{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# <span style=\"color:rgb(213,80,0)\">Tutorial 3 \\- Modify Structural Parameters</span>\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this tutorial, we will use a P2D model to simulate the effects of changing structural parameters (like electrode thickness or porosity) on cell discharge. After completing this tutorial, you should have a working knowledge of:\n",
    "\n",
    "-  Basics of how structural properties are defined in BattMo\n",
    "-  How changing the thickness of one electrode can affect the overall capacity of the cell\n",
    "-  How to setup and execute a parameter sweep\n",
    "\n",
    "We'll use the same model from Tutorial 1.\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "source": [
    "jsonstruct = parseBattmoJson('Examples/jsondatafiles/sample_input.json');"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Structural Parameters\n",
    "\n",
    "Structural parameters are defined in the JSON parameter file and parsed into the MATLAB structure. Once the JSON parameter file has been read into MATLAB as a jsonstruct, its properties can be modified programmatically.\n",
    "\n",
    "\n",
    "Let's begin by exploring the parameters of the negative electrode coating with the following command:\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "source": [
    "disp(jsonstruct.NegativeElectrode.Coating)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               thickness: 6.4000e-05\n",
       "                       N: 10\n",
       "        effectiveDensity: 1900\n",
       "    bruggemanCoefficient: 1.5000\n",
       "          ActiveMaterial: [1x1 struct]\n",
       "                  Binder: [1x1 struct]\n",
       "      ConductingAdditive: [1x1 struct]"
      ]
     },
     "metadata": {},
     "execution_count":  2,
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Here, we can see that the structural and material properties of the coating are defined. Let's try increasing the thickness of the negative electrode from 64 µm to 72 µm.\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "source": [
    "jsonstruct.NegativeElectrode.Coating.thickness = 72*micro;"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Now we can run the simulation and plot the discharge curve against the discharged capacity of the cell.\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "source": [
    "% instantiate an empty figure\n",
    "figure()\n",
    "output = runBatteryJson(jsonstruct);"
   ],
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "retrieve the states from the simulation result\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "source": [
    "states = output.states;\n",
    "\n",
    "% extract the time, voltage, and current quantities\n",
    "time = cellfun(@(state) state.time, states);\n",
    "voltage = cellfun(@(state) state.('Control').E, states);\n",
    "current = cellfun(@(state) state.('Control').I, states);\n",
    "\n",
    "% calculate the capacity\n",
    "capacity = time .* current;\n",
    "\n",
    "% plot the discharge curve in the figure\n",
    "plot((capacity/(hour*milli)), voltage, '-', 'linewidth', 3)\n",
    "xlabel('Capacity  /  mA \\cdot h')\n",
    "ylabel('Voltage  /  V')\n",
    "legend('t_{NE} = 72 µm')"
   ],
   "outputs": [
    {
     "data": {
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K927d5eFCxfKgQMHPAe1nPunp0x79uyZql2O+3O8nhLVDyCwxc1MQwQQQAABS4EJEybI+PHjc21l8+bNllvNnOZco/afvvrpp5/k6quvlh49ekibNm1k586dnoNapheMl1dTKVOTmuXlgWbVpVGNcplT4ewpAggggECoBJhRC1V3ej+Y0aNHy7Zt22TixImmkUtBLaLkdZaNGTbvdcWaCCCAAAKJFSCoJdYzY7Z2ySWXyOHDh6VEiRJmn3/99Vf54YcfpGzZsnLbbbeZf6ItYSwYr4FNPfTGg8Y1yjPLljGVzo4igAACmS0QxnG3sB7h1Od/dPbu3StHjx7Nttq1a5e583P+/PlSpUoVOf74450Jan5n2HR9Ztky+xcfe48AAghkigBBLVN6Ksn76eKpz4JI/cywEdiSXJhsHgEEEHBcgKDmeAH4PXzXCsbrjQfMsvmtJNZHAAEEEPAi4Nq4y6lPL1VRyDquFQynRS0LhuYIIIAAAlYCro27BDWrchFxrWDycnFa1LKAaI4AAggg4EvAtXGXoOarPPKv7FrBFMTlJ7BxWtSy6GiOAAIIOCzg2rhLULMsdtcKxguXn9DGzQdeRFkHAQQQQCAi4Nq4S1CzrH3XCsYPl5/AxiybH1nWRQABBNwVcG3cJahZ1rprBRMvl94tunzbd7J86/6Ym2CWLSYRKyCAAALOCrg27hLULEvdtYKx5BJm2WwFaY8AAgi4LeDauEtQs6x31wrGkiu7OYEtUZJsBwEEEHBLwLVxl6BmWd+uFYwlV9TmhLZkqLJNBBBAIJwCro27BDXLOnatYCy5Cm2uge21zfvk0WVfePoYvZatcY1yvBTekxYrIYAAAuEQcG3cJahZ1q1rBWPJ5bk5s2yeqVgRAQQQcErAtXGXoGZZ3q4VjCWX7+aRwKYNvdwxqutx16hvZhoggAACGSPg2rhLULMsTdcKxpLLqnk8s2ycGrUipzECCCAQOAHXxl2CmmUJulYwllwJaa6BTZeh+mw2D89lY5YtIexsBAEEEAiEgGvjLkHNsuxcKxhLroQ39zvLRmhLeBewQQQQQCClAq6NuwQ1y/JyrWAsuZLanNCWVF42jgACCARCwLVxl6BmWXauFYwlV0qax3tq1JxObVZdGtUol5L95EMQQAABBPwLuDbuEtT810iuFq4VjCVXypvbzLLpzhLaUt5lfCACCCBQqIBr4y5BzfIL4VrBWHKltblNaCOwpbXr+HAEEEAgW8C1cZegZln8rhWMJVdgmhPaAtMV7AgCCCDgS8C1cZeg5qs88q/sWsFYcgWuuQa25dv2y3L9t8dHfehBXF+nsnS/uCqnRgPXo+wQAgiEXcC1cZegZlnRrhWMJVegm8fzFgQ9IN6EEOhuZecQQCBkAq6NuwQ1ywJ2rWAsuTKmeTynRgltGdO97CgCCGSwgGvjLkHNslhdKxhLroxsHm9ou+PiqtKxTmVOj2Zkr7PTCCAQVAHXxl2CmmUlulYwllwZ3zze0Mbp0Yzveg4AAQQCIuDauEtQsyw81wrGkitUzQltoepODgYBBDJEwLVxl6BmWZiuFYwlV2ibxxvaOtSpLD24ezS0dcGBIYBA4gVcG3cJapY15FrBWHI50Tze0MbpUSfKg4NEAAFLAdfGXYIaBWMpQPPCBGxDm26btyJQYwgggMB/BQhqVIMvAdcKxhcOK+cSsAltjWuUk8Y1yhPaqCkEEHBewLVxlxk1y5J3rWAsuWj+HwGb0NawWllp/scKhDaqCQEEnBRwbdwlqFmWuWsFY8lF8ygC8YY23RTXtVFSCCDgmoBr4y5BzbLCXSsYSy6axxCI9zVWOUOb/jfXtVFqCCAQVgHXxl2CmmUlu1Ywllw09yGgoU2Xoa/v8PXC+Eho47o2H9isigACGSPg2rhLULMsTdcKxpKL5nEK2IQ2TpHGiU4zBBAIpIBr4y5BzbIMXSsYSy6aJ0ggEde1cYo0QZ3BZhBAIKUCro27BDXL8nKtYCy5aJ4EAdvr2szp1WbVua4tCX3DJhFAIPECro27BDXLGnKtYCy5aJ5kgUSdImW2LckdxeYRQCBuAdfGXYJa3KXyW0PXCsaSi+YpFrA9RVq9wjHyl3qnMNuW4n7j4xBAoGAB18Zdgprlt8G1grHkonkaBWxOkepuR57ZxmxbGjuRj0YAAecmSAhqlkVPULMEpHlaBGxPkRLc0tJtfCgCCDh4JougZln2BDVLQJoHQiARs216INyUEIjuZCcQCLWAa+MuQc2ynF0rGEsummeAQGS27eGlO2XJlm/j2mM9TUpwi4uORgggEEPAtXE31EFt2rRpMnPmTNm9e7dUqFBBWrZsKf369ZOSJUtGLYMVK1bI+PHjZfv27VKiRAlp0KCBDBw4UKpWrVpg2bhWMPwGcU/AdraN06Tu1QxHjEAyBVwbd0Mb1GbMmCHjxo2T4cOHy9lnny1bt26Vu+++Wzp06GDCWt5lx44dcuWVV8oDDzwg7dq1kwMHDsi9994ru3btkrlz5xLUkvmtY9sZI5Dz2jbd6eVb98e179yYEBcbjRBAgGvUwlMDy5Ytk+LFi0ujRo2yD2rAgAHy7bffyjPPPJPvQBcvXix9+/aVTz/9VIoWLWp+vnDhQhPqcv5Z3oauJfvwVAhHkgiBRN2UoPui17fpwgvlE9EzbAOB8Aq4Nu6GdkYtZ4kePXpU1q1bJz179pTevXtLx44d81Xwvn37pFWrVtKjRw/z80OHDsngwYOlWLFi8uSTTzKjFt7vPEeWQIFEnSYluCWwU9gUAiETIKiFrEP1mrMJEyZI6dKlzYxZly5dCjzCDz74wIS577//3qxTu3ZtmTJlipQv/9uF0dEWLZicS69evcw2WBBAQESD2z/Wfy2f7D5odZqU4EY1IeCugI7hOpbnXDZv3uwMSOhn1PRU5549e2TDhg0yevRo6datm3Tt2jVfB+s1atddd53cc8890qZNGzl48KA88sgj8tVXX8msWbOyT4fmbehasnfmm8GBJlwgEadJdady3lGq/8+p0oR3FRtEINACro27oQ9qOatNbzAYM2aMrFmzJl8RaohbuXKlvPTSS9k/+9e//iVNmjSRF198UerUqVPgjJpLyT7Q3152LqMEEhXcIuGNa9wyqvvZWQTiFiCoxU0XrIY6c3beeeeZa84iiz6qY9SoUQUGtbfeekvmzZuXvf6XX34pTZs2ldmzZ5vToNEW1womWL3M3oRJgOAWpt7kWBBInoBr425oZ9QmTZpkri97+OGHzWzYzp07zc0B+my0ESNGmAoaOXKk1K9f38yarV27Vm644QZ56KGHpG3btnL48GEZNmyYCXWLFi2SUqVKEdSS971jywjkE0h0cGtco5w0rlGeU6XUGgIZLkBQy/AOjOx+VlaWCWpz5szJfuBts2bNpE+fPlKmTBmzWsOGDaVTp06iNwDosmTJEtGAp9er6UNx69atK/3795fq1X97bAAzaiEpDg4jIwUSHdwUgdOlGVkK7LTjAgQ1xwvA7+G7VjB+fVgfgWQJJOrhu7p/3KCQrF5iuwgkXsC1cTe0pz4TXxrMqKXKlM9BIB6BRAY3/fzLa5aXP3G6NJ6uoA0CSRUgqCWVN3wbd61gwteDHFFYBRId3Jh1C2ulcFyZJuDauMuMmmWFulYwllw0RyCtAom8zk0PpOWZFeXCaidwk0Jae5UPd03AtXGXoGZZ4a4VjCUXzREIlACzboHqDnYGAU8Cro27BDVPZVHwSq4VjCUXzREItEAkuL284Wv5+F/xv/Yq50E2/2NFaXgas26B7nh2LqMEXBt3CWqW5elawVhy0RyBjBPQ8LZ8235Zrv/eut96/7nWzZqQDTgu4Nq4S1CzLHjXCsaSi+YIZLxAZNZtxuo9snXfYcJbxvcoB5BpAq6NuwQ1ywp1rWAsuWiOQCgFEj3rpkg688bbFEJZLhyUpYBr4y5BjYKxFKA5AgjkFYjMus36eK98+u9DzLpRIggkUICglkBMFzblWsG40KccIwLJEEjWrJvuK6/CSkaPsc2gCrg27jKjZlmJrhWMJRfNEUDgPwKRWbfXt3wrK3d+n5BZN06ZUl4uCLg27hLULKvatYKx5KI5AggUIpDo57pFghuzbpRdmARcG3cJapbV61rBWHLRHAEEfAok+m0KhDefHcDqgRNwbdwlqFmWoGsFY8lFcwQQsBRIxqxb3lOm+v+NapSz3FOaI5AcAdfGXYKaZR25VjCWXDRHAIEkCCQjvEUezMsjQpLQYWzSSsC1cZegZlUuIq4VjCUXzRFAIEUCOe8y1Y/krQopgudjki7g2rhLULMsKdcKxpKL5gggkEaBZD0iJDLrxinTNHauQx/t2rhLULMsbtcKxpKL5gggECCBZJwy1cPjrQoB6uQQ7opr4y5BzbKIXSsYSy6aI4BAwAWSEd54EX3AOz3Dds+1cZegZlmgrhWMJRfNEUAgAwWSfcqUO0wzsCjSuMuujbsENctic61gLLlojgACIRCIzLot37Zflm/7jhsVQtCnmXQIro27BDXL6nStYCy5aI4AAiEV4JRpSDs2gIfl2rhLULMsQtcKxpKL5ggg4JBAok+Zcq2bQ8VTyKG6Nu4S1Czr3rWCseSiOQIIOCyQrFk3HsrrVlG5Nu4S1Czr27WCseSiOQIIIJBLINHhjUeDhL/AXBt3CWqWNe1awVhy0RwBBBCIKZDIU6Y5T5dyd2lM+oxYwbVxl6BmWZauFYwlF80RQAAB3wI5Z91sX4VFcPPNH7gGro27BDXLEnStYCy5aI4AAghYCyTydCnBzbo7Ur4B18ZdgppliblWMJZcNEcAAQSSIpCo06Vc45aU7knoRl0bdwlqluXjWsFYctEcAQQQSIlAooLbFWdUkHuvOM3sM9e4paTrYn6Ia+NuIILa22+/LZdccokULVo0ZgcFbQXXCiZo/uwPAggg4EUgEcGN06RepJO/jmvjbiCCmqKfdNJJcvXVV0u7du2kevXqye/pBH2CawWTIDY2gwACCKRVIFHBbWiz38YrZttS152ujbuBCGorVqyQuXPnytKlS+Xnn3+WunXryjXXXCOtWrWSMmXKpK734/gk1womDiKaIIAAAoEX0OA29PUdZj/jubOU2bbUdbFr424gglqkew8ePCivvfaazJ8/X9577z0pVaqUtGjRwoS2iy66SIoUKZK6SvD4Sa4VjEcWVkMAAQQyWkCD24vr/i2b9v6/uIObzrYx05b4MnBt3A1UUMvZnbt37zaBbd68ebJt2zapWrWqCWz6z6mnnpr4no9zi64VTJxMNEMAAQQyVsD2OW4620ZoS1z3uzbuBjao5ezSTZs2mZm2OXPmyN69e2XLli2J63HLLblWMJZcNEcAAQQyXsDmNCmhzb77XRt3Ax/UduzYYWbVlixZIlu3bpUaNWrIokWL7Hs6QVtwrWASxMZmEEAAgVAI2My2EdriKwHXxt1ABrX9+/fLwoULTUD75JNP5IQTTjA3Flx77bVSu3bt+Ho2Sa1cK5gkMbJZBBBAIBQCkdk2Pzck8JBdf13v2rgbmKCmd3u++eab5rq05cuXy5EjR8yz1TScXXHFFebGgiAurhVMEPuAfUIAAQSCKBBvaON6tsJ707VxNxBB7YEHHpDFixfL999/L6eddpp5lpo+U61y5cpB/O7l2ifXCibwHcIOIoAAAgEUILQlrlNcG3cDEdTOP/98ad26tbmj84ILLkhcb6ZgS64VTApI+QgEEEAg1AJ+QxvXsuUuB9fG3UAEtcOHD8uxxx6bkV9M1womIzuJnUYAAQQCKOD3RoTItWwP/OdtCAE8pJTskmvjbiCCWkp6Nkkf4lrBJImRzSKAAAJOCzDL5r37XRt3Qx3Upk2bJjNnzhR9eG6FChWkZcuW0q9fPylZsmTUivjpp59k+PDh5vEf+t96h6leP6ePBCloca1gvH+VWBMBBBBAIB4BP6FNZ9ne7H5+PB+TsW1cG3dDG9RmzJgh48aNM8Hr7LPPNs9gu/vuu6VDhw4mrEVbBg4caN6U7+Y/AAAgAElEQVSCoOHsuOOOk4kTJ8p3330nTz/9NEEtY7/S7DgCCCCQmQJ+A5srd4sS1DKznvPt9bJly6R48eLSqFGj7J8NGDBAvv32W3nmmWfyrf/vf/9bmjRpYu4+rVatmmcF1wrGMwwrIoAAAggkTODB13fI8m3fxXzvqAs3Hrg27oZ2Ri3nt+Po0aOybt066dmzp/Tu3Vs6duyY78uzYMECGTt2rNxyyy0yefJkOXDggDRo0ECGDh0qp5xyCjNqCft1w4YQQAABBOIV8DrLNrR5dQnrTQcEtXirJ6Dtxo8fLxMmTJDSpUtL3759pUuXLlH3VMPZk08+ad6AoDNvP/74o/m3Xqs2a9YsKVKkSNR2WjA5l169eplAyIIAAggggECyBLwGtuU96kqjGuWStRsp2a6O4TqW51w2b96cks8OwoeEfkZNT3Xu2bNHNmzYIKNHj5Zu3bpJ165d89lrSNN/Vq9enf2okI0bN5qH77766qtSvXr1AoOaSwUThKJlHxBAAAEEfhPwEtjCdjqUGbUQV7/eYDBmzBhZs2ZNvqPUu0NHjBhhTpFGlh9++EHq168vzz//vDkNGm1xrWBCXB4cGgIIIJCxAl4CW1hOh7o27gZiRm3QoEFy6aWXykUXXSQVK1ZMyBdFZ87OO+886dGjR/b2NIyNGjUqalCLzJ7NnTtXzjrrLNNGXwjfvn178w7SKlWqENQS0jNsBAEEEEAgWQIa2BpPzD8ZEfm8MMyuEdSSVT2FbLd///6ycuVK+frrr01I0pex6z9169aN+40FkyZNkilTpsjDDz8sderUkZ07d8rgwYPNzJjOnOkycuRIM2Omd3vqcuONN4q+JUEf6aHXpOn6ZcuW5fEcaagJPhIBBBBAIH4BvUt06Gs7CtxAJs+uEdTirwvrlps2bZJVq1bJO++8Ix9++KH8+uuvJkg1bNjQBDd9HlrRokU9fU5WVpYJanPmzMl+4G2zZs2kT58+UqZMGbMN3W6nTp1EbwDQRa9ne+ihh2TFihVSokQJ82gPne0rX758gZ/pWsF4wmclBBBAAIG0C+js2pgVX8r8jd9E3ZdMnV1zbdwNxKnPaBWkd1vqhf3vvvuuCW4a4jQwabh6/PHH0/4FiOyAawUTGHh2BAEEEEDAk0Cs06GZNrvm2rgb2KCWt/p0tktn295++21zajIoi2sFExR39gMBBBBAwJ9Ak6fWFvjA3Ex6FZVr427GBDV/5Zi6tV0rmNTJ8kkIIIAAAokWiDW7lgnPXXNt3CWoWX4LXCsYSy6aI4AAAgikWSDWozyCfirUtXGXoGb5hXGtYCy5aI4AAgggEBCBwu4MDXJYc23cJahZfmFcKxhLLpojgAACCARIoLBToUENa66NuwQ1yy+MawVjyUVzBBBAAIGACRR2KjSIYc21cZegZvmFca1gLLlojgACCCAQUIGC7goNWlhzbdwlqFl+YVwrGEsumiOAAAIIBFigoOvWghTWXBt3CWqWXxjXCsaSi+YIIIAAAgEXCHpYc23cJahZfmFcKxhLLpojgAACCGSAQJDDmmvjbmCD2saNG2Xt2rXm/ZudO3eWihUryr59+8y/g7S4VjBBsmdfEEAAAQSSJxDUsObauBu4oPbDDz/IXXfdZV4XFVkWLVokRYoUkfbt28uMGTPkrLPOSl5l+tyyawXjk4fVEUAAAQQyWCCIYc21cTdwQW3QoEHy0Ucfyb333isNGjSQevXqyfz586V69eoyZMgQ+eabb+Tpp58OTNm7VjCBgWdHEEAAAQRSIjDnk73S/n835PusrNGXp+Tz836Ia+Nu4ILahRdeKE888YTov3WpVauWCWo1atSQzZs3y4033igffvhhWooj2oe6VjCBgWdHEEAAAQRSJhDt0R3pepG7a+Nu4ILaOeecIwsXLpRq1arlC2rbt2+XNm3aiF6/FpTFtYIJijv7gQACCCCQWoEi/d7I94HpeGyHa+Nu4ILaVVddJZdeeqkMGDAgX1AbP368LF26VObNm5fa6izk01wrmMDAsyMIIIAAAikVKOh1U8t71JVGNcqlbF9cG3cDF9T0xoG+ffvKueeea05/Pvvss3LDDTfIV199JcuXL5dx48ZJy5YtU1YQsT7ItYKJ5cHPEUAAAQTCK1DQzQWpvF7NtXE3cEFNy3vJkiUyZcoUc4rz6NGjUrx4cXOt2u233y5XXHFFoL4BrhVMoPDZGQQQQACBlAtEC2upPAXq2rgbyKAWqToNaYcPH5bSpUtL0aJFU16MXj7QtYLxYsI6CCCAAALhFoh2c0Gqwppr427ggpre2akzaAUtpUqVklNOOUWKFSsWiG+BawUTCHR2AgEEEEAgrQIFXa+WilOgro27gQtq2gGxFg1yV199tegz18qUKRNr9aT+3LWCSSomG0cAAQQQyBiBdJ0CdW3cDVxQe//99+X++++Xs88+Wxo3biyVKlUyr5