solution03.ipynb 335 KB
Newer Older
sp2668's avatar
sp2668 committed
1 2 3 4
{
 "cells": [
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
5 6 7 8 9
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
10 11 12 13 14 15
   "source": [
    "# Energy System Modelling - Solutions to Tutorial III"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
16 17 18 19 20
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
21 22 23 24 25 26
   "source": [
    "**Settings**"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
27 28 29 30 31 32
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
33 34 35 36 37 38 39 40 41 42
   "outputs": [],
   "source": [
    "import pypsa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
43 44 45 46 47 48
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
49 50 51 52 53 54 55
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
56 57 58 59 60 61
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
62 63
   "outputs": [],
   "source": [
sp2668's avatar
sp2668 committed
64
    "solver = \"glpk\""
sp2668's avatar
sp2668 committed
65 66 67 68
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
69 70 71 72 73
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
74 75 76 77 78 79 80
   "source": [
    "***\n",
    "**(a) Build a network in PyPSA with the two buses North and South and attach the load at each bus and attach the wind and solar generators with availability according to $G_{N,w}(t) = Cf_w(1+A_w\\sin \\omega_w t)$ and $G_{S,s}(t) = Cf_s(1+A_s\\sin \\omega_s t)$ for a year (you have to call `set_snapshots` for the year) and with `p_nom_extendable` set to `True`. As help you should have a look at the [minimal lopf example](https://www.pypsa.org/examples/minimal_example_lopf.html), understand what the [components documentation](https://pypsa.org/doc/components.html) of PyPSA gives you and that you can find the underlying objective function and constraints in the [LOPF documentation](https://pypsa.org/doc/optimal_power_flow.html#linear-optimal-power-flow).**"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
81 82 83 84 85
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
86 87 88 89 90 91
   "source": [
    "Initialize network"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
92 93 94 95 96 97
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
98 99 100 101 102 103 104
   "outputs": [],
   "source": [
    "network = pypsa.Network()"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
105 106 107 108 109
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
110 111 112 113 114 115
   "source": [
    "Add North and South bus"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
116 117 118 119 120 121
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
122 123 124 125 126 127 128 129 130
   "outputs": [],
   "source": [
    "network.add(\"Bus\",\n",
    "            \"North\",\n",
    "            carrier=\"AC\")"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
131 132 133 134 135 136
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
137 138 139 140 141 142 143 144 145
   "outputs": [],
   "source": [
    "network.add(\"Bus\",\n",
    "            \"South\",\n",
    "            carrier=\"AC\")"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
146 147 148 149 150
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
151 152 153 154 155 156
   "source": [
    "Attach constant load"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
157 158 159 160 161 162
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
163 164 165 166 167 168 169 170 171 172
   "outputs": [],
   "source": [
    "network.add(\"Load\",\n",
    "            \"North Load\",\n",
    "            bus=\"North\",\n",
    "            p_set=20e3)"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
173 174 175 176 177 178
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
179 180 181 182 183 184 185 186 187 188
   "outputs": [],
   "source": [
    "network.add(\"Load\",\n",
    "            \"South Load\",\n",
    "            bus=\"South\",\n",
    "            p_set=30e3)"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
189 190 191 192 193
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
194 195 196 197 198 199
   "source": [
    "Attach renewable generators according to given parameters"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
200 201 202 203 204 205
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
206 207 208 209 210 211 212
   "outputs": [],
   "source": [
    "network.set_snapshots(np.arange(0, 4*7*24))"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
213 214 215 216 217 218
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
   "outputs": [],
   "source": [
    "Cfw = 0.3\n",
    "Aw = 0.9\n",
    "omegaw = 2*np.pi/(7*24)\n",
    "\n",
    "Cfs = 0.12\n",
    "As = 1.\n",
    "omegas = 2*np.pi/24\n",
    "\n",
    "GNwt = Cfw * (1+Aw*np.sin(omegaw*network.snapshots.to_series()))\n",
    "GSst = Cfs * (1+As*np.sin(omegas*network.snapshots.to_series()))"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
235
   "execution_count": 11,
sp2668's avatar
sp2668 committed
236
   "metadata": {
sp2668's avatar
sp2668 committed
237 238 239 240
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
sp2668's avatar
sp2668 committed
241 242 243 244 245
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
minor  
sp2668 committed
246
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57c09ac358>"
sp2668's avatar
sp2668 committed
247 248
      ]
     },
sp2668's avatar
sp2668 committed
249
     "execution_count": 11,
sp2668's avatar
sp2668 committed
250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAD8CAYAAACW/ATfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXmcZEd1JvpF7pVZWXt1VXdVV1W3elN3q7U1MgIkITCbwcJjYwxjNvuNeXjgYXsePOPxGNvy2OPBMM9jhocNfgMeMAYxjG3ZSIAlkJHAQmpJjaRW79W1dXftW+5rzB9xI/PemxH3Rlblcm9Wfr9f/7oqK/LkyRMRX5xz4kRcQilFG2200UYbrQ1PsxVoo4022mij/miTfRtttNHGDkCb7Ntoo402dgDaZN9GG220sQPQJvs22mijjR2ANtm30UYbbewAtMm+jTbaaGMHoE32bbTRRhs7AG2yb6ONNtrYAfA164MHBgboxMREsz6+jTbaaMOVeOaZZ5YppYPVvq9pZD8xMYFTp0416+PbaKONNlwJQsj0Vt7XTuO00UYbbewAtMm+jTbaaGMHoE32bbTRRhs7AE3L2bfRRhttVItcLoe5uTmk0+lmq1J3hEIhjI6Owu/310Rem+zbaKMN12Bubg7RaBQTExMghDRbnbqBUoqVlRXMzc1h3759NZHZTuO00UYbrkE6nUZ/f39LEz0AEELQ399f0wimTfZttNGGq9DqRM9R6++5Y9M4xSLFt8/MY3I5gXsODeL4SHezVXI8Ts+u44mLSzg+0o17Dg3umEm3VcyuJvGPz1/H7u4Q3nxiN/zetm9lhY1UDv/r2Tn4PAT33TKC7o7a5KrbYNiRZJ/NF/FrX30OD784DwD41HfO4z/+zE341z8x1mTNnIs/feQC/vSRi6Xf3/3ycfz+fcfg8bQJX4RvvXgdH/7qaWTzRQDA156exeffexKdwR055Wxxfj6GX/zLJ7EczwIA/vKJK/gfv3wHxvsjTdasOvzUT/0UvvKVr6Cnp0ep/dTUFN7ylrfgxRdfrLNmOzSN86l/Oo+HX5zHv/+pI3j2d16Huw8N4j/83Qs4NbXabNUciQd/fA1/+shF/OytI3jud16Hf/OqffjSk9P40pNbOsjX8jg3v4lf++ppHNvThR9+7DX4xNtO4EdXVvDxv6//hHYjYukcfvmLT8NDCP7x/3oVHvg/78RGKocPfPnZ0mLpFjz00EPKRN9o7DiyP3t9E5/7/iTeecdevP/uG9AXCeC//evbMNLbgf/nG88jX3DX4Ko34pk8/uAfX8LNe3vwn992Ar2RAH77zTfi3sOD+E8Pn8XCZuuXwFUDSil+9+/PIBzw4vPvOYk9PR14+8m9+NC9B/C/nr2KJy4uN1tFx+HT372Eaxsp/Pm7b8fxkW7csa8Pf/K2m3H2+ib+8onJZqtnwCc+8Qn82Z/9GQDgN37jN/Ca17wGAPDoo4/iXe96FyYmJrC8vIypqSnceOON+JVf+RUcO3YMr3/965FKpQAAzzzzDG6++Wbceeed+MxnPtMw3XdcTPlfH7mIzoAPH3vjjaXXOoM+/PZP3YgPfPlZ/MPz1/Cvbh1toobOwl/9cApLsQw+/56TpZwzIQS/f99x3Pupx/Dn/3wZv/vTx5qspXPw+MVl/OjKKv7gZ45joDNYev2DrzmArz8zhz995AJeeaD1q0lUsbiZxhd+cAVvu20Ut431ll5/3dEh3Ht4EJ/7/iTec+eEMP31+/9wBi9d26ypPkf3dFmO57vvvhuf+tSn8OEPfxinTp1CJpNBLpfDE088gbvuugtPPPFEqe3FixfxN3/zN/j85z+Pt7/97fjGN76Bd73rXfilX/olfPrTn8Y999yDj370ozXV3wo7yrOfXkngW2fm8b5XTqA7bNz8ef3RYRwZjuIv/nkSlNImaegsZPNF/I9/mcJdBwdwy15jaDrWH8bP3jqCv3lqBhupXHMUdCD++w+uYDAaxNtPGh2GoM+Lf/vqG3Bqeg3Pzqw3STvn4ctPTiNfpPi39x6o+Nuv/eQhrCdz+Pqp2SZoJsbtt9+OZ555BrFYDMFgEHfeeSdOnTqFxx9/HHfddZeh7b59+3DLLbeU3jc1NYWNjQ2sr6/jnnvuAQC8+93vbpjuO8qz/9rTs/AQ4Bd/Yrzibx4PwbvvHMdv/+2LeH5uAzfvdWberZF45OwCFjYz+OOfPSH8+3vunMDXn5nDg6ev4t13TjRWOQdiZiWJx84v4Td+8hCCPm/F33/2tlH88cPn8NWnZnD7eK9Aws5CvlDEV56awWsO78K+gcqN2Fv29uDEaDe++tQs3veKykNUzYgo/X4/JiYm8IUvfAGveMUrcOLECXzve9/D5cuXceONNxraBoPlyM7r9SKVSoFS2rSobsd49sUixTeencO9h3dhuDskbHPfzXvQ4ffiAQd5Es3E3z53FbuiQdx9SHx19vGRLhzd3YUHTs01WDNn4u9PXwUAvO2kOA0YCfrw0zfvwT8+fx2JTL6RqjkSP7i8guV4Fj9/cq+0zS+8bC/OL8TwwtWNBmpmjbvvvhuf/OQncffdd+Ouu+7Cn//5n+OWW25RIvGenh50d3eX0j1//dd/XW91S9gxZP/c7DoWNjO475Y90jbRkB+vuXEXvn1mHoXizk7lbCRzeOz8Iu67eQ+8kvJKQgh+5tY9eOHqBmZWkg3W0FmglOLvTl/FHfv6MNLTIW331ltGkMoV8Nj5pQZq50z8/XNX0RXy4d4j8udwvOWmPfB5CL75wvUGamaNu+66C9evX8edd96JoaEhhEKhihSOFb7whS/ggx/8IO688050dMjHSq2xY9I4//TSAvxegnuP7LJs96bjw/jm89fx9NQqXr6/v0HaOQ+PXVhErkDx5hO7Ldu96fhu/NFD5/DtM/P4lbv3N0g75+HKcgKXlxJ47ysmLNvdsa8P/ZEAvnVm3ta2rYx8oYhHzy3idUeHhSkvju6wH3fe0I9vvTiPj73xSAM1lOO1r30tcrnyPtWFCxdKP09NTQEABgYGDLXzH/nIR0o/33777fjxj39c+v33fu/36qesDjvGs//OS/N4+f5+dIWsT+Xde3gXAj4PvnNmoUGaORPfO7eI/kgAN49a713s7Qvj2J4ufOel+QZp5kx8T/PU7z1s7Ux4PQSvPzaE751bRG4Hl/menl3HRiqH19g4XwBzKKZXkriwEG+AZq2LHUH2lxbjmFxK4PVHh2zbRoI+3DHRhycu7dwwu1Ck+P7FZdx9aFDphOw9hwbx3Mw64js4D/3Y+UXcMBjB3r6wbdt7Dg0insnj9OzOrcp57PwSvB6CVx0csG17z2GW5nn84s6dk7XAjiB7PkjsUjgcrzo4gAsLcSzu0ANDz8+tYzWRxasPqz3T+FUHB5AvUjx5eaXOmjkTyWweP5pctfXqOe7cPwAPYTX5OxXfO7+I28d6le6/GenpwP6BCH5waefaqxbYEWT/5OQKxvrCGO2197oA4FUHmLfxg8s7c3A9dn4JhAB3H1Qj+9vHexHye/DEDp2M/3J5BdlCEa9WJPvusB83jfbsWPJajKVx5tpmyWNXwSsPDOBHV1bbZ2C2gZYn+2KR4kdXVvHy/X3K7zm6uwu9Yf+O9byeurKKY3u60BsJKLUP+ry4Y1//jiX7p66sIuD14OSEeu38XQcGcHp2HZvpnXcg7akr7A4q7lSp4FUHB5DMFpDdwfsc20XLk/25+RjWk7mqKms8HoJXHBjADy+t7DhPIpsv4rnZNbxsQn1xBIBXHejHpcX4jrwr56mpVZwY7UbIL68qMeOVBwZQKFI8NbnzLt87NbWGDr8XR/d0Kb/n5fv74SFAJtcm+62i5cn+yUmWR662jPKOiT7Mb6ZxbWNnkdeZaxtI54pVkz1v/+z0Wj3UcixS2QJevLqBk1Xa69axHvg8BM/M7Cx7Acyzv228p6r7/bs7/Dgy3OWqWzBf/epX49SpU81Wo4SWJ/sfXWH5+j0WB11E4Jcy7TTyelq75rmalAQAHNvTjYDPg2d3GHmdnl1HrkBxx77q7BXye3FsT9eOG1+b6RzOzm/i5Hh1iyMA3Dbeg2yh2LLRdqFQqKv8lif707PruG2s+ntujuyOosPvxTM7bDI+dWUNE/1h7IqKr5SQIeDz4MRI946z19NTqyAEuH1sK+TVix/Pre+oevtnp9dAKTtcVi1uH+9FkQLpJqZyEokE3vzmN+Pmm2/G8ePH8bWvfQ2PPvoobr31Vtx000345V/+ZWQymYr3/eqv/ipOnjyJY8eO4Xd/93dLr09MTOD+++/Hq171Knz961+vq+4tfYJ2fiONhc3Mli4183s9ODHajed2kKdKKcWzM2tKB11EuG28F1/8wRQy+YLlqchWwqnpNRzaFa24RVUFt4314gs/mMK56zHcNLozHov5zPQavB5ScYuqCm4b68WF84tIZvPoCHiBhz8GzL9QWwWHbwLe9MfSP3/rW9/Cnj178M1vfhMAsLGxgePHj+PRRx/FoUOH8J73vAef/exn8eu//uuG9/3hH/4h+vr6UCgU8NrXvhbPP/88TpxgFwyGQiHD1cj1Qkt79qdnGVFvZWABjLzOXNtEOlff8MopmFtLYTWR3bq9xliYfabGd4w7FZRSvDC3vq3xBWBHpb5+PLeBg7s6EdnC4xnH+sLwEiCZbd58vOmmm/DII4/gN3/zN/H4449jamoK+/btw6FDhwAA733ve/H973+/4n0PPPAAbrvtNtx66604c+YMXnrppdLffuEXfqEhure0Z396dgN+L8GNu9V3/fW4bawX+SLFC1c3qt6wdCP4zYIntuhl6vc59A+iaFXMraWwlsxt2Svf0x3CUFcQz86s2d6p0wrgi+PrFE6yi0AIQcDnKZO9hQdeLxw6dAjPPPMMHnroIfzWb/0WXv/619u+58qVK/jkJz+Jp59+Gr29vXjf+96HdLpc+BGJNOY5u0qePSHkjYSQ84SQS4SQjwn+/j5CyBIh5LT279/UXtXqcXp2DUd3d1VVEqfHzdokftFB16vWE8/PscXx8HB0S+/f1RXCcFdox3j2210cCSE4MdqzY8ZXeXHc+rMi/D4PMvkCCsXm5O2vXbuGcDiMd73rXfjIRz6CH/7wh5iamsKlS5cAAF/60pdKDybh2NzcRCQSQXd3NxYWFvDwww83Q3V7z54Q4gXwGQCvAzAH4GlCyIOU0pdMTb9GKf1QHXTcEgpFihfmNvBzt2/9EYO7ukIYjAbx4tWdQl7rODLcta18+/GRrh1DXttdHAHg+J5uPHJ2AYlMfkupDTehtDiObH1/IqCVa6ZzRUSCjc9Cv/DCC/joRz8Kj8cDv9+Pz372s9jY2MDP//zPI5/P42Uvexk+8IEPGN5z880349Zbb8WxY8ewf/9+vPKVr2y43oBaGucOAJcopZMAQAj5KoC3AjCTvaMwtZJAIlvA8W0MLAA4vqcLZ661PnlRSvH83AZ++mb5ff8qOLqnG989xzbRwoFWJ6/tL47H9nSBUuDs9c2qa/XdBr44Htm99cXR7/WgCHa+oRmL4xve8Aa84Q1vqHj9ueeeq3jtscceK/38xS9+USiPX4ncCKgsjSMA9I9umtNeM+PnCCHPE0L+JyFE/uiZBuHc9RgAdvXBdnB8pBsXF+Mtv0k7vZJELJ3fltcFsMWxSIGzmv1bFSz/vLHtKhrujOyEaOjFqxvbXhy9HgK/14NUi8/HekCF7EV33JpPNfwDgAlK6QkAjwD4K6EgQt5PCDlFCDm1tFTf60rPXt+E10NwYFfntuQc29ONQpHi3Hxrk9eLWvSy7UhIe/9LLR4Nza2lsJnO4/ie7dlrqCuIgc5Ay+9zUErx4rUNHB/ZnvMFAB1+b5vstwAVsp8DoPfURwFc0zeglK5QSvlJgs8DuF0kiFL6OUrpSUrpycFB9RvvtoKz1zdxw2Bky5uzHMe0+zta3fM6Px+ryeK4uzuE3rC/5fc5+OK/nZQEwDZpj+7pxostTvaLsQzWkzkcGd4+2Qd9HmRyRRRb/NGhtT4prEL2TwM4SAjZRwgJAHgHgAf1DQgh+uer3QfgbO1U3BrOXt/ccsmlHqO9Heju8Ld83v7s9Rgm+sPbXhwJITg+0l2KFFoV564zcj40tD2yB1jq6+JCDJl863qrZzV7bWczG2AHkLLJDRRpEekWthelFCsrKwiFqjvJbgXbHQ5KaZ4Q8iEA3wbgBfDfKaVnCCH3AzhFKX0QwIcJIfcByANYBfC+mmm4Bawns7i2ka6JF0EIwZHhKM63eBrn/MImTmyjJE6PI8NR/NW/TKNQpNKHlbsd5xZiGOsLo7MGm4RHdnchX6SYXErUxEFxIvj8ObJNsh8dHcXFyWksrFxFbiXQ0hVMoVAIo6NbryY0Q8lSlNKHADxkeu3jup9/C8Bv1UyrbYJvDt64zRCb4/BwFH/77FVQSkFI65FXPJPH7GoKb7+9Nvvqh4aiyOaLmF5JYP/g9tJCTsX5+di2vVSOw1p0cGEh1tJkP9QVRE9Y7RkJMvj9fhw5eAN+9svfwi/+xDh+5y031kjD1kdLXpfAQ8btVuJwHBqKIpbJ43qLXnd8YYEtjjUjr2FOXq35gOh0roAry4lte6kc+wYi8HlIqR9aEefmYzWJtAH2vImDu6Itba96oGXJvj8SwGA0WBN5PC97vkUHFy9TrdVk5Ju8rToZLy3GUSjSmi2OAZ8H+wYiOD/fmotjrlDEpcV4zRZHgM3JVk+t1hotSfbn5lk4XKuUy6EhjbxadHCdn99EJODFaG91d/7LEA74MNYXbtnFsZx/rl3K5dBw63qqU8sJZAvFmi2OAHB4uFOr8MnWTGaro+XIvlikuLgYq0mVBEdPOIChrmDLkte5+RgODUfhqeFm6qGhaMsujufmNxHweTDRr/YAexUcHopiZjWJZDZfM5lOAS9TrSXZHxxq7VRhPdByZH91PYV0rrjtenEzDg21pudFKcX5hVhNQ2yAeV5XlhOueoycKs7Nx3BwVyd8VTxWzw7cObnYguRVqzMcehxu8dRqPdByZH9piU2WWpP94aFoKVfbSljSDrscrmEkBDDyyhcpriwnairXCbiwULtKHI7ypnbrkdf5hRj2DURq+kCb3d0hRIO+lo0e64HWI/uF+pD9oaEo0rkiZleTNZXbbJQXx9qTPdB6nlcsncPCZqbm42usL4ygz9OSZH95KY4DNS7BJYTg0HC05cZXPdF6ZL8YR38kgL7I9up5zTg03JrkdXmJed437KrtAxT2D0bg9ZCW87wmub1qTF48zXG+xdI4uUIRMyvJmo8vgBVOXFiItewDyGuN1iP7pThuqLHXBQAHNZmXFltrMl5ejCMc8GK4q3bHsgEg6PNioj+Mi4utRfaXtUio1mQPsGjoUos5E9MrSeSLtC72OrgrivVkDsvxdkWOClqK7CmluLQYr3mIDQCRoA/DXaHSZG8VTC4nsH8wUpeTwfsHO0uecKtgcikBr4dgrK92lTgc+wciuLaRbqmKHD5f6nGSev8gixYmW2xO1gstRfZL8Qw2Urma5wc59g9GWo68Li/G6+J1Acxe0yvJltrUvrwUx3hfGAFf7acOJ8RW2tTm84UTcy3Bx+1kC9mrnmgpsucploND9SGvfQMRTC7FWyZHmMoWcHU9VT+yH4ggWyji6lqqLvKbgctL8brd97NvgBFiK5H95aU4dkWD6Ar5ay57T08HAj5PS9mrnmgpsr+8WJ9KHI79g53YTOexmmiNHCGfJPXwuphc1g+Xl1sjzC4UKaaWk7ihTvbiZN9K0SNbHOtjL6+HYKI/3E7jKKKlyP7SYhydWm69HijlCFvEk6jnZiPAPHugdchrbi2JbKFYN3t1BLwY6eloGfKilNY1TQgA+wdab1+oXmgpsq/nZiMA3DCg5QhbZDJeXoqDkLJHWWv0RQLo7vC3lL2A2pep6rF/MNIyzsRKIovNdL6+ZD8YwcxqErlC653UrjVai+yXEpjor99EHOntQMDraRlPYnIpgZGejm0/nUoGQkhLbWqXNhsH6umpMnu1wr4QT6vWK43DZHciX6SYabHDjvVAy5B9OlfAtY1U3bxUgOUIx/vDLeN5XV6qb4gNsKihVTbQLi/F0RcJoLfGB/b02DcQQTyTx1I8Y9/Y4bhcpwNoevCF5EqLOBT1RMuQ/exqEpTWLyXBwTxV96clitpj8OpN9jcMdmJ+M41Exv2145cXE3XbnOXgm9qtEA1NLsUR9Hkw0lObq7NFKO0LtUgRQD3RMmTPvceJupN9J2ZWk8i7PEc4v5lGKleoa/4ZKE/GVvDuJ5frHwmVDwq53168TLWWV2eb0RNmV6O0gr3qjZYh+6kV1tn76pizBxh55QoUsy6vHS+dbKxj/hnQlV+6PBpaT2axHM/WNf8MAHu6OxD0eVoiery8lKi7vYDyPkcb1mgZsr+ynGDVH+HaH97Qo5QjdHnYOFXnGnuO8f4wCHG/Zz+1wjYA99V5cfR4SEvsc2TzRcytJUuRXT3RShVM9URLkX0tnxwkw/6B1sipzqwmEfR5sKtGz+mVIeTntePutxfAFq96oxXI69p6CkWKutwhZMb+wU4sxzPYTOfq/lluRsuQ/dRysu75egDojQTQG/aXKg3cipnVJMb6wnU7k6DH/sFO12+g8ecY7O1tjEMxs5p09VO++OLYELJvscN79UJLkH0qW8D8Zrru+XoOFma7m7ymV5INmYgAm4xXXF47PrOSxGA0iI5Afc4k6LFvIIJCkWJ2zb214yWyb1AkBLg/tVpvtATZlzZnG7AZBADj/RHMrLh3IlJKMbuaxN4Gkf1YXxiJbAErLr5TaHo10bDFkaeK3DzGZlaTCHg9GIrW5+oSPUZ72b7QtIvt1Qi0BNmXyi4b5NmP9YVxfTONTL7QkM+rNVYTWSSyhYaR18QA+xw3T8bZ1VQDyZ6N4+kV96YlZlaSGO3rqGvZJUfIzx6+4+bFsRFQIntCyBsJIecJIZcIIR+zaPc2QgglhJysnYr2aFSNPcfEQBiUMgJwIxqZT2WfE9E+153klc0XcW0j1bBIaKAzgHDAi2kXXwHA94QahfH+sKvt1QjYkj0hxAvgMwDeBOAogHcSQo4K2kUBfBjAj2qtpB2mlhMYjAbRGfQ15PPcTl6NrCwBgL19Ha4Os6+up0ApMN4g8iKEPQnLrZ4qTxM2yl4AMN4Xce34ahRUPPs7AFyilE5SSrMAvgrgrYJ2fwDgEwDSNdRPCVMribpfk6AHJ0m3Di5eWTLagMoSgD2PdreLw+xGbjZyuNlTXU/mEMvkGxYJAaxvluOZlriWo15QIfsRALO63+e010oghNwKYC+l9B+tBBFC3k8IOUUIObW0tFS1sjJcWU42rBIHAPojAUQCXteS/fRKErsaVFnCMdYfLm2kuw2NTnsBWhHAahJFFz7SsTn2crcD1giokL1oh6U0AgkhHgD/L4D/204QpfRzlNKTlNKTg4OD6lpaIJnNYzmeaajXRQjBWH/EtRtojc6nAmzz3K3X0M6sJBD0eTDYWd8DaHqM94eRzRcxv9nwQHnbmG5GJOTy1GojoEL2cwD26n4fBXBN93sUwHEAjxFCpgC8HMCDjdqk5ZukjSav8T73htmzTSB7FmZnEXdhmD2jlak2orKEg5OXGz3VRh5A4xhre/a2UCH7pwEcJITsI4QEALwDwIP8j5TSDUrpAKV0glI6AeBJAPdRSk/VRWMTmhEyAsD4QBhzqykUXBZmZ/IFXN9MNzSfCug8LxdOxpkGll1ylGrtXeipzqwkMdAZQKRBBRMA0N3hR2/Y71oHrBGwJXtKaR7AhwB8G8BZAA9QSs8QQu4nhNxXbwXt0DSy74sgW3BfmH11TassaWCIDbiXvHhlSaPH1+7uEHwe4kpPtRlpQgAYc/lhx3pDaemllD4E4CHTax+XtH319tVSx+xqEtGgDz11vu3SjNKG0HKirg9nqDWatTjyMHvKZZNxLZlDPJNvuL18Xg/29oVdS/Yvm+ht+OeO94Xx7Mxawz/XLXD9CVqeT23EhV568MnvtrCxWWTfFfKjLxJwHXk1y178M6ddFgll80Vc32h82gtgDti19ZSrL5CrJ1qC7JsxsPb0dMDvdV+YPbPCrjYerPPVxiKM9YVdl8ZpRo09x3g/8+zddIEcv9q