schach.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotnine as p9\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawPosition(fen):\n",
    "    figuren = pd.DataFrame({\n",
    "    \"f\": [\"p\", \"r\", \"n\", \"b\", \"q\", \"k\", \"P\", \"R\", \"N\", \"B\", \"Q\", \"K\"],\n",
    "    \"gif\": [\n",
    "        \"bp1024.gif\", \"br1024.gif\", \"bn1024.gif\", \"bb1024.gif\", \"bq1024.gif\", \n",
    "        \"bk1024.gif\", \"wP1024.gif\", \"wR1024.gif\", \"wN1024.gif\", \"wB1024.gif\", \n",
    "        \"wQ1024.gif\", \"wK1024.gif\"\n",
    "        ]\n",
    "    })\n",
    "\n",
    "    def square_color(s, z):\n",
    "        return \"black\" if (s + z) % 2 == 0 else \"white\"\n",
    "\n",
    "    def fen_to_dataframe(fen):\n",
    "        board_fen = fen.split()[0]\n",
    "        board = []\n",
    "        rows = board_fen.split('/')\n",
    "        for z, row in enumerate(rows):\n",
    "            s = 1  \n",
    "            for char in row:\n",
    "                if char.isdigit():\n",
    "                    for _ in range(int(char)):\n",
    "                        color = square_color(s, 8 - z)\n",
    "                        board.append({'s': s, 'z': 8 - z, 'f': None, 'c': color})\n",
    "                        s += 1\n",
    "                else:\n",
    "                    color = square_color(s, 8 - z)\n",
    "                    board.append({'s': s, 'z': 8 - z, 'f': char, 'c': color})\n",
    "                    s += 1\n",
    "        \n",
    "        df = pd.DataFrame(board)\n",
    "        return df\n",
    "    \n",
    "    df_board = fen_to_dataframe(fen).merge(figuren, on='f', how='left')\n",
    "\n",
    "    p=(p9.ggplot(df_board)\n",
    "    +p9.geom_tile(p9.aes(x='s-.5',y='z-.5', fill='c'))\n",
    "    #+p9.geom_text(p9.aes(x='s-.5',y='z-.5', label='f'), size=15)\n",
    "    +p9.theme_bw()\n",
    "    +p9.theme(figure_size=[8,8])\n",
    "    +p9.coord_fixed()\n",
    "    +p9.scale_fill_manual(values=['darkgray','lightgray'])\n",
    "    +p9.theme(axis_line=p9.element_blank())\n",
    "    +p9.theme(panel_background=p9.element_blank())\n",
    "    +p9.theme(axis_text=p9.element_blank())\n",
    "    +p9.theme(axis_ticks=p9.element_blank())\n",
    "    +p9.theme(panel_grid=p9.element_blank())\n",
    "    +p9.labs(x='',y='')\n",
    "    +p9.theme(legend_position='none')\n",
    "    )\n",
    "\n",
    "    fig = p.draw()\n",
    "    ax = fig.axes[0]\n",
    "\n",
    "\n",
    "    dff_board = df_board.dropna()\n",
    "\n",
    "    for i, row in dff_board.iterrows():\n",
    "        img = matplotlib.offsetbox.OffsetImage(\n",
    "            plt.imread(row['gif']), \n",
    "            zoom = 0.06)\n",
    "        ax.add_artist(matplotlib.offsetbox.AnnotationBbox(img,\n",
    "            (row['s']-.5 ,row['z']-.5)\n",
    "            ,pad = 0.0\n",
    "            ,frameon =False\n",
    "            )\n",
    "            )\n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvPOOsVzKpVI1TZO9e/famrVLnatSzLbNJ9x/+9vfJohE1KBBg0r6v7fznqqqKt8EERH/hxAEEyew5xGLxVy5QosTOB4Z5SAUICjs9NX//d//lRNPPLGsdnp7ey1fLxRUUj9rbGwsq30En9Vn8tSpUy0Pr7IbRFT3q3Xr1skpp5yitIZixeNxX4/d2traVJfhO8yM5BCEQ6GCUCMAOK2hoSFtVqPcIJJPavYj9bjyyitdawvh8+KLL6Y937Rpk60gkkwmlQcREQnceX5BOBQqCDV6jTBiYdOmTcay6uMM8x1/KCLyxz/+UcU/EQB4LhUI9u3bV3DdPXv2SENDg+19akNDg6xbt67gdpctW+bafU0QTolEwnjY6TeapklLS4sHlRX2/PPPqy7BNvOV7VSP0QqN3Xbv3q3in8i3CCNAxKg8vI9DC1GKQoP/wYMHZ33oDxkypKirD+3fv19OPfXUrO2krh6Ur65izzcB8hk/frzqEgzd3d2qS0AEsAcFIiiRSMjkyZM9a2/y5MkEEZTEKoToup4WGPbu3eta+0eOHElr67333stap7e3VyZOnOhaDYgOvx1+bedGjEC5OIEdiKh///d/FxGRgwcPuhZM/vCHP0jfvn1d2Taixw8XXjjhhBOMZXNQamhoUFEOQsRvQQTwCjMjQMSl7nqdepx99tklb+vss89O2xZBBOWyOt66kP/8z/8s+cacxdwzwlzbc889V8qvB4iIyMKFC1WXYInDtOAFZkaAiKipqTEuX9rb2ys9PT3y8MMPy4oVK2TPnj2OtPH0008bN38TERk5cqTMmjVLLr/8cqmqqjLuqF1ZWSmHDx92pE1Em9Mnkp9++umW2/TDrAzCa8WKFapLsNSnTx/VJSACIh9GKioqZNy4cVmvB+VKKVYfkG+88YYkk0kF1UAVv07v79ixQ5YuXSpLly4t+r0bN25kAIgstbW10tXV5Xm75s+EYcOGya5duzyvAeH06KOPqi4hcPr06SOzZs3Kej3IY7df/OIXkZ2JivxhWlZBJOjC+DvBWiwW820QKVdzc7PEYjH55je/qboU+EDqMKpCQWTnzp