F68803ZevWreZGgz179sjEiROlVatWJqylc3GtYNJpzWcjgAACCARLIB2P7HBt3A1cUIvc8dm1a9d81Tht2jT54osv5MEHHzTBbdiwYbJs2bK0Vq1rBZNWbD4cAQQQQCBQAum4C9S1cTdwQa1OnTrmOWmRB97mrMhdu3aZU576ZgINbDqjlu6H37pWMIH6DcHOIIAAAgikXSDVp0BdG3cDF9T0/Z46q6bPTsu7zJ07V/72t7/JmjVrRP9bXzX1xhv5n5Scyqp1rWBSactnIYAAAghkhkC0U6DJurHAtXE3cEFNT2f+/e9/lyZNmpjr1MqWLSs//vijbNmyxbxaqnXr1nLPPfeY69duueUW6dOnT1qr2LWCSSs2H44AAgggEEiBVM6quTbuBi6oHTlyRCZPniwLFiyQHTt2mILMysqSU089VVq0aCG9evUyj+/QB+LecccdaX9Mh2sFE8jfEOwUAggggEDaBVI1q+bauBu4oJaz0jS06WyaPjutRIkS8tNPP4k+Z6127dppL8jIDrhWMIGBZ0cQQAABBAIlkKpZNdfG3UAHtbwV+Mknn5jTnXozQVAW1womKO7sBwIIIIBA8ARSMavm2rgbuKC2f/9+eeihh+Tdd9+V77//PlcV6inQmjVrmmvVgrK4VjBBcWc/EEAAAQSCJ5CKWTXXxt3ABTW9UUDv6tTHcEydOlVuuukmc/rz1VdflYsvvlgGDBggJ5xwQmCq07WCCQw8O4IAAgggEEiBZM+quTbuBi6oXXLJJTJu3DipV6+eNGzYUObPny8nnniiuT6tZ8+ectVVV0mbNm0CU5yuFUxg4NkRBBBAAIFACiR7Vs21cTdwQa1WrVry2muvmbs8L7vsMnnhhRekatWqphg/++wz8zgOnV0LyuJawQTFnf1AAAEEEAiuQDJn1VwbdwMX1P785z9Lv379zFsH2rZtKzfffLM5DaqLPq5DZ9TWr18fmOp0rWACA8+OIIAAAggEViCZs2qujbuBC2rjx483z1F75ZVX5OWXX5bnnntOunTpYh58q28jKFmypMyZMycwxelawQQGnh1BAAEEEAi0QN5ZtaHNq8sDzapb77Nr427ggtqvv/4q06dPN7Noxx57rDz66KMmtB06dEjOOecceeSRR0Q7KSiLawUTFHf2AwEEEEAg2ALRZtUS8Vop18bdwAW1gsrul19+EQ1x3333nVSuXDkw1elawQQGnh1BAAEEEAi0wIpt30njiWty7WMiZtVcG3cDF9TOPPNMWbVqlZQvXz5fAW7cuNG8Nurtt98OTHG6VjCBgWdHEEAAAQQCL5CMmwpcG3cDE9RmzpxpCm7o0KHmpet62jPnorNpH330kSxfvlzWrl0bmOJ0rWACA8+OIIAAAggEXiAZNxW4Nu4GJqj17dtX9BVR//znPwssvNKlS0u3bt3MrFpQFtcKJiju7AcCCCCAQGYIJPqmAtfG3cAEtUi5aQe88847UU99Fi9ePHBV6VrBBK4D2CEEEEAAgUALJPqmAtfG3cAFtUBXW5Sdc61gMq1/2F8EEEAAgfQKJPr0p2vjbiCC2g033OCriiLXs8VqNG3aNNF1d+/eLRUqVJCWLVuah+nqs9hiLUOGDDHPa9MbG7RtQYtrBRPLjZ8jgAACCCCQVyCRpz9dG3cDEdT0+jQ/y9ixY2OuPmPGDPPO0OHDh8vZZ58tW7dulbvvvls6dOhgwlphy8qVK6VXr15y8OBBglpMaVZAAAEEEECgcIFEnv4kqIWk2pYtWyZ6TVujRo2yj2jAgAHy7bffyjPPPFPgUeqDdfU1VZ07d5YRI0YQ1EJSDxwGAggggED6BBL5TDWCWvr6MfuTf/rpJ/MYjk2bNplZLX19lL6VQF/SHs8NBUePHpV169ZJz549pXfv3tKxY8cCj3LYsGGyb98+s17z5s0JagGoB3YBAQQQQCDzBfKe/mxcs7y82f183wdGUPNNltgG+niOv/zlL+YxHVlZWXLccceZ10cVKVJE9GG4et1ZxYoVPX+ovjt0woQJoo/20FOs+t7QghZ9Plv37t1l4cKFcuDAAYKaZ2VWRAABBBBAoHCBRJ3+JKiludLuvPNOE9L0wbe1a9eWYsWKyZEjR2T16tXmz84991wZOXKk573UU5179uyRDRs2yOjRo81z2Lp27Zqvvc7i6ftFe/ToIW3atJGdO3d6Dmo5N6bXtunMHQsCCCCAAAII/FcgWlBb3qOuNKpRrlAmnWzRSZecy+bNm52hDcTNBDm1L7jgAnMN2fnn558O/eCDD+Suu+6S9957L64O0hsMxowZI2vW5H73mG5MQ9y2bdtk4sSJZtt+gppLBRMXPI0QQAABBBAQkUTc/cmMWppLqVatWrJo0SKpVq1avj354osvpHXr1mZ2LNaiM2fnnXeemSGLLPqojlGjRkUNapdccokcPnxYSpQoYVbXV1b98MMP5vq42267zfwTbXGtYGK583MEEEAAAQQKEmjy1FpZvnV/9o/juU7NtXE3cDNqrVq1MmFMT4HmXZ588kkT4vQasljLpEmTZMqUKfLwww9LnTp1zAzZ4MGDpUGDBuZuTl30FGr9+vWlSZMmsnfvXtGbDiLLrl27zJ2f8+fPlypVqsjxxx9PUIuFzs8RQAABBBAoRCAR16kR1NJcYi+//LJ5KfvFF19sAtYJJ5xgZrY+/PBD84/OiLVt2zbmXuqNCBrU9KG1kQfeNmvWTPr06SNlypQx7Rs2bCidOnUyz0zLu3DqMyYxKyCAAAIIIOBLIBFvKSCo+SJPzsoLFiyQ6dOnm8dz6CyXPpJDH1p76623mgv8g7S4VjBBsmdfEEAAAQQyT8D2OjXXxt1AnPps3769XHvtteaUZ85TjBrS9LoxfbRG0aJFA1mNrhVMIDuBnUIAAQQQyBgB2+vUXBt3AxHUrrvuOlm/fr15B2fTpk1NaNPTkkENZzm/Da4VTMb8JmBHEUAAAQQCKWB7nZpr424ggppW0vbt22XevHmipz31Qv6TTz5ZrrnmGvNPtDtAg1J9rhVMUNzZDwQQQACBzBSwfZ2Ua+NuYIJapNz0JgB9uO3cuXNl8eLF5hVS9erVk3bt2pnr0yI3AgSlPF0rmKC4sx8IIIAAApkrYHOdmmvjbuCCWs6y07cFvPHGG/LKK6/IW2+9Zd5S0KJFCxk+fHhgqtO1ggkMPDuCAAIIIJCxAjanP10bdwMd1CIVqO/6XLJkiTz22GPy9ddfS5DeBOBawWTsbwV2HAEEEEAgMAIENe9dEdigpnd8vvPOO+a6NQ1pv/zyi1x22WXmFGjLli29H2GS1ySoJRmYzSOAAAIIhE7A5jo118bdwAW1zz77zFyfpjcVfPPNN3L66aebl6XrTQUnnXRS4IrVtYIJXAewQwgggAACGSkQ73Vqro27gQhq+vomfVWTzp5t2bJFjjvuOPNMNZ09i/Zy9iBVpGsFEyR79gUBBBBAIHMF4j396dq4G4igduaZZ4re7anPTtNnqOmrnkqVKpUR1edawWREp7CTCCCAAAKBF4h2+jNr9OUx99u1cTcQQU1ftq6nNvXl55m2uFYwmdY/7C8CCCCAQDAFogW15T3qSqMa5QrdYdfG3UAEtWCWkLe9cq1gvKmwFgIIIIAAArEF4rlOzbVxl6AWu45I9pZGNEcAAQQQQCCaQN73fg5tXl0eaFadcTeHAEHN8rvjWrK35KI5AggggAAC2QLx3FDg2rhLULP8wrhWMJZcNEcAAQQQQICg5qMGCGo+sKKtSlCzBKQ5AggggICzAvHcUODauEtQs/x6uFYwllw0RwABBBBAIJeA3xsKXBt3CWqWXxjXCsaSi+YIIIAAAggQ1HzUAEHNBxanPi2xaI4AAggggEAegbx3fjauWV7e7H5+gU6uTZAQ1Cy/Mq4VjCUXzRFAAAEEEMgl4PfOT9fGXYKa5RfGtYKx5KI5AggggAACMYNaYW8ocG3cJahZfmFcKxhLLpojgAACCCCQSyDanZ+FPfjWtXGXoGb5hXGtYCy5aI4AAggggEA+AT93fro27hLULL8wrhWMJRfNEUAAAQQQyCfg54YC18ZdgprlF8a1grHkojkCCCCAAAL5BP6x/mu5dvr6XH+eNfryqFKujbsENcsvjGsFY8lFcwQQQAABBPIJ+Lnz07Vxl6Bm+YVxrWAsuWiOAAIIIIBAPgE/r5JybdwlqFl+YVwrGEsumiOAAAIIIOApqBV056dr4y5BzfIL41rBWHLRHAEEEEAAgagCXu/8dG3cJahZfmFcKxhLLpojgAACCCBAUPNRAwQ1H1jRViWoWQLSHAEEEEAAARHx+ogO18Zdgprl18O1grHkojkCCCCAAAJRBbze+enauEtQs/zCuFYwllw0RwABBBBAgKDmowYIaj6wOPVpiUVzBBBAAAEEChDw+ogO1yZICGqWXxnXCsaSi+YIIIAAAggUKJD3zs/lPepKoxrlcq3v2rhLULP8wrhWMJZcNEcAAQQQQMBzUIv2LDXXxl2CmuUXxrWCseSiOQIIIIAAAgQ1HzVAUPOBFW1VgpolIM0RQAABBBD4j4CXR3S4Nu4S1Cy/Hq4VjCUXzRFAAAEEEChQIO8jOhrXLC9vdj8/1/qujbsENcsvjGsFY8lFcwQQQAABBDwHNV0xa/TlBDVqJn4Bglr8drREAAEEEEAgp4CXh966Nu4yo2b5HXGtYCy5aI4AAggggECBAl6epebauEtQs/zCuFYwllw0RwABBBBAgKDmowZCHdSmTZsmM2fOlN27d0uFChWkZcuW0q9fPylZsmRUog0bNshjjz0mH3/8sVmnYcOGMmjQIKlcuXKBpAQ1H9XGqggggAACCMQQyPvQ27zPUnNt3A1tUJsxY4aMGzdOhg8fLmeffbZs3bpV7r77bunQoYMJa3mXvXv3SuvWrU2Yu/XWW+XAgQMyZMgQKVeunEyfPp2gxq8WBBBAAAEEUiBAUMuNHNqgtmzZMilevLg0atQo+4gHDBgg3377rTzzzDP5Sm316tXy/PPPy+jRo6VYsWLm5wsWLDAzauvXr5ciRYpELU/Xkn0KvqN8BAIIIICAwwJ5n6XGjFpWVlbY6+Ho0aOybt066dmzp/Tu3Vs6duzo6ZCffvppeeGFF0RDX0ELQc0TJSshgAACCCDgSSDWQ29dG3dDO6MWqYbx48fLhAkTpHTp0tK3b1/p0qWLp0LZuHGj3HjjjebUafPmzQlqntRYCQEEEEAAATuBWI/oIKjZ+QautZ7q3LNnj+iNAnpas1u3btK1a9dC91Nn0AYOHCj9+/c317QVtmjB5Fx69eplZu5YEEAAAQQQQMC/QLSgdsYr3XNtaPPmzf43nKEtQj+jlrNf9AaDMWPGyJo1awrsLj3dOXXqVBk7dqy56zPW4lqyj+XBzxFAAAEEELARYEYtt15og5rOnJ133nnSo0eP7CPWR3WMGjWqwKCmj/PQMPfss89KtWrVPNUZQc0TEyshgAACCCDgSSDWQ29dG3dDG9QmTZokU6ZMkYcffljq1KkjO3fulMGDB0uDBg1kxIgRplhGjhwp9evXlyZNmsiWLVvkmmuuEW1Xs2bNXMVUqVIlKVGiRNQCc61gPH3LWAkBBBBAAAELgcIe0eHauBvaoKY3s2pQmzNnTvYDb5s1ayZ9+vSRMmXKmPLRU5udOnUSva7s8ccfl6eeeipqWc2ePVtq165NULP40tEUAQQQQAABrwJ5g9ryHnWlUY1ypjlBzasi6zlZMHQ7AggggAACyRZgRu2/wqGdUUt2EUW271qyT5Urn4MAAggg4K5AYc9Sc23cJahZfg9cKxhLLpojgAACCCAQU4CgxoxazCLxugJBzasU6yGAAAIIIOBNIO8jOhrXLC9vdj/fNHZt3GVGzVvNFLiWawVjyUVzBBBAAAEEYgoU9iw118ZdglrMcil8BdcKxpKL5ggggAACCMQUIKj9l4igFrNcCGqWRDRHAAEEEEDAl0C0h95mjb7cbMO1CRKCmq/Syb+yawVjyUVzBBBAAAEEYgoU9nYC18ZdglrMcmFGzZKI5ggggAACCPgSIKj9l4ug5qt0mFGz5KI5AggggAACngQKeugtM2qe+FgpIuBawdDzCCCAAAIIpEKAoPabMjNqltVGULMEpDkCCCCAAAJRBPI+9HZo8+ryQLPq3ExAtfgTIKj582JtBBBAAAEEvAgQ1JhR81InMdchqMUkYgUEEEAAAQR8CxT0GinXxl1OffoundwNXCsYSy6aI4AAAggg4EmgoNdIuTbuEtQ8lUvBK7lWMJZcNEcAAQQQQMCTQEFvJ3Bt3CWoeSoXgpolE80RQAABBBDwJUBQ+42LoOarbPKv7Fqyt+SiOQIIIIAAAp4ECGoENU+FEmslglosIX6OAAIIIICAf4GC3k5we6sLZfPmzf43mKEtmFGz7DiCmiUgzRFAAAEEEIgiQFBjRi0hXwyCWkIY2QgCCCCAAAL5BPK+nWB5j7rCjBqF4kuAoOaLi5URQAABBBDwLBDtNVJ/79WCU5+eBVnRuVdZ0OUIIIAAAgikSoCgxl2f1rXGjJo1IRtAAAEEEEAgqgBBjaBm/dUgqFkTsgEEEEAAAQSiCkR73yenPikWXwIENV9crIwAAggggIBngWjv+/zX4x25Rs2zICtyjRo1gAACCCCAQJIECGqc+rQuLWbUrAnZAAIIIIAAAlEFor2d4IxXujOjRr14FyCoebdiTQQQQAABBPwIENSYUfNTL1HXJahZE7IBBBBAAAEEmFEroAZ4hZTll4OgZglIcwQQQAABBAoQYEaNGTXrLwdBzZqQDSCAAAIIIBBVINr7PrlGjWLxJUBQ88XFyggggAACCHgWiBbUfrdqrHy5coHnbWT6ipz6tOxBgpolIM0RQAABBBAoQICgxqlP6y8HQc2akA0ggAACCCBQoEDe10gxo0ax+BIgqPniYmUEEEAAAQR8CeQNahW3LJRvFoz2tY1MXplTn5a9R1CzBKQ5AggggAAChQgQ1LKysqiQ+AUIavHb0RIBBBBAAIFYAgQ1glqsGin05wQ1Kz4aI4AAAgggUKhA3vd9cuqTgvElQFDzxcXKCCCAAAII+BIgqDGj5qtg8q5MULPiozECCCCAAAK+ZtRK7/tcDk3v5owaNxNYdjVBzRKQ5ggggAACCBQikHdGjaBGufgSIKj54mJlBBBAAAEEfAnkfd8nQc0XHysT1KgBBBBAAAEEkidAUOMatezqWrFihYwfP162bt0qpUqVkosuukjuueceqVKlSoEVSFBL3peTLSOAAAIIIJA3qKlI1ujLnYHhGrX/dPWnn34q7du3l379+kmLFi1k//79MnToUClSpIjMmjWLoObMV4IDRQABBBAIkgBBjRk1U48a1D755BPp2LFjdn3Onj1bHnzwQdmwYQNBLUjfWvYFAQQQQMAZAYIaQS1qsX/xxRcyaNAgOfnkk2XMmDEENWd+JXCgCCCAAAJBElix7TtpPHFNrl3i1GeQeijF+7Jq1Sq55ZZb5MiRI9KuXTsZNmyYFC9enKCW4n7g4xBAAAEEEFABghozarm+CYcPH5avvvpKdEZNbyw49dRTZeLEiYUGtZw/7NWrl/Ts2ZNvFwIIIIAAAggkQGDUzNdlwEe5J0yYUUsAbBg2sWXLFmnTpo384x//kFq1akU9JO76DENPcwwIIIAAAkEVYEaNGTVTm5MmTZK1a9fK5MmTs2t127Zt0qpVK4JaUL+97BcCCCCAgBMCRfq9kes4mVFzottzH+Tq1aulc+fOoqcu27ZtKwcPHpThw4fLrl27ZOHChVKyZElm1BysCw4ZAQQQQCD9AgS19PdBIPZg6dKlZmYt8sDbevXqSf/+/eW0004rcP849RmIrmMnEEAAAQRCLEBQC3HnJvvQCGrJFmb7CCCAAAKuC+iL2SPL+kUz5JsFo50h4c0