40XtCABtfRcoOwbVRCVeTfbGo5VObMBG9HoK9ve4kr0ZdbWwGJy83YUYrr21kZQnHWF8YsXQe60n33NM+3cRIiF+X4taS6HrD1WS/FM8gky82xYsAtINCyy4jryZFQgDLqbotzJ5ZbdzVxmbwC9HcdBitqZFQX7v80gquJvtm5lMBNrhmVt0TZpcqS5owEQFGXkUKzLnonvaZBt/xosdEqYLJPfaaXU0i4GvM1cZmDEaD6PC792R7veFusl9pLtmP9UcQz+Sx6pJ72ht9tbEZpQomV5FXczYbgXLe203kNbOSxN7exlxtbEbpAjmXpVYbBXeT/WoShAB7ehrvRQC6sNEl5NXMfCpQDu3dUgvd6KuNzeD3tLuK7JuYJgTYGHOTvRoJV5P97GoSu7tCCPoan08F9JcvucOTmG0y2Q92BhEOeF2Tg+ZXGzefvNxhr2YdQNODp1bdeIFcveFqsuc19s0C/+yZFXeUenGPulFXG5vBw+xZl0RCzdxs5ODk5QY042pjM8b7w8jki1iMZZqmg1PherJvphdRehyaSybjzGrjrzY2Y8xF5MXLLpvqqfaHsRjLIJV1/iMwm50mBNg+GuCuTe1GwbVkn8oWsBjLNHVgAXCdp+oEe7mlgmlmNdnwq43N4F6yGyqYnBAJ8fHdJvtKuJbs+eBv5sAC2GR0y8BqZtklx1h/GOlcEUtx54fZfHFsRmUJh5vIq9l7QgAw0sMegekGezUariX7WY3sm5kfBNjAnt9MI51zdpjNrzZutmfP+8sN0VAzrjY2w01kz642DiIcaNzVxmYEfB7s6e5wxfhqNFxL9s2usecY62c3Xs6tOXuTdm6t+ZUlgHvIi1eWNNuZ6NMegel0ewE8Emr+DbB7+zpcYa9Gw71kv5pCOOBFfyTQVD3GXPI4tGafNuYY7WVhttNroZvGr19UAAAgAElEQVR1tbEZhBCWKnS4vQBn7AkB7FkTTh9fzYCLyb55F3rpUfJUHT64nJBPBYCgz4vdLqhgmnZAJQ7HeL/z94WaebWxGewRmBkks+57BGY94Vqyd0KIDQADnQF0+L2YWXV2GmdmJYmQvzlXG5ux1wUVTE6oLOFwQwXT1SZebWxGeV/I2XOy0XAl2VNKHRMylu/jcD55OSESAtxRa9+Mh2bLMNbHDgotOfigEO9PflNnM+GWfaFGw5VkvxzPIpVr3oVeZrjFU3WKvcb6wljYzDi6gskJB9A49rqAvJyyJ6TXwcn2agZcSfZOGliA88NsHgk5IcQGyqkRJx8UctriCDibvPjVxrsckCbsDfvRGfQ53gFrNFxJ9qUQ2yGTcbw/jFSu4NiDQiuJLJJNvNrYDDdc3dvMq43NGHFBBVMzrzY2g6dW3XKBXKPgSrLnHs5ob/NreoGy5+VUT8JpkdC4wz3VZl9tbEbQ53X8QSEnRUKAO/aFGg3Xkv1QVxAhf/PzqYDzc6pOKbvkcPpBobm1pCMOoOnh5INCTiqY4BjrD2N2LdW+6lgH15K9kwYWjzCcetUxPwPgFE+VHxRyqqdarixxhr0AZ3uqpQNoDqjE4djbF0a2fdWxAa4ke6fU2HM4/arjaYdFQoCzyctpkRDAdHHqVcdOSxMC7tjUbjSUyJ4Q8kZCyHlCyCVCyMcEf/8AIeQFQshpQsgThJCjtVeVIZ0rYN4BF3qZ4eSrjp0WCQHOrmAqXW3sgMoSDidfddwme3fAluwJIV4AnwHwJgBHAbxTQOZfoZTeRCm9BcAnAPyXmmuqwQmPihNhrD+MaYfej+O0SAjQXXXswDDbSQfQOPh4d2JFTrk6zhkFEwC76thDyg+gaUPNs78DwCVK6SSlNAvgqwDeqm9AKd3U/RoBUDd3zYleBODcg0JOjYScvKnthKuNzXCyp+qEq43NCPg82N3t3E3tZkCF7EcAzOp+n9NeM4AQ8kFCyGUwz/7DtVGvEk7MpwJlfZwWZjs1EnJq+SWlFDMrCcdFQn2RADqDPsfZCwCmVxOOuNrYDCfvCzUDKmQvimUrPHdK6WcopTcA+E0A/0EoiJD3E0JOEUJOLS0tVaephtHeDtx38x5H5VMB53qqTqwsAcoHhZxmr9VEFolswXH2cnIF0+xqyhF34pjByN6ZFXLNgArZzwHYq/t9FMA1i/ZfBfAzoj9QSj9HKT1JKT05ODiorqUOrzkyhD97562OyqcCzr3q2GlllxxOverYqWlCABhzYK290w6g6dG+6tgIFbJ/GsBBQsg+QkgAwDsAPKhvQAg5qPv1zQAu1k5Fd2CgM4BwwHlXHc+salcbN/Gh2TI40VN1Ntk7r4LJqWlCwJlXHReKFF/50QyuLDd+49iW7CmleQAfAvBtAGcBPEApPUMIuZ8Qcp/W7EOEkDOEkNMA/h2A99ZNY4fCqVcdO7GyhMOJ9uKLz6gDrjY2w4lXHTt5cXTivtD8Zhr//m9fwJOTKw3/bKXtc0rpQwAeMr32cd3Pv1ZjvVyJvQ68fGnWgTX2HPoKJqcc+HLS1cZmlC6QW01iV1eoydowOJnsy+WqzpmTzXxOgitP0DoVTguznXa1sRn8qmMnpXKceACNg2+COmlfyElXG5vRE/Yj6rCrjpu5OLbJvoYY69MOCjnkqmN+tfG4Q8nLibXjMytJRzyKUISRHudVME2vJDDWF3bE1cZmlB7W7iB7za4m4SHA7p7GR2Ztsq8hnHbVMT9t6VTychrZZ/IFXHfgATSOgM/juKuOnXgATQ+n7QvNriaxp6cDfm/jqbdN9jWE02rtnXoAjcNpVx1fXXNuZQmHk646ppQ6ek8IcN5VxzOryaY917hN9jXEKD8o5JCrjmccXFkCOO+gkJM3Gzmc5Knyq42duicEMHs56arj2bXmRUJtsq8h+FXHTrkQzWkPeRGBPT7OGeTl9EgIcNZVx25ZHAFnVOSksgUsxTJNuzCuTfY1htM8VSdPRMBZFUxOvNrYjNJBIQfcweQmsndCNMTvzWpWJNQm+xrDSWG2E682NmO83zkHhZx8AI3DSeWXTrza2Iw92lXHTnDAZlbbZN9ScMpVx/xq4/E+511QpYeTNrWnV5KOuwDNDCd5qtMrCQxGnXW1sRlOuuq42WnCNtnXGE656niOV5b0O9frApxDXryyxOmRUG/Y75irjt2QJgScE23PrKbQ4feiPxJoyue3yb7G4DXtzR5czfYiVOGUq4751cZOt5eTKphmHV5jzzHe74yrjmfXktjb19G0NGGb7GsMpzw+rtn5QVWUrjp2iL3cQF5jfR2YbjLZO/lqYzP29rGrjhOZ5l513OwzCW2yrzH6I/yq4+aTl1OvNjbDCUfa3UX2zLNv5kEhJ19tbMaYAyqYeJqwmWde2mRfY/CrjpsdZruhsoTDCTnVWZdEQoDuquMm3sHktsURaG4FkxPShG2yrwMc4amuJDHm8EocjvH+5h8Uml5x/gE0jjFeftnEMTajHVJyevUS4IwigNk1tmfQTGfC3WT/5Z8DvvgWtbb/7WXA3/6qfbtCDvijUeCxP7Zvm1wFfq8beO6vDS8LDwotXWBtJx+zlzv5z6zt4jn7tqe/wtomyg9DKF9tbKrEeew/A380wr6jHf7ug8CnT9q3A4C/+mngS//Kvh2lwH+9GfgH4+MP9ooqmPIZ4D8OA9//pL3c+CKzwfNft2+7cIa1nf6h4WXhnSWXHmFtVy7by33mi6xtat2+7aP3A388BhQVFrdv/Arw/73C8JLUU/3/3wB85R32MikF/stR4KGP2rfNpYD7B4Afftrw8ox2tbEhTbh5jdngzN/Zy712mrWdfdq+7fmHWdu1afu2T30e+L0eIBMvvSS96vg7/wH4kwNAsWgv94H3AJ97tX07APjcvcDX3m14qbyH1rzqOPeSfXqDTcapxw1EJ8TyJWD5AvDjr9gT3fQPgGwMeOw/2etw/mH2/z/9juHl8X7BVcfPf439/+Rn7eXyNi88YN/2O9pnX3i49NJSPINUTnC18WN/BGTjwNQT1jILOeD0l4GVi8CyzRMmk6vAle8Dl79rT3SLZ4G1KUaMOqITel5Xvg/kU8B3/8BaJgCc+yb7/7v327c9/RX2/1OfM7w8syq42vgHf8b+f/Eb9nIf/hj7/+J37Ns+/ik2fmeetG6Xz7AxsHgGWL1Sell41XF8EZh9ko2DTMxa7vUfA5tXmQ3siO7yd4FijhGjDtMrycqrjc/+A/tfxVF67kvs/2e+YN/28U+x/19SWEQe+ggAClz6p9JLhBCM9ZuibUrZApZYAq4+Yy0zlwJe+nvg2nPAxpx1242rwLVngbMPAtny5/FIqJ3G2QoWzpR/nv+xddtrz5V/1k0a27ZZm/s0rj3L/qfGCVN+9qVucHF9N65aywSAzTnje6xANdK8+mzpJe7xjQ/o0ji6gVfSWwa9B3XVpu388+WfF160bqu37fpM6Uch2evb5rNqcnNp63YAW3AAw3dM5wq4vpHGRL8p7RVfYP/rv6MMHi39Y2ev9Eb5Z7t+0EcUOnsIrzq+rpsD/DvKoLdt7Lp6W90CPbOaxIR5cbx2mv1vt9gAzPkCgJVL9m2Tq+z/6zbznFLA42c/m/qhYl8oqXMQ7fph6byu7Wnrttd1f18uv296hT0BrZkH0NxL9pvXdD/bDNhNHcHGrsnbVS1Xa5taY6u/BmH5JdfB7vP1cjdt2uZS7LMBw6Tln2vw7PWTutn2MunDrzoW2svU1lJuYsk+cuNtdTI5aVbkn0v9YPP56U0WMQENsRfA0gGG8ku9vezGjYVcy7bxRQAsTTgt2hPiOsTn7SMGVdtSqt42ucqiEKDie431ma46boS9dPpOrzb/dLaLyb4RhFSN3PLPwjCbD5LkirUHmkuXvQ67QWgg8LI9plcS8BDT1cZbHdy2bavpB7EOwoNCW5pgFIjNW7flOsbmSwtD6SEv+sUxEwMym5W6CGXq+6Ea29pEeRZ9VuGpVtVn2+vfpZiWJpQtjsU8W3hl0BN47Lr1wpBaY+k8sy4ixOTfa6921fFCTJt7m1X0WWyrfWack80umHAx2V8DAlEg3K82YHvGyz/bte2dKH+GnQ6Ctvyq49JkzGfY4OdtrUiRE0fvBJBctl4Y+Gf2Thg+f3o1id3dHQj4PJK2igO2Z1zNBh19QLBry/YC+CnHpKRtjfosE2dplN4JALSUppkuefa6ybip64f4PFCwOJDD9TP1g7ituM/kbQnQPVZhg/H+CCNdXsG0eRXoHAJ8obqPcW4vwx4HJ3CVPkuvA7kka1vMsXFu9fkAa2u3MFiM8YpN7Wr7zOMDukbV2naNsPZa23SugIXNTNuz3zLiC0B0CIjutvfm4ousU0M9am1338J+tvIoi0VG4Ltv1toa5Ro8Ve7lSNpWfD5Q1oHnjYVtF8ptE0slQhJe6KVvq2KDYDfQt9/eq44vAtFh9s+27QLQfxDwRyp0qKhgii/o+sFC33yGkYdKnyW4bY39MLOSQDToQ2/Yb9SVt6VFa09V32exeUZ80rbV9MMCc2Z6xoTjC9AdFDL0g0L/7jrK8tsqfWbqB2GaMJsAcgm1PjOPcSsd9P1QyJbTlnZtBeML0EXbXIfhE2o2iOwCuvaote3awxZePr5kacIGw71kn94AQt1AR699FYi+bVqhbdcewBuwlpvZBEDLnoxJ7rg+zOabcrytlVxzWyt9K9qy32dWk0Yvlf+NeIHuUTUbbMW22+iH0kEhftVxegPo2QuA2NhLS7VsxV6a3OnVJMYHTAfQttoPtGC9QZneYN53dLiKfuipsEGFp1rtGA/12LctFpl9TfaaEaUJtzNu6zEf8mlDVDzSa7rqOL3BotHIQP3mgya3tDia52SD4WKy32SdFeq276zMJhDqEk4aA4oFttEW7GKTwUouz+fy9JBgMpauOk6b2qrI7RXLNSBtaptex2Y6h9VEttKLSG8CwSgQ7tMmgsXFUCV7KRJHULGtRT8YrjrOpZkXF+qx79+t2MvUD9MrycqroG36Vyi3V7F/ub0ym9bpIYt+qPBU0zq5doSkl2vVNhsHQFl04Y+UdJhaYQ/NNqQJtzluLXUFqugHAnTvrZDr93qwp0d31XHGZC+raCxdzXyo7Af+lKyKUugGw71kn9nUrbYWoR2gdVaPfVs+sFTk8gEbGWATwdSW5zNnV5OCiWAlV/NOehTaZja1XOJIqe2MKMTmbUPdzA6A/aTRe5RWE0Ev10pXSnVyK21rqGCqqh80e3UOa9GYQv/q+qFQpJhbE9TYmwnJUu4G4OsAIoP2bdOmftCXYsradlTatuKq44w2xu36oVhkkYdErvF7iftBWFnC7dXNozEbewFqY7yqftCINtwnbDvWFy5XMKU3GIGHeuyjsYxpPlihNB+6y/ZaSSIa8qFHnyZsAtxL9qXV1mbAFnIsl8i9dRXiUJHL25ZWcWNbg6fK23arpCWqDFv55wNAar1cWSKajCFdW0tvSifXNi2h6CXm02wzjkdjJnsZrjou9YMCIenbqvZvzwT7P7WGa+sp5Aq0cnE0L7p2/cAJEbDvX+V+0HmJprQEr2AyePYq3mc2BoBKx61RV072xvkwI6osKfVDj7B/hW1LaRybtsRT9tbt2oa6pP1guLPK3Geq8yyzaX3yWdAPfHFs9j1VSmRPCHkjIeQ8IeQSIeRjgr//O0LIS4SQ5wkhjxJCxmuvqgm8A0I91mkJTlR8wFp2qja4SwuDQngpkTsmInvVtITHz3K6gIIH3qXzEtdLDzsX5uyDGnnayt2okCsEpbqJ0GOdljAvpCaZ/Krj2dVkTfvBqIPWNtynRWPr5Qu9zItjZhPwh1nkBlTdD5b6cnvZyrXuh7E+LS1RLDAS53KtojF9P9ilKvUOjdYPG6kc1pK5Ss9+K/3QOcTGul3/BqOKpLxZ1lXQll11nGVXHVfVD+b+lURjuTRQyBj7AWxxdMIT42zJnhDiBfAZAG8CcBTAOwkhR03NngNwklJ6AsD/BPCJWitqQCHHam9VvCmRBy6bCIYBa+OplghJnGowXHVcIdcuzO9iRKOSljB49iyNM9AZQGfQV9k2ZGxrqUNQoW0uyTx/g6cqmQg29gJQPtKe0S8Mit6niqeaqWw7peVTK07P8kUs2A37tISivbi+VfVDt7TteH+EXXWcMo0vq2iswl4qC2k5wuJpworTszaRboVc4gUCEbX+DerTjzZyLWxruOq4mn5Q7V9z2iufQj6TxNxaqumVOICaZ38HgEuU0klKaRbAVwG8Vd+AUvo9Sikvkn4SwGht1TQhrTNqqJv9LBvc+g4IdrEyupzk9juD3K7ye1Xkmj7fcNVxepMRty+kybXJDwa7AEKEciv05bpq7+V3lkjbBnlbiVxKyzndkE3btMkGXH/Z9yq17WbRmOkahNJBoYp+sLGXXgc7ewU6Aa+v1L8zK+xCr+GuUKXcUDfg8Sj2Q5e9vfRy7exVyLP0o0U/7NUqmFZWtbJQlf7NCGwrq103z7NMrBQ5VqRxqu2HkDbGVfo31A34AmxfxGpO8tSMhBM44c7wfaFgl729SsUCxnkm/V6AoR8WlpaRL1LXkP0IgFnd73PaazL8HwAetvj79pHReREBbdBl4+K2+rC11FZy503aJNfqbhweInK5gs8vk9dGmcADnYYb+YQ68EElkWts280WEo8PyMTFZZdAOQ8f6GS/y+RmE2Vv3c5eek9Z1bZ6uTlj27E+dtVxNrGmk9tp0w+8z6IKfbZRntxa2+mVJPb2dhgv9OJtDf1gU04Z1KIxwL5/VcatPhqU9Blf1BeWtJpxpX7Qy40AoOUTqhU6mOdD3GJPaIONQX+HWp+Z+sGybaiKtnobmAi8XK6aKMvdCn/I+tdgL9Zn1xaXtc92QRoHgGhXQZgHIYS8C8BJAH8i+fv7CSGnCCGnlpYsDqnYIW0/ESra6olOxesJRNiGYl7ygIj0JuANAr4gaysYAKWDQjy8BBQIfFM3EWwmjT4KCESQz8RxbUPwXNBSBUYVJKNir7TJXqpyg1yusS3f1N5Y066L4PrmEtbeZyDKLiKzW0gzlQvptGxxNPSDDcnw/vV4GeHLbFDIsajSYK8qFtKMmOyXl7UTqIb5YDPGg9325GWeZ9k4ZpYT4jRh2jgW7b11/RhXnQ/ieVYhlzs/Jtt2d/gRDflwfWWNXemg4vyI7KW8kAILWt+4xbOfA7BX9/sogIozw4SQnwTw2wDuo5QKGZJS+jlK6UlK6cnBwcGt6Mtg9sAB+0kT0pGMXWcFuxiB2MnlAzYoJuUx7arjbHzdROB2A7a7LFclHAaAQBTJ2DooFQysbJylr0reJ1H0wO3speuHILfX1iMsTl6xjVWmYyBa1iG39X4o62BcSGk2od1ZIpiIZrmW3rquz6z6l/elYSFVsZe4H/gdTOs8jRNUGeN6uQp95vGz9GMgAhTzmFtZV7OXbT8o2AswLtDBqFwuL+3VR9AmuTy1urysT3vZ9YOewBXHuK4fVtdWxWnCJkCF7J8GcJAQso8QEgDwDgAP6hsQQm4F8BdgRL9YezVNMOQHtQ6w7aweBe9Tq5f2BaShoEGufoLnUxWVKNxTzSXXdW3tQtFNtbbFonFhCESQTrDvWuGp6u3l8Vh7SFV56xuVbZXkiicNJ5FUbJVNGK6rpdwNk71i1hvwuj4rpmNIZguVm41cXwOBS/ohn2V9H1ToM32ZqC/APFC7iDTULfU++VXHic1VXVsFewHqqaRQt7Z/xPpsZXW1cjOb62voB0WHxs5br5Ary63zYgHrPhvrC2O9FDl22zs/ojFum3or99n6+mrlvf9Ngi3ZU0rzAD4E4NsAzgJ4gFJ6hhByPyHkPq3ZnwDoBPB1QshpQsiDEnG1gSBcsl9to/beuj4/qOIh6b11QJiDBoBiat3onajkdLlcaW5dVy+t6ZtNcrKXHHhRCZ0NYatiJKQSBWQ2Wb10oFPaZ30Rlh7IJUz2stTBZC9aZJu/sra6/qVaP4jTOOZ+sEv9KUQBeq+6JLeKtJdkXygV0/Y4DAupRT94g4A/pBbp6tNeAOKxjcp8Pf9uJXtFWR9YleGqRAGUllOVgPWia7atJCoe6wsjyRdHiyigQq5F+rHctnI+bG5sNP3kLIfSTfqU0ocAPGR67eO6n3+yxnpZQz8R+EMjrCaNPwx4/WreupnALb0e40RAJl72LACMageFSCZm8vzibCCbD1nweumQyuA2E3gExfV1RAJe9EcClboCphy0AiF5fSyEt9vjCHaV9zasPNVgtJzTBSomDT8oRFMbQNRkWyuy7RzS2uomo1/w+DdT/pfk2OdXkFc+w+qlVTYG9WTAdVAh8FJbhf71hdhCKdkXys6vl+Xy+/xl9jIQuA15ZYz2AoAIJGWEmU12cZ6uLbLxch27WQclh0ZLP+r7QfakqLRojItTq2eL2uep7KPp+8GvkjLm6UfWNhVfx9iNziB7d56gFW26qnh+dp6MeeMIsCEva++THxTy500EDio+BKbP6XJ9lYkjCmTjGOuPVJ7U06cESnIVNga5vlaExOulLbzPkly+4Fl462N9HSDmBU/StqRDRf8KdOBeom6Pw1vMIUDyGO01LQz6MwFcrsqeENdXdYFW6l8tjRIQ56vH+sPwZGOgvhBLDamMcVWHxrQnBAARpMWVJcJ+EOhQSj+a7CXagDcTuFXOPmMa4xZpnChSJrmK88HrY2leO4fG4ymNcV8h5RjP3p1kn9lkq6zXxzx7qw4QErgCcahs/Jo9e1GY3RtEqJgSyBXoW0EcKrn1cltvPoH9A4KJuFWPkutgSRyaty6pgDDIVbFXXxj+fAy0gpCq6QdB23yG1Uub+uGGHoKgz1spEzCOG7t+UImaRBFWFf0rkru3L4wuJFHwawsoL/+sqh9UFgbWNkwyuGFQMsZU5hm/XM0gV1L+KZoPKlV3XAfJ+IqSZKVcy34g5e9k178VkVAa+wY7xe0bDHeSfTZeHqiAdZ40myi3tUsJiNpayu00thUMggM9HmMbK2+KTw69DsWc+BmspbZMXsEfRrCYwn7RROSfpZcrS81kEwBImTQsw2ydDUoldxYhuYINxvrC6KBpZDwho85W+lb0r5VtjX12pFcwBUT2kpV/VvSZxUIq0kHaNs4WUK9f11ZsrzBJI+vVohNe/mlpL/txW3qdt9W89aFgDj1hU5qQUlP/Wswz0RgHxONGaK+4eANe1GeC77WnpwOdJG1qa9NngQjz1ktyLfqMy/QGUCQ+REhK7IA1AS4l+yQQ0IVGlh2QLOfafEFWSiZrm9MRh1UpIaVMhp4QAaHcfd0spZL1aJPRapOHPxSc62tV6sU3gzUdNotBRJCWkL0mN6AncJkNkkwmTwVZpRqyCVM/WITZXK5OZ1HbvX1hhEkGsUKw/PmStigW2EZgNfbS9KVanx0QpJTL/WDqX1H5Jz+NXRo3No6HTgfLhVRvLy5XkpYII4MUguUXLftXNG5l+urmmfb9xqMCos1nWG5dYT5U2suiz7LGMY5AJ6uPLwicn5xpjEv6we/1YCikLdp+PdnLqnwSpn6wKLDQ24sQZDwdiHqyGOkR7B81Ae4k+5yOwIFSvlrc1kxIVt6nboJZeT2FLCvzCpgGt0Dufo3slzJek1zRRDCTgZWnahzcK9kAOkgW+/sEA6u0MFh7PSW9VO1lJiSr0rhssvx9PB5pauSGwU6EkcFa3leWCUgI3LyIWXmJRgJfyzOPeX+XgLxyEu9TmJYwE5KF98n19Wl9ZBk16ezF2wq+V2/Yjy5vFvGinuyt+ldHSLz8U6V/tYV0b1hw46OZwFW8dZV5VtG/Vo6SaIyLF7zhcBF5eNn3t2krdiyt7FXusyRCGA7lHFF2CbiV7EWEZDW4zSuzlffJB59V/a15YFlsDI5rKbzrSR4GWnkyJs/eKgowEfhChpHXvu7KpsgmWT6dD247GxgI3CrENRGS1UaX2UOyCLM7kMFyhpO9Vf7X5IFbRQEmAr+aZPLHIqLUjFmuxVmOCqLrZI6AqPyTR4M8JWAVBVTYS7wwEELQ48tjI6+7K92yH8yOkqRtsWiYD5uUjZ3hsEUqy2wvS89eIQqoYp5Vyo0Kz74AwK5gHkkaLD8C07IfRPZSc5RiNISBQE7ctglwJ9mLQlyrzjJ4SBLvkw9ufzkMk27MyTxKwSAY0chklo9lS09GEOYD1kSntZ1Psa7s8ggOL1cMWKvcZwIVHqVV1GQmcJUQ10Kut5iDnxSwoH0feP2sLlyUg67wwK32Q4wEPhNn3tbuajxV1VSD/nWz3IoFb/seZZc3i9Wcroo6IK4xL+ml0A+lDVNN3ytrRRQowa6gVQqlipy939Rn25xnlak3ra0g9dYfKCCJIOY3ee7eLmev2Ge6/s0ViljPB9DrFdirSXAn2WeT5UEC2EwakYckqtbQBnfAtIioeOAWEzxYZANqelOXAweUUg2W95zkzOTltZArmOC0KC7/FOaKVQncLmIwLzhyD/xaUjc0ZZ6q1F72Hvik1h+9PovNb1W5xMP2g7iugJhsRfYqZMq18Wa5frWoKeLJYD3vx2Y6V9ZXNXKzG+PcXisJJBBCv1+ga7ULHmDMrQOK88wqYkiw8wj83I3FPOvxZZGkQUwu6TaArVJvVeXsma4zq0nEaQhRkfPVJLiU7OPVkYzKymweWFZtzR64VfmnJndyQwt/q9m8sssV6wb3tOapSgnJ7FFK5Vax+S2LGMzgG9oB06Sx6IdrSQ8y+YK1DmZ7+YKs7l8ht35xnU1ssu1Ug2YDvqFttydjthcgjxgqPHBxP4RoxkReEnsVtM3NikjXYoxr9ppcSiCBDjF5ST1whXlWyz0ss1MnkRv1ZpFCEJeX9BU8VHz1ecXeiVXOPmGwVxIhhInkNHcT4E6yV90Y5IPb4CFJVmbzwALkIa7Zozd1gcgAACAASURBVATkqSRtAF1coygWqbW3XpH7tAlxtXbrySzm01oYL9PXTLQyHSoIqZPZUFb+qRIJ5dMAqCDlI49Y4sVA6UEZ0g14s70Ise0Hru+5VW3xrWrvRGIv85ixkmu2F2DbvyW52biw/NNfSCGFEC4vxstyFSq4SnKtosxAmbyyng54RRVJZntZlX9KU5UyHQhzavj3AuT2MqcfAeEYDxTTyBCTvaRyJXsnwhRoUmevOOLoQLAouT66CXAn2cs2Bs0dICRwycosInCZN2X2KEty5R7lWs6H65tptkkqK//MaYPbr6vWACyIQwuxlxNI0JDh84xyZR6limdvk8JQyUFnRfaSpWbYaxWel8qmnKXcMiFl8gVcWudkL7GXN8AO7en1lnr227CXlVzz+OIy9KAUJJ9EmujtZZeaUeiznJHAJ5cTKPoVI10rudWm3gKKUVNFulbeluSSIIEILuvTODIdRJGuqPyzWGApOc1eV5YTKPrC4sWxSXAf2VMq3hgs5ivvnhcSuB1xmCaYMPcq8ZAs5CYRxKSevGTelL7G3W4z1xAyajljmVzzgJW1lRKSqS2vcTcTeC5Z+UBmoUdp3Q9JhHST0cJegCCVZO1RTq8kUaQe5L0d1v2glwlYE5K5rXTvROR9qniUEh3yGRBaRLAjakzjZATeZ05kL0mNue5MQLFIcWU5DiKLmrYzz7jzI7NXNQup6hjPJuENRYzz0Uqu2V4iuaYzFJNLCQQ6bC49bDDcR/b8AIe+Y3lnmL0eEYH7w+XBqYeQwMPiPJ7Io/SHLT0ZQ5gdiEjkmlICvB5bqK8xZMySjrIMkb5+kb1UPMqwWIecYILzn83fTepRSr4XgI5w1OjZy+xllivtX+01X0dpklN/RG4vw5jhZCAZN+Yxw2WI5Irspbp3ImqrfU5ntKtsL3+YlX+avU/zoS7+s+h76fr3+mYa6VwR/lCn3F56HQFmM5m9PP7yyWCug8xe5r7Vfw+DXMH+kV43g9wEAh2duLaRZg8f90vGuGivqdS/4n4oOWDLcXREuljhh+zBOw2G+8jeXLoF6AgpIWlrmjSiDhASuMWAFekgGVjU40MopPNUrRYGva4eD/tdShxaiL2UQF9fT1mGSF8VAtf0VVoYhAQuWxgE9vKHJcTBXhvo61GzF/97SQfZwpAs1bhzud6QRf/qZfq0e3+kC4PAXkoELlkYikU2Rs32ErXVPqerqwdTKwnkC0XbhaFiwRFuTJajBL44BsPRKh0lBXsBFvMsqWYDoNJRslkYwhF2AObKsm5umPXlhyeFC7S4HxCIYCOVw3I8i2hXl1zfJsB9ZC/z5gALz17F+5QRuEK1Bn+fZMASfwT7BzsxuRwvv0/qyUSMr0mJrjy4J5fj2D3Qr8lQ8ewlNshnWTpsywRe5cJQzFdu/Gptd/X3Y3Ipzg6+WCykTJY5crPO6U4uJTDUFYTHamHYKiFZevYJsb1kpKzUD+z3vp5u5AoUc2sp+zFuTjvlknLnxx8upYcinVHrSNc8xlTsxb+bbOHX28Dj0areVPZOrDz7JDqj7PTh5aW4EoFXypUvpHxx7O7ukevQBLiP7IWbfZJJIyRwu5RPFSkBJU+GDdgbBiO4vKjTR8WjLMmVe6r5QhFTK0mMDfZoZYeylI+CvSwJXCU1Y5fyUVgYtN/3DPYhls5jKZ6xJlriZZupeh1sCHxyOY59AxGbCGuLhCQjDv6aimcvJE/ZQsp+H+zvA6CRV6l/q0i9mW+d1M2zyaU4OoM+hMKyctkEI2GPjk5UU5VcB+nCUMV8MC/6XDc9tMOTXV1d8BCwKM+OE5Q8+/J84ItjXw+Ptp2xSes+spelBAA1ApcuDAJCCnSyWyfNh17MBzi4XIsBe8NgJ+Y304hn8tpEqCbElefWp1eTyOaLODTcJZ5glKp79pYELvNkVBYGSa5Y/5kmHfYODwAAWyD59xJtOOqrNQBtw1G2kEZAKcXFhTgOD0WrIySb/i3B42VjoyIlkGNjSejZq3iU1gvpkJ7sS/0ri7BEC78scgvjwkIcB4c6QaqJhKyqcSoIvFM90pX2gzn1pp25qIhe2aLmC3Vib1/YtDhWYa8Krinz0oWFGAJeDwZ6e8VtmwT3kb1ss0//N3NbpYXBipAEg6Ai3WI1ESKlO8CvLCXkm2Lmk8FcB2lKIIyLC6yS4vBQVOyp5lIAqJq9hARulxKoZmEQeapiHcaHdwFgXjiTK3jgiygSku4FsH64tsEW3EPDUWsCF6XTpP3bWdlWSuDVb/axn60jrM5oNwY6A8yrtE29iRZ+wRjXTgZfWIiVF8dCtvK+GVEkZGkvQdQkTVWK5pnCpjq/clvqgXdi/0CE2cuOE1Q21XW8dGEhhht2dcIb6jT+rclwH9lbEpIkLaG8MOgOcBjkCghJFOaLav21AXtgF+v4Cwsxi4VBQl4WKYHz83EQAiZftDCI7OXxsvtmJCmB6haGaiIGxU11bxC7eyIIB7y4uBC3XhgqPEpre12YZ4vjIb44Svc4RB6lqa2oWoO3VUkJ+EJgl+1tw146QrphsLM8vvSfWSFXMfXmj2A5kcVKIouD3F76z9TroBwJyRZoycIgSuOY5YpOBpfkyu11YFcnLi/FWQkuYJmaMXw+182sK6B59nEcGuqU26tJcB/ZZ3WbnBw2KQElT0aYErAIs0V5R9DK2w61ATvRH0HA68H5hZiFJyMJh8266g5wXFiIsQd+BLzi3LaIwPnvVRF4bVIClXLFBO7xEBwaiuLc/KZ1hFXhUUaYbUS1/v4Isz+AQ7uiFguDpH8rUgKCk8G8rUpKgHuf1XiUFgR+ZDiKCwtxUH4ob7upN93iyDx7i3kmi7CEqbcqUmQqEZbIXoC4f3X2OjzchWy+iKm1jNj5sbKXZJ7FaQBX11PMmbCqemsCXEj21acElHKfMo8DEA+CioFlETEEwvB5PTiwqxPn5mM2noyoGkdO4OcXYmxg8e+mkhIAxNGFiJD8HRB6n9WmBPQng/WfISQO9rcjw1Gcn4+BWoXZokUMkKTeGHkNd4XQHfZbp96qIg7RZq4iIVkuDNWl3g4PdyGeyeN6ymv8W0luwngyWP8ZooXBz1ISAHBouNMmwhLYgBbFBx2FKR9B1CSKdEURlshegLh/dfY6Mszmzfn5mKR/q0+9XVpjVU0srWpREdQEuI/sLQlccKrNfICj2pQAICFwwYAFxGGjgbw22XvzaaP3KU0JyFMzOW8Hriwn2MDiOlRDSGZ7iQiJkCoIyZo4DFGTX9JnOgI/MhzFWjJXvqtd1aPknynQ4fxCjOXrub7m1JvoZDAgzhVLCdwqV6zevwZS9Pq1azbkJzcPa9+LE460Hwy6Wi2OEZxfiKM37MdgZ9B6nolsoP8uJR0kY9xsL9HhSa6vir1KcuWpygO7OuEhYHPScmHQp96sUz7nVtmcPjwcldurSXAf2YsqFWQdYEngiikBQELg1SwM7G+Hh6NY2GQ3FJb04xAd4OA6SDyOpYwXhSLFwSHdMzorvDndqUqDXEVPhv8uy+8rhbhxeT+Iogs/txc7lDLFT/PLUm8GufLKiqI/jEuLcRzS9k/gD1eeNJVFQjYpgYq20oVBMMZE9pLKlfSZr6NE9mdX8sa/6dvK7CUaN5pnf3AoCkJ0zyXe9jwTjHHzQUdLAlcct5YLQxghvxcTAxEWbVsuDPr9LslBx2wc8PhxfimNDr+XPYrQqgy3CXAf2b/iw8DHZowbqR6PPC0hChmBKlMCcgIvy5UtDOXBfWQ3Iy/+oBGDXNHA4r9LQsY57VrjwyVPVRDiispPAQjL3UQEXpIrsJf+6VdA+Z4TURpHVG6n/0y9voFyJAQAVzZoWY5ZX2XPPoHNQgCZfFHn2QsWBmlKwCo1o2Av6cIg6F9RqhKAsKyUjy+PB51BH/b2deDFxVz5bwa5krQI/5tJLtUqS0qRo+xZvKrzjJ8MFkWZ5rZSApfPB3HKR7YwMB1uHO5i+zg2C0OlXNG4ZfY6NNTJHkVo9eziJsB9ZO/1AaFuY0oAEK/MsioBQNHjsEoJSAZs1jS4BeR1NUHK+ull6j9TL9d80lT7jOkY4PMQ7B/QearKBC6yVxW5T9EEL8lVyOlalXRqf+uNBDDUFcQFnpYQevYKC7R2MngxzXLZhrSXWQcrAs8ljd7nlnLFChGD6GQwf68owtLJPDzUhXMLCXHqTWQvi43fDAkhls4b017a3yr0VZln0v0jUT9UE2FVYS+T3MPDUUyvJFHwWURuKtVD2nw4Px8v76Hxazbann2NIe0AU0d5A+LDFlWlBETVGoJw2PRot13RIHrCfkzzFJ7Bs5fk1kURg/bz5XWKfQMRBHye8ntlG3gqnqrUo1RcSLm+NoRk+AyblMDh4S6cXS6I26qm3rSfeURlSHuZ5Vp59oDxpOmWcsWivQDB9zKfDC7JtbbXkeEoJpcTbFNbZYG2OE3N90osF8eS3CoIXGWeWdnLfNBxS6k3JpdHxXEaEKfezCeD+XsF9ir4OrAcz5Qjba6vmzZoCSFvJIScJ4RcIoR8TPD3uwkhzxJC8oSQt9VeTQVIQyvTYJEetqgmJSBKSwhylKaBRQjBkeEoLmtPSRJ7lAqeKt8MWimYBpY2wfUbjlkZIUlSM+aTwXq5eogmONdXJafL7zmxSQkcGY6Wc9B6HfjJYBXPXvt5JgaM94cRDvjK3wuQRFgyQlJJNVSTEpB54JHK6FW4MCSM9todRaFIkfN2qHngMucnm8RKltmpnMYRkDI/GSwlcIG9tuvZm3WwcpRsqqJu1PaFNvJ+tXQtf6/AXimw1LJhTsrKrJsAW7InhHgBfAbAmwAcBfBOQshRU7MZAO8D8JVaK6gMWQpDREiy8E4aiurkWh3g4HJKMisH95HhLnG1hFVKoEJuOY1zfKRbp28YFSdNRQfLuE4quXVAHjFUMRHUUz5GQjoyHMV6XvNwDSkB7WSwShWI9vPlDYrje8z2gjqBc/1McoU5XfNJUykhVTFupQuD0V4Ae/iLUv/KnJ9cEvMpL/b2dbAyVf33FOXWVcqWpftHVhHW1uZZ6XPM1VbZpOGZwaO9HQgHvFjN+tXStSW5lW1jRWanY/oxJiuzbgJUPPs7AFyilE5SSrMAvgrgrfoGlNIpSunzAJp3cbOqRylrKyIk0UlT6cBSSwkcGY5iNecv66fXFbDYC6gMcZM0aCQvEdHxGnf9hjaXq5JbByxyxaKJIA5x5Skf64jhyHAXMvCDgiimBOT2mosTHBvpMn6+Xpb+5yoiLKUy3FyCjSVz1FSThbTcdqKfpfXixcDW+1crA76aIKbFUTDGpQRuYS/pZm7t5ln5vaaDjpzAtajJ4yGsSi7tUUvXcv0F82Et58dITwf6IqaL+VyUsx8BMKv7fU57rWoQQt5PCDlFCDm1tLS0FRFyCDtAsjKbSYYf4FBZGOxyujYpgeMj3czr0ssCFDzKygmWQhDH9ujIS0R0VikB80lTS4+ydoRklKvTlZ8M1hHSgV2dCPi8yHo6xPaqIiWQRNDodVWbEqiQW2XqTWovgfcpXUhFkVC5rc/rwY27u7CWE3iqqv2rnQy+nvQYx5fI+ZESuIW9qlkYtjjP2HslqSSTzON7unE14QFVSddynQTzYTnrM9qL6+SiahwieE3wtF17UEo/Ryk9SSk9OTg4uBURciikBIxtdR3AD3AIUz4RIXFUDm7BSVMBgR8aiiLnETxVyiolAAgnQk93D3r1XoQszJYNWL2O/H1b9MANcqvyKK0JPOBj5FWRlthCSiBFTYtjtSkBvY4luaaTwYA8YpClBMwnTaWRkFrq7cRIN5YzXlCVahyub0U0qC2O+jQh1zcrsJdKnb20usUqwlKoehOdDNZ/jnncmD7/ptFubOT9IOaDjlnFcQugmE1gOeM1plW5vi7y7OcA7NX9PgrgWn3U2Qaq8SjNbWUpAaByYZB54PzQiU1KIODzlK7vFS8MaiFuDj7cONpn0lUSZsuIQ68jf580R2nyPk0eZVmuyZPhJ4NV9gIkBH5ipBuxQgBUJSXAT5oKUgLhzi4MdAaNnw8o7p1IIgZ/WFwGXCHXIiVglrvNze+bRrsRKwaQS+tObvKTwVb9W5KppQlhShNyfQULQ4W+/KDjNlNvSudZrDxwvY78Z9Pn3zzaU36Gs3nhV9zDKqQTLK06Yvbs3ZWzfxrAQULIPkJIAMA7ADxYX7W2AHMHyA5wiNrKUgL8NZWcLmCxMBh1ODQyxFRUqUEWDNhsKo4kDQgmomBhsEoJ6HXk75MtDOaTpqqExE8Gq3iqkjMBN412I04DSMQ3jbryz7PTQft575Apmqy2zl6vI/9ZdSG1JSRB6q1CbqTypKmgf0+MdiNJQyikFSIhrq+AwAOhTgxGg8a2koWhQl9+0lQ0H6qpxvGZoiZFAmdyJWW4ps+/YTAivvmyitQbySWRRKhyTpr3sNKbwN+8E7j4SKXcOsOW7CmleQAfAvBtAGcBPEApPUMIuZ8Qch8AEEJeRgiZA/DzAP6CEHKmnkoLYSYOK2/dH4bhvgpLzz4iIfDOyrbmwS2Re3RsADnqxcbGuk6uTYir03dtfQ0JhHB8VBAy6nUELDxwiWcvs0GF3CpTMyJ7VaQExPY6Mcr2OZLxDaOugrZMB2OfpZJskdi/Z1elrnoduVyP33gyWNZWZoNAZZ+xhUEyZip0kPRZwNRnpb0mY9sDg53IekJiD1zF+dHm0S7+qEuzvqpyFeeD1F7+SGWNu7AfbOxl7l9TW5/Xo3uqlLnPJHKL+bLzUyzCV0yDBCLY1WUqhDDbK70BnH8IiDU+OeKzbwJQSh8C8JDptY/rfn4aLL3TPPAOyGfZRC3dLyIhGX0HZLS2wWhlW38YSOg2k7ncoEhuRCzXpAMnr9W1NfTq5QY6BQc4Kkk5trkOSjtw295eY1u/gJQzcSBkCi31cg2eTNyakHJJAH0sJZBLiu2lP2nq8Vj3gzklkBHb9sBgJ54iIWSSsfKLJbkiHYwL//XFZewHcGyfqa5A9EzTbFzet0ClvqLPFx0Yy8aB6G4LuSYd7Poh2Mny/MV8hb4+rweRzi74k7oSXCt7mRyl2MY6ogDGdu+qbFsRFW9hnpnb8lp/s1zLfjCNcdn4AgT9MFTRdGigD9gE8ul4mRSzCYm9dPPMFyzZoLe3r7KtLIsg0rfOaKETtKZBYOlRdkoGrMTrEXqq1UQBlZ5XEiFsbOg91bhYpoA4UvENFHzhcv1z6fMlG7SqIa7M+zTvBVjZgOvLT5pa2kuW9qokL18oinzGRIhSucYJtriygiIluGmicpIL+9fSBmZStvDsKxYGtf6Ve5QmHSzGeE9PN3zII5vJlHXVyzDINUZYV64vAABuGBHYS5Z6k84zk728gcqoiWj3yKiMW2mFjVU/2C+ko1qKb2Zhmb1AKZCNKUVYK2trAIAhq0iIp3ysFsc6o3XI3uypZjQvUOQdVNMB5pVZ5p3o5XJkY6xUzWskZZ/Xg6I/jFhMR/YyMjA905RSilwqBl9HFZ6MzOvSt6XUfsEpLaQ29tLLtYuazPaSyI10dsGTSyKVLZjk2kcM62urSJEQoh2ByrbmVFJGMsFFJ01tF2iV1IxpYSjktSuW7SIs6OxVKXdYI55zM9e176Vur5nriwCAA6PDAn0F9pLKNfevZCyW5Jq9ddX0Y0xtjFvIPTDCophLV9l3Rz6tXbFs4Shpcl+avgoAGB0akOigO+holRmoM1qH7M1VDXYepaEDbPLwZs9A1lYUskk61ReMIJ+OYzOdK7eVTQR/eYJdWU4gWEwhFOmubBcwkTLX1yrE5fbKpdjgFobOpkljZy+9DnYeuP6kqUWf9XR3owMZnJ5dN7a1qS4pFini8Q3kfYJ2/P0q0Y3opKmsf6URllXKx2QvqxSGQj+MDjGyf37qum1bc7XV/NIKAKBDNMYqqnES2onUUGXbio1ymzFesTAI7MWdH/NCKlvEAKXIbZf2wPYr15fK7QBJqtI4xi/NsUhoZEiQ9jLrkLGYD3VG65C9eTPGMkdp7gAL76RiwMaZh+cLVrY1Lwwy7wRAMBxFBzJ4dnqtLFfq9ZQn2LMz64ggja7unsp2omeaynSQ2kth0tjZC9ARkk0kBJRtZhEFDPT2ogMZPD21qsmNsXy7ubaay9V0vbQUR7CQhEfmSYn6VzYRhf2rEGFZRU3mhcFqcZTZS6BDV5SNj7PT85pcedSkP2maLxSxtqbZWDofzM5EtLL8FKhcSDMxuUdbdT9sYYwXctqhPXmfXV1YBqW0PMYVUm/TVxnZBzosFgaF1Fu90TpkL/OQlIjOKgcdMZ405V6EaHALB6xgAADojHYhTPTkJdmQ4t9N0/FHkyvo9KQR7eqtbGd+pmk+yy6pkoXY/PvwzwdswuG48T2WIa6CbaULTmXbYDjK7HVlpfweGRnoPPAfTa4gjAwCYcEmNSCuHhJ5c1xflRSZ18fSPpzockkA1Nr7VImaZPay6N/JqwsoFqlNP5T77MVrm/AXeWWYQoWNFSkLPfsqIizpwiCIsET24s6PeSG1cFJy6ThmV1M2/VB2flLZAq4tLcvlVswzi0W3zmgdsjfnPi1zlBJvSpYSAMqdJfPm+PtVcroAvMEIev15PH1lTSfXYtLkkqCU4olLy4iSDIiV16NE4FWEl+ZNRJWFoeR98ijAqqpBJ9fjF0dNgTC8KOLFmUXkC0Wmr9XiqH3+4xeX0efPIBCWEbhpU12Ws+f65hS8da6Ded/C0rO399aF9rKRm08ncGkprjYfsgk8cXEJEaTZFcnmyjD+WXrnx7YfqsnZK3jrJbmavYpFrfRSIfWmMMbDyOCpqVW1CCubwNNTqwgVtXSwyjyzWnTrjNYh+4oVVMWT0XWAqKZXL1e/MNhtMpU2fi28E38E3b4snptdQyyds8lnsgF7eSmBhY0kgjRt7X1WpARE3id/pqnJXiplhyo5e5XKHTPR2Xl+AGg2iedm163tpRFHvlDEv1xewWAgDyKzl8izV9lELGSFZY/lthFBPwh0MD9SU8H7VKo40+wVJhk8fnFZLWLIJfH4xWWMhIsgVrl1/WcrRli2bYX9oBBh5SzGbUmuSoUe06s/kMcTF5d0/WCVs0/iiUvL6PJk5HLN9srEwa7YCIv1rSNah+wrcp9VbiJaTVq9PLsBqz9pajkRwoiQLHIFih9cWrEnmVwCP7i0jDDSRr0qdIioEW1JbjUEXsUmokHu/27v3IPjqu47/jmrlbR6rR5+Sn7KvN/gGAolBRpCU2gCfSQzkE6apumkbZpOk3QmhaQlaTqZJmmbV4cpCSGP5klCQkIoBBxeQ4EAtgHbYGzZARvbsmVZllavXb1O/zjnaO+u7j3nrJCslXS/M5q9e/er3/729zvnd37nd869N6Jxhw0MtkEMqE+M8NjuLvugq6803X7wBP25MZqSIbekDsqdTkBy1V5DZ1hhu60SEXJ9ZljuAN6eJm+vkJ1hSq76ruxQhm0HTrCuIWKhPiC3QF/romtRySfSv4EZlnPWVOefKQf9a7OXTn7OaKng8T3HGM/aMvt8THiio5vTWxLR3FB7RSSWs4wFFOx1IzJTtYhtj0C+IRuutYRiHDuQf43khsmNHkSSY4M0VCd5fPdRIvf0Grm5AZ7o6Oa0JkvDMr/N1AVtJQFQNgvaK0puZS1q4Tfwu6K4kzbQ8kxQDlvjMB1/Uq7DBsAlbZU8+soxhx/U+WdfOYAQUCsjbpth5JrvHx/Tt9iwDPy5gK6B75r62wJynQGpvtBe5twUmSH2ipKr/3/jykqe+U0Po8MZu67AK/s7GR2XrEyNWXQN6WdR9qquV7Of0Wz+f6yzAC1zLKtvsWHh5nzbeNAPDp9V1bGhUXJiaJSDR7uiudpeA/297OrMcIZZPgsddEuw1yxj4QR7c5VoTu9dt5VQqg03k+dGOSClt59lDdeSnRjupA6WYJ9qRIxlufLURp565VD0tkeAVBqZy/DUvm6uWKe3uEVNcavThbqCVW6BDSBc30SiSK6Fmyq2rWXWZPyQDfgs0l6K++Y1VbzcmWEsa5Gr/bBl937OW9VIYnQo2l6pRhU4TO0XLHKb/Oxlfpuxl+1aA/PbckU+i8o+kzWF9hIJptx10/wu4NylMDI+QXdPj9MPu147SKoyQVNy1GKvWexn2Uz+xnlg72c+9grKNd8P1nazKjVKQsA+vZ0ycrusqOD1TrXTqT2NWgwO2xkWZq85qNfDQgr2yWqVyQc7WJRRwwK4rdNCvnFZ5ZrgFZQblU0pHa5pr2GgX+8dt+gwMdzH0Mg4V7XraaFNhykdIUqHdD5wuDKkArl622PxgzjM/4uEpx9CBgaHvTatVB0qN9jnHBgOdB7hbWevcMhNA/pqSdce6FTIQOplLx8/FA0Mvv51zJrW1o5RV1XB8Z4ep706DhzmytOXUTHqshel9zPbtkcjd2JUZfWuWVOoHyz65vzbeNXYABetbebAEUtmLwSk0nQe7WJVUw3LqkIeyxj8fvCz1yxj4QR7CO8IYaguHm1LCeCW7CQ4MIyNqNq9LasGrlxfbV/g0dyKiRGWpuC8ZTrA2rIpnwzc6FAckHzlRtlLCBVoch7c4o7gkdmvqR1l/ZJapK3TaLkNDHHdmY1EbnsMyCWb8cvWR/rVThSnvRqnOTA4Sj7FM7comRWVUFlLcnSAq89aQX+ml4mw3WZGJkA2w7Xnttp9NmWA9ukPfe6gXKofcsWzAIttff1QrWYM1567ksH+XmQiGb4zDJioStPf18PbzlmJiNoNBGrWlUgW2itqxjLLWGDBvtGvIySr9HQ4MNraOi34ZZ8FDdbVsBS3KTHMFevVNHwiYoU+W6Eaxw1n1ZMc0ws9tmzKdIScY09vsb0Sen+4TS7Y7RUmN3JXRZ26QG1SruWiG50lilyGGy5oo3pimAEZctVmgHvOEmhvkPnvCkNwgHbtlc+sYgAAE85JREFUgZ4MdD6zgEb/AF5sr8ra8FlTsVxXlphqhGwfN1zYRvXEECfGInyrbdBcMczVZy23+zeYrdu2PRZzbVs/obCf+QwME2Pqqm9XG68OSWgcM4a3n99GHTlyoiZ81gT0ylrq5CBvv6DVvjYnRJF/LetSs4yFFeyr034ZJWjHetTRJgN4n/1uj+b7Ddcnq9bca05VnO3HxkOp246q839ydmCxyVZTHR9R02FX1lNdbIOIkoCRm+0t5EahutFvcNTTYa8ZQ8C2f3TBMirFONu7xkKpe/rUb7imPeVnLy3Xy17FXJt/RwbUoq+rfFDgB0vgMHKzHvXygNwrTl9GOjHC/v5w3w6OwaBMcf7SBA2pSv/Za9S97IO6QpG9fLiONY5S/VCwFmDZ9qjttbIxxYa0pHe8mtHx8MdqHxquZEVVjovWNNnXpQJyAXffmUUsrGAf7Aiu7LN4xI8KBno67NdgS8lO8lnPpatUxvWzl/qm0CYmJA90qGz+rGb8apRaruJaGrcpH0hpz06M3ILsxDMg+fjBJ9CZ6XA2w/p6la0/+XqO3NjUAfK7L6hBaVNrRUl+8MrWoci/HkEx6m6PQbm+2bpvGQcm/VtZkWBZ9Siv9gte7R6cQrvrudfJUMs5S2Tgbo9Rv0v3k2yfR7ZeZAOwL5QXy52pfibHVd81to3a9phqnCztntGSoH+imgd2HplC27q/h85sJatqRhFC2BMamFqmizP7GcCUurIrIGXce3qN3JxnLREK646u7CSXmSzNPHUwx69/c7yAdt+OTnb3qoxM5DymwwUdwZGtVwemw67spJQG61vfD8q13e0R9FpAuqCDd+Uq+M7T+wto+44N8OOd6rtrxgc9svUie4HHLCAQZFxrAUZf10A6OqQWMV3Zuu+6lJGr/dAgcmRFDV/cvKeAMjwyzu2P72Ossp6lyaz9bo+gyktVDX619dCypk85zSU3MED79kkfP6TSqiwkJcurRxlL1vKVhzsKsnspJV/YvIeRZD1NicCNFH0TpZxlN98sY2EF+4I6qSv71Fzb3R4nuWm/OnxFUl1wkcv47SiAArkNDU3c+vOdDOZUeaJncIRP/+JllizVd9MLZjJRi20FmaqjPlicITnrv4FZgKtmX7D4bWncqSa/4BnUQXPXta7gy7/qYP9x1eHHJyQfu3s7yeoaZKJS+8FjcRT0zM21dz4QOHID0Xd7NLpCfsbgaoug/OUMSI2FgcPlM90fEqMDnLZmJfe+eFhdZKXx2Qd20dWfo6llWVFt3eazRr81jqoGQBT6wWuh3HfW1KdsFrXtMUyuK0mREzAygBgZYMXSJeztGuD2x/ZNUn6y7RBP7j1O++o2EsG9/j6xxiexnEUsvGBvFo6yfflOFAZTPjAlhJnimtq2ixusO2ruR66/mL1dA7zvW8/xy52dvPuOX5MZHuWj79hUyK2qj27ck3J73TYITp197GWmw0656fx2u5EBt9xcpgTb5u31rsvPoaJC8J47n+UXLx7mr76zla37T/CvN5yHSHn6LMQPkdwwe9lmTcVc2+8C7bNeh70a1YVfYyOefuhTCc34CBedtp4zVzbwoe8/z/ee2c9n/vdlvv30fv7i8nYa0i3T9kMkd/L6jOn6IR39/UGuy16+3CK5zUuWcf0Fbfzn5j18cfMevvXkq3z8pzu4pL2Fs9avVvbyiTV6oVwNvNLOnUUsrGBfnVaLRtleNULXhNwG2MAEpGF9I7KUjatHZsO1ym0slFsTcndKyE+HjVyR4M1nt/Mf77qAlw9n+OvvbuNw7zB3/vkmTlvTpv4nm4Hh3miZ5neB7rgObrDsNHzCX+7wCbu9TAAf1gu6M+WHSbn6yUArVnLney9mfELydz94nsd2d/HJd5zNH160aqofouRWptT1GUZuRXX4RUpQmK2XYq9sr90GBX7o85Ob7fPzb8AGlfXNfPN9F3PKsjo+cc9O7njiVW66ZC23XHfmG/LDjPjXXJ9h5FbVh1/9DiX6ITB79eXqfiZqmvn8O8/nD85v5csPd/CpX7zMxnVNfO09byJRo7f0ZnvVDKcEP1i5swivZ9DOGxhn9eo6rsuxpiM6uWnoPRAIXg7HBuVaB5FABpxqhESCP964mrecuZyOrgHOXNmgdkdMTDA5HXYF2uKOkF7l5ub61G/zkTtwVF0c47KtnIDMQfXexTWBy4fb82qBH97U1szD/3AlOw/1sbalNv/A52I/+AQkkyC4svWc1rcUPyw9zc0txb+Zgx4JTaPyVb9eZKxpprWxhns+eDk7DvWRrqmkfWldnluKHwaO+PWHYD8TibwNi5FIqNKRKT/6JClGrquPgfptw73QvN5PrvZDqrKC2969kY+8tZ/s6ATntKXVouyUWOPww0g/DOn1OJu+s4gFFuy1s3oP6Pe27KRRLYoN6PqlK/PK9nnOAtLKqa4scVJur1oUCzTuptoqLl7fkudNdoS+ErLEPpUlrjjXriuoTuDMEnXjPuHTuEvxgw7KQz0echtD/ZCqrGBT0F5GB8O1ZYlGh2yfWqy22SBZperDRq6PvUyQ8cnWh477ZYkQ8INHplrkh0RCcMGaIjuXmq137/GfBRh76YQmmmsGnD67THN9hpGbXm3/fvDzmbFX/5EpCc2py4vWMEryg+kPr7u5s4iFV8aBEh1wwI/rmyVOTtl0B4/KEovluhpAcCpo41bVUzAL8AkcGX1vHi97edi2ZD9I6POYBQSzdVuWaLg+ZS+jQ8l+KKGc5usHn2AwHT94zXTT6vqMwCzAKtfYy5XQTA66pfrB0sfMVdqzUfYC/5jgyy3FD7OIhRXsp3QEjwzYcF2zgLGsKmG4skTTYIdP2L/f6JD1mLoXyHV0BLMoNtSjskSfKa5Ptl4cwH3kzrQfzNa4oePuLDG4zuKy7bT84PCvuWmZKXv5lGZKsVcpfijFv5N+8FiAH+qxl72M3On4wdV3fP2gb1o2mdDMWLst8tlM+WEWsbCCvRkxuzv0+xY/biJpzxKNc7o77DKN3OETMNjtye1R3FoP7tBxxXfKbYLje9WxTW5VvXqAyfES7GW4NrnT9UMyZc8Sa5oBCcf3+dl2qAeGuqHWkUlN+uG4nx8Gu3WW6MHt9rBXdRoQpdmrFD+U4t/uDqWPLaGpaVY3Les94OmHE9oPJfQHH7n9nbrsZeEK4e+HVKCfg8NeRdyZ8sMsYmEF+wa9a+XQVjWa14c87d0g3ZrnNrTas8R0QK45tukwMQZHtru56VbIdELmsIfcVjiyQ021bYuuRt9DWwt1D4MQSu6hbW5u3VI1KE5yLToE/VBRbe8Ixba1ZYkNrYVcGxpa1RbFrl1+9uo7qBYdfeQefl5liT7+nfSDRYeKJNSv8PNZ/QpA+Pls0l7b1M6vqK2MQTm+bdyXm25VC9rde/24J15TA4PLZw1tfjYwcn1sW5lSgdjHZ8G2GHwfpSsofWta1PfMAbyCvRDi94UQu4UQe4UQN4d8Xi2EuEt//owQYv1MK+qF2hYVXEYGdACPuJkU5B05MuDRWNpmibtKZUgTo34ByVxs4qPDtLgWHRIVyqbm0v/aJdHc+uVqsDU2sAXwAnu5bLCqBG6JPjNXjvr4zMdeRu5M+yxZpexrLr6yzUhNAJrrNg5qS7SPvcwTnWa0jZfisyDXEsCralXGPjKgXqsibkkSlOPTbmcRzmAvhKgAbgOuBc4GbhJCnF1Eez9wQkp5KvBF4HMzragXhMgb1tUAapfk7/Do22C9uG3hxy6uLTOYooOLG5TrqUOi0h7AIa+jK4AnKqBhpeY6GnfdcrXYGtQlUtfA754tn/naq1gflw5O/2q5lbXui24Mt6HV7odkFdQtK/yfKDSUYtsg15WBnwSfzWg/09xUo/tKV6Ovq81U1eVLRK42M4vwyewvAfZKKX8jpRwBfgjcUMS5Afi2Pr4buFoIWyucRTSuUa9Na+w8IaB2aeH/RMHwAJrW2rnBz11yg5+79A1+7pQb0MF0dpfc2iXu52Iaruv7ARpXF/5PFCqS+eDmklu/cqouUSiwl8Nnwc9LkmvZ8geFv8e1A8O0m/rl9gAelOvSFfIBycVNVucffu6yVzC4zWi7DfaHEvqZK9gH5dpuxwF5fV2DQlCulx/apupykuET7FcBrwfeH9TnQjlSyjGgD3CkibOE5XrSseZSN7d5nXo95XftvEQif++LU662c+sCA8P6N9u5K88LHJ9v5wZl2dYiAE55i3pN1rgD+Ab921va7TyAtZep1+XFE7sQLD1dva77bTfXdFyjdxQqkoDw4wYDgEuHtgvzx8vOsnPbr8wfuwL4qbqt1C5xB/ANV6nXZg8/rL5YvbraDOQv5lr/O26uCVou2wZrzhscfadpXf54raNPrt6UP15yqp274ar8sevGYsYPPoHW+LflFDd31Ub12naRm9uyQb1uuNLOm01IKa1/wLuArwfevwf4ryLOS8DqwPt9wJIQWR8AtgBb1q5dK2cFA91SPnCzlNl+N/f4Pikf+mcpx0bd3INbpXz883467H1Yymfv8OPuuFvK7T/24z57h5Qdm/24j/+7lAe3uHljo1I+dKuyhQu5AWXbgWNubv9RKR+4RcrcoJt7rEPKzZ+UcnzMzT3wrJRPfMHNk1LKPQ9J+dw3/Lgv/kjKnff4cX/9VSn3PermTUxI+ehnpTz8gps7NiLlg/8kZc9rbm42o/wweNzNzXRK+cuPSzky7OYe3SXlrz4t5fi4m7v/aSn/78tunpRSvnK/lFv/x4/7/PelfPleP+5Tt0n56hNu3sSElI98RsrOHW7uaE7Zq/d1N3e4V8r7/1HKoRNubu9BJXc06+Y6AGyRjrgd9ifU/0ZDCHEZ8Ckp5dv0+1v0IPFvAc6DmvO0ECIJHAGWSYvwTZs2yS1btpQ+OsWIESPGIoYQYquUcpObWQifMs5zwGlCiHYhRBVwI3BvEede4L36+J3AI7ZAHyNGjBgxTi6c98aRUo4JIT4EPAhUAN+QUr4khPg0ajpxL3An8B0hxF6gBzUgxIgRI0aMMoHXjdCklPcD9xeduzVwnEXV9mPEiBEjRhliYV1BGyNGjBgxQhEH+xgxYsRYBIiDfYwYMWIsAsTBPkaMGDEWAeJgHyNGjBiLAM6Lqmbti4U4Buyf5r8vBbpnUJ2Thfmod6zzycN81DvW+eTB6L1OSum46dVUzFmwfyMQQmyZzhVkc435qHes88nDfNQ71vnk4Y3qHZdxYsSIEWMRIA72MWLEiLEIMF+D/dfmWoFpYj7qHet88jAf9Y51Pnl4Q3rPy5p9jBgxYsQoDfM1s48RI0aMGCVg3gV718PP5wpCiG8IIbqEEDsD51qEEJuFEB36tVmfF0KIr+jfsF0IsXGOdF4jhHhUCLFLCPGSEOLv54neKSHEs0KIF7Xe/6LPt+sH3ncIIe7St+RGCFGt3+/Vn6+fC721LhVCiOeFEPfNB52FEK8JIXYIIV4QQmzR58q6fWhdmoQQdwshXtHt+7Jy1lsIcYa2sfnLCCE+PKM6T+eJJ3P1h7rF8j5gA1AFvAicPdd6ad2uADYCOwPnPg/crI9vBj6nj68DHkA9Y+9S4Jk50rkV2KiPG4A9qIfKl7veAqjXx5XAM1qfHwE36vO3A3+jjz8I3K6PbwTumsN28lHg+8B9+n1Z6wy8BiwtOlfW7UPr8m3gL/VxFdA0H/TW+lSgHgC1biZ1nrMfNE0jXAY8GHh/C3DLXOsV0Gd9UbDfDbTq41Zgtz7+KnBTGG+O9f85cM180huoBbYBv4W64CRZ3FZQz2K4TB8nNU/Mga6rgYeBtwD36Y5a7jqHBfuybh9AGni12F7lrnfg+38PeHKmdZ5vZRyfh5+XE1ZIKTsB9Kt5UnjZ/Q5dJrgIlSWXvd66HPIC0AVsRs34eqV64H2xbpN668/7gCUnV2MAvgR8DJjQ75dQ/jpL4CEhxFYhxAf0uXJvHxuAY8A3dcns60KIOspfb4MbgR/o4xnTeb4FexFybj5uJyqr3yGEqAd+AnxYSpmxUUPOzYneUspxKeWFqGz5EuCsMJp+nXO9hRBvB7qklFuDp0OoZaOzxuVSyo3AtcDfCiGusHDLReckqqT631LKi4BBVAkkCuWiN3rN5nrgxy5qyDmrzvMt2B8E1gTerwYOz5EuPjgqhGgF0K9d+nzZ/A4hRCUq0H9PSvlTfbrs9TaQUvYCj6Hqlk1CPfAeCnWb1Ft/3oh6fObJxOXA9UKI14Afoko5X6K8dUZKeVi/dgH3oAbWcm8fB4GDUspn9Pu7UcG/3PUGNahuk1Ie1e9nTOf5Fux9Hn5eTgg+iP29qJq4Of9nekX9UqDPTNVOJoQQAvX84F1Syi8EPip3vZcJIZr0cQ3wVmAX8CjqgfcwVW/ze94JPCJ1ofNkQUp5i5RytZRyPardPiKl/FPKWGchRJ0QosEco2rJOynz9iGlPAK8LoQ4Q5+6GniZMtdb4ybyJRyYSZ3nahHiDSxeXIfaNbIP+MRc6xPQ6wdAJzCKGnXfj6qxPgx06NcWzRXAbfo37AA2zZHOb0ZN/bYDL+i/6+aB3ucDz2u9dwK36vMbgGeBvahpcLU+n9Lv9+rPN8xxW7mK/G6cstVZ6/ai/nvJ9Ldybx9alwuBLbqN/AxoLne9UZsNjgONgXMzpnN8BW2MGDFiLALMtzJOjBgxYsSYBuJgHyNGjBiLAHGwjxEjRoxFgDjYx4gRI8YiQBzsY8SIEWMRIA72MWLEiLEIEAf7GDFixFgEiIN9jBgxYiwC/D8dyS2o7xf4eAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.concat([GNwt, GSst], keys=['wind', 'solar'], axis=1).loc[:4*7*24].plot()"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
270 271 272 273 274 275
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
276 277 278 279 280 281 282 283 284 285 286 287
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Wind\",\n",
    "            bus=\"North\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=1.2e6,\n",
    "            p_max_pu=GNwt)"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
288 289 290 291 292 293
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
294 295 296 297 298 299 300 301 302 303 304 305
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Solar\",\n",
    "            bus=\"South\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=0.6e6,\n",
    "            p_max_pu=GSst)"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
306 307 308 309 310
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
311 312
   "source": [
    "***\n",
jonas.hoersch's avatar
jonas.hoersch committed
313
    "**(b) Attach extendable storage units at the North and the South! The storages have to be modelled as an `H2-bus` (a bus with `carrier='H2'`) linked to the `AC-bus` North with a `Link` where `p_nom_extendable=True` with the `capital_cost` of the power capacity and an also extendable `Store` with the `capital_cost` of the energy capacity, for instance. The losses can be set on the links as `efficiency`.**"
sp2668's avatar
sp2668 committed
314 315 316 317
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
318 319 320 321 322 323
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
   "outputs": [],
   "source": [
    "for bus in [\"North\", \"South\"]:\n",
    "    \n",
    "    # H2 storage\n",
    "    network.add(\"Bus\",\n",
    "                bus + \" H2\",\n",
