaqGtvYf7WU1HQNZ/KrMNwhbob0dSVukB8zBvnh72rX47H0Zrr1HbRjx46fnBkMBlasWCFGzgUCwUOR+8NpvvnwPVobG1Db2jF56UoGWPBunKa6Nk7vvEHWqVIA1LYqhsb1J2ZCH1RWllNea83KonLtOhoOHOg8c3rmGTxXLMd2gBnKP3dQ0ljCpuub2J6znbqOMpCtypa44DgSIxOJcI8wbUCSBCUX5MzN5a3Q2tXIS+AIGLJIHg237fkBlpqmdr65WkpqhoZTeVXo71A3sX1ciIuRxU0fN8v9JcDS6NbU1b24fv06Tz/9NBqNxlhvaXTE1JVAYBno2ts5suFTLn2TBoBPcChxr/8BN19/M0f28+h1BjK+L+KHtHy0rXJpJXKkLyPmhODgYjmNni1XrlK5bh2N330nHygUOE2dKgucCBMLiDuQJImzpWdJzkzmcNFhDB2ZkgDHABIiEpgbNhcXGxfTBtVQChmbZYFTkdV17hwgl6YGJ5mkNFXXrOWba7K4OZFbeZe4GRQgi5u4QX4Euv+6xY1Jp67uxY0bN9B1jCcKBALBvagqKiTtvb9SUXATgCfj5zI28QWL3Y1TcLWKY1tyqC2TPZ68+zkxNiEc3/4mfjD/Ai0ZGVSuWUvjkSPygUKB84wZeC5fhk2Y+TzAmrXNpOalkpKVQm5t1wj2cL/hJEUmMb7PeFRKE/p76drg+j5Z3OQehI5+IKxsZYfwwUnQfzz0cEz1rVq+vVpGakYJx3Mr0eq7xM0AP+dOcRPkabkO9r2Fbgmd3/3ud3d9LEkSGo2GtLQ0lixZYpTABALB44ckSVz5/lsOffERurY27JxdmL7yDfoPecrcof0sdRUtnNiaQ366vJzOzknNiDkhRI30Q6G0jHHx5osXqVy7jqZjx+QDpRKXmfF4LFuGTbDpdrj8mMKGQjZlbWJH7g4a2uU+JjsrO2aFzCIxMpEQVxO2OUgSaC51lKa+hpY7+kz7DJNLUwPngm3PCteGVi3fZZaTmlHC0exK2vWGzmuRvk5yz02MHyFepvW8etzpltC5ePHiXR8rlUq8vLx499137zuRJRAIfp20NTfx7ccfcP2U/EDuO2gw01f9Dkc3dzNH9lO0bXrO77/JpW8L0esMKJUKBk3ow9C4IGzsLSPr1Hz+PJVr1tB08pR8oFLhMmsWnst+i3VQkFlikiSJU5pTpGSmcKToCFKH/UGgUyAJEQnMCZuDs7UJWwYayyFji9xYXN61iwcnf3lianASePZstqupTcd3WeWkppdwOLuCdl2XuAn1diS+I3MT5mN5yyQfF7oldL7//ntjxyEQCB5jSrKzSPvH29RXlKFUqRi98HmGzpxncbtxJEki91w5J7bl0lQrG1n2iXRj7IJw3P0to4TQdPYslWvW0nzmjHxgZYXLnNl4LluGdWCgWWJq1jaz+8ZukrOSya/rWig72n80SVFJjAkYg1Jhoq+1rh1yvoGLGyHnQFdpSmUDUfGyuAme0KOlqeZ2Hd9nVZCaUcKhrHLa7hA3wZ4OsriJ8Sfcx9HiFkk+jnRL6LS0tCBJEvb2cmPUrVu32LFjBwMGDGDKlClGDVAgEPReJIOBs7u3cWLzeiSDAWcvH+Je+z3+4ZHmDu0nVBY1cHRTNppceQrIycOWMc+F0T/W0+wPI0mSaD5zhsoP1tB87px8qFbjOncuHr/9LdZ9AswSV0F9ASlZKezM3UmjVnZlt7eyZ3bobBIjE+nvcm+7IKOjyegoTW2B5qqu84CnOkpT88Cu5zZrt2r1HL5ezp4MDYcyy2nR6juv9fOw78jc+BPl52T276dfG90SOrNnz2bevHksX76c2tpahg0bhrW1NZWVlfztb39jxYoVxo5TIBD0Mhprqtm35m8UXL4EQMTIsTzz29XY2FtGZuQ2rY1azuzO4+qxYiQJrNRKnpzej8GT+2JlbcIm2Z9BjH+kMwAAIABJREFUkiSaTp6kcs1aWi5cAEChVuP63LN4LF2K2t/0E2oGycDJkpMkZyZzrPhY53k/534kRiYyO2Q2jtYm6jFpqpR7bi5uhLLLXeeOvl2lKa+emzRr1eo5ml1BaoaG7zLLaGrvEjd93OyIj/EnPsaPgf7OQtyYkW4JnQsXLvB///d/AGzduhVfX18uXrzItm3beOutt4TQEQh+5eRfPMe+tf9HS30dVjY2THxxGdFPP2NRP+wNBolrx4o5vTuPtiZ5WjT0SW9GzQ/Fyd2822UlSaLp2DFZ4KSnA6CwtsZ1wQI8lr6M2tfX5DE1tjey68YuUrJSuFV/q/N8bMBYkqKSGOU/yjTlKb0Wcr6V+26yvwGDvJEalTVEzIAhi+XSlKpnFg226fQcz6kkNUPDt9fKaGzrmjQOcLXrnJaK6eNiUd/vv2a69Z3Q3NyMk5PcOHXgwAHmzZuHUqlkxIgR3Lp16z6vFggEjyt6nZZjyV9yPm0nAF59g4h7/Y949DFP78i9KMmp4ejmHKqK5HKLR4ADYxeEExBhBpuBO5AkicbDh6lcu47Wy3KGQmFjg1vCQtxfehm1j7fJY8qvyyclK4Vdubto1snj9Y5qR+aEziEhMoF+zv1ME0jZNVncZGyGpoquc/8hMHgRRM8H+55pbNfqDRzPrSQtQ8M3V0tpaO0SN77OtrK4ifFjSKCrEDcWSLeETmhoKDt37mTu3Ll88803vPHGGwCUl5eLJXwCwa+UGk0xaf94m7I8eVfK4KnxjF/8ElbW1maOrIvGmlZObr9Bzg9lANjYWzFsZjDR4/xRqszXGC1JEo2HDlG5Zi2t1+TpIIWtLW6JiXi89CJWXl4mjccgGThefJyNmRs5WXKy87y/S3+SIpOYGTITB7UJSpDN1fKm4ksb5fHw2zh4QcxCWeD49MyWZ53ewKm8KlLTNXxzrZTaZm3nNS8nG+IG+REf48cTfd1QWsiqAcHP0y2h89Zbb5GUlMQbb7zBpEmTGDlyJCBnd4YMMb/7rUAgMC3Xjn3PwU/Wom1twdbRianLXyd06Ahzh9WJXmvg0ncFnNt3C12bHhQwcIw/w2cHY+doPiEmGQw0HDxI5dp1tGXJm3kV9va4JyXi/uKLWHmY1jerob2Bnbk7SclKobChUI4HBeP7jCcxKpGRfiN7PmOh18keU5c2yIv99O3yudIKwqfJpanQyaAy/pi/3iBxJr+K1AwN+6+UUt3U3nnN09Ga6dFy5mZokDsqIW56Dd22gCgtLUWj0RAbG4uyY0T07NmzODs7ExlpeRMVtxEWEAKB8WhvbeG7T9dx7eghAPpERTPj1X/DycPTzJF1cTOjkmNf51Bf0QKAb7AL4xLC8eprvr0lksFAw4EDssDJll2xlfb2uC1ejPuLv8HKzbQltLzaPJKzktl9YzctOvnfyUntxNywuSREJhDoZILSY8V1OXOTvhkaS7vOfQfB4MUw6FlwMP73lcEg8cPNatIua9h7uZTKxrbOa+4O1kyL9iV+kB/Dgz2EuDEzJreA8PX1xfdHDXHDhg3r7tsJBIJeRln+DdLe+ys1mhIUCiUjn01k+LwFKE25zv8XqC1r5vjXOdy6Io8a27tYM2peKOHDfMzWRyHp9dTv30/lunW0594AQOnoiNvzi/FYsgSVa8+NP/8YvUHfWZ46pTnVeR7iEkJSVBLxwfHYq3vYW6mlBq5slwVO8fmuc3sPGLRAnpryizH6bQ0GiYuFNexJ17Dvioay+i5x42KnZtpAX+Jj/RgZ7IGVGUuaAuPQLaHT1NTE//zP//Ddd99RXl6OwWC463peXp5RghMIBJaHJElc3J/K0Q2fotfpcPTwJO7Vf6NPVLS5QwOgvVXH+X03uXSwEINeQqlSEDspkKdmBGFt2zOTOPdD0umo37ePynUf0t7x81Hp7Iz7Cy/g/vxiVC6m88yqb69nR84ONmVtoqixCJDLU08HPs2iqEUM8x3Ws0LQoIe8w7K4yUwFfYfIUKggfKosbsKmgpVxS4qSJHGpsJa0DA17L2soqWvtvOZka8XUgb7ExfgxOsQTawtyoRc8Ot36X7906VKOHDnC888/j5+fn+gyFwh+JbQ0NvDNuve4ce40ACFPDWfq8texczJ/GViSJLLPlnFqey5NdXJvRd+B7ox5Lgw3X/Ps7pF0OupSU6la9yHtHROpShcXPH6zBLfFi1E5ma58dqP2BsmZyezJ29NVnrJ2Yn7YfBZGLKSPU5+eDaDqRkdpahPUF3edew+Qm4pjFoCjcafKJEniSnE9qZdLSMvQUFTT0nnN0caKZwb4EB/jx5gwT2ysLCMTKTA+3RI6+/btIy0tjdGjRxs7HoFAYKEUZ10j7R9v01BVgcrKinGLX2LItJkW8YtORUEDxzZno7khbzV29rRlzIJwggZ5mCU+SaulbvceKj/6CG1BAQAqV1fcX3wRt0VJqBxNs1BPb9BztOgoyVnJnNac7jwPdQ0lKSqJuP5xPVueamuAqzvkhX6FXffH1hUGPSdnb/yHgBG/RpIkkalpIDWjhLTLGm5VNXdes7dWMSlKFjfjw72wVQtx82ugW0LHzc0Nd3fLM+ITCATGx2DQc3bnVk5+vRHJYMDV14/41/+IT3CouUOjtVHL6Y6txkhgZa3kyelBDJ4ciJUZHmJSezu1u3ZR9dHHaIvkspDKzQ2Pl1/CNSERlaNpMkt1bXWd01PFjXL2RKlQMiFwAkmRSQz1HdpzAtBggFsn5OzNtV2g7RAaCiWETJLtGMKng9q4SxmzyxpITS8h9bKGvIqmznNbtZJJkbK4eTrCGzszb7sWmJ5uCZ3/+I//4K233uLLL7/s9LsSCASPH4011ez74F0KrsjbeaPGPM3kpSuxtjPv/3uD3sDVYyWc2Z1HW7O8vC3sKW9GzjPPVmOpvZ3a7Tuo+vhjtCUlAKg8PPB4+WXcEhaiNNHPydyaXJKzkknNS+0sTzlbOzM/XC5PBTj2oCdWzS1IT5H9pmrvWBzrESZnbmITwNm4lhU3KhpJy9CQmlFCdllj57m1lZIJEV7ExfgzKdIbBxvz9GYJLINuffXfffddbty4gY+PD0FBQajVd+8zuNDhySIQCHovNy+dZ++av3XaOEx+eSUDxk00e6mqJKeGo5tyqCq+Y6vxwnACwk2/1djQ3k7t1q1U/fMTdBoNACovTzyXLsV1wQKUdnY9HoPeoOdI0RGSM5M5U3qm8zzMLYxFkYuYETwDO6seiqO9GTJ3w8UNcLPL9wprJ4ieJ++86TPUqKWpgqpm9mSUkJqhIVNT33muVikYH+5FfIw/k6K8cbI1/p4dQe+kW0Jnzpw5xo5DIBBYCHqdjhOb1/PD7m1Ah43Dv/wRjwDz2jg01rRyclsuOefKAXmr8fBZwQwca/qtxoa2Nmq/3krVP/+Jrkzesmzl7Y3H0qW4LngOpW3PZ5Xq2urk6anrm+4qT00MnEhSVBJP+TzVM6JUkqDwjFyaurID2hs6Liig/zhZ3ETGg7XxslhFNc2kZWhIu6who6iu89xKqWBMmCdxg/yYMtAXFzshbgQ/pdsLA3srYmGgQHBv6spLSXvvbTS51wGInRLH08+/bFYbB51Wz6WDhZzfdxNdu8GsW40Nra3UbtkiZ3AqZL8lK19fPF5Ziuuzz6K0senxGHJqckjOSiYtL62zPOVi49I5PeXv2EOO5vUlXaWpqtyuc7cgeWoqNgFc+xrtdpq6FvZeLiU1o4SLBbWd50oFjArxJD7Gj6kDfXFzsByLEUHPYvKFgQDnz58nMzMTgIEDBwr7B4GgF5N9+jgHPnqftuYmbBwcmLrsdcKGjzJrTD/eauwX4sLYhabfamxoaaFm02aqPv0UfWUlAFZ+fngu+y0u8+ah7GEhqDfoOVx0mJTMlLvKUxFuESRFJTGj/wxsrXogi6Rthetp8tRU3vcgdexMU9vDgDlyY3HfUaA0TkatvKGVfR3i5oebNZ3nCgUM7+9OfIw/06J98XTseUEpeHzoltApLy8nISGBw4cP49qxybO2tpYJEyawadMmvExsQCcQCLqPtr2NI199Qvq3+wDwC48k7tXf4+LtY7aYass7thpfNu9WY0NzMzUpm6j67DP0VXIsan9/PJYtw3XuHBQ9LHDuVZ6a1HcSSZFJPOnzpPH/PSQJSi7KpanLW6G1K5tC31GyuBkwG2yMIzarGtvYd6WUtAwNZ/KrMNxRYxga5EbcID9mDPLD29n0TeaCx4NuCZ1XX32VhoYGrl69SlRUFADXrl1jyZIlvPbaa6SkpBg1SIFA0DNUFRWS+t5fqSy4CQoFw2Y/y6jnFqGyMs+UirZNz/n9N7n4bQEGXcdW44mBPBVn2q3GhqYmqpOTqf7sc/Q1cmZB3acPnsuX4TJ7Ngp1z/aC/Nz0lIuNC8+GPcvCiIX4OfoZ/6aNFZCxWRY45de6zp37wOBEiE0EjxCj3Kq2uZ1vrpaSmqHh5I0q9Heom8GBrsTHyOaZfi4938wtePzpVo+Oi4sLBw8eZOjQoXednz17lilTplBbW3uPV5of0aMjEHRsjD38LYc+/whdWxv2Lq5MX/2vBMWYp/wsSRI3LlRwYmsOjTWyJUBglBtjF4abdKuxvrGRmg0bqf7iC/QdP8fUffviuXw5LjPje1Tg3Gt6KtwtnEVRi3qmPKXXQs4BuTSV8w0Y5FF9VDYQNVPO3vQfD0bwL6tv1fLt1TJSM0o4llOJ7g5xMyjAhfgYOXMT6C5Wlgh+HpP26BgMhp+MlAOo1eqf+F4JBALLoq25mYOfrCHrxBEA+sUMYfqq3+HgavrxbIDqkiaObs6m+LqcOXFyt2XMc2H0H+xpsjKVvqGBmg0bqPriSwx18lSPdVAQniuW4xwXh6IHM1xmKU+VXZMzNxmboami6zzgSbmxOHo+2D26wWhTm46DmWWkZmg4cr2Cdn3X8yHS14mZsf7EDfIjyNM8Fh2CXwfd+t87ceJEXn/9dVJSUvD3lzv8i4uLeeONN5g0aZJRAxQIBMajLC+X1Pf+Sm2pBoVSyeiFzzNs1nwURmomfRjaW3ScTcvn8qEiDAYJlZWSJ6b2ZcjUfqhNtL1WX19P9Vfrqf7qKwz18k4W6+BgWeDMmIFC1XNx3Ks8NT9sPgkRCcYvT7XUwJVtcvam5I5dZw5eELNQHgv3jnrk27Rq9XyfVU5qhobvsspo1XaJm1BvR+Jj/IiP8SfU2zQ2GAJBt4TOBx98wKxZswgKCiIwUN6tUVhYSHR0NBs2bDBqgAKB4NGRJIkLe3dzdOPnGPQ6nDy9iHvtDwREPPqD7aFjMUhcP1vKye03aKmXzTf7x3oy+tkwXLxM05Ohr6uj+suvqF6/HkODvAfGOjQEzxUrcJ42rccETmd5KiuZMxoTlKfu5RSutILwaXL2JuwZUD1aSa5Np+dodiWpGSUcvFZGU7u+81o/D3tmxvgTH+tHhI+T2RdOCn59dEvoBAYGcuHCBQ4ePEhWVhYAUVFRTJ482ajBCQSCR6e5vo5v1v2dvAs/ABA6dCRTl7+OrYmMJe+koqCBo5uyKc2Ty0Mu3naMXRBOv2gPk9xfX1tL1ZdfUrN+A4ZGebOyTVgYnqtW4jRlSo9ltu7lPTWp7yQSIxONv9yv6oa87yY95UdO4QPlvptBC8Dx0aZjtXoDJ3Ir2ZOu4cC1UhpadZ3XAlztOjM30QHOQtwIzMpDNSMfOnSI1atXc/r06Z80AtXV1TFq1Cg+/PBDxo4da/RAjYVoRhb8mijKukraP96msaoSlVrN088vJXbKDJM/eFqbtJzZJZtvShJY2agYOiOI2ImBqNQ9XzbT1dRQfVvgNMmGjzbh4XiuWoXTM5N7TOD8UnnK6Mv92hrh2k65NFVwsuv8tlP4kEXgN/iR7Bj0BonTeVWkZpSw/0opNc3azms+zjbEDZIzN0MCXYW4ERgdkzQj//3vf+eVV1752Ru4uLiwbNky/va3v1m00BEIfg1IBgNnd23lxJYNSAYDbv59iH/9D3gHBZs0DoNB4trxEs7syqO1SX4ohj3lzaj5oTi69fxeFF1NDdVffEnN+vUYmmUXbZvISDxXrsBpcs8IHL1Bz9Gio2zM2tjz5SlJglsn5dLU1Z2gve3arYCQiXLfTcSMR3IKNxgkfrhZTWqGhn1XNFQ2tnde83S0Znq0H/ExfgwNckepFOJGYHk8lNBJT0/nr3/96z2vT5kyhXfeeeeRgxIIBN2nua6WvR+8y62MiwAMGDuBSUtXYm1r2p0kpXl1HN2UTUWB3APj7u/AuIXhBET0/HSXrqaG6s8+p2bjxi6BExWF16qVOE6c2CMCp769np05cnmqqLEIkMtTEwInsChqkXHLU3XFkJ4sl6eq87rO3YM77BgSwaX7TuWSJHGxsJbUdA1pl0soq2/rvOZqr2Z6tC/xMf4M7++OlYl9xgSCh+WhhE5ZWdnPjpV3vpmVFRUVFfe8LhAIepbCqxmkvf8OTTXVWFnbMOml5Qx8erJJywjN9e2c2pFL1qlSAKxtVQybFcyg8QE9br6pq66m+vPPqd6YjHRb4AyIwmvVKlng9MC/Q15dHsmZyey+sbuzPOVs7cz8cHl6ymjlKW0rXN8rO4Xfacdg7QgD58DgxdB3RLdLU5IkcbWknj0ZJaRlaCiqaem85mRrxZQBvsyM9WN0qCdqIW4EvYiHEjoBAQFcuXKF0NDQn72ekZGBn18PbOwUCAS/iMGg5/S2zZzetglJMuDRpy8z3/gTHn2MZ7J4P/R6A1cOF3N2Tx7trfLUTeQoP0bOCcHeuWetEnRVVVR99hk1KZs6BY7tgAF4rl6F44QJRhc4BsnA8eLjbMzcyMmSrn6YUNdQFkUtIi44DjsrI2TQJAk06R07b7bcbcfQb7RcmoqaBTbdbyzPKWtgT3oJezI05Fc2dZ7bW6t4ZoAP8TH+jAv3xMbKNCP/AoGxeSihM2PGDP785z8zbdo0bG3vrvm2tLTw7//+78THxxs1QIFA8Ms01daw9/23KbiSAUD0hGeY+OIy1Dam8wYqvl7D0c3ZVJfID0qvvk6MSwjHN9ilR++rq6qi6tPPqElJQWqRMxC2AwfKAufpp40ucBrbG9l1YxfJmckUNBQAoEDB04FPszhqMUN9hxrnnk1VcHmL3Fhcdrnr3DlALksNTnokO4ablU2kZpSwJ13D9bKGznMbKyUTI72ZGevPhAhv7Ey0z0gg6EkeauqqrKyMJ554ApVKxerVq4mIiAAgKyuLNWvWoNfruXDhAj4+5jMDvB9i6krwOHEr4xJ7P3iH5rpa1Da2TH5lFQPGTjDZ/Rtr2jixLYfcc+UA2DqoGTEnmKjR/j3amKqrqJAFzqZNSK2t8r0HDcJz1Uocx483usC5WXeTlKwUdubupFknZ4yc1E7MC5tHQmQCfZz6PPpN9Dq4cQgurofr+8DQMdGksobIeHlqKnhCt+0YimqaScvQkJqh4XJxXee5WqVgfLgX8TH+TB7gg6ONeXzOBIL7YZKpKx8fH06ePMmKFSt48803ua2RFAoFU6dOZc2aNRYtcgSCxwWDXs/Jr5M5s3MLSBJefYOI+5c/4hEQaJL763UG0g8V8kPaTXRtehQKGDgugOGzgrF16Dk/KF1FBVWffErN5s1dAicmBq9VK3EYN86oAscgGThZcpKNmRs5Xny88zzYJZhFUYuID47HXm0EX6bKXLi0AS6lQGNp17nfYLk0FT0f7N279dbl9a2kXdawJ72ECwVdZS+VUsGoEA9mxvgzdaAvLvY9a1IqEJiTh5bu/fr1Y+/evdTU1JCbm4skSYSFheHmZh6fHIHg10ZDdSV7//EORZlXAIiZPI2nl7yC2trGJPcvyqrm6KZsakrlzIZvsDPjEiLw6uvUY/fUlpdT9ckn1G7egtQmTwDZxsbgtXo1DmPGGFXgNGmb2H1jN8mZydysvwnI5alxfcaRFJXESL+Rj36/tga4ukMuTRWe7jq3c4fYBHlyyje6W29d3dTOviuyuDmTX83tnL1CAcOC3JkZ68/0aF88HE3z/SIQmJtu5yjd3Nx+4l4uEAh6lvxL59n3wbu0NNRjbWfHM6+sJnL0eJPcu7GmjZPbcsjpKFPZOakZOTeUyBG+KHqoTPVzAscuNhbP1atxGDPaqAKnsL6Q5KxkdubupFErb012VDsyJ3QOiZGJ9HV+xMbu2ztvLm6QF/tpZaGIQgmhz8jZm/BpYPXwjdt1LVq+uVpKaoaGE7mV6O9wBn+iryvxMf7Exfjh42y6vi2BwFIQxViBoBeg1+k4sWUDP+zaCoB3UAjxb/wRN18jbta91731BjIOFfFDaj7ajjJV9LgAhvVgmepnBc6QIXiuWoXD6FFGEziSJHFKc4rkzGSOFh1FQhYIQc5BJEYmMjt0Ng7qR3TWriuSrRguboSa/K5zjzC57yYmAZwfflr1tjP4nvQSjmZX3uUMHh3gzMwOcdPHzQjlNYGgFyOEjkBg4dRXlpP23tuUZGcCMHhqPOMXv4SVdc+ObIM8TXVkUzY1Gnmayqe/M+MTe65MpauspOqfn8hNxncKnNWrcBhlPIHTrG0mNS+VjZkbyavrWrg3JmAMi6IWMcp/FErFI+yK0bVBVpqcvblxCDoEFNaOED1P3nkTOOyhd960avUcvl7OnvSfOoOH+zh2mGf609/zEcWZQPAYIYSOQGDB3Dh/hv1r/05rYwM29g5MWf4a4cNH9/h9m2rbOLEtl5wfygCwdVQzcm4IUSP9eqRMpauqkpuMU1I6m4ztYmPxfPVVo2ZwNI0aUrJS2Jazjfr2egDsreyZHTqbpMgkglyCHvEGGbK4ubwFWmq6zvuNkUtTA2aB9cOJEK3ewPHcSvakl3DgahmNbV3mmUEe9sTH+DMz1p8I357rkRIIejNC6AgEFohep+VY8pecT9sJgG9IGHGv/xFXH9+eva/ewOXvizibmo+2VQ8KiB4bwPDZPVOm0lVXU/Xpp9Qk37EHJzYGr9WvGq0HR5IkLpZfZEPmBr4r+A5Dx0bhPo59SIpKYk7oHJysH0EkNFfD5a3yWHhpRte5c4C872ZwkmzN8BDoDRJn8qvYky77S9XeYZ7p72JLfKw/M4UzuEDwQAihIxBYGHXlpaS+97+U5mYD8MSM2Yxb9BtUVj07AlySU8ORlK6lfz79nRmXEI53P+Pvm9LV1FD96aeyVcNtgTNoEF6vrsZh7FijPLzb9e3sy9/HxsyNZFZndp4P9x3OoqhFjOszDlU3d9Jg0EPeYTl7k5UK+g6jS5U1RMbJ2ZuH3Hlz219qT7pswVDe0OUv5eloTdwgP2bG+vNEXzdhnikQPARC6AgEFkTO2ZN8s+492pqbsHFwYNqKNwgdOqJH79lU18bJ7blkn+koUzl0lKlGGb9Mddtss3rjxi6rhuhoeZOxkRb9VbZUsvn6ZrZc30J1azUANiob4oPjSYpKItwtvPtvXp0vG2leSob6oq5zn0HwxPMw6LmH2nkjSRLXNPXsSZfHwYtru/ylXOxk88yZscI8UyB4FITQEQgsAJ1Wy9ENn3Fx/x4A/MIiiH/9jzh7effYPQ16A5cPF3NmT15nmWrgGH9GzA7B1tG42SN9bS1Vn39Bzfr1nW7ishfVahwnGMeq4WrVVTZe28i+m/vQGeQ+Fm97bxIjE5kfNh83227u+mpvhsw9cmnq5rGuc1tXiFkgZ2/8Yh/qLXPLGzv8pUrIq7jbX2rKAB9mxvozNswLayshbgSCR0UIHYHAzNSWakh976+U5eUC8NTMeYxJeAGVVc/99yzJqeXoputUFcsPWe9+ToxPijB6mUpfV0fVF19Q89V6DE3yvWwGROG1erVRzDZ1Bh3fFXzHxsyNXCy/2Hke6xXL4qjFTOo3CbWyG6JNkqD4gixurmyDtvqOCwoImSCLm4g4UD/4XprC6mZSM+TMzTVNfee5tZWSScJfSiDoMYTQEQjMSM6Zk+xf93faW5qxdXJm+so3CH6i5xZxNtW1cWr7Da6fka0GbBysGDknhAGj/Y1aptLX11P9xZdUf/UVhkZ5+Z5NZCReq1fhOGnSIwucurY6tmZvZdP1TZQ2yX8XK6UVU4OmsjhqMdGe3dsqTGMFZGyWe28quvp6cO0LQ56XDTVdH9xmo7y+VRY3GSVcvMOCwUqpYGyYJ7MG+zM5ygcnW2HBIBD0FELoCARmQK/TcnTD51zYtxsA/4gBxL/+B5w8PHvkfga9gctHijm7O4/2jjLVgDH+jDRymUrf0ED1l19R/eWXGBpkV2yb8HA8V6/CafJkFMpHK8Xk1uSyMWsjqTdSadXLY+jutu48F/4cCyMW4mXv1Y2gdZB7UM7eZO+HjrIXVrYwYLacvek3Bh4w9trmdvZdKWX3pRJO51fdZcEwMtiDmbH+TBvoi5tDz+9BEggEQugIBCanvqKcPX//n86pqqGz5jN64fM9VqrS5NZyJCWbqmI5s+Ldz4lxCRH49DdemUrf0ED1V19R/eVXGOrlsoxNWBieq1bhNOWZRxI4BsnAsaJjbMjcwGlNly9UpHski6IWMb3/dGxU3fBtqsyVxU16CjSWdZ37PyE3Fg+cB3auD/RWTW06vr1Wxu70Eo5mV6D7kQXDrFh/Zgzyw1tYMAgEJkcIHYHAhNw4f5b9a/5Ga1Mjtg6OTFv1BiFPDu+Re7U0tnNq+w0yT2oAsLG3YsScEAaM8TfaeLK+sYmaDeup+vwLDHV18n3CQjsEzpRHEjjN2mZ25O4gOTOZgoYCAJQKJRMDJ7IoahFP+jz58CWw9ia4tgsurIeCk13n9h5yWWrwIvAZ8EBv1arVcyS7gt3pJXyXefeW4ig/Z2bF+hMf40egu7BgEAjMiRA6AoEJMOj1HN+8vtOryjckjPh/+RMu3j5Gv5dkkMg8peHk9lzamuQyTNRoP0bODcHO0TjlEkNLCzXJyVT98xP0tXLviXVc7LCRAAAgAElEQVRICF6rVuI0bdojCRxNo4bkrGS2ZW+jQSuXv5ysnZgfNp+EyAQCHAMe7g07G4u/gsvboF1+T9lMc3KHmeb0BzLT1OkNnLxRxe70Er65WkpD691bimfF+jNrsD+h3mJLsUBgKZhV6Bw9epS3336b8+fPo9Fo2LFjB3PmzLnn52/fvp1169Zx6dIl2traGDhwIP/v//0/pk6dasKoBYKHo6G6krT33qY46yoAQ6bPZPzil3pkAWBlUSNHkq9TmidnVzwCHBmfFIFfiItR3t/Q1kbt5i1Ufvwx+spKAKyDgvBctQrnGdNRqLo/MZRekc76a+s5eOsgekkPQD/nfiyOWsyskFnYqx8yM9JcLTcWX1gP5Ve7zt2CZHETmwQu9xdNBoPE+YIa9qSXsPeyhsrG9s5rvs62zIz1Y1ZsgNhSLBBYKGYVOk1NTcTGxvLSSy8xb968+37+0aNHeeaZZ/iv//ovXF1d+fzzz5k5cyZnzpxhyJAhJohYIHg4bqZfYO8H79JSX4e1nT1Tl79G+IgxRr9Pe6uOs6n5ZBwqQjJIWNmoGD6zPzET+qA0wqI5qb2d2u07qPzwQ3Sl8pSTuk8fPFetwmVmPIpu9hfpDDoO3jrI+mvryajssk8Y7juc5wc8z9g+Yx/OXNNggLzv5d6brLQ7NhbbyD5TT7zwQI3FkiRxtaSePeklpGZo7lrk52avZsYgP2bF+jM0yF1sKRYILByFJEnS/T+t51EoFPfN6PwcAwcOZOHChbz11lsP9Pn19fW4uLhQV1eHs7PxV9sLBAAGg55TWzdxevsmkCS8goKZ+cafcPP1N+p9JEki72IFx7bk0FQrWwaEDPFizIIwHN0evfFV0umo272HyjVr0BYXA2Dl64vnihW4zpuLQt29rFR9ez3bsreRnJXcOR6uVqqZ0X8Gzw94ngj3iId7w9oCuLgRLm2EusKuc98YWdwMehbs7r8wMK+ikd3pJexOv3uRn6ONFVMGyov8xoR6ohZbigUCk9Pd53ev7tExGAw0NDTg7v7gK9cFgp6mqbaGve+/TcEVOUMRM3kaE5b8Fitr444T11W0cHRTNgVXqwBw9rRlXEIE/aI9Hvm9Jb2e+r37qFyzhvabNwFQeXni+dtluC54DqVNN6acgIL6AjZkbmBn7k5adHKWxN3WnYURC1kQsQBPu4cYr9e1yT5TF9bLvlN0/M5m6wKDFsiTUw+wsbiktoU9HeLmaslPF/nNivVnQqQ3tmqxyE8g6I30aqHzzjvv0NjYyIIFC+75OW1tbbS1dZnj1dfX3/NzBYJHpfBqBmn/eJum2hrUNrb/P3v3HV1VtS1+/HtKeu89pEFCCb0FpYP0IoI06YhguQp6Vbzv9+7zvatYUCyggCJNRYr0oiAYinRI6C0QAumN9HbK/v2xMYihBAgEwvyMwRg366y9s85255x5115rTro+/xJ123as0t9hMpiJ2ZLAwU0JmAxmtHoNTZ+qRbPutdDfY1ZdRVHI37KFzC+/pPScmqlZ5+KC2/jxuAwbitbG5q7OeTDtIItOLmL75e0oVwOSMOcwRtYbSc+Qnne2PTz1uPpo6uhSKL5yrT24HTQZCXV7g8Wtx5ldWMbGYymsjU1m/8Xs8nbd1UR+fRr68lR9SeQnRE3wyAY6P/74I++++y5r1qzB0/Pm9YCmTZvGu++++wBHJh5HitnM/jUr+GPp9yiKGTf/QPpMmYqbX+Wz6FbG5dPZ7Fhylpw0tV6Uf4QL7YbUwcXb7p7OqygKBdHRZHz5JaUn1YzAWkdH3MaOweW5Eejs7/z8ZaYyfrn4C4tPLuZ09uny9rZ+bRlRbwStfVpXfvFuSa5aiuHwYkg+fK3dwReaDFe3hbsG3/IUhaVGfjuVxprYirluWga7lue6cZVEfkLUKI9koPPTTz8xfvx4li9fTpcuXW7Zd+rUqUyZMqX857y8PAICqvbLRzzeivJy2TTrUy7GHgKgfvvOdB47CQvrqksOV5hbyh8r4jh3QE1sZ+toyRODwqjd3OuedvooikLh7t1kfPEFJUfUR21aW1tcR4/CdfRodHexji27JJtlZ5ax9MxSMovVnVnWOmv6hvZleL3hhDiFVHZwcGkPHF4EJ1bD1UddaPUQ3kOdvQnrDNqbz2IZTGZ2nM1gTWwyW06mUWwwlb9W39eRfo196d3QF1/nO5+pEkI8Gh65QGfJkiWMHTuWn376iV69et22v5WVFVZ3uZ5AiNtJOnOK9Z9/SEFWJnoLSzqPm0SDjl2r7Pxms8KJHUnsXXOBsmIjGg00aO9Pq34hWNnc259v0YEDZHz+BUUHDwKgsbbG9bnhuI4bh97lzit9x12J4/tT37P+wnpKTerjYk8bT4bWHcrA2gNxtq5clmEKs9RsxYcXQubZa+3u4eq6m4ZDwP7mpR7MZoWDCVdYE5vExmMpXCkylL9Wy82WfpLrRojHSrUGOgUFBcTFxZX/HB8fT2xsLK6urgQGBjJ16lSSkpJYtGgRoD6uGjVqFJ9//jmtWrUi9eo2VxsbG5ycqiZPiBCVoSgKhzasZuePCzCbTLj4+NFn8tt41Lr145M7kZ6QR/QPZ8i4pCa4q6oK48WxsWR88QWFu/cAoLG0xHnIYNyffx69x53VilIUhd3Ju1l0chG7k69lGq7nVo8R9UbQrVY3LHSVWOdiNsPFnWpwc2rdtW3hFrbQYAA0HQX+LdSCUTcZx6mUfNYcSWJdbDLJuSXlr7nbW9GnkQ/9GvvRyN9Jct0I8Zip1u3l0dHRdOxYcaHmqFGjWLBgAaNHj+bixYtER0cD0KFDB7Zv337T/pUh28vFvSopKOCXrz/j/EG17lJ4m3Y8NeFlLG2qJtV/aZGBvWsucHxHEihgaaOndb8Q6rfzu6ecLSUnT5Lx+RcU/Pk3pNfjPPAZ3CdOxMLb+47OVWYqY2P8RhaeWEhcjvp/Vv4szzCi3giaeDapXECRn6ZuCT+8CK7EX2v3aaQGN5GDwPrmf6eXsopYeySJNbHJnEsvKG93sNLTvYE3/Rr70TrEFb1sBxfikXe3398PTR6dB0UCHXEvUs+fY92MD8jLSEOn19Nh1AQade1RJbMEiqJwdn8af/wcR3GeOqNRp5UXbQaEYed0949fS8+dI+PLmeRv3qw26HQ49euH+4uTsPT3v6Nz5Zbmsvzscn489SMZxRkA2OptGVB7AMPqDiPAoRLr38wmOP87HF4AZzZdqxZu6QANB6kBjm/jmx6eWVDKhqMprIlN4vClnPL2P7eD92vsS4dw2Q4uRE3zWObREeJBURSF2M0b2L7oW0xGI06eXvSZPBWvkLAqOf+V1EK2LzlL0hl1u7Szly3th9bBP+Luc0SVJSaS+eWX5K5dpy7s1Whw7NUL95dexCr4zh6xJRUk8f3J7/n53M/l+W88bTwZXm84A+sMxNGyEh86eckQ8726cyr30rV2/xZqcFP/abCyv+Gh+SUGNp9IY82RZP6Iy8R0dceUVgNtQt3p29iXbvW9cbKR7eBCiOtJoCPEbZQWFbF57pec3bMTgLAWrek26TWs7W78pXwnjAYThzYlcPjXBMwmBZ2FluY9g2jSJRCdxd09bjFmZJD59WyuLF8OBnUhrkPXrnj84xWsate+o3MdzzzOghML2JKwBbOiVueu41KHUfVH0SOox+3X35iMELcFDi2Ac5vh6jmwdlIXFTcbBV71b3homdHM9rMZrI5J4rdTaZQar1UHbxTgTL+r1cE9Hatud5sQouaRQEeIW8i8dJG1n07jSkoSWp2OdsPH0rRn3yp5VJV4OpvoH8+Qm67OkNSKdKPd4Do4ut/dVmdTXh5Z384je/FilGL1nHZt2uAxeTI2kQ0qfR6zYmZH4g4WnFjAobRD5e1tfNswqv4oonyibv/+rySoSf1ivof8lGvtgW2g2Wi17tQNkvopikLM5RxWHU5i/dHk63ZMhXjY0b+xH30b+RLkfm95g4QQjw8JdIS4iZM7f2fLNzMxlpZi7+ZOn9fewrdO3Xs+b3FBGX+siOPMXnXXoK2TJe0G1yGkicddBVDm4mKyF39P1rffYr6a+du6UUM8J0/BrnWrSp+n1FTKuvPrWHhiIRfzLgKg1+jpGdKTkfVG3r7+lMkAZzbCoYVwfhvlJRlsXKHxMPXxlEedGx56MbOQ1bFJrI5J4mJWUXm7h4MV/Rr50r+JH/V9pTq4EOLOSaAjxN8YDQaiF37DkS0bAajVsAk9X3kDW8d7S2GgKAqn96Sy++c4SgoNoIHIdn606h96VzlxlLIyrqxYQebXX2PKUBPzWdUOw+O117Dv1KnSQcGVkissPbOUJaeXkF2ilkOwt7BnUPgghkUMw9vuNjuyss6ru6Zif4TC9Gvtwe3VR1MRvUFfcTH1lcIy1h9NZlXM9YuKbS11dK/vTf8mfjwR5o5OqoMLIe6BBDpC/EVeRjrrZkwj9fw5AFo/M5SogUPQ3iL7bmXkpBUR/eNpks6oX+hufvZ0GB6Od8idB0+KyUTehg1kfDkTw2W1UreFnx8e/3gFx9690egqN9ZLeZdYdHIRa+LWUGJS88742PnwXN3nGFB7APaWt1iDZDKqszcH510tqHmVnadakqHpSHCtmAG5xGBi2+l0Vh5OYvvZdAyma4uKn6ztwYAmfjxV3wtbS/loEkJUDfk0EeKq+JiDbJz5CSUF+VjbO9Dz5dcJbtL8ns5pMpg5vDmBQ5sSMBnN6C20tOgdTKMuAejuMLeLoigU/B5NxowZlJ5TAzGduzvukybiMmgQmkpWR49Nj2XBiQVsu7StvMBmXde6jGkwhq61uqLX3uJjITdJnb05vPAva280aimGZqOhTnf42wJls1nhwMVsVsUkseFYCvklxvLX6vs68nQTP/o29sXTQRYVCyGqngQ64rFnNpvYs2IJe1cuBUXBK6Q2fadMxdHj5sViKyP5XA7RP5zmSqq65iSwnivth4Xf1WLjwn37yZgxg+LYWAC0Dg64jR+P64jn0NrePlGhyWzi98u/s+DEAo5kHClvb+ffjtH1R9Pcq/nNH3WZzXDhdzj4nZr3RrlaL8rWXS3J0Gw0uARVOCwuPZ9VMUmsjkkmKae4vN3XyZp+Tfx4uokfdbykDIMQ4v6SQEc81oryctn45XQSjsYA0KhrTzqMeh69xd3nYykpNLBnZRwn/1BnPGwcLGj7bB3Cmnve8WLa4uMnyJgxg8I//gCu1qMaMQK38ePQVaLsSYmxhDVxa1h4ciGX868+5tJa0Ce0DyPrjSTUOfTmBxdmQez3cHD+9VmLaz0BzcdC3T4V1t5k5Jey7oi67uZYUm55u4OVnp6RPvRv4kerYNd7yvAshBB3QgId8dhKPnuadZ99oBbktLSi64SXqde2YkmSylIUhXMH0ti1/BzF+eq26HpP+hL1dCjWdncWOJVeiCfjiy/I/+UXtUGvx+XZQbhNnIiF5+1nmvLK8lh2ZhmLTy4uX2DsZOXE4PDBDI0YiruN+83eBFzeBwfmwcnV12pOWTlCo6HQfAx4Xr/zrLjMxOaTqayKSWLnuWvJ/PRaDR3CPXi6iT+d60qmYiFE9ZBARzx2FEUh5pf1bF88D7PJiIuPH32nTMU9MOiuz5mbUcyOJWe4dFINKlx87OgwPBzfsEpW7L7KkJJCxqxZ5K5aDSaTms24T288XnkFy4Dbl1dIL0rn+5Pfs+zsMgoNhQD42vkyqv4o+of1x9biJo+5SvLg6FJ19ib9xLV2n8bQYhw0eAYsr+WuURS1Qvjyg5fZcDSFwjJT+WuNA5wZ0NSPXpE+uNnffekKIYSoChLoiMdKWUkxm+d8yZndOwCo0+oJnpr4KlaVWOdyIyaTmSO/XebA+niMBjM6vZbmPWvR5Kla6PSVX2xszM4ma85crvz4I8rVbMb2nTrh8eqrWIffOPfMXyXkJTD/+HzWnl+LwaweH+YcxrjIcXQL6oaF9iYzSilH1Z1TR5fD1cAIvQ1EPqM+nvJrdl33pJxiVh5KZMXhRBL+ku8m0NWW/lfX3QRLMj8hxENEAh3x2MhKvMzaT98nO+lylWQ5Tr2QS/QPp8lKUgMEv3AXOgwLx9mr8kGTubCQrPkLyP7uO8xFauBg26IFHlMmY9ukyW2PP5F1gu+OfceWhC3lO6iaejZlXOQ42vq1vfF7MxTDidVqgJN44Fq7ex1oPg4aDQYbl/Lm4jITv55IZcWhRP44n8mfZYDtLHX0aujDwGYBtAhykWR+QoiHkgQ64rFw+o/tbJ7zJYbSEuxdXOn92tv4RdS7q3OVFhvZu/o8x3ckgQLWdhY8MSiM8Fbelf6yVwwGcn7+mYyZszBlqsn+rOvVw2PyZOyefOKW51EUhf2p+5l3bB57UvaUt7f3b8/YBmNp6tX0xgdmnVd3TsX+AMVq8VC0Fuqi4uZjIehJuPp7FUXhUMIVVhxKZP3RFApKr20JbxPqxsBm/nRv4C35boQQDz35lBI1msloIHrRPGJ/XQ9AQP2G9PrHP7FzdrnNkRUpisL5wxnsXHaWolx1kW5Ea2/aDAzDxr5yOWwURSH/t9/I+HQGZfHqTiaLwEA8J7+GQ7duaLQ3f9xlVsxsu7SNecfmcTzrOAA6jY4ewT0Y02AMdVxu8Ijrz8R+B76F+O3X2p0C1azFTUeC/bXFzck5xayKSWLFoUTiMwvL2wNcbRjYNIABTf0IcL27x3xCCFEdJNARNVZeZgbrP/uAlHNnAGjZfxBPPPsc2kpmDv6r/OwSdiw5w8VjWQA4edrQYXgE/uGVD5iKDseQ/vHHFMeoW9l1Li64v/giLoOfvWWyvzJTGRsubOC749+V16Cy0lkxoPYARtUfhZ+9X8WDCrPUpH4H5kFe4tVGDdTpps7ehHWBq9meSwzXHk3tirv2aMrWUkfPSB8GNfOnRZBsCRdCPJok0BE10sWjMWz84mOK8/OwsrOjx0tTCG1W+QKXfzKbFY79nsjetRcwlprQ6jQ07VaLZj1qoa/kdunSC/FkzPiU/C2/AVdz4Ywehdv48ejsb15modBQyIqzK1h0chHpRWoNKQdLB4ZGDGVYxDDcbNwqHpRyBPbNhWPLwVSqttm6qQU1m48B50Dg6s6zS+qjqXVHkq/LVtwq2JVBzQPo0cAbOyv5iBBCPNrkU0zUKIrZzN6VS9m94kdQFDyDQukzZSrOXrcpTHkDWUkF/P79adLi1YrgPmFOdBgWgatv5XYVGTMyyJg1i5zlK9St4lotzs8MwP3ll7Hw8rrpcdkl2fxw6geWnF5Cflk+AJ42noysP5KBdQZiZ/G3328ywKm1aoBzee+1dp/G0OoFqD8ALNTyCqm5JayMSWTFoUQuZFx7NOXnbMPAZv4809SfQDd5NCWEqDkk0BE1RnF+HptmfkJ87CEAIjs9RacxE9FXsgbUn0wGMwd/ucjhXxIwmxQsrXVEDQij/pO+aCrx+MZUUEj2/PlkzZ+PcnUnlX3Hjni+PgWrsLCbHpdUkMTCEwtZdW5VeZHNIMcgxjYYS6+QXljq/vY+CjLg0AJ199Sfdae0eqjXD1pNBP8WoNFQYjCx5UgyKw4lsvNcBlfz+WFjoaNHpDcDm/nTOthNHk0JIWokCXREjZAad5a1M6aRn5mB3sKSzuNfpEGHLnd+ngu5bFt0qrw+VVBDd9oPDcfe5faJ7xSDgZwVK9SdVFnqWh7rhg3x+ucb2LZocdPj4q7EMe/4PDbFb8J0tY5Ufbf6jI8cT8eAjuj+Xjk96ZA6e3Ni5bXMxXae6qOpZmPA0QeAM6n5LNl/iVUxSeQWG8oPbxnkysDm/vSM9MFeHk0JIWo4+ZQTjzRFUTi29Ve2zZ+NyWjE2cuHPlOm4hkUckfnKSsxsnfNBY5FJ4Ki1qdqNySc0KYet90yrigK+Vu2qDupLl4EwKJWIJ6Tp+DQ7ambHn86+zRzj85lS8KW8rYonyjGRY6jpXfL648zlsHJNbBvNiQdvNbu11x9PFWvH+itKCozsv7gZX7af4nDl3LKu/k6WTOwmT8DmvoTJAn9hBCPEQl0xCPLUFbK1nlfcyJaXeQb2rw13V98DWu7my/wvZGE41lE/3iagmx18W5ElDdPDKxdqfpURYcOkf7x9PKq4jpXV3Un1bODbrqT6njmceYcmUN0YnR5W9daXRkXOY76bvWv75yfqpZlODQfCtLUNq0FNBgALV8AfzVz8fGkXJbsP8va2GTyr+a80Ws1dKnrxZCWAbSt7YFOHk0JIR5DEuiIR1JueiprP5lG+sXzaDRanhw6khZ9n7mj7LzFBWXsWnaOs/vVAMLBzZqOwyMIqOd622NLz58n/dMZFGzdCoDGxkbdSTVu3E13UsWkxzDnyBz+SFYrkWs1WroFdWNC5ATCXP6ydkdR1IzF++aohTXNV3dE2XurdaeajQZ7T/JLDKzdl8BP+y9fVym8lpstQ1oE8kwzPzwdrCt9PYQQoiaSQEc8cuJjDrLxy+mUFBZg4+BIr1ffpFZk40of/2eV8Z3LzlFSYECjgYadAmjVNwQLq1tvGTekp5M5cxY5K1aA2azupBo4EPeXX7phVXFFUTiQeoA5R+ewP3U/oCb56xXSi+cjnyfIKegvJy9R193smwMpsdfaA1qpj6fq9kXR6om5nMNPvxxh3ZEUig3qmh5LnZZuDbwZ2iKA1iGysFgIIf4kgY54ZPx967h3WB36TJ6Ko7tHpc+Rn13C9h/PkHBcXSzs6mtHpxF18Qp2vOVxpoJCsr+bR9b8BSjFxQDYd+6M55TJWIWGVhyrorA7eTdzjs4hJl1NEKjX6ukX2o9xkeMIcPhLJfK8FDVz8aEFUKSWg0BnBZEDoeUE8G1MbpGBlXsT+Wn/Zc6k5ZcfGuZpz5AWAQxo6o+r3Z3tLhNCiMeBBDrikVBSUMDGmdOJj1EX4jbq2oMOoyagt7j9OhoAxaxwfEcSe1adx1BqQqvX0KJn0G2rjCtGo7qT6suZ13ZSNWqI1z//iW3z5hX7KwrbE7cz9+hcjmUeA8BCa8GA2gMY12AcPvY+1zqnHoPdM+H4z3C14jiOfurjqaajUGzd2B+fzU9LY9l4LIVSoxkAK72WXg19GNoykOa1pJimEELcigQ64qGXfvECaz99n9y01LvaOp6dUsjvi0+TekFdx+IT6kSH5yJw9bn17qOCnbtI/+hDSs/FAWBZqxYeU6bg8FTXCsGFWTGz9dJW5h6dy+ns0wBY66wZWGcgYxqMwdP26mMtsxnifoM9M6+vPRUYpea+iehNVrGJlYeSWHLg+HVJ/SK8HRjWKpB+jf1wsqlcgCeEEI87CXTEQ+3E9q389s0sjIYynDy96DPlHbyCKz4quhGT0czhXxM4uOkiZqOChZWOqKdDadDO75aJ/0rj4kj78CMKd+4EQOfkhPvLL+MyZDCav80gmcwmNidsZu7RucTlqAGRjd6GIRFDGFlvJO427mpHQwkcXQp7ZkGmWnsLjU7dFh71Mmbfpuw+n8WSpUfZfCIVg0nN6mdrqaNvI1+GtAykkb+TzN4IIcQdkkBHPJSMBgPRC7/hyJaNAAQ3bkaPV97Axt6hUsenxeexbfEpspPVGZFaDdxoPywcB9eb70IyZmeT8eWX5CxbrpZssLDAddgw3F+chM7J6fq+ZiMb4zfyzdFvygtt2lvYM6zuMEbUHYGztbPasTBTLax54BsozFDbLB3UquGtJ5Jt4c3SA5dZ8mM0l7KLys/f0N+JoS0D6dPIV5L6CSHEPZBPUPHQyc/KZN2n00iJOwMaDVHPDCXqmSFotDdfS/MnQ6mJfWsvcHTbZRQFrO0taDu4NrWbe910NsRcVsaVxYvJ/Ho25oICABy6dsHz9dexDAq6/vwmA2vPr+XbY9+SWKBWBXe0dOS5es8xvO5wHC2vLmrOPKfO3hxZAka1nAOO/tB6IjQdydFMhYWbE1h39ARlV9feOFjp6d/EjyEtA6jve31gJYQQ4u5IoCMeKpeOH2H95x9RnJeLlZ0dPV95g5AmNy+f8FeXT2bz+w+nyc9SA4s6rbx4clBtbOxvvBtJURTyf91M+vTpGBLVoMWqXl283nobu1Ytr+tbaipl1blVfHf8O1IK1bpSLlYujKw/kiHhQ7C3tFfz38TvVNffnP3l2sE+jaHNK5TU7s2GE5ksmnecI5evZS1u4OfIyNZB9G7kg62l/EkKIURVkk9V8VBQFIUDa39m15JFKIoZj6AQ+k55p1JVx0sKDfyx4hyn96QCYO9qRYdhEdRq4HbTY4qPHSPtgw8pPqQWANV7eOAxeTJO/ftdN3NUZipjxdkVzDs2j/TidADcbdwZXX80g+oMwtbCVq0efnQ57PkSUo5cPVID4T0g6iUSHZvww/7LLF29k+xCtTaVpU7dOTUiqhZNApxl7Y0QQtwnEuiIaldaVMSvX3/Guf27AajfvjOdx7+IheXtC2leiM1g+49nKMorAw1EdvCndb8QLK1vfGsbUlJInzGDvLXrANBYW+M2dixu48ehtbW91s9kYFXcKuYenUtakZo52cvWi7ENxjKg9gCs9dZQkgv7P1cT/OUlqQfqraHxMJTWL7LrijOLdiSw9VR0ecVwXydrhreuxeAWAbjb3/79CSGEuDcS6IhqlXk5gbWfvM+VlCS0Oj2dxrxAwy7dbzvDUZxfxs6lZzl3UJ1lcfG2peOIuviE3nhti7mwkKx588j6bj5Kifpoy6lfXzwmT8bC+9qskdFsZN35dcw5OoekAjV48bTx5PmGzzOg9gAsdZZwJUEtrnl4EZSpa3qw84CWE8iPHMmKU8UsXpjAhYyz5ed9IsyNkVFBdI7wRK+7/VojIYQQVUMCHVFtTu/ewebZX2AoLcHezZ2+k6fiUzv8lscoikLcoXR2Lj1Lcb4BjVZDk66BtOgdhN6iYvkGxWQid/UaMj77DGOGuuvJpnkzvN56G5vIBuX9TGYTG+M3MvvIbCeKYHkAACAASURBVC7lXwLAzdqN8ZHjGRQ+CCudFSQeVNffnFwDirqAGI8IiHqZs57dWXgglVWfxVJUppZlsLfS80xTP0ZE1SLMs3K7xYQQQlQtCXTEA2cyGtn543wObVgDQGCDhvR69S1sHW+906gwt5QdS85yIVYNWNz87Og0si6etW5cvqFw7z7SPvyQ0lOnALAICMDzjTeuS/hnVsxsvriZr458RXxuPKAuMh7bYCyDIwZjo7WCc7/Crs/g8t5rJw/pgLHVS/xa0oCFexPYH7+//KXanvaMbBPE0038ZGu4EEJUM/kUFg9UYc4V1s34gKTTJwBo2W8gTwwegVZ382KaiqJwdl8qO5edo7TIiFaroVmPWjTrEXTD8g2l8fGkT/+kvLK41sEB90mTcHluOFpLy/Jzbr20lVmxs8oT/TlaOjKmwRiGRgzFTmulFtjcNQPST6on1lpA5CCyG45ncbwjP/6cQFqeWsdKp9XQrb4XI1oH0TrEVRYXCyHEQ0ICHfHAJJ0+ybrPPqDwSjaWNjZ0f3EytVu2ueUxBVdKiP7hWhFOj0AHOo2MwN2/4qMgU04OmV9/TfYPP4LRCDodLoMH4/7yS+hdXYFrtai+iv2KU9nqTI+9hT0j64/kubrP4aCxgNjv4Y8vICdBPbGlA0rzsRzxH8q8IyVsmpeC0azu8HK3t2JYywCGtgrEx8mmqi6VEEKIKiKBjrjvFEUh5pd1bF88D7PJhJt/IH1f/xeuvn63PObUHyn8seIcZSVqEc6WvYNp0jUQ7d8W8yoGA1eW/ETmrFmYctV6Vnbt2+H1z39iFRZWfr7dybuZFTurvNimrd6W4XWHM6r+KJwUDez/FvZ8BYXqAmds3TC2nMg6y57MPXCFU9sulP/O5rVcGBFVix4NfLC8RVFQIYQQ1UsCHXFfGUpL2DJ3Jqd2RQMQHtWWpyb+A0vrm89+5GUW8/v3p0k8fQUAr2BHOo2se8MinAW7/iBt2jTKzp8HwKp2GJ5vvY39k0+U99mXso9ZsbOISVcfM/1Zi2pM/TG4GI2w63M1yClVgyScAihsNomFJe34blcamQXqzI61hZb+jdXFxZK5WAghHg0S6Ij7JictlbWfvEdGQjwarZb2z42lac9+N12/opgVju9IYveq8xhLTegstLTuF0LDTgFo/1aEsywhgbQPP6Jg2zYAdC4ueLz6Ks4Dn0GjV2/rQ2mHmBU7iwOpBwCw0lnxbPizjG0wFvfSItj2PhxeDMZi9aTu4aQ1nMgXGU1YsTmVUqO6+8rHyZrRbYIY3CIAZ9sbZ1kWQgjxcJJAR9wX8bGH2PjFx5QUFmDr5Ezv194ioF7kTfvnpBfx++LTJJ9TSyP41nam43MROHvZXtfPVFBI1uyvyVq4CAwG0OtxHT4M95deQueo7r46knGEWTGz2JOyBwALrQUD6wxkfOR4PAuy4Zf/gmPLwWwEQPFtyumw8Xx8MZRtG7OAZAAi/ZwY3zaYnpE+WEjuGyGEeCRJoCOqlGI2s2/VMv5Y/gMoCj5h4fSZMhUHN/cb9jebFY5uu8y+NRcwGszorXS0eTqUBu380PxlFkcxm8ldvYb0GZ9iysgEwO7JJ/Ga+jZWoaEAnMg6wayYWexM2gmAXqOnf+3+TIicgE9OMqydDKfXX/vdwe3Z5TWS9095cHpzAZCFRgNd63oxvm0ILYJcZPeUEEI84iTQEVWmtKiQTbNmcP6gmm+mYZfudBz9AnoLixv2z04pZNuiU6TF5wHgH+FCx+cicHS/fv1OcWwsqe+9T8kxdRGxRa1AvN5+G/sOHdBoNJy7co6ZMTPZdvnqYyyNjj6hfXghcgL+GXHw8wSI31F+vrLavVht/yzTj9uTfqoUKMDGQsezzf0Z80QwQe4V1wIJIYR4NEmgI6pEVuIl1kx/jyspSej0ejqPe5HITk/dsK/ZZCZmyyX2r4/HbFSwtNbxxMDa1H3C57oZFENaOhmffkLumrUAaO3scH9xEi4jRqC1tCQxP5GvYr9i/YX1KCho0NArpBcTIydQK/kY/DQCktUFyGj15NV5mu+Ufsw+qafEYAZK8XK0YnSbYIa1DMTJ9sYBmRBCiEeXBDrinp3du4tfvvoMQ2kJDm4e9J0yFe+wOjfsm5lYwLZFp8i4lA9ArQZudBgejr2LdXkfc2kp2QsWkjlnDkpREQBOAwbgOfk19B4eZBZnMnffXJafXY7x6jqbLoFdeLnhREIvHYTvn4VMtc6UorchJexZZhQ8xfLYP4MoMw38HBn/ZAg9I2V7uBBC1GQS6Ii7ZjaZ2PXTIg6s/Rm4dSkHk9HMoU0XObQpAbNZwcpWT9tna1OnlXf5LI6iKBRs3Urahx9huHwZAJvGjfH61zvYREaSV5bHgsNf8P2p7ym+ulOqtU9rXm04kQYJB2HhAMhLVM9l7cSZwCH8T1o79l4NcDQa6Bzhxfi2wbQKluzFQgjxOJBAR9yVorxcNnz+EZeOHwGgeZ8BtB066oalHDIu5bN14SmyktRK3yGNPWg3tA52TlblfUrOniVt2jSK9qjre/Senni+8TqOffpQYiph3rF5fHf8O/LK1PU8ke6RvBr5Aq0Sj8HiIeVJ/sx2nuz1Gsq/LjUn/qg6FmsLLYOaBTDmiSBCPOzv30URQgjx0JFAR9yxtAtxrPnkPfIzM7CwsqbbpFcJj2pboZ/JaObgposcvjqLY21vQbshdQhr5lk+m2LKySHjy5lc+eknMJnQWFriOnYM7s8/j8nGkuVnlzP7yGwyitVCnqFOobwS+Tydks+i+WksFKk7sAwO/mxyGsL/S2hEbpYa4Hg6WDGqTRDDWwVK/hshhHhMSaAj7sjx6N/47dtZmAwGXHx86fv6v