HW_5_pair_coding_with_sonny.ipynb

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  "language": "Julia",
  "name": ""
 },
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Robert Driving Sonny Nav\n",
      "function Axb(A::Array{Float64,2},b::Array{Float64,1})\n",
      "  return A*b\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "Axb (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = rand((2,2))\n",
      "b = rand((2))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "2-element Array{Float64,1}:\n",
        " 0.664424\n",
        " 0.293108"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Axb(A,b),A*b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".7778711252014386\n",
        ".751041934911207\n",
        ".7778711252014386\n",
        ".751041934911207\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sonny driving, Robert Nav\n",
      "function Axb_2(A::Array{Float64,2},b::Array{Float64,1})\n",
      "    x = zeros(size(b))\n",
      "    for i in 1:length(A[:,1])\n",
      "        for j in 1:length(A[1,:])\n",
      "            x[i] += A[i,j]*b[j]\n",
      "        end\n",
      "    end\n",
      "    return x\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "Axb_2 (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Axb_2(A,b),A*b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".7778711252014386\n",
        ".751041934911207\n",
        ".7778711252014386\n",
        ".751041934911207\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sonny driving, Robert Nav\n",
      "function Axb_3(A::Array{Float64,2},b::Array{Float64,1})\n",
      "    x = zeros(size(b))\n",
      "    for j in 1:length(A[1,:])\n",
      "        for i in 1:length(A[:,1])\n",
      "            x[i] += A[i,j]*b[j]\n",
      "        end\n",
      "    end\n",
      "    return x\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "Axb_3 (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(Axb_3(A,b),A*b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".7778711252014386\n",
        ".751041934911207\n",
        ".7778711252014386\n",
        ".751041934911207\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##1d. \n",
      "b will be faster because it is in column major form. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##1e. \n",
      "Should be about the same for small matrices as they will fit on the L1 cache so they will be qucik either way.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function testing_function(f1,f2,f3,dimension)\n",
      "  A = rand((dimension,dimension))\n",
      "  b = rand((dimension))\n",
      "  println(dimension,\"x\",dimension, \" Matrix\")\n",
      "  println(\"Function 1    Function 2    Function 3\")\n",
      "  println((@elapsed f1(A,b)),\"      \",(@elapsed f2(A,b)),\"      \",(@elapsed f2(A,b)))\n",
      "  println()\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "testing_function (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "testing_function(Axb,Axb_2,Axb_3,8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x8 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "5.856e-6      9.537e-6      2.273e-6\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in [4,8,32,264,512,1024,1025,10000]\n",
      "  testing_function(Axb,Axb_2,Axb_3,i)\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4x4 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "5.037e-6      8.714e-6      2.281e-6\n",
        "\n",
        "8x8 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "8.09e-6      4.729e-6      2.988e-6\n",
        "\n",
        "32x32 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "2.728e-6      2.8385e-5      2.1947e-5\n",
        "\n",
        "264x264 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "0.000339658      0.001088721      0.001148038\n",
        "\n",
        "512x512 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "0.000504836      0.00347624      0.006173517\n",
        "\n",
        "1024"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x1024 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "0.001708682      0.041160704      0.039387101\n",
        "\n",
        "1025x1025 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "0."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "001048628      0.037675767      0.023529767\n",
        "\n",
        "10000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x10000 Matrix\n",
        "Function 1    Function 2    Function 3\n",
        "0."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "065427243      5.855805872      5.523546687\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ooopse we got our terminology mixed-up with or rows and columns. -R \n",
      "\n",
      "Note the thrashing in the 1024 and 1025 cases. -S"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##2a.)\n",
      "The use of the abstract type is sufficient since the lookup() function has an implicit capability to parse different table types. -S"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#2b) \n",
      "#Sonny driving\n",
      "#Pkg.add(\"Calculus\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "include(\"CLASS_REPO/Astro585/Lab5/HW5_Q2_lookup_table.jl\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "lookup (generic function with 3 methods)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lookup_table = make_table_linear(x->tanh(x),0.,2.,10);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#2c)\n",
      "#Sonny driving\n",
      "using PyPlot\n",
      "x = linspace(0.,2.,1000);\n",
      "y1 = tanh(x);\n",
      "y2 = lookup(lookup_table,x);\n",
      "res = y2 - y1;\n",
      "plot(x,y1,label=\"Function\");\n",
      "plot(x,y2,label=\"Lookup\");\n",
      "legend(loc=\"lower right\");\n",
      "#figure();\n",
      "#plot(x,res,'o',label=\"Residuals\")\n",
      "#legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
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      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using PyPlot\n",
      "plot(x,res,label=\"Residuals\")\n",