Ell1NULMEpDkCCCCAAAI+BFwbdwlqPooj2qquFYwlF80RQAABBBCwEnBt3CWoWZWLiGsFY8lFcwQQQAABBKwEXBt3CWpW5UJQs+SjOQIIIIAAAr4ECGq+uFjZtYKhxxFAAAEEEEingGvjLjNqltXmWsFYctEcAQQQQAABKwHXxl2CmlW5cOrTko/mCCCAAAII+BIgqPniYmXXCoYeRwABBBBAIJ0Cro27zKhZVptrBWPJRXMEEEAAAQSsBFwbdwlqVuXCqU9LPpojgAACCCDgS4Cg5ouLlV0rGHocAQQQQACBdAq4Nu4yo2ZZba4VjCUXzRFAAAEEELAScG3cJahZlQunPi35aI4AAggggIAvAYKaLy5Wdq1g6HEEEEAAAQTSKeDauMuMmmW1uVYwllw0RwABBBBAwErAtXGXoGZVLpz6tOSjOQIIIIAAAr4ECGq+uFjZtYKhxxFAAAEEEEingGvjLjNqltXmWsFYctEcAQQQQAABKwHXxl2CmlW5cOrTko/mCCCAAAII+BIgqPniYmXXCoYeRwABBD8KRawAABYOSURBVBBAIJ0Cro27zKhZVptrBWPJRXMEEEAAAQSsBFwbdwlqVuXCqU9LPpojgAACCCDgS4Cg5ouLlV0rGHocAQQQQACBdAq4Nu4yo2ZZba4VjCUXzRFAAAEEELAScG3cJahZlQunPi35aI4AAggggIAvAYKaLy5Wdq1g6HEEEEAAAQTSKeDauMuMmmW1uVYwllw0RwABBBBAwErAtXGXoGZVLpz6tOSjOQIIIIAAAr4ECGq+uFjZtYKhxxFAAAEEEEingGvjLjNqltXmWsFYctEcAQQQQAABKwHXxl2CmlW5cOrTko/mCCCAAAII+BIgqPniYmXXCoYeRwABBBBAIJ0Cro27zKhZVptrBWPJRXMEEEAAAQSsBFwbdwlqVuXCqU9LPpojgAACCCDgS4Cg5ouLlV0rGHocAQQQQACBdAq4Nu4yo2ZZba4VjCUXzRFAAAEEELAScG3cJahZlQunPi35aI4AAggggIAvAYKaLy5Wdq1g6HEEEEAAAQTSKeDauMuMmmW1uVYwllw0RwABBBBAwErAtXGXoGZVLpz6tOSjOQIIIIAAAr4ECGq+uFjZtYKhxxFAAAEEEEingGvjLjNqltXmWsFYctEcAQQQQAABKwHXxl2CmlW5cOrTko/mCCCAAAII+BIgqPniCvbKCxculMmTJ8uOHTukQoUK0rp1a+nbt6+UKFEi6o6vWLFCxo8fL9u3bzfrNGjQQAYOHChVq1Yt8EBdK5hE9viECROkZ8+eidykU9vCz6678cPPTsCuNfUXv59r425oZ9Q0dHXv3l3uu+8+adSokXz++efy17/+VW655Rbp0aNHvgrRMHfllVfKAw88IO3atZMDBw7IvffeK7t27ZK5c+cS1OL/TmGXBDvdpGu/rBLNiJ+dKH742QnE39q12gttUJs1a5b861//kj59+mRXg4a2r776SqZPn56vQhYvXmxm2z799FMpWrSo+bnOyPXr1y/Xn+Vt6FrBxP/Vyt8SOztN/PCzE7BrTf3hZycQf2vXai+0QS1aCdx2221SqVIlefTRR/P9eN++fdKqVSsz29axY0c5dOiQDB48WIoVKyZPPvkks0Lxf6ewS4IdM2r2qK79srcXy70F/OxE8YvfzzU7Z4KazrCNGDFC5s2bJ6eeemrUCvnggw/MNVPff/+9+Xnt2rVlypQpUr58+QIrqkuXLvL+++/HX3G0RAABBBBAAAHPAhdeeKE899xzntfP9BVDH9SysrJEL9r8+9//bm4s0PAVbdFr1K677jq55557pE2bNnLw4EF55JFHzKlSDXmR06GZ3uHsPwIIIIAAAghkjkCog9rPP/9sbiD48ssv5amnnir07s3Ro0fLypUr5aWXXsruPb3GrUmTJvLiiy9KnTp1MqdX2VMEEEAAAQQQCIVAaIOazqTpXZ9HjhyRcePGyXHHHVdoh2lQe+utt8yp0ciiAa9p06Yye/bsAmfiQlEFHAQCCCCAAAIIBFIgtEFt5syZMnHiRHMe+5hjjsnG11OYlStXNv8/cuRIqV+/vpk1W7t2rdxwww3y0EMPSdu2beXw4cMybNgwWbNmjSxatEhKlSoVyA5kpxBAAAEEEEAgvAKhDWqdOnWS1atX5+u5Y489VtatW2f+vGHDhqLr9erVy/z/kiVLZNKkSeYBuSVLlpS6detK//79pXr16uGtAI4MAQQQQAABBAIrENqgFlhxdgwBBBBAAAEEEPAoQFDzCMVqCCCAAAIIIIBAqgUIaqkW5/MQQAABBBBAAAGPAgQ1j1CshgACCCCAAAIIpFqAoBaHuL654OGHH5Zly5aJPqtN7xwdOnSo/P73v49ja+FtsnnzZnMX7ccffyx6E0fz5s3Na7n0v6MtV111lWibnEuRIkXks88+Cy9SnEemN7zow5nVNq9ZnJsMZbO3337b1Jx+N//v//6v0GOk/mKXwNdffy2jRo0yjzLS331nnnmmueHq/PPPj93YoTU2bNggjz32mPl+6o1peuPaoEGDsp84kJeC2vNWHNu2bTNPa9CnMRw9etTUnz4rtV69et42kKFrEdTi6Lhu3brJ7t27zeM7ypQpI/oMtu3bt8uCBQukePHicWwxfE30zQ4tWrQwv6D0/akabvUXeoMGDUzIjbb86U9/kptvvtm0iyw5H6cSPqX4jmjx4sXG8JJLLpG5c+cS1Apg1FfGvfnmm+aVcfq4nVhBjfqLXY/t27eXEiVKyJAhQ8zvPn0P8vLly81fWo8//vjYG3Bgjb1790rr1q2lZcuWcuutt8qBAweMV7ly5WT69On87ouzBn766Se54oor5KKLLjLPSNX3cOsrHvXxWW+88Uahr3qM8yMD04yg5rMrIm8ryPkQXP0iaiDRX1qNGjXyucVwrq4BQsPEu+++a/5GqcuKFStMaHvvvfei/lLXv5WPGTPGPNeOpWABtdW/QX7++edyxx13ENQKoJo2bZp5NuLjjz8uOsMRK6hRf4V/6/QvW/fee6/cdddd8oc//MGsrKHksssuM6/ou+CCC/jaipjHQj3//PPmL/AaJnTRv8TrjNr69etFzxLkXai92KWzb98+efnll6Vjx47mLwm6RB5KH/a3BxHUYtdHrjX0WWt9+/aVTz75JNf7P6+++mr585//nP1MNp+bDd3qOtu4devWXH+D1Fk2/WWuDyHWl+rmXPQNErVq1TLvW9VfZvv37zf/P2DAADn99NND55OIA9LZIoJabMlHH300ZlCj/mI7RltDfw/qLJvOaFStWjW+jTjQ6umnn5YXXnjBzDzmXai9+ApAx4gnnnjCnIZ/5ZVXCrykJr6tB6sVQc1nf+iXTV9JtWrVqlwtu3btKtWqVZMHHnjA5xbDubpeN6Cv8Ro7dmyuA9Twpddu6GmBnIt+6Vq1amX+dq6zIPo30QkTJphArKf6ypcvH04oi6MiqHnD8xLUqD9vljnX0hk2/a5efPHFZqaNJbrAxo0b5cYbb5Thw4eb63TzLtSev8rRa9POO+88+eWXX8xf/HU8qVKlir+NZNjaBDWfHaZBbfz48eaUXs5Fr63SNxjcf//9PrcYztULCmrnnHOOuRg5b1CLpnDo0CET3Pr16yedO3cOJ5TFURHUvOF5CWrUnzfLyFp6M4vO5uop+L/97W/Zp/j8bSX8a+sM2sCBA831uR06dPB8wPzuK5xKz9bojS16OcOmTZtkzpw5of7LPEHN81fntxWXLl0qvXv3NjM9kesP9M/1/aDNmjWTO++80+cWw7m6Xp+2ZcsW+d///d/sA9Rr+fQXu365vN6lo3dD6XV/GtZYcgsQ1LxVRLxBTbdO/eU3XrlypfTp08cENT2TwBJdQE93Tp061ZxV0GuY/S7UXmwxnV3Tm6puv/32UNciQS12LeRaQ1O8FsasWbOkTp065mc6dX3ppZeaO1D0ZywiCxcuNLOL+ks98kJ7/dulhly9mSByMWjESkOdXrt23333Za8f+Vul3jF17bXXwppHgKDmrSS8BDXqz5vlRx99ZG4I0jtquemnYDO9kWXGjBny7LPPmktiCluoPW+1p2OJjg/z5s3LHj/08hode2+55RaCmjdGd9bS03o69aoXzJcuXdpce6ABTu9I0cdJsIjordR6zZmG2Z49e8q3335rpv/1l7t+2XTRsKs3GOjfyjXs6mM59BEJvXr1Er3AVu8A1ecQ6TVqeYOdy8Zaa+qjgVdPq+jjEXTRxyPg9Ftl6N+09Y5EXZ566ilzekQvPNalYsWK5k5k6s/ft0gfcXLllVdKu3btzD85F2rvvxoavK655hqZNGmS1KxZM5dTpUqVzONNqD1/tadr//DDD+aSGb1DVv/CrxMAerex3mE7f/58qVGjhv+NZkgLZtTi6CgNF3pq77XXXjMDpl5MqzcRnHLKKXFsLbxN9DqWBx98UPRv4Rog9Je83sUZeVyH3j2rg2nksQl60a1ev6anlfWXmV4oqg91jfU30vAKRj8yDbv6mJi8i3rq6SgWkX/+85/mLuxoi15nqr/sqT9/laIzGnotbrRF/xKmzwxjEfM4GP3LQbQl8lgnai++StEQrDcPrFu3zoy9GoQ1tIX9TBZBLb56oRUCCCCAAAIIIJB0AYJa0on5AAQQQAABBBBAID4Bglp8brRCAAEEEEAAAQSSLkBQSzoxH4AAAggggAACCMQnQFCLz41WCCCAAAIIIIBA0gUIakkn5gMQQAABBBBAAIH4BAhq8bnRCoHACujjUPSBm6tXrzbPqTvppJPkwgsvNI9PyPtcpyAchD4YVJ9J+Nlnn6V1d3Q/9PlW+kymdC/6yBp9NVPr1q3N8wRTveir2/RZafoYCRYEEEivAEEtvf58OgIJFdBnhOkz/erWrWseunniiSfKl19+aR4Mqc+s04dwamgL0vL555+bUNmxY0ezWxqY1q9fb55+n8pFX//2u9/9zjxEON3LddddJz///LPoswj1+WX6QNlULgS1VGrzWQgULkBQo0IQCInAtm3bzLspmzdvLqNHj5YiRYpkH5nOrOmL7fWNEYsWLQr0GzQ0KOmrYVIZ1PRNBvXr1zfvZdR3y6Zz0X7Ut3pouNZZ0EGDBvl6oXci9p2glghFtoFAYgQIaolxZCsIpF3goYceMqfu3nnnHSlbtmy+/fniiy/k2GOPNadCddE3QOhpNX0jxC+//GJOi+qprshTvvX0qT5hXZ+0rm/i0Jm5ypUrm1d8XX311dnb13fv6bq6fX2ti742bPDgwbneKLF27VoTvDZs2CDly5c3b6nQz9K3VOQ89alhUk/dRhZ9s4XOEGpo0TdVRBZ9JZTuwzPPPCMaKmwX3b8bb7xRPvjgAznuuOOsNqevl9NTlmqiL+bW49HXVkXesqHvwNUnrFevXl0eeeQROeecc3J93siRI0Xf46qvTtM3eezatSv77R1WO+ajsZq2b99eihUrZvpHA369evVMHegsLQsCCKROgKCWOms+CYGkCuhsmgYCfRF0rEUHXh2MdRZJX7Ktgemll14SPXWqr0bTQPbcc8+Z99g2aNDAXENWrlw5efLJJ00AmTt3rpx11lnmVS7XX3+9eZ+rzgIdOnTIhD99t2vkWi99nZMGs2bNmskNN9wg33zzjXnfq4YZ/XfOoHbgwAHp0qWLnHbaaeZnGji1nYZHvWYrsmh41P1dsWJFQmYH9bj0FGPkdWax/Ar7+c6dO82s5rnnnitDhw41gUxD2qpVq7L/rEKFCuZ1X/oaHA3XkUVn9ho3bmxmP/Xnuk833XSTvPHGG3Lqqafa7Javtlob+tq3iy66SPQ07J49e6Rfv37mdLoGZxYEEEidAEEtddZ8EgJJFWjYsKF5v6WGqliLBoJ///vfcsIJJ2S/yF1Pj+qslZ7+09ClAUpn6TT46ftsddGZN73GTYOEDtwa+HQ7GiKKFi1q1tHZoEjI0OCoM0Qa2jRU6QyNLhr0dKZJ9zXvzQQ6k3P66adnn/rU6+qmTp0q7777bvZ7YvXlzFdccYXZh0QsnTp1MmFQr1OzXSJB7e6775bbbrvNbG7JkiUmzEZs9c/0uDUI6yxjZFGj22+/Xd566y0TlvUU8OWXX24u7NeZzFQtGtT08+fMmZP9kXpKWmcLc/5ZqvaHz0HAZQGCmsu9z7GHSkAH10svvVQeffRRT8el4UFDkJ6G0wvXI4vOAunMVySo6YX+OrsSWXRWpUqVKmZ2TYPE5MmTzeC9e/duM0MUWfRauBo1ashf/vIXE7D0NGC0JVZQ05sg/vSnP8n48eOladOmJizobNyrr75qZqtsFw2oGj51P/Rl7bZLJKipbZMmTczm3n//fTNTuGDBAjnjjDPMn2lY1Zk2PQWtp4x16dOnj3z33XfmlG5kGTdunDkNunTpUttd89xea0nDul4fF1k0VOo+pHI/PO8wKyIQYgGCWog7l0NzS0BnokqUKGGu54q16J2Weo2X3mnZrVs3c92RzrLVqlXLnK7LGdT0sRk5b0zQ2adjjjnGnALV03Z6zZXOvLVo0cLcnagzX127djU3LWhQ02BXtWpVmTBhQlxBTRvpPhYvXtyEQ/3n7bffNqdpE7HoDOBf//pX+fDDD81n2C6RoKY+kev9IkFNTyvrad1oQe2HH34wM5c6axltyXudntf9PHz4sJmRa9u2rZnp9LJEu5lA/wKgIW3ZsmVeNsE6CCCQIAGCWoIg2QwC6RbQOz31FKEOpjrjlXfRcKY3GuhF89OnT5eJEyfmCic6I6bXR+UNanqBfc6bE3TA18dYaPDSU3sa8DSURJbITFEkqOnn6aIzVtGWWDNq2kbDQe/eveW9994TDYq6zQ4dOiSEXE+/6nV0OgOWiCXeoKbXx40aNcr0TeQ0cmR/9Hq92rVrm0Dsd9HT0zfffLOZIYv0RaxtENRiCfFzBFInQFBLnTWfhEBSBTRs6GCs15np6Ug93RhZIhfp66Ctd2nqrJTOSGnwiSwa3PQ0m86Q6TVokVOfU6ZMyX5khc7O6GlCnTHT03QamvQ6ND0tGVn04ne9cH7hwoXmTlK92/PFF180M21616kuGuZmz54tzz//vJkBzPnAW50Z1FOaem1bZIlcZK8/01OoiXy2mF7vpjOIemoyEUu8QU0v2tcArNex5V30mDVI6nFHTpMmYl8L2gZBLZm6bBsBfwIENX9erI1AoAU0AOmjMfSUo96NefLJJ5vHauhszY8//miufdK7NSMXresMjT43TK9X02vRdPZMb0jo37+/CXT6+Ah93IaGMj09qgFQP0NDmH6GhikNfDqTp4FNt6+nSfXPNKDp3Y96s4He9amfo88F27dvn7lzUG8G0Mdv5J1R03W0jV4Tdcopp4jeIamLzhjqzJ2eYtX/TsSi179pKImEykRsM56gFgnZTzzxhDHLu+jPtV9y3oyQiH0lqCVTkW0jkBgBglpiHNkKAoER0Edm6J2aa9asMY/J0LCmF7XraUq9ky+yaNjRa8z0migNTTqTpu00dOnsjl5LpUFNZ7300Rj6IFZtr3da6sydLnpdlV5wrjM9esOBzkzpdVAatnQ/dKZM19U7PDXU6TPb9DlqejOAXhems0N5g5qGSH1+mC6PPfZY9nPS9KJ7nVHLeReqLbqGTnXQa94StcQT1DSgaZjWmcjIrGPe/bn22mtNGNYZzmQvzKglW5jtI+BdgKDm3Yo1EXBKICjv4Iyg6/Vbeq2a3gGZ8+YGpzqFg0UAAecECGrOdTkHjIA3gaAENX0ch15Lp7N7Ovul15SxIIAAAq4IENRc6WmOEwGfAkEJanrzgs6g6YNg9SYGFgQQQMAlAYKaS73NsSKAAAIIIIBARgkQ1DKqu9hZBBBAAAEEEHBJgKDmUm9zrAgggAACCCCQUQIEtYzqLnYWAQQQQAABBFwSIKi51NscKwIIIIAAAghklABBLaO6i51FAAEEEEAAAZcECGou9TbHigACCCCAAAIZJUBQy6juYmcRQAABBBBAwCUBgppLvc2xIoAAAggggEBGCRDUMqq72FkEEEAAAQQQcEmAoOZSb3OsCCCAAAIIIJBRAv8fT3acGRh0CskAAAAASUVORK5CYII=\" width=\"620\" alt=\"figure_0.png\"></center>"