1lX37zqaeeytvGxx9/zOV94Zh77rlHdQk5+fVQW6sgEnRh/J3sivzMSC58Iws/C2sAsbJu3TqJxWJcjSuiCg343dhXz5w5M+u1ZDJp2VaqvqamJsfrAFQL2mFajN2CKfIzI0DQRCmImMViMenXr5/qMuChkSNHWr5eyknt5aqoqMjb5k9/+lPPagG8ErQwgmAijAAB8vDDD6suQamTTjrJsZPt4X/mWZEtW7Z4HkBySdVxxRVXGK9xnh5K0dzcrLqEvK6//nrVJSACCCNAgCxZskR1CcrNmDFDdQnw0KZNm0TTNGlsbFRdSpaHH35YNE2TTZs2qS4FAeX3847mzZunugREAOeMALDtj3/8o4iILweGCK/U+UJ+O0SR85gQdjU1NapLQAQQRoAASSQStgZkr776qq1jffv27Su6rsuBAwdE5OhlU/NdWrCnp8eoI5c+ffqkbU/TNDl06FDBWrq7u2XSpEkF12MAGF2p//s77rhD2tralNQwYMAAeemll5S0DQBhRBgBAiY1ILv88svllVdekQsuuEBuueWWkraVCgmpmxE6cY3z7u7ukrbXp0+frKDxve99T5544gmZMmWKPPTQQ2XXhnC49dZb5dZbbxURkfvvv19++MMfutre1KlTZdmyZa62gWjasWNHzgs1+MHixYtVl4AIiHwY4VtWBFUUTma/5ZZbSg5aiIb58+fL/Pnzjed9+vSR0aNHl7XNN998Uw4fPlxuaUBBHR0dgRuHWF362muqZkbhjsiHEQBAeHR3d5c9uCOIAIB3uJoWAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlKhSXUCpNm/erLqE0GtqalJdQujRj91HP3Yf/dh99GP30Y/dRz+GFWZGAAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoQRAAAAAEoQRgAAAAAoQRgBAAAAoARhBAAAAIAShBEAAAAASlSpLgAAACBqBgwYIJ988klR74nH48ZyW1ub0yUBShBGAAAAPKJpmsyePTvttULBom/fvvLlL3857bW6ujrp6upyvD7AaxymBQAA4JHMICKSPuORqaamJiuIiIjMnDnT0boAVQgjAAAAiuUKJF/5ylc8rgTwFodpueSWW26Rt99+2/JnVVVV8uMf/9jjigD40axZs/LuK15//XWPKwKKRz8uja7romma8fyaa66RBx54wHieGVAy14ez6MdqMDPigng8nrMzi4j09PTknZIFEA2xWKzgviIWi3lYEVA8+nF5dF03lvfs2WMsjxgxwnK9+++/35vCIoZ+rA5hxEHDhw8vKmTE43G5+uqrXawIgB91dnYW9aEWi8Vk+vTpLlYEFI9+XJpFixYZy9dee62IHB0/pMTjcYnH43L66acbr5kDyz/8wz9krZt6/OY3v3Gz9FCiH6tHGHHQjBkzin5PR0cHU65AxJx22mlFv6ejo8OFSoDS0Y9L89nPftZYTs1yfPjhh45s+7HHHksLJyiMfqweYcQh5fzRW11ZA0A4lTPNzyEC8Av6sfN0XZfGxsa0x4wZM9JmRVI2bdpka5upUHLhhRc6XW4o0I/9gRPYHdC/f/+yt1FVVSU9PT0OVAPAr6qrq8vextatW2X06NEOVAOUhn7sns2bN9tab9y4cZYh5eWXX5ZTTjkl6/Xe3l7jS1NulngU/dg/mBlxQGtra9nbOO+88xyoBICfjR07tuxtsK+AavTj8rh5+NSUKVNE13Xjkav99evXu1ZDUNCP/YMwAgAAoECuwODk9nVdl5EjR6a9fuedd3JOCXyDMAIAAOABcwCor6/3rN3333/fMvgQSOAHhBEAAACXZQ78Ozs7Pa/B6vCteDwudXV1ntcCpBBGAAAAXPSNb3wj7XkqEGiaZjxWrVrlWT2ZgWTmzJnS0NDgWfuAGWEEAADARd3d3cayOYiYeX2Z/8xA8nd/93eetg+kEEYc4MRl8rjUHhB+iUTCF9sAykE/Lo758KwhQ4aIiPjmUv5Wh2xFBf3YPwgjDnDijzdKOwAgqpy4SRY32oJq9OPS7dq1S0Ss73HxySefeF2OiEQ3kNCP/YMwUiYn/2ijsgMAosjJDy0+AKEK/bg45s9186FaVpy4gXKpohZI6Mf+Qhgpgxt/rGHfAQBR5MaHFR+A8Br9uDhPPvlk2vOampqc6yaTSbfLKcjte574Bf3YfwgjJXr11Vdd2/bVV1/t2rYBeGvFihWubXv69OmubRswox8X7/HHHzeW8w30zzrrrKyT2VVZsmSJsRzGL0fpx/5EGCnRXXfd5dq2Ozo6XNs2AG8tXLjQtW2zr4BX6MfFKTSQT4UTXdflmWeeKbu91OWBy3XttdemPf/sZz9b9jb9hH7sT4SRElx44YWutxHGbySAqJkwYYLrbXB4ANxGPy5PrlkRpw6LqqysdGQ7Kea6/uqv/srRbatEP/YvwkgJent7VZcAIADYVyAM6MfFMX+ZOGzYMNfbc/t8kz59+ri6fa/Qj/2LMOJjfjmGFAAAFO+jjz5SXUJJzLMjs2bNUlcIIoEwUqRvfOMbnrXl9d1YAThn0qRJnrXFoQFwC/24OOZZkaAGkTCiH/sbYaRIha4TDgAi7CsQDvTj0nlxiJb5CAqnL81rPvwr6Oex0o/9jTACAABQJvOA3Yv7hrh9KDeHisMrhBEAAIAy9OvXL+252wP56667ztXtA14ijAAAAJTh3HPPNZbdvpP5gQMH0m5O6Cbz7xL0Q7XgX4QRAACAEvXv39/T9urq6jxtD3AbYaRIbW1tnrW1YMECz9oC4KxEIuFZW4sXL/asLUQL/biw1tZWY9nNWZFdu3ZxHkeJ6Mf+RhjxsfHjx6suAUAAmAdDQFDRj3NbvHhx1tW5vPpy9NChQ8ayV4eHBRn9uHhVqgsAAAAIIvN5FG7NiljNhnh5lIb5DuwvvfSSfOtb3/KsbUQDMyMl8GIn4OWOBoA7vDg0wMvDDxBN9OPiaJrm2OFUmdupq6szxgeVlZXG65dccokj7YUZ/di/mBkBAAAo0s9+9jNj+fjjjxeR9PCgaVpZsyWZQSTzS8rzzz/fWH7kkUdKbgdQjZmRErk5c8GsCBAebn5Txrdw8Ar9ONvKlSuN5e3bt1uuU8oMyfz58wsGEa+1tLQYy0G+mhf92J+YGSlDRUWFJ3dZBRBs7CsQBvTj4m3YsKGo9VWfH5LLhg0bjNpmzpzpi5pKRT/2H2ZGShSPx13rzNxYCAiPWCzm2r4iFou5sl0gE/3Ynq6urrTn5hmFQjKDSEVFRc5Bv/kO7BdddJH9AiOOfuxPhJEi7dmzx5OwEI/HZeHCha63A8Ad69ev9+TDKRaLMRiBa+jHxamtrRVd142HHVYnvLe1tckTTzyR8z0ffPCBsfyjH/2otGIjhH7sb4SRIsybN0+uueYaz9rbuHGjXHDBBZ61B8AZZ5xxhsydO9ez9jZs2FDUN7CAHfTj8v361782wsa4ceOyfl7KYVkPP/ywY/VFAf3Y/wgjNv3zP/+z7N692/N2k8mkzJkzx/N2AZRmzpw5yvYVzc3NnreLcKIfO+Pcc881ljdv3pz2s8wgsnbtWlvnYvz2t781lt2843sY0I+DgTBiwwknnCBvvPGGsvZ1XZff/e53ytoHYM+AAQOU7yuWLl2qrH2EA/3YHnNwKPaqWVaHZe3YsaPg+zin1D76cXAQRmyYMmWK6hLkBz/4geoSABQwatQo1SXIsmXLVJeAgKMfl6aiwt6QqqenJ+253StTXXHFFWnPvZ4VMZ/Dcswxx3jadinox8FBGCnAT99C+KkWAOn8dCUVP9WCYPFT3/FTLbmYg4Su68b5IVYnpd96660iIlJdXW28tmbNGlvt1NbWyv79+9Pa8pr5kPE//OEPnrdfDD/1HT/V4leEkTz8OPj3Y01A1Pnxw8aPNcHf/Nhn/FhTpueee87WerfffntWQDFfFSuXmpoaOeecc4znmTMkKtx8882qS8jJj33GjzX5CWEEAACgRB9//LGtQ61Kuat637595Stf+Uraaz/84Q+LKxDwOe7AnoOfZyDi8Xig734KhImfv/GKxWKSSCRUl4EAoB+Xz+pz+Yorrkg7vCrfupkGDBggX/rSl9Je4+pZ+dGPg4mZEQAAABc89NBDac9XrVplK4jU1dURRBAZhBELfp4VSQlCjUDY+flbuJQg1Ai1gtBHglBjLm1tbcYjmUwWXP+YY46RmTNnpr1GECksCH0kCDWqQBgBAADwiS9+8YvGsqZpBBGEHmEkw9ChQ1WXYNuTTz6pugQgsqqqgnPK3Z133qm6BPgU/dhfMo96sDOTAvpx0BFGMpx55pmqS7Dt8ccfV10CEFmNjY2qS7CNfQVyoR/7R2YQYUbEPvpxsBFGAAAAFFq1alXac4IIooQwEnD9+vVTXQKAAAjSYQxALmHtxz/96U+NZYJI+IW1H5eKMGLyj//4j6pLKNq5556rugQgclpbW1WXULQgHcYAb9CP/eGll15SXUKg0Y+DjzBi8t5776kuAUAAsK9AGNCP/WHJkiXGMrMixaMfBx9hBAAAAIAShBEAAADFWlpaVJcAKEEYAQAAUGDOnDnG8oYNG9QVAihEGAEAAFCAc0QAEa4tBgAAoJimaapLAJRgZiQEjjvuONUlAAgABjsIA/oxwoB+/GeEkRDYuXOn6hIABAD7CoRBmPpxe3u76hKgSJj6cbk4TAsAAECBLVu2SFtbm+oyAKUIIyZB3SEkk0nVJQCRkkgkVJcAlI1+jDCgHwcfh2kBAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABAiSrVBZSqqalJdQmht3nzZtUlhB792H30Y/fRj91HP3Yf/dh99GP3jR8/XnUJRWNmBAAAAIAShBEAAAAAShBGAAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoG9AzsAhE08Hk973tbWpqgSAAC8QRgBAMUyQ0jm64QSAEBYcZgWACiUK4gUuw4AAEFEGLGpoqL4f6pYLGY8ACDTpZdemvZc1/W0R751AQAIAw7TsiEzTCQSibzrV1RUyLhx49JeO3DggNTW1jpeG4Dg6urqMpYzw0fqNU3TstYFACAsmBkpwGpWI99MR2VlZVYQERH5whe+4GhdAAAAQNARRkqUK5A0NTV5XAkAAAAQTISRMsyYMSPtOeeGAAAAAPYRRsqwZ88eY7mzs1NhJQCC7l/+5V+yXlu8eLGCSgAA8A5hpIBFixbl/XnqalmnnXZawW2Zr64Vi8VkyZIlTpUJIIDM9w+56aabjJPVRUQ0TZObb77Zcl0AAMKCMFLAzJkzXdv2ww8/zOV/gYirr69Pe65pWlooERFpaGjwsiQAADzDpX0VMl/KU9M0I5BUVlbKhg0bFFUFwEu9vb0F1+np6fGgEgAAvMfMiCKZ9xQw3+ist7eX2RIg5DZu3CjxeNzW/UO6urokHo8XvMcRAABBw8xIASoCQSqopGZLbrrpJvna177meR0A3BGPxy1fz/ySIvNwrTvuuENEOH8EABAezIwokjoufPTo0TnX0XVduru7ZfHixcySACHw5ptvWgaR1Kyo3dfj8bhs3brVlRoBAPASMyN5eBEAtm3bZnz7aTXoqKmpEV3XjVkSDtMAgilXCLHDPFuasmDBAhFhlgQAEGzMjOSgYibC6io6KanBSCwWk+rqai/LAlCmzCBSUVFhO4iY6bouFRXpu+1ch3wBABAEhBELkyZNsnzdfJL5+eef71r7mqbJb3/7W8v2RUTGjh0rNTU1rrUPwDmZYSF1kYpS9fb2ZgUZAgkAIKgIIxa6u7uzXsv88F+1apWrNZx11lmWsySpOsaMGeNq+wDKZxVEnEIgAQCEAWEkg9XhWZWVlQoqOSpfIOGkdsC//umf/intud0gku9wzUyZ28xsEwAAvyOM2GB1w7H+/ft71j6BBAiet956y1hOJpOutWPetrlNAACCgDBiUszA3s6NypxkFUgeffRRESGQAH5jPmRq0KBBtmc6zDZu3GhrPU3TZNCgQZZtAwDgd4SRP7n77rttr1vKwMIJmYdkzJ07V0kdAOzr6OgIRRsAALiBMPIny5cvt7XeM888424heWRe0lOEw7UAvzHPTDh5wnoh5raYHQEABAU3PZTCA3lN0ySZTFqGAa9pmpZzgNPb26v0ZHsAxck3y9rS0pL1mpfhBgAAL6gfXQeEH4JISuaAJPV8woQJCqoBkGKekdi5c6fn7e/YscOyFgAA/Mo/I2xFgnh4U75gxMwI4A/HHnus522OGDHC8zYBAChH5MNIUH300Udpz1OzI01NTSrKAVACXdezHint7e05fwYAQFhEOowEcVYk5TOf+YzqEgAAAICyRDqMhE3q5oxBDlkAAACIjsiGkTAM2DOvxMP5IkB4NDc3qy4BAADXRfLSvlVVkfy1AQTAxRdfrLoEAAA8E8mZkcbGRtUlOGbBggVpz5PJpIiEY+YHiKLly5fbvgkrAABBF7kwUl1drboER91xxx1pz/PdRA0AAADwk8iFkbFjx6ouAUAEfPDBB5FoEwCAckQujKiSSCQ8a+uzn/2siIhceeWVnrUJQKStrc1YVnEDQnOb5loAAPCrSIURVedRuB1EMg/Nevvtt0VE5KWXXnK1XQD5eXnYJIdoAgCCKFJhxEoymXTtzsZ1dXVGEGGgAERD5ozEX//1X7ve5plnnpm3BgAA/Coy17i9/fbbs14zhxBd1x0NDJmzIePHj3ds2wD87bXXXpOJEyeKiMjvfvc70TTNtS89Mvdbr732mmzbts2VtgAAcFpkZkZWrlxZcB2nBgtenh9SSNiuHgYEwbZt27IuIa5pmisPs8bGRoIIACBQIhNG7GhpaSl7G6qCyOuvv572/NlnnxURrh4GqHLbbbfZ+hLEKStXrpTbbrvNs/YAAHBCpMNIbW1t2vONGzeWvK2KioqcQeSss84qebt2pQ4JSck8hhyA99w6NEt1WwAAOCXSYeTgwYM5D3coRiKRkPb29pw/59r/QDTNmTMn7XlHR4fouu7Io6OjI29bAAAEQWROYHdLocOybrzxRo8qAeA3mRfJcNKgQYPSLrzBzAgAIIgiPTNSjm3bttk6P+SZZ57xoBoAfhOPx43lNWvWuNbO6tWrLdsEACAIIhNGnDyxPJFIyKFDhwqup+omiwD85fTTT3dt29OmTXNt2wAAuC0yYcQpdkPN1KlTXa7Evn79+qkuAYgU8/kbXV1drrdnboNzRwAAQRKpMFLu7MjLL79sa73KykrZv39/WW05oaamRkREVq1apbgSIFrM52/U1dW5do+R1KOurs6ybQAA/C5SYUSkvGO3zR/4uVRWVkpTU1PJbTgpdc+Bm2++WW0hAAAAgIXIhZFBgwaVNENi5z1VVVW+CSIihBAAAAD4W2Qv7WsVLqZOnWp5eJXdINLY2OhIbaVat26dnHLKKUprAJDOq8OmyrlXEgAAqkRuZiSfF198Me35pk2bbAWRZDKpPIiIiNx7772qSwAAAABsi+zMSC7m8GHnG01N06SlpcXNkmx7/vnnVZcAIIPbNyVkRgQAEGTMjJRp/PjxqkswdHd3qy4BgIi0tbVlvWa++lW58m3Lqm0AAPyKMFIGv93U0M6NGAF4o62tTSZPnmz5s3Iv5Wtl8uTJBBEAQOBwmFaJ/BZEAPjPt7/9bRER6d+/v7S2trrSxlNPPSWffvqpK9sGAMBthJESLFy4UHUJljhMC/CnTz/9NG3WYvny5fL000+XtK2zzz5b5s6dm7ZtAACCijBSghUrVqguwVKfPn1UlwBEWkNDg/T29oqISG9vr/T29sr//M//yIoVK2TPnj2OtPH000+nBZmRI0fKrFmz5Itf/KJUVlZKZWWliBy9AWtnZ6cjbQIA4BbCSJEeffRR1SUAUCAej7vexpo1a+T0008XEZEXXnhBpk2bVvA9O3bskKVLl8rSpUuLbq+9vV22bNlS9PsAAHAKYaRI99xzj+oScurbt6/qEoDQcTuEdHR0yKBBg7JeP/30043LAe/du1cGDx7seNupy5Kfcsopct111zm+fQAACuFqWiHCYVqAc+LxuO0gctttt8lzzz0nuq4X/bAKIpkGDRpU0rafe+45ue222wpuf926dZ7M/AAAkImZkRAhjADOsBqYJ5NJT24w6ORNEmfMmCEzZsyQ7373u3nbSonH47JmzRr54IMPym4bAAA7mBkpQnNzs+oS8rr++utVlwAE3tq1a9Oer1u3TnRdtwwiixYtkvr6eqmrq5M+ffpIXV2d1NXVSX19vbS2tsqiRYvkhRdeyHrfCy+8IGeffbZUVVXlvIdI5utVVVVy9tln59zeokWLpLW11ajHXFN9fb0sWrQo632pGRSzadOmGSfBAwDgNmZGiuDEN5VumjdvnuoSgMC7//77jeU9e/YY52rYmRU5fPiwsfyb3/xGfvOb3zhWV29vr/zXf/2X/Nd//VdR70vVdMstt8gtt9yS9fNUIDH/fueffz43UAQAeIIwEiI1NTWqSwBC5ZhjjvG8zV/+8pciIvLlL3/Zk/a8OPQMAIBcCCMAYNLW1mbrZO5f/epXcvDgwYLrDRkyRCorK+XAgQMiIlJbWysfffRRzvUPHTpk1JHL8OHD07bX29sru3fvLlhLv3795Nxzzy24HrMiAACvEEaKsGPHDhk5cqTqMnJavHix6hKAUEgNxu+55x555ZVX5IILLpBZs2alrWMniIhIVkj45JNPyq7PHGaK2d7BgwezgsYvfvELeeKJJ2TKlCmcdwYA8BxhpAgdHR2SSCRUl1GUmTNnqi4BCKwbbrhBdQmumzVrVlbQAgDAK1xNCwAAAIAShBEAAAAAShBGAAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoQRAAAAAEoQRgAAAAAoUaW6gFJt3rxZdQmh19TUpLqE0KMfu49+7D76sfvox+6jH7uPfgwrzIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUIIwAAAACUqFJdAACESUVFhSSTyaLeE4vFjOVEIuF0SQAA+BZhBAAcYg4VIoWDRUVFhYwbNy7ttQMHDkhtba3jtQEA4EccpgUADsgMIrleS6msrMwKIiIiX/jCFxytCwAAPyOMAICLcgWSpqYmjysBAMB/OEwLgTVr1ix5++23LX9WVVUlr7/+uscVAUfpui6aphnPZ8yYIc8//7zxPDOgZK4PZ91yyy159xU//vGPPa4IgB8xrlCDmREEUiwWy7nDEBHp6enJe4gM4DZd143lPXv2GMudnZ2W691///3eFBYx8Xi84L4iHo97WBEAP2JcoQ5hBIHS2dlZ1M4gFovJ9OnTXawIOGrRokXG8rXXXisiIsOHDzdei8ViEovF5LTTTjNeMweWf/iHf8haN/VYsmSJm6WH0vDhw4sKGfF4XK6++moXKwLgR4wr1COMIFDMAzm7Ojo6XKgESDdz5kxjOTXL8eGHHzqy7YcffjgtnKCwGTNmpD3Xdd3yYdbR0cHhckDEZI4r7O4r4BzCCAKjnEEYAzioouu6NDY2pj1mzJiR9eEmIrJp0yZb20yFkgkTJjhcbThkzohY/Vubf3bxxRcbz2fPnu1aXQD8xer8vVwy9xWMK5xDGEEgVFdXl72NrVu3OlAJULzNmzenPZ577jnL9caNG2f5jdy6dess1+/t7WW2JEP//v3TnucbXKQsX7487XlVFdd2AcIuc1xRyr6CcYUzCCMIhLFjx5a9jfPOO8+BSgBrbgaCKVOm5DxcwNw+V4USaW1tNZbtDC6s1mVfAYSfeVzBvkItwggAOKyYD7ZSt6/ruowcOTLt9cWLFzNLAgAIFMIIAJTJHADq6+s9a/f999+3DD4EEgBAUBBGAKAMmQP/zPuIeMHq8K1YLObIuVYAALiJMAIAJZo0aVLa81Qg0DTNeKxatcqzejIDydixY6Wmpsaz9gEAKBZhBABK1N3dbSybg4iZ15eKzQwkY8aM8bR9AACKQRhBICQSCV9sA0gxH541ZMgQERHp6elRVU4aq0O2oqKtrc1YLuYGhuZ1zdsAEE7mMUGp+wrGFc4gjCAQnBhMRWlABm/t2rVLRKzvh/PJJ594XY6IRDeQZN7w0M4gI3OdzG0ACJ/MfWIp+4qo7FfdRhiB7zn5x86OA04w9yPzoVpWMm/C56WoBRKrEGHnMstW6xBIgPCy2heWuq8I+37VC4QR+Jobf+TsOFCOu+++O+15vhPEk8mk2+UU5PY9T/wiMzzku0GkFav1CSRA+GSOAZzYVzCuKA9hBL61YsUK17Y9ffp017aNcFu+fLmxnO8D7KyzzirqOGQ3LVmyxFgO44fmq6++mva8nACW+d6rr7665G0B8JfMcYWT+wrGFaUjjMC3Fi5c6Nq2Ozo6XNs2wqvQQD714aTrujzzzDNlt5e6PHC5rr322rTnvb29ZW/TT+666y5j2YmZIPM22FcA4WEeV7Cv8A/CCHxpwoQJrrcRxm+I4Z1cH2ROHRZVWVnpyHZSzHV58ffllQsvvNCV7Y4YMcJY5nAtIPjc2u+Z9xWMK0pDGIEvhe2bWwSf+UNm2LBhrrfn9vkmTocdVcz7CifPj9mxY4dj2wKgHvsK/yKMAECRPvroI9UllMT8AdzU1KSwkuDxy/k/ABA2hBH4zqRJkzxriylV2GHuJ0ENImH0jW98w1h246ph5m3Onj3b8e0D8IZ5XOH2voJxRfEII/CdQvdtAFTy4hAt87fwTn9wmg//CvqHJvsKAHawr/A3wggA5GEesHtx3xC3DwficCMAgJ8QRgAgh6qqqrTnbg/kr7vuOle3DwCA3xBGACCHxsZGY9ntO5kfOHAg7eaEbuL4ZgCAXxBGAMBCdXW1p+3V1dV52h4AAH5AGIHvJBIJz9pavHixZ20hWMaOHWssuzkrsmvXLs7jKFFbW5ux7Ma/4dy5c43lBQsWOL59AN4wjyvc3lcwrigeYQSR1traqroERNjixYuzrs7lVRg/dOiQsXzllVd60mbQPPbYY8by+PHjFVYCwM/M+wrGFcUjjABABvN5FG7NimiaJjfffHPaa17OCvbp08dYfumllzxr101PPPGEY9sy360ZQLiwr/AXwgh8yYtBmZcDPwSfpmmOTe9nbqeurs7oj+afXXLJJY60F2bmQ7UuvPBCx7ZrvpKauQ0AwWT+zHdrX8G4ojRVhVcBgOi4/fbbjeXjjz9eRNIDgqZpZc2WZAaRzA8v8+FAjzzySMntRFW5/z+pbQAIN/YV/sHMCHzLzW8Y+PYCuaxcudJY3r59u+U6pXwAzZ8/v2AQ8VpLS4ux7PXVw5yUOXNRzgAh873MigDhkbnPdXJfoXp/HmTMjMDXKioqPLnrNVCMDRs2FLW+1QeeHz64NmzYYNQ2duxYX9RUqsx9RSnfevItJxB+7Cv8h5kR+FYsFnMtiHCjNxSjq6sr7bl5RqGQzA+tioqKnIP+s846y1i+6KKLiqgw2uLxuOW+4q233rK9jf379+fcNoBwyDWucGJfwbiidIQR+M769es9+aOOxWIM+GBLbW2t6LpuPOywOuE9kUhIe3t7zvd88MEHxvKPfvSj0oqNkD179liGhdT/00knnWR7W/X19cb7zCekihwNJAsXLiy7XgBq5BpXOL2vYFxRGsIIfOWMM85Iu3mQ2zZs2FDUt9yAiMivf/1rI2yMGzcu6+elHJZ14403OlZfFMybN0+uueaatNeKCYv5HDlyJGs7GzdulAsuuKDsbQPwltW4ws19BeOK4hFG4Btz5syR3bt3e95uMpmU5uZmz9tFcJ177rnG8ubNm9N+lhlEtm3bZutcjGeeecZYdvOO72Hwz//8z2n7ipqaGlf+zXRdl6lTpxrPk8mkzJkzx/F2ALgjc1zh5b6CcYV9hBH4woABA+SNN95Q1r6u67J06VJl7cM/zMGh2JMUrQ7LMt/pPBeONbbvhBNOSNtXzJ8/X7q7u11rb+3atbJlyxbjua7r8rvf/c619gA4I3NcoWJfwbjCHsIIfGHUqFGqS5Bly5apLgE+VFFhbzfZ09OT9tzulanM36aJeD8rYr4Tcb9+/TxtuxRTpkwxlqdNmyb33Xef622OGTNG1q9fbzz/wQ9+4HqbAMpjHleo2lcwrrCHMALl/PStsJ9qgTrmIKHrunF+iNVJ6bfeequIpN+n4+WXX7bVTmVlZdqVWVQcnmU+7GjVqlWet1+MzJPVV69e7VnbEydOlPr6+py1APCPzM9ylfsKxhWFEUaglB//SP1YE7y3Zs0aW+vdfvvtWQGlrq6u4PsqKyulqanJeH7FFVcUV6ALbr75ZtUl5JQ5+FcR3Do7O9OeE0gA/8n8DPfDvoJxRX6EEQCwMGjQIFuHWpVyF96qqqq0ICIi8sMf/rC4AiNM5Qn+XFwACA72FcFAGIEyfv6mwM+1wVuJRCLrYZ6Cz1y3kKqqKmlsbEx7jQ+t/MwzEObD4VR59tlnjWVmRwD/MH92+21fwbgiN8IIABTpxRdfTHu+adMmW0EkmUwSRMp0+PBh1SXImWeeqboEAAWwrwgOwgiUCMI3BEGoEeqYZ0rsBApN07JuhEUQKcw883DVVVcprCSd+f+O2RFAPfNntl/3FYwrrBFGAMAD48ePN5Y1TSOIlODBBx9UXQKAAGBfESyEEXiuqqpKdQm23XnnnapLQAhkfhuWTCYVVRIsQ4cOVV1CXoMHDzaWn3zySYWVANHm93GFeV/BuCIbYQSeyzxm3s8ef/xx1SUg4PxwmcmgMh9v7cd/tz179hjL7CsAdczjCvYVwUMYAQCXLFy4MO25Hz8kAQBQiTACFOD36V/414oVK4xlgkg4DRw40Fju16+fukIA+Jp5X8G4Ih1hBJ5qbW1VXULRgnRYGfzj0UcfVV1CoP3jP/6jseznILd3715j+dxzz1VYCRBN5nFFUPYVjCvSEUbgqffee091CYAn7rnnHmPZzx+QfsW+AoAd7CuCjzACAAAAQAnCCAC4KPNGhwAA4M8IIwDgsObmZmN5w4YN6goBAMDnCCMA4DDOEYmWpqYm1SUACAD2FdYIIwDgIk3TfP/ws1GjRqkuoaAbbrhBdQlA5LGvCC4udAzYoGka33YDCrz77ru+D0xmxx13nLz//vuqywAiJ2j7CsYVf8bMCGDDzp07VZeAANmxY4fqEqAI+woAdrCv+DNmRgDAYR0dHZJIJFSXAQCA7xFG4CkGaADsaGtrU11CSZLJpOoSgEhhXBF8HKYFAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJapUF1CqpqYm1SWE3ubNm1WXEHr0Y/fRj91HP3Yf/dh99GP30Y/dN378eNUlFI2ZEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoQRAAAAAEoQRgAAAAAoQRgBAAAAoARhBAAAAIAShBEAAAAAShBGAAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAElWqCwAAAEB+s2bNkrffftvyZ1VVVfL66697XBHgDGZGAAAAfCwWi+UMIiIiPT09EovFPKwIcA5hBAAAwIc6OzuLChmxWEymT5/uYkWA8zhMCwAAwIdOO+20tOe6rluup2masdzR0eFqTYDTmBkBAADwmcwZkVxBJPWziy++OOd7AT8jjAAAAPhIdXV12vN8QSRl+fLlac+3bt3qZEmAawgjAAAAPjJ27Fhj2U4QsVr3vPPOc7QmwC2EEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoQRAAAAAEoQRgAAAHwkkUgYy+YbGhZiXte8DcDPCCMAAAA+knnTQjuBJHMdbnyIoCCMAAAA+IRViLBzrxGrdQgkCALCCAAAgA9khgdd14u+6WHm+gQS+B1hBAAAQLEVK1akPS8mhGTKfO/06dNL3hbgNsIIAACAYgsXLjSWywkiVtvo6Ogoe3uAWwgjAAAACk2YMMGV7Y4YMcJY5nAt+BVhBAAAQKHe3l5j2YlZkZQdO3Y4ti3ALYQRAAAAAEoQRgAAABSZNGmSsezkrIjVNjlUC35EGAEAAFCku7tbdQmAUoQRAAAAAEoQRgAAAAAoQRgBAAAAoARhBAAAAIAShBEAAABFEomEsaxpmuPbnzt3rrG8ePFix7cPlIswAgAAEFKPPfaYsdza2qqwEsAaYQQAAMAnnnjiCce2Zb6zO+BXhBEAAACFzIdqXXjhhY5tt6qqyrINwE8IIwAAAD7ixLkjbpx/AriBMAIAAKBY5sxFOWEi873MisDPCCMAAAA+UFGRPiwrJZAwI4KgIYwAAAAoFovFJJlMZr3+1ltv2d7G/v37c24b8CvCCAAAgCLr16+3DAu6rouu63LSSSfZ3lZ9fb3xPvPJ6yJHA8lFF11Udr2A06oKrwIAAACnnXHGGbJ79+6013Rdd2TbR44cEZH0w7Y2bNggLS0t0t7e7kgbgBOYGQEAAPDYnDlz0oJITU2NY0HETNd1mTp1qvE8mUxKc3Oz4+0ApSKMAAAAeGjAgAHyxhtvGM/nz58v3d3drrW3du1a2bJli/Fc13VZunSpa+0BxSCMAAAAeGjUqFHG8rRp0+S+++5zvc0xY8bI+vXrjefLli1zvU3ADsIIAACARzJPVl+9erVnbU+cOFHq6+tz1gKoQBgBAADwQObg341zRArp7OxMe04ggWqEEQAAAI+pCCJ+aBvIRBgBAABwmXkGorq6WmElRz377LPGMrMjUIkwAgAA4KHDhw+rLkHOPPNM1SUAIkIYAQAAcJV55uGqq65SWEk68+FazI5AFcIIAACARx588EHVJQC+QhgBAABwSVVVleoS8ho8eLCxfOeddyqsBFFFGAEAAHBJY2OjsezHq1jt2bPHWH788ccVVoKoIowAAAAAUIIwAgAAEGEDBw40lv1+WBnChzACAADggtbWVmPZj4dopezdu9dYNh9WBniBMAIAAOCC9957T3UJgO8RRgAAAAAoQRgBAAAAoARhBAAAAIAShBEAAICIa2pqUl0CIoowAgAA4KJRo0apLqGgG264QXUJiCguJg0AAOCid999VzRNU12GbZqm+fpSxAgXZkYAAABg2Llzp+oSECGEEQAAAABKcJgWAACACxKJhOoSAN9jZgQAAACAEoQRAAAAAEoQRgAAAAAoQRgBAAAAoARhBAAAAIAShBEAAAAAShBGAAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKVKkuoFSbN29WXULoNTU1qS4h9OjH7qMfu49+7D76sfvox+6jH8MKMyMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQI7B3YAQCA/8RisbTniURCUSUAgoAwAgAAyjZx4kQ5cuRI1uuxWEyqq6vltddeU1AVAL8jjAAAABERWblypdx+++1pr9mZ2ejXr59lEEk5cuSI9OvXTw4ePFh2jQDChXNGAACAxGKxrCCSej1f0BAROemkk4zl++67T3RdF13XZcmSJZbrFKrD/IjH4zZ/AwBBRBgBACDiMs/zyDRx4kTb25o/f76xfO2119p+36ZNmyzr2Lx5c8H6AAQXYQQAABhSsxq6rqe97nYguOCCC/L+nEAChBNhBACACOvq6jKWr7jiirSfZQYSt5iDRk1NTc5ABCB8OIEdABSaNWuWvP3225Y/q6qqktdff93jihA177zzjrH8wx/+sOztaZpmhAhN04p+f3d3d9rzhx56yAhJBw4ckNra2rJrBOAfzIwAgCKxWCxnEBER6enp4dAUuG78+PHGcinhQST7iluapmVtq9T7jVx++eXG8vbt20vaBgD/YmYEADzW2dkpp512mu31Y7GYDB48WFavXu1iVQiDYcOGyfDhw7NeLyYIpGY2rrjiCnn44YeN17/73e/mfV8ikcgZnsu58aE51DQ2NuZd16r9jz76SD7++OOS2wfgLmZGAMBjmUHEfHx8rmPlOzo6vCwRAXTZZZdZBhGRo4P0Dz/8MOd733jjjbTnmqalBRERkfPPP79gDVahw04Qeeqpp9LaHjNmjLFsx4cffpgzCA0fPlwuu+wyW9sB4D3CCAB4KHPAlO8EXV3X5eKLL875XsDs97//fdrzzFmEv/mbv8n53mQyKZs3b87583JmNuw4/vjj055v3bq1qMO8Mn+3zN89898GgH8QRgDAI9XV1WnP7VwpaPny5WnPt27d6mRJCInm5mZjefTo