      "legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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/8MILLyA7Oxv33ntvxHXIy8vD+vXrsWDBAnzlK1/BggULUFZWhrfeegv/93//h0svvTSkCp2eno4lS5bgJz/5CaZMmYJvfvObGBgYQF1dHU477TRUVFSETIl833334e6778aUKVMwZ84cZGRk4O2338bHH388PIJFNPv27cOcOXMwffp0nHPOOaioqEBjYyO2b9+OwcHB4R5gK2Pll8jCBgeBb34T+Phjs9eEiMi5HnzwQeTm5uIXv/jFcBX1O9/5Dt5//3185zvfwQcffIBf/vKX+N3vfodDhw5h/vz5WLdu3fD9b7zxRtx2221oaGjAs88+i8cffxz79u3DjTfeiPfeey9gDGGXy6VZYf32t7+Nl19+GRdeeCG2bt2K9evXY+TIkXjnnXdw5plnat5nxYoVeOyxx5CdnY0NGzbg5Zdfxty5c/Hyyy8jIyMj5D6LFy9GdXU1ysvLUVNTgy1btuALX/gCdu/ejalTp+qq/E6bNg0PPvgg0tPT8corr2D16tV45ZVXMG3aNLz00ktRg74VuLzBhwUOt2fPHlx44YV4//33LT8DCVFbG1BcDPz2t8Ctt5q9NtbwL/8CTJwI3Hef2WtCZA/c75GV6H0/Gvm+ZdsDkYXJ4Re7usxdDyvZuRP4fHQhIiKimLHtgcjC+vvFT4Zfn64u30EBERFRrBh+iSyM4TcUwy8RESWC4ZfIwhh+QzH8EhFRIhh+iSxMhl/2uPp4PAy/REQUP4ZfIgtj5TfQ4CDQ28vwS0RE8WP4JbIwht9A3d3iJ8MvERHFi+GXyMIYfgPJ7cDwS0RE8WL4JbIwjvMbSPY+M/wSEVG8OMkFkYXxgrdArPwSxe9jzpNOFmCF9yHDL5GFse0hkNwOvb3mrgeRnRQUFAAAbrrpJpPXhMhHvi/NwPBLZGEMv4FY+SWK3cSJE3HgwAF0dHSYvSpEAETwnThxomnPz/BLZGEMv4EYfoniY2bQILIaXvBGZGEMv4F4wRsRESWK4ZfIwhh+A7HyS0REiWL4JbIwGX57esTsZqpj+CUiokQx/BJZmH/Ik7ObqUyG38FBHgwQEVF8GH6JLExWfgG2PgCB4x37bxsiIiK9GH6JLIzhN5D/NmDrAxERxYPhl8jCGH4DMfwSEVGiGH6JLMw//HKKY4ZfIiJKHMMvkYWx8huI4ZeIiBLF8EtkYf39QEaG+DvDr6h+y+ngGX6JiCgeDL9EFtbXBxQVib8z/IptUFws/s7wS0RE8WD4JbKw/n5f2GP4FdugpET8neGXiIjiwfBLZGH9/UB+PuB284I3gJVfIiJKHMMvkYXJnt/cXFZ+AXEAICu/PT3mrgsREdkTwy+Rhcnwm5fH8AuIbTBqlPg7K+FERBQPhl8iC2PlN5B/+OX2ICKieDD8ElkYw28gVn6JiChRDL9EFtbdDeTkiPCretgbGhJ9vkVF4oBA9e1BRETxYfglsjCPR/T7svLre/25uTwYICKi+DH8ElmYDL+84C0w/HJ7EBFRvBh+iSzM4/FVOlUPe8Hhl5VfIiKKB8MvkYV1dbHtQZKvX1bCGX6JiCgeDL9EFubf86t62Avu+VX9YICIiOLD8EtkYbzgzUeGf7Y9EBFRIhh+iSzK62XPrz/2/BIRUTIw/BJZVG+vCMAc7UFg2wMRESUDwy+RRcnKJtseBF7wRkREycDwS2RRweG3u1vMcqYqjwdISxOzuzH8EhFRvBh+iSzK/wKv3Fzx9+5u89bHbF1dYju4XAy/REQUP4ZfIovyP80vw6/KrQ8y/AJsAyEiovgx/BJZVHDbA6B24JMTfgCs/BIRUfwYfoksyj/8ytCneviVBwF5eeyBJiKi+DD8ElmUVs+vyuFXjnkMsAeaiIjix/BLZFFaPb8qn+oPrvwCam8PIiKKD8MvkUV5PEB6OpCZycovwPAbycKFwHPPmb0WRET2wPBLZFEejy/kMfyGXvAmbyPgT38C9u0zey2IiOyB4ZfIovx7XBn2tHt+WfkVPB5gcNDstSAisgeGXyKL8q/8ZmWJyR1UDr9se9A2MAD09jL8EhHpxfBLZFH+p/ldLhH8VA57DL/a5DZg+CUi0ofhl8ii/Cu/AGc1C57hTd6mOoZfIqLYMPwSWZR/jyvA8Kt1wRsrv0Bnp/jJ8EtEpE9M4be3txfLly9HRUUFcnNzUVVVhbq6Ol33bW1txeLFizFq1Cjk5+dj5syZ2Lt3r+ayO3bswIwZM5CXl4fy8nIsWbIEnih7uWeeeQZutxsFBQWxvCQiy2LlN5D/wYAcAo7hl5VfIqJYxRR+b7vtNqxZswY333wznnjiCaSlpeGaa67B22+/HfF+Q0NDuPbaa7Flyxbcc889WLlyJRoaGnD55Zfjk08+CVh23759mDVrFnp6erBmzRrcddddWL9+PebNmxf28Ts7O7Fs2TLk5eXB5XLF8pKILMu/0gmIv6safoeGgJ6ewEq4ytvDHyu/RESxSde74O7du/H73/8eq1atwtKlSwEAN998MyZPnoxly5ZFDMC1tbXYuXMnamtrMWfOHADA/PnzMWnSJDz88MN45plnhpf94Q9/iNLSUrz++uvIz88HAJxxxhlYtGgRXn31VVx55ZUhj//Tn/4UhYWFmDlzJv7whz/ofUlElqZV+VW10imnMQ5uA1F1e/hj5ZeIKDa6K7+1tbVIT0/H4sWLh2/LysrCnXfeiZ07d+LYsWMR71tWVjYcfAFg5MiRmD9/PrZv347+/n4AQHt7O+rq6nDTTTcNB18AuOWWW5Cfn4+tW7eGPPbBgwexdu1arFmzBmlpaXpfDpHlse3BR77u4Movwy8rv0REsdIdfvfu3YtJkyYFhFIAmDZtGgDRrhDpvlOnTg25fdq0aejq6sKBAwcAAB9++CEGBgZw0UUXBSyXkZGByspKzR7he++9FzNnzsTVV1+t96UQ2QIvePORr5ttIKFY+SUiio3u8FtfX4/y8vKQ2+Vtx48fT/i+9fX1Abf7KysrC3mO//7v/8arr76K1atX63wVRPYR3POrcviVAY9tD6EYfomIYqM7/HZ3dyMrKyvk9uzs7OHfh9PT06PrvvJnuGX9n6Ovrw/33Xcfvvvd7+Kcc87R+zKIbCO47UHlSifbHsJj2wMRUWx0X/CWk5OD3t7ekNt7enqGf5/ofeXPcMvm+u351qxZg+bmZqxYsULvSwhw7733ori4OOC2hQsXYuHChXE9HlEyDQ1pV35VDXsMv+Gx8ktEdrdlyxZs2bIl4LbW1lbDnk93+C0vL9dsbZCtChUVFQnfV7Y7yNuDl5XLtbW14ac//Sm+973vobW1dXgDdXZ2wuv14tNPP0VOTg5Gjx4ddp3Wrl2r2YdM5uruBrKyALfi069ojW6gctiTrzu4En7qlDnrYyWs/BKR3WkVH/fs2YMLL7zQkOfTHTGmTJmCAwcOoKOjI+D2Xbt2AQAqKyvD3reyshJ79uyB1+sNuW9eXh4mTZoEAJg8eTLS09Px7rvvBizX19eHffv2DT9HS0sLPB4PVq5cifHjxw//2bZtG7q6unDmmWfi7rvv1vvSyEIuuADYuNHstTBfuAu8GH59t6lcCffHyi8RUWx0h9+5c+dicHAQ69evH76tt7cX1dXVqKqqwmmnnQYAOHHiBPbv34+BgYGA+548eRLbtm0bvq2pqQnPPvssrrvuOmRkZAAAioqKcMUVV2Dz5s3olOUMAJs2bYLH4xme6GLMmDF4/vnn8Yc//CHgz1e/+lVkZ2fjD3/4Ax588ME4NwmZ6eRJ4PBhs9fCfOEqnaqGPW6P8Fj5JSKKje62h+nTp2PevHl48MEH0dDQgAkTJmDjxo04cuQIqqurh5d74IEHUFNTg8OHD2PcuHEARPitqqrC7bffjo8++gilpaVYt24dvF5vSM/uo48+iosvvhiXXXYZFi1ahM8++wyrV6/GVVddhdmzZwMQvcHXX399yDpu27YNu3fvxje+8Y24NgaZb2jItzNXWbiw19srQo5qQ1p7PEBGhvgjqXwBoD9WfomIYhNTZ2VNTQ3uvfdebNq0CUuWLMHg4CBefPFFzJgxY3gZl8sVMsWw2+3GSy+9hAULFuCJJ57AsmXLMHr0aPz5z3/GxIkTA5adMmUK6urqkJOTg6VLl+Kpp57CXXfdhdra2qjrp/XcZC9eL8MvoD20lwzCKlY7g0e+ANj2ILHyS0QUG92VX0AMQbZy5UqsXLky7DLV1dUBlWCpuLgYGzZswIYNG6I+zyWXXIK33norllWL+NxkH0NDQFBbuZLC9fwCIvAVFqZ+ncykFX7Z9iCw8ktEFBvFr6knq2HlVwjX9uD/O5WEC789PQx9DL9ERLFh+CVLYc+vwPAbKFz4BXzDwqmKbQ9ERLFh+CVLYeVXYM9voHA9v/J3KmPll4goNgy/ZCns+RW6uoDMTCDdryuf4TfwNpW3hz95sDg0ZO56EBHZBcMvWQorvwLDXqBI20Pl4c7kNNguFyu/RER6MfySpbDnV2D4DcS2B23d3eKAsaCA4ZeISC+GX7IMOft1Z6fv76ryeAL7fQEgJ0dU+FQMezwY0CZfe1ERwy8RkV4Mv2QZMvAODIiZzFSmFfZcLnUndmDbgzZ5lqSwkOGXiEgvhl+yDP9qr+qtD11doWEPUHdih85OVn61sPJLRBQ7hl+yDP+r1VUPv1qVTkDd8MueX23ytbPyS0SkH8MvWYZ/5Vf14c60en4Bhl9/aWlAVpaa20OSB4ms/BIR6cfwS5bByq8PK78+/f3iT7jtoXLPLyu/RESxY/gly2DPrw97fn20pnqWVL0AUGLll4godgy/ZBms/Pqw8usTKfyquD38eTxiFJC8PIZfIiK9GH7JMtjz68OeX59o4Vfltgc5CkZ6OsMvEZFeDL9kGaz8+rDy68PKb3geD5CfLy7+Y/glItKH4Zcsgz2/Puz59WHPb3jyIInhl4hIP4Zfsgy2PQiDg0BPD8OvxMpveJ2drPwSEcWK4Zcsg20PguxhZc+vECn85uertz38sfJLRBQ7hl+yDLY9CKx0Boq2PVR+r8gL3hh+iYj0Y/gly2DlV5CV30ijG/hvK6fzeAC3W8zmFoyVX7Y9EBHFiuGXLENWfl0utXt+ZfDPzw/9nQzE3d2pWx+zyVP7Llfo7/Lz1T5QYuWXiCh2DL9kGbKaWVCgdqCJdprffxkVhBv2DVCzDcRfZ6f4vKSlic+Pf+sQERFpY/gly5A77sJCtcOvnsqvSoEvUviVlV9VQ5//aA+AWu0wRETxYvgly5A7boZf8ZPhV4gWfoeGxNBwKgoOv2x9ICKKjuGXLENW7woK1O75ZdtDoGhtD3IZFXV0+NoeAIZfIiI9GH7JMlj5FTo7RZjRGt1AxbAXrfILqPl+GRoKHO0BYPglItKD4ZcsI7jnV/U+Tq3RDRh+A8nbVQy/3d3iM8LwS0QUG4Zfsgz/yu/QkFrDefnjaf5Aeiq/Km0Pyb83nOGXiEg/hl+yDP+eX0DNah7gq/xqkVMeqxT22PagTfbFM/wSEcWG4Zcsw7/yC6gZaIDI4TctDcjOZviVVKyES/LzwQveiIhiw/BLluHf8wuoG34jhT1AvYkdWPnV5t/24P78m5zhl4goOoZfsozgyq+qw51FqvwCDL/+cnLEhYGqh19WfomI9GP4Jctg5Vdg+A0UKfy6XOptD8k//GZni7+rOtkHEVEsGH7JMtjzK3R2su1BGhwUgS7S9pBTHKvG/4I3eZGoqmdLiIhiwfBLlhE82oOqO3I5cUE4KoXfri7xkwcDoTo7RcU3PZ2fGSKiWDD8kmXIym9WFpCZqWY1D2Dbg79IUz1LqlZ+/d8nDL9ERPox/JJlyMqvyyV25ioGGoDh1598nZG2B8Mvx8YmIooFwy9Zhqz8ut3qBhqAQ53501P5VWl7+Ovo8IXe3FzxuWHll4goOoZfsgz/ym9+vpo78qEh9vz6Y9tDeP6VX5U/M0REsWL4Jctg5dd3gRfDr6C38qvieyW4PaaggOGXiEgPhl+yDPb88jR/ML2VX1W2hz+GXyKi+DDOrRxIAAAgAElEQVT8kmWw8hs4cUE4DL+BVH2vdHQw/BIRxYPhlyyDPb+xhV+5vZzM4xHvh5yc8MuodDDgr7PTd8EbwPBLRKQXwy9Zhn/ll20P4ZfJyxPbqrc3NetkJo9HjGTgcoVfRtXKL9seiIjiw/BLlhFc+VU10ADRK7+AGtXOaMO+Ab73igqVcH/B4VfVsyVERLFi+CXLCO75VXFHzvAbSE/4VakS7o+VXyKi+DD8kmWw8qu/7cF/WSfTW/kF1Hq/DA2x55eIKF4Mv2QZsvIrhzrzeHy3qaKzE0hPBzIzwy/D8BtIpe0haY0HzfBLRKQPwy9Zhqz8yrYHrxfo7jZ3nVJNnsqOdIGXSmGPlV9tWu0xDL9ERPow/JJl+Fd+5U5dtZ15Zycrnf4YfrWFC79dXcDgoDnrRERkFwy/ZBn+lV/Zy6hSoAFE2It0sRvg+z3Dr6DSwYAkDwqDe34B9T4zRESxYvgly9Cq/Kq2Iw++gl+LDHsqbBs920PF90q4yi+g3tkSIqJYMfySZQT3/ALq7cj1hL30dCA7W42wx4MBbQy/RETxY/glywge7QFQK9AA+k7zA+oMBacn/ObkiPeMSm0PDL9ERPFj+CXL0Kr8qhDw/OkJewDDrz+3WxwwqLA9JBlw/Q+UVD1gJCKKFcMvWYZ/5VfFU9kAw28wvdsjL0+9ym9OjmiBkVj5JSLSh+GXLMO/8iv7WlXbkbPtwaevT/zhwUAorYMChl8iIn0Yfsky/Cu/gNiZqxRoAFZ+/clKLrdHKK33SVaWOGhk+CUiiozhlyzDv/ILqBdoAIZff1oXdYWjYttD8HaRF4oy/BIRRcbwS5YRXPnNz1dvR862B59Ywq8K28NfR0fgBBcSw6/PX/6i1nuCiPRj+CXLCK78FhaqtSMfHBTT0zLsCbFWfp2+PfyFO0Og4gGjloEBYOZMoLbW7DUhIiti+CXL0Or5bW83b31SratL/GT4FWKt/Kre9gCw8iu1t4uDyd5es9eEiKyI4ZcsI7jyq9qOXIY3tj0IbHsIj+E3srY28XNw0Nz1ICJrYvglywiu/KrW9sCwF4gXvIXHnt/I5Bkjhl8i0sLwS5ahVflVqe0hnvArt5kTdXaKA6GcnOjLqnAw4I+V38hY+SWiSBh+yTK0en5V2pHL8Ka37WFoCOjpMXadzNTZKbaFW8e3FMOvoNpnJhxWfokoEoZfsgzVR3uIdVIHwNmBT++Yx4Cv7cHJlXB/DL+RycqvPKAmIvLH8EuWEa7yq1KgARh+pVjCb36+Olf3Dw2JoM/wGx4rv0QUCcMvWYZWz+/QkG8IMKeLte3B/z5OFGvlF1Djojf5GnnBW3js+SWiSBh+yTJk+PUf7QFQZ2fu8QAZGUBmZvRlGX4DqbA9pEhnCAoKRPW7vz+162Q1rPwSUSQMv2QZsu3Bv/ILqDPiA8NeIG4PbdHCL6DOAWM4rPwSUSQMv2QZwZVf1XbkDHuBuD20MfxGJw+YecEbEWlh+CXL0JrkAlBnR+7x6Ov3BXzBx8k9rgy/2uTnIVL4VWE7RMLKLxFFwvBLlhGu8su2h1CZmUB6urNDTizbQ6WKp3yN8uDQn0rbIRL2/BJRJAy/ZBlDQ4ETGqi2I5eTOujhcjl/Yod4Kr8qvFdksAs32gOgxnaIhJVfIoqE4Zcsw+v1VX0BMa1tWpo6O/KODu1AEw7Dr09WlvMr4VJHh/icaB0oMfwKrPwSUSQMv2QZwZVfl0vszFVpe2D4DRRL+JXvFRVCX0eH2C7+B4qSShXwSDjDGxFFwvBLlhFc+QXUCTSACHsMv8LgoJjcRG/4BcSyKrxXOjq0+30B0QuemanGdgjH62Xll4giY/glyxgaCg2/hYXq7MhZ+fWRs/rFEn4LCpy7PfxFe5+odMCopafHN8kHwy8RaWH4JcvwegPbHgD12h5irXQ6NexFGss2HFVCX3s7w28k/t8XDL9EpIXhlyxDq/Kr0o6clV+feMKvSm0PDL/hyX5fgOGXiLTFFH57e3uxfPlyVFRUIDc3F1VVVairq9N139bWVixevBijRo1Cfn4+Zs6cib1792ouu2PHDsyYMQN5eXkoLy/HkiVL4Akazf/48eO46aabcPbZZ6OwsBDFxcWYOnUqnnzySQzyG8+WtCq/qrQ9eL3s+fUXb+XXqdvDX7TwW1ioztkSLfK15+Yy/BKRtvRYFr7tttvw3HPP4b777sPEiRNRXV2Na665Bq+99houueSSsPcbGhrCtddeiw8++ADLli1DaWkp1q1bh8svvxzvv/8+zjrrrOFl9+3bh1mzZuH888/HmjVrcPToUaxatQoHDx7ESy+9NLzcqVOncOzYMcyfPx/jxo1Df38//vSnP+Gee+7BX//6V2zYsCGOzUFmClf5ra83Z31SqatLBGCGXyHe8Hv4sCGrYykdHcAZZ4T/vSoHjOHIyu+IERztgYi06Q6/u3fvxu9//3usWrUKS5cuBQDcfPPNmDx5MpYtW4a333477H1ra2uxc+dO1NbWYs6cOQCA+fPnY9KkSXj44YfxzDPPDC/7wx/+EKWlpXj99deR//me74wzzsCiRYvw6quv4sorrwQAfPGLX8Rrr70W8Dz/8i//gm984xvYuHEj1q1bh4yMDL0vjyxA5Z7fSFPWhpOXx/DrT5W2h2g9v4WFQFNT6tbHauT3RUkJK79EpE1320NtbS3S09OxePHi4duysrJw5513YufOnTh27FjE+5aVlQ0HXwAYOXIk5s+fj+3bt6P/80tz29vbUVdXh5tuumk4+ALALbfcgvz8fGzdujXqen7hC19AWloa0tLS9L40sgiVR3uQr5GVX4FtD+Gx7SEyWfktLmb4JSJtusPv3r17MWnSpIBQCgDTpk0DINoVIt136tSpIbdPmzYNXV1dOHDgAADgww8/xMDAAC666KKA5TIyMlBZWanZI9zT04OmpiYcPnwYGzduxG9/+1ssXboU7uASIlleuMqvCuFXhjaGX0G+Lr3TPQPqvFcYfiNrbxezQ2ZnM/wSkTbdCbG+vh7l5eUht8vbjh8/nvB96z9v7tRatqysTPM51q5di9GjR2P8+PG4/fbbsWTJEjz66KM6XhFZTbie3/Z2EYydLN7Kb2+vb0xTJ+nsFOElPYarElRpe4g0yQXA8NvWJrZBWhrDLxFp071r6e7uRlZWVsjt2dnZw78Pp6enR9d95c9wy2o9x4033ojp06ejsbER27dvx89+9jNkZWXhoYce0vGqyErCVX4HBkTI+/zt4kjx9PzKZT0ecYrXSWKZ2lgqKBAXDg4OiuDjRP39YhIHVn7Da28HiorEe4AXvBGRFt3hNycnB729vSG39/T0DP8+0fvKn+GWzc3NDbl93LhxGDduHABgwYIFKCgowI9//GPceuutw7druffee1EclBgWLlyIhQsXhr0PGStczy8gwqEK4TfWyi8ggiLDr2/beTyRK6N2pud9UlgotoGTDwIiYeWXyH62bNmCLVu2BNzW2tpq2PPpDr/l5eWabQeyVaGioiLh+8p2h3qNsa3q6+sjPof07W9/Gxs2bMCePXsiht+1a9dq9iGTecJVfgFRzRk1KvXrlCrx9Lj6h1+niSf8yuWjtQXYmd7wK5d12kGRHm1tvsqvE1uCiJxIq/i4Z88eXHjhhYY8n+6e3ylTpuDAgQPoCGqq27VrFwCgsrIy7H0rKyuxZ88eeIMaN3ft2oW8vDxMmjQJADB58mSkp6fj3XffDViur68P+/bti/gckmyN4AVv9hOu5xdwfi+nnNo4lret08NvLFVwwLe8E7eHFEv4VbX1ob1dbAO3m5VfItKme1c7d+5cDA4OYv369cO39fb2orq6GlVVVTjttNMAACdOnMD+/fsxMDAQcN+TJ09i27Ztw7c1NTXh2WefxXXXXTc8Hm9RURGuuOIKbN68GZ1+e7BNmzbB4/Fg3rx5AffX8pvf/AaZmZmoqqrS+9LIIsLN8AaoE35j4fTwG2/bg5PfK/K1RbvgDVA3/PpXfhl+iUiL7raH6dOnY968eXjwwQfR0NCACRMmYOPGjThy5Aiqq6uHl3vggQdQU1ODw4cPD7cdzJ07F1VVVbj99tvx0UcfDc/w5vV6sWLFioDnefTRR3HxxRfjsssuw6JFi/DZZ59h9erVuOqqqzB79uzh5X76059ix44duPrqqzF27Fg0Nzfjueeew3vvvYcVK1Zg9OjRiW4bSrFIlV+n78ijDV+lheE3kH/bg1PJzwErv+HJym9vLy94IyJtMU1vXFNTg4ceegibNm1CS0sLLrjgArz44ouYMWPG8DIulwuuoATjdrvx0ksv4f7778cTTzyB7u5uTJ8+HTU1NZg4cWLAslOmTEFdXR2WL1+OpUuXorCwEHfddRcee+yxgOW+/vWv49ChQ3j66afR2NiInJwcTJkyBVu3bsXcuXNj3Q5kAZF6fp0caID4TvM7PfyWlsZ2H7Y9CKqHX1n5bW5m5ZeItMUUfrOysrBy5UqsXLky7DLV1dUBlWCpuLgYGzZswIYNG6I+zyWXXIK33nor4jJXXHEFrrjiiugrTbahVfnNyxO3OT38xlP5lYOfODHssfKrjeE3urY2caEf2x6IKBxeFUaWoVX5dbtFqHH6jjyenl+3WxwcMPwKublimzg9/ObkRJ78Q243p39mtAwOim3Enl8iioThlyxDq/ILqDFtbTyVX8C5UxzHE35dLuduD6m9Pfr7xO32zYyoGvmai4s52gMRhcfwS5ahVfkFxGlcp4ffeHp+AeeGvXjCL+D8KY71HiQVForT/6qRY+JzhjciioThlywjUuXX6VUsVn59vN74w6/TzxLEEn6d/pnRIgM/2x6IKBKGX7KMcJVfpwcaIL6eX8CZ4benRxwIxRt+nbY9/Omdva6oSM3wKyu/vOCNiCJh+CXLCFf5VaHtgZVfH/l62PYQipXfyFj5JSI9GH7JMvr6gM8n+wvg9LYHeZqf4VdIJPw6/SyBngveAHXDr3/PLy94I6JwGH7JMuT4nMGcHmi6u+M/zc/wG0iFtgeG3/Da2oDsbCArixe8EVF4DL9kGa2t2uHX6W0PeiYuCMeJp/nl68nLi/2+Ttwe/hh+I/M/gGbbAxGFw/BLliGnJQ3m9LaHRMKvE6vi3B7h6b3gTdXw29rq+w5h+CWicBh+yTLCVX6dHmjkaXqGPSHR8Ovktgf2/EbGyi8R6cHwS5YRqe2ht1dcEOdEMuzxAi+BbSDahob0XxgpW4VU63ll5ZeI9GD4JcsI1/YgT/M6NdQkEvbkgUF/f3LXyUwdHUBODpCeHvt9ZeXX603+epnN4xE/9YZfr9d3H1X4f4dwtAciCofhlyxhcFCcpg1X+QWcO11roqf5/R/DCfSe2tdSUCBCX1dXctfJCuT/sd6eX0C91gf/s0cc7YGIwmH4JUuQO2mt8CsrOU7dkcse1XhGN5Ah0UnbJt4JPwBf64iTDgYk+X+st/Lrfx9V+Fd+2fZAROEw/JIl+M/MFEyFym9urthZx8qJld9Ewq8Tt4cUyxkCVcNvcOWX4ZeItDD8kiXImZlUrPwy7AVKxvZw4ogPDL/RsfJLRHow/JIlRAq/KlR+GX592PagjeE3sp4eMSIMK79EFA3DL1mCDLZa4Tc7G8jIcO6OXO/wVVqcGn71XNSlxYnbQ4rlgjcn9oJHIw+g/Ud74AVvRKSF4ZcsIXjH5c/lEjt8J1d+4xnjF3Bm2Et0tAfAmW0P7e1i+LesrOjLpqeLPnKVwm/wdQOs/BJROAy/ZAmtrWJs18xM7d87ecaqRE7zp6eLyriTwm8i20OOmOGk7SHJ7eJy6VveyZ8ZLcGtUwy/RBQOwy9ZQrjZ3aSiIufuyBMJe4BvNi+nSGR7uN0iADtpe0ixbhfVwq9W5Rdg6wMRhWL4JUsIN7ub5OS2h0R6fgHnTXGc6MGAnOXNaWLthVYt/AZXfnNzxU/VZrkjougYfskSVK/8xtvzC4iw55Rt4/UmfjCQn++sgwEp1l5o1cJvW5toCZHbqKRE/GxpMW+diMiaGH7JEqKFXydXfpNR6XRK2PN4RABm5TcU2x4ia20Vr9n9+V6N4ZeIwmH4JUuI1vbg9Movw68Qy3Be4Thpe/hj+I0s+DuE4ZeIwmH4JUtQtfKbjNP8Tgp7Mqwluj2cGPqiHSAGUzH8+n+HjBghfjY3m7M+RGRdDL9kCar2/Pb0iOGYEu35dUr4jWUWs3CcNvqF1N7OC94iaW0NPDiQ3yes/BJRMIZfsgS9oz14valbp1RIRthj+A3k1NDX3s7KbyTB3yFpaWIbMPwSUTCGXzKd16uv8jswICqlTsJKZyCG3/Da2mKv/DrxgDEcre+QkhKGXyIKxfBLpvN4xKn/aD2/gPP6fmVIi6WiF4yV30BO7PkdHBS94bFWfgcHge5u49bLSrTOHjH8EpEWhl8ynQy00Sq/gPNCjXw9yRjdwAkVvo4OMVRVTk78j+GkSrgUzygYTj1gDIeVXyLSi+GXTCdnZorW8ws4b0eerPDr9TpjJis5i5nLFf9jFBYCXV2iTcYp4jlDIIOg0w4Yw2Hll4j0Yvgl0wVPS6qFld/wZIuAE6qdsc5ipkVuSydsD0ke9MXyPpGfGfn5crKhIfHeYeWXiPRg+CXT6Wl7cHLlNz0dyM6O/zGcFH4TnfAD8L1XnHSgFE/lVy7rtM+MFtn2w8ovEenB8Eumi6XtwUmBBvCN3ZrIaX6G30BOfK/EU/mVB5MqVH7DHUAz/BKRFoZfMl1rq6h+5uaGXyYjQ1wE5bQqVqwTF2hh+A3kxPAbT+W3oEAcVDntM6Ml3AG0DL9OuBiUiJKH4ZdMJ6cljVb9dOL4rQy/gRh+tbW1iVEw8vL038ftFttShfArX6NW+JXDxBERSQy/ZLrgaUnDceIUx8kIv066wIvhV1u87THFxWq0PYS7aLakRPxk6wMR+WP4JdNFm91NkjNWOUkywm9OjqjyOSX8Jro98vJESHRS+I11djepqMh5nxktkdoeAIZfIgrE8Eum0xt+WfnV5nI5Z1azZAx1Jk/3O2F7SPG+T4qL1Qm/WVmhk6Mw/BKRFoZfMp3W4PRaWPkNzylTHCej7QFwXn94e3t8U2AXFanR9tDS4gu6/uRtzc2pXR8isjaGXzKdypXfeE9nB2P4DeS08Mu2h8jChV/5vcLKLxH5Y/gl07HnN/HHcUL47esTfxh+Q8Vb+VXlgreWFu3vkPR08X5i+CUifwy/ZDq9bQ9OrPwy/PrI9Wf4DcXKb2StrdqVX4ATXRBRKIZfMp2qld/+fqC7O76KXjCG30BOC7+J9Pw66TMTTri2B4Dhl4hCMfySqXp7gZ4e/ZXfjg5gaMj49UoFGfZY+RWSuT2cFn7jrfzK0R6c8pkJh+GXiGLB8EumkjulcDsuf4WFYppSp8zWJMNZssKe3cOv3B6s/IZKpPLrpM9MOJHOHo0YwfArvfwyMDBg9loQmY/hl0wld0ojRkRf1mkzdyUz/DphXFu2PWjr6xNnR+Kt/ALOb31g5Te6vXuBr30N+OUvzV4TIvMx/JKpYqn8ysqXU3bkyQ6/dq/8JjP8OuFgQJKvI97KL+DsER/6+0Vlm+E3ss8+Ez+dfhaASA+GXzJVrG0PgPNCDcOvINc/Pz/xx5JtIF5v4o9lNnmwF+9oD/6P4UQy2Idre2D4FaJtJyKVMPySqVj5TV747e0VVTC76ugQ09Ompyf+WLI/3ONJ/LHMlkjlVwYdJ1d+5WuLVvl1woFQIuR2SsboMkR2x/BLpmppAbKyROiJxomVX5cLyMtL/LFkq4Cdq78dHck5EACc9V5h5TeyaAfQJSXA4CBP98vwm5lp7noQWQHDL5mquVlf1RfwBTwnBBrAN8GFy5X4Yzkh/La3J6ffF3BW+E3kDIGspKsefv2XU5V8/YOD5q4HkRUw/JKpWlr0jfQAAG63CEdO2ZEna3Y3wBnht6OD4VeLfL/Hc7ra5XL+FMcy1EXq+fVfTlXyPcDwS8TwSyaLNESRFifNWJXM8Csfh+FXcFL4bW8HMjJEe1A8nPSZ0dLaCqSlhX/vMPwKMvxynF8ihl8yGcNvch7LCS0hDL/a5AQX8bbHOOkzo6WlRVR9w20f+f3S3Jy6dbIiVn6JfBh+yVSxhl85XasTGFH5tXPY48GAtninNpZUaHuI9B0i2yFUr/yeOiV+MvwSMfySyeIJv07ZkTPsBWprS94wTBkZ4mIvO28PKd6pjSVVKr/hpKeLz4fq4bepSfxk+CVi+CWTMfwm57HS0sTkEHYOOcncHoBzpjhm5Tey1tbo3yEjRqjd9uD1Ao2N4u/s+SVi+CWTNTfrH+0BEFUsp+zIkx327F7hS2blF3BO+GXlNzI9B9Cqh9+ODt8EOKz8EjH8kol6esQfVSu/iVb0ghUV2TvsJRrygjkl/Cb6PlEh/Eabsre0VO3wK1seAIZfIoDhl0wUy9TGEi94C6+w0L7bpqcH6Otj24OWRA8KnHTAqEVv24O84EtFDL9EgRh+yTSJhN+hIWPWKVXkdKvJrHTaufIr15uV31DJqPx2dflOezuNnrYH1Su/st8XYM8vEcDwSyaKN/x6vfaezAEQwRdg5VeS683Kb6hEzxDIAwonbItgQ0P6Kr+lpaz8AmI7sfJLxPBLJoon/Modud1P48ogwp5fIZEpfMNxQvj1ehO/EFD2w9r9M6OlvV1so2g9v2x7EMO95eYy/BIBDL9kongrv4D9d+RGhF87V36N2h52D7/d3SKsJKPya9f3RiTye0BP5berS/SWq6ixERg1SgyJyPBLxPBLJmpuFhMRZGfrv48Mv3bfkbPyG8iIym9BgX23h5SM7eKUA0Yteg+g5XCKqvb9NjUBI0eK8MueXyKGXzJRrBNcAM7ZkbPyG8jIyq/Xm7zHTLVkbBcnV35l+NUz1BnA8JuezsovEcDwSyaKJ/yy5zc8O1/V39YmzgBkZibvMQsLxbbo7U3eY6ZaMiq/KoRfPW0PgLp9v01NbHsg8sfwS6aJJ/xmZYlWCaeE3/z85D2mDNJ2HAkj2bO7AfbeHpIMrNEqm5FkZIgLnez+mdEiX5OeC94AdcNvYyPbHoj8MfySaeIJv4AzZqxqbxfBNy0teY9p5wpfsif8AHyPZ8ftIekNd9E44TOjpaVF/D9H+xzJ7xnV2x5Y+SUSGH7JNC0tvopMLJwwY5URYc/O47kaUfm18/aQWlsBl0tcvJcIJ3xmtOiZ2hgQoa+4WM3K78CA2E7s+SXyYfgl0zQ3x1f5dcKOnJXOQIlO4avFCf3hra3idbgT/KZ2wmdGi54JLiRVZ3lrbhYXfbLnl8iH4ZdME2/bgxN25IlOWavFzpVOI7aHE4bFa21NvOUBEJ8zeXGYk8TyHaLqRBdydjf2/BL5MPyaaM4cYPVqs9fCPKqH32SEGn+s/AaS28PO7xWG38hi+Q5RtfLrH37Z9kAkMPya6MAB4L33zF4Lc3R3iyGoVL3gTZ7OTqacHLFzY+VXyMgA8vLs/V5JVvgtLnZu+NW7fUpL1az8NjaKn7zgjciH4ddEXi9w4oTZa2GOeKY2lpxQ+U1WqPHnctl3ogsjLngDxGPa+b3Cym9kbHuIrqlJ9IyXlDD8EkkMvyZi+FV3tAcj2h4A+05xbMQFgIDYxnY8GJCSdYbAqeG3udk3gUU0Krc9lJaKAMyeXyKB4ddEDL+JVX7tPG2tEW0PgD0rv0NDYiIKVn5DJbPy6/HYc/a/cIaGRJjVewAtK792/t6IhxzjF2DPL5HE8Gsir1eEQDtPvxovWYGJN/wODoqduR15vcZWfu0Wfjs7xTYxKvzabXv4S9b7RH7OnFT97egQAVhv+C0tBfr6xBTgKpGzuwFseyCSGH5NJCsQKlZ/E6n82nkmMwDo6RE7YaMqv3Zre5D/j0a1PbDy68zwKw+gY6n8Aur1/TY1iTF+AYZfIinm8Nvb24vly5ejoqICubm5qKqqQl1dna77tra2YvHixRg1ahTy8/Mxc+ZM7N27V3PZHTt2YMaMGcjLy0N5eTmWLFkCT1Cpb//+/Vi2bBkqKytRWFiIiooKfP3rX8f7778f68syherhNzcXyMyM/b4yDNg11CRrylotdqx0yrDOym+g/n5xdiOZ4deunxktMsTG0vPrfz9VBLc9sOeXKI7we9ttt2HNmjW4+eab8cQTTyAtLQ3XXHMN3n777Yj3GxoawrXXXostW7bgnnvuwcqVK9HQ0IDLL78cn3zyScCy+/btw6xZs9DT04M1a9bgrrvuwvr16zFv3ryA5Z566ik89dRTmD59OlavXo2lS5fi73//O6qqqvC///u/sb60lFM9/MZT9QXsH35lGOMFbwIrv9qS+T5h5dcXflW76I1tD0Sh0mNZePfu3fj973+PVatWYenSpQCAm2++GZMnT8ayZcsiBuDa2lrs3LkTtbW1mDNnDgBg/vz5mDRpEh5++GE888wzw8v+8Ic/RGlpKV5//XXk5+cDAM444wwsWrQIr776Kq688koAwI033ogf//jHyM3NHb7vHXfcgXPPPRePPPIIZs2aFcvLSzmVw++pU/orNsHsHn7levOCN0GuLyu/gZJ5hoDhV+22B4ZfokAxVX5ra2uRnp6OxYsXD9+WlZWFO++8Ezt37sSxY8ci3resrGw4+ALAyJEjMX/+fGzfvh39n1+G3N7ejrq6Otx0003DwRcAbrnlFuTn52Pr1q3Dt02dOjUg+ALAiBEjMGPGDHz88cexvDRTqBx+YxmiKJjdwy8rv4Hk+hpZ+bXjFf7JDL95eeKUt9PCb3o64LebiKioSIQ/lSq/XV3ij3/PL9seiGIMv3v37sWkSZMCQikATJs2DYBoV4h036lTp4bcPm3aNHR1deHAgQMAgA8//BADAwO46KKLApbLyMhAZWVl2B5hfydOnMAo+Wm3MLlDrq83dz3MkEjlNztb9Arbvd4wJdcAACAASURBVKJnZOXXTmGvrU1M0KE3xMSiqEhUuux4hX8yw6/L5byxfuUBtMulb3m5DVSq/PpPbQxwqDMiKabwW19fj/Ly8pDb5W3Hjx9P+L71nydBrWXLysoiPgcAvPnmm3jnnXewYMGCiMtZgcqV31On4pvgAhA7MTuP39raKgacNyrsDQyIESXsQk5w4TZg7Bk7nyVI9oWRTgu/8XyHqDbRRXD4ZdsDkRDT7qa7uxtZWVkht2dnZw//Ppyenh5d95U/wy0b6TkaGhpw4403Yvz48Vi2bFmEV2INqoffeCu/gP0vZDIq7MnWATtVxeX2MIKdh8VrbfVNWZ0MTgu/sUxwIZWWqlX5bWwUP0ePFj8ZfomEmC54y8nJQa/GjAw9n5eZcnJyEr6v/Blu2eAeX8nj8eDrX/86PB4PXnnllbDLSffeey+Kg0oqCxcuxMKFCyPezwgMv7Gzc/hN1titWvzDXlmZMc+RbG1txrSAAL7tbNfwm8yDpOJihl85y5sqGhrET/b8ktVt2bIFW7ZsCbit1cCdfEzht7y8XLPtQLYqVFRUJHxf2e5Qr9EIW19fr/kcfX19mDNnDv72t7/hlVdewXnnnRf1taxdu1azBzmVvF6xQzpxQvxdb++a3fX1idmZVA2/Rs3uBviqhHa66E22PRhBhmo7vleSfZBUUuKsA+3mZmDixNjuU1oKHDxozPpYUUODaK+SdSn2/JJVaRUf9+zZgwsvvNCQ54uppjBlyhQcOHAAHR0dAbfv2rULAFBZWRn2vpWVldizZw+8QVfi7Nq1C3l5eZg0aRIAYPLkyUhPT8e7774bsFxfXx/27dsX8hxDQ0O45ZZb8Nprr+F3v/sdvvKVr8Tykkzl9QLl5WJ6YztWpuIle+4SDb923WatrcZVOu14mp+VX23JPkhyYttDrN8hKlZ+ZcsDwLYHIimm8Dt37lwMDg5i/fr1w7f19vaiuroaVVVVOO200wCI0Rb279+PAb/zK3PnzsXJkyexbdu24duamprw7LPP4rrrrkNGRgYAoKioCFdccQU2b96Mzs7O4WU3bdoEj8cTMtHF97//fWzduhXr1q3DN7/5zVhejulk+AXUGvEh1pmZtNj9gjdWfn2MrPzm5Ykdvh3fK8k+SHJa+OUFb9Ex/BJpi6ntYfr06Zg3bx4efPBBNDQ0YMKECdi4cSOOHDmC6urq4eUeeOAB1NTU4PDhwxg3bhwAEX6rqqpw++2346OPPkJpaSnWrVsHr9eLFStWBDzPo48+iosvvhiXXXYZFi1ahM8++wyrV6/GVVddhdmzZw8vt3btWvzqV7/Cl7/8ZeTk5GDz5s0BjzNnzpyovb9m8g+/J04A555r7vqkSjLCr93bHiZMMOax7XrB21lnGfPYcmQQO20PyYi2B6eEX683/gvempuBoSFjLji1msbGwPDL6Y2JhJjCLwDU1NTgoYcewqZNm9DS0oILLrgAL774ImbMmDG8jMvlgiuogdXtduOll17C/fffjyeeeALd3d2YPn06ampqMDGocWvKlCmoq6vD8uXLsXTpUhQWFuKuu+7CY489FrDcX//6V7hcLrzzzjvYuXNnwO9cLhcuvfTS4fBtRV4vIFuYndSLF43q4dfIym9GBpCba7/Kr1FtD4B9zxK0tgLjxyfv8UpKRK/9wIAIQXbW0SEqmLGG35Ejxf3a2uKfXt1OGhqACy7w/ZuVXyIh5q/ArKwsrFy5EitXrgy7THV1dUAlWCouLsaGDRuwYcOGqM9zySWX4K233oq4TLjnsQuvFygoEKdmVQy/iQRAO/f8GnnBG2C/KY6NHOoMYOVXkmGvrS2xA08riPe6ATnebWOjOuGXbQ9EoRQ48WNdcoSHsjL1wm9xcWLVp+JicaGgXWfuMrrSaafKr5EXvAH2PUtgVPh1QuuDDL+xVn7lkF9y8gcn83pF+PWf7DQ9HejvN2+diKyC4ddEcuCLsjK1LniL5yrtYHKnZ7cd+cAA0NnJyq/U2yv+sPIbiuE3PHn2KJ62B0CN8OvxAN3dgZVfOcmHnaY/JzICw6+JVK78Jhp+7bojlxVZVn6FVGwPO1Z+jThIsutnRku8lV+5vJz5zMmCZ3cDxL6mv98Z7wGiRDD8mojhN35yR263YYtkCGPlV0jVwYBdtock15fhV1tzsziFX1AQ2/0yMsR2UKHyK2d3Cw6/gFr7GyItDL8mkuG3vFytL6NkhF+7tj0YEWqC2Wl0A7k9jGx7sGPl14iDpIICMbyX3T4zWuQwZ/HMijlyJMOvSvsbIi0Mvybyr/w2NalzIUIywq8MBXat/BpZ6SwpsU+lMxWVcDtWfo3YLi6XeDwnhd94jBypRtuDDL+yzxkAxowRPxl+SXUMvybyD7/yylwVJCP8ZmSISpbdduSpCHt2Cjhyexg57FRxseiftdPg/kadIXDKRBfxzO4mjRqlTuW3tDRwVJ38fPGH4ZdUx/BrIv/wC6jxheT1Jif8AvbckctQwwu8BLmeRo/2ANjnIkDAuIMkO35mtCRa+VUl/PoPcyapdo0JkRaGXxOpGH7lDFPx7rj8jRhhz7aH3FxRuTaKHAO5p8e450iW1lZRwTdyxjEZIO1yQAAYd1DgpPAb7wG0Km0PwVMbSwy/RAy/ppLhd/Ro8VOFL6RkTG0s2XFHbvTsboCvhcAOYc/IqZ4lWfm1U9+vPChIS0vu49rxM6MlkcqvSm0PDL9E2hh+TSTDb0aGqEao8IWU7PBrx8qvkS0PgC9M2iHkpCL82rXya8R2YfgV37Xt7UBfX3LXyWrChd8xY4CTJ1O/PkRWwvBrIhl+AXVmeUtm+B0xwn47coa9QKz8ajMy/NrhfRGJvG4gkcov4PzqLyu/ROEx/JqI4Tcxdqz8trUZX/ll20Mghl8fJ1R+5cgdifT8As4Ov0NDkXt+GxvtNfoJUbIx/JrIP/xWVKgTfjMzgby8xB+LlV9trPwGyswEcnLssT2klhbjwm9bmwhHdhXv1MaSCuG3tVWE23Dh1+tV46I/onAYfk0UHH6PHzd3fVJBXqUdz8xMwWQVy0478lRc8JabK0ZPsMOBQSrCL2C/iS5aWpIzIkqwkhLxvWOnA4FgiYZf2fbg5PAnX1u4oc4Atj6Q2hh+TaQVfr1ec9fJaMka4xcQO7+hITF8ml2k4oI3OZOXHQJOqsKvXbaHlMgFXZHIz55sP7KjRMNvQYG4yNjJlV+tqY0lhl8ihl9TBYff/n5775T0SGb4lb2tdur7TVXYs8uFTams/Nphe0hGh187fWaCye/IeLePy+X8iS4ihV95G8MvqYzh10TB4RdwfutDIldpB5Ph1w6n9wHx/52KC94Ae0xxPDAgqvY8GAjk9Yr/OyOmfHZC5bepSVRuE5kAZNQoZ7c9NDSI1ietz1ZmpngfMPySyhh+TaRq+E1m2wNgnyqWxwMMDvI0vyR7cI0IecHsNMpBV5cYg9aIyq98TLuH35EjE7tuQIXK76hRgDvMHn7MGIZfUhvDr4mChzoDGH5jYbfKrwyjDL9CKreHnabCTrSnNZKcHPHHCeE3ESqEX62WB4lj/ZLqGH5N5B9+MzPFkTrDr36FhaKyYbdQk6pKJ8Ovj50qv0a/T0pLGX6d3vbQ2Kg90oNUVsZZ3khtDL8m8g+/gPOHO+vvF9OKJiv8ut326G2V5HoaUdELZoftkurwa5eDJKPfJ3YPv42NrPxGc/KkaG0Ih5Vf4ehRYPJke38eKD4MvyZSLfzKnU2kikSs7DTRhVzPVFR+2fYQaMQI0XPd32/8cyXKyLYHQIRfuxwIaElm24NTh5Y8ccLXSqeF4VfYswf4v/8Djh0ze00o1Rh+TaRa+I008Hq87FTRk+uZytENrLxzl+E3kav29bJTf3hzs/heMGpUELtXfpPV9tDXZ68xwmOhJ/y2tQHd3albJyv67DPxk1M9q4fh10QMv4mzUy9nS4tvgH2jFReLkSU6O41/rni1torgm5Zm/HPZaWSQ5mYRfI3aLnYOv15v8iq/gDNbH7q7RXtZtLYHgH2/Mvza4YwQJRfDr8mCw++JEyK0OJER4ddOV/EbNXarFlldtnLrQ6omuADsVfk1ampjyc7ht6sL6Olh+I1EBtpolV+ArQ+s/KqL4dck8nR0cPgdHHTmFzIgwm9mpqh+JoudKr/NzQy//swIv3Y4UDJqdjdpxAj7hl/53ZiMtgfAmSM+yEAbKfzKqjDDr/jJ8Ksehl+ThAu/gHNbH+TwO4kMTh/MbpXfVIz0APjCHsOvILe7HQ6UjD5IKi0VF//19hr3HEZJVviVI844sdAgK7+R2h5GjhQzwNXXp2adrIptD+pi+DWJyuE3mexU+TWj7cHK2yaV4TcnB8jKsvb2kIyu/MrgZ5eDRn/JCr9ZWeIMlFMrv2lpkYeUdLtFZVjl8Ov1svKrMoZfk2iF39GjxZcSw69+I0aIK7btcOSeyrYHOVIAK78+djlLkIqeX8CerQ/JCr+A+L51avgdPTr6BZNOv8A6muZm0T8OMPyqiOHXJFrhNz1dnKpy6heSUZVfwNohT0pl20N2tvhj5e3S2pq6gwHAPmcJUlX5tWv4zckBcnMTf6wxY5w52sHJk5H7fSXVw6+s+gIMvypi+DWJVvgFnP2FZFTlF7BPRS/VYc/q4TeVlV+7jAmdip5fwL7hNxlVX0BURxsakvNYVnLiROR+X6miQu3JHfzDrx3OHFJyMfyajOE3MXYZwmpoKPXh1+pTHJvR9mDl7QGInXBHh7GV3+Ji8b2jevh1auU32gQXkpP3NXqw8qs2hl+TqFb5HRwUO1ujwq/VK3odHSIApzr8WrXyOzAgJuBIdeXX6uFX/n8ZGX7T0sR2Vz38Ornyqzf8NjXZc9SPZDh6VLwHAIZfFTH8mkS18NvcLF6zUW0PVg81cv1S1fMLWLvtQa4XL3gLJNfP6IOk0lLrbwstya78NjRYewrwWHm9opqtt+0BUHes36NHgfHjxd/Z9qAehl+TRAq/J08670jUiNndAHHxS2am9XfkMvyy7UGQ/1+pPhiw6vaQUrVd7DrLW7Irv319QFtbch7PCjo7xSx4eiu/gDOLLXocOQKccYbYBzttf0vRMfyaJFL4lUfvTmJU+HW5WNELx8ptD2aEX/k+sXKlL1VnCBh+fae8nfRdq2d2N0n18Hv0KDB2rBhlieFXPQy/Jgm3A3bqF5JR4Rewx47cjLYHK4dfMyrhJSWi0tfdnbrnjFUq2x6s/pkJ5vUmv+0BcFbfr57Z3aQRI8RZM6fta/QYGhLhd9w4ICODbQ8qYvg1SaTKL+C8L6TGRt+FNsk2cqT1d+QtLeL/Wk4+kQpW7vk1q+3B/7mtqLlZjM+ck2Ps89gx/La3iwpdsg6gZeXXSeE3lsqvy+Xca0yiaWwUB8Ks/KqL4dck4cKvnHPdaV9IjY3itbkNeMeVlvpmfrKq5mYRfI14/eEUF4t+xsHB1D2nXs3NYopZo0OePztcHGn0BBeSHcOvPHuUrMpvSYn4rnVa20Nmpv4ig6rh98gR8XPcOIZfVTH8miRc+JVzrjvtC8mIMX4lu1R+U1nlBHw7wPb21D6vHmZsDzuMCZ2q7WKH/udgyZzaGBDfvU4b7kzO7ha8XwlH1fB79Kj4OXYs2x5UxfBrknDhF3DmF5KR4dcOld9UT3AB+MKvFVsfUlXh9GeH2QCNnt1NKi0V1a6ODuOfK1mSHX4BEX6dVvnV0+8rOXFfo8eRI6K9qLSUlV9VMfyahOE3eexQ+U1VqPFn5UqnGdtDHgxYcXtIqWx7AKz/ufEnw69c92SQY/06hd4JLiQn7mv0kBe7uVwMv6pi+DVJpPB72mmBUy86gdGV385Oa89UZEbl18rh14y2h4wMID/f+pVfhl9tTU1AYaHoaU0Wp1V+ZduDXhUV4sxQV5dx62RFR46IlgeAbQ+qYvg1SaTwe/rpDL+xkKdBrbwjNyPsWfk0vxltD4B4TiseDEipep/YNfwms+UBcGblN9a2BwCorzdmfaxKVn4BVn5VxfBrkkjhd+xYcTTe2ZnadTKKHJ/TyMovYO2+XzNO8xcWigsorRp+U709AOvP8pbKnl+A4ddJF7zJyZFirfwC6rU++Fd+GX7VxPBrkmiVXwA4dix162Ok1tbkjs8ZzC6V31SHPbdbPKcVw68ZlXDA2rMBer2pq4jn5oqh5qy6LbQYVfltawN6epL7uGZobRVj1zL8RtbXJyrksvKbkcHwqyKGX5PoCb9yOBa7M3J2N8D6ld+BAbGDTeaFOnpZMezJkMfKb6CODjEmcyrCr5wW3MoHjMGampL/GZITXcjvKDuTE1zE0vZQWCgOhFQKv8ePi+8g/8ove37Vw/BrkmgXvAHO6fs1OvwWFYnZ46y6I5fh04zwa8XJDDwesbMxo/Jr1Uo4kPopn0eOtO4Bo5aGBl9YTRb5eE646E327cpqrh4qzvLmP8EFwLYHVTH8miRS+M3OFkGR4Vcft9vaY/3K8MnKryBDHi94CyTfJ6naLqNG2aviaUT4lVVSJ/T9ygBbXh7b/VQLv/4TXABse1AVw69JIoVfwFkjPjQ2itdpZPizYoVTYvgNJNfHrLYHq20PyYhJHCKxU/jt7RWtQ8kOv/KA3AmV3+PHxVjWsU4Zrlr4PXJEfC/m5Yl/s+1BTQy/JokWfseOdVbP74gRojXBKFY+hcvwG0iuj1mV39ZWYGgo9c8djXyfMPyGkusZSz+rHpmZ4oDIKZXfWFoepIoK51xcrcfRo76qL8C2B1Ux/JpEpcrvyZPJ32kFs0Pll6MbCKnubfVXUiKCrxWn9W1qEiMwyIqU0ewUfmVlNtmVX/mYKodfua+R+ySn8x/mDGDbg6oYfk3C8JtcVq/8FhaKL9lUkwcFVtqxyTAupxtOJStP/CGH8gr3nZBso0aJ57TSeyMcGU6NCL9jxjin7SHe8Nvdbd1e+GTzn+ACYNuDqhh+TaIn/DY3O2PaSVZ+zWl5AETY6+uz1vuouVkEXyPbYMKx8uQOqX6fjBolKl6tral7zngZGX5Vr/zKKqhT2uyiCa78su1BTQy/JtHT8ws4o/ob66xD8bB65dfM8AtYq9JpxoQfkpXHhDZiEodI5HPZofWhoUEMaZiVlfzHHj3a/pVfrzfx8OuEfU00bW3iYO+MM3y3se1BTQy/JtFT+QWc8YUU63zz8SgtBdrbrXn66tSp1IYaf1YMv6maxUyLlWcDTHX4lSMd2CH8njxpTNUXcEbbQ0uLGBEjnvBbVibOwqhQ+f30U/HTP/yy7UFNDL8miRZ+nTLRRV+f+GJORc8vYM1QY2bl14qn+c0Mv7m5YhxtVn7tFX6NGONXGjNGbIPBQWMePxXkUGXxhN+0NHE/u+9r9Dh8WPz8whd8t7HtQU0MvyaJFn5zcsSO0O5H47KXLhWVX8BaIU9i20MgM9seAPG54vtEPJfLZc0DgWANDcZ9h5SXixFA7HAQEE68E1xIp59u/32NHocPi9YZ//dSZqaompNaGH5NEi38As4Y8UGeTkxFzy9gzR25meG3qEi8x6wUfs2s/ALWnA3Q60195TctTfw/2CH0Gdn2IAOjnSd6kFMbxxt+x461/75Gj08/FVVft1/yqahQI/hTIIZfk6gWflWt/Hq95oZft9t6s5o1N7PyG6yrC+jpSX1vuF3G+jWy7UEGRhkg7ej4cfEdE+8FgSpVfv37fQFgwgTx/rLi2N9kHIZfk+gJv06Y5e3ECfHTqB2XVFwsgp7VKnqdneJiCrPCL2C9iS5Y+Q1l1iyAdgi/Xq+xbQ9jxojvYbuH33j6fSVZ+bXDmM+JOHw4sN8XEOEXAA4dSvnqkIkYfk2iUuV3xAjjJ3hISxPVRKtV9Myc2liy0hjIfX2iwmLW6BeANSu/Moyz8huqtVVckGTUAXRGhtjuKodfOdGFlQ6SjaBV+R0/Xvxk+FULw69J9IbfU6fEl5JdpWKCC8mKY/1aIfxaqfIrt4fZ4ddq7xOG3/CMnNpYKi9XO/yqMNZvR4f4HgwOv6NHiynF//EPU1aLTMLwaxK9bQ8AcOyY8etjlFRMcCFZqcIpMfwGkiHPCpVwK53iNeugwA7hNxUjxqgefuW48nZvs4tEa4xfQOyDJ0xg+FUNw69J9FZ+AXt/IaViggvJihU9ht9AVqn89vRYa8rnpiZxsVJubmqfV4ZfKx0IBDNyamPJzuF3aEise7wjPQC+iS6cXPmVY/wGh1+A4VdFDL8m0RN+nTDRRSrbHqxa+c3MFKfVzGKl7WLW6X1/VhwZRA5zFun7wAjyQMDjSe3zxqKhQUxEUFxs3HNUVNg3/J46JS6qTaTyKye6sHOhJZrDh8V3sdaZyAkT2POrGoZfk+gJv7m5omrH8KuPVSu/cjIBs8jKrxWqe6dOiVE5iorMWwcrjglt1hTYdpjlTY7xa+RnqLxcnKWywmckVonM7vb/2zvz6KqKdO0/JzMkzAQykQRIwiwJYQgBARlEVLQvzaBebUUF21Zbu/si4sXPqzbtam21l8tWG1ojYKsMzgoO2IKCIQxJmEMgkEAGkoCEKSNJfX+8VgY4OTnD3lW1T+q3Fuvoyd67Kju1az/11js0x9tz/ebnA9HRLXP8cvr1I7cIXemt/aDFryScEb+AtdOd1dWR6BLl86uy+JVJ9+5UwUiFwMnTp+l+2HsBiUJVy6+McWIF8WtmmjNOeDhlIlHFPcgVjBK/3p7r116mB07//iR8T5wQ2SONTLT4lYSz4tfK6c5ElTbm9OxJaZHq6sS05wyqiF/eF9mocD9UtPyKru7GsYr4NTtPuJULXXDx66mRoT1Yfh2JX0C7PrQntPiVRHsQv7zAhSjxy1+QKokaFcQeFzgq3BdZIq85wcHk+6fCYoCj3R5ax8zSxhyri99evTzPpc4tv1Z0/XCGgoLWxW90NPk966C39oMWv5Jxxu3Bqlsxokobc/gLUqUXuQriVyVLp6zt/ebYbOq5yMhaFAQEkP8136VREVFuD4B1xa+nLg8AvWuqq63p+tEWFy/SM3ZldTeOvz8JYC1+2w9a/ErCWctvTAwJKJWjsVtDVGljDm9HpRd5eXmTdU0WKolfWRbOK1EpAwZjchcFvXs3LVRVRITbQ1AQZZOwovgtKvIszRnHG1JrtkZrOX6bo9OdtS+0+JWEs1tLfKXKH14rUVJCQicgQEx7XGSqJH7LyuSL3+BgyiGrgkVcBbcHQC3Lb2UlWdxk3ReVxW9NDXDunJgFtFVz/RYWNhVE8gR+DW8Uv45y/HJ0ujOiogJ4803ZvTAfLX4l4YrlF7Cm+DVqO85ZgoMpPZwq4vfSJcqwIMry3Ro2GwlwFcSeCm4ggFqWXxFFHByhsvgVeW+sKn5PnjRG/PbuTdv/3ip+/fwcW8i55ddbfZ6dZe1a4MEHgbNnZffEXLT4lYSz4jcighzxrSh+Pa065A69eqkjfrmlVbblF1DD0llXR1Y8VSy/WvwSVhC/IuIGrCh+q6vpuTZC/Pr60nW4ldSbOHaMrL6+vq0f068fcOGC/HlSNgcP0mdtrdx+mI0Wv5JwVvz6+ZEvlhXFr2jLL6DFb2uoIH55II0qll/Z94PDx4kWv1fD+6Utv/bhmYC4v66nxMZa813TFnl5TenMWoP/vL37/WrxqzEVZ8UvQK4PVpyQZFl+VfBtBdQTv7LviwqljTkqLAY4fLEm0+f39Gmgvl5O+44QmS7RiuKXuygYYfkF6F3jrZbffv0cHxMXR59Hj5rfH5U5dIg+tfjVmIIr4jc62nrpzhiTI35DQ9Wx/MoWNc1RweeXuxmoYPnt2ZMCzSorZfeExkn37rTLI4OwMKChQf74sAcPmvU0h60zhIeTn/6FC+a3ZRTa8ts2jJH4bcvyGxJCYyA3V0y/VOT8+aYxpcWvxhS83fJ75gz5eLZ3t4cuXSjTgmxUsHSqZPlVKSe0iFRejuBWVRVdH0QuoK2Y6/fkSVo4dexozPViYmgcqFAK3SjKymhR05blFwASEtq3+OVWX0CLX41JuCp+i4vVKtvbFvwF0t4D3lRweQCaxK/MSOYzZwAfH8qnKhuVckJr8ds6Wvw6xqg0ZxyeCsxqO42O4D68bVl+AS1+tfjVmI6r4rehwVpljnm9eRmW34sX1bBcqCR+Q0OBy5cp24IsTp8GunVzHHEtCm35bUJl8XvqlBa/jjh50jiXB6AptaY3+f3y3L19+7Z9bEICcORI+013dvBgkybR4rcZNTU1WLx4MSIiItCxY0ekpKRg06ZNTp1bUVGBhQsXIjQ0FCEhIZg8eTKysrLsHvvTTz9h/PjxCA4ORnh4OB599FFcslPibNmyZbjlllvQu3dv+Pj44JlnnnHl15GKq+IXsJbrA3+BhIWJbVclUaOS+FWhypsq1d2Apn6oYPktL5crfjt0ADp1UlP8lpSIm0M6daJc4XzhbgWMyvHLiYqybmrN1sjLo+erU6e2j01IIOMJD7Rsbxw6RPcA0OK3Bffccw9eeeUV3HXXXXj11Vfh6+uLG2+8Edu2bXN4XkNDA2666Sa8//77+P3vf48XXngBZWVlmDRpEo5eEVqZnZ2NKVOmoLq6Gq+88gruv/9+LF++HHPmzLnquk899RR2796NESNGAABszihJRXA14A2w1oRUXEyBTaL9XVWq8qZCdTeOCuJXZgnfKwkIIPcLVcaJ7EIoKqY7Ex00a7MBkZFULtgqGO324OdH98DbLL/OuDwATcKvvbo+HDwIJCbSf3u7+HU6vnjHjh1Ys2YN/va3v+GPf/wjAOCuu+7C0KFD8fjjjzsUwOvXr0d6ejrWr1+PWbNmAQDmzp2LhIQEPP300/j3v//deOyTTz6JHj16YPPmzQgJCQEAxMbGYsGCBfj2228xbdq0xmPz8/MRHR2NM2fOIFQVleEkrojfDh3o5Wgl8Ssj0wOgli+ntvy2RJXSxhwV/MMZU2ORpKL41IbdjgAAIABJREFUPX+eijiInEeioqzjXlZZSbspRro9AN6X8SEvz7lgN4CO8/Eh8Ttxorn9Uo2qKuD4ceD++4E1a7xf/Dpt+V2/fj38/PywcOHCxu8CAwNx3333IT09HUUOlsvr169HWFhYo/AFgJ49e2Lu3Ln49NNPUfdLJNf58+exadMm3HnnnY3CFwB+85vfICQkBGvXrm1x3ehfTKLMgg46rohfwHoZH2SJX5Usv7K3s5vDRadMdxBVShtzVEiLV1FBvtiyx4mK4ldG0KyVxC9/5Rpp+QW8L9evK5bfgAAS/+3R8puTQ7pk+HD6fy1+fyErKwsJCQktRCkAjBo1CgC5Kzg6l7smXHluZWUlcn8Zafv27cPly5cxcuTIFsf5+/sjMTGxVR9hK+Lt4ldGdTdAne3sqipKryPbosfx96e0azItvyps7zdHhYIosksbc1QWvyLjBqwkfnmBC235bZ3KShpHzlp+gfab8WH/fvpMSqJPLX5/oaSkBOF2luD8u2IHUQLOnlvyy2xn79iwsDCHbVgNbxe/siy/gBrb2SpVd+PIzvWroviVPU60+G0dGZbfyEhauDc0iGvTXYwucMHhqTW9QfwcP06fzlp+gaaMD+2N/fspvojvEnrD398RTovfqqoqBNqJXgoKCmr8eWtUV1c7dS7/bO1YR21YDXfE74kT1piUGZNn+QXUsuipJH5lVnmrrKQoatkirzkqiF8+TmXfl9696V6oNL+cOkVVt67YbDSVqChyQ5E9Lpzh5EkSKh06GHvd2Fiaw7ll2crwNGeuWn6PHlWz3LeZ7N8PDBvWVGnS28Wv0wFvHTp0QE1NzVXfV1dXN/7c03P5Z2vHdjSqjA2Axx57DF2vyLZ/++234/bbbzesDUe4I35ra8k6I8ui6ixnz1JfZfVTBV9OVS2/shYFqoi85vBxwpjzz6HRlJVRainZhT9696aX/c8/qxOUKGP3iFtRCwvFp2l0FaNz/HKa5/p1xWKqInl5QFCQa+MoIYEKShUUuCaarc7+/cDtt9NcGBAgXvy+//77eP/991t8V1FRYVp7Tovf8PBwu24H3FUhwoGZz9lzubtDiZ0s4yUlJQ7bcJW///3vdv2QReGO+AXogVRd/PI/tUy3hx075LTNUVX8Hj4sp21Vtveb06sXTfAXLgCdO8vpA8/04CO53FDzQhcqiV/RArS5+L0i9EQ5jE5zxunTh95LVnKza41jx0jAurK4bZ7urL2I3/PnaWd56FD6fxni157xMTMzE8nJyaa05/SUm5SUhNzcXFy4cKHF9xkZGQCARJ4czg6JiYnIzMy8KitDRkYGgoODkfDLaBs6dCj8/Pywc+fOFsfV1tYiOzvbYRtWwxPxqzo8CjkyUk77KvgvlpdTUvVfPHuUQKbPr6riF5C7S6CKHzQXvyol9xdZ3Y3Tsye9+K2Q69foAhecwEC6796Q8SEvz3XrdZ8+dA/aU9AbD3aTKX5F47T4nT17Nurr67F8+fLG72pqapCWloaUlBRE/qJ0Tp06hZycHFy+fLnFuaWlpfjoo48avzt9+jTWrVuHmTNnwt/fHwDQpUsXTJ06Fe+++y4uXrzYeOzq1atx6dIlu4UurIqr4rdrV/rHHfhVprCQfi9ZPr9hYSR+ZfovqpTjlxMaKs/tgQtMVayKgBpp8VQRvyqW9pXh9uDjQ/OWFTI+FBaa4/YAeE/GB275dQUfHyAurv2JX19fYOBA+v/2IH6ddnsYPXo05syZgyVLlqCsrAz9+/fHypUrceLECaSlpTUe98QTT2DVqlWNBSgAEr8pKSmYP38+Dh48iB49euD1118HY+yqksTLli1DamoqJk6ciAULFqCwsBAvv/wypk+fjuuvv77FsatXr0ZBQQEqKysBAFu2bMGf//xnAJQbmLevIq6KX4AeYu7ArzKFhWRJCgiQ0354OPkvnjkjT4CqULjgSkJDKa9sba34v01ZGdCtm7wxYQ9VLL+ydkia07EjpcJr7+IXsEa6s8pK8s82w/ILeEeu3/p613L8Nqe9pTvbvx+Ij2/aqdTi9wpWrVqFp556CqtXr8bZs2cxfPhwfPHFFxg/fnzjMTab7aoywz4+PtiwYQMWLVqEV199FVVVVRg9ejRWrVqF+Pj4FscmJSVh06ZNWLx4Mf74xz+ic+fOuP/++/H8889f1Z+3334bW7ZsaWx38+bN+P7772Gz2TBhwgQtfiVhpkXCGbif4KlTcsWvCha95vCt7fJy8YJLxfvRowc9fzLFb3l5UzlR2URENPnry6a6mgJnZQSdWUH8mpXjl9O3L7B1qznXFsWJEyTgBgxw/dyBA4HVq43vk6rs39/k8gBo8XsVgYGBeOGFF/DCCy+0ekxaWloLSzCna9euWLFiBVasWNFmO+PGjcNWJ56877//vs1jVMVd8btunTn9MRKVxO+wYXL6UFralCxcFZoHNWnxS9t8MjNgAPS3UOW+hIerY/nlPvuyLL+7d4tv1xW4S0JsrDnX79+f5vGaGvJ/tSLccssD2Fxh8GD6/c+flxcMK5L9+4GHHmr6//YgfiXHGLdf3BG/ffvSaraZO7WSqCJ+Zb7IT51SL1VSc/ErGhXFLyA3LV5tLbnmqJK9RSXLr8yg2chImsOuiM9Wivx88k016/7070+/vxViTFojN5dEnDuuIYMG0WdOjrF9UpHSUjIAtDfLrxa/knDX8ltfr37ycdniNyiIggNlRa43NNCEopr45S4gWvw2IbPQhUzrpj1UEr/c7UCG+I2KovLkZ8+Kb9tZCgqon7/EihsO95PNyzPn+iLIzaXANV9f18/lgV+HDhnbJxXhmR6a75Jq8asxDXfFL6C23+/FixRUJVP8AiQ8ZYnfs2cpSbpq4jcwkBYFWvw2IVP88p0JVcYJd3tQweJZVNQUhCea5rl+VSU/vyn9pRlERNB8YWXxe+SIey4PABAcTPf34EFj+6Qi+/fT37p5YKAWvxrTcEf8RkfTVpfK4pe/MMyKQnYWmeKXt6uKqGmOjBzIjGnxaw/VxklEBGUROH9edk9I/EZFyam8ZxXxa5a/L0DvmX79rC1+c3PdF78A+f22F8vv4MEtLeRa/GpMwx3xy/2XrCB+VbD8yvL55eJSFVHTHBnit6KC/NRVFL8yC6KcOkUiQ5WUeNz9QgXXh8JCeSngwsMBPz+Kr1CVggJzLb8AWQKPHjW3DbOoqaEFgifid9Cg9mH53bPn6sDwjh3VWASbiRa/knBH/ALqpzvj4ldWgQtOeLh8yy8PMFOJ3r3FWzpVrO7GCQujYI+6OvFtl5TQPXHHJ9EM+DOrQsaHoiJ54tfXlxbvqhZ5qK2lBYqZll+AxK9VLb95efSOvSKTqksMHkwBf1VVxvVLNS5fBvbtuzrd4tChJIq9GS1+JeHN4jc0VH5ZX9luDyEh5DemGjIsnSqLX27tlOH6IKN8ryNUsvzKFL8AuZipKn5PnqT3hwjxe/w4BVlbDU/SnHEGDaLgZW8udnHkCOXUvlL8jhxJz6BK5c6NRotfSXgiflVOPyM70wMnLIy226urxbetYpozTq9eWvw2R2ZaPNXGCQ8wky1+GWvy+ZVFTIy6bg+88poIt4fa2qa0c1biyBGgUyfPdt94ujNv9vvNzqbP4cNbfj9yJH2qnu/aE7T4lYS74rdvX8oNeu6c8X0yApXELyBn5aqaqGlO797A6dNirTnl5eRD2bWruDadhVs7ZYjfkhL1xklEhHy3h9OnSXRpy699CgrovWF2ULGV053xYDdPAia7daPn05v9frOzaax3797y++hoqoC5a5ecfolAi19JeGL5BdS1/hYWys/0AGjx2xq9e9NW3unT4tosKyNXGB8FZ5tevegZlDVOVHJ7AKg/si2/MnP8cmJi6D7I8AVvi/x8+juZXXktNpaeWSuLX0/x9owPe/ZcbfUFaE4cOVKLX40JeCp+VfX7VcXyy0WFDFGjYoELjowqb6qmOQPIIh0aKt7ayZi6ll/Z4pdvs8t2e2hoUHPL3+w0Z5zAQDJkWFX8ehLsxvH2jA/Z2Vf7+3JGjtRuDxoTcFf89uxJwVQqit+qKnLJkGmx4fToQVHb2vLbEhni99QpdcUvICczSEUFbe2rNk54oQuZFBXRsyszW0p0NH2q6PogIs0Zx4oZH86fp+fZKMvvkSNq7gB4yqlT9B5wJH5LSuQvhs1Ci19JuCt+bTZ1Mz7wABFRE7MjfHzo5Sn6RX75Mvm4qpjmDJAjfktK1Nveb46MnNBcbKt2X7jlV2aVt8JC+pvITAHHxa+KQW+iLL+ANXP98v4aIX4HDSLha7UFgDPwVGb23B4AIDmZPr3V9UGLX0m4K34BdcWvqChkZ5GR7qy8nP62qln0OB070s6ByNReqotfGZZf1UobcyIiaAdHZkCt7DRnAD0noaHqWX7r6uj+iLb8qlDy2ll4ajIj3B6GDKHPAwc8v5ZqZGdTRoy+fe3/PCqKduy0+NUog6plJwsKyOIq+8XFkbGFq1rJWnuITHfGfVtVF796nBC80IVMX1fZac440dHqWX5PnKBMLTz2w2z69yc3gjNnxLRnBIcO0XNlRHaZXr1ot2zvXs+vpRrZ2WT1bS0Q2duD3rT4lYQnlt/4eMr2oJofUn4+vbT8/WX3hIiIEP8SV7m0MUdkoYtz5yjXssril+8QiLRulZSQBT4kRFybzsAztZw8Ka8PMksbNycmRj3LL9/x42nIzMaK6c4OHiRfXaO45hrvFb+t+ftykpMp6M1Kln9n0eJXEp6I34QEWv1zNwNVEBmI4QxRUeLFL7foqRzgJVL8couqyuI3PByoqaEgNFGoGhQZEUFzkkzxq4LbA6Cm+M3LI19oUekk4+Lo88gRMe0ZwaFDTQUqjMAbxW9lJbmHtObvyxk5klzkZM4HZqHFryQ8tfwC6pVdFBmI4QyRkfTg1taKa/PUKco0ERAgrk1X0eK3JTKqvKmY4xegXZvwcHkvuwsXaLdAJbcHlaxex46RKBe1u9apE40H1d41rVFXR3012vJ77BiNTW9h715K5deW5Xf0aPrMyDC/T6LR4lcSnojfyEigQwf1JiQVLb/c51QUxcVqWvSaI9LH1QriV0ZOaBVz/HKiopoKTYiGW1pVmEdiYij4T2RBmLbIyxPn78sZMADIyRHbprvk5ZEANlr8AsD+/cZdUza7d9MCatgwx8eFhZFBa/t2Id0Siha/kvBE/Pr40HaUSltRtbUk/FSz/AJiXR9U2bJ1RGQkWX4vXza/rZISsh4FB5vflrvIsPyqPE769JFn+VUpXSKfy1RyL8vLE+fvyxkwADh8WGyb7sILUhgpfgcNIlcTb3J92LWLRL0zVQJTUoD0dPP7JBotfiXhifgFyO9XJcvvyZP0O6nw0uJwcSHSiqVKpLojIiLobyXC9UH1TA9AU+CZKMsvY/S8qDpOZIrfggISGiqMGdWqaTJGfZEhfo8coW1y1Tl4kNzOQkONu2ZgIDBwoPeJ35EjnTt27FggM5PiIrwJLX4lYYT4Vcnyq9J2JadrV8rXKdLyq0qkuiN4OisRlXusIH4Bsa4g588Dly6pO064+JXh63riBC0K/PzEt30lXboA3burI35Pnya/UxluD1VV1gh64pke3H2vtsawYcC+fcZeUxaXLtF9clb8pqSQ8M3ONrdfotHiVxKeit/4eHpRVFUZ1ydP4FuDvDKSCthsJDBEid/6erIeqipqOCJzuVpF/IosiMJ3IlS2/FZWAmfPim+7oECtOUSlnOqi05xxBgygTyu4Phid6YHDMz6oFPzoLtnZZMV3VvwmJpL129v8frX4lYQRll9AndKTBQUkcpzxIRKJyOCd0lISwKqL3549ybKmLb9NiMwJzdtRWfwCcix9J06otXukUjVNLsJFW35jYyl7jerit76eAvOM9PflXHMNZSGxgvW7LXbtovc0r17XFgEBlO/X2/x+tfiVhBGWX0Ad1wfV0pxxRFp+eTuqi18fHxKkWvw2ITInNF+McQu8anDxKyPjg2oZY1QSv8eO0cK1c2ex7fr6UoC16uI3P58K6pglfgHv8PvdtYusua6kyxs7Vlt+NQbhqfgNDSWfNFWC3lR7aXFEil8uFlQXvwAJL7PFb2Ul+bdaRfwWForZ1iwspFzLquaCDgsjwSPaylVXR2NSJbeH/v3pPojMFd4aMtKccayQ8cGMTA+cqCiKIfEW8eusywMnJYXe8aLLwJuJFr+S8FT82mxqZXxQ1fLLLXoiRE1REQmanj3Nb8tTRIhfK+T45URFkf/8zz+b35bqQZG+vjQ+RIvfoiLyRVRpEd2vH/WJp2CTiYxMDxyriN/Onc3ZUbHZyPq7Z4/x1xbJ+fP0d3RV/I4dS5/eZP3V4lcSnopfgFwfVHB7qK2lF6WK4jcykvonIlF9URFNvD4WeKoiI7X4bY7Irf7CQnX9fTky0p1xgamS5VeldGdHj8oVvydPUqYAVTEr0wMnMRHIyjLn2qLIyiLt4ar4jYykOcub/H4t8Jr2TowQv6pYfgsKyDrC68CrhMhCFyoXLrgSEQFeVhK/XIyKEL9WyAUtQ/zydIkqiV+edk12xoeLF2mxygOdRTNwIH2qbP01K9MDJzmZjE3nzpnXhtns2kXVYfnf0xXGjQO2bTO+T7LQ4lcSRll+y8rkP4z8xSDLKuEIkaJG9e3s5kRE0BZ/dbV5bZSUUFRx167mtWEU3M9VW34JWZbfHj3Uqgbo50duGLItv3yHT5b45aKS+9WqRn09cOCA8xkM3CE5mT6tbP3dtQtISnIvj/a11wI7d1Ishzegxa8kjLL8AvJdH44epchRvnWsEr17k6jRlt+WcL84MwMYioupHbO2IY2EVxUzW/xWVtKiQ/Vx0qcP3QuRVb1UDZpVIeMDt7jKEr9dutCC7cABOe23xbFj9GwNH25eGwMHUtGkXbvMa8Ns0tMpeM0dJkygoNSMDGP7JAstfiVhpPjNyfG8P56Qlwf07UsCQjVEiRrGrLGdzRFR5e3kSTUXRK0RFWW+tVP1HL+c2Fiq6iSiBDZHi9/Wyc2lDD/dusnrw5Ah6opfnoWBpyQzA19fspru3m1eG2ZSUkLPGA9ec5UhQ2j8/fCDsf2ShRa/kjBC/HbuTBakQ4eM6ZO7HD2qpr8vR8QW7rlzapesvRItfq+GWzvNRPXqbpy+fenz+HFxbapW3Y3Dq7zJrO6VmyvP6stRXfz27g306mVuO8nJ1hW/PFjNXfHr40OuD1r8ajzCqIl08GD5flh5eWr6+3JiYprKL5uFVQpccLp2BYKCtPhtjohqgFYZJ6LFb0MDPaOy8tg6Ii4OuHABKC+X14fc3KYyw7IYOpQs4CpmfNizx1yrL8fKQW/p6TQfezL3TJhA11Eh77WnaPErCcaM8YWULX4bGmhCVNnyGxvbFEluFlYRNRybzdyMDw0NdG0ril8zLXyFhbTwUCmoyx4hIZSvWtR2f3ExuVmoKH65xVVWZh3GyOdXBcsvIH+n0R5795rr78uxctBberr7Vl/OhAmUD92q1u/maPErCSPF79Gj9OKQQVERta265ffkSeDyZfPaUL1krT3MrH5XWkrBEVYTv5cumWvVsVJGkL59xVl+Vc4YExdHc7WsNF9lZVScQLb45ZXTVHN9OH+exqkIy69Vg95qa6nPnorfpCRauHuD64MWv5IwUvw2NMjL+MBfWqpbfuvrzc34UFBAgXWBgea1YTSRkeZt83MfayuJX95XM/3DVQ3qsodI8XvsGM2H3N1CJYKC6G8my/LL25Xt9hASQvdBNfG7fz99ihC/Vg16y84mI5Wn4tfPj/L9avGrcRujxK/s/ItHj9LvoWJ1Nw4XG2a6PlhJ1HBiYswr22pF8SsiJ3R+vpoCzx6iLb+RkSQ0VURmed/Dh2mOVcEqrmLQ2969JMrcKdzgDlYMektPJ8NMUpLn15owAdi6lQxKVkaLX0kYJX579KAoV1niNy+PIrRVtnhyUWpm0Ft+vtoLAHvExJDQM2MSO3mSKgl17278tc0iLIwims0Sv4xZa5z07Ut/x7o689vKy1PT35cjs5pmbi6NGRXmWBXF7549JHxF3R8rBr2lp1O/AwI8v9aECeRqYkW/5+Zo8SsJo8QvIDfoTWa9eWfp2JFyZJpp+bWSqOHExJAftBkZH06cIKuvFQpccPz9SQCbJX5//pnK1FplnPTtSy5VIiq9qZ4xZsAAmuvMjBtoDRWC3ThDh9I8ev687J40sWePmGA3zsiR9Gklv18jgt04Y8aQ3++mTcZcTxZa/ErCm8Svyv6+nNhY8yy/ly+TYLKKqOHwnKpmuD5YLc0ZJzravEUSH39WGSci050dO6a2+E1IIAu42Vlj7HHwYJN7m2y4yNy3T24/OJcvkz/riBHi2hw4kCrebd8urk1PKCqiOd4o8RsQAEycqMWvxk2MFr+5uWK2J5vT0EBWCVG+Vp4QE2Pei6uoiFwHrCJqOGb6QltV/Jrp58qva5VxEh1Nc5TZ4vfcOeDMGbXFLw82E+33W1VFVnGeZkw2gwbRDkl2tuyeEDk5dI94CjIR+PiQ9ZMXjVAdHpw2frxx15w2jfx+q6qMu6ZotPiVhNHit66OrLAiOXGCBr8VxK+Zll9+XasFvHXqROUqtfhtwkzxm59P24U9ephzfaMJDKQgNLPFr8ppzjhRURSMJ9rvNyeH3hWqiN+AAOqLKuKXB54ZEcjlCmPHkuVXZtU/Z9myhd7RvXsbd82pUyl7xNatxl1TNFr8SsJI8Tt0KH2K3oriyc5V2ZJzBM9s0NBg/LWtKn4Bsu4Z7fZQV0d15K0qfgsLzalgxDM9WMkPWkTGB15IQ+WANx8fID5evPjlwWU8x64KJCaqJX7j44HOncW2O3Ys7VbISjHqClu2AJMmGXvNIUMoPsLKrg9a/ErCSPHbsycVV9izx5jrOUtODkX0c99RlYmNbRJlRlNQQKvqDh2Mv7bZmOEOUlxM49uq4pcxc/ygrRgUKUL85uWRD6XqmUFkpDs7cICszl26iG3XEYmJZGiREfx3JZmZYl0eOGPG0Kfqrg+nTtF7euJEY69rs5H199tvjb2uSLT4lYSR4hegQATR4vfQIXoh+FhgFJnp32pFUcMxQ/xaMccvhwd5mVHW14rjpH9/892peKYH1S3iAwaQkBDJgQPquDxwEhNpy1tW3mNOfT2l25Ihfrt2pR1P1cUv9/c1WvwC5PeblQWcPm38tUVgAdninRgtfq+5hpJ9iyQnxxouD4C5uX6tKGo43B3ESN81K4vfPn1oMWe0tdNqOX45CQn0cvv5Z/PasEK6RIBEaHExcPasuDZVFL8844Ns14fDh4HKSjniFyDXB9XF75Yt5BYSHm78tadMoc/vvjP+2iLQ4lcSe/YY6+M2fDiJDjNfUldy6JB1xG/nzhRoZJZFz4r+vgC5rFy8aOwL/fhx2sIW7YdnBP7+dE+MFr+nTwOXLllP/PIsB2b6ulolYwyPrRBV5KGyksahauK3a1cax7LFr6xgN87YsVRaWaWcx1eyZYs5Vl+AgmEHDwa++cac65uNFr8SaGgAvv4amD7duGvy1bgo6+/p0/TPCi8tTny88QEK9fW06LCaqOGY4Q5y7Jh1Svjawww/V77jYLX7Eh9Pn2aJ3/PnyZpqhXlkwAAqo7t/v5j2Dh1SK9NDc1QIetu9m3YMunaV0/6119K7/Kef5LTfFuXltFAzOtitOTfeCGzYYE4gudlo8SuBvXuB0lJjxW9CAqUmEiV+ue+bVSy/gDnit7iYAj+sLn6NDPA6flztyP22MFP8Wm2chISQhccs/04+j1hB/AYE0DwrSvyqmOmBk5hIwWYyU31lZDQFnskgIYECnbdskdcHR5jp78u5+WYKquNWeCuhxa8Evv6a8n2OG2fcNf38yEIgKujt0KGm9D9WwQzxy0WSVd0eevWiRZPRll8tflty/HhTXmWrMWCAeZZfLn65e4XqDB0qVvxGR9O4UY3Ro8nFTkT1P3vU1JD4TkmR0z5AMTsTJ6orfrdsoXk4Ksq8NsaNI8v7F1+Y14ZZaPErgQ0bgOuuI9FhJCKD3nJy6MEy+ncwk/h4ctUw0r/16FGaBK0q9mw2Eu5GBQLW1ZEbiNW295vTty+Nk4sXjbsmLwOuekYDeyQkmGv5jY4mY4AV4OJXhMVzzx6a01Vk1Cj63LFDTvuZmZSL26iSve4yYQKwcyf586vGt99SOjIz8fMDZswAPv/c3HbMQItfwZw5Q1VRbrnF+GsPH04Ts4j8i/v3q7kd5whupTbS+nv0KGUICAoy7pqi6d/fuEDAkyfJD9qqiwGgSbgbadU6csRauyTNGTCA+m+GX19OjjVcHjhDh9IcXlpqbjuMURopWcFcbdGzJz3jGRly2t++neZcHusii4kT6X2rWtaHEyfo2br+evPbmjmTxmpRkfltGYkWv4LhzuE332z8tZOSgOrqpsprZrJnj/yJx1XMEL9WFjUcI3O5csFodcsvYGxmECuPk4QEmld4CjsjsaL4Bcx3fSgpAcrK1BW/ALk+yLL8pqcDI0dSdhaZDB5MWYS4f60qfPstuSVOnmx+WzfcAPj6Ws/1QYtfwXz2GTnpm5F3b8QI2lbdudP4azfn1CmyfFhN/HbuTD6uRovfuDjjrieDuDgqNGCEZe/YMZp0rVD1rzXCwqhan1Hi99IlsopYVfyale6sro6eHyuJ3379yOJotvjNyqLPESPMbccTxowh94O6OvFtb98u3+UBoLluwgT1/H6//poWJyJiDLp1A8aP1+JX44CaGuCrr8xxeQAoMGLQIGDXLnOuz+FBdYmJ5rZjBkYGvTFGFlOrihpOXBxZ9oqLPb/W8eMUYBEQ4Pm1ZGGz0T0xSuxxq7pVx0lMDFnYjBa/x47RlrGVxK+vL1n7zBa/mZkkKlReRI4eTfOGqABATlER7ULIDHZrzqQMSx1GAAAgAElEQVRJJMZV8futrwc2bRLj8sC5+WZqs7JSXJueosWvQL7/noJozBK/AG0FmW353bOHUiBZcWs7IcE48VtSQhOeVUUNh1uu8/I8v5ZVqnW1xYABxgV58fGWkGDM9UTj50djxOigNyulOWvOsGHm57jl/r4qB0gmJdFiQLTrw/bt9KmK+L3+egq+U8X1YfduCuoWKX5/9StaCG3YIK5NT9HiVyAff0yC0cyk5aNGkTitqTGvDR6F7GPB0cMtv0ZEa1vdoseJjaWXrBF+v7m51klb5Qgj03sdOULpgHr0MOZ6MhgwoEmsGkVODtClC7mZWInkZGDfPhI8ZqFysBunQwd6D4gWv1u30m5ERITYdltjwAAKelal0tlXX5GL3+jR4tqMiyMXnbVrxbXpKRaUL9akrg5Yvx6YN8/c1fyoUdSWmSnPsrOt6fIAkFCtqKCIbU85csTaac44gYG0veqp+G1oIMFoVQtncwYMoO1VI9Kd8WA3la14bTFkiPHb2wcPktXXavclOZmEr1nb/WfPUupB1cUvQNZX0RXONm82t2qZq9hsZGVVRfx+/jkV0BIdDDh3Lvn9quL+0RZa/Api0yZKCn7bbea2M3w4bVOa5fdbXU3bn1YLduMYmfEhN5csEFbKddwaPOjNE4qKgKoq7xG/gDHW38OHrX9Phg0jNx8jFo0cK2aMAWjh7+NjXlUr7lJhBfF77bVkwS8rE9Pe2bM0blQSvwCJ34MHgcJCuf0oLqZ3/8yZ4tueM4fmf6sEvmnxK4gPPiArh9lJy4OCqA2z/H4PHCCHeiu+tIAmC5wR6eAOHLBeruPWMCLAi59vdaEHNP0Onvq5MkYvRTNdnUQwbBh9GmXtrKuj+6JqEQdHdOxIz71ZBobMTHIpsIL70LXX0uePP4pp78cf6ZlSTfxOmULvlW+/lduPL7+khdmNN4pvu18/2nm2iuuDFr8CqK4mf9/bbhOzxWdm0Ft2Nv0OPN+l1ejYkR7SAwc8v9bBg94jfgcOJKFXX+/+NQ4fpl0HKwZCXknXrpQWz1Pxe+oUudlYfZzEx9M26r59xlzv8GESwFYUvwDNsWZZfrdvJxHh62vO9Y0kKormU1HBXps3025bbKyY9pylRw8aE19/Lbcfn39OJYdlxRfMnUtBbxcuyGnfFbT4FQAfDGa7PHBSUkjcVVQYf+3MTLKKWaUcqT14iVJPuHSJ/PKsbtHjDB5Mi7SCAvevkZtLmR78/Izrl0yMyPjAF1lWF7/+/pRG0SjLL0+XyC3KViM5meIqjA4sZox8aFXIYessEyaIFb8TJ4ppy1VuuIHEr4y8xwClGdu0SY7LA2fOHHqPWMH1QYtfAbz9Nq3kRW1jXXtt0yRqNDt2UHJzKzNkiOeW38OH6R5bXdRwBg2iT0/cQbwl2I1jRJDXwYPkE271oEiAFo1GWX737iULXteuxlxPNCNHksgxOujt5Eny27Sa+N2zxxxjS3MqKmjnUTWXB86tt1IfRbmAXMmmTeRzK1P8xsTQ2F29Wl4fnEWLX5MpLAQ2bgTuv19cm/37U/ogox/C6mqa5KwufocOpeAsTybrgwfpk4tGqxMVRbmbPRG/OTnW8FN0lqFD6XfyJKXVgQPkUmKFLey2GDaMxK8RlQD37rWuywNAMQ++vsa7PqSn06fVxC9jwLZt5razZQu1o6rld8QImkc//VRO++vXkzFGdt7se+4hC3hRkdx+tIUWvybzzjsUvHD77eLatNnI+mu0+M3KImuHyPyBZsBdFTyx/h48SLkdO3Uypk+ysdlo0uSi3lUuXKDqblb1BbfHsGFUgcyTQEBv8gtPSqK/s6dZQRgj9ymrBs0CNKcPGWJ8bEV6OhkvevUy9rpm0q8f5dw1u8TvV1/RvVF1F8VmI+vvp58ak0feFaqrgU8+IZ9b2cybR7tdqlt/tfg1kYYG4K23aDCIFknXXksTc3W1cdfcsYMGtZUtNgBZJ319PduyPHDAe6y+nEGD3Lf8ctFsVR9Oe3Ah7+5WP2PelREkOZk+PbV2njxJqbFGjfK8TzJJTTXe2mk1f1+ARN/kyeZmOmCMdlBlZDFwhVtvpbgJM/Ps2+Prr2lhqoL47dIFmDULSEsTvwhwBS1+TeSbbygo6r77xLd97bW0XWtk9Z2MDNraCQgw7poyCAwk31QedOMO2dnWtlzZY/BgEr/uTFj79lGKHW9aEHTvThYtd8XvyZOUl9RbxknPnuTT56n45dZSq4vfcePoeTEq93FVFe2uWU38AsCMGTQnlpSYc/2cHBKVM2aYc32jmDiRqqt98onYdteuJcODKvPvvffSjpnoAiiuoMWvibzyColFGZPZsGH0EBrp+rBtmzUnZnuMGEFbr+5w5gxw4oQ1ktC7wpAhwLlz7iVq37ePcgV36GB8v2QybJj7OwR8fI0YYVx/ZJOcbIz4jYwEwsON6ZMsxo+nT6Ne8Lt3k5uNFefYadPIAmxWqq+NGymHvarBbpyAAAo4W7tWnNWzqgr47DM1rL6cSZMo5eWbb8ruSeto8WsS+/aR5fdPf5JTvtPXlywTRqWgyc8nwTdhgjHXk01yMll+L192/VwrVWByBf77ZGW5fu6+fd7l8sAZNsz9LcysLCA0lKzH3kJyMol6T17su3ZZ3+oLkBU8MhLYutWY623eTAYLKz5HoaGUAeOrr8y5/oYNJKissLi+4w5yAxPl+vDZZ1SGfd48Me05g48P8LvfAWvWAKWlsntjHy1+TeLllynyc84ceX2YMoXEb1WV59fiIppX9LE6ycnkD+2Oj2tWFuU55qWSvYXISEqO7o743b/fmi/ttkhKou1Wd7a2s7LI6itj8WsWycm0O3D0qHvnNzR4j/i12cjAYNTu2n/+QwLPqnmyZ8wgg487BgVHXLxI91h1lwfOtGnkIvTvf4tp7+23aRdCtffRvffSWF6xQnZP7KPFrwkUFQHvvQc8+iglh5fFDTeQwDPC+rtlC4mb7t09v5YKJCbSpzuuDzxS3RvSVzXHZiOx56r4LSkBysu9U/yOHEmf7pSyzcz0vt0BnumFp+RylZwcEs9WzxjDmTSJ3Dg8rWhVVUXuE5MnG9ItKdxwA/m4G50B46uvKH7lppuMva5Z+PuTC8L77xuTFtARJ05QoOH8+ea24w7duwN33gm88Ya8wh+O0OLXBJYto5ypCxfK7cfgwZSOa+NGz6/1ww/q5ld0h86dKejNHf/FrCzvEzUcd8Qvf9lxoehNxMVR9LKrL/SyMloEe9s46daNsmC4u9X/ww+0aExJMbZfspg6lSydnhoYfvqJqsVNmWJMv2QwejQJHqOre61dSzso/fsbe10zueMOip0wu+DFypVAx45yd5gd8cgjVLRl3TrZPbkaLX4N5vhxMvMvXkwCSyY2G20VeeqHVVBA25yqBxu4ysiRrouac+eouhtP++RtJCWRNeHnn50/Z+dOoHdvWmh5Gz4+7o0TnmXFGxcE48e7L35//JGenZAQY/ski7g4IDra8zRfmzZRbl8rl0v39QV+9Stjg70uXiQxrVIwlzOkplLAV1qaeW00NND1585VN9/8sGG0I/CXv5hvBXcVLX4N5tlnyW/y4Ydl94S44QYSa8ePu3+NjRtpYrOyVcIeqalk+XXFJ3r7dprYx40zr18y4ZZKV9xBduwgH05v8m1tzqhRrovfbduoymLfvub0SSbjx5Ov/OnTrp3HGFlIvSVuAKAxP20aiVdP+PJLmqut/gzNm0eGEnfiBuzx5Zc0P6tq2WwNm412ftesMS4V3pVs3EjvdZHVY91h6VLKd/7ZZ7J70hItfg0kKwtYtYr+2B07yu4NMWUKOZ17Yv3duJGEYteuxvVLBcaPJ18kV3Ihb91Kkc2qBRcYRUIC/Z2dTd/EGAlDb/HhtMeoUeTX7EoKuG3baIFkdTFjD77wc7XAQ0EB3UNvyRjDmTqVXu7upAgE6L7s20cpsqzOddeR8WfNGmOut3YtPX+qVnVzxL33krXznXfMuf4rr9C9UT013rhxtGv85z+rVfRCi1+DaGgAHnyQ/GwfeEB2b5ro3JksLe6uumprge++s06krSsMHUr+nK5s4W7bRgsBbxQ1AG3zjxvnvLDJy6MgF28Wv1zsOeu/V1tLCwJv3R2IiaGt/v/8x7Xz/vOfpgwJ3sT06WRgcHeO/eILCpK6/npj+yUDf3/g1782xvXh3DlKcWY1qy+nVy/q+xtvGL/lv3cvvZf/8AdrvIuWLqVd1k8/ld2TJrT4NYi33qIKaG+8ITfDgz1mz6ZtOXe2X7ZsAS5doi05b4PnQnZW1NTV0d+YJ7f3VsaPJ8uvMymLeNS/N/q2cnr3pspJ33/v3PGZmZRlxVvHic1Ggs/V3aSNG4ExY8gy6E1060aWLXeren32WVNlMG9g3jzKC+9pddH33qOF5B13GNItKfzud2QgMDr/8SuvUCrV2bONva5ZTJ5M7kGLF6uT+UGLXwM4cQJ4/HHg7rvVfOH9+te08vzoI9fPXbeOtpx4ajBvgws9Zx7IrCygstL7LFdXMm4cBZo4U9b3u+8o7Zu3CZorue4658XvDz+Q25O3PjMALYZzc52PJairoxyw3riDBAD/9V80PioqXDuvvJyeof/6L3P6JYOJE0mYvfWWZ9dZsYLSm0VGGtMvGYwdS7tiy5YZt+Wfn085hB95RD1DW2vYbMDf/gYcOQL885+ye0No8esh9fWUy65zZ1qNqUjv3mSZcNUPq64O+PBDiia1wtaKO0ybRjk6nfFx3biR3CS8IUG/I0aNojKdbbmDMEYvbm8LhLTHdddRII8zfp0bN5KlwyovJneYMoV2TpwtZ5ueDpw/D9x4o7n9ksUtt9BOiavbuuvW0dxqtWwGjvD1BRYsIMvtuXPuXWPXLjI2LFhgbN9EY7MBTz9N75fvvjPmms88QynlHnrImOuJ4pprKB/x//2f68GyZqDFr4c89xz5R777Lm1/qcp//zf53OXnO3/Of/5DKa+8aWK+khEjKCr/yy/bPnbDhib/Pm8mKIj8mtsSNkeOkBhsD+KXp/lry8/1/HlaNHiryON06UJj5PPPnTv+88/JB3LECHP7JYuoKFogrVzp2nnvvUdzSs+e5vRLFvfdR64/777r3vkvvwzExnrHTsGMGeQW9swznlt/Dx1qCqoPDjamfyJZtox2oR97THZPtPj1iPffpwH9f/+nfvqeefMoF6ArpQbT0oABA7x7+9bHh4RKW4nZy8ooiMnbRQ3n5pvJUlFZ2fox331HCwFvi963R8+e5K/all/npk1kAfSGl3ZbzJlDrgxtxRI0NAAffED+iT5e/MaZP59cH5x1BTl8mAwnVvZpbY3ISGDWLBKxrpY7zs+ngLk//ck7DA3c+rt1q/M7Ja3xv/9L+dStahEPCwP+/ndy2zC6GIqruDQV1dTUYPHixYiIiEDHjh2RkpKCTU4mOKyoqMDChQsRGhqKkJAQTJ48GVmtJAP86aefMH78eAQHByM8PByPPvooLl26dNVxjDG88MIL6Nu3Lzp06IDhw4fjgw8+cOVXcpstW4B77gHuuotWYaoTHAz85jfkh1Vb2/bxp06Ry8ODD3qvywPn5ptpRZ2X1/oxGzfSqt0bA//sMXMmWW4cbdV9+in5B3tLwYK2mD2bxsHFi60f88UXFBwXGyusW9KYO5eeifXrHR/3ww+0Q3DnnWL6JYtZs+hZePtt545/7TWyhv/61+b2SxZLlgDHjtHCxxX++lfaWVCxZK+73HQT7R49+qhz7197bNgAfPwx8PzzQGCgod0Tyl13kXFgwQJKISkN5gK33XYb8/f3Z48//jhbsWIFS01NZf7+/mzr1q0Oz6uvr2epqaksJCSEPfvss+wf//gHGzJkCOvcuTM7cuRIi2OzsrJYUFAQS05OZv/85z/Z0qVLWVBQEJsxY8ZV133iiSeYzWZjDzzwAPvXv/7Fbr75Zmaz2dgHH3zQal92797NALDdu3e78qu34KuvGOvQgbGpUxmrqXH7MsI5eJAxm42xFSvaPvbZZxnr2JGxs2fN75dsLl5kLCSEsaefbv2YKVMYu/Za16/93nvvud0v2cTHM7Zggf2flZYy5uvL2BtviO2TTI4dYwxgbO1a+z+/eJGxTp0Ye+opsf3iyBhr06czNn6842P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        " <defs>\n",
        "  <clipPath id=\"pbaaead5c47\">\n",
        "   <rect height=\"345.6\" width=\"446.4\" x=\"44.089375\" y=\"13.006875\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
        "</svg>\n"
       ],
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x110526290>)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##2c.)\n",
      "Tada! It works pretty well. -S"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#robert driving\n",
      "println(\"Function \", (@elapsed tanh(x)))\n",
      "println(\"lookup \", (@elapsed lookup(lookup_table,x)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "Warning: Possible conflict in library symbol dtrtri_\n",
        "Warning: Possible conflict in library symbol dgetri_\n",
        "Warning: Possible conflict in library symbol dgetrf_\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Function 5"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ".2298e-5\n",
        "lookup 0.001251682\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Why would anyone do that? So slowwwwwwwwww! -R"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#I stole this from stack exchange -R\n",
      "function fact(n)\n",
      "    if n == 1\n",
      "        n \n",
      "    else \n",
      "        n * fact(n-1) \n",
      "    end                                                                      \n",
      "end;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function fn(x::Float64): \n",
      "  @assert x <= 20\n",
      "  @assert x >= 1.0\n",
      "  A = fact(iceil(x)) \n",
      "  x -= 1\n",
      "  for i in 1:100\n",
      "    x += 1/i\n",
      "  end\n",
      "  return A*tanh(x)/(sin(x) + cos(x))\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "fn (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LU1 = make_table_linear(x->fn(x),1.1,20.,100);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = linspace(1.1,19.9,10000)\n",
      "#robert driving\n",
      "println(\"Function \", (@elapsed [fn(i) for i in x]))\n",
      "println(\"lookup \", (@elapsed lookup(LU1,x)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Function 0.028917098\n",
        "lookup 0.016390423\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The more absurd we made the function, eventally we got the lookup table to beat it, but it shows the idea -R"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "include(\"CLASS_REPO/Astro585/Lab5/HW5_Q2_ecc_anom.jl\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "decc_anom_dM (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##2d part 2 The Return of the Julia"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Sonny Driving\n",
      "x = linspace(-2*pi,4*pi,1000);\n",
      "ecc_1 = ecc_anom(x,0.25);\n",
      "using PyPlot \n",
      "plot(x,ecc_1);\n",
      "xlabel(\"Mean Anomaly\");\n",
      "ylabel(\"Eccentric Anomaly\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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      "This would perform better than calculating a lot of eccentricity anomalies (there's a while statement in ecc_anom). -S"
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      "ecc_table1 = make_table_linear(x->ecc_anom(x,0.25),0.,2*pi,128);"
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      "#Sonny Driving\n",
      "x = linspace(0.,2*pi,1000);\n",
      "ecc_2 = ecc_anom(x,0.25);\n",
      "using PyPlot \n",
      "plot(x,ecc_2);\n",
      "plot(x,lookup(ecc_table1,x))\n",
      "ylim(-7,7);\n",
      "xlabel(\"Mean Anomaly\");\n",
      "ylabel(\"Eccentric Anomaly\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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        "    <path d=\"\n",
        "M43.56 352.8\n",
        "L43.56 7.2\" style=\"fill:none;stroke:#000000;\"/>\n",
        "   </g>\n",
        "  </g>\n",
        " </g>\n",
        " <defs>\n",
        "  <clipPath id=\"p9e520c46a2\">\n",
        "   <rect height=\"345.6\" width=\"446.4\" x=\"43.56\" y=\"7.2\"/>\n",
        "  </clipPath>\n",
        " </defs>\n",
        "</svg>\n"
       ],
       "text": [
        "Figure(PyObject <matplotlib.figure.Figure object at 0x1105bd290>)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The artifacting has to be something from the interpolation process - otherwise, the ecc_anom call would also show it. -S"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Robert Driving\n",
      "ecc_table2 = make_table_linear(x->ecc_anom(x,0.25),x->decc_anom_dM(x,0.25),0.,2*pi,128)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "lookup_table_linear_type([0.0,0.06594926834324932,0.13180330494829162,0.1974686520141046,0.26285531133600754,0.32787826188799074,0.39245874516739376,0.456525274597337,0.5200143477034695,0.5828708613683835  \u2026  5.700314445811203,5.763170959476115,5.826660032582249,5.890726562012193,5.955307045291593,6.020329995843578,6.085716655165482,6.151382002231294,6.217236038836336,0.0],[1.0,1.0167637056158338,1.034073578811775,1.0519308413114592,1.0703358668807594,1.0892885054667514,1.108788428861808,1.1288354821115478,1.1494300263451658,1.1705732612732902  \u2026  -2.3525060378415126,-2.268639906131681,-2.189788974769114,-2.1155888325507135,-2.045714513038787,-1.9798745790188708,-1.9178060404770738,-1.8592699928936012,-1.8040478911299431,1.0],0.0,6.283185307179586,128)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Robert Driving\n",
      "x = linspace(0.,2*pi,1000);\n",
      "ecc_2 = ecc_anom(x,0.25);\n",
      "using PyPlot \n",
      "plot(x,ecc_2);\n",
      "plot(x,lookup(ecc_table2,x))\n",
      "ylim(-2,7);\n",
      "xlabel(\"Mean Anomaly\");\n",
      "ylabel(\"Eccentric Anomaly\");\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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     "input": [
      "decc = [decc_anom_dM(i,0.25) for i in x ]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
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