    "                carrier=\"H2\")\n",
    "    network.add(\"Store\",\n",
    "                bus + \" H2 St.\",\n",
    "                bus=bus + \" H2\",\n",
    "                e_nom_extendable=True,\n",
    "                capital_cost=10e3)\n",
    "    network.add(\"Link\",\n",
    "                bus + \"->H2\",\n",
    "                bus0=bus, bus1=bus + \" H2\",\n",
    "                p_nom_extendable=True,\n",
    "                capital_cost=0.3e6,\n",
    "                efficiency=0.75)\n",
    "    network.add(\"Link\",\n",
    "                \"H2->\" + bus,\n",
    "                bus0=bus + \" H2\", bus1=bus,\n",
    "                p_nom_extendable=True,\n",
    "                capital_cost=0.45e6,\n",
    "                efficiency=0.58)\n",
    "    \n",
    "    # Battery storage\n",
    "    network.add(\"Bus\",\n",
    "                bus + \" Battery\",\n",
    "                carrier=\"Battery\")\n",
    "    network.add(\"Store\",\n",
    "                bus + \" Battery St.\",\n",
    "                bus=bus + \" Battery\",\n",
    "                e_nom_extendable=True,\n",
    "                capital_cost=0.2e6)\n",
    "    network.add(\"Link\",\n",
sp2668's avatar
sp2668 committed
360
    "                bus + \"->Battery\",\n",
sp2668's avatar
sp2668 committed
361 362
    "                bus0=bus, bus1=bus + \" Battery\",\n",
    "                p_nom_extendable=True,\n",
sp2668's avatar
sp2668 committed
363 364 365 366 367 368 369
    "                capital_cost=0.15e6,\n",
    "                efficiency=0.9)\n",
    "    network.add(\"Link\",\n",
    "                \"Battery->\" + bus,\n",
    "                bus0=bus + \" Battery\", bus1=bus,\n",
    "                p_nom_extendable=True,\n",
    "                capital_cost=0.15e6,\n",
sp2668's avatar
sp2668 committed
370 371 372 373 374
    "                efficiency=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
375 376 377 378 379
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
380 381 382 383 384 385 386
   "source": [
    "***\n",
    "**(c) Run an investment optimization by calling the `lopf` function.**"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
387 388 389 390 391 392
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pypsa.pf:Slack bus for sub-network 0 is North\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is South\n",
      "WARNING:pypsa.pf:No generators in sub-network 2, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 2 is North H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 3, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 3 is North Battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 4, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 4 is South H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 5, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 5 is South Battery\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
sp2668's avatar
minor  
sp2668 committed
410 411 412
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/components.py:758: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
413 414
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
415 416 417
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
418 419
      "\n",
      "\n",
sp2668's avatar
sp2668 committed
420
      "INFO:pypsa.opf:Solving model using glpk\n",
sp2668's avatar
sp2668 committed
421 422 423 424 425 426 427 428 429 430 431 432 433 434
      "INFO:pypsa.opf:Optimization successful\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
sp2668's avatar
sp2668 committed
435 436 437
      "- Name: unknown\n",
      "  Lower bound: 360476623771.945\n",
      "  Upper bound: 360476623771.945\n",
sp2668's avatar
sp2668 committed
438
      "  Number of objectives: 1\n",
sp2668's avatar
sp2668 committed
439 440 441
      "  Number of constraints: 29569\n",
      "  Number of variables: 16143\n",
      "  Number of nonzeros: 55073\n",
sp2668's avatar
sp2668 committed
442 443 444 445 446 447 448
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
sp2668's avatar
sp2668 committed
449 450 451 452
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 0\n",
      "      Number of created subproblems: 0\n",
sp2668's avatar
sp2668 committed
453
      "  Error rc: 0\n",
sp2668's avatar
minor  
sp2668 committed
454
      "  Time: 4.0027666091918945\n",
sp2668's avatar
sp2668 committed
455 456 457 458 459 460 461 462 463 464 465 466
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
sp2668's avatar
minor  
sp2668 committed
467 468 469
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/opf.py:1207: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
470 471
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
472
      "To accept the future behavior, pass 'sort=True'.\n",
sp2668's avatar
sp2668 committed
473
      "\n",
sp2668's avatar
minor  
sp2668 committed
474
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
475
      "\n",
sp2668's avatar
minor  
sp2668 committed
476
      "\n"
sp2668's avatar
sp2668 committed
477 478 479 480 481 482 483 484
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ok', 'optimal')"
      ]
     },
sp2668's avatar
sp2668 committed
485
     "execution_count": 15,
sp2668's avatar
sp2668 committed
486 487 488 489 490 491 492 493 494 495
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.lopf(solver_name=solver)"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
496 497 498 499 500
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
501 502 503 504 505 506 507
   "source": [
    "***\n",
    "**(d) How do your results `objective` and `{generators,stores,links}.p_nom_opt` compare with the results of III.1(d)?** "
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
508 509 510 511 512 513
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
514 515 516 517
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
518
       "360.476623771945"
sp2668's avatar
sp2668 committed
519 520
      ]
     },
sp2668's avatar
sp2668 committed
521
     "execution_count": 16,
sp2668's avatar
sp2668 committed
522 523 524 525 526 527 528 529 530 531 532
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_v1 = network.objective / 1e9 # Mio. Euro\n",
    "obj_v1"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
533 534 535 536 537 538
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
539 540 541 542
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
543 544
       "Wind      87.381871\n",
       "Solar    267.961955\n",
sp2668's avatar
sp2668 committed
545 546 547
       "Name: p_nom_opt, dtype: float64"
      ]
     },
sp2668's avatar
sp2668 committed
548
     "execution_count": 17,
sp2668's avatar
sp2668 committed
549 550 551 552 553 554 555 556 557 558 559 560
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (a) Capacities for wind and solar.\n",
    "res_cap_v1 = network.generators.p_nom_opt / 1e3 # GW\n",
    "res_cap_v1 "
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
561 562 563 564 565 566
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
567 568 569 570
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
571 572 573 574 575 576 577 578
       "North->H2         2.899018e+01\n",
       "H2->North         2.984926e+01\n",
       "North->Battery    1.357335e-02\n",
       "Battery->North    7.330123e-02\n",
       "South->H2        -5.788267e-15\n",
       "H2->South        -5.293786e-14\n",
       "South->Battery    3.431087e+01\n",
       "Battery->South    3.333333e+01\n",
sp2668's avatar
sp2668 committed
579 580 581
       "Name: p_nom_opt, dtype: float64"
      ]
     },
sp2668's avatar
sp2668 committed
582
     "execution_count": 18,
sp2668's avatar
sp2668 committed
583 584 585 586 587 588 589 590 591 592 593 594
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (b) Store and dispatch power capacity.\n",
    "sto_cap_v1 = network.links.p_nom_opt / 1e3 # GW\n",
    "sto_cap_v1 "
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
595
   "execution_count": 19,
sp2668's avatar
sp2668 committed
596
   "metadata": {
sp2668's avatar
sp2668 committed
597 598 599 600
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
sp2668's avatar
sp2668 committed
601 602 603 604 605
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
606 607 608 609
       "North H2 St.         1.350790e+00\n",
       "North Battery St.    1.832403e-04\n",
       "South H2 St.        -5.803564e-16\n",
       "South Battery St.    2.450389e-01\n",
sp2668's avatar
sp2668 committed
610 611 612
       "Name: e_nom_opt, dtype: float64"
      ]
     },
sp2668's avatar
sp2668 committed
613
     "execution_count": 19,
sp2668's avatar
sp2668 committed
614 615 616 617 618 619 620 621 622 623 624 625
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (c) Energy capacities.\n",
    "sto_engy_v1 = network.stores.e_nom_opt / 1e6 # TWh\n",
    "sto_engy_v1"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
626 627 628 629 630 631
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
632 633 634 635
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
minor  
sp2668 committed
636
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b1bd8278>"
sp2668's avatar
sp2668 committed
637 638
      ]
     },
sp2668's avatar
sp2668 committed
639
     "execution_count": 20,
sp2668's avatar
sp2668 committed
640 641 642 643 644
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
sp2668's avatar
sp2668 committed
645
      "image/png": "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\n",
sp2668's avatar
sp2668 committed
646 647 648 649 650 651 652 653 654 655 656 657 658 659
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network.stores_t.e.plot()"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
660 661 662 663 664
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
665 666 667 668 669 670 671 672
   "source": [
    "***\n",
    "**(e) Now we lift the restriction against transmission and allow North and South to bridge their 500 km\n",
    "separation with a transmission line. How does the cost optimal technology mix change?**"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
673 674 675 676 677
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
678 679 680 681 682 683
   "source": [
    "Add extendable link between North and South:"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
684 685 686 687 688 689
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
690 691 692 693 694 695 696 697 698 699 700 701
   "outputs": [],
   "source": [
    "network.add(\"Link\",\n",
    "            \"North<->South\",\n",
    "            bus0=\"North\", bus1=\"South\",\n",
    "            p_min_pu=-1,\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=0.2e6)"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
702 703 704 705 706
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
707 708 709 710 711 712
   "source": [
    "Run LOPF:"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
713 714 715 716 717 718
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
719 720 721 722 723
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
sp2668's avatar
minor  
sp2668 committed
724 725 726
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:6201: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
727 728
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
729 730 731
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
732 733 734 735 736 737 738 739 740 741 742 743 744 745
      "\n",
      "\n",
      "INFO:pypsa.pf:Slack bus for sub-network 0 is North\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is South\n",
      "WARNING:pypsa.pf:No generators in sub-network 2, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 2 is North H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 3, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 3 is North Battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 4, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 4 is South H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 5, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 5 is South Battery\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
sp2668's avatar
minor  
sp2668 committed
746 747 748
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/components.py:758: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
749 750
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
751 752 753
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
754 755
      "\n",
      "\n",
sp2668's avatar
sp2668 committed
756
      "INFO:pypsa.opf:Solving model using glpk\n",
sp2668's avatar
sp2668 committed
757 758 759 760 761 762 763 764 765 766 767 768 769 770
      "INFO:pypsa.opf:Optimization successful\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
sp2668's avatar
sp2668 committed
771 772 773
      "- Name: unknown\n",
      "  Lower bound: 338496411380.838\n",
      "  Upper bound: 338496411380.838\n",
sp2668's avatar
sp2668 committed
774
      "  Number of objectives: 1\n",
sp2668's avatar
sp2668 committed
775 776 777
      "  Number of constraints: 30913\n",
      "  Number of variables: 16816\n",
      "  Number of nonzeros: 59105\n",
sp2668's avatar
sp2668 committed
778 779 780 781 782 783 784
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
sp2668's avatar
sp2668 committed
785 786 787 788
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 0\n",
      "      Number of created subproblems: 0\n",
sp2668's avatar
sp2668 committed
789
      "  Error rc: 0\n",
sp2668's avatar
minor  
sp2668 committed
790
      "  Time: 8.729655981063843\n",
sp2668's avatar
sp2668 committed
791 792 793 794 795 796 797 798 799 800 801 802
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
sp2668's avatar
minor  
sp2668 committed
803 804 805
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/opf.py:1207: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
806 807
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
808
      "To accept the future behavior, pass 'sort=True'.\n",
sp2668's avatar
sp2668 committed
809
      "\n",
sp2668's avatar
minor  
sp2668 committed
810
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
811
      "\n",
sp2668's avatar
minor  
sp2668 committed
812
      "\n"
sp2668's avatar
sp2668 committed
813 814 815 816 817 818 819 820
     ]
    },
    {
     "data": {
      "text/plain": [
       "('ok', 'optimal')"
      ]
     },
sp2668's avatar
sp2668 committed
821
     "execution_count": 22,
sp2668's avatar
sp2668 committed
822 823 824 825 826 827 828 829 830 831
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "network.lopf(solver_name=solver)"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
832 833 834 835 836
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
837 838 839 840 841 842
   "source": [
    "Get the results `objective` and `{generators,stores,links}.p_nom_opt` with real availability:"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
843 844 845 846 847 848
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
849 850 851 852
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
853
       "338.496411380838"
sp2668's avatar
sp2668 committed
854 855
      ]
     },
sp2668's avatar
sp2668 committed
856
     "execution_count": 23,
sp2668's avatar
sp2668 committed
857 858 859 860 861 862 863 864 865 866 867
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_v2 = network.objective / 1e9 # Mio. Euro\n",
    "obj_v2"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
868 869 870 871 872 873
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
874 875 876 877
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
878 879
       "Wind     130.539736\n",