      ]
     },
     "metadata": {},
     "execution_count":  5,
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and Run a Parameter Sweep\n",
    "\n",
    "Now we can setup another parameter sweep to explore the effects of reducing the thickness of the negative electrode coating. Let's try simuating the cell performance with coating thickness values of 16 µm, 32 µm, and 64 µm. As in the previous tutorial, we will first create a vector contianing the desired thickness values. Then we will use a for\\-loop to iterate through the thickness values, modify the value in the jsonstruct, and run the simulation. We will then plot the results together for comparison.\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "source": [
    "% create a vector of diffent thickness values\n",
    "thickness = [16, 32, 64].*micro;\n",
    "\n",
    "% instantiate and empty cell array to store the outputs of the simulations\n",
    "output = cell(size(thickness));\n",
    "\n",
    "% instantiate an empty figure\n",
    "figure()\n",
    "\n",
    "% use a for-loop to iterate through the vector of c-rates\n",
    "for i = 1 : numel(thickness)\n",
    "    % modify the value for the c-rate in the control definition and update\n",
    "    % the total duration of the simulation accordingly\n",
    "    jsonstruct.NegativeElectrode.Coating.thickness = thickness(i);\n",
    "\n",
    "    % run the simulation and store the results in the output cell array\n",
    "    output{i} = runBatteryJson(jsonstruct);\n",
    "\n",
    "    % retrieve the states from the simulation result\n",
    "    states = output{i}.states;\n",
    "\n",
    "    % extract the time and voltage quantities\n",
    "    time = cellfun(@(state) state.time, states);\n",
    "    voltage = cellfun(@(state) state.('Control').E, states);\n",
    "    current = cellfun(@(state) state.('Control').I, states);\n",
    "\n",
    "    % calculate the capacity\n",
    "    capacity = time .* current;\n",
    "\n",
    "    % plot the discharge curve in the figure\n",
    "    plot((capacity/(hour*milli)), voltage, '-', 'linewidth', 3)\n",
    "    hold on\n",
    "end"
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "source": [
    "hold off\n",
    "xlabel('Capacity  /  mA \\cdot h')\n",
    "ylabel('Voltage  /  V')\n",
    "legend('t_{NE} = 16 µm', 't_{NE} = 32 µm', 't_{NE} = 64 µm')"
   ],
   "outputs": [
    {
     "data": {
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       "<center><img 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\" width=\"620\" alt=\"figure_1.png\"></center>"
      ]
     },
     "metadata": {},
     "execution_count":  7,
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The results of this parameter sweep show that reducing the thickness of the negative electrode to 32 µm and 16 µm reduces its capacity such that it becomes the limiting factor in the overall capacity of the cell. Note that in real Li\\-ion batteries, the capacity of the negative electrode is usually oversized with respect to the capacity of the positive electrode to avoid dangerous lithium plating.\n",
    "\n",
    "## Summary\n",
    "\n",
    "In this tutorial, we explored how to modify structural parameters in BattMo. We first learned that structural properties of the electrodes are defined in the structure (e.g. NegativeElectrode.Coating). From there, it is possible to change individual parameter values and simulate the effects on the cell discharge. We then reviewed how to setup a parameter sweep and explored how changing the thickness of the negative electrode coating affects the overall capacity of the cell.\n",
    "\n",
    ""
   ]
  }
 ],
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