0aLrumzevDmrj+Wb3ejt7S15ZsMJ+drJ97PM38n8u48ePdp43fxvBMA/CCMA4JGxY8cay8VcstS87nnnnedoTQgHcx958803bf/Mb0oJQ+bfKfPvKt/PAPgDYQQAAORUzOGBX/7yl7NeO/fcc50sB0DIEEYAAAi4qqo/Xxwz81wL8/Nly5a5Wsf//u//Zr1mvo9JIaWcF2X+nfL97uZ/IwD+QRgBACDgMm+OmbrPR+bgfOrUqSVt388D+czfKdfvzg1EAX8ijAAAEAKFzq0o50T0Qvf3cMK3v/3tkt/r5u8OwF2EEQAAQiLfoHvChAneFVKC//7v/y75vS0tLTl/RhAB/I0wAgAeMQ+K7N7MLXNdBlYoJJFIGA+z3t7eorf16KOPGsuFzueYNm2asZx5407zz9yQTCbTnuf6NwDgP4QRAPBI5mDOTiDJXIcbH6IcxfafuXPn2l537969Jf1MJL2uYi/By98EEGyEEQDwgNWAyc6gy2odBl+wy2pm4ODBgwoqcceBAweyXmM2BAgWwggAuCwzPGQewlKI1foEEpRq8uTJRa1v7ntu9Dvz1bDGjBlT1Hu/8IUvOF0OAI8RRgDARStWrEh7Xs5doDPfO3369JK3hWjzU5jdv3+/sbxlyxbb7/PT7wCgdIQRAHDRwoULjeVygojVNjo6OsreHqKrosL+EOCss84ylq1ORs88ed1q+eSTTy62xJyKqR2Av/HXDAAucetSqiNGjDCW+XYYxTCHg3Hjxtl+3zPPPGMsW52MXugEdRGRw4cPZ71W6onr5tqdCPkA1CGMAIBLzJdSdXLAtGPHDse2hWgLYpgNYs0AciOMAAAQYm+88Yax3NvbW3IwdvpEdvM2brnllpJrMod+8+8KIBgIIwDggkmTJhnLbhxG4vYVjhAe5hsCVlVViYjIZZddZrzmh/5jPrcqH3Otl19+uYj8+XcSyb75IQD/I4wAgAu6u7tVlwDk9PDDD6c9/9KXvmTrfRMnTjSWU1dzO/30043XrMKA+bVygk9mjQ899FDJ2wLgH4QRAAAiyDy79n//93+23rN+/XpjOXU1t3379hmvaZqW9R6r10o5cd1cIyetA+FBGAEAIKKOOeYYY9kPh2vlYq7NXDOA4COMAAAQUbt37057nkgkCr6n3POVzO8xz7TksnHjxrTnmTUDCDbCCAAAIbdp0yZj+eDBg2k/M4eLCy+80LOaRNLPQcnla1/7mrGceXjWgQMHjGXz7wggOAgjAOAC8zfMVsfMl2vu3LnG8uLFix3fPsLFPIivra11pY2enp6SflaOuro6Y5nzSIBgIowAQAA99thjxnJra6vCShAGxQ7krdavrKzMub7Vz5xoE0DwEUYAwANPPPGEY9sy3+QNcMr+/ftVl5DTJ598oroEAC4hjACAS8yHajl5LL75Jm92TjgG7BgwYEDOnx08eFB++ctfiqZpxiOT+WdWDzvrP/jgg2nngaT079+/vF8OgG8RRgDAI06cO+LG+SeItp6enoKBoba2VmbNmuV6Lddcc03aeSCZdWiaJkeOHHG9DgDeIYwAgIsyZy7KCROZ72VWBMXIvKhC6lFdXa2wquLV1NRkhSf+FoDgqiq8CgCgHBUVFZJMJo3nmqYVfTIuMyJQ6dChQ0YIcIOu63L48GHp27evK9sH4F/MjACAi2KxWFoQSXnrrbdsbyPXicV+vmM2gmPXrl2i63reR58+fVwNxJqmSZ8+fQrWsWvXLtdqAKAGYQQAXLB+/XrLsJAaVJ100km2t1VfX2+8z3zyusjRQHLRRReVXS+iJ9WnhgwZoroU24YMGWLUDSAcOEwLABx2xhlnyO7du9Nec2rwlDp51/wt9YYNG6SlpUXa29sdaQPRUMrhgn7BYYtAeDAzAgAOmjNnTloQqampcWXAp+u6TJ061XieTCalubnZ8XYQLk5eUEEVLuQAhAszIwDgkAEDBsgbb7xhPJ8/f77cd999rrW3du1aefPNN2Xs2LEicjSgLF26VObNm+damwi+RCKRdghhqTMkBw8elM2bN8sHH3wgO3fulK1bt8qHH36YdY5TfX29fOYzn5HRo0fLiBEj5Nhjj5Wmpibp169f0W0SRIDwIYwAgENGjRplLE+bNs3VIJIyZswYWb9+vZx88skiIrJs2TLCCArKDCTDhg2Tp556SiZPnqywqtx+//vfS2tra9prBBEgHDhMCwAckHmy+urVqz1re+LEiVJfX5+zFsCKeTC/a9cu3wYREZHJkyenXUmLIAKEB2EEAMqUOfhXcVJwZ2dn2nMCCewo9kpa1dXV0tDQIIsWLZIXX3yx4KV4cz1efPFFWbRokTQ0NBR908UgXf0LQGGEEQBwkMqrEwX1ykhQ57nnnkt7/tWvfjVviDh8+LDs27dPbrrpJjn11FNLbvfUU0+Vm266Sfbt2yeHDx/O2+ZXv/rVvDUDCDbCCACUwTwDUew3vG549tlnjWVmR2CH+ZCnVatWKazEmrkmDs8CwocwAgAOOXz4sOoS5Mwzz1RdAgAAthFGAKBE5pmHq666SmEl6cyHazE7AifNnj1bGhoaRNM0Rx4NDQ0ye/Zs1b8WAIW4tC8AOODBBx9UXQLgCC9vhLh//35ZtWpVIG++CMAZzIwAQAmqqvz9Xc7gwYON5TvvvFNhJfC7IM2eBalWAPYQRgCgBI2NjcayH69itWfPHmP58ccfV1gJ/Mzu4H7GjBny/PPPl3wp30KP559/XmbMmGGrlilTppTxGwPwG39/tQcAAFxRW1ub9lxlqJ4+fXreS/YeOnRI+vXrJyIiXV1dXpUFwAPMjABASA0cONBY9vthZfDe5z73OWPZj7N7Zn379pWhQ4caz5PJpMJqADiJMAIARWptbTWW/TyI27t3r7FsPqwMCKKPP/7YWDb/DQIINsIIABTpvffeU10CUJZTTjnFWD7jjDMUVmLfp59+aiy///77CisB4CTCCAAAEWMe2P/ud79TWIl9/fv3V10CABcQRgAAiKixY8eqLqEo//7v/24sc5lfIBwIIwAARNSWLVtUl1CUSy+9VHUJABxGGAGAEGtqalJdAnxO0zTRNE1GjRqlupScRo0aZdSZcuONNyqsCIBTuNYjAJTIz4O3lBtuuEEuv/xy1WXAZ7Zt2yZ/+Zd/mfba9u3b0wb7hTQ0NMioUaNk4MCBMnfuXGlpaRERkc9//vNp673++usiItLe3i7Lly+Xffv2ybvvviudnZ1l/Q4XX3xxWe8H4A+EEQAo0bvvvlvU4E01TdN8fSlieOfQoUOSSCTKOu+is7NT2tvbRURk9erVTpVmSyKR8LQ9AO4hjABAROzcuVOOPfZY1WXARzIH9X/84x9l9uzZiqqxtnLlSu6TA4QYYQQAAIjI0ZtjMusAwEuEEQAoEoM1AACcwdW0AAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoQRAAAAAEoQRgAAAAAoQRgBAAAAoESV6gJK1dTUpLqE0Nu8ebPqEkKPfuw++rH76Mfuox+7j37sPvqx+8aPH6+6hKIxMwIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAgEiJxWISi8VUlwEAEMIIACCiKisrVZcAAJFHGAEARNKRI0dUlwAAkUcYAQAAAKBEleoCAABwS75zQ1paWrJeSyQSbpYDAMjAzAgAAAAAJZgZccmsWbPk7bfftvxZVVWVvP766x5XBBSPfoygO+GEE2T79u221wUAeIsw4oJCl4zs6emRWCzG4QDwNfoxwuDJJ5/Mei3Vt9vb26WiggMEAEAl9sIO6uzsLOra9bFYTKZPn+5iRUDx6McAAMArzIw46LTTTkt7ruu65XqaphnLHR0drtYEFIt+DAAAvMLMiEMyv0nONYBL/eziiy/O+V5AFfoxAADwEmHEAdXV1WnP8w3gUpYvX572fOvWrU6WBBSNfgwAALxGGHHA2LFjjWU7Azirdc877zxHawKKRT9GmPXv319isVjaDF5LS4vEYjHp37+/wsoAINo4ZwQAEGrNzc15A/aJJ54oFRUV0t7e7mFVAAARZkYAACG2c+fOrCCi63rWa8lkUnbu3OllaQAAIYwAAELsb//2b43lH/zgB2khRNd1WbZsmeW6AABvEEYAAKGUeYW3b37zm1nrXHnllXnfAwBwF2EEABB6hS5TDQBQgzDigEQiYSybbwRXiHld8zYAFejHCJOrr77aWH766acLrv+b3/zGWL7mmmtcqQkAkI2raTkgc1pf07SC37RlDvZisRgDOShFP4afTJs2Tfbu3Zvz54X62dq1a43lv/u7vyvY3tlnn20sv/DCCznXe+CBB+TBBx/M+fOTTz456/47AIDcCCNlsjq+2M6Uv67rDOTgG/Rj+IXdczbM67nd3y688ELbbaxfv96ojb8DACiMMFKGzA/NYo87Tq1vHswxkIPX6Mfwg3JOHE+995133pFPP/3UqZJkypQp0tXVVfL7CSUAUBjnjJRoxYoVac/LOQEy873Tp08veVtAMejH8AOrILJ161bjfiBWDysnnniiY1fDisVilkHkP/7jP/LW9fjjj1tuCwBgjTBSooULFxrLTlyJxbyNjo6OsrcH2EE/hmqZA/Xe3l7RdV3+8i//Mu/78gWTWCwm//qv/2o83717d8E6Cq2TautrX/ta3vW+/vWvW9YVi8UcnbUBgLAgjJRgwoQJrmx3xIgRxjLfpMFt9GOoZnWIYEVF8R9LVoP/G2+80VgeOnRowW3kWiffTIydusxOOeWUoq5UBwBRQBgpQW9vr7Hs5PXpd+zY4di2gELox/ATp2bmSgkzVl544QXHajIHkPHjx5e9TQAIE05gBwB4zjwr8sQTTzi23VTILvVeOSLO3wQxmUxygQcAyIGZkSJNmjTJWHbjrr3mbXKIC9xCP4afzJkzx/FtZvbrXOHE7SDi9nYBIOgII0Xq7u5WXQJQNvoxVDIHVPPhgk6zG0hyre+0ffv2GcuEdAA4ijACAFDGqXM8cskMGC+//LKxbA4nXpzr1NDQ4HobABA0hBEAQKj19PQYy6eccoqIZM+SmK8CBwDwDmEEABBqlZWVac+9Ok8EAFAYYQQAoMTAgQM9aytX4Bg0aJBnNQAAshFGimS+HKMbN6+aO3eusbx48WLHtw+I0I/hD+YTur1gFUg6Ojo8rQEAkI4w4jOPPfaYsdza2qqwEqB09GMAAGAHYaRMbtysC/Aa/RhRYZ4daW9v97TtAwcOeNoeAAQBYaQE5kNcLrzwQse2W1VVZdkG4Ab6MVQZPHiwsTxs2DCFlXirrq7OWOZvAwCOIow4wIlj7t04bh8oBv0YXlm9erWxvGvXLoWVeGfhwoWqSwAAXyKMlCjzW61yBmGZ7+UbM3iFfgw/iEKIvfXWW41l/jYA4M8II2XIvHNwKR+oUfgQhr/Rj6FC5oA8zIdr8fcBALkRRkoUi8UkmUxmvf7WW2/Z3sb+/ftzbhvwAv0YKm3atMlY3rVrVyhnDJgxBID8CCNFWr9+veUgS9d10XVdTjrpJNvbqq+vN95nPulX5OhA7qKLLiq7XsAK/Rh+oOu6nHnmmcbz5uZmueWWWxRW5CyCCAAURhgpwhlnnJF2MzeRPw/eynXkyJGs7WzYsEFaWlrK3jZgRj+GnyxZsiTt+aJFi0o+rEnTNFuPlJaWlrzrffzxxyX/XgQRALCHMGLTnDlzZPfu3cbzmpoaRwZvmXRdl6lTpxrPk8mkNDc3O94Oool+DD9KJBLSt2/ftNcKBZJXX301Z8hwyvDhw9O2/8gjjxR8z6OPPkoQAYAiEEZsGDBggLzxxhvG8/nz50t3d7dr7a1du1a2bNliPNd1XZYuXepae4gG+jH87A9/+IP8/ve/T3tN0zSZPn268byrq8sIBn/1V39V1PZbW1vlrrvukp/+9Kfyq1/9Su666y5pbW2VoUOH2t7GZZddZrS/bt26rJ9rmiaXXnpp2msEEQDIjzBiw6hRo4zladOmyX333ed6m2PGjJH169cbz5ctW+Z6mwg3+jH8rl+/flmD9zVr1hgBoH///jnfe/rppxuHG1o9nnzySbnxxhvlvPPOk3POOUduvPFGefLJJ+Xjjz/O+Z4LLrggZ3unnnqqUddFF12UNRuiaRpBBABsIIwUkHmSr/lmXW6bOHGi1NfX56wFsIt+jCBJJBJSWVmZd52BAwemBYc1a9Y4XsdPfvKTtDaOP/54y/X+4z/+I+15IpGQjRs3Ol4PAIQRYSSPzEGTG8fWF9LZ2Zn2nIEcikU/RhBt2LBBnnnmmazXU8Fg7969nte0ffv2ghd7YDYEAIpDGLFJxQDOD20jXOjHCKpSrviWTCZl4MCBtq6w1dvbW1Y9jY2NBBEAKAFhJAfzN7fV1dUKKznq2WefNZb5Vhl20Y8RBSeeeKJlwKisrMyalculqqqq4KWAAQDOI4zYcPjwYdUlpN0YDCgF/RhhsW/fvrSw8O6777ranrktLsIAAM4ijFgwf2N71VVXKawknfmQAL5VRiH0Y4RNKhAMGjSo4LqDBw+Wffv25b3Clvmxb98+mTJlSsHtXn311cyYAICDqlQX4HcPPvig6hKAstGPEXT5Bv979uyRwYMHl7X9hoYGy3uHHD58WPr06VN0TQAAe5gZyVBV5e98Zv7AvfPOOxVWAj+jHyPszLMa5QaRfGpqatLa+ou/+AvX2gKAKCKMZGhsbDSW/Xj1nz179hjLjz/+uMJK4Gf0Y4SRORSo8t577ymvAQDChDACAPC9Ygb/s2bNsnU5X6tH6k7uTtcEALDm72M5YGngwIGyb98+ETl6OE5PT4/agoAS0I/hBKfP21i7dq1UVGR/T0fwAAB3MDNi0traaiz7+YPHfOdh8+E4gAj9GOHW1dWl5B4g5jY//vhjz9oFgLAjjJi89957qksAykY/RhilgkD//v3zrnfsscfavpxvrseXvvSlvG0MHz6cy/sCgEM4TAsAEFhuzP499dRTWa8RPADAHYQRAECgqDj80NwmwQQAnMNhWgAAXxoxYoTl63379vW4kj9ramqyfH3lypUeVwIA4UAYCahcH4hAkNCPUUgikch6rbu72zhnY8GCBa7X8Morrxjt/fGPf7RVIwDAHsKIhVGjRqkuoaAbbrhBdQnwOfoxwiKRSOQc8N9xxx1pV7qaM2dO2e3deOONaducMmVK0XUBAOzhnBEL7777bqCOCdY0zdeXcIUa9GOETWrgf8cdd0hbW5vlOm1tbTl/5oQBAwbISy+95Nr2ASBqmBkJgZ07d6ouASgb/Rh23XrrrcasxDe/+U3X25s6darRHkEEAJzFzAgAILDmz58v8+fPN5736dNHRo8eXdY233zzTTl8+HC5pQEAbCCMmHDsL8KAfowo6+7uLvtvgCACAN7hMC0AAAAAShBGAAAAAChBGAEAAACgBGEEAAAAgBKEEQAAAABKEEYAAAAAKEEYAQAAAKAEYQQAAACAEoQRAAAAAEoQRgAAAAAoUaW6gFJt3rxZdQmh19TUpLqE0KMfu49+7D76sfvox+6jH7uPfgwrzIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFAisHdgBwAAiKJ4PJ72vK2tTVElQPkIIx5gpwEAAMqVOZ7IfJ3xBYKIw7RcFI/HLXccuV4HAACwYmfcwNgCQcTMSAHz5s2T3bt3p71m55uH4447ztY677//fsH1MncuQ4cOlX/7t38r+D4AABB8l156aVHrPvLIIy5WAziLmZE84vF4VhBJvf7uu+/mfe/UqVMLbr/QOu+8847ltxy7du3i2w8AACKiq6vLlXUBPyCM5FBosP///t//c72Gm266Ke/PCSQAAAAIMsJIGdwMAwQNAAAAhB1hxEJNTY3qEmzr06eP6hIAAACAkhBGLPzf//2f6hJs27Fjh+oSAAAAgJKE/mpakyZNks997nNZr+e7Itbw4cPLbretra3goVZOXA98yJAheX9uVcPbb78tr776atltAwAA99kZU5jXBYIk1DMj3/ve9yyDiMjRQXrfvn3L2v7y5cvz/jzfDqHQzuLRRx8tpSRD3759c+64Pve5z8miRYvK2j4AAPBOfX19wXUaGho8qARwVqjDSHt7e96ff/nLX875MzvfLPTr16/gOlbbsbPt2trakradku93ExHZsGFDwe0DAAB/6O3tLbhOT0+PB5UAzgptGLE7nXnttdfm/Fk5MxtOKLX9fL+T2Zw5c4quCQAAeGfjxo0Sj8dt3T+kq6tL4vG4JBIJDyoDnBH6c0YK+eijj/L+PDXoN4eb73//+67WZPb9739frr/++qx68in0O6Xoul5yXQAAwF2lXub/jjvuEBHOH0EwhHZmxE3mcBCmtgAAgHpvvvmmI/cbi8fjsnXrVgcqAtwT2jBSWVlpa71/+qd/crWOuXPn2nrNSXZ/p6qqyE+MAQDgK/F4XL7zne84tr0FCxZwI2X4WmjDyE9+8hNb6zU3N5e0fbt/2AcPHrT1WjltZLL7O/34xz8uafsAAMB5boYGAgn8KrRhRKTwsZJhPpYyyr87AABB40VYIJDAj0IdRkTcHXQfd9xxgdw2QQQAAP/wMiQQSOA3oQ8jIkcH321tbbJw4cK018v9g5w6dWpZ73dz25m/2/e+9z3j3wEAAPhDqeeu6rpe8lUx3T5fFihGJMJIykknnZT12qWXXupae/nCjpvfTFxyySVZr+W6Ez0AAFDnrbfeikSbQC6RCiNW7NxEKJ+rr77aoUr+zO5NC3M5cOCAQ5UAAAC3OPHFZCwWU9Y24ITIhxGR8v4gOzo6HKzkKLs3LbTCzgUAAABBQRj5kzDcFOjNN99UXQIAALDBD18e+qEGIHJh5O6777Z8fcGCBSVv0+qP+dhjjy34Pqt1ytkx5LpJ0j333FPyNgEAgFqpk9XNj5SNGzfm/BkQBJELI/kumZsrCNi9m7vZtGnTHFknU0WF9X9ZvhAzcuTIotsBAADu8NOMhJ9qQTRFLowUUldXV9L7du/eXXbbn376aUnvq62tLbttAAAAwGuEkQwzZ84s6X3z5s0ru+3LLruspPedc845ZbcNAAD8SdO0rEdKc3Nzzp8BQUAYsZA5Zenn4y+ZXgUAAEBQRTKMLF26tOA6Y8eONZb79+9va7upYDBs2DDbtaTWtRsqzLU0NjYWXP+BBx6wXQsAAADgpUiGkSFDhhRcp6WlxVhes2ZNUds/44wzXFlXROSFF14wlpubmwuuf8wxxxS1fQAAEByJREJ1CUBZIhlG7ErNVqxdu1ZxJX+WCiMcngUAQHQtX75cli9frroMoGyEERvMsxGFlBISinlPMbUAAIBwuuSSS+SSSy5RXQZQNsJIAX6cgfBjTQAAAECxIhtG7rrrLtUluC4KvyMAAACCK7Jh5Pjjj1ddguui8DsCABA0bW1tqksw+KkWRFNkwwgAAAAAtQgjAAAAHvPDjIQfagAIIwAAAAq89tprkWwbMIt0GFmwYIHqElzzne98R3UJAAAgj23btkljY2PW67quO/rI1NjYKNu2bfPiVwQKqlJdgErjx49XXYJrxo0bp7oEAABQwG233Saapsns2bNFRCzDQ7l0XRdN00REZOXKla60AZQq0jMjbvnZz34m3d3dBb+p6O7ulp/97GeqywUAAAp5GQ4IIvCbSM+MlGPfvn3S0NBQ1jZqamrk7//+73PuGDo7O2XgwIFltQEAAPxtzpw5xnJqBsPNtlasWOFqG0AxmBmxYcqUKVmzGuUGETsaGhqy2p0yZYrr7QIAAO8wM4Ioi3wYyXVZu66uLiMArFu3zta2ksmkxONx0TStqEc8HpdkMmmrjXXr1hl1dXV1FfU7AQAAf4nH45FoE8gl8mFEJH3wnhro19bW5lx/8+bNlqGisrJSVq5cWXT7K1eulMrKSstttre353xfbW1t1pUyCCIAAAAICsLIn+QbxK9fvz4tIHh5paoJEyaktb1+/fqc6xJEAAAIDvO5IlFqGzAjjJiYB/MjR440AsCkSZMUVpVu0qRJRl0jR440XieIAAAQLPfee6/j9xSx+7j33ntV//qAiBBGsgwdOlQ0TZOdO3eqLqWgnTt3iqZpMnToUNWlAACAIl177bWRbBswI4xkWLp0qeoSihbEmgEAiDq3L+Pr17YBM8KIhdQhT6qmTu0+zLUCAIDgKfYKnE49AL8gjOQQhGMpv//976suAQAAACgZYSSH6667TnUJBV1//fWqSwAAAABKRhjJQ9XUKVOsAACEn8pDrTnMG35BGLEQpDuTBqlWAACQrq2tTSZPnuxZe5MnTyaIwFeqVBfgN/F4XIYMGSKdnZ1y5MgR1eXkVV1dLfX19XLppZfKI488orocAABQgm9/+9siItK/f39pbW11pY2nnnpKPv30U1e2DZSDMGIybNgwERHZtWtX2uvXXXedLFmyREVJWb71rW9lnVzPIVsAAATfp59+mjZrsXz5cnn66adL2tbZZ58tc+fOTds24EeEEZMzzjjD8vV77703LQA88MADMm/ePE9qWrp0qVxzzTUF1zv++OPlvffe86AiAABQjoaGBunt7RURkd7eXunt7ZX/+Z//kRUrVsiePXscaePpp59OCzIjR46UWbNmyRe/+EWprKyUyspKERGprKyUzs5OR9oESkEYsaBpmvT29kpFhfUpNddcc41lQNi+fbu0tLQU/Ufd0NAg7e3tcsIJJxT1vmQyaexMvvrVr8rdd99d1PsBAICz/Hou544dO2Tp0qUl3Si5vb1dtmzZ4kJVACewGy677LK055WVlcZVqy6++GJb2zjhhBNk3759Rd+8cN++fbaDyMUXX2zUlQoiIiLvv/++/V8WAAA4Kh6P+zaIlKulpUXi8Xgg7sGG4GFm5E/yHUv5ox/9SH70ox+lvZZMJl0/V0PX9ZyzMwAAQL2wBhAr69atk3Xr1nE1LjiKka5JaqbCjoqKCst7f5QSHsrdVqruKO0QAQBQLaqfu/F4XI499ljVZSAkCCMWzIdQlfLeYm9eWGo7pdYIAADKs3btWtUlKDVt2rS0w8WBUhFGCjAP+lVeFu/TTz8tGEC+9a1veVwVAADRdP/996suQbnzzz9fdQkIAcLIn/z3f/+3MVORS11dXdbJ5x988IHjtXzwwQdZ7dTV1eVcP1X3qaee6ngtAADAf375y1/KL3/5S9VlAGXjBPY/2bt3r7S1tUk8Hk8LJIUOg/rMZz6j5FCpzNDEyWQAAHgnNWYo5Fe/+pUcPHiw4HpDhgyRyspKOXDggIiI1NbWykcffZRz/UOHDhl15DJ8+PC07fX29sru3bsL1tKvXz8599xzC67H2ANOIIxkSP1hzZ07Vw4ePJg16Fd1jobVjE2/fv1k+fLl3hcDAACMMcM999wjr7zyilxwwQUya9astHXsBBERyQoJn3zySdn1mcNMMds7ePBgVtD4xS9+IU888YRMmTJFrr/++rJrA1IIIzmYB/lDhgyRv/7rvxYR61Bwzz33OPaH+f3vf19uuOGGnD9/9tlnbX2rAQAAvJHvczssZs2alRW0ACcQRmzYvXt31jcEV1xxhezfv19Eju6E3NgR1dfXy0MPPZRVCwAAABAGhJESZYYEs1/96leyatUqOXz4cN5t1NTUyFe/+lVbx2UCAAAAYUMYccG5555LwAAAAAAK4NK+AAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUCe9PD2bNnqy4BKNv48eNVlwCUjX6MMKAfA2owMwIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUIIwAgAAAEAJwggAAAAAJQgjAAAAAJQgjAAAAABQgjACAAAAQAnCCAAAAAAlqopZeffu3W7VAQAAACAk7OYGW2GktrZWqqur5ec//3lZRQEAAACIhurqaqmtrc27jqbrum5nY/v27ZMDBw44UhgAAACAcKutrZWBAwfmXcd2GAEAAAAAJ3ECOwAAAAAlCCMAAAAAlCCMAAAAAFCCMAIAAABACcIIAAAAACUIIwAAAACUIIwAAAAAUOL/A+yX1X2sX7a1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drawPosition(\"r1b1k1nr/p2p1pNp/n2B4/1p1NP2P/6P1/3P1Q2/P1P1K3/q5b1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}