       "Solar    174.567156\n",
sp2668's avatar
sp2668 committed
880 881 882
       "Name: p_nom_opt, dtype: float64"
      ]
     },
sp2668's avatar
sp2668 committed
883
     "execution_count": 24,
sp2668's avatar
sp2668 committed
884 885 886 887 888 889 890 891 892 893 894 895
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (a) Capacities for wind and solar.\n",
    "res_cap_v2 = network.generators.p_nom_opt / 1e3  # GW\n",
    "res_cap_v2"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
896 897 898 899 900 901
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
902 903 904 905
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
906 907 908 909 910 911 912 913 914
       "North->H2         3.050408e+01\n",
       "H2->North         4.681387e+01\n",
       "North->Battery   -2.328955e-13\n",
       "Battery->North   -6.652696e-14\n",
       "South->H2         2.253023e+01\n",
       "H2->South         1.654343e+01\n",
       "South->Battery    4.278347e+00\n",
       "Battery->South    6.495718e+00\n",
       "North<->South     2.390357e+01\n",
sp2668's avatar
sp2668 committed
915 916 917
       "Name: p_nom_opt, dtype: float64"
      ]
     },
sp2668's avatar
sp2668 committed
918
     "execution_count": 25,
sp2668's avatar
sp2668 committed
919 920 921 922 923 924 925 926 927 928 929 930
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (b) Store and dispatch power capacity.\n",
    "sto_cap_v2 = network.links.p_nom_opt / 1e3 # GW\n",
    "sto_cap_v2"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
931 932 933 934 935 936
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
937 938 939 940
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
sp2668 committed
941 942 943 944
       "North H2 St.         1.400510e+00\n",
       "North Battery St.   -1.191108e-15\n",
       "South H2 St.         7.131808e-01\n",
       "South Battery St.    2.576810e-02\n",
sp2668's avatar
sp2668 committed
945 946 947
       "Name: e_nom_opt, dtype: float64"
      ]
     },
sp2668's avatar
sp2668 committed
948
     "execution_count": 26,
sp2668's avatar
sp2668 committed
949 950 951 952 953 954 955 956 957 958 959 960
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (c) Energy capacities\n",
    "sto_engy_v2 = network.stores.e_nom_opt / 1e6 # TWh\n",
    "sto_engy_v2"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
961 962 963 964 965 966
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
967 968 969 970
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
minor  
sp2668 committed
971
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b2c832b0>"
sp2668's avatar
sp2668 committed
972 973
      ]
     },
sp2668's avatar
sp2668 committed
974
     "execution_count": 27,
sp2668's avatar
sp2668 committed
975 976 977 978 979
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
sp2668's avatar
sp2668 committed
980
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAD8CAYAAAC7IukgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXl4W9W19//ZmmzL85R5cEISYmdyQgbKUJKGAmkbCryhQHshKeRHuZept4Uy9PaFcl+G25ehhd5S8hJIKLS0ty0QKGUmzJA4ZI6dxCRy4ky2JY+SZU3794d0FNmWbckajqScz/P4sbW1z6BjnbP2Xmvt7xJSSjQ0NDQ0NOKJTu0T0NDQ0NDIPDTjoqGhoaERdzTjoqGhoaERdzTjoqGhoaERdzTjoqGhoaERdzTjoqGhoaERdzTjoqGhoaERdzTjoqGhoaERdzTjoqGhoaERdwxqn0CyKCsrkxUVFWqfhoaGhkZasWXLlhYpZXm0250yxqWiooKamhq1T0NDQ0MjrRBCNAxnO80tpqGhoaERdzTjoqGhoaERdzTjoqGhoaERd06ZmEs43G43jY2NOJ1OtU9FI0FkZ2czbtw4jEaj2qeioXFKcUobl8bGRvLz86moqEAIofbpaMQZKSVWq5XGxkYmTZqk9uloaJxSDOkWE0I8I4RoEkLsCvPebUIIKYQoC7wWQojHhRD1QogdQoh5IX1XCiH2B35WhrSfIYTYGdjmcRF4ygshSoQQbwf6vy2EKB7qGNHidDopLS3VDEuGIoSgtLRUm5lqaKhAJDGXdcBFfRuFEOOBbwKHQpqXAVMDP9cDTwb6lgD3AIuAhcA9irEI9Lk+ZDvlWHcC70oppwLvBl4PeIzhohmWzEb7/2poqMOQbjEp5YdCiIowbz0G/Ax4JaTtu8Bz0l87+XMhRJEQYjSwGHhbSmkDEEK8DVwkhNgIFEgpPwu0PwdcAvwzsK/Fgf2uBzYCdwx0DCnlscg/dnrT5fTQ1eMBQK8TlOaZ0GkP0QGREhqsdg7ZHGy2tJJl0HHt2ZPIMenVPrWUxen28swnB3G6vAghuHz+OMYVm9U+rZTFZnfR1Onko30tuH0+rj17EtnGU/v7NayYixDiYuCIlHJ7n5HhWOBwyOvGQNtg7Y1h2gFGKgZDSnlMCDFiiGP0My5CiOvxz26YMGFCFJ8weQgh+MlPfsIjjzwCwMMPP0xXVxf33ntv2P6OHg8HWrp6tX368QeUF+Zx1llnAbBq1Sq+853vsGLFikGPnZeXR1fXyX2tW7eOmpoafvvb3/Loo4/y9NNPYzAYKC8v55lnnmHixIn99nH//ffzxz/+Eb1ej06n46mnnmLRokX8+te/5vrrr8dsVveBJKWkpauHbz23sVf7hBIzy+eMUeek0oA3dx/nV2/sDb622nv4P5fMUvGMUpdXtx/l3/+8DY9PBtt0QnDDeaepeFbqE3UqshDCDPwc+N/h3g7TJofRPugpRLqNlHKNlHK+lHJ+eXnU6gVJISsri7///e+0tLRE1L+psweDTseMMQXMGluIHsl772/k008/jet5zZ07l5qaGnbs2MGKFSv42c9+1q/PZ599xmuvvcaXX37Jjh07eOeddxg/fjwAv/71r3E4HHE9p+HQ6nDT4/Fx45LTeOKquWz6+VJ0AvY3dQ298SnMlw2tmE166u9fxpLTy/m03qr2KaUkPR4vv3x1N9NH5/PrK6p59aZzqB5fxNt7Tqh9aqoznHUupwGTgO1CCAswDvhSCDEK/yxifEjfccDRIdrHhWkHOBFwqRH43RRoH2hfaYnBYOD666/nscce6/deQ0MDS5cuZfbs2SxdupSvDlrodHr45W03cvttt/GNb3yD2/71h7ywbi2PPfYY1dXVfPTRRwB8+OGHnHXWWUyePJm//vWvUZ/XkiVLgrOOM888k8bGxn59jh07RllZGVlZWQCUlZUxZswYHn/8cY4ePcqSJUtYsmRJ1MeOJ20OF0a94LYLTmf5nDGMyM9mYmkudcc6VD2vVOfLQ23MGVeEQa9j4aRSDrTYabW71D6tlOOt3Sdo6XJxx0XTuWTuWGaNK2TGmALqm7rwe+5PXaJ2i0kpdwKKi4qAgZkvpWwRQmwAbhJCvIg/eN8ecGm9CTwQEsS/ALhLSmkTQnQKIc4EvgCuAZ4I9NkArAQeCvx+JaS93zGi/Rx9+eWru9lzNL4PnKoxBdyzfMaQ/W688UZmz57db3Zw0003cc0117By5UqeeeYZbrr5Fv7r989hMujYt28f77zzDlaHm3vuuZdJo0v52e23A7B27VqOHTvGxx9/TF1dHRdffHFYF1l3dzfV1dXB1zabjYsvvrhfv7Vr17Js2bJ+7RdccAH33Xcf06ZN4/zzz+eKK67gvPPO45ZbbuHRRx/l/fffp6ysbMjPn0h6PD5Mel2vwP65U8v406ZDHLI6mFCqxRH6Yu3qYeeRdm5c4nfrzBlXCMCOI+2cNy01PQBq8faeE5TlmTjrtJPf86kj8mjvdtPY2s34klP3+xVJKvKfgM+A04UQjUKI6wbp/jpwAKgH/h/wbwCBQP5/ApsDP/cpwX3gX4GnA9t8hT+YD36j8k0hxH78WWkPDXaMdKagoIBrrrmGxx9/vFf7Z599xve//30Arr76ar747BOyjXr0OsHll1+OXq8n2+APGnq8vUdJl1xyCTqdjqqqKk6cCD9Fz8nJYdu2bcGf++67r1+f559/npqaGm4PGK5Q8vLy2LJlC2vWrKG8vJwrrriCdevWDecSJASfT+L2+tDre3tSb1wyBb1O8NSHX6l0ZqnNPRt2A7D4dP8Ycvb4IswmPS992X/2eirj9vp4f28TS04fgV538ju2ZPoIDDrB7z84tb9fkWSLXTXE+xUhf0vgxgH6PQM8E6a9BpgZpt0KLA3TPuAxYiGSGUYi+fGPf8y8efP44Q9/GPZ9t9cHCApz/CvNc3NzAYIZKf73T6K4qoBhT8/feecd7r//fj744INe+wtFr9ezePFiFi9ezKxZs1i/fj2rVq0a1vHiTY/HC4BB13sMNbIgm7NOK2PTQVu4zU5pbHYXb+4+zr+cOYEFFSUA5GUZ+Pas0bxX1zTE1qcWmy02Op0ezq8a2at9YmkuP1g0gee/OMSVCyYwKzDzO9XQtMVShJKSEr73ve+xdu3aYNtZZ53Fiy++CMDadc9RveDMoHFRMOoFeXl5tHfE16W3detWfvSjH7FhwwZGjBgRts/evXvZv39/8PW2bduCGWX5+fl0dnbG9ZyipcPpT9fOMvT/mlePL6K+uUuLI/Th5a1HcHslV59Z0at9cnkeVrsrmAKvAe/sacJk0HHu1P6u31uWTiUvy8DVz3zBJ/UtdDjdvPBFA794eRe/eHkXf/ziUMbHZDTjkkL89Kc/7ZU19vjjj/Pss88ye/Zs/vjC8/zywf/bL3deCME3L/oWr7+2oVdAP1Zuv/12urq6uPzyy6murg4bi+nq6mLlypVUVVUxe/Zs9uzZE0yhvv7661m2bFkwoL969eqk19NxuLzkBNyIfblgxkikhOc/H1apioxESslfag4zZ1whp4/K7/XexEBsau9xLREC/Nfq7drjnH1aKWZTfwdQaV4W669dSEe3mx88/QWz732Ln7+0ixc3H+IvNYe5+6WdGT8TFJluPRXmz58v+z7camtrqaysVOmMIqe9202D1c7EUjOFOaZ+7zdY7Tjdvn4PhFOduuMdmI167E2Hwv6ff/jsJj7Y18zY4hx63D56PH7XokEn+NfFp7H63MnJPmVV2dnYzvLffsx/XjKTq8/svabJ0mJn6aMfMLowm9dvPZeC7FNbCHTXkXa+88TH3H/pTH6wqP/6L4X6pi7qmzqxWB2U5pq4bN44PD4fs+99i+8vmqC6Oz4ShBBbpJTzo93ulBauTBf86bS6AW/oLIOOjm4PUkpN7iSAT0rcHokpR499gD73LJ/BnzYdovZ4J/lZBsrz/XGlzw9Y+dWbe/mXMyeeUqus/1JzmCyDjovDLC6tKMvlP75dyS9f3cOPX9zGM6sWqHCGqcMfPmsgx6jnO7MGX4g7ZUQeU0bk9WrT6/TMGV/EtsNtiTxF1dGMSxrgdHsxm/QDGg6TQY9E4vL6yDKcOg/DwbD3eJBIso0De34rynK561v9ZzRv7j7Oj/6whZ1H2oNB7UzH6fbyyrYjXDRzVL+4nsKqsyr4pL6Fd2qbOP0//tnrvQtmjOI3V1SjC+OCzDS6XV5e3naEy+aNpdA8vBncaeW5Gb/QUjMuKY7PJ+nx+Cgy93eHKSgBa5dHMy4KTZ09/tlejrG/LtAQVI8vAmDP0Y5Txri8tecEHU4P35s/fsA+Qgge+l+zWfeJBbfvZHbivuOdvLr9KNedMyl47TKZfSc66fH4YlrzM77ETEuXC3uPh9yszHwMZ+anyiC63f502sHcM6aAcenx+NCiLuDx+rD3eBhZkD0sQc8R+VlkGXQctqkvX5Ms/ralkbFFOXxtcumg/crysrjtwtN7tTV1OFn4wLtsPmg7ZYwLwNSRw7/bxgdEQA/ZHFSOLojLeaUaWrZYiqOkfuYOouBr0Al0QvRb63Kq4gpch+HGS4QQTCgxc+gUMi47j7Tz9Wllw3JrjSjIpjTX1E9QNVPZ0dhOtlHHxBhW348pygbgREfm1hrSjEuKY+/xkGPUY9AP/K8SQmDU63B5NOMCBK9DuPUtkTKuOIfG1u54nVJK02p3YbO7OK08b+jOA1CSa6LV7o7jWaUmUkreq2vinCllg96TQ1Ge5zcuzZ098Tq1lEMzLiojhOCnP/1p8PXDDz8cXCvi80nsLu+QPtmNGzey88tNwRH7qlWrIhKr1Ov1VFdXM2fOHObNmzeksnJbWxu/+93vgq8tFgt//OMfhzzOcLn//vuZMWMGs2fPprq6mi+++AIYWnHZ6fYhAFMMN39JbhZtjlNjgaUy45hcnjvsfRSbTbSeAtdr74lOjrR1s7Ry5NCdB6Es3x9Dbe7SjItGghhMct/h8qcX5w1iXDweDxs3bmR7zRe4PdGtWVK0xbZv386DDz7IXXfdNWj/eBgXr9cbUb9Y5Pydbi8mgz6mzKUis5G27swfiQN81exP1p5cNvyZS3Gu8ZQwLu/W+hc+fmN6eNWKSDGbDORlGbSZi0biGExyv3b/Af6/K7/LOYvOYOnSpRw65K8ovWrVKn7yk5+wZMkSrrjiCn7/+9/z9O9/y2XfPJuNH3wIRC+539HRQXGxX7S6q6uLpUuXMm/ePGbNmsUrr/gFqe+8806++uorqquruf3227nzzjv56KOPqK6u5rHHHsPr9XL77bezYMECZs+ezVNPPQX4Z1ZLlizh+9//PrNmzeIXv/gFv/nNb4LH/vnPf95PtDMWOX+n278yPxaKcow4XN6gPlkm81VzF0a9YFxxzrD34Z+5ZL4xfq+uiVljCxlZkB3zvsrzs2jKYOOiZYsp/PNOOL4zvvscNQuWPTRkt4Ek9+/46Y/5X1d8n5/d/COeeeYZbrnlFl5++WWAoOS+Xq/n3nvvRW/K5rvX3MC0kfmse/aZqCT3nU4nx44d47333gMgOzubl156iYKCAlpaWjjzzDO5+OKLeeihh9i1axfbtm0D/Ebj4Ycf5rXXXgNgzZo1FBYWsnnzZnp6ejj77LO54IILANi0aRO7du1i0qRJWCwWLrvsMm699VZ8Ph8vvvgimzZt6nVuw5Xz9/h8uLw+SkyxjZuKAusX2rvdjMjP7PTu/Se6mFiaG1MMoTjXRKvdldELedu73Ww91MqNS6bEZX/FZmNGu161mUsKEE5y3+uTbK3ZxJVX+UWpr776aj7++OPg+4rkvoJed3KtC0QnuV9XV8cbb7zBNddcg5QSKSV33303s2fP5vzzz+fIkSMD7iOUt956i+eee47q6moWLVqE1WoNClsuXLiQSZMmAVBRUUFpaSlbt27lrbfeYu7cuZSW9k6BHa6cv9Pt//yxzlwKA+uK2jN8NN7pdPNJfQtnnTZ4CvJQFJuNeHwyo4UtvzhgxSfhnCnxqVFUZDbRlsHfL23mohDBDCOR9JXcd7j8N2lovCV0RKhI7iso4oxKUD9ayf2vfe1rtLS00NzczOuvv05zczNbtmzBaDRSUVGB0zl0yqSUkieeeIILL7ywV/vGjRv7ne/q1atZt24dx48f59prrw27v+HI+SvXLVbZlqLAKvVMj7u8U3uCHo8vrORLNBQHjHGr3U1+huqOfVLfQo5Rz9wJxUN3joCiHCN7j6urHJ5ItJlLitBXct/e42HOGQt59e/+eMkLL7zAOeecE3bb/Px87F2dMa11qaurw+v1UlpaSnt7OyNGjMBoNPL+++/T0NAQPE6ojH7f1xdeeCFPPvkkbrf/gbxv3z7s9vDKXpdeeilvvPEGmzdv7meMYPhy/l1OD9lGPcYYXDxw0i2WySNLgH/uPM6YwmzmxfjADBqXDHbzfFzfwqLJJcFFy7FSaDbSnsGDF23mkkL89Kc/5be//S0AdpeXex98mPt+djOPPPIw5eXlPPvss2G3W758OStWrOB//v4y9z30cMTHCy1zLKVk/fr16PV6fvCDH7B8+XLmz59PdXU106dPB6C0tJSzzz6bmTNnsmzZMh544AEMBgNz5sxh1apV3HrrrVgsFubNm4eUkvLy8mCMqC8mk4klS5ZQVFTUy72n0NXVxc0330xbWxsGg4EpU6awZs0a4KSc/+jRo3n//fdZvXo1N9xwA3PnnYHd5aUsb2CpnEgpCqhPZ7JP3OXx8Ul9C5fMHRuzJlhxrv962TL0eh1vd/JVs50rF0yI2z6Lckx09Xhwe30xD4ZSEsXHnuk/Z5xxhuzLnj17+rWlAl6fT+5obJNHWh1RbXeguUvuO96RoLOKL16vV86ZM0fu27cvbvtsd7jk9sOtsqPb1at9OP/nNodLTrzjNfn/PvwqXqeXcnxS3ywn3vGafHPXsZj3daC5S0684zX59y8Px+HMUo+XtzbKiXe8Jnc2tsVtn+s+OSgn3vGabO50xm2fiQCokcN45maguUx/ul1epJRRC9plG3U4PT58KV6jZ8+ePUyZMoWlS5cyderUuO23q8eDTghywxRvipb8LAM6kdlusQ/2NWPUC86KQ4C6OOBGtGXoKv2th9rIMeqZHseaSZnueh3SuAghnhFCNAkhdoW0/V8hRJ0QYocQ4iUhRFHIe3cJIeqFEHuFEBeGtF8UaKsXQtwZ0j5JCPGFEGK/EOLPQghToD0r8Lo+8H7FUMfIFOyBoLR5ED2xcOQY9UgpcbpTe21GVVUVBw4c4JFHHonrfrucfoXZeMi+63SCwhwjbd2Z6eYB+GBvM/Mnlgy6SDdSCrKNAWOcmdfry0OtzB5XGFO6dl8UpfP2DP2ORXKl1gEX9Wl7G5gppZwN7APuAhBCVAFXAjMC2/xOCKEXQuiB/waWAVXAVYG+AP8FPCalnAq0AtcF2q8DWqWUU4DHAv0GPEaUnzulcfR4yTJEH5TOzTIggKaOHtocruBPqhubeOD2+nB6vORmxe+rUGQ20d6dmam11q4e6o53ck6Y+u/DQacTFJlN2OyZ96B0ur3sOdrBvInxyRJTCGYknqozFynlh4CtT9tbUkrlrvscGBf4+7vAi1LKHinlQaAeWBj4qZdSHpBSuoAXge8Kf27tNwBlCfl64JKQfa0P/P1XYGmg/0DHyBgcgeJg0WLU6ygym+hwujlkcwR/LC32iNKR0xl7YH1FXhxcYgqFOZm7yK2moRWARZPiV6/Gvygw8x6UOxrb8fhkzBl1fcl0t1g87sRrgT8H/h6L39goNAbaAA73aV8ElAJtIYYqtP9YZRsppUcI0R7oP9gx0h6314fH6yNnGMYF/Gq+IwqyUGxJp9PDsfZuut1ezHF88KYa9h4vOiHIHuZ1C0eR2ZiRI3GAGosNk0HHrHGFcdtncYbOXL485DfEcyfEt1ZNMCMxQ9ORY3IgCiF+DniAF5SmMN3kMNqHs69w53e9EKJGCFHT3NwcrkvK0e3yu7CGu8JcCEGWQU+20f9TmOM3KN0Z7hqzuwLxljhKjxTlZOZIHGCTpZXqcUVxrVxanJuZysh7jnYwtiiHsrysoTtHQX62ASGgPQOvGcRgXIQQK4HvAD+QJ30ujUBondRxwNFB2luAIiGEoU97r30F3i/E754baF/9kFKukVLOl1LOLy8ffknSRNJXVv7jzz5DEP0K840bN/aSzFdk9416HToh6HGHX1yZl9dbCXfdunXcdNNNADz66KNUVVUxe/Zsli5dGlxMOdRniFQaP154vD6c7vjGW0CR58i8G9/h8rD7SDvzK+Lr5ik2Z6YycoPVzqSy4ZcjGIiTSSOZOYAZlnERQlwE3AFcLKUMfXpsAK4MZHpNAqYCm4DNwNRAZpgJf0B+Q8AovQ8oqoorgVdC9rUy8PcK4L1A/4GOkXaEk5UvHTGGLIM+KOcSKX2Ni4J/JqOjZxiFxObOnUtNTQ07duxgxYoV/YQ1B/oMkUrjxwtHYLYXb7dfYY6RDqcHry+z4lXbDrfh8UkWVMQv3gLKzMWdcfE9i9XBxNLhV50cjEyeHUeSivwn4DPgdCFEoxDiOuC3QD7wthBimxDi9wBSyt3AX4A9wBvAjVJKbyCmchPwJlAL/CXQF/xG6idCiHr8MZW1gfa1QGmg/SfAnYMdI8broArhZOXzS0eQY9Lz7rvvMnfuXGbNmsW1115LT49fmruioiJY+6WmpobFixdjsVj4/e9/z2OPPUZ1dTUfffQRcFJ2f+mi2bz68t+jPr8lS5ZgNvtvqjPPPJPGxsaIPkOk0vjxwuH2IohdrLIvSsC1I8NGlnuOdgAwO47xFvDHXFweX9DYZwJtDhft3e6EzFzAL5CaqTOXIYd6UsqrwjSvDdOm9L8fuD9M++vA62HaDxAm20tK6QQuj+YYsfBfm/6LOltdPHfJ9JLp3LHwjgHf7ysr/79WXE75tLkIr5tVq1bx7rvvMm3aNK655hqefPJJfvzjH4fdT0VFBTfccAN5eXncdtttAKxduzYou//Bpm2svGoFt6y+ut+2oRIwADabjYsvvrhfv7Vr17Js2bIhP0Ok0vjxpNvlJcsY/WxvKILZPN3uoLxJJlDf1EVJronSOMcQSkL0xaJdAJyqHGzxa+NNLE2McSnK4IxEbYW+ivSVlf/+VVfxyl/+SKOlnkmTJjFt2jQAVq5cyYcffhj1/hXZ/RlVVVibm/GFce8osvvKz3333devz/PPP09NTQ233377kJ8hUmn8eCGlxOHyDCt1eygyVV9sf1MXU0YMv+rkQCjGuDWDVuk3WP1u3YpEucUyuOJpZgwv4sBgM4xEEiorP/600/nDH57jW4u/NmB/g8GAz+ePnwwlg6+4qox6HVJK3F4fWbroHsLvvPMO999/Px988EEvGf+BPkOk0vjxwuXx4fXJYaduD0ZBBsruSympb+riO7NHx33fJRkoXmmx2hECxpdoMZdo0WYuKtJXVn779m2MGz+BGVWVWCwW6uvrAfjDH/7AeeedB/hdYFu2bAHgb3/7W3DbwWTo9Xq/u8gTZWB669at/OhHP2LDhg2MGBG+ZvhwpfHjhZJibTbGf5wUrEaZQTd/c1cP7d1upiZk5pJ5M70Gq4PRBdkx1wcaiMLAoudMSxoBzbioSldXFytXrgym++6treO2O39OdnY2zz77LJdffjmzZs1Cp9Nxww03AHDPPfdw6623cu655/aSql++fDkvvfRSr4C+giEQi4j2C3z77bfT1dXF5ZdfTnV1ddhYTN/PsGfPHu69917gpDS+EtBfvXo1NTU1UZ3DUDjdXoQQZBvj/1U+Kc+ROQ/L+hNdAEwdGT8BRoXiDFxxbrHaqUhQMB/83zEp/RVBMw3NLaYiZ5xxRjB92OeT7D7aTnl+NgBLly5l69at/bY599xz2bdvX7/2adOmsWPHjl79FPQ6wed7G8POXLq6unq9XrVqVdCl9c4770T1Gfpy8803c/PNNwdfP/3000PuL1p6PD5Mel1C6rYXZqBbbH9TwLgkYOZSmIFaWQ1WBxfOGJWw/YdeM2XmlyloM5cUocfjQ0JCRuDDnbmkAy6PL26VAfti0OvIzzJk1MOyvqmL/GwD5fnxzRQD//XKyzJkTHXF9m43NrsrYcF8CHG9Zsg1C0UzLilCj8cfO8hKgG9XJwQCgdc3vBLIqYqUkh6Pj6wEGRfIvFK0+5s6mToiLyEzPSCjyhQ0WBObhgy9090zDc24pAhOtw8BCXlQCiHQ60XUAf1Ux+OT+KRM2MwFAqmimRRzaepi6oj4x1sUCnOMGbPo1KKkIZclbuaiuMUyaQCjoBmXFMHl8WE06OIqvBiKQScyzi3mCkjaJNS45GTOCmqb3UVLl4upI+Mfb1EoyiDZ/QZlAWVJ4mYuhYG1VJkoXqkZlxShx+vFFMcqd33R6zJv5qLopWUl8LoVmo0Zk4pcHwjmJ2IBpUImCTFarA5GFWQnZA2VQiYmQShoxiUFkFLi8vjiKn/el0yduQgExoTOXDLnYakYl0SkISsUZVCMymK1J0ywUsFk0GE26TPmmoWiGReVuf/++5k5cyaXLj2LC887MyhXHy0DSe4r6HUCj7e/cdHr9VRXVzNnzhzmzZs3YFqxQltbG7/73e+Cry0WC3/84x+Hdc6RMJicf3tHF0aDSJgrEU4+LDNB6Xd/Uydmk54xhdkJO0Zhjon2DFFGbrDaqUhgMF8hkwYwoWjGRUUUufqPPtvEX9/+hFf/8UZQrj5aBpLcV1BmLn1vekVbbPv27Tz44IPcddddgx4nHsbF641MNXcoOf+2rq6EzvbAH3Px+iRdPZ6hO6c49QFNsURlioHfzePy+nAOUD8oXeh0umnpcjExgcF8hYKczJnthaIZFxVR5OqFwe93HT1qBGPGjAGIq+T+5MmTeX3Dy0j82VUD0dHRQXGxv4BUV1cXS5cuZd68ecyaNYtXXvGX2bnzzjv56quvqK6u5vbbb+fOO+/ko48+orq6msceewyv18vtt9/OggULmD17Nk899RTgN35Llizh+9//PrNmzeIXv/gFv/nNb4LH/vnPf87jjz8e9voMJOecJQ4WAAAgAElEQVR/9WXf5geXfiu2f8IQFGbQqvP9JxIjWBnKydTa9A5QnxSsTMLMJYPieqFoK/QDHH/gAXpq4yu5n1U5nVF33z3g+4pc/fzZM5h/1te54dqrWbJ4MU6nM66S+3V1dXxn+XJeXrIMj08SGv9WJPedTifHjh3jvffeAyA7O5uXXnqJgoICWlpaOPPMM7n44ot56KGH2LVrF9u2bQP8RuPhhx/mtddeA2DNmjUUFhayefNmenp6OPvss7ngggsA2LRpE7t27WLSpElYLBYuu+wybr31Vnw+Hy+++CKbNvWu+TaUnP//+/MGqiaNG94/J0KKQlJFhzenTA06nW6OdzgTblxCU2tHF+Yk9FiJJJnGpTDHGJT2zyS0mYuKKHL1Dzz6BGVl5Vx15ZWsW7eOvXv3xlVyv6qqiuamJqD/Kn3FLVZXV8cbb7zBNddcg5R+99ndd9/N7NmzOf/88zly5AgnTpwY8phvvfUWzz33HNXV1SxatAir1RoUtly4cCGTJk0C/AaxtLSUrVu38tZbbzF37lxKS0vDXp9wcv7KBCyRCyghVIwxvUeWysNyclmCZy4Zkv1kCS6gTLxbrCjHlJFuMW3mEmCwGUYi0ev1nHHmOSz42jksOWs+69ev71W8qy/DkdwHgrGWwdKRv/a1r9HS0kJzczOvv/46zc3NbNmyBaPRSEVFxZDHU47zxBNPcOGFF/Zq37hxI7m5vUeBq1evZt26dRw/fpxrr7027P4GkvNXPkUi17hA5rh5lIdlIhcEQkiZgnQ3Li12yvOzklL0rDCD1gaFos1cVESRq+/xeMky6IJy9dOnT4+r5H4og6Uj19XV4fV6KS0tpb29nREjRmA0Gnn//fdpaGgIe5y+ry+88EKefPJJ3G7/zbJv3z7s9vBT/ksvvZQ33niDzZs39zNGoddHIVTOPzcvD3tXV+KNSwY9LAEmJKguiUKmlIZusDoSqikWSmGOkR6PD6c7c8pDgzZzUZWuri5uuulmmqw2skxGpk+bypo1a3pJ7ns8HhYsWNBLcv+6667jgQceYNGiRcF9LV++nBUrVvDKK6/wxBNPDHjMvunIoWWOpZSsX78evV7PD37wA5YvX878+fOprq5m+vTpAJSWlnL22Wczc+ZMli1bxgMPPIDBYGDOnDmsWrWKW2+9FYvFwrx585BSUl5ezssvvxz2XEwmE0uWLKGoqKhX+YDQ63PzzTfT1taGwWBgypQprFmzBoArr/4hN11zORPHj+X9999n9erV3HDDDcyfPz+K/8DQFGSIPIfF6mBkQRZmU2Jv+aAbMQNmel+fVp6UY4WKVyaqbowqKP71gX6AZ4AmYFdIWwnwNrA/8Ls40C6Ax4F6YAcwL2SblYH++4GVIe1nADsD2zwOiOEeY7CfM844Q/Zlz549/dqSjb3HLbcfbpVtDldCj+Pz+eSOw63yWFt3Qo8TDV6vV86ZM0fu27cv6m33n+iUXzV1RtQ31v/z9P/4p/w/r+2OaR9qs+LJT+T3fv9pwo/j8/nk5Lv+IX/1Rm3Cj5Uo7D1uOfGO1+Rv39uflOO9uv2InHjHa7LuWEdSjhctQI2M4Bnb9ycSn8I64KI+bXcC70oppwLvBl4DLAOmBn6uB54EEEKUAPcAi4CFwD1CiOLANk8G+irbXTScY6Qrij5WogPTQgj0Ol3KKCPv2bOHKVOmsHTpUqZOnRr19i6PN+EuMYVM0MuyWB1JyXwSQqR96V4l+SEZwXzwB/Qh/WfHfRlyjiyl/FAIUdGn+bvA4sDf64GNwB2B9ucC1u5zIUSREGJ0oO/bUkobgBDibeAiIcRGoEBK+Vmg/