3APqFWhX8blq7M4ieosTmhTT9oPrYONw9WimkYjV5YtI/PzL8rLNjh07Yrnm/9E7+/HpvhNzIqdxeV89RGWr50vL9YfQ+/0S+hWvAzF2QCUOgSy1GYw/3c5EkOGejvX83FkfNtgejf0lfU3QgjxmJNAR1SKyWjg9wXfcGTLRgBCmrWkx0tTsLaz/1u/irM47YeGE9bMs7xP4d59pL3/PqVn1QXDVrVr4/XOVGxbt2Zn0k6+WDeZM1fOAOBq7cqEuiMYlJWO5Zq3oERNKFjkEMRC/UCmpzTChDqD0ynCk/Ftg4kKcZP1N0IIIQAJdEQl5Gdnsu7TaaScOwMaDW0GDaP104PRaK+fLak4i+NBuyHh2DqqszhliYmkf/gR+Vu2AKB1csLjH6/gMngwh7OO8MUvozmcfhhQK4qPrjOYEbm52G743/I6VPn2IXytDGB2RmPMaNFrNQxo7MvE9qHU8apY0VwIIcTjTQIdcUuJp46zbsYHFOXmYGVnR89X3iCkSYvr+vx9R9Wfa3FqN/cCwFxcTObcuWTP+w6lrAx0OlyGDMH95ZeIU9L4r+h/lGczttJZMSz0acbmF+G8eTqUqdvQr9iH8bmhP4sy1QDHxkLHkJYBjG8bgp/zzQuECiGEeLxJoCNuSFEUYn5Zx/bF8zCbTLgHBtHv9X/h7O1zXb8KszhNPGg3VJ3FURSF/F83k/bhhxhTUgCwbd0ar3emku5tzTsx09h0cROgZjMeENSTF4oMeG2bBYZCADLt6vBhcT9WZDZCQYuLrQWj2gQxKioIFztZYCyEEOLWJNARFRhKS9gydyandkUDEPFEe56a8AoW1teS+plMZg5tSuDQxovqLI6dBe2GXttRVRoXR+p/3qNor7pd3MLXF8+336KoTUM+OjaXlXtXYlJMAPTw78BLJVpq7fiuvJJ4im0E7xX2ZX1WI0CDn7MNz7cN5tkWAdhaym0rhBCicuQbQ1wnJy2VtZ+8R0ZCPBqtlvbPjaNpz77XLe7NTFRncTIvV5zFMeXnkzlzFtk//ABGIxpLS9yefx79iEF8G/cDP65+h1JTKQBtvVryjzJLIvYsA2MJAJds6vJ/+X3Zkt0Q0BDu5cAL7UPo08hXKogLIYS4YxLoiHLxsYfY+MXHlBQWYOvkTO/X3iKgXmT56zecxRlSh7DmnqAo5KxaTfonn2DKVHPb2HfpjPMbk1mWH828jU+Tb1DX2zRxrcerJjuaHVgLpjIAzlvX5928PuwoiQQ0tAhyYVKHUDqGe8oOKiGEEHdNAh2BYjazb/Vy/lj2PSgKPmHh9JkyFQc39/I+f5/FCWnsQfth6ixO8YkTpP3ffyiOjQXAMigIj3emstUni5kHJpBepGYtruMYxKtmR9rG/IrGbADglGUD/q+gL7tL6gMautT1ZGL7UJoHuT7YiyCEEKJGkkDnMVdaVMQvX31K3AF1LU3Dzt3pOOYF9BZqJW+TyczhXxI4uEGdxbGy09N+SDhhzT0x5eSQ8u/3yVm2DBQFja0t7pMmcbxzEK8f/ZTz8ecB8LHx4BWNG72ObUN7tQhnrL4h0wr7sa+krrpFvKlsERdCCFH1JNB5jGUnJ7Lm4/+QnZyITq+n09hJNOzcrfz1zMQCti48WWEWx8ZOR85PP5H+2eeYr2Y1duzdm8yxPXk9YQGHd36utlnYM0HvzZDTO7C6GuDs1zXmo6K+HCyJwMZCxxjZIi6EEOI+kkDnMRV3YC+bZn1CWXEx9q5u9J3yDj61w4G/zOJsvIjZpM7i/JkXp/jwYeL/8x6lp04BYBUejjJ5LNOV3/ntwD/UNq0lw618GXdmL46mkwDs0TTi45KnOazUwcXWgtdki7gQQogHQAKdx4xiNrN7xY/s/fknAPwi6tNn8tvYObsA6izOtkWnyLikLhwObuRO+2HhWJbmkvzWW+StXQeA1tERm0ljWVg7hZ8v/DcmxYQWLf2sfXnxfAzeZXEA7KMBH5cO4KASga+TNf9uF8Jg2SIuhBDiAZFvm8dISWEBm2Z+woXDBwBo0qMP7Z8bh06vx2QyE/NrAgc2/GUWZ3Adwhq5cOX7H0icNQtzURFoNNgN6Memp1yZlziP4vNq3psOll68mnCSsJKLABxU6vJx2UD2KXUJcrPlo45h9G/sJ0U2hRBCPFAS6DwmMi8nsGb6f8hJTUFvYUnXCS9Tr10nALKSC9i6oOIsjnL8IPFPv0/ZhQsAWDWM5MiIFnxWtJ7si2r18IYWLkxJjKdZ0SUADit1mG4YyG5zfcK9HPm8Yyi9G/qi08oWcSGEEA+eBDqPgbN7d/HLV59hKC3Bwd2Dfq//C6+QMMwmM7G/XWbfuguYjQpWtnraDq5DkK+RjP/6Z3nxTZ2bG2kjuzLNdQ+XsxcBEKS359XUFDrnX0IDxJrD+MQ4kJ3mSBr6OzOnYxhd63qhlQBHCCFENZJApwYzm03s+mkxB9asACCwQSN6vfomto5OXEktZOvCU6TF5wEQFOlGu4HBlCxfTPw336CUloJOR+nTnZnRKInDRSugCNy11kzKyuLpnEtYAEfNIXxqfIZoc2NaBLmysFNt2tV2lyR/QgghHgoS6NRQxfl5bPjiYxKOxgDQvM8A2g4dBRotsb9dYu+aC5gMZiytdTz5bG38ik6RNux1DElJ6gmaRvJ9NyvWsg2KwE6jZ0xuPiOyL2OrKJww1+JT40C2mpvStrYHSzuG0SrErRrfsRBCCFGRBDo1UPrFC6yZ/h55GWnorazoNvFVItq0Iye9iG2LTpESp+a+CajnypMdHSiY+S5J23cAoPHyILpfEF+5xqAAerQ8W1jMC5mJuJrNnDIH8pnxGX41N6dLXW9WdwqjcYBzNb5bIYQQ4uaqdQvMjh076NOnD76+vmg0GlavXn3bY6Kjo2natClWVlaEhYWxYMGC+z/QR8ipXdEs+X//JC8jDScvb4b95xPCW7fl6O+JLP3PflLicrGw0tF+UAitlR2kD3uawu07QK8nrndDxozMZ5ZbDIoGehQbWHs5kanpaWQafZhU9iq9DO9j0aAvm15tx7ejmkuQI4QQ4qFWrTM6hYWFNGrUiLFjxzJgwIDb9o+Pj6dXr15MnDiRH374ga1btzJ+/Hh8fHzo1q3bbY+vycwmEzt++I5DG9YAENy4GT1f+SdlJTrWfB5L0pkrAPjVcaZV2BUK3xtHVnIyADmNgvjoiWzinNTkfi1LTUzJzKB+WRnnzT68YnyGX4iib+MAtnQMJdTDvnrepBBCCHGHqjXQ6dGjBz169Kh0/9mzZxMcHMwnn3wCQN26ddm1axczZsx4rAOdotwc1n/2IZdPHgOg1dODiRo0lNO70/hjRRyGUhN6Sy0t27vi+utMcubuBMDg4cziLnp+qXUZNBrqGMxMycqkTXEJ8WZvXjUO4FfNkzzTIpBt7UMJcLWtzrcphBBC3LFHao3Onj176NKly3Vt3bp147XXXrvpMaWlpZSWlpb/nJeXd9/GVx1Sz59j7Sfvk5+VgYW1DT1emoxP7aZsnHWcSyfVXDfewQ400R6k7H++pshgQNHr2NnOlW+aZFNqqcHLBK9kZ9G7oJBEsydvGEfzq64dg9sEs71dCF6O1tX8LoUQQoi780gFOqmpqXh5eV3X5uXlRV5eHsXFxdjYVCwMOW3aNN59990HNcQH6vjvW/ht3leYDAZcfP3p+/o7ZFyyYMn/7qes2IhOr6VJfROuy6dSmqzuprpU15VP2uaS4nYFO0XDxOwcnsvLJ8vsyr+MQ/lV34lh7UPZ/kQwbvZW1fwOhRBCiHvzSAU6d2Pq1KlMmTKl/Oe8vDwCAgKqcUT3zmQ08PvCbzmyeQMAoc1b0e65l9izKpGLRzMB8PCxon7SSrSfb8QIFLraMrtDGfvq5KJHw9DcfCbm5GI0OfK+cSQbLboxomMdop8IwsnGohrfnRBCCFF1HqlAx9vbm7S0tOva0tLScHR0vOFsDoCVlRVWVjVnZqLgSjbrZnxA8pmToNHQZuAwXAPas/Lj45QWGdHqNNR3TcF9xUdoDKWYdVrWt9azvHUppZYauhQW82p2Dk4Ga742DmatZS9GdKnL71G1cLCWAEcIIUTN8kgFOlFRUWzcuPG6ti1bthAVFVVNI3qwks+eYu2n0yi8ko2VrR2dx73KxRMOHP7tNACuzgoRsXOx3noEgJOhlszpbCLFzUyjkjJeT75CaImOb439WWXVl2EdG/BbVC3srR6p20AIIYSotGr9hisoKCAuLq785/j4eGJjY3F1dSUwMJCpU6eSlJTEokVqfaWJEycyc+ZM3nzzTcaOHcu2bdtYtmwZGzZsqK638MAc/e0Xtn43G7PJiJt/II27T2L36hxKCjLQaKGO8Rg+a+aiVcxccdYzr5OZ/XVMBBiNfJKWwxOFZhaauvO61dMM7dyYX1sHYmspAY4QQoiarVq/6Q4ePEjHjh3Lf/5zLc2oUaNYsGABKSkpXLp0qfz14OBgNmzYwOTJk/n888/x9/fn22+/rdFby40GA9vmz+bY1l8BCG0WhZVjd/74OQMAJ6tiwvfNxD7nIkadhtWtNKyKUrDRKbydnUO/3FKWmrowwHogz3ZtxoaWgdhY6qrzLQkhhBAPjEZRFKW6B/Eg5eXl4eTkRG5uLo6OjtU9nFvKz85k3SfTSIk7AxoN9TsMJPl8CMX5BjQaheDMPwg8vgytYiI2WMP8rlqyXRSey8tn5JVCNhnbs9RmMM90bMXgFgFYW0iAI4QQ4tF0t9/f8uziIZV4+gTrPp1GUW4OVnb2+NYdzPlYJ8CAvZJLxKHZOOZfItMR5nfRcrA29CksZNLlPPYZonjBZih9uz/Jsmb+EuAIIYR4bEmg85BRFIUjmzfy+8K5mE0mnDz90Vj2JuW8LaAQmPQ7wefXYNYYWdlGw6ooLU1MJSxNyeF8cVPetBlO724d+aGZP5b6ai1lJoQQQlQ7CXQeIsayMn6b9xUnon8DwMW3MUVFbdEYLLAtyybi+Hyc8y5cfUylw87BwGfZWRQXRPCx3VS6932KhU39sNBJgCOEEEKABDoPjbzMDNZ9+j6p58+h0Wiwc+tIUVEjNBoN/onRhF5YQ7Z9GdMHaLkYYublnGxcLwXxo91kOvXvzZzGvhLgCCGEEH8jgc5DIPHkcdZ99gFFuTnoLW3RWPbAaKqFTWk2EacX45h3lrWtNGxqrWF4cR5jL3uxwvZF2g8YwBcNfdBLgCOEEELckAQ61UhRFGJ+Wc/2xd9iNpnQW3mhteqNVueET/If1D6/kpMBpcwfpKWNZSHvpzqyyXoyvk8PZnpDX3RaTXW/BSGEEOKhJoFONTGUlfLbN7M4uWMbADrLcHQ2T2FVVkjdE7PQlJ3kyz5aLALLeCvLgp2mF8jtP5L/RPqilQBHCCGEqBQJdKpBXmY6a6a/R3r8eUCD3qYdOqumeKcdIOTCMrY0KeFoCzND8k2czh9GUd8J/FeknwQ4QgghxB2SQOcBu3ziKOtmfEBxfh5orLGw64214kLEiW9Itz3CJ8Ohu9aATcEA9D1f4vXIQAlwhBBCiLskgc4DoigKMZvWEr14HorZjEbniaVdXzyzz+OVOIuf2xUS6lPGE2U98egymeGNgiXAEUIIIe6RBDoPgKG0hM1zZ3J6VzQAWsu62Fi0oc6ZFcQEHubMoFLqmdoT0uFtOjeuLQGOEEIIUUUk0LnPctPTWPXhf8hKjEddj9Mez0I9Fukf8nvnLHxtm9Kow7/p1LSuBDhCCCFEFZNA5z5KOBrLmunvYygtAo0N1tZdqZW8lyMRf2DfMoxOHWbTvlkjCXCEEEKI+0QCnftAURR2LfmJ/Wt+BBQ0Oi/cTfUoKlvI6U6WtGm/kPatWkmAI4QQQtxnEuhUsbKSYha98Sa5GfEA6Cwi8CzMJaX+cpp0e48OT3SSAEcIIYR4QCTQqUIHftnArgXfY1byAS12mvoY7Q/gNmYiQzrOlQBHCCGEeMAk0KkiP/3n3yQdOw6UgsYGe50T3gN96dNviwQ4QgghRDWRQKeKtBk4hBXH30WLLZ6N/Rn65ntotFJsUwghhKhOEuhUkcCIujTu24PWvfpj6+RU3cMRQgghBBLoVKlOw0ZV9xCEEEII8RfybEUIIYQQNZYEOkIIIYSosSTQEUIIIUSNJYGOEEIIIWosCXSEEEIIUWNJoCOEEEKIGksCHSGEEELUWBLoCCGEEKLGkkBHCCGEEDWWBDpCCCGEqLEk0BFCCCFEjSWBjhBCCCFqLAl0hBBCCFFjPXbVyxVFASAvL6+aRyKEEEKIyvrze/vP7/HKeuwCnfz8fAACAgKqeSRCCCGEuFP5+fk4OTlVur9GudPQ6BFnNptJTk7GwcEBjUZTpefOy8sjICCAy5cv4+joWKXnflTJNalIrklFck0qkmtSkVyTG3tcrouiKOTn5+Pr64tWW/mVN4/djI5Wq8Xf3/++/g5HR8cafbPdDbkmFck1qUiuSUVyTSqSa3Jjj8N1uZOZnD/JYmQhhBBC1FgS6AghhBCixtL9z//8z/9U9yBqEp1OR4cOHdDrH7ungjcl16QiuSYVyTWpSK5JRXJNbkyuy809douRhRBCCPH4kEdXQgghhKixJNARQgghRI0lgY4QQgghaiwJdIQQQghRY0mgcw8uXrzIuHHjCA4OxsbGhtDQUP79739TVlZ2y+NKSkp46aWXcHNzw97enmeeeYa0tLQHNOr777333qNNmzbY2tri7OxcqWNGjx6NRqO57l/37t3v80gfnLu5Joqi8N///d/4+PhgY2NDly5dOHfu3H0e6YOTnZ3N8OHDcXR0xNnZmXHjxlFQUHDLYzp06FDhPpk4ceIDGnHVmzVrFkFBQVhbW9OqVSv2799/y/7Lly8nIiICa2trIiMj2bhx4wMa6YNzJ9dkwYIFFe4Ha2vrBzja+2/Hjh306dMHX19fNBoNq1evvu0x0dHRNG3aFCsrK8LCwliwYMH9H+hDTAKde3D69GnMZjNz5szhxIkTzJgxg9mzZ/POO+/c8rjJkyezbt06li9fzvbt20lOTmbAgAEPaNT3X1lZGYMGDWLSpEl3dFz37t1JSUkp/7dkyZL7NMIH726uyUcffcQXX3zB7Nmz2bdvH3Z2dnTr1o2SkpL7ONIHZ/jw4Zw4cYItW7awfv16duzYwYQJE2573PPPP3/dffLRRx89gNFWvaVLlzJlyhT+/e9/c/jwYRo1akS3bt1IT0+/Yf/du3czdOhQxo0bR0xMDP3796d///4cP378AY/8/rnTawJqNuC/3g8JCQkPcMT3X2FhIY0aNWLWrFmV6h8fH0+vXr3o2LEjsbGxvPbaa4wfP55ff/31Po/0IaaIKvXRRx8pwcHBN309JydHsbCwUJYvX17edurUKQVQ9uzZ8yCG+MDMnz9fcXJyqlTfUaNGKf369bvPI6p+lb0mZrNZ8fb2Vj7++OPytpycHMXKykpZsmTJ/RziA3Hy5EkFUA4cOFDetmnTJkWj0ShJSUk3Pa59+/bKq6+++iCGeN+1bNlSeemll8p/NplMiq+vrzJt2rQb9n/22WeVXr16XdfWqlUr5YUXXriv43yQ7vSa3MlnTE0AKKtWrbplnzfffFOpX7/+dW2DBw9WunXrdj+H9lCTGZ0qlpubi6ur601fP3ToEAaDgS5dupS3RUREEBgYyJ49ex7EEB9a0dHReHp6Eh4ezqRJk8jKyqruIVWb+Ph4UlNTr7tPnJycaNWqVY24T/bs2YOzszPNmzcvb+vSpQtarZZ9+/bd8tgffvgBd3d3GjRowNSpUykqKrrfw61yZWVlHDp06Lr/vlqtli5dutz0v++ePXuu6w/QrVu3GnE/wN1dE4CCggJq1apFQEAA/fr148SJEw9iuA+tmn6f3A1JoViF4uLi+PLLL5k+ffpN+6SmpmJpaVlhnYaXlxepqan3e4gPre7duzNgwACCg4M5f/4877zzDj169GDPnj3odLrqHt4D9+e94OXldV17TblPUlNT8fT0vK5Nr9fj6up6y/c3bNgwatWqha+vL0ePHuWtt97izJkzrFy58n4PuUplZmZiMplu+N/3zwiK8gAACp5JREFU9OnTNzwmNTW1xt4PcHfXJDw8nO+++46GDRuSm5vL9OnTadOmDSdOnLjvxZsfVje7T/Ly8iguLsbGxqaaRlZ9ZEbnBt5+++0KC9z+/u/vf3hJSUl0796dQYMG8fzzz1fTyO+fu7kmd2LIkCH07duXyMhI+vfvz/r16zlw4ADR0dFV9yaq2P2+Jo+i+31NJkyYQLdu3YiMjGT48OEsWrSIVatWcf78+Sp8F+JRERUVxciRI2ncuDHt27dn5cqVeHh4MGfOnOoemniIyIzODbz++uuMHj36ln1CQkLK/3dycjIdO3akTZs2zJ0795bHeXt7U1ZWRk5OznWzOmlpaXh7e9/TuO+nO70m9yokJAR3d3fi4uLo3LlzlZ23Kt3Pa/LnvZCWloaPj095e1paGo0bN76rcz4Ilb0m3t7eFRaYGo1GsrOz7+jvoFWrVoA6mxoaGnrH460u7u7u6HS6Crstb/U54O3tfUf9HzV3c03+zsLCgiZNmhAXF3c/hvhIuNl94ujo+FjO5oAEOjfk4eGBh4dHpfomJSXRsWNHmjVrxvz589Fqbz1J1qxZMywsLNi6dSvPPPMMAGfOnOHSpUtERUXd89jvlzu5JlUhMTGRrKys677kHzb385oEBwfj7e3N1q1bywObvLw89u3bd8e72R6kyl6TqKgocnJyOHToEM2aNQNg27ZtmM3m8uClMmJjYwEe6vvkRiwtLWnWrBlbt26lf//+AJjNZrZu3crLL798w2OioqLYunUrr732Wnnbli1bHurPjTtxN9fk70wmE8eOHaNnz573c6gPtaioqAppB2rSfXJXqns19KMsMTFRCQsLUzp37qwkJiYqKSkp5f/+2ic8PFzZt29fedvEiROVwMBAZdu2bcrBgweVqKgoJSoqqjrewn2RkJCgxMTEKO+++65ib2+vxMTEKDExMUp+fn55n/DwcGXlypWKoihKfn6+8sYbbyh79uxR4uPjld9++01p2rSpUrt2baWkpKS63kaVutNroiiK8sEHHyjOzs7KmjVrlKNHjyr9+vVTgoODleLi4up4C1Wue/fuSpMmTZR9+/Ypu3btUmrXrq0MHTq0/PW//+3ExcUp//u//6scPHhQiY+PV9asWaOEhIQo7dq1+//t3WlIVOsfB/DvpLM4zogapRYqiSiU2YJoKChhZoiaZZuIaZJGStCLDK1MgiKUNEok6Y1KZKsYgmjkBuFCOUmG2RBuvdAWBpJyScPf/0U093rtXv6hzdw7fT8wMGfmOc8Gc/jynDk81hrCoty+fVvUarVUVlbKy5cvJTMzU5ydneXt27ciIpKSkiK5ubnm8u3t7WJvby+XLl2S/v5+KSgoEKVSKS9evLDWEJbcz87JuXPn5OHDhzIwMCAGg0EOHDggGo1G+vr6rDWEJffp0yfz9QKAlJSUSE9Pj4yMjIiISG5urqSkpJjLDw4OilarlZycHOnv75eysjKxs7OTxsZGaw3B6hh0FqGiokIA/PD13dDQkACQ1tZW82dTU1OSlZUlLi4uotVqZdeuXfPC0X9damrqD+fkz3MAQCoqKkREZHJyUrZv3y4rVqwQpVIp3t7ekpGRYb642YKfnRORb4+Y5+fni5ubm6jVaomMjBSj0Wj5zv8iJpNJkpKSRKfTiZOTkxw6dGhe8Pvrb+fNmzcSHh4urq6uolarxdfXV3JycmR8fNxKI1i80tJS8fLyEpVKJcHBwdLV1WX+LiIiQlJTU+eVv3v3rvj5+YlKpZJ169ZJfX29hXv86/3MnBw/ftxc1s3NTWJiYuTZs2dW6PWv09ra+sNrx/d5SE1NlYiIiAXnbNy4UVQqlfj4+My7rvyOFCIillo9IiIiIrIkPnVFRERENotBh4iIiGwWgw4RERHZLAYdIiIislkMOkRERGSzGHSIiIjIZjHoEBERkc1i0CEimzE8PAyFQmHeGoKIiEGHiCzmw4cPOHr0KLy8vKBWq+Hu7o7o6Gi0t7cvSf2enp4YGxtDQEDAktRHRP993NSTiCwmMTERMzMzqKqqgo+PD969e4fm5maYTKYlqd/Ozs5mdvMmoqXBFR0isoiPHz/i8ePHKCwsxNatW+Ht7Y3g4GDk5eUhPj4eAFBSUoL169fD0dERnp6eyMrKwufPnwF8273dwcEBDQ0N8+qtra2FXq/H5OTkgltXbW1tUCgUaG5uRlBQELRaLUJDQ2E0GufVcf78eaxcuRJ6vR6HDx9Gbm6uedf47/UEBwfD0dERzs7OCAsLw8jIyK+cLiJaIgw6RGQROp0OOp0ODx48wJcvX35YZtmyZbh69Sr6+vpQVVWFlpYWnDx5EgDg5OSE2NhYVFdXzzvn5s2bSEhIgFar/du2T58+jeLiYnR3d8Pe3h7p6enzzr9w4QIKCwthMBjg5eWFa9eumb//+vUrEhISEBERgd7eXnR2diIzMxMKhWIx00FElmLtXUWJ6Pdx//59cXFxEY1GI6GhoZKXlyfPnz//2/L37t2T5cuXm49ra2tFp9PJxMSEiIiMj4+LRqORhoYGEfljx/Oenh4R+WPn56amJnMd9fX1AkCmpqZERCQkJESys7PntRsWFiYbNmwQkW+7rAOQtra2JZgBIrI0rugQkcUkJiZidHQUdXV12LFjB9ra2rB582ZUVlYCAJqamhAZGYnVq1dDr9cjJSUFJpMJk5OTAICYmBgolUrU1dUBAGpqauDk5IRt27b9Y7uBgYHm9x4eHgCA9+/fAwCMRiOCg4Pnlf/zsaurK9LS0hAdHY24uDhcuXIFY2Nji5sIIrIYBh0isiiNRoOoqCjk5+ejo6MDaWlpKCgowPDwMGJjYxEYGIiamhoYDAaUlZUBAGZmZgAAKpUKe/bsMd++qq6uxv79+2Fv/8/PVSiVSvP777ec5ubm/u8+V1RUoLOzE6Ghobhz5w78/PzQ1dX1U+MmIutg0CEiq1q7di0mJiZgMBgwNzeH4uJibNmyBX5+fhgdHV1QPjk5GY2Njejr60NLSwuSk5MX1b6/vz+ePn0677O/HgPApk2bkJeXh46ODgQEBCz4rxAR/Tvx8XIisgiTyYS9e/ciPT0dgYGB0Ov16O7uRlFREXbu3AlfX1/Mzs6itLQUcXFxaG9vR3l5+YJ6wsPD4e7ujuTkZKxZswYhISGL6texY8eQkZGBoKAg84pNb28vfHx8AABDQ0O4fv064uPjsWrVKhiNRrx+/RoHDx5cVLtEZBkMOkRkETqdDiEhIbh8+TIGBgYwOzsLT09PZGRk4NSpU3BwcEBJSQkKCwuRl5eH8PBwXLx4cUGgUCgUSEpKQlFREc6ePbvofiUnJ2NwcBAnTpzA9PQ09u3bh7S0NDx58gQAoNVq8erVK1RVVcFkMsHDwwPZ2dk4cuTIotsmol9PISJi7U4QEf2bREVFwd3dHTdu3LB2V4hokbiiQ0S/tcnJSZSXlyM6Ohp2dna4desWmpqa8OjRI2t3jYiWAFd0iOi3NjU1hbi4OPT09GB6ehr+/v44c+YMdu/ebe2uEdESYNAhIiIim8XHy4mIiMhmMegQERGRzWLQISIiIpvFoENEREQ2i0GHiIiIbBaDDhEREdksBh0iIiKyWQw6REREZLMYdIiIiMhm/Q+KRIYtioU/wQAAAABJRU5ErkJggg==", + "text/plain": [ + "PyPlot.Figure(PyObject )" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Plot consumption policy function with different ages (income level 2)\n", + "inc_level=2\n", + "a_states=convert(Vector{Int64},linspace(10,15,6))\n", + "num_aplots=length(a_states)\n", + "dr_alist=[Dolo.tabulate(model, dr, :s, s0, a) for a in a_states]\n", + "c_alist=[exp(ygrid[inc_level])+dr_alist[a][:s]*r-dr_alist[a][Axis{:V}(:a)] for a in 1:num_aplots];\n", + "\n", + "for i=1:num_aplots\n", + " plt.plot(dr_alist[i][Axis{:V}(:s)][1:end_y],c_alist[i][1:end_y], label=i)\n", + "end\n", + "\n", + "plt.xlabel(\"Savings\")\n", + "plt.ylabel(\"Consumption\")\n", + "plt.title(\"Consumption Policy Function across different Ages\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Simulate the model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "T=10\n", + "hor=linspace(1,T,T)\n", + "mc_ar=model.exogenous\n", + "sim_armc = Dolo.simulate(model,dr,mc_ar;N=100,T=10);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + " Set-up the life-status and earnings path for the agents" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "N=100\n", + "T=10\n", + "\n", + "life_grid=ones(num_ages)\n", + "life_grid[num_ages]=0.0\n", + "income_path=zeros(T,N)\n", + "life_status=zeros(T,N)\n", + "age_status=zeros(T,N)\n", + "Tot_states=54.0 #(9 ages * 6 income states)\n", + "for j=1:N\n", + " for i=1:T\n", + " state=convert(Int64,sim_armc[Axis{:N}(j), Axis{:V}(:mc_process)][i])\n", + " state_y,state_a=Dolo.node(model.exogenous,state)\n", + " income_path[i,j]=exp(state_y)\n", + " life_status[i,j]=state_a\n", + " end\n", + "end" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Plot the life-cycle consumption and income profile for one agent." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "PyPlot.Figure(PyObject )" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pick an agent from the simulation: \n", + "import PyPlot\n", + "plt=PyPlot\n", + "j=9\n", + "t_death=maximum(find(life_status[:,j]))\n", + "horizon=t_death-2\n", + "hor_alive=linspace(1,horizon,horizon)\n", + "\n", + "c=(income_path[1:horizon,j]+sim_armc[Axis{:N}(j), Axis{:V}(:s)][1:horizon]*r-sim_armc[Axis{:N}(j), Axis{:V}(:a)][1:horizon]).*life_status[1:horizon,j]\n", + "plt.plot(hor_alive, income_path[1:horizon,j].*life_status[1:horizon,j], color=\"blue\", alpha=0.35, label=\"income\")\n", + "plt.plot(hor_alive, c, color=\"green\",label=\"consumption\")\n", + "plt.legend()\n", + "plt.xlabel(\"Time Period\")\n", + "plt.title(\"Simulated Paths for Consumption and Income\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 0.5.1", + "language": "julia", + "name": "julia-0.5" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "0.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/huggett_1993.yaml b/huggett_1993.yaml new file mode 100644 index 0000000..3b9476d --- /dev/null +++ b/huggett_1993.yaml @@ -0,0 +1,76 @@ +name: Huggett 1993 + +# s is the level of savings: c+s(+1)=(1+r)*s+y +# define a, the control, as: a=s(+1)-s +symbols: + exogenous: [lny] # exogenous states + states: [s] # endogenous states + controls: [a] # here the control is the difference in savings between tomorrow's assets and today's assets: a=s(+1)-s + expectations: [m] + values: [V] + parameters: [beta, sigma, r, rho, ybar,sig_y, abar] # abar is the borrowing constrain; # r is the interest rate, 1+r is the gross interest rate + rewards: [u] + +definitions: + y: exp(lny) + c: y+r*s-a + +equations: + + arbitrage: + - 1-beta*(c/c(1))^(sigma)*(1+r) | abar-s<= a <= inf + + transition: + - s=a(-1)+s(-1) + + value: + - V=c^(1-sigma)/(1-sigma) +beta*V(1) + + felicity: + - u=c^(1-sigma)/(1-sigma) + + expectation: + - m=beta/(c(1)^sigma)*(1+r) + + +calibration: + + # parameters taken from Huggett (1993) paper + beta: 0.97 + sigma: 2 + r: 0.0001 + rho: 0.6 + ybar: 0.0 + sig_y: 0.4 + abar: -2.0 + m: 0 + V0: (c^(1-sigma)/(1-sigma))/(1-beta) + + # endogenous variables - initial conditions + a: 0.0 + s: 0.0 + lny: ybar + y: exp(lny) + c: y+r*s-a # budget constraint + V: (c^(1-sigma)/(1-sigma))/(1-beta) + u: c^(1-sigma)/(1-sigma) + + +exogenous: !VAR1 + rho: 0.85 + Sigma: [[sig_y^2]] + N: 5 + +domain: + s: [-2.0,20.0] + +options: + grid: !Cartesian + order: [30] + + + + + + + \ No newline at end of file