TngEuCf0R5DSnksuo+eGij6WInUFVNIJX2xqqoqDhw4MKxtPV4fHp9MuEFWSHe9LHuPh+bOnqQsCAT/9UrnB6USn0qGMYZQfbH0diX2Zbh350jlYR74rRRYHwscDunXGGgbrL0xTPtwjjEspMoyFS6PD6Neh06XuFXTCpmiL+byKmrIQ/un4/H/TfdFbsFMsWQ9LNNcX0yR2p+QrJlLhhYMi/fQL9wTUg6jfTjH6N9RiOuFEDVCiJrm5uZ+72dnZ2O1WlU1MIkudhVKKs1cYiFSV6KUEqvVSnZ2bFpaftn99B1VJt/Nk97GpcFqpzTXREG2MSnHK8xQ4zLc1JETiisq4PZqCrQ3Qq+FzOOAo4H2xX3aNwbax4XpP5xj9ENKuQZYAzB//vx+T9Vx48bR2NhIOMOTLI61dZNt0tPTkvj62a0OF063D68tccKFyaDD6aaj24OhI3tInazs7GzGjYttFX+6x1ySPnPJMfJVc/quOLdY7VSUJedaAeSZDOhE+qe792W4xmUD/uyvhwK/Xwlpv0kI8SL+4H17wDi8CTwQEsS/ALhLSmkTQnQKIc4EvgCuAZ4YzjGG8yGMRmNwxbgatHe7WfbLt7hr2XR+dMZpCT/eg6/X8uynFvb+50UJFS9MND/9y3Y+/aqVz+5ampTjKTEXKWVaXreGFkfSFgSCPx05neMHDVYHXzutdOiOcUKnE2kfpwrHkN82IcSf8M86yoQQjfizvh4C/iKEuA44BFwe6P468C38acIO4IcAASPyn8DmQL/7lOA+8K/4M9Jy8Afy/xloj+oY6cihoLsiOaOkIrMJl8dHt9ub8PUOiaQhCXU2Qik0G3F5/Eq/iSwclSgOWu1JWxAI/rVBnT0evD6JPgmxxHjS7fJyrN2ZtFmeQronjYQjkmyxqwZ4q9+wMZDBdeMA+3kG/5qZvu01wMww7dZoj5FuHEySJIdCSa7ft9vqcKe1cbFY7ZxfOTJpx1NSRdu6XeSY0k+MscFq59ypyVkQCP6Yi5R+sUxlUWW6cMiW3PiUQqE58/TFNPkXFUlGje5QlBu91Z6+LotgnY0kjiyL0lh23+HycKKjh0lJjCEUprGqQbLjUwqFOUba09iVGA7NuKhIMmp0h1KSGzAuafwlPimFnryRZTrri6kxEk9nY9ygknFJ9wy7cGjGRUWSHTsoDtz0tjSeuTQkOU4Foami6Xfdkr0gENJ73YbF6qDYbAz+z5NFkTnzYi6acVERS5JqdCtkQm2SZNbZUEjn62ZJ8hoXCFlxnoYPS/+AL7mzFjipauDLgHVoCppxUQkldpDMfPrCNHbvKDRYk1dnQ6EojWMIDVY7ZXkm8pO0IBCgMI21siwtyZPaD6VQSYLo8ST92IlCMy4qoUbswKjXkZdlSMubXsGSxDobCmaTHqNepOVI3NLiSPpIPBjQT7PYntPt5Wh7t2ozFyCtZYb6ohkXlVAjdgBKPn163fShNCTZlQh+pd/CHFNazvgsSY7rgb86qNmkT7vr1djqQMrkLQ0IRXG9pvPAry+acVEJNWIH4Dcu6VolUEmrTaYrUaHIbEy7gL7Trc6CQEhPZWRLi+JNUOf7BemdydkXzbiohKXFzogkxw4gvXWy1FrgBqRljRLleqlhjNNxxblaa1wAis3pv0ygL5pxUYmGJBVv6ks63vQKao8s0824nExDVsPNk4YzF6udgmxDcBaRTMrzswBo7uxJ+rEThWZcVEINXzik502voIwsk1VnI5TCnPST5wi6XpOkABGKf8V5el2vBquDirJcVcRJC7INmAw6zbhoxIa9x0NTpzqxg4LATa92kbThkOw6G6H4Zy7p5bJQa0Eg+PXY0tEYq5EpBv6kkfK8LM24aMTGyTRkFdw7OSZcXr/Cb7rhT6tN/qwF/DEXu8sbLFSWDjQkuS5JKIXm9MpKdHl8HGntZpJK3y/wu8aaNOOiEQsNKmWKQYjuUxrd+ApqpCErpKOkiX9BoErGJceI0+3D6faqcvxoaWx14JPJXxoQyujCbI62d6t2/HijGRcVUEOSQyFdV+n7F7g5Vbv5C4PrENLDKJ9cEKjSTC/NjHHQm6DCGheF8SVmGlu7M0YCRjMuKmBpSb4kh0K6Spkctql786ebMnJwQaBKxrgsL72ynw4q5S9UnLmML87B5fHR3JUe12woNOOiAskWrAylIM0ekgoWlRQNFNJNRl5J21Zr5hI0LmnyoGyw2snLMlCaq15xswmB7/aBZrtq5xBPNOOiAg3W5Os9KSgPyXRbpa/EqSapdd2C1SjT47opacjJLBIWyog0W7dhsfqTRdRIQ1aYOaYAgO2NbaqdQzzRjEuS6XZ5Od7hVGVhG4TKoadH7EDBYrVTpFJaLZys6ZIu6cgWq53CHKNqZYbTzS2mZmadQmleFhNKzGw7pBkXhBD/LoTYLYTYJYT4kxAiWwgxSQjxhRBivxDiz0IIU6BvVuB1feD9ipD93BVo3yuEuDCk/aJAW70Q4s6Q9rDHSAcabAHfrkpf5LwsA3qdSBv3joKasz2A/CwDOpE+saoGFdSjQ8kx6cnPNtDU4VTtHCLF7fXR2Nqt6vVSqB5fxJeHWtU+jbgwbOMihBgL3ALMl1LOBPTAlcB/AY9JKacCrcB1gU2uA1qllFOAxwL9EEJUBbabAVwE/E4IoRdC6IH/BpYBVcBVgb4McoyUR/GFq+XeEUKkZUlVf5xKvZtfpxN+6Zw0McpqLghUGFuUQ2Nr6qfWHmntxuOTql8vgHkTimjq7GHyXf/glW1H1D6dmIjVLWYAcoQQBsAMHAO+Afw18P564JLA398NvCbw/lLhd3B+F3hRStkjpTwI1AMLAz/1UsoDUkoX8CLw3cA2Ax0j5WlQUcJEId30xZQFbmrf/Oly3ZTrpbabR0mtTXXUFKzsy+Xzx/Mf367EJ+GtPSfUPp2YGLZxkVIeAR4GDuE3Ku3AFqBNSqmUU2sExgb+HgscDmzrCfQvDW3vs81A7aWDHCPlsVgdlOSagrEPNSg0p5fs/uHAAje13RaFZlNaxFxS5XqNLzZzuNWR8lJDahTuG4jcLAOrz53Md2aP5h87jvGt33wUTMNPN2JxixXjn3VMAsYAufhdWH1Rvlnh0jBkHNvDneP1QogaIURNc3NzuC5Jx9KijmBlKOnk3oFQRQN1R5bp4k5Mlet12ohcHC5vys9eLFY7ZpM+qEycCvzr4tO4auEE9hzrYO3HB9U+nWERi1vsfOCglLJZSukG/g6cBRQF3GQA44Cjgb8bgfEAgfcLAVtoe59tBmpvGeQYvZBSrpFSzpdSzi8vL4/ho8aPBqtdtXiLQro8JBVOSu2ra5RLck3Y7Kk/c0mV6zVzTCEAu4+2q3oeQ6Eki6iZhtyXGWMKefCyWSydPoKP9qfGwDhaYjEuh4AzhRDmQBxkKbAHeB9YEeizEngl8PeGwGsC778n/fPlDcCVgWyyScBUYBOwGZgayAwz4Q/6bwhsM9AxUhq1JUwUitLEvaPQYLWTn2WgRMUFbuA3Lq1pYFwarHbys9W/XqePyifHqGfj3tR+OKqdLDIYs8cVcaDFjr3HM3TnFCOWmMsX+IPqXwI7A/taA9wB/EQIUY8/PrI2sMlaoDTQ/hPgzsB+dgN/wW+Y3gBulFJ6AzGVm4A3gVrgL4G+DHKMlEZtCROFghwjHU4P3jTRMLJYHUwsU3eBG/iNi93lTXkxxoOBQnRqX69so56LZo7ijd3HVT2PwfB4fRy2qZvmPhhVYwqQEvae6FT7VKImphq7Usp7gHv6NB/An+nVt68TuHyA/dwP3B+m/XXg9TDtYY+R6qSCfhGc1MnqdLpVW2QXDQ1WOzPGFqp9GsGZQKvDxejCHJXPZmAarHZmpcD1Apg+Kp+Xth6hvdutahLLQBxrd+L2ypSduSjx2cM2B/MmFKt8NtGhrdBPIkpWitoxl3RSRk6lBW5KnXNrV+q6xk5er9QYiU8oOflwTEWCacgqp20PxLhi/yAm1ZMiwqEZlySitoSJQjrJoR9t8y9wS4WH5dgi/41ee6xD5TMZmCOt3Xh9MmUeluMDxuWG57dw0a8/5H9qDg+xRXKxqFi4LxLMJr+YZmNrahrnwdCMSxJRW8JE4aS+WOobl+DNnwIPy5ljC5hYaub1ncfUPpUBORhcEKj+TA/8Qf2rFk5gxpgCTnQ4eXVHal27hhY72UZdUGgzFRlZkJ02Gm2hxBRz0YiOgy125leo7zctSiMRRjWrdvZFCMGMMQXUHUvd4GpDisT1FIx6HQ9eNguAf3thS8pdO4vVzsSSXHS61ElD7kuR2UhrGriw+6LNXJJEj8dfGTAVpt+FAfn4dFilf7AlsMAtLzVGluOLU7taoMXqINekpywv9RI1UvHaKVL7qUxxmi0dUNCMS5I4bOv2VwZUOQ0Z0iugn2oL3MaVmHF5fVz+1Gfc9fedKSdtokjHp8r1CmVyeS4ur4/a46kRs/L6JIesjpRwuQ5GkTm9FDUUNOOSJBIhyfHJkU94ctuTfNX2Vdj3dzbv5MltTwZ/lH4mgw6zSZ8WAf14LnB7uf5lNh7eGNM+Fk8r57xp5XQ63fxp06FgBmCq0BBY4xIPmhxNPL3zaQ62x0d+5IKqURj1gg3bwwpqJJ3jHU5cXl/crteBtgOs372eLldXXPanUGT2C6am2kBmKLSYS5KId1bKlye+5IZ3bgDgf/b9D/+47B/kGE6uvfjkyCfc9O5NeOTJlb372/bz6OJHgfRQ+PX6JIdtDi6oGhXzvvZY9/CLT34BwIvffpEZZTOGtZ/xJWbWX7uQgy12ljy8kU+/sqbMyNfj9XHI5uCimbFfL4/Pw3VvXoelw8K+1n386uu/inmfxbkmqkYXsONwasjBKPGpeAxeerw9rH5rNc3dzRxsP8i9Z90b8z4Vis0mvD5JZ4+HguzUWys0EJpxSRKWFjsF2QaK45SG/OLeFynMKuSXZ/2SH7//Y+7//H4mFEwAwOlx8nzt85xWdBprL1xLgamA2z64jd3W3cHt/eKVqe3HPdrWHbcFbv848I/g3x80fjBs46IwscRMlkEXXCeRChxtc8YlbbvV2colr1yCzWkDoNZaG4/TA2DG2EJe3X4UKaXqrjtlwBePwn3bmrbR3N1MnjGPjYc3xvXzKQud2+xuzbho9McSR1+4T/r44tgXfH3s11k8bjFVpVW88lVvebWZpTN5YukTFGb5V2pXllbyVsNbtPe0U5hVSHl+Fs0pvBgQTi46jYcrcfPxzSwYtYBWZyu7WnbFvD+dTjCuOIdDKeQWs8Qps67mRA02p41rqq4hz5TH77b9jk5XJ/mm/JjPcVJpLp1ODx1Oj+or9i1WOyaDjtEF2THvq+ZEDTqh47pZ1/GbL3/DCccJRuXGPoOEk4oarQ6XqnWgokUzLkmiwepgzviiuOzL0mHB5rSxYNQC9Do9f/7On/H6vPjwBfsYdb1v3MqSSgD22vaycPRCyvOz+Kopvr7heHNy9XRsN1S3p5u9rXtZPWs1J+wn+OjIR3EZWU4o8dcrSRWUuN6kGEfitdZa9ELPLfNuYfPxzQDU2epYMGpBzOc4osCf9dfU4VTfuLTYmVBijksa8u6W3ZxWdBoLRy0Mvo6XcSnOTZ91aaFoAf0k4PL4aGyNX01zxU0R6trR6/QYdcbgT1+ml0z3b2vzbzsiP5vmrp6UDhI2WO1kGXSMzI9tZLm/dT8+6aOqpIoZZTOwOW1sbdoa8/lVji5g7/FOmjpTo078wRYHOcbY65LU2eqYVDiJLH0WVaX+yuK7W3YPsVVkKOfWlAKLAuOZ/FBrq6WypJLTS07HIAzsssY+O1YIusVS3I3dF824JIHGYGXA+HyR62x1mHQmJhVOinib0pxSRppHBo1LeX4Wbq9M6RRHS+Dmj3VkWWerA2B66XTmjZgHwK82xx6g/s7sMXh8kk/qW2LeVzxosPoL0cU6I9tr2xuc6ZZklzA6dzR7rHvicYqMDLig1DbIPp+kwRafTMRmRzMt3S1UlVaRpc9iWsk0ao7XxOEs/QTdYmlQ7iEUzbgkgWAZ1Titcamz1TGleErYGcpgVJZWBmc9I1JoBDkQysMyVupsdeSb8hmTO4bTS07nmxO/yYH2A3h9sUnnK/7vEx2pcQ39aduxDWCs3VaaupuCM12AqtKqXskgsaDMXNSWM2nq7MHp9sUlmK8YXsUgnz/hfLY1b+OE/UTM+4b0kmsKRTMuScASxzUuUkrqbHW9bv5IqSyp5GD7QRxuR4hxSQ2XTl98Pul3W8Th5leulzKiXzx+Md2ebho6GmLab16WAbNJT1MKGBd/2nZ3zNcrOMvrY1wOdR6i0xW7dEt+lgGDTqguZ3IwjmnIe2x7EIjgNVs0ehFA3FxjBr2O/GxDSnsZwqEZlyTQYHWQl+VXN42VE44TtPW0Ddu4SCT7WvelzAhyII53OOnx+GKeuXh8Hva17ut1vZQR5h5b7K6eEflZKWGgj7Z1BxYExna9FONyesnpwTbleinvxYIQIlAJVd0HZUNQ4DP2wUuttZaKwgrMRv+1n1Y8Db3QxzWFOx0lYDTjkgQOttipiFMlReUGV274aKgs9W9Ta6tlRND3nZrGxRKnm7+ho4Eeb0+v66UEq+Nx84/Iz06JaxivtO06Wx1j88YGU9jh5PcmHsYFFDkTdR+UFqsDo14wpij2om9KMF8h25DNpMJJwfhmPFBW6acTmnFJAv7YQfyyUgSCacXTot52pHkkxVnF1FpryTXpyTGmhksnHCcflrGNxJUbPHQkbtAZmFY8LS43f2meCVsKBFrjlbZdZ6vj9OLTe7WV5ZQxImcE25u3x7RvhWKzkVaVjUuD1c74EjP6GJNFbE4bx+3Hg1l1ClWlVXGdufgXPWvGRSOEeFdS3Gvby8SCicEpeDQIIfxBfVstQggmlpr5qjk117pYrHZMel3M5YTrrOEz6ypLKqmz1sWcip0qooINVn9dkljSth1uBw0dDUwv7e9ynVYyjTctb7KtaVsspwn4VbnVvmaWOKUh11nDexKml0ynudufRRYPisymtNACDEUzLglGqaQYr5nLcIP5CpUlldS31ePyupg5tpBdR9pTcq1LQ4uD8SU5MY0snR4n7x56l2nF0/ovKi2tpNPdSWNXY0zn6b/pXapfw4Mtjpjrkuxr3YdEhnW5/tucfwMILqqMhWKVDbKUMm6ZiErcrq9BVq5hvGYvRWkg19SXmIyLEKJICPFXIUSdEKJWCPE1IUSJEOJtIcT+wO/iQF8hhHhcCFEvhNghhJgXsp+Vgf77hRArQ9rPEELsDGzzuAgELQY6RiqiZKXEumoaoL2nnSNdR3q5eKKlsrQSj89DfVs9cycUYbW7+PQra8znFm/ikVa7+fhmGrsaWTljZb/3gvGnGG/+ohwjbq/E4YotrTlW4vGwVNyE4QYvs8pnMT5/fFxcicW5JlXdYs2dPThc3rjMXPZY9zA+fzwFpoJe7X0XLcdKkdlIe7c7pWrhDEWsM5ffAG9IKacDc4Ba4E7gXSnlVODdwGuAZcDUwM/1wJPgNxTAPcAiYCFwT4ixeDLQV9nuokD7QMdIOeIVOwD/yBKGF8xXCB1RfWfWGHKMem59cSsuj2+ILZOHf2QZe0loJQB99tiz+703tWgqBmGI+eYPVvVU0WXhXxAYe9r2XtteirKKGGkeGfb9qtKquCymLMwx0uPx4XSrY5Atcbwna621Ye/HPFMeEwsmxm3mUphjxCehs8czdOcUYdjGRQhRAHwdWAsgpXRJKduA7wLrA93WA5cE/v4u8Jz08zlQJIQYDVwIvC2ltEkpW4G3gYsC7xVIKT+Tfp/Dc332Fe4YKYfFGr9KisoXNZaZy7j8ceQZ86i11VJoNnLP8ipaulzcs2E3n36VGivNmzt76HZ7Yw5Of3L0EybkTwgruGjSm5hSPCXmm1+p6qnm6unjHU5cntjrktTaanutB+pLZUklR7qO0N4Tm2R+cUDORK3ZiyVOGmztPe00djUGZ8F9qSypjOPMxX/N2lMgvhcpscxcJgPNwLNCiK1CiKeFELnASCnlMYDA7xGB/mOBwyHbNwbaBmtvDNPOIMfohRDieiFEjRCiprm5efifNAbiWUmxzlZHeU45ZTllw96HTuiYXjI9+KX/9uzRjCrI5k+bDnHH33bEfI7xwBKHtNrDHYfZcmILl0+7fMA+ynWIJV6izFzUDLZa4rAg0O1zs791/6DxvNBUdoBPj37Kpa9cyqWvXBrVanSl7IRacZcGqx2DTjA2xjTkvba9AFSVVIV9v7I0PsYYTkrAtHWnT9wlFuNiAOYBT0op5wJ2BndPhXu6ymG0R4yUco2Ucr6Ucn55eXk0m8YNS4udSfGSfWmNLZivML1kOvts+/D4PORnG/n87qXcu7yKw7ZuXtl2JA5nGhvBkWUMxmW3zS9XsnD0wgH7VJZUYnPaaHI0Dfs4xUFRQRWNSxzqkhxsP4jb5x7cuATcPzXHa/jyxJfc+t6tNHc3U99Wz6sHXo34WIXmkxLyamCxOhhXnINBH1tUQDGyg81cQvvFQpHKBnk4xHJ1G4FGKeUXgdd/xW9sTgRcWgR+N4X0Hx+y/Tjg6BDt48K0M8gxUgqP18fh1thjB+CvdHeg7UBcjEtVaRVOrxNLuyXYdnH1WEYWZPHQP+vweNWNvygjyzFFw0+r3Wvbi0EYmFI0ZcA+ytoE5eZft2sd3/jLN/iX1/8Fjy8y3/bJmIt6I8qGONQliWRxbnF2MWPzxvLUjqe47s3rGJU7ig2XbKCypJLPjn4W8bHUNsiWlvisO9tt9cvqF2eHzyeKZ8ZYKsT2omXYxkVKeRw4LIRQAgBLgT3ABkBJz1kJKFWsNgDXBLLGzgTaAy6tN4ELhBDFgUD+BcCbgfc6hRBnBrLErumzr3DHSCmOtTvjVkmxvrUer/TGxbiEG1GV5Jq4+1uVHGt3Unssdg2pWLC0OBhfYo5pZFlrq2Vy0WRM+oEld6YVT8OgM/DxkY9Zv3s9j2x5BIfHwfbm7Xx+7POIjhMUFVR15mJnYox1SWqttWTrs5lYMHHQfg+f9zCrZqziyulX8vQFT1OSXcKMshlRuRfVdCUqySLxuCdrrbUDuqZnd+QAACAASURBVMQAirKLGJ07updxOdB2gM3HN+P2RffZldheexqlI8daLOxm4AUhhAk4APwQv8H6ixDiOuAQoDi9Xwe+BdQDjkBfpJQ2IcR/AkoC/X1SSlvg738F1gE5wD8DPwAPDXCMlCKegpXBKXgMmWIKFYUVZOuzqbXVsvy05cH2ueP9I7BdR9uZNa5woM0TjiWKtFq7287anWs5aj+KXujRCR06oWN703a+MeEbg25rNpr5+tiv8+e9fwZg6YSlPHjug5z9p7P54tgXnDP2nCGPn23Uk23UqboGwdIS2+z4qe1P8Xzt88wum41epx+078yymcwsm9mrrbKkkr/u+ytH7UcZmzd2gC1PUpSj3szFanfR1eOJObPO7rbT0NHAtyd/e9B+lSWV7GjZQaerk//e9t+8UPsCALfNvy1sivxApMIgJlpiMi5Sym3A/DBvLQ3TVwI3DrCfZ4BnwrTXADPDtFvDHSPVsMRxjUuttZZ8Yz7j8scN3XkIgvInfabr40tyyM8yUHusI+ZjDBdlZLmgomTIvna3nRUbVtDY1chI80j0Qo9XepFSYjaYhzQuAHctuotzx51LjiGHb078Jia9Key1GYwiFVecK3VJzp0aWZLHH/b8gT/V/Ykebw8GYUCv03Os6xinF5/OdbOuG9Y5BN2L1tqIjEu2UYfJoFPFlRgvwco6Wx0S2U/2pS/njT+P9w6/x9df/Doe6WHphKVsa9rGPw78IyrjYjLoyDXp08otppU5TiAWq4Nsoy4obx8Le6x7qCytjEvWGfiDkP848A980odO+N1PQviF/I61q6fyq4wsI5m5vFz/Mo1djTx07kNDjiAHYlTuKFZMW9GrbXrJdN5ueDviUshqigpGU5fkuP04D9c8zMyymUwpmoLH58ErvVSXV3PLvFuGXZZ3avFUAP5947+z7eptQ85+hBAU5RhVSau1tMRJsy4w+BjKuFxYcSGfH/scl9fFimkrOGfsOTyx9QnW7lyL0+Mk2xB5nCwV1KSjQTMuCaQhsMo8VoPg9rnZ17qPq6ZfFacz80/X/7z3zxzpPML4gpP5FCMKslRV+Y1mZPneofeYUjRl2IZlIKpKq/jb/r9F7uYxq/OghFD16KEfltubt+OTPu5edDczSmcM2T9SsvRZfHPiN3m74W0Oth9kSvHASRQKammyNVjt6ASMK47NuOyx7oloWUCuMZdffb131dOqkiq80sv+1v3MKp8V8TELc4y0nyKpyBpDYLE64rIK+EDbAVw+15CjpGhQtJD61jQZkZ9Nc4d6M5dIR5Zen5edLTtZOGrgVOPhEm2WT1GOSbVssZNrXIY2xnW2OgzCwNSiqXE/jxur/R7vSGvkqHXNDlodjC3OwWSIPQ15uPdj3/VCkZIqIqmRohmXBOH1SQ7FqZJisIzqAPn0wyEof9LnATqiIIvmrh7VhBgjHVke6jxEt6c7LtlzfZlaPNVf7CnCm1/Nmz6auiSRZNANl4qCCnIMOexuiawcckGOkfbu5EuZNAxDs87r8+JwO+jx9uDxeXih9gXq2+qHfT+Ozh1NYVZh1FI66VbTRXOLJYhj7UplwOi+yEe7jvJIzSPY3XZ0Qode6LF0WDAbzEOmiUaDIn/StwBUWV4Wbq+kvdsdlJxIJpYIR5bK6uh4GlwFpdjTjubIFAsUX3ikMZp4Ek1dkjprXVidtXig1+mpLKmM+IFZZDay52jsK9ejQUrJwRY7l1QP7epU+Ou+v/LAFw/0Sx0uzynne9O+N6zzEEIMSxomFUoVRINmXBLEcAUr//cn/5uaEzVUlfr9sj7pw6g3csXpVwQD7/Fiesl0Pmz8sNdDsTi4elod4xLpyLLWVotBZ+C0wtMSch7njTuPtbvWsrN555B+8SKzEZfXR7fbi9mU3Fsq0rokzY5mrE5rXFLZB0KJVXl93iGD+kU5yR+FtzncdDojSxYB/8LlR2oeoaq0iqUTluKVXrw+/z35rcnfotw8fNWPypJKnq99HrfP3a8cxED4lZFdqgxihoNmXBLEcMr0WtotfHH8C35yxk/44cwfJurUglSWVPJy/cuccJwIZgqdXD3tAuJTgyYaLFYHF88ZM2S/OlsdU4qmYNRHdmNGyw9n/pBndj3Dx0c+Htq4hKxBSKZxUeqSfG1y6ZB9h5IqiQdVpVU8X/t8REH9IrMRh8tLj8dLlmFwQxQvor0nPz/6OV3uLv5tzr9x1tiz4noulaWVuH1uDrQdiFiINrS8Q25W6j+6tZhLgrC02Mky6BgVhSTHh40fAnBBxQWJOq1ehK5PUFBTw6jN4aK92z3kyFJKGXPRtKEozCpkesl0tjRtGbJvkUpaWcG6JBFo1wUVtYuHr6g9FMr3KZKgfqGi8pvE2Uu0paC/bPoSg87A/FHhlvLFhjKDjCbukm4SMJpxSRBKplg0khzbmrcxLm9cROmv8WBa8TQEolfcRU059IMRZj41dzdjc9oSalwAv6yJdWhZk6A0R5Jv+mjUo+tsdUzIn0CeKS9h5xNNUF9ZcZ7MFG5LiwMRRRpyrbWWqUVTE5IAMaFgAmaDOaq4S2FOqFch9dGMS4LwVwaMzq1Uax1+euNwMBvNVBRW9BppnjQuaqxB8D8shxpZKsYw0calsqSSDlcHR+1HB+0X1MpK8jWLZo2LUqslkUQT1Fdcick0yA1WO2MKc8g2Du2GU2bHiXIjBktfRKEEoYZBjgXNuCQAny96cbxOV+eghYcSRWVJJRsPb2RXyy4A8rMN6IQ6oyOL1R7RyFIxLol08UDk610KVXhQgt/1Gkldkg5XB0e6jiTlu1VVWsXe1r14fYNXmVTD/WqxOiJ2iZ1wnKC1pzWhBjnSa6WgucU0ON7hpMfji2qNS7JG431RpE/eOPgGADqdoMisTo3zBqsjopFlMlw8EPl6F7Vu+gZrZOrRStp2Mr5bVaVVdHu6Odh+cNB+QfHKJM9cIvUmKAOKRGbXVZZW0u3ppqGjIaL+6VbTRTMuCWA4mWLKlznZxmXBqAXMLpvNLuuuYFuR2aiKWyxSNeRaa21MpZ4jJduQzeSiyUPOXHKMekx6XdJv+miuFyTPuIC/1slgFAYflMkZxLQ73LQ63BF7E2ptteiEjmnF0xJ2Tsr/IxpVA0ifapSacUkAw1njUmerY0TOiJhKGA+XmWUz2WPdE5yeF+UYVXGLKSWhByPoPkzgiDKUSBa7CSEoNCdX9+lkXZLIgvnJ+m4pQf2h4i75WX73a7JcidGWv6i11lJRUIHZGJ8qsuGYXDiZLH0Wdda6oTtzUk1ai7mcwlisdkx6HaMLI6/RXWurDep9JZuZZTPp9nRzoP0A4A/qt9qT+wVu73Zjs7uGHFkm08UDfuPS0t1Cs6N50H6FOcmVgGnpCtQliTSYn6TvVqRBfZ1OUJDEaxYsnR2hq7rWVpvwGFWw9EWEGWOKmrTmFjuFsbTYGV+SE5EkB4DT4+Rg+8Gku8QUZpT5FXKVoL5fziS5M5dDEabVJjs2FanIYFGOMemZT8CQUvtqfLciDuon8Zop3oQJJUMbY5vTxgnHiaTMjitLKiNKd1fw64tpbrFTFkuLg0llkQeb97fuxyu9g5ZMTSQVBRXkGfP4+/6/I6WkWIWYS6QL3GpttZRml8YkvRENykN5qLhLssUrlTUuQ7nFlO9WstyIEHlQv9BsSlpA32K1M7owO6I0ZMVNlRTjUlpJp9vv6o0ENQvTRYtmXOKMzyexWO1MijDlEU6OitVyi+mEjqnFU9nWvI1tzdsozjXR7fbidEeWIhkPgiPxksEflntte5N6nXKNuUwsmDjkzKUwx5T0mYs+gjTk4HcryTMXGDqo7y8YlpxReEMU5S+UAHsykkaCM+MI17v4Y3uacTklOTaMNORaWy0FpgLG5P7/7b15eFvVtbD/bk2eYsuW7TiJ48QZHA9xRkJCGEoIZW5JKUOhLdCR2xZ623K/tkB7oV/5cW9bKLfl6+1EC4V7KS1tGdICoUAgkACBhJBJnucxtiXPsub1++NIshXLQxJbtoLe5/FjaZ8tnaWjrbP2XmvttSbOqTVd3HvOvQAc7jw8Y3sQ5qUlkmQae2bp9rmp6amhKCO6SjhouhgPbeUSPXNFvc1BbvrE2aPL7eWkmlKjlvUBRuzUn0i5RDGFfH3X5FPtl9vLyZ2TiznBPM1SjSh9MdnyDh8mn4tSSq+UOqCU+kfg+RKl1F6lVJVS6s9KKVOgPSHwvDpwPH/Ee9wZaK9QSl0yov3SQFu1UuqOEe0RzzEbCBZvmqzjELRleJGlaEYznS5OW8zc5LmU2ctmJAVMfdfEYbXVPdV4xRv1FV5xZjGtg630usZOEW9OMjLo9uHx+aMiU33X4KQmMMEcbNEcW3qdntVZq9l3bN+4/aJ1o+xzerANuic94YtmpgyT3sSy9GWTL0z3IfO5fAMYeWV+DPyXiBQA3cAXA+1fBLpFZDnwX4F+KKVKgOuBlcClwC8DCksP/DdwGVAC3BDoO945Zpy6E1QuwRLG0bSJj0Vwhj4TiRgnkzo+6MyP9rUK+V3GmV2GUsBEYSYuopleJ4oU8/q9Mza2zlpwFlXdVdiGbGP2MScZ6XN68PuntzBdY8g/NbFZrN/dT2N/Y1TNiMWZWrj7ZJz66ckmnB5/VE3WJ8spKRel1ELgCuB3gecK2Ar8NdDlMeATgcfbAs8JHL8w0H8b8CcRcYlIHVANbAz8VYtIrYi4gT8B2yY4x4xT1zVIolFHTurksiHX9dbh9rtnzN8ykuLMYur66khO0Gbf0Vp+D7i8dA24WDyBn8pqszLHOIe81LyoyBVkMmlgzEnRMyV2h+qSjK+M63vrcflcMxKFuCZ7DTC+QjYnmxCBfuf0VqQ8kT0uoSJ0UVTIxZZi7E47HY6OCfvOVKqhk+FUVy4/A74DBG0BmUCPiARHSzMQNPbmAk0AgeO9gf6h9uNeM1b7eOcIQyl1i1Jqn1JqX2fn+PsUpoqgbXey2ZBnajYeiWJLMX7xY/do6SiitXJpmGRGgyNdRyjJLJnyomkTkZGYwfyU+eMql/RQCvnpv2bD2aMnjqyDmRlbQYV2fKXTkYTq4EzzNTuRTc3RqHtzPKHSF5Pwu8RSCpiT/pUqpT4GdIjIyIIXke6oMsGxqWof3SjyWxHZICIbsrOjE7paZxs8IX9Lma2MRH0i+Wn50yfUJAkO8hZHFRC92dFkfvxun5uK7orQnpxoU2wpZv+x/Tg8jojHo7lyCfr1JvIhlNvLSdAnkG/On3aZjifVlEpeat64mymjdaOs7xpkbmrCpAq5ldvLyU7KjmqmjGDpi8n4XdJjKO3+qUwBzwGuVErVo5mstqKtZNKVUsFvcSEQzFfeDOQBBI6bAfvI9uNeM1Z71zjnmFE8Pj9NdscJR4qtsKyYsCxsNMhJziE9IZ3q3goSophmYjJmi6ruKrx+L6WZpVGR6Xg+VfQpOoY6+OorX8XpdY46Hs0U8vWBMOS8CbJHl9nLWJGxAoNuZqoWThRlF62En/WTLJ0Nmuk12pnJk43JLDEvmVSOsVjKjHzSykVE7hSRhSKSj+aQ3ykinwFeA64JdLsZeC7weHvgOYHjO0XzYG0Hrg9Eky0BCoB3gfeAgkBkmClwju2B14x1jhml6tgAHp9QPD9tUv394qfCXjErTGKgpZcY6dSP1tK7octB1pwE5oxTujUYebQ6e3VUZDqesxeczZXLruT9jvd5s+XNUcejaa6o7RpkYcb4YcjRqNY5EcWZxTQPNNPn7ot4PFrFryaban8mM2UUZ04c7g6xVdNlOozX3wVuV0pVo/lHfh9o/z2QGWi/HbgDQESOAk8BVmAHcKuI+AI+lduAl9Ci0Z4K9B3vHDPK0VYtVHXlgskpl5b+FgY8AzN6Azie4sxiqnqqSEtSUU0qOJH/4OWGlynIKGBeyryoyBSJuzffjUEZeLXx1VHHUhONKBWdGeVk9mw09TfR7+6fWeUSmDQFneTHE7xR9k3jNRt0eensd026WqdPfKzMjL7ptdhSzDHHsXGj62DkymX2m8WmZL0sIq8Drwce16JFeh3fxwlcO8br7wPui9D+AvBChPaI55hpjrb2kWzST34JHlgGR3sZPh7FmcV4/V6SkrvoGZq+jLAjabA5OGf5sI3b5/fxyJFHqO6pxqAzMOAe4GDnQb5z5neiIs9YJOgTyEvL4/na5/nK6q+E+TL0OkVa4vTvOBcR6rsGOTPfMm6/t1rfAmDjvJn7mYRSytusnDnvzFHHo+GnaphkmhwYzigwU8oFNAV3Tu45Y/abk2BAr1Mx4dCfGWPsaYq1tY+ieamTTlhptVkx6AwUpBdMs2STJzjIVWILPd3zp/18Q24f7X3OsJXLU5VP8dCBh8hOysagM+Dz+7ho8UVcV3jdtMszEd/e8G2+9urXONh5cJSj3Jw0/TvOO/tdDLp9o4JG9h/bz+6W3eiUDr3S81L9S+Sn5bM4bfG0yjMemUmZ5CTnjBkFZTLoSDHpp/WahdIKTSJS7GjXUbKTsslJyZk2ecYiuBWhzF42rnIJZUaOAZ9LXLlMEX6/YG3r46p1k0+zUWYroyC9AJN+1iQYIC81jxRjCl5pondozbSfr9EeiBQL3CxFhD+W/ZE12Wv4n8v+Z0azFkTi7AVnk2RIosxexja2hR1Lj0Lep7oIkWKNfY18+Z9fxuP3oFAIgkJx7zn3zvj1K7YU83zt83x51ZdZlr5s1HEtA/f0XbM62+Qi6wCO2I7MWDRimimNhXMWTs7vkmz80PpcPpQ02h0MuLyT9reISFRqRpwoOqWjyFLEII1R8bmEMhoEzBb1ffXU99XzsaUfm/EbYyT0Oj2FGYURQ2yjUdMlVJdkhJnnyfInAXj12lc5dPMhDt10iAM3HmDb8m0R3yOaXL3iagCeq4kcc2NOmt4ia5MJFgFoH2ynrreOdXPXTZssExHcqT8R2spl9vtc4splijjaqkXErFwwuWR3bYNt9Lh6ZizN/ngUW4rp9dXjcHtweac3zUTQbLEoYLb4oOMDADbN3zSt5z0VSjJLKLeX4/WH7yxPT57+zMh1XQ6MesWCdC0DhIjwetPrnL3gbOYmzwU008lsCG0H2JK3hTNyzmBv296Ix6dbIU8mWATghTrNtbslb8u0yTIRJZklNPU3jRldF2S6V3tTRVy5TBFHW3sx6BQr5k2ujktw+TvbVi4QcOqLG52pc9pvlvU2B5YUU8i5W24vJ9mQPKO+gonYOG8jQ94hdjXvCmuPRnnoms4BFmemYNBrP91jjmM0DzSzecHmaT3vqbB+7noq7BWR9wdNsylxrNLZT1U8xRVPX8EVT1/Blc9eyc/2/4xN8zex1Lx02mSZiImi64LESmbkuHKZIo629rF87hwSDJObMR61HUWv9KzIWDHNkp04wUGuS2yddttugy08G3KZvYxCS2HUU7ycCOfnnU9GQgY7G3eGtZsDlRWnMxFjTecAy7OHJzDBScpMRDhNltKsUnzii5gKZjrT7kcKFgHY07KHe9+5l/TEdFZmrWR5+nKuXHYl/3Huf0yLHJNlZHTdeMRKTZe4Q38KEBEONfdwUcnko0zebn2b0qxSEg2TS3AZTZaYl2DUmXAntkx7VEqDzcHGJVpYrV/8lNvLuWr5VdN6zlPFoDOwZu4attdsZ0POBq4q0ORNTzbiFxhwe0lLNE75ed1ePw02B5eXDkfxldnLUKhZOUkJsiprFQCHuw6zdu7asGPmJBO9Dg8iMuU+tuODRYK81vQayYZkHr3k0VkVTJOZlBkqezEe6UkmBlxePD4/Rv3snYTNXsliiLquQbodHtYvyphc/946jtiOcP7C86dZspPDoDOwaM7yaV+5OD0+WnuHQiuXhr4GhrxDs2pT6VhcseQKAO7be1+oVvx0755utA/i8wvL5g7fLMvsZeSb80k2RmdP0smQnZzNvJR5HOw8OOpYerIRt8/P0DSkkK/rGgBGJ/j8oOMDVmevnlWKJUiJpWRShelg9mdGjiuXKeD9xh4A1i+OrFyququ4/OnL2fzHzZz7p3PZ9uw2kgxJoRnvbKQwowh9YgvdDte0naO524HI8Aa3oNkkWoWaToVLl1zKfefeh8vn4vm65xGRUGbk6bKHV3doN8vl2amhtplO8TJZNs/fzJvNb45K/Jk+jRspazq1YJGlI8yILp+L6p7q0GpqtlGcWUx9X/2YCVIhdjIjx5XLFPB+YzepiYYwW/hIfvLeT+h2dnPlsiu5fMnlXFd4Hb/+6K+jmnn1RCnNKkHpXTT0NU3c+SSp7wrPhlxmK8OoM7I0feacqifC6iwtz9n3dn+Px44+NrzjfJrCRIdvlpoy7nZ20z7YPmty043HFUuvwOF18E7bO2Ht07lLv7ZzkJy08DDkqu4qfOKblYE0MFz2orK7csw+wzVdZnc4cly5TAHvN3SzNi89Yg2X9sF23ml7h8+Xfp47N93JXZvu4vtnfZ/1OetnQNLJsy5Hyz7c0F81beeoP66OS5m9jOXpyzHqpt5fMR3km/N5+sqnWWZexvN1z0+7uaKmY4D55kRSAjfLmag9crKsm7uORH0i33jtG2GzcvM0XrPargGWZoVP+ILO8tmqkIPf5fhVT6d3hTxVxJXLKTLg8lJ5rH9Mf0swx9PWvK3RFOuUWWEpANHROlQ9bedosDlISzSQnmwMZfGNBZPYSAoyCtiSt4XqnmqSTVqU2HT96Gu6BkOrFphdheYmwqQ3hTZ1joyGCtYnmepZuIhQ2xl+vUC7ZqmmVHLnTD6TRjTJSc7BkmgZv1RBFGsHnQpx5XKKvFdvxy+wIT+ycjnUeQhzgjli6ovZjElvQu+dT5e7dtrOUdc1SH5WCkop2gfb6XH1xIT/4HiKMovw+r10uRuB6ZmFazfL8Jl4ua2c+SnzMSdMbuPuTPOVNV8BjlMu0+Q/sA266R3ysOw4U3WZrYxiS/GszP4AI8pejLtyiY2aLnHlcorsqerCZNCNmaU26HCdrYN5PJJYTJ+/Hq2EztSj3SyHTWIQGyae4wlmWajprSDJqJ+WjZSdAy76nV6WZYdHisXCqiVIVlIWc5PCQ22n60ZZ0d4PQEHOsHLx+D1UdlfO+glMkaWI6u5q3L7I4yhY3iEeLXaas6fGxobFGSQaR2+eDA7mWLoBjCRdn49X9dPh6Jjy93a4vbT2OkORPGX2MnRKN6v3a4zFwtSFzDHOocxeNm3pTGqPi3xyeBw09DWEsunGCiWZJWErlySjHpNeN+XX7P2GbgBWL0wPtdX11uH2u2f9BEbLkOGlqieyvzNa5R1OlbhyOQW6BlyUtfWF1SIZSW1PLR6/Z9bPlMZiboIWtTWZZHonSjBhZdBsUW4rZ0naEpIMSVN+rukmmOyzzF42belMgmHIQR9CRXcFgsTcxKU4s5i63rqQU18pRdo0JK98v7GbgrlzQpFVMJzNYDbm8xtJUL7/2Dt2xoCMZCO2wbhyOW15q0arGnfuGMol5HCd5TOlschNXgaiJpUG/ESpPS6s1mq3xtwsfCRFliIq7ZWkJemmxRZ+qLkHS4qJ3HRN+c72qKexKMksQRAquofzZ021QhYRDjT1jAqyKbeXk2RImtV560BbCS+cs5BDnYfodHRG7DM3NZGOvunbgzYVxJXLKbC7qpO0RAOluZEdqqHBnDq7B/NYZCWn4ndnTZjr6GSo7RxEKViSlYJtyEaHoyPmbpQjKckswelzkpBkm5Yd+h809bB6oTnkuyu3l2NJtIQyIccKwWjA8IixqTUl1nYN0uPwsH5xeli71WZlRcaKWZMxeiyUUjy45UGAUfuCgswzJ9LeNzoR6GwirlxOEhHhzaouzi3IGrPyZKwM5rEwJ5vwOXOx2kYnHDxVKo71sTAjiUSjPqZCasciKLvf1Dzlmyh7HR4qjw2wYUQGiFgNFMlOyiYzMXNUxNhUKpegv2XkysUvfiq6K2LGRL0iYwVJhqQxJ3ZB5TJdwTZTwUkrF6VUnlLqNaVUmVLqqFLqG4F2i1LqZaVUVeB/RqBdKaUeUkpVK6UOKaXWj3ivmwP9q5RSN49oP0MpdTjwmodU4Jc01jmiSU3nAG29Ts4ryI54PDiYY/mGmZ5kxOdcQMdQOzU9NVP63gebelkTcLYGfTqFlsIpPUc0yTfnk6BPwKVrmnLn9P5GOwAbAhGJbp+b6u7qmBxbSqlRTn1z0tTWwdlX301aoiEsDLmpv4lBz2DM7KPS67SM6WMql7RE3F4/9lnsdzmVlYsX+DcRKQbOAm5VSpUAdwCvikgB8GrgOcBlQEHg7xbgV6ApCuAeYBOwEbhnhLL4VaBv8HWXBtrHOkfU2F3VBYztb2nub2bQMxiz/hbQ0kx4+9aQoE/icevjU/a+HX1OWnqGWJunKZdyezm5c3JjZr9GJAw6A4UZhfT563F5/TinMBHje/XdGPUqpIyre6rxijdmfVQlmSXU9tYy5B0CgiuXqblJigi7q7s4e1lWWMaMUP2kGFLIxZZiKror8It/1LH8LC1lUm0gMGY2ctLKRUTaROT9wON+oAzIBbYBjwW6PQZ8IvB4G/C4aLwDpCul5gOXAC+LiF1EuoGXgUsDx9JE5G3R1n6PH/dekc4RNfY1dLPAnEieJXI2Wqtdm3HEyjI8EunJRsRrZumc0in1u7xXr5ktgjPx4Ma2WKc4sxi7pw7wT/FM3E5prpkkk2ZejXUzYnGmlj8rWBTLnGRk0O3D4xt9Ez1RGmwOWnqGOGd5Zlh7mb0Mg87A8vTlp3yOaFGSWcKgZ5Cm/tH5/QrmaslLq44NRFusSTMlPhelVD6wDtgL5IhIG2gKCAh6HHOBkVepOdA2XntzhHbGOUfUeL+he8wsyKCF1hpUbA3m4wlucMtOWDrupq4T5b16O0lGPSsXpDHgHqCxvzGmV3hBii3FuPwOlLF7ykxjTo+Pg029YZt0rTYrKcYU8lLzpuQcsqMYSgAAIABJREFU0SZY2Cw4YZnKnGy7qzWLwvHbA8psZRSkF2DUx0beOhiRZyxCtGZuehKpCQY+aOqOtliT5pSVi1JqDvA34JsiMl7x50ieRzmJ9hOR7Ral1D6l1L7OzsghfSdDa88Qrb3Oceu3lNvLWZ6xfFbWjJgs5kDep3R9Pl7xUt0zNXnG9jXYWbcoHaNeF5qFx/IKL0jQTKVPbJkyM88HTT24ff5RzvzCjNldrXM8QvmzAr62qcyMvKe6iwXmRJaMKBAmIlo2gxibwCwzL8OgM4SsICPR6RQXleTw1L5mltz5fOjvM797B5d36mvjnAynNDqVUkY0xfKEiDwdaD4WMGkR+B/c3t0MjJxqLQRaJ2hfGKF9vHOEISK/FZENIrIhOzuy4/1kCM6Ozhhj5RIczLF+wwz+6JPRQqmnYr/LgMuLtbUvZBKLdRPPSArSC9ArPbrEVrqnaOWyq7ITg05x1jLNzOPz+6iwV8SMYzoSwfxZwyuXqUle6fMLb9XYOHt5VlgU3THHsZjMW2fUGylIL6B8jGjNb120gm9cWMDXL1jO1y9YzrY1C9hTbeO18qnPqHEynEq0mAJ+D5SJyIMjDm0HghFfNwPPjWi/KRA1dhbQGzBpvQRcrJTKCDjyLwZeChzrV0qdFTjXTce9V6RzRIWn3mti+dw5rBpjf0uHowO70x5zg/l4TAYdySY9fldGKL3JqXKgsRu/wJmBRJ9l9jKykrLITp465T9TmPQm8tOWoU9smbIontcrOlm/OCNUNrmutw6nzxnTygU0f0JNTw1Or3PKsvwebumld8jDR1aEj6VY3XAK2nUqs5dFDDnOsyTzrYtWcPvFhdx+cSEPXLsGc5KR1yumzkpzKpzKyuUc4EZgq1Lqg8Df5cCPgIuUUlXARYHnAC8AtUA18DDwNQARsQP3Au8F/n4YaAP4KvC7wGtqgBcD7WOdY9rx+4WjrX2cV5AVsX4LnF6z8fQkI71OXyi9yanyVo0Ng06xblGGtpO640DIBn86sDKzGF1iKx1TsMHtWJ+TsrY+thQO3yxDCT5jfGyVZJbgEx9V3VVTZhZ7s1K7qZ6zbLQzP1bz1hVbiulx9dA+2D5hX4Nex6pcM4dbeqMg2cQYJu4SGRHZTWS/CMCFEfoLcOsY7/UI8EiE9n1AaYR2W6RzRIOmbgdDHh9F81LH7GO1W1GomN63EcScbKLH4aF0SSlPlD3Bka4j7KjbQW1vLYJw18a7yEsb37Fcbi9ne812/OLn2cZmcpcrfnbgfXpcPTT1N/G5lZ+LzoeJAiuzSthe+xxN/a3Aqd3MdgVulltWDMerWG1WEvWJLDEvOaX3nmmC/g+rzcqli7UVfkf/qaUz2VXZSWluGplzEsLaj3QdYal5KcnGyJGds5mgH6/MXsb8OfMn7L8yN43fv1mH1+fHoJ9Zn1xsegRnkJetxwBYmzd+pNjitMWkGFPG7BMrpAeSCl68+GI8fg83PH8Dj1kfo76vnj0te/jVwV9N+B4PH3qYJ8qe4Lnq7QwY3sVh3M/LDS/zXvt7nJN7DlcsvSIKnyQ6BM1VjQOnXsHzn0ePkZOWQPH84YlMmb2MQkthzGZ9CLIgZQHmBDNWuxVzkpHFmckcaDz5yKeWniH2NXRzccm8sHYRwWqzxuzqeEXGCnRKN2mrwZLMFLx+oa135lPDnPTK5cPKK2XHKM1No3CclUu5vZzV2aujKNX0YU4yUts1QGlWKQ+c/wB2p531c9dTaCnk7j1383LDy/jFP27kUrm9nK15W9mcejvf+esh/vKN8yienxbFTxE9VmSsAFEcc59aRoNeh4ddlR3ctDk/5Jz2+DyU28v5+NKPT4WoM4pSihJLSShIZMNiS2ildjLsq9cs6ReV5IS1tw22YXfaKc0aZQCJCZIMSSw1L510MM3CDG111tw9NOYevGgRX7mcIA02B4U5Y98Ye129tA62xrwzP0gw75NSikvyL+GGohtC5r51c9cx4Bmgsa9xzNcH97EUWYrYVdlJTlrCuCbFWCfZmEyKbj69voZTep+XrO0oywts7/k8G5/YyBn/cwYbn9jIoGeQLXlbpkbYGaY4s5iqnircPjfF81PpGnDRfZKBEHVdw4lQR3K46zBAzCoX0Pwuk1cuWtbs5m7HdIo0KeIrlxPA6fHR1uskP3PsGUEwMiXWo3mCmJON9Ax5EJFRSRKDn7HMXka+OT/i6yu7KwFtH8vDtXY+UpAVc8kWT5T5ScupdB9kyO0L7ao/EXpdvTxZ9RsSsnaxOnsjJZkl6JQOndKx1LyUc3LPmQapo09JZglev5eq7iqWz9WCFr7zt0NUdwzg9mq79c9YnMFDN6yb8L1qOwdDiVBH8kbzG6SaUmPa/1lkKeLvtX+na6iLrKTI6aaCLEhPQilt5TLTxJXLCdBo12YDi7PG9qWcLtE8QdKTTLi9fpwe/6gb5dL0pZh0Jqw2K5ctuSzi6w92HgTArM+na+AI68bJanC6sNxcSLXjDQ63NbNx8YmXW3i2+lmq3c+hl1Qe2vrQaeG7i0Qo/b7dykcXbiM3PYm3a2wUz09lcWYKzd0Oth9sZVWumS9/ZOm471XbNUBK9tvc9urT+MWPHz8IvNf+Hh9f9nGMutjZmX88weCHcns55+aeO25fk0HHvLREmuIrl9iiPpAkbqKVy4KUBaQnpo/ZJ5YIhon2DnlGKRejzsiKjBVjLtn94ufRI4+yNnst9ce0165fdHpcl/HYuGAVO9rg9foPTkq5WG1liDeVKzN/fdoqFoCFcxaSakrFarNy7Ypr2XPH1rDjtgEX5/74Ne7/ZwXb1i5gblpixPdp6muiXj2MTncQ6Z5HRmIGOqVDKcXZC87ma2u/Fo2PM20ETexltrIJlQtoprH4yiXGaLAFVi6WcVYutrLTxiQGw3mfeobczDOP/nEXZxazo35HRLNZc38z3a5uvlnwTd639pBs0lOYc/r6W4J8dNl6frgfXqjcR17SsEkn0aijeH4aS7PmYDKM7e482lWGbyiXovmntyIOOvXHSoqaOSeBv3/9HD764Bu8ZD3GjWdFVtQ/2vtTVOoBFiSV8NhlD5GTkhOxX6ySakplUeqiSUeM5WUk806tbZqlmpi4cjkB6m2DpCcbMSdHXmL3u/tp7G9k2/JtUZZs+pho93RxZjF/qfwLzQPNoxIpjqzT8mRbLysXpM147H00yEgyM0c/l3ZnDXc+fXjU8avXL+Sn162J+Fqn10lTfz0+5xaK5p/+irgks4T/LftfPD5PxKSSy7LnsMiSzOvlHWMql6O2o3j6VvO9s39GTkrsZ3qIRJGlaNKZyRdmJNHe58Tj82Ocwd/b6f9Ln0IabA4WZ469agnuzD+dVi5BRTqWcolUtjZIhb0CgzJQkF5ATecgy+fOGdXndOWs3NUsnt/NO3deyDt3XsgvvziHf/l4Mxes8vPMgWYGXd6Ir6vqrsKPH4N3IcXzTs9w7ZGUZJbg8XvGTIqqlGJLYTZv1dgi1sjpc/dhc7Xjd85nafbpO76KM4tpHmim1zXx7vuFlmT8oiXYnUniyuUEqLcNjutvCYY9ni7OfBhOKtg3Rjr0gvQCDDpDRL9Lmb2MpelLGXCCfdAdVhnwdKc4s5jWwWaSk9y81PxnvvvWbfyx+he0Gh/FL9DWG/mHf6RLU9LnLFpzUpFmsUbQWX3UdnTMPpuXZjLk8VF5rH/UsWBNGL13IfPH8MmcDpRYtElc8POOR15gr0uTPa5cYgK3109rz9C4K5c9LXsoyCggMylzzD6xRijv0xgZa016EwXpBRHtwcE677WdWkGjD5NyOSPnDABufvFmHtj3ABctvohbVt9C61AVyminpSfyDupd9R8gvkSuW3N6bMKdiLzUPOYmz+Xed+7lnbZ3IvYJrkiCPs+RWLsC0ZkZxWPm+jsdGJkGZiLyLNpel5mOGIsrl0nS3O3AL1qkmIhQ11tHVXcVtT211PfW81bLW7zX/h5b87ZO/GYxRIpJj0Gnxk0qWJyppU8fmbm1a6iLrqEuiixF1HwIlcu6uetYYl5CdU81F+RdwI/O+xFbF2ljQ5/YTNsYJovDnWUoTy7nF0a9/t2MoFM6Htr6EH7xs7NxZ8Q+iwI7zesjlPTd3XQQvyeVm8+K3U2Sk8GSaCEnOWdSymVeWiJ6naLJPrPKJe7QnyShSLHMFF5pfIXbX799VJ80UxrXF10fbdGmFaUUlhQTXQNjJxUsthTzdNXTtA+2h5LrjSwC9lLtACaDjtzA7uEPAzql429X/o0h7xBpJs13UpBegEEZMKW08WZVF9dvXBT2mgGnm15fI0tTLxw3mux0Y2XmStbPXc+T5U/y1TVfJdGQyPd2f4/dLbsx6Az88sJfMi8tkfoIK5ejXWUod+6onGKnI8WZk9upb9DrWJCeSNMMhyPHlcskqbcN73F55oNdpJnSuHvz3fjFj098+MXP+rnrJ9xBG4vkWZLHtd+OdOofr1yWm1fw9UP72LTEgv40NltEwqgzYjQNR0CZ9CaWZyynW29jx9H2UZlrnzr0Pkrn4fz8yJFkpzNbF23l/Y73+fn7P6drqIs3mt/gkwWf5B+1/+Cl+pdYnPkRGmzhK5fdzXvo9zexJO0THwr/VImlhF1Nu3B4HBNmeM7LSI6vXGKFBpuD1AQDRqOLnY07OW/heVySf8lMixUVFlmSebfOPubxFRkr0Cs9VruVCxdrlRDKbGXkzsnl3doh2vuc/HBbbGalnWpKMkt42v40fv3FtPY4WTQiQOSlqv0AXF545kyJN2PcvPJm9rTs4W9VfwPg+5u+z6eKPkXrQCvvtr/L8szLePW4Cov37f1PfK65XFX66ZkQOeoUZxYjCJXdlaydu3bcvvlZKbx4uC1KkkXmw7P2PkXqbYMszkrm3fZ36ff0c13hdTMtUtTIsyTT1jsUFgrq9PgYdHlxenwkGhJZmr40LBy5oruCYksxb9fYSDbp2Vr04fAhTMSWhVsAMFneCqUTCtLQX4USA8szxk91crrywJYHePyyx3nuE8/xqaJPAVrCyZqeGhZmGukacNHv1Hx/Pc4emgca8PRsoGTeh2NshXbqT8LvsjQrhW6Hhx7H1FREPRniymWSBPe4WG1W9Eofs/UhTobVuWb8Aoeae/H7hYderaLk7h2svOcliu/ewQ+2H6Uwoyjk1B/0DNLQ10CRpYgG2yD5mSkfis2Tk+GCRRew0rIaXWIre+uGd1H3uQYZML5Djqk4pvNgnQpppjTWzV3HUvOwcg1WrExJ0VYtlce04JDgDdbvXMDS7NM3Rc5IcpJzsCRaJuV3CWaHDgbTzATxX/wk8Pj8NNkd5KS72FG/g2Xpy0g0nL4x9cezIT8DnYJv/OkAZ/9oJw++XMnWorncdXkRH1u9gD+8Vc+xzmzsTjudQ52hWPwiSxENdgf5WbFXAXA6WZezGmNSC08dGN69/7u9b6IMg1y66JoZlGz2EdwH4zE0AVDRru11Cfr0kiWP7OMqT56uKKW09PuTWLmsyUtHKdhTPXNpYOI+l0lQ3zWI1+/l7cEHsbm7+OWFv8Tb1UXv9r9jXJhL2sUXR3yduN30/v0feG3aF5xUupKUs8+O2NdZWcngG28gfi2cN2XTRpLWRHbseo510P/yy/gdDlLO2kTS6sh7IsTrpeeZZ/B192gNClIvuICE5cvH/bx9O3bgH3RgvvLjKKOR9GQTP756NX/Z30yCQcdtW5dz9SITjhdf5GqlkAXzOdhohgzNqd8y0ALA/KRlNHQd5Muearp++z4A+nQz6VddhTKOnp2LCAOvvYarWiu0ZZyXQ9rHPx4xRb+no4O+F15A3JqZJGHZUlIvjFz52u9w0Pfii3htmt8oae0aUjZujNi3/9VXcdXUhp4nrV5NylmbIvYVvx9Paxv+gX5cFRXMOf989OmR84H5HQ56nn4Gv8PBNR4DL/rdtOr20ufcRmqCgT/W/QiAz6zTxoerro6BnTtBr8e8bRuGjMjZpIcOHmTw3XdBQJeSTPpVV6FLHq3MRYS+7dvxHNNWAPqMdO17MIy+BYjHQ8/Tz+Dr7R23H8DQoUMMvrM39NyYu4C0yy5D6caet/pdLvr/+TJJa1ZjWrQoYh8Rofe550hNTSVDn0q7s5YU00Yq2vsATbkYxUJe5lzcNTX073xNe6GCtIsuwpSfH/F93U1N9L/8CuL1ovR60j72MYw5o81q4vfTv2MH7mZtLOsSTJg/8Qn0ZvOYn8tVW0v/K68CoE9LI/26a0ddB3dzC/07Xgz9zvVpqZi3bUOXNHYkpfa7eB1DpoXizGL+cPQPuH1uTHpTWL+BPXtwHtVM04lFhazKNbO7qot/vbBgzPeeTmJauSilLgV+DuiB34nIj6bjPA8feoTk/BdpdrRw//n3sz6thPrPfBZXmTaDkJ8+gPmK8FK9IkLrnXfR9/zzIwVm8eOPkXxmuMN26PARGm68EXEOb6zrSkhgyTPPkLA0vFa6t7ubhhtvxNOoFejqMplY9MjvSTrjjFE34WP/+SO6n3girM3269+wbMeLGLLDczC5m1sY2r8PZ2Ul9t8/osl15DDz77kHgGs35HHthrxhGW74NO76egA+N3cBn9r8FVIyFGW2MloHW7EkWni30s2/fPA0a+veYmSNQXddPTnf/c6o62z/w2N0/PjHYW2+nh4sN90Ufg1sNhpu+DSelpaw9tyHfj5K0YvHQ/NtX2fwrbeGGw0Gljz1ZxJLwtP09DzzLG133jlKrkWPPUbKpmFlJCI43n6bYz/+Ca6K4R3TiWtWk//HP6L04ZFL4vXS8q3bGdi1K9R26/IE7rm8jP/eWc3nz8/AhTYBsfRD24/+nd5nn0M8muIceHUnix5/bNT3O/j22zR+6cvgG/aFDX1wkNz7fzLqM3Q+9BC2X/06rM3T1Mzc278V1uZuaqLhppvxtg07gzt/+iCZt9xC+rXXoE8dznc2dPgwDTfehLjCw9S9HZ1kfv5zo2QA8DudNP/rvzL4xpvoLRaW/uPvGCyWceX9hVHH0TUvUXTWFspHrFx8zvms0Q/S8Nkb8fX0hF5rf/xxlr3wQpisoI3x+k9dj88+HJzS++wzLHnmmVHKs+P+B7A/+mhY28CuXeT9/vejvgevzUbz125l6ODBsHbdnDmYP3ZFWL+Gm27E2xruaHe8t4/cnz4w6hoMHTpE34s7cFqtOPZqCnzNz76F1++luqc6LM1U3z//Scs3vgkj9ppd9oW7+MVANn6/zMwGUxGJyT80hVIDLAVMwEGgZKz+Z5xxhpwMDo9DVv1htZT+oVQePvioDJWXS+WWC8RaXCJ9r7wiNZ+4Sqou/Kj4Xa6w13X+6ldiLSySjl/8QnxDQ+Kx26XyI+dL3aeuF7/fH+rnbm+XyvM+IlUXbBV3S4v4nE5xNzdL+br10vTNb4a9p9/lkvrP3ihlq1bLwN694m5tlfKNm8RaWCTHHvyvsL72J58Ua2GRtP/nj8TndIrP6ZSh8gqxriyV1u//e1hfV1OzVJy1WayFRWItLJLGr90qrT/4gViLimWovDysr8/plLobPh2SoffFF8VaWCT3fOxrcvXvLpHbXrlNLv/b5XLrK7fKQ5/9tlgLi6T1hz8Un8MhPqdTWu64U8pKV4m7uTnsfftee02sxSXSdNtt4hscFN/QkDR8/vNSsXGTePv7h8/vcmnnX71GBvfvF5/TKd7+Aan52Mel+uJLxO/xDF8vv19av//vYi0sEvuf/iw+h0M8HR1SvnGTNNxyS9j5B/fvl7LSVVJ/8+fENzAgPqdT+862XCC1V31S/D6f9p5er7TefY9YC4ukbPUaaf/JT6T76Wfk2IP/JdbCIul++pnw78zvl9Z7tP62J54Qn9MpXb/7nVgLi+SOr5bIxgf/Wx5852Ep/UOpfP2JP0rNJ66SslWrpe76G8RVVye2x/9HrIVF0rdzZ/h3Vlcn5Rs3SfUVV4ins1N8Tqccu/9+sRYWydDRo2F9u//yF7EWFknLXXeJb2hIfE6nNP/b/5GyVavF3dYW6uft7ZXqy6+QsnXrxf7UU+JzOqX7b0+HxljrD34Q6utub5fKc8/Txm17uzbGhoak4fNfkPINZ4rHbg+/vu++K83ful2sxSXae91zj1hLVkrbD++V4+n+61/FWlgkzd+6Xbr/+jd57fqLxVpYJM9fdYOsu+cFaevtkdJHV8nq+26VAxdeIuUbN4mzulp8TqcM7t+v/e4e+n9h7+ntH5Caj18p5RvOlL7XXpOu3/1eep57TvvO/vKXiNer9Qc/CF2vrkceFWthkfS/uTusr29wUGqvvU7K1qyV9v/4D02OoSFtPF52ufi9Xq1fcNyuWSuODz4I/SaPPfBTsRYWiePAgbAx0/bDe7Uxtmq1VJy1WVruuksqz/uIlF99lZQ+ulL+WvHXUH/HgQNyZFWp7PjoWrnmicvkmj9eJm+cvUr2nrlWzvjX/5WqY/1yKgD75GTu0SfzotnwB2wGXhrx/E7gzrH6n6xy2dOyR0r/UCpn/dcvxNPRIZVbLpDydeul9/nnRUSk/403QjeOIL07XtJ+HN/+dpgisf/5z9qN4lXtRuFzOKT26mukbN36UTfxjp//POxG4RsakrrPfEashUXSs317qJ+rqVkab7017EYx8PbbYi1ZKY23/EtocAdp+//uE2vJSnF88IG03XefNHzxS9pnOnOjDO7fL+7mZvH7/eLt6ZHyMzdKw5e/HHqt3+eTpm9+U6yFRdL7wgtam98vlZ/4pFgLi2THRzdK6aMrpfQPpfLYT7V+r33h6+HKtLVVylatlpY77xK/1yt9O3dK/+uvS/n6M6TmqqvENzgY6us4fES7Ufz8odC5Wr57R9j5g/S9+qqmRJ56SkREvD090vCFL0ZUvJ2/+W3Ye7iamqVi89lSdfHF4u3uDusbvAH1PPusDOzZI7XXXqfdqL97R1hfv98vtddcK5VbLhCf0xlq73r4YU2G++8P61tx06flnbVFsuY/vySlfyiV0kdKZd9NXxBrcYn079o13NftlqqLL5aaK7eFFJy3p0eqL7lUKs7aLK7GxlBfb19fSHEOvPWW9D7/vPS98opYS1ZKwxe+KH63O2zcWEtXSev3/136Xt0p3c88I3Wf/oxYV5bKwDt7w66Bz+WS1n+/W6wrS8XV0KCN209eLeXr1stQeUVYX2dlpViLiqX9xz8JfVbbE0+ItXSVWAuLpOGLX5Lel14SEdEmMIH3DNL/xpuavF/8UkjeV+pflkeuLBZrYZH88Ip/kVvv/7TcenuJ7N60UZP37XfCZGj6129I+br14rHZNBm8Xmn8l6+ItWSl9O8eVg5+v19qr7su7DsbfPddsZau0q7XyImKyyVVWy+U2k9eHRrPfo9HGr/y1dBEcyTBSVfP3/+hjdvvfFcbcy++GH5tBwak4pxzpf7Gm8TV1CQDb70lzd/6llgLi6Tt//4wbGIVVLq3ff8MufdtTSm7Ghrk0JnrZeemIvnin66Rb7/+bfn269+Wf/vNVXKouEgeP/8yeW5X+GTjRDlZ5aK018YeSqlrgEtF5EuB5zcCm0Tktkj9N2zYIPv27Tvh8+z+4VXIjgosfh2GIUH8Qv5nFpCYozkRRYTGJ9twNDkxZWp+BLfdQ9L8BBbdMB/diJ3W4hNqH2nG3e3BZDHic/rxDfpY+MkcUgvCI158Lj81v2nE7xGMZgN+tx9vv4/5l2eTvuq45X6vh9qHm1F6MKQacNs9JGQaWfzZXPQJ4TZf76CXmoeb8bu0MrImixF9sp7s8zJIWRRu97Xt7aHjdTvGdANKrxCf4OnxMneLhcxNw74Fn8vP0deOYTw4RF8SeAxgGQB/lp4Vn12I6bgNbsd22rC/14suQReSQ5+iZ8lNuRjTws0Tzc8eo79iEJPFqJ2/10vWORlknxvugxARGv63laFWFyaLEa/Dh9/px3KmmbkXWMJMGX63n+rfNOJz+DFmGPAN+kAp8m9cQEJmuB1b/ELdYy24OoZDOo///EEGG4Zo/FMbhjl6dIHr7rZ5SCtKYcGVc8NkGGpzUft4C24j9CdBulthdAo5WzOxnBlu1++1DtD69w70KXr0iTp8Qz58Tj+Lr59Pcl7k72wkCXNNLP70glFjof2VLrr394W1zb80i/Q1o7Mxewa81PymSftcIvgcfhZenUPq8tGRWq0vdNJ7uB9ThhGf04dvyI/JYiT/swvQJw2PBe+Al+rfNqH0CkOK1u7p9WKyGMPkFYRb6eC8vzhYWzt8v1JGxYLLskkrDk8r5OpyU/tIMzqjwpA6/NvJuSgTy/rwaxv8zvRJOvTJejw9XoxmA/k3LkCfGD5ue4700/Z8Z+j7Fa82HiO9r4hQ90gznj4vukQ93j4vWedmkH3OaN9Z94E+2v/ZFdaWsT6NnI9mho0Z8Wv3D0evh7Z0MKFIHRC8CrbfmMwPMnIwovW34+PXu5u4ao8ftwEqz8vi2l+9Oerck0EptV9ENpzw62JYuVwLXHKcctkoIl8f0ecW4BaARYsWndHQ0HDC52l+4HP0v3KARIMek1FHxnozyQvDI8U8fV46d3fjd2s3SsMcPVlnZ2BIHr1r2NXlpuudHsQrKAWpRSmkFUbOuTXU4sS+vxfR3pbU5cmYSyPX+BiocdBzWLNH6xN0ZJ2XgXFOZJeao8VJf/kAKUuTSclPGrOmvfiErre7cXUN5xVLyk3AssE86jV9Q25a3rRT53CRqtezwGgke3M6aebRkTx+r5/OXd14+r0YUvSIgGV9GglZplF9fUM+Ot6w4xvSLkLSggQsZ44+PwS/Bzt+t6B0YF6VypwlkSPV3N0eug/04enzovQKy5lmkuZFjjpy93ro3t+HyWIkJT8JU/rYocI9h/oYqB3OZmBMM5D9kYywSUaQN4520V01SBo6lhpNpC1IIn1t6qjPJiLY3+tlqFXzbYz32cQvdO7uRnzwAb1rAAAGg0lEQVSCKd3IUJuL7PMyMKaOHgt+j5+OXXb0SXpSC7T3Spw7duRV75F++qu1vTnmkjmkrogcAux3++nc0423zws6RUKWkcyN6Sj96O9ssGGInoN9oTGuM+kiyluNm99KL3kHXJh7/Jwxbw5FS8xhymok/ZWD9FqHw3BT8pPIWBu5hEHPwT4G6rTvTJ+oI2tzOkZz5IAT+7u9DLUN+5iSFyWOUixBHM1O7Pu0FPlj/W5C77uvF79LSMg2krQgMeL3BeCyuWl6p5tqjwsf4NdB58ZkPj3PQuJxwb9PSz+V1X0UHvWiXzGfK+9/OeJ7TsSHUblsBn4gIpcEnt8JICL/Gan/ya5c4sSJE+fDzMkql1je5/IeUKCUWqKUMgHXA9tnWKY4ceLEiUMMhyKLiFcpdRvwElrk2CMiMnbFoThx4sSJEzViVrkAiMgLwAszLUecOHHixAknls1iceLEiRNnlhJXLnHixIkTZ8qJK5c4ceLEiTPlxJVLnDhx4sSZcuLKJU6cOHHiTDkxu4nyRFFKdQInvkVfIwvomrDX7CMW5Y7LHD1iUe5YlBliU+6gzItFJHuizsfzoVEup4JSat/J7FCdaWJR7rjM0SMW5Y5FmSE25T5VmeNmsThx4sSJM+XElUucOHHixJly4splcvx2pgU4SWJR7rjM0SMW5Y5FmSE25T4lmeM+lzhx4sSJM+XEVy5x4sSJE2fKiSuXCVBKXaqUqlBKVSul7phpeYIopR5RSnUopY6MaLMopV5WSlUF/mcE2pVS6qHAZziklFo/QzLnKaVeU0qVKaWOKqW+ESNyJyql3lVKHQzI/X8D7UuUUnsDcv85UPoBpVRC4Hl14Hj+TMgdkEWvlDqglPpHDMlcr5Q6rJT6QCm1L9A228dIulLqr0qp8sD43jybZVZKFQaub/CvTyn1zSmV+WRqI39Y/tBS+dcASwETcBAomWm5ArJ9BFgPHBnR9hPgjsDjO4AfBx5fDrwIKOAsYO8MyTwfWB94nApUAiUxILcC5gQeG4G9AXmeAq4PtP8a+Grg8deAXwceXw/8eQbHye3AH4F/BJ7Hgsz1QNZxbbN9jDwGfCnw2ASkz3aZR8iuB9qBxVMp84x9oFj4AzYDL414fidw50zLNUKe/OOUSwUwP/B4PlARePwb4IZI/WZY/ueAi2JJbiAZeB/YhLbBzHD8WEGrMbQ58NgQ6KdmQNaFwKvAVuAfgRvDrJY5cP5IymXWjhEgDag7/nrNZpmPk/NiYM9Uyxw3i41PLtA04nlzoG22kiMibQCB/3MD7bPucwTMLuvQVgGzXu6AeekDoAN4GW1F2yMi3giyheQOHO8FMqMrMQA/A74DBCrUk8nslxlAgH8qpfYrpW4JtM3mMbIU6AQeDZggf6eUSmF2yzyS64EnA4+nTOa4chkfFaEtFsPrZtXnUErNAf4GfFNE+sbrGqFtRuQWEZ+IrEVbDWwEiiN1C/yfcbmVUh8DOkRk/8jmCF1njcwjOEdE1gOXAbcqpT4yTt/ZILcBzUT9KxFZBwyimZTGYjbIDEDA53Yl8JeJukZoG1fmuHIZn2Ygb8TzhUDrDMkyGY4ppeYDBP53BNpnzedQShnRFMsTIvJ0oHnWyx1ERHqA19HszulKqWA115GyheQOHDcD9uhKyjnAlUqpeuBPaKaxnzG7ZQZARFoD/zuAZ9CU+WweI81As4jsDTz/K5qymc0yB7kMeF9EjgWeT5nMceUyPu8BBYEIGxPa8nH7DMs0HtuBmwOPb0bzaQTbbwpEfJwF9AaXvtFEKaWA3wNlIvLgiEOzXe5spVR64HES8FGgDHgNuCbQ7Xi5g5/nGmCnBAzV0UJE7hSRhSKSjzZud4rIZ5jFMgMopVKUUqnBx2j+gCPM4jEiIu1Ak1KqMNB0IWCdzTKP4AaGTWIwlTLPlBMpVv7QoiQq0Wzs35tpeUbI9STQBnjQZhVfRLORvwpUBf5bAn0V8N+Bz3AY2DBDMp+LtpQ+BHwQ+Ls8BuReDRwIyH0EuDvQvhR4F6hGMyskBNoTA8+rA8eXzvBY2cJwtNisljkg38HA39Hgby4GxshaYF9gjDwLZMSAzMmADTCPaJsymeM79OPEiRMnzpQTN4vFiRMnTpwpJ65c4sSJEyfOlBNXLnHixIkTZ8qJK5c4ceLEiTPlxJVLnDhx4sSZcuLKJU6cOHHiTDlx5RInTpw4caacuHKJEydOnDhTzv8PHIuozQdXunoAAAAASUVORK5CYII=\n",
sp2668's avatar
sp2668 committed
981 982 983 984 985 986 987 988 989 990 991 992 993 994
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "network.stores_t.e.plot()"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
995 996 997 998 999
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
1000 1001
   "source": [
    "***\n",
jonas.hoersch's avatar
jonas.hoersch committed
1002
    "**(e) Replace the approximated availability time-series of the wind and the solar generators with the ones from `availability.csv` computed from reanalysis weather data and re-run the LOPF. Compare the results! Explain the differences by looking at the cumulative variations relative to the mean of the availability time-series!**"
sp2668's avatar
sp2668 committed
1003 1004 1005 1006
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
1007 1008 1009 1010 1011
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
1012 1013 1014 1015 1016 1017
   "source": [
    "Adapt the network to new availabiltiy data:"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1018 1019 1020 1021 1022 1023
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
1024 1025 1026 1027 1028 1029 1030 1031
   "outputs": [],
   "source": [
    "network.remove(\"Generator\", \"Wind\")\n",
    "network.remove(\"Generator\", \"Solar\")"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1032 1033 1034 1035 1036 1037
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>solar</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.402412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.480648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.586046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.641201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     solar      wind\n",
       "name                                \n",
       "2012-01-01 00:00:00    0.0  0.402412\n",
       "2012-01-01 01:00:00    0.0  0.480648\n",
       "2012-01-01 02:00:00    0.0  0.542354\n",
       "2012-01-01 03:00:00    0.0  0.586046\n",
       "2012-01-01 04:00:00    0.0  0.641201"
      ]
     },
sp2668's avatar
sp2668 committed
1109
     "execution_count": 29,
sp2668's avatar
sp2668 committed
1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "availability = pd.read_csv(\"availability.csv\", index_col=0, parse_dates=True)\n",
    "availability.head()"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1121 1122 1123 1124 1125 1126
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
1127 1128 1129 1130
   "outputs": [
    {
     "data": {
      "text/plain": [
sp2668's avatar
minor  
sp2668 committed
1131
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57b30b6320>"
sp2668's avatar
sp2668 committed
1132 1133
      ]
     },
sp2668's avatar
sp2668 committed
1134
     "execution_count": 30,
sp2668's avatar
sp2668 committed
1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "availability.loc[\"2012-7\"].plot()"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1155 1156 1157 1158 1159 1160
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
sp2668's avatar
sp2668 committed
1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-07-01 00:00:00', '2012-07-01 01:00:00',\n",
       "               '2012-07-01 02:00:00', '2012-07-01 03:00:00',\n",
       "               '2012-07-01 04:00:00', '2012-07-01 05:00:00',\n",
       "               '2012-07-01 06:00:00', '2012-07-01 07:00:00',\n",
       "               '2012-07-01 08:00:00', '2012-07-01 09:00:00',\n",
       "               ...\n",
       "               '2012-07-31 14:00:00', '2012-07-31 15:00:00',\n",
       "               '2012-07-31 16:00:00', '2012-07-31 17:00:00',\n",
       "               '2012-07-31 18:00:00', '2012-07-31 19:00:00',\n",
       "               '2012-07-31 20:00:00', '2012-07-31 21:00:00',\n",
       "               '2012-07-31 22:00:00', '2012-07-31 23:00:00'],\n",
       "              dtype='datetime64[ns]', name='name', length=744, freq=None)"
      ]
     },
sp2668's avatar
sp2668 committed
1179
     "execution_count": 31,
sp2668's avatar
sp2668 committed
1180 1181 1182 1183 1184 1185 1186 1187 1188 1189
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "availability.loc[\"2012-7\"].index"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1190 1191 1192 1193 1194 1195
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
sp2668's avatar
sp2668 committed
1196 1197 1198 1199 1200 1201 1202
   "outputs": [],
   "source": [
    "network.set_snapshots(availability.loc[\"2012-7\"].index)"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1203 1204 1205 1206 1207 1208
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Wind\",\n",
    "            bus=\"North\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=1.2e6,\n",
    "            p_max_pu=availability[\"wind\"])"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1221 1222 1223 1224 1225 1226
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238
   "outputs": [],
   "source": [
    "network.add(\"Generator\",\n",
    "            \"Solar\",\n",
    "            bus=\"South\",\n",
    "            p_nom_extendable=True,\n",
    "            capital_cost=0.6e6,\n",
    "            p_max_pu=availability[\"solar\"])"
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
1239 1240 1241 1242 1243
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
1244 1245 1246 1247 1248 1249
   "source": [
    "Run LOPF:"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
sp2668 committed
1250 1251 1252 1253 1254 1255
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
sp2668's avatar
sp2668 committed
1256 1257 1258 1259 1260
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
sp2668's avatar
minor  
sp2668 committed
1261 1262 1263
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:6201: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
1264 1265
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
1266 1267 1268
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282
      "\n",
      "\n",
      "INFO:pypsa.pf:Slack bus for sub-network 0 is North\n",
      "INFO:pypsa.pf:Slack bus for sub-network 1 is South\n",
      "WARNING:pypsa.pf:No generators in sub-network 2, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 2 is North H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 3, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 3 is North Battery\n",
      "WARNING:pypsa.pf:No generators in sub-network 4, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 4 is South H2\n",
      "WARNING:pypsa.pf:No generators in sub-network 5, better hope power is already balanced\n",
      "INFO:pypsa.pf:Slack bus for sub-network 5 is South Battery\n",
      "INFO:pypsa.opf:Performed preliminary steps\n",
      "INFO:pypsa.opf:Building pyomo model using `angles` formulation\n",
sp2668's avatar
minor  
sp2668 committed
1283 1284 1285
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/components.py:758: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
sp2668's avatar
sp2668 committed
1286 1287
      "of pandas will change to not sort by default.\n",
      "\n",
sp2668's avatar
minor  
sp2668 committed
1288
      "To accept the future behavior, pass 'sort=True'.\n",
sp2668's avatar
sp2668 committed
1289
      "\n",
sp2668's avatar
minor  
sp2668 committed
1290
      "To retain the current behavior and silence the warning, pass sort=False\n",
sp2668's avatar
sp2668 committed
1291
      "\n",
sp2668's avatar
minor  
sp2668 committed
1292 1293 1294
      "\n",
      "INFO:pypsa.opf:Solving model using glpk\n",
      "INFO:pypsa.opf:Optimization successful\n"
sp2668's avatar
sp2668 committed
1295 1296 1297 1298 1299 1300
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
sp2668's avatar
minor  
sp2668 committed
1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333
      "# ==========================================================\n",
      "# = Solver Results                                         =\n",
      "# ==========================================================\n",
      "# ----------------------------------------------------------\n",
      "#   Problem Information\n",
      "# ----------------------------------------------------------\n",
      "Problem: \n",
      "- Name: unknown\n",
      "  Lower bound: 525895790860.553\n",
      "  Upper bound: 525895790860.553\n",
      "  Number of objectives: 1\n",
      "  Number of constraints: 34225\n",
      "  Number of variables: 18616\n",
      "  Number of nonzeros: 65191\n",
      "  Sense: minimize\n",
      "# ----------------------------------------------------------\n",
      "#   Solver Information\n",
      "# ----------------------------------------------------------\n",
      "Solver: \n",
      "- Status: ok\n",
      "  Termination condition: optimal\n",
      "  Statistics: \n",
      "    Branch and bound: \n",
      "      Number of bounded subproblems: 0\n",
      "      Number of created subproblems: 0\n",
      "  Error rc: 0\n",
      "  Time: 7.67028284072876\n",
      "# ----------------------------------------------------------\n",
      "#   Solution Information\n",
      "# ----------------------------------------------------------\n",
      "Solution: \n",
      "- number of solutions: 0\n",
      "  number of solutions displayed: 0\n"
sp2668's avatar
sp2668 committed
1334 1335 1336
     ]
    },
    {
sp2668's avatar
minor  
sp2668 committed
1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ws/sp2668/software/anaconda3/lib/python3.6/site-packages/pypsa/opf.py:1207: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "\n"
sp2668's avatar
sp2668 committed
1350
     ]
sp2668's avatar
minor  
sp2668 committed
1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
    },
    {
     "data": {
      "text/plain": [
       "('ok', 'optimal')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
sp2668's avatar
sp2668 committed
1361 1362 1363
    }
   ],
   "source": [
sp2668's avatar
sp2668 committed
1364
    "network.lopf(solver_name=\"glpk\")"
sp2668's avatar
sp2668 committed
1365 1366 1367 1368
   ]
  },
  {
   "cell_type": "markdown",
sp2668's avatar
sp2668 committed
1369 1370 1371 1372 1373
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
sp2668's avatar
sp2668 committed
1374 1375 1376 1377 1378 1379
   "source": [
    "Get the results `objective` and `{generators,stores,links}.p_nom_opt` with real availability:"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
minor  
sp2668 committed
1380
   "execution_count": 36,
sp2668's avatar
sp2668 committed
1381 1382 1383
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
sp2668's avatar
sp2668 committed
1384
    }
sp2668's avatar
sp2668 committed
1385
   },
sp2668's avatar
minor  
sp2668 committed
1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397
   "outputs": [
    {
     "data": {
      "text/plain": [
       "525.895790860553"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
sp2668's avatar
sp2668 committed
1398 1399 1400 1401 1402 1403 1404
   "source": [
    "obj_v3 = network.objective / 1e9 # Mio. Euro\n",
    "obj_v3"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
minor  
sp2668 committed
1405
   "execution_count": 37,
sp2668's avatar
sp2668 committed
1406 1407 1408
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
sp2668's avatar
sp2668 committed
1409
    }
sp2668's avatar
sp2668 committed
1410
   },
sp2668's avatar
minor  
sp2668 committed
1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wind     365.287069\n",
       "Solar     11.207320\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
sp2668's avatar
sp2668 committed
1425 1426 1427 1428 1429 1430 1431 1432
   "source": [
    "# (a) Capacities for wind and solar.\n",
    "res_cap_v3 = network.generators.p_nom_opt / 1e3\n",
    "res_cap_v3"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
minor  
sp2668 committed
1433
   "execution_count": 38,
sp2668's avatar
sp2668 committed
1434 1435 1436
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
sp2668's avatar
sp2668 committed
1437
    }
sp2668's avatar
sp2668 committed
1438
   },
sp2668's avatar
minor  
sp2668 committed
1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North->H2         72.666054\n",
       "H2->North         69.478006\n",
       "North->Battery     0.907102\n",
       "Battery->North     3.344104\n",
       "South->H2          0.038317\n",
       "H2->South          0.069792\n",
       "South->Battery     0.049307\n",
       "Battery->South     0.133128\n",
       "North<->South     30.000000\n",
       "Name: p_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
sp2668's avatar
sp2668 committed
1460 1461 1462 1463 1464 1465 1466 1467
   "source": [
    "# (b) Store and dispatch power capacity.\n",
    "sto_cap_v3 = network.links.p_nom_opt / 1e3\n",
    "sto_cap_v3"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
minor  
sp2668 committed
1468
   "execution_count": 39,
sp2668's avatar
sp2668 committed
1469 1470 1471
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
sp2668's avatar
sp2668 committed
1472
    }
sp2668's avatar
sp2668 committed
1473
   },
sp2668's avatar
minor  
sp2668 committed
1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North H2 St.         1.956662\n",
       "North Battery St.    0.006984\n",
       "South H2 St.         0.001084\n",
       "South Battery St.    0.000399\n",
       "Name: e_nom_opt, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
sp2668's avatar
sp2668 committed
1490 1491 1492 1493 1494 1495 1496 1497
   "source": [
    "# (c) Energy capacities\n",
    "sto_engy_v3 = network.stores.e_nom_opt / 1e6\n",
    "sto_engy_v3"
   ]
  },
  {
   "cell_type": "code",
sp2668's avatar
minor  
sp2668 committed
1498
   "execution_count": 40,
sp2668's avatar
sp2668 committed
1499 1500 1501
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
sp2668's avatar
sp2668 committed
1502
    }
sp2668's avatar
sp2668 committed
1503
   },
sp2668's avatar